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Packages produced are intended to be used with AnnotationDbi. 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Package: r-bioc-annotatr Architecture: all Version: 1.38.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-annotationhub, r-cran-dplyr, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-seqinfo, r-cran-ggplot2, r-bioc-iranges, r-cran-readr, r-bioc-regioner, r-cran-reshape2, r-cran-rlang, r-bioc-rtracklayer, r-bioc-s4vectors Suggests: r-bioc-genomeinfodb, r-bioc-biocstyle, r-cran-devtools, r-cran-knitr, r-bioc-org.dm.eg.db, r-bioc-org.gg.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.rn.eg.db, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm6.ensgene, r-bioc-txdb.drerio.ucsc.danrer10.refgene, r-bioc-txdb.drerio.ucsc.danrer11.refgene, r-bioc-txdb.ggallus.ucsc.galgal5.refgene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm9.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.mmusculus.ucsc.mm39.knowngene, r-bioc-txdb.rnorvegicus.ucsc.rn4.ensgene, r-bioc-txdb.rnorvegicus.ucsc.rn5.refgene, r-bioc-txdb.rnorvegicus.ucsc.rn6.refgene, r-bioc-txdb.rnorvegicus.ucsc.rn7.refgene Filename: pool/dists/noble/main/r-bioc-annotatr_1.38.3-1.ca2404.1_all.deb Size: 2977058 MD5sum: af7b04aab5ce3980718b2d6418c7df4d SHA1: 50e27e1a4140cc9264c8dd21db48497b3ae9a63b SHA256: 23c2a598087d33467722c4a51cfe2f9e2a26b1aafdbf3bfef9d528976c3c5ded SHA512: e85f46fae541443db83cecbf25f02b578fdb563e0c7d6cc2e71b4100afa49577300e1f1153c89608bd5095478d9934bf27d63c100e6eced69e26e4704e684261 Homepage: https://cran.r-project.org/package=annotatr Description: Bioc Package 'annotatr' (Annotation of Genomic Regions to Genomic Annotations) Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. Package: r-bioc-anvil Architecture: all Version: 1.24.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-anvilbase, r-cran-futile.logger, r-bioc-gcptools, r-cran-jsonlite, r-cran-httr, r-cran-digest, r-cran-keyring, r-cran-rapiclient, r-cran-yaml, r-cran-tibble, r-cran-shiny, r-cran-dt, r-cran-miniui, r-cran-htmltools, r-bioc-biocbaseutils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-readr, r-bioc-biocstyle, r-cran-devtools, r-bioc-anvilaz, r-bioc-anvilgcp, r-cran-lifecycle Filename: pool/dists/noble/main/r-bioc-anvil_1.24.1-1.ca2404.1_all.deb Size: 552340 MD5sum: ed3a90d43bca565abed32fae48d7d780 SHA1: 3c6ec22e91fefa6bf25ce0a5323e0460cbbee809 SHA256: edbcfbf268e02197ae903253802170dec1f93da78f3214225116298761dd983c SHA512: b8906c786155735fdd0d462598c504a093be803c31d92b5de81977faf158ed7c28ed665a6f14f414a6e39e55805a905a5238ef6316a4c96886ae6ea8e5a5fa4d Homepage: https://cran.r-project.org/package=AnVIL Description: Bioc Package 'AnVIL' (Bioconductor on the AnVIL compute environment) The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides programatic access to the Dockstore, Leonardo, Rawls, TDR, and Terra RESTful programming interfaces. For platform-specific user-level functionality, see either the AnVILGCP or AnVILAz package. 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Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers. 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These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. 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One and two color array platforms are supported. Package: r-bioc-assorthead Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13218 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-assorthead_1.6.4-1.ca2404.1_all.deb Size: 1598190 MD5sum: 7f072075f5d9c506986305dedb7cac64 SHA1: 052f442e2bb5077721343be58855660f13c4ca52 SHA256: 1886c60f114f9e5e343c5826bf49c701aa2933998590fa3c6e96fd35627d0ef6 SHA512: 99a24204715f138d7444250d7231b660bd46b50fd1a46dcfeda606082de06e7d6fba8255bbecd713a3735cd2877e711ce328b653a8408eb57d98046ef2ad7ea6 Homepage: https://cran.r-project.org/package=assorthead Description: Bioc Package 'assorthead' (Assorted Header-Only C++ Libraries) Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries. Package: r-bioc-atacseqqc Architecture: all Version: 1.36.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16487 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-chippeakanno, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-genomeinfodb, r-bioc-genomicscores, r-bioc-limma, r-cran-polynom, r-bioc-rsamtools, r-cran-randomforest, r-bioc-rtracklayer, r-bioc-motifstack, r-cran-kernsmooth, r-bioc-edger, r-bioc-biocparallel Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-phastcons100way.ucsc.hg19, r-bioc-motifdb, r-bioc-trackviewer, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-atacseqqc_1.36.1-1.ca2404.1_all.deb Size: 14597130 MD5sum: 61e0a3e7ecf6af03f3656b155fbb2212 SHA1: dc6b3e39ecf663db0d10fdb956cfc359b3676ef1 SHA256: 2f001d75d66fc2c8ed4e9beb9d1a72503558876c0aa21d61a9ef711df2e6fd5c SHA512: 7f4223edf60555c4506e890c149351e3edc02e8c56543efaff66c337bf6e6bbaa56a4945979dc1109f2bda9fec35cde3bdb816046320fb40df73f03f9255c30e Homepage: https://cran.r-project.org/package=ATACseqQC Description: Bioc Package 'ATACseqQC' (ATAC-seq Quality Control) ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. Package: r-bioc-aucell Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3565 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-cran-data.table, r-bioc-gseabase, r-cran-matrix, r-cran-mixtools, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-biocgenerics Suggests: r-bioc-biobase, r-bioc-biocstyle, r-cran-dosnow, r-cran-dynamictreecut, r-cran-dt, r-bioc-geoquery, r-cran-knitr, r-cran-nmf, r-cran-plyr, r-cran-r2html, r-cran-rmarkdown, r-cran-reshape2, r-cran-plotly, r-cran-rtsne, r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-bioc-aucell_1.34.0-1.ca2404.1_all.deb Size: 2271340 MD5sum: 1ab7d19fcbf869b08b5cc0c3b91cdaed SHA1: f1f7fa01640ae590cb0147f9fb6fae670e40e0d6 SHA256: 18a3ba0a1f6a43cfb15f5bb9299a13438697381a0f3bc134af0d551d45f942d4 SHA512: 71448081535b0a6a70b6b3f2c6a60c820b377b7d89b8a0c2f0a05af1f64e1f4a458cc9c6895a5a30154b6e19a2b14076648226fde540d5a8b27960ae1c9d7ef9 Homepage: https://cran.r-project.org/package=AUCell Description: Bioc Package 'AUCell' (AUCell: Analysis of 'gene set' activity in single-cell RNA-seqdata (e.g. identify cells with specific gene signatures)) AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. 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Package: r-bioc-basilisk.utils Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-dir.expiry Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-basilisk.utils_1.24.0-1.ca2404.1_all.deb Size: 246608 MD5sum: a28be831c11d3c492666e321e391ebb3 SHA1: 719e14549bf3e39f3c3f318151d91215ebc65538 SHA256: 49aed75bac4d8673d6c6978bc625e274de351b0c301c3557633a05eb704e3132 SHA512: 97ff81eeda2c03b02e04fedf4edbe48110d9ca94d5b25315c5a720e3bb7527bc7c0a6b32ecb00e99bcf65b3dd0995aa743dc5d5234bf349c2c0e6ecd8012f51f Homepage: https://cran.r-project.org/package=basilisk.utils Description: Bioc Package 'basilisk.utils' (Centralized Conda Installation for Bioconductor Packages) Provides a centralized conda installation for use by other Bioconductor packages. If conda is not already available on the system, it is downloaded and installed from the Miniforge project; otherwise, no action is performed. Historically, this package was used to provide a Python installation for basilisk, hence the name. 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Package: r-bioc-bumphunter Architecture: all Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-foreach, r-cran-iterators, r-cran-locfit, r-cran-matrixstats, r-bioc-limma, r-cran-dorng, r-bioc-biocgenerics, r-bioc-genomicfeatures, r-bioc-annotationdbi Suggests: r-cran-testthat, r-cran-runit, r-cran-doparallel, r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/noble/main/r-bioc-bumphunter_1.54.0-1.ca2404.1_all.deb Size: 4303472 MD5sum: 76af90322624421f8497eb7b989e704e SHA1: 7dd91cd4aa65056c6b41f69feee6b099b64af55d SHA256: 7f65e9bc6558a2446eef08f5f08453b7cebfa94592fc40bb457406bdb7f5f1e1 SHA512: 058b46f325c066306ffc2e770f0d63cd5237bcb545ce7d6ce8d716ea10492d00d52aa242bd7ff06c97b6072ffbb7ad98a443befa08d16c80711ef07649540137 Homepage: https://cran.r-project.org/package=bumphunter Description: Bioc Package 'bumphunter' (Bump Hunter) Tools for finding bumps in genomic data Package: r-bioc-bumpymatrix Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-s4vectors, r-bioc-iranges Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-bumpymatrix_1.20.0-1.ca2404.1_all.deb Size: 877646 MD5sum: 9921d89e7524293351f22ea7879cd877 SHA1: b8cd01186616bc1398c31aa70606fae313d653fe SHA256: e3edb39df2bbc6004b03735bc60cb11cc4c34a44e2457f6b08e4fe992e61ffbf SHA512: d7953d81e91fbc6cfc8e66fc2c536b475b9e54f344ecbb00530bdad3332802cd3598e33020597017859becdecd6ebc511176fa41968fa1457b8412a1022f61b1 Homepage: https://cran.r-project.org/package=BumpyMatrix Description: Bioc Package 'BumpyMatrix' (Bumpy Matrix of Non-Scalar Objects) Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. Package: r-bioc-catalyst Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-circlize, r-bioc-complexheatmap, r-bioc-consensusclusterplus, r-cran-cowplot, r-cran-dplyr, r-cran-drc, r-bioc-flowcore, r-bioc-flowsom, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-gridextra, r-cran-matrix, r-cran-matrixstats, r-cran-nnls, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rtsne, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-scales, r-bioc-scater Suggests: r-bioc-biocstyle, r-bioc-diffcyt, r-bioc-flowworkspace, r-bioc-ggcyto, r-cran-knitr, r-bioc-opencyto, r-cran-rmarkdown, r-cran-testthat, r-cran-uwot Filename: pool/dists/noble/main/r-bioc-catalyst_1.36.0-1.ca2404.1_all.deb Size: 12187944 MD5sum: 84b072b51862ec3702a5142a5eb40969 SHA1: e3fbd1e391a4e0c493d93a18f02b1cb5bd25be14 SHA256: 6c049ee18c2324ab71e06fdcaa84ad135514e5ec7b302011a69ea1a8d4e9a985 SHA512: 6c2399f0c9b394d3ade195a987c5c775079358346b7fe34d0d3868d7391a7e40b3b8c3e857e65c30058ccb787c96efddcfbbb690195febeb71d43ea62945223b Homepage: https://cran.r-project.org/package=CATALYST Description: Bioc Package 'CATALYST' (Cytometry dATa anALYSis Tools) CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively. Package: r-bioc-category Architecture: all Version: 2.78.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-biobase, r-cran-matrix, r-bioc-graph, r-bioc-rbgl, r-bioc-gseabase, r-bioc-genefilter, r-bioc-annotate, r-cran-dbi Suggests: r-bioc-ebarrays, r-bioc-all, r-bioc-rgraphviz, r-cran-rcolorbrewer, r-cran-xtable, r-bioc-hgu95av2.db, r-bioc-keggrest, r-bioc-karyoploter, r-bioc-geneplotter, r-bioc-limma, r-cran-lattice, r-cran-runit, r-bioc-org.sc.sgd.db, r-bioc-gostats, r-bioc-go.db Filename: pool/dists/noble/main/r-bioc-category_2.78.0-1.ca2404.1_all.deb Size: 1357616 MD5sum: 638a841f5e7fecde79e8f90b5186f525 SHA1: f82bbe9272db4b7a85cf3c8d9e91fb2e84118617 SHA256: 542570f3d24a19e53d9e2cfa54dd91a6771cb1c5906f17327c8a44ba3748cb76 SHA512: 9d91a53d0dd076cb608485c104e923ab6392721630d0e7107f9024ce1db4e1452f659f0426f140846a73bceee65f0374995fc3f0024e6fecbac20ab0c402bb1e Homepage: https://cran.r-project.org/package=Category Description: Bioc Package 'Category' (Category Analysis) A collection of tools for performing category (gene set enrichment) analysis. Package: r-bioc-cbioportaldata Architecture: all Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3101 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-anvil, r-bioc-multiassayexperiment, r-bioc-biocbaseutils, r-bioc-biocfilecache, r-cran-digest, r-cran-dplyr, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-httr, r-bioc-iranges, r-cran-readr, r-bioc-raggedexperiment, r-bioc-rtcgatoolbox, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr, r-bioc-tcgautils Suggests: r-bioc-biocstyle, r-cran-jsonlite, r-cran-knitr, r-cran-survival, r-cran-survminer, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-cbioportaldata_2.24.0-1.ca2404.1_all.deb Size: 469794 MD5sum: 7b5877fe21b03862dd4eaaf13ee743dc SHA1: bdb617f954be42ea24d73a53a4e977c98a26bf88 SHA256: 3d331bb3bf1c19c0f13591e6b67204ed10b60c4887bc172ef81a2d64d9946283 SHA512: f6fb849484eaa966113bc824c74a4de75f56cf2c472811f30b6ac1017b4b9a7584c9ecf4f99ed37cdfa495a48e2d874681626c557e2267ba8e5c5152e2bd2279 Homepage: https://cran.r-project.org/package=cBioPortalData Description: Bioc Package 'cBioPortalData' (Exposes and Makes Available Data from the cBioPortal WebResources) The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. Package: r-bioc-celldex Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1370 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-experimenthub, r-bioc-annotationhub, r-bioc-annotationdbi, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-gypsum, r-bioc-alabaster.base, r-bioc-alabaster.matrix, r-bioc-alabaster.se, r-cran-dbi, r-cran-rsqlite, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-dt, r-cran-jsonvalidate, r-cran-biocmanager, r-bioc-ensembldb Filename: pool/dists/noble/main/r-bioc-celldex_1.22.0-1.ca2404.1_all.deb Size: 430144 MD5sum: 88f50dc4994be906151b07c2e562c55e SHA1: e0c56015d1a54b9881feefc76f71cc6764416e93 SHA256: 7ecee8fdcd158e259469b64fda8704f0677154983d252e591164bda84c8726bc SHA512: 650922342e5ca33ad0e658a0ec6bdd5004c9f09015a08193123269f14e13633b65c1715bd9d0353ce5aff0e0e9d7ff0d3ed93cb4b431db087d12631df5c5f206 Homepage: https://cran.r-project.org/package=celldex Description: Bioc Package 'celldex' (Index of Reference Cell Type Datasets) Provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq. Package: r-bioc-cghbase Architecture: all Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1386 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-marray Filename: pool/dists/noble/main/r-bioc-cghbase_1.72.0-1.ca2404.1_all.deb Size: 1105156 MD5sum: e33d71e6970478a15ec8e1d08fab7319 SHA1: 09dfbfa61c93c012c24896d4bc8ab3508b5248b5 SHA256: 92da6f136fd8b00210ad72d1a637fae72e218693feb9b7cbf97b8929909fd22f SHA512: b7233bad22b5c90164aa3a7ccaefe2ad9e29f7b245313b2de9894c0bad1fa49f351320c56d5b197052ed5b436c01dacbdb303a114647bd7a41275633140d13fe Homepage: https://cran.r-project.org/package=CGHbase Description: Bioc Package 'CGHbase' (CGHbase: Base functions and classes for arrayCGH data analysis.) Contains functions and classes that are needed by arrayCGH packages. Package: r-bioc-cghcall Architecture: all Version: 2.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-impute, r-bioc-dnacopy, r-bioc-biobase, r-bioc-cghbase, r-cran-snowfall Filename: pool/dists/noble/main/r-bioc-cghcall_2.74.0-1.ca2404.1_all.deb Size: 540288 MD5sum: 2fa870fb21ebe5c58a882c58a4a44f0c SHA1: 72fca5e57b8da9b475ce2784aa54858b1f950132 SHA256: f3cee503191bf56eb2168ea9f0e28829dd205a8b378a732d4b3c1a77428f5378 SHA512: 07b181c146757a34e01eb06952aa1f5417b6fee81f60e0a2e324eb390b19981ecf82a99ddf4701ff651b7d7507f22caad34241bed7b11744eba19366cf02a958 Homepage: https://cran.r-project.org/package=CGHcall Description: Bioc Package 'CGHcall' (Calling aberrations for array CGH tumor profiles.) Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. Package: r-bioc-chippeakanno Architecture: all Version: 3.46.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 28456 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-bioc-genomicranges, r-bioc-s4vectors, r-bioc-annotationdbi, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-pwalign, r-cran-dbi, r-cran-dplyr, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-rbgl, r-bioc-rsamtools, r-bioc-summarizedexperiment, r-cran-venndiagram, r-bioc-biomart, r-cran-ggplot2, r-bioc-graph, r-bioc-interactionset, r-bioc-keggrest, r-cran-matrixstats, r-bioc-multtest, r-bioc-regioner, r-bioc-rtracklayer, r-bioc-universalmotif, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-data.table, r-cran-scales, r-bioc-ensembldb Suggests: r-bioc-annotationhub, r-bioc-bsgenome, r-bioc-limma, r-bioc-reactome.db, r-cran-biocmanager, r-bioc-biocstyle, r-bioc-bsgenome.ecoli.ncbi.20080805, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-org.ce.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome.celegans.ucsc.ce10, r-bioc-bsgenome.drerio.ucsc.danrer7, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-delayedarray, r-cran-idr, r-cran-seqinr, r-bioc-ensdb.hsapiens.v75, r-bioc-ensdb.hsapiens.v79, r-bioc-ensdb.hsapiens.v86, r-bioc-txdb.hsapiens.ucsc.hg18.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-go.db, r-cran-gplots, r-cran-upsetr, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-testthat, r-bioc-trackviewer, r-bioc-motifstack, r-bioc-organismdbi, r-bioc-biocfilecache Filename: pool/dists/noble/main/r-bioc-chippeakanno_3.46.1-1.ca2404.1_all.deb Size: 22870810 MD5sum: 7541ec49d8ba3ef5ce784e8a925f9290 SHA1: 35a47a4eb97d593ef3fd800be485e489ace2642e SHA256: ce06413ab2ff3b8f281cdbcccfb2d7e28a3f490f8a7d89d12340aa77653d0dee SHA512: aa19b1be77f5eb9051c389cd44350517f4688e3f95231e05ee7c72a10ce887780a74634b577dc93d8adb1dffe4a66248420510b2f60b04cb5e76f1811c53824a Homepage: https://cran.r-project.org/package=ChIPpeakAnno Description: Bioc Package 'ChIPpeakAnno' (Batch annotation of the peaks identified from either ChIP-seq,ChIP-chip experiments, or any experiments that result in largenumber of genomic interval data) The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities. Package: r-bioc-chipseeker Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8082 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-aplot, r-bioc-biocgenerics, r-cran-boot, r-cran-dplyr, r-bioc-enrichplot, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-ggplot2, r-cran-gplots, r-cran-gtools, r-cran-magrittr, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-rlang, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-scales, r-cran-tibble, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-yulab.utils Suggests: r-bioc-clusterprofiler, r-cran-ggimage, r-cran-ggplotify, r-cran-ggupset, r-cran-ggvenndiagram, r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-bioc-reactomepa, r-cran-rmarkdown, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Filename: pool/dists/noble/main/r-bioc-chipseeker_1.48.0-1.ca2404.1_all.deb Size: 7479366 MD5sum: 8527e14ee94bbd5527e6a3e114dcf851 SHA1: d85157e864157c2cf4c4c538bb0a6475c64847d1 SHA256: 7b58de1ac394323f8855c699143b2806050e1b3edc5af18de8c40b9be5b92c99 SHA512: 32be5e254b3b2f3824e5f57e6530720768d7401d73c356ecd66de6177439acd3e020cf8aee6ea98a561b4dfeb1f6a28078b01d9051f21142900438f33b156ac8 Homepage: https://cran.r-project.org/package=ChIPseeker Description: Bioc Package 'ChIPseeker' (ChIPseeker for ChIP peak Annotation, Comparison, andVisualization) This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. Package: r-bioc-cicero Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1802 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-monocle, r-bioc-gviz, r-cran-assertthat, r-bioc-biobase, r-bioc-biocgenerics, r-cran-data.table, r-cran-dplyr, r-cran-fnn, r-bioc-genomicranges, r-cran-ggplot2, r-cran-glasso, r-cran-igraph, r-bioc-iranges, r-cran-matrix, r-cran-plyr, r-cran-reshape2, r-bioc-s4vectors, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vgam Suggests: r-bioc-annotationdbi, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-rtracklayer, r-cran-testthat, r-cran-vdiffr, r-cran-covr Filename: pool/dists/noble/main/r-bioc-cicero_1.30.0-1.ca2404.1_all.deb Size: 1126524 MD5sum: 02a900017b51b10843491bc4bacdb9aa SHA1: 853dc388ee29cb58df138f04027ab2d66b74e7b4 SHA256: b180ee84c8d85ebeca765471368c6ba35739e6ab10028aae1317cdda898726e7 SHA512: 1fdaa3d851a1eb2a26409dc7fbf0e7ad353d038775bd8fd4313cb290c24f571374587863b36b94be7e289f7211c2c618cbac24d32b7a92181cea7b909e340d32 Homepage: https://cran.r-project.org/package=cicero Description: Bioc Package 'cicero' (Predict cis-co-accessibility from single-cell chromatinaccessibility data) Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. Package: r-bioc-clusterprofiler Architecture: all Version: 4.20.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aisdk, r-bioc-annotationdbi, r-cran-dplyr, r-cran-enrichit, r-bioc-enrichplot, r-cran-ggplot2, r-bioc-go.db, r-bioc-gosemsim, r-cran-gson, r-cran-httr, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr, r-cran-plyr, r-bioc-qvalue, r-cran-rlang, r-cran-tidyr, r-cran-yulab.utils Suggests: r-bioc-annotationhub, r-cran-biocmanager, r-bioc-dose, r-cran-ggtangle, r-cran-readr, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-clusterprofiler_4.20.2-1.ca2404.1_all.deb Size: 799108 MD5sum: 41fde1934daba2991da93242b2bddf0e SHA1: bc5102694689a093ac16601c10042e89be5f2470 SHA256: 0fb2b0775034aeb327f25c2b9f2d33fe55e33ae881ca5e1941ad2a76d7f05ebb SHA512: 0832ee287c87f072aac757d3ecfb93d2f1fd5575899b8a9f0052cbe6eaa2074fd763b2b7c1c10876e3935b988ba70e69a358ee8c481f208d046ddfdd2a0f4779 Homepage: https://cran.r-project.org/package=clusterProfiler Description: Bioc Package 'clusterProfiler' (A Universal Enrichment Tool for Interpreting Omics Data) A universal tool for interpreting functional characteristics of omics data. It supports Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) for both coding and non-coding genomics data of thousands of species. It provides a unified and tidy interface to access, manipulate, and visualize enrichment results. A key capability is the simultaneous analysis and comparison of datasets from multiple treatments or time points. Furthermore, it integrates Large Language Model (LLM) capabilities to provide automated and insightful interpretation of enrichment results. Package: r-bioc-cmapr Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6090 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5, r-cran-data.table, r-bioc-flowcore, r-bioc-summarizedexperiment, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-cmapr_1.24.0-1.ca2404.1_all.deb Size: 3924344 MD5sum: 562fa66ae745afa23f29ed436940c775 SHA1: 858c847566c97c4e989962390c047f38853506b5 SHA256: be8d510f089f95dc54bcf60cc86cd3605ce57d1fdb0312b9f3ceaaf4116db4f9 SHA512: 0b44d378a69edb0f03644b44a1d068bdaea6d3e4230a3cea339867e00e70e90af28f4214b5f08bb6b2a25d0e353a1ff85690e1cb8a464ce23cd1ac144d4590b6 Homepage: https://cran.r-project.org/package=cmapR Description: Bioc Package 'cmapR' (CMap Tools in R) The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. Package: r-bioc-complexheatmap Architecture: all Version: 2.28.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3559 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-getoptlong, r-cran-colorspace, r-cran-clue, r-cran-rcolorbrewer, r-cran-globaloptions, r-cran-png, r-cran-digest, r-bioc-iranges, r-cran-matrixstats, r-cran-foreach, r-cran-doparallel, r-cran-codetools Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-dendsort, r-cran-jpeg, r-cran-tiff, r-cran-fastcluster, r-bioc-enrichedheatmap, r-cran-dendextend, r-cran-grimport, r-cran-grimport2, r-cran-glue, r-bioc-genomicranges, r-cran-gridtext, r-cran-pheatmap, r-cran-gridgraphics, r-cran-gplots, r-cran-rmarkdown, r-cran-cairo, r-cran-magick Filename: pool/dists/noble/main/r-bioc-complexheatmap_2.28.0-1.ca2404.1_all.deb Size: 3032710 MD5sum: 7349d25584a85a21c7581c0a151eaf46 SHA1: 889e2c0855be25edf4d2cd42897bf3b4157de9d0 SHA256: c9499264720471cd31dd226569b34a0ef01b431d6037d9e3878a22a764ba03fd SHA512: 148f64a8cdcc47d8ec5ac22dc96db949a2206eceb6a9c8f65ad55d52d72d4b236765ba29858bfa8ef4a72d188a6570847298ffe47fcabd676fa2b5e10244f959 Homepage: https://cran.r-project.org/package=ComplexHeatmap Description: Bioc Package 'ComplexHeatmap' (Make Complex Heatmaps) Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. Package: r-bioc-compounddb Architecture: all Version: 1.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3234 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationfilter, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-chemminer, r-cran-tibble, r-cran-jsonlite, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-rsqlite, r-bioc-biobase, r-bioc-protgenerics, r-cran-xml2, r-bioc-iranges, r-bioc-spectra, r-bioc-mscoreutils, r-bioc-metabocoreutils, r-bioc-biocparallel, r-cran-stringi, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-bioc-msbackendmgf Filename: pool/dists/noble/main/r-bioc-compounddb_1.16.0-1.ca2404.1_all.deb Size: 1137090 MD5sum: 27617509eefd673911cfb8609e33a6c1 SHA1: 130c1804054e80e1331a1e0d2e4554e124deefe5 SHA256: 9c2087159d512e5a6380ecdd08d68040810c5461bd655fcd6f509aa421e13bd6 SHA512: 8021458d51337b6c802a7b99589cae90e8d314bda7206a5f956de2a27862eb68a7eb71fa5b23d49f7bcbb3d73fe1168c0168dbf72f4fe2063185b01f133ff954 Homepage: https://cran.r-project.org/package=CompoundDb Description: Bioc Package 'CompoundDb' (Creating and Using (Chemical) Compound Annotation Databases) CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. Package: r-bioc-consensusclusterplus Architecture: all Version: 1.76.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-all, r-cran-cluster Filename: pool/dists/noble/main/r-bioc-consensusclusterplus_1.76.0-1.ca2404.1_all.deb Size: 446524 MD5sum: 5d160a8d51e2c167248e86cdf398e564 SHA1: d9de3c01bdc951eaf0a4feeb23506d384e64316b SHA256: fc67f6fd543dfd5a0a5f1fa062b1b185cef0352477cba8c20a073b8fd646f410 SHA512: 7c5c8cdbdc34b84adec95ee53dc6b74bb7eadb19b13e780796937cd38f8ce506c27308cb8b32a4df5239fa99a848e8dc45ad8258fc950b1923eccf4a579b3cd7 Homepage: https://cran.r-project.org/package=ConsensusClusterPlus Description: Bioc Package 'ConsensusClusterPlus' (ConsensusClusterPlus) algorithm for determining cluster count and membership by stability evidence in unsupervised analysis Package: r-bioc-cordon Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-bioc-biobase, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-data.table Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-cordon_1.30.0-1.ca2404.1_all.deb Size: 2647460 MD5sum: 5d0a2915841b8aded02140992169061f SHA1: f9a877891411de0a28cdb7d6dd7c8e242efb5f5c SHA256: 99589bc3244cae4f08e2d5372c05a284a86edfca83911288ff44f0b0bddaa016 SHA512: 38daf0655456fa026474c16bd5d62589c8fe9ce3c31bfb0547d7dc7ec61fe3f8e9187aed8f573b7b22285c1508dabe46aacb13acbfda008af6b73aae2a280ae4 Homepage: https://cran.r-project.org/package=coRdon Description: Bioc Package 'coRdon' (Codon Usage Analysis and Prediction of Gene Expressivity) Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. Package: r-bioc-coregx Architecture: all Version: 2.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3870 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-bioc-biobase, r-bioc-s4vectors, r-bioc-multiassayexperiment, r-bioc-matrixgenerics, r-bioc-piano, r-bioc-biocparallel, r-bioc-bumpymatrix, r-cran-checkmate, r-cran-lsa, r-cran-data.table, r-cran-crayon, r-cran-glue, r-cran-rlang, r-cran-bench Suggests: r-cran-pander, r-cran-markdown, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-knitr, r-cran-formatr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-coregx_2.16.0-1.ca2404.1_all.deb Size: 2267236 MD5sum: 90ed3d907786f946a82ffb8d6fa66318 SHA1: 3b1fddf988495dc97e4e958951a87d0aaabdc007 SHA256: 9710b46a8a781cc9918534993dbef0aa2524567e4468076a5a87d53150083bcd SHA512: 85b5f9ffc7a6d731cbf8fe732e3d36d6b66324ee9c7054098811474fafacbf804d546c826415dda00b93e8e2a112b4dbe345dbaadc00fc8153d34193ac0364da Homepage: https://cran.r-project.org/package=CoreGx Description: Bioc Package 'CoreGx' (Classes and Functions to Serve as the Basis for Other 'Gx'Packages) A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . Package: r-bioc-cqn Architecture: all Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mclust, r-cran-nor1mix, r-cran-quantreg Suggests: r-cran-scales, r-bioc-edger Filename: pool/dists/noble/main/r-bioc-cqn_1.58.0-1.ca2404.1_all.deb Size: 992412 MD5sum: 383ddfaa1bfdb4017331f24b1558f5ea SHA1: 46aa7ca2097928c22971485cf6038a639b7e532e SHA256: a71b93d0c4bec54c36b4da45073c2b5524ece2b9571ef79cd771f1776cbb8101 SHA512: 4f2de7d4fde3070528027cadaf8ccd218095af372876a9cdf3cc265a12c4433403fc6b27f4aaac5595d7cf555028f34fda017a79fc0876c22c97ce3da672005e Homepage: https://cran.r-project.org/package=cqn Description: Bioc Package 'cqn' (Conditional quantile normalization) A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. Package: r-bioc-cytomapper Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7667 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-ebimage, r-bioc-singlecellexperiment, r-bioc-spatialexperiment, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-delayedarray, r-cran-rcolorbrewer, r-cran-viridis, r-bioc-summarizedexperiment, r-cran-raster, r-cran-ggplot2, r-cran-ggbeeswarm, r-cran-svgpanzoom, r-cran-svglite, r-cran-shiny, r-cran-shinydashboard, r-cran-matrixstats, r-bioc-rhdf5, r-cran-nnls Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-cowplot, r-cran-testthat, r-cran-shinytest Filename: pool/dists/noble/main/r-bioc-cytomapper_1.24.0-1.ca2404.1_all.deb Size: 4170680 MD5sum: 52fc88a0c005f21a45b183d2d27ae870 SHA1: 0a41a634db98f46a1ece02273bcd7318cdb4a305 SHA256: 40015745a243093e69dc8d385f1c727ee2d7726da8cffea3f1e72e8b1d891c3a SHA512: de91a21fc2717f2982edd05d067e9594ff164c3e50cadec939d55299267acfd05cdf63304735b5f05fb3471c9e99dbd4f9dcdd5f6fe37d48fd50ab7c515b7327 Homepage: https://cran.r-project.org/package=cytomapper Description: Bioc Package 'cytomapper' (Visualization of highly multiplexed imaging data in R) Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. Package: r-bioc-decontam Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-phyloseq Filename: pool/dists/noble/main/r-bioc-decontam_1.32.0-1.ca2404.1_all.deb Size: 733996 MD5sum: 81645363c9eb1eeb68c04a1b774834d4 SHA1: 407b79efab21a9c46fd85dcc810e41f3dfd4f1d6 SHA256: b33fc9952a1b48a782d9e631ec5542de18c3bbd78b4c5ff41508ce6ee38f87e5 SHA512: 77cbebe8f314f3166322515eb45cb632f542e695f31a6719b87af7d751a9fa595e5626555026e1f7d4ab59eb2d580d7f162739439f7fe3b78a29446d461ef740 Homepage: https://cran.r-project.org/package=decontam Description: Bioc Package 'decontam' (Identify Contaminants in Marker-gene and Metagenomics SequencingData) Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. Package: r-bioc-decoupler Architecture: all Version: 2.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8526 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-cran-broom, r-cran-dplyr, r-cran-magrittr, r-cran-matrix, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-glmnet, r-bioc-gsva, r-bioc-viper, r-bioc-fgsea, r-bioc-aucell, r-bioc-summarizedexperiment, r-cran-rpart, r-cran-ranger, r-bioc-biocstyle, r-cran-covr, r-cran-knitr, r-cran-pkgdown, r-cran-refmanager, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sessioninfo, r-cran-pheatmap, r-cran-testthat, r-bioc-omnipathr, r-cran-seurat, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-magick Filename: pool/dists/noble/main/r-bioc-decoupler_2.17.0-1.ca2404.1_all.deb Size: 3537916 MD5sum: 3754b1768cd3fab0c26734be72014e06 SHA1: 6e2185720b4bf6a089d4fe2017e01bd3566a02d2 SHA256: 98fa0b2d9abcd9e099251016f4687b84596b35bf13398f0adc6dca8feacdda44 SHA512: d825682896f19dc9d151794cc29a41096e78be9a4d8df39e1302e48de076ed493411c3916de2b8e4b6a05eaf5545d47c187950aeca33981ffd5b545502a11236 Homepage: https://cran.r-project.org/package=decoupleR Description: Bioc Package 'decoupleR' (decoupleR: Ensemble of computational methods to infer biologicalactivities from omics data) Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. Package: r-bioc-degreport Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-broom, r-cran-circlize, r-bioc-complexheatmap, r-cran-cowplot, r-bioc-consensusclusterplus, r-cran-cluster, r-cran-dendextend, r-bioc-deseq2, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-ggdendro, r-cran-ggrepel, r-cran-knitr, r-cran-logging, r-cran-magrittr, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-stringi, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tidyr, r-cran-tibble Suggests: r-bioc-biocstyle, r-bioc-annotationdbi, r-bioc-limma, r-cran-pheatmap, r-cran-rmarkdown, r-cran-statmod, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-degreport_1.48.0-1.ca2404.1_all.deb Size: 3253392 MD5sum: 8f658cfb9f97995524023143b5b886fa SHA1: 239a4004b32f5e10c62fc33cd269e07e2978082e SHA256: 3c8906f952a05d4ac39d101b2dca5dac6f6b4d97dc5880f0356b13fd5ef3d816 SHA512: 812cdf29c12eb4af0be3ebac0dd04f3139f75de1039927d83617d98138f913c6dbcec1fef614a469410ab9092694d1b62dd71dea9a5829d57f6666046e5b0081 Homepage: https://cran.r-project.org/package=DEGreport Description: Bioc Package 'DEGreport' (Report of DEG analysis) Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. Package: r-bioc-delayedarray Architecture: all Version: 0.38.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3691 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-sparsearray Suggests: r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-zarrarray, r-bioc-genefilter, r-bioc-summarizedexperiment, r-bioc-airway, r-cran-lobstr, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-runit Filename: pool/dists/noble/main/r-bioc-delayedarray_0.38.2-1.ca2404.1_all.deb Size: 2182556 MD5sum: c8b43886ca2b2c4a91855db103706944 SHA1: ad35f2011a10dd033cb3d6ec598da745081f6837 SHA256: d6a7d81381383cb027c73a245aad03f2e19e4626a127c1ea9f5b7aa79cc48c33 SHA512: 7425f8e84ea926aed4e944129c8d2cd7955992d9132f7626ad3008432362f1db8753838d3a773c78d8a01c129e01ed4073aeb7bcb5f09873af88cd02ab40b65d Homepage: https://cran.r-project.org/package=DelayedArray Description: Bioc Package 'DelayedArray' (A unified framework for working transparently with on-disk andin-memory array-like datasets) Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-delayedmatrixstats Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-delayedarray, r-bioc-sparsematrixstats, r-cran-matrix, r-bioc-s4vectors, r-bioc-iranges, r-bioc-sparsearray Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-microbenchmark, r-cran-profmem, r-bioc-hdf5array, r-cran-matrixstats Filename: pool/dists/noble/main/r-bioc-delayedmatrixstats_1.34.0-1.ca2404.1_all.deb Size: 705058 MD5sum: e5ee4f3fdc1fb82672f07bb8e385318d SHA1: 90691d8726fa3737db4d17baa366b91a87d86163 SHA256: 38c17052ebb6bd444fafd892804b38d893294669fd6dc2a50627526175947a33 SHA512: 86d5eeed82eacc44c43bd6fd151b6da340b7ff148fc8a9a7f2c36e89bcba1998efce93de065d02eb9345ec9955cf988b5ccaa1dec581bc37b09535a393239a01 Homepage: https://cran.r-project.org/package=DelayedMatrixStats Description: Bioc Package 'DelayedMatrixStats' (Functions that Apply to Rows and Columns of 'DelayedMatrix'Objects) A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. Package: r-bioc-dep Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-bioc-summarizedexperiment, r-bioc-msnbase, r-bioc-limma, r-bioc-vsn, r-cran-fdrtool, r-cran-ggrepel, r-bioc-complexheatmap, r-cran-rcolorbrewer, r-cran-circlize, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-rmarkdown, r-cran-assertthat, r-cran-gridextra, r-cran-imputelcmd, r-cran-cluster Suggests: r-cran-testthat, r-cran-enrichr, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-dep_1.32.0-1.ca2404.1_all.deb Size: 4176580 MD5sum: 2bbf130b56f5f6d2164b008495dcaa09 SHA1: 1cec339252c616d14861c8b8f15c9063fd6517dc SHA256: c5321cc51226cd7871472332691ddf5985cda69e2cfc941877f7950300a1dd6c SHA512: 0cf1c540fb336f6ea45c85738b0dd663baae7f28e3fd01f7ee011c0d83720bc7969b51db24c32a07fd81786280ed7144a18f53cd7a6be4507520f24ad5e33844 Homepage: https://cran.r-project.org/package=DEP Description: Bioc Package 'DEP' (Differential Enrichment analysis of Proteomics data) This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. Package: r-bioc-depmap Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-experimenthub, r-bioc-annotationhub, r-bioc-biocfilecache, r-cran-httr2, r-cran-curl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-viridis, r-cran-gridextra, r-cran-ggplot2, r-cran-readr, r-cran-stringr, r-cran-tidyverse, r-cran-magick Filename: pool/dists/noble/main/r-bioc-depmap_1.26.0-1.ca2404.1_all.deb Size: 1507910 MD5sum: f02a7700d20cf17d46101441cd876464 SHA1: a7e80a7b57562dbc100b40ac9f6530a3a43624c4 SHA256: 544eb13a32a588e58904435f7d9f99f70705bf2c7f15b98d44a77f53dc427837 SHA512: fa2894ead26ddac52adfad36b132e51640589de29707f7b34fca3f361f49e0dadd48ebdf110daaeadec9f6d017e368f54e5d061bf2e6bc4f5b2e6407cbe125d5 Homepage: https://cran.r-project.org/package=depmap Description: Bioc Package 'depmap' (Cancer Dependency Map Data Package) The depmap package is a data package that accesses datsets from the Broad Institute DepMap cancer dependency study using ExperimentHub. Datasets from the most current release are available, including RNAI and CRISPR-Cas9 gene knockout screens quantifying the genetic dependency for select cancer cell lines. Additional datasets are also available pertaining to the log copy number of genes for select cell lines, protein expression of cell lines as measured by reverse phase protein lysate microarray (RPPA), 'Transcript Per Million' (TPM) data, as well as supplementary datasets which contain metadata and mutation calls for the other datasets found in the current release. The 19Q3 release adds the drug_dependency dataset, that contains cancer cell line dependency data with respect to drug and drug-candidate compounds. The 20Q2 release adds the proteomic dataset that contains quantitative profiling of proteins via mass spectrometry. This package will be updated on a quarterly basis to incorporate the latest Broad Institute DepMap Public cancer dependency datasets. All data made available in this package was generated by the Broad Institute DepMap for research purposes and not intended for clinical use. This data is distributed under the Creative Commons license (Attribution 4.0 International (CC BY 4.0)). Package: r-bioc-derfinder Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4872 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-biocparallel, r-bioc-bumphunter, r-bioc-derfinderhelper, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-genomicfiles, r-bioc-genomicranges, r-cran-hmisc, r-bioc-iranges, r-bioc-qvalue, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-s4vectors Suggests: r-bioc-biocstyle, r-cran-sessioninfo, r-bioc-derfinderdata, r-bioc-derfinderplot, r-bioc-deseq2, r-cran-ggplot2, r-cran-knitr, r-bioc-limma, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-covr Filename: pool/dists/noble/main/r-bioc-derfinder_1.46.0-1.ca2404.1_all.deb Size: 1934760 MD5sum: d36b5c1407558463b71c7baf0d688375 SHA1: af55f72e67fbe719d397b41a046acf078d729aee SHA256: 9703cefb4f2fc1c39acc02a68d1998c711e9a760120cf9bcf63463c0b8b2e2f0 SHA512: 4304dc4d631f66e1dbf343676d304aa66312587985d1ade03dfcdadb04d011185009293d44ec58bfd153a233b012ad289ac0d4320899e0c0bc85ee0915a06268 Homepage: https://cran.r-project.org/package=derfinder Description: Bioc Package 'derfinder' (Annotation-agnostic differential expression analysis of RNA-seqdata at base-pair resolution via the DER Finder approach) This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. Package: r-bioc-derfinderhelper Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-cran-matrix, r-bioc-s4vectors Suggests: r-cran-sessioninfo, r-cran-knitr, r-bioc-biocstyle, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-bioc-derfinderhelper_1.46.0-1.ca2404.1_all.deb Size: 304472 MD5sum: a5f4c8d89733e499be58b4735437c68f SHA1: d604c9dcb14ac83eb7fe832170b4473d0c41fa78 SHA256: b3f67aec601a343cd0114aceb4a37df715b5631dd60d5184a76cbaf174ce3132 SHA512: 61ffada6ca745a40b58c99cc577c68af7436e4dc161de6d10bf1ec3e0dc8210b4068a195dd075d8198c3c16b9d9c5b8dfe343b5857b83b0be8a0dbd095212b2d Homepage: https://cran.r-project.org/package=derfinderHelper Description: Bioc Package 'derfinderHelper' (derfinder helper package) Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. Package: r-bioc-dexseq Architecture: all Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3466 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-iranges, r-bioc-genomicranges, r-bioc-deseq2, r-bioc-annotationdbi, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biomart, r-cran-hwriter, r-cran-stringr, r-bioc-rsamtools, r-cran-statmod, r-bioc-geneplotter, r-bioc-genefilter Suggests: r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-pasilla, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-cran-roxygen2, r-bioc-glmgampoi Filename: pool/dists/noble/main/r-bioc-dexseq_1.58.0-1.ca2404.1_all.deb Size: 2051854 MD5sum: 4020f9e68d50ceae7f67fd716c2f2690 SHA1: 339c573baedb0b1a2b3cc41a772b1dd8028aecdd SHA256: 7715c35dbaceec9164aaa98046ddab0dbbf050370d2a3d042fff90788991faa4 SHA512: 0ca470cce9b10c963ba077f98ce4c46c292a00e78f8692380b2540bb1f78862983fcd9866d822e784d621258ccdb18bc5e4ca0448b1d27339f4be56b47831f4a Homepage: https://cran.r-project.org/package=DEXSeq Description: Bioc Package 'DEXSeq' (Inference of differential exon usage in RNA-Seq) The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. Package: r-bioc-diffcoexp Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-wgcna, r-bioc-summarizedexperiment, r-cran-diffcorr, r-cran-psych, r-cran-igraph, r-bioc-biocgenerics Suggests: r-bioc-geoquery, r-cran-runit Filename: pool/dists/noble/main/r-bioc-diffcoexp_1.32.0-1.ca2404.1_all.deb Size: 327606 MD5sum: 54ed8090826de4779f372806d54c460b SHA1: 573120fca442d1297eea2106aebf460c62698172 SHA256: 5f2dbfbd6f5c4db9b3065d8f887595a1a6972da5f12141dc1c0811ca5fc67697 SHA512: e76fc43821b96a0813eb1e743e23d85c2a03bbba513ff0a1b9562d3645a192893cab7a742f2b1f72b5b230c3c37bd337f0820e9d6d4643fba1f65473036a28de Homepage: https://cran.r-project.org/package=diffcoexp Description: Bioc Package 'diffcoexp' (Differential Co-expression Analysis) A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. Package: r-bioc-diffcyt Architecture: all Version: 1.32.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-flowcore, r-bioc-flowsom, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-limma, r-bioc-edger, r-cran-lme4, r-cran-multcomp, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-magrittr, r-bioc-complexheatmap, r-cran-circlize Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-hdcytodata, r-bioc-catalyst Filename: pool/dists/noble/main/r-bioc-diffcyt_1.32.1-1.ca2404.1_all.deb Size: 898654 MD5sum: 894e68c2309f84da9b7718aaf3e04022 SHA1: 0c1aa9281a04ec367548f9c47cf5755a6a13b75f SHA256: 96b5605cf15bdf8e870bf362b557d4bbec05052c63d500a5e83bb370fc3b6baa SHA512: 8b37433c5d7504f962b42b868a44b342d951c6c23d9d18079366d85f566705cf1a75e1ba12f279a9849d626302e556de03500ee08de04a95f32f039233d8b59a Homepage: https://cran.r-project.org/package=diffcyt Description: Bioc Package 'diffcyt' (Differential discovery in high-dimensional cytometry viahigh-resolution clustering) Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Package: r-bioc-dir.expiry Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-filelock Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-dir.expiry_1.20.0-1.ca2404.1_all.deb Size: 297284 MD5sum: 07f55de9f8ab69024cb41957785741c9 SHA1: cfdd5858c992367ee5d4fc7ca7ffe6f83301cdd2 SHA256: 6ce1acd8c06bb3dfeb25040a2496e1420aeba32bd7e17b94124848d49301aa67 SHA512: f15cf4b62438decdc000d8459c6a8bfe377f6a826243b508364d408afaed7ce019e8472da9523abbbdf04f7ce905bc62f6a677ac74a3f9dffce0e095b9c26795 Homepage: https://cran.r-project.org/package=dir.expiry Description: Bioc Package 'dir.expiry' (Managing Expiration for Cache Directories) Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. Package: r-bioc-dittoseq Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3421 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-colorspace, r-cran-gridextra, r-cran-cowplot, r-cran-reshape2, r-cran-pheatmap, r-cran-ggrepel, r-cran-ggridges, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-s4vectors Suggests: r-cran-plotly, r-cran-testthat, r-cran-seurat, r-bioc-deseq2, r-bioc-edger, r-cran-ggplot.multistats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-scrnaseq, r-cran-ggrastr, r-bioc-complexheatmap, r-bioc-bluster, r-bioc-scater, r-bioc-scran, r-cran-mass Filename: pool/dists/noble/main/r-bioc-dittoseq_1.24.0-1.ca2404.1_all.deb Size: 1947864 MD5sum: 4a445c2f8258ad7109cf9f2170e8cf2f SHA1: 0032b879334ab37e8445f4d1a50d2e6aa94de5c8 SHA256: 73d545a22f2c51566b2bd2bcbec02cd38c64f3cb60809dc96dd49ec8a1e8fce5 SHA512: fbd0e9c74fe9e555bfe20388097dd903138f29d1e5ae3fc646b939a18744a85c9fbeb44d2211cc4790860df165d6c43fd68096519730e7a922ab7d523a64953f Homepage: https://cran.r-project.org/package=dittoSeq Description: Bioc Package 'dittoSeq' (User Friendly Single-Cell and Bulk RNA Sequencing Visualization) A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). Package: r-bioc-dmrcate Architecture: all Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1283 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-bsseq, r-bioc-seqinfo, r-bioc-limma, r-bioc-edger, r-bioc-minfi, r-bioc-missmethyl, r-bioc-genomicranges, r-cran-plyr, r-bioc-gviz, r-bioc-iranges, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-biomart Suggests: r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-bioc-genomeinfodb, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-illuminahumanmethylationepicanno.ilm10b4.hg19, r-bioc-illuminahumanmethylationepicv2anno.20a1.hg38, r-bioc-flowsorted.blood.epic, r-bioc-tissuetreg, r-bioc-dmrcatedata, r-bioc-epicv2manifest Filename: pool/dists/noble/main/r-bioc-dmrcate_3.8.0-1.ca2404.1_all.deb Size: 1038748 MD5sum: 87984a011cfa5093bf668afb4f62169c SHA1: 4474418bc9c67095cafb4153b58bc7dbc7eec3f3 SHA256: 5c2a812f63c94567e8a3724134ff6d1ae48ffbf671adeec4c7e2acbb2f203f6e SHA512: 9a1ef0e08318d68b90a67d70549ed8b5e3e75737244986d3131e8c07dc5f7a2f9a1580a1acd43fe34d349f4a6171de3a058c745efcfee113bef4fc2c09700def Homepage: https://cran.r-project.org/package=DMRcate Description: Bioc Package 'DMRcate' (Methylation array and sequencing spatial analysis methods) De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. Package: r-bioc-dose Architecture: all Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5792 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-enrichit, r-cran-ggplot2, r-bioc-gosemsim, r-cran-reshape2, r-cran-yulab.utils Suggests: r-cran-prettydoc, r-bioc-clusterprofiler, r-cran-gson, r-cran-knitr, r-cran-memoise, r-bioc-org.hs.eg.db, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-dose_4.6.0-1.ca2404.1_all.deb Size: 5834202 MD5sum: 3689edd9d01204618a49b9a9b8ded45c SHA1: 3dec864b14ffc4547eff16e36105c73886afb9dd SHA256: 455bb40510e047b8c5a8fefd3d8c3808a76c1956efefba0f15a968c6b883e0fd SHA512: 92928768eff338434fd579b8b21a81dcc63938304afcda3e3a45dbd30126a8cbeee4a41ab754f14ad93b452cba178c827f02f00f960e5f0cfb6cd17b86a2caec Homepage: https://cran.r-project.org/package=DOSE Description: Bioc Package 'DOSE' (Disease Ontology Semantic and Enrichment analysis) This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. Package: r-bioc-drimseq Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-limma, r-bioc-edger, r-cran-ggplot2, r-cran-reshape2 Suggests: r-bioc-pasillatranscriptexpr, r-bioc-geuvadistranscriptexpr, r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-drimseq_1.40.0-1.ca2404.1_all.deb Size: 786210 MD5sum: 1884ae62397163f44e60a0c006fde062 SHA1: 820dceb1bfe6a7aa8488500ca4ded7fcd39431f6 SHA256: 6cf996786efb21e6dd48db9093416ec5a3b1e2e694a3a734dfb83d9aa3c7ceab SHA512: 4b91fbc8c299114c5e0c9af283ea24887278985a41c35113fc8fc9151b174d476839f196398b9b46eaee0b9272f0e5d5bde437a6b7595e57e615793f734fd097 Homepage: https://cran.r-project.org/package=DRIMSeq Description: Bioc Package 'DRIMSeq' (Differential transcript usage and tuQTL analyses withDirichlet-multinomial model in RNA-seq) The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. 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Package: r-bioc-edaseq Architecture: all Version: 2.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2306 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-shortread, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-aroma.light, r-bioc-rsamtools, r-bioc-biomart, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-genomicranges, r-cran-biocmanager Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-yeastrnaseq, r-bioc-leebamviews, r-bioc-edger, r-cran-kernsmooth, r-cran-testthat, r-bioc-deseq2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-edaseq_2.46.0-1.ca2404.1_all.deb Size: 1401548 MD5sum: 48ade32f4e565877cc2a9b65da0cad35 SHA1: 71b0afea63cf53c839734763816ecd728b498b3f SHA256: ff46a42d1e415d4fcc2df626cceec2a68ae8a41e450b28c87a01784d7387eb05 SHA512: 78f8f2411363e1bdc107a8f8347a74110e9d653e4dc4f1b4eb1053903dbdf1014ea28cd89b0e632c9b966f3fc6342ebc0d893856eab9c5de5fc45db9e2bd7a70 Homepage: https://cran.r-project.org/package=EDASeq Description: Bioc Package 'EDASeq' (Exploratory Data Analysis and Normalization for RNA-Seq) Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). Package: r-bioc-empiricalbrownsmethod Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-empiricalbrownsmethod_1.40.0-1.ca2404.1_all.deb Size: 53646 MD5sum: 251a51e9dd5db0191f75a5710f6113c5 SHA1: 681d121aa4b7937c44d55892139fe49979607e45 SHA256: 90d2291daba77dadaaf2d15db1803080ddfa069bb5446e49422add6a2bbad5ff SHA512: 5009f4b469aae27a188e31d3de99449b4bc6484ac02f90d7c75bc8869a31534654203180c0ed0ecc266d1395e5614d2620c1cbcdca0055ba22f766bdc427eae4 Homepage: https://cran.r-project.org/package=EmpiricalBrownsMethod Description: Bioc Package 'EmpiricalBrownsMethod' (Uses Brown's method to combine p-values from dependent tests) Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. Package: r-bioc-enhancedvolcano Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7923 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales Suggests: r-cran-runit, r-cran-ggrastr, r-bioc-biocgenerics, r-cran-knitr, r-bioc-deseq2, r-bioc-pasilla, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-gridextra, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-enhancedvolcano_1.30.0-1.ca2404.1_all.deb Size: 5602658 MD5sum: daac5c3f3c1279992fb38bd7f5a1b3a3 SHA1: 7356a027e152bac99c4de3fe078ffac10c21021c SHA256: 696587a9f9c5ba54fbfb3ad81cbbb113bab51426c1c3e32072ef5e412aefe0ea SHA512: 8b449d1781cad5c0ac5f8927d6f1d759d79e2889d221653e9393b4ad6824604966b0bccce2da06222209d7b4c796501b38db308f2e7bcab6cba441f52c7efe4b Homepage: https://cran.r-project.org/package=EnhancedVolcano Description: Bioc Package 'EnhancedVolcano' (Publication-ready volcano plots with enhanced colouring andlabeling) Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. Package: r-bioc-enrichmentbrowser Architecture: all Version: 2.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2739 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-graph, r-bioc-annotationdbi, r-bioc-biocfilecache, r-cran-biocmanager, r-bioc-gseabase, r-bioc-go.db, r-bioc-keggrest, r-bioc-kegggraph, r-bioc-rgraphviz, r-bioc-s4vectors, r-bioc-spia, r-bioc-edger, r-bioc-graphite, r-cran-hwriter, r-bioc-limma, r-bioc-pathview, r-bioc-safe Suggests: r-bioc-all, r-bioc-biocstyle, r-bioc-complexheatmap, r-bioc-deseq2, r-bioc-reportingtools, r-bioc-airway, r-bioc-biocgraph, r-bioc-hgu95av2.db, r-bioc-geneplotter, r-cran-knitr, r-cran-msigdbr, r-cran-rmarkdown, r-cran-statmod Filename: pool/dists/noble/main/r-bioc-enrichmentbrowser_2.42.0-1.ca2404.1_all.deb Size: 1480632 MD5sum: d1eef12f26813cd2b4e453cd46fa4e78 SHA1: 80b7e78fca6f5254a6636b040e9c1c072f36e617 SHA256: c5072ec66db19f92bf27537a7b367121c86f593a53e5f6f79a4fa64c8f166db7 SHA512: dbadd60389bf5d826736ac2f8ca651fc807277f950fe798519993c4670cb20afacc5ed76f486aa4c36635051955dcb030d253d0e37cfe7a23ac10cebf68a6a02 Homepage: https://cran.r-project.org/package=EnrichmentBrowser Description: Bioc Package 'EnrichmentBrowser' (Seamless navigation through combined results of set-based andnetwork-based enrichment analysis) The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. Package: r-bioc-enrichplot Architecture: all Version: 1.32.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-bioc-dose, r-cran-dplyr, r-cran-enrichit, r-cran-ggfun, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtangle, r-bioc-ggtree, r-bioc-gosemsim, r-cran-igraph, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-scatterpie, r-cran-tidydr, r-cran-yulab.utils Suggests: r-bioc-annotationdbi, r-bioc-clusterprofiler, r-cran-europepmc, r-cran-ggarchery, r-cran-ggforce, r-cran-gghoriplot, r-cran-ggplotify, r-cran-ggridges, r-cran-ggstar, r-bioc-ggtreeextra, r-cran-ggupset, r-cran-glue, r-cran-gridextra, r-cran-gson, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-bioc-enrichplot_1.32.1-1.ca2404.1_all.deb Size: 309038 MD5sum: e4992ae36fb62e16c8a3470518952fe3 SHA1: 5d5f2234191a988e2a8395785aae4fa5094c2ddb SHA256: 8c004b80ec1b1d6373205dbe965599c6612b13ac04ab86b2e8db874b3018ce41 SHA512: cdf8a786b227eb2d9451023f2675fd875f9dc495e63d145bce45ef15df7381e26a7a51b5304e7428e909bcdfd186176937e5390ad08b429b346cde30e5b4bef4 Homepage: https://cran.r-project.org/package=enrichplot Description: Bioc Package 'enrichplot' (Visualization of Functional Enrichment Result) The 'enrichplot' package provides visualization methods for interpreting functional enrichment results from ORA or GSEA analyses. It is designed to work with the 'clusterProfiler' ecosystem and builds on 'ggplot2' for flexible and extensible graphics. Package: r-bioc-ensdb.hsapiens.v75 Architecture: all Version: 2.99.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-ensembldb Filename: pool/dists/noble/main/r-bioc-ensdb.hsapiens.v75_2.99.0-1.ca2404.1_all.deb Size: 61897766 MD5sum: 5ac3a153ebf87c555fe16d14a8b02673 SHA1: 4fc69037cb26c9e5eab13fdf089e9de366d04c5d SHA256: 22e93d894c629e688a3a1e3f0127174eb123de7eb9e801190dcf7e62058523a6 SHA512: a8e9718b45ee42f62a6733b448d1fefdee907a36610c19b6bd0fa8b069102e37529758c1969153ee2d83b63e77435a78612f7b21e4641fb68832235490da7c05 Homepage: https://cran.r-project.org/package=EnsDb.Hsapiens.v75 Description: Bioc Package 'EnsDb.Hsapiens.v75' (Ensembl based annotation package) Exposes an annotation databases generated from Ensembl. Package: r-bioc-ensembldb Architecture: all Version: 2.36.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8053 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-annotationfilter, r-cran-rsqlite, r-cran-dbi, r-bioc-biobase, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-annotationdbi, r-bioc-rtracklayer, r-bioc-s4vectors, r-bioc-rsamtools, r-bioc-iranges, r-bioc-protgenerics, r-bioc-biostrings, r-cran-curl Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-ensdb.hsapiens.v86, r-cran-testthat, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-ggbio, r-bioc-gviz, r-cran-rmarkdown, r-bioc-annotationhub Filename: pool/dists/noble/main/r-bioc-ensembldb_2.36.1-1.ca2404.1_all.deb Size: 2475948 MD5sum: ea1826d3d95894869b35af87fa37c3f4 SHA1: ec63d06b5ec39bdfacb97992d6a05247c8ac28df SHA256: 40757cc57246755c208bde42af872b41c4a594def9c516c64f9696368ea2f8c2 SHA512: 277608856b6a9bb0d3099057f5c54ae9eac72c107f563d219734f2dde48aa3a7cbcea958d15d64276d3aaaee17538066582c2d06416df760d2aa0abc65b3edf1 Homepage: https://cran.r-project.org/package=ensembldb Description: Bioc Package 'ensembldb' (Utilities to create and use Ensembl-based annotation databases) The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. Package: r-bioc-epidish Architecture: all Version: 2.28.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3859 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-quadprog, r-cran-matrixstats, r-cran-stringr, r-cran-locfdr, r-cran-matrix, r-bioc-genefilter Suggests: r-cran-roxygen2, r-bioc-geoquery, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-biobase, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-epidish_2.28.0-1.ca2404.1_all.deb Size: 2142676 MD5sum: eb38f0b7c5019e16826cdfa9f696d1fc SHA1: 0dfe97e4663921c7e76accea9a4c0846ba0e922c SHA256: 8a56bc3f05cd31b952c84e870a64d42bd83a11c42ca5cb17c5bac8d301fc23cd SHA512: 8bad316093bdb03e2f0cf0e836b9f067cda7efc2eb0bfe9a3a1a81b99c57074d16b6dafce693e8d5c130630564139cc602cd3457822b2770138303044ea66607 Homepage: https://cran.r-project.org/package=EpiDISH Description: Bioc Package 'EpiDISH' (Epigenetic Dissection of Intra-Sample-Heterogeneity) EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. Package: r-bioc-escape Architecture: all Version: 2.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2969 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggdist, r-cran-ggplot2, r-cran-matrix, r-bioc-matrixgenerics, r-bioc-summarizedexperiment Suggests: r-bioc-aucell, r-bioc-biocparallel, r-bioc-biocstyle, r-bioc-delayedmatrixstats, r-cran-dplyr, r-bioc-fgsea, r-bioc-gseabase, r-cran-ggraph, r-cran-ggridges, r-cran-ggpointdensity, r-bioc-gsva, r-cran-hexbin, r-cran-igraph, r-cran-irlba, r-cran-knitr, r-bioc-msigdb, r-cran-patchwork, r-cran-rmarkdown, r-cran-rlang, r-bioc-scran, r-cran-seuratobject, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-bioc-ucell Filename: pool/dists/noble/main/r-bioc-escape_2.8.0-1.ca2404.1_all.deb Size: 1754094 MD5sum: 1fe44b5c0df0d358e28eb3c53ec93e6c SHA1: 01f75f71100962732eef8897451088135366005f SHA256: 5fa0726ebd89f8cb58e0082ad300d6cd67cb0f1dbf498344dd20f14e3bfb22b8 SHA512: adece73bbcc74f2fbd360db79bf7f81186f0195f55f7eb4a33fe429aa0a3f72f2bc43cd53665b208390d964b24238500ecb57597ac1f34c2c12541fa0ebecd2b Homepage: https://cran.r-project.org/package=escape Description: Bioc Package 'escape' (Easy single cell analysis platform for enrichment) A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. Alternatively, escape supports use of rank-based GSEA, such as the use of differential gene expression via fgsea. 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ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. 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Package: r-bioc-ggcyto Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-bioc-flowcore, r-bioc-ncdfflow, r-bioc-flowworkspace, r-cran-plyr, r-cran-scales, r-cran-hexbin, r-cran-data.table, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-rlang Suggests: r-cran-testthat, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-flowstats, r-bioc-opencyto, r-bioc-flowviz, r-cran-ggridges, r-cran-vdiffr Filename: pool/dists/noble/main/r-bioc-ggcyto_1.40.0-1.ca2404.1_all.deb Size: 3234250 MD5sum: d3a3e2cde9bdd7c7f3dcd9f76ca96e88 SHA1: 3fa0759744ddc0421fbf7540e5501d3062e7bc19 SHA256: a4b285f8606fc6f29fa85b1a58035a60a2f7731c2aa3d34848247654cd39e405 SHA512: 616d2510af6400c6ae20c61a552f3f1e34706d1e6870b127f150c7f01d172ac1de39ff3a6613ac97468ae5cabb6777d46a50dd1f737db73de84f0899ab16339d Homepage: https://cran.r-project.org/package=ggcyto Description: Bioc Package 'ggcyto' (Visualize Cytometry data with ggplot) With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. 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The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. Package: r-bioc-ggmsa Architecture: all Version: 1.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3266 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr, r-cran-aplot, r-cran-rcolorbrewer, r-cran-ggfun, r-cran-ggforce, r-cran-dplyr, r-bioc-r4rna, r-cran-seqmagick, r-bioc-ggtree Suggests: r-bioc-ggtreeextra, r-cran-ape, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-ggnewscale, r-cran-kableextra, r-cran-gggenes, r-cran-statebins, r-cran-prettydoc, r-cran-testthat, r-cran-yulab.utils Filename: pool/dists/noble/main/r-bioc-ggmsa_1.18.0-1.ca2404.1_all.deb Size: 2284956 MD5sum: 334dc3321775cc5415ab885e28250026 SHA1: d8b733195db5af394f4c0f20f001fab8bc523f19 SHA256: 759575d96430048ed6f6c6f31d3b54da94ab8a30abb16606db7ecf8535f4dbed SHA512: 3f931ada31a28ae6c2ec9535a3d29349554452ee5e7006e8b9c3c21486240f18a173b5130d0711a15064b953150819fdd0d18ac3d6dae741457d5487e241410d Homepage: https://cran.r-project.org/package=ggmsa Description: Bioc Package 'ggmsa' (Plot Multiple Sequence Alignment using 'ggplot2') A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. 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The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". 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Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. 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J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021. Package: r-bioc-gviz Architecture: all Version: 1.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-xvector, r-bioc-rtracklayer, r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biomart, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-genomicfeatures, r-bioc-ensembldb, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-biovizbase, r-bioc-rsamtools, r-cran-latticeextra, r-cran-matrixstats, r-bioc-genomicalignments, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-biocgenerics, r-cran-digest Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-xml2, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-gviz_1.56.0-1.ca2404.1_all.deb Size: 7129516 MD5sum: 5e53857f2bcbb00c9fa502f3d5302ca8 SHA1: 540be818162d3d3e15079e136d6a76eb98015bb5 SHA256: df772095972e34c3d43a567038381625a6e80e7727102c9b237b551168d22ee2 SHA512: 33acfe91b5b0cb0f814e7876a3079ee55ce8482843c38d9432fe9cdeafb8061fcc3b8864868442daa114fb02a449ae9a38239b36fc97fab0be6f39174b988d21 Homepage: https://cran.r-project.org/package=Gviz Description: Bioc Package 'Gviz' (Plotting data and annotation information along genomiccoordinates) Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. Package: r-bioc-gwascat Architecture: all Version: 2.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37093 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-readr, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-biocfilecache, r-bioc-snpstats, r-bioc-variantannotation, r-bioc-annotationhub, r-cran-data.table, r-cran-tibble Suggests: r-bioc-do.db, r-cran-dt, r-cran-knitr, r-bioc-rbgl, r-cran-testthat, r-cran-rmarkdown, r-cran-dplyr, r-bioc-gviz, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-graph, r-bioc-ggbio, r-bioc-delayedarray, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-org.hs.eg.db, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-gwascat_2.44.0-1.ca2404.1_all.deb Size: 35686082 MD5sum: 6c9dca26b84102e72d1848f7fb8bdc7d SHA1: 0a483fc98c4e643b9332060ae2d1d211ff4b349b SHA256: 046199bb6796e7f4ef2be63a4b72d2d56ef0cdaf49833e7d6065e6927f83e475 SHA512: 9d460af35e8c0445d1afed5aee341bd3b742d230e2975c6bcc903bc1a31eadc10c516330b6bab80a74859c6c5238f884d84d1cb43ea7e1504c7aaa7096f64b52 Homepage: https://cran.r-project.org/package=gwascat Description: Bioc Package 'gwascat' (representing and modeling data in the EMBL-EBI GWAS catalog) Represent and model data in the EMBL-EBI GWAS catalog. 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Package: r-bioc-gypsum Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-filelock, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-cran-digest, r-cran-jsonvalidate, r-cran-dbi, r-cran-rsqlite, r-bioc-s4vectors Filename: pool/dists/noble/main/r-bioc-gypsum_1.8.0-1.ca2404.1_all.deb Size: 412576 MD5sum: 20421d17d194e203d56f934e32b546aa SHA1: a4232ffb2e31275ef67c215f4f44bb908b0554e2 SHA256: 72ac7b93b74e7979255492cbc8b233c55bb5d17a970422d35d1edc48f05770db SHA512: b82385bc93e94f0f8ea5c4fb95012723c2562194116e0ce73c682856bdce2d8323f1505fdb1701ac05607caffb6d1388a5be1e02c7c100d4c058c3b96c44533e Homepage: https://cran.r-project.org/package=gypsum Description: Bioc Package 'gypsum' (Interface to the gypsum REST API) Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. 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It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. Package: r-bioc-hdo.db Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-cran-dbi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-hdo.db_1.0.0-1.ca2404.1_all.deb Size: 2208510 MD5sum: 4f6275b1d0b92201624f9798e062c976 SHA1: 73e88c85e0f8a5cc6e57b82270dc686be2f047ff SHA256: 41b50b43962d5c6cc202bf9bda1d9632210fc2d304ccf7ae663eac18fd7973f6 SHA512: 0cdaf40ed8263471f6d6fe03061a8f4cabbbcee782e086073f97aec86e652c149d72500e5431b14d358cace47ffce225b45051e857e9a5bfc6c90823221dd22f Homepage: https://cran.r-project.org/package=HDO.db Description: Bioc Package 'HDO.db' (A set of annotation maps describing the entire Human DiseaseOntology) A set of annotation maps describing the entire Human Disease Ontology assembled using data from DO. 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By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. Package: r-bioc-hgu95a.db Architecture: all Version: 3.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-org.hs.eg.db Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-hgu95a.db_3.13.0-1.ca2404.1_all.deb Size: 490860 MD5sum: dc99f949e913f4aa313067111533b2fa SHA1: 0ff78565342bbce18e944fdbd97c20c933aa5f5d SHA256: f3c5b95a9752d205304c333389afe24c2d3bc5adef6ae58d0ac9c34d927c8580 SHA512: 1a3ce451d679268a49513609f477e50a734678661b10ec1c177c40a1b52be247d70dba0642a11428dc03013cff000c58e2bc87006e6ea1a827c78741e76d67bd Homepage: https://cran.r-project.org/package=hgu95a.db Description: Bioc Package 'hgu95a.db' (Affymetrix Affymetrix HG_U95A Array annotation data (chiphgu95a)) Affymetrix Affymetrix HG_U95A Array annotation data (chip hgu95a) assembled using data from public repositories Package: r-bioc-hiccompare Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4116 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-mgcv, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-biocparallel, r-cran-kernsmooth, r-cran-pheatmap, r-cran-gtools, r-bioc-rhdf5 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-multihiccompare Filename: pool/dists/noble/main/r-bioc-hiccompare_1.34.0-1.ca2404.1_all.deb Size: 3883202 MD5sum: f9f80e190741f9b23c7bfddbbe9f7b4e SHA1: 32905ff3c188178529320a88f55b72bf2829e56f SHA256: 17692f7d799d2224d57250ae8abfa11bab298557fe20c8401766f947575ba590 SHA512: b895703a46313069de91f02dae2d123fe37115f5d185724b3f986ec4228c8ace43c696dc029e2cc40b13d69f3ecb24a2b33a7afa1f5d45d3b350d4e721e8ee4e Homepage: https://cran.r-project.org/package=HiCcompare Description: Bioc Package 'HiCcompare' (HiCcompare: Joint normalization and comparative analysis ofmultiple Hi-C datasets) HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. 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In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum-induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell. 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The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. Package: r-bioc-karyoploter Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3657 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-regioner, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-cran-memoise, r-bioc-rtracklayer, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-s4vectors, r-bioc-biovizbase, r-cran-digest, r-cran-bezier, r-bioc-genomicfeatures, r-bioc-bamsignals, r-bioc-annotationdbi, r-bioc-variantannotation Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-cran-magrittr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.hsapiens.ucsc.hg19.masked, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-pasillabamsubset Filename: pool/dists/noble/main/r-bioc-karyoploter_1.38.0-1.ca2404.1_all.deb Size: 2529768 MD5sum: 5f7f6fe4dde452e652dae1a705153e2c SHA1: 21280ece031ce16f9ee524066bf17c125b1825d3 SHA256: 6bacca91c3877cb7f85b501cbe033ab343dac5df92b6b56becbbaf28b86811ab SHA512: 5160b8ccc5b8fe8a1dba17dfb33c6950f825015d194a71b242820a36d2f0d1a7fc200a8d4afb900b5569ee519a8554c1f5ba27b292eb84a03707f6c151e66494 Homepage: https://cran.r-project.org/package=karyoploteR Description: Bioc Package 'karyoploteR' (Plot customizable linear genomes displaying arbitrary data) karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. Package: r-bioc-kegggraph Architecture: all Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2350 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-bioc-graph, r-cran-rcurl, r-bioc-rgraphviz Suggests: r-bioc-rbgl, r-cran-testthat, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-hgu133plus2.db, r-bioc-spia Filename: pool/dists/noble/main/r-bioc-kegggraph_1.72.0-1.ca2404.1_all.deb Size: 1659310 MD5sum: 6fb05846163ef4caf9ad209739f3a600 SHA1: 13d6ed27063a22c3b074a7eb5d0ed8ca3dae59fd SHA256: 9b43dcfc0c6aedc70117a2838182b0e4849632e614809c21b80e5261b1df0e6a SHA512: b7ccc72b8251357c9369370dcef96555ed94099563e323987ba3eb1d320dd5c1066674eabc5e454d1a555f0f9dd1f7bd5712e805967714bd64298e7e71b24b95 Homepage: https://cran.r-project.org/package=KEGGgraph Description: Bioc Package 'KEGGgraph' (KEGGgraph: A graph approach to KEGG PATHWAY in R andBioconductor) KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. Package: r-bioc-keggrest Architecture: all Version: 1.52.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-png, r-bioc-biostrings Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-keggrest_1.52.2-1.ca2404.1_all.deb Size: 385168 MD5sum: 77c8ab893f743bd3a58d1941be19fec7 SHA1: 308aee1c0bdde4cd8f8ce796578212d908598ae4 SHA256: 8fb5321001a00e5231a8a487a81a2206fc4fc6ee98065bf7fbbd9eaa3497ba5f SHA512: ee5a65ca16694fa5595889d1fc607f3d84bd15dc26229d66245bf279518804c956ccb9cbe6ca1811fe48798be4e9a4f319eac3598df638d2c08b9ddb84c0c2ad Homepage: https://cran.r-project.org/package=KEGGREST Description: Bioc Package 'KEGGREST' (Client-side REST access to the Kyoto Encyclopedia of Genes andGenomes (KEGG)) A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see ). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. Package: r-bioc-lbe Architecture: all Version: 1.80.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-qvalue Filename: pool/dists/noble/main/r-bioc-lbe_1.80.0-1.ca2404.1_all.deb Size: 615288 MD5sum: d1ec6d2da3049082d7e981c0c3bbc88b SHA1: eb24d3dea72210a3eb573b86537c5d54a16bbbb4 SHA256: 10cca22a64be1069cb7f2584023579f15f1e4f4c10dc7c741d96ae0356640a0e SHA512: e2cb83573b018ae8588e92dc99216f6d33bcd63fdda2e9a831cb599cd80e5cb58796c9f541816aa334d66a6ba0176e6ca3a74eb7d42f389552db57698498cc65 Homepage: https://cran.r-project.org/package=LBE Description: Bioc Package 'LBE' (Estimation of the false discovery rate) LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. Package: r-bioc-lefser Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-coin, r-cran-mass, r-cran-ggplot2, r-bioc-s4vectors, r-cran-dplyr, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-forcats, r-cran-stringr, r-bioc-ggtree, r-bioc-biocgenerics, r-cran-ape, r-cran-ggrepel, r-bioc-mia, r-cran-purrr, r-cran-tidyselect, r-bioc-treeio Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-curatedmetagenomicdata, r-bioc-biocstyle, r-bioc-phyloseq, r-cran-pkgdown, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-bioc-lefser_1.22.0-1.ca2404.1_all.deb Size: 879722 MD5sum: ff6025c584d0425afedd3c359d25f942 SHA1: 5d45b189f30799764da902490485e7c9e2188fc1 SHA256: 3784570bfa21ef0a80bfa97f7a3f499e4c4fe3938be9466d508c426cf3f956d0 SHA512: d50f3a46db96de26030a155419f3c2ca0b396027d60b8e5a4623ee50958aaea08782216c3394a8564f400cb1325a8e2db311aca4f85722d1516803fdb16ecb12 Homepage: https://cran.r-project.org/package=lefser Description: Bioc Package 'lefser' (R implementation of the LEfSE method for microbiome biomarkerdiscovery) lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes). 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Package: r-bioc-lumi Architecture: all Version: 2.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5029 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-affy, r-bioc-methylumi, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-annotate, r-cran-lattice, r-cran-mgcv, r-cran-nleqslv, r-cran-kernsmooth, r-bioc-preprocesscore, r-cran-rsqlite, r-cran-dbi, r-bioc-annotationdbi, r-cran-mass Suggests: r-bioc-beadarray, r-bioc-limma, r-bioc-vsn, r-bioc-lumibarnes, r-bioc-lumihumanall.db, r-bioc-lumihumanidmapping, r-bioc-genefilter, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-bioc-lumi_2.64.0-1.ca2404.1_all.deb Size: 4730852 MD5sum: ec54bab4d932cc36ec679f65728416f7 SHA1: 507ae731c27c18c31806c6172e15d06e6b2ad7a0 SHA256: add687726647953f8cc80a392139f5ec5bac747eaf3d75d54c6de8547892875c SHA512: c9e0a77975dba11ce83d41826934f5da57ddc8eaf02dda1afe0f75d13d28216f73a734a1a1bd296452d696d61409ab057dd55f26d1aa5ee14dd23fbf2ecb4cbd Homepage: https://cran.r-project.org/package=lumi Description: Bioc Package 'lumi' (BeadArray Specific Methods for Illumina Methylation andExpression Microarrays) The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. Package: r-bioc-m3c Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-dosnow, r-cran-cluster, r-cran-foreach, r-cran-doparallel, r-cran-matrixcalc, r-cran-rtsne, r-cran-corpcor, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-m3c_1.34.0-1.ca2404.1_all.deb Size: 775920 MD5sum: cb2636e1bfce856580fc0306d5d642c5 SHA1: 747e0b034c8a2c6f778f4303d6357353b5c9d0e1 SHA256: c58a17078ebcec69c31fa504ba697e5ad8722501e3bcfc7d69a129cf8fed4b8d SHA512: c58eb00ef1059fc2abc05cca2f10dd0a38b70d7d6dca772e5a6cd85385ca00f895178c9ca9df1c16d8bf9e339db4454c5ab942ba8d3ee932aad9847c0fb3ce2d Homepage: https://cran.r-project.org/package=M3C Description: Bioc Package 'M3C' (Monte Carlo Reference-based Consensus Clustering) M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. Package: r-bioc-m3drop Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13311 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-rcolorbrewer, r-cran-gplots, r-cran-bbmle, r-cran-statmod, r-cran-matrixstats, r-cran-matrix, r-cran-irlba, r-cran-reldist, r-cran-hmisc, r-bioc-scater Suggests: r-cran-rocr, r-cran-knitr, r-bioc-m3dexampledata, r-bioc-singlecellexperiment, r-cran-seurat, r-bioc-biobase Filename: pool/dists/noble/main/r-bioc-m3drop_1.38.0-1.ca2404.1_all.deb Size: 12821776 MD5sum: 03d542b52fd1f96d3de79e8f9c7c9331 SHA1: 409c79f7953b1afea0692f846c4f3c36b42d1e9b SHA256: e30960f18410ab04b7afc5f5b7b493a1f7aafab61f25e511ced407cb303e8827 SHA512: 341ab8fe3d63eae85b977c8df8b0f3b2dc2dca1274c725df9d17d8281d7e1db21dc55400d92af643729551d670819a36ba42e4b2a291b6349010305214f22941 Homepage: https://cran.r-project.org/package=M3Drop Description: Bioc Package 'M3Drop' (Michaelis-Menten Modelling of Dropouts in single-cell RNASeq) This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model. Package: r-bioc-maaslin2 Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-robustbase, r-cran-biglm, r-cran-pcapp, r-bioc-edger, r-bioc-metagenomeseq, r-cran-pbapply, r-cran-car, r-cran-dplyr, r-cran-vegan, r-cran-chemometrics, r-cran-ggplot2, r-cran-pheatmap, r-cran-logging, r-cran-data.table, r-cran-lmertest, r-cran-hash, r-cran-optparse, r-cran-glmmtmb, r-cran-mass, r-cran-cplm, r-cran-pscl, r-cran-lme4, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-maaslin2_1.26.0-1.ca2404.1_all.deb Size: 570400 MD5sum: 7f3281b79e35364892dfc42b8ea45696 SHA1: cb0c83e90160237434fe65c31266be74fdc750d8 SHA256: a5b730d4dcaa2b4c5bbb50a02b2cd2158f57d82c5101555f1bbef554be6dbe39 SHA512: d4158f6f2a309b8865b911fb3a2d8d4c1ac256534ec2eb64246ee41fad1cb7531389624984039493896527cc7769a98c01f94dcd68d5097c97281287985efc4e Homepage: https://cran.r-project.org/package=Maaslin2 Description: Bioc Package 'Maaslin2' ("Multivariable Association Discovery in Population-scaleMeta-omics Studies") MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. Package: r-bioc-mageckflute Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 27253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-gridextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-reshape2, r-bioc-dose, r-bioc-clusterprofiler, r-bioc-pathview, r-bioc-enrichplot, r-cran-msigdbr, r-bioc-depmap Suggests: r-bioc-biomart, r-bioc-biocstyle, r-cran-dendextend, r-cran-knitr, r-cran-pheatmap, r-cran-png, r-cran-scales, r-bioc-sva, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-mageckflute_2.9.0-1.ca2404.1_all.deb Size: 20085962 MD5sum: a3b08893041bc151ed2ea1bfe013a2f2 SHA1: 5c777dc971aa93fd243716abc8f48e896a44bfe3 SHA256: 874a35ab41c74c5bd5000b330c80d548108f8dcfd5a113df76932c67af9257bf SHA512: 3a211353f14cf383d8c3cbf198d52f488742bfa5180ea05d74a4fea0ad5735ebc1471a7fb02233f6bfe43ea327ef58d7126c96b9f69699c115a9db969c186c71 Homepage: https://cran.r-project.org/package=MAGeCKFlute Description: Bioc Package 'MAGeCKFlute' (Integrative Analysis Pipeline for Pooled CRISPR FunctionalGenetic Screens) CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. Package: r-bioc-marray Architecture: all Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9999 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma Suggests: r-bioc-tkwidgets Filename: pool/dists/noble/main/r-bioc-marray_1.90.0-1.ca2404.1_all.deb Size: 5645794 MD5sum: 4f95356be437f1581d1695296a6eb2bf SHA1: 0aec19229a6444fb7a9ec5f85738893feecba2ce SHA256: 585d095480f6d37a340966cf62a1c7598e324b98ee3f5d3e3ca064efa42fad43 SHA512: 59552dc93487d8fe33ae4bc450c5e8e2278db0e934072018f39226510a0ff7f0f5ec325677fcf32076ad289a1d5411750d457f1ff63a2bf66c32c4301b259ca0 Homepage: https://cran.r-project.org/package=marray Description: Bioc Package 'marray' (Exploratory analysis for two-color spotted microarray data) Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. Package: r-bioc-mast Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11185 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-data.table, r-cran-ggplot2, r-cran-plyr, r-cran-stringr, r-cran-abind, r-cran-reshape2, r-bioc-summarizedexperiment, r-cran-progress, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lme4, r-cran-blme, r-cran-roxygen2, r-cran-numderiv, r-cran-car, r-cran-gdata, r-cran-lattice, r-cran-ggally, r-bioc-gseabase, r-cran-nmf, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-rsvd, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-biocstyle, r-bioc-scater, r-bioc-delayedarray, r-bioc-hdf5array, r-bioc-zinbwave, r-cran-dplyr Filename: pool/dists/noble/main/r-bioc-mast_1.38.0-1.ca2404.1_all.deb Size: 7521172 MD5sum: 6e1729f3461029ff938acfe3c56afa0f SHA1: cea67f9e76e2467d9cd0b71e96f4fa961defa20a SHA256: a1ab20383ed1cc33b7192b09c7b38e05b6c9b87323b0afb5d862af3b135a5870 SHA512: 47b4cd6f51dc7c2eedf16bd8d8f297a5d894ad93e271aaf7fc4c77d585990528ba01a1ab7c9a2b9b65f9ac0c54b8664fcd2810d71357beb79422736e9887c40a Homepage: https://cran.r-project.org/package=MAST Description: Bioc Package 'MAST' (Model-based Analysis of Single Cell Transcriptomics) Methods and models for handling zero-inflated single cell assay data. Package: r-bioc-matrixgenerics Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-matrix, r-bioc-sparsematrixstats, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-summarizedexperiment, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-matrixgenerics_1.24.0-1.ca2404.1_all.deb Size: 438512 MD5sum: c4dc0d0c3952ad1a582524dfeb79e8b2 SHA1: 4c5e0dba9a279f5e0e3206ee1f83b61c13de0cb2 SHA256: 94c478d3ec3d4a85bc6e18650410b32e6f8fbff438e1a832d060d8d3a7db0826 SHA512: ded8351d38087f0246b1552478989c6302e327770c4aa75720e0b81eefb46b16714e10f1e2bdb5d31a89ddd8177932acee63b7657291c65e677b09efeb4c2289 Homepage: https://cran.r-project.org/package=MatrixGenerics Description: Bioc Package 'MatrixGenerics' (S4 Generic Summary Statistic Functions that Operate onMatrix-Like Objects) S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. Package: r-bioc-metaboannotation Architecture: all Version: 1.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4597 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-mscoreutils, r-bioc-metabocoreutils, r-bioc-protgenerics, r-bioc-s4vectors, r-bioc-spectra, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-bioc-qfeatures, r-bioc-annotationhub, r-bioc-compounddb Suggests: r-cran-testthat, r-cran-knitr, r-bioc-msdatahub, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-plotly, r-cran-shiny, r-cran-shinyjs, r-cran-msentropy, r-cran-dt, r-cran-microbenchmark, r-bioc-mzr Filename: pool/dists/noble/main/r-bioc-metaboannotation_1.16.0-1.ca2404.1_all.deb Size: 1443850 MD5sum: 58f190272d53794ee22fa40e72241c75 SHA1: cc5faba05ef8303323aca113b2a8452974120c69 SHA256: 1a5a0dccd35d07d3f66f62609b22f291e9475adffca8cdae93d6a7f60a74f39a SHA512: e180ec099f1977e11ccd86c32a4b510ec0412989e50d7f7fdb9ead02d2a33c223158a55d9b3c67c7a182e92f44bab42cf596d16f4380d5c6876088a9a7539eda Homepage: https://cran.r-project.org/package=MetaboAnnotation Description: Bioc Package 'MetaboAnnotation' (Utilities for Annotation of Metabolomics Data) High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. Package: r-bioc-metabocoreutils Architecture: all Version: 1.20.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2893 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-mscoreutils, r-bioc-biocparallel Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-robustbase Filename: pool/dists/noble/main/r-bioc-metabocoreutils_1.20.1-1.ca2404.1_all.deb Size: 1357822 MD5sum: 3b2f1d53f604c9597d5baa28c484733a SHA1: f218b443f6017d2bb288f8b9d63f5663e17f269b SHA256: 474077904ac84d9c348832933ace0cd3f3d8eb9811ea651370c66c18c664d715 SHA512: 65773850081c40e2b04d6a52b2d5ef013d0581460b9421fc798dc3cffc4be6af521315264f92341690125c2e85f5b3e92ede2ae7c708634ee48aa03e4b57ca82 Homepage: https://cran.r-project.org/package=MetaboCoreUtils Description: Bioc Package 'MetaboCoreUtils' (Core Utils for Metabolomics Data) MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. Package: r-bioc-metagenomeseq Architecture: all Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-limma, r-cran-glmnet, r-cran-rcolorbrewer, r-cran-matrixstats, r-cran-foreach, r-cran-matrix, r-cran-gplots, r-bioc-wrench Suggests: r-bioc-annotate, r-bioc-biocgenerics, r-bioc-biomformat, r-cran-knitr, r-cran-gss, r-cran-testthat, r-cran-vegan, r-bioc-ihw, r-bioc-sparsearray Filename: pool/dists/noble/main/r-bioc-metagenomeseq_1.54.0-1.ca2404.1_all.deb Size: 2139002 MD5sum: e5a647d538a614a465617470c53a9b59 SHA1: 98ca37c91f27c82bc6d974b8f706c2805a664733 SHA256: efc88a5dd1da6880ad4354bcdbed45851f46d22a83a786d9bb09ca94f6833efb SHA512: 53e5eb13e2603bc34dd90fddfa76833fc7fdaf0d9dffe2382308b8f3ed1b79c7f335bf376bcc8f4c60f99e12e86cb5fb439f4e12cb7f05eb098c513ff6e80c3f Homepage: https://cran.r-project.org/package=metagenomeSeq Description: Bioc Package 'metagenomeSeq' (Statistical analysis for sparse high-throughput sequencing) metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. Package: r-bioc-methylumi Architecture: all Version: 2.58.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11300 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-scales, r-cran-reshape2, r-cran-ggplot2, r-cran-matrixstats, r-bioc-fdb.infiniummethylation.hg19, r-bioc-minfi, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-lattice, r-bioc-annotate, r-bioc-genefilter, r-bioc-annotationdbi, r-bioc-illuminaio, r-bioc-genomicfeatures, r-bioc-biocparallel Suggests: r-bioc-lumi, r-bioc-limma, r-cran-sqn, r-cran-mass, r-bioc-rtracklayer, r-bioc-biostrings, r-cran-quarto, r-bioc-tcgamethylation450k, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-fdb.infiniummethylation.hg18, r-bioc-homo.sapiens, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-methylumi_2.58.1-1.ca2404.1_all.deb Size: 6225640 MD5sum: 4d027f729fe83298b12b04bd50365424 SHA1: 61167f34ea72448bbc88fa403a784922592f0673 SHA256: 17bf7e0b33bb109aa17a5657bff975738b66509a938ca13a9fe8b51412278ce8 SHA512: 829cd367189791a3e39dba9f791a273d25ef2c0ce49c8cf8df189e475f7d3e5183839ddad1c1f0612373f26c49e18e3feda077a4ab233cf0f2a831f9f5b971c5 Homepage: https://cran.r-project.org/package=methylumi Description: Bioc Package 'methylumi' (Handle Illumina methylation data) This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. Package: r-bioc-mfuzz Architecture: all Version: 2.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1365 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-e1071, r-bioc-tkwidgets Suggests: r-bioc-marray Filename: pool/dists/noble/main/r-bioc-mfuzz_2.72.0-1.ca2404.1_all.deb Size: 777162 MD5sum: ca1573ace3b2e34ea2d2c12138565c40 SHA1: aa53f5c0e47b4ead2090a1f740e2b4e19bd7a3f4 SHA256: 75586bae9b8a92de32f7afc86506d3ba860b59ce83982c25b2b82af21838e895 SHA512: ce602747a3193a601dd389c72f47310fc46cb430d9f368ce230f1cc737694db0a01573b841cd7345d2b32caa1c44bc453ce8fa5f58af0a2b61c4a82bfda6b76a Homepage: https://cran.r-project.org/package=Mfuzz Description: Bioc Package 'Mfuzz' (Soft clustering of omics time series data) The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface. Package: r-bioc-microbiome Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-phyloseq, r-cran-ggplot2, r-bioc-biostrings, r-cran-compositions, r-cran-dplyr, r-cran-reshape2, r-cran-rtsne, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-vegan Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-cairo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-microbiome_1.34.0-1.ca2404.1_all.deb Size: 1016012 MD5sum: dde4a042b48604f247ca1cdd3265b80d SHA1: edefc2b78a155ec6395aaf6d84c02b448f7a243f SHA256: eb150eeac95b60b000dec293251b3b10c18551849a95232acff789388870b656 SHA512: 6294a576f87df3160c55da1f587e43a6485c0d1940246c8333b3a675905a6f5908407f9fe5d178501cca7fbcb3df987aa76a7c0604f7bc308beb9d9bc84cae2b Homepage: https://cran.r-project.org/package=microbiome Description: Bioc Package 'microbiome' (Microbiome Analytics) Utilities for microbiome analysis. Package: r-bioc-microbiomemarker Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-phyloseq, r-cran-magrittr, r-cran-purrr, r-cran-mass, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-coin, r-bioc-ggtree, r-cran-tidytree, r-bioc-iranges, r-cran-tidyr, r-cran-patchwork, r-cran-ggsignif, r-bioc-metagenomeseq, r-bioc-deseq2, r-bioc-edger, r-bioc-biocgenerics, r-bioc-biostrings, r-cran-yaml, r-bioc-biomformat, r-bioc-s4vectors, r-bioc-biobase, r-bioc-complexheatmap, r-bioc-ancombc, r-cran-caret, r-bioc-limma, r-bioc-aldex2, r-bioc-multtest, r-cran-plotroc, r-cran-vegan, r-cran-proc, r-bioc-biocparallel Suggests: r-cran-testthat, r-cran-covr, r-cran-glmnet, r-cran-matrix, r-cran-kernlab, r-cran-e1071, r-cran-ranger, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-withr Filename: pool/dists/noble/main/r-bioc-microbiomemarker_1.10.0-1.ca2404.1_all.deb Size: 3330164 MD5sum: 8bb9dba56130dae12cb3a19481715236 SHA1: 56d1f0fbbc27886ca505e495d32682e4b49b3f7f SHA256: 92290b3ec625ff2a6cb6f7cff36e97a3802dfa31b748e8d70b282d16d3bd18b9 SHA512: ddcc93619578e0eb02173a10e6c2bf8fca1268b6c6e8024b6a4566633de31d822368750af7177f973f08ed4d4ec613fdf39f61efefb1610860b188d89709f8d0 Homepage: https://cran.r-project.org/package=microbiomeMarker Description: Bioc Package 'microbiomeMarker' (microbiome biomarker analysis toolkit) To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers. Package: r-bioc-microbiotaprocess Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8130 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-bioc-biostrings, r-cran-ggrepel, r-cran-vegan, r-cran-zoo, r-bioc-ggtree, r-cran-tidytree, r-cran-mass, r-cran-rlang, r-cran-tibble, r-cran-coin, r-cran-ggsignif, r-cran-patchwork, r-cran-ggstar, r-cran-tidyselect, r-bioc-summarizedexperiment, r-cran-foreach, r-bioc-treeio, r-cran-pillar, r-cran-cli, r-cran-plyr, r-cran-dtplyr, r-bioc-ggtreeextra, r-cran-data.table, r-cran-ggfun Suggests: r-cran-rmarkdown, r-cran-prettydoc, r-cran-testthat, r-cran-knitr, r-cran-nlme, r-cran-phangorn, r-bioc-decipher, r-cran-randomforest, r-cran-jsonlite, r-bioc-biomformat, r-cran-scales, r-cran-yaml, r-cran-withr, r-bioc-s4vectors, r-cran-purrr, r-cran-seqmagick, r-cran-glue, r-cran-ggupset, r-cran-ggvenndiagram, r-cran-ggalluvial, r-cran-forcats, r-bioc-phyloseq, r-cran-aplot, r-cran-ggnewscale, r-cran-ggside, r-cran-ggh4x, r-bioc-hopach, r-cran-shadowtext, r-bioc-dirichletmultinomial, r-cran-ggpp, r-cran-biocmanager, r-bioc-rhdf5 Filename: pool/dists/noble/main/r-bioc-microbiotaprocess_1.24.0-1.ca2404.1_all.deb Size: 5383438 MD5sum: 9870b72bb7f112d29e9ddbd4dd3b7909 SHA1: f00a914aeefa36e57edd76ead62a673a47fed3fa SHA256: 2f07a406e148038e0ecaba68e002db15fd43fb79d5a69c3bf61eb636ea557996 SHA512: 4fcc9ae14e22f6942e2fd16839c7d2b3adb250dceab05951da2a5fbac7078e7d13b2b69418bb27f86b3128875e87dd391f64f9855cd5eb81129e1859c004b769 Homepage: https://cran.r-project.org/package=MicrobiotaProcess Description: Bioc Package 'MicrobiotaProcess' (A comprehensive R package for managing and analyzing microbiomeand other ecological data within the tidy framework) MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). Package: r-bioc-minfi Architecture: all Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2581 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-bumphunter, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-biobase, r-bioc-iranges, r-cran-beanplot, r-cran-rcolorbrewer, r-cran-lattice, r-cran-nor1mix, r-bioc-siggenes, r-bioc-limma, r-bioc-preprocesscore, r-bioc-illuminaio, r-bioc-delayedmatrixstats, r-cran-mclust, r-bioc-genefilter, r-cran-nlme, r-cran-reshape, r-cran-mass, r-cran-quadprog, r-cran-data.table, r-bioc-geoquery, r-bioc-delayedarray, r-bioc-hdf5array, r-bioc-biocparallel Suggests: r-bioc-illuminahumanmethylation450kmanifest, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-minfidata, r-bioc-minfidataepic, r-bioc-flowsorted.blood.450k, r-cran-runit, r-cran-digest, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-minfi_1.58.0-1.ca2404.1_all.deb Size: 1341422 MD5sum: 29e38e7c75046f72006742d6b6fe39e0 SHA1: e083333f4fbd0d77ed3d8457cf06d1240128beff SHA256: 470370ee410b7459a38aed175ee5d625fb3fbf17f4f237e3970aa23e0f039428 SHA512: c238aa8b6da091c38f9f500e73f1e059e38936d1705d12e532e6d72f47492b62fa53ad3ef6fec141301f141a2fab046dce8270349abd6b86e00f4eba950aa7f9 Homepage: https://cran.r-project.org/package=minfi Description: Bioc Package 'minfi' (Analyze Illumina Infinium DNA methylation arrays) Tools to analyze & visualize Illumina Infinium methylation arrays. Package: r-bioc-missmethyl Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-illuminahumanmethylationepicanno.ilm10b4.hg19, r-bioc-illuminahumanmethylationepicv2anno.20a1.hg38, r-bioc-annotationdbi, r-cran-biasedurn, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-go.db, r-bioc-illuminahumanmethylation450kmanifest, r-bioc-illuminahumanmethylationepicmanifest, r-bioc-illuminahumanmethylationepicv2manifest, r-bioc-iranges, r-bioc-limma, r-bioc-methylumi, r-bioc-minfi, r-bioc-org.hs.eg.db, r-cran-ruv, r-bioc-s4vectors, r-cran-statmod, r-cran-stringr, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-bioc-edger, r-cran-knitr, r-bioc-minfidata, r-cran-rmarkdown, r-bioc-tweedeseqcountdata, r-bioc-dmrcate, r-bioc-experimenthub Filename: pool/dists/noble/main/r-bioc-missmethyl_1.46.0-1.ca2404.1_all.deb Size: 1570718 MD5sum: fb551827c14329f421e30b8b41deb027 SHA1: 1cb6873cb65404013e76d91dd1cae7ac471ae813 SHA256: 9bda58812278b5666c57246a5917b19a50bb9cddc14badd2d4364d3e69513e0e SHA512: 6d22a7112d6f86ef33f789e30613de50f6ca44f08f63baa2968803d0795d95337a0d67e22c8422454ed7faa3963397bd838d3644ddc59600284820080628afa7 Homepage: https://cran.r-project.org/package=missMethyl Description: Bioc Package 'missMethyl' (Analysing Illumina HumanMethylation BeadChip Data) Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. Package: r-bioc-mixomics Architecture: all Version: 6.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24943 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-lattice, r-cran-ggplot2, r-cran-igraph, r-cran-ellipse, r-cran-corpcor, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-matrixstats, r-cran-rarpack, r-cran-gridextra, r-cran-ggrepel, r-bioc-biocparallel, r-cran-rgl, r-cran-rlang Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-mime, r-cran-testthat, r-cran-microbenchmark, r-cran-magick, r-cran-vdiffr, r-cran-kableextra, r-cran-devtools Filename: pool/dists/noble/main/r-bioc-mixomics_6.36.0-1.ca2404.1_all.deb Size: 19438458 MD5sum: b519d3e47979caebac66bc3be2c5a5c4 SHA1: 5ae83c8ea2a1bed3c8ebd702423da426b63e5e92 SHA256: 38ddbd9532485e73fa2112d1d769eaa19b2181dae0a69d20685026912c396771 SHA512: 17eef2404e34d437b367acf447b69ffa74c429ad08e5f60fe96add2fc3f47acf29619b9899ec6d151b7719b374db520e77da782d567426393bce3cfef951751a Homepage: https://cran.r-project.org/package=mixOmics Description: Bioc Package 'mixOmics' (Omics Data Integration Project) Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. Package: r-bioc-mlinterfaces Architecture: all Version: 1.92.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5097 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-annotate, r-cran-cluster, r-cran-gdata, r-cran-pls, r-cran-sfsmisc, r-cran-mass, r-cran-rpart, r-bioc-genefilter, r-cran-fpc, r-cran-ggvis, r-cran-shiny, r-cran-gbm, r-cran-rcolorbrewer, r-cran-hwriter, r-cran-threejs, r-cran-mlbench, r-cran-magrittr, r-bioc-summarizedexperiment Suggests: r-cran-class, r-cran-e1071, r-cran-ipred, r-cran-randomforest, r-bioc-gpls, r-cran-pamr, r-cran-nnet, r-bioc-all, r-bioc-hgu95av2.db, r-cran-som, r-bioc-hu6800.db, r-cran-lattice, r-cran-caret, r-bioc-golubesets, r-cran-ada, r-bioc-keggorthology, r-cran-kernlab, r-cran-mboost, r-cran-party, r-cran-klar, r-bioc-biocstyle, r-cran-knitr, r-cran-testthat, r-bioc-airway Filename: pool/dists/noble/main/r-bioc-mlinterfaces_1.92.0-1.ca2404.1_all.deb Size: 3025760 MD5sum: 9ec34ae302b8afb1b8ae6f91c7b0a662 SHA1: c979f7d349c0821012036a2c41cdb9dd6d1ed849 SHA256: 1b54f5ba17e475caf2d617455b2c642186053ac678eea6b4ad9563c450e18d1a SHA512: 52d68db53bd89333ae13aa54775cc82cf3b9b648003c28885343a3df07a453c84737f5d02122aa5be331bb2a85bf8fcdfea2c184ea7b18639d029a6ca38eb961 Homepage: https://cran.r-project.org/package=MLInterfaces Description: Bioc Package 'MLInterfaces' (Uniform interfaces to R machine learning procedures for data inBioconductor containers) This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. 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This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports. 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Package: r-bioc-motifstack Architecture: all Version: 1.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ade4, r-bioc-biostrings, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-xml, r-bioc-tfbstools Suggests: r-cran-cairo, r-cran-grimport, r-cran-grimport2, r-bioc-biocgenerics, r-bioc-motifdb, r-cran-rcolorbrewer, r-bioc-biocstyle, r-cran-knitr, r-cran-runit, r-cran-rmarkdown, r-bioc-jaspar2020 Filename: pool/dists/noble/main/r-bioc-motifstack_1.56.0-1.ca2404.1_all.deb Size: 2718676 MD5sum: ffa88a625e78515727e75fb6bc77aaca SHA1: 7dd97942b4fda442a57399a26de44dc4d2b4b0e1 SHA256: 0d6bdded592c669332c61cccd547fefb310f9123e5457a7b424fac6c76f22177 SHA512: ebfb02890553bcb41a055fb970b5cfd21e621b5bc095f65253f62fa3291cb2def3a00877b3eb647816d6d6adbff8bf9501d4f50567fe6d640b44b6a87230fe82 Homepage: https://cran.r-project.org/package=motifStack Description: Bioc Package 'motifStack' (Plot stacked logos for single or multiple DNA, RNA and aminoacid sequence) The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. 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Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. 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The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. Package: r-bioc-msfeatures Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2640 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-protgenerics, r-bioc-mscoreutils, r-bioc-summarizedexperiment Suggests: r-cran-testthat, r-cran-roxygen2, r-bioc-biocstyle, r-cran-pheatmap, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-msfeatures_1.20.0-1.ca2404.1_all.deb Size: 1511390 MD5sum: e96bc6d8115b7ad45cfc2d2e8027153b SHA1: d2daff7a166c039cd852a66cc84b44f9fed97e0f SHA256: c9c3840189cf6faef21376e515e321967bec6e32c4b40d5b563e9722aeb4dc91 SHA512: 1b67898c4015920eba17f596c1636b4250d40984fa63845c15a677fee763e35c43fa06ee2cb3cdf30db127833995d78e11ef67d412e5a6a1f832321f798a0ca7 Homepage: https://cran.r-project.org/package=MsFeatures Description: Bioc Package 'MsFeatures' (Functionality for Mass Spectrometry Features) The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. 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Signatures are stored in GeneSet class objects form the GSEABase package and the entire database is stored in a GeneSetCollection object. These data are then hosted on the ExperimentHub. Data used in this package was obtained from the MSigDB of the Broad Institute. Metadata for each gene set is stored along with the gene set in the GeneSet class object. 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It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. Package: r-bioc-multidataset Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-ggrepel, r-cran-qqman, r-bioc-limma Suggests: r-bioc-brgedata, r-bioc-minfi, r-bioc-minfidata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-omicade4, r-bioc-iclusterplus, r-bioc-geoquery, r-bioc-multiassayexperiment, r-bioc-biocstyle, r-bioc-raggedexperiment Filename: pool/dists/noble/main/r-bioc-multidataset_1.40.0-1.ca2404.1_all.deb Size: 607282 MD5sum: f3159fc0e00bb4899e711ddc8657dbb9 SHA1: 35edb24d350c282624fc70b204c99bb9453801db SHA256: 08aee474b3b7a24cc02b7154cbe0408c4b0297add340d5260c04871093cc6a7f SHA512: 62ac8958b3bb84640ad98ea303c8eb0e6c718076c9112fbcc7c5f8ffbab34324cf53fc2c6d55bc6daf4ddec7364bc98e4d342598f02b49bd4fc588d662511604 Homepage: https://cran.r-project.org/package=MultiDataSet Description: Bioc Package 'MultiDataSet' (Implementation of MultiDataSet and ResultSet) Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. Package: r-bioc-multihiccompare Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7051 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-hiccompare, r-bioc-edger, r-bioc-biocparallel, r-cran-qqman, r-cran-pheatmap, r-bioc-genomicranges, r-cran-pbapply, r-bioc-genomeinfodbdata, r-bioc-genomeinfodb, r-cran-aggregation Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-multihiccompare_1.30.0-1.ca2404.1_all.deb Size: 5242290 MD5sum: 5d332ca7e1678d25fd86fbb13c24a68e SHA1: 46faffbf53b1baa55bf5bb7cea7d98923f930cce SHA256: 0e06c9daef32212d33262cd2d80ebb909b3833ebe9f80571f09e062deae2c1af SHA512: 0d47ca0b9fd3d4ab4d4ed52701ad6b72fc48bacd20c83f5af4f775d31f5907b8fbd1b8e3690534a6536fa6083a80058604defa686d52d8cdd37853995e114146 Homepage: https://cran.r-project.org/package=multiHiCcompare Description: Bioc Package 'multiHiCcompare' (Normalize and detect differences between Hi-C datasets whenreplicates of each experimental condition are available) multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. Package: r-bioc-mungesumstats Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4315 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-r.utils, r-cran-dplyr, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-iranges, r-cran-ieugwasr, r-bioc-bsgenome, r-bioc-biostrings, r-cran-stringr, r-bioc-variantannotation, r-bioc-rtracklayer, r-cran-rcurl Suggests: r-bioc-snplocs.hsapiens.dbsnp144.grch37, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-snplocs.hsapiens.dbsnp155.grch37, r-bioc-snplocs.hsapiens.dbsnp155.grch38, r-bioc-bsgenome.hsapiens.1000genomes.hs37d5, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-upsetr, r-bioc-biocstyle, r-cran-covr, r-bioc-rsamtools, r-bioc-matrixgenerics, r-cran-badger, r-bioc-biocparallel, r-bioc-genomicfiles Filename: pool/dists/noble/main/r-bioc-mungesumstats_1.20.0-1.ca2404.1_all.deb Size: 2410966 MD5sum: 0563fc55de2db8a86a50beccac5346fa SHA1: acc2f2efe655f374192d7473d1015fce3b6d3715 SHA256: 3eaec72d7e90df632476804afea8b1bbe70d2978fa02223e45bde849e04861e5 SHA512: 96f65e1f986158957d6a3f746a319dba2a1885920c2a3495d11611fb729e5bc5435341af62dd16d3d5053bdc981638d2a41148be92e1c4286268a25ce7e60d64 Homepage: https://cran.r-project.org/package=MungeSumstats Description: Bioc Package 'MungeSumstats' (Standardise summary statistics from GWAS) The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. Package: r-bioc-muscat Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-cran-blme, r-bioc-complexheatmap, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-glmmtmb, r-bioc-limma, r-cran-lmertest, r-cran-lme4, r-cran-matrix, r-bioc-matrixgenerics, r-cran-matrixstats, r-cran-progress, r-cran-rlang, r-bioc-s4vectors, r-cran-scales, r-bioc-scater, r-bioc-scuttle, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-variancepartition Suggests: r-bioc-biocstyle, r-bioc-countsimqc, r-bioc-deseq2, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-icobra, r-bioc-ihw, r-cran-knitr, r-cran-patchwork, r-cran-phylogram, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-sctransform, r-cran-statmod, r-bioc-stager, r-cran-testthat, r-cran-tidyr, r-cran-upsetr Filename: pool/dists/noble/main/r-bioc-muscat_1.26.0-1.ca2404.1_all.deb Size: 7275028 MD5sum: 8303a4fc4da7830501aab56b71055217 SHA1: acce5eb911f13e372259857c81812f2791cf4906 SHA256: 32cb25e21931103fe3e641ad803a24cb559b6f20f0ac621cdfbca6af46a95308 SHA512: 26bc9993c66d130882bf54f74d1b162d2b2102b6cf7c044ad4f3e4a829dc3b63ae649e04206a5852843f39c3cbb2ef27267e238f88d5acbd6ed47e24cb93d0e5 Homepage: https://cran.r-project.org/package=muscat Description: Bioc Package 'muscat' (Multi-sample multi-group scRNA-seq data analysis tools) `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. Package: r-bioc-mutationalpatterns Architecture: all Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-cran-nmf, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-bsgenome, r-bioc-variantannotation, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-ggplot2, r-cran-pracma, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-biostrings, r-cran-ggdendro, r-cran-cowplot, r-cran-ggalluvial, r-cran-rcolorbrewer Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-biocstyle, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-biomart, r-cran-gridextra, r-bioc-rtracklayer, r-bioc-ccfindr, r-bioc-genomicfeatures, r-bioc-annotationdbi, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-mutationalpatterns_3.22.0-1.ca2404.1_all.deb Size: 6244194 MD5sum: fe91410c39cfc75c6477ada73d8af5b3 SHA1: df2040668993bba6f48efbb87d1ae38a27113368 SHA256: 56f12763a77be3ee21ef823dfc0dd60b37b0ed3649e8aaef5d606a8c184563c8 SHA512: 404d185c058da3a38496965ab022299f168c91aaf6c8debb2d228617a0a10bde7b4ba34fd6be7d086d9b2cc3daccaa4a8ff7cf05ebd912841f16fb4a87355ee1 Homepage: https://cran.r-project.org/package=MutationalPatterns Description: Bioc Package 'MutationalPatterns' (Comprehensive genome-wide analysis of mutational processes) Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. Package: r-bioc-mygene Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-cran-httr, r-cran-jsonlite, r-cran-hmisc, r-cran-sqldf, r-cran-plyr, r-bioc-s4vectors Suggests: r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-mygene_1.48.0-1.ca2404.1_all.deb Size: 228182 MD5sum: b096343832935233f70045bbe353927a SHA1: 0378a4bfeed3587f8c4fbd45aa00553d94e55584 SHA256: a89758a3b17789b5c9725974f82e87d55a106e637149a5bd291202f42d06c2d6 SHA512: e578d6f0167f6066c4cffe4d9bb237a8ffb0abaad188850eb26860d808ebb4e6aabd93da891ec8946e3f89e7c3b0766c47fd402a52e87682a10bc79feee21161 Homepage: https://cran.r-project.org/package=mygene Description: Bioc Package 'mygene' (Access MyGene.Info_ services) MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. Package: r-bioc-mzid Architecture: all Version: 1.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3398 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-plyr, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-bioc-protgenerics Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-mzid_1.50.0-1.ca2404.1_all.deb Size: 753536 MD5sum: fe60da6afed28563071e4f60d05ba11e SHA1: abbf1f5b50998bbde7d804349595e056742825a7 SHA256: 240e328c74dd553e9e6aa3cd770692d2e20fcf5f01f5b9d415c264748158d3a1 SHA512: 9d402027b2de935a7f7672afe5128e38c7bbf68a8a370ad21aea793ba263897c05f85d408342b8c073679be80ca77a41934adc1f7f77998f9e172337f9aeaed8 Homepage: https://cran.r-project.org/package=mzID Description: Bioc Package 'mzID' (An mzIdentML parser for R) A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. Package: r-bioc-nanostringnctools Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-s4vectors, r-cran-ggplot2, r-bioc-biocgenerics, r-bioc-biostrings, r-cran-ggbeeswarm, r-cran-ggiraph, r-cran-ggthemes, r-bioc-iranges, r-cran-pheatmap, r-cran-rcolorbrewer Suggests: r-bioc-biovizbase, r-bioc-ggbio, r-cran-runit, r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/noble/main/r-bioc-nanostringnctools_1.20.0-1.ca2404.1_all.deb Size: 5010648 MD5sum: 715e1a49278cee411bf5a11e02cc4a61 SHA1: df2e16181abbaecb4357ef5f8074553916ed9e62 SHA256: 46e6e99cdf6479a44c9320a18319806e6e18201d7b74ca514440676934ba92a5 SHA512: 2f13e2ca591d228e41c9a408a140a7e6efac6384d63cd5f65b759285a2a37dcfa81fd97ac151ef115a5966ad2ca53bfd8c1f9e35c20052be26b19526f0231508 Homepage: https://cran.r-project.org/package=NanoStringNCTools Description: Bioc Package 'NanoStringNCTools' (NanoString nCounter Tools) Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. Package: r-bioc-nebulosa Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-seuratobject, r-cran-ks, r-cran-matrix, r-cran-ggrastr Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-bioc-scater, r-bioc-scran, r-bioc-dropletutils, r-cran-igraph, r-bioc-biocfilecache, r-cran-seurat Filename: pool/dists/noble/main/r-bioc-nebulosa_1.22.0-1.ca2404.1_all.deb Size: 2791160 MD5sum: 9462a7853e6fe1b497edc07ba00dc878 SHA1: baa2e443a5a87a1c3f471e8e5f134592e9a1fa21 SHA256: b97abcb1c3c0b09090b6a16cc50100f76b18946a9555933b6f98324e9daf7503 SHA512: be0c8bf0ada5124a5dab9fd1f3a9443724a365777aa7284787c409f057526a84d09710b938a05862afd3e28f2cb5cf2c6f47796a491b84d366b0143b2319bcc4 Homepage: https://cran.r-project.org/package=Nebulosa Description: Bioc Package 'Nebulosa' (Single-Cell Data Visualisation Using Kernel Gene-WeightedDensity Estimation) This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. Package: r-bioc-noiseq Architecture: all Version: 2.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2718 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-matrix Filename: pool/dists/noble/main/r-bioc-noiseq_2.56.0-1.ca2404.1_all.deb Size: 2507288 MD5sum: 32e415ed9df4cb0a214bd37de6558fa4 SHA1: 062665acf485f7dce5d1de54dc5cfbc21c3365b7 SHA256: 202c2c7c96ffc3b974ce5b91c71f1189f48b652fac3e402e6d668c17388a98c0 SHA512: 289d7070277d3d37bdcfa47c32d69953102887445df4ff2616112f36dc2eb3c91499ecb1d785a67efaffd96e1a09e049f3767ee8cdd1a0a180af78ebe1ccfe9b Homepage: https://cran.r-project.org/package=NOISeq Description: Bioc Package 'NOISeq' (Exploratory analysis and differential expression for RNA-seqdata) Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. Package: r-bioc-oligoclasses Architecture: all Version: 1.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-affyio, r-cran-foreach, r-cran-biocmanager, r-bioc-s4vectors, r-cran-rsqlite, r-cran-dbi, r-cran-ff Suggests: r-bioc-hapmapsnp5, r-bioc-hapmapsnp6, r-bioc-pd.genomewidesnp.6, r-bioc-pd.genomewidesnp.5, r-bioc-pd.mapping50k.hind240, r-bioc-pd.mapping50k.xba240, r-bioc-pd.mapping250k.sty, r-bioc-pd.mapping250k.nsp, r-bioc-genomewidesnp6crlmm, r-bioc-genomewidesnp5crlmm, r-cran-runit, r-bioc-human370v1ccrlmm, r-bioc-vanillaice, r-bioc-crlmm Filename: pool/dists/noble/main/r-bioc-oligoclasses_1.74.0-1.ca2404.1_all.deb Size: 1256796 MD5sum: ade203372452a8da6debeb77bd301865 SHA1: f0d5095a7f1798e10c0e29e8fdeb803a1f2eb107 SHA256: 80e7ffa8e5a8c31dff246805238336519083b7352c9f533c6b28644b680641f2 SHA512: bd179be476eb4625a703c55c0ecb24a97e52c58ca77cda302eb720f90ac0b918be698fa58327e1d1289a9bc3841a0393efa24989aa7584268af0d2cbe5a64aa1 Homepage: https://cran.r-project.org/package=oligoClasses Description: Bioc Package 'oligoClasses' (Classes for high-throughput arrays supported by oligo and crlmm) This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. Package: r-bioc-omnipathr Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8153 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-crayon, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-later, r-cran-logger, r-cran-lubridate, r-cran-magrittr, r-cran-progress, r-cran-purrr, r-cran-rappdirs, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-r.utils, r-cran-rvest, r-cran-sessioninfo, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-withr, r-cran-xml, r-cran-xml2, r-cran-yaml, r-cran-zip Suggests: r-bioc-biocstyle, r-cran-bookdown, r-cran-ggplot2, r-cran-ggraph, r-cran-gprofiler2, r-cran-knitr, r-cran-mlrmbo, r-cran-parallelmap, r-cran-paramhelpers, r-cran-r.matlab, r-bioc-sbmlr, r-cran-sigmajs, r-cran-smoof, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-omnipathr_4.0.0-1.ca2404.1_all.deb Size: 2552002 MD5sum: 8e105acbb287998ef84125779e0f15cd SHA1: 7c538c2f3ba5c177a85e39ba8afb469fc234b984 SHA256: 588e0f168df01f518853b3f3793fb7840782188880f97777293a752d06282310 SHA512: f926c3aa4f152262fa5f4ac6e50e9e07d3b3fe460460b480ee10e069ff33efe8adc7548b01acc72077c09c7fc6c8dfa46df1d337a110b89e701d94a18189ab7e Homepage: https://cran.r-project.org/package=OmnipathR Description: Bioc Package 'OmnipathR' (OmniPath web service client and more) A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). Package: r-bioc-org.dr.eg.db Architecture: all Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144077 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-org.dr.eg.db_3.22.0-1.ca2404.1_all.deb Size: 25092002 MD5sum: c8ae3461b9a0811719be1b4c72273dcb SHA1: ea8ece2dd5599b9b8ee61654afb1c3ded9fdca81 SHA256: 40c516a854e748cefb8a501119ad8bbccbb564d8cbd1d07ef59942a1337c361e SHA512: c7e100953b815ac8313ea1fe13659051cb9a5d48104ef5cb3b90b97649aff451409a3d4d9c927c1f457763cfab380754d49a3f0a337e915434226e7053dc65cf Homepage: https://cran.r-project.org/package=org.Dr.eg.db Description: Bioc Package 'org.Dr.eg.db' (Genome wide annotation for Zebrafish) Genome wide annotation for Zebrafish, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-org.hs.eg.db Architecture: all Version: 3.23.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407999 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-org.hs.eg.db_3.23.1-1.ca2404.1_all.deb Size: 67848116 MD5sum: 52ee45db694af01da759ada514c83d58 SHA1: 8869aba1d1036eb603079f899fa79198d6ba47c4 SHA256: e58097a19057d29c6c20775a79ce079547ccaa3937ae2fe1bde92332ad00c9f4 SHA512: a3b9b08f2b2682d0cec6d4225064ca2bd118e1057084378288a6d9bd0f5f793e709addba3622bd0f09d325badf3e1cff72ff305eceec9f44e49a0245f224f5e0 Homepage: https://cran.r-project.org/package=org.Hs.eg.db Description: Bioc Package 'org.Hs.eg.db' (Genome wide annotation for Human) Genome wide annotation for Human, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-org.mm.eg.db Architecture: all Version: 3.23.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389774 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-org.mm.eg.db_3.23.0-1.ca2404.1_all.deb Size: 62154034 MD5sum: 7540ebbf79d8d9c991930451c63d9c37 SHA1: b1e6647d780fd51d462455d4822ee74121f95124 SHA256: 08a34bf0b8704453102b5267eee5b9d5425faed2631ab07632488ea99425de6e SHA512: 6a65204b0e00935a2ba4a914f9518902a599293dbfbeea3584b97c486cef30a93736b3653beea7f104ade0814dd093ba7fb9ec196a6059abe6036817b6f959ba Homepage: https://cran.r-project.org/package=org.Mm.eg.db Description: Bioc Package 'org.Mm.eg.db' (Genome wide annotation for Mouse) Genome wide annotation for Mouse, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-organism.dplyr Architecture: all Version: 1.37.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-annotationfilter, r-cran-rsqlite, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-annotationdbi, r-cran-rlang, r-bioc-biocfilecache, r-cran-dbi, r-cran-dbplyr, r-cran-tibble Suggests: r-bioc-genomeinfodb, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-org.mm.eg.db, r-bioc-txdb.mmusculus.ucsc.mm10.ensgene, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magick, r-bioc-biocstyle, r-cran-ggplot2 Filename: pool/dists/noble/main/r-bioc-organism.dplyr_1.37.1-1.ca2404.1_all.deb Size: 796258 MD5sum: 2d3314d0e327a5ae4e87de473e093cb4 SHA1: af69145220d08f6b99a9b9d086cbd5c2e0110c8d SHA256: 114bd5eabb9139eb2d72403b35d2e55967827f98a017bd69b42be06fffdf561e SHA512: ff869027463f30b16575c6451da480f959269bc72144c24f72dfaf78cec83d2a41dc61fc04963594956ae598a70f8fdda11a9856e1ab9942c513c2687c34058d Homepage: https://cran.r-project.org/package=Organism.dplyr Description: Bioc Package 'Organism.dplyr' (dplyr-based Access to Bioconductor Annotation Resources) This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). Package: r-bioc-organismdbi Architecture: all Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-seqinfo, r-bioc-genomicfeatures, r-cran-dbi, r-cran-biocmanager, r-bioc-biobase, r-bioc-graph, r-bioc-rbgl, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges Suggests: r-bioc-txdbmaker, r-bioc-genomeinfodbdata, r-bioc-homo.sapiens, r-bioc-rattus.norvegicus, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-annotationhub, r-bioc-fdb.ucsc.trnas, r-bioc-rtracklayer, r-bioc-biomart, r-cran-runit, r-cran-rmariadb, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-organismdbi_1.54.0-1.ca2404.1_all.deb Size: 708072 MD5sum: e31136dc3809907893f6e9689da3dece SHA1: bbe1c2845fd666ae40fe21bf34e2fe4c92ae59bc SHA256: df2f998085d441ff8724c08b0c3bb16ef1b157c9a2ce60c41f192119056a3d0a SHA512: 489c4e32e81719d01f37bfc51f33a0a97b3f6ff93a714d507a05d3ca210c3c4ee94420d556fb5fa8beeed455c000ad1f5cfc348d536bab1aa995fa2779045218 Homepage: https://cran.r-project.org/package=OrganismDbi Description: Bioc Package 'OrganismDbi' (Software to enable the smooth interfacing of different databasepackages) The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. Package: r-bioc-orthogene Architecture: all Version: 1.15.02-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-jsonlite, r-cran-homologene, r-cran-gprofiler2, r-cran-babelgene, r-cran-data.table, r-cran-ggplot2, r-cran-ggpubr, r-cran-patchwork, r-bioc-delayedarray, r-cran-grr, r-cran-repmis, r-bioc-ggtree Suggests: r-cran-rworkflows, r-cran-remotes, r-cran-knitr, r-bioc-biocstyle, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-piggyback, r-cran-magick, r-bioc-genomeinfodbdata, r-cran-ape, r-cran-phytools, r-cran-rphylopic, r-cran-treetools, r-cran-ggimage, r-bioc-omadb Filename: pool/dists/noble/main/r-bioc-orthogene_1.15.02-1.ca2404.1_all.deb Size: 2491540 MD5sum: 67323e0e9f35ad305d2a9061b9f07c6a SHA1: 1b8892fd621008eefc33075e17abf134c24bb773 SHA256: 9be6d53506262a91da897856da1b038bd21e6476166b1c0af4b1bdacbf4f8f50 SHA512: eee85f52aeab19092febede13bb999daec094f6d7aeab0c11ef6d1f5e600acd86a43e66fdb004f4ba772ebabdad9ef916737f05ac16c8d660ba8f2804b632507 Homepage: https://cran.r-project.org/package=orthogene Description: Bioc Package 'orthogene' (Interspecies gene mapping) `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. Package: r-bioc-pathview Architecture: all Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-kegggraph, r-cran-xml, r-bioc-rgraphviz, r-bioc-graph, r-cran-png, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-bioc-keggrest Suggests: r-bioc-gage, r-bioc-org.mm.eg.db, r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-pathview_1.52.0-1.ca2404.1_all.deb Size: 2932854 MD5sum: 036b595c0f867a6b7dd3d07fbb336c6e SHA1: 574bfe665f6138c81aa1d8914ef1275af6a914ed SHA256: 38b7b0953cf29a4ac5408974eea917fa8f6ba60e5dee43ba12ca3fe7b26ff749 SHA512: 468a8079ab08ba37841350260aa0bfd102984f4ff3391c248aebad5e48f7f6040fe26113ce782be59fd0b9fb2e1e7cc40453c1d357f371264d9fc7dea71fb3a3 Homepage: https://cran.r-project.org/package=pathview Description: Bioc Package 'pathview' (a tool set for pathway based data integration and visualization) Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. Package: r-bioc-pcaexplorer Architecture: all Version: 3.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13783 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-deseq2, r-bioc-summarizedexperiment, r-bioc-mosdef, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-genefilter, r-cran-ggplot2, r-cran-heatmaply, r-cran-plotly, r-cran-scales, r-cran-nmf, r-cran-plyr, r-bioc-topgo, r-bioc-limma, r-bioc-gostats, r-bioc-go.db, r-bioc-annotationdbi, r-cran-shiny, r-cran-shinydashboard, r-cran-shinybs, r-cran-ggrepel, r-cran-dt, r-cran-shinyace, r-cran-threejs, r-bioc-biomart, r-cran-pheatmap, r-cran-knitr, r-cran-rmarkdown, r-cran-base64enc, r-cran-tidyr Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-markdown, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-htmltools Filename: pool/dists/noble/main/r-bioc-pcaexplorer_3.6.0-1.ca2404.1_all.deb Size: 6871776 MD5sum: 423aca853279e61e724d9bdadd96eead SHA1: 818aa5d9d24df67806ee60852b041272866b702f SHA256: b5ace0eea59fa85be0d0af62e1e6c22ac02d56ec9e4eb52389850f6a781e4d56 SHA512: 7198c97d65fdb5da063540feeb931cc6284d54e4428d8f466498cc3d0e4df8c3036243056058d7e4ebf316f1279ca13094d3fe7a96affe6fb74bb7569079276e Homepage: https://cran.r-project.org/package=pcaExplorer Description: Bioc Package 'pcaExplorer' (Interactive Visualization of RNA-seq Data Using a PrincipalComponents Approach) This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. Package: r-bioc-pedixplorer Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12188 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-quadprog, r-cran-matrix, r-bioc-s4vectors, r-cran-shiny, r-cran-readxl, r-cran-dt, r-cran-igraph, r-cran-shinycssloaders, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinyjqui, r-cran-shinywidgets, r-cran-htmlwidgets, r-cran-plotly, r-cran-colourpicker, r-cran-shinytoastr Suggests: r-cran-diffviewer, r-cran-gridextra, r-cran-testthat, r-cran-vdiffr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-knitr, r-cran-withr, r-cran-qpdf, r-cran-shinytest2, r-cran-devtools, r-cran-r.devices, r-cran-usethis, r-cran-rlang, r-cran-magick, r-cran-cowplot Filename: pool/dists/noble/main/r-bioc-pedixplorer_1.8.0-1.ca2404.1_all.deb Size: 4998648 MD5sum: 05b47f8295044d93fcd0b90584760e37 SHA1: 9bf82ba2312a4cbd337b5358a48af3946abf608f SHA256: 0ce7ba066c487899e52798716ce8620468ed139fe76a0ffa0591c3e0460bbca1 SHA512: fbec379c4f875686e71fcb2696442751126c15306c210815228b4234d1349ee644d67e481a8ea1cfcdfd0c895aa1a3bebe5d1b20bc4b4efc1538a2891bf98ab9 Homepage: https://cran.r-project.org/package=Pedixplorer Description: Bioc Package 'Pedixplorer' (Pedigree Functions) Routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome. 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There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. Package: r-bioc-quantiseqr Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5050 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-limsolve, r-cran-mass, r-bioc-preprocesscore, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang Suggests: r-bioc-annotationdbi, r-bioc-biocstyle, r-cran-dplyr, r-bioc-experimenthub, r-bioc-geoquery, r-cran-knitr, r-bioc-macrophage, r-bioc-org.hs.eg.db, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-bioc-quantiseqr_1.20.0-1.ca2404.1_all.deb Size: 3666140 MD5sum: 9a00c7e45ee9ddc9a085e434ba762be3 SHA1: cd7b619426a1bfd88ef601a0dc186451c19df1b7 SHA256: 496d2c4b214b89a9f415f099f7361d3fb14cdd64651ea1e81380311489780657 SHA512: 0600d2f8c2b04880d04fb4da6709e6ec502497405da34b939dc028e09d53af9e249d7ee8157357935a798597ca10d52c219605c57b853b371d6efb372f7ab386 Homepage: https://cran.r-project.org/package=quantiseqr Description: Bioc Package 'quantiseqr' (Quantification of the Tumor Immune contexture from RNA-seq data) This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. Package: r-bioc-quantro Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4047 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-minfi, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-quantro_1.46.0-1.ca2404.1_all.deb Size: 3153818 MD5sum: 4580dff872dd4196eeadda1ea91bcd5a SHA1: 35886209572b3957678feb12dd85bfd5f903606a SHA256: bc97c9b3c30f0179e03e0230da6cb3e12bb5f41f2ac79485165b5e79d661d09c SHA512: a3d199265d0041dd754fa5b0e7cacdbd1115ff76d22a1f3f813721996f976ae9694fd6040f18872a7785392d10cebdf59012009a9c6e17273273100cc9ab0b4a Homepage: https://cran.r-project.org/package=quantro Description: Bioc Package 'quantro' (A test for when to use quantile normalization) A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. Package: r-bioc-quantsmooth Architecture: all Version: 1.78.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/noble/main/r-bioc-quantsmooth_1.78.0-1.ca2404.1_all.deb Size: 430662 MD5sum: 6a9d6cd8b86f61a5835cc028157e4d10 SHA1: bc99884b4824aa844d5a48f718e14ca74f2ca6df SHA256: 4d77ec32a3bf437840a9d7655583b7e3512aaa7ba9bbf1a28d4bcd5432bc2474 SHA512: b117ade0d3132f5ae75f4dc279606c3bfe71f3ce814d8af49fc0245c7fc9efc5014484e85137a24de19a6c3f9e538bb9d1356a412666258fbec0af6873778796 Homepage: https://cran.r-project.org/package=quantsmooth Description: Bioc Package 'quantsmooth' (Quantile smoothing and genomic visualization of array data) Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. Package: r-bioc-qusage Architecture: all Version: 2.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10270 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma, r-bioc-biobase, r-cran-nlme, r-cran-emmeans, r-cran-fftw Filename: pool/dists/noble/main/r-bioc-qusage_2.46.0-1.ca2404.1_all.deb Size: 10238950 MD5sum: 08951d4a61f92e7ab2ce7f9894ec870c SHA1: b2510dbaf2352f3dbb84fa9ce4ac45e7ffbcbbbf SHA256: 2f5b0eb157f922bd03ebb194c6ce498bec7db21948171af672f20b991901efe1 SHA512: 2720ca3275b10db7ce416f153f7b2edb0c579c294baefe382cda84974864faa5375d27dcdd30d2692b12ebcb8f740472cba7cc1bfcac782e9e95df4615e4e20f Homepage: https://cran.r-project.org/package=qusage Description: Bioc Package 'qusage' (qusage: Quantitative Set Analysis for Gene Expression) This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) Package: r-bioc-qvalue Architecture: all Version: 2.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2823 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-bioc-qvalue_2.44.0-1.ca2404.1_all.deb Size: 2810588 MD5sum: 005e898b46f68cc2a8763214693ba47f SHA1: 790cf9eadcc66c0c75fb7c09de86e5c31173f64f SHA256: 0637f3b4735edf2aa2f6bb172846117a9479dae8bfddd30a8e8772ea1ae2e54c SHA512: 820f8176bc30b559c1322ecac0512ee783ebb0e406c3d9ac4de953e607206b4437afcf6843e1d7abc4571cee1e89690bd93027394a9755c4e8b88fcd76188aa5 Homepage: https://cran.r-project.org/package=qvalue Description: Bioc Package 'qvalue' (Q-value estimation for false discovery rate control) This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. Package: r-bioc-r4rna Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/noble/main/r-bioc-r4rna_1.40.0-1.ca2404.1_all.deb Size: 1039304 MD5sum: 528fe1e9dfbc326c1cfb2107eb277e12 SHA1: a790108027edb801fe00844a2cdeb1a80218dae8 SHA256: 66868e9d8c731a20b2a211a00e9db05ff79397f6f38b0456fdb79f2ca17821c7 SHA512: 75038ec8050f2aa5f05f489b28414df426f7f5e0d9eb2b5d43750d4ec1675cf91b317e122d1e16dc3c82273b05920f86e74e287dbd5dc70d5d840bd7b326d7ad Homepage: https://cran.r-project.org/package=R4RNA Description: Bioc Package 'R4RNA' (An R package for RNA visualization and analysis) A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. Package: r-bioc-raggedexperiment Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2952 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-biocbaseutils, r-bioc-biocgenerics, r-bioc-seqinfo, r-bioc-iranges, r-cran-matrix, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-genomeinfodb, r-bioc-multiassayexperiment Filename: pool/dists/noble/main/r-bioc-raggedexperiment_1.36.0-1.ca2404.1_all.deb Size: 776018 MD5sum: 69e6e6768b6377456fe5aba0afbf0780 SHA1: 202ffed388b9e81c2c4b06c6e6985b78ca31a98a SHA256: 450463df9a6fee1aad852b80fdea3a6939ff18ce5cd29fd168119f82ee5d9f71 SHA512: a51d7dee5588c6ff08e4d08a5cd6d3d9eefc0fa6e264ea6236f80dc24eee63538fe5dc6562ecd8dc9ad915f6fded6dd88b3789b6eb904c97cb09077e87066309 Homepage: https://cran.r-project.org/package=RaggedExperiment Description: Bioc Package 'RaggedExperiment' (Representation of Sparse Experiments and Assays Across Samples) This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. Package: r-bioc-rankprod Architecture: all Version: 3.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 959 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/noble/main/r-bioc-rankprod_3.38.0-1.ca2404.1_all.deb Size: 887938 MD5sum: ed8420714b85a2a278b317792c8f7da7 SHA1: dba1ce2338d8b20392b29eb6fad5515128911044 SHA256: d925be418dc91da25b9c0f150a698c0f0bd6c4df907b11949219a92b4ccc8325 SHA512: 5fd3295df8d3b849416e4724aa041664ea9cd8d6d4e538079c916f9abd33e314d9100f43f0cca5344a8c416603c3b39f28775ce1621df1aa7e67bac99a716210 Homepage: https://cran.r-project.org/package=RankProd Description: Bioc Package 'RankProd' (Rank Product method for identifying differentially expressedgenes with application in meta-analysis) Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. Package: r-bioc-rcistarget Architecture: all Version: 1.29.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-aucell, r-bioc-biocgenerics, r-cran-data.table, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-arrow, r-cran-dplyr, r-cran-tibble, r-bioc-gseabase, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-zoo Suggests: r-bioc-biobase, r-bioc-biocstyle, r-bioc-biocparallel, r-cran-doparallel, r-cran-dt, r-cran-foreach, r-cran-gplots, r-bioc-rtracklayer, r-cran-igraph, r-cran-knitr, r-bioc-rcistarget.hg19.motifdbs.cisbponly.500bp, r-cran-rmarkdown, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/noble/main/r-bioc-rcistarget_1.29.0-1.ca2404.1_all.deb Size: 12916128 MD5sum: 3ce8a792b71db997866fdc0c3dd57a65 SHA1: 0ea7479c0577ba61cee8fabe26833641635eeb47 SHA256: ab9f2881050f48334a39ac3cad75c79d1487bd402e370dbd2c43410934be0f3e SHA512: 872d3f3a4d58f139da2a052817f46d508eec9d1de6ee590b36368ca0b3f970210c8b85e7303d5d9844df421e6c7ae432ab3f0e65738b29b4172196a0d5b2a124 Homepage: https://cran.r-project.org/package=RcisTarget Description: Bioc Package 'RcisTarget' (RcisTarget Identify transcription factor binding motifs enrichedon a list of genes or genomic regions) RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). Package: r-bioc-rcy3 Architecture: all Version: 2.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17762 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rjsonio, r-cran-xml, r-bioc-biocgenerics, r-bioc-graph, r-cran-fs, r-cran-uuid, r-cran-stringi, r-cran-glue, r-cran-rcurl, r-cran-base64url, r-cran-base64enc, r-cran-irkernel, r-cran-irdisplay, r-cran-rcolorbrewer, r-cran-gplots Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/noble/main/r-bioc-rcy3_2.32.0-1.ca2404.1_all.deb Size: 3742688 MD5sum: 8381bbb9fc7c8649fb715480208d08dd SHA1: 3161ff7980e0540f1de28d449b884b5986cc61a2 SHA256: 7b42f88f8d0af4e91dac0b54b1f4f3baaebff124b0a13f34931ba835fd7503d1 SHA512: 92708dcc1f71d14be85c21e2bf5f777b99556720ff6c050efe3d1a752645083130754a381b13026c4efa820da979298bd44173968e370dd9700a023ec62a0ebe Homepage: https://cran.r-project.org/package=RCy3 Description: Bioc Package 'RCy3' (Functions to Access and Control Cytoscape) Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. Package: r-bioc-reactome.db Architecture: all Version: 1.96.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-rsqlite Filename: pool/dists/noble/main/r-bioc-reactome.db_1.96.0-1.ca2404.1_all.deb Size: 220047100 MD5sum: 0c9f6be79655c673710ced253f65a49c SHA1: b5fa6b50a5833049710c7188ecf8e8063be35782 SHA256: 499ea43ba72284fe2203dadbcac1ab8c93e857d87d42666f84cc4f31a0211032 SHA512: fed96d7de67cac8d0a36b00e50c6ce14ed5c5f57ff230032bf8e5993e599b403c49a7d6b62993548dd828096eb80e7f188c643fe2f076dc89d21460a6fd9f124 Homepage: https://cran.r-project.org/package=reactome.db Description: Bioc Package 'reactome.db' (A set of annotation maps for reactome) A set of annotation maps for reactome assembled using data from reactome. This package has been created by a third-party developer, and is not affiliated with the Reactome team. Package: r-bioc-reactomegsa Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocsingular, r-cran-dplyr, r-cran-ggplot2, r-cran-gplots, r-cran-httr, r-cran-igraph, r-cran-jsonlite, r-cran-progress, r-cran-rcolorbrewer, r-bioc-summarizedexperiment, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-bioc-reactomegsa.data, r-cran-rmarkdown, r-bioc-scater, r-bioc-scran, r-bioc-scrnaseq, r-bioc-scuttle, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-reactomegsa_1.26.0-1.ca2404.1_all.deb Size: 723670 MD5sum: 64650f30e714993c2211e5200b2e555f SHA1: 7d5268299f7f8b200b2d8f51537d8536e7407264 SHA256: dff0b5afc161658e0c38c443df59be89ceec9f2f5e2f78daa8500e22e90b5396 SHA512: e5c8297269933b1f788b062320cfdfa6a21629a0bd9177678381508752a0e8c6fc732a192dfe8aac9d3d841b2059090365def1d9b283cba32e48783d38312202 Homepage: https://cran.r-project.org/package=ReactomeGSA Description: Bioc Package 'ReactomeGSA' (Client for the Reactome Analysis Service for comparativemulti-omics gene set analysis) The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. Package: r-bioc-reactomepa Architecture: all Version: 1.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-enrichplot, r-cran-enrichit, r-cran-ggplot2, r-cran-ggraph, r-bioc-reactome.db, r-cran-igraph, r-bioc-graphite, r-cran-gson, r-cran-yulab.utils Suggests: r-bioc-clusterprofiler, r-cran-knitr, r-cran-rmarkdown, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-reactomepa_1.56.0-1.ca2404.1_all.deb Size: 85114 MD5sum: f857af8ff551cd96144524be6ae28691 SHA1: a46fe2a57e170cb1967d95d31ef7d67616d85e53 SHA256: b7d3c4b9b0c2eabc7e5cd838ab1ce33f3ecc8f4320f2ba8d8a9e2bdcde62a754 SHA512: 4fc2c8eb9af3ca34aa88ace77ba1f1b97e075f58ec2c698a678a813074140ff2b89f785022cc6f87e98e55d51380610df1d0d98cdc34f1eb158477d12adb09be Homepage: https://cran.r-project.org/package=ReactomePA Description: Bioc Package 'ReactomePA' (Reactome Pathway Analysis) This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team. Package: r-bioc-recount3 Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-biocfilecache, r-cran-data.table, r-bioc-genomicranges, r-cran-httr, r-cran-matrix, r-cran-r.utils, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-sessioninfo Suggests: r-bioc-biocstyle, r-cran-covr, r-cran-knitcitations, r-cran-knitr, r-cran-lobstr, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-bioc-recount Filename: pool/dists/noble/main/r-bioc-recount3_1.22.0-1.ca2404.1_all.deb Size: 613028 MD5sum: 83b655005b9cbe300379b6a6dd72aff9 SHA1: b5a6497daf6485f595f1f2cb3df274a9eaf5a766 SHA256: 41f85d62bf9d9019859ee88652dd99b6576f205f82e8df26d7d715c4db3b3c8f SHA512: 3bb80d9b99cb52930cada4fdeee032a2616f946f0a1faea72c390e9c592b845ec962209204d8d7fe1d54da8084274d14c5830e898f1d59d4fd9c6c84e0a57322 Homepage: https://cran.r-project.org/package=recount3 Description: Bioc Package 'recount3' (Explore and download data from the recount3 project) The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. 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Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules. 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The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. 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Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). Package: r-bioc-rrvgo Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2472 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-gosemsim, r-bioc-annotationdbi, r-bioc-go.db, r-cran-pheatmap, r-cran-ggplot2, r-cran-ggrepel, r-cran-treemap, r-cran-tm, r-cran-wordcloud, r-cran-shiny, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-testthat, r-cran-shinydashboard, r-cran-dt, r-cran-plotly, r-cran-heatmaply, r-cran-magrittr, r-bioc-clusterprofiler, r-bioc-dose, r-cran-slam, r-bioc-org.ag.eg.db, r-bioc-org.at.tair.db, r-bioc-org.bt.eg.db, r-bioc-org.ce.eg.db, r-bioc-org.cf.eg.db, r-bioc-org.dm.eg.db, r-bioc-org.dr.eg.db, r-bioc-org.eck12.eg.db, r-bioc-org.ecsakai.eg.db, r-bioc-org.gg.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.mmu.eg.db, r-bioc-org.pt.eg.db, r-bioc-org.rn.eg.db, r-bioc-org.sc.sgd.db, r-bioc-org.ss.eg.db, r-bioc-org.xl.eg.db Filename: pool/dists/noble/main/r-bioc-rrvgo_1.24.0-1.ca2404.1_all.deb Size: 1533674 MD5sum: 596664e86832d8b1171ab5c74adf49de SHA1: 6404e4a76f4bc5d10372e4096d4c5bc21d61063f SHA256: de87873d142c7c22f968610fecaff4b35c9ef7bcedc092222b64f134417415ce SHA512: 97fa8946d2396214ad0d872b4dd4eb9e62c9ea6a414d2469cff2b922837240218a44e293cc000dcce5ac3920fda7455cc83e184f47890cfdf678d2e383f74177 Homepage: https://cran.r-project.org/package=rrvgo Description: Bioc Package 'rrvgo' (Reduce + Visualize GO) Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. 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It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. 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Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. 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SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines. 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This includes chip tango addresses, mapping information, performance annotation, and trained predictor for Infinium array data. This package provides user access to essential annotation data for working with many generations of the Infinium DNA methylation array. Currently we support human array (HM27, HM450, EPIC), mouse array (MM285) and the HorvathMethylChip40 (Mammal40) array. 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Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. Package: r-bioc-siggenes Architecture: all Version: 1.86.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-multtest, r-cran-scrime Suggests: r-bioc-affy, r-bioc-annotate, r-bioc-genefilter, r-cran-kernsmooth Filename: pool/dists/noble/main/r-bioc-siggenes_1.86.0-1.ca2404.1_all.deb Size: 1275358 MD5sum: e966cf8534cc54855f7c646c3c6753f3 SHA1: 7f67fcf9b033b7d72bb5088f3d7cb48336d2414e SHA256: 07704afd6123a8fc5da8cd636c4fb3cb96af60935588fd4348c9ac6445a08b54 SHA512: 4273b9448f9d0810f614ba4e419a037872142d5f953890c1d3882de8193d7a417f5c74dbde9413a85a7886265fcf020a0e2d757a1240b2c725faddefe9a106c0 Homepage: https://cran.r-project.org/package=siggenes Description: Bioc Package 'siggenes' (Multiple Testing using SAM and Efron's Empirical BayesApproaches) Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). Package: r-bioc-signaturesearchdata Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-experimenthub, r-bioc-affy, r-bioc-limma, r-bioc-biobase, r-cran-magrittr, r-cran-dplyr, r-cran-r.utils, r-bioc-rhdf5 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-signaturesearchdata_1.26.0-1.ca2404.1_all.deb Size: 600048 MD5sum: ad0b09a177f9177817d9ae90f3c35e9d SHA1: a952b8f8e5a7f7f419613a27491973c534a3acd1 SHA256: 5d7c7370b7a5dfe40030ac543f0ea79d061c7854178817aa428bf59127159847 SHA512: 11e92686d81feb2d88b3541e8310cc4a2b7507363d95ba967c23ea19ee273f8e937003543e944b84450bb93f153f1ec8895519bf0cc699ae5435eaf71ad9e8f1 Homepage: https://cran.r-project.org/package=signatureSearchData Description: Bioc Package 'signatureSearchData' (Datasets for signatureSearch package) CMAP/LINCS hdf5 databases and other annotations used for signatureSearch software package. Package: r-bioc-simplifyenrichment Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-simona, r-bioc-complexheatmap, r-cran-circlize, r-cran-getoptlong, r-cran-digest, r-cran-tm, r-bioc-go.db, r-bioc-annotationdbi, r-cran-slam, r-cran-clue, r-cran-cluster, r-cran-colorspace, r-cran-globaloptions Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-cowplot, r-cran-mclust, r-cran-apcluster, r-cran-mcl, r-cran-dbscan, r-cran-igraph, r-cran-gridextra, r-cran-dynamictreecut, r-cran-testthat, r-cran-gridgraphics, r-cran-flexclust, r-cran-biocmanager, r-bioc-interactivecomplexheatmap, r-cran-shiny, r-cran-shinydashboard, r-bioc-cola, r-bioc-hu6800.db, r-cran-rmarkdown, r-bioc-genefilter, r-cran-gridtext, r-cran-fpc Filename: pool/dists/noble/main/r-bioc-simplifyenrichment_2.6.0-1.ca2404.1_all.deb Size: 1582224 MD5sum: dbabf68599a97b7f0f1a7965a8043b87 SHA1: 8bcf80be9b2d3950b243936c7e4d7328d91933fa SHA256: f9b8270e8c11e0c196f299da861065030f9a64b2a0b3fc0f10078d12d41a204a SHA512: 694a8345d0b15d64ed8529b8e6212fdbf90031d21076e85f9253b97b629aee120430543255afe74105eb22de27be89f44c210e691d79a1d7425068aff45ccb97 Homepage: https://cran.r-project.org/package=simplifyEnrichment Description: Bioc Package 'simplifyEnrichment' (Simplify Functional Enrichment Results) A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. Package: r-bioc-singlecellexperiment Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4202 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-delayedarray Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-bioc-scrnaseq, r-cran-rtsne Filename: pool/dists/noble/main/r-bioc-singlecellexperiment_1.34.0-1.ca2404.1_all.deb Size: 1110446 MD5sum: afae5a33349c84266650c842044b1ae7 SHA1: 822f8f3c57ac7fc75ddf59a78353b6b21c7f17ec SHA256: c7abda1039f1f144138acd3ccd08a4137993112fd9069a1636d034488b815b4a SHA512: df3b22e0c98df54de7946eadcfa2fbdc54ebbae30d6438231e9febc29fe9a9ac03601d8246efae3e7b34a358dc030105951d1f2094dfd6232857b05c94636818 Homepage: https://cran.r-project.org/package=SingleCellExperiment Description: Bioc Package 'SingleCellExperiment' (S4 Classes for Single Cell Data) Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. Package: r-bioc-singlecelltk Architecture: all Version: 2.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7336 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-delayedarray, r-bioc-biobase, r-cran-ape, r-cran-anndata, r-bioc-annotationhub, r-bioc-batchelor, r-bioc-biocparallel, r-bioc-celldex, r-cran-colourpicker, r-cran-colorspace, r-cran-cowplot, r-cran-cluster, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedmatrixstats, r-bioc-deseq2, r-cran-dplyr, r-cran-dt, r-bioc-experimenthub, r-bioc-ensembldb, r-cran-fields, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggrepel, r-bioc-ggtree, r-cran-gridextra, r-bioc-gsva, r-bioc-gsvadata, r-cran-igraph, r-cran-kernsmooth, r-bioc-limma, r-bioc-mast, r-cran-matrix, r-cran-matrixstats, r-cran-msigdbr, r-bioc-multtest, r-cran-plotly, r-cran-plyr, r-cran-rocr, r-cran-rtsne, r-bioc-s4vectors, r-bioc-scater, r-bioc-scmerge, r-bioc-scran, r-cran-seurat, r-cran-shiny, r-cran-shinyjs, r-bioc-singler, r-cran-stringr, r-cran-soupx, r-bioc-sva, r-cran-reshape2, r-cran-shinyalert, r-cran-circlize, r-cran-enrichr, r-bioc-celda, r-cran-shinycssloaders, r-bioc-dropletutils, r-bioc-scds, r-cran-reticulate, r-bioc-tximport, r-cran-tidyr, r-bioc-eds, r-cran-withr, r-bioc-gseabase, r-cran-r.utils, r-bioc-zinbwave, r-bioc-scrnaseq, r-bioc-tenxpbmcdata, r-cran-yaml, r-cran-rmarkdown, r-cran-magrittr, r-bioc-scdblfinder, r-cran-metap, r-cran-vam, r-cran-tibble, r-cran-rlang, r-bioc-tscan, r-bioc-trajectoryutils, r-bioc-scuttle, r-bioc-zellkonverter, r-cran-lifecycle Suggests: r-cran-testthat, r-bioc-rsubread, r-bioc-biocstyle, r-cran-knitr, r-cran-lintr, r-cran-spelling, r-bioc-org.mm.eg.db, r-cran-kableextra, r-cran-shinythemes, r-cran-shinybs, r-cran-shinyjqui, r-cran-shinywidgets, r-cran-shinyfiles, r-bioc-biocgenerics, r-cran-rcolorbrewer, r-cran-fastmap, r-cran-harmony, r-cran-seuratobject, r-cran-optparse Filename: pool/dists/noble/main/r-bioc-singlecelltk_2.22.0-1.ca2404.1_all.deb Size: 4488382 MD5sum: 5da53ccdf8eb657c1501d5d2696d3728 SHA1: e077b70fd8fdd5968a26885cc9ea953edcc3ae4e SHA256: 96699dd9e74c556f05663c9ff33a7ab49bea8db6d3e4f1e59bb60c505c2db17e SHA512: 34445b4810bc8cb89585abb5e4793c65d0e58ce288b1a949ec52cd481ca9fff84bfa4530ce6ccd27406fb980d7d37efc6961201829d06cdfa15efcce664b6c5c Homepage: https://cran.r-project.org/package=singleCellTK Description: Bioc Package 'singleCellTK' (Comprehensive and Interactive Analysis of Single Cell RNA-SeqData) The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. Package: r-bioc-singscore Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4620 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-bioc-gseabase, r-cran-plotly, r-cran-tidyr, r-cran-plyr, r-cran-magrittr, r-cran-reshape, r-bioc-edger, r-cran-rcolorbrewer, r-bioc-biobase, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-cran-matrixstats, r-cran-reshape2, r-bioc-s4vectors Suggests: r-cran-pkgdown, r-bioc-biocstyle, r-cran-hexbin, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-bioc-singscore_1.32.0-1.ca2404.1_all.deb Size: 3602552 MD5sum: 8f40968cbdf67efd8be4673bad1f583e SHA1: 322f493027a40064d8d8875482e2246e3ecd34ae SHA256: b187ca683a0bd20cbfe3c6fddcea4557c8f122ac43e95be82808724135a869b3 SHA512: 312aa6f3fc92b20a20a6f300ba35bdb36a35990455a4a8b4a4c0bf263f3f5dcb8197220376c8ac2fc301d980ab58bc76b7358b36829d8e8005560546c484ca50 Homepage: https://cran.r-project.org/package=singscore Description: Bioc Package 'singscore' (Rank-based single-sample gene set scoring method) A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. Package: r-bioc-slingshot Architecture: all Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3673 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-princurve, r-bioc-trajectoryutils, r-cran-igraph, r-cran-matrixstats, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-bioc-clusterexperiment, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-mclust, r-cran-mgcv, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat, r-cran-uwot, r-cran-covr Filename: pool/dists/noble/main/r-bioc-slingshot_2.20.0-1.ca2404.1_all.deb Size: 1990460 MD5sum: 8e1d91dc78c5b3fd13b3992716cce7b8 SHA1: 72f296c8dc93f3bd4946ff28fb3a8f10cd7b6f2f SHA256: 12f660e0d640715c6992bb8ed2821c658394f8fc68e9c3ffdfc3a2da9313f2dd SHA512: b8b2ebb645c49df46b0db56a9b24ca5ea79d626c627bdd36622ac5c08b74191185c209adbe42495f048989a08965637b71e22e7e2118dd6e9ac7e7bcd2d305d5 Homepage: https://cran.r-project.org/package=slingshot Description: Bioc Package 'slingshot' (Tools for ordering single-cell sequencing) Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. Package: r-bioc-soggi Architecture: all Version: 1.40.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-cran-reshape2, r-cran-ggplot2, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-rtracklayer, r-bioc-preprocesscore, r-bioc-chipseq, r-bioc-biocparallel Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-soggi_1.40.1-1.ca2404.1_all.deb Size: 3877340 MD5sum: 65fa197a828beb1b5fcec6a2692accba SHA1: 8b4fbbaee1ffc1d52f91433c4e297b193bacd1aa SHA256: baac675cc37bce3f304458006577da9eb740864aaf2cb8d0dea5aa283185f40c SHA512: 519169a66e9b344fa6f141896cb029c8a40b421f19bd965522aa7fb2e0ec186b614a2b0ff6ee5f3c4ac95ede368fa8b8a440dc0c791bc246d924353783f0c1dc Homepage: https://cran.r-project.org/package=soGGi Description: Bioc Package 'soGGi' (Visualise ChIP-seq, MNase-seq and motif occurrence as aggregateplots Summarised Over Grouped Genomic Intervals) The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. Package: r-bioc-spatialexperiment Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7716 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-rjson, r-cran-magick, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-bioc-biocfilecache Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-bioc-bumpymatrix, r-bioc-dropletutils, r-bioc-visiumio Filename: pool/dists/noble/main/r-bioc-spatialexperiment_1.22.0-1.ca2404.1_all.deb Size: 5064524 MD5sum: e83214ccfb17647ce9d51bbff80712d2 SHA1: 6a83379c9538fdcbab528f0fe37fcb8afeb39c23 SHA256: a561f6b1fa93ecb93109cfefddea357eb784a2c0d16cf83644e8c8cbdc7bd94b SHA512: 1fd3646b30bf5a6cf6cdc5f8d8f3d3a855c5b657f5581dc04bfcb68043db4ea89dc3a5d98bfcea0e29f1724951d1b15e3cd1ad281102f1de703e353d56f1214f Homepage: https://cran.r-project.org/package=SpatialExperiment Description: Bioc Package 'SpatialExperiment' (S4 Class for Spatially Resolved -omics Data) Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. Package: r-bioc-spectra Architecture: all Version: 1.22.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5403 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-protgenerics, r-bioc-iranges, r-bioc-mscoreutils, r-cran-fs, r-bioc-biocgenerics, r-bioc-metabocoreutils, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-bioc-msdatahub, r-cran-roxygen2, r-bioc-biocstyle, r-bioc-mzr, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-vdiffr, r-cran-msentropy, r-cran-patrick Filename: pool/dists/noble/main/r-bioc-spectra_1.22.2-1.ca2404.1_all.deb Size: 1853064 MD5sum: 10382c8899793ad57348da6163ef65d3 SHA1: 824ba8aa6885f25cf524275e121c51872191359a SHA256: 436c3607d24527203ba6435dcf2a67c28a463fa4ab3ea36d4014bb4f4342bdbb SHA512: 8aeb8bac2cce0dc2061067fcd1a1f7b674b398943e09f7664fb5012fc80b9d7e391c6ee379369c22b54705b69b2e12eb56ca18cc5b3fc584830fa724b00f18f1 Homepage: https://cran.r-project.org/package=Spectra Description: Bioc Package 'Spectra' (Spectra Infrastructure for Mass Spectrometry Data) The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. Package: r-bioc-spia Architecture: all Version: 2.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4910 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-kegggraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-bioc-hgu133plus2.db Filename: pool/dists/noble/main/r-bioc-spia_2.64.0-1.ca2404.1_all.deb Size: 2592410 MD5sum: ca96f191ed0c1d0c83a7cc061fdee349 SHA1: 7789e0eb53f117ef4ccf98555f05074ecbac5132 SHA256: e4c7a68ef45514e3aa2cd63edb7212aa79273e8c2836b5fa94aa7c0e803d524a SHA512: 97a05728e4a12d111ddadf7203095eb44badb9b41bffee56495d0a5ac38fa16dc9f360d5e496a477e7aebe7a6a941b75cbd55810c827dbbea97b3a66493be1ab Homepage: https://cran.r-project.org/package=SPIA Description: Bioc Package 'SPIA' (Signaling Pathway Impact Analysis (SPIA) using combined evidenceof pathway over-representation and unusual signalingperturbations) This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. Package: r-bioc-splatter Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10255 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-checkmate, r-cran-crayon, r-bioc-edger, r-cran-fitdistrplus, r-cran-lifecycle, r-cran-locfit, r-cran-matrixstats, r-cran-rlang, r-bioc-s4vectors, r-bioc-scrapper, r-bioc-scuttle, r-bioc-summarizedexperiment, r-cran-withr Suggests: r-bioc-basics, r-cran-biocmanager, r-bioc-biocsingular, r-bioc-biocstyle, r-bioc-biostrings, r-cran-covr, r-cran-cowplot, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-igraph, r-bioc-iranges, r-cran-knitr, r-cran-limsolve, r-cran-lme4, r-cran-magick, r-bioc-phenopath, r-bioc-preprocesscore, r-cran-progress, r-cran-pscl, r-cran-rmarkdown, r-cran-scales, r-bioc-scater, r-bioc-scdd, r-bioc-scran, r-cran-sparsedc, r-cran-spelling, r-cran-testthat, r-bioc-variantannotation, r-bioc-zinbwave Filename: pool/dists/noble/main/r-bioc-splatter_1.36.0-1.ca2404.1_all.deb Size: 5879094 MD5sum: 0b37ed6c3a105f46220edfeb6daa4dc6 SHA1: 53852f81467e629d8e4bad9a0dd344f3fe0708d5 SHA256: a969ad7900d670fae88e12fb6c9ee8504a1e1112ba36101e4c6a6bb3dd883506 SHA512: 3cbae8d832eee1d9826227453c576f0c4f73d152bb2b27fc3e02f0c942d8f5b5a2b99388d49f0e81c2a7d5294757b5b1c5cfa89c4a83ca2ef7cd37a3e1cc07af Homepage: https://cran.r-project.org/package=splatter Description: Bioc Package 'splatter' (Simple Simulation of Single-cell RNA Sequencing Data) Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. Package: r-bioc-sradb Architecture: all Version: 1.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5067 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rsqlite, r-bioc-graph, r-cran-rcurl, r-cran-r.utils Suggests: r-bioc-rgraphviz Filename: pool/dists/noble/main/r-bioc-sradb_1.74.0-1.ca2404.1_all.deb Size: 720996 MD5sum: 7cb8005827b36be934141c70c9f69361 SHA1: c996ddbdccde4a5d2aef24f7a5903c36d0418238 SHA256: d5ef7f4c8ade8dddccbdfaef8708af9abbccd8bef78d822ec7548d101e286bd9 SHA512: d03de05dbac9bc432652a3132a5dfae6f626a95b8ed3ab7cbf4ceaa9291bb99398057f41d5304496fff9938ec0f8b8133d42bb517c307290292b1784b4cd849c Homepage: https://cran.r-project.org/package=SRAdb Description: Bioc Package 'SRAdb' (A compilation of metadata from NCBI SRA and tools) The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. Package: r-bioc-stringdb Architecture: all Version: 2.24.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4540 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-png, r-cran-sqldf, r-cran-plyr, r-cran-igraph, r-cran-httr, r-cran-rcolorbrewer, r-cran-gplots, r-cran-hash, r-cran-plotrix Suggests: r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-stringdb_2.24.1-1.ca2404.1_all.deb Size: 4461990 MD5sum: ac31e5155bacd2fff8aeaf93619def5b SHA1: f5ecf6a48854cc00884dc5d5c3c80cd185c211ac SHA256: a777ebcecb3a4daca393bcc08ed4947acd018fecdb5230fb19c624ff32bffd8f SHA512: 386a8e3c0ad5dd6fa0061d76b00bb47927e15102aa4df940980aa4cfb25f5d8ca4714a535a0d1041d8d4a1b871c9ecb41d4b578983347fa63ded62c01d9f22ee Homepage: https://cran.r-project.org/package=STRINGdb Description: Bioc Package 'STRINGdb' (STRINGdb - Protein-Protein Interaction Networks and FunctionalEnrichment Analysis) The STRINGdb package provides an R interface to STRING, a protein-protein interaction database and functional enrichment analysis tool (https://string-db.org). Package: r-bioc-summarizedexperiment Architecture: all Version: 1.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3977 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-genomicranges, r-bioc-biobase, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-s4arrays, r-bioc-delayedarray Suggests: r-bioc-genomeinfodb, r-bioc-rhdf5, r-bioc-hdf5array, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-sparsearray, r-bioc-singlecellexperiment, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-hgu95av2.db, r-bioc-airway, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-testthat, r-cran-digest Filename: pool/dists/noble/main/r-bioc-summarizedexperiment_1.42.0-1.ca2404.1_all.deb Size: 1438018 MD5sum: 8cef6687754deadec9142df2ecc3224d SHA1: 4020c173f37e80327ea9a790467e285c6db5c83b SHA256: 17ef7821a43961b3f34bad80ec44fc9efbf096bc5ee3111905f2e81ae0a0f5be SHA512: 1d8b180cd124138ba8ca1f9f673cca40cbba74310b1c20886dfe73db6da3aefa97c283c9bb349fca56a61bd4a924bd63b15abfdb23489c9993ec8a9d5c5483d7 Homepage: https://cran.r-project.org/package=SummarizedExperiment Description: Bioc Package 'SummarizedExperiment' (A container (S4 class) for matrix-like assays) The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. Package: r-bioc-suprahex Architecture: all Version: 1.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3861 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hexbin, r-cran-ape, r-cran-mass, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-magrittr, r-cran-igraph Filename: pool/dists/noble/main/r-bioc-suprahex_1.42.0-1.ca2404.1_all.deb Size: 3438146 MD5sum: 13ef40c0b890c52a4fbbb3e7bfd90b2a SHA1: dcbc6593fa1d61dd29332deb899f7df5a66d679c SHA256: 7c25ef2ad4d922371c9ba181db299a1d009e6ae16389d23b4518a1879e5bae20 SHA512: ab5f4d60bbf56446d40341ac856155f9f0a31df303889c1e026b1e25dd35ec078e62c2f330755d8771bb68e91aeed0f66236b36ed436ae383ccc548b6cee7fc8 Homepage: https://cran.r-project.org/package=supraHex Description: Bioc Package 'supraHex' (supraHex: a supra-hexagonal map for analysing tabular omics data) A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. Package: r-bioc-systempiper Architecture: all Version: 2.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13515 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rsamtools, r-bioc-biostrings, r-bioc-shortread, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-yaml, r-cran-stringr, r-cran-magrittr, r-bioc-s4vectors, r-cran-crayon, r-bioc-biocgenerics, r-cran-htmlwidgets Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-systempiperdata, r-bioc-genomicalignments, r-cran-dplyr, r-cran-testthat, r-cran-rjson, r-bioc-annotate, r-bioc-annotationdbi, r-cran-kableextra, r-bioc-go.db, r-bioc-genomeinfodb, r-cran-dt, r-bioc-rtracklayer, r-bioc-limma, r-bioc-edger, r-bioc-deseq2, r-bioc-iranges, r-cran-batchtools, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-genomeinfodbdata, r-bioc-variantannotation Filename: pool/dists/noble/main/r-bioc-systempiper_2.18.0-1.ca2404.1_all.deb Size: 6363014 MD5sum: 2807dd6296205d4f3de2f658c2485370 SHA1: 7e43eb3f0a816dd576a111ddb73b017fa3acdcb8 SHA256: 4727868f7285f497b7217241eaaab4c1bf5fb8a9844f1e6c5de4cec0e3003131 SHA512: 5cdb94af6de5ffcde187cc278995037e7b3677bc84a7254e713a3cefedd640e61ff94a8487b3c5b02d1c5e29395d8928a24e923c30bc3df9f97a562cb051e108 Homepage: https://cran.r-project.org/package=systemPipeR Description: Bioc Package 'systemPipeR' (systemPipeR: A Multipurpose Workflow Management System forReproducible Data Analysis) systemPipeR is a workflow management environment for reproducible data analysis that integrates R with command-line software. It enables researchers to design, execute, and report complex workflows on local machines and HPC systems. The framework combines R-based analysis with external tools through a Common Workflow Language (CWL) interface, manages workflow dependencies and restart capabilities, and automatically generates reproducible scientific analysis reports. The companion package systemPipeRdata provides ready-to-use workflow templates that simplify workflow setup and customization. Alternatively, workflow templates can be loaded from dedicated GitHub repositories. Package: r-bioc-tcgabiolinks Architecture: all Version: 2.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110004 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-downloader, r-bioc-biomart, r-cran-dplyr, r-cran-tibble, r-bioc-genomicranges, r-cran-xml, r-cran-data.table, r-cran-jsonlite, r-cran-plyr, r-cran-knitr, r-cran-ggplot2, r-cran-stringr, r-bioc-iranges, r-cran-rvest, r-bioc-s4vectors, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-tcgabiolinksgui.data, r-cran-readr, r-cran-tidyr, r-cran-purrr, r-cran-xml2, r-cran-httr Suggests: r-cran-jpeg, r-cran-png, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-devtools, r-bioc-maftools, r-cran-parmigene, r-cran-c3net, r-bioc-minet, r-bioc-biobase, r-bioc-affy, r-cran-testthat, r-bioc-sesame, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-pathview, r-bioc-clusterprofiler, r-cran-seurat, r-bioc-complexheatmap, r-cran-circlize, r-bioc-consensusclusterplus, r-cran-igraph, r-bioc-limma, r-bioc-edger, r-bioc-sva, r-bioc-edaseq, r-cran-survminer, r-bioc-genefilter, r-cran-gridextra, r-cran-survival, r-cran-doparallel, r-cran-ggrepel, r-cran-scales, r-cran-dt Filename: pool/dists/noble/main/r-bioc-tcgabiolinks_2.40.0-1.ca2404.1_all.deb Size: 34633672 MD5sum: fb23120d3f7a3e12b77452e656957b15 SHA1: add99faa4e0aa62988a9b17470c5ca650c2b7d37 SHA256: cb173d3cee52ca45c7aa3b5bce7c22c3918a15f563eeb9338338145b181449fc SHA512: 414e14277f4db6effd7e559bc069f86f1655ee461195670e9a54b1e9c33f189333613854a23aaa7c6070131305beec19dbecbb50add3fa5f48442e3a289cd055 Homepage: https://cran.r-project.org/package=TCGAbiolinks Description: Bioc Package 'TCGAbiolinks' (TCGAbiolinks: An R/Bioconductor package for integrative analysiswith GDC data) The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. Package: r-bioc-tcgabiolinksgui.data Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 23171 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-dt Filename: pool/dists/noble/main/r-bioc-tcgabiolinksgui.data_1.32.0-1.ca2404.1_all.deb Size: 22789552 MD5sum: 6cbfd5eb59654378746ca40e5d77f1c0 SHA1: c295161dc7eab9fdfd8f54f179289751b018f63d SHA256: f539afc06a8999fd3627ab27b45ce009984bd7b2fcafb041d5d079111b34effc SHA512: bad7ad0396373c995a155068f93626a5f67e90e93a689c2c5aec4b775bb401e28b36e74e4765e01b76b9afd573357e280b51cafca458fbc6f6d59cc90f0a5fe2 Homepage: https://cran.r-project.org/package=TCGAbiolinksGUI.data Description: Bioc Package 'TCGAbiolinksGUI.data' (Data for the TCGAbiolinksGUI package) Supporting data for the TCGAbiolinksGUI package. Package: r-bioc-tcgautils Architecture: all Version: 1.32.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-biocgenerics, r-bioc-biocbaseutils, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-genomicdatacommons, r-cran-glue, r-bioc-iranges, r-bioc-multiassayexperiment, r-bioc-raggedexperiment, r-cran-rvest, r-bioc-s4vectors, r-bioc-seqinfo, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-xml2 Suggests: r-bioc-annotationhub, r-bioc-bioc.gff, r-bioc-biocfilecache, r-bioc-biocstyle, r-bioc-curatedtcgadata, r-bioc-complexheatmap, r-cran-devtools, r-cran-dplyr, r-cran-httr, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-impute, r-cran-knitr, r-cran-magrittr, r-bioc-mirnameconverter, r-bioc-org.hs.eg.db, r-cran-rcolorbrewer, r-cran-readr, r-cran-rmarkdown, r-bioc-rtcgatoolbox, r-bioc-rtracklayer, r-cran-r.utils, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg18.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/noble/main/r-bioc-tcgautils_1.32.2-1.ca2404.1_all.deb Size: 517528 MD5sum: 18f5f72d798e4214f2780f7e16c844f4 SHA1: 172c1da5e9d9ef6f78e018af2be7e70760ded230 SHA256: 54a0a4755cf7615275deda199bb11a99c303baffa1788436961b6a4929afa25a SHA512: b11679212684efb963ccf5a400c14f1463a2594e616bd03eca5c0325204c571b1f7623796740a842bad6d88ef9c08bc7b116ecb99e643ffe4ee43cdcef2dd6be Homepage: https://cran.r-project.org/package=TCGAutils Description: Bioc Package 'TCGAutils' (TCGA utility functions for data management) A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. Package: r-bioc-tcseq Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-edger, r-bioc-biocgenerics, r-cran-reshape2, r-bioc-genomicranges, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-genomicalignments, r-bioc-rsamtools, r-cran-e1071, r-cran-cluster, r-cran-ggplot2, r-cran-locfit Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-bioc-tcseq_1.36.0-1.ca2404.1_all.deb Size: 843202 MD5sum: 22c67366a9b9c48f8d942c274398501c SHA1: a220c0b6143ccb7a073caabebecbda751b962616 SHA256: 53561f6d6e8dcb58e8192f2f1fa90286af916600b10e180936f7c7ef288c4ad8 SHA512: 7c58db23319219a838a2477e51ac01b666215c7fb8566cdaeec8f56c58706c0276e46ae37c126da9177cea61252219c04eba98cfb7259936a97178b72ababf68 Homepage: https://cran.r-project.org/package=TCseq Description: Bioc Package 'TCseq' (Time course sequencing data analysis) Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data. 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SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis. Package: r-bioc-tkwidgets Architecture: all Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-widgettools, r-bioc-dyndoc Suggests: r-bioc-biobase, r-bioc-hgu95av2 Filename: pool/dists/noble/main/r-bioc-tkwidgets_1.90.0-1.ca2404.1_all.deb Size: 654106 MD5sum: a6558bf51c8c0e89cc347f6e9d8075bd SHA1: bf17be594188852181b85a327de265b199ebed60 SHA256: cec851c57b7dc44898e46c55ecaaaf12f437f500485a3f034c0a0309b3010df3 SHA512: f1aa1c06101afdff4ddaaf95b5b9b146051cded95eed311b9989bfc926b1621da122c93d10d6086efa1cac901089fde1bb5a5d2cae11adc4abcb416df391f60e Homepage: https://cran.r-project.org/package=tkWidgets Description: Bioc Package 'tkWidgets' (R based tk widgets) Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. Package: r-bioc-toast Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4593 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-epidish, r-bioc-limma, r-cran-nnls, r-cran-quadprog, r-bioc-summarizedexperiment, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-tidyr, r-cran-ggally Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-matrixstats, r-cran-matrix Filename: pool/dists/noble/main/r-bioc-toast_1.26.0-1.ca2404.1_all.deb Size: 3931832 MD5sum: 2a4db74413c65667c3d3ccd66edd4088 SHA1: e93be0eefbccd7debbd2142f4e1a5ba71d6f2583 SHA256: 29b306348bbbcf0368bb967d0ccbd9bb346519a1b2bd07c8295c0d87823230fc SHA512: 731c93e5d2ef02e1f85602ea926d7ad7ced63bad983f0d21b6572886a041f420949d8ae9829cbbdc76833a39289469819bc5b5e9ba4a458e94bf3fa3b296e7af Homepage: https://cran.r-project.org/package=TOAST Description: Bioc Package 'TOAST' (Tools for the analysis of heterogeneous tissues) This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. Package: r-bioc-topgo Architecture: all Version: 2.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4217 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-graph, r-bioc-biobase, r-bioc-go.db, r-bioc-annotationdbi, r-cran-sparsem, r-cran-lattice, r-cran-matrixstats, r-cran-dbi Suggests: r-bioc-all, r-bioc-hgu95av2.db, r-bioc-hgu133a.db, r-bioc-genefilter, r-bioc-multtest, r-bioc-rgraphviz, r-bioc-globaltest, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-topgo_2.64.0-1.ca2404.1_all.deb Size: 2571880 MD5sum: ee806c672ddf0f65d6ae2467d38c9118 SHA1: 81c83178791b79d3289539bab7a3c25901a3bb20 SHA256: 914b596c8ed3292888c9502bed0b4f14cee55d91b42127e3fc3999ec556a277a SHA512: e3a13693157c3dfd0bb6d5a7f6bd54eccabbb768dd0fd7b9d9557a5cbf7ae7e38579126d4e10df43d1d0cfbfcc85d7594caa1e7a02f92e649d7fa02603916e0a Homepage: https://cran.r-project.org/package=topGO Description: Bioc Package 'topGO' (Enrichment Analysis for Gene Ontology) topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. Package: r-bioc-trackviewer Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20480 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-gviz, r-bioc-rsamtools, r-bioc-s4vectors, r-bioc-rtracklayer, r-bioc-biocgenerics, r-cran-scales, r-bioc-iranges, r-bioc-annotationdbi, r-cran-grimport, r-cran-htmlwidgets, r-bioc-interactionset, r-bioc-rhdf5, r-cran-strawr, r-bioc-txdbmaker Suggests: r-bioc-biomart, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-runit, r-bioc-org.hs.eg.db, r-bioc-biocstyle, r-cran-knitr, r-bioc-variantannotation, r-cran-httr, r-cran-htmltools, r-cran-rmarkdown, r-bioc-motifstack Filename: pool/dists/noble/main/r-bioc-trackviewer_1.48.0-1.ca2404.1_all.deb Size: 9748190 MD5sum: a0f129df9a35372225609f0cc91bf0d8 SHA1: e56e510ffecf68379a8754c1ee3377225ac77b76 SHA256: f591ebf5601a0786e1a127b1c89b2be891cd11f24c89c6a6e504d36e1e621919 SHA512: 4dcb4862f91e5414f0b99c030878e4a0051db3d0938d9415b87136b23184949f1aee92c3bd7990b03b599d9380c9893f80a0d2b962c41fa19a906a70f65d07c3 Homepage: https://cran.r-project.org/package=trackViewer Description: Bioc Package 'trackViewer' (A R/Bioconductor package with web interface for drawing elegantinteractive tracks or lollipop plot to facilitate integratedanalysis of multi-omics data) Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. Package: r-bioc-tradeseq Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-bioc-edger, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-slingshot, r-cran-magrittr, r-cran-rcolorbrewer, r-bioc-biocparallel, r-bioc-biobase, r-cran-pbapply, r-cran-igraph, r-cran-ggplot2, r-cran-princurve, r-bioc-s4vectors, r-cran-tibble, r-cran-matrix, r-bioc-trajectoryutils, r-cran-viridis, r-cran-matrixstats, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-bioc-clusterexperiment, r-bioc-delayedmatrixstats Filename: pool/dists/noble/main/r-bioc-tradeseq_1.26.0-1.ca2404.1_all.deb Size: 4257412 MD5sum: fa6155d82fb3b2197e664319b1597381 SHA1: 0da4bd59ff893f010649b8c0f17716b526f41d9a SHA256: 396e94936ee5fc50c7451221c2504d15cdf5d5b5d240212a1af9dac41f325a0b SHA512: 300fa93f36f47f422dca65824d5d493851a0c514627a75d1f4b64235e34f4d3eccbc2f5380a02024a5bad504f5e9a4145cc2d0932a18cc5f8778087d6b1ba1f7 Homepage: https://cran.r-project.org/package=tradeSeq Description: Bioc Package 'tradeSeq' (trajectory-based differential expression analysis for sequencingdata) tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. Package: r-bioc-trajectoryutils Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-matrix, r-cran-igraph, r-bioc-s4vectors, r-bioc-summarizedexperiment Suggests: r-bioc-biocneighbors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-trajectoryutils_1.20.0-1.ca2404.1_all.deb Size: 510974 MD5sum: e7bf645bfee977c8f4b361e60613850f SHA1: 13766da93f1bcf59e9a251709daedf5d2d0c20cb SHA256: 7aa22bbde2996201c8d9337b841f255100e62ef6dd2a27583d19f60c09721412 SHA512: 6974265c12fb18862feb23c0739584c3bb405bf8c8567fba6c176a15ab6f2f9556febc18426c10dcedbca2ef922a032a63ea89788f3d4b2219350d84fb45285c Homepage: https://cran.r-project.org/package=TrajectoryUtils Description: Bioc Package 'TrajectoryUtils' (Single-Cell Trajectory Analysis Utilities) Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. Package: r-bioc-treeio Architecture: all Version: 1.36.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidytree, r-cran-yulab.utils Suggests: r-bioc-biostrings, r-cran-cli, r-cran-ggplot2, r-bioc-ggtree, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-phangorn, r-cran-prettydoc, r-cran-purrr, r-cran-testthat, r-cran-tidyr, r-cran-vroom, r-cran-xml2, r-cran-yaml Filename: pool/dists/noble/main/r-bioc-treeio_1.36.1-1.ca2404.1_all.deb Size: 735988 MD5sum: 9af4a1fbc6750d2d3f03a47aec5376f1 SHA1: 507251443b01b540e9094d566bb55a89206beb5b SHA256: 17cce1f480740520ad57d9b1726535498cb092d024185283c5d80ff4f730a627 SHA512: 77037001c548c47980e7a9258ad90a591a1eae5030e4428c40ca920a57ac6b0b2208b755dd52730c4557fa914350a6ea10bdc94a08cf13bbf85b33a3683c2f71 Homepage: https://cran.r-project.org/package=treeio Description: Bioc Package 'treeio' (Base Classes and Functions for Phylogenetic Tree Input andOutput) 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. Package: r-bioc-treesummarizedexperiment Architecture: all Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-s4vectors, r-bioc-biostrings, r-bioc-biocgenerics, r-cran-ape, r-cran-rlang, r-cran-dplyr, r-bioc-summarizedexperiment, r-bioc-biocparallel, r-bioc-iranges, r-bioc-treeio Suggests: r-bioc-ggtree, r-cran-ggplot2, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-treesummarizedexperiment_2.20.0-1.ca2404.1_all.deb Size: 1360950 MD5sum: fb2fa118a3c7ff833215faffee26e884 SHA1: b3de20ca2a039d2e41f47a0180f6b38ce2d56db1 SHA256: 8d086ddf48a18199ab81466a7acbf0847989f145c48ad4f7ff6bd740e58c9607 SHA512: eab617ca10f20f736d402ce1c1ceecbe6d52bfd65ba5804554ff8bf5a0089a39db520f18ab36aff199d04547036d429be7efa5df6fb8cdd4e59e4e6d56da6d9c Homepage: https://cran.r-project.org/package=TreeSummarizedExperiment Description: Bioc Package 'TreeSummarizedExperiment' (TreeSummarizedExperiment: a S4 Class for Data with TreeStructures) TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. Package: r-bioc-tscan Architecture: all Version: 1.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3034 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-trajectoryutils, r-cran-ggplot2, r-cran-shiny, r-cran-plyr, r-cran-fastica, r-cran-igraph, r-cran-combinat, r-cran-mgcv, r-cran-mclust, r-cran-gplots, r-cran-matrix, r-bioc-summarizedexperiment, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-s4vectors Suggests: r-cran-knitr, r-cran-testthat, r-bioc-scuttle, r-bioc-scran, r-bioc-metapod, r-bioc-biocparallel, r-bioc-biocneighbors, r-bioc-batchelor Filename: pool/dists/noble/main/r-bioc-tscan_1.50.0-1.ca2404.1_all.deb Size: 2893142 MD5sum: 11722700c2525b8add5c622b542f5d48 SHA1: e093de7ce0ea1cedb03dac9f077590388aefb35e SHA256: d51728c27e51e390ac7de672d6e2e5564d02aad9b0683cc91fd7eb604badccbd SHA512: 34ca04494729696fd0213b3aa63da22b60e2d834c91f61a426395372d10aa1d6608fe6bba4a9f45c0966e08525638a92db7a33ac106ff2436d93511c848192c5 Homepage: https://cran.r-project.org/package=TSCAN Description: Bioc Package 'TSCAN' (Tools for Single-Cell Analysis) Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. Package: r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Architecture: all Version: 3.22.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-annotationdbi Filename: pool/dists/noble/main/r-bioc-txdb.hsapiens.ucsc.hg19.knowngene_3.22.1-1.ca2404.1_all.deb Size: 37285808 MD5sum: 3aaba29fab75831f127abaebe5f64eca SHA1: 7d617d26f5dd14c49596868eece0465dd7eafb92 SHA256: 7101859a3c169eb4189c96c7748b2389516990ef64c58b7a3b191b9752c847df SHA512: b77ba4c8cefd4d8d5cda5c9f4c4d9ee5144420a38c7c2104cf59e4d0181ab7cb98fb204c932333dea1b6da2227e3c4c5b2641b12557f895265a589c7d2426fcc Homepage: https://cran.r-project.org/package=TxDb.Hsapiens.UCSC.hg19.knownGene Description: Bioc Package 'TxDb.Hsapiens.UCSC.hg19.knownGene' (Annotation package for TxDb object(s)) Exposes an annotation databases generated from UCSC by exposing these as TxDb objects Package: r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Architecture: all Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-annotationdbi Filename: pool/dists/noble/main/r-bioc-txdb.hsapiens.ucsc.hg38.knowngene_3.22.0-1.ca2404.1_all.deb Size: 40269750 MD5sum: d8c6939aa4f6f33ba51c5551416bc289 SHA1: 7e7351d0128038853f8192be01a0c4b4346f9fce SHA256: a8d02e637dc68d0193fbd4dfce021538b35daf67d3dadaf2a57c5853c151edd1 SHA512: 999fc32439a3ee5e9202bb3c0641b107345493374cf204d1e350cd3585f148d57c7d5a2a96702333b4a2fe9a1f57d3c75c1ad9f40dc6572162c24fc676fdf14d Homepage: https://cran.r-project.org/package=TxDb.Hsapiens.UCSC.hg38.knownGene Description: Bioc Package 'TxDb.Hsapiens.UCSC.hg38.knownGene' (Annotation package for TxDb object(s)) Exposes an annotation databases generated from UCSC by exposing these as TxDb objects Package: r-bioc-txdb.mmusculus.ucsc.mm10.knowngene Architecture: all Version: 3.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-annotationdbi Filename: pool/dists/noble/main/r-bioc-txdb.mmusculus.ucsc.mm10.knowngene_3.10.0-1.ca2404.1_all.deb Size: 17346986 MD5sum: 091d02a5ea88bdc8a86f560f66743815 SHA1: d06c318e5bf776030bfc7b084a0500608c0d34dc SHA256: 3494bca46f8d54a58664e2792fd99bdb89cb7fb9b1b10d62cf16a96abe69abe2 SHA512: 988c81d3d435e6ddc51cf145e6580e3d74dc83a34071dec447afa5cd4ec831726ae27a6aa1104e22cbcb4c1a74818838294f440c124a840a5aec2e71bf94ca86 Homepage: https://cran.r-project.org/package=TxDb.Mmusculus.UCSC.mm10.knownGene Description: Bioc Package 'TxDb.Mmusculus.UCSC.mm10.knownGene' (Annotation package for TxDb object(s)) Exposes an annotation databases generated from UCSC by exposing these as TxDb objects Package: r-bioc-txdbmaker Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3670 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-httr, r-cran-rjson, r-cran-dbi, r-cran-rsqlite, r-bioc-iranges, r-bioc-ucsc.utils, r-bioc-genomeinfodb, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocio, r-bioc-rtracklayer, r-bioc-biomart Suggests: r-cran-rmariadb, r-bioc-ensembldb, r-bioc-genomeinfodbdata, r-cran-runit, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-txdbmaker_1.8.0-1.ca2404.1_all.deb Size: 1067234 MD5sum: c8ee7b212ef784e75978d6d247cdfb0f SHA1: bb6451e97d774a55bd4307bebfc93d4a613ede57 SHA256: 9c8cafba3de6c8653899d16e353c2f3c2061a053bf1a7f7653a4222f5661ba6a SHA512: 28ab884596254feaf3e9bf57c68005aa6a3841921bfe5aa56b2b95aa9180c3c8e87d31e8173d2fd8cb744ad846c8c728e374970c881cc90c87f64555b68322f0 Homepage: https://cran.r-project.org/package=txdbmaker Description: Bioc Package 'txdbmaker' (Tools for making TxDb objects from genomic annotations) A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats. 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De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. 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Package: r-cran-abcrlda Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-abcrlda_1.0.3-1.ca2404.1_all.deb Size: 206042 MD5sum: 64b1513398105726550d6d041027bb61 SHA1: 5489289c51865a9734f29489e84fff6d683468d4 SHA256: b092721d077e21b05ac8409b4fc1e4e48dca66e9b6364d417660c797976ec22a SHA512: dbccdd3caf00537e0002dde224b7eb37c55cb67d2e8c79661270ec2dbead329469cd0d26329ef1a64f3467992bf8b0153a5e194f0502cab686a2c79387894ce5 Homepage: https://cran.r-project.org/package=abcrlda Description: CRAN Package 'abcrlda' (Asymptotically Bias-Corrected Regularized Linear DiscriminantAnalysis) Offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. 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This package overlaps considerably with other R packages such as 'vegan', 'gUniFrac', 'betapart', and 'fossil'. We also include a wide range of functions that are implemented in software outside the R ecosystem, such as 'scipy', 'Mothur', and 'scikit-bio'. The implementations here are designed to be basic and clear to the reader. 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This sampler was adapted from the original MATLAB routine proposed in Wang (2012) . Package: r-cran-abhgenotyper Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-abhgenotyper_1.0.1-1.ca2404.1_all.deb Size: 134496 MD5sum: 297642f1c50c2d8221a6d20e05dcf6bc SHA1: 74adc0464264d254b5f9529642b3b6c90053033e SHA256: 95658ea7a2b91bffbd3a2a9f8b51df254f3ebdbafa9780eea9d99caf779edcf5 SHA512: b723ab69ccb9617c8c392386de97bc66fc638c2c6e0a2541b0f47e697f24c88f50d0d6b952279dffdb2efa09663198f4c732dd73d54d1583a312674910bbcde2 Homepage: https://cran.r-project.org/package=ABHgenotypeR Description: CRAN Package 'ABHgenotypeR' (Easy Visualization of ABH Genotypes) Easy to use functions to visualize marker data from biparental populations. Useful for both analyzing and presenting genotypes in the ABH format. 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The AB method allows to make inference for composite null hypotheses of no mediation effect, providing valid type I error control and thus optimizes statistical power. For more technical details, refer to He, Song and Xu (2024) . Package: r-cran-abind Architecture: all Version: 1.4-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-abind_1.4-8-1.ca2404.1_all.deb Size: 66050 MD5sum: 64c9b53c86d989fbaa2b59a1df06c6cc SHA1: 70462e24f0cfa645e1788d0701b899a8678f42bf SHA256: cbedc4fd30d00d757f9499318670cdcddfede70e9cfb03332a6b44565809c6c7 SHA512: 2c76370c2b21872a711965deaaa16cb39b460518667177b9a0b2f7b9cc556372f3c996c1711cecfca2b9d2b9045ebb7dd7e0194e0871bf111c0263cc5f060c64 Homepage: https://cran.r-project.org/package=abind Description: CRAN Package 'abind' (Combine Multidimensional Arrays) Combine multidimensional arrays into a single array. 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Package: r-cran-abjdata Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3913 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-abjdata_1.1.2-1.ca2404.1_all.deb Size: 3966204 MD5sum: 055cf74cde95c18af0b00fb8c63e7850 SHA1: 141dd25dbfb2fde4764fbde96df4c640c2d4fe07 SHA256: 869cd9a30caac30edf81ad89dd93b2c58935f434e9a8a3f47f4be7e63701a84c SHA512: 35f1777cb9d0bb46374bce3248a9948968d5b79e41ceda19af7c1ddc1a551a1290b52b2a57087ab27d01346f054640c0cd3aa6518b8b4386995481c2b6d1cca4 Homepage: https://cran.r-project.org/package=abjData Description: CRAN Package 'abjData' (Databases Used Routinely by the Brazilian JurimetricsAssociation) The Brazilian Jurimetrics Association (ABJ in Portuguese, see for more information) is a non-profit organization which aims to investigate and promote the use of statistics and probability in the study of Law and its institutions. This package has a set of datasets commonly used in our book. 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This package implements general purpose tools used by ABJ, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal. Package: r-cran-ablasso Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1836 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdm, r-cran-matrixstats, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ablasso_1.1-1.ca2404.1_all.deb Size: 1665272 MD5sum: 2d4dfcee2fd1f906aa65edb17f36e3b1 SHA1: cb49e935f5a62751bcf51ba8276d17e4e9f0c6a7 SHA256: 4896061c6d3d0f18eaca79378c15b5947e44b5b54e62208f429a8680667b3067 SHA512: a67ea869ea8a88400c9f4b1aeaf28633a1b230ed7dffa6d5d30219bbf7ed4e8124e7a0817f98f795b17bfdeed6062be45d44d1fbf4de9681319909cc928fa0ce Homepage: https://cran.r-project.org/package=ablasso Description: CRAN Package 'ablasso' (Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models) Implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint . 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(2022) . The package contains two movement functions, each of which is based on the Ornstein-Uhlenbeck (OU) model (Ornstein & Uhlenbeck, 1930) . It also contains several visualization and data summarization functions to facilitate the presentation of simulation results. 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As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize. Package: r-cran-abodoutlier Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Filename: pool/dists/noble/main/r-cran-abodoutlier_0.1-1.ca2404.1_all.deb Size: 15522 MD5sum: 868b0ce9521f991cc35beacc3bedf994 SHA1: bec5e028b7428afb484236c2e18f4eef02e8a72a SHA256: b208931152bb0bd6753c28071b879d7ec67f2b4512951371e03c1a566bcfc742 SHA512: 9f43ba8a82be308ab3ae8a40e899cc1cc4f453b8dc529257b61afef3ace31ec334fa5f7bba267171adee177a66edd7b6d54d80154c8d5f1681b23fecb9b3ed8d Homepage: https://cran.r-project.org/package=abodOutlier Description: CRAN Package 'abodOutlier' (Angle-Based Outlier Detection) Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection. 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Applications include surface mine reclamation monitoring, sediment pond capacity tracking, highwall safety classification, and erosion channel detection. Built on 'lidR' for point cloud I/O and 'terra' for raster operations. Includes access utilities for 'KyFromAbove' cloud-native elevation data on Amazon Web Services ('AWS') . Methods for terrain change detection and volume estimation follow Li and others (2005) . 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(2006) ). The package also contains functions to calculate other scores used in anti-doping programs, such as the OFF-score (Gore et al. (2003) ), as well as example data. Package: r-cran-abrsqol Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-abrsqol_1.0.0-1.ca2404.1_all.deb Size: 28538 MD5sum: d8b8d492232b57ea97fe0d52eba5a804 SHA1: 9a97ebcd4979459bf01ddf83c062ad716e2d581c SHA256: 71769e561d28058c67151914565806797e863913a101270c4270019be69a3889 SHA512: 08f6777645c9cae462a5148ef4895b0234ead4b391f16e95b29e88bf56bb8ad732c85d7d0e5287a22f2fe1bc8d2c8536ceb5eff774d46e55f35e236e2d4bbdfd Homepage: https://cran.r-project.org/package=ABRSQOL Description: CRAN Package 'ABRSQOL' (Quality-of-Life Solver for "Measuring Quality of Life underSpatial Frictions") This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictions—generated by idiosyncratic tastes and local ties — and trade frictions — generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper. 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Package: r-cran-absolution Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-alakazam, r-cran-attachment, r-cran-benchmarkme, r-cran-bigassertr, r-cran-bigparallelr, r-cran-bigstatsr, r-bioc-biostrings, r-cran-bs4dash, r-cran-callr, r-cran-colourpicker, r-cran-config, r-cran-dashboardthemes, r-cran-data.table, r-cran-dockerfiler, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-fresh, r-cran-fs, r-cran-ggplot2, r-cran-golem, r-cran-htmlwidgets, r-bioc-iranges, r-cran-knitr, r-cran-peptides, r-cran-plotly, r-bioc-pwalign, r-cran-reactable, r-cran-rlang, r-cran-rmarkdown, r-cran-seqinr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinymanager, r-cran-shinymeta, r-cran-shinythemes, r-cran-shinywidgets, r-cran-sortable, r-cran-stringdist, r-cran-stringr, r-cran-sunburstr, r-cran-umap, r-cran-upsetjs, r-cran-viridis, r-cran-xfun Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-absolution_1.0.2-1.ca2404.1_all.deb Size: 1813132 MD5sum: 7a53168a91642b96ac08d02f4ef576fd SHA1: b02394257aa4e6d1d291c6afcfbf2cd9815e05fa SHA256: 95f721b2404ef4fab96d759e45c8c7ecd2b7c35f47669b395b1bcdf47ad7d407 SHA512: f641dc880b3f745b34d2814a505707b725e2141d374057d1f99c7373eb963fcc8a8a218c1ea464101a9fd47de6f7e3ab52a147a5a1bf8498dacc2a72eb00f85b Homepage: https://cran.r-project.org/package=AbSolution Description: CRAN Package 'AbSolution' (Interactive Feature-Based Analysis of AIRR-Seq Data) An interactive framework for the exploration and analysis of adaptive immune receptor repertoire sequencing (AIRR-seq) data. It enables large-scale computation and integrated analysis of sequence-derived features, including physicochemical properties, amino acid descriptor sets, sequence motifs, compositional patterns, and somatic hypermutation metrics. The application supports multiscale analysis across sequences, clones, and repertoires, with interactive visualizations and statistical feature selection. 'AbSolution' also facilitates reproducible research by enabling structured export of data, code, parameters, and computational environments. See for more details. Package: r-cran-absorber Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-sparsegl, r-cran-fda, r-cran-ggplot2, r-cran-mass, r-cran-irlba Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-absorber_1.0-1.ca2404.1_all.deb Size: 94050 MD5sum: 77820167e8471b0023f50eeb19d73b7a SHA1: b77672f5b115639e9c82148a30669dd1d60600ad SHA256: b694f66febb26241414e6f22f76ffbf32f322690857c65da3aea472b287ad04e SHA512: 7339263b91dfbf5567ebd3a98872b3dc343aff31b6cd27f152ab61f89c66779e981aeee8197917ffe2bba7cdf46b60f58e0457a3dd929dfdb97e51316f5e0346 Homepage: https://cran.r-project.org/package=absorber Description: CRAN Package 'absorber' (Variable Selection in Nonparametric Models using B-Splines) A variable selection method using B-Splines in multivariate nOnparametric Regression models Based on partial dErivatives Regularization (ABSORBER) implements a novel variable selection method in a nonlinear multivariate model using B-splines. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), . 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A variety of user defined options and formatting are included. Package: r-cran-absurvtdc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-readxl Filename: pool/dists/noble/main/r-cran-absurvtdc_0.1.0-1.ca2404.1_all.deb Size: 51102 MD5sum: d85f3e1199d9489382b43586bd6a76d1 SHA1: 37efda88b764c65d6ff47753151bc47100356093 SHA256: 2239f1ea89b1d1b805cb40dcd82c095f55d1aef5a29a9d9441b78d6b30c060dd SHA512: f63df60ca8a7ae060ebd86ce9b5e4f2f5de2320fd2b3dec02839f2adf3dfca35a4ca98e5466994c2a47b19b3f9d62ee025155333584a681c6d531b1225c2f7de Homepage: https://cran.r-project.org/package=ABSurvTDC Description: CRAN Package 'ABSurvTDC' (Survival Analysis using Time Dependent Covariate for AnimalBreeding) Survival analysis is employed to model the time it takes for events to occur. Survival model examines the relationship between survival and one or more predictors, usually termed covariates in the survival-analysis literature. To this end, Cox-proportional (Cox-PH) hazard rate model introduced in a seminal paper by Cox (1972) , is a broadly applicable and the most widely used method of survival analysis. This package can be used to estimate the effect of fixed and time-dependent covariates and also to compute the survival probabilities of the lactation of dairy animal. This package has been developed using algorithm of Klein and Moeschberger (2003) . 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This method does not make any assumptions about the missingness mechanisms and controls the Type I error regardless of the missing values by taking all possible missing values into account. Package: r-cran-ac3net Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ac3net_1.2.2-1.ca2404.1_all.deb Size: 1378684 MD5sum: 30654ee16c76eeac2e4304b16f649e32 SHA1: df0ba273518dff1cecf617209ad37f1a57ad6e8a SHA256: abb728d451b41016b937dafb8560de4303cfed6c97c0e1547380a64667d4e774 SHA512: bb646fc7be1f854711abfecb1ab0751dc2ea297d425fca984faff1bb5abbcc02f057a8bbe10a03d72b300590903ba7fbdfc36f76f66e0d0f50253fcffc914e26 Homepage: https://cran.r-project.org/package=Ac3net Description: CRAN Package 'Ac3net' (Inferring Directional Conservative Causal Core Gene Networks) Infers directional conservative causal core (gene) networks. 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Permutation inference for canonical correlation analysis. NeuroImage, . Furthermore, it provides plotting tools to visualize the results. 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A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit, allowing for the extrapolation of the degradation of a drug product - both in time and temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate t-distribution, and analytical formulae (the delta method) are available options to calculate the confidence and prediction intervals. The results (modelling, extrapolations and statistical intervals) can be visualised with multiple plots. The examples illustrate the accelerated stability modelling in drugs and vaccines development. Package: r-cran-accept Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyselect, r-cran-dplyr, r-cran-reldist, r-cran-tibble, r-cran-hardhat, r-cran-vctrs, r-cran-vetiver Suggests: r-cran-jsonlite, r-cran-plotly, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-accept_1.0.2-1.ca2404.1_all.deb Size: 737446 MD5sum: ef4ef3b95b32b9b43dc72cedc493a41c SHA1: 6031ab33f93fc659658769a3f653ed52f984d3b4 SHA256: 71f13b40d4262787fbb416dc440cb06005a87d6186520d12e6a3dfc4fad80df3 SHA512: d8684287a1c9988735b1e9f554f4946e76aa2cc9fb5fe1ff020de19cf6d5b670f43a04237ea6a7ccaaf42498fb0c5e965ae85dc1b74fddc741acb5613db21f43 Homepage: https://cran.r-project.org/package=accept Description: CRAN Package 'accept' (The Acute COPD Exacerbation Prediction Tool (ACCEPT)) Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction models published in Adibi et al. (2020) and Safari et al. (2022) . Package: r-cran-acceptancesampling Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-acceptancesampling_1.0.11-1.ca2404.1_all.deb Size: 418190 MD5sum: db5b5058b1723a50540abf848cee0429 SHA1: b35bc61d795d298736192a6fcd4b4af7adef6e12 SHA256: 5e9574a95b931f052f5be82c66655bc34bf6a4d08e906c72d34c5d42c2fe4589 SHA512: 815228bd9c773fbefa8f5bca8c04a681f8bdbb2d341ddc1d4b24df29f72e34540397972833d3183e02ef3c1206e88718aa298e44f4df6bd543dd2fe2ec2f90b3 Homepage: https://cran.r-project.org/package=AcceptanceSampling Description: CRAN Package 'AcceptanceSampling' (Creation and Evaluation of Acceptance Sampling Plans) Provides functionality for creating and evaluating acceptance sampling plans. Sampling plans can be single, double or multiple. Package: r-cran-accessibility Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3225 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-hmisc, r-cran-rdpack Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-accessibility_1.5.0-1.ca2404.1_all.deb Size: 2752592 MD5sum: 9c76e59cf06c5410b7074d4a04b4774d SHA1: c57d6da16e1f3beb78d55f16cf07cdd3f703f517 SHA256: 43c8ac2fa3a67423edeb2b104e30eac4f2f79f3ed5ce52e737543c823a68b0b5 SHA512: ceab80147944910b6196b46aafbfc9af820b583631381788663f58331f2c7382faf4c85ed79636f527388ed5b01eecc1430b14af4c518e0c486b2a0d51bc475a Homepage: https://cran.r-project.org/package=accessibility Description: CRAN Package 'accessibility' (Transport Accessibility Measures) A set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing for example the location of jobs, healthcare and population), the package allows one to calculate accessibility levels, and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), some cutting edge measures proposed in the literature (e.g. balancing cost and constrained accessibility) as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures). 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Accessible 'PDF' files are produced only on a 'Windows' Operating System. One aspect of accessibility is providing a headings structure that is recognised by a screen reader, providing a navigational tool for a blind or partially-sighted person. A key aim is to produce documents of different formats easily from each of a collection of 'R markdown' source files. Input 'R markdown' files are rendered using the render() function from the 'rmarkdown' package . A 'zip' file containing multiple output files can be produced from one function call. A user-supplied template 'Word' document can be used to determine the formatting of an output 'Word' document. Accessible 'PDF' files are produced from 'Word' documents using 'OfficeToPDF' . A convenience function, install_otp() is provided to install this software. The option to print 'HTML' output to (non-accessible) 'PDF' files is also available. 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Supports one or two primary variables, exponential-tilt sensitivity analysis, regression weights, and nonparametric bootstrap confidence intervals. Package: r-cran-accrual Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tcltk2, r-cran-fgui, r-cran-smpracticals Filename: pool/dists/noble/main/r-cran-accrual_1.4-1.ca2404.1_all.deb Size: 60256 MD5sum: 38daaf18ac3439dd6cb5565c5d0039b2 SHA1: d132906ab0fc4138aa88f6557642bcb06f9bffe5 SHA256: 09807acc54780c07f5e70fc5578de977d564ce3d86a4cd10bccf65fd6920af47 SHA512: f86e34362daac0571d157d4941418761800dc8c65038dc8b0fdb14b4285f7e9f5491993a6ca95668e5283b9740ae0578306fe962695b1f3a2c970a45c0ff4b16 Homepage: https://cran.r-project.org/package=accrual Description: CRAN Package 'accrual' (Bayesian Accrual Prediction) Participant recruitment for medical research is challenging. Slow accrual leads to delays in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. We developed a Bayesian method that integrates the researcher's experience with previous trials and data from the current study, providing reliable predictions on accrual rate for clinical studies. For more details and background on these methodologies, see the publications of Byron, Stephen and Susan (2008) , and Yu et al. (2015) . In this R package, Bayesian accrual prediction functions are presented, which can be easily used by statisticians and clinical researchers. Package: r-cran-accrualplot Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-markdown, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-accrualplot_1.0.10-1.ca2404.1_all.deb Size: 283018 MD5sum: 8d9908e7b7fa89575d09c26290f39bca SHA1: dcf6151196b82ab57f0336d885d3740972006181 SHA256: 7fd0871b273b2868cd80274726917c4ccea95f0905c5353587a8f2864e378384 SHA512: f7c1b5d6dd6d5e8a1362c757700d573565af6dc7a63397e4c0a4f1e5a66a0d2b6c6861aceab62a992717ec914a2e48a6d321c4292df43ca3d39c2a3f74fb5a2c Homepage: https://cran.r-project.org/package=accrualPlot Description: CRAN Package 'accrualPlot' (Accrual Plots and Predictions for Clinical Trials) Tracking accrual in clinical trials is important for trial success. 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Package: r-cran-accsamplingdesign Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-vgam, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-accsamplingdesign_0.1.0-1.ca2404.1_all.deb Size: 334028 MD5sum: b4728b053adccf6300005cad44d266e5 SHA1: c9763b53421b75e8f561e54dc0f486d4ba7f75cd SHA256: de86894371d5784ddbd0f91edaf710840eace23004903177cf75f09346db73d3 SHA512: 589f0e2feae21604c7ce8bd7d8ceafa0b867cfab155be8e2c52be3b35985e6600e38c0c38cf4623159b84bd860d7ed1badac3d70c5f4feb1dde85752ea4b363b Homepage: https://cran.r-project.org/package=AccSamplingDesign Description: CRAN Package 'AccSamplingDesign' (Acceptance Sampling Plans Design) Provides tools for designing and analyzing Acceptance Sampling plans. 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Four metrics are provided: Counted Squared Error (CSE), Counted Absolute Error (CAE), Counted Absolute Percentage Error (CAPE), and Symmetric Counted Absolute Percentage Error (SCAPE). These metrics offer robust, consistent, and interpretable evaluation on a 0-100% scale, addressing limitations of conventional metrics like RMSE, MAE, and MAPE. The package integrates with 'caret', 'tidymodels', and common forecasting frameworks. Based on Agustini, Fithriasari, and Prastyo (2026) . 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See the examples, testing versions and more details from: . Package: r-cran-ace.coco Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-mvtnorm, r-cran-mass Filename: pool/dists/noble/main/r-cran-ace.coco_0.1-1.ca2404.1_all.deb Size: 18668 MD5sum: 7753c586e5d759a55adc2bcd592ab906 SHA1: 27ff2b2947a9d9654696fd1c9ac2a6d24ec041bd SHA256: 28a64084363a72f85db434e26074474d04400f915b502205899dfe4182e44850 SHA512: 5356531452ec63684fbca3472a695c55abd2422700fb04651c09e228d4e45c573366e3ddf6b3b5842ffe0bc5838922b3746193826acf2923ad141314127c93a6 Homepage: https://cran.r-project.org/package=ACE.CoCo Description: CRAN Package 'ACE.CoCo' (Analysis of Correlated High-Dimensional Expression (ACE) Data) A function for estimating factor models. Give factor-adjusted statistics, factor-adjusted mean estimation (one-sample test) or factor-adjusted mean difference estimation (two-sample test). Package: r-cran-ace2fastq Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ace2fastq_0.6.0-1.ca2404.1_all.deb Size: 26862 MD5sum: f58b69ccb429db09928435de06390d9e SHA1: b58a5bdca09203f981e79d68c090f5a7b1f8a165 SHA256: 5db73e62865b2142127b9ac19ffc4c6139879234cb88db04c0e326b2b743e653 SHA512: dc05a77f8fdf38023766ecd2cb91249a3a0ffec05d78b480f55cba0545df1abe312d98066d1192670db9907c0709f9423291e6744c2cb6e7b43a0b84a9e9219c Homepage: https://cran.r-project.org/package=ace2fastq Description: CRAN Package 'ace2fastq' (ACE File to FASTQ Converter) The ACE file format is used in genomics to store contigs from sequencing machines. This tools converts it into FASTQ format. Both formats contain the sequence characters and their corresponding quality information. Unlike the FASTQ file, the ace file stores the quality values numerically. The conversion algorithm uses the standard Sanger formula. The package facilitates insertion into pipelines, and content inspection. Package: r-cran-aceeditor Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11922 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-aceeditor_1.0.1-1.ca2404.1_all.deb Size: 1660064 MD5sum: 483eb8c3f108bcb373aa730e08995319 SHA1: 416688b21bba5757e8a2635282d04fc564878a4e SHA256: 7da4c5009922717e06ddeb4cb45d11822884f19a40bb98e9b6404eaaeb826f23 SHA512: 1b99d6c12ce66aa97a7c3c6d984a489d5c38013decbf45ae46eabff89f2f0301e1667c82b5eb4c53bb3894d7cf324259b31d85068f1b732855a88e2c2c0d341f Homepage: https://cran.r-project.org/package=aceEditor Description: CRAN Package 'aceEditor' (The 'Ace' Editor as a HTML Widget) Wraps the 'Ace' editor in a HTML widget. The 'Ace' editor has support for many languages. It can be opened in the viewer pane of 'RStudio', and this provides a second source editor. Package: r-cran-acep Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4082 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr, r-cran-magrittr Suggests: r-cran-furrr, r-cran-ollamar, r-cran-future, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-reticulate, r-cran-rsyntax, r-cran-spacyr, r-cran-tidygeocoder, r-cran-udpipe, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acep_0.1.2-1.ca2404.1_all.deb Size: 3501814 MD5sum: e56860d0c08ab981a0ce85b07f53a253 SHA1: fc07170a7989431b803969342c80c44c58f2a3bd SHA256: df6472abd5116362abb84c22f2d2e108ef4c056fcd5b7b6e7d74535c044d1c8d SHA512: 890c02fcc436c02fb387ed59a61f412561c846c8f9273a3f1c6df7ae19fc039d0bb2d92053ce75b09778abdc0d55679feb4d68b957f8853c1ba3aebc74cb735a Homepage: https://cran.r-project.org/package=ACEP Description: CRAN Package 'ACEP' (Análisis Computacional de Eventos de Protesta) La librería 'ACEP' contiene funciones específicas para desarrollar análisis computacional de eventos de protesta. Asimismo, contiene bases de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. La colección de diccionarios reune diccionarios de diferentes orígenes. The 'ACEP' library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources. Package: r-cran-acesearch Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acesearch_1.0.0-1.ca2404.1_all.deb Size: 26526 MD5sum: 6682fe02cc14d2a67b6919990c570eea SHA1: 39fad9c238239c906b953e6dd1c9e4ab29307a26 SHA256: a7b2db1653555106bfb69662a2e7a73dc1ad8743bb92ea34d039b259d299cdf9 SHA512: cd7457e3397ab100792de1d0099c7357f6188d89722e7357a6e350405f075b547bc9ba0c26a655f5aca8d8d7d4e6ddc3da69230d9eb235e3ed3f7b476383df30 Homepage: https://cran.r-project.org/package=ACEsearch Description: CRAN Package 'ACEsearch' ('ACE' Search Engine API) 'ACE' (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of 'ACE' search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at . Package: r-cran-acesimfit Architecture: all Version: 0.0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-acesimfit_0.0.0.9-1.ca2404.1_all.deb Size: 53518 MD5sum: f57627dd1d1b62b15ee626c0745b27fc SHA1: cd2dd32b8e43ac5654292c27ac6ba2b6cea2295c SHA256: 870760149982aa2037e6dc520da1851e01f64aa9f59430d9faf286372eed939d SHA512: 3b8d8c61bcdd3bd755956ba497ed379c2f0855036d90493e205b240a0c72bd7b027862a0cecd693e311a53e94b5a03f2650b95d4dbb858a719d1876621ec5ddb Homepage: https://cran.r-project.org/package=ACEsimFit Description: CRAN Package 'ACEsimFit' (ACE Kin Pair Data Simulations and Model Fitting) A few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004). Package: r-cran-acfmperiod Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acfmperiod_1.1.0-1.ca2404.1_all.deb Size: 432482 MD5sum: 0c19a30e8ba6900948bf5b4a314e5e5a SHA1: a73f2a760b3d38b70dabdbaad487f39f7d96345c SHA256: 87d9ec5183048788368a6e8b81d53bdb99954ad065efb0f5bfe97606a72902ee SHA512: 76d28c98c8fb0928367495f8d02af33e1d450f6db06c26d5b98d80c6f500d067ee7910022902b236e085b675dd256a266a3f02d23640a6dc17683c2e5bd2df45 Homepage: https://cran.r-project.org/package=acfMPeriod Description: CRAN Package 'acfMPeriod' (Robust Estimation of the ACF from the M-Periodogram) Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) . The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) . Package: r-cran-achievegap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-lme4, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-achievegap_0.1.0-1.ca2404.1_all.deb Size: 142192 MD5sum: 20af0e42b688013ddbb1019754885680 SHA1: b06da4fd6d8d265bb5b37ada38c3136f96805a79 SHA256: cd2f5556cb7da1993a783f3ca0fb047839d01ba90366c6d620a3fab8b6d6e99c SHA512: f90ef044068d49712bd1f4310e80d182fe3c5fd8adfc084d6046a2d9cc022f36ad2e35cb2a7c60d0a160457d83340567b88d37a72aeca9c8cf007adeba5632c8 Homepage: https://cran.r-project.org/package=achieveGap Description: CRAN Package 'achieveGap' (Modeling Achievement Gap Trajectories with HierarchicalPenalized Splines) Implements a hierarchical penalized spline framework for estimating achievement gap trajectories in longitudinal educational data. The achievement gap between two groups (e.g., low versus high socioeconomic status) is modeled directly as a smooth function of grade while the baseline trajectory is estimated simultaneously within a mixed-effects model. Smoothing parameters are selected using restricted maximum likelihood (REML), and simultaneous confidence bands with correct joint coverage are constructed using posterior simulation. The package also includes functions for simulation-based benchmarking, visualization of gap trajectories, and hypothesis testing for global and grade-specific differences. The modeling framework builds on penalized spline methods (Eilers and Marx, 1996, ) and generalized additive modeling approaches (Wood, 2017, ), with uncertainty quantification following Marra and Wood (2012, ). Package: r-cran-achilles Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender, r-cran-dplyr, r-cran-jsonlite, r-cran-parallellogger, r-cran-readr, r-cran-data.table, r-cran-lubridate, r-cran-tseries, r-cran-rlang Suggests: r-cran-dt, r-cran-magrittr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-achilles_1.7.2-1.ca2404.1_all.deb Size: 703508 MD5sum: 76d13457f59c5cb679aa811537412536 SHA1: abb4c5f9e8ad57c554dddc482e4bb4bb167fe9d4 SHA256: 63ce1fd3f5f8d8c32e44621b09bab3d1711bb4db79f5ee4b4179c59f3485f760 SHA512: 072e62959d621e40fd12e92e03d44275eeffaf7f28880d8077cc803e00c09cc25dc22fcecbef82ff50c5a55998e3171a2c18079bdda75188810085b07bbd7e47 Homepage: https://cran.r-project.org/package=Achilles Description: CRAN Package 'Achilles' (Achilles Data Source Characterization) Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications. Package: r-cran-acid Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-hmisc Suggests: r-cran-ineq Filename: pool/dists/noble/main/r-cran-acid_1.1-1.ca2404.1_all.deb Size: 349846 MD5sum: 48225c3507a8aa53ea6ef5ca70967adb SHA1: 9b26b2df320024ac758f24b364dfda834732fb0c SHA256: e3fd39ce43e972350bb28975301534f5b4a35779907a4591abf13ed63331ce13 SHA512: 38fe09000afbb07efac0e2cc0ab821105861119be2bca16e0e61e95ddbdf9cd5015e154d8eab8616fd0fbf5126896667d0587813dc43489adcc84ad2697bcee5 Homepage: https://cran.r-project.org/package=acid Description: CRAN Package 'acid' (Analysing Conditional Income Distributions) Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level. Package: r-cran-ackwards Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-generics, r-cran-psych, r-cran-rlang Suggests: r-cran-covr, r-cran-efatools, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-lavaan, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ackwards_0.2.0-1.ca2404.1_all.deb Size: 2082140 MD5sum: 82cf636dcedc3d4b233a1ad80271c8bb SHA1: 4138255d48fc5e40ffdac528d4e03e09aa432d24 SHA256: 329baa940a8a585322bc6b1c536eb394fefb5e3ed6ba36e8da8c647a40c9718b SHA512: b7d3d3f4ab96455bec8f727f7762398f184b13a4571e74b2db79bf5cdcc3d45341d3406f67ee967456b040bd33c3ed0b89c7d1d9facd181c3526281928e472f6 Homepage: https://cran.r-project.org/package=ackwards Description: CRAN Package 'ackwards' (Bass-Ackwards Hierarchical Structural Analysis) Implements Goldberg's (2006) bass-ackwards method and modern descendants for hierarchical structural analysis. Extracts solutions from 1 to k factors using principal component analysis (PCA), exploratory factor analysis (EFA), or exploratory structural equation modeling (ESEM) engines, then characterizes the hierarchy via between-level factor-score correlations computed via exact linear algebra (Waller, 2007, ) or materialized scores. Includes the Forbes (2023) extension for redundancy pruning and all-levels cross-correlations. Package: r-cran-acled.api Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acled.api_1.1.8-1.ca2404.1_all.deb Size: 34064 MD5sum: ec7c674e0f537a6a31498e68bc9d4fb6 SHA1: 150f24f5b9c77790040482327655def55688bf73 SHA256: 37e69ec5bc2c2630d054480e37e01367673517ae25a4206eb4cd3fc86b36013b SHA512: 50526b0f931ee03631f67215a271fb90eaa8f1291f79f1513789a3c1272dd5ac0a73cfe9a5f25b565a3d388b5f8b3f84aa7e668c329ad0c5ac2cefe017cddd01 Homepage: https://cran.r-project.org/package=acled.api Description: CRAN Package 'acled.api' (Automated Retrieval of ACLED Conflict Event Data) Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at . The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements. Package: r-cran-acledr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-lubridate, r-cran-stringr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-janitor, r-cran-rmarkdown, r-cran-readr, r-cran-kableextra, r-cran-ggplot2, r-cran-covr, r-cran-here, r-cran-secret, r-cran-sf, r-cran-raster, r-cran-forcats, r-cran-igraph, r-cran-sjmisc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acledr_1.0.1-1.ca2404.1_all.deb Size: 1691358 MD5sum: 61e280bbe093bfabe56f775d6f8ff390 SHA1: 44d51a7def69aa2b3b9cdcbf171f9d85e9ed3989 SHA256: ba6277c6d4e5ef8c3c9a098731e8e609fa988c6ce59d8cf93b91e9316a86dc05 SHA512: 0b582798fb4a8d3d71f4e6ac8390accc41f040412009294ef9d6eaa7dd1fe8dedded31010c07847663bb09194e03cc577b53c4157050faddea3deefe04e2c047 Homepage: https://cran.r-project.org/package=acledR Description: CRAN Package 'acledR' (Manipulate ACLED Data) Tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations. Package: r-cran-aclhs Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-geor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aclhs_1.0.1-1.ca2404.1_all.deb Size: 68960 MD5sum: 7abf7a59191119a86d6ed3cc3e93a294 SHA1: 10856b38a337196332f6e58b9125759331fb0c14 SHA256: 22a010167d5b14001a1a08509fd23b7d823a621e16662ce17cbb5bafd9dddda7 SHA512: 0909c8ee4e15e365b5d57d43049516e907abd37b87f8fce4cea79e815f98d6674af83605d5729c16f088a9149912a7e5b3c3a1d63234d458edea6e01167635a0 Homepage: https://cran.r-project.org/package=aclhs Description: CRAN Package 'aclhs' (Autocorrelated Conditioned Latin Hypercube Sampling) Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), . For acLHS, see Le and Vargas (2024) . Package: r-cran-acmgscaler Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-acmgscaler_1.0.0-1.ca2404.1_all.deb Size: 55194 MD5sum: e13c6ffa4498b3aef919156a15f9f9f3 SHA1: 6e358affb8753672bdc43205b650b94a99d9c9b1 SHA256: 620b561b8f13330f65f3ea7bab0bf73fd3d82fd816e772d53f49ba2b8d070f71 SHA512: ae2c9dd1bdea4e977cb292575605aae91e734faa8f70eeaa073e60aba9ab2b81cec9e42bc49fa6eaa7fb137431dc828fadae95e6800e33a704e400bf4bb77aac Homepage: https://cran.r-project.org/package=acmgscaler Description: CRAN Package 'acmgscaler' (Variant Effect Calibration to ACMG/AMP Evidence Strength) Provides a function to calibrate variant effect scores against evidence strength categories defined by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines. The method computes likelihood ratios of pathogenicity via kernel density estimation of pathogenic and benign score distributions, and derives score intervals corresponding to ACMG/AMP evidence levels. This enables researchers and clinical geneticists to interpret functional and computational variant scores in a reproducible and standardised manner. For details, see Badonyi and Marsh (2025) . Package: r-cran-acne Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aroma.affymetrix, r-cran-mass, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-matrixstats, r-cran-r.filesets, r-cran-aroma.core Suggests: r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-acne_0.9.2-1.ca2404.1_all.deb Size: 131508 MD5sum: d6e0fb79199e13818c694b3c27b15ad4 SHA1: 1e697e8ff86d69ea096a2ac766ae9ee9adb19e8d SHA256: 3c5c64a714e7cc126bb97c0bf4eddc00e3a385e48eb9be108e8cff0158f9e094 SHA512: c72cdfca5007548c80cd3fe7918c6f1967606b02274fb9de1e31f05251c85b40ea9eeaec16ff775f931ded265f1200fac03996385ba287bdd2d11d55c101d302 Homepage: https://cran.r-project.org/package=ACNE Description: CRAN Package 'ACNE' (Affymetrix SNP Probe-Summarization using Non-Negative MatrixFactorization) A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF). Package: r-cran-acnr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3037 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.utils, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acnr_1.0.0-1.ca2404.1_all.deb Size: 2829770 MD5sum: 67ef5db0e0c2aa8509da0da19221a6c2 SHA1: 834ea631b6bc2a71cfd5155223f529a28c6c42ba SHA256: ed4f7c6394b03eb101523ac21ee826ea4c78fafcf962d88108686c39b2b8dd6a SHA512: 317579a90061bee869dd8390743998a58a53429087822b6cd4a242b31e2ecb826156a0585da0a38c52bf282261842697b30f86585529c7c416a373938e877980 Homepage: https://cran.r-project.org/package=acnr Description: CRAN Package 'acnr' (Annotated Copy-Number Regions) Provides SNP array data from different types of copy-number regions. These regions were identified manually by the authors of the package and may be used to generate realistic data sets with known truth. Package: r-cran-acopula Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-acopula_0.9.4-1.ca2404.1_all.deb Size: 620676 MD5sum: ca24bf6a11167386264dbbecf6a5c841 SHA1: fe0a65b0042dbd76707b65c6e46f0b34b6e77d79 SHA256: c203a639b909cdd5fa0a2f714b3d86ce85e63ff4d9a885fd9298c4ea470243da SHA512: 7262a55f5472f21ba735aa89fd84b943dd54f65294e5186b86e3e672711597d22fb2a239001fc34653723b694970cf1ab503e78231000d8c341a1f27a6f12ea9 Homepage: https://cran.r-project.org/package=acopula Description: CRAN Package 'acopula' (Modelling Dependence with Multivariate Archimax (or anyUser-Defined Continuous) Copulas) Archimax copulas are mixture of Archimedean and EV copulas. The package provides definitions of several parametric families of generator and dependence function, computes CDF and PDF, estimates parameters, tests for goodness of fit, generates random sample and checks copula properties for custom constructs. In 2-dimensional case explicit formulas for density are used, contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well. Package: r-cran-acorn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acorn_0.1.0-1.ca2404.1_all.deb Size: 110290 MD5sum: fc2bc16b2ff0e45b4f2c0e84f9cd84be SHA1: 7f8bc79dadc4ff89d2f8e3e13ae30cb0831adac4 SHA256: db1ed988c8cc437af369127c10fbcb78276ff7d59a5d68ed30d1bd49020d58c4 SHA512: 6981376bad5625dd1a6a0f9c635a396c92156506d6ed5f4147424b529f033ec1f3c8a465336addd5a09c00bb936125046e09d9dfad82fabc06121bd84bbd31e7 Homepage: https://cran.r-project.org/package=acoRn Description: CRAN Package 'acoRn' (Exclusion-Based Parentage Assignment Using Multilocus GenotypeData) Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), . The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. 'acoRn' includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers. Package: r-cran-acousticndlcoder Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tuner, r-cran-zoo, r-cran-seewave Filename: pool/dists/noble/main/r-cran-acousticndlcoder_1.0.2-1.ca2404.1_all.deb Size: 600752 MD5sum: 925daaa840901b211c822769e8a11308 SHA1: 86257232832f64cdbb5555a9a03e9c60c43a6beb SHA256: 7cad60d29720a6816938b8e8105a249bdc03169de80f51a11530176ecb09941b SHA512: 7ccb9aaa682068e2c2e05c9803391a858a9067441e2060c90f3482c46e84e3d9dec5d14f9269fe64a6aa25db8a43c850d7c8909dc2ac12d573f184eafbe021f8 Homepage: https://cran.r-project.org/package=AcousticNDLCodeR Description: CRAN Package 'AcousticNDLCodeR' (Coding Sound Files for Use with NDL) Make acoustic cues to use with the R packages 'ndl' or 'ndl2'. The package implements functions used in the PLoS ONE paper: Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, and R. Harald Baayen (2017). Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLoS ONE 12(4):e0174623 More details can be found in the paper and the supplement. 'ndl' is available on CRAN. 'ndl2' is available by request from . Package: r-cran-acp Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tseries, r-cran-quantmod Filename: pool/dists/noble/main/r-cran-acp_2.1-1.ca2404.1_all.deb Size: 59500 MD5sum: d546b52474eb34bb790a4eac8df2138e SHA1: 7c00742c39e4215f02e7eb6a888ee0a32b5a3adb SHA256: 784d46f9176dfc5229cb88d5af4c2df1592da198c5a283d5bd16d89b19816078 SHA512: 12cea02c8af902e68c1dda6c92312535c9a2a07ec339f98d65169c00b0c50b399a52e2de0750c1b3076b01264af9c57d324dc25a58f363bf752492b2d7a6bdf7 Homepage: https://cran.r-project.org/package=acp Description: CRAN Package 'acp' (Autoregressive Conditional Poisson) Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003). Package: r-cran-acr Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4997 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-irr, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-withr, r-cran-yaml Suggests: r-cran-callr, r-cran-cluster, r-cran-covr, r-cran-ellmer, r-cran-ggplot2, r-cran-ggraph, r-cran-ggwordcloud, r-cran-igraph, r-cran-ipeaplot, r-cran-knitr, r-cran-shiny, r-cran-openxlsx, r-cran-pkgdown, r-cran-quanteda, r-cran-readtext, r-cran-rmarkdown, r-cran-scales, r-cran-senatebr, r-cran-spelling, r-cran-stopwords, r-cran-stringi, r-cran-tesseract, r-cran-testthat, r-cran-tidytext, r-cran-topicmodels, r-cran-wordcloud, r-cran-writexl Filename: pool/dists/noble/main/r-cran-acr_0.3.4-1.ca2404.1_all.deb Size: 3225292 MD5sum: cff039ba061710b74098ae49cbb6355e SHA1: ac9dc65ae13f7eadeb17f0429ab9729822584a4f SHA256: b87bb5f031a6a99bc64add4ca0d5b4112e92bf024e0e733cd4cafbadd69e8dc3 SHA512: b45bb00b012d09704ebb92702ae09e73b06bd0369b417e40dd4da897e2bd3f0391ecd1e9aa2cae187523a9415f0420eeda90d060ce08ddfe30da86b2616ec941 Homepage: https://cran.r-project.org/package=acR Description: CRAN Package 'acR' (Content Analysis in R: Integrated Qualitative (LLMs) andQuantitative Pipeline) Provides an integrated pipeline for content analysis combining qualitative coding assisted by large language models (LLMs) with classical quantitative text analysis. Includes modules for pre-processing (tokenization, stopwords for Brazilian Portuguese), descriptive statistics, keyness, co-occurrence networks, word clouds (including comparative and X-ray variants), sentiment analysis via OpLexicon, Latent Dirichlet Allocation (LDA), inter-coder reliability metrics (Krippendorff, Gwet), and modern visualizations based on ggplot2. Special focus on Brazilian corpora and political-institutional codebooks. Inspired by Maerz and Benoit (2025) . 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The methodology is based on the accessibility model described in Bervaes et al. (1996) "Een model voor het gebruik van de groene ruimte in stadslandschappen (Fase I)" , with optimization procedures based on Goldfarb and Idnani (1983) and Vanderbei et al. (1986) . 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Package: r-cran-acroname Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-r.utils, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-hunspell Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acroname_0.1.0-1.ca2404.1_all.deb Size: 29792 MD5sum: 06ead9276d5f1e761cad2eb7826206c9 SHA1: cc430a784f8cf25687c0899dbf34431e381a535e SHA256: dfcec3397e3f90715c30581928aaf3e2b6067bb3b330304014d22eaa1669915f SHA512: 0bf163db70b2823d67f30feddfff7147d9f456be2dfba1b5bf1e234911265fea3c2c7298792fd8407f1c475c9e64818979804b6288cd327eaa76eb689b30b262 Homepage: https://cran.r-project.org/package=acroname Description: CRAN Package 'acroname' (Engine for Acronyms and Initialisms) A tool for generating acronyms and initialisms from arbitrary text input. 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Package: r-cran-acs Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-xml, r-cran-plyr, r-cran-httr Filename: pool/dists/noble/main/r-cran-acs_2.1.4-1.ca2404.1_all.deb Size: 1346678 MD5sum: 9a07681a35d19e636d8a0b8a7dc8a93a SHA1: 98374158333c82304f23d940924747f6f7e1e8f4 SHA256: 2f0af353c63256921eae1b47388c0f436b4e1e2eac051be8de221ae9f462c0c5 SHA512: e6e5a0970acd923a34fd39fa6be76fae75338719ef8764a9aa70e8728666231b511f70d46c882c7ea4fa01340f2cdd19639ca86693d5fcff52166571c0f1297d Homepage: https://cran.r-project.org/package=acs Description: CRAN Package 'acs' (Download, Manipulate, and Present American Community Survey andDecennial Data from the US Census) Provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census (), including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. Package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways. Package: r-cran-acsmoe Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-sf, r-cran-testthat, r-cran-tidycensus Filename: pool/dists/noble/main/r-cran-acsmoe_0.1.0-1.ca2404.1_all.deb Size: 77764 MD5sum: 5010a812343ce56b2b42f4c8c715ab50 SHA1: 70edf2f1e61d84faa0bb6f5c5ff6ae9b00fec62b SHA256: 2edd025bbdc6afdd5b6500189970a2a445d45512834ea9bb569a389baee0297b SHA512: 41da77735644de61995de5dbfb69b0eda6578714b005758753aa695299feb51764f146828c694125fd820d950f6debfdfc002bcd10c0634867957a30e424d3f3 Homepage: https://cran.r-project.org/package=acsmoe Description: CRAN Package 'acsmoe' (Propagate Uncertainty for ACS Tabular Estimates) Utilities for propagating uncertainty in American Community Survey tabular workflows that use published estimates and margins of error, following U.S. Census Bureau derived-estimate guidance and complementing 'tidycensus' margin-of-error workflows. Includes covariance-aware derived estimates, simulation helpers, geographic aggregation, confidence-interval conversion, and reliability diagnostics. Package: r-cran-acss.data Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13926 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-acss.data_1.2-1.ca2404.1_all.deb Size: 14223878 MD5sum: ac1e5fe04bf52978380dfc4c42157a55 SHA1: dadbe2d2af4e243ada2fdaf386d4ea6002ee82f4 SHA256: e6cca75072f13dc71fd45bb58c39a96603f27ac8ac1e17e0567e62489382cc9b SHA512: 07dbd31bd8cc6602d6840d41cffc141660b9747b2fef23a25b95560f9390891ec4eb6ea04e731b28610b896ea40d3af608a2ef6a1764b5fa9e93ba42f7879b0e Homepage: https://cran.r-project.org/package=acss.data Description: CRAN Package 'acss.data' (Data Only: Algorithmic Complexity of Short Strings (Computed viaCoding Theorem Method)) Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs. Package: r-cran-acss Architecture: all Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-acss.data, r-cran-zoo Suggests: r-cran-effects, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-acss_0.3-2-1.ca2404.1_all.deb Size: 1208174 MD5sum: 6c506f938bc128defe1c2ac2737ef0c7 SHA1: 0797f311d56f89f83208294005292834f04d06b8 SHA256: 2558ad24f8debc88c7126374ae5862eddb3d3443af1b684d89d1f8b34abc89b6 SHA512: 7e145e9702899deeccacfb643115b70f16457315e49838cd60883aede40e7c937d82ca3653a538db2b30b201a788feafb2f8cfd9189b55f30669f8e94872f3a9 Homepage: https://cran.r-project.org/package=acss Description: CRAN Package 'acss' (Algorithmic Complexity for Short Strings) Main functionality is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method (see ?acss). The database containing the complexity is provided in the data only package acss.data, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity. Package: r-cran-acswr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-acswr_1.0.1-1.ca2404.1_all.deb Size: 246816 MD5sum: 69dd5cc6143397bfcbf84cf9aa519955 SHA1: a2cb6c52a4b7fe6cf56f3cd30b9e1e8cfd8b86a9 SHA256: 41ef6bd201e536f9094010f859acf20216e7f750679244746d1f2b6f2ac8b9a5 SHA512: 015cecb9f40307e759f067a2ad74cb22145b06eb040e1aa4e452d43831979bc7d595a21d95480b2d24f76b34505a7ef617d15ff7f65dbfe7d58e105a2a0efb72 Homepage: https://cran.r-project.org/package=ACSWR Description: CRAN Package 'ACSWR' (A Companion Package for the Book "A Course in Statistics with R") A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9. Package: r-cran-act Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-textutils, r-cran-progress, r-cran-xml, r-cran-xml2, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-act_1.3.1-1.ca2404.1_all.deb Size: 2061384 MD5sum: 28a14a2a6b3dacde588ec67d8f0e56f0 SHA1: 1dce025a0dc1c03d5d0265eb8de948cbf08e64bc SHA256: 53868e7792cf808d3c53036fdbd054898cc1c3be131b726c6a2246153ed8a63a SHA512: 326ca16facb395e9720bbc9a9069177345e8134803261b001b7664c74ae030efbe80f9608bbe7e14f8cd6c14061a533c177d28e1b8a657565f13620e28d2e6f3 Homepage: https://cran.r-project.org/package=act Description: CRAN Package 'act' (Aligned Corpus Toolkit) The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN' .eaf, 'EXMARaLDA .exb and 'Praat' .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and 'Excel' .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using 'FFmpeg' and video sub titles in 'Subrib title' .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with 'Praat' using 'Praat'-scripts, and exchange data with the 'rPraat' package. The package is itself written in R and may be expanded by other users. Package: r-cran-actcr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-cosinor, r-cran-cosinor2, r-cran-dplyr, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-actcr_0.4.0-1.ca2404.1_all.deb Size: 420716 MD5sum: 93bc8c174ead878144bf52e36f142e23 SHA1: d863a7c2aa6a55e8f09648619401f3de08306c95 SHA256: 9558e0ea9e7922c48a35fbb32b9356cbd9d02a1402461a9cda4dfc3baac63cfa SHA512: 83c712f72e4142c8e55073d6ca99779b397ee0c1434e4a544783a1d03ddaaa9adae217471a45bb09a682a6b3539a58e02afd7862055e0f678e7ac93caf82e899 Homepage: https://cran.r-project.org/package=ActCR Description: CRAN Package 'ActCR' (Extract Circadian Rhythms Metrics from Actigraphy Data) Circadian rhythms are rhythms that oscillate about every 24 h, which has been observed in multiple physiological processes including core body temperature, hormone secretion, heart rate, blood pressure, and many others. Measuring circadian rhythm with wearables is based on a principle that there is increased movement during wake periods and reduced movement during sleep periods, and has been shown to be reliable and valid. This package can be used to extract nonparametric circadian metrics like intradaily variability (IV), interdaily stability (IS), and relative amplitude (RA); and parametric cosinor model and extended cosinor model coefficient. Details can be found in Junrui Di et al (2019) . Package: r-cran-actel Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-data.table, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-fasttime, r-cran-ggplot2, r-cran-knitr, r-cran-readr, r-cran-reshape2, r-cran-rmarkdown, r-cran-rsvg, r-cran-scales, r-cran-stringi, r-cran-stringr, r-cran-svglite Suggests: r-cran-gdistance, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actel_1.4.0-1.ca2404.1_all.deb Size: 1475978 MD5sum: 98d696f412e82c06afaaa6be3cb21141 SHA1: 2f27377be0a31fee0dd916f298586de367c48c63 SHA256: cd46547179c741050ed6bdd12531046fec44e28be84f3881e58b2ecf26ffb518 SHA512: 10fbe8304f44eb8dd701fd768336538f6901e9c3d5d2ebefa517d3c672b55fcbeb19b59a2e89bd628739d3e9ae07a60862c9a5429bf61923aea3ea1d2de5613e Homepage: https://cran.r-project.org/package=actel Description: CRAN Package 'actel' (Acoustic Telemetry Data Analysis) Designed for studies where animals tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of animal movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) . Package: r-cran-actfrag Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-accelerometry, r-cran-dplyr, r-cran-ineq, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actfrag_0.1.2-1.ca2404.1_all.deb Size: 399262 MD5sum: 32a28540cab02c8d82db59a18a84e964 SHA1: 1e7327eb7298e6ec3ab0d7715650935366911735 SHA256: 352ea90cc789884b490ce6ce39390b11dc346061a4add3e2ae14cf3fb8993f79 SHA512: 264dd7e3cd28f4e9b181a0bbc8dcf86013a909337ae56e9e75dd79663e9624d4571e71c7f5d38820f442455b6c487145a19338f610376a8eddaf7ad2a7087701 Homepage: https://cran.r-project.org/package=ActFrag Description: CRAN Package 'ActFrag' (Activity Fragmentation Metrics Extracted from Minute LevelActivity Data) Recent studies haven shown that, on top of total daily active/sedentary volumes, the time accumulation strategies provide more sensitive information. This package provides functions to extract commonly used fragmentation metrics to quantify such time accumulation strategies based on minute level actigraphy-measured activity counts data. Package: r-cran-actfts Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4033 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-plotly, r-cran-reactable, r-cran-tseries, r-cran-xts, r-cran-forecast, r-cran-lifecycle Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actfts_0.3.0-1.ca2404.1_all.deb Size: 1052834 MD5sum: 03e9e213ab54a6501777cdcac0bd0418 SHA1: 13375e6e7324112295f36188fb0047cbaf127837 SHA256: 046cae6d5eee7b668faf8191001b074cf4d1a91b60ad20230126bae8470c17f2 SHA512: 811f32c832dffd2dad554d11d99154b6c5852011b834b177318b45254a3f6013be34631b0adfe3df2fc18adf5d93bf1ab789f54ee24e9491476f6c4b417b7200 Homepage: https://cran.r-project.org/package=actfts Description: CRAN Package 'actfts' (Autocorrelation Tools Featured for Time Series) The 'actfts' package provides tools for performing autocorrelation analysis of time series data. It includes functions to compute and visualize the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Additionally, it performs the Dickey-Fuller, KPSS, and Phillips-Perron unit root tests to assess the stationarity of time series. Theoretical foundations are based on Box and Cox (1964) , Box and Jenkins (1976) , and Box and Pierce (1970) . Statistical methods are also drawn from Kolmogorov (1933) , Kwiatkowski et al. (1992) , and Ljung and Box (1978) . The package integrates functions from 'forecast' (Hyndman & Khandakar, 2008) , 'tseries' (Trapletti & Hornik, 2020) , 'xts' (Ryan & Ulrich, 2020) , and 'stats' (R Core Team, 2023) . Additionally, it provides visualization tools via 'plotly' (Sievert, 2020) and 'reactable' (Glaz, 2023) . The package also incorporates macroeconomic datasets from the U.S. Bureau of Economic Analysis: Disposable Personal Income (DPI) , Gross Domestic Product (GDP) , and Personal Consumption Expenditures (PCEC) . Package: r-cran-actibase Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-hms, r-cran-tidyr, r-cran-janitor, r-cran-assertthat, r-cran-vctrs Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-readr Filename: pool/dists/noble/main/r-cran-actibase_0.6.0-1.ca2404.1_all.deb Size: 313618 MD5sum: 051c388d8007d4315f9013269e04a4be SHA1: bd96e794a208a8e202a454c5a0f28952b37f1084 SHA256: 48af62d824cc9b4d2d9ce5bde604a7a666f023a6a02c0d53e7a46f67e2492a76 SHA512: a9d8c641fb2b0c6eaca4942131cff853fd3830df5e69fa9a4395761ff66f58a454d3621ae7d36e88c4898a57248d78bee91390d568867d9e19db01742ffa22ac Homepage: https://cran.r-project.org/package=actibase Description: CRAN Package 'actibase' (Baseline Functions for Actigraphy and Activity Processing andAnalysis) Provides baseline functions for actigraphy and activity data. This package is intended to be extended by downstream overlays such as 'actiread', 'actimetrics', and 'stepcount'. Package: r-cran-actilifecounts Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gsignal, r-cran-pracma, r-cran-ggirread Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actilifecounts_1.1.1-1.ca2404.1_all.deb Size: 561298 MD5sum: 557e617a24ec5d426c4af3cc5b54d5ec SHA1: da7e703bf52ca936526ce5641a09d783e49ea0b5 SHA256: 113efbd8a91e9c7876b6ffcb925d740d6195d2093b48548d247c12f69fb924a3 SHA512: 41e06cc31c5d6ef06302c432ca231c72b54d7918461f36bd8ab128dc40845399d781defd2e1199da766a461ac5cb3e492940f3d0ce0bf96640987227058deebd Homepage: https://cran.r-project.org/package=actilifecounts Description: CRAN Package 'actilifecounts' (Generate Activity Counts from Raw Accelerometer Data) A tool to obtain activity counts, originally a translation of the 'python' package 'agcounts' . This tool allows the processing of data from any accelerometer brand, with a more flexible approach to handle different sampling frequencies. Package: r-cran-actimetrics Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-actibase, r-cran-actiread, r-cran-dplyr, r-cran-lubridate, r-cran-assertthat, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-tidyr, r-cran-walking Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-httr, r-cran-rmarkdown, r-cran-mimsunit, r-cran-data.table, r-cran-reticulate, r-cran-stepcount, r-cran-actigraph.sleepr, r-cran-agcounts, r-cran-callr Filename: pool/dists/noble/main/r-cran-actimetrics_0.4.0-1.ca2404.1_all.deb Size: 112612 MD5sum: 1dc6ba9c7363c991aa9274abead1e360 SHA1: 915d2d250702cd21d357c47c76fc432ff26ced77 SHA256: 5f30f3a06a08cd4cb6d380ca8900a2b3df6819f9946e6fce1399ceea8807bfe1 SHA512: 2b1feca4e3aba3a1e0bfa970d212371be330f29950a6cc5290d76031e2c86fedffcaff1e810596192dc407593fb6ba5fd4fd0799632483a7960cdec1bafe51b3 Homepage: https://cran.r-project.org/package=actimetrics Description: CRAN Package 'actimetrics' (Create Metrics Actigraphy and Activity Analysis) Provides functions for calibrating, counting, and summarizing actigraphy and activity data into specific metrics and sleep measures. The metrics include activity counts, step counts, activity index, Monitor Independent Movement Summary Unit (MIMS), mean amplitude deviation (MAD), and provides wrappers for sleep estimation from activity counts using Tudor-Locke (2014) and Sadeh (1994) . Package: r-cran-actinet Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2526 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-lubridate, r-cran-readr, r-cran-reticulate, r-cran-rlang Suggests: r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-callr Filename: pool/dists/noble/main/r-cran-actinet_0.4.0-1.ca2404.1_all.deb Size: 2547056 MD5sum: 8f6e9f83bb537be4a84113559a63db89 SHA1: e1968151cd4229fa6a7a9c13e890558fbcf6cf84 SHA256: 8aeb4f9414fca515f76a1c1b69a2a204a8f3cc37b748be1aa808ed810ba6455f SHA512: 944e08b1233592ee48172e23d4d7c96c873ba33e97674c938146ed647e4920b7e5ba05d53b1a2b5db54f17487082586cc20b711cda4cfdb3b3d9cb2069458491 Homepage: https://cran.r-project.org/package=actinet Description: CRAN Package 'actinet' (Estimate Human Activity from Accelerometry Data) Interfaces the 'actinet' Python module for an activity classification model based on self-supervised learning for wrist-worn accelerometer data. Package: r-cran-actiquantiles Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mapnhanespa Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-actiquantiles_0.1.0-1.ca2404.1_all.deb Size: 12802 MD5sum: 0e07dc4c038e5feb864fe9b8266066f0 SHA1: ed53b0246160cb84f009dfbc3d2fc80352ecb905 SHA256: 0bbe198e5d3a8d04100530a8daf84fecb9e0adb722224272991dbcaa9cb31a63 SHA512: 70a7e6438c9ef6d31a893f2d5877b159ce065f608661ddb3173dec5b1241070c08453534e8fe1f3570fdec8fcbe8953a391c558da43ab09b67962c2f57f23a5e Homepage: https://cran.r-project.org/package=actiquantiles Description: CRAN Package 'actiquantiles' (Map Activity Data to Normalized Quantiles) Provides functions mapping physical activity measures to normalized quantiles. Currently, only maps the 'NHANES' quantiles, but other quantiles will be integrated. Package: r-cran-actiread Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7184 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-actibase, r-cran-ggirread, r-cran-read.gt3x, r-cran-r.utils, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-assertthat, r-cran-cli, r-cran-tibble, r-cran-janitor, r-cran-purrr, r-cran-readr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-reticulate, r-cran-arrow Filename: pool/dists/noble/main/r-cran-actiread_0.5.0-1.ca2404.1_all.deb Size: 7244534 MD5sum: 6b7579b8df1773e024c4d520a3171ac1 SHA1: 8c81d3c2c0b3493c0c94e42c65f5d653a3ff64ce SHA256: 9e8f946c92df8e8c89597094087a9ea612147e4afc30d29fcb58db5bdf221d59 SHA512: 5f8dca825ba16afbacab7f498101669cb996370bb5409376cf580fb3ec4c7d6d0c5080108b071738276925e6e911c29b058cc6d166ee72d898423a6175320d6f Homepage: https://cran.r-project.org/package=actiread Description: CRAN Package 'actiread' (Baseline Package for Reading Actigraphy and Activity Data) Provides baseline functions for reading actigraphy and activity data, relying on baseline functions from 'actibase'. Reads data from 'Axivity' 'CWA' , ActiGraph 'GT3X' , 'SensorLog' , and 'SensorLogger' zipped CSV files. Package: r-cran-actisensorlog Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-actibase, r-cran-actiread, r-cran-assertthat, r-cran-dplyr, r-cran-geosphere, r-cran-janitor, r-cran-lubridate, r-cran-lutz, r-cran-purrr, r-cran-readr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-actisensorlog_0.2.0-1.ca2404.1_all.deb Size: 64568 MD5sum: 20940556bc1b5a41feb48dfa6098da7d SHA1: 63361dd9486761f735bfebfdeec24df4ccc3ac9e SHA256: e4570a9af703745ded5432df22ac449e15ba16b0c5bcf45455d845b79ded25bc SHA512: 6c8b08fbc9cedf64ed402d568fe1730ee7d22dbda8b0b11fe46e7ec552c294e247445037b76962ea4edfa27f73d454019e5718a67dd17daca76fbe6264777689 Homepage: https://cran.r-project.org/package=actisensorlog Description: CRAN Package 'actisensorlog' (Summarize 'SensorLog'/'SensorLogger' Activity Data) Provides functions for analyzing 'SensorLog' and 'SensorLogger' data. Package: r-cran-activanalyzer Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dbplyr, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-hms, r-cran-lubridate, r-cran-magrittr, r-cran-modelr, r-cran-patchwork, r-cran-physicalactivity, r-cran-plyr, r-cran-reactable, r-cran-rmarkdown, r-cran-rsqlite, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-stringr, r-cran-tidyr, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-processx, r-cran-globals, r-cran-config, r-cran-tidyselect, r-cran-dbi, r-cran-htmltools, r-cran-officer, r-cran-pkgload, r-cran-scales, r-cran-tibble, r-cran-rlang, r-cran-tinytex, r-cran-shinytest2, r-cran-pkgdown, r-cran-callr Filename: pool/dists/noble/main/r-cran-activanalyzer_2.1.2-1.ca2404.1_all.deb Size: 2159340 MD5sum: aa293b501b09890aa128f79ca4a81065 SHA1: a3299ae90e36a2d809cf95a2f46a6434f4d802c3 SHA256: 0638f65c6e2d6390d6aa9a842a194d6d8f165b6aa085361b5ff1368066c010e7 SHA512: b04342327003bba02e9a313fff135ffaeb2da67d1b0c477c42eb5cec2b4f4b7740d22f05da15c501294e6b517bda555ed6264a731e217197d73a851adda4b472 Homepage: https://cran.r-project.org/package=activAnalyzer Description: CRAN Package 'activAnalyzer' (A 'Shiny' App to Analyze Accelerometer-Measured Daily PhysicalBehavior Data) A tool to analyse 'ActiGraph' accelerometer data and to implement the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis is completed, the app allows to export results to .csv files and to generate a report of the measurement. All the configured inputs relevant for interpreting the results are recorded in the report. In addition to the existing 'R' packages that are fully integrated with the app, the app uses some functions from the 'actigraph.sleepr' package developed by Petkova (2021) . Package: r-cran-activatr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggmap, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-rlang, r-cran-tibble, r-cran-slider, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-activatr_0.2.1-1.ca2404.1_all.deb Size: 2791112 MD5sum: cae8bd316945647af2a1593ecfedc53a SHA1: 7abb3a0154cd53bcb073e862557751db60e4ea3f SHA256: 867a1a48657757568d45a4c7c0905c58117caab3aaf77ffbde77dfc3bacf3780 SHA512: 89ae8832798d048d7e11fc42e4f09ff310d907790ab10f1220c55c00c2c47c011e55da95bf7fc7097e97fc32bc2c34766ad04987e1960b9d74fadbb5d8fae464 Homepage: https://cran.r-project.org/package=activatr Description: CRAN Package 'activatr' (Utilities for Parsing and Plotting Activities) This contains helpful functions for parsing, managing, plotting, and visualizing activities, most often from GPX (GPS Exchange Format) files recorded by GPS devices. It allows easy parsing of the source files into standard R data formats, along with functions to compute derived data for the activity, and to plot the activity in a variety of ways. Package: r-cran-activedriver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-activedriver_1.0.0-1.ca2404.1_all.deb Size: 180012 MD5sum: ff42e39ec4d74b36cafa04083f96abb5 SHA1: df2475e563303762264869a976e30c5844551ecc SHA256: bdcdd209e68b8ec26530d6f5a253a85d580934fec7a815662671ea9d03e5aa0c SHA512: 53f5783cff285ff11e9cae991bbf173fc718026f73645bcabfc27be28fd3bdb86b908607fce57cce3ee26245a4ab9cbd79fc89890cfec0cb5a82debf124f0f8d Homepage: https://cran.r-project.org/package=ActiveDriver Description: CRAN Package 'ActiveDriver' (Finding Cancer Driver Proteins with Enriched Mutations inPost-Translational Modification Sites) A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 . Package: r-cran-activedriverwgs Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm9, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-activedriverwgs_1.2.1-1.ca2404.1_all.deb Size: 858896 MD5sum: 1aa81ac956ee27b606b84e35a6ac2b68 SHA1: 209cb6c93ca2d02172c826dac6864250533bb544 SHA256: e688f4828cca5246a1d5c4a553e887c60fdd78be2cce75bed90fb67d230fc49f SHA512: dadc921c46a624845ea091435171715f8e7656c64493603de0a90528ffdabf64a7f50c105519ce549de93c43d3ca89c838af6eb1ea4b5cb68575bb904a0f01b7 Homepage: https://cran.r-project.org/package=ActiveDriverWGS Description: CRAN Package 'ActiveDriverWGS' (A Driver Discovery Tool for Cancer Whole Genomes) A method for finding enrichments of somatic single nucleotide variants (SNVs) and small insertions-deletions (Indels) in functional elements in the human genome. 'ActiveDriverWGS' detects coding and noncoding cancer driver elements using whole genome sequencing data. The method is part of the publication H. Zhu et al. (2020) "Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks" in Molecular Cell. Package: r-cran-activepathways Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-activepathways_2.0.6-1.ca2404.1_all.deb Size: 1747290 MD5sum: 6c37c2bd6eed181bf554c05cba0371cb SHA1: ffd993847464d1501e6c9f424ac2e74d535c2547 SHA256: 8657eecf4b3fd56cbeda1b36fdffa5cf3131e03962d5e152a654e7897e4907a1 SHA512: 993f9e9144b9ea25d76df882915e6d441cdd2d21a8f46106c3b66bf48aa4e3121a7e49290b85178921adb6fe50b0ca913eb2de00368d9fc06fb18b8ff6bda496 Homepage: https://cran.r-project.org/package=ActivePathways Description: CRAN Package 'ActivePathways' (Integrative Pathway Enrichment Analysis of Multivariate OmicsData) Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) . Package: r-cran-activity Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-activity_1.3.4-1.ca2404.1_all.deb Size: 259040 MD5sum: c242571e8839fa0925be153069338137 SHA1: d2d8b505869df64d0262fc9ffbfab813203c9bfc SHA256: d30c104f624a426ea68d7f5c43b66c5c1266aa974492a83106463b7cb108ea1b SHA512: 13fb7a0d05d0531f59d3706dbeb1dfb095f36052aaa7f29a25fad5feff0abd94666b54ea48661388e49de35a6ce04193ba2ba52197e47c1adaf6340710332a62 Homepage: https://cran.r-project.org/package=activity Description: CRAN Package 'activity' (Animal Activity Statistics) Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables). Package: r-cran-activitycounts Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seewave, r-cran-signal, r-cran-tibble, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-activitycounts_0.2.1-1.ca2404.1_all.deb Size: 1011566 MD5sum: e1dec28cad6093050f5465f1e491b502 SHA1: 4958a5aba0b6b4f7ae183f97c5c449fd6c9b1e1b SHA256: 5404f54b64db9d6cf5023af59f979959a964d396c3b3d5b87636b10d92297f69 SHA512: 97c27800a4d8732a28dd950f4f9712e15dc86999b5f4a0522df1e69f27b1bd583d3f93fbebeee14fe11cdcdfac45c1f5985a35af3c54b0f1d1ca8f2f5d8a1d3c Homepage: https://cran.r-project.org/package=activityCounts Description: CRAN Package 'activityCounts' (Generate ActiLife Counts) ActiLife software generates activity counts from data collected by Actigraph accelerometers . Actigraph is one of the most common research-grade accelerometers. There is considerable research validating and developing algorithms for human activity using ActiLife counts. Unfortunately, ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands. The code creates ActiLife counts from raw acceleration data for different accelerometer brands and it is developed based on the study done by Brond and others (2017) . Package: r-cran-activityindex Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-data.table, r-cran-r.utils Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-activityindex_0.3.7-1.ca2404.1_all.deb Size: 4085284 MD5sum: 29f426c290d8daf305d7389fe19087ff SHA1: 529dd74bc3cd9b75da475795a7752f758a28b331 SHA256: 0007d519b6835f8203d2931a5b567aa2699b6f250d73b8fc8a11d4889409e97f SHA512: df3e7425bde6d134119ac165060f2003e2ffd37fa5b67c96f5ba2eebc5c63dfda2fbf26e8ac29ee6608256baa5e68b9588ea301a2190502682da789ba7ce4237 Homepage: https://cran.r-project.org/package=ActivityIndex Description: CRAN Package 'ActivityIndex' (Activity Index Calculation using Raw 'Accelerometry' Data) Reads raw 'accelerometry' from 'GT3X+' data and plain table data to calculate Activity Index from 'Bai et al.' (2016) . The Activity Index refers to the square root of the second-level average variance of the three 'accelerometry' axes. Package: r-cran-activpal Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-lubridate, r-cran-magrittr, r-cran-devtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-activpal_0.1.4-1.ca2404.1_all.deb Size: 339644 MD5sum: 65eeb941673b7a449129a35078a7883b SHA1: ad10e857fe22cf3c33e3f0329fa07829a73ee367 SHA256: 267ca56afe96d408ca1afab69fd1f31527349d47287126a1937d3ad68212bc71 SHA512: 1110ba651d48308fb10904314146019ad7cec3b8ce72b1d9d2a90657f8d13858f7c26e777ab908b8260d8648af07acabea51c1c23784b90b53b354cbc886bf2c Homepage: https://cran.r-project.org/package=activPAL Description: CRAN Package 'activPAL' (Advanced Processing and Chart Generation from activPAL EventsFiles) Contains functions to generate pre-defined summary statistics from activPAL events files . The package also contains functions to produce informative graphics that visualise physical activity behaviour and trends. This includes generating graphs that align physical activity behaviour with additional time based observations described by other data sets, such as sleep diaries and continuous glucose monitoring data. Package: r-cran-actiwalkability Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arcgislayers, r-cran-assertthat, r-cran-dplyr, r-cran-rlang Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-withr, r-cran-testthat, r-cran-rmarkdown, r-cran-curl Filename: pool/dists/noble/main/r-cran-actiwalkability_0.1.0-1.ca2404.1_all.deb Size: 66528 MD5sum: bcb85a7eb3041d4998ca69777b824777 SHA1: 65d2bc0e283c41415c0d74b2f9a387360c8428e8 SHA256: 9807bc2ebb4838cf14699d725f48d3e6c64910fb72d2637922f7894b5339adb4 SHA512: 55540485294562fe6322c4a50235d52d416b258b20f505c25f19a36d313a3afc2495caa64f5c0aa862fb90b56f6b5a6cc80bcc6b283181fe571f3c6150af5e1e Homepage: https://cran.r-project.org/package=actiwalkability Description: CRAN Package 'actiwalkability' (Access the EPA Walkability Index) Provides helpers for querying the EPA Walkability Index and working with Census GEOID/FIPS identifiers . Package: r-cran-actlifer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-actlifer_1.0.0-1.ca2404.1_all.deb Size: 459966 MD5sum: fdb68cbd643bc7742d44b6cae74775b1 SHA1: a3ebefe66ecaf74a73fa9c879a6d252401c53a06 SHA256: 285bc1fbe195892a02e4d2db7134e70cfd2377e1b17142e0a923e025269d6ebc SHA512: 9ecc7f6698c4f559f78cc5c61b80594d2a753898d8569c3a7b2f51a4f5e060aec22923f744f3a7352b8beccc1c6491491310ece7e2c5101609d881f0da62db6a Homepage: https://cran.r-project.org/package=actLifer Description: CRAN Package 'actLifer' (Creating Actuarial Life Tables) Contains data and functions that can be used to make actuarial life tables. Each function adds a column to the inputted dataset for each intermediate calculation between mortality rate and life expectancy. Users can run any of our functions to complete the life table until that step, or run lifetable() to output a full life table that can be customized to remove optional columns. Methods for creating lifetables are as described in Zedstatistics (2021) . Package: r-cran-actogrammr Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-actogrammr_0.2.3-1.ca2404.1_all.deb Size: 174096 MD5sum: eb687a585205fe7cfb48d0ee8ea337f2 SHA1: 19bd1c05b0b537eb48cfa11bb6c38b5933f854eb SHA256: 23e7d96513d9fe9ce352053973fe94660dffec7a0156e3430db20f2e86b9932d SHA512: fc5dc090351898c71b289747c9304293c45cd8cb13d8537621307087a6519851c250985635aff5ec4513a88aed28bee537a5cde13ab4da68995446110bc5519a Homepage: https://cran.r-project.org/package=actogrammr Description: CRAN Package 'actogrammr' (Read in Activity Data and Plot Actograms) Read in activity measurements from standard file formats used by circadian rhythm researchers, currently only 'ClockLab' format, and process and plot the data. The central type of plot is the actogram, as first described by in "Activity and distribution of certain wild mice in relation to biotic communities" by MS Johnson (1926) . Package: r-cran-actuare Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cplm, r-cran-statmod, r-cran-nlme, r-cran-lme4, r-cran-magrittr, r-cran-data.table, r-cran-ggplot2, r-cran-reformulas Suggests: r-cran-plyr, r-cran-knitr, r-cran-bookdown, r-cran-insurancedata, r-cran-actuar, r-cran-lattice, r-cran-minqa, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-actuare_1.0.1-1.ca2404.1_all.deb Size: 1486616 MD5sum: 2baf61f2ebb4c0e2ad6d34bb86fbf22e SHA1: 06af7fda2655ba4c7b487ead68c6b35f8b06dd99 SHA256: 018ef5914c5389272707e10bcb9fc5560bef48ddd2bcae42c6a47b5b6ac719ff SHA512: d8b0dafbd6851a75fc652c8df4e1b8d5ba1df7f9af18b1074081d2759c4486ed8c2757b4f517cba8bbe167e26003b791ddeeaf0a84271c5c5158fa2853c1ebbc Homepage: https://cran.r-project.org/package=actuaRE Description: CRAN Package 'actuaRE' (Handling Single-Level and Hierarchically Structured Risk Factorsusing Credibility and Random Effects Models) Fits random effects models for multi-level/high-cardinality factors using credibility theory (Buhlmann-Straub for single-level, Jewell for hierarchical structures), GLM extensions following Ohlsson (2008) , or Tweedie generalized linear mixed models. Provides functions for model fitting, visualization, and prediction. See Campo, B.D.C. and Antonio, K. (2023) . Package: r-cran-actuarialm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-actuarialm_0.1.0-1.ca2404.1_all.deb Size: 51334 MD5sum: 1a696a8afcdcd56b96bf1965a6f83af0 SHA1: 7c02a1a697eb8bcfb243384532727d43be8cc7fb SHA256: da5dea203a8a0e93e972c456a9b297192778a789f8ce7e9a1c3767a77d4c9879 SHA512: f71eada39acc731ba98703b2dfd27db876847eb635f134dd8123b4b7158af39ee376143464e155a0203cf95986621d5c931507304b1fbab0245a2835e7a4f1ea Homepage: https://cran.r-project.org/package=ActuarialM Description: CRAN Package 'ActuarialM' (Computation of Actuarial Measures Using Bell G Family) It computes two frequently applied actuarial measures, the expected shortfall and the value at risk. Seven well-known classical distributions in connection to the Bell generalized family are used as follows: Bell-exponential distribution, Bell-extended exponential distribution, Bell-Weibull distribution, Bell-extended Weibull distribution, Bell-Lomax distribution, Bell-Burr-12 distribution, and Bell-Burr-X distribution. Related works include: a) Fayomi, A., Tahir, M. H., Algarni, A., Imran, M., & Jamal, F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. . b) Alsadat, N., Imran, M., Tahir, M. H., Jamal, F., Ahmad, H., & Elgarhy, M. (2023). "Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data". Open Physics, 21(1), 20220242. . Package: r-cran-actuary Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dtplyr, r-cran-forcats, r-cran-furrr, r-cran-future, r-cran-gamlss.dist, r-cran-ggplot2, r-cran-ggtext, r-cran-mass, r-cran-pracma, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yardstick Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-actuary_0.1.2-1.ca2404.1_all.deb Size: 971790 MD5sum: 69abe87c325f0752bc8d4347cb332792 SHA1: c2aa62820677e88816a325e665e79426bb2e3586 SHA256: 5060098abb7f29aed6ba06944caca3287ffe6eb27d60ccccd177fd2eed3541bd SHA512: 3f935ab8f3f3a91312a97d7450ba59295e20b48b15570224e3c8473459f4fe846d32ec2628e49a18c46501bb32bff7d64641cf8dac1e528ed73d8fc17cfe5153 Homepage: https://cran.r-project.org/package=actuary Description: CRAN Package 'actuary' (Actuarial Functions and Utilities) Provides actuarial modeling tools for Monte Carlo loss simulations, loss reserving, and reinsurance layer loss calculations. It enables users to generate stochastic loss datasets with customisable frequency and severity distributions, fit development patterns to claim triangles, and calculate reinsurance losses for occurrence and aggregate layers with user-defined retentions, limits, and reinstatements. For development pattern selection, the package includes a machine learning approach that evaluates multiple reserving models using holdout validation to identify the best-fitting pattern based on predictive accuracy, this is based on the algorithm described in Richman, R and Balona, C (2020). Package: r-cran-actuaryr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-crayon, r-cran-purrr, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-actuaryr_1.1.1-1.ca2404.1_all.deb Size: 464630 MD5sum: a0cc3f181b124c762edf2bd7aa8e4a56 SHA1: c6343fbcf5329de0902f2d864d05d18829febdd9 SHA256: 3e55fcbf6b6c0227ec08967aa0fd1184f4c1b68c3c143701f9ef7baafbdaa4bf SHA512: 1c6f4cffdfc6e14ccd9e0cd776fee23305c025893b86552e0792138326008be876088c78c5f846d5aa104c1bef87c4ff2a4e8fc0dd214b404f3b6a5a9fa2e8ef Homepage: https://cran.r-project.org/package=actuaryr Description: CRAN Package 'actuaryr' (Develop Actuarial Models) Actuarial reports are prepared for the last day of a specific period, such as a month, a quarter or a year. Actuarial models assume that certain events happen at the beginning or end of periods. The package contains functions to easily refer to the first or last (working) day within a specific period relative to a base date to facilitate actuarial reporting and to compare results. Package: r-cran-actxps Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-glue, r-cran-purrr, r-cran-scales, r-cran-gt, r-cran-paletteer, r-cran-recipes, r-cran-generics, r-cran-readr, r-cran-tidyr, r-cran-vctrs, r-cran-clock, r-cran-cli Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-bslib, r-cran-thematic Filename: pool/dists/noble/main/r-cran-actxps_1.6.1-1.ca2404.1_all.deb Size: 2606614 MD5sum: f14771661e38b1a17b6cde8b04f89ae6 SHA1: 9fbb487de175119c4461b0a6a0ad0b0ef9137bd8 SHA256: d5b525a41bdb5a7528e867f00167f76ee49556f2dcd5746c9fb1ed953a8eb4be SHA512: 6d5ad2cc191d52bc8f9c4d98601f8ea51818e420e033ca06c29b2e4b91437d81be9a6a958606ec2c1c4115a4b73c8f24ab5b2ec473027b72112106195153c6a1 Homepage: https://cran.r-project.org/package=actxps Description: CRAN Package 'actxps' (Create Actuarial Experience Studies: Prepare Data, SummarizeResults, and Create Reports) Experience studies are used by actuaries to explore historical experience across blocks of business and to inform assumption setting activities. This package provides functions for preparing data, creating studies, visualizing results, and beginning assumption development. Experience study methods, including exposure calculations, are described in: Atkinson & McGarry (2016) "Experience Study Calculations" . The limited fluctuation credibility method used by the 'exp_stats()' function is described in: Herzog (1999, ISBN:1-56698-374-6) "Introduction to Credibility Theory". Package: r-cran-acuityview Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-imager, r-cran-fftwtools, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-acuityview_1.1.1-1.ca2404.1_all.deb Size: 167294 MD5sum: 2ab0a333fc462906c66d1820eb805792 SHA1: 3842ebf724cf77735767f308ac8634426f62367a SHA256: b10afdd3b0fc71647ea6bb490002767ca8757d98047859c2e4220d7bf46200de SHA512: 29aa284e617f3468891e3871065af110ad7ea9abb5e500d9b36b9cbd454fc32bb8f54047724192aa4dfeecceb815310473739213ee48fd5eff95ff6684809616 Homepage: https://cran.r-project.org/package=AcuityView Description: CRAN Package 'AcuityView' (A Package for Displaying Visual Scenes as They May Appear to anAnimal with Lower Acuity) This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication. Package: r-cran-acumos Architecture: all Version: 0.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rprotobuf, r-cran-rserve, r-cran-restrserve, r-cran-yaml Suggests: r-cran-randomforest, r-cran-testthat, r-cran-callr Filename: pool/dists/noble/main/r-cran-acumos_0.4-4-1.ca2404.1_all.deb Size: 70630 MD5sum: d021cbf024d5c49f18d54f464b281439 SHA1: 191cc6d34eedb3e012d1e1e157fe73766aa42cfb SHA256: 7e13046e00496b35a5bca6c0727234a65c6ff36e574c0a33a2e880ba5fc4b727 SHA512: f634589547ce285b7247f6d7c8c922bcfa4b298e9a45b89d02a29200f7e96eaecfc6719aecdeb50ba29bb392ab3df953c12ad6c5c2dfb822c858367482ef1896 Homepage: https://cran.r-project.org/package=acumos Description: CRAN Package 'acumos' ('Acumos' R Interface) Create, upload and run 'Acumos' R models. 'Acumos' () is a platform and open source framework intended to make it easy to build, share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella organization within 'The Linux Foundation'. With this package, user can create a component, and push it to an 'Acumos' platform. Package: r-cran-acv Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acv_1.0.2-1.ca2404.1_all.deb Size: 51380 MD5sum: 7d1b5b03408942756e81788039aec81b SHA1: e272cb54a8f055c482336646e49889ab7560cf57 SHA256: e4a3b7faca036035accb6c8a905b796993944c669f85aa2e2000c1beb14d2938 SHA512: 91ee0e9c0f70cf54864421b977aaa58ebf3fc1193a7e59824edca92b55acc211659f8b798bb0a1eaedd15195e3fbc379104f8ee130dfdf44852a1d2afd3151fb Homepage: https://cran.r-project.org/package=ACV Description: CRAN Package 'ACV' (Optimal Out-of-Sample Forecast Evaluation and Testing underStationarity) Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) . Package: r-cran-acwr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2d3 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acwr_0.1.0-1.ca2404.1_all.deb Size: 54194 MD5sum: e154b6ed2fb8bf777f6205bc3f6d52fd SHA1: 1e819763e07a85fcb4409b6153ac003a58ff3616 SHA256: fd89870642198eb495c9f4e9c4c3ccd0f1b328bf17c17ff367cf06355c419e14 SHA512: 42f2cbb72c340c7f93044b6b218556d349fb87e1ce8323387c843834eaf7c4d0f9490b44f360f5ea69e0ed26324d529e9b69215e77d070ccd66835ab672369ad Homepage: https://cran.r-project.org/package=ACWR Description: CRAN Package 'ACWR' (Acute Chronic Workload Ratio Calculation) Functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) for EWMA and Windt & Gabbet (2018) for RAC and RAU . Package: r-cran-ada Architecture: all Version: 2.0-5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-ada_2.0-5.1-1.ca2404.1_all.deb Size: 735082 MD5sum: 616e20befae4abfa6cd511e60296e374 SHA1: 073c2dfa602e3bac0107c8d500fa5a13976c7f1e SHA256: 3c5ed5af68aff3a6bfb1988740204ed7a0ae0440c16ef1382c5b7e9c2e69fc5d SHA512: 017b15ea6c2c80371124dd5de2966937b34197944dcb7f54b2358c0b72fc9ffa7b0d84fd2fab91e43c310555360c3c7ffcb797468a4826ec1a62afacf27d63b9 Homepage: https://cran.r-project.org/package=ada Description: CRAN Package 'ada' (The R Package Ada for Stochastic Boosting) Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. 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Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. 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Package: r-cran-adace Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-pracma Suggests: r-cran-testthat, r-cran-cubature, r-cran-mass Filename: pool/dists/noble/main/r-cran-adace_1.0.2-1.ca2404.1_all.deb Size: 116922 MD5sum: a2d333e7ee0b574e107e231d07652083 SHA1: 56c92862bb5e982607420f885ef68eaf1b452c7d SHA256: 22e7e585f402eeda04f16b818d7e4c7e3194d5eb7d0f5534e5f848834f7444c4 SHA512: dfc0407fa1af25ed5f1784f72bcce4a058efdd7a0205aba9490d2aa56239bbb33b5394f2856008ebb9bd7727aa9b7eb131c1f2bccf402765e7fd0adf630ce909 Homepage: https://cran.r-project.org/package=adace Description: CRAN Package 'adace' (Estimator of the Adherer Average Causal Effect) Estimate the causal treatment effect for subjects that can adhere to one or both of the treatments. Given longitudinal data with missing observations, consistent causal effects are calculated. 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It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. 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Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value that quantifies the degree of proximity between the Uda-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Extreme values in the data can be eliminated using the 1.5IQR rule to create its robust version if user demands. Full description of the methodology can be found in the article by Wijesuriya (2025a) . Further, the development of Ad-plot and Ud-plot is contained in both article and the 'adplots' R package by Wijesuriya (2025b & 2025c) and . Package: r-cran-adapsamp Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-adapsamp_1.2.0-1.ca2404.1_all.deb Size: 47806 MD5sum: 0c4e9f3c73bb5bc0cdeeca9dd1704c43 SHA1: b0c577fb16df2f274b485517441cdecf7f686bc2 SHA256: ca0843ca653eb9c9eb277b44186d6a939210574f8634c504ef4586c65812d260 SHA512: 4bfcc8e86e6496096c9ec8799b016a8c62d783b53f99a2e179e78d0f5c36a4bd06981417a5684340f6ce41011dbee8b7963cac57ea32c72800bd44655ea230c9 Homepage: https://cran.r-project.org/package=AdapSamp Description: CRAN Package 'AdapSamp' (Adaptive Sampling Algorithms) For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above. Version 1.2.0 fixes several correctness bugs and improves numerical robustness. Package: r-cran-adapt4pv Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-speedglm, r-cran-xgboost, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-adapt4pv_0.2-3-1.ca2404.1_all.deb Size: 421892 MD5sum: 3c774fefcadca96d93fd0b336d7ca378 SHA1: 3167cf8b24440abfab740377a4d20ab0f308cafc SHA256: a7052a7332b7d1bfcf74c62f95fee66896169d2b946c15dae6277692b50b76a8 SHA512: 2221c14fab653156d2fe5cf40b5b79469b23eec5cb65f37b2cfe2e90576589464586ef104a4dfa152015a6db1041b1f0e341b9dd58a0897b34ff541fc0503b78 Homepage: https://cran.r-project.org/package=adapt4pv Description: CRAN Package 'adapt4pv' (Adaptive Approaches for Signal Detection in Pharmacovigilance) A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al . 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This makes it easier to find and correct the most important problems first. Package: r-cran-adaptdiag Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-extradistr, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vgam, r-cran-covr Filename: pool/dists/noble/main/r-cran-adaptdiag_0.1.1-1.ca2404.1_all.deb Size: 197516 MD5sum: bf107d8f598fbda38eb5c0f89e045124 SHA1: 917d19891519998949f9988b916a00b0e8a0c8c9 SHA256: e78c2360422a9e28def59f7ca5c8ce2857fe2b480b33f14e38ea2b966da22410 SHA512: f72e0f4e3c7b4f99a975976c8b86f97f62959745c6cf71ae20e00ed5ef42e2ae9c88bf119d3c525d4448dcda7127763a30c3a07e0d9e2f1475f24f4907b0bb7f Homepage: https://cran.r-project.org/package=adaptDiag Description: CRAN Package 'adaptDiag' (Bayesian Adaptive Designs for Diagnostic Trials) Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities. Package: r-cran-adapthycensor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-adapthycensor_0.1.0-1.ca2404.1_all.deb Size: 122276 MD5sum: 231e22585c141b8b2d7613891a683c8f SHA1: 861bbd00192b148a435ef67eef2152fa47ad7814 SHA256: cb3821950288f471a4974957ef0bee16f90140c43179a2e88ec39f8e5fd8ba36 SHA512: 641bb9e914e77c63260ad304dada14efda7a30b9e82a038efbb6ea42185b38e3d002f52e2660a9a7a895db646bb8abf4bba08012a7fd21011f6a1aab95a2155e Homepage: https://cran.r-project.org/package=AdaptHyCensor Description: CRAN Package 'AdaptHyCensor' (Generalized Inference and Data Generation for AdaptiveProgressive Hybrid Censoring Schemes) Comprehensive computational tools for data generation, statistical inference, and visual diagnostics under Adaptive Type-I and Adaptive Type-II Progressive Hybrid Censoring Schemes. Users can supply custom probability density functions (PDF), cumulative distribution functions (CDF), survival functions, parameter ranges, and progressive schemes for any univariate lifetime distribution. Parameter estimation methods include Maximum Likelihood Estimation (MLE) using multiple optimization algorithms (Newton-Raphson (NR), Broyden-Fletcher-Goldfarb-Shanno (BFGS), BFGS in R (BFGSR), Berndt-Hall-Hall-Hausman (BHHH), Simulated Annealing (SANN), Conjugate Gradients (CG), and Nelder-Mead (NM)), Bayesian estimation via Gibbs and Metropolis-Hastings (M-H) MCMC sampling, Importance Sampling (IS), and Lindley's approximation. Diagnostic tools provide histograms, dot plots, and autocorrelation function (ACF) plots for model validation. Methods are based on Balakrishnan, Cramer, and Kundu (2023, ISBN:978-0-12-398387-9), Ng, Kundu, and Chan (2009, IEEE Transactions on Reliability, 58, 634-642), Lin and Huang (2012, Journal of Statistical Computation and Simulation, 82, 1005-1018), Lindley (1980, Journal of the Royal Statistical Society, Series B, 42, 223-237), and Berndt, Hall, Hall, and Hausman (1974, Annals of Economic and Social Measurement, 3, 653-665). Package: r-cran-adaptiveboxplot Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adaptiveboxplot_0.1.1-1.ca2404.1_all.deb Size: 25818 MD5sum: 38563bef62253e911d6dc3e002939d5a SHA1: e6bc8f33685495111e6ac6e9706e7ebbcbf6d3d2 SHA256: 6f99f1e14b79deadc45311c04f52dea81e85d4bac912a9a7cac283c1d446a30d SHA512: 61b3a452537e7dfdbb732371c346b1db4ffe836889015d2277f357f928b2208bb742f629baebbae83807e1a51c39d29e137e7ac7f8e200f70537177bfa1c18a8 Homepage: https://cran.r-project.org/package=AdaptiveBoxplot Description: CRAN Package 'AdaptiveBoxplot' (FDR(BH) Boxplot and FWER(Holm) Boxplot) Implements a framework for creating boxplots where the whisker lengths are determined by formal multiple testing procedures, making them adaptive to sample size and data characteristics. The function bh_boxplot() generates boxplots that control the False Discovery Rate (FDR) via the Benjamini-Hochberg procedure, and the function holm_boxplot() generates boxplots that control the Family-Wise Error Rate (FWER) via the Holm procedure. The methods are based on the framework in Gang, Lin, and Tong (2025) . Package: r-cran-adaptivegpca Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-shiny, r-bioc-phyloseq Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adaptivegpca_0.1.3-1.ca2404.1_all.deb Size: 1333142 MD5sum: 47596e6ea741b76acf331688897fbc04 SHA1: 4b0a3780b4ce1e9bd0bd0100c49da4608812a7a4 SHA256: 92867ca00982c0135623df12b1c560c322c061d7f9a81cdcbe6c435a8652614a SHA512: 3ec7dbe62ff623af75fb43b134f2cfe2801ed9fc568c9178460f5216f771595ddafc1731d551525d995348f38bc3afd3772467163b9e0ed14cd3b7af5958225f Homepage: https://cran.r-project.org/package=adaptiveGPCA Description: CRAN Package 'adaptiveGPCA' (Adaptive Generalized PCA) Implements adaptive gPCA, as described in: Fukuyama, J. (2017) . The package also includes functionality for applying the method to 'phyloseq' objects so that the method can be easily applied to microbiome data and a 'shiny' app for interactive visualization. Package: r-cran-adaptivetrialsr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-adaptivetrialsr_0.1.2-1.ca2404.1_all.deb Size: 856440 MD5sum: a409e0f7790d95a97b0a1526e0be21f7 SHA1: d8b6d8a5f32b4a372e7ff4db0ebc5a49df42d73b SHA256: f0ccdb5f95bb24bc31107a2bd7d4bdbbeb2d44a60e80fb9848cf9f9e897b444a SHA512: 19ff9277d6d574016456c9547c594535e004fdcd85158cc8da588cfe2d718d1a175989ba2a0fea1716323acfff02d67a59b519ab65dc60ee22d223c9a6df7315 Homepage: https://cran.r-project.org/package=AdaptiveTrialsR Description: CRAN Package 'AdaptiveTrialsR' (Bayesian and Adaptive Clinical Trial Simulation and Monitoring) Provides tools for simulating, monitoring, and evaluating Bayesian and adaptive clinical trial designs with binary outcomes. The package includes methods for trial simulation, posterior and predictive probability monitoring, adaptive randomization, Bayesian treatment-effect estimation, dose-finding designs, two-stage phase II trial designs, group sequential monitoring, operating-characteristic evaluation, interim analyses, and decision-support procedures. The methodological foundation includes the two-stage phase II design described by Simon (1989) . 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The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate. Package: r-cran-adaptmt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-glmnet, r-cran-hdtweedie, r-cran-mgcv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-adaptmt_1.0.0-1.ca2404.1_all.deb Size: 1014726 MD5sum: 8bbede3ed8fc7f74aa755baf89039ff9 SHA1: 7dfcb53ff7289ea0e8b770872617ce8189df2a4c SHA256: cde748f202f102eeaa2584fd0fb7a6003e774ae7fa021b56aa311f2a6870e250 SHA512: 0c9cba7159c0fdf9508a8c2cacc2588edbb0ea1342ea3999c04a59fa90302188ba053d3ab7c2400d421cc0e1dce94ff547aba6e6cb1b2e753a75db6b1c534286 Homepage: https://cran.r-project.org/package=adaptMT Description: CRAN Package 'adaptMT' (Adaptive P-Value Thresholding for Multiple Hypothesis Testingwith Side Information) Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any algorithm to learn local false discovery rate and a pool of convenient functions that implement specific algorithms. See Lei, Lihua and Fithian, William (2016) . Package: r-cran-adaptr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4201 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-covr, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-adaptr_1.5.0-1.ca2404.1_all.deb Size: 3795396 MD5sum: fac873b3247c43dc0701d5cb3ac2c311 SHA1: 04808950487f78c979d9363da45e2f7bb1007649 SHA256: 28c26b4e5d4b5f739d032135dce5630486e8c70fa66fd823e3f45a973ff37956 SHA512: 02245ff8a11546048ea728df282425acb9aed02c79311bc6fd52f162b073c56a78185c069a234455780c7c64b0bfe990848247f8341477b2b5196bd95b68cea3 Homepage: https://cran.r-project.org/package=adaptr Description: CRAN Package 'adaptr' (Adaptive Trial Simulator) Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (), primarily supported by a grant from Sygeforsikringen "danmark" (). 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Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells . Package: r-cran-adaptsmofmri Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-matrix, r-cran-coda, r-cran-mvtnorm, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-adaptsmofmri_1.2-1.ca2404.1_all.deb Size: 1346578 MD5sum: 4d0b2d4706915d308399e31b1424a6ae SHA1: e33dec9354ba163c109edfce516d3e333efbce07 SHA256: 1929be0c7d22eb6f89f4a6fa8019dc1d46212fd7604a6f4b13cf9ff4af3cb696 SHA512: a4e802a0c5a91e17bbf50829471b3e5ef5a2d5fc45e7474532b0143c1690775986fe75b80fb252b8fcfc2c2bdaaca6db1b263abc57258367303ccc5a625471a0 Homepage: https://cran.r-project.org/package=adaptsmoFMRI Description: CRAN Package 'adaptsmoFMRI' (Adaptive Smoothing of FMRI Data) Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods. Implemented methods are based on the research developed by A. Brezger, L. Fahrmeir, A. Hennerfeind (2007) . Package: r-cran-adapttest Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-adapttest_1.2-1.ca2404.1_all.deb Size: 131804 MD5sum: 0e971af3050ed0174afc2f05a0110bac SHA1: 9432a8965072a2d37ddc13b2ce6f06e472586923 SHA256: db9ae56344822874619c9167458bf102e2ae9b64d1c5ae71306f2468e7a5d611 SHA512: 5acac0875943b396535b85b3fdca0a70c828f5b247d038b8b7ce5ffac77f2b9c2ae005e7b1749b6c9a0c60929caa19deeb1ecb82121d8aa1a4277de8cf38a16a Homepage: https://cran.r-project.org/package=adaptTest Description: CRAN Package 'adaptTest' (Adaptive Two-Stage Tests) The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006. 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DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy. 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Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) . 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The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017). Package: r-cran-ade4tkgui Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-adegraphics, r-cran-lattice Suggests: r-cran-pixmap, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-ade4tkgui_0.3.4-1.ca2404.1_all.deb Size: 433852 MD5sum: b7bb55914a28dc1fbb6f63835e3e8988 SHA1: e2b234d0919b83b6beaf1c4803fbcfda72a26b4e SHA256: 27a38034e1f5d0dd70bf527c33892efde622090810bae74aafea7e4c2a560ee2 SHA512: f3b1d6f33942cfde660d83a276f78ac683fbaffe5ba2bd580a6929e8bd307eff655d4e2647c1d28e40373c26a0ea277a41996316e17cdd2ec328845e8deb1931 Homepage: https://cran.r-project.org/package=ade4TkGUI Description: CRAN Package 'ade4TkGUI' ('ade4' Tcl/Tk Graphical User Interface) A Tcl/Tk GUI for some basic functions in the 'ade4' package. 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This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. . A full functional on-line and interactive version is available at . 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Package: r-cran-ader Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ader_1.5-1.ca2404.1_all.deb Size: 62990 MD5sum: 931ba172c6ab6bfec72d5015506e53ff SHA1: 92aba207fd3cada9ab28656bdc8b47dd34a23456 SHA256: f6d053ff24b6ca6e52ab3209bb1f8dc7953fa62376d157d296acc5326ab463e7 SHA512: 979636cffb481eee376874b7d1724698ab3df361858721d89e2051d60afe152df5d6bf4716eeaebc0c0b24ca3ed9f4f65d2cad85c142bd0298938b9208170431 Homepage: https://cran.r-project.org/package=ADER Description: CRAN Package 'ADER' (Data Analysis in Ecology) Data sets used in Cayuela and De la Cruz (2022, ISBN:978-84-8476-833-3). Package: r-cran-adformr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-adformr_0.1.0-1.ca2404.1_all.deb Size: 22904 MD5sum: 38afb018e0e6d211aaed12e14734223b SHA1: 9834b6f4983208795891752c492fec89bca0c725 SHA256: 8de150d5a0b1ba5a154be0cdb7adfbcc61f53131593aa1659e22764c036fa29d SHA512: 419eb8f24138414e231f86ef5d39a03db09657fe42f59c0285605e5028ebff097e49042112931a60f0f16dff8dd592dd6388c90b70e61d92d265aad5fc7eb263 Homepage: https://cran.r-project.org/package=adformR Description: CRAN Package 'adformR' (Get Adform Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Adform Ads using the 'Windsor.ai' API . Package: r-cran-adgoftest Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adgoftest_0.3-1.ca2404.1_all.deb Size: 15872 MD5sum: 2d7e34f8f711d1b3aa17e427ae7e2443 SHA1: 99fb831f5adc868a315d7a43c00b128d0f2a1d10 SHA256: 6a4a6ac823da3ab61b3bc1d9df53cfdd439ff8a1ba7db059dc3ff6183bfe739f SHA512: 3bccd62b2dc7e65e7f9376f04ad3133352c9e5e9abf34d16817386771103c5d26be3d68ebad15877dbb4c94006f6cc356b91a9299de9e73a43b7b699f400a237 Homepage: https://cran.r-project.org/package=ADGofTest Description: CRAN Package 'ADGofTest' (Anderson-Darling GoF test) Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution" Package: r-cran-adheaping Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-kernelheaping, r-cran-foreign, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adheaping_1.1.0-1.ca2404.1_all.deb Size: 86442 MD5sum: ba6996f37f58b325d14ff483edaed385 SHA1: 8a3ff60b770a8748517d322639706ffed533025f SHA256: 7ccb802602e19f255484d4d6a1764ca2833dc3f2d4197ee4700b278d8db52e27 SHA512: 862f177ab17a7c3e90d122dae3997a60872ea675e4e1a38cbaeb9be57032d8746496c60c4251198d4c350342515a0be61e672d6c63eec179bdf28fa3f6148543 Homepage: https://cran.r-project.org/package=adheaping Description: CRAN Package 'adheaping' (Characteristic-Function De-Heaping Density Estimation) Tuning-free kernel density estimation for heaped and rounded data using a characteristic-function theory of heaping. Rounding to a grid is convolution with a box followed by lattice sampling, so the density is recovered by deconvolving the known box and tapering against a data-driven noise floor. Provides a box-deconvolution de-heaping estimator, a superposition variant, and a single combined estimator selected by a band-capacity gate; grid, heaped-fraction, and mixed-grain readers; and a spectral higher-order comb detector. Base-R replicas of the Heitjan-Rubin multiple-imputation and measurement-error deconvolution methods are included for comparison, and the 'Kernelheaping' stochastic expectation-maximization estimator is used when installed. Package: r-cran-adherer Architecture: all Version: 0.8.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4467 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-data.table, r-cran-rsvg, r-cran-jpeg, r-cran-png, r-cran-webp Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r.rsp, r-cran-base64, r-cran-viridislite, r-cran-adhererviz Filename: pool/dists/noble/main/r-cran-adherer_0.8.3-1.ca2404.2_all.deb Size: 2829504 MD5sum: 05d87137227ef2f1cc6f09404aa93fec SHA1: b589adc38c2905990b234b9dc5fd66e1df37b1d8 SHA256: ee3b987fa5615aa5eac3c6fe2fd90431146ee026308eef4d530586f30d3ff5ad SHA512: 632bd541dda74faa0c5ecff2d3af9834e7bde8067cfe61a542e81291e12674d30e15665b5b00d43a4348343cb902fa3e4917a8723af462d0df13412eb8afabda Homepage: https://cran.r-project.org/package=AdhereR Description: CRAN Package 'AdhereR' (Adherence to Medications) Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python. 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It is implemented using Shiny and HTML/CSS/JavaScript. Package: r-cran-adismf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aiccmodavg, r-cran-ggplot2, r-cran-nls2 Filename: pool/dists/noble/main/r-cran-adismf_0.1.1-1.ca2404.1_all.deb Size: 38942 MD5sum: b536a696683498fe3f54bf9b335f8e2a SHA1: f62fb512e0ce049129c0f660d38d6e0565dd95e5 SHA256: 59be2dedb76a5c0eaf23fb822e2685c3ee2b331009e2fc52725c64d0137a2fd7 SHA512: 190567f22dd67ec3b1d422621d879f728a43f3a76536b12e039a0699656951ed4f51774dd836d8d971b5ca9c5275ed4e4cd7fb6f0d370551ce8343dd11c6750b Homepage: https://cran.r-project.org/package=AdIsMF Description: CRAN Package 'AdIsMF' (Adsorption Isotherm Model Fitting) The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. 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Package: r-cran-adiv Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-adegraphics, r-cran-ape, r-cran-cluster, r-cran-lpsolve, r-cran-phylobase, r-cran-phytools, r-cran-rgl Filename: pool/dists/noble/main/r-cran-adiv_2.2.1-1.ca2404.1_all.deb Size: 999258 MD5sum: 73a9c57faa988c78aaf0c96d93391ce8 SHA1: 8c45c7237197cf6513d44584b0803d406c8259e5 SHA256: 766e5274fb6335c56a55cf5cfd0999ebce177bb8f04717420501b9dc40611b3d SHA512: eedcf395450f80ef8a018e5f62c195f8992d3dfcd01bc2b77f3ce8fde2e21306f21f01b34cd8c6d7d70958f98c553fa6902c32b99740bc11731a4f743eb3b3ec Homepage: https://cran.r-project.org/package=adiv Description: CRAN Package 'adiv' (Analysis of Diversity) Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. 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If the threshold for either sensitivity or specificity is not given, the crossing point between the sensitivity and specificity curves are returned. For bootstrap procedures, mean and CI bootstrap values of sensitivity, specificity, crossing point between specificity and specificity as well as AUC and AUCPR can be evaluated. Package: r-cran-adjustedcranlogs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cranlogs, r-cran-xml2, r-cran-lubridate, r-cran-dplyr, r-cran-rvest Filename: pool/dists/noble/main/r-cran-adjustedcranlogs_0.1.0-1.ca2404.1_all.deb Size: 228420 MD5sum: 3341a349ef08cb265c7efc436ca16f53 SHA1: 94c630b94e4f5a11d8f79de5123c0db2379f9d05 SHA256: 2e92110294ace0e0b2c88576dd0d386f3f3744e81bf4ee5e8d13ec7752e6b509 SHA512: c57e091b5dace4979ea06bc51e52c5e388d37888884f9f3eec93baefc677572bf9b085c8dee0db4b58726b8e346e1e86fc1d084768a16f68206321461d7f1d03 Homepage: https://cran.r-project.org/package=adjustedcranlogs Description: CRAN Package 'adjustedcranlogs' (Remove Automated and Repeated Downloads from 'RStudio' 'CRAN'Download Logs) Adjusts output of 'cranlogs' package to account for 'CRAN'-wide daily automated downloads and re-downloads caused by package updates. Package: r-cran-adjustedcurves Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1704 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r.utils, r-cran-doparallel, r-cran-dorng, r-cran-dplyr, r-cran-foreach, r-cran-rlang, r-cran-survival Suggests: r-cran-mass, r-cran-matching, r-cran-weightit, r-cran-cmprsk, r-cran-eventglm, r-cran-geepack, r-cran-ggplot2, r-cran-knitr, r-cran-mets, r-cran-mice, r-cran-nnet, r-cran-pammtools, r-cran-pec, r-cran-prodlim, r-cran-riskregression, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-ggpp, r-cran-vdiffr, r-cran-covr, r-cran-data.table, r-cran-numderiv, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-adjustedcurves_0.12.0-1.ca2404.1_all.deb Size: 1306320 MD5sum: 1ee92c6e6c45c71f3f4d5ce425fa96cf SHA1: 61a47662fdbede2cd52d5f15a1f554170a8290c1 SHA256: aac0d2ecbafcecabafb779811505dcf9bab3a37ca68968634aa768ee8dfd6936 SHA512: 0a56f5fac1403d0f242bce264e27a0471edfe08ea30a0f533a63c246bd08f764e53b5a7c0b3a5625a9f2cbddbfdc8a6f92229c318a6fa0e498744caf0e6b71de Homepage: https://cran.r-project.org/package=adjustedCurves Description: CRAN Package 'adjustedCurves' (Confounder-Adjusted Survival Curves and Cumulative IncidenceFunctions) Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) . Package: r-cran-adjustr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyselect, r-cran-rstan, r-cran-loo Suggests: r-cran-ggplot2, r-cran-extradistr, r-cran-tidyr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adjustr_0.2.0-1.ca2404.1_all.deb Size: 378166 MD5sum: 760fc415b7bc64415ca0683b6586651e SHA1: c5f9298a0aec9ac22fc4f579e46e6cca99a71331 SHA256: 6bdc85421739d7e2f1794c3b00a8756f723801e36a769ecce638477c15f51e8a SHA512: f52ade5b7adc7c9c50471f7313e6f26315ded038eb66b8952dfe3ec014f669c386f8b77a1dc522e393fc8b7dc9918c02eef28d7b6d04a18be05c16e45e3f1f6f Homepage: https://cran.r-project.org/package=adjustr Description: CRAN Package 'adjustr' (Stan Model Adjustments and Sensitivity Analyses using ImportanceSampling) Assess the sensitivity of a Bayesian model (fitted using 'Stan' via 'rstan', 'brms', or 'cmdstanr') to the specification of its likelihood and priors. Users provide a series of alternate sampling specifications, and the package uses Pareto-smoothed importance sampling (PSIS) to estimate posterior quantities of interest under each specification, without needing to refit the model. Methods are based on Vehtari, Simpson, Gelman, Yao, and Gabry (2024) . Package: r-cran-adklakedata Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rappdirs, r-cran-tibble Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adklakedata_0.7.1-1.ca2404.1_all.deb Size: 246128 MD5sum: 572af424ae25346c1fd907cc1a58d6d2 SHA1: 351683fc077f571cb3513254ea07f28cfcc25f06 SHA256: 7a9964bf8f62c431560d52ecadffb28d78a09f2a85c22608d3ad41f42324e23a SHA512: 5a3aa9fd5d0b01eef9cfa93835939ad27ab108bac2d087af7eb0f8484dbed0d948ae8fe7a47d7408bf51fd16ec31cb8110f294a52b8fa376ccf28d9e983c4f0d Homepage: https://cran.r-project.org/package=adklakedata Description: CRAN Package 'adklakedata' (Adirondack Long-Term Lake Data) Package for the access and distribution of long-term lake datasets from 28 lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters originally described in Farrell et al. 2018 . Water chemistry and nutrient records are extended through 2024 using data from the USGS AQ Samples database, including new columns for surface temperature, UV-254 absorbance, and a program flag distinguishing AEAP integrated samples from ALTM surface grabs. The underlying figshare archive additionally contains chemistry records for 25 ALTM-only lakes; the package restricts to the 28 originals for consistency with the published dataset. Package: r-cran-adlp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-synthetic Filename: pool/dists/noble/main/r-cran-adlp_0.1.0-1.ca2404.1_all.deb Size: 540912 MD5sum: dd84908d0100c1fda5876c6c08d7e081 SHA1: 739d59cfb470fdd4a38c1a03b783349281bf4b16 SHA256: 5e889ec7d9cd67198b85a968accedce1001adc69d5546dc56a167dfbabc92da8 SHA512: 750bc26df42a3396f71c1b534e4b53ce1b11dc9c64ee7026fcfc04d48d82d3d854e610348175263e6d498c50d6ffd8b4514c5dcfc02f225dfb46c457d1a83252 Homepage: https://cran.r-project.org/package=ADLP Description: CRAN Package 'ADLP' (Accident and Development Period Adjusted Linear Pools forActuarial Stochastic Reserving) Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" . Package: r-cran-admetshiny Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1270 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-dplyr, r-cran-dt, r-cran-fingerprint, r-cran-fmsb, r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-openxlsx, r-cran-rcdk, r-cran-rmarkdown, r-cran-rtsne, r-cran-shiny, r-cran-uwot, r-cran-viridislite, r-cran-webchem Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admetshiny_1.0.0-1.ca2404.1_all.deb Size: 1187632 MD5sum: 4bf999a068d2d9a641656109d6aa6d85 SHA1: da0530fed8c0ca9141b73b5c83487b732a099751 SHA256: 8dd2294528de41d054c4da2e8178aaaff402e1972db728d5194ef9b63924f5dd SHA512: 3b4e50cd66d549d6b7b119e7c81c9ce060ae43dfab5071edde03e5e91109ba67f52922a6b39e820cf72f315984efe0bccbb262ee81c38198daf47386a5ff8cc2 Homepage: https://cran.r-project.org/package=admetshiny Description: CRAN Package 'admetshiny' (Interactive ADMET and Drug-Likeness Analysis of Small Molecules) Provides an interactive Shiny application and a toolbox of R functions for the management, calculation, filtering, visualization and exploratory analysis of molecular descriptors and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties of small molecules. Computes descriptors locally via the Chemistry Development Kit (CDK), and offers drug-likeness filters (Lipinski, Veber, Ghose, Egan, Muegge), the BOILED-Egg model for gastrointestinal absorption and blood-brain barrier permeability, a P-glycoprotein (P-gp, also known as ATP-binding cassette sub-family B member 1, ABCB1) substrate Random Forest classifier, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), radar plots and Tanimoto / AGglomerative NESting (AGNES) clustering to support compound prioritization in early-stage drug discovery. Package: r-cran-admiral.test Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3768 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-lintr, r-cran-pkgdown, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-usethis, r-cran-covr Filename: pool/dists/noble/main/r-cran-admiral.test_0.7.0-1.ca2404.1_all.deb Size: 3270638 MD5sum: 4b59363fde7bd28fb91d7600152c9bf1 SHA1: 581a26483e48df76a217da464229fa0a1a64f3bf SHA256: 2007abcc47e528b0319d3c94ac1c5b7f714706c720f5293c2dc87c4184614071 SHA512: 3de6eb39b21c4a9cc403a186a0fd815367dc7efb8da0b7fc3097011e39d1571c667a66bd4d4755950a659b9a97fe839d14be29094c4de2b694e3f179bcfdf703 Homepage: https://cran.r-project.org/package=admiral.test Description: CRAN Package 'admiral.test' (Test Data for the 'admiral' Package) A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the 'admiral' package. Package: r-cran-admiral Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4835 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-ggnewscale, r-cran-ggplot2, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-pharmaversesdtm, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-admiral_1.5.0-1.ca2404.1_all.deb Size: 2099112 MD5sum: 6b059112ae1f75bb47640b255661509c SHA1: 3b338115b067859194d9eafa0e77b81831bf0dff SHA256: 9d1018a98f80f4d6a0181a72a9e78a862d0540ca0ca7cd8d45ab5c9bb2728e54 SHA512: 7c767abbcacab0bc41317f6f6be70dd8d29e2c372715266de4eada0e3e4ae1a1fdb01f81e24a6322661d01c3a4abc440ac31e3b52745b7ce184ffbb6d5910e1e Homepage: https://cran.r-project.org/package=admiral Description: CRAN Package 'admiral' (ADaM in R Asset Library) A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). Package: r-cran-admiraldev Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-roxygen2, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admiraldev_1.5.0-1.ca2404.1_all.deb Size: 1235970 MD5sum: 2e4dd49114a97d8f9fceebaa2014aa8a SHA1: af6fab113f27b692dbe2715d2b5ca66ec8ae43be SHA256: dbd6175e5ef40da14944be231410e499890f565e9b272cdc228d9079c79ad17d SHA512: 6638a66b42a9cf15acdef377b7c437810f9b9e9e98ff71625d9a1c6538bda0f7900e86990ea8139c5a2d593be1b0cea7062a78e54e82af274c564ed6fb09a1b2 Homepage: https://cran.r-project.org/package=admiraldev Description: CRAN Package 'admiraldev' (Utility Functions and Development Tools for the Admiral PackageFamily) Utility functions to check data, variables and conditions for functions used in 'admiral' and 'admiral' extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages. Package: r-cran-admiralmetabolic Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-pharmaversesdtm, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-admiralmetabolic_0.3.0-1.ca2404.1_all.deb Size: 225964 MD5sum: d7d73b53a0472fb7f5814cd76d57fcc5 SHA1: 8cf3280fac2212bc33b5f5e101cb8656cc8bb1d1 SHA256: 3ec55c7197876e6053eafc0269d018197de3ed0c9c524c8ce1194985f0d49b2b SHA512: b836020b3741d1bd58bdc005e6db7bf522c724ca57cb0f38ce462fa7418ffa1d9eba6ec1addcf963bd8138b03aa16681ebd0ab2aef50657db225f92ccc616c86 Homepage: https://cran.r-project.org/package=admiralmetabolic Description: CRAN Package 'admiralmetabolic' (Metabolism Extension Package for ADaM in 'R' Asset Library) A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package focusing on the metabolism therapeutic area. Package: r-cran-admiralneuro Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-metatools, r-cran-pharmaversesdtm, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admiralneuro_0.3.0-1.ca2404.1_all.deb Size: 214586 MD5sum: f05f22c64b020a69b88849a0591f4984 SHA1: 3529a7285b901d4cc462e46ac449c6cfc1c24ed2 SHA256: 770dd18d9e2c9f40cc2e5004704f6b42c3663217dd14a571a9f8c364924e26a5 SHA512: 7924f6431a060cd558f97ee896290858b2a4399cbf90acd604820e662b3422bb102f6c6681872bce153829ea65b56581e815fe0ad7200d5f2cc1cda6d4be6081 Homepage: https://cran.r-project.org/package=admiralneuro Description: CRAN Package 'admiralneuro' (Neuroscience Extension Package for ADaM in 'R' Asset Library) Programming neuroscience specific Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). This package extends the 'admiral' package. Package: r-cran-admiralonco Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-covr, r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-ggplot2, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-lintr, r-cran-metatools, r-cran-miniui, r-cran-pharmaverseadam, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-admiralonco_1.5.0-1.ca2404.1_all.deb Size: 456104 MD5sum: fdbd9f6245845e08591086448f4f6227 SHA1: 2d84586a94022701f06b263a609a8b08476a5593 SHA256: 4c70e828760735d779b60f933a176da7e3d36d157a6035643ab0eb4d8472e756 SHA512: acbd82438583deb748ad8d95370298f7850072ae7f7bd54e44d4427e3f40ddd1bd85b1446f078674f0156a6251da0aa650b60021c0457368d89df5f33f265ef2 Homepage: https://cran.r-project.org/package=admiralonco Description: CRAN Package 'admiralonco' (Oncology Extension Package for ADaM in 'R' Asset Library) Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), ). The package is an extension package of the 'admiral' package. Package: r-cran-admiralophtha Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-lintr, r-cran-miniui, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-admiralophtha_1.5.0-1.ca2404.1_all.deb Size: 758616 MD5sum: 8f77cb76dc1d847cae707d19cd29f497 SHA1: 2f6fc02fa6ac0d9dc3f7a48c5b3e20b36b3b1ea0 SHA256: 4cebc1cd5f743ca3a3b20945d562f49fc0369a8859ba8d6b808875b526e5809d SHA512: e107495baba26600e56a2639253acb4be61323cacbf340f5e5d9cb5f92a8e13abc3eec84d996a619537e76764166741945d45bda2cf7b7136281b74a0feb3835 Homepage: https://cran.r-project.org/package=admiralophtha Description: CRAN Package 'admiralophtha' (ADaM in R Asset Library - Ophthalmology) Aids the programming of Clinical Data Standards Interchange Consortium (CDISC) compliant Ophthalmology Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). Package: r-cran-admiralpeds Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-dt, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-lubridate, r-cran-pharmaversesdtm, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-yaml Filename: pool/dists/noble/main/r-cran-admiralpeds_0.4.0-1.ca2404.1_all.deb Size: 452190 MD5sum: 04b39399458612a43add55c882bbc17d SHA1: 6fd4b1c0d5a33835d38ffd4f38b36bab0fc5a3ef SHA256: bfd1e660ef48dfdd30a0ee1d64db8aa26e821d56e2bc365b21ead55538e7b5ed SHA512: bed461f65b7674033722ddb0fe399292694b52c9719ce57abdabbea1e5db59c11f1761f524d66671dd3071993ccbe9ebe69e97ae82b8e46ce6505285638f77c6 Homepage: https://cran.r-project.org/package=admiralpeds Description: CRAN Package 'admiralpeds' (Pediatrics Extension Package for ADaM in 'R' Asset Library) A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package for pediatric clinical trials. Package: r-cran-admiralvaccine Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-knitr, r-cran-lintr, r-cran-metatools, r-cran-miniui, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-admiralvaccine_0.6.0-1.ca2404.1_all.deb Size: 205078 MD5sum: 25e4730f00e30613126e260251fd15f5 SHA1: dcfc817709311ef967c894b69c51d4f892e299ee SHA256: 251562720deaf5c1737de334c61fcebfd8876101b95cc0ce641dde6587005a13 SHA512: 60292264174c1d45086c1fad13b86f49a781d4a6bad34e00cafcf5b0943ecb2e91dfeb95571e057a800be872bccdc6c42757a9fb0ca1a01ff6b07d2627a3e682 Homepage: https://cran.r-project.org/package=admiralvaccine Description: CRAN Package 'admiralvaccine' (Vaccine Extension Package for ADaM in 'R' Asset Library) Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), ). The package is an extension package of the 'admiral' package. Package: r-cran-admixr Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-rlang Suggests: r-cran-covr, r-cran-glue, r-cran-testthat, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-admixr_0.9.2-1.ca2404.1_all.deb Size: 562394 MD5sum: eea457dbf2e22c8f5cf4c022505517be SHA1: 4eab1c24ee00ee908a650bd92ef27983ae50d85d SHA256: d034c769fdaeee7e9cc4913d38a1de3db4cdf62567f4f4069e80917f6a01e49d SHA512: 2e5d5e1503cacba49068573240b965a7c17f408e3fdb0fb8fe2cb18f74d5113b9a804df60a9788fd210da754393fe14cccdb0e59f616b3d8372af9832feeccfe Homepage: https://cran.r-project.org/package=admixr Description: CRAN Package 'admixr' (An Interface for Running 'ADMIXTOOLS' Analyses) An interface for performing all stages of 'ADMIXTOOLS' analyses () entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run 'ADMIXTOOLS' programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the 'EIGENSTRAT' format is also provided. Package: r-cran-admmdensestsubmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-admmdensestsubmatrix_0.1.0-1.ca2404.1_all.deb Size: 497136 MD5sum: 31325ca34071bf2e5708740ae1d53478 SHA1: c91798186c75dba1e8d7d6c5e1bf2196a87d0499 SHA256: 744f9094f88d26381651d9afcfb1996ce21464b3091a682749afde0be67eeae0 SHA512: ee84c27d9bcae36766246aec123107ce3f49284394a722d9efab846523894d8d79c0ca23d5b56cbf8aa63546cfff7fcdedbc712e30f368b5eb6a6da2db53bd24 Homepage: https://cran.r-project.org/package=admmDensestSubmatrix Description: CRAN Package 'admmDensestSubmatrix' (Alternating Direction Method of Multipliers to Solve DenseDubmatrix Problem) Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) . Package: r-cran-admtools Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape Suggests: r-cran-fossilsim, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admtools_0.7.0-1.ca2404.1_all.deb Size: 439286 MD5sum: 0ce204ed1ec7df45d91de1c09fed8666 SHA1: e222462690afa302125e7b95a62f76e477a53a4d SHA256: 2ec1e17abfdb9155ce50857b05aa1e61595497dc4c4faea20d65917129ae958f SHA512: 72206ceeaed7933f9f75087bfbdc3ed36b900f077bed29b988fe673020f2e7f9272465269b0c97c2822f06191875b172561e45e4a3e4734d126625b8e688a4c3 Homepage: https://cran.r-project.org/package=admtools Description: CRAN Package 'admtools' (Estimate and Manipulate Age-Depth Models) Estimate age-depth models from stratigraphic and sedimentological data, and transform data between the time and stratigraphic domain. Package: r-cran-admur Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mathjaxr, r-cran-scales, r-cran-zoo Suggests: r-cran-deoptimr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-admur_1.0.3-1.ca2404.1_all.deb Size: 1807512 MD5sum: 01384336e0b60fd864538876c613a613 SHA1: 7caa6c718505862eb8e058f52a705d13c969c193 SHA256: bac79a2eeadd4f1cb6c308fa38e57274d51c78b946647b55e15d55f97dcabe05 SHA512: ace88420b4f9220aa3f3a1217ad3c4ffb8e818862837c18d271429a172759d5aa6b57d5e5fb4650b76ad36931d6dcdf153291b3352eb2e3308a2a786230cfe7f Homepage: https://cran.r-project.org/package=ADMUR Description: CRAN Package 'ADMUR' (Ancient Demographic Modelling Using Radiocarbon) Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model. Details about the method can be found in Timpson A., Barberena R., Thomas M. G., Mendez C., Manning K. (2020) . Package: r-cran-adnuts Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall, r-cran-ellipse, r-cran-rstan, r-cran-r2admb, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-shinystan, r-cran-matrixcalc, r-cran-knitr, r-cran-tmb, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adnuts_1.1.2-1.ca2404.1_all.deb Size: 779362 MD5sum: 8b07124dedf72c440b0ad43c64829b4a SHA1: 3483d177e59627c40f867def9a3a67802d31bcbe SHA256: f7ec22c9a13ac6b4fb3d068be00d3af276ec65e03fd585b0c4a2eba6b6dd1928 SHA512: f22df4c9d3a00d30c18896deaac3143d217166d6ce020f964a8b172f45a4f598e79340feae91105ab62dea8e83527766279f92feea6b4d9d6355e326b18c0553 Homepage: https://cran.r-project.org/package=adnuts Description: CRAN Package 'adnuts' (No-U-Turn MCMC Sampling for 'ADMB' Models) Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) . Designed for 'AD Model Builder' ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as 'Template Model Builder' models and other special cases. Functionality is similar to 'Stan', and the 'rstan' and 'shinystan' packages are used for diagnostics and inference. Package: r-cran-adobeanalyticsr Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-jsonlite, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-httr, r-cran-tidyr, r-cran-rlang, r-cran-lubridate, r-cran-ggplot2, r-cran-scales, r-cran-r6, r-cran-jose, r-cran-openssl, r-cran-lifecycle, r-cran-glue, r-cran-vctrs, r-cran-progress, r-cran-memoise, r-cran-httr2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adobeanalyticsr_0.5.1-1.ca2404.1_all.deb Size: 1235108 MD5sum: 772bf2ba715424d58d496d5f44240aca SHA1: 9e2849c93cd897f4eb84d2c310cc2a4c2bdb6161 SHA256: 6540ff87eb96d2298a1fafd4973ae375feaf235d36e1225184a5d84039c6d056 SHA512: d5274dab61c69120a9369ad6b0533acd8dc039648316730860a0ae953265027adcc87aa360c94400fc7589457e765018b4897c3cbf33529d9b298afc60916e4b Homepage: https://cran.r-project.org/package=adobeanalyticsr Description: CRAN Package 'adobeanalyticsr' (R Client for 'Adobe Analytics' API 2.0) Connect to the 'Adobe Analytics' API v2.0 which powers 'Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. Package: r-cran-adoptr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-rpact, r-cran-vdiffr, r-cran-pwr, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-gridextra, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-adoptr_1.1.2-1.ca2404.1_all.deb Size: 906008 MD5sum: 61df0d779111ab399c3a04019639ec8d SHA1: 892bce35c40ad1b64cffb0de6462d19c767c8e84 SHA256: f96ad2b52501eda9e75f4e9e5bf6f084b7cd86fcb444465d13a38c6e73f29fad SHA512: 8db720c05d3a55f46bdb321fa237e3f0cf2b8d1bdb2dba29f5a9af6e0c7670b660687c1e4f230993cad62b57de2bfdf061d4d3fe94e6c3d85e9039d27722d233 Homepage: https://cran.r-project.org/package=adoptr Description: CRAN Package 'adoptr' (Adaptive Optimal Two-Stage Designs) Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) and Kunzmann et al. (2021) for details. Package: r-cran-adp Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adp_0.1.6-1.ca2404.1_all.deb Size: 25504 MD5sum: 6e36a0ac65148f11d41cb084e3c71a8a SHA1: b8686e2156575775e956643f40ab2a9a03471d70 SHA256: c5cd6f8f7b321eee8f3e300596788f31046fce71aa774f702b0d5e930e7a45ba SHA512: cd1c195b2e62250ebb2c975e8448d4a67138ee40f25a6ea1563bb95f78c5381a8b21b56a3dd1f7daec145bbc3dfac7b637fb3c6565f8d4aeabd8a899be937258 Homepage: https://cran.r-project.org/package=ADP Description: CRAN Package 'ADP' (Adoption Probability, Triers and Users Rate of a New Product) Calculate users prevalence of a product based on the prevalence of triers in the population. The measurement of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. On the other hand, The measurement of people who also adopt it as part of their life is more complicated since adopting an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculate the prevalence of a product's users to overcome this difficulty. The current package assists in calculating the users prevalence of a product based on the prevalence of triers in the population. See for: Kislev, M. M., and S. Kislev (2020) . Package: r-cran-adpclust Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-cluster, r-cran-fields, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adpclust_0.7-1.ca2404.1_all.deb Size: 1154924 MD5sum: d857c404a8865dcfcc948e52500891ab SHA1: c7d3ceed5a72d92e33d778de31f8975d98f81d22 SHA256: 827997678772a6d793acdbfd3c8c6462a65a426136776dd28ea28e02618c6cc8 SHA512: 07ac22e13006ab9c77e71a114d2d6b91daa5d041872e3a30614c89cf0e133da7b6e135240f368b67ed3d45a6864af48f1eece9c4838417656ac15aa82bf9de91 Homepage: https://cran.r-project.org/package=ADPclust Description: CRAN Package 'ADPclust' (Fast Clustering Using Adaptive Density Peak Detection) An implementation of ADPclust clustering procedures (Fast Clustering Using Adaptive Density Peak Detection). The work is built and improved upon the idea of Rodriguez and Laio (2014). ADPclust clusters data by finding density peaks in a density-distance plot generated from local multivariate Gaussian density estimation. It includes an automatic centroids selection and parameter optimization algorithm, which finds the number of clusters and cluster centroids by comparing average silhouettes on a grid of testing clustering results; It also includes a user interactive algorithm that allows the user to manually selects cluster centroids from a two dimensional "density-distance plot". Here is the research article associated with this package: "Wang, Xiao-Feng, and Yifan Xu (2015) Fast clustering using adaptive density peak detection." Statistical methods in medical research". url: http://smm.sagepub.com/content/early/2015/10/15/0962280215609948.abstract. Package: r-cran-adpf Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adpf_0.0.1-1.ca2404.1_all.deb Size: 37206 MD5sum: b39b30ff874c08fac11e31b920c5f159 SHA1: 12344b2da294a70fe160094ffc0cb55a9f0c20ae SHA256: 9db96696ca5da683dd04d3b8fe77a9ba30284532cff64aee792fd2aee28c815b SHA512: 58b5aa661711fe757fb00ca920822229dcea7352a186bf977a5b602b72ff4d04bb0c56211bf08e9dbeaa82566d48d0d0742d959beaa3b317c97c3f138c56c62e Homepage: https://cran.r-project.org/package=ADPF Description: CRAN Package 'ADPF' (Use Least Squares Polynomial Regression and Statistical Testingto Improve Savitzky-Golay) This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) for more information. 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The Ad-plot can identify symmetry, skewness, and outliers of the data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Full description of this methodology can be found in the article by Wijesuriya (2025) . 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Also contains the low dimensional ADPROCLUS method for simultaneous dimension reduction and overlapping clustering. For reference see Depril, Van Mechelen, Mirkin (2008) and Depril, Van Mechelen, Wilderjans (2012) . 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The proposed clinical trial must be written by the user in the form of a function that takes as argument a sample size and returns a boolean (for whether or not the trial is a success). The 'adsasi' functions will then use it to find the correct sample size empirically. The unavoidable mis-specification is obviated by trying sample size values close to the right value, the latter being understood as the value that gives the probability of success the user wants (usually 80 or 90% in biostatistics, corresponding to 20 or 10% type II error). 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The package supports parameter estimation through both linearized and non-linear fitting techniques and generates high-quality plots for model diagnostics. It is intended for environmental scientists, chemists, and researchers working on adsorption phenomena in soils, water treatment, and material sciences. Functions are compatible with base 'R' and 'ggplot2' for visualization. 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This package allows users to fit these models to experimental data, providing parameter estimates along with fit statistics such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. Researchers and engineers in material science, environmental engineering, and chemical engineering can rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. For more details, see Harding (1907) . 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This package enables users to fit non-linear and linear adsorption isotherm models—Freundlich, Langmuir, and Temkin—within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) , and Gamerman & Lopes (2006) . 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The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data. The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results for the association of adverse event biomarkers and overall survival and progression free survival. The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events. Plots and tables of results are downloadable. 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In addition to this, we also provide a simple way (Jones and Hulme, 1996) to grid the irregularly-spaced data points onto regular latitude-longitude grids by averaging all stations in grid-boxes. This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177). 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Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 . 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Package: r-cran-aeenrich Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-bioc-qvalue, r-cran-doparallel, r-cran-tidyr, r-cran-modelr, r-cran-foreach, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aeenrich_1.1.1-1.ca2404.1_all.deb Size: 475976 MD5sum: 0ae85c2d539d47e42e09ef30e4aab196 SHA1: f70ed83422f1656a2954a8af01a0c8e28d0589f1 SHA256: 89f1d2e82094fa9d945337deefbcaac73fd82409c695550da4438a4f1aca669f SHA512: f4e563a8d1d01bc8b90906172c36b5e0a38ceed1ea4f8839930e667403a718abbbc069bdb7835c64f973ef60737c2a995ff989d29afb69dbad70359e51978d61 Homepage: https://cran.r-project.org/package=AEenrich Description: CRAN Package 'AEenrich' (Adverse Event Enrichment Tests) We extend existing gene enrichment tests to perform adverse event enrichment analysis. 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Provides tidy access to 5-minute and 30-minute wholesale electricity prices, regional demand, dispatch-unit output, interconnector flows, rooftop photovoltaic generation, generator bids, predispatch forecasts, frequency control ancillary services markets, and gas market data across the National Electricity Market (NEM) regions. Data is published by AEMO under its Copyright Permissions Notice . 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ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.) Package: r-cran-aerobiology Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-writexl, r-cran-ggvis, r-cran-lubridate, r-cran-plotly, r-cran-ggplot2, r-cran-tidyr, r-cran-circular, r-cran-scales, r-cran-zoo, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aerobiology_2.0.4-1.ca2404.1_all.deb Size: 729442 MD5sum: 6d5d19ffa1f55471e0aee0842c0995ad SHA1: 7424c46f5f1798260ce729b78bac48f99ded3b01 SHA256: cf8067aa27e86109ce619d9e1a6675d74dcf8280bc08229a3d2eb4c54dedd74f SHA512: 6eae58e1b8af932df398c445f4b1212a1895d53f5c83ade83240adde4bcee280d58e3e8e2d59cc40e689c1511d3ac56aedf5fe2553d7d3a7958b968de2345525 Homepage: https://cran.r-project.org/package=AeRobiology Description: CRAN Package 'AeRobiology' (A Computational Tool for Aerobiological Data) Different tools for managing databases of airborne particles, elaborating the main calculations and visualization of results. In a first step, data are checked using tools for quality control and all missing gaps are completed. Then, the main parameters of the pollen season are calculated and represented graphically. Multiple graphical tools are available: pollen calendars, phenological plots, time series, tendencies, interactive plots, abundance plots... Package: r-cran-aeroevapr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-data.table, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-aeroevapr_0.1.6-1.ca2404.1_all.deb Size: 83156 MD5sum: 13b24fca74d513bf18639b9b7156a2a1 SHA1: c23f0262fcb544f3bcfdee33c3662fe60e93397a SHA256: 2ee66d5aac59da37dd5f0d772103ed34fcd078f657e3290b8d379d8ba543aa89 SHA512: a9bc8e2dda2d47f3a028fecf88ca36486b64e0c08d3cff76d80ad10568b90511caf64d5f7cc7c13855b15f6805ed0576dc835c375bb70bcdadef86ab4f3d2659 Homepage: https://cran.r-project.org/package=AeroEvapR Description: CRAN Package 'AeroEvapR' (Estimating Reservoir Evaporation via Aerodynamic Approach) Developed as an 'R' alternative to the 'AeroEvap' model developed by the Desert Research Institute (DRI) in 'python' which estimates open water evaporation using the aerodynamic mass transfer approach. Package: r-cran-aerosampler Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-stringr, r-cran-tidyselect, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aerosampler_0.3.0-1.ca2404.1_all.deb Size: 170700 MD5sum: 2dccd41a55febdbb0f4a91a88ecb2174 SHA1: 6d4d111e98482f3cfe0b57667e6780dcd6bf9daf SHA256: 437acd7ad774c98aab6abe8f01dafd3d1d613dd7133c140b9e6816adceb0394c SHA512: cc788a6df8b230cc9a5ced8020caf748b541e7be122a6a3053089c0d5142ee17977a5b20eb144019e3cbe923afa197a43d22702d80edd6064344c87b5074617a Homepage: https://cran.r-project.org/package=AeroSampleR Description: CRAN Package 'AeroSampleR' (Estimate Aerosol Particle Collection Through Sample Lines) Estimate ideal efficiencies of aerosol sampling through sample lines. Functions were developed consistent with the approach described in Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), "Hand Calculations for Transport of Radioactive Aerosols through Sampling Systems" Health Phys 106, 5, S78-S87, . Package: r-cran-aersn Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aersn_0.2.3-1.ca2404.1_all.deb Size: 617370 MD5sum: 07760d844b6c94a1baea28d6bd87900e SHA1: 3a1bdfdad92110464cfcad2d33eabca110b293e1 SHA256: 11437dd1e45d93a7bd17a018ca163c5ea9aee121f09ca29458c22796733d91bb SHA512: e9fa8162aac7fac67702a1aa7e9efc4222ef15959be8c6695f0a7bbc5b76ad06bbde62be82a82233b566f715bae2f0fa64cfb960ed37ad8c0094bcbba0423e0a Homepage: https://cran.r-project.org/package=aersn Description: CRAN Package 'aersn' (Affine-Equivariant Adjusted-Range Self-Normalization forTime-Series Inference) Tuning-free inference on fixed-dimensional parameters of dependent time series using affine-equivariant adjusted-range self-normalization. The centered partial-sum path of estimated influence contributions is normalized by its increment hull, the convex hull of all path increments. The gauge of the hull provides an asymptotically pivotal test statistic and an affine-equivariant confidence region without estimating the long-run covariance matrix, and its support function gives simultaneous confidence intervals for linear contrasts. For a single parameter the construction reduces exactly to adjusted-range self-normalization, whose limiting distribution is available in closed form. The Brownian reference law is simulated on a grid matched to the sample size or a supplied common variance-accumulation profile; inference for dependent observations remains asymptotic. Five further methods are provided for comparison on the same estimate and influence contributions: componentwise adjusted ranges after lag-zero partial prewhitening, quadratic self-normalization following Shao (2010) , kernel long-run covariance estimation with automatic bandwidth selection following Andrews (1991) and Newey and West (1994) , Bartlett fixed-b inference following Kiefer and Vogelsang (2005) , and the equal-weighted cosine method of Lazarus, Lewis, Stock and Watson (2018) . Model interfaces are provided for sample means, linear regression, smooth generalized method of moments, and conditional likelihood scores; other estimators are handled through user-supplied influence contributions. The methods follow Hong, Lin, Linton, Newey and Sun (2026), Cambridge Working Papers in Economics No. 2678 and, for the scalar case, Hong, Linton, McCabe, Sun and Wang (2024) . Package: r-cran-aesopr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-aesopr_0.1.0-1.ca2404.1_all.deb Size: 170028 MD5sum: 6a0d033e73a2313ec289e1167da5619e SHA1: c8a6b204da72cd3d1b26eef3cc2d88fb1cc40fb8 SHA256: 04a918a0a3958aa4f1ea490267f308fa2e802f8ce07fa4a81cd8fc49f17a7849 SHA512: a34b1ec3dd3f02412fae487411fcdbd1eecf7e12b8e4d0707f412558dc4dc79996a862e916a1fe8a7dbf27031bf351853447503fb1ae2d2b8e8e46a9f7ab7955 Homepage: https://cran.r-project.org/package=aesopR Description: CRAN Package 'aesopR' (Tools for Text Analysis of Aesop's Fables) Provides a tidy text corpus of Aesop's Fables sourced from the Library of Congress, along with analysis-ready datasets for sentiment, emotion, and linguistic analysis of moral storytelling. The package includes both full narrative texts and word-level representations to support exploratory text analysis and teaching workflows. Package: r-cran-af Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-drgee, r-cran-stdreg, r-cran-data.table, r-cran-ivtools Filename: pool/dists/noble/main/r-cran-af_0.1.5-1.ca2404.1_all.deb Size: 257362 MD5sum: 7ca48535f1ceef25ca7326127539e11d SHA1: c57ff54a4781c0e8cbfda80baaaa38d845f7c8b9 SHA256: d6fbfc292a4933f33c315d51d44c477c08004190305d898924dfa465ac60b986 SHA512: c47c6529097a4f75912ac62a7f2f53f52e75637e50b5c24705bc2e6bf157f919bb6c39d6a423e157bfe227fbb404308f6042bcc3fccab928bacdaa45c972a5b2 Homepage: https://cran.r-project.org/package=AF Description: CRAN Package 'AF' (Model-Based Estimation of Confounder-Adjusted AttributableFractions) Estimates the attributable fraction in different sampling designs adjusted for measured confounders using logistic regression (cross-sectional and case-control designs), conditional logistic regression (matched case-control design), Cox proportional hazard regression (cohort design with time-to- event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis. An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) . Package: r-cran-afc Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-afc_1.5.0-1.ca2404.1_all.deb Size: 118112 MD5sum: 2f2c0cd4b1bdbf94b92223ae7b8cee0d SHA1: 395567490174efa9b7d816be82c4e67021359309 SHA256: 890b31ffea709a904c9da221aa0c1462b30910bbf4625ecb25516630a04b601d SHA512: 532e5a5c0c7887eb30c313e00cf103d1016a0f67bb929ea90263e363a5583992f8fee63e8b4b99052e72a24000decd6ac7a69a6869a82a12742495d9eaa53f50 Homepage: https://cran.r-project.org/package=afc Description: CRAN Package 'afc' (Generalized Discrimination Score) This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. 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Package: r-cran-afdx Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-desctools, r-cran-kableextra, r-cran-coda, r-cran-rjags, r-cran-ggmcmc, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-afdx_1.1.2-1.ca2404.1_all.deb Size: 323126 MD5sum: c5523af8e3d7d5ba9c70ed5b85eb3207 SHA1: a6e67090be04d8aaf1e6d19353571fbc824db20f SHA256: e33e15785b83b6ee386f80fcbc52d95c8d77b36a555b367daeeb2e8469aae1b3 SHA512: 7266fb22dc62383a9f9ae93829d3ac606f81757382edd3c8e57a0c862d13c503ea066bc69a6d72a70325fe69ed6c373db2c03071313c980bef98090e8b59aae1 Homepage: https://cran.r-project.org/package=afdx Description: CRAN Package 'afdx' (Diagnosis Performance Using Attributable Fraction) Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction. Package: r-cran-afex Architecture: all Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4740 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-pbkrtest, r-cran-lmertest, r-cran-car, r-cran-reshape2, r-cran-reformulas, r-cran-rlang Suggests: r-cran-emmeans, r-cran-coin, r-cran-xtable, r-cran-plyr, r-cran-optimx, r-cran-nloptr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-lattice, r-cran-latticeextra, r-cran-multcomp, r-cran-testthat, r-cran-mlmrev, r-cran-dplyr, r-cran-tidyr, r-cran-dfoptim, r-cran-matrix, r-cran-psychtools, r-cran-ggplot2, r-cran-memss, r-cran-effects, r-cran-cardata, r-cran-ggbeeswarm, r-cran-nlme, r-cran-cowplot, r-cran-jtools, r-cran-ggpubr, r-cran-mass, r-cran-glmmtmb, r-cran-brms, r-cran-rstanarm, r-cran-statmod, r-cran-performance, r-cran-see, r-cran-ez, r-cran-ggresidpanel, r-cran-vdiffr, r-cran-glmmadaptive, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-afex_1.5-1-1.ca2404.1_all.deb Size: 3582408 MD5sum: eeea4dffb024677cf74407908a10cc54 SHA1: 291b2d218caf4f1aba80c3e732a6ef5855931d95 SHA256: a01ec0915a398ea954af8b944eed15ccb348684fcfc333bcc7d169cc31c4fab0 SHA512: fd64d70f2f5bc3ee05d283d2af3d90f16d599b1525c1e5b03f0b88c9f25da9dc9e25fe7dfea4c352ec7c66ccd915f4d3743948c0c33fc9f6dbffd2b7577d4fae Homepage: https://cran.r-project.org/package=afex Description: CRAN Package 'afex' (Analysis of Factorial Experiments) Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software). Package: r-cran-affect Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-affect_0.1.2-1.ca2404.1_all.deb Size: 41740 MD5sum: ad004575bc0e0bd8f49e320382a95b96 SHA1: 1fbf7f43fe2911baa788ea6b2dff79c706dad757 SHA256: 98831161a97c6c7eb02994e6ca51dbe593e102edcdbd2356ebb8fa0b9d77e1cf SHA512: 1dc7d66fc1e2546c95b47b06724221fb2083deafa60256e47778e71a81f73d25ec92d623d5c1225fef9d5156803fbdfc24d16dd2ce8270d73b5b79cd6f3c4567 Homepage: https://cran.r-project.org/package=AFFECT Description: CRAN Package 'AFFECT' (Accelerated Functional Failure Time Model withError-Contaminated Survival Times) We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model. Package: r-cran-affiner Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-artsy, r-cran-ggplot2, r-cran-gridpattern, r-cran-gtable, r-cran-knitr, r-cran-ragg, r-cran-rgl, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-affiner_0.3.1-1.ca2404.1_all.deb Size: 1585576 MD5sum: b16e74255f02ee1050a2df818dc7aac2 SHA1: e32078e799808578996eda57247b6c1baccf3ee6 SHA256: cecdcaa40d96f9a1b7e12dd5435d9c5f34e0ef9202bed984b9133f1ef7ac50fd SHA512: 852b01bd82673fcf1a1e3a02afbe838218b543a56213c2b3313d96b3e138b8ac7df8448f7d08fa9793ddc9201458c74737927482ae1dacffc1a1e7c43d784978 Homepage: https://cran.r-project.org/package=affiner Description: CRAN Package 'affiner' (A Finer Way to Render 3D Illustrated Objects in 'grid' UsingAffine Transformations) Dilate, permute, project, reflect, rotate, shear, and translate 2D and 3D points. Supports parallel projections including oblique projections such as the cabinet projection as well as axonometric projections such as the isometric projection. Use 'grid's "affine transformation" feature to render illustrated flat surfaces. Package: r-cran-affinity Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-reproj Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-affinity_0.2.5-1.ca2404.1_all.deb Size: 622396 MD5sum: e7cd1494029958185dc189bf0434b4d8 SHA1: d432e989eebe6134c1babae0a3948e98a9c87c4e SHA256: 16df1a0b9f4aa51d1b5ffce91ca7de01ee922a7e97663209302158b792592f47 SHA512: e5d7b80fc4261a0bf137a7f7dd1f5c0e47f9acc9d286aea04639c25782da64e948029d46e8fe25cc1a957d63f97788615639ea68e99c69634822ac23f63dd6fc Homepage: https://cran.r-project.org/package=affinity Description: CRAN Package 'affinity' (Raster Georeferencing, Grid Affine Transforms, Cell Abstraction) Tools for raster georeferencing, grid affine transforms, and general raster logic. These functions provide converters between raster specifications, world vector, geotransform, 'RasterIO' window, and 'RasterIO window' in 'sf' package list format. There are functions to offset a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and helper functions to harvesting user clicks on a graphics device to use for simple georeferencing of images. Methods used are available from and . Package: r-cran-affinitymatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm, r-cran-mass, r-cran-hmisc, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-affinitymatrix_0.1.0-1.ca2404.1_all.deb Size: 125902 MD5sum: c30384bff00c0f183420b5fa9a9ed46c SHA1: c1ecb252e38cf2d42dd2f9d30396770986914d4b SHA256: 1e6ce274bf77a826a1faebdb5782f386853737a7a6319b0a108f574fc2d54d84 SHA512: 93fe70b428d04e3c4ff1a212f627d8b11f27cbf0ee8a8600518aece1fb8ce067a079b2112842d459932a63795df59557996aa19d89f7754c86f644610286004b Homepage: https://cran.r-project.org/package=affinitymatrix Description: CRAN Package 'affinitymatrix' (Estimation of Affinity Matrix) Tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by 'Dupuy' and 'Galichon' (2014) and its 'unipartite' variant by 'Ciscato', 'Galichon' and 'Gousse' (2020) . It also contains all the necessary tools to implement the 'saliency' analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings. Package: r-cran-affluenceindex Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.univar Filename: pool/dists/noble/main/r-cran-affluenceindex_2.2-1.ca2404.1_all.deb Size: 88984 MD5sum: 8321419074f4ed6b5e65a85c98914af5 SHA1: a64cb8c5f7364759bb3034b2613f0529680221ae SHA256: c80d9f3f4cea7b13c878ad44c36782f2f4950873ec1fc4e1a0eabc9e8f807959 SHA512: 8975e4efdd8386e3ec9553da9ed36070fd84c80c834761b9dd0ae6af9ed2ead3501af97b7261d087b9292223035b53cae13af7d16a29c3efd7e2bb4629997d6d Homepage: https://cran.r-project.org/package=affluenceIndex Description: CRAN Package 'affluenceIndex' (Affluence (Richness) Indices) Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of affluence line. Some simple errors are fixed and it works with new version of Spatial Statistics packaged. Package: r-cran-afheritability Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-mvtnorm, r-cran-ggplot2, r-cran-shiny Filename: pool/dists/noble/main/r-cran-afheritability_0.1.0-1.ca2404.1_all.deb Size: 65174 MD5sum: 84528bf3dcef2f22b00b737e215eff70 SHA1: 3daab71d6cb844e55824b1613a1099c54fff057b SHA256: 6aaf82d1aaa697a58ce5c72b5caa9da988b7518f904ed5c743c1f34a06826e17 SHA512: d776dc9bd0365aa53872b2c70eeab8aca1931bb5497edf6d1f2df4b91cfaa5b925d856e18860b733c4d9e16191712a0a1a825c0060ac0b08243502cf3059e721 Homepage: https://cran.r-project.org/package=AFheritability Description: CRAN Package 'AFheritability' (The Attributable Fraction (AF) Described as a Function ofDisease Heritability, Prevalence and Intervention SpecificFactors) The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp(). For more information see Dahlqwist E et al. (2019) . Package: r-cran-afm Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-gstat, r-cran-fractaldim, r-cran-rgl, r-cran-pracma, r-cran-gridextra, r-cran-moments, r-cran-ggplot2, r-cran-sp, r-cran-png, r-cran-plyr, r-cran-igraph, r-cran-shiny, r-cran-shinyjs, r-cran-scales, r-cran-dbscan, r-cran-mixtools, r-cran-fftwtools Filename: pool/dists/noble/main/r-cran-afm_2.0-1.ca2404.1_all.deb Size: 1889856 MD5sum: 5d6869c60145bc035e55e3dd84b0ed85 SHA1: 2aa875b4a151639206ae5b0e458e7c608e0496fb SHA256: 1ab28c96ae27062671fcbbfc5c90f95e5007ac5ced3927408ba0737d72fdcb36 SHA512: 757ddc34f584480d400815d0acb056862ae6ef46fd561b2edc6ac1952881f7cb3b9d577b65d99673f38cebe3c7b8ace0edc58bbc2d82e1d3b413e8ead7ce8214 Homepage: https://cran.r-project.org/package=AFM Description: CRAN Package 'AFM' (Atomic Force Microscope Image Analysis) Provides Atomic Force Microscope images analysis such as Gaussian mixes identification, Power Spectral Density, roughness against lengthscale, experimental variogram and variogram models, fractal dimension and scale, 2D network analysis. The AFM images can be exported to STL format for 3D printing. Package: r-cran-afmpar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-afmpar_0.2.0-1.ca2404.1_all.deb Size: 16542 MD5sum: 3c6c84a84080c6f3397ac7749e1cb3df SHA1: d039c24c9813839db3da5476d7571507a1fe2ecf SHA256: 8007b68f01a3a4eaeaeba3371c02b14e6918a12646391784959cb342ed7a7721 SHA512: 04a0093ea953bb1c90e70f6eac6f095d9504b60e49f5043c49a5a6ffe9581254d786eb8b1790667cd1beae0c2d12240ae34e7608005b9894baf12eb32f8162a3 Homepage: https://cran.r-project.org/package=afmpar Description: CRAN Package 'afmpar' (Check Validity of Greek AFM and PA Numbers) With the functions in this package you can check the validity of the Greek Tax Identification Number (AFM) and the Greek Personal Number (PA) . The PA is a new universal ID for Greek citizens across all public services and it is to replace older numbers issued by various Greek state agencies. Its format is a 12-character ID consisting of three alphanumeric characters followed by the nine numerical digits of the AFM. Package: r-cran-afmtoolkit Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-gridextra, r-cran-scales, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-afmtoolkit_1.0.0-1.ca2404.1_all.deb Size: 4635628 MD5sum: be8a1106610a96dea5159979908638dc SHA1: 5405006883e60805f02023b69399501679d856e2 SHA256: b522f0dc296c45341c27ba2967e0982bc9b47b90ddc9a8ad8dbfeb9ba65326ad SHA512: a22225d1428549007c4391f716778f79a0a629aa6deedd25587b8c827b8f599c8e3886aa3a6db228b70ae7629e435dc3f735d30a34ea1ed56672e878e8a1f455 Homepage: https://cran.r-project.org/package=afmToolkit Description: CRAN Package 'afmToolkit' (Functions for Atomic Force Microscope Force-Distance CurvesAnalysis) Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch mode. Package: r-cran-afpt Architecture: all Version: 1.1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-afpt_1.1.0.4-1.ca2404.1_all.deb Size: 428468 MD5sum: 71b3297c0a7835c3dd553c49e07e5749 SHA1: 8379935d8b8cf442fd637ad6d2d3d9d3e651f150 SHA256: b04624f38ae8f0d5e4b19577f832daff5fbc40477dcc2e68ca3887643582c62b SHA512: 1ad76ebb21276bb72d0b4f0405abaab5357c619b30e1622505bd8d0ef519b8226a6c6d6be17a82c9ecce44c0384991bca799b04e77146c0f0ef33dc0dd8e437e Homepage: https://cran.r-project.org/package=afpt Description: CRAN Package 'afpt' (Tools for Modelling of Animal Flight Performance) Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) . Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions. Package: r-cran-afr Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-forecast, r-cran-zoo, r-cran-olsrr, r-cran-lmtest, r-cran-nlme, r-cran-ggplot2, r-cran-tseries, r-cran-gridextra, r-cran-rlang, r-cran-xts, r-cran-nortest, r-cran-goftest, r-cran-cli, r-cran-arrow Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-afr_0.3.9-1.ca2404.1_all.deb Size: 396450 MD5sum: 47ea1b3dd0c508b97b1a7d538de5ce11 SHA1: 3e428772ed06703610a7ceb2de0b0c30a9479ba3 SHA256: d8740d68cbed6b40532310c8dbd270d675f7ddb987addf6b15d99dcfcf5cccce SHA512: a6dd4a94c89ac7271e1e25effd6cd1e3f49b0bab9d5a54078a30f6914ea02b1b9c5d67ca7cf10dd91884bc8e153727aac5f5c8ef23421a31da3167b874e7c013 Homepage: https://cran.r-project.org/package=AFR Description: CRAN Package 'AFR' (Toolkit for Regression Analysis of Kazakhstan Banking SectorData) Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians. The package also provides helper functions for loading an insurance scoring dataset, a past case competition dataset for insurance risk scoring and fair pricing. Package: r-cran-africamonitor Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-data.table, r-cran-collapse Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-africamonitor_0.2.4-1.ca2404.1_all.deb Size: 75376 MD5sum: 849624c13dc2fec92bb08c86b61af6ae SHA1: 23c505fb90f812fbcbd187a6488ab2a878394f50 SHA256: ffd4841804528d3938decb6b89c2d1124f05650c3a75fcc2b83f1d3c3829b45e SHA512: e3db8976417c0f3a705e9a12bf0df69e880522f11998b133d7335e55a37b47948bd44033e36ddd7c494ca863bed0bff1fe140b53ce8099f0e561c1d320276139 Homepage: https://cran.r-project.org/package=africamonitor Description: CRAN Package 'africamonitor' (Africa Macroeconomic Monitor Database API) An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the 'Kiel Institute Africa Initiative' , which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at . The database is maintained at the Kiel Institute for the World Economy . Package: r-cran-aftables Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-openxlsx2, r-cran-pillar, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-tidyr, r-cran-rlang, r-cran-yaml, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aftables_2.1.0-1.ca2404.1_all.deb Size: 1095040 MD5sum: 525c9822863a4f7aa3903ccb3dbca6ea SHA1: 22df91635df9a2745e8c44d7f5d5903792d39378 SHA256: f7a305879ac5a4255ca9eaa14e3e96567674f54cf21938c2782a08f4b64d8095 SHA512: afae333b9b16ab966971534b93b7ee46c66b50e51ff2744d7610081871c4ef5768d44dee7f0e46e5abd37f5c518fbc0588186f4c68f4cbfd466ec96646902d3a Homepage: https://cran.r-project.org/package=aftables Description: CRAN Package 'aftables' (Create Spreadsheet Publications Following Best Practice) Generate spreadsheet publications that follow best practice guidance from the UK government's Analysis Function, available at , with a focus on accessibility. See also the 'Python' package 'gptables'. Package: r-cran-afthd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-photobiology, r-cran-r2jags, r-cran-rstpm2, r-cran-survival Filename: pool/dists/noble/main/r-cran-afthd_1.1.0-1.ca2404.1_all.deb Size: 531424 MD5sum: 170effed57354faa37a99d9dd3cb61a6 SHA1: e120ed5011042ca13b3b13960ea3bc4b80776692 SHA256: d085dea2465ff4340027d51cabde841ce506f7b4c9cd9e9defec288d038ad09f SHA512: 9cdfc861a92b7655174efd3d83571b968753a68c34f7d3cdce3db320409eade5f31d4b9fccdf18f3536874ed693c7c7a6392ac971fee53805d19edeba7676cdb Homepage: https://cran.r-project.org/package=afthd Description: CRAN Package 'afthd' (Accelerated Failure Time for High Dimensional Data with MCMC) Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) . Package: r-cran-aftr2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-aftr2_0.1.0-1.ca2404.1_all.deb Size: 10428 MD5sum: 9ec7fc034ed0a938b81e418590225299 SHA1: 96cc0a12f5ef633b05328a3b987308d9d909fe2b SHA256: 33198ced836655c2debf94e4870f9fc11b1fd870ed001828a98a6b668fe9df5b SHA512: 75cf3e8523abd0ab7fc4030a2e9332299729e16c43bbab3240bb7aad7d5800b6f0d2fa2d311c1ed67b8a81d3358cf80af90add12e577bd1e6c7deb311be7811a Homepage: https://cran.r-project.org/package=aftR2 Description: CRAN Package 'aftR2' (R-Squared Measure under Accelerated Failure Time (AFT) Models) Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) . Package: r-cran-ag5tools Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-reticulate, r-cran-fs, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ag5tools_0.0.3-1.ca2404.1_all.deb Size: 531296 MD5sum: d7a54ed3705057d7a21e45f287bb5963 SHA1: 18be38c57090183e08fcbfc2dbb1db12f0e12c87 SHA256: 237f92d2bd45004e3771baf72835dedda75ed9f4f1e445dd88d4a15a646cd07a SHA512: 0ff6581c000fc9a8dc6c4ce276377b43858dfbb8c2b81f6b2b43bfc48ae29a0b9f66d97a17be1a2a4cb7d573aa26971454042586b4b23cc41153253b3035afe5 Homepage: https://cran.r-project.org/package=ag5Tools Description: CRAN Package 'ag5Tools' (Toolbox for Downloading and Extracting Copernicus AgERA5 Data) Tools for downloading and extracting data from the Copernicus "Agrometeorological indicators from 1979 to present derived from reanalysis" (AgERA5). Package: r-cran-agbqr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agbqr_0.1.0-1.ca2404.1_all.deb Size: 24394 MD5sum: c251a6b85c77d36d8be0a5a40cb45a3c SHA1: 174b7f435fa0cae1494dbeffeef9f73fff512762 SHA256: cefc3cb32f148f7f0b98d6f8a11829afbc8e5178acaf98b9d53a3dd1cd031c2e SHA512: a8c8652a3f0c808d100614d2012fc1de4cc96875080a8e0afed28448eb35848eae1bf3e28c5fcc00f7050b8e61a3a6a9b4b521dd0fec97a5353819a2ea693234 Homepage: https://cran.r-project.org/package=AGBQR Description: CRAN Package 'AGBQR' (Adaptive Generalized Bayesian Quantile Regression) Implements adaptive generalized Bayesian quantile regression with quantile-specific learning rates, HAC-based calibration, Gibbs posterior simulation, posterior summaries, predictive evaluation, and visualization tools. The package builds on the generalized Bayesian composite quantile regression framework of Hardy and Korobilis (2026) by allowing learning rates to vary across quantile levels. The implementation is designed for empirical work with small and moderate time-series samples where posterior calibration and tail-specific inference are important. Package: r-cran-agd Architecture: all Version: 0.45.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist Filename: pool/dists/noble/main/r-cran-agd_0.45.0-1.ca2404.1_all.deb Size: 449140 MD5sum: d7a4995961ab998edaacdc1eabbf1300 SHA1: 16e8b22386f6a9304cd1e9c5874aef0725bef5c2 SHA256: bb2e04ceb2e82bad90d140aeb6466c80a3c6bcfde7c670ee7071cad1d2716951 SHA512: d680772bae401c6b714d06f1bf99cdbb65d9e30b4c02ff3947a59a0ac64044aaf600e6ab6be86820a54ac1c64b1231b980f2525b8b5429b02fdcd0cf16949e4d Homepage: https://cran.r-project.org/package=AGD Description: CRAN Package 'AGD' (Analysis of Growth Data) Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands. Package: r-cran-agebanddecomposition Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-readxl, r-cran-ggplot2, r-cran-dplr, r-cran-patchwork Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agebanddecomposition_2.0.1-1.ca2404.1_all.deb Size: 1958496 MD5sum: dda57e106121ef37e913fd7e8acbf2ee SHA1: d1095b61a09ad48e531cd389fbca781ecdb3cc2f SHA256: 09a128af81eba285cdc5d029a0b25cdd816be50dfd759cbdfcbae81674398b8f SHA512: e486762ec7fd6918a20d2eb1d9ff7927ddac6a93f00e531d6c37fd86b7c58acb9bedff2bd379e2eb705cf6a0e4852c3f7d82d5006a6ddc37e7a00874b8dfd723 Homepage: https://cran.r-project.org/package=AgeBandDecomposition Description: CRAN Package 'AgeBandDecomposition' (Age Band Decomposition Method for Tree Ring Standardization) Implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability. Package: r-cran-ageg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ageg_1.0.0-1.ca2404.1_all.deb Size: 14608 MD5sum: b24ec35fba699da56651a6db7767b123 SHA1: 1bcfec8ffa2efa72e0d8f412bc5b1d0179ccaeeb SHA256: d053b1c4068e6490bba0ccea5ad219b9ae73cac4047f71c11711a57b529bc1fd SHA512: 07ceeadd16cf7b81401ff21eedda394e3ee9b8398eab8e04ffdf02f5ae690662a36bf56d218c0094d3ae15fc858b794fca25696e38ac46c18629ecba1bc6584d Homepage: https://cran.r-project.org/package=ageg Description: CRAN Package 'ageg' (Age Grouping Functions) Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates. Package: r-cran-agena.ai Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-httr, r-cran-openxlsx, r-bioc-rgraphviz Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-agena.ai_1.1.2-1.ca2404.1_all.deb Size: 234478 MD5sum: 03e16fe8024ff23c2d83db7542707090 SHA1: 65c9eb917f9ff2bbd4f883d489588241a76061cd SHA256: 0bdd18b757bc12c47c1dac992294a6645e6375e9fea0bcab6aa351c654110237 SHA512: 6d096504f35a343b74d49170a4477652314920720096495365359e02a62c4ba731b5faa5d61ca0fc95f05cc47453070abc15346a66adb733a9b8192cf886ace4 Homepage: https://cran.r-project.org/package=agena.ai Description: CRAN Package 'agena.ai' (R Wrapper for 'agena.ai' API) An R wrapper for 'agena.ai' which provides users capabilities to work with 'agena.ai' using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to 'agena.ai' cloud or local API for calculations. Note: running calculations requires a valid 'agena.ai' API license (past the initial trial period of the local API). Package: r-cran-agenticr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-cli, r-cran-yaml, r-cran-processx Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agenticr_0.3.3-1.ca2404.1_all.deb Size: 233678 MD5sum: d16125d458539ad58502796de6fe11ac SHA1: 0cf389d52818bda78ade58a0f0070f6c59703bd4 SHA256: 7cd5a93ad9ed003d5300f8c9d3826b745bc01d59aef6c9d725fa5581b64d1368 SHA512: 3c6584d6e06c49e636861a13c3bd0276cd83b2bda26ec300fcad5a3a7b4dcfee168fa731a5a090ac9de54d2fd03823f86fcea6c97b7968d2b82578c8cf4e5a2c Homepage: https://cran.r-project.org/package=agenticr Description: CRAN Package 'agenticr' (AI-Powered R Console Assistant) An AI-powered assistant for the R console. Type natural language or incorrect R code directly in the console, and 'agenticr' routes it to an AI agent for processing. Focused on statistical analysis, data transformation, and visualization, . Package: r-cran-agentr Architecture: all Version: 0.2.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-jsonlite, r-cran-rlang, r-cran-yaml Suggests: r-cran-diagrammer, r-cran-diagrammersvg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agentr_0.2.8.4-1.ca2404.1_all.deb Size: 1528782 MD5sum: 0a23732fcfd348eebcb9168509dd61c8 SHA1: 4a93b75403cdd8b331b493aedd38e586deddfa03 SHA256: 0841f1b245acf37603ee21bbdc4d8ef9baa799b757a4f35c3379b322d8fd96f6 SHA512: 2a105a64b80d97a50226760f397ebed34391f8e39311d45758968bd2455f56de24533cab09f1b95f64738d0935c2644670b6a694bb465ce6f026eeb205a65de3 Homepage: https://cran.r-project.org/package=agentr Description: CRAN Package 'agentr' (Specification and Review Scaffolding for AI Agent Workflows) Specification, review, and scaffolding helpers for AI agent systems. The package standardizes workflow, memory, knowledge, interface, proposal, and review artifacts so humans and coding assistants can infer, inspect, revise, and hand off task designs. It intentionally excludes communication layers, provider-specific model client code, and full runtime execution engines so that design artifacts and implementation transport remain cleanly separated. Package: r-cran-agepopdenom Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-glue, r-cran-terra, r-cran-sf, r-cran-here, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-rdhs, r-cran-tmb, r-cran-curl, r-cran-gstat, r-cran-haven, r-cran-crayon, r-cran-purrr, r-cran-countrycode Suggests: r-cran-sp, r-cran-tibble, r-cran-rstudioapi, r-cran-future, r-cran-future.apply, r-cran-pbmcapply, r-cran-knitr, r-cran-httr, r-cran-scales, r-cran-rmarkdown, r-cran-exactextractr, r-cran-pdist, r-cran-numderiv, r-cran-openxlsx2, r-cran-matrixstats, r-cran-testthat, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-agepopdenom_1.2.3-1.ca2404.1_all.deb Size: 684252 MD5sum: edd1d0b4f19859463a8dcbebdcc4b3bb SHA1: 4b0ba752b4c69a3b9e84e07323188745726fa60c SHA256: a9a713b4c4ccbb6b18b6c7cba7bda4ee122044530a22be5e19435e2ea6da00ec SHA512: 799986cf31bd5ec5a6184259e8fc797e174391972d611235f5d70c5027dacf9dea704ab74d6c42cdac3b8852b4315c3727e86ff9365b0ca26638fbae60f75476 Homepage: https://cran.r-project.org/package=AgePopDenom Description: CRAN Package 'AgePopDenom' (Model Fine-Scale Age-Structured Population Data usingOpen-Source Data) Automate the modelling of age-structured population data using survey data, grid population estimates and urban-rural extents. Package: r-cran-agetopicmodels Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-magrittr, r-cran-proc, r-cran-reshape2, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agetopicmodels_0.3.0-1.ca2404.1_all.deb Size: 5064672 MD5sum: 4fbfcafb2a1ba45038bf40080e1a0b2a SHA1: 49c2d16b3909d20475af2236c9bbba30093a381a SHA256: 9504ea56b6e0682f784be6980c3d9d1b1c40a2bf9614e5699a1826d0c78b5ec0 SHA512: 33557e5a39f853eb99ce72b1bd60b603272add38f0ac983582dfaaf0d62eb767720917b46d0d8825c9be85cbf54f5daa8cdbe8ed5199d09f6426c658b6f5d1e5 Homepage: https://cran.r-project.org/package=AgeTopicModels Description: CRAN Package 'AgeTopicModels' (Inferring Age-Dependent Disease Topic from Diagnosis Data) We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individual’s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individual’s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) . Package: r-cran-ageutils Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-litedown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ageutils_0.1.2-1.ca2404.1_all.deb Size: 54096 MD5sum: 9dcdd4d37a501af3456e9ef50c558fbd SHA1: 6362c7752e1ff6866048a7e37cd327fb85b0f839 SHA256: cbaf8c07dbe8cbde4e623e591cf7d12d79ccd4edde8956e82490f29ea258464e SHA512: acfb0086aa929d8b9eb24db722b20f9e5a724700c2e4a2e784a37fb0cb484ceb59b00d7a13dbc8070e50af258df4f50f79059c752b435f747cc4e0af4844b130 Homepage: https://cran.r-project.org/package=ageutils Description: CRAN Package 'ageutils' (Collection of Functions for Working with Age Intervals) Provides a collection of efficient functions for working with individual ages and corresponding intervals. These include functions for conversion from an age to an interval, aggregation of ages with associated counts in to intervals and the splitting of interval counts based on specified age distributions. Package: r-cran-agfh Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-goftest, r-cran-ks, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agfh_0.2.1-1.ca2404.1_all.deb Size: 213886 MD5sum: 64a113abe76048e3b953ab2d8168b600 SHA1: 8bf47e03bd9e63192fd7042770155a8f45819e39 SHA256: 2349718046ee15d31681e70818cb932fabb99ad929406c86b7d1f4fd336572bc SHA512: 54925b20e0042d22bd957844b7748da4dd5da316caaf5f65cfda50e97b1cdff25a961c71a7594063e61c5740395e7ed3dde3ab1524044aded69f7ea1e1d33804 Homepage: https://cran.r-project.org/package=agfh Description: CRAN Package 'agfh' (Agnostic Fay-Herriot Model for Small Area Statistics) Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors. Package: r-cran-agghoo Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-class, r-cran-r6, r-cran-rpart, r-cran-fnn Suggests: r-cran-roxygen2, r-cran-mlbench Filename: pool/dists/noble/main/r-cran-agghoo_0.1-0-1.ca2404.1_all.deb Size: 110910 MD5sum: b70ff252fbdeb5cbe317be50b8a60a15 SHA1: b2f06b3295257891838d3fae44b8dee640b43542 SHA256: 0ae6a7ad6785acfe613726c32f75746e56e0d7d8857d46f8b4c29f7d7aa6840a SHA512: 9b3134251e19c537f36fca08d2e739cb1c8583779072d9a80fad8d4f2286cd053f28268e56b50c92c299b39014f07bfd3126ed0a98447ee425feffd6b194ad12 Homepage: https://cran.r-project.org/package=agghoo Description: CRAN Package 'agghoo' (Aggregated Hold-Out Cross Validation) The 'agghoo' procedure is an alternative to usual cross-validation. Instead of choosing the best model trained on V subsamples, it determines a winner model for each subsample, and then aggregates the V outputs. For the details, see "Aggregated hold-out" by Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (2021) published in Journal of Machine Learning Research 22(20):1--55. Package: r-cran-aggrecat Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-gofkernel, r-cran-purrr, r-cran-r2jags, r-cran-coda, r-cran-precrec, r-cran-mathjaxr, r-cran-cli, r-cran-vgam, r-cran-crayon, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-insight, r-cran-desctools, r-cran-mlmetrics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-pointblank, r-cran-janitor, r-cran-qualtrics, r-cran-here, r-cran-readxl, r-cran-readr, r-cran-lubridate, r-cran-forcats, r-cran-ggforce, r-cran-ggpubr, r-cran-ggridges, r-cran-rjags, r-cran-tidybayes, r-cran-tidyverse, r-cran-usethis, r-cran-nlme, r-cran-gt, r-cran-gtextras, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-aggrecat_1.1.0-1.ca2404.1_all.deb Size: 1911834 MD5sum: 99abc9beb4316528e107224b292aa01f SHA1: 99389e4d8616db3654111cdaa170821e8b8e37d6 SHA256: 30a990b1ff71cd5f0e30f98e4af009d345bff70430416968b5747079272772eb SHA512: eca589235501116b69d231867b99d1948b113052c63ab3f3c5f9c926b11b52afe2c5f8c887749b91d65b5c15ddb066d55270914bf52459a62eb5e9d18ae7ced8 Homepage: https://cran.r-project.org/package=aggreCAT Description: CRAN Package 'aggreCAT' (Mathematically Aggregating Expert Judgments) The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) . The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data. Package: r-cran-aggregater Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tibble, r-cran-ncmisc Filename: pool/dists/noble/main/r-cran-aggregater_0.1.1-1.ca2404.1_all.deb Size: 36648 MD5sum: 0f01ff7a09a6375f1dc2220231f6be72 SHA1: c2c4e1a7d31fa6a8e73babe794aff207e9213a5f SHA256: 9eee985a764dd0d0e19befa885d39580c3c3418e13aee2e3ccee16c0b2aea34b SHA512: 1c464b436fe2f518811e09455a8f8c99de88f47537b2e4597ec4cf6537083e924023fd7c9a4ab5405d0b5747756b98edf9f60e5a5ec0622dce6e6cab10dfdffb Homepage: https://cran.r-project.org/package=AggregateR Description: CRAN Package 'AggregateR' (Aggregate Numeric, Date and Categorical Variables) Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. 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Package: r-cran-aggregation Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aggregation_1.0.2-1.ca2404.1_all.deb Size: 16446 MD5sum: 56d8b151226d819ef880282fcc8ac118 SHA1: eca9a86f4af3d9d2485c500332d8955269d958c4 SHA256: 7634094640c16adbd260fbff3142500b2020af8e23e864e47740b85a195797f5 SHA512: 0de79714534c86d049f2005e2a4897b162a60b1dd1dc237903f92bf9dd838fd0fbf8a5e07c29b7ee754de9fcc74985595a6caf4478a6bc682cfd992d2b117f0d Homepage: https://cran.r-project.org/package=aggregation Description: CRAN Package 'aggregation' (p-Value Aggregation Methods) Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, )], and Sidak correction [Sidak, Z (1967, )]. Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), ]. Package: r-cran-aggtrees Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-broom, r-cran-car, r-cran-caret, r-cran-estimatr, r-cran-grf, r-cran-rpart, r-cran-rpart.plot, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aggtrees_2.1.0-1.ca2404.1_all.deb Size: 663444 MD5sum: ea782304ba2846b4641130a5877987e4 SHA1: 59d517c3a11960c3376ca246bdc7daeeee25daf7 SHA256: 8756b0a4a50c7b47fb1f66e4e38b4a4d6c9641164a701eb91826c2cdb8b5147e SHA512: 6a76a7c250b07f137ad772bf51ce766389b8f514b83c35e1ad30b46b69a4b830c2cc0503e8d02b026e45818cb5111cee0af1f38d3248e64fcd9fc4cf270a2a94 Homepage: https://cran.r-project.org/package=aggTrees Description: CRAN Package 'aggTrees' (Aggregation Trees) Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. 'aggTrees' allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022) . Package: r-cran-aggutils Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-docstring Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aggutils_1.0.2-1.ca2404.1_all.deb Size: 25576 MD5sum: 362214a369e12fd73e617069a057218c SHA1: ac3f203bc8792b6b23069ff40420c2319ca8235c SHA256: e562699972f9ebe3f0cbc5a5a9e4e87ca42ace21c8dab74e75d180abfe7eee6d SHA512: 7e38992e0f2ba0cb3d427a6b8e458129f4b1ab1a408f28aed5668007ec0422d2aeb4635b38c704cb914b99c3a99cbbf3b9820f373ae45ddae1c0810201b5fd16 Homepage: https://cran.r-project.org/package=aggutils Description: CRAN Package 'aggutils' (Utilities for Aggregating Probabilistic Forecasts) Provides several methods for aggregating probabilistic forecasts. You have a group of people who have made probabilistic forecasts for the same event. You want to take advantage of the "wisdom of the crowd" and combine these forecasts in some sensible way. This package provides implementations of several strategies, including geometric mean of odds, an extremized aggregate (Neyman, Roughgarden (2021) ), and "high-density trimmed mean" (Powell et al. (2022) ). Package: r-cran-aghmatrix Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2293 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-zoo Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aghmatrix_2.1.4-1.ca2404.1_all.deb Size: 2155488 MD5sum: 0b368760c85c9f0a791617d35149a891 SHA1: 0e293c8b1589f5677adf7211c1bbbc9213f90972 SHA256: 99cd367b57b4317de7fd3ad504dedf791fe7e2b66bff68a989579e8d919d08d3 SHA512: 5e563b919b3900fdc460ed0c7995cee636d72f041f7ae253e43e832383f92c764ab04c1ac05e8df156704f7a03fc1a10c7c560f26d2e7ad8a10ad8568a34554b Homepage: https://cran.r-project.org/package=AGHmatrix Description: CRAN Package 'AGHmatrix' (Relationship Matrices for Diploid and Autopolyploid Species) Computation of A (pedigree), G (genomic-base), and H (A corrected by G) relationship matrices for diploid and autopolyploid species. Several methods are implemented considering additive and non-additive models. Package: r-cran-aghq Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvquad, r-cran-matrix, r-cran-rlang, r-cran-polynom, r-cran-numderiv Suggests: r-cran-trustoptim, r-cran-trust, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aghq_0.4.1-1.ca2404.1_all.deb Size: 330340 MD5sum: d8a223105540e54409957160081722ac SHA1: 41ff051b6af09bb86db9d517f2140d7dbe5c0634 SHA256: 7acd012dee1e8c0075d525517e634cf4915c97a586d53a6c73d13afebe1c78ae SHA512: 66107f73f4055de95ff48f19d70d533d638a654c5e6f6daac46cc507985449d2fc3e0273c653f2c3afb5c7d3c46edadc252a47b1181c1463dae72565146fa127 Homepage: https://cran.r-project.org/package=aghq Description: CRAN Package 'aghq' (Adaptive Gauss Hermite Quadrature for Bayesian Inference) Adaptive Gauss Hermite Quadrature for Bayesian inference. The AGHQ method for normalizing posterior distributions and making Bayesian inferences based on them. Functions are provided for doing quadrature and marginal Laplace approximations, and summary methods are provided for making inferences based on the results. See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package" . Package: r-cran-aglm Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-assertthat, r-cran-mathjaxr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-faraway Filename: pool/dists/noble/main/r-cran-aglm_0.4.1-1.ca2404.1_all.deb Size: 123554 MD5sum: 8f635616dce7a99c255a2252260b81cc SHA1: b46cf6ca5696f391802a73acdfaaebe316bd723b SHA256: a203334ef9ddd5b5d5d543af2ae914c121d466cccc85896da1cfbe007e98b7fe SHA512: 02a6f82ad9e228e096e1b134c9a4046e960acd6d14d8dfb6060655118108bcdb3309fb52c5d5cc3241450ec13b1ff1e796488c81cc883e4762b55db5dcf1cb23 Homepage: https://cran.r-project.org/package=aglm Description: CRAN Package 'aglm' (Accurate Generalized Linear Model) Provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) . 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The equations developed in Ingel and Jahn-Eimermacher (2014) and their consequences are employed. Package: r-cran-agpris Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-plyr, r-cran-sp, r-cran-spdep, r-cran-spacetime, r-cran-matrixcalc, r-cran-maxlik Suggests: r-cran-terra Filename: pool/dists/noble/main/r-cran-agpris_2.0-1.ca2404.1_all.deb Size: 1168960 MD5sum: e68fb3281f04e8ceb8687694d417aaa8 SHA1: 05e8b41fc70ec92d0732c4a003a310ff4c11f340 SHA256: b1cb2f8726f5d2e274c3d5e9bc0f69d5ac79f7acfd38a7eeea9cbe8e3cee3adc SHA512: 65ec5a741f4a2d8c88d6de1d6740ca7c36b4c61415f5e562a56eeb190d318009097da8d53889bfebc8abc145ea90412a74fde642e5ccf26722c33ee70a350d5f Homepage: https://cran.r-project.org/package=AGPRIS Description: CRAN Package 'AGPRIS' (AGricultural PRoductivity in Space) Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. Estimators implemented are the BCML (Elhorst (2010), ), the MML (Elhorst (2010) ) and the INLA Bayesian estimator (Lindgren and Rue, (2015) ; Bivand, Gomez-Rubio and Rue, (2015) ) adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (2021), )). Package: r-cran-agree Architecture: all Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-miscf, r-cran-lme4, r-cran-r2jags, r-cran-coda Filename: pool/dists/noble/main/r-cran-agree_0.5-3-1.ca2404.1_all.deb Size: 72058 MD5sum: c07282dd726dc2ddfbfc92a3a0da5c99 SHA1: 6d5875336ef6c0c97ae944d261b644c55df98161 SHA256: 66c034d84559b36c37a10a4aa028e228ecf66d407582f8e026f80128ea089776 SHA512: 4dcf58a4193a70d52133ea4be7baf12e7bfbe37a6ad4d4ff3b5e02644ff2671abd03c1b4b5997d62d678f48a1991467df4b6e36e7e2fd919c95f1de3483e35b4 Homepage: https://cran.r-project.org/package=agRee Description: CRAN Package 'agRee' (Various Methods for Measuring Agreement) Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference. Package: r-cran-agreementinterval Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych Suggests: r-cran-testthat, r-cran-mass, r-cran-matrix, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-agreementinterval_0.1.1-1.ca2404.1_all.deb Size: 48950 MD5sum: d3dd750492b81acea358bc00db7c484a SHA1: a2bae9e69dec5059d5c55ebea954d3aba0cb6cff SHA256: 293063f8af83108d339268913f554f8f3ba1a65c5ef9b88632aee677fca96c66 SHA512: 72f72492cfb98edbadd1e1e3aa0e57d86b8fb1071600b48f1aac79d080b47e0cb9c5fb7000f90f544e2fcbb8dd8bf07694e03a84a5794979110d08fc45526d49 Homepage: https://cran.r-project.org/package=AgreementInterval Description: CRAN Package 'AgreementInterval' (Agreement Interval of Two Measurement Methods) A tool for calculating agreement interval of two measurement methods (Jason Liao (2015) ) and present results in plots with discordance rate and/or clinically meaningful limit to quantify agreement quality. Package: r-cran-agricolae Architecture: all Version: 1.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-cluster, r-cran-algdesign Filename: pool/dists/noble/main/r-cran-agricolae_1.3-7-1.ca2404.1_all.deb Size: 1177266 MD5sum: e3ca3636f3cdbb4c74a55d96727a5f02 SHA1: a949d8460219965fa3a8979bb6f5a170282ca6bf SHA256: 7871db9b20184ade23b21bd70ab343f750e88743dc860da3c255acf8d487b556 SHA512: e186d5aa1d00a5152f2059daf7b5439ae3a8cb70935b7e3c2bc5a113dccde11dc4669cb23ae7c3114dd4c77f77e9c4ef9dc8f1db7f2b625b51cc5dbfe0bf33e9 Homepage: https://cran.r-project.org/package=agricolae Description: CRAN Package 'agricolae' (Statistical Procedures for Agricultural Research) Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster. Package: r-cran-agricolaeplotr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-agricolae, r-cran-raster, r-cran-sp, r-cran-fieldhub, r-cran-tibble, r-cran-sf, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-stplanr, r-cran-ggspatial Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-agricolaeplotr_1.0.0-1.ca2404.1_all.deb Size: 2298578 MD5sum: 7969d71df8f68c9c7150445467ed0400 SHA1: 9a34259074546bec237f323dcb40f501759c5581 SHA256: a3648704a091eed0022b5cb716e9a456f14a529e2a8b0e919faae8bbff6eab68 SHA512: dff2af1c5d48217635981714fc56ed102e975453ee45e8e6fbd4d464f6b010832341d81c89f177d6c53a09c56d33d6b07eb491c48cd8f4a9118de2bf2e03db79 Homepage: https://cran.r-project.org/package=agricolaeplotr Description: CRAN Package 'agricolaeplotr' (Visualization of Design of Experiments from the 'agricolae'Package) Visualization of Design of Experiments from the 'agricolae' package with 'ggplot2' framework The user provides an experiment design from the 'agricolae' package, calls the corresponding function and will receive a visualization with 'ggplot2' based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard 'ggplot2' commands or with other packages with 'ggplot2' function extensions. Package: r-cran-agridat Architecture: all Version: 1.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3931 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-aer, r-cran-agricolae, r-cran-betareg, r-cran-broom, r-cran-car, r-cran-coin, r-cran-corrgram, r-cran-desplot, r-cran-dplyr, r-cran-effects, r-cran-emmeans, r-cran-equivalence, r-cran-frf2, r-cran-gam, r-cran-gge, r-cran-ggplot2, r-cran-gnm, r-cran-gstat, r-cran-hh, r-cran-knitr, r-cran-lattice, r-cran-latticeextra, r-cran-lme4, r-cran-lucid, r-cran-mapproj, r-cran-maps, r-cran-mass, r-cran-mcmcglmm, r-cran-metafor, r-cran-mgcv, r-cran-nlme, r-cran-nullabor, r-cran-ordinal, r-cran-pbkrtest, r-cran-pls, r-cran-pscl, r-cran-qicharts, r-cran-qtl, r-cran-reshape2, r-cran-rmarkdown, r-cran-sp, r-cran-spats, r-cran-survival, r-cran-testthat, r-cran-vcd Filename: pool/dists/noble/main/r-cran-agridat_1.26-1.ca2404.1_all.deb Size: 3298032 MD5sum: 377f79cd097315c1190e60f898c4aaef SHA1: 62e44a2ad2aede4ebfa6fd643ac2424ee780050f SHA256: b4796af3007f160b889e678604a08a752abbf9232f301bd1c09fde59dbf692eb SHA512: d64b7cd4bfcf212a873754e6a5082e1d71f6b3bf38aab030ef1d00ce2f01116b4cfd500e08ff15a4d08e5a83fd87dd42cd9df341d4099ab0746e3c26e6fa7832 Homepage: https://cran.r-project.org/package=agridat Description: CRAN Package 'agridat' (Agricultural Datasets) Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more. Package: r-cran-agridatasets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-agridatasets_0.1.1-1.ca2404.1_all.deb Size: 836168 MD5sum: cd817d6d8839c03a485267f1101fb473 SHA1: eb5a49f926abde69acec43696f0f0df693439a54 SHA256: 254977373ee2a9b00fa28e500751776d9e35ac96d429b0a1f9a2dcaf97ab0863 SHA512: f01547136a985a0455b73b877e39b1259c2587eeae24bf0699d25d51ed43299b31d5e965861be6a7e66925cc2d593c845e6f33df62bc2180881d79b3ba115721 Homepage: https://cran.r-project.org/package=agridatasets Description: CRAN Package 'agridatasets' (A Comprehensive Collection of Agricultural and AgronomicDatasets) Offers a rich and diverse collection of datasets focused on agriculture, agronomy, animal science, and related fields. The package includes experimental, observational, and field-trial data on crops such as rice, wheat, corn, soybean, cotton, coffee, avocado, and orange, as well as forestry species including bamboo, eucalyptus, and timber. Datasets cover plant breeding and genetics, factorial and randomized block experiments, herbicide and insecticide efficacy trials, pest and disease infestation, soil characteristics and land suitability, plant growth regulators, seed germination, and crop yield modeling. Additional datasets address animal science topics such as cattle insemination and conception, pig and broiler growth, lamb births, and toxicology studies on aquatic and non-target species. Data sources include peer-reviewed agronomic studies, uniformity and Latin square field trials, glasshouse experiments, and international agricultural surveys. Designed for agronomists, researchers, plant and animal scientists, data scientists, and students, this package facilitates exploratory data analysis, statistical modeling, and hypothesis testing in agricultural and biological sciences. The package includes datasets originally distributed in other R packages. The original authors and contributors associated with these source packages and datasets are acknowledged in Authors@R, and the original sources and applicable licensing terms are documented in LICENSES_DETAILS.md. Package: r-cran-agridatatools Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-factoextra, r-cran-dendextend, r-cran-circlize, r-cran-ggrepel, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agridatatools_0.2.1-1.ca2404.1_all.deb Size: 398128 MD5sum: 60062bc1f89bad2c53bb61cdb7928281 SHA1: eef4262887aa7e1cb8f0623f24162c868c60b8e0 SHA256: 51b753694a466ae699680506e6e76724f7ef86f3e237d4b1001c4e8fab2329df SHA512: fbf9f56f83cf74fd9c403ce114a3dbaa12923a4f7a2b6056b80d0ff9f4cbd5fdc886656970e01693091831a1b003c72c6aebb42c1f8acefa0e02b2630a95da1b Homepage: https://cran.r-project.org/package=AgriDataTools Description: CRAN Package 'AgriDataTools' (Automated Statistical Analysis and Tools for AgriculturalResearch) A comprehensive suite of statistical tools tailored for agricultural and plant breeding research. Provides automated pipelines for analysis of variance and covariance under randomized complete block designs and completely randomized designs, descriptive summary statistics, and post-hoc multiple range tests including Least Significant Difference, Tukey, and Scheffe based on Steel et al. (1997) . Quantitative genetic parameters including genotypic, phenotypic, and environmental variance components and broad-sense heritability follow Burton and Devane (1953) . Genetic advance and genetic advance as percentage of mean estimation follow Johnson et al. (1955) . Genotypic, phenotypic, and environmental correlations follow Miller et al. (1958) . Genotypic and phenotypic path coefficient analysis direct and indirect effects decomposition follows Dewey and Lu (1959) . Principal component analysis follows Jolliffe (2002) and hierarchical clustering follows Sneath and Sokal (1973) . Package: r-cran-agridiversix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-mcmcpack, r-cran-ggplot2, r-cran-tidyr, r-cran-viridis Filename: pool/dists/noble/main/r-cran-agridiversix_0.1.0-1.ca2404.1_all.deb Size: 15310 MD5sum: c2ba54107c5a0ad62daefbf9ebae9d3f SHA1: 030ee30fc8879fe64d207cfc9dc378c8e00004c3 SHA256: c56e0e64475835e806153fd8166d644a7eb7710a58d5b73cfcb4b54f9e6a7e66 SHA512: 79e33453a9991d78f72aa980b77696f82f4fd813ba920b32d0e6ff970dd8e2cba7508a53051982c1d790e405de30da4bf0a5fa45201e2e668b900f619bd6925d Homepage: https://cran.r-project.org/package=AgriDiversiX Description: CRAN Package 'AgriDiversiX' (Agricultural Crop Diversification Indices Analysis) Provides functions to compute agricultural crop diversification indices for crop data across zones and years. The package implements widely used diversification and concentration measures including Herfindahl Index,Simpson Index, Entropy Index, Ogive Index, and Maximum Proportion Index. Package: r-cran-agridq Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-car, r-cran-lmtest, r-cran-tseries, r-cran-stringdist Suggests: r-cran-testthat, r-cran-covr, r-cran-mass Filename: pool/dists/noble/main/r-cran-agridq_0.1.3-1.ca2404.1_all.deb Size: 144990 MD5sum: 8c1e9eaad4e6077fb70862bcd27eb517 SHA1: 4ff82d105f1072279f33e62b0a3e7eac2f6c622f SHA256: 3bb769b04c707da9b043a2b973bb6d96e9e0bf2bda0ed0ea1e1ddf521a04b95d SHA512: e4e9fe4bacd36f5b26c4c4bbf87bde531e0a8e3a3997d04b91fdcaba56d470cbfbdd7bb447a85f35dc38f6e516dac6276fa27645a51d02d07ec1b34daaca6cbc Homepage: https://cran.r-project.org/package=agriDQ Description: CRAN Package 'agriDQ' (Data Quality Checks and Statistical Assumption Testing forAgricultural Experiments) Provides a comprehensive pipeline for data quality checks and statistical assumption diagnostics in agricultural experimental data. Functions cover outlier detection using Interquartile Range (IQR) fence, Z-score, modified Z-score (Hampel identifier), Grubbs test and Dixon Q-test with consensus flagging; missing data pattern analysis and mechanism classification (Missing Completely At Random/Missing At Random/Missing Not At Random (MCAR/MAR/MNAR)) via Little's test; normality testing using Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, Lilliefors, Pearson chi-square and Jarque-Bera tests; homogeneity of variance via Bartlett, Levene and Fligner-Killeen tests; independence of errors via Durbin-Watson, Breusch-Godfrey and Wald-Wolfowitz runs tests; experimental design validation for Completely Randomised Design (CRD), Randomised Complete Block Design (RCBD), Latin Square Design (LSD) and factorial designs; qualitative variable consistency checks; and automated HyperText Markup Language (HTML) report generation. Designed to align with Findable, Accessible, Interoperable and Reusable (FAIR) data principles. Methods follow Gomez and Gomez (1984, ISBN:978-0471870920) and Montgomery (2017, ISBN:978-1119492443). Package: r-cran-agrifeature Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-agrifeature_1.0.3-1.ca2404.1_all.deb Size: 24816 MD5sum: 6907e0f930e886487a12e1fb3983edcb SHA1: bfe3e66fca402b9889bbb1578b4def0447c1babb SHA256: 09742b75398095c8cafc5de698e4a26c8eb481cd2c0d2385f77dc3a26a0e2511 SHA512: 8dee54f771e8abd146b0a6a9502fb91e43d6384866b149167eb4e4108901c845d16b6a9757865e74501d4067841626624e07e6a39b60952bc84b3f9fd5e8c770 Homepage: https://cran.r-project.org/package=agrifeature Description: CRAN Package 'agrifeature' (Agriculture Image Feature) Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) . Package: r-cran-agrifusionr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ranger, r-cran-xgboost, r-cran-cubist, r-cran-glmnet, r-cran-kernlab, r-cran-mgcv, r-cran-treeshap, r-cran-nasapower, r-cran-chirps, r-cran-daymetr, r-cran-geodata, r-cran-terra, r-cran-agridat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-agrifusionr_0.1.0-1.ca2404.1_all.deb Size: 233478 MD5sum: 10b946c9bb8b390e4deae8133cc0acc6 SHA1: 28106f3b40818362c1f0707ce8db7cffc1122b3e SHA256: 6b6366eec6cd455a31787791eee443b21a9f019568fa5d12c53094b741123c9e SHA512: 04d05f4ea96ac1f39bae58cd618f32f64653b739105e32e2f336266ee3da8008eb5ae86b6c4f21e6ac7d6c31e00d7891712061b15d30ae9306afb8513af7399c Homepage: https://cran.r-project.org/package=AgriFusionR Description: CRAN Package 'AgriFusionR' (An Integration Framework for Agricultural Analytics) Assembles agricultural analyses around a single unit of observation, the management unit within a season, and keeps climate, soil and remote-sensing covariates aligned to it. Covariates are aggregated over phenological windows derived from accumulated growing degree days rather than calendar months, following McMaster and Wilhelm (1997) . Models are validated with spatial resampling by default, since random cross-validation inflates apparent skill when observations are spatially autocorrelated, as shown by Roberts and others (2017) . Prediction intervals use split conformal inference after Lei and others (2018) . Data sources and learning algorithms are supplied through registries so that new providers and methods can be added without modifying the package. Package: r-cran-agrime Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-agrime_0.1.0-1.ca2404.1_all.deb Size: 218374 MD5sum: 98caebdec15c810fe60b5f2b702c1eda SHA1: 061d103be148a68dcd2d21036ca3178b47806d0f SHA256: f2658b200e161beef977f34af689b440042e65b45eedbf40faddd9685cffc7b4 SHA512: 003864e4a7680a5c1d909fd478acf474da98574a258058ebbb50dc8afca03db1dc267d028070a60d3c58defa5c70cbde5cab50154eae069d965a0941fb450e04 Homepage: https://cran.r-project.org/package=agriME Description: CRAN Package 'agriME' (Agricultural Marketing Efficiency and Price Spread Analysis) Provides reproducible tools for analysing agricultural marketing channels, price spread, the producer's share in the consumer price, intermediary costs and margins, and alternative indices of marketing efficiency. Implements conventional, Shepherd, and Acharya measures; validates stage-level channel accounts; compares and ranks channels; and supplies bootstrap confidence intervals, scenario sensitivity analysis, loss-adjusted margins, break-even calculations, and base-graphics methods. Methodological context is provided by Acharya and Agarwal (2021, ISBN:9789389688061) and Shepherd (2007) . Package: r-cran-agripam Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agripam_0.1.0-1.ca2404.1_all.deb Size: 115066 MD5sum: cba299eb90b590c6b2da6bf43f95d600 SHA1: 067e4ccdc86143cb63dd12c272186a5db9d9eec0 SHA256: b4ceba8d1bb7a907cb94cec647318df47e645d16c1d34ecc72e168e3d03caaa7 SHA512: ce692e3e8afa3b9c4492680bec02d966d4b1aa5c0011292dfbe1e648da351c58b6d7330a7e4c3e260527189e3defdc45d36cf5f5604670cdce960846ebb0c024 Homepage: https://cran.r-project.org/package=agriPAM Description: CRAN Package 'agriPAM' (Agricultural Policy Analysis Matrix Toolkit) Builds and analyses Policy Analysis Matrices ('PAMs') for agricultural production systems. Computes private and social profitability, policy transfers, the domestic resource cost ratio, nominal protection coefficients for outputs and inputs, the effective protection coefficient, the private cost ratio, the profitability coefficient, subsidy ratios, and social cost-benefit ratios. Supports itemised farm budgets, parity prices, grouped analysis, deterministic sensitivity analysis, switching values, and correlated Monte Carlo simulation. Methods follow Monke and Pearson (1989, ISBN:0801419530) and the Food and Agriculture Organization of the United Nations (2007, ISBN:9789251057476). Package: r-cran-agrireg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-drc, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-emmeans, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lmertest, r-cran-car, r-cran-multcomp Filename: pool/dists/noble/main/r-cran-agrireg_0.1.0-1.ca2404.1_all.deb Size: 197586 MD5sum: 4e0dc7972e8cd4a2bafa4f107ec1143d SHA1: 0872b2f37607bb94add066c9b39ebbef77e5e33d SHA256: de478e6692b42e3e637185cb231dc0099e7bf000c4787dc9ffd9e50e14ae4af1 SHA512: ead3e1b8e9d4f0fa4847c5171a95a3e7c07faf9eaccb82ebf7c17fb85ba464d66f9773ab1b68e3f1b010d203752343535676e89470510e712b8294f1a13599a1 Homepage: https://cran.r-project.org/package=agriReg Description: CRAN Package 'agriReg' (Linear and Nonlinear Regression for Agricultural Data) Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via 'lme4'), nonlinear growth curves (logistic, 'Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via 'drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and 'ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) , and Bates and others (2015) . Package: r-cran-agritutorial Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmertest, r-cran-emmeans, r-cran-pbkrtest, r-cran-lattice, r-cran-nlme, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-agritutorial_0.1.5-1.ca2404.1_all.deb Size: 294800 MD5sum: e2d0d0b25107eb17a67aad7d792d936c SHA1: 043a7f79961c6477cbdfe55a2bd0c1119bdc0c28 SHA256: 0c3a7a64d3417a17121de88f67a7137a08a377b75426a9641eeeeab2b9eea116 SHA512: 0567152e7931e0c0d4c03e2c2519674e509e23f166f6070ba518136ac0cb2aba14fca5334e2fef00d8b0dc6f7a19cf2b2307065d8c9935177fdb5b90ab4a04e6 Homepage: https://cran.r-project.org/package=agriTutorial Description: CRAN Package 'agriTutorial' (Tutorial Analysis of Some Agricultural Experiments) Example software for the analysis of data from designed experiments, especially agricultural crop experiments. The basics of the analysis of designed experiments are discussed using real examples from agricultural field trials. A range of statistical methods using a range of R statistical packages are exemplified . The experimental data is made available as separate data sets for each example and the R analysis code is made available as example code. The example code can be readily extended, as required. Package: r-cran-agriutilities Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-psych, r-cran-dplyr, r-cran-tidyr, r-cran-lme4, r-cran-matrix, r-cran-ggpubr, r-cran-lmertest, r-cran-data.table, r-cran-magrittr, r-cran-emmeans, r-cran-ggrepel, r-cran-tibble, r-cran-rlang, r-cran-statgensta, r-cran-spats Suggests: r-cran-knitr, r-cran-lattice, r-cran-cluster, r-cran-rmarkdown, r-cran-agridat, r-cran-gt Filename: pool/dists/noble/main/r-cran-agriutilities_1.2.3-1.ca2404.1_all.deb Size: 1699536 MD5sum: dcc3f0cd8864e68b0a6bae4839d065a3 SHA1: 648b30ec12d17effd9b169f911db340e65cb2a72 SHA256: c01e3fc978ca5c0299c5b4d6374387c62f192ab6636888f3a9b5796cef876855 SHA512: eea2fe6bbe2cfca6cb3f1c321fa20dad5aa3b1d9a5fb065139d1a4a7b60d43cf813aec161fb04510a61c1cb6a54adb2c860d46bf30febc855b286f624474bcce Homepage: https://cran.r-project.org/package=agriutilities Description: CRAN Package 'agriutilities' (Utilities for Data Analysis in Agriculture) Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, 'agriutilities' will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package 'asreml' for the 'ASReml' software, this can be obtained upon purchase from 'VSN' international . Package: r-cran-agriwater Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-agriwater_1.0.2-1.ca2404.1_all.deb Size: 310026 MD5sum: d19401aaee76b006d6609bf934190063 SHA1: 149b63fc1780dd3b25f3a392d2308461c0c48c73 SHA256: 5d43848e7d1236d4802b21e9ad93b1751099dfc92b4d38800e169d656863afa7 SHA512: c6851531ec81b92d5e8b42b87002d9bf68cff4569d238ae2011d62885fa3c6a878cc8fd3b9d0f5d6ee1a1be93d7a765ad1c446ea60d2595a48af91f93469ffcb Homepage: https://cran.r-project.org/package=agriwater Description: CRAN Package 'agriwater' (Evapotranspiration and Energy Fluxes Spatial Analysis) Spatial modeling of energy balance and actual evapotranspiration using satellite images and meteorological data. Options of satellite are: Landsat-8 (with and without thermal bands), Sentinel-2 and MODIS. Respectively spatial resolutions are 30, 100, 10 and 250 meters. User can use data from a single meteorological station or a grid of meteorological stations (using any spatial interpolation method). Silva, Teixeira, and Manzione (2019) . Package: r-cran-agrmt Architecture: all Version: 1.42.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-agrmt_1.42.21-1.ca2404.1_all.deb Size: 277722 MD5sum: d2b586aabd54d57ef147a158577992b1 SHA1: fa95b745ba0c3f4b4303288d5d31ea61de008fcc SHA256: daeb0a17aad4753ffb4efbbea9b53f82424742503ad6e55dddcedc19bdcf4178 SHA512: e56eb8ba6c81723219b40d26a8b5ce936118d2d0f74a50f8333167d3bd8f7439416ed652264e79eafd260414bc17ef1d5f32c549bf0338332a8ca91f4866ef41 Homepage: https://cran.r-project.org/package=agrmt Description: CRAN Package 'agrmt' (Calculate Concentration and Dispersion in Ordered Rating Scales) Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes. Package: r-cran-agrobox Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-stringr, r-cran-agricolae, r-cran-pwr, r-cran-rlang, r-cran-openxlsx, r-cran-rstatix, r-cran-multcompview Suggests: r-cran-kableextra, r-cran-magick, r-cran-tinytex, r-cran-cli, r-cran-officer, r-cran-rvg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agrobox_0.4.0-1.ca2404.1_all.deb Size: 139658 MD5sum: f9dec5f5a5d02710c7a736d3f0cc8b34 SHA1: 563a0119d62be14639c302b5da5c36a7fd362e7f SHA256: 20da1c026ad432dbaf58da2fe1535df472553d79fef1e24806bf6a01639afeba SHA512: 7567fdc53496bb0c4a0130266213af05547032ffc0c48f33d08695a07fb2a850f4ffdafd61ac9d6bc52b0dde566592c431ee9f2af2186be1cb391b0198255dc6 Homepage: https://cran.r-project.org/package=agrobox Description: CRAN Package 'agrobox' (Data Visualization and Statistical Tools for AgroindustrialExperiments) Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform one-way and two-way ANOVA with post-hoc tests (Tukey HSD and Duncan MRT), Welch ANOVA for heteroscedastic data, and the Games-Howell post-hoc test as a robust alternative when variance homogeneity fails. When residual normality fails, the Kruskal-Wallis test (with 'agricolae' or Dunn post-hoc letters) or, for blocked designs, the Friedman test is used, so that letters are always obtained through a defensible route. Normality of residuals is assessed with the Shapiro-Wilk test and homoscedasticity with the Fligner-Killeen test; the appropriate statistical path is selected automatically based on these diagnostics. Coefficients of variation and statistical power (via one-way ANOVA power analysis) are reported alongside the post-hoc letter display, and each figure includes a note describing the statistical route used, its rationale, advantages, limitations and scope. High-level wrappers allow automated multi-variable analysis with optional clustering by one or two experimental factors, with support for custom level ordering and relabeling. Results are returned as 'ggplot2' boxplots with mean and letter annotations, wide-format summary tables ready for publication or LaTeX rendering, and structured decision summaries for rapid agronomic interpretation. Direct export to Excel spreadsheets and high-resolution image tables is also supported. Functions follow methods widely used in agronomy, field trials, and plant breeding. Key references: Tukey (1949) ; Duncan (1955) ; Welch (1951) ; Games and Howell (1976) ; Shapiro and Wilk (1965) ; Fligner and Killeen (1976) ; Kruskal and Wallis (1952) ; Dunn (1964) ; Friedman (1937) ; Cohen (1988, ISBN:9781138892899); Wickham (2016, ISBN:9783319242750) for 'ggplot2'; see also 'agricolae' and 'rstatix' . Version en espanol: Conjunto de herramientas para el analisis estadistico, visualizacion y generacion de reportes en ensayos agroindustriales y agricolas. Incluye ANOVA univariado y bifactorial con pruebas post-hoc (Tukey HSD y Duncan MRT), ANOVA de Welch para datos heterocedasticos y la prueba post-hoc de Games-Howell como alternativa robusta cuando falla la homogeneidad de varianzas. Cuando falla la normalidad de residuos se usa la prueba de Kruskal-Wallis (con letras de 'agricolae' o de Dunn) o, en disenos en bloques, la prueba de Friedman, de modo que las letras se obtienen siempre por una ruta defendible. La normalidad de residuos se evalua con la prueba de Shapiro-Wilk y la homogeneidad de varianzas con la prueba de Fligner-Killeen; la ruta estadistica apropiada se selecciona automaticamente segun estos diagnosticos. Se reportan coeficientes de variacion y potencia estadistica junto con las letras de separacion de medias, y cada grafico incluye una nota que describe la ruta estadistica usada, por que, sus ventajas, desventajas y alcance. Los envoltorios de alto nivel permiten analisis multivariable automatizado con agrupamiento opcional por uno o dos factores experimentales, con soporte para orden y etiquetado personalizado de niveles. Los resultados se devuelven como boxplots con anotaciones de medias y letras, tablas resumen en formato ancho listas para publicacion o renderizado en LaTeX, y resumenes de decision para interpretacion agronomica rapida. Tambien se soporta exportacion directa a Excel e imagenes de alta resolucion para informes tecnicos. Package: r-cran-agrometindices Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agrometindices_0.1.0-1.ca2404.1_all.deb Size: 74570 MD5sum: ddd964aa21f21eb4eeac784b338c5983 SHA1: ad2eaacaebb22b36f080ad6e779b55e633223995 SHA256: 42e4bd4ac1c616a49f9b9abe19b7cd31fcc8c88ba3e44ab108d53616360ff59d SHA512: 80296aca6008d571e20fbf8ab49490c5cc0d3816c75a48ced0aa59557a8b44453b9a90ca672a65260a03e99ff6e07b9ab4e9b50d9d2395d92e507d92ee7387a8 Homepage: https://cran.r-project.org/package=AgroMetIndices Description: CRAN Package 'AgroMetIndices' (Agrometeorological Indices for Daily, Monthly, and Seasonal TimeScales) Provides methods for calculating agrometeorological indices from daily weather data at daily, monthly, and user-defined seasonal time scales. The package includes temperature, rainfall, growing degree days, heat and cold stress, wet and dry spells, extreme rainfall, and seasonal completeness indices, along with functions for meteorological data quality assessment and crop-season definition. For method details see and . Package: r-cran-agror Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-ggplot2, r-cran-nortest, r-cran-lme4, r-cran-crayon, r-cran-lmtest, r-cran-emmeans, r-cran-multcomp, r-cran-ggrepel, r-cran-mass, r-cran-cowplot, r-cran-multcompview, r-cran-rcolorbrewer, r-cran-drc, r-cran-dunn.test, r-cran-gtools, r-cran-gridextra Suggests: r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-agror_1.3.7-1.ca2404.1_all.deb Size: 1605176 MD5sum: f2c4aeb2dfa92bb3286a2a2afff5002b SHA1: f5a73249f1db4c5280e672a540f1e1a8ed446d81 SHA256: b021253d0dee589f1fa809e42fbe23484b19f3c9f7e239b54150b98499307a18 SHA512: 115257a3084e3778f3937d79b8b01139a2d2c32fc72dc015db111d9c1f10a265a13a754557538d36766581d3c0df3072bc2b75428c727aac1bd5a0873e8b08f3 Homepage: https://cran.r-project.org/package=AgroR Description: CRAN Package 'AgroR' (Experimental Statistics and Graphics for Agricultural Sciences) Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), ). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) . Package: r-cran-agroreg Architecture: all Version: 1.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-ggplot2, r-cran-boot, r-cran-minpack.lm, r-cran-dplyr, r-cran-rcompanion, r-cran-broom, r-cran-egg, r-cran-purrr Filename: pool/dists/noble/main/r-cran-agroreg_1.2.11-1.ca2404.1_all.deb Size: 673706 MD5sum: 80424aafb4cd98e2388f19bc29b8499c SHA1: 42ccdcdb4951d523bbd3a89ab82b3b605993b300 SHA256: 2efb46976666ce38efae44a2fdf5e6df8d40f78392af628a867b9e3029c224b2 SHA512: 08ee78ead9f35445f2b313b0e4a7bcb204eeb54d19ae66564698f35959b535c99efcd3d76d25ed0aa1dfd286ddc2df12af99ff4d4b681532444a90b8e671b776 Homepage: https://cran.r-project.org/package=AgroReg Description: CRAN Package 'AgroReg' (Regression Analysis Linear and Nonlinear for Agriculture) Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). ). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically. Package: r-cran-agrostab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-agrostab_0.1.0-1.ca2404.1_all.deb Size: 92354 MD5sum: 1a28b9ab32728342d01f3c18e7384736 SHA1: 531ad29a14f583ed6b1f873eec539f37f959ac9d SHA256: 3d3f8b63612af9fdd2a32c4828075aeef1cc471f858a6ce9bb9268450193f982 SHA512: 129b7bae662a613a51a5756d71f9dd8ae8dd71babc180cb65305a25cad8514a41637d38cd41f6a1ba5b926fc4924250dc9eb29b7ca8cc2f95a172f94329f2993 Homepage: https://cran.r-project.org/package=agrostab Description: CRAN Package 'agrostab' (Stability Analysis for Agricultural Research) Statistical procedures to perform stability analysis in plant breeding and to identify stable genotypes under diverse environments. It is possible to calculate coefficient of homeostaticity by Khangildin et al. (1979), variance of specific adaptive ability by Kilchevsky&Khotyleva (1989), weighted homeostaticity index by Martynov (1990), steadiness of stability index by Udachin (1990), superiority measure by Lin&Binn (1988) , regression on environmental index by Erberhart&Rassel (1966) , Tai's (1971) stability parameters , stability variance by Shukla (1972) , ecovalence by Wricke (1962), nonparametric stability parameters by Nassar&Huehn (1987) , Francis&Kannenberg's parameters of stability (1978) . 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Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), ) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), ). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology. 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Package: r-cran-ahmbook Architecture: all Version: 0.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-unmarked, r-cran-mvtnorm Suggests: r-cran-coda, r-cran-fields, r-cran-raster, r-cran-sp, r-cran-spdep Filename: pool/dists/noble/main/r-cran-ahmbook_0.2.12-1.ca2404.1_all.deb Size: 2230006 MD5sum: 13a39e23167eb68d16a01d287bfad911 SHA1: f9ff30577810d76ea6ad042fedcfdfe194f27390 SHA256: 01b609fc24064dbefef5848ebe99aa6a400bd57bea4dc3a98910db7ece68f47c SHA512: a4a765d01a625d15ca240dce7492cfa67fc1f342447060b5aa0a01a7e213cb3c26d060537641d4732a1f9a989b89ddc879114bc64a0ed78f690cd040e319a8b8 Homepage: https://cran.r-project.org/package=AHMbook Description: CRAN Package 'AHMbook' (Functions and Data for the Book 'Applied Hierarchical Modelingin Ecology' Vols 1 and 2) Provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication 'Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS' by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), . 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Package: r-cran-ahpgaussian Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ahpgaussian_0.1.3-1.ca2404.1_all.deb Size: 35136 MD5sum: 26efdefdd2ec771c404d3d1a68553b14 SHA1: ad5e72433ae50f3c16530beecac84a26ad525997 SHA256: 6b226fcf810ea965efe5ff9c0f6f80466171da093d479809d9f301964a5edd17 SHA512: 1935e103a231a24f21b3f130a27f47b8b9961a5970e4671b82fc8708d7cca9d3e2c64f06b458f1ca9771e228843529be48821d0b8d5035e04eb6d948a8b865df Homepage: https://cran.r-project.org/package=AHPGaussian Description: CRAN Package 'AHPGaussian' (New Multicriteria Method: AHPGaussian) Implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. 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Stage 1 validates a clinically proposed truncation time against the planned design using simulation-based diagnostics for risk-set support, follow-up coverage, estimability, and estimator stability, classifying it as Pass, Borderline, or Fail. Stage 2 performs constrained optimization over a grid of candidate truncation times within a clinical-distance window, maximizing a utility subject to feasibility constraints, with an independent evaluation run to assess the selected time. Supports proportional-hazards, early-, and delayed-effect patterns, uniform accrual with administrative censoring, and calibrated exponential random censoring. Package: r-cran-ai4officialstats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/noble/main/r-cran-ai4officialstats_0.2.0-1.ca2404.1_all.deb Size: 433720 MD5sum: ece479c599945306369ad2ba17f0eecb SHA1: bdbdbcab3d68f39157007b1acd2bc114e2ffeefa SHA256: 2eb683bf79446d02aab1e058707cdc61bf727a1f47a90a903aa846d325f35c8c SHA512: 3883beb2444acb44b7cad37e3be6cc9022c8e0f879d1f8396032201de87493d49b372e7eb8e9e7652436c60a50df4c5c22082dcb427be4da909b4ed35f17af17 Homepage: https://cran.r-project.org/package=AI4OfficialStats Description: CRAN Package 'AI4OfficialStats' (Audit Statistical Fidelity of AI-Mediated Official Statistics) Provides deterministic tools for auditing whether artificial intelligence systems preserve the numerical, semantic, contextual, temporal, geographic, unit, provenance, revision, transformation, and uncertainty properties of official statistics. Structured reference statistics and machine-generated claims can be compared using non-compensatory critical-error rules, weakest-link and geometric fidelity summaries, provenance graphs, and portable SHA-256 proof bundles. The package provides bounded connectors for official statistical services, an easy schema-detection and file-import layer for arbitrary official organisations, extensible provider registries, and a search-first natural- language verification layer that classifies statistical claims, selects suitable official sources, retrieves candidate evidence, matches statistical dimensions, and compares claimed values. If no reference year is stated, verification uses the latest available matching official observation and discloses the resolved year. Source attribution is optional: automatic routing can choose suitable providers when none is named, while explicitly named supported sources are respected by default. Automatic catalogue-to-observation verification is implemented for the World Bank, WHO, the United Nations Statistics Division Sustainable Development Goals service, and the European Commission statistical service, while other providers remain available through bounded direct connectors or generic official-data import. Prompt perturbation, statistical red-team generation, minimal-pair tests, and benchmark data support reproducible evaluation of generative, retrieval-augmented, and agentic statistical systems. An embedded alignment layer maps claim-level controls to relevant activities of the Generic Statistical Business Process Model (GSBPM) 5.2, including Analyse, Disseminate, Evaluate, Quality Management, and Metadata Management. Package: r-cran-ai Architecture: all Version: 1.0.4.44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-class, r-cran-catools, r-cran-mass, r-cran-party, r-cran-metrics Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ai_1.0.4.44-1.ca2404.1_all.deb Size: 25086 MD5sum: 05f1701774f27794bc9ae459e9e4a572 SHA1: ff79e1f44bf14118aa29a85f79b8747a8aac7706 SHA256: 4ed6a026d6ca8789c9eb12eed99aa377ce70892813ae7a3cc41479192036c845 SHA512: c9c65457aacb5207b0dd14105f2b8e2c5766e3c8b664411bacf7c01d792e47ab4900346b03d6458b21d9ba477c35b1c1a52332553cc6063f09fb60b7158dfa01 Homepage: https://cran.r-project.org/package=ai Description: CRAN Package 'ai' (Build, Predict and Analyse Artificial Intelligence Models) An interface for data processing, building models, predicting values and analysing outcomes. Fitting Linear Models, Robust Fitting of Linear Models, k-Nearest Neighbor Classification, 1-Nearest Neighbor Classification, and Conditional Inference Trees are available. Package: r-cran-aibias Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-cli, r-cran-purrr, r-cran-tibble Suggests: r-cran-mgcv, r-cran-lme4, r-cran-boot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aibias_0.1.1-1.ca2404.1_all.deb Size: 159494 MD5sum: 71fadca04e479da8efb3e18836c6a53d SHA1: 0bd279324aa29ab02d6a8973a1283f038dfcf120 SHA256: dbdb4f2a8b7b1152028de651dfb5043d16dc7bbadad8cf3a1db6c256f09a1b6f SHA512: 4e2c6958f603734dc50652622d18c75f6ca4f608822284291c7599379f68fe9cf80676fa7eea2915df9b86c2450bbe08c5394352a87c24395b9aee9a9dbc41f9 Homepage: https://cran.r-project.org/package=AIBias Description: CRAN Package 'AIBias' (Longitudinal Bias Auditing for Sequential Decision Systems) Provides tools for detecting, quantifying, and visualizing algorithmic bias as a longitudinal process in repeated decision systems. Existing fairness metrics treat bias as a single-period snapshot; this package operationalizes the view that bias in sequential systems must be measured over time. Implements group-specific decision-rate trajectories, standardized disparity measures analogous to the standardized mean difference (Cohen, 1988, ISBN:0-8058-0283-5), cumulative bias burden, Markov-based transition disparity (recovery and retention gaps), and a dynamic amplification index that quantifies whether prior decisions compound current group inequality. The amplification framework extends longitudinal causal inference ideas from Robins (1986) and the sequential decision-process perspective in the fairness literature (see ) to the audit setting. Covariate-adjusted trajectories are estimated via logistic regression, generalized additive models (Wood, 2017, ), or generalized linear mixed models (Bates, 2015, ). Uncertainty quantification uses the cluster bootstrap (Cameron, 2008, ). Package: r-cran-aic Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3685 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc, r-cran-zcompositions, r-cran-shiny, r-bioc-edger, r-bioc-aldex2, r-cran-vegan Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aic_1.0-1.ca2404.1_all.deb Size: 2797366 MD5sum: aceba70d64a3c48a6fb22525165154ac SHA1: 2924636542aa044161cbf011c5ee8277ffca0a9e SHA256: 7953906ae5f6cc401a5130904c571ee26164e4e896c822cbd92c9a1c6664805f SHA512: 5d6e4db3d3ef47c4934ac4f733991c409f8d28c51360dc72eb69af8afd807ce23abc3d889cdac73555aad5a1df5dce099ab18cf47371fda6754bc1da53b10d72 Homepage: https://cran.r-project.org/package=aIc Description: CRAN Package 'aIc' (Testing for Compositional Pathologies in Datasets) A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) ), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) ) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) , Anders et al. (2013)). Package: r-cran-aiccmodavg Architecture: all Version: 2.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4045 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-survival, r-cran-unmarked, r-cran-vgam, r-cran-xtable Suggests: r-cran-betareg, r-cran-coxme, r-cran-fitdistrplus, r-cran-glmmtmb, r-cran-lavaan, r-cran-lme4, r-cran-maxlike, r-cran-nnet, r-cran-ordinal, r-cran-pscl, r-cran-r2jags, r-cran-r2openbugs, r-cran-r2winbugs, r-cran-jagsui, r-cran-lmertest Filename: pool/dists/noble/main/r-cran-aiccmodavg_2.3-4-1.ca2404.1_all.deb Size: 2936546 MD5sum: 34b42c22a1c0f5bfe8662ff68fedbc02 SHA1: 9fab6f85e948293db5fc5b0674686e2ec30b267a SHA256: 0f40284afb5ffe926ed087f31874f27625371188cc66a367868402d3770a8d91 SHA512: c552c3dd2f09463aad53b6f42aa6dc1e86424528e309d938331b1463607d1ef559869aa9aa05aad8543fdc510c18b4581ad8aa2a36e9f0ed1ffcbfd7ac1a125d Homepage: https://cran.r-project.org/package=AICcmodavg Description: CRAN Package 'AICcmodavg' (Model Selection and Multimodel Inference Based on (Q)AIC(c)) Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package. Package: r-cran-aiccpermanova Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-stringr, r-cran-tidyr, r-cran-vegan Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aiccpermanova_0.0.2-1.ca2404.1_all.deb Size: 42260 MD5sum: 6f233e4a117c9d198d8ca690e25a1d7d SHA1: 7287e96ac921b291ad208e96eda95f05d50e1b5e SHA256: 5e391e9f9669c8219670383e321bedd9f492a53fae367108ff7807a1690956e0 SHA512: 6ef0599454fd8d1af494a43890de51f579105158c49b24fafc4fc25403c1d292c5701dc2f7fe236b38f741f3676c0bdf397f39714d7e0d1c7a38321132d5c41f Homepage: https://cran.r-project.org/package=AICcPermanova Description: CRAN Package 'AICcPermanova' (Model Selection of PERMANOVA Models Using AICc) Provides tools for model selection and model averaging of PerMANOVA models using Akaike Information Criterion corrected for small sample sizes (AICc) and Information Theoretic criteria principles. The package is built around the PERMANOVA analysis from the 'vegan' package and provides a streamlined workflow for generating and comparing models, obtaining model weights, and summarizing results using model averaging approaches. The methods implemented in this package are based on the practical information- theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) (). Package: r-cran-aid Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-tseries, r-cran-nortest, r-cran-ggplot2, r-cran-psych, r-cran-meta, r-cran-stringr Suggests: r-cran-onewaytests Filename: pool/dists/noble/main/r-cran-aid_3.1-1.ca2404.1_all.deb Size: 83234 MD5sum: 7a7da2d1269cfa419d364ef838bc671f SHA1: f7b4441b06c6f9e53a81c7265c5b77efe9358e17 SHA256: 6da2f0433b8de1862ec3304f55f8778f24d0b6056f64398759ef5ee57c3f7638 SHA512: a7dad69e1ef3af7e431021029b28462940b6a20b8505c54f68382cbed5d3961c8707061ec880cd1c72d14ca100c5b85a1b86e5824105adb5865b3978e30cb521 Homepage: https://cran.r-project.org/package=AID Description: CRAN Package 'AID' (Box-Cox Power Transformation) Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data. 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The package provides the 'intData' class for representing interval-valued data, along with functions to aggregate microdata and to estimate parameters of latent distributions. Barycenter and covariance matrix estimation is implemented based on the Mallows distance (Oliveira et al. (2025) ). Robust estimation of the symbolic covariance matrix is implemented via the Interval Minimum Covariance Determinant (IMCD) estimator, enabling outlier detection based on the robust squared Interval-Mahalanobis distance, as proposed by Loureiro et al. (2026b) . Explainable outlier detection is supported through Shapley value based decomposition of the squared robust Interval-Mahalanobis distance, allowing assessment of variable contributions to outlyingness (Loureiro et al. (2026a) ). Shapley interaction indices are also implemented, along with visualization tools to support interpretation of the results. Package: r-cran-aidar Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aidar_1.0.5-1.ca2404.1_all.deb Size: 76182 MD5sum: e51d8770e7911f1a59c9967b17fdc54e SHA1: f20a18ccf08917f7f7bce818e3678595c2587aca SHA256: b74e2770f19bb82fe190514b22f3fcb154b3de43d4fcf7286d97ec3a6e203d00 SHA512: 6b305e419d9a2558e7a3fd37326afd386cf89c9b68767e2ab9a15c22fca041e05ce71be3af0ae9796cd440c5e034a5913ee91b94956bb756aef705be499d8ce9 Homepage: https://cran.r-project.org/package=aidar Description: CRAN Package 'aidar' (Tools for Reading AIDA Files) Read objects from the AIDA () file and make them available as dataframes in R. Package: r-cran-aides Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-meta Suggests: r-cran-bookdown, r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aides_1.3.3-1.ca2404.1_all.deb Size: 461216 MD5sum: 016ce5530708896c1fe15071912a4159 SHA1: d15baae54fd36c5b1ac21b9115d0606c00f0b7ea SHA256: 8d50a0c8c6a48f57ec71fa7cb3fe754b254939314fe07f0ca82674fcb9df2d6f SHA512: 8a6232ee810b0328e540f9b079b6a50d9d2d35e51a328f5d83b67e9690761191a563993a74dc3a01a380e9b36438cbed3da97e72ec2412adcf579d6598931621 Homepage: https://cran.r-project.org/package=aides Description: CRAN Package 'aides' (Additive Information & Details of Evidence Synthesis) A supportive collection of functions for pooled analysis of aggregate data. The current version supports users to test assumptions before relevant analysis of bias from study size and sequential analysis such as mentioned by Wetterslev, J., Jakobsen, J. C., & Gluud, C. (2017) . Package: r-cran-aidif Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-mirt, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aidif_0.2.0-1.ca2404.1_all.deb Size: 189680 MD5sum: 5e8b9b922ba412e363d1b5f10d6c3137 SHA1: c042201e5a1263acf6563b2faa44e2f3694fd44a SHA256: 1659cd2c075d52d44f7ab6a93f1733b55ff6f863a322f441aec39838cd20b5e8 SHA512: c6f46b181483f02e91f3b4344b3d332d8ab9c540a33ec50eaf6a45740ab338b7cf31385778002218601364ee9e217108e4a557d91133b178b6a94dab54d02db6 Homepage: https://cran.r-project.org/package=aiDIF Description: CRAN Package 'aiDIF' (Differential Item Functioning for AI-Scored Assessments) Detects and quantifies differential item functioning (DIF) in AI-scored educational and psychological assessments. Provides a fully self-contained robust DIF engine (M-estimation via iteratively re-weighted least squares with the bi-square loss) alongside the Differential AI Scoring Bias (DASB) test, which detects item-level scoring shifts that differ across subgroups when comparing human and AI scoring conditions. Supports independent and paired scoring designs, robust linking of the cross-condition contrast, multiplicity control, conversion of fitted 'mirt' models to package inputs, simulation utilities, anchor weight diagnostics, and an AI-effect classification framework. Methods follow Halpin (2024) . Package: r-cran-aieconindex Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-aieconindex_0.2.0-1.ca2404.1_all.deb Size: 139816 MD5sum: d966c41be03930227bc7de8512a793bf SHA1: 4f2da38d591e490b48a9c4465246d35078a012b3 SHA256: 3bbf49b21da64d2f951e485deb55dca5cf5fb83fba158725ec0c86778d5a9cf2 SHA512: 68f369e28a8f89c48f6fb23754bf204247c31790026f2ae2c57c38bed7394f8954a713dba5a7889333d4845a541721b832515e31fa2111e5426ea584e84ee707 Homepage: https://cran.r-project.org/package=aieconindex Description: CRAN Package 'aieconindex' (Access the 'Anthropic Economic Index' Dataset) Provides clean, tidy access to the 'Anthropic Economic Index' (AEI) dataset hosted on 'Hugging Face' . The AEI is a recurring release from 'Anthropic' that maps usage of the 'Claude' family of large language models to occupations and tasks using the 'O*NET' taxonomy and the 'Standard Occupational Classification' system, following the methodology of Handa et al. (2025) and the privacy-preserving system 'Clio' of Tamkin et al. (2024) . Functions list available releases, fetch raw and enriched usage tables, retrieve task statements, request hierarchies, country-level breakdowns, and the standalone labor market impacts tables (job exposure and task penetration), compare two releases, join the index to user-supplied data on a shared key, and compute usage-concentration metrics (Herfindahl-Hirschman Index, top-N concentration ratios, Shannon entropy). Data is cached locally for subsequent calls. Reproducibility helpers produce 'BibTeX' or plain-text citations that include the methodological source paper. This product uses the 'Anthropic Economic Index' data (released under CC-BY by 'Anthropic') but is not endorsed or certified by 'Anthropic'. 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The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) . Package: r-cran-airgriwrm Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1897 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-png, r-cran-rlang, r-cran-zlib Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-airgriwrm_0.7.0-1.ca2404.1_all.deb Size: 1338024 MD5sum: b95066717abaae2ad344e0f723201ab0 SHA1: 24ab3e6ac1466ee89a021ad488fc8321768c01ad SHA256: 1522bb93c00177d63a50cd94ddebaacb48fede4298b9ec49f143aa44b95d0c3a SHA512: 6966e57e922e9d22674a8c71b35298018c8b91762c8df399ca58c5ce8798bb057d8be994b915ce799031837c44df9f482bb5659c93d10e6f851057e40c6a5ae3 Homepage: https://cran.r-project.org/package=airGRiwrm Description: CRAN Package 'airGRiwrm' ('airGR' Integrated Water Resource Management) Semi-distributed Precipitation-Runoff Modeling based on 'airGR' package models integrating human infrastructures and their managements. Package: r-cran-airgrteaching Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7103 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-airgr, r-cran-dygraphs, r-cran-markdown, r-cran-plotrix, r-cran-shiny, r-cran-shinyjs, r-cran-xts Suggests: r-cran-airgrdatasets, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-airgrteaching_0.3.8-1.ca2404.1_all.deb Size: 3513614 MD5sum: 84fa1c225bcc914fea5f41578bf272a2 SHA1: fc9565b33546199b29fc00115dd4e7e54596723c SHA256: 78a3f19d259fbf4aeacf3e87863311805392c35ab1e1b744868ce8ed03434da0 SHA512: 88255c2bb06c3d8de0fb3a88a4c2e87ebb4b021debb5a7742cd2490058961387f53fc34d02e0d098aa39b3feba3244ed32d9effd28eb95fd588586a86d30e086 Homepage: https://cran.r-project.org/package=airGRteaching Description: CRAN Package 'airGRteaching' (Teaching Hydrological Modelling with the GR Rainfall-RunoffModels ('Shiny' Interface Included)) Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables. Package: r-cran-airly Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-reshape2, r-cran-tibble Suggests: r-cran-testthat, r-cran-httptest, r-cran-covr Filename: pool/dists/noble/main/r-cran-airly_0.1.0-1.ca2404.1_all.deb Size: 118852 MD5sum: c0aa4bed1382347f7002477e0ab5971b SHA1: cd8439bde9979d3fdde4aa606cf9ac4c4aaad533 SHA256: 6fa026abb33405d469ba9a704b6de5cfb125c88f8312cf42a1798257cfa96c15 SHA512: 703dbac7bae1df27e25c181886f471937970a6580b6d4ab3e9fc99809ea529ee7981b983046dc0bb1545573c0f57022fa4f73075262fdad91f64e0e49546fda3 Homepage: https://cran.r-project.org/package=aiRly Description: CRAN Package 'aiRly' (R Wrapper for 'Airly' API) Get information about air quality using 'Airly' API through R. Package: r-cran-airmonitor Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dygraphs, r-cran-leaflet, r-cran-lubridate, r-cran-magrittr, r-cran-mazamacoreutils, r-cran-mazamarollutils, r-cran-mazamatimeseries, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyselect, r-cran-xts Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-airmonitor_0.4.3-1.ca2404.1_all.deb Size: 992878 MD5sum: d4a8e56d8453db5476e12105fb22c296 SHA1: 47c174c62f2bd119cf799656086b5b110fc46df5 SHA256: a9520416a1a785f655538c40ecc2c6341fdcef15e3f426cecea6492d28d62c34 SHA512: 4094474add0cd43924dc75749c8ea838f2fe28ae8ebd52300d29723fa97e62fb5a4fa8ae48dcaa645ae171f6311e43a0396d9ce4035503edd4766c35c22cace9 Homepage: https://cran.r-project.org/package=AirMonitor Description: CRAN Package 'AirMonitor' (Air Quality Data Analysis) Utilities for working with hourly air quality monitoring data with a focus on small particulates (PM2.5). A compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment". Algorithms to calculate NowCast and the associated Air Quality Index (AQI) are defined at the US Environmental Projection Agency AirNow program: . 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Package: r-cran-airportproblems Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plotly Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airportproblems_0.1.0-1.ca2404.1_all.deb Size: 335508 MD5sum: 29d8c4f3873618d71ca30181e619a032 SHA1: d7c3303cc02713f2ab2e89964dfa42a37c4e95f4 SHA256: dece73732c5a5f3e233b48b53caef8488121ecba57a06cf22031f2555a1a7c2a SHA512: 38b04d43fafa124cb76b12395c33159e068fae1ff5541155f293f1334f55a12cb42ddfe385da01b22e7733b2282c0a03ed7fe3b59d199c688fbba3e8b1e27b3d Homepage: https://cran.r-project.org/package=AirportProblems Description: CRAN Package 'AirportProblems' (Analysis of Cost Allocation for Airport Problems) Airport problems, introduced by Littlechild and Owen (1973) , are cost allocation problems where agents share the cost of a facility (or service) based on their ordered needs. Valid allocations must satisfy no-subsidy constraints, meaning that no group of agents contributes more than the highest cost of its members (i.e., no agent is allowed to subsidize another). A rule is a mechanism that selects an allocation vector for a given problem. This package computes several rules proposed in the literature, including both standard rules and their variants, such as weighted versions, rules for clones, and rules based on the agents’ hierarchy order. These rules can be applied to various problems of interest, including the allocation of liabilities and the maintenance of irrigation systems, among others. Moreover, the package provides functions for graphical representation, enabling users to visually compare the outcomes produced by each rule, or to display the no-subsidy set. In addition, it includes four datasets illustrating different applications and examples of airport problems. For a more detailed explanation of all concepts, see Thomson (2024) . Package: r-cran-airportr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airportr_0.1.3-1.ca2404.1_all.deb Size: 371240 MD5sum: bf557f4e1a9d3d315880088b38935ea7 SHA1: 62d48218f693822c4296439bd66ccefd27524abd SHA256: d31caffaf33888b9185043d3c091c8cd048543cba4cea5e5d601325e75b6349b SHA512: f7491f3a56ade5626034c4851f19151019a93c17cac89de1e89118ffe5a5877383555d21e66e55e8206df34cc69f9c23c9c88082777671bc065f83a7459607ba Homepage: https://cran.r-project.org/package=airportr Description: CRAN Package 'airportr' (Convenience Tools for Working with Airport Data) Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License. Package: r-cran-airports Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-airports_0.1.0-1.ca2404.1_all.deb Size: 791168 MD5sum: 8ec799209e438ced1c283f47ef41d244 SHA1: 14955ab5d71067c24b768a13f837fee96d04b6f0 SHA256: bac690e87571a8e596b812e8189221c762c66f549c80f6b87de69ace0251c320 SHA512: ff806cb296c116170da9d34f100ae978eb0a593e40c027314d2a4908db51d6f6e25dc00b46c0852e63a7ec3e30f36316801d7cc985b21d86cd1ca55ea3829212 Homepage: https://cran.r-project.org/package=airports Description: CRAN Package 'airports' (Data on Airports) Geographic, use, and property related data on airports. Package: r-cran-airqualityes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3808 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airqualityes_1.0.0-1.ca2404.1_all.deb Size: 3810674 MD5sum: 8f0c4432f21c2a53ee918422c3183158 SHA1: 9c9069499bd3ccb25f0febc859447c4fb0d9a196 SHA256: 6ece0231238cc717320bb7b30e2f41b5e3eb3757984c4dec17bd12f1a6f5547c SHA512: a57cd61e6a3adacd31e4f34cc72f34c2d6054d67bf52965aabe4adcd381a0fd3000a87a7759c4a4458dbf99e2145610095fece7f22079a816966e50ed342c849 Homepage: https://cran.r-project.org/package=airqualityES Description: CRAN Package 'airqualityES' (Air Quality Measurements in Spain from 2011 to 2018) These dataset contains daily quality air measurements in Spain over a period of 18 years (from 2001 to 2018). The measurements refer to several pollutants. These data are openly published by the Government of Spain. The datasets were originally spread over a number of files and formats. Here, the same information is contained in simple dataframe for convenience of researches, journalists or general public. See the Spanish Government website for more information. Package: r-cran-airr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1537 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-readr, r-cran-stringi, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-airr_2.0.0-1.ca2404.1_all.deb Size: 598166 MD5sum: 1912ca1866d57c1a52acd0a44d5819c9 SHA1: c6442a665efa06f67affc7b74f270a6ea009465c SHA256: 746d7deb3ffd646ac71bda6546b2115ececd59d473bd9aef334771c29c370d4a SHA512: 0701078dcc83ec98fe5fb2f22adbacb3b5ab1b6f790a7f5181ebc956cf88c56014de908bb487b0b4866852434e893b66333845a1ec597e4e5997769db7f80da5 Homepage: https://cran.r-project.org/package=airr Description: CRAN Package 'airr' (AIRR Data Representation Reference Library) Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community . Package: r-cran-airscreen Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-airscreen_0.1.0-1.ca2404.1_all.deb Size: 62210 MD5sum: 7e3639bae3e0068b33d31c44bc6ad7ae SHA1: 150705135294cd0c8f5b08e082d8b40b2afa1e57 SHA256: 43ae6737dd0aaee67e337f2c3aded8806ac10e08a19efb523131ca78597d699d SHA512: c251480c37bca458e9d28f8a7dd0356906e4d7bef86d2c80e6c8900c8e5053cdfbcd33abbf628540269f41c3176b2de3d770971de01c59cdc4ae8fe00797774b Homepage: https://cran.r-project.org/package=AirScreen Description: CRAN Package 'AirScreen' (Feature Screening via Adaptive Iterative Ridge (Air-HOLP andAir-OLS)) Implements two complementary high-dimensional feature screening methods, Adaptive Iterative Ridge High-dimensional Ordinary Least-squares Projection (Air-HOLP, suitable when the number of predictors p is greater than or equal to the sample size n) and Adaptive Iterative Ridge Ordinary Least Squares (Air-OLS, for n greater than p). Also provides helper functions to generate compound-symmetry and AR(1) correlated data, plus a unified Air() front end and a summary method. For methodological details see Joudah, Muller and Zhu (2025) . 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We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) . Package: r-cran-aisanalyze Architecture: all Version: 3.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-purrr, r-cran-stringr, r-cran-doparallel, r-cran-foreach, r-cran-assertthat, r-cran-data.table, r-cran-magrittr Suggests: r-cran-knitr, r-cran-units, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aisanalyze_3.1.4-1.ca2404.1_all.deb Size: 1138272 MD5sum: 2331008d0e437378aec395074d412102 SHA1: e784dc3fc656d1361829c2a9c4251ef384e0bb10 SHA256: ca0205049e3e061c8dd1fb17db804059484599e64a1d95330144751d95b5a773 SHA512: e8f6f5a4f7c1666583e49738a3e47f63b1e7f1e0e2ea2cd7be99605b7342a8d1c030f541fa2994dfc0e473a7ee95f7336c291e34f5a5fa6b007a416d98ee76be Homepage: https://cran.r-project.org/package=AISanalyze Description: CRAN Package 'AISanalyze' (Processing and Analyzing AIS Vessel Tracking Data) Processes Automatic Identification System (AIS) vessel tracking data, including travel estimation, trajectory correction, interpolation, extraction, and summarising vessel information. The package is designed to facilitate reproducible analyses of maritime traffic in ecological, environmental, and marine spatial planning applications. For more details see . Package: r-cran-aiscreenr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6008 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-httr2, r-cran-stringr, r-cran-furrr, r-cran-tidyr, r-cran-tictoc, r-cran-askpass, r-cran-curl, r-cran-purrr, r-cran-lifecycle, r-cran-jsonlite, r-cran-htmltools, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-testthat, r-cran-withr, r-cran-readtext, r-cran-quarto Filename: pool/dists/noble/main/r-cran-aiscreenr_0.4.0-1.ca2404.1_all.deb Size: 5151324 MD5sum: f390d566cdff011164eba428e026e03e SHA1: 7aeed6cf32de708ebc57a07aed7113e5ef99a781 SHA256: 16889e79b9e004ef4aef79756e454762fc6421096d2f95c69f86df50efdead89 SHA512: 48c26324892b97175f679e0474d318bb4be2e4f84fd171a01ef5aa833f08736e02871ba0f968e85d358b9739d73869a68f5a479132a3bf453d3d4f87fcda5111 Homepage: https://cran.r-project.org/package=AIscreenR Description: CRAN Package 'AIscreenR' (AI Screening Tools in R for Systematic Reviewing) Provides functions to conduct title and abstract screening in systematic reviews using large language models, such as the Generative Pre-trained Transformer (GPT) models from 'OpenAI' . These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) . 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Providers register themselves with the core 'aisdk' provider registry on load. 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Implements the sustainability indicators of Froese (2004) , empirical biological reference points from Froese and Binohlan (2000) , and the decision framework of Cope and Punt (2009) . Incorporates a three-tier Monte Carlo and bootstrap uncertainty propagation framework for sustainability indicators, optimum bin size calculations following Wang et al. (2020) , multi-month length-frequency harmonization, and length-weight relationship fitting. Methodology is detailed in Ali et al. (2025) . 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Package: r-cran-alcoholsurv Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sensitivitymv Suggests: r-cran-survival, r-cran-itos, r-cran-coin Filename: pool/dists/noble/main/r-cran-alcoholsurv_0.7.2-1.ca2404.1_all.deb Size: 122178 MD5sum: f165fdafbf706896ce9ac0be3a61c4a4 SHA1: e48c4de192a5c1d5366cae1c082f8d3db9c4bb8c SHA256: e7de63cc917abde77b3eafdce219faa9edd5d6867e9475af5ef7cae1749b581c SHA512: 6271a2311d738217707b51e2485412eb86a6886630c496c7923159acdfa9e1602aae2a5608eb1c3ec9ee4f2b86464362025bcec80de7b83dd860edf1baaea4ee Homepage: https://cran.r-project.org/package=alcoholSurv Description: CRAN Package 'alcoholSurv' (Light Daily Alcohol and Longevity) Contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025) . Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs in Rosenbaum (2026) ). Package: r-cran-ald Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ald_1.3.1-1.ca2404.1_all.deb Size: 44386 MD5sum: 08767f2f48746bcd5df5f7a6b0cce6d1 SHA1: e6d8492c86502f19a28dcdb0cc41a712b36c5bde SHA256: f1d91b94e49ad54a95537ff71157f6268174f7b33d0653ad0bafc82aad993164 SHA512: c38e24a92285812460ba7d58f1a18577217d748cac3e56a8b2c83b83ce59e6ebfccea1922c5c07dd730362a3bff5d9968946fb3e7658572eaaed42b8141ac0fc Homepage: https://cran.r-project.org/package=ald Description: CRAN Package 'ald' (The Asymmetric Laplace Distribution) It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) useful for quantile regression. Package: r-cran-aldex3 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-lme4, r-cran-lmertest, r-cran-mass, r-cran-nlme, r-cran-abind, r-cran-matrixstats Suggests: r-cran-rbeta2009, r-cran-testthat, r-cran-lmtest, r-cran-sandwich, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aldex3_1.0.2-1.ca2404.1_all.deb Size: 137778 MD5sum: 69eb8819a0900735f3d9ed178d83951c SHA1: 1d1d38220b4ad7199008a5d3a545eb994e3b1eae SHA256: 88d49e51cf3cbed84bae20b20f9816e7888b7745a5326e52edfd5352a8815e05 SHA512: 2cff4ec6f47a2f7a6b2e043e748a1716ab34a123d30957f52992f29c9d270e2be55b991ea539b51c760c34bff4967b2f8d79f4f7f126aa992f3756e494f576ba Homepage: https://cran.r-project.org/package=ALDEx3 Description: CRAN Package 'ALDEx3' (Linear Models for Sequence Count Data) Provides scalable generalized linear and mixed effects models tailored for sequence count data analysis (e.g., analysis of 16S or RNA-seq data). Uses Dirichlet-multinomial sampling to quantify uncertainty in relative abundance or relative expression conditioned on observed count data. Implements scale models as a generalization of normalizations which account for uncertainty in scale (e.g., total abundances) as described in Nixon et al. (2025) and McGovern et al. (2025) . 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Package: r-cran-aldvmm Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-checkmate, r-cran-optimx, r-cran-formula, r-cran-sandwich, r-cran-lmtest Suggests: r-cran-knitr, r-cran-kableextra, r-cran-markdown, r-cran-tinytex, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-bookdown, r-cran-xtable, r-cran-ggplot2, r-cran-scales, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-aldvmm_0.9.0-1.ca2404.1_all.deb Size: 419982 MD5sum: 59739319ece1c0a3768e7adec3c5b492 SHA1: 2e14a6024d3e1efd42564afa361ed245d447fda9 SHA256: 3840edffd7d811615c69f7199faecb85c953fb7f6d46b99f19d0c81c95b1ca09 SHA512: c1aa47bafe08d4402f4db05d8006ee270c4435a30dbea40ddcf17da1d695a57fd785cf165d70015f2aaa1ef547420e9d80e7287b2ac55fd8c3984647851e20cb Homepage: https://cran.r-project.org/package=aldvmm Description: CRAN Package 'aldvmm' (Adjusted Limited Dependent Variable Mixture Models) The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership. 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ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. . Package: r-cran-alepe Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-readr, r-cran-rlang, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-httptest2, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-alepe_0.1.1-1.ca2404.1_all.deb Size: 87334 MD5sum: 432d8255c604c2423197b2d82df498b4 SHA1: 3bf86ae0e75f2b65bec62a60d13df43d875a8604 SHA256: 63129fdeee74f33cc2d2362aab530507b4f076b6e834a6dd47b5fd209c274947 SHA512: 7a256349554cdd6bbaf770db2b021f60af6004c350d724d911946644f585bf0e33299907579d907200bcd60656fdabce39699c7815bd34b65fdfbad88116727a Homepage: https://cran.r-project.org/package=alepe Description: CRAN Package 'alepe' (Access the Open Data API of the Legislative Assembly ofPernambuco) A tidy interface to the open data API of the Legislative Assembly of the State of Pernambuco, Brazil ('ALEPE', ). Retrieve data on representatives, staff, positions, departments, remuneration, contracts, procurement processes, and legislative propositions as tibbles with clean names and parsed column types. Requests are cached locally and retried with exponential backoff; network failures are handled gracefully. Package: r-cran-aleplot Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaimpute Suggests: r-cran-r.rsp, r-cran-nnet Filename: pool/dists/noble/main/r-cran-aleplot_1.1-1.ca2404.1_all.deb Size: 637314 MD5sum: b81f7c22ca8773df668ca07cc7a0b150 SHA1: 9e6afe2398837ac51bff639aec911f2135055ae4 SHA256: 037eff678f793064af88f362207709871335ec2c480ae2d812b982ceb9c2dd33 SHA512: 27102ac9e85a3585ac79c8c004552769076c8ae2a2aa304489fe8286eb7f07ab41672c3f3ac0b33f17f8828ae1eef6bb86a8a57a83c1bfec4c7d7dec61a16da9 Homepage: https://cran.r-project.org/package=ALEPlot Description: CRAN Package 'ALEPlot' (Accumulated Local Effects (ALE) Plots and Partial Dependence(PD) Plots) Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model. Package: r-cran-alfq Architecture: all Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-plyr, r-cran-caret, r-cran-seqinr, r-cran-lattice, r-cran-randomforest, r-cran-rocr, r-cran-reshape2, r-cran-bio3d Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-alfq_1.3.6-1.ca2404.1_all.deb Size: 264894 MD5sum: e89cc0a3be44c872fb47addf71ba800d SHA1: 0bd426dc89c2193402d7587c163f2a012f3c0e1c SHA256: 1b01a534926e1cb00e93a788200ee5d5084bf0128c0b52aa7eb7cd6f65171a86 SHA512: 458d6b7bf298f06f7ffc2675c6d9e93540a66b4f75c41eabf55ec1bbaa25970f5e057728e7973dc685a5f5c70629b37249cda6d8647ccec6c039f844a7e19b3b Homepage: https://cran.r-project.org/package=aLFQ Description: CRAN Package 'aLFQ' (Estimating Absolute Protein Quantities from Label-Free LC-MS/MSProteomics Data) Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample. Package: r-cran-alfr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-stringr Suggests: r-cran-devtools, r-cran-httptest, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-remotes, r-cran-spelling, r-cran-fs Filename: pool/dists/noble/main/r-cran-alfr_1.2.1-1.ca2404.1_all.deb Size: 52416 MD5sum: f7e52355e1e3f88d5c489ebb881eebcf SHA1: a3a7139336123de2b96539309b5372e04e18da9a SHA256: f92c9fd52dd47f397c43cb06c5b424555c9f88d98dd6aa3da664a237c9f802c3 SHA512: 810d8578a94923869cb544c5956fe27f1d052d4fc2b9b92f76a0fe16da412272cf368b58644d986ce6f09ed80a9fa0bc3d94a81f3be68e43f89d81252cc09d26 Homepage: https://cran.r-project.org/package=alfr Description: CRAN Package 'alfr' (Connectivity to 'Alfresco' Content Management Repositories) Allows you to connect to an 'Alfresco' content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the 'Alfresco' repository, read and upload content and manage folder hierarchies. For more details on the 'Alfresco' content management repository see . Package: r-cran-alfred Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-lubridate, r-cran-jsonlite, r-cran-rlang, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-covr Filename: pool/dists/noble/main/r-cran-alfred_0.2.1-1.ca2404.1_all.deb Size: 19872 MD5sum: 6be84bf4da1e14ffc2a9add995b18f3a SHA1: 5f199ce0411f22f4d4ec17f38c04e37df137837b SHA256: c3c061bc3d872aee24f32844882cee9d7323ab8bc10263d08f14535397e0c31b SHA512: b7ac4f9a02ee3cab9f93e018712054250ff1f0f8bfbdce4c4ad79dc44efac103e50fa0e4e44d55564f4ec98b4850ca37c041134be81994d927b478c5c8bb0c0a Homepage: https://cran.r-project.org/package=alfred Description: CRAN Package 'alfred' (Downloading Time Series from ALFRED Database for VariousVintages) Provides direct access to the ALFRED () and FRED () databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis. Package: r-cran-algaeclassify Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-algaeclassify_2.0.6-1.ca2404.1_all.deb Size: 238646 MD5sum: 64bd544211aa5231f37af93e9e87ec3c SHA1: 961ea8d73b8c6fc46f1e2d47aa44aed496417a75 SHA256: 5bdf581d9e554dc1e7c3dbf591bad7ef61094e398ac04ba6bd6f156fe88fe63a SHA512: 42e79bf288697278c7b9d7785834cf633fbab10d134398ca06bdddce5529e535a5df133037a922044de644be205bd268d39329ed434c4f2411f22b792b84446f Homepage: https://cran.r-project.org/package=algaeClassify Description: CRAN Package 'algaeClassify' (Tools to Query the 'Algaebase' Online Database, StandardizePhytoplankton Taxonomic Data, and Perform Functional GroupClassifications) Functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 ) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the 'algaebase' online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the 'algaebase' administrators. Note that none of the 'algaeClassify' authors are affiliated with 'algaebase' in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the 'Global Names Resolver' service via its API (). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the 'ITIS' database via its JSON web service API. The 'algaeClassify' package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use. Package: r-cran-algebraic.dist Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-algebraic.dist_1.0.0-1.ca2404.1_all.deb Size: 863086 MD5sum: db3d4690178c2fb993ee0aaca769f256 SHA1: 535a41ea948917308bb955b80972283a349e2a19 SHA256: 8a537e2caa5c88986e1186cb8ba2d3196815a8b50104f7b3783980b113a14d46 SHA512: b6fe615db410b9e11b01a433c9c4680af03fa798a9f190adeec450acc783694d91bee2eec5c2c3ed6c1d19c4b8d3bda6d1729f1615fb0b5e2b7d61694335359f Homepage: https://cran.r-project.org/package=algebraic.dist Description: CRAN Package 'algebraic.dist' (Algebra over Probability Distributions) Provides an algebra over probability distributions enabling composition, sampling, and automatic simplification to closed forms. Supports normal, exponential, gamma, Weibull, chi-squared, uniform, beta, log-normal, Poisson, multivariate normal, empirical, and mixture distributions with algebraic operators (addition, subtraction, multiplication, division, power, exp, log, min, max) that automatically simplify when mathematical identities apply. Includes closed-form MVN conditioning (Schur complement), affine transformations, mixture marginals/conditionals (Bayes rule), and limiting distribution builders (CLT, LLN, delta method). Uses S3 classes for distributions and R6 for support objects. Package: r-cran-algebraic.mle Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algebraic.dist, r-cran-boot, r-cran-mvtnorm, r-cran-mass, r-cran-numderiv Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-cdft Filename: pool/dists/noble/main/r-cran-algebraic.mle_2.0.2-1.ca2404.1_all.deb Size: 480302 MD5sum: 71faf9b5970b66915ff6882493efaadb SHA1: a5a3d53b9991697f38f0f71b1b00891724ac1807 SHA256: c2f312cefde8cefaa95666f47c5d09f4a39f9521a853635138462d46d6ce9d7d SHA512: 094a811cba7fde3da2b0da2ef00d94cf38a92a554f722607f3e8337e13fc93e22b90c39fde7dcddadba70ef569de9ad4f7e407a84629d5292ed614ad18e55fac Homepage: https://cran.r-project.org/package=algebraic.mle Description: CRAN Package 'algebraic.mle' (Algebraic Maximum Likelihood Estimators) The maximum likelihood estimator (MLE) is a technology: under regularity conditions, any MLE is asymptotically normal with variance given by the inverse Fisher information. This package exploits that structure by defining an algebra over MLEs. Compose independent estimators into joint MLEs via block-diagonal covariance ('joint'), optimally combine repeated estimates via inverse-variance weighting ('combine'), propagate transformations via the delta method ('rmap'), and bridge to distribution algebra via conversion to normal or multivariate normal objects ('as_dist'). Supports asymptotic ('mle', 'mle_numerical') and bootstrap ('mle_boot') estimators with a unified interface for inference: confidence intervals, standard errors, AIC, Fisher information, and predictive intervals. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022). Package: r-cran-algebraichaplopackage Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-algebraichaplopackage_1.2-1.ca2404.1_all.deb Size: 89112 MD5sum: 9bb600bd4bd02d45f32327b0776745bb SHA1: 0cd719690c7149709942e01bd12f4794d41088bb SHA256: 2622c4333cff144161d3ca310fa2e3316e6e631a74053ddd08ee6e01b6ea9e08 SHA512: 31e7e174e674a8acce8efc2160f575b83d740f4faf2a4106f25ca628c79e28201046f97a0dfe4de7d79d47fd174c0a3e7d2442a82c55a9c4b2ba9eb6a311fea2 Homepage: https://cran.r-project.org/package=AlgebraicHaploPackage Description: CRAN Package 'AlgebraicHaploPackage' (Haplotype Two Snips Out of a Paired Group of Patients) Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations. Package: r-cran-algeriapis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-algeriapis_0.1.0-1.ca2404.1_all.deb Size: 304376 MD5sum: 126bd1a7fcfd793bf765b19283adc742 SHA1: e1d33723f5ef08bf8f766f7a468a9a382b0a9609 SHA256: 22b7f28e1c87f482388be8f9bd6373dfe72fdce1510e0239e0463a22fc71ab61 SHA512: e93b3f22a6b0e3e71515f04e63659fd5035069c548eb1b639cd34253b245cb4a4de895d248b003d64764e686871333e3c0e4dc1f2ee3b48cac1c0098fcb5a1e1 Homepage: https://cran.r-project.org/package=AlgeriAPIs Description: CRAN Package 'AlgeriAPIs' (Access Algerian Data via Public APIs) Provides functions to access data from public RESTful APIs including 'World Bank API' and 'REST Countries API', retrieving real-time or historical information related to Algeria. The package enables users to query economic indicators and international demographic and geopolitical statistics in a reproducible way. It is designed for researchers, analysts, and developers who require reliable and programmatic access to Algerian data through established APIs. For more information on the APIs, see: 'World Bank API' and 'REST Countries API' . Package: r-cran-algo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-algo_0.1.0-1.ca2404.1_all.deb Size: 17912 MD5sum: 6ad99d6e1d9c999d0c944317f7ac92c4 SHA1: 1dc60d9d67a3edfcf38399ee053712e758e6bf25 SHA256: 005bca3156301e63a58b42a9824b8c4753d56aac0b4de43b8a88a38d849dbb2a SHA512: 738fc2691504763c7bc77776aa8ef2b4d30ace4f6eeb57b3b5b35ad2bfb7947f515c2fa10af942046ede2c3155fec1384e2191799a95bbec368524d43e4908b2 Homepage: https://cran.r-project.org/package=algo Description: CRAN Package 'algo' (Implement an Address Search Auto Completion Menu on 'Shiny' TextInputs Using the 'Algolia Places' 'Javascript' Library) Allows the user to implement an address search auto completion menu on 'shiny' text inputs. This is done using the 'Algolia Places' 'JavaScript' library. See . Package: r-cran-algorithmia Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-httr, r-cran-rjson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit Filename: pool/dists/noble/main/r-cran-algorithmia_0.3.0-1.ca2404.1_all.deb Size: 224148 MD5sum: ed4732808bcb8de87f97061c7ba30ce4 SHA1: 5f1ca08a846ed936a8156a8d6f723fa49617a054 SHA256: 67e18f6f26c9b3c6071448a1853d9887ed8399c8ac6c44c7c23ac9783286ebfe SHA512: d9f61141fea1bb586cad2c2784a730e36842e5141b69eb802d0e515d89591865994516df86a4fd18e81e2c693d50072a02250a378371a6c1f697d5ff8eca9bc4 Homepage: https://cran.r-project.org/package=algorithmia Description: CRAN Package 'algorithmia' (Allows you to Easily Interact with the Algorithmia Platform) The company, Algorithmia, houses the largest marketplace of online algorithms. This package essentially holds a bunch of REST wrappers that make it very easy to call algorithms in the Algorithmia platform and access files and directories in the Algorithmia data API. To learn more about the services they offer and the algorithms in the platform visit . More information for developers can be found at . Package: r-cran-aliases2entrez Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2011 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-bioc-limma, r-bioc-org.hs.eg.db, r-bioc-annotationdbi, r-cran-foreach, r-cran-readr, r-cran-rcurl Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-aliases2entrez_0.1.2-1.ca2404.1_all.deb Size: 610336 MD5sum: c5a9c3e90c23cc7c23779c228cfeed20 SHA1: 740df57250b539741ac8270a32d9be7f7aa55036 SHA256: 0d2678eef85d6a947017a4d55c772d8829f69bcfc76f956455235c2b0f041850 SHA512: 85ed4f7c63e7d469ea07f85d61af48bc95c98edef9755ae5d1439a6fe0d03b583b5e180e1e2a7bd5b0d77541207828b2c17e2c120b1dd765aff4327ccad00602 Homepage: https://cran.r-project.org/package=aliases2entrez Description: CRAN Package 'aliases2entrez' (Converts Human gene symbols to entrez IDs) Queries multiple resources authors HGNC (2019) , authors limma (2015) to find the correspondence between evolving nomenclature of human gene symbols, aliases, previous symbols or synonyms with stable, curated gene entrezID from NCBI database. This allows fast, accurate and up-to-date correspondence between human gene expression datasets from various date and platform (e.g: gene symbol: BRCA1 - ID: 672). Package: r-cran-alien Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-helpersmg, r-cran-rlang, r-cran-tidyr Suggests: r-cran-bh, r-cran-knitr, r-cran-purrr, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-alien_1.0.2-1.ca2404.1_all.deb Size: 138984 MD5sum: a064f50235cc1ee77a6e767993e42290 SHA1: 702ba2b109b73a4ba3caf2e23b401ec0b0397a28 SHA256: f574decbd8702312d0497c0640ee9d7994b7d4c0875c40b09b3312c332855c4f SHA512: aed5566a597434c0935686499dfc7d61e056d7b63a16459308ee6dcb1248847d1f434e711c56e673477b01180ab846dfab3368fa044d3fe32c7b16d90a2c56c8 Homepage: https://cran.r-project.org/package=alien Description: CRAN Package 'alien' (Estimate Invasive and Alien Species (IAS) Introduction Rates) Easily estimate the introduction rates of alien species given first records data. It specializes in addressing the role of sampling on the pattern of discoveries, thus providing better estimates than using Generalized Linear Models which assume perfect immediate detection of newly introduced species. Package: r-cran-align Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matlab Filename: pool/dists/noble/main/r-cran-align_0.1.0-1.ca2404.1_all.deb Size: 30030 MD5sum: 2f18d270f72a472c3ec50c0fc6f5e528 SHA1: 9b5c19c33d20e9fcb2b6695c46e93948e23ed7f3 SHA256: 66b151a854ca5ee547c0c451937d45902430212dbb86376ad308f9acef2ba826 SHA512: c94a2407d052e8f1a327168eba2f9f0f88144d35198f7e5a36d3b9e007ff8be6f1afb21a38b5299968fae8bc57b81f576d6911a5a93346ba321f47ce79f17438 Homepage: https://cran.r-project.org/package=align Description: CRAN Package 'align' (A Modified DTW Algorithm for Stratigraphic Time Series Alignment) A dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published 'MATLAB' code by Hay et al. (2019) . The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review). Package: r-cran-alignlv Architecture: all Version: 0.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-mirt, r-cran-lavaan, r-cran-magrittr, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alignlv_0.1.0.0-1.ca2404.1_all.deb Size: 91606 MD5sum: 41343bdbd5220c04706a2782e327cb3b SHA1: 6f81aa631ac7f7a35a7cae9509889b4cc5803c86 SHA256: 49acca06c3d4f095bcbe648cd3fdec83aa97959ee7eeb88e1355e40679dded1e SHA512: ab6ee2060af0c6bf91cf1c24e9a5f6ebe9d97bf95fd7e53145586d2580fb7627a1369f03ad89dfb2661d75800467e84ccef7b40e54841b949fe76a7dc7eeabf1 Homepage: https://cran.r-project.org/package=AlignLV Description: CRAN Package 'AlignLV' (Multiple Group Item Response Theory Alignment Helpers for'lavaan' and 'mirt') Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments. Package: r-cran-alkahest Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-matrix, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-alkahest_1.3.0-1.ca2404.1_all.deb Size: 1067052 MD5sum: 1c4189a6c565fcf83127dafa7ca2069d SHA1: c7875e1134a9309421ae2326b2f297a044f46627 SHA256: f6e7427b2d206b6c580fceb79136bed72435b35e3e9e9aa5642fc44bbf5085c9 SHA512: 7ed658fec657fba209e4676ddd00b45bb60abd5c7f5ac50c75494c42198ba83e3d66b676fce361a5ca9155dbbd1137fae794645b0b711f0ee0f4d7e13a09fea1 Homepage: https://cran.r-project.org/package=alkahest Description: CRAN Package 'alkahest' (Pre-Processing XY Data from Experimental Methods) A lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) , Rolling Ball algorithm after Kneen and Annegarn (1996) , SNIP algorithm after Ryan et al. (1988) , 4S Peak Filling after Liland (2015) and more. Package: r-cran-allcontributors Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clipr, r-cran-curl, r-cran-gert, r-cran-gh, r-cran-gitcreds, r-cran-magrittr Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-allcontributors_0.2.3-1.ca2404.1_all.deb Size: 482140 MD5sum: 00580bbf8baf91c6ef98555358e961ed SHA1: f25a41bf7cf7db8677ecbea5140b9c5cbac51ad4 SHA256: a6e7dd9efe131b0e61d202f7405a6d62aeaed6e28f9a477309a9e06baf26d801 SHA512: e8578c8f15021596c217b2aa2fe995d0642e3237a0121211cd11883bdbd1cce21cb0c4fec6cfc8c9b9e9eb2d3c1e6137674d0620de662400018047e32b0efdd7 Homepage: https://cran.r-project.org/package=allcontributors Description: CRAN Package 'allcontributors' (Acknowledge all Contributors to a Project) Acknowledge all contributors to a project via a single function call. The function appends to a 'README' or other specified file(s) a table with names of all individuals who contributed via code or repository issues. The package also includes several additional functions to extract and quantify contributions to any repository. Package: r-cran-allehap Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-allehap_0.9.9-1.ca2404.1_all.deb Size: 580246 MD5sum: 6db25b72a2ac85e29558831c0a208ee6 SHA1: 656da90f908cec3108a0e6350e09955aef7a4ad4 SHA256: 96e816176793d68afeed7d4de66dc054e24e931aee2127a5c164d6ac559b60d6 SHA512: d9f008dbe21b4083f591a1454d00656c77e5560e61bf4f55d24f6fc0c76c4c1e3f491295a9bb69d0097c8faeacecc27a6759dd867c5f0ab20ec0f642f31ad817 Homepage: https://cran.r-project.org/package=alleHap Description: CRAN Package 'alleHap' (Allele Imputation and Haplotype Reconstruction from PedigreeDatabases) Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated. Package: r-cran-allelematch Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7874 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dynamictreecut Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allelematch_3.0.0-1.ca2404.1_all.deb Size: 5111114 MD5sum: 15f6c7e22b9e3564da574016edabed5f SHA1: 20b4cd1d71f944121049485276b3b544285a9b26 SHA256: d4706dc0ca24f7592a4549ec37d8834918bae8bbac2819e018344ae490f4a160 SHA512: a6b093b8d6e29141228fe21264410f64adac14493ca882d1c9daca4136f4afb4a5549fcfc7e8a14931fb279f47c39b0b78471832aa831808d890fef6d1ddf4ac Homepage: https://cran.r-project.org/package=allelematch Description: CRAN Package 'allelematch' (Identifying Unique Multilocus Genotypes where Genotyping Errorand Missing Data may be Present) Tools for the identification of unique multilocus genotypes when both genotyping error and missing data may be present. Includes a data pre-screening utility to analyze pairwise locus overlap and protect against underlying mathematical sorting constraints. Targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, particularly in non-invasive wildlife sampling applications). Functions explicitly incorporate missing data and can tolerate allele mismatches created by genotyping error. If you use this package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). The complete user manual and analytical tutorials are included locally as an R vignette and can be accessed within an active R session using vignette('allelematch'). Package: r-cran-alleleretain Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pedigree Filename: pool/dists/noble/main/r-cran-alleleretain_2.0.2-1.ca2404.1_all.deb Size: 350192 MD5sum: e3e5804ded9f7eaa1221a6374e96933d SHA1: f158af77fcb7e78be6fc1e23d61126fab79d2662 SHA256: 34e3504ba27ffb0eaa45404bd87bdd01fead8628e1cccc8722ce871076f2ea60 SHA512: dc45c467227d494e5130a0e3dc30bf8766f8a1b4b7c2ecb836d90bcd76a8e1cfdc1e1c466fa4fb225251c853a861238543bb385da7795ca5b8b74b62aeff68bf Homepage: https://cran.r-project.org/package=AlleleRetain Description: CRAN Package 'AlleleRetain' (Allele Retention, Inbreeding, and Demography) Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations. Package: r-cran-alleleshift Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-biodiversityr, r-cran-adegenet Suggests: r-cran-poppr, r-cran-mgcv, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-ggforce, r-cran-ggrepel, r-cran-ggsci, r-cran-gggibbous, r-cran-gganimate, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-alleleshift_1.1-3-1.ca2404.1_all.deb Size: 573126 MD5sum: b8b2c717cc3cc56fb625b97f10dfad10 SHA1: 2c46d7b383bbf2f2c07bf9dc576b39f1602c62f6 SHA256: fdafba92e548f5c42df22ca107ccf9cc0d674b2db6c0c029969a2fe894fdeed8 SHA512: 700c8becae3527731626830b0271a7dba266656ebe2d2cb3eb1215fcb27d44db5bf1076322fd1fdd7085237df3ae757278522c1b4f4d237e46dee0c29068937f Homepage: https://cran.r-project.org/package=AlleleShift Description: CRAN Package 'AlleleShift' (Predict and Visualize Population-Level Changes in AlleleFrequencies in Response to Climate Change) Methods () are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis. Package: r-cran-allelobin Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1552 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-readxl, r-cran-openxlsx Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-allelobin_1.0.0-1.ca2404.1_all.deb Size: 201494 MD5sum: 65555005065c929de43b9f8d3c192973 SHA1: 046465fce5ed50aa80c969685a74ff7b39af5f43 SHA256: 07d219ab2bf3038d68fb6cb318ec9a8d3f13715484c0b416b95bea5edd72793c SHA512: b7304fad158c8fd94a730db56d2719125f37c11546f4f5bcbf56bd0218ab1d7f46ea5b9dc93cdb2af6144b9d9c746c4cfea13df545b6ae4151e6193c39e61b38 Homepage: https://cran.r-project.org/package=AlleloBin Description: CRAN Package 'AlleloBin' (A Shiny Application for Allele Binning in Microsatellite Markers) Provides allele binning functionality for SSR/microsatellite markers using least-squares minimization (Idury & Cardon (1997) ). Includes a 'Shiny' application for interactive use, summary statistics, and visualization. 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It can be used for assessing treatment effects in clinical trials or risk factors in bio-medical and epidemiological research. Like Stata command 'confall' (Wang Z (2007) ), 'allestimates' calculates and stores all effect estimates, and plots them against p values or Akaike information criterion (AIC) values. It currently has functions for linear regression: all_lm(), logistic and Poisson regression: all_glm(), and Cox proportional hazards regression: all_cox(). Package: r-cran-allmetrics Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-allmetrics_0.2.1-1.ca2404.1_all.deb Size: 12868 MD5sum: b284699626eac9995ba60537c964fff7 SHA1: 78146cd05593347a15fbdf9a977311d7525fb0a2 SHA256: fd5623bfa487d174cb84c3a7f75826a548cf7fa151ecae17f12c2830d3b7fb0b SHA512: 27f2094709816162f1b895819e3ba552b010a6125389547492088c40925e69e4783f0d5381f806f824a528aa657819062dff7996c7955dcf96b2774f007efd00 Homepage: https://cran.r-project.org/package=AllMetrics Description: CRAN Package 'AllMetrics' (Calculating Multiple Performance Metrics of a Prediction Model) Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) and Garai et al. (2024) . 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Package: r-cran-allocation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr Suggests: r-cran-quarto Filename: pool/dists/noble/main/r-cran-allocation_0.1.0-1.ca2404.1_all.deb Size: 107228 MD5sum: f864f9b6f67f7e11db540706782bc3b6 SHA1: 8c2fc696b9115795e3aaa5bf945b389ff43c9559 SHA256: a4015d4155f279366c715f39fd812c2ba444d72b20200bee118a2fec8a129b52 SHA512: ada0f232d7a78defd763055f3972577b22501be149447b75265c52e36f60247da9acd5cadd014f9969c7eddcba572221942436073cad9b396e95c48afce020af Homepage: https://cran.r-project.org/package=allocation Description: CRAN Package 'allocation' (Exact Optimal Allocation Algorithms for Stratified Sampling) Implements several exact methods for allocating optimal sample sizes when designing stratified samples. These methods are discussed in Wright (2012) and Wright (2017) . Package: r-cran-allofus Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1613 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-glue, r-cran-bigrquery, r-cran-purrr, r-cran-dbplyr, r-cran-sessioninfo, r-cran-rlang, r-cran-stringr, r-cran-dbi, r-cran-lifecycle, r-cran-bit64, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-dt, r-cran-googlesheets4, r-cran-tibble, r-cran-forcats, r-cran-gh, r-cran-sqlrender, r-cran-duckdb, r-cran-withr Filename: pool/dists/noble/main/r-cran-allofus_1.3.0-1.ca2404.1_all.deb Size: 1036288 MD5sum: d6ed66e85d382dab19c36a6dc127a4e0 SHA1: 69b5464e7e89faf89cf3ad2af4792fb3ddbb15df SHA256: 9e7bbdd0e1cffadd0cba6e16ed91a63b4f882b0f28b27d1e8873f95d5dbf328f SHA512: 8d7a281cac34a8621a532a322737c240debd16a51a0dde372e12d3b3db3d3abcb883e72eba89786acb5dfa874d92b42db642b12845389c612cfc195d56bf4031 Homepage: https://cran.r-project.org/package=allofus Description: CRAN Package 'allofus' (Interface for 'All of Us' Researcher Workbench) Streamline use of the 'All of Us' Researcher Workbench ()with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'. Package: r-cran-allometric Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-units, r-cran-refmanager, r-cran-magrittr, r-cran-purrr, r-cran-isocodes, r-cran-tidyr, r-cran-progress, r-cran-vctrs, r-cran-openssl, r-cran-curl, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allometric_2.3.0-1.ca2404.1_all.deb Size: 498448 MD5sum: 870ba4a420ee3da204c47186a984fd65 SHA1: 3ea4895618d3cd316ca4653d86965d54d0578400 SHA256: 844bb8eb4d671f44e68fc9affbfdaf2c72110e91dc7948e1f2a45c3b7c71c403 SHA512: f0e381082da69760cad0f158b4b8f64414b1283648bf0b50484d5452379905117d2e5d9569660b64a5ad81fc990e15264ede7a4359977f61fc69a6edbbc478da Homepage: https://cran.r-project.org/package=allometric Description: CRAN Package 'allometric' (Structured Allometric Models for Trees) Access allometric models used in forest resource analysis, such as volume equations, taper equations, biomass models, among many others. Users are able to efficiently find and select allometric models suitable for their project area and use them in analysis. Additionally, 'allometric' provides a structured framework for adding new models to an open-source models repository. Package: r-cran-allometry Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-allometry_0.2.0-1.ca2404.1_all.deb Size: 36010 MD5sum: 884db346be3fdb2753ad76d29fd49e31 SHA1: 3f207597508644efa7deeea062d5d3f0451fc798 SHA256: 8a5af713e96084fc9f836492db384fd171e29b9743f93403d1f233eb66343169 SHA512: 4e3409a4285a1d9e1c0164b2222ec40bd122b0c031470e43a036664a6341cd1e90b91a3808b0af8d00e938c258f0f642c019ab4f864db6443aab32121de1de4a Homepage: https://cran.r-project.org/package=allometry Description: CRAN Package 'allometry' (Examples of Datasets on Allometry) Examples of datasets on allometry, the study of the relationship of biological traits to body size. This package contains the datasets of morphological measurement taken from 113 maritime earwigs (Anisolabis maritima) by Matsuzawa and Konuma (2025) , and taken from 507 Helm’s stag beetles (Geodorcus helmsi) collected by Grey et al. (2025) . Package: r-cran-allomr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-allomr_0.3.0-1.ca2404.1_all.deb Size: 15460 MD5sum: 8fc0205e201a3a466ed02a2ec4788033 SHA1: 0a382d2cea1545d39bf7c1777d3f1f3ef699aed3 SHA256: 45bfb6c35501d6a918dff6169ae7014df4f1ac7f896f66b6ffdd2610c371079e SHA512: e1ea6db7af6b93bbe96be6881168325e39ce85684999e6db96c4a645df61166f87f5168780c2544124eea771448abad0bd73841c8fa9e88282ea9ba06edd6788 Homepage: https://cran.r-project.org/package=allomr Description: CRAN Package 'allomr' (Removing Allometric Effects of Body Size in MorphologicalAnalysis) Implementation of the technique of Lleonart et al. (2000) to scale body measurements that exhibit an allometric growth. This procedure is a theoretical generalization of the technique used by Thorpe (1975) and Thorpe (1976) . Package: r-cran-allspice Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allspice_1.0.7-1.ca2404.1_all.deb Size: 1526424 MD5sum: e861c0ca6cadedeae92c6fb21598b2a0 SHA1: 27df6e240e76509aee6fe5e7cc9c868952847f06 SHA256: 910a3ea0d8fdcd508a5f362c766b05a23b5e23ef37fcec351f6915db95f08a2e SHA512: bce92fdfb7a7462b12b893098a85c5bee328be509a939b9f206a374735eb597007e2aef4d5424857a1cea6dd165d63b3f8abf9ed2d2f2f08992f53cf55c18155 Homepage: https://cran.r-project.org/package=Allspice Description: CRAN Package 'Allspice' (RNA-Seq Profile Classifier) We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, . The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified. Package: r-cran-allspicer Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-allspicer_0.1.9-1.ca2404.1_all.deb Size: 43578 MD5sum: 3309225c758d64778c6baf4464323829 SHA1: 6d812391d3c98d054ac57a7029b23804e9cea36b SHA256: 0c301c650768b25927c7ea597af7f9c68ccebf77312819ec7280041013cdd55a SHA512: 2c0f407f9c2735ab2a83a35cfacb3fc4d87d25f391e88dbcc9c168140d03c99c9c20ef166b56e3087741cfc66eb69faa1f1a058a5eed1609b48df8d4d26aaa83 Homepage: https://cran.r-project.org/package=ALLSPICER Description: CRAN Package 'ALLSPICER' (ALLelic Spectrum of Pleiotropy Informed Correlated Effects) Provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) . Package: r-cran-alluvial Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-testthat, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-alluvial_0.1-2-1.ca2404.1_all.deb Size: 537604 MD5sum: 3617c8c1af4f362aa597bee054835856 SHA1: 70c506746b9963126706a84aec40bc433b9b425e SHA256: 9df34dc3c67e35b168715e49140ee0abd4662d164079152ce30a3c2489a750e8 SHA512: 31884d49e597a1fe05635c75c4cee1779303ef2f40651d0d43db332f8b2306278b2f0504874fdfb7b1f83a9444ce9cd9e6704eb7b9412c90f04e64caa2c55cce Homepage: https://cran.r-project.org/package=alluvial Description: CRAN Package 'alluvial' (Alluvial Diagrams) Creating alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data. Package: r-cran-alone Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/noble/main/r-cran-alone_0.7-1.ca2404.1_all.deb Size: 55386 MD5sum: 9d8f73b77d458011067403b3515adc58 SHA1: 30b737d4f2328f934d93d8d7f2b9220b47a0c867 SHA256: 6047cb24ea325f1112e1956842e0a31f96946643a34779f51a44486c1f89f32d SHA512: d36602aac7c276c3452662a8a1318f035edc93936c8c8e1ecc1860f89366423698560e3746ae633cd1910463b3a622cca08e67216aac745912bac0381aef6166 Homepage: https://cran.r-project.org/package=alone Description: CRAN Package 'alone' (Datasets from the Survival TV Series Alone) A collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information. 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Package: r-cran-alpha.correction.bh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alpha.correction.bh_0.1.0-1.ca2404.1_all.deb Size: 18990 MD5sum: 0dde64ed0d499a3e4da07b71c4185857 SHA1: f90166e679ec57ba9c3dfdf2ab9261a35abd3944 SHA256: 05e36730246fdc8a3e45dbd44516d6478b15d05ff6b7fab6f774375b461d28bc SHA512: 4f2b004ee2a8211d12af770b20805b4d2207c502e8c5dea60a8c16aee8ce4b2bb36032703d09af559dbaee956d3dd9cf928860ad56bed9fdfd44ba1b026e0cb0 Homepage: https://cran.r-project.org/package=alpha.correction.bh Description: CRAN Package 'alpha.correction.bh' (Benjamini-Hochberg Alpha Correction) Provides the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300". For researchers interested in using the exact mathematical formulas and procedures as used in the original paper. Package: r-cran-alphaci Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future.apply, r-cran-matrixcalc Suggests: r-cran-covr, r-cran-extradistr, r-cran-knitr, r-cran-lavaan, r-cran-psychtools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alphaci_1.0.1-1.ca2404.1_all.deb Size: 150354 MD5sum: a4e530205d28654589b798b226460d23 SHA1: 1cc343ba2705d96b868069190d0fd8d2297d0600 SHA256: cdbeb536580fd2d9db72d932670ad202e7e290e5bf3f027a1a9893265bde36b0 SHA512: e41f672ad48813830646ee9d5d6cad3528336019b451c5d6f0d3380dd703e079643b4e2e2f54a5dd684a9bddf952824af4880f06c2a90d36dc4446ba1353da2c Homepage: https://cran.r-project.org/package=alphaci Description: CRAN Package 'alphaci' (Confidence Intervals for Coefficient Alpha and StandardizedAlpha) Calculate confidence intervals for alpha and standardized alpha using asymptotic theory or the studentized bootstrap, with or without transformations. Supports the asymptotic distribution-free method of Maydeu-Olivares, et al. (2007) , the pseudo-elliptical method of Yuan & Bentler (2002) , and the normal method of van Zyl et al. (1999) , for both coefficient alpha and standardized alpha. Package: r-cran-alphahull Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-interp, r-cran-r.utils, r-cran-sgeostat, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-splancs Filename: pool/dists/noble/main/r-cran-alphahull_2.5-1.ca2404.1_all.deb Size: 602460 MD5sum: a1d262adc5b5f7c68fbe1a79648384ee SHA1: e550698a36bc58179b849b645ebe3d1c9d834546 SHA256: ca6228f350de5f110b4d1d628b8997d83a008ba89e897275febb6180e0345e45 SHA512: 5d2158a5bbfe2eb2c5e80bd76418d71802f8647945ecd7fa463ab667f24b9696a7d917b54d0f6bf0012a95922106cd3e497274a3ff9a90d6073830c3c778fd17 Homepage: https://cran.r-project.org/package=alphahull Description: CRAN Package 'alphahull' (Generalization of the Convex Hull of a Sample of Points in thePlane) Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object. Package: r-cran-alphan Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bayesfactor, r-cran-justifyalpha, r-cran-knitr, r-cran-pwrss, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alphan_0.3.0-1.ca2404.1_all.deb Size: 181042 MD5sum: 356f743790e8b4f28f432b37d0a7c981 SHA1: e0e4c6b8edadbf2fa672ef456abca596311af0ec SHA256: f816d32d198ac48a28a25fc0c99023f7f4dd2ea98e5691858cd6432b76452a0c SHA512: 47706339b5e58d8feb221d04938beeefe8da3775b5df5048d7241a2e43d5c633625ddb9400cef16172124108ad5afb23cf49b2b5ab71bfe2f590d430bb0f3915 Homepage: https://cran.r-project.org/package=alphaN Description: CRAN Package 'alphaN' (Set Alpha Based on Sample Size Using Bayes Factors) Sets the alpha level for coefficients in a regression model as a decreasing function of the sample size through the use of Jeffreys' Approximate Bayes factor. You tell alphaN() your sample size, and it tells you to which value you must lower alpha to avoid Lindley's Paradox. For details, see Wulff and Taylor (2024) . Alpha can also be calibrated to the effect-size and moment Bayes factors of Klauer, Meyer-Grant, and Kellen (2025) , which center the alternative hypothesis on an effect size of your choosing. Package: r-cran-alphaoutlier Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-nleqslv, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-alphaoutlier_1.2.2-1.ca2404.1_all.deb Size: 112682 MD5sum: 309c7a149d47cdc15dcf1663719f2b45 SHA1: 42d990e8f3da61720bfa04dfb8341493c5500ac5 SHA256: 96af3eaf60f8f3363e1006a2677ae16f670953872b9d32f785c7b79b4e38080d SHA512: d9c56ddeec51858225df06c794eaa99b8fe53821a7549bed723c3274e8121a5551e6f3d7baab24fee768477698f24e2e353ab215b24c7e39952376ae7f21fd50 Homepage: https://cran.r-project.org/package=alphaOutlier Description: CRAN Package 'alphaOutlier' (Obtain Alpha-Outlier Regions for Well-Known ProbabilityDistributions) Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, for details. Package: r-cran-alphapowerhazard Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-maxlik, r-cran-numderiv, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-alphapowerhazard_0.1.0-1.ca2404.1_all.deb Size: 199026 MD5sum: d8f1aecdc7337fdb73c9f92aae35e675 SHA1: f93073914ad51b5061f1d5619145222e2401b8b6 SHA256: 73f01a6acb78c943a46623ddeb8c792100d4e8d6e5b0c10ea8ef63ea37b53f33 SHA512: 7511bd53f056a63412340282ffd76951acffcec179cd7661fec01d96eb9d7860df9132251753ed2aaeabb4b06c83d40735762349e7174ece2c1c6aa34c421687 Homepage: https://cran.r-project.org/package=AlphaPowerHazard Description: CRAN Package 'AlphaPowerHazard' (Alpha-Power Hazard Regression Models for Survival Data) Implements the alpha-power hazard model and regression frameworks for survival data based on the flexible hazard rate function h(x; alpha, beta) = alpha^x + x^(beta-1) (Pal et al., 2026 ). Provides standard distribution functions (d, p, q, r, h, H, s) and distributional properties including raw/central moments, variance, skewness, kurtosis, quantile statistics (Bowley's skewness, Moors's kurtosis), Lambert W hazard rate function minimum (Corless et al., 1996), order statistics, and stochastic ordering (Shaked & Shanthikumar, 1994). Computes five classical estimation methods for baseline parameters: Maximum Likelihood Estimation (Casella & Berger, 2002), Least Squares Estimation (Swain et al., 1988), Weighted Least Squares Estimation (Styan, 1973), Maximum Product of Spacings Estimation (Cheng & Amin, 1983 ), and Cramer-von Mises Estimation (Macdonald, 1971). Supports four hazard regression models (M1-M4) within proportional hazards and parametric frameworks across uncensored data, right censoring, left censoring, interval censoring, and progressive Type-I and Type-II censoring schemes (Lee & Wang, 2003; Lawless, 2011; Balakrishnan & Aggarwala, 2000). Includes comprehensive model diagnostics, Cox-Snell, martingale, deviance, standardized, and studentized residuals, leverage, Cook's distance, DFFITS, DFBETAS, model comparisons (AIC, BIC, WAIC), k-fold cross-validation, prediction suites, random data generators, and an eight-panel diagnostic visualization suite. Package: r-cran-alphasdm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1968 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-reticulate, r-cran-sf Suggests: r-cran-stars, r-cran-withr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-alphasdm_0.2.0-1.ca2404.1_all.deb Size: 1516034 MD5sum: f728c03cba6179d6c15235b5c744d454 SHA1: 71bcb019e6b4e2b3d43ad8c882798d87ea11a4fa SHA256: 26dcd5d214c092e0c29a2e17cba2ceb750856b6cc2dd35717cc0cd08e3a843ab SHA512: 220da487414f8cc103f151ebe5246aace5616d81884ad20e315092aa87f1eab2c67a7894b6a9cdc5afdb0ccefbb5bf6b7fe62b91a65c1fd2779b11608499f98c Homepage: https://cran.r-project.org/package=AlphaSDM Description: CRAN Package 'AlphaSDM' (Species Distribution Models on 'AlphaEarth' Satellite Embeddings) Fits species distribution models and maps habitat suitability at up to 10 m resolution from occurrence records alone, using the 'AlphaEarth' Foundations satellite embeddings (Brown et al. 2025) . The embeddings, 64 values per pixel per year from a geospatial foundation model, replace environmental layers, so none need to be sourced or aligned. Provides tools to format occurrence records, place pseudo-absences, train and evaluate an ensemble of machine learning models, and export habitat-suitability rasters. Sampling, model training and prediction all run on 'Google Earth Engine', which requires a free account for noncommercial use. Package: r-cran-alphastable Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-nnls, r-cran-stabledist, r-cran-nlme Suggests: r-cran-matrix, r-cran-fbasics, r-cran-fmstable, r-cran-runit, r-cran-rmpfr, r-cran-sfsmisc Filename: pool/dists/noble/main/r-cran-alphastable_0.2.1-1.ca2404.1_all.deb Size: 126320 MD5sum: 7ea78012271ae1db48f705966fec0c8c SHA1: 1770a5836d99a630f3a8ceb72fc349f871449ba8 SHA256: b2fe505e0c4f212ec42209fce7cda861234a785780ec7ab2ef57d90dacfeb6da SHA512: 3b353d495377632640444a29b0e3a605c335f806147de75ccc9297e549b0356a7e7ed83f1bb24928879d93e8ddc409d4c0a42b3803264243c2ba106cd5a7bc20 Homepage: https://cran.r-project.org/package=alphastable Description: CRAN Package 'alphastable' (Inference for Stable Distribution) Developed to perform the tasks given by the following. 1-computing the probability density function and distribution function of a univariate stable distribution; 2- generating from univariate stable, truncated stable, multivariate elliptically contoured stable, and bivariate strictly stable distributions; 3- estimating the parameters of univariate symmetric stable, skew stable, Cauchy, multivariate elliptically contoured stable, and multivariate strictly stable distributions; 4- estimating the parameters of the mixture of symmetric stable and mixture of Cauchy distributions. Package: r-cran-alphavantagepf Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3447 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-timedate, r-cran-lubridate, r-cran-fst, r-cran-shiny, r-cran-shinyjs, r-cran-shinyfeedback, r-cran-dygraphs, r-cran-gt, r-cran-gtextras, r-cran-clipr, r-cran-rlang, r-cran-ttr, r-cran-patchwork, r-cran-progressr, r-cran-usethis, r-cran-ggplot2, r-cran-financegraphs Suggests: r-cran-bslib, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-alphavantagepf_0.9.0-1.ca2404.1_all.deb Size: 2067396 MD5sum: 94b1ed5a4356ffd09a0bc1f9c3f9d679 SHA1: 2ee1764c058835767703ee046e2ccd15e6dec54d SHA256: 7c5fbabbadb23188650c68a771136d1c45c7faf8ce78152fbfbdbb0b9e5b5139 SHA512: fdc465cc36bbbb8e6f9ed386a73b3323521221281fca9597bc6ab0a1aa1793f4df92b97d98d8b7f1fb932f2f86588dc722e5812241580b8860224fdd9c5bd782 Homepage: https://cran.r-project.org/package=alphavantagepf Description: CRAN Package 'alphavantagepf' ('Alphavantage Financial Data' API R Wrapper and Shiny Interface) Download, manage, and visualize via Shiny App 'Alphavantage financial data' . Data is downloaded into `data.table`s using one parameterized function. Results can be piped to optional helper functions to extract and simplify more complex data. A Shiny interface is also provided to download, manage, analyze and visualize market data. Package: r-cran-alphavantager Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-timetk Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-alphavantager_0.1.3-1.ca2404.1_all.deb Size: 22582 MD5sum: 5bebbd30f00bff03e96c9f457ae565f4 SHA1: a0f92edec28dbf47a6ed9a9bea3ae934817e7934 SHA256: 0f6e1d728933860e234a585cfda16294b1a801ada49e229d601c439a47960792 SHA512: dc74b09dcc8d57ad02009607cb3cbef4e35c794ea83d00d3fedc7f15ed037c5014b5540dca93661360d8b5ad4900ebbdd6af5909c0d093b17109a357aee8c9f0 Homepage: https://cran.r-project.org/package=alphavantager Description: CRAN Package 'alphavantager' (Lightweight Interface to the Alpha Vantage API) Alpha Vantage has free historical financial information. All you need to do is get a free API key at . Then you can use the R interface to retrieve free equity information. Refer to the Alpha Vantage website for more information. Package: r-cran-alpmixbayes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-alpmixbayes_0.1.0-1.ca2404.1_all.deb Size: 789634 MD5sum: 91a4691487bee5ef38a6c156a6ede0ae SHA1: 4a0092f4d478ae5585a35b8d72a682124676f075 SHA256: fb46a2fb466feb2e0faa826cf1640c3c3a6c93cb05e34fca04c9791c8499c175 SHA512: e8703a5d69198c19e0b30bb5639df918061f1f5c0930117d44ddd2560e7a9c91271326694375fcf5346fcbd72c3ebbcef72dce47fcbdeb0f23fa38c3b5b54353 Homepage: https://cran.r-project.org/package=alpmixBayes Description: CRAN Package 'alpmixBayes' (Bayesian Estimation for Alpha-Mixture Survival Models) Implements Bayesian estimation and inference for alpha-mixture survival models, including Weibull and Exponential based components, with tools for simulation and posterior summaries. The methods target applications in reliability and biomedical survival analysis. The package implements Bayesian estimation for the alpha-mixture methodology introduced in Asadi et al. (2019) . Package: r-cran-alr4 Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-effects Filename: pool/dists/noble/main/r-cran-alr4_1.0.7-1.ca2404.1_all.deb Size: 638072 MD5sum: c2f932ae28e56f582bf5022dbb687723 SHA1: eec20c24a5e6582c70a8c6b56c6bf4906e1cd1c8 SHA256: ab6b9804df393fd54c837ca9c5c835cdc4fca4fd65f914971f7fdfc6b8a92f11 SHA512: 301d845446efcfc15a3d3f6d20b671819af3791a9d8f46e82984e2a447696116cecd5327990e6828dee3db662b8dc432722ea8acc26d42df8f569d4a3ccb39d4 Homepage: https://cran.r-project.org/package=alr4 Description: CRAN Package 'alr4' (Data to Accompany Applied Linear Regression 4th Edition) Datasets to Accompany S. Weisberg (2014), "Applied Linear Regression," 4th edition. Many data files in this package are included in the alr3 package as well, so only one of them should be used. Package: r-cran-als Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnls, r-cran-iso Filename: pool/dists/noble/main/r-cran-als_0.0.7-1.ca2404.1_all.deb Size: 499844 MD5sum: 7553660de8745b66fd4e50b38e0dcc73 SHA1: eab9b00cffc0e095f9ba3feacedf51d771fc1303 SHA256: d2b26d9e7153e3b8320b98d4f8f15e93c883b8222c85d0d17b9e6078c640a7bf SHA512: 5043aeb0cbbfbbc1baa05c8b24b6acc7d0cedaff0908948da4cd8059d4c0bf862a8ad1a7718509fbbaac2acba3dcbcfaca0b93a69bd9edf4c6ffd9364da68c62 Homepage: https://cran.r-project.org/package=ALS Description: CRAN Package 'ALS' (Multivariate Curve Resolution Alternating Least Squares(MCR-ALS)) Alternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares. Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets. Package: r-cran-alsbinary Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-readr, r-cran-readxl, r-cran-dt Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-alsbinary_1.0.0-1.ca2404.1_all.deb Size: 236578 MD5sum: 6937cb9b979c5bb0532e2cc1abd3761d SHA1: 8dbeac349dd031cb1366705d0167eb337dbbe005 SHA256: 9a3d9151ee9bf542d219b8809f632a88b72156dbfc2f7c151fc7d373184504b1 SHA512: 440198e01f6723d11b74d81c23d29961cb45d246c5081297a621452603adfa678b80455c662a6c56257e89ff2adb571d20db792802e7da4bb544739faae7a5ad Homepage: https://cran.r-project.org/package=ALSBinary Description: CRAN Package 'ALSBinary' ('ALS-Binary': Allele Size to Binary Converter) Converts microsatellite allele sizes into binary matrices and provides a 'Shiny' interface. Package: r-cran-alscpc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-alscpc_1.0-1.ca2404.1_all.deb Size: 24404 MD5sum: 7b452702471d9462b6e99dea2e233266 SHA1: 94f4867365206143bdda627fb9675864e03296be SHA256: 5d60726b0efdebebbe0264a476770850608e7ded6687a9287e5dde0cede051e2 SHA512: 174f75b64afb7cd601ad864c5b9dfd2d55b54ef33041ea7df8afaff6657101ed10783156e0c087d74f1fa0557ddf803da186a1a803a476f05e099e61dd38a94a Homepage: https://cran.r-project.org/package=ALSCPC Description: CRAN Package 'ALSCPC' (Accelerated line search algorithm for simultaneous orthogonaltransformation of several positive definite symmetric matricesto nearly diagonal form) Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices. Package: r-cran-alsi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-homals Suggests: r-cran-paran, r-cran-readxl, r-cran-openxlsx, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-alsi_0.2.0-1.ca2404.1_all.deb Size: 156692 MD5sum: f461934700c984be194130edb423d977 SHA1: 0f0a6a3712593f40efbd06df2513dd912e44a34e SHA256: bfd03854f124569223458b8820c7e2ef519199e89c1c25b84531df63ac014899 SHA512: aa82d44490afa192b6557843ed6aa45f9b49a4be5c5cf474dc09c973e78809522efda4699e9a80d38d02c925c2360855c8fc7b76077a184ac76e2153ac857d1e Homepage: https://cran.r-project.org/package=alsi Description: CRAN Package 'alsi' (Aggregated Latent Space Index for Binary, Ordinal, andContinuous Data) Provides three stability-validated pipelines for computing an Aggregated Latent Space Index (ALSI): a binary MCA pipeline (alsi_workflow()), an ordinal pipeline using homals alternating least squares optimal scaling (alsi_workflow_ordinal()), and a continuous ipsatized SVD pipeline (calsi_workflow()). All three pipelines share a common bootstrap dual-criterion stability framework (principal angles and Tucker congruence phi) for determining the number of dimensions to retain before index construction. The package is designed to complement Segmented Profile Analysis (SEPA) and is intended for psychometric scale construction and dimensional reduction in survey and clinical research. Package: r-cran-altadata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-altadata_0.1.1-1.ca2404.1_all.deb Size: 39842 MD5sum: 2ae9db6c1680ac3842c9d5239cee23cd SHA1: 0defa583b78bcc767c945c2b9386156db2db239f SHA256: c7408b6570b5c3dece274294eaf320179e27c01a6fc8b33881612859211ae42e SHA512: 950fcb4450d899860937d936710e66ea61eb279357e1be2a1a7b4de959b1b20449ea31a82ba338689ddad4a98155157ffd1951223916a6e4668ef369c11192ae Homepage: https://cran.r-project.org/package=altadata Description: CRAN Package 'altadata' (API Wrapper for Altadata.io) Functions for interacting directly with the 'ALTADATA' API. With this R package, developers can build applications around the 'ALTADATA' API without having to deal with accessing and managing requests and responses. 'ALTADATA' is a curated data marketplace for more information go to . Package: r-cran-altair Architecture: all Version: 4.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-htmlwidgets, r-cran-assertthat, r-cran-magrittr, r-cran-vegawidget, r-cran-repr Suggests: r-cran-httr, r-cran-rprojroot, r-cran-purrr, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-listviewer, r-cran-testthat, r-cran-pryr, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-pkgdown, r-cran-v8, r-cran-rsvg, r-cran-png, r-cran-fs Filename: pool/dists/noble/main/r-cran-altair_4.2.3-1.ca2404.1_all.deb Size: 114734 MD5sum: 956058f59b1225ab8610eefba432e8e7 SHA1: 8d87a27e17732e2aad5e3130eb3f847ed02a24bd SHA256: 85cbe6312cbe2cbafc174948d946db1d26118e91f80927a16da6d776d116c8ee SHA512: 005472552854549b5711a3a9ed89a1849f6e195cf63b6f317ed29f7936d23e1003620d05e15dfbbf485ac72c3e6c10ca9f2f73a428b53dec8470107c14f83fbd Homepage: https://cran.r-project.org/package=altair Description: CRAN Package 'altair' (Interface to 'Altair') Interface to 'Altair' , which itself is a 'Python' interface to 'Vega-Lite' . This package uses the 'Reticulate' framework to manage the interface between R and 'Python'. Package: r-cran-altdoc Architecture: all Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-desc, r-cran-evaluate, r-cran-fs, r-cran-quarto, r-cran-rmarkdown Suggests: r-cran-covr, r-cran-digest, r-cran-downlit, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-servr, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-altdoc_0.7.3-1.ca2404.1_all.deb Size: 443058 MD5sum: 90484f35e7ea6630ad7891840f66d459 SHA1: 70d4d389fcd51f048c0f26ceabb0ec1a74920532 SHA256: 855f7594f5ca7e62e1681c6dcfcb1b3e66c650ebc527da19baa104d1d70ab786 SHA512: 81bb1c879d249cbcf1c2fcbe4d9f9b23a7f368bc6443a0ee15709792de462bedccb14912a2c5ae2e512d48c2f45d6fede87b579dfb935446f7800777a5a7ca27 Homepage: https://cran.r-project.org/package=altdoc Description: CRAN Package 'altdoc' (Package Documentation Websites with 'Quarto', 'Docsify','Docute', or 'MkDocs') This is a simple and powerful package to create, render, preview, and deploy documentation websites for 'R' packages. It is a lightweight and flexible alternative to 'pkgdown', with support for many documentation generators, including 'Quarto', 'Docute', 'Docsify', and 'MkDocs'. Package: r-cran-alteredpqr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-alteredpqr_0.1.0-1.ca2404.1_all.deb Size: 451180 MD5sum: 2f2825238009202ed9f44a7716a1996c SHA1: 1a4531828545781532ee019f563f393b923dd0e9 SHA256: fc617815875643387ec8f2b9cc5ade4d26562dd1e543296ac10d592645a6ac20 SHA512: cfba8259772c1049444675df817254788c3fbc1f14032165c4bea8cae5482c76784ae3351880c5d665022bad395514bede9eb12256227b90502e96191f03001b Homepage: https://cran.r-project.org/package=AlteredPQR Description: CRAN Package 'AlteredPQR' (Detection of Altered Protein Quantitative Relationships) Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition. Package: r-cran-altfuelr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-altfuelr_0.1.0-1.ca2404.1_all.deb Size: 2410708 MD5sum: c230663bab582d734c893d2b47197e1d SHA1: 9ea647a8ff6f5b91ec27a2ffaf08a4c4dcd20ff0 SHA256: 1394f5fe8a518a3aeee72cfe370541c62ad7fb3ea2156301554b7e0d74b7d213 SHA512: 6bdd35e8caed62f40a70b17fe639464975f3ef9c2248350c79dd09b262eddb6c3e42019b027f180be269798922c1e6f41e38b419a826c7f8f750edab7a8334a5 Homepage: https://cran.r-project.org/package=altfuelr Description: CRAN Package 'altfuelr' (Provides an Interface to the NREL Alternate Fuels Locator) Provides a number of functions to access the National Energy Research Laboratory Alternate Fuel Locator API . 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Package: r-cran-altmeta Architecture: all Version: 4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-lme4, r-cran-matrix, r-cran-metafor Filename: pool/dists/noble/main/r-cran-altmeta_4.4-1.ca2404.1_all.deb Size: 567600 MD5sum: c004cf41ac817d555ec913221ee0a33e SHA1: 67b894d1b6e85ec2a9c1180d242ec9bb82d25dd3 SHA256: ba60c54be090d186506d75d360201edd00d184855ac9018d1739ab0ba84fee6e SHA512: 4f26f372afb757f9aa2908f0230841c2f440eca55ff5eac2ada2bedbc11338e8fe4c24ecf7a6fc6f097e0bc9f535b1c1149a8271d914ba573c2793666d7ae5fb Homepage: https://cran.r-project.org/package=altmeta Description: CRAN Package 'altmeta' (Alternative Meta-Analysis Methods) Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 ) - tests and measures for between-study heterogeneity (Lin et al., 2017 ; Wang et al., 2022 ; Yu et al., 2025 ); - measures, tests, and visualization tools for publication bias, small-study effects, or related bias (Lin and Chu, 2018 ; Lin, 2019 ; Lin, 2020 ; Shi et al., 2020 ); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 ); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 ; Chu and Cole, 2006 ); - meta-analysis methods for synthesizing proportions (Lin and Chu, 2020 ); - models for multivariate meta-analysis, measures of inconsistency degrees of freedom in Bayesian network meta-analysis, and predictive P-score (Lin and Chu, 2018 ; Lin, 2020 ; Rosenberger et al., 2021 ). Package: r-cran-altopt Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-lattice Filename: pool/dists/noble/main/r-cran-altopt_0.1.2-1.ca2404.1_all.deb Size: 116124 MD5sum: 8c56892f24695434217978dd58c206e5 SHA1: 051b9e06d1106ee8cc9228c2cb49afa090d327e7 SHA256: aa8928abe7dcfd9b6d9169a99ed0149acf35343350526e3a0e9904a81309627e SHA512: 21a876b0f72e0664aece56091b0d14f87bd54741836ccb4170bc3c3c7b1ca79771bab5ea648d1a7cbaaf2446df6a02a329d17b0af7c5258a45cb09f3edcff4ba Homepage: https://cran.r-project.org/package=ALTopt Description: CRAN Package 'ALTopt' (Optimal Experimental Designs for Accelerated Life Testing) Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. For more details, see Seo and Pan (2015) . Package: r-cran-altr2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gsl, r-cran-purrr Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-altr2_1.1.0-1.ca2404.1_all.deb Size: 22958 MD5sum: 9addc3be48b06601e208437f86d7c28b SHA1: 0588314b221d025f76b7a808cf7a0d02d6a4277d SHA256: be5c41ab58b1e41610fbfce5775ffeae078da0f2a1e764911f4e143f38af05c8 SHA512: b287e61e84936376cdd3edd7701aff49904928f7f932611b19e3442c33bf44572841b7e123f877e79d1dc9a2c27faf002c26bcf634953e840c1963fd9319223a Homepage: https://cran.r-project.org/package=altR2 Description: CRAN Package 'altR2' (Alternative Estimators to Adjusted R-Squared) Provides alternatives to the normal adjusted R-squared estimator for the estimation of the multiple squared correlation in regression models, as fitted by the lm() function. The alternative estimators are described in Karch (2020) . Package: r-cran-amadeus Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1941 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-sftime, r-cran-terra, r-cran-data.table, r-cran-httr2, r-cran-exactextractr, r-cran-stringi, r-cran-stars, r-cran-tidyr, r-cran-rlang, r-cran-archive, r-cran-collapse, r-cran-rdpack Suggests: r-cran-covr, r-cran-devtools, r-cran-dorng, r-cran-fnn, r-cran-furrr, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mirai, r-cran-nhdplustools, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-targets, r-cran-testthat, r-cran-tigris, r-cran-withr Filename: pool/dists/noble/main/r-cran-amadeus_2.0.0-1.ca2404.1_all.deb Size: 1487544 MD5sum: 18afa45ecc72cea9f76ebfb9bb3b0267 SHA1: 3d70a07de25073e7d62a71646ff63ca8be9eab6c SHA256: b2fff86d85983025fcae05cbc61d08bfb88966605a0572085686e5990da99e94 SHA512: 555d8c926bd5ea4e8148c3aa6ac57c94e0c092b330fe1e665b9e9d129f601b00cbc2060d360087308336cde4cb77f8893f4e939813f4fbe690d68a980332d446 Homepage: https://cran.r-project.org/package=amadeus Description: CRAN Package 'amadeus' (Accessing and Analyzing Large-Scale Environmental Data) Functions are designed to facilitate access to and utility with large scale, publicly available environmental data in R. The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) . Package: r-cran-amanida Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse, r-cran-webchem Suggests: r-cran-markdown, r-bioc-metaboliteidmapping, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-amanida_0.3.0-1.ca2404.1_all.deb Size: 377166 MD5sum: a728a61c6fe1a4664f45056dc2a128d6 SHA1: 3c5bf66fa22f0edd1f591bbd930392867385b72e SHA256: aa4513e69fbcce66563f370f35a6ac8f4f0dadcd41b613de833699459d75a99e SHA512: ab5824420e57ce306cabc725cecf1e3473dc8349d91454729dfb5c370b270a470f163ac0a688fe9e0517c1735ef3e04c0b0d6ac52094d454c1e054d4426ce879 Homepage: https://cran.r-project.org/package=amanida Description: CRAN Package 'amanida' (Meta-Analysis for Non-Integral Data) Combination of results for meta-analysis using significance and effect size only. P-values and fold-change are combined to obtain a global significance on each metabolite. Produces a volcano plot summarising the relevant results from meta-analysis. Vote-counting reports for metabolites. And explore plot to detect discrepancies between studies at a first glance. Methodology is described in the Llambrich et al. (2021) . Package: r-cran-amanpg Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4164 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-amanpg_0.3.4-1.ca2404.1_all.deb Size: 4192794 MD5sum: 345704041a856b40a3fa6871758ef4da SHA1: 6873442c43b669b3df374e880dcd83bf3e1d503b SHA256: a8dcd953abb82fc24cc1abb92d061da5bbc162b7763b4a5cb4108302ecfd656e SHA512: 0d0dc46c880de1922c40580625fd392c9370ae15880fcd375f5115a25bad4ef58fd44cd4b4787bad4b837cf40621a256f5601a8c003424f9b0d42064ba51b3ce Homepage: https://cran.r-project.org/package=amanpg Description: CRAN Package 'amanpg' (Alternating Manifold Proximal Gradient Method for Sparse PCA) Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides a novel algorithm for solving the sparse principal component analysis problem which provides advantages over existing methods in terms of efficiency and convergence guarantees. Chen, S., Ma, S., Xue, L., & Zou, H. (2020) . Zou, H., Hastie, T., & Tibshirani, R. (2006) . Zou, H., & Xue, L. (2018) . Package: r-cran-amapgeocode Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-digest, r-cran-xml2, r-cran-tibble, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httptest2, r-cran-vcr, r-cran-spelling, r-cran-covr, r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-readr Filename: pool/dists/noble/main/r-cran-amapgeocode_1.0.0-1.ca2404.1_all.deb Size: 215632 MD5sum: 92541c8e988c7dcbc49ca88a91b5640d SHA1: fe5cbb04720ba3a269392a967f326ecadeffd2ae SHA256: 5fef6bedf9853df30cdc83aadfd6c2626deba7dc7716f92817f28f33f0ed72ff SHA512: 81a490c5d07b67628d6959e353e7ae5761359dc5dde47fdca635b1ab27961dd6d967dbd8b45994c6c215626447de3c0806d9e483aeedc8449636778416bdbe5c Homepage: https://cran.r-project.org/package=amapGeocode Description: CRAN Package 'amapGeocode' (An Interface to the 'AutoNavi Maps' API Geocoding Services) Getting and parsing data of location geocode/reverse-geocode and administrative regions from 'AutoNavi Maps' API. Package: r-cran-amapro Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3956 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes, r-cran-jsonlite, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-amapro_0.1.5-1.ca2404.1_all.deb Size: 980716 MD5sum: 0aafa76fde2cd48ed5ca655531d612d8 SHA1: dbb3158cc6697f752abdf4a54e2400d988eaf55f SHA256: f370a57d38e2fd1154a53cfc6cb517fad560f4f1e59938ffb48c37f243a8fa6f SHA512: 4b85746712f0dca3b2b9f5511fa7a08eb55cb690bde421a924890c6b30be2b7cb8168f62b1307c113d7fe9abe3142de2f5dba3321a9abc40dcf3f447dbf3e10c Homepage: https://cran.r-project.org/package=amapro Description: CRAN Package 'amapro' (Thin Wrapper for Mapping Library 'AMap'('Gaode')) Build and control interactive 2D and 3D maps with 'R/Shiny'. Lean set of powerful commands wrapping native calls to 'AMap' . 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Package: r-cran-amapvox Architecture: all Version: 2.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-jsonlite, r-cran-rappdirs, r-cran-stringr Suggests: r-cran-fields, r-cran-ggplot2, r-cran-knitr, r-cran-rann, r-cran-rgl, r-cran-refmanager, r-cran-rmarkdown, r-cran-sf, r-cran-terra Filename: pool/dists/noble/main/r-cran-amapvox_2.4.2-1.ca2404.1_all.deb Size: 529732 MD5sum: a7753a43da3e95efb3fc9e3d23ae3905 SHA1: 9db797088987f5869a34a72ae95b85d909e56545 SHA256: 2cb43b03522641a260209074d6e2f9f0749b8fb8d78f9329bb3196717410fc76 SHA512: 53a9320f612c54541bb5bf35873e0f12351d632a7e2e917d7cf1550de77263a9f6598edbd3bcf7d44f5e2c1d04cacfe7328effda076c7c12b5d1ef274e548cb3 Homepage: https://cran.r-project.org/package=AMAPVox Description: CRAN Package 'AMAPVox' (LiDAR Data Voxelisation) Read, manipulate and write voxel spaces. Voxel spaces are read from text-based output files of the 'AMAPVox' software. 'AMAPVox' is a LiDAR point cloud voxelisation software that aims at estimating leaf area through several theoretical/numerical approaches. See more in the article Vincent et al. (2017) and the technical note Vincent et al. (2021) . Package: r-cran-amazonadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-amazonadsr_0.1.0-1.ca2404.1_all.deb Size: 23006 MD5sum: debb6ae53506be2a5e8c5d6b7b99c098 SHA1: b56a78b272373ef55a60073bef46514b03cdeb2e SHA256: bb1921d1dd43c881313440c88776c760972a9d4ccc4e29fd613b03fd9641c3e8 SHA512: 67acdfa150dc49eac7e23a375ad5433b514d188d692a5d31e2378eeb82176f6ce1ffb726e9b428fde10c54e5705832533b7e6b151ec9065ac06f0b073b06202c Homepage: https://cran.r-project.org/package=amazonadsR Description: CRAN Package 'amazonadsR' (Get Amazon Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Amazon Ads using the 'Windsor.ai' API . 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Package: r-cran-ambir Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-cli, r-cran-magrittr, r-cran-lifecycle Suggests: r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ambir_0.2.0-1.ca2404.1_all.deb Size: 680476 MD5sum: 49749fd6a4bc16376174b6f2067615f9 SHA1: 875693e6d9472e0f21d8b2200e9175b576487542 SHA256: 74a6e9e6d4840d61e9cbf45e1e6a8f54d8d2e4dcc04ed4664b61716dcabd0054 SHA512: eb1d342f91978e51ba9b7df9b37bd166e115587ca645232da7cfd4806ed9b15826d6e237dd78928cbd7dc8cc030592ad13cda37bdce976b6249c01cb7192d199 Homepage: https://cran.r-project.org/package=ambiR Description: CRAN Package 'ambiR' (Calculate AZTI’s Marine Biotic Index) Calculate AZTI’s Marine Biotic Index - AMBI. The included list of benthic fauna species according to their sensitivity to pollution. Matching species in sample data to the list allows the calculation of fractions of individuals in the different sensitivity categories and thereafter the AMBI index. The Shannon Diversity Index H' and the Danish benthic fauna quality index DKI (Dansk Kvalitetsindeks) can also be calculated, as well as the multivariate M-AMBI index. Borja, A., Franco, J. ,Pérez, V. (2000) "A marine biotic index to establish the ecological quality of soft bottom benthos within European estuarine and coastal environments" . 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Following a multi-barrier approach, the package simulates pathogen inflow and removal along a treatment train, estimates human exposure, and computes health risk indicators such as infection probability, illness probability, and Disability-Adjusted Life Years (DALYs). It also supports an economic analysis of the treatment scenarios considered. For more details see . Package: r-cran-ambs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ambs_0.1.0-1.ca2404.1_all.deb Size: 174628 MD5sum: d388cab03ba9ea10fe5e80d0d6c1411c SHA1: d1e4cdbd2c825d3c36876fd095e1dc868c88e49c SHA256: 3a14a4ec076028d25570304a56add3b47bb4614b1d5d32c92032105e9edeabac SHA512: 19a4040985862c2d74699e14c5dfb5504358e216db862a01b9b5efc53fe937c6d6f8f4b5c1f8975e4ed7aa4c7bf1fd26c7d968dae8ea1d8cd121428ed3f8002f Homepage: https://cran.r-project.org/package=ambs Description: CRAN Package 'ambs' (Bayesian Alpha-Mixture Survival Models) Implements Bayesian estimation for alpha-mixture survival models with right-censored data. Weibull-Weibull, Gamma-Weibull, and Lognormal-Lognormal component specifications are supported, with all component parameters treated as unknown. The package provides identifiability handling, adaptive Markov chain Monte Carlo sampling, convergence diagnostics, model comparison criteria, and posterior survival, hazard, and density estimation. The methodology extends the framework described by Luan et al. (2026) . Danish Ezwan, David Goldberg, and Ting Huang contributed equally to the package. Package: r-cran-amcp Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 857 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-amcp_2.0.0-1.ca2404.1_all.deb Size: 547196 MD5sum: 3c310f233ebce0e2eae1dec4488acd53 SHA1: 623c8e885dc86296b177dd4d0782c87ea5a99409 SHA256: 231f3b7b805729c6f3451ca78719e2e1a09ed0a5044878fbedc641e04fe480bf SHA512: de4e834251286cd9850a148426576ae22ee8e3699bab5ec1c95b3bbe148b1b0f973392bdd536ca0a5a8664b9e255ea95df831157e4dd08981711b540aa88fcea Homepage: https://cran.r-project.org/package=AMCP Description: CRAN Package 'AMCP' (Data Sets to Accompany Designing Experiments and Analyzing Data:A Model Comparison Perspective (Maxwell, Delaney, and Kelley,2027, 4th Edition)) Data sets that accompany the book "Designing experiments and analyzing data: A model comparison perspective" (4th ed.) by Maxwell, Delaney, and Kelley (2027; ISBN 978-1-041-25384-6; Routledge). Contains all of the data sets in the book's chapters and end-of-chapter exercises. Beginning with version 2.0, the package is tailored to the 4th edition of the book; for the data as distributed with the 3rd edition (2018), install the archived version 1.0.2 from CRAN. We recommend the 'DMAR' package as the companion for carrying out the book's analyses; these analyses are illustrated in the book itself using the 'MBESS' package, which may be used as well. The book's companion website is available at and its publisher page at . 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The package supports a domain-agnostic approach to studying the shape, dispersion, and internal structure of point clouds, with applications across biological and ecological datasets, including those derived from deep-time records. The AMD framework builds on the idea that strongly coupled systems may occupy a limited set of recurrent regimes in state space, producing high-occupancy regions separated by sparsely populated transitional configurations. The package focuses on detecting these concentration patterns and quantifying their geometric definition without assuming any underlying dynamical model. It provides AMD curve computation, cluster assignment, and sigma-equivalent estimation, together with S3 methods for plotting, printing, and summarising AMD and sigma-equivalent objects. Mendoza (2025) . Package: r-cran-amelie Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-amelie_0.2.1-1.ca2404.1_all.deb Size: 41388 MD5sum: 5a1fb6cdc5c3d113200ebd2d6a02b21b SHA1: e1f4858892b294ef05fe528043de1243b0122ad4 SHA256: 72b9995ea91823711f9e9dd6dc90d4506bf9bda6b3bc8be2807611aecc29f10b SHA512: 9090038cf26707964d69d74b8f4cc69f9ce6870e64f1d2fe75e6bf105ce4772c7f14e3e182c5859c6a3dbaa2ef091907be2dfa55699a8d0d506d7982762b5fd9 Homepage: https://cran.r-project.org/package=amelie Description: CRAN Package 'amelie' (Anomaly Detection with Normal Probability Functions) Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: , and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) . 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Package: r-cran-amerifluxr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 704 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-heatmaply, r-cran-httr, r-cran-jsonlite, r-cran-memoise, r-cran-rcurl, r-cran-readxl Suggests: r-cran-covr, r-cran-data.table, r-cran-knitr, r-cran-pander, r-cran-qpdf, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat, r-cran-mockr Filename: pool/dists/noble/main/r-cran-amerifluxr_1.0.0-1.ca2404.1_all.deb Size: 444406 MD5sum: d5fa6053f01d55fb96ba405c780ad3a7 SHA1: e263e2881c5ccc7ff281ec23e87605be87a56f2e SHA256: 47f817075166fb2e2841d146d617d34547a5e8604fc9d9bc1b62d3d415b0ed6e SHA512: e241ef7574c55a9632a83c34e9623af2f502e5bc187c73c25e7352427465655aa7fd50806a985431670b5b92cb1821e6c83089720e16e08a1e1904e4db034819 Homepage: https://cran.r-project.org/package=amerifluxr Description: CRAN Package 'amerifluxr' (Interface to 'AmeriFlux' Data Services) Programmatic interface to the 'AmeriFlux' database (). 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Package: r-cran-ami Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-config, r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-ami_0.2.1-1.ca2404.1_all.deb Size: 64038 MD5sum: 58b31c8f5f7a5a60a7a441c81ce76aec SHA1: fe2faa3f7d5c11853227188f0ad7bbb24c6623b6 SHA256: 0030cfab5fe1aa1c35375e08f0086f9f98ba2b8c3b4aff6ecf85fd5579deb4e8 SHA512: ec6d0db5e84549671419fdc3d59ad9a04d7a4f7b2ec8350509f7bd262023bff6f4581246d1b1eda9f45f5549c3b4e22c50ff8ffb753770c789e412ad72240cbd Homepage: https://cran.r-project.org/package=ami Description: CRAN Package 'ami' (Checks for Various Computing Environments) A collection of lightweight functions that can be used to determine the computing environment in which your code is running. This includes operating systems, continuous integration (CI) environments, containers, and more. Package: r-cran-amigaffh Architecture: all Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 930 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tune, r-cran-vctrs Suggests: r-cran-adfexplorer, r-cran-protrackr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-amigaffh_0.4.8-1.ca2404.1_all.deb Size: 802656 MD5sum: 53e7359696266c706d7d3066a1be73e5 SHA1: 5bbbf6277e4e69c05a9cd92774869f964441b29d SHA256: ec70647096eeecde4725d63619a218435b72673617c702e32dc8af8546632d8a SHA512: bf487b320dc4bb75e7608fdec3b1f583d983b6c38c6b830baba3cea14ead63a4d53f904e69fe159d5d52b2b01d2e6c4c4d98010ba2d08bac603c6fc9bcc7955d Homepage: https://cran.r-project.org/package=AmigaFFH Description: CRAN Package 'AmigaFFH' (Commodore Amiga File Format Handler) Modern software often poorly support older file formats. This package intends to handle many file formats that were native to the antiquated Commodore Amiga machine. This package focuses on file types from the older Amiga operating systems (<= 3.0). It will read and write specific file formats and coerces them into more contemporary data. 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Package: r-cran-ammibayes Architecture: all Version: 2.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1598 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-distfree.cr, r-cran-coda, r-cran-spam, r-cran-movmf, r-cran-msm, r-cran-bayesplot, r-cran-hmisc, r-cran-mass Suggests: r-cran-ggpubr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ammibayes_2.2-0-1.ca2404.1_all.deb Size: 1482484 MD5sum: 0de0d656eeefad23b09b396d9883e343 SHA1: 16f65051ed2ef36056dfcae6addc7da6a326fec9 SHA256: e2f92ca504419f3d641cf817f62d1027ef73993a2d123ca903681c81e94b02b5 SHA512: 0be381c002a9d14ebba807e468a9a1712229970b3b9b77b3696ca0982dfcfff32ecc20845928dff080ae52a8efb9db30db3c2daff837bbaeceb31bff23a438dc Homepage: https://cran.r-project.org/package=ammiBayes Description: CRAN Package 'ammiBayes' (Bayesian Ammi Model for Continuous Data with or without Additiveand Dominance Effect) Flexible multi-environment trials analysis via MCMC method for Additive Main Effects and Multiplicative Interaction Model (AMMI) for continuous data. Biplot with the averages and regions of confidence can be generated. The chains run in parallel on Linux systems and run serially on Windows. Package: r-cran-ammistability Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-agricolae, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-reshape2, r-cran-rdpack, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-xml, r-cran-httr, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-ammistability_0.1.4-1.ca2404.1_all.deb Size: 472732 MD5sum: 6097ce883dde46f981668ae15bc39d22 SHA1: eb1f2fe29f9fe68e12b943112177725a830125c1 SHA256: b9479936de8fe2c5875b88331b76c9ad67a5efc50a55076d5b38033b7d79c218 SHA512: 48ef201bc1fbc26a56bf745898fd4d46999662f65c81c6dfd262f43eea427040dadfb8955548226e59df787fb82e7a2672c6f0b3f87fe16e54a23eb7b6e0da77 Homepage: https://cran.r-project.org/package=ammistability Description: CRAN Package 'ammistability' (Additive Main Effects and Multiplicative Interaction ModelStability Parameters) Computes various stability parameters from Additive Main Effects and Multiplicative Interaction (AMMI) analysis results such as Modified AMMI Stability Value (MASV), Sums of the Absolute Value of the Interaction Principal Component Scores (SIPC), Sum Across Environments of Genotype-Environment Interaction Modelled by AMMI (AMGE), Sum Across Environments of Absolute Value of Genotype-Environment Interaction Modelled by AMMI (AV_(AMGE)), AMMI Stability Index (ASI), Modified ASI (MASI), AMMI Based Stability Parameter (ASTAB), Annicchiarico's D Parameter (DA), Zhang's D Parameter (DZ), Averages of the Squared Eigenvector Values (EV), Stability Measure Based on Fitted AMMI Model (FA), Absolute Value of the Relative Contribution of IPCs to the Interaction (Za). Further calculates the Simultaneous Selection Index for Yield and Stability from the computed stability parameters. See the vignette for complete list of citations for the methods implemented. Package: r-cran-ammodels Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-unmarked Suggests: r-cran-r.rsp, r-cran-shiny, r-cran-shinybs, r-cran-knitr, r-cran-aiccmodavg Filename: pool/dists/noble/main/r-cran-ammodels_0.1.4-1.ca2404.1_all.deb Size: 1222132 MD5sum: 45f3093ec8e76774324b4aad2bef64c6 SHA1: 788cc406583c18f2a8aa5ac9b8b0292f5971015b SHA256: 98d45cafd590120b5fdc405a24fc7e82f615f9d9dcc04a6d7c1637f1f3e178e9 SHA512: 15b419d83c73f7865237f2af221be508ad53ab6fbf2f3588c2c374be54edf570b7cd025936fef6037671e212da9a4ccb6977a469248132f868bafe061866a508 Homepage: https://cran.r-project.org/package=AMModels Description: CRAN Package 'AMModels' (Adaptive Management Model Manager) Helps enable adaptive management by codifying knowledge in the form of models generated from numerous analyses and data sets. Facilitates this process by storing all models and data sets in a single object that can be updated and saved, thus tracking changes in knowledge through time. A shiny application called AM Model Manager (modelMgr()) enables the use of these functions via a GUI. Package: r-cran-ammoniaconcentration Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ammoniaconcentration_0.1-1.ca2404.1_all.deb Size: 11224 MD5sum: b92ccaab8506025e4c1f0e3bd4d943cf SHA1: 96e56adcf9a0abe69e845b8863c8d340c708700d SHA256: 7b8cc40dd94093a017f93a2eeeef52336ce6621c582589c4623c10c389a0e272 SHA512: 19bb03cdf7688278bc8509a04419502321b720422104fef5be55aa71f9721340752fdd153e5e8a62fe6cda3a207baf66436d4ba38d0ab581317ec1c5508056b5 Homepage: https://cran.r-project.org/package=AmmoniaConcentration Description: CRAN Package 'AmmoniaConcentration' (Un-Ionized Ammonia Concentration) Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values. Package: r-cran-amnlfa Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mplusautomation, r-cran-reshape2, r-cran-gridextra, r-cran-stringr, r-cran-plyr, r-cran-devtools, r-cran-dplyr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-amnlfa_1.1.2-1.ca2404.1_all.deb Size: 235436 MD5sum: 448a83a400a50536aadd0565756d49ec SHA1: 1fb1ce8991a80ca5d2e676ee9251d41221510567 SHA256: 29499c5c773e823478788bf5fababb9cfa4ccd0d4b9ec0f37bfcaf26e540cab5 SHA512: dfbfc30a67c3f92043bf544333ce02f00b22cfa549d2bd612ea82a81fb6c78efdbebe45ebc7f5055f4bbf66fd3e40df9579e66b40e1b5940705eb54f74557e49 Homepage: https://cran.r-project.org/package=aMNLFA Description: CRAN Package 'aMNLFA' (Automated Moderated Nonlinear Factor Analysis Using 'M-plus') Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment. Package: r-cran-amoudsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ahsurv, r-cran-flexsurv, r-cran-pracma Filename: pool/dists/noble/main/r-cran-amoudsurv_0.1.0-1.ca2404.1_all.deb Size: 196380 MD5sum: 7aaba500fe5da7ddba65b3ba36b913e7 SHA1: f9544352631c2eb96ad1de632f09fc598a54e57e SHA256: 21735cfa885c71624b6958f85f48812547fe16e7f005bd2c1a9b87db6f27a697 SHA512: caeb9c740df3679ab8b94e51051f75702d95abe357a5fb58f2882632d2db736f20d84a1f594fc4654e39d15cfda6b604ea54ae1251bf224e6dd969ec5b5767df Homepage: https://cran.r-project.org/package=AmoudSurv Description: CRAN Package 'AmoudSurv' (Tractable Parametric Odds-Based Regression Models) Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) , and Muse et al., (2022) . 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Additionally, functionality is included to aid simulations performed directly in 'NONMEM' and to automatically create shiny apps for simulation models. Package: r-cran-amp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-superlearner, r-cran-glmnet, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-amp_1.0.0-1.ca2404.1_all.deb Size: 237256 MD5sum: cbb2bf7573eb56fe319e36f326ceb0d0 SHA1: c39369ffb8832b8a0797c2c4b5c442b5fd6eb1de SHA256: 8ae4fa9b4ecb0935b244bb7df68bed27879a1ea5e016e7dfd414f81509b1ed0b SHA512: ac4b7331144406b167a5b6e8ee836c34f07af7025b5ff147f31590541417f45e36d035a4395408ef2c5559bb5e24a40f5c6dff6feeed9fec2c831a24d380330d Homepage: https://cran.r-project.org/package=amp Description: CRAN Package 'amp' (Statistical Test for the Multivariate Point Null Hypotheses) A testing framework for testing the multivariate point null hypothesis. A testing framework described in Elder et al. (2022) to test the multivariate point null hypothesis. After the user selects a parameter of interest and defines the assumed data generating mechanism, this information should be encoded in functions for the parameter estimator and its corresponding influence curve. Some parameter and data generating mechanism combinations have codings in this package, and are explained in detail in the article. Package: r-cran-ampd Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ampd_0.2-1.ca2404.1_all.deb Size: 16912 MD5sum: 3a6ff9e5eeb33cd460493fa67119b0d3 SHA1: 5995ff6a2941ec9203a52537321c649ca41731eb SHA256: 693fe48702a439d539c47de51ba087be6580fff7ab7ce44f8618aa5fd327c92b SHA512: dc8a4fd243dc6996e5902108f11f987ca722e5c166f09d4d79b5415580f8929572c59b80f7362df99c25b9d5a46ea25e97a73f7d704b0898e5b54bb74c4dad4e Homepage: https://cran.r-project.org/package=ampd Description: CRAN Package 'ampd' (An Algorithm for Automatic Peak Detection in Noisy Periodic andQuasi-Periodic Signals) A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. 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Package: r-cran-ampgram Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biogram, r-cran-devtools, r-cran-pbapply, r-cran-ranger, r-cran-shiny, r-cran-stringi Suggests: r-cran-dt, r-cran-ggplot2, r-cran-pander, r-cran-rmarkdown, r-cran-shinythemes, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ampgram_1.0-1.ca2404.1_all.deb Size: 86746 MD5sum: 843ace5073865f3858273570d983b917 SHA1: 14794385a4fffee623340115e7d6804e82d6f74a SHA256: fbff42459ef0d262afab28baf123f5c978ffcabd6c707671f40aa932db11987c SHA512: 74c1cc8cf848ee5f28c9ab4875cfe2cdea2cc165f80f73447855f5993787dc84c05e51644681cd1356cc17f349749ca34161cf318151b660964848810439a795 Homepage: https://cran.r-project.org/package=AmpGram Description: CRAN Package 'AmpGram' (Prediction of Antimicrobial Peptides) Predicts antimicrobial peptides using random forests trained on the n-gram encoded peptides. 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Package: r-cran-ample Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3942 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-markdown, r-cran-rcolorbrewer, r-cran-r6, r-cran-scales, r-cran-shinyjs, r-cran-ggplot2, r-cran-shinyscreenshot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ample_1.0.2-1.ca2404.1_all.deb Size: 1511168 MD5sum: 82d57de008981ab8ad28a4e19458d60d SHA1: c7f6d8ff4694fc9d64e3f190496d30feb26a1519 SHA256: 98d18c7b07f3115e90ab584f52ce9926ca3228c508888fe2660ce779c2ac176d SHA512: b25d3cdd876baade6f11da53b38d15974ae603ce6823809671a1e73b9ab88e0777ba030e506d46bbcb091180c6602a3a61be46428d4c602d77db3ca9d1b22b5a Homepage: https://cran.r-project.org/package=AMPLE Description: CRAN Package 'AMPLE' (Shiny Apps to Support Capacity Building on Harvest Control Rules) Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. 'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. 'Measuring performance' builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. 'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected. Package: r-cran-ampliconduo Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable Filename: pool/dists/noble/main/r-cran-ampliconduo_1.1.1-1.ca2404.1_all.deb Size: 564934 MD5sum: 95ea3f366f3a244a93f7809444cce898 SHA1: a0595788678f8ec6bf87b2d7a47221bcf4813ca6 SHA256: 83739a1f558caffa0e0429606a2afe91135665658aa6be180b4529334264a0fa SHA512: 5c94857d47eda4d69f3eb3e095e9d80b6eed93706b235c942676412fafe27c65c0f482e72427b12ab9063215ddac75ea6de4e18dbec3c105df6c0b036e000822 Homepage: https://cran.r-project.org/package=AmpliconDuo Description: CRAN Package 'AmpliconDuo' (Statistical Analysis of Amplicon Data of the Same Sample toIdentify Artefacts) Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads. Package: r-cran-amr Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5001 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cleaner, r-cran-cli, r-cran-crayon, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-openxlsx, r-cran-parallelly, r-cran-pillar, r-cran-progress, r-cran-readxl, r-cran-recipes, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rvest, r-cran-skimr, r-cran-testthat, r-cran-tibble, r-cran-tidymodels, r-cran-tidyselect, r-cran-tinytest, r-cran-vctrs, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-amr_3.0.1-1.ca2404.1_all.deb Size: 4799708 MD5sum: 92a902f03d72bbbf17bab3027899e128 SHA1: 4d069f1f4aad493c47080b996b3624afeb15ff40 SHA256: 384e4f3717bedcf1cea8620bc1d632ae612787185353902ac560f524fbf49ead SHA512: cc066e8a905218e678b8675e8b84ad4246eac16d1e75ab427bb028c717b57594a1bdff898e1710f57e92f8f9adc1d4f984397d49051bb1567c0eeb06b3c3d56c Homepage: https://cran.r-project.org/package=AMR Description: CRAN Package 'AMR' (Antimicrobial Resistance Data Analysis) Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in . Package: r-cran-amregtest Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-r6, r-cran-remotes, r-cran-testthat, r-cran-withr Suggests: r-cran-allelematch, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-amregtest_1.3.2-1.ca2404.1_all.deb Size: 178502 MD5sum: 3e0786f432c0d08e3809112c3029712e SHA1: 64c9380a5e3f4be1e4054e0ea1c341aa684e31e1 SHA256: b828c63d14ebce59c962a9078e81111fba4e1583c5716698d1331463178546df SHA512: 8d1f0a464c6413a193e76daae5ae617e2ce812717705179e356cc3c0b0048cb73510fef82b8811b935505635aab3076236d501b8a2a3843f0d6c608e0cd8b14d Homepage: https://cran.r-project.org/package=amregtest Description: CRAN Package 'amregtest' (Runs Allelematch Regression Tests) Automates regression testing of package 'allelematch'. Over 2500 tests cover all functions in 'allelematch', reproduce the examples from the documentation, and include negative tests. The implementation is based on 'testthat'. Package: r-cran-amrsurveilr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-sf, r-cran-spdep Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-amrsurveilr_0.1.0-1.ca2404.1_all.deb Size: 51816 MD5sum: 354fbad53a7eabe6c963fbaf85d7c4df SHA1: a9abe9e31245e99f3c30f26ab1b38bf78753dc42 SHA256: ceeff19313841f434712ab81f32576cd63a3cbfb21d87f2c14641abbd85af35c SHA512: 45b14499a2ca520f7f3708589a77fefe55c7459e53bc2cbb81643111293999d6e500b110331200285c811ffd5272b17012ab4531853ecd1c0a1b1195d2e4fd69 Homepage: https://cran.r-project.org/package=AMRsurveilR Description: CRAN Package 'AMRsurveilR' (Antimicrobial Resistance Surveillance, Epidemiology and RiskAnalysis) Provides tools for antimicrobial resistance surveillance, epidemiological analysis, temporal trend detection, early warning detection, spatial cluster identification, and risk factor analysis. The package supports analysis of antimicrobial resistance patterns, resistance to multiple antimicrobial classes, temporal surveillance, and spatial epidemiology for applications in veterinary, medical, and One Health research. Antimicrobial resistance surveillance approaches are informed by guidelines from WHO (2023) and WOAH (2024) . Statistical methods include cumulative sum (CUSUM) monitoring (Page, 1954) , exponentially weighted moving average (EWMA) monitoring (Roberts, 1959) , Local Moran's I spatial analysis (Anselin, 1995) , and multidrug- and extensively drug-resistant classification (Magiorakos et al., 2012) . Package: r-cran-amscorer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-amscorer_0.1.0-1.ca2404.1_all.deb Size: 69346 MD5sum: 7962b3f56fce1e5bf92e063bb427ffcc SHA1: a74e1c294405ec56f358f17ba63dcc0d0de5e740 SHA256: e37e91d984d43eb86bbc129bdabc3164c3a0cb7956bfdbbd579876e8ff880c8e SHA512: aa99aace4565f100a221ab69a0f0a8b76f7fba8d0d4dbafd937c571c6c57e8b787f025a9e764883f5e02e03aa865b5d97d73b4a728863abb8de77ca7ce45ee88 Homepage: https://cran.r-project.org/package=amscorer Description: CRAN Package 'amscorer' (Clinical Scores Calculator for Healthcare) Provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) , Sass et al. (2006) , Kamath et al. (2001) , Kim et al. (2008) Kim et al. (2021) , Mungroop et al. (2019) , and de Pastena et al. (2023) .. Package: r-cran-amt Architecture: all Version: 0.3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4765 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-circular, r-cran-ctmm, r-cran-data.table, r-cran-dplyr, r-cran-fitdistrplus, r-cran-fnn, r-cran-kernsmooth, r-cran-lubridate, r-cran-mass, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-survival, r-cran-terra, r-cran-tibble, r-cran-tidyr Suggests: r-cran-adehabitatlt, r-cran-broom, r-cran-ggplot2, r-cran-ggraph, r-cran-geosphere, r-cran-knitr, r-cran-leaflet, r-cran-movehmm, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-suncalc, r-cran-tidygraph, r-cran-tinytest, r-cran-units Filename: pool/dists/noble/main/r-cran-amt_0.3.1.0-1.ca2404.1_all.deb Size: 3505264 MD5sum: 72a376ca3035af8803fc1b5763138b58 SHA1: ca6088b39e25b526be941fe4a24aed56386595b1 SHA256: 696c920fa6aa0cf1d022bee2337e2a4eceb84fa8752bd7f0130f4ec2d7d72065 SHA512: a3bc776376dcb6d2a72286300e39fb2ab94ca7c05e5a1e55a5237e2f1fb8d464382c63021ea1d1a684fc0674bbead02a6a9254c647282e320dbe8d68c7c43f25 Homepage: https://cran.r-project.org/package=amt Description: CRAN Package 'amt' (Animal Movement Tools) Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions. Package: r-cran-amvenndiagram5 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1628 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-partitions, r-cran-venn Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-amvenndiagram5_1.0.0-1.ca2404.1_all.deb Size: 1119970 MD5sum: 57ca6785fd173deb2eb77fc3201f9bec SHA1: 9e47ee4895b16d228e0089a336a7e8283a3dc62a SHA256: 68b5fddddd39a8fbd917f5e34be708e7e14c525e4476ff8ddedfa265a81d6f9e SHA512: e558bd61657ded76e476c98e8ae45ecf16a09002bdb99feadbb1f4849e080cc5960d6362d7456109e22dad883249abec07835c23e53fe1aeaaf5ef6d6cb5555b Homepage: https://cran.r-project.org/package=amVennDiagram5 Description: CRAN Package 'amVennDiagram5' (Interactive Venn Diagrams) Creates interactive Venn diagrams using the 'amCharts5' library for 'JavaScript'. They can be used directly from the R console, from 'RStudio', in 'shiny' applications, and in 'rmarkdown' documents. Package: r-cran-amylogram Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biogram, r-cran-ranger, r-cran-seqinr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-amylogram_1.1-1.ca2404.1_all.deb Size: 644594 MD5sum: ae85acddecbf454d3817c88545746989 SHA1: fb82e4c89314cd532066e0e68067be9202025e53 SHA256: 712ee3ae211b6c3044bbab2f0042e2e19c2afd73a404ffd9a1a7fcee8e3944a7 SHA512: bd3c523d80f38741a52d4609b1d46170eac0342a9b6a1fc697602864ee5a71b31bcfa29b693caa949eb0546601e65629d3a38cead1cbc9d5e93c301ef90cd146 Homepage: https://cran.r-project.org/package=AmyloGram Description: CRAN Package 'AmyloGram' (Prediction of Amyloid Proteins) Predicts amyloid proteins using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. Package: r-cran-anabel Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra, r-cran-minpack.lm, r-cran-openxlsx, r-cran-progress, r-cran-purrr, r-cran-qpdf, r-cran-reshape2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-anabel_3.0.2-1.ca2404.1_all.deb Size: 797424 MD5sum: 1c6918cd9b90d0bbf2b63aac8030c3bd SHA1: 430dacc9b555c4363ce56e1473eb5745ee119412 SHA256: 55923d14ceee86d4191a9b3729c41d665f3d7f300f04fb11c73290a570915070 SHA512: 7e64d43cf9372186ccc7fb29e009c4430ae08844884cc8379e0f5b015d8676b5f686e11e83696af7c38ed4f9a50d339eed3b397bebbd20d12c26e1070982adda Homepage: https://cran.r-project.org/package=anabel Description: CRAN Package 'anabel' (Analysis of Binding Events + l) A free software for a fast and easy analysis of 1:1 molecular interaction studies. This package is suitable for a high-throughput data analysis. Both the online app and the package are completely open source. You provide a table of sensogram, tell 'anabel' which method to use, and it takes care of all fitting details. The first two releases of 'anabel' were created and implemented as in (, ). Package: r-cran-anaconda Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-bioc-deseq2, r-cran-ggplot2, r-cran-ggrepel, r-cran-pheatmap, r-cran-lookup, r-cran-plyr, r-cran-data.table, r-cran-rafalib, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-anaconda_0.1.5-1.ca2404.1_all.deb Size: 176224 MD5sum: 9fb4432663ec4a36481b613a1ba06159 SHA1: d0502f2d32f0add548f4ce9a4bdb53d4e3f4ddb3 SHA256: 1bcddfeb1534e09e7f72c7ff9156d38f5348aad52c046094c3318b4b9e60eeff SHA512: b77d2bf5627ec78d72524ff953c7038dc34568e181871050858563795e3a83316b376dc5f4206453b2d35859bc1f99b98d98c21cd24646145085028c22c61374 Homepage: https://cran.r-project.org/package=Anaconda Description: CRAN Package 'Anaconda' (Targeted Differential and Global Enrichment Analysis ofTaxonomic Rank by Shared Asvs) Targeted differential and global enrichment analysis of taxonomic rank by shared ASVs (Amplicon Sequence Variant), for high-throughput eDNA sequencing of fungi, bacteria, and metazoan. Actually works in two steps: I) Targeted differential analysis from QIIME2 data and II) Global analysis by Taxon Mann-Whitney U test analysis from targeted analysis (I) (I) Estimate variance-mean dependence in count/abundance ASVs data from high-throughput sequencing assays and test for differential represented ASVs based on a model using the negative binomial distribution. (II) NCBITaxon_MWU uses continuous measure of significance (such as fold-change or -log(p-value)) to identify NCBITaxon that are significantly enriches with either up- or down-represented ASVs. If the measure is binary (0 or 1) the script will perform a typical 'NCBITaxon enrichment' analysis based Fisher's exact test: it will show NCBITaxon over-represented among the ASVs that have 1 as their measure. On the plot, different fonts are used to indicate significance and color indicates enrichment with either up (red) or down (blue) regulated ASVs. No colors are shown for binary measure analysis. The tree on the plot is hierarchical clustering of NCBITaxon based on shared ASVs. Categories with no branch length between them are subsets of each other. The fraction next to the category name indicates the fraction of 'good' ASVs in it; 'good' ASVs are the ones exceeding the arbitrary absValue cutoff (option in taxon_mwuPlot()). For Fisher's based test, specify absValue=0.5. This value does not affect statistics and is used for plotting only. The original idea was for genes differential expression analysis from Wright et al (2015) ; adapted here for taxonomic analysis. The 'Anaconda' package makes it possible to carry out these analyses by automatically creating several graphs and tables and storing them in specially created subfolders. You will need your QIIME2 pipeline output for each kingdom (eg; Fungi and/or Bacteria and/or Metazoan): i) taxonomy.tsv, ii) taxonomy_RepSeq.tsv, iii) ASV.tsv and iv) SampleSheet_comparison.txt (the latter being created by you). Package: r-cran-anacor Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-colorspace, r-cran-fda Filename: pool/dists/noble/main/r-cran-anacor_1.1-5-1.ca2404.1_all.deb Size: 339140 MD5sum: 09d9a8ce1df211e6b1196e29b781ed06 SHA1: 44f66a95ba0d35f505a2dd0ebceb7eec1a00618c SHA256: 2f88c3e73bd4d46532d6a268404e39bee44a8553fdcd24d1827eecc9c02ebde6 SHA512: 9eafe158f7893d33bcdf9f4e2b89fdf72c5e1a9109e3339d5c3476dc0f79a2ef16fd6db603c08c05380fb9d582c3e09dcd44d50a446287726ee4a71e877b0b7d Homepage: https://cran.r-project.org/package=anacor Description: CRAN Package 'anacor' (Simple and Canonical Correspondence Analysis) Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided. Package: r-cran-analitica Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-ggridges, r-cran-patchwork, r-cran-moments, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect, r-cran-multcompview Suggests: r-cran-car, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-analitica_2.2.0-1.ca2404.1_all.deb Size: 328892 MD5sum: b84790f0308233c25fae1c7c6a36a31a SHA1: 1ed51e4a56cafecb1ae822d296778de91c1d808e SHA256: 0e72ff8bb5a442b70bdb3fe20c66c937671aa5d18c490be79592543ea3bbcec8 SHA512: 506a2b2e6fb3ef2b667c667ba0f81d276904490ecfeccb1fe0102daa5208a2660ab45a3b39c55390cf43467d9fada81f29b0e935bcf0a6efb28e9f56ada8b422 Homepage: https://cran.r-project.org/package=Analitica Description: CRAN Package 'Analitica' (Exploratory Data Analysis, Group Comparison Tools, and OtherProcedures) Provides a comprehensive set of tools for descriptive statistics, graphical data exploration, outlier detection, homoscedasticity testing, and multiple comparison procedures. Includes manual implementations of Levene's test, Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others. This version introduces two new procedures: the Jonckheere-Terpstra trend test and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in teaching, applied statistical analysis, and reproducible research. Additionally you can find a post hoc Test Planner, which helps you to make a decision on which procedure is most suitable. Package: r-cran-analogsea Architecture: all Version: 1.0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-yaml Suggests: r-cran-testthat, r-cran-knitr, r-cran-ssh, r-cran-aws.s3, r-cran-arrow Filename: pool/dists/noble/main/r-cran-analogsea_1.0.7.2-1.ca2404.1_all.deb Size: 373864 MD5sum: bd34d7319861e6921f7cd2395da18bc5 SHA1: d36b3f2797389d0a4d91beb61ece164266aa78d6 SHA256: b5703700988fc778f12ef013aedd0d39baa92edfbb33a37b10eb7f74d5bcb0cf SHA512: 965b155f46738c67f584b016cfdc98dec31e055879f04f8572a02ba7796450c15521b4cfaf6c95e46e00d1f9e8bf87ab7fbaad49112d0b1f67d7b64313bbe878 Homepage: https://cran.r-project.org/package=analogsea Description: CRAN Package 'analogsea' (Interface to 'DigitalOcean') Provides a set of functions for interacting with the 'DigitalOcean' API , including creating images, destroying them, rebooting, getting details on regions, and available images. 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It provides essential features, including a descriptive statistics table for a quick overview of your dataset, interactive distribution plots to visualize variable patterns, Principal Component Analysis for dimensionality reduction and feature analysis, missing value imputation methods, and correlation analysis. 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Package: r-cran-angstroms Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1971 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-nabor, r-cran-ncdf4, r-cran-proj4, r-cran-sp, r-cran-spbabel Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-angstroms_0.0.1-1.ca2404.1_all.deb Size: 1276926 MD5sum: 8672af93cf0a33395e94623f4b8017c4 SHA1: cfd9a81699bfd726e01ec9bcab2fd65375bbc160 SHA256: f8418714d4a5f57bfe16d98b22967ac9e26fbd26555ed4a72e09d0ba54ee3fb7 SHA512: 348c2b220c7bfb04d552fb26122e81cef2bc986590f4290034a66a4999ae24e99c1b09c7510b4d82463b6dc81cadf78fd752e841172360282a81679c0d28b03b Homepage: https://cran.r-project.org/package=angstroms Description: CRAN Package 'angstroms' (Tools for 'ROMS' the Regional Ocean Modeling System) Helper functions for working with Regional Ocean Modeling System 'ROMS' output. See for more information about 'ROMS'. Package: r-cran-anidom Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rptr Filename: pool/dists/noble/main/r-cran-anidom_0.1.5-1.ca2404.1_all.deb Size: 57524 MD5sum: 2905cfc5cbdaa708ead6334b944a5ee4 SHA1: 10cdf59c37bde3cc9b150ceced3b65665ccf9521 SHA256: 7fc963039b880287c8bd830010dcd9c2df6d4b41d196b2141a5f178638c3e0c0 SHA512: d5be53e50ce5d09c8d11c60c71ce7c2dfd740c68331d2f0ea62802fb9cd45eacb73aaf28f87cef19eae78b466e211061287b79e406dc8648c7c30f0503b08f95 Homepage: https://cran.r-project.org/package=aniDom Description: CRAN Package 'aniDom' (Inferring Dominance Hierarchies and Estimating Uncertainty) Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set. Package: r-cran-anim.plots Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-animation Suggests: r-cran-maps, r-cran-knitr, r-cran-mapdata, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-anim.plots_0.2.3-1.ca2404.1_all.deb Size: 628992 MD5sum: ff1a979246e145c3c9cd93863a1688db SHA1: 5157557f65a94002de212ee7f3f320f9eecb25fb SHA256: c9688726267cfcf8eff33cdbfec31d5c33e55ef2bfe357dffe6fe66be2ac630c SHA512: b0dc5f4e2d4795a38197bff64d61efb3078587d7f73f01b5e4fe0b08c48daca50b93d448e30f5b38397c673d6be73e96e5302431c36743e4fcb0719101d94956 Homepage: https://cran.r-project.org/package=anim.plots Description: CRAN Package 'anim.plots' (Simple Animated Plots for R) Simple animated versions of basic R plots, using the 'animation' package. 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See Ackerman (2018) . 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The network generating algorithm first determines the X and Y coordinates of N nodes within a rectangle with a side length of L and an area of A. Then it computes the pair-wise Euclidean distance Dij between node i and j, and then a complete network with 1/Dij as link weights is constructed. Then, the algorithm removes links from the complete network with the probability as shown in the function ahn_prob(). Such link removals can make the network disconnected whereas a connected network is wanted. In such cases, the algorithm rewires one network component to its spatially nearest neighbouring component and repeat doing this until the network is connected again. Finally, it outputs an undirected network (weighted or unweighted, connected or disconnected). This package came with a manuscript on modelling the physical configurations of animal habitats using networks (in preparation). 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Package: r-cran-animation Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick Suggests: r-cran-mass, r-cran-class, r-cran-cairo, r-cran-fimport, r-cran-rgl, r-cran-zoo, r-cran-testit Filename: pool/dists/noble/main/r-cran-animation_2.8-1.ca2404.1_all.deb Size: 533918 MD5sum: dd7dc69ccdd2ae28c69c19a54e2a342b SHA1: 49e8bd670a8acf384b3f6cbb61e38f20be3dcc7d SHA256: bdb62cab3d702d86da16e96ca83e5487316019aeea2ba57668c146d4172d57f3 SHA512: 391974a470138f7fbf91f65129c7da44407586ec3b2b077cffa64e21990b13fb14ab30aa8b81523ac33f609e82a28c1ecba2d37ac901efbb21426c2de8425e10 Homepage: https://cran.r-project.org/package=animation Description: CRAN Package 'animation' (A Gallery of Animations in Statistics and Utilities to CreateAnimations) Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted into 'Sweave' / 'knitr' easily. Package: r-cran-animbook Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gganimate, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-animbook_1.0.1-1.ca2404.1_all.deb Size: 804308 MD5sum: a3fbd89a33afe4d6a8dbe6e3a489e47b SHA1: 0455aa8f990d819f37be8086a5222d553ee77c07 SHA256: 2b5df556f8fc1654a2acca19d6f8889e2121dfa7364a1f6cb681b205f0780116 SHA512: df441b24f0e3949410c876f948f7272443d0ae469415fbcc8e7521d98230a3ffe6c0932442ab7a39d3a273c2d274134e55bd7dcda8208b2ee8271ce61d092595 Homepage: https://cran.r-project.org/package=animbook Description: CRAN Package 'animbook' (Visualizing Changes in Performance Measures and DemographicAffiliations using Animation) Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data. Krisanat Anukarnsakulchularp (2023) . 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Package: r-cran-animint2 Architecture: all Version: 2025.10.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-servr, r-cran-digest, r-cran-rjsonio, r-cran-gtable, r-cran-mass, r-cran-plyr, r-cran-reshape2, r-cran-scales, r-cran-knitr, r-cran-data.table Suggests: r-cran-gert, r-cran-gitcreds, r-cran-gh, r-cran-sp, r-cran-gistr, r-cran-shiny, r-cran-covr, r-cran-rcolorbrewer, r-cran-htmltools, r-cran-rmarkdown, r-cran-testthat, r-cran-xml, r-cran-devtools, r-cran-httr, r-cran-maps, r-cran-ggplot2movies, r-cran-hexbin, r-cran-hmisc, r-cran-lattice, r-cran-mapproj, r-cran-mgcv, r-cran-nlme, r-cran-rpart, r-cran-svglite, r-cran-ggplot2, r-cran-chromote, r-cran-magick Filename: pool/dists/noble/main/r-cran-animint2_2025.10.17-1.ca2404.1_all.deb Size: 4195064 MD5sum: 41b0d65785a8e08bd8e214e864b06024 SHA1: 5ab1883263f1b4a912bcec2985b2c23dd1527657 SHA256: 663d86f0c1fd071acaaf97d84675233aaa2424732b2f70414f9e075afbe0790c SHA512: 6299629a6d280692e40619882d68d3606bc35aaa02b1e13a83bf87a07771c0554d3f8185ef84d0e2546386389a75cf4befef7f994d89bcfac13b02dc101df666 Homepage: https://cran.r-project.org/package=animint2 Description: CRAN Package 'animint2' (Animated Interactive Grammar of Graphics) Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, describes the concepts implemented. Package: r-cran-animl Architecture: all Version: 3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pbapply, r-cran-reticulate Suggests: r-cran-jsonlite, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-animl_3.3.1-1.ca2404.1_all.deb Size: 155308 MD5sum: 1fe9bacb44079b64a3b65e0e4d508d47 SHA1: 0936b4ffac36ee0663bc5a61794c041df66bb046 SHA256: 77a1dd3cffeabe833e9944c39cc8c27400ce5c3a980a804b1f360723d9029d35 SHA512: c6da5559d675d82803e79967347c3b75c350b1548bd42310344e8e5c26b6f2a125a8cb19df501ad81ef04ede2a8ab215d00b10d4a7a21c25dd9046890463b7ba Homepage: https://cran.r-project.org/package=animl Description: CRAN Package 'animl' (A Collection of ML Tools for Species Detection andClassification in Camera Trap Images and Videos) Functions required to classify subjects within camera trap field data. The package can handle both images and videos. 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E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations. Package: r-cran-aniview Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-htmltools Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-aniview_0.1.0-1.ca2404.1_all.deb Size: 87732 MD5sum: fa943c564968022d986fb9e6b55ae136 SHA1: 3d483674261129a462df4fedbbbd79b52478d375 SHA256: c7f29bfcf148f795c328bca34762b84af4e0a9ade68383ac70d22f51c16521c9 SHA512: d5221e17529272c3c6835249f839a94cb3137dfebae02f7b3ba90872d2b9acd6500a04b4442921446a119d34932b64c4016158e713918b414630d7f118463e4b Homepage: https://cran.r-project.org/package=aniview Description: CRAN Package 'aniview' (Animate Shiny and R Markdown Content when it Comes into View) Animate Shiny and R Markdown content when it comes into view using 'animate-css' effects thanks to 'jQuery AniView'. Package: r-cran-ankir Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-jsonlite, r-cran-dbi, r-cran-rsqlite, r-cran-tibble, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-withr, r-cran-dplyr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-ankir_0.6.0-1.ca2404.1_all.deb Size: 1226996 MD5sum: abcfee5fac7412f1e030fc86d9331d2c SHA1: 398c8fee8816c14cacebdab828c4930aad2d60c2 SHA256: 4a7e2812160e615d25f474693c6bfa3830bfab4f03241277fa6c2cbdf80abf59 SHA512: bf6fe3a2c751200ada11504963968f3c991e54439f471eecb664ddff9f9e2af2baf12546a933f6121c0ab41a3c80e693ed4ab79f6b1255674c244eba451944d3 Homepage: https://cran.r-project.org/package=ankiR Description: CRAN Package 'ankiR' (Read and Analyze 'Anki' Flashcard Databases) Comprehensive toolkit for reading and analyzing 'Anki' flashcard collection databases. Provides functions to access notes, cards, decks, note types, and review logs with a tidy interface. Features extensive analytics including retention rates, learning curves, forgetting curve fitting, and review patterns. Supports 'FSRS' (Free Spaced Repetition Scheduler) analysis with stability, difficulty, retrievability metrics, parameter comparison, and workload predictions. Includes visualization functions, comparative analysis, time-based analytics, card quality assessment, sibling card analysis, interference detection, predictive features, session simulation, and an interactive Shiny dashboard. Academic/exam preparation tools for medical students and board exam preparation. Export capabilities include CSV, Org-mode, Markdown, SuperMemo, Mochi, Obsidian SR, and JSON formats with progress reports. Package: r-cran-anndata Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-lifecycle, r-cran-matrix, r-cran-r6, r-cran-reticulate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-anndata_0.8.0-1.ca2404.1_all.deb Size: 229890 MD5sum: 568b14fe6ceb74f1c0b987811b192650 SHA1: 2c8a26bc1f2331c1338a9009eaf6e82c061871b4 SHA256: c2f55626a300b27e1800e83473ad3948e78f4401374a123f420a9802b87bce9d SHA512: 4086a570d67eaebfdafc89cd64fdd717dbffc47273570eff2e7194e4318a7303910f7967f0a9ad306ba2b981e73451a3ca092579e29485cf17be87edc6476748 Homepage: https://cran.r-project.org/package=anndata Description: CRAN Package 'anndata' ('anndata' for R) A 'reticulate' wrapper for the Python package 'anndata'. Provides a scalable way of keeping track of data and learned annotations. Used to read from and write to the h5ad file format. Package: r-cran-annmatrix Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-annmatrix_0.1.2-1.ca2404.1_all.deb Size: 67420 MD5sum: 7e293258891b1c9907112fbf3c333681 SHA1: eb90ee2c5c553c0f4ae64282e692e2dfc57151d4 SHA256: c322acd71388ca2a3e1f52edd2c256fd8196bbfdd9fda3a590ffa9573323e6ea SHA512: 2c92e81291b3f486e9aa4adbfbe322c706b9d2307cf7f7c828ed120568db862dd3e4554d8199b242e9f83466d537a129866b34a2d4840f60e5448afae4f830c7 Homepage: https://cran.r-project.org/package=annmatrix Description: CRAN Package 'annmatrix' (Annotated Matrix: Matrices with Persistent Row and ColumnAnnotations) Implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But 'annmatrix' objects are also expected to be useful in various other contexts. Package: r-cran-annoprobe Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2042 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dt, r-cran-ggpubr, r-cran-pheatmap, r-bioc-biobase, r-cran-xml2, r-cran-httr, r-cran-curl Suggests: r-bioc-limma, r-bioc-geoquery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-annoprobe_0.1.7-1.ca2404.1_all.deb Size: 2054538 MD5sum: f62328faeaaf8595f3d02f01b17eaa31 SHA1: 664c44ed971de28da21bffaf39c99f3b1620f4e4 SHA256: 9e6fc0b8d305c4951e5654a49306a3da59961a6b4e1072226a7bb20d4733f73a SHA512: 894f32d0ef61cc302b7755f08ba116b7051f7205a94fcf1de399b607ba7bbccd4180defa6e84871686f4cc268c633e0c497bb90c05c1878f637427e11e18706f Homepage: https://cran.r-project.org/package=AnnoProbe Description: CRAN Package 'AnnoProbe' (Annotate the Gene Symbols for Probes in Expression Array) We curated 147 of expression array, from 3 species(human,mouse,rat), 3 companies('Affymetrix','Illumina','Agilent'), by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, and then annotate them to genes. Package: r-cran-annotar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gprofiler2, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-bioc-biomart, r-cran-tibble, r-cran-magrittr, r-cran-later, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-annotar_0.1.1-1.ca2404.1_all.deb Size: 130446 MD5sum: 1b8f71bb77b0267106e817a31fd75e05 SHA1: 0cb12c3f7acc55632f6be722bc041c183bbb67b3 SHA256: 6c61545f762a7a05dd25e4a56d7f7b5d90cf997e270912d051feba4151b7dc4b SHA512: 8fc319f6c73b6195674e454af7e2d87f85bd5a1090c3976633e9e0c26b9e142d44c7dcbd25e4d915155aa589c0ccee54167180e9d55e6f526f36363cf4244320 Homepage: https://cran.r-project.org/package=annotaR Description: CRAN Package 'annotaR' (Tidy, Integrated Gene Annotation) A framework for intuitive, multi-source gene and protein annotation, with a focus on integrating functional genomics with disease and drug data for translational insights. Methods used include g:Profiler (Raudvere et al. (2019) ), biomaRt (Durinck et al. (2009) ), and the Open Targets Platform (Koscielny et al. (2017) ). 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Borstein & O'Meara (2018) . 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Annotator has the same function as 'graphics::locator()' but achieves its purpose through drawing, rather than multiple mouse clicks. It is based on the 'htmlwidgets' package and 'fabric.js' JavaScript library . Package: r-cran-annuityrir Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mc2d, r-cran-tseries, r-cran-envstats, r-cran-fitdistrplus, r-cran-actuar Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-annuityrir_1.0-0-1.ca2404.1_all.deb Size: 172374 MD5sum: c688e13ea56abd6e124682d9f54321e1 SHA1: cbd76a1186c9950baa0a44b967959baed4043a3d SHA256: 726c4273a135be0da7535b81f07f11bac9a368199d51dbfb10d5d1204d07e37a SHA512: 52d99dc482bf00007b17d58bf2e30ff67cab6c95174babef86d617f0dcc5a9bef2d7931b4b53ce1d5f1f6893d434740a6d2fab2e2b222567442ff19c8a79d141 Homepage: https://cran.r-project.org/package=AnnuityRIR Description: CRAN Package 'AnnuityRIR' (Annuity Random Interest Rates) Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) . Cruz Rambaud et al. (2015) . Package: r-cran-anocva Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Suggests: r-cran-mass, r-cran-igraph Filename: pool/dists/noble/main/r-cran-anocva_0.1.1-1.ca2404.1_all.deb Size: 41616 MD5sum: 041d4a73ccd8651f9bb547404e5c2da9 SHA1: df9439271360e4807b1be498ce758a34c1e0b3b7 SHA256: a325608fe2be9d250a4ba097579115a5b4c01492302e85a3196ab0612c17bd06 SHA512: 19f074a971c753785a719679decb0dff923408ec5fd2a78578dc0334c6bd9547f3a8ed04e7debfd77e4985c954bd953fc5cdd1d2d392e705c3f9c872463c6ed5 Homepage: https://cran.r-project.org/package=anocva Description: CRAN Package 'anocva' (A Non-Parametric Statistical Test to Compare ClusteringStructures) Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. Package: r-cran-anofa Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrapply, r-cran-superb, r-cran-rdpack, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-anofa_0.1.3-1.ca2404.1_all.deb Size: 181678 MD5sum: 5905da5f4ca9a560d29820e61e0c1909 SHA1: 084cc97dfbc6ca4a2a78194ecbee86f908484f02 SHA256: 562dd4388ea0acbef6046ca4065bd8a66114607b7c29e98d73ada9a62b4c57c9 SHA512: 3d47b0a3475a711ead461e7e74fc797104d0958406a7642bcbcea74b4ecfd504d782acbbc2ec39deac2c21c18f97b49a56d4b2630b55194b4069bc5e28683a84 Homepage: https://cran.r-project.org/package=ANOFA Description: CRAN Package 'ANOFA' (Analyses of Frequency Data) Analyses of frequencies can be performed using an alternative test based on the G statistic. The test has similar type-I error rates and power as the chi-square test. However, it is based on a total statistic that can be decomposed in an additive fashion into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its similarities with ANOVA. This framework also renders plots of frequencies along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models. See Laurencelle and Cousineau (2023) . Package: r-cran-anoint Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-anoint_1.5-1.ca2404.1_all.deb Size: 261310 MD5sum: 58e7fc8390817e685aceba1b6bf2a583 SHA1: 409f2c97069b867368642c783969a3a5dec23375 SHA256: 43aa93002dd0e86934018bcb096c4669fcd0bcb16d2eb5ec117ea5be9bac1db0 SHA512: 23377ac3daf27a569e880e30d46409934038590d831a7c99fdb0a9965bab1946da559a4635da3278b4282cff5b2689ad531244ab3741599fecf5175ebdbf9f4c Homepage: https://cran.r-project.org/package=anoint Description: CRAN Package 'anoint' (Analysis of Interactions) The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) . Package: r-cran-anom Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mcpan, r-cran-multcomp, r-cran-nparcomp, r-cran-simcomp Suggests: r-cran-knitr, r-cran-lme4, r-cran-nlme, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-anom_0.5-1.ca2404.1_all.deb Size: 251684 MD5sum: ed5f73dfa8dc53cbda6f59efd4a83abc SHA1: 0340d597ff6f3bc5b13f295ab210e900065709e8 SHA256: 568ac0c19d03fcbf8834d0e94cb8fe2839da28d9f1741dd6540495a2a1ea8255 SHA512: 91037b03efa76eca9047a49cf40ec5d75c002384fb9689bd1a8f959909c0ae99c4c6bda37cb615d6bd74252ebad590bad5f72061ce7d4cc8061e7f083645c713 Homepage: https://cran.r-project.org/package=ANOM Description: CRAN Package 'ANOM' (Analysis of Means) Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean. Package: r-cran-anomalize Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-timetk, r-cran-sweep, r-cran-tibbletime, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-tidyverse, r-cran-tidyquant, r-cran-stringr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-anomalize_0.3.0-1.ca2404.1_all.deb Size: 3057280 MD5sum: fdc49c05b0d498de5e3c9ea71465ad4a SHA1: bee372a39be7c73c4c5ff266f147c5a8943eec06 SHA256: 2a52e014e9ca600e74a9d6d8c51f7a37d45136602e1cf4515f2565af38693688 SHA512: 471e8e5d7b7dc536085687aaf6323ca075de11f76cda91cdf2f382ba61d97c24d9daa39600b0821d668278ca82908c09d72010fe95e8a8fe1479395c41dda1fb Homepage: https://cran.r-project.org/package=anomalize Description: CRAN Package 'anomalize' (Tidy Anomaly Detection) The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods. Package: r-cran-anomalyscore Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dtw, r-cran-astsa, r-cran-transport, r-cran-tsa, r-cran-rann, r-cran-mass, r-cran-mvlsw Filename: pool/dists/noble/main/r-cran-anomalyscore_0.1.3-1.ca2404.1_all.deb Size: 124818 MD5sum: 63481d305951257cb7be1f082fb5a6a7 SHA1: 10eed5ae9392adddc0398d09a6ecc5722da8dfd5 SHA256: 7f666018b204a1c600f7960efaac5b4db3d2d98659908126c065bb8049768026 SHA512: d0f7cedd6d6fae4454e065033c313e820d3c47849afd1f15f7641bd0d0b90d397ae8816674365879df9ea70a0a24b1b4e41a2c8ce817475650528e3a5af9b171 Homepage: https://cran.r-project.org/package=AnomalyScore Description: CRAN Package 'AnomalyScore' (Anomaly Scoring for Multivariate Time Series) Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided. 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Package: r-cran-anopa Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superb, r-cran-rdpack, r-cran-ggplot2, r-cran-scales, r-cran-rrapply, r-cran-plyr Suggests: r-cran-rmarkdown, r-cran-ggh4x, r-cran-gridextra, r-cran-psych, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-anopa_0.2.3-1.ca2404.1_all.deb Size: 286642 MD5sum: 051fa6024d8738c0a735b5114b5fa7f1 SHA1: 72895f7e4e5811f022a897fcceb84c8acdc379af SHA256: 6426b750046f78830dd26337d60a9fb07609c9c157053e372657a904109d5c2a SHA512: 854985a0f200033538e44e69223cec1bb92be3bab1a4b4bb930d7f23974e156379b4ee0e43080ef2273ce4671cacb292769f780687be5f2ea6f62d8309ae8cf9 Homepage: https://cran.r-project.org/package=ANOPA Description: CRAN Package 'ANOPA' (Analyses of Proportions using Anscombe Transform) Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed data. The 'ANOPA' package can analyze proportions obtained from up to four factors. The factors can be within-subject or between-subject or a mix of within- and between-subject. The main, omnibus analysis can be followed by additive decompositions into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For that reason, we call this set of tools 'ANOPA' (Analysis of Proportion using Anscombe transform) to highlight its similarities with ANOVA. The 'ANOPA' framework also allows plots of proportions easy to obtain along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. Only particularity, the 'ANOPA' computes F statistics which have an infinite degree of freedom on the denominator. See Laurencelle and Cousineau (2023) . 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Supports complex designs with more than two factors and their interactions with a single function call. 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Runtime examples are provided in the package function as well as at . 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Runtime examples are provided in the package function as well as at . Package: r-cran-anovir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-anovir_0.1.0-1.ca2404.1_all.deb Size: 402916 MD5sum: da413e17a9393b47cf89e1f9648a0765 SHA1: 635cd9f5055f9f011db261c52d72dc45679768d8 SHA256: f8db1495506268709390ef367b663444e57fceac8b00790d7a446a31719c005c SHA512: 0b16363baf129b2436b3c83a3bce4bd7e1d24da4e0801680aa3e358d4ebfb1b8531ebfd14204b446234296b84591660042d559c3c6d3b003adbba5241b716a3d Homepage: https://cran.r-project.org/package=anovir Description: CRAN Package 'anovir' (Analysis of Virulence) Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) . Package: r-cran-anscombiser Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-datasaurus, r-cran-gganimate, r-cran-ggplot2, r-cran-maps, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-anscombiser_1.1.0-1.ca2404.1_all.deb Size: 192962 MD5sum: 75dca464b13fe54c56a6cbb9706b4b9b SHA1: f814bb33bb69052b9802c393b1de4a5a9f9cd8c1 SHA256: 2197e70a9b9baec0ac04d22ada8294c5ebdc8fe7d9b0ca4dbc0c458634d197bc SHA512: 3c0fade1fc8bc0a97755e00cd6f2385a2f2a03e98ce1a595cc5e3be687d976b6ba3ad0161c38aa1eafd82dd8e8b29bdb3a7e6f33772cb31cc3a68e7e188c78b4 Homepage: https://cran.r-project.org/package=anscombiser Description: CRAN Package 'anscombiser' (Create Datasets with Identical Summary Statistics) Anscombe's quartet are a set of four two-variable datasets that have several common summary statistics but which have very different joint distributions. This becomes apparent when the data are plotted, which illustrates the importance of using graphical displays in Statistics. This package enables the creation of datasets that have identical marginal sample means and sample variances, sample correlation, least squares regression coefficients and coefficient of determination. The user supplies an initial dataset, which is shifted, scaled and rotated in order to achieve target summary statistics. The general shape of the initial dataset is retained. The target statistics can be supplied directly or calculated based on a user-supplied dataset. The 'datasauRus' package provides further examples of datasets that have markedly different scatter plots but share many sample summary statistics. Package: r-cran-ansm5 Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ansm5_1.1.1-1.ca2404.1_all.deb Size: 568426 MD5sum: 008bf7d500594ff390e88c96203129de SHA1: 8893f8c1fce2a690f05f72a26ce732d9a0d4513a SHA256: b9f4906c4c6a722d3c5ba810140e5cc0bd752399acccca41de42ea1529db5f6b SHA512: 924cb4e91cc7a7e9a1a8f5aed3be9450cf9536ba856a105b4c65a2ca68f56b12c0bcecb78fd882ba61f4df7706525fd43d6314effac26289ed0bab31fa1e8973 Homepage: https://cran.r-project.org/package=ANSM5 Description: CRAN Package 'ANSM5' (Functions and Data for the Book "Applied NonparametricStatistical Methods", 5th Edition) Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive. Package: r-cran-antangiocool Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-rjava, r-cran-rweka, r-cran-rpart Filename: pool/dists/noble/main/r-cran-antangiocool_1.2-1.ca2404.1_all.deb Size: 16819556 MD5sum: fd82424fb7585fa220f981f47d84b250 SHA1: 0125bd1832dc849e9adebd33d629f3d763305135 SHA256: e1be0cdbd313cab5466d3f7365899f4c30da9f12f9d4b18338b3f8037eda4caa SHA512: 7d31f30f9f5ab94fdfd9b74cd1e12f110296b84b42cf9d7bc5783dc3db16698ce32d2c389d32cbaf1e0e751fe5c4319cbd75d5b4024d6a784bc4af72df6cc18c Homepage: https://cran.r-project.org/package=AntAngioCOOL Description: CRAN Package 'AntAngioCOOL' (Anti-Angiogenic Peptide Prediction) Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset. Package: r-cran-antareseditobject Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1845 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-antaresread, r-cran-assertthat, r-cran-cli, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-whisker, r-cran-doparallel, r-cran-dofuture, r-cran-memuse, r-cran-progressr, r-cran-pbapply, r-cran-future, r-cran-plyr, r-cran-yaml, r-cran-lifecycle, r-cran-zip Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-antareseditobject_1.0.1-1.ca2404.1_all.deb Size: 933804 MD5sum: 08d592bdd632593b2c0f1e2b320da180 SHA1: 590a21835d81ec33b34049b2f6d9ff0ac0673337 SHA256: c3fd463f9bf90e62e891f12a6cfd6886491bb94e074bc66ac637df31ae9bae44 SHA512: ca10008ed5a9dfd602afb211ec25d447418f111cd3d8ef7059fcbc4b53852d07d6eb7827d2ec24275f5330ca6eb9c01b9ed17a4b4dbbe7dbab0071eac6a7c001 Homepage: https://cran.r-project.org/package=antaresEditObject Description: CRAN Package 'antaresEditObject' (Edit an 'Antares' Simulation) Edit an 'Antares' simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings. 'Antares' is an open source power system generator, more information available here : . 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This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors. 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This package provides functions that create interactive charts to help 'Antares' users visually explore the results of their simulations. 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The package automates the assignment of species to functional guilds based on trophic strategies, feeding habits, and foraging behavior, using established classification frameworks (Silva et al., 2015 ; Silvestre et al., 2003 ; Delabie et al., 2000 ), and also includes a novel classification system implemented within the package, developed from ant species occurring in urban environments. It also includes routines to flag exotic species of Brazil (Vieira, 2025, unpublished master's thesis), identify endemic species (Silva et al., 2025 ), and classify species rarity and rarity forms of the Atlantic Forest (Silva et al., 2024 ). The package reduces manual effort and improves reproducibility, supporting research and biodiversity management of Neotropical ant communities. 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(2024) , Lenth (2024) ), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA. 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These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod. 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The 'aopdata' package brings annual estimates of access to employment, health, education and social assistance services by transport mode, as well as data on the spatial distribution of population, jobs, health care, schools and social assistance facilities at a fine spatial resolution for all cities included in the project. More info on the 'AOP' website . Package: r-cran-aoptbdtvc Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-mass Filename: pool/dists/noble/main/r-cran-aoptbdtvc_0.0.3-1.ca2404.1_all.deb Size: 89076 MD5sum: b2b7fe8597bfe3a791f8084fdc12b209 SHA1: 696597befdd5fe948231a2be0623eef16631361c SHA256: 513a7af3b802e17de6a18174f016a1616505c48c0d3032fafe88428465f80c05 SHA512: af906faaff5cb0c2e4a93696cd6fb3394d3e3297d970ceb061a51ba2199017f70f419400d2001319b09e6454dd0e2c40f5422afa66637f041fb37b6ba8eec39b Homepage: https://cran.r-project.org/package=Aoptbdtvc Description: CRAN Package 'Aoptbdtvc' (A-Optimal Block Designs for Comparing Test Treatments withControls) A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737. 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As a result, police databases often record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. For events with a location described by either a latitude/longitude or X/Y coordinate pair, and a start and end date/time, this package generates an aoristic data frame with aoristic weighted probability values for each hour of the week, for each observation. The coordinates are not necessary for the program to calculate aoristic weights; however, they are part of this package because a spatial component has been integral to aoristic analysis from the start. Dummy coordinates can be introduced if the user only has temporal data. Outputs include an aoristic data frame, as well as summary graphs and displays. For more information see: Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, Journal of Quantitative Criminology. 18 (1): 23-43. Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that has been discontinued with his permission. 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For more details see - Koleck TA, Dreisbach C, Zhang C, Grayson S, Lor M, Deng Z, Conway A, Higgins PDR, Bakken S (2024) . 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References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) ; Lin & Liao (2008) . 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Package: r-cran-apathe Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-papaja, r-cran-bookdown, r-cran-rmdfiltr, r-cran-rmarkdown, r-cran-assertthat Suggests: r-cran-stringr, r-cran-bibtex, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apathe_0.1.0-1.ca2404.1_all.deb Size: 86108 MD5sum: 44808cbc75370d7aaa3f3c38ae370c61 SHA1: aa97d8a702d6798da636c605068c6626a80f76ec SHA256: 89c09f0078a9ba25131b6706595937b8d0dd2a80dda9e6a5b18fecb8128b452c SHA512: 16588fc063e7771bfa10fa793a7a61cdebfddec1900dec8eaa02d5fefc94bb876fe4a6010752c49fcb5866938868be95a7140f31dd08f8ab1cf9799fb4f731d6 Homepage: https://cran.r-project.org/package=apathe Description: CRAN Package 'apathe' (American Psychological Association Thesis Templates for RMarkdown) Facilitates writing computationally reproducible student theses in PDF format that conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package currently provides two R Markdown templates for homework and theses at the Psychology Department of the University of Cologne. The package builds on the package 'papaja' but is tailored to the requirements of student theses and omits features for simplicity. Package: r-cran-apc Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-plyr, r-cran-reshape, r-cran-plm, r-cran-survey, r-cran-lmtest, r-cran-car, r-cran-aer, r-cran-islr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-apc_3.0.0-1.ca2404.1_all.deb Size: 635810 MD5sum: e08e21be5818fbc0c22db9e61fd7ebbd SHA1: b784aa1031a2a74c3fa8bacf075fc45a1ccbcc1c SHA256: ed292401ad8048b67ea96708c7b0dd01fa6ce7e4a00970629e89c06c8a6437d9 SHA512: 2cff98a167d15417dbc715a0b26aae1eb89f026ce78489bd53a9e754f03a750f15d21744e233465d7fb483645c813ef2c8442030c62fce3de9b6c65cd8e7dfdb Homepage: https://cran.r-project.org/package=apc Description: CRAN Package 'apc' (Age-Period-Cohort Analysis) Functions for age-period-cohort analysis. Aggregate data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses. The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. 2-sample analysis is possible. Mixed frequency data are possible. Individual-level data should have a row for each individual and columns for each of age, period, and cohort. The statistical model for repeated cross-section is a generalized linear model. The statistical model for panel data is ordinary least squares. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) is used. Thus, the analysis does not rely on ad hoc identification. Package: r-cran-apca Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-apca_1.0.0-1.ca2404.1_all.deb Size: 42306 MD5sum: 7a8dbaca71bbb493b9760db8f44faaa3 SHA1: 084cdf7f90169c4916840bd0f7d7dd1f9afc0044 SHA256: 2f9238ddd6461b00ecc033e2effa899d01e507e9b03492c96591db389e63c603 SHA512: 9f6a23a39bf8ce77d70ddce4fde791b546ba940bd15c8fe690949d76bb091b30ff7b66599948ff2ecb11ec42a1303ddd57dcdf2516c63f35028ea85140109a9d Homepage: https://cran.r-project.org/package=apca Description: CRAN Package 'apca' (Advanced Principal Component Analysis) Provides nine computational algorithms for dimensionality reduction via Principal Component Analysis (PCA), built using an object-oriented (S3) architecture. The package includes classical and modern methods: Singular Value Decomposition (SVD) based on Eckart and Young (1936) , Power Iteration based on Hotelling (1933) , QR Algorithm based on Francis (1961) , Jacobi Algorithm based on Jacobi (1846) , Arnoldi Iteration based on Arnoldi (1951) , 'NIPALS' based on Wold (1975) , Alternating Least Squares (ALS) based on Kolda and Bader (2009) , Probabilistic PCA (PPCA) with EM Algorithm based on Tipping and Bishop (1999) , and Generalized Hebbian Algorithm (GHA) based on Sanger (1989) . Package: r-cran-apcalign Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4557 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readr, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-stringi, r-cran-stringdist, r-cran-crayon, r-cran-httr, r-cran-jsonlite, r-cran-curl, r-cran-arrow, r-cran-rlang Suggests: r-cran-janitor, r-cran-tidyr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-here, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apcalign_2.0.1-1.ca2404.1_all.deb Size: 3559680 MD5sum: 5aa9330ad74390bbc80f6cfa7e54f32c SHA1: d5472a03d4838222985e22adb6b3867f2d0a113f SHA256: 32467ebedcd4cba10c8bc8edacc8a1b72392b9bf846a1f79e8bc95c1f0fc4f2a SHA512: a500132614c522f0bbb5f3301bd20a6b8fc7ce0449ac79d94d22406921561731e806d5ccf6f3c0e7dda656f9336b9112ebb1d6173555eb5f8af415ec6eb68e23 Homepage: https://cran.r-project.org/package=APCalign Description: CRAN Package 'APCalign' (Resolving Plant Taxon Names Using the Australian Plant Census) The process of resolving and updating taxon names is necessary when working with biodiversity data. 'APCalign' uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. 'APCalign' also supplies information about the establishment status (i.e. native or introduced) of plant taxa across different states/territories. Package: r-cran-apcanalysis Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-apcanalysis_1.0-1.ca2404.1_all.deb Size: 51356 MD5sum: 5937b8ef35864587729655a2d323c2d8 SHA1: 244333b4c811babc646f2179b573792b0a4de316 SHA256: 42f842643ba5048664240c67222f9bd5f66bfaebff4109f7b1e0bf05efb4123c SHA512: 8e06fa58761f17549b898b1778dbca1d536d58315175dc46cc68498548a9edbfd41f01e6a2cf6981f2391bfa4cc70035e8ce9b2a497d802188b7146c996c5fc9 Homepage: https://cran.r-project.org/package=APCanalysis Description: CRAN Package 'APCanalysis' (Analysis of Unreplicated Orthogonal Experiments using AllPossible Comparisons) Analysis of data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) . Package: r-cran-apci Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-data.table, r-cran-ggpubr, r-cran-stringr, r-cran-gee Filename: pool/dists/noble/main/r-cran-apci_1.0.8-1.ca2404.1_all.deb Size: 1195600 MD5sum: 3ea4cd0643ab60ff412ac30ea2c7559a SHA1: 5e6692f664f208de8226cd843fd3e24327121bcd SHA256: ff1ab59804c3d34e86277b495212b02d7d7343962601d3ff2cd2391d7848c917 SHA512: fd94b4bf4f023dda96fbc1e5c3b569117eca86b7b75671de8f356a62e02f312c9a6099ef728cdff3d90e0f3e549235d0aad128138ce787331e33e72b2ceb202c Homepage: https://cran.r-project.org/package=APCI Description: CRAN Package 'APCI' (A New Age-Period-Cohort Model for Describing and InvestigatingInter-Cohort Differences and Life Course Dynamics) It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics. Package: r-cran-apcinteraction Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-pbapply, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-apcinteraction_0.1.0-1.ca2404.1_all.deb Size: 519588 MD5sum: d2ce0dbe688e8e5a7041cab1fa5f2cb1 SHA1: 9fbace35484b963a11b6afaee987145cc47dc0f8 SHA256: e57bf0dfba5ffd100b45ac6b1a4ea0e03bbb280c095221d3cc847593df161e2c SHA512: c1a2ad6f1fb3a5953350040d2cee41c3723b103174c771b3e16301b4fc20dfc2b4ce14e17c864b05a5d05225711910ddb28e7cf2f0066d0896d450e220a44dac Homepage: https://cran.r-project.org/package=APCinteraction Description: CRAN Package 'APCinteraction' (Nonparametric Interaction Tests in Balanced Two-Way ANOVA Models) Provides novel nonparametric tests, 'APCSSA' and 'APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package 'APCinteractionData' is available on 'GitHub' . Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) . Package: r-cran-apcoa Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-randomcolor, r-cran-ape, r-cran-car, r-cran-cluster Filename: pool/dists/noble/main/r-cran-apcoa_1.3-1.ca2404.1_all.deb Size: 29452 MD5sum: e04de5c0e1ea7cac1d0a82d15f80fda0 SHA1: 63c027c2d97abc687466c638dba814800dc5c27a SHA256: 66fa830183ed1499df4fd7dcddff33b547a3bbb2717bbc2412d9390267ac7991 SHA512: f7747b558dd4ba4e3503c34e645685d9bc1b0a42a6b8eed61b36bf038f63c3cbb889ab5535604889f7faa8deb1514e7c261d54de1fe9090a7542d2eb2cab4b34 Homepage: https://cran.r-project.org/package=aPCoA Description: CRAN Package 'aPCoA' (Covariate Adjusted PCoA Plot) In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101. Package: r-cran-apctools Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6011 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggpubr, r-cran-checkmate, r-cran-knitr, r-cran-ggplot2, r-cran-colorspace, r-cran-dplyr, r-cran-mgcv, r-cran-scales, r-cran-tidyr, r-cran-stringr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-apctools_1.0.8-1.ca2404.1_all.deb Size: 4072214 MD5sum: eb30767d9569efed28cfa54683b0f4d0 SHA1: 8199dfca38652a419f0739692b4db7129cf71503 SHA256: cb52e1ec750b0713a1045aeb0281118e511e243b566f61b60809b0ae9937e614 SHA512: fda39aae1bb32aa46f8db0d5be84d2051b09834ec6be2bbbe661edd04e4c7ffa44db3e1210ef2f734a6245982592cc150a16ad8bfce28c33e2a749051df43da0 Homepage: https://cran.r-project.org/package=APCtools Description: CRAN Package 'APCtools' (Routines for Descriptive and Model-Based APC Analysis) Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) for methodological details. Package: r-cran-apd Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-psych Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apd_1.0.1-1.ca2404.1_all.deb Size: 102020 MD5sum: b789a86fd1f49061171ddc25b052d9a1 SHA1: dd018c91ca96a99594cc86e2898b6a9803aecf2a SHA256: 54a8685aa3930725f25a8fc09925bb8df49d96e1e50ada2cfd10f9c25053cbb9 SHA512: 65c6ea3f6e118638697cf0aa13c579ed3f121505fd900af893a2bc80f5f33a1600130e6907a189740280095ebc57e7c59d7750afc7637240982aa86e32cb4b9d Homepage: https://cran.r-project.org/package=APD Description: CRAN Package 'APD' (Average Proportional Distance) Estimation of average proportional distance for repeatability of responses in scales items, more other supplemental information. Package: r-cran-apdesign Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-apdesign_1.0.0-1.ca2404.1_all.deb Size: 22848 MD5sum: 98898ff7c5555ba31d2c6f272725ab9c SHA1: 053c6f319c4141fdbfa04cb8fa0e2da9df6028cb SHA256: a78a675863380ae5b2c29c395710bb011afd428280358ebb43afac8f9a66e0a4 SHA512: 30c17201d53f03bdb2e794e71d1346b0001ff68a7100ce721c6413048616ff018b341c4aac6d099ade4501cf2e39b09cce17643aba6d266815a2f380ab6f1793 Homepage: https://cran.r-project.org/package=apdesign Description: CRAN Package 'apdesign' (An Implementation of the Additive Polynomial Design Matrix) An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality. 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This reduces the redundancy of the overlapping pathways and helps to notice the most important biological themes in the data (Kerseviciute and Gordevicius (2023) ). Package: r-cran-apercu Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pls Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-apercu_0.2.5-1.ca2404.1_all.deb Size: 27850 MD5sum: 2384fc3da5aa6ba63b293498149198e8 SHA1: f5125168eb76851fe0bbd5d0e650c78e8a7cf94b SHA256: fe27e642c934da6185c20cc93afd3c9b0465fd653bf99ea3e78636c22f7bf5e7 SHA512: ebe24ee33ec2f55b2a5bfbbdecf7def2589eb502947f65459fa03a3c01074449646fcc00ece42a781d06afe146f91f46235e0c22c1ac34a19b676bf4d3074424 Homepage: https://cran.r-project.org/package=apercu Description: CRAN Package 'apercu' (Quick Look at your Data) The goal is to print an "aperçu", a short view of a vector, a matrix, a data.frame, a list or an array. 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'apex' implements the new S4 classes 'multidna', 'multiphyDat' and associated methods to handle aligned DNA sequences from multiple genes. Package: r-cran-apexcharter Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3060 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-rlang, r-cran-ggplot2, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-scales, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-apexcharter_0.5.1-1.ca2404.1_all.deb Size: 754216 MD5sum: b2c45ed1672ee7b8e320e669fabc2f6b SHA1: 332400f464d1121b770b0bb7c2aff8fecd6b4cdf SHA256: e1443741d7b52ff34d087e4e942c149c6d2607efc2f8fcee81a17524bb0a3a2f SHA512: 09e9cefbb22886485ebeed53db4b43bc145e99b73d0bd44a38edd4eeeff8446b9a0214469a72227600d04352f994d0d5f6ec474e919f6f0396d350e5637fac1a Homepage: https://cran.r-project.org/package=apexcharter Description: CRAN Package 'apexcharter' (Create Interactive Chart with the JavaScript 'ApexCharts'Library) Provides an 'htmlwidgets' interface to 'apexcharts.js'. 'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API. 'Apexcharts' examples and documentation are available here: . Package: r-cran-apfr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-apfr_1.0.2-1.ca2404.1_all.deb Size: 63108 MD5sum: c6378eb7cedf55b61f2bd1bf57574446 SHA1: b3a721366e914f6ff22aa973fac93b639c97d8f9 SHA256: 0b21c4f0250308da5643df080b3ae2d150da7b6f57d962103b4392fe6b2fb523 SHA512: b233b53ffe3e4efb6dd6b77a6a5a5eac52041e3d3c1da7fa98137acea6f79bded254fe2812055011bb620c637b951c5ebcaffc0aded645a9980f5dc77da9a0ce Homepage: https://cran.r-project.org/package=APFr Description: CRAN Package 'APFr' (Multiple Testing Approach using Average Power Function (APF) andBayes FDR Robust Estimation) Implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) . Package: r-cran-api2lm Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-api2lm_0.2-1.ca2404.1_all.deb Size: 293748 MD5sum: 12cd3586f4bb55edf792340bda6efe0a SHA1: 1ae51ad65c49beea58443e8771b6e7fd43889bc9 SHA256: e5f6eb5e89958f84e4b0d7619348a11d46b5e10e8edae95e68b9c29db815d01b SHA512: 42d4ef7c8b5ab2eec5ec93e3f9ecc89e190a994c6b55a5c902e128d3a516f47828f98b7f70ee317872daa1f598f451b671898a1124478e72930cca0206ee804d Homepage: https://cran.r-project.org/package=api2lm Description: CRAN Package 'api2lm' (Functions and Data Sets for the Book "A Progressive Introductionto Linear Models") Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (). Package: r-cran-apifetch Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apifetch_0.2.0-1.ca2404.1_all.deb Size: 89218 MD5sum: 4b9ffbd4a9efb9b098cde311ee7bda9c SHA1: 2f955bce44ec3497f906c01a25e7a3e4d3cec6a2 SHA256: 2095cbbd0a8f73087b83d5a40b1c4834d7b2865ff919f7aad751cfdbd9a6e88b SHA512: 110cd948ebffe808ae90a7957a7a3e0fa98547794974be4e2cbf82d9ff30a0b33fe46d046892ef3371b7399bb7ed77ac66521fdf64b08e1d00c18c2877e980f0 Homepage: https://cran.r-project.org/package=apifetch Description: CRAN Package 'apifetch' (Token-Authenticated REST API Retrieval Toolkit) A small, dependency-light toolkit for talking to token-authenticated REST APIs. It manages authentication tokens in process environment variables (never written to disk), builds requests with configurable authentication and pagination strategies, and retrieves paginated data either one page at a time or in chunks combined into a single tibble. The design is API-agnostic: a single 'apifetch_api' profile describes an endpoint together with how it authenticates and paginates, so the same verbs work across different services. 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See . Package: r-cran-aplms Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-mass, r-cran-matrix, r-cran-rlist, r-cran-psych, r-cran-rmutil Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aplms_0.1.0-1.ca2404.1_all.deb Size: 265838 MD5sum: 356b7c127165887c1e873e7ce14a1b3e SHA1: e5bb397b82696900c03a99c78c3220a668777f09 SHA256: ad763e09f97149fe0da38bbc9941815dcb3b9c81142807b909c2fb5aea8575ec SHA512: 8f6932744816bb4b57f6301e18f0cb12781e85b39fcda379fa89ffa873126e248045232a45cde8a62262e73499396806f24270af36bb7e462eb8f2c7c69d79eb Homepage: https://cran.r-project.org/package=aplms Description: CRAN Package 'aplms' (Additive Partial Linear Models with Symmetric AutoregressiveErrors) Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). This setup enables the modeling of a time series response variable using linear and nonlinear structures of a set of explanatory variables, with nonparametric components approximated by natural cubic splines or P-splines. It also accounts for autoregressive error terms with distributions that have lighter or heavier tails than the normal distribution. The package includes various error distributions, such as normal, generalized normal, Student's t, generalized Student's t, power-exponential, and Cauchy distributions. Chou-Chen, S.W., Oliveira, R.A., Raicher, I., Gilberto A. Paula (2024) . Package: r-cran-aplore3 Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-vcdextra, r-cran-nnet, r-cran-survival, r-cran-proc Filename: pool/dists/noble/main/r-cran-aplore3_0.9-1.ca2404.1_all.deb Size: 444194 MD5sum: 4e334569bff836fc480e7cede89ff3c1 SHA1: 54883f101cabcd298e3cf3049887b6642a98be13 SHA256: 3db968f1eee0d6efb5d56417efb2e95ccf5c7a622f9d365fca91c2d9f51b47d0 SHA512: a7d83bfb8073802eb504de55a6b6af703a8c8e4b97c434fcbf4c25db0d9f3c00fe90d477a418eb4ed31bd8eec4ca390ab4fe10f60ad6392776fbd1b7a9cb3c39 Homepage: https://cran.r-project.org/package=aplore3 Description: CRAN Package 'aplore3' (Datasets from Hosmer, Lemeshow and Sturdivant, "Applied LogisticRegression" (3rd Ed., 2013)) An unofficial companion to "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book. Package: r-cran-aplosnca Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-httptest2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-downloader Filename: pool/dists/noble/main/r-cran-aplosnca_1.0.1-1.ca2404.1_all.deb Size: 51026 MD5sum: 83aa1e82384bfe065463625821f1e9fa SHA1: 73f025b69cf9401eb3074606c61eca2cc4457ce4 SHA256: 9cecdda379b48071944618843ea5dc00e36626eca032b0ba2043d89203d5442e SHA512: 7ff95669a472ba3b9d6a5fda781ccb6fa3cd71688c720cebfa8edfbb779e4ab32ccfcb0068b7da36c41cdc0a68ba45162aa628a94c88d22bac44d9395a623c8f Homepage: https://cran.r-project.org/package=AplosNCA Description: CRAN Package 'AplosNCA' (Use 'Aplos NCA API' for Pharmacokinetic Analysis) Using this package you can interact with the 'Aplos NCA API' using standard R functions. 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Package: r-cran-aplotextra Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-cran-dplyr, r-cran-forcats, r-cran-ggfun, r-cran-ggplot2, r-bioc-maftools, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-ggstar, r-cran-yulab.utils Suggests: r-bioc-ggtree, r-cran-data.table, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aplotextra_0.0.6-1.ca2404.1_all.deb Size: 72744 MD5sum: d75461fa1c9e5defc4d68bed78a79a93 SHA1: de23ba05173b275211cea212ce3a9d4dfe035d3c SHA256: 1bbd2896b271bc2869f05259ee343d6f4500ce02389885de71b2e6a55e5c6a5b SHA512: 47d3fc2754b28079c2aefec3fa0fd09a17fabe071586061e52786aae8128bd730ea7c8264f3b2ef2853a1388995c8971a428fa226aaa4b380b0615cf13ff187b Homepage: https://cran.r-project.org/package=aplotExtra Description: CRAN Package 'aplotExtra' (Creating Composite Plots using 'aplot') Many complex plots are actually composite plots, such as 'oncoplot', 'funkyheatmap', 'upsetplot', etc. We can produce subplots using 'ggplot2' and combine them to create composite plots using 'aplot'. 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Package: r-cran-aplpack Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4374 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tkrplot, r-cran-jpeg, r-cran-png Filename: pool/dists/noble/main/r-cran-aplpack_1.3.5-1.ca2404.1_all.deb Size: 3531844 MD5sum: 3cb0115360ef58c9c872039c5a8f7a18 SHA1: 48b757580c6acfe77a40b70d76b524595e4c71d1 SHA256: d00110c7d22a621bea3d2bef2bb790bdfb72020e8abb76ad4e0238a00dd5d381 SHA512: f71cc94dfe7fcb21e12367764b5bb3594e26c5fcfd5461212286a0d6a9e4ce272028bed100ea8229dc66b7ccc077a699cb71ba960dbaeceee071cb97d9cbbf61 Homepage: https://cran.r-project.org/package=aplpack Description: CRAN Package 'aplpack' (Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots',Slider Functions and Others) Some functions for drawing some special plots: The function 'bagplot' plots a bagplot, 'faces' plots chernoff faces, 'iconplot' plots a representation of a frequency table or a data matrix, 'plothulls' plots hulls of a bivariate data set, 'plotsummary' plots a graphical summary of a data set, 'puticon' adds icons to a plot, 'skyline.hist' combines several histograms of a one dimensional data set in one plot, 'slider' functions supports some interactive graphics, 'spin3R' helps an inspection of a 3-dim point cloud, 'stem.leaf' plots a stem and leaf plot, 'stem.leaf.backback' plots back-to-back versions of stem and leaf plot. Package: r-cran-apm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggh4x, r-cran-ggrepel, r-cran-mass, r-cran-sandwich, r-cran-pbapply, r-cran-fwb, r-cran-chk Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-apm_0.1.1-1.ca2404.1_all.deb Size: 504148 MD5sum: 927fc72006c7538d468c503667167577 SHA1: 3d28cd9c0729443591063185a9966b25949ca31b SHA256: 5602aee3dbe4c19d33e91beef99eec6ce650452b8bd3be941afc12a7938932e5 SHA512: 913bfbf497b053d7752466aab3d3812ce809acf5ea10b2e3a56c1a33567d7b70e444f82e5ac9789f7028e77d0a081177e41d0bd1f1bb12563ee047189d36070d Homepage: https://cran.r-project.org/package=apm Description: CRAN Package 'apm' (Averaged Prediction Models) In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models' differential average prediction errors and the posterior distribution of which model is most robust. Package: r-cran-apmx Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-this.path, r-cran-flextable, r-cran-officer, r-cran-tidyselect, r-cran-arsenal Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-apmx_1.1.1-1.ca2404.1_all.deb Size: 242046 MD5sum: 19dc9eb80dd4ba83c5b98fba4530e587 SHA1: 135c64e95e73f8d8c6f31ec5522388f482201a62 SHA256: 5dba31e1839e9499e39f8d9ee619cfaac093eec00e43d50c33cf2e2b82966c02 SHA512: d4c1ea51cfcef2796615095518a950d0db770cd19cf99a1e70644a14ac7313d4735e4383b7f54108304ed9bfc74240c7166034c04801cb84264e73ebc627ead5 Homepage: https://cran.r-project.org/package=apmx Description: CRAN Package 'apmx' (Automated Population Pharmacokinetic Dataset Assembly) Automated methods to assemble population PK (pharmacokinetic) and PKPD (pharmacodynamic) datasets for analysis in 'NONMEM' (non-linear mixed effects modeling) by Bauer (2019) . The package includes functions to build datasets from SDTM (study data tabulation module) , ADaM (analysis dataset module) , or other dataset formats. The package will combine population datasets, add covariates, and create documentation to support regulatory submission and internal communication. Package: r-cran-apng Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops Filename: pool/dists/noble/main/r-cran-apng_1.1-1.ca2404.1_all.deb Size: 47172 MD5sum: cda3f14e0c8711fac1d0673dfe036abb SHA1: d9191ecdd2d135221b2eacc80a80ee0f3513d4b5 SHA256: b1d31d7b20b71cf99e79b2852eca0c74c6c4af211e2557ebb47dfdd8796bde73 SHA512: 0ed6fd20108f15da6c0aa71788e56e1b601f7d6120ddd8347a9ce63f9908e522466a908a4d264300e0de45d78d4e23fdd75089046f420c250d798a19a0eb084d Homepage: https://cran.r-project.org/package=apng Description: CRAN Package 'apng' (Convert Png Files into Animated Png) Convert several png files into an animated png file. This package exports only a single function `apng'. Call the apng function with a vector of file names (which should be png files) to convert them to a single animated png file. Package: r-cran-apor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-apor_0.1.1-1.ca2404.1_all.deb Size: 32852 MD5sum: aee7759ae88d9b5fa09ab1e6af6773a7 SHA1: 98ccdee121463181deffe2b85304b26557f674e6 SHA256: ad135414b10793e91cd9b9267a8c5e2a0c1318336cc5b66677f91d01dae1ce31 SHA512: 2584caf569e5354c5973b04e55c064b1a695ad993eb35dac33debe77ee58706e162e873af95bd555b59f0c212bcc9155ae9af02030e9a9d7cfc8644c288a6e8b Homepage: https://cran.r-project.org/package=apor Description: CRAN Package 'apor' (Assessment of Predictions for an Ordinal Response) Produces several metrics to assess the prediction of ordinal categories based on the estimated probability distribution for each unit of analysis produced by any model returning a matrix with these probabilities. Package: r-cran-appac Architecture: all Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2038 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-kza, r-cran-robustbase, r-cran-strucchange Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-appac_4.0.3-1.ca2404.1_all.deb Size: 1833586 MD5sum: f2c37225ce272cbcafe4d4f40ccf8834 SHA1: 2d815952dd393f4f2c78afba42d2a1b8767496e8 SHA256: 9f8c3934e2af362ecd0ae14e710e9927cc3e38b3e05deec56dcb14b52023cb4d SHA512: e26afd83b9a7e8b1cf7d4d0681ef7330d4a7d89ed0b4711c4789aa41723e4681a2349fec3ba00063eb6c7ac7e8357cdb878670acc5646909b2830e960683f6ff Homepage: https://cran.r-project.org/package=appac Description: CRAN Package 'appac' (Atmospheric Pressure Peak Area Correction for Gas Chromatographywith Standard Detectors) Corrects gas-chromatography peak areas for the influence of ambient air pressure on standard detectors open to the ambient atmosphere, such as the flame ionization detector, whose pressure sensitivity was characterised by Bocek, Novak and Janak (1969) . Unlike the pressure compensation of Ayers and Clardy (1985) , which is combined with a calibration and valid only for a single calibration period of a few days, per-cylinder peak areas are decomposed by principal components into a pressure-correlated component and per-peak drift; a common pressure-sensitivity coefficient (kappa) is estimated with a heavy-tail-robust fit on a drift-reduced signal, and slow drift plus a daily factor are removed. Returns the corrected areas together with a chi-square goodness-of-fit diagnostic. Structural-break detection (package 'strucchange', Zeileis and others (2002) ) is provided for episode-level and variance breakpoint analysis. 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Package: r-cran-aps Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ranger, r-cran-covr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aps_1.0.1-1.ca2404.1_all.deb Size: 486536 MD5sum: b0f439babd4ee001e6e13fcf469812ce SHA1: 153094ecf308e5135b16cdb1ad3a1b00fd4e9dab SHA256: 61d6d4397212909dfd5209d83f2244e3b673fd987f7bada572bd35879e6f21e8 SHA512: da6404fd6e6783b198b70ed002c1dc9f320a50f2b98f68719194913a9770def8de1e0d5e416d9f9a605add7ef6cc911535dc79abbfae5fde88a4f0f794673c72 Homepage: https://cran.r-project.org/package=APS Description: CRAN Package 'APS' (Analysing Prediction Stability of Non-Deterministic PredictionModels) Provides methods to analyse the stability of non-deterministic prediction models. Prediction stability is quantified either as data-based prediction stability (phi) or as model-based prediction stability (psi). The package implements measures for categorical, ordinal, and metric predictions based on repeated model fitting and corresponding predictions. Methods are based on Lange et al. (2025) . Package: r-cran-apsimeval Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-patchwork, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-yaml, r-cran-readxl, r-cran-multcompview, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-apsimeval_0.1.0-1.ca2404.1_all.deb Size: 427148 MD5sum: 405904e513c470b100830c40e992fe59 SHA1: 943879ca7e444c224bf3409d76739d769167908f SHA256: cb1c7f87c94663fb050a3b4c26aee2ea4a74c5bde5a06fd2f21f248696be615e SHA512: 252fcad8dce9974174eb07e86bddaa77b61473ccc61da794aae1cb985b5ce07b683b321eba7328d7f373e96882d60dc3e81dff98f7edae39187debb2af0e1c83 Homepage: https://cran.r-project.org/package=apsimeval Description: CRAN Package 'apsimeval' (Evaluation, Visualisation and Sensitivity Analysis of 'APSIM'Classic Output) Reads Agricultural Production Systems sIMulator ('APSIM') Classic 7.x '.out' files, pairs simulated series with sparse observed measurements, and computes the goodness-of-fit statistics used in crop-model calibration and validation, including the decomposition of root mean squared error into systematic and unsystematic components. Produces publication grade figures with 'ggplot2': one-to-one scatter plots, residual and Taylor diagrams, probability of exceedance curves, multi-variable timelines with a secondary axis, and distribution plots, assembled into multi-panel figures at journal column widths in colour, greyscale or line art. Treatment structure encoded in simulation names is parsed into factor columns, and the sensitivity of an output to those factors is quantified by variance decomposition, one-at-a-time analysis or range screening. A built-in 'shiny' interface watches the output directory and refreshes incrementally as 'APSIM' regenerates its files. Package: r-cran-apsimx Architecture: all Version: 2.8.271-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6894 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-jsonlite, r-cran-knitr, r-cran-rsqlite, r-cran-xml2 Suggests: r-cran-bayesiantools, r-cran-chirps, r-cran-daymetr, r-cran-future, r-cran-ggplot2, r-cran-gsodr, r-cran-listviewer, r-cran-maps, r-cran-metrica, r-cran-mgcv, r-cran-mvtnorm, r-cran-nasapower, r-cran-nloptr, r-cran-parallelly, r-cran-reactr, r-cran-rmarkdown, r-cran-sensitivity, r-cran-soildb, r-cran-sp, r-cran-spdata, r-cran-sf, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-apsimx_2.8.271-1.ca2404.1_all.deb Size: 1375098 MD5sum: 100d493c815a284c39d5940be844c176 SHA1: 9d460709d9ff0f5d5578bc37dd874a08d1581454 SHA256: 12c302c1dd6374c7bc6cd80d5170485ccb3bd46603eecaa8112fea841b011b00 SHA512: a0847945f56d0a2431b4bff727c2a4aa5daea660922f60650b3789a08f581e4a457d24a9f59316911755107e4083794d0d00795f62228d7e154fbfdd2c86a995 Homepage: https://cran.r-project.org/package=apsimx Description: CRAN Package 'apsimx' (Inspect, Read, Edit and Run 'APSIM' "Next Generation" and'APSIM' Classic) The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON') and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to 'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation". For more information about 'APSIM' see () and for 'APSIM' next generation (). Package: r-cran-apt Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-erer, r-cran-car, r-cran-urca Filename: pool/dists/noble/main/r-cran-apt_4.0-1.ca2404.1_all.deb Size: 94462 MD5sum: c17527b9ae88e74001850bca497970c1 SHA1: b3747ec9dc86af7c36b8abee307dae34a519091a SHA256: 6f6acb90855328d9fe89817ada7904d9284954156d6855e1c0ceb4120b8693f1 SHA512: eecbef6e6c87b3555c2e258b7f4e957e398ea155ad5a5997cdb3dd92baef18710c0f6e2327ad1983844fb77a61d9b37b8b03195080a570e07bf55ea36da239b6 Homepage: https://cran.r-project.org/package=apt Description: CRAN Package 'apt' (Asymmetric Price Transmission) The transmission between two time-series prices is assessed. It contains several functions for linear and nonlinear threshold co-integration, and furthermore, symmetric and asymmetric error correction models. Package: r-cran-aptg Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-rotl, r-cran-taxize Suggests: r-cran-fishtree, r-cran-rgbif, r-cran-geosphere, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aptg_0.4.0-1.ca2404.1_all.deb Size: 55872 MD5sum: b7f843cfdc68ca79b37a16145e042efa SHA1: bb704fd48fb5810f33d5d65437c3e088efedd6de SHA256: 91546790f593965a51005b61c89c7c69780876be774a8fc4fb575118cd013649 SHA512: b6db9c3c90ea400eb5b50bab41119f87cb2236b16dc331f39f2adfcbaad8f74c9027bdd359353af0605d667a20e60afa915752c72dac9231205e8b9fe54cf17a Homepage: https://cran.r-project.org/package=aptg Description: CRAN Package 'aptg' (Automatic Phylogenetic Tree Generator) Generates phylogenetic trees and distance matrices from a list of taxon names, or from a higher taxon expanded down to a chosen lower rank. Trees are obtained as induced subtrees of the Open Tree of Life synthetic tree using the 'rotl' package (Michonneau, Brown and Winter, 2016, ). Expansion of a higher taxon to its descendants uses 'taxize' (Chamberlain and Szocs, 2013, ). Package: r-cran-apticalc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2 Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apticalc_0.1.1-1.ca2404.1_all.deb Size: 30038 MD5sum: 1818cc0acb75d02413a07e3516aead5e SHA1: 6daac27f460e11b0e4be6f4648bdf87ba333b4de SHA256: dabbbb4069d94e5a38f6ff76d71573a2c2605e9655600938c6a8b7ad52661886 SHA512: 426ef6ff833e1f131b3993ce4e879360a67c01eadce01a7a7a7977416e7c9f60371b925f744140923d9c093e74659df1219f80b9a61898dc678b1e719769cae1 Homepage: https://cran.r-project.org/package=APTIcalc Description: CRAN Package 'APTIcalc' (Air Pollution Tolerance Index (APTI) Calculator) It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a 'shiny' app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).. Package: r-cran-aptools Architecture: all Version: 6.8.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-cmprsk Filename: pool/dists/noble/main/r-cran-aptools_6.8.8-1.ca2404.1_all.deb Size: 75240 MD5sum: 5315421ac212df458ec238e3180f8aa6 SHA1: 1f95ef43115a647ef3bb026e1996fe6d98dacccc SHA256: 0679524f2fee1f747bf5662c15b322f61696e0a116076d626c5731c220765b5b SHA512: 7fde0a138745970e1ac3a0a0cd8db299a6a841bf9e9b847c1fdb99a552ca90c06f5fd312bb6f791f9d6218b897091fc2df34ee03011e33ae6a2b239b4f9ce90b Homepage: https://cran.r-project.org/package=APtools Description: CRAN Package 'APtools' (Average Positive Predictive Values (AP) for Binary Outcomes andCensored Event Times) We provide tools to estimate two prediction accuracy metrics, the average positive predictive values (AP) as well as the well-known AUC (the area under the receiver operator characteristic curve) for risk scores. The outcome of interest is either binary or censored event time. Note that for censored event time, our functions' estimates, the AP and the AUC, are time-dependent for pre-specified time interval(s). A function that compares the APs of two risk scores/markers is also included. Optional outputs include positive predictive values and true positive fractions at the specified marker cut-off values, and a plot of the time-dependent AP versus time (available for event time data). Package: r-cran-apyramid Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1081 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyselect, r-cran-rlang, r-cran-forcats, r-cran-dplyr, r-cran-scales, r-cran-glue Suggests: r-cran-testthat, r-cran-survey, r-cran-srvyr, r-cran-vdiffr, r-cran-covr, r-cran-outbreaks, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-apyramid_0.1.3-1.ca2404.1_all.deb Size: 808762 MD5sum: dc4f5ec57115cb34afef0c250e15123c SHA1: fcc8421de17724d4a9fa122a2c8a51187ee48fe6 SHA256: 35317640625ff92e0b4190f1ce884850ab0e427ccffa8b6d79892f39c3640f70 SHA512: d313b11603175e5d7484a70b3a53f08ec2b5d659ff5187a539bd4b8e7ff9671f7d1d9eaa53fd7614fcc0f73f202c96c629d68829974e5fe50e1ffb097a81015e Homepage: https://cran.r-project.org/package=apyramid Description: CRAN Package 'apyramid' (Visualize Population Pyramids Aggregated by Age) Provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a 'ggplot' object, allowing the user to further customize the output. This package is part of the 'R4Epis' project . Package: r-cran-aqeval Architecture: all Version: 0.6.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openair, r-cran-dplyr, r-cran-loa, r-cran-ggplot2, r-cran-strucchange, r-cran-segmented, r-cran-mgcv, r-cran-tidyr, r-cran-lubridate, r-cran-purrr, r-cran-ggtext, r-cran-data.table Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-aqeval_0.6.11-1.ca2404.1_all.deb Size: 853932 MD5sum: e1e94fd729ae7214b21eeecaccccf9cd SHA1: 84971e8e3d1f6f86f5406111aeb345a5e1fe992d SHA256: ea762cbf8b4aaba26321cbdbc1cf05cf39110a1979e4046f081c70c1aa46b460 SHA512: 2abe09545b1a7df05a005d9d5cb917ce699f2441fdb7c988f07665929daca0c2c8e7e4d269e5ca4868dd87412eedf38c8db82b7c83f23303f8fae9d0e6469f15 Homepage: https://cran.r-project.org/package=AQEval Description: CRAN Package 'AQEval' (Air Quality Evaluation) Developed for use by those tasked with the routine detection, characterisation and quantification of discrete changes in air quality time-series, such as identifying the impacts of air quality policy interventions. The main functions use signal isolation then break-point/segment (BP/S) methods based on 'strucchange' and 'segmented' methods to detect and quantify change events (Ropkins & Tate, 2021, ; Ropkins et al., 2026, ). Package: r-cran-aqfig Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor Suggests: r-cran-maps Filename: pool/dists/noble/main/r-cran-aqfig_0.9-1.ca2404.1_all.deb Size: 54016 MD5sum: dc904bca663a27661e6a33c1080cacbb SHA1: bd248aa3338b698b3bceb614db6aa6fa1facd8fc SHA256: 9969d081231435785faecc77b8663107d236e9e0a130e586d126cdd2f8f8118c SHA512: 666fc64e16a3c499ce35662ca76160b160454bf4efad3c2d957dffdb2ea8fd98e0cb31a8cb3a7340ab10980df669b7d712463520f83e3679b29fb3eee88398ea Homepage: https://cran.r-project.org/package=aqfig Description: CRAN Package 'aqfig' (Display Air Quality Model Output and Monitoring Data) Display air quality model output and monitoring data using scatterplots, grids, and legends. Package: r-cran-aqir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate Suggests: r-cran-plotly, r-cran-readxl Filename: pool/dists/noble/main/r-cran-aqir_0.1.0-1.ca2404.1_all.deb Size: 33056 MD5sum: 65f439e892f234620af2f3ba60e8deb3 SHA1: 19776284b12bf0afbc69182383569bdab7b2f6f9 SHA256: e0bfa48983c992be8ff7ed354b2d42ee05125b80b7a274e21fa75386a5117df4 SHA512: e0a8f155fda13a7920e7d92263514abd06503c79297d126e4846138bb8518d75e1eb5de9a646f306819a595b900c31207a0fe110af1a2bd7940f35d11852cb8e Homepage: https://cran.r-project.org/package=AQIR Description: CRAN Package 'AQIR' (Air Quality Indexing and Statistical Reporting) Provides a comprehensive framework for Air Quality Index (AQI) analysis from air pollutant concentration data using Central Pollution Control Board (CPCB) criteria. Calculates pollutant-specific AQI sub-indices and derives the overall AQI and its category, identifies the primary pollutant, and summarises, ranks, and visualizes results across monitoring locations. Methodology follows Central Pollution Control Board (2014,"National Air Quality Index") . Package: r-cran-aqlschemes Architecture: all Version: 1.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-aqlschemes_1.7-2-1.ca2404.1_all.deb Size: 353390 MD5sum: fabe9cee756434b901b5ff57b13222cd SHA1: 394c849af239e79cab3960ef5a69c735a21e3f43 SHA256: f11cc100e6d752e90a89feea93c6f835bd3a3b1f0e57dbf38beecc6f934f8068 SHA512: 51cc9fd9eda390d4bd97feecc620e73b52c674e7d32922afe9917abe5e19b175b72db4b7b92e3d283e9ff6ef221cbb6c966dadac2d76fa5788a2ec94895625ec Homepage: https://cran.r-project.org/package=AQLSchemes Description: CRAN Package 'AQLSchemes' (Retrieving Acceptance Sampling Schemes) Functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes were the most used and copied set of standards in the world. They are intended to be used for sampling a stream of lots, and were used in contract agreements between supplier and customer companies. When the US military dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This package is useful because its computer implementation of these tables duplicates that available in other commercial software and subscription online calculators. Package: r-cran-aqp Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-cluster, r-cran-data.table, r-cran-farver, r-cran-digest, r-cran-colorspace, r-cran-ape Suggests: r-cran-mvtnorm, r-cran-soildb, r-cran-sp, r-cran-sf, r-cran-latticeextra, r-cran-tactile, r-cran-compositions, r-cran-markovchain, r-cran-xtable, r-cran-testthat, r-cran-gmedian, r-cran-hmisc, r-cran-tibble, r-cran-rcolorbrewer, r-cran-scales, r-cran-mpspline2, r-cran-soiltexture, r-cran-gower, r-cran-knitr, r-cran-rmarkdown, r-cran-dendextend Filename: pool/dists/noble/main/r-cran-aqp_2.3.2-1.ca2404.1_all.deb Size: 4928584 MD5sum: 681143b62374d3db0ddcb9a88a24f237 SHA1: 1623b154d7fa02b5798f0f9db7da1936a1f18488 SHA256: c849af985377d31fa58e99b2fb9bdd0417c366f3245fff8fc78d440e9ffb331c SHA512: aed7f07c5c3db27052f46894f1b3ec2d2eb788474485ef54d16a1b5932afef88aee6dd8af040bc26b275c42fbde39a73cb9b7a2ed480b50b2e19407061a8b447 Homepage: https://cran.r-project.org/package=aqp Description: CRAN Package 'aqp' (Algorithms for Quantitative Pedology) The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks . These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb . Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice. Package: r-cran-aquaanalytix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aquaanalytix_0.1.0-1.ca2404.1_all.deb Size: 36038 MD5sum: 7aa63969691d3d1cf742252700b24b70 SHA1: 0267310a120fb9321df972d15affe8617d3d7c02 SHA256: 1cc2f5a2e938bd11a071b1847f11405bb52cc618d939d4083d2cc510dd227202 SHA512: 303ce9a8bbb39aa80915f0278d1245e60a53258e6b343b7f4824592b3e3bd57d20c817285d503e7c9da102a37d9dbb7eacafa4c6a3475a11d429d78da6de59f3 Homepage: https://cran.r-project.org/package=AquaAnalytix Description: CRAN Package 'AquaAnalytix' (Water Quality Analysis) A varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875). Package: r-cran-aquabeher Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-sp, r-cran-terra, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-learnr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aquabeher_1.4.0-1.ca2404.1_all.deb Size: 3230258 MD5sum: bf7ef18b777d42db690fae3985b78c60 SHA1: c1a803326338ca6ca34c2464b4607211fb741214 SHA256: 52fd6327eaf4bd3e7a6fa87b0e371b9de7e95fbad9527340f22f9f1e2169f85c SHA512: ab9e51c8bb064c5f72e96c93f61f3a67c1ba83291210065642d636058df2d105b6dbaee5d6a0ff162415ebf927638eaaeb3a0ef34ff338455cca9948365f9c29 Homepage: https://cran.r-project.org/package=AquaBEHER Description: CRAN Package 'AquaBEHER' (Estimation and Prediction of Wet Season Calendar and Soil WaterBalance for Agriculture) Computes and integrates daily potential evapotranspiration (PET) and a soil water balance model. It allows users to estimate and predict the wet season calendar, including onset, cessation, and duration, based on an agroclimatic approach for a specified period. This functionality helps in managing agricultural water resources more effectively. For detailed methodologies, users can refer to Allen et al. (1998, ISBN:92-5-104219-5); Allen (2005, ISBN:9780784408056); Doorenbos and Pruitt (1975, ISBN:9251002797); Guo et al. (2016) ; Hargreaves and Samani (1985) ; Priestley and Taylor (1972) . Package: r-cran-aquacultur Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2942 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-lubridate, r-cran-tidyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aquacultur_1.1.1-1.ca2404.1_all.deb Size: 419038 MD5sum: 10bee02e7ff24eb606b45e04573b9b8d SHA1: 1101c9588ae7558ef4b6d5be50addd04cc92cc6b SHA256: 21f04cc0a36e21149b3aa1c2bfcb4177afecb97120747fbcf6ae46e3051c55eb SHA512: 3d8457b7e3556cc568789ba3d290b58ea5814b4e104b1634651c8425911fe682f8664b28711645a28caec4132c36c1f4948eba4e9838828ac375475b529d029f Homepage: https://cran.r-project.org/package=aquacultuR Description: CRAN Package 'aquacultuR' (A Comprehensive R Tool for Zootechnical Metrics) A collection of functions to compute frequently used metrics for nutrition trials in aquaculture. Implementations include metrics to calculate growth, feed conversion, nutrient use efficiency, and feed digestibility. The package supports reproducible workflows for summarising experimental results and reduces manual calculation errors. For additional information see Machado e Silva, Karthikeyan and Tellbüscher (2025) . Package: r-cran-aquadtree Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-dplyr, r-cran-rlang Suggests: r-cran-sf, r-cran-knitr, r-cran-devtools, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aquadtree_1.0.6-1.ca2404.1_all.deb Size: 4495794 MD5sum: 8e7f05e6748e5f12c40f64a19ae803cf SHA1: cbcd18f5cb8a4e386eaa82aa58758becdaf0bb3f SHA256: 39a0b0091f6239de01419d575187c91575dd702e1ea3728a9495f9e2b70ff16a SHA512: 14ac1f9b9dd90007b0610b67e3792be56374d617025aa637b56b09d1abafd9414c81a6edaced46e69dceb107b65bc8e5f6fa2e8a8af8ce039e0d05baaf53669b Homepage: https://cran.r-project.org/package=AQuadtree Description: CRAN Package 'AQuadtree' (Confidentiality of Spatial Point Data) Provides an automatic aggregation tool to manage point data privacy, intended to be helpful for the production of official spatial data and for researchers. The package pursues the data accuracy at the smallest possible areas preventing individual information disclosure. The methodology, based on hierarchical geographic data structures performs aggregation and local suppression of point data to ensure privacy as described in Lagonigro, R., Oller, R., Martori J.C. (2017) . The data structures are created following the guidelines for grid datasets from the European Forum for Geography and Statistics. Package: r-cran-aquaenv Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Suggests: r-cran-desolve Filename: pool/dists/noble/main/r-cran-aquaenv_1.0-5-1.ca2404.1_all.deb Size: 724926 MD5sum: 8739595ef0eb73551b3354acbd5c30be SHA1: 9f2fa67c1e27d2abb12d35fd735a5b753b9d1db2 SHA256: de2d9f06ebab4204adffa0f62177a8ed64b561f77d12814d1185b15af42a1ab6 SHA512: 121eeedd352ad170776322c9e40fd2a251294e4bcae862a27d9af66b1e90f68e6b9d66924ba78f1a364c6b2269c7ea1df696d08dc5b694b19af42de8788ff225 Homepage: https://cran.r-project.org/package=AquaEnv Description: CRAN Package 'AquaEnv' (Integrated Development Toolbox for Aquatic Chemical ModelGeneration) Toolbox for the experimental aquatic chemist, focused on acidification and CO2 air-water exchange. It contains all elements to model the pH, the related CO2 air-water exchange, and aquatic acid-base chemistry for an arbitrary marine, estuarine or freshwater system. It contains a suite of tools for sensitivity analysis, visualisation, modelling of chemical batches, and can be used to build dynamic models of aquatic systems. As from version 1.0-4, it also contains functions to calculate the buffer factors. Package: r-cran-aquality Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aquality_1.4-1.ca2404.1_all.deb Size: 122572 MD5sum: 534025423f3044ed292077894e27bc40 SHA1: 5f2859882e12cc037e550feed9e099181fddab42 SHA256: b8207de1467aac76c9d48f1607b32f35a8a1acc9192c28c0ee888d4516c89362 SHA512: 1e08957b7be6ddcdc6e25feb19373861555eefe98248044ae2f031da653d65513069ece98fe1e2b1c84f43a511964cbfdc3dd5e87e2d258c337c23f5868f3d5e Homepage: https://cran.r-project.org/package=AQuality Description: CRAN Package 'AQuality' (Water and Measurements Quality) The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component. Package: r-cran-aquaticlifehistory Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-readr, r-cran-broom, r-cran-mumin, r-cran-magrittr, r-cran-rlist, r-cran-minpack.lm, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aquaticlifehistory_1.0.6-1.ca2404.1_all.deb Size: 964096 MD5sum: ca770b4b5aac638eb380e9693446286d SHA1: f789ef8500025864cc2ea7219bcad2eedb745ab3 SHA256: d1ca0bf11b84486e1c3880a4d8bd33b74ba7acdd8d93b12d9201e2ec8a52ab63 SHA512: 5644f1900185b4bc3bf9dd7f028d472dea64b088fe4fff401b86ac8a36d3941c479ee8c5fead9b2dc8b39579249e2f0ab5c2c938bf26eab917d0fbe7ce4336f1 Homepage: https://cran.r-project.org/package=AquaticLifeHistory Description: CRAN Package 'AquaticLifeHistory' (Life History Analysis Tools) Estimate aquatic species life history using robust techniques. This package supports users undertaking two types of analysis: 1) Growth from length-at-age data, and 2) maturity analyses for length and/or age data. Maturity analyses are performed using generalised linear model approaches incorporating either a binomial or quasibinomial distribution. Growth modelling is performed using the multimodel approach presented by Smart et al. (2016) "Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future" . Package: r-cran-aquodom Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-httr, r-cran-memoise, r-cran-cachem, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-lubridate, r-cran-readr, r-cran-glue, r-cran-rlang, r-cran-openxlsx Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/noble/main/r-cran-aquodom_0.1.1-1.ca2404.1_all.deb Size: 33630 MD5sum: 72215abc335aed8604fffd01d6b37565 SHA1: e49ffe7974012e9707d626a5d76cd02bbc527ebe SHA256: 0a40ad6d2a04a57a78d6beebaf0d864cdb379df668dd02ddc5ebf4ffea748613 SHA512: d295961931d8b44e6d16ff295a08b3ba72b2ec4f828e31d1293daa6dfb1bb6edee79b8a648a6f4d94bf14a0737a71dcd1a8252bce7ef9cd3071430c50bd7d22a Homepage: https://cran.r-project.org/package=aquodom Description: CRAN Package 'aquodom' (Access to Aquo domaintables from R (Dutch)) The Aquo Standard is the Dutch Standard for the exchange of data in water management. With *aquodom* (short for aquo domaintables) it is easy to exploit the API () to download domaintables of the Aquo Standard and use them in R. Package: r-cran-ar.matrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-sparsemvn, r-cran-sp Suggests: r-cran-ggplot2, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-ar.matrix_0.1.0-1.ca2404.1_all.deb Size: 441818 MD5sum: fe44e78b526c25346c6c1343d87802dc SHA1: 643d548fa259a4ea446eca066b4bac47ef2d17d4 SHA256: 8ab8f1a9242792c80ce6f13ff36ae32dabc21a51f2ac134bfa15699e41e8b891 SHA512: dd53002014c003593a502d835805b5ae0113f00aaac19f4824da62462210aa07f672fc1b16bca0dbba1ba3dda10752c12902eb38eb2eecd44c912ddc3189f5f2 Homepage: https://cran.r-project.org/package=ar.matrix Description: CRAN Package 'ar.matrix' (Simulate Auto Regressive Data from Precision Matricies) Using sparse precision matricies and Choleski factorization simulates data that is auto-regressive. Package: r-cran-ar Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distrib Filename: pool/dists/noble/main/r-cran-ar_1.1-1.ca2404.1_all.deb Size: 26862 MD5sum: 49a077cf44d4299aab343ee7e31df738 SHA1: d16c7ab68be51ec3317062d3445937609696a263 SHA256: c4964649590ffc7e4327b953ed3257349071d9ffcac83a28091fe259379cfdaa SHA512: 0d8bb227614a57ca8724c5a6e8ef7a611903a51059106f6cbbd35ef59699bceb85257d7a9f7a96b060a7821d0a996586e42f5351f0fd621a50654823d0ac149f Homepage: https://cran.r-project.org/package=AR Description: CRAN Package 'AR' (Another Look at the Acceptance-Rejection Method) In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method. Package: r-cran-arabic2kansuji Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-arabic2kansuji_0.1.3-1.ca2404.1_all.deb Size: 20886 MD5sum: 1d3c4f75540f50feefe0c6c071c2ff30 SHA1: b148b783d33ec3c9dd302cc76be5f8e4bd7db451 SHA256: 2bab3db483d2c11b8bcd7017889bca3fbf42a27b10c5c6c24a286b09a74d4215 SHA512: e4dd5215d3dde6f6e1df856e91b8747dcf254bff0b4148495cbb9843129ddcffd4b30377eb1c388d5edb49942ef9210e51028489e99e994a4f8d1ecaf51f3037 Homepage: https://cran.r-project.org/package=arabic2kansuji Description: CRAN Package 'arabic2kansuji' (Convert Arabic Numerals to Kansuji) Simple functions to convert given Arabic numerals to Kansuji numerical figures that represent numbers written in Chinese characters. Package: r-cran-arabicstemr Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-arabicstemr_1.3-1.ca2404.1_all.deb Size: 78564 MD5sum: ebc0b91bb6007b5c3d7c8a89d3ddfc98 SHA1: 4d329ed589bd092eedb38c30dc8d60dcaad0fde7 SHA256: be124f64c879b6a1d5ac9e31a251643a6450a3c38f6dfb9a9dbd8763f44e26f9 SHA512: c0342652fd83e361013e60c4e900d22aa2cd8b862ba8367daeb89ba67025cda77b5f28b3b886b50ec2ee61946b25c22ca84c271d25af70aeb3ee3b73f963e1ad Homepage: https://cran.r-project.org/package=arabicStemR Description: CRAN Package 'arabicStemR' (Arabic Stemmer for Text Analysis) Allows users to stem Arabic texts for text analysis. Package: r-cran-arakno Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-httr, r-cran-jsonlite, r-cran-phytools, r-cran-rgbif, r-cran-rworldmap, r-cran-rworldxtra Filename: pool/dists/noble/main/r-cran-arakno_1.3.3-1.ca2404.1_all.deb Size: 138116 MD5sum: d5a07b7e92f12793433b5d85388ba33d SHA1: 31c5bf54a5d062bb5779538270fca3efa11cf085 SHA256: 12f454d59dcf8a048d1ca4713cfdc58831c9d1fc75facec5055378a9eff7933b SHA512: 83a6733a3e7e443b280dfed805fa6e95da3afc546041a4a04ca78f7cc52f167b651f100f8a1aa6d0a417b6ac2608206a64cfbe4a5738c722d796387ab52e3014 Homepage: https://cran.r-project.org/package=arakno Description: CRAN Package 'arakno' (ARAchnid KNowledge Online) Allows the user to connect with the World Spider Catalogue (WSC; ) and the World Spider Trait (WST; ) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; ), and mapping. Package: r-cran-aramappings Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clarabel, r-cran-cvxr, r-cran-matrix, r-cran-pracma, r-cran-rglpk, r-cran-slam, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-parallelly, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aramappings_0.2.0-1.ca2404.1_all.deb Size: 1108658 MD5sum: d2de1fba05842f823d3cc8e52a19c7bd SHA1: b260c9975ab809ad52fbed0ca97f485f536a9565 SHA256: 232f7dbc5b2e540bf9c272bfdd53d7e7756455e6c114e8bc354ed05a29cb16e5 SHA512: 753d92fd5faf0113c6a2114e31b84264d7c20e2e2cbf1fbf7f625b0e174edfe478e79cca74dd74b59be44153956961a6b3a23435b5dc4148637e93ef7a30d375 Homepage: https://cran.r-project.org/package=aramappings Description: CRAN Package 'aramappings' (Computes Adaptable Radial Axes Mappings) Computes low-dimensional point representations of high-dimensional numerical data according to the data visualization method Adaptable Radial Axes described in: Manuel Rubio-Sánchez, Alberto Sanchez, and Dirk J. Lehmann (2017) "Adaptable radial axes plots for improved multivariate data visualization" . Package: r-cran-araponga Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3222 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-rlang Suggests: r-cran-curl, r-cran-httr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-araponga_1.1.0-1.ca2404.1_all.deb Size: 2355284 MD5sum: 0357b4230fb2dc151ab763af99bef0b7 SHA1: ebedae638e25d6bca77995dcb616a6e28d991ad0 SHA256: ec337db62d22e0722a2c329e99983e5781ef5277398c842b5205e172cf869386 SHA512: 569e0fc1a27738d3f764cf6e585352e683120dabeee3c338da1254d15d1a74117e9d336f134ddd14b7919c3b52407fc1b5004283b4237289dc1bf9e1defb25f5 Homepage: https://cran.r-project.org/package=araponga Description: CRAN Package 'araponga' (Estimate 3D Orientations from 2D Landmarks) Estimates possible 3D orientations of an object from the 2D image coordinates of two landmarks and an approximate camera-object elevation angle. Because a projected 2D angle can be compatible with multiple 3D pitch, yaw, and view-elevation angles, the package returns compatible sets of orientations rather than a single unconstrained estimate. Package: r-cran-ararredux Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ararredux_1.1-1.ca2404.1_all.deb Size: 276250 MD5sum: 51c93b2df45ade3325dc39c13239765d SHA1: 5b38ba8be493e5ba96431487d24304240bf72d62 SHA256: 18cdf53ec62bd342a3cc58a96a084158d69f088a5bee764c6f9095c5077fef99 SHA512: 49af9c05ff40b4072d657a6587d5ce96b1b192c33577f560753d518646239a1ac293abdc251b39d8ca3b763e59a8ef03f226c26859be8f012754128876496fc7 Homepage: https://cran.r-project.org/package=ArArRedux Description: CRAN Package 'ArArRedux' (Rigorous Data Reduction and Error Propagation of Ar40 / Ar39Data) Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall]. Package: r-cran-arc Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arules, r-cran-r.utils, r-cran-discretization, r-cran-matrix Suggests: r-cran-qcba Filename: pool/dists/noble/main/r-cran-arc_1.4.2-1.ca2404.1_all.deb Size: 224142 MD5sum: c1324e2d42c3000e3071869283088f44 SHA1: 04a547cd2109adb94cec62a8fb4704a4a7998438 SHA256: 4a987402ac6ae4192c25d79004270cc0887a9af351deaa4c9ac87c9c3e2d51a4 SHA512: 00be3f075401a820fde49fa391f5bcc2cc198b92497cbf454884a5c3983bf83b461edb45919ec8ef8fffba175f798fc4c2a5ee47ab782e0cdfc2fa1b3a89a74d Homepage: https://cran.r-project.org/package=arc Description: CRAN Package 'arc' (Association Rule Classification) Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) , contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested. Package: r-cran-arcgeocoder Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidygeocoder Filename: pool/dists/noble/main/r-cran-arcgeocoder_0.4.1-1.ca2404.1_all.deb Size: 534804 MD5sum: 26d1f9c6c399d0598b5c7ae8c2d08807 SHA1: 06d48969bc9cd51562710f88193751a370d2605c SHA256: 5668721f589aec6f53f96f6394ae66bbd6bbb373ceb910602e3346119f372bdd SHA512: 27554baee9504a21e15690d773f0e98044118b3ce33daf998e7a453ab6fa350826cc4fdd04cc2fd4887cabd75a9fa828bf6c44455076fe2a57fc9f37fae68333 Homepage: https://cran.r-project.org/package=arcgeocoder Description: CRAN Package 'arcgeocoder' (Geocoding with the 'ArcGIS' REST API Service) Lightweight interface for converting addresses into geographic coordinates and coordinates into addresses using the 'ArcGIS' REST API service . 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Package: r-cran-arcgis Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arcgisgeocode, r-cran-arcgislayers, r-cran-arcgisplaces, r-cran-arcgisutils, r-cran-cli, r-cran-httr2 Suggests: r-cran-arcpbf, r-cran-calcite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arcgis_0.2.0-1.ca2404.1_all.deb Size: 15246 MD5sum: 849a857dde230dcafb3f7124aaae6e30 SHA1: 5b78cdfa3ff397eb63ef621d3eaaee6c86d38dc6 SHA256: b05b1e47c0263d303476e9a9a6a52e125587ef48d2191e6d5399dac61153e771 SHA512: f7a93a860223624225381d6ae5f41a6a01706f2978240d3c2372cdb7d133f67075d718f8c76e48c508c5a954ad93047289774eba8fbc2e0e88c51191830a9066 Homepage: https://cran.r-project.org/package=arcgis Description: CRAN Package 'arcgis' (ArcGIS Location Services Meta-Package) Provides easy installation and loading of core ArcGIS location services packages 'arcgislayers', 'arcgisutils', 'arcgisgeocode', and 'arcgisplaces'. Enabling developers to interact with spatial data and services from 'ArcGIS Online', 'ArcGIS Enterprise', and 'ArcGIS Platform'. Learn more about the 'arcgis' meta-package at . Package: r-cran-arcgislayers Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arcgisutils, r-cran-arcpbf, r-cran-cli, r-cran-httr2, r-cran-jsonify, r-cran-lifecycle, r-cran-rcppsimdjson, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-yyjsonr Suggests: r-cran-testthat, r-cran-vctrs, r-cran-curl, r-cran-dplyr, r-cran-arrow, r-cran-geoarrow Filename: pool/dists/noble/main/r-cran-arcgislayers_0.7.0-1.ca2404.1_all.deb Size: 356774 MD5sum: f59e44c98a9cc3daca7d39c0ff8ad7b5 SHA1: a610b752812015df9e873491cc1c36d616d0d2bc SHA256: f404e53c0509291e73f958920c123f94cf25e3f4b34d23ab8cd7d50b875c6241 SHA512: 45bc9310c41e5fd6c259f205c77bc703f48a335466cd2289c13ab4a924038ff352f1fa73b1e946883aedb94a78786f574c925f8e0f5ece8522124757fd48c660 Homepage: https://cran.r-project.org/package=arcgislayers Description: CRAN Package 'arcgislayers' (Harness ArcGIS Data Services) Enables users of 'ArcGIS Enterprise', 'ArcGIS Online', or 'ArcGIS Platform' to read, write, publish, or manage vector and raster data via ArcGIS location services REST API endpoints . Package: r-cran-archaeophases.dataset Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-archaeophases.dataset_0.2.0-1.ca2404.1_all.deb Size: 3398934 MD5sum: e42eb5147a95739733a90e938354be24 SHA1: 6c5158c8050cd7ae8434de52804ae88769ba6e49 SHA256: 67e3714bcd3ecd13746a927ea71ab8713d7da9e5a7994aeba75455338b2da908 SHA512: b475c798b5080028a7a8d11f683f5368dfcdb56d302f34836f9381f25fe189a61c8e1aeb31518aed1a5cce9b2896bc92940e32d680fbc68732c17cdaca63036b Homepage: https://cran.r-project.org/package=ArchaeoPhases.dataset Description: CRAN Package 'ArchaeoPhases.dataset' (Data Sets for 'ArchaeoPhases' Vignettes) Provides the data sets used to build the 'ArchaeoPhases' vignettes. The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size. Package: r-cran-archaeophases Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3232 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aion, r-cran-arkhe Suggests: r-cran-coda, r-cran-fontquiver, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-archaeophases_2.1.1-1.ca2404.1_all.deb Size: 1935340 MD5sum: 93a548e0a5ce8c41e88d41579010baac SHA1: 3e649223c9bf0fdb79cf46b04c40e9cd81503e84 SHA256: c39086cb2321e3dbbd3bc1fece1291cf4f0441dfb6d41e76d953f0d0930f5ff0 SHA512: 95e720a3af629a43358fb1a59d0a15d9f4e73d27b1fe74394e8f22a8b20333d5b1afe1f07a01660c7eb37798ae3c42601d74c91aa81ade92332c8f7e1b376c58 Homepage: https://cran.r-project.org/package=ArchaeoPhases Description: CRAN Package 'ArchaeoPhases' (Post-Processing of Markov Chain Monte Carlo Simulations forChronological Modelling) Statistical analysis of archaeological dates and groups of dates. This package allows to post-process Markov Chain Monte Carlo (MCMC) simulations from 'ChronoModel' , 'Oxcal' or 'BCal' . It provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases) as described in Philippe and Vibet (2020) . Package: r-cran-archdata Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ca, r-cran-circular, r-cran-plotrix, r-cran-mass, r-cran-spatstat Filename: pool/dists/noble/main/r-cran-archdata_1.2-1-1.ca2404.1_all.deb Size: 160340 MD5sum: cecf643208e5ed6e01171a546275650d SHA1: 1315620eb251a4f62f48dfb42523220968b2aaa0 SHA256: 0decd2c1e54c126ffa7eabe7b75abdbdcdf2cdcd70d8625a851ca40821e62da6 SHA512: a720a840900b83b0b6352ffac3701f3cfa9ab0714da07bdb09dd3d4ca00e01161db15a06cf42180a3271efa5a54b7fb5cc93f379e3d9fe6e51832026d7de56f5 Homepage: https://cran.r-project.org/package=archdata Description: CRAN Package 'archdata' (Example Datasets from Archaeological Research) The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology. Package: r-cran-archeofrag.gui Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-archeofrag, r-cran-dendextend, r-cran-doparallel, r-cran-dorng, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-shiny, r-cran-shinythemes Suggests: r-bioc-rbgl Filename: pool/dists/noble/main/r-cran-archeofrag.gui_1.2.0-1.ca2404.1_all.deb Size: 410800 MD5sum: 820102b6c114305f9da734ff46dc7dd9 SHA1: 1313897f1d42e95042f8c4ad22b05c56683047de SHA256: 3e88ca575e5002c332c01e296590d212f6f6c72dd481fe9a68c894294b618eac SHA512: 27502fb1798e60d352d43436a90a25bbef3c8c86071a1f1903108b49cef63014d07c9e1b3d560ccf4a070159ae59540cc56f9e861f95aa65a3d5f7bce03222c5 Homepage: https://cran.r-project.org/package=archeofrag.gui Description: CRAN Package 'archeofrag.gui' (Spatial Analysis in Archaeology from Refitting Fragments (GUI)) A 'Shiny' application to access the functionalities and datasets of the 'archeofrag' package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and export, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, data export to the 'fabryka' application for spatial orientation analysis, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the 'openMOLE' model exploration software. A demonstration of the app is available at . Package: r-cran-archeofrag Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph Suggests: r-bioc-rbgl, r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archeofrag_1.3.0-1.ca2404.1_all.deb Size: 876142 MD5sum: ddf485e50d2cedb32d03eb53678c4a92 SHA1: 76d6243bd8b4b34336f7a0235fcfa054039a0db9 SHA256: 93c074467a2275566d419e5fdd06205c8e3759fa2ea905172c473b318265637b SHA512: 099b0d62a5342ce44fc9b1e579ef82f3eed83c0d3f95fb2fe9bc8cac72a697003d18f40fa9f07381d2678034cdd93bbd61ee627fa97d27684f3303193f03d222 Homepage: https://cran.r-project.org/package=archeofrag Description: CRAN Package 'archeofrag' (Spatial Analysis in Archaeology from Refitting Fragments) Methods to analyse spatial units in archaeology from the refitting relationships between fragments of objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, visualise, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. A series of published empirical datasets is included. Documentation about 'archeofrag' is provided by a vignette and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, ) and Plutniak (2022, Journal of Open Source Software, ). This package is complemented by the 'archeofrag.gui' R package, a companion GUI application available at . Package: r-cran-archeoviz Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-mgcv, r-cran-geometry, r-cran-mass, r-cran-reshape2, r-cran-svglite, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinythemes, r-cran-knitr Suggests: r-cran-covr, r-cran-seahors, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archeoviz_1.4.3-1.ca2404.1_all.deb Size: 364462 MD5sum: 00e552d12825bb1b3c846b27c23db222 SHA1: 06251456636fcded7b8c4d80a4fc3eb6f98226a1 SHA256: 33cb0eb8e163122743822220a95ea71e4e0776ae337e596dde8c4f89bc1d00c6 SHA512: a28f2c77f11ac3b829d8f56c9569ffa69b6e8c70b3aa99250f55771533ff724903dbb86bfb7123c15152a67ce7d8aa05db4a772c54ade052958fe55d99bb9964 Homepage: https://cran.r-project.org/package=archeoViz Description: CRAN Package 'archeoViz' (Visualisation, Exploration, and Web Communication ofArchaeological Spatial Data) An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at . Package: r-cran-archetypal Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-geometry, r-cran-inflection, r-cran-doparallel, r-cran-lpsolve, r-cran-plot3d, r-cran-entropy Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-archetypal_1.3.1-1.ca2404.1_all.deb Size: 3970238 MD5sum: 54c575ac49341a09454458ab74bddeb0 SHA1: 2ad801dcbab0d6723b290ee50c585ab1e0d31e2e SHA256: da7fc85360525c01a20ea5314c636527bbeb962f6817d9fb834296ec7fb3f5f0 SHA512: 40d105946fa26cf4f0dc25886d2e5dda2f5a34c935b77ee8fa63bee7250983dca69a7f20815c648aa703c0563190e7f46a8e54fb149fd7e1a99fdae240c10f90 Homepage: https://cran.r-project.org/package=archetypal Description: CRAN Package 'archetypal' (Finds the Archetypal Analysis of a Data Frame) Performs archetypal analysis by using Principal Convex Hull Analysis under a full control of all algorithmic parameters. It contains a set of functions for determining the initial solution, the optimal algorithmic parameters and the optimal number of archetypes. Post run tools are also available for the assessment of the derived solution. Morup, M., Hansen, LK (2012) . Hochbaum, DS, Shmoys, DB (1985) . Eddy, WF (1977) . Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) . Christopoulos, DT (2016) . Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), . Christopoulos, DT (2015) . Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), . Package: r-cran-archetyper Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-here, r-cran-knitr, r-cran-log4r, r-cran-config, r-cran-tidyverse, r-cran-rmarkdown, r-cran-skimr, r-cran-stringr, r-cran-snakecase, r-cran-testthat, r-cran-bannercommenter, r-cran-feather, r-cran-readr, r-cran-ps Suggests: r-cran-rcurl, r-cran-mass, r-cran-broom, r-cran-caret, r-cran-covr, r-cran-gtsummary, r-cran-performance, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-archetyper_0.1.0-1.ca2404.1_all.deb Size: 1047428 MD5sum: ed5273d6f569ac67e5abd363be09e56f SHA1: 451f3957f48a700d98eecd40201cf00eef4bfa91 SHA256: 6bf4058122199802e9015942c35b1bb57df1a92c4041526fda34ba1c5ac16015 SHA512: 1ab3e4474058f2ef522a16c52aa634344c3a9589ee4939045e826f05a85128e03a80b2c8ceeec0a4f03d87571dd360f4ed907093bdf6f5d6c55af02bfb209ff9 Homepage: https://cran.r-project.org/package=archetyper Description: CRAN Package 'archetyper' (An Archetype for Data Mining and Data Science Projects) A project template to support the data science workflow. Package: r-cran-archetypes Architecture: all Version: 2.2-0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltools, r-cran-nnls Suggests: r-cran-mass, r-cran-vcd, r-cran-mlbench, r-cran-ggplot2, r-cran-tsp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-archetypes_2.2-0.2-1.ca2404.1_all.deb Size: 1190804 MD5sum: ad8e20198e17b40cf206f47736977a69 SHA1: 4a857f24bbad4697fc6f4f1b1c4b90c1b37d5cf1 SHA256: 62625f877fcdd285bbac3f26e33ab09d8a983a4e47e3f1f2c09437676de90823 SHA512: 9ce3faae398e95205382bb1d9b408cd21f1e0687570af74edae5f829c8e490c4030751f65ab332e5c07dd2ac7c8e8b9ee75456636637a3050e3574e61fa6a2aa Homepage: https://cran.r-project.org/package=archetypes Description: CRAN Package 'archetypes' (Archetypal Analysis) The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm. Package: r-cran-archidart Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-xml, r-cran-geometry, r-cran-sp Suggests: r-cran-rgl, r-cran-tda Filename: pool/dists/noble/main/r-cran-archidart_3.4-1.ca2404.1_all.deb Size: 1671690 MD5sum: 8d0efee9df9d84e7b8e0741e608e3358 SHA1: fc4ca5574ec65833d74890d031b45b4078a447b1 SHA256: 405995f3e5fba123e1d10c817a5a5108b7d8c93716d1dfa8938eb86ce536c162 SHA512: 9d46a16eaf3c2cc51aa9a3c936e603ed80844b863a373b1df1d33bb2eca5a742ea47c15c328b2d52bf59a5229de93965640fbfdbb2f72a0908599f697fb09ef0 Homepage: https://cran.r-project.org/package=archiDART Description: CRAN Package 'archiDART' (Plant Root System Architecture Analysis Using DART and RSMLFiles) Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, ), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, ). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, . Package: r-cran-archipelago Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archipelago_0.1.0-1.ca2404.1_all.deb Size: 967744 MD5sum: e9d173a472582e8b976251c55e37fa18 SHA1: 0c2ecc9bc284046b1e46acabf568dacd8cce609c SHA256: 624573988eb3f7d5dbcc1688d4bd3e11a820db84bf11449beace8d3d4caa8e07 SHA512: c7192973d8618cadead79f9a43c514b53077e2467e17e94aa2697b497fad46e5ea2c4c120e4965710ea381e8669c0f72d3b92f5244ab75435f0be5a2bed08d18 Homepage: https://cran.r-project.org/package=archipelago Description: CRAN Package 'archipelago' (Visualising Variant Set Association Test Results) Provides a graphical method for joint visualisation of Variant Set Association Test (VSAT) results and individual variant association statistics. The Archipelago method assigns genomic coordinates to variant set statistics, allowing simultaneous display of variant-level and set-level signals in a unified plot. This supports interpretation of both collective and individual variant contributions in genetic association studies using variant aggregation approaches. For more see Lawless et al. (2026) . Package: r-cran-archipelagoengine Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-magrittr Suggests: r-cran-splm, r-cran-spatialreg, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archipelagoengine_0.1.1-1.ca2404.1_all.deb Size: 406374 MD5sum: a12be4c18951afe279aa485d3430511b SHA1: b6797a68f9d5feca1010a3daf5a6fd0c4af8f565 SHA256: 15e25093a89fa1ce3bc56808c9c7a5c3d07cdda70a5e1179f546be2ed3494aee SHA512: b6a23d974430164a5c7b8dcc78353a86f54ab15571959ddf6cba8e1188ad063eaf0db2c253d27360415e8dbf32210e1d5f6b7d0da669a47e9645d04393abd09a Homepage: https://cran.r-project.org/package=ArchipelagoEngine Description: CRAN Package 'ArchipelagoEngine' (Spatial Weight Construction for Archipelagic Geographies) Implements specialized K-Nearest Neighbor (KNN) logic to address the unique challenges of spatial modeling in archipelagic environments. Standard contiguity models often leave significant portions of island nations (e.g., 20% of the Philippines) mathematically isolated. This package provides tools to ensure 100% network connectivity, neutralizing spatial bias and enabling robust econometric inference. Methodology follows Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) . Package: r-cran-archissur Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gpcsign, r-cran-dicekriging, r-cran-kriginv, r-cran-future.apply, r-cran-truncatednormal, r-cran-randtoolbox, r-cran-rgenoud Suggests: r-cran-future, r-cran-dicedesign, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-archissur_0.0.1-1.ca2404.1_all.deb Size: 91040 MD5sum: 13c0ae45ae87500b409632c0773458f6 SHA1: d081e468deef8600905d2958c74cc51ad0f825e2 SHA256: 1acc55bdd75d7003ef89fcd8bc2f1b5564e7c577c95b5fd3b3f102f25d8116ef SHA512: 7b2be215ef90959d826fae5bce8fbe3d6d7c14886e0f8ae45bcd4b82b953e41dcd5509cc01facf8cf3702e557929c3557516bb0731ff5e59ff45b49d5b804941 Homepage: https://cran.r-project.org/package=ARCHISSUR Description: CRAN Package 'ARCHISSUR' (Active Recovery of a Constrained and Hidden Set by StepwiseUncertainty Reduction Strategy) Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025). Package: r-cran-archiveretriever Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-anytime, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-vcr, r-cran-testthat, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-archiveretriever_0.4.1-1.ca2404.1_all.deb Size: 282966 MD5sum: ec77813fffc6ad5d3e8fe82fe3cd044e SHA1: 33b6618866fe518e02d8ff0ebd5eadcdc2ead375 SHA256: f6786cc2dcd7e25819c4463eadac717dcb10caa9acd747887d7069912d3d4bd7 SHA512: 7a605c5367e796f4e547341c761edec8daeabc369f1625e24a355cfe894fc17bc7c38ca4974e6341520f562eb068deafec43cd544e593dd60556f7dd3fe5599a Homepage: https://cran.r-project.org/package=archiveRetriever Description: CRAN Package 'archiveRetriever' (Retrieve Archived Web Pages from the 'Internet Archive') Scraping content from archived web pages stored in the 'Internet Archive' () using a systematic workflow. Get an overview of the mementos available from the respective homepage, retrieve the Urls and links of the page and finally scrape the content. The final output is stored in tibbles, which can be then easily used for further analysis. Package: r-cran-archivist.github Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-archivist, r-cran-httr, r-cran-git2r, r-cran-jsonlite, r-cran-digest Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-archivist.github_0.2.6-1.ca2404.1_all.deb Size: 996944 MD5sum: 5fa7c86ac639ffea6cca35118bbd198b SHA1: 112cd678f8d3cfbcac589f9836502219ee7133f3 SHA256: cd4b7b648d4e92e0913043fd6b04bb57e217cd9f4f201d3503b20909d98a2e47 SHA512: 27d957c6dfe869008d0229aef1f94389ad644ace28ab2d6c1bdc475826c86857c775e073bd97cd6c50c1408f81389d4911b9706fe0a0fe869185d7c4a17f97d4 Homepage: https://cran.r-project.org/package=archivist.github Description: CRAN Package 'archivist.github' (Tools for Archiving, Managing and Sharing R Objects via GitHub) The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package. 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Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. The more projects we work with the more artifacts are produced and the harder it is to manage these artifacts. Archivist helps to store and manage artifacts created in R. Archivist allows you to store selected artifacts as a binary files together with their metadata and relations. Archivist allows to share artifacts with others, either through shared folder or github. Archivist allows to look for already created artifacts by using it's class, name, date of the creation or other properties. Makes it easy to restore such artifacts. Archivist allows to check if new artifact is the exact copy that was produced some time ago. That might be useful either for testing or caching. Package: r-cran-arco Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-boot Filename: pool/dists/noble/main/r-cran-arco_0.3-1-1.ca2404.1_all.deb Size: 92890 MD5sum: a0773e08ef1514dd47a4c69e663d53ef SHA1: bf60ee7b555ec738ca5e8df4e42e043ceb70a672 SHA256: c359a003ae71382805aa099c6263cac2b183866f5b07287d7ebc3a45f05dff55 SHA512: f1ed484058fd6d3e5af7edd68ddd5a97d4f7641ebfb2f5dace3f1e06d8d85663d1db4b7cbcf63e0550a17d3941224a8c82f011ae12ef97d45db4c393f6d0b94a Homepage: https://cran.r-project.org/package=ArCo Description: CRAN Package 'ArCo' (Artificial Counterfactual Package) Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) . 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Package: r-cran-arctools Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2803 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-runstats Suggests: r-cran-testthat, r-cran-data.table, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-arctools_1.1.6-1.ca2404.1_all.deb Size: 486638 MD5sum: 62c141e438f4556317b024aa3ed4cdfb SHA1: 4fe545a2d5cb9bde890832eb48decd80086dd453 SHA256: ab40b40be42c9885161e28999b1cee657ae8110dde548aea3663e4b78250e2cd SHA512: 00b4c83fd71b624f995d7a1e906aa7bb8e46ff02d62ba6c311fe4ef59d2bdcfc9058a8f9c352d84a73b4cd06efc72417beef0fa8dc7e38eb0ca0fd45494a2b49 Homepage: https://cran.r-project.org/package=arctools Description: CRAN Package 'arctools' (Processing and Physical Activity Summaries of Minute LevelActivity Data) Provides functions to process minute level actigraphy-measured activity counts data and extract commonly used physical activity volume and fragmentation metrics. 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Package: r-cran-ard Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-osqp, r-cran-quadprog, r-cran-boot, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ard_0.1.0-1.ca2404.1_all.deb Size: 90542 MD5sum: 21418029250e425969112a6eb1301724 SHA1: 91eb6530201a24ff07177ddab659b89230441e30 SHA256: 3ae0ae1b509cc217565d1b976fbf9f3a4e3fa4dd54cd2bc840d5c5e97d73f0bd SHA512: 223b159b9b2ecff73a95d05c3042b140609f9a405810f0f513b703e84bb5bc239602e4d6299545fd597efb8b06261513d449f38f48ca627b611c02b5cfe08613 Homepage: https://cran.r-project.org/package=aRD Description: CRAN Package 'aRD' (Adjusted Risk Differences via Specifically Penalized Likelihood) Fits a linear-binomial model using a modified Newton-type algorithm for solving the maximum likelihood estimation problem under linear box constraints. Similar methods are described in Wagenpfeil, Schöpe and Bekhit (2025, ISBN:9783111341972) "Estimation of adjusted relative risks in log-binomial regression using the BSW algorithm". In: Mau, Mukhin, Wang and Xu (Eds.), Biokybernetika. De Gruyter, Berlin, pp. 665–676. Package: r-cran-ardec Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ardec_2.1-1-1.ca2404.1_all.deb Size: 407628 MD5sum: 9e395e4eb1215b8a539e1666d9511afb SHA1: 47b1061cd7cc9a6acf5d71fe3e758be3524f7242 SHA256: 4e81b6982974e8b529015c76df2cc162b12da84351cfa01cdd244eb0e6ce508c SHA512: 11a08e551a3c18574dd6b6c0545f3b129653042604833427a87b4e142fbd4d2b545c12a5da60c4aca05423f259f1688003ed571b7dfe223ec9cfb47ced023fc1 Homepage: https://cran.r-project.org/package=ArDec Description: CRAN Package 'ArDec' (Time Series Autoregressive-Based Decomposition) Autoregressive-based decomposition of a time series based on the approach in West (1997). Particular cases include the extraction of trend and seasonal components. Package: r-cran-ardeco Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-ghql, r-cran-jsonlite, r-cran-stringr, r-cran-dplyr, r-cran-arrow, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-httptest2 Filename: pool/dists/noble/main/r-cran-ardeco_2.2.3-1.ca2404.1_all.deb Size: 74200 MD5sum: 85b6c187aca331ac3a51b7035a6df111 SHA1: f00d485ff21d12b46d80dabaa7c3efff1e4afeb2 SHA256: a986224861a8f0e78d0fc25323034179ad96e0a9b51eb678acee270d8610ff07 SHA512: fc61081e0cb580617599c4cd08c961e8dd4dff47358167fbd71bec9c47400a06cb3e940cbd9d04dc6d7ae52bac8c2c4d2fa09dfb37ee2141002bc221fcda5e72 Homepage: https://cran.r-project.org/package=ARDECO Description: CRAN Package 'ARDECO' (Annual Regional Database of the European Commission (ARDECO)) A set of functions to access the 'ARDECO' (Annual Regional Database of the European Commission) data directly from the official ARDECO public repository through the exploitation of the 'ARDECO' APIs. The APIs are completely transparent to the user and the provided functions provide a direct access to the 'ARDECO' data. The 'ARDECO' database is a collection of variables related to demography, employment, labour market, domestic product, capital formation. Each variable can be exposed in one or more units of measure as well as refers to total values plus additional dimensions like economic sectors, gender, age classes. Data can be also aggregated at country level according to the tercet classes as defined by EUROSTAT. The description of the 'ARDECO' database can be found at the following URL . Package: r-cran-ardl.nardl Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gets, r-cran-plyr, r-cran-dplyr, r-cran-rlist, r-cran-nardl, r-cran-car, r-cran-lmtest, r-cran-texreg, r-cran-stringr, r-cran-tseries, r-cran-sandwich, r-cran-purrr, r-cran-tidyselect Suggests: r-cran-dynamac Filename: pool/dists/noble/main/r-cran-ardl.nardl_1.3.0-1.ca2404.1_all.deb Size: 297998 MD5sum: c322aabf9e50df829d042b3a124ce4f8 SHA1: e2336eaad8088e8769a097c6447ee9eda40b94ec SHA256: 3caa450ff2fae866a7327d543e8f3b85573f8bb57dbbfb5e092799c8001933d2 SHA512: b2546c9b1f7f9405dd6e68142228b1572ab6c650d8e561ec32a9f966834ac873caff41837cac94a7e57fe846eb853530e1ea07e42d581a7b4ed41ed25c3a0c89 Homepage: https://cran.r-project.org/package=ardl.nardl Description: CRAN Package 'ardl.nardl' (Linear and Nonlinear Autoregressive Distributed Lag Models:General-to-Specific Approach) Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) () bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) and their corresponding tests. 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It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) . Package: r-cran-ardldml Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tseries Filename: pool/dists/noble/main/r-cran-ardldml_0.1.0-1.ca2404.1_all.deb Size: 313046 MD5sum: 8fa91b40680347117079882ca4792e6a SHA1: f04d1aceb496a33809109b1376cf28b5d603586a SHA256: cb300520753c6331bf2fd79487b14d84902ba5d3a11e44130436f387c05fa830 SHA512: 7ccdc494ba8f2540fd0fc22304e258f8314185a74074457bf68467788616f0fc9d868e20621fca9c2134869c438685497ac20ce08723c26726a2736feca5ce1a Homepage: https://cran.r-project.org/package=ardldml Description: CRAN Package 'ardldml' (Bounds Testing for Cointegration with Many Persistent Controls) An implementation of the DML-Bounds procedure of Villena (2026) for testing cointegration in data-rich time-series settings. The Autoregressive Distributed Lag (ARDL) bounds test of Pesaran, Shin and Smith (2001) avoids pretesting the integration order of the regressors but is not designed for a high-dimensional conditioning set. Residualising the lagged levels against persistent controls can absorb stochastic trends and thereby change the finite-sample null distribution, so what governs the null is the effective number of stochastic trends surviving residualisation rather than the integration order of the original regressors. The procedure combines h-block cross-fitting, a balanced nuisance projection in the Double Machine Learning (DML) style of Chernozhukov and others (2018) , adaptive weighting after Zou (2006) , and a restricted system wild bootstrap that regenerates the dependent variable and the focal regressor jointly. No critical-value table is shipped: the classical bracket is regenerated by simulation and the operational critical value is bootstrapped. A trend-absorption diagnostic and a penalty-sensitivity sweep report whether a verdict survives a change of conditioning set. Monthly United States macroeconomic series from the 'FRED-MD' database of McCracken and Ng (2016) are bundled so every example runs offline. Package: r-cran-ardlverse Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-quantreg, r-cran-lmtest, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ardlverse_2.1.0-1.ca2404.1_all.deb Size: 766686 MD5sum: eeffbb3d5e34bcc86b81e2ca86df609f SHA1: a70d5ae5b603b1993216636e6aa68ace5bfcd9d6 SHA256: b472d5bc9c0639be136815dd30088b020819aa7ed44ee380dc6d5f720beb5cb5 SHA512: aaeea70dcebcaa4473a299488a094e3fe4fcbd66aeee7bf9af923f435b58c084684425a054b24e87af5eb374a1b00a7769d696baeeb0f37eaf14b0f9ba33e96e Homepage: https://cran.r-project.org/package=ardlverse Description: CRAN Package 'ardlverse' (Comprehensive ARDL: Panel, Bootstrap and Fourier Methods) A unified framework for Autoregressive Distributed Lag (ARDL) modeling and cointegration analysis. Implements Panel ARDL with Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators following Pesaran, Shin and Smith (1999) . Provides the bounds test of Pesaran, Shin and Smith (2001) with the critical values of Kripfganz and Schneider (2020) and with recursive bootstrap critical values following Bertelli, Vacca and Zoia (2022) and McNown, Sam and Goh (2018) . Includes Quantile Nonlinear ARDL (QNARDL) combining distributional and asymmetric effects based on Shin, Yu and Greenwood-Nimmo (2014) , Fourier ARDL for modeling smooth structural breaks following Enders and Lee (2012) , and Fourier (bootstrap) nonlinear ARDL. Features include Augmented ARDL (AARDL) with deferred t and F tests, Multiple-Threshold NARDL for complex asymmetries, Rolling/Recursive ARDL for time-varying relationships, and Panel NARDL for nonlinear panel cointegration. All methods include comprehensive diagnostics, publication-ready outputs, and visualization tools. Package: r-cran-ards Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ards_0.1.3-1.ca2404.1_all.deb Size: 250224 MD5sum: b5fad7629a58a3f68443d2954cedd687 SHA1: 9be9ad586452ec1cd2b7de96ff6dd7df077c01f8 SHA256: e6c4ae02fbe91502fe16d3f5bdcf4111dfcbd9ceb02d2406b60161257467b22e SHA512: 0b44018eecf65640cbf9eef98ca13e758934633befbd2db28c3b795060d88c69037825a4663738fa9dedb9c2a7f25e45bc241b76c0460978ed06cb95afa7a1b7 Homepage: https://cran.r-project.org/package=ards Description: CRAN Package 'ards' (Creates Analysis Results Datasets) Contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure. Package: r-cran-areabiplot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nipals Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-areabiplot_1.0.0-1.ca2404.1_all.deb Size: 22530 MD5sum: 5a2c4e0e64551fb4939363bbbb70f1f5 SHA1: 1d71dbe3ef3f67b4f221056b3e7feb7c8b5639b5 SHA256: 682c9ade4b8aa4ed872d2d4d6533423f73d585b31a7058559a3ac477bfb8c64e SHA512: 665467267c576860dc4eaba822ce1a06efa491eeedbffdde1e388f0b83863263f5beebaddc86292c305e561fa0bf07e2c84bd24fee54c29ab44124f2d79c91f8 Homepage: https://cran.r-project.org/package=areabiplot Description: CRAN Package 'areabiplot' (Area Biplot) Considering an (n x m) data matrix X, this package is based on the method proposed by Gower, Groener, and Velden (2010) , and utilize the resulting matrices from the extended version of the NIPALS decomposition to determine n triangles whose areas are used to visually estimate the elements of a specific column of X. After a 90-degree rotation of the sample points, the triangles are drawn regarding the following points: 1.the origin of the axes; 2.the sample points; 3. the vector endpoint representing some variable. Package: r-cran-areal Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-areal_0.1.8-1.ca2404.1_all.deb Size: 1399902 MD5sum: 1fae658346fc1cacad9c2c82de7bf805 SHA1: 01cee74806e1e803286bde530fc541875f6d3b83 SHA256: 98abba4e12480d0f6d429d711edd8fd8e51e3711ede71a8dc505c191f7793e69 SHA512: dc274717c265784c497747a3d4cbc9c2cce33a1ff650ec408fbcb26bc4ab7cb4a91cd9aa88db9cd71f3cef5e2b48d94cb9b3818b1361c59e80a18ede663bda84 Homepage: https://cran.r-project.org/package=areal Description: CRAN Package 'areal' (Areal Weighted Interpolation) A pipeable, transparent implementation of areal weighted interpolation with support for interpolating multiple variables in a single function call. These tools provide a full-featured workflow for validation and estimation that fits into both modern data management (e.g. tidyverse) and spatial data (e.g. sf) frameworks. 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For applications that surpass the spatial, temporal or thematic scope of any single data source, data must be integrated from several heterogeneous sources. Inconsistent concepts, definitions, or messy data tables make this a tedious and error-prone process. 'arealDB' tackles those problems and helps the user to integrate a harmonised databases of areal data. Read the paper at Ehrmann, Seppelt & Meyer (2020) . Package: r-cran-areaofeffect Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-lwgeom, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-svglite Filename: pool/dists/noble/main/r-cran-areaofeffect_0.2.4-1.ca2404.1_all.deb Size: 3559296 MD5sum: bc73e76404f1f44d7327dae524ba0755 SHA1: e32860914b4aa6c85edad04b8719bc4d58490580 SHA256: 73598734f3de29f9181b9e27e45cc147d3a634228e1685a4e10681809eb14551 SHA512: 095cde0cc985bb51db9e2d5f82a9367ea76806e259f40ab2ed8d83851312bd52be959e024964256c422fcc0683a4aa54eb4f65955763a9c1bca180899d822ddf Homepage: https://cran.r-project.org/package=areaOfEffect Description: CRAN Package 'areaOfEffect' (Spatial Support at Scale) Formalizes spatial support at scale for ecological and geographical analysis. Given points and support polygons, classifies points as "core" (inside original support) or "halo" (inside scaled support but outside original), pruning all others. The default scale produces equal core and halo areas - a geometrically derived choice requiring no tuning. An optional mask enforces hard boundaries such as coastlines. Political borders are treated as soft boundaries with no ecological meaning. 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A variety of input formats are supported, including vectors, matrices, data frames, formulas, etc. Package: r-cran-arena2r Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1826 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-shiny, r-cran-shinydashboard, r-cran-shinybs, r-cran-shinyjs, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arena2r_1.0.0-1.ca2404.1_all.deb Size: 399538 MD5sum: ae0985b8684db92e21f64b991a6ca960 SHA1: fb343fac241a7b99a2d8cd90cfa184a55b16556f SHA256: eed822dcc5dcd33cdb96aa974862d4a487d78666ead9060cc667ea35783a9695 SHA512: 33cbf6db2027824428e1807d1b64d7cc58a7561e3df915f5854293f02728887d2fcbe62ac2bc5525a18092a632c63b119dacd14764e418b243f111ae6696ac12 Homepage: https://cran.r-project.org/package=arena2r Description: CRAN Package 'arena2r' (Plots, Summary Statistics and Tools for Arena Simulation Users) Reads Arena CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results. Package: r-cran-arenar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ingredients, r-cran-ibreakdown, r-cran-gistr, r-cran-jsonlite, r-cran-plumber, r-cran-auditor, r-cran-dalex, r-cran-fairmodels Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-pkgdown, r-cran-covr, r-cran-ranger Filename: pool/dists/noble/main/r-cran-arenar_0.2.0-1.ca2404.1_all.deb Size: 173892 MD5sum: dd731642de19e980a8053b71170e783f SHA1: 60935917a808c2b69d082975bc5f5b8c951bb826 SHA256: af4eea38fcc917e045617c9b6a229c60dbfcbba9c9157d26846cc5217a0296c5 SHA512: c1062734bacff73e0ed3e96a7b53079fa88facf8cced3ba1e255802f59d809f9e6f75248a5abeccb1c22a2b7a9849ce05a63f17902db5e40062157bdb3f97736 Homepage: https://cran.r-project.org/package=arenar Description: CRAN Package 'arenar' (Arena for the Exploration and Comparison of any ML Models) Generates data for challenging machine learning models in 'Arena' - an interactive web application. You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable 'Arena' URL. Package: r-cran-arete Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1932 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-cld2, r-cran-stringr, r-cran-reticulate, r-cran-pdftools, r-cran-fedmatch, r-cran-kableextra, r-cran-dplyr, r-cran-gecko, r-cran-ggplot2, r-cran-jsonlite, r-cran-googledrive, r-cran-irr, r-cran-rmarkdown Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-arete_0.2-1.ca2404.1_all.deb Size: 911620 MD5sum: b5588ffbd74844264e3f45ade4896f69 SHA1: ed90123f52d19bbc8abe2cfc7c190502e44705fe SHA256: 0ed8878c62e488ce3f802ec6da269484260902253c4661e41b4a33450777ad82 SHA512: 32e387fceeb4c0825434047e4c454b8bd9e5b2db60e3bc06640b9c1bfce6f9fe3d5c95f041c0255986cfca7b87f36d0c1d655101cacbf9f8040b2ae92b2cd225 Homepage: https://cran.r-project.org/package=arete Description: CRAN Package 'arete' (Automated REtrieval from TExt) A Python based pipeline for extraction of species occurrence data through the usage of large language models. Includes validation tools designed to handle model hallucinations for a scientific, rigorous use of LLM. Currently supports usage of GPT with more planned, including local and non-proprietary models. For more details on the methodology used please consult the references listed under each function, such as Kent, A. et al. (1995) , van Rijsbergen, C.J. (1979, ISBN:978-0408709293, Levenshtein, V.I. (1966) and Klaus Krippendorff (2011) . Package: r-cran-arf Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-ranger, r-cran-stringr, r-cran-truncnorm Suggests: r-cran-dofuture, r-cran-doparallel, r-cran-ggplot2, r-cran-knitr, r-cran-mlbench, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-arf_0.2.5-1.ca2404.1_all.deb Size: 615180 MD5sum: 4554023bb3fb3cba060874c0b2cc8b3b SHA1: acd8272f679f55748180db134ac45bdbb8f398db SHA256: 171daefc09b7345813889048850c5e7d7506d2ba7bffa532f782470046815c78 SHA512: 46e34ea7d0c4fd5cb380468495a2ff9c1188637382964211416819a1aea773f0c4971186038ac3423a62584f4b4b0b38a806bb6b9ca23c1d95a23dfefc7e62ba Homepage: https://cran.r-project.org/package=arf Description: CRAN Package 'arf' (Adversarial Random Forests) Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2023) . Package: r-cran-arg Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-arg_0.2.1-1.ca2404.1_all.deb Size: 272562 MD5sum: 15c5d1272f9fe8416631019138d00f93 SHA1: 0c786afcb1b310bae520e171ab8ad3d79d2faeeb SHA256: 60cec25c3f4a09714e3b474952ce2a1aff13896f82cf7b1acadf8a3dcd55d677 SHA512: 6da1db10cd4cd6559e8be2bd55724fa9de5b87c6dd0d0c80c21e5b0bda737adfb83533e7899ae54733aa551688c102d2e00071920f5bf90adfd7f184cd3c43ad Homepage: https://cran.r-project.org/package=arg Description: CRAN Package 'arg' (Clean and Simple Argument Checking) Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using 'cli'. Package: r-cran-argentinapi Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-lubridate, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-argentinapi_0.2.1-1.ca2404.1_all.deb Size: 962162 MD5sum: 9ab6c7d1e41629f372c8991e2deb439b SHA1: 0c0ec61996ef3d18f4fded209de3b47edc46fa0b SHA256: ba68e62340b79323e303f35a233c65d02adb781bdf43c342dd6757b9763782f1 SHA512: c1bffd828dbccd326508b35a14dfd4cb95a73ee63f6f9e451426fc901fb705ec923032b93407826bd4f9cd9cdacd77cc27c8317500df70216cc95896f0c6625d Homepage: https://cran.r-project.org/package=ArgentinAPI Description: CRAN Package 'ArgentinAPI' (Access Argentinian Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including the 'ArgentinaDatos API', 'REST Countries API', and 'World Bank API' related to Argentina's exchange rates, inflation, political figures, holidays, economic indicators, and general country-level statistics. Additionally, the package includes curated datasets related to Argentina, covering topics such as economic indicators, biodiversity, agriculture, human rights, genetic data, and consumer prices. The package supports research and analysis focused on Argentina by integrating open APIs with high-quality datasets from various domains. For more details on the APIs, see: 'ArgentinaDatos API' , 'REST Countries API' , and 'World Bank API' . Package: r-cran-argentum Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-argentum_2.1.0-1.ca2404.1_all.deb Size: 312686 MD5sum: c611fc7bd7f890337fb97bb264232ecd SHA1: 70dcb8d71878ce5e993d27c6f9edb5e02715b328 SHA256: 3f4f3b08c9ec8cad8725a9fe4f882ab6315ef04803b1096944c9355efbe41920 SHA512: 3485ffcb22709123ff11fdb63100f253d81acc109d8b65c38b5570e5e45826a1c6c8a1430a1bf5a85393bd960fecb3679c29a2acab6587f96348e64f53f8898d Homepage: https://cran.r-project.org/package=Argentum Description: CRAN Package 'Argentum' (Access Argentine WFS and WMS Geospatial Web Services) Discovers and reads geospatial layers published by Argentine public organizations through the Open Geospatial Consortium standards Web Feature Service (WFS) and Web Map Service (WMS). Provides a cached catalogue of endpoints, capability parsing with version negotiation, paginated vector downloads returned as 'sf' objects, and raster map retrieval returned as 'terra' objects. For the underlying standards see and . Package: r-cran-argmincs Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsda, r-cran-glue, r-cran-ldats, r-cran-mass, r-cran-rdpack, r-cran-withr Filename: pool/dists/noble/main/r-cran-argmincs_1.1.0-1.ca2404.1_all.deb Size: 99368 MD5sum: 60b42c96f3de57ca47c6652963a6db2d SHA1: 0a7cc372fc67369127fa3252f1fd622a4531a66c SHA256: 517ff327a86b0871675952c257b19da7a176b5524238c25cf0b0766c82139705 SHA512: 7555b4e682ad25020f6579a1f47dc136b6e4687d0901dc20237ed4faa21858ae26758221e1a77aec1780cb2e987472136efe24dad391c813fb8dbbac5e0ce6f9 Homepage: https://cran.r-project.org/package=argminCS Description: CRAN Package 'argminCS' (Argmin Inference over a Discrete Candidate Set) Provides methods to construct frequentist confidence sets with valid marginal coverage for identifying the population-level argmin or argmax based on IID data. For instance, given an n by p loss matrix—where n is the sample size and p is the number of models—the CS.argmin() method produces a discrete confidence set that contains the model with the minimal (best) expected risk with desired probability. The argmin.HT() method helps check if a specific model should be included in such a confidence set. The main implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) "Winners with confidence: Discrete argmin inference with an application to model selection". Package: r-cran-argo Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-glmnet, r-cran-zoo, r-cran-xml, r-cran-xtable, r-cran-matrix, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-argo_3.0.3-1.ca2404.1_all.deb Size: 880002 MD5sum: 95ac6e73bc8c04eb80d81229ca7a7310 SHA1: 070951277cb80bdeeb26d043ae858e4f2bd628af SHA256: b04710717bef4fbb62b6aa0b56ec15f2d7288fa72c2dde3a402f8145c4359451 SHA512: 544d451a384511185d38601d737da7eb2f18128c6dbe4e88a1ec463bace485254a7a384d30628f0feaa23c3e8592bc9cbb2bce0b2c962f1b334be77628c0945b Homepage: https://cran.r-project.org/package=argo Description: CRAN Package 'argo' (Accurate Estimation of Influenza Epidemics using Google SearchData) Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) ; Ning, S., Yang, S. and Kou, S.C. (2019) ; Yang, S., Ning, S. and Kou, S.C. (2021) . 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Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) for more on the scientific context and applications. Package: r-cran-argondash Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4377 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-argonr Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-argondash_0.2.5-1.ca2404.1_all.deb Size: 1844990 MD5sum: ad65a2e509e93319ee4c50e83810a392 SHA1: 9743b32aa921cfa364fa483b9e4f45377fd4195e SHA256: 78d1fae3513ca0461573e57fdab479c932804cf70c541025c97651b1378ed078 SHA512: d5799cf0bb6893964c56b868e43dd80de1ee0ddf082e22519658198b962fe7165a9a631f01da4ea73cf83b66227caac5492a251231fcb40cafc193fd5cb0b490 Homepage: https://cran.r-project.org/package=argonDash Description: CRAN Package 'argonDash' (Argon Shiny Dashboard Template) Create awesome 'Bootstrap 4' dashboards powered by 'Argon'. 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The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis. Package: r-cran-argosfilter Architecture: all Version: 0.71-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-argosfilter_0.71-1.ca2404.1_all.deb Size: 56728 MD5sum: b75a877e8b27116a352895fa1d2f65db SHA1: a0101d681302a78f0722d148563f288bf74c8b2f SHA256: a605d16f24e1676246bf4a721513e306812c507f2923d2ba82da073997f0fd17 SHA512: f856898ab7c631048232fd1c038f3f41350f3f9253dd8ac9d642150c435427a00a416b2ffb6766569bed40fab49762e339621d3e46fab23ea79bfd92b731a5b9 Homepage: https://cran.r-project.org/package=argosfilter Description: CRAN Package 'argosfilter' (Argos Locations Filter) Filters animal satellite tracking data obtained from the Argos system(), following the algorithm described in Freitas et al (2008) . It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality. 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Package: r-cran-arht Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-arht_0.1.0-1.ca2404.1_all.deb Size: 54132 MD5sum: 685e01f888b24b7ccaf9bd0d1e6ec80c SHA1: 5266f024031c64dea38be88d3d28486e2888a8f7 SHA256: b2d15ca5d273443880ccf83d66f6075eb83a0ef9d97963f5bb18e5dfbdf94c81 SHA512: 857bce87bdff0ce8ff4b80832fa34d8d227d363984d9d01c74c1dad6c169f09b8a85197593497eebfe64d9d122c915e048c7e7bf684f62bcceb27434690cc3b6 Homepage: https://cran.r-project.org/package=ARHT Description: CRAN Package 'ARHT' (Adaptable Regularized Hotelling's T^2 Test for High-DimensionalData) Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by Li et al., (2016) . Both one-sample and two-sample mean test are available with various probabilistic alternative prior models. It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix (Bai et al. (2010) ). In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large. Package: r-cran-ari Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-text2speech, r-cran-tuner, r-cran-webshot, r-cran-purrr, r-cran-rmarkdown, r-cran-xml2, r-cran-rvest, r-cran-progress, r-cran-hms Suggests: r-cran-testthat, r-cran-xaringan, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ari_0.3.5-1.ca2404.1_all.deb Size: 235946 MD5sum: d80e0a9de550952e74e5a8e3987f16d2 SHA1: 485e415acf475f38273437357ee48d6c97c25910 SHA256: 340bc9b9b6da3947309353cadb360e9e28738ab6814ea9ca9110a173db16e023 SHA512: aa210d1c66f765fb8a59cda21a12ff38310a4ed5c377819e84d9397a4df86b4de3636c3366fc5645d96b84e89e5687f6075f66aba9f09ba879b4cd766feeadf8 Homepage: https://cran.r-project.org/package=ari Description: CRAN Package 'ari' (Automated R Instructor) Create videos from 'R Markdown' documents, or images and audio files. These images can come from image files or HTML slides, and the audio files can be provided by the user or computer voice narration can be created using 'Amazon Polly'. The purpose of this package is to allow users to create accessible, translatable, and reproducible lecture videos. See for more information. Package: r-cran-aribrain Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hommel, r-cran-rnifti, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aribrain_0.2-1.ca2404.1_all.deb Size: 1237666 MD5sum: 8460159284ad4d64c4d0707589148ac8 SHA1: 012c96a3cb069e7ee3326a7c900a27a58b923b0b SHA256: 6090cedbca536b6019cc708984aaf7be3dc5e8b748ca0925cbfefba718671104 SHA512: 233e6da97585ac4a7396be5935d1584b0903361f22889cbff19919fa92cfec5121dc581f06a39b52d34783b495ebc17a367daff347e27409e95cc1437bdd5cea Homepage: https://cran.r-project.org/package=ARIbrain Description: CRAN Package 'ARIbrain' (All-Resolution Inference) It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) . Package: r-cran-aridagri Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-emmeans, r-cran-multcomp, r-cran-corrplot, r-cran-factoextra, r-cran-factominer, r-cran-lavaan, r-cran-semplot, r-cran-agricolae, r-cran-car, r-cran-writexl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aridagri_2.0.4-1.ca2404.1_all.deb Size: 273850 MD5sum: dc79caf65ba9e4f25e587f9daaa519a7 SHA1: 76df1a1da9418088d2e8ba227498c5e69832e08c SHA256: bac9ab4d8e490c39348e855f82927acb7a5c1346e934988b02d7064cf2efe7ef SHA512: 9972fecd5ff23991097339a2b19e8cbf94099809d486847cfe4eb19818a4fde883346ae32429380bbefed886f88391a7220d293d9207e1a5e578d845df5a1315 Homepage: https://cran.r-project.org/package=aridagri Description: CRAN Package 'aridagri' (Comprehensive Statistical Tools for Agricultural Research) A comprehensive suite of statistical and analytical tools for agricultural research. Includes complete analysis of variance (ANOVA) functions for all experimental designs: Completely Randomized Design (CRD), Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha Lattice, with proper error terms and comprehensive Standard Error (SE) and Critical Difference (CD) calculations. Features multiple post-hoc tests: Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), Scheffe, Bonferroni, and Dunnett, along with assumption checking and publication-ready output. Advanced methods include stability analysis using Eberhart-Russell regression, Additive Main Effects and Multiplicative Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority Index as described in Eberhart and Russell (1966) . Thermal indices include Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency calculations, correlation matrix, Principal Component Analysis (PCA), path analysis, and Structural Equation Modeling (SEM). Statistical methods follow Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, ISBN:8170271169). Package: r-cran-arigamyannsvr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-allmetrics, r-cran-describedf, r-cran-dplyr, r-cran-psych, r-cran-fints, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-neuralnet, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-arigamyannsvr_0.1.0-1.ca2404.1_all.deb Size: 46120 MD5sum: 32ee7e54bcc82e2a859abb4ebd0a8342 SHA1: 708a13c856ce84cf58d9aaf1931cd0b590a99754 SHA256: 5419fe916cfb4d387affe63a00330b7c546cb4dd3ec50e094610b3ea2334f87e SHA512: 7fdf56262ccf1a679e8b2c033ed4589c8bb6caa9e1f3f0d6ea93b10be65e3090c2f85048388e111fc16f56175069de939cd3673ffa7926393cb63fca2e9fabaa Homepage: https://cran.r-project.org/package=AriGaMyANNSVR Description: CRAN Package 'AriGaMyANNSVR' (Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models) Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) . 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The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) . Package: r-cran-arimasel Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tseries, r-cran-forecast, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arimasel_0.2.0-1.ca2404.1_all.deb Size: 639316 MD5sum: bff8db9610805b23cd64e02cc259a15a SHA1: f2236d48f83682c0d14127d25e3ad659a2f17f33 SHA256: 4fdc365b7eb0cfb8d8d64e90e214e68f308df3e245d551948d2f5977fac361c1 SHA512: e255f3099ccfc921354ebf9d0c840d4faf17b0d50e4f31708eac1d5e9cc13b8fe71283f1ea35186274acd3ed7338d11f7b3999c279eb771d3ee6055a102e43b7 Homepage: https://cran.r-project.org/package=arimasel Description: CRAN Package 'arimasel' (Cartesian Product-Based ARIMA Model Identification and Selection) Provides an alternative algorithm for ARIMA and seasonal ARIMA model identification based on Cartesian products of user-supplied parameter sets. Rather than relying on ACF/PACF plots or stepwise search (as in auto.arima()), the package exhaustively evaluates every candidate (p,d,q)(P,D,Q)[m] combination in the requested index sets, ranks all converged models by AIC, AICc, BIC, and HQIC simultaneously, computes Akaike weights for model uncertainty quantification, supports exogenous regressors, produces ensemble forecasts, evaluates candidate models by rolling-origin (expanding window) cross-validation, and provides publication-quality diagnostic and comparison plots. A feature-based exploratory data analysis suite computes scale-free time series characteristics (trend and seasonal strength, spectral entropy, autocorrelation, lumpiness, stability) in the spirit of Hyndman, Wang and Laptev (2015), and a feature-guided automatic search narrows the Cartesian product model space before the exhaustive search runs. The algorithm is flexible, transparent, and widely applicable for quick, reproducible ARIMA model selection in both academic research and industry forecasting pipelines. Applications are demonstrated with Nigerian macroeconomic time series data. 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First step is dedicated to eliminate dependence between variables (clustering of variables, followed by factor analysis inside each cluster). Second step is a variable selection using by aggregation of adapted methods. Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles associated with outcome of a non-small-cell lung cancer treatment. 2018. . Package: r-cran-armadillo4r Architecture: all Version: 15.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp4r Suggests: r-cran-matrix, r-cran-tinyroxygen, r-cran-tinytest, r-cran-litedown, r-cran-rbibutils Filename: pool/dists/noble/main/r-cran-armadillo4r_15.4.2-1.ca2404.1_all.deb Size: 576610 MD5sum: ca634739be7e8b72cefcea435715228b SHA1: f7aceece63b7baa1c2456fa5e5f8d2e7d710d513 SHA256: c84fdacd9d9d9b67ecd53b410632adb9a4a4506db813d2cfaf0e335525d322b1 SHA512: 437c95e0c7bb926b61bf55ca5724b09d09cfd00fd7b8eea3bbad391e238dfe6713ab82cee258d9da101e981c4e881c3607c521989be436bd30f8558285d5ec5f Homepage: https://cran.r-project.org/package=armadillo4r Description: CRAN Package 'armadillo4r' (An 'Armadillo' Interface) Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. 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Package: r-cran-armalstm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rugarch, r-cran-tseries, r-cran-tensorflow, r-cran-keras, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-armalstm_0.1.0-1.ca2404.1_all.deb Size: 25216 MD5sum: 395c9d32768846a669c501afeb85548d SHA1: 8a52ae208e5d4e644fbd051fbb00bcb248ca7bdb SHA256: e165430f4a7121003f7baa1e4f83597f73715dd3214b69a01e9186b4ca6dfcb0 SHA512: 22af22d00f278333ab1306d91ddf56cd10fa26a7f8f4bcf6f829ec28f4008db19e474be6ae0f53a0cf9aae904e6920e362fd8fbdcbb96d02ce776689efe758ac Homepage: https://cran.r-project.org/package=ARMALSTM Description: CRAN Package 'ARMALSTM' (Fitting of Hybrid ARMA-LSTM Models) The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) . 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Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) . NOTE: We have created a new package, 'ROCnReg', with more functionalities. It also implements all the methods included in 'AROC'. We, therefore, recommend using 'ROCnReg' ('AROC' will no longer be maintained). 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The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006. Package: r-cran-aroma.apd Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-r.huge Suggests: r-bioc-affxparser Filename: pool/dists/noble/main/r-cran-aroma.apd_0.7.1-1.ca2404.1_all.deb Size: 118926 MD5sum: 9ea62f7994bfb854a272eba1ac4f2e79 SHA1: d75305105e1173f35a8337ea21ba6916970dc0c3 SHA256: 5793506e56f3eea2d530d308a580a3c1561f3a1786b6a10bb51d9b202b2e57bf SHA512: f1ca04469098d8b339be02c9c7f27e42caa5c54cb7e65458119c7ec74c22d2e74a68c1ee42d5f51a43baffeb503e0c37e3c963e268bcd352bcb244bd50b26e32 Homepage: https://cran.r-project.org/package=aroma.apd Description: CRAN Package 'aroma.apd' (A Probe-Level Data File Format Used by 'aroma.affymetrix'[deprecated]) DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.) Package: r-cran-aroma.cn Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-aroma.core, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.filesets, r-cran-r.cache, r-cran-matrixstats, r-cran-pscbs, r-cran-future.apply Suggests: r-bioc-aroma.light, r-bioc-dnacopy, r-bioc-glad Filename: pool/dists/noble/main/r-cran-aroma.cn_1.7.1-1.ca2404.1_all.deb Size: 555270 MD5sum: 52d76d3e97e9fd7639f2cd0c280f4d07 SHA1: 4dc9a40e21605a947a59c9b196c8e327e5bdd9d4 SHA256: d4b874b44f18a0b95a7fd03a93ebd00a509584cdb492d5d2837c634ef716e456 SHA512: 0be97e23db3de18adb0d43911d59334f54dc6adefd7e2e243ba8937bebfc5f71547b82ddb4df99307f99bce27d5410f6041d976328344cf206dcc2b6d1cbb2cf Homepage: https://cran.r-project.org/package=aroma.cn Description: CRAN Package 'aroma.cn' (Copy-Number Analysis of Large Microarray Data Sets) Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data. Package: r-cran-aroma.core Architecture: all Version: 3.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-r.filesets, r-cran-r.devices, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.cache, r-cran-r.rsp, r-cran-matrixstats, r-cran-rcolorbrewer, r-cran-pscbs, r-cran-listenv, r-cran-future, r-cran-biocmanager Suggests: r-cran-kernsmooth, r-cran-png, r-cran-cairo, r-bioc-ebimage, r-bioc-preprocesscore, r-bioc-aroma.light, r-bioc-dnacopy, r-bioc-glad Filename: pool/dists/noble/main/r-cran-aroma.core_3.3.2-1.ca2404.1_all.deb Size: 1886598 MD5sum: fef3dbfaa5abf996c5655181617a9c24 SHA1: 28b6043e291d0416c560e722422199a8feae8482 SHA256: af7541801e47d08e7cb5df282f6251be1f7e53fad077f4af64c5bee41745d545 SHA512: 9631dd99540c92eb81f90bceb64c08fc03964b941aa0b53746dedec083412d1b689ba171892332013a2a3406d6bf39b92a1004b24363186d2094415f2cbad170 Homepage: https://cran.r-project.org/package=aroma.core Description: CRAN Package 'aroma.core' (Core Methods and Classes Used by 'aroma.*' Packages Part of theAroma Framework) Core methods and classes used by higher-level 'aroma.*' packages part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'. Package: r-cran-arothron Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-alphashape3d, r-cran-compositions, r-cran-doparallel, r-cran-foreach, r-cran-geometry, r-cran-morpho, r-cran-rgl, r-cran-rvcg, r-cran-stringr, r-cran-vegan Filename: pool/dists/noble/main/r-cran-arothron_2.0.5-1.ca2404.1_all.deb Size: 5493212 MD5sum: 8369aa0eb89b32322c8d9ffddf3b6013 SHA1: 57338305213f0a929ae033b1ae8c3539793eb906 SHA256: c5925a8f51f490a6f4275ccfa9a623e7894e97ea168a8ecf51919ead224f7795 SHA512: 6c80fc31e0565841b3f54618e398ba8caf40566fae71cd479b499d84868be14f962af97638bfb8a91ba0386f39caaf67c636fa9a839c439f27cf371b6f8039a4 Homepage: https://cran.r-project.org/package=Arothron Description: CRAN Package 'Arothron' (Geometric Morphometric Methods and Virtual Anthropology Tools) Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 ). Package: r-cran-arpaldata Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyselect, r-cran-dplyr, r-cran-rlang, r-cran-lubridate, r-cran-readr, r-cran-stringr, r-cran-tm, r-cran-tidyr, r-cran-eurostat, r-cran-sf, r-cran-ggplot2, r-cran-tibble, r-cran-aweek, r-cran-curl, r-cran-future, r-cran-future.apply, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arpaldata_2.0.1-1.ca2404.1_all.deb Size: 15482010 MD5sum: dd6d9aa4ad441b2828ce1c2a26f8ee17 SHA1: 2838063a311072f7689f986bbe6d7934f35e41c5 SHA256: 1cab9130e0698febb18d46b0f94f0c585385d54c05a32f3953514e90e773ee99 SHA512: aa4cb6cfc759b8e868313182265246fd6b4cb4be922bd07841ec4e6383c79a862dba713989e0ae13ed310a29a91ec8ba82dd9261ec2b4de11f7454ec56f2a414 Homepage: https://cran.r-project.org/package=ARPALData Description: CRAN Package 'ARPALData' (Retrieving and Analyzing Air Quality and Weather Data from ARPALombardia) Contains functions for retrieving, managing, and analyzing air quality and weather data from the Regione Lombardia open database (). 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Package: r-cran-arplmec Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-mass, r-cran-mnormt, r-cran-expm, r-cran-relliptical, r-cran-truncatednormal, r-cran-laplacesdemon Filename: pool/dists/noble/main/r-cran-arplmec_2.4.1-1.ca2404.1_all.deb Size: 236134 MD5sum: 68ad84fea77fe8a79a068988659511c1 SHA1: 09738f4c6efcc4f05e213d14936356c4e8cf7643 SHA256: b6c2f6b57265c93d60329ef90b988d0f58d3f9320816931ebdc4f0b18d9e08be SHA512: dc195f7de2fd418bd1b97a1c1a3691c60597bd8f3b4b2e7510472c99e14b755aaf23ad9b0a5f5235c79719bd3eccdf4ba417acee7c5da7be4f5ab9e4c4651236 Homepage: https://cran.r-project.org/package=ARpLMEC Description: CRAN Package 'ARpLMEC' (Censored Mixed-Effects Models with Different CorrelationStructures) Left, right or interval censored mixed-effects linear model with autoregressive errors of order p or DEC correlation structure using the type-EM algorithm. The error distribution can be Normal or t-Student. It provides the parameter estimates, the standard errors and prediction of future observations (available only for the normal case). Olivari et all (2021) . 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The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document. Package: r-cran-art Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car Filename: pool/dists/noble/main/r-cran-art_1.0-1.ca2404.1_all.deb Size: 26120 MD5sum: dccf264ef0361e5786001c2454b7459b SHA1: 0484ce75ac76ddf643a2f14e5b99a8aee291f89b SHA256: 0392da4312b2927d0f97ad00623a7082b94b4cbab6e175961b70fe6888f52d2b SHA512: e1f22fd441982db7f37954b1a6402b5e08f57e7beaff38f00b6c78564ec4937c299e84eb0ffbd6a3b0320e76468787e9630534cbdec25efd3460ca04e0a143e3 Homepage: https://cran.r-project.org/package=ART Description: CRAN Package 'ART' (Aligned Rank Transform for Nonparametric Factorial Analysis) An implementation of the Aligned Rank Transform technique for factorial analysis (see references below for details) including models with missing terms (unsaturated factorial models). The function first computes a separate aligned ranked response variable for each effect of the user-specified model, and then runs a classic ANOVA on each of the aligned ranked responses. For further details, see Higgins, J. J. and Tashtoush, S. (1994). An aligned rank transform test for interaction. Nonlinear World 1 (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp. 143-146. . Package: r-cran-artfima Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ltsa, r-cran-gsl Filename: pool/dists/noble/main/r-cran-artfima_1.5-1.ca2404.1_all.deb Size: 140356 MD5sum: c684ee49758300d84ecfb43e8c271f49 SHA1: 701741ba765b327c076adade984fa5f432030d70 SHA256: c3b218c0d3f5eaa2e1028d833f31e6ed2c0c618c9dc3d3989be30f3c198ac4fe SHA512: f9098d72421c9e1a22677d63c857813fef132447b2e9b61ae7abe3647dbd3147a0f37efd1969e71c9a4926da3b7cb8dcb1631f0b630da4dafaa2e91c257e2f5b Homepage: https://cran.r-project.org/package=artfima Description: CRAN Package 'artfima' (ARTFIMA Model Estimation) Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stationary ARTFIMA. Package: r-cran-arthistory Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-arthistory_0.1.0-1.ca2404.1_all.deb Size: 254766 MD5sum: 474f0570ff2685c7270cdc894e152689 SHA1: 26bc89a33eb972a3bfaa731eaebc77f02b642661 SHA256: f54d0e2fb53c5fd8f7bc1df0d25ad964599b75afb80b0c9a91b910f0a0640a03 SHA512: f0cfd3ffdf5eaf37e3f3e38a191c2a4b75abee24d59f910149453f5de0687996964ce40d87a2a6c71a01a87d1190fb92a6ae82596adedcb9bb9c13f012d82065 Homepage: https://cran.r-project.org/package=arthistory Description: CRAN Package 'arthistory' (Art History Textbook Data) Data from Gardner and Janson art history textbooks about both the artists featured in these books as well as their works. See Helen Gardner ("Art through the ages; an introduction to its history and significance," 1926, . Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1980, ISBN: 0155037587). Fred S. Kleiner ("Gardner’s art through the ages: a global history," 2020, ISBN: 9781337630702). Horst de la Croix and Richard G. Tansey ("Gardner's art through the ages," 1986, ISBN: 0155037633). Helen Gardner ("Art through the ages; an introduction to its history and significance," 1936, ). Helen Gardner ("Art through the ages," 1948, ). Helen Gardner, revised under the editorship of Sumner M. Crosby ("Art through the ages," 1959, ). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1975, ISBN: 0155037560). Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2013, ISBN: 9780495915423. Fred S. Kleiner, Christin J. Mamiya, Richard G. Tansey ("Gardner’s art through the ages," 2001, ISBN: 0155083155). Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2016, ISBN: 9781285837840). Fred S. Kleiner, Christin J. Mamiya ("Gardner’s art through the ages," 2005, ISBN: 0534640958). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1970, ISBN: 0155037528). Helen Gardner, Richard G. Tansey, Fred S. Kleiner ("Gardner’s Art through the ages," 1996, ISBN: 0155011413). Helen Gardner, Horst de la Croix, Richard G. Tansey, Diane Kirkpatrick ("Gardner’s Art through the ages," 1991, ISBN: 0155037692). Helen Gardner, Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2009, ISBN: 9780495093077). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Janson’s history of art: the western tradition," 2007, ISBN: 0131934554). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Janson’s history of art: the western tradition," 2011, ISBN: 9780205685172). H. W. Janson, Anthony F. Janson ("History of Art," 2001, ISBN: 0810934469). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1986, ISBN: 013389388). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1977, ISBN: 0810910527). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1969, ). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1963, ). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1991, ISBN: 0810934019). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1995, ISBN: 0810934213). Package: r-cran-artma Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-lintr, r-cran-metafor, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyverse, r-cran-usethis, r-cran-withr, r-cran-yaml Suggests: r-cran-box, r-cran-box.linters, r-cran-covr, r-cran-devtools, r-cran-fs, r-cran-here, r-cran-knitr, r-cran-languageserver, r-cran-mathjaxr, r-cran-optparse, r-cran-pkgbuild, r-cran-remotes, r-cran-rex, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-artma_0.2.1-1.ca2404.1_all.deb Size: 125272 MD5sum: b61e19d1910a86112a3f06926ccb563b SHA1: 9987bc958df0dd83097b1dccdf64dc511eb033b9 SHA256: 743f81063e3f55586dd59ce2989bb915248d641b26a1a6aeba44de4e349134df SHA512: aa2cc4039ed078ed942fb3ec9e0dc3528dcc0dfcb8b9671cc8ed4c6f0e0879141985e77e8734c7cd83cce7beec60b9cd39ed193bfd0e05df64475df916d43864 Homepage: https://cran.r-project.org/package=artma Description: CRAN Package 'artma' (Automatic Replication Tools for Meta-Analysis) Provides a unified and straightforward interface for performing a variety of meta-analysis methods directly from user data. 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Package: r-cran-artofr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bannercommenter, r-cran-clipr, r-cran-rstudioapi, r-cran-shiny Suggests: r-cran-rmarkdown, r-cran-miniui, r-cran-knitr Filename: pool/dists/noble/main/r-cran-artofr_0.4.1-1.ca2404.1_all.deb Size: 49004 MD5sum: db50b71b761286a432c31b4420f2b1bd SHA1: fcc1cadefec6fc7f7312081e24688d7e35b4f84c SHA256: c7d64153cf69f1a3381610452208abb1c47f997d2bf885ba934c09bbecfcedea SHA512: 6f3d5f15678b2229555360cfab05470399818a511936ccd3a1cf2d3f489bfc85671ad475a5600fc722ba51310c34bdc35a99936cba7b58e16f3d39178ba5dde0 Homepage: https://cran.r-project.org/package=ARTofR Description: CRAN Package 'ARTofR' (To Insert Title, Divider, and Block of Comments) For instructions, check . This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers. Package: r-cran-artoo Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-hms, r-cran-jsonlite, r-cran-nanoparquet, r-cran-rlang, r-cran-s7, r-cran-utf8 Suggests: r-cran-callr, r-cran-digest, r-cran-quarto, r-cran-readxl, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-writexl, r-cran-xml2, r-cran-xslt Filename: pool/dists/noble/main/r-cran-artoo_0.2.0-1.ca2404.1_all.deb Size: 1285546 MD5sum: 3f966fa90e40288bf028b776394a089f SHA1: ae372e34f9ca3a5f9467c0ce4a598f7f65278d88 SHA256: fde01d2c26fa259c628c6506acb78cf684b1a679d195d94b1ba58dc3a4927f72 SHA512: 0a5306d880d340250b47ec3bf5af0f14afcb34ff3f53c10db4a7c4ffd5a8e5a6616d63eeeffa792eca52664b0708f9554aa808c909938665eacf9e3698aefee7 Homepage: https://cran.r-project.org/package=artoo Description: CRAN Package 'artoo' (Lossless CDISC-Native Input and Output for Clinical Datasets) Reads and writes clinical-trial datasets losslessly across 'SAS' XPORT (XPT), Clinical Data Interchange Standards Consortium (CDISC) Dataset-JSON, and 'Apache Parquet', applying a specification to produce submission-ready Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) datasets. A single canonical metadata model carries labels, CDISC data types, lengths, 'SAS' display formats, controlled-terminology references, and sort keys identically across every format, so conversion between any two formats is lossless by construction. Pure 'R' and lightweight, with no external 'SAS' or 'Java' runtime. Reads and writes CDISC Define-XML, the specification document that accompanies a submission, and renders it as HTML through the bundled Define-XML stylesheet. Implements the published format specifications for CDISC Dataset-JSON (), CDISC Define-XML (), and 'SAS' XPORT (). Package: r-cran-artool Architecture: all Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-car, r-cran-plyr, r-cran-magrittr, r-cran-dplyr, r-cran-emmeans Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-pander, r-cran-lmertest, r-cran-cluster, r-cran-phia, r-cran-survival, r-cran-psych, r-cran-stringi, r-cran-desctools, r-cran-tibble, r-cran-covr Filename: pool/dists/noble/main/r-cran-artool_0.11.2-1.ca2404.1_all.deb Size: 162298 MD5sum: f2928210e1d853764a30eae572a238a3 SHA1: 746ed1af7f2479c64acacfd96d3f44775192cb98 SHA256: 353d9ba63e95a3d83d395878944bc1a2c929811aefb6c3fcd47723ed1ddce225 SHA512: f0c9899d4b6ec656f4312e9b2b992677e5feefae01c5d8ac845755410ecfcde11a0e9b3e052e165761078575a7ce69e9ecf421d6020ac31e5ecae54f68abd142 Homepage: https://cran.r-project.org/package=ARTool Description: CRAN Package 'ARTool' (Aligned Rank Transform) The aligned rank transform for nonparametric factorial ANOVAs as described by Wobbrock, Findlater, Gergle, and Higgins (2011) . 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Package: r-cran-artpack Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-knitr, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-ggplot2, r-cran-withr Filename: pool/dists/noble/main/r-cran-artpack_0.2.0-1.ca2404.1_all.deb Size: 1282092 MD5sum: 412decac56125a1d31c640144338ca55 SHA1: 2d3a721d8881ff5f17bac07716d9d0126ab7a2f8 SHA256: 62f5b5338f5d4846f53ed4cd6fc9c94b8bbff6142020e8094fbbe25f6032ab55 SHA512: 8a348f0556d1ed9e203218c2374d275c932ddb15718b691e05d18526a5f12087a4548e2966aa2b973869241ffcb0f86ef82e896269ffbee4394c4ee617303bf6 Homepage: https://cran.r-project.org/package=artpack Description: CRAN Package 'artpack' (Creates Generative Art Data) Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art. Package: r-cran-arttransfer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gbm, r-cran-glmnet, r-cran-nnet, r-cran-randomforest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arttransfer_1.0.0-1.ca2404.1_all.deb Size: 78248 MD5sum: 6a7594fd233cd3576a5e37b589be9e33 SHA1: 6687291b2bc14ea622e972c1f07fe735118dbc19 SHA256: 153b5656005f252d3ec46825074bb8d881017580377b804c7c4996cd099c6c2f SHA512: 405737076fe50c738420885a3e86bec5e371b2215012006d0b4e0250921c5ba5f2c4ecd67c119bc95826ab756ad8df404beecd4e259058580894888f32e0ac49 Homepage: https://cran.r-project.org/package=ARTtransfer Description: CRAN Package 'ARTtransfer' (Adaptive and Robust Pipeline for Transfer Learning) Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) . Package: r-cran-arulesnbminer Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arules, r-cran-rjava Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arulesnbminer_0.1.10-1.ca2404.1_all.deb Size: 611964 MD5sum: 398fea6edd41e2a1dbb79d21dbf209f4 SHA1: b6f88bde3a8fd325baed10aa50b7f9ea3993608e SHA256: a2230fdc6ef442ab4b78811229f217ef3d60be68ff7e67b9da32a6b66cb8ffa8 SHA512: abc98d040ec9c03d6ac8dcd12bf846f14c487776fef0d29d3c60f1113533219b545efe38b26a8c45278bdc5cdc7b310de8699a3992e9b107ca74a88a9000729b Homepage: https://cran.r-project.org/package=arulesNBMiner Description: CRAN Package 'arulesNBMiner' (Mining NB-Frequent Itemsets and NB-Precise Rules) NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) . Package: r-cran-arulesviz Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-plotly, r-cran-scatterplot3d, r-cran-seriation, r-cran-tibble, r-cran-tidyr, r-cran-vcd, r-cran-visnetwork Suggests: r-bioc-graph, r-cran-htmlwidgets, r-bioc-rgraphviz, r-cran-shiny, r-cran-shinythemes, r-cran-testthat, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-arulesviz_1.5.4-1.ca2404.1_all.deb Size: 1645952 MD5sum: 2a80ff1543dd408605945a4b032c5981 SHA1: e5a8f60699b1919ce547b5c4ca2b7b9896d7f202 SHA256: 33c9231a6eba2656c89f3636f05d718efe0453008f97bd522ee67927ddcb1dd8 SHA512: 05ef3a598bd999e041456e169ebf9eeb7a28096176775c2bf8d4b5fd2e9a33dafd1af60a55b5a70c6b872cec6e33644ae52a60268dbbe6a48be31033e32c9589 Homepage: https://cran.r-project.org/package=arulesViz Description: CRAN Package 'arulesViz' (Visualizing Association Rules and Frequent Itemsets) Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) . Package: r-cran-arutools Architecture: all Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-here, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-lutz, r-cran-parzer, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-seewave, r-cran-sf, r-cran-spsurvey, r-cran-stringr, r-cran-suncalc, r-cran-tidyr, r-cran-units, r-cran-withr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-patchwork, r-cran-readxl, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-soundecology, r-cran-testthat, r-cran-tuner, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-arutools_0.7.3-1.ca2404.1_all.deb Size: 549998 MD5sum: 89e969a0dbfb177e9820196af8e45234 SHA1: 896e3bec9edb005bf69d1fc85a24618128ac3929 SHA256: 4833390b47d9ddc406dfb861958b214d6f8f7f3336b029303649912f7b15df6c SHA512: 70359e247b925b5a2281fe8b398d60fa281e99158815f78a92e7bfb8ab9849331bcf1986278686af0c95f0f7edf3f310b7eaac8b79b92ed74daf9405f61fd7b7 Homepage: https://cran.r-project.org/package=ARUtools Description: CRAN Package 'ARUtools' (Management and Processing of Autonomous Recording Unit (ARU)Data) Parse Autonomous Recording Unit (ARU) data and for sub-sampling recordings. Extract Metadata from your recordings, select a subset of recordings for interpretation, and prepare files for processing on the 'WildTrax' platform. Read and process metadata from recordings collected using the SongMeter and BAR-LT types of ARUs. Package: r-cran-arvindrf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger, r-cran-coda, r-cran-goftest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-arvindrf_1.0.0-1.ca2404.1_all.deb Size: 71738 MD5sum: f68e7b5d4c91180bb8769c5364125157 SHA1: 3b3e70bd54880f418f796999bbf195046d10f0c7 SHA256: a82b1f7d9073a393aa999b4cd08f803877229ce12d0a5f91227e05094daaf422 SHA512: ae5254c64403d04c37b256fe054a0d4bbbb43200fc95124221aef5ecb05d0501516c88b78d9a8ed362d4736710589b77c698be22906fcd5e40a2ce9c0f699bc1 Homepage: https://cran.r-project.org/package=ArvindRF Description: CRAN Package 'ArvindRF' (Random Forest Regression with Arvind Distribution Error Model) Implements Random Forest regression under the Arvind distribution error model. Provides core distribution functions (density, cumulative distribution, quantile, random generation, hazard, survival), parameter estimation via Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC), non-parametric bootstrap confidence intervals (at 90%, 95%, and 99% levels), Highest Posterior Density (HPD) intervals, model evaluation metrics (estimated values, bias, mean squared error, risk value), homoscedastic prediction intervals, and goodness-of-fit diagnostic tests (Kolmogorov-Smirnov and Anderson-Darling tests, Akaike Information Criterion, and Bayesian Information Criterion). References: Breiman (2001) ; Wright and Ziegler (2017) . Package: r-cran-arvindst Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-forecast, r-cran-tvreg, r-cran-lme4, r-cran-reshape2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arvindst_1.1.0-1.ca2404.1_all.deb Size: 130432 MD5sum: 5670c10d5d0def8850b21ba4b2102f01 SHA1: 45c2b79ff15e13fba5502ef88e3dd684ae152464 SHA256: dea3de2c40d73b36e47e617cdc2c829d4c42f1b07873ea10d40f9bf92bdd79e2 SHA512: 45fb1753f94417fe2321cae2a00e2e839888a259c2af7f5f6e7e8e84a266d1032c319e273344e0f66432a9bf3c07a4e5c58b3012047a0b2be9265f46fbbc7ac8 Homepage: https://cran.r-project.org/package=ArvindSt Description: CRAN Package 'ArvindSt' (Five Novel Stochastic Regression Models with Arvind-DistributedErrors and Effects) Implements the 'Arvind' distribution and five novel stochastic regression models that replace the traditional Gaussian error assumption with 'Arvind'-distributed errors. The 'Arvind' distribution is a flexible single-parameter continuous distribution on the positive real line characterised by a polynomial numerator with Gaussian-type decay. The package provides complete distribution functions (darvind(), parvind(), qarvind(), rarvind()), maximum likelihood estimation via fit_arvind_mle(), and five model-fitting routines: Random Walk on Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via fit_hmm(). Additionally provides Monte Carlo forecasting with prediction intervals via forecast_arvind(), comprehensive goodness-of-fit diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and plot_arvind(), k-fold and rolling-window cross-validation via cv_arvind(), and unified model comparison via summary_arvind(). For more details see Pandey, Singh, Tyagi, and Tyagi (2024), "Modelling climate, COVID-19, and reliability data: A new continuous lifetime model under different methods of estimation", 'Statistics and Applications', 22(2). Package: r-cran-arxiv Architecture: all Version: 0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-xml Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arxiv_0.20-1.ca2404.1_all.deb Size: 88580 MD5sum: 60ec2381bb9fd03f8ed8106851a990ac SHA1: 2f0f1499d794ac3816e7e4deae262fe5e2cfcae2 SHA256: 76856c0f69802f76c30f502284c8df3a0aa3abe7d8a6cf1e5e854289633bebe1 SHA512: 27ca5a65c38780482ffe9b5b925102928145cd801e9ad15a822dc4c797c361a9f81232d76efd8ee010b87598899c21777c222597b02a1b395a604bfc69872890 Homepage: https://cran.r-project.org/package=aRxiv Description: CRAN Package 'aRxiv' (Interface to the arXiv API) An interface to the API for 'arXiv', a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics. Package: r-cran-asaur Architecture: all Version: 0.50-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asaur_0.50-1.ca2404.1_all.deb Size: 103744 MD5sum: 01be9e1136320529352f47ad016faf70 SHA1: 5c1cb5d5ec50a4a0345ec4f8001a71584adc3448 SHA256: 381673e5b102538ff858280c04258346624aa86e4603116cce0f841740b4c68d SHA512: b3efffdc687d7a1a57f6e6685d33764fe87dd9106c5ed582e95a4437ff121c2f3340c5cf0c261a1ba9f7f416104772050077ab59ea7f6d82159e4aa4f3594291 Homepage: https://cran.r-project.org/package=asaur Description: CRAN Package 'asaur' (Data Sets for "Applied Survival Analysis Using R"") Data sets are referred to in the text "Applied Survival Analysis Using R" by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, . Package: r-cran-asbio Architecture: all Version: 1.13-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scatterplot3d, r-cran-pixmap, r-cran-plotrix, r-cran-mvtnorm, r-cran-desolve, r-cran-lattice, r-cran-multcompview, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-tkrplot Suggests: r-cran-boot, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-asbio_1.13-1-1.ca2404.1_all.deb Size: 3473476 MD5sum: eeb35b749dfbcc5766bd2ff67fbfd028 SHA1: 7adfd502bce92fab95cf3bba7ff38c31444eaa2d SHA256: ca3b1185c3abf825806633d9a094262d6b4425f967a99982aeb41806551f22d7 SHA512: f37d6fe28aab8230309030a18c5003918ce58a8f788847d4403d28975665781be8a6c2ae8790dc4de24e31ce1c8b5dd80c367acb215e85f3cab27286bdcf0348 Homepage: https://cran.r-project.org/package=asbio Description: CRAN Package 'asbio' (A Collection of Statistical Tools for Biologists) Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0. Package: r-cran-ascent Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-ggplot2, r-cran-vegan, r-cran-geometry Suggests: r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ascent_0.1.1-1.ca2404.1_all.deb Size: 171460 MD5sum: 2d53a0cd31f9863cbe2537705700c19f SHA1: 7c477542191a4a3e00125b66ae053a5d0250dbce SHA256: b9d68ad1cc0ff761358454ead4ffb8f4c17867dc6fd7648a7d949ad0ee6ddaad SHA512: 8d241a16469c84be221a90985b876c8ea942f55692d7374b989a82048332b5f817339c568dc44725832feef479350b8e8ca1c0d45866eac1b0202d8f1872405c Homepage: https://cran.r-project.org/package=ascent Description: CRAN Package 'ascent' (Multi-Layer Decomposition of Functional Community Restructuring) Implements the 'ASC-CFD' (Assemblage Shift Characterization - Community Functional Dynamics) framework for decomposing functional community restructuring into positional (centroid displacement), dispersive (functional dispersion), and boundary (convex hull volume) components. Provides hierarchical null models (structural, quantitative, identity) to evaluate statistical significance and species-level leverage analysis to identify taxa driving functional shifts. Supports both temporal (paired) and spatial (pairwise) comparisons. Package: r-cran-ascenttraining Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1971 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ascenttraining_1.0.0-1.ca2404.1_all.deb Size: 1890622 MD5sum: 5aeecf3b0dd68ae44bebeee530f6529e SHA1: 2ba580df659109ed6dfbb8d935a5633758a4ab71 SHA256: c34bfb85a52064c8fd6b5904e3c71064f6fd20df0224cd1a438af6380ea726ab SHA512: 7870e00ecb7e8877bfe02fede0001f4f091230c9cb872c28d44d5fa24110446cbf38c8b78233bb554fd83ae2b05018d716d4c1cc4a753cad35db698dc1249998 Homepage: https://cran.r-project.org/package=ascentTraining Description: CRAN Package 'ascentTraining' (Ascent Training Datasets) Datasets to be used primarily in conjunction with Ascent training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book. 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Package: r-cran-ascotracer Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-lubridate, r-cran-lutz, r-cran-circular, r-cran-purrr, r-cran-sf, r-cran-terra Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ascotracer_0.0.1-1.ca2404.1_all.deb Size: 598046 MD5sum: 0b91e62d3a776994a413885d18cbc41d SHA1: 2521135044d46255c846577d45c032fb9c02a505 SHA256: 697778dcf8705bc4fbec90f41462404072983985502c90ca0da9d903936c2052 SHA512: ccf2943a87ff1ca032d8082d609b99ca5146d1986c1679abe10d87900fd161dc97997a330d246b69bc57edfe8534a5ba4b005f660cae1c4076c0b5c8cf01d490 Homepage: https://cran.r-project.org/package=ascotraceR Description: CRAN Package 'ascotraceR' (Simulate the Spread of Ascochyta Blight in Chickpea) A spatiotemporal model that simulates the spread of Ascochyta blight in chickpea fields based on location-specific weather conditions. This model is adapted from a model developed by Diggle et al. (2002) for simulating the spread of anthracnose in a lupin field. Package: r-cran-ascribe Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-quarto, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ascribe_0.2.0-1.ca2404.1_all.deb Size: 103550 MD5sum: 041f83a28a7ee9d10e1dd8c39a9b3107 SHA1: feabd98bfbbc09c5785c0f0dbd3abbf6a41bd1f5 SHA256: f3562bfe867e205ac4b9377b91baa98a5739d9f2e7f6a367eba9e7156616d7db SHA512: 6939cea1923130874517e543f418d5ae0132f7e167ab75fb77afb8eff3a2c10acee1a8a54a8290fc1cd25836684780b15f08e0fff07bdfed797e7de15eb3bf67 Homepage: https://cran.r-project.org/package=ascribe Description: CRAN Package 'ascribe' (Static Detection and Citation of R Package and Function Usage) Scans R source files for package and function use, then builds citations from configurable package universes. Supports .R, .Rmd, and .qmd files, resolves unqualified calls by attachment order and re-export origin, and leaves each package collection to define its own citations. See 'stanflow' for an example usage. Package: r-cran-asd Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-asd_2.2-1.ca2404.1_all.deb Size: 121900 MD5sum: 187f2df0e13fe2457275695ca7e35f16 SHA1: 79c4f1c2c797c3e93fdb62bc6be5b06fbe077b21 SHA256: 51ced501070cd26c1fedf1d71f2ce4c370f7e62cd3bc341636aeab02ec4b41cb SHA512: f5adf9389660520c877b938ef5a2b2b5b155aeb740431ddc66ba7a1d5e19aa2dd8c37ff4700267406662d084d72c8af9ba0f8cebad3b6b7c132afa946142c8e9 Homepage: https://cran.r-project.org/package=asd Description: CRAN Package 'asd' (Simulations for Adaptive Seamless Designs) Package runs simulations for adaptive seamless designs with and without early outcomes for treatment selection and subpopulation type designs. Package: r-cran-asdreader Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asdreader_0.1-3-1.ca2404.1_all.deb Size: 67146 MD5sum: 388e3eb32dd1e7ee3b5158147827460b SHA1: aef4caa2d3b54b7617369f3a00fa977dedb359f1 SHA256: 0317650ea403cde0afc59efbc7fc91707905b4996eb40fb12addc8bbe32c2548 SHA512: f154c4d75dd916efa96ebe8e895779a23bed8521e115a95ad884234c975354e2d535613607771a9bf8c1cee644a43502460e35129cea107af3974f8c887a6843 Homepage: https://cran.r-project.org/package=asdreader Description: CRAN Package 'asdreader' (Reading ASD Binary Files in R) A simple driver that reads binary data created by the ASD Inc. portable spectrometer instruments, such as the FieldSpec (for more information, see ). Spectral data can be extracted from the ASD files as raw (DN), white reference, radiance, or reflectance. Additionally, the metadata information contained in the ASD file header can also be accessed. Package: r-cran-asgs.foyer Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2664 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Suggests: r-cran-testthat, r-cran-spdep, r-cran-codetools Filename: pool/dists/noble/main/r-cran-asgs.foyer_0.3.3-1.ca2404.1_all.deb Size: 2642404 MD5sum: 6714557df3cd2681edbe620da1757c24 SHA1: 82882dae4c1c1b77b1d943f154af020260c7bea2 SHA256: 56fdccf2170f0d2216cf2e6d8abaa82ebc0f65a0af5a1764e9e394e87e6d91f0 SHA512: d7c7d8e563d006080d1bc197a994481da28b95ec60ee1d99d4ccdbeea216c1f49e92e94b09be6fd97a5c344bfa4c93a17b6f468daa9955ecb8298b91e5b2f7f1 Homepage: https://cran.r-project.org/package=ASGS.foyer Description: CRAN Package 'ASGS.foyer' (Interface to the Australian Statistical Geography Standard) The Australian Statistical Geography Standard ('ASGS') is a set of shapefiles by the Australian Bureau of Statistics. This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles. Package: r-cran-ashapesampler Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-alphahull, r-cran-alphashape3d, r-cran-truncnorm, r-cran-rvcg, r-cran-tda, r-cran-doparallel, r-cran-foreach, r-cran-dplyr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rgl, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ashapesampler_1.0.0-1.ca2404.1_all.deb Size: 805392 MD5sum: 71fca92d3b1341936f5002053aaa1d6b SHA1: 9185f2a4c144a23a8f343ced938be7c7254017f9 SHA256: 5a76b203d9227d10d1a66ab5e48b1c9c504c8269bffbe79e356f201e3d004a43 SHA512: ef1ed9db88a45407bf7f18e67aa0986a1bf5e47a09e4bc2e03224e7b688b8cd1ebe5d56258ec6f7c721fdb3ec686a463cc43adb7d99f752714a38e4c934adfe0 Homepage: https://cran.r-project.org/package=ashapesampler Description: CRAN Package 'ashapesampler' (Generating Alpha Shapes) Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) . Package: r-cran-asht Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exact2x2, r-cran-exactci, r-cran-bpcp, r-cran-coin, r-cran-perm, r-cran-ssanv Suggests: r-cran-bootstrap Filename: pool/dists/noble/main/r-cran-asht_1.0.3-1.ca2404.1_all.deb Size: 263842 MD5sum: 3da459e63720b72f58e1b61b1da48e11 SHA1: bfa80165a93d424d6bfaf61e906b5b213ef40e01 SHA256: cde659746e446144f40362d3869c3ab01e2ea346db8ad741c09c97b5460f0f44 SHA512: 3e317bd0265d43bdbc6e6708a80763ed4e18102ebb554b65e2a98aec60e2f42fe1c647a2d338033bf535b2c4c0d14975aaea571c2b3302e0a94ec628509bbd51 Homepage: https://cran.r-project.org/package=asht Description: CRAN Package 'asht' (Applied Statistical Hypothesis Tests) Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim ], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky ], two-sample melding tests [see Fay, Proschan, and Brittain ], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 ] or Bayer, Fay, and Graubard, 2023 ). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp). Package: r-cran-asiaverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chinapis, r-cran-japanapis, r-cran-southkoreapis, r-cran-indiapis, r-cran-indonesiapis, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-asiaverse_0.1.0-1.ca2404.1_all.deb Size: 104742 MD5sum: 2ef51b51c88e65de44899afe7d4463e1 SHA1: ff3a81f9aefa42419fdc596a9079b1dd78ab1509 SHA256: 013de8567cc7ac85790a0eb93274f36769c2b889cfab6dad1cfc4acd106b19a2 SHA512: 84d181a5abe8c6815170a94b6fb4e20a7c5f477dbe5a9f5cf14523957184ced4158fbb17033179be3de08a4c27f38949d45d407bf596e99d0684449793d23615 Homepage: https://cran.r-project.org/package=Asiaverse Description: CRAN Package 'Asiaverse' (A Metapackage for Asian Countries RESTful APIs and CuratedDatasets) A metapackage that brings together a comprehensive collection of R packages providing access to APIs functions and curated datasets from China, Japan, South Korea, India, and Indonesia. It includes real-time and historical data through public RESTful APIs (Nager.Date, World Bank API, REST Countries API) and extensive curated collections of open datasets covering economics, demographics, public health, environmental data, natural disasters, political indicators, and social metrics. Designed to provide researchers, analysts, educators, and data scientists with centralized access to Asian data sources, this metapackage facilitates reproducible research, comparative analysis, and teaching applications focused on these five major Asian countries. Included packages: - 'ChinAPIs': APIs functions and curated datasets for China and Hong Kong covering air quality, demographics, input-output tables, epidemiology, political structure, and social indicators. - 'JapanAPIs': APIs functions and curated datasets for Japan including natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. - 'SouthKoreAPIs': APIs functions and curated datasets for South Korea covering public health outbreaks, social surveys, elections, economic indicators, natural disasters, climate data, energy consumption, cultural information, and financial markets. - 'IndiAPIs': APIs functions and curated datasets for India with comprehensive collections and real-time access to economic, demographic, and geopolitical indicators. - 'IndonesiAPIs': APIs functions and curated datasets for Indonesia covering holidays, economic indicators, consumer prices, poverty probability, food prices by region, tourism destinations, and minimum wage statistics. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-asioheaders Architecture: all Version: 1.30.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5320 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asioheaders_1.30.2-1-1.ca2404.1_all.deb Size: 411764 MD5sum: 65b6f19b9b27ab2ac5dafe8b1c0e1ac8 SHA1: c7d77bd6eebce1cec279ce4e6c3b602764548bf4 SHA256: a32ba59fbfee6b37fb3394378898e04ba76b0f0c1ec7e960c7ea871fe25a1957 SHA512: e87fe74452facc1e30caebe408a33427d25777c6bdc4b30dcdf80b3ee98c8820d1d7f129359827062b8f25a2cbe160b6c24516260ba8c75498d9bfc3ef47d8ac Homepage: https://cran.r-project.org/package=AsioHeaders Description: CRAN Package 'AsioHeaders' ('Asio' C++ Header Files) 'Asio' is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and released under the 'Boost Software License', Version 1.0. Package: r-cran-askgpt Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3538 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-callr, r-cran-dplyr, r-cran-glue, r-cran-rlang, r-cran-httr2, r-cran-rappdirs, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-shinycssloaders, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-askgpt_0.1.3-1.ca2404.1_all.deb Size: 2393660 MD5sum: 470bf0b6bd7b4d6dad2e484ef87d2270 SHA1: 5e361c367760928e54e1571a172cfe27a2a3867e SHA256: 793bde0de10bd6801dd9f4e1a65eb0626caf477f252f8420b2315c98346f949b SHA512: 072318f3c87fd3989481e117695bf87b4071b0530f9d4f4dbdb3fe9faff6cd3b5e26c15a1014f510b3f343ae18639eb15a7c8270d74a48d5fdbf53ce90f11eaa Homepage: https://cran.r-project.org/package=askgpt Description: CRAN Package 'askgpt' (Asking GPT About R Stuff) A chat package connecting to API endpoints by 'OpenAI' () to answer questions (about R). Package: r-cran-asleep Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3352 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-lubridate, r-cran-readr, r-cran-reticulate, r-cran-rlang Suggests: r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-callr Filename: pool/dists/noble/main/r-cran-asleep_0.3.0-1.ca2404.1_all.deb Size: 2475202 MD5sum: 58ace43a3e9fe7a5da416a2156e5d6a4 SHA1: 9993fc7a2d3ef496ba8290f8fe82777190bc6489 SHA256: c5c0a29d5c646026d28ce719348415223ca6483640bfc343caeb9a215ea6e897 SHA512: 15c7a7afc2a29060e9df248de396dc111706ca6a72ab05ed8d5c8eb472090719fe5f1a0bf2c67e47b7d1cffe2275e9318cff8992a9d35bf64743e1df5e8da4c4 Homepage: https://cran.r-project.org/package=asleep Description: CRAN Package 'asleep' (Estimate Sleep from 'Accelerometry' Data) Interfaces the 'asleep' python module from Yuan (2024) to estimate sleep from 'accelerometry' data. Package: r-cran-aslib Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-data.table, r-cran-bbmisc, r-cran-checkmate, r-cran-corrplot, r-cran-ggplot2, r-cran-llama, r-cran-mlr, r-cran-parallelmap, r-cran-paramhelpers, r-cran-plyr, r-cran-reshape2, r-cran-rweka, r-cran-stringr, r-cran-yaml Suggests: r-cran-testthat, r-cran-rpart Filename: pool/dists/noble/main/r-cran-aslib_0.1.3-1.ca2404.1_all.deb Size: 170960 MD5sum: 6ef6d2d2274eed042cb0e358a7204bed SHA1: c81fa168062dd1a917f1c6d781f735654980cedc SHA256: 23b4412d386aee5ad515f66eff70f8e1a0a697ff8d12c5017b9ce74332d9928b SHA512: e0c05f21c33d7c34ad31204024022aa025f2295d1b4c93e5bf7b4511fe4f4abca45c22c7801fc1f020c0fe60586d97b603769110f3b28d35a50a5c170cb0a98b Homepage: https://cran.r-project.org/package=aslib Description: CRAN Package 'aslib' (Interface to the Algorithm Selection Benchmark Library) Provides an interface to the algorithm selection benchmark library at and the 'LLAMA' package () for building algorithm selection models; see Bischl et al. (2016) . Package: r-cran-asm Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-pracma, r-cran-iso, r-cran-mass, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-asm_0.2.4-1.ca2404.1_all.deb Size: 72692 MD5sum: f12596d8b147194ff30a158e6dbda461 SHA1: 1ff2c92e8e4500a28be6405e316eb56a1160b550 SHA256: b684858dedd57ce769e165f94dec7af4cfd8ca9398fc1f4019a12651a12ec73f SHA512: 40847bf9f4f57fbfc94bf8bb8e46e18ceb6f5dfe2c025f72cadc85da27e42001f1e5765438f596869c28e4eb63c5ed77d5d76e956857c44a8c542fff63973a31 Homepage: https://cran.r-project.org/package=asm Description: CRAN Package 'asm' (Optimal Convex M-Estimation for Linear Regression via AntitonicScore Matching) Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) . Package: r-cran-asmbook Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-lattice, r-cran-mass, r-cran-tmb Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-asmbook_1.0.2-1.ca2404.1_all.deb Size: 130966 MD5sum: 3ac5a67506944edc547291efe55e218f SHA1: 95d9012365e81bb44f2ee1b791eb2842d2a1c201 SHA256: 7d8de89f1f3dc9f500ae2ad91d006e06ec6508483dbbc079f8f262bfd093c055 SHA512: 4fcaf2bb8e7ca6b3e6e3606983678dfb253a3e024e6cb986f7721d61e128f91208fc6e05978aa4a90cbb954e5bfe67bc705ba093d756f9943d7eb9c84719a7dd Homepage: https://cran.r-project.org/package=ASMbook Description: CRAN Package 'ASMbook' (Functions for the Book "Applied Statistical Modeling forEcologists") Provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo. Package: r-cran-asml Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-ggplot2, r-cran-dalex, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-reshape2, r-cran-polychrome, r-cran-scales, r-cran-rlang Suggests: r-cran-snow Filename: pool/dists/noble/main/r-cran-asml_1.1.0-1.ca2404.1_all.deb Size: 661558 MD5sum: 9dcb76b2c952422c361ce595b1ef9960 SHA1: 6ab28b13b304f385f9f7fd37276ca30790ca1db3 SHA256: d853a863aaaa145952c163786d1e64007f0f73531ba31617b444e6602fe26d8a SHA512: 043c2feac9721053d9f1a8cac325a427586f7805dd4bc4c4507ebc398bb3803e70d27c8efb014d63845fcf4daeb3e55bc8b154b39bd6fa85333697fb8622eb18 Homepage: https://cran.r-project.org/package=ASML Description: CRAN Package 'ASML' (Algorithm Portfolio Selection with Machine Learning) A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) ). Package: r-cran-asnipe Architecture: all Version: 1.1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-ape, r-cran-raster, r-cran-sna Filename: pool/dists/noble/main/r-cran-asnipe_1.1.17-1.ca2404.1_all.deb Size: 440152 MD5sum: 7374f976c4608dcaa4a3e1b9a41a5a81 SHA1: 07c99309cc1f2c8985185b0a816e2a6d226d9dd8 SHA256: 4862df393074b03914e999dbe208c046f4553f253edd9ee21caf7769d263a20a SHA512: 3025abd41dd0edc99cdf36426d32d5d40c25494561ff6977e8edad89b78270a06fa44c7c68048d9f4c0b68d6f3f96452df52a94b0e1b5e5cd21e9c99d2ddc89c Homepage: https://cran.r-project.org/package=asnipe Description: CRAN Package 'asnipe' (Animal Social Network Inference and Permutations for Ecologists) Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies and Farine & Whitehead (2015) . In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data. Package: r-cran-aspace Architecture: all Version: 4.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splancs, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-aspace_4.1.2-1.ca2404.1_all.deb Size: 124792 MD5sum: fd34639bc9fc6604206a9ba634671162 SHA1: 7416637179bdfb38bd7c19e3de59be256d89a208 SHA256: 6cdb2ae1de876150180779928efd5be55e486bf1a87f3a92dfab580ce498d659 SHA512: 85b0c3befc6600d2c468a675327cae243c664617a3d6b53b03de3b8dc27a5a4fdabe0220d888e28e6d67f74f15f7ca5f4cf4b7a78eedcc4dc1e5f392c137d3c7 Homepage: https://cran.r-project.org/package=aspace Description: CRAN Package 'aspace' (Functions for Estimating Centrographic Statistics) A collection of functions for computing centrographic statistics (e.g., standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations. Separate plotting functions have been developed for each measure. Users interested in writing results to ESRI shapefiles can do so by using results from 'aspace' functions as inputs to the convert.to.shapefile() and write.shapefile() functions in the 'shapefiles' library. We intend to provide 'terra' integration for geographic data in a future release. The 'aspace' package was originally conceived to aid in the analysis of spatial patterns of travel behaviour (see Buliung and Remmel 2008 ). Package: r-cran-aspect Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sem, r-cran-polycor Filename: pool/dists/noble/main/r-cran-aspect_1.0-7-1.ca2404.1_all.deb Size: 94532 MD5sum: 4e0c382a8c08ac0156858c6bc3496d45 SHA1: 531222c447d962ddcb9850912ae7b73f4ef88a8a SHA256: 068da97125073a2e1efe74ea3fc6904371109d45c66996287871a18c4e851c9e SHA512: 42d520b5eb5f9cc0d96f25bc907b62d9e2b3de356f5e77cfcb3455a42651a4dde2351651e5c014bbcd04ff34519ad348ed95aaa842fa5990f0a2bd93b49d5753 Homepage: https://cran.r-project.org/package=aspect Description: CRAN Package 'aspect' (A General Framework for Multivariate Analysis with OptimalScaling) Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models. Package: r-cran-aspi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aspi_0.2.0-1.ca2404.1_all.deb Size: 76440 MD5sum: 6ab25f8cc27d7ef88aed13fcab631ab0 SHA1: 53e766a770b8aed170e7ece46b5299ed7055531a SHA256: 6827546ba515ebb7d80ab55045dcef7fdf49f873c60347d8b6343abbc73df71b SHA512: 135f27eb3a2ee710a532a7727f94fc662e43a5173fc663c54baac9c72dd5cc0e00f1760f6c08d00e380cf7cc1c708bfd65fb87a2500e25abbe0f454de8e0753a Homepage: https://cran.r-project.org/package=aspi Description: CRAN Package 'aspi' (Analysis of Symmetry of Parasitic Infections) Tools for the analysis and visualization of bilateral asymmetry in parasitic infections. Package: r-cran-aspu Architecture: all Version: 1.50-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-mass, r-cran-mvtnorm, r-cran-fields, r-cran-matrixstats Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-aspu_1.50-1.ca2404.1_all.deb Size: 747724 MD5sum: 6bafce5eec1b9c29743e0afc3abbc516 SHA1: 794a2f74a692f6e98871e9576915b23449431fae SHA256: 76c4ec2a6aa2cd4e0227a45a2555de7463185c0f52d02823c6f861f19917c549 SHA512: a878d6e95bf3969aed907d1adbf8b83b8fb3246f0339bcd5de35b462bc60ace0c498ab1f2406997e5b389b3953b8dd0175196f280c6a43b6cb001f5c531d1e1e Homepage: https://cran.r-project.org/package=aSPU Description: CRAN Package 'aSPU' (Adaptive Sum of Powered Score Test) R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU' test for multiple traits - single 'SNP' (single nucleotide polymorphism) association in generalized estimation equations, 'MTaSPUs' test for multiple traits - single 'SNP' association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended 'Simes' procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of 'GATES' test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP' set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single 'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits- single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath'). Package: r-cran-asremlplus Architecture: all Version: 4.4.65-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3294 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dae, r-cran-devtools, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-nloptr, r-cran-qqplotr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-sticky, r-cran-stringr, r-cran-trycatchlog Suggests: r-cran-emmeans, r-cran-lattice, r-cran-lmertest, r-cran-pbkrtest, r-cran-r.rsp, r-cran-testthat, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-asremlplus_4.4.65-1.ca2404.1_all.deb Size: 3135990 MD5sum: 42ede120aee13644a30fb56e34b143eb SHA1: 657b07d09ae5ba19a778cbaee77b4ce17726d84a SHA256: d58353a6468a675431dd2cbdf4c5f1e9901280ce5e4e4fd67a3a647834352cdd SHA512: 049b101dafcad38411571ab48fcbea4853564c604237a96ca6977b180ae60f1590e3b93c446f0947309182e903c281b1bc7042e06cc7a44aa5ad40f59a00e567 Homepage: https://cran.r-project.org/package=asremlPlus Description: CRAN Package 'asremlPlus' (Augments 'ASReml-R' in Fitting Mixed Models and PackagesGenerally in Exploring Prediction Differences) Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' as 'asreml-R', who will supply a zip file for local installation/updating (see ). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from . Package: r-cran-asrgenomics Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4918 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aghmatrix, r-cran-cowplot, r-cran-crayon, r-cran-data.table, r-cran-ellipse, r-cran-factoextra, r-cran-ggplot2, r-cran-matrix, r-cran-scattermore Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-v8, r-cran-testthat Filename: pool/dists/noble/main/r-cran-asrgenomics_1.1.6-1.ca2404.1_all.deb Size: 4841278 MD5sum: 37949451fd2de3f0d5045ddccd2fbabe SHA1: 48d773a25a08ed4d769cfd249d53d6b19beefa2f SHA256: 993ba72467af42c31436f2a6a0700bea1cab12cd1f9a036c5c10b07c377840bd SHA512: c0bf4b375c95b72b19ee09a793b5380bae2308296b1d01414f507d8e4b15aa8ea4cafaa2f2b5ddcaa9fc2420a571925a44aca10f0a1c93103e686a73249f2f6d Homepage: https://cran.r-project.org/package=ASRgenomics Description: CRAN Package 'ASRgenomics' (Complementary Genomic Functions) Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of 'asreml' or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) . 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Package: r-cran-assert Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-assert_1.0.1-1.ca2404.1_all.deb Size: 11588 MD5sum: ec8c301939bbf6bc6c40b6fa37dbd2c0 SHA1: e66794c15b8ae85531740373a3d7dd67ec175da7 SHA256: d506506f29cc4db724128a3b992ffbcc2b42541a3d64fe8fd95e361643953547 SHA512: b4865cb652bc55a69ec231727222cb078b27cd1a919408375303c88e2ddd7488dfed7735ace39fa7b73a38527b57035a8dc7944ea40b56ebd37a2f8c4ea8de21 Homepage: https://cran.r-project.org/package=assert Description: CRAN Package 'assert' (Validate Function Arguments) Lightweight validation tool for checking function arguments and validating data analysis scripts. This is an alternative to stopifnot() from the 'base' package and to assert_that() from the 'assertthat' package. It provides more informative error messages and facilitates debugging. 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Package: r-cran-assertions Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-assertions_0.3.0-1.ca2404.1_all.deb Size: 572542 MD5sum: bcf08d9e345d946986789bcb8b68732c SHA1: f2a0ffadece364840f23ac1807ebcc1bf512afa9 SHA256: 551149ba5aa0c926f851693070358bbc813bc9b074e797fc2c5d1b82df9a582c SHA512: c1323e8b53f5f848c6d42913314da2b9600d2dee16d40dbc38c919e4a6489e7a6dad5199c88d477306c91f1053b2c42ec5894c5c898a61b1c3faf6d866c13e2c Homepage: https://cran.r-project.org/package=assertions Description: CRAN Package 'assertions' (Simple Assertions for Beautiful and Customisable Error Messages) Provides simple assertions with sensible defaults and customisable error messages. 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Package: r-cran-assetallocation Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4676 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-performanceanalytics, r-cran-quantmod, r-cran-riskportfolios, r-cran-xts, r-cran-zoo, r-cran-nmof, r-cran-riskparityportfolio, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-assetallocation_1.1.1-1.ca2404.1_all.deb Size: 2646124 MD5sum: 30ba766dafeb7082941fa0781eb19e78 SHA1: 2e95480826cf4d307296f1a715b8712d73781772 SHA256: b4477b770c19bc8e7d5591d828ac9ff69b9be7c589c8600c9c27e4b518544870 SHA512: 325498ee429ea25b6279cd879b830e1cdcdafe7ecab8c9a14c8af042d8692f84413c8fb636fe4b594b8d725854dcc8afc650966139b1238a0c854417fff53ffb Homepage: https://cran.r-project.org/package=AssetAllocation Description: CRAN Package 'AssetAllocation' (Backtesting Simple Asset Allocation Strategies) Easy and quick testing of customizable asset allocation strategies. 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Package: r-cran-assignr Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvnfast, r-cran-rlang, r-cran-geosphere, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-assignr_2.4.3-1.ca2404.1_all.deb Size: 795986 MD5sum: a541623ce1914c46f93121f6235af77e SHA1: a5432ee6bc19cc7c9e54a2fd06b2c482ddd3c3d2 SHA256: a9109df4feb54ac2030f98bef397b583c675f78a684726bc5c4da22f7468b5dd SHA512: 0e3160c409d442f012d6d00a33d74bd7e9d6b992ad22ac7583b97a7653afd5a5c850190f00d8ab4c95fabe223b1fc143238eda9f62d52f4535143e3db3058de8 Homepage: https://cran.r-project.org/package=assignR Description: CRAN Package 'assignR' (Infer Geographic Origin from Isotopic Data) Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) as described in Ma, C. et al. (2020) . Package: r-cran-assistant Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-mvtnorm, r-cran-knitr, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-assistant_1.4.3-1.ca2404.1_all.deb Size: 605314 MD5sum: ff802729fa7314c3b63304b894d7a1f6 SHA1: 9b91c739449c406f039bdb81d8b784efe5bdf4ba SHA256: c0e9c7d1c40eb4f8771beaacb95ccf11447d4f0b5479e842f29f8ddd7cfce570 SHA512: a538dfb124b6fdcb49f309dea012aee73a6085a2920f3250b3a780ff254e978e48170435dec25b715fdbbe73b5b25b71d9d9f446016602ce9b43c1b5b30e617d Homepage: https://cran.r-project.org/package=ASSISTant Description: CRAN Package 'ASSISTant' (Adaptive Subgroup Selection in Group Sequential Trials) Clinical trial design for subgroup selection in three-stage group sequential trial as described in Lai, Lavori and Liao (2014, ). Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette. Package: r-cran-assocafc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform Filename: pool/dists/noble/main/r-cran-assocafc_1.0.2-1.ca2404.1_all.deb Size: 127120 MD5sum: 405c147af9fe54da700bc9e4eefd39af SHA1: b159232ec93e643c9acc743dea4106e2ba53c1cb SHA256: cdba87568faad4abb3ed27f099fe59df660dd9c248820dce643338aadb67e7d0 SHA512: 77c03d06cba6efd32844bbec46a3ec9676782c7aa9a236184e2d95949fc4740f98c7a3f7683f1b59b03c9e7c8f7f14d9931b24ae013417b2ca26199ce07fc119 Homepage: https://cran.r-project.org/package=AssocAFC Description: CRAN Package 'AssocAFC' (Allele Frequency Comparison) When doing association analysis one does not always have the genotypes for the control population. In such cases it may be necessary to fall back on frequency based tests using well known sources for the frequencies in the control population, for instance, from the 1000 Genomes Project. The Allele Frequency Comparison ('AssocAFC') package performs multiple rare variant association analyses in both population and family-based GWAS (Genome-Wide Association Study) designs. It includes three score tests that are based on the difference of the sum of allele frequencies between cases and controls. Two of these tests, Wcorrected() and Wqls(), are collapsing-based tests and suffer from having protective and risk variants. The third test, afcSKAT(), is a score test that overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect directions. For more details see Saad M and Wijsman EM (2017) . Package: r-cran-assocbin Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-assocbin_1.1-2-1.ca2404.1_all.deb Size: 946106 MD5sum: 28aad0896c1273b31d1015e950af217e SHA1: 28d59848962cfb501213525015beac6ba1b6aca8 SHA256: f59c866ea73439cbd10faffe22d98be44f513839ece6ea109b63d474a093a936 SHA512: a394313a55f04a08e2db030b910996c1cfa662ad07c6d11d41dcd5a1a19999b740bb381723d37fcc67e299d23afbef9e1344e9a4729304f9c547bcc4d51d056a Homepage: https://cran.r-project.org/package=AssocBin Description: CRAN Package 'AssocBin' (Measuring Association with Recursive Binning) An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a thorough discussion and demonstration of the algorithm, see Salahub and Oldford (2025) . 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The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) . Package: r-cran-assocind Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-assocind_1.0.1-1.ca2404.1_all.deb Size: 115486 MD5sum: bb64c2022cd4fb1ca16f76befe6bb1ca SHA1: c971623e1902eddf2e8b4d18a701b355975304a8 SHA256: 1b0c409d546917a63833ee660a757f62afd8d77b20be9b46fbc1205a59065b9c SHA512: c60daea240babfe2098c0f7afb648ab3924a5e7835ff59cec684d57b4fdeb5e05b062a7836cd53ffe0a8c552726a68dee1b836c9d72ce4c920b79d4020b88589 Homepage: https://cran.r-project.org/package=assocInd Description: CRAN Package 'assocInd' (Implements New and Existing Association Indices for ConstructingAnimal Social Networks) Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method. Package: r-cran-assoctests Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-mvtnorm, r-cran-combinat, r-cran-fextremes Filename: pool/dists/noble/main/r-cran-assoctests_1.0-1-1.ca2404.1_all.deb Size: 895316 MD5sum: c30df526eec070730af51e7c93718c69 SHA1: b8e41fe8c4fa2d9629bb8756a83166641debd172 SHA256: ddaca6813a2b3c60b1aed4537921a25172dfd6341bffb515540b8b3fd56a6d22 SHA512: f47ad56fa6b02606fb9594cb9116305bcd5ca14968a0ea88a5ba9395e76c439ebaa01aac7de0eb5d83f8e99ea41e8495e010ffd9c05de4e97b9aa47059ecce12 Homepage: https://cran.r-project.org/package=AssocTests Description: CRAN Package 'AssocTests' (Genetic Association Studies) Some procedures including EIGENSTRAT (a procedure for detecting and correcting for population stratification through searching for the eigenvectors in genetic association studies), PCoC (a procedure for correcting for population stratification through calculating the principal coordinates and the clustering of the subjects), Tracy-Widom test (a procedure for detecting the significant eigenvalues of a matrix), distance regression (a procedure for detecting the association between a distance matrix and some independent variants of interest), single-marker test (a procedure for identifying the association between the genotype at a biallelic marker and a trait using the Wald test or the Fisher's exact test), MAX3 (a procedure for testing for the association between a single nucleotide polymorphism and a binary phenotype using the maximum value of the three test statistics derived for the recessive, additive, and dominant models), nonparametric trend test (a procedure for testing for the association between a genetic variant and a non-normal distributed quantitative trait based on the nonparametric risk), and nonparametric MAX3 (a procedure for testing for the association between a biallelic single nucleotide polymorphism and a quantitative trait using the maximum value of the three nonparametric trend tests derived for the recessive, additive, and dominant models), which are commonly used in genetic association studies. To cite this package in publications use: Lin Wang, Wei Zhang, and Qizhai Li. AssocTests: An R Package for Genetic Association Studies. Journal of Statistical Software. 2020; 94(5): 1-26. Package: r-cran-assortnet Architecture: all Version: 0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-assortnet_0.20-1.ca2404.1_all.deb Size: 23244 MD5sum: 8c352fdb9c38e24578588b9f0895fb62 SHA1: 8ff88124b1655a652dde83959ec741fe3fa40519 SHA256: f8943eb0891101021c3615ce0c1af558cd07ff25fa911157b193fb64b3732277 SHA512: d60da0424b87e4d3527a02f6dc8bb4ca988bd061b28adc81c07e963ddc480fafd12d36bedea6f6fdf277ccd192652cc3d96a33fcde184431e8a08a1510bd06d1 Homepage: https://cran.r-project.org/package=assortnet Description: CRAN Package 'assortnet' (Calculate the Assortativity Coefficient of Weighted and BinaryNetworks) Functions to calculate the assortment of vertices in social networks. This can be measured on both weighted and binary networks, with discrete or continuous vertex values. 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The user has to define a R function which gets translated. For a full list of possible functions check the documentation. After translation an R function is returned which is a shallow wrapper around the C++ code. Alternatively an external pointer to the C++ function is returned to the user. The intention of the package is to generate fast functions which can be used as ode-system or during optimization. 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Package: r-cran-asthmanhanes Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 966 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asthmanhanes_1.1.0-1.ca2404.1_all.deb Size: 955380 MD5sum: 98496735a62eafdaa7ed2b0c277533c2 SHA1: b21194f3a87c002013f9f6fa5de85650d62cb283 SHA256: 967bc3faa4281796ddd476fce2e58e456d4c2229172ec65b36abbd331006725a SHA512: e4ff11fb14f9c6c64da5780ad7a0657fd154b37499272b7ab1d4085f9756c1c79d6732b65f7272f9feaf8901bf898bf28fd3e63fb8ec985697eaeb2b3792e0a9 Homepage: https://cran.r-project.org/package=AsthmaNHANES Description: CRAN Package 'AsthmaNHANES' (Asthma Data Sets from NHANES) Data sets and examples from National Health and Nutritional Examination Survey (NHANES). Package: r-cran-astraeadb Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-r6 Suggests: r-cran-arrow, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-astraeadb_0.2.1-1.ca2404.1_all.deb Size: 338596 MD5sum: 86e7118f99585947de2d7ea2e40f66f3 SHA1: 2c3cd349a765c4c0525491e50d045ab010d8d97d SHA256: 8ef7ebcb78cf0f498ff65a3be213907bbc14fff44c9080c8e3b4a42ddfb979ee SHA512: bc15eb56f87cc2898bfb01bbe58c4fb46d5370f7beccc61aae18d31c5993edf8a0dc4fd19e54e48418f96614619d4102a5239a09f9b4000e1ef811393011cddf Homepage: https://cran.r-project.org/package=AstraeaDB Description: CRAN Package 'AstraeaDB' (Client for the 'AstraeaDB' Graph Database) Provides a client for 'AstraeaDB', a graph database with vector search capabilities. Supports node and edge create, read, update, and delete operations, label and edge-type lookups, graph traversals (breadth-first search, depth-first search, shortest path), temporal (time-travel) queries, graph algorithms (PageRank, Louvain community detection, connected components, and degree and betweenness centrality), vector similarity search, hybrid graph-vector search, Graph Query Language (GQL) execution, and graph-based retrieval-augmented generation (subgraph extraction with large language model integration). Communicates with the 'AstraeaDB' server over a JSON-over-TCP protocol. An optional 'Apache Arrow Flight' transport is available for high-performance bulk operations when the 'arrow' package is installed. Package: r-cran-astrodatr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3464 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-astrodatr_0.1-1.ca2404.1_all.deb Size: 3306158 MD5sum: 802a4f98337f2f14e9baa637b49c25f9 SHA1: 3a1e9e4c58562c26adeb00a6b9e4c2501cae382a SHA256: 1886b117532527eecffd196d549ec0e182bcfd8197c09130b78309824fbbddba SHA512: 03bb0ae081054026ea8a5df62ea9ee52f20054e47b2c08bb6dacc5c27338abba67443cfc436703f0c44f84ca5a22c5b214bca15ef35d8b4410ff7105d5edf17f Homepage: https://cran.r-project.org/package=astrodatR Description: CRAN Package 'astrodatR' (Astronomical Data) A collection of 19 datasets from contemporary astronomical research. They are described the textbook `Modern Statistical Methods for Astronomy with R Applications' by Eric D. Feigelson and G. Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State's Center for Astrostatistics (http://astrostatistics.psu.edu/datasets). These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis. 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Package: r-cran-astronomr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-pracma, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-desolve, r-cran-testthat Filename: pool/dists/noble/main/r-cran-astronomr_0.3.0-1.ca2404.1_all.deb Size: 115696 MD5sum: 6ca56c09ea39bacecf5fd3027fc0c0c5 SHA1: 3525c8f54f653aab96f7540bbf0e9d99d2f7b62c SHA256: 58858e313d5e3071b735fd99fe818366ee4b065c6b373dfaec38849e97521755 SHA512: 81b4cc90126fcf83a85cfca5dfd240eac05bc6e348833422d241f838e439585c44f6c672c7662156f4ec89cc59814e8ff93592061708cada7844304e17900a33 Homepage: https://cran.r-project.org/package=astronomR Description: CRAN Package 'astronomR' (Cosmic Insights: Statistical Frameworks for Astronomers) A comprehensive toolkit for astronomical and cosmological computations. Provides functions for angular coordinate conversions (degrees, hours-minutes-seconds, degrees-minutes-seconds, and radians), access to fundamental physical constants, queries to the Gaia Archive TAP (Table Access Protocol) service, cosmological distance calculations, early-universe thermal physics including photon density, 'Saha' equation solutions, and a full thermal-cosmology module covering the Hubble rate in the radiation-dominated era, effective relativistic degrees of freedom, entropy density, equilibrium yields, the Boltzmann relic-abundance ('pebble') equation for WIMP freeze-out, the freeze-out temperature solver, and the Peebles equation for hydrogen recombination. Also includes the Drake equation for estimating the number of communicating extraterrestrial civilisations in the Milky Way. Package: r-cran-astsa Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1409 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-astsa_2.5-1.ca2404.1_all.deb Size: 1370878 MD5sum: f1cc38a4734a8ed94b4e72c130e9f5df SHA1: 2f4d2c512100f40e31b70c495cae6da9d789a7ae SHA256: 577a9bd011190c23bed201f0ecfdba6ad095a55e41cf33522127d114ddf59a8d SHA512: e28267bc7a08611a154b76abf8420e2413aa7274eae3ca1cc41c87e82777e22130fb0d382a9076dded876cfc2477f38cad69132e8efebbdce3b3235fce5c1790 Homepage: https://cran.r-project.org/package=astsa Description: CRAN Package 'astsa' (Applied Statistical Time Series Analysis) Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, , and Time Series: A Data Analysis Approach Using R (2nd ed). Chapman-Hall, 2026, . Most scripts are designed to require minimal input to produce aesthetically pleasing output for ease of use in live demonstrations and course work. Package: r-cran-asus Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-asus_1.5.0-1.ca2404.1_all.deb Size: 62600 MD5sum: a01e6b632eb908943a3e079e446dd827 SHA1: 0ab2d4e39dcf9d8817891ce80ef8e56af529f1ae SHA256: 9019176f9bb11f18b9f7e2daaf63c17a76da12908fd2ff7b79b3c29353606c8e SHA512: 5390d2a54d0769f2c26bbfd03f08bf14d1ce6f579aae7f96da932584657898c37ef9616fb3ce482b2f582424d2403efc728ea6520159b4a3ca0846a1b2ebcec4 Homepage: https://cran.r-project.org/package=asus Description: CRAN Package 'asus' (Adaptive SURE Thresholding Using Side Information) Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020). Package: r-cran-asyk Architecture: all Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asyk_1.5.6-1.ca2404.1_all.deb Size: 32920 MD5sum: e7776c215558e65a6a0fe827846e6b24 SHA1: d625a4fa0e9028276cba626601e6d028079882e8 SHA256: 82c6b6d134ace5a9c3b5a9c9cb3d767b58a9fee0d37783bf8f5c5c8d9314f84d SHA512: 99e6d56b861c9965eb952239d2f31ec0679f61c77dc6404cd09f44e23d33532115330eb04aa26c3fb9fc55fff1c239c12fbae1afd744bb52f34de245208829de Homepage: https://cran.r-project.org/package=AsyK Description: CRAN Package 'AsyK' (Kernel Density Estimation) A collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) and Khan and Akbar. Package: r-cran-asylum Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4746 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asylum_1.1.2-1.ca2404.1_all.deb Size: 4230466 MD5sum: 9f613f20f4306b6c0c844a44bc249859 SHA1: ddb9d9fa107bf0a109d6e991a810ee1971e992ce SHA256: d3cb10715b823e7b8ea546bfbae8ea65a6afa74309307c96005a617ca8f467c6 SHA512: 35d060c5e37f6006585e911ce506d9bbca7b213f8dc7711973bd8f00643e457221079e2b9c8a54229bf719ece9f49e1545a817d25301b23e6a01c4be670892f9 Homepage: https://cran.r-project.org/package=asylum Description: CRAN Package 'asylum' (Data on Asylum and Resettlement for the UK) Data on Asylum and Resettlement for the UK, provided by the Home Office . Package: r-cran-asymld Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-haplo.stats, r-cran-fields Filename: pool/dists/noble/main/r-cran-asymld_0.1-1.ca2404.1_all.deb Size: 53218 MD5sum: a9baa5907003198e43ad469754647a7c SHA1: 92becf2e60b4c8a2d38263e654378997162126e2 SHA256: 9320e9675bab03a1f959b7f581c21e98c75f21b81b7b4b099507fad402d0b148 SHA512: 6a4ecd6f6b7a6f69828b0a0233878f38f198c0cb5990883787aef51c8bcb7d6bd9321b8433ff48a5d3ec8b49865c9c5c7173346d22e7e267f37b0a817001e029 Homepage: https://cran.r-project.org/package=asymLD Description: CRAN Package 'asymLD' (Asymmetric Linkage Disequilibrium (ALD) for Polymorphic GeneticData) Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data. Package: r-cran-asymmetricsords Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asymmetricsords_1.0.0-1.ca2404.1_all.deb Size: 161212 MD5sum: b8b116534ec147f7ea57181d762d03a1 SHA1: b88be9d38f9a5d3ac29ad180af55ba4cc801b050 SHA256: 46d66d1b11e692d2515fbf6c16f46db4f62922693b98c7d62163d8cad4b6b715 SHA512: bc6a83b4276d484561df33417ddec18b0d3930962de084b01191fa7d26c11591f0bc9466f9c1696fa6f33c16e4c8c638e4147b7c1cf1e1618a6f377461b5ec91 Homepage: https://cran.r-project.org/package=AsymmetricSORDs Description: CRAN Package 'AsymmetricSORDs' (Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)) Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) ."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) . "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model. Package: r-cran-asymmetry.measures Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sn, r-cran-skewt, r-cran-gamlss.dist Filename: pool/dists/noble/main/r-cran-asymmetry.measures_0.3-1.ca2404.1_all.deb Size: 144052 MD5sum: cbcc3831b10a620453baff1c5cb1ddd2 SHA1: 5069cef9df3da57e32db77cf18d73bfd4262d1be SHA256: f9776bca582f2bcf723011bd2dfe19c06a924656e49885e5a0945c64fbd3a364 SHA512: a22b5fefd5cfb36952a4090338f759035f9c68d2a1e92f4e3959e185cc629e0f60ef4fe0d3dd2fa22def893c904e960286d9822cb7dd2712308263deb8926199 Homepage: https://cran.r-project.org/package=asymmetry.measures Description: CRAN Package 'asymmetry.measures' (Asymmetry Measures for Probability Density Functions) Provides functions and examples for the weak and strong density asymmetry measures in the articles: "A measure of asymmetry", Patil, Patil and Bagkavos (2012) and "A measure of asymmetry based on a new necessary and sufficient condition for symmetry", Patil, Bagkavos and Wood (2014) . The measures provided here are useful for quantifying the asymmetry of the shape of a density of a random variable. The package facilitates implementation of the measures which are applicable in a variety of fields including e.g. probability theory, statistics and economics. Package: r-cran-asymmetry Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacof Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-asymmetry_2.0.6-1.ca2404.1_all.deb Size: 153964 MD5sum: 922b21528e0ace4cd756a108d97343cd SHA1: f1ce933f9105b9cd5a9dfe4b1c3ef8aeed802893 SHA256: 5f6ea6025ee95f8147306c3dd6a717f820a15ff93cfaee4209195dafa2dd9ae3 SHA512: 34409d9dd473c531ea47066ab54f687377a5bbfc07be237cfa126879b14ca6382eeea7ca126e150081f3041716f37945c5fe0a5694810fde62ae83c4233c1d3b Homepage: https://cran.r-project.org/package=asymmetry Description: CRAN Package 'asymmetry' (Multidimensional Scaling of Asymmetric Proximities) Multidimensional scaling models and methods for the visualization and analysis of asymmetric proximity data. An asymmetric data matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example of an asymmetric matrix is a student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides algorithms for three multidimensional scaling models, the slide-vector model, a scaling model with unique dimensions and the asymscal model. Furthermore, some other procedures, such as a heat map for skew-symmetric data, and the decomposition of asymmetry are also provided for the exploratory analysis of asymmetric tables. Package: r-cran-asympdiag Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-glmmtmb, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-rlang, r-cran-sandwich, r-cran-survival, r-cran-testthat, r-cran-vctrs, r-cran-withr Filename: pool/dists/noble/main/r-cran-asympdiag_0.3.3-1.ca2404.1_all.deb Size: 107638 MD5sum: 6689f38e712e49b8bbc52bcd5a874dee SHA1: 9c5a0b75bb8cbe6aeb2c97f2d63a5fae2861fc6a SHA256: 155299144f60ba719b0ffcfec45eae3cd02a3f5d2aa822a99d3cefd734aeae80 SHA512: 2431fd0bb3a6e55815d755df1e15763d927f69a77119f661a2b9e3aac5231f5e60b551ec4cbc1bdf1d1205071a216eb89b2542b3ca286d83220580a204d501ef Homepage: https://cran.r-project.org/package=asympDiag Description: CRAN Package 'asympDiag' (Diagnostic Tools for Asymptotic Theory) Leveraging Monte Carlo simulations, this package provides tools for diagnosing regression models. It implements a parametric bootstrap framework to compute statistics, generates diagnostic envelopes to assess goodness-of-fit, and evaluates type I error control for Wald tests. By simulating data under the assumption that the model is true, it helps to identify model mis-specifications and enhances the reliability of the model inferences. Package: r-cran-asymptest Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asymptest_0.1.4-1.ca2404.1_all.deb Size: 345730 MD5sum: 46301bc01dddea34b0ecb88ac5ca2e97 SHA1: b2521df790518560e9d351e0ce429bc11ffdb0cf SHA256: 042fd83774c88c58dae276bfaaaae2932216623e78fc052a8785ba0cec66f48b SHA512: 873835691f86f0a55a0292ba654aee0e36343b4208a3bcb81b6fadacac957368d5bf8e66047d0198bbceb84c9d166304c51c012622ee2be34e783eeb2721d3e9 Homepage: https://cran.r-project.org/package=asympTest Description: CRAN Package 'asympTest' (A Simple R Package for Classical Parametric Statistical Testsand Confidence Intervals in Large Samples) One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software. Package: r-cran-asymptor Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-asymptor_1.1.0-1.ca2404.1_all.deb Size: 136318 MD5sum: d0c6e5d6c34095351b3413261b345de0 SHA1: e8be8f41fdf5184e3593b4ae80de76bfb06bc273 SHA256: f5a507cabb60d487fa2da58411786a1fe2a966aab3274edc14bbf26ad18b88f5 SHA512: 8462c4dc5edc65293f2b09804eae88831fde3934934d75e115c62dd73951e191a1b3bf66bdb13f85efcf251a6038e688f3e26edd4bae9a8ee8f735ad5b6a7204 Homepage: https://cran.r-project.org/package=asymptor Description: CRAN Package 'asymptor' (Estimate Asymptomatic Cases via Capture/Recapture Methods) Estimate the lower and upper bound of asymptomatic cases in an epidemic using the capture/recapture methods from Böhning et al. (2020) and Rocchetti et al. (2020) . Note there is currently some discussion about the validity of the methods implemented in this package. You should read carefully the original articles, alongside this answer from Li et al. (2022) before using this package in your project. 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These work similarly to generator and async constructs from 'Python' or 'JavaScript'. Objects produced are compatible with the 'iterators' and 'promises' packages. Version 0.3 supports on.exit, single-step debugging, stream() for making asynchronous iterators, and delimited goto() in switch() calls. Package: r-cran-asynchlong Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asynchlong_2.4-1.ca2404.1_all.deb Size: 257838 MD5sum: 564bfccd7b968e09a9141ba53546abca SHA1: ee132d9c3f70cfc2157c66e50481934a0a547db7 SHA256: 3f1815fd02a15673803b290d61d955f16b7eab5872c8947fe30ef91474c9611b SHA512: 73544448743be95d07c62ca421f44e318f9a195069c6c4e8d9565721f17b8db0f047d0468b58021f703a59576a3d736e1878e4ccc26c7ea52c832b468e7f49ae Homepage: https://cran.r-project.org/package=AsynchLong Description: CRAN Package 'AsynchLong' (Regression Analysis of Sparse Asynchronous Longitudinal Data) Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. Cao, H., Li, J., and Fine, J. P. (2016) . Cao, H., Zeng, D., and Fine, J. P. (2015) . 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Package: r-cran-atable Architecture: all Version: 0.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doby, r-cran-plyr, r-cran-reshape2, r-cran-hmisc, r-cran-settings, r-cran-desctools, r-cran-effsize Suggests: r-cran-testthat, r-cran-knitr, r-cran-survival, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-atable_0.1.15-1.ca2404.1_all.deb Size: 674424 MD5sum: 2f39d0c9e621ebdcbe07d53d8b967caf SHA1: 9cc90ac329106a3d0179ee9fe1e160aa761f9ca4 SHA256: f5b5e90acbc328a69e1656091ee49553a8367f34ad7f4b626893459ec111e129 SHA512: 13547e13eeb7b0e190dff4ef3fb320b50281b6887fc2e52365453f2ad66eb7b66d6252bff932c4657e58749df939a340e4b2b174dc9974c7484daaa747c44647 Homepage: https://cran.r-project.org/package=atable Description: CRAN Package 'atable' (Create Tables for Reporting Clinical Trials) Create Tables for Reporting Clinical Trials. Calculates descriptive statistics and hypothesis tests, arranges the results in a table ready for reporting with LaTeX, HTML or Word. Package: r-cran-atbounds Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-atbounds_0.1.1-1.ca2404.1_all.deb Size: 436446 MD5sum: 38b915e04c3371cf9be11f9816fab0e8 SHA1: d19090c9b9c4107e3b0f67dd97d0a61f0150eeae SHA256: 025bb5e7d9343e6e042b3a9e680612b0b7872a36a38badd3bd9b820161964366 SHA512: 0c03bdd94127bc8af16a51b58104dc0a48dff5c25a032301e2dd1afd4e1fa40d48be0ceeea206ee0471e55d57bb2019698132ad65a5d6dc158f97d15861005ad Homepage: https://cran.r-project.org/package=ATbounds Description: CRAN Package 'ATbounds' (Bounding Treatment Effects by Limited Information Pooling) Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. For more details, see the paper by Lee and Weidner, "Bounding Treatment Effects by Pooling Limited Information across Observations," forthcoming at the Journal of Econometrics, . Package: r-cran-ate.error Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ate.error_1.0.0-1.ca2404.1_all.deb Size: 232392 MD5sum: a5f82d9e813bb708fc20e280b0e9bfe1 SHA1: 878dda034a544a9743a9a99d778be04cf8f5a6d3 SHA256: 610c69264b05569a50d0d11d0da5ed0b671048a148c0c4d0aef62911714f38e0 SHA512: e29565a10cf7f15cd4a24980664d140e815e5d7f324c4a404c31923ab8d8a16f6266f76e21c0dd9763b1b61ca2f06a49f42fc01d473dc840abe4c592cabe3c2b Homepage: https://cran.r-project.org/package=ATE.ERROR Description: CRAN Package 'ATE.ERROR' (Estimating ATE with Misclassified Outcomes and MismeasuredCovariates) Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, ; 2019, ). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration. 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Package: r-cran-atlas Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-atlas_1.0.0-1.ca2404.1_all.deb Size: 26164 MD5sum: c9141ae376ec634d287e47f556aea8ec SHA1: 26a4d16457556abee5f34fd4bbebaa4cd7aa30b8 SHA256: 82f7c5cb86106385f605792e81ac0a7c86f29ec6f07c44b8f89c1dde9298e84f SHA512: 796c906ee67a5146c6215e15e565d0d01ce4ba53666c18e6b054e3f1805d4fa678d38c914ff3675cd41ad1a02d8e67ff8485e220da46f2ccd8876ea14f58f6bd Homepage: https://cran.r-project.org/package=atlas Description: CRAN Package 'atlas' (Stanford 'ATLAS' Search Engine API) Stanford 'ATLAS' (Advanced Temporal Search Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. 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Additional interfaces and resources are available at . Package: r-cran-atlasmaker Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-atlasmaker_0.1.0-1.ca2404.1_all.deb Size: 3181346 MD5sum: 0cd313783c931766f319f47c289dc79a SHA1: 8c6f7756f5c737eeba0baed35c4d8670fe80bfda SHA256: 3fc9ab34a5e6cf0aa765910f0bfcbd4b916c5c6e6e974c760a3f8df08f6e5809 SHA512: 83d63322660a1828b89bb5bd385554dd4004b90705d19b1e24c1e889c6fb5c21f9a2221cbff620b6579ee394106eeb6e046724280550d9b7b031d8f897357069 Homepage: https://cran.r-project.org/package=AtlasMaker Description: CRAN Package 'AtlasMaker' (Make Multiple 'leaflet' Maps in 'Shiny') Simplify creating multiple, related 'leaflet' maps across tabs for a 'shiny' application. 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Package: r-cran-atmchile Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-plotly, r-cran-shiny, r-cran-openair, r-cran-lubridate, r-cran-shinycssloaders, r-cran-dt Filename: pool/dists/noble/main/r-cran-atmchile_1.2.0-1.ca2404.1_all.deb Size: 390178 MD5sum: 130f829cf2f4377e3dd5c3cd7acfe07f SHA1: 299eb9e1bf43c8a14eefa8fe023b4856d1c8b540 SHA256: 11e9cc80dbbdcdd7fbe0c174182b42bb9559c41fd1b88e023cd9685d7153b1b3 SHA512: b540342ec869a6cd66aaed30110845dc8fb01fc190636a48719532cd2167fe457f9e51935d8e2a8e79bdc7ebb838ab7c12cb9c10046a0c1d72465ed805d94f07 Homepage: https://cran.r-project.org/package=AtmChile Description: CRAN Package 'AtmChile' (Download Air Quality and Meteorological Information of Chile) Download air quality and meteorological information of Chile from the National Air Quality System (S.I.N.C.A.) dependent on the Ministry of the Environment and the Meteorological Directorate of Chile (D.M.C.) dependent on the Directorate General of Civil Aeronautics. Package: r-cran-atmopt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doe.base, r-cran-hiernet, r-cran-gtools Filename: pool/dists/noble/main/r-cran-atmopt_0.1.0-1.ca2404.1_all.deb Size: 53132 MD5sum: ddeea0331e220705c204146f24820589 SHA1: 02d62dd29a02c7a987701891a8eada6f254f51df SHA256: 18413fca5d9e94b6316701d2b0eb31b614d0eb09be2cdb85135d6098f88ef38e SHA512: 3ecea7f2b033c8336803e6a0e9adba761448256da53b9202305ce2bf1eab0bb0fd40105f395a4ee29c4733968bbeb789054ddd2497a32bd824983b08ca2e338e Homepage: https://cran.r-project.org/package=atmopt Description: CRAN Package 'atmopt' (Analysis-of-Marginal-Tail-Means) Provides functions for implementing the Analysis-of-marginal-Tail-Means (ATM) method, a robust optimization method for discrete black-box optimization. Technical details can be found in Mak and Wu (2018+) . This work was supported by USARO grant W911NF-17-1-0007. Package: r-cran-ato Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-readxl Suggests: r-cran-digest, r-cran-knitr, r-cran-openssl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ato_0.1.1-1.ca2404.1_all.deb Size: 319498 MD5sum: 840bb6f065d9067658fbd9c51b7e8faf SHA1: e219a6fe7b857a792239bea4ef339c0ec7bb0b9a SHA256: 6e62ba767c98e41a214faf6f64a67c04d53ad0105a4991f7e7dea7f50737ba09 SHA512: 9d44f2dadb1bc3005a73d8ea3d9548a8c54722c3d7db06a6c86b1475a0695ffc76b70dce3e51d6340768d8578cf473eb183d6501ca441e089f00f3f2a4661535 Homepage: https://cran.r-project.org/package=ato Description: CRAN Package 'ato' (Download and Tidy Australian Taxation Office Data) Fetch Australian Taxation Office (ATO) Taxation Statistics and related datasets via the data.gov.au Comprehensive Knowledge Archive Network ('CKAN') API . Provides tidy access to individual, company, superannuation, goods and services tax (GST), fringe benefits tax (FBT), Voluntary Tax Transparency Code (VTTC), Pay As You Go (PAYG) withholding, charity, excise, and Corporate Tax Transparency data, plus Petroleum Resource Rent Tax, Medicare Levy Surcharge, and fuel tax credit aggregates. Includes reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional 'Zenodo' deposit), classification crosswalks (ANZSIC 2006 to 2020, ANZSCO 2013 to 2021), panel harmonisation, reconciliation against Final Budget Outcome totals, and real-terms and per-capita helpers backed by bundled Australian Bureau of Statistics (ABS) Consumer Price Index and Estimated Resident Population series. Bridges to the 'taxstats' 2 per cent microdata sample via column-schema mapping. Data is published by the Australian Taxation Office under Creative Commons Attribution 2.5 Australia or 3.0 Australia licences (dataset-dependent). Package: r-cran-atom4r Architecture: all Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-jsonlite, r-cran-readr, r-cran-xml, r-cran-httr, r-cran-zip, r-cran-rdflib, r-cran-keyring Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-atom4r_0.3-4-1.ca2404.1_all.deb Size: 1878076 MD5sum: 3851cfb736968a2116a5d38f82a1ada5 SHA1: 84f6a8e64b36bf69f7f2ffc60bd0cf232a9a9e6e SHA256: 84f913e18baac49744bb44556179ffc0f4021d35307965c97f1cf16f96d34d09 SHA512: 067cbb0a2088376bb4700e8760558c59ea4ef778a975cf478741a6666f6a8407527ccf2fdc2868f7c693aa1e3dcd5768b8b16bbe020a4e68678531439f3548c4 Homepage: https://cran.r-project.org/package=atom4R Description: CRAN Package 'atom4R' (Tools to Handle and Publish Metadata as 'Atom' XML Format) Provides tools to read/write/publish metadata based on the 'Atom' XML syndication format. 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Package: r-cran-atpolr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2759 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-sf, r-cran-stringr, r-cran-terra Suggests: r-cran-colorspace, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-atpolr_0.1.1-1.ca2404.1_all.deb Size: 1973696 MD5sum: f0bc4fe717b22728e45d547d93e5e754 SHA1: 1af3554c13fb7f8987e7918b87f76e040eb7758b SHA256: b6d08f54a745c6ee3a0ae48c04b9f62d29bf5535773f68fb1aa43b22a1739d06 SHA512: 14bfeaee7621b4541a35cc083a28dc5dcd150c2ae043fa9f0af8866f4ff9dd7830aaeb906bb7aac22a8957b0f0a09707c8532fbf0e73427aec2b43ce2745e556 Homepage: https://cran.r-project.org/package=atpolR Description: CRAN Package 'atpolR' (ATPOL Grid Implementation) ATPOL is a rectangular grid system used for botanical studies in Poland. The ATPOL grid was developed in Institute of Botany, Jagiellonian University, Krakow, Poland in '70. 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Package: r-cran-atq Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-zoo, r-cran-ggplot2, r-cran-gridextra, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-fansi, r-cran-farver, r-cran-utf8, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-atq_0.2.3-1.ca2404.1_all.deb Size: 465740 MD5sum: 54e7b7656439ad0e3ee87160a9c04d0c SHA1: a746291564f05ed8f88cfbe845af5906aef4cd27 SHA256: a1b2e2050154ece471b8a62aa321d98d02c4d878d9d5065f461d8ce997f0ca9b SHA512: 55ed1a1ed2bb7e8cbd5cbf8da750ee99880e4ac1765ebf6ce6b7da83febe668db325f012e3d7470c1be6bd503f6da7d23e1f113538e9b40df47f1617ba894785 Homepage: https://cran.r-project.org/package=ATQ Description: CRAN Package 'ATQ' (Alert Time Quality - Evaluating Timely Epidemic Metrics) Provides tools for evaluating timely epidemic detection models within school absenteeism-based surveillance systems. 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Package: r-cran-atrisk Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-sn, r-cran-dfoptim, r-cran-ggplot2, r-cran-ggridges Filename: pool/dists/noble/main/r-cran-atrisk_0.2.0-1.ca2404.1_all.deb Size: 67424 MD5sum: 0f299c0ce57e62b747cdc6a215eeb9ae SHA1: 5b9094f1231422696a203375c3003b1421faf699 SHA256: d192dd7fe2641c98dc4a6530c17424c4f6555a694bafa4d4fa2e93044bddc21a SHA512: 1e7373631b20780641b9df0572d02c368bf641ec9eb95a81fbc613f185b196040a9ea3bbce4daabc66f23c3ccdcdf85a0e486f474c6fa497d6636b281a84ea1f Homepage: https://cran.r-project.org/package=atRisk Description: CRAN Package 'atRisk' (At-Risk) The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers. 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This R and 'Shiny'-based statistical package provides methods for the analysis of augmented randomized complete block designs (augmented RCBDs) across multiple environments. The package performs environment-wise augmented RCBD analysis and pooled analysis across environments, including analysis of variance (ANOVA), testing for homogeneity of error variances across environments, adjusted treatment means, treatment sum-of-squares partitioning, standard error of the mean (SEM), critical difference (CD), and treatment ranking. For pooled analysis, environment-specific error variances are assessed for homogeneity and used for appropriate transformation where required, followed by a general linear model incorporating environment, block nested within environment, treatment, and environment x treatment interaction. This 'Shiny' interface provides a user-friendly platform that enables researchers and plant breeders to perform these analyses without requiring extensive programming knowledge. For method details see, Federer, W. T. (1961) . It consists of the function augmentedPooledRCBD() which launches the application interface. 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It produces title pages, acknowledgements, conflicts of interest, and contribution sections for large author lists, with helpers for validating and reading common spreadsheet formats. Package: r-cran-authoritative Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-authoritative_0.2.0-1.ca2404.1_all.deb Size: 72770 MD5sum: d80b78c4b8ff6bd770da68b816528f9b SHA1: f46a96360b35ad85c78c8056af3a69f7cef663ca SHA256: 4f3a8cfbd499d9ff66e0b6cad2e6b167382f0e60764dc6b6fae33ac6e0703b61 SHA512: dd5e12ce1c56c053e9c637f7ad9f1d8254998f4d50981fe7716f37c6fd28257c5e05e0c920a98a1a5aad1b040e60aca72640b0696c36b72835bdcd3489245430 Homepage: https://cran.r-project.org/package=authoritative Description: CRAN Package 'authoritative' (Parse and Deduplicate Author Names) Utilities to parse authors fields from DESCRIPTION files and general purpose functions to deduplicate names in database, beyond the specific case of R package authors. Package: r-cran-auto.pca Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-plyr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-auto.pca_0.3-1.ca2404.1_all.deb Size: 15528 MD5sum: 36c236cc94f333e3d9062f0cafa924db SHA1: 5b0657af6259f77e6d27768aec77a9c1676acb40 SHA256: 63ca9458f8947ea48525f79ad231169c440ab63ec022aa419b0ec02ac8f51ffa SHA512: db9866f1964269434710f84440d26a0e338b620369b6a07cf048405408d8c423a4b0808995986b3bf76a29b5d3cf46e7cdec07f9cb6345790e383d37ac6f5b36 Homepage: https://cran.r-project.org/package=auto.pca Description: CRAN Package 'auto.pca' (Automatic Variable Reduction Using Principal Component Analysis) PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the web page. Package: r-cran-autoads Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-autoads_0.1.0-1.ca2404.1_all.deb Size: 36788 MD5sum: b56af7a2443b45f328b729f9b038d4b1 SHA1: 8624bc9c8d8ac0ce372d443037380176aa2e39ff SHA256: 744c651045aed791b9559d398d71cdec728ad7708e2846a9cd2df1fc176c2186 SHA512: 06d6b8241b219b55982cc542d14e6eef323c1795ba2b2e94c539379cb1cc283dfdbe8b92455ebdd8cdae34de9f498768f56ce0785d9431ec802c348ce40be87d Homepage: https://cran.r-project.org/package=AutoAds Description: CRAN Package 'AutoAds' (Advertisement Metrics Calculation) Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages 'forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" and Rob J Hyndman et al "Documentation for 'forecast'" (2003) , and 'ggplot2', described in Hadley Wickham et al "Documentation for 'ggplot2'" (2015) , and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" . Package: r-cran-autoann Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Filename: pool/dists/noble/main/r-cran-autoann_0.1.0-1.ca2404.1_all.deb Size: 16428 MD5sum: eb0186ba6f1231e8dfc756d4f1595bde SHA1: ccaa9c19bd9ca8e2c63bb8d70a44d65a95a83d47 SHA256: f9c3d333149e451d7e15c6da664c467c41b8fe1de9e7ed01186c23637aed1051 SHA512: 2d48bd6ee9981bcb9f09c9ba58e888cedb78e63e4ebff391703450b76c7d7a4bf971b0d795170d3df1e90279a5525c34ef26c7c879e2d6ce691c44ae48d42c7c Homepage: https://cran.r-project.org/package=autoann Description: CRAN Package 'autoann' (Neural Network–Based Model Selection and Forecasting) Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest. Package: r-cran-autobagging Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2038 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-xgboost, r-cran-e1071, r-cran-rpart, r-cran-abind, r-cran-caret, r-cran-mass, r-cran-entropy, r-cran-lsr, r-cran-corelearn, r-cran-infotheo, r-cran-minerva, r-cran-party Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autobagging_0.1.0-1.ca2404.1_all.deb Size: 1970984 MD5sum: 13bf13517a1f429f28c8305391b7bd86 SHA1: f512b0897251caecaad6291b181512f965965c84 SHA256: b7f78c1248fa55501031db315f15cd6b62396889c32632e2efa360887537b936 SHA512: 00b76caf7bfb0ad80e11d68d25088bdb809671f1206c641a3dca7c8173c93081d5c4efaa2befdc40b60fa13c2257061e80fc83c7a543827b8751b6ee736ab190 Homepage: https://cran.r-project.org/package=autoBagging Description: CRAN Package 'autoBagging' (Learning to Rank Bagging Workflows with Metalearning) A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367. Package: r-cran-autocodebook Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-gt Suggests: r-cran-sparklyr, r-cran-dbplyr, r-cran-testthat, r-cran-tidyplots, r-cran-ggplot2, r-cran-patchwork, r-cran-rmarkdown, r-cran-knitr, r-cran-officer, r-cran-flextable, r-cran-openxlsx, r-cran-scales, r-cran-rvg, r-cran-devemf, r-cran-svglite Filename: pool/dists/noble/main/r-cran-autocodebook_0.1.0-1.ca2404.1_all.deb Size: 629342 MD5sum: 2b4bf6c6d1d14d6919551a616ab7cbd7 SHA1: 03bc2286dab27612976b655592e234169a3cd965 SHA256: 0ee811573b5125286e6f1db6e06b27d7b4a38ed8cdfed16937f39309f766184b SHA512: f418fe477fd8da6b8b61114650b0f9b868e5117f4f39c500ae8ee8f448fca7e8fbd62cf577591374001caa0f70af81cd029a2df7d9b937523c84c2679728c5ab Homepage: https://cran.r-project.org/package=autocodebook Description: CRAN Package 'autocodebook' (Automatic Codebook and Tracking for 'Spark' and 'dplyr'Pipelines) Wraps 'dplyr' verbs (mutate, summarise, filter) to automatically capture variable metadata (type, source columns, categories, and source code), producing a codebook and eligibility tracking table with zero manual documentation. Works with both 'sparklyr' (tbl_spark) and local data frames. Adds big-data optimizations (caching, assume-unique counting, checkpointing) and a standardized report module with an eligibility flowchart, editable codebook export (HTML, DOCX, XLSX), and cross-sectional or longitudinal variable inspection. The eligibility flowchart follows the CONSORT statement (Schulz, Altman and Moher (2010) ) and the reporting of observational cohort studies follows the STROBE recommendations (von Elm and others (2007) ). Package: r-cran-autocogs Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-checkmate, r-cran-diptest, r-cran-dplyr, r-cran-ggplot2, r-cran-hexbin, r-cran-mass, r-cran-mclust, r-cran-moments, r-cran-progress, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autocogs_0.1.5-1.ca2404.1_all.deb Size: 822710 MD5sum: 3747630c9ff75f9ec0bb493cb4e53223 SHA1: 407d09203d35e3a00ddf4b5adab41ceea06d9be0 SHA256: 2fb02ae47b3e4481332284b6d77f35f9098d9f821cc8891c599cbb05b0c3f4d4 SHA512: b3465f18ae27e688b5249138e530458d1304ba32a05879f3bb0b4956ec2a485f1ca86b3f5181c354bc8dc37f89780c6bdb2e8dc5c267086808475d2546eadeac Homepage: https://cran.r-project.org/package=autocogs Description: CRAN Package 'autocogs' (Automatic Cognostic Summaries) Automatically calculates cognostic groups for plot objects and list column plot objects. Results are returned in a nested data frame. Package: r-cran-autocovariateselection Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autocovariateselection_1.0.1-1.ca2404.1_all.deb Size: 546574 MD5sum: 0df3332ecef09007a16548c01f74c334 SHA1: 440e801d34e77faa77957cdd32f99ea1c61bd2b7 SHA256: 8911b515c10ac5d9dbb5b83a85e15eb0a01e218aba46f48178e4ae281fa63037 SHA512: 19afd060db2ac1ce9750add5987bb0e8571dcf75c58e081be281366bda170f2bdb5b6c66a7709dab12365dae9ca96ce50c7d3839836041cf12da3d336eb0d890 Homepage: https://cran.r-project.org/package=autoCovariateSelection Description: CRAN Package 'autoCovariateSelection' (R Package to Implement Automated Covariate Selection for TwoExposure Cohorts Using High-Dimensional Propensity ScoreAlgorithm) Contains functions to implement automated covariate selection using methods described in the high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. The functions in this package help to implement the three major steps of automated covariate selection as described by Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is also available for running examples and also for further exploration. The original article where the algorithm is described by Schneeweiss et.al. (2009) . Package: r-cran-autodb Architecture: all Version: 3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16869 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-rlang, r-cran-spelling, r-cran-diagrammer, r-cran-testthat, r-cran-r.utils, r-cran-hedgehog, r-cran-tibble, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autodb_3.3.1-1.ca2404.1_all.deb Size: 1351836 MD5sum: a761b348ce7e0fc2dac2c45566dda34d SHA1: 052894faa203e653f1172a389c68e0d28ebb47e4 SHA256: deab9bf23f0d142e92203f494422b78cfca6ea043fe5189fe2a84f62f6d5295e SHA512: aad9ba85ed2865c8e2e4169e63676c0ad5d24789d51e7dc9ce6e5040895aad24ea000c463b186e83ed0a13d53fbf2c4038c3bdc80497da31602b0249131e8d0e Homepage: https://cran.r-project.org/package=autodb Description: CRAN Package 'autodb' (Automatic Database Normalisation for Data Frames) Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time. Package: r-cran-autodeskr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3302 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-ellmer, r-cran-knitr, r-cran-mcptools, r-cran-rmarkdown, r-cran-testthat, r-cran-httptest2, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-autodeskr_0.5.0-1.ca2404.1_all.deb Size: 2322510 MD5sum: f3ce27acce6b48b74f5e7082b116a48a SHA1: 4059e13247e30907ec8ad071e9f747a5a6038b95 SHA256: 7be1b00d91c21cd296064e1433724c5400646c06e1af3b04c4f73e6d559327e0 SHA512: d223041e567fcf5373ef61bc7ca02374cc2796641e935aa78aaee5a7f260046deaec22364a2473e5a7a9bccf5e7be672a5eb56a43bdb159033aa569625b0724f Homepage: https://cran.r-project.org/package=AutoDeskR Description: CRAN Package 'AutoDeskR' (An Interface to the 'AutoDesk' 'API' Platform) An interface to the 'AutoDesk' Platform Services ('APS') 'API' including the Authentication 'API' for obtaining 'OAuth2' tokens with expiry tracking, Data Management 'API' for managing buckets and objects across the platform's cloud services, Design Automation 'API' for running automated tasks on design files in the cloud, Model Derivative 'API' for translating design files into 'SVF', 'SVF2', 'OBJ', and 'STL' formats and extracting model data, Reality Capture 'API' for generating 3D models from photogrammetry image sets, and Viewer for rendering 2D and 3D models in 'Shiny' applications. Package: r-cran-autoeda Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-e1071, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-psych, r-cran-factoextra, r-cran-openxlsx, r-cran-ggally, r-cran-visdat, r-cran-igraph Suggests: r-cran-knitr, r-cran-mice, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-autoeda_0.1.1-1.ca2404.1_all.deb Size: 146912 MD5sum: 89ff830f7ef2510e4fd0b13158f6cd80 SHA1: b129fecad0b8b6210755f05b26e00a5c4d228188 SHA256: a69ddc37a684a22c3d55116634d55430bd9dcdad194d7eddb0d3f21e88db3ec6 SHA512: 3fd898d411bab9023f576932e4f412acfd205b96adad09b0715213de05ed6a5f822ab5887c505bea17218fae742b5f029523a124b4596cd437858f85e48d31bd Homepage: https://cran.r-project.org/package=AutoEDA Description: CRAN Package 'AutoEDA' (Automatic Exploratory Data Analysis) Automatically performs exploratory data analysis for tabular datasets, including data summaries, missing value analysis, descriptive statistics, visualizations, correlation analysis, outlier detection, and automated report generation. The package provides a streamlined workflow for rapid data exploration and produces publication-ready tables and graphics. For methodological details, see Tukey (1977, ISBN:9780201076165) and the cited methodological literature in the package documentation. Package: r-cran-autoensemble Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-h2o, r-cran-h2otools, r-cran-curl Filename: pool/dists/noble/main/r-cran-autoensemble_0.3-1.ca2404.1_all.deb Size: 258358 MD5sum: ec65dcd877ceef9f7114aceefa9b9cad SHA1: 1682d2ea332f929abda5b0079ed5fd535da717dc SHA256: 744255f4923e19ad170c76a2075b384716aa3e6acde2b3d0a5d9f2085bb78071 SHA512: 49aacc94bfa7d9897a59ec203a14cbd6494292129885ee1881ec617a5ce04b2642151ad62a38ed94f94f6b7aebe12d089843979555da430fcc514f5c187a4018 Homepage: https://cran.r-project.org/package=autoEnsemble Description: CRAN Package 'autoEnsemble' (Automated Stacked Ensemble Classifier for Severe Class Imbalance) A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability. Package: r-cran-autofc Architecture: all Version: 1.0.0.1100-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-mass, r-cran-mplusautomation, r-cran-pbapply, r-cran-rstan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autofc_1.0.0.1100-1.ca2404.1_all.deb Size: 481022 MD5sum: 0c1f4e222569751227ce0e0495e8ea0d SHA1: a3d12241cc41a88d1285812bfb1b750b2536b9bf SHA256: c179bd94850182fdf181cb6f95880cd43784108dc9b1c6448fb8457fa5ec0a34 SHA512: 8d0d3dedc9b40b31e0184f7470cf26750df994adf46146e87c648c53f1d5209de76dda27784eb58b7c268e13acc6f6534821ed43492c5e45d5c54908411a03c8 Homepage: https://cran.r-project.org/package=autoFC Description: CRAN Package 'autoFC' (Automatic Toolkit for Construction, Optimization, Scoring andSimulation of Forced-Choice Tests) Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 ). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 ). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 ). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 ) or minimize item location differences (Cao & Drasgow, 2019 ) depending on scoring models. Decision of which items should be assigned to the same block, also called as item pairing, is thus critical to the quality of an FC test. Because such pairing process often requires researchers to meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per elevates. To address these problems, autoFC is developed as a automatic and efficient tool for facilitating the automatic construction of FC tests (Li et al., 2022 ), essentially exempting users from the burden of manual item pairing. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2025 ). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 ) and predict trait scores of simulated/actual respondents based on an estimated model. Package: r-cran-autoflagr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isotree, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-cran-proc, r-cran-prroc, r-cran-knitr, r-cran-gt, r-cran-scales, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-ggnewscale Filename: pool/dists/noble/main/r-cran-autoflagr_1.0.0-1.ca2404.1_all.deb Size: 702328 MD5sum: e83606dc94af8eee410c265611ae0e3e SHA1: 7449375ae5131a846c478240f6064588b1c3a03a SHA256: e5659568024e76bb8e1373d8b0d442c87f458fad0e3f0ab69c4209734a3d844b SHA512: a1983b04e5d58f6d4ca932e3136b09e6c58fe1ff4c58bb42f9a2bcbc442c7a42598de3be138453c57055e7f996e22a0fbb75d4bd3a3cbf4b8217e018f8ab8da5 Homepage: https://cran.r-project.org/package=autoFlagR Description: CRAN Package 'autoFlagR' (AI-Driven Anomaly Detection for Data Quality) Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) , Breunig et al. (2000) . Package: r-cran-autogam Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-staccuracy, r-cran-stringr, r-cran-univariateml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autogam_0.1.0-1.ca2404.1_all.deb Size: 111976 MD5sum: 67c3b558cfe03458f9c550294bdb352a SHA1: 1cffdc1eb7a3f3982a1f2d23804b4181f4c6dd8a SHA256: 60550b82d731f389fbca79f36ac05717319c92b021de4202259d7faf886dbad5 SHA512: a04380f679a1c55d740d7e548cd4a959c460ac972c8e34f9830cbaacb01d1d113d4e8f428e1fbabf0d08fa6baf195b6ffddfa8dbb666a851ba7638cb35390726 Homepage: https://cran.r-project.org/package=autogam Description: CRAN Package 'autogam' (Automate the Creation of Generalized Additive Models (GAMs)) This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets. Package: r-cran-autogenai Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autogenai_0.1.0-1.ca2404.1_all.deb Size: 105860 MD5sum: 1396e76e96082a8ad4c5bef6be22a0d2 SHA1: d282238312fa38629ee7fbbd36e6ba89d62be1a9 SHA256: a93b85f4986a517c90feffcbbda566deb268f3925a1c1751cefbbe2eea29d65e SHA512: b034bf27d9061f049e2ea7f707b9130c4fea74c74bc982438fecf93e42b0f7b87ccd62425066d6a5a2eb6ec192ff581c144b2d048380d5636b5ea427ac29a658 Homepage: https://cran.r-project.org/package=AutoGenAI Description: CRAN Package 'AutoGenAI' (Adaptive Optimization of Prompts, Models and GenerationStrategies) Provides provider-agnostic tools for jointly comparing and optimizing prompts, language-model providers, and generation strategies for generative artificial intelligence workflows. Candidate configurations can be evaluated using user-supplied scoring functions, cost and latency measurements, robustness perturbations, Pareto-front screening, budget and latency constraints, prompt evolution, adaptive routing, self-consistency, and text-output ensembles. The core workflow is designed to run offline with deterministic mock providers, while external model application programming interfaces can be connected through user-defined provider functions. Evolutionary search concepts are described by Goldberg (1989, ISBN:0201157675), and multi-objective optimization concepts are related to Deb, Pratap, Agarwal and Meyarivan (2002) . Package: r-cran-autogo Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-dplyr, r-cran-ape, r-bioc-complexheatmap, r-bioc-deseq2, r-cran-dichromat, r-cran-ggplot2, r-cran-ggrepel, r-bioc-gsva, r-cran-msigdbr, r-cran-openxlsx, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-textshape, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autogo_1.0.3-1.ca2404.1_all.deb Size: 3310090 MD5sum: f024fe6f02fa5357186c2efc728cf0ad SHA1: fc690f71ccc6f5362e80f6d2474122a735cc6f65 SHA256: af1ef07c71eb4587a8266020d74f5c8418876f1915541d5d972e7fc34db7022d SHA512: 99db5dfab2430099155007e581312d28af6e9e4c9a2992ecd739ce61a0cfa1e584e6324971b3a9fbb972880be8ee3357e9e4fda7d173973f4f7caa43dc4a49e3 Homepage: https://cran.r-project.org/package=autoGO Description: CRAN Package 'autoGO' (Auto-GO: Reproducible, Robust and High Quality OntologyEnrichment Visualizations) Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) . The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce. Package: r-cran-autograph Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-manynet, r-cran-dplyr, r-cran-ggraph, r-cran-ggplot2, r-cran-graphlayouts, r-cran-igraph, r-cran-patchwork Suggests: r-cran-gganimate, r-cran-ggforce, r-cran-gifski, r-cran-messydates, r-cran-migraph, r-cran-netrics, r-cran-systemfonts, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autograph_1.2.6-1.ca2404.1_all.deb Size: 1725186 MD5sum: 82b58d94ab209598b49d055451454ad2 SHA1: 78e7cea743f976790906670b720cfba468388c96 SHA256: cc2b887137d5503a2163f41287b74a2fed7293a2fe2989778f5228056bc5d141 SHA512: 2b0a2ac99782dc9f9b07ca27db7fde16ae994bf57c4eb0b8a2a1ecf242dcdf8f11646b87d8df02cf2829c31d206869d9d37cdc1f3b98340bc27ad1d344c4c864 Homepage: https://cran.r-project.org/package=autograph Description: CRAN Package 'autograph' (Automatic Plotting and Theming of Many Graphs) Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes 'ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes. Package: r-cran-autoharp Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-lintr, r-cran-igraph, r-cran-xfun Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autoharp_0.3.2-1.ca2404.1_all.deb Size: 3321254 MD5sum: 198c06929b64e27c6d55e3b575a409fc SHA1: cfb8e4c3ce17aa519d40b2204f644e24e88d7ba9 SHA256: 4411a89b2207e9357115642dd3f58d3d31765aae4e104d9b6d6065b8a74fdaf0 SHA512: 060aae878408135ef38857bf78e4d44871a151cd21795a9c43ad6835e18614afa3694e0afc9d72ce44883272c8ff6f4ebe965c0ec0e83ad89abb54ffb208c78e Homepage: https://cran.r-project.org/package=autoharp Description: CRAN Package 'autoharp' (Semi-Automatic Grading of R and Rmd Scripts) A customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure. Package: r-cran-autohrf Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2899 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-gtools, r-cran-lubridate, r-cran-magrittr, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autohrf_1.1.3-1.ca2404.1_all.deb Size: 1894168 MD5sum: 63c083102bfc8f2300a0e33cfa46e635 SHA1: ebf139648ebb515e301dee756df4aa68f8fe4dfa SHA256: c02afb61cee56dbb32a24f1de66f677c723bb52db0337df04600e3ccb4529c97 SHA512: 3d0c4e9af33d8e5bea641641eacf027df9f533da9b33d0ec99ff218c447479ef98a2c2a29757a734dd5d09d74b06318598e71e9a55f8c20adc64cb296985e863 Homepage: https://cran.r-project.org/package=autohrf Description: CRAN Package 'autohrf' (Automated Generation of Data-Informed GLM Models in Task-BasedfMRI Data Analysis) Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner. Package: r-cran-autoimage Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-fields, r-cran-mapproj, r-cran-ggplot2, r-cran-maps, r-cran-mba Suggests: r-cran-abind, r-cran-testthat, r-cran-spatstat.geom, r-cran-gear, r-cran-broom, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autoimage_2.2.3-1.ca2404.1_all.deb Size: 2165942 MD5sum: fdf6b1ed60c84ec5e3a887dce7859d6a SHA1: 8d115b4fa3f5c5c2a4588c5ce4e8aa52558b548d SHA256: 4f871115e4ef027b492364e6251acd46a3ae7e9c39457ce5a7cd39794326fb57 SHA512: c6dad8f45bf5c37e6f59ac0d3959ed82f2386a7f02ed2ef04405e80f78047d626e9b880650be553877bb5e8d2037cd232e1636e83158a65facdf9fc5da78dabd Homepage: https://cran.r-project.org/package=autoimage Description: CRAN Package 'autoimage' (Multiple Heat Maps for Projected Coordinates) Functions for displaying multiple images or scatterplots with a color scale, i.e., heat maps, possibly with projected coordinates. The package relies on the base graphics system, so graphics are rendered rapidly. Package: r-cran-autoimport Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-desc, r-cran-diffviewer, r-cran-digest, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-callr, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-pkgload, r-cran-rstudioapi, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-autoimport_0.1.1-1.ca2404.1_all.deb Size: 235362 MD5sum: d40bf8dd05afb07044edf4970e21bae3 SHA1: 87639a794b4d81a013d98f62a86db7efb3e2aa23 SHA256: 21aa595fc1be425672006451e113b37adb499a827befa2aa9e3870f6e10ef05a SHA512: 71d212f460084150321f227ba8878c9218599d3b1ea4308885f3e985b8797e6adf23f181312f4c278ee7fd4b7df13e2342b624fae98439b33e71f344861cbc71 Homepage: https://cran.r-project.org/package=autoimport Description: CRAN Package 'autoimport' (Automatic Generation of @importFrom Tags) A toolbox to read all R files inside a package and automatically generate @importFrom 'roxygen2' tags in the right place. Includes a 'shiny' application to review the changes before applying them. Package: r-cran-autokeras Architecture: all Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-reticulate Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-autokeras_1.0.12-1.ca2404.1_all.deb Size: 63526 MD5sum: b06fb84080fbc41e3af6a9ff7ac6ef05 SHA1: 32650cf63828076c7ef7e317fa7f38518bfec9e6 SHA256: 8de4c7728d2001a95891d19c9fdd2af6a4653e8baac08bab6473581f4ec00b60 SHA512: 3b3e8453ebc43ced8bfb60cb64c3c440f68acbd294226f25ecf65fe60bcb236a506f19f3514d3716e4b28e0d6251091e8275b22e04285a8917fc9d7211d24947 Homepage: https://cran.r-project.org/package=autokeras Description: CRAN Package 'autokeras' (R Interface to 'AutoKeras') R Interface to 'AutoKeras' . 'AutoKeras' is an open source software library for Automated Machine Learning (AutoML). The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. 'AutoKeras' provides functions to automatically search for architecture and hyperparameters of deep learning models. Package: r-cran-autolibload Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-devtools, r-cran-vprint Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autolibload_1.0-1.ca2404.1_all.deb Size: 30446 MD5sum: 00cd44148cc4f5107dfc9113a4ec757e SHA1: 49130e2b06d683039cb170e4526ea21b12a48b0c SHA256: 1f5564556477f1799a7603b99a92c493dc64a57d6b8f93c135f562cc0db888b0 SHA512: 8a5e2f92e26ac48bfc13118807fdafec6ece471cbb96cf397f034672f9a616716009a8ad2939bd0653fec5a2e7cd57458ba246cacffbbed33a5b3a99615c571f Homepage: https://cran.r-project.org/package=autoLibLoad Description: CRAN Package 'autoLibLoad' (Automate Retrieving, Building, Installing and Loading SpecifiedPackages) Packages required for the search path may be located in the CRAN repository, the system library, or a local directory. We automate determining the disposition of each required package, retrieving it, and loading it as needed. Package: r-cran-automagic Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-formatr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-remotes, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-automagic_0.5.1-1.ca2404.1_all.deb Size: 30944 MD5sum: bdb6272aba0fe08a1b1fc5be3dd99441 SHA1: be2c006e0064f35acb019e8a045bf11afd90589f SHA256: da73d031197766725fe60c0c24988f8cb7a22211b16b7455568822d88450c820 SHA512: 05666f2a4b59c7b362a3e7cb71503aebf744030e911dc2c0906a4eedebd1ad6d3ed6fa6faae661c4648be3ce3c6babf15aceaefacf674b5c421a660acea1446e Homepage: https://cran.r-project.org/package=automagic Description: CRAN Package 'automagic' (Automagically Document and Install Packages Necessary to Run RCode) Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install. Package: r-cran-automap Architecture: all Version: 1.1-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-lattice, r-cran-reshape, r-cran-ggplot2, r-cran-sp, r-cran-sf, r-cran-stars Filename: pool/dists/noble/main/r-cran-automap_1.1-20-1.ca2404.1_all.deb Size: 91108 MD5sum: db2659b08ae75a74228c476be88ab84c SHA1: 51894967c461e83913826774a3cd18d768855d5f SHA256: 565f4d012a5018e15fd9fe6b228f2471f0eb6e9c18c5ad234d1522760ee25b66 SHA512: 8fc70720d6c2b2005ad16ab0d99644005186e0428b5925db655233206b68767fa5a2c9913eb9a5dcae34b509e361a6717ad405997f0a53dbf2b3a65b589e6b12 Homepage: https://cran.r-project.org/package=automap Description: CRAN Package 'automap' (Automatic Interpolation Package) An automatic interpolation is done by automatically estimating the variogram and then calling gstat. An overview is given by Hiemstra et al (2008) . Package: r-cran-automatedreclin Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1280 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blocking, r-cran-data.table, r-cran-densityratio, r-cran-fixedpoint, r-cran-nleqslv, r-cran-purrr, r-cran-reclin2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-automatedreclin_1.1.2-1.ca2404.1_all.deb Size: 1168562 MD5sum: 1b0094c9177b3d16fe5268bbc1ab5d09 SHA1: 9545c9bd99c0839b98606a45a477a0213c0dfdd9 SHA256: 1635e9c66c0db3b3aede85da350dc8215957162bab3584ca086a4b583a7da6ae SHA512: e43ffc1657e27766fc753b4d2747f2c7dfb8e47adffa0a50cce15ef72de0e472c0f9535c9a1268d2eb88c70e7885bb60069905715b7ad4c9eb53eaa702c2d340 Homepage: https://cran.r-project.org/package=automatedRecLin Description: CRAN Package 'automatedRecLin' (Record Linkage Based on an Entropy-Maximizing Classifier) The goal of 'automatedRecLin' is to perform record linkage (also known as entity resolution) in unsupervised or supervised settings. It compares pairs of records from two datasets using selected comparison functions to estimate the probability or density ratio between matched and non-matched records. Based on these estimates, it predicts a set of matches that maximizes entropy. For details see: Lee et al. (2022) , Vo et al. (2023) , Sugiyama et al. (2008) . Package: r-cran-automatedtests Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-nnet, r-cran-nortest, r-cran-desctools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-automatedtests_0.1.2-1.ca2404.1_all.deb Size: 1195782 MD5sum: 137a2c8a9022c954b5533d36351910f6 SHA1: d2e4b49f35109033a367f77600d910f5cb2d400c SHA256: 675cf4cec028934851b049497be9ab7dc7e4c9613d2733bb41455fdd8637778e SHA512: 084755efd31c97c475bb3471c9f5ddf68ed618ac43a19f588bac0b8c89e998b38a5e33220985cf17ea56fecbb0ba12022222f0627c975221528095c47aa0c301 Homepage: https://cran.r-project.org/package=automatedtests Description: CRAN Package 'automatedtests' (Automating Choosing Statistical Tests) Automatically selects and runs the most appropriate statistical test for your data, returning clear, easy-to-read results. Ideal for all experience levels. Package: r-cran-automfa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-mass, r-cran-matrix, r-cran-rfast, r-cran-expm, r-cran-rdpack, r-cran-pracma, r-cran-usethis Filename: pool/dists/noble/main/r-cran-automfa_1.0.0-1.ca2404.1_all.deb Size: 267262 MD5sum: bfecbebafaf355cdcd2f26daeac0a76d SHA1: cccd52ceb31c935bb6f3d8eed924eb66488362f9 SHA256: 6d9ca821ba5ede38a9b688973c077fdde12a65059b280c033464d28918c1ba59 SHA512: 60d84121bf820fbd63189abb76184fb08fcceb30ab4039f9680f06d90a80a7b781be27498af86ed3b09dc0864f0e7b23b9eead30fd0fa8bf2bfef747f33af5c9 Homepage: https://cran.r-project.org/package=autoMFA Description: CRAN Package 'autoMFA' (Algorithms for Automatically Fitting MFA Models) Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically. The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible. Package: r-cran-automl Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-automl_1.3.2-1.ca2404.1_all.deb Size: 419100 MD5sum: de13a70313eaf889fad283b0ee732511 SHA1: 4be53e71c8022454845b5c09b8e2d704c54f43fa SHA256: 29bfe60c4125c77c6f78006f307f2fcd26959e9aeaf45f5d8f34deaf9d0c72f4 SHA512: ffdc523c25bf16955eb7e99496b61d7b23cd7a496a3220ec8a31fdcfe746d4b602c356ce0a22c35ba293d5e4d6ea6a4ebf2b8af31546291ec7e835cf3234b189 Homepage: https://cran.r-project.org/package=automl Description: CRAN Package 'automl' (Deep Learning with Metaheuristic) Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization. Package: r-cran-automlr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4951 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-cran-coxboost, r-cran-digest, r-cran-future, r-cran-future.apply, r-cran-glmnet, r-cran-gbm, r-cran-log4r, r-cran-plsrcox, r-cran-quadprog, r-cran-randomforestsrc, r-cran-superpc, r-cran-survivalsvm, r-cran-testthat, r-cran-timeroc Filename: pool/dists/noble/main/r-cran-automlr_1.0.0-1.ca2404.1_all.deb Size: 4550248 MD5sum: 0944166ec429fd5fd37553fcaa94abc3 SHA1: 19af6631e5f16cd45e8f59e28764f6c6fca96056 SHA256: 7b95f250e0fee59dd4e83ffa0e485348c0b18b3b453b692e86ab126a66ddcfb0 SHA512: 0a77ae23c8ae49b2a3db2cf58d17b242d1a8c4c108e6134fedacb6d84be41ac6e75e04129fe2e1f2e3c216a99985a550a8a685f5f19c756e84dba7818de8e753 Homepage: https://cran.r-project.org/package=AutoMLR Description: CRAN Package 'AutoMLR' (Automated Multi-Outcome Machine Learning Combination Models) Provides automated machine learning workflows for survival analysis, binary classification, continuous outcomes, and ordinal outcomes. The package trains and combines model variants across user-supplied multi-cohort data, evaluates survival models by leave-one-out cross-validation using Harrell's concordance index, binary models by leave-one-out cross-validation using receiver operating characteristic area under the curve, continuous models by out-of-fold root mean squared error and R-squared, and ordinal models by out-of-fold quadratic weighted kappa. It renders reproducible reports in Hypertext Markup Language (HTML) with figures and diagnostics. The survival workflow supports penalized and tree-based Cox proportional hazards models, stepwise Cox models, partial least squares regression for Cox models, supervised principal components, gradient boosting machine Cox models, survival support vector machines (survival-SVM), random survival forests, and optional 'CoxBoost'. The binary workflow supports penalized logistic regression, logistic baselines, gradient boosting machines, random forests, principal component analysis (PCA) logistic regression, and Gaussian naive Bayes variants. Continuous and ordinal workflows reuse an 18-variant regression registry with penalized, linear, boosted, forest, PCA, and baseline families. The optional 'CoxBoost' model is enabled when the suggested 'CoxBoost' package is installed; it is used conditionally and is not a strong dependency. Optional model backends are checked at run time so missing backend packages skip only the affected model variants rather than blocking installation of the whole package. Methods build on Friedman et al. (2010) , Bair and Tibshirani (2004) , Ishwaran et al. (2008) , Blanche et al. (2013) , and Binder and Schumacher (2008) . Package: r-cran-automr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-mendelianrandomization, r-cran-r2jags, r-cran-coda, r-cran-nortest Filename: pool/dists/noble/main/r-cran-automr_1.2.1-1.ca2404.1_all.deb Size: 199420 MD5sum: f81d0733db3c1ecf0134881ac05f7e8e SHA1: cc0d7f27c5e72430c0ebf18006a46fe823bfcd80 SHA256: aa02641558ca44cc4a8ebcdbd832a2edbcee96754819ef875397dee976c7482e SHA512: 4848a905ffd42036defaf034100134c705360bce676764fc63c046e812a272fbd133cfb50cec8105c3d0e1e7e9470cc992e5f80c415cc7398d4f637cc97ab76e Homepage: https://cran.r-project.org/package=autoMR Description: CRAN Package 'autoMR' (Automated Mendelian Randomization Pipelines and Visualizations) Provides tools to summarize, analyze, and visualize results from Mendelian randomization studies using summarized genetic association data. The package includes functions for generating forest plots and scatter plots at the single-nucleotide polymorphism and Mendelian randomization method levels, and for fitting multiple estimators in a unified pipeline, including inverse-variance weighted estimation, Mendelian randomization Egger regression, the weighted median estimator, the robust adjusted profile score, Mendelian randomization pleiotropy residual sum and outlier, Mendelian randomization with the genotype recoding invariance property, and a Bayesian horseshoe method. Related methods are described by Burgess (2013) , Bowden (2015) , Bowden (2016) , Zhao (2020) , Verbanck (2018) , Dudbridge (2025) , and Grant and Burgess (2024) . Related open-source software includes 'TwoSampleMR' , 'mr.raps' , 'MR-PRESSO' , and 'MR-Horse' . Package: r-cran-automrp Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-lme4, r-cran-gbm, r-cran-e1071, r-cran-tibble, r-cran-glmmlasso, r-cran-ebmaforecast, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-ggplot2, r-cran-knitr, r-cran-tidyr, r-cran-purrr, r-cran-forcats, r-cran-vglmer, r-cran-stringr Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-automrp_1.0.6-1.ca2404.1_all.deb Size: 676498 MD5sum: b269e2b7e95dda3c6e1e043822fa2c93 SHA1: fa13e6ce7fe76be091ddc2ca9498a4f8e43f359a SHA256: fa75bc9f27d03e7197888095b3aa05d26b3c80234591ee6771b3fe6c9f839429 SHA512: e235b046faa5b5330f4d4213909c1ec26388da93b54702e54f26e5eba19ef1cdb3126c52804940d63cc80ada2d4d67a2ee6f6b99be95ee2dd1b74ad183236291 Homepage: https://cran.r-project.org/package=autoMrP Description: CRAN Package 'autoMrP' (Improving MrP with Ensemble Learning) A tool that improves the prediction performance of multilevel regression with post-stratification (MrP) by combining a number of machine learning methods. For information on the method, please refer to Broniecki, Wüest, Leemann (2020) ''Improving Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)'' in the 'Journal of Politics'. Final pre-print version: . Package: r-cran-autonewsmd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-git2r, r-cran-quarto, r-cran-r6 Suggests: r-cran-lintr, r-cran-precommit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autonewsmd_0.1.0-1.ca2404.1_all.deb Size: 79184 MD5sum: f4645c56fae0cd3156ab23c8c83c9edd SHA1: f034533e50350c209edbb04686896c0b9587818c SHA256: 8709f11a0e043328ab46ea8b1bd5635e67f02938c89f64fca8cb73e64d6a0e98 SHA512: 9c0c8892328858bae62530bf2e8b7b08c7160ce1f98fafde5268a24438c32aebe815d68de12aa9efa36560dadda36d1655725b22b2847b55b79a8d3925644dcf Homepage: https://cran.r-project.org/package=autonewsmd Description: CRAN Package 'autonewsmd' (Auto-Generate Changelog using Conventional Commits) Automatically generate a changelog file (NEWS.md / CHANGELOG.md) from the git history using conventional commit messages (). 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Package: r-cran-autopipe Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-org.hs.eg.db, r-bioc-clusterprofiler, r-cran-msigdbr Filename: pool/dists/noble/main/r-cran-autopipe_0.1.6-1.ca2404.1_all.deb Size: 157134 MD5sum: de5c79e7c3746673e362d93aa1cfecfe SHA1: 9d31e73bc44dce1e8e3ee2dcf1fd217f34f14966 SHA256: af38b28445bad0536de63a5a6dc87d3fbc3d4a20a50de7f026754c2e76a881c1 SHA512: 284d2bbdfb44920cb95b9313b15a67ad396a94ce5adafcdfabcbc525184e61216868dcdaa4edc79716b19a54c2bbd095f86b65b64072614f4377e455bdc02acc Homepage: https://cran.r-project.org/package=AutoPipe Description: CRAN Package 'AutoPipe' (Automated Transcriptome Classifier Pipeline: ComprehensiveTranscriptome Analysis) An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. 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Package: r-cran-autoplotly Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-ggfortify Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-autoplotly_0.1.7-1.ca2404.1_all.deb Size: 32904 MD5sum: cb3c794e50eb0023c9923f1446c97ea2 SHA1: 800a1ef7386760dd2723e595712fff2499096c0f SHA256: 3dd261362d32464469e6005d03882a09857b704aac4d4ffc910d366e5a1301f2 SHA512: bf717cb0013c8d7801333769975e08c8732a9d54e0e6d051ef4ffbe172bab963b2e8be781fdb5b73e7b19aedbab9a5a9c209c8ba621f77a9aafcf4fbe688c696 Homepage: https://cran.r-project.org/package=autoplotly Description: CRAN Package 'autoplotly' (Automatic Generation of Interactive Visualizations forStatistical Results) Functionalities to automatically generate interactive visualizations for statistical results supported by 'ggfortify', such as time series, PCA, clustering and survival analysis, with 'plotly.js' and 'ggplot2' style. 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The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation). Package: r-cran-autoreg Architecture: all Version: 0.3.5-1.ca2404.3 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3569 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-moonbook, r-cran-nortest, r-cran-dplyr, r-cran-crayon, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-survival, r-cran-mice, r-cran-officer, r-cran-flextable, r-cran-rlang, r-cran-patchwork, r-cran-ggplot2, r-cran-boot, r-cran-broom, r-cran-tidycmprsk, r-cran-scales, r-cran-maxstat, r-cran-pammtools Suggests: r-cran-knitr, r-cran-finalfit, r-cran-lme4, r-cran-th.data, r-cran-rmarkdown, r-cran-survminer, r-cran-asaur, r-cran-cmprsk, r-cran-paireddata Filename: pool/dists/noble/main/r-cran-autoreg_0.3.5-1.ca2404.3_all.deb Size: 2451088 MD5sum: a3daee263096232596ab3755a4b40961 SHA1: 3579f18c3784e84bb38c0fbcae8588a1bc7307eb SHA256: 14216e60333c7fab88a6519fc33939609ae0e094b5f66e574aa5d326c3210014 SHA512: 6a1ed5e8d62fb14d6949924470b70af92895f1155415939e175ab863c6356fd8b04310101ae96e52c80ffb7e83e8be98ff95a83a8929a72d4fd06a925ef84b5b Homepage: https://cran.r-project.org/package=autoReg Description: CRAN Package 'autoReg' (Automatic Linear and Logistic Regression and Survival Analysis) Make summary tables for descriptive statistics and select explanatory variables automatically in various regression models. Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents. Package: r-cran-autoregressionmde Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-autoregressionmde_1.0-1.ca2404.1_all.deb Size: 19168 MD5sum: 845bc0ccf034a14b0e3f6c4fcd596325 SHA1: a486dd760c659893c651b1df7797a2071f74e948 SHA256: 43ca1158b829a5af3ca7445ee283f35509ffc79ce9b0884305ca674eafe5018c SHA512: b49d684b86ce8e20464fe5320d048bbcac67ace8fb440dcced3da36652e5ca56348cb21df2dfde577ebd1ef69b92151e3490d27f5b82f749d07ec9bc294b2414 Homepage: https://cran.r-project.org/package=AutoregressionMDE Description: CRAN Package 'AutoregressionMDE' (Minimum Distance Estimation in Autoregressive Model) Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model. Package: r-cran-autorelevate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-autorelevate_0.1.0-1.ca2404.1_all.deb Size: 147238 MD5sum: 74070cfd2279f0cc3172e5369bef4d60 SHA1: a0be99869eb94f8e7bcb1e600689fbc616d19fdc SHA256: 5fa14292e795c65b0a6a419adbc1f96f74f997dcb7f1107e84f70c4f8cdde54c SHA512: 2058da8c109904105c65a351fe713f1f68037a6f9080c35571005e24912d590755fb98136d05fcc6ed35ee9f3674fadb38174a1243c6039abc0a59f3a3744b37 Homepage: https://cran.r-project.org/package=autorelevate Description: CRAN Package 'autorelevate' (The Autorelevated Family of Probability Distributions andEstimation Methods) Implements the autorelevated family of probability distributions, obtained by applying the autorelevation transformation of Krakowski (1973) and Dileepkumar and Sankaran (2022) to ten baseline probability distributions: Weibull, Lomax, Burr XII, Gompertz, Log-Logistic, Chen, Exponentiated Exponential, Power Lindley, Log-normal, and Gamma. The Weibull member of the family is studied in detail by Dileep Kumar, Shabeer, and Sankaran (2025) . The Lomax member is studied by Sharma, Pal, Bhardwaj, and Tyagi (2026, submitted), who establish its upside-down bathtub hazard shape. Supplies vectorized density, distribution, survival, hazard, quantile (via the negative branch of the Lambert W function), and random-generation functions for all ten members of the family. It also implements Maximum Likelihood, Maximum Product of Spacings, Least Squares, Weighted Least Squares, and Cramer-von Mises estimation methods along with a Kolmogorov-Smirnov goodness-of-fit test, a Total Time on Test plot, and model selection by AIC, BIC, CAIC, and HQIC. It also includes a bundled bladder cancer remission dataset (Lee and Wang, 2003) for illustration. Package: r-cran-autoscore Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tableone, r-cran-proc, r-cran-randomforest, r-cran-ggplot2, r-cran-knitr, r-cran-hmisc, r-cran-car, r-cran-dplyr, r-cran-ordinal, r-cran-survival, r-cran-tidyr, r-cran-plotly, r-cran-magrittr, r-cran-randomforestsrc, r-cran-rlang, r-cran-survauc, r-cran-survminer Suggests: r-cran-rpart, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autoscore_1.1.0-1.ca2404.1_all.deb Size: 1923698 MD5sum: 09dc285642e40a1e0c8fa5d1fb231dc7 SHA1: b782192ea4eded9c07abeff18129d3d22e9b0ca3 SHA256: 1e03e8dd01a1050a1403b3b6571b50b6bdead3129b759e390cafeab4cf1bf35a SHA512: 336e58b34055bd7563f753b0650c068134a291c7b13c2e9de0a82ecc5ceff7df72d36d4b2d3b430a4f5e1d98e2e0f12526aca66495a51faea3ec4f7ffcb0a9bb Homepage: https://cran.r-project.org/package=AutoScore Description: CRAN Package 'AutoScore' (An Interpretable Machine Learning-Based Automatic Clinical ScoreGenerator) A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies. Package: r-cran-autoscorecard Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-infotheo, r-cran-rocr, r-cran-rpart, r-cran-discretization, r-cran-corrplot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autoscorecard_0.3.0-1.ca2404.1_all.deb Size: 709720 MD5sum: 9a00745e8580b8bcb5db98963a7199ac SHA1: cf25499d89096297f319bc6c1ce47da6f5d717a7 SHA256: 8ebdf006673f082a2e2d3fb83ffdd3fe1ad5037196d561c226f5d42e7dc60138 SHA512: bf4184aebdfba5f32ef21e2aa98f83458a90f75388ef0e188b78abb9bd75e50af36771f14fdd55ce267b66b321d09f6e06ea9cf1f358556414f5458c6aa80476 Homepage: https://cran.r-project.org/package=autoScorecard Description: CRAN Package 'autoScorecard' (Fully Automatic Generation of Scorecards) Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. . 2. Dong-feng Li(Peking University),Class PPT. 3. . 4. . Package: r-cran-autoseed Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-autoseed_0.1.0-1.ca2404.1_all.deb Size: 1505692 MD5sum: 612993c3e1a09be76006715d8c899439 SHA1: 060d1e45a27854c5cc689fb8bef470330d15373b SHA256: 8e92de6330d933fbc52a67f08381772163288f2850556f2f70ab7f1212120258 SHA512: 06b90c35a88280990d7f163de7479543add013d12bfba9b311e2c12469ddcafe3109f77d04a2d90eaac33941d4d58ff7b41420c1b99110b2dea4dbacd9c21873 Homepage: https://cran.r-project.org/package=Autoseed Description: CRAN Package 'Autoseed' (Retrieve Disease-Related Genes from Public Sources) For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease, we developed this program to acquire disease-related genes. The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank' and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes. 'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes. Package: r-cran-autoshiny Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-roxygen2, r-cran-magrittr, r-cran-webshot Filename: pool/dists/noble/main/r-cran-autoshiny_0.0.3-1.ca2404.1_all.deb Size: 40340 MD5sum: 75fec4e546d3fd0b9712909ca76a4e01 SHA1: a4d9f2cb060aee357201aa2f8f300fc91c5a9eee SHA256: 542d1a28738989efd2e897cb3d5dd60b92b1c6af2f390c0590fd19e61c05eab0 SHA512: 7901dce24fd650bb0bf1c4ec8cfc29f6cc36674100b78eacd2a3747757e063b96c4292f5bd785eddd58ed84bec80dd67f3550287963239fc7c9a436ba3d77a67 Homepage: https://cran.r-project.org/package=autoshiny Description: CRAN Package 'autoshiny' (Automatic Transformation of an 'R' Function into a 'shiny' App) Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at . Package: r-cran-autoslider.core Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11703 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-formatters, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-gtsummary, r-cran-jsonlite, r-cran-officer, r-cran-rlang, r-cran-rlistings, r-cran-rtables, r-cran-rvg, r-cran-stringr, r-cran-survival, r-cran-tern, r-cran-tidyr, r-cran-yaml Suggests: r-cran-crane, r-cran-data.table, r-cran-devtools, r-cran-ellmer, r-cran-filters, r-cran-glue, r-cran-htmltools, r-cran-httr, r-cran-knitr, r-cran-lubridate, r-cran-mime, r-cran-nestcolor, r-cran-purrr, r-cran-r.utils, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsvg, r-cran-svglite, r-cran-testthat, r-cran-tmb, r-cran-withr Filename: pool/dists/noble/main/r-cran-autoslider.core_0.3.4-1.ca2404.1_all.deb Size: 2502960 MD5sum: dda9d10c32fe24fff06018fa0bf2d819 SHA1: 485c85c4bb9f4e9ff947bbd11a707ae32855e126 SHA256: 138bd9a48d46c4c0ff76cd997defb4a0c38fb87f330c8af7aa378261b0648a0c SHA512: 92b78ccc890dacae5d2accce9a132f7db10608a03e24a018a7b54324e91a08c365e82f1e17ac641750ea8328988aa3538c27d970eb24b9b8224bb319354ae5ea Homepage: https://cran.r-project.org/package=autoslider.core Description: CRAN Package 'autoslider.core' (Slide Automation for Tables, Listings and Figures) The normal process of creating clinical study slides is that a statistician manually type in the numbers from outputs and a separate statistician to double check the typed in numbers. This process is time consuming, resource intensive, and error prone. Automatic slide generation is a solution to address these issues. It reduces the amount of work and the required time when creating slides, and reduces the risk of errors from manually typing or copying numbers from the output to slides. It also helps users to avoid unnecessary stress when creating large amounts of slide decks in a short time window. Package: r-cran-autoslider.trade Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-autoslider.core, r-cran-cowplot, r-cran-formatters, r-cran-ggplot2, r-cran-rlistings, r-cran-rtables Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autoslider.trade_0.0.1-1.ca2404.1_all.deb Size: 70622 MD5sum: 62c36a1ec9786c91e7f3616d9949334c SHA1: 25e5a660525c7282d4f925442a6312211eb0ec07 SHA256: 113f5c26d73d9dbaeb00da428ca1a2e916c129d83e643e6f330d128dd30d6fe3 SHA512: da04cbc052e8330eeeb207a2d2caf54af7bca8deb6ef49ed3ad4dbeec94fbe55fd93434397125d5e46e9ac1c240f1e1ffb3493d91e0d18e3c24e8fa01cbe200d Homepage: https://cran.r-project.org/package=autoslider.trade Description: CRAN Package 'autoslider.trade' (Slide Automation for Trading Tables, Listings and Figures) A downstream package of 'autoslider.core' that produces tables, listings and figures for finance trading, in the same style as 'autoslider'. Where 'autoslider.core' automates clinical study outputs, this package automates trading outputs from price and trade data: performance tables, equity curves and trade listings. Package: r-cran-autostats Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-purrr, r-cran-janitor, r-cran-tibble, r-cran-rlang, r-cran-rlist, r-cran-broom, r-cran-magrittr, r-cran-ggeasy, r-cran-ggplot2, r-cran-jtools, r-cran-gtools, r-cran-ggthemes, r-cran-patchwork, r-cran-tidyr, r-cran-xgboost, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-framecleaner, r-cran-presenter, r-cran-yardstick, r-cran-dials, r-cran-party, r-cran-data.table, r-cran-nnet, r-cran-recosystem, r-cran-ckmeans.1d.dp, r-cran-broom.mixed, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forcats, r-cran-doparallel, r-cran-hardhat, r-cran-flextable, r-cran-glmnet, r-cran-ggstance, r-cran-matrix, r-cran-bbmisc, r-cran-readr, r-cran-lubridate, r-cran-ranger, r-cran-xicor Filename: pool/dists/noble/main/r-cran-autostats_0.4.2-1.ca2404.1_all.deb Size: 332886 MD5sum: 2585a9c344d770401236629af9d052d4 SHA1: 8b39446f949c7721322e084df2df406cf49f5ed6 SHA256: a9a49d7212976997a3280651ced16ffa90ade872e21e5e93ed30caaecdc70c86 SHA512: f8393a044c4e05f0900908c4f84e25e0a79155e6f68400e44da5a743daa92178300b02b4ad3b0b19595c8e96dfaf88a465e0681d6cfc2ec4eb4be1bccd0f5893 Homepage: https://cran.r-project.org/package=autostats Description: CRAN Package 'autostats' (Auto Stats) Automatically do statistical exploration. Create formulas using 'tidyselect' syntax, and then determine cross-validated model accuracy and variable contributions using 'glm' and 'xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set. Package: r-cran-autostepwiseglm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-formula.tools Filename: pool/dists/noble/main/r-cran-autostepwiseglm_0.2.0-1.ca2404.1_all.deb Size: 29000 MD5sum: 91c7c33e135980b66f92e3c93bdb79e3 SHA1: b4c9cd2f697230ca16c59b414bf92842c332b4df SHA256: 2dcd48335c5de1f46a8e4c2fbe0d3b15d73713ee57b0a1b79790035f11f162ca SHA512: 02863e38d3081a39317f9b349b8f83258a239bcb03b5279bb63c2e1902da19f1a517c295530dae1c97c1db0f2ddd2260fdd780f3f7cd9aa44dccbd2cfaab3dbc Homepage: https://cran.r-project.org/package=AutoStepwiseGLM Description: CRAN Package 'AutoStepwiseGLM' (Builds Stepwise GLMs via Train and Test Approach) Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5). Package: r-cran-autostratak Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autostratak_0.1.4-1.ca2404.1_all.deb Size: 31186 MD5sum: 3a032a9910dbe1fa5b71226106a6de92 SHA1: 995a41f5f10452a8bd5f5936a4793f9dee4e0236 SHA256: 87dc85f5fb719b0a62d7a90102694d452a2e9e232a55144e6a5fad2268da3d71 SHA512: b4b70f8b664852e874a9ed48a4972c680bb915e00f53bdbde65fd0f6eddadf78455fbcc4feb46d686040b940eb3143079e5b9013ab2f3f478134089285d31fbc Homepage: https://cran.r-project.org/package=AutoStrataK Description: CRAN Package 'AutoStrataK' (Automatic Optimal Stratification for Survey Sampling) Provides tools for the automatic stratification of survey populations using clustering and optimization techniques. The package assists researchers and survey practitioners in constructing homogeneous strata to improve the efficiency and precision of survey estimates. Functions are provided for generating strata, evaluating stratification quality, summarizing stratified populations, and visualizing stratification results. These tools support the design and implementation of efficient survey sampling strategies. The package utilizes standard statistical methods from survey sampling, clustering, and optimization for automatic stratification. Methods are described in Cochran (1977, ISBN:9780471162405) and Lohr (2021, ISBN:9780367354556). Package: r-cran-autostsm Architecture: all Version: 3.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik, r-cran-forecast, r-cran-lubridate, r-cran-ggplot2, r-cran-gridextra, r-cran-strucchange, r-cran-foreach, r-cran-dosnow, r-cran-lmtest, r-cran-ggrepel, r-cran-progress, r-cran-sandwich, r-cran-data.table, r-cran-kalmanfilter Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autostsm_3.1.5-1.ca2404.1_all.deb Size: 350096 MD5sum: 92d308b7e49e54bef085cc7f1d16f083 SHA1: 784b36db06c761a6fdec3b26004180503277f60c SHA256: 793794083b88e8ce4aa27e2d7f6d9d90937a176b68c4715764d55ac7de3b2d48 SHA512: 96dfd1ea0fe88316222f6a27f63b1b46bfba1139d0bc38bc54ad37bef3a9c876bb17407b97eb4a5d334fa071afc9d7ca30d48c7df0f3974a125c3526c87e1a60 Homepage: https://cran.r-project.org/package=autostsm Description: CRAN Package 'autostsm' (Automatic Structural Time Series Models) Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" . Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-autosync Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1491 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-automerge, r-cran-httr2, r-cran-jose, r-cran-later, r-cran-nanonext, r-cran-promises, r-cran-secretbase Suggests: r-cran-openssl, r-cran-shiny, r-cran-shinyreact, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autosync_0.2.0-1.ca2404.1_all.deb Size: 545066 MD5sum: 5b075c86b814f2417ac480522b7384a2 SHA1: a9462136ebe8b8ec6cb9f9e0a44ad6e5b18303e0 SHA256: ca070567d81049b05af3e5a73c44a65941e2ad3343285c47e99f261090cdab00 SHA512: 6b515ff490b50542b7f9188c7e83117ecc290fc2b0fbdd1ff3df81cf91d1ccfb38377920cb8b0809a2484c29daa1272a14a6a31abd69811ce2f3818ce45569a0 Homepage: https://cran.r-project.org/package=autosync Description: CRAN Package 'autosync' ('Automerge' Sync Server and Client) A WebSocket-based implementation of the 'automerge-repo' synchronization protocol used by 'sync.automerge.org'. 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Package: r-cran-autotab Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-keras, r-cran-magrittr, r-cran-r6, r-cran-reticulate, r-cran-tensorflow Suggests: r-cran-dplyr, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autotab_1.1-1.ca2404.1_all.deb Size: 659136 MD5sum: 1787d2f3aabddbdb24b7aecb2fb026cb SHA1: 99ae73f09b322442386b1d6c79a9f4d0025fd5a1 SHA256: bab9b4b83ee3b0a29a4e1a96beba81a4022a009f4f7293aa874c826dfdd1012d SHA512: 9dab91d2f631a0b182ee293b1a822e330644f4a2a8ef01aeeea85dcc2dd7b96be493dd85862bb9c2ada5f6384cffbf9d6bf81b62d9ab6b36cd90642385597503 Homepage: https://cran.r-project.org/package=autotab Description: CRAN Package 'autotab' (Variational Autoencoders for Heterogeneous Tabular Data) Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' interface, enabling reproducible VAE training for heterogeneous tabular datasets. 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Ye, C.,and Yang,Y. (2019) . 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Package: r-cran-aws.alexa Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-aws.signature, r-cran-xml2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-lintr Filename: pool/dists/noble/main/r-cran-aws.alexa_0.1.8-1.ca2404.1_all.deb Size: 252034 MD5sum: ff2b6052ec378a6f8fa3cb9a491d7bce SHA1: 1a9a0675da9eb2e78aaae9b5c283c611af0952a4 SHA256: cdc76b2297dc8f4ebd1a634165aa725d0b4b0d183774cc850f8c359020d51b87 SHA512: 72612a310b354335d5188f781ac5451393cb988bade3459833ed21a1d9952a24d3c6dc1abc5a57b3449e8249d5da74d761af8141a175d0b8732f91c2c3c84114 Homepage: https://cran.r-project.org/package=aws.alexa Description: CRAN Package 'aws.alexa' (Client for the Amazon Alexa Web Information Services API) Use the Amazon Alexa Web Information Services API to find information about domains, including the kind of content that they carry, how popular are they---rank and traffic history, sites linking to them, among other things. See for more information. Package: r-cran-aws.comprehend Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.comprehend_0.2.1-1.ca2404.1_all.deb Size: 42408 MD5sum: 6674a2b37dc73c51c9bd19cfd9e8d8ea SHA1: bc707414a8077c2f53c5f60c1991b602c7ba4591 SHA256: efedb75708b98f2f5a72c199b121794eaf056bd68f7308ece1cf69ef61d0de78 SHA512: 0709c7db6aea8949052230ca06493a90f30c34c683f85687afe024d0509f9642a082a0f59132ca66ee046ca3012c676d3dbdd8aec2f5afa8569442a90902a4b5 Homepage: https://cran.r-project.org/package=aws.comprehend Description: CRAN Package 'aws.comprehend' (Client for 'AWS Comprehend') Client for 'AWS Comprehend' , a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction. Package: r-cran-aws.ec2metadata Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-aws.ec2metadata_0.2.2-1.ca2404.1_all.deb Size: 22750 MD5sum: 58cdd9de34f5d937e56756a0e5344d8c SHA1: 8de7fd25b12f047017c83d21ac2eeeeb0165af79 SHA256: c4836a1eaa4fcf60e903ffff929c3932d91cf34e1f216e16ae00b45734a4b864 SHA512: 9d5f978ced0c7c32b7e854dc734fc06221994f2e714a6c939db5d45ee6821a9c22506e80382349b4bdb0184ae1f58a86f4d5e4f811cdd326345774e24b601748 Homepage: https://cran.r-project.org/package=aws.ec2metadata Description: CRAN Package 'aws.ec2metadata' (Get EC2 Instance Metadata) Retrieve Amazon EC2 instance metadata from within the running instance. Package: r-cran-aws.ecx Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-aws.signature, r-cran-httr, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.ecx_1.0.6-1.ca2404.1_all.deb Size: 2268920 MD5sum: e4d99d90951bef65064cf2ac9609f605 SHA1: 4d4e96ba8422904191ae8ec551f90c2820f52f74 SHA256: 109f31347e3553ba51722a99633c07bc3af817619df06622db9366cc5d7e0ff1 SHA512: bebf0744635f24660e067361226153d78480917bda60b3d8b386b9222012ba89df3357c747a4cbb0176f6d968319ddcd9fa5f97a13016fd814773ed1148cc1f7 Homepage: https://cran.r-project.org/package=aws.ecx Description: CRAN Package 'aws.ecx' (Communicating with AWS EC2 and ECS using AWS REST APIs) Providing the functions for communicating with Amazon Web Services(AWS) Elastic Compute Cloud(EC2) and Elastic Container Service(ECS). The functions will have the prefix 'ecs_' or 'ec2_' depending on the class of the API. The request will be sent via the REST API and the parameters are given by the function argument. The credentials can be set via 'aws_set_credentials'. The EC2 documentation can be found at and ECS can be found at . Package: r-cran-aws.iam Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml2, r-cran-jsonlite, r-cran-aws.signature Filename: pool/dists/noble/main/r-cran-aws.iam_0.1.8-1.ca2404.1_all.deb Size: 131010 MD5sum: 65a14f871ccad1b3d7aec0426c040fd4 SHA1: 60770e489550ab1a5390debeb865b6fda0758c3f SHA256: eeb3ad95cc5e19dc87f83464092d1eb9f8ef3eead6d0f403c0e7972f46cfda31 SHA512: 7154306c0bdaaeaf3b5b34016e65f6a970acd74951f03cf54caebebcd1255ce4a3b9fbe2455d93c3c7e4166731b48d85f3bd67fca13ec5f946cd2d00e60c156b Homepage: https://cran.r-project.org/package=aws.iam Description: CRAN Package 'aws.iam' (AWS IAM Client Package) A simple client for the Amazon Web Services ('AWS') Identity and Access Management ('IAM') 'API' . Package: r-cran-aws.kms Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-base64enc, r-cran-aws.signature Filename: pool/dists/noble/main/r-cran-aws.kms_0.1.4-1.ca2404.1_all.deb Size: 57218 MD5sum: ad7ec274fb050811f182009a3c6061f3 SHA1: d567ab3758ed44607a5e517e78fa9006f631d8e2 SHA256: 1f82d6b5cf3e6f195939c881e0989fdd7cb66ad706a2a434de135f97e854aef7 SHA512: 27dd6dd6acaf6f249b6c0e3f8f42f08a02df0a1abea13ade88fd7e5dbacb8a5e24b3f3eba9b1f2cc610537ffbc3f8442406fae75c127ecf6e88226fba74a3b06 Homepage: https://cran.r-project.org/package=aws.kms Description: CRAN Package 'aws.kms' ('AWS Key Management Service' Client Package) Client package for the 'AWS Key Management Service' , a cloud service for managing encryption keys. Package: r-cran-aws.lambda Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature, r-cran-base64enc Suggests: r-cran-testthat, r-cran-aws.s3, r-cran-aws.iam Filename: pool/dists/noble/main/r-cran-aws.lambda_0.2.0-1.ca2404.1_all.deb Size: 59628 MD5sum: 660f0ecea591f37d729a25db64c38e0a SHA1: 6fe123e80f1bfe5d8175f12cff604c00334ceb40 SHA256: a597feea23b61dd213bfe5f1989f45941fdfdd31bab5d4f935b5220df52c21bd SHA512: 0ce32be2fa5b14b6e60c83a9aeb6234325a4212e52ac1ecbd0bf8f94262d32ab0bd2f268da3434813d90301aea55ab060c6a6f2ab81b1d9819533a74d89c5623 Homepage: https://cran.r-project.org/package=aws.lambda Description: CRAN Package 'aws.lambda' (AWS Lambda Client Package) A simple client package for the Amazon Web Services ('AWS') Lambda API . Package: r-cran-aws.polly Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature, r-cran-tuner Filename: pool/dists/noble/main/r-cran-aws.polly_0.1.5-1.ca2404.1_all.deb Size: 30142 MD5sum: 81301dff18d962f37b5b9a2e2578bcf9 SHA1: 09125813107bfaca88235b7a0b9439dfb2ca6c89 SHA256: c9265fd564a6d791413585ebfc648200f7c4cd64a495ccb71ef1f602d58f068a SHA512: 7f98e762c8ff955db8e57549c772ad57322a473b17a2c86f833fc9188784a4605f05fec25dfd182725c5dacfaca107d8f18dd5702fe30fda9d6ea60b2f4500d2 Homepage: https://cran.r-project.org/package=aws.polly Description: CRAN Package 'aws.polly' (Client for AWS Polly) A client for AWS Polly , a speech synthesis service. Package: r-cran-aws.s3 Architecture: all Version: 0.3.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-xml2, r-cran-base64enc, r-cran-digest, r-cran-aws.signature Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.s3_0.3.22-1.ca2404.1_all.deb Size: 205296 MD5sum: fb1183fc14f259cbe6916254430c97f6 SHA1: f5ebb4633b53b3d353af1b1b663e809fa49dd3a4 SHA256: e6c02146ab7c5265c58af166a310974abcc73cb609770aa8283654a328fedd96 SHA512: 800d06e365c324e6eafcdba375db0194b4693af52f7d4bf846e164ef2c4a032805b992143bd1dce5fee5e712fb81dce65f1028e4c2cfe47d86d56a406fe75e5c Homepage: https://cran.r-project.org/package=aws.s3 Description: CRAN Package 'aws.s3' ('AWS S3' Client Package) A simple client package for the Amazon Web Services ('AWS') Simple Storage Service ('S3') 'REST' 'API' . Package: r-cran-aws.signature Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-base64enc, r-cran-jsonlite, r-cran-curl Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.signature_0.6.2-1.ca2404.1_all.deb Size: 87932 MD5sum: 7a0a7492a734d0c9a2015aba471abec9 SHA1: 343a1dbc477dc4de1abb4efb9a0b10c1bd47d6a5 SHA256: 2e50bb5a749193df2b82cae3f6164b1de149f099c4864a0ec8cb09adb474c690 SHA512: ee01964ad755b6d6b978872b6382804cf65eeede409862a35fcabe99c4656ddc9e53a09509bc0ea2cece6e37b492584c7fda634cdcfe019953497b8f42d1dff1 Homepage: https://cran.r-project.org/package=aws.signature Description: CRAN Package 'aws.signature' (Amazon Web Services Request Signatures) Generates version 2 and version 4 request signatures for Amazon Web Services ('AWS') Application Programming Interfaces ('APIs') and provides a mechanism for retrieving credentials from environment variables, 'AWS' credentials files, and 'EC2' instance metadata. For use on 'EC2' instances, users will need to install the suggested package 'aws.ec2metadata' . Package: r-cran-aws.transcribe Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.transcribe_0.1.3-1.ca2404.1_all.deb Size: 28576 MD5sum: 8126864c2c49dcd169638c106efad63e SHA1: 4c0bd60d4396f3a855dd236701da5efe18505537 SHA256: 80bf0faa9f4411a7caf4988b92921ee22eb711d20467bae5d86e44ecc6f5e191 SHA512: e8b4bfa1798a05b50d0a0db75d58ba19678abd7d744380d6c48876ac3bb656053010853a73fe1cdc695a7afd9eed35a426064b99c2cf3896487d8eb206fbaf03 Homepage: https://cran.r-project.org/package=aws.transcribe Description: CRAN Package 'aws.transcribe' (Client for 'AWS Transcribe') Client for 'AWS Transcribe' , a cloud transcription service that can convert an audio media file in English and other languages into a text transcript. Package: r-cran-aws.translate Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Filename: pool/dists/noble/main/r-cran-aws.translate_0.1.4-1.ca2404.1_all.deb Size: 19640 MD5sum: d4948ead312793c0454ef73344bb6236 SHA1: 2c9ea4c5315bd87c714aac37928acb4924bff52a SHA256: 82cafc0fa0d684eb6cc87004e54d650bd260008bedd3987c903cf550f20607cb SHA512: b37eaa9390e862057285c74cd8ebab207b4a07996916e1099bc7bf8b650aed97db29595b1f8326e9f65e47377eb96f5c62244377d3afd927764c5bd0e052f6f8 Homepage: https://cran.r-project.org/package=aws.translate Description: CRAN Package 'aws.translate' (Client for 'AWS Translate') A client for 'AWS Translate' , a machine translation service that will convert a text input in one language into a text output in another language. Package: r-cran-aws.wrfsmn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aws.s3, r-cran-lubridate, r-cran-terra, r-cran-dplyr, r-cran-ggplot2, r-cran-hydrogof, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.wrfsmn_0.1.0-1.ca2404.1_all.deb Size: 129006 MD5sum: ba0843f3b55e0062d18e3bc559a7da7a SHA1: be1a9943cf33b7822f3ca638f34d9c48f3461f8f SHA256: 591930a67719517184214a466a8745f8946085f1dc70cb3fb72fd5277f6f96a0 SHA512: 6526abc44fbf09ccb3a520a744710207c5382685e1fd93c2e9b170fd55d149161c2f7e0322eaf5a6ef2d8b17752684f7df0ab792e3eda3d3a2e40b57d2b4d9db Homepage: https://cran.r-project.org/package=aws.wrfsmn Description: CRAN Package 'aws.wrfsmn' (Data Processing of SMN Hi-Res Weather Forecast from 'AWS') Exploration of Weather Research & Forecasting ('WRF') Model data of Servicio Meteorologico Nacional (SMN) from Amazon Web Services () cloud. The package provides the possibility of data downloading, processing and correction methods. It also has map management and series exploration of available meteorological variables of 'WRF' forecast. Package: r-cran-axisandallies Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-axisandallies_0.1.1-1.ca2404.1_all.deb Size: 38936 MD5sum: 72c49d15b12750d75d3fdf2ea8c6fcec SHA1: eeb53383237a1178bb96d4da27267e98595bea0a SHA256: dc84da2087f6c170db2eea1bcb7668c7c6683fc217272a76b948f7acd623f0b1 SHA512: c138e068a1adaf2f256a8f1fbccd72f7b77a18c7c7d1d65cf556ba859e17ccb1d2c93fe3981f00e5c2fba911f3c16754c7f99b8156cba817343ed116126e1284 Homepage: https://cran.r-project.org/package=axisandallies Description: CRAN Package 'axisandallies' (Axis and Allies Spring) Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly. Package: r-cran-axprism Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-axprism_0.2.0-1.ca2404.1_all.deb Size: 106382 MD5sum: c5c025eceba7751b700002dd98ada277 SHA1: 663011938f2857c0d3826fc010c473791fd2fde8 SHA256: 766b067f9442573da4a772a1240617a3bd607c3c0de81f233b61466e8603b4ad SHA512: 9977a8faec50f36a6d4c470815267db47d715cadb4c3b9ecb9de4a90d41063eb9849b23324bd2d5b3fbd362006ded8cff33607cc5221ef4a42c9c974c6d98419 Homepage: https://cran.r-project.org/package=axprism Description: CRAN Package 'axprism' (Client for the 'AxPrism' Institutional XBRL and ShariahCompliance API) Provides an R client for the 'AxPrism' Application Programming Interface (API) (), which serves institutional financial data in the eXtensible Business Reporting Language (XBRL) format together with Shariah compliance screening. Supported compliance rulesets include those of the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI), the Morgan Stanley Capital International (MSCI) Islamic methodology, the Dow Jones Islamic Market (DJIM), the Financial Times Stock Exchange (FTSE) and the Saudi Capital Market Authority (CMA). Convenience functions wrap company fundamentals, compliance verdicts and portfolio screening, company profiles, equity screeners, regulatory disclosures and filing text search, and the Tadawul (Saudi Exchange), Bursa Malaysia and Indonesia Stock Exchange (IDX) markets, as well as webhooks and bulk data export. Requests use 'X-API-Key' header authentication, automatic retries with exponential backoff, and a generic request helper that covers all endpoints. Package: r-cran-aziad Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-foreach, r-cran-doparallel, r-cran-qrm, r-cran-corpcor, r-cran-envstats Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aziad_0.0.3-1.ca2404.1_all.deb Size: 216196 MD5sum: 277e2761e6ef9a2efc800252682ef88d SHA1: bbf44a358f586c9ec99f9f35f290155aada7d072 SHA256: 0fa2bd15ba0709d2c02326df225dff00caaff823560434557661ed0ccb412dbd SHA512: 9c0f6aa29d66260ffe8b3d88a57fc9039e669ac8bd9351d9327e31e1f6d11f1e9ceb0b8da5d88dc8e0ef89c3e2fd2781577e50296f64ec8bbb8934591a4844a0 Homepage: https://cran.r-project.org/package=AZIAD Description: CRAN Package 'AZIAD' (Analyzing Zero-Inflated and Zero-Altered Data) Description: Computes maximum likelihood estimates of general, zero-inflated, and zero-altered models for discrete and continuous distributions. It also performs Kolmogorov-Smirnov (KS) tests and likelihood ratio tests for general, zero-inflated, and zero-altered data. Additionally, it obtains the inverse of the Fisher information matrix and confidence intervals for the parameters of general, zero-inflated, and zero-altered models. The package simulates random deviates from zero-inflated or hurdle models to obtain maximum likelihood estimates. Based on the work of Aldirawi et al. (2022) and Dousti Mousavi et al. (2023) . Package: r-cran-azlogr Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-catools, r-cran-digest, r-cran-httr, r-cran-jsonlite, r-cran-logger Suggests: r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azlogr_0.0.6-1.ca2404.1_all.deb Size: 52672 MD5sum: 4fec2bbd2a4564263ff8c11d4141143f SHA1: 2eb225912c8961975588c3b270d4eec64a1e90a6 SHA256: 311884aa90d9421ba7225ce89823a0d0e359b0b36aeb84dcbc36532783821a7b SHA512: 134cca0e0a57b553028706f8b09852a67c8feddce901138f9e02bde4abe604ea6cac15037492386916c5059a2bac4604f1a026c98d393df428c8ffff2f5564c6 Homepage: https://cran.r-project.org/package=azlogr Description: CRAN Package 'azlogr' (Logging in 'R' and Post to 'Azure Log Analytics' Workspace) It extends the functionality of 'logger' package. Additional logging metadata can be configured to be collected. Logging messages are displayed on console and optionally they are sent to 'Azure Log Analytics' workspace in real-time. Package: r-cran-azr Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-s7, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-data.table, r-cran-httpuv, r-cran-clipr, r-cran-knitr, r-cran-processx, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-azr_0.3.6-1.ca2404.1_all.deb Size: 938796 MD5sum: 097b9fede41c39fbf35ba33f7d732c2c SHA1: 4397b7e7b444fe47f099705c379b5084f0af9f11 SHA256: 5e1d96c7086f8dd84aa56428f874050f927aa889535bfe9fdfbe4fd41123cb7a SHA512: 096b085dae5672a044e7d334b3115863bec2ed03cf75828bc0056b398a74bc09c99acc47308b98cea5e595151575100fdc5874009f49aa8fb508a363c50f7e7a Homepage: https://cran.r-project.org/package=azr Description: CRAN Package 'azr' (Credential Chain for Seamless 'OAuth 2.0' Authentication to'Azure Services') Implements a credential chain for 'Azure OAuth 2.0' authentication based on the package 'httr2''s 'OAuth' framework. Sequentially attempts authentication methods until one succeeds. During development allows interactive browser-based flows ('Device Code' and 'Auth Code' flows) and non-interactive flow ('Client Secret') in batch mode. Package: r-cran-azureappinsights Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rlang, r-cran-assertthat, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-testthat, r-cran-here Filename: pool/dists/noble/main/r-cran-azureappinsights_0.3.1-1.ca2404.1_all.deb Size: 302794 MD5sum: fdd65592ffd1209bb84d2e538bd72ad1 SHA1: f41199f3b4da40769b50423799f0a7b6f90bed33 SHA256: 0dd5035e1d5a907f6f22c01693fed2b39ad5c8cca52e0e831b5abe66c697a58f SHA512: d7d28cecf70595b3a5315a7abb202bbd463644a63ac133079ba288d955f05065ccf32a414b7e0b1a0c6973e8484a561b900f6d7bb39e93fbb467d6c7e4f0b781 Homepage: https://cran.r-project.org/package=AzureAppInsights Description: CRAN Package 'AzureAppInsights' (Include Azure Application Insights in Shiny Apps) Imports Azure Application Insights for web pages into Shiny apps via Microsoft's JavaScript snippet. Allows app developers to submit page tracking and submit events. Package: r-cran-azureauth Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-openssl, r-cran-jsonlite, r-cran-jose, r-cran-r6, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httpuv, r-cran-shiny, r-cran-shinyjs, r-cran-azurermr, r-cran-azuregraph Filename: pool/dists/noble/main/r-cran-azureauth_1.3.5-1.ca2404.1_all.deb Size: 460300 MD5sum: a56534d3829583b0d9d42b3eca39a0b5 SHA1: 438c22bd67e8909614b38bade8d3744948cb09a1 SHA256: 5b417b20a84da2eacdb2ec23c7ea0556359473694ff185a0ac6dedf75533819f SHA512: 75a13d0e79ab0b134361f3dbcc60cacd1452647811fc63af2bd48225bd5d553952d2e9bc616dd1d48a53cbf350bdebe0056719a05247fecf49089821f80ac708 Homepage: https://cran.r-project.org/package=AzureAuth Description: CRAN Package 'AzureAuth' (Authentication Services for Azure Active Directory) Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud . Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages. Package: r-cran-azurecognitive Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azureauth, r-cran-azurermr, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azurecognitive_1.0.2-1.ca2404.1_all.deb Size: 147172 MD5sum: cba2a71961f00ab9d637e0afcd7f1434 SHA1: c739bada73ede09686d8f073f20fc5b35293d0d2 SHA256: 871ff238f9271ca073caa59e5d1e2f5d2f80c6b9cd7e97e8bd693a0c5971b83b SHA512: b9f2a73416de423211322a4d846ee038647bb13af6f8504a55453883b11f4a3f7d2e9afb5cd5e7d1752ee7bb7f480ce8b3e633c9343f849baca585379809c9c0 Homepage: https://cran.r-project.org/package=AzureCognitive Description: CRAN Package 'AzureCognitive' (Interface to Azure Cognitive Services) An interface to Azure Cognitive Services . Both an 'Azure Resource Manager' interface, for deploying Cognitive Services resources, and a client framework are supplied. While 'AzureCognitive' can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the 'AzureR' family of packages. Package: r-cran-azurecontainers Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azuregraph, r-cran-openssl, r-cran-httr, r-cran-r6, r-cran-processx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid, r-cran-mass, r-cran-bcrypt, r-cran-randomforest, r-cran-plumber, r-cran-restrserve, r-cran-azurekeyvault Filename: pool/dists/noble/main/r-cran-azurecontainers_1.3.3-1.ca2404.1_all.deb Size: 474564 MD5sum: be9e7d1d003b86de6f4fb13cd5060bf6 SHA1: 0abff3c1ee631a27fcd659d77ef0e33186791fbf SHA256: b331ad1f4bc0d4505ec1bb78efe3bd2da5b181d1f2a7e3c842b31d01762b84a4 SHA512: bb6fb7c29e3bba5ba85188d71e3615b08e31abcdd4dbbac7ed33f09eb810d14c39b5fbe7839857d8db06919edfe9d0389301486b8da3365103aab4300497ba93 Homepage: https://cran.r-project.org/package=AzureContainers Description: CRAN Package 'AzureContainers' (Interface to 'Container Instances', 'Docker Registry' and'Kubernetes' in 'Azure') An interface to container functionality in Microsoft's 'Azure' cloud: . Manage 'Azure Container Instance' (ACI), 'Azure Container Registry' (ACR) and 'Azure Kubernetes Service' (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the 'docker', 'docker-compose', 'kubectl' and 'helm' commandline tools are provided. Part of the 'AzureR' family of packages. Package: r-cran-azurecosmosr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-curl, r-cran-openssl, r-cran-jsonlite, r-cran-httr, r-cran-uuid, r-cran-vctrs Suggests: r-cran-azuretablestor, r-cran-mongolite, r-cran-dbi, r-cran-odbc, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azurecosmosr_1.0.0-1.ca2404.1_all.deb Size: 222752 MD5sum: ca6f25d4737d736de4dd7e737ffea7c4 SHA1: 309cc691a7d321b6cf887a3be4cf6a42d9d42968 SHA256: 693470711ff4df9aa77145638fae34be07ee6cfb70a931bbfe42feb254a000a5 SHA512: 90b9c9295550cce92dd82831c1fdbc2f34c99243315603888db4dd778c197544df96b52b550a4a0db44495495f9eb98c90efb9747ec555c4463d55b03eb65d5d Homepage: https://cran.r-project.org/package=AzureCosmosR Description: CRAN Package 'AzureCosmosR' (Interface to the 'Azure Cosmos DB' 'NoSQL' Database Service) An interface to 'Azure CosmosDB': . On the admin side, 'AzureCosmosR' provides functionality to create and manage 'Cosmos DB' instances in Microsoft's 'Azure' cloud. On the client side, it provides an interface to the 'Cosmos DB' SQL API, letting the user store and query documents and attachments in 'Cosmos DB'. Part of the 'AzureR' family of packages. Package: r-cran-azuregraph Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-curl, r-cran-r6 Suggests: r-cran-azurermr, r-cran-vctrs, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azuregraph_1.3.5-1.ca2404.1_all.deb Size: 540924 MD5sum: cfbe2cdcb46aaccd6d54710f04206ec0 SHA1: f8c623612b03d6fe99f0f03b81e41a4a062f585e SHA256: b2fcc6b05457a2907fc3bb2edf7f95506f4dc7fb1d239ce5cda42af986878d01 SHA512: cb5318512b2a84f2643142a4c15c9e73cdbf45b93e292f8fabbfb68ded76289718a00f3aefe3b7a76b22e2d19c7a684e17ab4c6a3cd064dbbb6a0a1c36ae753d Homepage: https://cran.r-project.org/package=AzureGraph Description: CRAN Package 'AzureGraph' (Simple Interface to 'Microsoft Graph') A simple interface to the 'Microsoft Graph' API . 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages. Package: r-cran-azurekeyvault Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-jose, r-cran-azurermr, r-cran-azuregraph, r-cran-azureauth Suggests: r-cran-azurestor, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azurekeyvault_1.0.6-1.ca2404.1_all.deb Size: 511898 MD5sum: c923b2b178eed64546233a66fac8560c SHA1: 877e0d0161e0819a3cb4908e2a58878c7854108a SHA256: 17db0a58b01ccdea94e39a6272d991a511ae2723057c476291a887eaa6e2adce SHA512: d314c28cebb106688349b60ec5f6e83ee35db1681c73af5537cbc6965c991a32229b03a51388f58e3199b7ffc860e5836b888285c1cfdee85eb69560d899e9e6 Homepage: https://cran.r-project.org/package=AzureKeyVault Description: CRAN Package 'AzureKeyVault' (Key and Secret Management in 'Azure') Manage keys, certificates, secrets, and storage accounts in Microsoft's 'Key Vault' service: . Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the 'AzureAuth' package to enable authentication with a certificate, and with the 'openssl' package for importing and exporting cryptographic objects. Part of the 'AzureR' family of packages. Package: r-cran-azurekusto Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-openssl, r-cran-azureauth, r-cran-azurermr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-dbi Suggests: r-cran-bit64, r-cran-knitr, r-cran-testthat, r-cran-azuregraph, r-cran-azurestor, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azurekusto_1.1.4-1.ca2404.1_all.deb Size: 576514 MD5sum: b2fbda375b6da704bc25a8d386a274ec SHA1: 43f390aa321f3f9d636808c1e2570efe411e2651 SHA256: 0eab94d67a1487d059021e1d71ae668dd39c10433c3f00427a2f4a2f76889ae7 SHA512: 383d7bea4b681f211aca8f21c3a9b2cb7bc492ec5754dab6fce3ffa0b15d1a2500db804bfbd506f5e6bc64a778bf5eb5b04282ca452913c2247f32e5429a17e9 Homepage: https://cran.r-project.org/package=AzureKusto Description: CRAN Package 'AzureKusto' (Interface to 'Kusto'/'Azure Data Explorer') An interface to 'Azure Data Explorer', also known as 'Kusto', a fast, distributed data exploration service from Microsoft: . Includes 'DBI' and 'dplyr' interfaces, with the latter modelled after the 'dbplyr' package, whereby queries are translated from R into the native 'KQL' query language and executed lazily. On the admin side, the package extends the object framework provided by 'AzureRMR' to support creation and deletion of databases, and management of database principals. Part of the 'AzureR' family of packages. Package: r-cran-azuremapsr Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-rlist, r-cran-sf, r-cran-stringr Filename: pool/dists/noble/main/r-cran-azuremapsr_0.0.2-1.ca2404.1_all.deb Size: 50458 MD5sum: 4b6f7e13685e90d6e57e116d0eaff5c5 SHA1: eb3bd856ab4a5271408d8a1f1c16c3db717d5f79 SHA256: 119a951a4708203f93c7ba4f2d0cf3c5a77c6197ea64072d4242ecc08e1074ad SHA512: 6e1dce62fd423e3f6cb3bc88331bd8c37ccb7b282a8d4a623ffd0856937bf9161fcb9d750b581ff130f60fef4ffece63471a3643b16950963e730a84ec7928a2 Homepage: https://cran.r-project.org/package=azuremapsr Description: CRAN Package 'azuremapsr' (Interface to the 'Azure Maps' API) Provides a wrapper for the Microsoft 'Azure Maps' REST APIs , enabling users to access mapping and geospatial services directly from R. This package simplifies authenticating, building, and sending requests for services like route directions. It handles conversions between R objects (such as 'sf' objects) and the GeoJSON+JSON format required by the API, making it easier to integrate 'Azure Maps' into R-based data analysis workflows. Package: r-cran-azuremlsdk Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reticulate, r-cran-plyr, r-cran-dt, r-cran-rstudioapi, r-cran-htmltools, r-cran-servr, r-cran-shiny, r-cran-shinycssloaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr, r-cran-jsonlite, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-azuremlsdk_1.10.0-1.ca2404.1_all.deb Size: 501382 MD5sum: 63589eb168844bc1ddce127e019c30a2 SHA1: 4cd170af23bc3c1a825a26238f3924897a410635 SHA256: fec9bf3b40a6dbc0d175400bbd0893d57045a46b1d6941e7fe609feddd6d5399 SHA512: 0468fe523c9ad1fa4963f26fedf4534ba0d4b7391a8aa4c4388a394f0b403a51450811196354b697c84a97dfcaae618b97f1b4207ec13fadc64b2b14a9a02602 Homepage: https://cran.r-project.org/package=azuremlsdk Description: CRAN Package 'azuremlsdk' (Interface to the 'Azure Machine Learning' 'SDK') Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: . Package: r-cran-azureqstor Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-openssl, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azureqstor_1.0.2-1.ca2404.1_all.deb Size: 165342 MD5sum: 28d52fde867de1c7b036f3468886bf68 SHA1: 4fecdd14833426b3578891bcd7c0ff7c4411efd2 SHA256: 06c1550fc476d7abef5a87306311e629b1f795c19cc83fb109af300ffda446da SHA512: 6b18457298063e90729e7abcce6f5ec361ad7adb82f226d93851a208032b6757bf8d331669a9f9e5e05c8561bd2d9765f87790375a6eeddc1ba67ef388ac0bbf Homepage: https://cran.r-project.org/package=AzureQstor Description: CRAN Package 'AzureQstor' (Interface to 'Azure Queue Storage') An interface to 'Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the 'AzureR' family of packages. Package: r-cran-azurermr Architecture: all Version: 2.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azuregraph, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httpuv, r-cran-azurestor Filename: pool/dists/noble/main/r-cran-azurermr_2.4.5-1.ca2404.1_all.deb Size: 466178 MD5sum: 025db181557925abbd8ea71fb8b65ee7 SHA1: b5fae076166c9b3bebbcb03e6b3ea9765109f502 SHA256: b3f3864cf733424779c6d09387986a411f140b913aa208aed795addda01a31f3 SHA512: cdf4c58b0ac35fdaa4b111b3d01bc37a4e3760bf10eb0f584ef72bfd2d289b5ecb65039afe2dc134cfe312fdbb17c0e0a8ebf816f34b9b26f6fa43181061a728 Homepage: https://cran.r-project.org/package=AzureRMR Description: CRAN Package 'AzureRMR' (Interface to 'Azure Resource Manager') A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages. Package: r-cran-azurestor Architecture: all Version: 3.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-mime, r-cran-openssl, r-cran-xml2, r-cran-vctrs, r-cran-azurermr Suggests: r-cran-azureauth, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-testthat, r-cran-processx, r-cran-uuid Filename: pool/dists/noble/main/r-cran-azurestor_3.7.1-1.ca2404.1_all.deb Size: 469708 MD5sum: c4f005719d34986d37a3320813601704 SHA1: cb1e9c273d69dc41839e00f33e6aef193568c252 SHA256: add25bd90a93e43a0dadfb40e7bba2358698792994dabe4a69c195bb7c1ba767 SHA512: 4961c0f286a887afb6ffacfd3f42ae799c7a6ca4112e6ffc066f7dcf500d2d4348f77e27eb0c1b52e9e1f988066d7dacdf5550814f406d94e8242c3906555034 Homepage: https://cran.r-project.org/package=AzureStor Description: CRAN Package 'AzureStor' (Storage Management in 'Azure') Manage storage in Microsoft's 'Azure' cloud: . On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages. Package: r-cran-azuretablestor Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-jsonlite, r-cran-openssl, r-cran-httr, r-cran-uuid, r-cran-vctrs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azuretablestor_1.0.0-1.ca2404.1_all.deb Size: 113820 MD5sum: e0606bca629fba5bec95b29759e7d243 SHA1: 70ceba687b0e0be28da8877660734b26ee15d320 SHA256: b2f430cb362c021963d5fe72013003f8bfbb1d53ac2aa7a29637bb24167ff2f4 SHA512: 5e1d732f7cf6251783b2779ddc2ed64714ff3abaf2045b1e4a0f915b1ea3063b2c51a83d6c3fe85d6080f3ae861eba1f74120800d2f114cff06f8435700bb388 Homepage: https://cran.r-project.org/package=AzureTableStor Description: CRAN Package 'AzureTableStor' (Interface to the Table Storage Service in 'Azure') An interface to the table storage service in 'Azure': . Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a 'CosmosDB' database with the table service API. Part of the 'AzureR' family of packages. Package: r-cran-azurevision Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1851 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurecognitive, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-azureauth, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azurevision_1.0.2-1.ca2404.1_all.deb Size: 1548542 MD5sum: 862548ab72d933694d1b9891d29f56d0 SHA1: 4b9244591d7a85f2206eb8962f82fe1413b9afa1 SHA256: e8f832e0e7cb92ee6202cff5a97781c1d6b1fc0f76c83c9099551dcae05a24c6 SHA512: 20143538dc3eaacbf0a22088e9745bafd1a9da54b0c85becbb07eeb7e035671fc2e2b2222290e6d0f84b4401efb1d092a602088d5cc71e7016609fcca63d1230 Homepage: https://cran.r-project.org/package=AzureVision Description: CRAN Package 'AzureVision' (Interface to Azure Computer Vision Services) An interface to 'Azure Computer Vision' and 'Azure Custom Vision' , building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages. Package: r-cran-azurevm Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-azurermr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-azurekeyvault, r-cran-azurevmmetadata Filename: pool/dists/noble/main/r-cran-azurevm_2.2.2-1.ca2404.1_all.deb Size: 424396 MD5sum: 1cc28bce7cd54621c30d19938a88585e SHA1: 853bc2113b96ca149aacd425751b9bc704275e04 SHA256: 06b9ab7fad5a4ff8e47c340d84fc249b76a187ad72959223bb73f41469b78ec7 SHA512: 119a74e72903697abe0839ad65cf7c6a0ae865f68f669397bec64f7c79ab9cfad6a8dc8bd0d3b2e033521a9034e11d987a9d96479e1f25b9e50e0acfcc226b98 Homepage: https://cran.r-project.org/package=AzureVM Description: CRAN Package 'AzureVM' (Virtual Machines in 'Azure') Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: . Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages. Package: r-cran-azurevmmetadata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openssl, r-cran-httr Suggests: r-cran-azureauth, r-cran-azurevm Filename: pool/dists/noble/main/r-cran-azurevmmetadata_1.0.1-1.ca2404.1_all.deb Size: 91944 MD5sum: 806b31cb1b305892627213b5c34fdba4 SHA1: a9b59c30c87b69793321cf5d5b392ccc18dc402d SHA256: 6fe2572e04fa74d1bec4a1b3cc939235b6481c393aa05b8c9bb1ffb9c97f51ee SHA512: 2f8497a3556fccba231735242358c1eb595e1e919283efd6dbe3c3a6534cbca77fc9399fd0f6401f391ad3bceeaefb4f742e1131746cd248bd130fb8c7e8d640 Homepage: https://cran.r-project.org/package=AzureVMmetadata Description: CRAN Package 'AzureVMmetadata' (Interface to Azure Virtual Machine Instance Metadata) A simple interface to the instance metadata for a virtual machine running in Microsoft's 'Azure' cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the 'AzureR' family of packages. Package: r-cran-babebi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-babebi_0.1.0-1.ca2404.1_all.deb Size: 72190 MD5sum: 3ade7d19c2d25a29eb322b386c80b0f1 SHA1: d4bb09e823d2f656e90a845c4539ebf081c7ce0f SHA256: f4e8da65dafbbebaa8948148b20b82f058cf031d606e35846c562df39f10ff34 SHA512: 7b31b2dfe62015f0b0a55dcb4cdf416852f823a29a20d130568f841b911c5cfab58b3fbba953291d3ce4b04c560d41271f43caf085feef12e211e88587e62270 Homepage: https://cran.r-project.org/package=babebi Description: CRAN Package 'babebi' (Bayesian Estimation and Validation for Small-N Designs withRater Bias) Approximate Bayesian inference and Monte Carlo validation for small-N repeated-measures designs with two time points and two raters. The package is intended for applications in which sample size is limited and the observed outcome may be affected by rater-specific bias. User-supplied data are standardised into a common long-format structure. Pre-post effects are analysed using difference scores in a linear model with a rater indicator as covariate. Posterior summaries for the regression coefficients are obtained from a large-sample normal approximation centred at the least-squares estimate with plug-in covariance under a flat improper prior. Evidence for a non-zero pre-post effect, adjusted for rater differences, is summarised using a BIC-based approximation to the Bayes factor for comparison between models with and without the pre-post effect. Monte Carlo validation uses design quantities estimated from the observed data, including sample size, mean pre-post change, and second-rater additive discrepancy, and summarises inferential performance in terms of bias, root mean squared error, credible interval coverage, posterior tail probabilities, and mean Bayes factor values. For background on the BIC approximation and Bayes factors, see Schwarz (1978) and Kass and Raftery (1995) . 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Package: r-cran-babelgene Architecture: all Version: 22.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3625 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-babelgene_22.9-1.ca2404.1_all.deb Size: 3651122 MD5sum: 5a8be0d2ebfd3b2adfa3dfef38cf4345 SHA1: 8fda00babee1bb96328e4766705942819b1e6a9e SHA256: bedada633d01fd36a3ab877f09dd197bfbc9a2f0a3cfdf806d75e0ec1dc24732 SHA512: e82fe6990a21bd0fa3c132608a4c34e2ad867d67c601bbd71a3db1c58188151836d56962993c4831302f95481fd9c79c24249ae756160a5fdbcfc6e21d8cf1dd Homepage: https://cran.r-project.org/package=babelgene Description: CRAN Package 'babelgene' (Gene Orthologs for Model Organisms in a Tidy Data Format) Genomic analysis of model organisms frequently requires the use of databases based on human data or making comparisons to patient-derived resources. 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'BEAST2' is commonly accompanied by 'BEAUti 2', 'Tracer' and 'DensiTree'. 'babette' provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from 'R'. Package: r-cran-babynames Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-babynames_1.0.1-1.ca2404.1_all.deb Size: 5533092 MD5sum: aebb3d9eea52b78b79b90eeed5a19810 SHA1: 362b48f6ca01e24b501cf4a3c7d6c5baf086c34d SHA256: 56ac68ce4fe232c2dff42f210f3f2bb72e2a91b03bbb452f95bc1651daf58512 SHA512: 449e6880162c46b72c4569e413d50862964abf115a00a14f4ed954fe8b6773583483b97c77975ed458e92f524fd77b22cc62452f23d7610d94bee27fdc522712 Homepage: https://cran.r-project.org/package=babynames Description: CRAN Package 'babynames' (US Baby Names 1880-2017) US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex. 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Contains names used for at least 5 children in a given year, covering sectors "Jewish", "Muslim", "Christian-Arab", and "Druze" from 1949-2024. Legacy 1948 data and archived "Other" sector data are provided as separate datasets. Primary data source: CBS Release 391/2025 . Package: r-cran-babytimer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-snakecase, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-babytimer_0.1.0-1.ca2404.1_all.deb Size: 31060 MD5sum: c703d0c3c98e896a4ed86d21a2d18e60 SHA1: 9497af4c22c4ddf26bab6baed8833fef8b9009f6 SHA256: 47a96318e4267c72edda9ed240e7f316de7e3731f0656146c8dd88c5b2570bd9 SHA512: 228cce1380dbbeb204f741a6ee1105a0ab40cc0118147f870deb191e7b6c4ff8530b3d9aa4575c13db40e79e9b4b766d1133b7b10b54d02ec69e4baf2a7aea3b Homepage: https://cran.r-project.org/package=babyTimeR Description: CRAN Package 'babyTimeR' (Parse Output from 'BabyTime' Application) 'BabyTime' is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time. Package: r-cran-bacco Architecture: all Version: 2.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emulator, r-cran-calibrator, r-cran-approximator Filename: pool/dists/noble/main/r-cran-bacco_2.1-0-1.ca2404.1_all.deb Size: 305998 MD5sum: d6c83e5c9c148a412ab3dbecc8835457 SHA1: b9ae85ef4135a4ef0729041a6a72df297bbcc7b3 SHA256: 73527ab5d4268dcaad24f660979ea4bdbdfcfc758eff2e8e7aefe4980a82f392 SHA512: 0be3cd7c8f14a7316949913828393d171245206f860b6748fa32a1e6c581923deaf8438056dfdf6bdc45bb7ba9e9ae49e530a4b060c75212ce6fb30cc328cc70 Homepage: https://cran.r-project.org/package=BACCO Description: CRAN Package 'BACCO' (Bayesian Analysis of Computer Code Output (BACCO)) The BACCO bundle of packages is replaced by the BACCO package, which provides a vignette that illustrates the constituent packages (emulator, approximator, calibrator) in use. Package: r-cran-bacct Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-bacct_1.0-1.ca2404.1_all.deb Size: 43652 MD5sum: c315452ca942a3db9af304b05a99c678 SHA1: 670f282acb88131a2704501dbc9fae77a8d6332c SHA256: 643fb939c3a6056a875e5b098af058112add2e054f8e2bf86f798911aeb50a97 SHA512: fd4efedfb20a38faf92313ac2ae2a1ca475396321a23278ca2de48cd92e211e87cd814f5d3c7955fc7c2241f8e850932c68ad7f67fc4467d9fef811acb54cd53 Homepage: https://cran.r-project.org/package=BACCT Description: CRAN Package 'BACCT' (Bayesian Augmented Control for Clinical Trials) Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package. Package: r-cran-bacenapi Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-bacenapi_0.3.1-1.ca2404.1_all.deb Size: 185068 MD5sum: c86e6c02d8a179885cb0fb25b2882ef4 SHA1: 0ff7e89d24ef88ccf5a78f450cdb257bb825c597 SHA256: 91a02214d889ba620e31c15c460996905d960f43a99f486b899e4f33e0f67f80 SHA512: 2cd4599aeeb61288e80082cae2e4544b6623f63cfb2b6c8c3f4682c95232b541b410829b17518907d7440cdbb9d4325fdcd32503964b775f419a7908becd9aac Homepage: https://cran.r-project.org/package=BacenAPI Description: CRAN Package 'BacenAPI' (Data Collection from the Central Bank of Brazil) Provides tools to facilitate the access and processing of data from the Central Bank of Brazil API. The package allows users to retrieve economic and financial data, transforming them into usable tabular formats for further analysis. The data is obtained from the Central Bank of Brazil API: . Package: r-cran-bacenr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bacenr_0.5.0-1.ca2404.1_all.deb Size: 638298 MD5sum: a1190b5dad6626983000410fa2edf962 SHA1: f3a676e819ac802d9b5afd14ec16637cf821f643 SHA256: 84ac4216bd10a2823c47f6044520a5b22981a70f0bafa43ba83ff47bd9e30b40 SHA512: 0e6129e7110f4a19036888f100b4377e028b3fdd9032c7c0fa71045267963b4cb84b3980c9f1584285cf76e20f82bed2921e1775fd6fba8cffdcff3716a529ad Homepage: https://cran.r-project.org/package=bacenR Description: CRAN Package 'bacenR' (Access Data from Brazilian Central Bank: IFdata, ActiveInstitutions, Balance Sheets and Normative Acts) Provides functions to query, retrieve, and tidy economic and financial data from Brazilian Central Bank web services for use in R analyses and workflows. Active institutions information, balance sheets and normative acts. 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Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multi-group clinical trials with binary outcomes. Reference: Nan Chen and J. Jack Lee (2019) . 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For more details, please refer to the paper "The Bag-and-Whisker Plot: A New Bagplot for Bivariate Data" by Qin, Gang, Tong and Cui (2025) . 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Package: r-cran-banditsci Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-mvtnorm, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-banditsci_1.0.0-1.ca2404.1_all.deb Size: 1041094 MD5sum: 394e08e4a9531ee17fbc3c7a73694b75 SHA1: b7972528df493ef39a21c9487dfa8d56bbeda335 SHA256: e9f6622808711c1d4b356f219c644a165b3cd0d4845495e2b465c21a80640d49 SHA512: 6f406b4ade7130bb49cfbf330f434dba8c1dbab2a9717840deb8cfe67b611840a3bf8fec68d8e7b04e559a421d881f6d18518e9bf7f7b1bbb731d334bb9ba862 Homepage: https://cran.r-project.org/package=banditsCI Description: CRAN Package 'banditsCI' (Bandit-Based Experiments and Policy Evaluation) Frequentist inference on adaptively generated data. The methods implemented are based on Zhan et al. (2021) and Hadad et al. (2021) . For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied. Package: r-cran-bandsfdp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bandsfdp_1.1.0-1.ca2404.1_all.deb Size: 54598 MD5sum: fa103b707e86f1ff92cf5aa10e04a36a SHA1: fedad73ab9d6e0af98d6db2053267fdb3a6c7812 SHA256: 22d4f663e25c62a095c1fb897540bbdb687ad4900948289d2230e29882923f3e SHA512: 1bed9eba3448c279be581428a05e4ccc60c510247e2a5c0d023ddebba2732b7b39cffebb1d2c0640e94c070719abd9f3bb28a6d08ccf423c607b4017985ef7fa Homepage: https://cran.r-project.org/package=bandsfdp Description: CRAN Package 'bandsfdp' (Compute Upper Prediction Bounds on the FDP in Competition-BasedSetups) Implements functions that calculate upper prediction bounds on the false discovery proportion (FDP) in the list of discoveries returned by competition-based setups, implementing Ebadi et al. (2022) . Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression (note this package typically uses the terminology of TDC). Included is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the simultaneous standardized and uniform bands. Requires pre-computed Monte Carlo statistics available at . This data can be downloaded by running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R and restarting R after installation. The size of this data is roughly 81Mb. Package: r-cran-banffit Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-crayon, r-cran-stringr, r-cran-fs, r-cran-lubridate, r-cran-tidyr, r-cran-fabr, r-cran-madshapr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-usethis Filename: pool/dists/noble/main/r-cran-banffit_2.0.0-1.ca2404.1_all.deb Size: 263974 MD5sum: d22f4c1808df91961cc52c9651db9a28 SHA1: 8dd281e737e400d3e8a688c5284c631da6e21e08 SHA256: 6901e2ea6783171e5734055d59ab2dc5db3daed6e6b0b1111df0fa96de692f73 SHA512: 2ae1e00e53c259daf5c40a6fbeca1f0b1569aba3bd2a10941b675768e936cce96f173cccf8adc629e872037d0da3908ca05d1f47a0dad977a7d0a401d866632a Homepage: https://cran.r-project.org/package=banffIT Description: CRAN Package 'banffIT' (Automated Standardized Assignment of the Banff Classification) Assigns standardized diagnoses using the Banff Classification (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell mediated rejection as well as Active, Chronic active, and Chronic antibody mediated rejection). The main function considers a minimal dataset containing biopsies information in a specific format (described by a data dictionary), verifies its content and format (based on the data dictionary), assigns diagnoses, and creates a summary report. The package is developed on the reference guide to the Banff classification of renal allograft pathology Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) . The full description of the Banff classification is available at . Package: r-cran-bang Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-rust Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bang_1.0.4-1.ca2404.1_all.deb Size: 313160 MD5sum: fe04af30bf98fb2d28d4584afddbe6ff SHA1: 2e94b2cd8989ea61375a2b6e822edc4f645d04a8 SHA256: 83077325dc6fccef19cc4aa9245ff5b6adfe1586d09c82f5820749b1c128429f SHA512: 4e1323e513074f9cda4a971eac707b75ebd1ff17d81a7e9222a90abbd87bb03af7ef5ac36d2ce5d6cb37375ab254d6e7a35570c35fab4c966a1cbe5935528911 Homepage: https://cran.r-project.org/package=bang Description: CRAN Package 'bang' (Bayesian Analysis, No Gibbs) Provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. 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Package: r-cran-bangladesh Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmap, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-viridis Filename: pool/dists/noble/main/r-cran-bangladesh_1.0.0-1.ca2404.1_all.deb Size: 5258724 MD5sum: 07ae2c49bbe7a39dc3a2f17bfd866fb4 SHA1: 90346e0a367f21fc2d1976ba048677535387d079 SHA256: 9316b5b8f30608a5d8427cdf3dd3a927975b0ed2c697e44400e99dc26ed348c6 SHA512: f30f94ec989efd4d5cbae04e5981bce67cb3a06b4de82f78766e16262d86c9a21fa648f16833f5a42192f8fdf48787fc00f7b762e824666fd736ca7d0d2f1ac5 Homepage: https://cran.r-project.org/package=bangladesh Description: CRAN Package 'bangladesh' (Provides Ready to Use Shapefiles for Geographical Map ofBangladesh) Usually, it is difficult to plot choropleth maps for Bangladesh in 'R'. 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Package: r-cran-bannercommenter Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-bannercommenter_1.0.0-1.ca2404.1_all.deb Size: 202522 MD5sum: ff17c31d610a440fc5bafa042716470a SHA1: 55af11b81bf949ef10b24df0e93a91120abcc68e SHA256: 15cafd4a827e48275024ec5be065c04d4cc81759c65a910a2b56011879ee516e SHA512: 06ffeb088f41912de88a392201261b1507d202f26bd7f443507ff578ede912beb4f114881b62cc63f782147bc83a0f97be94b815c4859a8d5a096c9d25771bca Homepage: https://cran.r-project.org/package=bannerCommenter Description: CRAN Package 'bannerCommenter' (Make Banner Comments with a Consistent Format) A convenience package for use while drafting code. It facilitates making stand-out comment lines decorated with bands of characters. 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Package: r-cran-banter Architecture: all Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1872 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-randomforest, r-cran-rfpermute, r-cran-rlang, r-cran-swfscmisc, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-banter_0.9.8-1.ca2404.1_all.deb Size: 1841716 MD5sum: f028063f2de684c5d449f11f4e06e9d3 SHA1: d41cb56cbd49fda49f4379e8cd5ea3c289242820 SHA256: 794911f84fa52cefb5199757c00417c9c642a2d55e0f44c39f478fba02f32a70 SHA512: eb370cc6d8fcd1a331edad68c3a2b238ba69a8fe294afa35e208f5ff20e78562242da217df85e1c76b4009d0aa1b4e3095ac5c7ac72442da98fc342aca9808bb Homepage: https://cran.r-project.org/package=banter Description: CRAN Package 'banter' (BioAcoustic eveNT classifiER) Create a hierarchical acoustic event species classifier out of multiple call type detectors as described in Rankin et al (2017) . Package: r-cran-banxicor Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvest, r-cran-stringr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-banxicor_0.9.0-1.ca2404.1_all.deb Size: 24702 MD5sum: 801cc0759ac4bb393a5ee0307b330784 SHA1: 1339926d44dc12053eefbcba0a33737a18d1b236 SHA256: 700e67700665698bcf96ea053d54703165c52fd78f78faf8e031aa64dcbae592 SHA512: ce9830884d161eb4af6cea7d1ad7f9508399241e66a9549b1c83d2684353a54b693d90aadddec35453d432694b7344b1c270dfc84de976890ded1c55b796d036 Homepage: https://cran.r-project.org/package=banxicoR Description: CRAN Package 'banxicoR' (Download Data from the Bank of Mexico) Provides functions to scrape IQY calls to Bank of Mexico, downloading and ordering the data conveniently. Package: r-cran-baorista Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-baorista_0.2.1-1.ca2404.1_all.deb Size: 186866 MD5sum: fb7ff09aaa99b02fc9bae0b5d6e90341 SHA1: 7101a5ebc273d5d04e443a55894ca32621007715 SHA256: f53a6eba84a17f1705d82017e0a0836bf0e4af63d6ef35e0f6faef2e11622bab SHA512: fc70624ef7f7c9b519917fb09153f8ec2d45c87959428371956e47a27bebce345df3aa6753f0388542fd64c07860b4b596374e5b50404141c371d1904c6dd3b8 Homepage: https://cran.r-project.org/package=baorista Description: CRAN Package 'baorista' (Bayesian Aoristic Analyses) Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)). 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Package: r-cran-bapred Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-lme4, r-cran-mass, r-bioc-sva, r-bioc-affyplm, r-cran-fnn, r-cran-fuzzyranktests, r-cran-mnormt, r-bioc-affy, r-bioc-biobase Suggests: r-bioc-arrayexpress Filename: pool/dists/noble/main/r-cran-bapred_1.1-1.ca2404.1_all.deb Size: 897384 MD5sum: 1a9caa4a0d3880d32556ccdcbcd1fbe3 SHA1: e44c746ef630f6de9ca631b524c0068ece078d3b SHA256: bae0a05647a0c9f6699f9b322e52fff7bd3a850dcf5ef647dccf4d655e9eeb93 SHA512: 59fb8bd243ba549b76b5023c43b8622de0016de22ce59372588cf5c5d0b997e270c6ed53ad4aac13fdc4bc71c059a91eb1ba3d2eef32af1c79a8f25faf858131 Homepage: https://cran.r-project.org/package=bapred Description: CRAN Package 'bapred' (Batch Effect Removal and Addon Normalization (in PhenotypePrediction using Gene Data)) Various tools dealing with batch effects, in particular enabling the removal of discrepancies between training and test sets in prediction scenarios. Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008) is implemented to enable integrating the quantile normalization step into prediction rules. The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide an additional function which enables a posteriori ('addon') batch effect removal in independent batches ('test data'). Here, the (already batch effect adjusted) training data is not altered. For evaluating the success of batch effect adjustment several metrics are provided. Moreover, the package implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba() and baaddon() which enable batch effect removal and addon batch effect removal, respectively, with one of the seven methods mentioned above. 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Two more methods are in this package, simple gradient method (Gradmod) and Powell method (Powell). These are not recommended for use, their purpose are purely for comparison. 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There are multiple models implemented for two bark beetle species. The models can be customized and their submodels (onset of infestation, beetle development, diapause initiation, mortality) can be combined. The following models are available in the package: PHENIPS-Clim (first-time release in this package), PHENIPS (Baier et al. 2007) , RITY (Ogris et al. 2019) , CHAPY (Ogris et al. 2020) , BSO (Jakoby et al. 2019) , Lange et al. (2008) , Jönsson et al. (2011) . The package may be expanded by models for other bark beetle species in the future. Package: r-cran-barry Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-barry_0.2.2-1.ca2404.1_all.deb Size: 97708 MD5sum: 5012b831b4d650834bd37a2c8903a7d6 SHA1: 7320dd1e212eefffa828ee0823bd83bf9058f725 SHA256: 109032c8ddd15062c80a8cc4e70022b9c9aabe5804c216f10d344b2c0e32b8f5 SHA512: ffe9f90a385657f36aa8ed55aeaf94b3abf4334f610170a9c9838c6ef69317983e132987f7f553b6f0c8a79cc298f04382b51d182bf6df7d0157321ce7c5f383 Homepage: https://cran.r-project.org/package=barry Description: CRAN Package 'barry' (Your Go-to Motif Accountant) Provides the 'C++' header-only library 'barry' for use in R packages. 'barry' is a 'C++' template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. 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Package: r-cran-bartmachine Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bartmachinejars, r-cran-rjava, r-cran-randomforest, r-cran-missforest, r-cran-ggplot2, r-cran-checkmate, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bartmachine_1.4.2-1.ca2404.1_all.deb Size: 1366312 MD5sum: b798a78b73a9b5aafe035ec9a987efab SHA1: f042ce9ed687c279fe1e45b39528d5b7b37e65ee SHA256: 9e880afaa1240be4092ed5bea3cff92f81d5db38e310a52129093a201bc2409f SHA512: d10efcae1500365ac017edfa81c86760ee7c90c3e005d76d06b028a4985f616be47a28bd525f5d0e550c73132cc0a9af390b0ed7c08e3f6db7b39cd0c9418df1 Homepage: https://cran.r-project.org/package=bartMachine Description: CRAN Package 'bartMachine' (Bayesian Additive Regression Trees) An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization. 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A., George, E. I., & McCulloch, R. E. 2010) model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 , 'dbarts' (Vincent Dorie 2023) , and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) . Package: r-cran-barulho Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7842 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-warbler, r-cran-ohun, r-cran-seewave, r-cran-tuner, r-cran-fftw, r-cran-viridis, r-cran-sim.diffproc, r-cran-png, r-cran-checkmate, r-cran-cli, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr, r-cran-kableextra, r-cran-testthat, r-cran-covr, r-cran-formatr, r-cran-rraven, r-cran-monitor Filename: pool/dists/noble/main/r-cran-barulho_2.2.0-1.ca2404.1_all.deb Size: 4018770 MD5sum: 70c0d2121ac0675dd7d41fab2a66f140 SHA1: 1cd0297ec966d0ad85cbf65212b69158b5a8df42 SHA256: cba7043a2feb0b5b8b53a13cdbff0226cd8f24b3fb26c25eafa7f39b0f133657 SHA512: 072278e7bdd9099e9b8dbfa06f6f4bdcf2986223f12263cf6c163fdc674fefddd37be25c3e191f56e8e02fccb1c4820fb5a88d2b96d9d3be1c30aa197b8f95b9 Homepage: https://cran.r-project.org/package=baRulho Description: CRAN Package 'baRulho' (Quantifying (Animal) Sound Degradation) Intended to facilitate acoustic analysis of (animal) sound propagation experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 ). 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Likewise, we can use ols(), lrm() and cph() from the 'rms' package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. 'base.rms' package can help you to switch between the two sets of commands easily. 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Manipulating imputed datasets and fitting models on them. Summarizing models. 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This package provides nowcasting methods based on using empirical delay distributions and uncertainty from past performance. It is also designed to be used as a baseline method for developers of new nowcasting methods. For more details on the performance of the method(s) in this package applied to case studies of COVID-19 and norovirus, see our recent paper at . The package supports standard data frame inputs with reference date, report date, and count columns, as well as the direct use of reporting triangles, and is compatible with 'epinowcast' objects. Alongside an opinionated default workflow, it has a low-level pipe-friendly modular interface, allowing context-specific workflows. It can accommodate a wide spectrum of reporting schedules, including mixed patterns of reference and reporting (daily-weekly, weekly-daily). It also supports sharing delay distributions and uncertainty estimates between strata, as well as custom uncertainty models and delay estimation methods. Package: r-cran-baselinr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-baselinr_0.6.0-1.ca2404.1_all.deb Size: 175282 MD5sum: cb3d0a8efa0feb1ce3e255360e278266 SHA1: 71b3c14cb7285f89d15797a5cd3dfddace85110b SHA256: 75aa5c7e512269ff8868ac1e9f396426f226e417b0f19dcb7c8fd1902dd25385 SHA512: 2cb95c87c898f5c10536402250200448a8cb32597b529a7d52af171be0c9e56224832ddafa127cd472376e393c983cbb243480ccafcf0d11310357ab456884e7 Homepage: https://cran.r-project.org/package=baselinr Description: CRAN Package 'baselinr' (What Works Clearinghouse Standards for Education ImpactEvaluations) Applies the group-design determinations of the What Works Clearinghouse (WWC) to education impact studies. Computes WWC effect sizes (Hedges' g with the small-sample correction, and the Cox index) and classifies baseline equivalence; classifies overall and differential attrition against the WWC attrition boundary; returns the group-design study rating; and reports the robustness of the baseline-equivalence verdict as a multiverse over the computation choices an analyst could defensibly make differently. Provides report-ready tables and Love plots. 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Seeds germinate by following accumulation of thermal time in degree days/hours, quantified by multiplying the time of germination with excess of base temperature required by each seed for its germination, which follows log-normal distribution. The theoretical germination course can be obtained by regressing the rate of germination at various fractions against temperature (Garcia et al., 1982), where the fraction-wise regression lines intersect the temperature axis at base temperature and the methodology of determining optimum base temperature has been described by Ellis et al. (1987). This package helps to find the base temperature of seed germination using algorithms of Garcia et al. (1982) and Ellis et al. (1982) . 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These plotting functions have a variety of options, such as figure sizes, legends, parameters to plot, and saving plots to file. Functions interface with the NIMBLE software package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017) . 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The package provides common chart types and analyses with built-in validation, customization options, and publication-friendly themes. 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Create/manipulate tables row-by-row, column-by-column or cell-by-cell. Use common formatting/styling to output rich tables as 'HTML', 'HTML widgets' or to 'Excel'. Package: r-cran-basifor Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6470 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-foreign, r-cran-hmisc, r-cran-httr, r-cran-measurements, r-cran-rodbc, r-cran-rvest, r-cran-sf Suggests: r-cran-odbc, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-basifor_0.8.0-1.ca2404.1_all.deb Size: 6127514 MD5sum: 7754fba6915573574349e320eacfa320 SHA1: 1af15005546365cba9a3ff31dd0f50f8e44d4673 SHA256: b819cc207c5478d7b7a0443b74f44e1973a027e8e874b5b15f721a3cf005cf3c SHA512: 49e5255efa0334bfc992090e4ef61108b1c2420a31fea6371a19fc8488f919356be25221243a5064bff6abc7266a7a12b4f7e3f0976280d05a4f72af775f4c90 Homepage: https://cran.r-project.org/package=basifoR Description: CRAN Package 'basifoR' (Retrieval and Processing of the Spanish National ForestInventory) Fetches, harmonizes, and analyses data from the Spanish National Forest Inventory for reproducible, design-aware forest inventory workflows. Computes tree- and stand-level metrics, applies sampling-based expansion factors, estimates volume, and supports extensible processing for external inventory designs with custom sampling schemes and volume equations. Spatial extensions can attach plot geometries, preserve geometry sidecars through metric workflows, and return georeferenced sf outputs for mapping and remote-sensing integration. Package: r-cran-basil Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-basil_0.1.0-1.ca2404.1_all.deb Size: 27600 MD5sum: e01979a0334f8be69bc3432010c6e875 SHA1: 49d053f82f7ce98a13b61d8fd8073c131509eefc SHA256: 6fc84b81ad19640c464b607e60504d53c6ffb2698e82d92a4b8936c9d995bb40 SHA512: abce8e958216711dfb5ead5a69e22b52a06034b7e9b12e3515b7b4bc7285493d0b8ebf907c2221695d106e367bb6b3c0b1483dee4ef736b821f13e28579a44c6 Homepage: https://cran.r-project.org/package=basil Description: CRAN Package 'basil' (Survival Prediction for Severe Limb Ischaemia (BASIL Model)) Predicts survival for patients with severe limb ischaemia using the prognostic model developed from the Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial (Bradbury and others (2010) ). The model is an accelerated failure time Weibull regression. The package is intended for research and audit; it is not a substitute for clinical judgement and its predictions should not be used as the sole basis for clinical decisions. Package: r-cran-basinet Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-biostrings, r-cran-rweka, r-cran-randomforest, r-cran-rmcfs, r-cran-rjava Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-basinet_0.0.5-1.ca2404.1_all.deb Size: 263932 MD5sum: 22391006592f937ec2c7bf69f5d1d031 SHA1: 6d8f93b5cc9e8e40430c4f7de0feb98904d41cfd SHA256: dbd9303923fee282292585f2d74088c03e2ae2f66340620aa9f2fad525a9e855 SHA512: 2ebae75b291e9fd1db0c64942e0851a10b9633a343b95af21f9efafcf0e630cf1d88e01e8f0548fbc60a7b68041ef74f93fc1099e2d2ced7f340ad5f87373cc2 Homepage: https://cran.r-project.org/package=BASiNET Description: CRAN Package 'BASiNET' (Classification of RNA Sequences using Complex Network Theory) It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) , these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) . Package: r-cran-basinetentropy Architecture: all Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-biostrings, r-cran-randomforest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-basinetentropy_0.99.6-1.ca2404.1_all.deb Size: 152836 MD5sum: d4d6f890802fbb590b50cb5d2aee54bc SHA1: 6c06d52a90de40517825f87c2394ade24d461f99 SHA256: c53a9eacd207be061cd81191b9207f8b3044e1644806064d7c01eb69232b13a0 SHA512: 65f5743296f7eeaa80c8b77638e1da59eae6fecb91b2b6054f92dfd2680444d216f5f376c5b3ea5727439b1bc09a5126fcafeb4e98f248b2dfb237dbf32f8abd Homepage: https://cran.r-project.org/package=BASiNETEntropy Description: CRAN Package 'BASiNETEntropy' (Classification of RNA Sequences using Complex Network andInformation Theory) It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) , these sequences are used to run examples. Package: r-cran-baskepro Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-baskepro_1.1.1-1.ca2404.1_all.deb Size: 22016 MD5sum: bbe1168d3c01474b3f1913ae90a2b09a SHA1: cbb0a0d63c00e8d411054ebde0e22b14fc94fe5c SHA256: bb096ae19afe7d1b6f43549f2681fdb483f058ab11c0c27bc8b07ffa69cc5b85 SHA512: 06c3e6b38858bffccb77b9064a4097d1f09f8fcb9740d89c73b4149fb2182b653d7884178252f0eabe29b589ec55c6e64114a299dc232395ece2942a9c03c834 Homepage: https://cran.r-project.org/package=BaSkePro Description: CRAN Package 'BaSkePro' (Bayesian Model to Archaeological Faunal Skeletal Profiles) Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).. Package: r-cran-basket Architecture: all Version: 0.10.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gensa, r-cran-foreach, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-igraph, r-cran-gridextra, r-cran-itertools, r-cran-crayon, r-cran-cli, r-cran-rcolorbrewer, r-cran-tidygraph, r-cran-ggraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-basket_0.10.11-1.ca2404.1_all.deb Size: 577698 MD5sum: 90b93301453b51ef7b2c3006f8972a0a SHA1: eda731441df47c4776ed1c511aa7a4694f0c144f SHA256: af3eb4404ce361a9ce3611b716d3fec145b8f9b82778fe0ce8f613f73218caf6 SHA512: 08b52dcf9555539d32a17244331d856b19c58d7f63df8f070a972c04c53a34a1bfc3b0ef2457084284ddd0d0caf6b1ba8ce9f2bb1b7c5980fb7c58aa09280cd6 Homepage: https://cran.r-project.org/package=basket Description: CRAN Package 'basket' (Basket Trial Analysis) Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) ; Hobbs, B.P. and Landin, R. (2018) ; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) ; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) . Package: r-cran-basketballanalyzer Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-directlabels, r-cran-corrplot, r-cran-pbsmapping, r-cran-sp, r-cran-operators, r-cran-stringr, r-cran-ggally, r-cran-statnet.common, r-cran-readr, r-cran-gtools, r-cran-data.table, r-cran-mathjaxr Suggests: r-cran-dendextend, r-cran-ggnetwork, r-cran-ggplotify, r-cran-network Filename: pool/dists/noble/main/r-cran-basketballanalyzer_0.8.1-1.ca2404.1_all.deb Size: 2797292 MD5sum: acc0c7bf75502b4f6f4baab4fbf42a38 SHA1: 32f6a44f2c037ae5887d7b4f2c629fc7441947f8 SHA256: 342b19c1c63867e114123adf613e69b3c2f5c6ed26e05ecc6f393061cab3e8c7 SHA512: 2d8c6f377f5557c5f7fc4a474fc0fa9ebf132dc9e9bac8779761e6660d8c4512be2ff3583d27d53b22c9ff9eb31558e8f2051b197528c2e861ad718667e79da0 Homepage: https://cran.r-project.org/package=BasketballAnalyzeR Description: CRAN Package 'BasketballAnalyzeR' (Analysis and Visualization of Basketball Data) Contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799. Package: r-cran-baskettrial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-baskettrial_0.1.0-1.ca2404.1_all.deb Size: 46618 MD5sum: 1f42d5720a71371a5b3c6bd655d10c1a SHA1: c3117829825ac0262a40bd470c6eb5d853ec6600 SHA256: be4a34ce58a886ff190ded9fb4d3537d9ffd717a1f86a512f410b3e1daaf972f SHA512: d50d68f7ca1723c730819129490baa820f0c69c265101d3c34413677dd2fdba89c2182c2efc2639d673967017ff57270b18791176eb8b85836aeb54f33776bfb Homepage: https://cran.r-project.org/package=BasketTrial Description: CRAN Package 'BasketTrial' (Bayesian Basket Trial Design and Analysis) Provides tools for Bayesian basket trial design and analysis using a novel three-component local power prior framework with global borrowing control, pairwise similarity assessment and a borrowing threshold. Supports simulation-based evaluation of operating characteristics and comparison with other methods. Applicable to both equal and unequal sample size settings in early-phase oncology trials. For more details see Zhou et al. (2023) . Package: r-cran-baskoptr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-baskwrap, r-cran-future.apply Suggests: r-cran-optimizr, r-cran-baskexact, r-cran-testthat, r-cran-progressr, r-cran-basksim Filename: pool/dists/noble/main/r-cran-baskoptr_1.0.4-1.ca2404.1_all.deb Size: 179134 MD5sum: 808ecbac8cb4280dd7344dfedf879dd7 SHA1: 3036a821bf2777e4f6f318d32336331d4f2c6f86 SHA256: 308cfe08869e503146409b79d9e58f1e42c5a54674ad3954b2c0fcd3a84acc34 SHA512: 9e56c338d73bc1acdc999a4c089c3baa5d9fdf0942392736d046871a00a4fc7541fbbf5ffc26bd430216afc8fa9dcf52f9b7ead003f543f216b8e7c47001c6f9 Homepage: https://cran.r-project.org/package=baskoptr Description: CRAN Package 'baskoptr' (Utility-Based Optimization for Basket Trial Designs) A unified framework for optimizing basket trial designs. To this end, the package supplies several utility functions and also a function for executing optimization algorithms on basket trial designs. The considered utility functions are discussed in Sauer et al. (2025) . Package: r-cran-basksim Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrangements, r-cran-bhmbasket, r-cran-dofuture, r-cran-extradistr, r-cran-foreach, r-cran-hdinterval, r-cran-progressr, r-cran-purrr Suggests: r-cran-covr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-basksim_2.2.0-1.ca2404.1_all.deb Size: 281016 MD5sum: 332ff039fe6f7f4726fc6cbf3b3485db SHA1: 53350a2fa9472e4fd7b110c9676c4e5c416a4dc2 SHA256: 319eb9b9ce0b3b8a92f673a6b9af342976f2e023f3343119a155006a1267d91c SHA512: 71c66344f5dbfd43ccbaac19d50d4e4259618b35d016c08a6b17c3207bf0b256d6bf4ce777c44d287127273f4de8526116cf21697c25641f4f58caa6f0dda913 Homepage: https://cran.r-project.org/package=basksim Description: CRAN Package 'basksim' (Simulation-Based Calculation of Basket Trial OperatingCharacteristics) Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) , Schmitt and Baumann (2025) , Fujikawa et al. (2020) , Berry et al. (2020) , and Neuenschwander et al. (2016) . For the latter two designs, the functions are mostly wrappers for functions provided by the package 'bhmbasket'. Package: r-cran-baskwrap Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-basksim, r-cran-baskexact Suggests: r-cran-testthat, r-cran-here, r-cran-reticulate, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-baskwrap_1.0.3-1.ca2404.1_all.deb Size: 241670 MD5sum: 72e9b6f55f31c20a37de1bc80a17ec2a SHA1: 6436bcf165216ad7e73c87926dd9e56908daf888 SHA256: d9f058b4d916a821aa008df3755dbc2b179e8be0a237bb1a23cacd1fbde942b2 SHA512: 15efa511d223596b00e5ba67698c38619533dbce76a73b58acfe48f2f1ad519fb526d2814b10e97de59da77d81bf0dfd2dba90654e1fd184d84681aad075b536 Homepage: https://cran.r-project.org/package=baskwrap Description: CRAN Package 'baskwrap' (Wrapper Package for Several Basket Trial R Packages) A simple interface to switch between two methods for calculating basket trial characteristics, numerical integration ("exact") and Monte Carlo simulation ("simulated") for the basket trial design by Fujikawa et al. 2020 . The exact implementation is from the 'baskexact' package, see Baumann (2024) . The simulated implementation is from the 'basksim' package, which was developed for Baumann et al. (2024) . The package's syntax is compatible with the 'basksim' syntax and easily extendable. 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Package: r-cran-bawir Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-plyr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-reshape2, r-cran-rvest, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-anthropometry, r-cran-cowplot, r-cran-ggimage, r-cran-ggrepel, r-cran-ggtext, r-cran-ggupset, r-cran-knitr, r-cran-markdown, r-cran-packagerank, r-cran-png, r-cran-qdapregex, r-cran-rcurl, r-cran-readr, r-cran-rmarkdown, r-cran-rworldmap, r-cran-scales Filename: pool/dists/noble/main/r-cran-bawir_1.5.5-1.ca2404.1_all.deb Size: 883542 MD5sum: 1e5fb6c0d77d2e2acaaf374b597d546e SHA1: f1983434ca744e2c5bd36e7bc9601fc80514e1eb SHA256: dd9469832a5bf9b83a23c823f81e486da6489b8c41b852a9f38425b30cfef94e SHA512: f3d9ccfe9f94822dfea42492aba2987e101b7df7921b77e9bc59b5e95e35adcf81aabbab702da510789d8878cdac40608d5aaf40b412a6a17dbeddb4b2bee415 Homepage: https://cran.r-project.org/package=BAwiR Description: CRAN Package 'BAwiR' (Analysis of Basketball Data) Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from , and , following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls, offensive rebounds and different types of shooting charts. Please see Vinue (2020) , Vinue (2024) and Vinue (2026) . 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Package: r-cran-bayesbrainmap Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-fmritools, r-cran-foreach, r-cran-matrix, r-cran-matrixstats, r-cran-pesel, r-cran-squarem Suggests: r-cran-ciftitools, r-cran-excursions, r-cran-rnifti, r-cran-oro.nifti, r-cran-gifti, r-cran-ggplot2, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesbrainmap_0.3.1-1.ca2404.1_all.deb Size: 494014 MD5sum: a239678ee947a5c1070060e8ff5f73c8 SHA1: 87d7d5080b6807f5b824a9ba79a8d2903f24865d SHA256: 5a21f8115b68beebe2029ab92b54370ad6b8bb0a48c86c4aab95ba7ec313ec0f SHA512: 3b2e818074d1245520e3dda486ccfd5284476d932948735716988d68c2da671fce94a891bf9f796fd3738c2691c2cba868b5f8920b733deaf83466b1f5173a12 Homepage: https://cran.r-project.org/package=BayesBrainMap Description: CRAN Package 'BayesBrainMap' (Estimate Brain Networks and Connectivity with Population-DerivedPriors) Implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) , the model with spatial priors described in Mejia et al. (2022) , and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) . Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods.  Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Package: r-cran-bayescace Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-rdpack, r-cran-forestplot, r-cran-metafor, r-cran-lme4 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-bayescace_1.2.3-1.ca2404.1_all.deb Size: 1817984 MD5sum: c0f2cdfd31f62b7d11d494a01a4ac0da SHA1: 3b2889a6cd79d73ca21f9a7f9dd89264b48b1245 SHA256: 9d052d5fba6693ebf0723908aa8a5e7cfad82667dc9cabb35035f2fd08a32dcd SHA512: d79c0cabed630ec0002bec0380c15a1ea165f76b80237688022bab3a849d31a1ca721be34c48005d406954f5041ffb6c81ccf6e7058166bed70ad58d22737637 Homepage: https://cran.r-project.org/package=BayesCACE Description: CRAN Package 'BayesCACE' (Bayesian Model for CACE Analysis) Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) , which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) , with an application example on Epidural Analgesia. Package: r-cran-bayescombo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-labstats, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayescombo_1.0-1.ca2404.1_all.deb Size: 189032 MD5sum: f4c7bcbd9963e652f926ba1d6114669e SHA1: 83fdb137a59cfa4092c9034fb1b5b9a60455973a SHA256: 74c1ca47b6ce2768d42bde7920c879d07f6a907594ea4ff2302d694b7480c2d7 SHA512: 90910e1f9ec10723d49ecc9a74f64d768a9dba6ccceb036728fb76e6baefc568141351e0dc3cad737a46e303e1f0cf6ed954505fd46ee8343005a4a54c0e390d Homepage: https://cran.r-project.org/package=BayesCombo Description: CRAN Package 'BayesCombo' (Bayesian Evidence Combination) Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis. Package: r-cran-bayescpclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-rcppalgos Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayescpclust_0.1.0-1.ca2404.1_all.deb Size: 97466 MD5sum: 2ad5e41dcb1136fce854d1f9de174ee9 SHA1: e5e06ea498039af764af74ddf38d8586ec94fe41 SHA256: 86ce5e6c62eca976b1fccfda7508d781e979a8c4683b5f77859a934403069d79 SHA512: 118ad7cccdd3559598ff5c7951a1220d09ae53f18fa6b2d77561e3333410877d15b4a45eddc18cfd93be3d3d20c415825c8fab0d598cfaf32a87998ff72d25c8 Homepage: https://cran.r-project.org/package=BayesCPclust Description: CRAN Package 'BayesCPclust' (A Bayesian Approach for Clustering Constant-Wise Change-PointData) A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" . Package: r-cran-bayescr Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve, r-cran-truncdist, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-bayescr_2.1-1.ca2404.1_all.deb Size: 97604 MD5sum: cf0b0580fdd48bb8cc9d10beff86c122 SHA1: 197df41bb97f9dd81adfdb4264b318586916be4d SHA256: 9a8d05f86cb655b0dccb1ecb1095b5c5cf5034c56fdf23a159d43612be8bcfd0 SHA512: 503c56331b72c80c3c32ae93713c7a6f60a4dbe558db0bf975b8b21e1d2f8da8800c0fa1373dc54cf8f8a35bd04037e66235a9173c3e1ab909b32f4edc2d38f5 Homepage: https://cran.r-project.org/package=BayesCR Description: CRAN Package 'BayesCR' (Bayesian Analysis of Censored Regression Models Under ScaleMixture of Skew Normal Distributions) Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables. Package: r-cran-bayesct Architecture: all Version: 0.99.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesdp, r-cran-dplyr, r-cran-purrr, r-cran-survival, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-devtools, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bayesct_0.99.3-1.ca2404.1_all.deb Size: 270108 MD5sum: 6710ddc9341bc826bfaa60bc0c792b56 SHA1: 301b3e45402cc0a8918d419e7e622f76cde1d427 SHA256: 31d234691cc668555666dade76bb819e6a3fc805d8c566a61c7c804c326a61c6 SHA512: 8545e70d9c67c021c0991d9b721c8b021c87be7debda59a0c289b9e4fe77fc1c1538760a403a7fa7252061921f573e1bbeb49bc84442277da0bd465466641b33 Homepage: https://cran.r-project.org/package=bayesCT Description: CRAN Package 'bayesCT' (Simulation and Analysis of Adaptive Bayesian Clinical Trials) Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors. Package: r-cran-bayesctdesign Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eha, r-cran-ggplot2, r-cran-survival, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-bayesctdesign_0.6.1-1.ca2404.1_all.deb Size: 313450 MD5sum: 7636340da2ca071bb4d018d4e682d15c SHA1: 13c9007b075a0116bad2a310a25bd2615f19536d SHA256: e12e7c145da6fda95475acb35f094fa6c0219f11c49c36fbbe75dedc1c6617c3 SHA512: 18f0c692c8310a78e7e627e88771329dcc0f7475e8d233a248cff35756729f4509a6eddbd8b59d061e6886cc5817de214d1e15b9cd310793c698906f4013f2da Homepage: https://cran.r-project.org/package=BayesCTDesign Description: CRAN Package 'BayesCTDesign' (Two Arm Bayesian Clinical Trial Design with and WithoutHistorical Control Data) A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) . Package: r-cran-bayescvi Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mclust, r-cran-ggplot2, r-cran-universalcvi Filename: pool/dists/noble/main/r-cran-bayescvi_1.0.2-1.ca2404.1_all.deb Size: 458992 MD5sum: 5f7e84c32acbf9a1fe51f3680ca91c55 SHA1: 0277206412db9ade86a7cb18d7043c77f1fe93d4 SHA256: d91f9867aee118401e6ff2fac62462e6b2932f2f1fb83453e1e6eca0611b0852 SHA512: 8b29bf3312105b8ed8375a3286752bf8ae0cf1a711ac77fc453b88d8b094c6055fb949eba400553d1a9b0c7202375dac5411aeb455fabc9deb0d8b0dee4b3be7 Homepage: https://cran.r-project.org/package=BayesCVI Description: CRAN Package 'BayesCVI' (Bayesian Cluster Validity Index) Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. ) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI. Package: r-cran-bayesda Architecture: all Version: 2012.04-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bayesda_2012.04-1-1.ca2404.1_all.deb Size: 69366 MD5sum: e9ba82a52bb319621b2f6240700fab1e SHA1: 3829b891eb663c884235b531d38d2a56f5a9b013 SHA256: 3ef4c6040f7e5666097ad44b58389bc2aaeaf21e718c18cdaae730d486bf628f SHA512: c65fb05669ac3e2ebafa2d94114f92792770ad9fa8f9ca1f07678947cef53f9c6216d1b40b6d4793c375cd3abab7ec23b6038c2a91ef5b77ec8573e74917d7c5 Homepage: https://cran.r-project.org/package=BayesDA Description: CRAN Package 'BayesDA' (Functions and Datasets for the book "Bayesian Data Analysis") Functions for Bayesian Data Analysis, with datasets from the book "Bayesian data Analysis (second edition)" by Gelman, Carlin, Stern and Rubin. Not all datasets yet, hopefully completed soon. Package: r-cran-bayesdesign Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayesdesign_0.1.1-1.ca2404.1_all.deb Size: 35424 MD5sum: 2abf12bd3c0c060037d0e8741c13cc8e SHA1: 6aa81748ffc96978c59d2bb1e7e5867a877582d9 SHA256: e4b467e639b887dbaa8910c39c8aba06c202b3a9733490ffeb89e65eade6ca22 SHA512: ba13b8aa5835a8e5920cee29b4446893c55e9fc33ff06659d093a945419ba1b20be755e19052daa0f581d72d8fc272c93d6efd4459cb56448c03d7b5b7efaa2d Homepage: https://cran.r-project.org/package=BayesDesign Description: CRAN Package 'BayesDesign' (Bayesian Single-Arm Design with Survival Endpoints) The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint. Package: r-cran-bayesdiagnostics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-checkmate, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-posterior, r-cran-matrixstats, r-cran-bridgesampling, r-cran-rstan, r-cran-tidyr Suggests: r-cran-bayesplot, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-bayesdiagnostics_0.1.0-1.ca2404.1_all.deb Size: 255986 MD5sum: bdda6b697f0eb4efd24449260bbde5e5 SHA1: aa76655514f79e4d8fb54ceb1b9cb60ba02d48af SHA256: 989c71b6bcc9df17479ed5facd30d3197ca668ce9aca639140182c1d8fd43eef SHA512: 3f5f69c0887de70dc24dfb2a8c82175e8aed23e292b8d6bd567476da23854a532b1f991bd2de4a3d0b1f72fad8e08775464c4f69f2bf1cff51077193e2ca8bf0 Homepage: https://cran.r-project.org/package=bayesDiagnostics Description: CRAN Package 'bayesDiagnostics' (Comprehensive Bayesian Model Diagnostics and Comparison Tools) Provides comprehensive tools for Bayesian model diagnostics and comparison. Includes prior sensitivity analysis, posterior predictive checks (Gelman et al. (2013) ), advanced model comparison using Pareto-smoothed importance sampling leave-one-out cross-validation (Vehtari et al. (2017) ), convergence diagnostics, and prior elicitation tools. Integrates with 'brms' (Burkner (2017) ), 'rstan', and 'rstanarm' packages for comprehensive Bayesian workflow diagnostics. Package: r-cran-bayesdip Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayesdip_0.1.1-1.ca2404.1_all.deb Size: 124994 MD5sum: dcc7d7d95ba77f0f22f1474255d520ef SHA1: 734e176913d49cd3fa9d8568e35fe16d292b358b SHA256: 5eec5a121f228d07d39496631cf5ce9859f9ff9cbb760e5b2e098d28f29862ef SHA512: b62a58bcc8f7618e01c62128d1791f3b4031487e2103d7f0532d363c3b6d22a755501b0370ddcbda16bc7a464bbf08cb44d54939a376bf8bfa9c5e17d500d0c9 Homepage: https://cran.r-project.org/package=BayesDIP Description: CRAN Package 'BayesDIP' (Bayesian Decreasingly Informative Priors for Early TerminationPhase II Trials) Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials. See Wang C and Sabo RT (2022) ; Sabo RT (2014) . Package: r-cran-bayesdissolution Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-geor, r-cran-mcmcpack, r-cran-mnormt, r-cran-pscl, r-cran-shiny Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-bayesdissolution_0.2.1-1.ca2404.1_all.deb Size: 107524 MD5sum: 463abcfb9bc3c193723fd30175be66a9 SHA1: 12e53868865955654e9d010009d71087d038fb43 SHA256: 7138c3f669d45c40e4bb8d90ddb59c09fdf942eee4125a22312147b6ea5b293c SHA512: 08c83c4ddc13d120aee436d31429f9a7ae18b35dc935a53ae4bfe88ef3fc64968be8e2d87c00e3f5083bcf25f5e87f7465adcde84637ed381a38ae1d99d0fa70 Homepage: https://cran.r-project.org/package=BayesDissolution Description: CRAN Package 'BayesDissolution' (Bayesian Models for Dissolution Testing) Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) . However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time. Package: r-cran-bayesdistreg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-bayesdistreg_0.1.0-1.ca2404.1_all.deb Size: 86750 MD5sum: 1239e7570cb892de4525b878359c8d0d SHA1: 0d6c624e88a3dab16c902c3c36549c3a89f7f66f SHA256: 17b10901a79a99576681eec9b5c5733930979355d71ca2fce5b4b3db54e3b45f SHA512: 7a7e115d4b98e0b13f18021968a27d1a232e60468f3eb315aac2c0299adf5e0c3534dd383630c781de81cf2170a0426380dc238de22de00384ae6254e298c995 Homepage: https://cran.r-project.org/package=bayesdistreg Description: CRAN Package 'bayesdistreg' (Bayesian Distribution Regression) Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference. Package: r-cran-bayesertools Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1559 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-gt, r-cran-cli, r-cran-rlang, r-cran-rstanarm, r-cran-rstanemax, r-cran-rstantools, r-cran-loo, r-cran-ggdist, r-cran-posterior Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-htmltools, r-cran-digest, r-cran-ggforce, r-cran-xgxr, r-cran-scales, r-cran-readr, r-cran-bayestestr, r-cran-patchwork, r-cran-projpred, r-cran-rsample, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-bayesertools_0.2.7-1.ca2404.1_all.deb Size: 1242704 MD5sum: 175137aea6b9aa52907eb89633a15936 SHA1: a98c9a0f9f81fb770619b6ea105891d0c1ed0606 SHA256: cd4131bf6122f67458dea63d515c74b3dd4eeb5c5440c385fff90274f399d015 SHA512: d9da28d36549ffcea06610f91ce45cee02e310d4aa6afef41158a7138b1bb9871b04a845d0893cced5f872386a245b26c510c72b53f8f7ddf5cd501c04bc7670 Homepage: https://cran.r-project.org/package=BayesERtools Description: CRAN Package 'BayesERtools' (Bayesian Exposure-Response Analysis Tools) Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at . Package: r-cran-bayesestdft Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-bayesestdft_1.0.0-1.ca2404.1_all.deb Size: 877454 MD5sum: bfb1c840eee716bf75a2a684959822f7 SHA1: 1f83c28fa7b9c5a14f4110f62a4959c5bb170bd8 SHA256: 716fa117f82a3d4aaaf56c03263e6d411268184069e07b4d2ecd5d07f7ea410a SHA512: 6d3cd311ebe0c7ace144d85f0b5456b98d480e5cf83771e157accecb6d0f96a10a7ed1466d7f9f2c0190bd4611e13b1f03fc216e67732264e8fb0fef7f56c5a2 Homepage: https://cran.r-project.org/package=bayesestdft Description: CRAN Package 'bayesestdft' (Estimating the Degrees of Freedom of the Student'st-Distribution under a Bayesian Framework) A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior. Package: r-cran-bayesfbhborrow Architecture: all Version: 2.0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-invgamma, r-cran-mvtnorm, r-cran-checkmate, r-cran-magrittr, r-cran-ggplot2, r-cran-patchwork, r-cran-kableextra, r-cran-survival, r-cran-survminer, r-cran-extradistr, r-cran-bayestestr Suggests: r-cran-tibble, r-cran-readxl, r-cran-testthat, r-cran-rmarkdown, r-cran-ggfortify, r-cran-condsurv Filename: pool/dists/noble/main/r-cran-bayesfbhborrow_2.0.14-1.ca2404.1_all.deb Size: 861722 MD5sum: 97a712518cd39576ec318648da43759f SHA1: 1a074cf168bc539d30bd3cf6a2b76f90a218b406 SHA256: fb7a0b039e02dd92a750dfbd70ac7bd27a020a680f584916c4ccae665dd48775 SHA512: 45971f85fd09dd58a25942fde57cb80251d385d7de932a5c8a60ecf41ec179e4d91a8a4b8499f471b3e19fe21eb141ecad82ed6a43be2ae94b67cd3327829f8b Homepage: https://cran.r-project.org/package=BayesFBHborrow Description: CRAN Package 'BayesFBHborrow' (Bayesian Dynamic Borrowing with Flexible Baseline HazardFunction) Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2026) , and a paper focused on the software is in Scott, Axillus, Lewin and Izmirlian (2026) . Package: r-cran-bayesfluxr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesfluxr_0.1.3-1.ca2404.1_all.deb Size: 1067136 MD5sum: 1fae3e1037e47e7577d6e5bf855dd58c SHA1: 187be390025df8f8ff1488db0f3c5ef6af14a3b6 SHA256: 89688136c760f2fd370fddb053a4550e20e3e2fa5a34a9d435669634ab2c4453 SHA512: a137590af0b4aaa18902b425a4a4782506383f90802834dfa1b61619477f81889d6d02ef097cc661e81deb5eaeceaf0dc1f7c76213b445995a504da6c630b8eb Homepage: https://cran.r-project.org/package=BayesFluxR Description: CRAN Package 'BayesFluxR' (Implementation of Bayesian Neural Networks) Implementation of 'BayesFlux.jl' for R; It extends the famous 'Flux.jl' machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks. Package: r-cran-bayesforge Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate, r-cran-abind Suggests: r-cran-testthat, r-cran-brms, r-cran-rstan Filename: pool/dists/noble/main/r-cran-bayesforge_0.0.1-1.ca2404.1_all.deb Size: 425904 MD5sum: adc03e0c409596f1b03b31dfe26fc0d4 SHA1: 208871e410088ca6422c4f8780048ddc2a5038c9 SHA256: 50d27c9d4ac54f5e9c410c1684e679651403a92eb655fd6ffd9cb81ca34efb68 SHA512: 168fe1397615ccaf303b2bf1ec5301ca9c2ca7f9804e8d0ce35a1d8c70205da16d6cf459f0c40665f367906bea56b91958fb0f43f9296c2bba1d4464604eb15e Homepage: https://cran.r-project.org/package=BayesForge Description: CRAN Package 'BayesForge' (Bayesian Inference using 'numpyro' and 'XLA') A high-performance probabilistic programming library that aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU (Central Processing Unit), GPU (Graphics Processing Unit), or TPU (Tensor Processing Unit) using 'JAX' (Just-In-Time compiled Accelerated linear algebra) as the computational backend: Sosa (2026) . Package: r-cran-bayesfr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesfr_1.0.1-1.ca2404.1_all.deb Size: 2783206 MD5sum: f799219d236f80eba630349fee4a8232 SHA1: 3f151de70bd377ab3c25cfd62839884947596d6d SHA256: 61b1abd23f706aa083aaab3804651e0101d17dbaa8ed79c580edbd0db25b90e5 SHA512: 1223b3a911251aabe43a855f76909a8d716b06d15743e8e8c440eb1ab1b9454bd901978478679b171ebcae953c14fefc0855b6a68e6a3452dfda47dd005d506a Homepage: https://cran.r-project.org/package=BayesFR Description: CRAN Package 'BayesFR' (Fitting Functional Responses in 1- and 2-Prey Systems) Easy application of Bayesian inference for functional responses via 'brms'. This package allows to fit various FR models for single- and multi-prey experiments by providing nonlinear prediction functions for 'brms'. It uses dynamical prediction models to correct for prey depletion. The 'brms' framework facilitates statistical modeling and enables users to conveniently incorporate covariates such as temperature gradients, experimental treatment variables, or random effects that account for grouping in experimental units. Default 'brms' functions make it easy to perform model checking, model comparison and hypothesis testing. Potential statistical issues with data from feeding trials, such as overdispersion, can be resolved by effortlessly switching between likelihood functions. This package, together with its tutorials, should provide students and researchers with a comprehensive and integrated statistical framework for easily testing their hypotheses on trophic interactions. References: Rosenbaum and Rall (2018) ; Rosenbaum et al. (2024) . Package: r-cran-bayesgof Architecture: all Version: 5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orthopolynom, r-cran-vgam, r-cran-bolstad2, r-cran-nleqslv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesgof_5.2-1.ca2404.1_all.deb Size: 312652 MD5sum: 204d4a842cab2f08cdbff71293c37a2c SHA1: 1c63905405194fd432071474a9916557e0dc68cb SHA256: 9f56860ef80ae1a57c718fcf6d0ba04c1d2e483306b15c7d7cff5420b4464fd3 SHA512: 7761dc3c584964ee229bb371ed5f2f176357c3335ec3285ae34fdd23af0fb22c7a6a714ea3eb36607574c8754c332ab03b3c6599ff5d5812b9e62ff70abe014e Homepage: https://cran.r-project.org/package=BayesGOF Description: CRAN Package 'BayesGOF' (Bayesian Modeling via Frequentist Goodness-of-Fit) A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (). Package: r-cran-bayesgwqs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesgwqs_0.1.1-1.ca2404.1_all.deb Size: 146250 MD5sum: fc9c2585add8a9da952f8bbe44053ee1 SHA1: 5179e72bc376aee0f64cb78d4fcb7f9812abc70b SHA256: 37247e7f6ef3ea344ba6e30f38b04d0bf25aebbcff066f018aaaa42bbe49bce7 SHA512: 50964dec5d7fb500280c41125c9fe572bc296b1b7573160e6df216c42bf2b4b85bf18fcd526f608f05ae2aa9614f58bec1fc305dd034e14ee2125ba5c4c255ee Homepage: https://cran.r-project.org/package=BayesGWQS Description: CRAN Package 'BayesGWQS' (Bayesian Grouped Weighted Quantile Sum Regression) Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) . Package: r-cran-bayesian Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-parsnip, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-devtools, r-cran-future, r-cran-knitr, r-cran-recipes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstan, r-cran-spelling, r-cran-testthat, r-cran-workflows Filename: pool/dists/noble/main/r-cran-bayesian_1.0.1-1.ca2404.1_all.deb Size: 127688 MD5sum: bd3c38a8bd32c2a4ba5bdd693c3859d8 SHA1: 84383e3ba78fb794334a8a2f47e268669f438ae4 SHA256: 9baae52300860966447ca512d1218bac4a96fe22c1b1185193f5f7ad1d63695c SHA512: d782ee19eeae272fb8256aeda59aa494d532aae487b3c6158e24986c825bf1f1d89f2689551cb927fe5c42c3355fd5f75cfaf40d94845a10372ae76f2fc9340d Homepage: https://cran.r-project.org/package=bayesian Description: CRAN Package 'bayesian' (Bindings for Bayesian TidyModels) Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) , Bürkner (2018) , and Carpenter et al. (2017) . Package: r-cran-bayesiandeb Architecture: all Version: 0.2.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2965 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-posterior, r-cran-rlang, r-cran-cli, r-cran-ggplot2, r-cran-bayesplot, r-cran-desolve Suggests: r-cran-testthat, r-cran-loo, r-cran-digest, r-cran-dplyr, r-cran-tidyr, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-bayesiandeb_0.2.1-1.ca2404.2_all.deb Size: 2238896 MD5sum: a15cf0cd6ee41b6da88550cf15f886af SHA1: 97c59bc3965743a7c4b7d8eb38cfe82addb82afe SHA256: 16e74f000bf6083410597240c0a93c6225b66c42b23b3060fc53e7a8ce882ce5 SHA512: 191687af46cfacc7560c9510f60949c849f4ea9863b0a40b498e5f5b4ba95bb2b3049dafcfd93761ed26936641ed60a6f4bb0859fc3f57eadcf9045ab9f7dcc4 Homepage: https://cran.r-project.org/package=BayesianDEB Description: CRAN Package 'BayesianDEB' (Bayesian Dynamic Energy Budget Modelling) Provides a Bayesian framework for Dynamic Energy Budget (DEB) modelling via 'Stan'. Implements the standard DEB model of Kooijman (2010, ) as a state-space model with Hamiltonian Monte Carlo inference (Carpenter et al., 2017, ). Includes individual-level growth models, growth-reproduction models, hierarchical multi-individual models with partial pooling, and toxicokinetic-toxicodynamic (TKTD) models for ecotoxicology following the DEBtox framework (Jager et al., 2006, ). Supports prior specification from biological knowledge, convergence diagnostics (Vehtari et al., 2021, ), posterior predictive checks, derived quantity estimation, and visualisation via 'ggplot2'. Package: r-cran-bayesiandisaggregation Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr Suggests: r-cran-rstan, r-cran-posterior, r-cran-loo, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesiandisaggregation_0.2.1-1.ca2404.1_all.deb Size: 107470 MD5sum: 84a6afbc8b679df79bede618e3984710 SHA1: a247ad05a4bf93817dbdb16dfa71e40ff3e7891c SHA256: 1c76d3d97f2d39286a34ae3c17664d78f149373984ccde476a317d4b15be82ec SHA512: a29c9dcd86967e02211f85e9c1dbe0d5060eddea88098865c8b745e46aed1d98b08576e4dfa088f0321ed0f275657efd65f1e32d408c3deb03480131c057a9a6 Homepage: https://cran.r-project.org/package=BayesianDisaggregation Description: CRAN Package 'BayesianDisaggregation' (Evidence-Based Bayesian Disaggregation of Aggregate Indices) Disaggregates an observed aggregate price index into sectoral components with a Bayesian state-space model in which the aggregate enters as a genuine observation density rather than as a renormalization identity. A random-walk-with-drift transition in log space (with partial pooling on the drift and the innovation scale) and an estimable cross-sectional concentration produce posterior draws of the sectoral indices with credible intervals, suitable as multiple-imputation input for downstream dynamic models. The Hamiltonian Monte Carlo engine follows Stan (Carpenter et al., 2017) ; model comparison uses Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (Vehtari, Gelman and Gabry, 2017) . A closed-form linear-Gaussian Kalman/RTS smoother provides an exact, MCMC-free Bayesian alternative for the same aggregate evidence. Package: r-cran-bayesianfactorzoo Architecture: all Version: 0.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-reshape2, r-cran-mass, r-cran-timeseries, r-cran-coda, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-ggplot2, r-cran-nse, r-cran-rdpack, r-cran-matrix Filename: pool/dists/noble/main/r-cran-bayesianfactorzoo_0.0.0.3-1.ca2404.1_all.deb Size: 333964 MD5sum: 698e5b39e1c4b407fcc83b50c5a8762f SHA1: ce70a61e5804d96a4a6ee81de6b1345fa1f35a9c SHA256: 89f5cd34198cb83617bfe53ec38dae52e2142e1e062a7a366f694bd5230bbdb7 SHA512: 8d92c1d10a4927b7164c3fd5347fa0a85fdbd5c1a6b31a8074801bfe47d70086e4ed8ad282f8c07c7bab7b00739635624da8bfd4a7f97d99f9c3da85362a7924 Homepage: https://cran.r-project.org/package=BayesianFactorZoo Description: CRAN Package 'BayesianFactorZoo' (Bayesian Solutions for the Factor Zoo: We Just Ran TwoQuadrillion Models) Contains the functions to use the econometric methods in the paper Bryzgalova, Huang, and Julliard (2023) . In this package, we provide a novel Bayesian framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a stand-alone model, we provide functions including BayesianFM() and BayesianSDF() to deliver reliable price of risk estimates for both tradable and nontradable factors. For competing factors and possibly nonnested models, we provide functions including continuous_ss_sdf(), continuous_ss_sdf_v2(), and dirac_ss_sdf_pvalue() to analyze high-dimensional models. If you use this package, please cite the paper. We are thankful to Yunan Ding and Jingtong Zhang for their research assistance. Any errors or omissions are the responsibility of the authors. Package: r-cran-bayesianfitforecast Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayesplot, r-cran-readxl, r-cran-loo, r-cran-openxlsx, r-cran-rstan, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianfitforecast_1.1.1-1.ca2404.1_all.deb Size: 3061074 MD5sum: 7feada5504def5dd424eb396d802e383 SHA1: e4f0b06815a63b206c3235233be4cb4d934e4cc1 SHA256: c0504861064bc05ab23ebc2f9d87c533aa737c06145263cdce73a49a734338c3 SHA512: 5c9697c55f5c4c1ba369af9f204783815e2c3f21905b3216db4ec10750f45f0bd8d85e5d79c73caa05a49c7e10d0b13ed95886e1c7c2d4024f28bbd19c73fcf0 Homepage: https://cran.r-project.org/package=BayesianFitForecast Description: CRAN Package 'BayesianFitForecast' (Bayesian Parameter Estimation and Forecasting forEpidemiological Models) Methods for Bayesian parameter estimation and forecasting in epidemiological models. Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. Implements approaches described in and . Package: r-cran-bayesiangammareg Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayesiangammareg_0.1.1-1.ca2404.1_all.deb Size: 66618 MD5sum: cd675bcb8fc4e092ddfbbb5f4fd3eecb SHA1: 3913f965bc2cc85c5d056f3bc71024627adc6abc SHA256: 2a9364d8fd99a66ee5a64a58a4cf08c297ec9f8b3fe2337aa794977b4d2a9fef SHA512: 52b02c30543af4770c714f98d11c060e024d4b6b20c1ccebb5e1991bbc275f955e87297e4f90618843662169add6aab2044060e8a3c55556a8d9a6c631b04bec Homepage: https://cran.r-project.org/package=Bayesiangammareg Description: CRAN Package 'Bayesiangammareg' (Double Generalized Gamma Regression Models) Fits double generalized Gamma regression models from a Bayesian perspective, where both the mean and shape parameters are modeled simultaneously using flexible link functions. The methodology is based on Cepeda-Cuervo and Urdinola (2012) and extended in Cepeda-Cuervo (2026), 'Double Generalized Linear Models: Likelihood and Bayesian Methods' (ISBN: 9781041169970). The package provides parameter estimation, model fitting, and model comparison tools, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Package: r-cran-bayesianglasso Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayesianglasso_0.2.0-1.ca2404.1_all.deb Size: 16124 MD5sum: 9ac290ef06e9a06e4f969e25a0cb53c1 SHA1: 5f91694f509bf069fa1ee744840e4de8136d43cf SHA256: c640e6ff814de1bb4679f833b1d2dfa0a478f68ea70c2b402e1f21f3c8142345 SHA512: f5f006d2f7bdbf6b426bf82ebdae1bb4dcf7601e1c33851f836437fe3c3dc91f78d3cf21dba7dec9d0a68b9a6fded3b0dfc19a5a123ce0a453e6dfb571e78fc3 Homepage: https://cran.r-project.org/package=BayesianGLasso Description: CRAN Package 'BayesianGLasso' (Bayesian Graphical Lasso) Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) . Package: r-cran-bayesianhybriddesign Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-checkmate, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-metrics, r-cran-rbest Suggests: r-cran-broom, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bayesianhybriddesign_0.1.0-1.ca2404.1_all.deb Size: 187038 MD5sum: 1a743a0f089d989960b912b239500e60 SHA1: ecde785866b92ae4f8c427645418f4ed7f67dd4f SHA256: 854ff8d6c5c02fee4c1725bb90abeb1156b8a53329e9f8ea0afbeb81330436a7 SHA512: 37d283bafc0af3762c109463ae52a3ce725ccb3723caf6a60efbe29693ab8c0f0973e708d469e8e82227295a651fb7cc1a8014b45bbf58833d176df2eecd5aa0 Homepage: https://cran.r-project.org/package=BayesianHybridDesign Description: CRAN Package 'BayesianHybridDesign' (Bayesian Hybrid Design and Analysis) Implements Bayesian hybrid designs that incorporate historical control data into a current clinical trial. The package uses a dynamic power prior method to determine the degree of borrowing from the historical data, creating a 'hybrid' control arm. This approach is primarily designed for studies with a binary primary endpoint, such as the overall response rate (ORR). Functions are provided for design calibration, sample size calculation, power evaluation, and final analysis. Additionally, it includes functions adapted from the 'SAMprior' package (v1.1.1) by Yang et al. (2023) to support the Self-Adapting Mixture (SAM) prior framework for comparison. Package: r-cran-bayesianinference Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianinference_0.0.1-1.ca2404.1_all.deb Size: 410230 MD5sum: 4848cd9b9d70872c87fa72a3793b7519 SHA1: 7075c19ef10834b198137e7cf1b3ad48898389b4 SHA256: c0c640bdf9206be2e60c87304fe66c3e3495d26936b84f4a2b95bb193b6f8e28 SHA512: c62d3a9fef9b56ccd5a28aa9710a2494e6ba2d771fa9c4fc253b2bf835199f355956c5f009d3b2d99af99fb4a2523049478b6e9a44768c7bed8dceb38ad84ae3 Homepage: https://cran.r-project.org/package=BayesianInference Description: CRAN Package 'BayesianInference' (Bayesian Inference) Beta version of 'Bayesian Inference' (BI) using 'python' and BI. It aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU, GPU, or TPU. Package: r-cran-bayesianlaterality Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tmvtnorm, r-cran-rdpack Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianlaterality_0.1.2-1.ca2404.1_all.deb Size: 29798 MD5sum: 63562865ecc3783d96756957ee369cd9 SHA1: a67418f809cdccf08faa8c21cf74637293484eb9 SHA256: b6f19ca4eb652e70e71ad8f463e1911a7d6e863fe4c60e05541e76ca521e4772 SHA512: 046d663a3141d1eb1ed9471c81e3547b48d057bc333e876fd0380abc577bfc52ba55b79ece566509bb6dc5570421988a8b473769ae54c2cad8381428c0049c24 Homepage: https://cran.r-project.org/package=BayesianLaterality Description: CRAN Package 'BayesianLaterality' (Predict Brain Asymmetry Based on Handedness and DichoticListening) Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, ). Package: r-cran-bayesianmcpmod Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dosefinding, r-cran-dplyr, r-cran-ggplot2, r-cran-logistf, r-cran-nloptr, r-cran-rbest, r-cran-tidyr Suggests: r-cran-clindr, r-cran-dofuture, r-cran-future.apply, r-cran-kableextra, r-cran-knitr, r-cran-mcpmodpack, r-cran-reactable, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-bayesianmcpmod_1.3.2-1.ca2404.1_all.deb Size: 1103696 MD5sum: 43199d22c05adfa15e6692a71ff11624 SHA1: c693ff2c4e66af3e235887c41953aba20e2aad12 SHA256: fc0baa7a0df76c2701bcb2b53d22d4d29d12eabbefda34bcfed2212ff4f4b9c5 SHA512: f18483dd916df94677ef3bb7d87b438d5a016d893a0344185e4f48b466a5ba414b94bc75df9aedcb710aa04f93352fbf5ad9d342f5fb26444898ab0a407f1bf7 Homepage: https://cran.r-project.org/package=BayesianMCPMod Description: CRAN Package 'BayesianMCPMod' (Simulate, Evaluate, and Analyze Dose Finding Trials withBayesian MCPMod) Bayesian MCPMod (Fleischer et al. (2022) ) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally and binary distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) ), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) ). Estimated dose-response relationships can be bootstrapped and visualized. Package: r-cran-bayesianmediationa Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-r2jags, r-cran-car, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesianmediationa_1.0.1-1.ca2404.1_all.deb Size: 212868 MD5sum: d3664ca2fabe5d3b1a77b1048084a6fd SHA1: e9e5538627a119d1d4f909b088802d66b27025e8 SHA256: 9c78af9239bf8f377c3edb25840b8f4a84963789378ceb828e18161b7a74ad12 SHA512: 3993d66e92a7cce55db3775b5f93e34b0b44d748730b796c41481ada4b71e31c934d3ef2ad9bf5f2200fc0bfbfa2c2cda843b8027a18ee002a7a09ce8897e88e Homepage: https://cran.r-project.org/package=BayesianMediationA Description: CRAN Package 'BayesianMediationA' (Bayesian Mediation Analysis) We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results. Package: r-cran-bayesiannetwork Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-heatmaply, r-cran-lattice, r-cran-networkd3, r-cran-plotly, r-cran-rintrojs, r-cran-shiny, r-cran-shinyace, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-chromote, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest2 Filename: pool/dists/noble/main/r-cran-bayesiannetwork_0.4-1.ca2404.1_all.deb Size: 2797410 MD5sum: a14068b752e59e552eb9b482a7f7f771 SHA1: c31f05f73ab6c6d66fd4be6a3fa5f90f1c581b2c SHA256: befb2720d0c216277baaedcd3c668361cc4fffb2ddc4d0aad0141b66e96826e5 SHA512: 04f0af0d5bab0423c958c3aed26336a95123d11a26e8bc19f534a7716abb366db9038abbf77d8c508624680c861ab432a5f0cf71cc609a8dbd41d2ed1003eb5d Homepage: https://cran.r-project.org/package=BayesianNetwork Description: CRAN Package 'BayesianNetwork' (Bayesian Network Modeling and Analysis) A "Shiny"" web application for creating interactive Bayesian Network models, learning the structure and parameters of Bayesian networks, and utilities for classic network analysis. Package: r-cran-bayesianou Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-rstan, r-cran-loo, r-cran-posterior, r-cran-ggplot2, r-cran-tidyr, r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianou_0.2.0-1.ca2404.1_all.deb Size: 346810 MD5sum: 9535daacd71ff732facfdc436069df29 SHA1: 4e7836efdd7a50525c950792aa58a9451912f5e5 SHA256: a4a749b71cb7d5731c6276ddb4ab650affc55c9e7470e21db6ee4c189d6fa1d2 SHA512: d38e27ca7ba62c90f68727461d04060d3f81309bad29c7e846971957bbc46309d47638764e1da92dc71deacef307e035717f57d69763a71bbc65353dbbab2cc7 Homepage: https://cran.r-project.org/package=bayesianOU Description: CRAN Package 'bayesianOU' (Bayesian Nonlinear Ornstein-Uhlenbeck Models with StochasticVolatility) Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via 'Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) . Prior specifications follow recommendations from Gelman (2006) for scale parameters. Package: r-cran-bayesianpower Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianpower_0.2.3-1.ca2404.1_all.deb Size: 46538 MD5sum: f37529217000818daf2d1373cca7b1bb SHA1: f810d41eb62b32124a0b3e8bfe56c0638623a9e0 SHA256: bc3ea3495012f20adc841ef14e1f7b47d90617fa35f2127f23b03436894c60f7 SHA512: a398d0ab2d006b05cc14e6eeb464819a84aa2c26ca3f238124855e994bb222f5a2cc4cd5bde731cade1d7521c3e7f3ae7a4933ea2debdbf80b829af8d3b6d949 Homepage: https://cran.r-project.org/package=BayesianPower Description: CRAN Package 'BayesianPower' (Sample Size and Power for Comparing Inequality ConstrainedHypotheses) A collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) . Package: r-cran-bayesianqdm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-bayesianqdm_0.1.0-1.ca2404.1_all.deb Size: 745544 MD5sum: e85506a1cbb8f1590c0f5969d5271fe3 SHA1: e77ea2273afcea0cdd36872cbffee41edb37df69 SHA256: d18550a09d4fe532a6922bab757cfaac348da4bec57619f1c1ddc8432268c20d SHA512: 4e63312ee1c80023ddbe29329c12d2f770431db7dba88c7c03ac0f6162548c3b0d9826349c852d332203b708fc9b2a8f8254b03f7fd263a8838b812d83aa53cb Homepage: https://cran.r-project.org/package=BayesianQDM Description: CRAN Package 'BayesianQDM' (Bayesian Quantitative Decision-Making Framework for Binary andContinuous Endpoints) Provides comprehensive methods to calculate posterior probabilities, posterior predictive probabilities, and Go/NoGo/Gray decision probabilities for quantitative decision-making under a Bayesian paradigm in clinical trials. The package supports both single and two-endpoint analyses for binary and continuous outcomes, with controlled, uncontrolled, and external designs. For single continuous endpoints, three calculation methods are available: numerical integration (NI), Monte Carlo simulation (MC), and Moment-Matching approximation (MM). For two continuous endpoints, a bivariate Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods. For two binary endpoints, a Dirichlet-multinomial model is implemented. External designs incorporate historical data through power priors using exact conjugate representations (Normal-Inverse-Chi-squared for single continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for binary endpoints), enabling closed-form posterior computation without Markov chain Monte Carlo (MCMC) sampling. This approach significantly reduces computational burden while preserving complete Bayesian rigor. The package also provides grid-search functions to find optimal Go and NoGo thresholds that satisfy user-specified operating characteristic criteria for all supported endpoint types and study designs. S3 print() and plot() methods are provided for all decision probability classes, enabling formatted display and visualisation of Go/NoGo/Gray operating characteristics across treatment scenarios. See Kang, Yamaguchi, and Han (2026) for the methodological framework. Package: r-cran-bayesianreasoning Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2009 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext, r-cran-gt, r-cran-magrittr, r-cran-png, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-cran-tidyr Suggests: r-cran-curl, r-cran-httr, r-cran-knitr, r-cran-patchwork, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-webshot2 Filename: pool/dists/noble/main/r-cran-bayesianreasoning_0.4.3-1.ca2404.1_all.deb Size: 1457752 MD5sum: d4766373ee1c0f2c10d620259f3fbe9f SHA1: 9b2cadd8f27bd986c9ccf6403ba836a24fdb7aad SHA256: 8119ac53b545e1d9fb68d374589171f788b27129fd2b7d001bcaeb465f75a78c SHA512: d6a517ee514b67f9af56a3d6ebf7c2b1d14bc7ef4c112787db070b141a1357a8a28d969f8b623097109dda52a1ce15f39ae42b64148f59943a70f760825cb905 Homepage: https://cran.r-project.org/package=BayesianReasoning Description: CRAN Package 'BayesianReasoning' (Plot Positive and Negative Predictive Values for Medical Tests) Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding . Package: r-cran-bayesiansurpriser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-mass, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tibble, r-cran-vdiffr, r-cran-tidycensus, r-cran-tigris, r-cran-cancensus, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-bayesiansurpriser_0.1.0-1.ca2404.1_all.deb Size: 2342188 MD5sum: 322f2767235704d220367fa74559b101 SHA1: 229312149080ac4ca76e6fdca73a245beb78f194 SHA256: 52d866ed4ebcea7eda944ecc0cd73b518fc7f4c8d3bd5ac447e061eea2b1e478 SHA512: ee7337924850e6524d6aca575f127f8497f169d500da0b667edc25b84c5be7635fb90e8b246ea8c35eb5c04ebe55c5a4f54fa43f6496b6cf2b9dcb0399a1755e Homepage: https://cran.r-project.org/package=bayesiansurpriser Description: CRAN Package 'bayesiansurpriser' (Bayesian Surprise for De-Biasing Thematic Maps) Implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with 'sf' for spatial data and 'ggplot2' for visualization. Supports temporal/streaming data analysis. Package: r-cran-bayesiantreg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayesiantreg_1.0.1-1.ca2404.1_all.deb Size: 103074 MD5sum: 9365146eff89a8f206cd26e989c5e756 SHA1: c017405291510dccf73dc9d387e179dc94937dd4 SHA256: 95d0de4fba853b8a49496a3ae81207883dd5edff8ff6d8cf484f08c298c5fc0e SHA512: ac85d27aec7e9ea987e32b5fa7aeb585676392944e79d2d347a7a9d4b5608da048fabb07be7bf2a951517bf9ea94c081b239d3381add757522c254ad3fd89518 Homepage: https://cran.r-project.org/package=Bayesiantreg Description: CRAN Package 'Bayesiantreg' (Bayesian t Regression for Modeling Mean and Scale Parameters) Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated. Package: r-cran-bayesics Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-janitor, r-cran-extradistr, r-cran-mvtnorm, r-cran-matrix, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-patchwork, r-cran-bms, r-cran-cluster, r-cran-dfba, r-cran-tibble, r-cran-survival, r-cran-stringr Suggests: r-cran-rstanarm, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesics_3.1.0-1.ca2404.1_all.deb Size: 554476 MD5sum: 9b78138ab2b2e6a4c726739376d90d02 SHA1: 8a62cd3a35bcedfcee131e07e936c70655964a6b SHA256: ce12e19bbcb2107b2a9d3eeba6b75f7c390b6defee76393378e799147e2e1568 SHA512: 49a506edbfa4ba346a689fd4094213373b7e92131c941b46bcd707d3f367fe90875332815498a369e00d7130437a5fe8156df851cd1c85895be8ccbb3fc1ae5a Homepage: https://cran.r-project.org/package=bayesics Description: CRAN Package 'bayesics' (Bayesian Analyses for One- and Two-Sample Inference andRegression Methods) Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) or by Chib's method as given in . Interpretations are from Kass and Raftery (1995) . ROPE bounds are based on discussions in Kruschke (2018) . Methods for determining the number of posterior samples required are described in Doss et al. (2014) . Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) . Methods for contingency table analysis is described in Gunel et al. (1974) . Variational Bayes (VB) methods are described in Salimans and Knowles (2013) . Mediation analysis uses the framework described in Imai et al. (2010) . The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) . Non-parametric survival methods are described in Qing et al. (2023) . Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) . Correlation analysis methods are carried out by Barch and Chechile (2023) , and described in Lindley and Phillips (1976) and Chechile and Barch (2021) . See also Chechile (2020, ISBN: 9780262044585). Package: r-cran-bayeslca Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-coda, r-cran-fields, r-cran-nlme, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bayeslca_1.9-1.ca2404.1_all.deb Size: 168762 MD5sum: 33bdce7ad08497aeaab30fd469d114e7 SHA1: 3802a8acf0704a005147dd8069f25b4309a0adc8 SHA256: fc32e469b5f2609b724587ef38894711d0d70ef928563fb2b609105b23d144db SHA512: 7c5d1bb7ce5078bc1e117f93aad7559b9f1879368e5f2bedf4c8769760727b61afd33a20a325d8497fd14e0ed6f9ca84a28a21007c48e5cf5a666d7f6d060996 Homepage: https://cran.r-project.org/package=BayesLCA Description: CRAN Package 'BayesLCA' (Bayesian Latent Class Analysis) Bayesian Latent Class Analysis using several different methods. 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Details for this method can be found in: Lynch, Scott, et al., (2022) ; Zang, Emma, et al., (2022) . 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Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) , the Cairns-Blake-Dowd (2009) , the Li-Lee (2005) , and the Plat (2009) models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p × A × T (strata × age × year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported. Package: r-cran-bayesmortalityplus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-progress, r-cran-tidyr, r-cran-scales, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayesmortalityplus_1.0.0-1.ca2404.1_all.deb Size: 683052 MD5sum: 2e408cda2d4ca092cec89326edc09dba SHA1: 12b197aec5f71d37ba2409fd4376e39ae2e364fc SHA256: c3b334d322a6b93be876843e7f5a2534fa87b17b147567211d138c54cdce1b3f SHA512: 6f8b4921cc5bf61c64a4901817f5ff6274b84e163b98d6ee19e1a9a4a2fd047a9c2b5fe3c28d474235a17fedad09024ff55d8eeaf99f804a1db47bce00b4fddc Homepage: https://cran.r-project.org/package=BayesMortalityPlus Description: CRAN Package 'BayesMortalityPlus' (Bayesian Mortality Modelling) Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) and Dellaportas, Petros, et al. (2001) , and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) ). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) . The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) and Pedroza, C. (2006) . Journal publication available at . 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First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference. Package: r-cran-bayesmultmeta Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-rdpack Suggests: r-cran-mvmeta, r-cran-gplots, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesmultmeta_0.1.1-1.ca2404.1_all.deb Size: 91646 MD5sum: 9b4cb22530ec25e93ae3ae03e5a88227 SHA1: dbf7cb8a2ad74920835b6787c49a75ba76a4e749 SHA256: 58f9a44d7e7cfdde618784cf92d664173cfac4c20cccf5b45be3269d5cc51072 SHA512: 6ecc0a9a6dbe83bdbe3ffb679a59a7b488905825c9773d1554dd65953b7a3a353d22952d775464f362da0959f3cbdea90a9335af6681951477094c1ae643602c Homepage: https://cran.r-project.org/package=BayesMultMeta Description: CRAN Package 'BayesMultMeta' (Bayesian Multivariate Meta-Analysis) Objective Bayesian inference procedures for the parameters of the multivariate random effects model with application to multivariate meta-analysis. The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (). Package: r-cran-bayesnec Architecture: all Version: 2.1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-ggplot2, r-cran-formula.tools, r-cran-loo, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-evaluate, r-cran-rlang, r-cran-chk Suggests: r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesnec_2.1.3.1-1.ca2404.1_all.deb Size: 4644214 MD5sum: f3822e86bf34dc0ea61f54cad36689b2 SHA1: c6b9cd684437d222e62aacdf2d405f4388b447d2 SHA256: 80ea7dbc4f46acf34a7723c4800a4025722736d036a96a911331aa2114c01ebe SHA512: f75eb7dcc76eb4c973da3dfc05a4a870cc422c4421379e9023b7a09df6fcb2de88b5d6de4ab7b71de28a4c88ccfb75aa66859042083e81eab0be225587819a15 Homepage: https://cran.r-project.org/package=bayesnec Description: CRAN Package 'bayesnec' (A Bayesian No-Effect- Concentration (NEC) Algorithm) Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017); Burkner (2018); Carpenter 'et al.' (2017) to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)), 'NSEC' (see Fisher and Fox (2023)), and 'N(S)EC (see Fisher et al. 2023). A full description of this package can be found in Fisher 'et al.' (2024). This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020); Fisher et al. (2020)). Package: r-cran-bayesnetbp Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcolorbrewer, r-cran-fields, r-cran-doby, r-bioc-graph, r-cran-bnlearn Suggests: r-bioc-rgraphviz, r-cran-shiny, r-cran-googlevis, r-cran-cyjshiny, r-cran-qtl, r-cran-qtlnet Filename: pool/dists/noble/main/r-cran-bayesnetbp_1.6.1-1.ca2404.1_all.deb Size: 285280 MD5sum: 985fe28e477a5a034d0c5b4017f4324c SHA1: c43cbf3252f72250ab3fbb6794d7c81cd0e94b34 SHA256: d86ef2fcbd21c05397262bc9d2c5e052749ff6a7b7b6f0b3530094a577439fb5 SHA512: 5a7aa6bd87bf434e6c9022ec444eb7cf3c26ae9a77b1e2b609b5ad7a96c7357f800d37aef23c3bc23c5580dbabd2895032078701ce89eba6a307bad46c8ab25b Homepage: https://cran.r-project.org/package=BayesNetBP Description: CRAN Package 'BayesNetBP' (Bayesian Network Belief Propagation) Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks . For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks . The optional 'cyjShiny' package for running the Shiny app is available at . Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub. Package: r-cran-bayesnsgp Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-fnn, r-cran-matrix, r-cran-statmatch, r-cran-sf, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesnsgp_0.3.1-1.ca2404.1_all.deb Size: 709990 MD5sum: 3a96c31947922ef7e58c91924bfda8b4 SHA1: e6196cdcf921594cdef3334b95d74d8cd0f35c3b SHA256: c33e1cc707ebc2f7889f6e1cf80679a7e391dcabf4bc3c6d4bdc18a326177eb8 SHA512: 5b06137f796b25d5dd217b5d113359bcc42a091011501662469c7aef4c2340dbfa5c1234a710852f1fa71cf75495aa3ea0b2afe6dd1409b14a2fd0ae701f2e4a Homepage: https://cran.r-project.org/package=BayesNSGP Description: CRAN Package 'BayesNSGP' (Bayesian Analysis of Non-Stationary Gaussian Process Models) Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) ). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step. Also provided are nearest-neighbor Gaussian process components for use directly in user-written model code, where the spatial process is retained as a latent field: neighbor-structure construction, a latent-field density and matching simulation function, a purpose-built Metropolis-Hastings sampler that updates the field one node at a time, and posterior prediction at unobserved locations. Package: r-cran-bayesorddesign Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ordinal, r-cran-schoolmath, r-cran-coda, r-cran-gsdesign, r-cran-superdiag, r-cran-ggplot2, r-cran-madness, r-cran-rjmcmc, r-cran-r2jags, r-cran-rjags Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesorddesign_0.1.2-1.ca2404.1_all.deb Size: 111278 MD5sum: e844c0e411590c7c8b8901e5bc87f464 SHA1: 5e3db9c07cfe5cc76b7a65255488723bef5dff91 SHA256: 32720915144b40865cdc5560bc49cef832eab061f2bcdc1ee8d8d05d68a8c131 SHA512: 1c5342f31dc9c09326eaabed4afe8841ec07395b8e27db8b5cb294e2fbd2e9571f9e5ba286c7e706b1a8b233c44f46a8ff59c3d668a50485cb97fc0816fd8d80 Homepage: https://cran.r-project.org/package=BayesOrdDesign Description: CRAN Package 'BayesOrdDesign' (Bayesian Group Sequential Design for Ordinal Data) The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) . Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) . Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) . Package: r-cran-bayespanelur Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayespanelur_0.1.0-1.ca2404.1_all.deb Size: 62210 MD5sum: cf83a775635c9ddbab5f221f32508117 SHA1: b10f0f14c626a6c076076be9d3eaa5fd0cb4bce9 SHA256: 3f0cd4f04563e4e27563d0d73372312cf3f9ea58c1a622c13b908cbd65ae746e SHA512: c4de4e67eb07b93b8fd1540ff8c95da7696fd6bafc8119937a26d0ab57dc1f472f4558f270a8b08ccc3e7b30e12e94d3dd0a3ddfc7f943fd8af496bd70529872 Homepage: https://cran.r-project.org/package=BayesPanelUR Description: CRAN Package 'BayesPanelUR' (Bayesian Unit Root Test for Panel Data Models) Implements the Bayesian unit root test for Panel Autoregressive (PAR) time series models developed by Kumar et al. (2016) . The package evaluates the unit root hypothesis (difference stationarity versus trend stationarity) in panel data using the Posterior Odds Ratio (POR). It accommodates PAR models with linear time trend as well as linear time trend with augmentation terms of arbitrary order. Full posterior probabilities, Bayes factors, and individual panel estimates are computed automatically. Package: r-cran-bayespiecehazselect Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayespiecehazselect_1.1.0-1.ca2404.1_all.deb Size: 90230 MD5sum: d76bb7013103c75d4f31bac40db00a1a SHA1: 15eefa1ed933ecaae9c5ca02ad948745b4acefc1 SHA256: 5850f12f00a0bee66af13370b16874618ac5d1330d9461204a842c4813ddb784 SHA512: 1f6181342b766f236415dd0c586a077138c1f2740556d4d5e269e89a2b1ab10fe8f3aa2d1eb819fbc5d26cb9eef341f4c0b4eb8f41595ceb797798034a499e48 Homepage: https://cran.r-project.org/package=BayesPieceHazSelect Description: CRAN Package 'BayesPieceHazSelect' (Variable Selection in a Hierarchical Bayesian Model for a HazardFunction) Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path. Package: r-cran-bayesplay Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1484 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gginnards Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-vdiffr, r-cran-ggplot2, r-cran-patrick Filename: pool/dists/noble/main/r-cran-bayesplay_0.9.3-1.ca2404.1_all.deb Size: 928318 MD5sum: 5cf2c7ccbbf681d94af03da9c080003f SHA1: 4290b8eb9d2a987f8725b55389bb2eb1e666a667 SHA256: a2bc9db8384d05ef59d53966fb5b990caa6b8504327738506ea641562a328fa7 SHA512: 6dadaa3f37841a714054055998cdc29c0b340ff02bab2cc6a23257355efff88f0290cee7ee397793f8f24b6486918885f9fb11f2e33796e082aef9e9b4dceb6c Homepage: https://cran.r-project.org/package=bayesplay Description: CRAN Package 'bayesplay' (The Bayes Factor Playground) A lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors. Package: r-cran-bayesplot Architecture: all Version: 1.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7204 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggdist, r-cran-ggplot2, r-cran-ggridges, r-cran-glue, r-cran-lifecycle, r-cran-posterior, r-cran-reshape2, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggfortify, r-cran-gridextra, r-cran-hexbin, r-cran-knitr, r-cran-loo, r-cran-monotone, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-scales, r-cran-shinystan, r-cran-survival, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-bayesplot_1.16.0-1.ca2404.1_all.deb Size: 5827492 MD5sum: a3d0706db89658ed0a6cd595b12f5e87 SHA1: 9bf59859c6b98af67f9f6a61116bbdf3498b1327 SHA256: 6ea98087362a3c6ea00adb98d01da12114f8d410b3408cb05fdd14a733159d56 SHA512: 8612d5cf9f2034c65606d3a20a62786c7a38765ee3926a4494e61c4af9badce8ae72a5a9da9044f16503fda39f4a2be8d3422e1b542f7d89afde1a683894212c Homepage: https://cran.r-project.org/package=bayesplot Description: CRAN Package 'bayesplot' (Plotting for Bayesian Models) Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) . The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. Package: r-cran-bayespm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-extradistr, r-cran-rmutil, r-cran-invgamma Filename: pool/dists/noble/main/r-cran-bayespm_0.2.0-1.ca2404.1_all.deb Size: 447052 MD5sum: 7238f31a5e8c771287e6f9afaff1eba9 SHA1: 9cb6fab2c86a7bef0b7bd7e0fd0a37e624c63276 SHA256: 7899688f93729990f4e3077617290f092e14bdb58aed066b0a0659b976b0385d SHA512: 120aa1b701b395df10c7d2ecb2d87aee024a30ff029139363ecdf378a68915f6352704a11cc87f72662a7a7c9ad72dcef5d4a732485ff9181c3c4a2a6606ba83 Homepage: https://cran.r-project.org/package=bayespm Description: CRAN Package 'bayespm' (Bayesian Statistical Process Monitoring) The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" , Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" , Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" . Package: r-cran-bayespmtools Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fastlogisticregressionwrap, r-cran-logitnorm, r-cran-mc2d, r-cran-mcmapper, r-cran-proc, r-cran-cobs, r-cran-oor, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayespmtools_0.0.2-1.ca2404.1_all.deb Size: 175580 MD5sum: 7f6490e45f102159fd7c053fe960ee1a SHA1: be70270209203fdd0b980eb58276b31dc01f3619 SHA256: e8b6f92ab44b2a51cbcd0e0e7232eefbfd3b1492a27cc93af6e75b372c4080e4 SHA512: 4bddc9ef9bcfea54a7566953de7be636e9bd8c44614002ab5d8799f78b3d80e99c135c2fdfb7d8c0f1db6bb1d618df136aa458284de126efb4ccb4f308475869 Homepage: https://cran.r-project.org/package=bayespmtools Description: CRAN Package 'bayespmtools' (Bayesian Sample Size and Precision Considerations for RiskPrediction Models) Performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2026) . Package: r-cran-bayespocket Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gigrvg, r-cran-truncnorm, r-cran-progress, r-cran-stochtree, r-cran-softbart, r-cran-pbmcapply Filename: pool/dists/noble/main/r-cran-bayespocket_0.1.0-1.ca2404.1_all.deb Size: 63934 MD5sum: fe413e3c9535fea7ccdcbc1ef975ea0e SHA1: 98e9166dd417371e0f80394fb871a191fa655eb1 SHA256: 86ceb7e75626003cdc54a1a4636b034f57d846efbd59ef49ae5191698710c6cc SHA512: 3159e321f55dda8b6a756980b60b504348040986ca32e98355dfccc150ace80bea9f6c88cb5394fb06304e3c17cab44a0c64d46122cb0698e7e2161bb33a27b1 Homepage: https://cran.r-project.org/package=BayesPocket Description: CRAN Package 'BayesPocket' (Bayesian Causal Inference for Periodontal Diseases inLongitudinal Studies) Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation. Package: r-cran-bayespostest Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-catools, r-cran-coda, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2, r-cran-rlang, r-cran-texreg, r-cran-tidyr, r-cran-rocr, r-cran-r2jags Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-covr, r-cran-runjags, r-cran-rstanarm, r-cran-mcmcpack, r-cran-r2winbugs, r-cran-brms, r-cran-cardata, r-cran-hdinterval, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bayespostest_0.4.1-1.ca2404.1_all.deb Size: 881090 MD5sum: a79cffdd6c2122f14e150cc2604c610b SHA1: 832db52fae940fda587039f18e10df85fa833634 SHA256: c511c873af29d91b21de98b820df40a71dad237df45a2e7b8c8ecdcc9df940dd SHA512: 04229650b25b52ae850111a730607c0e217fb822ad30127a97fd34af72d46888d721e66812dc46de829a86dcb0bb8aa73372c4959d42556377218a533d7efe8c Homepage: https://cran.r-project.org/package=BayesPostEst Description: CRAN Package 'BayesPostEst' (Generate Postestimation Quantities for Bayesian MCMC Estimation) An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 ), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) , the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 ), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 ). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'. Package: r-cran-bayesppr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesppr_0.2.0-1.ca2404.1_all.deb Size: 314124 MD5sum: 2821fb9478c28076c9abc6bf5437cae3 SHA1: 3ad057586d4f21084bcec1ffb4903e9b77b212d0 SHA256: 78619568b06ec7c2b3e77db5d334e428c59668150152a910de18137b6e3e9372 SHA512: a396314e8bac199bc771245cacd4276a0901e47cd2b913704a51c2d21c9e44b8195e8227ecf6ba4860f9fc418c1f3ceb54acca0b321a906b6959d62f1bd1dad6 Homepage: https://cran.r-project.org/package=BayesPPR Description: CRAN Package 'BayesPPR' (Bayesian Projection Pursuit Regression) Bayesian fitting of projection pursuit regression model. Built to handle continuous and categorical inputs and scalar output (Collins et al., 2023 ). Package: r-cran-bayesqm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-clue, r-cran-posterior Suggests: r-cran-loo, r-cran-readxl, r-cran-jsonlite, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesqm_0.2.0-1.ca2404.1_all.deb Size: 751608 MD5sum: 251a4e7000b79ebefbcf146d562ad0d3 SHA1: 9d078a455a3593f8580bc93ca09163fdb4ed46ce SHA256: e7254cfaa405a14837a33d6fd1cec3995073881fe8f4e7e3b0a47a1e3367eb9f SHA512: 39626346fb91a1ab3358bc00e390fc42d5f2fde36c73322db43dcb1a66ad650bfb286bd8a753626c326598925f24d4c45f85317b2e4d48c82a4576717ed35d0f Homepage: https://cran.r-project.org/package=bayesqm Description: CRAN Package 'bayesqm' (Bayesian Q Methodology: Exact Rank-Order Likelihood for Forced QSorts) A Bayesian analysis for Q methodology, alongside the classical one. Models the forced Q sort as an ordered partition of the statements through an exact rank-order likelihood (the design quotas fix the partition margins, so the likelihood of the observed sorting event is exact), fits it by a parameter-expanded Gibbs sampler in R with no compiled code and a convergence gate on rotation-invariant functionals, resolves rotational ambiguity via the MatchAlign post-processing of Poworoznek et al. (2025) , and returns the familiar Q tables as posterior summaries: credible intervals for bounded participant loadings, flag probabilities with an explicit unclassified state, quota-respecting factor arrays, distinguishing and consensus statements judged against a posterior critical difference and a grid-width equivalence region, one posterior false-discovery rule for all published claims, and a two-signal posterior-predictive workflow for the number of factors. Package: r-cran-bayesqrcount Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesqrcount_0.1.0-1.ca2404.1_all.deb Size: 96690 MD5sum: 5c09552bf38e62d1ed9fde937cf6085b SHA1: 0577732a643be50b6605124f617f54aeb1e8cf96 SHA256: 62c339095ebb0b9b76340a79d240757915dc1356dc12e9d0fe402f18443728c5 SHA512: 214ae409034d6cc2f728ac149b073b462cf6098fe3209ed34648fd7618586af140e4edc55c220fc49f46055ada442f2f0e41b095840bf495e4fd12cea344f69f Homepage: https://cran.r-project.org/package=BayesQRCount Description: CRAN Package 'BayesQRCount' (Adaptive Bayesian Quantile Regression for Count Data) Implements Bayesian quantile regression for count data using the jittering technique for discrete data smoothing and an asymmetric Laplace distribution likelihood. Supports adaptive variable selection via a random-bridge penalty with a beta prior on the power parameter, as well as fixed-bridge and Lasso penalties. Utilizes Markov chain Monte Carlo with Gibbs sampling and adaptive Metropolis-Hastings algorithms for posterior inference, provides Gelman-Rubin convergence diagnostics, and predicts conditional quantiles for count responses. Methodology and applications are based on the following key references: Luo, Zhou, Hu, and Li (2026, Journal of Mathematics, 2026:1543166, ), Koenker and Bassett (1978, Econometrica, 46, 33-50, ), Machado and Santos Silva (2005, Journal of the American Statistical Association, 100, 1226-1237, ), Yu and Moyeed (2001, Statistics and Probability Letters, 54, 437-447, ), Polson, Scott, and Windle (2014, Journal of the Royal Statistical Society Series B, 76, 713-733, ), Park and Casella (2008, Journal of the American Statistical Association, 103, 681-686, ), and Roberts and Rosenthal (2009, Journal of Computational and Graphical Statistics, 18, 349-367, ). 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Posterior predictions and regional sensitivity coefficients are returned and can be visualised with built-in plotting utilities. Methods are based on Stan (Carpenter et al. (2017) ) and Bayesian workflow described in Gelman et al. (2013, ISBN:9781439840955). 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The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2024) , Bottom-Up Importance Sampling (Zambon et al., 2024) , methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) , analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) . 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Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) . 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Methodological details are elaborated by Hoefler and Miller (). Besides generic functions, the package also provides an intuitive 'Shiny' application, that can be run in local R environments. Package: r-cran-bayesrs Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-ggplot2, r-cran-metrology, r-cran-reshape, r-cran-coda Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesrs_0.1.3-1.ca2404.1_all.deb Size: 785322 MD5sum: 88678c0c71fc42955bcb52bd46e69fad SHA1: 7987b24e0411e8661d9c33b65d92f6317134f09d SHA256: 126bc421c05354a246b98e06904e5f2bee408aeda4905ab2b3ff0a59bb05f0ce SHA512: 151900bfc30961052dbc4778860f5268cc9c45dabd36e37f8bc6aaa20728b2bdad38480e173e7bdc1e72b11ed0bf9c22ed702db0ea8ab2eb57fad983ca005717 Homepage: https://cran.r-project.org/package=BayesRS Description: CRAN Package 'BayesRS' (Bayes Factors for Hierarchical Linear Models with ContinuousPredictors) Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, ). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, ). Package: r-cran-bayesrtmb Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3734 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rtmb, r-cran-r6, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gparotation, r-cran-future Filename: pool/dists/noble/main/r-cran-bayesrtmb_0.4.0-1.ca2404.1_all.deb Size: 2274054 MD5sum: 3da3fd6f45ef534ee8a99ef4e240a439 SHA1: 93875c33f621aa71ff1128209af178811fa130ce SHA256: 38ba9f8e26144706ee210c07d348f42bf693e217230741e628ebf300fe6d711e SHA512: 95001b909944c4f0638c84cc696ca6481fc53a65bd22f46df4c916858c6215888e29714e8c0aca0baaf29317ff70a124dff24772f15e195d9a515445578d2a68 Homepage: https://cran.r-project.org/package=BayesRTMB Description: CRAN Package 'BayesRTMB' (Bayesian Inference Using 'RTMB') Provides tools for Markov chain Monte Carlo (MCMC) and Maximum A Posteriori (MAP) estimation utilizing the 'RTMB' package. It supports various statistical models including generalized linear mixed models, factor analysis, item response theory, and multidimensional unfolding. The package allows users to easily transition between frequentist and Bayesian paradigms using a unified interface. Automatic differentiation and Laplace approximation follow Kristensen et al. (2016) , and MCMC sampling uses the No-U-Turn Sampler described by Hoffman and Gelman (2014) . Package: r-cran-bayesrules Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rstanarm, r-cran-e1071, r-cran-groupdata2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesrules_0.0.3-1.ca2404.1_all.deb Size: 2146484 MD5sum: e8b0348798eb1f64eb20dd7771fe8a59 SHA1: d72159648d195f395256a024554a40ee2de76ee7 SHA256: f9896d79fc5ee4c442b58c4ed7153daa822a078384a402110569a43d893882ec SHA512: 4171fb01d3f5c9718c99175a2d066797f0fe154888c404244d02ec026c598461fa1de0416c1e09a369301eef9ebe009e693caa3dc796ebbea5ec73d0bb067d5a Homepage: https://cran.r-project.org/package=bayesrules Description: CRAN Package 'bayesrules' (Datasets and Supplemental Functions from Bayes Rules! Book) Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models. Package: r-cran-bayess5 Architecture: all Version: 1.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-snowfall, r-cran-abind, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-bayess5_1.41-1.ca2404.1_all.deb Size: 158772 MD5sum: a7a0928cdbd60089ce6caa98dbe6aba5 SHA1: dde72ae7dae1f7b6329363abe9a1e93f9b01d493 SHA256: 96f1718ea1b4d1959f7ca608f92954567ab905913245fa37c367506fe65383d3 SHA512: 53a4c824eb6db5eb46f67c2288a014b404cd0d63cf618fba87a347c65b5f5bdcf8a79237cf66b2ebcc996ab7d5f347c173fd43a340d01896ba04659cdd744f83 Homepage: https://cran.r-project.org/package=BayesS5 Description: CRAN Package 'BayesS5' (Bayesian Variable Selection Using Simplified Shotgun StochasticSearch with Screening (S5)) In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya. Package: r-cran-bayess Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mnormt, r-cran-gplots, r-cran-combinat Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayess_1.7-1.ca2404.1_all.deb Size: 322080 MD5sum: 64cf0025a3555236d13f0537f37069fd SHA1: 3d3c9c7f056f6a0235656f328d214db41b1ec21e SHA256: 74699801cabfd0804b1994a401af16a4661e3625ac4ddb396273e49169bfae91 SHA512: bee91ccbea62521e84828ede95795ec79d2fdfb78b54b4d4eef10e8b917ba54abf28c948c6c8dcdefb29d5c5b15a2cf0130ae0f7075d894451a145f8611fd4f8 Homepage: https://cran.r-project.org/package=bayess Description: CRAN Package 'bayess' (Bayesian Essentials with R) Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) . 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The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) . Package: r-cran-bayessim Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1930 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-magrittr, r-cran-mass, r-cran-mvtnorm, r-cran-patchwork, r-cran-tidyr, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayessim_1.0.4-1.ca2404.1_all.deb Size: 1539468 MD5sum: 430c18c032fea1ec9c327f8db5a70d2a SHA1: 2b9d6286a25fd16e8c78ebbdfa6bdb2971cace21 SHA256: dcde350d5cc367b75545ea765c125b153c998e4b6a84e767bc1970fd3630fac5 SHA512: cfb0700f0a695c22c73b392611815b7b5732f885d3f0af0689f25fda2ddb3b88264fe0e38939dc1d65cc3dfc4eb19235069dc024c71bee8afc588871b8b445c7 Homepage: https://cran.r-project.org/package=BayesSIM Description: CRAN Package 'BayesSIM' (Integrated Interface of Bayesian Single Index Models using'nimble') Provides tools for fitting Bayesian single index models with flexible choices of priors for both the index and the link function. The package implements model estimation and posterior inference using efficient MCMC algorithms built on the 'nimble' framework, allowing users to specify, extend, and simulate models in a unified and reproducible manner. The following methods are implemented in the package: Antoniadis et al. (2004) , Wang (2009) , Choi et al. (2011) , Dhara et al. (2019) , McGee et al. (2023) . Package: r-cran-bayessplineur Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayessplineur_0.1.0-1.ca2404.1_all.deb Size: 92264 MD5sum: 375408d1de851cd73086c7adb5f78368 SHA1: 78283070fa5b6c53362f03203d09e40f3e645a0e SHA256: a5e4c37d96e433f7206962e606d60a3c0812683ab61bf623d9e470ee20295208 SHA512: 374a2e5f48f9ef570e6aa9a0608eda8ed4f2c028e50bd28f3672d79a6f53ef11348bd30a0159fb3c385e95b467eaac2caa3384941c6a81525dc84617b285360f Homepage: https://cran.r-project.org/package=BayesSplineUR Description: CRAN Package 'BayesSplineUR' (Bayesian Unit Root Test for AR(1) Model with Trend Approximatedby Linear Spline Function) Performs Bayesian unit root testing for autoregressive time series models with non-linear trend components approximated by linear spline functions, as proposed by Kumar et al. (2020) . The package 'BayesSplineUR' computes posterior odds ratios, Bayes factors, and posterior probabilities for the unit root hypothesis against trend-stationary alternatives in models with linear spline trends or maintained polynomial trends as developed by Chaturvedi and Kumar (2005) . Includes automatic knot selection using information criteria (AIC/BIC) and theoretical foundations for Bayesian unit root testing under structural breaks and maintained trends drawing from Schotman and van Dijk (1991) , Phillips and Perron (1988) , Ouliaris et al. (1988) , and Perron (1989) . Package: r-cran-bayesssm Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-extradistr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-bayesssm_0.5.0-1.ca2404.1_all.deb Size: 160540 MD5sum: de0411e01c2f1521ee51c9a50766c736 SHA1: 08173073ce0a17913120e0effa7cb04c431927c8 SHA256: 333c9ff06e3efe3425a857fb4fa773f0b47d3886fb418b4fe2176e683cbb66ba SHA512: 95ff03b28967935be234e815cd84e36f17530db96e99114275b102165509d4df46587c5ee8c67f8ed85af194aeb9ed3ad01e4b26c7fb4a17dbd494ccba8bbaa9 Homepage: https://cran.r-project.org/package=bayesSSM Description: CRAN Package 'bayesSSM' (Bayesian Methods for State Space Models) Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) and automatic tuning inspired by Pitt et al. (2012) and J. Dahlin and T. B. Schön (2019) . Package: r-cran-bayessurveillance Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayessurveillance_0.0.2-1.ca2404.1_all.deb Size: 46314 MD5sum: 058f87432421904b8cf0064f8c2e7458 SHA1: 5863c4d6707a00c487e33a97add31da84c25a4d1 SHA256: cb6dd17295540cd495ffdc8e7f681c8e37ef31e3c0995b31471a7006b044c1fa SHA512: befac61e2df144ac9e9cda273901b36092c7c12dc8591e0164c4b4ffbefa1c14540c6e9b97605b25a30ba1ff2ac8b0c8d789ae74d3b24aae3b5a3e5087f8fe2f Homepage: https://cran.r-project.org/package=BayesSurveillance Description: CRAN Package 'BayesSurveillance' (Bayesian Surveillance Methods for Healthcare PerformanceMonitoring) Provides Bayesian surveillance methods for prospective monitoring of healthcare performance, patient safety, and clinical quality indicators. The package implements beta-binomial monitoring for binary outcomes, gamma-Poisson monitoring for count outcomes, posterior predictive alert probabilities, Bayesian early-warning signal detection, risk-adjusted surveillance, simulation tools, decision-support methods, and graphical summaries. These methods support continuous performance monitoring and timely detection of adverse trends in healthcare systems. The methodology is motivated by established risk-adjusted monitoring, sequential surveillance, and healthcare quality-improvement frameworks , , , and . Package: r-cran-bayessurvival Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2 Suggests: r-cran-simsurv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayessurvival_0.2.0-1.ca2404.1_all.deb Size: 339110 MD5sum: c9c0268e7b1836d27b452b022287a842 SHA1: b812df7dc97bcd8c0ecabb53b58828ac76457a3a SHA256: ccae2c842ade89f74764408ede7cae22ae3482d25c353e712bcfe9efaccd4f4e SHA512: d73478285577355f0f7a2798d03e017be0f8125eae04a05440185dc0d1b08d30cd7138c9fe7c60b8444a255c93ce374361c5f28ac18f5eee219d76434c9e89bf Homepage: https://cran.r-project.org/package=BayesSurvival Description: CRAN Package 'BayesSurvival' (Bayesian Survival Analysis for Right Censored Data) Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" . In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification. Package: r-cran-bayest Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-coda, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayest_1.5-1.ca2404.1_all.deb Size: 35954 MD5sum: b6fa5d270cb22de04af04f694dbc6650 SHA1: 6d5ffabbdfaf170b859524154e2b199cf3d4917b SHA256: 830981b88216640c2d98caf731a44bb4ce86551b42f2b2e788e505ba784b40b4 SHA512: 2cf4aa466c0916a3dc7732dff245da7282b5f830cff2982941bc1ed9c4a8eb7834cedb9ab21ec29326084533e408a9c85de483481091afbf810a79ab91a115e8 Homepage: https://cran.r-project.org/package=bayest Description: CRAN Package 'bayest' (Effect Size Targeted Bayesian Two-Sample t-Tests via MarkovChain Monte Carlo in Gaussian Mixture Models) Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) . Package: r-cran-bayestestr Architecture: all Version: 0.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1747 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-insight, r-cran-datawizard Suggests: r-cran-bayesfactor, r-cran-bayesqr, r-cran-bayesplot, r-cran-betareg, r-cran-bh, r-cran-blavaan, r-cran-boot, r-cran-bridgesampling, r-cran-brms, r-cran-collapse, r-cran-curl, r-cran-effectsize, r-cran-emmeans, r-cran-gamm4, r-cran-ggdist, r-cran-ggplot2, r-cran-glmmtmb, r-cran-httr2, r-cran-kernsmooth, r-cran-knitr, r-cran-lavaan, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-marginaleffects, r-cran-mass, r-cran-mclust, r-cran-mediation, r-cran-modelbased, r-cran-ordbetareg, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-posterior, r-cran-quadprog, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-see, r-cran-testthat, r-cran-tinytable, r-cran-tweedie, r-cran-withr Filename: pool/dists/noble/main/r-cran-bayestestr_0.19.0-1.ca2404.1_all.deb Size: 1283974 MD5sum: fd6c8ea6a57628dff6528358f10ab929 SHA1: f02f61ba9a39d81e6fbce6fef6ebd850c1182d68 SHA256: 2f7ad219618625928ce26a05047a8777cb7bc95a4c489e4007d95801cf7198df SHA512: 06109592c84400186b13488085f87ae75bec272eba331b41099b6ff8766abd3c1057a4f563a57b465602c52e5355ab3fc9ae0d5cff0cecece2d48a06d07bc61a Homepage: https://cran.r-project.org/package=bayestestR Description: CRAN Package 'bayestestR' (Understand and Describe Bayesian Models and PosteriorDistributions) Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) . Package: r-cran-bayestls Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-patchwork, r-cran-posterior, r-cran-tibble Suggests: r-cran-glmmtmb, r-cran-here, r-cran-pkgload, r-cran-readxl, r-cran-testthat, r-cran-tidybayes, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bayestls_1.0.0-1.ca2404.1_all.deb Size: 469016 MD5sum: 5fb7569397af4685f2e787c65a351453 SHA1: 6f8b6ee8a721c7e8b00373ce620bda8faa102a41 SHA256: 7312d0df8f9c40da780830334d9d455610eb23e0545592abc764316c6bbc5b31 SHA512: 85f5f853183f6ae7d9b5980905e6a938c9e0109f2fdd542c097f273edfc0feb5642de9b56c0256adaacf8d72faf3eba49977c66c13be2cdcde6d13b5b2b94bd5 Homepage: https://cran.r-project.org/package=bayesTLS Description: CRAN Package 'bayesTLS' (Joint Bayesian 4PL Models for Thermal Load Sensitivity) Fits joint Bayesian four-parameter logistic (4PL) models to thermal-tolerance proportion data, extracts the classical thermal load sensitivity quantities (z, CTmax at 1 hour, T_crit) with full posterior uncertainty, and predicts heat-injury accumulation and survival under fluctuating temperature regimes with optional Sharpe-Schoolfield repair. Models are fitted with 'Stan' via the 'brms' package. Implements the framework described in Noble, Arnold, Nakagawa and Pottier (in preparation). Package: r-cran-bayestools Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2173 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-extradistr, r-cran-mvtnorm, r-cran-coda, r-cran-bridgesampling, r-cran-ggplot2, r-cran-rdpack, r-cran-rlang Suggests: r-cran-scales, r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-knitr, r-cran-rstan, r-cran-rjags, r-cran-runjags, r-cran-lme4, r-cran-bayesfactor, r-cran-robma, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayestools_0.3.0-1.ca2404.1_all.deb Size: 1806568 MD5sum: 678456f8c5dc36ac78a52f330e2f0b5d SHA1: c6acabfeeae0bdbb3b947ef77ff9879e0a54d3d9 SHA256: ca29bde4f65275b6b4b462065b48af4508d57e3e8540614482cc642c47af0d81 SHA512: 1b318368933715f81b692d3c2fa5ad64fe07a582d4739ea12615cb08069fd36295f0db9e2271bfdd68cc8c78e9111cff4a268f8d5b67533f7519bd0db42c20a0 Homepage: https://cran.r-project.org/package=BayesTools Description: CRAN Package 'BayesTools' (Tools for Bayesian Analyses) Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, , Hoeting et al., 1999, ). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions. Package: r-cran-bayestreeprior Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tgp, r-cran-bayestree, r-cran-bartmachine, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayestreeprior_1.0.1-1.ca2404.1_all.deb Size: 58694 MD5sum: a3336adf5afa3601536bd366c4b91756 SHA1: 67f85673f22a5bc9d7e6b287d291d69299f3d9ce SHA256: 2bad3d7829c36a7f3a0db4f817bae971fa94a15c84f47f7fedbd2060b70184ad SHA512: 4b135e3aea3c2f21e6444672284b21afa02a92374677f12c77bec352214a3cc97b745769bbf6d665e940ceecab15e1ea0d3eb85629d7efdbee4c5fb8380a4202 Homepage: https://cran.r-project.org/package=BayesTreePrior Description: CRAN Package 'BayesTreePrior' (Bayesian Tree Prior Simulation) Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) . The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016). Package: r-cran-bayestwin Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-coda, r-cran-matrixstats, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bayestwin_1.0-1.ca2404.1_all.deb Size: 524804 MD5sum: 177a66707c6968f35c9d4f6acd8fb355 SHA1: e095991107b4d58db01b455f29e3ff94b656347a SHA256: a6c2226ffb37aa6cc7664d9c24bbc3f0acda9c31e9d5c782d19d023c4273ecd2 SHA512: 8ea3a787e8b06c3ec8a72a7e5e6e91b5afad37a9bde0e04525bceb766336ea832e37b563317f610b83b62e229b7ce4d7a2dac0d9c069333a1a54cc8d93ae4e96 Homepage: https://cran.r-project.org/package=BayesTwin Description: CRAN Package 'BayesTwin' (Bayesian Analysis of Item-Level Twin Data) Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) , Molenaar & Dolan (2014) , Schwabe, Jonker & van den Berg (2016) and Schwabe, Boomsma & van den Berg (2016) . Package: r-cran-bayesurtrend Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesurtrend_0.1.0-1.ca2404.1_all.deb Size: 57004 MD5sum: d4676f8d8de531f2d185a85293329826 SHA1: 5874680546e482a4ed82cde0d2ae07d27b4e2910 SHA256: 4f30d7f20785940991fa51a3712463e1d6913594160a91279db04f5af30142f8 SHA512: 6c90361c0028f8874bc2ffdfdb9713f329896b1189ea8e3328c95528e5e4a879f665a5ed90aa4e2cde09fb66b3a39a30ed4a5ee5208343b59129305dff27510b Homepage: https://cran.r-project.org/package=BayesURTrend Description: CRAN Package 'BayesURTrend' (Bayesian Unit Root Test for Model with Maintained Trend) Performs Bayesian unit root testing for time series models with maintained polynomial trend components as proposed by Chaturvedi and Kumar (2005) . The package 'BayesURTrend' computes posterior odds ratios, Bayes factors, and posterior probabilities for unit root hypotheses against stationary alternatives in autoregressive models augmented with polynomial trends. Methodological foundations for Bayesian unit root testing under structural breaks and maintained trends are drawn from Schotman and van Dijk (1991) , Phillips and Perron (1988) , and Ouliaris et al. (1988) . Package: r-cran-bayesvl Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-stanheaders, r-cran-coda, r-cran-bnlearn, r-cran-ggplot2, r-cran-bayesplot, r-cran-viridis, r-cran-reshape2 Suggests: r-cran-loo Filename: pool/dists/noble/main/r-cran-bayesvl_1.0.0-1.ca2404.1_all.deb Size: 562720 MD5sum: edbad0b39406bd700aaa3a1b42630102 SHA1: ed0a52873e3e3c87e34ba7efa4559f372ce56d3b SHA256: 52bd0d5ac1729338c336e09b3178faed7fd9d8d412dd6e9aef8d39391dbae971 SHA512: f26f9eef4221d2634fe27d0e14711d98a8336e2836767a3e577fd6aadd3a5575cd49f59450553c157462ee39a185acd2de8f5057bdf474862b369f57bd00331b Homepage: https://cran.r-project.org/package=bayesvl Description: CRAN Package 'bayesvl' (Visually Learning the Graphical Structure of Bayesian Networksand Performing MCMC with 'Stan') Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate Stan code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18). Package: r-cran-bayesvolcano Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hdinterval, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-brms, r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesvolcano_1.0.1-1.ca2404.1_all.deb Size: 1359980 MD5sum: 332d32c8e8fd43bd7ee117b91efdb0bf SHA1: c2f7ace79217211f2e81ed44245e489fb2d0281e SHA256: 9a72f4819cb551cad2ca7bab1c6d952a0c905eea5c0911e5b57c3f2208087083 SHA512: 2d2a8f478129959f1e45f7648caefddcb7ac82df152d080f292f47f3b6ee5ad5ad2de6b95f800363c614d9851c4b26ebfe8c25a0bf9712fcb4543c16392614eb Homepage: https://cran.r-project.org/package=BayesVolcano Description: CRAN Package 'BayesVolcano' (Creating Volcano Plots from Bayesian Model Posteriors) Bayesian models are used to estimate effect sizes (e.g., gene expression changes, protein abundance differences, drug response effects) while accounting for uncertainty, small sample sizes, and complex experimental designs. However, Bayesian posteriors of models with many parameters are often difficult to interpret at a glance. One way to quickly identify important biological changes based on frequentist analysis are volcano plots (using fold-changes and p-values). Bayesian volcano plots bring together the explicit treatment of uncertainty in Bayesian models and the familiar visualization of volcano plots. Package: r-cran-bayesx Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3842 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shapefiles, r-cran-sp, r-cran-sf, r-cran-colorspace, r-cran-coda, r-cran-interp Suggests: r-cran-spdep Filename: pool/dists/noble/main/r-cran-bayesx_0.3-3-1.ca2404.1_all.deb Size: 1298822 MD5sum: fc67b96b16502a3c3bedaec780418762 SHA1: 0068ea9f86c206f923e711b16cfb220aefc98c3a SHA256: 3246be979e414cd67c008890b0fdb32f58f31ceb61bbad15c5334cb3c3b4274e SHA512: e10d175532a96c4292f591409b01f639bce97fcb5bbbe39f6c0d051829329cdad82fc29ae9392ef7b0a7a3674f7544fd812d7a8de65147ee595d79d2867ead2e Homepage: https://cran.r-project.org/package=BayesX Description: CRAN Package 'BayesX' (R Utilities Accompanying the Software Package BayesX) Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX. 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Package: r-cran-baylum Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-cli, r-cran-hexbin, r-cran-kernsmooth, r-cran-luminescence, r-cran-rjags, r-cran-runjags, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-baylum_0.3.3-1.ca2404.1_all.deb Size: 2152858 MD5sum: 8217b4459d950f422cee43728dce909c SHA1: 58c245eae4ab23f0fcf2a5716cf51eda31e02e62 SHA256: 1ab7a71bc8f7867c8514a76c79899dda7e029a3d536d7de11925075492095125 SHA512: 534e0e0a0dbcebe8f78ed55bcc5687f80c205745f38b0165300be1d53b9d1a54990754a2501caf1701790f6ef7bbdea4efb82508d4b4631e8a9b6a61ea8eaa05 Homepage: https://cran.r-project.org/package=BayLum Description: CRAN Package 'BayLum' (Chronological Bayesian Models Integrating Optically StimulatedLuminescence and Radiocarbon Age Dating) Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) and Combes et al. (2015) . This includes, amongst others, data import, export, application of age models and palaeodose model. Package: r-cran-baymedr Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-pso, r-cran-rms, r-cran-stringr, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-baymedr_0.2-1.ca2404.1_all.deb Size: 233290 MD5sum: 08832572c12848f949bf3afbf2049f44 SHA1: 8b17cebdde82528dbfb4767e9686a5b4d5a5fb1a SHA256: 3bc74338433d3068f3902667b49228ea4979d307a86634fad9552e53949c252f SHA512: 1089ed8f3bdac4f600f5d73f32c0e1d77f9a779d7984bb031303bf1a5686442e809602033fedb72a4d7d7c33604155655b5de933bdc6bc031b5ebf8b5b756e8a Homepage: https://cran.r-project.org/package=baymedr Description: CRAN Package 'baymedr' (Computation of Bayes Factors for Common Biomedical Designs) BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure, as well as functions for simulating survival data and calculating a Bayes factor for Cox proportional hazards models. Bayes factors for these tests can be computed based on raw data or summary statistics. Package: r-cran-bayprior Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4029 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-glue, r-cran-golem, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-brms, r-cran-htmlwidgets, r-cran-knitr, r-cran-metafor, r-cran-patchwork, r-cran-quarto, r-cran-rmarkdown, r-cran-rstanarm, r-cran-shinytest2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-bayprior_0.4.0-1.ca2404.1_all.deb Size: 2611734 MD5sum: 55e65511daede0a4953b2aa54074e175 SHA1: fb42e1824e9f5ccf508351db87a78c1e6f8116cc SHA256: d703eae1d8abb30eda65079fada5d6f10cc3334e5a649bdbed7c52de1fb7fe50 SHA512: 432b620c95d8abaf60d73b6b8f2a7e9f5f00592e1065f7d54ae0c7b8d4597c73fd18042be83a0dd99bf0d082edb0b7e2032080d7c3b30c269c0c34a43467c154 Homepage: https://cran.r-project.org/package=bayprior Description: CRAN Package 'bayprior' (Bayesian Prior Elicitation, Diagnostics, and RegulatoryReporting) A toolkit for constructing, validating, and justifying Bayesian priors in clinical trial settings. 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Based on reference ecological weights publicly available for a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, ). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio. 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See Cameron et al (2008) for application of bootstrap to cluster samples. See Aaron et al (2016) and Aaron et al (2016) for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys. 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For methodological details, see Ghosh, Marques, and Chakraborty (2025) , Ghosh, Marques, and Chakraborty (2023) , and Ghosh, Marques, and Chakraborty (2021) . Package: r-cran-bcdag Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-graph, r-cran-grbase, r-bioc-rgraphviz, r-cran-lattice, r-cran-mvtnorm Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bcdag_1.1.4-1.ca2404.1_all.deb Size: 403474 MD5sum: 7e52097081203651251c07823b4491ad SHA1: 4b9f9b141d5ff97bd3da0ebe9d23feb0d131b910 SHA256: 8cabc6ef3dac07cfdf0a69b4cd33686766ba32c22056c1fb926d9f8dd203a5a0 SHA512: f51f2f38bcabb5963d81c83cf1e87f4409249bb76b5188b1f0d03a0de301bd39c924ba0e6d361ebd3ddb80920bbe23667ec02f32e6b3d3db6cbdcb5a4b2d8cdc Homepage: https://cran.r-project.org/package=BCDAG Description: CRAN Package 'BCDAG' (Bayesian Structure and Causal Learning of Gaussian DirectedGraphs) A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) , F. Castelletti and A. Mascaro (2022) , F. Castelletti and A. Mascaro (2026) . Package: r-cran-bcdata Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2594 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-crul, r-cran-dbplyr, r-cran-dplyr, r-cran-tibble, r-cran-glue, r-cran-jsonlite, r-cran-leaflet, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-sf, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bcdata_0.5.3-1.ca2404.1_all.deb Size: 1675290 MD5sum: 4c806d91ca57efde59a220b5554d88b0 SHA1: aef93d5f5cbaa22e53d3b5a945a2f8b01e35b400 SHA256: 6a16c31d564e25a7f5288c062e03725a476fb94a5c0ddbc8dd4c27c80cd40e8a SHA512: d2c56cdc763c151e82629f5009359e4ce9c2d8935aa4cf4e0a4b654ceb2ae37af272f9b632fee7c64dbf75e260087241f5e5e15f97189162c867b80df4fc48ba Homepage: https://cran.r-project.org/package=bcdata Description: CRAN Package 'bcdata' (Search and Retrieve Data from the BC Data Catalogue) Search, query, and download tabular and 'geospatial' data from the British Columbia Data Catalogue (). Search catalogue data records based on keywords, data licence, sector, data format, and B.C. government organization. View metadata directly in R, download many data formats, and query 'geospatial' data available via the B.C. government Web Feature Service ('WFS') using 'dplyr' syntax. Package: r-cran-bcdating Architecture: all Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bcdating_0.9.8-1.ca2404.1_all.deb Size: 187818 MD5sum: df2159e0be10c099d176f302697483e5 SHA1: f3103bd6bef2f8dbf9d01f38a56d83d0f99732d4 SHA256: 2eaf453d36bdf141a5e2221595d651ad62eda7be6ff0819f1820bdb344c26205 SHA512: b848147a660ad491e51a62c8d77802601b8c64d44cd44ef5b334f375aa0decf30bf3f8f4ef119016897e32493cbe647f24bcb0b299278827170008f8319ce912 Homepage: https://cran.r-project.org/package=BCDating Description: CRAN Package 'BCDating' (Business Cycle Dating and Plotting Tools) Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features. Package: r-cran-bcdiag Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-fabia Suggests: r-cran-biclust, r-cran-isa2 Filename: pool/dists/noble/main/r-cran-bcdiag_1.0.10-1.ca2404.1_all.deb Size: 979948 MD5sum: 164ae63870f45c84a84c8605b538adfa SHA1: c6074099289c2c4b3dc466486562ee63fc3dd812 SHA256: 014b2b28e442b1152919bfe79fd6a702ec74d5585af93c019c18d78f3f0d1258 SHA512: 76fe05415ab8cff1ca4fc39575fb36717b8917e10724e5548a09b09b757d1c5f6abd42c92519c03ccbe2c314bffd4c08072638c407267668a8bbb238e5f690bd Homepage: https://cran.r-project.org/package=BcDiag Description: CRAN Package 'BcDiag' (Diagnostics Plots for Bicluster Data) Diagnostic tools based on two-way anova and median-polish residual plots for Bicluster output obtained from packages; "biclust" by Kaiser et al.(2008),"isa2" by Csardi et al. (2010) and "fabia" by Hochreiter et al. (2010). Moreover, It provides visualization tools for bicluster output and corresponding non-bicluster rows- or columns outcomes. It has also extended the idea of Kaiser et al.(2008) which is, extracting bicluster output in a text format, by adding two bicluster methods from the fabia and isa2 R packages. Package: r-cran-bcea Architecture: all Version: 2.4.83-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-matrix, r-cran-plotly, r-cran-purrr, r-cran-rdpack, r-cran-scales, r-cran-tidyr, r-cran-voi Suggests: r-cran-knitr, r-cran-mgcv, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bcea_2.4.83-1.ca2404.1_all.deb Size: 2782344 MD5sum: 2796fe1fa95c6569fd37e84991735d38 SHA1: d5b60f5e231716a351ee7e3e7bbc5feb8275b893 SHA256: b6ffb3814a74e617ec07da3236364ae258ef0aa2a00e6f3ac31030cdacbeb336 SHA512: afb07b6f84ebba38e3ce884d516bb92b8bfca44602d202863c73aba622d488c5564c372c41d42011cbe122e766fc2945c69dc661dddb88281896ab5f207c5536 Homepage: https://cran.r-project.org/package=BCEA Description: CRAN Package 'BCEA' (Bayesian Cost Effectiveness Analysis) Produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) . Package: r-cran-bcfrailph Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-bcfrailph_0.1.2-1.ca2404.1_all.deb Size: 327410 MD5sum: c40a1f5003c7aabd1fa28ff615d0186a SHA1: 5b81a15579648282fa0529a6a898421f3754a451 SHA256: 9dfb65afad37b39178d20bf9b5ca2d3446023049c724d69ada02866475d2b3a6 SHA512: dd4d56dcf9d12e1a4e1d1305e8cfaed2bd1d25577cacd68a67218cc00209d880780942171336a3d545614f89c7c5ac36a70391002ddb6062d3389520d7e65223 Homepage: https://cran.r-project.org/package=bcfrailph Description: CRAN Package 'bcfrailph' (Semiparametric Bivariate Correlated Frailty Models Fit) Fit semiparametric bivariate correlated frailty models. Package: r-cran-bcfrailphdv Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-bcfrailph Filename: pool/dists/noble/main/r-cran-bcfrailphdv_0.1.2-1.ca2404.1_all.deb Size: 462734 MD5sum: f7c85eb5e192f438079dcf0cd9f6ac05 SHA1: a8ae86272480e047446186fdf7bde6ed188bfdcb SHA256: b7656f94184f7d79f42e449c574448372e48cc4191a787722637d9aefb394d36 SHA512: ce254d52627b03f80ff9db65642cc56d645ac5901e89e43bda8fe8e97bcd0e875205ce9c8b7a5f87f8a0684b9a051671d5ebfc8ea0ff7508cea30a6a574718ff Homepage: https://cran.r-project.org/package=bcfrailphdv Description: CRAN Package 'bcfrailphdv' (Bivariate Correlated Frailty Models with Varied Variances) Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Kifle et al (2023) with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) . Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach. Package: r-cran-bcgcalc Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2570 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2 Suggests: r-cran-biomontools, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-shiny, r-cran-dt, r-cran-httr, r-cran-testthat, r-cran-plyr, r-cran-tidyr, r-cran-tibble, r-cran-rioja, r-cran-nhdplustools, r-cran-complexupset Filename: pool/dists/noble/main/r-cran-bcgcalc_2.3.1-1.ca2404.1_all.deb Size: 2076336 MD5sum: 817e2f34d1dfdb88a2d634e9e629b7eb SHA1: 3824f99f0422dcfb4d59aed98bef506f5c2aa363 SHA256: f434a32ea667e6c44d5b4aeed8057f84df659af61ba420296d0095d8e7457126 SHA512: 0f71c2c7270fd2572029bbcab31248400564ef2b6869f0a14c2d0900a21a27dbeb73c40392d2d301856e4f5303e9a9f670e44b3281bb2ec68590e8a5d323a0d1 Homepage: https://cran.r-project.org/package=BCGcalc Description: CRAN Package 'BCGcalc' (Biological Condition Gradient, Calculator) Functions to calculate Biological Condition Gradient (BCG) using input files with one row per sample with metric values and site classes as columns. A second file with the BCG Rules (example included) to define the memberships is also needed. The three main functions convert metric scores to metric memberships following fuzzy set BCG Rules (BCG.Level.Assignment), combine metric memberships to level memberships according to BCG Rules (BCG.Level.Membership), and then assign a BCG primary and secondary level based on level memberships (BCG.Level.Assignment). Originally developed as a package for use with BCG for Puget Lowland/Willamette Valley but has been further enhanced for use with multiple communities (benthic macroinvertebrates, fish, periphyton, or coral) and different rule sets. Oregon and Washington reference - "Stamp, J. and J. Gerritsen. 2018. Calibration of the Biological Condition Gradient (BCG) for Macroinvertebrate Assemblages in Puget Lowland/Willamette Valley Freshwater Wadeable Streams. Prepared by Tetra Tech for the US EPA Office of Water, Office of Science and Technology and US EPA Region 10." BCG process documentation - "USEPA. 2016. A Practitioner’s Guide to the Biological Condition Gradient - A Framework to Describe Incremental Change in Aquatic Ecosystems. EPA 842-R-16-001. Office of Science and Technology, Washington, DC 20460." Package: r-cran-bcgee Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gee Filename: pool/dists/noble/main/r-cran-bcgee_0.1.1-1.ca2404.1_all.deb Size: 41604 MD5sum: 0fc30fd06263b772d2e6189cd0f80a8a SHA1: 7e99236f2f36f9cdb46831ccb9e6b89a3faa73c5 SHA256: 77a548da09ba10127a332dc956e1e8415bbd1d84e647718678e641cf81cfe7a6 SHA512: 4b76f678009725a4c808560e46a9cd9bfcbf2d12dc24f384b5fef28254a78320d86d0441c3b69d4a3eed17deb6e5e0277d890de980a7296bc7a7dc03f152101a Homepage: https://cran.r-project.org/package=BCgee Description: CRAN Package 'BCgee' (Bias-Corrected Estimates for Generalized Linear Models forDependent Data) Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) . Package: r-cran-bchm Architecture: all Version: 1.00-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-cluster, r-cran-coda, r-cran-knitr, r-cran-crayon, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bchm_1.00-1.ca2404.1_all.deb Size: 305546 MD5sum: adea0004e1049e68c45754fc9ce81f60 SHA1: 5c464a6ba69ff25df6da756bd57055d0717df603 SHA256: d75fa334e42c8818b74e4fff0abdd516794eec1b0d6faa9235063905c8a9a656 SHA512: afc0fea4a0af240dcbaeba7e3bfbaae749513fa16de7e69d6a5c89ed4c956e0ea02c1957f5c098317fa29baa5440c244164f93b3463f312dee904a23f5e7823d Homepage: https://cran.r-project.org/package=BCHM Description: CRAN Package 'BCHM' (Clinical Trial Calculation Based on BCHM Design) Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them. 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The layers are sourced from the British Columbia and Canadian government under open licenses, including B.C. Data Catalogue (), the Government of Canada Open Data Portal (), and Statistics Canada (). Package: r-cran-bcmixed Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-nlme Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bcmixed_0.1.6-1.ca2404.1_all.deb Size: 152094 MD5sum: 0b46e4965a4d5e226fe0efb95358f407 SHA1: ad11c1f371222c2436974e27141275d24b3c18eb SHA256: b9b172a5d045dcd7cd25f4f9feb4f671dd614e22f675832e2781d6952501b8d2 SHA512: 530022af6c797008be1bf80e759eae09994a65751d4775a5c924f67095e0c800193520588985ecee8befc19d8f6a4353d9fa43fede2472d906a3541a2d9f82d8 Homepage: https://cran.r-project.org/package=bcmixed Description: CRAN Package 'bcmixed' (Mixed Effect Model with the Box-Cox Transformation) Inference on the marginal model of the mixed effect model with the Box-Cox transformation and on the model median differences between treatment groups for longitudinal randomized clinical trials. These statistical methods are proposed by Maruo et al. (2017) . Package: r-cran-bcputility Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-sf Suggests: r-cran-blob Filename: pool/dists/noble/main/r-cran-bcputility_0.4.6-1.ca2404.1_all.deb Size: 267016 MD5sum: 9bc20d80eca6822eea63f81d27690007 SHA1: 5678d274e937b111a54e02005b3803b025a3015c SHA256: 4c7a69535124ba45bc976b4ce3d70fa8500b8059b40de3b84bd21c2d736df8bd SHA512: d0484b352aa2b168c574e966e6606f28ee16085d29b6b8601738be009bfbf69c6e7b8a54de93bb2cfd174c30ce8d43d0ff6f0f10baedd99f6c442c896f9dd124 Homepage: https://cran.r-project.org/package=bcputility Description: CRAN Package 'bcputility' (Wrapper for SQL Server bcp Utility) Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases. Package: r-cran-bcra Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bcra_2.1.2-1.ca2404.1_all.deb Size: 88106 MD5sum: 01317861c039f3f7988cfc5800e1c0a1 SHA1: e3ce0d1342a9cd6231cee3c611a2bb1d6071bb64 SHA256: cf038dd82888a628e5702572edd10fd69770e7c441164f0b2305afa5a6da1f3c SHA512: 6f94f3b605522139f5b4bd6e3c84831817f34184bd2b38c39569de950f0ee024f0c63fa79251ad464aaebffbe3b2cb967db01831d074ff42164a5f87366765ae Homepage: https://cran.r-project.org/package=BCRA Description: CRAN Package 'BCRA' (Breast Cancer Risk Assessment) Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) . Marthew PB, Gail MH, et al (2016) . Package: r-cran-bcrm Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rlang, r-cran-ggplot2, r-cran-knitr Suggests: r-cran-brugs, r-cran-r2winbugs, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bcrm_0.5.4-1.ca2404.1_all.deb Size: 222410 MD5sum: 7d7053b2011aee7a0c9be028cf6ae070 SHA1: 1700eee8f44da828395169a592715e8dba2cd229 SHA256: 02e681fcee28e152ab5866780c838fb8367022ce707aacd973017dd50dc6e63d SHA512: 66f98131e2bcc8c26fcaf203204128fd8cc411be62eb301eacc0ff2ee7a0ede7c849d4747b11427c44af75bc361a0cc8319f0490cb44b40032be7696a6fcff40 Homepage: https://cran.r-project.org/package=bcrm Description: CRAN Package 'bcrm' (Bayesian Continual Reassessment Method for Phase IDose-Escalation Trials) Implements a wide variety of one- and two-parameter Bayesian CRM designs. The program can run interactively, allowing the user to enter outcomes after each cohort has been recruited, or via simulation to assess operating characteristics. See Sweeting et al. (2013): . Package: r-cran-bcrp Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-readr, r-cran-tibble, r-cran-yyjsonr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bcrp_1.0.2-1.ca2404.1_all.deb Size: 18494 MD5sum: dcecaeb4b5db0c72b3a97b894d2dd57b SHA1: 7245762065119e282c112169b60a263d4137b6ab SHA256: e42ab954fc12184d9636122c9ebfe5a3170d94936d31cfefefea41989d362d5f SHA512: d2e70db9a19bdd1170d301e58b5b288ba81774c024317b5004edf1d15aa178e9392d4471481fa202049ff409c94e5611be3bc0eb27a150955b8dfd6e6097fb64 Homepage: https://cran.r-project.org/package=bcRP Description: CRAN Package 'bcRP' (Access 'BCRPDATA' API) Search and access more than ten thousand datasets included in 'BCRPDATA' (see for more information). 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The BCS distributions are a class of flexible probability models capable of describing different levels of skewness and tail-heaviness. The package offers a comprehensive regression modeling framework, including estimation and tools for evaluating goodness-of-fit. 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Supports Bayesian Tobit quantile regression with double adaptive Lasso penalty ('PDAL-BTQR'), double Lasso penalty ('PDL-BTQR'), and unpenalized mixed-effects ('P-BTQR'). Handles left, right, interval, and bilateral censoring schemes in longitudinal and clustered structures. Includes Gibbs sampling algorithms, parameter estimation, standard error computation, posterior credible intervals, forecast predictions, DIC, LPML, and diagnostic plotting. References: Tobin (1958) ; Koenker and Bassett (1978) ; Zou (2006) ; Alhamzawi and Yu (2012) ; Zhao et al. (2024) . 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This process can improve the accuracy and precision of species distribution models (SDM, also known as ecological niche models, ENM). The package offers a data-driven protocol to determine thinning parameters using kernel-density bandwidth selection. Two thinning methods are provided (stochastic and deterministic) to reduce over-sampled environmental conditions and down-weight outlier observations. The name 'bean' reflects the core principle of the method: each 'pod' (a grid cell in E-space) is allowed to contain only a limited number of 'beans' (occurrence points). See Silverman (1986, ISBN:978-0-412-24620-3) and Rousseeuw and Leroy (2003, ISBN:978-0-471-48855-2) for the underlying statistical methods. 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Package: r-cran-beans Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-beans_0.1.0-1.ca2404.1_all.deb Size: 1394462 MD5sum: 0c3e9d8a6e4d88ddd7a2552def937612 SHA1: 8c03f20ff69b6db30cd3592da3e0828abf6067e3 SHA256: c815d9801570c9adc5de8e5f5bf47fc346cc935643bd4f0314789c1d46668d6c SHA512: 220a8d9569fd29680cf7ea6836f25aef4deb90390629950cddd03a2729cc7905b3c6eb823cbfc88e983f5f5f1debcda0b999b380b48f32416c1069348e71a4ca Homepage: https://cran.r-project.org/package=beans Description: CRAN Package 'beans' (Data on Dried Beans) These data contain morphological image measurements for dried beans from Koklu and Ozkan (2020) . Package: r-cran-bearishtrader Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bearishtrader_1.0.2-1.ca2404.1_all.deb Size: 188824 MD5sum: 31a81be5d40bb7665c17ba4ee04304d7 SHA1: 9dea67b854787fca0f96fbdd6f7a7b80ffbba34d SHA256: 5c73ae3f187c89af54afc79cc4d8108012789c23915e6f118f76cc2a7bdf16a8 SHA512: 5df1a713a5c0b6d66c940aa4fb8c764435ea53ae89d5d2ac997e3b4a3fd57ed0699c54198578d95914e11361cc764fb009080ee02058e7a4a05359f10c7ad57f Homepage: https://cran.r-project.org/package=bearishTrader Description: CRAN Package 'bearishTrader' (Trading Strategies for Bearish Outlook) Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Juan A. Serur, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). Package: r-cran-beast Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-beast_1.2-1.ca2404.1_all.deb Size: 307010 MD5sum: 1d60909f6af60fb2ccff81b99ea62d48 SHA1: a482f098e214c54c1e38026a3620321cd86a9831 SHA256: 448dc9a901473787ea82bbc1fd2494166656de9774a861ea8769957634c764cc SHA512: f000ce8d77a6e010f558559b11c52048e02090121912c39543730fec57b8641c042027488d02b07018c14178854a85db51cd50e585f431a5ea3650460166d3d0 Homepage: https://cran.r-project.org/package=beast Description: CRAN Package 'beast' (Bayesian Estimation of Change-Points in the Slope ofMultivariate Time-Series) Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) for a detailed presentation of the method. Package: r-cran-beastier Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-beautier, r-cran-phangorn, r-cran-rappdirs, r-cran-readr, r-cran-rjava, r-cran-rlang, r-cran-sessioninfo, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tracerer Filename: pool/dists/noble/main/r-cran-beastier_2.5.2-1.ca2404.1_all.deb Size: 524986 MD5sum: d43be918f65e75aaf5ccdf8ff7cb5f93 SHA1: 5a2a3cea3d059c07631cb0b0cb9404b1737140e8 SHA256: 2bfcc65af2c941f4a0d538ac695f885b425009d4ba750629e48ef2a52fbc33ef SHA512: 1dec7bc043091a3e52121646564d73ec6b99c18b262a90e406b306627eed9b64e9a12c2d602706f894226f2da7cce71058deab2990d2a3b0fe6cc549e194ff66 Homepage: https://cran.r-project.org/package=beastier Description: CRAN Package 'beastier' (Call 'BEAST2') 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is a command-line tool. This package provides a way to call 'BEAST2' from an 'R' function call. Package: r-cran-beastjar Architecture: all Version: 10.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8814 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-beastjar_10.5.1-1.ca2404.1_all.deb Size: 7957286 MD5sum: 97dbd25899ee3bd6f6f88d8cc87fa6e1 SHA1: ae7304f638c3d18ca4f5a2e36c323ce3ccb1a29a SHA256: ea269bbb69d0a86c2e04664572bdc8128b27e07325e8a7898014f69f88b7af5b SHA512: add5516ef5e2c31f3040390bc3cb4de7b912dc075e4ae1874326e1951f3e8ca84fa52b8d0385dbe0086a1f75dbd8a693be29eb78cf2ec0351d7cf261fd80adb4 Homepage: https://cran.r-project.org/package=BeastJar Description: CRAN Package 'BeastJar' (JAR Dependency for MCMC Using 'BEAST') Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST X' software library of Baele et al (2025) . 'BEAST X' supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the 'BEAST X' JAR in this package, we offer an efficient distribution system for 'BEAST X' use by other R packages using CRAN. Package: r-cran-beastt Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cobalt, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-distributional, r-cran-tidyr, r-cran-ggdist, r-cran-mixtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-beastt_0.0.1-1.ca2404.1_all.deb Size: 353840 MD5sum: 2ade46faa0657fa17cc04021c0ee832a SHA1: 1814368313770a224c99ac9a697c84677e473556 SHA256: b77ed7521371400aa038b921c24d427b4160a083aec00036c85e16cc1566b83f SHA512: 78c8dac4daee929225b37d6120df9ed80dd74a3a7876e1df77fb03b7b3699714f488be42604e8a821c6384ed87b2239b73473fbbf8723af9492c377d0d54c4c3 Homepage: https://cran.r-project.org/package=beastt Description: CRAN Package 'beastt' (Bayesian Evaluation, Analysis, and Simulation Software Tools forTrials) Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. Package: r-cran-beautier Architecture: all Version: 2.6.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4791 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-rappdirs, r-cran-purrr, r-cran-rlang, r-cran-seqinr, r-cran-stringr Suggests: r-cran-knitr, r-cran-markdown, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-beautier_2.6.12-1.ca2404.1_all.deb Size: 1718858 MD5sum: 4bb7cf15b7be1a57b646408e7dae5435 SHA1: 0b917b7d5a177b73ce9fba15f5ca7b63450a195e SHA256: 0f8ae192921b43f661bf4a0748142ff8ad4b9a72d4bece2a910db19d01bd9d4e SHA512: d036fde73e9df7daeee2b1c2b5530aa1d4a347019c57148c0770f3427b7ee44c716621a74d3b032723f4c0330614218737aa3d4b201823df9c6b18f4f57abaee Homepage: https://cran.r-project.org/package=beautier Description: CRAN Package 'beautier' ('BEAUti' from R) 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAUti 2' (which is part of 'BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file 'BEAST2' needs to run. This package provides a way to create 'BEAST2' input files without active user input, but using R function calls instead. Package: r-cran-beautils Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3484 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-fieldhub, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-qrencoder, r-cran-rlang, r-cran-rstudioapi, r-cran-tidyr, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-beautils_0.2.0-1.ca2404.1_all.deb Size: 2102486 MD5sum: ea4b7c4523f90b50fe1b531c4f15ebe3 SHA1: f3d570704fd20952ddc55194d5ec1a8cc1cb9fe0 SHA256: 4bb7992b7f58ee811caac5e2aafebf5077576901228c5bee1875e79a4c0dba8a SHA512: 565455824a66538e70d798580a553de37bf7b700245d1d711201d57fb19b53c66624342f8016e8c25fe5274894ad0f36cec736e955437f831f507e2f676f2d3a Homepage: https://cran.r-project.org/package=beautils Description: CRAN Package 'beautils' (Field Planning and Biostatistics Utilities) Provides a collection of utility functions for biostatistics, agricultural trial planning, and experimental design. Key features include generating experimental designs (like Latin Square, Alpha-Lattice by Patterson and Williams (1976) , and Factorial), fieldbook creation, layout sketching, QR code-based label generation, and descriptive statistical tools to easily handle most common descriptive statistics for quantitative variables as described by Field, A., Miles, J., & Field, Z. (2012, ISBN:978-1-4462-0045-2). Package: r-cran-beaver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ellipsis, r-cran-fs, r-cran-ggplot2, r-cran-purrr, r-cran-rjags, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-yodel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-beaver_1.0.0-1.ca2404.1_all.deb Size: 214208 MD5sum: 8a261a85e61cc27c003081977216f808 SHA1: 08a89b5eaedd471a5d55e47365c19f6f2640cb1b SHA256: 54b63640244bf1d71d2f289d9ce5e5d7002413968457431e8bf0dfda2991ac39 SHA512: 6fd71de265899d81e464447b422d7fb5409c9feeda59ab0e3069073e654507b3cb08dc053f3c07a679d82d5a4c2efc75aae2ca95554c7f608f841a4391eca258 Homepage: https://cran.r-project.org/package=beaver Description: CRAN Package 'beaver' (Bayesian Model Averaging of Covariate Adjusted Negative-BinomialDose-Response) Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. 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Package: r-cran-bed Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-shiny, r-cran-dt, r-cran-miniui, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biomart, r-bioc-geoquery, r-cran-base64enc, r-cran-htmltools, r-cran-webshot2, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-bed_1.6.0-1.ca2404.1_all.deb Size: 1607922 MD5sum: 73a7fbf732ca7688138088c25f3d46d9 SHA1: 4e992bdba53eeff82ed88aa17006150425ad7744 SHA256: 6fb5f64df2b5420ee939684876a251aec427c239066363c087353ae93202b411 SHA512: c8509633297c40c5823425a7052e353746ab31c499717aa1ab028ce09072bd73714dc4da33c73fe846f1e42088a721698116c52dc8ede83b832897537d005257 Homepage: https://cran.r-project.org/package=BED Description: CRAN Package 'BED' (Biological Entity Dictionary (BED)) An interface for the 'Neo4j' database providing mapping between different identifiers of biological entities. This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information. The method has been published by Godard and van Eyll (2018) . Package: r-cran-bedassle Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-emdbook Filename: pool/dists/noble/main/r-cran-bedassle_1.6.1-1.ca2404.1_all.deb Size: 235284 MD5sum: 05922cab4b2486a9117f3fbdea6634c0 SHA1: b568f74ad1803110458f6c2d3d66b262630f3adc SHA256: a839d44600fa232ab6913f263a9e2217703233d33a67c9bd83698b95a9d11f2b SHA512: 28d9f03ffbe4f4cb18f76b5ceea385373c27dd1b6259f5529db478e0724c116a2d631c75316cb7f2220c21229997b13b8d31849030542682ebfc454767062f30 Homepage: https://cran.r-project.org/package=BEDASSLE Description: CRAN Package 'BEDASSLE' (Quantifies Effects of Geo/Eco Distance on GeneticDifferentiation) Provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy. Package: r-cran-bedr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-venndiagram, r-cran-data.table, r-cran-r.utils, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bedr_1.1.5-1.ca2404.1_all.deb Size: 967986 MD5sum: ac55096ea881cd4f5563b42524a68393 SHA1: d97bd7d0691e32d61cb71ea96eecd5a9e4725133 SHA256: 0f5eeaf9f81ca6ea180f770cc029225616bf70e819bacdc9a7a0cc337cf061f8 SHA512: f425f21dd280ea4090d4ca0c5e6d6d62104fa18aa19d4e7aa819661dbf92adb564144ca7762de3356945e98d89c44e52582c7c19f0ae5d33b26290749d8a82b3 Homepage: https://cran.r-project.org/package=bedr Description: CRAN Package 'bedr' (Genomic Region Processing using Tools Such as 'BEDTools','BEDOPS' and 'Tabix') Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing. Package: r-cran-bedrockbio Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bedrockbio_2.0.0-1.ca2404.1_all.deb Size: 29648 MD5sum: b5f87dedd089086b9e1155badfe5cbc5 SHA1: 961805fca18dfdf21a04152b3ed2ce3b15699e78 SHA256: 46f627de6e0a84a94a913141ef29354681566e362f70276e3771644b7747b947 SHA512: 78c172bf5f038d99b509a829c24afca4349f2f280051547d88e3ff472b068d096cd27bf4faacd5f16c7b4ae96d428295bcc4a49329e4633ff8bf8fb44f1ca083 Homepage: https://cran.r-project.org/package=bedrockbio Description: CRAN Package 'bedrockbio' (Open-Access Computational Biology Datasets) Efficiently access the 'Bedrock Bio' library of open-access computational biology datasets. Lazily query datasets backed by 'DuckDB' and 'Apache Iceberg', with support for predicate pushdown and column projection to the cloud storage backend. This enables quick, iterative access to otherwise massive, unwieldy datasets without downloading them in full. See for available datasets and documentation. Package: r-cran-beebdc Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1142 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-coordinatecleaner, r-cran-cowplot, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggspatial, r-cran-here, r-cran-igraph, r-cran-lubridate, r-cran-mgsub, r-cran-openxlsx, r-cran-paletteer, r-cran-readr, r-cran-rnaturalearth, r-cran-sf, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-bdc, r-cran-biocmanager, r-cran-classint, r-bioc-complexheatmap, r-cran-countrycode, r-cran-curl, r-cran-devtools, r-cran-emld, r-cran-formatr, r-cran-galah, r-cran-hexbin, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr, r-cran-inext, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-leaflet, r-cran-magrittr, r-cran-mosaic, r-cran-pkgdown, r-cran-plotly, r-cran-prettydoc, r-cran-purrr, r-cran-r.utils, r-cran-renv, r-cran-rgnparser, r-cran-rlang, r-cran-rmarkdown, r-cran-rmdformats, r-cran-rnaturalearthdata, r-cran-rvest, r-cran-spader, r-cran-taxadb, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-beebdc_1.3.5-1.ca2404.1_all.deb Size: 1020404 MD5sum: f86d3673b9ce182b86a7b70209ad987e SHA1: 0698b5e83cbc9843f1543e8b120ef7669e57ce54 SHA256: f8464bfd0743fb3cd86bbb819723b49f2d1d33cf0f067e470ba5e97c3a2ef6de SHA512: 27b6fb7017d231b3fb150cb6bc6ecd4599f9ff436321a982c957e9226303bb3a283b5a92902005ef6984db30effcd8b9953ad43b0fef37205a39f0ac569d7338 Homepage: https://cran.r-project.org/package=BeeBDC Description: CRAN Package 'BeeBDC' (Occurrence Data Cleaning) Flags and checks occurrence data that are in Darwin Core format. The package includes generic functions and data as well as some that are specific to bees. This package is meant to build upon and be complimentary to other excellent occurrence cleaning packages, including 'bdc' and 'CoordinateCleaner'. This package uses datasets from several sources and particularly from the Discover Life Website, created by Ascher and Pickering (2020). For further information, please see the original publication and package website. Publication - Dorey et al. (2023) and package website - Dorey et al. (2023) . Package: r-cran-beeca Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-sandwich Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-marginaleffects, r-cran-margins, r-cran-robincar Filename: pool/dists/noble/main/r-cran-beeca_0.2.0-1.ca2404.1_all.deb Size: 120910 MD5sum: bd5d11efc7f113a204e869a4630b4ea0 SHA1: 906bf6531268df7069f19892ddb01ebb8c4ec28c SHA256: 726d577afbdf316ec2d710efb5e82979887893c102cee979e711f47f1ec40515 SHA512: 65a082a11fd4bd6fb0752baa6b53c35343c89735b76fad80e1601d590948ff4a69f2fea8ed403fd0d3b82035ecd3d994f1720025bd6f781528e95332f532339b Homepage: https://cran.r-project.org/package=beeca Description: CRAN Package 'beeca' (Binary Endpoint Estimation with Covariate Adjustment) Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) ). 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Package: r-cran-beezdemand Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlsr, r-cran-nlstools, r-cran-nls2, r-cran-ggplot2, r-cran-reshape2, r-cran-optimx Suggests: r-cran-openxlsx, r-cran-knitr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-beezdemand_0.1.2-1.ca2404.1_all.deb Size: 755066 MD5sum: a4c12179c1f6f01ced39f978edec4009 SHA1: a971cc0538e523a60007eef07cbdd2b393dbdde1 SHA256: 5d69af8477c253a3de8df60dadfa58dbacf2b9497cdfe0849101012928ab6834 SHA512: dcab695064a8400d2f3bd3f2da219cf67c261ae796d2055f9b15fcf9afa8368b1496bdbaa584c9a107102a982f56fced0756eef2253253c333c1799634bbfc91 Homepage: https://cran.r-project.org/package=beezdemand Description: CRAN Package 'beezdemand' (Behavioral Economic Easy Demand) Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; ), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, ), exponential (Hursh & Silberberg, 2008, ) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, ), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data. 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The package supports scoring of the 27-Item Monetary Choice Questionnaire (see Kaplan et al., 2016; ), calculating k values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and two-stage approaches, checking for unsystematic discounting (Johnson & Bickel, 2008; ) and scoring of the minute discounting task (see Koffarnus & Bickel, 2014; ) using the Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; ), which is also available as a .qsf file in this package. 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Package: r-cran-behaviorchange Architecture: all Version: 25.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4032 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn, r-cran-data.tree, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-ggplot2, r-cran-googlesheets4, r-cran-gridextra, r-cran-gtable, r-cran-knitr, r-cran-rmdpartials, r-cran-ufs, r-cran-viridis, r-cran-yum Suggests: r-cran-htmltools, r-cran-kableextra, r-cran-openxlsx, r-cran-patchwork, r-cran-png, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rsvg, r-cran-webshot Filename: pool/dists/noble/main/r-cran-behaviorchange_25.8.0-1.ca2404.1_all.deb Size: 1797056 MD5sum: 4998022a985342b0fe39b2b6ba5ddb5c SHA1: 6d40859f8ecc720b0fd6dcb3fd55d815007fb44b SHA256: 42d678eb5fbde76adba9c4d92426578eed92e50b39845f00615df0a0acd90793 SHA512: 14219471710e41ffe2826a5ec114ac9d3938551d24ef9e969b95b61b8fb98ba533e873f8451f4e08391adc0dfa078e2011b548ec0dd68241cd8a61be2f2c6447 Homepage: https://cran.r-project.org/package=behaviorchange Description: CRAN Package 'behaviorchange' (Tools for Behavior Change Researchers and Professionals) Contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) ), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) ), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) ). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers. 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Package: r-cran-belikelihood Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-nlme, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-belikelihood_1.1-1.ca2404.1_all.deb Size: 328352 MD5sum: 1fc6cfac0226659d392f4fcde311d1e4 SHA1: 673b7c552413646ec0c0252bd2c0749f88ce762e SHA256: 3fba28d999aaabf8f4ca80c7731310d1d4665c6da58e59b434efd6177b0a12f7 SHA512: c6cbd4304a567664dd76374fc80b89fce300ddfe69bbe714cba45ffc9bfe9ab58b90938631b167b06d3bd47154175f49c5c0a2538bb5d94dfa977807ff19ab7d Homepage: https://cran.r-project.org/package=BElikelihood Description: CRAN Package 'BElikelihood' (Likelihood Method for Evaluating Bioequivalence) A likelihood method is implemented to present evidence for evaluating bioequivalence (BE). The functions use bioequivalence data [area under the blood concentration-time curve (AUC) and peak concentration (Cmax)] from various crossover designs commonly used in BE studies including a fully replicated, a partially replicated design, and a conventional 2x2 crossover design. They will calculate the profile likelihoods for the mean difference, total standard deviation ratio, and within subject standard deviation ratio for a test and a reference drug. A plot of a standardized profile likelihood can be generated along with the maximum likelihood estimate and likelihood intervals, which present evidence for bioequivalence. See Liping Du and Leena Choi (2015) . 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These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) and Lock et al., (2018) ), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) ), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) ). 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Package: r-cran-betaarma Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast, r-cran-ggplot2, r-cran-rlang, r-cran-gridextra Suggests: r-cran-knitr, r-cran-zoo, r-cran-xtable, r-cran-here, r-cran-moments, r-cran-rmarkdown, r-cran-tseries, r-cran-lbfgs, r-cran-dplyr, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-betaarma_1.2.0-1.ca2404.1_all.deb Size: 498334 MD5sum: 2207f1e087e8de37d308b71a36992eb9 SHA1: b6aa379c32a1f93070fe5a90cc202de1af3acdb4 SHA256: 10fb3ed47eedb88b2f37d1113554cd4a74f04ec7b0d49ae8c8e8cf46bcb0398c SHA512: 8311effcde05861ccde54ba181f2ad2190a78bd6796477206e95ea6678e3a1ac5f82343d29650ca37bd819d0078785782fc4250f444989b65eea0b4e03d052bc Homepage: https://cran.r-project.org/package=betaARMA Description: CRAN Package 'betaARMA' (Beta Autoregressive Moving Average Models) Fits Beta Autoregressive Moving Average (BARMA) models for time series data distributed in the standard unit interval (0, 1). The estimation is performed via the conditional maximum likelihood method using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton algorithm. A ridge penalization scheme is available to improve numerical stability of the estimation, as proposed by Cribari-Neto, Costa and Fonseca (2025) . The package includes tools for model fitting, diagnostic checking, and forecasting, along with two hydro-environmental datasets from Brazil. Based on the work of Rocha and Cribari-Neto (2009) and the associated erratum Rocha and Cribari-Neto (2017) . The original code was developed by Fabio M. Bayer. Package: r-cran-betabit Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3416 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Suggests: r-cran-xml, r-cran-sp, r-cran-raster, r-cran-dalex, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-betabit_2.2-1.ca2404.1_all.deb Size: 3154438 MD5sum: 32b999c3516b67198fbb47106a0eb5fc SHA1: a4285acc0760d8be6d8a4d5d5755eb5c7742f9a9 SHA256: 334ad65074651ad3b24e0015f6564ccfa42fca7edc4a86df078fc889ce6b803c SHA512: 1c976c1ea05cf00299a556c9cad6f94cdfe395c1ded9d119cf63eb51a5595f1ac07b7ca85bfec341ab55567103ef3aed6e8b8198ba7a4b4f1e152f3cc2ed1ede Homepage: https://cran.r-project.org/package=BetaBit Description: CRAN Package 'BetaBit' (Mini Games from Adventures of Beta and Bit) Three games: proton, frequon and regression. Each one is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. In proton you have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages. This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge? In regression you will test your modeling skills in a series of eight sub-tasks. Try only if ANOVA is your close friend. It's a part of Beta and Bit project. You will find more about the Beta and Bit project at . Package: r-cran-betacal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-betacal_0.1.0-1.ca2404.1_all.deb Size: 15198 MD5sum: 329662d86abc23166721750e1a8cebcc SHA1: eaedb84d196806696e6016dec3cc5c0bac4504b8 SHA256: 303ef2760a6e48c42952c8bc80bd7b78be037b329a06de619292c57013cc10dd SHA512: cbdeaaf4ed012a57ff8abdd21b6b3dfc7310403107e165b67c526d463194c5bd551bc316baf3956ba7fdc89ff861598f0bff6c6838d60c4caa5c8a9656911ab4 Homepage: https://cran.r-project.org/package=betacal Description: CRAN Package 'betacal' (Beta Calibration) Fit beta calibration models and obtain calibrated probabilities from them. Package: r-cran-betaclust Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-plotly, r-cran-scales, r-cran-proc Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-betaclust_1.0.5-1.ca2404.1_all.deb Size: 647332 MD5sum: 6ab1f98cf0df5f70ed1f90f2079b8bb0 SHA1: 8484b27985719271e67ea2b657e5a8ea3f2bbb47 SHA256: 57b6ce0ba20baeb55afc7e12f251be9efa6907fb10162f003c4629c27efc418a SHA512: 530ee985c9f43228d983273304556f75794b3fc1728b6398ec94128f5a073786c0c0aeea37fce190a1b22856b403d5ab7cbae2d4a89562d862aeb0bdf0ed187e Homepage: https://cran.r-project.org/package=betaclust Description: CRAN Package 'betaclust' (A Family of Beta Mixture Models for Clustering Beta-Valued DNAMethylation Data) A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility. Package: r-cran-betadanish Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maxlik, r-cran-survival Suggests: r-cran-readxl, r-cran-flexsurv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cmprsk, r-cran-coda, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-betadanish_0.3.0-1.ca2404.1_all.deb Size: 466720 MD5sum: f5c05e26edbb1d3154a5342dc2d7cc17 SHA1: 3b077bc6aca4083cf0675cca063ca8bea592d718 SHA256: 4aa6289dd36372f891677828e274c7026191704c637423406d2b9192b2d7aa04 SHA512: d56dfd92b78249f4169dc3b04767b29f02aae21eec5ac54cdb3de13284b2c0a823a796e3279ebf0948fddb5a30246cba398b94736fbd239387c3cd21670f3839 Homepage: https://cran.r-project.org/package=BetaDanish Description: CRAN Package 'BetaDanish' (The Beta-Danish Distribution for Lifetime Data Analysis) Implements the four-parameter Beta-Danish distribution and its three-parameter Exponentiated Danish submodel for survival, reliability and lifetime data analysis, following Ahmad and Danish (2025) . Density, distribution, quantile, survival, hazard and random generation functions are evaluated so as to retain accuracy in the heavy upper tail, where the survival function is regularly varying. Estimation covers maximum likelihood for complete and right-censored samples, ridge-penalized fitting for weakly identified regimes, a grouped likelihood for times recorded on a coarse grid, and Bayesian sampling. Inference provides log-scale Wald and profile likelihood intervals, together with a reparameterization in terms of the identified composite of the two shape parameters. Structural properties include raw, incomplete and conditional moments with their existence conditions, Shannon, Renyi and Tsallis entropies, mean residual life, mean deviations, Lorenz and Bonferroni curves, probability weighted moments, order statistics, stress-strength reliability, hazard shape classification and the tail index. Regression modules cover accelerated failure time models, mixture and promotion-time cure models, and competing risks with Aalen-Johansen comparison and Gray's test. Analyses can be run directly from a delimited text file or spreadsheet. Package: r-cran-betadelta Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-betasandwich, r-cran-betamc, r-cran-betanb Filename: pool/dists/noble/main/r-cran-betadelta_1.0.7-1.ca2404.1_all.deb Size: 98710 MD5sum: 48b8e673953076ac2c6b864e6ebb2844 SHA1: b01dc3e3279ca48f534ac5057c08c2ba4a0b9f28 SHA256: 3dac48430020dc34825f145d2c2d903afd233befc9efb27305a7ce0e91cb4da3 SHA512: fae5d50a9605fe1cdc4cfa6e0ce984d645455aff14a86be3388467532684b45a4ba550364fb7e7c8d8ae708ea1e37e03bdd9f689356b9a7015ce3bdafe04f6a1 Homepage: https://cran.r-project.org/package=betaDelta Description: CRAN Package 'betaDelta' (Confidence Intervals for Standardized Regression Coefficients) Generates confidence intervals for standardized regression coefficients using delta method standard errors for models fitted by lm() as described in Yuan and Chan (2011) and Jones and Waller (2015) . The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) . Package: r-cran-betafunctions Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-betafunctions_1.9.0-1.ca2404.1_all.deb Size: 313338 MD5sum: 1b45cfc54082cbb808a253bb44ac5a93 SHA1: 9331547c2c03c6dd6b5df4206625aebed9bf170e SHA256: 287e6744ae6e359683c1471bac79f0a7352bb8a4df10187034c62b26e0d3d108 SHA512: 625af86bf39a74392a6fde709e5ce67873f3f6f06f13c38b1f6e8e54ab1dc9ece6cfbcc952b2f981e6dafcce607f3de0570f1241f3c4778cb91d9187d9753205 Homepage: https://cran.r-project.org/package=betafunctions Description: CRAN Package 'betafunctions' (Functions for Working with Two- And Four-Parameter BetaProbability Distributions and Psychometric Analysis ofClassifications) Package providing a number of functions for working with Two- and Four-parameter Beta and closely related distributions (i.e., the Gamma- Binomial-, and Beta-Binomial distributions). Includes, among other things: - d/p/q/r functions for Four-Parameter Beta distributions and Generalized "Binomial" (continuous) distributions, and d/p/r- functions for Beta- Binomial distributions. - d/p/q/r functions for Two- and Four-Parameter Beta distributions parameterized in terms of their means and variances rather than their shape-parameters. - Moment generating functions for Binomial distributions, Beta-Binomial distributions, and observed value distributions. - Functions for estimating classification accuracy and consistency, making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L) and 'Hanson and Brennan' approaches. A shiny app is available, providing a GUI for the L&L approach when used for binary classifications. For url to the app, see documentation for the LL.CA() function. Livingston and Lewis (1995) . Lord (1965) . Hanson (1991) . Package: r-cran-betamc Architecture: all Version: 1.3.4-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-mice, r-cran-amelia, r-cran-betadelta, r-cran-betasandwich, r-cran-betanb Filename: pool/dists/noble/main/r-cran-betamc_1.3.4-1.ca2404.2_all.deb Size: 134796 MD5sum: 6e328f24a4f13e8125639c0488fb3cf1 SHA1: 50610f4174bc59615de4776b8ace6992aa0797b8 SHA256: 2c023499fd68d46c4eb990f5948fe605d20134a8a182d910aaff824fe1aa2dbf SHA512: b5c2d5c18e657db43741044f00ee76716b14fdffb59140acef28cc2884673c2600ff1454e11e75442aef5e0d6c51db236db2b06ca3c92eed5c6d537dac07f8a9 Homepage: https://cran.r-project.org/package=betaMC Description: CRAN Package 'betaMC' (Monte Carlo for Regression Effect Sizes) Generates Monte Carlo confidence intervals for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). 'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2024 ) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 ) to generate confidence intervals effect sizes in regression. Package: r-cran-betanb Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-betadelta, r-cran-betasandwich, r-cran-betamc Filename: pool/dists/noble/main/r-cran-betanb_1.0.7-1.ca2404.1_all.deb Size: 90404 MD5sum: ef3e817f85b8e46cea4cb577969dc5b2 SHA1: b26445672627598369135c980f8627143bbf9870 SHA256: 5b06a25f6833d1cdd790eabca7b27758913f3361b52842cca3711c0fefdf1570 SHA512: 9c57cdd7c5f0e67c0badfcaae59eefb17426d8593b4051fa50cb25ec634560194f03d71cfafe069d24b4720d2fc075de887a4602a2288aeaa1f6c15eff73cd37 Homepage: https://cran.r-project.org/package=betaNB Description: CRAN Package 'betaNB' (Bootstrap for Regression Effect Sizes) Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: ) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). 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Package: r-cran-betapass Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-betareg, r-cran-ggplot2, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-betapass_1.1-2-1.ca2404.1_all.deb Size: 71578 MD5sum: 9c1b2c77b0dfedb3ffb927dcd077feed SHA1: e621a67ea0545582288772301247aea0b5eccab7 SHA256: 9dc777a25cf84395e2363a4413b36d2ba1fc20f71c32c91c61d456e96844a71d SHA512: d81f3d7658ced00ba5d00833186fbf4eb10de1e537ba2a72231c11c44dc7f098d6bb9a3a006a5363a2d6266ddf6e81d24e8c307756aa73389980d36cd6dc30a1 Homepage: https://cran.r-project.org/package=BetaPASS Description: CRAN Package 'BetaPASS' (Calculate Power and Sample Size with Beta Regression) Power calculations are a critical component of any research study to determine the minimum sample size necessary to detect differences between multiple groups. 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Package: r-cran-betasandwich Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-betadelta, r-cran-betamc, r-cran-betanb Filename: pool/dists/noble/main/r-cran-betasandwich_1.0.9-1.ca2404.1_all.deb Size: 118054 MD5sum: 221a05dbade1ecc81cb04286261f2a27 SHA1: 4855e6f3f2f0026025543e953bd2ece7c4704e02 SHA256: f05d26656f323c3311cc85c893d6683bcb93c52b856c0cc03d0e9860064ffb50 SHA512: 52b993f10c7bf8232613d7d70244d60b3dfa976ff11f5e07a0bfe472e49593605cae5042dde10de7ff1b13ac3ccd351d10c8143df4d468c62740ccba4d7f2d63 Homepage: https://cran.r-project.org/package=betaSandwich Description: CRAN Package 'betaSandwich' (Robust Confidence Intervals for Standardized RegressionCoefficients) Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) . The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) . 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Package: r-cran-bfm Architecture: all Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-psych Suggests: r-cran-testthat, r-cran-spelling, r-cran-betareg, r-cran-zoib Filename: pool/dists/noble/main/r-cran-bfm_0.2.11-1.ca2404.1_all.deb Size: 32940 MD5sum: b81dba604a672c43d50856c819255d64 SHA1: 3344749835693a8cc7bde0d026225f8cd1151fec SHA256: ac2a8713815933736b36a6218c0de25b558bfe3629a8ceb6f02eae223e6e6766 SHA512: a5f8bc1b72d79f71efc073ec5bf73ea301161417256ea0a76e6c22f5ae3995176486f0229e7795878f6dea5478e221c7e537f7da1912b8d0ea489d30b9671b52 Homepage: https://cran.r-project.org/package=BFM Description: CRAN Package 'BFM' (Beta Factor Model) Provides tools for factor analysis in financial and econometric settings under Beta factor models. 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Package: r-cran-bfpwr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lamw Suggests: r-cran-roxygen2, r-cran-tinytest, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bfpwr_0.1.6-1.ca2404.1_all.deb Size: 442990 MD5sum: f7ed1183c7847e82f443c6a2f4679bd2 SHA1: b1c45c22a790c5022924a23d47166461b4442dd8 SHA256: 7470663951337e0a003b5a90aea9f4678871f322a23c0100b348ede108cbe1e1 SHA512: afb83096b9c0f921429bcdcf2e5fb9e25f3fe62f5869bc184734fac41d76ee76cf1fd983a2c01a806ea4cb33c920c25b238f62b538bd778bba53cace463653a3 Homepage: https://cran.r-project.org/package=bfpwr Description: CRAN Package 'bfpwr' (Power and Sample Size Calculations for Bayes Factor Analysis) Implements z-test, t-test, and normal moment prior Bayes factors based on summary statistics, along with functionality to perform corresponding power and sample size calculations as described in Pawel and Held (2025) . 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This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package 'sf' and to plot them easily and quickly without having to worry too much about the technical details. It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs. Package: r-cran-bfw Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circlize, r-cran-coda, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mass, r-cran-officer, r-cran-plyr, r-cran-png, r-cran-runjags, r-cran-rvg, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-lavaan, r-cran-psych, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bfw_0.4.2-1.ca2404.1_all.deb Size: 732174 MD5sum: 7a4545aa94b2173c5b3d909ea70070a5 SHA1: eda3b855c34e51370f11a19dc825c2c6c6b11b28 SHA256: 51d004a92003054114e8511e07011d09b433e6b65b8b17e892eb49c5c7151bea SHA512: 938d25f8b84b383bc7f52ceafac81782febb03a79e1764287dd3df0402f8639a88f2699c7361c6c72dfec91d7f431f0adca3b683168739ffdfb4847065cd51cb Homepage: https://cran.r-project.org/package=bfw Description: CRAN Package 'bfw' (Bayesian Framework for Computational Modeling) Derived from the work of Kruschke (2015, ), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, ). 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Package: r-cran-bgev Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-envstats, r-cran-mass, r-cran-nleqslv, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bgev_0.3-1.ca2404.1_all.deb Size: 71666 MD5sum: 5b246193631d01ee0d37ef7c809bbee8 SHA1: 637922450f9c8932d212409a627e2281ba484f15 SHA256: d83eeae7888363cebcd23e527cf8bc59075a1b67998a82eb9754ef593a587003 SHA512: 8a72744f7993fa36cc991267520b6b02901239645916dbd6b944ff7bd03383a135252f157d30fc26f893bffc1f8f98a1876a1d91b2a3d6e51b7ee49d9ed9d010 Homepage: https://cran.r-project.org/package=bgev Description: CRAN Package 'bgev' (Bimodal GEV Distribution with Location Parameter) Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) . 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Package: r-cran-bgfanalyzer Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6476 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-zoo, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bgfanalyzer_1.1.0-1.ca2404.1_all.deb Size: 2337960 MD5sum: b4cc1c1f0ae499eac3f0d787144f7bb2 SHA1: ed774750175e22a5dffba14236f2c4da6141bd0c SHA256: 73fa49397ed8954ad9073e0b4088303aa3aaa2302e9d61307a70feb9a3c744f6 SHA512: 035dfdd2543f60a56d47d64ddd9518a62726751c46be396137aa9c6fe66a90245028313d2754ab292ba5b4ecf3120e62aa74c1aa7879fa754bd976cd3cd2d8a3 Homepage: https://cran.r-project.org/package=bgfanalyzer Description: CRAN Package 'bgfanalyzer' (Analyze Microbial Biogas Fermentation Data) Provides a new S3 class object and relevant methods to analyze biogas fermentation data. It includes three workflows. 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Package: r-cran-bgfd Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adequacymodel Filename: pool/dists/noble/main/r-cran-bgfd_0.1-1.ca2404.1_all.deb Size: 242108 MD5sum: 2ab6067820c334c0d8f3577c47b6d4ce SHA1: 884c3f40f0c195c31b0f921441224c7e6ce01031 SHA256: 21dd461644d15a7d71cf8a3e9675d8f62453f70e82be0d0bb0a5cbf3e8f1b8f2 SHA512: c035a8a75ad711bf05fcf4bcf7d9d0857ea2db589bf160a99464a097594e76f3bf83cbc9b2fedf2b8be2bc55f719275168cd19a6d6c4d758c5a30e555d4fd3c4 Homepage: https://cran.r-project.org/package=BGFD Description: CRAN Package 'BGFD' (Bell-G and Complementary Bell-G Family of Distributions) Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes: a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. . b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. . c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. . 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(2021) ], calculation of relatedness coefficients using path-tracing methods [Wright (1922) ; McArdle & McDonald (1984) ], inference of relatedness, pedigree conversion, and simulation of multi-generational family data [Lyu et al. (2025) ]. For a full overview, see [Garrison et al. (2024) ]. For a big data application see [Burt et al. (2025) . 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For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software . 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Package: r-cran-bgsmtr Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4043 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-misctools, r-cran-coda, r-cran-edison, r-cran-statmod, r-cran-sparsemvn, r-cran-inline, r-cran-laplacesdemon, r-cran-glmnet, r-cran-cholwishart, r-cran-mnormt, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-bgsmtr_0.7-1.ca2404.1_all.deb Size: 4000878 MD5sum: 76c8a5ac8e7f8c2fd8fcca22a535c5cd SHA1: cfefb6502c5594ef191be224cfaa9f260245da4c SHA256: c6cecb2f428ad409f22a6c1926f2dfb5562e0e121f0ca2709e7a191d9bda06c2 SHA512: 4e35b060f7e5b3756f195ff3be3a8feae293d9acfe7468d05d550af39a721c8815af479c4256954cf2603bdd632b8e25f37d73ff987ec20d9d4d8ed68259ea84 Homepage: https://cran.r-project.org/package=bgsmtr Description: CRAN Package 'bgsmtr' (Bayesian Group Sparse Multi-Task Regression) Implementation of Bayesian multi-task regression models and was developed within the context of imaging genetics. The package can currently fit two models. The Bayesian group sparse multi-task regression model of Greenlaw et al. (2017) can be fit with implementation using Gibbs sampling. An extension of this model developed by Song, Ge et al. to accommodate both spatial correlation as well as correlation across brain hemispheres can also be fit using either mean-field variational Bayes or Gibbs sampling. The model can also be used more generally for multivariate (non-imaging) phenotypes with spatial correlation. Package: r-cran-bh Architecture: all Version: 1.90.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132358 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bh_1.90.0-1-1.ca2404.1_all.deb Size: 9043000 MD5sum: 50d106979bdc4c6d0446176b0f36e697 SHA1: c418d69d9861e3cfe3353f8c25badae445c52571 SHA256: b80e87cb8acc4d181bffd0d8b762f9cd756dba562b9e0bcf478bafcfd9517590 SHA512: 50a8c1e4309ffa2ad3ae8433bb86a5e233bd858a430a4006b5febed20a078fad4b64d76dfbb06ca57dfe0590d262ceb3f92017531df32d7e8cdacd43f9f9166f Homepage: https://cran.r-project.org/package=BH Description: CRAN Package 'BH' (Boost C++ Header Files) Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN packages. 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The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables. Package: r-cran-bhat Architecture: all Version: 0.9-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bhat_0.9-12-1.ca2404.1_all.deb Size: 95704 MD5sum: 71b682c00e4de37d4db994bf609a9df7 SHA1: 0f2f482c86fc00fe3cd48c44242b60ce3f7153a3 SHA256: 3826d3a2a6e337a628e3e5c290f4b819c757ffd61b7c6cb5b36469d469a20fcb SHA512: 79983b904650041b399e164fdcf6a15f5d06d3839b19b7ad7de92cc554767440b66b87e48b7890e1a782b98cf163e92b246021b78265d9836a045e84e6981726 Homepage: https://cran.r-project.org/package=Bhat Description: CRAN Package 'Bhat' (General Likelihood Exploration) Provides functions for Maximum Likelihood Estimation, Markov Chain Monte Carlo, finding confidence intervals. The implementation is heavily based on the original Fortran source code translated to R. Package: r-cran-bhh2 Architecture: all Version: 2016.05.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-frf2 Filename: pool/dists/noble/main/r-cran-bhh2_2016.05.31-1.ca2404.1_all.deb Size: 243910 MD5sum: a3f1ebaa99e53fea3e0de551e56c99a4 SHA1: 35d225424867d393e62ae3dd5c6594b5f9c91597 SHA256: cc9ce8d3e24a5252675f9566da91f46f6546167d85820a0713e403a6c44f7e68 SHA512: 5e18f3c57da191d35ede4141086fdcd36d3efc4eaa2b4dc578764feed577a410acc8a0889ffa4c1f090c115ae0257ed748b1eeefd1a0163073acb1d040bccac0 Homepage: https://cran.r-project.org/package=BHH2 Description: CRAN Package 'BHH2' (Useful Functions for Box, Hunter and Hunter II) Functions and data sets reproducing some examples in Box, Hunter and Hunter II. Useful for statistical design of experiments, especially factorial experiments. Package: r-cran-bhm Architecture: all Version: 1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-lpl, r-cran-mass, r-cran-survival Filename: pool/dists/noble/main/r-cran-bhm_1.19-1.ca2404.1_all.deb Size: 307650 MD5sum: 084b9035c8b197903b1f27fcef136953 SHA1: 004cc6b78ff8f470f47e6fa9511e21b041817449 SHA256: cfb4d46ceb63fe9a7a4cf57e1192283036360df285b49dfd68d37e7c9ff01a03 SHA512: bf560f070245249761e4c49996f247246c92fcb8e58a03cf1b0c019f4f575ec429686892d14be6c96deee28cf2608c5d0b1e9fd4a4eaf4cb1bb7a38b30cbb807 Homepage: https://cran.r-project.org/package=bhm Description: CRAN Package 'bhm' (Biomarker Threshold Models) Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method. 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Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) and Neuenschwander et al. (2016) , as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) . In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided. Package: r-cran-bi Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bi_1.2.0-1.ca2404.1_all.deb Size: 22586 MD5sum: f9090063f0cd99e24f7d529e2740e5a1 SHA1: 8ab309c1e789090ff1f0cc6e311233552fe5a59a SHA256: 012ade49987dc46388f2e01833aa02e3b8d9c1442e19a5138cee163e103a7dc1 SHA512: 0527b743cd4ed10afbea35f0b4f1dbcf6964410b6529aeb3d2a337668ee67d0d066e0247fb82562c9dbb42dc36fe1936120e0b1e2d0aa0c05d27dd260dd03f90 Homepage: https://cran.r-project.org/package=BI Description: CRAN Package 'BI' (Blinding Assessment Indexes for Randomized, Controlled, ClinicalTrials) Generate the James Blinding Index, as described in James et al (1996) and the Bang Blinding Index, as described in Bang et al (2004) . These are measures to assess whether or not satisfactory blinding has been maintained in a randomized, controlled, clinical trial. These can be generated for trial subjects, research coordinators and principal investigators, based upon standardized questionnaires that have been administered, to assess whether or not they can correctly guess to which treatment arm (e.g. placebo or treatment) subjects were assigned at randomization. Package: r-cran-biascorrector Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-magrittr, r-cran-rbiascorrection, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs Suggests: r-cran-lintr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biascorrector_0.2.3-1.ca2404.1_all.deb Size: 165590 MD5sum: c046ef48246dbc2e41f406e9f7372f69 SHA1: c1d91e87006ccda745755dfec408dde195b4d2c9 SHA256: 0c43bd5583a5823145a51aa55b3b4f606358ab5770ff0fe0235f49fcd5800274 SHA512: 698da1950f3514675ee0fda62045c3d4bd47779623fd24dd3d04316569db1cf6773f2dcec94ffe68ee65c169796ad909eb60e2a29afc89d1899c8f70428f617f Homepage: https://cran.r-project.org/package=BiasCorrector Description: CRAN Package 'BiasCorrector' (A GUI to Correct Measurement Bias in DNA Methylation Analyses) A GUI to correct measurement bias in DNA methylation analyses. The 'BiasCorrector' package just wraps the functions implemented in the 'R' package 'rBiasCorrection' into a shiny web application in order to make them more easily accessible. Publication: Kapsner et al. (2021) . 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Package: r-cran-bibliometrix Architecture: all Version: 5.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4685 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bibliometrixdata, r-cran-contentanalysis, r-cran-dimensionsr, r-cran-dplyr, r-cran-ca, r-cran-forcats, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-matrix, r-cran-plotly, r-cran-openalexr, r-cran-openxlsx, r-cran-pubmedr, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rscopus, r-cran-shiny, r-cran-shinycssloaders, r-cran-snowballc, r-cran-stringdist, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidytext, r-cran-visnetwork, r-cran-xml2 Suggests: r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat, r-cran-wordcloud2 Filename: pool/dists/noble/main/r-cran-bibliometrix_5.5.0-1.ca2404.1_all.deb Size: 3019276 MD5sum: 46030c7b55c78ca67983d8d51299379b SHA1: 6132a3a9f2afd9f4e69016eb9ccecbd8229ca28e SHA256: 2eedfebccec49bd838553446baf37b21d99f20e87ed413635ec7d982c9d2dd0b SHA512: e67abc8fe5cc60d1d90dff4208bcad533ef8ab2e17a06c34a1bd0c1e7922819679168c923e3f170989326650665cebcc23199840eef8be00939666095e1cb751 Homepage: https://cran.r-project.org/package=bibliometrix Description: CRAN Package 'bibliometrix' (Comprehensive Science Mapping Analysis) Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) . 'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS', 'Clarivate Analytics Web of Science' (), 'Digital Science Dimensions' (), 'OpenAlex' (), 'Cochrane Library' (), 'Lens' (), and 'PubMed' () databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis. Package: r-cran-bibliometrixdata Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bibliometrix Filename: pool/dists/noble/main/r-cran-bibliometrixdata_0.3.0-1.ca2404.1_all.deb Size: 3946554 MD5sum: fea431e2b75c7060b166c9ea27b58318 SHA1: a6c9f33cca14eface965de5702291955eccc5b58 SHA256: d1a5a193ca078d560039e2b82b7db56d25079f2e394efa3d7aafc63da050f90b SHA512: 564035321b1ab23534a26c36b954d6243974d220cacb6a654ab1b825cf309303ef89b4c506fe7ae3722432dda5c4017610e1ba1ca0d7709ffc4776445bcbcb1d Homepage: https://cran.r-project.org/package=bibliometrixData Description: CRAN Package 'bibliometrixData' (Bibliometrix Example Datasets) It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' () and 'Clarivate Analytics Web of Science' () databases. They can be used to test the different features of the package 'bibliometrix' (). Package: r-cran-biblionetwork Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-biblionetwork_0.1.0-1.ca2404.1_all.deb Size: 206308 MD5sum: b8866b7f8f0ea8457b6f2655f7653d19 SHA1: e5044dd80c453d40591b45ff9fbcd903fece1c7d SHA256: 7d017992e402bea8793508ae74ac43de74ae3e9bb9d594786d2a64fc26df090d SHA512: e75929a662738741c13456c7e3763475e1224549a2f7323574a23d8cc0208db77cf1e56b3efa029e35de4aae0fc90af6ff35ab366a3ca1d0dca0673794de7cb0 Homepage: https://cran.r-project.org/package=biblionetwork Description: CRAN Package 'biblionetwork' (Create Different Types of Bibliometric Networks) Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) ; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) ; Sen, Subir K. and Shymal K. Gan (1983) ; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) ; Zhao, Dangzhi and Strotmann, Andreas (2008) . Package: r-cran-bibliorefer Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bibliorefer_0.1.4-1.ca2404.1_all.deb Size: 402194 MD5sum: cc3eb5d162a99472460da9597ce0722d SHA1: 34dabff67065c92fc8a804ce6fbe35bfe8698871 SHA256: ace09c829bc97b7ccbc751cb9ec199277e6b124a5a3c40821dc4475ee71a9456 SHA512: 8c99ac2b043935672bee2519356693c46f0a8c1ff2bd83647ed49a2e1103641e6e8a8fbd4c10b9180e7cde348d98945d5e470bafccb638189d08006bd296a618 Homepage: https://cran.r-project.org/package=bibliorefer Description: CRAN Package 'bibliorefer' (Generator of Main Scientific References) Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) . Caibo Zhou, Wenyan Song. (2021) . Package: r-cran-biblioverlap Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-magrittr, r-cran-matrix, r-cran-rlang, r-cran-shiny, r-cran-stringdist, r-cran-upsetr, r-cran-uuid Suggests: r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biblioverlap_1.0.2-1.ca2404.1_all.deb Size: 527844 MD5sum: 8fc724df379c1aa4490acfdb4c572319 SHA1: 44bcc9901d7c7b5c1593bd467fde9ad071bd62f0 SHA256: 0b33d6f4e92bf695abc7f437ed0f494b80e21339fae28892f6e02fd79a0aa536 SHA512: b825dfa1d74d7d163e7ab8ad895afb99bf78cf29ee7980fe7a5cd8ee52e3e2c25e58ac7a05b4e63885c6d0d38049b167e41ddd3759b4414bbf0898a9709f9b4a Homepage: https://cran.r-project.org/package=biblioverlap Description: CRAN Package 'biblioverlap' (Document-Level Matching Between Bibliographic Datasets) Identifies and visualizes document overlap in any number of bibliographic datasets. This package implements the identification of overlapping documents through the exact match of a unique identifier (e.g. Digital Object Identifier - DOI) and, for records where the identifier is absent, through a score calculated from a set of fields commonly found in bibliographic datasets (Title, Source, Authors and Publication Year). Additionally, it provides functions to visualize the results of the document matching through a Venn diagram and/or UpSet plot, as well as a summary of the matching procedure. Package: r-cran-bibnets Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1697 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cograph, r-cran-igraph, r-cran-knitr, r-cran-openalexr, r-cran-rcrossref, r-cran-rmarkdown, r-cran-testthat, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-bibnets_0.6.0-1.ca2404.1_all.deb Size: 1392960 MD5sum: dec99edeee782afd296106cab5ab6b1e SHA1: 04aa2d62d5901eb7bb193a8c0c8ea2a57e8e9087 SHA256: 0d17151a86c288e0d8abbde2dda05bcb221f90b93f051940b5804d532e8ae02a SHA512: 448f4c060db3475d0242f3c9c55f3220dcebfc7143b34df1e9b2d5337fcc2c8307b249800d203bf16b8b946565c76c63919107b7011be039b43b0f9eb3bcf22b Homepage: https://cran.r-project.org/package=bibnets Description: CRAN Package 'bibnets' (Importing, Constructing, and Exporting Bibliometric Networks) Imports, constructs, and exports bibliometric networks from scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX', 'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref' exports. Goes beyond standard co-networks with attention-weighted networks (lead, last, proximity, circular position weights), position-aware counting (harmonic, arithmetic, geometric, golden-ratio), similarity and dissimilarity normalisations, temporal networks with fixed, sliding, and cumulative windows, disparity-filter backbone extraction, historiograph construction, and local citation scoring. Methods described in López-Pernas, Saqr & Apiola (2023) . Package: r-cran-bibplots Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bibplots_0.0.8-1.ca2404.1_all.deb Size: 75476 MD5sum: bf521953facd125ad75d2eeafa495530 SHA1: d8ff849d472665e585335c63ef75c58f8ccfa4a0 SHA256: 2469c0d9307f668bd95613ec18ce367303f4a94433cce86fbf94f1f7c750d143 SHA512: 0ba0dd1a247530c79b2b773c3e94a2782f4bd92c76448d5f05f9ede532f06fe00bc8ca3c83b5c3d665e434f2c3cae5730e2d272dbefd6626f287fc0474688fbc Homepage: https://cran.r-project.org/package=BibPlots Description: CRAN Package 'BibPlots' (Plot Functions for Use in Bibliometrics) Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see ). Further extension to more plot variants is planned. Package: r-cran-bibs Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gigrvg Filename: pool/dists/noble/main/r-cran-bibs_1.1.1-1.ca2404.1_all.deb Size: 74928 MD5sum: 7c0a62efadf7d56698a7a012f0812d17 SHA1: ba0d4f8f33d99225d60124a93e3fb1e9d1a2fdcd SHA256: 94692f4ab2cb001e314362843183b4593d2cb645436483d076f5b0b403720030 SHA512: 0fb730c647c54d9e30fa0f26759fe66d250914aadfd42b3a4632964ea5243f4aa91aed903f89069f9bb301e7ddec9a011519911e51cd8e774e76f3dedde637b8 Homepage: https://cran.r-project.org/package=bibs Description: CRAN Package 'bibs' (Bayesian Inference for the Birnbaum-Saunders Distribution) Developed for the following tasks. 1- Simulating and computing the maximum likelihood estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing the Bayesian estimator for the parameters of the BS distribution based on reference prior proposed by Xu and Tang (2010) and conjugate prior. 3- Computing the Bayesian estimator for the BS distribution based on conjugate prior. 4- Computing the Bayesian estimator for the BS distribution based on Jeffrey prior given by Achcar (1993) 5- Computing the Bayesian estimator for the BS distribution under progressive type-II censoring scheme. Package: r-cran-bibtex Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bibtex_0.5.3-1.ca2404.1_all.deb Size: 73756 MD5sum: 8253fa33d0314f1f611300a12006774d SHA1: 2e408e18b719daefa2a4d5d33ebdd012019cfe60 SHA256: ef58414a1e649901501d507f35508a5a3b86fa875eeff652c54e08ccad557567 SHA512: 400b13570d19b888e315c005d560c37e1ad14e0d03870e55f1eebb42aa01a588b585b225211960056c076a5d16ff92c075948c55fe8e3249a6414432906b0a5e Homepage: https://cran.r-project.org/package=bibtex Description: CRAN Package 'bibtex' (Bibtex Parser) Utility to parse a bibtex file. Package: r-cran-bicausality Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bicausality_0.1.4-1.ca2404.1_all.deb Size: 141570 MD5sum: d86f9436702004c6ba48a2eec5833faf SHA1: 276f4c33157b66efd1a58b733776182948e35696 SHA256: d0a2973e87c87d4aee6b9b294dfc0b247ad08f8a17f6e830c2fbace2614c9db0 SHA512: 75439a3a3a947a230925524f31394d223a4182aec804e03a62abb04328560b94e308692b7520e4d4b32243f58b8c541e8cbe364f9bf8f08d2edc053fccca1290 Homepage: https://cran.r-project.org/package=BiCausality Description: CRAN Package 'BiCausality' (Binary Causality Inference Framework) A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) . Package: r-cran-biclustermd Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-biclust, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-magrittr, r-cran-nycflights13, r-cran-phyclust Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biclustermd_0.2.4-1.ca2404.1_all.deb Size: 227752 MD5sum: 257654cbbadee2bdd20ec1c02931b4e0 SHA1: 6681cfd4f66f3a8e2498e671972cdf4820200028 SHA256: 04a99c3677eb353780dc38b8ef4721e4df13f0f49e4cd211a343f48c2cac83d3 SHA512: 6a7982ec37c36c4e0941b76b2db24e24184192111701d9bfa0a563fef4537e1eea9c8b7ce5eb236a10ecbe2b13b2909a1fc2fadb400e23e9152d72b5d135600c Homepage: https://cran.r-project.org/package=biclustermd Description: CRAN Package 'biclustermd' (Biclustering with Missing Data) Biclustering is a statistical learning technique that simultaneously partitions and clusters rows and columns of a data matrix. Since the solution space of biclustering is in infeasible to completely search with current computational mechanisms, this package uses a greedy heuristic. The algorithm featured in this package is, to the best our knowledge, the first biclustering algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information Sciences, 510, 304–316. Package: r-cran-bicorn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bicorn_0.1.0-1.ca2404.1_all.deb Size: 78830 MD5sum: 000a18aca33bbe3e7147fb8f7847c6a8 SHA1: a05de6e1bca844ee9002cc79354edfff1438697a SHA256: 72cba360ee23392d38c3f9cdc5258fb18f4042eeb0bcccae6c850c3167d80e17 SHA512: 7d80f7a942e736fa1f926017299f6bbd2ac05447660e123ad49293f2c81dc86a96694035f0710de6298e894b11c59fa3a87e3dc12edd7a8add55e911fd69e3d1 Homepage: https://cran.r-project.org/package=BICORN Description: CRAN Package 'BICORN' (Integrative Inference of De Novo Cis-Regulatory Modules) Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor. Package: r-cran-bidask Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-xts, r-cran-zoo, r-cran-dplyr, r-cran-crypto2, r-cran-quantmod, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bidask_2.1.5-1.ca2404.1_all.deb Size: 1165090 MD5sum: 705a7f05f442a463a988b517f21d41b2 SHA1: ec972514ebebdc3933c0444bf9ed412f9423ebaa SHA256: 60f7e8d0dfa6fa257f96742745024acbfc1d3fc4f594d37ff4f30d9c5702344e SHA512: 90a132d48e170d429924414fd8939ddc860b26a19553bbd749b92eb0a1339249da6bbc8bad2f27035c9ce935f5ccbc28ae25c32511eb157d9514ae9dedcd2861 Homepage: https://cran.r-project.org/package=bidask Description: CRAN Package 'bidask' (Efficient Estimation of Bid-Ask Spreads from Open, High, Low,and Close Prices) Implements the efficient estimator of bid-ask spreads from open, high, low, and close prices described in Ardia, Guidotti, & Kroencke (JFE, 2024) . It also provides an implementation of the estimators described in Roll (JF, 1984) , Corwin & Schultz (JF, 2012) , and Abdi & Ranaldo (RFS, 2017) . Package: r-cran-bidimregression Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bidimregression_2.0.1-1.ca2404.1_all.deb Size: 129508 MD5sum: 2ce1e121d6ed51d5674052b3b807106b SHA1: 430889e04c004367df9779a900b52571a115213b SHA256: 93919a3edd961ac79c66970eaeda1994f1cf288301a4170e260e63d7770e3ebf SHA512: 86f3fd698209300edc284dd53cec3c7eecb6d34a9454a670d60bd2b43358a94eae37b3eba1445db3f2d45fbc7b9fced45da99b64728cdb8817a5c5c39d5bb933 Homepage: https://cran.r-project.org/package=BiDimRegression Description: CRAN Package 'BiDimRegression' (Calculates the Bidimensional Regression Between Two 2DConfigurations) Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965). Package: r-cran-bidser Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1886 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-data.tree, r-cran-tidyselect, r-cran-dplyr, r-cran-fs, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-neuroim2, r-cran-httr, r-cran-crayon, r-cran-stringdist, r-cran-ggplot2, r-cran-plotly, r-cran-patchwork, r-cran-viridis, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg, r-cran-systemfonts, r-cran-testthat, r-cran-covr, r-cran-lintr, r-cran-gluedown, r-cran-rnifti, r-cran-future, r-cran-future.apply, r-cran-pkgdown, r-cran-albersdown Filename: pool/dists/noble/main/r-cran-bidser_0.5.2-1.ca2404.1_all.deb Size: 1058358 MD5sum: 9bfdbd35e9ec0cd635298da731d9b349 SHA1: 890096d88188002491e669666e315450832c2f1c SHA256: 462ab7ede3896a1c46f89acc78c7d2bdbd4178a97d286df27cdde16bc02070f5 SHA512: df4fa4f32a02a5d3dc537599d8755a0e5a2a87011b3bcaace659a5031f767b08da85ac38835f80036c11cb0dac8d5b7dd3310a549e79c6547606a5e20aad1bc1 Homepage: https://cran.r-project.org/package=bidser Description: CRAN Package 'bidser' (Work with 'BIDS' (Brain Imaging Data Structure) Projects) Tools for working with 'BIDS' (Brain Imaging Data Structure) formatted neuroimaging datasets. The package provides functionality for reading and querying 'BIDS'-compliant projects, creating mock 'BIDS' datasets for testing, and extracting preprocessed data from 'fMRIPrep' derivatives. It supports searching and filtering 'BIDS' files by various entities such as subject, session, task, and run to streamline neuroimaging data workflows. See Gorgolewski et al. (2016) for the 'BIDS' specification. Package: r-cran-bidsr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-fastmap, r-cran-fs, r-cran-jsonlite, r-cran-nanotime, r-cran-s7, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bidsr_0.1.1-1.ca2404.1_all.deb Size: 698672 MD5sum: 1ff8dd0cdb9bf563ced6865b7830cc0e SHA1: a7513f8fc8de378aef1c92a47436fb26b7898ddd SHA256: 9a32b86de2ce4f45d510883449b8586bc43d8d6b1ded4ce2993623a513f1a1e7 SHA512: 4e9aff55c404d93f14f49f7f34091e6a436db9d6aead7ea940649706795f840f9b3b957b15ae135bbe50294ddf22572a9c98e464d7e68b210123323e37b087f4 Homepage: https://cran.r-project.org/package=bidsr Description: CRAN Package 'bidsr' (A Brain Imaging Data Structure ('BIDS') Parser) Parse and read the files that comply with the brain imaging data structure, or 'BIDS' format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) . Provides query functions to extract and check the 'BIDS' entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or 'RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) ; see 'citation("bidsr")' for details and attributions. Package: r-cran-bidux Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-jsonlite, r-cran-memoise, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-stringdist, r-cran-tibble Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-otel, r-cran-otelsdk, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bidux_0.4.0-1.ca2404.1_all.deb Size: 1198886 MD5sum: 5b8075526bc04d756227295083be38e0 SHA1: 7a4ef0139694c0b31bf8280a4f11ffddfe7b1629 SHA256: e9288862a833edfae5ff25e7aa53a86038deccf14e47d248cb69af6c5878ad87 SHA512: 076479ec5dd6ca713e7b46a88c794544e750a22f7e1bc0d3b9089f34e5b4f8b4494227bc29e930192da57ab3a84357dde21f99a7245c8d46f8f6dae4a776e512 Homepage: https://cran.r-project.org/package=bidux Description: CRAN Package 'bidux' (Behavioral Insight Design: A Toolkit for Integrating BehavioralScience in UI/UX Design) Provides a framework and toolkit to guide R dashboard developers in implementing the Behavioral Insight Design (BID) framework. The package offers functions for documenting each of the five stages (Interpret, Notice, Anticipate, Structure, and Validate), along with a comprehensive concept dictionary. Works with both 'shiny' applications and 'Quarto' dashboards. Package: r-cran-bien Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpostgresql, r-cran-dbi, r-cran-ape, r-cran-sf, r-cran-terra, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-curl, r-cran-maps Filename: pool/dists/noble/main/r-cran-bien_1.2.8-1.ca2404.1_all.deb Size: 380994 MD5sum: a621985212426dfd20ccfcd61b046a98 SHA1: d5ec19acc51bcee1423b3db4843512c23ece9c4d SHA256: e8212616e4fbfef23b9be7664fe87d96653befff4fc06db444196fa02182381b SHA512: 9189dc53bc495b4f733f1ba655af7dfa98e54645ffaf4fbf52d8adf0938a282a4f09448293f784e31e6564c42ff1c9e9b13ebc31b80b0256f2e66bcd6af293c7 Homepage: https://cran.r-project.org/package=BIEN Description: CRAN Package 'BIEN' (Tools for Accessing the Botanical Information and EcologyNetwork Database) Provides Tools for Accessing the Botanical Information and Ecology Network Database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See for more Information). This package provides functions that query the BIEN database by constructing and executing optimized SQL queries. Package: r-cran-bifactorindicescalculator Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-lavaan, r-cran-mirt, r-cran-mplusautomation, r-cran-mnormt Suggests: r-cran-testthat, r-cran-psych Filename: pool/dists/noble/main/r-cran-bifactorindicescalculator_0.2.2-1.ca2404.1_all.deb Size: 227212 MD5sum: 2c580396d8269f22fcbe6af88a9ee07a SHA1: 5a1eb6c7ef35b87166bbe4a401d05e46b593238d SHA256: d3ad0996c3d44fefcce195475994a05eee38f7b523bae1dc72b5dce0d457d488 SHA512: e303c182e28bf9eebc5871bb08ce16910ad89d71b554c84e9810e4c63fce6783d2a1e07d88b9e27404d7c42b3567008e2b28dac5d7a8578dd490f299b0e7ccfa Homepage: https://cran.r-project.org/package=BifactorIndicesCalculator Description: CRAN Package 'BifactorIndicesCalculator' (Bifactor Indices Calculator) The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017) with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models. Package: r-cran-bifactory Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-gparotation, r-cran-psych, r-cran-mass, r-cran-numderiv, r-cran-withr Suggests: r-cran-mplusautomation, r-cran-openxlsx2, r-cran-pkgload, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bifactory_0.6.0-1.ca2404.1_all.deb Size: 726834 MD5sum: 7a9e92b2d28b9c53c43fe748f5504144 SHA1: 540e4ee9706e53f255ff2de3e1d44f6fb9d7d5bd SHA256: 6025632272d0178d9faccd61a7520ab52653e5172b7bbb22079c0de339bd2b18 SHA512: 1c005af1b51471ce5081d36df9675d6d168272beace92d2579afd81e8830e5f958e70ee173c3cc1fadacc1ffd12195a2b9349d3483109017bf7a21263147ca70 Homepage: https://cran.r-project.org/package=bifactory Description: CRAN Package 'bifactory' ((Bifactor) ESEM with Continuous (MLR) or Ordinal (WLSMV) Data) Fits bifactor exploratory structural equation models (B-ESEM), together with standard exploratory structural equation modeling (ESEM) and confirmatory factor analysis (CFA), for continuous and ordinal data. Continuous models use 'lavaan' native efa() blocks with robust maximum likelihood (MLR) estimation. Ordinal ESEM defaults to the 'lavaan' weighted least squares mean- and variance-adjusted (WLSMV) estimator; ordinal B-ESEM uses a custom diagonally weighted least squares (DWLS) path with polychoric correlations from 'psych', rotation-delta standard errors via 'numDeriv', and a mean- and variance-adjusted chi-square. Target, geomin, and oblimin rotations use 'GPArotation'; the bifactor ESEM approach follows Morin, Arens and Marsh (2016) . Additional features include multi-group measurement invariance (configural through strict, with partial invariance), ESEM-within-CFA conversion, McDonald's omega reliability suite, and the Mehrvarz and Rouder (2026) alignment ratio check for independent cluster model confirmatory factor analysis (ICM-CFA) misspecification. An optional 'MplusAutomation' interface allows side-by-side comparison with 'Mplus' output. Package: r-cran-bifrost Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-cli, r-cran-digest, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-phytools, r-cran-plotrix, r-cran-rrphylo, r-cran-jsonlite, r-cran-mvmorph, r-cran-viridis, r-cran-txtplot Suggests: r-bioc-complexheatmap, r-cran-rcolorbrewer, r-cran-circlize, r-cran-evd, r-cran-phylolm, r-cran-rmarkdown, r-cran-testthat, r-cran-univariateml, r-cran-withr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-bifrost_0.2.0-1.ca2404.1_all.deb Size: 2065710 MD5sum: 8ff037f15021890a61768dec9d877315 SHA1: ebed4080683532f7abd13fdb07873f917354fac1 SHA256: a4413279fd9649edaa7623b72e1b48b67846f12eab55e879e6d1ffb4a6b0601f SHA512: badeee550b614e2c3a29d229474ba21a4f20199c0316a26121a6faf370023737a27239400fa89c19540f56a2eaa3e8b7afd39f843ae81ddc654aa67e3459c16a Homepage: https://cran.r-project.org/package=bifrost Description: CRAN Package 'bifrost' (Branch-Level Inference Framework for Recognizing Optimal Shiftsin Traits) Methods for detecting, visualizing, and evaluating cladogenic shifts in multivariate trait data on phylogenies. Implements penalized-likelihood multivariate generalized least squares models and a greedy step-wise shift search for high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Provides tools for inspecting search trajectories, summarizing branch and lineage rates, analyzing shift timing and magnitudes, estimating post-hoc regime covariance and integration, and running simulation-based calibration and tuning. The search follows approaches developed in Smith et al. (2023) and Berv et al. (2024) . Methods build on multivariate generalized least squares approaches described in Clavel et al. (2019) and implemented in the mvgls() function from the 'mvMORPH' package. Documentation and worked examples are available at . Package: r-cran-bifurcatingr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmultivar Suggests: r-cran-igraph, r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bifurcatingr_2.1.0-1.ca2404.1_all.deb Size: 861278 MD5sum: a3996f06c80cf186b5f39fc9f3efd611 SHA1: f05712fcff5a7340c891bec72c5d14466c51c453 SHA256: 2def151eb76cc3666865a24b1af728fda434dc670b96675fdcf495a34867242b SHA512: c86ed661b857cccf59ed34361689b42e354f77d348c8ca3effa84bc9ade60658b8a70179f8f1dd93548b3f5363a3168e7bd865592dd68f5e1955f2931d6e0310 Homepage: https://cran.r-project.org/package=bifurcatingr Description: CRAN Package 'bifurcatingr' (Bifurcating Autoregressive Models) Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) and Elbayoumi and Mostafa (2020) . Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) . Package: r-cran-bigassertr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-bigassertr_0.2.0-1.ca2404.1_all.deb Size: 34328 MD5sum: 3e9c2129ed3066c95404d7b7b08a153b SHA1: 5e4130c17009f3ed5f283a693755397ee687a02a SHA256: 6a8f1d6cdbf2eed0f3ce8ca827e245161e87e70584da4348934dccbd930ee431 SHA512: 41578d1a5be63a9424992a7132ec52bd5fa8e5287606925831912d1cad5bdbb18de9d8fcc150b9a295caf6191c4383aebbde7d05981e5d3f774a28dc671938ef Homepage: https://cran.r-project.org/package=bigassertr Description: CRAN Package 'bigassertr' (Assertion and Message Functions) Enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). Also, multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.). Package: r-cran-bigbang Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-glue, r-cran-whisker Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgbuild, r-cran-pkgload, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bigbang_0.5.1-1.ca2404.1_all.deb Size: 716616 MD5sum: 039583a3f18deec8f3b787449c4407f0 SHA1: c93e7ce8ae12565a18305b9915ab40b49c4e32f4 SHA256: 0fc713b9a36362ab23830b3c1330e9a1112695319b3283ea351254fa87b076d2 SHA512: dc9483d4cda0fb77468cf3a42f8f9e8a52d155dcb1e273423b4ced66f1b33f4160be070e01b5c5e5d030b9941fa246f7b6bbf13326f7dba9b54b44b5ca752544 Homepage: https://cran.r-project.org/package=bigbang Description: CRAN Package 'bigbang' (Build 'Tidyverse'-Style Meta-Packages from Local Package Files) Turns a curated set of package archives (.tar.gz, .zip) into one meta-package in the style of the 'tidyverse', so that a group of interdependent packages can be distributed and installed as a single unit. 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Package: r-cran-bigbits Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/noble/main/r-cran-bigbits_1.4-1.ca2404.1_all.deb Size: 63914 MD5sum: 10d4437dde6d9d75af6b25096277fc15 SHA1: ab3863d4d9197bd143341c70394bb0b00d7d54b8 SHA256: 7df4e40bdec4b5e17639060db05377e08216eb193310185754710f2e21061fd4 SHA512: 1a81c33db265b278f1780570fc88be7dadaeb2665572cdd27eb5d2935d2d487b442d768ce08f795628e2b67f5cd457b7ef3503fe82347bbd072380e7ed0b95e0 Homepage: https://cran.r-project.org/package=bigBits Description: CRAN Package 'bigBits' (Perform Boolean Operations on Large Numbers) A set of Boolean operators which accept integers of any size, in any base from 2 to 36, including 2's complement format, and perform actions like "AND," "OR", "NOT", "SHIFTR/L" etc. The output can be in any base specified. A direct base to base converter is included. Package: r-cran-bigchess Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-processx Suggests: r-cran-ff, r-cran-rsqlite, r-cran-rjson, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-bigchess_1.9.1-1.ca2404.1_all.deb Size: 1133114 MD5sum: f03f430d0d06a1849663bc01d87e63e3 SHA1: 1f5a16c5fc0ce0137fcf4a331b7815c17c620327 SHA256: 3c0e03d6ab470fc13277a057b060cf5e6b3d783d08c72dc518e677fcd9a5ea0e SHA512: 3e74a176a782e5c8a82b01b52e354d67c7a1814d504d1ee476c5844f0952c5b98b2fcb3b343367a3b5944576882e1e593c1d4e2e5be467ed17915ae66b274b74 Homepage: https://cran.r-project.org/package=bigchess Description: CRAN Package 'bigchess' (Read, Write, Manipulate, Explore Chess PGN Files and R API toUCI Chess Engines) Provides functions for reading *.PGN files with more than one game, including large files without copying it into RAM (using 'ff' package or 'RSQLite' package). Handle chess data and chess aggregated data, count figure moves statistics, create player profile, plot winning chances, browse openings. Set of functions of R API to communicate with UCI-protocol based chess engines. Package: r-cran-bigd Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-bigd_0.3.1-1.ca2404.1_all.deb Size: 1165086 MD5sum: 5094d7d2aa8c781ed66686ded5296cea SHA1: 84c765bb4972dd2f04c8188669ed8a51eb6b2ce9 SHA256: df6642e009ba3afe9f1a71db2aa45a990a66ca61d9e7439668a5aea99d3fc0a6 SHA512: f2829d768d4b1cadd695b1f346150e36ae31a52c2c8ec8d81c055250a48a750051a69ca0ecc76453b74dd2dfc046d0dd16a5fa026cf2d5bc088573884cfe4232 Homepage: https://cran.r-project.org/package=bigD Description: CRAN Package 'bigD' (Flexibly Format Dates and Times to a Given Locale) Format dates and times flexibly and to whichever locales make sense. Parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). 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Package: r-cran-bigdatape Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2379 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-apifetch Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-tibble, r-cran-httr2, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bigdatape_0.3.0-1.ca2404.1_all.deb Size: 102380 MD5sum: 340896901e88a9bf2b10ae1f4d99dc00 SHA1: f601a2fc7a31de6d8866edea380423ecc0c9e6c6 SHA256: ad43cc9b97f7dfb106e420aa3058280d16682cf3b75d3a91637ea58e223e1f06 SHA512: 1681e9a9cbae5f09056c1cdb0018fa7eea14fb3da8157837fe7eb0c31626abdeb19a0670312f02b507f769a307e7f1c2b239fbd057a1eb97e6c56e5ecaaf5c05 Homepage: https://cran.r-project.org/package=BigDataPE Description: CRAN Package 'BigDataPE' (Secure and Intuitive Access to 'BigDataPE' 'API' Datasets) Designed to simplify the process of retrieving datasets from the 'Big Data PE' platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data. The data-retrieval engine is provided by the generic 'apifetch' package, which 'BigDataPE' configures for the Big Data PE service. Package: r-cran-bigdawg Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-haplo.stats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bigdawg_3.1.0-1.ca2404.1_all.deb Size: 1164846 MD5sum: 11a1e61d3c40be3025d49d0357bb4f41 SHA1: 9923c30ea3e09cb6b7ac949379e69911a488453e SHA256: 34c2def92fad0cefd2b44785e17dd2596f1c04625d22bddb0250f7412d3d79fd SHA512: 930508447608507c081cb1d43d695e461b5e6e6f4c9fc091c2453466648012fd2607fd4eef2a1d1182e6d9bb2c4fd50ebd5740219aa2ba9ea72cd566d47b7bcd Homepage: https://cran.r-project.org/package=BIGDAWG Description: CRAN Package 'BIGDAWG' (Case-Cotrol Analysis of Multi-Allelic Loci) Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) . Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) . Package: r-cran-bigdm Architecture: all Version: 0.5.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crayon, r-cran-doparallel, r-cran-fastdummies, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-geos, r-cran-mass, r-cran-matrix, r-cran-parallelly, r-cran-rcolorbrewer, r-cran-rdpack, r-cran-sf, r-cran-spatialreg, r-cran-spdep, r-cran-rlist Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-bigdm_0.5.9-1.ca2404.1_all.deb Size: 5029428 MD5sum: dc3c41dd3314df9e8d917eef5177fba6 SHA1: 53973fff9d2645ae8ca047e8729c7e562e814c82 SHA256: 7c4cd19995d3157b0b88e3dd76a57f6ba9981fdc911e3e93ea8260452046df70 SHA512: 537ebddc1df992577ab290059fafdeffa4eba4e9f6d2167737b180581e44c68fd51e58784f3d0fe59d56540f00857e18aff6aa35147e74dca5f84930297b047e Homepage: https://cran.r-project.org/package=bigDM Description: CRAN Package 'bigDM' (Scalable Bayesian Disease Mapping Models for High-DimensionalData) Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 ; Orozco-Acosta et al., 2023 and Vicente et al., 2023 ). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001). Package: r-cran-bigl Architecture: all Version: 1.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-minpack.lm, r-cran-numderiv, r-cran-progress, r-cran-plotly, r-cran-robustbase, r-cran-scales, r-cran-nleqslv, r-cran-data.table, r-cran-lifecycle, r-cran-htmlwidgets, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-dt Filename: pool/dists/noble/main/r-cran-bigl_1.9.3-1.ca2404.1_all.deb Size: 1448758 MD5sum: 8603ab69e845c525b9215d637b5d9373 SHA1: 308a68be7f51b815b4262cffb0bfae8b1199071c SHA256: 102aa1a225b4e83a11663466c3baa459edca51054077bdd16c72fcdc5f195ebc SHA512: d2f2e01596f83f1d147b330ee42e0b4380644959e2e3b6c510dd6474a0c35e439d83f93d5a883e6ba912d864f1080c9a7e5478997b494f879b8550aacb24019d Homepage: https://cran.r-project.org/package=BIGL Description: CRAN Package 'BIGL' (Biochemically Intuitive Generalized Loewe Model) Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) . Package: r-cran-bigleaf Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-solartime Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigleaf_0.8.2-1.ca2404.1_all.deb Size: 1028224 MD5sum: dbc73ffb748f69bc6e65e337bdc01443 SHA1: 2c8a1724fed7053acc000055792aa6f68e751d20 SHA256: 0f7bad47d99720db6807363ebac3bc04619f9db19a3127e8a11f98cb43f07123 SHA512: 5f13f479ebe1cd187dbd713cfb6be0a243b3061044ae446986384ef493656243cb86cc11f512a3192881414927293f00e0c981068772f3b2a0072ca304165e2d Homepage: https://cran.r-project.org/package=bigleaf Description: CRAN Package 'bigleaf' (Physical and Physiological Ecosystem Properties from EddyCovariance Data) Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables. Package: r-cran-bigmatch Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcbalance, r-cran-liqueuer, r-cran-plyr, r-cran-mvnfast Suggests: r-cran-optmatch Filename: pool/dists/noble/main/r-cran-bigmatch_0.6.4-1.ca2404.1_all.deb Size: 200288 MD5sum: 5a7eb80aa7a156158ab2f7c2a0095150 SHA1: 79ff717aa0c0917aae98a3e889a7bd24c6809de3 SHA256: aa9a03ffaf325561c39209467b568d6efee3a1aa6a90554e2d533573d1f05fd4 SHA512: 925c43e7fe45d66a430456de75815931ae3ce8dcd1471fd8317d409eb9573dd99938e4521669f61f096c0524f6bf597b7d196079a1db3b114a51a4aecf423f3c Homepage: https://cran.r-project.org/package=bigmatch Description: CRAN Package 'bigmatch' (Making Optimal Matching Size-Scalable Using Optimal Calipers) Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). . Package: r-cran-bigmds Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-svd, r-cran-corpcor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigmds_3.0.0-1.ca2404.1_all.deb Size: 70342 MD5sum: c78cc4fa6c4d1fcd167547ced27ac5c5 SHA1: a2eb0b9562b1d9b73456d342cbedc5d90a4e948c SHA256: f21bea3720207f43a69bbdb08d3174126d1672895e749192bd354e24499cd7d7 SHA512: 56c59e463785a9234448446a62e75eb09ca1ebc4cd40da34919d1ebe791124d50c0ec216e531095b7c50712585060242290bca675a14be62f6d825b911ec2a38 Homepage: https://cran.r-project.org/package=bigmds Description: CRAN Package 'bigmds' (Multidimensional Scaling for Big Data) MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006). Package: r-cran-bigmemory.sri Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bigmemory.sri_0.1.8-1.ca2404.1_all.deb Size: 12746 MD5sum: e73135fcb6f3f65bd0a7279c858ae399 SHA1: 5e6a37d2a6d7602045716793db7817cbae13bbaa SHA256: 122dbc596d694333d1c0e7017975a0e31d514947cfe58767d6807ba7886ad7c5 SHA512: 358e0d0547a9a16d59681ce4efd4ad2e6081d718dd7cd7f8221f9dc150863ce5be3f4793679513a0e268af5b00edc4f02135a3669aa809b27f56a4ba567f3e94 Homepage: https://cran.r-project.org/package=bigmemory.sri Description: CRAN Package 'bigmemory.sri' (A Shared Resource Interface for Bigmemory Project Packages) A shared resource interface for the bigmemory and synchronicity packages. Package: r-cran-bigmice Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-sparklyr, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bigmice_1.0.0-1.ca2404.1_all.deb Size: 163246 MD5sum: c6695d97028172d2fc6a996c9e5a3216 SHA1: b145d376777f9138765c6001733e162e9bea95ec SHA256: 894dc5ad1a1c1675159d89f2701ab59c5b50922a9c1b426fb8fcfbc97760211d SHA512: e0d9ddedb4fe8d8a64d1addf0cb8766f1b894f0aa362e44425b06b5357e60a674e8749c590dfb0d78df0caa995d55c8e16054d4172b1cd846a6407eb03710d26 Homepage: https://cran.r-project.org/package=bigMICE Description: CRAN Package 'bigMICE' (Multiple Imputation of Big Data) A computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using 'Apache Spark'. The methodology is described in Morvan et al. (2026) . Package: r-cran-bigparallelr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-bigassertr, r-cran-doparallel, r-cran-flock, r-cran-parallelly, r-cran-rhpcblasctl Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-bigparallelr_0.3.2-1.ca2404.1_all.deb Size: 42574 MD5sum: 05173ac5186c3a6d19711e9228e72bf9 SHA1: 0401d2eb3d2250b3b26d5132f7cc3b50f8b46943 SHA256: e24218448e7b0bfa41ed2e03387121a0c54cbbbfe3d30ca6c5ab28c863f4a459 SHA512: f0fa387c0703ea20c5fdb4f1bc6ca408a014e401f9b5545688ccd96db4245297eb52abf2e68c8ca12d158f6cde057707fa405aa65b5daafe0852319fef54c9d1 Homepage: https://cran.r-project.org/package=bigparallelr Description: CRAN Package 'bigparallelr' (Easy Parallel Tools) Utility functions for easy parallelism in R. Include some reexports from other packages, utility functions for splitting and parallelizing over blocks, and choosing and setting the number of cores used. Package: r-cran-bigpopa Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-quadprog, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-vcfr, r-cran-withr Filename: pool/dists/noble/main/r-cran-bigpopa_2.1.0-1.ca2404.1_all.deb Size: 268638 MD5sum: cd8f9b4a47220ea00f67147e18eccad6 SHA1: 4c5d558be7d342891b233542c7b777c112366b8f SHA256: 98d5ffa3951ce5f52a07eb3300fde962da49cff467afbec60a0094c2d4ca749b SHA512: 07811519d33b81584d84338630e840e9c739e891a56cef7e8fd80dcf4734d5a747c820df2494d1ef8bd631fe240ad71e29a3ce1413d11f997f78c4bb25443637 Homepage: https://cran.r-project.org/package=BIGpopA Description: CRAN Package 'BIGpopA' (Pedigree Validation Genetic Composition of Diploids & Polyploids) Tools for pedigree quality control and genomic breed/line composition estimation in diploid and polyploid breeding populations. 'BIGpopA' provides functions to check and correct common pedigree errors, assign parentage from SNP genotype data using Mendelian error rates, validate parent-offspring trios, and estimate genome-wide breed or line composition using quadratic programming. Pedigree validation and parentage assignment support any ploidy, using a polysomic Mendelian test for even ploidy and a homozygosity-based check for odd ploidy. Genotypes can be supplied as dosage tables, VCF files, or 'PLINK' .ped files. For more details about the included 'breedTools' functions, see Funkhouser et al. (2017) . Package: r-cran-bigr Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2971 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rdpack, r-cran-readr, r-cran-reshape2, r-cran-rlang, r-cran-tidyr, r-cran-vcfr, r-bioc-rsamtools, r-bioc-biostrings, r-bioc-pwalign, r-cran-janitor, r-cran-quadprog, r-cran-tibble, r-cran-stringr, r-cran-data.table Suggests: r-cran-covr, r-cran-ggplot2, r-cran-spelling, r-cran-rmdformats, r-cran-knitr, r-cran-rmarkdown, r-cran-polyrad, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigr_0.7.2-1.ca2404.1_all.deb Size: 1424942 MD5sum: 984ddfe1e2789c1a2a9b9c2218ff5b82 SHA1: add6fb0f59c33a3ba4bcd78016c440f14912e3f4 SHA256: 5424a102016400babb2ae6335ea64760dc108515fc20d9aad4436d04f21a9388 SHA512: 6ed6d05f6e58d10ebcc3090e3288cc3df948cf4ef6ebe2f97da72462dc11eb570aa327e94bd7aaf8cefae98699b350593c7403142d8c8304ea77290022a88ed9 Homepage: https://cran.r-project.org/package=BIGr Description: CRAN Package 'BIGr' (Breeding Insight Genomics Functions for Polyploid and DiploidSpecies) Functions developed within Breeding Insight to analyze diploid and polyploid breeding and genetic data. 'BIGr' provides the ability to filter variant call format (VCF) files, extract single nucleotide polymorphisms (SNPs) from diversity arrays technology missing allele discovery count (DArT MADC) files, and manipulate genotype data for both diploid and polyploid species. It also serves as the core dependency for the 'BIGapp' 'Shiny' app, which provides a user-friendly interface for performing routine genotype analysis tasks such as dosage calling, filtering, principal component analysis (PCA), genome-wide association studies (GWAS), and genomic prediction. For more details about the included 'breedTools' functions, see Funkhouser et al. (2017) , and the 'updog' output format, see Gerard et al. (2018) . Package: r-cran-bigsimr Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigsimr_0.12.0-1.ca2404.1_all.deb Size: 22416 MD5sum: e6245ae973a7ce51eb759a34b721de35 SHA1: d44ea70ab6a640b63c2142310eb4c9d38022fa69 SHA256: e58eb22657836585f8cc76179c50e6f9c0a9c5d5a552a4e2a53a47689ebddaeb SHA512: dd4a61c9ec79ee37d32e5877bfe0e36fdae7b73bfc4075e5ad80971e3ba1751aa975cc3d89c5b75c7c2bf529da2ac8327f028c53fffe76dc9803c7dc54e5a5c6 Homepage: https://cran.r-project.org/package=bigsimr Description: CRAN Package 'bigsimr' (Fast Generation of High-Dimensional Random Vectors) Simulate multivariate data with arbitrary marginal distributions. 'bigsimr' is a package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. It utilizes the Julia package 'Bigsimr.jl' for its core routines. Package: r-cran-bigstep Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-magrittr, r-cran-matrixstats, r-cran-r.utils, r-cran-rcppeigen, r-cran-speedglm Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigstep_1.1.2-1.ca2404.1_all.deb Size: 88636 MD5sum: 0cbf49b58e73f89ee00cc2bb57e57e7d SHA1: f58f4d8b498447a73cdbc2131991e096784c5370 SHA256: 6a5cbb998e6758a29ed46b6a1ec3655ed3428ca4c1db25b8d2ed58381bedadd9 SHA512: 9890d41393c3a9c9db8fd928b7943eb0a4a36271efd219430c00ff9a23a528dfa095270f2833f5525222bab83c41b872f032f9e5b798308f2a3f0c5bd3d416e9 Homepage: https://cran.r-project.org/package=bigstep Description: CRAN Package 'bigstep' (Stepwise Selection for Large Data Sets) Selecting linear and generalized linear models for large data sets using modified stepwise procedure and modern selection criteria (like modifications of Bayesian Information Criterion). Selection can be performed on data which exceed RAM capacity. Bogdan et al., (2004) . Package: r-cran-bikeshare14 Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3524 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table Filename: pool/dists/noble/main/r-cran-bikeshare14_0.1.4-1.ca2404.1_all.deb Size: 3577032 MD5sum: f16aaa7c01b6216638c3a45cdeebc196 SHA1: 0439c0c2a8defe932be09c0dafc1334236817ee0 SHA256: d13922feafe46a8ff9041086e7f631ff4645708dfc495f804b465f36b520e651 SHA512: 6b2f115a114bcf3c08660eb65fa4807ffb3d4290bc7342fae2255eec5adf8eace66dd3c3c72f944a75aa4dc29c4506d79274b513243bfcab8d1faddb737d7973 Homepage: https://cran.r-project.org/package=bikeshare14 Description: CRAN Package 'bikeshare14' (Bay Area Bike Share Trips in 2014) Anonymised Bay Area bike share trip data for the year 2014. Also contains additional metadata on stations and weather. Package: r-cran-bikm1 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-ade4, r-cran-pracma, r-cran-ggplot2, r-cran-reshape2, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-bikm1_1.1.0-1.ca2404.1_all.deb Size: 449228 MD5sum: b5b5ab71b278c8408b0323975c62f521 SHA1: bc45f6affff7cc7e32cfa476f2d63f1eb59d74c9 SHA256: ac1a4161469f9b254602dae670453d38aa18a6a33434e79a60acd3091dd03391 SHA512: 0352538fb1e8d23617784fbd1c0918b15730e42eaa10da6414a85c2f2a258d07982e63833166efb08494291d7bfb5555b54f00be54fad1378aadf3a2f9ab4fe3 Homepage: https://cran.r-project.org/package=bikm1 Description: CRAN Package 'bikm1' (Co-Clustering Adjusted Rand Index and Bikm1 Procedure forContingency and Binary Data-Sets) Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) . 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BiMaU stands for the Biostatistics and Mathematics Research Unit at the Sant Joan de Déu - Pediatric Cancer Center Barcelona . 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Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control, by A. Luciani (2022) . 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Package: r-cran-binarybalancedcut Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-binarybalancedcut_0.2-1.ca2404.1_all.deb Size: 14878 MD5sum: 0368c4f8206681fd254cb35019b9260a SHA1: ad8b6d94233eeeb11afecb332527af6e60149a69 SHA256: 22335916d4f70f96313d589136ae6157a60a632d875d6fc0fe345d51ef4ba8af SHA512: 3b8133b335b2dcba508a8a1fa63b156322d802a972e804fbefdfa6111d87301c857567c32939b54d9fcf4a8626d021f98252d68723ecc61aa15c326a9dcd38a4 Homepage: https://cran.r-project.org/package=BinarybalancedCut Description: CRAN Package 'BinarybalancedCut' (Threshold Cut Point of Probability for a Binary Classifier Model) Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold. 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Package: r-cran-binaryeppm Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-expm, r-cran-numderiv, r-cran-lmtest Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-binaryeppm_3.0-1.ca2404.1_all.deb Size: 430004 MD5sum: 777e3673902a0428c4fecc083c045f6d SHA1: e2bb12ef3114a12dcbe2cf5b5a238329f2c4f766 SHA256: ab621e18a8aca85dbe29fcf97b0692d9d5dbe4586af46d2d3315dee075592902 SHA512: 8b52fed37315d87e75c892096465f82f7c76346177b88547bb9ca1d5ecd938d7bd382851def9076f29e51e7a6edf5875b18f2d0589619ee700805c74e5d7bdd4 Homepage: https://cran.r-project.org/package=BinaryEPPM Description: CRAN Package 'BinaryEPPM' (Mean and Scale-Factor Modeling of Under- And Over-DispersedBinary Data) Under- and over-dispersed binary data are modeled using an extended Poisson process model (EPPM) appropriate for binary data. A feature of the model is that the under-dispersion relative to the binomial distribution only needs to be greater than zero, but the over-dispersion is restricted compared to other distributional models such as the beta and correlated binomials. Because of this, the examples focus on under-dispersed data and how, in combination with the beta or correlated distributions, flexible models can be fitted to data displaying both under- and over-dispersion. Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for the probability of success and scale-factor with various link functions for p, and log link for scale-factor, to fit a variety of models relevant to areas such as bioassay. Details of the EPPM are in Faddy and Smith (2012) and Smith and Faddy (2019) . 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Package: r-cran-binequality Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.cens, r-cran-gamlss.dist, r-cran-survival, r-cran-ineq Filename: pool/dists/noble/main/r-cran-binequality_1.0.4-1.ca2404.1_all.deb Size: 2508932 MD5sum: f3cc39a5b06284df6067619a6b9a1e06 SHA1: 7de6759da8af14c462752a253bbe25aa85caa9a4 SHA256: 5a2980e0183b99f5f1c485b661028096ba8c9dbc8e9858e974a04f8eea89a48c SHA512: 2dbb2a3f9480d57c2a049a6d235202230dfce3cf8c99253d9d093cf1c83adcb403d346def393faf67efd2afbb0f3e7f1cdf86037b6189dc06ec85021fdab2e1b Homepage: https://cran.r-project.org/package=binequality Description: CRAN Package 'binequality' (Methods for Analyzing Binned Income Data) Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states. 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The functions in the binest package translate those bin counts into estimates of the mean and standard deviation (SD). They do so using the heteroskedastic ordered probit (HETOP) model, which assumes that scores follow a normal distribution within each school or district, each of which has its own mean and SD. The binest package includes the fast_hetop() function, which fits the model much more quickly than previous implementations. The model is described by Reardon, Shear, Castellano and Ho (2017) ; a Bayesian variant is described by Lockwood, Castellano and Shear (2018) . 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High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, )), polarization shifted QPSK (Agrell & Karlsson (2009, )), and utility functions to work with the performance functions. 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For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations. 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It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: . The GUI version of this package is available on the R Shiny online server at: or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) , and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) . The authors of these publications are thanked for allowing the use of their data. 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The confidence intervals are: Jeffreys, Wald, Wald corrected, Wald, Blyth and Still, Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. . 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Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) . At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included 'shiny' apps. 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Package: r-cran-binst Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Suggests: r-cran-discretization, r-cran-formula, r-cran-testthat, r-cran-bammtools, r-cran-earth Filename: pool/dists/noble/main/r-cran-binst_0.2.1-1.ca2404.1_all.deb Size: 28770 MD5sum: 6b179263b66eff3dd745aafd4d83a4be SHA1: f10456fb77bb6a24d01c0519a7f5289360ff6f02 SHA256: 892ee2af2f01d44e5ca6bb304e4dcf7aba72d39864ff70b07fceae8ce7f28152 SHA512: 1b982f14495bb3a451b29d0468ac8793213305f34cb094b0222b47d560331cdb89f8b11fef98d3d095ee3040ac9021fa8a2d98696580a3a244c690dacda0d38a Homepage: https://cran.r-project.org/package=binst Description: CRAN Package 'binst' (Data Preprocessing, Binning for Classification and Regression) Various supervised and unsupervised binning tools including using entropy, recursive partition methods and clustering. 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Provides functions to parse structured sample identifiers encoding pyrolysis conditions, read raw FTIR and XRD instrument output, compute adsorption capacity and removal efficiency, fit adsorption isotherms following Langmuir (1918) and Sips (1948) among other models, fit adsorption kinetics following Ho and McKay (1999) and Chien and Clayton (1980) among other models, fit batches of samples at once, compute van't Hoff thermodynamic parameters, baseline-correct and pick peaks in FTIR spectra, deconvolve XRD patterns into a crystallinity index, compute BET surface area following Brunauer, Emmett, and Teller (1938) , compute proximate and ultimate analysis summaries including directly from a thermogravimetric analysis (TGA) curve, compute a smoothed derivative thermogravimetric (DTG) curve and pick its decomposition peaks, fit non-isothermal decomposition kinetics from multi-heating-rate TGA data following Kissinger (1957) , build correlation matrices with p-values, and produce publication-style base-graphics figures including 600 dpi TIFF export. Built on base R ('stats', 'graphics', 'grDevices') so it has no dependency on packages that require external CRAN network access to install. Package: r-cran-biocharkitgui Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-biocharkit, r-cran-shiny, r-cran-readxl, r-cran-dt, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-biocharkitgui_0.3.1-1.ca2404.1_all.deb Size: 94036 MD5sum: 5b2b06a1dc845c61bbd8eb3008a79b3b SHA1: f0058d53551cd4ddac857158d64e394875f3f812 SHA256: b424462b853536ea2ddbf1de800a0db05edb5203d6a96781e137a159cf37370a SHA512: 902627701749bbad997bf5ba759ae75c26631d0567fbee0a051f30fcf64f74204a51af068888d720a6eb63739762e3e400d0c2ee2dd1546dbfe5fc4c904cee55 Homepage: https://cran.r-project.org/package=biocharkitgui Description: CRAN Package 'biocharkitgui' ('Shiny' GUI for the 'biocharkit' Biochar Analysis Toolkit) A point-and-click 'Shiny' interface to the 'biocharkit' package. Lets a user upload Excel workbooks of biochar characterisation and batch adsorption data, map spreadsheet columns to the required variables via dropdown menus, and run sample-ID parsing, adsorption capacity and removal efficiency calculations, isotherm fitting (Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Sips), kinetics fitting (pseudo-first/ second-order, Elovich, intraparticle diffusion), van't Hoff thermodynamics, batch fitting across many samples at once, FTIR baseline correction, automatic peak picking and functional-group analysis, XRD peak deconvolution and crystallinity index, BET surface area, TGA analysis (DTG curve with auto-detected decomposition peaks, moisture/volatile-matter/ash/fixed-carbon straight off a curve for a single sample or in batch across many, and Kissinger non-isothermal kinetics from multi-heating-rate data), proximate/ultimate analysis, and correlation matrices, without writing any R code. Results and 600 dpi TIFF figures can be downloaded directly from the browser, along with a combined analysis report. Package: r-cran-biocircos Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-biocircos_0.3.4-1.ca2404.1_all.deb Size: 250592 MD5sum: 03cbaf096c9c7cc40720f5d842cf17ad SHA1: 33b8e619afbc3be31b5f058b746276a830125cb4 SHA256: 0d2c4c641e09a11b436d94292abce4b935ea4e320850c03d6a81d959e5144805 SHA512: acc04693e04c9a011591cc9fc1b07f077785dedf40e6fd45a300cd7f51d36c53627ad07d1751bcc8165545de73fb17ea46bdce5d44551a3a91aa1c612dc24115 Homepage: https://cran.r-project.org/package=BioCircos Description: CRAN Package 'BioCircos' (Interactive Circular Visualization of Genomic Data using'htmlwidgets' and 'BioCircos.js') Implement in 'R' interactive Circos-like visualizations of genomic data, to map information such as genetic variants, genomic fusions and aberrations to a circular genome, as proposed by the 'JavaScript' library 'BioCircos.js', based on the 'JQuery' and 'D3' technologies. The output is by default displayed in stand-alone HTML documents or in the 'RStudio' viewer pane. Moreover it can be integrated in 'R Markdown' documents and 'Shiny' applications. Package: r-cran-bioclients Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-biohttp, r-cran-httr2, r-cran-tibble Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bioclients_0.1.1-1.ca2404.1_all.deb Size: 606800 MD5sum: 3f8a1cfcd7f7c6f563f995b1e1d68aab SHA1: 9b6b1fb7380659a0a644ba456b101b5bc55115dd SHA256: 2ac1e6fdd31dca598e1437ea60c4cc40b5cc1f6c52a98c548ca18ec8e061a3d5 SHA512: e6e3700f49151a5ae0f142861bb7ab5e2ef8c9a696ae905c61c6e3222101af458e1f0b53f87361c754d764f8593888cae5cab35757340a8049ac2fb91a890a6a Homepage: https://cran.r-project.org/package=bioclients Description: CRAN Package 'bioclients' (Clients for Biological Database Web Services) Look up genes, variants and proteins from R, without writing a client for every biological web service. Each service gets one client that makes the request and returns a table. Parsing is a separate function that needs no network, so it can run on a saved response and be tested offline. Transport, retries, caching and error handling are left to the 'biohttp' package. Dependencies for single services are optional, so you do not install what you will not use. The services covered include 'Ensembl', described in Dyer et al. (2025) , 'UniProt', in The UniProt Consortium (2025) , 'gnomAD', in Chen et al. (2024) , 'Open Targets', in Buniello et al. (2025) , and the 'AlphaFold' Protein Structure Database, in Varadi et al. (2024) . Each client's help page cites the service it calls. Package: r-cran-bioclim Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1442 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-ggplot2, r-cran-berryfunctions, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bioclim_0.4.0-1.ca2404.1_all.deb Size: 1426448 MD5sum: 6dda327f2a50167f9157fffbb88a2dec SHA1: 0b331c18fc7ad5164a34e34e76edb49745fe9c86 SHA256: 214fb21dd4aab863d22ec239f6cd01a296a2035d7556cff2031389950593340f SHA512: 039b5e66d59228fcd5bec45805812a600a22403c59cebd491f90141b85c8086d01b034f07e5ad8d109d3bef44dc5780b02b64d4b1fc2f34d86a007b91764a7b4 Homepage: https://cran.r-project.org/package=bioclim Description: CRAN Package 'bioclim' (Bioclimatic Analysis and Classification) Using numeric or raster data, this package contains functions to calculate: complete water balance, bioclimatic balance, bioclimatic intensities, reports for individual locations, multi-layered rasters for spatial analysis. Package: r-cran-biocmanager Architecture: all Version: 1.30.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1198 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-biocversion, r-bioc-biocstyle, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-curl, r-cran-knitr Filename: pool/dists/noble/main/r-cran-biocmanager_1.30.27-1.ca2404.1_all.deb Size: 633670 MD5sum: 2d84562901d334aaefddc39d006841f7 SHA1: 5c073836f9113b508681f4ecca75e4321118a76b SHA256: d33dbca9a0a0748f3a57563848d15f8bcd83507c918caf66dfb24b55c2df7f45 SHA512: b8e87823f3357c81fb00e3d5739849fb825ed80d20bc27380f380e58c4a188a8a6844daa097366ca508ec3480c37ad76cb72b8fd81535b2a33dc808e96e2ce84 Homepage: https://cran.r-project.org/package=BiocManager Description: CRAN Package 'BiocManager' (Access the Bioconductor Project Package Repository) A convenient tool to install and update Bioconductor packages. Package: r-cran-biocohort Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-cli, r-cran-rlang, r-cran-checkmate, r-cran-fs, r-cran-readr, r-cran-dplyr, r-cran-tibble Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-knitr, r-cran-rmarkdown, r-bioc-rtracklayer, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-cran-babelgene, r-cran-withr, r-cran-readxl, r-cran-writexl, r-cran-arrow, r-bioc-summarizedexperiment, r-cran-seuratobject, r-cran-yaml Filename: pool/dists/noble/main/r-cran-biocohort_0.1.1-1.ca2404.1_all.deb Size: 519530 MD5sum: 9ea65797508ca436849ee49bddb4d311 SHA1: f14f7d603634cdaa06d902a0be79cacc6f6639ea SHA256: 947aa1e14389e270172310c5cfe591dc5ebb782a63f40ce3a32e1d3b9973bbf7 SHA512: fdf1ab52033cf8c0f35e85725af69e5d2a8ae09046a7000de2664bf550fff4e34a088d2a70a42451e424ccc2b83d8268f259db2aa9a436645df150f12ab0cf19 Homepage: https://cran.r-project.org/package=biocohort Description: CRAN Package 'biocohort' (Cohort Objects for Subjects and Samples in Omics Studies) Keeps the subjects, samples, and analysis outputs of a study in one validated object. It starts from a sample manifest with one row per sample, which is read, checked, and split into a subject table and a sample map. Species and assay are plain values in those tables rather than fixed types, so the same object serves any organism and any omics assay. From that object the package writes the sample sheet a pipeline expects, pairs tumor and normal samples on demand, and records where each analysis writes its output so the files can be loaded back in by subject or by pair. Manual corrections are kept in an audit trail. Results can also be translated across genome builds or species, with liftover for coordinates and ortholog mapping for genes. Package: r-cran-biocompute Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1830 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-yaml, r-cran-digest, r-cran-uuid, r-cran-jsonvalidate, r-cran-httr, r-cran-curl, r-cran-crayon, r-cran-cli, r-cran-stringr, r-cran-magrittr, r-cran-rmarkdown Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-biocompute_1.1.1-1.ca2404.1_all.deb Size: 382638 MD5sum: 66aa9ba686b785ef0ce52e2c852eb0e2 SHA1: a84669be5f4bb945e69a20b79955dc9c98bfe8ba SHA256: c3e30ec4362a7ed1a2fe70f09953f0022754000962052d81b64ad87c80ed9ebd SHA512: a8429fd5b20ffdf1cdd713afaf500164c7454870da22da2b093fd7654ba6fc248014d3bdbdadddecccc2bce4ce8c7c44e58fa391ef87f0d8574dd76c988ca660 Homepage: https://cran.r-project.org/package=biocompute Description: CRAN Package 'biocompute' (Create and Manipulate BioCompute Objects) Tools to create, validate, and export BioCompute Objects described in King et al. (2019) . Users can encode information in data frames, and compose BioCompute Objects from the domains defined by the standard. A checksum validator and a JSON schema validator are provided. This package also supports exporting BioCompute Objects as JSON, PDF, HTML, or 'Word' documents, and exporting to cloud-based platforms. Package: r-cran-biodem Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-biodem_0.5-1.ca2404.1_all.deb Size: 86842 MD5sum: 8f83f53d50debb9431a5e18fee6e1f66 SHA1: 18ed740ba928d03e3e7103d6f4d0a548d637655f SHA256: ba73e089aaabf92aafa3b44d4d66fc877e5c498d1b90ad4c874cc6e62b542834 SHA512: 0250db7f39f310e9d536649e1c0f95c7c50f55e562535b282551be664e511fb9812fd17ba26a830859daaeb4c9484d148931e50f91b9422422cf53e5f66e24f0 Homepage: https://cran.r-project.org/package=Biodem Description: CRAN Package 'Biodem' (Biodemography Functions) The Biodem package provides a number of functions for Biodemographic analysis. Package: r-cran-biodiversityr Architecture: all Version: 2.18-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2794 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vegan, r-cran-rcmdr, r-cran-ggplot2 Suggests: r-cran-vegan3d, r-cran-rgl, r-cran-permute, r-cran-lattice, r-cran-mass, r-cran-mgcv, r-cran-cluster, r-cran-car, r-cran-rodbc, r-cran-rpart, r-cran-effects, r-cran-multcomp, r-cran-ellipse, r-cran-sp, r-cran-spatial, r-cran-nnet, r-cran-dismo, r-cran-raster, r-cran-terra, r-cran-maxlike, r-cran-gbm, r-cran-randomforest, r-cran-gam, r-cran-earth, r-cran-mda, r-cran-kernlab, r-cran-e1071, r-cran-glmnet, r-cran-bootstrap, r-cran-presenceabsence, r-cran-geosphere, r-cran-enmeval, r-cran-red, r-cran-igraph, r-cran-rlof, r-cran-maxnet, r-cran-party, r-cran-readxl, r-cran-colorspace, r-cran-dplyr, r-cran-rlang, r-cran-sf, r-cran-envirem, r-cran-concaveman, r-cran-pvclust, r-cran-blockcv Filename: pool/dists/noble/main/r-cran-biodiversityr_2.18-1-1.ca2404.1_all.deb Size: 1691732 MD5sum: b29b9ccb7b1afe93b583b9b93446481d SHA1: c940c4c942a4c6697e4e786129d6ebecbe582bfd SHA256: b61958ce0dc45e50761b351ca8993a1094e42e3c82fc751e51f332327e9fb5c9 SHA512: b0db08c600f11ed5be718c29ee6d929961cf41b8c7c434bdb6dd4535120340ddfb74169b1ca61fb475446051240009b32f59fd785bbab2a125cd055ad6e8574a Homepage: https://cran.r-project.org/package=BiodiversityR Description: CRAN Package 'BiodiversityR' (Package for Community Ecology and Suitability Analysis) Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package. Package: r-cran-biodosetools Architecture: all Version: 3.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-golem, r-cran-bsplus, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-maxlik, r-cran-mixtools, r-cran-msm, r-cran-pdftools, r-cran-rhandsontable, r-cran-rlang, r-cran-readr, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-tidyr, r-cran-config, r-cran-cli, r-cran-openxlsx, r-cran-mass, r-cran-gridextra, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-kableextra, r-cran-markdown, r-cran-pander, r-cran-tinytex, r-cran-xtable Filename: pool/dists/noble/main/r-cran-biodosetools_3.7.2-1.ca2404.1_all.deb Size: 2982068 MD5sum: f646b9eba583faeb7008503d1465af57 SHA1: cbe747009d5097be69f1b86a5f4689c1fbf7a4c3 SHA256: 1ac8cfef9cac7b0b7cb47a632f7f80fc7b98b399dfdc10621397ccec83ed83b0 SHA512: 6bb3914141c01751d5dd69a6df8e55cdc9c3a15e83b9267732355d50279b1ccbb1e65c63a8d9bc28de983d50356c73d555a2fad7052aab07b9c0cce9a9adfe90 Homepage: https://cran.r-project.org/package=biodosetools Description: CRAN Package 'biodosetools' ('shiny' Application for Biological Dosimetry) A tool to perform all different statistical tests and calculations needed by Biological dosimetry Laboratories. Detailed documentation is available in . Package: r-cran-biodry Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-ecodist Filename: pool/dists/noble/main/r-cran-biodry_0.9.1-1.ca2404.1_all.deb Size: 168648 MD5sum: 5d97b81c10547bc2a0fa0ba8a4ed2054 SHA1: e19921f3870c781f1c4eb533c54d5f1c706d7c42 SHA256: 31b911c99b965c4d51e14720c924caa6b6fe293a637700b2fea63b6aa94453c4 SHA512: a69a67b10b3486cf9d9b3a2406c9d5756dd36b0b2f66f460a1e3aa0cd87757c47307878a3fc53b4e97be99a38bb7211fe7e9dcf323f0dada9469f8bc67e76133 Homepage: https://cran.r-project.org/package=BIOdry Description: CRAN Package 'BIOdry' (Multilevel Modeling of Dendroclimatical Fluctuations) Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions. Package: r-cran-bioefic Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bioefic_0.1.1-1.ca2404.1_all.deb Size: 136868 MD5sum: 2080e1ee108aff9788847865fb2aa512 SHA1: 84b8200eb20b7a07d5e60927705147fcf3eea278 SHA256: e6b72cc353973fe924ede1cf6e26a32a02a1a0085ce02d3731aa25cbdf69f998 SHA512: ff7599bd99992da904be72dc3411c2cf94b13ce5d9d5bd658152279f76c14f26f3f7f9b2e0ac1e3082c2087c00ae3f928bf3ce994deddbe633ac61e13c3fbe38 Homepage: https://cran.r-project.org/package=BIOEFIC Description: CRAN Package 'BIOEFIC' (Relative Bioefficiency via Simultaneous Regressions) Fits simultaneous regression models to compare two sources (reference and test) and estimates relative bioefficiency. Includes simultaneous exponential model with common asymptote (model = 1), slope-ratio model (model = 2), quadratic model (model = 3), linear-response plateau model (model = 4), and Michaelis-Menten model (model = 5). Output style follows the 'easyreg' package. Methods are based on Finney (1978, ISBN:0-85264-252-0), Mercer et al. (1978) , Robbins et al. (1979) , Noll et al. (1984) , Gallant and Fuller (1973) , Littell et al. (1997) , and Burnham and Anderson (2002, ISBN:978-0-387-95364-9). Package: r-cran-biofetchr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4703 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-coordinatecleaner, r-cran-countrycode, r-cran-curl, r-cran-dplyr, r-cran-geodata, r-cran-jsonlite, r-cran-mregions2, r-cran-readr, r-cran-rgbif, r-cran-rlang, r-cran-sf, r-cran-tibble Suggests: r-cran-geosphere, r-cran-ggplot2, r-cran-httr, r-cran-knitr, r-cran-lwgeom, r-cran-mapme.biodiversity, r-cran-osmdata, r-cran-readxl, r-cran-remotes, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-stringi, r-cran-stringr, r-cran-terra, r-cran-testthat, r-cran-tidyr, r-cran-worrms Filename: pool/dists/noble/main/r-cran-biofetchr_0.1.2-1.ca2404.1_all.deb Size: 3795320 MD5sum: a078b2dfe7d5f90c2d002bbd769c5443 SHA1: d05c025484d8134dbaf2f52d0c8f6f5ce7f1befa SHA256: e7dc0f9cc10b3bcc0419bc91e375cc1e313c800ec265036158e1e8568a240817 SHA512: cd5be5641d12a3160337819d6776b3cb00d93d297bf0991580deee137cefaed2061d8c22cf8d7f06b971eaf4b7c59e104a2cf373f9bfdb667b6c61d32cbd3eea Homepage: https://cran.r-project.org/package=biofetchR Description: CRAN Package 'biofetchR' (Download, Clean, Classify, Enrich and Export BiodiversityOccurrence Data) Downloads, imports, cleans, classifies, enriches and exports biodiversity occurrence data, with an emphasis on reproducible Global Biodiversity Information Facility (GBIF) workflows. The package supports batch occurrence downloads, taxonomic standardisation, coordinate cleaning, optional spatial thinning, spatial attribution and structured export of processed occurrence records and audit outputs. Terrestrial and freshwater workflows can join records to administrative units, protected areas, freshwater ecoregions, basins, rivers, lakes, reservoirs, wetlands and other contextual spatial overlays. Marine workflows support offshore and coastal records through joins to Marine Regions style layers, Exclusive Economic Zone (EEZ) units, marine ecoregions, Large Marine Ecosystems and user-supplied marine overlays. The package also supports native-range and invasive-status evidence workflows using the World Register of Marine Species (WoRMS) , evidence derived from Standardising and Integrating Alien Species (SInAS) , and Global Register of Introduced and Invasive Species (GRIIS) style species-country records. These tools are intended for biodiversity, macroecological and invasion-biology analyses where occurrence records need to be processed consistently, transparently and reproducibly. Package: r-cran-biogas Architecture: all Version: 1.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-minpack.lm, r-cran-knitr, r-cran-ggplot2, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-biogas_1.64.0-1.ca2404.1_all.deb Size: 1392732 MD5sum: dd4244e01eaf1270ecdd96b60e44fa25 SHA1: f4f5e608121365360d5243aa9515a8e220510221 SHA256: 42931cf34f73f88fcdb2bbe18d35bfab15b867a9e5ca4de90b52ad85272df31d SHA512: 3b25d93be55528f111e269a55fb2dc290b8f5c47c6227816d5b166a810fa922adad1560719dd5cb6b8a2f0067ac9f0e371b3e9f207efd3182728909e3deaaef2 Homepage: https://cran.r-project.org/package=biogas Description: CRAN Package 'biogas' (Process Biogas Data and Predict Biogas Production) Functions for calculating biochemical methane potential (BMP) from laboratory measurements and other types of data processing and prediction useful for biogas research. Raw laboratory measurements for diverse methods (volumetric, manometric, gravimetric, gas density) can be processed to calculate BMP. Theoretical maximum BMP or methane or biogas yield can be predicted from various measures of substrate composition. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to standard (or user-defined) temperature and pressure. Gas quantity can be converted between volume, mass, and moles. A function for planning BMP experiments can consider multiple constraints in suggesting substrate or inoculum quantities, and check for problems. Inoculum and substrate mass can be determined for planning BMP experiments. Finally, a set of first-order models can be fit to measured methane production rate or cumulative yield in order to extract estimates of ultimate yield and kinetic constants. See Hafner et al. (2018) for details. OBA is a web application that provides access to some of the package functionality: . The Standard BMP Methods website documents the calculations in detail: . Package: r-cran-biogeom Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1986 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bmp, r-cran-spatstat.geom Filename: pool/dists/noble/main/r-cran-biogeom_1.5.2-1.ca2404.1_all.deb Size: 1668830 MD5sum: 6a6b675812f142f791d5eab6615b9fc4 SHA1: f5472d7cf701224c78851ef6af289deaf19d032f SHA256: 1e92f58d627b53c68d33df2a34d09277bea45ca402047734f462ea0bd7a68e90 SHA512: 7166baaa53b5c19b17c5f098e83d5fe31b2cbc92e22a2b58faf0344eb6d0b2e81d17746445622024019c899713ff25e760a77a06a62069ea31aa218db6acc9dd Homepage: https://cran.r-project.org/package=biogeom Description: CRAN Package 'biogeom' (Biological Geometries) Is used to simulate and fit biological geometries. 'biogeom' incorporates several novel universal parametric equations that can generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings (Gielis (2003) ; Shi et al. (2020) ), three growth-rate curves representing the ontogenetic growth trajectories of animals and plants against time, and the axially symmetrical and integral forms of all these functions (Shi et al. (2017) ; Shi et al. (2021) ). The optimization method proposed by Nelder and Mead (1965) was used to estimate model parameters. 'biogeom' includes several real data sets of the boundary coordinates of natural shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings, seeds, and sea stars,and can be potentially applied to other natural shapes. 'biogeom' can quantify the conspecific or interspecific similarity of natural outlines, and provides information with important ecological and evolutionary implications for the growth and form of living organisms. Please see Shi et al. (2022) for details. Package: r-cran-biogram Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam, r-cran-combinat, r-cran-entropy, r-cran-partitions Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biogram_1.6.3-1.ca2404.1_all.deb Size: 393570 MD5sum: 0b1ff4377d642c7e4f9492341769c1b2 SHA1: f73859bb2fe6f89509a90da1b8a3fdbcb61d96bd SHA256: 737f17394697adfa3b3daf079309437a9470872a057e5db38ebd40863b69fe39 SHA512: c03418ee0a7678b0d3a4a052c070caeca3c3780c1f3cd740a9341d6b4c8b14ba5b1dddea66f227177e42d0c4885292c4e3ac57b3173ebf2a134324b97d01cd82 Homepage: https://cran.r-project.org/package=biogram Description: CRAN Package 'biogram' (N-Gram Analysis of Biological Sequences) Tools for extraction and analysis of various n-grams (k-mers) derived from biological sequences (proteins or nucleic acids). Contains QuiPT (quick permutation test) for fast feature-filtering of the n-gram data. Package: r-cran-biogrowth Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-tibble, r-cran-dplyr, r-cran-fme, r-cran-mass, r-cran-rlang, r-cran-purrr, r-cran-ggplot2, r-cran-cowplot, r-cran-lamw, r-cran-tidyr, r-cran-formula.tools, r-cran-mvtnorm, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-biogrowth_1.0.8-1.ca2404.1_all.deb Size: 1213802 MD5sum: 9d6bc7dacf6f4f8619b31c9117040b33 SHA1: 10ab7a98d17a6bda9a49e7dfcdc8eb7d9a832346 SHA256: cb874bccbcc1171a14d48326357c0fc7e12c157a0ff00341f9b5139ab70a12c5 SHA512: d86ae034b834ff0b438c9b83f6d7d32a9961e83576f27cad94431bae66e962cc0689f20af6018ee7151a39a0c823025c1a43074260ced33bc0736e087e2fcbb4 Homepage: https://cran.r-project.org/package=biogrowth Description: CRAN Package 'biogrowth' (Modelling of Population Growth) Modelling of population growth under static and dynamic environmental conditions. Includes functions for model fitting and making prediction under isothermal and dynamic conditions. The methods (algorithms & models) are based on predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)). Package: r-cran-biogsp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2091 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-rann, r-cran-rspectra, r-cran-ggplot2, r-cran-patchwork, r-cran-gridextra, r-cran-viridis, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-biogsp_1.0.1-1.ca2404.1_all.deb Size: 1300438 MD5sum: 881d919035c7b1e7c86f5e527d54939c SHA1: 97d428b9c8bb0a839ff52384193c0a4c63a3422c SHA256: 55b21cba4c1bdbec0f9b027c25a1b949a7515dc848b41e9b5ba522ec227b3f36 SHA512: 301cc0ff28827a421024d0bccd30f99c93920f53fd7513f998ec7ab4c50c97128b5c79339a32fc231559042b9e756a80162710f07b1265bd65622b6209ffd3cf Homepage: https://cran.r-project.org/package=BioGSP Description: CRAN Package 'BioGSP' (Biological Graph Signal Processing for Spatial Data Analysis) Implementation of Graph Signal Processing (GSP) methods including Spectral Graph Wavelet Transform (SGWT) for analyzing spatial patterns in biological data. Based on Hammond, Vandergheynst, and Gribonval (2011) . Provides tools for multi-scale analysis of biology spatial signals, including forward and inverse transforms, energy analysis, and visualization functions tailored for biological applications. Biological application example is on Stephanie, Yao, Yuzhou (2024) . Package: r-cran-biohttp Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cachem, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-webfakes, r-cran-withr Filename: pool/dists/noble/main/r-cran-biohttp_0.1.2-1.ca2404.1_all.deb Size: 171172 MD5sum: 6a940972face20b434604c447cfeb1de SHA1: 0d10af4ab939f22f39394265bdc2a56674551bf4 SHA256: feb786c126d00210cace33e641d332be5bd4133ff98e35ebce6d9cc750637bc0 SHA512: dbf0b6a0fd4d83b9359026dbbe36264f018f2151558e31b7aeb462569a93e26b9b292ec4f91f49fc78f231c42686f75b69f3ef976e945bf8c0ddc247c11b6023 Homepage: https://cran.r-project.org/package=biohttp Description: CRAN Package 'biohttp' (Normalized HTTP Transport with Circuit Breaking and Caching) Web service calls return a normalized result value instead of raising a condition, so a caller branches on data rather than on an error handler. Transport failure, a non-success status code, and an unreadable response body are reported as three distinct outcomes. Per-host circuit breaking, retry with a transient-failure predicate, optional throttling, redacted request headers, and a success-only cache come as defaults. Many questions to one source can be asked as a single batch, where only the entries the cache is missing reach the network. Service-specific knowledge is left to the client packages built on top. The circuit breaker is the pattern described in Nygard (2018, ISBN:9781680502398). Package: r-cran-bioinactivation Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-desolve, r-cran-fme, r-cran-lazyeval, r-cran-ggplot2, r-cran-mass, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bioinactivation_1.3.1-1.ca2404.1_all.deb Size: 286568 MD5sum: 22a7d6289d286e1133843e48a8e144d5 SHA1: 00f5ec7cacd3d975afe0dac12851c4b13dab575a SHA256: 4b7f91085f132315c42afd624ffde0150e9f76a6f49f5021e8f2c79abf3f789d SHA512: fa0f12e8526cfbc033870ebecb1dd5260a71e53ee77308346e5d1002afa757303e9a79d71a9eabecedae8cd2269dea94cdd1fb55a2f2b4077f939b98fd45697d Homepage: https://cran.r-project.org/package=bioinactivation Description: CRAN Package 'bioinactivation' (Mathematical Modelling of (Dynamic) Microbial Inactivation) Functions for modelling microbial inactivation under isothermal or dynamic conditions. The calculations are based on several mathematical models broadly used by the scientific community and industry. Functions enable to make predictions for cases where the kinetic parameters are known. It also implements functions for parameter estimation for isothermal and dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian approach to parameter estimation. Package: r-cran-bioindex Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4529 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hms, r-cran-magrittr, r-cran-marmap, r-cran-mgcv, r-cran-reshape2, r-cran-shiny, r-cran-shinyjs, r-cran-stringr, r-cran-terra, r-cran-tidyterra, r-cran-zip Suggests: r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bioindex_0.6.5-1.ca2404.1_all.deb Size: 4512366 MD5sum: 2db2ee650276222ecedb11262d12e239 SHA1: e4477c795ed3d1de95b761670f7aa372e1c1f3ef SHA256: 6c8fe9b74216980972e1df925cab745800b2e90191d03619a863d340d9619395 SHA512: 7da298f2cb2d578a7ca456c3c39b3680209b0875ae3f5ff6a8df6867593bf51a98cd3a5a520c449d3391fa3a64b68b04826b134018b21820f80408c475d22317 Homepage: https://cran.r-project.org/package=BioIndex Description: CRAN Package 'BioIndex' (Biological Indicators and Indices for MEDITS Survey Data) Supports the standardized analysis of Mediterranean International Bottom Trawl Survey (MEDITS) data and the calculation of biological indicators for selected species and population components. The package provides functions to estimate abundance and biomass indices, analyse size structure and length frequency distributions, derive sex ratio and maturity related metrics, explore spatial patterns, and assess temporal trends across surveys. Developed for integration within the Regional Database for Fisheries (RDBFIS) framework, it is intended to work on quality checked input data and to produce reproducible outputs that can support monitoring, comparative analyses among Geographical Sub-Areas (GSAs) and countries, and fishery management. Package: r-cran-bioinsight Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3912 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-edger, r-bioc-limma, r-cran-knitr, r-cran-wordcloud, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-bioc-biomart Filename: pool/dists/noble/main/r-cran-bioinsight_0.3.1-1.ca2404.1_all.deb Size: 873016 MD5sum: 8b0b9c47d5e56ba7b07115f5fa0c6b0b SHA1: c43cad602eb8e1842ae1d82258871ef79a558b9d SHA256: 04c9a27e9170ed16b38bc15be14b10728beff96831b056d687696290158781fc SHA512: 3014c4ce3a3630408ad96ec2c4699da01d36faed0b82174d7dd83d85a5baf714a77be74ed79beea56cace6a8ddea6adffca06128cbe8d09e898e8a68e8e866b6 Homepage: https://cran.r-project.org/package=BioInsight Description: CRAN Package 'BioInsight' (Filter and Plot RNA Biotypes) Analyze and plot the abundance of different RNA biotypes present in a count matrix, this evaluation can be useful if you want to test different strategies of normalization or analyze a particular biotype in a differential gene expression analysis. Package: r-cran-bioiot Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-singlecellexperiment, r-cran-seurat, r-bioc-summarizedexperiment, r-bioc-s4vectors Filename: pool/dists/noble/main/r-cran-bioiot_0.2.2-1.ca2404.1_all.deb Size: 286728 MD5sum: f76c9546393abcb89a19ac5794364d35 SHA1: 27417fe832c3ab1e088d02fe96c794be695a6416 SHA256: d778de61e795dd7797105f25a26b0d24b5cb4df70fbc3aabfc3395e7711033bb SHA512: 2b8d90c6fc4456df4b3c39b9769251a434f9698cc39eb6bd311cd8df0d9c3492923b32ce8e63448dfc7bd2d8ca8c4c35ee55aae2b9e9e9319cf431e7b5edcd2f Homepage: https://cran.r-project.org/package=bioIOT Description: CRAN Package 'bioIOT' (Inverse Optimal Transport for Single-Cell Trajectory Analysis) Semi-relaxed inverse optimal transport (IOT) for single-cell state-transition and pseudotime analysis: a self-contained R solver (Anderson-accelerated soft Sinkhorn with exact implicit gradients), feature-weight fitting with a two-stage bias-corrected refit and multi-restart, state transition matrices, random-walk pseudotime, 'ggplot2' visualisation, soft-gated 'Seurat' and 'SingleCellExperiment' interfaces, reproducible simulated demo data, and bulk-cohort pathway scoring utilities. Package: r-cran-bioleak Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2888 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-generics, r-bioc-summarizedexperiment, r-cran-hardhat, r-cran-parsnip Suggests: r-bioc-biocparallel, r-cran-splitgraph, r-cran-cli, r-cran-dials, r-cran-fnn, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-mice, r-cran-missforest, r-cran-pkgload, r-cran-ranger, r-cran-randomforest, r-cran-recipes, r-cran-rann, r-cran-rsample, r-cran-tune, r-cran-vim, r-cran-withr, r-cran-workflows, r-cran-xgboost, r-cran-yardstick, r-cran-proc, r-cran-prroc, r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bioleak_0.3.8-1.ca2404.1_all.deb Size: 1385328 MD5sum: 72e0ad16bb068a6fb5088c9220c20929 SHA1: 417de5e1f2de0cca2bae36901d3d2dd14a8c4fde SHA256: df5dfed444b95d1323c1189e788a2f884f0048cf92345c6c198c19db1f96dee6 SHA512: 5794dd4846bbb0102c6543087535642440eae9941073bd6e281e0bd8a79b6c6d68c4a51f76e9a90b27d702270b08c30a2f928a345ce36f0b823f7dae34964444 Homepage: https://cran.r-project.org/package=bioLeak Description: CRAN Package 'bioLeak' (Leakage-Safe Modeling and Auditing for Genomic and Clinical Data) Prevents and detects information leakage in biomedical machine learning. Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered), guarded preprocessing (train-only imputation, normalization, filtering, feature selection), cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests, and duplicate detection. Package: r-cran-biolink Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-xml2, r-cran-dbi, r-cran-rmysql, r-cran-glue, r-cran-memoise Suggests: r-cran-testthat, r-cran-lintr, r-cran-httr, r-cran-covr Filename: pool/dists/noble/main/r-cran-biolink_0.1.8-1.ca2404.1_all.deb Size: 72334 MD5sum: 683f9fcc05e02d26865cb4ecc1669a8e SHA1: 932f9e8f9599dbfc32494fb8d79a6e31db49cd13 SHA256: fff0bc497f904df263bccb34481eef43ff200d9eeff0f79c9594a999e6cffbe8 SHA512: 83036bfbe9b460f8aa518419c6c8734c724219f81237f1101b63f9d4b194324a21c8d5f82c43bd2b174f8bc87ca51e4a4d8e4a269e7ead6338d166ce66571009 Homepage: https://cran.r-project.org/package=biolink Description: CRAN Package 'biolink' (Create Hyperlinks to Biological Databases and Resources) Generate urls and hyperlinks to commonly used biological databases and resources based on standard identifiers. This is primarily useful when writing dynamic reports that reference things like gene symbols in text or tables, allowing you to, for example, convert gene identifiers to hyperlinks pointing to their entry in the 'NCBI' Gene database. Currently supports 'NCBI' Gene, 'PubMed', Gene Ontology, 'KEGG', CRAN and Bioconductor. Package: r-cran-biologicalactivityindices Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-biologicalactivityindices_0.1.0-1.ca2404.1_all.deb Size: 12322 MD5sum: f8146ecdb4ce3e8ad905dfe1699da323 SHA1: 473f49cbde7ee39a9c540865c3383df848041b7d SHA256: 7dc8db93d1aa4a2800423f477f0bba219dcbbfd98e607a4414f9f18181b215eb SHA512: 7bbef4a1450879519eaef163a82696f38e168c28ecb930cc545fb3846cc5d6e8ec0959b5b9cd02965539d5e79a12d61b908cefbcbf99bc0384748b0d3eabfd87 Homepage: https://cran.r-project.org/package=biologicalActivityIndices Description: CRAN Package 'biologicalActivityIndices' (Biological Activity Indices) Ecological alteration of degraded lands can improve their sustainability by addition of large amount of biomass to soil resulting in improved soil health. Soil biological parameters (such as carbon, nitrogen and phosphorus cycling enzyme activity) are reactive to minute variations in soils [Ghosh et al. (2021) ]. Hence, biological activity index combining Urease, Alkaline Phosphatase, Dehydrogenase (DHA) & Beta-Glucosidase activity will assist in detecting early changes in restored land use systems [Patidar et al. (2023) ]. This package helps to calculate Biological Activity Index (BAI) based on vectors of Land Use System/treatment and control/reference Land Use System containing four values of Urease, Alkaline Phosphatase, DHA & Beta-Glucosidase. (DHA), urease (URE), fluorescein diacetate hydrolysis (FDA) and alkaline phosphatase (ALP) activities are measured in soil samples using triphenyl tetrazolium chloride, urea, fluorescein diacetate and p-nitro phenyl-phosphate as substrates, respectively. Package: r-cran-biom2 Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wgcna, r-cran-mlr3, r-cran-cmplot, r-cran-ggsci, r-cran-rocr, r-cran-caret, r-cran-ggplot2, r-cran-ggpubr, r-cran-viridis, r-cran-ggthemes, r-cran-ggstatsplot, r-cran-htmlwidgets, r-cran-mlr3verse, r-cran-uwot, r-cran-webshot, r-cran-wordcloud2, r-cran-ggforce, r-cran-igraph, r-cran-ggnetwork Filename: pool/dists/noble/main/r-cran-biom2_1.1.3-1.ca2404.1_all.deb Size: 4170190 MD5sum: 8c98c331a223465129666723275ddfc4 SHA1: 531a5bd2c33154a83c96d1ab0fb700a357c959a2 SHA256: 5551e5b72f1b3c9eee511a7e22e37f258d3b744c685b5306f9ba5e4851f3bae2 SHA512: c766f2d1b2d02de88d622aae930591603427351bb3c77b2e625ad03a10948d5611a84d878626cc5d41c0ae7c40ec6b9a2a3ae7cb1244d2f89243c277f65fafcf Homepage: https://cran.r-project.org/package=BioM2 Description: CRAN Package 'BioM2' (Biologically Explainable Machine Learning Framework) Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) .Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) ) methods. Package: r-cran-biomark Architecture: all Version: 0.4.5-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-glmnet, r-cran-mass, r-cran-st Filename: pool/dists/noble/main/r-cran-biomark_0.4.5-1.ca2404.2_all.deb Size: 1017102 MD5sum: c37793e493aa553e16281af85dfaa2fd SHA1: 9822971c009ebbc76d742250bc51b31193d7680b SHA256: f816e18e705e4d26e8492752b32739b00cdd0419274249420374a4764adefad7 SHA512: 86b3a6134fe96ae48b7452a85f87a7007043bc08b7ce1b802f2655575538b0d6b8cc7e15a7fc9cd9f7613330407e9f790f7b3887391fe6b74f2b958ff2d6f74d Homepage: https://cran.r-project.org/package=BioMark Description: CRAN Package 'BioMark' (Find Biomarkers in Two-Class Discrimination Problems) Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism. Package: r-cran-biomass Architecture: all Version: 2.2.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4735 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-minpack.lm, r-cran-jsonlite, r-cran-proj4, r-cran-data.table, r-cran-rappdirs, r-cran-sf, r-cran-terra, r-cran-ggplot2, r-cran-ggnewscale Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-testthat, r-cran-vdiffr, r-cran-curl, r-cran-geodata, r-cran-httr2, r-cran-pkgdown, r-cran-dplyr, r-cran-brms, r-cran-bh, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-biomass_2.2.7-1-1.ca2404.1_all.deb Size: 3618054 MD5sum: 6b0c4d3965c715bf23617ef809c7eeb8 SHA1: 9b9485dd7d4685c4e6950b3a2d357d0efee53743 SHA256: 43a810584da53c70ed7d4268ff0e0709e13495a084e9d4e1a1b6cd9bf8ff461b SHA512: 354f36afa01fd2f068016dbe54eb59ee5ea6785c79a9be68d9377ecb3bfefb31778c22e545001600d8b17340bd74d74e57ac29111c01d67245347db11c5a3d2c Homepage: https://cran.r-project.org/package=BIOMASS Description: CRAN Package 'BIOMASS' (Estimating Aboveground Biomass and Its Uncertainty in TropicalForests) Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017) . Package: r-cran-biomes Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-readr, r-cran-checkmate, r-cran-rlang, r-cran-ggplot2, r-cran-sf, r-cran-viridis, r-cran-tidyterra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-rgbif, r-cran-coordinatecleaner, r-cran-cowplot, r-cran-ggforce, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-biomes_0.9.5-1.ca2404.1_all.deb Size: 385304 MD5sum: c9eb0e547ee586440f05dd632dffc945 SHA1: a1419501f9266934b95d98727851c60b2340679c SHA256: ca255c89e4d7c67f283d226e9b71bea6e8b81c5d4a6405e176ac67e0c5a45048 SHA512: ef32c1af2a769a1980b88ee7d1866d17b9f338c4a34798c84a320513e5497628e40908d4c74145b692141c217de3c5eaaa74a1b0452199a04dd17a9d207c9738 Homepage: https://cran.r-project.org/package=biomes Description: CRAN Package 'biomes' (Reproducible Occurrence-to-Biome Classification Using 31 GlobalBiome Schemes) Reproducibly classifies occurrence records into biomes using 31 published global biome schemes compiled by Fischer and colleagues (2022) , provided as harmonised raster layers at 10x10 km resolution globally. Includes functions to choose the most suitable biome scheme for a dataset by a data-driven ranking, to classify occurrence records, and to tabulate and visualise the result. Works with user-provided occurrences or a taxon name, in which case occurrences are downloaded from GBIF () and cleaned automatically. Package: r-cran-biometrics Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-nlme Suggests: r-cran-datana, r-cran-lattice Filename: pool/dists/noble/main/r-cran-biometrics_1.0.4-1.ca2404.1_all.deb Size: 3644776 MD5sum: 4ffd81bffa83e9cc52e109ef15b1de96 SHA1: 8724056659b109dbefcebd20cc374d659b741438 SHA256: 6d1f9f4ba7c0db4d355b6f447c8fc115f7991a72f816f45fd3441d9d75b08b37 SHA512: ef2fc9fc470e84fbb21582926a1bffc60fc5feac94e2ed56cff0c9f436cb4560597f01920b3c2de0472d9b0fdbc98ebf12b262bd8100f4494b9911ab013114c6 Homepage: https://cran.r-project.org/package=biometrics Description: CRAN Package 'biometrics' (A Package for Biometrics and Modelling) A system of functions and datasets to carry out quantitative analyses in the biological sciences. The package facilitates data management, exploratory analyses, and model assessment. Although it currently focuses on forest ecology, silviculture and decision-making, most of the package functions are applicable across several disciplines, including economics, environmental science, and healthcare. Package: r-cran-biometryassist Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-agricolae, r-cran-curl, r-cran-emmeans, r-cran-ggplot2, r-cran-jsonlite, r-cran-lattice, r-cran-multcompview, r-cran-pracma, r-cran-patchwork, r-cran-rlang, r-cran-scales, r-cran-stringi Suggests: r-cran-covr, r-cran-crayon, r-cran-ggspatial, r-cran-knitr, r-cran-matrix, r-cran-mockery, r-cran-mvtnorm, r-cran-openxlsx2, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-biometryassist_1.5.1-1.ca2404.1_all.deb Size: 697502 MD5sum: 751083f8ebc9701e34f345589c89dad1 SHA1: fb47e7be94b82409cd17af63731d4d91383283f3 SHA256: 3007a00db46e695633b59880dfb1c884b78be31adb668c01c56474b5a4d32c27 SHA512: a18e24fe6a3706db90618ffa4dd40a26560db03fb8d561572c8e030e16074e4f99d3149887e11729b343ed38a4cea8a6a05a6419fd4d838bffada6e464ad8a14 Homepage: https://cran.r-project.org/package=biometryassist Description: CRAN Package 'biometryassist' (Functions to Assist Design and Analysis of Agronomic Experiments) Provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the `asreml` package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as 'ASReml-R' from 'VSNi' , who will supply a zip file for local installation/updating (see ). Package: r-cran-biomixmodel Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-glmmtmb, r-cran-nlme, r-cran-mgcv, r-cran-survival, r-cran-coxme, r-cran-brms Filename: pool/dists/noble/main/r-cran-biomixmodel_1.0.0-1.ca2404.1_all.deb Size: 61872 MD5sum: 98b0cc81ef2d49b4087d35954466f5cc SHA1: 3fb636671408e07ff1481f120f7adaaae92625c9 SHA256: 12a9e288e5f5ba2e314ea0e16bc38b01b9f80c53e2870cedd47b5f7b6c4aed3f SHA512: 1dcd8510c9a66785fc1c09690bf6c81558cac3ee992a054626fad6b4876850db6811d8414f2d729ada3ec2a60701245fbae2c41c961b88cf09961175e0036478 Homepage: https://cran.r-project.org/package=BioMixModel Description: CRAN Package 'BioMixModel' (Mixed Models for Biological, Clustered and Longitudinal Data) Fits and interprets mixed-effects models for clustered, longitudinal and heterogeneous biological data. Provides variance partitioning, intraclass correlation, penalized likelihood summaries, a heterogeneous-data information criterion, model comparison, diagnostics, and ensemble-style summaries for multilevel data. The package is designed as a complementary, interpretable workflow around established mixed-model methods. Methods for intraclass correlation and variance partitioning are informed by Nakagawa and Schielzeth (2010) and Nakagawa et al. (2017) . Mixed-effects modeling approaches are described by Zuur et al. (2009) . Package: r-cran-biomod2 Architecture: all Version: 4.3-4-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6753 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-sp, r-cran-reshape, r-cran-reshape2, r-cran-abind, r-cran-foreach, r-cran-ggplot2, r-cran-gbm, r-cran-rpart, r-cran-mass, r-cran-proc, r-cran-presenceabsence, r-cran-dplyr, r-cran-rlang, r-cran-scales Suggests: r-cran-hmisc, r-cran-gam, r-cran-mgcv, r-cran-earth, r-cran-maxnet, r-cran-mda, r-cran-nnet, r-cran-randomforest, r-cran-xgboost, r-cran-cito, r-cran-torch, r-cran-car, r-cran-caret, r-cran-dismo, r-cran-enmeval, r-cran-doparallel, r-cran-raster, r-cran-ggpubr, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-tidyterra, r-cran-ggtext, r-cran-patchwork, r-cran-kableextra, r-cran-dt, r-cran-r.utils, r-cran-paletteer, r-cran-viridis Filename: pool/dists/noble/main/r-cran-biomod2_4.3-4-7-1.ca2404.1_all.deb Size: 3310168 MD5sum: 845a6f191c948828d44df882cbbc78e1 SHA1: aae8c23338e74053b2d8f91c9083e54a27bf2fe1 SHA256: 5b55c23c3b12d0f434e1a7ee45ae704f09cbcf32328a94969a76fc59dc38e7f3 SHA512: 0a1bab320ec5a617d167c05dceb698d5a61fb6dcb8f73f618aabe2f22647499c4088d5baef82931a1407085c5906fd274eb7e6b466d670fb098eb2882caf29b1 Homepage: https://cran.r-project.org/package=biomod2 Description: CRAN Package 'biomod2' (Ensemble Platform for Species Distribution Modeling) Functions for species distribution modelling, to calibrate, evaluate, and project species-environment relationships across space and time using multiple modelling algorithms and ensemble forecasting. It accommodates diverse ecological data types (presence-only, presence-absence, counts, multi-class abundance, or relative/absolute abundance) within a unified modelling workflow which includes cross-validation schemes, pseudo-absence selection strategies, expanded model parametrization options, a dozen of algorithms, and tools for exploring and visualizing outputs. Package: r-cran-biomontools Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-maps, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-dataexplorer, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-lazyeval, r-cran-readxl, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyjs, r-cran-shinywidgets, r-cran-writexl, r-cran-shinyalert, r-cran-stringr, r-cran-stringdist Filename: pool/dists/noble/main/r-cran-biomontools_1.3.2-1.ca2404.1_all.deb Size: 1877300 MD5sum: 75b13717c008c1388be1cc5f9845efed SHA1: 888280792def9961051d074bfea86f73c88ae581 SHA256: 1031d518f40bd66b3899dfaf9e78d01843f2c57f2550cdd3b1ca9e12e46900f6 SHA512: 0f8607a031727421a804f59581346668623dd9792ac9e6d6f22b1f8e5962aa3fe6869e2de163d9fcee61d187ecc6eb763f111f464c63b1922c57b05895678e88 Homepage: https://cran.r-project.org/package=BioMonTools Description: CRAN Package 'BioMonTools' (Biomonitoring and Bioassessment Calculations) An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, subsampling, and multimetric index calculation. Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral. As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) . Package: r-cran-biomor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-recipes, r-cran-themis, r-cran-xgboost, r-cran-magrittr, r-cran-dplyr, r-cran-proc Suggests: r-cran-randomforest, r-cran-testthat, r-cran-prroc, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-biomor_0.1.1-1.ca2404.1_all.deb Size: 48828 MD5sum: 8aee9f9deafc113212bc64a1cf0e86e8 SHA1: 8e4369cf38822aae3c081b7ed8b51f0fd99fb28c SHA256: 9817217df9f44628f6d7abbdb8f8dc738448504dbb8a29d56034a1d697205007 SHA512: bb2c83404c91084f850e1fee53247e8f060db7f23c5da23f7da8170f4519118a8b6625588405c7917ae39aca4244bdca66bc79639c1e151b515c7eedb1d6a818 Homepage: https://cran.r-project.org/package=BioMoR Description: CRAN Package 'BioMoR' (Bioinformatics Modeling with Recursion and Autoencoder-BasedEnsemble) Tools for bioinformatics modeling using recursive transformer-inspired architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. Includes utilities for cross-validation, calibration, benchmarking, and threshold optimization in predictive modeling workflows. The methodology builds on ensemble learning (Breiman 2001 ), gradient boosting (Chen and Guestrin 2016 ), autoencoders (Hinton and Salakhutdinov 2006 ), and recursive transformer efficiency approaches such as Mixture-of-Recursions (Bae et al. 2025 ). Package: r-cran-bionetdata Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3273 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bionetdata_1.1-1.ca2404.1_all.deb Size: 3313434 MD5sum: 83a0bf29c9ee30c5aa8fac43e5f9e831 SHA1: 7b83443d489404a823a2c1f07bee1590b0aa3995 SHA256: 41a5a9c2fe9a45a78ecd74e5c265967313e144559c9647f3672ab56461a52e85 SHA512: 72d6d7de7efe075f60dbdd4d1ee04456d221b6173d1b757d62884e4553de1c943a81f03385396405445a1f8ac5ada13855516de96b21d40e9e6e43fd6ff8b1fe Homepage: https://cran.r-project.org/package=bionetdata Description: CRAN Package 'bionetdata' (Biological and Chemical Data Networks) Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured. Package: r-cran-biopalette Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-jsonlite, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biopalette_0.2.2-1.ca2404.1_all.deb Size: 316508 MD5sum: a5bde6a2c60950dda6df6071740126d5 SHA1: b09b4b290d333aea26c2810c1e9c4445e99a5852 SHA256: 18f0265d3c47ef15972061d93ceef02f4e770329d041478d69365796f8f3e55b SHA512: 0d07485a6c4b6c80700685e37b76c51a9cfc08e535d459e1b30feee648eed8697cfb7a26fbc7b7a95c12e844ecfb09603dbaf6985bfae3c96f8c700fd24415f4 Homepage: https://cran.r-project.org/package=biopalette Description: CRAN Package 'biopalette' (Image-Inspired Color Palettes for Biomedical Visualization) Provides a curated collection of image-inspired color palettes for biomedical visualization. The palettes are organized as qualitative, sequential, or diverging scales and include documented source context and intended use. The package provides functions to retrieve, inspect, preview, and apply these palettes in base R and 'ggplot2' graphics, together with utilities for working with palette definitions. Package: r-cran-biopet Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-proc, r-cran-vgam Filename: pool/dists/noble/main/r-cran-biopet_0.2.2-1.ca2404.1_all.deb Size: 78202 MD5sum: 890415bf3382cc83bdfa4a88c7e786fe SHA1: 0aca2da4a5439fcba6370341a508b5dbe1436ce2 SHA256: ef80b0b3d19f718a15d36a96a2ccaf19af6a36942a60acc5225b9a7390762083 SHA512: b713cb0a8d1836dd3c07b02c65badbbbd5eb95a719e6414a168ea3264b6a8edf3986046e53acd5de5424b8737a3463293ef476de8c815b222327c71f896ce4f7 Homepage: https://cran.r-project.org/package=BioPET Description: CRAN Package 'BioPET' (Biomarker Prognostic Enrichment Tool) Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) ). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials. Package: r-cran-biopetsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-biopetsurv_0.1.0-1.ca2404.1_all.deb Size: 112370 MD5sum: 76ff1111978f8b9b3e413764c57fcc0d SHA1: 892bb6b205017ff34ff6a14004936320d4582d15 SHA256: 32947b8f3dbfedda48068e66e2acd7f685fb98f85e840b9bd2b4c5e44be0c530 SHA512: 72ba63afe90aec6c0b4b4d765b76ad1aff146f5f2da9cc13233f595297613fab1b76c8f1cc6e31e6e3c5037e2a4eb76e54b94a3e901cd1fed2673499bb0a6667 Homepage: https://cran.r-project.org/package=BioPETsurv Description: CRAN Package 'BioPETsurv' (Biomarker Prognostic Enrichment Tool for Time-to-Event Trial) Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event. A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) ). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes. 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By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) ). 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For further details, see Liu et al. (2024) . Package: r-cran-bioprobability Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bioprobability_1.0-1.ca2404.1_all.deb Size: 34932 MD5sum: 426b1958bff481b2f5be67aad7af4a8d SHA1: 854c474d25432e3c308c91488fdf350c72033c53 SHA256: 518dac6595c2fa4e739fca12e027381295de34c40816da8a52acd07a123beade SHA512: ae82747fd6b7314e582102304be4d0c1c03e9690964c855162242ac67001cd8100ebb019b23c27a1ef44c76e8eec6b0b3cfe93be693d8d02b0f1ff38f14813e0 Homepage: https://cran.r-project.org/package=BioProbability Description: CRAN Package 'BioProbability' (Probability in Biostatistics) Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided. Package: r-cran-biorad Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5687 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-lutz, r-cran-raster, r-cran-readr, r-bioc-rhdf5, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stringr, r-cran-suntools, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-viridis, r-cran-viridislite, r-cran-xml2 Suggests: r-cran-elevatr, r-cran-getrad, r-cran-ggspatial, r-cran-knitr, r-cran-prettymapr, r-cran-rmarkdown, r-cran-rosm, r-cran-testthat, r-cran-vdiffr, r-cran-vol2birdr, r-cran-withr Filename: pool/dists/noble/main/r-cran-biorad_0.12.0-1.ca2404.1_all.deb Size: 4900074 MD5sum: 1a2e01b5e3790d409509a237c09bcc21 SHA1: ac548318cdecc8313e088d90191b67b4bd8bd2c6 SHA256: ac45880b52e19bad656b555ebb9cc8efab2c91456dbbcd69a1c95dbccc52f93a SHA512: 6a4c18c78f296b1973a41aba6837789bfbca19710803a24b73f85082ced078b11ce62fab45cd18303455d3a32af38abd17b4e64ae27fdd86323d05ad1940f6a5 Homepage: https://cran.r-project.org/package=bioRad Description: CRAN Package 'bioRad' (Biological Analysis and Visualization of Weather Radar Data) Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" for a software paper describing package and methodologies. Package: r-cran-biorssay Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-biorssay_1.1.0-1.ca2404.1_all.deb Size: 239748 MD5sum: 86a196cb49e5c0ae88b71e1714936409 SHA1: 5ba9ccf7c33b6303cb408a5971bfea37f2d32925 SHA256: abf6594dd0928f015689322aee01b1e0e311cffa7011008f65372f2e3baf7ce4 SHA512: e8f6d2d4e3ac18602782b5d556f33b69ef5935c93ec95eebc515d86ade574b6e94608ea44f843f466ef873b5f45ee08fd3504d35cbf97baa95b0758dedf82b81 Homepage: https://cran.r-project.org/package=BioRssay Description: CRAN Package 'BioRssay' (Analyze Bioassays and Probit Graphs) A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations. Package: r-cran-bios2cor Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bio3d, r-cran-circular, r-cran-bigmemory, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bios2cor_2.2.2-1.ca2404.1_all.deb Size: 774476 MD5sum: 8e8f0df34a4e6b9789dd5000b75c9bca SHA1: f778d0105eb9563eef8fd34584bb510087fa1ff1 SHA256: cd531bb64af6b187a1700a7268f5f1331f7404ad750786269e48b68e908196fb SHA512: f83bdb3252af6266c587089001b119c48d908988bfe973484249971a4234469a806b4e9c23b3244a505f217e46849e776180a49adb4e458fe82080769e654d42 Homepage: https://cran.r-project.org/package=Bios2cor Description: CRAN Package 'Bios2cor' (From Biological Sequences and Simulations to CorrelationAnalysis) Utilities for computation and analysis of correlation/covariation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/covariation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and utilities to analyze the correlation/covariation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence covariation analysis are provided in: (1) Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M (2014) and (2) Taddese B, Deniaud M, Garnier A, Tiss A, Guissouma H, Abdi H, Henrion D, Chabbert M (2018) . An example of side chain correlated motion analysis is provided in: Taddese B, Garnier A, Abdi H, Henrion D, Chabbert M (2020) . This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026) and by GENCI (Grant number: 100567). Package: r-cran-bios2mds Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3035 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-amap, r-cran-e1071, r-cran-scales, r-cran-cluster, r-cran-rgl Filename: pool/dists/noble/main/r-cran-bios2mds_1.2.4-1.ca2404.1_all.deb Size: 2804488 MD5sum: 3d6f10e1662692cd916dfafd81290182 SHA1: b36deb10ad7b5da8e8fbe322dda89667b2397ed3 SHA256: c48e0f46ce7f7cad6d28557ae427baf995c2cd359e20826ce0b708b5b9eb39f8 SHA512: c56803f9ab27c051bb77d01ccdf77b3a65706c404e0ee84b1fae9c9c2a79086afbf5f6a899f89c77d1b0d2a402530ed57488c26e41d589402e760efc19a63611 Homepage: https://cran.r-project.org/package=bios2mds Description: CRAN Package 'bios2mds' (From Biological Sequences to Multidimensional Scaling) Utilities dedicated to the analysis of biological sequences by metric MultiDimensional Scaling with projection of supplementary data. It contains functions for reading multiple sequence alignment files, calculating distance matrices, performing metric multidimensional scaling and visualizing results. Package: r-cran-biosampler Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-biosampler_1.0.4-1.ca2404.1_all.deb Size: 56944 MD5sum: 6d8d3fc2cdbc4fc2cf448b116de39774 SHA1: 338afda21e812b3451658ceff6af0c869854e4e9 SHA256: 9b8d123c2e0694a859711300c092fcdab773b09bc5a1d4b8ec634b2e3300cb43 SHA512: 1df2abc71c78cb69f335277f62bd355391deea142ea77efd01fc386e17cce20215d8ae03696afc738d1c727e2e1cacb9f98dd55e5e05f3bd959980efaf7d6175 Homepage: https://cran.r-project.org/package=biosampleR Description: CRAN Package 'biosampleR' (Biodiversity Index Calculation and Bootstrap Confidence IntervalEstimation) Provides tools for the calculation of common biodiversity indices from count data. 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Package: r-cran-bioseq Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-tibble, r-cran-ape, r-cran-crayon, r-cran-dplyr, r-cran-pillar, r-cran-stringi, r-cran-stringr, r-cran-stringdist, r-cran-readr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-bioseq_0.1.5-1.ca2404.1_all.deb Size: 750272 MD5sum: c6387bc96db98044a75467b34b9110fe SHA1: cc52cdb7d2e6324fbb93cf2a901bcef97bb16cca SHA256: 9fb4770b2b27299912aca1f57129c33e667065cda3ca0a25afb085ed12ae8ba4 SHA512: 516d67aa52bc6ab6fce615e9ede1db7c09aee0ec6331884dd6fb3d34a16391c72981b56ae2d2e1280691bffaae31964b61c59b0acdefb69e744f85b181cb55ed Homepage: https://cran.r-project.org/package=bioseq Description: CRAN Package 'bioseq' (A Toolbox for Manipulating Biological Sequences) Classes and functions to work with biological sequences (DNA, RNA and amino acid sequences). 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The package has a number of utilizations from calculating frequency from waveform, performing operations in dB, and determining acoustic range of recorders. The majority of this package is based on key concepts learned from the K. Lisa Yang Center for Conservation Bioacoustics at Cornell University and their associated course: Introduction to Bioacoustics course. More information can be found within the walk through vignettes at . Package: r-cran-biospear Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pkgconfig, r-cran-cobs, r-cran-corpcor, r-cran-devtools, r-cran-glmnet, r-cran-grplasso, r-cran-mass, r-cran-matrix, r-cran-mboost, r-cran-plsrcox, r-cran-proc, r-cran-prroc, r-cran-rcurl, r-cran-survauc, r-cran-survival Filename: pool/dists/noble/main/r-cran-biospear_1.0.2-1.ca2404.1_all.deb Size: 3294676 MD5sum: 92bb95e1465c9cc4cbeb70cdc9d2b766 SHA1: c834599e8025d04ba086c97c8bbdc3e763d9d460 SHA256: 9bb469efdefd5f9749a648c87b8a3776cc24802c646eb72118317043ff94fe40 SHA512: 66ec8455f2764f38d7027631b698c40883e6212a4f5a2b49f6c4646108c3c8f803508e5e3bcd296faafdd81adb2a76905850ec84487e94c8d3db0c76aa1b6814 Homepage: https://cran.r-project.org/package=biospear Description: CRAN Package 'biospear' (Biomarker Selection in Penalized Regression Models) Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) ), the adaptive lasso (Zou H (2006) ), the stability selection (Meinshausen N et al. (2010) ), some extensions of the lasso (Ternes et al. (2016) ), some methods for the interaction setting (Ternes N et al. (2016) ), or others. A function generating simulated survival data set is also provided. 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Designed for use with the 'bipartite' analysis package. Includes open source 'viz-js' library Adapted from examples at (released under GPL-3). Package: r-cran-bipd Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-mvtnorm, r-cran-dplyr Suggests: r-cran-dclone, r-cran-r2winbugs, r-cran-mice, r-cran-micemd, r-cran-miceadds, r-cran-mitools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bipd_0.3-1.ca2404.1_all.deb Size: 126866 MD5sum: 3258cebbc6296570e66995403ea4f979 SHA1: b5fb6c4f14513649b1a2fc2cb5a82a069bae4516 SHA256: ba2ec7b83eca90b0d647d0897bd246af89490d5fee279c8efd5a1e2baf380306 SHA512: 5867d295eddd81196fbf879337ac6fbfbbe7bb0b40f41ca0ff83537b98a0c9fa23683b4d0fd69e78e20921aa4188c519e5a33e0f2c735b0dcea79c3b87c0be6d Homepage: https://cran.r-project.org/package=bipd Description: CRAN Package 'bipd' (Bayesian Individual Patient Data Meta-Analysis using 'JAGS') We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) . This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages. Package: r-cran-bipl5 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-crayon, r-cran-htmlwidgets, r-cran-knitr, r-cran-plotly Filename: pool/dists/noble/main/r-cran-bipl5_1.0.2-1.ca2404.1_all.deb Size: 274260 MD5sum: e7366371eacca1c127ba60cc2f4b8fba SHA1: b9252f617fe6553a505aee8ec946afac47b04e24 SHA256: 24a40a32778114f28cdcfe365fe133c1f29ba73739f7dc649756e69b0180afdc SHA512: c8cdeee6ea484bc655c7198875f41e5fcefafb207aaf344709612efae4c506429ecf71b391656a3a3fc223b0674abb2185931ec631bc40c003923de39c4b2229 Homepage: https://cran.r-project.org/package=bipl5 Description: CRAN Package 'bipl5' (Construct Reactive Calibrated Axes Biplots) A modern view on the principal component analysis biplot with calibrated axes. Create principal component analysis biplots rendered in HTML with significant reactivity embedded within the plot. Furthermore, the traditional biplot view is enhanced by translated axes with inter-class kernel densities superimposed. For more information on biplots, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0). Package: r-cran-biplotbootgui Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-dendroextras, r-cran-mass, r-cran-matlib, r-cran-rgl, r-cran-shapes, r-cran-tcltk2, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-biplotbootgui_1.3-1.ca2404.1_all.deb Size: 292694 MD5sum: 564582b197195352a0c2e8f5fd4a63d0 SHA1: 6df2015869ce3c96cd2310e2fed2f713f3af85fd SHA256: 2f48fb23d7aaf4ca25af8bdbf6f7d3fcc2ff9af6e12ce7be6d38cfce9d85a077 SHA512: 04ed0b9e46bbe490f0365221f009af7cf89a3798e520d52b9a01191e6b75d6e1be326825e709c6d2d9a27b9efa82ca9eaebe70446bdd800cf54ed6c7e3a216a7 Homepage: https://cran.r-project.org/package=biplotbootGUI Description: CRAN Package 'biplotbootGUI' (Bootstrap on Classical Biplots and Clustering Disjoint Biplot) A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology. Package: r-cran-biplotml Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-optimx, r-cran-rspectra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-pracma, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-biplotml_1.1.1-1.ca2404.1_all.deb Size: 129282 MD5sum: a7de007668cbd468be0eeb035df3bf1b SHA1: 585adbcba9ccd23637c66a43bd09699779639196 SHA256: d2b7abca7d8cb70968a36dd87f95edb1c58e744933abcadd88c6f5e4ca288db9 SHA512: bb686d5fccfe6747f198a52e82809bb9439baf6333f3ec661e35a50c864511b6747bcc22aa9d5bc880a78732dd478d06d5deec77dde22607954247c1bb3f526b Homepage: https://cran.r-project.org/package=BiplotML Description: CRAN Package 'BiplotML' (Logistic Biplot Estimation Using Machine Learning Algorithms) Implements methods for fitting logistic biplot models to multivariate binary data. The logistic biplot represents individuals as points and binary variables as directed vectors in a low-dimensional subspace; the orthogonal projection of each individual onto a variable vector approximates the expected probability that the corresponding characteristic is present. Available fitting methods include conjugate gradient algorithms, a coordinate descent Majorization-Minimization (MM) algorithm, and a block coordinate descent algorithm based on data projection that supports matrices with missing values and allows new individuals to be projected as supplementary rows without refitting the model. A cross-validation procedure is provided to select the number of latent dimensions k. References: Babativa-Marquez and Vicente-Villardon (2021) ; Vicente-Villardon and Galindo (2006, ISBN:9780470973196). Package: r-cran-birankr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-birankr_1.0.1-1.ca2404.1_all.deb Size: 67730 MD5sum: f09d12339a00de29237275c2a9cd9363 SHA1: 0eda2026e04254b444c4fb94b19b4322f45ccd73 SHA256: 496d9abdfdecc5f61b1bd055dda51c14e450c56a4418575a873c920da7efe7eb SHA512: 9657392665c7cefd9a1970be5aa6bf72ae5fe444ae9eacfe244e13fdb3a8fd8db97f76416dc90404e84ece4c4d07a155d147d297366fd07af7ced9cc6cd8be8b Homepage: https://cran.r-project.org/package=birankr Description: CRAN Package 'birankr' (Ranking Nodes in Bipartite and Weighted Networks) Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) . Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix). Package: r-cran-birdcolors Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-birdcolors_1.0.1-1.ca2404.1_all.deb Size: 29370 MD5sum: 4ffaf9a7a3da799b7d1b86f6707f1035 SHA1: c654b2ea70fbaa016b70b143e5cda27eb42461e8 SHA256: 8cef9cb83f9882c30c92024f5bd244591682cb3243a6f75b5a46b2c541c99f99 SHA512: 0aa7a8e76dbab01a31ab83ab462a82874981b148c9f13549f4cb7b799df4288583833088c9da3078d44e90163004be06f90d80742eacf188724c0b301eba3b28 Homepage: https://cran.r-project.org/package=birdcolors Description: CRAN Package 'birdcolors' (Create Palettes from the Colors of the World's Birds) Create attractive palettes based on the colors of the world's birds. Palettes are composed of 2 to 9 colors, with options to expand palettes via interpolation. Compatible with the package 'ggplot2' and base R graphics. Package: r-cran-birddog Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggraph, r-cran-ggplot2, r-cran-plotly, r-cran-igraph, r-cran-tidygraph, r-cran-tidyr, r-cran-tibble, r-cran-matrix, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-glue, r-cran-openalexr, r-cran-rcolorbrewer, r-cran-scales, r-cran-stringr Suggests: r-cran-benchmarkme, r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-gghoriplot, r-cran-ggrepel, r-cran-ggthemes, r-cran-janitor, r-cran-gt, r-cran-testthat, r-cran-tictoc, r-cran-viridis, r-cran-zoo, r-cran-stm, r-cran-tidytext, r-cran-udpipe Filename: pool/dists/noble/main/r-cran-birddog_1.0.4-1.ca2404.1_all.deb Size: 2437958 MD5sum: 35a4b6d5ba271ca0750fcc86fdb4ae72 SHA1: 63859145ff3ecec3bded14bffc9ed9c78701df3c SHA256: 8f3ba82cf14848b74e6a7e5bee09cd47fc94449c2e419f5839e82362b7634e80 SHA512: e113b726b65d01761d8129857641f108107fb7666326369b2f0c3e4a85acdee8e284dab89eee04d6170599e600ea6a10a2ceb0f9eb1534ae42cb77b00e3e1ea7 Homepage: https://cran.r-project.org/package=birddog Description: CRAN Package 'birddog' (Sniffing Emergence and Trajectories in Academic Papers andPatents) Provides a unified set of methods to detect scientific emergence and technological trajectories in academic papers and patents. The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) , Souza et al. (2022) , Matos et al. (2023) , Maria et al. (2023) , Biazatti et al. (2024) , Felizardo et al. (2025) , and Miranda et al. (2025) . Package: r-cran-birdnetr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-arrow, r-cran-curl, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-birdnetr_0.3.2-1.ca2404.1_all.deb Size: 574390 MD5sum: f1d81a7efe5a355d1742109c4b59b4d7 SHA1: d863ba353b09b49b2f8546ca2ecc7d8bd358f64c SHA256: 60dfe1c2fea00f9b587591a6ccd69378626039d5feee154cf37d9d6340a315d4 SHA512: fca0363ccc1f7126cc69046d64176881a52e9f43f6e025e751d72d8bc1fbeea65769ccd294d0c28b7854370127e206bcd93a2342437756caa19e1d6f787100a7 Homepage: https://cran.r-project.org/package=birdnetR Description: CRAN Package 'birdnetR' (Deep Learning for Automated (Bird) Sound Identification) Use 'BirdNET', a state-of-the-art deep learning classifier, to automatically identify (bird) sounds. Analyze bioacoustic datasets without any computer science background using a pre-trained model or a custom trained classifier. Predict bird species occurrence based on location and week of the year. Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) . Package: r-cran-birdring Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-ks, r-cran-lazydata, r-cran-raster Filename: pool/dists/noble/main/r-cran-birdring_1.6-1.ca2404.1_all.deb Size: 582720 MD5sum: 409b92adb604d2b283658c4046f42598 SHA1: 63dde5c5bf6e92ae4a6a1100ca50924763f744ca SHA256: 1aa907109cd4c65c74475083bfcf8f0236975b8a6136a5c9e4419c0c6a858e3e SHA512: be784628a6c0fa0dfdf9384eb3bf92d7b2eca352bb487a844efab3b11aa92bfbe1f23faaca0747c579b67f6e64ee1d10fb7dc21a0c721dba81220f72cf455c9f Homepage: https://cran.r-project.org/package=birdring Description: CRAN Package 'birdring' (Methods to Analyse Ring Re-Encounter Data) R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to . Package: r-cran-birdscanr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-suntools, r-cran-modi, r-cran-reshape2, r-cran-rodbc, r-cran-rpostgresql, r-cran-rlang, r-cran-rstudioapi, r-cran-sp, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-birdscanr_0.3.0-1.ca2404.1_all.deb Size: 694028 MD5sum: daf21c43732634724541a8902154165a SHA1: b3d93a9ad52080811c68ebd3655e0bdb15f3763e SHA256: e726c62c7d0a2021bc6cfdb87b69b89e91405e1f04bb9861d5648d6a631f9b0b SHA512: 8388dd446c4930b6679e319419cee032540802125c94e9cea09286956cd7b66ac15cb0dbec2a161e12c92d8346fcc9c95537a189d1adda0f84bfc69becbd62ea Homepage: https://cran.r-project.org/package=birdscanR Description: CRAN Package 'birdscanR' (Migration Traffic Rate Calculation Package for 'Birdscan MR1'Radars) Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) ). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) . Package: r-cran-birk Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-birk_2.1.2-1.ca2404.1_all.deb Size: 47274 MD5sum: 9926e401b3863c33deff6b3d6f1c60ca SHA1: ba87f3cdc8381f211f0f45e8306aad8773f8b744 SHA256: dfb88e50c9840b36945c8505a54d5a7859c34544bf9e1487f714da13fe9c5ac7 SHA512: 25c28c26ad3440777e7c551749d48eda66d9de6b082093c1c6b0c295f4b25a1cfe19caea9f2d392cfc73ddf04edd5df66464f45063670a1f12cfc84317bbf2a4 Homepage: https://cran.r-project.org/package=birk Description: CRAN Package 'birk' (MA Birk's Functions) Collection of tools to make R more convenient. Includes tools to summarize data using statistics not available with base R and manipulate objects for analyses. Package: r-cran-birtr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-birtr_1.0.0-1.ca2404.1_all.deb Size: 57008 MD5sum: 630c54a0ce71e48a7621b55b8d917246 SHA1: 2a7cfe36197a1834705ee0fd748135b92a275fc9 SHA256: 58e664aeed861f90b0b11e0d7b7c26f6f139476465d0c19fc4c99465c6124bd0 SHA512: d179215a5774cef8b2ca606d977ab1b002b273507cd249f5665bd8d38d830dc0c5825b1e6a5712e850ae41010b3efde6446397c7a5c18e91900f2df72e775ca7 Homepage: https://cran.r-project.org/package=birtr Description: CRAN Package 'birtr' (The R Package for "The Basics of Item Response Theory Using R") R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch(). For example, iccplot() plots an item characteristic curve under the two-parameter logistic model. Package: r-cran-bis Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-rvest, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-zoo Filename: pool/dists/noble/main/r-cran-bis_0.4-1.ca2404.1_all.deb Size: 24326 MD5sum: 7a8afd795594c231ae02167615f56d5a SHA1: 07e90e5b96ccef0ea30dd6ce17690fa24a466058 SHA256: 4c97e41a48d69e47b8cebbb0fc8b90f942c5f4d7c7d1fbe57236fd8e436ebd1b SHA512: e9ce8908b6e61c440bd1adfd19322dd1fac2ca1098b1a0a44a1e924bec3c38b58d72f00833c5330cdcd199e671cb7b30b794cbcd558c91234cd8313dfbb5c19d Homepage: https://cran.r-project.org/package=BIS Description: CRAN Package 'BIS' (Programmatic Access to Bank for International Settlements Data) Provides an interface to data provided by the Bank for International Settlements , allowing for programmatic retrieval of a large quantity of (central) banking data. Package: r-cran-biscale Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biscale_1.1.0-1.ca2404.1_all.deb Size: 2587106 MD5sum: 503236164dfbfb9f09c4892a792531ed SHA1: e99619d035f0c3fd6b2b9128607390e473881f25 SHA256: 9f2b5d9150eca3c66239ea435e1970af395edfa7559266cd9dd36a6b7dbb4335 SHA512: 760ffc02e9719f7f18029ba6c4108f70ebf0aea0a796bd7a69a5bd048451e752a85b510b2b23127a3f67199dcb92b9edc858feaec11da82a366e22eb1bfde8ab Homepage: https://cran.r-project.org/package=biscale Description: CRAN Package 'biscale' (Tools and Palettes for Bivariate Thematic Mapping) Provides a 'ggplot2' centric approach to bivariate mapping. This is a technique that maps two quantities simultaneously rather than the single value that most thematic maps display. The package provides a suite of tools for calculating breaks using multiple different approaches, a selection of palettes appropriate for bivariate mapping and scale functions for 'ggplot2' calls that adds those palettes to maps. Tools for creating bivariate legends are also included. Package: r-cran-bisdata Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-zoo Filename: pool/dists/noble/main/r-cran-bisdata_0.2-3-1.ca2404.1_all.deb Size: 22048 MD5sum: 95e7cb0ff9f7c45f17ae75680304f4b3 SHA1: 0888ccb166be7c424ca5148491294218e62154fa SHA256: e7e56743bdd3aa83d1f18461d90c43b9305cef82e8dec3c769b8d04217177660 SHA512: b9d7b4fe43255aa02a0976a2e440f268dd9b1e787b8a3da56ec0fac33a06f2fbb478403927877d5ee8ec6d406e395d7bcc1911b076805f829f15b9ab3ea56ba6 Homepage: https://cran.r-project.org/package=BISdata Description: CRAN Package 'BISdata' (Download Data from the Bank for International Settlements (BIS)) Functions for downloading data from the Bank for International Settlements (BIS; ) in Basel. Supported are only full datasets in (typically) CSV format. The package is lightweight and without dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into 'zoo' objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. Package: r-cran-bisectr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools Filename: pool/dists/noble/main/r-cran-bisectr_0.1.0-1.ca2404.1_all.deb Size: 25758 MD5sum: 6c7ed707a048b255ea8498b7056600af SHA1: 915edf17f3135217a5c66c89af0d25f044539360 SHA256: 322d9097bc2f81381290352f9c0d29851f82f2dcfb404338803d304c057225d7 SHA512: 1a2f386de5fed7dc6ee0307451d45dd786aa1b5bb72ba922bb5df55a2eee9a7e72b021bc04c7812f908093865077cae0a3721ccd1c05182b4a427ad6977cc56c Homepage: https://cran.r-project.org/package=bisectr Description: CRAN Package 'bisectr' (Tools to find bad commits with git bisect) Tools to find bad commits with git bisect. See https://github.com/wch/bisectr for examples and test script templates. Package: r-cran-bisep Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-cran-mclust, r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-gosemsim Filename: pool/dists/noble/main/r-cran-bisep_2.3-1.ca2404.1_all.deb Size: 323896 MD5sum: 4c33b77ba460d5f6352844c1024f7fd6 SHA1: c0a20cba72d598a7d0a66a2075363216fcc958a7 SHA256: 7e4883759b0542e783212646782aaf3178c0ef4f58dd97c945f5c010bd4ac6ec SHA512: 7153b163aa6b06132d19371a667bbc17763aacabfea6c3619d5176b4dec9aab23e7c00e7b3602f19dd16f7d8913c810fe820fef7132aea5771ff58cee283fd12 Homepage: https://cran.r-project.org/package=BiSEp Description: CRAN Package 'BiSEp' (Toolkit to Identify Candidate Synthetic Lethality) Enables the user to infer potential synthetic lethal relationships by analysing relationships between bimodally distributed gene pairs in big gene expression datasets. Enables the user to visualise these candidate synthetic lethal relationships. Package: r-cran-bispdep Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdata, r-cran-sf, r-cran-spdep, r-cran-spatialreg, r-cran-combinat, r-cran-boot, r-cran-ggplot2, r-cran-sp, r-cran-rcolorbrewer Suggests: r-cran-spam Filename: pool/dists/noble/main/r-cran-bispdep_1.0-2-1.ca2404.1_all.deb Size: 184470 MD5sum: fe10f9af04395ef3c4225d6ab227f5b7 SHA1: 81fbe8e996fba9e38580a20f23e2ecd97279986b SHA256: c7e1c70f0072e263f871a964060df69624b875edb159f9df9070a3e46ca691ac SHA512: 0dda1b04281f504540934c17d22e2628064e55201082b196f4d5c7e9e0cf68681db301c7dfa1075c7db5b503f353434578cabbbd7b545a632895a6e320fcd3b4 Homepage: https://cran.r-project.org/package=bispdep Description: CRAN Package 'bispdep' (Statistical Tools for Bivariate Spatial Dependence Analysis) A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established. Package: r-cran-bisquerna Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-limsolve Suggests: r-cran-seurat, r-cran-plyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bisquerna_1.0.5-1.ca2404.1_all.deb Size: 100346 MD5sum: 6a219a187d5ca3416c01a34f52666822 SHA1: 4a66aa567dd4bd78b19a04fbba147f69e5b5d5ff SHA256: fcb0b93ca3b8b1e98b716ce414deaa8f201c0ad81cbc1a4b91c245d7e27d8ebe SHA512: 887920318ed482eb51b82dae53b793d93a5e4e5b8755426b9876d04a9c12e9387906c18867d1f2fd1f3e9265149ed05803fe419b337f4f9245d5333c5791ce3d Homepage: https://cran.r-project.org/package=BisqueRNA Description: CRAN Package 'BisqueRNA' (Decomposition of Bulk Expression with Single-Cell Sequencing) Provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples. For more details, see Jew and Alvarez et al (2019) . Package: r-cran-bisrna Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-bioc-ihw, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bisrna_0.2.2-1.ca2404.1_all.deb Size: 54802 MD5sum: 2bf5e696d4e240dda6e9a4ac3dddb5ec SHA1: 676a9f0526ef83cf583851bacf8c5a8309572478 SHA256: cac93c6ed12966f732b9df446f7a2f28743132d8fc5faa7f069d12a102f552fa SHA512: 4de6ac9350db18f715cddaa4188652e905ce9d809f0e80ba32250a65c1590da691477251ba7f025515648982cd3cfa1a48ea0d752dab13a3d26946a859957dbd Homepage: https://cran.r-project.org/package=BisRNA Description: CRAN Package 'BisRNA' (Analysis of RNA Cytosine-5 Methylation) Bisulfite-treated RNA non-conversion in a set of samples is analysed as follows : each sample's non-conversion distribution is identified to a Poisson distribution. P-values adjusted for multiple testing are calculated in each sample. Combined non-conversion P-values and standard errors are calculated on the intersection of the set of samples. For further details, see C Legrand, F Tuorto, M Hartmann, R Liebers, D Jakob, M Helm and F Lyko (2017) . Package: r-cran-bite Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-mass, r-cran-phytools, r-cran-coda, r-cran-sm, r-cran-vioplot, r-cran-xml2 Suggests: r-cran-mvmorph Filename: pool/dists/noble/main/r-cran-bite_0.3-1.ca2404.1_all.deb Size: 360898 MD5sum: 5d3214ae0178bc94a55eec3b2651f7d4 SHA1: dc4fd61dddc0104168acd25d2cb922523b0b659c SHA256: dcf7b0d8d2782ce20760ff64b01551aece447a99d1ab53c70325eb50273e2073 SHA512: 9fa7afc72f414ce16cde256a24591600a0ceb724d32ba5b00489f7419101c66260a6faf835263b26ae0298d54267a909886d1a277bcb9b0641b1c92ebfae7d1d Homepage: https://cran.r-project.org/package=bite Description: CRAN Package 'bite' (Bayesian Integrative Models of Trait Evolution) Contains the JIVE (joint inter and intra-specific model of variance evolution) model and other Bayesian models aimed at understanding trait evolution. The goal of the package is to join phylogenetic comparative models (PCM) that tend to integrate various type of data (individual observations, environmental data, fossil data) into a hierarchical Bayesian framework. It contains various PCMs as well as functions to join those models into a hierarchical Bayesian framework in a flexible and user friendly way. It contains various Markov chain Monte-Carlo (MCMC) algorithms, methods for model comparison and many plotting function for pre- and post-processing data visualization. Finally, this package integrates functions allowing bridges between 'R' and the 'BEAST2' implementations of PCMs. Kostikova A, Silvestro D, Pearman PB, Salamin N (2016) . Gaboriau T, Mendes FK, Joly S, Silvestro D, Salamin N (in prep). Package: r-cran-bitfield Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-checkmate, r-cran-codetools, r-cran-dplyr, r-cran-gh, r-cran-gitcreds, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bitfield_1.0.0-1.ca2404.1_all.deb Size: 369262 MD5sum: 2520256c0ea7296ff7c447dc55c06fd4 SHA1: d6e4de0eae92a225db7ba16a9dedbcf65bc6fa59 SHA256: bae702bbcf0d989489fd5cdba469a970dc5879c1196d82299f64c71f0618ef57 SHA512: 7c22693ad43f22629b1af6118767e73638030d6a2e202b3b2c46586a6c2f0aaaa08aaf99749e0c577c3e3cb4e76d8239a1e7c84b7d55ab1452cb3a23a2cd62fb Homepage: https://cran.r-project.org/package=bitfield Description: CRAN Package 'bitfield' (Handle Bitfields to Record Meta Data) Record algorithmic and analytic meta data along a workflow to store that in a bitfield, which can be published alongside any (modelled) data products. 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Package: r-cran-bitstreamio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bitstreamio_0.1.0-1.ca2404.1_all.deb Size: 71150 MD5sum: fa59e6335263765b4d5908582e2b9422 SHA1: 89405cf0e8302271f2783f150fcb80953f5a9155 SHA256: 4b34733d6bfa13c6dc2594c91dccf01027f490cf9433ff8d93c63e972133a8c7 SHA512: f4d02c36d4926357a3d05958dc671597a723988172cd5361797e8b4789b8be25f17bf67f1cbf5394a4291656c47250880f16c89649284976d99b435c1bb74d8e Homepage: https://cran.r-project.org/package=bitstreamio Description: CRAN Package 'bitstreamio' (Read and Write Bits from Files, Connections and Raw Vectors) Bit-level reading and writing are necessary when dealing with many file formats e.g. compressed data and binary files. Currently, R connections are manipulated at the byte level. 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Package: r-cran-bittermelon Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-png, r-cran-unicode Suggests: r-cran-colorfast, r-cran-farver, r-cran-gridpattern, r-cran-hexfont, r-cran-knitr, r-cran-magick, r-cran-mazing, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-bittermelon_2.3.1-1.ca2404.1_all.deb Size: 902766 MD5sum: b7f65fb38d3bd7625f196df6967ea151 SHA1: d5f1aded0c6cd5295c1ac0cbe78ea63613db604d SHA256: e5258e6cb0b1a3ad4b780815da4c6929fb97da961cce10f495a48cc5869904bb SHA512: f4dbd0c72636ae1dcd447548f8125d84690ad9abe04f6da15e93b3041ff8f4e078930b30e7d9ec403bc2e36a9e34f35ad7c8a749595a17c3628bd41432193ae9 Homepage: https://cran.r-project.org/package=bittermelon Description: CRAN Package 'bittermelon' (Bitmap Tools) Provides functions for creating, modifying, and displaying bitmaps including printing them in the terminal. 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Package: r-cran-bivarhr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-data.table, r-cran-tidyr, r-cran-tibble, r-cran-readr, r-cran-cli, r-cran-furrr, r-cran-future, r-cran-future.apply, r-cran-posterior, r-cran-loo, r-cran-progressr Suggests: r-cran-testthat, r-cran-mass, r-cran-rtransferentropy, r-cran-bnlearn, r-cran-sensemakr, r-cran-causalimpact, r-cran-bsts, r-cran-vars, r-cran-tsdyn, r-cran-openxlsx, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-bivarhr_0.1.6-1.ca2404.1_all.deb Size: 301274 MD5sum: 1493f14685aeab509399a29bc1d0fab9 SHA1: 2a7e0c0dbfdbec764aa96dfe6028cdb964bee72d SHA256: dd55d38d0682324d5bd097607e39962a3a32ad4e47bd4a5995112e9fca7391a8 SHA512: e332c3027b1f391f0124b0ba06dc156f16026e4569e1933d39e7ac139aa7221416ce4b30548d598f9c88c8d23e4974ae6d83a19c55248ff7cd382fc934aff4ae Homepage: https://cran.r-project.org/package=bivarhr Description: CRAN Package 'bivarhr' (Bivariate Hurdle Regression with Bayesian Model Averaging) Provides tools for fitting bivariate hurdle negative binomial models with horseshoe priors, Bayesian Model Averaging (BMA) via stacking, and comprehensive causal inference methods including G-computation, transfer entropy, Threshold Vector Autoregressive (TVAR) and Smooth Transition Autoregressive (STAR) models, Dynamic Bayesian Networks (DBN), Hidden Markov Models (HMM), and sensitivity analysis. 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It supports tests for continuous and dichotomous data, as well as stepwise regression for linear, logistic, and Firth penalized logistic models. While not a substitute for tailored analysis, 'BiVariAn' accelerates workflows and is expanding features like multilingual interpretations of results.The methods for selecting significant statistical tests, as well as the predictor selection in prediction functions, can be referenced in the works of Marc Kery (2003) and Rainer Puhr (2017) . 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Package: r-cran-bivpois Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-bivpois_1.2-1.ca2404.1_all.deb Size: 55942 MD5sum: 2befcdc6a0594b4760a1c41d3bddbc99 SHA1: cf1100692c512a2ff79aa58adad0d4420c1288b6 SHA256: 67c9bd6c4730457ae6023c72ed8bd6bda30da768c2e191b1cd4a66ac684eb209 SHA512: 4011789dc4707b74128ae8b7d4c42ca05ab6eff4691183b59023003eeb4f581ebed6b5fc2b8a2a73d24d01a12cf8c34cdc2ca5d561df8b576c13e77c331cf572 Homepage: https://cran.r-project.org/package=bivpois Description: CRAN Package 'bivpois' (Bivariate Poisson Distribution) Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. . Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. . Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. . 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The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. 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There are a number of functions in this package that extend Bayesian kernel machine regression fits to allow multiple-chain inference and diagnostics, which leverage functions from the 'future', 'rstan', and 'coda' packages. Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. ; . Package: r-cran-bkmutate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-ggrepel, r-cran-patchwork, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bkmutate_0.1.0-1.ca2404.1_all.deb Size: 101474 MD5sum: c2ce87424b4c4f2a9e51cfaa967e9497 SHA1: a4b8d2e45d207c21693d2a2fcaef3347e1690f68 SHA256: 0f32c5d8b80b8cd77e254659744a001814bbddde1a02d923c8637ed756d1ec44 SHA512: 9dcb6aca8e636a5f97c720f282b93c55d6147a5fab362cfa84f221f9a2be0142402ef761089823739608567ce0c5873b4e6cdcc113cbf369e8764b8d9d4390be Homepage: https://cran.r-project.org/package=BKMutate Description: CRAN Package 'BKMutate' (Statistical Analysis of Induced Mutagenesis Experiments in CropPlants) A colour-first toolkit for the statistical analysis of induced mutagenesis experiments in crop plants. It fits dose-response models to physical and chemical mutagen data and estimates the median lethal and growth-reduction doses (LD50, GR50) with confidence intervals obtained from Fieller's theorem; quantifies first-generation biological damage (lethality, injury and pollen sterility); and estimates mutagenic effectiveness and mutagenic efficiency. Effectiveness and efficiency are conventionally reported as point estimates only; this package treats them as functions of binomial proportions and supplies interval estimates by the delta method on the logarithmic scale and by the nonparametric bootstrap. It further provides chlorophyll mutation spectrum analysis with tests of homogeneity and diversity, generalised linear models for second-generation mutant counts with formal assessment of overdispersion, and formal comparison of mutagens including relative biological effectiveness. Every analysis returns a tidy result object and a publication-ready 'ggplot2' figure. Methods follow Konzak et al. (1965, ISBN:9789201150653), Fieller (1954) and Katz et al. (1978) . Package: r-cran-bkp Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dirmult, r-cran-gridextra, r-cran-lattice, r-cran-optimx, r-cran-tgp Suggests: r-cran-knitr, r-cran-mlbench, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bkp_0.2.3-1.ca2404.1_all.deb Size: 219912 MD5sum: 4fc87c84afb00a83031ec10e93377adb SHA1: 5859266de742441bdb73b682d58f4ddf16c3deff SHA256: 54aa8a42e51201045dfa7af93a0e05c3aad72518b0d5a6572a748f5e78eddfbd SHA512: 5acd6595ec03c818ff83265a6622b2f897eef017247bbe39d48475c81ca3e16b0bb1cc84984758344f0a0909b3da9a6aba2e30274ff301c355a5d4b8e1b04d3a Homepage: https://cran.r-project.org/package=BKP Description: CRAN Package 'BKP' (Beta Kernel Process Modeling) Implements the Beta Kernel Process (BKP) for nonparametric modeling of spatially varying binomial probabilities, together with its extension, the Dirichlet Kernel Process (DKP), for categorical or multinomial data. The package provides functions for model fitting, predictive inference with uncertainty quantification, posterior simulation, and visualization in one-and two-dimensional input spaces. Multiple kernel functions (Gaussian, Matern 5/2, and Matern 3/2) are supported, with hyperparameters optimized through multi-start gradient-based search. For more details, see Zhao, Qing, and Xu (2025) . 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It tests observed segregation against the classical Mendelian expectations, fitting every standard mono-, di- and trihybrid ratio automatically and ranking them by goodness of fit, with Yates continuity correction and Monte Carlo exact tests for sparse tables. It partitions chi-square across families into pooled and heterogeneity components, so that a poor overall fit can be attributed either to the hypothesised ratio or to variation between families. Genetic linkage is estimated by maximum likelihood from two-locus second filial generation and backcross data, with logarithm of odds scores and likelihood-ratio confidence intervals for the recombination fraction. The package further computes Shannon-Weaver and Simpson diversity for descriptor states used in distinctness, uniformity and stability testing, and performs multiple correspondence analysis and Gower-distance clustering of mixed categorical and quantitative descriptors. 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The important features of this package are: (a) Enabling local temporal and spatial modeling of the relationship between the response variable and covariates. (b) Implementing the model described by Lei et al. (2023) . (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the model parameters. (d) Employing a tensor decomposition to reduce the number of estimated parameters. (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration with the 'torch' package. 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Analysis of variance tables, precision statistics and genetic variability parameters are jointly over-determined by exact algebraic identities; 'BKVerify' recomputes every derivable quantity using rounding-interval arithmetic and reports a value as inconsistent only when no combination of values inside the reported rounding intervals can satisfy the identity. The package deliberately restricts itself to relationships that hold irrespective of which variance-component definition an author adopted, so that flagged results reflect arithmetic inconsistency rather than methodological disagreement. Implemented checks cover analysis of variance internal structure, coefficient of variation, standard error of mean and critical difference, the genetic advance identity of Johnson, Robinson and Comstock (1955) , the relation between genotypic and phenotypic coefficients of variation and broad-sense heritability, and admissibility of reported correlation matrices. 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Package: r-cran-blockcov Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rdpack, r-cran-bbmisc, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-blockcov_0.1.1-1.ca2404.1_all.deb Size: 163080 MD5sum: d70839c3c330ee2535b78e28400b89b9 SHA1: a65b0e9d5670866f447ef2d28a243e95de4d69b5 SHA256: 4697ee4701aa351872df0aee43f5b2b033407018fa7a616dee81b11f216a4580 SHA512: 411990d0a4f6aba62c146a800058b8fc18f8d8d369dae6d797b958467460367b6516fec77ad337b27e1fd7e5bc01011e7d55db9d065f21a8d7a5d5e6ee24c0c1 Homepage: https://cran.r-project.org/package=BlockCov Description: CRAN Package 'BlockCov' (Estimation of Large Block Covariance Matrices) Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Dockès et al. (2019) . Package: r-cran-blockedff Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blockedff_0.1.0-1.ca2404.1_all.deb Size: 70994 MD5sum: 96661710c33a5c240b539c71c98061ac SHA1: 794f754d9741dccd087fc7da171dcc25b6c1495f SHA256: 8fe874285db73a70ee6fedb2a00d3f61590ba1e81686b9eed183788c0ca79bc8 SHA512: 115149705a9a4edd5e4c15816829c002a32d1cbe7c12cd2c8ef9af3f33533be2c2c9e1786f79e1b7378177e07ab13d8c42a34012238c41e9e45f40521fe36aa4 Homepage: https://cran.r-project.org/package=blockedFF Description: CRAN Package 'blockedFF' (Generation of Blocked Fractional Factorial Designs (Two-Leveland Three-Level)) Provides computational tools to generate efficient blocked and unblocked fractional factorial designs for two-level and three-level factors using the generalized Minimum Aberration (MA) criterion and related optimization algorithms. Methodological foundations include the general theory of minimum aberration as described by Cheng and Tang (2005) , and the catalogue of three-level regular fractional factorial designs developed by Xu (2005) . The main functions dol2() and dol3() generate blocked two-level and three-level fractional factorial designs, respectively, using beam search, optimization-based ranking, confounding assessment, and structured output suitable for complete factorial situations. Package: r-cran-blocking Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2626 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-text2vec, r-cran-tokenizers, r-cran-rcpphnsw, r-cran-rcppannoy, r-cran-mlpack, r-cran-rnndescent, r-cran-igraph, r-cran-data.table, r-cran-readr, r-cran-matrix Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-reclin2 Filename: pool/dists/noble/main/r-cran-blocking_1.0.3-1.ca2404.1_all.deb Size: 2342278 MD5sum: 958d281ffdfbd2331bc048d25ae4cb47 SHA1: 1db2a5961b054bb222cd2418262cee4ff749a30a SHA256: 65b4004813017f98d9b58b038185183588a5cf218993bd1ac8c2f6c7c37f44dc SHA512: 1acb67169e3950aa5c49cc1a656a65996009327d661af8c72bfed95db6fc111c5383b02df9c495623c520a55e5f02ec95606c0834ee0aed8346fb280f5fdf905 Homepage: https://cran.r-project.org/package=blocking Description: CRAN Package 'blocking' (Various Blocking Methods for Entity Resolution) The goal of 'blocking' is to provide blocking methods for record linkage and deduplication using approximate nearest neighbour (ANN) algorithms and graph techniques. It supports multiple ANN implementations via 'rnndescent', 'RcppHNSW', 'RcppAnnoy', and 'mlpack' packages, and provides integration with the 'reclin2' package. The package generates shingles from character strings and similarity vectors for record comparison, and includes evaluation metrics for assessing blocking performance including false positive rate (FPR) and false negative rate (FNR) estimates. For details see: Papadakis et al. (2020) , Steorts et al. (2014) , Dasylva and Goussanou (2021) , Dasylva and Goussanou (2022) . Package: r-cran-blocklength Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tseries Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-blocklength_0.2.2-1.ca2404.1_all.deb Size: 126652 MD5sum: cfb426e92c553282acb354aadcc030c0 SHA1: 82249da89b54b6cfc544cdfb68b5a6758ed5226b SHA256: b82c1a88f5646c740fd3f14d57283361211f692c0c53ad62e0c058c777c9395f SHA512: 2401bff4236008e7238cb72a578957515b3fb72c2a10d95e4cbef5f3ab29374e04d777a7a1a42ea89a9dc56c3a151a3d1e74867b54364345f20592696f4568ea Homepage: https://cran.r-project.org/package=blocklength Description: CRAN Package 'blocklength' (Select an Optimal Block-Length to Bootstrap Dependent Data(Block Bootstrap)) A set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) subsampling-based cross-validation method, the Politis and White (2004) Spectral Density Plug-in method, including the Patton, Politis, and White (2009) correction, and the Lahiri, Furukawa, and Lee (2007) nonparametric plug-in method, with a corresponding set of S3 plot methods. Package: r-cran-blockmatrix Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blockmatrix_1.0-1.ca2404.1_all.deb Size: 65676 MD5sum: 8aa2e67b2eb905860236487de7dfccff SHA1: 7e4693d1f13245f188b32cb640d9d6b11e2f6670 SHA256: 987a35098fe30e4cde6edc3019d0e16a8dbe8589b928ef8645eee566913cdc5b SHA512: bd7b2ab2817dea521395d92d71bbb5e7ff144ef0b9100d1c31d971ea03bf71fe86640cf308668090c0914274dfe194fe306d8300b3948879e09c65b386ce0a73 Homepage: https://cran.r-project.org/package=blockmatrix Description: CRAN Package 'blockmatrix' (blockmatrix: Tools to solve algebraic systems with partitionedmatrices) Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed. Package: r-cran-blockmissingdata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-glmnetcr, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-blockmissingdata_0.1.1-1.ca2404.1_all.deb Size: 78620 MD5sum: 2020dc643402723c8c0061be9f15b3f1 SHA1: c4926a367de37fca6db936010be3e415a23bf63c SHA256: ae680f75a699903be0523074422f9047986c2c6da8ccb05958897100b718b750 SHA512: ce228c628d6b8965b91251e733fb5c5090d52a3523709ce77a73a96e56672cc16f6a5e40f8286317619ae0ef449fb507ed3a1b42a4d160310d39bdf4e85c86ca Homepage: https://cran.r-project.org/package=BlockMissingData Description: CRAN Package 'BlockMissingData' (Integrating Multi-Source Block-Wise Missing Data in ModelSelection) Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) . Package: r-cran-blockmodelinggui Architecture: all Version: 1.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockmodeling, r-cran-shiny, r-cran-dt, r-cran-htmlwidgets, r-cran-igraph, r-cran-intergraph, r-cran-network, r-cran-shinybusy, r-cran-shinyjs, r-cran-shinythemes, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-blockmodelinggui_1.8.4-1.ca2404.1_all.deb Size: 46648 MD5sum: 4d62738d988fbbf28b173d45c3861416 SHA1: 6f05dc74f6650bb6379572e3a4308e6e6071631f SHA256: 9881e1f36ee6ce9dfc00c3d496acbb5a143609527f4011b6a2a2104ebf1fd4fa SHA512: eb77be7f4a473834700f76ed238624016ece39b32165ef85139bcb69417f36f3916c3394cee4d8444edf4563e652891ad4e7eec3b9c7003e5692d97c70175f12 Homepage: https://cran.r-project.org/package=BlockmodelingGUI Description: CRAN Package 'BlockmodelingGUI' (GUI for the Generalised Blockmodeling of Valued Networks) This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-blockr.core Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-bsicons, r-cran-jsonlite, r-cran-vctrs, r-cran-generics, r-cran-rlang, r-cran-htmltools, r-cran-evaluate, r-cran-shinyfiles, r-cran-digest, r-cran-cli, r-cran-glue, r-cran-yaml Suggests: r-cran-testthat, r-cran-memuse, r-cran-withr, r-cran-shinytest2, r-cran-chromote, r-cran-roxy.shinylive, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-scoutbar, r-cran-thematic, r-cran-ids, r-cran-ellmer Filename: pool/dists/noble/main/r-cran-blockr.core_0.1.3-1.ca2404.1_all.deb Size: 851162 MD5sum: f886f290be5ae58ae35d3ac0afd2cd1c SHA1: 2896956d8437ff03fa39639a76c02669052fcb6c SHA256: b29febf5758dbab9c82bc7c7f485c2294132e4b15815ec52370c2b6059fd88fb SHA512: 687d3d357a3981b9b32e76b713f4b4e9b28ae09c27800372e607c3b70643df4d41cc3b5c74408a5e4ffcd9daa3a7a670950504430674fa5eff37809893b60b18 Homepage: https://cran.r-project.org/package=blockr.core Description: CRAN Package 'blockr.core' (Graphical Web-Framework for Data Manipulation and Visualization) A framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks. Package: r-cran-blockr.dag Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2933 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blockr.core, r-cran-blockr.dock, r-cran-shiny, r-cran-g6r, r-cran-jsonlite, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxy.shinylive, r-cran-webshot2, r-cran-quarto, r-cran-cyclocomp, r-cran-shinytest2, r-cran-chromote, r-cran-withr, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-blockr.dag_0.1.5-1.ca2404.1_all.deb Size: 810358 MD5sum: 8a6b53ebb01dc94d2416b307d768c3e8 SHA1: 6318a085833cd8ee4d2036829714e79ff63828e0 SHA256: 68b9d8c7de94efc7aa455630e60de40fbb7a04d253d0c2828e88600b74a750c4 SHA512: 26e45c7a91aac5301db8ef42980ef573cf894008057f9cedc94516a6091ad1234369ddf8e2a21a2b8a1f20c204a951eb2f43dc5a3da374d8cdf466e26b799986 Homepage: https://cran.r-project.org/package=blockr.dag Description: CRAN Package 'blockr.dag' (A Directed Acyclic Graph Extension for 'blockr') Building on the docking layout manager provided by 'blockr.dock', this provides an extension that allows for visualizing and manipulating a 'blockr' board using a DAG-based user interface powered by the 'g6R' graph visualisation HTML widget. Package: r-cran-blockr.dock Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blockr.core, r-cran-bsicons, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-glue, r-cran-dockviewr, r-cran-htmltools, r-cran-cli, r-cran-shinywidgets, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-dt, r-cran-colorspace, r-cran-withr, r-cran-shinytest2, r-cran-chromote, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-blockr.dock_0.1.2-1.ca2404.1_all.deb Size: 666632 MD5sum: d95f773eda831d443afe5ba69cea7be2 SHA1: 71816c60df8326678cb3655a02b4e39395bdcf7b SHA256: b5c54dc41293ed2f68a307b0461f81d03e4d6fdfad597a853f23ac7a8790c8e5 SHA512: 06a3d39a57eb780d9e76409c4bec9049a70c806d04f0bc5ddbe564da15b3424203e841e02947904b31d9e310036e77fc2f3d023b2df0e55d4e36d6ad891225e0 Homepage: https://cran.r-project.org/package=blockr.dock Description: CRAN Package 'blockr.dock' (A Docking Layout Manager for 'blockr') Building on the docking layout manager provided by 'dockViewR', this provides a flexible front-end to 'blockr.core'. It provides an extension mechanism which allows for providing means to manipulate a board object via panel-based user interface components. Package: r-cran-blockr.dplyr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-blockr.core, r-cran-dplyr, r-cran-tidyr, r-cran-shinyace, r-cran-glue, r-cran-htmltools, r-cran-bslib, r-cran-jsonlite, r-cran-shinyjs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-blockr.dplyr_0.1.0-1.ca2404.1_all.deb Size: 1739050 MD5sum: 755a4505196e1e112ee1c1ea77b98cbe SHA1: 81f81c50c9e5af5275257e6858d123ce667b08e9 SHA256: ba5e8f3405de5148da04f2f5b528a1dae153ffff286d28d4195a7a5bc04a294a SHA512: e7dd6680956ce29f03ff247c8721c62a67eff2e9139adc9f7ef2e22b3fed7d58d9c559d0af493395ae327b50ca700b85d21276ccd514a77048c58ecb5769db81 Homepage: https://cran.r-project.org/package=blockr.dplyr Description: CRAN Package 'blockr.dplyr' (Interactive 'dplyr' Data Transformation Blocks) Extends 'blockr.core' with interactive blocks for visual data wrangling using 'dplyr' and 'tidyr' operations. Users can build data transformation pipelines through a graphical interface without writing code directly. Includes blocks for filtering, selecting, mutating, summarizing, joining, and arranging data, with support for complex expressions, grouping operations, and real-time validation. Package: r-cran-blockr.ggplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockr.core, r-cran-colourpicker, r-cran-ggplot2, r-cran-glue, r-cran-patchwork, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-cowplot, r-cran-ggpubr, r-cran-ggthemes, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rlang, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blockr.ggplot_0.1.0-1.ca2404.1_all.deb Size: 1723532 MD5sum: b4a5805b3320cce2341536102fc085b5 SHA1: 4429a778ed6d6a9e01496ffaeec2ebc3f4984b4a SHA256: bcfd1ac0e35ff829ca67231cf62b9fabfa5c194e26926fcf78b5ed48ca883b07 SHA512: 0a20fcea770fa7bdd35db91dc177a24c56ef80c736a29303fa231210d2737d5c206d522523dcd05b89610ae1339ed4d79487f583156d16890052cebd8dc8bc41 Homepage: https://cran.r-project.org/package=blockr.ggplot Description: CRAN Package 'blockr.ggplot' (Interactive 'ggplot2' Visualization Blocks) Extends 'blockr.core' with interactive blocks for data visualization using 'ggplot2'. Users can build charts through a graphical interface without writing code directly. Includes common chart types (bar charts, line charts, pie charts, scatter plots) as well as statistical plots (boxplots, histograms, density plots, violin plots) with rich customization options and intuitive user interfaces. Package: r-cran-blockr.io Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockr.core, r-cran-readxl, r-cran-shiny, r-cran-readr, r-cran-shinyfiles, r-cran-rio, r-cran-arrow, r-cran-bslib, r-cran-rappdirs, r-cran-shinyjs, r-cran-writexl, r-cran-zip Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blockr.io_0.1.0-1.ca2404.1_all.deb Size: 274518 MD5sum: 9f53bf15eb3b9d89b17d98af1fa747a6 SHA1: 30289389dfc8f72b3ee809144bc13c2ce44ba309 SHA256: cb281092b666e4496b8724bcaca68a174a57a8307467785e7c0fe71af6d487aa SHA512: c9564ad524be6802fce994be8f0ade84bffa8228bdb9a2dd456e4c68b7acb4d2aad00f919a3faef02c31dd9fd5f34968fe3cb961b48117752df443bc20555c2e Homepage: https://cran.r-project.org/package=blockr.io Description: CRAN Package 'blockr.io' (Interactive File Import and Export Blocks) Extends 'blockr.core' with interactive blocks for reading and writing data files. Supports CSV, Excel, Parquet, RDS, and other formats through a graphical interface without writing code directly. Includes file browser integration and configurable import/export options. Package: r-cran-blockr.session Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blockr.core, r-cran-shiny, r-cran-pins, r-cran-jsonlite, r-cran-glue, r-cran-rlang, r-cran-bsicons, r-cran-htmltools, r-cran-httr2, r-cran-zip Suggests: r-cran-testthat, r-cran-blockr.dock, r-cran-connectapi, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-blockr.session_0.1.0-1.ca2404.1_all.deb Size: 210778 MD5sum: 25bccacf2e0cf376c3e39ec35cedb9aa SHA1: ec0be5a085d86ad6476012027f2c3b72a3d8cd57 SHA256: ab1180777dd3fe9a6f7d7b3fef1475ce769ae77e0d7dd1d0d4346b906d7da07b SHA512: c18561ff777a9f7fa84daa4fc0a5f964ff7f2c40fbbf7c04db281e49fb5be9b2b5eb89e55c1878d8b3a05335b503bc093d899a8cabe54e70b9a1ef56aaa80a47 Homepage: https://cran.r-project.org/package=blockr.session Description: CRAN Package 'blockr.session' (Session Management for 'blockr') Persist, restore and manage 'blockr' boards from within a running app. Provides a 'manage_project' plugin for 'blockr.core' that replaces the built-in file upload and download interface with storage backed by the 'pins' package. On 'Posit Connect' each visitor reads and writes pins under their own account, with board sharing, visibility controls and version history. Package: r-cran-blockr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockr.core, r-cran-blockr.dock, r-cran-blockr.dag, r-cran-blockr.dplyr, r-cran-blockr.ggplot, r-cran-blockr.io, r-cran-cli, r-cran-rlang Suggests: r-cran-testthat, r-cran-quarto, r-cran-knitr, r-cran-shinytest2, r-cran-withr Filename: pool/dists/noble/main/r-cran-blockr_0.1.0-1.ca2404.1_all.deb Size: 189320 MD5sum: 98b1b94bc23191a5f8487772d43e9dce SHA1: 45b5e066d1176472f29b1adf08b73f1e1177277b SHA256: 30facd5e0b7933e0b52e72072e9350582dd1a04b74f69bd3a44d16906250f0ef SHA512: 7ef5fa960a6006b67c614cfddaeaff6a0be859366f41b24b7dcb796bb58f7b0c58de6f5e77c23e2a9fc785e98e6a8959464e61f95faf313bedf1a4534c1bf074 Homepage: https://cran.r-project.org/package=blockr Description: CRAN Package 'blockr' (A Block-Based Framework for Data Manipulation and Visualization) A framework for building interactive dashboards and document-based reports. Underlying data manipulation and visualization is possible using a web-based point and click user interface. Package: r-cran-blockrand Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blockrand_1.5-1.ca2404.1_all.deb Size: 34222 MD5sum: cbf4bf01e46408e834f7c1dd3284eb19 SHA1: 1811cf36ef26b3d584d691f650669391c04a0ada SHA256: 6c47da75612434ac8a19aaa5653ccc531ab4d12711a1a92bf64843f3dbdd690a SHA512: 4eefd163d419daa69c3335aba584f32309227895642af119b3469eb2fd2de24685457ff4f8e686bd78ad049edd999919292c42b2306a841f0f297427c18fb0fc Homepage: https://cran.r-project.org/package=blockrand Description: CRAN Package 'blockrand' (Randomization for Block Random Clinical Trials) Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards. Package: r-cran-blockrar Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-ldbounds, r-cran-arm Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr Filename: pool/dists/noble/main/r-cran-blockrar_1.0.3-1.ca2404.1_all.deb Size: 126702 MD5sum: 745a76edbe2bc67ba57d0e8159064ec2 SHA1: b85b2b62fc57d945f4359f51c9facde6572e88b8 SHA256: 9ede06b03ad57893843d262380a6eea18b6856b93cc9e9c695cfa94b63c691f6 SHA512: 8d4bdaf804224f34586e537da2b691c0f7cc50c7b5f6dea154f19730c70c8b5f0ea747f33caa03725063aa88c67c847dabf78e1ca966785afb3a92abdd2b570c Homepage: https://cran.r-project.org/package=blockRAR Description: CRAN Package 'blockRAR' (Block Design for Response-Adaptive Randomization) Computes power for response-adaptive randomization with a block design that captures both the time and treatment effect. T. Chandereng, R. Chappell (2019) . Package: r-cran-blocksdesign Architecture: all Version: 4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-polynomf Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-blocksdesign_4.9-1.ca2404.1_all.deb Size: 485388 MD5sum: ab163f0249d97ce450446ac8af4aba17 SHA1: 59a6ad84ed3de9d32c26db3695991ecf7c654d95 SHA256: 867e8b78101ec4cdafb531321be16b11b04d1120d65ab59895ab23a725645cad SHA512: 95ae98696663511e97dbe6b8c437607f56357e592e7b7cad889df9ff169201c5026758da45419873b33d0bccdf62ad8d0ba63dacda73a6fa0550d90631f8ed15 Homepage: https://cran.r-project.org/package=blocksdesign Description: CRAN Package 'blocksdesign' (Nested and Crossed Block Designs for Factorial and UnstructuredTreatment Sets) Constructs treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any feasible linear model and the block design can be any feasible combination of crossed or nested block factors. The block design is a sum of one or more block factors and the block design is optimized sequentially with the levels of each successive block factor optimized conditional on all previously optimized block factors. D-optimality is used throughout except for square or rectangular lattice block designs which are constructed algebraically using mutually orthogonal Latin squares. Crossed block designs with interaction effects are optimized using a weighting scheme which allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020) . Package: r-cran-blockstrap Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-rlang, r-cran-vctrs Suggests: r-cran-hospitalnetwork, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blockstrap_1.0.0-1.ca2404.1_all.deb Size: 19818 MD5sum: bb3b897149588b0951022b0ece6d9f5f SHA1: 2a5638f9b7a18b29206ad74a80d98a457161ed0c SHA256: 348b2f46010f9c1c11535c8da51d78170d1982234d703c896ec1adca2a02155d SHA512: 618a2633fd74ccd85dd7e46ea9cbcd58b260b26b379ed2e2bb203c0046dce3ce72ea08040a5cf0fe34d28d4c7bc912fedd0460cd1d9c970f41b0c2eb319ed78f Homepage: https://cran.r-project.org/package=blockstrap Description: CRAN Package 'blockstrap' (Sample Dataframes by a Group) Sample dataframes by group, in the form of a 'block bootstrap'. Entire groups are returned allowing for a single 'observation' to span multiple rows of the dataframe. Package: r-cran-blockwise Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vim, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-ranger, r-cran-gbm, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-blockwise_0.1.2-1.ca2404.1_all.deb Size: 713106 MD5sum: ec4e6c08859e1e8d7bead81b3b31448b SHA1: 7707c1a3d6fda5d0ed91a3b379501a7b33d8e30b SHA256: 704505662b499f7bb4c6fbfd9d7ea4a556356b7b84674360c51272dc8bafe107 SHA512: 8cb75218c18537955061d56f07c0b60fdcc8b29397b1b04b1adfcbca93e80baf9144e3adc6e91e32c73720e52a55b91c48b7ad2ccd0419f184fa7225a2a3b2b3 Homepage: https://cran.r-project.org/package=blockwise Description: CRAN Package 'blockwise' (Reduced Modeling for Tabular Data with Blockwise Missingness) Supervised learning on tabular data with blockwise missing patterns, using the Blockwise Reduced Modeling (BRM) method of Srinivasan, Currim, and Ram (2025) . BRM partitions the training data into overlapping subsets based on per-row feature-missing patterns, fits one user-supplied learner per subset with minimal imputation, and at prediction time routes each test instance to the best-matching subset model. The interface is learner-agnostic: any fit-and-predict pair can be plugged in, and convenience specifications are provided for linear models, tree models, random forests, and gradient boosting. Package: r-cran-blockwiseranktest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-blockwiseranktest_0.1.0-1.ca2404.1_all.deb Size: 64612 MD5sum: 564f8636bc225e4c4b73c828eb0630b7 SHA1: af0df6fc25c12b78ea8163d2f5e68925a476b3bf SHA256: fe2386bda9358cd514f64c5188ef83dd33bc73a4d8a3ec9a73cd237ade2e6b4c SHA512: 2fc150053b3c9d59564a8118fee177dfe720d8bf9004b0e3c8ad87e473012ed43689ff4d23bc41af5fd7fca9a3efcb982e4e898a3ed3423414541cdd54b461c5 Homepage: https://cran.r-project.org/package=BlockwiseRankTest Description: CRAN Package 'BlockwiseRankTest' (Block-Wise Rank in Similarity Graph Edge-Count Two-Sample Test(BRISE)) Implements the Block-wise Rank in Similarity Graph Edge-count test (BRISE), a rank-based two-sample test designed for block-wise missing data. The method constructs (pattern) pair-wise similarity graphs and derives quadratic test statistics with asymptotic chi-square distribution or permutation-based p-values. It provides both vectorized and congregated versions for flexible inference. The methodology is described in Zhang, Liang, Maile, and Zhou (2025) . Package: r-cran-blocs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-collapse, r-cran-dplyr, r-cran-ggplot2, r-cran-ks, r-cran-mgcv, r-cran-rlang, r-cran-tibble Suggests: r-cran-devtools, r-cran-questionr, r-cran-reldist, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blocs_0.1.1-1.ca2404.1_all.deb Size: 428096 MD5sum: 1ac1ebc42ef2e011e528eb4cf9c0be47 SHA1: f62fa4f0e4881a215eb85f2a68fdd653013e3495 SHA256: d0987e775d376b56fab044d40d58dffc5c1f706fa8749d6207e92cb7dd49babd SHA512: 4721c7d3425e8cb831c688a48e330f01cf9c0205c219138c2642ab603f40c2af413a91f4a2e7ce030c36afbe82c241e5c32b68d4a99a10fb57fb99565519c9ea Homepage: https://cran.r-project.org/package=blocs Description: CRAN Package 'blocs' (Estimate and Visualize Voting Blocs' Partisan Contributions) Functions to combine data on voting blocs' size, turnout, and vote choice to estimate each bloc's vote contributions to the Democratic and Republican parties. 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Two approaches are implemented: direct estimation using censored maximum likelihood, and a two-step approach that first imputes BLOQ values using various methods and then computes the AUC using the imputed data. Technical details are described in Barnett et al. (2020), "Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification," Statistics in Biopharmaceutical Research. . Package: r-cran-blrm Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-rjags, r-cran-mvtnorm, r-cran-openxlsx, r-cran-reshape2, r-cran-shiny Filename: pool/dists/noble/main/r-cran-blrm_1.0-2-1.ca2404.1_all.deb Size: 135904 MD5sum: 9249e6025de417583b2e105e90282872 SHA1: 01139154f8cd9c621b437304caac659348280af7 SHA256: 399113976ae88e1d2729705a43c579e85a0f7b6149955ad33c6ac31b5e5558de SHA512: df8e79d846bea1e1e90e1f2cb4fc78bc4326dc8a4fde9335bf3ba8a1c07f77421547e5c2ed9df9768ecd02334b25c99492b8ad9ea7f9da72710d7326ce12c87d Homepage: https://cran.r-project.org/package=blrm Description: CRAN Package 'blrm' (Dose Escalation Design in Phase I Oncology Trial Using BayesianLogistic Regression Modeling) Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial. 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This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) . It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level. Package: r-cran-blrshiny2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-dplyr, r-cran-caret, r-cran-e1071, r-cran-rhandsontable, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-blrshiny2_0.1.0-1.ca2404.1_all.deb Size: 71518 MD5sum: c281dcef9c48be404e179e8b753d0609 SHA1: b4aa10d4eb4cbeca2e64d563c49db5dde65f54c1 SHA256: 8157b66fe460801b8eaa72d2379a5552cdcc4c0948502b136d0a13ef8fc06e39 SHA512: 4fb612110b543b370c8e57421775704100d9f4067308de2b96c92f416120f28f5e65846753e1005b04f4768ec917ec76110e9a410dd3093e69e6e2e90a1c9f82 Homepage: https://cran.r-project.org/package=BLRShiny2 Description: CRAN Package 'BLRShiny2' (Interactive Document for Working with Binary Logistic RegressionAnalysis) An interactive document on the topic of binary logistic regression analysis using 'rmarkdown' and 'shiny' packages. 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Runtime examples are provided in the package function as well as at . Package: r-cran-blsbandit Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-jsonlite, r-cran-plotly, r-cran-rsqlite, r-cran-shiny, r-cran-zoo Filename: pool/dists/noble/main/r-cran-blsbandit_0.1-1.ca2404.1_all.deb Size: 48842 MD5sum: e3d6c8e9f8f555983f2a0c939724804e SHA1: 909390bd501d17ff28967ea128e21689e5736f83 SHA256: 9a3080503dfe113b8cf59d2413a1698609721b9c7a773fb8d148cc379c486b0e SHA512: f791be4cbbe7b4b921e2ebdb53069ef261d6572880423ced1cc3b8444d3577c40e98f5def25f614398bffee05a58ccbc2973c68f7bfab47329599e5022d26c4a Homepage: https://cran.r-project.org/package=blsBandit Description: CRAN Package 'blsBandit' (Data Viewer for Bureau of Labor Statistics Data) Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) . 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Package: r-cran-blsr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-purrr, r-cran-rlang, r-cran-readr, r-cran-stringr Suggests: r-cran-testthat, r-cran-stringi, r-cran-zoo, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-blsr_0.5.0-1.ca2404.1_all.deb Size: 128306 MD5sum: 2e9298a010479866084c62f1152f7189 SHA1: 163f80fb92805754847b5295a802937660ef229c SHA256: 705fce60170cf978cce170d8a8a7989a4e9ee86af7985b00edadc1b6a8740e2a SHA512: 7f7878f88dfab098001e7c00ab23414de0b60ebf58f5d69148f24f068d7eb8bc680fc0dec8cf32b370a6c47872b663bebab89b8dfb386212d8225d4cfaf09b79 Homepage: https://cran.r-project.org/package=blsR Description: CRAN Package 'blsR' (Make Requests from the Bureau of Labor Statistics API) Implements v2 of the B.L.S. 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Package: r-cran-bmabart Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bart, r-cran-survival, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bmabart_2.0-1.ca2404.1_all.deb Size: 263340 MD5sum: 085620209f302418d323a9453653655a SHA1: 1c01507a23d66be654eb5fb2eb22b1b69d005f4c SHA256: 8e9e5f7833ed9b04639b4e17b5b8ee5358ac256f365ef2bf588224fc194eec80 SHA512: 6d349d17ba3df6ad68d2f57de00f79328b8f52610df372b600ea13d1732e9c372ea048fde30888cbbdf2680a22d9ce3401817252c8ad0c18b56c8d12f90febb0 Homepage: https://cran.r-project.org/package=bmabart Description: CRAN Package 'bmabart' (Bayesian Mediation Analysis Using BART) Used for Bayesian mediation analysis based on Bayesian additive Regression Trees (BART). The analysis method is described in Yu and Li (2025) "Mediation Analysis with Bayesian Additive Regression Trees", submitted for publication. 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The goal of 'bmass' is to comprehensively test all possible multivariate models given the phenotypes and datasets provided. Multivariate models are determined by assigning each phenotype to being either Unassociated (U), Directly associated (D) or Indirectly associated (I) with the genetic variant of interest. Test results for each model are presented in the form of Bayes factors, thereby allowing direct comparisons between models. The underlying framework implemented here is based on the modeling developed in "A Unified Framework for Association Analysis with Multiple Related Phenotypes", M. Stephens (2013) . Package: r-cran-bmco Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-coda, r-cran-mcmcpack, r-cran-msm, r-cran-pgdraw, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-refmanager Filename: pool/dists/noble/main/r-cran-bmco_0.1.0-1.ca2404.1_all.deb Size: 3440624 MD5sum: e8c423c5194df3193cfadb112cc652bb SHA1: 4c3a915a8351851221f61b71d87d6f6093980362 SHA256: efed2e263dbc0fff4225dc1a7d3fed267521094c6b01bd33af65a759623d0f66 SHA512: a686834a2a62d4b9c7926cf4f3a4de9dd1883981f5f5f85a70fe5ef4460c462f7f33a300ec508e30ec783864f3b662d69a36123c4e97bf152506c6e31df781ef Homepage: https://cran.r-project.org/package=bmco Description: CRAN Package 'bmco' (Bayesian Analysis for Multivariate Categorical Outcomes) Provides Bayesian methods for comparing groups on multiple binary outcomes. Includes basic tests using multivariate Bernoulli distributions, subgroup analysis via generalized linear models, and multilevel models for clustered data. For statistical underpinnings, see Kavelaars, Mulder, and Kaptein (2020) , Kavelaars, Mulder, and Kaptein (2024) , and Kavelaars, Mulder, and Kaptein (2023) . An interactive shiny app to perform sample size computations is available. Package: r-cran-bmconcor Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4229 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bmconcor_2.0.0-1.ca2404.1_all.deb Size: 659578 MD5sum: 6ba6a900838a7f6b59ea4eea796d49fd SHA1: c93eb07b755c1c25ba5623296ef1dbf82d1f72ee SHA256: 67a4ef820aac766e4a4c091a879eab4316f365b76d74e27f5db6d4f264ee8a5f SHA512: e784d6168caa2efa22a4f83cc40a7641d8d03081727d6e145df35d4c98745f16780fa6b73353b751e78546349eb1ee4aa7c75a6e91a2df1823224ac31d8c4aee Homepage: https://cran.r-project.org/package=BMconcor Description: CRAN Package 'BMconcor' (CONCOR for Structural- And Regular-Equivalence Blockmodeling) The four functions svdcp() ('cp' for column partitioned), svdbip() or svdbip2() ('bip' for bipartitioned), and svdbips() ('s' for a simultaneous optimization of a set of 'r' solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) and Hanafi M. & Lafosse, R. (2001) . Package: r-cran-bmem Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-amelia, r-cran-mass, r-cran-snowfall, r-cran-lavaan, r-cran-sem Filename: pool/dists/noble/main/r-cran-bmem_2.3-1.ca2404.1_all.deb Size: 996644 MD5sum: 3084445fc84e62768dbdec0da31cb431 SHA1: 67cd47bf7f432c8c7d9c46c92f494dca56440473 SHA256: 6f8bd2d0c87a9c87d1b925722fbed36f65e0aa9887876230dac6149a2e85228c SHA512: 8c1c08a968ec588e0a60fac62c60e6e1c5c74811957ec0b0c3a3d5becf39f24a9cf3672ecb555963832d6cf854a61401404982b93132f250e65eeb0c896d205a Homepage: https://cran.r-project.org/package=bmem Description: CRAN Package 'bmem' (Mediation Analysis with Missing Data Using Bootstrap) Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) . Zhang (2014) . Package: r-cran-bmemapping Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-gstat, r-cran-sf, r-cran-mvtnorm Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bmemapping_2.0.0-1.ca2404.1_all.deb Size: 193328 MD5sum: 7b47b4e3ffa95cd89a5b6348527dbcd1 SHA1: cf20bbfb6dce1b091b6d29de3a9ef0439d790d71 SHA256: e0c0a0f52c7071139d8fe8eede8968180cb6c57ce5467cdf4b55a27e13017331 SHA512: 23a19bba62ee59e6d81dd902a9a8fe85079e12e4b8a973f780f1939360dd6cee86d8ab55551741120fd1277ba502c1c7d1652fbfdcb91521978c7eea38bb48fd Homepage: https://cran.r-project.org/package=BMEmapping Description: CRAN Package 'BMEmapping' (Spatial Interpolation using Bayesian Maximum Entropy (BME)) Provides an accessible and robust implementation of core BME methodologies for spatial prediction. It enables the systematic integration of heterogeneous data sources including both hard data (precise measurements) and soft interval data (bounded or uncertain observations) while incorporating prior knowledge and supporting variogram-based spatial modeling. The BME methodology is described in Christakos (1990) , Serre and Christakos (1999) and Duah (2025, 2026) . Package: r-cran-bmemlavaan Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-mass, r-cran-snowfall, r-cran-rsem, r-cran-lavaan, r-cran-sem Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-bmemlavaan_0.7-1.ca2404.1_all.deb Size: 514108 MD5sum: 16c43542eb22c104b744f9a92b0f70b4 SHA1: 300e7e4025b4797f317c6a7b851b01808e775341 SHA256: 87cc7a514d8de93501eb1d625dee201945302b7ec451b8cc7617a42ee9d45603 SHA512: f57b6d528dc28aeaf18c27ca09d59fe54564740c6f6ba26e393c7ac59abaa2a49d59a715097c5f78ead6638538ab0e4bcccfd489dbbe6642f07e8868854bf2de Homepage: https://cran.r-project.org/package=bmemLavaan Description: CRAN Package 'bmemLavaan' (Mediation Analysis with Missing Data and Non-Normal Data) Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, ), Zhang (2014, ), and Yuan and Zhang (2012, ). Package: r-cran-bmet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bmet_0.1.0-1.ca2404.1_all.deb Size: 23252 MD5sum: 85040e7d22d9d9a55eb0f5a738e2e9b4 SHA1: a8553860343ea9c823024ed93e415758dfcd6601 SHA256: 78362db188d4e2378d54d7902810aa77fb911682eca32295cfdc568b26d8731f SHA512: 2630514f507d58a576a4e4869796648d98eefa521865ef9f8de927e14235b4f18198bddbcd4b1afe19e44fe4225da34d57e2d518cb35c882ce5fa68f101d8af4 Homepage: https://cran.r-project.org/package=bmet Description: CRAN Package 'bmet' (Bayesian Multigroup Equivalence Testing) Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) , but more models and equivalence testing features may be added over time. Package: r-cran-bmiselect Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvnfast, r-cran-gigrvg, r-cran-mass, r-cran-rfast, r-cran-foreach, r-cran-doparallel, r-cran-arm, r-cran-mice, r-cran-abind, r-cran-stringr, r-cran-posterior Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bmiselect_1.0.3-1.ca2404.1_all.deb Size: 193610 MD5sum: 341cce58d50476de8992d96479a5d1ae SHA1: bcbbc50e5ceba5d48861a1125a6806e49e820a13 SHA256: 0ed101b4dd42f10ae713817d25e6c5b8ed83f232ddafd2e65708548945a74504 SHA512: 1e2839a6d06316d466a21d02ba3df27d3634a082835c66dd2b92832b5c02e812f2103903bca820a595ec968818dc67d040b187dcf8d341b747e6411c33f03d4f Homepage: https://cran.r-project.org/package=BMIselect Description: CRAN Package 'BMIselect' (Bayesian MI-LASSO for Variable Selection on Multiply-ImputedDatasets) Provides a suite of Bayesian MI-LASSO for variable selection methods for multiply-imputed datasets. The package includes four Bayesian MI-LASSO models using shrinkage (Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab (Spike-and-Laplace) priors, along with tools for model fitting via MCMC, four-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random or missing-completely-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data. ArXiv, 2211.00114. for more details. We also provide the frequentist`s MI-LASSO function. Package: r-cran-bml Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-r2jags, r-cran-ggplot2, r-cran-ggmcmc, r-cran-coda, r-cran-patchwork, r-cran-tibble, r-cran-purrr, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bml_0.9.0-1.ca2404.1_all.deb Size: 575942 MD5sum: de2c6890ee53167afffba2c0a6291191 SHA1: 882d9ab874f4e4304fd75a3e37fc5ffd5f04eeec SHA256: 6bb59c6b6f8b1ae4199a4869c141366600b930fc0a690a473aaf7067635f947a SHA512: 776c4f7b81e4d1b72ae2676e646ff4c9737e802e1a8233c8dd1004a8689f31349cf29b07548ddeb3342ba59b24e679a176eb62ad6efa963abe2fc4dd3a38d6c5 Homepage: https://cran.r-project.org/package=bml Description: CRAN Package 'bml' (Bayesian Multiple-Membership Multilevel Models withParameterizable Weight Functions) Implements Bayesian multiple-membership multilevel models with parameterizable weight functions via 'JAGS' to model how lower-level units jointly shape higher-level outcomes (micro-macro link) across a range of outcome types (e.g., linear, logit, and survival models). Supports estimation and comparison of alternative aggregation mechanisms, allows weight matrices to be endogenized through parameters and covariates, and accommodates complex dependence structures that extend beyond traditional multilevel frameworks. For details, see Rosche (2026) "A Multilevel Model for Coalition Governments. Uncovering Party-Level Dependencies Within and Between Governments" . Package: r-cran-bmm Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1509 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayesplot, r-cran-brms, r-cran-crayon, r-cran-fs, r-cran-glue, r-cran-matrixstats, r-cran-rtdists, r-cran-rlang, r-cran-withr Suggests: r-cran-bookdown, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-fansi, r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-mixtur, r-cran-remotes, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidybayes, r-cran-tidyr, r-cran-usethis, r-cran-waldo, r-cran-yaml Filename: pool/dists/noble/main/r-cran-bmm_1.3.2-1.ca2404.1_all.deb Size: 1343944 MD5sum: 11a8c9ff5ab2b75b50e8fa59933a5934 SHA1: 099ba7df2a273dd8f6556119a3485f4fe2063906 SHA256: 438eca8fbed753bd14d01a521a8b0db637fe5ae5f9841cebcb6ddef852d2c03c SHA512: 2751c632673eb37bd6d8db4937f2e04bb054457049798d44f061464777ffe6a036327805f40986f161b37bf6674c4445a3c25849efbf8202fa16c8e238c7ed8d Homepage: https://cran.r-project.org/package=bmm Description: CRAN Package 'bmm' (Easy and Accessible Bayesian Measurement Models Using 'brms') Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2025) . Package: r-cran-bmp Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pixmap, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bmp_0.3.1-1.ca2404.1_all.deb Size: 108650 MD5sum: 5db2c89b4bcf21332787ff6efb404978 SHA1: 3c0629869fdfdada5999ebf920b493120ef9973b SHA256: d5595a3bb7567c1c373a381c4b8314743600ff7d47d1a7ca2bdce796cbc1e876 SHA512: 1ee0f9d37416c62ccdbc9b4bb2b56b25936680cbd5ee4008da87a9c07cfc5f2259c9ebb7791a369cc55bec2bb6b29a52436551aafd0647142b8275e91e31fc0b Homepage: https://cran.r-project.org/package=bmp Description: CRAN Package 'bmp' (Read Windows Bitmap (BMP) Images) Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. Pure R implementation without external dependencies. Package: r-cran-bmrbr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-rvest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bmrbr_0.2.0-1.ca2404.1_all.deb Size: 23206 MD5sum: 6d0c957736372a03456ee8a5a0cd70ab SHA1: ebcd5db799b43fce8aaba698e326b4013eafa9e2 SHA256: 905c710b328d467eb389768ceacea1ee0a869992c2e3ed1fd5fbc88057a4c869 SHA512: c640b46a4f15e2a46e2041f9a247c039de7dd9b868ded3a8770220ca60ead2d62fdb8c522b8f7667dc6889f4b3e3d233f6a84e0a5b7f4dba9c251088a5b899e8 Homepage: https://cran.r-project.org/package=BMRBr Description: CRAN Package 'BMRBr' ('BMRB' File Downloader) Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, 'BMRB' offers an R package 'RBMRB' to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using 'RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, 'BMRBr' File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the 'BMRB' have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual 'BMRB' data file from the 'BMRB' database . The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402–8. . [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13–22. . Package: r-cran-bmrmm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-logofgamma, r-cran-mcmcpack, r-cran-multicool, r-cran-pracma Filename: pool/dists/noble/main/r-cran-bmrmm_1.0.1-1.ca2404.1_all.deb Size: 233890 MD5sum: 93c631ee2c67036fdaf6e6d68a11e8d3 SHA1: 92dfd7565fad27fbc9b87808f004fe4634231c84 SHA256: 339c97d2d525f723363c2184065df8c1bc52552f331831eda8acdaa759feafd3 SHA512: 020a4fd9deecc48d5f721b6e45c90519a50b5b5fe60edb8cafa6c4eeee702c42e601d69cec42cdc54d85f5daf5b78f96761184803dc426cf94fe686d200b9ff1 Homepage: https://cran.r-project.org/package=BMRMM Description: CRAN Package 'BMRMM' (An Implementation of the Bayesian Markov (Renewal) Mixed Models) The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) and Yutong Wu et al., (2022) . Package: r-cran-bms Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bms_0.3.5-1.ca2404.1_all.deb Size: 2914762 MD5sum: 6df92185025fa7a19d4ac2796ac35258 SHA1: 3439c33e7d29926e801c674915c3d085222ae373 SHA256: 36934f83c5d0dd7796fccb4a2a6b72b39f732f820168c0be77f2c4cf39e7271c SHA512: d0f320c751f31c8df2f252897c6eeb350c7e0178ad0a6b4c6ad2e5832ecfdbb405c459d8b8ee962d973f29e7819e78b2b27346179361c9cfb0f89c32164e162d Homepage: https://cran.r-project.org/package=BMS Description: CRAN Package 'BMS' (Bayesian Model Averaging Library) Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods. Package: r-cran-bmscstan Architecture: all Version: 1.2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-bayesplot, r-cran-loo, r-cran-logspline, r-cran-laplacesdemon Suggests: r-cran-reshape2, r-cran-gridextra, r-cran-bridgesampling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-bmscstan_1.2.1.0-1.ca2404.1_all.deb Size: 306334 MD5sum: a53dc51c3e18cbc94f401d26f1aceb52 SHA1: c7b86f30fa417617f0a1bd47e4bb656e0d1e3e1e SHA256: 21df837d545dec153ae354e73ff58db5e94db5634aeba6095ee99f85a74be2be SHA512: 3ab2759b70fb899113ee476d41501eb59ade28108a360c04ccdd7b5fd1afe80d9f01c100b95c68eaa8705bceba2d3543f6ab6d521210544106a50d7b266bb03f Homepage: https://cran.r-project.org/package=bmscstan Description: CRAN Package 'bmscstan' (Bayesian Multilevel Single Case Models using 'Stan') Analyse single case analyses against a control group. Its purpose is to provide a flexible, with good power and low first type error approach that can manage at the same time controls' and patient's data. The use of Bayesian statistics allows to test both the alternative and null hypothesis. Scandola, M., & Romano, D. (2020, August 3). Scandola, M., & Romano, D. (2021). . Package: r-cran-bmt Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partitions, r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-bmt_0.1.3-1.ca2404.1_all.deb Size: 338376 MD5sum: 1d1f27b36fa5bea53201490448b32383 SHA1: b82569ef00b7a592f9255a0b276ed55e094e3e42 SHA256: 6f2e3f4a920f211fd842708109dd5da0e4a49089aa486989146d7898bf3a6abb SHA512: b39809dece07160625271067658aa50fe061ef67570978a98f069ab0d0089dab9789af45f748cc6353323cae42615e560b4dce35f9d37d7688d21c43c5973783 Homepage: https://cran.r-project.org/package=BMT Description: CRAN Package 'BMT' (The BMT Distribution) Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) . Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included. Package: r-cran-bmtar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brobdingnag, r-cran-mass, r-cran-mcmcpack, r-cran-expm, r-cran-ks, r-cran-mvtnorm, r-cran-doparallel, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bmtar_0.1.1-1.ca2404.1_all.deb Size: 533604 MD5sum: 7926465158485364c954a6ff2b6ce8c8 SHA1: ec315df4cb5291847563cd1bfac0035a98171ad3 SHA256: b7962fcc1decf10e2ca4e7d0d97accd420e0626ddcec9098de91756208f84923 SHA512: 6fe980dc21bd6432f3f14a621e490c8f6c1be80613a56ad2a07d74d952376eb045f9c9b36270a77e2247fe483215860b685080fcb7e746180d48934769a3d000 Homepage: https://cran.r-project.org/package=BMTAR Description: CRAN Package 'BMTAR' (Bayesian Approach for MTAR Models with Missing Data) Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) . Package: r-cran-bndesr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-lubridate, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bndesr_1.0.4-1.ca2404.1_all.deb Size: 40408 MD5sum: 68a77d9850cf1dcd2737b9a05a6dc8de SHA1: a8ab98a979a0a38937c8c7bee027ec3492ea2f5d SHA256: 561ce8603da41fc8e6c0325216cd85317c2fa1d0f9c6e1add0008e76a4aa386f SHA512: 346e8dbbead3983aa97a84ed4837b5795bf7d5093bec2f758376ea38cbbc29267ae9719f3af0f0ed828d569b428c9f778cc7364726f42c55a8d568b8b6d02e96 Homepage: https://cran.r-project.org/package=bndesr Description: CRAN Package 'bndesr' (Access Data from the Brazilian Development Bank (BNDES)) Allows access to data on BNDES disbursements and contracts since 1995. The package makes it easy to import data from the bank into R.. Package: r-cran-bndovb Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-pracma, r-cran-mass, r-cran-dplyr, r-cran-factormodel, r-cran-nnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bndovb_1.1-1.ca2404.1_all.deb Size: 603860 MD5sum: a28e40a950bff1a87d415a2563923bf4 SHA1: c5f382f38b12db0b4bfdff2f0ec02d86a6439715 SHA256: 9b218fb0bd3f8b8dae4851d0b660831d7d1ea8e534e1f4f6ef7ba500e1fe1c9a SHA512: 0f3a0312b2d997772bcb2889f2803a30f6ad9420f247453cfddd25dfda8a449de7831b9f5ff729b387beef7afdf69c4fc896ffe88cf13ec810072ad5a0939482 Homepage: https://cran.r-project.org/package=bndovb Description: CRAN Package 'bndovb' (Bounding Omitted Variable Bias Using Auxiliary Data) Functions to implement a Hwang(2021) estimator, which bounds an omitted variable bias using auxiliary data. Package: r-cran-bnma Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bnma_1.6.1-1.ca2404.1_all.deb Size: 382270 MD5sum: 250887192343361321b72e826097c9f9 SHA1: 7bfe4753017130ccd01f7f6c13c30ad1f73a0ac1 SHA256: f870610394acf239120017c3ec2e7380b2662455bad380114b7528ac63261969 SHA512: 5e92616ec72ac8ecdebf2438a01e4ff51e2e01c8e2fe2c287b0bac5d5f42633d982f266159aecf0783c92e9b54aa795cf928b4357126038b7e03f2db13516237 Homepage: https://cran.r-project.org/package=bnma Description: CRAN Package 'bnma' (Bayesian Network Meta-Analysis using 'JAGS') Network meta-analyses using Bayesian framework following Dias et al. (2013) . Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results. 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It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) . 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The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) . 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For this we propose a hybrid approach, which uses Bayesian network structure learning algorithms from data to create the input file for creation of a PA model. The process is performed in a semi-automatic way by our intermediate algorithm, allowing novice researchers to create and evaluate their own PA models from a data set. The references used for this project are: Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. . Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. . Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. . Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. . Package: r-cran-bnpdensity Architecture: all Version: 2025.7.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-survival, r-cran-coda, r-cran-dplyr, r-cran-tidyr, r-cran-viridis Suggests: r-cran-greedyepl, r-cran-rmpfr, r-cran-gmp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bnpmix, r-cran-salso Filename: pool/dists/noble/main/r-cran-bnpdensity_2025.7.29-1.ca2404.1_all.deb Size: 4540194 MD5sum: 2d5db4b5e192abaf103724c7bfb499d1 SHA1: e9437113b8a5a2a4b3e2f19df764fdcf4e2aa09b SHA256: 8131c878514ac24ba5871d55b06af6ff42b2b85f3733f7eb59d9edf121a6ab30 SHA512: 8126fd49c70bdaa6a634e01c1db7dfbdcb6a4b9df7de45e8211e55db4b49140ddf32e5409619de58dbb5f9f0724b7839cad4b749474f0834a044f710bd97cdcd Homepage: https://cran.r-project.org/package=BNPdensity Description: CRAN Package 'BNPdensity' (Ferguson-Klass Type Algorithm for Posterior Normalized RandomMeasures) Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures. 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Package: r-cran-bnpsd Architecture: all Version: 1.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-nnls Suggests: r-cran-popkin, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-bnpsd_1.3.13-1.ca2404.1_all.deb Size: 408362 MD5sum: 830a31b9d569778f089fde2606270bed SHA1: 8169a2a69f4883ce927cb311381fc5e56188811d SHA256: 70f821a9986fadf2e963e6ef5e00bf09e355c969fc79e1e4e4d2a93e78b4a35f SHA512: 02ff96ac5973d46187889b80b9cc54fd8c243593d935cb125a708f886dc19f25ef64f81c820671bb8d2b1b76dd16a6388fc0ad4abe669621b7f5c9c171248902 Homepage: https://cran.r-project.org/package=bnpsd Description: CRAN Package 'bnpsd' (Simulate Genotypes from the BN-PSD Admixture Model) The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions. 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The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at . Reference: 'M. Leonelli' (2025) . Package: r-cran-bnrich Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bnlearn, r-cran-corpcor, r-cran-glmnet, r-bioc-graph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bnrich_0.1.1-1.ca2404.1_all.deb Size: 916596 MD5sum: 6e46d9e15d766a43da69e80268de920c SHA1: 92c59b8ee422c47744871991e18dc8018aed376f SHA256: 5dae732193fdb50832196b3654bd6f323405e7f3e79f7e4937b8527122efce2f SHA512: b51878e73d25f582435e507459ff4fb224011e06f21d93431aa696eb1df5a43e289441b4027a68c9766b6b8ee7ac7363df19ce15f41c90be61b1d0dd9825d9c1 Homepage: https://cran.r-project.org/package=BNrich Description: CRAN Package 'BNrich' (Pathway Enrichment Analysis Based on Bayesian Network) Maleknia et al. (2020) . A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters. 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Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. . 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Functionality requires installing the data packages 'adiposerefdata' and 'musclerefdata' from p-mq.github.io/drat. For more information on the underlying research, please visit our website which also includes a graphical interface. The models and underlying data are described in Marquardt J. Peter et al (2025), "Subcutaneous and Visceral adipose tissue Reference Values from Framingham Heart Study Thoracic and Abdominal CT", *Investigative Radiology* and Tonnesen PE et al. (2023), "Muscle Reference Values from Thoracic and Abdominal CT for Sarcopenia Assessment [column] The Framingham Heart Study", *Investigative Radiology*, . Package: r-cran-boe Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-scales, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-boe_0.4.0-1.ca2404.1_all.deb Size: 192300 MD5sum: 74a60aa2399759c416f9791d6501dc06 SHA1: 8a2bf33617fdbf88c5398e39ed0725d30b0f68c8 SHA256: 7031644e74103d7d060a0d934d3e1afac908fbd3cd7c8c99ad9bcac2a8d1ba6f SHA512: 935902095b3a9b4a6dfdf0c7633bcd4b51ae7641d72acfa4ce98f62d735238b84afb927eb1df71377d4e94ae7ce11efcb563163ffba52ae95f0328b78243ca30 Homepage: https://cran.r-project.org/package=boe Description: CRAN Package 'boe' (Download Data from the 'Bank of England' Statistical Database) Provides functions to download and tidy statistical data published by the 'Bank of England' . 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Package: r-cran-boin Architecture: all Version: 2.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso Filename: pool/dists/noble/main/r-cran-boin_2.7.2-1.ca2404.1_all.deb Size: 202186 MD5sum: b48bc4ed21ead171cfd6149f68dc7433 SHA1: 45fc0b170b7d6f095d7dc63be3685ba1ce052d13 SHA256: 74332aff2e990f7ed9966aebafbaad5e395df29cf3a5f04a180e68f9fd2c7ac2 SHA512: b2127ed95467be7fd7f64cbd7df68540c08216778383040dfc3ff513550bb9eb8644f37901a4bfceb5964ca02d70ff71141a82d9d702baa304624ae5a156ca55 Homepage: https://cran.r-project.org/package=BOIN Description: CRAN Package 'BOIN' (Bayesian Optimal INterval (BOIN) Design for Single-Agent andDrug- Combination Phase I Clinical Trials) The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding the maximum tolerated dose (MTD). It can be used to design both single-agent and drug-combination trials. The BOIN design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the BOIN design is that it achieves simplicity and superior performance at the same time. The BOIN design is algorithm-based and can be implemented in a simple way similar to the traditional 3+3 design. The BOIN design yields an average performance that is comparable to that of the continual reassessment method (CRM, one of the best model-based designs) in terms of selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses. For tutorial, please check Yan et al. (2020) . 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Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application. Package: r-cran-boiwsa Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-hmisc, r-cran-lubridate, r-cran-patchwork, r-cran-tidyr, r-cran-rlang, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-boiwsa_1.1.5-1.ca2404.1_all.deb Size: 302776 MD5sum: 7b66dbb549c6f7ce7926dc04c125ab44 SHA1: 9f3b64d38bf74d6eabd082e49cf703af0951362a SHA256: 17efe6b2d27e43476ef35f00e1d023bb4d51433f77310ff037be0683a3fe8a99 SHA512: 7f7e1b3136ab5c0415c8f20cbd6a0d2efe104ebd9f8b23732143e9158e55e8d1d3e8bf61ed3c2c0d0f450f8b073efa843f7c92fa901eb7125415b61bb0165203 Homepage: https://cran.r-project.org/package=boiwsa Description: CRAN Package 'boiwsa' (Seasonal Adjustment of Weekly Data) Perform seasonal adjustment and forecasting of weekly data. 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Package: r-cran-boostingdea Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rglpk, r-cran-dplyr, r-cran-lpsolveapi, r-cran-mlmetrics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-boostingdea_0.1.0-1.ca2404.1_all.deb Size: 221468 MD5sum: d113a875ebe2832e457c6b0b9aad3259 SHA1: 40efb09f6d9681285466c8e5419d7ae0aed10fd6 SHA256: f839ac002a65667fe0d6ac315780ea6a4373f8c9c8edfc0208a5594067758707 SHA512: fbaa5edb49f58047b2fedda6798b42b3c48e88427ba9f190a9819a1a8b0aaea989327b267f6c9a9690d96acc2ac686294c4a6e24d840725ad5b2bd83eba09f13 Homepage: https://cran.r-project.org/package=boostingDEA Description: CRAN Package 'boostingDEA' (A Boosting Approach to Data Envelopment Analysis) Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. 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Package: r-cran-boostrq Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mboost, r-cran-stabs, r-cran-quantreg, r-cran-checkmate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-boostrq_1.0.0-1.ca2404.1_all.deb Size: 76778 MD5sum: df0078d6af0a692aaced797dbd3a1671 SHA1: 7b7d95eba8fdc085054c105fcfe9b3d87be1d614 SHA256: bec70c692969516e335fdc9551a6b4d0953c3b7188576810ed57730cfb73616b SHA512: dc245ff8ea3057f70af1c9e4586f509ae3a03601db86ba282cc612796cd702db796663cead0cd3177071b9407c7928b9219cbca833234f05870fd8b07bc0a14f Homepage: https://cran.r-project.org/package=boostrq Description: CRAN Package 'boostrq' (Boosting Regression Quantiles) Boosting Regression Quantiles is a component-wise boosting algorithm, that embeds all boosting steps in the well-established framework of quantile regression. 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Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihoods, so installation of R package 'metafor' is required. This approach refers to the studies of Anscombe (1956) , Haldane (1940) , Hedges (1981) , Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) , Viechtbauer (2010) , and Zuckerman (1994, ISBN:978-0521432009). 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Package: r-cran-boot Architecture: all Version: 1.3-32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-survival Filename: pool/dists/noble/main/r-cran-boot_1.3-32-1.ca2404.1_all.deb Size: 636236 MD5sum: c719a40019dd5cafcd1a411f5880f4fa SHA1: 63d4d68e95e54f689f73d9d45d7a8fd7d8ad63b1 SHA256: 5f24470dacc742f1b822ee7130f1cf65654e849574932e0f4bbb85c5e0cb697b SHA512: 0173c5487d341d82f7b05dfd17bc9f09b2f221c2b668cf5c820e778a16b32f349838fb7e2b87c495c4c9af169876db27b6c8005befbcdd1d58afd92711d60544 Homepage: https://cran.r-project.org/package=boot Description: CRAN Package 'boot' (Bootstrap Functions) Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S. Package: r-cran-bootcluster Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-mclust, r-cran-flexclust, r-cran-fpc, r-cran-dplyr, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-ggplot2, r-cran-gridextra, r-cran-intergraph, r-cran-ggally, r-cran-network, r-cran-kernlab, r-cran-sna, r-cran-progress Filename: pool/dists/noble/main/r-cran-bootcluster_0.4.3-1.ca2404.1_all.deb Size: 195112 MD5sum: 8a699f71be87726a593f23cbd21ee128 SHA1: c18cd5084abbd2d5e1ecc35d73dfe3aab84be5ea SHA256: 1621dc5e58414c5a913a0282282e1d267126537fdc77944c910be3dcdf83d244 SHA512: dc0ed629b9faa3fe3e0611a1e4a5757b62551d3c8b633c4190e7eefef423aebdb7d65522af6c29526591cf38aefa478860240e963497b15eccb7770beb6e1ea1 Homepage: https://cran.r-project.org/package=bootcluster Description: CRAN Package 'bootcluster' (Bootstrapping Estimates of Clustering Stability) Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016). Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) . Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) . Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method. Package: r-cran-bootcomb Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-hdinterval Filename: pool/dists/noble/main/r-cran-bootcomb_1.1.2-1.ca2404.1_all.deb Size: 102552 MD5sum: 390e9f0eb2de1765c0c56f32c5a0bce4 SHA1: 1cc5b289161bb921248199146c4c1a79a6957db4 SHA256: 69be787b3bd802bebde54db8be79b75bf9d7f0f3aaa4772aadfd4cd327811f23 SHA512: b2b433675b1430b60df9c6b8f8a6af7c8546d2da8ce08b8ba581031df7efd21d9fc27828477ec46b88a1efe75dfa8582c8c1ee5fbd3c06a7bf2b4d79dfd26f84 Homepage: https://cran.r-project.org/package=bootComb Description: CRAN Package 'bootComb' (Combine Parameter Estimates via Parametric Bootstrap) Propagate uncertainty from several estimates when combining these estimates via a function. This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter. Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage. Gaussian copulas are used for when parameters are assumed to be dependent / correlated. References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020) for an example of combining conditional prevalences. Package: r-cran-bootes Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootes_1.3.1-1.ca2404.1_all.deb Size: 55406 MD5sum: 0e6e9d294b971f23419b7fc93417ffb3 SHA1: f97d053a372cdd7e0ec2094477d4fa6e7cd276b4 SHA256: 0ee873c96efbcb11356856405e5e5df441ba17012fcdfc215dd2b17993b9d936 SHA512: ffcf520e0357b02b3673ae28683f471185b91742516ebedf6fdee83a0b39c707104a0b413d8656df03b2e08a5b103f42cf0201fe69470a8237a3c39880a9c9eb Homepage: https://cran.r-project.org/package=bootES Description: CRAN Package 'bootES' (Bootstrap Confidence Intervals on Effect Sizes) Calculate robust measures of effect sizes using the bootstrap. 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(1998) . The package can also be used to simulate dissolution profiles based on mathematical modelling and multivariate normal distribution. 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From paper by Ghashti, J.S., Andrews, J.L. Thompson, J.R.J., Epp, J. and H.S. Kochar (2025), "A bootstrap augmented k-means algorithm for fuzzy partitions" (Submitted). Package: r-cran-bootlr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-binom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootlr_1.0.2-1.ca2404.1_all.deb Size: 57664 MD5sum: bb1dea4a02265096835d1ffe5fe2e12d SHA1: f5fac5f659be28a755a8a84768f1bb2c0c5ad2ab SHA256: e99ea849cc3dc4a38d13343973dd9fb5f38383d46ac54baa66961c23c98b121a SHA512: 972a1685d3c5e4318f21efb33223cdfa14dad0bb66133b34b7d72191fca6f54e968edac1eead3e3654c94d86f973ffe31eaf52c01375fd77ef4c4f890e3af82d Homepage: https://cran.r-project.org/package=bootLR Description: CRAN Package 'bootLR' (Bootstrapped Confidence Intervals for (Negative) LikelihoodRatio Tests) Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) . It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'. Package: r-cran-bootlrtpairwise Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bootlrtpairwise_0.2.0-1.ca2404.1_all.deb Size: 25226 MD5sum: 1c89f08d62c7662f02f6661d1e11b540 SHA1: 5ffe8d0e6ae3950ae59f0beb945a9bbed349f4cb SHA256: f6d7b0668fdade975923df0e1c1fb67555aab73b4bf87b0dddce93de27d0aa70 SHA512: fed1c9e75e71dd36bbb69f91d798930fa1ad8ee1066e94a866181167caabba2b25f08252a905e465109b409e2843cf87cf8fa69bed0efbf4824db4c583100889 Homepage: https://cran.r-project.org/package=bootLRTpairwise Description: CRAN Package 'bootLRTpairwise' (Bootstrap Hypothesis Tests for Treatment Effects in One-WayANOVA with Unequal Variances) Implements three test procedures using bootstrap resampling techniques for assessing treatment effects in one-way ANOVA models with unequal variances (heteroscedasticity). It includes a parametric bootstrap likelihood ratio test (PB_LRT()), a pairwise parametric bootstrap mean test (PPBMT()), and a Rademacher wild pairwise non-parametric bootstrap test (RWPNPBT()). These methods provide robust alternatives to classical ANOVA and standard pairwise comparisons when the assumption of homogeneity of variances is violated. Package: r-cran-bootmlm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-lme4, r-cran-matrix, r-cran-numderiv Suggests: r-cran-nlme, r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-msm, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bootmlm_0.1.1-1.ca2404.1_all.deb Size: 140918 MD5sum: 2ed9993ac11060c0f795b7b1fa11e4b2 SHA1: 1633efb36cb53146133dad3ff85c286bb2b71276 SHA256: 848dec8a6291c138e08f17076bf89fee6b80de2d8b49ef5eb61672e0782e18f6 SHA512: 0f894c36755d6f8162d81d3dbca3563d76f6c194cdee26c8d703e3a81b9d67b3477297f73bb9f7acf7a312fb0441d5dd96369ce43fb1df34d8c3407910b51032 Homepage: https://cran.r-project.org/package=bootmlm Description: CRAN Package 'bootmlm' (Bootstrap Resampling for Multilevel Models) Functions for bootstrapping with multilevel data and models (and mixed-effect models). It implements multiple bootstrap methods under the parametric, residual, and case bootstrap categories, as discussed in Van der Leeden, Meijer, and Busing (2008) and Carpenter, Goldstein, and Rasbash (2003) . Currently it supports fitted objects from the 'lme4' package. Package: r-cran-bootmrmr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bootmrmr_0.1-1.ca2404.1_all.deb Size: 130866 MD5sum: 5cf39239f947173922344e6be243fdd9 SHA1: 93f4c85a1483d549d37d720ea69fd06148d3d38b SHA256: b8cb591ac71d10b3d021ec748866dffc782fb8218ee498edbfb23db1bef230ca SHA512: d5c6ecc43acf6293a74ff725c1174f7cc7e8a37ba6d98b5edacd510218fe9cd6cd1088e0adf4cda66021fedbcfdc74392c048844ef22a505895839bec1826f1e Homepage: https://cran.r-project.org/package=BootMRMR Description: CRAN Package 'BootMRMR' (Bootstrap-MRMR Technique for Informative Gene Selection) Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications). Package: r-cran-bootnet Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-isingfit, r-cran-qgraph, r-cran-mantar, r-cran-dplyr, r-cran-tidyr, r-cran-gtools, r-cran-corpcor, r-cran-isingsampler, r-cran-mvtnorm, r-cran-abind, r-cran-matrix, r-cran-snow, r-cran-mgm, r-cran-networktoolbox, r-cran-pbapply, r-cran-networktools, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-glasso, r-cran-bdgraph, r-cran-graphicalvar, r-cran-relaimpo, r-cran-lavaan, r-cran-psychtools Filename: pool/dists/noble/main/r-cran-bootnet_1.9.1-1.ca2404.1_all.deb Size: 352738 MD5sum: 74f7b54468be8509eb694ce9b6230138 SHA1: dc7c1604986ad74e8f4b57141696bfb44c2a17b4 SHA256: 016e76728ac97813fe36d3638ab78286a18e8198f8ee5d23ca489f4c8d1d75f4 SHA512: f50851d62192e876633c865e90188e98ddeb28aba052d69d6038e793d48b60e9592592012815e1c4fa29eb4bd1cc8afed7ca14b6a08baa6a4e59ee31cedc32ee Homepage: https://cran.r-project.org/package=bootnet Description: CRAN Package 'bootnet' (Bootstrap Methods for Various Network Estimation Routines) Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices . Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines. Package: r-cran-bootpls Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-doparallel, r-cran-foreach, r-cran-plsrglm, r-cran-pls, r-cran-spls, r-cran-bipartite, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-markdown, r-cran-plsdof, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bootpls_1.2.0-1.ca2404.1_all.deb Size: 228474 MD5sum: c2e3c5bd13eea499a4f92d5183297f0d SHA1: 262fa61becae481812b825490dd9752d35280db7 SHA256: d316b4ee6a6ad01cf07593d97d20e9f7227413cb884b2f8e989e49df086dbe7f SHA512: 89496cb54edc18d5dd8c5261becd74487a57bb74328b9111356c730c1627c521b0ad95dbfe56debecde071be662824b39be6690f950670e6c1618d6c036f9781 Homepage: https://cran.r-project.org/package=bootPLS Description: CRAN Package 'bootPLS' (Bootstrap Hyperparameter Selection for PLS Models and Extensions) Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', ) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', ) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', ). Package: r-cran-bootpr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bootpr_1.0-1.ca2404.1_all.deb Size: 153782 MD5sum: faa1c248493862b3769ad7a8544a789f SHA1: f9eda2abc999f7a5e7ec5ee165158b6f12b44b52 SHA256: d908eb5d34b90bab8fb7c1f7ca40d82f1ef0f0e6b25ae0ff71fd4bc80a1dd2bc SHA512: 98f8d816044bc307e3173965ffd7f3be062141dabdbeeff36e468144db6af8115b7811f437fa7c3a857e66f68e44a57014d74f9e820dd2af8e5b795d8f430643 Homepage: https://cran.r-project.org/package=BootPR Description: CRAN Package 'BootPR' (Bootstrap Prediction Intervals and Bias-Corrected Forecasting) Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series. Package: r-cran-bootstatespace Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simstatespace, r-cran-dynr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootstatespace_1.0.3-1.ca2404.1_all.deb Size: 168388 MD5sum: e4c6db3bf8e50badaa2a1fccf07dedd4 SHA1: 54a471016ab5e0b4d423565303b7aa6d70e820aa SHA256: 35cad7ba2337d58c138091249f4274e74aeb97e14a70eae02df72ef1bd35327a SHA512: bf3666eb015f10191e61facd6040755eeb24f6dd3f6bea75fab97cef1391694daf38ccad15467e21ab443748790146fa3c7371dba01cdf878b21ac04d2173306 Homepage: https://cran.r-project.org/package=bootStateSpace Description: CRAN Package 'bootStateSpace' (Bootstrap for State Space Models) Provides a streamlined and user-friendly framework for bootstrapping in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. The parametric bootstrap implemented here was developed and applied in Pesigan, Russell, and Chow (2025) . Package: r-cran-bootstepaic Architecture: all Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-bootstepaic_1.4-0-1.ca2404.1_all.deb Size: 23070 MD5sum: 1635c46e1a658825109cdd871e8ed4e0 SHA1: 23c7272953ec1bfa01c7621a6e68cdf8c13a7cb2 SHA256: 9df3017952cf672e8c268582226f13d3cc2e930b1ac8d5a64860901443fd62d0 SHA512: 9bb785182439d264605957852dc9324b53adb18d8ff3fbaa0f8eb46ee1ada208032c724bf1adf16d60f2a03ffc5d66f07a9f90a976bacb765a767c2a73a23029 Homepage: https://cran.r-project.org/package=bootStepAIC Description: CRAN Package 'bootStepAIC' (Bootstrap stepAIC) Model selection by bootstrapping the stepAIC() procedure. Package: r-cran-bootstrapfp Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampling Filename: pool/dists/noble/main/r-cran-bootstrapfp_0.4.6-1.ca2404.1_all.deb Size: 67214 MD5sum: c3b92d2ff56c3f34ffa6456ca0068801 SHA1: 5214df0d6a1047192fd9de0bbdc9cabf288100f9 SHA256: 3e183167684fb9b86f84c4cd1aaa5d2a597cdb410fad1e64ceae2cf468846900 SHA512: b71ea6ec70638a973471992aea945f551ae3a7721af05cd6d6daf8b50ef18058ca3b52f419385463be16964bc5a0ce6be5f1f38be57dffa1754d0bf018a863ca Homepage: https://cran.r-project.org/package=bootstrapFP Description: CRAN Package 'bootstrapFP' (Bootstrap Algorithms for Finite Population Inference) Finite Population bootstrap algorithms to estimate the variance of the Horvitz-Thompson estimator for single-stage sampling. For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) . Package: r-cran-bootstrapqtl Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixeqtl, r-cran-foreach, r-cran-data.table Suggests: r-cran-domc, r-cran-doparallel, r-bioc-qvalue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootstrapqtl_1.0.5-1.ca2404.1_all.deb Size: 49406 MD5sum: 545e6fe34b66c4aab384e6abe68bb83d SHA1: 4fd278cee2d7b5e5b15a5c3120547d2a86e5bdfb SHA256: 2391df6987e1df2a78270ad78071a60d2b7eb22292d128b1aad4c31e650606a7 SHA512: 25d7e03f8ae542683f148dd8fce54e2862960585c3cd7a90bd1296608e57ec12fd4a886c4f4c09dd264c9484123bc5748194cf68a7bf37e2a3820023e5e783a9 Homepage: https://cran.r-project.org/package=BootstrapQTL Description: CRAN Package 'BootstrapQTL' (Bootstrap cis-QTL Method that Corrects for the Winner's Curse) Identifies genome-related molecular traits with significant evidence of genetic regulation and performs a bootstrap procedure to correct estimated effect sizes for over-estimation present in cis-QTL mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 2018 . Package: r-cran-bootstraptests Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootstraptests_0.1.0-1.ca2404.1_all.deb Size: 136368 MD5sum: 5da24057608a308463cf314ee0c3f1fb SHA1: 06d37d1798ff556a0a54940b1d6e22512febd44a SHA256: f374206262370bcd637c7beb3ec4c6c131fc11abe6a479db07f4b9bf87368068 SHA512: cd3307b98fdc025efa3ce4fc23cb758af66c3f66c90590fddbbabe8b8172702f69bb2a6f2ec87cec1caa03bd5f70417c2faf86bd6aae3ed0b8eed48fa233d655 Homepage: https://cran.r-project.org/package=BootstrapTests Description: CRAN Package 'BootstrapTests' (Bootstrap-Based Hypothesis Testing using Different ResamplingSchemes) Perform bootstrap-based hypothesis testing procedures on three statistical problems. In particular, it covers independence testing, testing the slope in a linear regression setting, and goodness-of-fit testing, following (Derumigny, Galanis, Schipper and Van der Vaart, 2025) . Package: r-cran-bootsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootsurv_0.1.0-1.ca2404.1_all.deb Size: 585674 MD5sum: 95cc126ed15c6e1afa949159b7228f47 SHA1: 8c90a51105807567e09456e6f9343a48a57b78bd SHA256: d86652588182de57b27c6771ad9a13b6017f1ee92aabba72dda0a71f7b39348d SHA512: d4be2dd967004b928249046102e1dcc0346278bf62b7ed4edac7d544dc220bc4a0822880299808b3eb5e340bc78580aa4bdaf9cb11d0e895690670ea1e9dc484 Homepage: https://cran.r-project.org/package=bootsurv Description: CRAN Package 'bootsurv' (Bootstrap Methods for Complete Survey Data) Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis. Package: r-cran-bootsvd Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff Filename: pool/dists/noble/main/r-cran-bootsvd_1.2-1.ca2404.1_all.deb Size: 150278 MD5sum: e033ac91be52791806031e82d0dc168a SHA1: bbef1e1a8d4b4526b88198d22e22a31869ae2105 SHA256: 8a321d902e5588df07c15ee3879514ac0deab3f7831faf4ea7db97ec05f7efa0 SHA512: 8e2e8fd450fda3b3b2c45590a6fb06ec3b3694b461dfd773049a3a4841776b1b8d93a86361ed949e1f5ce5481232b7df6057e49ad46e77ad41299ba8f0a2bed4 Homepage: https://cran.r-project.org/package=bootSVD Description: CRAN Package 'bootSVD' (Fast, Exact Bootstrap Principal Component Analysis for HighDimensional Data) Implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in (see also ). For data matrices that are too large to operate on in memory, users can input objects with class 'ff' (see the 'ff' package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the 'parallel' package can be used to parallelize the calculation of the bootstrap PCs. Package: r-cran-bootwar Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2044 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mmcards, r-cran-npboottprm, r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootwar_0.2.1-1.ca2404.1_all.deb Size: 1767980 MD5sum: f9e25668e2592c9d820927659d9edc83 SHA1: 700ffc579e2db2cceeb7ab655080d7bf05e2de44 SHA256: b8fe5d6bc26bf0d0ab15678c8604361d27427881b99f978e477de9a3aab24261 SHA512: 75c39f6bc72727be949dfff07467942001b7d4d452e4148facb8953a5bd4864dac13ca882520d3b36686a38800d113b4593dad0fbb6570b9aa01dd7c069812a1 Homepage: https://cran.r-project.org/package=bootwar Description: CRAN Package 'bootwar' (Nonparametric Bootstrap Test with Pooled Resampling Card Game) The card game War is simple in its rules but can be lengthy. In another domain, the nonparametric bootstrap test with pooled resampling (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) , is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals. Package: r-cran-bootwptos Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh, r-cran-tseries Filename: pool/dists/noble/main/r-cran-bootwptos_1.2.1-1.ca2404.1_all.deb Size: 56794 MD5sum: 613a4a69a776eeb51188a178da94138b SHA1: e9e0188f32eb58fa460435c064caa4a25de56353 SHA256: dcb21bbf6e41960369ff0e5eee94001e5e521c731c611687b0f10a0c8c803903 SHA512: 3f3e1ea616fd53f381c6165d99337913042ed9d65f82f28745c4c32e50627e1dd5f7858837ce4159715f4b684ed47bb239dd26b5a6bb9f69195fab6757c55e74 Homepage: https://cran.r-project.org/package=BootWPTOS Description: CRAN Package 'BootWPTOS' (Test Stationarity using Bootstrap Wavelet Packet Tests) Provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 . Package: r-cran-bop2fe Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-patchwork, r-cran-ggplot2 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-bop2fe_1.0.3-1.ca2404.1_all.deb Size: 606916 MD5sum: 255b0b46a90a26691a3bbb1efcfdf047 SHA1: 40e1c8624be00b986a6bc1c6f9bfe0fb1d056043 SHA256: 171dec61452db75505c1d284088d82fddae60816460440df2bbfcb0446263287 SHA512: c6ffdc4a6ce8aab1965811ce989355c0cc5f1e3e3a7a15006438d0c392dfeb66206b06d358e2c29e8f1f8c2bf37352cf29861b15a56b6fb7bc91082033c7e2ec Homepage: https://cran.r-project.org/package=BOP2FE Description: CRAN Package 'BOP2FE' (Bayesian Optimal Phase II Design with Futility and EfficacyStopping Boundaries) Bayesian optimal design with futility and efficacy stopping boundaries (BOP2-FE) is a novel statistical framework for single-arm Phase II clinical trials. It enables early termination for efficacy when interim data are promising, while explicitly controlling Type I and Type II error rates. The design supports a variety of endpoint structures, including single binary endpoints, nested endpoints, co-primary endpoints, and joint monitoring of efficacy and toxicity. The package provides tools for enumerating stopping boundaries prior to trial initiation and for conducting simulation studies to evaluate the design’s operating characteristics. Users can flexibly specify design parameters to suit their specific applications. For methodological details, refer to Xu et al. (2025) . Package: r-cran-boptbd Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-boptbd_1.0.7-1.ca2404.1_all.deb Size: 84248 MD5sum: 786a227a52d7d5e03cb88ea5419df4ad SHA1: 1191c94d524dd8a97b3f412bfc1d7f23651ddd20 SHA256: 14a0296004a6f489e0dc7620c99c6d6f8b3ffabc62712ebcaafc37682e51dbce SHA512: 738c8a28c14bbc29949dbf10cb87e76f68f15b723e13586ddbcea6adc63eb7d25e494365ea52b41377c6a0e7de6ed2f52fcf6bd651018a7a9155a79ed8a1950e Homepage: https://cran.r-project.org/package=Boptbd Description: CRAN Package 'Boptbd' (Bayesian Optimal Block Designs) Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) and Gemechu, Debusho and Haines (2025) where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly. Package: r-cran-bor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bor_0.1.0-1.ca2404.1_all.deb Size: 22154 MD5sum: b047e18688e4d76ce3889cf777c36ddd SHA1: e9aa7a554f6a9e23273bede2df9ffa966d957436 SHA256: f82b000fcaf1f14ff03a3ee7aed77419e2d2b942ad547149cad73ed4ddff7102 SHA512: 76af7ff894c9000b6c67755a07ab8a37bb597dc7c817261383e5e29313e2b100f0c1a7d81c8961425ef634b5f30b3ce7c4ef643958c0ce9a5051fdfdd94d3674 Homepage: https://cran.r-project.org/package=bor Description: CRAN Package 'bor' (Transforming Behavioral Observation Records into Data Matrices) Transforms focal observations' data, where different types of social interactions can be recorded by multiple observers, into asymmetric data matrices. 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Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix. Package: r-cran-bordereffect Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-shiny, r-cran-ape, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bordereffect_0.1.0-1.ca2404.1_all.deb Size: 158474 MD5sum: db02b089f08a6481cc80e11a4aa14e39 SHA1: 19ef6e9a89e8a70a40ff3d4e627e1694ea4a7b75 SHA256: 2ee919a11485627507cb24738632f78dd649125eab21c52cdd39a40bfbb60b07 SHA512: 50f4b4f0ecbf90716f9faabb8a7949d5ad091900994e9174216f3a1f56db0e49b492388c39a10ef7ce7ef6f5683859e5273611d654da61f3a3e3df7b877f2915 Homepage: https://cran.r-project.org/package=BorderEffect Description: CRAN Package 'BorderEffect' (Detection of Edge Effects in Field Trials via Besag-KemptonCompetition) Estimates and evaluates the intraspecific competition coefficient associated with the edge (border) effect in agricultural field trials, using the Besag-Kempton autoregressive model and a least-squares estimator following Darghan, Rivera, Gonzalez and Castellanos (2022) . 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Package: r-cran-boruta Architecture: all Version: 10.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fru Suggests: r-cran-rferns, r-cran-randomforest, r-cran-ranger, r-cran-survival Filename: pool/dists/noble/main/r-cran-boruta_10.0.0-1.ca2404.1_all.deb Size: 415852 MD5sum: 554ddabaa2620280e15116b4160cce36 SHA1: c5e0c84b35c091f579dee2fe2f6cd0ef62f467b2 SHA256: 7358dc02be84c04b25b9476d4d2f9f4e213a165036ff9627b8aee9d36db5567f SHA512: fac51b9c9a99c47478f7f9b4398778aad06e2997511c0b37e333f4502f69c237c2bfe4aa34792a34930fdd07218b2caad62baa060cb5a2f412919d7ac9767391 Homepage: https://cran.r-project.org/package=Boruta Description: CRAN Package 'Boruta' (Wrapper Algorithm for All Relevant Feature Selection) An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows). Package: r-cran-bosfr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bosfr_0.1.0-1.ca2404.1_all.deb Size: 54842 MD5sum: c21046cf63579d948c76607d271395da SHA1: 6b82060a4d4222debfcee2d34527b9ce7d71b892 SHA256: c75986b389291e7fc56367ec0cbd4db9775f36eba16768bd69d90f208f5c6bd7 SHA512: 8c74dc738cd0fefee9a2f975b4c41e2d5208cd5777acef59971bbabf40b795f853f5429d555b38f6adfbc8fd8f0fecd6d87547423fe636d9a3c502e3d22c947d Homepage: https://cran.r-project.org/package=bosfr Description: CRAN Package 'bosfr' (Computes Exact Bounds of Spearman's Footrule with Missing Data) Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed. Package: r-cran-boso Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass Suggests: r-cran-testthat, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggpubr, r-cran-dplyr, r-cran-kableextra, r-cran-devtools, r-bioc-biocstyle Filename: pool/dists/noble/main/r-cran-boso_1.0.4-1.ca2404.1_all.deb Size: 2700114 MD5sum: 36dfebe05433c9a15e12801a1a266d1c SHA1: 8f9a75e63583204e3cfbba233169dbefb66829e2 SHA256: c1200802e6c91d15cf4d499bb5674433be5be5635ee85f118b3dbeec0d3a06e4 SHA512: 9811d3a89400a9b05b1f64374839dac6d5aac6d1ba2f984fddacc9243717dc36a00c497803e6f0638fac24becf2aadaaf6a400e3368c4603d665bbc6cc199c14 Homepage: https://cran.r-project.org/package=BOSO Description: CRAN Package 'BOSO' (Bilevel Optimization Selector Operator) A novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). The main contribution is the use a bilevel optimization problem to select the variables in the training problem that minimize the error in the validation set. Preprint available: [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection algorithm for linear regression with high-dimensional data." bioRxiv. ]. In order to run the vignette, it is recommended to install the 'bestsubset' package, using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset"). If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset"). Moreover, to install cplexAPI you can check . 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If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) . 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This package provides modified ADF and M-type tests (MZ-alpha, MZ-t, MSB) with p-values computed via Monte Carlo simulation of bounded Brownian motion. Supports one-sided (lower bound only) and two-sided bounds, with automatic lag selection using the MAIC criterion of Ng and Perron (2001) . 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Output the coordinates, and images annotated with boxes and labels. For an example study that uses bounding boxes for image localization and classification see Ibrahim, Badr, Abdallah, and Eissa (2012) "Bounding Box Object Localization Based on Image Superpixelization" . Package: r-cran-boussinesq Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-boussinesq_1.0.6-1.ca2404.1_all.deb Size: 29972 MD5sum: 133a34d5bed2e291bcb885fbbb8dfa40 SHA1: 9b4ceede20f2cdf32c7774bb53ff06de8bff3ac7 SHA256: bb78f2f8ef51cb1a2f9c14a7fb27573d4978cf95dca1e5a439e839f3982610ea SHA512: cf0601cc70d5a7f90868a26471f815af7da62f9851c488e74b7dc5cb85081f3cb4dd8cc4f6e8bd6e788da7cf9ef402617f288de1d66964031da07fd2b5baeb23 Homepage: https://cran.r-project.org/package=boussinesq Description: CRAN Package 'boussinesq' (Analytic Solutions for (Ground-Water) Boussinesq Equation) A collection of R functions were implemented from published and available analytic solutions for the One-Dimensional Boussinesq Equation (ground-water). In particular, the function "beq.lin()" is the analytic solution of the linearized form of Boussinesq Equation between two different head-based boundary (Dirichlet) conditions; "beq.song" is the non-linear power-series analytic solution of the motion of a wetting front over a dry bedrock (Song at al, 2007, see complete reference on function documentation). Bugs/comments/questions/collaboration of any kind are warmly welcomed. 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These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) , Nakamura and Noma (2021) ). 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The main functions are 'optim.boxcox()' for linear models with random effects and 'boxcoxtype()' for logistic models with random effects. Package: r-cran-boxdensityplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-boxdensityplot_0.1.0-1.ca2404.1_all.deb Size: 34398 MD5sum: b73df676ef7c404a858f07a5921d79f9 SHA1: a5e06fc938a23caea9c0e60681be24269e5828f7 SHA256: cf3e6158f909d2755cabc12135ef68f07d0156b765c3e86b5beec723d92f469a SHA512: 304d2507388e8dec7beeb060cfff4e2ceffa3e9107eb23be0b586df33062596c6bbce9a79219ab146ec73f6b5cb94f162c3adf796360839620f992d60af08a9a Homepage: https://cran.r-project.org/package=BoxDensityPlot Description: CRAN Package 'BoxDensityPlot' (Multi-Trait Density and Boxplot Visualization) Reads wide-format phenotypic data (one row per genotype or sample, one column per trait) from CSV or 'Excel' files, reshapes it to long format, and produces faceted figures that combine a mirrored density curve with a boxplot for each trait. Density curves can be drawn on the left, right, or both sides of the box, and figures can be saved automatically at publication resolution. Package: r-cran-boxfilter Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-boxfilter_0.2-1.ca2404.1_all.deb Size: 512608 MD5sum: e30a78153f857a50c244a20f392fbb42 SHA1: 5cdda6893fc9bd6db70225fabe66808ff16c4560 SHA256: d7ee1e6b62ee713db8ed10b0ebd391fcff2c072ea0aff53ea92669e976348f2d SHA512: ebc52df0ac017421f0a39c213095b7d7c7acc3fee8b8b283a18f478bf566bb4468ce453522d3f931657d545c76bbfaf2be11b724b95c1eb2737c12c9aac34281 Homepage: https://cran.r-project.org/package=boxfilter Description: CRAN Package 'boxfilter' (Filter Noisy Data) Noise filter based on determining the proportion of neighboring points. 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Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain ]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options. 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Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio. 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Package: r-cran-bqror Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-gigrvg, r-cran-truncnorm, r-cran-npflow, r-cran-invgamma, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bqror_1.7.1-1.ca2404.1_all.deb Size: 291870 MD5sum: 7d6b9d2dbae4b290297727d1fc0a4be9 SHA1: 3c23f806723c34ce0218fa15438f9593cb408473 SHA256: 6e57d5cc8d0e31f07b418db60295a86d737af5ef0bab4b3d0c018818a6327aaa SHA512: 5edd95c6e622406f377e3e6b9ebe806b93e5c5c8532c98b5e5924f37bda6e127ef6240e4f818126a9f55d7e9945f8b0bada80b74387c1411fede847337b86495 Homepage: https://cran.r-project.org/package=bqror Description: CRAN Package 'bqror' (Bayesian Quantile Regression for Ordinal Models) Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 . Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 . Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 . Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. . Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. . 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Also provides functions for producing full BRAID analysis reports with custom layouts and aesthetics, using the BRAID method originally described in Twarog et al. (2016) . 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Package: r-cran-braids Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maybe Filename: pool/dists/noble/main/r-cran-braids_1.0.0-1.ca2404.1_all.deb Size: 75228 MD5sum: 52cbf16b78012d7ae086d57e9905a009 SHA1: 8de6b6aedfff82968f7b530e57fde93f0c593c2f SHA256: 9ef4f072ab0d5ac4d563be871413f5b8292f9daf4bfae777629a336cb4b8d0ec SHA512: c84e7337ce5701bc94c81da5d7ab2a1fa430882e11866a66c10ab97cecc2019993613a1baf994bf300936b2ed2a8f5f73514fb6641b834087cc75e32f7300d3a Homepage: https://cran.r-project.org/package=braids Description: CRAN Package 'braids' (The Braid Groups) Deals with the braid groups. Includes creation of some specific braids, group operations, free reduction, and Bronfman polynomials. Braid theory has applications in fluid mechanics and quantum physics. The code is adapted from the 'Haskell' library 'combinat', and is based on Birman and Brendle (2005) . Package: r-cran-brailler Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3662 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-dplyr, r-cran-extrafont, r-cran-ggplot2, r-cran-gridgraphics, r-cran-gridsvg, r-cran-hunspell, r-cran-knitr, r-cran-mathjaxr, r-cran-moments, r-cran-quarto, r-cran-rdpack, r-cran-rmarkdown, r-cran-roloc, r-cran-rolocisccnbs, r-cran-tidyr, r-cran-whisker, r-cran-xml, r-cran-xtable Suggests: r-cran-broom, r-cran-emmeans, r-cran-ggfortify, r-cran-installr, r-cran-lmtest, r-cran-markdown, r-cran-multcomp, r-cran-nortest, r-cran-rstatix, r-cran-spelling, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-brailler_1.0.2-1.ca2404.1_all.deb Size: 1258332 MD5sum: f38429c502bd598f461ecae2ad38de00 SHA1: 1bd1233d72cdc7ccf70e661a4134c5a6166d9aec SHA256: 38929306a360b23b9ef99ffed74b6d8c241a5832594e8c01a73ca68b726df835 SHA512: bec5a2e82cab601b6688ef0b290b61da7f3ab825f77f2389e664fc1f6662b90af1691d2d0825fff629c88e143439fe9185b26d1faf8ad329985de5d866a8bd63 Homepage: https://cran.r-project.org/package=BrailleR Description: CRAN Package 'BrailleR' (Improved Access for Blind Users) Blind users do not have access to the graphical output from R without printing the content of graphics windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images. Package: r-cran-braincon Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-braincon_0.3.0-1.ca2404.1_all.deb Size: 197234 MD5sum: f85b2f37165a9da0357c3e6e4309b3f1 SHA1: f6a6e2872277ec6ffb177c6683fcb8c033130bf5 SHA256: 99ca26c949bf411cae8298976aad20f1b8de7881d4bf1bd6d2d964e190f963e0 SHA512: 696520f32f620f8860d8a01a0c1f6d751033c68318abf7cb6e10b4d52f94011c796f47c5495e54d8a39bfa11e7d442513f9f0117f1a32616f016c181e1967c4a Homepage: https://cran.r-project.org/package=BrainCon Description: CRAN Package 'BrainCon' (Inference the Partial Correlations Based on Time Series Data) A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using the methods of Qiu and Zhou. (2021) and Genovese and Wasserman. (2006). It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations. Package: r-cran-braingraph Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-abind, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-permute Suggests: r-cran-hmisc, r-cran-ade4, r-cran-boot, r-cran-car, r-cran-expm, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-mediation, r-cran-oro.nifti, r-cran-scales Filename: pool/dists/noble/main/r-cran-braingraph_3.1.2-1.ca2404.1_all.deb Size: 2278466 MD5sum: 7843c47b53a85dbf8da05e36324348bc SHA1: 267df24e5d1b534efd85683eaf7f58f0bd83cf62 SHA256: b36b23ced165712201882c55c29199b20d51f48553799cf34c31341ea8ee8450 SHA512: ca0451e275b6d3cc403a3d87252d86c0f10daad432ed43cb8f19dd6469a73cce520b490d4af683359e032680cdebf6b6a2973ff3b769a6e1513f11ec435b2a06 Homepage: https://cran.r-project.org/package=brainGraph Description: CRAN Package 'brainGraph' (Graph Theory Analysis of Brain MRI Data) A set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures. Package: r-cran-brainkcca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2650 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cca, r-cran-kernlab, r-cran-elasticnet, r-cran-rgl, r-cran-brainr, r-cran-misc3d, r-cran-oro.nifti, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brainkcca_0.1.0-1.ca2404.1_all.deb Size: 2466588 MD5sum: 1af31499e8dcd1a2842dc0625d30f47d SHA1: c057fe2ec1776abe722a866afb11c711781afbda SHA256: ce83350f2c5edb86987a227bae7cb555434f61a3491d32ae9b9fe489ee08d202 SHA512: c2bcf2b1331afa6df34c4cb0c318bb3eed4c38e0ad9cc01ce41edea107aca7cc3210cb4bf4457c38f4ca22bae908a976879dc6515b037e4d0dfe4b0aca6ab92e Homepage: https://cran.r-project.org/package=brainKCCA Description: CRAN Package 'brainKCCA' (Region-Level Connectivity Network Construction via KernelCanonical Correlation Analysis) It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) . Package: r-cran-brainnettest Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brainnettest_0.2.2-1.ca2404.1_all.deb Size: 345430 MD5sum: 685b44257b47c7081880656cebeb3c3a SHA1: 9bb8fe3a78cc165eb337ba2249067eeaf84dbdee SHA256: 93a031a635c3785d851054269f0b0a08c261ec11e292edba05244c13213b819e SHA512: 45ea00bd3da184b5717623e3c4bcc8256f46a77a9a1dd7b1ef9042869c7988578885cf83ff5cdb875060ffd1f24ad50c98add21e761491f1a830dcc792331dd0 Homepage: https://cran.r-project.org/package=BrainNetTest Description: CRAN Package 'BrainNetTest' (Hypothesis Testing for Populations of Brain Networks) Non-parametric hypothesis testing for populations of brain networks represented as graphs, following the L1-distance ANOVA framework of Fraiman and Fraiman (2018) . The package builds on this nonparametric graph-comparison framework, extending it with procedures for edge-level inference and identification of the specific connections driving group differences. In particular, it provides utilities to compute central (mean) graphs, pairwise Manhattan distances between adjacency matrices, the group test statistic T and its permutation p-value, and a fast permutation procedure to identify the critical edges that drive between-group differences. Helper functions to generate synthetic community-structured graphs and to visualise brain networks with communities are also included. Package: r-cran-brainr Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-misc3d, r-cran-oro.nifti Suggests: r-cran-servr Filename: pool/dists/noble/main/r-cran-brainr_1.7.0-1.ca2404.1_all.deb Size: 3924046 MD5sum: 475b2e908708ef7c64ef3a0f8b35f072 SHA1: e213331852702267268f445b501ceecd0a01b395 SHA256: bc257dd6fc25026ea117aab88b11fe475d1dc0dda3c1cc3ac98dde898b24a86a SHA512: e5dae278abd47450c4f7022522d2f3c40b4c246c4b2af33fa4927311297091a5f01cfc2170a98c7cde533d2e8163fb938af4aef2955ba7a13b635e5155e4122b Homepage: https://cran.r-project.org/package=brainR Description: CRAN Package 'brainR' (Helper Functions to 'misc3d' and 'rgl' Packages for BrainImaging) This includes functions for creating 3D and 4D images using 'WebGL', 'rgl', and 'JavaScript' commands. This package relies on the X toolkit ('XTK', ). Package: r-cran-branchingprocess Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-branchingprocess_0.1.0-1.ca2404.1_all.deb Size: 52828 MD5sum: c9923826df3f2afe37d8e8374729b25c SHA1: f22c5221de6bd596266e47e2f3d74a756c68650a SHA256: bd22154cc1c20ba9761227bc32db5418a6cad17885fcf05ef697aac5d8cf0aa0 SHA512: c1784e375f7047ea54c08439e975f7d09c294844bdf50de263c9e08c5204a8f702ef0140d86854744d634eda8123ecd9c16796c7ca1401c50aa982b452c16446 Homepage: https://cran.r-project.org/package=branchingprocess Description: CRAN Package 'branchingprocess' (Calculate Outbreak Probabilities for a Branching Process Model) Quantify outbreak risk posed by individual importers of a transmissible pathogen. Input parameters of negative binomial offspring distributions for the number of transmissions from each infected individual and initial number of infected. Calculate probabilities of final outbreak size and generations of transmission, as described in Toth et al. (2015) and Toth et al. (2016) . 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'brand.yml' is a simple, portable 'YAML' file that codifies your company's brand guidelines into a format that can be used by 'Quarto', 'Shiny' and 'R' tooling to create branded outputs. Maintain unified, branded theming for web applications to printed reports to dashboards and presentations with a consistent look and feel. 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Package: r-cran-brandwatchr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brandwatchr_0.3.0-1.ca2404.1_all.deb Size: 106434 MD5sum: d81ed7599bdd09c0396b46e5016d7b42 SHA1: febc6d186d2f8c40ceb31bbcab9cea8f815374dd SHA256: 7c7ad26c1772dcbf2b4b2e0af8a072d62602597d2fb7c3110a444be8b5ff350e SHA512: b250a177bc1e90bbf07c2b15b205327ac4cb6c5c35b852a9bec2ac357de7a6f79758c46a631d4cc0c8c94954cd3f79cb986280315e01ddd38fcefe9c18d62845 Homepage: https://cran.r-project.org/package=brandwatchR Description: CRAN Package 'brandwatchR' ('Brandwatch' API to R) Interact with the 'Brandwatch' API . Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories. Also allows you to directly obtain mentions and aggregate data for a specified query or query group. Package: r-cran-brant Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-brant_0.3-0-1.ca2404.1_all.deb Size: 26176 MD5sum: b3ed361cf91486f83683618a5080d52f SHA1: 13e4b3acc429d1c9f2e134bb1c4b23d8eae1ed90 SHA256: 8c71bd8b808aa30d4b9acb5f17368b311452f2a19077437e92168c98cc520036 SHA512: 4e05bc93010388422528b15560100950ba44f387839d8082341959da10bf390cb8f3022644f3431600b9508a4eae321e1c0e45c7cdebf768910c635da791729f Homepage: https://cran.r-project.org/package=brant Description: CRAN Package 'brant' (Test for Parallel Regression Assumption) Tests the parallel regression assumption wit the brant test by Brant (1990) for ordinal logit models generated with the function polr() from the package 'MASS'. Package: r-cran-brapir2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 470 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-furrr, r-cran-future, r-cran-httptest2, r-cran-keyring, r-cran-knitr, r-cran-rappdirs, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-brapir2_0.2.0-1.ca2404.1_all.deb Size: 325892 MD5sum: 8844d4c4d4286b87225d32db640a4750 SHA1: 25c46d7e28bbf8724e2cf8c9fef4f423d0ed19c0 SHA256: f9367d742aa3bc962e22bc48c8fb589fd1a8d12a730d1a3b5a8d6bb0eafc704a SHA512: d6b8b81b8765b7b6cea5f37c019a9380cacfd2e93e6125c55c46ef6b04535cef968d59cc2640c42f81d1dbe359f04d328eb224ae8109a0c82d9bd003323744be Homepage: https://cran.r-project.org/package=brapiR2 Description: CRAN Package 'brapiR2' (A Tidyverse-Native Client for the 'BrAPI' v2 (Breeding API)Specification) Provides pipe-friendly, stateless read access to the Breeding API ('BrAPI') v2.1 specification, an open community standard for plant breeding data interchange maintained by the BrAPI project . Wraps 32 of the 37 'BrAPI' v2.1 entities across all four modules, Core, Germplasm, Phenotyping, and Genotyping, covering 49 of the specification's 138 retrieval ('GET' and search) endpoints and returning tidy tibbles ready for analysis. Write and update endpoints are out of scope by design. Features include automatic pagination, async search handling, response caching, parallel batch fetching, and convenience functions for genomic selection workflows (e.g. dosage matrix extraction). Designed for plant breeders and bioinformaticians who need programmatic access to plant breeding databases that implement the 'BrAPI' v2 specification. Package: r-cran-braqca Architecture: all Version: 1.4.11.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qca, r-cran-bootstrap Filename: pool/dists/noble/main/r-cran-braqca_1.4.11.27-1.ca2404.1_all.deb Size: 47270 MD5sum: e136fbe082a63bcd03d9771924654a5e SHA1: fb60d593c1c6fc52ad1e91242393b98918a29496 SHA256: e245b13929a9ebd0c9f7387a730f94f608610f6f6ce1fcccf569dfb250355269 SHA512: c16274d5d577d5cf472f46617086bf14f046e0406f9dbc253e222fba06787e4a9abe6e06b2fc851b3f72d462b25ae131653a8ed53c4dba90a2f88619cb5394a3 Homepage: https://cran.r-project.org/package=braQCA Description: CRAN Package 'braQCA' (Bootstrapped Robustness Assessment for Qualitative ComparativeAnalysis) Test the robustness of a user's Qualitative Comparative Analysis solutions to randomness, using the bootstrapped assessment: baQCA(). This package also includes a function that provides recommendations for improving solutions to reach typical significance levels: brQCA(). Data included come from McVeigh et al. (2014) . 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It also provides functions to compute the dimensions of the vertices, the intrinsic kernels and the intrinsic distances. Intrinsic kernels and distances were introduced by Vershik (2014) . 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Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames. 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'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) , Willis et al. (2017) , and Willis (2016) , as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) . Package: r-cran-breakdown Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1260 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-e1071, r-cran-kernlab, r-cran-xgboost, r-cran-caret, r-cran-randomforest, r-cran-dalex, r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-breakdown_0.2.2-1.ca2404.1_all.deb Size: 851852 MD5sum: 3dbc9b1d9a4a8ff31b6bc3cca64cd79b SHA1: 8ad328a2f8910179661013aecdfa8275510f2804 SHA256: 1b03a0e53c1bb5dfcb44aad809be76d288e0449f835df77a50a16bd265781f3a SHA512: d4f44a6de0e46e55dd919593abd6749fe817bc85cbfb41e5c9c5c58651c16f2b8357d77124d3ee6ab6a1b083c9a76111d633f2aa99350196fbea27d4ae2f3da9 Homepage: https://cran.r-project.org/package=breakDown Description: CRAN Package 'breakDown' (Model Agnostic Explainers for Individual Predictions) Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models. Package: r-cran-breakpoint Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-msm, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-breakpoint_1.2-1.ca2404.1_all.deb Size: 232532 MD5sum: 1d1fc0a97d1d6536e7c7ed0cc956e001 SHA1: ce714012219c7ce70345182041119c2acd686c76 SHA256: 94862e2af88e6f310d43205ff7d41535687fc8995aae99735ee43611f46a4cbb SHA512: 2bbfc7bdb5bb656f0002a18a868a4bcc8b29cea3b2c3f918b52c4719cebd82fa2b29d28cdff18521318de5135e20379f441019327cf83de65258c1656927bb28 Homepage: https://cran.r-project.org/package=breakpoint Description: CRAN Package 'breakpoint' (An R Package for Multiple Break-Point Detection via theCross-Entropy Method) Implements the Cross-Entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number and their corresponding locations of break-points in continuous and discrete measurements (Priyadarshana and Sofronov (2015), Priyadarshana and Sofronov (2012a), Priyadarshana and Sofronov (2012b)). Package: r-cran-breakpoints Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-zoo Filename: pool/dists/noble/main/r-cran-breakpoints_1.2-1.ca2404.1_all.deb Size: 48024 MD5sum: 57356a5e41cd3186f105f6b0be61c8a7 SHA1: 6969d076e2d70619441445031d12abd8b9a9f75d SHA256: 2c961aed87dba360f41071d59cc13bcf0efefc179dbfa92ed68adc2ef6716192 SHA512: a3f4d6573f8115e0603fe551588a8df75d425a38277a3c1cd61a7f20694b2493ac48d7cd5ab116fdf03a583cb6d476ad5b3c2553814f86e45a9cf9f63d6d6864 Homepage: https://cran.r-project.org/package=BreakPoints Description: CRAN Package 'BreakPoints' (Identify Breakpoints in Series of Data) Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) , Buishand, T. (1982) , Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) , Mann, H. B., Whitney, D. R. (1947) , Pettitt, A. N. (1979) , Ruxton, G. D., jul (2006) , Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) . 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Package: r-cran-breeze Architecture: all Version: 0.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1858 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-rcolorbrewer, r-cran-rgooglemaps, r-cran-xml Filename: pool/dists/noble/main/r-cran-breeze_0.4-4-1.ca2404.1_all.deb Size: 1298292 MD5sum: 7ab2b7ba2629dba82d6c846d2aaddc9f SHA1: 2bceb599c5c491fb3ebb6f1bb04c420591ff6fdd SHA256: e888e55461094896473adb3f967c32f5f2a3d3c1a2829690f0e9167f6b4065c0 SHA512: a1b807df9f059136c9c93fce8933ae5022b687ef2f9c362c2a4fe34bdd4b9a4a202e51501294e98a88ee7b83063598f6f919d196a1b0a78a607a77f552c569f1 Homepage: https://cran.r-project.org/package=bReeze Description: CRAN Package 'bReeze' (Functions for Wind Resource Assessment) A collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines. 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Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) . For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) . The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses. Package: r-cran-brew Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brew_1.0-10-1.ca2404.1_all.deb Size: 55018 MD5sum: 3042761b33da175096ce0731661e8e82 SHA1: a8edea46d462d43611661b8110f6f43f70b4976f SHA256: 3b3d096a297d7f4a49674aa8c6e23732f5e703b3eb9245e37da22c9eacd93a99 SHA512: 5f30a465afd2c8fc0f1ffe708f4a348d39f01004330ac58f8579b2407c71ecd43e0d9a95f9469aebaa92369b8417a290f52642a80f4e3cb56af05093175bd753 Homepage: https://cran.r-project.org/package=brew Description: CRAN Package 'brew' (Templating Framework for Report Generation) Implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. Package: r-cran-brfinance Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-httr2, r-cran-lubridate, r-cran-labelled, r-cran-yfr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-brfinance_0.9.0-1.ca2404.1_all.deb Size: 414124 MD5sum: d59d7266eaefd819c7d312d4d9e17f80 SHA1: 63a91766e153720cf3e69bc550f8850c0c5bfcc4 SHA256: ff85d480f40c7be3dbe4a068101cd54343bb9c0e9dd27a4d37124ae07df04714 SHA512: 0946ea4b94dc0b8749b14a799ed5012f6e98983c2bde5efaaf0ec21099c58767f4a6505ffa467c9354f94ca44e69a2d055e3ec565f043edc46f9db0497abe2e3 Homepage: https://cran.r-project.org/package=brfinance Description: CRAN Package 'brfinance' (Access to Brazilian Macroeconomic and Financial Time Series) Provides simplified access to selected Brazilian macroeconomic and financial time series from official sources, primarily the Central Bank of Brazil through the SGS (Sistema Gerenciador de Séries Temporais) API. The package enables users to quickly retrieve and visualize indicators such as the unemployment rate and the Selic interest rate using a standardized data structure. It is designed for data access and visualization purposes, without performing forecasts or statistical modeling. For more information, see the official API: . 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Each requested survey year is downloaded once as a compact file hosted on public releases, verified against a published checksum, and cached locally; queries then run through 'DuckDB' (via the 'duckdb' package), so column selection and repeat analyses never re-transfer data. Survey-design helpers construct 'srvyr' design objects with year-appropriate weights, strata, and primary sampling units, including explicit handling of the 2011 weighting methodology change and of the codes CDC uses for missing-type answers. Package: r-cran-brickset Architecture: all Version: 2026.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-piggyback Suggests: r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-shiny Filename: pool/dists/noble/main/r-cran-brickset_2026.0.0-1.ca2404.1_all.deb Size: 1105070 MD5sum: 8b458adefc367257724a4dd22b3664de SHA1: 5cf3908ef8d4bed991bdca8864256c741484d1f0 SHA256: 501107305f67bd7d6743a4f6d6846c5aec92da826bf97da12e180831e9e9efd2 SHA512: 50ab616dca11319e4be55924f1db2ff83494b9313867134ebe89b4d1e2a62478d3a25aec9480d7143e23b7e7566e58272018b5c0a260cae5d352959b988f83db Homepage: https://cran.r-project.org/package=brickset Description: CRAN Package 'brickset' (Interface with the Brickset API for Getting Data About LEGO Sets) Interface with the 'Brickset' API for getting data about LEGO sets. Data sets that can be used for teaching and learning without the need of a 'Brickset' account and API key are also included. Includes all LEGO since through the end of 2025. Package: r-cran-brickster Architecture: all Version: 0.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2295 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-ini, r-cran-jsonlite, r-cran-lifecycle, r-cran-nanoarrow, r-cran-processx, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-tibble Suggests: r-cran-arrow, r-cran-htmltools, r-cran-huxtable, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-brickster_0.2.15-1.ca2404.1_all.deb Size: 1874212 MD5sum: 4aa5fff4de43f82849795db39b1d70a2 SHA1: 1fc0337f88187dc61aa3bd62b8bfc8c81b01abe6 SHA256: f852fb923bbf1785a2e6026228f6e0e9ec37196b8782542a2c405fa9868a839d SHA512: e97c0261950ff213a613dce06dccd7c47b2cd877a7a134eaec7705f9b39767be7d5089ba0c2a78694e1e4845847a5306b21f32dbff9ad40bbd93e1a34ce26790 Homepage: https://cran.r-project.org/package=brickster Description: CRAN Package 'brickster' (R Toolkit for 'Databricks') Collection of utilities that improve using 'Databricks' from R. 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In Wang and Louis (2003) , such a univariate bridge distribution was derived as the distribution of the random intercept that 'bridged' a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian. Package: r-cran-bridger2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-plotly, r-cran-bsda, r-cran-outliers Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bridger2_0.1.0-1.ca2404.1_all.deb Size: 304216 MD5sum: f4395766a7ea4393b70315ae9428c8b4 SHA1: a3c04a84533da0e284592f166206c2a3235aae0a SHA256: 235cb7c4c5a556f72e7bc3128102768f6aa41781e642d13d66c1bd8b87ad429c SHA512: 15a41c67d9771300f62c1373d09093500a75ad97131aba39ed795b88612af6c9414b8a3b25882af00030384edd8bc29c9e3122940904dd8a00e29b2f4f2888c2 Homepage: https://cran.r-project.org/package=bridger2 Description: CRAN Package 'bridger2' (Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data) BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions. Package: r-cran-bridger Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-patchwork, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-ggedit, r-cran-glue, r-cran-gridextra, r-cran-kableextra, r-cran-pdftools, r-cran-scales, r-cran-stringr Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-bridger_0.1.0-1.ca2404.1_all.deb Size: 545968 MD5sum: 06d945539d459e943912b2ee2a1d9423 SHA1: defb2c7ac8d4960cd563a2720809966f4a489aee SHA256: 024cfa8dd5d8cdb0a09be2a327bfcc15c7b6a7564ff19f0816f5c514849f1f97 SHA512: 8c16b1a8b05c5fda0ead1650597f80d426fba1ff177e7406d1b6117bc17119795d53cc5c6c69b7863285b984d8be758106c17e6051add5ac35bef5552536c571 Homepage: https://cran.r-project.org/package=bridger Description: CRAN Package 'bridger' (Bridge Hand Generator with Criteria Selector) Produce bridge hands, allowing parameters for hands to offer specific for bidding sequences. Package: r-cran-bridgesampling Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix, r-cran-brobdingnag, r-cran-stringr, r-cran-coda, r-cran-scales Suggests: r-cran-testthat, r-cran-rcpp, r-cran-rcppeigen, r-cran-r2jags, r-cran-rjags, r-cran-runjags, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-bayesfactor, r-cran-rstan, r-cran-rstanarm, r-cran-nimble, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bridgesampling_1.2-1-1.ca2404.1_all.deb Size: 1176910 MD5sum: 67ef94916c3d9d56436c8e936adf3d25 SHA1: b7ddf7f7ed1691dcb1b8c26494a49cba083cb1e7 SHA256: 785a0ebeae49a14232b45140660aa76967cf8c81b31a5b22c74c985997f0ee7f SHA512: 4aed9765001aea3f358be545f3f84ae14d70386058faa6779d296a11eb067c9a56d52225d3f5aa27ab01f450e0dcad9072e701b1fa1ca6b8f71e853a8ad9c9d5 Homepage: https://cran.r-project.org/package=bridgesampling Description: CRAN Package 'bridgesampling' (Bridge Sampling for Marginal Likelihoods and Bayes Factors) Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, ). Gronau, Singmann, & Wagenmakers (2020) . Package: r-cran-bridgr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-rlang, r-cran-scales, r-cran-tsbox, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-srr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bridgr_1.0.0-1.ca2404.1_all.deb Size: 1360504 MD5sum: 4f1e74a5c3a496c7d3c015ddb03a05da SHA1: 56a16b92d368364409fc52f8c78e3326ceac54dd SHA256: 1e55c94067b4bf4ccf5581b3303741aa1f45a3965fab460e53f263e16b96e41a SHA512: 6c501be4a1943af3d8bc73d4f979668cecee4d4176feccc8b896fdffe92626859daf1bcd0752c4fb2c159ac85d3d3957eab793a6807fe5e229af229b76f9454d Homepage: https://cran.r-project.org/package=bridgr Description: CRAN Package 'bridgr' (Bridging Data Frequencies for Timely Economic Forecasts) Implements bridge and MIDAS-style mixed-frequency models for nowcasting and forecasting macroeconomic variables by linking higher-frequency indicator variables to a lower-frequency target series. The package standardizes input data, infers regular frequencies, forecasts missing indicator observations, and aggregates indicators to the target frequency before fitting a regression with autoregressive target dynamics. Frequency alignment can be customized through user-supplied conversion rules. For more on bridge and MIDAS models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) , Ghysels, Sinko, & Valkanov (2007) , Andreou, Ghysels, & Kourtellos (2010) , Schumacher (2016) , and Burri (2026) . Package: r-cran-brightspacer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-config, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-openssl, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-brightspacer_0.1.0-1.ca2404.1_all.deb Size: 288254 MD5sum: 1d48df933e8467de1d26fdef86c11406 SHA1: 3b350a86c7250a5c8bc67c69552bc4b4c69e3c47 SHA256: 715b6b7368c6d3d8f9d4c4ebdfaf7d08b9fee24b36992a6506908ba5c3468ffe SHA512: d32a079d9924e41f1244c5a7d956d86d4cc8845894c42074a606691aad37c8ee79dca15b9bbf4acd0dc39ff0a81a483e85e0a75a31ecf3f596ba91128df0ae26 Homepage: https://cran.r-project.org/package=brightspaceR Description: CRAN Package 'brightspaceR' (Access D2L 'Brightspace' Data Sets via the 'BDS' API) Connect to the D2L 'Brightspace' Data Sets ('BDS') API via 'OAuth2', download all available datasets as tidy data frames with proper types, join them using convenience functions that know the foreign key relationships, and analyse student engagement, performance, and retention with ready-made analytics functions. Package: r-cran-brikmeans Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-cluster, r-cran-depthtools, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-brikmeans_1.0-1.ca2404.1_all.deb Size: 142274 MD5sum: 65f299b1ba9be9f38cf0dfbf632d245e SHA1: 3a79103e489239e39e87bfa008557140f23370f5 SHA256: d133ab3a039df69e70821052aa1f3f3437ee91e1e8f193dda6c75e02efac54e3 SHA512: d169e25864ba74f4bd8fe1d701d1adb50d934eada3bd0c0779a9a1da763df5dbd8c6d57093a4c6fe3c44e552c65076541935d4afdb71bae7db1a2bc7ae443982 Homepage: https://cran.r-project.org/package=briKmeans Description: CRAN Package 'briKmeans' (Package for Brik, Fabrik and Fdebrik Algorithms to InitialiseKmeans) Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al. Package: r-cran-brinda Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-berryfunctions, r-cran-data.table, r-cran-dplyr, r-cran-hmisc, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brinda_0.1.5-1.ca2404.1_all.deb Size: 52784 MD5sum: 17fe14ce546de2990f7c48d31623b689 SHA1: 4cb8993bcb4407004e7ef70d071cb5c1b65fa612 SHA256: c0507347df16ec3a42064ecf39f6b50f2225abe4e40662cfb006216b57bab8e6 SHA512: 301eef0b1e205d9c32341e85a758125aad3fe22f5ed9a8070a7bd057598a0c4e6425e68c495a6ff496b0c6104a3878fa44cc9a559a94553d55bd1ca8881ac8c1 Homepage: https://cran.r-project.org/package=BRINDA Description: CRAN Package 'BRINDA' (Computation of BRINDA Adjusted Micronutrient Biomarkers forInflammation) Inflammation can affect many micronutrient biomarkers and can thus lead to incorrect diagnosis of individuals and to over- or under-estimate the prevalence of deficiency in a population. Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country partnership designed to improve the interpretation of nutrient biomarkers in settings of inflammation and to generate context-specific estimates of risk factors for anemia (Suchdev (2016) ). In the past few years, BRINDA published a series of papers to provide guidance on how to adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum ferritin by Namaste (2020), soluble transferrin receptor (sTfR), serum zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by Namaste (2020) , Rohner (2017) , McDonald (2020) , and Young (2020) . The BRINDA inflammation adjustment method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age Children (PSC); however, the general principle of the BRINDA method might apply to other population groups. The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method, as described above. The BRINDA R package will first carry out rigorous checks and provides users guidance to correct data or input errors (if they occur) prior to inflammation adjustments. After no errors are detected, the package implements the BRINDA inflammation adjustment for up to five micronutrient biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin, sTfR, and serum zinc (when appropriate), using inflammation indicators of AGP and/or CRP for various population groups. Of note, adjustment for serum and RBC folate and serum B-12 is not included in the R package, since evidence shows that no adjustment is needed for these micronutrient biomarkers in either WRA or PSC groups (Young (2020) ). Package: r-cran-brinton Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3746 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rmarkdown, r-cran-glue, r-cran-pander, r-cran-lubridate, r-cran-tibble, r-cran-sm, r-cran-rcolorbrewer, r-cran-forcats, r-cran-ggally, r-cran-patchwork, r-cran-scales Suggests: r-cran-knitr, r-cran-mass, r-cran-hexbin, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brinton_0.2.7-1.ca2404.1_all.deb Size: 2705156 MD5sum: d493b72be6d95caad3b1f053c83117ce SHA1: dd8934cb1c92c40353bb6cbbb704e8f37d9ae6f1 SHA256: 15aea64d3737731351ecbeaecc81ee26c3abf80704342c140a0dc49db0e87438 SHA512: 463308ce425a6838340f28fbf284212f7e27f436fed00fe257826e6dc20d34c96abac176824851f5665db0222295cc85ba65d7747a3c6b3d0ebced8b469c96da Homepage: https://cran.r-project.org/package=brinton Description: CRAN Package 'brinton' (A Graphical EDA Tool) An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed. d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships between all pairs of variables of a certain(s) type(s) in a multivariate data set. Package: r-cran-briqr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-httptest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-briqr_0.1.0-1.ca2404.1_all.deb Size: 18016 MD5sum: 2405443d4dda3b040c3c500486df8acb SHA1: 9aa4947343d2def43cf25c364c5aaf4efc52f1dc SHA256: b20539d8bdd3f37cd1248f5bc2870a895d4893fe463f2d9e6751cfcf24471f11 SHA512: 32b6821536d1a6fed5a1cde8a583b12786ee77de52a6a3c2c4cf381b634891b000d59b655c19772539492a2894196ab9597db4b21d473d1befa6ea02b75f9317 Homepage: https://cran.r-project.org/package=briqr Description: CRAN Package 'briqr' (Interface to the 'Briq' API) An interface to the 'Briq' API . 'Briq' is a tool that aims to promote employee engagement by helping employees recognize and reward each other. Employees can praise and thank one another (for achieving a company goal, for example) by giving virtual credits (known as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq' API lets you create, read, update and delete users, user groups, transactions and messages. This package provides functions that simplify getting the users, user groups and transactions of your organization into R. Package: r-cran-brisk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-hitandrun, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-fs, r-cran-testthat, r-cran-tibble, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brisk_0.1.1-1.ca2404.1_all.deb Size: 208636 MD5sum: 92442e15517abcee24eca78ca59337c9 SHA1: 5df25ceb08b7299425c2c87a5ab839f626eba4e1 SHA256: 0e33a0d53ad468d9801748a7856440c31c5aa52aad956b1b81c9564154741f4b SHA512: 9b5b5b61a9388bf82b2dc762fb0f8c7f16308f8532df69e9352f2b7fa7e028867d37088fa4816d490f1b7499e47ae293d20b5fb5b6e3b7670573e5d0faf13698 Homepage: https://cran.r-project.org/package=brisk Description: CRAN Package 'brisk' (Bayesian Benefit Risk Analysis) Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric describing the overall benefit relative to risk. One approach is to use the multi-criteria decision analysis framework (MCDA), as in Mussen, Salek, and Walker (2007) . Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. The brisk package provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users input posterior samples, utility functions, weights, and the package outputs quantitative benefit-risk scores. The posterior of the benefit-risk scores for each group can be compared. Some plotting capabilities are also included. Package: r-cran-brlrmr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-brglm, r-cran-mass, r-cran-profilemodel, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-brlrmr_0.1.7-1.ca2404.1_all.deb Size: 68972 MD5sum: 6fb706cad26b03bb7d7e88d8cfd118b1 SHA1: 720cf3d7f69f890432431057f4fe1b1170bdec4b SHA256: c3f50fb6debb11826f13ee7b2f24f682fc011f051dd903a60a40bb5aab51c70e SHA512: 96802d2b828bcc1a00ba56627c553d388e65ac9f78f54c08bcffd08d2383981d381db0cad3a7ac82a1bc86e850d0ce5b60196d0a44fb28cbea886d863b74ac82 Homepage: https://cran.r-project.org/package=brlrmr Description: CRAN Package 'brlrmr' (Bias Reduction with Missing Binary Response) Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) . Package: r-cran-brm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brm_1.1.1-1.ca2404.1_all.deb Size: 82066 MD5sum: f0fff2a4863487c627e39bffd90e9939 SHA1: f16e9c737be31618c4c1eb2b68b9f80a414be823 SHA256: 9ec9faaedf75d667527007970b3e0a9f932cfb639cf5be8432c8886a65467487 SHA512: 11b7801887b1eec872573aec8dff7e638322ab98fbeaad274540fcab1a5f76f2977581da6c87257965325e7a416bcbb0473b882ae5bb52ecbe1bbd3fd35e76b9 Homepage: https://cran.r-project.org/package=brm Description: CRAN Package 'brm' (Binary Regression Model) Fits novel models for the conditional relative risk, risk difference and odds ratio . Package: r-cran-brms.mmrm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4890 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-mass, r-cran-posterior, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-trialr, r-cran-zoo Suggests: r-cran-bh, r-cran-emmeans, r-cran-fst, r-cran-gt, r-cran-gtsummary, r-cran-knitr, r-cran-markdown, r-cran-mmrm, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rstan, r-cran-stanheaders, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brms.mmrm_1.1.1-1.ca2404.1_all.deb Size: 1548994 MD5sum: 6f7cf235d9733385911fda10b86a1d5f SHA1: 63048f5ee4c1305b25ade7e14570c8b44e0e26cd SHA256: d709745138905519f920b237f11dec8b09abbbb9a8a13a6bc3affebe008bb9b1 SHA512: 149973ffaf38647c237f0b0e2e269d0c2bd39be9fdcc0c2ee5941fcde1da30107431fa535384414344674b9b006d7acdfe004ef7b36e40de93372896f73c3c02 Homepage: https://cran.r-project.org/package=brms.mmrm Description: CRAN Package 'brms.mmrm' (Bayesian MMRMs using 'brms') The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) , Mallinckrodt (2008) . Package: r-cran-brms Architecture: all Version: 2.23.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-loo, r-cran-posterior, r-cran-matrix, r-cran-mgcv, r-cran-rstantools, r-cran-bayesplot, r-cran-bridgesampling, r-cran-glue, r-cran-rlang, r-cran-future, r-cran-future.apply, r-cran-matrixstats, r-cran-nleqslv, r-cran-nlme, r-cran-coda, r-cran-abind, r-cran-backports Suggests: r-cran-testthat, r-cran-emmeans, r-cran-projpred, r-cran-priorsense, r-cran-shinystan, r-cran-splines2, r-cran-rwiener, r-cran-rtdists, r-cran-extradistr, r-cran-processx, r-cran-mice, r-cran-spdep, r-cran-mnormt, r-cran-lme4, r-cran-mcmcglmm, r-cran-ape, r-cran-arm, r-cran-statmod, r-cran-digest, r-cran-diffobj, r-cran-betareg, r-cran-r.rsp, r-cran-gtable, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg, r-cran-colorspace, r-cran-mirai, r-cran-future.mirai Filename: pool/dists/noble/main/r-cran-brms_2.23.0-1.ca2404.1_all.deb Size: 7140264 MD5sum: 2cece98920d8f7126cbd14f22d338626 SHA1: 9e316c62a73d9027cfa6d7f9dbaa87bab855cc9b SHA256: 59a56cbf857201005e5c603606019ef3535b105544bc1287a8d48da0ab16a4a4 SHA512: 5e9ee045a0eb8426e6d13e9b3c3b3b7dd9f280bf22fd3d0fd5d446a504112c7d386ff679a791fca63b90d47f10679b0a55afab4a4e43ae7ac23a266651aff126 Homepage: https://cran.r-project.org/package=brms Description: CRAN Package 'brms' (Bayesian Regression Models using 'Stan') Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) ; Bürkner (2018) ; Bürkner (2021) ; Carpenter et al. (2017) . Package: r-cran-brobdingnag Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cubature, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brobdingnag_1.3-1-1.ca2404.1_all.deb Size: 983830 MD5sum: 6352482285d8314a35e38e53e3db74f0 SHA1: f5c0fbd1ee91a2d8aeb12bfb42da63fca886ded0 SHA256: 5945a5c721001356e650d955def45c4d609165b5ef8505315349ed08adad23c6 SHA512: cc9338612a1b788f27001f9f6c525216a2904489b2976b8f128f2fe75bd563ecdf7b7cad86beb607b12c439a6c4dd159defb1cdf0ec4fc356153965cb4e93469 Homepage: https://cran.r-project.org/package=Brobdingnag Description: CRAN Package 'Brobdingnag' (Very Large Numbers in R) Very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. Functionality for complex numbers is also provided. The package includes a vignette that gives a step-by-step introduction to using S4 methods. Package: r-cran-brokenadaptiveridge Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cyclops, r-cran-parallellogger, r-cran-bit64 Suggests: r-cran-testthat, r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brokenadaptiveridge_1.0.2-1.ca2404.1_all.deb Size: 28124 MD5sum: 68b97cbb4a46630bf8e6884f1feb6af2 SHA1: c6071dca90d3a3b0b0aa1e5c21abd6d7e8824298 SHA256: 3102e5e595b53540f82b0987362851511cda42e646a9809f2472d72a232e9558 SHA512: 4923b7ebd5cf69845f387dffcbfb76cd1d6fb4c5ac0f84a933410ee98ac444e1cf87943adc1532d0ab2dacfaae3a81e1d0f1f13e541cddfad0310fb5758deb05 Homepage: https://cran.r-project.org/package=BrokenAdaptiveRidge Description: CRAN Package 'BrokenAdaptiveRidge' (Broken Adaptive Ridge Regression with Cyclops) Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) and Li et al (2021) . Package: r-cran-brokenstick Architecture: all Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-lme4, r-cran-matrixsampling, r-cran-rlang, r-cran-tidyr Suggests: r-cran-agd, r-cran-bookdown, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-mice, r-cran-mvtnorm, r-cran-plyr, r-cran-svglite, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brokenstick_2.7.0-1.ca2404.1_all.deb Size: 1211832 MD5sum: 6d0ca2f974bb52deab4c520b2039bdbd SHA1: ec1904180d46993da14d870da0f5e6fc30b3dabb SHA256: e75dcac30778e4a58b70ccde226fd0d8a1867b011a0effa4a993ed77a9c90e36 SHA512: c2865aa8783916c0a9d33e5019208eb49747b0b3ac8a980b3dfbcf184965b3383efc1359f737b742dac8a778eb00ff1ab90d3000ea1e04b924553657acd7a900 Homepage: https://cran.r-project.org/package=brokenstick Description: CRAN Package 'brokenstick' (Broken Stick Model for Irregular Longitudinal Data) Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See for additional documentation on background, methodology and applications. Package: r-cran-brolgar Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fabletools, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-vctrs Suggests: r-cran-gapminder, r-cran-gghighlight, r-cran-knitr, r-cran-matrix, r-cran-lme4, r-cran-modelr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-brolgar_1.0.2-1.ca2404.1_all.deb Size: 3694038 MD5sum: e9412e88a4eaa56d88f953795d1cbe45 SHA1: 729716699883a2d5cf96b993f7eed7d0ee114db1 SHA256: b0ca640289dad4d1a0ed5f25ce33af8f3873c39a20842ce64e40a4116765af21 SHA512: 077b84815955f6a0b2ce5c1ea3751f5746ef0d638a53ffd061660a4c5eb657854e6235ccecbb7b6437186b19ca5bb6c985ac09447e3c6a3d0db9d9410dbc9579 Homepage: https://cran.r-project.org/package=brolgar Description: CRAN Package 'brolgar' (Browse Over Longitudinal Data Graphically and Analytically in R) Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) . Package: r-cran-broom.helpers Architecture: all Version: 1.23.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cards, r-cran-cli, r-cran-dplyr, r-cran-labelled, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-betareg, r-cran-biglm, r-cran-brms, r-cran-broom.mixed, r-cran-cmprsk, r-cran-covr, r-cran-effects, r-cran-emmeans, r-cran-fixest, r-cran-forcats, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-ggeffects, r-cran-ggstats, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-glue, r-cran-gt, r-cran-gtsummary, r-cran-knitr, r-cran-lavaan, r-cran-lfe, r-cran-lme4, r-cran-logitr, r-cran-marginaleffects, r-cran-margins, r-cran-mass, r-cran-mgcv, r-cran-mice, r-cran-mmrm, r-cran-multgee, r-cran-nnet, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-patchwork, r-cran-plm, r-cran-pscl, r-cran-quantreg, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tidycmprsk, r-cran-vgam, r-cran-svyvgam Filename: pool/dists/noble/main/r-cran-broom.helpers_1.23.0-1.ca2404.1_all.deb Size: 572048 MD5sum: d947b35517fec029b49330d083701604 SHA1: 11143420597f8e769d5323ab6990ea24dc511e41 SHA256: 398e75985379cea952c2ebc446397db2912d8a67c91937697669d92df32f653a SHA512: 53b0b822b51a42017f3574000cad5bd2897c5bd860efef6c0ceb61b669a5c9f1ff9c48bf3af9cfee8f34c04771991fb625221807d8376f2d2a27334d6e76b660 Homepage: https://cran.r-project.org/package=broom.helpers Description: CRAN Package 'broom.helpers' (Helpers for Model Coefficients Tibbles) Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more. Package: r-cran-broom.mixed Architecture: all Version: 0.2.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-coda, r-cran-dplyr, r-cran-forcats, r-cran-nlme, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-furrr Suggests: r-cran-brms, r-cran-dotwhisker, r-cran-knitr, r-cran-testthat, r-cran-gamlss, r-cran-gamlss.data, r-cran-ggplot2, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-lmertest, r-cran-lme4, r-cran-matrix, r-cran-mcmcglmm, r-cran-mediation, r-cran-mgcv, r-cran-mice, r-cran-ordinal, r-cran-pander, r-cran-pbkrtest, r-cran-posterior, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-r2jags, r-cran-tmb, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-broom.mixed_0.2.9.7-1.ca2404.1_all.deb Size: 5321854 MD5sum: d04f18d6ab171c0aba5f3d14119ac806 SHA1: 347e70b7d848c911d96fdb9af68cb7dd25ec2dbb SHA256: 76bd1506668dbef7ce2d8ebd856ad7e0961b6e2bb7b363fbc00b8b3a49e73b1f SHA512: 34938084633bd540337571d0bd5669cc9e651c126f49c53b7e574167d45f7efe13b707bf45f24e346c9f80f8e35b5f31da4ebe9a04f277ef1f2086e998ee8f8d Homepage: https://cran.r-project.org/package=broom.mixed Description: CRAN Package 'broom.mixed' (Tidying Methods for Mixed Models) Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics. Package: r-cran-broom Architecture: all Version: 1.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1856 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-backports, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-aer, r-cran-auc, r-cran-bbmle, r-cran-betareg, r-cran-biglm, r-cran-bingroup, r-cran-boot, r-cran-btergm, r-cran-car, r-cran-cardata, r-cran-caret, r-cran-cluster, r-cran-cmprsk, r-cran-coda, r-cran-covr, r-cran-drc, r-cran-e1071, r-cran-emmeans, r-cran-epir, r-cran-ergm, r-cran-fixest, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-glmnet, r-cran-glmnetutils, r-cran-gmm, r-cran-hmisc, r-cran-interp, r-cran-irlba, r-cran-joinerml, r-cran-kendall, r-cran-knitr, r-cran-ks, r-cran-lahman, r-cran-lavaan, r-cran-leaps, r-cran-lfe, r-cran-lm.beta, r-cran-lme4, r-cran-lmodel2, r-cran-lmtest, r-cran-lsmeans, r-cran-maps, r-cran-margins, r-cran-mass, r-cran-mclust, r-cran-mediation, r-cran-metafor, r-cran-mfx, r-cran-mgcv, r-cran-mlogit, r-cran-modeldata, r-cran-modeltests, r-cran-muhaz, r-cran-multcomp, r-cran-network, r-cran-nnet, r-cran-ordinal, r-cran-plm, r-cran-polca, r-cran-psych, r-cran-quantreg, r-cran-rmarkdown, r-cran-robust, r-cran-robustbase, r-cran-rsample, r-cran-sandwich, r-cran-spatialreg, r-cran-spdep, r-cran-speedglm, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-systemfit, r-cran-testthat, r-cran-tseries, r-cran-vars, r-cran-zoo Filename: pool/dists/noble/main/r-cran-broom_1.0.13-1.ca2404.1_all.deb Size: 1536102 MD5sum: 916202e0bef12d28a6acbdaecde4c8c2 SHA1: 617d3779593a3e059d4167faa7b5dc474d093d49 SHA256: ebf7c6fb69624d4d72dfe35d2631de6c4d703c62a1acedeaa891519d51e703c9 SHA512: fc680feaa9c8bfac1f8f9b1bb40c9f9176c9ea847d068053609e1f0b6db9dc09f29438644510b47ade4608dfa39102ec378d569c20e075f86b02729703783bac Homepage: https://cran.r-project.org/package=broom Description: CRAN Package 'broom' (Convert Statistical Objects into Tidy Tibbles) Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures. Package: r-cran-browndog Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-jsonlite, r-cran-httpuv Filename: pool/dists/noble/main/r-cran-browndog_0.2.1-1.ca2404.1_all.deb Size: 30792 MD5sum: 453f35d96da1be96cff8ceaf9ca386b3 SHA1: 2eda8888db9be9863eaaedee54e3df17467ec126 SHA256: 2693e8da3b38b6bf8cf3f28930a9eb0cb07aca9de9c2f22453e29bd959104d36 SHA512: 8d366016245484e2ff9c572c82b6c9af58e9efd7034f6aac660f21c0f6f3f9c0aa6583c170dcaaefb2a20976d6254d15693ce58bd2450547c06fb692b6df0802 Homepage: https://cran.r-project.org/package=BrownDog Description: CRAN Package 'BrownDog' (Brown Dog R Interface) An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services that provides modules to identify the conversion options for a file, to convert file to appropriate format, or to extract data from a file. See for more information. Package: r-cran-brpl Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brpl_1.0.2-1.ca2404.1_all.deb Size: 1130080 MD5sum: 7b75a589027122dcb1aa29fe089f5ea6 SHA1: f6736a981f2c3a89890c62d236b4808f6f54ff4c SHA256: dac1622a696fd01963372ed6cedc635af844e9bb3cb376ed6ae67ed23e658207 SHA512: eb147f484a44dfe5e1837e59a3996019c701d92b552ccfed7fd106d83b92fe0e5d15394e21f6c2a55c86c0f5dbbc639a705f58ea75f34b862de38b5458c8c33e Homepage: https://cran.r-project.org/package=BRPL Description: CRAN Package 'BRPL' (Methods for Bivariate Poverty Line Calculations) Provides tools for identifying subgroups within populations based on individual response patterns to specific interventions or treatments. Designed to support researchers and clinicians in exploring heterogeneous treatment effects and developing personalized therapeutic strategies. Offers functionality for analyzing and visualizing the interplay between two variables, thereby enhancing the interpretation of social sustainability metrics. The package focuses on bivariate discriminant analysis and aims to clarify relationships between indicator variables. Package: r-cran-brpop Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-zendown Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-brpop_0.7.0-1.ca2404.1_all.deb Size: 380166 MD5sum: 5a5f0a0319d68afca6a71e09551a965b SHA1: 2a3a72f505dee86b6dc4b185ad28e5d6c309c661 SHA256: 29ab48241b4b935ca07b396e73a22a631a34a486c67bbfe65ba8f0cefb415103 SHA512: 652453cb0d660a517e02120899e041e404d54b8d71b8b6d11bd1a8b1c46042deea96801234b08176a4d90534f504fcd74918d419757e36ab3062a2f561ecb346 Homepage: https://cran.r-project.org/package=brpop Description: CRAN Package 'brpop' (Brazilian Population Estimates) Provides Brazilian municipality population estimates from official and research sources, with functions to aggregate the data by state, health region, sex, and age group. 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Package: r-cran-brsim Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-corrplot, r-cran-rcmdrmisc Filename: pool/dists/noble/main/r-cran-brsim_0.3-1.ca2404.1_all.deb Size: 28176 MD5sum: a92a9a705a576e5ebfef6e69206eb270 SHA1: c200fa90e37a6eaaaf740978eff8b00d5c18c8d5 SHA256: d0cb8ae78c4b93805a53da29114c62481709802a27e354c7dffbbfac1974807f SHA512: 1f55c090f1f22365280c9b6fcc6bfb4602050e3c8623ae075c36bb516989f2e198af6a4157c47ab6e4b737e505192aa32edea892098e0f09b1eba1845ecde2b8 Homepage: https://cran.r-project.org/package=brsim Description: CRAN Package 'brsim' (Brainerd-Robinson Similarity Coefficient Matrix) Provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; a heatmap is produced to visually represent the similarity matrix. Optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters; the results of the clustering can be optionally used to sort the heatmap. Package: r-cran-brucer Architecture: all Version: 2026.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-data.table, r-cran-rio, r-cran-crayon, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-psych, r-cran-afex, r-cran-emmeans, r-cran-effectsize, r-cran-mediation, r-cran-interactions, r-cran-lavaan, r-cran-jtools, r-cran-texreg Suggests: r-cran-pacman, r-cran-glue, r-cran-tibble, r-cran-forcats, r-cran-haven, r-cran-foreign, r-cran-readxl, r-cran-openxlsx, r-cran-clipr, r-cran-cowplot, r-cran-ggtext, r-cran-see, r-cran-car, r-cran-lmtest, r-cran-lme4, r-cran-lmertest, r-cran-nnet, r-cran-vars, r-cran-phia, r-cran-performance, r-cran-mass, r-cran-mumin, r-cran-bayesfactor, r-cran-ggally, r-cran-gparotation Filename: pool/dists/noble/main/r-cran-brucer_2026.1-1.ca2404.1_all.deb Size: 543940 MD5sum: cd06e004e3db0d5335966c98f25aec78 SHA1: fc3d1dab7f72551808e39702187a4adb97d1de57 SHA256: e17877e380416ba607c921546a43ad62d00758b5f60660330139f798ebe3a9bb SHA512: 8f3b04c84fd3a27b173fd559d0e4c8a691f78360f15f855f64ccb8dfcd8880a28cd4b5300194e129aff6af1c5594cc849265e38b75026b7daebd2dffcc3c8adc Homepage: https://cran.r-project.org/package=bruceR Description: CRAN Package 'bruceR' (Broadly Useful Convenient and Efficient R Functions) Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics. Package: r-cran-brulee Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-coro, r-cran-curl, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-hardhat, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-safetensors, r-cran-tibble, r-cran-tidyselect, r-cran-torch, r-cran-withr Suggests: r-cran-covr, r-cran-lubridate, r-cran-modeldata, r-cran-recipes, r-cran-spelling, r-cran-testthat, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-brulee_1.2.0-1.ca2404.1_all.deb Size: 973400 MD5sum: 28717f6bf553655deadc73ac1314f04d SHA1: 19cf9eacc310d617a8f734364b96d291e65f16e6 SHA256: f4af7a1c48eec4d57efbab009af139e5e700d9261eb248e287b3d4b5ab09bd14 SHA512: f2429ffa50cce2610ee6358f098902724261e25e55dd98ef6baf0da76b99291da290eccf60e4ac61c0d97a7ae954e2756d8bbafc3f501d947e0a8c2a5a9b80a3 Homepage: https://cran.r-project.org/package=brulee Description: CRAN Package 'brulee' (High-Level Modeling Functions with 'torch') Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons. Package: r-cran-bruneimap Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2957 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-lifecycle Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-bruneimap_0.3.1-1.ca2404.1_all.deb Size: 2945684 MD5sum: 13f248c2d923e79d660f959be2b39362 SHA1: 7cdb1b877a6ab2b3e6bfdaee9ce23fcf035f399d SHA256: 418f74209ed514e8b40f6f9e90e62c2cc2601758308272303260696e3221ac1a SHA512: 6cd4041cdf423c205ffcc5686591d04639175cc56143c3987cb8eda62d64c62542145a9957171d0b5f3d43b07c669dec2d790720de74583a4568e9618b5468b6 Homepage: https://cran.r-project.org/package=bruneimap Description: CRAN Package 'bruneimap' (Maps and Spatial Data of Brunei) Provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Brunei’s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies. Package: r-cran-bruno Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bruno_0.1.0-1.ca2404.1_all.deb Size: 69634 MD5sum: 541063dffac7d7877eaebfe4a8107bb0 SHA1: 32cdc48d5b7c715574c832687b4d1d082ef01c9d SHA256: 597573d3de4c198d6d7104fce469569166a9d5fdb393998c4b95ec657fdebe47 SHA512: 3f2e64ed21904117558deefdb3c949a9b095668008931ce4e9aa7fbe390d8556fd77cd7b7aed1b603fe6e54cfeb4e6b670e7b6c7162277efc96387837668b979 Homepage: https://cran.r-project.org/package=bruno Description: CRAN Package 'bruno' (Predicting User-Defined Event Recurrence under Exchangeability) Implements analytical prediction of recurrence for user-defined binary events; 'bruno' abbreviates Beta-Bernoulli Recurrence for Unobserved Next Outcomes. The procedure applies when the observed and future event indicators are judged exchangeable for the intended prediction. For an indefinitely extendible exchangeable binary sequence, de Finetti's representation theorem expresses the assigned joint probabilities as a mixture of Bernoulli laws over a mixing distribution on the unit interval (de Finetti, 1931) . The package adopts a beta distribution as an additional parametric specification of this mixing distribution. Users specify an initial probability mu0 assigned to the event and a positive concentration parameter tau, giving beta parameters a = mu0 * tau and b = (1 - mu0) * tau. If the declared event occurs s times among n observed cases, conditioning gives Beta(a + s, b + n - s). From this conditional assessment, the package computes analytically the probability assigned to occurrence of the same event in the next exchangeable case and, for a prespecified future sample size, the exact beta-binomial predictive distribution of the number of future event occurrences. Events may be supplied directly as logical or binary indicators or defined from paired pre-post measurements through a user-specified logical expression. Prediction may be performed for a single predictive class or separately across user-defined predictive classes, using common or class-specific initial probabilities and concentration parameters. Cases for which event status cannot be determined, and cases with missing predictive-class membership in grouped analyses, are excluded without imputation; case-level classification and inclusion information are retained for audit purposes. Summary methods provide central probability intervals for the conditional beta assessment and, for future samples larger than one case, predictive intervals for the future recurrence count. The package is intended for psychological, educational, pilot-study, and research decision-making applications in which recurrence of an explicitly defined event is the predictive target and the predictive relevance of observed cases for future cases can be substantively justified. The resulting probabilities concern recurrence of the declared event within the stated predictive class and do not independently establish latent change, intervention efficacy, causal effects, measurement validity, or a research decision. 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A recommended solution for this common problem has been Bayesian model estimation. Bayesian methods rely on user specified information from historical data or researcher intuition to more accurately estimate the parameters. This package provides a user friendly interface for estimating test reliability. Here, reliability is modeled as a beta distributed random variable with shape parameters alpha=true score variance and beta=error variance (Tanzer & Harlow, 2020) . 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Supports configurable sidebar brand display modes, hover-expand behavior, and theme customization using CSS variables. Includes complete navbar item helpers, navbar structure validation, reusable brand configuration, prototype top-navigation support, and helpers for common navigation bar and footer layouts. Package: r-cran-bsagri Architecture: all Version: 0.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-multcomp, r-cran-mcpan, r-cran-mvtnorm, r-cran-boot, r-cran-mratios Filename: pool/dists/noble/main/r-cran-bsagri_0.1-10-1.ca2404.1_all.deb Size: 191212 MD5sum: 614cd18384a4e9746741b77ad8846909 SHA1: 9e2b28fea3c6d5c5e3c5c11b9935917a9b32c58b SHA256: 408803190b1fcaa21325be3dba9b02c4ac09cf3045ae715ea1731a241c41122d SHA512: a7cce31e536f25702b243caf7480b18fdb76cedfcfda38c3e60b7605b4e155695d75a0e2a7147cd5640d0b1d6a9caf04b788a2685b1b994b31f1a606aa500905 Homepage: https://cran.r-project.org/package=BSagri Description: CRAN Package 'BSagri' (Safety Assessment in Agricultural Field Trials) Collection of functions, data sets and code examples for evaluations of field trials with the objective of equivalence assessment. 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Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as 'coda' and 'boa'. Package: r-cran-bscui Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-webshot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-xml2, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-glue, r-cran-scales, r-cran-shiny, r-cran-reactable, r-cran-reactable.extras Filename: pool/dists/noble/main/r-cran-bscui_0.1.6-1.ca2404.1_all.deb Size: 1352822 MD5sum: 8510738be7da0f3e352dfc65b47a8f1d SHA1: 4f24ae4fd4356db51935b6fc4f4c3d32aa54aed0 SHA256: 27ab7a27ed1764e507ee0e43b108e751f4dad0bf2763bda4a719ac6fd27d82c0 SHA512: b91fce4c56a67c80e2f6675e702c5ded124075433466095ad9871cdfdc8153af2ef261273902f5e34d9eab320ffdfa9eacdee2faecc712e87e1ff9cbd25d5546 Homepage: https://cran.r-project.org/package=bscui Description: CRAN Package 'bscui' (Build SVG Custom User Interface) Render SVG as interactive figures to display contextual information, with selectable and clickable user interface elements. These figures can be seamlessly integrated into 'rmarkdown' and 'Quarto' documents, as well as 'shiny' applications, allowing manipulation of elements and reporting actions performed on them. Additional features include pan, zoom in/out functionality, and the ability to export the figures in SVG or PNG formats. Package: r-cran-bsda Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-e1071 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bsda_1.2.2-1.ca2404.1_all.deb Size: 878844 MD5sum: e182c25ae27d922179b32a306cb3463b SHA1: 811522a2be20eb3a886339d5baa28126ad5b492b SHA256: 0c58057004bceb42dd03fb02e89755b0d502de526914f5d8b1ac79589eef3a21 SHA512: d61e62afc866e6e42fe72dacf604b946e0226a456e9430812c64190a6d07ca7a9df5d52fd70b9c20a7cb5cb0a832a4d08e82cdbef3f9d47f175d336c7e608daa Homepage: https://cran.r-project.org/package=BSDA Description: CRAN Package 'BSDA' (Basic Statistics and Data Analysis) Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens. Package: r-cran-bset Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-future, r-cran-mvtnorm, r-cran-dplyr, r-cran-surrogaterank, r-cran-rdpack, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-bset_1.0-1.ca2404.1_all.deb Size: 722344 MD5sum: 8a435c0981641bc2ff54f092c0c8a1f8 SHA1: fd39bcee80dd3598fc62ea5f2b2b37fd313fd5d7 SHA256: 7efd3a4acc01d5266d9b735ca09dd458338a8f3390ad23e0c5d9f421c2109c31 SHA512: 17232d4cc8d14476667ed161dbc699620a54417dc830a50eac0897000a9187bfeeb8dfca1ae82361158f518c7ff914a990e03160dd5281ec2302ad02bf4f1524 Homepage: https://cran.r-project.org/package=BSET Description: CRAN Package 'BSET' (A Bayesian Surrogate Evaluation Test) An implementation of the Bayesian Surrogate Evaluation Test (BSET) for assessing the validity of surrogate markers in clinical trials. Provides hypothesis testing tools to evaluate whether a surrogate can reliably estimate the causal effect of a treatment on a primary outcome. Implements the imputation-based Bayesian methodology of Carlotti and Parast (2026) , extending the frequentist rank-based approach of Parast et al. (2024) . Addresses key limitations of the frequentist method, including the lack of causal interpretability and the inability to adjust for covariates in the estimation process. Package: r-cran-bsgof Architecture: all Version: 0.23.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2398 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bsgof_0.23.8-1.ca2404.1_all.deb Size: 2281046 MD5sum: b3389bb9741d4f3828668988899386bd SHA1: bcbeebb1bcf1ab1acd6aa6519751cda871d98e80 SHA256: 7cf8bcf7deac082e741344480c803b99a672b01151a209a0ffc31b5d35901551 SHA512: 024cbb7d39fcf22b10667e8ca221e8dbf793a266a3cc8ba2290d66d30c15e1a5e2199bc02510129cb8d2bffe701876de94230962c4ecafe78615ce61dc70a7f3 Homepage: https://cran.r-project.org/package=bsgof Description: CRAN Package 'bsgof' (Birnbaum-Saunders Goodness-of-Fit Test) Performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) . This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528). Package: r-cran-bsgw Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-survival, r-cran-mfusampler Filename: pool/dists/noble/main/r-cran-bsgw_0.9.4-1.ca2404.1_all.deb Size: 95604 MD5sum: 82cda910ebe9daf759170d7404614a39 SHA1: e4d2d446a7e6bedeb2140258b304fe045aedb2ec SHA256: 5d3e24bfef91d6c5f5d52d5f0ed2a03f6d948c4db811b50dc0d77cbe745df069 SHA512: 4aaf0a731e4be7abb127731a04cdcb5cbb675a48c806fdcdef22024dbabb25e6ef6e8cbd7f78b2901987d3aea7c0eec4cbe61b3355247489e223fc551fdc7ff6 Homepage: https://cran.r-project.org/package=BSGW Description: CRAN Package 'BSGW' (Bayesian Survival Model with Lasso Shrinkage Using GeneralizedWeibull Regression) Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation. Package: r-cran-bshazard Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-epi Filename: pool/dists/noble/main/r-cran-bshazard_1.2-1.ca2404.1_all.deb Size: 51554 MD5sum: 01608a8d77587fdc61cd6698d9261494 SHA1: 49bdcbe685c8116a8ac4d280d2147c6822c9ba32 SHA256: 20aad7cf10965e8d163a253c2009a45a95287fe97a44a2cf87a05b5aa3005296 SHA512: 62e235348c0d7b3a4c04c4be84d77ceec13428c8b26d5e74f3c90a4d711dd05654202d129c098043a32fc6c0676335e0894a2d3ad6b356509fc04628eb26d54e Homepage: https://cran.r-project.org/package=bshazard Description: CRAN Package 'bshazard' (Nonparametric Smoothing of the Hazard Function) The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed. The package is based on the work in Rebora P (2014) . Package: r-cran-bsi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bsi_1.0.0-1.ca2404.1_all.deb Size: 45102 MD5sum: 8d1be470f49a8e120de9fe680db9b746 SHA1: 9354e4269cc4fd988ab71fcfb5f41c62df566fef SHA256: 51df07bb348c5aca4258d9cd7f41e8ae6384271721247c548788b51553819d9a SHA512: 31e41661593dbdb1799f147c5ebb669ccc7e3525774b2d73220c251fe69521a5371d1e14950ab04de9d58034693c13939048bdbca42e857b4f1dcc38891bd133 Homepage: https://cran.r-project.org/package=bSi Description: CRAN Package 'bSi' (Modeling and Computing Biogenic Silica ('bSi') from Inland andPelagic Sediments) A collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica 'bSi' in inland and marine sediments. These tools share common data representations and follow a consistent 'API' design. The primary goal of the 'bSi' package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire 'bSi' analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems. Package: r-cran-bsicons Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-htmltools, r-cran-rlang Suggests: r-cran-bslib, r-cran-processx, r-cran-testthat, r-cran-webshot2, r-cran-withr Filename: pool/dists/noble/main/r-cran-bsicons_0.1.2-1.ca2404.1_all.deb Size: 255362 MD5sum: 1a5c22e8800642ce79ce50b823c79cdf SHA1: adbdb1d4e6a2da4088f0e562635530bf773d933e SHA256: d3d7f1ff3b84918c2f54abe07ad0d72d1252a9bc041bdf6619c840e24dd4d3ef SHA512: 2f89b51930a8da4203f6d68e287bd645f9c2edc1df63e8afc71fadd881f32d3cd4ef93dfc0580914be436a942b02594a9a7f7763c9fe601cff362825f881eba1 Homepage: https://cran.r-project.org/package=bsicons Description: CRAN Package 'bsicons' (Easily Work with 'Bootstrap' Icons) Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R Markdown' documents. More generally, icons can be inserted in any 'htmltools' document through inline 'SVG'. Package: r-cran-bsims Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-intrval, r-cran-mefa4, r-cran-mass, r-cran-deldir, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-detect, r-cran-shiny Filename: pool/dists/noble/main/r-cran-bsims_0.3-3-1.ca2404.1_all.deb Size: 1875950 MD5sum: 4c9fc6574933150096d29be292951d33 SHA1: 438cbf74eb7ac39dffb4c88395f91341bacbbb56 SHA256: 2afb4c88c8a4e5e82159a09001fab4d3b4aaaf7e0cc4e2c49b317a40646e6b11 SHA512: 1b077272071ded2bd6b6a81fca41ff5af4227c8fcd4ccad66bdfb707ea7683e98a78b9a35f25d29c3453209112253e48e9bb4ddcb34e9813824d505dcfd48b98 Homepage: https://cran.r-project.org/package=bSims Description: CRAN Package 'bSims' (Agent-Based Bird Point Count Simulator) A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 ). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) , Solymos et al. (2013) , and Solymos et al. (2018) , and sound attenuation experiments by Yip et al. (2017) . Package: r-cran-bsitar Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9026 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-rstan, r-cran-loo, r-cran-dplyr, r-cran-rlang, r-cran-rdpack, r-cran-insight, r-cran-data.table, r-cran-collapse, r-cran-marginaleffects, r-cran-magrittr Suggests: r-cran-sitar, r-cran-ggplot2, r-cran-tidybayes, r-cran-bayesplot, r-cran-posterior, r-cran-testthat, r-cran-ggridges, r-cran-jtools, r-cran-splines2, r-cran-scales, r-cran-kableextra, r-cran-knitr, r-cran-future, r-cran-future.callr, r-cran-future.apply, r-cran-future.mirai, r-cran-mirai, r-cran-doparallel, r-cran-foreach, r-cran-dofuture, r-cran-fastplyr, r-cran-cheapr, r-cran-dtplyr, r-cran-checkmate, r-cran-installr, r-cran-bayestestr, r-cran-rmarkdown, r-cran-modelsummary, r-cran-performance, r-cran-reformulas, r-cran-growthcleanr, r-cran-r.rsp, r-cran-openxlsx2, r-cran-priorsense, r-cran-distributional, r-cran-extradistr, r-cran-ggdist, r-cran-flextable, r-cran-flexlsx, r-cran-hmisc, r-cran-r.utils, r-cran-mass, r-cran-matrix, r-cran-tidyr, r-cran-nlme, r-cran-purrr, r-cran-forcats, r-cran-patchwork, r-cran-tibble, r-cran-pracma, r-cran-bookdown, r-cran-spelling, r-cran-boot, r-cran-abind, r-cran-glue Filename: pool/dists/noble/main/r-cran-bsitar_0.4.0-1.ca2404.1_all.deb Size: 8477172 MD5sum: 3b10f52bcd49e4d9a346d3f48fd09938 SHA1: fca3da80d325573c004031f1d8cf99bdc89ce25d SHA256: 1a259f9e55ad28e5e2f4517859d5d7d18ad3cf2c645aa63ebc177ae1293808be SHA512: d0ddb8eeab4945cb36cd0a10baa43e9181d9a009b96feaa352a6c5c7a9e947419847e40270e4490db18213bce8d5753b7d748d4b616f92b4a236f19638524fa6 Homepage: https://cran.r-project.org/package=bsitar Description: CRAN Package 'bsitar' (Bayesian Super Imposition by Translation and Rotation GrowthCurve Analysis) The Super Imposition by Translation and Rotation (SITAR) model is a shape-invariant nonlinear mixed effect model that fits a natural cubic spline mean curve to the growth data and aligns individual-specific growth curves to the underlying mean curve via a set of random effects (see Cole, 2010 for details). The non-Bayesian version of the SITAR model can be fit by using the already available R package 'sitar'. Unlike the 'sitar' package which allows modelling of a single outcome only, the 'bsitar' package offers great flexibility in fitting models of varying complexities, including joint modelling of multiple outcomes such as height and weight (multivariate model). Additionally, the 'bsitar' package allows for the simultaneous analysis of an outcome separately for subgroups defined by a factor variable such as gender. This is achieved by fitting separate models for each subgroup (for example males and females for gender variable). An advantage of this approach is that posterior draws for each subgroup are part of a single model object, making it possible to compare coefficients across subgroups and test hypotheses. Since the 'bsitar' package is a front-end to the R package 'brms', it offers excellent support for post-processing of posterior draws via various functions that are directly available from the 'brms' package. In addition, the 'bsitar' package includes various customized functions that allow for the visualization of distance (increase in size with age) and velocity (change in growth rate as a function of age), as well as the estimation of growth spurt parameters such as age at peak growth velocity and peak growth velocity. Package: r-cran-bskyr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-lubridate, r-cran-magick, r-cran-mime, r-cran-opengraph, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-emoji, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vcr, r-cran-withr Filename: pool/dists/noble/main/r-cran-bskyr_0.4.0-1.ca2404.1_all.deb Size: 442354 MD5sum: 5e8fb2dc244c4a95e5667d5ca2c766d1 SHA1: 2b659e5c048fcee4c23c0dabd7b9e3eee8729183 SHA256: 3f8a70197e3a7da4b0c1227e1e3567b4b41ec47f3a2ec3a7ac2d9d55f9d6eb95 SHA512: 437ab63971a16be735201e4ef85e48d1f029db5aeb77238aa144a4c1d734c0a1a22ab9e8422f8803d62e49a0c4daec778951b02725e8e556afeae343513dd402 Homepage: https://cran.r-project.org/package=bskyr Description: CRAN Package 'bskyr' (Interact with 'Bluesky' Social) Collect data from and make posts on 'Bluesky' Social via the Hypertext Transfer Protocol (HTTP) Application Programming Interface (API), as documented at . This further supports broader queries to the Authenticated Transfer (AT) Protocol which 'Bluesky' Social relies on. Data is returned in a tidy format and posts can be made using a simple interface. Package: r-cran-bslib Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12075 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-cachem, r-cran-fastmap, r-cran-htmltools, r-cran-jquerylib, r-cran-jsonlite, r-cran-lifecycle, r-cran-memoise, r-cran-mime, r-cran-rlang, r-cran-sass Suggests: r-cran-brand.yml, r-cran-bsicons, r-cran-curl, r-cran-fontawesome, r-cran-future, r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-magrittr, r-cran-rappdirs, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-thematic, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-bslib_0.12.0-1.ca2404.1_all.deb Size: 5441264 MD5sum: fe12f9e026318fdae01aa5224b049f87 SHA1: bff7a278a31c0c6a787c24cd3e30de855c0ada73 SHA256: f378f643c9959cf4bf5fd4fb6a3bc02640fc0717f9f50da147dd640110a65d8f SHA512: c941b9c1bee45dd0df69e0f135aa900d7686ba1d9fc3e603bca55bb83b9e81e9a8beab2b3dd80cd2b158ae8b75995838cf52f3b47d6d06ee5ba56d0d0e2ab68b Homepage: https://cran.r-project.org/package=bslib Description: CRAN Package 'bslib' (Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown') Simplifies custom 'CSS' styling of both 'shiny' and 'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as well as their various 'Bootswatch' themes. An interactive widget is also provided for previewing themes in real time. Package: r-cran-bslibdash Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-htmltools, r-cran-bslib, r-cran-bsicons, r-cran-rlang, r-cran-glue, r-cran-sass Suggests: r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-bslibdash_0.7.5-1.ca2404.1_all.deb Size: 434704 MD5sum: a8ad813ac46fe4fb1c329a117c361d8b SHA1: 08c2f5fb151dbea81d5f9fc1f4f2748f4feba9dc SHA256: 127852426be1f4b8602e433d775fff2ec1655054bb4465c5855fb84a43531f48 SHA512: 22a301e56393f4e29f479640827f694219e0037326a1d9f126f2403da3a2109e99335a36e1e87621166843372f8d5b6443e2b20c1789e8447118b68d39abc7a1 Homepage: https://cran.r-project.org/package=bslibdash Description: CRAN Package 'bslibdash' ('Bootstrap' 5 Dashboard Framework for 'shiny' Apps) Provides a dashboard layer for 'shiny' applications built on 'bslib' and 'Bootstrap' 5. Includes a dashboard page shell, sidebar navigation, cards, value boxes, header drop-down menus and feedback components that inherit the active 'bslib' theme and follow 'Bootstrap' design patterns. Function names mirror those of the 'shinydashboard' package wherever the underlying concepts are shared, allowing existing applications to migrate with minimal changes. Package: r-cran-bsocialv2 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-growthcurver, r-cran-igraph, r-cran-magrittr, r-cran-reshape2, r-cran-rlang, r-cran-tidyr, r-cran-viridis Suggests: r-cran-readr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bsocialv2_0.2.1-1.ca2404.1_all.deb Size: 271722 MD5sum: 4598f3a026efea58ae6f8493713c76f4 SHA1: 97c05f54225c59056933b69e72846d2c52fb8b9c SHA256: 152e9f7543d9d616f04ea019a2d3699159dfafed13c4101e17f96884f09fb23d SHA512: 5783336f75d2fa21788d4686aa346bae409d8fad7f860daedc975252c02c9a631adcae7a45e1198beaef790ffffda5621097a49c409c2309f8070c09879bc3ef Homepage: https://cran.r-project.org/package=bsocialv2 Description: CRAN Package 'bsocialv2' (Analysis of Microbial Social Behavior in Bacterial Consortia) Provides an S4 class and methods for analyzing microbial social behavior in bacterial consortia. Includes growth parameter extraction, social behavior classification (cooperators/cheaters/neutrals), diversity effect analysis, consortium assembly path finding, and stability analysis via coefficient of variation. Methods are described in Purswani et al. (2017) . Package: r-cran-bspadata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-spdep, r-cran-pscl, r-cran-pbapply, r-cran-coda Filename: pool/dists/noble/main/r-cran-bspadata_1.1.0-1.ca2404.1_all.deb Size: 172738 MD5sum: 894544c1b4e7d1cfd62f0b478565212a SHA1: 33862107cbf6fec325c08ba02d0b90e54355c234 SHA256: 5572c28027373200f33872134e4c15876f26171ecbf4967919565f61892adb1d SHA512: 683037066c82299b3f3cf90915600d914e29cfce65255cd07b4bd2d6fdb6e8aa7a842364ed3d382a9778c244fe886314f0d6d5a6857ae53d3977539cb48246bd Homepage: https://cran.r-project.org/package=BSPADATA Description: CRAN Package 'BSPADATA' (Bayesian Proposal to Fit Spatial Econometric Models) The purpose of this package is to fit the three Spatial Econometric Models proposed in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. 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Package: r-cran-bspcov Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gigrvg, r-cran-coda, r-cran-progress, r-cran-bayesfactor, r-cran-mass, r-cran-mvnfast, r-cran-matrixcalc, r-cran-matrixstats, r-cran-purrr, r-cran-dplyr, r-cran-rspectra, r-cran-matrix, r-cran-plyr, r-cran-cholwishart, r-cran-magrittr, r-cran-future, r-cran-furrr, r-cran-ks, r-cran-ggplot2, r-cran-ggmcmc, r-cran-caret, r-cran-fincovregularization, r-cran-mvtnorm, r-cran-patchwork, r-cran-reshape2, r-cran-future.apply Suggests: r-cran-hdbinseg, r-cran-poet, r-cran-tidyquant, r-cran-tidyr, r-cran-timetk, r-cran-quantmod Filename: pool/dists/noble/main/r-cran-bspcov_1.0.3-1.ca2404.1_all.deb Size: 4213544 MD5sum: 79ac0eff432bfa7f60873e47f5c4cd73 SHA1: 8f7cf49880eea15a752999d74bcee15aa7aa9436 SHA256: a4f6ba8bb844df0c0823f9cf3c454fa65f9f3d1a94031cd4c34d5f450552b40c SHA512: af61dea21bc00e3d736635d2a0f361912cfae8bb5a9edf53cd743436b7f57b377885858d14215025cd4f00ccea417712674114a91aa404f3954cc46922316522 Homepage: https://cran.r-project.org/package=bspcov Description: CRAN Package 'bspcov' (Bayesian Sparse Estimation of a Covariance Matrix) Bayesian estimations of a covariance matrix for multivariate normal data. Assumes that the covariance matrix is sparse or band matrix and positive-definite. Methods implemented include the beta-mixture shrinkage prior (Lee et al. (2022) ), screened beta-mixture prior (Lee et al. (2024) ), and post-processed posteriors for banded and sparse covariances (Lee et al. (2023) ; Lee and Lee (2023) ). This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea ('NRF') funded by the Ministry of Education ('RS-2023-00211979', 'NRF-2022R1A5A7033499', 'NRF-2020R1A4A1018207' and 'NRF-2020R1C1C1A01013338'). Package: r-cran-bspec Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bspec_1.6-1.ca2404.1_all.deb Size: 154632 MD5sum: 1106dcc98c07d89164c1a9d6d2243613 SHA1: a9f88f4093fb4b0339d2eaad46663000c98d401e SHA256: cabdb532da821bd1e9a475437702ef9d327503688dabfbe9469462101973091e SHA512: b3968f7861faf4ff4cf7255142b951bbf755067ebb93b0e6083b8ac0753355bc9ab19e4b05c5016f4d73376c8543032792d0e2114c8d397cba60bc4d8bf82e5a Homepage: https://cran.r-project.org/package=bspec Description: CRAN Package 'bspec' (Bayesian Spectral Inference) Bayesian inference on the (discrete) power spectrum of time series. Package: r-cran-bsplinequantreg Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cvxr, r-cran-ecosolver Suggests: r-cran-clarabel, r-cran-cobs, r-cran-quantreg, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bsplinequantreg_0.2.5-1.ca2404.1_all.deb Size: 2056316 MD5sum: 5b599e9512ec749e5f917f705c02c97d SHA1: fb5b8defc9e324e934e5de8091c5a9e6e1a9795c SHA256: 105c4e5b7753b9c1a3a02c3d2d7a3c617b876bbf7cae0f1f0af5d3e338f9d36d SHA512: f0490588bb8ebd7a07673f690157caf1880fe354b1ccf85a354e1c53461bd7e7d6490db942509a8fef09d5d512d3ec695cf29773c7ed54604ca893dcf493f3c0 Homepage: https://cran.r-project.org/package=BsplineQuantReg Description: CRAN Package 'BsplineQuantReg' ('Constrained Quantile Regression with B-Splines') Quantile regression with B-splines under shape constraints. The initial version with cubic splines is now augmented with splines of degree 1 to 4. Constraints for degrees 3 (monotone) and 4 (monotone and convex) use the Karlin-Studden SOCP characterization for the sign of the polynomial, while other constraints applied at the knots are added as linear problems. The method for cubic splines is described in 'Abbes (2026)' . Other formulations are simple consequences of the other given references. All B-spline and polynomial functions have been rewritten for consistency. This package provides an original B-spline library for conversion between PP-form and B-spline representation, evaluation, differentiation, callable and non-callable objects, print human readable pp forms, view basis, all based on "De Boor\'s" theory. It also extends to multiple knots to catch up singularities. This feature is robust in the package including for constrained regression. This R implementation is intended for demonstration and prototyping. An equivalent Python package is available at . 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There is also an interactive 'shiny' application for monitoring job status. Package: r-cran-bsw Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-quadprog, r-cran-boot, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bsw_0.1.2-1.ca2404.1_all.deb Size: 75596 MD5sum: 01db74cf9933f955e7abeb9ba6748dd4 SHA1: c5a631c1bc921e6f3d509fbf20bc06900840efda SHA256: 1f6dafaec663f031f0e9c17047a92c80ff299192bd35e413142619f9beefecd8 SHA512: 7e9fa7ae767579825fc5b486dde88f7634dedce4c2b9e1fe79376eab2139487138b48d712c3e5daae0a845b1b42506b49ec230d9328de9f22d76180f2bf463c8 Homepage: https://cran.r-project.org/package=BSW Description: CRAN Package 'BSW' (Fitting a Log-Binomial Model Using the Bekhit–Schöpe–Wagenpfeil(BSW) Algorithm) Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints. Package: r-cran-bt Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-statmod Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bt_0.4-1.ca2404.1_all.deb Size: 571818 MD5sum: 40c1bc49154c79e2e6ac345fadcad8ff SHA1: 083a2639af92d2455e54e335b504f17c0a3993c2 SHA256: d4cca705840683dd7f3584463a71e64d1ca8ecdd47d6d4e0714b2c2b3594a2ae SHA512: cfbe9298473f0949db0bc6235cec8603944ccb5c9f8d8c792dd56a725d51b131862996558f67c15cac0d1cdc4c8c3912fcfaf597908408565ff20d3117ba7446 Homepage: https://cran.r-project.org/package=BT Description: CRAN Package 'BT' ((Adaptive) Boosting Trees Algorithm) Performs (Adaptive) Boosting Trees for Poisson distributed response variables, using log-link function. The code approach is similar to the one used in 'gbm'/'gbm3'. Moreover, each tree in the expansion is built thanks to the 'rpart' package. This package is based on following books and articles Denuit, M., Hainaut, D., Trufin, J. (2019) Denuit, M., Hainaut, D., Trufin, J. (2019) Denuit, M., Hainaut, D., Trufin, J. (2019) Denuit, M., Hainaut, D., Trufin, J. (2022) Denuit, M., Huyghe, J., Trufin, J. (2022) Denuit, M., Trufin, J., Verdebout, T. (2022) . 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Package: r-cran-btw Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1927 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-clipr, r-cran-commonmark, r-cran-dplyr, r-cran-ellmer, r-cran-frontmatter, r-cran-fs, r-cran-jsonlite, r-cran-lifecycle, r-cran-mcptools, r-cran-pkgsearch, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-s7, r-cran-sessioninfo, r-cran-skimr, r-cran-withr, r-cran-xml2 Suggests: r-cran-bslib, r-cran-callr, r-cran-chromote, r-cran-covr, r-cran-curl, r-cran-dbi, r-cran-desc, r-cran-devtools, r-cran-diffviewer, r-cran-evaluate, r-cran-fansi, r-cran-gert, r-cran-gh, r-cran-htmltools, r-cran-pandoc, r-cran-pkgload, r-cran-processx, r-cran-ragg, r-cran-r6, r-cran-rapp, r-cran-renv, r-cran-roxygen2, r-cran-rsqlite, r-cran-shiny, r-cran-shinychat, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-btw_1.5.0-1.ca2404.1_all.deb Size: 1593448 MD5sum: eea2acc93769ed2cafac798b8daeb2d9 SHA1: 70564ed70b827bb0c7474c3c7468defff4511a3c SHA256: 30719cce9d883c6ea1c3505f8c0695f618f3f832f823ccef5ddba95495936fc4 SHA512: d04a55e32eaad2383561fa28c1663de22e01c8ab60278cb383bcf7c751532b1d66a61c553269e6e25b6a52ea6340eb41f626353c3662a74cd568697e60e64f9c Homepage: https://cran.r-project.org/package=btw Description: CRAN Package 'btw' (A Toolkit for Connecting R and Large Language Models) A complete toolkit for connecting 'R' environments with Large Language Models (LLMs). Provides utilities for describing 'R' objects, package documentation, and workspace state in plain text formats optimized for LLM consumption. Supports multiple workflows: interactive copy-paste to external chat interfaces, programmatic tool registration with 'ellmer' chat clients, batteries-included chat applications via 'shinychat', and exposure to external coding agents through the Model Context Protocol. Project configuration files enable stable, repeatable conversations with project-specific context and preferred LLM settings. Package: r-cran-btwar Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pracma, r-cran-tseries, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-btwar_1.0.1-1.ca2404.1_all.deb Size: 389224 MD5sum: f8424a5a08d3e2f33dcd9f7bd5dd3bfc SHA1: 262021592436964f4fc0a93e578bea1c7d563cf5 SHA256: 9ec872efa180526a560a628da6576c79db18c5e9c7d4e026e534ddda6bf11f95 SHA512: 218d416f4692d6c009ac6786bdada1400befe23a5a4918a42aa5fae3520ea856c28d911ff708ec8b7a76f43d8db786089f250190c4df12994172ac1653cbbf3b Homepage: https://cran.r-project.org/package=BTWAR Description: CRAN Package 'BTWAR' (Butterworth-Induced Autoregressive Model) Implements the Butterworth-Induced Autoregressive ('BTWAR') model, where autoregressive coefficients are obtained from analog Butterworth filter prototypes mapped into the discrete-time domain using the Matched Z-Transform. The framework establishes a structured connection between frequency-domain filter design and time-domain autoregressive modeling. Model order selection is performed via nested rolling-origin cross-validation. Method described in Bras-Geraldes, Rocha and Martins (2026) . Package: r-cran-btyd Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hypergeo, r-cran-optimx, r-cran-dplyr, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-btyd_2.4.3-1.ca2404.1_all.deb Size: 1092384 MD5sum: 3653540b753810f690df0916ee3f2501 SHA1: 76269448ad44c198374b4f4bf1d3f82c4e2d55dd SHA256: 46b0ec5c91970dcf30bfbdfece2118fc6dff26e5831ba870df8b8b6f2badcf01 SHA512: 2d7e7dab63baa74a3d35b056201d242d2f9cb20bac68e09b91f1a6b1e77ce836a7eeb7d433da8ba4a432402c68a7126f22c3d6faf1e0fd42c33e10daf845e81a Homepage: https://cran.r-project.org/package=BTYD Description: CRAN Package 'BTYD' (Implementing BTYD Models with the Log Sum Exp Patch) Functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 ), BG/NBD (Fader, Hardie, and Lee 2005 ) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 ) models. Package: r-cran-bubbleheatmap Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bubbleheatmap_0.1.1-1.ca2404.1_all.deb Size: 263188 MD5sum: fb47c114ddea27580c5cfb28af79f3c8 SHA1: 78c0bf1e3f3ddd7257bbaa2e8484ae295a1fdd1f SHA256: 0c9bd593d361eaf9297d81d7b5fcc95860714783c8a23539b7f8de0f372d7e97 SHA512: 4769a076c17f5cf7e595683fcebf77378aacb8914e18d3b48c79fd237e15e2dcd99420c09f57a331d1f987317c19f430e08fafee189a792873eb07770d5e0704 Homepage: https://cran.r-project.org/package=bubbleHeatmap Description: CRAN Package 'bubbleHeatmap' (Produces 'bubbleHeatmap' Plots for Visualising Metabolomics Data) Plotting package based on the grid system, combining elements of a bubble plot and heatmap to conveniently display two numerical variables, (represented by color and size) grouped by categorical variables on the x and y axes. This is a useful alternative to a forest plot when the data can be grouped in two dimensions, such as predictors x outcomes. It has particular advantages for visualising the metabolic measures produced by the 'Nightingale Health' metabolomics platform, and templates are included for automatically generating figures from these datasets. 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Package: r-cran-bucky Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest Suggests: r-cran-mass, r-cran-amelia, r-cran-mice Filename: pool/dists/noble/main/r-cran-bucky_1.0.7-1.ca2404.1_all.deb Size: 77284 MD5sum: 783db7f57caf6f8d5472a22ea4f4ea4d SHA1: 815f7a2b80961b636467cce1be9bda3eed31b69a SHA256: 1936fa8fe607370a357ea17337c90542f5a87ef5a5f4a01df0ab8f1d46faeb23 SHA512: f613d5669adbf2cdc9da7819902b1e7f27372f350ece143f40dd9c75bf40937165be57bba6f85ba8873f159584d7b7c95448373302d4596b6cf1c492d2eecb7f Homepage: https://cran.r-project.org/package=bucky Description: CRAN Package 'bucky' (Bucky's Archive for Data Analysis in the Social Sciences) Provides functions for various statistical techniques commonly used in the social sciences, including functions to compute clustered robust standard errors, combine results across multiply-imputed data sets, and simplify the addition of robust and clustered robust standard errors. Package: r-cran-bucss Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bucss_1.2.1-1.ca2404.1_all.deb Size: 139972 MD5sum: dcd7eb0e7f193babf334b25593ee132b SHA1: 16d6ec31352ff044ba7d5d87c8615d5203748f0e SHA256: 870a5c0c8fa97492a47cead178441cae755b58d0a23da66704167ddf556de40b SHA512: 728dd8de030f4ccb5f0fa72ec71170b5c502027bb6d7fb731ad3769097d4f37fcc2b992e5c3d3b191c5a298b57570d2a69f3d709b358b8fb6c18abcc278abc13 Homepage: https://cran.r-project.org/package=BUCSS Description: CRAN Package 'BUCSS' (Bias and Uncertainty Corrected Sample Size) Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562). Package: r-cran-budgetivr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-arrangements, r-cran-mass, r-cran-rglpk Filename: pool/dists/noble/main/r-cran-budgetivr_0.1.2-1.ca2404.1_all.deb Size: 116638 MD5sum: ba43daf45f43e52869235c221964a74a SHA1: cfe1ff0aee9cc825597cb89b6a60fe645dacbf67 SHA256: 43f6b94bf01d29f75110b8e4154412683c59feed86086f397b75b60b5ba50391 SHA512: f27de74d110d20c9f16cc322d2cf00722f64f00c45763494c65ad23385500cf144de5df4c7dd8ec37e45514109dd58f9067cb6118c1453735191b483bd2b9497 Homepage: https://cran.r-project.org/package=budgetIVr Description: CRAN Package 'budgetIVr' (Partial Identification of Causal Effects with Mostly InvalidInstruments) A tuneable and interpretable method for relaxing the instrumental variables (IV) assumptions to infer treatment effects in the presence of unobserved confounding. For a treatment-associated covariate to be a valid IV, it must be (a) unconfounded with the outcome and (b) have a causal effect on the outcome that is exclusively mediated by the exposure. There is no general test of the validity of these IV assumptions for any particular pre-treatment covariate. However, if different pre-treatment covariates give differing causal effect estimates when treated as IVs, then we know at least some of the covariates violate these assumptions. 'budgetIVr' exploits this fact by taking as input a minimum budget of pre-treatment covariates assumed to be valid IVs and idenfiying the set of causal effects that are consistent with the user's data and budget assumption. The following generalizations of this principle can be used in this package: (1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; (2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis; (3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost). The methods in this package require only summary statistics. Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature. For further methodological details, please refer to Penn et al. (2024) . 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For each site and each class it returns the exact surface area inside the buffer and a distance-decay weighted "effective" area in which the kernel is integrated over polygon geometry rather than evaluated at the polygon centroid, avoiding the large bias the centroid approximation introduces for elongated features passing close to the site. Polygons may overlap, so class areas are not constrained to sum to the buffer area. Intended for buffer-based exposure assessment and fine-scale spatial epidemiology, where the relevant scale is tens of metres and global land-cover products are too coarse: land-use regression around air-quality monitors, green space around residential addresses, vector-surveillance traps, and comparable designs. The classification dictionary is user-supplied, and point features and distances to off-buffer reference features are recorded alongside the areas. 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Package: r-cran-bujar Architecture: all Version: 0.2-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2900 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mda, r-cran-mpath, r-cran-mboost, r-cran-gbm, r-cran-earth, r-cran-elasticnet, r-cran-rms, r-cran-modeltools, r-cran-bst, r-cran-survival Suggests: r-cran-th.data, r-cran-r.rsp, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-bujar_0.2-11-1.ca2404.1_all.deb Size: 2809692 MD5sum: bbf4fd454b49325e975ea5d96740124e SHA1: 216424ca21e173641f9aec3463d45e87410a7acd SHA256: f5a3f8fedecb3fa1df089af77bdb29550e92b01dcbf99d5207ae9ae266307c4f SHA512: 3a630ceeb51a369c64029d12c4af754af6a7c3b98250257b5390a7abfec19998412ef9db25b53ef899bf4bd41f45c8638e1a81997eb76cf5b8ddc7c01b738838 Homepage: https://cran.r-project.org/package=bujar Description: CRAN Package 'bujar' (Buckley-James Regression for Survival Data with High-DimensionalCovariates) Buckley-James regression for right-censoring survival data with high-dimensional covariates. 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We provide a reading routine for x3p files (see for more information) and a host of analysis functions designed to assess the probability that two bullets were fired from the same gun barrel. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Package: r-cran-bullwhipgame Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Filename: pool/dists/noble/main/r-cran-bullwhipgame_0.1.0-1.ca2404.1_all.deb Size: 74320 MD5sum: c256baca8ae843436f4ef18e671efe20 SHA1: bfae377896ce857e0e12917a515eed0517c183dd SHA256: 802a54b7baa952e5a20134157753dc76e2e376775d824212898ca3de50b3b668 SHA512: 34089155e3bc61407edeb34e860058c3df1ebfc41135b6ae6d747a790984dd9e429409ee08917fecbd1e223260be061ded372f476d669d48692f627a6d9ccb51 Homepage: https://cran.r-project.org/package=bullwhipgame Description: CRAN Package 'bullwhipgame' (Bullwhip Effect Demo in Shiny) The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) . Package: r-cran-bumbl Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-lifecycle, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-car, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rsq, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bumbl_1.0.4-1.ca2404.1_all.deb Size: 254978 MD5sum: 3aef717dd750ac7be2951c963708f99b SHA1: 083fc8ded8a9bc53042e08057908db45580c35bd SHA256: f344d9dac774d100156d1c1be33d11c53364ce78ed0888c81036ef245c36e9bd SHA512: 479c318fcc913d49c7a983230c43d71f45c9c1d6a9a462ccf0ebe441514036c2708b692ffae82de73b01771c7f1dd9d3898a03bcdd43f707eca6e87972f5c77d Homepage: https://cran.r-project.org/package=bumbl Description: CRAN Package 'bumbl' (Tools for Modeling Bumblebee Colony Growth and Decline) Bumblebee colonies grow during worker production, then decline after switching to production of reproductive individuals (drones and gynes). This package provides tools for modeling and visualizing this pattern by identifying a switchpoint with a growth rate before and a decline rate after the switchpoint. The mathematical models fit by bumbl are described in Crone and Williams (2016) . Package: r-cran-bumblebee Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gtools, r-cran-hmisc, r-cran-magrittr, r-cran-rmarkdown Suggests: r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bumblebee_0.1.0-1.ca2404.1_all.deb Size: 268074 MD5sum: db30c4ddbfa8cc93887f980035f6886c SHA1: 299d8508c19f3bc732acc5e3a867978dc6768b43 SHA256: 32591ab9f5ac25a693487eb4355c2a20ed2ce411a8ed5393dd3b4f4f9383af37 SHA512: 4222b50ecb8088b0ba2f152f54edf55dd9edd2e345d47890dc0d26911403e0bfcb78ff89626385a9cf6474d0ddee496f69e074a4bf453cdd3a2b5bdb304a10a0 Homepage: https://cran.r-project.org/package=bumblebee Description: CRAN Package 'bumblebee' (Quantify Disease Transmission Within and Between PopulationGroups) A simple tool to quantify the amount of transmission of an infectious disease of interest occurring within and between population groups. 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Package: r-cran-bunching Architecture: all Version: 0.8.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bunching_0.8.6-1.ca2404.1_all.deb Size: 519300 MD5sum: 9949a75f59dab20510afefb4cd30dcda SHA1: 0d88e1de84475ee0bce1e1f21aa1d9a45f64dcaf SHA256: a09876698ab507b295634bd636c82a248af59b6b165dca782b5ad34d8ccb145a SHA512: 1a863585a61a626f94bd2c539557e70b6f2141721e4c79ad511625bec2451f2a1f5bc50cff7540441312493835a426d6a10073200d44c556bd1eea68c1391a96 Homepage: https://cran.r-project.org/package=bunching Description: CRAN Package 'bunching' (Estimate Bunching) Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) , with lots of functionality to set plot options. Package: r-cran-bunchr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-bunchr_1.2.1-1.ca2404.1_all.deb Size: 392882 MD5sum: 5985f2c2df5862e18bd2d515ea6a71a8 SHA1: ba529fc7ac2dc290c61631af2ead5dec09c165d1 SHA256: 8c91fbbe0bd31e5d632e07948326d4811b5b6b1746d048079c5a6d70e9602e5a SHA512: 9eed95945ff2dfaf443eef42680853b6b16eaec86bdf4e829ac71304f6d35309497f4ba71f040bb8e374941f30d039461dc9085efeedf18bd204b67a7c3aebe4 Homepage: https://cran.r-project.org/package=bunchr Description: CRAN Package 'bunchr' (Analyze Bunching in a Kink or Notch Setting) View and analyze data where bunching is expected. Estimate counter- factual distributions. For earnings data, estimate the compensated elasticity of earnings w.r.t. the net-of-tax rate. 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Adapter helpers return tidy data frames for supported APIs, with optional response caching and rate limiting guidance. Package: r-cran-bundesbank Architecture: all Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-zoo Filename: pool/dists/noble/main/r-cran-bundesbank_0.1-12-1.ca2404.1_all.deb Size: 23518 MD5sum: c3bae5bc4e858a97dd6cd1f1d834139c SHA1: e09d038527179d364adc30c42354505d35b171a5 SHA256: a00ff0caa6142211d00bb33e15c33729afb43aa196b4225ac61a7d487ff808b6 SHA512: 388d182847ac9dc007c9a1800e29dbf3e1abc484a3c9b2549d84f4f4c03fe73c2229e0923c78675545361ec169a93fd36b4d96670292965c357e713ba24f5339 Homepage: https://cran.r-project.org/package=bundesbank Description: CRAN Package 'bundesbank' (Download Data from Bundesbank) Download data from the time-series databases of the Bundesbank, the German central bank. 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Package: r-cran-bundesligr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bundesligr_0.1.0-1.ca2404.1_all.deb Size: 21436 MD5sum: ebbe85acde67dff85e3131f89261af9e SHA1: c6a9308a37c385a866dbcb9e7ca721c2614c97da SHA256: 9bc8600041e989521b99c2c110b9d74c17e6bb70ae56466a3365c54c537ac806 SHA512: ddd260e081b309190ecd7fecd458e59e8ac928d2ef8bcb031d83d27a926c2fdf6b0f6f9b1d0a5f068b74a822cdee0869bd3ea5b4d9be4b68a4b837c893a397e9 Homepage: https://cran.r-project.org/package=bundesligR Description: CRAN Package 'bundesligR' (All Final Tables of the Bundesliga) All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016. 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Package: r-cran-buoyant Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-analogsea, r-cran-jsonlite, r-cran-renv, r-cran-ssh, r-cran-withr, r-cran-yaml Suggests: r-cran-knitr, r-cran-quarto, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-buoyant_0.1.0-1.ca2404.1_all.deb Size: 81336 MD5sum: c06443256ac23d6ee3fb23255fa5d6d4 SHA1: 980372170daf98e0547be6ae7c769701c2647229 SHA256: ceb493ef6f274a4101ad00d34443eb303686d32d33a12aceff12498c92e8c82b SHA512: 5909edc75ace484a790195c478ae87107d142e8f6ee86cf67e5576482d0dcb90a01449f4f319e1d374f9c3a873ca49dc48d0a33072f10033a66d1284b0dbe425 Homepage: https://cran.r-project.org/package=buoyant Description: CRAN Package 'buoyant' (Deploy '_server.yml' Compliant Applications to 'DigitalOcean') Provides tools to deploy R web server applications that follow the '_server.yml' standard. 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Package: r-cran-bupar Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-data.table, r-cran-shiny, r-cran-miniui, r-cran-pillar, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-glue, r-cran-forcats, r-cran-rlang, r-cran-cli, r-cran-eventdatar, r-cran-stringr, r-cran-stringi, r-cran-lubridate, r-cran-lifecycle, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-lintr, r-cran-edear, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bupar_1.0.1-1.ca2404.1_all.deb Size: 366518 MD5sum: f83a462f78c75234f87bd3f99d948dc5 SHA1: d93679c928fe1db3b149ecc2670920c431ed7242 SHA256: 0360c47ef9e3af581ad860f599bda52d8bc66460bbf93fdb541115dbd244f441 SHA512: e09e2697fcda17615fb058bde034e045760a459465f300acda5fec5817c7acfc51a4721d9b7333447d72322ee3dba504f3e8ef43027e35748f09d0412eaf779e Homepage: https://cran.r-project.org/package=bupaR Description: CRAN Package 'bupaR' (Business Process Analysis in R) Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of 'Petri Net' objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'. 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Package: r-cran-burakdiagrams Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-magrittr, r-cran-plotly, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-burakdiagrams_0.1.0-1.ca2404.1_all.deb Size: 73746 MD5sum: 0ccdf830145003fe768be76c9c9be51a SHA1: fcd8b7463a506f009a4cbeec576709718f3c6c1a SHA256: 786dabd6c2737dbce249fd0e09857df5edc1e99b7ccd9f00b1b46584ae69a7c8 SHA512: 40f4b568e216c81ca2305cc61a2ee550f2991035d132794f3271c79360281c7cb9ec225fc8e65822d8e59bbee24b786fde3b051c6c4fe93874cccc90adc46d8b Homepage: https://cran.r-project.org/package=burakDiagrams Description: CRAN Package 'burakDiagrams' (Interactive Burak Diagrams for Model Performance Evaluation) Creates interactive 3D Burak Diagrams for evaluating climatological and hydrological simulations. The BD-Clim framework evaluates climatological simulations by representing correlation, standard deviation, centered root mean-square difference, bias, and root mean-square difference. The BD-HydNSE framework evaluates hydrological simulations using correlation, standard deviation, centered root mean-square difference, percent bias, and Nash-Sutcliffe efficiency. The BD-HydKGE framework evaluates hydrological simulations using correlation, standard deviation, centered root mean-square difference, percent bias, and Kling-Gupta efficiency. The frameworks extend the Taylor Diagram ( Taylor (2001) ) by introducing an orthogonal axis for bias and representing selected performance metrics as surfaces in the resulting 3D space. Nash-Sutcliffe efficiency was introduced by Nash and Sutcliffe (1970) , and Kling-Gupta efficiency was proposed by Gupta et al. (2009) . The diagrams are interactive and can optionally be saved as HTML files. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Package: r-cran-cabcanalysis Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-plotrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cabcanalysis_1.0.2-1.ca2404.1_all.deb Size: 84292 MD5sum: bf1abe1c5c435c59afe753a95af4452f SHA1: c46d77c85e25d527354b65ceb19796b2362197ab SHA256: 90b5a688f47fce00ecb66e985f793ea98e894cae1e088e946d03c15410b30c35 SHA512: 13f812817830f525e31ec9b48d65369a36cc88bf03425a9853049bfcf40d46ba6994166a327f520a7f555f55e1cb1b278518143c72ff7e5d584f9620a7f4504d Homepage: https://cran.r-project.org/package=cABCanalysis Description: CRAN Package 'cABCanalysis' (Computed ABC Analysis) Identify the most relative data points by dividing a numeric data set into three classes A, B, and C, where class A items are the "import few", class C items are the "trivial many" with class B items being something in between, resembling the idea of the Pareto principle. This ABC classification is done using an ABC curve, which plots cumulative "Yield" against "Effort", similar to a Lorenz curve. Class borders are then precisely mathematically defined on that curve, aiding in interpretation. Based on: Ultsch A, Lotsch J (2015) "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data". PLoS ONE 10(6): e0129767. . Package: r-cran-cabinets Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-glue, r-cran-rstudioapi, r-cran-crayon, r-cran-fs, r-cran-stringr, r-cran-git2r, r-cran-cli, r-cran-renv Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cabinets_0.6.0-1.ca2404.1_all.deb Size: 84190 MD5sum: ae8d63f3cd68b0aed9f1ee46bde44535 SHA1: 2ac7574bfcc199cc9ab2972983129e8a24a32368 SHA256: 79a6abc5c38572909ba919754acae30ed6f3af3500e3833af11b70b915c037db SHA512: e45b21b35f55b6c00790b51f6cb79ddc1abf94bdf690c744259bbb42b0a88a3dd7ca946658707a29765bc8a553906a5cf8354c5001343ad17a3fc1d5b6945c00 Homepage: https://cran.r-project.org/package=cabinets Description: CRAN Package 'cabinets' (Project Specific Workspace Organization Templates) Creates project specific directory and file templates that are written to a .Rprofile file. 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Package: r-cran-cabiplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factominer, r-cran-factoextra, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cabiplot_0.1.0-1.ca2404.1_all.deb Size: 48528 MD5sum: 4c8f4ee37212cf9cdbff1bab9cc1d667 SHA1: 7c65df8c9ec191dd6a3124ae53e78076bd78b1d1 SHA256: e410339d999f920de0df69a44b383e39c2f1df764b774a2dff80841a3c1e760d SHA512: 2fe38b6860f99bac9da31b13466ccb055402d6ecf8d8f16a8e2b4ecadd41fbffbd5517e679c42611d525728e8ce5fb408577e07a19f20ae1d97aa7badb0ecb34 Homepage: https://cran.r-project.org/package=CAbiplot Description: CRAN Package 'CAbiplot' (Correspondence Analysis Biplots and Diagnostic Reports) A convenience wrapper around 'FactoMineR' and 'factoextra' for running Correspondence Analysis (CA) on a numeric data table (e.g. a genotype-by-trait or contingency-style matrix) and producing a full set of publication-ready diagnostic plots: scree plot, symmetric biplot, row-only and column-only plots, row/column contribution plots, and row/column cos2 (quality-of-representation) plots. A single top-level function runs the whole pipeline, prints formatted result tables, and optionally saves every plot as a high-resolution image, mirroring a typical CA reporting workflow used in agronomy and plant-breeding studies. An example genotype-by-trait data set is included. Package: r-cran-cabootcrs Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-colorspace Suggests: r-cran-ellipse Filename: pool/dists/noble/main/r-cran-cabootcrs_2.1.0-1.ca2404.1_all.deb Size: 281454 MD5sum: c412baea97b8569496bd094db5a4a0b9 SHA1: c267b8b094f30ad5dfb752d1a7819ff8aeeb3801 SHA256: af692b2bdc6e3927136edd4b38261f9bdcc6669fa05ad193c33f91426cc0bbab SHA512: c92880335aef827cad3778bc6f345cae22d4ef14a12be7ca5349c863c583f1d1849a87ef93d4fc641277ae6a2a7b99837bc0b857ff5d187a4e45086d0bbfdd95 Homepage: https://cran.r-project.org/package=cabootcrs Description: CRAN Package 'cabootcrs' (Bootstrap Confidence Regions for Simple and MultipleCorrespondence Analysis) Performs simple correspondence analysis on a two-way contingency table, or multiple correspondence analysis (homogeneity analysis) on data with p categorical variables, and produces bootstrap-based elliptical confidence regions around the projected coordinates for the category points. Includes routines to plot the results in a variety of styles. Also reports the standard numerical output for correspondence analysis. Package: r-cran-cacc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-cacc_0.1.1-1.ca2404.1_all.deb Size: 60470 MD5sum: 0e704278b1c99031c43420e1f8e63ada SHA1: dfb3ce53d57c0fff0b61103eeca6b7d7001d9fc3 SHA256: 83cc7cef1bb5cceecf290b9e32681aca58c0e55f8144dbbad58809c0f8fc1133 SHA512: 7dcd05648d8d18f85bc8834dc5f67a6f26fcce34ce3432ee9263a2b60f1e64d2919f14d72acde749452f8bc27fb60476f3679389a8254426357b79ee5aefe417 Homepage: https://cran.r-project.org/package=cacc Description: CRAN Package 'cacc' (Conjunctive Analysis of Case Configurations) A set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) , and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) . Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods. 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Zero counts are treated as censored observations below sample-specific detection limits, avoiding the use of pseudocounts. The package implements estimation and hypothesis testing procedures for assessing associations between microbial taxa and covariates while accounting for the compositional structure of sequencing count data. It supports taxon-level differential abundance analysis, estimation of regression effects under censoring induced by detection limits, and inference based on rank-based methods that remain applicable in the presence of excess zeros. Functions are provided for model fitting, significance testing, extraction of effect estimates, and summarization of results across taxa. The package also provides optional bootstrap calibration of taxon-level p-values for sensitivity analysis in small-taxon settings. 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Lists the reference months available on the server, downloads the three monthly files (movements declared on time, declared late, and exclusions) with an idempotent local cache, and reads the national 7z archives as a stream, filtering by state and selecting columns before anything is kept in memory, so that a single state can be extracted without loading the full national file. Also provides the official record layout and a helper to consolidate admissions, separations and net balance by reference month. 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Package: r-cran-cairovolt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cairovolt_1.0.0-1.ca2404.1_all.deb Size: 16094 MD5sum: 8f6e351a81c5fcaf69c26f06bcb6966a SHA1: 3e560a5c723a11e028effaac0e8da21e0c89d9ff SHA256: 426f093719362f9f4bbb098477b4b0f5504fdf9dcd9ee751c64cae0f041271d5 SHA512: 98101ddcd32f9aad6019e93dde8994833b01ff1e07caac660091739ae8e36f1dd6956523918e52768d93dc27c1c84955aa1c55a55a00dfa85a6aa249aca63a2d Homepage: https://cran.r-project.org/package=cairovolt Description: CRAN Package 'cairovolt' (E-Commerce Charging & Audio Equipment Analysis Utilities) Standard metrics converter and comparator for consumer electronics. Provides utility functions for converting battery capacity (mAh to Wh), comparing wall charger output times, and validating product specifications using standard formulas. Includes a sample dataset of electronic accessories compiled from CairoVolt's catalog. 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See the project website for more information, documentation and examples, and for the full paper. 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Package: r-cran-caliberrfimpute Architecture: all Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-mvtnorm, r-cran-randomforest Suggests: r-cran-missforest, r-cran-rpart, r-cran-survival, r-cran-xtable, r-cran-ranger Filename: pool/dists/noble/main/r-cran-caliberrfimpute_1.0-8-1.ca2404.1_all.deb Size: 510008 MD5sum: 7eacf7ca8b7d77b6a8462bf1cd920952 SHA1: b8dc00a3cbe421b7fde6b4bfb2babc02d6b00cea SHA256: 166706e63215f7958e1247693acf6ac28bfe557d01bdca8b152f82973e4b5a08 SHA512: 6d25df759aa7e96b286dc866df9b6e9f30776a3a641b12033614a7725f919bf055bef991410e04c8eadb63fcc645f9783372d14f9bb705dc2d41148483a123b0 Homepage: https://cran.r-project.org/package=CALIBERrfimpute Description: CRAN Package 'CALIBERrfimpute' (Multiple Imputation Using MICE and Random Forest) Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) . Package: r-cran-calibmsm Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggextra, r-cran-gridextra, r-cran-hmisc, r-cran-mstate, r-cran-rms, r-cran-survival, r-cran-tidyr, r-cran-vgam Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-survminer, r-cran-flexsurv Filename: pool/dists/noble/main/r-cran-calibmsm_1.1.3-1.ca2404.1_all.deb Size: 1941868 MD5sum: 865980b7a22fe59e95f92286c34ba6f7 SHA1: c004d2d2beb831294bfcb260884330fdda48dfa4 SHA256: 88e7ccfad8fb100a7b2d454616fd01bb9430c034224c76890fd67812342ad4e4 SHA512: 1c58ff639439091b8deb18c4f2e4acfbc198e5886e9ce5f322aa1cffe0c83b5e30953ad302cab4b34878fea19c49eca95fc10a6932d4ba78f76c923d9ad14fa7 Homepage: https://cran.r-project.org/package=calibmsm Description: CRAN Package 'calibmsm' (Calibration Plots for the Transition Probabilities fromMultistate Models) Assess the calibration of an existing (i.e. previously developed) multistate model through calibration plots. Calibration is assessed using one of three methods. 1) Calibration methods for binary logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 2) Calibration methods for multinomial logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 3) Pseudo-values estimated using the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction with landmarking when required. These calibration plots evaluate the calibration (in a validation cohort of interest) of the transition probabilities estimated from an existing multistate model. While package development has focused on multistate models, calibration plots can be produced for any model which utilises information post baseline to update predictions (e.g. dynamic models); competing risks models; or standard single outcome survival models, where predictions can be made at any landmark time. Please see Pate et al. (2024) and Pate et al. (2024) . Package: r-cran-calibrar Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-cmaes, r-cran-deoptim, r-cran-dfoptim, r-cran-gensa, r-cran-minqa, r-cran-optimx, r-cran-foreach, r-cran-lbfgsb3c, r-cran-pso, r-cran-rgenoud, r-cran-soma, r-cran-stringr Suggests: r-cran-desolve, r-cran-ibm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-calibrar_0.9.0-1.ca2404.1_all.deb Size: 561452 MD5sum: b248c65b9010b5a76d5b5d8e5a775f4c SHA1: d104a8f7580149668680f7b4a76eaa376bbb86ac SHA256: b033b7e854e462c1b38162ce40d0d7f4903ee2c4f2e601bdfb770d98846e14a6 SHA512: c0b2f6c8fdb0405ee18a32a10d1cb34a0e79c1bc33b5c098df93c064aefa6ba40c77be8817ee5cb9c71fe793dbd1d326b8484a2786510cd1376c9246357ddbb4 Homepage: https://cran.r-project.org/package=calibrar Description: CRAN Package 'calibrar' (Automated Parameter Estimation for Complex Models) General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See for more details. Package: r-cran-calibrate Architecture: all Version: 1.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-calibrate_1.7.7-1.ca2404.1_all.deb Size: 350926 MD5sum: 2ee0ac65303abeb652aec98e40f54cb7 SHA1: 1c2f79bb098964c31123c6066b791396c3b9334c SHA256: cae8cb0030e37f3ec265a41c07bcfdfac10f301bb721eb74bef43583242e2cb2 SHA512: ebd9de67c393905b9b3d51c81c6716597cc26a17dd385c519bdf4dbe1b65d96508c63c229e05cb3375c1dddf51cd82d2c691308a7ca3a861e767c7efc97b7912 Homepage: https://cran.r-project.org/package=calibrate Description: CRAN Package 'calibrate' (Calibration of Scatterplot and Biplot Axes) Package for drawing calibrated scales with tick marks on (non-orthogonal) variable vectors in scatterplots and biplots. Also provides some functions for biplot creation and for multivariate analysis such as principal coordinate analysis. Package: r-cran-calibratebinary Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gpfit, r-cran-gelnet, r-cran-kernlab, r-cran-randtoolbox Filename: pool/dists/noble/main/r-cran-calibratebinary_0.1-1.ca2404.1_all.deb Size: 37420 MD5sum: 455970a254e764035b18c9cd42ef0658 SHA1: 50ae2c5bf93b8e37f3c316f87fffd251bdcaa918 SHA256: 3d7bdade67b86983a8f22a78aba6c6bc97c14dc200e8f40ad38e50b7139c6438 SHA512: a2dd1822289099e45d9be32075af17e36910b9b3d7ce9e6b0e28c3593e33ae656afba60cbabeec72acccf2f30de4cb9cc423f0d0338ab5716d1643526b7db75e Homepage: https://cran.r-project.org/package=calibrateBinary Description: CRAN Package 'calibrateBinary' (Calibration for Computer Experiments with Binary Responses) Performs the calibration procedure proposed by Sung et al. (2018+) . This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest. Package: r-cran-calibratessb Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-calibratessb_1.4.0-1.ca2404.1_all.deb Size: 190554 MD5sum: 6400d80e8143042e6d226f0ff18ccbfd SHA1: 5c92655cdf22674fe1a45ca4e0b676155cc7f8e6 SHA256: 9540bb88b7a9823e81f1df845d23d9610f670ab190b4295f7e53d2fb9f5cb7ed SHA512: 7da9d5cc1f1cd1b5407a84b01ab696cb6328d9f9be3f8a989ea4dff48865a2517e316ba0ff4ba426a85150bf8b0023766df686fb0b50bc5ae15842b54bd209da Homepage: https://cran.r-project.org/package=CalibrateSSB Description: CRAN Package 'CalibrateSSB' (Weighting and Estimation for Panel Data with Non-Response) Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the 'survey' package is used for this purpose. It is also possible to use 'ReGenesees', which can be installed from . Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) . Package: r-cran-calibrationcurves Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-ggplot2, r-cran-survival, r-cran-hmisc, r-cran-pec, r-cran-riskregression, r-cran-meta, r-cran-metafor, r-cran-zoo, r-cran-lme4, r-cran-mertools, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-rstudioapi, r-cran-mgcv, r-cran-mass, r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-calibrationcurves_3.1.0-1.ca2404.1_all.deb Size: 2273264 MD5sum: 4a38d2c1845dec3c355b3d2fa0cb44c1 SHA1: d442420ca1da71d904a32205bffb6087a02d6d5c SHA256: d0f1ac85250e1c0502b5619f853fc7afcde7f62fd5a9252a7f6d28951a1978f2 SHA512: 70903776e8351fcbdbeea52a65cce00b647a833ab30a6b776ff6b8a849f5d2d58b78362e1a9e63336e19a52a21fb70d8ae280af0860bdce9c0b8a323073e6e2f Homepage: https://cran.r-project.org/package=CalibrationCurves Description: CRAN Package 'CalibrationCurves' (Calibration Performance) Plots calibration curves and computes statistics for assessing calibration performance. See Lasai et al. (2025) , De Cock Campo (2023) and Van Calster et al. (2016) . Package: r-cran-calibrator Architecture: all Version: 1.2-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-emulator, r-cran-mvtnorm, r-cran-cubature Filename: pool/dists/noble/main/r-cran-calibrator_1.2-9-1.ca2404.1_all.deb Size: 416320 MD5sum: 85fb815779de1bd8df9d307fbbff31d8 SHA1: d08b7861ef75c678fe015f02319709df0051a85f SHA256: 755eaf5b0622d9aea9afb1c485351eb6169b53f8b34447635adfd8abe6aafa91 SHA512: e3dbafb7f2440e2189d22cbe1c2205f5a4f4d9353912701985ee3fd0e90f49ab47b5e137168b0e03f5d611f0932b062b2e727db014a4dc9fb7ad039b6221c7a2 Homepage: https://cran.r-project.org/package=calibrator Description: CRAN Package 'calibrator' (Bayesian Calibration of Complex Computer Codes) Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/. Package: r-cran-calibratr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-reshape2, r-cran-foreach, r-cran-fitdistrplus, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-calibratr_0.1.2-1.ca2404.1_all.deb Size: 248080 MD5sum: 71af89373a62db0b1f0f4b8a1651e342 SHA1: 43ca86cd5b86d58662e7fa992979eb5166f37227 SHA256: 1639b6ddbde46618c9078cdf7d31ee4e89c94fd65506d9dbba0aec1ab70d4d01 SHA512: a0f959cabd19cca37d841c453db5e20a0f3a78f53a44ead3f140b5a7f896689f131828f08bd8bfc4ea8b4d9d7f443083a2884f930b17e829236175d75aa8bdbb Homepage: https://cran.r-project.org/package=CalibratR Description: CRAN Package 'CalibratR' (Mapping ML Scores to Calibrated Predictions) Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465. Package: r-cran-calidad Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4603 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-purrr, r-cran-survey, r-cran-kableextra, r-cran-stringr, r-cran-haven Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-srvyr Filename: pool/dists/noble/main/r-cran-calidad_0.8.2-1.ca2404.1_all.deb Size: 4537322 MD5sum: 99f43a2b5497f965cd0088bf5c8a7a51 SHA1: 50b530629bd7aed7d427a726f593da5de8c718a5 SHA256: 9dc8c71cfb2a3431ea9b60fb766380c3206ec9f296d78dd8672a452f005f770b SHA512: ebaab3931557b12ac0406d230eb5a8ac452eeebf69a448b0a0044a846a47697e7ad3d726e81e19020a16b8e0dc7aa25220a3861cd52bb3a1eaba4ef27357bc4d Homepage: https://cran.r-project.org/package=calidad Description: CRAN Package 'calidad' (Assesses the Quality of Estimates Made by Complex Sample Designs) Assesses the quality of estimates made by complex sample designs, following the methodology developed by the National Institute of Statistics Chile (Household Survey Standard 2020, ), (Economics Survey Standard 2024, ) and by Economic Commission for Latin America and Caribbean (2020, ), (2024, ). Package: r-cran-callback Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-callback_0.1.3-1.ca2404.1_all.deb Size: 323156 MD5sum: 62dfbd600a35bea4caee6dcded429e44 SHA1: 51fdf9466eaea32a0b7710267ff381545b65b8d7 SHA256: 5fc22ba4643d35ad811d68376074c1609b922f21fe657bd050821db9a438548b SHA512: 2b007246f6b8c868697b044d5483f694b0e03b4865cb10672eff7bc39b91851bdd7232054c67e521c9cc65d677509c74d3a73bd13b0a3dcf1706ada3ac2a3170 Homepage: https://cran.r-project.org/package=callback Description: CRAN Package 'callback' (Computes Statistics from Discrimination Experimental Data) In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments. Package: r-cran-callme Architecture: all Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-callme_0.1.11-1.ca2404.1_all.deb Size: 48090 MD5sum: c92848ac2ba4b2d98bd11a521bd645f6 SHA1: 0fa4a0a400f27093846abef18576e8a2a8b956a4 SHA256: 4ad2d033bd2eefbf6817671d2d1bcd91f97c71067ec51f98fbb94fb08837d5f1 SHA512: 1691a7abe9772466e5d1a4c238da2d0f03a72ce96d542d1e797217ed9187d328452cf5b1cade72677e0a302859765f8f7c189ceaa91e9f3cd3b4781732fa6e12 Homepage: https://cran.r-project.org/package=callme Description: CRAN Package 'callme' (Easily Compile and Call Inline 'C' Functions) Compile inline 'C' code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file. 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This packages does exactly that. Package: r-cran-callsync Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-oce, r-cran-seewave, r-cran-signal, r-cran-stringr, r-cran-tuner, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-callsync_0.2.3-1.ca2404.1_all.deb Size: 164966 MD5sum: 396e5e9e6d9c0494a5f1402d242c0d74 SHA1: a8c1ca4c25f3ccbec8570f50bde967e8cbf5f818 SHA256: 12ab5ee41dca0f85203df26ba3be81f5b51c9414e0bbac9d03620054512c1f51 SHA512: 38af0080ca7f28c63a2a17e190f173a2f93b6888ad4d76e62c25900b4563f187d283ccfa937f17a208df29d0e506d3d8ed45c60cea8a37e57977eaa767671795 Homepage: https://cran.r-project.org/package=callsync Description: CRAN Package 'callsync' (Recording Synchronisation, Call Detection and Assignment, AudioAnalysis) Intended to analyse recordings from multiple microphones (e.g., backpack microphones in captive setting). It allows users to align recordings even if there is non-linear drift of several minutes between them. A call detection and assignment pipeline can be used to find vocalisations and assign them to the vocalising individuals (even if the vocalisation is picked up on multiple microphones). The tracing and measurement functions allow for detailed analysis of the vocalisations and filtering of noise. Finally, the package includes a function to run spectrographic cross correlation, which can be used to compare vocalisations. It also includes multiple other functions related to analysis of vocal behaviour. Package: r-cran-calmate Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-aroma.core, r-cran-mass, r-cran-r.methodss3, r-cran-r.oo, r-cran-matrixstats, r-cran-r.filesets Suggests: r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-calmate_0.13.0-1.ca2404.1_all.deb Size: 205288 MD5sum: 3ceac2de149ab254d7276ddf6bc65319 SHA1: afb8dbd799cd52925e2de6892eaffe6aef59a72f SHA256: c87af026f70df9ff18f16b92ac9acec27f6d3aa4f9a9b9021c60d5554704f0ed SHA512: e65e04d49a368a255f05629a7edd865b898608783e5de1ef4ae5f1f483f75a375884e9e969f8eabc54075e8f104d9a5d6c7faa70b9dd90333b74b598ae880b8e Homepage: https://cran.r-project.org/package=calmate Description: CRAN Package 'calmate' (Improved Allele-Specific Copy Number of SNP Microarrays forDownstream Segmentation) The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina. Package: r-cran-calmr Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2014 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-future, r-cran-future.apply, r-cran-ga, r-cran-ggnetwork, r-cran-ggplot2, r-cran-lifecycle, r-cran-network, r-cran-patchwork, r-cran-progressr, r-cran-rlang Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-calmr_0.8.1-1.ca2404.1_all.deb Size: 893428 MD5sum: 9a09552b362ecf5eec31a8dc95ff5436 SHA1: 7207631388cc65c80647c99c212401954e5c6377 SHA256: bab3f530b0f3abdb38c387cae8a6bc73a74150d4265d957a78e7dd278a28f2d1 SHA512: e95b0e1f8d0e3aa3d5f6cfa0f38a638320c59359446a886c1a225a6eca062680f01870850369aef914128307ea3ec579c61a728c82b57f6f5d0b17e944d866bb Homepage: https://cran.r-project.org/package=calmr Description: CRAN Package 'calmr' (Canonical Associative Learning Models and their Representations) Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods. Package: r-cran-calms Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-foreign, r-cran-lavaan, r-cran-lsr, r-cran-matchit, r-cran-shiny, r-cran-shinyjs, r-cran-stringr, r-cran-bslib Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgenoud, r-cran-matching Filename: pool/dists/noble/main/r-cran-calms_1.0-3-1.ca2404.1_all.deb Size: 1754192 MD5sum: 8260fb4f2ccfe22b1675089958e56608 SHA1: ddcbdee3dd318edd2927e8bdca32bac0a993d655 SHA256: 3763be26fa498c2600b830a0f5c7b9dcddc8bbc3681c4a8f2a6e05535747e692 SHA512: 7106364e3d60c7094810bdde2aa5ffc285f887870679fd9fef989e61898949f1eb4a05dffbd902f7d0e70ff3cd31b30e685e9c041ef198bb00548057aa861cb8 Homepage: https://cran.r-project.org/package=calms Description: CRAN Package 'calms' (Comprehensive Analysis of Latent Means) Provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, measurement invariance analysis, and assessment of latent mean differences of equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the 'MatchIt' package [Ho et al. (2011) ], ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the 'lavaan' package [Rosseel (2012) ], providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) to support factor ratio testing and the list-and-delete procedure. Package: r-cran-calpassapi Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-digest, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-calpassapi_0.0.3-1.ca2404.1_all.deb Size: 37308 MD5sum: fd5d0d18dae5096b2337c49ffb6a14f5 SHA1: bc1b29781a3c5ef3ae0b79218acaded16616e742 SHA256: 3fe362010175a87aac8ae13b7516b2612db555a322b04b15055ecb34092a59a9 SHA512: 0d0b59f154595eff529478e00376662ef6e9dbfd6d71b8d88aa7c7a6c047d333f18acc3d15a089b39874d9e1230d512b7cf4089b50e9ae2fd36057ad1eb31e07 Homepage: https://cran.r-project.org/package=calpassapi Description: CRAN Package 'calpassapi' (R Interface to Access CalPASS API) Implements methods for querying data from CalPASS using its API. CalPASS Plus. MMAP API V1. . Package: r-cran-camcorder Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gifski, r-cran-magick, r-cran-rsvg, r-cran-jsonlite, r-cran-rlang, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-ragg, r-cran-covr, r-cran-testthat, r-cran-patchwork, r-cran-withr, r-cran-dplyr, r-cran-systemfonts, r-cran-scales, r-cran-extrafont Filename: pool/dists/noble/main/r-cran-camcorder_0.1.0-1.ca2404.1_all.deb Size: 1418976 MD5sum: 06b82c92d613c8adccf7a43aa88191d3 SHA1: 089edfc91851d26dd3a4b76dc0e27c6c6e7a5a95 SHA256: cc6412d92bf631472129bcf23d91e99a032a74c332dcc6a5df42b764ba178443 SHA512: 500e0fb318271aedacfd0f772414aed87cf46649a1439a8683ca7741d6e1afe8c9e007af5a07b97da8ccec7edd00c6227bb21309f05e03d0de8b9329742b1ad6 Homepage: https://cran.r-project.org/package=camcorder Description: CRAN Package 'camcorder' (Record Your Plot History) Record and generate a 'gif' of your 'R' sessions plots. When creating a visualization, there is inevitably iteration and refinement that occurs. Automatically save the plots made to a specified directory, previewing them as they would be saved. Then combine all plots generated into a 'gif' to show the plot refinement over time. Package: r-cran-camea Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-metafor, r-cran-tibble, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-camea_0.1.2-1.ca2404.1_all.deb Size: 88516 MD5sum: 54db1ec0e9f2ce40062a38a86b7dd46b SHA1: ea7a77d8b7c62004d2dc9aa47850979f27b15190 SHA256: 056796b812a9c25d37dda3e76ffb022ce1d89b88199b8bd7f35a305af679ab65 SHA512: 4da4a09cbf376afdafd7c618924c774eda64876468e46e51c84e634667e47c04e2fe2cc35d968c0aa482d1a88dc023737767f7a3fd275259c7dfe4638634f7a2 Homepage: https://cran.r-project.org/package=CaMeA Description: CRAN Package 'CaMeA' (Causal Meta-Analysis for Aggregated Data) A tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) . Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the 'metafor' package . Package: r-cran-camelratiosindex Architecture: all Version: 1.0.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-robustfa, r-cran-rrcov, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-camelratiosindex_1.0.0-1.ca2404.2_all.deb Size: 129328 MD5sum: f89e48f24c91a9f21f5e02ff299dfe01 SHA1: 58f1428464158e7040b016c39870c6090d0467db SHA256: 3e4736ceca7868c68f8ed0461edd41c57df753e90541e89abe96151eb7829bb6 SHA512: bd1c81ac59fb72380d68d6328a999accfdca8576f605f119bbec30e859401188593289d4860434dd057377eb3ec271d13ebed393fc96ec9f9f9cc23eaf09cd90 Homepage: https://cran.r-project.org/package=CamelRatiosIndex Description: CRAN Package 'CamelRatiosIndex' (Multivariate-Weighted Indexing of CAMEL Ratios for BankPerformance) Computes a composite year-on-year index for bank performance assessment using the CAMEL framework (Capital Adequacy, Asset Quality, Management Efficiency, Earnings, Liquidity). The multivariate weighting scheme employs factor analysis with robust covariance estimation to derive communality-based weights from the correlation matrix of CAMEL ratios. Provides functions for index computation, visualization, and comparison across banks and time periods.The methodology is described in Ayimah et al. (2023a) and Ayimah et al. (2023b) . Package: r-cran-camelscl Architecture: all Version: 0.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-zoo, r-cran-hydrotsm, r-cran-terra Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-camelscl_0.1-10-1.ca2404.1_all.deb Size: 266338 MD5sum: 83d1f3afb237caf068e2b53d49b8b268 SHA1: 009a969230fe738a192d2ac8b8c1fa1b8c1f8648 SHA256: 45097fbac56aad57e5e3b2f3f81f38aebcf575c9d4b2e46dcccb4b1aae6f6e9f SHA512: 9063304745b9a76d684df966e306ad58de0ad78a3ea6b3cd09e06e166e850bdc59ed40c5fd9e281572ea194efabf9c83b93ef764f63b33b8152e6f0bd3cc69da Homepage: https://cran.r-project.org/package=camelsCL Description: CRAN Package 'camelsCL' (Easy Handling of the CAMELS-CL Dataset) Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) , developed by Alvarez-Garreton et al. (2018) . The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses. Package: r-cran-camerondata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-camerondata_1.0.0-1.ca2404.1_all.deb Size: 1311266 MD5sum: dcdb5f5648ab6d2661a730f2f5e208ae SHA1: 16a5c1895eda95968e71b19b204fc28f7db6dc40 SHA256: 1de9be0d0190e1e96d9f740038ccd3e6068187fdfa076d2596fe1c440baffb22 SHA512: 6eb8f93ff4ee6cc5c20d533260a270f97f17d10cb701445c1ea7d2fd773ff5ccfa97d65d799bcb283a140645458946e920902b774e5a42211f5da29ef78538a2 Homepage: https://cran.r-project.org/package=camerondata Description: CRAN Package 'camerondata' (Datasets from "Microeconometrics: Methods and Applications" byCameron and Trivedi) Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used. Package: r-cran-camml Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vam, r-cran-seurat, r-cran-mass, r-cran-matrix, r-cran-biocmanager, r-bioc-org.hs.eg.db, r-bioc-org.dr.eg.db, r-bioc-org.mm.eg.db, r-bioc-annotationdbi, r-cran-seuratobject, r-bioc-edger Suggests: r-cran-sctransform Filename: pool/dists/noble/main/r-cran-camml_1.0.0-1.ca2404.1_all.deb Size: 643810 MD5sum: 4ab940de9431b752c01767420addb7ed SHA1: ed15aa36572a0e376a3393ef26b9b62f0da196c6 SHA256: bad91ab244f851a1a1747becd0b59c740ad1232e8e7892858282995bba7401f5 SHA512: d35f8956641599419f7e2d69303104d7ab8f2a921aa9edf23999a4cbe70c0742eb10e746452955f402aa6b85852e50fa6a0fa9eb8a6a940cdb03e2b225219a7b Homepage: https://cran.r-project.org/package=CAMML Description: CRAN Package 'CAMML' (Cell-Typing using Variance Adjusted Mahalanobis Distances withMulti-Labeling) Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) . Package: r-cran-campaignmanager Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-campaignmanager_0.1.0-1.ca2404.1_all.deb Size: 22896 MD5sum: 957d53581e3d48ad6ba702178e245fbe SHA1: 370162a7d848d0317fcab3ca0413744f4bf32f8a SHA256: d0d75435725402550aea1a9b1359c488c53807a1e5c3502f33a8f26f10dcb389 SHA512: a4c5883a9f330520a7c8660dadfee0165cb644495d89282b97c2f85e9d01cf2966700866d88a8c19c76d36483883c40888efac427d55e3f8391932a064dc56c1 Homepage: https://cran.r-project.org/package=campaignmanageR Description: CRAN Package 'campaignmanageR' (Connect to Campaign Manager via the 'Windsor.ai' API) Collect marketing data from Campaign Manager using the 'Windsor.ai' API . Package: r-cran-campfin Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringdist, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-campfin_1.0.11-1.ca2404.1_all.deb Size: 646670 MD5sum: a084e85594511ec556da9c3fead95b68 SHA1: ee98c28bd5e26670dc7999909280aab005ca6417 SHA256: d912177addcb13e2fefda2280946c09ad6671a8eaaf5dec08503da989d3b83d6 SHA512: 775e4e3a02ebed2e65c95ffaafdd50fbee462b20d1ed3a202a8cf12fc53b297a7b5d7db09355908d5b59b3a224e87f3214b5b02b4eb05402f87697ced74421be Homepage: https://cran.r-project.org/package=campfin Description: CRAN Package 'campfin' (Wrangle Campaign Finance Data) Explore and normalize American campaign finance data. Created by the Investigative Reporting Workshop to facilitate work on The Accountability Project, an effort to collect public data into a central, standard database that is more easily searched: . Package: r-cran-campsis Architecture: all Version: 1.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-campsismod, r-cran-assertthat, r-cran-digest, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-lifecycle, r-cran-mass, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bookdown, r-cran-devtools, r-cran-gridextra, r-cran-knitr, r-cran-mrgsolve, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rxode2, r-cran-stringr, r-cran-testthat, r-cran-tictoc, r-cran-vdiffr, r-cran-xfun Filename: pool/dists/noble/main/r-cran-campsis_1.9.2-1.ca2404.1_all.deb Size: 1138430 MD5sum: 6c3c3c31a4e9a0711ba456688f1d485b SHA1: 6da7d453de5765b5dbcf6bd854fe4b7967feaa92 SHA256: 0a8c3bf1e7bad8f8af3dd427de12c813fb9b3a3c1da3daf44dbda98955f80b74 SHA512: 626c0cf93fbd0fc0b481a4cecf9a84123031536117da5bd3a4e514c8e4b52a7f24a58f0462a233e057128ec30e4f0764a48db1094e8dcc29aa044f1442e37026 Homepage: https://cran.r-project.org/package=campsis Description: CRAN Package 'campsis' (Generic PK/PD Simulation Platform Campsis) A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on the R packages 'rxode2' and 'mrgsolve'. Campsis provides an abstraction layer over the underlying processes of defining a PK/PD model, assembling a custom dataset and running a simulation. The package has a strong dependency on the R package 'campsismod', which allows models to be read from and written to files, including through a JSON-based interface, and to be adapted further on the fly in the R environment. In addition, 'campsis' allows users to assemble datasets in an intuitive manner, including via a JSON-based interface to import Campsis datasets defined using formal JSON schemas distributed with the package. Once the dataset is ready, the package prepares the simulation, calls 'rxode2' or 'mrgsolve' (at the user's choice), and returns the results for the given model, dataset and desired simulation settings. The package itself is licensed under the GPL (>= 3); the JSON schema files shipped in inst/extdata are licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). Package: r-cran-campsismod Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-laplacesdemon, r-cran-lifecycle, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-xfun Filename: pool/dists/noble/main/r-cran-campsismod_1.4.2-1.ca2404.1_all.deb Size: 1118582 MD5sum: a24a164a2bef70637eb430cbe91aa8d4 SHA1: 8819b24575f92ca99143ef90e8e96eee98bda0ea SHA256: 93d28e77d727bb1c53b540d55122858c8afed7c86521ff78003a2202d1fad7f8 SHA512: 9f04712837a7c22852b7a90dd261613064f22b38ebf116a1d58c10836bb038b9667e3daec7e9b6a6cd92e694c3e999feca5846e6d5aabbb2d4c52ffd740f89cd Homepage: https://cran.r-project.org/package=campsismod Description: CRAN Package 'campsismod' (Generic Implementation of a PK/PD Model) A generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read and write pharmacometric models from and to files, including a JSON-based interface to import Campsis models defined using a formal JSON schema distributed with the package. Models can be adapted further on the fly in the R environment using an intuitive API to add, modify or delete equations, ordinary differential equations (ODEs), model parameters or compartment properties (such as infusion duration or rate, bioavailability and initial values). The package also provides export facilities for use with the simulation packages 'rxode2' and 'mrgsolve'. The package itself is licensed under the GPL (>= 3); the JSON schema file shipped in inst/extdata is licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). This package is designed and intended to be used with the package 'campsis', a PK/PD simulation platform built on top of 'rxode2' and 'mrgsolve'. Package: r-cran-campsisnca Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-campsismod, r-cran-assertthat, r-cran-campsis, r-cran-cards, r-cran-dplyr, r-cran-glue, r-cran-gt, r-cran-gtsummary, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-ggplot2, r-cran-mrgsolve, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rxode2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-campsisnca_1.7.2-1.ca2404.1_all.deb Size: 885892 MD5sum: aab05312fee5a79adb1aa53ab14bccf7 SHA1: d88547d3e2d3763046d129201e60826d1026f441 SHA256: 3bf77741efe6ea437aa40cc0150481f91a7e208ed95b5d995ee8c99f4207dafb SHA512: 60cbced0000a5e2420fcf7d71aefc946643e4ff64515319d4b1637f881306dd06ada3fe4296f3e50aabc6b1ac9cbd633984979ef2127b64b899aed7bb27c559e Homepage: https://cran.r-project.org/package=campsisnca Description: CRAN Package 'campsisnca' (Non-Compartmental Analysis for Campsis Simulation Platform) A flexible and user-friendly non-compartmental analysis (NCA) toolkit designed to work seamlessly with simulated pharmacokinetic data generated using the 'campsis' ecosystem. The package provides a comprehensive framework to compute standard and custom NCA metrics, including exposure (AUC), peak/trough concentrations, half-life and time-above/below thresholds, with support for configurable time windows and summary statistics. 'campsisnca' integrates tightly with 'campsis' and 'campsismod', enabling streamlined workflows from simulation to analysis. In addition, the package provides a JSON-based interface to define NCA analyses, metrics and options using formal schemas, allowing analyses to be created, validated and executed outside of R and facilitating reproducibility, automation and system integration. The package also includes utilities for generating formatted summary tables and exporting results in multiple formats suitable for reporting. Trapezoidal rule implementation for AUC calculation is based on the 'qpNCA' package by Huisman, Jolling, Mehta and Bergsma (2021) , following methodology from Rowland and Tozer (2011, ISBN:978-0-683-07404-8). The package itself is licensed under the GPL (>= 3); the JSON schema files shipped in inst/extdata are licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). Package: r-cran-camsrad Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml2 Suggests: r-cran-ncdf4, r-cran-roxygen2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-camsrad_0.3.0-1.ca2404.1_all.deb Size: 32894 MD5sum: 1a21c65a73ea546af5fe41552b5714b0 SHA1: d11e7c6b64eecd11b51f06c149741d8c1dc9b478 SHA256: 247483874b2d996aaf21b088007dc47f04ff76beabb1e25ecd9975be0a0bd044 SHA512: 872aadf822b2c0f590db58c411037b23d61d176e002df66fc6f981811819f142dcef6353bda57d3806632ad107a0c8333cf1a59c6aba23c2a316304b0a3208b4 Homepage: https://cran.r-project.org/package=camsRad Description: CRAN Package 'camsRad' (Client for CAMS Radiation Service) Copernicus Atmosphere Monitoring Service (CAMS) radiations service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see . Package: r-cran-camtrapdp Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-eml, r-cran-frictionless, r-cran-memoise, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-uuid Suggests: r-cran-jsonlite, r-cran-lubridate, r-cran-testthat, r-cran-tibble, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-camtrapdp_0.6.0-1.ca2404.1_all.deb Size: 199622 MD5sum: d88aecf0a9e5d2484b4243e69281568a SHA1: 374893caf3d4737dab20fa4776947fb0fe798ea2 SHA256: ce8981957d6ce26eff46dbd035347366df093218aacd9e33f0c681faea7be12f SHA512: 13ee72eca91dc26c1b10c751c797f3a350c599e6a8b8db3db3bb7b6d33486be2e91707b4338e633dde27be88c1e0ca3aa1624f9e4a8b68a0627da84fc7e7f42c Homepage: https://cran.r-project.org/package=camtrapdp Description: CRAN Package 'camtrapdp' (Read and Manipulate Camera Trap Data Packages) Read and manipulate Camera Trap Data Packages ('Camtrap DP'). 'Camtrap DP' () is a data exchange format for camera trap data. With 'camtrapdp' you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. 'camtraptor' or 'camtrapR'. Package: r-cran-camtrapr Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9752 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-crayon, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-generics, r-cran-ggplot2, r-cran-htmltools, r-cran-leaflet, r-cran-lubridate, r-cran-reshape2, r-cran-secr, r-cran-sf, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-terra, r-cran-tibble Suggests: r-cran-abind, r-cran-bayesplot, r-cran-callr, r-cran-camtrapdp, r-cran-coda, r-cran-corrplot, r-cran-elevatr, r-cran-inext, r-cran-jsonify, r-cran-jsonlite, r-cran-knitr, r-cran-lattice, r-cran-magick, r-cran-mapview, r-cran-mockery, r-cran-nimble, r-cran-nimbleecology, r-cran-overlap, r-cran-patchwork, r-cran-pbapply, r-cran-plotly, r-cran-psych, r-cran-r.rsp, r-cran-raster, r-cran-ritis, r-cran-rjags, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-rstudioapi, r-cran-scales, r-cran-shinytest2, r-cran-shinywidgets, r-cran-stringr, r-cran-taxize, r-cran-tesseract, r-cran-testthat, r-cran-ubms, r-cran-units, r-cran-unmarked, r-cran-viridislite, r-cran-withr, r-cran-zip Filename: pool/dists/noble/main/r-cran-camtrapr_3.1.0-1.ca2404.1_all.deb Size: 5420202 MD5sum: 9fb22031b19261391814b0fd0c84a5be SHA1: 5f6ffb1ef2881313628df18f9f9ed359f9b3887d SHA256: aa1401e2f4eeedeb34d394f32d7e30295ba6048bf1f037d0ce942eecf1fe94cc SHA512: 949b92997229f7e228402a52d77159b485be0965bbe052795ad778b9304d1791a75c17423d7042c31a3ec976d23de02bd0e05691926faab091bfc9e13a05ea52 Homepage: https://cran.r-project.org/package=camtrapR Description: CRAN Package 'camtrapR' (Camera Trap Data Management and Analysis Framework) Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs. Package: r-cran-canadamaps Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4025 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rmapshaper, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-canadamaps_0.1-1.ca2404.1_all.deb Size: 4084738 MD5sum: 7a69621afd72a30f78c96574a83e0c65 SHA1: a25fd30ea0f0f7521230b3057d7775c950ceacc2 SHA256: 30444906388f95d076938d675217787a92ad521e70f39427e6b4feb4c9ba0f37 SHA512: cb07b658fa54e48b0c4fc02db63dcd76b02ee3c387a062d4a4441a73b3d03194105856f20f4ae3ae060ca2da255de7db78921a71d58fa3c2351f5c30b7e07eba Homepage: https://cran.r-project.org/package=canadamaps Description: CRAN Package 'canadamaps' (Maps of the Political and Administrative Divisions of Canada) Terrestrial maps with simplified topologies for Census Divisions, Agricultural Regions, Economic Regions, Federal Electoral Divisions and Provinces. Package: r-cran-canadianmaps Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-sp, r-cran-sf, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-canadianmaps_2.0.0-1.ca2404.1_all.deb Size: 4182436 MD5sum: 1471ec052026d951af4486d70c80b2a9 SHA1: 539b85ecc23a2c93cfdf2fcda837e62566960414 SHA256: 79f2c316d0fa4284347fd4896e81c25cbe81d823a37a40605cd38e0112bae2ed SHA512: f84fb9295a3f8927212d84a98c407772e8a3698700de559c35653b770f21fe811fcd176df6a1245dde27890a32b0a2558b39f20a15a73f120fb4299abe5d62b4 Homepage: https://cran.r-project.org/package=canadianmaps Description: CRAN Package 'canadianmaps' (Effortlessly Create Stunning Canadian Maps) Simple and seamless access to a variety of 'StatCan' shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular 'ggplot2' package. Package: r-cran-canaper Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-assertr, r-cran-assertthat, r-cran-dplyr, r-cran-future.apply, r-cran-phyloregion, r-cran-progressr, r-cran-purrr, r-cran-tibble, r-cran-vegan Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-future, r-cran-tictoc, r-cran-patchwork, r-cran-testthat, r-cran-stringr, r-cran-magrittr, r-cran-covr, r-cran-picante, r-cran-withr, r-cran-fs, r-cran-readr, r-cran-usethis, r-cran-scales, r-cran-colorblindness Filename: pool/dists/noble/main/r-cran-canaper_1.0.1-1.ca2404.1_all.deb Size: 535012 MD5sum: 548e43e5eb8db57ce2a80a4bf72cb904 SHA1: f08838550b0e2142a679a0148439a9704be61c6d SHA256: 72a52bb737711e38c3a00259120d1292fd2617f6b62e6d76d0fd66bc39d615a6 SHA512: 5ce5a8c4f9b17a7842e467f642ccda858c7c04a5643ebac401a2a988582558bb6b03f25ee5b3d30c282390ea17341f95307b494d3c052bf77272f0f50b92ded1 Homepage: https://cran.r-project.org/package=canaper Description: CRAN Package 'canaper' (Categorical Analysis of Neo- And Paleo-Endemism) Provides functions to analyze the spatial distribution of biodiversity, in particular categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al (2014) . 'canaper' conducts statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species. Package: r-cran-cancensus Architecture: all Version: 0.6.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-sf, r-cran-geojsonsf, r-cran-rlang, r-cran-readr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-leaflet, r-cran-mapdeck, r-cran-rmarkdown, r-cran-scales, r-cran-sp, r-cran-tidyr, r-cran-lwgeom, r-cran-xml2, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-cancensus_0.6.1-1.ca2404.2_all.deb Size: 1032642 MD5sum: 827d274dff53092cfb645b2ecd96a913 SHA1: a9d40560c9af9c51bc92edf3ad4c698668111ae6 SHA256: d03ce076f83c34f9aed49256f1e3d0dd3c43e8dc897253f29421485d013449d3 SHA512: 7fe105dee29ccb4c7370541d7b1d8aed24387df59a35fc51fb5615e2017dd5543f5ef08b0f48ec5926bfde918bf453fa1d74898e90b26aef768cc0a4cd25258d Homepage: https://cran.r-project.org/package=cancensus Description: CRAN Package 'cancensus' (Access, Retrieve, and Work with Canadian Census Data andGeography) Integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready tidy data frames and spatial data in multiple formats, as well as convenience functions for working with Census variables, variable hierarchies, and region selection. API keys are freely available with free registration at . Census data and boundary geometries are reproduced and distributed on an "as is" basis with the permission of Statistics Canada (Statistics Canada 1996; 2001; 2006; 2011; 2016; 2021). Package: r-cran-cancerevolutionvisualization Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-gtable, r-cran-plyr, r-cran-stringr, r-cran-boutroslab.plotting.general Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cancerevolutionvisualization_2.0.1-1.ca2404.1_all.deb Size: 270440 MD5sum: d00d987343226168f186f9d4a9d955cf SHA1: 51d7481542747c7e4c286c188969a876cb20f808 SHA256: 6fc4b61cce2ce5818acd3fa1e185921025133bda4244cc21e8f8c8dd1f44d046 SHA512: c8287b66e7778be49a13048a4acf2667179d49831210e58a072c9e11b216c6c61b6a0b843823686d5522ffd0b87261fb865e3e33f7196737ae1e34c0c4d495f4 Homepage: https://cran.r-project.org/package=CancerEvolutionVisualization Description: CRAN Package 'CancerEvolutionVisualization' (Publication Quality Phylogenetic Tree Plots) Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user. Package: r-cran-cancergi Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-systemfit, r-bioc-qvalue, r-cran-survival, r-cran-reshape2, r-cran-igraph Filename: pool/dists/noble/main/r-cran-cancergi_1.0.1-1.ca2404.1_all.deb Size: 729080 MD5sum: c973ce2b94e98c9a00238f58fd0699d8 SHA1: 37127e74603b94c0f2b593eaadce65db3f01deea SHA256: 6f529f3a3e2e2440001dd6018fe2e17532bdfd28dd04378a59e00234998482b0 SHA512: c430d51542aadeb6b14fe5b871078abdfe3fa8d2bbedd3eb41ca9983f64b751faf27072bb69bac9f25e1f252b12f2708344dd0b1000bc25002fcfeb8c791b538 Homepage: https://cran.r-project.org/package=cancerGI Description: CRAN Package 'cancerGI' (Analyses of Cancer Gene Interaction) Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. . Package: r-cran-cancergram Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biogram, r-cran-devtools, r-cran-pbapply, r-cran-ranger, r-cran-shiny, r-cran-stringi, r-cran-dplyr Suggests: r-cran-dt, r-cran-ggplot2, r-cran-pander, r-cran-rmarkdown, r-cran-shinythemes, r-cran-spelling Filename: pool/dists/noble/main/r-cran-cancergram_1.0.0-1.ca2404.1_all.deb Size: 213382 MD5sum: dbe867f1fe5b68b6415c585d585c5bad SHA1: 40b07502f2e4339e2480a958df50fa8a155c5a19 SHA256: 4bbb948ff7dac036146939ed10adc80632f538c9e6e9937c90bd531ae12687dc SHA512: 9a7d4351dadb39855e87c353db49dcc29046992be9714684871447446feceb5d42684bea92d58e4c110333c78ee75bcbf8c36a6c5cd23dfab25ed7c596422142 Homepage: https://cran.r-project.org/package=CancerGram Description: CRAN Package 'CancerGram' (Prediction of Anticancer Peptides) Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: . For more information see: Burdukiewicz et al. (2020) . Package: r-cran-cancerr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cancerr_0.1.1-1.ca2404.1_all.deb Size: 268584 MD5sum: b2767f0d609493f5c6d08fb44e584a52 SHA1: 9cf1b17d8949e94db2211a40b68ff6a95def7733 SHA256: 541f73b803d065d310628e38b0269137f122f0145ef7d8256132cbe73d22b875 SHA512: b83e6f936918ef9a92d25d0cfdb444b1b44a95e85e6cd115f6031233fb80b74ff1320718ccca37bb6bafd5fd2b060f892c057ae27e8404675d3d877a9c409002 Homepage: https://cran.r-project.org/package=cancerR Description: CRAN Package 'cancerR' (Classification of Cancer Using Administrative Data) Classifies the type of cancer using routinely collected data commonly found in cancer registries from pathology reports. The package implements the International Classification of Diseases for Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type . Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) . Adolescent and young adult cancer classification is based on Barr et al. (2020) . Package: r-cran-cancerradarr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7833 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-epitools, r-cran-gtools, r-cran-magrittr, r-cran-openxlsx, r-cran-plyr, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr Suggests: r-cran-plotly, r-cran-shiny, r-cran-tidyverse, r-cran-dt, r-cran-testthat, r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-cancerradarr_3.0.0-1.ca2404.1_all.deb Size: 7170862 MD5sum: d3853927ec1ad447603edb6bdb918a38 SHA1: d68d6e74013cd3510b7a57b87abfe85569db7007 SHA256: 335c3d5d286bf493ce5be2ec6273f52484aaf23d697d193b965c9408291777a6 SHA512: 85348fef81205fec426377527ca736d4e6393101cd71edcd6b294718315590e51b7b658b15a9d15dc9192cbe5ffe7e8fadc24b457c37dae84963a11309e33242 Homepage: https://cran.r-project.org/package=cancerradarr Description: CRAN Package 'cancerradarr' (Cancer RADAR Project Tool) Cancer RADAR is a project which aim is to develop an infrastructure that allows quantifying the risk of cancer by migration background across Europe. This package contains a set of functions cancer registries partners should use to reshape 5 year-age group cancer incidence data into a set of summary statistics (see Boyle & Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data protections rules. Package: r-cran-cancerscreening Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-khisr, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cancerscreening_1.1.1-1.ca2404.1_all.deb Size: 285642 MD5sum: 26879985438a5ced0d89f45d34f46631 SHA1: ba8ab10da664256333af7752003f446ee0f6a3bd SHA256: f52d8915760ce27309ccfea30ebf253d43ff5beb3efc25533f3bdfbefda7fde7 SHA512: b49d3bd94278de4d32e94d8ac568c61c78450e82cf1e864477919e2df350fa429ac771432e2cd2313af1a73974001a152a7b7310d48809f54c4f738349bd0b33 Homepage: https://cran.r-project.org/package=cancerscreening Description: CRAN Package 'cancerscreening' (Streamline Access to Cancer Screening Data) Retrieve cancer screening data for cervical, breast and colorectal cancers from the Kenya Health Information System in a consistent way. Package: r-cran-candisc Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-heplots, r-cran-car, r-cran-insight, r-cran-dplyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-rgl, r-cran-cardata, r-cran-corrplot, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-rpart.plot, r-cran-klar, r-cran-lifecycle, r-cran-tidyr, r-cran-testthat, r-cran-rcolorbrewer, r-cran-tinytable Filename: pool/dists/noble/main/r-cran-candisc_1.1.1-1.ca2404.1_all.deb Size: 590282 MD5sum: 52e0f0dbf456ff81a4925167a69c8851 SHA1: 1a7cdb632b0173cdfc382965d0d595c469f650bf SHA256: c04632e22cad22553e959b613128d12ddd1d2521dc2764e27adb10911a9180d6 SHA512: 82066398fecf4dd1d45d110f2e5ba26d3fc8cad0cb6c8867cf1e4b55bc3f6b79b030e55f5c9d9eb89fe144cf0236124d0a46dbcbe421360f44b285cd531c57bd Homepage: https://cran.r-project.org/package=candisc Description: CRAN Package 'candisc' (Visualizing Generalized Canonical Discriminant and CanonicalCorrelation Analysis) Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included. Package: r-cran-cane Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-agricolae, r-cran-dplyr, r-cran-emmeans Filename: pool/dists/noble/main/r-cran-cane_0.1.1-1.ca2404.1_all.deb Size: 49740 MD5sum: 9c17a3e965e86b089e001cb9df40ada4 SHA1: 24afc8b89d057534feedf8d532b93d7f3792e76a SHA256: 3d54662c50dbbfd7cb9671d13c5de077bbc8b1c4c75eae7b90f1c2311e37fb63 SHA512: ea22988b4cdfb1a9f0784034398f00a44580f0bd4f412f835022b98fd6ea6329e09a522d332fbe9ebf2d95e815230f45418bf44bea976fc97addac20e9223adb Homepage: https://cran.r-project.org/package=CANE Description: CRAN Package 'CANE' (Comprehensive Groups of Experiments Analysis for NumerousEnvironments) In many cases, experiments must be repeated across multiple seasons or locations to ensure applicability of findings. A single experiment conducted in one location and season may yield limited conclusions, as results can vary under different environmental conditions. In agricultural research, treatment × location and treatment × season interactions play a crucial role. Analyzing a series of experiments across diverse conditions allows for more generalized and reliable recommendations. The 'CANE' package facilitates the pooled analysis of experiments conducted over multiple years, seasons, or locations. It is designed to assess treatment interactions with environmental factors (such as location and season) using various experimental designs. The package supports pooled analysis of variance (ANOVA) for the following designs: (1) 'PooledCRD()': completely randomized design; (2) 'PooledRBD()': randomized block design; (3) 'PooledLSD()': Latin square design; (4) 'PooledSPD()': split plot design; and (5) 'PooledStPD()': strip plot design. Each function provides the following outputs: (i) Individual ANOVA tables based on independent analysis for each location or year; (ii) Testing of homogeneity of error variances among distinct locations using Bartlett’s Chi-Square test; (iii) If Bartlett’s test is significant, 'Aitken’s' transformation, defined as the ratio of the response to the square root of the error mean square, is applied to the response variable; otherwise, the data is used as is; (iv) Combined analysis to obtain a pooled ANOVA table; (v) Multiple comparison tests, including Tukey's honestly significant difference (Tukey's HSD) test, Duncan’s multiple range test (DMRT), and the least significant difference (LSD) test, for treatment comparisons. The statistical theory and steps of analysis of these designs are available in Dean et al. (2017) and Ruíz et al. (2024). By broadening the scope of experimental conclusions, 'CANE' enables researchers to derive robust, widely applicable recommendations. This package is particularly valuable in agricultural research, where accounting for treatment × location and treatment × season interactions is essential for ensuring the validity of findings across multiple settings. Package: r-cran-canek Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-irlba, r-cran-numbers, r-cran-fpc, r-bioc-bluster, r-cran-igraph, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-seurat, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-scater, r-bioc-batchelor, r-bioc-scran, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-canek_0.3.1-1.ca2404.1_all.deb Size: 3134562 MD5sum: 3e16237e1ebdc83b91fa4c1e84d29908 SHA1: 037043b4f203938d5e18f6c87b8806c843edebe5 SHA256: ce18acf86a83ecb9867f5c1a3988c52cf076e0ba0ca85880b88453143f361c90 SHA512: 0901d38e6c73735f570e6675763bdfba46dbca4ccde91ecf34218834d8f63deeaab1f5d415d20772fb7ce8f256547fca9514ffb3d6f05209581eb745f88e2ac0 Homepage: https://cran.r-project.org/package=Canek Description: CRAN Package 'Canek' (Batch Correction of Single Cell Transcriptome Data) Non-linear/linear hybrid method for batch-effect correction that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) . Package: r-cran-canonicalfamilyextra Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-gee, r-cran-geepack, r-cran-superlearner, r-cran-ipred, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-canonicalfamilyextra_1.0.0-1.ca2404.1_all.deb Size: 38098 MD5sum: 71d34f8c7d230f71599a4c680416d352 SHA1: e0451e36a8ca1d00cc95bb1ea603b9afbb95fac5 SHA256: 64708d87c77ebe662dd1b260f441c3bb47fff0a89cb7abb53539d2dc7d62613e SHA512: d1f366e05de13e6e1b0e6b0d5cb4e9d9e4d8b8c3b8a61b7b14f00b38068c013a231b9d41701e4ac221ddbe08759e13597a66ac31a4722ed621bbac1ec64b9978 Homepage: https://cran.r-project.org/package=CanonicalFamilyExtra Description: CRAN Package 'CanonicalFamilyExtra' (Extra Canonical Link Family Objects for Generalized LinearModels) Extra family objects in "weird" scenarios, particularly logistic or log-linear model with unbounded or non-binary/non-integer outcomes. Provides binomial_extra() and poisson_extra() as generalizations of binomial() and poisson(). The use of canonical link with the corresponding working likelihood in glm() ensures convexity, making model fitting reliable and independent of starting value. Robert WM Wedderburn (1974) and Peter McCullagh (1983) justified this method to fit generalized linear (mean) models with quasi-/working likelihood. Package: r-cran-canopy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-fields, r-cran-pheatmap, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-canopy_1.3.0-1.ca2404.1_all.deb Size: 969690 MD5sum: 6953c246c3e38c14c36af89e855d5c22 SHA1: e65b5453570846045db3ce673971c7d8d3b932eb SHA256: fa262f669f64b0a7104b243aabeaeeb496e84ca0b756cf443d0c491bfa4f5b84 SHA512: dcb6d5cae23010cdb29cc403d27308e93f029ce3d41005d32ab1f0f30f39b1459c0de254c6d9ef6901787d1ea6c251f996d9c4ee01702be4087ddf15d9e08f21 Homepage: https://cran.r-project.org/package=Canopy Description: CRAN Package 'Canopy' (Accessing Intra-Tumor Heterogeneity and Tracking Longitudinaland Spatial Clonal Evolutionary History by Next-GenerationSequencing) A statistical framework and computational procedure for identifying the sub-populations within a tumor, determining the mutation profiles of each subpopulation, and inferring the tumor's phylogenetic history. The input are variant allele frequencies (VAFs) of somatic single nucleotide alterations (SNAs) along with allele-specific coverage ratios between the tumor and matched normal sample for somatic copy number alterations (CNAs). These quantities can be directly taken from the output of existing software. Canopy provides a general mathematical framework for pooling data across samples and sites to infer the underlying parameters. For SNAs that fall within CNA regions, Canopy infers their temporal ordering and resolves their phase. When there are multiple evolutionary configurations consistent with the data, Canopy outputs all configurations along with their confidence assessment. Package: r-cran-canprot Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1682 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-chnosz Filename: pool/dists/noble/main/r-cran-canprot_2.0.0-1.ca2404.1_all.deb Size: 1056880 MD5sum: e972f1b2578becde9b4b6bda8a93fdc7 SHA1: 6d9ca566910debd7e8d163c05f501adeb0b6e4c2 SHA256: f335f38d47c3768a5058835535c24385030c9c2c95a5fb6806f41d0286ba489d SHA512: 5b36d54b7510462e15f77864ce26a49e95593e9936b0d8d9b5b6a155e9533c31d4542a0992c52fbdb2592ca66a53d0a2ba2478b55434eaf4b1c2c034de956c4f Homepage: https://cran.r-project.org/package=canprot Description: CRAN Package 'canprot' (Chemical Analysis of Proteins) Chemical analysis of proteins based on their amino acid compositions. Amino acid compositions can be read from FASTA files and used to calculate chemical metrics including carbon oxidation state and stoichiometric hydration state, as described in Dick et al. (2020) . Other properties that can be calculated include protein length, grand average of hydropathy (GRAVY), isoelectric point (pI), molecular weight (MW), standard molal volume (V0), and metabolic costs (Akashi and Gojobori, 2002 ; Wagner, 2005 ; Zhang et al., 2018 ). A database of amino acid compositions of human proteins derived from UniProt is provided. Package: r-cran-canpumf Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2226 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-rvest, r-cran-httr, r-cran-curl, r-cran-jsonlite, r-cran-purrr, r-cran-dbi, r-cran-duckdb, r-cran-dbplyr, r-cran-haven, r-cran-zip Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-scales, r-cran-ggplot2, r-cran-testthat, r-cran-withr, r-cran-microbenchmark, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-rsvg, r-cran-pdftools, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-canpumf_0.6.0-1.ca2404.1_all.deb Size: 1392470 MD5sum: 41c17ecb15638d50f048002c74cf96f7 SHA1: 9d980e75fbc84bdd405b9c217e725ad020186eb3 SHA256: ded96c8ce32487b28d417e96538a0ab1f344f11e613073da9498b99b4a72f300 SHA512: 7f739505eceae6a9935a718bf699d86b642c07a031f66770fdbfdd210d9c64df5a5cb753372117c586837be8e58a7cb54aa416425247034c87d52e08b235d7c6 Homepage: https://cran.r-project.org/package=canpumf Description: CRAN Package 'canpumf' (Parse StatCan PUMF Files) Facilitate working with Statistics Canada (StatCan) Public Use Microdata Files (PUMF). Enables downloading of available PUMF data, parsing of metadata from command files or other sources to infer the layout structure, variable labels and value labels as well as missing data values, and returns a connection to a 'DuckDB' database with the labelled data. Data and documentation come from Statistics Canada's Public Use Microdata Files , distributed under the Statistics Canada Open Licence . Package: r-cran-cansim2r Architecture: all Version: 1.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-hmisc, r-cran-downloader Filename: pool/dists/noble/main/r-cran-cansim2r_1.14.1-1.ca2404.1_all.deb Size: 27712 MD5sum: 0a8ea9a13e248652726e492bfc81f2d8 SHA1: bf2ab9c7ee4281961fbc1ba5126f6414e508ae45 SHA256: 524710015d958dfd934ccec35b2c135fa778e24268f2c7a1a472209a9270851b SHA512: 4cf834b61e46a5c862c73f987bd79690a1a4681cb8350bb303cbfd2c3cf12ce2cd0e79433964bfe7c87bbffaad959d43560c7d9eef25aae9d43a5907afe980bf Homepage: https://cran.r-project.org/package=CANSIM2R Description: CRAN Package 'CANSIM2R' (Directly Extracts Complete CANSIM Data Tables) Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. 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This package enriches the tables with metadata, deals with encoding issues, allows for bilingual English or French language data retrieval, and bundles convenience functions to make it easier to work with retrieved table data. For more efficient data access the package allows for caching data in a local database and database level filtering, data manipulation and summarizing. Package: r-cran-canton Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-systemfonts Suggests: r-bioc-complexheatmap, r-cran-pheatmap, r-cran-testthat Filename: pool/dists/noble/main/r-cran-canton_0.0.8-1.ca2404.1_all.deb Size: 81148 MD5sum: b21b5341d6cce50a336ea4ef6ea7fdb1 SHA1: 29820c18d2d9e57b5a58cda9800232b3342f8007 SHA256: a72b865ae0c0a5b1b3e672fee0cb39536dcf8e949e7bce229cbfbcd2b2dc05f9 SHA512: ba631fa04587ba237aefc27dd175a4a436cb3f0fcffca3f05f430e60a4e81935be45c2600f460f69fd4705d7d08d3b7138a3890c342ceebd12ee107cf6fce02d Homepage: https://cran.r-project.org/package=Canton Description: CRAN Package 'Canton' (Consistent Fonts and Figure Export for Scientific PublicationWorkflows) Provides a unified interface for exporting figures created with base graphics, 'ggplot2', 'grid', 'pheatmap', and 'ComplexHeatmap' to PDF, PNG, JPEG, and TIFF files. Selects an appropriate rendering strategy based on the plot object and supports exporting a figure to multiple formats in one call. Also provides font configuration and diagnostics, reusable figure presets, publication-oriented themes, and colour palettes. The 'ggplot2' graphics framework is described by Wickham (2016, ISBN:978-3-319-24277-4). Package: r-cran-cantrends Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-carrier, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lspline, r-cran-mirai, r-cran-purrr, r-cran-rlang, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cantrends_0.1.0-1.ca2404.1_all.deb Size: 60064 MD5sum: 66018acdc20e0fa599f7d92b5415e2e2 SHA1: ce4229a7299c469bc193d4069d403cc85cc14dc9 SHA256: 7a743f7c7f77373f82ebfae5dc019c9879d7be4987e91ff9631ba83367f7eb22 SHA512: c2e606a5ba1297a0ffbf2b1fb9ac39ed7689395040ec49c9242eb9d44abf0576b3d78458b30c385ee31d4145d81802004fc1ed3bda0c92d2a411312dc13480b5 Homepage: https://cran.r-project.org/package=cantrends Description: CRAN Package 'cantrends' (Fit Segmented Regression Models) Estimates piecewise linear spline models for assessing temporal trends in cancer incidence and mortality rates. Provides tools for identifying and reporting knot locations, annual percent changes (APCs), and average annual percent changes (AAPCs), facilitating the analysis and communication of changes in cancer rates over time. 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Package: r-cran-caracas Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-matrix, r-cran-doby, r-cran-magrittr Suggests: r-cran-ryacas, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytex, r-cran-magick, r-cran-pdftools, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-caracas_2.1.1-1.ca2404.1_all.deb Size: 455288 MD5sum: 594213468d4cdbf896120dac0cfaf9bb SHA1: 725b6665733068ba7db9e69226bd614043425884 SHA256: b332891a1a87582767662877f1b3b617edc5b53c1ad97a77c5a3c46f893d52ce SHA512: 37ccb5b6757b46e6aec1136679615fc8e49a2918c45c400c9684565caf7911faee0abcccaea8deb5ed6e17429654b03b4015dd0430d31a15cc52bbceded0dedf Homepage: https://cran.r-project.org/package=caracas Description: CRAN Package 'caracas' (Computer Algebra) Computer algebra via the 'SymPy' library (). This makes it possible to solve equations symbolically, find symbolic integrals, symbolic sums and other important quantities. Package: r-cran-caradpt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-caradpt_0.1.0-1.ca2404.1_all.deb Size: 197164 MD5sum: 3cd65b8ecec24249d3ff1e23bf454799 SHA1: 347b294aac0fea2d560a422f582e6c47061cdec8 SHA256: 6c75d3883fae82b983da8e94acaa0dafca1c6008188bb9454b5115d6563dc770 SHA512: 24a0a96d73c2576bb42bd6f1e14b5f15be7a51ec75d770e2b9fbe8534be250ec21d347e81ede047c12910cacea5e2e2bee578f39e492051b95fc1f3671d7830d Homepage: https://cran.r-project.org/package=caradpt Description: CRAN Package 'caradpt' (Covariate-Adjusted Response-Adaptive Designs for Clinical Trials) Tools for implementing covariate-adjusted response-adaptive procedures for binary, continuous and survival responses. Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references: Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007) Zhang, L. X. & Hu, F. (2009) Hu, J., Zhu, H., & Hu, F. (2015) Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) Mukherjee, A., Jana, S., & Coad, S. (2024) . Package: r-cran-carbayesdata Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Filename: pool/dists/noble/main/r-cran-carbayesdata_3.0-1.ca2404.1_all.deb Size: 355876 MD5sum: 8754ebeeebf6b963347d2f64da42a2f8 SHA1: 9b9a28e4b32cc0988bfbf4be31b2a69acbaa25a5 SHA256: 8d6cfc816e0ac6baa47535ec93bacc45ed19d3137294e5214056d0301b1288c6 SHA512: e20582f6308e92141dbf91a49bb8c5e2f3b782bdbd9c123ac29d0fde25fd39d54a28e18fcf57ad816dfde25143a6b6f02d4df8f13d9bfcf9ab02d8cf05997582 Homepage: https://cran.r-project.org/package=CARBayesdata Description: CRAN Package 'CARBayesdata' (Data Used in the Vignettes Accompanying the CARBayes andCARBayesST Packages) Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. 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Package: r-cran-carbondata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-readxl Suggests: r-cran-arrow, r-cran-nanoparquet, r-cran-testthat, r-cran-writexl Filename: pool/dists/noble/main/r-cran-carbondata_0.2.0-1.ca2404.1_all.deb Size: 153946 MD5sum: 6e62b100ec103a5f05fd7db01be508a2 SHA1: 87c919188f5f8630faa5ffb6b52c9ba23b457a65 SHA256: e831147beeb9f0d003d074d912cbb4372fd42b9c36d5bb10dd3af774da20a20e SHA512: 0866fb522bc17f8cf99ca1df863fc86532237286706ae0de557f7c4f346385b29ce307d9d522a17ecd252d69f288cfa62dc7dca2aa2838c5326d207867b06b36 Homepage: https://cran.r-project.org/package=carbondata Description: CRAN Package 'carbondata' (Access Carbon Market Data from Emissions Trading Systems andVoluntary Registries) Unified access to carbon market data from compliance emissions trading systems ('EU ETS', 'UK ETS', 'RGGI', California Cap-and-Trade) and voluntary carbon markets (Verra, Gold Standard, American Carbon Registry, Climate Action Reserve, via the Berkeley Voluntary Registry Offsets Database and the 'CarbonPlan' 'OffsetsDB' API). Includes cross-market price data from the 'International Carbon Action Partnership' ('ICAP') Allowance Price Explorer , global carbon pricing from the World Bank Carbon Pricing Dashboard , and the historical 'RFF' World Carbon Pricing Database following Dolphin, Pollitt and Newbery (2020) . Data is downloaded from public sources on first use and cached locally. Package: r-cran-carbonpredict Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5049 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-progress, r-cran-lmertest, r-cran-networkd3, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-mockery, r-cran-png Filename: pool/dists/noble/main/r-cran-carbonpredict_2.0.1-1.ca2404.1_all.deb Size: 4777186 MD5sum: 4da92e68d961f1f539984023baaf7ecd SHA1: 68fd33b9c386339914958da4511f4594f260da06 SHA256: d9d1327804e54e24b51ef005bf26ef46c854b8381182f8111723fe34db397a39 SHA512: 7cfc4b4579496c1092d6869b8e58780ff66db8b7f189856f252fb8344cec7557432675bb3786497c3d95db700349be686e3c78673bb1d53467817d10ddc1ffff Homepage: https://cran.r-project.org/package=carbonpredict Description: CRAN Package 'carbonpredict' (Predict Carbon Emissions for UK SMEs) Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data, as well as Scope 1 carbon emissions for UK farms. The 'carbonpredict' package provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. It also provides Scope 1 carbon emissions predictions for UK farms of types: Cereals ex. rice, Dairy, Mixed farming, Sheep and goats, Cattle & buffaloes, Poultry, Animal production and Support for crop production. The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publications: Phillpotts, A., Owen. A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D. (2025) "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions" and Wells, J., Trendl, A., Owen, A., Barrett, J., Gridley, J., Jobst, N., Leake, D. (2025) "A Scalable Tool for Farm-Level Carbon Accounting: Evidence from UK Agriculture". 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Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report , it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for 'shiny' applications and community-driven extensions. Package: r-cran-carcass Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-survival, r-cran-expm, r-cran-arm, r-cran-mass Filename: pool/dists/noble/main/r-cran-carcass_1.9-1.ca2404.1_all.deb Size: 107236 MD5sum: 44c65c7976800b305450956d7fe895eb SHA1: b24b6decfc2408514e986d41877e214723497344 SHA256: 8afd662cb830b83d98f9615c9da55ff655abaf84873c07454a315f1c10b2febd SHA512: e8286b54e10234fce60bfdbdc04f4209d3f2cc7f9fb7327586e74cb6e46cc6a5aaf4b9f682b4b41c8c990ee8c4d8222e18ea41ede2baa3c77047b841b3742ee0 Homepage: https://cran.r-project.org/package=carcass Description: CRAN Package 'carcass' (Estimation of the Number of Fatalities from Carcass Searches) The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability. 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Additional methods exist for analysis of procedural billing codes. 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Package: r-cran-cardiacdp Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-purrr, r-cran-rcolorbrewer, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cardiacdp_0.4.2-1.ca2404.1_all.deb Size: 241662 MD5sum: be43025b4ed62f6285492dd256e1ba86 SHA1: bbe163301e023f2adf4d0f6c351ecd802c55254b SHA256: 43bac0d6cd2766fa6036475c1821249b72a9d591e20327cf1d7d3c1d0fcffd62 SHA512: 5b130c39e02d31789b04a58b393175797ab20a51ff0484790ff2acf3bfa91fe8bdd82138e0e96561c3ed56436548a535ab1957e3d1eb84ddde3718fd07763cc6 Homepage: https://cran.r-project.org/package=CardiacDP Description: CRAN Package 'CardiacDP' (Automated Cardiac Data Processing via ACF, GA & Tracking Index) An algorithm developed to efficiently and accurately process complex and variable cardiac data with three key features: 1. employing autocorrelation to identify recurrent heartbeats and use their periods to compute heart rates; 2. incorporating a genetic algorithm framework to minimize data loss due to noise interference and accommodate within-sequence variations; and 3. introducing a tracking index as a moving reference to reduce errors. Lau, Wong, & Gu (2026) . Package: r-cran-cardidates Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-pastecs, r-cran-lattice Filename: pool/dists/noble/main/r-cran-cardidates_0.4.9-1.ca2404.1_all.deb Size: 251964 MD5sum: 297a406a9923cc6be148662abc4ff006 SHA1: 8d3a888e9cf026fd490196f1d95ad654e1802c18 SHA256: f8914576bfdb120ab4bf9e668c5e16cbfd9a5e38effbd51cd952a3f2b8f1bdd8 SHA512: 95bb415d587231f8a3b1ad0bf6f93dde994c69512f4f1b1f3f228e0de2944001cb35893eb0a060844e28e7453988ab87f72342253226e972249ae65aa7a10364 Homepage: https://cran.r-project.org/package=cardidates Description: CRAN Package 'cardidates' (Identification of Cardinal Dates in Ecological Time Series) Identification of cardinal dates (begin, time of maximum, end of mass developments) in ecological time series using fitted Weibull functions. Package: r-cran-cardinalfda Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cards, r-cran-cardx, r-cran-crane, r-cran-gtsummary, r-cran-dplyr Suggests: r-cran-cowplot, r-cran-forcats, r-cran-ggplot2, r-cran-ggsurvfit, r-cran-gt, r-cran-knitr, r-cran-labelled, r-cran-lubridate, r-cran-pharmaverseadam, r-cran-purrr, r-cran-random.cdisc.data, r-cran-rlang, r-cran-rtables, r-cran-svglite, r-cran-tern, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-cardinalfda_0.3.0-1.ca2404.1_all.deb Size: 52486 MD5sum: 0245ac535a3b6ec60be41791b3776002 SHA1: 22d45ba5b611f5b3f534ee8bce5ab5e4a6366080 SHA256: a5b6f2fe72d14d698d3ff9154bd19a0880424c69756b34d2ad7635cff8d75265 SHA512: 1f350c4252255a849481f1f850378f1dcb1ad22900003e94b3f7445d338979d5a4281392739cb56e40910d6011e3cd6296a960e77398a2f6853a4f38e6965b8c Homepage: https://cran.r-project.org/package=cardinalfda Description: CRAN Package 'cardinalfda' (FDA Safety Tables and Figures) Provides implementations of safety tables and figures recommended by the FDA (U.S. Food and Drug Administration) for clinical trial reporting. 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Package: r-cran-cardiocurver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1748 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal, r-cran-ggplot2, r-cran-gridextra, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cardiocurver_1.0.0-1.ca2404.1_all.deb Size: 262210 MD5sum: 6df66ebd92e70563b98baca0abcd6aca SHA1: 6bb306cbbbaf1fbcba38b1630e5130f4f4e34659 SHA256: e5ccb706558fc07289c6e355283888eef895709fee6c3cac0fd1fdb8d51e122c SHA512: e35a084145cfd3dfac385b8123e2cadc618db803b12d1a60bee549905e8add5748a8c2938b4507e70d4aff44b4fc7ed4b25d3202f20707462a8e413643bed2d7 Homepage: https://cran.r-project.org/package=CardioCurveR Description: CRAN Package 'CardioCurveR' (Nonlinear Modeling of R-R Interval Dynamics) Automated and robust framework for analyzing R-R interval (RRi) signals using advanced nonlinear modeling and preprocessing techniques. The package implements a dual-logistic model to capture the rapid drop and subsequent recovery of RRi during exercise, as described by Castillo-Aguilar et al. (2025) . In addition, 'CardioCurveR' includes tools for filtering RRi signals using zero-phase Butterworth low-pass filtering and for cleaning ectopic beats via adaptive outlier replacement using local regression and robust statistics. These integrated methods preserve the dynamic features of RRi signals and facilitate accurate cardiovascular monitoring and clinical research. Package: r-cran-cardiodatasets Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cardiodatasets_0.2.0-1.ca2404.1_all.deb Size: 537662 MD5sum: ee466f1dbb62be1d2a01e087006955ce SHA1: b2e66bd9bc342a5504f6b4e6f20f6d2bce9de831 SHA256: c4a6b08faf9e68533a8a722d67d56cb9fe509e7a57c15678645e224c36ccacea SHA512: 41090a4f5e23a6463519c49c8932feedb9151ba298dbe5cec859d30acbe20b18b478700d7a346764d584f32a4ef294ceef6bf929081d527fc32ed2793fd6b412 Homepage: https://cran.r-project.org/package=CardioDataSets Description: CRAN Package 'CardioDataSets' (A Comprehensive Collection of Cardiovascular and Heart DiseaseDatasets) Offers a diverse collection of datasets focused on cardiovascular and heart disease research, including heart failure, myocardial infarction, aortic dissection, transplant outcomes, cardiovascular risk factors, drug efficacy, and mortality trends. Designed for researchers, clinicians, epidemiologists, and data scientists, the package features clinical, epidemiological, and simulated datasets covering a wide range of conditions and treatments such as statins, anticoagulants, and beta blockers. It supports analyses related to disease progression, treatment effects, rehospitalization, and public health outcomes across various cardiovascular patient populations. Package: r-cran-cardiovagal Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-equivalence Filename: pool/dists/noble/main/r-cran-cardiovagal_0.1.5-1.ca2404.1_all.deb Size: 11350 MD5sum: 4e41a0aa817654f664333cc218fc2052 SHA1: 6c58107ca20704a7ec0cd730847083155231dc76 SHA256: 4f9ecf13898b0c6638d3ef5ae1f28244d4c95b5378622149e5e5bd2ba864bac1 SHA512: d622d94fe52829b3cf0d7e986c35cb70bbcf70d7308599e6164aa90734c50cc44644c086f01843086f6bb6560d53490cf2ae20a597655ac4ff169250bd977049 Homepage: https://cran.r-project.org/package=cardiovagal Description: CRAN Package 'cardiovagal' (Automatic Equivalence Testing for Cross-Species CardiovagalHomeostasis) Automates cardiovagal state regulation mapping by translating raw, noisy mammalian heart rate variability intervals into a standardized linear index using fixed physiological anchors. The package incorporates natural log data compression and utilizes two-one-sided tests (TOST) and Bayesian Region of Practical Equivalence (ROPE) thresholds to mathematically verify cross-species homeostatic synchronization. Methodologies for equivalence testing and regional practical equivalence bounds follow Lakens (2017) and Kruschke (2018) . 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Package: r-cran-care4cmodel Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-care4cmodel_1.0.3-1.ca2404.1_all.deb Size: 248136 MD5sum: b20c9050bc6d464d0c1f46588aff18db SHA1: da30fdcffd8860526b38635d15f941fddde1c12e SHA256: 34cc183c92183a66249500cf6757bfa600cd6df47eed75dcd96967c264bfb86c SHA512: 026220a577e0ef24d30d553124880b7ef39c56f2331646a744d49e006dfb3ee8d51da04fc6d18bd3028f8a3e470cfb9f1cd9f918757f555e6fe643ac63228bc8 Homepage: https://cran.r-project.org/package=care4cmodel Description: CRAN Package 'care4cmodel' (Carbon-Related Assessment of Silvicultural Concepts) A simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. 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Package: r-cran-care Architecture: all Version: 1.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor Suggests: r-cran-crossval Filename: pool/dists/noble/main/r-cran-care_1.1.11-1.ca2404.1_all.deb Size: 153940 MD5sum: a7562ab5a153375aa0b3ee592e478765 SHA1: 68a3df2fb80935c4c1b902407cf3ccaded46dc4f SHA256: 95c8352ebd882f52dd663994d3f0b5a9c679dc94a277a9ea8afd4e6480a88054 SHA512: f5985c2f4d8c3a4f359372ec2fb486b0e1cf311a1787a5dc858b2c6f07a2062e721b9563810103a9e11a674e8770bd422a2f767452c31d05aea64b558a7e7db7 Homepage: https://cran.r-project.org/package=care Description: CRAN Package 'care' (High-Dimensional Regression and CAR Score Variable Selection) Implements the regression approach of Zuber and Strimmer (2011) "High-dimensional regression and variable selection using CAR scores" SAGMB 10: 34, . CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available. Package: r-cran-carecall Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-spelling, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-carecall_0.1.0-1.ca2404.1_all.deb Size: 91934 MD5sum: 38f4d39c18033b6f4c33f74d614aad2c SHA1: 4fe0d0ffde74f966bef3021f3b628f16c6c30d4e SHA256: efa83abe6d1ea6f183ac17dc565f9b946b965187e5f1c884e734114a173c5eca SHA512: 21f533191c04aacc9fcf3f9a13f4acd6770b5fd64884aad1b65d83a198cc5cd62170612a7c75d9af80d4eef6bd149450480db3ff6b3499eddf215ff2ce3df3c8 Homepage: https://cran.r-project.org/package=caRecall Description: CRAN Package 'caRecall' (Government of Canada Vehicle Recalls Database API Wrapper) Provides API access to the Government of Canada Vehicle Recalls Database used by the Defect Investigations and Recalls Division for vehicles, tires, and child car seats. 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Package: r-cran-caredensity Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixextra, r-cran-data.table, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-caredensity_0.1.0-1.ca2404.1_all.deb Size: 58168 MD5sum: 816e0a1c70003d9705544d3d13a20862 SHA1: 3c49b5aba897395d31253653f3dd8317ae0f50ec SHA256: 88fb71489a34b6b811e693d5bb7fba30a0921377aeb609a4b3e7d175ab4a5727 SHA512: 87d97d2e0426d9fd372ba37252d595cd65aed99d336e71f1fd06747d1a4dc05747337c96af1959efc38955009a176d0aaac275a44a4f34a5b2b4d5567d3eaeda Homepage: https://cran.r-project.org/package=CareDensity Description: CRAN Package 'CareDensity' (Calculate the Care Density or Fragmented Care Density Given aPatient-Sharing Network) Given a patient-sharing network, calculate either the classic care density as proposed by Pollack et al. (2013) or the fragmented care density as proposed by Engels et al. (2024) . By utilizing the 'igraph' and 'data.table' packages, the provided functions scale well for very large graphs. Package: r-cran-careless Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-careless_1.2.2-1.ca2404.1_all.deb Size: 114092 MD5sum: 4a6bae0e992655ce2fbd4d6b629c70d3 SHA1: 613c5807ddca958137b65ce4de1a77e9f3f5657c SHA256: e093c4d95e74dfc9f79ea96faaa44cb3ca0d7c719e4de7f215f60711bb326a9d SHA512: d77fda40a8fc29ccf0f36061dc69c463ffdeb155aa2a4df92c47218f874488c1cb728addb2892971affb3d4b3bc59719d1af10a4cc6a22d9689f8c7bb600a778 Homepage: https://cran.r-project.org/package=careless Description: CRAN Package 'careless' (Procedures for Computing Indices of Careless Responding) When taking online surveys, participants sometimes respond to items without regard to their content. These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The 'R' package 'careless' provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) . Package: r-cran-caresid Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ca, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-caresid_0.1-1.ca2404.1_all.deb Size: 24254 MD5sum: b0ae7b2ce16ac862b489dc79f58cdc8e SHA1: a63a00a072e02d1ea68e98b044e7faab6c313493 SHA256: a9ca884e5a09e0fe237dbe9c1b4f793abbc170653d6a99452949946081330177 SHA512: e25f2b3be471478cd6c6d2c8848a1ad52db088f086d0e132c425a2d12ba6a0bfc21d52cbd89df186dd32b232f79c241bf9760591e4849d23d07acb318254899d Homepage: https://cran.r-project.org/package=caresid Description: CRAN Package 'caresid' (Correspondence Analysis Plot and Associations Visualisation) Performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold. Package: r-cran-caretensemble Architecture: all Version: 4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3506 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-ggplot2, r-cran-lattice, r-cran-patchwork, r-cran-pbapply, r-cran-rlang Suggests: r-cran-mass, r-cran-catools, r-cran-covr, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-klar, r-cran-knitr, r-cran-lintr, r-cran-mgcv, r-cran-mlbench, r-cran-nnet, r-cran-randomforest, r-cran-rmarkdown, r-cran-rhub, r-cran-rpart, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-devtools, r-cran-roxygen2, r-cran-xml2, r-cran-htmltools, r-cran-dt, r-cran-pkgdown, r-cran-rcmdcheck, r-cran-cyclocomp Filename: pool/dists/noble/main/r-cran-caretensemble_4.0.2-1.ca2404.1_all.deb Size: 3181500 MD5sum: dba90b1990086f29e855033dfaa6f6fa SHA1: b6ce1c24f55da83d67633244ed3f1810cfa43715 SHA256: c3da08eb7418095a5036b4fe741c13807060f99f0097dd4241fd27beafbe9080 SHA512: dec536d709eac0240156f462d5730cbe00872332ffb160383e419cb1b72f472a56576c9805ab9e47c78c95e6d610bccf21ef2d82d00e635d1fba3e09d22fc24e Homepage: https://cran.r-project.org/package=caretEnsemble Description: CRAN Package 'caretEnsemble' (Ensembles of Caret Models) Functions for creating ensembles of caret models: caretList() and caretStack(). caretList() is a convenience function for fitting multiple caret::train() models to the same dataset. caretStack() will make linear or non-linear combinations of these models, using a caret::train() model as a meta-model. Package: r-cran-caretforecast Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-caret, r-cran-magrittr, r-cran-dplyr, r-cran-generics Suggests: r-cran-cubist, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-caretforecast_0.1.3-1.ca2404.1_all.deb Size: 2173740 MD5sum: 4ae3f567b2871e27ad8a608db742eaec SHA1: 3aa89ab642835c98671f3045657e9b1668826846 SHA256: faa1bc7457a9e285518862608aaef45b08f3cb57cce05f7999fad5b325e09981 SHA512: 30489ccdfd9b25c500bd14fcf83f75e23bc562919bcb96fea0d88cdcf4940c6eb3c4e8126bd367bf05f5bb82c1a89b0e24cb1fce297010ed9119984610105cc8 Homepage: https://cran.r-project.org/package=caretForecast Description: CRAN Package 'caretForecast' (Conformal Time Series Forecasting Using State of Art MachineLearning Algorithms) Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively. Package: r-cran-caretmultimodal Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2911 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-ggplot2, r-cran-proc, r-cran-foreach, r-cran-viridis, r-bioc-multiassayexperiment, r-cran-glmnet Suggests: r-cran-testthat, r-cran-randomforest, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-caretmultimodal_1.0.0-1.ca2404.1_all.deb Size: 2878702 MD5sum: f582509cd92473d0eb54f02fca368796 SHA1: a4dfcfb081d18c959a521f6433496f44bdeeb666 SHA256: 35a4e4ca8b8edc8fb35639b84f44169f5dc6e03e7edddd755f9a46b232cb49c9 SHA512: 3976ca996eed981f07eef201fee1b64803945c3c2f9bc8fb289836cb4d1c30741ddc171332766c8bb72f22928d1476c9c1a3b250d59ce83617f12a9625afb1c6 Homepage: https://cran.r-project.org/package=caretMultimodal Description: CRAN Package 'caretMultimodal' (Multimodal Late Fusion with 'caret') Extends the 'caret' framework to support late fusion workflows, enabling users to train models independently across multiple data modalities and combine their predictions into a single meta-model. Designed for developers, data scientists, and biomedical researchers alike, 'caretMultimodal' aims to make late fusion ensemble modelling as accessible and flexible as single-dataset workflows in 'caret'. Late fusion methods are based on Wolpert (1992) . Package: r-cran-caretsdm Architecture: all Version: 1.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-checkcli, r-cran-checkmate, r-cran-cli, r-cran-coordinatecleaner, r-cran-data.table, r-cran-dismo, r-cran-dplyr, r-cran-ecdfniche, r-cran-ecospat, r-cran-fs, r-cran-ggplot2, r-cran-ggspatial, r-cran-glue, r-cran-gtools, r-cran-lwgeom, r-cran-maxnet, r-cran-proc, r-cran-purrr, r-cran-raster, r-cran-sf, r-cran-stars, r-cran-stringdist, r-cran-stringr, r-cran-terra, r-cran-tidyr Suggests: r-cran-bench, r-cran-biomod2, r-cran-blockcv, r-cran-gbm, r-cran-cito, r-cran-covr, r-cran-e1071, r-cran-earth, r-cran-furrr, r-cran-future, r-cran-gam, r-cran-here, r-cran-httr2, r-cran-kknn, r-cran-knitr, r-cran-mapview, r-cran-mda, r-cran-naivebayes, r-cran-nnet, r-cran-parallelly, r-cran-pdp, r-cran-progressr, r-cran-r.utils, r-cran-randomforest, r-cran-rgbif, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-rpart, r-cran-rsnns, r-cran-rtsne, r-cran-sdm, r-cran-tibble, r-cran-usdm, r-cran-withr, r-cran-xgboost, r-cran-testthat Filename: pool/dists/noble/main/r-cran-caretsdm_1.9.7-1.ca2404.1_all.deb Size: 776440 MD5sum: 0f255d0e80191a7d59670f64ef415953 SHA1: 48db8f282ffefb66442bbad25a4905d320013898 SHA256: 86245f91a7e1b26722b1c454184717f8d3e896b4e533fff31f2e14491d4bd643 SHA512: e3a668fdf8411b0fb902c043ff7d156ccd055ed128a06ac08ccf75b4ee63de1d55bcde92a031c968159de30b2a3a20473cd050f4a7fa38dd4bb1679ce7ecba4c Homepage: https://cran.r-project.org/package=caretSDM Description: CRAN Package 'caretSDM' (Build Species Distribution Modeling using 'caret') Use machine learning algorithms and advanced geographic information system tools to build Species Distribution Modeling in a extensible and modern fashion. Package: r-cran-carfima Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-deoptim, r-cran-pracma, r-cran-truncnorm, r-cran-invgamma Filename: pool/dists/noble/main/r-cran-carfima_2.0.2-1.ca2404.1_all.deb Size: 71626 MD5sum: e33cb1f39622ed1f68010a9a6d5fc3dc SHA1: b55abcfa0e12beee71b091657a40a3ef7bfac78b SHA256: c15e1df617d8f307d722fe2ddb50c8f116035a22d294a22a4782909513a37939 SHA512: cc1b33956ef769ae2b3bf39a86ca70014ec617251dac25f8e21998b92e3e13fcc6323416a85ee732103c2b439a88613ac4931bf9ec335aa0db6ac47f952d0880 Homepage: https://cran.r-project.org/package=carfima Description: CRAN Package 'carfima' (Continuous-Time Fractionally Integrated ARMA Process forIrregularly Spaced Long-Memory Time Series Data) We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times. Package: r-cran-cargo Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-cargo_0.4.9-1.ca2404.1_all.deb Size: 254806 MD5sum: f86a41ba65e4064122715ed6a1f377f3 SHA1: 219d5913487d040fd6c354caf95b91c4a30311a8 SHA256: 9823c1613f8bbf0fc8687073596c7043c4c3ab2bd3aae375c84164df31269b55 SHA512: f7c5ac71b12286cbf1ee2a5ed4212e6fc1af7add83a2850303cbe9c7fa0c91b43e2568c57752810689f5fb492319bc7a0c5f65171e0d3e2ba4e6951e452573ed Homepage: https://cran.r-project.org/package=cargo Description: CRAN Package 'cargo' (Develop R Packages using Rust) A framework is provided to develop R packages using 'Rust' with minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' in a manner consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with, nor endorsed by the Rust project. Package: r-cran-caribou Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-caribou_1.1-1-1.ca2404.1_all.deb Size: 55736 MD5sum: 946aa5a4d1651f56a4cc648d2c844c58 SHA1: fdc349d7824db2af4b883c4b6c9cdf145e2d22dd SHA256: 5629617b543f709927b8d62817f4d61152a07db62ab132868d4bdcff6be9b6f4 SHA512: ef10e34e36385727995ec01b74f129afaa283aee234a63d36e8242f605460de43637b4bcd3fe755fea753c1b35d4d8d5e91fc09306281284834be35d645b60ce Homepage: https://cran.r-project.org/package=caribou Description: CRAN Package 'caribou' (Estimation of Caribou Abundance Based on Radio Telemetry Data) Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990). Package: r-cran-carletonstats Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-patchwork Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carletonstats_2.3-1.ca2404.1_all.deb Size: 174124 MD5sum: 04e6cf4a118abaeba42511e17d8cf614 SHA1: c105b60a756eb378b9e42f1c8d062e2c5973650a SHA256: b3b58ec9e033b49ffa4587d7cb0d0e458781679d00ac263a155cc06862590d83 SHA512: e80af8991f6af116f074cca7545d06ca9842247769e4c53eae95dc51faada868f220db07cdc6ba12a6a30412a0acc02521a88021ef11fadc6f276f6563d764e9 Homepage: https://cran.r-project.org/package=CarletonStats Description: CRAN Package 'CarletonStats' (Functions for Statistics Classes at Carleton College) Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions. Package: r-cran-carm Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-dplyr, r-cran-mass Filename: pool/dists/noble/main/r-cran-carm_2.0.0-1.ca2404.1_all.deb Size: 43524 MD5sum: 4364411d6e824cbc9ca4e5af97f98601 SHA1: 266abf3c9cff29cf6147181e32da29bddb2e3202 SHA256: 6d5c196b147e4f0d3f037125952361e68e5b87e744bbdb552d5455e81c56407e SHA512: 55f896347f82155b26ddee707dfa234a2628b7b94b511f0824bd77d433f6032c7fd07c9a98352bcaf43a091cbd167dbc428d092c5d4fd48a5650f3f32fff650d Homepage: https://cran.r-project.org/package=CARM Description: CRAN Package 'CARM' (Covariate-Adjusted Adaptive Randomization viaMahalanobis-Distance) In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2024). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. . In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.. Package: r-cran-caroc Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-caroc_0.1.5-1.ca2404.1_all.deb Size: 229672 MD5sum: aa7fd007d36979d86bd067110f90ba6a SHA1: 5f7b0d7f7adb5574fc087bce59040c19d35cd5c2 SHA256: 0e63ece1cf7ce65debcdc00a5f83692fdcc0cddd90f09047c5266ab6658faa71 SHA512: c2e22cd150269aa1fbde0ae67b02d1685fa3aeb3b229b91a883fe76565a141b846f6d48fe877408c9b4059fb08771ba322714155936f82030b92c88b717a15c2 Homepage: https://cran.r-project.org/package=caROC Description: CRAN Package 'caROC' (Continuous Biomarker Evaluation with Adjustment of Covariates) Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment". Package: r-cran-caroline Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3748 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mass, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-caroline_1.1.2-1.ca2404.1_all.deb Size: 2146082 MD5sum: 9020f32a28402cd6b8ea69e1e8b1834b SHA1: 661a05fd8d0ec6744fd3a0dab11f5f6d8e30c257 SHA256: dd682aa8a8abcc7b706e7f60c7e1dc6b6584e3fff39221108005fa4d20c0b563 SHA512: 6fa358051f9810fbadb4e991cb29bc5b183a85d4ceaf16928868bf9975e044ef8704a84514878946a27b66efaac63424270bee307e0af9793a2d0655f893ff2d Homepage: https://cran.r-project.org/package=caroline Description: CRAN Package 'caroline' (A Collection of Database, Data Structure, Data Conversion,Visualization, Reporting, and General Utility Functions) This R-extension package contains dozens of functions useful for: database style joins [nerge()] & aggregation [bestBy(), groupBy() & regroup()], database migration [dbWriteTable2()], file I/O [write.delim(), read.tab()], text parsing / data mining [m()], data structure conversion [nv(), tab2df()], summarizing & reporting [pct(), fit.1ln.rprt()], character string manipulation [m() & pad()], legend table making [sstable() & leghead()] & plot placement [legend.position()], plot annotation [labsegs() & mvlabs()], data visualization [pies(), spie(), & heatmatrix()], and data exploration [hyperplot(), plot.xy.ab.p()], batch scripting [parseArgString()]. The package's greatest contributions stem from its database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions. The latest additions are plotting functions [confound.grid() & sparge()] that intake a dataframe & formulas to visually resolve variable confounding (Simpson, 1951). Package: r-cran-carpenter Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-pander, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carpenter_0.2.3-1.ca2404.1_all.deb Size: 59150 MD5sum: 06b977ee2c0b2efb33d2c1eb0e6123fb SHA1: 211d01df6158c018a88c8e4038e61d94ecbeba66 SHA256: 0bf8e0509d7be073eab7e23f59880a775299c898e78149c6801f09bfd1317cd8 SHA512: 914cc28a2b0742585846093cb021f5537f925ec6f8eb1c65d59adf9b43a95c63686436a57a3b2a2f5f638ab5de1c5440b1134b2b4063dcc36046c3907d63657f Homepage: https://cran.r-project.org/package=carpenter Description: CRAN Package 'carpenter' (Build Common Tables of Summary Statistics for Reports) Mainly used to build tables that are commonly presented for bio-medical/health research, such as basic characteristic tables or descriptive statistics. Package: r-cran-carrier Architecture: all Version: 0.3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lobstr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-carrier_0.3.0.4-1.ca2404.1_all.deb Size: 26672 MD5sum: d3f8fde5bab5f13bd549ede14969b0a1 SHA1: 4c218039c6e0e6af7939e745d882bf8f700b1a21 SHA256: 1c366f325ae9ae868b8d9e115c1cf5b0a25a2065df0a1749676e90ef930c6a0a SHA512: 611942238c6322ee0ba91a8118f5f24907c7181de3b8aa182b1688d22c0f0daba744abc769aef537e59195d7ef32f5af20a2c851652faafa5e765bce986962e6 Homepage: https://cran.r-project.org/package=carrier Description: CRAN Package 'carrier' (Isolate Functions for Remote Execution) Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With the carrier package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print at the console with their total size and can be easily tested locally before being sent to a remote. Package: r-cran-carsalgo Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pls, r-cran-rlang Filename: pool/dists/noble/main/r-cran-carsalgo_0.5.0-1.ca2404.1_all.deb Size: 42368 MD5sum: 6707cfae260cb703451b4a39800bd63a SHA1: 05597cb878f3f8e1d1b470e987bcf9a56117994a SHA256: 475dda297eb007f2a2fc3053a55d736065bbc4f1e986c0cb5d95811d2f6c5147 SHA512: d2a24606340e85b2c8867551b78abc73fc0eecf32e32bf39c0b1fca2550cc9f4f21fe9371a1f195aa8e0c58ae6ab689074b6e1398904ce26ae4efea39e1ecc3e Homepage: https://cran.r-project.org/package=carsAlgo Description: CRAN Package 'carsAlgo' (Competitive Adaptive Reweighted Sampling (CARS) Algorithm) Implements Competitive Adaptive Reweighted Sampling (CARS) algorithm for variable selection from high-dimensional dataset using Partial Least Squares (PLS) regression models. CARS algorithm iteratively applies the Monte Carlo sub-sampling and exponential variable elimination techniques to identify/select the most informative variables/features subjected to minimal cross-validated RMSE score. The implementation of CARS algorithm is inspired from the work of Li et al. (2009) . This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), hyperspectral chemometrics areas, etc. Package: r-cran-cartograflow Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-reshape2, r-cran-rlang, r-cran-sf, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-cartography Filename: pool/dists/noble/main/r-cran-cartograflow_1.0.5-1.ca2404.1_all.deb Size: 372172 MD5sum: ab671b731339c9588e0bbf0fbcb60b47 SHA1: 4ed9b65643c41d4b9990dc65dd26bde179c88dc0 SHA256: b4d70329de76f90e2e623707ad507a05d0a416409e153544b889f84d25c80764 SHA512: 289bf0884c23feca466644d267ab75a11720c7d5d6b620c157e7f527788ed90fe82a152f0c5621a85ed45e8d7cbff64a435d4264f75fd6ab8127b1f009235f29 Homepage: https://cran.r-project.org/package=cartograflow Description: CRAN Package 'cartograflow' (Filtering Matrix for Flow Mapping) Functions to prepare and filter an origin-destination matrix for thematic flow mapping purposes. 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Package: r-cran-cartographer Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-rnaturalearth, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cartographer_0.2.2-1.ca2404.1_all.deb Size: 162320 MD5sum: 548103b74f838ccb77f96bf391f9a0af SHA1: 25c57d7b618c304b54eb17fa155ec29467bafa44 SHA256: be56c13bc31167a76fdf32a4f30b5acccfaeadb1989b1265283075ec8ebb005e SHA512: 0a7532fb5d825d58e74abee0bbf0036c78e1ea790a4c3bdce4d4593bc9807a8c30103e2b11037716482a4b92d0efff8818d80f8a74e097d38869facbf974ca7d Homepage: https://cran.r-project.org/package=cartographer Description: CRAN Package 'cartographer' (Turn Place Names into Map Data) A tool for easily matching spatial data when you have a list of place/region names. 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Package: r-cran-carwatch Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-clock, r-cran-digest, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-jsonlite, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-shiny, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carwatch_1.0.1-1.ca2404.1_all.deb Size: 1079148 MD5sum: 7da24ca6a6339ab2397736da67d1bf9b SHA1: 203ae23537bb3523763e4c230fb6d18cf71bd646 SHA256: e5616e94c806532cdc1b5ce627ce88214fffcdcf3b6046411ae84942fe060e6a SHA512: fb07d29ab6e5ba89327c6abf388eadbefe7009954583b16df9aa8314b9e3c8650c17f3098290cb306277c9fe606c66bf4b649950913c886f647bf6cc4944a978 Homepage: https://cran.r-project.org/package=carwatch Description: CRAN Package 'carwatch' (Processing of 'CARWatch' Sampling Logs and Saliva Data) Import and reconstruct saliva-sampling studies recorded by the 'CARWatch' application. 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Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) . 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Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) . Package: r-cran-cascadeselect Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-reactr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-cascadeselect_1.1.0-1.ca2404.1_all.deb Size: 830922 MD5sum: 628237dc6f9385755534e1d601f23c9c SHA1: eedd7acb49ff27732e21ee0118ba3fc05bec3e71 SHA256: 51105a13e70c06320d94e2fc0e17e2340559ec87f8880546ee70a22892595ca2 SHA512: 05eae5107d9c5b2c724baab2503fcac1489e1ac5303ec6299c15fb0f79f36968d1e82d446e55d8b66f56a80ec8a4760e2183bc87525e3c47f10109ddcb860f77 Homepage: https://cran.r-project.org/package=cascadeSelect Description: CRAN Package 'cascadeSelect' (A Cascade Select Input for 'Shiny') Provides a cascade select widget for usage in 'Shiny' applications. 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This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) . In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks. Package: r-cran-cascsim Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2html, r-cran-fitdistrplus, r-cran-moments, r-cran-copula, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cascsim_0.4-1.ca2404.1_all.deb Size: 666166 MD5sum: 1b2d6884ea7538937e593674998b6052 SHA1: def9a286a84baa2177f0b7d5ec7027fb2490691c SHA256: 4f9520013011ea0a175059a02a341ff7fd921009c7ae8010ef8e02c7a38c5386 SHA512: f0bb59db87acfeee4dbf37f5988050c74bf398eb3bbd49a3e057d235839b00ae130b4ee2121df7d909ba69ac7319ff938e9c3dc2976e5eb40dbb836041225391 Homepage: https://cran.r-project.org/package=cascsim Description: CRAN Package 'cascsim' (Casualty Actuarial Society Individual Claim Simulator) It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. 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Package: r-cran-casebase Architecture: all Version: 0.10.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5024 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-mgcv, r-cran-survival, r-cran-vgam Suggests: r-cran-colorspace, r-cran-covr, r-cran-dplyr, r-cran-eha, r-cran-glmnet, r-cran-knitr, r-cran-lubridate, r-cran-progress, r-cran-rmarkdown, r-cran-testthat, r-cran-visreg Filename: pool/dists/noble/main/r-cran-casebase_0.10.7-1.ca2404.1_all.deb Size: 3712558 MD5sum: e575ead21850d6970395ff0611ed27eb SHA1: 67e8c92f60bea58ca1b368e638c850dee0d31642 SHA256: 63445fd81c179f69ecf41de9708ea9e2f61267f1d9503a743ba373690194022f SHA512: db294adca85ee858da1cd99f487a7e3bc74ebad5031476a5a403dbf85ebf98f7710283fb918e81a434c3d41eb1679ef018190f14bd27f6e01a83a4d8c2dc4a74 Homepage: https://cran.r-project.org/package=casebase Description: CRAN Package 'casebase' (Fitting Flexible Smooth-in-Time Hazards and Risk Functions viaLogistic and Multinomial Regression) Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) , Saarela and Arjas (2015) , and Saarela (2015) . Package: r-cran-casematch Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-casematch_1.1.0-1.ca2404.1_all.deb Size: 48032 MD5sum: 9ddc567f501ea767be201711b2a2cb85 SHA1: 1569347bc85817a89965b1e252f9370257d65640 SHA256: 20432f5f85810ae4d1297fe8d24a17e686ef80e6fa28efcec40266a23070e448 SHA512: 4d09d1cf4f9e3efda93c73350c18474bccf4ddb5187081daa0084d3ffc2360f0b663ee2b77a537c1b8152ade8fb9d351675b8584b59d1c6b17ccc8303543bfad Homepage: https://cran.r-project.org/package=caseMatch Description: CRAN Package 'caseMatch' (Identify Similar Cases for Qualitative Case Studies) Allows users to identify similar cases for qualitative case studies using statistical matching methods. Package: r-cran-cases Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bindata, r-cran-boot, r-cran-copula, r-cran-corrplot, r-cran-dplyr, r-cran-extradistr, r-cran-magrittr, r-cran-matrix, r-cran-multcomp, r-cran-mvtnorm, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-badger, r-cran-glmnet, r-cran-splitstackshape Filename: pool/dists/noble/main/r-cran-cases_0.2.0-1.ca2404.1_all.deb Size: 252492 MD5sum: e3720702c9506623fb27bc0e5a13ac35 SHA1: f54af2a63b01296b58913fcc91172b41ee5befc5 SHA256: 63359a169b1d18ddc0b702f5ef4dd0c3a2b9ec6d3496c814652035816fc2bd57 SHA512: bb6cf7b6617214a5c4a90984edfbec5f0cd83f10bdc215b7329be426b936c68546e079e77ea6f7df99f972fbfe18d237ccad959f8c62cfc623ea542ca68f6966 Homepage: https://cran.r-project.org/package=cases Description: CRAN Package 'cases' (Stratified Evaluation of Subgroup Classification Accuracy) Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). ). Package: r-cran-casidata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5132 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vcdextra, r-cran-car Filename: pool/dists/noble/main/r-cran-casidata_0.2.1-1.ca2404.1_all.deb Size: 4710278 MD5sum: 6de259e2a4c57fce0ee6f4f40321bce4 SHA1: 5974ddadbba8a8ed71f63050d65d6d2875b70e11 SHA256: e5f5f6161efd7317f4b6013e5c4e074f710195b26e70bcd925b57295388ce8f9 SHA512: 9a85dfa05f4c2d8cb0d5f8ce51f08006edb5b7491569137b9c39a812636caa9d373eca87568acef103f525dc526859d8521ca3a41d4c3d48aee43efc7c1e8111 Homepage: https://cran.r-project.org/package=CASIdata Description: CRAN Package 'CASIdata' (Datasets from Computer Age Statistical Inference) Provides the datasets from Efron & Hastie (2016, ISBN: 9781108107952), "Computer Age Statistical Inference: Algorithms, Evidence, and Data Science", in an accessible R format for those who want to use them for study or to try to reproduce analyses from the book. Package: r-cran-casimir Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-purrr, r-cran-rsample, r-cran-tidyr, r-cran-rlang, r-cran-collapse, r-cran-stringr, r-cran-options, r-cran-withr Suggests: r-cran-testthat, r-cran-tibble, r-cran-tidyverse, r-cran-ggplot2, r-cran-future Filename: pool/dists/noble/main/r-cran-casimir_0.3.3-1.ca2404.1_all.deb Size: 327558 MD5sum: abc7cf768e52f0eec43ff124d1d82178 SHA1: f27b73bdc388f72df5a4f40d97cc9d84ca4da546 SHA256: 65c958aa34fe726ccbea0ac908ab3f9ad52d8d567be2e32f891b1e6144511f0a SHA512: 219366a2719971a2d18c844a5721803a2630fe52871b41c42dd070172007dae25bce2a1afa0f5d4e5ac84ea4a85cf567e9514dfc1774f6531aee165dc971762c Homepage: https://cran.r-project.org/package=casimir Description: CRAN Package 'casimir' (Comparing Automated Subject Indexing Methods in R) Perform evaluation of automatic subject indexing methods. The main focus of the package is to enable efficient computation of set retrieval and ranked retrieval metrics across multiple dimensions of a dataset, e.g. document strata or subsets of the label set. The package also provides the possibility of computing bootstrap confidence intervals for all major metrics, with seamless integration of parallel computation and propensity scored variants of standard metrics. Package: r-cran-casino Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-crayon, r-cran-r6, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-beepr Filename: pool/dists/noble/main/r-cran-casino_0.1.0-1.ca2404.1_all.deb Size: 344264 MD5sum: 7463f49fa55d5b00e879b4577ef52953 SHA1: 8e363802e6792059f4aad8c70916b8e2c73a257f SHA256: c405194fd56336c257f82799bdad4197d766919bad7dc0e7cfdedca35c80b527 SHA512: 5e026ea4bf05f83daf656543511e2fccd1f37c265754a2e1a18a49b33631940d7c3a8144a39be95104cfb56bde5a8eb0f76b2bdbf47c496773034dd49764c9c5 Homepage: https://cran.r-project.org/package=casino Description: CRAN Package 'casino' (Play Casino Games) Play casino games in the R console, including poker, blackjack, and a slot machine. Try to build your fortune before you succumb to the gambler's ruin! Package: r-cran-casmi Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-entropyestimation, r-cran-entropy Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-casmi_2.0.0-1.ca2404.1_all.deb Size: 63338 MD5sum: 54507e4980e2260c047a8ce3353639ac SHA1: ee01440b0392885f401268b45e7e50ced1045581 SHA256: 57bc370cc8bd643eaaddfb2704c587374f476398d1122d30fcca4728b8fb70c6 SHA512: ebcb8b3f823633d6d70003cd91c71b60979c304e04b8b453b5eacc085b89be972136dda10512d963ac351e467c59e8ac408b1998dbac181f9fbc7991cb73f607 Homepage: https://cran.r-project.org/package=CASMI Description: CRAN Package 'CASMI' ('CASMI'-Based Functions) Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) , and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) ) or contact the package author for the latest updates. Package: r-cran-cassandra Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bipartite, r-cran-reshape2, r-cran-magrittr, r-cran-vegan, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-boot, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-proc, r-cran-lattice Filename: pool/dists/noble/main/r-cran-cassandra_0.2.0-1.ca2404.1_all.deb Size: 168052 MD5sum: 9f7dc70d483e48ba58e86ef8747e1d36 SHA1: 89fcf50dae5fcedd0c5b7f1f7bbef9093c60ea10 SHA256: f1dc06e5e4f1e3e32a97f219d3c984b0291251a70a866311442b3c41150cf5ea SHA512: 6eddd83fff3ed05cb43719a415159c1243fabb419c43e398c57f0c78f149472351bab8307262bd9cf01038fd21f81d577bf60cf377024481021d6bb0b4061512 Homepage: https://cran.r-project.org/package=cassandRa Description: CRAN Package 'cassandRa' (Finds Missing Links and Metric Confidence Intervals inEcological Bipartite Networks) Provides methods to deal with under sampling in ecological bipartite networks from Terry and Lewis (2020) Ecology Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. 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Package: r-cran-cassowaryr Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-alphahull, r-cran-splancs, r-cran-interp, r-cran-energy, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-progress, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mgcv, r-cran-ggally, r-cran-tidyr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-cassowaryr_2.0.2-1.ca2404.1_all.deb Size: 506930 MD5sum: 31d817f5fc1dd88ad7ad653a0ef62bd4 SHA1: 007d300c0e5c1fbc84abdcd6fb28ae619e5f1675 SHA256: 298222d9989efa44cf7f3182d018c0c26c2e4acef6e2d9a9ac73533e1da971b8 SHA512: 9b912d1b95a1415d1b83ea82897da26f4e67c24919eac347a83aa9fbd84c83e42cb7a22dd739fc17a2c423ec29d6562b52db9215e8b3cb8dde13597d82e0db73 Homepage: https://cran.r-project.org/package=cassowaryr Description: CRAN Package 'cassowaryr' (Compute Scagnostics on Pairs of Numeric Variables in a Data Set) Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided. 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'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) ; Meyer et al. (2019) ; Meyer and Pebesma (2021) ; Milà et al. (2022) ; Meyer and Pebesma (2022) ; Linnenbrink et al. (2024) ; Schumacher et al. (2025) . The package is described in detail in Meyer et al. (2026) . Package: r-cran-castgen Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-rdpack, r-cran-vcfr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-castgen_1.0.2-1.ca2404.1_all.deb Size: 29764 MD5sum: f2ab34e1ff011690cc3eae95f6301b73 SHA1: 8fe10b220d24f99760834a6f0cf330f291f51414 SHA256: 18c7c423b3f395cf80743f0cdbb264c1cf2199e38dca7e3e2ad2df649c6b84a0 SHA512: a91191d242dfc9fcf50b3a336a34f35b91dfa9fd40cd7c696ce8ef5e4b308226c7dbd270a205fdfea568f43cee7079fcf1af2a31b27945ec1e64f36442000ff1 Homepage: https://cran.r-project.org/package=castgen Description: CRAN Package 'castgen' (Estimate Sample Size for Population Genomic Studies) Estimate sample sizes needed to capture target levels of genetic diversity from a population (multivariate allele frequencies) for applications like germplasm conservation and breeding efforts. 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Package: r-cran-cat2cat Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-caret, r-cran-dplyr, r-cran-e1071, r-cran-fixest, r-cran-forcats, r-cran-knitr, r-cran-magrittr, r-cran-randomforest, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-cat2cat_0.6.1-1.ca2404.1_all.deb Size: 2627554 MD5sum: 2274fdbf16a4f6522e5189c3959ad238 SHA1: 74a778ab9fc52e0f5a837a30e7d9173838a6948d SHA256: 23a781224983da3e2fa76d84272f74192db10be30c93c50f4e62d61021a6f457 SHA512: c9f4113ccf79bb1ab5ace804e154772dc806b9baae8fb963dd6e7c28e665e658d2b6845963114f6c6cf362f04be5fa9ad35154b7fdb19b1104896ef4764995b4 Homepage: https://cran.r-project.org/package=cat2cat Description: CRAN Package 'cat2cat' (Handling an Inconsistently Coded Categorical Variable in aLongitudinal Dataset) Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. The main rule is to replicate the observation if it could be assigned to a few categories. Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. This procedure was invented and implemented in the paper by 'Nasinski', 'Majchrowska', and 'Broniatowska' (2020) . Package: r-cran-cata Architecture: all Version: 0.1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cata_0.1.2.0-1.ca2404.1_all.deb Size: 213808 MD5sum: 9b44d3217095803218b02cc89d1012fd SHA1: d8f5cccf31db24f1049f175be68e033cb452a76b SHA256: f66a274d0f61c6bdd47a60f96206a710d1e7b59fb31494e2eace219918fb7d39 SHA512: c93f7e5366e977601c83159069ab4d8b4c95942242e0f93d2ff48465fb7387acbc867bb8e90dc80894885d04aca2af9eae0586020139f3977980f6a689579cd7 Homepage: https://cran.r-project.org/package=cata Description: CRAN Package 'cata' (Analysis of Check-All-that-Apply (CATA) Data) Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) . Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022) . Consumers can also be clustered on their product-related hedonic responses; see Castura, Meyners, Pohjanheimo, Varela & Næs (2023) . Permutation tests based on the L1-norm methods are provided; for details, see Chaya, Castura & Greenacre (2025) . 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Bundles hands-on modules for importing data, missing values, outliers, text cleaning, merging, visualization, and basic statistics, plus a set of AI-skills pages (prompting levels, a prompt gallery, AI safety rules, and a methodology case study) and an interactive quiz with per-session topic filters. Launch the app with run_cata(). Package: r-cran-catastro Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1427 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-mapspain, r-cran-rappdirs, r-cran-sf, r-cran-terra, r-cran-xml2 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-tibble, r-cran-tidyterra, r-cran-withr Filename: pool/dists/noble/main/r-cran-catastro_1.1.0-1.ca2404.1_all.deb Size: 1094840 MD5sum: 17f54cc709323cc198514a303f2384b9 SHA1: 702177e7f8ba4149fd5c2090a8cba94230d32018 SHA256: 605c12a9485b5a2c6edaa0b7a19e508107888bcf4dbf935e1ac89ed3053e54b9 SHA512: 06ea1b6e3f11ff6f13aecf7dfdc221381ce242bf0d631a5551c3dec532a6aeabfe2efa70eed735dcbede496811b7fe51815638ae6aa73de8b71ca1377acacb52 Homepage: https://cran.r-project.org/package=CatastRo Description: CRAN Package 'CatastRo' (Interface to the Spanish 'Catastro' Web Services) Access public spatial data from the Spanish 'Catastro' through its Infrastructure for Spatial Information in Europe ('INSPIRE') and related web services. Retrieve parcel, building, address and map image data and convert between property reference codes and coordinates. Package: r-cran-catchmentacs Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidycensus, r-cran-tidyr, r-cran-tigris Suggests: r-cran-classint, r-cran-htmltools, r-cran-knitr, r-cran-leaflet, r-cran-openrouteservice, r-cran-osrm, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-catchmentacs_0.6.0-1.ca2404.1_all.deb Size: 2356700 MD5sum: 1fac9a960c9ae4ed5a1860062145a0f3 SHA1: 9f6463bffc1aeae1706e0cefb5be7d149e720255 SHA256: 1dfc81c0f5d1f68946a5e898864533f8c7e294893fe583ac82f3be812a3e5ab2 SHA512: 64a77e8407963e7c529f8f0f7838a399d6dfb280843755cf993e9ba18908e16f8863a1cbcb44fbd10491b257c92c2e8ee78b7505e75adb3cccbf92123c1276e4 Homepage: https://cran.r-project.org/package=catchmentACS Description: CRAN Package 'catchmentACS' (Isochrone-Based Area-Weighted ACS Aggregation) Computes American Community Survey (ACS) estimates for the area within a given drive time of each of a set of points, such as the locations of pre-kindergarten classrooms. Such an area is called an isochrone. The drive-time areas come from a routing service, either the 'Open Source Routing Machine' ('OSRM', ) or 'openrouteservice' (), and the ACS 5-year estimates of census tracts come from the Census Bureau () through the 'tidycensus' package, one state at a time. The tracts that overlap an area are combined by area weighting, which uses two weights: the share of each tract's area that lies inside, for counts and rates, and each tract's share of the overlapping area, for the medians and per-person values of three ACS tables (median household income, median home value, and per capita income). A median or per-person value from any other table is added up like a count. Counts, medians, per-person values, and five rates are returned with a margin of error at a chosen confidence level, 90 percent by default, and with a record of the settings, inputs, and package versions behind the run. The five rates are the poverty rate, the shares of households receiving Supplemental Nutrition Assistance Program (SNAP) benefits and Supplemental Security Income, the unemployment rate, and the labor force participation rate. The margins of error of counts and rates use the approximation formulas in chapter 8 of U.S. Census Bureau (2020) "Understanding and Using American Community Survey Data: What All Data Users Need to Know" . For a count the formula for a sum is applied to the weighted tract estimates, with the weights treated as fixed. For a rate the default is the formula for a ratio. Its margin of error is at least as wide as that of the formula for a proportion, which the handbook gives for ratios whose numerator is part of the denominator, as it is in all five rates. The margins of error of medians and per-person values are an approximation made by the package. Package: r-cran-catcont Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat, r-cran-data.table Filename: pool/dists/noble/main/r-cran-catcont_0.5.0-1.ca2404.1_all.deb Size: 28498 MD5sum: 3336af35269379bc72cd1a2bc514ab68 SHA1: b44fe4c7e10b792e11884c4fe1dccf91649533ac SHA256: 4502e0bdc36e58cefb34f214f02211ce24ed33ba55ba60474655ceea99631e24 SHA512: 75793bbabd303c79e9e8e3ac0a2c9e68beaedc9904325000c7f16a0efc91f58d593f123414a1325b7adfa96501a1940a4e5e7fd328258591edfd6fa7473e8cd4 Homepage: https://cran.r-project.org/package=catcont Description: CRAN Package 'catcont' (Test, Identify, Select and Mutate Categorical or ContinuousValues) Methods and utilities for testing, identifying, selecting and mutating objects as categorical or continous types. 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The names of the examples refer to the chapter and the data set that is used. Package: r-cran-catdataanalysis Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-catdataanalysis_0.1-5-1.ca2404.1_all.deb Size: 72520 MD5sum: 030e6287d4debed63513b8ff2b039e58 SHA1: c986eefd7f0ff8e2d4d42ad91b436876bb3cc364 SHA256: 2a33dd1da7d507740a55a81af4c59ec1017af4d597b07b16b312415e6344837b SHA512: a975bdd75949b2e18f2d7e1ef7f4733499c00c4a6d7d357957b3a7d8225461eb62da8c720c498d07c424cf109f72e679ef5b54fd9798b8c2de233a333d1242e9 Homepage: https://cran.r-project.org/package=CatDataAnalysis Description: CRAN Package 'CatDataAnalysis' (Datasets for Categorical Data Analysis by Agresti) Datasets used in the book "Categorical Data Analysis" by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book. 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It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data. Package: r-cran-cate Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-esabcv, r-cran-ruv, r-bioc-sva, r-cran-corpcor, r-cran-leapp Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-cate_1.1.1-1.ca2404.1_all.deb Size: 619272 MD5sum: 3f1388702a759c24a8df1788dca9992a SHA1: 0430a594853e927070603f957a2d4a80ffdf769e SHA256: e82d77108816e5dedf6f87fe68a117d3eb9c0b339c8a40c9c1bf65ca465c9f80 SHA512: 0a285102ee0e14c03cfbdf1bcc09bcc98d2a1ef7cfcb3e44739b3b562326f54ba609180bd94cceabddb5e4f28cb3850df2b1b2b03837e06f53c9977c9dbb78d7 Homepage: https://cran.r-project.org/package=cate Description: CRAN Package 'cate' (High Dimensional Factor Analysis and Confounder Adjusted Testingand Estimation) Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing. Package: r-cran-categoryencodings Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-sparsepca, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-categoryencodings_1.4.3-1.ca2404.1_all.deb Size: 60016 MD5sum: 4d37aeadcd28bee4d72eea25a816e2df SHA1: 4305e10d826b62b9c36527ed85e147571049e684 SHA256: 0a6abcc9771d192d0691d0fdacd3fc42b2d3df10cc5df233f5a6820c93c3aa52 SHA512: b8dec902a72d3f7ecc003e5356d8d762d32c515e52db39f20ecd1d3707069def60d51edf29071e7925332d6fcaeade9bc7b0b7d9c6e2c2dd2f2473a797a74998 Homepage: https://cran.r-project.org/package=categoryEncodings Description: CRAN Package 'categoryEncodings' (Category Variable Encodings) Simple, fast, and automatic encodings for category data using a data.table backend. Most of the methods are an implementation of "Sufficient Representation for Categorical Variables" by Johannemann, Hadad, Athey, Wager (2019) , particularly their mean, sparse principal component analysis, low rank representation, and multinomial logit encodings. 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Package: r-cran-catfda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-rlang, r-cran-foreach, r-cran-dorng Suggests: r-cran-doparallel, r-cran-testthat Filename: pool/dists/noble/main/r-cran-catfda_0.1.0-1.ca2404.1_all.deb Size: 62258 MD5sum: 0eba40ed9fba0c922cc1a04b3c008637 SHA1: 3d4a9875f7d0860f54bf49bdc3ffdcdaeeb8716c SHA256: ae0e19df04e5c0ec2a5944f21812af6f87ae0c38ec6a22ab17ca3cc6c8d91966 SHA512: f1e05eda702c39d8c5d7f15077dfe305016cd0b0e6f03088927d5e715188c00e3e9da007f8fc2b316614495b23b870358c62ac9f7f8d5b0964166b9fac63ff91 Homepage: https://cran.r-project.org/package=catfda Description: CRAN Package 'catfda' (Statistical Analysis for Categorical Functional Data) Implements methods for estimating latent Gaussian processes from categorical functional data using binomial, probit, or multinomial GAM-based approaches, and for clustering individuals via multivariate functional principal component scores. Methods are described in Champon et al. (2026) . Package: r-cran-catfun Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-epitools, r-cran-desctools, r-cran-cli, r-cran-magrittr, r-cran-hmisc, r-cran-broom, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-forcats Filename: pool/dists/noble/main/r-cran-catfun_0.1.4-1.ca2404.1_all.deb Size: 99934 MD5sum: ab1976fdc6a18fa54d449a5c5931c8d6 SHA1: a06cf5828cfacda3371f59d899d69fc8372c8706 SHA256: 9c11b2a8884dfe818adde67e6ac38b7f2f9c303df1229ed8fe7b5d6b23a0dc2f SHA512: 46b15917ecb8fb15cc502b42fe0a7150afe109bd26fe7ee5075f90347c07271fd277f82ce3388f0ed868c7b317cc8c23bfa46b61050025de9978e0c1a10e0546 Homepage: https://cran.r-project.org/package=catfun Description: CRAN Package 'catfun' (Categorical Data Analysis) Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) and Alan Agresti (2002) . 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Provides tools to analyse 'intraspecific' trait variability and its consequences for community assembly. Implements a framework using individual-level trait data to decompose variance at the population, species, and community levels. Methods are described in 'Taudiere' and 'Violle' (2016) . Package: r-cran-catmap Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forestplot, r-cran-metafor Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-catmap_1.6.4-1.ca2404.1_all.deb Size: 57584 MD5sum: 659092c6b1b63d6f4c1c3b1ff36f3e76 SHA1: 36cdb34d235e97c7baefa0e315c8763e2c8038d4 SHA256: 088848225609a025b1f6f408424ab2a7716c5e6395e3e7d463aef92b7f2d79c6 SHA512: f6c77e82c553dd3fa623aa7b5cf0315fa62202e133ee8ae33e01ce6159ca1dd15031df2daadea619cd9a7566dc5dfb7a19c1742dd7ebfa59d2092381a4dc6651 Homepage: https://cran.r-project.org/package=catmap Description: CRAN Package 'catmap' (Case-Control and TDT Meta-Analysis Package) Although many software tools can perform meta-analyses on genetic case-control data, none of these apply to combined case-control and family-based (TDT) studies. This package conducts fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986) ] meta-analyses on combined genetic data. Specifically, this package implements a fixed-effects model [Kazeem and Farrall (2005) ] and a random-effects model [Nicodemus (2008) ] for combined studies. Package: r-cran-catmaply Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 21351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-viridis, r-cran-lubridate, r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-catmaply_0.9.5-1.ca2404.1_all.deb Size: 1447854 MD5sum: 49a029bd400b5053f610a88e06148535 SHA1: 00c1b1f76ab7feaf8987205b04f5188d349c0070 SHA256: 4ed79ca78d79cb22cc300f943797d415ea5f855ba61f692fbb42afaec470ed70 SHA512: d56b2f868dfbb1b10b7ae0a5a17f7dc2c2a5d9459e480c55b850364fcfc73dacb30d64ac4f7ba82612330670e0ec6aeb0316fd9538e429cb5adbf797874a3b9c Homepage: https://cran.r-project.org/package=catmaply Description: CRAN Package 'catmaply' (Heatmap for Categorical Data using 'plotly') Methods and plotting functions for displaying categorical data on an interactive heatmap using 'plotly'. Provides functionality for strictly categorical heatmaps, heatmaps illustrating categorized continuous data and annotated heatmaps. Also, there are various options to interact with the x-axis to prevent overlapping axis labels, e.g. via simple sliders or range sliders. Besides the viewer pane, resulting plots can be saved as a standalone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app. Package: r-cran-catmodeling Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-pracma, r-cran-geosphere Filename: pool/dists/noble/main/r-cran-catmodeling_0.0.2-1.ca2404.1_all.deb Size: 284060 MD5sum: f314a514b0aa54ba02f3e74539966a94 SHA1: 60ec82327ef1612a6949004cb92899d3aa6a02d3 SHA256: ead47057dc9e796c7d09176230fb11fef2d23f1ecdccb1d7d9616f19a26cb99a SHA512: 98466bb285681b0d2a43afbd1f6596228ab2edc6c4cd05594d2e8213ebfe2068d1616b730ac944ce7f3cbfa043042fd912e0c59d7aeebbc88add27d64778b20a Homepage: https://cran.r-project.org/package=catmodeling Description: CRAN Package 'catmodeling' (Catastrophe Model Simulation and Adjustment) Manipulation of catastrophe model outputs, including tasks such as simulating year loss tables (YLTs) from event loss tables (ELTs), adjusting the frequencies of events in YLTs to create new YLTs, applying catastrophe exceedance of loss contracts (catXL), applying hours clauses, and calculating diagnostics from ELTs and YLTs, such as average annual loss and exceedance probability curves. Frequency adjustment routines are based on the paper "A new simulation algorithm for more precise estimates of change in catastrophe risk models, with application to hurricanes and climate change", Jewson, S. (2023); . Version 0.0.2 no longer uses rust. Package: r-cran-catool Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-catool_1.0.1-1.ca2404.1_all.deb Size: 82714 MD5sum: 471c6492c190cd162c33c73dcaa1f282 SHA1: 2fc40febf6d2acb21d32d58d2d03b00fe185a036 SHA256: 67350b2de87ce7fcf9d2b8a4be434ce23c8c7ef0344ed1536aad4a94b6a320ac SHA512: 2cd86f7000b99c07382de0dea5bc682812b9c94fb2782f7bb436a7c6da9fdc7928f3f9e94ff19072566f3ed330103b37bc34b9f936986a8f5427679e12ed320b Homepage: https://cran.r-project.org/package=catool Description: CRAN Package 'catool' (Compensation Analysis Tool for Instructor Overload Pay) Calculates equitable overload compensation for college instructors based on institutional policies, enrollment thresholds, and regular teaching load limits. Compensation is awarded only for credit hours that exceed the regular load and meet minimum enrollment criteria. When enrollment is below a specified threshold, pay is prorated accordingly. The package prioritizes compensation from high-enrollment courses, or optionally from low-enrollment courses for fairness, depending on user-defined strategy. Includes tools for flexible policy settings, instructor filtering, and produces clean, audit-ready summary tables suitable for payroll and administrative reporting. Package: r-cran-catpredi Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpe, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-mgcv, r-cran-rgenoud, r-cran-rms, r-cran-survival Filename: pool/dists/noble/main/r-cran-catpredi_2.0-1.ca2404.1_all.deb Size: 135860 MD5sum: e493845cf42e02226cdd6adaa516812b SHA1: 459bce11786be975787d1bbebaf1a9ade6211402 SHA256: 61947eefb5098dd3efccb26c89026c0d3281b86f630453243a547ed76b76be60 SHA512: b0a9ebb6a6ad67ec795b1cbac8461cafee1819fc4aafd1937ab7ad4b4fb6799a263968c6f9c59c451d7b4b59ef37955c8dbefd936e1eb31d55575852139e9940 Homepage: https://cran.r-project.org/package=CatPredi Description: CRAN Package 'CatPredi' (Optimal Categorisation of Continuous Variables in PredictionModels) Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) . I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) . Package: r-cran-catr Architecture: all Version: 3.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-catr_3.17-1.ca2404.1_all.deb Size: 552582 MD5sum: 1d40374e5466f2267c4f5bd568250aa8 SHA1: e99e64f1d43dfa7ac7b1ff2874addc8b7067b587 SHA256: 173172e8b45fdc0396d6482d2c8bd9257de1e5bcd40e92f003c31aad862776fd SHA512: bde1532be9317ebbb1f859a49a137b74d5cf2f8a4f3e3dec84264fe3ac40bd8e829d5e08ddd49b6b14d86bf849f7a6e27ac4b7048131736e22e500db9bcb89dc Homepage: https://cran.r-project.org/package=catR Description: CRAN Package 'catR' (Generation of IRT Response Patterns under Computerized AdaptiveTesting) Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) ). Package: r-cran-catregs Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2072 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tidyverse, r-cran-ggplot2, r-cran-mass, r-cran-emmeans, r-cran-pscl, r-cran-nnet, r-cran-marginaleffects, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-epi, r-cran-dplyr, r-cran-ordinal, r-cran-nlme, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-catregs_1.3-1.ca2404.1_all.deb Size: 1881496 MD5sum: e536440fef4c4febbea61749786766ac SHA1: 3a54b090e26caf18a67f5ab47b89728abd2d64c3 SHA256: 73fb7189d015d3953d2a5f7909f904de674b52dd23d18612302b4118d85a84c2 SHA512: 242978e1ccbadab42090da03686cfffc97f0865030081345ebf6ba3cb83a60f78e315860364cfed4be742e86f426818fd810f601b721806c1f3fb3f9e463bcf7 Homepage: https://cran.r-project.org/package=catregs Description: CRAN Package 'catregs' (Post-Estimation Functions for Generalized Linear Mixed Models) Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518). Package: r-cran-cats Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-openxlsx, r-cran-forcats, r-cran-epitools, r-cran-zoo, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-gtools Filename: pool/dists/noble/main/r-cran-cats_1.0.2-1.ca2404.1_all.deb Size: 72328 MD5sum: 00566e1a57a82668abd48c9e75ad48d9 SHA1: 3d3987a74f0095d843e51829b4c9c11da33dbaf9 SHA256: 347ea76a0373ec48a0763db3261a8de66b4e0783f5056103f8e0ed4501471e32 SHA512: a66bd215a4503a7b04a06de1299e7c27b74140f5f83496e08d64f4ef59c1ad417f6c7e0fe1bfb1c1b87970341d282b053ad2e45fb9fc0d1f383cbf7e2db7e3ec Homepage: https://cran.r-project.org/package=cats Description: CRAN Package 'cats' (Cohort Platform Trial Simulation) Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated. Package: r-cran-catseyes Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-emmeans Filename: pool/dists/noble/main/r-cran-catseyes_0.2.5-1.ca2404.1_all.deb Size: 27624 MD5sum: 403451c0a054100ce1f1b848e0a95121 SHA1: f2533abf77c96a07811dff491e509435cf4842c7 SHA256: 0445564dcdd8e486b0cd951d3e0ffd4a1549fb5cc5991028a3610d17d91b404c SHA512: 70399d05013a4f6525c5d7e6b91973c85baa4ed6c6a7c620c3ca0a3cd8394c23494f9884f35c21458df25dde835a8ecaaf2c615ca5323a86bf0555e19e66c76c Homepage: https://cran.r-project.org/package=catseyes Description: CRAN Package 'catseyes' (Create Catseye Plots Illustrating the Normal Distribution of theMeans) Provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. pmid:24220629. Package: r-cran-catt Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-catt_2.0-1.ca2404.1_all.deb Size: 12166 MD5sum: ae141208cd0c4170f6bdee7a9887b55e SHA1: f34bfc15a2f8c1cc373cd35dddfac949d31a13d4 SHA256: c61e58a4ab9b8efe95f6feb5a7e68ab87c48270458f22f9bab3e7de7dcebd40f SHA512: 4b737f47c27799bc8d355f18af1534896df90bab096ce63ed216ce8f2524eb8781dc03fa7f3c71e1dcda76a1890635c8291be97f540fe5d1fc1d89e0fc696842 Homepage: https://cran.r-project.org/package=CATT Description: CRAN Package 'CATT' (The Cochran-Armitage Trend Test) This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default. Package: r-cran-cattexact Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cattexact_0.1.1-1.ca2404.1_all.deb Size: 27154 MD5sum: 8e16de06d27278578770b2874a964b3a SHA1: 971276e45a588f8593cc41b46494634eaee6f1fd SHA256: 6dc92a62024ef9bb8121addff09383b1d8a1ed0fb6c24ac8c5317581bce4bf44 SHA512: 3967e98265f3ae13da397cf0bc004c188c8c4dce57c02686f0b397dd2fc1f45d32fde36be53c4c6b794761018b6baf63f13dd36d4193f40e104a042152351c13 Homepage: https://cran.r-project.org/package=CATTexact Description: CRAN Package 'CATTexact' (Computation of the p-Value for the Exact ConditionalCochran-Armitage Trend Test) Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) . Package: r-cran-catviz Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-catviz_0.1.1-1.ca2404.1_all.deb Size: 103716 MD5sum: 38424fa6a1b0c29566edfdd1d3ce853b SHA1: b1831376960ab426809cae19e0e7c3079422f313 SHA256: 23daf0bea39b82d8a1ecaec8b332b4caae995e498b5468c82996757879633141 SHA512: 4dd3771190dd9fe0c1095e72b06cbbe87923bc5a32a988e2bc7568b0e292852f0f029c9740248bd8a7b00e6c8643f6f8ee0de461a636225eb865e37659a4b531 Homepage: https://cran.r-project.org/package=catviz Description: CRAN Package 'catviz' (Visualizing Causal Assignment Trees for CSDiD and DR-DDD Designs) Tools for constructing, labeling, and visualizing Causal Assignment Trees (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs. not-yet-treated composition, and subgroup structure, and produces publication-quality diagrams and summary tables. Current functionality focuses on data-to-node mapping, node counts, cohort-year summaries, and high-quality tree plots suitable for empirical applications prior to estimation. Methods are based on Callaway and Sant'Anna (2021) , Sant'Anna and Zhao (2020) , and Kilanko (2026) . Package: r-cran-cauchycp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-cauchycp_0.1.1-1.ca2404.1_all.deb Size: 19108 MD5sum: ef97f731b5d9648f084c7346ebfaa292 SHA1: 628959e6198033f59d9b2c8a34e611e3f8d38966 SHA256: d7643afa0119b5adcb3f539720afe10a7cf198f60addbf0f05ce6e1bdd6a5fa1 SHA512: 0a726f749eac200fc27e2f2ea23295ab6d6768049bc3af0427af90b90dc39a69ba5462d918cc0b9690d32b7fd857003a37217f910896da39d4e7b7ce9d929c57 Homepage: https://cran.r-project.org/package=CauchyCP Description: CRAN Package 'CauchyCP' (Powerful Test for Survival Data under Non-Proportional Hazards) An omnibus test of change-point Cox regression models to improve the statistical power of detecting signals of non-proportional hazards patterns. The technical details can be found in Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen (2021) . Extensive simulation studies demonstrate that, compared to existing tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small alpha levels; 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient. Package: r-cran-cauchypca Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-cauchypca_1.4-1.ca2404.1_all.deb Size: 25034 MD5sum: 36f756d188a69a029f1a7f4e72531030 SHA1: 0156cca24f520c198229090b0556468fcc603baa SHA256: b59f8f463d22598a791454fe38af117e3554fe61f6f57bb5e4f09706e65404e4 SHA512: 11fc5eeaadcb669bdcef9346393916b81239bbdf45119c07a5616b0d06db4ab73ac34ab0350cb80ec6463f34eb79edf65c9a2063d477e590c782fa2aa770fd7a Homepage: https://cran.r-project.org/package=cauchypca Description: CRAN Package 'cauchypca' (Robust Principal Component Analysis Using the CauchyDistribution) A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. . Package: r-cran-cauchyreg Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositional, r-cran-glmnet, r-cran-rfast Filename: pool/dists/noble/main/r-cran-cauchyreg_1.0-1.ca2404.1_all.deb Size: 33498 MD5sum: d064032fa3a1c30a3dc5068943f0b000 SHA1: 78ed450c03d423e568c95e49bfb9ef44aae4954b SHA256: 493ed5b6ef1f562f7cd919d440f24148dfee68753695461d12efe5cffdc8e72b SHA512: c645085da770324c8db19cf42f5f7204faaac660d7ec581f3dbd04903f4668362d90906939063e28ef346d1749c9440a5aec07d2b536c993cdedfc92b7652b15 Homepage: https://cran.r-project.org/package=cauchyreg Description: CRAN Package 'cauchyreg' (Cauchy Regression) Cauchy regression modelling and LASSO to perform variable selection are included in this package. Cross-validation is performed to choose the optimal value of the lambda parameter. LASSO is based on the IRLS algorithm. A relevant paper is . Package: r-cran-caumedi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-caumedi_0.1.1-1.ca2404.1_all.deb Size: 41342 MD5sum: 8a71e95d8c00c15c91a506079353ec4d SHA1: 7071db429d0d283125843c95ce36d7e3395b4625 SHA256: a942becb1284c3f062a9d9fabe5c98633520bd07509296c254329118d4292df1 SHA512: ef0bd54790aaee8dc51a9b6cb559b028c8c405eefcbbde9fc33a5fe31f4d51508b57bedfa08c7877d59e559420f9af3c59ac001ff2cb3cb2057eef8eec91750d Homepage: https://cran.r-project.org/package=CauMedi Description: CRAN Package 'CauMedi' (Cell Type-Specific Causal Mediation Models for Single-Cell Data) A causal mediation framework for cell type-specific single-cell data based on joint mediator multilevel models. The framework jointly models overdispersed counts and zero inflation for the mediators. 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Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package. Package: r-cran-causal.decomp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-suppdists, r-cran-psweight, r-cran-rlang, r-cran-dyntxregime, r-cran-distr, r-cran-rpart, r-cran-dplyr, r-cran-modelobj, r-cran-magrittr, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-rpart.plot, r-cran-spelling, r-cran-cbps, r-cran-pbmcapply Filename: pool/dists/noble/main/r-cran-causal.decomp_0.2.0-1.ca2404.1_all.deb Size: 272934 MD5sum: e2a1e103cf5380450d1715d10b73c136 SHA1: f9b7932b5501c67f775a4803ca65b63fd9230706 SHA256: f389fcebaf50dab6730cd02ab0cd79dbfe3424b5219bef27e7cac0a64c33a88c SHA512: 232e5f9e953f840875a849d22799b2e6b6dba2e65839ed755c4b00dc5288d080da45df126494ccd4f69b6b058d04ed8b0a3d507891cd1d9f30cd7e0c58a03d88 Homepage: https://cran.r-project.org/package=causal.decomp Description: CRAN Package 'causal.decomp' (Causal Decomposition Analysis) We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (; ). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 ). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) . Package: r-cran-causalbatch Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdcsis, r-bioc-sva, r-cran-matchit, r-cran-nnet, r-cran-dplyr, r-cran-magrittr, r-bioc-genefilter, r-bioc-biocparallel Suggests: r-cran-tidyr, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-roxygen2, r-cran-ks, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-causalbatch_1.3.0-1.ca2404.1_all.deb Size: 1093200 MD5sum: 8f67c3c0dcf3c303b051ed48ab056814 SHA1: 7b596e37cc22b47d43e51a3bd14aa735952eb9eb SHA256: 55dfe24f7a87e5185fd5b65369340c863b3abce5148ce6ddcba1768db6a9bfed SHA512: b53e58bdad00753e7e7ab97d436034309d830e83575925700107bf9042ed109fcddefc075c4c8cd83e51a776cc1327d08d23eec4a0de0a7cbcdd0a5b03210ff7 Homepage: https://cran.r-project.org/package=causalBatch Description: CRAN Package 'causalBatch' (Causal Batch Effects) Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) and Bridgeford et al. (2023) . Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups. 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The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) . Package: r-cran-causaldata Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-causaldata_0.1.4-1.ca2404.1_all.deb Size: 3137134 MD5sum: dc2ffc22e711cb6f2fca9177b112c24b SHA1: 67272bd6004370a8687d3ef416d4665694edf4ca SHA256: 69383c3793adaf61683fff36dcb5c05c8f2414d0d49e6d8ba57474361745c3da SHA512: b81fc6c224e042c58d8e3e2530a18c354f9b5fee9856606b4675510f934d9873968ca4f1da04ce4dea4d2fcf3bd3dce66fda5c6d948abfde8e2664a2937957f6 Homepage: https://cran.r-project.org/package=causaldata Description: CRAN Package 'causaldata' (Example Data Sets for Causal Inference Textbooks) Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" , first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" . 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Provides theorem-backed bounds together with computable proxy diagnostics for information loss from confounding, selection bias, and distributional shift. Supports continuous, binary, count, survival, and competing risks outcomes. Key features include propensity-score total-variation deficiency proxies, negative control diagnostics, policy regret bounds, and sensitivity analysis via confounding frontiers. 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The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021; ), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen, 2025; ) and Temporal Fast Causal Inference (TFCI). It provides a unified framework for specifying background knowledge, which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010; ) and 'pcalg' (Kalish et al., 2012; ), as well as the Java library 'Tetrad' (Scheines et al., 1998; ). The package further includes utilities for visualization, comparison, and evaluation of graph structures, facilitating performance evaluation and methodological studies. Package: r-cran-causaldrf Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-survey Suggests: r-cran-bayestree, r-cran-dplyr, r-cran-foreign, r-cran-hmisc, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-reshape2, r-cran-rmarkdown, r-cran-sas7bdat, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-causaldrf_0.4.2-1.ca2404.1_all.deb Size: 829602 MD5sum: 39fdbb36f5f9da8afd5b8e5b9e783e9e SHA1: 44f7b7b6c1e772c4e2d0123633478cee507d0572 SHA256: e095f6fbef62ec4f2aa0be40b77b1e48235fa790eb5e46c9c7d7a97fa8131361 SHA512: a17ea3533e42299188a1fd12216a7daf18013e5a9eead932f78dfc2003d3f49172f40e475d0891ae42e83a0c9d44c8f93369a95db5d70bad3d167bf82c98cb06 Homepage: https://cran.r-project.org/package=causaldrf Description: CRAN Package 'causaldrf' (Estimating Causal Dose Response Functions) Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) . Package: r-cran-causaleffect Architecture: all Version: 1.3.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-r.rsp, r-cran-xml Filename: pool/dists/noble/main/r-cran-causaleffect_1.3.15-1.ca2404.1_all.deb Size: 872906 MD5sum: 48ba9f71df0e8d298ea26553b615434e SHA1: ac58fcfe8ea8f6930be4db612501067a5288af92 SHA256: d2a23d04d188e00b30dd55bed1279209688169d78d1b68d8c2c3678fd1a53e89 SHA512: 726146cd16accaa3f0be417edeefeb6bfdd22f3090c44b0cf3d2d214c9935cfb804121007e9f01c79ea392060ec57acb46350bc29cb6ad44e14d0e35680c29e5 Homepage: https://cran.r-project.org/package=causaleffect Description: CRAN Package 'causaleffect' (Deriving Expressions of Joint Interventional Distributions andTransport Formulas in Causal Models) Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) , an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) , and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) . All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) . Package: r-cran-causalfrag Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-jsonlite, r-cran-glue Suggests: r-cran-httr2, r-cran-confoundvis, r-cran-sensemakr, r-cran-evalue, r-cran-konfound, r-cran-rbounds, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-causalfrag_0.2.0-1.ca2404.1_all.deb Size: 162874 MD5sum: aaafbe2e8ef5e9e4ec4a54be4c15cdb8 SHA1: 264d801a48a33f570577956807c4353262b4bada SHA256: 4c6b768ff534f0c6e82431b9c5f1817f5098c12dfcdea29efcae8105567b4aed SHA512: cc2e86c92826b202ad77e1bd2a7832d3f7fcd93fe48ce8abffbb5ea0344c4aac1d57786ca70d56909bcb74181bea01c5709e369ec79460ac63bdc7e1f9f705a6 Homepage: https://cran.r-project.org/package=causalfrag Description: CRAN Package 'causalfrag' (Cross-Framework Sensitivity Analysis with an OLS Crosswalk) Runs, classifies, interprets and reports sensitivity analyses for unmeasured confounding across the partial R-squared robustness value approach (Cinelli and Hazlett, 2020, ), E-values (VanderWeele and Ding, 2017, ), and the impact threshold for a confounding variable and robustness of inference to replacement (Frank, 2000, ; Frank, Maroulis, Duong and Kelcey, 2013, ). An ordinary least squares crosswalk reports the robustness values, impact threshold and replacement percentage computed from the focal t statistic and residual degrees of freedom, makes explicit that their agreement is largely fixed by that shared input, and flags the boundary band in which they disagree. Template-based plain-language reports are included, with optional integration with the 'confoundvis' package for plots. Package: r-cran-causalgam Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gam Filename: pool/dists/noble/main/r-cran-causalgam_0.1-4-1.ca2404.1_all.deb Size: 50866 MD5sum: e00b66e2258bba2a838afbcb10f5db02 SHA1: 3757a33c2058844ae1aec4f88630b1dcb215deb6 SHA256: 05925f6493a31e05e4cacd2ec97966f360dc239bd3c215387358da34fdd1a9bf SHA512: f679dfd44c9e02b3b144efc8b804d03ebcb64d865eb6616e2a22d257abee49d645dbe44aae87542647598cea375fbbf68f144475c6bb6561557ba877242c8dcb Homepage: https://cran.r-project.org/package=CausalGAM Description: CRAN Package 'CausalGAM' (Estimation of Causal Effects with Generalized Additive Models) Implements various estimators for average treatment effects - an inverse probability weighted (IPW) estimator, an augmented inverse probability weighted (AIPW) estimator, and a standard regression estimator - that make use of generalized additive models for the treatment assignment model and/or outcome model. See: Glynn, Adam N. and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis. 18: 36-56. Package: r-cran-causalgenerics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vctrs Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-causalgenerics_0.1.0-1.ca2404.1_all.deb Size: 61468 MD5sum: 90b1c6d1fc1dae59dfed9c53e40a4016 SHA1: 871ad2d5252daf0fbe7a6bc4d2f199dd31358de8 SHA256: e96eda08614b811e75038773f95a532ebf1c3d8f3a682774015bbb1931a5f32b SHA512: 897a12e677ab9c03deec01663a2f1c383804ddd04e07e2ad776bd41587fcb060a91b17ecfe8956fd9be57ec3ebc43b9a44430b6846b00d160f3b2daaf36babd2 Homepage: https://cran.r-project.org/package=causalgenerics Description: CRAN Package 'causalgenerics' (Shared Generics for the 'r-causal' Ecosystem) A home for the S3 generics shared across the 'r-causal' ecosystem, including 'propensity', 'halfmoon', 'positively', and 'balancing'. Owning the generic definitions in one place lets those packages register methods without masking one another when several are attached at once. The generics cover inverse probability weighted estimation, effective sample size, and the metadata carried by causal weight vectors, and the package supplies the abstract causal weight class and the result class those estimates are returned in, together with the methods both classes carry, so that the ecosystem packages inherit them rather than writing their own. The design follows that of the 'generics' package, which provides commonly used S3 generics for the same purpose: letting packages share a single definition instead of each defining its own. Package: r-cran-causalhypergraph Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-stringr, r-cran-useful, r-cran-cna Filename: pool/dists/noble/main/r-cran-causalhypergraph_0.1.0-1.ca2404.1_all.deb Size: 47408 MD5sum: bd7ed96b31ba4296685122f9cbccbff3 SHA1: 74fe9721f70ef6be5fd54ea64dbd80512b4bdd23 SHA256: 3352001a313cbdb318e1045dca07ed40027ef965ffc038e48851ba2489a70f96 SHA512: 90d07474a1daa92c599b5f89b74e3a36527b9ec5de1a35509efaf38c5d7dce8b3db65952cd0df5db18e81c9734735d068a75a56c167e26b68c1e510389169f77 Homepage: https://cran.r-project.org/package=causalHyperGraph Description: CRAN Package 'causalHyperGraph' (Drawing Causal Hypergraphs) Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA). Package: r-cran-causalimpact Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsts, r-cran-assertthat, r-cran-boom, r-cran-ggplot2, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-causalimpact_1.4.1-1.ca2404.1_all.deb Size: 424862 MD5sum: b91f8eac6104e34bfa105ce392fa62ed SHA1: 66c8494c13b60d9348e9525e14041001f65c1350 SHA256: aa412ff51286f607e548d05d422fbbfd8722298843c0fe6db48e94ce55779f77 SHA512: f6dae2a927cecf03418745c8a05864a9abf1cb7febfe0aec5392790b80d855d32f2db8c5243152768ce66ba81a98896cf4dde480032cede8e53f93590bc51aee Homepage: https://cran.r-project.org/package=CausalImpact Description: CRAN Package 'CausalImpact' (Inferring Causal Effects using Bayesian Structural Time-SeriesModels) Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) . See the package documentation on GitHub to get started. Package: r-cran-causaljudgment Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-causaljudgment_0.1.0-1.ca2404.1_all.deb Size: 60202 MD5sum: c1349e6a4aed7d05a3a6f363f706704f SHA1: 855d91b9e23defe6a27b50ffcb037f25e964b1c0 SHA256: 1c9320c872b51d10274fa9c146a79eed9ddb993a0bf40d9c4bd48f90a3a7a9fd SHA512: 1afce20050a910e49b95fe4559feecb6922f6371397ea12acf962cdfeb8773a54eed75950887ddf10ef8afb66f2a81897b8c9e219659efa965b78932d9b386ad Homepage: https://cran.r-project.org/package=causaljudgment Description: CRAN Package 'causaljudgment' (Computational Models of Causal Judgment) Provides computational implementations of models of causal judgment, including the Counterfactual Effect Size (CES) model of Quillien and Lucas (2023) and the Necessity-Sufficiency (NS) model of Icard, Kominsky and Knobe (2017) . The package represents causal structures as binary Structural Causal Models and analytically computes causal judgments from counterfactual probability distributions. Package: r-cran-causalloopanalytics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-causalloopanalytics_1.0.0-1.ca2404.1_all.deb Size: 87262 MD5sum: 52ba527e33521030c509e8cfe7812b1d SHA1: 4494684372dcdbc8b7b7ae2ccc6cbb270d8647a2 SHA256: ff9eb9ed2852f0dbe08bdc99113587acc3510241bc8b652243ec4319a4186811 SHA512: ae8fe63ba2cb9ba623ae0224b375be4b45cda10ba0b163011470487adfe493aad8e2b0ccae3f6368ff1633913f946e6ea00a8d183cdb60558a7885663273099b Homepage: https://cran.r-project.org/package=CausalLoopAnalytics Description: CRAN Package 'CausalLoopAnalytics' (Data-Driven Causal Loop and Feedback Network Analysis) Provides tools for constructing signed causal-loop models, discovering directed causal relationships from time-series data using Granger-style tests, identifying and classifying reinforcing and balancing feedback loops, quantifying loop strength, assessing loop stability by bootstrap resampling, calculating network centrality and leverage-point scores, comparing causal-loop models, and producing publication-ready base R visualizations and summaries. The package is domain-agnostic and can be used in human medicine, veterinary medicine, agriculture, epidemiology, ecology, public health, and One Health. Methods are based on Granger (1969) and Efron (1979) . Package: r-cran-causalmbsts Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-cholwishart, r-cran-forecast, r-cran-mass, r-cran-matrix, r-cran-mixmatrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causalmbsts_0.1.1-1.ca2404.1_all.deb Size: 144790 MD5sum: 06af06d0f7c218ad55e675386cabce7d SHA1: 4435224934167d6971f763707c79411952a7d7ff SHA256: ea510ae499a9a912cf893487cd64afb632dce411b4c738e991db25148bbf25c8 SHA512: d797c01ba5fb3c843466e9bbd56380f74487cb1abddfd3cefb06abb245d4713fba7ac42c3e237d99a1805d69e1e65a2fc551b1c5ecc82c57fb0de2086abd49c5 Homepage: https://cran.r-project.org/package=CausalMBSTS Description: CRAN Package 'CausalMBSTS' (MBSTS Models for Causal Inference and Forecasting) Infers the causal effect of an intervention on a multivariate response through the use of Multivariate Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) . The package also includes functions for model building and forecasting. Package: r-cran-causalmetar Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-metafor, r-cran-nnet, r-cran-progress, r-cran-superlearner Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-causalmetar_0.1.3-1.ca2404.1_all.deb Size: 1169098 MD5sum: d7ccd88b84188d6fed224be4804fb98c SHA1: 96629a0a0f673daa7c853884076d8df111c76cb6 SHA256: df42cba6e9f07036bd086c04242ce6a1fa371df4433605e80bd55505bd24f993 SHA512: aafdba95cfa786695291bd8249cd0368430396d8699a65d6ca3218baaacd7897d07d82625e7ace9189b86cb9f08845558513f2dccc3979029a26bad5233372d1 Homepage: https://cran.r-project.org/package=CausalMetaR Description: CRAN Package 'CausalMetaR' (Causally Interpretable Meta-Analysis) Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) , Robertson et al. (2021) , and Wang et al. (2024) . The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) for a detailed guide on using the package. Package: r-cran-causalmixgpd Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-cli, r-cran-coda, r-cran-ggmcmc, r-cran-future, r-cran-future.apply, r-cran-crayon, r-cran-dt, r-cran-kableextra, r-cran-plotly, r-cran-matchit, r-cran-codetools Filename: pool/dists/noble/main/r-cran-causalmixgpd_0.8.0-1.ca2404.1_all.deb Size: 3036516 MD5sum: d63f18b5c372657c4cea0a3e14bdab4e SHA1: eed9be69f29ba341ad245ff367945fbbcfb64081 SHA256: 626160b75aea0bb95cae8a15171b42bbfdc5c2a46ab19b14b625b5d11542b954 SHA512: edc3673047102e9f0e1a90c0f060e0d30b42779ca165f9575af0f4d3fb87bdfb020d2fbf8786c88567d9af840517719f7a537d5691a9cb56466a0934028922f1 Homepage: https://cran.r-project.org/package=CausalMixGPD Description: CRAN Package 'CausalMixGPD' (Bayesian Nonparametric Conditional Density Modeling in CausalInference and Clustering with a Heavy-Tail Extension) The presence of a heavy tail is a feature of many scenarios when risk management involves extremely rare events. While parametric distributions may give adequate representation of the mode of data, they are likely to misrepresent heavy tails, and completely nonparametric approaches lack a rigorous mechanism for tail extrapolation; see Pickands (1975) . The package 'CausalMixGPD' implements tools for Bayesian analysis of heavy-tailed outcomes by combining Dirichlet process mixture models for the body of the distribution with optional generalized Pareto tails. The method allows for unconditional and covariate-modulated mixtures, implements MCMC estimation using 'nimble', and extends to mixtures of different arms' outcomes with application to causal inference in the Rubin (1974) framework. Posterior summaries include density functions, quantiles, expected values, survival functions, and causal effects, with an emphasis on tail quantiles and functional measures sensitive to the tail. Package: r-cran-causalmodels Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-causaldata, r-cran-boot, r-cran-multcomp, r-cran-geepack Filename: pool/dists/noble/main/r-cran-causalmodels_0.2.1-1.ca2404.1_all.deb Size: 105170 MD5sum: 104bcb6bc77745ed71dc9b1f3b760d33 SHA1: 96b847b3e1aeb40016ac3d693087b85d779fef3f SHA256: ebd550670fa2ed8f66aa56729ecb9e1d1c6f70e64af68d3be17b7fdcee124282 SHA512: 23f8d67a3fc0fdf9f093e09ee35b5307e6bd2ecbd3e7c050a58345ff4ec210e6a70a5f1a9c853b2dcd3b799477fd98c6b8e12197dfd7ea82ba267a339ec4b5bd Homepage: https://cran.r-project.org/package=CausalModels Description: CRAN Package 'CausalModels' (Causal Inference Modeling for Estimation of Causal Effects) Provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) ). Package: r-cran-causalnet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-scales, r-cran-cowplot Suggests: r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causalnet_0.1.0-1.ca2404.1_all.deb Size: 66554 MD5sum: 072958851266417d251f0e34e28066db SHA1: aed452a9aad37c0cdcd3e2ebe174d5afbd0f3ddf SHA256: eb530ab2a117b74a001507b40ae83cc3509266513e2847b7efa3804e67049e47 SHA512: 877214f5148b67e2ffd3c946467412cf6dd828e9b961b7a80e6b9a09d6ecd605b3d7490701da10d8ca87c305821c2c9fc55a6e42c273469d55ea95fa66712e68 Homepage: https://cran.r-project.org/package=causalnet Description: CRAN Package 'causalnet' (Directed Causal Network Enumeration and Simulation) Enumerate orientation-consistent directed networks from an undirected or partially directed skeleton, detect feedback loops, summarize topology, and simulate node dynamics via stochastic differential equations. Package: r-cran-causaloptim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2067 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-shiny, r-cran-rcdd Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causaloptim_1.0.0-1.ca2404.1_all.deb Size: 847264 MD5sum: c8f343c01131d3e4974c6ac9cb4523da SHA1: b102a1b7b77144c3dff9f4f9d77d3618b19b25f0 SHA256: 50b4dd25d526e06e81503b7e72a5f3e4c25072962f4b8da8c2741ee42341dd7f SHA512: 61415f2f6c97bf0cfc0f58f2fc41b94a2865a7a254e77bbe04c9502f826f5b3ee49ed180979dedbc4023711cd9f068f4a192ba226de38aad60ef191cada18786 Homepage: https://cran.r-project.org/package=causaloptim Description: CRAN Package 'causaloptim' (An Interface to Specify Causal Graphs and Compute Bounds onCausal Effects) When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) . Package: r-cran-causalpaf Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dagitty, r-cran-dplyr, r-cran-forestplot, r-cran-ggdag, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-mass, r-cran-reshape2, r-cran-rlist Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-causalpaf_1.2.5-1.ca2404.1_all.deb Size: 1149672 MD5sum: 859a649729df9539bf4f8c70b5eaa65c SHA1: cbba5e5bd98e85a20a83d6b9515f2b39dc19c9d9 SHA256: 5deb000b0d468b4641e207d5d1d8b12a0ac1e5c3b72663db75448f9ef0edea00 SHA512: 84468a5f75c3b0f38163b6f1774103015a08383c16e835408ef57e45d0ab9c8c91158f20ab7ca3a5643fce3cb0561bfe28233a43b6fe9875b09573fb1ede0b00 Homepage: https://cran.r-project.org/package=causalPAF Description: CRAN Package 'causalPAF' (Causal Effect for Population Attributable Fractions (PAF)) Calculates population attributable fraction causal effects. The 'causalPAF' package contains a suite of functions for causal analysis calculations of population attributable fractions (PAF) given a causal diagram which apply both: Pathway-specific population attributable fractions (PS-PAFs) O’Connell and Ferguson (2022) and Sequential population attributable fractions Ferguson, O’Connell, and O’Donnell (2020) . Results are presentable in both table and plot format. Package: r-cran-causalplot Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6541 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext Suggests: r-cran-shiny, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-causalplot_0.2.1-1.ca2404.1_all.deb Size: 2415318 MD5sum: 7ac4f8af2dbbb0ec47bb7c3c2b6efa20 SHA1: 93db87034fb602d585efd9ca221c1a743d1b81bc SHA256: 29c7a0e0d3ec0067883a98d8d01eaca69433951d081209a845c60f72c23e59cc SHA512: 9486c6a821f1da3d64f3f46fd52ef406d23ccc5f1bcdf8d02f94d255ea940efbfd62c3d8c9e28d36932841263ff7c8cfa8db27ef81d3a383e9fccf92981c64af Homepage: https://cran.r-project.org/package=causalplot Description: CRAN Package 'causalplot' (Create Publication-Ready Causal Diagrams) Creates publication-ready causal diagrams using 'ggplot2'. Provides simple templates for common causal diagrams (e.g., mediating mechanisms and parallel pathways) with customizable labels, colors, fonts, and export-friendly defaults. Package: r-cran-causalqual Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-caret, r-cran-cli, r-cran-ggplot2, r-cran-ggsci, r-cran-grf, r-cran-lmtest, r-cran-magrittr, r-cran-ocf, r-cran-rdrobust, r-cran-sandwich, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causalqual_1.0.0-1.ca2404.1_all.deb Size: 638374 MD5sum: 5bd3c6d1c288f31d31594cc6af914d95 SHA1: fdd3c450e3184879ee38f5c3b812f34c57c83509 SHA256: 3c04ddcfd1e9180686258df574d95d64f63f67ac7af44260172aff40c7330dd4 SHA512: 81c7c5b89be1c593a16fd251b772d107e77e33bb74e5f6e7205600dc967b5a67d55c81c6e9b74769c7586e0a2e4635537b617aeefecb3c8bdbe22f317ea7453f Homepage: https://cran.r-project.org/package=causalQual Description: CRAN Package 'causalQual' (Causal Inference for Qualitative Outcomes) Implements the framework introduced in Di Francesco and Mellace (2025) , shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs. Package: r-cran-causalreg Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-causalreg_0.1.2-1.ca2404.1_all.deb Size: 43920 MD5sum: 1aee12a83cba830b77f8db7b50ee2c8f SHA1: 03afa4dfae37f843dc8532a67bba3381b9bcb3b1 SHA256: bcec7e674cb492df0bbe4703545812dff8b25768d20056922cc2bf8932538a92 SHA512: 81aa8d612f810073d1e143bae334939196a521fd5e3940cb2252137b76f22b21f30ae0e93c09677aaae90727132c77176c838d16714a9591cfd048e7582e6b2c Homepage: https://cran.r-project.org/package=causalreg Description: CRAN Package 'causalreg' (Causal Generalized Linear Models) An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents. Package: r-cran-causalsens Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-causalsens_0.1.3-1.ca2404.1_all.deb Size: 419766 MD5sum: dd73014fe22255245963281c9018022d SHA1: 392ebf30d9f44fddf0b9b04c4a16e55d2c501001 SHA256: 6e1db71680172c7f73092adb2a5f458419f28f39a6a6ba9c4a05ad8a5fb54517 SHA512: cecbb607be3069e6bed5188fcd0f7ea79ccca950ca4e076b29f2046612270cea8557c1dd7f51d3688524b38f4a8623897e3abbf2e49ab906de91ef71a48af4fd Homepage: https://cran.r-project.org/package=causalsens Description: CRAN Package 'causalsens' (Selection Bias Approach to Sensitivity Analysis for CausalEffects) The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects. Package: r-cran-causalsim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-causalsim_0.1.0-1.ca2404.1_all.deb Size: 122424 MD5sum: 463c3fbad2bc2167c602298e8138ee67 SHA1: 3ce90a834dfd55fbbc741e5a1ab5b37e24164ab8 SHA256: 451bd16476b1f0363ac0f412b0f6474ae1d92308d0923a0f9b2b0b90127ca962 SHA512: 879b07e9c50a92e3ac4408eca730bc1b2ba0ac6b89cedb27963a3bd49e9d2513a0a74260f60d1d4c3b1c1ec72f1606986c4b86d3ad5d4757f482eef097267bbf Homepage: https://cran.r-project.org/package=causalsim Description: CRAN Package 'causalsim' (Simulation-Ready Causal Data Generating Processes) Construct, simulate, and evaluate causal data generating processes (DGPs) with known ground truth. Designed for benchmarking causal estimators, studying confounding and treatment-effect heterogeneity, and building reproducible teaching examples. Covariate roles (confounder, effect modifier, noise) and heterogeneous treatment effects are first-class concepts in the API, and estimator performance is summarised with bias, root mean squared error, confidence-interval coverage, and power. Package: r-cran-causalspline Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sandwich, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-cobalt, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-causalspline_0.1.0-1.ca2404.1_all.deb Size: 187174 MD5sum: d3d08274fb5b4d91fc935cb64c452f99 SHA1: 48bd7cc1bfd6899c2e4ebabe815e4c9329259358 SHA256: b4f0c1070a51001c142f618a2cf28ebac9498e97b6b7d6279d042d28ca89739e SHA512: 47111e756f98394edd30285f64f3191ceadfad6b396d26574676c0592c64863b5533df5091c45dc58505a601b6a06a9771c61735efa6cf2922c016779c088da5 Homepage: https://cran.r-project.org/package=CausalSpline Description: CRAN Package 'CausalSpline' (Nonlinear Causal Dose-Response Estimation via Splines) Estimates nonlinear causal dose-response functions for continuous treatments using spline-based methods under standard causal assumptions (unconfoundedness / ignorability). Implements three identification strategies: Inverse Probability Weighting (IPW) via the generalised propensity score (GPS), G-computation (outcome regression), and a doubly-robust combination. Natural cubic splines and B-splines are supported for both the exposure-response curve f(T) and the propensity nuisance model. Pointwise confidence bands are obtained via the sandwich estimator or nonparametric bootstrap. Also provides fragility diagnostics including pointwise curvature-based fragility, uncertainty-normalised fragility, and regional integration over user-defined treatment intervals. Builds on the framework of Hirano and Imbens (2004) for continuous treatments and extends it to fully nonparametric spline estimation. Package: r-cran-causalstate Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-superlearner, r-cran-origami, r-cran-glmnet, r-cran-xgboost, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-stringr, r-cran-hal9001, r-cran-dbarts, r-cran-mgcv, r-cran-earth, r-cran-nnls Filename: pool/dists/noble/main/r-cran-causalstate_0.10.2-1.ca2404.1_all.deb Size: 519640 MD5sum: 1de2bfe2aaf40cd53944390b007c4a0c SHA1: b14d0cf045d083334d822cacb500134ae9a1b2e7 SHA256: 6a31a7787f1d5ce16f7e1c965edcb10f712d48be06f33e90f3655df17ef137ee SHA512: 6748be463e141d9f37279e055f7e5cf8ce0c11f130ebddf79cfcf2cc638d23640e2953c26997087c720f08c41a48031a0ab43931ac7bc76f8406fade30102731 Homepage: https://cran.r-project.org/package=CausalState Description: CRAN Package 'CausalState' (Causal Inference in a Longitudinal Transitioning StateEnvironment) Implements Sequential Doubly Robust (SDR) and infinite-dimensional Targeted Maximum Likelihood (iTMLE) estimators for longitudinal modified treatment policies in settings with transitioning states, such as ICU, ward, or emergency department care episodes. Treatment is permitted in active states and becomes structurally inapplicable after a state transition (e.g. discharge or death). Supports asymmetric g- and Q-model regularisation, k-fold cross-fitting, and pluggable SuperLearner ensembles. Includes specialised SuperLearner wrappers (SL.tgt.* and SL.tmle_* families) for the iTMLE targeting step, which pass the logit offset as a covariate column to preserve correct subsetting during SuperLearner cross-validation. Methods based on Diaz et al. (2021) and Luedtke et al. (2017) . Package: r-cran-causalweight Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2898 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger, r-cran-mvtnorm, r-cran-np, r-cran-hdm, r-cran-clubsandwich, r-cran-glmnet, r-cran-xgboost, r-cran-kernlab, r-cran-fastdummies, r-cran-grf, r-cran-checkmate, r-cran-nnls, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-causalweight_1.1.6-1.ca2404.1_all.deb Size: 2700912 MD5sum: 17162832dae3c228b2edb916875ab07d SHA1: de604d456f0e78a9efc35a4eba112f5fd3020189 SHA256: 823968bde7440d385b182d47c615c10145921aa5c21dcb6fef53500ffb819811 SHA512: b4f12f6b2a7b2435607ddbbe13ce874d60062b4d5483899a14546feca699a6938bf77b7fdca77388edd8468ce781ba13f3a69c53f2d9923c9f8b9678c9b2d88b Homepage: https://cran.r-project.org/package=causalweight Description: CRAN Package 'causalweight' (Estimation Methods for Causal Inference Based on InverseProbability Weighting and Doubly Robust Estimation) Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects based on different identification strategies (unconfoundedness, instruments, difference-in-differences, regression discontinuity designs). Package: r-cran-causalwins Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drf, r-cran-factominer, r-cran-grf, r-cran-matchit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-wins, r-cran-mass Filename: pool/dists/noble/main/r-cran-causalwins_0.1.0-1.ca2404.1_all.deb Size: 212726 MD5sum: c12d01733d8bdab5c30c9f85f079f96d SHA1: 876da75a4950871a54b10551845e9d15e917dbc0 SHA256: c0fcd824baafe70bc7a713ddd23b512da63e52331fc1bddc75d54ee997300141 SHA512: 5dcbe520a06053c501b3aa41d6bc429be6f9819bf30f11f80977629fc48e1408e74e5308b765a5679af9b331a00ba0e89204a92455b9ad97255ec23a9b5e549a Homepage: https://cran.r-project.org/package=causalWins Description: CRAN Package 'causalWins' (Compute the Causal Win Ratio Using Nearest Neighbor Matching) Based on “Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis” (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit. Package: r-cran-causcor Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-writexls Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-causcor_0.1.3-1.ca2404.1_all.deb Size: 36480 MD5sum: 7737802d9ac7532dee183581f4438eb5 SHA1: af8cb29136bfeb27ba513d8fa638dc68e1f5971b SHA256: b3dae2379fabb900fa92d8dee15562b2fe680d824cf8196bff61c9ce699f21a0 SHA512: dd77a29120e01183983c4ba31e35ac93263a35df2a6d72e381e06291bf39cfe1378b4b01cb139e414daa93d7b89edd9c580a6d534628961eac1d15ed365516ca Homepage: https://cran.r-project.org/package=CausCor Description: CRAN Package 'CausCor' (Calculate Correlations and Estimate Causality) This tool performs pairwise correlation analysis and estimate causality. Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria. Package: r-cran-causens Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgdown, r-cran-psweight, r-cran-rjags, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-waldo Filename: pool/dists/noble/main/r-cran-causens_0.0.3-1.ca2404.1_all.deb Size: 570020 MD5sum: b71c108e90af4399de35c0cf7fca4e45 SHA1: ce42517b849de4f0740278e00a579ea8d9b601f2 SHA256: 7abfe2fdbd9bf0dfbe46cb8501ef7283e23c58aa0aea8ddc26115b048611a0b6 SHA512: 84db66aab948247cce79a6426f8caa77fbeabfa01543c1ee4a61868a66129fdc42b1b5167175e41b48ce16866db35343420b6e5030e40634a01465bcddfb584d Homepage: https://cran.r-project.org/package=causens Description: CRAN Package 'causens' (Perform Causal Sensitivity Analyses Using Various StatisticalMethods) While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) and Li, Shen, Wu, and Li (2011) ), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) ). Package: r-cran-caustests Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-caustests_1.1.4-1.ca2404.1_all.deb Size: 161996 MD5sum: 5e4e5d53c2f2ce38f189f15a538d8e6f SHA1: 963f8885e991e06b29685d69c51c1f571c0218ea SHA256: 0cf1bb6457db07d8438b6e7e0460b185243b60d4cb2e31d0787585a4098298cf SHA512: ca57f75e9803ca28f9d9c516243827ea96478a610ba59d0dbcf71c89980e3144e9512e5ccdfe2a1851fc055bddb051340847ccde52b7a27bf145e15e95363480 Homepage: https://cran.r-project.org/package=caustests Description: CRAN Package 'caustests' (Multiple Granger Causality Tests for Time Series and Panel Data) Comprehensive suite of Granger causality tests for time series and panel data. For time series: Toda-Yamamoto (1995) , Fourier-based tests with single frequency (Enders and Jones, 2016) and cumulative frequencies (Nazlioglu et al., 2019) , quantile causality tests (Cai et al., 2023) , and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) . For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) and Panel Quantile Causality tests (Wang and Nguyen, 2022) , as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) . All tests include bootstrap inference for robust p-values. Package: r-cran-cavariants Architecture: all Version: 6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggforce, r-cran-ggrepel, r-cran-gridextra, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/noble/main/r-cran-cavariants_6.0-1.ca2404.1_all.deb Size: 159318 MD5sum: 5c56c533300ee684baae582eeb1de4c3 SHA1: 38757c1440b81cb98e9fec9906274bcaf6687081 SHA256: a4e71d88bc4ae4eb0e55fa67818970c664a8f88d25386f0363f2ff0d7d13715a SHA512: ec38f86bf8bae07c96879c583c2f297b3bcc5e2c77e7ed35af69e43063a13aed95bb42c03ee409b29bd27e071f25436d9c89fa8941600b1083136aba9ca9c2fb Homepage: https://cran.r-project.org/package=CAvariants Description: CRAN Package 'CAvariants' (Correspondence Analysis Variants) Provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca. Package: r-cran-cayleyr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cayleyr_0.1.0-1.ca2404.1_all.deb Size: 40710 MD5sum: ee701adb65619f5194c32e527d18459c SHA1: 09157e87e59db4256172bd619a8154c54d358e58 SHA256: a097c1dc16ec3c82b9ff38cd274b8e1d241a5ffd7f826071d7950d88c4b13fde SHA512: 36001accfc4e1076501b09509ee8186854d220a95cc9260eddc961be7412dce524ace1d6381b59d85a262dda680e5098f4936e3ea54e8c4a99703693ab7ad728 Homepage: https://cran.r-project.org/package=cayleyR Description: CRAN Package 'cayleyR' (Cayley Graph Analysis for Permutation Puzzles) Implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, and finding optimal operation sequences in permutation groups generated by shift and reverse operations. Package: r-cran-cbamodel Architecture: all Version: 0.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-cbamodel_0.0.1.2-1.ca2404.1_all.deb Size: 22704 MD5sum: e674a068d66f77df0a33b29d0880feef SHA1: 70ddd7a512510cfce76a7ba3a6d625ab064c88e0 SHA256: 23518b50494866c888b0e6add8a8d2b41ea100bc65fcecae3b974426be50fd7f SHA512: d84ed4e0eacb613bdf4f74950494b401906f5503cf26c8026c658b37e210714be565db19a7de71d598674f5972233fc87161727852514501c2c1898f93ac3d13 Homepage: https://cran.r-project.org/package=CBAModel Description: CRAN Package 'CBAModel' (Stochastic 3D Structure Model for Binder-Conductive AdditivePhase) Simulation of the stochastic 3D structure model for the nanoporous binder-conductive additive phase in battery cathodes introduced in P. Gräfensteiner, M. Osenberg, A. Hilger, N. Bohn, J. R. Binder, I. Manke, V. Schmidt, M. Neumann (2024) . The model is developed for a binder-conductive additive phase of consisting of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. Package: r-cran-cbanalysis Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cbanalysis_0.2.0-1.ca2404.1_all.deb Size: 18202 MD5sum: 36e1862f624b3dfb900946eba6fb7b84 SHA1: 6b99cd962483d582ef0e80c06d294fae7f48c20d SHA256: 9f138e5ffda56f2004fb7b5ecb9ecad7c6b5ffdd7d584e582eb435222207b36d SHA512: 12ab899ef81caeab1ef8fa391b93b0ec2d3b303636e393121365795310ec8308636c82216f9d9ec54cb5e5ebaf8cf641c626572f07c3649794f01a07a9b44922 Homepage: https://cran.r-project.org/package=cbanalysis Description: CRAN Package 'cbanalysis' (Coffee Break Descriptive Analysis) A set of functions that helps you to generate descriptive statistics based on the variable types. Package: r-cran-cbass Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cbass_0.1-1.ca2404.1_all.deb Size: 75814 MD5sum: a89b0fa193920c1c3b665cb7d5d09004 SHA1: 5ae2ad923639c5ca48b58b1ad5f889f9bbd0c665 SHA256: b135313bfc3cea05b57137b3bd22a18a810bdf2332c62790a4540b0b3f668800 SHA512: aa99746a0adc23c16f1810a26d9f6691feb4f2458d138ac2d05abdd9cb61873469e77cc14297cbaea44ca1d4729841d5de3b00aee38f641ccdea4c63088f62c8 Homepage: https://cran.r-project.org/package=cbass Description: CRAN Package 'cbass' (Classification -- Bayesian Adaptive Smoothing Splines) Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review. Package: r-cran-cbassed50 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-rlog, r-cran-dplyr, r-cran-ggplot2, r-cran-readxl, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cbassed50_0.2.0-1.ca2404.1_all.deb Size: 105214 MD5sum: d3619fcfb9290f26dac374e206572de2 SHA1: 6c4679a32c90691c1d34e586a4109ed65e3aaf0e SHA256: 6377764b4a2ca926b417a9c39617ae761b859e048a7852ecea3d8828ff0fc4bd SHA512: b87d7c91c9422508575447001d7189aa47f243a48a1f2d36529f1e59e7817fd307bcd7c863541f64c46c151e204245fbf8b06d21cefa7823ac869e7693221f0d Homepage: https://cran.r-project.org/package=CBASSED50 Description: CRAN Package 'CBASSED50' (Process CBASS-Derived PAM Data) Tools to process CBASS-derived PAM data efficiently. Minimal requirements are PAM-based photosynthetic efficiency data (or data from any other continuous variable that changes with temperature, e.g. relative bleaching scores) from 4 coral samples (nubbins) subjected to 4 temperature profiles of at least 2 colonies from 1 coral species from 1 site. Please refer to the following CBASS (Coral Bleaching Automated Stress System) papers for in-depth information regarding CBASS acute thermal stress assays, experimental design considerations, and ED5/ED50/ED95 thermal parameters: Nicolas R. Evensen et al. (2023) Christian R. Voolstra et al. (2020) Christian R. Voolstra et al. (2025) . Package: r-cran-cbcgrps Architecture: all Version: 2.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nortest Filename: pool/dists/noble/main/r-cran-cbcgrps_2.8.2-1.ca2404.1_all.deb Size: 78718 MD5sum: 8c89868eeee9592eb61c8479e1bcc731 SHA1: 7a71ff8d94a9311dddd817ace21d3b97c8a24008 SHA256: 4315c293192b88ef39bdf0b87397e3397a7b155ab46b20e795bb272e2a5b8f2f SHA512: cbd9790920bfc4d93c4572b61eacff1c90937c7879c2214f3837aca7d20200301a3de7acc3cc09550cac5404e6069de3f820c93f6d49638acb3453afa7b9ae12 Homepage: https://cran.r-project.org/package=CBCgrps Description: CRAN Package 'CBCgrps' (Compare Baseline Characteristics Between Groups) Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis. Package: r-cran-cbctools Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-ggplot2, r-cran-idefix, r-cran-logitr, r-cran-randtoolbox, r-cran-rlang Suggests: r-cran-here, r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cbctools_0.7.1-1.ca2404.1_all.deb Size: 1318930 MD5sum: e38d4594bb48720e26b7e98bb4487d3d SHA1: d55d9b1eba55b0bcc9202f7b88e47422d336a2b7 SHA256: 7cebd74bd097b7cab5d357d33d0579411824e08b7a8a610726062630a73dd38f SHA512: 858ff5703f0251846e577a197eec69646d5bcbc4ac8be9a111607aed8bb90648f09f0df00b03cb9b91501fa891ef926d9612082d1aa28c1fb5af05a5960ea5c3 Homepage: https://cran.r-project.org/package=cbcTools Description: CRAN Package 'cbcTools' (Design and Analyze Choice-Based Conjoint Experiments) Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, frequency-based designs, and D-optimal designs, as well as "labeled" designs (also known as "alternative-specific designs"), designs with "no choice" options, and designs with dominant alternatives removed. Conveniently inspect and compare designs using a variety of metrics, including design balance, overlap, and D-error, and simulate choice data for a survey design either randomly or according to a utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Bayesian D-efficient designs using the 'cea' and 'modfed' methods are obtained using the 'idefix' package by Traets et al (2020) . Choice simulation and model estimation in power analyses are handled using the 'logitr' package by Helveston (2023) . Package: r-cran-cbioportalr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2319 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-magrittr, r-cran-rlang, r-cran-glue, r-cran-jsonlite, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-cbioportalr_1.1.1-1.ca2404.1_all.deb Size: 2071640 MD5sum: 42feee188cc413e1577a226239efb6ea SHA1: 186c176e2c0c3b5a23662c47787c86bb04f9fdde SHA256: 288d0d90388626a21cd2a9c3c31a3848758deecf39403ee7bd14ee65195da79a SHA512: 975d602b8b6bde7a02384c19a06f927b29e3cfa49a91a5d6487af931320bf1fa3e4dd04b88c29813c63c3560b3c4f3468f0fb3c15a263035542b7e0ca48f3cf9 Homepage: https://cran.r-project.org/package=cbioportalR Description: CRAN Package 'cbioportalR' (Browse and Query Clinical and Genomic Data from cBioPortal) Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly functions that wrap 'cBioPortal's' existing API endpoints . Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). See and Gao et al., (2013) for more information on the cBioPortal web resource. 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The package focuses on the mappings between competence and performance level (skill (multi) map, problem function etc.). 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The confounder blanket learner (CBL) uses sparse regression techniques to simultaneously perform many conditional independence tests, with complementary pairs stability selection to guarantee finite sample error control. CBL is sound and complete with respect to a so-called "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) . 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This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) and Algamal and Lee (2015) . 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It contains the lists of all data categories and data groups for searching the available variables (data series). As of February 17, 2026, there were 47,986 variables in the dataset. The lists of data categories and data groups can be updated by the user at any time. A specific variable, a group of variables, or all variables in a data group can be downloaded at different frequencies using a variety of aggregation methods. Package: r-cran-cbsodatar Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-whisker, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-shiny, r-cran-testthat, r-cran-sf Filename: pool/dists/noble/main/r-cran-cbsodatar_1.2.1-1.ca2404.1_all.deb Size: 584822 MD5sum: 7c1c03f0a49ebc59cc34299b17586c26 SHA1: 2c5b277e4b60019d6b549ce59efdf7534d9d0c5d SHA256: 07d9508a1fe8c415ec39c9f985b89223cea331ffdb1702471613286c449ffed3 SHA512: eddefc0264d83369d6a3cf5b98ec823eb707adc1f7b0bbe7decc1f1c11b1176786c39829e91694b9eb1b62ca2bf8023ef6e940c68fbb41d0c65a7668616b193e Homepage: https://cran.r-project.org/package=cbsodataR Description: CRAN Package 'cbsodataR' (Statistics Netherlands (CBS) Open Data API Client) The data and meta data from Statistics Netherlands () can be browsed and downloaded. The client uses the open data API of Statistics Netherlands. Package: r-cran-cbsr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-nlcoptim Filename: pool/dists/noble/main/r-cran-cbsr_1.0.5-1.ca2404.1_all.deb Size: 42498 MD5sum: 0153858184b35cd8efd7b430df612bb8 SHA1: a19504ddecd3abe09ac75c1dfd6d8f4e75c977d3 SHA256: 059aeb8fda72a7d4bc858fa5b2b2378aff4d93b58829175ed4ccd1a9ca8ca575 SHA512: 89fb038710a286218eee6671ce43bf84b3991d1fc3d4bbaa78abcdfbf7a2b140e46d87287443eb93a427089bff55b17ab539b86dbafa79dec4460876c6e19625 Homepage: https://cran.r-project.org/package=CBSr Description: CRAN Package 'CBSr' (Fits Cubic Bezier Spline Functions to Intertemporal and RiskyChoice Data) Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable . Package: r-cran-cbsreps Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-assertthat, r-cran-kfas, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cbsreps_0.1.0-1.ca2404.1_all.deb Size: 151818 MD5sum: 4b30b079e39fa4b420154386d03d37b9 SHA1: 99917a363922329ba8e0a616df61817ff3380b9c SHA256: fe6918315bc21aed9f2acd7de4880aa826094fa7a2cff9425c5244d77f9f820b SHA512: 280112457c9dfa48251c5191a0e1d4a783297741b2e78623dd7659fd078dec8105660ffdf2f19d01bd155b5af6959fde94703f48129f3135bc7177960b44fc88 Homepage: https://cran.r-project.org/package=cbsREPS Description: CRAN Package 'cbsREPS' (Hedonic and Multilateral Index Methods for Real Estate PriceStatistics) Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_price_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows for real estate and other domains where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) . For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, ). 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Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) and Wu, Tuzhilina and Donnat (2025) . 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The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) and Carrasco et al. (2013) . 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"The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. . 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Environmental Modelling & Software. . 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'ccdR' was developed to streamline the process of accessing the information available through the 'CTX' APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') and other resources found at the EPA Computational Toxicology and Exposure Online Resources . 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This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling. Package: r-cran-cchsflow Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-dplyr, r-cran-sjlabelled, r-cran-stringr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cchsflow_2.1.0-1.ca2404.1_all.deb Size: 1255214 MD5sum: f8b5fa1b08e5b271bf69dd468f2bf14c SHA1: ce79ec88d66c3b714fc9ab7338bceeecc50e62eb SHA256: 707e3186a7fbe4cf788da6dc49ac07a9d98993753ae0dbb0cba17aa392ba4d82 SHA512: 225d490566f65c7852a5ca0d601258f7451f0abcc8aba2c2ee3c7f56fbe7719bc68abca6f53a9e68199abae4f1ccb380041bf0795a8ef6ab754a04505e247875 Homepage: https://cran.r-project.org/package=cchsflow Description: CRAN Package 'cchsflow' (Transforming and Harmonizing CCHS Variables) Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from 'sjmisc' rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation Functions". Journal of Open Source Software, 3(26), 754. . 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'CCI' implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) . Package: r-cran-ccid Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-idetect, r-cran-hdbinseg, r-cran-genenet, r-cran-gdata Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccid_1.2.0-1.ca2404.1_all.deb Size: 85014 MD5sum: c5a1339f53c8a6a265164688edd9249b SHA1: 0a47223c55e8a6f852a6d1fba03e737fe7e9d99e SHA256: 06f00c724143a3037ac7c20be440f0a09dc7ef7d6063ad66befd34afec455c24 SHA512: c3e52f7e625afc8a4e0d5852b7194ad747df3c06b3d8ba88502a1f1f9f7853060c1daf3c038464a20a8a62b76e78d8448ecafebaedd705f4e56c8da051f56586 Homepage: https://cran.r-project.org/package=ccid Description: CRAN Package 'ccid' (Cross-Covariance Isolate Detect: a New Change-Point Method forEstimating Dynamic Functional Connectivity) Provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2022), Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Medical Image Analysis, Volume 75. Package: r-cran-cclustr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-e1071, r-cran-fpc, r-cran-viridislite, r-cran-klar, r-cran-clustmixtype, r-cran-proxy, r-cran-mclust Suggests: r-cran-knitr, r-cran-mice, r-cran-mlbench, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cclustr_0.1.2-1.ca2404.1_all.deb Size: 196096 MD5sum: a56aac9586cc09a227232b34b0a28c31 SHA1: 038363f9b40fc0af6b8326c8338467331a41d29c SHA256: 18db171ef299de9c32ae793cd311ca76bbb03956602aa382bc4fd3132c87db72 SHA512: 67e06069a80c8768e844672283c54870183d6c64245350620137c93e7f33230de39bb5c45bef0084c9a5ea0889118702c6e502b116cccdbc880ff69b19579286 Homepage: https://cran.r-project.org/package=cclustr Description: CRAN Package 'cclustr' (Consensus Clustering Methods for Multiple Imputed Data) Provides tools for performing consensus clustering on multiple imputed datasets. The package supports a range of clustering algorithms across imputations, including hierarchical methods (e.g., Ward, single, complete, average) and partition-based approaches such as k-means, k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'), and methods for mixed or categorical data (k-modes and k-prototypes). A co-assignment matrix is constructed to quantify agreement between partitions, and consensus solutions are derived via hierarchical clustering applied to the resulting dissimilarity matrix. Additional functions are provided for validation and visualization of clustering results, facilitating robust analysis in the presence of missing data. Consensus clustering framework is based on Monti et al. (2003) , rank aggregation methods follow Pihur et al. (2007) , and the PAC (Proportion of Ambiguous Clustering) metric is based on Senbabaoglu et al. (2014) . Package: r-cran-ccm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ccm_1.2-1.ca2404.1_all.deb Size: 62352 MD5sum: eb799d871dc3647ddc41a7c05c772ac7 SHA1: 7241661c708f5a48f94edde19c3cded9aae03cc6 SHA256: 7e82d294708b6b529d540b4be9492c707f13267bde2f27a214c79c146715468f SHA512: 822d08584f70338eb09758d7981aab064fd24d0d43555b7e8072bad80a5968f71c3e44593764a716edb5817912963fea7d850f79f33084af1c8baf93a8752092 Homepage: https://cran.r-project.org/package=CCM Description: CRAN Package 'CCM' (Correlation Classification Method) Classification method described in Dancik et al (2011) that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes. Package: r-cran-ccmestimator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccmestimator_1.0.0-1.ca2404.1_all.deb Size: 102310 MD5sum: f6e1a76b6440f280947b74be53793f54 SHA1: 80d052597f84491e2530fb9cca0d8b7b3cbf880f SHA256: 3142f2b1f92601e4d6efb4ee199be4c356a91640ce1eaf5c5d323f93cb692c2a SHA512: 07fde7c112c6f65bfa95a8a532c6a83a98861460ae5dca71f765192fb2c83a4db42544e0039f6e4ea92a2c8994a3f23a4a53522a982d16cfadd9232627260df7 Homepage: https://cran.r-project.org/package=ccmEstimator Description: CRAN Package 'ccmEstimator' (Comparative Causal Mediation Estimation) Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020). Package: r-cran-ccml Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dicer, r-cran-tidyr, r-cran-snftool, r-cran-plyr, r-bioc-consensusclusterplus Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccml_1.4.0-1.ca2404.1_all.deb Size: 52504 MD5sum: a359eff60563af963af272ce3627b64e SHA1: 7227d2204a2589e19b4c31e76280a0bb2db2e997 SHA256: 4399b04dd9328477b6efd47274c6351488677896bd1270594a9f0e5b0c44d6a8 SHA512: c8e9635fd81713da5a6f0239590bb16fe2e7e92377c03b94c953c6a51a6997032697d326b8bdd5b7a4744e35088df4398ecdc07bc445a7af6da3497be15f2c45 Homepage: https://cran.r-project.org/package=ccml Description: CRAN Package 'ccml' (Consensus Clustering for Different Sample Coverage Data) Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly used in clinical data. Current consensus clustering methods tend to ensemble a number of different clusters from mathematical replicates with similar sample coverage. As the fact of common variety of sample coverage in the real-world data, a new consensus clustering strategy dealing with such biological replicates is required. This is a two-step consensus clustering package, which is used to input multiple predictive labels with different sample coverage (missing labels). Package: r-cran-ccmm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ccmm_1.0-1.ca2404.1_all.deb Size: 94748 MD5sum: 3065e0e672508de4a8f6e9110fa6021f SHA1: c4607af363c26be1d360c2839b74e0bd53d1fbe3 SHA256: d44d18fa15d7f00711717c296cad0b9cb036449dc59882f205dcf134645e112e SHA512: 76fbeda01de0d3e4df5499772e7553759847f986008864f0668deba6ad93b23ddc8258b39df81793c2435e208e1e8cbc56f5ffc2e9e99964487b0f03c49e8383 Homepage: https://cran.r-project.org/package=ccmm Description: CRAN Package 'ccmm' (Compositional Mediation Model) Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision). 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It uses the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The user can select between replacement and without replacement, the number of controls, and several covariates to match upon. See Mamouris (2021) for an overview. 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Including the estimators BT from Bang and Tsiatis (2000) and ZT from Zhao and Tian (2001) . Package: r-cran-ccp Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ccp_1.2-1.ca2404.1_all.deb Size: 45238 MD5sum: 7b093a67e539d17e1f75b845949d6fe3 SHA1: c5b39687dcca0667b403612178e52e0b477ba87d SHA256: 111214c0e96b9f345820300c6bbce44c42bc3d7f8cd9808465916ca8c7baf52b SHA512: 23553d0aeef43f56fca59811d887c94ab43fb99226927a316581fda499f76f6dc72e0f7dd5bd6a3c72cc0eff8d2b5e2a4e467752e51f53fbe5941ce3df321a44 Homepage: https://cran.r-project.org/package=CCP Description: CRAN Package 'CCP' (Significance Tests for Canonical Correlation Analysis (CCA)) Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method. Package: r-cran-ccpsyc Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-lavaan, r-cran-magrittr, r-cran-mcmcpack, r-cran-psych, r-cran-rcppalgos, r-cran-readr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccpsyc_0.2.8-1.ca2404.1_all.deb Size: 145828 MD5sum: 74dd39d6242c8175c4a2135bc4084f1f SHA1: 1601d4b580933db48c64dc2e07d93701f862d4e2 SHA256: 97f877ac279e958dfff1e80db73e856e606b9501e0d75faa0c72d70498a92f7a SHA512: b4aadadc54336ba5420e9230d90875aeb2f9044742af189430466057eff6cccf73869c86a3d4b1b0ede82a09ef72eb2dba46e1be4d8bb55e9fa4b2611441f1c1 Homepage: https://cran.r-project.org/package=ccpsyc Description: CRAN Package 'ccpsyc' (Methods for Cross-Cultural Psychology) Combines multiple functions that automate and simplify methods commonly employed in cross-cultural psychology, providing a unified analysis approach for measurement invariance testing, effect sizes for differential item functioning, factor congruence and multi-group reliability. Methods follow Fischer and Karl (2019) and Gunn, Grimm and Edwards (2020) . Package: r-cran-ccremover Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2272 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ccremover_1.0.4-1.ca2404.1_all.deb Size: 2232380 MD5sum: dbb5ed611846a0c8e78fe3fd1402c319 SHA1: 937a820bf8562edd126a30d199fc11b6dba06848 SHA256: 359688cf797ae84f3cccea42843936dd1841b885536ad6b5a1e7540fb0892d40 SHA512: 01b4704ff8a470477822c85e1e104ef0ddd1d4692f84135cd1727d2aefd03fa2bf5677436d1e930a4121c1773e989baf9cdcc25399f1097bb199599ef180d36e Homepage: https://cran.r-project.org/package=ccRemover Description: CRAN Package 'ccRemover' (Removes the Cell-Cycle Effect from Single-Cell RNA-SequencingData) Implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) . Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data. Package: r-cran-ccsrfind Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ccsrfind_0.1.0-1.ca2404.1_all.deb Size: 684764 MD5sum: 57c2b3b4c1db93e16e993f13fe1d128f SHA1: 4b40d43612046617b005f5f6c5f1039a2f278120 SHA256: c0b9544cfaf29b3b322fc7d1a5ed016ed6077d2935aea2be603e68144b08dc0d SHA512: f42475995b429949759c3ffe1cfdf61d6637fc4df376a9e23749279e44e6b6bc77d02012f45081a1f4f89fd3b595ef06a6a835d02109994c773d42f6614e24cf Homepage: https://cran.r-project.org/package=CCSRfind Description: CRAN Package 'CCSRfind' (Convert ICD-10 Codes to CCSR Codes) Provides a tool for matching ICD-10 codes to corresponding Clinical Classification Software Refined (CCSR) codes. The main function, CCSRfind(), identifies each CCSR code that applies to an individual given their diagnosis codes. 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Tools clone participants across strategies, apply strategy-specific artificial censoring, and estimate inverse probability of censoring weights using pooled logistic or Cox models, fit weighted outcome models, and obtain subject-level bootstrap confidence intervals by repeating the complete analysis. The methods are described by Maringe et al. (2020) and Gaber et al. (2024) . Package: r-cran-cd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cd_0.1.0-1.ca2404.1_all.deb Size: 14664 MD5sum: 877440f73616bf7bf4791eed682a9e09 SHA1: a768ab41799ae80bc870db4469b1c2d8e43f8e46 SHA256: d73b2e465026ef53eff1ea56eaa824a42d4f761d78d5ef9f32f018fc317f1eee SHA512: 659062c95b23407e6e51bbdfcdd0253ee0bc66437b0ad7b3e9dd23cfbea2e2a258573dce0b0ebf002cd5de1d16d4c07d19511f706bdc4d5df94bc6bc2a08d45d Homepage: https://cran.r-project.org/package=cd Description: CRAN Package 'cd' (CD Data for Entity Resolution) Duplicated music data (pre-processed and formatted) for entity resolution. The total size of the data set is 9763. There are respective gold standard records that are labeled and can be considered as a unique identifier. 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The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", , . This package introduces the idea of explicit control table specification of data transforms. Works on in-memory data or on remote data using 'rquery' and 'SQL' database interfaces. Package: r-cran-cdcanthro Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-cdcanthro_0.4.0-1.ca2404.1_all.deb Size: 581248 MD5sum: 6487d6d9243f61b3e25966319c1041cb SHA1: e7a8165ce312e15e17ca34c8e04d2c26dd659de1 SHA256: 3766e9687bc7878e5aa3a32ad6bad0b73dadb96857201519af6c6486f7834276 SHA512: 7554630ff103e85cf14b62e7ab8ac1f4b8567a9435c4e96addabc581299c25608e635a4dcf0bf25983d2e261047b524782658a9a000a0484e361cac36769bc65 Homepage: https://cran.r-project.org/package=cdcanthro Description: CRAN Package 'cdcanthro' (Standardized Metrics Based on the CDC and WHO Growth Charts) Calculation of sex- and age-standardized growth metrics using the LMS method (lambda-mu-sigma). The package includes functions for the CDC Growth Charts (cdc_z) and the WHO Charts (who_z). Because CDC recommends using the WHO Charts for children under 24 months and the CDC Charts among older children, there can be large differences at age 2.0 years. For example, a girl weighing 9.9 kg would be at the WHO 10th percentile on the day before her second birthday, but at the CDC 2nd percentile the following day. The 'gradual_z' function reduces the differences among 2- to 5-year-olds by taking a weighted average of the CDC and WHO z-scores. Package: r-cran-cdcat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 963 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-gdina, r-cran-usethis Filename: pool/dists/noble/main/r-cran-cdcat_0.1.0-1.ca2404.1_all.deb Size: 734620 MD5sum: 40c93682fb3d7412f3875428047bae87 SHA1: 9ee9c35e9c4b8f732877127a36aa398e77ad5380 SHA256: 54af56d458c129a7cd03de6f78e2575d544fc1d566fa24a2ffc439c04099104c SHA512: 0a38a111dab8217491ee593025c9249fcf3c43115e6d41649e4c16a18f349da636d4fa7867508ce1098e0fe0d3513f582b1150cee6360d40ac3bfb36097a0b9a Homepage: https://cran.r-project.org/package=cdCAT Description: CRAN Package 'cdCAT' (Computerized Adaptive Testing with Cognitive Diagnostic Models) A session-based engine for cognitive diagnostic computerized adaptive testing (CD-CAT), the application of adaptive testing to cognitive diagnosis models. Three models are supported: the deterministic inputs, noisy "and" gate (DINA), the deterministic inputs, noisy "or" gate (DINO), and the generalized DINA (GDINA) model. Item selection criteria include Kullback-Leibler (KL) information, posterior-weighted Kullback-Leibler (PWKL), modified posterior-weighted Kullback-Leibler (MPWKL), and Shannon entropy (SHE). Latent attribute profiles are estimated by maximum likelihood estimation (MLE), maximum a posteriori (MAP), or expected a posteriori (EAP). Content balancing, item exposure control, and shadow testing are configurable through constraint functions. The implemented methods follow Cheng (2009) and de la Torre (2011) . Designed for real-time, item-by-item adaptive applications. 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It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) ) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) ), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) . 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Geophysical Research Letters, 36, L11708, . ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, . 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In addition, pairwise normalization (PN) and range normalization (RN) models are supported. Both fixed-parameter and random-parameter specifications can be estimated, the latter allowing for preference heterogeneity. All models are formulated within a logit framework under the assumption of independently and identically distributed (i.i.d.) Gumbel error terms. The implemented methods are based on Tversky and Simonson (1993) , Chorus et al. (2014) , Rooderkerk et al. (2011) , Guevara and Fukushi (2016) , Landry and Webb (2021) , and Daviet and Webb (2023) . 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The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, ) and the discrete factor loading method (Wang, Song, & Ding, 2018, ). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, ). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; ), the general nonparametric classification method (Chiu et al., 2018; ), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; ) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices. Package: r-cran-cdnbcr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-pracma Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdnbcr_1.1.1-1.ca2404.1_all.deb Size: 140628 MD5sum: 911371aab822ae4c22286363bd37c06a SHA1: 449062e38d18667eed48600e09cbdb9ca4ea3cb4 SHA256: 9b1b1aec3e8548d442a95e20c94e4e7e0b43a14bb246dd1f4f720e7560bf9565 SHA512: e0b77e0e0d83ac808a202e7d057376606c05efb909e3321c9cf4535b17c89d6e0e7ac2d2383e9c0f30a2c69acebe49f4fd5806cb8056c6bd18d890d6d3044a4f Homepage: https://cran.r-project.org/package=cdnbcr Description: CRAN Package 'cdnbcr' (Correlated Destructive Negative Binomial Cure Rate Model) Provides tools for modeling time-to-event data with a cure fraction under correlated destructive negative binomial cure rate models. The models assume multiple latent competing causes with possible dependence and allow for elimination (inactivation) of some initial causes. Estimation is performed via an Expectation-Maximization algorithm, and diagnostic tools based on Cox-Snell residuals are provided. 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Package: r-cran-cdparcoord Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-plotly, r-cran-freqparcoord, r-cran-partools Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cdparcoord_1.0.1-1.ca2404.1_all.deb Size: 1749470 MD5sum: 418734600b610dcbaa5edcffa7bb4581 SHA1: ed1b5c96194ff18e92fe4c10484d2e0c1d0ce1d4 SHA256: dfc9179abb2e5a9b23159d2dcc4c0bf39118b04a2e031fc7530e374b8d318f25 SHA512: 852a11a823631a49e0fbbabc54c692e836e08d6c47d0758181806d9bdd2a99eb4ba44b76e4150dcb72bb12e210a6c504a16f81d0b5dfc5bc40bee6ec9bf85177 Homepage: https://cran.r-project.org/package=cdparcoord Description: CRAN Package 'cdparcoord' (Top Frequency-Based Parallel Coordinates) Parallel coordinate plotting with resolutions for large data sets and missing values. Package: r-cran-cdrcr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-sf, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-purrr, r-cran-rlist, r-cran-rjson, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdrcr_0.1.1-1.ca2404.1_all.deb Size: 2074402 MD5sum: f9ce4fb81160bc2723d3444e0a275fe1 SHA1: a2feeb14cc5d8b0403c1c9e9830a420df37eb152 SHA256: 4b3c1e2bee23e83e63d0a7dcaf60899544ca3387b3e5ad47b82055980c6d1c28 SHA512: 2765ce45079621d6eb0fb00b64b64b61c5571324c044e403e4031a411682484a7a117990f1e025f7f8ab56daaa25f57dc69b62dfb1ae0630628af2e2734c7c07 Homepage: https://cran.r-project.org/package=cdrcR Description: CRAN Package 'cdrcR' (Load 'CDRC' Data) A wrapper for the 'CDRC' 'API' that returns data frames or 'sf' of 'CDRC' data. The 'API' web reference is:. Package: r-cran-cds Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-limsolve, r-cran-clue, r-cran-colorspace, r-cran-copula Filename: pool/dists/noble/main/r-cran-cds_1.0.4-1.ca2404.1_all.deb Size: 172376 MD5sum: 4caa5a473a980dbaa13605e49c911686 SHA1: 9408b8a2f935f73c3113eb0c99a01f13ff97abcf SHA256: 21dd256af47247f6152cd2ba1c37f455b15588e4bd87244aa0a0971ff9142c41 SHA512: 87b0c270ffd417477efb873042f26b8c205c62cb99f8da7b0e91f26915d75869bc11a3eaf549c75ac2fb8a6bc82fe4f6061a9644450719b8aa73d6b71a16d037 Homepage: https://cran.r-project.org/package=cds Description: CRAN Package 'cds' (Constrained Dual Scaling for Detecting Response Styles) This is an implementation of constrained dual scaling for detecting response styles in categorical data, including utility functions. The procedure involves adding additional columns to the data matrix representing the boundaries between the rating categories. The resulting matrix is then doubled and analyzed by dual scaling. One-dimensional solutions are sought which provide optimal scores for the rating categories. These optimal scores are constrained to follow monotone quadratic splines. Clusters are introduced within which the response styles can vary. The type of response style present in a cluster can be diagnosed from the optimal scores for said cluster, and this can be used to construct an imputed version of the data set which adjusts for response styles. Package: r-cran-cdsampling Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rglpk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdsampling_0.1.6-1.ca2404.1_all.deb Size: 135544 MD5sum: 17edabf63e7b42cb1540d16536dcef01 SHA1: 42850065ebcd12a8149ffa554f73eaf7cf056eab SHA256: 2af8e21ea621bf6b21b4856199fa1390e5b3f487c1da8c6548c9fb6028953407 SHA512: 573528d5b0b3f2ae5fef099ca17b9e9008a541a2b62f7dc0d3e549fb6dcbf284f09420df96c0307ecb252764d903caf4845c2ca38c2e2dd2ce1742c012a6ee79 Homepage: https://cran.r-project.org/package=CDsampling Description: CRAN Package 'CDsampling' (Constrained Sampling in Paid Research Studies) In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the 'CDsampling' package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025). Package: r-cran-cdse Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-lutz, r-cran-sf, r-cran-terra Suggests: r-cran-maps, r-cran-rsi, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cdse_0.3.2-1.ca2404.1_all.deb Size: 696134 MD5sum: badd89a74ae368c39a13a312f83ad2a5 SHA1: 8a3d893d70bb82c3011045f819f5079a78bd5bc1 SHA256: f34fcc72fa360dc9aa142488ec17314d7a59881a0b406ad2bbfa19d58bc7fd92 SHA512: 4ce65404bdde6bda5fe5f117dc46125372371b0aa4265ec29939c19161c1e2ed02beac70ec384f52b688cc931ce6fcbffc733f1bc3277a105d345537b6ffe6ad Homepage: https://cran.r-project.org/package=CDSE Description: CRAN Package 'CDSE' ('Copernicus Data Space Ecosystem' API Wrapper) Provides interface to the 'Copernicus Data Space Ecosystem' API , mainly for searching the catalog of available data from Copernicus Sentinel missions and obtaining the images for just the area of interest based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface that provides access to various satellite imagery archives. It allows you to access raw satellite data, rendered images, statistical analysis, and other features. This package is in no way officially related to or endorsed by Copernicus. Package: r-cran-cdsim Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trend, r-cran-truncnorm, r-cran-ncdf4, r-cran-lubridate, r-cran-readr, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr, r-cran-vroom, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cdsim_0.1.2-1.ca2404.1_all.deb Size: 1159764 MD5sum: 41035f9db1fc542ffdf799705eb3a595 SHA1: 3ca92416ce1a2e218db3a1ff7fa10c5a8c5ffb87 SHA256: 0a919052c0480f0f1d39fec92d3900dc08b11cb79de0097ee4c4867e910db47e SHA512: fd0fc217f218663276a2160c44e845fd6e05d0db48f44fcb171445fd22aa0cf4bfa049a18e0f83dbe12bab95baaf50a3e9488e8846132b861a1343b7c746fc76 Homepage: https://cran.r-project.org/package=CDSim Description: CRAN Package 'CDSim' (Simulating Climate Data for Research and Modelling) Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1999) and Richardson (1981) . 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Generates synthetic climate datasets for single or multiple weather stations using stochastic weather generation techniques. 'CDSimX' simulates daily climate variables including minimum and maximum temperature, rainfall, relative humidity, solar radiation, wind speed, wind direction, dew point temperature, and potential evapotranspiration. The package incorporates seasonal harmonic models, Markov chain rainfall occurrence processes, Gamma-distributed rainfall amounts, copula-based dependence structures, bias-correction procedures, and physical consistency constraints. 'CDSimX' supports climate data generation, environmental modeling, machine learning benchmarking, sensitivity analysis, and educational applications. Methods are based on established stochastic weather generation approaches described in Richardson (1981) , Wilks (1999) , and Osei et al. (2026) . Package: r-cran-cdss Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readods, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-diagrammersvg, r-bioc-rgraphviz, r-cran-rsvg, r-cran-png, r-cran-kstmatrix, r-cran-cbkst Filename: pool/dists/noble/main/r-cran-cdss_1.0-0-1.ca2404.1_all.deb Size: 712566 MD5sum: d2f59da84bd2229616f9e1d2de5cf4a6 SHA1: 787106eab598bb6df44c2bc9051c0ff0b8bede39 SHA256: 374f876f3a55da8167b41892b5055b04b74d39dfb7858030328b6a82f9148b26 SHA512: 0dde0bc8c969384e662e87991efdc63b2472e7bf8abc279d6910acc0beaf55e4e4fb1adcf05fe3d966e50a1727f47d7a42e7e78014a757da7df9ddea420d25d0 Homepage: https://cran.r-project.org/package=CDSS Description: CRAN Package 'CDSS' (Course-Dependent Skill Structures) Deriving skill structures from skill assignment data for courses (sets of learning objects). Package: r-cran-cdtmbnma Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-posterior Suggests: r-cran-rstan, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-loo Filename: pool/dists/noble/main/r-cran-cdtmbnma_0.2.1-1.ca2404.1_all.deb Size: 145338 MD5sum: bc93b0f277bc4c6d0a74f375a70306e0 SHA1: 02c8258c9c0c14a7f3a814c83434a265f206e376 SHA256: fd742bc077339ea90c38d8664b48f53b01cbe9fa70135876c162f9c9538b543e SHA512: 1ab0d69a5faed793c75849b551ceda35fde69301bb7ab9575de01bd8e48155b79804fa7d48e9610869acc7c0803d4bc4cde3c7ed986011ca1dee45d8239056da Homepage: https://cran.r-project.org/package=cdtmbnma Description: CRAN Package 'cdtmbnma' (Component, Dose, and Time Network Meta-Analysis withDose-Dependent Interactions) Bayesian component model-based network meta-analysis for treatment combinations with explicit component dose-response and dose-dependent interaction surfaces. The main interface fits single-timepoint arm-level networks with continuous or binary outcomes, any number of components, additive, bilinear, or saturating pairwise interactions, study-level random effects, and prediction at unobserved dose combinations. A stage-one longitudinal interface fits a two-component exponential time-course model with a bilinear interaction on the asymptote. Estimation uses 'Stan' through 'cmdstanr' or 'rstan'. Methods are described in Welton et al. (2009) , Mawdsley et al. (2016) , Wicha et al. (2017) , and Pedder et al. (2019) . Package: r-cran-cdvi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cdvi_0.1.0-1.ca2404.1_all.deb Size: 11880 MD5sum: b7bd4b44a1a4b3374654af0d9469c6af SHA1: bcf1a0b3d525f8cd8941e40b1778c8958d6bbf3f SHA256: acdab130f7c1f283d3ef136d63cdf701d506c0f92988f8705320ed8e13d1f858 SHA512: 452826d12139f0347fb47fbfeda8595ee868021ebb08414f37f48b17af3ba7404b84e4aa12005518c77f25eb1f0f343c045a36bde642315edffaa5d4645be1f1 Homepage: https://cran.r-project.org/package=CDVI Description: CRAN Package 'CDVI' (Cuddy-Della Valle Index for Capturing the Instability in TimeSeries Data) Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022). Package: r-cran-cdvinecopulaconditional Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-vinecopula Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdvinecopulaconditional_0.1.1-1.ca2404.1_all.deb Size: 134916 MD5sum: 013abba10025bd0fc8cfd0c2df974e92 SHA1: fea532a7fbc01233635c6f70e70dc29e324e06da SHA256: f91958b0c097a950c2ada9831d6d993127df72a84b8713883672c3e71796c39e SHA512: 0aa9c64f792f85ca9164969cff3f325fb7cf8a113f86249544b4a95a87e46ac54ce4b967f0f70131546a61e13a488b291228850d23ebc933a9cb8a9d014ec732 Homepage: https://cran.r-project.org/package=CDVineCopulaConditional Description: CRAN Package 'CDVineCopulaConditional' (Sampling from Conditional C- and D-Vine Copulas) Provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) ). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) , and it is a modified version of that from Aas et al. (2009) . A function is available to select the best vine (based on information criteria) among those which allow for such a conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets. Package: r-cran-ceact Architecture: all Version: 0.5.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceact_0.5.0-1.ca2404.2_all.deb Size: 427572 MD5sum: c930729c99696d09b7ee8e1e98c0656e SHA1: 8548fe0b67bb172fedf0468e0c07dfd10589ad84 SHA256: 422550679b99c5b19204b430af0ba10e8fd5cd2218ab50b14b019c150dcf61b7 SHA512: a5bd86b4eff6de2d4b5aaf7cc32f9fa32fc7cc3ed345bdb097bca65f0f0403dfd7db45223d474581b706d99dc34b9112e823412639c8496e7880e4f9ed283419 Homepage: https://cran.r-project.org/package=CEACT Description: CRAN Package 'CEACT' (Cost-Effectiveness Analysis Toolkit for Clinical Trials) Provides tools for trial-based economic evaluation of healthcare interventions. Computes and visualizes incremental cost-effectiveness ratios, cost-effectiveness acceptability curves, cost-effectiveness planes, net monetary benefit tables, and one-way deterministic sensitivity analyses. Supports cost-utility analyses using observed summaries and non-parametric bootstrap uncertainty. Package: r-cran-ceas Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3956 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-lme4, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceas_1.3.0-1.ca2404.1_all.deb Size: 2591928 MD5sum: 8288473b5a3e66ba4e5a283d19cdf1a5 SHA1: e58911ed1070753a1d2023edf3d1f032e8ae5b87 SHA256: 491365d0180b45c4aa12337954ffd50edb95f81735373fef41e402c22406cd58 SHA512: b184088670cb3788cd553d3babf2210eabc63616a05cba27efb9e1ccdfad1eea207682ce8434d56fb1cc7903214b83833b1309ff1c601e49364c0b4bfb614e2f Homepage: https://cran.r-project.org/package=ceas Description: CRAN Package 'ceas' (Cellular Energetics Analysis Software) Measuring cellular energetics is essential to understanding a matrix’s (e.g. cell, tissue or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization using the methods described by Mookerjee et al. (2017) . Package: r-cran-ceblr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2990 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceblr_1.0.0-1.ca2404.1_all.deb Size: 2877742 MD5sum: e48d91ef016ca4b3134fb3463b91cfd9 SHA1: 58d098a6eb068412cb26a2a0af2c3961f959f587 SHA256: f1879af2a1e9d9ec116b90a05747799a55a69d83f5993d40899087abdeb32ac9 SHA512: 19386f5353c384b7a4df16701a1bbede730fa67a2710793ce0d00cfa42ecda6cc6c311d88c626a03a0bf4edea1e28a0cd96e3f3c24cdca45d14c7600196cbe32 Homepage: https://cran.r-project.org/package=ceblR Description: CRAN Package 'ceblR' (Extract Data from the Canadian Elite Basketball League) Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) to create a repository of basic and advanced statistics for teams and players. Package: r-cran-ceda Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-limma, r-cran-mixtools, r-cran-ggplot2, r-cran-dplyr, r-cran-ggsci, r-cran-ggridges, r-cran-ggprism Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ceda_1.1.1-1.ca2404.1_all.deb Size: 3259440 MD5sum: d05639d1e766f42511fd4c110427d7a4 SHA1: e5343b7e3dd039dd2385336565bb443ca828b117 SHA256: 7d8bdd073676cba495f730bb9481e2cb7252b5d9ff99de6d09c4cc5d8e0170a9 SHA512: 073f065c4a6e2f18306a45f8e190c54b183b1ce6b0f75cb961f9327403a0ae50e50674ba15a54f5ce28f94584405bb67e7b6d6093111b9380541a1e75a956d28 Homepage: https://cran.r-project.org/package=CEDA Description: CRAN Package 'CEDA' (CRISPR Screen and Gene Expression Differential Analysis) Provides analytical methods for analyzing CRISPR screen data at different levels of gene expression. Multi-component normal mixture models and EM algorithms are used for modeling. Package: r-cran-cedmr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-mediation, r-cran-rlang, r-cran-rms, r-cran-tidyr Suggests: r-cran-cluster, r-cran-konfound, r-cran-mice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cedmr_0.1.0-1.ca2404.1_all.deb Size: 111978 MD5sum: 98c79aa3196289ae6e94f1ac31b81445 SHA1: 5f3f9278a71724dc37665f6d2c4b701d57e4c025 SHA256: 635180db94fb200ce55417d96fadb40697bb8b0d5069e16aa433c12dd5db2062 SHA512: 44f9e5e0dfd40a54ece4dc999bc62a92188a70b33f5df344358fc54b3de5a19f76871a4b3e42d6a756e1e8fc70a7493b87773e21e8077176f7b450f6cbd318eb Homepage: https://cran.r-project.org/package=CEDMr Description: CRAN Package 'CEDMr' (Capability-Ecological Developmental Model (CEDM) Analysis) Implements the Capability-Ecological Developmental Model (CEDM) for longitudinal and multilevel data. The package supports estimation and interpretation of models examining how socioeconomic status (SES), health indicators, and contextual factors jointly relate to academic outcomes. Functionality includes: (1) classification of ecological capability regimes (amplifying, neutral, compensatory); (2) estimation of moderated multilevel models with higher-order interaction terms; (3) causal mediation analysis using doubly robust estimation; (4) random-effects within-between (REWB) decomposition; (5) nonlinear moderation using restricted cubic splines; (6) clustering of longitudinal health trajectories; and (7) sensitivity analysis using the impact threshold for a confounding variable (ITCV) and robustness-to-replacement (RIR) measures. The package is designed for use with general longitudinal multilevel datasets. Package: r-cran-ceemdanml Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlibeemd, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-fints, r-cran-lsts, r-cran-earth, r-cran-caret, r-cran-neuralnet, r-cran-e1071, r-cran-pso Filename: pool/dists/noble/main/r-cran-ceemdanml_0.1.0-1.ca2404.1_all.deb Size: 49542 MD5sum: e3d607c6b188d0134fddf3de1aa4d212 SHA1: 1149fe5589b059a93deb969faab94ebeae76846e SHA256: 5dfdb221ae40e131518cf2557a2418c3412bdbae295433dd2e529a37ebd94926 SHA512: 3ee4d77226053e1ec1489374025e06f69ca20e235fa6d374574612cb82c012cac8aa3c4f993b8d1d46bc81ae2c336adf4b97d3c36bdbbde948512c5d6e74f3c8 Homepage: https://cran.r-project.org/package=CEEMDANML Description: CRAN Package 'CEEMDANML' (CEEMDAN Decomposition Based Hybrid Machine Learning Models) Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) . Package: r-cran-ceg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-bioc-rgraphviz Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceg_0.1.0-1.ca2404.1_all.deb Size: 260542 MD5sum: 4ff551cedff0b0dd05fb8c64f0e05131 SHA1: 4e5c45bac9d3260cb719321ad4d9b11784934b86 SHA256: 653dba58e02ef899390ad7174a306824d22c6333371b4b12bb00d9fc711bbfc3 SHA512: b045277b11230c5794f1d42c455874a0efe356409372ec28bd20a9ec88a7c29379d1277e0873b38473551ee8b6d930e3c76fe591cf797001fefa18a128a8bb10 Homepage: https://cran.r-project.org/package=ceg Description: CRAN Package 'ceg' (Chain Event Graph) Create and learn Chain Event Graph (CEG) models using a Bayesian framework. It provides us with a Hierarchical Agglomerative algorithm to search the CEG model space. The package also includes several facilities for visualisations of the objects associated with a CEG. The CEG class can represent a range of relational data types, and supports arbitrary vertex, edge and graph attributes. A Chain Event Graph is a tree-based graphical model that provides a powerful graphical interface through which domain experts can easily translate a process into sequences of observed events using plain language. CEGs have been a useful class of graphical model especially to capture context-specific conditional independences. References: Collazo R, Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018 (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169 . Package: r-cran-celestial Architecture: all Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-nistunits, r-cran-pracma Filename: pool/dists/noble/main/r-cran-celestial_1.4.6-1.ca2404.1_all.deb Size: 364362 MD5sum: b1049de58aa3267fd492a5296e5cdfad SHA1: 89cda04989c7fd9a96eed601551c62ce2d9c9c9e SHA256: b56612386f501e61010ae61d610408af1ab70f96fdfe5b8da484d23d1fb37c37 SHA512: b0c914f69736874cf80db3b67b3348e3a9314c91274e0e76f6d15080677abaadbca083ecdbbca061e6eaf33ffa43adcacdacf715e55ee8fff2d205bbd3380685 Homepage: https://cran.r-project.org/package=celestial Description: CRAN Package 'celestial' (Collection of Common Astronomical Conversion Routines andFunctions) Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations. Package: r-cran-celldeep Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-celldeep_1.0.1-1.ca2404.1_all.deb Size: 1490144 MD5sum: 005b1896a10804d706d65b8ffadbd81c SHA1: c6f7a2a31a91aad42da6bbb3bbb8cf905a902642 SHA256: f1e4b49b61bcd9bb81474760082f80c0a8fdee18b000c40eef3b8a524a86c36c SHA512: 05beec28a7e1009570d71db8128a32664f1709dcb6b6679a1c86bfbc8b8979877c94db8d566416da7de5a57cfc2f7b920b6f6bc82c90b5d5635aee3bb7490d31 Homepage: https://cran.r-project.org/package=CellDEEP Description: CRAN Package 'CellDEEP' (Cell DiffErential Expression by Pooling ('CellDEEP')) Pool cells together before running differentially expression (DE) analysis. Tell 'CellDEEP' how many cells you want to pool together (which shall be determined by the overall cell number of data), then run DE analysis. Cheng et al. (2026) . Package: r-cran-cellgeometry Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4310 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-bioc-complexheatmap, r-bioc-delayedarray, r-cran-dplyr, r-bioc-ensembldb, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-matrixstats, r-cran-mcprogress, r-cran-rlang, r-cran-scales Suggests: r-cran-future.apply, r-cran-ggforce, r-cran-ggsci, r-cran-knitr, r-cran-plotly, r-cran-rfast2, r-cran-rmarkdown, r-cran-seriation, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-cellgeometry_0.6.4-1.ca2404.1_all.deb Size: 2190748 MD5sum: 564ad4310538ab6f8ebc263887b780f8 SHA1: 3c9cb9622670359792fc5ed416236d0944733335 SHA256: 88643266423aa9240c344ff0fb91336b4d563f5476c1b6eee8fd309c74f98282 SHA512: 05523c11ddb046e9c4a434df937ef6ef65a0e19326a0cab7863bfd95171700aba8769398c31bcfb49ba5435b2b0fb55790348784caf74907bff75c31786cb6e8 Homepage: https://cran.r-project.org/package=cellGeometry Description: CRAN Package 'cellGeometry' (Geometric Single Cell Deconvolution) Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology . Package: r-cran-cellkey Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5013 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sdchierarchies, r-cran-rlang, r-cran-digest, r-cran-sdctable, r-cran-ptable, r-cran-cli, r-cran-yaml, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cellkey_1.0.3-1.ca2404.1_all.deb Size: 4968400 MD5sum: db4968badeb3f6399cf8cde418d8291c SHA1: ce1144180a4e8a8a429a11fdde38987efb795e94 SHA256: 2354b4e699de10fb21a9090aa90d38def89307a333a20cdda7a1f928ab76a9c6 SHA512: 66878db1c6c8f32d09804a16625304cc7c192afb351582043b2cd66fe8e5630215e761b5db13071b0ee79343699539dad3dcf48f95ebf668c2813556c5faf58c Homepage: https://cran.r-project.org/package=cellKey Description: CRAN Package 'cellKey' (Consistent Perturbation of Statistical Frequency- And MagnitudeTables) Data from statistical agencies and other institutions often need to be protected before they can be published. This package can be used to perturb statistical tables in a consistent way. The main idea is to add - at the micro data level - a record key for each unit. Based on these keys, for any cell in a statistical table a cell key is computed as a function on the record keys contributing to a specific cell. Values that are added to the cell in order to perturb it are derived from a lookup-table that maps values of cell keys to specific perturbation values. The theoretical basis for the methods implemented can be found in Thompson, Broadfoot and Elazar (2013) which was extended and enhanced by Giessing and Tent (2019) . Package: r-cran-cellkeyperturbation Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table Suggests: r-cran-bigrquery, r-cran-dbi, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cellkeyperturbation_3.0.0-1.ca2404.1_all.deb Size: 88782 MD5sum: bc986444eaa5095b31dc3abdce0de27d SHA1: b45e50375f59e7633a0dbf7aa50c213946f38f94 SHA256: da01d9366425c18e52463da51feb11b916213f35b3e9599580c03b7ab87f9d70 SHA512: 842abb0ac21f7d2df308ce0b3ed12901d77ea71dc6c70caf33070295647a80d6d6090b8e9fb347f099884b7473000d35f1257a794519ea15ed2d6949b850a790 Homepage: https://cran.r-project.org/package=cellkeyperturbation Description: CRAN Package 'cellkeyperturbation' (Cell Key Perturbation) Provides functions to generate frequency tables and apply cell key perturbation to protect against statistical disclosure in tabular outputs. The implemented methods are described in "Cell Key Perturbation User Guide" . Developed at the UK Office for National Statistics. Package: r-cran-cellorigins Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iterpc Filename: pool/dists/noble/main/r-cran-cellorigins_0.1.3-1.ca2404.1_all.deb Size: 346668 MD5sum: 84db4114345178f961ac5dd007c6d008 SHA1: 87f5138264db635265e9792e11ee38d2acace639 SHA256: a0ecf150729a9ca4e1f82bd190ceb0d82392696a57a5021b457251ae2c7319b7 SHA512: 09b720431f180663d95ab8b3b5aa364c1ac9c41f1e1d861266b185325f64ec68a009af1438f92d560fae869f2274a056ba0698c0a767dcdafd21e99507d3661f Homepage: https://cran.r-project.org/package=cellOrigins Description: CRAN Package 'cellOrigins' (Finds RNASeq Source Tissues Using In Situ Hybridisation Data) Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products. Package: r-cran-cellpypes Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scutils, r-cran-ggplot2, r-cran-matrix, r-cran-rlang, r-cran-viridis, r-cran-cowplot, r-cran-dplyr, r-cran-scales, r-cran-scattermore Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-seurat, r-bioc-deseq2, r-cran-rcppannoy, r-cran-tibble, r-cran-seuratobject Filename: pool/dists/noble/main/r-cran-cellpypes_0.3.0-1.ca2404.1_all.deb Size: 1066838 MD5sum: b604537b29aa3704be896809c72d2b2f SHA1: b130b58f41f398b2b9a6d730488c0bfbf446243d SHA256: 362efb55cc1e58cfe6c8c450199607cef7224291ba75f45445aaaf8c790ee101 SHA512: f9237c164b4bf85d9c50a9c954a28e8463e7d6c94c4f18486bd2862f755f60707579d0c7a0d96f9015dfcb8f5a8e59e65791b1fee316eea46faf688dfe77e885 Homepage: https://cran.r-project.org/package=cellpypes Description: CRAN Package 'cellpypes' (Cell Type Pipes for Single-Cell RNA Sequencing Data) Annotate single-cell RNA sequencing data manually based on marker gene thresholds. Find cell type rules (gene+threshold) through exploration, use the popular piping operator '%>%' to reconstruct complex cell type hierarchies. 'cellpypes' models technical noise to find positive and negative cells for a given expression threshold and returns cell type labels or pseudobulks. Cite this package as Frauhammer (2022) and visit for tutorials and newest features. Package: r-cran-cellranger Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rematch, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cellranger_1.1.0-1.ca2404.1_all.deb Size: 103102 MD5sum: 3ba31e894c8b40552a4f383a3be4b451 SHA1: 4d2797daaa61572b4dda6e01eff286e67544e37d SHA256: c36e80a081ed87be007d76006a1721dba03f00879057a7726940074c02869257 SHA512: aff72b4e99f678a1673266a177deceb0a60976238eea3cffb23ee7212ca4e57751c5150405d607e18207234dd7d8aaf308ade653c764e761b332282a8109fb80 Homepage: https://cran.r-project.org/package=cellranger Description: CRAN Package 'cellranger' (Translate Spreadsheet Cell Ranges to Rows and Columns) Helper functions to work with spreadsheets and the "A1:D10" style of cell range specification. Package: r-cran-celltrackr Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ellipse, r-cran-pracma Suggests: r-cran-scatterplot3d, r-cran-fractaldim, r-cran-testthat, r-cran-wordspace, r-cran-knitr, r-cran-rmarkdown, r-cran-rspectra, r-cran-uwot, r-cran-dendextend, r-cran-ggplot2, r-cran-ggbeeswarm, r-cran-gridextra, r-cran-mvtnorm, r-cran-jsonlite, r-cran-httr, r-cran-curl Filename: pool/dists/noble/main/r-cran-celltrackr_1.2.2-1.ca2404.1_all.deb Size: 3426762 MD5sum: 62802220e0825461255bfddd0d81ec37 SHA1: 31e632842a4a7a970b70a65c4a4568120e73fa1b SHA256: e6a3b92b2152803f87e9bd2a0d78fea2b56b51a6893db831d5150e65e6f55acc SHA512: 39bb5397c6b6b81f2781912d26707af369c0637c2282a5c65f979cfb1f1efcf88c899d1a60373f5c53d41303d9ea94bcdf2b4577ad58edd59cb65f7475aac9d0 Homepage: https://cran.r-project.org/package=celltrackR Description: CRAN Package 'celltrackR' (Motion Trajectory Analysis) Methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) , Beltman, Maree, and de Boer (2009) , Gneiting and Schlather (2004) , Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) , Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) , Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) , Textor, Sinn, and de Boer (2013) , Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) . Package: r-cran-cellularautomata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gganimate, r-cran-ggplot2, r-cran-patchwork, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cellularautomata_0.1.0-1.ca2404.1_all.deb Size: 212882 MD5sum: f17c9911d8ea6d47fa93e73d29dda05c SHA1: 879f40e057e05645f724062db326d9caf60bdf12 SHA256: f2f419c24d55801cd9d70d59c2c3dff4e1e747be7cf8a5e482824b854e72593d SHA512: 7b98c336a53d726e497d8c86b6fab1c2467f74da047626c77b2b207a45d6aa2de5eedeb9ee23203c9adcbcc39692f5dd121f99e7d58d09547b9d343e0b57096d Homepage: https://cran.r-project.org/package=cellularautomata Description: CRAN Package 'cellularautomata' (Cellular Automata) Create cellular automata from 'Wolfram' rules. Allows the creation of 'Wolfram' style plots, as well as of animations. Easy to create multiple plots, for example the output of a rule with different initial states, or the output of many different rules from the same state. The output of a cellular automaton is given as a matrix, making it easy to try to explore the possibility of predicting its time evolution using various statistical tools available in R. Wolfram S. (2002, ISBN:1579550088) "A New Kind of Science". Package: r-cran-cellvolumedist Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-gplots Filename: pool/dists/noble/main/r-cran-cellvolumedist_1.5-1.ca2404.1_all.deb Size: 52852 MD5sum: b455e814ec3cb873b492863b69add655 SHA1: 17c7e79e250f596e5d54efcc839f9fab6d383de5 SHA256: 12f89bbea598a3ed35561ef53af2df1f66d55213ce2e17e049807783467dda0f SHA512: 9361af887d9e0feb0468d8306bfb710c017f905ca1df28ef788ec0fa4fc59757dca7f020b33267e62bf99ecb9a0c89efe5122f0f57897c340b953fe7ef0356e8 Homepage: https://cran.r-project.org/package=cellVolumeDist Description: CRAN Package 'cellVolumeDist' (Functions to Fit Cell Volume Distributions and Thereby EstimateCell Growth Rates and Division Times) Implements a methodology for using cell volume distributions to estimate cell growth rates and division times that is described in the paper, "Cell Volume Distributions Reveal Cell Growth Rates and Division Times", by Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona and Anne L. Plant, which appeared in the Journal of Theoretical Biology. In order to reproduce the analysis used to obtain Table 1 in the paper, execute the command "example(fitVolDist)". Package: r-cran-cellwindx Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-bioc-complexheatmap, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-patchwork, r-cran-seurat, r-cran-tidyr Suggests: r-cran-seuratobject Filename: pool/dists/noble/main/r-cran-cellwindx_1.0.0-1.ca2404.1_all.deb Size: 92440 MD5sum: 6e4cb9ec638bafab137695f97f984eb2 SHA1: 9a062705039d4f2dba818ebe9ebced95ba10f495 SHA256: 6c9a4eae4a3b3933e2bdbd925cca0e18c277a73e29ace78594f4b9f6cc36aab6 SHA512: 4c4fa5e7c983e70ecd26d82e7eb85489b7835b8541a17a8a73d20ef28c4ad1f8a5a2de53c430b783a4bd321579fdf915d707a319e262cb7fccbca83c2856f834 Homepage: https://cran.r-project.org/package=CellWindX Description: CRAN Package 'CellWindX' (Marker Gene Analysis and Visualization for Single-Cell Data) Provides a 'Seurat'-compatible toolkit for marker gene identification, expression summarization, and visualization of annotated single-cell transcriptomic data. 'CellWindX' identifies top cell-type-enriched markers, calculates marker expression percentages and average expression values across cell groups, and generates publication-oriented dimensional reduction plots, marker heatmaps, and gene-level radar plots. The package includes built-in aesthetic palettes and supports both exploratory analysis and downstream figure preparation for single-cell atlas studies. The workflow is designed to complement single-cell analysis frameworks such as 'Seurat' described by Satija et al. (2015) and Hao et al. (2021) , as well as heatmap visualization methods implemented in 'ComplexHeatmap' described by Gu et al. (2016) . Package: r-cran-cem Architecture: all Version: 1.1.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-matchit, r-cran-combinat, r-cran-randomforest, r-cran-nlme Suggests: r-cran-amelia Filename: pool/dists/noble/main/r-cran-cem_1.1.31-1.ca2404.1_all.deb Size: 677224 MD5sum: 5a158ced099ff5b42221a96c9b907e14 SHA1: 95dc06a0150e47066eabd4424a61ed5425dc3e70 SHA256: 9290d0aec58839b5e8405cb358d3d96dc87be42d821d4f42204bd1fe46cac44e SHA512: 4e2f0695890373910348024680fc950bd3fdf1839810731585f5b36540cb36dea18dab3961d5084199c3a8c879482894502da105cc3095b35d7212ddb7872eb3 Homepage: https://cran.r-project.org/package=cem Description: CRAN Package 'cem' (Coarsened Exact Matching) Implementation of the Coarsened Exact Matching algorithm discussed along with its properties in Iacus, King, Porro (2011) ; Iacus, King, Porro (2012) and Iacus, King, Porro (2019) . Package: r-cran-cemco Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-nnet, r-cran-rootsolve, r-cran-foreach, r-cran-mass, r-cran-mclust, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-cemco_0.2-1.ca2404.1_all.deb Size: 50974 MD5sum: 7ffb630d0e7ae625c0e767bedd6b16fa SHA1: 630920ba73230895ed39024fce2ab5ec77077576 SHA256: 0a2d080c798ec5dc3d4a49eac13ee2f84253358b086b6d75bf073eee99bc1f92 SHA512: ae305c6f6b1ecbffd7a59687799537761eb8a81433b446e3883962722be32b8a896d1642fe79293bf281689cb8f05eeec978ec8d50e39828dbfe092f951c73ca Homepage: https://cran.r-project.org/package=cemco Description: CRAN Package 'cemco' (Fit 'CemCO' Algorithm) 'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) ). Package: r-cran-cenbar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-mass, r-cran-mvtnorm, r-cran-glmnet, r-cran-survival, r-cran-cvtools Filename: pool/dists/noble/main/r-cran-cenbar_0.1.1-1.ca2404.1_all.deb Size: 43266 MD5sum: f7265ea7e580c5a745b6bad1de3253f6 SHA1: 5e2e944ff880a9c1e41bbe23e8864f673956ffdc SHA256: c58ba80a44ac3f5c316afea72e0ed5becac8d573ca3202982d21731ee366576c SHA512: c95442847d4187530252d85f20e9fb76678c17783e0db514946fbaa6d5f21a68aaf1946c0748727455ecc419806e153c3343d810af1ad2e83e2512464efa49c7 Homepage: https://cran.r-project.org/package=CenBAR Description: CRAN Package 'CenBAR' (Broken Adaptive Ridge AFT Model with Censored Data) Broken adaptive ridge estimator for censored data is used to select variables and estimate their coefficients in the semi-parametric accelerated failure time model for right-censored survival data. Package: r-cran-cencrne Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cencrne_1.0.0-1.ca2404.1_all.deb Size: 2038822 MD5sum: c919aa1cedc370cac2f70c9b0c389d21 SHA1: 7ffd2aef9b9d2550da2e952cc70406c36c365506 SHA256: 4ac766c02f25fa6a959c8675c29d51d67ff1d688589a656f12af3b51d0e3a404 SHA512: 0fceaa2e8b65648518c47d1da6bb3633da9dff3b93e34fffee24c5c5e867315b1b1fbfe232dd2ed2577de9870b7acd52b6840d668176df8061a028438a4fd37e Homepage: https://cran.r-project.org/package=cencrne Description: CRAN Package 'cencrne' (Consistent Estimation of the Number of Communities viaRegularized Network Embedding) The network analysis plays an important role in numerous application domains including biomedicine. Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. Reference: Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, . Package: r-cran-cengam Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-cengam_0.5.4-1.ca2404.1_all.deb Size: 260924 MD5sum: 408ef4712f3238d472529e23c042a06a SHA1: 5e331a8409e9295391636d77daf61b9bbe8fb8fb SHA256: bbf7297e8928ca78bfdd2617dc547e7d26c6f9f0f653d6bb3c26242b3911a95c SHA512: 21232e3399cd32e10495240d0d93a57ad307ea0eb2c5420b72f5f7987c871c888bdecbf83500175f0574f9bb8c275d5960621459ea786ce0ad2c4e1186e7ae46 Homepage: https://cran.r-project.org/package=cenGAM Description: CRAN Package 'cenGAM' (Censored Regression with Smooth Terms) Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) . Package: r-cran-censable Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-stringr, r-cran-memoise, r-cran-purrr, r-cran-censusapi, r-cran-tinytiger Suggests: r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-censable_0.0.8-1.ca2404.1_all.deb Size: 445784 MD5sum: fde9b57faebb8d1efcf34dd98d87876f SHA1: c6990265c521d5d7a3664260820f7e3adcad11ae SHA256: c2a3ecf944ab21b361ab1ea3dd9759b75ed7d1adef9baf45860b6cf5d930e495 SHA512: 2e6c38801658280743b47ce6a0de20655c1587f2ac4b9bb5711524906cbc87727d125056d605a392c20455f0907473fc1175e50674723db6178506376d77f2f6 Homepage: https://cran.r-project.org/package=censable Description: CRAN Package 'censable' (Making Census Data More Usable) Creates a common framework for organizing, naming, and gathering population, age, race, and ethnicity data from the Census Bureau. Accesses the API . Provides tools for adding information to existing data to line up with Census data. Package: r-cran-censcov Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-censcov_1.0-0-1.ca2404.1_all.deb Size: 49690 MD5sum: 11e1025aeb50af63ef9e6ec8456567f4 SHA1: 58972fbd03d9425ead9c07cc2a1f048b6f38fbfa SHA256: bcf7ce312bde95f2ede2b91a26379a2e0f931f8123f75e99ece8fe6417363ee4 SHA512: f255b755fe17d9ba0ce644cc7a598e733c26ca00e5489ab918286c13ca6a818339369fd33c2ebea973d534a7c02e16817d186508fcf581d2315d5764f4ed90e3 Homepage: https://cran.r-project.org/package=censCov Description: CRAN Package 'censCov' (Linear Regression with a Randomly Censored Covariate) Implementations of threshold regression approaches for linear regression models with a covariate subject to random censoring, including deletion threshold regression and completion threshold regression. Reverse survival regression, which flip the role of response variable and the covariate, is also considered. Package: r-cran-censmfm Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-momtrunc, r-cran-mvtnorm, r-cran-gridextra, r-cran-ggplot2, r-cran-tlrmvnmvt Suggests: r-cran-mixsmsn Filename: pool/dists/noble/main/r-cran-censmfm_3.1-1.ca2404.1_all.deb Size: 153672 MD5sum: 994bc9d4d61e4380a7495a8b55c4e8d4 SHA1: f9fa082d1e464572010b4bd3976ad9b7713cf2fb SHA256: f960f2e7075b94f65c5dd9fafa82d7e84366b1941edc5dc84fe6f7647596a342 SHA512: 04fd573d7603504ec79e9cf5c4ba13594c560781656f362a921dd94312a5b40190b01c9e6bc91e4018591123a52718126066868e4ebcb3c2fd9928922c00e3e7 Homepage: https://cran.r-project.org/package=CensMFM Description: CRAN Package 'CensMFM' (Finite Mixture of Multivariate Censored/Missing Data) It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) for the multivariate skew-normal case. Package: r-cran-censo2017 Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-cli, r-cran-crayon, r-cran-rstudioapi Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-dplyr, r-cran-dbplyr, r-cran-ggplot2, r-cran-chilemapas Filename: pool/dists/noble/main/r-cran-censo2017_0.6.2-1.ca2404.1_all.deb Size: 59860 MD5sum: 751fa72e3c1a685dfcae16d3dceb0ae9 SHA1: fa489b9bda69541ee929d4d4a18de14770bab94f SHA256: 426d98e4d09ab924651680aeb0d1fee5d23b243bf992aee69f94bfd68076395e SHA512: ba26ca3ce2328b22ec941c59b3d34570452abe97a06845ad7ad0ac81f2b064dbec91dc85483451e330d4efefeeba0da55c4455672f61b5b4481726bcbf9467f7 Homepage: https://cran.r-project.org/package=censo2017 Description: CRAN Package 'censo2017' (Base de Datos de Facil Acceso del Censo 2017 de Chile (2017Chilean Census Easy Access Database)) Provee un acceso conveniente a mas de 17 millones de registros de la base de datos del Censo 2017. Los datos fueron importados desde el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De Grande. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Provides convenient access to more than 17 million records from the Chilean Census 2017 database. The datasets were imported from the official DVD provided by the Chilean National Bureau of Statistics by using the REDATAM converter created by Pablo De Grande and in addition it includes the maps accompanying these datasets.) Package: r-cran-censobr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-rlang Suggests: r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-geobr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-kableextra, r-cran-knitr, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-censobr_1.0.0-1.ca2404.1_all.deb Size: 508250 MD5sum: 1cf54a19a3c1efe7ea0b4e8a9828c078 SHA1: 56040ab848e2629f760a5d86a406173041de52f2 SHA256: 1431c782da579e24a020da130a67b1ce2a45eee5b80f7c441f48e029c145d35a SHA512: 93ed7934bcb053b9304455f0e9ab6f0b28fa6e40a0b8272c1d55243045c5836467be930330d43ecc3650d63c398a91dc1c4ebd7eb1134d4f73a2a8ee14fa374e Homepage: https://cran.r-project.org/package=censobr Description: CRAN Package 'censobr' (Download Data from Brazil's Population Census) Easy access to data from Brazil's population censuses. The package provides a simple and efficient way to download and read the data sets and the documentation of all the population censuses taken in and after 1960 in the country. The package is built on top of the 'Arrow' platform , which allows users to work with larger-than-memory census data using 'dplyr' familiar functions. . Package: r-cran-censorcopula Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula Filename: pool/dists/noble/main/r-cran-censorcopula_2.0-1.ca2404.1_all.deb Size: 22738 MD5sum: e1c40f379a544b40a9a3ed22c0923acc SHA1: 859a309370feaddb9658c26b8b08f66d680e8c53 SHA256: dd1d0a30d78515c259719f9d14a1768a9acc1ff7a0a1d05cfd3f853ffb7c5438 SHA512: 67f77c68eca60942338d14736b49f3d0215aee2c09b49ac9a5be5678bb76251d5722b8c82484291ecee8be8a205a128a978d7b37ce0b8fcfb5c6b9384047e4dc Homepage: https://cran.r-project.org/package=censorcopula Description: CRAN Package 'censorcopula' (Estimate Parameter of Bivariate Copula) Implement an interval censor method to break ties when using data with ties to fitting a bivariate copula. Package: r-cran-censored Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-parsnip, r-cran-survival, r-cran-cli, r-cran-dials, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-mboost, r-cran-prodlim, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-aorsf, r-cran-coin, r-cran-covr, r-cran-flexsurv, r-cran-glmnet, r-cran-ipred, r-cran-partykit, r-cran-pec, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-censored_0.3.5-1.ca2404.1_all.deb Size: 215642 MD5sum: 6f3c20680fc2884765c67b84c2d21c8e SHA1: 0f3ec5f3cc2af45d3e13baea8930dcb6940afc2a SHA256: fd25192f1d5a48909e1d81e9a29817a7148d5fefed96c0e826a10ed1e4dc8d3f SHA512: e7b02767588e66d622e23155336522b865d5a31947436148f35d166739992072e96b44a38621f05cb8a0c9ef3e7b158a1d5ab8468a1b79a8074f243ef92899fb Homepage: https://cran.r-project.org/package=censored Description: CRAN Package 'censored' ('parsnip' Engines for Survival Models) Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) ), semi-parametric (e.g., Simon et al (2011) ), and tree-based models (e.g., Buehlmann and Hothorn (2007) ). Package: r-cran-censoredaids Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mnormt, r-cran-mvtnorm, r-cran-matrixcalc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-censoredaids_1.0.0-1.ca2404.1_all.deb Size: 434504 MD5sum: cde124355fe634a774a530808fba561a SHA1: 1db56b71e3499d034b2fa7c33b37f58af1eb3e12 SHA256: 5bfe6e2e03d56ce8c0adae650a760b4e8eafc88f2704c91b1a7b654962d972ee SHA512: 0c73b2f965587fbd7632c51959416454259ddbab2dbebf44301910b3a641dfc47c627838e62014cc9956ce4b27592085f556ad493c622205e558c24f59cc440a Homepage: https://cran.r-project.org/package=censoredAIDS Description: CRAN Package 'censoredAIDS' (Estimation of Censored AI/QUAI Demand System via MaximumLikelihood Estimation (MLE)) Tools for estimating censored Almost Ideal (AI) and Quadratic Almost Ideal (QUAI) demand systems using Maximum Likelihood Estimation (MLE). It includes functions for calculating demand share equations and the truncated log-likelihood function for a system of equations, incorporating demographic variables. The package is designed to handle censored data, where some observations may be zero due to non-purchase of certain goods. Package also contains a procedure to approximate demand elasticities numerically and estimate standard errors via Delta Method. It is particularly useful for applied researchers analyzing household consumption data. Package: r-cran-censosbo Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-curl, r-cran-cli, r-cran-fs, r-cran-rlang, r-cran-dplyr, r-cran-sf Suggests: r-cran-duckdb, r-cran-dbi, r-cran-ggplot2, r-cran-dt, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-scales Filename: pool/dists/noble/main/r-cran-censosbo_2.0.0-1.ca2404.1_all.deb Size: 1238318 MD5sum: e943cfe0a0cd8cde80de66893bfe1d80 SHA1: 59acdf68606ad9d135837fe2faf64db0887cc20c SHA256: 3d093977b410d346633ced0734847f9e19bb75dc81fc3855b8593ade3fc8208f SHA512: a66eab82133289562a8a9dc28f8ccbd5d91b57eee2983aa7cb9cce74a2cfce2df934a445dc61fc760941673a1e8f97646ea65f9a619526a6522fdf3fb780dd4f Homepage: https://cran.r-project.org/package=censosbo Description: CRAN Package 'censosbo' (Access and Analysis of Bolivian Census Microdata) Programmatic access to the microdata of the Bolivian population and housing censuses of 1976, 1992, 2001, 2012 and 2024, published by the National Statistics Institute of Bolivia (INE, ). Data files in Apache Parquet format are downloaded on demand from a companion data repository, cached locally, and can be filtered by department, province or municipality. Supports 'dplyr' workflows through Apache Arrow and SQL queries through 'DuckDB'. Includes variable dictionaries for every census year with a thematic taxonomy and contextual metadata (reference population, questionnaire item number and provenance of each variable), derived from the census questionnaires and from the Data Documentation Initiative (DDI) files of the INE ANDA catalogue; functions to harmonise variables across censuses for temporal comparison; and choropleth maps at the department and municipality level. Also includes the 2024 census aggregates for urban blocks and rural communities, with their geometries. Documentation and messages are in Spanish, the language of the source data. Package: r-cran-censreg Architecture: all Version: 0.5-40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maxlik, r-cran-glmmml, r-cran-sandwich, r-cran-misctools, r-cran-plm Suggests: r-cran-aer, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-censreg_0.5-40-1.ca2404.1_all.deb Size: 205066 MD5sum: 08a92c2322b1cf405411b4916d4c220d SHA1: 616af574cacf5f3c97273e3d1a9f7c9d2493262e SHA256: 359ead9b387c2cf1daff0cc859b28709f9f4d7de0f3480720f5e105295542d62 SHA512: 5ea14a040b5b0de3b4aec9bcc43073eaa16a557651951de2e4c5273ccba4b81826f33cc1b6fc3c07beff857a4158b10e570b72b687b8c652647e5f6e508eeda9 Homepage: https://cran.r-project.org/package=censReg Description: CRAN Package 'censReg' (Censored Regression (Tobit) Models) Maximum Likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data. Package: r-cran-censregsmsn Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mnormt, r-cran-cubature, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-censregsmsn_0.0.1-1.ca2404.1_all.deb Size: 124602 MD5sum: 1560fe22f95e221f1a34aaae4926bf4c SHA1: ae6163bab754c9d4fc21f42bf47f0365a1428d09 SHA256: 36bafc7a945c8189daf6726c575f1829fef9cc375b59924950a0dd6e3e6ee393 SHA512: 9a904bcad416b0de9489bdf72e7203c6839e4634a2f98189509bd290994d402e56520930115a6cfa3a81501057eeba7db9405114483db1f9a2ad383e3ec793e0 Homepage: https://cran.r-project.org/package=CensRegSMSN Description: CRAN Package 'CensRegSMSN' (Censored Linear Regression Models under Heavy‑tailedDistributions) Functions for fitting univariate linear regression models under Scale Mixtures of Skew-Normal (SMSN) distributions, considering left, right or interval censoring and missing responses. Estimation is performed via an EM-type algorithm. Includes selection criteria, sample generation and envelope. For details, see Gil, Y.A., Garay, A.M., and Lachos, V.H. (2025) . Package: r-cran-censspatial Architecture: all Version: 3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor, r-cran-rcpp, r-cran-mvtnorm, r-cran-optimx, r-cran-tmvtnorm, r-cran-msm, r-cran-psych, r-cran-numderiv, r-cran-raster, r-cran-moments, r-cran-lattice, r-cran-tlrmvnmvt Filename: pool/dists/noble/main/r-cran-censspatial_3.6-1.ca2404.1_all.deb Size: 228240 MD5sum: e742bcfb8597819117af22222fcf971a SHA1: 0e2add380545476127c5bf0f26844da2989b1600 SHA256: 5483f3083d6c0e8b7b1100a45faf463a8bb8703b2c2855e02cc0ddffee48d486 SHA512: dead4defddd8434ad7c198300f8dacde785aa6ad3c88ce5392634129ec903bf4ca507b4d02f0b0744da88c2fc8ba2d4943346dec2ab6cd92f754c9f5ee9339ab Homepage: https://cran.r-project.org/package=CensSpatial Description: CRAN Package 'CensSpatial' (Censored Spatial Models) It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) . Package: r-cran-census2016 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3575 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-scales, r-cran-testthat, r-cran-png Filename: pool/dists/noble/main/r-cran-census2016_0.2.0-1.ca2404.1_all.deb Size: 3439210 MD5sum: a184cb05fb2830e328db641ca37c8623 SHA1: 5f9f6d8149a5655d138bb3ed8e955514651cf06c SHA256: 4cbe1c797d596c35744031ef122eecb663541a0c82df67c5f619448a0c26f4e9 SHA512: 9b46efb66937125e07e4e222fea8dac7b51b68231e7b82aad85cbddc9ecc08f9dff5a5b7c5db6e6fb8747755f05a8ed5334f7ef3f19c6dd936ffdb54e366a91c Homepage: https://cran.r-project.org/package=Census2016 Description: CRAN Package 'Census2016' (Data from the Australian Census 2016) Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis. Package: r-cran-censusapi Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-censusapi_0.10.0-1.ca2404.1_all.deb Size: 68408 MD5sum: 4ec66a24cbdb63d399c9b69230f0840f SHA1: de561f7ff01d82cd99c0a7f8c4915c8ac59dfdd1 SHA256: 936cce41c5f6e44f7de146622c233151f51fb5cc9137f3823c46b12e4565ccdc SHA512: 83b38b18af7a9dcc10e9ea5419a102bb0e4a84a5dbc825af65a9555e55e29a664d61913670b06672c9207350c6cb40479cedb71c5a596a75c08fb71c0e859774 Homepage: https://cran.r-project.org/package=censusapi Description: CRAN Package 'censusapi' (Retrieve Data from the Census APIs) A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. 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Package: r-cran-censuspyrid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-dplyr, r-cran-tidyr, r-cran-dt, r-cran-ggplot2, r-cran-ggthemes, r-cran-scales, r-cran-networkd3 Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-censuspyrid_1.0.2-1.ca2404.1_all.deb Size: 461146 MD5sum: 11d37d6fa0a99dfdbdca16f1125c4355 SHA1: 2cdd849c050ff247893e34c3c13bd2cb0d2311ef SHA256: 002dee6eaa7916b8c3950250231c5f3632518bea53238add68d32af77beb4715 SHA512: 269e259a325f106451d8c0de2860990d4f6e80bb1e1ff8c3deb49cd64a4bd89649b747fcd8156201e5fd1a5012e5578e6f11a05d607bdb75525e622ed0faa6b3 Homepage: https://cran.r-project.org/package=censuspyrID Description: CRAN Package 'censuspyrID' (Explorer of Indonesian Population Pyramids from Harmonized andNon-Harmonized Census Data) Provides harmonized and non-harmonized population pyramid datasets from the Indonesian population censuses (1971–2020), along with tools for visualization and an interactive 'shiny'-based explorer application. Data are processed from IPUMS International (1971–2010) and the Population Census 2020 (BPS Indonesia). Package: r-cran-censusr Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-censusr_0.0.4-1.ca2404.1_all.deb Size: 42248 MD5sum: 0cc396e27f5ce98e777312bc1966cdb4 SHA1: 9e8a9b15b37b746718ae8fd70e6e6e04386249d3 SHA256: 0da6ed8d9469b379d43e1e0d8d80cbe99ba95154b131f21c971bf7f4bc3882cd SHA512: e6018bfd06b4432887633db701da3fc608fcb659128dd9ebfb40b2335b28fba1a0d359fda44e54208a5e2f452827154d2784778c5ec1d51297f7455c8d9522a1 Homepage: https://cran.r-project.org/package=censusr Description: CRAN Package 'censusr' (Collect Data from the Census API) Use the US Census API to collect summary data tables for SF1 and ACS datasets at arbitrary geographies. Package: r-cran-centerline Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wk, r-cran-sf, r-cran-geos, r-cran-sfnetworks, r-cran-checkmate Suggests: r-cran-smoothr, r-cran-testthat, r-cran-geomtextpath, r-cran-terra, r-cran-igraph, r-cran-ggplot2, r-cran-raybevel Filename: pool/dists/noble/main/r-cran-centerline_0.2.5-1.ca2404.1_all.deb Size: 2224790 MD5sum: 873751b9c7daf0392dae6aeb4164bd34 SHA1: 74dd8095fa285cca5cb7ac0d072092a6a83a57fa SHA256: 922c53328bc69af4435d945af83e43c79ec841ec788851fd9aaf8f850ea42f75 SHA512: 64dbebaeca2eaeb7610d2671b8303993a0a0eecff6172edd5fec596ddbeb4ddf0266b604b4013947e6a43f5ccc5e48a4507cee0181ba55637796b32198f71fd7 Homepage: https://cran.r-project.org/package=centerline Description: CRAN Package 'centerline' (Extract Centerline from Closed Polygons) Generates skeletons of closed 2D polygons using Voronoi diagrams. It provides methods for 'sf', 'terra', and 'geos' objects to compute polygon centerlines based on the generated skeletons. Voronoi, G. (1908) . 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Package: r-cran-centr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chk, r-cran-dplyr, r-cran-sf, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-centr_0.2.4-1.ca2404.1_all.deb Size: 89044 MD5sum: f7729664c2cd24e31bb085d2c81b09f9 SHA1: 5549fa176167d8f68dd6678bd9105fb1423c2fee SHA256: ba1066a880fe3a1adbdefc0a4f5da399ffed076b6d9c817ed97207faaa5a6a71 SHA512: 0cd4bab1d0fd8041144e9209bc588f817fee02cda94d98939997a1e319203ced8ea76cafba7a88838ac51b52a93970af7d565957eef44201497fb9dabecdb878 Homepage: https://cran.r-project.org/package=centr Description: CRAN Package 'centr' (Weighted and Unweighted Spatial Centers) Generate mean and median weighted or unweighted spatial centers. Functions are analogous to their identically named counterparts within 'ArcGIS Pro'. Median center methodology based off of Kuhn and Kuenne (1962) . Package: r-cran-centrifuger Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-shiny, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-centrifuger_0.1.7-1.ca2404.1_all.deb Size: 99844 MD5sum: f7fd4b1f40a65b9d29679b468d8a643d SHA1: d88f5dad231df92e215a059bfa14965ba875d384 SHA256: 6cbc06fabc880d63f5a577dd237fcdbe002ea49bd37e0fb7a565890191527a6f SHA512: 53b160f922b49159aa8528a26babde19ba8cdd574f835d93229cc95a6c30354c9401c245c9f381ab4f916c4fc99e7c8c5dd633255273dc6cb2c5c2ea9daefa62 Homepage: https://cran.r-project.org/package=centrifugeR Description: CRAN Package 'centrifugeR' (Non-Trivial Balance of Centrifuge Rotors) Find the numbers of test tubes that can be balanced in centrifuge rotors and show various ways to load them. Refer to Pham (2020) for more information on package functionality. Package: r-cran-ceodata Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-haven, r-cran-dplyr, r-cran-urltools, r-cran-stringr, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl, r-cran-rmarkdown, r-cran-vtable, r-cran-comparegroups Filename: pool/dists/noble/main/r-cran-ceodata_1.4.0-1.ca2404.1_all.deb Size: 212222 MD5sum: 6cb1f8b5a7d1c3c241f90c42e3c4b420 SHA1: f74e5501616aa5acc88788bb42e8dbb77ed13eed SHA256: 3557fb575e8aebe8cf78ec7ce90a2aff42958ef58ae6127f65eef2f876dd0b0c SHA512: ed67f8ba099fe259b0801b81a4b0c16b7025a88a21e592b12ded19422a872161b8c86706c59dc513131463283884cbe1a1d721c7998c6f6d4659a4edd1fb199c Homepage: https://cran.r-project.org/package=CEOdata Description: CRAN Package 'CEOdata' (Datasets of the CEO (Centre d'Estudis d'Opinio) for OpinionPolls in Catalonia) Easy and convenient access to the datasets of the "Centre d'Estudis d'Opinio", the Catalan institution for polling and public opinion. The package retrieves microdata directly from the open data platform of the Generalitat de Catalunya and returns it in a tidy format. Package: r-cran-ceoptim Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-msm, r-cran-sna Filename: pool/dists/noble/main/r-cran-ceoptim_1.3-1.ca2404.1_all.deb Size: 60024 MD5sum: c3a89bc03b83419d37a28f2a382f534f SHA1: 496446b381f1f5674864e3ded964f033e7dbb7a7 SHA256: 19012234f5122cbf282fdb68c089c35dcae00b904226afd55b3b76b56d5f4992 SHA512: 0b5a2e623c8af0cb3520db45921a3eb460b22f367cc489bc370fa7497319b5872242ba4eacf9d0d6436adfa3f74a28c7b5cd16b2f6c7fbd1a617d839ee42baf2 Homepage: https://cran.r-project.org/package=CEoptim Description: CRAN Package 'CEoptim' (Cross-Entropy R Package for Optimization) Optimization solver based on the Cross-Entropy method. Package: r-cran-cepa Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-bioc-rgraphviz, r-bioc-graph Filename: pool/dists/noble/main/r-cran-cepa_0.8.2-1.ca2404.1_all.deb Size: 3117330 MD5sum: 43e92a8338dc5e9e49d834786ede44d7 SHA1: 9a240e3105411901fe573b01b71c33e7747ffcff SHA256: e88c0dc2ecb9c16fafa1debf114f1ec7f57d9352f6200709dc4aa0f9e1e98bb4 SHA512: f6ed8a632b8a8d9d6ebde39ae010352ef83b706da98038dd4447fbf38e9a1c8ee2eec7dc69825d587b28e0ff29e1a23c13750fc04a81bad163d09cbf17d95cad Homepage: https://cran.r-project.org/package=CePa Description: CRAN Package 'CePa' (Centrality-Based Pathway Enrichment) It aims to find significant pathways through network topology information. It has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centrality measures are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. . Package: r-cran-cepalstatr Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-dplyr, r-cran-httr2, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-reactable, r-cran-stringr, r-cran-tidyselect, r-cran-rlang, r-cran-collapsibletree, r-cran-gridextra, r-cran-htmlwidgets Suggests: r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-svglite, r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-cepalstatr_0.9.0-1.ca2404.1_all.deb Size: 985832 MD5sum: 94276068407cc501298d7dc0c707c153 SHA1: 0d40c6154a58f264d9eac100a686553676b327e9 SHA256: e831285f2a0d2b49c15730b30042985d84636778fda2100fdcaf6ccd16ca9180 SHA512: f8d663f82e2ed1083b96a91ebbd3beaaf61cbd3ecb4685906e2f98e388c6108620650305980521ab3428804c6445dd5cca9217cd246b542f4762923956de7a12 Homepage: https://cran.r-project.org/package=CepalStatR Description: CRAN Package 'CepalStatR' (Access to the 'CEPALSTAT API') Explore metadata and retrieve indicators from the statistical portal of the Economic Commission for Latin America and the Caribbean . 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Package: r-cran-cepiigeodist Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gravity Filename: pool/dists/noble/main/r-cran-cepiigeodist_0.1-1.ca2404.1_all.deb Size: 958072 MD5sum: 57cd39906113ba3834a1522e9729cb00 SHA1: 78c86ea71c32b112f44685a95e8dc347d6de6f6f SHA256: a88a466adc78fb0824bd83f6156ce82034a114dd96336d068fb19e0fffea8176 SHA512: 7118e1d36a797cf3a0bdf4c638bd7c27b9d245d2e44bb43d97592a6478ff5d805fdb157f96b90d0e15c96fb051c61947b5985876367435f748c280789bde4b50 Homepage: https://cran.r-project.org/package=cepiigeodist Description: CRAN Package 'cepiigeodist' (CEPII's GeoDist Datasets) Provides data on countries and their main city or agglomeration and the different distance measures and dummy variables indicating whether two countries are contiguous, share a common language or a colonial relationship. The reference article for these datasets is Mayer and Zignago (2011) . Package: r-cran-cepiweek Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cepiweek_0.1.2-1.ca2404.1_all.deb Size: 13732 MD5sum: 36b6bdc75eccd32c8cd53db447b49db9 SHA1: ee590841b49bfd56c569bba06e1a0fab8b1a648e SHA256: fc9d78a77b084d703daa4c78daff72d10b6df16789f7472b78e0fdebab443f32 SHA512: 0af8a4be0d402814566688138fa64452ffea0bbb2bef4f50076107f04349cd8ff3b0d73eb9e87c3253b498d9c7c4e55c284e899e641a38c3f7957828f62bcb53 Homepage: https://cran.r-project.org/package=cepiweek Description: CRAN Package 'cepiweek' (Continuous Epidemiological Week Indexing for Time-SeriesAnalysis) Provides a simple algorithm to generate a continuous epidemiological week index from date variables in a dataframe. Weeks are computed as sequential 7-day intervals starting from the earliest observed date. They do not reset at calendar year boundaries and are not ISO 8601 nor MMWR calendar weeks. The approach is intended for epidemiological modeling and time-series analysis where temporal continuity is required. The generated weeks are sequential and do not reset at calendar year boundaries. Package: r-cran-cepp Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-trust, r-cran-randtoolbox Filename: pool/dists/noble/main/r-cran-cepp_1.7-1.ca2404.1_all.deb Size: 444184 MD5sum: 8c9a73ce4b3d41d07957799a46eb2704 SHA1: de0ef85fe47d84f641d7de9688b6b51a9bf35fd0 SHA256: 9f1350bae28b3af7ffb2daef5e61eb33266eeaa9accc51d6f66b523eb782e0bf SHA512: be268b16bc35b9ee5c0ed5004064375238bbba976f3dd92d451c0262eb0467a0ab07a7ab25c07e9ec55412ad2fa2848711ceaf84cf88bb37d17b3310d1b9c56d Homepage: https://cran.r-project.org/package=cepp Description: CRAN Package 'cepp' (Context Driven Exploratory Projection Pursuit) Functions and Data to support Context Driven Exploratory Projection Pursuit. 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Package: r-cran-cepreg Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-renvlp, r-cran-psych Filename: pool/dists/noble/main/r-cran-cepreg_0.1.3-1.ca2404.1_all.deb Size: 59006 MD5sum: 548e5151c4e7a570cd8d8b8e22a22418 SHA1: 42631a945d0c99a4d413f857eca6be638ee2f13f SHA256: f4bab77b605d54547af17430535d1ac8689f04ac2d276463f2b341d85bf47327 SHA512: 37050dc9b6dc3ed999d9120a570a73fde35ca5f9a58a99ec2697d674b4ca3a2c6dcee533e495661b834ca995a95526394ad56168b6dbcb80b4f269353e496769 Homepage: https://cran.r-project.org/package=CepReg Description: CRAN Package 'CepReg' (A Cepstral Model for Covariate-Dependent Time Series) Modeling associations between covariates and power spectra of replicated time series using a cepstral-based semiparametric framework. 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For more information on the CE please visit . For further reading on CE estimate calculations please see the CE Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of Methods at . For further information about CE PUMD please visit . 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Provides tidy access to the Australian Carbon Credit Unit ('ACCU') Scheme project register, Safeguard Mechanism baselines and covered emissions, Large-scale Renewable Energy Target ('LRET') power station accreditations, Small-scale Renewable Energy Scheme ('SRES') installation data, the National Greenhouse and Energy Reporting ('NGER') scheme, and Quarterly Carbon Market Reports . Includes a post-Chubb ACCU integrity layer (Chubb 2022 Independent Review), Safeguard reform handling (declining industry baselines from July 2023), National Greenhouse and Energy Reporting scope discipline (Scope 1 / Scope 2 market vs location / Climate Active), reconciliation against the Quarterly Carbon Market Report, and reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional Zenodo deposit). Data is published by the Clean Energy Regulator under a Creative Commons Attribution 4.0 International licence. Package: r-cran-ceramic Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-purrr, r-cran-rappdirs, r-cran-rlang, r-cran-sp, r-cran-slippymath, r-cran-tibble, r-cran-crsmeta, r-cran-vapour, r-cran-stringr, r-cran-wk Suggests: r-cran-covr, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceramic_0.9.5-1.ca2404.1_all.deb Size: 4204002 MD5sum: 8d0720a9bcdc51e76b09cd97d0524ba3 SHA1: 899bcacff7020e2d02435037bfaefeb5b942fd44 SHA256: 43feff747f6dabc686c39e07b1d042e3f9859d21dd43e6f5ff99c1a1d45c9f82 SHA512: 5d0df7820b59d191432384e42c722af620a148f31bab3c9da5e163bedd662862e30a24646e955191f3c89caa516fec312b814f69a2d0569ac88421555e17f1d8 Homepage: https://cran.r-project.org/package=ceramic Description: CRAN Package 'ceramic' (Download Online Imagery Tiles) Download imagery tiles to a standard cache and load the data into raster objects. Facilities for 'AWS' terrain terrain and 'Mapbox' servers are provided. 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Package: r-cran-cereal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cereal_0.1.0-1.ca2404.1_all.deb Size: 47774 MD5sum: 6c65c3f7479137651419454e477f7e63 SHA1: 2174f812de8a241099d65ac38ae1890419ed7475 SHA256: aca0a2c58c0ddaacf21f5fef1fbdb5d538309375fe42d1a2c818cb204e1d76a3 SHA512: e4cf32cd5349ec5e3ac1bce0edb1cb86dddf33875b2b52c3297123fdc9f3e16c0292647251a79cdc99883c10d2564c7378f51b3fc8c4d8b76d5a66949fdfc77d Homepage: https://cran.r-project.org/package=cereal Description: CRAN Package 'cereal' (Serialize 'vctrs' Objects to 'JSON') The 'vctrs' package provides a concept of vector prototype that can be especially useful when deploying models and code. Serialize these object prototypes to 'JSON' so they can be used to check and coerce data in production systems, and deserialize 'JSON' back to the correct object prototypes. Package: r-cran-ceriolioutlierdetection Architecture: all Version: 1.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase Suggests: r-cran-rrcov, r-cran-mvtnorm, r-cran-mclust Filename: pool/dists/noble/main/r-cran-ceriolioutlierdetection_1.1.15-1.ca2404.1_all.deb Size: 64396 MD5sum: a76035eee789869016ca0ba570543f6c SHA1: b0259e2a8cc6a74f5405a5cb63dd10df4e318f89 SHA256: f7efad47fd254d8f41f8aea920d476826c4ed0f1bbb32ae2d66accf2012e8f50 SHA512: 9bcaa13af801900197e9f038454386ee79ae452b05e1a49ae44441b33ffad83d801a7d2320b9d87f493236d96f74bc3f651156374d1fc86bafca01ff237b6e33 Homepage: https://cran.r-project.org/package=CerioliOutlierDetection Description: CRAN Package 'CerioliOutlierDetection' (Outlier Detection Using the Iterated RMCD Method of Cerioli(2010)) Implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) and Green and Martin (2017) . See also Chapter 2 of Green (2017) . Package: r-cran-cernaseek Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-survival Filename: pool/dists/noble/main/r-cran-cernaseek_2.1.3-1.ca2404.1_all.deb Size: 542948 MD5sum: e9cac53e8df3ad64e7b2e4a908f4c85e SHA1: 07c054c6d5a70c59aef1e41c26be56091d6966de SHA256: 9b2c2ff292a76522553b4740546950dff86d51c00e0f74b6f892c17a6a7c2e09 SHA512: 2d96983e0490ac98297e703d62f51a75791947c80bc5b40f049ab3706e200b8a182e2e9b7774a96a554fdcf26d25db9fd5a64f345f8c66629241e1ffb1c9a398 Homepage: https://cran.r-project.org/package=CeRNASeek Description: CRAN Package 'CeRNASeek' (Identification and Analysis of ceRNA Regulation) Provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) ), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair. Package: r-cran-certainty Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-certainty_1.0.0-1.ca2404.1_all.deb Size: 348770 MD5sum: 0115617656c752d04cad625dc7dcf48d SHA1: b0c7cab8a53980955f68f205adf184d8243b424a SHA256: 71dbb987273673ed62ae41565a9cdf8ec4201e2133dd16a9dc089a4ba8e5510b SHA512: 4450dc294d9a64934219777ac9cac9e64aac4e8c96b801a5c4607e4e6a10fc24d8fdd2fc696fde785b09a07165a3345d9267054438b29f5f35bea52123b8790a Homepage: https://cran.r-project.org/package=ceRtainty Description: CRAN Package 'ceRtainty' (Certainty Equivalent) Compute the certainty equivalents and premium risks as tools for risk-efficiency analysis. For more technical information, please refer to: Hardaker, Richardson, Lien, & Schumann (2004) , and Richardson, & Outlaw (2008) . Package: r-cran-certara.darwinreporter Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-colourpicker, r-cran-shinyace, r-cran-shinymeta, r-cran-ggplot2, r-cran-xpose, r-cran-certara.xpose.nlme, r-cran-dplyr, r-cran-jsonlite, r-cran-tidyr, r-cran-flextable, r-cran-shinyjqui, r-cran-plotly, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-bslib, r-cran-shinytree, r-cran-sortable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-certara.darwinreporter_2.0.1-1.ca2404.1_all.deb Size: 342214 MD5sum: 49d5c27fe69313e5f46de8e1a2e6660c SHA1: 33c19fbce940d722c44e489ed6287721533ed013 SHA256: 653916dcc10596893aaaa8f989b7bb4f5bd96e955f9213f43335e58eab904adb SHA512: 38b8831ee51f74bcbd015aad239bcb1068cff362f1146ae4b074a58a23c665eca412372b094d6ae2dc811d17566ac66642b79c52617fabda92fa4bfaf2b90eca Homepage: https://cran.r-project.org/package=Certara.DarwinReporter Description: CRAN Package 'Certara.DarwinReporter' (Data Visualization Utilities for 'pyDarwin' Machine LearningPharmacometric Model Development) Utilize the 'shiny' interface for visualizing results from a 'pyDarwin' () machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models, allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an R Markdown document and rendered in various output formats. Package: r-cran-certara.modelresults Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colourpicker, r-cran-shinyace, r-cran-shinymeta, r-cran-certara.xpose.nlme, r-cran-xpose, r-cran-dplyr, r-cran-flextable, r-cran-shinyjqui, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinytree, r-cran-sortable, r-cran-tidyr, r-cran-rlang, r-cran-bslib Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-certara.rsnlme Filename: pool/dists/noble/main/r-cran-certara.modelresults_3.0.1-1.ca2404.1_all.deb Size: 790582 MD5sum: 4a189654362b4fc995f79d9aafe13550 SHA1: 085e697c344e654b5d5ed53eb7f60965bc9804bc SHA256: 3403421881d02adeace424ee89b3ebd3ce6e6c9c6bca3b2598d6bfd86a122690 SHA512: 0fa1c71fd8448ddf737e1643a1c92dcb8dca509c72022b6aa592c87046dcfa81f56fe2c59e2681f3ef42f1b91855ceaf6a9e1e1199098dad0fea09e24e4494b3 Homepage: https://cran.r-project.org/package=Certara.ModelResults Description: CRAN Package 'Certara.ModelResults' (Generate Diagnostics for Pharmacometric Models Using 'shiny') Utilize the 'shiny' interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the 'shiny' session. Model diagnostics can be included in a 'rmarkdown' document and rendered to desired output format. Package: r-cran-certara.nlme8 Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml2, r-cran-batchtools, r-cran-data.table Suggests: r-cran-testthat, r-cran-brew, r-cran-withr Filename: pool/dists/noble/main/r-cran-certara.nlme8_3.2.0-1.ca2404.1_all.deb Size: 653086 MD5sum: d7a58204c7c0347232fdc190a17b6331 SHA1: c7119719602875d50f30f233189b0082b5e8006f SHA256: 3c2fc21d39817584758e815700ca5907d032ae47ef4277f158eb2134db0fafda SHA512: 4d21b5fe595c9e43af19dd01ef38ceb82818f040028a021ac26da29cb589c7db5b667a3a32e4b285e10d6c4f664bc19afde850fc427396ad5f723d2a69ba99df Homepage: https://cran.r-project.org/package=Certara.NLME8 Description: CRAN Package 'Certara.NLME8' (Utilities for Certara's Nonlinear Mixed-Effects Modeling Engine) Interface to Certara's Nonlinear Mixed-Effects (NLME) modeling engine ('NLME-Engine') for pharmacokinetic and pharmacodynamic (PK/PD) modeling and simulation. Provides access to the Maximum Likelihood estimation algorithms available in the 'Phoenix' NLME platform for population, individual, and pooled analyses using parametric methods. Includes utilities for setting up NLME installations and parallel settings, running estimation, bootstrap, and covariate search workflows, and updating model files from engine output. Jobs can be executed locally or across high-performance computing resources, including Linux Sun Grid Engine (SGE) and Simple Linux Utility for Resource Management (SLURM) grids as well as multicore Linux and Windows hosts. Package: r-cran-certara.r Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-batchtools, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-plotly, r-cran-reshape, r-cran-remotes, r-cran-rlang, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinymaterial, r-cran-shinyjqui, r-cran-sortable, r-cran-ssh, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-certara.r_1.1.0-1.ca2404.1_all.deb Size: 26886 MD5sum: 2236591ccc21e14e26e20f1fdd749ea7 SHA1: 21956ab6a31fbf5d4264bf366a630582c129bb69 SHA256: b573f1c702eae3288ffbeb655eeae401f0d2bf449da962f2216709b157e015ec SHA512: b7e6a7654c8caade81a740ae5c9e66d2cf8d43a7099aba0aa7f58b43e9e376e153ca4e408fdacb547390ab0fc35ed6b02b46fc684d908497db1029e129bcb3f4 Homepage: https://cran.r-project.org/package=Certara.R Description: CRAN Package 'Certara.R' (Easily Install Pharmacometric Packages and Shiny ApplicationsDeveloped by Certara) A convenient set of wrapper functions to install pharmacometric packages and Shiny applications developed by Certara PMX and Integrated Drug Development (iDD). The functions ensure the successful installation of packages from non-standard repositories. Package: r-cran-certara.rdarwin Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-jsonlite, r-cran-ssh Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-certara.darwinreporter, r-cran-certara.rsnlme, r-cran-geometry Filename: pool/dists/noble/main/r-cran-certara.rdarwin_1.2.0-1.ca2404.1_all.deb Size: 761132 MD5sum: 1dd38e2463543d6085879cbd27b87193 SHA1: 2ef609e089217b6eed59ea6780db2056d85a8ab5 SHA256: a0168596f09fd89b544944cfd5ea977d0a1eaadf16ece08d15dc45ddea09ad3f SHA512: c5277860b0878790f2004ae784e4796cbc0842d468071ee0903eb93dcfe638e62fbfbd2dc61e466aed9910230cd6e534efe762bf86a056cd055aca67615169c8 Homepage: https://cran.r-project.org/package=Certara.RDarwin Description: CRAN Package 'Certara.RDarwin' (Interface for 'pyDarwin' Machine Learning Pharmacometric ModelDevelopment) Utilities that support the usage of 'pyDarwin' () for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine. Package: r-cran-certara.rsnlme.modelbuilder Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-certara.rsnlme, r-cran-shinymeta, r-cran-shinyace, r-cran-bslib, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggforce, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-fs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-certara.rsnlme.modelbuilder_3.0.1-1.ca2404.1_all.deb Size: 588406 MD5sum: 42fe45caa8b4f78f52e8ca13c4a071da SHA1: f53ebe02d1e60662400bfd7ea3741527649d1123 SHA256: ac8ac26598182797bfa5abe4a7fee7e0c43055d9dfb06e9aeff1dae04d965b95 SHA512: 15b3e8f5bb16d958cdb99a639a6d2a08d9d5a6959e357f46fec87a04e0697497948920f09b6f3ad79fdbb3c566aefb3d9c112122d89d4f8b17ab8d52e45841d6 Homepage: https://cran.r-project.org/package=Certara.RsNLME.ModelBuilder Description: CRAN Package 'Certara.RsNLME.ModelBuilder' (Pharmacometric Model Building Using 'shiny') Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny' interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the 'Certara.RsNLME' package. Additional support to generate the underlying 'Certara.RsNLME' code to recreate the corresponding model in R is provided in the user interface. 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Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the 'Certara.RsNLME' package. 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The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench . Execution is supported both locally or on remote machines. 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Generate the underlying 'tidyvpc' and 'ggplot2' code directly from the user interface and download R or Rmd scripts to reproduce the VPCs in R. 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This integration enables users to utilize all 'ggplot2'-based plotting functions available in 'xpose' for thorough model diagnostics and data visualization. Additionally, the package introduces specialized plotting functions tailored for covariate model evaluation, extending the analytical capabilities beyond those offered by 'xpose' alone. 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Suitable for academic awards, professional recognition, and similar uses. Package: r-cran-ces Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-ces_1.1.0-1.ca2404.1_all.deb Size: 109608 MD5sum: 7ae9f430ffea7120af53a41529f399a9 SHA1: efd34cb4bcca637014990d2b4a77f3ce484f740d SHA256: 586c31c9e30390bbe0d12f8fb9f1b485725471b44be7c634e19ebacc50f1dffa SHA512: fa2ab715c72bf003444cd80e4b4eb3d9ff818d779478d1447ae4223c0accaeffc0e3f41623f5315f9255691aefc81fa0bacfbfdb7e05260ce29876df45d55dc1 Homepage: https://cran.r-project.org/package=ces Description: CRAN Package 'ces' (Access to Canadian Election Study Data) Provides tools to easily access and analyze Canadian Election Study data. The package simplifies the process of downloading, cleaning, and using 'CES' datasets for political science research and analysis. The Canadian Election Study ('CES') has been conducted during federal elections since 1965, surveying Canadians on their political preferences, engagement, and demographics. Data is accessed from multiple sources including the 'Borealis' Data repository and the official 'Canadian Election Study' website . This package is not officially affiliated with the Canadian Election Study, 'Borealis' Data, or the University of British Columbia, and users should cite the original data sources in their work. 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The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) for more information. 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Package: r-cran-cfmortality Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cfmortality_0.3.0-1.ca2404.1_all.deb Size: 14100 MD5sum: 0873cfa2add90add9f71749bd601ce02 SHA1: 6356f37ba7a2f4525169a0c3a942414a4fb98332 SHA256: f6e89af027152bbbdd7fbe4bc3f7a0696ec92e13c745d929e809f2a13bfc1fde SHA512: 98ccdba360649c8873c9eee6ede639f614b684732815d6766bee801a65b41b4f8cd5505644f385cd5b2f7b7e60e97c80f47203e40de9d4732508744ada8bfdc6 Homepage: https://cran.r-project.org/package=cfmortality Description: CRAN Package 'cfmortality' (Cystic Fibrosis Survival Prediction Model Based on StanojevicModel) Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) . 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The subsequent phase II trials are designed to examine the potential efficacy of the drug based on the MTD obtained from the phase I trials, with the aim of identifying the optimal biological dose (OBD). The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) ), randomized CFO (rCFO), precision CFO (pCFO), two-dimensional CFO (2dCFO) (Wang et al. (2023) ), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) ), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO (Fang and Yin (2024) ). It supports phase I/II trials for the CFO design and only phase I trials for the other CFO-type designs. The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization. An interactive web application for CFO is available at the provided URL. Package: r-cran-cforecast Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bvar, r-cran-dplyr, r-cran-fkf, r-cran-kfas, r-cran-misctools, r-cran-tibble, r-cran-vars, r-cran-wex Filename: pool/dists/noble/main/r-cran-cforecast_0.1.1-1.ca2404.1_all.deb Size: 104458 MD5sum: b149a43c61ff32bff1dc8eb317d0ee29 SHA1: c24d399476d5f125ea37444ddbf5cbe6ee72d916 SHA256: a646732152b185bd8e7f2ba4d3f3aeb89546e4a1efc5b6fc81f85b457b2f927c SHA512: c01edc20f24a136d0f2061f17fc644144e433772b8af6fe28f0c4d933664aae9cfe136f13dc8cb7f619b4514431b23f3ba208f72fd6316eb1e555c8e23a132c0 Homepage: https://cran.r-project.org/package=cforecast Description: CRAN Package 'cforecast' (Conditional Forecasting and Scenario Analysis Using VAR Models) Provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman smoothing framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) . Package: r-cran-cforward Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cforward_0.2.0-1.ca2404.1_all.deb Size: 98110 MD5sum: 88a1938e184bd4626d30cff99b278326 SHA1: ff6c7d74ef008ef3fede2c38b6e933ae29aadc73 SHA256: 755acad6f283f9e8e201ebca0a0f5de07e5d7785d8fd35efd33c9b72c81f4be2 SHA512: dffac8d5f2e5a1bd4f64f49244535e2548ff043c374f7e6863c49181490fee93cc3d46f6c9411a36dfb930969ff6f679a836e7e28c200d1699d8d6f8d157ac11 Homepage: https://cran.r-project.org/package=cforward Description: CRAN Package 'cforward' (Forward Selection using Concordance/C-Index) Performs forward model selection, using the C-index/concordance in survival analysis models. Package: r-cran-cfr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-bookdown, r-cran-data.table, r-cran-distcrete, r-cran-distributional, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-incidence2, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-cfr_0.2.0-1.ca2404.1_all.deb Size: 854238 MD5sum: ef09c8bf06b285a0a1474c26b3bfffeb SHA1: 3bad16d482435690a289f9eae023b805fbc2ff74 SHA256: 842dc8ae039ba3d52b860fc8fc2ee0a20ebb3ac9f735010b8b6f23c2f20e56e5 SHA512: a6e0388792c9719be9a1171aba10051c79d0dc0c61b1297fb80cc6e69783b2846e3bcfe0c8957646f93bd0de4a28a5ef3f0624097fa57da271d27498ec58d206 Homepage: https://cran.r-project.org/package=cfr Description: CRAN Package 'cfr' (Estimate Disease Severity and Case Ascertainment) Estimate the severity of a disease and ascertainment of cases, as discussed in Nishiura et al. (2009) . 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The CF Metadata Conventions is widely used for distributing files with climate observations or projections, including the Coupled Model Intercomparison Project (CMIP) data used by climate change scientists and the Intergovernmental Panel on Climate Change (IPCC). This package specifically allows the user to work with any of the CF-compliant calendars (many of which are not compliant with POSIXt). The CF time coordinate is formally defined in the CF Metadata Conventions document available at . Package: r-cran-cg Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-vgam, r-cran-mass, r-cran-lattice, r-cran-survival, r-cran-multcomp, r-cran-nlme, r-cran-rms Filename: pool/dists/noble/main/r-cran-cg_1.0-4-1.ca2404.1_all.deb Size: 1382402 MD5sum: d1873695fb1cbf34709dbd1607be26d8 SHA1: d19ae77a504161b9e0f0de84a75e4cea3c090f03 SHA256: 419e9a3b3461cdb5c0db57b3f2c2cbd0799f05c6dc2cde41b4da86f7f3c7a8f7 SHA512: 4b034f4cb216691d7646e72fd4b3435d895377f17a984fbba79f17fe57c3745b8ece0f1d8a844078a26b4b708d92d7c03dbd3aebf296476af1d9e0a518df81fd Homepage: https://cran.r-project.org/package=cg Description: CRAN Package 'cg' (Compare Groups, Analytically and Graphically) Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized. Package: r-cran-cgaim Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-scam, r-cran-scar, r-cran-quadprog, r-cran-osqp, r-cran-matrix, r-cran-mass, r-cran-cgam, r-cran-mgcv, r-cran-gratia, r-cran-doparallel, r-cran-coneproj, r-cran-truncatednormal, r-cran-foreach, r-cran-nnls Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cgaim_1.0.4-1.ca2404.1_all.deb Size: 140862 MD5sum: 29a90a21310ec072334d01d07f0a9352 SHA1: 89cdc832a21fc3acf26f71bba70ab52dd5153bb1 SHA256: 5fae49a0d7d366fd22240582b6898f1e82d8f1458415aba630b0a49668e20210 SHA512: 35509b706d4762ffa41a0ed3209c0261817c428e53a5b7b6b1f7b4ae54ea275a9795655cb3026dae2438677718a3c1f5f9689007134a63d0c1c4e356d386aea4 Homepage: https://cran.r-project.org/package=cgaim Description: CRAN Package 'cgaim' (Constrained Groupwise Additive Index Models) Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" . Package: r-cran-cgal4h Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34635 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgal4h_0.1.0-1.ca2404.1_all.deb Size: 4099392 MD5sum: 10d33644ca16e570effd4e6ad3702b77 SHA1: 408761de78e7560ec051808078cd4bd277a6df3e SHA256: a21942ed59f2897d83781eb27a2fc350067b9d708a69e44f2cdfbf993f0f40af SHA512: 425a4221eb7a37e8837506052db12515aee53099bb08cd77d03921b683d32a4a3bdba125d5d9bdb2a775c0ec4b313caf316b34da33bad54401983db179af6a7f Homepage: https://cran.r-project.org/package=cgal4h Description: CRAN Package 'cgal4h' ('CGAL' Version 4 C++ Header Files) 'CGAL' is a C++ library that aims to provide easy access to efficient and reliable algorithms in computational geometry. Since its version 4, 'CGAL' can be used as standalone header-only library and is available under a double GPL-3|LGPL license. . Package: r-cran-cgam Architecture: all Version: 1.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coneproj, r-cran-splines2, r-cran-svdialogs, r-cran-statmod, r-cran-lme4, r-cran-matrix, r-cran-ggplot2, r-cran-dplyr, r-cran-zeallot, r-cran-rlang, r-cran-quadprog, r-cran-mass Suggests: r-cran-roxygen2, r-cran-patchwork, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-cgam_1.32-1.ca2404.1_all.deb Size: 1295724 MD5sum: f9f2772b4287eadba8e1f8c8a4b6e577 SHA1: 501e21f12d79f6671956fb0b34b988a53cb26fd1 SHA256: d8a819f6cbc1de94dd8aa7819ff158cb1ccf3011163e7f2408ac31a09e31be03 SHA512: 36618389f81ee9d381f0362b75ee42d92f2f4044052c32d24ad011f9bf57dd77f3f84fc043c00ff647a32da70ab4cdb94e27a98031e4a3de0133cc9f7ddb90f1 Homepage: https://cran.r-project.org/package=cgam Description: CRAN Package 'cgam' (Constrained Generalized Additive Model) A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019) for more details. Package: r-cran-cge Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cge_0.3.3-1.ca2404.1_all.deb Size: 289604 MD5sum: 1bf353428711996c7363361a74905838 SHA1: 27302aa0e910b0d8c35ce9346f34760ac71c9cbb SHA256: 22fade92c4be1dac2305caf5766eb77bf597aae10ba1c74f965e89e37579b601 SHA512: 6267996c22927f2c9fd85b0c08ce6e2fb9df3e9e1076a37c1d93b0a0c7797b68a337a20a63963e6cf4dd8480a869aed142d7d30283d74e75bfaf398ff2551f08 Homepage: https://cran.r-project.org/package=CGE Description: CRAN Package 'CGE' (Computing General Equilibrium) Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package. Package: r-cran-cgmanalysis Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-parsedate, r-cran-lubridate, r-cran-pracma, r-cran-zoo, r-cran-pastecs, r-cran-readxl, r-cran-readr, r-cran-xml, r-cran-mess, r-cran-rlang Filename: pool/dists/noble/main/r-cran-cgmanalysis_3.2.0-1.ca2404.1_all.deb Size: 207238 MD5sum: 9c5758dd963147f8d89f7ab492e4e9a4 SHA1: 1d7c8e0ffa1742f8a8bc0f943bccd81779051097 SHA256: d7c30ce47e62bd23a6538ee61363e36221de3f3cba54cd6c7735f2ea99006baa SHA512: 986733e9fce543664c332e23ec4d9687a68b9bca77bd97e55513e7e3c84a08d2c9d89412bd73c63e30338a90a2f9a706a4a96dddcf940c4a3c265dca897271f2 Homepage: https://cran.r-project.org/package=cgmanalysis Description: CRAN Package 'cgmanalysis' (Clean and Analyze Continuous Glucose Monitor Data) This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit to download the new-user guide. Package: r-cran-cgmissingdatar Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mice, r-cran-fnn, r-cran-ranger, r-cran-data.table, r-cran-xgboost, r-cran-lightgbm, r-cran-forecast, r-cran-cgmanalyzer, r-cran-lifecycle, r-cran-reticulate, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cgmissingdatar_0.0.2-1.ca2404.1_all.deb Size: 181434 MD5sum: 1e8db51b12f8327f7d7e8dda7f29dc22 SHA1: 7ca8d82159c7079c10b13aa6e6b8ff392be92027 SHA256: dc0fdf8fb43381df0125f4eb4343736fa23ffeaa52b81463f6b7b4b3004f0329 SHA512: d33b5eef620df28f2c05272bda1e7c620ed5e9bd71a9576e3279f86127102b1dc8284ee066b9c90553907093bf15be883ef0efa899ead490bd48c9c46820e0f8 Homepage: https://cran.r-project.org/package=CGMissingDataR Description: CRAN Package 'CGMissingDataR' (Impute Missing Glucose Values in CGM Data) Imputes missing glucose values in repeated-measures continuous glucose monitoring (CGM) data. Workflows create time-series features from raw timestamps, support model selection, and return the user's original columns plus an imputed glucose column. Methods include multiple imputation by chained equations (MICE; Azur et al. (2011) ), Random Forest regression (Breiman (2001) ), k-nearest-neighbor regression (Zhang (2016) ), XGBoost (Chen and Guestrin (2016) ), LightGBM (Ke et al. (2017) ), and ARIMA forecasting with the forecast framework (Hyndman and Khandakar (2008) ). A Python-compatible backend uses 'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', Python 'xgboost', and optional Python 'lightgbm'. Package: r-cran-cgmquantify Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyverse, r-cran-ggplot2, r-cran-hms, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cgmquantify_0.1.0-1.ca2404.1_all.deb Size: 64934 MD5sum: 3991c398366ad1dd90f84a91e7731e61 SHA1: d269dba726285f8447553c2f7b91b020f4e8b166 SHA256: 45103d1ded42abdb8225021957c31c4c6eb819615e69d5ceb072f30108156165 SHA512: c86ac31c6919def0d76c8b8390eb6dfcf5f84cdbc3e821a746b011ded218e810f01a54367d821f2aa4194b883cc28441119fd29e7734644bb0517b3cefcbbd37 Homepage: https://cran.r-project.org/package=cgmquantify Description: CRAN Package 'cgmquantify' (Analyzing Glucose and Glucose Variability) Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) American Diabetes Association (2020) Kovatchev B (2019) Kovdeatchev BP (2017) Tamborlane W V., Beck RW, Bode BW, et al. (2008) Umpierrez GE, P. Kovatchev B (2018) . Package: r-cran-cgnm Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rxode2 Filename: pool/dists/noble/main/r-cran-cgnm_0.10.0-1.ca2404.1_all.deb Size: 774276 MD5sum: 10675c826c01385471ef786f89060f7f SHA1: dd77a2337c9aaf064bee92aecafb70ea2195fa56 SHA256: 9f295c5d5fb2a6e5c1a702ee207c02ec5845158158e1e4df95c9a19862465a01 SHA512: 043bd581bf0207a5ffc73a3e8f349be0a902039b41107c0bce23e5d54e2653a1371f2c155240be9f4728144bd5cbfadcb7841038ed3ddf81f54cea9a317f23c6 Homepage: https://cran.r-project.org/package=CGNM Description: CRAN Package 'CGNM' (Cluster Gauss-Newton Method) Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) . Cluster Gauss–Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) . Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based helper bot available at Package: r-cran-cgp Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgp_2.1-1-1.ca2404.1_all.deb Size: 48474 MD5sum: 732728e5332c6cf71a8591cd47abfa53 SHA1: 48e361677a128d8df124aa0313440e3d6f6d17cf SHA256: 64be76341428351aeb955f62ed148dc1c187f722834fcf243f2c039df7a8f3bc SHA512: 5d4c8a5427e88116a33887961d2efd596803f0911e97454c8f5054799ec29beb9fe7b8993f1ab1f9367f6143212f99a855659f6674cf7b4817bd2c17fab8dc72 Homepage: https://cran.r-project.org/package=CGP Description: CRAN Package 'CGP' (Composite Gaussian Process Models) Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP. Package: r-cran-cgpfunctions Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesfactor, r-cran-desctools, r-cran-dplyr, r-cran-forcats, r-cran-ggmosaic, r-cran-ggplot2, r-cran-ggrepel, r-cran-paletteer, r-cran-partykit, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-sjstats, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bsda, r-cran-ggthemes, r-cran-hrbrthemes, r-cran-janitor, r-cran-knitr, r-cran-lsr, r-cran-magrittr, r-cran-productplots, r-cran-pwr, r-cran-rmarkdown, r-cran-stringi, r-cran-tibble, r-cran-testthat, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-cgpfunctions_0.6.3-1.ca2404.1_all.deb Size: 1831808 MD5sum: 375277402986c67be28bf85dee24cb23 SHA1: 50346fac99268bde07bfffc2060e20ff582486ac SHA256: 9fde718417c509baa898d484998a01dfffa684a6a75f888fbd2bbacffbd507be SHA512: b7078eea3b22ad8a1b30e619de5c30d1bd1df74a8121b3fc17b890921cbf8dd6ad10c9f2ce817cd40b94a604de8527e25939ac10190f87cadcff385852f2f6c9 Homepage: https://cran.r-project.org/package=CGPfunctions Description: CRAN Package 'CGPfunctions' (Powell Miscellaneous Functions for Teaching and LearningStatistics) Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not new methods but rather wrappers around either base R or other packages. Package: r-cran-cgr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgr_0.1.0-1.ca2404.1_all.deb Size: 11320 MD5sum: 4eb354d4633f111c23fb6905e7b1a4fe SHA1: 77d003cd7ecc9aec0b7db3f839d1c1d1028514bb SHA256: 2148b5ad55af5147fc02a7b6d6d21518c04ccdfbd996590c57ee06a79593b908 SHA512: 2eb95e41c0fb1050df79039141cc516f69b0d2dc52ab63d4b4f8c3edcbf1ed342e65053a59723acc306429170c68285cb3d9bfc645315783aeff0ca99200b4dc Homepage: https://cran.r-project.org/package=CGR Description: CRAN Package 'CGR' (Compound Growth Rate for Capturing the Growth Rate Over thePeriod) The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022). Package: r-cran-cgwtools Architecture: all Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp Filename: pool/dists/noble/main/r-cran-cgwtools_4.1-1.ca2404.1_all.deb Size: 132118 MD5sum: c808e52f08adb93ee27ab1a454068989 SHA1: 6d8e2d76da0e277705c9c7abef14e957f1de64db SHA256: 396d18636623d07d5782d9a14af5d5d36e00934e28a08628aa6376c2f181798d SHA512: f400c42bcdc76cc9381c1e2869f03630097dd01bdd1baceaa964536906c48fa0d67b5a3b94e9fe856714f4bcccb8cfc9e219128eb6d3dfb645dbbcee0f5e6be9 Homepage: https://cran.r-project.org/package=cgwtools Description: CRAN Package 'cgwtools' (Miscellaneous Tools) Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' . Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min. In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. Various other goodies included as well. Package: r-cran-ch Architecture: all Version: 0.1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-clipr, r-cran-ryacas, r-cran-magrittr, r-cran-mass, r-cran-crayon, r-cran-polynom, r-cran-pracma Suggests: r-cran-scales Filename: pool/dists/noble/main/r-cran-ch_0.1.0.2-1.ca2404.1_all.deb Size: 72520 MD5sum: 68a89151c286e1a4a3eb72e39288a818 SHA1: 4e4d0ce2f31a4c0a971edd5527174691fb519f86 SHA256: 79ea0fa56563dcaf074e06d045878ab0ac094345b080802792f804b4f146d949 SHA512: 147026e5154c81ae7d16f365d50bdb025f2060f27487dada4604176dc2ef41452e6e9bba56515f014ca156dea87aa237dbe2928b66b425feadddc075f7e61b1c Homepage: https://cran.r-project.org/package=ch Description: CRAN Package 'ch' (About some Small Functions) The solution to some common problems is proposed, as well as a summary of some small functions. In particular, it provides a useful function for some problems in chemistry. For example, monoa(), monob() and mono() function can be used to calculate The pH of weak acid/base. The ggpng() function can save the PNG format with transparent background. The period_table() function will show the periodic table. Also the show_ruler() function will show the ruler. The show_color() function is funny and easier to show colors. I also provide the symb() function to generate multiple symbols at once. The csv2vcf() function provides an easy method to generate a file. The sym2poly() and sym2coef() function can extract coefficients from polynomials. Package: r-cran-chaidr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-diagrammer, r-cran-ggparty, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-partykit, r-cran-plotly, r-cran-rmarkdown, r-cran-rpart, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-chaidr_0.1.0-1.ca2404.1_all.deb Size: 516932 MD5sum: a4cd4e4e5e1a173dc199aca4851afea5 SHA1: 14449d02ade3dd58ad37f254e19539c4de3e2873 SHA256: 2d105858c4841f57147fcf15c5811cb7f7f0536161b910fe08495199139cd0e6 SHA512: 024e1766d05c6ea938ce224388612ef993743f212b4d8b477b7fb38202678101bac672d85519a94a08ae981a0f2d1c1b7c3b0fdef5f48ad4426cc18ef28c760c Homepage: https://cran.r-project.org/package=chaidr Description: CRAN Package 'chaidr' (CHAID and Exhaustive CHAID Decision Trees) An implementation in base 'R' of the CHAID (Chi-squared Automatic Interaction Detection) decision tree algorithm of Kass (1980) and the Exhaustive CHAID variant of Biggs, de Ville, and Suen (1991) , as specified in the 'IBM SPSS' Statistics Algorithms documentation. Supports nominal, ordinal (with floating missing category), and continuous predictors, and nominal, ordinal, and continuous response variables using Pearson chi-squared, Goodman row-effects, and one-way ANOVA F tests respectively. Includes prediction, rule extraction, gains and lift analysis, validation on holdout data, and visualization via base graphics, 'Graphviz' DOT, 'plotly', and conversion to 'partykit' objects. Package: r-cran-chainbinomial Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics Suggests: r-cran-testthat, r-cran-numderiv, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chainbinomial_0.1.5-1.ca2404.1_all.deb Size: 90552 MD5sum: d43ab0eb1b6c293df97b4bb2b34d6326 SHA1: 331d82d350fd0c665027e226e77a4579c740e018 SHA256: 3f865b045efea7dff79635be30ac5267ef5fc8287e1ea4cb51f8e5972ecea717 SHA512: 09c3c84f72bffaf19498063576a9b0aa1bef14b1a79ca5abce3dc5afefb036bbfd659425b408af6bd69a1a366428bc523e04a7386f83930e5733240bc22b358f Homepage: https://cran.r-project.org/package=chainbinomial Description: CRAN Package 'chainbinomial' (Chain Binomial Models for Analysis of Infectious Disease Data) Implements the chain binomial model for analysis of infectious disease data. Contains functions for calculating probabilities of the final size of infectious disease outbreaks using the method from D. Ludwig (1975) and for outbreaks that are not concluded, from Lindstrøm et al. (2024) . The package also contains methods for estimation and regression analysis of secondary attack rates. Package: r-cran-chainladder Architecture: all Version: 0.2.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3512 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-actuar, r-cran-lattice, r-cran-tweedie, r-cran-systemfit, r-cran-statmod, r-cran-cplm, r-cran-ggplot2, r-cran-mass Suggests: r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chainladder_0.2.21-1.ca2404.1_all.deb Size: 2193122 MD5sum: f801b558face3efdf64b2e6817770ad6 SHA1: 8efda0c2be9b73869c757a45147b411222e9177c SHA256: f4e76ad730e20f967276473260e987f3eef2b818f626a060e741114316224cd6 SHA512: 6e739d47b3badabc61cd131f97478f2ddf9365045924eec535cc0a76238d3db53359c6325a205b8c38ed81e847e97a59fedc58cbcc63f8275bb56de95736a8c2 Homepage: https://cran.r-project.org/package=ChainLadder Description: CRAN Package 'ChainLadder' (Statistical Methods and Models for Claims Reserving in GeneralInsurance) Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II. Package: r-cran-chameleon Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-ggplot2, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chameleon_0.2-3-1.ca2404.1_all.deb Size: 155088 MD5sum: 3a87c9c200d89c7e544c7cdb14137c5a SHA1: a74b430e347c46b90b1d5ad4cb69f7665c0b9472 SHA256: 7c3dba7a67141c0d314605206bdfa58a9459a1d23ff74abc92e0665c7befba45 SHA512: f642fc72507aa1d9a3aa2604c187a56fad65a63475d3a36524b1a2251be0b0cfa2502cd29e3a461f189b63daa5eda008fcf88ed6eee02a8a928f8d8ebdad59dc Homepage: https://cran.r-project.org/package=chameleon Description: CRAN Package 'chameleon' (Automatic Colors for Multi-Dimensional Data) Assign distinct colors to arbitrary multi-dimensional data, considering its structure. Package: r-cran-chandwich Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chandwich_1.1.6-1.ca2404.1_all.deb Size: 245200 MD5sum: 7d20b1c03f580dfe0d9a2069ab9e0baf SHA1: 48aa5a244ca807af31cf2460190bb9b8f7ede9be SHA256: bd12467535bbde75f6ea07cfb51edbf48b18d1365b0198c1321077c624dfee0e SHA512: 3845633733757c0e48d11b269c03e9bfed7434dd9f55043ba9145097a1996cadb4c357070376ae68eb4c052c297c0b7f0489e91bdb3720798d685f5f30949204 Homepage: https://cran.r-project.org/package=chandwich Description: CRAN Package 'chandwich' (Chandler-Bate Sandwich Loglikelihood Adjustment) Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) . This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test. Package: r-cran-changepoint.geo Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-changepoint.np, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-changepoint.geo_1.0.3-1.ca2404.1_all.deb Size: 262674 MD5sum: 61ff64503d730404925586047af64c65 SHA1: 937bc83b62a1cd88cee07dbb7861f47421c32d13 SHA256: 94624e2ceee0e0727448f64f0cad31c837f187e5a4d48144e023e89e5a8b817a SHA512: d2a94fb2c2bd09df5ae840a2a149a38318aa36e2da00a420d9126e99860c823b0cb4fdcdb0ff781bfa66db19c2a6f8706b12a561165971d80118cee5a5a74069 Homepage: https://cran.r-project.org/package=changepoint.geo Description: CRAN Package 'changepoint.geo' (Geometrically Inspired Multivariate Changepoint Detection) Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) . Package: r-cran-changepoint.influence Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-changepoint.influence_1.0.2-1.ca2404.1_all.deb Size: 98838 MD5sum: cab63ececc8c71149bdff9bd687786db SHA1: 29595323f23eea90a3b5703f94540869a6859066 SHA256: 51397b62a692413c93d6c09cbb339de79c84c8ff31c7288d52f576a09cacad3c SHA512: 07a3e0041dcd92cef5834bd0900b412573d83e430864daeae88baa4389b9e41c21be0706f1bc61985039a115d73945aebe6a395661294516b7cb228bc75e5ecc Homepage: https://cran.r-project.org/package=changepoint.influence Description: CRAN Package 'changepoint.influence' (Package to Calculate the Influence of the Data on a ChangepointSegmentation) Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) . Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this. There are options for different types of graphics to assess the influence. Package: r-cran-changepointsvar Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lars Filename: pool/dists/noble/main/r-cran-changepointsvar_0.1.2-1.ca2404.1_all.deb Size: 44582 MD5sum: 0a602ac6bf0d703b067b5d679a82d06e SHA1: 16c349979c44a06527fc4aabfdc050ac45beb5b0 SHA256: bc594ec433b8636964981bbe394500e438ac6c498b24a8cde071bfdd8a806064 SHA512: ee512faf5d43ba70e8f82717c33fe89fe6c8e98534d173726ceb292185caeff70099d5c68d23bc5877c3f756379e99d10766feb5bd7d4d5221d9dc6cdd08823f Homepage: https://cran.r-project.org/package=changepointsVar Description: CRAN Package 'changepointsVar' (Change-Points Detections for Changes in Variance) Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, . Package: r-cran-changepointtesting Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-changepointtesting_1.2-1.ca2404.1_all.deb Size: 163106 MD5sum: 5c224cda314d5709e73611e8b1360306 SHA1: 4a91dd5e5bb0edfa74c82e424e72e154d696d16c SHA256: c54b55c39a5666c6619d30cb2149803b9c909d2a2ec08ffb207e786222e752ec SHA512: ce306e30a019e358b75345a7d1c190d73f75ac2ad5b801c2664b2eec1c16bd6715f173ea97189fad4b4595ea34561378b197cc58872a832d6e4a746158ebf837 Homepage: https://cran.r-project.org/package=ChangepointTesting Description: CRAN Package 'ChangepointTesting' (Change Point Estimation for Clustered Signals) A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values. Package: r-cran-changer Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-available, r-cran-devtools, r-cran-git2r Filename: pool/dists/noble/main/r-cran-changer_0.0.5-1.ca2404.1_all.deb Size: 20224 MD5sum: 2efa4132ec04b5a115822632f1e900f0 SHA1: de8744268180f431c9009ffe2d8062e85f91368e SHA256: 93852f772896cda9533a7aedc2e54eee7943bbf0ce8f0f696c9b9d495896afe0 SHA512: fac56be685fb2f2a1271fe30aa557431a7b85fa1449a41d0b4125873bcf4b323686ca015a4240f38d1a1c679bf7c584ed0cee7d874f5a54fb5d39d751e8fd7db Homepage: https://cran.r-project.org/package=changer Description: CRAN Package 'changer' (Change R Package Name) Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task. 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For example, combining predictions from species distribution models with other maps of environmental data to characterize the proportion of a species’ range that is under protection, calculating metrics used under the International Union for Conservation of Nature (IUCN) Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and calculating more general metrics such as taxonomic and phylogenetic diversity, as well as endemism. Also facilitates temporal comparisons among biodiversity metrics to inform efforts towards complementarity and consideration of future scenarios in conservation decisions. 'changeRangeR' also provides tools to determine the effects of modeling decisions through sensitivity tests. Package: r-cran-changes Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nls.multstart, r-cran-ggplot2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-changes_1.0.1-1.ca2404.1_all.deb Size: 102788 MD5sum: 7071fc3a53dba9bc0cf1830bb25946e8 SHA1: d90e2a72082af6e704224f9651d5edd97ce8354e SHA256: 3d54c9df103a870f1a96810bbc733e876d4240fd16145e3691c6c87a0954de00 SHA512: 842e7181c56d94d77768d772a6777c65cbe08d94dd365bd2c0ffd6501c7f38735815fce4a0ff8c980c16d77eecae54702fb942f67cf6272228de4ec7e58eb5ab Homepage: https://cran.r-project.org/package=changeS Description: CRAN Package 'changeS' (S-Curve Fit for Changepoint Analysis) Estimation of changepoints using an "S-curve" approximation. Formation of confidence intervals for changepoint locations and magnitudes. Both abrupt and gradual changes can be modeled. Package: r-cran-channelattributionapp Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-channelattribution, r-cran-shiny, r-cran-data.table, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-channelattributionapp_1.3-1.ca2404.1_all.deb Size: 127460 MD5sum: 72b7f67785d5715c75f7fb239592c557 SHA1: d10297183eda604446ad9a9e6e90060d17f4d82b SHA256: 0a9782e26317c6d67d7d8d691b8992f88f47c6c98c0ac66566a39746938312db SHA512: c4a6520ec3ba5b07061006ce6fc4b9525d8945a1617f8c9317736f6c6339c4ab6332a61b61818a858e0316d34bc6849aa0b118c774cedf381779cf39c4d77806 Homepage: https://cran.r-project.org/package=ChannelAttributionApp Description: CRAN Package 'ChannelAttributionApp' (Shiny Web Application for the Multichannel Attribution Problem) Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package 'ChannelAttribution'. 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Package: r-cran-chaosgame Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-colorramps, r-cran-ggplot2, r-cran-gridextra, r-cran-plot3d, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-chaosgame_1.5-1.ca2404.1_all.deb Size: 93604 MD5sum: 69043f4d0b0afa6a0bfb9c6ee050206c SHA1: aa23cbfa7d6044c9b53dca39e2e9f87e2daa2411 SHA256: 00e16b70b3b4e8ce526d76947fb5629c02def242c30ab82a7cd4e8bfcf1ebf8d SHA512: 7cca06fde24e4d7404b3e96a9dc8f9a4a54f324e4cc7155aa8bdf3ddfe39c9999a5cb597f8f027e625cecfd078277743b326c739cb61550dc38f7cc769f8d839 Homepage: https://cran.r-project.org/package=ChaosGame Description: CRAN Package 'ChaosGame' (Chaos Game) The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. 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Package: r-cran-chapensk Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bessel Filename: pool/dists/noble/main/r-cran-chapensk_0.5-1.ca2404.1_all.deb Size: 257188 MD5sum: 89c911040ef495543191c51f588c975a SHA1: e4b243506403088e552b23f1c5118713eb7be3a0 SHA256: d549db177fdad517d2677f49e18f97f7eb3459895f731750421319738926d0e7 SHA512: 67e2ab5d19c8584f302d55d01e74602399ad3f43db9659253d99442c6d3308057d49d923be887c60d3a031bed0c98bd2d34270e391c4748fba8f4028b364efd3 Homepage: https://cran.r-project.org/package=chapensk Description: CRAN Package 'chapensk' (Estimation of Gas Properties from the Lennard-Jones Potential) Calculation of gas transport properties (viscosity, diffusion, thermal conductivity) using Chapman-Enskok theory (Chapman 1918, ) and of the second virial coefficient (Vargas et al. 2001, ) using the Lennard-Jones (12-6) potential. 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Package: r-cran-chapgwas Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-plyr Filename: pool/dists/noble/main/r-cran-chapgwas_0.1.3-1.ca2404.1_all.deb Size: 37206 MD5sum: 4913c4cebd930de288df7e7547c64982 SHA1: a7f6cddde2ce5bebc75a887a7bf5901f9abb4325 SHA256: 8200d6fe5d0d1ff2aa37efbaab6751a5382e39d96faef11f34c790c6a607aec1 SHA512: f35851b22fe47897efec194464747c1994113fffcfd1338daefe2541120d3c10f331169103b5536fc9311ab7d9c3989a9418173bfb9bf22059f6d0828afd90db Homepage: https://cran.r-project.org/package=CHAPGWAS Description: CRAN Package 'CHAPGWAS' (CHAP-GWAS: Leveraging Chromosomal Haplotypes to ImproveGenome-Wide Association Studies) CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association Study) provides a dynamically adaptive framework for genome-wide association studies (GWAS) that integrates chromosome-scale haplotypes with single nucleotide polymorphism (SNP) analysis. 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Package: r-cran-charanalysis Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-ggtext, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-charanalysis_2.0.3-1.ca2404.1_all.deb Size: 573778 MD5sum: ad16e0de3b8b46b3cf517f6a4b32bff5 SHA1: 574f7537341809522b6ebac31e8f33237b38a95e SHA256: 6bd5d9a576f64539a4ed2719a9fbbd8be94125e45097e9999ed3b50cfbd65678 SHA512: 8847f0162471df21b0f7348c850e32c682339a5a07cb321e59fdd7f6f2b9525ce79cc59ce526c5e0a03aa92e545575c73f72d3a3c1b9d781cca38ebdea710ff9 Homepage: https://cran.r-project.org/package=CharAnalysis Description: CRAN Package 'CharAnalysis' (Peak Detection and Fire History from Sediment-Charcoal Records) A program for reconstructing local fire histories from high-resolution, continuously sampled lake-sediment charcoal records. 'CharAnalysis' decomposes a charcoal record into low- and high-frequency components and uses locally defined thresholds to separate fire signal from noise, following the approach of Higuera et al. (2009) , with underlying assumptions and rationale described in Higuera et al. (2010) . The package is designed for macroscopic charcoal records with contiguous sampling fine enough to resolve individual fire events, and is not appropriate for low-resolution or discontinuously sampled records. See the package URL for the User's Guide and application examples. Package: r-cran-charcuterie Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-charcuterie_0.0.6-1.ca2404.1_all.deb Size: 319052 MD5sum: 30f16fda65e164ec31f208104d4ccb93 SHA1: d04ea380ffb463c42678e62f1a538f529bb94308 SHA256: 82aea0251a0617b5a9eee963ece61442a479820800647427e4a5ad42d483ba37 SHA512: 8ab4829ff5f195777b84b48a26446ce6f06d591260007903bc683c53b40538325effff84c7fb34b4ed183798c1235a569d6e2102513e4c54d8ee765c01607292 Homepage: https://cran.r-project.org/package=charcuterie Description: CRAN Package 'charcuterie' (Handle Strings as Vectors of Characters) Creates a new chars class which looks like a string but is actually a vector of individual characters, making 'strings' iterable. 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Package: r-cran-charisma Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plyr, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-recolorize, r-cran-imager, r-cran-abind, r-cran-jpeg, r-cran-png Suggests: r-cran-pavo, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-charisma_1.0.0-1.ca2404.1_all.deb Size: 4268298 MD5sum: 1e568bdafbb8145b13c61f8d1a358afd SHA1: 5c37fccfce589523d62f7d29fda549b570284c38 SHA256: c0922b2e6f043e34ecba7aa49fd47633205d3a1e701bb4f7266c8d796c9b839c SHA512: 9ad5f18c4249f241a8a5fd44c51566590900b47b92643a2eeb5bf72dc64b97b50fd43bd527fd377ebf085bc4f4be073070a61f720d5d1645717fd8552e888d60 Homepage: https://cran.r-project.org/package=charisma Description: CRAN Package 'charisma' (Reproducible Color Characterization of Digital Images forBiological Studies) Provides a standardized and reproducible framework for characterizing and classifying discrete color classes from digital images of biological organisms. 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Package: r-cran-charlatan Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-tibble, r-cran-whisker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ipaddress, r-cran-stringi, r-cran-spelling Filename: pool/dists/noble/main/r-cran-charlatan_0.6.2-1.ca2404.1_all.deb Size: 4143914 MD5sum: 8d3841086e7bcccb949e51d4b15ca939 SHA1: e5be86572198e16883bfdea598a86d769df6b908 SHA256: 41bb1950a38c622d3895de2844ed32686003161b038b04e1a6a451518b7e2f4c SHA512: e85566d66469050c7cb4ec0922e3553c0298c3b34953f4613b5b6db867dffcf1f74901e4d4853c4e6864e7c95b82497db9add639d8e97f4e93322d307ab1b15a Homepage: https://cran.r-project.org/package=charlatan Description: CRAN Package 'charlatan' (Make Fake Data) Make fake data that looks realistic, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers from distributions and within a range. Package: r-cran-charlesschwabapi Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-lubridate, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-anytime, r-cran-dplyr, r-cran-openssl, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-charlesschwabapi_1.0.5-1.ca2404.1_all.deb Size: 131552 MD5sum: b8c92c0c19d02b17c612eae32c94956e SHA1: 7a4ea1db7788c2b9e400598f1afca04ba2f37811 SHA256: 984d1b72276ef5244d0fe3257bc9640816c257d99af55da9be52ae0f20a99da8 SHA512: 69086a6ec80300c6028de8b99fe0132d6609a91c8c900b7e8204c4b0dc498405d4d5e8a82c66c9a0f39df802da68568a8b6abc8428bd35dcf458871a4b53deea Homepage: https://cran.r-project.org/package=charlesschwabapi Description: CRAN Package 'charlesschwabapi' (Wrapper Functions Around 'Charles Schwab Individual Trader API') For those wishing to interact with the 'Charles Schwab Individual Trader API' () with R in a simplified manner, this package offers wrapper functions around authentication and the available API calls to streamline the process. 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Code allows one to define strata, adaptively sample using several types of confidence bounds for the quantity of interest (Lai's confidence bands, Bayesian credible intervals, normal confidence intervals), and sampling strategies (random sampling, stratified random sampling, Neyman's sampling, see Neyman (1934) and Neyman (1938) ). 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Package: r-cran-chatgpt Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clipr, r-cran-httr, r-cran-jsonlite, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny Filename: pool/dists/noble/main/r-cran-chatgpt_0.2.3-1.ca2404.1_all.deb Size: 146230 MD5sum: 719f5d46cd2c5cff030f73fd2c4eb364 SHA1: 8e77a272260e308ceafc9217a4e0b5072b9d8908 SHA256: bd9a46a4383fa4dc5153dced28c5120f67b5a0e973ce58495d629d00a523300b SHA512: b3382b320f5022372dced556b136c087cbd8e84dc856b2519d52d57ef4e1e372725fb6c5fb26f3e8489b41a332108a245fffa817e8c5dfe7d33c3b6ecc90e296 Homepage: https://cran.r-project.org/package=chatgpt Description: CRAN Package 'chatgpt' (Interface to 'ChatGPT' from R) 'OpenAI's 'ChatGPT' coding assistant for 'RStudio'. A set of functions and 'RStudio' addins that aim to help the R developer in tedious coding tasks. Package: r-cran-chatllm Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-aws.signature, r-cran-future, r-cran-promises, r-cran-later, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-chatllm_0.1.4-1.ca2404.1_all.deb Size: 1334384 MD5sum: 2eeac775e41a3af0a4c0205c59d2795e SHA1: a7a7aa0caa61e9e78a9142db3ef9c8496f5e6f78 SHA256: 16e9d44d97b123a69273a40f3e62d3dd4a7d7a04f6bb17b93c60bed4eb954874 SHA512: 4779927fd5fba1b06fe457e6458917fdc7eb06d7f2eafc61f5311be5fe49741eea2058c6fa5a75f8ac6fc4b9547269705dac2c19e8cafe21c787834da06d741f Homepage: https://cran.r-project.org/package=chatLLM Description: CRAN Package 'chatLLM' (A Flexible Interface for 'LLM' API Interactions) Provides a flexible interface for interacting with Large Language Model ('LLM') providers including 'OpenAI', 'Azure OpenAI', 'Azure AI Foundry', 'Groq', 'Anthropic', 'DeepSeek', 'DashScope', 'Gemini', 'Grok', 'GitHub Models', and AWS Bedrock. Supports both synchronous and asynchronous chat-completion APIs, with features such as retry logic, dynamic model selection, customizable parameters, and multi-message conversation handling. Designed to streamline integration with state-of-the-art LLM services across multiple platforms. 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Package: r-cran-checkluhn Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-checkluhn_1.1.0-1.ca2404.1_all.deb Size: 17328 MD5sum: 026500f718c44a53a91f3b069f89ad1f SHA1: f0dbf7387d4c5480a231e6729b85de71c10f63c1 SHA256: 965c3e6913c94f02ebda7016da78a060b12c7b8c8d492801de20116e4e8d4b7b SHA512: 52d89dc182d862061667b5ade8df49727e5d3a98229e4adceb11acc7a15e540439854c43860d2e34e0dd25117a562ade711d54c488d1bd9e0d32f201a63ed247 Homepage: https://cran.r-project.org/package=checkLuhn Description: CRAN Package 'checkLuhn' (Checks if a Number is Valid Using the Luhn Algorithm) Confirms if the number is Luhn compliant. Can check if credit card, IMEI number or any other Luhn based number is correct. For more info see: . 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Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint's checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the "Microsoft R Archived Network" server (). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17. 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Package: r-cran-checkthat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-checkthat_0.1.0-1.ca2404.1_all.deb Size: 83326 MD5sum: 89c46a49d4f6abcf3f31bb9c4dee5256 SHA1: f26a9cbe8b0337849139f467f59a639cbb0f6948 SHA256: 18aaadddc3055df4214172b969151f227e5c708f8cdfdf710cc6a059c18be9ca SHA512: ef4a9b50b496c961175b87b3ca521678a61416f13cc296784f7ffb8bdbdbf832fadede63ee5b3eec9d38364555f7ba7cc14dc3c50792c314378626a3f0607cc0 Homepage: https://cran.r-project.org/package=checkthat Description: CRAN Package 'checkthat' (Intuitive Unit Testing Tools for Data Manipulation) Provides a lightweight data validation and testing toolkit for R. 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Consolidates ad-hoc requirements that CRAN reviewers enforce but standard checks do not surface, helping 'R' package maintainers identify and fix issues before submission to reduce rejection rates. Covers code-pattern issues, DESCRIPTION-field formatting, documentation problems, and general package structure concerns. 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Package: r-cran-cheese Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cheese_0.1.3-1.ca2404.1_all.deb Size: 158800 MD5sum: 2e80257de5a8d776945f8993f574a64c SHA1: 19b0e3453ac759ebf2778497c87c00ab2b6c4b53 SHA256: 7a43ea71255faddc8bbfb9ae3d416a2b5ef149ae88e40464234447aaac5bc06c SHA512: adc5fd22529fd748099bdb2898ed6dc361e3c0c616fab03ea89ec63e13ee6b4d20686f993e9764adac167164b716949f4dd7943105ec759e609f49bdb2f37d00 Homepage: https://cran.r-project.org/package=cheese Description: CRAN Package 'cheese' (Tools for Working with Data During Statistical Analysis) Contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. 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Package: r-cran-cheetahr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3974 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-testthat, r-cran-shiny, r-cran-quarto, r-cran-knitr, r-cran-dplyr, r-cran-palmerpenguins Filename: pool/dists/noble/main/r-cran-cheetahr_0.4.0-1.ca2404.1_all.deb Size: 1204258 MD5sum: 95c0f1724aeb06187674f00bb1307e34 SHA1: e6e8e5e4872e22e94581350acb7f2561c041892f SHA256: f8160d2a5a5098527e76160994f12695e9bc57e1b64c8f87cfcb6f4cd0a1f195 SHA512: 7a756be0fb6a48ef89287b8c3b367cf5ce4eae96efa28a32f1c16e789e7f01bed51e3da771722fe82b049306ca7d64a91c5631218dd437d48d98bd0df85cd7b8 Homepage: https://cran.r-project.org/package=cheetahR Description: CRAN Package 'cheetahR' (High Performance Tables Using 'Cheetah Grid') An R interface to 'Cheetah Grid', a high-performance JavaScript table widget. 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Package: r-cran-chem.databases Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2461 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-install.load, r-cran-spelling Filename: pool/dists/noble/main/r-cran-chem.databases_1.0.0-1.ca2404.1_all.deb Size: 2483170 MD5sum: 899201b53bf53b7a3c4f7098c8f64e74 SHA1: 04ef1abf4f376f33e65fd416f204f1019377c37a SHA256: 34075ba3c8309471fdda3bf263d95261bb4374207e6d5c4c37bd21dedc7a72ef SHA512: 8d614e19b7116bbefe3a7c78e893ba1f834e6673ec59ee014d3df8fb57fa4bfe53097e036e76dd31b6ab27517d4af6a73a130c8e77258f9fc51cba3c541cd72a Homepage: https://cran.r-project.org/package=chem.databases Description: CRAN Package 'chem.databases' (Collection of 3 Chemical Databases from Public Sources) Contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) [United States (US) Department of Health and Human Services (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI), "Multi-Species Acute Toxicity Database", ] combined with the Toxic Substances Control Act (TSCA) Inventory [United States Environmental Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical Substance Inventory", ] and the Agency for Toxic Substances and Disease Registry (ATSDR) Database [United States (US) Department of Health and Human Services (DHHS) Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), "Agency for Toxic Substances and Disease Registry (ATSDR) Database", ] in 2 data sets. One data set has a focus on the latter 2 databases and one data set focuses on the former database. Also contains the collection of chemical data from Wikipedia compiled in the US EPA CompTox Chemicals Dashboard [United States Environmental Protection Agency (US EPA) / Wikimedia Foundation, Inc. "CompTox Chemicals Dashboard v2.2.1", ]. Package: r-cran-chem16s Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4807 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-ggplot2, r-cran-rlang, r-cran-reshape2, r-bioc-phyloseq, r-cran-canprot Suggests: r-cran-tinytest, r-cran-knitr, r-cran-patchwork, r-cran-ggpmisc, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chem16s_1.2.0-1.ca2404.1_all.deb Size: 3456002 MD5sum: 14cdb2cce8196683489d761959ee90ce SHA1: 7bc9ccff4be8126f87aa92537ec652df4874534f SHA256: 267d330e3175a6ab269fb801a902d9dfa3c051f4e06595764ca9088817733783 SHA512: 38b7ad275e874db4f719b766114a3968fcde11cb6c39af448eeca850fa21fa6586e125b1e6269d3279422fe79e34cd23af6f116c9df80c8d2485a71f8c770bc3 Homepage: https://cran.r-project.org/package=chem16S Description: CRAN Package 'chem16S' (Chemical Metrics for Microbial Communities) Combines taxonomic classifications of high-throughput 16S rRNA gene sequences with reference proteomes of archaeal and bacterial taxa to generate amino acid compositions of community reference proteomes. 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Package: r-cran-chemcal Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-knitr, r-cran-testthat, r-cran-investr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chemcal_0.2.3-1.ca2404.1_all.deb Size: 408388 MD5sum: dccbf6cd8289918909d4f28ab55d3fd7 SHA1: 6e35ef1bbf8088882e73f75b1ee408bf7cc61d54 SHA256: 057109df968788ca6bad98dac4985f196871ef2e498660c3217ca8b325fe6fc7 SHA512: c8037793448e84bc44e296adf068fd92be5e125f8fbd77aea83335834cf77a67a5b9cfec4b0e8a93a56d25923c1ae947bc41ca0344b4c4bc9ade1d7d524445ee Homepage: https://cran.r-project.org/package=chemCal Description: CRAN Package 'chemCal' (Calibration Functions for Analytical Chemistry) Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. (1997) There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm' from the 'MASS' package). 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This package primarily contains two functions, which are used to generate artificial data and estimate ATE with high-dimensional and error-prone data accommodated. Package: r-cran-chemmodlab Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-msqc, r-cran-class, r-cran-e1071, r-cran-elasticnet, r-cran-lars, r-cran-mass, r-cran-nnet, r-cran-proc, r-cran-randomforest, r-cran-rpart, r-cran-tree, r-cran-pls, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-chemmodlab_2.0.0-1.ca2404.1_all.deb Size: 381906 MD5sum: cfaaaa0f48240c4ba330bef38dc93988 SHA1: 74e2732c6b8065bb17b3e7e1da6a4a672d3483ff SHA256: c06645f5644d9952bc26da3081558a4cef3127d07fa872cd1788048a05ff6a17 SHA512: 60b9ef63c325927c09490928365c52054ac31242eb901758810420290ae13e983a7799b38a6f4e3e655216d6025f82b1188edec1b5ad1095afaa44526a7ed972 Homepage: https://cran.r-project.org/package=chemmodlab Description: CRAN Package 'chemmodlab' (A Cheminformatics Modeling Laboratory for Fitting and AssessingMachine Learning Models) Contains a set of methods for fitting models and methods for validating the resulting models. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the Cheminformatics field. As promising new methodologies emerge from the statistical and data-mining communities, they will be incorporated into the laboratory. These methods are aimed at discovering quantitative structure-activity relationships (QSARs). However, the user can directly input their own choices of descriptors and responses, so the capability for comparing models is effectively unlimited. Package: r-cran-chemodiv Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-vegan, r-cran-webchem, r-bioc-fmcsr, r-bioc-chemminer, r-cran-hillr, r-cran-ape, r-cran-gunifrac, r-cran-tidygraph, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-gridextra, r-cran-ggdendro, r-cran-tidyr, r-cran-rlang, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chemodiv_0.3.1-1.ca2404.1_all.deb Size: 579894 MD5sum: bb1464c2b7659e5c63f0c40d86e23ed1 SHA1: 960ee588c318a12e136546f68ece1dfa2ef1b145 SHA256: 1253e049378a26cba9a87e39320b16a67f5f93ffbdb279798d1b7a71e152c5e7 SHA512: 091b5ab8c9474474b15eb9802643d24f503471beaa18db1e55950ef3290e508759328494a894c1b1daf68718e94c33760ec3b38b43711ad13ad4662acd418c6f Homepage: https://cran.r-project.org/package=chemodiv Description: CRAN Package 'chemodiv' (Analysing Chemodiversity of Phytochemical Data) Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) . Package: r-cran-chemometrics Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-class, r-cran-e1071, r-cran-mass, r-cran-nnet, r-cran-pcapp, r-cran-robustbase, r-cran-som, r-cran-lars, r-cran-pls, r-cran-mclust Suggests: r-cran-gclus Filename: pool/dists/noble/main/r-cran-chemometrics_1.4.4-1.ca2404.1_all.deb Size: 4086196 MD5sum: 5a1d67378da210417151ded2fb00ef32 SHA1: 491f286c47e6b88e6695a9b8526035fa46f7d4c3 SHA256: 75359978a1fb9e784e5ab1abf100f3a69be214cce71234e2e7d096dc53819114 SHA512: 971cd0f0bf191601dd2246f3df93a98f25af02e5a19280b9e4362b28478dc12b8b4c080f9097fea2f11d69f96b34cb2c98e793aa90a7b141f33a43b0f3972a10 Homepage: https://cran.r-project.org/package=chemometrics Description: CRAN Package 'chemometrics' (Multivariate Statistical Analysis in Chemometrics) R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009). Package: r-cran-chemospec2d Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chemospecutils, r-cran-colorspace, r-cran-readjdx, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-tinytest, r-cran-irlba, r-cran-threeway, r-cran-multiway, r-cran-matrixstats, r-cran-r.utils, r-cran-mlrmbo, r-cran-paramhelpers, r-cran-smoof, r-cran-mlr, r-cran-lhs, r-cran-rcpproll, r-cran-rmarkdown, r-cran-robustbase, r-cran-bookdown, r-cran-cmls Filename: pool/dists/noble/main/r-cran-chemospec2d_0.5.1-1.ca2404.1_all.deb Size: 386674 MD5sum: f09ec14cdacf5485a204918a5b0907f2 SHA1: b42216f5dfeec23b8e85ff058544f71e0b54977f SHA256: 05e56d889f258eb016b3292b31c50d4acf056a73648c8ae0fb2fb0ec9f5714da SHA512: 152b4734f6a8c2a5bd6d4c59642fc39b33c9d5bf69b0a697f778361a54f4f8109070649abb104e7bc72d373b087bc002f205413dc00b16f6b3d8506635896abd Homepage: https://cran.r-project.org/package=ChemoSpec2D Description: CRAN Package 'ChemoSpec2D' (Exploratory Chemometrics for 2D Spectroscopy) A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly. Package: r-cran-chemospec Architecture: all Version: 6.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chemospecutils, r-cran-reshape2, r-cran-readjdx, r-cran-patchwork, r-cran-ggplot2, r-cran-plotly, r-cran-lattice, r-cran-magrittr Suggests: r-cran-idpmisc, r-cran-js, r-cran-nbclust, r-cran-clustercrit, r-cran-baseline, r-cran-mclust, r-cran-pls, r-cran-r.utils, r-cran-rcolorbrewer, r-cran-seriation, r-cran-mass, r-cran-pcapp, r-cran-jsonlite, r-cran-signal, r-cran-speaq, r-cran-elasticnet, r-cran-irlba, r-cran-chemometrics, r-cran-amap, r-cran-tinytest, r-cran-roxut Filename: pool/dists/noble/main/r-cran-chemospec_6.3.1-1.ca2404.1_all.deb Size: 3134784 MD5sum: 97883b59b3f32784a71ba4afe79cb6b1 SHA1: c1c0f9c3743790d3665281d833e09e11e16d75b8 SHA256: 95369b2b10f2534eb98f2d2f77fe976a6de42fae46ba46fe0b4d2a319ef4fa31 SHA512: f207b49d29065d1ca084f604e6041a922d45705f9146e8c60db62cd01f01be68decda8c06c82fa9af7b20abf06916e019c45c64d5abafb6c655ac571a3b0c138 Homepage: https://cran.r-project.org/package=ChemoSpec Description: CRAN Package 'ChemoSpec' (Exploratory Chemometrics for Spectroscopy) A collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available. Package: r-cran-chemospecutils Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-plotly, r-cran-magrittr Suggests: r-cran-chemospec, r-cran-chemospec2d, r-cran-tinytest, r-cran-robustbase, r-cran-rcolorbrewer, r-cran-amap, r-cran-irlba, r-cran-lattice, r-cran-roxut, r-cran-patchwork, r-cran-threeway, r-cran-multiway, r-cran-mvoutlier Filename: pool/dists/noble/main/r-cran-chemospecutils_1.0.5-1.ca2404.1_all.deb Size: 235260 MD5sum: 2b3f01110376f114b6fee3fefdbf6de4 SHA1: f1b570c8d15d9486869b93b8337d2ad213b5cb04 SHA256: df6e3066f7bcc8fbc9f200d368f2458068aef34befb33ee51ed7a49012e3c313 SHA512: b8dd46835a83167d2cceb68e949f987f3f8b931ae96b5fe0211fc3e8b785e0cefdefa9dc4b2d49bcbbc254eea7a6a92384df9ecc09bc95adfdda034ca863fccb Homepage: https://cran.r-project.org/package=ChemoSpecUtils Description: CRAN Package 'ChemoSpecUtils' (Functions Supporting Packages ChemoSpec and ChemoSpec2D) Functions supporting the common needs of packages 'ChemoSpec' and 'ChemoSpec2D'. Package: r-cran-chernoffdist Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gsl Filename: pool/dists/noble/main/r-cran-chernoffdist_0.1.0-1.ca2404.1_all.deb Size: 19278 MD5sum: 57348de169b018a4ff64ab0aaa2279f3 SHA1: 64472b4148e73daea1f4b3e09653ac1cdc23dbe1 SHA256: 11a4d6f83a4bdf9c4a3ecebf31dc4bae3f5ea9277832b40b0d944fc0546f8d07 SHA512: 439a35614aec7abd0a6181a3cc5c396f8fbb0f1132873374b0a62040d7b26716cc3a514fe5c132539f7d4e45f16e03785a7f1df713a76e489e08286e6e1f8a73 Homepage: https://cran.r-project.org/package=ChernoffDist Description: CRAN Package 'ChernoffDist' (Chernoff's Distribution) Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, . Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2). Package: r-cran-cherry Architecture: all Version: 0.6-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-lpsolve, r-cran-hommel Suggests: r-cran-mass, r-bioc-multtest Filename: pool/dists/noble/main/r-cran-cherry_0.6-15-1.ca2404.1_all.deb Size: 540098 MD5sum: 5161ce63c91488144a6b1ae0ebe095a9 SHA1: 1ee5d41436043e6dca7e651c7176dd94aedd53ac SHA256: c93a50710a6519580e3e5dba8bad763a3a8a316c37a009ccaeed1917eaf4a788 SHA512: 68d0cc9a14a647a6d10b7ad565f5dc8534e15e86208073ff8c66171ed001118ebef2942f7f5a38f55cec60118fcd3d27096cfa880f1924e16e38e39834161774 Homepage: https://cran.r-project.org/package=cherry Description: CRAN Package 'cherry' (Multiple Testing Methods for Exploratory Research) Provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) . Package: r-cran-cherryblossom Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cherryblossom_0.1.0-1.ca2404.1_all.deb Size: 1333548 MD5sum: 32a486173da617c1530731e233ae8c96 SHA1: 917b0016d4ed4cec66a84258f75a6fa2a6722770 SHA256: 2ebf27d3f81930b5d7c06634a88ea49ed2e52c6ce3bc8f09b629162eb1b11fec SHA512: e7db5421e2d4abc649c5e2c612508907289a6cc1722ce5e34d7e799578dc3292fad035e4d68f3962f7bc10fc84bf139dc9fa20964ee6f8d4feee3e19cf5a1528 Homepage: https://cran.r-project.org/package=cherryblossom Description: CRAN Package 'cherryblossom' (Cherry Blossom Run Race Results) Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC. Package: r-cran-chess2plyrs Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chess2plyrs_0.3.0-1.ca2404.1_all.deb Size: 145134 MD5sum: 68891c77df2edd172d617a13a16d2fda SHA1: 035bee575a5acabdd9bea90cd517fcd59b0d2ebc SHA256: 872860c84ed643117079f064cb71cb93448d48f14677dfeb50f6984104630c8c SHA512: c8973bf59426672862475c4eb9523d5cde2779913ce56ada1130c466843fb13e343b391f7dd42c4ba3caebbf0b31a677b7e66a4c15af49a3af616a7d97e12faf Homepage: https://cran.r-project.org/package=chess2plyrs Description: CRAN Package 'chess2plyrs' (Chess Game Creation and Tools) A chess program which allows the user to create a game, add moves, check for legal moves and game result, plot the board, take back, read and write FEN (Forsyth–Edwards Notation). A basic chess engine based on minimax is implemented. Package: r-cran-chess Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-magrittr, r-cran-purrr, r-cran-reticulate, r-cran-rsvg Suggests: r-cran-covr, r-cran-knitr, r-cran-png, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chess_1.0.1-1.ca2404.1_all.deb Size: 219228 MD5sum: 86c61cd86e818210d1e2af83bd51e008 SHA1: 80b376ed8032a187f692f56b21f4b090fe26da8a SHA256: 918cf16edab9671d6fe9427fb6d9f216080b609ec3b3e7665b968ee7f3842ee7 SHA512: 63df208115ecd5b77500f72e7cfdc40cb41f184c782a0f768cf33377b515fe279b1e625349a66d77cf28e3617e4874d0667525c954a21da36b3e5d3d8e227cc5 Homepage: https://cran.r-project.org/package=chess Description: CRAN Package 'chess' (Read, Write, Create and Explore Chess Games) This is an opinionated wrapper around the python-chess package. It allows users to read and write PGN files as well as create and explore game trees such as the ones seen in chess books. 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It makes the creation of connectivity matrices easier, i.e. a binary matrix of dimension n x n, where n is the number of nodes (sampling units) indicating the presence (1) or the absence (0) of an edge (link) between pairs of nodes. Different network objects can be produced by 'chessboard': node list, neighbor list, edge list, connectivity matrix. It can also produce objects that will be used later in Moran's Eigenvector Maps (Dray et al. (2006) ) and Asymetric Eigenvector Maps (Blanchet et al. (2008) ), methods available in the package 'adespatial' (Dray et al. (2023) ). This work is part of the FRB-CESAB working group Bridge . Package: r-cran-chessgmoog Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3939 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-chessgmoog_0.1.0-1.ca2404.1_all.deb Size: 3984516 MD5sum: 595c0ff24781b12c6192369c9bc1d096 SHA1: 79d2d3e75ac34aaa37867ebc03f2ccac3b218fbc SHA256: 9f9c2c6ae902547272dd340764013ee49b656b8c29e95451fcabdeef19fce502 SHA512: cf55302a900e1977c113e87a11a6924c3f0e1312a940c0ac04716335a7935938d4bd127e0f8bbfc7360c13ec583090814ba8661c8b4a831bb5fecaae74319a36 Homepage: https://cran.r-project.org/package=ChessGmooG Description: CRAN Package 'ChessGmooG' (FIDE Chess Players Ratings for 2015 and 2020) Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". Package: r-cran-chessresults Architecture: all Version: 2026.08.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-readr, r-cran-rvest Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-chessresults_2026.08.27-1.ca2404.1_all.deb Size: 35492 MD5sum: e923cb6bfc70f55bda6995b200634178 SHA1: a880c4cf22a3df97b57d9c93e979b9ded68b5a42 SHA256: 2d31ad11538db0ded94522f57344c2287dc3b16e3aa9d1a0683ac22cbc35232b SHA512: b69c5416ad12f449f78dc99a702106b00c71789913ab0b5b359ceff2ec1647e2f1802bf95ab3479e4a016f02d0f621ea703bca36c4eb83e8c3562af7b5c68a10 Homepage: https://cran.r-project.org/package=chessResults Description: CRAN Package 'chessResults' (Scraper for Chess-Results.com) Scrape data from and get a clean 'tibble'. Currently supports tournament information, starting rank, playing schedule, pairings/results for rounds, and closing rank. 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It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' package has functions for linear regression, logistic regression, negative binomial regression, Cox proportional hazards model and conditional logistic regression. 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Package: r-cran-chiledataapi Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2601 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-scales Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chiledataapi_0.3.0-1.ca2404.1_all.deb Size: 1392926 MD5sum: 9a39ad13bbb801c8647e5d73d0647eba SHA1: bdf41f39e9e7925b046c74aaa8885a7de18da957 SHA256: 50cc2b9511b3b43f63d01aac1e107b3e8d96f84c7c1d75e36b6b6ca5c5fb53e1 SHA512: 8feee4abb61f6d6b4be272a0c846333db65674dd8660d6db7a640b05413466a863edc9c9c8fe71e7b3a5f907bc5c53c27601bb08701225ef07595083f41be5fe Homepage: https://cran.r-project.org/package=ChileDataAPI Description: CRAN Package 'ChileDataAPI' (Access Chilean Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'FINDIC API', 'World Bank API', and 'Nager.Date', retrieving real-time or historical data related to Chile such as financial indicators, holidays, and more. Additionally, the package includes curated datasets related to Chile, covering topics such as human rights violations during the Pinochet regime, electoral data, census samples, health surveys, seismic events, territorial codes, and environmental measurements. The package supports research and analysis focused on Chile by integrating open APIs with high-quality datasets from multiple domains. For more information on the APIs, see: 'FINDIC' , 'World Bank API' , and 'Nager.Date' . 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Estos mapas no tienen precision geodesica, por lo que aplica el DFL-83 de 1979 de la Republica de Chile y se consideran referenciales sin validez legal. No se incluyen los territorios antarticos y bajo ningun evento estos mapas significan que exista una cesion u ocupacion de territorios soberanos en contra del Derecho Internacional por parte de Chile. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Terrestrial maps with simplified toplogies. These maps lack geodesic precision, therefore DFL-83 1979 of the Republic of Chile applies and are considered to have no legal validity. Antartic territories are excluded and under no event these maps mean there is a cession or occupation of sovereign territories against International Laws from Chile. This package was intentionally documented in asciified spanish to make it work without problem on different platforms.) Package: r-cran-chillmodels Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-chillr, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-chillmodels_1.0.2-1.ca2404.1_all.deb Size: 78674 MD5sum: 43142124bf8d2f09a1adb5e2c074c638 SHA1: 161c2522d8041194fba0078cb9c3c61f29951394 SHA256: ff511d07c1e655f090083d85237f29076faad28612332a8341e7e2c82b7eed49 SHA512: 054b7d01ee4469838c583bd6543d92f22b4fcdbd4485a7feae31bdf0fbae43fed7498352acb4a9422cdf8289d6ee623976cd5f0a62e0130dca1996a921732c40 Homepage: https://cran.r-project.org/package=ChillModels Description: CRAN Package 'ChillModels' (Processing Chill and Heat Models for Temperate Fruit Trees) Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 ), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 ), Model for apple "Gala" (Guak and Nielsen, 2013 ), Taiwan Model (Lu et al., 2012 ), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 ) and Heat Restriction model. Package: r-cran-chinapis Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3821 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chinapis_0.2.0-1.ca2404.1_all.deb Size: 1988952 MD5sum: bd80989829752a54520180c4c16d63a8 SHA1: ff24eb89fc2cb05ba590825f78b7acb74e9fce3c SHA256: 8801e88d7e2293b9679061490189e41a0771a059a62d0aa05ff6a205372012ea SHA512: 8729eee056e0c715ea73712919f3311bdeb255378db644bfc6e4c40ef5104bd17a366223fecba796995c2a35a4490ee7d11e44978a90be92801e87cd5b209eeb Homepage: https://cran.r-project.org/package=ChinAPIs Description: CRAN Package 'ChinAPIs' (Access Chinese Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date' and 'World Bank API', retrieving real-time or historical data related to China, such as holidays and economic indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on China and Hong Kong, covering topics such as air quality, demographics, input-output tables, epidemiology, political structure, names, and social indicators. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' . Package: r-cran-chinese.misc Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jiebar, r-cran-nlp, r-cran-tm, r-cran-stringi, r-cran-slam, r-cran-matrix, r-cran-purrr Filename: pool/dists/noble/main/r-cran-chinese.misc_0.2.3-1.ca2404.1_all.deb Size: 240722 MD5sum: cf7a1cad0aeee432105a6b54889d236d SHA1: 3d84e81335de0a26e19ec8fd0bb6c7d1e733e41a SHA256: 07f897a4e556fda532c98b025ca18af53cc32c73ce2f89c449dbdb9972052619 SHA512: 1804315095295d942a6320ee0085bf9f704d8a3fd162c8b1d4253a1d456275415c114f21a71c13f95afceb6af22e860177e311ba720075e3fc734ccee1678915 Homepage: https://cran.r-project.org/package=chinese.misc Description: CRAN Package 'chinese.misc' (Miscellaneous Tools for Chinese Text Mining and More) Efforts are made to make Chinese text mining easier, faster, and robust to errors. Document term matrix can be generated by only one line of code; detecting encoding, segmenting and removing stop words are done automatically. Some convenient tools are also supplied. Package: r-cran-chinesenames Architecture: all Version: 2025.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brucer, r-cran-data.table Suggests: r-cran-babynames, r-cran-car, r-cran-dplyr, r-cran-glue Filename: pool/dists/noble/main/r-cran-chinesenames_2025.8-1.ca2404.1_all.deb Size: 408432 MD5sum: 6431f5a015e1b0d9df03408686e40c98 SHA1: 1a885239f692bb7b39162943c4525ad200bea5f9 SHA256: 519c3ead73a2bbec9ac3eac52bec26e9d38e01e2f8c9454c4d35aa808f8e1967 SHA512: 790a3b34b4013f47a8791082b04cd7ae82988babd0f3ed9ea68f5a71c860c5c875e433529f678c4f068fb2cd665d675dca961c927d57d03fccd02d78aee09068 Homepage: https://cran.r-project.org/package=ChineseNames Description: CRAN Package 'ChineseNames' (Chinese Name Database 1930-2008) A database of Chinese surnames and given names (1930-2008). This database contains nationwide frequency statistics of 1,806 Chinese surnames and 2,614 Chinese characters used in given names, covering about 1.2 billion Han Chinese population (96.8 percent of the Han Chinese household-registered population born from 1930 to 2008 and still alive in 2008). This package also contains a function for computing multiple indices of Chinese surnames and given names for social science research (e.g., name uniqueness, name gender, name valence, and name warmth/competence). Details are provided at . Package: r-cran-chiopendata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-jsonlite, r-cran-httr, r-cran-janitor, r-cran-rlang Suggests: r-cran-curl, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chiopendata_0.1.1-1.ca2404.1_all.deb Size: 49602 MD5sum: 5cdf74288c611d0f228d05299e9d4052 SHA1: c788b9c296740614f95972cec9944413d2452670 SHA256: 214afb09b06bc1b4ce3917fc45c8230094fe83a209330a0c490cae0f70c2b070 SHA512: 0e76dea411a828658608343a33bb9206e0dd6083f3d4df713f69182d7dcbd7c2b41a70c747a4aef895ebf351d788021984e2d9a619f6c64190fb0c55428fb7bb Homepage: https://cran.r-project.org/package=chiOpenData Description: CRAN Package 'chiOpenData' (Convenient Access to Chicago Open Data API Endpoints) Provides simple, reproducible access to datasets from the Chicago Open Data portal . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. 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Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) . Package: r-cran-chirps Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-sf, r-cran-terra Suggests: r-cran-climatrends, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chirps_1.1-1.ca2404.1_all.deb Size: 635314 MD5sum: 10531e0c667d710178ab2eddccb83791 SHA1: e3c9b43bbdaeabae7532b033dee6903a7f9009a4 SHA256: c9bde44c0628c4ec3577673f1af81aea641bc97f0992794e718c24505f4c4d31 SHA512: b9523c31be1e65f9adafc7d15425c5f90e8a6ae903ed0666c780b2f9d2f989cab2a96b6e08cf712c5069ad44de2a63b9358fc9d6c4eb7aa5358b49383c480c79 Homepage: https://cran.r-project.org/package=chirps Description: CRAN Package 'chirps' (API Client for CHIRPS and CHIRTS) API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit its official home page . Package: r-cran-chisq.posthoc.test Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chisq.posthoc.test_0.1.2-1.ca2404.1_all.deb Size: 17384 MD5sum: ec3ae3437088c8acf43d4dbda09afe62 SHA1: 1d731bf33685322bcb8e434ff603087132bd5fab SHA256: 5ddcbff290a6ebfb723153b09532251aec52924a0f6f3b3714f4f7ce38502af7 SHA512: 2e6b923f7465368244afe25f9b1a7225859e4ac37f4d72e8fb106ec84704920f1f29eacfcf32e5fabdbf853070e40fe86779337b43ef4ae0c78fe528b49ac30b Homepage: https://cran.r-project.org/package=chisq.posthoc.test Description: CRAN Package 'chisq.posthoc.test' (A Post Hoc Analysis for Pearson's Chi-Squared Test for CountData) Perform post hoc analysis based on residuals of Pearson's Chi-squared Test for Count Data based on T. Mark Beasley & Randall E. Schumacker (1995) . 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It also calculates standardised, moment-corrected standardised, and adjusted standardised residuals, and their significance, as well as the Quetelet Index, IJ association factor, and adjusted standardised counts. It also computes the chi-square-maximising version of the input table. Different outputs are returned in nicely formatted tables. 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Package: r-cran-chkptstanr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-abind, r-cran-rdpack, r-cran-rstan Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-posterior Filename: pool/dists/noble/main/r-cran-chkptstanr_0.1.1-1.ca2404.1_all.deb Size: 874644 MD5sum: 7310d107ada67be2eba6bbe30404ef42 SHA1: 2b32206da13052952466361fad1d0669c7d2683b SHA256: 8cba405fb0c31b8f0aa6688bf4cf5ebb521000acb6c3aa53fe080309c2bd93d6 SHA512: 200917f9a4597f0067250e6c13cb735c5a951f980788761727b51d510b0906af93184ca06555cf0e315ef11c05fe651b60d728d502f4e7db4359d2abf7002e8e Homepage: https://cran.r-project.org/package=chkptstanr Description: CRAN Package 'chkptstanr' (Checkpoint MCMC Sampling with 'Stan') Fit Bayesian models in Stan with checkpointing, that is, the ability to stop the MCMC sampler at will, and then pick right back up where the MCMC sampler left off. Custom 'Stan' models can be fitted, or the popular package 'brms' can be used to generate the 'Stan' code. This package is fully compatible with the R packages 'brms', 'posterior', 'cmdstanr', and 'bayesplot'. Package: r-cran-chlorpromaziner Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-chlorpromaziner_0.2.0-1.ca2404.1_all.deb Size: 77580 MD5sum: 64fc40a24c777f4d94c6c068784817b9 SHA1: 709618738b204e4b81e7bff17378e951bfd80fe1 SHA256: 54c7ec950f7bf9fbfd501a5f699042c390e92878871de3b5c2dc129e72bc3e0c SHA512: f119a8ab2c32f8e86390173961af189bbd28b3016c4da2741773e3f18165a6042e93675a67d852337accf2e06ecee579f59838d30988f835e5c9a1a9ce05eaf6 Homepage: https://cran.r-project.org/package=chlorpromazineR Description: CRAN Package 'chlorpromazineR' (Convert Antipsychotic Doses to Chlorpromazine Equivalents) As different antipsychotic medications have different potencies, the doses of different medications cannot be directly compared. Various strategies are used to convert doses into a common reference so that comparison is meaningful. Chlorpromazine (CPZ) has historically been used as a reference medication into which other antipsychotic doses can be converted, as "chlorpromazine-equivalent doses". Using conversion keys generated from widely-cited scientific papers, e.g. Gardner et. al 2010 and Leucht et al. 2016 , antipsychotic doses are converted to CPZ (or any specified antipsychotic) equivalents. The use of the package is described in the included vignette. Not for clinical use. Package: r-cran-chms Architecture: all Version: 7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24733 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-haven, r-cran-hms, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-mirai, r-cran-mori, r-cran-parallelly, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-stringr, r-cran-tidyr, r-cran-zoo Suggests: r-cran-config, r-cran-ggplot2, r-cran-janitor, r-cran-kableextra, r-cran-physicalactivity, r-cran-quarto, r-cran-scales, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-chms_7.1-1.ca2404.1_all.deb Size: 3890528 MD5sum: 8040ee2fa3c0643096b44ccf33387ec0 SHA1: 2182d25d01de9f405405ab3ed6d60fa1218a3862 SHA256: 19541289fe9baaeac7a3882dfa3327e4a0e7e8c08be8c5e740d8e6afe5cc2364 SHA512: e5ea7a30c5a2ef84b385964f991ce53f34e2cef55726b52ca5fc9f6eca50453e6df2aa8fc836dc99b583b6d6b88a93f12a7f61cafede02a5309f94957cf9500f Homepage: https://cran.r-project.org/package=chms Description: CRAN Package 'chms' (Accelerometer Processing Methods for Cycle 7 of the CHMS) 'ActiGraph wGT3X-BT' accelerometer processing methods using the standardized workflow developed by Statistics Canada for cycle 7 of the Canadian Health Measures Survey (CHMS). 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Package: r-cran-christmas Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-animation Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-christmas_1.4.1-1.ca2404.1_all.deb Size: 178004 MD5sum: 0e1432af25cc76467528461f15ca6ebf SHA1: 285b84598221e1242abe72da9c469a4f704fb4bd SHA256: 4fc48aaa7615eff01c30d49c55da51d71c31422f218c372d847591e23c684751 SHA512: ca9775648af6fd58e09b8191b4aca13153489c8d958bdcc2392ed82d1ff5ad348d65b7998e01c80af7c0a6d03a7b202687a81e8c60c4326222ad896960e04fcf Homepage: https://cran.r-project.org/package=christmas Description: CRAN Package 'christmas' (Generation of Different Animated Christmas Cards) Generation of different Christmas cards, most of them being animated. Most of the cards can be generated in three languages (English, Catalan and Spanish). The collection started in 2009. Package: r-cran-chromconverter Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1733 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bitops, r-cran-fs, r-cran-purrr, r-cran-readxl, r-cran-reticulate, r-cran-stringr, r-cran-tidyr, r-cran-rams, r-cran-tibble, r-cran-xml2, r-cran-bit64, r-cran-data.table, r-cran-base64enc, r-cran-jsonlite, r-cran-digest Suggests: r-cran-ncdf4, r-cran-pbapply, r-cran-testthat, r-bioc-mzr Filename: pool/dists/noble/main/r-cran-chromconverter_0.9.0-1.ca2404.1_all.deb Size: 855468 MD5sum: b8c05706f577b642de36b6c78c7a8ada SHA1: 680a216408bb25fdd36259b3fff0e0d68b6fbfbb SHA256: 9c1780d58641fa10d9e23a4c178ca3af5a7a7e9b70338ba5d7c9d4ddf1adc52a SHA512: 4b0366a7b2f3c660f70fef89c487bd994e6f355750cf2de2093f3f37d312befa3ee8f245f8cf1368c0f22d6dbc7d24ffcec2e089e7b0294ccf04c9bfd7f70b3f Homepage: https://cran.r-project.org/package=chromConverter Description: CRAN Package 'chromConverter' (Chromatographic File Converter) Reads chromatograms from binary formats into R objects. Currently supports conversion of 'Agilent ChemStation', 'Agilent MassHunter', 'Agilent OpenLab', 'Shimadzu LabSolutions', 'ThermoRaw', 'Varian Workstation', and 'Waters Empower' files as well as various other formats. In addition to its internal parsers, chromConverter contains bindings to parsers in external libraries, such as 'Aston' , 'Entab' , 'rainbow' , and 'ThermoRawFileParser' . Package: r-cran-chromer Architecture: all Version: 0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-httr Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chromer_0.10-1.ca2404.1_all.deb Size: 43924 MD5sum: 9abe1f2fb03cbd5f80a9ba11ff8874d5 SHA1: f02407301bd6527a7607c49df6c9c42cbced5b9f SHA256: 55cedde6512c12df5d2c4ab35091480625cd9d07449ab358e4032c1efff4becb SHA512: 010e384821a0d32c8b6b969ca3aa2d38110a411260394188e7c8ad166da1c4bf7df69bce12375a621256a1000c996634ceae736b1ce83f99c69cd65ee09acd10 Homepage: https://cran.r-project.org/package=chromer Description: CRAN Package 'chromer' (Interface to Chromosome Counts Database API) A programmatic interface to the Chromosome Counts Database (), Rice et al. (2014) . This package is part of the 'ROpenSci' suite (). Package: r-cran-chromomap Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3383 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chromomap_4.1.1-1.ca2404.1_all.deb Size: 768818 MD5sum: 7d33a0fda0ca3c47a9f32d633e7c369a SHA1: 095ba0a6f21894e2c34d7a7639767e800d0f4619 SHA256: f726bd33dc9b81c7362cfa09eae760dc5b267831f25e3cbe164167e5643cd029 SHA512: 5e878322a3230fbcf2d7d59160032ef7cbf3d6c0a30d777916d7b9716219c6f17dc47ba7c96e89a474664aa3643a58902bc4302fb90162d78b3b01dea5b6d960 Homepage: https://cran.r-project.org/package=chromoMap Description: CRAN Package 'chromoMap' (Interactive Genomic Visualization of Biological Data) Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes, it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each of chromosome locus to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications. 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Package: r-cran-chromseq Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-chromseq_0.1.3-1.ca2404.1_all.deb Size: 88514 MD5sum: fe85dc64e6a5912d358b71a4d8fbe58b SHA1: e80e3fa7d79e45a04bb0883a677c733596692881 SHA256: d5c73a32bea6cfd76c5cd287085757fd00b18fa4a7a4ec3937d855e0f5b26df6 SHA512: 3b9fe26221221a0dd3fd98ab99143ddc4798f4cf7b23e3dc6a7b60ad3b82841a543b7f0c758268d33e6bacfc0e7a38e2cb8885134a96ad7931d7150a23ebf4f4 Homepage: https://cran.r-project.org/package=chromseq Description: CRAN Package 'chromseq' (Split Chromosome 'Fasta' File) Chromosome files in the 'Fasta' format usually contain large sequences like human genome. Sometimes users have to split these chromosomes into different files according to their chromosome number. The 'chromseq' can help to handle this. So the selected chromosome sequence can be used for downstream analysis like motif finding. Howard Y. Chang(2019) . 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Includes 'ggplot2' geoms and theme for chronological charts. Package: r-cran-chronometre Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcppspdlog, r-cran-xptr, r-cran-reticulate, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-chronometre_0.0.2-1.ca2404.1_all.deb Size: 10094 MD5sum: b4ce88984ca3acff1f8e9511523adbef SHA1: 629143bbd04aae4e98813c46c793c8c58b9f58f1 SHA256: e5d839d65cbbf5c0aed392119987cab322c6abedf9b8014cac19532357d9002b SHA512: 96c542d4b2f190d48d9d4a8290d56e5b5d1195ae56003aa6075decd45535abafa05bb7005a1d8a81dff8a6a0c11fd951f3d47e8d26e0ae727450e3e292fa908d Homepage: https://cran.r-project.org/package=chronometre Description: CRAN Package 'chronometre' (A 'chronomètre' is a 'stopwatch') As a 'chronomètre' is a 'stopwatch', this package offers a simple stopwatch, and in particular one that can be shared with Python (using the corresponding package of the same name available via 'PyPi') such that both interpreters operate on the same object instance and shown in the demo file, as well as in the unit tests. Package: r-cran-chronosphere Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-curl, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-chronosphere_0.6.1-1.ca2404.1_all.deb Size: 593974 MD5sum: 167f7291b65351a73b8dd27b3473aec4 SHA1: fb3eb7530b5c166f0b636efefe9151e2d5a7ab9a SHA256: 3b125e7f50c783f9e8dd903fbeec3d751e9f2f9393cfca9343a119b6d645c919 SHA512: e8ca4f7c0cd39214c14cc2b0084403c7211ddad7d03f84b74fba55adb27b978209ef8c1ec7c095204018c7f4a3efb28d92b7e5bd17b4b8fda288b969b96e47cf Homepage: https://cran.r-project.org/package=chronosphere Description: CRAN Package 'chronosphere' (Evolving Earth System Variables) The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. The inherent meta-database maps data files and import code to programming classes and allows access to these items via files deposited in public repositories. The purpose of the project is to increase reproducibility and establish version tracking of results from (paleo)environmental/ecological research. Package: r-cran-chunked Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-laf, r-cran-rlang, r-cran-dbi, r-cran-progress Suggests: r-cran-testthat, r-cran-rsqlite, r-cran-dbplyr Filename: pool/dists/noble/main/r-cran-chunked_0.6.2-1.ca2404.1_all.deb Size: 56570 MD5sum: 5d743a79b5e69f0f17c463ea6290f042 SHA1: ecabd0d2a47d12d28afaf1ebe8d2765c1000a33e SHA256: b05dac9aa06483f041173d8e13cdcc2920fd7eaab08324804a6c15f3927b1827 SHA512: 6fe66038400fcfc2949c8d06b8e47c2b6c1a3e7b3a7b132f66ca734a248265e5ddd3eaa79af5ed95766dcefb1b5711da16ef7e9a690573b3a10bf5c2e40dbd1e Homepage: https://cran.r-project.org/package=chunked Description: CRAN Package 'chunked' (Chunkwise Text-File Processing for 'dplyr') Data stored in text file can be processed chunkwise using 'dplyr' commands. 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Package: r-cran-chyper Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chyper_0.3.1-1.ca2404.1_all.deb Size: 66946 MD5sum: 825eaf2b0fca1e3a04c3106c7a118756 SHA1: cc14daf13a14a807acc74cb7725b2fbf300808b4 SHA256: 6a2b06fb7cab5a04243317d176f7fe0feb8eba6f670721479bdd3c7fac0fd53e SHA512: f11a3b6cadad2a8b4965dc48a0e72fa134ef3aca8e8a07af8910f36caaf41871dae52bda7572d58ec265bfdedc8a7507de45eb1f6270f94b8cee78f1460630d1 Homepage: https://cran.r-project.org/package=chyper Description: CRAN Package 'chyper' (Functions for Conditional Hypergeometric Distributions) An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size. 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See Hu Z, Ahmed A, Yau C (2021) for more details. 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Includes optimized 'SQL' search with 'SQLite', fuzzy matching of medical terms ('Jaro-Winkler'), Charlson and Elixhauser comorbidity calculation, 'WHO' 'ICD-11' 'API' integration, and hierarchical code validation. Data from Centro FIC Chile 'DEIS' . Package: r-cran-ciee Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-lavaan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ciee_0.1.1-1.ca2404.1_all.deb Size: 369708 MD5sum: 5bcbcdb24591ee9e0b29a90c2ff71917 SHA1: 4701cb6a7f3b37ecae4dbd6c7f32b14b4866ef87 SHA256: 06f99ab8e295a2e76f25955ca75588ad0838b3cba25ae0ed7805300a9f2edac2 SHA512: 10c0a7cca31528d0c6d63cceacbf13816657a861d7b995f051c6bc2fb85d5d277962d0e5642de6b1c72aa103ca8ef77cab1531e37b16d94a7c35c9c79d243493 Homepage: https://cran.r-project.org/package=CIEE Description: CRAN Package 'CIEE' (Estimating and Testing Direct Effects in Directed Acyclic Graphsusing Estimating Equations) In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, ) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison. 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"Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, . 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In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions. Package: r-cran-cifti Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-oro.nifti, r-cran-gifti, r-cran-r.utils Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-cifti_0.5.0-1.ca2404.1_all.deb Size: 1308996 MD5sum: bf632ee16dd50f2f336da2e351aaec4c SHA1: c6632633d062a78d7ed57a0a091dc1d7c5b08d12 SHA256: 0d317acf093a5e82631de8d14ad8eb39d1a810d027c076b21877508da5846f66 SHA512: 12f900a0e5e4db05d7dde49c26062e85bdf009cc4a627e442f6e5d6b3ba9d2545471def35d8030e4ddbd2c0ef1398c4635e6523c033c38b85210602160a5fe7c Homepage: https://cran.r-project.org/package=cifti Description: CRAN Package 'cifti' (Toolbox for Connectivity Informatics Technology Initiative('CIFTI') Files) Functions for the input/output and visualization of medical imaging data in the form of 'CIFTI' files . 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'ciftiTools' provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing. Package: r-cran-cim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cim_1.0.0-1.ca2404.1_all.deb Size: 51654 MD5sum: be82e682cd533240964b9c643ec72bcb SHA1: ce0e619c99566b380a8a92e0ecc9cf3e4c520fc7 SHA256: 949fa5c051fdf7ba8c3a01874f5223a685317e5bfbef175454c9a6838daa8477 SHA512: b389b257cb416b674a8df8dc7798d7c3545e2639b4bf0d27fba9f02eae280095e70843e536a06087fda5fe14894fab6f6098f713e75463a1ed75b5944955bb08 Homepage: https://cran.r-project.org/package=CIM Description: CRAN Package 'CIM' (Compositional Impact of Migration) Produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) for technical details. Package: r-cran-cimir Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-glue, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-jsonlite, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cimir_0.4-1-1.ca2404.1_all.deb Size: 109710 MD5sum: d813fc0501c382551ecfa723da8ab0ab SHA1: cebd24cb91256e65338d47d81ec0771328f46fd1 SHA256: 6f7f324944495b68436c53ec63a53bb87fa6ac62d7861dc721fe8df190aef3bc SHA512: 70b84efa17c69c5ba165671aa6115f86b95d9ea23caa4800aa95d5fa07cf3d1dbc07c2ecaf8e210d4f53122db97d406e9a5a2eb092d0feefc5937ec4f369fdfb Homepage: https://cran.r-project.org/package=cimir Description: CRAN Package 'cimir' (Interface to the CIMIS Web API) Connect to the California Irrigation Management Information System (CIMIS) Web API. See the CIMIS main page and web API documentation for more information. Package: r-cran-cimple Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-jmbayes2, r-cran-lme4, r-cran-mice, r-cran-nleqslv, r-cran-nlme, r-cran-statmod, r-cran-survival Filename: pool/dists/noble/main/r-cran-cimple_0.1.0-1.ca2404.1_all.deb Size: 243768 MD5sum: 8cfceffe526cdc8cb06ee4e9e945ae17 SHA1: 4908d477e325e6dd59915764ea94980a9e93efc8 SHA256: e3ffcaad7574a817b50e1b888ef39b4191a6c31f2d096879547d866ddc32d53e SHA512: 162074fb686fed22a4abf9a42a734680c31aeec76affc3d9944c2bdd171bb7cb9a76414e75c2c2ce770a097f9c1b6f8757575de84a7c49f16493cac1af55bace Homepage: https://cran.r-project.org/package=CIMPLE Description: CRAN Package 'CIMPLE' (Analysis of Longitudinal Electronic Health Record (EHR) Datawith Possibly Informative Observational Time) Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) . Package: r-cran-cimpleg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-assertthat, r-cran-broom, r-cran-butcher, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-gtools, r-cran-magrittr, r-cran-matrixstats, r-cran-nnls, r-cran-oner, r-cran-parsnip, r-cran-patchwork, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-scales, r-cran-tibble, r-cran-tictoc, r-cran-tidyr, r-cran-tidyselect, r-cran-tsutils, r-cran-tune, r-cran-vroom, r-cran-workflows, r-cran-yardstick Suggests: r-bioc-biobase, r-bioc-biomart, r-cran-c50, r-cran-circlize, r-bioc-epidish, r-cran-furrr, r-cran-future, r-cran-future.apply, r-bioc-geoquery, r-cran-ggbeeswarm, r-cran-ggsignif, r-cran-glmnet, r-cran-knitr, r-bioc-minfi, r-cran-mltools, r-cran-nmf, r-cran-nnet, r-cran-plyr, r-cran-ranger, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rfast, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-cimpleg_1.0.1-1.ca2404.1_all.deb Size: 4615874 MD5sum: cd3ef3fbf48cf85b7f8ccecf1ef0eb9e SHA1: 6207329c4ae35068c51429fb9e2fc9aae823b7f4 SHA256: 545a512d1de4de527b64fa46d30cc906243ccd496e72c7bda34d614ee630a44b SHA512: 5bae45e7b8b999ae2cde4b2b3fd5b3dc8150f6cd13a80a8dff25a203323b96ff0d94cb0b688a550199f65664531c99b8bea14ba077021f68a8072981db611a35 Homepage: https://cran.r-project.org/package=CimpleG Description: CRAN Package 'CimpleG' (A Method to Identify Single CpG Sites for Classification andDeconvolution) DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. 'CimpleG' is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. 'CimpleG' is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG' based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, 'CimpleG' provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) . Package: r-cran-cimtx Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-bart, r-cran-twang, r-cran-arm, r-cran-dplyr, r-cran-matching, r-cran-magrittr, r-cran-weightit, r-cran-tmle, r-cran-tidyr, r-cran-ggplot2, r-cran-cowplot, r-cran-mgcv, r-cran-metr, r-cran-stringr, r-cran-superlearner, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-cimtx_1.2.0-1.ca2404.1_all.deb Size: 265338 MD5sum: 62c0cda00f0361fd079c0abfcdd66b55 SHA1: 3aad84311ea1b6c6d4113a53c5078beddb09abd0 SHA256: 9ff3973c79df97bdf2df327807e2437b3ebedca8dee63ba5d7c61b70f7dca98f SHA512: 937982548f5c139f4adff320e6b11824cc8078145e11613b4f09786922a7d775a673371779dc5858edc92bc8a300864402b4f4df240018c30d8fe52641043982 Homepage: https://cran.r-project.org/package=CIMTx Description: CRAN Package 'CIMTx' (Causal Inference for Multiple Treatments with a Binary Outcome) Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. . Package: r-cran-cinar Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biocmanager, r-bioc-deseq2, r-cran-dplyr, r-bioc-edger, r-bioc-fgsea, r-bioc-genomicranges, r-cran-ggplot2, r-cran-ggrepel, r-bioc-limma, r-cran-pheatmap, r-bioc-preprocesscore, r-cran-rcolorbrewer, r-bioc-sva, r-cran-writexl Suggests: r-bioc-chipseeker, r-cran-knitr, r-cran-matrix, r-cran-seurat, r-cran-rmarkdown, r-cran-markdown, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene Filename: pool/dists/noble/main/r-cran-cinar_0.2.6-1.ca2404.1_all.deb Size: 3202112 MD5sum: bc660c19a7116a0327daac4f4aa9bfa0 SHA1: e6366d4002c53d9c1bc9e8d9caff560ef7fc2789 SHA256: 49d13869af70ed21b8ef88c51d4cedec2385ba0c39cd7fb1077b31985f1f181d SHA512: 320cf31a3e0622bf691a0ea4a7232dad17873dbfcdca2d0ccfacf2d57f7ece7a57ab4f015905bceea34b889adb29b557bdb359a5967250c4830c054d4c917b6a Homepage: https://cran.r-project.org/package=cinaR Description: CRAN Package 'cinaR' (A Computational Pipeline for Bulk 'ATAC-Seq' Profiles) Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data analyses. This package combines different packages to have an ultimate package for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 'Karakaslar et al.' (2021) . Package: r-cran-cinargenesets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cinargenesets_0.1.1-1.ca2404.1_all.deb Size: 262784 MD5sum: 305482262c8a5bc611678775a8d474ab SHA1: 2b569b19ac12fdb75c09cbe4b7f7205763e0e92d SHA256: fb4f7f3b30e4a938054180dd5824ac5dc7f8d11fa52c0d212974247f64f7521c SHA512: be608df960cd902cfee080af38b5a1f660c64871c1e115731402cfb976ef84edc391f780d25edf96d6cbdafbdf6d44a94d62434828c264115448e6577c288955 Homepage: https://cran.r-project.org/package=cinaRgenesets Description: CRAN Package 'cinaRgenesets' (Ready-to-Use Curated Gene Sets for 'cinaR') Immune related gene sets provided along with the 'cinaR' package. 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Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package provides a consistent interface for discovering and downloading datasets from the Cincinnati Open Data Portal using human-readable dataset keys or official Socrata dataset identifiers. 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'CINmetrics' (Chromosomal INstability metrics) provides functions to calculate various chromosomal instability metrics on masked Copy Number Variation(CNV) data at individual sample level. The chromosomal instability metrics have been implemented as described in the following studies: Baumbusch LO et al. 2013 , Davidson JM et al. 2014 , Chin SF et al. 2007 . 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"CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) . 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For dev version and change history, see GitHub assaforon/cir. Package: r-cran-circacompare Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-withr Suggests: r-cran-testthat, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circacompare_0.2.0-1.ca2404.1_all.deb Size: 145668 MD5sum: ba24cdceffa41239610c026aa318cac8 SHA1: 4a9a8714a4acde9c9068cf1fdbe074a691c0921b SHA256: 9d09788da9aa8fce1881431bc5270ef882f088cf8b15e6ae0f3d8b2d5458586b SHA512: ab80a4c10869aafe783c91fe26b3918ebbeb5e75f2d8449a54d10c39c6db33471eed09f7347e69ce1ac903370c7076e967814ad660c3d588cecfc90f3fd96907 Homepage: https://cran.r-project.org/package=circacompare Description: CRAN Package 'circacompare' (Analyses of Circadian Data) Uses non-linear regression to statistically compare two circadian rhythms. 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The algorithm has been validated against event markers using data from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, and its methodological details are described in Chen and Sun (2024) . The package provides functions to estimate sleep metrics (e.g., sleep and wake onset times) and circadian rhythm metrics (e.g., mesor, phasor, interdaily stability, intradaily variability), as well as tools for screening actigraphy quality, fitting cosinor models, and performing parametric change point detection. The workflow can also be used to segment long actigraphy sequences into regularized structures for physical activity research. Package: r-cran-circda Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circular, r-cran-directional, r-cran-glmnet, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-circda_1.1-1.ca2404.1_all.deb Size: 180520 MD5sum: dc5b1612e7d74500363b4f03aca207d0 SHA1: 75baa32e65b64626374d6ae225ae8d2bcf1c9e87 SHA256: 6ae587ec33947896baf74fc6b91b802ca90725c97c1de16ed6044c7f63b5cd75 SHA512: 2f7b9ec34cfe27cf6e84830bb28f74d6200b3dda443de33db53d96358513df178a223ddf81fae773305f32322009d1d13992a874a687da00feabfb0d9577847b Homepage: https://cran.r-project.org/package=circda Description: CRAN Package 'circda' (Circular Data Analysis) Functions to perform maximum likelihood estimation, model-based clustering, discriminant and regression analysis with a circular response variable. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include: Tsagris M. and Alzeley O. (2025). "Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling". Australian & New Zealand Journal of Statistics, 67(1): 77--103. . Tsagris M., Papastamoulis P. and Kato S. (2025). "Directional data analysis: spherical Cauchy or Poisson kernel-based distribution". Statistics and Computing, 35:51 . Alzeley O. and Tsagris (2026). "On the generalized circular projected Cauchy distribution". Mathematics, 14(11): 1934 . 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Package: r-cran-circlizeplus Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5766 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize Suggests: r-cran-png, r-cran-ape, r-cran-dendextend, r-bioc-complexheatmap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circlizeplus_0.9.1-1.ca2404.1_all.deb Size: 3858958 MD5sum: ce777d191b0383adb0603bff8cf3d1d4 SHA1: d2b394f755793415de19df7a548cb908c23ae589 SHA256: b394357b099bd9cd7f39998f73f9e926ba416da5dfc00848039930f50cab9490 SHA512: c12257a8b12eb085985845a017fe1fc8d907e645911605f7b0538f402503c736e33b5980625987d17bf92ab8dcfadb3c03627b4ef98988ac8445a96573d36a86 Homepage: https://cran.r-project.org/package=circlizePlus Description: CRAN Package 'circlizePlus' (Using 'ggplot2' Feature to Write Readable R Code for CircularVisualization) A wrapper for 'circlize'. All components are based on classes and objects. 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Package: r-cran-circmle Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-energy Filename: pool/dists/noble/main/r-cran-circmle_0.3.0-1.ca2404.1_all.deb Size: 126894 MD5sum: 8d11ed48483dff610dcdd5cc354e2b4c SHA1: 004e0be944b293756e8939330622641fe16eff46 SHA256: 61d0ebd41411379b65074bcc9d71ba05678abc0b8c2b95f089102bff65208bc9 SHA512: 44ede1f20f55bebe48cd01b71ecd97f9b898c869a93b5b60cc003edb1497459cbeee4fbac28b912c5fc2ae462ab353e3dfdd610beb40f51608eb1555d436f9d4 Homepage: https://cran.r-project.org/package=CircMLE Description: CRAN Package 'CircMLE' (Maximum Likelihood Analysis of Circular Data) A series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) . The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). The ability to perform variants of the Hermans-Rasson test and Pycke test is also included as described in Landler et al. (2019) . The latest version also includes a new method to calculate circular-circular and circular-linear distance correlations. Package: r-cran-circnntsr Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-circnntsr_2.3-1.ca2404.1_all.deb Size: 253388 MD5sum: f51096f8695e05f570e86471b9ebae54 SHA1: 02185bbf07a70ffce092a52498d2223001c7840a SHA256: 9ae7ec74950c92e9d79bbd3a04b18e3f5bc514eab6868167fd7ae6a0d32b7f08 SHA512: c46875280d367719d1f4995ca0e6017e4eff005594c2835451436f6502364b3a07ec1cfbd74462a12185efd4ef09fef0847710af6386e9b5a1b544cd5075b597 Homepage: https://cran.r-project.org/package=CircNNTSR Description: CRAN Package 'CircNNTSR' (Statistical Analysis of Circular Data using NonnegativeTrigonometric Sums (NNTS) Models) Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more. Package: r-cran-circnntsraxial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psychtools, r-cran-circnntsr Filename: pool/dists/noble/main/r-cran-circnntsraxial_0.1.0-1.ca2404.1_all.deb Size: 77346 MD5sum: 93ac6e5e4f286c666e69f0a8269a66c0 SHA1: b17cf5005673057e5f1781c4906eed103f715c0e SHA256: 620f824f64411204873c911f3f5da60b7c6a8ba94638983d56bb3e6b1aac7d66 SHA512: 9cc02cd8e4f2146a253c862a512d851df7da891808d7312924ac7c9f341631048e63b8334579710fcc59abdd9dc064a1caf71ec6b076305be6d01cd126403329 Homepage: https://cran.r-project.org/package=CircNNTSRaxial Description: CRAN Package 'CircNNTSRaxial' (Axial Data using NNTS Models) Statistical analysis of axial using distributions Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), ''Multimodal distributions for circular axial data", . 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Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), "Multimodal Symmetric Circular Distributions Based on Nonnegative Trigonometric Sums and a Likelihood Ratio Test for Reflective Symmetry", . 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Package: r-cran-circuitscaper Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-juliacall Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circuitscaper_0.1.1-1.ca2404.1_all.deb Size: 257242 MD5sum: db003970503c580e83e50e500fc5bd3c SHA1: 64e170e642d2a26bc49a03e9c683e38bc5af97de SHA256: 1ac25e8bdd305e11038b1139744b338fcf9d51434708cad5b73cf7b2ac4b2cfa SHA512: f00b1be0c4be38be980c8f8ca383bd00919aadbf8485f5a8b725132e1514488dd955ca6ca14ab95028cdeba80042378d12c3aff0088d62f955c892a71e52df37 Homepage: https://cran.r-project.org/package=circuitscaper Description: CRAN Package 'circuitscaper' ('Circuitscape' and 'Omniscape' Connectivity Analysis via 'Julia') Provides an R-native interface to the 'Circuitscape.jl' and 'Omniscape.jl' 'Julia' packages for landscape connectivity modeling using circuit theory. 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The primary 2-dimensional version creates concentric circular boxplots for specified groups, scaling the width of each boxplot to adjust for human perception. The 3-dimensional version maps these plots onto a torus which is suitable for periodic circular data such as wind direction over the course of a year. An example dataset of this type is provided for reference. For examples of circular boxplots and additional implementation details, see Berlinski et al. (2026) . Package: r-cran-circularev Architecture: all Version: 0.1.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-mgcv, r-cran-circular, r-cran-npcirc, r-cran-ggplot2 Suggests: r-cran-plotly, r-cran-formatr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circularev_0.1.2-1.ca2404.2_all.deb Size: 495768 MD5sum: 063c14d958051266e3af43ac1f518a62 SHA1: c78f62cfab6f99fb98d19bcdf50cd8935c7d4c38 SHA256: da2ff39f699f91115069ce400e1dfb0ae43d3a1cd545f7d22daf4598b4cb1c51 SHA512: 15963d2e173a921d0a68a9f40304e3db40ec44a66801121ad8611414e6eca340bd673e6d45cb9115017977c6f7db23495d35d903215e42624ff745624770a0e0 Homepage: https://cran.r-project.org/package=circularEV Description: CRAN Package 'circularEV' (Extreme Value Analysis for Circular Data) General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667 . 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The main methodology follows Rivest et al. (2016) "A General Angular Regression Model for the Analysis of Data on Animal Movement in Ecology" . 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Package: r-cran-cis.dglm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dglm, r-cran-rcolorbrewer, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-cis.dglm_0.1.0-1.ca2404.1_all.deb Size: 59650 MD5sum: 1de1f51504ee059416e6b33d1e924db8 SHA1: d01d9612fe8439396d00439998ad77e6006856d7 SHA256: 5b63a81028b69a961b3a96e5411e4995d862ae3e44674ab1364b41dc5b7006f2 SHA512: befce624747e5ffcd8c6fa04f9fff1c13a86dd2523f99d63a679a14db77608b5a9f19e333d6497a3d613e10162c50ca350bf327b3ac2d3731ffea0c364d41408 Homepage: https://cran.r-project.org/package=CIS.DGLM Description: CRAN Package 'CIS.DGLM' (Covariates, Interaction, and Selection for DGLM) An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set. Package: r-cran-cisp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-gdverse, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spedm Filename: pool/dists/noble/main/r-cran-cisp_0.2.0-1.ca2404.1_all.deb Size: 107864 MD5sum: bdf198a3d9d129c012b8efc64377be9c SHA1: 5f8da15204bd95836bbb299ce60a6b2d2c2f767f SHA256: d9188e7b1790bf2af5edbeca0584dfc41aef538dfe9b38084cd866f07e360121 SHA512: e4ffe790920d2be1b05665de08134bc7c98f63de920d5e254157e5cad3504e58769cdf0275eb21d807d6a3772f1a07a4afb25baf799cbe2ff3fa403a8f086ee2 Homepage: https://cran.r-project.org/package=cisp Description: CRAN Package 'cisp' (A Correlation Indicator Based on Spatial Patterns) Utilizes spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns. Package: r-cran-citan Architecture: all Version: 2025.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-agop, r-cran-rsqlite, r-cran-stringi, r-cran-dbi Filename: pool/dists/noble/main/r-cran-citan_2025.7.1-1.ca2404.1_all.deb Size: 944644 MD5sum: fafed4b0f489f790a78d60199328457a SHA1: 8db3f72a48d76a07a633018602b785ff65f61b44 SHA256: 6d84bbd745714e1ac379f91d888dd990de6fb3d961c710282e6ca88957650f3d SHA512: a1f0d266ecf371db98f49f5ab09dbb936b2e41c1b477455a4a30c81a5a985d1bd13410e7f1712bf071b0dd0ea2f0c7f63d1799fae8c33c254d231450df3fef92 Homepage: https://cran.r-project.org/package=CITAN Description: CRAN Package 'CITAN' (CITation ANalysis Toolpack) Supports quantitative research in scientometrics and bibliometrics. Provides various tools for preprocessing bibliographic data retrieved, e.g., from Elsevier's Scopus, computing bibliometric impact of individuals, or modelling phenomena encountered in the social sciences. This package is deprecated; see 'agop' instead. Package: r-cran-citation Architecture: all Version: 0.12.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desc, r-cran-jsonlite, r-cran-withr, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citation_0.12.2-1.ca2404.1_all.deb Size: 58650 MD5sum: d66b539e3362a4c7fc9d0f820dbc698f SHA1: 17a29994a44794890fda263a014bc415bca0054c SHA256: b86c289509f122ff84fb2dbed982b4e03ce5ec8008483f7ede3b60e7eed49396 SHA512: 1d764b8dea0b3ced1b7571d9d2a6c1c620ad3b31fb77ebb16fac01e9f953c1352d94a0dfdf6c045663e682da77c6ab6efa5161eb2c241d9d9b2023ab7c39e7b5 Homepage: https://cran.r-project.org/package=citation Description: CRAN Package 'citation' (Software Citation Tools) A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (), extending the functionality already provided by the citation() function in the 'utils' package. Package: r-cran-citationchaser Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-maditr, r-cran-mess, r-cran-networkd3, r-cran-scales, r-cran-tibble, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citationchaser_0.0.4-1.ca2404.1_all.deb Size: 242822 MD5sum: bd113d8debfd5c7e45701493263e4d7d SHA1: 5c08a15b5b149735d460c6397ea948332d940b6a SHA256: 485a670bc50c6d3b979f4667efdb23159cd3fde36db19349c86a3570f9212a37 SHA512: ca861d838e7af4a0fe1ce44e2e60807537d609069fb93f45f78369545e0b862c3045d40c3a331a1156da709bd6cea721b273dcc01f44494bccf59e87fc3c4d0b Homepage: https://cran.r-project.org/package=citationchaser Description: CRAN Package 'citationchaser' (Perform Forward and Backwards Chasing in Evidence Syntheses) In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study. In systematic reviews, this supplementary search technique is known as 'citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles. Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records that should be included in a review. This package contains functions to automate this process by making use of the Lens.org API. An input article list can be used to return a list of all referenced records, and/or all citing records in the Lens.org database (consisting of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE; ). Package: r-cran-citcdf Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-pbapply, r-cran-patchwork, r-cran-rcppnumerical, r-cran-survey, r-cran-viridislite Suggests: r-bioc-biocset, r-cran-bnlearn, r-cran-gsa, r-cran-knitr, r-cran-quarto, r-cran-reactable, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-seuratobject, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citcdf_1.1.0-1.ca2404.1_all.deb Size: 633210 MD5sum: c87ef55ddaaa7341c0808274ea9b9752 SHA1: 3a68825a8fed9f1c36b71fc60739ff63024961b8 SHA256: da3385a7abdc860c8841fbfafc6265683c237ed847036ad1a580b39c2a8895ba SHA512: 34472266745a097cbdc93c735205f49c36393cff87bfe8a1c3819c6c378ee0786d514b0e69b3d1d11ad913dd538a077498bf32c13df7fb947c7cf19a45b5bc88 Homepage: https://cran.r-project.org/package=citcdf Description: CRAN Package 'citcdf' (Conditional Independence Testing with Cumulative DistributionFunctions) Complex hypothesis testing through conditional cumulative distribution function estimation. Method is detailed in: Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2021). "Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis", bioRxiv . Package: r-cran-citecorp Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-data.table, r-cran-fauxpas, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-citecorp_0.3.0-1.ca2404.1_all.deb Size: 38802 MD5sum: 2f28478caecbb6fcb721a45355431551 SHA1: 97beef69450845407ae13dc35b4b52c05f52f538 SHA256: a26736b7fcdc0281e384f031d1683b223f12ea3c12715ae33575bd5fe345b858 SHA512: bfc657f17b711bfeb8e10275f24ea0a5c4839c68aa4a9ac5ab3372b612a7d6f2c74407ea5da9370a543cf7d59279501489c3cfb93c5db203049e28fa811cc93b Homepage: https://cran.r-project.org/package=citecorp Description: CRAN Package 'citecorp' (Client for the Open Citations Corpus) Client for the Open Citations Corpus (). Includes a set of functions for getting one identifier type from another, as well as getting references and citations for a given identifier. 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Search results can be analyzed using plots and tables, and imported or exported in 'RIS' and 'CSV' formats. An interactive 'shiny' application is included for exploratory use. Package: r-cran-citesperu Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citesperu_0.1.0-1.ca2404.1_all.deb Size: 457820 MD5sum: 95d3ab19b665220e86ba4e0944e24ee1 SHA1: d320f094b20e6aa8ae1d5503f1b26754feaf2d20 SHA256: 25a74a59719b779f78c05346ae0a0a91508680b8c04c69e525abbef728e70b76 SHA512: 1f482bde85e5450d30c06194c13f01f43bc6fecabab94675a16804098df2902fcbfc31064d35c6b1714a7d29a13053a87e60af03513cbffdbd3392dd556f96f4 Homepage: https://cran.r-project.org/package=citesperu Description: CRAN Package 'citesperu' (Peruvian Species Checklists from CITES Publications) Provides tools and official datasets for working with Peruvian fauna and flora checklists published by the Ministry of the Environment (MINAM) under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Includes official datasets for fauna (2018, 2019, 2023) and flora (2018), with tools for reproducible data preparation, checklist queries, scientific name matching and summaries by appendix and taxonomic group. Package: r-cran-citestr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-processx, r-cran-rlang Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-reticulate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citestr_0.1.1-1.ca2404.1_all.deb Size: 100956 MD5sum: 4b4a279e7ff26ed9401213015db81b56 SHA1: 3ddc37160e5c17d2838f8ef2a3bc6f2d38df1fe9 SHA256: 09ea26f9a658deff5fa6db26c16b39977fe1206ed29d5c61218d881f2c353835 SHA512: 33e8091176fcb3ab42629fcc46376c990dc9685b71aad320772ec888de38d0b30c8ef095bd75aa404ba61dc6827f51567994b7017fed9536c45d865eae00f613 Homepage: https://cran.r-project.org/package=citestR Description: CRAN Package 'citestR' (Conditional Independence of Missingness Test) Tests whether missingness in explanatory variables is conditionally independent of the outcome, given observed data. Uses multiply-imputed datasets and cross-validated classifiers to produce a test statistic and p-value, with a sensitivity parameter (kappa) for calibrating interpretation. Wraps the 'citest' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. Package: r-cran-cities Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-ggthemes Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cities_0.1.3-1.ca2404.1_all.deb Size: 519018 MD5sum: 2d4ac5aa32092049617a746d8211bfc9 SHA1: fc0ca1ed830de970cef116a46cd8b0b2f18f5d35 SHA256: 05b4e6729e41209ceac30b95f5f2e6ae1f0f714e089fcc8aa4fe1b7d35b7c428 SHA512: 9daa8a4a10ec6a80ebae33d963a98aea04fe465fae83a84031b06244ed4523764a81aa3ea63148e5981d1d076126d446dfefdb99bd043cab4bc2c64429905576 Homepage: https://cran.r-project.org/package=cities Description: CRAN Package 'cities' (Clinical Trials with Intercurrent Events Simulator) Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner. Package: r-cran-citmic Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1868 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatch, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citmic_0.1.3-1.ca2404.1_all.deb Size: 1852418 MD5sum: 678305cf0aaf1127cdb899d222d62efc SHA1: 5bdcd3a858ef12de222d8f2fe54ba02bed3502b6 SHA256: 07e8c29060070bff4a3e5ce93c50ac1abf24141302cb027a93e26c8e6a6048f1 SHA512: b20e3bb3abc35d50a350d88d6e379c403b932153a04eab3629872d0506c4a6eda9b9ab6d80d738b4d6c15e4fc4e1095a5ba2797e308c02078f8a5d3707f74fd9 Homepage: https://cran.r-project.org/package=CITMIC Description: CRAN Package 'CITMIC' (Estimation of Cell Infiltration Based on Cell Crosstalk) A systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. 'CITMIC' first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells. Package: r-cran-citmre Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-plotly, r-cran-rvest, r-cran-xml2, r-cran-xts Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citmre_0.1.0-1.ca2404.1_all.deb Size: 241902 MD5sum: 87d6ab63b98c220f201d713be585816e SHA1: 5093f2c3e259666d62eb040b94a78dcb59f5e912 SHA256: 0a436f6059c8d763d148426157e7d56cc14872252d7e60e43c62e854ddb959b8 SHA512: e8e8f89d4bf4ff56ecb0b45c200351043ffa643c43be1a2f008ebfa4f8f21c68a7fa69e58321da1ae63e1cc2acb65e619ffafbe930d01b0f32268689881ea5b5 Homepage: https://cran.r-project.org/package=citmre Description: CRAN Package 'citmre' (Colombian Index Tool Market Rate Exchange) Downloads the Representative Market Rate Exchange (RMRE) from the source. Allows setting the data series in time frequencies, splitting the time series through start and end functions, transforming the data set in log returns or levels, and making a Dynamic graph. Package: r-cran-cito Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3654 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coro, r-cran-checkmate, r-cran-torch, r-cran-gridextra, r-cran-parabar, r-cran-abind, r-cran-progress, r-cran-cli, r-cran-torchvision, r-cran-tibble, r-cran-lme4 Suggests: r-cran-spelling, r-cran-rmarkdown, r-cran-testthat, r-cran-plotly, r-cran-ggraph, r-cran-igraph, r-cran-ggplot2, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cito_1.1-1.ca2404.1_all.deb Size: 2580918 MD5sum: 2e4c91d09c6c351edad2cf783a60c533 SHA1: 68a9265daee341186f423df542bd594dcd36215b SHA256: 94a0a4c647733de374f75247dcad301c16010c066f4cb74b39e33eaca57ca7ad SHA512: d5d0dfaa186e8c37dcd1d13cb746d59dbc53468a3121a524b44165495bc3b3b7910118231745013c70c0667be934aaf4c5afaa394483e129aaa7ce79c68958f3 Homepage: https://cran.r-project.org/package=cito Description: CRAN Package 'cito' (Building and Training Neural Networks) The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package. Package: r-cran-citools Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arm, r-cran-boot, r-cran-dplyr, r-cran-lme4, r-cran-mass, r-cran-survival Suggests: r-cran-here, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spreda Filename: pool/dists/noble/main/r-cran-citools_0.6.1-1.ca2404.1_all.deb Size: 735938 MD5sum: 6d129606c3c91fdd153294c0463165b7 SHA1: 459f172e6ac13632242323f42d9a8af177614901 SHA256: a6be368162b6f19e6bb0f6ae2e3d4fe2004629648215dc2449b2b06c621deca3 SHA512: 0c6147b879613bd43a14affda6bcd2cf1d680a731f63d66c015c0a0c7b736d2d172782d727c273cd713855bb4599b5cf5a9f305d79ef466c8f5ff51bda5db28b Homepage: https://cran.r-project.org/package=ciTools Description: CRAN Package 'ciTools' (Confidence or Prediction Intervals, Quantiles, and Probabilitiesfor Statistical Models) Functions to append confidence intervals, prediction intervals, and other quantities of interest to data frames. All appended quantities are for the response variable, after conditioning on the model and covariates. This package has a data frame first syntax that allows for easy piping. Currently supported models include (log-) linear, (log-) linear mixed, generalized linear models, generalized linear mixed models, and accelerated failure time models. Package: r-cran-citrus Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggally, r-cran-clustmixtype, r-cran-treeclust, r-cran-rpart, r-cran-tibble, r-cran-rpart.plot, r-cran-stringr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-citrus_1.0.2-1.ca2404.1_all.deb Size: 183916 MD5sum: 604e2502a1bbabeec5cade7927f37ed0 SHA1: 4c30db2ff3d8fb83df5b4c698bbca166307cad96 SHA256: 6cb2dd800d7001c861a1bdd5929e7c20001d145ba4e4edc2325bf87a370d7159 SHA512: c85cb07c0a1f437e0bfe1405c32070b20b3b20f2b66cf9700f8d7584f76fecfe2bf64863773d27950186520d2004ebe7f9251b204c15a51f6c7bddb6fbd142d0 Homepage: https://cran.r-project.org/package=citrus Description: CRAN Package 'citrus' (Customer Intelligence Tool for Rapid Understandable Segmentation) A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models. Package: r-cran-citsr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-nlme, r-cran-clubsandwich, r-cran-ggplot2, r-cran-aiccmodavg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citsr_0.1.4-1.ca2404.1_all.deb Size: 41662 MD5sum: 63c7c886afb583cd1b4234d055ceddc2 SHA1: a8c3c23aab305e31224617170517f724e1f35041 SHA256: 9eaacfb1fac9563ba7178cc85029742fbe3509382af748e27d7dbf0f8fb02faa SHA512: 7a60dfdaf7af3357cbf66ac5f39efee2f5c8a5276c5b6cf6a66a572e5cc803687798a39b7cfd9af9756317351e0dadc40ebb7b41a72a374417f76b4529a43b2c Homepage: https://cran.r-project.org/package=citsr Description: CRAN Package 'citsr' (Controlled Interrupted Time Series Analysis and Visualization) Implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressive–moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) and Lopez Bernal, Cummins, and Gasparrini (2018) . Package: r-cran-citydistr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-citydistr_0.1.0-1.ca2404.1_all.deb Size: 54494 MD5sum: 727b5b078df432cc922d9bd18a3cb2e3 SHA1: 876b94c35063d61f8de155a2eb00e1329a5672ac SHA256: fa686cac9e3980d32c7f016e3bd7a23dba3aa7a6872bf88174b97a28a06120c1 SHA512: 877d33be147ed2aef41144a95f06f7ad6e563e2ae8f7092c797599f1e7bcd35da630f180674d15b0b045d6502dc623e459de72a5098c7fdc55ade346b0b4197e Homepage: https://cran.r-project.org/package=citydistR Description: CRAN Package 'citydistR' (City-Adaptive Distance Modelling Utilities) Tools for distribution-aware and city-adaptive analysis of learning-based road-network distance estimates. Provides detour-factor diagnostics, a Topological Predictability Index, upper-tail summaries, modular robust losses, hybrid objective components, adaptive validation weights, and multi-criteria model evaluation. The functions are designed as reusable building blocks rather than a fixed model specification. Package: r-cran-ciu Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-crayon, r-cran-ggplot2, r-cran-ggbeeswarm Suggests: r-cran-mass, r-cran-caret, r-cran-gbm Filename: pool/dists/noble/main/r-cran-ciu_0.8-1.ca2404.1_all.deb Size: 176808 MD5sum: 894e49626863163e4af66140eb8699da SHA1: a1b274365ffa9265e04510bafad7902209ec85e3 SHA256: fba4c29c039d56ba17000a3f8befd3079483f25aadaeae7a60f2eab245477624 SHA512: 7710ce944eea9d85ee309564027c044f11292758547c55f462938f11202ec7b0389612ea6c27489c625c2470b8d2cfa3e62ac52a55d3053494554f3aecaab0a5 Homepage: https://cran.r-project.org/package=ciu Description: CRAN Package 'ciu' (Contextual Importance and Utility) Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) . Package: r-cran-ciuupi2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-pracma, r-cran-precisesums, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ciuupi2_1.0.2-1.ca2404.1_all.deb Size: 98026 MD5sum: aee0cf1effc409feb2ee88263d320b82 SHA1: b5db06ef28af1cb5e55f3c1c7c08e202b2c25165 SHA256: 0e443a4208742db3477c5649641a2326a514f224c2679e22fa0b61663348c88a SHA512: 19509e1404f6349ca2421715138bb9954faae988f306c1d0710aad2034f9ed2e850727daf2f2ab1a7088bd907802b2968ffeb1f32618c34262fe47699235758f Homepage: https://cran.r-project.org/package=ciuupi2 Description: CRAN Package 'ciuupi2' (Kabaila and Giri (2009) Confidence Interval) Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with unknown variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a specified number. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval is proposed by Kabaila, P. and Giri, K. (2009) . Package: r-cran-ciuupi Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-statmod, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ciuupi_1.2.4-1.ca2404.1_all.deb Size: 133066 MD5sum: 7c47e5f5c62ff66acdde865d2703e04e SHA1: b7b29322abbcceb6bad48f7dd535f3f284cc1c65 SHA256: 789d8e3e787c5e7ccb3ecb33511ac1d05af0b223092026b0386bed05c862c4d3 SHA512: 46e9036993ad915d2bc00c84c0df8e27b7ff0e33144e24e4c4e0a2cfe2889b68cd665f0fc86328f4bab41efb3d3381770053aee28a832aa32d32196102b53785 Homepage: https://cran.r-project.org/package=ciuupi Description: CRAN Package 'ciuupi' (Confidence Intervals Utilizing Uncertain Prior Information) Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then the resulting interval has, to a very good approximation, the same coverage probability and expected length properties as when the error variance is known. Thirdly, some more complicated models can be approximated by the linear regression model with error variance known when certain unknown parameters are replaced by estimates. This confidence interval is described in Mainzer, R. and Kabaila, P. (2019) , and is a member of the family of confidence intervals proposed by Kabaila, P. and Giri, K. (2009) . Package: r-cran-civ Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-kcmeans Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-civ_0.1.0-1.ca2404.1_all.deb Size: 40398 MD5sum: cdfe831177f2748fe367af0670df9b4e SHA1: e47dcbc26b38ea2080f27e8355b363795a0c8131 SHA256: b277146f878e47f3f656a39810acc018be5b061c84106210b96baa1f591a6d4e SHA512: c48a68188240f1ad61d55019f13ca53ad91ace93405e87458b5b16594c8b120f993bbb1146c7a35305ad7086cd0f871eebb6deeb93f663450896628cc8917fbc Homepage: https://cran.r-project.org/package=civ Description: CRAN Package 'civ' (Categorical Instrumental Variables) Implementation of the categorical instrumental variable (CIV) estimator proposed by Wiemann (2023) . CIV allows for optimal instrumental variable estimation in settings with relatively few observations per category. To obtain valid inference in these challenging settings, CIV leverages a regularization assumption that implies existence of a latent categorical variable with fixed finite support achieving the same first stage fit as the observed instrument. Package: r-cran-civic.icarm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rpart, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-jsonlite, r-cran-digest Suggests: r-cran-dalex, r-cran-glmnet, r-cran-mgcv, r-cran-proc, r-cran-nnet, r-cran-mboost, r-cran-partykit, r-cran-testthat, r-cran-covr, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-civic.icarm_0.4.0-1.ca2404.1_all.deb Size: 197754 MD5sum: aa2d7bef0c6683017debf30b2c656ed3 SHA1: 223760ba21b52f63b0035d8528809d0ce46b63f8 SHA256: cfc6fd7709a23f544225a26aa78ae84f2d5c9f3ac486c2f5713c00d18abcedbe SHA512: 74881064fd66ba2b2630cc558a22bd6af1d72d2af88707cd55f2f51eb392c2cc185575376c5819a305cb8730a9259872bf69987cde84582008170286f822eafc Homepage: https://cran.r-project.org/package=civic.icarm Description: CRAN Package 'civic.icarm' (Interpretable Civic-Accountable and Responsible Machine Learning) A general-purpose framework for Interpretable Civic-Accountable and Responsible Machine Learning (ICARM). Works with any clean tabular data and automatically detects whether a task is binary classification, multi-class classification, or regression from the target variable type. Provides a single unified entry point civic_fit() alongside tidy interfaces for global and local model explanations, group-level fairness auditing, probability calibration, multi-model comparison, threshold analysis, and reproducible audit trails. Designed to support the DataCitizen-Pro research agenda at Ludwigsburg University of Education: developing data literacy, statistical reasoning, and democratic judgment formation in civic and political teacher education. References: Biecek (2018) , Kuhn (2008) , Awe (2025) . Package: r-cran-civis Architecture: all Version: 3.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4276 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-httr, r-cran-jsonlite, r-cran-memoise Suggests: r-cran-feather, r-cran-ggplot2, r-cran-knitr, r-cran-lifecycle, r-cran-rmarkdown, r-cran-roxygen2, r-cran-mockery, r-cran-r.utils, r-cran-rstudioapi, r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-civis_3.1.4-1.ca2404.1_all.deb Size: 3293780 MD5sum: 4083b69af7bfecec9b117ba375e17420 SHA1: 24c9001b23817b442f52afd0d3a46b56bbb18dd7 SHA256: 97eb1923d0228f94260a8e504a26e872b3c0fb1543603cdf52bcb63ff97a463a SHA512: 4090f2cd6bfc4893b8e770dc8db7600d2846b3cec7ecada389795d79e22bb1572f3fd6710b8b6d7765797f3139359af58750dfd3872ffff1051d6f4fbc1e248a Homepage: https://cran.r-project.org/package=civis Description: CRAN Package 'civis' (R Client for the 'Civis Platform API') A convenient interface for making requests directly to the 'Civis Platform API' . Full documentation available 'here' . Package: r-cran-ciw Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-xml, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ciw_0.0.2-1.ca2404.1_all.deb Size: 18696 MD5sum: 37259cde277b70c86ae8c4cda2b7a997 SHA1: 1024fddcb08e7ed6a8c2ebfe6037d8132f26473f SHA256: 968731d5bfa3f3922c2a68b5c30cde8ddff72a55da05255e8a1e213c13026095 SHA512: 36b20e54b89ff6dc300413915ac22d48af84db2080a24bd0d4617325033325492bc480f511021e68766fe0ef4a3ca44e46d3b2835277cce3ebbb47a964ad6695 Homepage: https://cran.r-project.org/package=ciw Description: CRAN Package 'ciw' (Watch the CRAN Incoming Directories) Directory reads and summaries are provided for one or more of the subdirectories of the directory, and a compact summary object is returned. The package name is a contraption of 'CRAN Incoming Watcher'. Package: r-cran-cjamp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cjamp_0.1.1-1.ca2404.1_all.deb Size: 678956 MD5sum: b58b147cfebded6b1f991f17b58e61ed SHA1: 3c934baa58771c3dbcbf8f05eedc9e88c1fb7262 SHA256: 3feeb2006c63faed7413f9fe68172a5be785918bc6bff728ccd9ac5b02c89dd1 SHA512: c0f6793b581c5dc62f97045feba9c557aaa353221788f50e39e59909c822982d09c3dfcec0b50ef470040291a5f83f6e5e8bc101249f9124adad00aca572cf20 Homepage: https://cran.r-project.org/package=CJAMP Description: CRAN Package 'CJAMP' (Copula-Based Joint Analysis of Multiple Phenotypes) We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, ; Konigorski, Yilmaz, Bull, 2014, ). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers. Package: r-cran-cjar Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-httr, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-dplyr, r-cran-jsonlite, r-cran-glue, r-cran-jose, r-cran-tibble, r-cran-lubridate, r-cran-progress, r-cran-vctrs, r-cran-stringr, r-cran-rlang, r-cran-memoise, r-cran-openssl, r-cran-httr2 Filename: pool/dists/noble/main/r-cran-cjar_0.2.1-1.ca2404.1_all.deb Size: 840164 MD5sum: f69abd734fd39dc7139bd1545c966c5a SHA1: ce4bd09ddac6871467e023dcb50e8e94be40161c SHA256: de71ffacafdd6b86025ae092a7ebb701b192d368111c091ca7d14dd003dfd280 SHA512: 6895196386d958bb11659031242de05bc6f809a76777ec1821e13b83fc4ce12111c4d57cd7117b56603f8e85b1bc48a1e52ab171632c19cf465398dee79110c6 Homepage: https://cran.r-project.org/package=cjar Description: CRAN Package 'cjar' (R Client for 'Customer Journey Analytics' ('CJA') API) Connect and pull data from the 'CJA' API, which powers 'CJA Workspace' . The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process. Package: r-cran-cjbart Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bart, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-randomforestsrc, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cjbart_0.3.2-1.ca2404.1_all.deb Size: 301382 MD5sum: add9918658bb17619cbf40848ad61552 SHA1: e370618986969bf483f5df1878d8d9ee3076694f SHA256: f73a7209c10ddafe03ef316a2cd00d7417cde3d1450b87a7e639a1dd029a7902 SHA512: dddcb2c02d1abd47990f1c2c2b52d7ca19e296cd3775cf45f7b6496bf7098b15c92b274c9a2b7599825ac5d11c1b50e3efd37cc798248ed24030299f098022ff Homepage: https://cran.r-project.org/package=cjbart Description: CRAN Package 'cjbart' (Heterogeneous Effects Analysis of Conjoint Experiments) A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) . This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) , to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects. Package: r-cran-cjive Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve, r-cran-ggplot2, r-cran-reshape2, r-cran-fields, r-cran-gplots, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cjive_0.1.0-1.ca2404.1_all.deb Size: 108022 MD5sum: 3e541c7c37cc213d694e2664b2fd9af6 SHA1: 4ba6513d778a291beb7d06b7b85c54aeb489fa91 SHA256: 7643f53eb2c740235e1e36b083741d8100627160a7f35f8069fab7f119c171bf SHA512: a04fa56025a09b5cc4b7032b65392ff1205d9c2aca5db39acf5893034913f8b692dbb90157ae1992a9f5e7f5f2a6bfde708de79adea2e188eb26eb90ddc7a0d5 Homepage: https://cran.r-project.org/package=CJIVE Description: CRAN Package 'CJIVE' (Canonical Joint and Individual Variation Explained (CJIVE)) Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations. Please cite the following article when utilizing this package: Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) . Package: r-cran-cjoint Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2, r-cran-survey, r-cran-matrix, r-cran-dt, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cjoint_2.1.3-1.ca2404.1_all.deb Size: 418736 MD5sum: 4d28a17dfa30e76a9b4b599731a85a34 SHA1: 18ffc24e9065e0d37f0d49c00e005663897aa252 SHA256: 35e260afaae86cedd19ff42ec0438aa54f9edd5197fce4db682b6999c512743f SHA512: f502aebd1d64306af955bab8d812e50c4f89a2ad010dcc6b6823553d3e4b83ae5a9e551ffb4d0e0022b98d2470311e2ba388b989061f1490d16e18f5076f1447 Homepage: https://cran.r-project.org/package=cjoint Description: CRAN Package 'cjoint' (AMCE Estimator for Conjoint Experiments) An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30. Package: r-cran-ckanr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-crul, r-cran-jsonlite, r-cran-dplyr, r-cran-dbplyr, r-cran-magrittr Suggests: r-cran-sf, r-cran-readxl, r-cran-testthat, r-cran-xml2, r-cran-lazyeval Filename: pool/dists/noble/main/r-cran-ckanr_0.7.0-1.ca2404.1_all.deb Size: 435142 MD5sum: da57e425eb1c5fa693ac7209bef8afca SHA1: 0c011da3637f45480f667b0e4b4e42c1761a5661 SHA256: 7152dc282720f931f85cf9ef7bf3e8237083114961589d1ebffaf2f54fb595ad SHA512: 1afeab0b5be2af46448252bdfa23fd8b446e222e8f429b8c0ebb42f064ffd97e81d408c78e799f73baf66eebd787d2ed140763ab0f2f17c2dd4213ad9760b01a Homepage: https://cran.r-project.org/package=ckanr Description: CRAN Package 'ckanr' (Client for the Comprehensive Knowledge Archive Network ('CKAN')API) Client for 'CKAN' API (). Includes interface to 'CKAN' 'APIs' for search, list, show for packages, organizations, and resources. In addition, provides an interface to the 'datastore' API. Package: r-cran-ckat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform Filename: pool/dists/noble/main/r-cran-ckat_0.1.0-1.ca2404.1_all.deb Size: 35934 MD5sum: 1a09d5b9cffe477b1ac7ba4428df5597 SHA1: 9a660205ed891f28c56e01aeb959923c4cd67080 SHA256: 788f5e2458562528735e78ee14e5d745163661cfbe831399b9d2440fbac423a4 SHA512: 481074cd031dcd546b7f55865661c240cc7555c7f699e162d18dc3673d8022f2223ba6e8b144a4dde0c0d7ed393acf33916820aac02e00d40da725993191145f Homepage: https://cran.r-project.org/package=CKAT Description: CRAN Package 'CKAT' (Composite Kernel Association Test for Pharmacogenetics Studies) Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials. Package: r-cran-cknnrld Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-directional, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cknnrld_0.2.2-1.ca2404.1_all.deb Size: 88888 MD5sum: 0ee61f5761fe1ffa8cbd7c46cf02e6f6 SHA1: ef65373735c04b0991085c0b3dda6536945b15fd SHA256: 05ea9252fdec01e6439abd0170d79cc5084a80e9ecc35f1ebf7371529562a5df SHA512: 5870137c1cd64cd2091690423265759b8bc8a2950fdc9d2e9fd6322ad6ce177320fe02a120422d26d45656650ca6d1c1ef4c68bc85d911f39cffe4934810b370 Homepage: https://cran.r-project.org/package=CKNNRLD Description: CRAN Package 'CKNNRLD' (Clustering-Based K-Nearest Neighbor Regression for LongitudinalData) Implements the 'CKNNRLD' algorithm (Clustering-Based K-Nearest Neighbor Regression for Longitudinal Data) for improving K-Nearest Neighbor ('KNN') regression on longitudinal data through cluster-based partitioning and localized prediction. Offers enhanced computational efficiency and accuracy for high-volume longitudinal datasets. The acronym 'KNN' stands for K-Nearest Neighbor. References: Loeloe MS, Tabatabaei SM, Sefidkar R, Mehrparvar AH, Jambarsang S (2025). "Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach." BMC Bioinformatics, 26, 232. . Package: r-cran-cla Architecture: all Version: 0.96-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-fgarch, r-cran-frapo, r-cran-matrix, r-cran-sfsmisc Filename: pool/dists/noble/main/r-cran-cla_0.96-3-1.ca2404.1_all.deb Size: 1197210 MD5sum: f3a3d8df584cbab2b7496399cb6e31b6 SHA1: b0618f2bee16207a16fae0e57cd500c7e9dcb32f SHA256: 0eb6e171f17310fa449b36c60f9eb10429f23f7f7e92313e2f57297d0f9a98aa SHA512: d8b597ac11e6a2dbbe4639608b612f687f5fd9d4f724c1c99bf8813f2378220ab58c7aee52acd1363ce6bbf99f63530f40fcda0feaeecfc175c01203e2858251 Homepage: https://cran.r-project.org/package=CLA Description: CRAN Package 'CLA' (Critical Line Algorithm in Pure R) Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) . Care has been taken for correctness in light of previous buggy implementations. Package: r-cran-claddis Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-strap, r-cran-clipr, r-cran-geoscale, r-cran-multicool, r-cran-partitions Suggests: r-cran-rgl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-claddis_0.7.0-1.ca2404.1_all.deb Size: 1378374 MD5sum: ce019f8cc458f4999cbb33fce26eb3a2 SHA1: e8c308159c4a4b373449f97fb37518f560fd633f SHA256: ef839191d4de9d239a34a800410fcdd3e8de456156afcdb5bb98baa6a47f3fbc SHA512: 0373a81378bfa9f0611f78a991d37aca80737154c00b9ba7abc96e3b05fb9e40c360f2827cea51aa588cdc49a9387536c6e6988dee053ff2461131db02924db3 Homepage: https://cran.r-project.org/package=Claddis Description: CRAN Package 'Claddis' (Measuring Morphological Diversity and Evolutionary Tempo) Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) ) format with read_nexus_matrix(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) ). Main functions are test_rates(), which implements AIC and likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) , Brusatte et al. (2014) , Close et al. (2015) , and Lloyd (2016) , and calculate_morphological_distances(), which implements multiple discrete character distance metrics from Gower (1971) , Wills (1998) , Lloyd (2016) , and Hopkins and St John (2018) . This also includes the GED correction from Lehmann et al. (2019) . Multiple functions implement morphospace plots: plot_chronophylomorphospace() implements Sakamoto and Ruta (2012) , plot_morphospace() implements Wills et al. (1994) , plot_changes_on_tree() implements Wang and Lloyd (2016) , and plot_morphospace_stack() implements Foote (1993) . Other functions include safe_taxonomic_reduction(), which implements Wilkinson (1995) , map_dollo_changes() implements the Dollo stochastic character mapping of Tarver et al. (2018) , and estimate_ancestral_states() implements the ancestral state options of Lloyd (2018) . calculate_tree_length() and reconstruct_ancestral_states() implements the generalised algorithms from Swofford and Maddison (1992; no doi). Package: r-cran-claimsproblems Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geometry, r-cran-pracma, r-cran-rgl Filename: pool/dists/noble/main/r-cran-claimsproblems_1.0.0-1.ca2404.1_all.deb Size: 267228 MD5sum: d9db5eb75bfd438d6b43dde6ae811e1f SHA1: 4ed8c3f75e0c9ae7571c7441cca6d0cf89f505d6 SHA256: 9c54ddf8d030c2d2f5bedb0a1735299c37832c771af0ba44b60a789949df2832 SHA512: ed66c46e6ccaee2079e2022c8faffe3954bd97abd5259fb522bb5d44a1cb592f5bd3c8b386d401eae36e93d9863254523075080477106366dd0f2126b0b7743b Homepage: https://cran.r-project.org/package=ClaimsProblems Description: CRAN Package 'ClaimsProblems' (Analysis of Conflicting Claims) The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a division among the claimants. This package computes the main rules introduced in the literature from ancient times to the present. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the constrained egalitarian, the Piniles’ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average-of-awards rules are also included. All of them can be found in the book by W. Thomson (2019), How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', except for the average-of-awards rule, introduced by Mirás Calvo et al. (2022), . In addition, graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, the schedules of awards of a rule, and some indexes. A good understanding of the similarities and differences between the rules is useful for better decision-making. Therefore, this package could be helpful to students, researchers, and managers alike. For a more detailed explanation of the package, see Mirás Calvo et al. (2023), . Package: r-cran-clam Architecture: all Version: 2.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rintcal, r-cran-rice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-clam_2.6.3-1.ca2404.1_all.deb Size: 180586 MD5sum: eb680750d54a473b25246c1ca22aa21c SHA1: c3e30beec0435dd063f895e2e5b4a2e667971617 SHA256: 6f1ffc60991d6725e19ca048cb73e0914dae7158429109e46d7385aeb51583c3 SHA512: 6e9a7b906e3e99989de7499ee40535db7812a23dca9884a68b2dd05933fc892711eb1d6070fb64ecaa4e595f9c50c413118353cd1ca244c5eb42e3cbf4903d50 Homepage: https://cran.r-project.org/package=clam Description: CRAN Package 'clam' (Classical Age-Depth Modelling of Cores from Deposits) Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010) . Package: r-cran-clampseg Architecture: all Version: 1.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stepr, r-cran-lowpassfilter Suggests: r-cran-testthat, r-cran-r.cache Filename: pool/dists/noble/main/r-cran-clampseg_1.3-0-1.ca2404.1_all.deb Size: 317684 MD5sum: d59cabc97e8069f9976a28faecfe6dcd SHA1: f544dc8c327f5caef6e33d0ca17e157f4c21d2c7 SHA256: 5aca4ecfd845e780b4694695dcd8744bf5fb0b6346f01f5370167b3f75b769e0 SHA512: 22a0b2713d75fcbc0eb5351b054eb8a7cf7c5afe082f8f4599f9205bf0267da218d5cf3a0a65c40c9acec48e72c622fa8da16a098dfbbd47f0b9d87e075044a9 Homepage: https://cran.r-project.org/package=clampSeg Description: CRAN Package 'clampSeg' (Idealisation of Patch Clamp Recordings) Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) , JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) , and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) . Further details on how to use them are given in Pein, Eltzner and Munk (2021) . Package: r-cran-clamr Architecture: all Version: 2.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clamr_2.1-3-1.ca2404.1_all.deb Size: 82402 MD5sum: 9059b6f5b3d9b8f5c2a6c9b1a7ea677e SHA1: d3829409222e587829b579271447df7caa318877 SHA256: 217a0c9ab6dc0b12feae2495a600f579b1e5a2729d28294ef928da718373916b SHA512: 34a4a14287688c6310613b13b007906aa6920d2d33d5caa5435a461727ba46fc9a2b76386754e039cda4e2e833362d3a01f10ec30bae3b9603c36798cbfd1ac8 Homepage: https://cran.r-project.org/package=ClamR Description: CRAN Package 'ClamR' (Time Series Modeling for Climate Change Proxies) Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams. Package: r-cran-clap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-fnn, r-cran-dplyr, r-cran-rlang Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clap_0.1.0-1.ca2404.1_all.deb Size: 216140 MD5sum: 7f6ba4780191acc45250209dd5385ad0 SHA1: bcaefeaeec948c5719d30a2d8079ea9437c86c17 SHA256: 10d4d81440ab7b3f01cebe489a3e52a153bb29a4dd4972812e929988385a27e4 SHA512: 6fc46580d31610854a3c13f20fba980311bdac38eb4980f06a57ef79e92792f1ed2f3eaaee5be6a481d682a61348841ae7ec42aeee83485f475960878e6f3713 Homepage: https://cran.r-project.org/package=clap Description: CRAN Package 'clap' (Detecting Class Overlapping Regions in Multidimensional Data) The issue of overlapping regions in multidimensional data arises when different classes or clusters share similar feature representations, making it challenging to delineate distinct boundaries between them accurately. This package provides methods for detecting and visualizing these overlapping regions using partitional clustering techniques based on nearest neighbor distances. Package: r-cran-clarifai Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clarifai_0.4.2-1.ca2404.1_all.deb Size: 1266860 MD5sum: 43e66191723ae5de58da9485af9c4532 SHA1: 5e61071904d04da419afbde92d74867c38c84165 SHA256: 8beac5cac7d1f2308e4e3e0b33753a82bb4d65d9ed3cf61c74ffc408d4773e1b SHA512: 317a79dfab353ec523360cf73fed1ccca8fe7fcd7c7568cbfca64fa47474e883845e8398f7abe4c5981dcbb3939750e27a95f2839b0a4d18aa72369289ef06da Homepage: https://cran.r-project.org/package=clarifai Description: CRAN Package 'clarifai' (Access to Clarifai API) Get description of images from Clarifai API. For more information, see . Clarifai uses a large deep learning cloud to come up with descriptive labels of the things in an image. It also provides how confident it is about each of the labels. 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This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) . 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality. Package: r-cran-clarketest Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-ordinal, r-cran-nnet Filename: pool/dists/noble/main/r-cran-clarketest_0.2.0-1.ca2404.1_all.deb Size: 145534 MD5sum: 7366f9fbd73eb05b6c8ea6a8351f04db SHA1: 4657124ddeddc835a2a846a6facd8c429cfdb3ed SHA256: 253a533c37d6aa12d9a989852ac81c23378f96d3d1599a4f7430b8e21f8adde7 SHA512: 95b1048a812171460a481dc70056aa0a7aaf259a4e718bdb06f850c4f1cbba74728c027b3240c0cb4d918b4b2fac9f061eb24fea8bf1c490fb5a197de53d7a27 Homepage: https://cran.r-project.org/package=clarkeTest Description: CRAN Package 'clarkeTest' (Distribution-Free Tests of Non-Nested Models) Implementation of Clarke's distribution-free test of non-nested models. Currently supported model functions are: lm(), glm() ('binomial', 'poisson', 'negative binomial' links), polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet'). For more information on the test, see Clarke (2007) . Package: r-cran-classbound Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1179 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-class, r-cran-ggplot2, r-cran-rlang, r-cran-ggnewscale, r-cran-shiny, r-cran-dt, r-cran-rcolorbrewer Suggests: r-cran-tidymodels, r-cran-workflowsets, r-cran-workflows, r-cran-parsnip, r-cran-tourr, r-cran-rpart, r-cran-roxygen2, r-cran-pptreeviz, r-cran-pptreeext, r-cran-ppforest2, r-cran-randomforest, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-palmerpenguins, r-cran-e1071, r-cran-mixsim, r-cran-mass, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-classbound_1.0.0-1.ca2404.1_all.deb Size: 872798 MD5sum: ca1cbaebfe55ea0325a51c1046865f9f SHA1: 90b585251fc4d2c61e34ecf94a9a9fa1eb0b8c72 SHA256: 217977c2554aaf7a5476092728f2c42e58c5403bfd0d11cdfce5179ec2512ab1 SHA512: 3c5c12360f58c7956cbfdc2b14f1258ab0999d438eb569c07ca7a2c48c65ae1f2003c7bb7e8f578a8c759fb88025986fff42644500dfc6343c0391a80a801324 Homepage: https://cran.r-project.org/package=classbound Description: CRAN Package 'classbound' (Visualization for Classification Decision Boundaries) Exploring, visualizing, and comparing classification decision boundaries. Provides a unified interface for fitting classifiers and rendering 2D decision boundary plots, with support for 2D slice visualization (fixing non-plotted dimensions at reference values) and projection-based visualization for high-dimensional data (including Principal Component Analysis (PCA) and tour projections from the 'tourr' package). Supports native 'R' classifiers, 'tidymodels' workflows, and custom user-supplied models via a flexible adapter system. Includes an interactive 'Shiny' application ('explorapp') for visual exploration, data simulation, drawing, model comparison, probability surfaces, and reproducible exports. Package: r-cran-classcomparison Architecture: all Version: 3.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-oompabase, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-classcomparison_3.3.6-1.ca2404.1_all.deb Size: 983008 MD5sum: 009a3ae5f8bd0a514ec24386e4032cde SHA1: df897dbd3c41a45da986f70f021d721e2bd9e919 SHA256: edcd4c9cf3b65be9070f0de563ef71e0b9dc8211b2f3fe323713658cf47f2dba SHA512: 2312d94655dfef9bdf0bf1ffb791845791aead55e2038d0c7296be8cb5d00bb5772ee8877c2921ad16636c5e440a395d9581c5081d6de55795437a78b11a3f6a Homepage: https://cran.r-project.org/package=ClassComparison Description: CRAN Package 'ClassComparison' (Classes and Methods for "Class Comparison" Problems onMicroarrays) Defines the classes used for "class comparison" problems in the OOMPA project (). Class comparison includes tests for differential expression; see Simon's book for details on typical problem types. 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Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance. Package: r-cran-classgraph Architecture: all Version: 0.7-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-bioc-rgraphviz Suggests: r-cran-matrix Filename: pool/dists/noble/main/r-cran-classgraph_0.7-7-1.ca2404.1_all.deb Size: 45884 MD5sum: b25abe99be36c4bc09d70ce157d32116 SHA1: 1c9a34b7eaab7a0196a9c70dea4819c24d81a569 SHA256: c6bc812387fe37bfc758407f378d0f71eadc50bee2629dffc780cc2f178a4c0b SHA512: 5a57eef532ee8a303c12709371626a896423b90d8272994da264af0a2b7eb3e47adf452d692fe3dfe3cced8ec221c79de2378cc0857c40ecf9a351d5c19aed58 Homepage: https://cran.r-project.org/package=classGraph Description: CRAN Package 'classGraph' (Construct Graphs of S4 Class Hierarchies) Construct directed graphs of S4 class hierarchies and visualize them. In general, these graphs typically are DAGs (directed acyclic graphs), often simple trees in practice. Package: r-cran-classicaltest Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-classicaltest_0.7.5-1.ca2404.1_all.deb Size: 182946 MD5sum: 9ec5d604b1846e9bf83114b54559c21c SHA1: fbc0c9a0bd047e56bc1e072c824fee9164459e80 SHA256: 0bff6ac85dc26c4f4db2dbf15cfe0a8829664f7c879b80cb47c6806d5fb46753 SHA512: e7d35b6fed09fba7b37832437839eabccd02d46bf24ef0565739d722168e5c766b26f2fccd367cc27edc07c765175da2e6ab555c43ac9b325f74b800545f112d Homepage: https://cran.r-project.org/package=classicaltest Description: CRAN Package 'classicaltest' (Classical Test Theory (CTT) Analysis) Functions for classical test theory analysis, following methods presented by Wu et al. (2006) . 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The package also returns 25 plots, 5 tables and a summary report. Package: r-cran-classifierplots Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-rcpp, r-cran-rocr, r-cran-caret, r-cran-gridextra, r-cran-png Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-classifierplots_1.4.0-1.ca2404.1_all.deb Size: 333270 MD5sum: 85e5052dec5fbe20b56c236e0cc3f083 SHA1: f1675960eb40f60f84578bb029aa3ffe77570439 SHA256: 561375a1c85fbcadf504f95843f5e52049edf09a500b6bd2ccf0221a5691240a SHA512: d1a49ad86086606e4d56006e390b31039c3d3f91e6fb2a43c1bb9c71e6a1984b2cfcac5337edf6e242660f95fe5713b74ec333f6b8e60116dffe3a4e62ae1844 Homepage: https://cran.r-project.org/package=classifierplots Description: CRAN Package 'classifierplots' (Generates a Visualization of Classifier Performance as a Grid ofDiagnostic Plots) Generates a visualization of binary classifier performance as a grid of diagnostic plots with just one function call. Includes ROC curves, prediction density, accuracy, precision, recall and calibration plots, all using ggplot2 for easy modification. Debug your binary classifiers faster and easier! 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Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps non-linear. This package implements methods for understanding the division of space between the groups. Package: r-cran-classifyits Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-data.table, r-cran-seqinr Suggests: r-cran-formatr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-classifyits_1.0.3-1.ca2404.1_all.deb Size: 200658 MD5sum: 3c9696a8dffb89b0ff18ec08d68c931f SHA1: 0ee62c1e67d161c554eaa3e824b2c8ffe0ec9c06 SHA256: c3c59a593944c52e469dcb61f46131a5fb49398fccfaede2bf33ae7bfcf75f84 SHA512: 3311d386999e74502acffe9c5f852447cdf4fb800cdfb69173c65f41b7945e06487d72e61ef6300996164f288216878a4d5c22e60a0b9a39350984e38dae178d Homepage: https://cran.r-project.org/package=ClassifyITS Description: CRAN Package 'ClassifyITS' (Fungal Assignment Pipeline) Fungi are ubiquitous in Earth's wonderfully diverse ecosystems. The 'ClassifyITS' package aids in the taxonomic classification of internal transcribed spacer (ITS) fungal sequences. Unlike previous methods, it employs taxon-specific e-value and percent identity cutoffs at each taxonomic rank from kingdom to species. The package takes a conservative approach and outputs both graphics and user-friendly files to help users manually inspect fungal operational taxonomic units (OTUs) that fail classification at relevant levels (e.g., Phylum). 'ClassifyITS' is based on taxonomic cutoff criteria from "The Global Soil Mycobiome consortium dataset for boosting fungal diversity research" (Fungal Diversity, Tedersoo, 2021, ) and "Best practices in metabarcoding of fungi: From experimental design to results" (Molecular Ecology, Tedersoo, 2022, ). Package: r-cran-classmap Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-robustbase, r-cran-e1071, r-cran-cellwise, r-cran-cluster, r-cran-kernlab, r-cran-gridextra, r-cran-rpart, r-cran-randomforest, r-cran-scales, r-cran-lpsolve, r-cran-diptest Suggests: r-cran-knitr, r-cran-reshape2, r-cran-svd, r-cran-rpart.plot, r-cran-nnet, r-cran-robcompositions, r-cran-rmarkdown, r-cran-fairml, r-cran-robusthd, r-cran-vioplot Filename: pool/dists/noble/main/r-cran-classmap_1.2.7-1.ca2404.1_all.deb Size: 2048634 MD5sum: b0a8cc83e93d3a40c43898739b603d26 SHA1: 50f56609a6d1a9d5a8c8d9bacab83ba9e2cfb940 SHA256: 1ae267ca1e29a1f9383e762b7816e8a01c890797ee0c8391086ffbada9de37f0 SHA512: 4c4a6d552a8e597bcd35b6d5041b3c45d38f63837382c38a2bc90e4e015dac69af699398bd905fa0b2ca42b1ba110944a7439f5115ffd3ec052ac0ab1b4023ec Homepage: https://cran.r-project.org/package=classmap Description: CRAN Package 'classmap' (Visualizing Classification Results) Tools to visualize the results of a classification or a regression. The graphical displays include stacked plots, silhouette plots, quasi residual plots, class maps, predictions plots, and predictions correlation plots. Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2022). Class maps for visualizing classification results. \emph{Technometrics}, 64(2), 151–165. (open access), Raymaekers J., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. \emph{Journal of Computational and Graphical Statistics}, 31(4), 1332–1343. , and Rousseeuw, P.J. (2026). Explainable Linear and Generalized Linear Models by the Predictions Plot. The American Statistician, 80, 157-163, (open access), and Montalcini, C., Rousseeuw, P.J. (2025). The bixplot: A variation on the boxplot suited for bimodal data, (open access). Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", "Neural_net_examples", "predsplot_examples", and "bixplot_examples". Package: r-cran-clast Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clast_1.0.1-1.ca2404.1_all.deb Size: 97476 MD5sum: 254fd041b754c76a0d30423a9ee2a145 SHA1: 5c8d39ce9b2e59f9ff71ebd38a7ead365f0e53ce SHA256: e0423ad2a037abea41e3f6d8fe3b94916788c87912e6a4af4656f97579dea2f9 SHA512: 1c2ba2765719877e53ca3665cfd274e0dfa74db381dfae8b623a2246b3c59d332fbe9e128dcf8321e035ff7c14d6bdccef8372f27dbae44040458c03e5694ce2 Homepage: https://cran.r-project.org/package=CLAST Description: CRAN Package 'CLAST' (Exact Confidence Limits after a Sequential Trial) The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, . Package: r-cran-clayringsmiletus Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cluster, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clayringsmiletus_1.0.2-1.ca2404.1_all.deb Size: 65244 MD5sum: 79325b2b2d8721ec59732321c03807b6 SHA1: cbcf17128e41c4579b87813189e3f0805b65389b SHA256: f5ceff1afbfbf89f7a45e8ef8c95d9c566b8b80a0a20c0ce4a81b7ba8a5581d4 SHA512: d48ce6b6f064de657472e89b62028dc8704eb9aa45fd8febb0c9f40c01b9167f0e0ffc88d97205c5742d5e1722ebe31e16285c9891902adb46e862a71fbfd173 Homepage: https://cran.r-project.org/package=clayringsmiletus Description: CRAN Package 'clayringsmiletus' (Clay Stacking Rings Found in Miletus (Data)) Stacking rings are tools used to stack pottery in a Kiln. A relatively large group of stacking rings was found in the area of the sanctuary of Dionysos in Miletus in the 1970s. Measurements and additional info is gathered in this package and made available for use by other researchers. The data along with its archaeological context and analysis has been published in "Archäologischer Anzeiger" (2020/1, ). 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Groups sharing the same letter are not significantly different from each other. Supports multiple input formats including results from 'stats' pairwise tests, 'DescTools', 'PMCMRplus', 'rstatix', symmetric matrices of p-values, and data frames. Provides a consistent interface for visualizing statistical groupings across different testing frameworks. 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Package: r-cran-cldedgelister Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml2 Suggests: r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-writexl, r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cldedgelister_1.0.3-1.ca2404.1_all.deb Size: 116372 MD5sum: 7003d92cfe0ac127c2fd7b8df3d100d3 SHA1: 090873d16fd8a25a94c1da859050b6463f68842d SHA256: 71afb130740167135af6360f6cd4bb98a3c119c9595d52aa4554e68054cb6730 SHA512: 73965952e72247c7cdff6bc517fd0cd03b5eee8d9999fc376c7640dffb630ecde0f19769d2da07b6716f5a7d6ddfd602e4d0b06d258a9e863e1fe1f85742d331 Homepage: https://cran.r-project.org/package=CLDedgelister Description: CRAN Package 'CLDedgelister' (Import System-Dynamics Models and Convert Them to Edge Lists) Imports causal and system-dynamics models from 'Vensim', 'Stella' / 'iThink' and 'Powersim Studio' ('XMILE'), 'AnyLogic' and 'GoldSim' (exported 'XML'), and converts them into a two-column edge list of cause-to-effect links. 'Vensim' models are read from their native diagram files; 'Powersim Studio' and 'GoldSim' are also now read from their native diagram files in addition to exported 'XMILE' or 'XML'. Provides an 'RStudio' add-in with a simple point-and-click interface, together with command-line functions that return a data frame or write it to 'CSV' or 'Excel'. 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Package: r-cran-cleangeo Architecture: all Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sp, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-cleangeo_0.3-2-1.ca2404.1_all.deb Size: 71368 MD5sum: 6059ba821ea48ee5e1866e281ec1e9b6 SHA1: 4cdafd020f525459c4678ab2dd33986e17f10b9a SHA256: 5cfd9e680e612271c8078293debf44e4d1a25c868545dffe65d7b41d5de87b1d SHA512: 1f841a3278d9c5a5694ff02747fc713193a9ba373fa5d7112ca304eb3a20312841c190319ff2bc10b40c54f93e5e9d6f02512adb7746dca041b8442e623935ca Homepage: https://cran.r-project.org/package=cleangeo Description: CRAN Package 'cleangeo' (Cleaning Geometries from Spatial Objects) Provides a set of utility tools to inspect spatial objects, facilitate handling and reporting of topology errors and geometry validity issue with sp objects. 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The package includes non-parametric methods to analyze drug active-ingredient residue (DAR), cleaning agent residue (CAR), and microbial colonies (Mic) for non-Poisson distributions. Additionally, Poisson methods are provided for Mic analysis when Mic data follow a Poisson distribution. 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Package: r-cran-cleanr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-fritools, r-cran-pkgload, r-cran-rprojroot Suggests: r-cran-devtools, r-cran-rasciidoc, r-cran-rmarkdown, r-cran-runit, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-cleanr_1.4.0-1.ca2404.1_all.deb Size: 96746 MD5sum: 881fa1f76e76d462c537c72176988860 SHA1: 21249f5d906fd10d0068777f19020185614ec153 SHA256: 07b69ba4bfd43ee1e80be88e4cab4a6f4be762c2e1b4014ef4f5fb39cc37c894 SHA512: 4cf424d78f097ea43957aa9acee2f907c0b7e3a4acbfa1245f4087387d3edfa2df6ded63c35f000397ee262a6ceaaf7389c015c97ed5f3e00ed3a0b84ef174b7 Homepage: https://cran.r-project.org/package=cleanr Description: CRAN Package 'cleanr' (Helps You to Code Cleaner) Check your R code for some of the most common layout flaws. Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) in 'The C Programming Language' did, be it explicitly as R.C. Martin (2008) in 'Clean Code: A Handbook of Agile Software Craftsmanship' did. So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout instead. Package: r-cran-cleanrmd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2967 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cleanrmd_0.2.0-1.ca2404.1_all.deb Size: 1873222 MD5sum: a5e80c7ba9d4b72cdb830bd32b389539 SHA1: c974231f8d86a5e57e299214770e8f4a1a88ba84 SHA256: 117f28093d4ebd0a84f3e14d499c89d6d355df2a756b62e8bbdc5ca430104db6 SHA512: b01cba3582068bb150b4817d05bc4021ce0b271f6b4e99c35c4b1532a2d624c6cfa1a0edabd66002b0ded03aa24597bee65eb09c7b6b4e2d931b3d3073517b3a Homepage: https://cran.r-project.org/package=cleanrmd Description: CRAN Package 'cleanrmd' (Clean Class-Less 'R Markdown' HTML Documents) A collection of clean 'R Markdown' HTML document templates using classy-looking classless CSS styles. 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It implements reliable and efficient procedures for automating the process of cleaning univariate time series data. The package provides integration with already developed and deployed tools for missing value imputation and outlier detection. It also provides a way of visualizing large time-series data in different resolutions. 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Package: r-cran-clespr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-pbivnorm, r-cran-mass, r-cran-magic, r-cran-survival, r-cran-clordr, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-clespr_1.1.2-1.ca2404.1_all.deb Size: 79054 MD5sum: f038678316f5cba33f8d1c934228fe3e SHA1: fc300cf724da697c2c48f63f8b8e79df31961d54 SHA256: db6f289370356d308ac106e4639d4576286760997c21e8bb1483dfdf27059e14 SHA512: 02e301f89b0746a1d0152097c922194cbada37a56ada91e918eab87262bd88718ee5121898d6630d4f06c007c79c689fbad4b393aa730c4e82caf5e6ebddd02f Homepage: https://cran.r-project.org/package=clespr Description: CRAN Package 'clespr' (Composite Likelihood Estimation for Spatial Data) Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) . 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This package is now superseded by the 'cli' package. Please use 'cli' instead in new projects. 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Package: r-cran-clickableimagemap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-ggplotify, r-cran-ggplot2, r-cran-gtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clickableimagemap_1.0-1.ca2404.1_all.deb Size: 428876 MD5sum: 07d306a5fd69766470e4ba0cd7583c82 SHA1: 40f69ba7cce4de48313fa49d413dba5cc3e0cd6d SHA256: 89da84701e26ad39796954309c26cae470115fc18ab202fcdaa5985547c5a22b SHA512: cf1739f1b4fa44da931c32ac99adef0492cb3fd4ecf0cb8adfe3fa97d7390624be997b0967418ab96038de321ad169c0559b6d751ce534315e29a9d758b5d04f Homepage: https://cran.r-project.org/package=clickableImageMap Description: CRAN Package 'clickableImageMap' (Implement 'tableGrob' Object as a Clickable Image Map) Implement 'tableGrob' object as a clickable image map. The 'clickableImageMap' package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a 'tableGrob', all of these features are controllable. In particular, the 'ggplot2' grid system allows exact positioning of the table relative to other graphics etc. Package: r-cran-clickb Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-discreteweibull, r-cran-mclust, r-cran-mcmcpack Suggests: r-cran-seqhmm Filename: pool/dists/noble/main/r-cran-clickb_0.1-1.ca2404.1_all.deb Size: 37556 MD5sum: 336a3638eba8b5022b803d6dafd7971d SHA1: 7f9604b15b6308b86d572bf4fd8a4d020dae5b32 SHA256: d2f399c8ed4188962ec6408dc61d557909469a13464f50afcdab99016d05af5b SHA512: 8d0383b46daa8fc05c7470bad5a48796211b60ec2969adf793a5c99c41f81e77ab6bdaf95aa2e483758a388dc1cd05bc6dbe20aa870a9c7452cf913a9e230ff3 Homepage: https://cran.r-project.org/package=clickb Description: CRAN Package 'clickb' (Web Data Analysis by Bayesian Mixture of Markov Models) Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences' clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) . Package: r-cran-clickclustcont Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-clickclustcont_0.1.7-1.ca2404.1_all.deb Size: 176330 MD5sum: a8dd066b19e4053a6e551d6203a8dde0 SHA1: b04905dccb8b118bc7147459b6177f5df3a049b4 SHA256: c1cdf50912c3ce5476fb7054900c69b79dee03dfe3d1f31404b206ea387b22af SHA512: a482212f28154471c1fd3f34332e5e8c15ae24c4fd1f0e72e1cc101011194dd7f53424c3c95d3a93d6a1c75b598224fe751de3e4cfe8dc89cf1a9d88f8197ede Homepage: https://cran.r-project.org/package=ClickClustCont Description: CRAN Package 'ClickClustCont' (Mixtures of Continuous Time Markov Models) Provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) . Package: r-cran-clickhousehttp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-httr2, r-cran-jsonlite, r-cran-arrow, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-dbplyr, r-cran-stringi, r-cran-stringr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-clickhousehttp_1.1.2-1.ca2404.1_all.deb Size: 584934 MD5sum: 3254452c2085b4d048054e24271a1152 SHA1: d1e039e8593ebb05c86147bce295b3741b25d07e SHA256: 57d840d3b8ebecdef84716e52e4b9e2ef20db5820839f83dede2c100a6c4b650 SHA512: 89b6aa15023b581d6b797e19fb4473b5ba77da7bd9aee50c0489e7aa433c2c9c96d3659e3895366c4dd19b2dd63a2c1eea77feffb38e93c9d7405facb7c6d3fa Homepage: https://cran.r-project.org/package=ClickHouseHTTP Description: CRAN Package 'ClickHouseHTTP' (A Simple HTTP Database Interface to 'ClickHouse') 'ClickHouse' () is an open-source, high performance columnar OLAP (online analytical processing of queries) database management system for real-time analytics using SQL. This 'DBI' backend relies on the 'ClickHouse' HTTP interface and support HTTPS protocol. Package: r-cran-clickr Architecture: all Version: 0.9.45-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beeswarm, r-cran-future, r-cran-future.apply, r-cran-stringdist Filename: pool/dists/noble/main/r-cran-clickr_0.9.45-1.ca2404.1_all.deb Size: 185714 MD5sum: 5043e30903d3fde13ff4ba6c055aff02 SHA1: bea9128c094e982ae2a343d3728b9864d620c4ef SHA256: d0055be823b0978410790c4b39bea9dd3bad1ec261f1a5663824515da56d5820 SHA512: 5896d0e537bec125ed0fb971323706fa21e0108d2045b6997e49513864159bb94a2f9ffc391125cfed2ff8c9cd1ff196f375e71b22e87708f20602c51030760d Homepage: https://cran.r-project.org/package=clickR Description: CRAN Package 'clickR' (Semi-Automatic Preprocessing of Messy Data with Change Trackingfor Dataset Cleaning) Tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning. Package: r-cran-clickstream Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-reshape2, r-cran-mass, r-cran-plyr, r-cran-rsolnp, r-cran-arules, r-cran-linprog, r-cran-ggplot2, r-cran-clickclust, r-cran-data.table Filename: pool/dists/noble/main/r-cran-clickstream_1.3.4-1.ca2404.1_all.deb Size: 319482 MD5sum: c96cf44ec88a5c071227c6c78f105ef2 SHA1: bd4082619bf37ffa8526d401409fa588ff5bd68e SHA256: 3f480095830e0a51d46fcce8d41b9a50d8e9b36c88747e8042a9e407f1f8c631 SHA512: a96c6b3ef89cc5aaae2a8506f77f1dc4c4694329648731163f66a305ae676787775e57ab709690f2b11904510e3161df382bda0cb9b6a751eb07a028e450b8eb Homepage: https://cran.r-project.org/package=clickstream Description: CRAN Package 'clickstream' (Analyzes Clickstreams Based on Markov Chains) A set of tools to read, analyze and write lists of click sequences on websites (i.e., clickstream). A click can be represented by a number, character or string. Clickstreams can be modeled as zero- (only computes occurrence probabilities), first- or higher-order Markov chains. Package: r-cran-clidamonger Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clidamonger_1.6.0-1.ca2404.1_all.deb Size: 3047462 MD5sum: f12fa608c79510b58202e554b1e84a06 SHA1: c020ab70d1947fbb0781749767c96becca668b43 SHA256: 83eeeeddf88816cf714f07227fe211c703ed73ff24de86f929fc9c3a36675705 SHA512: 4eacb9df08e9c6492f2b8938ee6ef19eeb74764820c0be2c82ca16eba5dd2cde97cd133f154261ae61bfaf11b9fa667737aa94af39cb9fe5f73da9dbc4737eb8 Homepage: https://cran.r-project.org/package=clidamonger Description: CRAN Package 'clidamonger' (Monthly Climate Data for Germany, Usable for Heating and CoolingCalculations) This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation). Package: r-cran-clidatajp Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1805 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-rlang, r-cran-rvest, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clidatajp_0.5.3-1.ca2404.1_all.deb Size: 1610036 MD5sum: 1abb18c119dbf5189d492ae3890f861b SHA1: f74d175f515ad4c2e55344dec195925478087f85 SHA256: 39da0bb071d35c466b1e928d8feb3beb3a161f13a5692e39a04e691fda0e9333 SHA512: fd691485f7fe0900ae0e9674b0a5590d47b012a59b5fb8502fce9ce41576f36c295caae23a7595d707e457bcb9f173cc0818bf6eec8483061ad7134e8a627bc3 Homepage: https://cran.r-project.org/package=clidatajp Description: CRAN Package 'clidatajp' (Data from Japan Meteorological Agency) Includes climate data from Japan Meteorological Agency ('JMA') . Can download climate data from 'JMA'. Package: r-cran-cliff Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ellipsis, r-cran-processx, r-cran-rlang Suggests: r-cran-withr, r-cran-crayon, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cliff_0.1.2-1.ca2404.1_all.deb Size: 20228 MD5sum: da72ca88de92482efb7a311cd06b8951 SHA1: ed411747a86f7c70e03df44162c3d3f180a7ec86 SHA256: 5e6f5b88f41e09c6548571a6aebaa887a1c556b25e31ac151503eae6160d0e82 SHA512: 715188dc28e663058cbf0a165e7f5a009753c1d3cd5ec41a85871528262828aabbf84f3834426159c46a31087dfd744964bd2bbf9926bf82aa9c70849bd2b87e Homepage: https://cran.r-project.org/package=cliff Description: CRAN Package 'cliff' (Execute Command Line Programs Interactively) Execute command line programs and format results for interactive use. 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Package: r-cran-clifro Architecture: all Version: 3.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-xml2, r-cran-magrittr, r-cran-ggplot2, r-cran-scales, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rvest, r-cran-httr, r-cran-stringr Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clifro_3.2-5-1.ca2404.1_all.deb Size: 731628 MD5sum: a3d628564d3ddf14ba4688c5e400f0da SHA1: ce87a9f3c14a7519a40a59d2b5e366491cb67339 SHA256: 8a39bd55a851b73f37823c5d641228e7b0a1cff25a87d4af8e5f164dc0bed5db SHA512: 21bf3a23dae4d46fbe1b62c8bf5ad913806ffa67797b2918441a7c24cd82e0d9ccea2876225a75ac72c13353b731b28e7b5c340f946a4b101f0e9c5a743ad52a Homepage: https://cran.r-project.org/package=clifro Description: CRAN Package 'clifro' (Easily Download and Visualise Climate Data from CliFlo) CliFlo is a web portal to the New Zealand National Climate Database and provides public access (via subscription) to around 6,500 various climate stations (see for more information). Collating and manipulating data from CliFlo (hence clifro) and importing into R for further analysis, exploration and visualisation is now straightforward and coherent. The user is required to have an internet connection, and a current CliFlo subscription (free) if data from stations, other than the public Reefton electronic weather station, is sought. Package: r-cran-cliftlrd Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cnltreg, r-cran-liftlrd Suggests: r-cran-fracdiff Filename: pool/dists/noble/main/r-cran-cliftlrd_0.1-2-1.ca2404.1_all.deb Size: 41950 MD5sum: a8f16a8f7557defa51b6e2bfc7138578 SHA1: 5bc2d72afd82957bc311394dbda0edaace5112b7 SHA256: 30c0289e8ebbeaf34cc5493e11061f69053ccfd3dacb74855bb8e052883b1f7f SHA512: 55d7e026444c4ec5fbcc709f9bed0815be91d6a971efed2cc22d83cdbc1c50fa7ed694860336aaf144129d6de2b00771fc501425ce65eb2a5009218faa4e6664 Homepage: https://cran.r-project.org/package=CliftLRD Description: CRAN Package 'CliftLRD' (Complex-Valued Wavelet Lifting Estimators of the Hurst Exponentfor Irregularly Sampled Time Series) Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) . Package: r-cran-clikcorr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-clikcorr_1.0-1.ca2404.1_all.deb Size: 144408 MD5sum: 6007c49e7cc4b8e29afd195c4e8a7fa0 SHA1: 9b75336939cb9c17e4579b35b2722fcb1f43597e SHA256: 0f03e1c48dcf6ba1454244bcd296b7dc614f5b493f8b013eedf03df2a1e116aa SHA512: 22835bd70f04076ba67e9fe9e83d56f23d808d1766f3bb3eb0aaa7d7e06fa6313e76f78b32c6c699b416894e3ed98e80e1e3c0dca61c2ff940ab9e9c94ce891a Homepage: https://cran.r-project.org/package=clikcorr Description: CRAN Package 'clikcorr' (Censoring Data and Likelihood-Based Correlation Estimation) A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness. Package: r-cran-clim4health Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6212 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cstools, r-cran-ecmwfr, r-cran-ggplot2, r-cran-ghrexplore, r-cran-lubridate, r-cran-s2dv, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-data.table, r-cran-units, r-cran-csdownscale, r-cran-ncdf4, r-cran-ggpattern, r-cran-startr, r-cran-rlang Suggests: r-cran-testthat, r-cran-spdata, r-cran-cubelyr, r-cran-knitr, r-cran-withr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clim4health_0.1.0-1.ca2404.1_all.deb Size: 2261170 MD5sum: 004470a1bafdc9554d21789bc90ae745 SHA1: babe5afc49b45966e197dc875486e52bf4218855 SHA256: 1b8baf906663823e58dc1738aae32edbcfe0a2544a0536fb60a6ef4f8348a3f0 SHA512: b10a44d1e2555cd88fe3dea2c37a570a1d16ea1de3e3d91efa7725efd9f9b6276371323e461d56db73f110f644f2d070edbcd1a83e034dd90abd529b4088c400 Homepage: https://cran.r-project.org/package=clim4health Description: CRAN Package 'clim4health' (Post-Processing of Climate Data for Health Applications) Obtain, transform and export climate data including reanalyses, (seasonal) forecasts and hindcasts, and weather stations for their use in epidemiological analyses. It is organised in three sequential blocks, input (download and load data), transform (downscaling, verification, spatiotemporal aggregation and threshold-based indicators) and output (visualising and exporting data). Downscaling methods include those described in Duzenli et al. (2026) and verification methods are based on those in Manubens et al. (2018) . Package: r-cran-climaemet Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-httr2, r-cran-jsonlite, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-climatol, r-cran-gganimate, r-cran-jpeg, r-cran-knitr, r-cran-lifecycle, r-cran-lubridate, r-cran-mapspain, r-cran-quarto, r-cran-scales, r-cran-sf, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-climaemet_1.6.0-1.ca2404.1_all.deb Size: 866718 MD5sum: 38e29109a3eb0a7defdf016f75ecd783 SHA1: 9ed7cbbd48c56c2fb92ff718f9e11f73a2beea3a SHA256: 0044c950f870d4c679943f310cc7338062e6481a988d421a0587b828c53fd155 SHA512: 58468680a4ee802c0bca6e83eef729b675b7e49daaeff7c2767bcff69220667b8cc6c850515b6416ade865f2c69f02851c44f14e58105a4f4dc1b8f1f0642ff4 Homepage: https://cran.r-project.org/package=climaemet Description: CRAN Package 'climaemet' (Tools for AEMET Climate Data) Download meteorological and climate data from the Spanish Meteorological Agency (AEMET) directly in R using the AEMET API. Create scientific visualizations, including climate charts, climate time series trend analyses, temperature and precipitation anomaly maps, warming stripes and climatograms. Package: r-cran-climarep Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3807 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-terra, r-cran-sf, r-cran-tidyterra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-climarep_1.1-1.ca2404.1_all.deb Size: 2339346 MD5sum: 9ee510b74d234c33bef4ac3865336845 SHA1: c041fcc14d55713c426209fd964b81cfe7ae8d75 SHA256: fd72fd06f1ab9663f4e559cbf959240b138cbb9af3fdd7110dd07df56757147e SHA512: 4d4764874e7f116e8716a6663a9a354518d42a2f61e6340f21a6c880e2e5789ca66aac472c11de4fe1925bbe48bf9f9907160f9d8e2fad0d61f55d5fc00abb32 Homepage: https://cran.r-project.org/package=ClimaRep Description: CRAN Package 'ClimaRep' (Estimating Climate Analogue Areas) Offers tools to identify the climate analogues of reference polygons and quantifies their transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) and Mingarro and Lobo (2022) . 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This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations. Package: r-cran-climatebr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1974 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-sf, r-cran-gstat, r-cran-glue, r-cran-arrow, r-cran-dplyr, r-cran-tidyr, r-cran-janitor, r-cran-data.table, r-cran-pracma, r-cran-purrr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-geobr Filename: pool/dists/noble/main/r-cran-climatebr_0.2.0-1.ca2404.1_all.deb Size: 1809812 MD5sum: 231cad2ada24d98989cad3b34fb249ea SHA1: 35db165cf7bee8ea0e0ed3feb13e7ce4c2709c14 SHA256: 787e9f099c0282bd9a319254f66ebd94fcea759726680a5429d108037b92f80b SHA512: 9d0532ce3178ee26085db6820d607f348c4dbcad59eaad18d2db673bd923754f52f61b83785319fab0e7b7eadb5e7786cdc27541b861f734eb1183d5d47fd1b8 Homepage: https://cran.r-project.org/package=climateBR Description: CRAN Package 'climateBR' (Download Rainfall, Temperature, and Wind Data from Brazil) Provides functions to download and import meteorological data from Brazil's National Institute of Meteorology (INMET) . Package: r-cran-climatehealth Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-data.table, r-cran-dlnm, r-cran-dplyr, r-cran-epi, r-cran-forcats, r-cran-exactextractr, r-cran-ggplot2, r-cran-gnm, r-cran-lifecycle, r-cran-lme4, r-cran-lubridate, r-cran-metafor, r-cran-mgcv, r-cran-mixmeta, r-cran-ncdf4, r-cran-patchwork, r-cran-pkgbuild, r-cran-png, r-cran-purrr, r-cran-raster, r-cran-rcolorbrewer, r-cran-readr, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tseries, r-cran-tsmodel, r-cran-xfun, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-dt, r-cran-htmltools, r-cran-mockery, r-cran-mvmeta, r-cran-openxlsx, r-cran-patrick, r-cran-pkgload, r-cran-stringdist, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-climatehealth_1.0.3-1.ca2404.1_all.deb Size: 1271610 MD5sum: 93a616095164369c6a61807e0245bc52 SHA1: 0b25c487801ae96dedb7822848e5cad0b8898189 SHA256: c4a1094fa8c8340c534e04dc5de55aa423b1453cf16cf29a917f927507ee4150 SHA512: 93bf784eab97e5b88b40e64ff2d94fe51977e2066fce4834735e6602932acfe8b0d8c7681d943c5aa9ede1f7cd9aaa6128dd5b5b6c4ab5df8b50495c1b447e38 Homepage: https://cran.r-project.org/package=climatehealth Description: CRAN Package 'climatehealth' (Statistical Tools for Modelling Climate-Health Impacts) Tools for producing climate-health indicators and supporting official statistics from health and climate data. Implements analytical workflows for temperature-related mortality, wildfire smoke exposure, air pollution, suicides related to extreme heat, malaria, and diarrhoeal disease outcomes, with utilities for descriptive statistics, model validation, attributable fraction and attributable number estimation, relative risk estimation, minimum mortality temperature estimation, and plotting for reporting. These six indicators are endorsed by the United Nations Statistical Commission for inclusion in the Global Set of Environment and Climate Change Statistics. Implemented methods include distributed lag non-linear models (DLNM), quasi-Poisson time-series regression, case-crossover analysis, Bayesian spatio-temporal models using the Integrated Nested Laplace Approximation ('INLA'), and multivariate meta-analysis for sub-national estimates. The package is based on methods developed in the Standards for Official Statistics on Climate-Health Interactions (SOSCHI) project . For methodologies, see Watkins et al. (2026) , Jose et al. (2026) , Pearce et al. (2026) , Byukusenge et al. (2026) , Dzakpa et al. (2026) , and Dzakpa et al. (2026) . Package: r-cran-climatekit Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-terra, r-cran-ncdf4, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climatekit_0.2.2-1.ca2404.1_all.deb Size: 305692 MD5sum: 15b5decd9e30071dfbbef78d109571be SHA1: 782e731a183bdb7551c63b1374c7205630ff38c1 SHA256: 308a917ebf13869c4431c18319a2a6359ff955d5777cfd84944f4dbc71130596 SHA512: bd13a67f3dcb2cdcccae36ce9742606e491ed580117233f55fdbf753a5867be8d9d0c54c8cd041420b3a66e375dff3b20c75bb8270eb893c205f0801d623150f Homepage: https://cran.r-project.org/package=climatekit Description: CRAN Package 'climatekit' (Unified Climate Indices for Temperature, Precipitation, andDrought) Compute the standard suite of climate indices from daily weather observations. Provides the canonical 'ETCCDI' 27 (Expert Team on Climate Change Detection and Indices), the 'ET-SCI' heatwave and cold-wave families plus the Excess Heat Factor of Nairn and Fawcett (2013), and agroclimatic, drought, and human-comfort families. Drought indices ('SPI', 'SPEI') accept a choice of distribution (gamma or Pearson III for SPI; log-logistic or generalised extreme value for SPEI). Reference evapotranspiration is available via Hargreaves and the FAO-56 Penman-Monteith method (Allen et al. 1998). Percentile-based indices support the Zhang (2005) in-base bootstrap. Daily inputs are numeric vectors plus a 'Date' vector; outputs are tidy data frames. Optional gridded support via 'terra' applies any index over a 'SpatRaster' and reads 'netCDF' input. No external API calls; pairs with data packages such as 'readnoaa'. References: Alexander et al. (2006) ; Zhang et al. (2011) ; Zhang et al. (2005) . Package: r-cran-climatestability Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-rangebuilder, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-climatestability_0.1.4-1.ca2404.1_all.deb Size: 230030 MD5sum: 984c13f730e69a87c802b56c599a442f SHA1: 3a004dc1c299af079620fd112ad44e50ddc6477d SHA256: 4cb143052b87fb6ff967d7202ba187ee8203b81af9e27586764e6f02deffe538 SHA512: a6a98b72e0b6d460ed7635e547e8ee8f767b4b177de790b8970c0c8a620ffaf402445d504675a49fa61af5b7d1e610bb55b2771d65b8f196456739117990215e Homepage: https://cran.r-project.org/package=climateStability Description: CRAN Package 'climateStability' (Estimating Climate Stability from Climate Model Data) Climate stability measures are not formalized in the literature and tools for generating stability metrics from existing data are nascent. This package provides tools for calculating climate stability from raster data encapsulating climate change as a series of time slices. The methods follow Owens and Guralnick Biodiversity Informatics. Package: r-cran-climatestatsr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-sf, r-cran-ncdf4, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climatestatsr_0.1.2-1.ca2404.1_all.deb Size: 451716 MD5sum: fd759298de14ec7fd997e147b902db32 SHA1: 18474f4d51a84dc882d37b9e820d7919856a4c50 SHA256: 445e22c893acde6e06c8c33201f11d8ea1c92084cf01aa91993aeb40994ed3ec SHA512: 9e7be127f6cceee85f8a650faf7bb8483959fe041bc022d2a970a2c6d3361e1ed18d7c0fa44be8d0ebb6518708e6a5986ac5bb887b4d001245882ab1a32a753e Homepage: https://cran.r-project.org/package=climatestatsr Description: CRAN Package 'climatestatsr' (Statistical Tools for Climate Change Analysis) A comprehensive collection of statistical functions for climate change research. Provides tools for temporal trend detection based on the Mann-Kendall (MK) test (Mann 1945 ; Kendall 1975, ISBN:0852641990) and Sen's slope (Sen 1968 ), spatial autocorrelation using Moran's I (Moran 1950 ), extreme value analysis using the Generalised Extreme Value (GEV) distribution and Peaks-Over-Threshold (POT) method (Coles 2001 ), standardised drought indices including the Standardised Precipitation Index (SPI; McKee et al. 1993) and the Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al. 2010 ), and formal detection-attribution methods via optimal fingerprint regression and Empirical Orthogonal Function (EOF) analysis (Allen and Tett 1999 ), and apparent temperature via the heat index (Steadman 1979 ). Suitable for both station-level time series and gridded climate fields. Package: r-cran-climatol Architecture: all Version: 4.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2726 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-evd, r-cran-fields, r-cran-gstat, r-cran-maps, r-cran-mapdata, r-cran-ncdf4, r-cran-raster, r-cran-readxl, r-cran-rodbc, r-cran-sp Filename: pool/dists/noble/main/r-cran-climatol_4.5-0-1.ca2404.1_all.deb Size: 2364894 MD5sum: 54ea59c083ae1698f4182280afbced04 SHA1: 1a6538a83e57068c493d455f8c57e8de3d3f4069 SHA256: 5d974e3beb3d5e8993aec48989d8380a94e72ec9211fcdda53f10d472c92cb10 SHA512: 84a7805687bd4ee993c8774c5fed44cd60cb1e7c17c090688c1f3488c753e20fa0dc10b4ed31cafd414f8832a90ac48263c648ff10e0e507298b67833a03ec47 Homepage: https://cran.r-project.org/package=climatol Description: CRAN Package 'climatol' (Climate Tools (Series Homogenization and Derived Products)) Functions for the quality control, homogenization and missing data filling of climatological series and to obtain climatological summaries and grids from the results. Also functions to display wind-roses, meteograms, Walter&Lieth diagrams, and more. Package: r-cran-climatrends Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nasapower Suggests: r-cran-chirps, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climatrends_1.2-1.ca2404.1_all.deb Size: 3693404 MD5sum: d1941b68cb7bdda924fb8461ca41e510 SHA1: ff02f84c9b0e03c64ea00e7db0bc5890e5826b1a SHA256: a041a0e3fa63694afac84e254358ead61dba655124682fefc4fe959c008bbd3b SHA512: a56b2302a56a51906c8a98af4f682a2d7e745c52f5a88b6b72111df1fa1eca3f5d2ad14e1852c54b04bf2f931874242d632a6d1eb861b9b44b7e3dbd4a23a088 Homepage: https://cran.r-project.org/package=climatrends Description: CRAN Package 'climatrends' (Climate Variability Indices for Ecological Modelling) Supports analysis of trends in climate change, ecological and crop modelling. Package: r-cran-climclass Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-stringr Filename: pool/dists/noble/main/r-cran-climclass_2.1.1-1.ca2404.1_all.deb Size: 2172886 MD5sum: a9af65bbc733d908d1c4a2429be88f8c SHA1: 3e5b005f53a79916f47654921f1d5b23d93f219a SHA256: 559ee5c2c78951f776da7aac8f9371c86334715a965c46b848c2bc1a24ad0eb4 SHA512: 5617ae6489b75b815a8d9b6037908a06747a72f271454dfa3765cae701805d4835ddb8f36be924f497861049f02e08f964931eb6d2812eed6bc94b53ff6dfe37 Homepage: https://cran.r-project.org/package=ClimClass Description: CRAN Package 'ClimClass' (Climate Classification According to Several Indices) Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen. Package: r-cran-climd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-ncdf4, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climd_0.1.0-1.ca2404.1_all.deb Size: 2566294 MD5sum: 56976ecaa04a8e58b4900e2a6007c1b5 SHA1: 4f8b819539fe20d4bb3c75275c7fc697f3de2953 SHA256: 0eb8062c021b05418a3598678251c3fcc45a0f25d695251a0526405fd616f4b7 SHA512: 8da9506b098d0758b25721ed403aa63c4cc276038ce76a9580dbd76fca5458404dd3b303b7c7767702b182db79ee18ede36ab51157cb8bbb63dc5b17e84bae22 Homepage: https://cran.r-project.org/package=CLimd Description: CRAN Package 'CLimd' (Generating Rainfall Rasters from IMD NetCDF Data) The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly rainfall. For method details see, Malik, A. (2019).. It supports different aggregation methods, such as sum, min, max, mean, and standard deviation. These functions are designed for spatio-temporal analysis of rainfall patterns, trend analysis,geostatistical modeling of rainfall variability, identifying rainfall anomalies and extreme events and can be an input for hydrological and agricultural models. Package: r-cran-clime Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-clime_0.5.0-1.ca2404.1_all.deb Size: 43566 MD5sum: 1195e0786970c0ac6e69fc56982df9fc SHA1: 15fa0a3bc5347824834aa0cdefd840abbb470c5c SHA256: 1aaebb423af9bf2b3c7611b390e58f47962bb7e32fde2716b5945060fa928988 SHA512: b54f6f9ba3fe75a0a5fcdcaca2ac3756074f0d8927df95f45d53503791fca46e4895404285851dada52c8f6d2846d2471282643fe344db05ff1ef0f81d65b07b Homepage: https://cran.r-project.org/package=clime Description: CRAN Package 'clime' (Constrained L1-Minimization for Inverse (Covariance) MatrixEstimation) A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models. The computation uses linear programming. The method was published in TT Cai, W Liu, X Luo (2011) . Package: r-cran-climenu Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-keypress Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climenu_0.2.0-1.ca2404.1_all.deb Size: 64212 MD5sum: e725ba45c4f4961bb0f0268ce3c41665 SHA1: a498d735beb66480dbd4e406a94c9139d99289c3 SHA256: bd81fb59bdd8868e8acb89322e1ad1d309ffea5bb565805b5867bd6ee1cd63bb SHA512: 114f4187ec6ca31c8384de951ecd02d9f81724be4a851ae8d21a85e18197f63b42215d88f792732c996be66bc8891208183a576aa41435db1f8c5d7d3df64506 Homepage: https://cran.r-project.org/package=climenu Description: CRAN Package 'climenu' (Interactive Command-Line Menus) Provides interactive command-line menu functionality with single and multiple selection menus, keyboard navigation (arrow keys or vi-style j/k), preselection, and graceful fallback for non-interactive environments. Inspired by tools such as 'inquirer.js' , 'pick' , and 'survey' . Designed to be lightweight and easy to integrate into 'R' packages and scripts. Package: r-cran-climenv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-climaemet, r-cran-dismo, r-cran-dplyr, r-cran-elevatr, r-cran-exactextractr, r-cran-geodata, r-cran-glue, r-cran-plyr, r-cran-randomforest, r-cran-sf, r-cran-sp, r-cran-ternary, r-cran-terra Suggests: r-cran-covr, r-cran-fs, r-cran-knitr, r-cran-progress, r-cran-raster, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-climenv_1.0.0-1.ca2404.1_all.deb Size: 2039792 MD5sum: 0f49672b31aed12444a74f17b0b6471d SHA1: 461a642fc9e882f3239ee9ae250338e2ce917b9b SHA256: cf86d32fe067e427edec6b7cb55354eaedaa7207ea1e207445244395c43339b9 SHA512: 6c45c8fc1574d85fef137e58484088dfc9893aa9783832d45ef1935bedea9dad7290f02c082b0dcd9f3e63666c0d67b0112ca023091b35bee5a7a9078c15e934 Homepage: https://cran.r-project.org/package=climenv Description: CRAN Package 'climenv' (Download, Extract and Visualise Climate and Elevation Data) Grants access to three widely recognised modelled data sets, namely Global Climate Data (WorldClim 2), Climatologies at high resolution for the earth's land surface areas (CHELSA), and National Aeronautics and Space Administration's (NASA) Shuttle Radar Topography Mission (SRTM). It handles both multi and single geospatial polygon and point data, extracts outputs that can serve as covariates in various ecological studies. Provides two common graphic options – the Walter-Lieth (1960) climate diagram and the Holdridge (1967) life zone classification scheme. Provides one new graphic scheme of our own design which incorporates aspects of both Walter-Leigh and Holdridge. Provides user-friendly access and extraction of globally recognisable data sets to enhance their usability across a broad spectrum of applications. Package: r-cran-climetrics Architecture: all Version: 1.0-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rts, r-cran-raster, r-cran-terra, r-cran-zoo, r-cran-yaimpute, r-cran-xts Suggests: r-cran-r.rsp, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-climetrics_1.0-15-1.ca2404.1_all.deb Size: 3170712 MD5sum: 93bdae605a15cdee7d89cea53f5fb489 SHA1: 8572d3fea4d3d94735395a186a21e7bfbe8e5300 SHA256: 2629a3c4a174f47abf37b44f5ece81a706810ccdbc0509a152f7647eb2e05f0f SHA512: 9cd674474d86c9f32146473a067c0d8caf37046631713787a9ab71f25a3cb49dc0945b99d2931036d1e4bc3f235d65812cfe81ead0bdcf7a61cafdf27af227bc Homepage: https://cran.r-project.org/package=climetrics Description: CRAN Package 'climetrics' (Climate Change Metrics) A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time. Package: r-cran-climextremes Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extremes, r-cran-boot Filename: pool/dists/noble/main/r-cran-climextremes_0.3.1-1.ca2404.1_all.deb Size: 735128 MD5sum: 04bbad94ebf2e7de15c6cd40e2931b10 SHA1: ab7ae6dbfb653ed1c9962d2a585a6f0f45aaf474 SHA256: 9f88439b40e74a837ba7de9821943c01179a0393498a6456026d87bdbebe6966 SHA512: 99ecabfe08dfa7bd8a7c36836836f10fe0de222c909667b7d3004866113d1fea23d565210f1b8f0ed9e224bfd7bc6b11fa282e2b00f688bdc23be39f74e1b62c Homepage: https://cran.r-project.org/package=climextRemes Description: CRAN Package 'climextRemes' (Tools for Analyzing Climate Extremes) Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) . Package: r-cran-climind Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spei, r-cran-chron, r-cran-weathermetrics Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climind_0.1-3-1.ca2404.1_all.deb Size: 1634318 MD5sum: 54242f3599ee363c4363971bb10a8c11 SHA1: 06db306312bb9028899bff429267a3628c2c94ce SHA256: 7e223ae367e15227fcf9a454ad445aaad2c69658c97c82670fc52a980d626cf1 SHA512: 8690b86a93038efe5640f1aa3424b6a2361bc409528db45674f7ca50599c2d281654f3a21f21a872cecd638f8dc7301f76d89f1205a56e3d0766cde7be54a2fa Homepage: https://cran.r-project.org/package=ClimInd Description: CRAN Package 'ClimInd' (Climate Indices) Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5). Package: r-cran-climmobtools Architecture: all Version: 1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lpsolve, r-cran-matrix, r-cran-rspectra Suggests: r-cran-climatrends, r-cran-gosset, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-plackettluce, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climmobtools_1.8.2-1.ca2404.1_all.deb Size: 636178 MD5sum: 1b99fbe2d1e588e52f0426dc3d5a1fe3 SHA1: 2b0575da500b8300a3b2a4b50d703bd86d0b20b9 SHA256: 429cb01f4e044adcb28ce8ff356d9dbc55b657087c99228a689073dd2d9efae8 SHA512: 27f64bd6042bc1cfe3510e5b988e25299529fd1e6bda70daff17f4d61cc2db994ee3d9492abcf1e3579e82de236c4d643b733f02ff8ac771da9165502f0109d8 Homepage: https://cran.r-project.org/package=ClimMobTools Description: CRAN Package 'ClimMobTools' (API Client for the 'ClimMob' Platform) API client for 'ClimMob', an open source software for decentralized large-N trials with the 'tricot' approach . Developed by van Etten et al. (2019) , it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. 'ClimMobTools' enables project managers to deep explore and analyse their 'ClimMob' data in R. Package: r-cran-climniche Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5089 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-raster, r-cran-rmarkdown, r-cran-sf, r-cran-terra Filename: pool/dists/noble/main/r-cran-climniche_0.3.8-1.ca2404.1_all.deb Size: 3513308 MD5sum: e91aefb7c3497519f362c7270c78e650 SHA1: cb9bc5cb1cf6596591fefa59af2cd833cfaba195 SHA256: c55a85d5e85aee897ab5c7caed88ddd4b33d0a73da6744ce3de9cdea9c65b245 SHA512: c1c75949d98d129a0d64fb6d102aa85a7c93c82285c382178f7ee07b220113853bd75e191b80d3c70face04d772e49572a5ddafc1ce9c37d8cfa492e1fbda3bc Homepage: https://cran.r-project.org/package=climniche Description: CRAN Package 'climniche' (Climate Exposure Relative to a Species' Climatic Niche) Quantifies projected climatic change relative to the climatic niche represented by a species' current distribution. A weighted current reference defines the niche centre and empirical radial boundary. Present and projected conditions at each location give local climatic displacement, signed change in niche distance, a derived non-radial reconfiguration term, and exceedance beyond the niche boundary. Occurrence records, range maps, and binary or continuous species distribution model outputs can define reference weights. Matrix and spatial workflows return location-level values, weighted summaries, maps and climatic-variable contributions. Package: r-cran-climodr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cast, r-cran-corrplot, r-cran-doparallel, r-cran-dplyr, r-cran-lares, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-terra, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pls, r-cran-randomforest, r-cran-sp Filename: pool/dists/noble/main/r-cran-climodr_1.0.0-1.ca2404.1_all.deb Size: 1180696 MD5sum: 010d1bb5a03b89bb032866217b29e884 SHA1: 3ecdaba6c8503fefefd1a3055ecf55c4cc2d1d2e SHA256: 10c4fcf528b8674ec586389fa0dad8e015778882abfd24ad8369cca27d24eb18 SHA512: 3d871740815ca227da603c08d1187967c63793dd922220f9ffa9970946ea1a96022d7d1c63664d5d85b2e07a1542caef067e5992cb895f64366123061ccbe4d0 Homepage: https://cran.r-project.org/package=climodr Description: CRAN Package 'climodr' (Climate Modeling with Point Data from Climate Stations) An automated and streamlined workflow for predictive climate mapping using climate station data. Works within an environment the user provides a destined path to - otherwise it's tempdir(). Quick and relatively easy creation of resilient and reproducible climate models, predictions and climate maps, shortening the usually long and complicated work of predictive modelling. For more information, please find the provided URL. Many methods in this package are new, but the main method is based on a workflow from Meyer (2019) and Meyer (2022) , however, it was generalized and adjusted in the context of this package. Package: r-cran-climprojdiags Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multiapply, r-cran-pcict Suggests: r-cran-knitr, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climprojdiags_0.3.3-1.ca2404.1_all.deb Size: 1021618 MD5sum: 35303f3623f5e591748c3f57f0461575 SHA1: fca72f5c50da02b52822bc50b69901bc133d4a42 SHA256: f69507304cfa22dc6c969b21b154ae5f2a0d0fc5db67e7a0fae28b698bf114df SHA512: a5cb05cf30aa1d34638efe6858b2f60873e4cbd88f3149686e18e247251c652e4b872d3e310aa886176ebd90d7eafb15582438ad93165794956cd3ae7b1ab485 Homepage: https://cran.r-project.org/package=ClimProjDiags Description: CRAN Package 'ClimProjDiags' (Set of Tools to Compute Various Climate Indices) Set of tools to compute metrics and indices for climate analysis. The package provides functions to compute extreme indices, evaluate the agreement between models and combine theses models into an ensemble. Multi-model time series of climate indices can be computed either after averaging the 2-D fields from different models provided they share a common grid or by combining time series computed on the model native grid. Indices can be assigned weights and/or combined to construct new indices. Package: r-cran-climwin Architecture: all Version: 1.2.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-matrix, r-cran-evd, r-cran-lubridate, r-cran-lme4, r-cran-mumin, r-cran-reshape, r-cran-numderiv, r-cran-rcpproll, r-cran-nlme Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climwin_1.2.33-1.ca2404.1_all.deb Size: 1207698 MD5sum: 78ea4ae56c79887d069d9b203cae3ad7 SHA1: ab0c04c24382c995ef805326127c924a8189798e SHA256: 4709c20904ae7d7b9fb32cb5aca845f4708a51c7706857b4ed62b911089ba100 SHA512: 0f6ca7a934217fe79b9479a9d2e9beff3b4e7cda37ece40ab86b8874a1a47b88564aae9267e355a6ae0c3eedff7ddbefd912f7808e58511dd005591cc721153f Homepage: https://cran.r-project.org/package=climwin Description: CRAN Package 'climwin' (Climate Window Analysis) Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) and Bailey and van de Pol (2016) for details. Package: r-cran-clincompare Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-rlang, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-openxlsx, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clincompare_1.0.0-1.ca2404.1_all.deb Size: 943690 MD5sum: 804ec41820ebf64d5a96f840d0772ca2 SHA1: 859d7d8039a363ff4538282d6af7abe41531906e SHA256: 3749263654ecbfd7b8112a74849ceb92c5dec3c4e65e95bc503e9a981f4826c4 SHA512: 14f06ca46fd986bd4ede9a9cd79fccea67baf54c71231a9a3df37351f686d0c52f1787664342ef8f9546a3ed64f9b5f4913b58e1e85e5561b10f0e683f418e3d Homepage: https://cran.r-project.org/package=clinCompare Description: CRAN Package 'clinCompare' (Dataset Comparison with 'CDISC' Validation for Clinical TrialData) A general-purpose toolkit for comparing any two data frames with optional 'CDISC' (Clinical Data Interchange Standards Consortium) validation for clinical trial data. Core comparison functions work on arbitrary datasets: variable-level and observation-level comparison, data type checking, metadata attribute analysis (types, labels, lengths, formats), missing value handling, key-based row matching, tolerance-based numeric comparisons, and group-wise comparisons. Optional z-score outlier detection is available when enabled. When working with clinical data, the package additionally validates 'SDTM' (Study Data Tabulation Model) and 'ADaM' (Analysis Data Model) datasets against CDISC standards (SDTM IG 3.3/3.4, ADaM IG 1.1/1.2/1.3), automatically detecting domains and flagging non-conformant variables. Generates unified comparison reports in text or HTML format with interactive dashboards. For CDISC standards, see . Package: r-cran-clindatareview Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5608 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bookdown, r-cran-clinutils, r-cran-crosstalk, r-cran-data.table, r-cran-ggplot2, r-cran-haven, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-plotly, r-cran-plyr, r-cran-rmarkdown, r-cran-stringr, r-cran-yaml, r-cran-xml2, r-cran-xfun, r-cran-base64enc Suggests: r-cran-countrycode, r-cran-intextsummarytable, r-cran-patientprofilesvis, r-cran-testthat, r-cran-dt, r-cran-scales Filename: pool/dists/noble/main/r-cran-clindatareview_1.6.2-1.ca2404.1_all.deb Size: 1886992 MD5sum: f5196c07b85653dac691f55b463a69aa SHA1: b9168302bba508c1bb8a6039c99b4ebbceb6a72a SHA256: e10b18ae0a4c46b8d2157559dc2b33e56cd9fdedd4790604dcf35e8e4522020d SHA512: 15c7bfd1b40bdcac495a85f4686905e72352f5b86a4318d38f4d4efe8921d2193de025cee86d4b11473435d8876ca099535d4a0641dcccd3f29bf7078e74d823 Homepage: https://cran.r-project.org/package=clinDataReview Description: CRAN Package 'clinDataReview' (Clinical Data Review Tool) Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template 'R Markdown' reports contained in the package. Package: r-cran-clindr Architecture: all Version: 2.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstan, r-cran-shiny, r-cran-foreach, r-cran-dosefinding, r-cran-mvtnorm, r-cran-doparallel, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-glue, r-cran-waiter, r-cran-officer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clindr_2.5.3-1.ca2404.1_all.deb Size: 2314504 MD5sum: 498c178505bc6ef3141e74a2e81e1c35 SHA1: 770de2e261cafd7c571823ac79c7bcebc1bbc8dd SHA256: d5964412756ff04fa7dcc4671d7e95d8a20c08e7c8e0faa1171d4c71e8cccb1a SHA512: 526e7b40b33f25802333f388ba2ccbe3ce5af701386a9f06d435e5838fd0f318804cabedc192c40c0000b3270a54f9f009b74e5f77bd78cab99d2499416f0e6d Homepage: https://cran.r-project.org/package=clinDR Description: CRAN Package 'clinDR' (Simulation and Analysis Tools for Clinical Dose ResponseModeling) Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) and Thomas and Roy (2016) Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references. Package: r-cran-clinicalfair Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-clinicalfair_0.1.0-1.ca2404.1_all.deb Size: 245744 MD5sum: 0d813c97c42eebee8acd83ad1885ad0d SHA1: 360a069e3459ca8a5b235190e236f40539de641a SHA256: a7fd4e1e93a5ffeca1197fc3577aa68bfeb1d980f1fce98e382f2cf2b5dbb2bb SHA512: 89dd19198a881589a2fa82d6db1e2c05c9bd198bc39354e15261eb22cdb46ecdae760c4ae18e91752fb07575e4cc619fd794a3ebd2a6994504b2c2f704d3f22a Homepage: https://cran.r-project.org/package=clinicalfair Description: CRAN Package 'clinicalfair' (Algorithmic Fairness Assessment for Clinical Prediction Models) Post-hoc fairness auditing toolkit for clinical prediction models. Unlike in-processing approaches that modify model training, this package evaluates existing models by computing group-wise fairness metrics (demographic parity, equalized odds, predictive parity, calibration disparity), visualizing disparities across protected attributes, and performing threshold-based mitigation. Supports intersectional analysis across multiple attributes and generates audit reports useful for fairness-oriented auditing in clinical AI settings. Methods described in Obermeyer et al. (2019) and Hardt, Price, and Srebro (2016) . Package: r-cran-clinicalomicsdbr Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-r6, r-cran-dplyr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-clinicalomicsdbr_1.0.6-1.ca2404.1_all.deb Size: 55568 MD5sum: b593f62ea16e9c05bd77568b867679af SHA1: 8246116b483c4ab4cd1775bfa05ea6c83dc7a7c6 SHA256: 3923be6113270825b83a6744ae7591607f473a5d4a35ae4737eda954f3f32145 SHA512: 6e51c9e1dcf9d76b3ccc6442cb781c7d9383463288717e504961390f32f84bbff07b4263dadfa6418a45c3c9b832d7c41c13457abf86839ae6ecd59903aada80 Homepage: https://cran.r-project.org/package=clinicalomicsdbR Description: CRAN Package 'clinicalomicsdbR' (Interface with the 'ClinicalOmicsDB' API, Allowing for Easy DataDownloading and Importing) Provides an interface to the 'ClinicalOmicsDB' API, allowing for easy data downloading and importing. 'ClinicalOmicsDB' is a database of clinical and 'omics' data from cancer patients. The database is accessible at . Package: r-cran-clinicalsignificance Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesfactor, r-cran-bayestestr, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-insight, r-cran-lme4, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-clinicalsignificance_3.0.0-1.ca2404.1_all.deb Size: 737526 MD5sum: 5f11ef577ed182900461a833b112ab40 SHA1: 1f1cc66ddbd4701466d0f45aff20cef350e386dd SHA256: aff9431b4726c6bc11b41282669f4270eea74a519ebdc78cc5a6eb80bfe9bf9b SHA512: a233aad899c1172c43e43fcdc723223e7711fc78f1788274b670a4e829bd93c37e58c205403d31ab631e57690227250a227ac951fcbe41d28a6558e8e7858c3f Homepage: https://cran.r-project.org/package=clinicalsignificance Description: CRAN Package 'clinicalsignificance' (A Toolbox for Clinical Significance Analyses in InterventionStudies) A clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. . Package: r-cran-clinicalutilityrecal Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lattice, r-cran-caret, r-cran-ggplot2, r-cran-cowplot, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-clinicalutilityrecal_0.1.1-1.ca2404.1_all.deb Size: 105374 MD5sum: e78302becdc83b6a736f0b1e3df10981 SHA1: bb82a1bbe8db066e821573e146a9b81becca4104 SHA256: 11faff659e21f12ae091092f1bc655ffaf305dd9bec7065a2028545a66f8d540 SHA512: aecb3c3299539b1a67113b2b578772cd390de69d05d2b521cc36c51418d3d18eb8c2d7619b7f6027ed48b239803944a064b1c703c8cddd2b30dbb7cad0789c8c Homepage: https://cran.r-project.org/package=ClinicalUtilityRecal Description: CRAN Package 'ClinicalUtilityRecal' (Recalibration Methods for Improved Clinical Utility of RiskScores) Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" . Package: r-cran-clinify Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1622 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-flextable, r-cran-officer, r-cran-magrittr, r-cran-dplyr, r-cran-knitr, r-cran-htmltools, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-tplyr, r-cran-rvest, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-clinify_0.4.0-1.ca2404.1_all.deb Size: 636480 MD5sum: 76e21086e885db59b2557be4845d83e4 SHA1: fab8dab650b20888b0fc5250fa28270db14e7f72 SHA256: 6b6ba7041fad8dbca05cd5180a99b718c4aa7bcd8c39c2959bd14d09f166bf1b SHA512: e0135e6a8fd20463fdf640e1e4b96c660569292c6dfba5f54da613a62858521d0ec98c17f6c5358b93945d716d61a2910a57cf3c909c761f1af2c54940e0b2a5 Homepage: https://cran.r-project.org/package=clinify Description: CRAN Package 'clinify' (Clinical Table Styling Tools and Utilities) The primary motivation of this package is to take the things that are great about the R packages 'flextable' and 'officer' , take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces. Package: r-cran-clinmon Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clinmon_0.6.0-1.ca2404.1_all.deb Size: 513506 MD5sum: 83b6acb917ba820364fb9ad0414cc2d6 SHA1: 2172a4c343064894bb4163c4a068ad4f7415bad1 SHA256: 9973c5ae4ca08e60622574d022230d826930071df4477b93b5b4ba7c012a9749 SHA512: 3afafb9fd932ff3a502b844acfdbc2b332f8485e7f6dd7984a72293e5e304c67665f85c49e6c246e45f1eadacfcfb3e80077da9001f752cf28f0fa73f5acb60c Homepage: https://cran.r-project.org/package=clinmon Description: CRAN Package 'clinmon' (Hemodynamic Calculations from Clinical Monitoring) Every research team have their own script for calculation of hemodynamic indexes. This package makes it possible to insert a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Package: r-cran-clinpk Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clinpk_0.13.0-1.ca2404.1_all.deb Size: 794974 MD5sum: 9c47df6a9060463ee7c20f5bc11e3f9d SHA1: bbfdf9f63ad985d722116ae045024c9221174a41 SHA256: 1a3ed19c4fb5c6fdf1fa0214278fedd2fc27f970246368c9ddc07d60d3ab9802 SHA512: 35483ef1ec983707aff2abb108fdd0c72285b915ecfe397fd127ebc8195a75710edb7aac2155d9b5295ed0fe69a1802a5b06465baa6b3601cd23da557196d19a Homepage: https://cran.r-project.org/package=clinPK Description: CRAN Package 'clinPK' (Clinical Pharmacokinetics Toolkit) Provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function. Package: r-cran-clinpubr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1840 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-data.table, r-cran-desctools, r-cran-dplyr, r-cran-fbasics, r-cran-forestploter, r-cran-ggplot2, r-cran-hmisc, r-cran-rlang, r-cran-rms, r-cran-stringi, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tidyr Suggests: r-cran-caret, r-cran-dcurves, r-cran-dtplyr, r-cran-geepack, r-cran-knitr, r-cran-proc, r-cran-resourceselection, r-cran-rmarkdown, r-cran-rstatix, r-cran-tableone, r-cran-testthat, r-cran-timeroc, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-clinpubr_1.4.0-1.ca2404.1_all.deb Size: 1195260 MD5sum: 7e28366d28f524973ac7354c526efa5b SHA1: 0c50b20c71d29f8e619864ce6988c37954fd5c64 SHA256: ffae8650b0868f68e9e62dbda7f53e22b13192cf3c2aad3d1b4bf59b8710e5bf SHA512: 3c4d17f741771ec8c137aa64f41576f92ffb19ef76094ffbba49ad1b4378fb8de015def4dfa7e0f66efd75850c3531f600bd38fd2adbabdf183b03f7a9c171c5 Homepage: https://cran.r-project.org/package=clinpubr Description: CRAN Package 'clinpubr' (Clinical Publication) Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures. Package: r-cran-clinsig Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clinsig_1.2-1.ca2404.1_all.deb Size: 42646 MD5sum: d92b75c39c00ac83aa817dd53072f36c SHA1: 98c5d1291f8ad2826fa8ad5c6c9d6f3cbcb39934 SHA256: 765cdd099ffb6596088ab292196b55d90966963ac6fe9319f1d7fe845fe8f483 SHA512: 730b08a7ba5665bfb6941a0d81fc95baa63f66d7c5265c1f60b5bd655d0c7db7de144437f3ec44f5e6aec405150c95a128457bff95d4001f82e703bc48b3c994 Homepage: https://cran.r-project.org/package=clinsig Description: CRAN Package 'clinsig' (Clinical Significance Functions) Functions for calculating clinical significance. Package: r-cran-clinsigmeasures Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clinsigmeasures_1.3-1.ca2404.1_all.deb Size: 49104 MD5sum: 3660f88be232bc98b5a8ef94fa0a9c32 SHA1: 3651bae8492100d0791df449d40eafc6cbec5572 SHA256: 486ad1dfb9a85af6f9172ac54855eb55d21c623c683522253175a2188d9029eb SHA512: 5bc5e8b28eaa9f1be9b9d541f9c03c031be43b43741be74663ed06aff02f2a51073eb958f113146f06d540e13c86cacaf180ffd0e983f643966af89c0e91c813 Homepage: https://cran.r-project.org/package=ClinSigMeasures Description: CRAN Package 'ClinSigMeasures' (Clinical Significance Measures) Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. 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Package: r-cran-clinspacy Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-data.table, r-cran-assertthat, r-cran-rappdirs, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clinspacy_1.0.2-1.ca2404.1_all.deb Size: 62942 MD5sum: 53f11f80375e28a06dc9728bb3dfc0c9 SHA1: e29464f04d468ce91289247b93b6132a5f124c0d SHA256: 7eb2c193ae84175c94f010b9e02dc82575d2547fc121128c8b89279da061819f SHA512: e9ba54d91ad53e4e65604dc3f73ef8f47ab53c97bff86397a99250170bfbdeb3e8dc1d11b33de7d775e9febc36dd8a01efa4ec07fd85f732aacff2f6b089bf30 Homepage: https://cran.r-project.org/package=clinspacy Description: CRAN Package 'clinspacy' (Clinical Natural Language Processing using 'spaCy', 'scispaCy',and 'medspaCy') Performs biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and transforms extracted data into a wide format for inclusion in machine learning models. The development of the 'scispaCy' package is described by Neumann (2019) . The 'medspacy' package uses 'ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) . Clinspacy also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept embeddings developed by Beam (2018) . Package: r-cran-clintools Architecture: all Version: 0.9.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal, r-cran-xml2, r-cran-lme4, r-cran-ggplot2, r-cran-proc, r-cran-irr, r-cran-nlme, r-cran-parameters, r-cran-stringi, r-cran-scales, r-cran-dplyr, r-cran-survival, r-cran-pander Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clintools_0.9.10.1-1.ca2404.1_all.deb Size: 791822 MD5sum: 6105abea87e08b0a00b64b705b0232e7 SHA1: 0c67b24cdb444be5eb146ab7527e109ed49ac236 SHA256: ba76118db618741114d7e360c2b0d689f115a9da877ba5ae6547904ef9b8fd83 SHA512: 45f5a0dd9a6fcb78184fdd0a5e8f7e1980ec34a425a495a7342a3c07e455eabc524f5d6fb6e5c7a4a3cf66bd06b9a8bb5347a242e94021cb1541ffb83bba7e8e Homepage: https://cran.r-project.org/package=clintools Description: CRAN Package 'clintools' (Tools for Clinical Research) Every research team have their own script for data management, statistics and most importantly hemodynamic indices. The purpose is to standardize scripts utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Transfer function analysis (Claassen et al. (2016) ) and Mx (Czosnyka et al. (1996) ) can be calculated using this package. Package: r-cran-clintrialdata Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-connector, r-cran-httr, r-cran-jsonlite, r-cran-piggyback Suggests: r-cran-arrow, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-clintrialdata_0.1.3-1.ca2404.1_all.deb Size: 2950798 MD5sum: 95414604bdb3d5e6a9127d805e7e218e SHA1: d6ad3b977e8f27186555c44edb64aebe95d79cec SHA256: 969a48ed51b9a828cf17444a86e8496ddcdc9b874828d7dfb5b9ac866ac8e8b2 SHA512: 20aa1a1c18945f9fe4695cb338950b85ad300b179983b983db0abdbb818356696d550a69c979ce8558738613cefa8343789e35999236261cc3891c8c40220066 Homepage: https://cran.r-project.org/package=clinTrialData Description: CRAN Package 'clinTrialData' (Clinical Trial Example Datasets) A collection of clinical trial example datasets from multiple sources including the CDISC Pilot 01 study (CDISC ). All datasets are provided in Parquet format for efficient storage and can be accessed using the 'connector' package. Designed for training, testing, prototyping, and demonstrating clinical data analysis workflows. 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Package: r-cran-clintrialx Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-lubridate, r-cran-readr, r-cran-dplyr, r-cran-progress, r-cran-rpostgresql, r-cran-tibble, r-cran-dbi, r-cran-rmarkdown Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-clintrialx_0.1.1-1.ca2404.1_all.deb Size: 387958 MD5sum: 10a8606ffae9166f51a0c93193edff05 SHA1: f34f7c6439a86de50c6c5a130017ef4eb23971ae SHA256: 2613af3fd006418e0f06893599dc86999a9293938a19da7648544c8911884bdb SHA512: e2ceea5640accc1e1502a23c28d217ffeec8c3d1c23bd13bc0e5a266c0832b9ddb83f1021f0cf069964f6ed6911ab3327370c9f9a44bd10fc13dd45fee84fb3e Homepage: https://cran.r-project.org/package=clintrialx Description: CRAN Package 'clintrialx' (Connect and Work with Clinical Trials Data Sources) Are you spending too much time fetching and managing clinical trial data? 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Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities. 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See README on for more details. P Yang et al. (2015) . 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See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) . Package: r-cran-clusscluster Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-venndiagram, r-cran-scales, r-cran-reshape2, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clusscluster_0.1.0-1.ca2404.1_all.deb Size: 210294 MD5sum: 275552be0a86ea61746eea42c4b67daf SHA1: 14a7886d877be7987412179d30569649ab44f4ad SHA256: 5a99594cfd80f7f73877991d8f92cefb07b336701c0c0909d680af1f5d3d02fc SHA512: 63d55ad3fcdf4eb62ba0602d28644a089b9a0ebe501866ec2fd4d4ed4a20b820629048e593ed500ba15d36c1a2cfbf7fc8ab382d1d6be23f8953c4cf7aa10a58 Homepage: https://cran.r-project.org/package=ClussCluster Description: CRAN Package 'ClussCluster' (Simultaneous Detection of Clusters and Cluster-Specific Genes inHigh-Throughput Transcriptome Data) Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, 'ClussCluster' incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells. Package: r-cran-clust.bin.pair Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clust.bin.pair_0.1.2-1.ca2404.1_all.deb Size: 35144 MD5sum: 2659c7868c3d87dcf6266cf798f7c856 SHA1: 46da3f04f126be805b0fc484aa7d614a74d76542 SHA256: df75a2ff981975255672b9ccb47e753ca4d9055199d4079c8dc99c74e05e7aef SHA512: 846c1b851277228555b347bb727f0235bb50d88a17fabb802ad7a273f139596985a85adf0c30b4cad831887fd66720c622b58cd0e41e8fd291f83b405c27c2fa Homepage: https://cran.r-project.org/package=clust.bin.pair Description: CRAN Package 'clust.bin.pair' (Statistical Methods for Analyzing Clustered Matched Pair Data) Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) , Obuchowski (1998) , Durkalski (2003) , and Yang (2010) with McNemar (1947) included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) respectively. Package: r-cran-clustblock Architecture: all Version: 6.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factominer Suggests: r-cran-clustvarlv Filename: pool/dists/noble/main/r-cran-clustblock_6.1.0-1.ca2404.1_all.deb Size: 419678 MD5sum: 18e8e1dd51e151d41624beeef5a573f6 SHA1: 9794ad627b5cd6d93bbce376aae9a3a6c1abbd0c SHA256: 249822fc10245b30f54af52b25dbebbedb3e2e07467662c14807689a1548b230 SHA512: 1bc36ae3f84bd8072ecca7e7a403a4ef754a9a5e575d8ac2660a9edaf83f9cae7026451b02ff91f2e71e6e24a2fa960c6d5a5878c2d5f8b19fc573a21d3cd934 Homepage: https://cran.r-project.org/package=ClustBlock Description: CRAN Package 'ClustBlock' (Clustering of Datasets) Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) , Llobell, Vigneau & Qannari (2019) ) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) , Llobell, Giacalone, Labenne & Qannari (2019) ) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data (Llobell, Bonnet & Giacalone (2024) ) . Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of observations (products in sensory analysis) in multi-block context (notably with ClusMB strategy) is also included (Llobell & Giacalone (2025) ).Performing clustering based on CATA and liking at the same time is possible thanks to cluscata_liking function (Vigneau, Cariou, Giacalone, Berget & Llobell (2022) ). Clustering of variables (quantitative, qualitative or mixed) can be done thanks to the MixCluStatis() function. Clustering on JAR + Liking can be achieved thanks to preprocess_JAR_liking function. Package: r-cran-clustcr2 Architecture: all Version: 1.7.3.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desctools, r-cran-ggplot2, r-cran-ggseqlogo, r-cran-network, r-cran-plyr, r-cran-rcolorbrewer, r-cran-stringr, r-cran-scales, r-cran-sna, r-cran-vlf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustcr2_1.7.3.01-1.ca2404.1_all.deb Size: 253044 MD5sum: afcbf59f672c70560bc24e5fa37b0ca9 SHA1: 2a984ca794742ea9ad63806f4bb2f9729c4abc2c SHA256: fff9704d575093ca84588b8a73c668efbc22407dd2ca29c7c28c4bd5346a00e0 SHA512: c04b5ea002c1f2976289e22ff350b81a3c6eeb431ba04ed99304692e58154a4b6fb1ec5228cfa789b5847b02d26613579174d5269d0e98aacd6fabb16c5de2a7 Homepage: https://cran.r-project.org/package=ClusTCR2 Description: CRAN Package 'ClusTCR2' (Identifying Similar T Cell Receptor Hyper-Variable Sequenceswith 'ClusTCR2') Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) , Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) . Package: r-cran-clustcurv Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-ggfortify, r-cran-ggplot2, r-cran-gmedian, r-cran-npregfast, r-cran-rcolorbrewer, r-cran-survival, r-cran-survminer Suggests: r-cran-covr, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustcurv_3.0.1-1.ca2404.1_all.deb Size: 1068770 MD5sum: c207c9cb20bf573bfa1da80eb5991814 SHA1: f8929e553a555e2c3f240105ec0796cf2469b2e2 SHA256: 4466eb81f2f786f2e3d00b3d3742b7c078512fdb8700e41b23bf7f486b3c07cb SHA512: 0bc3afe2d9e206ed48b2141bc2d4d91d0f4330ccc69e74b40767317f846b776d8f82032383de8ac3d9c9cfe9339b9e5a7918ac6e5bdf84aed8ffe549e2b6018d Homepage: https://cran.r-project.org/package=clustcurv Description: CRAN Package 'clustcurv' (Determining Groups in Multiples Curves) A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) . Package: r-cran-clusteff Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qrcm, r-cran-cluster, r-cran-fda, r-cran-ggpubr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clusteff_0.3.1-1.ca2404.1_all.deb Size: 117300 MD5sum: 1f23648378a07e1de3ba6b3dabdbc146 SHA1: 811d76f24e85bae1d87901d0d1293e735d71f113 SHA256: a470c190a3302924d25e698ba8433c6d94c77c8fc50ec37c543e52bd2e5b6d5b SHA512: a7fae2ebddfb299917699a80908c9db3e7eb4bb7a3034dad04ae6e5982942293d146e6a7801a67d0bebbe520334b5714b602371384e63f7e2f78553408abe1f2 Homepage: https://cran.r-project.org/package=clustEff Description: CRAN Package 'clustEff' (Clusters of Effects Curves in Quantile Regression Models) Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) . Package: r-cran-cluster.datasets Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cluster.datasets_1.0-1-1.ca2404.1_all.deb Size: 213086 MD5sum: e49afaf548ba6488bd43891b3fb7316a SHA1: a7804bcb688f4e91c02902d54111a25439a6a62c SHA256: 3fd08dec837acefd5eb01a892e8a0f923437ce31890cde903b8cbe7ca153e15b SHA512: fd69eddedf226bc8162856ed9e6896cfe811141fae87351e87dbbcf39e8f795b8a2b326e7456f7233010f672135fc7673601d7176e621fe6423355bfd14c2399 Homepage: https://cran.r-project.org/package=cluster.datasets Description: CRAN Package 'cluster.datasets' (Cluster Analysis Data Sets) A collection of data sets for teaching cluster analysis. Package: r-cran-cluster.obeu Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-cluster, r-cran-clvalid, r-cran-data.tree, r-cran-dendextend, r-cran-jsonlite, r-cran-mclust, r-cran-rcurl, r-cran-reshape, r-cran-reshape2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cluster.obeu_1.2.3-1.ca2404.1_all.deb Size: 64802 MD5sum: f48a395b0650a49d900db25d437a7a05 SHA1: bb0d10933e296da551a29aaeeb7633753c275f85 SHA256: 567f3c6919527362af1aa4e10b681cdbacdb3baa283c752d89f1698c97a31e3d SHA512: db5e9749efaac7a0a666377a53810735c5d594f122cfa4acc0ac489cd3ac23bce7eb800f65bfb6a03245dda2fc9627a0aa70405b0147c519c09a119f755c1ac3 Homepage: https://cran.r-project.org/package=Cluster.OBeu Description: CRAN Package 'Cluster.OBeu' (Cluster Analysis 'OpenBudgets.eu') Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface. Package: r-cran-clusterability Architecture: all Version: 0.2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diptest, r-cran-sparsepca, r-cran-elasticnet, r-cran-cluster Suggests: r-cran-testthat, r-cran-plotly, r-cran-bench Filename: pool/dists/noble/main/r-cran-clusterability_0.2.3.0-1.ca2404.1_all.deb Size: 92304 MD5sum: 12c328439e6463502964ea36b6f47837 SHA1: bdf9607a782f7d9411fceab12ed4117dc92330be SHA256: 14f7f08e8175087b9ac1bf3205089fb94a825e6f19befb2ad026e4f1503da62b SHA512: 0035b7ac4766351c0c20e80b1b3c4dac1a071c3d6d34f4fdde675422393b61c5770ee7cdcbb2c7695bbe4e4b44dad1fcb02c15375be860406aabca59d0354088 Homepage: https://cran.r-project.org/package=clusterability Description: CRAN Package 'clusterability' (Performs Tests for Cluster Tendency of a Data Set) Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) and Laborde et al. (2023) . Such methods can inform whether clustering algorithms are appropriate for a data set. Package: r-cran-clusterbootstrap Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-clusterbootstrap_2.0.0-1.ca2404.1_all.deb Size: 107448 MD5sum: 72947534201253c3b5518620d2e6c527 SHA1: d08902220718b09db2bf1e9806e994f8f2f58d11 SHA256: b5fab9fe311029d33a23e6664a0a8aa7d0ef5320b7eff0ba5c437a462cb5de03 SHA512: 4843c299f18d587ed42f95557a09d2024ba9b069f3cbbda92441a0b07920b7f298483990a0f9b21e5bd2fe65de57367262c6311bc38fff2f64e7edf05b93e885 Homepage: https://cran.r-project.org/package=ClusterBootstrap Description: CRAN Package 'ClusterBootstrap' (Analyze Clustered Data using the Cluster Bootstrap) Provides functionality for the analysis of clustered data using the cluster bootstrap. Package: r-cran-clustercons Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-lattice, r-cran-rcolorbrewer, r-cran-apcluster Suggests: r-cran-latticeextra Filename: pool/dists/noble/main/r-cran-clustercons_1.2-1.ca2404.1_all.deb Size: 268408 MD5sum: 86448b0a6b10e58ce18e4ed8ab5c72bd SHA1: ba49469b7011c18b22a318ed0742ca6424df50b6 SHA256: ae7d421a8e0e3547f4df06a3763f2a9acec9c50889dfc6472943667de693ae62 SHA512: e8934278648cb6b8c9bde7744bf21e411fccae6ea734355297312110ac90952c76369f7aef9fc30e525323e332ad19c2432b3c15eb49a12dd86ed4032229f4a8 Homepage: https://cran.r-project.org/package=clusterCons Description: CRAN Package 'clusterCons' (Consensus Clustering using Multiple Algorithms and Parameters) Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) . Package: r-cran-clusteredinterference Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-cubature, r-cran-lme4, r-cran-numderiv, r-cran-rootsolve Suggests: r-cran-testthat, r-cran-rprojroot, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-clusteredinterference_1.0.1-1.ca2404.1_all.deb Size: 146440 MD5sum: 9937faac9af45c5eff4e9e2d2f8b008f SHA1: 273111265bc9bbf45fc1efa4b85f9797266e68df SHA256: 0020c6b4a2922ee030e68d8b688e277770e220a6296a3599bf5565bab72fac74 SHA512: 98d1e947e51905eefc8f078a454c9c8983240700249ced956228c6e024d59dc391e98ee47ef9e7e829537b127df02205739d67b2612d104c7d7d45e8c0740da1 Homepage: https://cran.r-project.org/package=clusteredinterference Description: CRAN Package 'clusteredinterference' (Causal Effects from Observational Studies with ClusteredInterference) Estimating causal effects from observational studies assuming clustered (or partial) interference. These inverse probability-weighted estimators target new estimands arising from population-level treatment policies. The estimands and estimators are introduced in Barkley et al. (2017) . Package: r-cran-clusteredmsm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-cran-mstate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clusteredmsm_0.1.0-1.ca2404.1_all.deb Size: 152888 MD5sum: 406d7b60fb6bf310526f4f1866d506ee SHA1: fe70fbf2eeaf54844b86cd15c13aa4666997564b SHA256: a43f52db58e0cd832d1f55ffadedd11532f80d300bde1c78be138469c76e431a SHA512: fa72ac1f4913644a050e055e5791e39927181bd8eafbd71044d88b10673d71f95c042af2b433f3f18d3252cbe63d3249d80708873a490580cd8fd2b5184143a2 Homepage: https://cran.r-project.org/package=clusteredMSM Description: CRAN Package 'clusteredMSM' (Nonparametric Analysis of Clustered Multistate Processes) Nonparametric estimation of population-averaged transition probabilities, with cluster-bootstrap pointwise confidence intervals, simultaneous confidence bands, and two-sample Kolmogorov-Smirnov-type tests for clustered or independent multistate process data. Estimation follows Bakoyannis (2021) ; two-sample inference for the cluster-randomized and independent-samples designs follows Bakoyannis and Bandyopadhyay (2022) . Both methods use the working-independence Aalen-Johansen estimator. The package supports both progressive (acyclic) and non-monotone (e.g., illness-death with recovery) multistate processes, right censoring, left truncation, and informative cluster size. The user supplies data in interval format (one row per mutually-exclusive time interval per subject) and interacts with the package through a single formula-based function, patp(). Package: r-cran-clusteredmutations Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seriation, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clusteredmutations_1.0.2-1.ca2404.1_all.deb Size: 954598 MD5sum: 267d6ee44fc8acd4bb955f1e983e7f60 SHA1: 62934e4cf763ab0148e7edc34124d8f071d585d7 SHA256: 9552ed9c3fe7eb42b78280a372f93e538c79cb909c83dabc133e84451855dc81 SHA512: ef7c5ecb724427c8ab826e28e293142f4fd807ea87a53e8d73737d9579591d77622d60cc27ba40cf1f94abd88ab7a3127cd71ec05fe067fb5a99733539004ada Homepage: https://cran.r-project.org/package=ClusteredMutations Description: CRAN Package 'ClusteredMutations' (Location and Visualization of Clustered Somatic Mutations) Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information. Package: r-cran-clustergeneration Architecture: all Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-clustergeneration_1.3.8-1.ca2404.1_all.deb Size: 269914 MD5sum: 394d5004f1548cd2f17db70394321eb0 SHA1: 6632ec190aad8072f6214cc94887aa513ac0213d SHA256: 6011996cc20db0405b29502209c0f31533a5aaf410a9af63da93b3436cb0cf6d SHA512: 75135d81df9ab39d9142538a25ddbd909fb8bfd9d00d645d78d8f7ce9a47dcc28d97fc158be0c55c20cee038979709f7903c89dfc11c992dcb5002b6df76710b Homepage: https://cran.r-project.org/package=clusterGeneration Description: CRAN Package 'clusterGeneration' (Random Cluster Generation (with Specified Degree of Separation)) We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables. Package: r-cran-clustergvis Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorramps, r-cran-dplyr, r-cran-e1071, r-cran-factoextra, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-bioc-singlecellexperiment Suggests: r-bioc-biobase, r-bioc-complexheatmap, r-bioc-clusterprofiler, r-bioc-summarizedexperiment, r-bioc-tcseq, r-cran-circlize, r-cran-igraph, r-cran-knitr, r-bioc-monocle, r-cran-pheatmap, r-cran-rmarkdown, r-cran-seurat, r-cran-wgcna, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-clustergvis_0.1.4-1.ca2404.1_all.deb Size: 866152 MD5sum: ff1a34582c7d5af3c540e07366aebc5f SHA1: 156b07a2503d760b32db8dc3cd9ca00507d12545 SHA256: 32ad77bd524988a29fc9508a28de6e2dab407dcd7977047bf713899e0811ce18 SHA512: a2822e52654c0946ebeaa9625f08cd607fee94b45f4225cf443f4261e88c277edbc055104e688406465a1d03680d2f0ff179f26ec20677357300c7b1ce61939c Homepage: https://cran.r-project.org/package=ClusterGVis Description: CRAN Package 'ClusterGVis' (One-Step to Cluster and Visualize Gene Expression Data) Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as 'Seurat', 'Monocle', and 'WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) for more details. Package: r-cran-clusterhap Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clusterhap_0.1-1.ca2404.1_all.deb Size: 46796 MD5sum: 7f97c0a551f6b34246084accfd6bd6d4 SHA1: c1d5e8a104275d0cf9913a983788dfdac95ce95a SHA256: 3c0bcca43ee452e8ad1566605e9adf7b1affaf289235a5e3eac933e0b9a33e26 SHA512: a722c3172a910100a4d10d9d0b3a3044c00ed5c24b460d1d4a150568941a3d8277c4796d0f0a72ac36baeaa1ad61fa8e759e700f8049043a89f6d7f967f2a499 Homepage: https://cran.r-project.org/package=clusterhap Description: CRAN Package 'clusterhap' (Clustering Genotypes in Haplotypes) One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci). clusterhap groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, clusterhap groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, clusterhap calculates such probability from relative frequencies. Package: r-cran-clusterindices Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factoextra, r-cran-lowmemtkmeans, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-clusterindices_1.0-1.ca2404.1_all.deb Size: 37674 MD5sum: 136bb0a5b34439731c8450da87cf3282 SHA1: e2f2014d63632b907c2217c51733b07f997b40bc SHA256: f6fa99a38433a10c866be8093e2328cb9f138953d377b2bb9439b33587733f38 SHA512: 52a9f7f403d32702190ff9682c71badde754e6bf5abd20deeac678fb3338b492163684c7310a41f41311c7813108c23bfcb0652070381fcc2f53149c75aab1f5 Homepage: https://cran.r-project.org/package=clusterindices Description: CRAN Package 'clusterindices' (Cluster Validity Indices) Numerous indices to choose the optimal number of clusters when performing k-means. Relevant papers include: Tsagris M. and Kontemeniotis N. (2025). Lobachevskii Journal of Mathematics . Garcia-Escudero Luis A., Gordaliza Alfonso, Matran Carlos, Mayo-Iscar Agustin. (2008) . Package: r-cran-clustering Architecture: all Version: 1.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-amap, r-cran-apcluster, r-cran-cluster, r-cran-clusterr, r-cran-data.table, r-cran-dplyr, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-gmp, r-cran-pracma, r-cran-pvclust, r-cran-shiny, r-cran-sqldf, r-cran-xtable, r-cran-toordinal Suggests: r-cran-dt, r-cran-shinyalert, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-tidyverse, r-cran-shinycssloaders Filename: pool/dists/noble/main/r-cran-clustering_1.7.10-1.ca2404.1_all.deb Size: 395286 MD5sum: a5633c969fefe3a785d5e161a3210087 SHA1: acce5850640ac798e16f943625e65abe620b8b45 SHA256: d9d8f821c9768d81719a6e525e2376e3e963776779e39729f43b6d00dca3f6d2 SHA512: 148608a0768039815ba98f56ebabf5d8a280202e840e9921e31e04238155e3cebe80813131f3f69d03b758fb1158190decf91a4e6ae5be701ad8232bcb98b466 Homepage: https://cran.r-project.org/package=Clustering Description: CRAN Package 'Clustering' (Techniques for Evaluating Clustering) The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided. See Martos, L.A.P., García-Vico, Á.M., González, P. et al.(2023) "Clustering: an R library to facilitate the analysis and comparison of cluster algorithms.", Martos, L.A.P., García-Vico, Á.M., González, P. et al. "A Multiclustering Evolutionary Hyperrectangle-Based Algorithm" and L.A.P., García-Vico, Á.M., González, P. et al. "An Evolutionary Fuzzy System for Multiclustering in Data Streaming" . Package: r-cran-clusteriv Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-generics, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clusteriv_0.2.0-1.ca2404.1_all.deb Size: 533518 MD5sum: 2ffeef0e66ca9d79a4358d38fde60222 SHA1: 359af32e9b3f03099d68bc181a0d3892659b0f68 SHA256: b94cf0e4fc916be3bce4fba2f8d6129903681608ea4928e1a1d02165b32121aa SHA512: 48663312b548ba12198ccda8e0e371584b43c87281d66627e54ea703afe3da2691dd20b43c5f22a1873f91313ceb4fab398d7978e2bae6b8cc15c10850d8a348 Homepage: https://cran.r-project.org/package=clusterIV Description: CRAN Package 'clusterIV' (Clustered Instrumental Variables Estimation and Inference) Implements instrumental variables estimation and inference for one endogenous regressor and one-way clustered errors. Includes the cluster-jackknife IV estimator (CJIVE) of Frandsen, Leslie and McIntyre (2025) and the cluster-jackknife Anderson-Rubin and score tests of Ligtenberg (2025) , which are robust to weak and many instruments. Supports multiple excluded instruments, covariates, precision weights, and high-dimensional fixed effects. Package: r-cran-clustermole Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2373 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-bioc-gseabase, r-bioc-gsva, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-bioc-singscore, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustermole_1.2.0-1.ca2404.1_all.deb Size: 2393466 MD5sum: ba266308e4e18edffa991129d3ebe98e SHA1: 9d18466553fe7925ba1cad0272a790719414cf49 SHA256: ee13375b69f3c24b071749ad4cd548f853d84a0d14b9f3c288652e85f5794ea5 SHA512: c9932a6a304a436591247a2af6b7f76608e6225076d95fce1594617cc3ce5af37dcc37fd112e1908487d71699425dab28fb991c3f5ebce3300f6608b0e986cc9 Homepage: https://cran.r-project.org/package=clustermole Description: CRAN Package 'clustermole' (Cell Type Marker Database for Single-Cell RNA-Seq Data) Provides a meta-database of thousands of human and mouse cell identity markers curated from multiple sources, along with methods for cell type prediction based on marker gene overlaps or gene set enrichment. Package: r-cran-clusternomics Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-plyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mclust, r-cran-gplots, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clusternomics_0.1.1-1.ca2404.1_all.deb Size: 308620 MD5sum: b0a680548adb4e7c5efad62ff5f17e42 SHA1: 02cc348d85cfbd2cd900889b794516209ea00bfd SHA256: 69498859a5dc652f357b40502bb093978e425c875e81e585b90dbc000866df9c SHA512: 5b215a87e17b45429ce3469a0f90c0e0e21670f98f6134f5db5826bf24856690bdf103b3daf72f887479de8dad853c21ace960735ed3c77f0b4036197f0bca20 Homepage: https://cran.r-project.org/package=clusternomics Description: CRAN Package 'clusternomics' (Integrative Clustering for Heterogeneous Biomedical Datasets) Integrative context-dependent clustering for heterogeneous biomedical datasets. Identifies local clustering structures in related datasets, and a global clusters that exist across the datasets. Package: r-cran-clusterrandssadj Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-car, r-cran-emmeans, r-cran-lmtest, r-cran-matrix, r-cran-multcomp, r-cran-sandwich Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clusterrandssadj_1.0.0-1.ca2404.1_all.deb Size: 92588 MD5sum: 7634fc7d62d1553fc91c59f4cbedb984 SHA1: 9e816d55e451a2458f302182b857cd17a112a4e9 SHA256: d92cc2610b06fbc255469c5170238f7c4167686f0e10818aa3a207e1cd752a63 SHA512: 6ad952acab941f2e5bc1192b0e76a89981a01767e0fb7c8aebf657cd115175d055d4ab3c55fb9c266910f98b9b4d67ac911cbee11ad7bb9a56b71ce195c420ca Homepage: https://cran.r-project.org/package=ClusterRandSSAdj Description: CRAN Package 'ClusterRandSSAdj' (Small Sample Adjustment of Cluster Randomized Trial) A set of functions to apply 'HC3' (FIRORES) and 'HC2' (ROOT) sandwich estimators to make small-sample adjustments to standard errors of Generalized Linear Model statistics used to analyze cluster randomized trial data. The functions in the 'ClusterRandSSAdj' package make small-sample adjustments to Generalized Linear Model parameter estimates, least squares means and pair-wise comparison of least squares means, Type III tests, and estimates from linear combinations of Generalized Linear Model parameters. For more details see Ford (2017) and Westgate (2022) . Package: r-cran-clusterranktest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clusterranktest_1.0-1.ca2404.1_all.deb Size: 28898 MD5sum: 53feec732f3f6ede941e53b9823a5aa6 SHA1: 1d9038d9ad8cd17c73a06212386193e750dd191f SHA256: 0688ebb5d72a1d42f6e9c6ffce0a98e8964ad16eceea540472c967d2aa3979d3 SHA512: 71c25ef2c335bda703e5185d5dfdfd707f272b9c64f9f0e06ba436fe810c3857afab3ce1d940ea1c0583bc3bfbe796a9ed9500c568cd8eba52bf012cf5fd8841 Homepage: https://cran.r-project.org/package=ClusterRankTest Description: CRAN Package 'ClusterRankTest' (Rank Tests for Clustered Data) Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size. Package: r-cran-clusterrepro Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clusterrepro_0.9-1.ca2404.1_all.deb Size: 20194 MD5sum: f244e7c43234956853abb1ea48d26211 SHA1: 20c06f5beef4f8be159351853769e13d8144e4ef SHA256: a2e069d3971beb6e5258e979ba55bcded3c2eb95d9370d300795a0a04e67aa02 SHA512: 5dac6a67894d7ab8818f19d6b7febb2d3ddc98206120f16c72c215d3bfacf2e469e4eb935be9a94485090cf9d0a173b76f1b71930c99085d60ee3cae91317e72 Homepage: https://cran.r-project.org/package=clusterRepro Description: CRAN Package 'clusterRepro' (Reproducibility of Gene Expression Clusters) This is a function for validating microarray clusters via reproducibility, based on the paper referenced below. Package: r-cran-clusterses Architecture: all Version: 2.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-dfidx, r-cran-lmtest, r-cran-mlogit, r-cran-plm, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-clusterses_2.6.6-1.ca2404.1_all.deb Size: 113520 MD5sum: ad744c12f320f36f935623bdfc626d8d SHA1: d893eab344b94a16f5678aeca2229d0e43ac0dd6 SHA256: 1d84622c9ac9e25e91ef1b1be7d7e7c4436afb3bb0bc30f26eaee312bc04d362 SHA512: ea2e7ea7ba444507fecaaf1f2d837a61dbf3d92347212461fe605422186349f51ba78f45f992be52b53a237caf2c1c1b8f08c886e934de1eaf2823c09bb3d277 Homepage: https://cran.r-project.org/package=clusterSEs Description: CRAN Package 'clusterSEs' (Calculate Cluster-Robust p-Values and Confidence Intervals) Calculate p-values and confidence intervals using cluster-adjusted t-statistics (based on Ibragimov and Muller (2010) , pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) . Procedures are included for use with GLM, plm (pooling or fixed effects), and mlogit models. Package: r-cran-clustertend Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clustertend_1.7-1.ca2404.1_all.deb Size: 13054 MD5sum: 270cfcb763bbf8b766aaa1d5d376384f SHA1: a77cb3284e8816acf441e9bcf181c9d5829012db SHA256: 67d37ebae88f1307a27666f81a627e5b53b6eadcfd220c4d71d1a5afbccd164f SHA512: ee4cd9c09af62830fb4e559de365f9e57d46aa204720e4bcf7c7b67ef1fc5f684d054898afde74d1de8b38f73d9a5adbf347b0186c2632532774ab44946b4b08 Homepage: https://cran.r-project.org/package=clustertend Description: CRAN Package 'clustertend' (Check the Clustering Tendency) Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) . Package: r-cran-clusterv Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-cluster Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-clusterv_1.1.2-1.ca2404.1_all.deb Size: 752954 MD5sum: a37d5d162bdf8593de1e93eca72ccbd6 SHA1: fd3cc3960e6b47c462761e0ff9901a86a1762292 SHA256: 7c776ff7b593acc70fee417b3e4409e94417de8554e68eaa26bb2ec637dcc271 SHA512: 5f16a6bead141963ffca835644ca9e945c26e5cd9920cadee4f2762cb7ab5fbf06a569acc3f39354502e3d16b1b06508769fd0e026c558375485d98ca829f031 Homepage: https://cran.r-project.org/package=clusterv Description: CRAN Package 'clusterv' (Assessment of Cluster Stability by Randomized Maps) The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), . Package: r-cran-clustervar Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1388 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastdummies, r-cran-mass, r-cran-mvtnorm, r-cran-scales, r-cran-foreach, r-cran-doparallel, r-cran-parabar, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustervar_0.0.8-1.ca2404.1_all.deb Size: 1376400 MD5sum: e345f59238f3c68e24d2841bf73db243 SHA1: 930aaffdc0c97d6779ee4ba0f7a9c725bd7b0de2 SHA256: b699b6dd2ec7ca1e29b60d600c1e6ae41550e4f3897567323b8284b0f1f489a5 SHA512: 9555b4256ff690cb85e5b2becb39d758438359662e5a702b7ddfc2e1989adb4c8598076bf0ca0d42d4cfe25b228033e039c70d8e73926bd41d733e53d0922197 Homepage: https://cran.r-project.org/package=ClusterVAR Description: CRAN Package 'ClusterVAR' (Fitting Latent Class Vector-Autoregressive (VAR) Models) Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters. Package: r-cran-clusterwebapp Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-cluster, r-cran-factoextra, r-cran-ggplot2, r-cran-dbscan, r-cran-mclust, r-cran-kernlab, r-cran-rtsne, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-mlbench, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-clusterwebapp_0.1.3-1.ca2404.1_all.deb Size: 1290720 MD5sum: f81b257c3bf3038b14646ea2340e72e0 SHA1: 26a3586d66b6bcc1ddf151f69eac7320a1d95ae8 SHA256: d9105e2eb926222588815ee2116ca2e2e3bc5fccf251b3f067143de64cb47089 SHA512: 151cc9fcfa4d3b1c15cf029d3937313527b1b8278b07cf10e18d3fc3f857f36ccb22f3b9d1cc03edd46105647fa92143f23635c776f2662b21f4ace65517d6ee Homepage: https://cran.r-project.org/package=clusterWebApp Description: CRAN Package 'clusterWebApp' (Universal Clustering Analysis Platform) An interactive platform for clustering analysis and teaching based on the 'shiny' web application framework. Supports multiple popular clustering algorithms including k-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids), GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones, visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots, and explore method-specific visualizations and guides. For details on implemented methods, see: Reynolds (2009, ISBN:9781598296975) for GMM; Luxburg (2007) for spectral clustering. Package: r-cran-clustgeo Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustgeo_2.1-1.ca2404.1_all.deb Size: 1172344 MD5sum: 70eea58fc4e1f564307e4ebaebf994e2 SHA1: 1bd32296a477acb0f35e9bfb0bf4ef5ef948ffb2 SHA256: 48961b778202f90437797426d1837d5bf50e7fa0c5fe4abc245d462266167770 SHA512: 8b67f0a9bccc5ef4d669ad0b1c69712ec7f02925e9758b3205fea6f5623824231fc4a53ee5a74a89eaaed4e5f54ad77332cfaefd2278629d40928eb62ee560ab Homepage: https://cran.r-project.org/package=ClustGeo Description: CRAN Package 'ClustGeo' (Hierarchical Clustering with Spatial Constraints) Implements a Ward-like hierarchical clustering algorithm including soft spatial/geographical constraints. Package: r-cran-clustimpute Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clusterr, r-cran-copula, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-knitr Suggests: r-cran-ggextra, r-cran-rmarkdown, r-cran-testthat, r-cran-hmisc, r-cran-tictoc, r-cran-spelling, r-cran-corrplot, r-cran-covr Filename: pool/dists/noble/main/r-cran-clustimpute_0.2.4-1.ca2404.1_all.deb Size: 611292 MD5sum: b5f83c502fb645d2cbd37513d4ce5cf0 SHA1: 52135dff73fc6922e07b22d966491977e72ffc14 SHA256: ddb84969655a7a7dc3fc51d101094c11bcde6c686538599918fc8868a9802a26 SHA512: f44cffeb7f40fce762fec4d23e72806828b9c29391569640620508b5420ec30a927367016407eb231efb29ac8f1715bec2b6b8f5b0a51ebed32cf6040bb40d66 Homepage: https://cran.r-project.org/package=ClustImpute Description: CRAN Package 'ClustImpute' (K-Means Clustering with Build-in Missing Data Imputation) This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details. Package: r-cran-clustlearn Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proxy, r-cran-cli Suggests: r-cran-deldir Filename: pool/dists/noble/main/r-cran-clustlearn_1.0.0-1.ca2404.1_all.deb Size: 130944 MD5sum: fa536bf2cf941b4297279d56287034bd SHA1: d737b4c2e29e71cb864ba3dc0a551d26e8718140 SHA256: 2903d3c3447933ab1ca977207f725d07979986c1aaf473b59b8f5d528ca66cd9 SHA512: 04793c44a0a4ad79592aeb265cdd7d8780b976f7f6fac5e8a1a928eddf374de7d7fc854fbd525da088d4cdeef4deb0ec1346d775dcb0e81ab45b6b6f565ea117 Homepage: https://cran.r-project.org/package=clustlearn Description: CRAN Package 'clustlearn' (Learn Clustering Techniques Through Examples and Code) Clustering methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in Ezugwu et. al., (2022) ; and datasets to test them on, which highlight the strengths and weaknesses of each technique, as presented in the clustering section of 'scikit-learn' (Pedregosa et al., 2011) . Package: r-cran-clustmc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-procs, r-cran-psych, r-cran-usedist Suggests: r-cran-ggdendro, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustmc_0.1.2-1.ca2404.1_all.deb Size: 108930 MD5sum: a9b38176e3e2c5632b844ccf0129082a SHA1: 4767daf46044cf8b381d9bc4c07b656fa1974354 SHA256: b78f3606d32ef2064f1199adf2be9ad95e1acae62757b06ae9e5fc5cb61fdafd SHA512: e0c0c62614af405690bfb482ac8f85bb3521ccff85f69e2351e4240b34465db6362769ea4916b416832ca305e0242a917d4d550c8f3376b2f46cc73be9080f9a Homepage: https://cran.r-project.org/package=ClustMC Description: CRAN Package 'ClustMC' (Cluster-Based Multiple Comparisons) Multiple comparison techniques are typically applied following an F test from an ANOVA to decide which means are significantly different from one another. As an alternative to traditional methods, cluster analysis can be performed to group the means of different treatments into non-overlapping clusters. Treatments in different groups are considered statistically different. Several approaches have been proposed, with varying clustering methods and cut-off criteria. This package implements cluster-based multiple comparisons tests and also provides a visual representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., & Casanoves, F. (2002) . Bautista, M. G., Smith, D. W., & Steiner, R. L. (1997) . Package: r-cran-clustmd Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mclust, r-cran-reshape2, r-cran-mass, r-cran-msm, r-cran-mvtnorm, r-cran-truncnorm, r-cran-viridis Filename: pool/dists/noble/main/r-cran-clustmd_1.2.2-1.ca2404.1_all.deb Size: 170336 MD5sum: 9e8031ebf55daf7d8e8b3ee62af3cbf7 SHA1: b35b896a85c29d559cfa80c50ee64950f553a464 SHA256: e8623f96ad778a4a4d637f0dfaf1dc07aaf7a1dabbed7ba86f2360ff633a55a7 SHA512: e47b7dc2efe963ef38004d9072b75058d4ad3035f0f64ef120dbcb5c8fc8e32776c81925c323febe79c456beb93be9f2a7756ff34e1e503578f0de9377460a3a Homepage: https://cran.r-project.org/package=clustMD Description: CRAN Package 'clustMD' (Model Based Clustering for Mixed Data) Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models. Package: r-cran-clustmixtype Architecture: all Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-tibble, r-cran-combinat, r-cran-dplyr, r-cran-rlang, r-cran-cluster, r-cran-mclust Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustmixtype_0.5-2-1.ca2404.1_all.deb Size: 248500 MD5sum: 5ef1625be59e887d5073721a2a677c0e SHA1: 9778d887704f357ddbd541f33baaf3099fd0af20 SHA256: 431c8db35a8843722b8dbe9b62b1f788c7283a1b3fea38ab2e67d0268207d609 SHA512: f957d9e0ccde9892e89ac592c9f8f8a76948f17c44dcb9b8a95432184afd2050a20aa3dcd839d868700abc71da2bc4cb0f1936fd5d9edf819969c241b801d0ec Homepage: https://cran.r-project.org/package=clustMixType Description: CRAN Package 'clustMixType' (k-Prototypes Clustering for Mixed Variable-Type Data) Functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304 and G.Szepannek et al. (2025): Clustering Large Mixed-Type Data with Ordinal Variables, Adv Data Anal Classif 19, 749–767. Package: r-cran-clustnet Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bidag, r-cran-pcalg, r-bioc-rbgl, r-cran-clue, r-bioc-graph, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggraph, r-cran-ggpubr, r-cran-ggplot2, r-cran-reshape2, r-cran-car, r-cran-ks, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustnet_1.2.0-1.ca2404.1_all.deb Size: 178544 MD5sum: aa6f288062f4e205849f6c7818f19f1e SHA1: 30d37a953f503b5a3cd770b03293eb257d037d2d SHA256: 2167011a8ebb457128bc8ebd8732f94b6163b22c3b98a3f3fbe1c25bf252dffa SHA512: 1600fdcbaaf6bb4f87af1777ad66dff106c2e163f527024e6a96b6a9e8e3871eec9c415fffa224e4acff59cd35d7840c5a227a2d2541ef7d1c7df192ed705365 Homepage: https://cran.r-project.org/package=clustNet Description: CRAN Package 'clustNet' (Network-Based Clustering) Network-based clustering using a Bayesian network mixture model with optional covariate adjustment. Package: r-cran-clustofvar Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcamixdata Filename: pool/dists/noble/main/r-cran-clustofvar_1.2-1.ca2404.1_all.deb Size: 191664 MD5sum: b6aedb63935f1d34fcf7b5ae91d189db SHA1: d525c922ecec989fb70d531aa2a2af1c213bfbb9 SHA256: 96faf03f4aefda819e779675b7eea0c1743babb08565cb1e39299386ffd9622b SHA512: d6b961d34ea0f9e2621e35a789cba1d07e05232de7fb9fe4458cd30a418c9a437805afd84eb77c0ed381876a830fdc5578f1edb74fe846006c4f39cc7635e2eb Homepage: https://cran.r-project.org/package=ClustOfVar Description: CRAN Package 'ClustOfVar' (Clustering of Variables) Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both. Package: r-cran-clustorus Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2684 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bambi, r-cran-igraph, r-cran-purrr, r-cran-ggplot2, r-cran-rlang, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-clustorus_0.2.2-1.ca2404.1_all.deb Size: 1894336 MD5sum: a3d698f74c285334582f40b68e801c29 SHA1: e8b290ea1576fa01368c6962462b24f4a100799f SHA256: ddcd07038c373614db6308a1d39a2e1f368e772baa4c4ce2d6bb3ace1c67848b SHA512: e8cde62a55510cea7bf8c9c51d61dd0926b975b21b000862feb8f8abc1f69affb10e655edae5382fa19a4af602439373d17a1a8753b70b2481ae32deae973203 Homepage: https://cran.r-project.org/package=ClusTorus Description: CRAN Package 'ClusTorus' (Prediction and Clustering on the Torus by Conformal Prediction) Provides various tools of for clustering multivariate angular data on the torus. The package provides angular adaptations of usual clustering methods such as the k-means clustering, pairwise angular distances, which can be used as an input for distance-based clustering algorithms, and implements clustering based on the conformal prediction framework. Options for the conformal scores include scores based on a kernel density estimate, multivariate von Mises mixtures, and naive k-means clusters. Moreover, the package provides some basic data handling tools for angular data. Package: r-cran-clustra Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3563 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mgcv, r-cran-mixsim Suggests: r-cran-haven, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustra_0.2.1-1.ca2404.1_all.deb Size: 2834616 MD5sum: 72ea79aa3cdcae6d3f39d95b8278e2e6 SHA1: b781742d341620bacb9d4fd362d29d4b764da697 SHA256: 6d9e0ca534ccaa1ca1f4a45597c5d209064edbad3ed2db530647acab8f8f6854 SHA512: 3dc520880c3d30d9fd80fb0d674ed2f45438b38fd351a1ebd097c33b70ef04f2693682055ad57e5476cb0c83464a73bef572bca80438b1f0970098a3d5589ea9 Homepage: https://cran.r-project.org/package=clustra Description: CRAN Package 'clustra' (Clustering Longitudinal Trajectories) Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation. Package: r-cran-clustransition Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexclust Filename: pool/dists/noble/main/r-cran-clustransition_1.0-1.ca2404.1_all.deb Size: 976318 MD5sum: d3d2087319d3ab028af5bd5c589d1493 SHA1: d6eb04cab117a5c993dc445f42a3b9930c4324d3 SHA256: bc5b15c4ce95b9fbd0959340a27962024e38dab204c87e9cc21cd833fab91320 SHA512: 595bc1828283039733c61f250078ba57d4deea5d016d361738f121f6694d228bf8e39151eab1f22153bd1b61e4b93ae2f54639674ff9a05a69b8a424f26208ba Homepage: https://cran.r-project.org/package=clusTransition Description: CRAN Package 'clusTransition' (Monitor Changes in Cluster Solutions of Dynamic Datasets) Monitor and trace changes in clustering solutions of accumulating datasets at successive time points. The clusters can adopt External and Internal transition at succeeding time points. The External transitions comprise of Survived, Merged, Split, Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes in location and cohesion of the survived clusters. The package uses MONIC framework developed by Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006) . Package: r-cran-clustrd Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rarpack, r-cran-tibble, r-cran-corpcor, r-cran-ggally, r-cran-fpc, r-cran-cluster, r-cran-dplyr, r-cran-plyr, r-cran-ggrepel, r-cran-ca Filename: pool/dists/noble/main/r-cran-clustrd_1.4.0-1.ca2404.1_all.deb Size: 338176 MD5sum: 0c4b76dff4254c2c81b5d5098df4f5f7 SHA1: 3418a88705491a16ae070b4cdc42902aad57963e SHA256: 06e8df2ee0cc4bc0872fd2d09d22f8841f9266bedb56a31ce609a69e335f1137 SHA512: 237821019ab8979b8df692d46ab58e3c05317d677aba9cd910ff6fdda00ccad8bde39f21f146c4e204f4245bd2399d65149e012c21cf6597c56eae8f71f26535 Homepage: https://cran.r-project.org/package=clustrd Description: CRAN Package 'clustrd' (Methods for Joint Dimension Reduction and Clustering) A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; ). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; ) and reduced K-means (De Soete and Carroll 1994; ); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; ), i-FCB (Iodice D'Enza and Palumbo 2013, ) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; ), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; ), which combine PCA for mixed-type data with K-means. Package: r-cran-clustree Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggraph, r-cran-checkmate, r-cran-igraph, r-cran-dplyr, r-cran-ggplot2, r-cran-viridis, r-cran-rlang, r-cran-tidygraph, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-singlecellexperiment, r-cran-seurat, r-cran-covr, r-bioc-summarizedexperiment, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-clustree_0.5.1-1.ca2404.1_all.deb Size: 1883608 MD5sum: 43fc00cb15a1784b0f8800561344966d SHA1: d78a723d894da0bdbc3406468d8cdd9e073c5302 SHA256: fba8cedfbe589958b0b09707f1730880f7a8fec51061ee858eb5ab2989d96e2c SHA512: 9c2dc1297c2ea4fd8b743c18e0d9ee856a9bab52a164d593d8263d394e4ed2a711d31f26c14db96f73a9389e80fa7676b1b9184e0b0a11573fd1467e32340289 Homepage: https://cran.r-project.org/package=clustree Description: CRAN Package 'clustree' (Visualise Clusterings at Different Resolutions) Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases. Package: r-cran-clustringr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-stringdist, r-cran-igraph, r-cran-assertthat, r-cran-forcats, r-cran-rlang, r-cran-tidygraph, r-cran-ggraph, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clustringr_1.0-1.ca2404.1_all.deb Size: 417814 MD5sum: 7995661034aa75662fdacbed1363c650 SHA1: 8bf4d6da4f251f415ada02cbfcbb7df5bac784e0 SHA256: db448d67a67bb19818ef576e22476bee4b5b75328f6d444a31034900f3cbc871 SHA512: 4bfd168ee06aff8a32b899a369d8e05afdbe745b1044b69a403ad4e0dfd1c5f3e60c010000afb6bf60aa735c43e33879e177eef5dbbbe8dcf744bcf8e15fa2e4 Homepage: https://cran.r-project.org/package=clustringr Description: CRAN Package 'clustringr' (Cluster Strings by Edit-Distance) Returns an edit-distance based clusterization of an input vector of strings. Each cluster will contain a set of strings w/ small mutual edit-distance (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by stringdist::stringdist(). The set of all mutual edit-distances is then used by graph algorithms (from package 'igraph') to single out subsets of high connectivity. Package: r-cran-clustshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-klar, r-cran-mass, r-cran-dplyr, r-cran-psycho, r-cran-cluster Filename: pool/dists/noble/main/r-cran-clustshiny_0.1.0-1.ca2404.1_all.deb Size: 70932 MD5sum: 1c2f80a4049cdfc4d4f3e94ff1aa3083 SHA1: 3f88fb7154883e9438f1ec795384845219eb4ae3 SHA256: 588d50a0a30026eb90db0927b4e139f4ee9b67dd0c2fe4716ab2406cfd7a7c6c SHA512: c42bbfadaf0f5d566efa05db65f697ba2dd8bb67af7972253e268c4ba1c15d42858530dfe64a6d6f36685dc6884feaf8831ea4fe09d6eab2a3e42284851d3713 Homepage: https://cran.r-project.org/package=CLUSTShiny Description: CRAN Package 'CLUSTShiny' (Interactive Document for Working with Cluster Analysis) An interactive document on the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-clustvarsel Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-matrix, r-cran-bma, r-cran-foreach, r-cran-iterators Suggests: r-cran-mass, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustvarsel_2.3.5-1.ca2404.1_all.deb Size: 502814 MD5sum: 671d619a2d5a7cbb3a853eff76049c77 SHA1: b52253816b05349c610e13be61d0cab5a39409da SHA256: 7a51342a88555adacb4fa2e4de9d3d349b7b70a0edb236907025477ff38ecbfd SHA512: 09c0ee6e294c8d1c8ca5f79c884f88ef633091a63368e406b830830a0045f0aa597661a8056dc682635bc5c1545cbb3039e39fdef190b7fa02c6060709c6ffa9 Homepage: https://cran.r-project.org/package=clustvarsel Description: CRAN Package 'clustvarsel' (Variable Selection for Gaussian Model-Based Clustering) Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available. Package: r-cran-clvalid Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-class Suggests: r-bioc-biobase, r-bioc-annotate, r-bioc-go.db, r-bioc-moe430a.db, r-cran-rankaggreg, r-cran-kohonen, r-cran-mclust Filename: pool/dists/noble/main/r-cran-clvalid_0.7-1.ca2404.1_all.deb Size: 606454 MD5sum: c79e58cfa96a20ba5d28577df24fef15 SHA1: 53544a382d112ed8e71e3640fe135029fc0eb47a SHA256: 41f55022c361e2a522214d9ee74c36c6a5f3f1b59726383d414f8282cc40ab5d SHA512: 0bbf51a32c6b3d2db5fe6c8d474b64863b63d83ce27a91cefadd91fd7f05bd8d282824e4a911a7fbadc9604029b10429635f16bab3b1098190bae3174def48d0 Homepage: https://cran.r-project.org/package=clValid Description: CRAN Package 'clValid' (Validation of Clustering Results) Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) . Package: r-cran-cmaes Architecture: all Version: 1.0-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-cmaes_1.0-12-1.ca2404.1_all.deb Size: 43110 MD5sum: fd1d2ea982d98ce2c4abd1d9f6e69668 SHA1: 0fb99c3e0a5c03101bea773dc06fe3c18f908cc6 SHA256: 6e80c6af9b803376d632be9a06c28c7e03e687dc1967e617821f3cbca0ce3f18 SHA512: 2ea3694d6a87f8e2befb7c236ad926332852dd34a123c7f822c54bfd0074251fa098b52e31f3a280c021b42a943f308d988f41e07986910f3747ba60acab9623 Homepage: https://cran.r-project.org/package=cmaes Description: CRAN Package 'cmaes' (Covariance Matrix Adapting Evolutionary Strategy) Single objective optimization using a CMA-ES. Package: r-cran-cmaesr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paramhelpers, r-cran-bbmisc, r-cran-checkmate, r-cran-smoof, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cmaesr_1.0.3-1.ca2404.1_all.deb Size: 83518 MD5sum: 823fde703fc8a09b4798a4567f036f37 SHA1: 36d299b8542ebabcecfa5cee86e8911ac4915f17 SHA256: 320f0db7b82b05e146a6e109d10f302840399e75a506c617876c25f83df73756 SHA512: 7c3c3dd322b89ee49dbd2bd44180b71c574b48fbe5320613ba666cf081e332739b95b3a1398ff5cbb578a49581a00a1bfa40578315f2ea149b07e7df5311e4b5 Homepage: https://cran.r-project.org/package=cmaesr Description: CRAN Package 'cmaesr' (Covariance Matrix Adaptation Evolution Strategy) Pure R implementation of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES). Package: r-cran-cmahalanobis Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cmahalanobis_1.1.0-1.ca2404.1_all.deb Size: 387074 MD5sum: 495ef95de57dae8593d37aa3ec3fc610 SHA1: d7f0aeb71bb802a923dcea0815aadb081a6a7e7c SHA256: 253c35d8e49a89ffe3a9ca4f15ef1106b5f8c1249c35bc1abd11dec1d70909ca SHA512: d0deafa70d747bd1e0b5243e0071837e9facc45713eabb78a7508390f6a3a320f31a8beabb323d18fab88c2fd1ce5e66b9b5070f853397b7bcbd8b7df1312322 Homepage: https://cran.r-project.org/package=cmahalanobis Description: CRAN Package 'cmahalanobis' (Calculate Distance Measures for DataFrames) It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts. Package: r-cran-cmanalysis Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-ggplot2, r-cran-factoextra, r-cran-clue, r-cran-igraph, r-cran-stringr, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-cmanalysis_1.0.3-1.ca2404.1_all.deb Size: 107304 MD5sum: a5d84cf5afc328e61bc9a6f84c566717 SHA1: 3f5210fb72951a4eff883a5062dc40a7137e8126 SHA256: 41487cbc98436e10b1d7a080ae0f1a3bce473cc6cb976fbc650c7e854369699b SHA512: 9426974ce1d163fe2197d61b9f776e19a3a0765716b9d039fe14556e51bfc54ca3d27cfb6397d09b70773f111612785d25c829a21e2531183413afe3462f4149 Homepage: https://cran.r-project.org/package=cmAnalysis Description: CRAN Package 'cmAnalysis' (Process and Visualise Concept Mapping Data) Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. . Package: r-cran-cmapviz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-reshape2, r-cran-stringr Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-cmapviz_0.1.0-1.ca2404.1_all.deb Size: 161940 MD5sum: fe890c673778d65d2df02839539c3202 SHA1: 5e658def24234ab726cce7c700e4371199c6dd0b SHA256: d139d2e2c8a90588d9ffac75f188bdc01c65ff630ded8cd2895e86c81b27f0cb SHA512: 28d3b10c858580a8a6aa70d8c6759093e337c8adeeb01d2db3427a517921bf4124c7d02608d560d22509db1abb8245ee5f79469e332b35cf366851bb9a247304 Homepage: https://cran.r-project.org/package=CMapViz Description: CRAN Package 'CMapViz' (Representation Tool For Output Of Connectivity Map (CMap)Analysis) Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) . It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot. Package: r-cran-cmars Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-earth, r-cran-rmosek, r-cran-stringr, r-cran-matrix, r-cran-auc, r-cran-ryacas, r-cran-rocr, r-cran-mpv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cmars_0.1.4-1.ca2404.1_all.deb Size: 461486 MD5sum: 8a956cf92378e5de4a2afdc1351ac864 SHA1: 576d98a9d0af6fc687ae1a0460b650f726a60cac SHA256: 391e6e959fa485261be3da8d4b1d00197ef0e03812b5f957b45b0c0af56fd39a SHA512: fd69bc8b8e788af8031486f79a7f29e250ca0887d83f655771da4d55cdca53e1dc1ffceca13230e559b72e53e5e17064509670c47cd7f4b8c33b095dd7a801c6 Homepage: https://cran.r-project.org/package=cmaRs Description: CRAN Package 'cmaRs' (Implementation of the Conic Multivariate Adaptive RegressionSplines in R) An implementation of 'Conic Multivariate Adaptive Regression Splines (CMARS)' in R. See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, . It constructs models by using the terms obtained from the forward step of MARS and then estimates parameters by using 'Tikhonov' regularization and conic quadratic optimization. It is possible to construct models for prediction and binary classification. It provides performance measures for the model developed. The package needs the optimisation software 'MOSEK' to construct the models. Please follow the instructions in 'Rmosek' for the installation. Package: r-cran-cmatching Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matching, r-cran-lmtest, r-cran-multiwayvcov, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-cmatching_2.4-1.ca2404.1_all.deb Size: 86512 MD5sum: 2505292dfbe1c37a780c57f3b6c50a20 SHA1: 11242aa4b5d51fac34a3b10a3d6120bd0c8e73e3 SHA256: 422587af0866d61ebdb8925da9ec19ab1bb4d12d217473b1aa02f20ca6876823 SHA512: 65a0508d046a69cf90840aac08e621c9196d92a2629cca1538dd2836260c3882f26549e98069e0f0df9aeaa79b68fdec9a5a1f01f34a340fa8ca171ff925a565 Homepage: https://cran.r-project.org/package=CMatching Description: CRAN Package 'CMatching' (Matching Algorithms for Causal Inference with Clustered Data) Provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) . Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors. Package: r-cran-cmce Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cmce_0.1.0-1.ca2404.1_all.deb Size: 92994 MD5sum: 3063b092b214ca990cb1892c44f3b14c SHA1: 64043ee685aa8889ce219fa35afd50f764a7d4f1 SHA256: f1eb6b2d28466dd1cef5e13d57460f83aff01cd9404e1d29b5aaf09b7177c70a SHA512: 794361546d44da1c36790a331c6902d6e93799e0182f03269e0d25b52bdb3019cb325e01617bfa9110ce21a9837843edbc187984fd03f986e9f53a2ce3605fb6 Homepage: https://cran.r-project.org/package=cmce Description: CRAN Package 'cmce' (Computer Model Calibration for Deterministic and StochasticSimulators) Implements the Bayesian calibration model described in Pratola and Chkrebtii (2018) for stochastic and deterministic simulators. Additive and multiplicative discrepancy models are currently supported. See for more information and examples. 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(2015) . Provides a wide range of pre, inter, and post-processing options when working with cartridge case scan data and their associated comparisons. See the cmcR package website for more details and examples. Package: r-cran-cmdfun Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-purrr, r-cran-r.utils, r-cran-rlang, r-cran-testthat, r-cran-usethis Suggests: r-cran-cli, r-cran-covr, r-cran-knitr, r-cran-processx, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cmdfun_1.0.2-1.ca2404.1_all.deb Size: 87608 MD5sum: 38dab8e50880dc83d70249d41e972b6f SHA1: 7fd1db8f226f82efe55a6a5b72f43e357faebcb2 SHA256: 625dd9c818859be24901d5fc1ccab145a3fa20764dec984c8e94bf3be8d0b644 SHA512: d969d6d6a38753a85e72565207ab255ad05edf965973c3a637152dd38d041863dd2ae5dd86d9e151202ac40294c7ff23005680b10d7e3059324140060e06aacd Homepage: https://cran.r-project.org/package=cmdfun Description: CRAN Package 'cmdfun' (Framework for Building Interfaces to Shell Commands) Writing interfaces to command line software is cumbersome. 'cmdfun' provides a framework for building function calls to seamlessly interface with shell commands by allowing lazy evaluation of command line arguments. 'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys. Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside R when they might usually leave to execute non-R software. Package: r-cran-cmfsurrogate Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-cmfsurrogate_1.1-1.ca2404.1_all.deb Size: 50520 MD5sum: b70d9f45b8b95be7b8522321eed7a0e9 SHA1: 0445704b06df86bd1373a404c0d21ca7f61f69c7 SHA256: ffb5a696a6b1dbeeda1719b7daad1af84c08db6a4fcb48a44590a1167454b47d SHA512: 39f671752e10c5041f91dce7ecda3415f020e309b5ead9b5b3bbedf08560e17c0df3223b23f3006d144de9f2ff97d075a6a6966306c0090cd460f08e48c5461c Homepage: https://cran.r-project.org/package=CMFsurrogate Description: CRAN Package 'CMFsurrogate' (Calibrated Model Fusion Approach to Combine Surrogate Markers) Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) . A tutorial for the package is available at and a Shiny App is available at . Package: r-cran-cmgnd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppalgos, r-cran-lubridate, r-cran-gnorm, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cmgnd_0.1.1-1.ca2404.1_all.deb Size: 192854 MD5sum: 537515e108d41550a3de6661ddab678c SHA1: df126fceefbefb4c8ae3bf574be753cacddcaf26 SHA256: 49425074ff8b385cb75c26251b75f46500c3d03752780b04e575e9404572bcb7 SHA512: 96ea7d75b73f067275e4bf1c4823b844e3a20b2c79f19997015b18c47e40ce8f81ef2688f3d85788edf345c70ab313f754744765945b115b986f8f20b9cde3f9 Homepage: https://cran.r-project.org/package=cmgnd Description: CRAN Package 'cmgnd' (Constrained Mixture of Generalized Normal Distributions) The 'cmgnd' implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the 'cmgnd' reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) and Duttilo et al (2025) . 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Package: r-cran-cmsafops Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-fields, r-cran-fnn, r-cran-ncdf4, r-cran-rainfarmr, r-cran-raster, r-cran-progress, r-cran-trend, r-cran-searchtrees Suggests: r-cran-cmsaf, r-cran-cmsafvis, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cmsafops_1.4.3-1.ca2404.1_all.deb Size: 1115238 MD5sum: 2cc62eb27c1221b0ace4da7c9cb0c996 SHA1: 18edac4c3c053091d27e0e193e8029fa0fe8f9d7 SHA256: 1bfad61b40b9cff35190a5d2f184034884bf11a2f4e767b7fce305dd9d82c219 SHA512: 8540bd9d9f70962281f8f4fc8a14e1cd22d9ca1a096e711ae89a04c39c4364dd96f5b8645b530cdf369e8bc255df42fa2771cc47715ff1c46f7827a0acdd7615 Homepage: https://cran.r-project.org/package=cmsafops Description: CRAN Package 'cmsafops' (Tools for CM SAF NetCDF Data) The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (). It is a joint cooperation of eight National Meteorological and Hydrological Services. The 'cmsafops' R-package provides a collection of R-operators for the analysis and manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (). Detailed information and test data are provided on the CM SAF webpage (). Package: r-cran-cmsafvis Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cmsafops, r-cran-colorspace, r-cran-countrycode, r-cran-fields, r-cran-mapproj, r-cran-maps, r-cran-ncdf4, r-cran-png, r-cran-progress, r-cran-raster, r-cran-sf, r-cran-sp, r-cran-rcolorbrewer, r-cran-rastervis, r-cran-gridextra Suggests: r-cran-animation, r-cran-fnn, r-cran-lwgeom, r-cran-rainfarmr, r-cran-rnaturalearth, r-cran-searchtrees, r-cran-spelling, r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-cmsafvis_1.3.0-1.ca2404.1_all.deb Size: 799546 MD5sum: 56cd28b2a1b5e4128b0cf701d7a2da98 SHA1: 9efd414fb88239d6c293edc787c8517919700f71 SHA256: a2b42003f22560d81def4e1be35e163a56d19cdadede1b7dbd7b270b2387c138 SHA512: 45b4adc09cda96e6ad50a5ca451910987d41dd31e52c54696d2727ade4913f3e8a61c66d822006f768e9648514ef54310dcf538b306a908e9727effbf6233284 Homepage: https://cran.r-project.org/package=cmsafvis Description: CRAN Package 'cmsafvis' (Tools to Visualize CM SAF NetCDF Data) The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (). It is a joint cooperation of eight National Meteorological and Hydrological Services. The 'cmsafvis' R-package provides a collection of R-operators for the analysis and visualization of CM SAF NetCDF data. CM SAF climate data records are provided for free via (). Detailed information and test data are provided on the CM SAF webpage (). Package: r-cran-cmshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-shinymatrix, r-cran-matrix, r-cran-epitools, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-cmshiny_0.1.0-1.ca2404.1_all.deb Size: 70134 MD5sum: 5898053b7381290644b7fc5199fc6640 SHA1: b78cd5feff7dccd232184eed15f6f788ceb01d93 SHA256: 37e0c2aa3d5841aeca947e7e33823e540e87d4c294788f7bc789961f4c796ebe SHA512: 460ce607c30084c14cb1cdba1cdf356852c63d2e71db5a792ed52d53f6558375b29055cef6b416a7a3de23ee0b50d4ce7f1d77d4318b8b1285f86a8a8a62815b Homepage: https://cran.r-project.org/package=CMShiny Description: CRAN Package 'CMShiny' (Interactive Document for Working with Confusion Matrix) An interactive document on the topic of confusion matrix analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-cmstatr Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-ksamples, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-cmstatr_0.10.0-1.ca2404.1_all.deb Size: 839582 MD5sum: 6a13b0415c993034d6f3c8abc6ed7593 SHA1: b70817fada6789fa7f6d7a6e35ebfc39fb2506ac SHA256: e8534d3d663c872ff5c4c4b258dfcaf9439d30250dfd31e9cf512997ebd13d34 SHA512: e917343c5ab30981e0d3e1b9689728008343ef172369e6f9d3ae01110e67e65bb7e3e2d8e24c29d2324029eb2d64440f4a64cf4ad80398e713b02ee049e02607 Homepage: https://cran.r-project.org/package=cmstatr Description: CRAN Package 'cmstatr' (Statistical Methods for Composite Material Data) An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. 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Package: r-cran-cmtest Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-censreg, r-cran-aer, r-cran-knitr, r-cran-rmarkdown, r-cran-maxlik, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-cmtest_0.1-2-1.ca2404.1_all.deb Size: 297982 MD5sum: 0ef55cacbae7e2f25ed6b6d02a2d97bf SHA1: bc30b59f2a0da2a0427c858eb74a3eb2d3847e00 SHA256: 69013a3a4bd11fc2844fc2fe0ab140851d1b25232c17a24ecda63c771dcddfe0 SHA512: e35cc4123ab3c932de010ad3326868cbb2261c09aa5f8b269d59cc0182f0afaebdca43483b26b6946ef0655b2bfeffd382fe4d3d5b8542f84715cc59c7fa2fa9 Homepage: https://cran.r-project.org/package=cmtest Description: CRAN Package 'cmtest' (Conditional Moments Test) Conditional moments test, as proposed by Newey (1985) and Tauchen (1985) , useful to detect specification violations for models estimated by maximum likelihood. 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Selection of the optimal number of components can be done using 'ACMTF_modelSelection()' and 'ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 and Acar et al., 2014 , respectively. Package: r-cran-cmvnorm Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-elliptic, r-cran-emulator, r-cran-quadform Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cmvnorm_1.1-1-1.ca2404.1_all.deb Size: 228154 MD5sum: 832a4e5556904523c220abd9290c953d SHA1: 9720ae14d2962eb01ba19e08a216d384c883fc5d SHA256: 4ed003265b5cb5013badfc0bcdc701821ba5a1f24d0027a4096ebcb3c3a716a5 SHA512: 496a45a434afba654160b8d2eff8aee0cb9717d90e240ff239836a15717f5c648614fb8a7079bbd717f1ab87db21e9f95ff6c20b0d6a667122a240c15a13f092 Homepage: https://cran.r-project.org/package=cmvnorm Description: CRAN Package 'cmvnorm' (The Complex Multivariate Gaussian Distribution) Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes. 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Package: r-cran-cncagui Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-tkrplot, r-cran-tcltk2, r-cran-shapes, r-cran-plotrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-cncagui_1.1-1.ca2404.1_all.deb Size: 273036 MD5sum: 2d869716f874fa56c763dc38e637cead SHA1: d7ecf2c192f634f6392a27f00e28aeac975a3dd5 SHA256: 5aca2e61bba891252fd3d3b26565515d1902951802640d6560c970ed0a9dd740 SHA512: 3cf521766459919d1e9f69061cbb4dd2a7becb193045ea999da51363e704adbcf5b0370deaa7c1295bc4b8ee421ba1e87276174b878036af74ce8fb458809554 Homepage: https://cran.r-project.org/package=cncaGUI Description: CRAN Package 'cncaGUI' (Canonical Non-Symmetrical Correspondence Analysis in R) A GUI with which users can construct and interact with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods. 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Package: r-cran-cnd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-cli, r-cran-here, r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cnd_0.2.0-1.ca2404.1_all.deb Size: 259292 MD5sum: 9b824d110c50060d4cc691ce0b365aea SHA1: 74e7ce30e6612cd1cebfa01782c2b7987a45938a SHA256: 0b9a22a5375d42f05e23407b8defbe4d415a4a4e689d1ed14ce4228ad935736d SHA512: a2d643db3a13bd06b793161827c4a3681ae8e95531f5c72aa2dc20fbff831db7e6ff025e2c3f26d407b9d9ea65fd5cdaf1c91f4e0b5aab58c424eccb87986de6 Homepage: https://cran.r-project.org/package=cnd Description: CRAN Package 'cnd' (Create and Register Conditions) An interface for creating new condition generators objects. Generators are special functions that can be saved in registries and linked to other functions. 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Package: r-cran-cnefetools Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3561 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-sf, r-cran-geobr, r-cran-lifecycle, r-cran-rlang, r-cran-h3jsr, r-cran-tidyr, r-cran-dbi, r-cran-duckdb, r-cran-duckspatial, r-cran-cli, r-cran-checkmate, r-cran-fs, r-cran-httr2, r-cran-piggyback, r-cran-withr Suggests: r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-leafsync, r-cran-mapview, r-cran-odbr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-zip Filename: pool/dists/noble/main/r-cran-cnefetools_0.3.0-1.ca2404.1_all.deb Size: 2316804 MD5sum: fdb307617f3eb004b0e8429ff6d5f398 SHA1: 5a80c24896a22413710e5e05cb7036a22f767829 SHA256: f209e7472dad7c1afe22f69e29022963a71c896fd7191bb338bc0c3b7ca92e1f SHA512: bcb75ea8dd38ff22643dea502a9ba55202913bedc8b018aa158af6f81e195f1ef0e0b898ca410ab586da69cb74faf52b3447e5ce81844cbb15f55db379e2ee24 Homepage: https://cran.r-project.org/package=cnefetools Description: CRAN Package 'cnefetools' (Access and Analysis of Brazilian CNEFE Address Data) Download, cache and read municipality-level address data from the Cadastro Nacional de Enderecos para Fins Estatisticos (CNEFE) of the 2022 Brazilian Census, published by the Instituto Brasileiro de Geografia e Estatistica (IBGE) . Beyond data access, provides spatial aggregation of addresses, computation of land-use mix indices, and dasymetric interpolation of census tract variables using CNEFE dwelling points as ancillary data. Results can be produced on 'H3' hexagonal grids or user-supplied polygons, and heavy operations leverage a 'DuckDB' backend with extensions for fast, in-process execution. Package: r-cran-cnid Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cnid_2.1.1-1.ca2404.1_all.deb Size: 73970 MD5sum: dcc733de30bb09706d25340a0b2f58c0 SHA1: 90219dac3b53e910130ca3a64b993a496cc2da67 SHA256: b23d0f755a7c8ea6d54f5066d74faa8df91ec3e2bc3a8c356d3996f6dd430322 SHA512: cbe64a783db1b07c70dada7d93aff4d3821cb62f22a9f6228d22aa159708fb5cfe11f97e2484dc360d33e5717e3b763711a08320176893235832f60b3528de61 Homepage: https://cran.r-project.org/package=CNID Description: CRAN Package 'CNID' (Get Basic Information from Chinese ID Number) The Chinese ID number contains a lot of information, this package helps you get the region, date of birth, age, age based on year, gender, zodiac, constellation information from the Chinese ID number. 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Package: r-cran-cnmap Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cnmap_0.1.2-1.ca2404.1_all.deb Size: 425296 MD5sum: 4e9508fd1b0dd5935d1d58abe8ebc2b5 SHA1: 91bba6d4c1e907deb1335d00a8f1020e9b09211b SHA256: 1af2885edd59ba75efa89ad1848d77d14a3ad9450ef6ededa18ed2c2775aa80f SHA512: 74c853bade3407de5cea479a7581961ce70a0cc24df28e071c9085de51efdbea43b23c04311f73a674cf53903ae176ad0c1a33fb355c42e33eee7eaab6d47841 Homepage: https://cran.r-project.org/package=cnmap Description: CRAN Package 'cnmap' (China Map Data from AutoNavi Map) According to the codes and names of county-level and above administrative divisions released in 2022 by the Ministry of Civil Affairs of the People's Republic of China, the online vector map files were retrieved from the website (available at: ). This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177). Package: r-cran-cnorm Architecture: all Version: 3.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2529 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-leaps Suggests: r-cran-dt, r-cran-haven, r-cran-foreign, r-cran-knitr, r-cran-markdown, r-cran-numderiv, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinycssloaders, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cnorm_3.7.0-1.ca2404.1_all.deb Size: 1845738 MD5sum: 05f1e3d1ae90f64b9bb897e6c0ef22bf SHA1: 4c82419b245cffda865f566379fb7d2dcfb58cd4 SHA256: fe5f770509802affa368569aed83f6826690b481c24ed3e7587712ab997d59a2 SHA512: 4340c7beb279f8701721dfabc48998325653ce2d88e3b0612396e9d3b3d799e2fe7dd8516766c9ad2cdbb1716e2610677351e21e550739ed81dd39d4cecb5c22 Homepage: https://cran.r-project.org/package=cNORM Description: CRAN Package 'cNORM' (Continuous Norming) Generates continuous test norms in psychometrics and biometrics, and analyzes model fit. The package offers distribution-free modeling using Taylor polynomials, as well as parametric modeling using the beta-binomial distribution (for bounded accuracy tests), the Conway-Maxwell-Poisson distribution (for speeded tests and count data with over-, equi-, or under-dispersion), and the 'Sinh-Arcsinh' (SHASH) distribution. Originally developed for psychological and educational assessment, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and supports conventional norming. It generates norm tables including confidence intervals and provides methods for addressing representativeness issues through Iterative Proportional Fitting. Based on Lenhard et al. (2016) , Lenhard et al. (2019) , Lenhard and Lenhard (2021) , and Gary et al. (2023) . Package: r-cran-cnprep Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4077 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-rlecuyer Filename: pool/dists/noble/main/r-cran-cnprep_2.2-1.ca2404.1_all.deb Size: 4140468 MD5sum: e0031a76d60f5859069a99af3a42c2ae SHA1: 462ed9fb14f77994b8d7f6f738d2712010a4a415 SHA256: 07af7497214df4ba951f19581f760a4b0596554a88ed9a2969794d5a70f6b9ff SHA512: aa7c8946e7e879dc01378ebf2a2abfab0b42d38bd489abe3e8bfe680d9f02296a6455adb47c4718cc13ad7e686bd4e22c48180269caa00dc14ab00f926bd378f Homepage: https://cran.r-project.org/package=CNprep Description: CRAN Package 'CNprep' (Pre-Process DNA Copy Number (CN) Data for Detection of CN Events) DNA copy number data evaluation using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm. Package: r-cran-cnps Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-e1071 Filename: pool/dists/noble/main/r-cran-cnps_1.0.0-1.ca2404.1_all.deb Size: 128014 MD5sum: e899be98f20465e721c8b9650ff6bb6b SHA1: 275e4d39b88bf6af0d67e5bbabdfbfaafb4e29d4 SHA256: aae117f6dd756932be9a9f5fed2a77a5b48c8c5ecbbfe1b5f72838e41fc8abee SHA512: 51f7af4dd15fc51dac24177a5dd0207c570d2ab4669e574c6419aa2785e44dff21e03e42148b6c9e1dcab27be5c1962cb5f1096d6e25ca808aaf82f6b5ce3a17 Homepage: https://cran.r-project.org/package=CNPS Description: CRAN Package 'CNPS' (Nonparametric Statistics) We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use. We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative. 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It can be used to extract features from Copy number segment data and use that to find a subset of copy number signatures which can be further used to correlate with other relevant data. For more on 'NMF' see Gaujoux (2013) . 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Creates a "CNV profile curve" to represent an individual’s CNV events across a genomic region so to capture variations in CNV length and dosage. When evaluating association, the CNV profile curve is directly used as a predictor in the regression model, avoiding the need to predefine CNV loci. CNV profile regression estimates CNV effects at each genome position, making the results comparable across different studies. The penalization encourages sparsity in variable selection with a Lasso penalty and encourages effect smoothness between consecutive CNV events with a weighted fusion penalty, where the weight controls the level of smoothing between adjacent CNVs. For more details, see Si (2024) . Package: r-cran-cnvscope Architecture: all Version: 3.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5073 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-reshape2, r-cran-magrittr, r-cran-jointseg, r-cran-shiny, r-cran-rcurl, r-cran-foreach, r-cran-matrix, r-cran-openimager, r-cran-matrixstats, r-cran-plyr, r-cran-data.table, r-cran-dplyr, r-cran-doparallel, r-cran-stringr, r-bioc-rtracklayer Suggests: r-cran-knitr, r-cran-remotes, r-cran-pwr, r-bioc-complexheatmap, r-cran-rmarkdown, r-cran-igraph, r-cran-visnetwork, r-cran-circlize, r-cran-plotly, r-bioc-biomart, r-bioc-genomicinteractions, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-iranges, r-cran-rslurm, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-dt, r-cran-logging, r-cran-heatmaply, r-bioc-s4vectors, r-cran-biocmanager, r-cran-shinyjs, r-cran-htmltools, r-cran-htmlwidgets, r-bioc-genomeinfodb, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-tibble, r-cran-smoothie Filename: pool/dists/noble/main/r-cran-cnvscope_3.7.7-1.ca2404.1_all.deb Size: 2967040 MD5sum: ed31ff961fad33e37a3decb89fcbebcf SHA1: 65de5347015a3f7735eef2dd75f1bb305414a7a2 SHA256: 37abdaa68634fda561eb31c3e4ea412547e97e3d51550ce01aa7c815a05f4e1a SHA512: d1b66cd5fbaac579b9fe9ea2fce7b944bc85d73cfd8e37a7dc2f6105ef9d054bac1314cf8ce2989e27993cd01489a4a1af3043e75ea6ba7c9a54b368018cedd2 Homepage: https://cran.r-project.org/package=CNVScope Description: CRAN Package 'CNVScope' (A Versatile Toolkit for Copy Number Variation Relationship DataAnalysis and Visualization) Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps. 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It attaches the main stable packages of the 'coda' ecosystem, currently 'coda.base' and 'coda.plot', and provides helper tools to install development extensions from 'GitHub'. Package: r-cran-coda.plot Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda.base, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-coda.plot_0.2.2-1.ca2404.1_all.deb Size: 100938 MD5sum: a39ebd2812dfb6b933bf72d15d97d49f SHA1: 3d8d7403511809f4821bb65ebccf6c2b1af6e78e SHA256: 428b4afc4c7c868f3f528e47405788a040d8d1c560f1f50a7f327efbd4a84064 SHA512: 59bb99dfc156e0ac13d97778fa655c3e6a44f9f3fe79931b3fbd5e2e3104f46e420644b12c86ce59ada76bef7ffc8a750ac2f777a7747926c9658a4784cde0fb Homepage: https://cran.r-project.org/package=coda.plot Description: CRAN Package 'coda.plot' (Plots for Compositional Data) Provides a collection of easy-to-use functions for creating visualizations of compositional data using 'ggplot2'. Includes support for common plotting techniques in compositional data analysis. Package: r-cran-coda4microbiome Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corrplot, r-cran-glmnet, r-cran-plyr, r-cran-proc, r-cran-ggpubr, r-cran-ggplot2, r-bioc-complexheatmap, r-cran-circlize, r-cran-survival, r-cran-survminer Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-coda4microbiome_0.2.4-1.ca2404.1_all.deb Size: 369022 MD5sum: e270263c09e3066c44fbf24dcc03ce36 SHA1: 77ceb06330dbb198582f1c597b8213d263821a27 SHA256: 25d48308c6d6f49add8edf7432b9bab3a6bcb638a03e73cf0796e9f2d9509005 SHA512: fb734efcefc6d6239b2a1be48647458e0884bb876bf81e2cdf924d4d1995de37b4465091215b41a3ddea849f544e0c679553c64a911f3ddb1fef3943ee36e2e8 Homepage: https://cran.r-project.org/package=coda4microbiome Description: CRAN Package 'coda4microbiome' (Compositional Data Analysis for Microbiome Studies) Functions for microbiome data analysis that take into account its compositional nature. Performs variable selection through penalized regression for both, cross-sectional and longitudinal studies, and for binary and continuous outcomes. Package: r-cran-coda Architecture: all Version: 0.19-4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-coda_0.19-4.1-1.ca2404.1_all.deb Size: 326028 MD5sum: dd5d67fa80dac23a9a7ee414924264a6 SHA1: 6684fbd71e5146e1ee517c4859e76c9a2e77c8c6 SHA256: bc845a51603750383cb701b628aa57856107793b04c23a7b36496ba9d7b14da9 SHA512: 865729b4b36098162a5da9b365de1bbf430f3a30a80c220649253b4a53f26724e87c0a2ea2272dea0bb7c0069a437e0a6fefbed9f291ef8fefc2de43cb429942 Homepage: https://cran.r-project.org/package=coda Description: CRAN Package 'coda' (Output Analysis and Diagnostics for MCMC) Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. Package: r-cran-codacore Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1910 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tensorflow, r-cran-keras, r-cran-proc, r-cran-r6, r-cran-gtools Suggests: r-cran-zcompositions, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-codacore_0.0.4-1.ca2404.1_all.deb Size: 1085986 MD5sum: 403add36ca9d295437316a01bdaef2b4 SHA1: 092570ad37881bebcd7e585cfc255800e8d7f066 SHA256: 6ce84b4029933e6267db565a0e259e2abd6706e244d47658ee4c5aca908f7732 SHA512: 40ebded73ab5bf3fd95c33a9a26c41bc41e88ae5aae734b6a8ae28123f0907d7489c1da79f7f278ffccaaf2bff48ebe903832d60a7f01df40c2261ea49f02363 Homepage: https://cran.r-project.org/package=codacore Description: CRAN Package 'codacore' (Learning Sparse Log-Ratios for Compositional Data) In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) . 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Package: r-cran-codaimpact Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compositions Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-sf, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-codaimpact_0.1.0-1.ca2404.1_all.deb Size: 1070740 MD5sum: b2487baafbb675a4bb9b487bcfe95a4e SHA1: 01746e121990b448553553314b551578a150ec6a SHA256: 37fbf148d072080d090d42cf28c72404e3ec5a9e4d081503842d72cd3bbc908e SHA512: df7a92ae18b1f86470aa6f89929105abe3b4f89022b9a40adb950e920cf73971f9e4803823314f65a2890bb2dfd454b527a8e4f375602fde04fadfafb00daacf Homepage: https://cran.r-project.org/package=CoDaImpact Description: CRAN Package 'CoDaImpact' (Interpreting CoDa Regression Models) Provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) . The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities. These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares. 'CoDaImpact' is designed to be used with the 'compositions' package and its ecosystem. Package: r-cran-codalm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-squarem, r-cran-future, r-cran-future.apply Suggests: r-cran-knitr, r-cran-gtools, r-cran-remotes, r-cran-testthat, r-cran-rmarkdown, r-cran-ggtern, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-codalm_0.1.3-1.ca2404.1_all.deb Size: 35300 MD5sum: 0b9c1b435aa2b7701a1f95f8819258c0 SHA1: 1073de2b18072b85ea08a9472a7fb723f5a75228 SHA256: 6f9073622a26a52deb70c516982a3d875ed884e45f9206b9c825ba6c9f77a2b4 SHA512: 53c1f053616953a6ce22288bd90a146749503c787815247147c914ff398bcb5ebd870e697a952e42801d8cb14f5f1038e0de28fe75313666eeaeafa3f197100d Homepage: https://cran.r-project.org/package=codalm Description: CRAN Package 'codalm' (Transformation-Free Linear Regression for Compositional Outcomesand Predictors) Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. 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The models implemented are described in Creus-Martí et al (2018, ISBN:978-84-09-07541-6), Creus-Martí et al (2021) and Creus-Martí et al (2022) . Package: r-cran-codaredistlm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compositions, r-cran-ggplot2, r-cran-broom, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-codaredistlm_0.1.0-1.ca2404.1_all.deb Size: 186562 MD5sum: 55e640e2223e2d82ce6d8d2063ed66a1 SHA1: 1d93ebc5a6c92661c45a7e5c9adca917c38faf89 SHA256: 425acafa676e01a19d96e038c5cb596d3a0916cede9d208a6ec4df84f6ddaa73 SHA512: 28e2fde89fdeba402ae27fb379a7f61432592a2887496560f880815d69b4710953d6f8f45396304742aa5f1bb5d6177f89705a34e42a57cdd0442d15d4814d2b Homepage: https://cran.r-project.org/package=codaredistlm Description: CRAN Package 'codaredistlm' (Compositional Data Linear Models with Composition Redistribution) Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) and Dumuid et al. (2017b) . 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The method is described in Mouresan, Selle and Ronnegard (2019) . Package: r-cran-code Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-testthat Filename: pool/dists/noble/main/r-cran-code_1.1.1-1.ca2404.1_all.deb Size: 130056 MD5sum: bd283c82df1a9244f4885c970d824b7b SHA1: 6f8e0217c25375cb3202fa0efd05ca16c267321c SHA256: d2b930d9501ea77856af370107e457b536135352e56558e7042360fb1d63a574 SHA512: 59db1a3632944e4b3bba10052bb5a0cf35ed4d15df9c8207c3421cd23b549f04fc0028f60fb6d92a02715b3cb6e030beb65199e08952f803fd209e3f091e9648 Homepage: https://cran.r-project.org/package=cOde Description: CRAN Package 'cOde' (Automated C Code Generation for 'deSolve', 'bvpSolve') Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". 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Package: r-cran-codecountr Architecture: all Version: 0.0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-codecountr_0.0.4.9-1.ca2404.1_all.deb Size: 42808 MD5sum: 3508cb7d14b44033c0c8287003fc3217 SHA1: 9f2d4ec442e5a841fa0fea7a40f99a1e6416f278 SHA256: 4332132f3401e44f633a101a358d2f16c6dd36950a766cd09a66f59d1725b054 SHA512: f01cff86e8bc484a73b2881b68c3b832a33dc0a9548b530f75faf6f2ea0f45b1dab5fa3b0188a8425a6fb1614c33b2af59acab40746163b14852c37cbb13b9e7 Homepage: https://cran.r-project.org/package=codecountR Description: CRAN Package 'codecountR' (Counting Codes in a Text and Preparing Data for Analysis) Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, ), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes. Package: r-cran-codedepends Architecture: all Version: 0.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-codetools, r-bioc-graph, r-cran-xml Suggests: r-bioc-rgraphviz, r-cran-runit, r-cran-knitr, r-cran-highlight, r-cran-rjsonio, r-cran-rcurl, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-codedepends_0.6.7-1.ca2404.1_all.deb Size: 890166 MD5sum: a69104654c532c965ce8e69e7fca557e SHA1: d7b9eac181e0cd80472876bd57fb9e313f0e592b SHA256: 73c6d3acebd7bd4902a25efef7504f58c9ed0f4682472f8d5db84b6204c9d538 SHA512: e9da4f4ea759a7bfdf7bf739c2c1ae491ffb8372fd9479d658363d053ac5b2a04c40bd6d8ccca6c8ec1ff897947c282dd1c2f35765438da5155c1030ba218060 Homepage: https://cran.r-project.org/package=CodeDepends Description: CRAN Package 'CodeDepends' (Analysis of R Code for Reproducible Research and CodeComprehension) Tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions. Package: r-cran-codelist Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-codelist_0.1.0-1.ca2404.1_all.deb Size: 106120 MD5sum: ec83b41fde8a8d595678fc23521af418 SHA1: f0b2d95b519e62df2e7a8e0c34e40885bc3c603c SHA256: 74acb9c79d330d1fef90070a222f4ed3104906c92401f9952613835966a2598f SHA512: 61d891817378fa4880f28b6c8c571169fe0e45af262774079ace80de73717190f347ba6f17ac6aac90b11330074d7a69e8f88193a13b895cda85fdfb8482fb82 Homepage: https://cran.r-project.org/package=codelist Description: CRAN Package 'codelist' (Working with Code Lists) Functions for working with code lists and vectors with codes. These are an alternative for factor that keep track of both the codes and labels. Methods allow for transforming between codes and labels. Also supports hierarchical code lists. Package: r-cran-codelistgenerator Architecture: all Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3054 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dbi, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-omock, r-cran-covr, r-cran-duckdb, r-cran-cdmconnector, r-cran-visomopresults, r-cran-cohortconstructor, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rpostgres, r-cran-odbc, r-cran-openxlsx, r-cran-spelling, r-cran-tibble, r-cran-gt, r-cran-flextable, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-codelistgenerator_4.1.0-1.ca2404.1_all.deb Size: 1822784 MD5sum: c0d735f800982659dae0f8fa40b6b9c6 SHA1: b09d0dd743967a8e8ea597c6237d2cac078d3a0f SHA256: 2a1c00e04af059c98c123382cff0aa4e967c6e1379bc8a7bc3bc6027c67ca26c SHA512: 78ca399244e25a612e10206f14b35ae4455debf9eb31fef777b497da7f5eb77e6ca4ed6d9b336bbad9acc3a35294cb6368c8b5490ecf23b94fd269bddb5f544d Homepage: https://cran.r-project.org/package=CodelistGenerator Description: CRAN Package 'CodelistGenerator' (Identify Relevant Clinical Codes and Evaluate Their Use) Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned. Package: r-cran-codemeta Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desc, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-codemeta_0.1.1-1.ca2404.1_all.deb Size: 164708 MD5sum: 1a14a93f97f2fc6e1aa2fd8b2361a2aa SHA1: 2d7ecf3470f51d4bbc549d3711327d09f281f1cd SHA256: 65bbee09e63733cbc311b1510bdb9371a1ea1d3d5469e6e968ac310892602b74 SHA512: 0c48468169e75c59791537c94849567969dd80f9312951e19821b9435b32619fb98d3f929226f19553129750b4c687039ae14ce955f9aa8a66bf17e404b5b0be Homepage: https://cran.r-project.org/package=codemeta Description: CRAN Package 'codemeta' (A Smaller 'codemetar' Package) The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at . This package provides core utilities to generate this metadata with a minimum of dependencies. 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This package provides utilities to generate, parse, and modify 'codemeta.json' files automatically for R packages, as well as tools and examples for working with 'codemeta.json' 'JSON-LD' more generally. Package: r-cran-codename Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-codename_0.5.0-1.ca2404.1_all.deb Size: 86188 MD5sum: 5835616b683de719878d2a9c6e1dc09b SHA1: f3496d4a0094b86611089c8e107c41d84b75353e SHA256: 963c9121550292d1ae0ca20b1f86e49e6b47029f752f3d4c4b4e4032d8c710ff SHA512: 48860c36455c606cf14168823854ea47f8a5daffd163cd367497db8fceefb8472fe124533877304b9f4fa5ef529625e7da0b923bd583c0e29697014d5b906273 Homepage: https://cran.r-project.org/package=codename Description: CRAN Package 'codename' (Generation of Code Names for Organizations, People, Projects,and Whatever Else) This creates code names that a user can consider for their organizations, their projects, themselves, people in their organizations or projects, or whatever else. The user can also supply a numeric seed (and even a character seed) for maximum reproducibility. Use is simple and the code names produced come in various types too, contingent on what the user may be desiring as a code name or nickname. Package: r-cran-coder Architecture: all Version: 0.13.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-decoder, r-cran-generics, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-writexl Filename: pool/dists/noble/main/r-cran-coder_0.13.10-1.ca2404.1_all.deb Size: 343648 MD5sum: f15feae940aa8f64f995e3b0195f61eb SHA1: 94b19896a201bac506d1885d6be33808d531d18c SHA256: 58da9a159a231c7ec3a81d004400ef3a04decc39020778d86afb011523744126 SHA512: 40f612b3c604fe7121c9669bd76d561e1e06051702dcd5924964e263e48e7b7cf8751b8b15e56fac5c20a7bc97ef790af81c9b7e5a220546d8d8a8ef65adef1f Homepage: https://cran.r-project.org/package=coder Description: CRAN Package 'coder' (Deterministic Categorization of Items Based on External CodeData) Fast categorization of items based on external code data identified by regular expressions. A typical use case considers patient with medically coded data, such as codes from the International Classification of Diseases ('ICD') or the Anatomic Therapeutic Chemical ('ATC') classification system. Functions of the package relies on a triad of objects: (1) case data with unit id:s and possible dates of interest; (2) external code data for corresponding units in (1) and with optional dates of interest and; (3) a classification scheme ('classcodes' object) with regular expressions to identify and categorize relevant codes from (2). It is easy to introduce new classification schemes ('classcodes' objects) or to use default schemes included in the package. Use cases includes patient categorization based on 'comorbidity indices' such as 'Charlson', 'Elixhauser', 'RxRisk V', or the 'comorbidity-polypharmacy' score (CPS), as well as adverse events after hip and knee replacement surgery. Package: r-cran-codestral Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rstudioapi, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-codestral_0.0.2-1.ca2404.1_all.deb Size: 58854 MD5sum: 693d9184dec1462b1abab9291f761d3e SHA1: e1e1b71d25faccb110bdd0bb16428ab29c81a038 SHA256: ba85780ddb3cdf5f08c6b8ed8e084a1fc547243d6945806a299de9723805d3dc SHA512: 2b8a746b3a403270b1e9be158d6eeabc408e34d99b970622684715f93127174d8470ed4ae3f782c437437ab9ea500c4e5d305a49c684a812aad34af71e875b9f Homepage: https://cran.r-project.org/package=codestral Description: CRAN Package 'codestral' (Chat and FIM with 'Codestral') Create an addin in 'Rstudio' to do fill-in-the-middle (FIM) and chat with latest Mistral AI models for coding, 'Codestral' and 'Codestral Mamba'. For more details about 'Mistral AI API': and . For more details about 'Codestral' model: ; about 'Codestral Mamba': . Package: r-cran-codetools Architecture: all Version: 0.2-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-codetools_0.2-20-1.ca2404.1_all.deb Size: 90224 MD5sum: 427cfbac34e1f68e3c7e563a424aa15d SHA1: 1a928c094e42e65af9f2f368069906d35b61436e SHA256: eb4822d65f3601b729cb6910789384364c07eb77cf6889d59013411e1de9b2ec SHA512: 32b02af455dc16499ca1b9e1cdc71db1b258a2d9ef5e52922511d7ba9d5157825a1fa48d632141a1254303d25cd55c894a23bf39c726235db256758b7400e6b2 Homepage: https://cran.r-project.org/package=codetools Description: CRAN Package 'codetools' (Code Analysis Tools for R) Code analysis tools for R. 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Checks on 'CRAN' based on information in the 'URL' field or 'BioConductor' and 'GitHub' based on constructing a URL, and verifies all paths via testing for a successful response. This can be useful when automating static code analysis based on a list of package names, and similar tasks. Package: r-cran-codexcopd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-codexcopd_0.1.0-1.ca2404.1_all.deb Size: 11470 MD5sum: 17f4d96e871cab08a6b99b23a3f1d2e5 SHA1: 42995df4c25b7b4fc9dc020311eea0ce9bf439a3 SHA256: d6df306353f67048470125d1f5c8b4b2810b1a30d9602dcd4f830934394924bf SHA512: 13feaede501d06eef97026a37d1ef8930f6f040f6f53f0c3c5f45f5f1d50941ed109d917b4ba2440537af087ccdd89dd85adc1f58ba134db1ef765f6e2b505c2 Homepage: https://cran.r-project.org/package=codexcopd Description: CRAN Package 'codexcopd' (The CODEX (Comorbidity, Obstruction, Dyspnea, and PreviousSevere Exacerbations) Index: Short and Medium-Term Prognosis inPatients Hospitalized for Chronic Obstructive Pulmonary Disease(COPD) Exacerbations) Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) . 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Package: r-cran-codified Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-readr, r-cran-redcapr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-codified_0.3.0-1.ca2404.1_all.deb Size: 345778 MD5sum: 3cc65623d9430866daf0052f665bba02 SHA1: 02e722d0d1c5d6781eac9325cf69c88472710529 SHA256: 1a3ea1342a046ee3c676f9fae50b54acc32ce0075debc3261f0e4ae8f6fdfb15 SHA512: 920a00d0454653988d052137633a6b14e065b6c6e90a76151b130acf13b650de13c4dc38c618d7dcfa4c453974492f3c33b513ad18c5a52c30cf2c24bdd49b7b Homepage: https://cran.r-project.org/package=codified Description: CRAN Package 'codified' (Produce Standard/Formalized Demographics Tables) Augment clinical data with metadata to create output used in conventional publications and reports. Package: r-cran-codina Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 830 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-magrittr, r-cran-plyr, r-cran-visnetwork, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-codina_1.1.2-1.ca2404.1_all.deb Size: 799438 MD5sum: 7eab51d3e2b2ca5afef771a2a11f973f SHA1: 63ea22fcb85cec747d3035e5def41d704116361b SHA256: 57df2c0d3973135b059b8f49f7038261d1804b4f4b4f83554d9eeb37f5c6e7f2 SHA512: 092a286133ed98c4ec6879707fddb5a35115cefd855e001f5bfe76ec9a170ae227996a076bf46d269b983e1cb2b1f518bf8c8df150e27fc3848ddba3f668a533 Homepage: https://cran.r-project.org/package=CoDiNA Description: CRAN Package 'CoDiNA' (Co-Expression Differential Network Analysis) Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). 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Their main advantage is that they provide a consistent method for defining marginal effects in factorial models. In a simple one-way ANOVA model the intercept term is always the simple average of the class means. 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This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 . Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 ; Nicholls and Jones 2001 ). The paper accompanying this R package is Blaauw et al. 2024 . Package: r-cran-cofid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1513 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cofid_1.0.0-1.ca2404.1_all.deb Size: 1436674 MD5sum: 8fd294ab8469e6cfd4eaa0ea58025bff SHA1: 742f5e26a31d26cae8d463b3043247545be627ea SHA256: 728c75bc23d8e5411438f8105daafab9652e74e5d30071cea80cec5605f16151 SHA512: d5522b325f1b29fb9f4e9279b2b650659959191e0f45b345da5468691104682fc415e2ddf2adfd2ab7bb1e497f14bea4670ac1a96d0fbacca8ee5ce81f3a1ab9 Homepage: https://cran.r-project.org/package=cofid Description: CRAN Package 'cofid' (Copepod Fish Interaction Database) A curated list of copepod-fish ecological interaction records. It contains the taxonomy of the copepod and the fish and the publication from which the information was obtained. This database contains only marine and brackish water fish species. It excludes fish species that inhabit only freshwater. 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It includes functions to simulate factor-model data with copula-distributed idiosyncratic errors (e.g., Clayton, Gumbel, Frank, Student t and Gaussian copulas) and to perform diagnostic tests such as the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity. Estimation routines include principal component based factor analysis, projected principal component analysis, and principal orthogonal complement thresholding for large covariance matrix estimation. The philosophy of the package is described in Guo G. (2023) . 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The provided example data is for biological data but the methodology can be used for large data sets to compare quantitative entities that can be grouped. For example, a store might divide entities into cloth, food, car products etc and want to see how sales changes in the groups after some event. The theoretical background for the calculations are provided in New insights into functional regulation in MS-based drug profiling, Ana Sofia Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports . 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(2020) . Package: r-cran-coglyphr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sp Suggests: r-cran-imager, r-cran-png, r-cran-jpeg, r-cran-tiff, r-cran-bmp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coglyphr_1.1.0-1.ca2404.1_all.deb Size: 128550 MD5sum: 2e186c6aedc2adc3de6a9898e45cff87 SHA1: 578ff4769015b81bd0a72ddfb92d1564bc16c8c2 SHA256: 7edffcd1c459207d853291c17374aeac685f08ecdd3e6f7d6a06a82fd7b6bc37 SHA512: b29999f62c14f49b24023c749a3439fdac4bca1e4b0e259dc8db85f65f4a68ddad2883f4a196f05fd31d1cb47d1bda6b95dc628f5e53a55904c1a4f84224c5e2 Homepage: https://cran.r-project.org/package=coglyphr Description: CRAN Package 'coglyphr' (Compute Glyph Centers of Gravity from Image Data) Computes the center of gravity (COG) of character-like binary images using three different methods. This package provides functions for estimating stroke-based, contour-based, and potential energy-based COG. It is useful for analyzing glyph structure in areas such as visual cognition research and font development. The contour-based method was originally proposed by Kotani et al. (2004) and Kotani (2011) , while the potential energy-based method was introduced by Kotani et al. (2006) . Package: r-cran-cogmapr Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-bioc-rgraphviz, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-car Filename: pool/dists/noble/main/r-cran-cogmapr_0.9.5-1.ca2404.1_all.deb Size: 319902 MD5sum: 631c07112f9750e886a722e6bf6dc5e7 SHA1: 6b16501fe173a2e781dd845ae87a0f4dd455d3b1 SHA256: 1c7a5e24828918dfb800c3067e17944f80b36ad65edd7ca855d335fa985fec41 SHA512: 6f79b927b5a94109df822536c3daacf38b3cbb761cbc21bf6631e836991a019c57549decfdf51210deed9627e923efae50438f7f48b11a877e2389bfecf2665b Homepage: https://cran.r-project.org/package=cogmapr Description: CRAN Package 'cogmapr' (Cognitive Mapping Tools Based on Coding of Textual Sources) Functions for building cognitive maps based on qualitative data. Inputs are textual sources (articles, transcription of qualitative interviews of agents,...). These sources have been coded using relations and are linked to (i) a table describing the variables (or concepts) used for the coding and (ii) a table describing the sources (typology of agents, ...). Main outputs are Individual Cognitive Maps (ICM), Social Cognitive Maps (all sources or group of sources) and a list of quotes linked to relations. This package is linked to the work done during the PhD of Frederic M. Vanwindekens (CRA-W / UCL) hold the 13 of May 2014 at University of Louvain in collaboration with the Walloon Agricultural Research Centre (project MIMOSA, MOERMAN fund). Package: r-cran-cogmod Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-insight Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-loo, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-easystats, r-cran-datawizard, r-cran-bayestestr, r-cran-parameters, r-cran-performance, r-cran-modelbased, r-cran-report, r-cran-reformulas, r-cran-lme4, r-cran-rwiener, r-cran-statmod, r-cran-rtdists Filename: pool/dists/noble/main/r-cran-cogmod_0.3.3-1.ca2404.1_all.deb Size: 1096570 MD5sum: facf28395d6feefeb84afca22bc64c48 SHA1: 80f616470de0fa301f3fbeda78a866d4fe758b5a SHA256: c5eac6e2e31ea0b2cab4f602d2f41036841e7bb5bcd807dd7685a45d3b9c3dfc SHA512: 85995902eec6e02eae8b27995e5e8d2e910114ceda951a3a1ebfdca7f9c8e24a44b8964120434f40d898dd5d9411afec8ac517e71e9e0a22ed1cb7b5488c4e9a Homepage: https://cran.r-project.org/package=cogmod Description: CRAN Package 'cogmod' (Cognitive Models for Subjective Scales and Decision Making Tasks) Implements cognitive models for data from subjective (Likert or analog) scales and from decision making tasks with reaction times and choice data. Provides random generation, density functions, and custom response distributions for Bayesian estimation with 'brms', covering discrete beta, ordered beta and choice-confidence models for subjective ratings; reaction time distributions such as the ex-Gaussian and the shifted log-normal, Wald, gamma and Weibull; and sequential sampling models of choice and reaction time, including the drift diffusion model (DDM), the racing diffusion model (RDM), the lognormal race model (LNR) and the linear ballistic accumulator (LBA). The website provides examples and tutorials for using and interpreting the models. Methods are described in Ratcliff and McKoon (2008) , Brown and Heathcote (2008) , Rouder et al. (2015) , Tillman et al. (2020) , Kubinec (2023) , and Sciandra et al. (2024) . Package: r-cran-cognitor Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-config, r-cran-shinyjs, r-cran-httr, r-cran-dplyr, r-cran-base64enc, r-cran-jsonlite, r-cran-paws Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cognitor_1.0.5-1.ca2404.1_all.deb Size: 1311002 MD5sum: 27ef3fd4d19b724f36fc83930fbbbe0d SHA1: ea972e2c0fde85cf227305db3e6f4d4e776b2872 SHA256: 7ea6128d31038e9e732885c631c7306127ce57a728b013fb5ad674024bc1cf32 SHA512: ad5d2439bdd8dbc0de56a359939feb3a9dfc3bba7a57e71eefdc5609ade5ad6f3b912192e40206ebf2d777ecfc45d2a01fb8f41135e3acf047ce851364a723c0 Homepage: https://cran.r-project.org/package=cognitoR Description: CRAN Package 'cognitoR' (Authentication for 'Shiny' Apps with 'Amazon Cognito') Provides authentication for Shiny applications using 'Amazon Cognito' ( ). Package: r-cran-cograph Architecture: all Version: 2.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5562 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-r6 Suggests: r-cran-matrix, r-cran-backbone, r-cran-braingraph, r-cran-centiserve, r-cran-colorspace, r-cran-digest, r-cran-dplyr, r-cran-gifski, r-cran-gridextra, r-cran-grimport2, r-cran-igraph, r-cran-influencer, r-cran-jsonlite, r-cran-keyplayer, r-cran-knitr, r-cran-nestimate, r-cran-netrankr, r-cran-network, r-cran-qgraph, r-cran-rcolorbrewer, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsvg, r-cran-sna, r-cran-testthat, r-cran-tidygraph, r-cran-tna, r-cran-tnet, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-cograph_2.7.2-1.ca2404.1_all.deb Size: 4656938 MD5sum: cc811117269d45c70d0660a79e74e71d SHA1: a4c494d1b34adaa57d7da0b4f1800e81337294f8 SHA256: a5740cf13936e0e5badfc4dcd19940eccc90fb4dc7e139330cd51f1574d033e2 SHA512: 734ebbb20b9129d9faecf39d1dc51fab1811ea4c508b902b6299ee725b45450d30b04447e0754b2112a1f3922b31e6b28080004e38b0a081ac36e5a7925430f4 Homepage: https://cran.r-project.org/package=cograph Description: CRAN Package 'cograph' (Analysis and Visualization of Complex Networks) Provides tools for the analysis, visualization, and manipulation of dynamical, social (Saqr et al. (2024) ) and complex networks (Saqr et al. (2025) ). The package supports multiple network formats and offers flexible tools for heterogeneous, multi-layer, and hierarchical network analysis with simple syntax and extensive toolset. Package: r-cran-cohetsurr Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-mvtnorm, r-cran-mgcv, r-cran-grf Filename: pool/dists/noble/main/r-cran-cohetsurr_2.0-1.ca2404.1_all.deb Size: 239076 MD5sum: db4ce09240ecbb9e263929936ffe374c SHA1: 76a6553e3b72fcfb521c4bb547d307c80b561d69 SHA256: 90f85b676e1c2a7f0c94ce91d60ec84045f8066a436b2d2fc30c7fa9c4075f43 SHA512: 06e754a031ae86128185ad448f21137e325214f4769990372aea9fa8dd85bfd0ad206e81e0a9c255d758cc7c57afac8a011f3db2867d3fce134437c65636b83c Homepage: https://cran.r-project.org/package=cohetsurr Description: CRAN Package 'cohetsurr' (Assessing Complex Heterogeneity in Surrogacy) Provides functions to assess complex heterogeneity in the strength of a surrogate marker with respect to multiple baseline covariates, in either a randomized treatment setting or observational setting. For a randomized treatment setting, the functions assess and test for heterogeneity using both a parametric model and a semiparametric two-step model. More details for the randomized setting are available in: Knowlton, R., Tian, L., & Parast, L. (2025). "A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker," Statistics in Medicine, 44(5), e70001 . For an observational setting, functions in this package assess complex heterogeneity in the strength of a surrogate marker using meta-learners, with options for different base learners. More details for the observational setting will be available in the future in: Knowlton, R., Parast, L. (2025) "Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners." A tutorial for this package can be found at . Package: r-cran-cohortalgebra Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-rlang, r-cran-sqlrender Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortalgebra_0.3.1-1.ca2404.1_all.deb Size: 376224 MD5sum: 868ef869041f995594f4d83f1089da96 SHA1: 331b4079121e5835542b4f93dbbae792fb8aee0d SHA256: fdbb9d976e56f742475a9bcb9c5981051367971c731598fbabb76542c4d25950 SHA512: 3ae7da90f992fa0a90db3843036d30b33ca1fe02064b3f5fa231a3cfebce6baecb5918b6ef85d8239f2a0a4ae290c5eec69af80785a5451a38dc3eb3c82745ef Homepage: https://cran.r-project.org/package=CohortAlgebra Description: CRAN Package 'CohortAlgebra' (Use of Interval Algebra to Create New Cohort(s) from ExistingCohorts) This software tool is designed to generate new cohorts utilizing data from previously instantiated cohorts. It employs interval algebra operators such as UNION, INTERSECT, and MINUS to manipulate the data within the instantiated cohorts and create new cohorts. Package: r-cran-cohortbuilder Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1593 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-s7, r-cran-jsonlite, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-ggplot2, r-cran-rlang, r-cran-formatr, r-cran-collapse Suggests: r-cran-querybuilder, r-cran-vdiffr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ellmer, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortbuilder_1.0.0-1.ca2404.1_all.deb Size: 1016088 MD5sum: e460f79e17e80e5a994515dde07aa33a SHA1: 8937aecf2cdc25b42f12551db650c2033507dbe7 SHA256: 62adc16ba554a0587ee827ef0f97bba0648036d8017ff241b22eaea077f5879f SHA512: f173d765e397924d6a221dd713fcf9c09f342eaed6f28ea582e136cc602c318b9bf6e7362d3807866c8f79b4804406353c2c4dbf3c6cba7f70b11295d8f84704 Homepage: https://cran.r-project.org/package=cohortBuilder Description: CRAN Package 'cohortBuilder' (Data Source Agnostic Filtering Tools) Common API for filtering data stored in different data models. Provides multiple filter types and reproducible R code. Works standalone or with 'shinyCohortBuilder' as the GUI for interactive Shiny apps. Package: r-cran-cohortcharacteristics Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-cli, r-cran-stringr, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-snakecase, r-cran-lifecycle, r-cran-purrr, r-cran-clock Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-visomopresults, r-cran-cohortconstructor, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-drugutilisation, r-cran-dt, r-cran-duckdb, r-cran-flextable, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-gt, r-cran-here, r-cran-hmisc, r-cran-htmltools, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-plotly, r-cran-png, r-cran-reactable, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsvg, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-withr, r-cran-extrafont Filename: pool/dists/noble/main/r-cran-cohortcharacteristics_1.1.3-1.ca2404.1_all.deb Size: 2681318 MD5sum: da16daa96aff1881bebff5b7c5eceb19 SHA1: cd1f53ef11eebcd4bb745e2f506431c92e7441b4 SHA256: 837063410ea7d9fe5231ac8e856695b0ed7f7ab2b872fba57d6b9bec32029936 SHA512: ae912f22853307eccfc039c8969825288581d29b5a2768c6bb71de6de85b6d57893c90b0d48d2c7e90cba59bdd1bba030bcc3ae8949045a35b3cd7ebe99e4880 Homepage: https://cran.r-project.org/package=CohortCharacteristics Description: CRAN Package 'CohortCharacteristics' (Summarise and Visualise Characteristics of Patients in the OMOPCDM) Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Package: r-cran-cohortconstructor Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-codelistgenerator, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cdmconnector, r-cran-circer, r-cran-cohortcharacteristics, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-drugutilisation, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-here, r-cran-incidenceprevalence, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-sqlrender, r-cran-stringr, r-cran-testthat, r-cran-tictoc, r-cran-visomopresults, r-cran-systemfonts Filename: pool/dists/noble/main/r-cran-cohortconstructor_0.6.3-1.ca2404.1_all.deb Size: 1209032 MD5sum: 907c059ed2ad6b9602b6caa961af3deb SHA1: ddc73b57c580b795902478126446956353dbb6ef SHA256: c28ff4de5f8f1ac147072551d333e60f253e7a1dddc664d6edb5d874b420d457 SHA512: e30f5ae3261fbd25b8953762dbafbbb0d021d3e35f8b2e32c3bf6e89d8d1db33d17ca714d4041d35b19452ecf262caacd65f5bfaaecf1a496d6400b7dabbd2bf Homepage: https://cran.r-project.org/package=CohortConstructor Description: CRAN Package 'CohortConstructor' (Build and Manipulate Study Cohorts Using a Common Data Model) Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Package: r-cran-cohortcontrast Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-cdmconnector, r-cran-cohortconstructor, r-cran-omopgenerics, r-cran-lubridate, r-cran-doparallel, r-cran-foreach, r-cran-data.table, r-cran-cli, r-cran-jsonlite, r-cran-nanoparquet Suggests: r-cran-testthat, r-cran-patientprofiles, r-cran-duckdb, r-cran-dbi, r-cran-rpostgres, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-processx, r-cran-bit64, r-cran-reshape2, r-cran-igraph, r-cran-matrix, r-cran-cluster, r-cran-vegan, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-cohortcontrast_1.0.0-1.ca2404.1_all.deb Size: 4307524 MD5sum: 53fcd2e2b0eccddea120b5fa02f625a2 SHA1: 05b99c71169546fe477df1cd49d43b19327cdb6a SHA256: 078604ca36a2b9efa832eb12d58df2221ad98462040884b928442ededdc98a54 SHA512: 3b23a5cb49d369d57c16fdd3878736bb45073ee54c1e9fcb923a3a4e6470dcdf9616bd64eed784e227dbe89905d1521ff2789cd2279f709a4b1d76877e83b770 Homepage: https://cran.r-project.org/package=CohortContrast Description: CRAN Package 'CohortContrast' (Enrichment Analysis of Clinically Relevant Concepts in CommonData Model Cohort Data) Identifies clinically relevant concepts in Observational Medical Outcomes Partnership Common Data Model cohorts using an enrichment-based workflow. Defines target and control cohorts and extracts medical interventions that are over-represented in the target cohort during the observation period. Users can tune filtering and selection thresholds. The workflow includes chi-squared tests for two proportions with Yates continuity correction, logistic tests, and hierarchy and correlation mappings for relevant concepts. The results can be optionally explored using the bundled graphical user interface. For workflow details and examples, see . Package: r-cran-cohortcosts Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cdmconnector, r-cran-omopgenerics, r-cran-dbplyr, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-cli, r-cran-glue Suggests: r-cran-dbi, r-cran-tibble, r-cran-gt, r-cran-patientprofiles, r-cran-visomopresults, r-cran-duckdb, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortcosts_0.6.1-1.ca2404.1_all.deb Size: 37216 MD5sum: c0a072791f067031186123b7afcac22a SHA1: 93075738f9826e7a0f0e6850e59267e44b338ee2 SHA256: a55c04d3db9c1c5354cd994bed52afa93124d59b5c982e2a852dae0191111b00 SHA512: ee2295a6b43f9742a651509b4eb3233b66f3d69093c8bff6a2eb1703c030cee83bc4b98e3c4ae2ee68c0fa731900bd5c38cdd06bf1c95f8c9f08ec8b68e54ebc Homepage: https://cran.r-project.org/package=CohortCosts Description: CRAN Package 'CohortCosts' (Direct Medical Cost Extraction and Health Economics Costing onOMOP CDM) Links polymorphic Observational Medical Outcomes Partnership (OMOP) COST records to clinical events and tracks medical expenditures, unit cost tariffs, and healthcare price indices for OMOP Common Data Model (CDM) cohorts following DARWIN EU standards. Package: r-cran-cohortexplorer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-parallellogger, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortexplorer_0.1.0-1.ca2404.1_all.deb Size: 297136 MD5sum: 216f45c9848245370f73f46750d89a7d SHA1: 42a744824978989cea2a9a12c9c32c1822b1c855 SHA256: e5f591976066f6b5ce3d164df9756976a607b16c98266f7fb3aa6278f9e3ed29 SHA512: 466c5b662452b755e92d3151df8ac355bcad37d0976859a8b6eeadcfdcbb3d39d3a5bae8da620873e569866fd3803b2da3adcb91a2e904a9eb49076b421a3f0d Homepage: https://cran.r-project.org/package=CohortExplorer Description: CRAN Package 'CohortExplorer' (Explorer of Profiles of Patients in a Cohort) This software tool is designed to extract data from a randomized subset of individuals within a cohort and make it available for exploration in a shiny application environment. It retrieves date-stamped, event-level records from one or more data sources that represent patient data in the Observational Medical Outcomes Partnership (OMOP) data model format. This tool features a user-friendly interface that enables users to efficiently explore the extracted profiles, thereby facilitating applications, such as reviewing structured profiles. Package: r-cran-cohortgenerator Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2879 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-databaseconnector, r-cran-r6, r-cran-checkmate, r-cran-digest, r-cran-dplyr, r-cran-lubridate, r-cran-parallellogger, r-cran-readr, r-cran-rlang, r-cran-jsonlite, r-cran-resultmodelmanager, r-cran-sqlrender, r-cran-stringi, r-cran-tibble Suggests: r-cran-circer, r-cran-duckdb, r-cran-eunomia, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-zip Filename: pool/dists/noble/main/r-cran-cohortgenerator_1.1.1-1.ca2404.1_all.deb Size: 1413902 MD5sum: 15685a23f6e5ccbe37814ba4b7f539ab SHA1: 3062ad737c0ece2b53afd6e9ff0c88a0902b765b SHA256: 2d16ba8732a9041ba3da7de82de8a6fe9cf5271034103fbc9d7bc4a2bad49964 SHA512: 4ecb14db3de5dcda9e6e17b126964d32dbed5c777f69a26593867a73d7e993e2db4fe2ae6c624826770d427856cd1ebdf19a333344928a847719cb24c5e9e9bb Homepage: https://cran.r-project.org/package=CohortGenerator Description: CRAN Package 'CohortGenerator' (Cohort Generation for the OMOP Common Data Model) Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using 'CIRCE' () or SQL compatible with 'SqlRender' (). Package: r-cran-cohortpathways Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-rlang, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortpathways_0.0.1-1.ca2404.1_all.deb Size: 32742 MD5sum: 95ac1388fe4a38ebc20527a1b702b990 SHA1: bb773468020af2a371217790db00b2878617dde4 SHA256: c628fa11b8926fa1358504bee5803226363ef55755836031971197e777514e65 SHA512: 2fcf3dda1c85751ba51d5f951b225167c353017d4b1ac61736a0e98ac642b97ea8518a317519f244344c322bbb32293641526689d4869cb9af6a8c6c0279dd60 Homepage: https://cran.r-project.org/package=CohortPathways Description: CRAN Package 'CohortPathways' (Create Pathways from Target to Event Cohorts) Software tool designed to compute the temporal relationship defined as pathways between any two instantiated cohorts. The cohorts are input as Target and event cohorts. 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In order to obtain approval for this combination therapy, superiority of the combination over the two active compounds and superiority of the two active compounds over placebo need to be demonstrated. A more detailed description of the design can be found in Meyer et al. and a manual in Meyer et al. . Package: r-cran-cohorts Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1233 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-tibble, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cohorts_1.3.0-1.ca2404.1_all.deb Size: 1139424 MD5sum: c92a3c184c691b9d8d7406be7782bf66 SHA1: be1da7a9bbceafbb20b4cd12ce37bc9090065d56 SHA256: 4d619589b2d15c149e1831bd1498ce1b0ca083256ab2de5a8716030ccc3c7a9d SHA512: a355d21712142023be307cda41fc7842173f7b05a59f80293143acd2738f7ef196594b1d0c9936bcf29037e129c3f311b9a45cc19906d0b15927f45b818c53e1 Homepage: https://cran.r-project.org/package=cohorts Description: CRAN Package 'cohorts' (Cohort Analysis Made Easy) Functions to simplify the process of preparing event and transaction for cohort analysis. Package: r-cran-cohortsurvival Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3167 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-survival, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-codelistgenerator, r-cran-dbi, r-cran-roxygen2, r-cran-omock, r-cran-knitr, r-cran-tictoc, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-cmprsk, r-cran-duckdb, r-cran-gt, r-cran-flextable, r-cran-scales, r-cran-visomopresults, r-cran-extrafont, r-cran-cdmconnector Filename: pool/dists/noble/main/r-cran-cohortsurvival_1.2.0-1.ca2404.1_all.deb Size: 1779006 MD5sum: 2629acbd407f2fefe77621882802d372 SHA1: 2f4a2da9380436f4ef00b2a74936e1b351a170ee SHA256: 9182343e1807c6ff7093b38353df8cb7e204feee6976acd67b021f2e59eee0fc SHA512: 892d316128cdbd07dc70cdd76b2461fa25fb644a431f73a0c0d9148d745dcc944c160a1e37c1ede3e9eddcd5d3178c1132a907f158ec44f880765c8edc1e8d97 Homepage: https://cran.r-project.org/package=CohortSurvival Description: CRAN Package 'CohortSurvival' (Estimate Survival from Common Data Model Cohorts) Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. 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Package: r-cran-cohorttools Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epi, r-cran-cmprsk, r-cran-ggplot2, r-cran-survival, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-rsvg, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-mstate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cohorttools_0.1.8-1.ca2404.1_all.deb Size: 60350 MD5sum: d354310fe6ef4b4b6d2bcdd64995ffe5 SHA1: f7e73aa0c112fe2750e149c5ecbb5fd1b63a70ee SHA256: dfc02042830fec0391876423ddf7f322a2cd0e498b15bd2e54fc40b8e2d2af78 SHA512: 3b67882367796c76de7262fd5047313be3df85430c902fca84bf9cac0fc904297d6799dde47f1fe9e172114ca5b1cb8335dfed60144e7da59899316858607b36 Homepage: https://cran.r-project.org/package=cohorttools Description: CRAN Package 'cohorttools' (Cohort Data Analyses) Functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data. 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Package: r-cran-coil Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-aphid, r-cran-seqinr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coil_1.2.4-1.ca2404.1_all.deb Size: 172918 MD5sum: 3092480f49b56d8912af4e8307bedbd0 SHA1: 58026982b93903237c42bf029e56341d7f5e4552 SHA256: e460fdbea4a00fcbc7724370e3cc2022f4d0862ca1f3de66f84d4b50df39c934 SHA512: 8c6fbb3167a6ba79dd32713ea19aa8716affe857349fa3bebb5f39bca68599adea287fa1e4bf9ab54953a8807231733d7fb33363b5b25a24046516a0adfa0534 Homepage: https://cran.r-project.org/package=coil Description: CRAN Package 'coil' (Contextualization and Evaluation of COI-5P Barcode Data) Designed for the cleaning, contextualization and assessment of cytochrome c oxidase I DNA barcode data (COI-5P, or the five prime portion of COI). It contains functions for placing COI-5P barcode sequences into a common reading frame, translating DNA sequences to amino acids and for assessing the likelihood that a given barcode sequence includes an insertion or deletion error. The error assessment relies on the comparison of input sequences against nucleotide and amino acid profile hidden Markov models (PHMMs) (for details see Durbin et al. 1998, ISBN: 9780521629713) trained on a taxonomically diverse set of reference sequences. The functions are provided as a complete pipeline and are also available individually for efficient and targeted analysis of barcode data. Package: r-cran-coimp Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-cluster, r-cran-nnet, r-cran-gtools, r-cran-locfit Filename: pool/dists/noble/main/r-cran-coimp_2.2-1.ca2404.1_all.deb Size: 229232 MD5sum: ae6b91178245f40017404fc50ba16458 SHA1: d5a0a045511caf8ce10eccac0d46b533f78ebe30 SHA256: 56871b1ce46c8322fd7a27d65e02de3ab97b42429e48d01bcb4ff49b6156d004 SHA512: c01a763591627e8a109b8f4d5c00c61db5d1ea7765c3e811ebb4cb4aeadbd09a75c4e79eec1119aeb3c246c576c34accbb0cf5e8caa059c99816dd51711e7bc5 Homepage: https://cran.r-project.org/package=CoImp Description: CRAN Package 'CoImp' (Parametric and Nonparametric Copula-Based Imputation Methods) Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas. Package: r-cran-coinmarketcapr Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-ggplot2, r-cran-data.table, r-cran-curl, r-cran-cli, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-coinmarketcapr_0.4-1.ca2404.1_all.deb Size: 75906 MD5sum: f3e57783d3c768d0aa9733dd35cb9075 SHA1: dc83f7e274575b00e4c30b7c9a18467b44328992 SHA256: 7eaf6ded72b4cc448a0e7263dd114a52fce407e6c8dbf8aae2b050cf6b56f5cf SHA512: d21d93dc041a4e3f4a10257684809600471469dc3ae354d4884082d8f30646c411368d37530703565c00f343ca07bd4c6cdd7a0fdc391d349c21f04a1cf53d2e Homepage: https://cran.r-project.org/package=coinmarketcapr Description: CRAN Package 'coinmarketcapr' (Get 'Cryptocurrencies' Market Cap Prices from Coin Market Cap) Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' . 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The package implements seven classical approaches—Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glaz—along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation. Package: r-cran-coinprofile Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-plyr, r-cran-coin, r-cran-rdpack, r-cran-exactranktests, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-coinprofile_0.1.9-1.ca2404.1_all.deb Size: 188034 MD5sum: 0b6031bf98f18730c01225a24e038d2b SHA1: 3ee077f438f8560ed1b5b3ccb04894884674c4d2 SHA256: 292c93c8e43261d2f540d22decb9f186e905254fa9c99337237df699342c2203 SHA512: 2cbe851f0255392a71f114494f024bfe28fcb2796e5bc8d4856e7514af116022d1dfde829eaa364c2941987a751b9fee593bccac370abfd6f4542f8efa80b62e Homepage: https://cran.r-project.org/package=Coinprofile Description: CRAN Package 'Coinprofile' (Coincident Profile) Builds the coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) . 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It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results. Package: r-cran-coint Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-timeseries Suggests: r-cran-cointreg, r-cran-forecast, r-cran-timedate, r-cran-urca, r-cran-zoo Filename: pool/dists/noble/main/r-cran-coint_0.0.4-1.ca2404.1_all.deb Size: 439376 MD5sum: 51130db28142bf7ae55272ce55166f2f SHA1: 41f087cf832e7819e0bb48644c82056eedadfb59 SHA256: cec0d2731cfccf8ac47ce9ced052150314579909a3d286a7710555b7d2f57941 SHA512: c5b96b9e1002987a0718977df97fbc3334ecbb5759e13e1a996f1e83af5783bc1771c67d7734ddc5257983b658086619f702f184b8b7d08969262b36dd9d04f2 Homepage: https://cran.r-project.org/package=COINT Description: CRAN Package 'COINT' (Unit Root Tests with Structural Breaks and Fully-ModifiedEstimators) Procedures include Phillips (1995) FMVAR , Kitamura and Phillips (1997) FMGMM , Park (1992) CCR , and so on. Tests with 1 or 2 structural breaks include Gregory and Hansen (1996) , Zivot and Andrews (1992) , and Kurozumi (2002) . 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It is well known that in a cointegrating regression the ordinary least squares (OLS) estimator of the parameters is super-consistent, i.e. converges at rate equal to the sample size T. When the regressors are endogenous, the limiting distribution of the OLS estimator is contaminated by so-called second order bias terms, see e.g. Phillips and Hansen (1990) . The presence of these bias terms renders inference difficult. Consequently, several modifications to OLS that lead to zero mean Gaussian mixture limiting distributions have been proposed, which in turn make standard asymptotic inference feasible. These methods include the fully modified OLS (FM-OLS) approach of Phillips and Hansen (1990) , the dynamic OLS (D-OLS) approach of Phillips and Loretan (1991) , Saikkonen (1991) and Stock and Watson (1993) and the new estimation approach called integrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014) . 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Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions. 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Two sequences are colocalized if they are within the cut-off distance of each other, and clusters are sets of sequences where each sequence is colocalized to at least one other sequence in the cluster. For a set of .bed annotation tables provided in a list along with a cut-off distance, the program will output a file containing the locations of each cluster. Annotated .bed files are from the 'pwmscan' application at . Personal machines might crash or take excessively long depending on the number of annotated sequences in each file and whether chromsearch() or gensearch() is used. 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The package integrates three different APIs: 'API-Colombia' for Colombian-specific data including geography, culture, tourism, and government information; 'World Bank API' for economic and demographic indicators; and 'Nager.Date' for public holidays. The package enables users to explore various aspects of Colombia such as geographic locations, cultural attractions, economic indicators, demographic data, and public holidays. Additionally, 'ColombiAPI' includes curated datasets covering Bogota air stations, business and holiday dates, public schools, Colombian coffee exports, cannabis licenses, Medellin rainfall, malls in Bogota, as well as datasets on indigenous languages, student admissions and school statistics, forest liana mortality, municipal and regional data, connectivity and digital infrastructure, program graduates, vehicle counts, international visitors, and GDP projections. 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Uses CIELAB, RGB, or HSV color spaces. Originally written for use with organism coloration (reef fish color diversity, butterfly mimicry, etc), but easily applicable for any image set. 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Color palettes can also be created. Package: r-cran-colorhcplot Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-colorhcplot_1.5.1-1.ca2404.1_all.deb Size: 476346 MD5sum: f048c044a692e0ff6abb34d5f7f2a03d SHA1: 42dc4462834dc3c68e3b08898a5356b241b08710 SHA256: 4c352d344fc6f56e304fdafc966759ef4881e4d9ea8e2102d087724fa60b7d91 SHA512: 94bb17b62ce2f3e585cf1f9896ae07f91eb85059f4fda376af3b84606bdc3923b1555b94dc5545d8db1ca77fef49bc03bfbaf2fc486d30bd500fe5365f02ca40 Homepage: https://cran.r-project.org/package=colorhcplot Description: CRAN Package 'colorhcplot' (Colorful Hierarchical Clustering Dendrograms) Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters. 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Includes palettes and functionality from popular packages such as 'viridis', 'RColorBrewer', and base R 'grDevices', as well as 'ggplot2' plot bindings. Users can generate perceptually uniform and colorblind-friendly palettes, adjust palettes in HSL and RGB color spaces, map color gradients to value ranges, and create color-generating functions. 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Package: r-cran-colorize Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-knitr Suggests: r-cran-quarto, r-cran-glue, r-cran-crayon Filename: pool/dists/noble/main/r-cran-colorize_0.2.1-1.ca2404.1_all.deb Size: 632728 MD5sum: 69dab9b20e3e224bd0cd3fd5c4eeaeb5 SHA1: 15f81b4cc068f5d5c2159272f871d827f6e57685 SHA256: ab028c1ff47113be1a4ea87c072ae968395e229f40fb1f7a5a2e146a22d840b9 SHA512: 91180a342e3b17c1a4bdc085f6597c19ecc312396f530ace70ff01b6aaeb257d2d29bcdf6fe3708b28b5c38cf936fe2fb45c942f306c7c441c45f37c8d0aa44a Homepage: https://cran.r-project.org/package=colorize Description: CRAN Package 'colorize' (Render Text in Color for Markdown/Quarto Documents) Provides some simple functions for printing text in color in 'markdown' or 'Quarto' documents, to be rendered as HTML or LaTeX. This is useful when writing about the use of colors in graphs or tables, where you want to print their names in their actual color to give a direct impression of the color, like “red” shown in red, or “blue” shown in blue. 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Package: r-cran-colormap Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-v8, r-cran-stringr, r-cran-ggplot2 Suggests: r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-colormap_0.1.4-1.ca2404.1_all.deb Size: 41144 MD5sum: 86b17abe1a3f020e8a8789d67ea55307 SHA1: 2d2457c7c6fd93a59141ce547477a1a11f4e37eb SHA256: 81d719e66b9f996587a6c737252770b429e0f0f5163e9e5ff73d43467ff64d93 SHA512: dbb6c8a05afec5b19b5e34732a8c9c793367bdfad4e272d17bc636b2347160292e93e2f14ea88da7cded9596a3032843008b0c89299908be5976f631863332af Homepage: https://cran.r-project.org/package=colormap Description: CRAN Package 'colormap' (Color Palettes using Colormaps Node Module) Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. 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Package: r-cran-colorpatch Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-colorspace, r-cran-gridextra, r-cran-tsp Suggests: r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-colorpatch_0.1.2-1.ca2404.1_all.deb Size: 192698 MD5sum: 8942533278f486320384cdbd759370c2 SHA1: d8e01ee3a10f3e92a83d3cb8b06f20a750aed3ae SHA256: ebbed68eed1c27d2ac93a9a0e615e90d4374203e20cfa7228022d216dddd2452 SHA512: 0cb9c6228215a61cd4256513787acc12cdf1f68dbfa1834c7bfb8c55807101077bc03fb9f52c46c670dbf5d0c92e5a3daced760b4e293048ce9889a9146fbff6 Homepage: https://cran.r-project.org/package=colorpatch Description: CRAN Package 'colorpatch' (Optimized Rendering of Fold Changes and Confidence Values) Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values within a single diagram. This is especially useful for rendering gene expression data as well as other types of differential experiments. In addition to different rendering methods (ggplot extensions) functionality for perceptually optimizing color palettes are provided. Furthermore the package provides extension methods of the colorspace color-class in order to simplify the work with palettes (a.o. length, as.list, and append are supported). Package: r-cran-colorplane Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-colorplane_0.6.0-1.ca2404.1_all.deb Size: 152062 MD5sum: 561d9016e98be791f52c9caf5ee09ec6 SHA1: f69dc66b0ce880a59711f84c7dadca55a39b82d8 SHA256: 6c90e446735c4a6511840d21c982b6a96da2ebfd80f4f4a3c2cb5e02474eb718 SHA512: 10ae9c4cedf48b4d3e059128197b9d6e56cfb20209cd52f5c4a49cbf3a78b4252a3e37cfeecc939a053916e065c9a0188c1786f1b6bdb33a6180fcfd7e865e4e Homepage: https://cran.r-project.org/package=colorplane Description: CRAN Package 'colorplane' (Basic S4 Classes and Methods for Mapping Between Numeric Valuesand Colors) A simple set of classes and methods for mapping between scalar intensity values and colors. There is also support for layering maps on top of one another using alpha composition. Package: r-cran-colorr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-colorr_1.1.0-1.ca2404.1_all.deb Size: 89710 MD5sum: 445f69520d381a50a8a048c16f06c350 SHA1: 90c966590976863fc6f397aea572515f2704bc2a SHA256: db5cf5f7d3d416a2dd2d91987a97920e26a37e4ba623055153f58f80aadacd56 SHA512: f0101d5415ddd6bef266e4c09c5e3577484c5f1cef5b59366f9a315925f6716519195e87141955b83877dc72753a66a3d452ebe019ee0d95673ad1410d30dded Homepage: https://cran.r-project.org/package=colorr Description: CRAN Package 'colorr' (Color Palettes for Soccer, MLB, NBA, WNBA, NHL, and NFL Teams) Current-season color palettes for soccer clubs in the English Premier League ('EPL'), 'LaLiga', 'Serie A', the 'Bundesliga', 'Ligue 1' and Major League Soccer ('MLS'), and for Major League Baseball ('MLB'), National Basketball Association ('NBA'), Women's National Basketball Association ('WNBA'), National Hockey League ('NHL') and National Football League ('NFL') teams. Palettes are returned as named character vectors of hex colors, and as 'ggplot2' colour and fill scales. The palettes shipped in earlier versions of the package remain available so that older figures stay reproducible. Package: r-cran-colorramp2 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-colorramp2_0.1.1-1.ca2404.1_all.deb Size: 63738 MD5sum: b999171081f641215f10a9ed1fc7b39d SHA1: 19f1020265a460c1ecdf271d2b92582fb2efad48 SHA256: e1b2b37dcfe3b47efee54fbb8cec34ea8e1ab511f7b146ac920603e8f2ce3f7d SHA512: 5dc25cefc0c376f83ad7e05054f317f301ef0add433c338e8d1f3b993c3a7d3326480dfb92986148ad0cab8e7226263b271571b2426216935947a9766641b69f Homepage: https://cran.r-project.org/package=colorRamp2 Description: CRAN Package 'colorRamp2' (Generate Color Mapping Functions) A color mapping is generated according to the break values and corresponding colors. Other colors are generated by interpolating in a certain color space. The functions were part of the 'circlize' package . Package: r-cran-colorramps Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-colorramps_2.3.4-1.ca2404.1_all.deb Size: 29028 MD5sum: 4665d000cc32d710b5845a1ef3b8c505 SHA1: cfdb9498472748a62919a82d66acc3ce5e05df04 SHA256: 630435897ec6f9010e06996db485a9db3b161070133af3a86d910f839ae448b1 SHA512: 8cb2084180de045fba8f28771b66ee9a7ecd7733f94b4d69aebc374b3b62912796c8500bc877c5d6f1c5d3edb8dcd35fe1c93cecc79ab574ad69dd900e2773d1 Homepage: https://cran.r-project.org/package=colorRamps Description: CRAN Package 'colorRamps' (Builds Color Tables) Builds gradient color maps. 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When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) . Package: r-cran-colors3d Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-fnn, r-cran-plyr, r-cran-scales Filename: pool/dists/noble/main/r-cran-colors3d_1.0.1-1.ca2404.1_all.deb Size: 247996 MD5sum: f11e9979ce5ce72a483cb8b8d788f60f SHA1: 5dc1402d846880b18fd929309184bae6ef7c2f0c SHA256: e614834c7ce9ddf46a63f8a1784cca7203691ca21d0eca469d1870a6563d2000 SHA512: 01f40fba1f360d788ec7edd3ed2cb0ec5b30a10dea1f1fa6856b6ab24f013287bf1174cafe9591b7166484ad723eb7afcefc6853701929310d3c9f868b57ea29 Homepage: https://cran.r-project.org/package=colors3d Description: CRAN Package 'colors3d' (Generate 2D and 3D Color Palettes) Generate multivariate color palettes to represent two-dimensional or three-dimensional data in graphics (in contrast to standard color palettes that represent just one variable). You tell 'colors3d' how to map color space onto your data, and it gives you a color for each data point. You can then use these colors to make plots in base 'R', 'ggplot2', or other graphics frameworks. Package: r-cran-colorscience Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-pracma, r-cran-sp Filename: pool/dists/noble/main/r-cran-colorscience_1.0.9-1.ca2404.1_all.deb Size: 2121948 MD5sum: 34b1df55fe65b2dec2732c539cac153c SHA1: a4ccf7066fb955bc078c5784ebe61a17af957621 SHA256: b06955b414c696af899673cfbbece66181f80bf7637a4c969263fb661ee59265 SHA512: 7636a0c53780fdf4b0737155e4686f839654834fffed17c620ad34d1c71fadd66651faab50b311169a4681043f0398a4d26b7bf1f268c1e63e80fb8550099752 Homepage: https://cran.r-project.org/package=colorscience Description: CRAN Package 'colorscience' (Color Science Methods and Data) Methods and data for color science - color conversions by observer, illuminant, and gamma. Color matching functions and chromaticity diagrams. Color indices, color differences, and spectral data conversion/analysis. This package is deprecated and will someday be removed; for reasons and details please see the README file. Package: r-cran-colorsgen Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-colorsgen_1.0.0-1.ca2404.1_all.deb Size: 23008 MD5sum: d038bd5a6e9e562f1ea0adc4925fd4ff SHA1: 1605756ae3350fff972103a4c5700b3318d2cd80 SHA256: 5844b5f4116104ce50dab856012e2e99d874dda9e9c91ff0fd453f31eff6f587 SHA512: 06ccd8fb4987ab342dbdd4a509b8b3297281343fb26202e3faada63a38243fe62641243c315ff2e26f2826784c16be408006ee634bb57e62988c455251b383da Homepage: https://cran.r-project.org/package=colorsGen Description: CRAN Package 'colorsGen' (Generation of Random Colors) Generation of random colors, possibly with a given hue or a given luminosity. This is a port of the JavaScript library 'randomColor' . Package: r-cran-colorspec Architecture: all Version: 1.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5039 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-logger Suggests: r-cran-spacesxyz, r-cran-rootsolve, r-cran-mass, r-cran-quadprog, r-cran-rgl, r-cran-spacesrgb, r-cran-zonohedra, r-cran-microbenchmark, r-cran-arrangements, r-cran-knitr, r-cran-rmarkdown, r-cran-png Filename: pool/dists/noble/main/r-cran-colorspec_1.8-0-1.ca2404.1_all.deb Size: 3220540 MD5sum: 28ee4f2ebd67feb79ad0ae6db3baff12 SHA1: b196d04d4b5c3bdb544c2ad73401a15c132ca8f2 SHA256: 4a238484d81934e7ea3800d037bc5f994e9cd2714d086af1ba2a327781c0dbf9 SHA512: 3927f74eaa1692e18e2cb749cc14a2e5fc13fa9277a183eb31a667d02f44bed02e2d81e98f2a039539821279d7126d88d07815c30a7dc015bf669af0ebe8e54b Homepage: https://cran.r-project.org/package=colorSpec Description: CRAN Package 'colorSpec' (Color Calculations with Emphasis on Spectral Data) Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners. Build complex systems from simpler parts using a spectral product algebra. For light sources, compute CCT, CRI, SSI, and IES TM-30 reports. For object colors, compute optimal colors and Logvinenko coordinates. Work with the standard CIE illuminants and color matching functions, and read spectra from text files, including CGATS files. Estimate a spectrum from its response. A user guide and 9 vignettes are included. Package: r-cran-colour Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-jpeg, r-cran-png, r-cran-httr, r-cran-pixmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales Filename: pool/dists/noble/main/r-cran-colour_0.1.1-1.ca2404.1_all.deb Size: 2526358 MD5sum: a3495ac62b879be24b67588654eaab09 SHA1: 64db3d90af11b8f2f4de56dad33733277156346c SHA256: 1753fd7124c54aa9112fb7f21a292a8265e55bd7cd2714596f3f62af9c73ac9c SHA512: facecc850955b5dbfeb9863c8ce314f1bb42ad3785054b7cec3eede68e48e3331e78f9b70ad64a30a7cf44e09c40322a7ab4d508f63f9ae4db270bc5826c8891 Homepage: https://cran.r-project.org/package=colouR Description: CRAN Package 'colouR' (Create Colour Palettes from Images) Can take in images in either .jpg, .jpeg, or .png format and creates a colour palette of the most frequent colours used in the image. Also provides some custom colour palettes. Package: r-cran-colourlovers Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-jsonlite, r-cran-httr, r-cran-png Suggests: r-cran-httptest, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-colourlovers_0.3.6-1.ca2404.1_all.deb Size: 73638 MD5sum: f212db86c5ff78b49d0179813abc7754 SHA1: 29c86929171febb3b7bcade4ab567dde62e8b319 SHA256: 400dcdb323567039c798eb31e01102f8c162c969c1c6f562a514fe000a34ffcb SHA512: a9438bccd453351b94f9e9a7bfe3eb8da8177c11d91783edf6e778995edce1eeeec1c59514b4c1a2aa742ce1f8ffe972b07b3949c0c6eedf4cb54a96032e0278 Homepage: https://cran.r-project.org/package=colourlovers Description: CRAN Package 'colourlovers' (R Client for the COLOURlovers API) Provides access to the COLOURlovers API, which offers color inspiration and color palettes. Package: r-cran-colourpicker Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-miniui, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shinydisconnect Filename: pool/dists/noble/main/r-cran-colourpicker_1.3.0-1.ca2404.1_all.deb Size: 1265680 MD5sum: b713975d2796da955afc37af40e8b468 SHA1: 9af7e6d49cd2fa7f6ce7556bd759e37c9ae9b29b SHA256: ec0c0fff815f01ee01ad83b28e4fd79317bef68b1fb0be22f734c655900ddc14 SHA512: c3367d0be1fcdc952b46aad56194ebbd8f6a25bfeb55cd5790f05c2424adb156dd4de3e0a76f8a92e240ea2ea1b007a6f1cd4a94628b2156303316aec7dc8644 Homepage: https://cran.r-project.org/package=colourpicker Description: CRAN Package 'colourpicker' (A Colour Picker Tool for Shiny and for Selecting Colours inPlots) A colour picker that can be used as an input in 'Shiny' apps or Rmarkdown documents. The colour picker supports alpha opacity, custom colour palettes, and many more options. A Plot Colour Helper tool is available as an 'RStudio' Addin, which helps you pick colours to use in your plots. A more generic Colour Picker 'RStudio' Addin is also provided to let you select colours to use in your R code. Package: r-cran-colourspace Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-farver, r-cran-rann Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-colourspace_0.1.1-1.ca2404.1_all.deb Size: 2537882 MD5sum: 43504a669f18fd92cb7bda1b55afb8ae SHA1: 7358ee71cb925b7f47876b8d915956be941e6718 SHA256: ef8ac7a16a1e3120533ef7241340b45a8410fdb5acb4fd9a8fdf9c00e9067dde SHA512: b1178ba56262000ac88a65b515053716be5d1c547f1f79dd3825afc7e789e3b14b0d10f28c9c2d7503d7b6cf153a7da6e75ee3ab1e1979309a920f97a27a3782 Homepage: https://cran.r-project.org/package=colourspace Description: CRAN Package 'colourspace' (Convert from One Colour Space to Another, Print a Ready-to-PasteModern 'CSS' Syntax) Provides a comprehensive 'API' for colour conversion between popular colour spaces ('RGB', 'HSL', 'OKLab', 'OKLch', 'hex', and named colours) along with clean, modern 'CSS' Color Level 4 syntax output. Integrates seamlessly into 'Shiny' and 'Quarto' workflows. Includes nearest colour name lookup powered by a curated database of over 30,000 colour names. 'OKLab'/'OKLCh' colour spaces are described in Ottosson (2020) . 'CSS' Color Level 4 syntax follows the W3C specification . Package: r-cran-colourvision Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1055 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-corrplot, r-cran-rgl Filename: pool/dists/noble/main/r-cran-colourvision_2.1.0-1.ca2404.1_all.deb Size: 702134 MD5sum: f9072f4d1a957a91124334c67e7be6f9 SHA1: f7c82980abccb1b5acb7ddd85a99658101565c8b SHA256: bd09f7b51f4dfa8c20aa821a3c9420ff92c4b3ac44eef64270f1219163a4ebb5 SHA512: 777c5e2795dd3460f4ee4f05d72bfe175551ca07d0a050ed5cd3483e95edacc1d0a401553fddbbcda2c63fbd448e1240d391df928830c07aa395539502413c24 Homepage: https://cran.r-project.org/package=colourvision Description: CRAN Package 'colourvision' (Colour Vision Models) Colour vision models, colour spaces and colour thresholds. Provides flexibility to build user-defined colour vision models for n number of photoreceptor types. Includes Vorobyev & Osorio (1998) Receptor Noise Limited models , Chittka (1992) colour hexagon , and Endler & Mielke (2005) model . Models have been extended to accept any number of photoreceptor types. Package: r-cran-colp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-combinat Filename: pool/dists/noble/main/r-cran-colp_1.0.0-1.ca2404.1_all.deb Size: 22334 MD5sum: ecfea29edd6ebfa5842bca5e2323b568 SHA1: 4125cce1efef7d6bb790e9e999693b6d82a69138 SHA256: 00116ec9388afae841944def2e472443327aa587839713839fcfc29a590fd7e5 SHA512: 0949194d33ca2a7093990730811e99746b8568155c35f2049e6a32e4f60e8b9f694b7907a86410a684adf7cd6a9566a7f76c42d3d0e6e99e012a8507cd1a208f Homepage: https://cran.r-project.org/package=COLP Description: CRAN Package 'COLP' (Causal Discovery for Categorical Data with Label Permutation) Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)". Package: r-cran-cols4all Architecture: all Version: 0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3276 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-png, r-cran-stringdist, r-cran-colorspace, r-cran-spacesxyz Suggests: r-cran-colorblindcheck, r-cran-kableextra, r-cran-knitr, r-cran-shiny, r-cran-shinyjs, r-cran-ggplot2, r-cran-scales, r-cran-rmarkdown, r-cran-bookdown, r-cran-bibtex, r-cran-plotly Filename: pool/dists/noble/main/r-cran-cols4all_0.10-1.ca2404.1_all.deb Size: 3055012 MD5sum: d16b169499aa562f9c6f065b93d3b23f SHA1: 3718f201d51f639784ba632d653b98e8a6b6ed48 SHA256: b620f99de88f4b9abe37eb9b7a57b276f335d0e361b7f410dcb0e175f0824e56 SHA512: c147689b5c2ecd0e37b376be5bc8bb494f7f1a34c6744fa4b69b846673907d080537c7b9f94643ab0b295059fe63d6389f239b9fbeda1b4d0ce667cb1d0e832f Homepage: https://cran.r-project.org/package=cols4all Description: CRAN Package 'cols4all' (Colors for all) Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette. Package: r-cran-cols Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnsolve, r-cran-quadprog, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-cols_1.7-1.ca2404.1_all.deb Size: 31600 MD5sum: abf968fb3cd0d366c68c7dc8df0845bb SHA1: 91d50aca27fefd5025b5723dc77db064ce7556e8 SHA256: 08ccdc309d426455fa26c16f1880f103aec71a5f1db4e2e187fc3598fd5ebb2d SHA512: c76ec9f3b16833597d18b85205cd251fe4b06b46af06702308dd0ddb6383e74d3f701cffe8367ad2e8810a00530b0938dbeed5b45e1b63ffeebd49b08775eb92 Homepage: https://cran.r-project.org/package=cols Description: CRAN Package 'cols' (Constrained Ordinary Least Squares) Constrained ordinary least squares is performed. One constraint is that all beta coefficients (including the constant) cannot be negative. They can be either 0 or strictly positive. Another constraint is that the sum of the beta coefficients equals a constant. References: Hansen, B. E. (2022). Econometrics, Princeton University Press. . Package: r-cran-colt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-colt_0.1.1-1.ca2404.1_all.deb Size: 165764 MD5sum: d31631cb7be5b74b8e1655da0baab8f7 SHA1: d2a3536e243a657c15154810873652910dbe9c5c SHA256: 7c176b669e6b1b3f46b1f59697154ffde7ae2476cd6495d7b6210f124d61f023 SHA512: 277a3bf0d2004c00dbb7e5199322c266f59a11591aa3b0ba7cab417637fceba0392593ba4f58fbaacef0425a4a11949b32a35bf4db5551dfcbc920b58adc3da6 Homepage: https://cran.r-project.org/package=colt Description: CRAN Package 'colt' (Command-Line Color Themes) A collection of command-line color styles based on the 'crayon' package. 'Colt' styles are defined in themes that can easily be switched, to ensure command line output looks nice on dark as well as light consoles. Package: r-cran-comato Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-lattice, r-cran-gdata, r-cran-xml, r-cran-cluster, r-cran-clustersim Filename: pool/dists/noble/main/r-cran-comato_1.1-1.ca2404.1_all.deb Size: 156674 MD5sum: c0a76b2af5bfed2c15cdf1a5232149ce SHA1: fe894fa7de56f2303c9168c7660fb12d25eb076d SHA256: 31f39a110d32c1fb155637d7cb58f9e21ed0b05d5928a982a68505379fde797f SHA512: 723cb1e3451795045c1bf214ecbd63ecb4901d2f941a03d48d4c8fe1ee278a00b1223be6cb33214fe6acf80f02faf535577bee68c244a5a897c3afe390965abc Homepage: https://cran.r-project.org/package=comato Description: CRAN Package 'comato' (Analysis of Concept Maps and Concept Landscapes) Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps). Package: r-cran-combat.enigma Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-caret, r-cran-matrix, r-cran-nlme Filename: pool/dists/noble/main/r-cran-combat.enigma_1.1.1-1.ca2404.1_all.deb Size: 102604 MD5sum: 60bc9a5a6cc45a62fdb49ebb00a655e6 SHA1: 40df77316fcdf6ee42edd07a4d9b8ae5cd9e76ba SHA256: 31897bad79f0dbc3799bbe973cc18e7aeb9ef356ab01f805a72e69f948f3df43 SHA512: 9997f7a8b589883597cf7694b6fce4e0845cbb2442d9f9885145c1888c721308623b3f496a6ec49c903aa7240e11f89ca9db61099ffda38c38e446d38de86e11 Homepage: https://cran.r-project.org/package=combat.enigma Description: CRAN Package 'combat.enigma' (Fit and Apply ComBat, LMM, or Prescaling Harmonization forENIGMA and Other Multisite MRI Data) Fit and apply ComBat, linear mixed-effects models (LMM), or prescaling to harmonize magnetic resonance imaging (MRI) data from different sites. Briefly, these methods remove differences between sites due to using different scanning devices, and LMM additionally tests linear hypotheses. As detailed in the manual, the original ComBat function was first modified for the harmonization of MRI data (Fortin et al. (2017) ) and then modified again to create separate functions for fitting and applying the harmonization and allow missing values and constant rows for its use within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (Radua et al. (2020) ); this package includes the latter version. LMM calls "lme" massively considering specific brain imaging details. Finally, prescaling is a good option for fMRI, where different devices can have varying units of measurement. Package: r-cran-combat Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-combat_0.0.4-1.ca2404.1_all.deb Size: 31228 MD5sum: 7581d8b5157f90648cf309b442edb2fe SHA1: 536982efcb9b7e851a85a4c2862afc6f6d823f2e SHA256: 980444db17b33a856253d386168e9e47211360d36dae62b930c4895040d01101 SHA512: e6fc2384896df76b974eb8e0cba7cb5439f68120301f3555068298309dfdaddcb77e285613c736c224bd255bb1a1d08d53f5ed11395c1f1bddc2cf167ec7739d Homepage: https://cran.r-project.org/package=COMBAT Description: CRAN Package 'COMBAT' (A Combined Association Test for Genes using Summary Statistics) Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) . Package: r-cran-combatfamqc Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-dt, r-cran-shiny, r-cran-car, r-cran-broom, r-cran-pbkrtest, r-cran-rtsne, r-cran-mdmr, r-cran-gamlss, r-cran-lme4, r-cran-mgcv, r-cran-bslib, r-cran-shinydashboard, r-cran-gamlss.dist, r-cran-invgamma, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat, r-cran-remotes, r-cran-plotly, r-cran-quarto, r-cran-spelling, r-cran-systemfonts Filename: pool/dists/noble/main/r-cran-combatfamqc_1.0.6-1.ca2404.1_all.deb Size: 2236450 MD5sum: f8e59c99db6b7ae7a9011b436045929c SHA1: bacb2523971559c70e41e920889e248788ee157d SHA256: 52b2aabe0b7c3a9befddfd005f20d51eeca7bdaed5ae942d843adf03008fa0e7 SHA512: 4756638d6a80a968d5dcafec6d808f7c2387e02ca4c35b8e4b51c345dbbab3a28db80127dcb092b13fdc92756467904a3dd45f8fbf1519351b10ed91d85568e6 Homepage: https://cran.r-project.org/package=ComBatFamQC Description: CRAN Package 'ComBatFamQC' (Comprehensive Batch Effect Diagnostics and Harmonization) Provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, 'ComBatFamQC' enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes. Methods for harmonization are based on approaches described in Johnson et al., (2007) , Beer et al., (2020) , Pomponio et al., (2020) , and Chen et al., (2021) . 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Also see Bayer and Hanck (2013) . Package: r-cran-combinat Architecture: all Version: 0.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-combinat_0.0-9-1.ca2404.1_all.deb Size: 43324 MD5sum: e32a6face7d607831df506841dec7733 SHA1: 85cceff02427e8366469b6fde53b88370ba5052c SHA256: 1c7a67be7dfb0e02e26729023478b1f32096061eb52bfa869d3d370100cbe373 SHA512: 1dee2449032103d7c6c669535b7d409fe78788f84b881fcdd72470a59e997bf73d080dec0c65a8f44d4a79208533db7d03a6bf4cd8aa2f29d41eff1dfb6bf12a Homepage: https://cran.r-project.org/package=combinat Description: CRAN Package 'combinat' (Combinatorics Utilities) Provides routines for combinatorial enumeration including generation of all combinations, permutations, and lattice points on hypercuboids and simplex lattices. 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Includes tools for choropleth visualisation using 'ggplot2' and 'leaflet', and functions to retrieve the underlying spatial datasets as 'sf' objects. Package: r-cran-comp2roc Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-boot Filename: pool/dists/noble/main/r-cran-comp2roc_1.1.4-1.ca2404.1_all.deb Size: 92448 MD5sum: 734187c8b548fe090d689dc3a6702fe3 SHA1: 52b7c678a53ae1121b96d919222f39dc87980ebe SHA256: d37160e6143f53d686624557c039f4801d2806a5b578473d4e057d9d23fefa00 SHA512: e3d1853b3b1350375863ad0e1b327e7f18f10712165dce0f940fb4f9e11f09f646746f0137727f9d880f45365451b7a16e64d8bb940faa4209ed3c7a2faf4f6d Homepage: https://cran.r-project.org/package=Comp2ROC Description: CRAN Package 'Comp2ROC' (Compare Two ROC Curves that Intersect) Comparison of two ROC curves through the methodology proposed by Ana C. Braga. Package: r-cran-compare Architecture: all Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-compare_0.2-6-1.ca2404.1_all.deb Size: 504056 MD5sum: a966399c6339160d96caa98735b2f9ca SHA1: 7b07b31f740cf0c64744a22a99f05f2622702a3e SHA256: e4ae13a051bc8e4adc4e9b19c08805a87a5ec12a2f23288e478cbe6ffcf5542c SHA512: 5687521ff0a283ac53b79310b3b4ee027d40646d725c61937da3964780a061034e9114d3f3247f69263393a8a4bffc28bd83127ad936e9d99b50138a052e1064 Homepage: https://cran.r-project.org/package=compare Description: CRAN Package 'compare' (Comparing Objects for Differences) Functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical. Package: r-cran-comparecausalnetworks Architecture: all Version: 0.2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-data.table Suggests: r-cran-pcalg, r-cran-invariantcausalprediction, r-cran-glmnet, r-cran-backshift, r-cran-kernlab, r-cran-mgcv, r-cran-mboost, r-cran-bnlearn, r-cran-testthat, r-cran-huge, r-cran-flare Filename: pool/dists/noble/main/r-cran-comparecausalnetworks_0.2.6.2-1.ca2404.1_all.deb Size: 234804 MD5sum: b5cfde0b55c4912870bacd4bf193b38b SHA1: d5ac4daf8420a6caba63b31d2539691b711d5258 SHA256: 6b148828c6b84365b244e8e3ccb9db5ea2f47392a211e7e702fc167749adf0c6 SHA512: 453bb5746f7ffc41512b9a8cf20782f8fdfb0ac8946431450ef8a985a52574c0f58347391bc28bb751b3efaad5a7e7458a49c3be1e55a054d5440ee59f688b83 Homepage: https://cran.r-project.org/package=CompareCausalNetworks Description: CRAN Package 'CompareCausalNetworks' (Interface to Diverse Estimation Methods of Causal Networks) Unified interface for the estimation of causal networks, including the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate additive noise model), 'bivariateCAM' (bivariate causal additive model), 'CAM' (causal additive model) (from package 'CAM'; the package is temporarily unavailable on the CRAN repository; formerly available versions can be obtained from the archive), 'hiddenICP' (invariant causal prediction with hidden variables), 'ICP' (invariant causal prediction) (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC Algorithm), 'FCI' (fast causal inference), 'RFCI' (really fast causal inference) (all from package 'pcalg') and regression. Package: r-cran-comparecstat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-survival, r-cran-survc1 Filename: pool/dists/noble/main/r-cran-comparecstat_0.1.0-1.ca2404.1_all.deb Size: 17520 MD5sum: b3062399e09c113d3d59c2e1b40c8a6b SHA1: ce4c7079c5e6905d5fd365fb52993882c92b682b SHA256: c71521e053d019d4a7497077b4353a1b59cf0fbef9caa035bfde7f068dfbe044 SHA512: 741f674e98619f2a7b779446de803ef30fc139c16b9e15ff11ae25e0daddd9dc8391e062fa2f0926d06a1a0cec43583e3802a2f090070caff2818b270a9e2a27 Homepage: https://cran.r-project.org/package=compareCstat Description: CRAN Package 'compareCstat' (Compare C-Statistics (Concordance) Between Survival Models) Compare C-statistics (concordance statistics) between two survival models, using either bootstrap resampling (Harrell's C) or Uno's C with perturbation-resampling (from the survC1 package). Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) . Package: r-cran-comparedesign Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-ggplot2, r-cran-ggpubr, r-cran-rootsolve, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-comparedesign_2.4.0-1.ca2404.1_all.deb Size: 314588 MD5sum: 53e2a4789b89660745c6b29edbf63459 SHA1: eb4605665a9d96025f643b9abee933bea310bc8d SHA256: afc6dfee0ca3bc8136db925202d951098ea9be88c476c1be71cfb62f7f55dc1f SHA512: cf0c86a42c7cd9d8d431711adce8d3ab4e63cea2d6c928becb1b077b2167487b59657a5f75d1a531764b922705f1d6bf40179a1bd0b667db710b36be33a1da33 Homepage: https://cran.r-project.org/package=CompAREdesign Description: CRAN Package 'CompAREdesign' (Statistical Functions for the Design of Studies with CompositeEndpoints) It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. 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Package: r-cran-comparedf Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-htmltable, r-cran-openxlsx, r-cran-tidyr, r-cran-stringr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-futile.logger, r-cran-covr Filename: pool/dists/noble/main/r-cran-comparedf_2.3.5-1.ca2404.1_all.deb Size: 1069798 MD5sum: 472ae7d568c1b40c92852a2be0471c92 SHA1: a98808fa8b03d5f55852659ca500f876ca606e8a SHA256: 8c15a46065b30702648616ac1f2c19ba9ee41cb83be5125a86d7339f70023b24 SHA512: 587990a085828b9c4335d1d934340bad885808af884264e5c8554d78e320721a6a22740d558300deaa2aa7dcc82e4ad05ff231a581c3f503b26876cda9932e21 Homepage: https://cran.r-project.org/package=compareDF Description: CRAN Package 'compareDF' (Do a Git Style Diff of the Rows Between Two Dataframes withSimilar Structure) Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics. Package: r-cran-comparegroups Architecture: all Version: 4.10.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4465 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-hardyweinberg, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-chron, r-cran-writexl, r-cran-flextable, r-cran-officer, r-cran-pmcmrplus, r-cran-rmdformats, r-cran-rstatix Suggests: r-cran-tcltk2, r-cran-shiny, r-cran-shinybs, r-cran-shinyjs, r-cran-shinyjqui, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinydashboardplus, r-cran-dt, r-cran-readxl, r-cran-haven Filename: pool/dists/noble/main/r-cran-comparegroups_4.10.4-1.ca2404.1_all.deb Size: 3368138 MD5sum: f8f5abb3156cf810b81268f5b9dc9a01 SHA1: a5ff09a08d816fe0aac76dcb8246b791476ef10c SHA256: 16f6c8d73019029da089e7b72bf7f511841e0db461a7d0277cdfeb4a36ef49b0 SHA512: 0b41ce9b841f0ceaffb359ba66e4d2945aceda91fa15b89022ee723a13028ee3c1929c5b53a95c7047ca5cb135bc2aa6847a0a75640b4c28cade8971763d54ee Homepage: https://cran.r-project.org/package=compareGroups Description: CRAN Package 'compareGroups' (Descriptive Analysis by Groups) Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data. Package: r-cran-comparemcmcs Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-r6, r-cran-ggplot2, r-cran-reshape2, r-cran-xtable, r-cran-coda, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rjags, r-cran-rstan Filename: pool/dists/noble/main/r-cran-comparemcmcs_0.6.0-1.ca2404.1_all.deb Size: 373888 MD5sum: b887873b0432748cd5dde203fe11f77c SHA1: 6280a0accc7ca051c46daea12b3e3c68026b6f77 SHA256: f17537ec4c5f8b468e6fd2a98ba20409c6b1b17838bddf3bc126cd64f19b94dc SHA512: 754ab2cd9c71c063cb85d863664e55e723bc69c1473cfedf7817fff2016b83b7521a5dad7f3d0b96b44e68c517c964319cabc77e024da8f63dcd5ee00d384504 Homepage: https://cran.r-project.org/package=compareMCMCs Description: CRAN Package 'compareMCMCs' (Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines) Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the 'nimble' package or from other packages. Plug-ins for JAGS via 'rjags' and Stan via 'rstan' are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components. Package: r-cran-comparemultiplemodels Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ceemdanml Filename: pool/dists/noble/main/r-cran-comparemultiplemodels_0.1.0-1.ca2404.1_all.deb Size: 15340 MD5sum: cab4c2c33eaa4b0925e1402b7356934f SHA1: 8c66bef0886794eada265bc511cb119bb03fd98a SHA256: a70b153c28700ba83f987f3f5a2441b4de3f3bf59cf2bb46e787542fe517ae46 SHA512: a49f2fc556bc3009c9db5cacc8a3cf0834fe1995e6df7c4c7888539994b2808c5eed6a8ef97213b6d2a88719ef37a86c7eaa942ae7adf545a2e487f51086b698 Homepage: https://cran.r-project.org/package=CompareMultipleModels Description: CRAN Package 'CompareMultipleModels' (Finding the Best Model Using Eight Metrics Values) In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from 'AllMetrics' package. ‘input_df’ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular model’s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as ‘MIN’ and other values are denoted as ‘NA’. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as ‘MAX’ and other values are denoted as ‘NA’. ‘output_df’ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of ‘input_df’. Values in ‘output_df’ are corresponding ‘NA’, ‘MIN’ or ‘MAX’. Finally, the column containing minimum number of ‘NA’ values is denoted as the best column. ‘min_NA_col’ gives the name of the best column (model). ‘min_NA_values’ are the corresponding metrics values. ‘BestColumn_metrics’ is the data frame (dimension: 1*8) containing different metrics of the best column (model). ‘best_column_results’ is the final result (a list) containing all of these output elements. In special case, if two columns having equal 'NA', it will be checked among these two column which one is having least 'NA' in first five rows and will be inferred as the best. More details about 'AllMetrics' can be found in Garai (2023) . Package: r-cran-comparer Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gaupro, r-cran-mixopt, r-cran-rmarkdown, r-cran-plyr, r-cran-progress, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-ggplot2, r-cran-ggally, r-cran-ggpubr, r-cran-contourfunctions, r-cran-snow, r-cran-tibble, r-cran-lhs, r-cran-dicekriging, r-cran-diceoptim, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-comparer_0.2.4-1.ca2404.1_all.deb Size: 499402 MD5sum: f12437bf4a674b67030b2bfae889fd80 SHA1: a57f9aa50413084a06f3f6af0e29970c1c5b5dbc SHA256: 7e8a48a0f782da2e9e3fa981ee151839c054ce62f0c7bcc6e629c181002334be SHA512: 7ee12bc20f2561692f149d4da0bf0f079790101a0ea4c58586dea8a626ba0bf0e3ac4fd1df8860a69aef0e7ee2f75b1a4b89a1a17ebc785b4d90491a54ed9b8f Homepage: https://cran.r-project.org/package=comparer Description: CRAN Package 'comparer' (Compare Output and Run Time) Quickly run experiments to compare the run time and output of code blocks. The function mbc() can make fast comparisons of code, and will calculate statistics comparing the resulting outputs. It can be used to compare model fits to the same data or see which function runs faster. The R6 class ffexp$new() runs a function using all possible combinations of selected inputs. This is useful for comparing the effect of different parameter values. It can also run in parallel and automatically save intermediate results, which is very useful for long computations. Package: r-cran-comparetests Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-comparetests_1.3-1.ca2404.1_all.deb Size: 38242 MD5sum: 47ca3cd8d0d7d505c1c5a5bf5d606a73 SHA1: 0e917dd156b309873c5fa8674592cb6201a550a9 SHA256: a801677b620884ee31578dc8f93d6d5fdfc1b594a201306a3d5cf5f1374fbeff SHA512: f1b199350ee7d98129f6a0f4a7ba01e598cd2cb6da0eb52c5c13602798a02da5e8e460e15405a44c9773b22eeef5f83f008ea942d1cac9cf33df28d9de2cb3f9 Homepage: https://cran.r-project.org/package=CompareTests Description: CRAN Package 'CompareTests' (Correct for Verification Bias in Diagnostic Accuracy & Agreement) A standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) . Package: r-cran-comparison Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-isotone, r-cran-cvglasso Filename: pool/dists/noble/main/r-cran-comparison_1.0.8-1.ca2404.1_all.deb Size: 159170 MD5sum: 2c5e4e6efb4ab6daf357f10b49021de2 SHA1: d9ea4b9c2fc400992155bacb11d320403db4caf3 SHA256: 31bb9857d4ac6eebbb8a6490ab6081dbc85f0ea4374285ed5a43324f4fb4ed7e SHA512: 818d0b77fe39a48dad18cf16504f96b7cbe9176c4d6317336aba15381294b3c27e18da56b974e514dee7e018b6497d221e9a9a584f99bc5e7254406353bc4169 Homepage: https://cran.r-project.org/package=comparison Description: CRAN Package 'comparison' (Multivariate Likelihood Ratio Calculation and Evaluation) Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations. Package: r-cran-comparisonsurv Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survrm2, r-cran-tshrc, r-cran-muhaz Filename: pool/dists/noble/main/r-cran-comparisonsurv_1.1.1-1.ca2404.1_all.deb Size: 124240 MD5sum: 129787dbf835927b3c9edc72b8d3d63a SHA1: 3472d11e7bdf19bafd78eda252fbc72e76a18130 SHA256: b9d199c20af077179beee132c2e9ffaae0815392929189f31c20307831f73801 SHA512: 25020ba84cfd77c8b7f4c6fd2548515ad12cc7fd0070e38140193a9a12490bd13096edfe6e92309635a8eef553132aac00b1f2d1ee4d2b19b56276e221ebfb7f Homepage: https://cran.r-project.org/package=ComparisonSurv Description: CRAN Package 'ComparisonSurv' (Comparison of Survival Curves Between Two Groups) Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) and Huang et al. (2020) , fixed-point tests in Klein et al. (2007) , short-term tests, and long-term tests in Logan et al. (2008) . Some commonly used descriptive statistics and plots are also included. Package: r-cran-compclassmetrics Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-pracma, r-cran-cubature Filename: pool/dists/noble/main/r-cran-compclassmetrics_1.0.1-1.ca2404.1_all.deb Size: 99410 MD5sum: 7a12ad42ed5a0d05bbca9a1654359cce SHA1: 2de671a2137825b5c756df3e4517874e5e0d424b SHA256: 1d5d55c6db3688854da7a21a11e59304e293e464e3527c5538fb8c4e8b3d3f5c SHA512: 71947bc9c47a0a7c60ec4870375ea3a941fce50b57c613354982299f478844523be5ad8ac0e7d3f379920e3bba3781ace175c77e93f89457bc0ab8747b257ad1 Homepage: https://cran.r-project.org/package=CompClassMetrics Description: CRAN Package 'CompClassMetrics' (Classification Measures when Subclasses are Involved) Accuracy metrics are commonly used to assess the discriminating ability of diagnostic tests or biomarkers. Among them, metrics based on the ROC framework are particularly popular. When classification involves subclasses, the package 'CompClassMetrics' includes functions that can provide the point estimate, confidence interval as well as true values if a parametric setting is known. For more details see Nan and Tian (2025) , Nan and Tian (2023) , Feng and Tian (2020) and Wang et al (2016) . 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Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects. 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For method details see Sendhil, R., Jha, A., Kumar, A. and Singh, S. (2018). , and Wu, T. (2021). . 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Package: r-cran-complexr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3109 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-haven, r-cran-rlang, r-cran-purrr, r-cran-shiny, r-cran-srvyr, r-cran-survey, r-cran-magrittr, r-cran-ggplot2, r-cran-jsonlite, r-cran-writexl, r-cran-dt, r-cran-tidyselect Suggests: r-cran-convey, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-printr, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-complexr_1.0.1-1.ca2404.1_all.deb Size: 1589724 MD5sum: 49b76a1bbe15ce0e6a7bf12648703b07 SHA1: c483dab7e2fecf5296eae539582f3380bbdd797d SHA256: d54c2ddfb9c85c0274bffa55d652591ad549022eeaa609928403e37e116593f0 SHA512: acfe49c6ac0336c09317a4771d2a917a4c33e82080c2629c1b9faf118ab22463ea97e6bb5ec3af3bc26ae3c9ed9e4b0ba1bca11344c6759291e21141871a0f7a Homepage: https://cran.r-project.org/package=complexr Description: CRAN Package 'complexr' ('Shiny' Interface for Complex Survey Data Analysis) Provides a 'Shiny'-based interactive interface for the analysis of complex survey data. The package supports data import from multiple file formats, construction and diagnosis of complex survey designs with stratification, clustering, and sampling weights, as well as estimation of means, totals, proportions, associated standard errors, confidence intervals, and design effects. The multilingual interface facilitates the analysis and visualization of survey results without requiring specialized programming knowledge. Package: r-cran-complexupset Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-scales, r-cran-colorspace Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tibble, r-cran-ggplot2movies, r-cran-vdiffr, r-cran-jsonlite, r-cran-data.table Filename: pool/dists/noble/main/r-cran-complexupset_1.3.3-1.ca2404.1_all.deb Size: 2665036 MD5sum: 1132848463b93e72f12b0585eebbe7e4 SHA1: 7b1150318f7b53eab9603bb5eeddd4f387f7d25a SHA256: 0528194f3132b4000ffab4a7e9603aa43b9d6e5c45b6b600d2e81a116f07a498 SHA512: 00ac648b226e4590a7c6aa27f82f31af6ebf0edadd703e87b93fbb8c771144a5ce948346206a85ff3d041deab10234fdfd4dc80c823fd23c0cf407ab816f6cc8 Homepage: https://cran.r-project.org/package=ComplexUpset Description: CRAN Package 'ComplexUpset' (Create Complex UpSet Plots Using 'ggplot2' Components) UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'. For introduction to UpSet concept, see Lex et al. (2014) . Package: r-cran-complmrob Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-ggplot2, r-cran-boot, r-cran-scales Filename: pool/dists/noble/main/r-cran-complmrob_0.7.1-1.ca2404.1_all.deb Size: 94800 MD5sum: bf4457f4f2069bb5f58c5abcdc778dda SHA1: af870b83950b6963a87688b8e2c18b019d7ca720 SHA256: 6af3c4c1c4ae7510413d67799657a88ff90d6d48f1d3f34f1068a136b8a6c682 SHA512: 730e55da1bc705bda88107de840c0803d26d3971c51a59c6c4d830a41098390de48ec7f7c7b71a13ce149a270402d7802496c81bea9510c665bb179d092b6917 Homepage: https://cran.r-project.org/package=complmrob Description: CRAN Package 'complmrob' (Robust Linear Regression with Compositional Data as Covariates) Robust regression methods for compositional data. The distribution of the estimates can be approximated with various bootstrap methods. These bootstrap methods are available for the compositional as well as for standard robust regression estimates. This allows for direct comparison between them. Package: r-cran-compmix Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-hiernet, r-cran-glmnet, r-cran-superlearner, r-cran-bkmr, r-cran-qgcomp, r-cran-gwqs, r-cran-proc, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-compmix_1.1.0-1.ca2404.1_all.deb Size: 88362 MD5sum: e13c3fa5b4026c995a9bab220e9cec53 SHA1: 49e91301c5049d6fb71a084fcf24d8dcd5629864 SHA256: b3683365b54fb110ee00048155f50f82a511475ef811139a288858852e53b909 SHA512: a2e875e4566978af0c20c2a51937d9426afe8bdf9ae21e39d2a4deb75d08fb1be6fe07dce689281fdf2b83327b5eaad429a9daeb4a2d31a52593d6313d3c34cb Homepage: https://cran.r-project.org/package=CompMix Description: CRAN Package 'CompMix' (A Comprehensive Toolkit for Environmental Mixtures Analysis) Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. 'CompMix' package allows practitioners to estimate the health impacts from exposure to chemical mixtures data through various statistical approaches, including Lasso, Elastic net, Bayesian kernel machine regression (BKMR), hierNet, Quantile g-computation, Weighted quantile sum (WQS) and Random forest. Methods and recommendations are described in Hao et al. (2025) . Package: r-cran-compositereliability Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-plyr, r-cran-psych, r-cran-reshape2, r-cran-tidyr, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-compositereliability_1.0.3-1.ca2404.1_all.deb Size: 43972 MD5sum: f434ef9ff90989b349b6154fb45bcfe0 SHA1: 1aa792a4152d8dc0967d09db69ad4c9d5d9f587f SHA256: 15c838cf6aef5e6d3da02d8ae1940281e43c2b6cbf5732eceb0b5b84c1fac45b SHA512: 6d3297005f747063c6ada5444f9f969b92b899386b711abd7780c12985dc845bc58aa809939bb7a036cb3f7b546171ceb10f9355aa0bf07200f574f0f45c6759 Homepage: https://cran.r-project.org/package=CompositeReliability Description: CRAN Package 'CompositeReliability' (Determine the Composite Reliability of a Naturalistic,Unbalanced Dataset) The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts. Package: r-cran-compositereliabilityinnesteddesigns Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-plyr, r-cran-psych, r-cran-reshape2, r-cran-tidyr, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-compositereliabilityinnesteddesigns_1.0.4-1.ca2404.1_all.deb Size: 44954 MD5sum: 239aa8c0f6e30de678fa1599abe975ba SHA1: 9550229c56e5885969de5b818cf97b16def91f9e SHA256: dba8800105942a04c98a4dd32b4b89aa3d7400c69d2dee317b6a27d93c4ae5ca SHA512: 5fa7fe2c25afb51e54decc826269efe0bd53f39d6587de1a77195447af2914e992bde7c49dc8227eda1f7482f1a87c965b3f815e556dac9291af526ef301d8d6 Homepage: https://cran.r-project.org/package=compositeReliabilityInNestedDesigns Description: CRAN Package 'compositeReliabilityInNestedDesigns' (Optimizing the Composite Reliability in Multivariate NestedDesigns) The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM). Package: r-cran-compositional.mle Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algebraic.mle, r-cran-mass, r-cran-numderiv Suggests: r-cran-rmarkdown, r-cran-dplyr, r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-testthat, r-cran-cli, r-cran-future, r-cran-hypothesize, r-cran-likelihood.model Filename: pool/dists/noble/main/r-cran-compositional.mle_2.0.0-1.ca2404.1_all.deb Size: 553614 MD5sum: 69ec1198f38c821d6281c38466d8ae47 SHA1: 4bc8f140f950505aa6726b82261815a5db20155c SHA256: 41612aec1039c2474747dd4ad61b7b44258010b5538420eb3f78cbbd2b74feb9 SHA512: 75d7c17950bef64c829a8f509500909da7fa25b1e8f35a3489e494b2af1477422c86f3e10980ecaff4177573c6a7627360e5d0755418ed221b9dec634133107d Homepage: https://cran.r-project.org/package=compositional.mle Description: CRAN Package 'compositional.mle' (Compositional Maximum Likelihood Estimation) Provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns 'mle' objects compatible with 'algebraic.mle' for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" . Package: r-cran-compositional Architecture: all Version: 8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bigstatsr, r-cran-cluster, r-cran-emplik, r-cran-glmnet, r-cran-kernreg, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-minpack.lm, r-cran-mixture, r-cran-nnet, r-cran-osqp, r-cran-quadprog, r-cran-quantreg, r-cran-rangen, r-cran-rfast, r-cran-rfast2, r-cran-rgl, r-cran-rnanoflann, r-cran-sn Suggests: r-cran-bigparallelr, r-cran-codalm, r-cran-flexdir Filename: pool/dists/noble/main/r-cran-compositional_8.4-1.ca2404.1_all.deb Size: 1210676 MD5sum: d3aa85ece638da5ca1adcada4b93eb9f SHA1: 8cfb9a23593bd4f90ef7adeaa78bd4bf4f25eac3 SHA256: 7345d4a9237aedac2fdecad2e93b5c55a0a1a5d6b22d409431f978812c2722a5 SHA512: fdd4eb43e7758a78c7b0432f829ee7bd7097089ad8c7c80797fe148f91ea6b2bf7a2a1a6217cb98c07a6f09646a70ef4b511b9c6e71f0ded6222325cbcf2e153 Homepage: https://cran.r-project.org/package=Compositional Description: CRAN Package 'Compositional' (Compositional Data Analysis) Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data--based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. . b) Tsagris M. (2014). "The k--NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. . c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. . d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. . e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. . f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. . g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. . h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. . i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. . j) Alenazi A.A. (2022). "f--divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. . k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. . l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. . m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non--parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). . n) Tsagris. M. (2025). "Constrained least squares simplicial--simplicial regression". Statistics and Computing, 35(27). . o) Tsagris M. and Alzeley O. (2026). "Scalable approximation of the transformation--free linear simplicial--simplicial regression via constrained iterative reweighted least squares". Statistical Analysis and Data Mining, 19(4):e70100. . p) Sevinc V. and Tsagris. M. (2026). "Energy Based Equality of Distributions Testing for Compositional Data". Communications in Statistics--Simulation and Computation. . Package: r-cran-compositionalasmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositional, r-cran-minpack.lm, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalasmr_1.0-1.ca2404.1_all.deb Size: 35414 MD5sum: 8d99bbc925ba10eeec4950d2a4f6c770 SHA1: e7a892ba9a328df47e5e8d28e10add4ba1a76075 SHA256: 352072ab96904be771f8a300593a9258cbf032a5d37e5793fef7ef2d741f36f6 SHA512: 91839dda7d33d8f40a31e11a26b079a84a41782869a00826b6d9d6e2b03e4efb76f1f0eaa8b77e452586c48dc9db4d04e01173f42dd80d5a96ef0054ab7be73e Homepage: https://cran.r-project.org/package=Compositionalasmr Description: CRAN Package 'Compositionalasmr' (The alpha-Spatial Median Regression for Compositional Data) The alpha-spatial median regression is performed via the iteretively reweighted least squares algorithm. At first the alpha-transformation of Tsagris, Preston and Wood (2011) is applied and then the non-linear regression model is fitted. Package: r-cran-compositionalcln Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-mass, r-cran-rangen, r-cran-rfast Suggests: r-cran-compositional, r-cran-compositionalzadr, r-cran-mziln, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalcln_1.1-1.ca2404.1_all.deb Size: 154988 MD5sum: 534a93da6fef182f028441a8e27ff99a SHA1: 593c07674e04466d0180f7bc9371d7a72b9c343e SHA256: 030ac01ab59a31597a4db52e23253bfd0f44006940a689ff5a96559d6a9e0d92 SHA512: f70c0a0d3bc493fcd28a0ee344402a7fde420daef99823e2cf7741010035e2a67ac26eb2e9063ed8df183494bc7269a9f8e59394b9b4473289c8805839ae5452 Homepage: https://cran.r-project.org/package=Compositionalcln Description: CRAN Package 'Compositionalcln' (Modelling Compositional Data with Zero Values) Modelling structural zeros in compositional data using a conditional logistic normal model as described by Aitchison (1986), where MLE (Maximum Likelihood Estimation) is performed via the EM (Expectation-Maximization) algorithm. The relevant paper is Alzeley and Tsagris (2026) . Package: r-cran-compositionalclust Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositional, r-cran-factoextra, r-cran-lowmemtkmeans, r-cran-mixture, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalclust_1.3-1.ca2404.1_all.deb Size: 69180 MD5sum: d109e204674292512a3728070933d020 SHA1: 6fc306a6ee4e47ec001e0837e1e36aee61fd4d04 SHA256: dca46540968d0007ee093f69a51a4f38d919acc8bded9739a515442cd3242f4d SHA512: 8a628a6fc9b453adedfd02ceda17e32c7050f1b2b403d7cc550af309676e7c9f7ce4d0204fcec41a2d25d0fecfeb88e527fff1a99161fa5702c426ab53161a04 Homepage: https://cran.r-project.org/package=CompositionalClust Description: CRAN Package 'CompositionalClust' (Clustering with Compositional Data) Cluster analysis with compositional data using the alpha--transformation. Relevant papers include: Tsagris M. and Kontemeniotis N. (2025). Lobachevskii Journal of Mathematics . Tsagris M.T., Preston S. and Wood A.T.A. (2011), . Garcia-Escudero Luis A., Gordaliza Alfonso, Matran Carlos, Mayo-Iscar Agustin. (2008), . Package: r-cran-compositionalhdda Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-hdclassif, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalhdda_1.0-1.ca2404.1_all.deb Size: 24268 MD5sum: 8c5265f42c81aae088d746a8f1d0983b SHA1: b0c2570ff4783e59630c0b2f550c54799e7a2ea4 SHA256: 15e57038bf7a7542762e130537b992b4d996df5bd626cb1bf4a9c9da0e266d00 SHA512: cf75cd8b2328bde3fbdf62db0342d7b3b00dd2a736ad59f201e56cfd2a82ffef4ff6b4c0df62f9009ef971268905327ed1d4faa6324cbc1841cae478c450c211 Homepage: https://cran.r-project.org/package=CompositionalHDDA Description: CRAN Package 'CompositionalHDDA' (High Dimensional Discriminant Analysis with Compositional Data) High dimensional discriminant analysis with compositional data is performed. The compositional data are first transformed using the alpha-transformation of Tsagris M., Preston S. and Wood A.T.A. (2011) , and then the High Dimensional Discriminant Analysis (HDDA) algorithm of Bouveyron C. Girard S. and Schmid C. (2007) is applied. Package: r-cran-compositionalml Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boruta, r-cran-compositional, r-cran-doparallel, r-cran-e1071, r-cran-foreach, r-cran-ranger, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalml_1.0-1.ca2404.1_all.deb Size: 68978 MD5sum: 2f800d30775f9b8eef70f8746b1ce15d SHA1: 2db31ea8a46156eef1a02c9911200c0397dafb78 SHA256: e396ade17597d4fb38472f32da0bbac5a024ed013d2797587c8f45b64937d1c8 SHA512: 52637c6ed59d6cd144eab2cbf294d9de2970ee178b4e6d701fb9b63affb0866845b61298427295dde451e82c27bd129cd14f2d609f344b9b88d5feb91e597943 Homepage: https://cran.r-project.org/package=CompositionalML Description: CRAN Package 'CompositionalML' (Machine Learning with Compositional Data) Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. . Package: r-cran-compositionalmpt Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositional, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalmpt_1.0-1.ca2404.1_all.deb Size: 19092 MD5sum: 3c103adfe142f8d1e1e46c4cd221035a SHA1: 0435f0dab828b6f659409d394190f197a18a029a SHA256: 3ce0016582d39f49c4eabfa1be3d37935582eb7a680dc2df4ddae80f2609fd4c SHA512: 7d8b07399c714bd446a9972bf6be046e68d8b05502da19a7981b50ebccd607e0bfef6be15e6f096b3cbfacfbfd2d7faebb742cdab4d6f551cc948af722f65ca2 Homepage: https://cran.r-project.org/package=CompositionalMPT Description: CRAN Package 'CompositionalMPT' (Compositional Data Two-Sample Test of Equal Distributions) Two-sample tests of equal distributions for compositional data, with zero values present. The p-value is computed via permutations. The relevant papers are Stewart C., Iverson S. and Field C. (2014). "Testing for a diet change using fatty acid signatures". Environmental and Ecological Statistics, and Sevinc V. and Tsagris. M. (2026). "Energy Based Equality of Distributions Testing for Compositional Data". Communications in Statistics--Simulation and Computation, . Package: r-cran-compositionalnaimp Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositional, r-cran-rfast, r-cran-rnanoflann Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalnaimp_1.1-1.ca2404.1_all.deb Size: 37808 MD5sum: 7b71f4b1cdc256bdcfecfb65dc236b32 SHA1: 09f1bdde687af81ecaa9a676fd2c075e7d5bd7fe SHA256: 46c5209bc56b25408c2b5459162922910feffaf426a8430bcee673a295aff1fd SHA512: 813efd1a85f69df84cfe66c3115f562f3d2db71852fa391349bd7c1a6f817159da966afa62ea836f5e223ad509a89e486c09b6065d12b940114d560af28fd007 Homepage: https://cran.r-project.org/package=CompositionalNAimp Description: CRAN Package 'CompositionalNAimp' (Missing Value Imputation with Compositional Data) Functions to perform missing value imputation with compositional data using the Jensen-Shannon divergence based k--NN and a--k--NN algorithms. The functions are based on the following paper: Tsagris M., Alenazi A. and Stewart C. (2026). "A Jensen--Shannon divergence based k--NN algorithm for missing value imputation in compositional data", . Package: r-cran-compositionalrf Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compositional, r-cran-doparallel, r-cran-foreach, r-cran-multivariaterandomforest, r-cran-rfast Filename: pool/dists/noble/main/r-cran-compositionalrf_1.0-1.ca2404.1_all.deb Size: 23278 MD5sum: 09e3261c659d4167cafc822dfa04bbba SHA1: 9f8db26bb83ccf8e13fedb38d992a1253f470af1 SHA256: e2620a598ce9996f0ee1d709ad32081caa15c84de7de4586036ac1a9c5abbc10 SHA512: 40bd51bfd51866ea3235d4901a265c1201825aad98f0ffb7fcdc9a728bf43eeac10e0a5cc5a0e8ee55a094ec753d55b2203e869c93d903678e17fdd20f94d076 Homepage: https://cran.r-project.org/package=CompositionalRF Description: CRAN Package 'CompositionalRF' (Multivariate Random Forest with Compositional Responses) Non linear regression with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), , is applied. Package: r-cran-compositionalscsmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-compositional, r-cran-mass, r-cran-matrix, r-cran-quadprog, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalscsmr_1.0-1.ca2404.1_all.deb Size: 47990 MD5sum: 2891b21f0cab987f3fd88ebae68a95de SHA1: bfef8e03e111c70922af98aff23552a8cf0020cb SHA256: b03ece771c33057ffb6371f478bffd87f8f560e1a5c865d4c75622b5e0fb8e5f SHA512: 476fc06318d9cb245058f5b884f0a4e907a63cde61f13b26d2541e8edc9a66abfef12a209955a68b461a9ae165a6598142ce3d96db84c6fa5f7e15c44010edee Homepage: https://cran.r-project.org/package=Compositionalscsmr Description: CRAN Package 'Compositionalscsmr' (Simplical-Simplicial Spatial Median Regression for CompositionalData) Simplicial-simplicial regression is performed via the simplicially constrained spatial median regression model. The regression coefficients are constrained to be non-negative and sum to 1. For the spatial median regression the iteratively reweighted least squares algorithm is adopted, where quadratic programming is used to impose the constraints. Package: r-cran-compositionalsr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockcv, r-cran-compositional, r-cran-doparallel, r-cran-foreach, r-cran-gslnls, r-cran-minpack.lm, r-cran-rangen, r-cran-rfast, r-cran-sf, r-cran-spmoran Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalsr_1.4-1.ca2404.1_all.deb Size: 273036 MD5sum: f5e623e38f0c312f6e630184f17b1292 SHA1: 0ccfa387dbbd01c3eee11e899faf9184bb86d291 SHA256: fcdebd1a8135fdcd3063eea31fbdeec5d11d9479ad57a85b66f7a97c21da88bf SHA512: c53d96e03c2ad0842f62a085dbd1a124fe4a55e110396aedb24631bc8e232eed8d6d1937cb30a08ccde272747614e83ed7ab6d1724ea5ff3a1c5a66cdb82001d Homepage: https://cran.r-project.org/package=CompositionalSR Description: CRAN Package 'CompositionalSR' (Spatial Regression Models with Compositional Data) Spatial and non-spatial regression models with compositional responses (and compositional predictors) using the alpha--transformation. Relevant papers include: Tsagris M. and Pantazis Y. (2026), , Tsagris M. (2015), , Tsagris M.T., Preston S. and Wood A.T.A. (2011), . 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The relevant paper is Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. . 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The relevant paper is Tsagris and Alharbi (2026) . 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Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) , statistical methods in Emura et al (2012 PLoS ONE) , Emura & Chen (2016 Stat Methods Med Res) , and Emura et al (2019). Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) . 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A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data. 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Package: r-cran-compstatslib Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lattice, r-cran-miniui, r-cran-plotly, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-compstatslib_0.8.0-1.ca2404.1_all.deb Size: 160954 MD5sum: 1bd630061037103c876df3cfc4fb7fdd SHA1: 1c6e71e1e7a77b01685dbfb3e7feb0c3edf96495 SHA256: 42d831ce4d7f853a286381d7e1fd22ffb8021c0ff658750ac0b2719dce588c37 SHA512: fcfbd16afcb56df6d5051f9e0e1eee30fceb15efdc70440cb3bab011a83f2c0f5792ca1885055bb805bbae4b01c3062b7b39eddc2dc560c8b0e4c0573ff6d057 Homepage: https://cran.r-project.org/package=compstatslib Description: CRAN Package 'compstatslib' (Interactive 2D and 3D Visualization of Data and StatisticalConcepts) Interactive gadgets and plotting functions for visualizing data sets and statistical concepts in two and three dimensions. 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The output includes ES's of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). A free web application is available at . Package: r-cran-comradesm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4368 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-comradesm_0.1.1-1.ca2404.1_all.deb Size: 4441930 MD5sum: 7f12c68ff6f7d1e0e29a484f5fc44250 SHA1: 6899d03aa41e71e9597e6b7b3cae95f195004d7f SHA256: 2e773728a2650eef7f720f73425b7d41671c58d3877a09c22670f019a5f71730 SHA512: 836dcc6f31d928f0892fae680e74f5585302c19dde9ebf0353217c44000212adc46650c74c70a725dfbe4f5fbb84277257cf8818f53fe648a9c55d41f071173b Homepage: https://cran.r-project.org/package=ComradesM Description: CRAN Package 'ComradesM' (The Comrades Marathon 1921 to 2019) Datasets related to the Comrades Marathon used in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". The main dataset contains the times of every runner that finished in the time limit for each year the race was run. 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The package is suitable for the analysis of RNA structure cross-linking data and chemical probing data. Package: r-cran-comriskmodel Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adequacymodel Filename: pool/dists/noble/main/r-cran-comriskmodel_0.2.0-1.ca2404.1_all.deb Size: 290478 MD5sum: 1a5b784a998b40d882135b030973b65b SHA1: 915df6378f71842953b1aebc111cc809b191e8d4 SHA256: 4eb4754bd1e4e5bd4932cda589b002bf68d973f27a8cee7a95d11cd4a295ec7d SHA512: e3645df489531e6373ccb6be697c0559545a4f9e5443aafe75c4f142082eeec498d037bec7a4eed0f5da52c58e38a4db5b94ff2893916ca977811907b0d054e0 Homepage: https://cran.r-project.org/package=ComRiskModel Description: CRAN Package 'ComRiskModel' (Fitting of Complementary Risk Models) Evaluates the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF), random numbers and maximum likelihood estimates (MLEs) of well-known complementary binomial-G, complementary negative binomial-G and complementary geometric-G families of distributions taking baseline models such as exponential, extended exponential, Weibull, extended Weibull, Fisk, Lomax, Burr-XII and Burr-X. The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. . 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'Comtrade' provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame. Package: r-cran-con2aqi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-con2aqi_0.1.0-1.ca2404.1_all.deb Size: 14230 MD5sum: 808210c5c1031ef3b2b291b18f247582 SHA1: cdaa020a6f8f2cc7dbcae4bfa50650f5a665150a SHA256: 648f5523795dc015bb03648af20780157d4c0e606bedb0c8ef0b3d88a852cbbb SHA512: 150080154377faebf7709e976130807c117db226e32f6176f25b6ea67aa7d1241e84e8f47047bfc0241b90623fab1f840ac09865f7107a48a2f15a538936913d Homepage: https://cran.r-project.org/package=con2aqi Description: CRAN Package 'con2aqi' (Calculate the AQI from Pollutant Concentration) To calculate the AQI (Air Quality Index) from pollutant concentration data. O3, PM2.5, PM10, CO, SO2, and NO2 are available currently. The method can be referenced at Environmental Protection Agency, United States as follows: EPA (2016) . Package: r-cran-con2lki Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-con2lki_0.1.0-1.ca2404.1_all.deb Size: 18518 MD5sum: 785384e99dd65c5b917ec777a7c61dce SHA1: 28a2ff123cb444046a215b94a86c1a923194fa78 SHA256: b38a892fbfda4ef85600c208f9d1395e003062362ccbe2ce537bee80bd59eff3 SHA512: 790a94af9cf08e574f1c2843b5d78a777ff28e356afa8471b9b4e8915a4d63595e80e18e50be23cf89e9e3cdd82dead0ad81b66e989db050376921cdc071ac59 Homepage: https://cran.r-project.org/package=con2lki Description: CRAN Package 'con2lki' (Calculate the Dutch Air Quality Index (LKI)) Calculates the dutch air quality index (LKI). This index was created on the basis of scientific studies of the health effects of air pollution. From these studies it can be deduced at what concentrations a certain percentage of the population can be affected. For more information see: . 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Package: r-cran-conclust Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-conclust_1.1-1.ca2404.1_all.deb Size: 64700 MD5sum: cf6dc2fd2d172b0dfcd89a328de73bf7 SHA1: fa809e9d0b8b0ee16e22b039eabd211ce5af970e SHA256: 9082d0df6e3576b851e56b15fbf39b47986cc975ed97a94a37e77862dc2f6b2b SHA512: 3b9f082f45670b4d3a463c5025f7e59a26c54487e3026d6aafc2ecf334c8b96e334196d150f3b045a2e4cab1dbeb8633cc8e419afc628a7e86bfcfbaca0a76e3 Homepage: https://cran.r-project.org/package=conclust Description: CRAN Package 'conclust' (Pairwise Constraints Clustering) There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. Package: r-cran-conconianaerobicthresholdtest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tracker, r-cran-sizer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conconianaerobicthresholdtest_1.0.0-1.ca2404.1_all.deb Size: 723186 MD5sum: bd511c9f18d2d6d443c428fca610e139 SHA1: 3d7efb9ab1c7f6800d9b9cabee3176929ac7f317 SHA256: 684df167f659dbb76ecda168bcf139eaeb821b964751e7d34b6f4541a2dd9028 SHA512: 8d2009141138e737a1276f40f1d915186d8d7dce90270062ebcf7a2bc757a201ca1b63a9379e0ad6aaf3c099fc8866a6a97f72090a7c62d16c9070b3e82ae62e Homepage: https://cran.r-project.org/package=ConconiAnaerobicThresholdTest Description: CRAN Package 'ConconiAnaerobicThresholdTest' (Conconi Estimate of Anaerobic Threshold from a TCX File) Analyzes data from a Conconi et al. 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Package: r-cran-concordance Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3475 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-concordance_2.0.0-1.ca2404.1_all.deb Size: 3060118 MD5sum: 03d125c2d4c21ad1b32e5c8a15d8fd77 SHA1: b1211f169624e4c496c2a6d739e1476702c2bdda SHA256: 35fcca92bd324c1dace616a2b2db6c9422fdda4981e75f2f700121d39dbd946b SHA512: e1e4e2e8719af7ac2d13a09bfe8e2790b88fbf4b0f37896ef2c3b94c5e5869ec81aaa737c4597ea235b4968ef793c5e0eb482aaaeead66750ec6c678c2a2cd15 Homepage: https://cran.r-project.org/package=concordance Description: CRAN Package 'concordance' (Product Concordance) A set of utilities for matching products in different classification codes used in international trade research. 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For details, see Alcaraz J., Anton-Sanchez L., Monge J.F. (2022) The Concordance Test, an Alternative to Kruskal-Wallis Based on the Kendall-tau Distance: An R Package. The R Journal 14, 26–53 . 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The CONCOR algorithm is used on social network data to identify network positions based off a definition of structural equivalence; see Breiger, Boorman, and Arabie (1975) and Wasserman and Faust's book Social Network Analysis: Methods and Applications (1994). This version allows multiple relationships for the same set of nodes and uses both incoming and outgoing ties to find positions. 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Measures can be calculated in groups or individually. The calculated measure or the resulting vector in table format should help practitioners make more informed decisions. Methods used in this package are from: 1. Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M. (2005) "Banking concentration: the Brazilian case". 2. Cobham, A. and A. Summer (2013). "Is It All About the Tails? The Palma Measure of Income Inequality". 3. Garcia Alba Idunate, P. (1994). "Un Indice de dominancia para el analisis de la estructura de los mercados". 4. Ginevicius, R. and S. Cirba (2009). "Additive measurement of market concentration" . 5. Herfindahl, O. C. (1950), "Concentration in the steel industry" (PhD thesis). 6. Hirschmann, A. O. (1945), "National power and structure of foreign trade". 7. Melnik, A., O. Shy, and R. Stenbacka (2008), "Assessing market dominance" . 8. Palma, J. G. (2006). "Globalizing Inequality: 'Centrifugal' and 'Centripetal' Forces at Work". 9. Shannon, C. E. (1948). "A Mathematical Theory of Communication". 10. Simpson, E. H. (1949). "Measurement of Diversity" . 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Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020), and Kinyanjui, Tamba, and Okenye(2021) for more details. Package: r-cran-condor Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ssh Filename: pool/dists/noble/main/r-cran-condor_3.0.3-1.ca2404.1_all.deb Size: 70016 MD5sum: f25933d9824ef49d8860c15249d1e548 SHA1: 1fe745e981644bb2860f2325c4eb03e9469386b1 SHA256: f23b2e3a2b54c53a5baa705f49598efbd9c9e282719e5e7e8dd02948353732d1 SHA512: 1c22797c65e0b8f1cf491fb2c19237cb2e3425db39175a183a8c03de19019f82280344f72f5b71ece79fd9ac9b0722b5d56061f16f0399081901f6d580819511 Homepage: https://cran.r-project.org/package=condor Description: CRAN Package 'condor' (Interact with 'Condor' from R via SSH) Interact with 'Condor' from R via SSH connection. Files are first uploaded from user machine to submitter machine, and the job is then submitted from the submitter machine to 'Condor'. Functions are provided to submit, list, and download 'Condor' jobs from R. 'Condor' is an open source high-throughput computing software framework for distributed parallelization of computationally intensive tasks. Package: r-cran-condoroptions Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-condoroptions_1.0.1-1.ca2404.1_all.deb Size: 48698 MD5sum: 965e481884f69bdd86603e65c24c2bea SHA1: 1c6912256dd73da5f00b480414d51fc6096bbeb8 SHA256: 06df65b783655649b8de345623811d1da0f74b3004fc1565124ba34424757fb9 SHA512: 22cdb0929f6df78040ae15f4f4e9ccaa0eedd1481eb5d89ab666aa94ecb213704ea2b6549804799a38375e8892ece766b7d6836092d96ef8553078a6b767f08a Homepage: https://cran.r-project.org/package=condorOptions Description: CRAN Package 'condorOptions' (Trading Condor Options Strategies) Trading of Condor Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). Package: r-cran-condtruncmvn Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-condmvnorm, r-cran-matrixnormal, r-cran-tmvmixnorm, r-cran-tmvtnorm, r-cran-truncnorm Suggests: r-cran-formatr, r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sessioninfo, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-condtruncmvn_0.0.3-1.ca2404.1_all.deb Size: 47504 MD5sum: f66869e8744451b4a7811b4998ffa7b8 SHA1: c954dd099ad14ca34504dd06f492ce0bd8812bee SHA256: 4b2c706cfdbe9b199bff83d21ed48ff8f1443fa0d4c0fa42532f67fda9fcee0b SHA512: 1619cd19a95ab568e79609feece96f735c6c577f6f2bb110514b7ca1899eec32cd47fd8e7dbbeec2a003b08723a9fb94d97a65cf4ea230d07132388fcb1749e9 Homepage: https://cran.r-project.org/package=condTruncMVN Description: CRAN Package 'condTruncMVN' (Conditional Truncated Multivariate Normal Distribution) Computes the density and probability for the conditional truncated multivariate normal (Horrace (2005) p. 4, ). Also draws random samples from this distribution. Package: r-cran-conductor Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-r6, r-cran-shiny Suggests: r-cran-altdoc, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-conductor_0.1.2-1.ca2404.1_all.deb Size: 98110 MD5sum: da1c5b4f8990519811880795c36b608c SHA1: ff130ac2a9469c21e53acf5a75d94dd3bdd58bae SHA256: 10f95d48ea54d8f46a0035148c2a0574135855524e68ba9dc10ead5f8d93ee7c SHA512: 9adc59edf68cb694664186bbc991be9cba94a3f608714c5d115f2f0e86836d79f2b66df687550df5ab7dcdb666edd54241716d4e1692713f29d03429f03255aa Homepage: https://cran.r-project.org/package=conductor Description: CRAN Package 'conductor' (Create Tours in 'Shiny' Apps Using 'Shepherd.js') Enable the use of 'Shepherd.js' to create tours in 'Shiny' applications. Package: r-cran-condusco Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-assertthat, r-cran-bigrquery, r-cran-dbi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-whisker, r-cran-testthat, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-condusco_0.1.0-1.ca2404.1_all.deb Size: 24918 MD5sum: 2ff4b7605ef5ae2aede0ec9b3bedc6b6 SHA1: 8ef71521c751d38628772615d29571a530d82b05 SHA256: 85ebd8df0a990d6daa2d3fc8a5f085c03e6699d2711af1878a94be31b0fdf9f5 SHA512: f0e914c41c3a17b94ea1ad9b223f0dfce5ed79a41e6d3492a6a6f85ce2978d66494c97413cecd9961188f3ea9ffbb44eebc6b5baf82af3110c4f7bfb465b2ecb Homepage: https://cran.r-project.org/package=condusco Description: CRAN Package 'condusco' (Query-Driven Pipeline Execution and Query Templates) Runs a function iteratively over each row of either a dataframe or the results of a query. Use the 'BigQuery' and 'DBI' wrappers to iteratively pass each row of query results to a function. If a field contains a 'JSON' string, it will be converted to an object. This is helpful for queries that return 'JSON' strings that represent objects. These fields can then be treated as objects by the pipeline. Package: r-cran-condvis2 Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2968 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-scales, r-cran-cluster, r-cran-dendser, r-cran-plyr, r-cran-colorspace, r-cran-gower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-hdrcde, r-cran-scagnostics, r-cran-keras, r-cran-kernlab, r-cran-mclust, r-cran-mass, r-cran-ks, r-cran-mgcv, r-cran-randomforest, r-cran-parsnip, r-cran-mlr, r-cran-c50, r-cran-bartmachine, r-cran-bart, r-cran-caret, r-cran-e1071, r-cran-gbm, r-cran-glmnet, r-cran-glmnetutils, r-cran-mlr3, r-cran-nnet, r-cran-rpart, r-cran-tree, r-cran-testthat Filename: pool/dists/noble/main/r-cran-condvis2_0.1.2-1.ca2404.1_all.deb Size: 2153958 MD5sum: ed5073ff17ccd364499c4c7c420490f7 SHA1: 4ea2a1dc513ca6c57211016bf81bb972cf77e46b SHA256: 9b1b7cddeafb1a418c9a68d641d3b7ee89f40ea860857cccc8b70be519ab6098 SHA512: 10f77ff1d16430e82cdd190290eed7fdb37dabe7d46832f82b6b92dfcf1804538c9b15db6e6b86fdc2fdbee36595f1b263ad1978bd64dfb4b4d0ebf9bb9e348c Homepage: https://cran.r-project.org/package=condvis2 Description: CRAN Package 'condvis2' (Interactive Conditional Visualization for Supervised andUnsupervised Models in Shiny) Constructs a shiny app function with interactive displays for conditional visualization of models, data and density functions. An extended version of package 'condvis'. Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) . Package: r-cran-condvis Architecture: all Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-rcolorbrewer, r-cran-shiny, r-cran-scagnostics, r-cran-cluster, r-cran-hdrcde, r-cran-gplots, r-cran-tsp, r-cran-dendser, r-cran-testthat, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-condvis_0.5-2-1.ca2404.1_all.deb Size: 374462 MD5sum: c4466187fc9ffdb05318663014d48095 SHA1: 1d2cadf4467f66d7d376ae3ce5bc1772370a614b SHA256: da10fb6828b1082598a1776081c63bf7d50b28500e1bb02af6ed6c772f1a5428 SHA512: bbab2403455912cb55d160aac579bdeee932cc9be4d72c877d8d2005306665e9e65c1306ec911cc0ca0dd224c7d94ab93e2da33f8175f428132036c8e36da179 Homepage: https://cran.r-project.org/package=condvis Description: CRAN Package 'condvis' (Conditional Visualization for Statistical Models) Exploring fitted models by interactively taking 2-D and 3-D sections in data space. Package: r-cran-conf.design Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-conf.design_2.0.0-1.ca2404.1_all.deb Size: 45202 MD5sum: 71f7e64f9274b572236f09d6ea23be47 SHA1: 570768a0d7bc0a6398182986f850933cf6edf23d SHA256: 6385969215338aea41c3523be097a3308075235f377f1a857f882f920ddfa4ac SHA512: 98186f8d48d4fdc52c22e9d974842d16e9f2fec64016631d9913ea8368a31fb484d950a6ac880555860d1715c509d67b7b6ba472e4b1d18f21a47fc2b1131261 Homepage: https://cran.r-project.org/package=conf.design Description: CRAN Package 'conf.design' (Construction of factorial designs) This small library contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs. Package: r-cran-conf Architecture: all Version: 1.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-fitdistrplus, r-cran-pracma, r-cran-rootsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conf_1.9.3-1.ca2404.1_all.deb Size: 2112400 MD5sum: 4b48b45f5cddaa0b491aa7c80fa59dcb SHA1: 1663237ada95293ff19aea0b71678b10119a445f SHA256: 8856fc1444a7746a0f31f20466a850ee078e175ad8d7e407a85c9cae8c2eccc3 SHA512: 7a7773eb72444ae1269fc5cd3c0c6d8dd64a33aa659bdc3395b53df77544ea0caa691a774a4c8447244d22cf39830fd02c38ba1643bef5c7c23ca6467562b38f Homepage: https://cran.r-project.org/package=conf Description: CRAN Package 'conf' (Visualization and Analysis of Statistical Measures of Confidence) Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis of confidence region simulations, (3) calculating confidence intervals and the associated actual coverage for binomial proportions, (4) calculating the support values and the probability mass function of the Kaplan-Meier product-limit estimator, and (5) plotting the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set. Each is given in greater detail next. (1) Plots the two-dimensional confidence region for probability distribution parameters (supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif, weibull) corresponding to a user-given complete or right-censored dataset and level of significance. The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth appearance throughout the confidence region boundary. An alternative heuristic plots a specified number of points at roughly uniform intervals along its boundary. Both heuristics build upon the radial profile log-likelihood ratio technique for plotting confidence regions given by Jaeger (2016) , and are detailed in a publication by Weld et al. (2019) . (2) Performs confidence region coverage simulations for a random sample drawn from a user- specified parametric population distribution, or for a user-specified dataset and point of interest with coversim(). (3) Calculates confidence interval bounds for a binomial proportion with binomTest(), calculates the actual coverage with binomTestCoverage(), and plots the actual coverage with binomTestCoveragePlot(). Calculates confidence interval bounds for the binomial proportion using an ensemble of constituent confidence intervals with binomTestEnsemble(). Calculates confidence interval bounds for the binomial proportion using a complete enumeration of all possible transitions from one actual coverage acceptance curve to another which minimizes the root mean square error for n <= 15 and follows the transitions for well-known confidence intervals for n > 15 using binomTestMSE(). (4) The km.support() function calculates the support values of the Kaplan-Meier product-limit estimator for a given sample size n using an induction algorithm described in Qin et al. (2023) . The km.outcomes() function generates a matrix containing all possible outcomes (all possible sequences of failure times and right-censoring times) of the value of the Kaplan-Meier product-limit estimator for a particular sample size n. The km.pmf() function generates the probability mass function for the support values of the Kaplan-Meier product-limit estimator for a particular sample size n, probability of observing a failure h at the time of interest expressed as the cumulative probability percentile associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme. The km.surv() function generates multiple probability mass functions of the Kaplan-Meier product-limit estimator for the same arguments as those given for km.pmf(). (5) The km.coverage() function plots the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set for one or more of the following confidence intervals: Greenwood, log-minus-log, Peto, arcsine, and exponential Greenwood. The actual coverage function is plotted for a small number of items on test, stated coverage, failure rate, and censoring rate. The km.coverage() function can print an optional table containing all possible failure/censoring orderings, along with their contribution to the actual coverage function. Package: r-cran-confcons Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mockery, r-cran-vctrs, r-cran-withr, r-cran-rocr, r-cran-covr, r-cran-terra, r-cran-sf, r-cran-blockcv, r-cran-ggplot2, r-cran-ranger, r-cran-ecospat, r-cran-enmeval Filename: pool/dists/noble/main/r-cran-confcons_0.3.2-1.ca2404.1_all.deb Size: 250116 MD5sum: 86d341d3382dcbe82f4324cc6d9f7dd4 SHA1: 83163d3256426a1abbdaf68d9ddb6d04a09e67c3 SHA256: 7f7eba3aabb83e8ef49c066fd98655ceaa51e5b26653c492ea65e1f9a995c1fc SHA512: 6e40607df94068f3cdd809849cf5ee18a88f8e42eab6c22b3b5cafca4bce59c383484e00da4889692da4b57e2e9a8da01241fdece7879ac29a20cbd78d281af0 Homepage: https://cran.r-project.org/package=confcons Description: CRAN Package 'confcons' (Confidence and Consistency of Predictive Distribution Models) Calculate confidence and consistency that measure the goodness-of-fit and transferability of predictive/potential distribution models (including species distribution models) as described by Somodi & Bede-Fazekas et al. (2024) . Package: r-cran-confidence Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-markdown, r-cran-plyr, r-cran-xtable, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidence_1.1-3-1.ca2404.1_all.deb Size: 281132 MD5sum: fd97e92fb8aaf943bbf103f12336a0c2 SHA1: b50811ce8ad3044ac9f3ced56131b46db20e005f SHA256: a3f664589240fe4ee33814f2800724a3bc558a09cbbb2a9616bf9742587800a2 SHA512: fece17515bcb65311e0b7bf9417df664bcc3d36359918dea55e596298e92e2038f402bb4d71ec23024269c6f0e14173501e4192fa1f13cbfa0f91d7cd49ba0ab Homepage: https://cran.r-project.org/package=confidence Description: CRAN Package 'confidence' (Confidence Estimation of Environmental State Classifications) Functions for estimating and reporting multi-year averages and corresponding confidence intervals and distributions. A potential use case is reporting the chemical and ecological status of surface waters according to the European Water Framework Directive. Package: r-cran-confidencecurves Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cowplot, r-cran-desctools, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidencecurves_0.2.1-1.ca2404.1_all.deb Size: 40178 MD5sum: 99c39de563c719b9484de1f0b8c3d7cd SHA1: 6ac790c648e1eb479474a2dc3c25c78c14d67340 SHA256: 569e7339105aa006971d00182e4185f34cd1306911fdc70aa8fbb810c2f738be SHA512: 2b6db0bb0d2dc1b2657fde6b79ef5372c775bdfbf57d8c309812e455a6f3b2c8d5f29b682287fc923f29373d8c41c7b9f2d1484da26990f91ff4e432e9b607cd Homepage: https://cran.r-project.org/package=confidenceCurves Description: CRAN Package 'confidenceCurves' (Frequentist Confidence Analysis for Clinical Trials) Frequentist confidence analysis answers the question: How confident are we in a particular treatment effect? This package calculates the frequentist confidence in a treatment effect of interest given observed data, and returns the family of confidence curves associated with that data. Package: r-cran-confidenceellipse Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cellwise, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-pcapp, r-cran-purrr, r-cran-rgl, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidenceellipse_1.1.0-1.ca2404.1_all.deb Size: 2038164 MD5sum: d4415c8ff3ec1e232dd314925414e6b2 SHA1: 07e7ef938c3378711879d20febc239904a7179ee SHA256: 3aa1147b7e908f362de5e45e0e9f1806cc9792ded16dca408b62f7ce61b71bbb SHA512: 3bc2d34583a758bd5f2fd3c052bff2ad753c3202cb1279f2614f6a7fe6400aa37cdc5d8731b4c7e0dfe6a1bdcb22612fbfa9695a07aedbfcb2d1c2540c6e3a0f Homepage: https://cran.r-project.org/package=ConfidenceEllipse Description: CRAN Package 'ConfidenceEllipse' (Computation of 2D and 3D Elliptical Joint Confidence Regions) Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) . Package: r-cran-confidencesim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-confidencecurves, r-cran-genodds, r-cran-rpact Suggests: r-cran-dplyr, r-cran-knitr, r-cran-pbapply, r-cran-plyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidencesim_0.1.1-1.ca2404.1_all.deb Size: 107106 MD5sum: 94446f77e199a6720790cf29600c3627 SHA1: ade8e7ad1dfdb7aeb95908e3b8138e2bbf0246e8 SHA256: e3ccac618e1cb65f09d9a0513ec61864c22cd532b6906d47b4a1b4d57573f051 SHA512: 60c17cf82c7b604c7efcdd99ec06fb35e9a081cc182263079ccccc69e3265ea123d0e3243599f24a48bb8d0e561819ef0289898de4cc9e77f33831ea233bba5e Homepage: https://cran.r-project.org/package=confidenceSim Description: CRAN Package 'confidenceSim' (Highly Customizable, Parallelized Simulations of FrequentistConfidence Clinical Trials) Simulate one or many frequentist confidence clinical trials based on a specified set of parameters. From a two-arm, single-stage trial to a perpetually run Adaptive Platform Trial, this package offers vast flexibility to customize your trial and observe operational characteristics over thousands of instances. Package: r-cran-config Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaml Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-config_0.3.2-1.ca2404.1_all.deb Size: 96080 MD5sum: 77938cd86aab5b441a106d5a0fd8eeb8 SHA1: c13601543ff329f19e76fce0134d6a87feefa0a0 SHA256: 3dde366cc8bb40ee5084d993b03be7db98319cc0017c141e3882598d880dd4bc SHA512: ebebc713a76813dae77be81c5cce7ad9577bc53c86057cdb1b2a7c9e90aefc19bd9a019d514602f876408d9c2ae4ddd1c742a5de0d09fb7177078b8beaf73010 Homepage: https://cran.r-project.org/package=config Description: CRAN Package 'config' (Manage Environment Specific Configuration Values) Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value. Package: r-cran-configparser Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ini, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-configparser_1.0.0-1.ca2404.1_all.deb Size: 56062 MD5sum: f0b786ef415abe14b746ca08a240f946 SHA1: 64712e3da3bb6b38c4e8fdc16b2a6d948959c5ee SHA256: 2fb951f88802ef5735a18f39ed13d560e599776e2fc4098fe0d96c45df7828ed SHA512: f293c7648d2696df206aa53fba85b0b82762286c3b43a9fe45ec98bee3426ca13960d28feee27021a902e4f45469e07737fe86db0a91616c5490a41e5b6eeeb6 Homepage: https://cran.r-project.org/package=ConfigParser Description: CRAN Package 'ConfigParser' (Package to Parse an INI File, Including Variable Interpolation) Enhances the 'ini' package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables. Package: r-cran-configr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-ini, r-cran-yaml, r-cran-rcpptoml, r-cran-stringr, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-configr_0.3.5-1.ca2404.1_all.deb Size: 112548 MD5sum: c70a47cfa62dce17abcf4c26ea6a6f04 SHA1: 4c4aec509a267d8ba73ff744606fda836283f01a SHA256: 15c8ea6b55c293f150d80b7d89912211094f1115da3c99788c3c7c10059e7896 SHA512: dcda982ed76f24e6791e04058a0af0dd7994b605120789fc69c759c9990c8af6158631bcfa10415e3ec0b421e97189b303b895fc3d5cac400897f3a1fd4fd637 Homepage: https://cran.r-project.org/package=configr Description: CRAN Package 'configr' (An Implementation of Parsing and Writing Configuration File(JSON/INI/YAML/TOML)) Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'. Package: r-cran-configular Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-here, r-cran-magrittr, r-cran-stringr Suggests: r-cran-covr, r-cran-docopt, r-cran-git2r, r-cran-lintr, r-cran-precommit, r-cran-roxygen2, r-cran-styler, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-configular_0.1.1-1.ca2404.1_all.deb Size: 36830 MD5sum: 1648c42c8445d339c4f7abb2f73e9c40 SHA1: 3f08bd0ae5ea9bdebd06b4ad3d01a2686db33c1f SHA256: 658506c4d9aefb7ce8f7fe732d1b7cffadfb37360751ac7b2d345821246c57e5 SHA512: b332c663869cb72aa138240a8060843415d29673aa3295ef92514318d58ac2b372a0f92cc6a1181cbda6f960bafeb25dacac5d18b81e0e7177264cfb26c6a5dd Homepage: https://cran.r-project.org/package=configulaR Description: CRAN Package 'configulaR' (Manage Application Settings via '.env' or '.ini' Files) Provides a simple way to manage application settings by loading configuration values from '.env' or '.ini' files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings. Package: r-cran-configural Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-crayon Filename: pool/dists/noble/main/r-cran-configural_0.1.5-1.ca2404.1_all.deb Size: 170360 MD5sum: af07d2a7fb6236f1eeb888b5f3366aef SHA1: d2f47b31bd90a357f80637668819f38ad9c4446e SHA256: 7def786e7ae1a1e940f04b01b462c5f73a8909b2034c5df6f8bd6724538eef9d SHA512: 8a24d1fccc7859bf323a3aa2fd0c90c1d9a90b58c938d4eed88a290fd7b46dd9ee62bdc89574a32adb0c40278156d2cb21fdc9625aeace0714871394d2aaccdb Homepage: https://cran.r-project.org/package=configural Description: CRAN Package 'configural' (Multivariate Profile Analysis) R functions for criterion profile analysis, Davison and Davenport (2002) and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) . Sensitivity analyses to aid in interpreting criterion profile analysis results are also included. Package: r-cran-confinterpret Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-confinterpret_1.0.0-1.ca2404.1_all.deb Size: 119154 MD5sum: b3d759ef243602c1f39079fe1a7ce097 SHA1: cd1706ffbee2436ceccaa1188c2ad41462603e7e SHA256: 3f214391c6067beab165d3c93282c88684b6e92b45f77bb167736d295803751e SHA512: 8f99a23b257fd1e305daf7c9dbc4dfdeabfbc4a1e93d465524eda2de54a71ca04064a131801833153a646d100fa83f8342bf7d566e308206f9d20a1b809b8942 Homepage: https://cran.r-project.org/package=confinterpret Description: CRAN Package 'confinterpret' (Descriptive Interpretations of Confidence Intervals) Produces descriptive interpretations of confidence intervals. Includes (extensible) support for various test types, specified as sets of interpretations dependent on where the lower and upper confidence limits sit. Provides plotting functions for graphical display of interpretations. Package: r-cran-confintr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confintr_1.0.2-1.ca2404.1_all.deb Size: 168536 MD5sum: c9c5048d9ec55e4c999b660789a3091b SHA1: 800c02093ef21fb65b6e4221637a6c4976f7ed48 SHA256: 9000c66003213983b2757decba3f438ee4e8d4000db973f0a67b730ac2b83366 SHA512: f94a554f0f1bb86ccb5d669f78ad3a2503434c50c3b6bea3ebf1009114326c6ac05c823146199f9b9f176adf2ba2ff52431503e382e6291fafa3a4fa910a58e9 Homepage: https://cran.r-project.org/package=confintr Description: CRAN Package 'confintr' (Confidence Intervals) Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package 'boot'. Both one- and two-sided intervals are supported. Package: r-cran-confintrob Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-lme4, r-cran-mass, r-cran-mvtnorm, r-cran-tidyr Suggests: r-cran-robustlmm, r-cran-robustvarcomp, r-cran-lmertest, r-cran-testthat, r-cran-xtable, r-cran-ggplot2, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-confintrob_1.1-1-1.ca2404.1_all.deb Size: 66898 MD5sum: 08ac36222ca4c6cff380072214ef3190 SHA1: 726bcefdad2805323eaf4e45d6a700704d596096 SHA256: a07bf20b0bdbb57b550d361610cafcb3ac8ae69b3d59cdf0af10b21d22f046b5 SHA512: 845f4f47a4c948930c5f161af637548a50ea8af10f9ed5a5e67b6ca04416bd97957cf45d8ee1932a8a39c95f821d21b8c61a0d923b17f6c6e664a3b7484f8b2e Homepage: https://cran.r-project.org/package=confintROB Description: CRAN Package 'confintROB' (Confidence Intervals for Robust and Classical Linear Mixed ModelEstimators) The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the 'lme4' package and robust estimators from the rlmer() function in the 'robustlmm' package, as well as the varComprob() function in the 'robustvarComp' package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) and Mason, F., Cantoni, E., & Ghisletta, P. (2024) . Package: r-cran-confintvariance Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-confintvariance_1.0.2-1.ca2404.1_all.deb Size: 24680 MD5sum: ca2bc5f4a02d900c00eec31f5ba3ced8 SHA1: 35d9e44cac2768c2dd16f7fbafa1594268ce38ca SHA256: d3199795acc43ae5a411ba94aefc67114d9fee4965079bb9b98cb02e502a4e78 SHA512: e6a07a796b08653e82be0ff049d62afdef5c9fd33f1242b2e243075826d99020f2a8033d6e0d9e8512626d19f5be3e8a67b5cfabd283033d4f22a7341b56ca42 Homepage: https://cran.r-project.org/package=ConfIntVariance Description: CRAN Package 'ConfIntVariance' (Confidence Interval for the Univariate Population Variancewithout Normality Assumption) Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) and the references therein for an overview of unbiased estimation of variances of U-statistics. Package: r-cran-conflibertr Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-reticulate, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-conflibertr_0.5.3-1.ca2404.1_all.deb Size: 481778 MD5sum: 1c277dad4354c7c58f48c612f09a8580 SHA1: a11779aa859299b9be4ec84eb9af54cfe11f26b0 SHA256: db2318517ff526ab21d3f86fe93d42bc83e64af311fba0ddd605d5c7da037066 SHA512: 6423bcc938eb4add57c933014d2b85c053f535f554c45e42e583af82d99ca3218ca6e045574bd2a2431968adb040590b63603517acc5c1fbf2cdb77c6ec81a6b Homepage: https://cran.r-project.org/package=conflibertR Description: CRAN Package 'conflibertR' (Inference and Fine-Tuning with 'ConfliBERT' Conflict Text Models) An interface to 'ConfliBERT', a pretrained language model for analyzing text about conflict and political violence (Hu et al. (2022) ). Provides functions for named entity recognition, binary and multilabel classification, and question answering, plus tools to fine-tune custom classifiers, compare several base model architectures, and run an interactive active-learning loop for efficiently labeling new data. Models are downloaded from 'Hugging Face' and run through the 'transformers' library for 'Python' via the 'reticulate' package. 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This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use. Package: r-cran-conflr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-askpass, r-cran-commonmark, r-cran-curl, r-cran-glue, r-cran-httr, r-cran-knitr, r-cran-miniui, r-cran-purrr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-stringi, r-cran-xml2, r-cran-r6, r-cran-rlang Suggests: r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-conflr_0.1.1-1.ca2404.1_all.deb Size: 658892 MD5sum: 6722b8f6b0297b15066d6a519aebc869 SHA1: 242e84a947af6e87e54526bae2e0a3e2ca5157cc SHA256: d7f4c354adf16be0baf81151aa5e3388ee6dc761bb656e5480bc0e0378a92e39 SHA512: 22b5eeeb58fdb72b14ef90b6d15bb125b84bf62ba297aa214e00b9e1fae79b335873e30fb074678b09a1cdda059f43e51e4749a8256748c9a084feeb9e91e4eb Homepage: https://cran.r-project.org/package=conflr Description: CRAN Package 'conflr' (Client for 'Confluence' API) Provides utilities for working with various 'Confluence' API , including a functionality to convert an R Markdown document to 'Confluence' format and upload it to 'Confluence' automatically. Package: r-cran-confluxpro Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-rlang, r-cran-furrr, r-cran-progressr, r-cran-ggplot2, r-cran-scales, r-cran-lifecycle Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-diagrammer, r-cran-purrr Filename: pool/dists/noble/main/r-cran-confluxpro_1.3.1-1.ca2404.1_all.deb Size: 1089028 MD5sum: afe945525135eea32d9ebe086cc2c710 SHA1: 6c28438bcafaea24e6f84daf1abd0d932c22882a SHA256: a4093ca371b1ba6d3c2001eb26a8ac1d706addcf2ab09a3c3fa2d4b3863231c8 SHA512: 9b50627ad77eb2ef80d15d4535cff2d6b93f7bff669183e7eb7bb365ccc4f2222a51b67cb5806ded2ab3258df24da098588ffc897d2f540516d925da9ad9303f Homepage: https://cran.r-project.org/package=ConFluxPro Description: CRAN Package 'ConFluxPro' (Soil Gas Analysis and Flux Modeling) Model soil gas fluxes with the Flux-Gradient Method. It includes functions for data handling, a forward and an inverse model for flux modeling and methods for calibration and uncertainty estimation. For more details see Gartiser et al. (2025a) and Gartiser et al. (2025b) . Package: r-cran-confmatrix Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-rdpack, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confmatrix_0.2.1-1.ca2404.1_all.deb Size: 268140 MD5sum: 6cab136e09c315a86c903989cc1812b6 SHA1: 77ae3dbe6e1e07c8a74154cd03ab36baa9cd2bde SHA256: 25a47d94b957cf88ad35a60baa9c1d08bec5970e9155af8b203687c8332ab1d1 SHA512: 8d8d36687cf263f5763f9ac976fa46b1a4b7a7cdf42d7c0bbc71882753c218f164f48d67105d03a81ded896edb57f9b9025131ba40fd8b6d82d7c8cd840e93d8 Homepage: https://cran.r-project.org/package=ConfMatrix Description: CRAN Package 'ConfMatrix' (Confusion Matrix) Thematic quality indices are provided to facilitate the evaluation and quality control of geospatial data products (e.g. thematic maps, remote sensing classifications, etc.). The indices offered are based on the so-called confusion matrix. This matrix is constructed by comparing the assigned classes or attributes of a set of pairs of positions or objects in the product and the ground truth. In this package it is considered that the classes of the ground truth correspond to the columns and that the classes of the product to be valued correspond to the rows. The package offers two object classes with their methods: 'ConfMatrix' (Confusion matrix) and 'QCCS' (Quality Control Columns Set). The 'ConfMatrix' class of objects offers more than 20 methods based on the confusion matrix. The 'QCCS' class of objects offers a different perspective in which the ground truth is considered to allow the values of the column marginals to be fixed, see Ariza López et al. (2019) and Canran Liu et al. (2007) for more details. The package was created with 'R6'. Package: r-cran-confmeta Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-meta, r-cran-metafor, r-cran-patchwork, r-cran-replicationsuccess, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bayesmeta, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confmeta_0.1.1-1.ca2404.1_all.deb Size: 294008 MD5sum: 7f283ff7e3ac45831038b66871f92ada SHA1: fae00c57f1016c6f4a741e5266f3cd803f101374 SHA256: 211f545272bf85ceec4edb70959c739462b331d72f4a1a625e520eb0d8227bf3 SHA512: 29993f72d34a7290558b16c1deb5b5c98dc59238f4137b7d3f71c06b11e6e7f48760d68c7b60f6a2f14e28a0f035cdadf5fe8669985ecf5d468a441bc975e565 Homepage: https://cran.r-project.org/package=confMeta Description: CRAN Package 'confMeta' (Confidence Curves and P-Value Functions for Meta-Analysis) Provides tools for the combination of individual study results in meta-analyses using 'p-value' functions. Implements various combination methods including those by Fisher, Stouffer, Tippett, Edgington along with weighted generalizations. Contains functionality for the visualization and calculation of confidence curves and drapery plots to summarize evidence across studies. Package: r-cran-conforest Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-randomforest, r-cran-rann Filename: pool/dists/noble/main/r-cran-conforest_2.0.1-1.ca2404.1_all.deb Size: 49192 MD5sum: a9905a91e838cafd912b5feb12b884c1 SHA1: cbd2f9cc12ca5cff7404b4239deefcbc80247c6e SHA256: ed985e3bc76571df5eb4cfa2c2f7e67228999dd1c0e6a50ef5fa0b108eeaeb69 SHA512: 46531cd0cc474d4fe0ef9e5f4f536614475879121231f11ea30d338d49bb6f0c558fff14e6a58f9fab24dcca98cbd2b0f8a3d69e915bf9f2f9142343a5365ffa Homepage: https://cran.r-project.org/package=conforest Description: CRAN Package 'conforest' (Conformal Random Forests for Response Surface Emulation) Fits emulators, also known as surrogates or response surfaces, using conformal inference with random forests. The conformal calibration is performed using out-of-bag samples from the forest, eliminating the need for a separate hold-out set. The method is based on Johansson et al. (2014 ). Package: r-cran-conformalbayes Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rstantools, r-cran-loo, r-cran-matrixstats Suggests: r-cran-rstanarm, r-cran-brms, r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conformalbayes_0.1.4-1.ca2404.1_all.deb Size: 130620 MD5sum: ba85ab25274a1d93a791d74a77a8253c SHA1: 30d80fd784a243800563b83db75844b871aff9ea SHA256: e51bb7c7e25bf39d68437247860bd998d17e57eb6b9bccf58069fe7ce4a2f567 SHA512: df83a9e829ab19b445464a96327e71aa9fa710306c44d45ea48026375c0393f59bfb7fddec73e33a1bea3e6a570e4d45d01933788dad66927024ef6e53cc2a3b Homepage: https://cran.r-project.org/package=conformalbayes Description: CRAN Package 'conformalbayes' (Jackknife(+) Predictive Intervals for Bayesian Models) Provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021) . These intervals are calculated efficiently using importance sampling for the leave-one-out residuals. By default, the intervals will also reflect the relative uncertainty in the Bayesian model, using the locally-weighted conformal methods of Lei et al. (2018) . Package: r-cran-conformalclassification Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-doparallel, r-cran-mlbench Filename: pool/dists/noble/main/r-cran-conformalclassification_1.0.0-1.ca2404.1_all.deb Size: 51452 MD5sum: 260207601fcda6d83cae4a3858c9e254 SHA1: e933e389ff93400f8ac5dfc57b7572160c373971 SHA256: 1a38f409bed8a7879cd17019e56a26da061230f553b60b9649380aa8732619c5 SHA512: b9174244c6c39ee7e4c50d1678b0122221a01ed1298851f0f36a09b90c3dae3f0b6e55cb2af80643933f2a3a4419ae9f66aac257150fc7244545383528bae1ae Homepage: https://cran.r-project.org/package=conformalClassification Description: CRAN Package 'conformalClassification' (Transductive and Inductive Conformal Predictions forClassification Problems) Implementation of transductive conformal prediction (see Vovk, 2013, ) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems. Package: r-cran-conformalforecast Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-ggdist, r-cran-rlang, r-cran-zoo Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tsibble Filename: pool/dists/noble/main/r-cran-conformalforecast_0.1.1-1.ca2404.1_all.deb Size: 639880 MD5sum: db0d234a5ef59720513fb0efb7d5506c SHA1: 9b6838bbe81e9264fe2aa63f402c5775ec4e10f8 SHA256: 23f3ac530ebd7885323b617a40b74fe04cf238d8e29ebd6b728903065d239627 SHA512: 732d4d85889c3d67f07eae815edeea147bc308b34dd05a2f99f12a2d26801fa2df0482bb18734a92604329386b62b7747149a572077bd983136ce04274d70f08 Homepage: https://cran.r-project.org/package=conformalForecast Description: CRAN Package 'conformalForecast' (Conformal Prediction Methods for Multistep-Ahead Time SeriesForecasting) Methods and tools for performing multistep-ahead time series forecasting using conformal prediction methods including classical conformal prediction, adaptive conformal prediction, conformal PID (Proportional-Integral-Derivative) control, and autocorrelated multistep-ahead conformal prediction. The methods were described by Wang and Hyndman (2024) . Package: r-cran-conformalinference.fd Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggpubr, r-cran-scales Suggests: r-cran-roahd, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-conformalinference.fd_1.1.1-1.ca2404.1_all.deb Size: 187710 MD5sum: 19a2da2fd8ca94bb2c2f84a017cd4df0 SHA1: 2da3b1278736daf58ca09e0393cff446693efcd9 SHA256: b41ba041a84030570132033a7a7e010f3cf422e401e53fd0027d97cb2c8baff7 SHA512: 4123b1d4f5687604298a4adbcc2d4e23f31d23263399bd164b9387dec0c76c49b27e6313e34f69db66be4bbbf0a97dd562be6b61e43ce8514a68f67381718252 Homepage: https://cran.r-project.org/package=conformalInference.fd Description: CRAN Package 'conformalInference.fd' (Tools for Conformal Inference for Regression in MultivariateFunctional Setting) It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate 'conformalInference' package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) , Diquigiovanni, Fontana, and Vantini (2021) , Solari, and Djordjilovic (2021) . Package: r-cran-conformalinference.multi Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-conformalinference.multi_1.1.2-1.ca2404.1_all.deb Size: 90076 MD5sum: d844f65a8479174a208c15d620230d49 SHA1: dc0b3a82a081c3a7e849a969601a1a259639de7b SHA256: 92796d7a891f73ad945accb38dbb1bb1d342e5161bc8e56abde976367fbfe6fe SHA512: 9f5fa9649d793d8b83a6e6c64aedf50717f55abcc6bab5a36241634d2f0a6e195ee9b4d345cd91e09a698253ef4b4387c2ac892d349c2d64c7e63485a980b4ba Homepage: https://cran.r-project.org/package=conformalInference.multi Description: CRAN Package 'conformalInference.multi' (Conformal Inference Tools for Regression with MultivariateResponse) It computes full conformal, split conformal and multi-split conformal prediction regions when the response variable is multivariate (i.e. dimension is greater than one). Moreover, the package also contains plot functions to visualize the output of the full and split conformal functions. To guarantee consistency, the package structure mimics the univariate package 'conformalInference' by Ryan Tibshirani. See Lei, G’sell, Rinaldo, Tibshirani, & Wasserman (2018) for full and split conformal prediction in regression, and Barber, Candès, Ramdas, & Tibshirani (2023) for extensions beyond exchangeability. Package: r-cran-conformalpvalue Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-conformalpvalue_0.1.0-1.ca2404.1_all.deb Size: 14172 MD5sum: f9f3be8e975a9cf7e040bdb1e2c2ad2e SHA1: 79f65a8f719f621b0e51433fae709bb2f1302da2 SHA256: 30f5fc68cf1ea71a77aee0e9eafedeeae8adbed9807915c64dd3b8c39b15d73a SHA512: b825fa5f51a64cd52737c766ef0fffcacfa36c32e98280b296206eafcf233b5c52030068c6a914ed92823d54baf21b9e0157de635ba36a312a7e61d5d8874639 Homepage: https://cran.r-project.org/package=conformalpvalue Description: CRAN Package 'conformalpvalue' (Computes Conformal p-Values) Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) . Package: r-cran-conformalsmallest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm, r-cran-mass, r-cran-quantregforest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-repr Filename: pool/dists/noble/main/r-cran-conformalsmallest_1.0-1.ca2404.1_all.deb Size: 840134 MD5sum: 07456a5315c49c1f11229c936430f668 SHA1: e6ecc72a219d0373829cd5427b8e69d7c7ce8c87 SHA256: 5daba521a9c13993bc19cbc979f592bd916b1343cb67d90abf7614754e50133a SHA512: fdcb672a5d73e6dc9e0ac1e12b45fd299dfe23dff6a3101f7b60acf2d800a7cd080363e6c8e9c76ae201e25704d5474c877dd72aaef273fcb854f7e780de46ec Homepage: https://cran.r-project.org/package=ConformalSmallest Description: CRAN Package 'ConformalSmallest' (Efficient Tuning-Free Conformal Prediction) An implementation of efficiency first conformal prediction (EFCP) and validity first conformal prediction (VFCP) that demonstrates both validity (coverage guarantee) and efficiency (width guarantee). To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) . 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A common 'confoundsens' object stores a sensitivity path (the treatment effect as a function of hypothetical confounder strength) regardless of the framework that produced it, so the same robustness curves, contour plots, covariate benchmark ("sensitivity Love") plots, and plain-language reports can be drawn for impact threshold analysis (Frank, 2000, ), partial R-squared omitted-variable bias analysis (Cinelli and Hazlett, 2020, ), and E-values (VanderWeele and Ding, 2017, ). Paths can be computed directly from fitted linear models or converted from results produced by the 'sensemakr', 'konfound', and 'EValue' packages. 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A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115. Package: r-cran-confsam Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-penalized, r-cran-survival, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-confsam_0.2-1.ca2404.1_all.deb Size: 303162 MD5sum: 78f4ff3e7c33a7f64c1092d15d7c95c4 SHA1: fddc19fccd4b1462416d7dbf9e36c46d43825968 SHA256: 8a66b735d829ec031f9b19dd219b95b351ee5d492ffa9fd3c7623c6db01da0c1 SHA512: add82d5a879d84743765f059c88fb23b370bc713edcf8eae082c6e57b4f5d2f56f4a778632e39568c8983824cff87d64f5505b83cf5fa17214687d5b9ef98dc5 Homepage: https://cran.r-project.org/package=confSAM Description: CRAN Package 'confSAM' (Estimates and Bounds for the False Discovery Proportion, byPermutation) For multiple testing. Computes estimates and confidence bounds for the False Discovery Proportion (FDP), the fraction of false positives among all rejected hypotheses. The methods in the package use permutations of the data. Doing so, they take into account the dependence structure in the data. 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Package: r-cran-confzic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cmna, r-cran-ltsa, r-cran-mumin, r-cran-mvtnorm, r-cran-tidytable, r-cran-psych Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confzic_1.0.1-1.ca2404.1_all.deb Size: 68066 MD5sum: 55aaad379b397fad67e41e0d0fbd8c34 SHA1: 8dae7d95969693c05ef03067e4977c4330e88568 SHA256: 6752d4c68f9c680873e64f4f2dfecdd7f122a8c36e435e7257577f643b9fa1ff SHA512: a4f58b3eb2ee77c35a8f363a8a0e889859f33c65c70c6cf9d3ff730b06a1d6cd9b286d152aaa5edeb6784efed6e29d18fd2f39b571207a3cd8580c8720f197d3 Homepage: https://cran.r-project.org/package=ConfZIC Description: CRAN Package 'ConfZIC' (Confidence Envelopes for Model Selection Criteria Based onMinimum ZIC) Narrow down the number of models to look at in model selection using the confidence envelopes based on the minimum ZIC (Generalized Information Criteria) values for regression and time series data. Functions involve the computation of multivariate normal-probabilities with covariance matrices based on minimum ZIC inverting the CDF of the minimum ZIC. It involves both the computation of singular and non-singular probabilities as described in Genz (1992) <[https:doi.org/10.2307/1390838]https:doi.org/10.2307/1390838>. 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The dataset tracks district characteristics, members of Congress, and the political behavior of those members. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, identify the citations associated with their searches, and more. 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The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) . 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Package: r-cran-conjoint Architecture: all Version: 1.42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algdesign, r-cran-fpc, r-cran-broom, r-cran-ggplot2, r-cran-cluster, r-cran-ggfortify Filename: pool/dists/noble/main/r-cran-conjoint_1.42-1.ca2404.1_all.deb Size: 132772 MD5sum: a9c51708d040d1890bd26e94d8a1bdc8 SHA1: e38219882ce6d258ca24e73d13ced0581a05fab9 SHA256: 767fb2a3100123d27ee2965fdd747e10e1aac828346334dbc90140847e960ab6 SHA512: 0bce84e40db27968b24c3fa4537399c4d70fd7d3da5647447266ff4bc4c06600c9d52bc082e20837d9533db56809dead2fd69e5b9addf0427f4695a43acc6cd3 Homepage: https://cran.r-project.org/package=conjoint Description: CRAN Package 'conjoint' (An Implementation of Conjoint Analysis Method) This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method. Package: r-cran-conjurer Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conjurer_1.7.1-1.ca2404.1_all.deb Size: 354918 MD5sum: 1d7c8a00582211fbefa4e30d6f930198 SHA1: 043ae6bf389657df6496217710806fc5c9f49e73 SHA256: eea4ccdb32f299935e01a0f16069fc11979f2bc2cf31c9d5687e74a9779b4f58 SHA512: 63168d8825527fbabb23e4f7871c6f322f4eeb076e0dd6feabcef96ce6b2e64df1a2eb9a89dc615a836306e38ee3f70559ff4c6866e066f1349fc0026d364b74 Homepage: https://cran.r-project.org/package=conjurer Description: CRAN Package 'conjurer' (A Parametric Method for Generating Synthetic Data) Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. 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'ConMET' is an R-shiny application that facilitates performing and evaluating confirmatory factor analyses (CFAs) and is useful for running and reporting typical measurement models in applied psychology and management journals. 'ConMET' automatically creates, compares and summarizes CFA models. Most common fit indices (E.g., CFI and SRMR) are put in an overview table. ConMET also allows to test for common method variance. The application is particularly useful for teaching and instruction of measurement issues in survey research. The application uses the 'lavaan' package (Rosseel, 2012) to run CFAs. Package: r-cran-connect Architecture: all Version: 0.7.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qgraph Suggests: r-cran-covr, r-cran-lintr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-connect_0.7.27-1.ca2404.1_all.deb Size: 94940 MD5sum: 189d1023dbd3dd9c66c78be29718c9dd SHA1: 9eb06ff1bf69b5a7b808e1ca6bf70adca41c552b SHA256: c426a5b479a5fc0d14347dd45eb50aa5d0ba473652d7ef1a5705817832fd8b6e SHA512: 3ef711d15ecc6af3bc2304ec3b66b282dc390cf671f0affd53fac7670cb770289161495e2c6148f39dfec9b49ec1c995c612b857f3c569e999640b2776b3d739 Homepage: https://cran.r-project.org/package=ConNEcT Description: CRAN Package 'ConNEcT' (Contingency Measure-Based Networks for Binary Time Series) The ConNEcT approach investigates the pairwise association strength of binary time series by calculating contingency measures and depicts the results in a network. The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) .Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. The results of the non-parametric significance test can be included by depicting either all links or only the significant ones. Tutorial see Bodner et al.(2021) . 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Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers. 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It automates the display of schemata, tables, views, as well as the preview of the table's top 1000 records. 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Package: r-cran-connector.databricks Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-brickster, r-cran-checkmate, r-cran-cli, r-cran-connector, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-fs, r-cran-hms, r-cran-odbc, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-withr, r-cran-zephyr Suggests: r-cran-glue, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-whirl Filename: pool/dists/noble/main/r-cran-connector.databricks_0.1.0-1.ca2404.1_all.deb Size: 249926 MD5sum: 3bf510b1f587d7040966a998a92ab5a7 SHA1: 46edca32433d5313b4a28af5461e5565d3bc4166 SHA256: d69b47b65ed4dbf25f105ff22d621346d6fe5a87db07802fd5d559e38e6d3141 SHA512: 0034ff550a22982160b5f419eb707ac70d554f854b722f20cf8188c307d1d8c3f62708d32dfb9f913bf0a29aeb02d0e3f5dce153dec99ca830d9ecffef02a4a8 Homepage: https://cran.r-project.org/package=connector.databricks Description: CRAN Package 'connector.databricks' (Expand 'connector' Package for 'Databricks' Tables and Volumes) Expands the 'connector' package and provides a convenient interface for accessing and interacting with 'Databricks' volumes and tables directly from R. Package: r-cran-connector.sharepoint Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-azureauth, r-cran-checkmate, r-cran-cli, r-cran-connector, r-cran-microsoft365r, r-cran-r6, r-cran-zephyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-whirl, r-cran-glue, r-cran-mockery Filename: pool/dists/noble/main/r-cran-connector.sharepoint_0.1.0-1.ca2404.1_all.deb Size: 181576 MD5sum: 5ffe880928c13a5e649228fe9dcca2a4 SHA1: f025a98fb7a21d5207db5c5b166b2fcdf5d50c9e SHA256: ea9791882cd15546011992e8a13f7c0f33915a1b2f89347da72ea29c6a4f4aba SHA512: 1cd4fb6f2c9876ea9ab20191b8b062c62646c1de57d17370230a2c9b6f6914751bd51e00a1352c5575786877f1d26d0517c177a7d110bd7545ae250191f6897a Homepage: https://cran.r-project.org/package=connector.sharepoint Description: CRAN Package 'connector.sharepoint' ('Microsoft SharePoint' Interface for the 'connector' Package) Extends the 'connector' package to provide a convenient interface for accessing and interacting with 'Microsoft SharePoint' directly from 'R'. Supports listing, reading, writing, uploading, downloading, and removing files and directories on 'SharePoint' document libraries. Authentication is handled through 'Azure' tokens via the 'AzureAuth' package. Package: r-cran-connector Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-haven, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-vroom, r-cran-writexl, r-cran-yaml, r-cran-zephyr Suggests: r-cran-dbplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-whirl, r-cran-withr Filename: pool/dists/noble/main/r-cran-connector_1.0.0-1.ca2404.1_all.deb Size: 442354 MD5sum: 1750a78f1067e24b054412c1362fa537 SHA1: b2245bb2b3bb30f27a880b99e3e5e612a2252181 SHA256: 9817b9de519fbdd884f598183f1c9821d13163a99d6e8b7448de5f48f6b2e87c SHA512: 8a57371c3396a556d04e2cc33e9d1c05d9fb0bad6aa0ec7d2c3566ab34a42b8bc0096168b3a20412085890de25bd21f576101a806e46591c6430214656a7ce1a Homepage: https://cran.r-project.org/package=connector Description: CRAN Package 'connector' (Streamlining Data Access in Clinical Research) Provides a consistent interface for connecting R to various data sources including file systems and databases. Designed for clinical research, 'connector' streamlines access to 'ADAM', 'SDTM' for example. It helps to deal with multiple data formats through a standardized API and centralized configuration. Package: r-cran-connectwidgets Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4959 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-tibble, r-cran-htmlwidgets, r-cran-reactable, r-cran-dplyr, r-cran-magrittr, r-cran-htmltools, r-cran-glue, r-cran-rlang, r-cran-digest, r-cran-crosstalk, r-cran-reactr, r-cran-purrr, r-cran-bslib, r-cran-sass Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat, r-cran-webmockr, r-cran-withr Filename: pool/dists/noble/main/r-cran-connectwidgets_0.2.1-1.ca2404.1_all.deb Size: 1097036 MD5sum: b5fb8e571643f32e3e77d4dd77056356 SHA1: bc589854f39369651a8a729136283d5a1109c587 SHA256: 8cbf4567ec8fe086798e1297a761d95c64bf172ebcaffeb0e9a5cc62783a28f2 SHA512: f83265a44ece1d1535dcd0e902196958e1e3c55639d5ab85130e7c7b7abf3249a8a5b3870a990afc5e415155dd5ae84716b0ddfce319c6d41d8354ceac6317f0 Homepage: https://cran.r-project.org/package=connectwidgets Description: CRAN Package 'connectwidgets' (Organize and Curate Your Content Within 'Posit Connect') A collection of helper functions and 'htmlwidgets' to help publishers curate content collections on 'Posit Connect'. The components, Card, Grid, Table, Search, and Filter can be used to produce a showcase page or gallery contained within a static or interactive R Markdown page. Package: r-cran-connmattools Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-mcmc Filename: pool/dists/noble/main/r-cran-connmattools_0.3.5-1.ca2404.1_all.deb Size: 267202 MD5sum: bbb9127b5e7cbbd266adf0d651d97917 SHA1: e94490cc7c0def109ef6ccbd649983009d93e741 SHA256: e314720c639f881e37ff2b64e826d38ba2d92c328b21c4ba3ea34ce20ca7dae1 SHA512: 8b6cdd051174ed5cd6de78c87cf752ace132a500e6da927d60f1cd8888f014fb8de80bdc59150c0e9cdf3a2843029ef4a1543a849b05671ae9b35bfe1603b055 Homepage: https://cran.r-project.org/package=ConnMatTools Description: CRAN Package 'ConnMatTools' (Tools for Working with Connectivity Data) Collects several different methods for analyzing and working with connectivity data in R. Though primarily oriented towards marine larval dispersal, many of the methods are general and useful for terrestrial systems as well. Package: r-cran-conogive Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-mvtnorm, r-cran-checkmate, r-cran-assertthat Suggests: r-cran-testthat, r-cran-psychtools, r-cran-covr Filename: pool/dists/noble/main/r-cran-conogive_1.0.0-1.ca2404.1_all.deb Size: 115884 MD5sum: bb87dc1715b04f8d3c024cddf78ccead SHA1: dadc9c9121d5b6a76d77cf77b732f8ff04f60c6c SHA256: 78683effe8963a68816cd44618b86c73a0aa23b8d8cc847bf0bf73b32e8aa54b SHA512: 6d6958ee6f16b987fe35fa8e56466b761e91de8f19c9c8aec722ef5b82d314193ade188f936e3d3ad78f4938f92ceaee95028455c3f87c6a690dfa435cad150c Homepage: https://cran.r-project.org/package=conogive Description: CRAN Package 'conogive' (Congeneric Normal-Ogive Model) The congeneric normal-ogive model is a popular model for psychometric data (McDonald, R. P. (1997) ). This model estimates the model, calculates theoretical and concrete reliability coefficients, and predicts the latent variable of the model. This is the companion package to Moss (2020) . Package: r-cran-conover.test Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang Filename: pool/dists/noble/main/r-cran-conover.test_1.2.1-1.ca2404.1_all.deb Size: 69162 MD5sum: ac2e489e65f3f85b3b0ad8e4efb77b9c SHA1: 57be0ec4ec1d862d95396b07d30858523b25b61f SHA256: 91d9f0d8eb78992f5fc1e9b5bc48dd7100bed6f7a12a45ba9b9bba9d80c59811 SHA512: 4794975e3efca8a322359b5169cb4bb7ee301b33652467d857227dbdb0df8ff50d875689689761b011d4039b4dbaa5eb0764ad716e2edc5c7b1b5f26d6b2aa73 Homepage: https://cran.r-project.org/package=conover.test Description: CRAN Package 'conover.test' (Conover-Iman Test of Multiple Comparisons Using Rank Sums) Computes the Conover-Iman test (1979) for stochastic superiority and reports the results among multiple pairwise comparisons after a Kruskal-Wallis omnibus test for stochastic superiority among k groups (Kruskal and Wallis, 1952). conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected. Package: r-cran-conrad Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-conrad_1.0.0.1-1.ca2404.1_all.deb Size: 45928 MD5sum: b81315d7d5d000343ad83b2eea1ce6bf SHA1: c1d7754db82189e955452968148411844a78b9d1 SHA256: f8f5ae5693c5cf97e9351316042525f843145a96d1b0001065238d9b199a779f SHA512: 4190f0cae4e543cf0b34a3f7be595d0c3c18ca6442872146b45f2449c4008990365179ec7b4255fcebf9c219cffef11fda99997a8e939d5065b610b2bdc00f0f Homepage: https://cran.r-project.org/package=conrad Description: CRAN Package 'conrad' (Client for the Microsoft's 'Cognitive Services Text to SpeechREST' API) Convert text into synthesized speech and get a list of supported voices for a region. Microsoft's 'Cognitive Services Text to Speech REST' API supports neural text to speech voices, which support specific languages and dialects that are identified by locale. Package: r-cran-conscir Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-rlang, r-cran-shiny, r-cran-ggplot2, r-cran-dplyr, r-cran-lubridate, r-cran-padr, r-cran-openair Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bslib, r-cran-iapws95, r-cran-ternary Filename: pool/dists/noble/main/r-cran-conscir_0.3.0-1.ca2404.1_all.deb Size: 3939506 MD5sum: 8bae2272349455cc4e37a3397244c31c SHA1: afc01a7bf798329fbad325756195fe8ae44cdc40 SHA256: 0025756a1269f0a30456c2d82888059f96e0a57bf5d5472b3a38cfa9e81bc5de SHA512: 580b9758a192a2fee2539abcb0604eb6d5cb28493726077b6a41a7b789c9c726cbb8f8aef87846d1a54e8d2df6f7d637e31a3b6c15bc7591fcea378c5a9793ef Homepage: https://cran.r-project.org/package=ConSciR Description: CRAN Package 'ConSciR' (Tools for Conservation Science) Provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" . Package: r-cran-consensusclustering Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-igraph, r-cran-cluster, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-consensusclustering_1.5.0-1.ca2404.1_all.deb Size: 121176 MD5sum: 199df335c247ce0e8c446acc36273fef SHA1: 6e43e383543fe41fd2b887e44182181510909bc6 SHA256: 877ddff52b0eb92f1d7017bba1767bd34e05cff4ec176282f510b5539841d01a SHA512: de5237653afa395d7ba7575cdcfb8a113ef0b5514855ebd68f283c24cd6c9f06569ab993f6b73bd6b3baf4b5aa2c5c970574327447c0313cfad62184405f700e Homepage: https://cran.r-project.org/package=ConsensusClustering Description: CRAN Package 'ConsensusClustering' (Consensus Clustering) Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) . Package: r-cran-consensuscpa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-trend, r-cran-openxlsx, r-cran-zoo Filename: pool/dists/noble/main/r-cran-consensuscpa_0.1.0-1.ca2404.1_all.deb Size: 25880 MD5sum: acf25e34ceb9993c48f1902d60602f4d SHA1: f9cfacaf8219f74afc5332035b41db7f28b37da6 SHA256: 7adbaffa573c69ef34ec94b911efc4f7394e048aa6883e6683df9630702c396b SHA512: 01de4c34d451afac5df03783d55c9a4b4512c4d41f58adef2ff3544e2d1a604ce97a4b962bf7bd1da096469f69dffa417542293053facc89951a2a784300be49 Homepage: https://cran.r-project.org/package=ConsensusCPA Description: CRAN Package 'ConsensusCPA' (Consensus-Based Change-Point Analysis Using Multiple StatisticalTests) Provides a unified framework for detecting change points in univariate time series using multiple statistical methods, including Pettitt's test, Buishand Range test, Buishand U test, and the Standard Normal Homogeneity Test (SNHT). The package summarizes individual test results, determines a consensus change point using majority, median, or weighted agreement approaches, exports results with graphical comparisons of observations for before and after the detected change point. The methodology is further described in Laasya et al. (2026) . Package: r-cran-consensusopls Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-plotly, r-cran-psych, r-cran-dt, r-bioc-complexheatmap Filename: pool/dists/noble/main/r-cran-consensusopls_1.1.0-1.ca2404.1_all.deb Size: 3570494 MD5sum: 13daa662fd76a233be56f77b60515e3c SHA1: 1540d899d223e1ca72297a00c901ef8bef484262 SHA256: ff47a71e48574b7ed50a8f5bbe0f5d85791434c016838d7c9deb1e2da84c5242 SHA512: fe18a64ae80dfb4db521438f48894d16224148e7c804cf1295b30c4377501077c8256d17b22318d4e6c50202d5e2f16af141a0f4a0072da4d014b6ecec7e8f5b Homepage: https://cran.r-project.org/package=ConsensusOPLS Description: CRAN Package 'ConsensusOPLS' (Consensus OPLS for Multi-Block Data Fusion) Merging data from multiple sources is a relevant approach for comprehensively evaluating complex systems. However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strength of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structures (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools include score and loading plots, Variable Importance in Projection (VIP), functionality predict for SHAP computing, and performance coefficients such as R2, Q2, and DQ2 coefficients. J. Boccard and D.N. Rutledge (2013) . Package: r-cran-conserver Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-network, r-cran-rlang, r-cran-scales, r-cran-sna Filename: pool/dists/noble/main/r-cran-conserver_1.0.4-1.ca2404.1_all.deb Size: 175846 MD5sum: c20a407e7eb79110c5e46ec51cfa8712 SHA1: 0a002ede1ef917ab0923db8e5f3272fd8d1802e3 SHA256: 586920258b420431820fc8f4f81a73e4b6ef31dd4c92b2b7d221ff9aa29ae2ec SHA512: 44d6179b98f3efb14614a4703768a50375c91055267e5637974d7e592ee6d7b46dc955bb968d66687d45d5ac9ae92bc94a08c476db1912137417089008f2fc8f Homepage: https://cran.r-project.org/package=conserveR Description: CRAN Package 'conserveR' (Identifying Conservation Prioritization Methods Based on DataAvailability) Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, 'conserveR' ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of 'conserveR' contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology. Package: r-cran-consibiocloudclient Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-magrittr Suggests: r-cran-dotenv, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-consibiocloudclient_1.0.0-1.ca2404.1_all.deb Size: 79078 MD5sum: f0dc0ad7a27ebf3babbc70624d5d9917 SHA1: 5446d8bc622fa12fc0be400e69b1384d8ecca79f SHA256: 5e6dcf8e8fac8a34a8977464ec335b2bf02e6e2c313910199692d0f64da5b4c8 SHA512: 439c8ff07e0fb4627fe3f111f8683433ac79581c144ce4c0a4bebbe3f0a99e5dd6b9c646f1cc1c339ba53c2adb9e4402b18dedfc82916e655869711cdc4973e6 Homepage: https://cran.r-project.org/package=consibiocloudclient Description: CRAN Package 'consibiocloudclient' (A Client for the 'Consibio Cloud' API) Enable seamless interaction with 'Consibio Cloud' 'API' . This package provides tools to query data from resources like projects, elements, devices, and datalogs. 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Package: r-cran-consolidatepacks Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vprint, r-cran-stringr, r-cran-devtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-consolidatepacks_1.0.0-1.ca2404.1_all.deb Size: 69116 MD5sum: b85c4b9bc2bab377d7b7abd29ff5721a SHA1: b5d69b671f5572db7dceaecb6d658083e4103d8b SHA256: 9feb4fb108cdf53d3137023915b6513f05098884205e82dd10c956482fd7ef5e SHA512: bfe6957fc0437aec78de614034bdc168d17761250eb690b1e9205c12bd14c32c4e833a45948a1a556425471cbfafa0ba016595ecccc688f34c1da8fb946a41fb Homepage: https://cran.r-project.org/package=consolidatePacks Description: CRAN Package 'consolidatePacks' (Eliminate '@import' by Incorporating Dependencies Directly intothe Package) The purpose of this package is to remove the '@import' dependence of an external package by consolidating the functions into your package. This may be necessary when the '@import' package is decommissioned by CRAN, and you do not want your dependent package to also be decommissioned. The functions in this package recursively retrieve dependencies in the external package. It also performs the other needed bookkeeping, such as retrieving .Rd files in the man subdirectory. Package: r-cran-consort Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-diagrammer, r-cran-rmarkdown, r-cran-covr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-consort_1.2.4-1.ca2404.1_all.deb Size: 1039540 MD5sum: c5cca834b78773bc5115f2191c368bda SHA1: bdc601dffe04cadee4eb49543a59ce47ea34455b SHA256: d11be77452e00169958ad85a38c56cdd716305fe436aa576d21b35441e88305f SHA512: 8ed3f06aaf9032da8cf81bc49fbf21c004cbfcec4c11b5d87ae9fc1b1094e4d8745a95c767b3fe402405b43feeb4a9ef56493d2d8d198e7cd715409329f12261 Homepage: https://cran.r-project.org/package=consort Description: CRAN Package 'consort' (Create Consort Diagram) To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved. Package: r-cran-consortr Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-shiny, r-cran-shinydashboard, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-magrittr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-consortr_0.9.1-1.ca2404.1_all.deb Size: 32268 MD5sum: 2e7c4da6c69ba4d54b8949c483d1f9d9 SHA1: ff4e6b6c73cffeab2ea289e6fba7b8f4513a01ef SHA256: 3199308c757ffcd3347d757fcac788677c96f0eb55e15e16584320da13309df8 SHA512: 5d8b51b93e6af1abba03a15ef62c0f00a644d8366b8f5f5e9c597f52ef1dca360f2f7cc3d130f9a76f3cf65f599268ca7c696294f4a43c82d7a9ab13793a44bd Homepage: https://cran.r-project.org/package=consortr Description: CRAN Package 'consortr' (Interactive Consort Flow Diagrams) Shiny app for creating interactive consort flow diagrams and other types of flow diagrams, see Moher, Schulz and Altman (2001) . Package: r-cran-conspline Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coneproj Filename: pool/dists/noble/main/r-cran-conspline_1.2-1.ca2404.1_all.deb Size: 58008 MD5sum: afc23a0907392a480ecc2b1b70dff529 SHA1: bf123c432bcc26fac91def72ee776ec8a3775ac0 SHA256: dcf730f9064652c52342693caabcbd3903070349796025071f30737e4ce093c8 SHA512: 5c79c4379d240845a7184a44fd5ac638ca783ea1f3782586f0f52be1afc3483cd46330991062a1562925aeb93e5212e26cc086347be131c486080399f919cf13 Homepage: https://cran.r-project.org/package=ConSpline Description: CRAN Package 'ConSpline' (Partial Linear Least-Squares Regression using ConstrainedSplines) Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline. Function outputs fits and various types of inference. Package: r-cran-consrank Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-rlist, r-cran-proxy, r-cran-gtools, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-consrank_2.1.5-1.ca2404.1_all.deb Size: 230886 MD5sum: 19f490a0f6e2f4040454b2f419642bb6 SHA1: 61c1fe7aea0d0cd4252d0ec0d5ba6e0fe56d7d47 SHA256: 6623e69a0ef463c6bd08e1e84320336ca6c5f263483f32e4e0adf0ce38a1b916 SHA512: eedfcaa37d0665fba9065c46ba1979cdc4568dde8eb0aa70ef7bf0b80048edeec0649c00f059091bfcc406c17974b926da3255b48118693737fcaa9459768323 Homepage: https://cran.r-project.org/package=ConsRank Description: CRAN Package 'ConsRank' (Compute the Median Ranking(s) According to the Kemeny'sAxiomatic Approach) Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions. Please type '?ConsRank-deprecated' Essential references: Emond, E.J., and Mason, D.W. (2002) ; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) ; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) ; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) ; Albano, A., and Plaia, A. (2021) . Package: r-cran-consrankclass Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-consrank, r-cran-janitor, r-cran-pracma, r-cran-rlist, r-cran-proxy, r-cran-smacof, r-cran-gtools Filename: pool/dists/noble/main/r-cran-consrankclass_1.0.2-1.ca2404.1_all.deb Size: 241194 MD5sum: 501418a7733da4cb7dbe41b02d7f5c82 SHA1: 854268c3f5d91fdc84c17169013ab81602e09a38 SHA256: 78493566c0f7d4e03af9148917da1f5c40151427a3fbe2db031baf4eab042872 SHA512: ca0bfaa34a13e72fa29bb0425d9978c5514f5c4bc1c8fa54f67e49fa60646f373b5cdc87c08d67c317f4687a8c0ada169120f632a1c61867fe007eed7a9aa577 Homepage: https://cran.r-project.org/package=ConsRankClass Description: CRAN Package 'ConsRankClass' (Classification and Clustering of Preference Rankings) Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding. It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the 'ConsRank' R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function 'sacofSym' from the package 'smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) ; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) ; D'Ambrosio, A., and Heiser, W.J. (2019) ; D'Ambrosio, A., and Heiser W.J. (2016) ; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) ; Marden, J.J. . Package: r-cran-consrq Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-rfast Suggests: r-cran-rfast2, r-cran-cols Filename: pool/dists/noble/main/r-cran-consrq_1.0-1.ca2404.1_all.deb Size: 30764 MD5sum: d1bdcd5f2fc1d15fee2386cc01df031f SHA1: 6cfbdb20c802c47f515a863f12955c712f3eff1f SHA256: 1bc3a54b15569dc7a7d9735e2bf842866107ae5c6b9bfaa0486e94d69dd1244d SHA512: a043eab4097e2d3b88a048ffee4e7bbb66203d78a821bc3b5cfc4c10b9c93eb5779782efc9894e6fb671b4b7fa6c0b1ad71501c90aaff32f581c06d855644d5a Homepage: https://cran.r-project.org/package=consrq Description: CRAN Package 'consrq' (Constrained Quantile Regression) Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. . Package: r-cran-constants Architecture: all Version: 2022.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-errors, r-cran-units, r-cran-quantities, r-cran-testthat Filename: pool/dists/noble/main/r-cran-constants_2022.0-1.ca2404.1_all.deb Size: 114410 MD5sum: c52ccafef6de723c991581fc1eaffbbc SHA1: 68bb4a360edf5c9f69a5a61bc804647dd21685f7 SHA256: 64c6d0d12bd88295549b30ca0f04d6db587704014e6b70b0188190c07355a12a SHA512: e4f326b1ecf0f94d4212b2b1cffae9f8254b8769d0a8df655f4f00c4671e60a4f007cc5b4754a50c1b97b5e8b0944fc3288b3f22dba1a287c736ec5d235b3075 Homepage: https://cran.r-project.org/package=constants Description: CRAN Package 'constants' (Reference on Constants, Units and Uncertainty) CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with uncertainties and/or units are also provided if the 'errors', 'units' and/or 'quantities' packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2022 CODATA" version, published on May 2024: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024) . Package: r-cran-constellation Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-constellation_0.2.0-1.ca2404.1_all.deb Size: 558708 MD5sum: 21cd71fc6d9d4c0e4e4e8168495ba97d SHA1: b24be0ba00c270b634c4214161b42821d5654ebb SHA256: 3d6d48e52a1d6a863590792718d20692535e84543a4aafbd369195f675f4496d SHA512: ba6c9151529636cf3eea0d3c166588328ddf1cf386b22bc6e2e22fb41d6e54969fd4847af7316025c7adcd7de3aadb1b5464eaaef37650a601d609c25bf05d79 Homepage: https://cran.r-project.org/package=constellation Description: CRAN Package 'constellation' (Identify Event Sequences Using Time Series Joins) Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows. Package: r-cran-contactdata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4224 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-countrycode, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-contactdata_1.1.0-1.ca2404.1_all.deb Size: 4135042 MD5sum: 36b4b9cdd54b82ffe4264e3da51eeb09 SHA1: 8588e6a14200834cc060859e86a2765c8cd08801 SHA256: aae00ae320afc6c1fffbad19c9534344cf7a34f4c71cbc36a828f03054e32c22 SHA512: 4199f91c6828d1d8c1c886f3a49e15c3b36768dc97df2df69b2efe227da51902ec48afe6486ebd0ba3e396fc72204678a485ab9e5df54500108fe1cc9a4bae7d Homepage: https://cran.r-project.org/package=contactdata Description: CRAN Package 'contactdata' (Social Contact Matrices for 177 Countries) Data package for the supplementary data in Prem et al. (2017) and Prem et al. . Provides easy access to contact data for 177 countries, for use in epidemiological, demographic or social sciences research. Package: r-cran-contactsurveys Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-oai, r-cran-purrr, r-cran-rlang, r-cran-yesno, r-cran-zen4r, r-cran-jsonlite Suggests: r-cran-socialmixr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-withr, r-cran-vcr Filename: pool/dists/noble/main/r-cran-contactsurveys_0.2.0-1.ca2404.1_all.deb Size: 65934 MD5sum: 16eff0ffce8656c16eca79de5b1b4a8a SHA1: c9845ebcf84236360fb3c949236087c97358468f SHA256: ec772a84c8705b708730bb416db44fda1889aa7271d2838f65555abbe42f0122 SHA512: 0a309070a82e277fd191f38d1d5a97be1dc706e5e904d6e75e1f6ace3015866e7bc14e13e0448c2aa1d1c0f45a14fbac52aa48525e51161d4027f50d132cd85e Homepage: https://cran.r-project.org/package=contactsurveys Description: CRAN Package 'contactsurveys' (Download Contact Surveys for Use in Infectious Disease Modelling) Download, cache, and manage social contact survey data from the social contact data community on Zenodo () for use in infectious disease modelling. Provides functions to list available surveys, download survey files with automatic caching, and retrieve citations. Contact survey data describe who contacts whom in a population and are used to parameterise age-structured transmission models, for example via the 'socialmixr' package. The surveys available include those from the POLYMOD study (Mossong et al. (2008) ) and other social contact data shared on Zenodo. Package: r-cran-contagionchannels Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3082 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-waveslim, r-cran-quantreg, r-cran-igraph, r-cran-mass Suggests: r-cran-hdm, r-cran-glmnet, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-viridis, r-cran-scales, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-contagionchannels_0.1.3-1.ca2404.1_all.deb Size: 2573144 MD5sum: fb15ec8ca51a32535d68414f235cd03e SHA1: 6540d112df8eee8aa92b055d733955e92c426d28 SHA256: e10c04ec88ea59c32ea88c06d5e32bcf920cfb35de544810d3836082c684c9e4 SHA512: 368e05e157f614b3d7fd38941e866c7b711e7f78c37dd5b006b8395679c84d7aa9583504cd1c2abb0234e9ec2fbf85739d6e88a87b76eba761de6b66ce8eb297 Homepage: https://cran.r-project.org/package=contagionchannels Description: CRAN Package 'contagionchannels' (Two-Stage Detection and Attribution of Cross-Border FinancialContagion Channels) Implementation of a two-stage framework for the joint detection-and-attribution of cross-border financial contagion. Stage one detects directional information flows between equity markets via Wavelet-Quantile Transfer Entropy, combining maximal-overlap discrete wavelet decomposition (Percival and Walden, 2000, ISBN:9780521685085) with the transfer-entropy estimator of Schreiber (2000) and quantile conditioning following Han, Linton, Oka and Whang (2016) . Stage two attributes each significant directional link to one of five mutually exclusive transmission channels (Trade, Financial, Geopolitical, Behavioural, Monetary Policy) through a multi-method structural identification architecture combining instrumental-variables two-stage least squares with channel-specific external instruments (Stock and Watson, 2018) , LASSO-based instrument selection (Belloni, Chernozhukov and Hansen, 2014) , local projections (Jorda, 2005) , heteroskedasticity-based identification (Rigobon, 2003) , and the Cinelli-Hazlett (2020) robustness-value sensitivity bound. Bundled datasets and replication scripts reproduce the headline findings of Bhandari, Parida and Sahu (2026) ; the package is general-purpose and accommodates user-supplied returns and channel proxies. 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The core function, 'generate_dockerfile()', analyzes an 'R' project's environment and dependencies via an 'renv' lock file and generates a ready-to-use 'Dockerfile' that encapsulates the computational setup. Designed to help researchers build portable, reproducible workflows that can be reliably shared, archived, and rerun across systems. See R Core Team (2025) , Ushey et al. (2025) , and Docker Inc. (2025) . 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Implements item-sort indices from Anderson and Gerbing (1991) , exact item-sort inference following Howard and Melloy (2016) , empirical interpretation benchmarks from Colquitt et al. (2019) , and the construct-rating procedure of Hinkin and Tracey (1999) with HTC/HTD indices and repeated-measures item screening. The expert-panel workflow combines Aiken's V with score confidence intervals, Lawshe content validity ratios with exact inference, content validity indices with modified kappa and score intervals, item-objective congruence, and panel-level agreement using Krippendorff's alpha as described by Hayes and Krippendorff (2007) . Also provides judge and rater heterogeneity analysis following the generalizability-theory treatment of content-validity ratings in Crocker, Llabre and Miller (1988) , content-domain coverage and expert-perceived content structure following Sireci and Geisinger (1992) , comparison across successive pretest rounds, and exact expert-panel planning. Where published methods compete, users choose among them through arguments with evidence-based defaults. User-facing workflows emphasize interpretable summaries and transparent review recommendations rather than isolated coefficients. 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Functions emphasize reproducibility (explicit seeds throughout) and clear, publication-ready summaries. 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Package: r-cran-coopgame Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geometry, r-cran-rcdd, r-cran-gtools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/noble/main/r-cran-coopgame_0.2.2-1.ca2404.1_all.deb Size: 894164 MD5sum: 896d6db9c4b1828a9c326c4b47090ca0 SHA1: 471945fa853bfdc7ec808573bc5ec98d47566b71 SHA256: 2c8a841b43a870c4c85a6823111ce35e9a276f39e9dd836893e04edf0ca0dcbb SHA512: 2d858a0996e412ec1b7e8232d4c69a82e690c92c7cbe49ba9afd9cce6a936c0ef8c0d97e27450f186fd362223ccb79de598dc0425f1aa64046b40f9df8c8aa63 Homepage: https://cran.r-project.org/package=CoopGame Description: CRAN Package 'CoopGame' (Important Concepts of Cooperative Game Theory) The theory of cooperative games with transferable utility offers useful insights into the way parties can share gains from cooperation and secure sustainable agreements, see e.g. one of the books by Chakravarty, Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, ISBN:978-9027727299) for more details. 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Package: r-cran-copbasic Architecture: all Version: 2.2.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4431 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lmomco, r-cran-mvtnorm, r-cran-randtoolbox Suggests: r-cran-copula Filename: pool/dists/noble/main/r-cran-copbasic_2.2.17-1.ca2404.1_all.deb Size: 3901034 MD5sum: 03f37ca10720f263d27c6ca4525409fb SHA1: 16897353167cc619c3036f637ee84903a58c17f0 SHA256: af85cdcb02e0ede139b7c74586883996ea92976f6d1e55b063b70506e12a72d5 SHA512: f0b928cd15e8aac4ca92dfc4d2c2b0e977798173b5bf2d4fc8b6915b4e7370e3365933bc1a0e58d82de3e0281e3746698600b851af0c767ebb1d63c74706e378 Homepage: https://cran.r-project.org/package=copBasic Description: CRAN Package 'copBasic' (General Bivariate Copula Theory and Many Utility Functions) Extensive functions for bivariate copula (bicopula) computations and related operations for bicopula theory. The lower, upper, product, and select other bicopula are implemented along with operations including the diagonal, survival copula, dual of a copula, co-copula, and numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical derivatives and inverses of a bicopula are provided through which simulation is implemented. Bicopula composition, convex combination, asymmetry extension, and products also are provided. Support extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer- Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and positive/negative quadrant dependency, left (right) increasing (decreasing) are available. Other features include Kullback-Leibler Divergence, Vuong Procedure, spectral measure, and Lcomoments for fit and inference, Lcomoment ratio diagrams, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit. Package: r-cran-copcor Architecture: all Version: 2024.7-31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-r.rsp, r-cran-survival Filename: pool/dists/noble/main/r-cran-copcor_2024.7-31-1.ca2404.1_all.deb Size: 58910 MD5sum: 9bef0eb917137d86215c14904ac67e98 SHA1: 7f6fc13affe75df89e3cb279fe63f5c1190340c6 SHA256: 76c81208b02c7c4dd51399202dea39fc6c90b6ac1487b0134b1521fef8c24d67 SHA512: 014959b42bfc995c7274c5734575f3bcc66f4a000a89a7c8e90f233782a94ea6b8b8079ba1c64b3d14b3f09cf718ae0735e446795cf7920d56369d3f58797359 Homepage: https://cran.r-project.org/package=copcor Description: CRAN Package 'copcor' (Correlates of Protection and Correlates of Risk Functions) Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. 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Package: r-cran-copcts Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-msm, r-cran-copbasic Filename: pool/dists/noble/main/r-cran-copcts_1.0.0-1.ca2404.1_all.deb Size: 109002 MD5sum: c8d0c395c543410dd4e90a48469691be SHA1: 4c41af7fe09e518e4ea1c536f260768be25f65c8 SHA256: eb0e6240faffc5c2815d12fac558e3987822e3f848c5938f8e025a186c128c03 SHA512: 2668128738c1828638843c1359393392f7890232c5af3b235040a15d4a2377a53da20c3f48fc26ac7bbc37869b3b62580feb7766f1555461b9520030813d4d6b Homepage: https://cran.r-project.org/package=CopCTS Description: CRAN Package 'CopCTS' (Copula-Based Semiparametric Analysis for Time Series Data withDetection Limits) Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. 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Package: r-cran-cope Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-abind, r-cran-fields, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-nlme Filename: pool/dists/noble/main/r-cran-cope_0.2.3-1.ca2404.1_all.deb Size: 76062 MD5sum: f6e35f4021dacb81d909baeb4ad2d196 SHA1: 9f797e9c88d127b9bfc8fd72858da596e931c5f9 SHA256: 6c58e893eaf161ebc5d1cbebd614db5a8a8bbdb62e8e2dd4cb29152207442451 SHA512: 84bae1f6aecb97bf09583260b8a791418467132f512d5ad4064b59da4751b0c69980c63f67efc6fe1c0798cd746ef1c3546e33156397fc53e79571da2b463800 Homepage: https://cran.r-project.org/package=cope Description: CRAN Package 'cope' (Coverage Probability Excursion (CoPE) Sets) Provides functions to compute and plot Coverage Probability Excursion (CoPE) sets for real valued functions on a 2-dimensional domain. CoPE sets are obtained from repeated noisy observations of the function on the entire domain. They are designed to bound the excursion set of the target function at a given level from above and below with a predefined probability. The target function can be a parameter in spatially-indexed linear regression. Support by NIH grant R01 CA157528 is gratefully acknowledged. Package: r-cran-copent Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mnormt Filename: pool/dists/noble/main/r-cran-copent_0.5-1.ca2404.1_all.deb Size: 42698 MD5sum: 2d48c8a43c9553fd372d3582a9a7574a SHA1: 7454e55800fbc5791111c0e6a1e039ff63ff814d SHA256: 1cbaa11a0c922d0584d557f0b1f6b32aa84e572ba7512244d05608bb11039213 SHA512: 7178f42ae7b8887595885d58372b6ac49fe035f163ab86294b5adc3b624362a673af2a00d9355dd65659b6efd1282e14c3c0988c57fd6e96c2027c1a7a9cca23 Homepage: https://cran.r-project.org/package=copent Description: CRAN Package 'copent' (Estimating Copula Entropy and Transfer Entropy) The nonparametric methods for estimating copula entropy, transfer entropy, and the statistics for multivariate normality test and two-sample test are implemented. The methods for estimating transfer entropy and the statistics for multivariate normality test and two-sample test are based on the method for estimating copula entropy. The method for change point detection with copula entropy based two-sample test is also implemented. Please refer to Ma and Sun (2011) , Ma (2019) , Ma (2022) , Ma (2023) , and Ma (2024) for more information. Package: r-cran-copernicusclimate Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2930 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-clipr, r-cran-ggplot2, r-cran-knitr, r-cran-refmanager, r-cran-rmarkdown, r-cran-sf, r-cran-stars, r-cran-testthat Filename: pool/dists/noble/main/r-cran-copernicusclimate_0.0.6-1.ca2404.1_all.deb Size: 2139232 MD5sum: 8d47cea85cd075f3249009914295cd01 SHA1: d0b028040344d6a2917b60e0bb185541725e695a SHA256: 981875559dcb7751dba763147a08b553079b798fe791b1302620bed749a0afa0 SHA512: bb6f05c5f6024db9b167b396358ea19d1f5760ed8e239d8bfafbc317b7cd26d5130cb2343684c6bb47e64d47f5ebb5422dcc96b8926ceb3d5ce7db1f3d548a42 Homepage: https://cran.r-project.org/package=CopernicusClimate Description: CRAN Package 'CopernicusClimate' (Search Download and Handle Data from Copernicus Climate DataService) Subset and download data from EU Copernicus Climate Data Service: . 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This package provides entry points to several APIs allowing users to access the data directly in R. Package: r-cran-copernicusdem Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-sf, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-data.table, r-cran-fitbitviz, r-cran-mapview, r-cran-terra Filename: pool/dists/noble/main/r-cran-copernicusdem_1.0.5-1.ca2404.1_all.deb Size: 2658468 MD5sum: 27a4c7e7cb48745661bd581fe1a0f92e SHA1: 74a791e794991077396f90c876cefb13ea64986e SHA256: f7cdcef242026b5a438fa7822d59b540b4cea1b2bd79c0b661cebfe97611321d SHA512: e617d98ba84ab337db870b7ed10d1e9bb726794c1e9e50319f84da8128f62317901303532b0ce4ce70efa0829c2eb6f1206567f3ee44eb2f1665d3dabe41545f Homepage: https://cran.r-project.org/package=CopernicusDEM Description: CRAN Package 'CopernicusDEM' (Copernicus Digital Elevation Models) Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation. Package: r-cran-copernicusmarine Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-leaflet, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-cftime, r-cran-clipr, r-cran-curl, r-cran-dt, r-cran-knitr, r-cran-lifecycle, r-cran-ncmeta, r-cran-ncdf4, r-cran-paws.storage, r-cran-rmarkdown, r-cran-units, r-cran-testthat Filename: pool/dists/noble/main/r-cran-copernicusmarine_0.4.9-1.ca2404.1_all.deb Size: 720828 MD5sum: 27298017265b332577c226512afbc4bc SHA1: 4ea545b3a83140ef4d8f0abb2f93fe0c8e8e35c9 SHA256: 04764b240244ab941c58f8f7b7f8b612deceded5b39ed96c5e5f0b6898931c05 SHA512: f0cc3138a8da59c9c40ecf6763244e842a555aed126504d0c4e80938f84881e297e991a91e8450c60cf7150bd42a6ba457b6614b973611172b237e617c7d6283 Homepage: https://cran.r-project.org/package=CopernicusMarine Description: CRAN Package 'CopernicusMarine' (Search Download and Handle Data from Copernicus Marine ServiceInformation) Subset and download data from EU Copernicus Marine Service Information: . Import data on the oceans physical and biogeochemical state from Copernicus into R without the need of external software. Package: r-cran-copernicusr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-getpass, r-cran-testthat, r-cran-withr, r-cran-terra, r-cran-stars, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-copernicusr_0.1.0-1.ca2404.1_all.deb Size: 72168 MD5sum: e951efd5dcfeab33bf807965656301d4 SHA1: a95871037ef337f567095d339cd04928c277fc88 SHA256: c86d5f8253bd4b01456418e135a6db549c673769c54e008c2a45acbd2953ac3b SHA512: db7e552a6c5537122a2009a418f0a8fc7b28e56d7955c027e77cb6e87a0b7259ad589d483c0f4345693f235436cdbb8da39912b1a62c19897f2c59be5bfee098 Homepage: https://cran.r-project.org/package=copernicusR Description: CRAN Package 'copernicusR' (R Interface to Copernicus Marine Service) Provides an R interface to the Copernicus Marine Service for downloading and accessing marine data. Integrates with the official 'copernicusmarine' Python library through 'reticulate'. Requires Python 3.7+ and a free Copernicus Marine account. See and for more information. Package: r-cran-coppecosenzar Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-coppecosenzar_0.1.3-1.ca2404.1_all.deb Size: 233342 MD5sum: 898aab17a993593ed0c1fcb80da96e56 SHA1: d970aef5f16a4d49417a86d65659414448356abb SHA256: 6cf956759893d5a3a5140dc1fb8f34425e5248c9ae5f97323fbb119cf8593e8c SHA512: 4abe7e2e38fcf807a5f155481b2591a157950612f2c688843aaacf129554c0fdf643fcd8b520c512365a84d9099d06e620d58156cfe76178c1a0c0be774c05a8 Homepage: https://cran.r-project.org/package=coppeCosenzaR Description: CRAN Package 'coppeCosenzaR' (COPPE-Cosenza Fuzzy Hierarchy Model) The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. The model was based on the evaluation of local alternatives, representing regional potentialities, so as to fulfill demands of economic projects. After defining demand profiles in terms of their technological coefficients, the degree of importance of factors is defined so as to represent the productive activity. The method can detect a surplus of supply without the restriction of the distance of classical algebra, defining a hierarchy of location alternatives. In COPPE-Cosenza Model, the distance between factors is measured in terms of the difference between grades of memberships of the same factors belonging to two or more sets under comparison. The required factors are classified under the following linguistic variables: Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant (I). And the alternatives can assume the following linguistic variables: Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and Inexistent (In). The model also provides flexibility, allowing different aggregation rules to be performed and defined by the Decision Maker. Such feature is considered in this package, allowing the user to define other aggregation matrices, since it considers the same linguistic variables mentioned. Package: r-cran-cops Architecture: all Version: 1.12-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cordillera, r-cran-smacofx, r-cran-smacof, r-cran-analogue, r-cran-cmaes, r-cran-crs, r-cran-dfoptim, r-cran-gensa, r-cran-minqa, r-cran-nlcoptim, r-cran-nloptr, r-cran-pso, r-cran-rgenoud, r-cran-rsolnp, r-cran-subplex Suggests: r-cran-r.rsp, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cops_1.12-1-1.ca2404.1_all.deb Size: 583416 MD5sum: 506c7cc5783431ce69040da9e9ad5f8c SHA1: 80460b4093c84fe5c09e4dd2d50a39970ef9d0e5 SHA256: e57b2bf91626f0969d8dfa508ccbfc16256e1106990dc6f31907bf899d4abf3e SHA512: b79188a4951a19f828f55c5f47f3e2fdeb8cc9fe9114c314bc488d0136ee03e2077ef0a859c71b66c6a48f773e21974558665c05b20d0a31d3e32d0faca5087b Homepage: https://cran.r-project.org/package=cops Description: CRAN Package 'cops' (Cluster Optimized Proximity Scaling) Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, ). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, ). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,) and a functions to calculate the phi-distances for count data or histograms. Package: r-cran-copsens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3624 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-pcamethods, r-cran-cvxr, r-cran-mass, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-copsens_0.1.0-1.ca2404.1_all.deb Size: 3616866 MD5sum: efe1a470d4797fd5605f6438a520a41a SHA1: fc7b61dea82c0816cdba1d27fa7aae29ec51bf0f SHA256: a698465ec34c239c6433baa6717aaddecc114a8a9699832ce3936db11b9a3ec1 SHA512: 254b38cdf1607dcb6a4c53835c24ceab16fb91acc240d334eabcb5ca6e907701495dc4a21c9863ff0d3ff5698299d35915337af5e80fa36912285c89d03bd9e0 Homepage: https://cran.r-project.org/package=CopSens Description: CRAN Package 'CopSens' (Copula-Based Sensitivity Analysis for Observational CausalInference) Implements the copula-based sensitivity analysis method, as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding , with Gaussian copula adopted in particular. Package: r-cran-copula.markov.survival Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-copula.markov.survival_1.0.0-1.ca2404.1_all.deb Size: 157560 MD5sum: 995498be1e28a068dd63f5c493b3dfaf SHA1: f6a06494e86be2a02a3f221e25b7a254430f2908 SHA256: 1066066d2783e1107c0347bcd4f6933c8a00975bafa55b096dfeb2a02b34f1af SHA512: 32bd49e620f42bad80a0f311c56040fc7c17093baa42c1f8e7eb507a4f9978eda4facc99c59f1a7666222cedbc1085b255a274868e483822a96e5ca0160da757 Homepage: https://cran.r-project.org/package=Copula.Markov.survival Description: CRAN Package 'Copula.Markov.survival' (Copula Markov Model with Dependent Censoring) Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted). Package: r-cran-copula.markov Architecture: all Version: 2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-copula.markov_2.9-1.ca2404.1_all.deb Size: 239476 MD5sum: a705e0ba1b363af86841b84f9824daea SHA1: 3419920bdc117f341cccdd86ff9800f1e42b1f5a SHA256: a5f9ffe3dbee6c89d83a4fcb3c6ec82dbf7aae2f46ac91c131f8ec3c5e612ba6 SHA512: 073d8e6f2ac2a173315169e768b4331cb5bb20a619436b132db869a1dd52e7cd7adefa0046ee0bd4859fdb804eb5dfa1fd097b18e37b4dcaf1448705c8344390 Homepage: https://cran.r-project.org/package=Copula.Markov Description: CRAN Package 'Copula.Markov' (Copula-Based Estimation and Statistical Process Control forSerially Correlated Time Series) Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) , Huang and Emura (2021 Commun Stat-Simul) , Lin et al. (2021 Comm Stat-Simul) , Sun et al. (2020 JSS Series in Statistics), and Huang and Emura (2021, in revision). Package: r-cran-copula.surv Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-copula.surv_3.1-1.ca2404.1_all.deb Size: 183878 MD5sum: 7bdc33b053915513ebf8278f0c0be862 SHA1: fa5cdee7f1dc15f4b741cd1e0e8036527125c497 SHA256: 25cc871b8828220d5e89f0ee82c750525e86f63334fd95381448901b267c0d13 SHA512: 4c52523259947064729cf4bbb3b869d7d26d08ab29410dd2d016d28d89170ebfcbb49ca04c64317459bc5aefd98c6d7e2c732120feb005e6bc6fd9721b8e8dd1 Homepage: https://cran.r-project.org/package=Copula.surv Description: CRAN Package 'Copula.surv' (Analysis of Bivariate Survival Data Based on Copulas) Simulating bivariate survival data from various copula models. Estimating bivariate copula models with semiparametric or Weibull margins under various copulas. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for the Gumbel and Clayton copulas is also implemented for semiparametric models. See Emura, Lin and Wang (2010) for details. Package: r-cran-copulaboost Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvinecopulib Filename: pool/dists/noble/main/r-cran-copulaboost_0.1.0-1.ca2404.1_all.deb Size: 85636 MD5sum: d87ac26f49326de8e84fab877c753087 SHA1: 01354280b8fa641a24e16e7f2045dc3d0dc7a699 SHA256: d1450bb0ebf20c80b27dff47b77a4b25501a2aa5a8f9847deeec375b8a90d8f9 SHA512: fd269439ece3aee362c62b748eed79b2858dc47323bd6f6d0ff80841b89c10ff0d0decfd7d1954a795430885ca4f1772a3474858c42b4337f8c95f1db9bf31f6 Homepage: https://cran.r-project.org/package=copulaboost Description: CRAN Package 'copulaboost' (Fitting Additive Copula Regression Models for Binary OutcomeRegression) Additive copula regression for regression problems with binary outcome via gradient boosting [Brant, Hobæk Haff (2022); ]. The fitting process includes a specialised model selection algorithm for each component, where each component is found (by greedy optimisation) among all the D-vines with only Gaussian pair-copulas of a fixed dimension, as specified by the user. When the variables and structure have been selected, the algorithm then re-fits the component where the pair-copula distributions can be different from Gaussian, if specified. Package: r-cran-copulacenr Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-caret, r-cran-copbasic, r-cran-copula, r-cran-corpcor, r-cran-flexsurv, r-cran-foreach, r-cran-icenreg, r-cran-magrittr, r-cran-plotly, r-cran-pracma, r-cran-survival, r-cran-vinecopula Filename: pool/dists/noble/main/r-cran-copulacenr_1.2.4-1.ca2404.1_all.deb Size: 723316 MD5sum: c4b7a56b440e88ddadfb7051ec78e446 SHA1: f744c3fe68deb7638210b67f68b2e3d6b04453ee SHA256: baaac4dfc2ea99c0fd4d86314a4c5138dd8770cba220e5a3676845913fa18550 SHA512: d6847e1b5b35c71b5ab867455ceba4b80338bf89bd9e8c96c6295c586483cdb70473218f1861380ad82c1479ddf7dcfb63b41d8eca2d32ea39b1db25bf252b60 Homepage: https://cran.r-project.org/package=CopulaCenR Description: CRAN Package 'CopulaCenR' (Copula-Based Regression Models for Multivariate Censored Data) Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC. Package: r-cran-copuladata Architecture: all Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-copuladata_0.0-2-1.ca2404.1_all.deb Size: 53566 MD5sum: 4be2d13d29c4d1a175c17c649fde0143 SHA1: 050892f773e4a16894cebea19f9173db67c64d16 SHA256: 38a2d2b552f0c903edb6698daf56bf8cf83032da2f06e4a3df4a2228dc1ab4bd SHA512: 95a15276a0b5517edac7cf5fadaf0d58d4d9f84ed47e47e147ab7b5ccd0660eaaf42e4e4a8376671e6f8f79bfa97b3034174256c76ecb91be34e7f07c8b6c379 Homepage: https://cran.r-project.org/package=copulaData Description: CRAN Package 'copulaData' (Data Sets for Copula Modeling) Data sets used for copula modeling in addition to those in the R package 'copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin. Package: r-cran-copulaedas Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-vines, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-copulaedas_1.4.3-1.ca2404.1_all.deb Size: 279796 MD5sum: 10607a83a2acc9efa993154a5e7f122d SHA1: 59e03047e65fc001c318718c9fd3a5c58fba8818 SHA256: 0b3a96ca4dab6896d6bcd0722c872f4f679c7a4025370a15f1fd639c33985eca SHA512: 9bc6fa6347113a7efff60a4b9f14b06848a707fd3326fe93fc27a2e5e28d940a42c2d5f63e0aeacb554ab9e13afe10e630d8236a6d41d656df29c0d0aa65dacd Homepage: https://cran.r-project.org/package=copulaedas Description: CRAN Package 'copulaedas' (Estimation of Distribution Algorithms Based on Copulas) Provides a platform where EDAs (estimation of distribution algorithms) based on copulas can be implemented and studied. The package offers complete implementations of various EDAs based on copulas and vines, a group of well-known optimization problems, and utility functions to study the performance of the algorithms. Newly developed EDAs can be easily integrated into the package by extending an S4 class with generic functions for their main components. Package: r-cran-copulareg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvinecopulib Filename: pool/dists/noble/main/r-cran-copulareg_0.1.0-1.ca2404.1_all.deb Size: 40878 MD5sum: f1cbb7409a7226ed535eea15723d575a SHA1: ad758a41b5d0db883c3bb7f5561233a85f8c54f7 SHA256: f918037e3ead60b0e7a0990d746e8c61ccba1360e3e0ee417cf6ecfee72bb7dc SHA512: 58247c9d94fd31309464c7b223ec0c9727674035aa1519b8a5f09cf5b67bd943f1847d43890ebe0b779dda6c44d40f9820f9a7f3afc064edf95d2426b68183a3 Homepage: https://cran.r-project.org/package=copulareg Description: CRAN Package 'copulareg' (Copula Regression) Fits multivariate models in an R-vine pair copula construction framework, in such a way that the conditional copula can be easily evaluated. In addition, the package implements functionality to compute or approximate the conditional expectation via the conditional copula. Package: r-cran-copularemada Architecture: all Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod, r-cran-matlab, r-cran-tensor, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-copularemada_1.8.1-1.ca2404.1_all.deb Size: 590458 MD5sum: ff361af08beaf13457b00e90aca6f69b SHA1: 4d2ebe154db73a9a1cadabc3a5f4e58786c1f345 SHA256: ca3297f54d1a2000c249c0ef545f2e91faa3dd3f6bc8510043c9f35cd4f569bd SHA512: 8eafde92f5a725f4ec262998319268208f0cb0b2963348b339eb320eb29b13ccef38dcba614dbb7d41e30895a18c43fe2e469fbc6a2cb40295e5e507f6fbb2ff Homepage: https://cran.r-project.org/package=CopulaREMADA Description: CRAN Package 'CopulaREMADA' (Copula Mixed Models for Multivariate Meta-Analysis of DiagnosticTest Accuracy Studies) The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) and Nikoloulopoulos (2018) . The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) and also accounting for non-evaluable subjects in Nikoloulopoulos (2020) . The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) . The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2019) . The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2020) . The one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests in Nikoloulopoulos (2022) . The multinomial six-variate 1-truncated D-vine copula mixed model for meta-analysis of two diagnostic tests accounting for within and between studies dependence in Nikoloulopoulos (2024) . The 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard (Nikoloulopoulos, 2025) . The 1-truncated C-vine copula mixed models for network meta-analysis of multiple diagnostic tests (Nikoloulopoulos, 2026) . Package: r-cran-copulasfm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm, r-cran-vinecopula, r-cran-mass Filename: pool/dists/noble/main/r-cran-copulasfm_0.2.0-1.ca2404.1_all.deb Size: 41284 MD5sum: 04c4342bd5049842c8a7731a8073e69a SHA1: 59c9f8edd54c1a00a04b2179eff21a2f60f6874a SHA256: 0e7d1a5064bfaa5b564db60168e0a9e4f2fff50d7c16a500776db96a7f876fb0 SHA512: 1b9af9d3f7b18013bed01840ddc7f32100c70fc7430bbc67d28d95eefb8e95b89e5d556e4866f0e66d25da2089f1fe35d2dcdead0951e8a7a85f9d9fb4cb8600 Homepage: https://cran.r-project.org/package=copulaSFM Description: CRAN Package 'copulaSFM' (Copula-Based Stochastic Frontier Models) Provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) and Maneejuk et al. (2017, ISBN:9783319562176). 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To make the outcome of optimization more realistic, relevant empirical patient level data should be utilized when it’s available. However, a few problems arise in simulating trials based on small empirical data, where the underlying marginal distributions and their dependence structure cannot be understood or verified thoroughly due to the limited sample size. To resolve this issue, we use the copula invariance property, which can generate the joint distribution without making a strong parametric assumption. The function copula.sim can generate virtual patient data with optional data validation methods that are based on energy distance and ball divergence measurement. The function compare.copula.sim can conduct comparison of marginal mean and covariance of simulated data. To simulate patient-level data from a hypothetical treatment arm that would perform differently from the observed data, the function new.arm.copula.sim can be used to generate new multivariate data with the same dependence structure of the original data but with a shifted mean vector. 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The package implements maximum likelihood estimation of quantile regression models allowing flexible dependence structures between error components through various copula families (e.g., Gaussian and Student-t). It enables estimation of conditional quantile effects, dependence parameters, log-likelihood values, and information criteria (AIC and BIC). The framework combines quantile regression methodology introduced by Koenker and Bassett (1978) with copula theory described in Joe (2014, ISBN:9781466583221). This approach allows modeling heterogeneous effects across quantiles while capturing nonlinear dependence structures between variables. 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The method, Combinational Regularity Analysis (CORA), borrows its Boolean minimisation algorithms from switching circuit analysis. Truth tables are minimised either with the classical Quine-McCluskey algorithm over positive and don't care terms or with McCluskey's modified algorithm over positive and negative terms, and the resulting prime implicant charts are solved with Petrick's method. Multi-value conditions and structures with simple as well as complex effects are supported, together with a configurational data-mining search and two-level logic diagrams. The package is an R port of the 'Python' packages 'CORA' and 'LOGIGRAM' described in Sebechlebská, Mkrtchyan and Thiem (2023) ; it computes in plain R and requires no 'Python' installation. It is an independent implementation and is not endorsed by the authors of the original packages. 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Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See for a detailed presentation of the method. Package: r-cran-corbouli Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rfast, r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corbouli_0.1.5-1.ca2404.1_all.deb Size: 310826 MD5sum: c6061d4b486a2457ec74a7cc9255f260 SHA1: 4bc88209cad6a627fdcc13ab3c244074309b4fe3 SHA256: 6dab54c15bf8932c7f0b636b184e4f3ded17044ba7bb15fc8e2f66aabd8624ed SHA512: ff52c90d2c2fd9c8e2415a449f861fe2cd75866e2803c54252076bdfd30e58db76c6b3734af4a6c173b4011e9bb0aeee941bb1399f6c0f02170e9835a62c4dbc Homepage: https://cran.r-project.org/package=corbouli Description: CRAN Package 'corbouli' (Corbae-Ouliaris Frequency Domain Filtering) Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) , this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series. Package: r-cran-corclass Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-cairo Filename: pool/dists/noble/main/r-cran-corclass_0.2.1-1.ca2404.1_all.deb Size: 37806 MD5sum: 1354d2f1c15c5468d31d51f6dc04f5bb SHA1: 1da000a131d0f5c51ff7025c8f52f0f14b9f5a86 SHA256: 7801169e42327a02949462af660fdacc16089db759f918bdea5403e458390d7b SHA512: 030264915c6d61801590412cac85eb78d8cf8086a21397751164bcd10945f8d1963b7e60b9dee5395736b9620db80859f1ee0d41ca92b44a1b4b6de07ed02c67 Homepage: https://cran.r-project.org/package=corclass Description: CRAN Package 'corclass' (Correlational Class Analysis) Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules. 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The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) . We provide an R native version with methods for printing, summarizing, and plotting the result. Package: r-cran-core Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-core_3.2-1.ca2404.1_all.deb Size: 99872 MD5sum: 85a7f03ab3510bda246ee1246a242aad SHA1: 2885e7fb25be2a3b3e76824473dd029face5ddab SHA256: 80ec77b0c1a261a1d37116f5a0e9e987cf1ae6aaa7a1173aadedff0de5955bdd SHA512: 4aaf481f0e215befb26ab1405b6254cca729b1e6adb75cc7c262fe1d7da45a2b83d9cb888c8a0e781dd9e68dcce3102471e6deb04c0972cf9914c1fb795f5c6c Homepage: https://cran.r-project.org/package=CORE Description: CRAN Package 'CORE' (Cores of Recurrent Events) Given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs. 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This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) . Package: r-cran-coreheat Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-cran-wgcna, r-cran-heatmapflex, r-cran-convertid, r-cran-rappdirs Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-biocmanager, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db Filename: pool/dists/noble/main/r-cran-coreheat_0.3.2-1.ca2404.1_all.deb Size: 3254428 MD5sum: 8dad044f45d26e4c6be44072b4bfefc5 SHA1: a2f41a488390b44fde04547c64b74fccf4644f01 SHA256: 8345cf3c6f429cc3cf2b7620128fb58accac12bc8e3a4310991e9654b4d00bc1 SHA512: f0fcb5dfa0b6dd9ec26b5bcf72299b33c91fe82266ba2ecebc79ffc233d6b24168692b64924174fc786300210b91f86cddb22cdb7742f166545e393c7e5892fd Homepage: https://cran.r-project.org/package=coreheat Description: CRAN Package 'coreheat' (Correlation Heatmaps) Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default. The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package. Package: r-cran-corehunter Architecture: all Version: 3.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-naturalsort Suggests: r-cran-testthat, r-cran-mockr, r-cran-statmatch Filename: pool/dists/noble/main/r-cran-corehunter_3.2.3-1.ca2404.1_all.deb Size: 677138 MD5sum: 680675980467aaf6b82cde28169d9797 SHA1: a249052256644d9a1e4df3190c1867b1be2d0a7f SHA256: 0891b096ab81c7d301acb30398a2a83e4db20f392769b543b2c78b5d126301ae SHA512: 5b1760c04f78d53d47c082fd6b88f39f49d728cf032d3c08a62ea8a601f2831717ae8544593f9bb1f4e6d173fe8d094db6b6fb97b2363c78da4b30f56b3d93d6 Homepage: https://cran.r-project.org/package=corehunter Description: CRAN Package 'corehunter' (Multi-Purpose Core Subset Selection) Core Hunter is a tool to sample diverse, representative subsets from large germplasm collections, with minimum redundancy. Such so-called core collections have applications in plant breeding and genetic resource management in general. Core Hunter can construct cores based on genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many provided evaluation measures depending on the precise purpose of the core (e.g. high diversity, representativeness, or allelic richness). In addition, multiple measures can be simultaneously optimized as part of a weighted index to bring the different perspectives closer together. The Core Hunter library is implemented in Java 8 as an open source project (see ). Package: r-cran-corella Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3844 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-hms, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uuid Suggests: r-cran-gt, r-cran-knitr, r-cran-nanoparquet, r-cran-ozmaps, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-corella_0.1.4-1.ca2404.1_all.deb Size: 1383862 MD5sum: 4d0f602019b532bd605d1c1b7ca89875 SHA1: c58348abb6301998d4a575a2b012ddad58e2a718 SHA256: fc3b9441cad0209c2e269946a252ceaee08da0040e02e6cfe52431ae647ecfa9 SHA512: 641b980e09660a16dbcbe3b1bb3bfdbd642440752068c2c478dc9ebcbdeaa1fe2604da67a95b8f0484fff7da5a5afc785cd678669d4fecaa2b8e742ac21f73be Homepage: https://cran.r-project.org/package=corella Description: CRAN Package 'corella' (Prepare, Manipulate and Check Data to Comply with Darwin CoreStandard) Helps users standardise data to the Darwin Core Standard, a global data standard to store, document, and share biodiversity data like species occurrence records. The package provides tools to manipulate data to conform with, and check validity against, the Darwin Core Standard. Using 'corella' allows users to verify that their data can be used to build 'Darwin Core Archives' using the 'galaxias' package. Package: r-cran-coremicrobiomer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatch, r-cran-vegan, r-cran-srs, r-bioc-edger, r-cran-ggplot2, r-cran-ggrepel, r-cran-plotly, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-coremicrobiomer_0.1.0-1.ca2404.1_all.deb Size: 388754 MD5sum: 304f6a9e97edfc48e8939e34a5f8b1c4 SHA1: 56121a3e8f797ecb0dc0dda8103dcd19447546e4 SHA256: 8897e0a042ea2f11bd41a6ac0a0117966af40dcb42565bd2908da96654533cf1 SHA512: e2b800b8dd2c56a6c5d9867b33e71fd37fd9688ca398bad229d57bda2b9dfcf177e8094189ecdd497d660085591d3641c61364e7cc452cab47de4ac1e665aa33 Homepage: https://cran.r-project.org/package=CoreMicrobiomeR Description: CRAN Package 'CoreMicrobiomeR' (Identification of Core Microbiome) The Core Microbiome refers to the group of microorganisms that are consistently present in a particular environment, habitat, or host species. 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Package: r-cran-corenlp Architecture: all Version: 0.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-xml Filename: pool/dists/noble/main/r-cran-corenlp_0.4-3-1.ca2404.1_all.deb Size: 68630 MD5sum: 3fe8da28d2db5c2570fea385f88a443b SHA1: e364ff7190f5873e4c58d5c40e10472b008e4877 SHA256: 8601e07eb3caa83be8d33d8623d257c8c06b908cc4ab7e51951f097f1de3d0f3 SHA512: fe09142031d3521aced34901a356920f4415bd5086b035896668466153e0263aac4a3e847cd1c4273b6f3edfede6b62d814165bb0682cc68887ee84ed3ff01bd Homepage: https://cran.r-project.org/package=coreNLP Description: CRAN Package 'coreNLP' (Wrappers Around Stanford CoreNLP Tools) Provides a minimal interface for applying annotators from the 'Stanford CoreNLP' java library. Methods are provided for tasks such as tokenisation, part of speech tagging, lemmatisation, named entity recognition, coreference detection and sentiment analysis. Package: r-cran-coresim Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-car, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coresim_0.2.4-1.ca2404.1_all.deb Size: 49578 MD5sum: 0338e8d054aad26afaab7d28f2ede1b7 SHA1: bede17f34a9049c4535bb13381b86c89166e7e9a SHA256: b9c9e4dc0847c3accf453794f4e466f02d85d40f4f84a11ef680fbff67cfcf62 SHA512: 16bdf59c37bf3fd39d38e76666b324a620f2e396f184ba585c3a5334b059f68e7b58cac6e10dd613d5985c6104ec81e9a23f1f79288ade20b083781e7ba25e4d Homepage: https://cran.r-project.org/package=coreSim Description: CRAN Package 'coreSim' (Core Functionality for Simulating Quantities of Interest fromGeneralised Linear Models) Core functions for simulating quantities of interest from generalised linear models (GLM). 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Package: r-cran-coreval Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-haven Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-quickjsr, r-cran-rmarkdown, r-cran-testthat, r-cran-writexl, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-coreval_0.3.0-1.ca2404.1_all.deb Size: 1709772 MD5sum: 931689f02569907aefcc8f8cc52515d7 SHA1: fadadba307cd220b07ca077070b3e6fdeda7ff16 SHA256: 222fcc27ef1ec2f5cb03b862d21c29418545cf7aaa2192791965c1539a33cd2f SHA512: 7d56250fc348b183b0690f19621bf75993fa06f6a4bb54b08b8eda7ffd1275303b97d7464260238aa1d6f5d51abac4dc6ffabe18d46ef63996bbdfbbd4ca9f76 Homepage: https://cran.r-project.org/package=coreval Description: CRAN Package 'coreval' (Check Clinical Trial Data Against 'CDISC' Open Rules) Finds conformance problems in clinical trial data without leaving R, using the openly published 'CDISC' Open Rules ('CORE'). Check a single dataset while you are still writing the code that builds it, or a whole study folder once it exists, and get the findings back as a tidy data frame pointing at the exact row and variable. Reads transport ('XPT'), 'SAS', comma-separated and 'Dataset-JSON' files, plus 'Define-XML' when present, and 'USDM' study-design documents. Covers rules for the 'SDTM', 'SEND', 'TIG' and 'USDM' standards. The rules are bundled inside the package, so nothing is downloaded and your data never leaves your machine: no internet, no API key, no account. When a rule cannot be checked - because it needs a dataset you did not supply, for instance - it is reported as skipped with the reason, never counted as a pass. Meant as a quick first pass before a qualified validation system, never as a replacement for one. An independent project: not affiliated with or endorsed by 'CDISC', and not a 'CORE'-certified conformance engine. 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Beaulieu et al (2013) . Package: r-cran-corkscrew Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gplots, r-cran-rcolorbrewer, r-cran-igraph Filename: pool/dists/noble/main/r-cran-corkscrew_1.1-1.ca2404.1_all.deb Size: 58000 MD5sum: 40aff04b5ca08f9c48684d4dcc8b567c SHA1: 6898a64be3cecec2a7f705715967d65d7f2aca52 SHA256: 84d9b0c0eae04e5657b118ccb3c9753c5e8783fd113c7d057948d1c631e74daf SHA512: bceb01320f10b80d2f6bec11d15b161225613b9c62fcd517fe1edc390c4d19e2f08f76f79e807299ce9803d773fec015b1ba2a096322c08d6d5ae1537ff51edb Homepage: https://cran.r-project.org/package=corkscrew Description: CRAN Package 'corkscrew' (Preprocessor for Data Modeling) Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables using data transformations. Package: r-cran-corlink Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corlink_1.0.0-1.ca2404.1_all.deb Size: 49696 MD5sum: 50b5809f3227f7dcdc8fe1b1b97eac3c SHA1: 8abad68d58c933238899bd7c098fac65bafa85bd SHA256: 0c452211fde91fae72abac4d0b9bc2c5fe45cd756c5739f4f1af6df2c04def4e SHA512: 33e2cc833c691d9d0f20f53e03dc33b7596c5789d030d4ee77f4e269a7d018f61ea594a377243d495b4066b8158728809e54f72ac81bcbd55af9c06f13e65aee Homepage: https://cran.r-project.org/package=corlink Description: CRAN Package 'corlink' (Record Linkage, Incorporating Imputation for Missing AgreementPatterns, and Modeling Correlation Patterns Between Fields) A matrix of agreement patterns and counts for record pairs is the input for the procedure. An EM algorithm is used to impute plausible values for missing record pairs. 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(2020) . It allows for both mean and overdispersion covariates. Package: r-cran-cornerstoner Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vcd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cornerstoner_2.0.2-1.ca2404.1_all.deb Size: 2235924 MD5sum: b48263795bb2e0080a408d1877d08266 SHA1: 00bc2fc630e4478c3c6b699bca77c20c5c6d48a8 SHA256: 062797613d0dd521924e2b4da1b041c178d591d81f74b04ff8985fe0faa9bb3b SHA512: d1087af9e3bea4507a92e185740d268713e85b8d604fcbaa4a6a5afebb2c0a5054d533ca4ac07800f0be6eb2e0755659ca9b22ae19ae65a0fe2575124e54c17e Homepage: https://cran.r-project.org/package=CornerstoneR Description: CRAN Package 'CornerstoneR' (Collection of Scripts for Interface Between 'Cornerstone' and'R') Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'. The desktop application 'Cornerstone' () is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster. The engineers incorporate intensified hands-on statistics into their projects. They benefit from an intuitive and uniquely designed graphical Workmap concept: you design experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon, immediately, interactively, and without any programming. While 'Cornerstone's' interface to the statistical programming language 'R' has been available since version 6.0, the latest interface with 'R' is even much more efficient. 'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the standard 'Cornerstone' GUI. Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R' routines, immediately and without the need to deal with programming code. Additionally, your 'R' programming team develops corresponding 'R' packages detached from 'Cornerstone' in their favorite 'R' environment. Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel () (available in German). 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Package: r-cran-coroica Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-coroica_1.0.2-1.ca2404.1_all.deb Size: 39374 MD5sum: c1315c64126a5b7b12322a79cdb4f309 SHA1: 8fef2700366f7ffb9e49c0d9b7ff874006497380 SHA256: 8fa88b8bcd6d2e90d78f7836247a84f3299c2672a2568f32b70a6724df76fcab SHA512: 29efb2671c8b5353b0e806d5e054a6b386437b835f02f99b966333db47b54ce68084e007756d90fa62e7b95dd6d027bbda2e9a8a1a9dd80e0bd63ee70c575262 Homepage: https://cran.r-project.org/package=coroICA Description: CRAN Package 'coroICA' (Confounding Robust Independent Component Analysis for Noisy andGrouped Data) Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. 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Package: r-cran-corona Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-gganimate, r-cran-ggplot2, r-cran-gridextra, r-cran-qicharts2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-corona_0.3.0-1.ca2404.1_all.deb Size: 509368 MD5sum: fcbe32e2e522e714a6887843a88b8387 SHA1: e269fd9b23feef52f222b019eeaf495c36ce1566 SHA256: 06ccb29a198be5283b40bcf42bd595ea4e27531801bb6e37af012f5f38167703 SHA512: eb5677411c89bac5380a8f531af06be2ad889feba22045b02d3728cacea60e25d07672fa89a1dfca6ce51edea0ec120c5ccd6daefb3e7ba96253a6f045603d22 Homepage: https://cran.r-project.org/package=corona Description: CRAN Package 'corona' (Coronavirus ('Rona') Data Exploration) Manipulate and view coronavirus data and other societally relevant data at a basic level. Package: r-cran-coronanetr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-r.utils, r-cran-readr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-coronanetr_0.3.0-1.ca2404.1_all.deb Size: 64374 MD5sum: 6725eb32d2372cd7ca7680fa141c7c89 SHA1: 7020dda6e2e4cf2080b4226864f565175d242e92 SHA256: 758338d84df2f64b874d23957f4578edede0da0646489a0cb450e776bc061043 SHA512: 2b671c953ae58261668bfe204ad71c51d9934b73e6fc58c5722d1cb84e4be0a961b125a185415b3a53aca3d438d53bf3958818e21b07bfcec8166710a93f4d38 Homepage: https://cran.r-project.org/package=CoronaNetR Description: CRAN Package 'CoronaNetR' (API Access to 'CoronaNet' Data) Offers access to a database on government responses to the COVID-19 pandemic. To date, the 'CoronaNet' dataset provides the most comprehensive and granular documentation of such government policies in the world, capturing data for 20 broad policy categories alongside many other dimensions, including the initiator, target, and timing of a policy. This package is a programmatic front-end to up-to-date 'CoronaNet' policy records and the 'CoronaNet' policy intensity index scores. For more information, see Cheng et al. (2020) . 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Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus . Package: r-cran-corpcor Architecture: all Version: 1.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corpcor_1.6.10-1.ca2404.1_all.deb Size: 116064 MD5sum: b57ae27a141519822cf1ac4b228c9476 SHA1: bb0ed85a9e1fe5d89b6c6b7426fa416fad82c6b3 SHA256: 8c6081c1c46c1a455cff4ea6647a5d6545353dca48474df13b0c7644f3223283 SHA512: a030c8caef65cd86b6a338ab93386116744d3fc99b17fa23b10a57b087c66fc8041d3450c3c1b70091fd36046affac991687fa5d3559daeca0429c02e61396ab Homepage: https://cran.r-project.org/package=corpcor Description: CRAN Package 'corpcor' (Efficient Estimation of Covariance and (Partial) Correlation) Implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schafer and Strimmer (2005) and Opgen-Rhein and Strimmer (2007) . The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix. Package: r-cran-corplot Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-vgam Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-corplot_1.0.2-1.ca2404.1_all.deb Size: 50798 MD5sum: 55589316989f96ffe14ce6ebcb46bfee SHA1: c228b2a238878198a07411ef5fefa1246fb42b60 SHA256: 5810b1da0bbf3c4bf8eada136bda2bdbbe610d91a7d7a41dbeb13a76597728e7 SHA512: 5bc2d74c1c9bf4f6b931a8746978263a93c75e2ef14028661a3d335146b7b45cd7b30efe678429e4b1d149134df2f60fcabb3bbe84c9ba6a6726d5b7eb18a8dc Homepage: https://cran.r-project.org/package=CORPlot Description: CRAN Package 'CORPlot' (Cumulative Odds Ratio Plot) Create cumulative odds ratio plot to visually inspect the proportional odds assumption from the proportional odds model. Package: r-cran-corpmetrics Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corpmetrics_1.0-1.ca2404.1_all.deb Size: 37532 MD5sum: df8803ed3317df3376adb4324a1f2e5d SHA1: 82715f5da41f1f173ec6ab5f2d27c1f6a5d425ff SHA256: 39d8610132ffb3d8cd58273057337be16ea2f6274167b767fce036ae7c24c051 SHA512: 68fe1809b44d74376590b05d6a3b1d2673de65ef90147bf5f0557f364780fa64d3a2fe0c08f5851bf69958c1ec34480b3e260422c17de088a4a5114c0acd7e81 Homepage: https://cran.r-project.org/package=corpmetrics Description: CRAN Package 'corpmetrics' (Tools for Valuation, Financial Metrics and Modeling in CorporateFinance) Balance sheet and income statement metrics, investment analysis methods, valuation methods, loan amortization schedules, and Capital Asset Pricing Model. Package: r-cran-corpora Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3436 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corpora_0.7-1.ca2404.1_all.deb Size: 3417848 MD5sum: 6cd4d428248def8bdab5d128c4a32f80 SHA1: 4c4e6c37f054d43c11de88247c7f29a6f54dd3ca SHA256: e55a6e007b2a3d89a7c5770b4f578d54515fc0f63a18733c7fc27e31ad0a5ee4 SHA512: cbe779eb81c549d710bfce148718d567ee8924365aa7ba71d0c331240775a7c10e52a156be3e1932bb647979648dec64994e1eefeac7611ce464371aff2522d9 Homepage: https://cran.r-project.org/package=corpora Description: CRAN Package 'corpora' (Statistics and Data Sets for Corpus Frequency Data) Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course "Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL'). Package: r-cran-corporaexplorer Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-magrittr, r-cran-padr, r-cran-plyr, r-cran-rcolorbrewer, r-cran-re2, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-janeaustenr, r-cran-shinytest2, r-cran-sotu, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corporaexplorer_0.9.0-1.ca2404.1_all.deb Size: 151990 MD5sum: 19b17dc1934f5e7efa958ec32c5516ee SHA1: da24982e3944860ca3c0d7dde8cec76b704153b1 SHA256: ef9a2dead8a4179cbe82f210d8a70077e8494e86c3cab1f4a2bbff15963b4ef4 SHA512: a0236da3fff2691de45976420e64a63e64b6be25d0a86a7f906d071ccdf518ad5160b5b9c2af5814eb30ee754b9bed5862d22fc21111b4c20292d19ba874ff10 Homepage: https://cran.r-project.org/package=corporaexplorer Description: CRAN Package 'corporaexplorer' (A 'Shiny' App for Exploration of Text Collections) Facilitates dynamic exploration of text collections through an intuitive graphical user interface and the power of regular expressions. The package contains 1) a helper function to convert a data frame to a 'corporaexplorerobject' and 2) a 'Shiny' app for fast and flexible exploration of a 'corporaexplorerobject'. The package also includes demo apps with which one can explore Jane Austen's novels and the State of the Union Addresses (data from the 'janeaustenr' and 'sotu' packages respectively). Package: r-cran-corpower Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-osdesign Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-corpower_1.0.4-1.ca2404.1_all.deb Size: 175000 MD5sum: 38eb6bd61c1a9c19def03b444042765f SHA1: fbc4eec33687a45884c4d57e154e5cfe32b02b3d SHA256: 501f39cbccf5bb87a884550404aa9456947cc33fc387e2d7289fdc0e377bb1b7 SHA512: 6f043c0b31ba18eb86e1375a666bcf257ba018f9a4c1300faf23945b7e6313bc5a452168ceb918f77cb1dd335cfb354468ba56205488784a54ed70c6c44e4085 Homepage: https://cran.r-project.org/package=CoRpower Description: CRAN Package 'CoRpower' (Power Calculations for Assessing Correlates of Risk in ClinicalEfficacy Trials) Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups. 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Noda (1993) ). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots. 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For two specified levels of the variable, 'corrarray' displays one level's correlation matrix in the lower triangular matrix and the other level's correlation matrix in the upper triangular matrix. Such an output can enable visualization of correlations from two samples in a single correlation matrix or corrgram. 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Package: r-cran-correctoverloadedpeaks Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bitops, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-bioc-xcms, r-cran-xml, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-correctoverloadedpeaks_1.3.5-1.ca2404.1_all.deb Size: 644436 MD5sum: aa672bc40b954a4519ad8b6c57d52a8f SHA1: 89225b89fe667b210ee926d66a1a4ff18ae41e0e SHA256: aa9288100ab2177f44d3d7f2310c2dca593d3c4f9c9326ab5d892d3c39d75d9d SHA512: f5f34c30bd066f5bce83e74730680adbc8e4680c0b6ffa1283bb3b11f069ee00fdf0863b94ba202e0ea085d213478e9d97e0d134047f2a79935de7f0473677aa Homepage: https://cran.r-project.org/package=CorrectOverloadedPeaks Description: CRAN Package 'CorrectOverloadedPeaks' (Correct Overloaded Peaks from GC-APCI-MS Data) Analyzes and modifies metabolomics raw data (generated using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in 'xcmsRaw' format are accepted as input and 'mzXML' files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or an Isotopic-Ratio approach. Quality control plots are generated and corrected data are stored within the original 'xcmsRaw' or 'mzXML' respectively to allow further processing. 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The package provides functions to retrieve missing authors, titles, journal details, volume, issue, and page numbers. Digital object identifiers (DOIs) are retrieved using the 'CrossRef' application programming interface (API) , and references are formatted following DOI-based citation standards as described by Paskin (2010) and the 'citation.doi.org' service . The package is intended to simplify reference preparation for scientific journal submissions. Package: r-cran-correlatio Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-correlatio_0.2.1-1.ca2404.1_all.deb Size: 543014 MD5sum: 41a5d4b74524b47a124e3bac99a6e089 SHA1: c252b49411f82760198119d882962b5860843a69 SHA256: 8616473278b1b9c7d536b43a76c10f48632dfa03668e0bd9d75faae8a43cd3c7 SHA512: c5d3b99b5f27251f01500d8877e13e9ff9a3e86b7d12ec925c577dda5f27ac6997c11fe30e1a0001098c32ca3f5c0808b44eeced330ddcf024334ca43d5d387a Homepage: https://cran.r-project.org/package=correlatio Description: CRAN Package 'correlatio' (Visualize Details Behind Pearson's Correlation Coefficient) Helps visualizing what is summarized in Pearson's correlation coefficient. That is, it visualizes its main constituent, namely the distances of the single values to their respective mean. The visualization thereby shows what the etymology of the word correlation contains: In pairwise combination, bringing back (see package Vignette for more details). I hope that the 'correlatio' package may benefit some people in understanding and critically evaluating what Pearson's correlation coefficient summarizes in a single number, i.e., to what degree and why Pearson's correlation coefficient may (or may not) be warranted as a measure of association. Package: r-cran-correlation Architecture: all Version: 0.8.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-insight, r-cran-parameters Suggests: r-cran-bayesfactor, r-cran-energy, r-cran-ggplot2, r-cran-ggraph, r-cran-gt, r-cran-hmisc, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-mbend, r-cran-polycor, r-cran-poorman, r-cran-ppcor, r-cran-psych, r-cran-rmarkdown, r-cran-rmcorr, r-cran-rstanarm, r-cran-see, r-cran-testthat, r-cran-tidygraph, r-cran-wdm, r-cran-wrs2, r-cran-openxlsx2 Filename: pool/dists/noble/main/r-cran-correlation_0.8.8-1.ca2404.1_all.deb Size: 510256 MD5sum: a1bd42fd6d2f9d2bdae060ecc601c2a7 SHA1: bb2f0d990fc1b4c098b551c5f1bdaa70c5f08292 SHA256: 4a7be4e358c30d96f527d38ebb466cfe2c1a5b4fb15946e1d5ebcdbc0f67a2ea SHA512: 3c8472c477dc1da1f74f9ae8a98714d3df2ea0cf3b09b7f190d450ade454277691decbe91d2f2761e82b189204e313d6f4a7e8992cb84b2858b9f8643873049e Homepage: https://cran.r-project.org/package=correlation Description: CRAN Package 'correlation' (Methods for Correlation Analysis) Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) . Package: r-cran-correlationfunnel Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3592 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-recipes, r-cran-magrittr, r-cran-plotly, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-ggrepel, r-cran-stringr, r-cran-forcats, r-cran-purrr, r-cran-cli, r-cran-crayon, r-cran-rstudioapi Suggests: r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-lubridate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-correlationfunnel_0.2.0-1.ca2404.1_all.deb Size: 2839378 MD5sum: 1da73629ab310a08084e7f05799ab216 SHA1: a1e819ceefe3eed8785c85ee0fb1b7f64bd1278b SHA256: 87faacd50c47492e066c1736b5f93889119a060365a265a8d514f0ef0069ff5d SHA512: c97db304756291c309d17ac1ba951296d76f5f8fd5ae4968bce412a61276e735891f0d7a7ec7b466d41e7139267989fbea5ed4994574e17f0c5ad8c88a1ebe02 Homepage: https://cran.r-project.org/package=correlationfunnel Description: CRAN Package 'correlationfunnel' (Speed Up Exploratory Data Analysis (EDA) with the CorrelationFunnel) Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) . Package: r-cran-correlationr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-correlationr_0.1.0-1.ca2404.1_all.deb Size: 21312 MD5sum: 892cf68b66a00d248234226a78954849 SHA1: 8e5e1b8edc410e7d621c3e11ff2209fe92077d42 SHA256: df0b5233f06f3a443ffb8bc2e0b2a413f0323808149622922381027599ea5ac4 SHA512: 70a3e23ebeee420248dfc13e2ddff370946573b79bf501f3e251c981f461843ddd99c9e125ee4033409c8c7aa2ccc8e93fb2fd2c0b65e7e63c0ce7b58b10184d Homepage: https://cran.r-project.org/package=correlationr Description: CRAN Package 'correlationr' (Conduct Robust Correlations on Non-Normal Data) Allows you to conduct robust correlations on your non-normal data set. The robust correlations included in the package are median-absolute-deviation and median-based correlations. Li, J.C.H. (2022) . Package: r-cran-correlbinom Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmpfr Filename: pool/dists/noble/main/r-cran-correlbinom_0.0.1-1.ca2404.1_all.deb Size: 18194 MD5sum: baa12fdbf2a91d7bd2c1413104bb2a38 SHA1: e37e077c5d6ca865c32b110d4da61c89201f93ef SHA256: ca44760d97c261ac54f0cf02d054671ce3f5e147670e272c0e0fa1136feada0c SHA512: 7454b21a7f6690dd83190272804f4025838b74a897cdaa515ca181239e033d4269675bc6a391b1597f6d605c093ff6a13ab72f5abe6765587a2c71ede35bdb07 Homepage: https://cran.r-project.org/package=correlbinom Description: CRAN Package 'correlbinom' (Correlated Binomial Probabilities) Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) or from Kuk (2004) . The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes. 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The correspondence table between two statistical classifications can be updated when one of the classifications gets updated to a new version. Package: r-cran-corrfuns Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-mvcor Filename: pool/dists/noble/main/r-cran-corrfuns_1.2-1.ca2404.1_all.deb Size: 84554 MD5sum: ddc0333fd9b99b77b2941d72ec39e4cf SHA1: cfc1924f20fbec9870131e47b5337787ce502764 SHA256: 42b1f4270aa22150effc264678b106dbfa2423e65ccd0e30f90e0032ffe5ba09 SHA512: 03039fd1f6f7470b100dc470546ce50f0ef8c4cec152aac87694f05b33d44a8e7c5c16271330a1c2e443ea2cba008f41ecab66d1e10df7115c09da83878db2a3 Homepage: https://cran.r-project.org/package=corrfuns Description: CRAN Package 'corrfuns' (Correlation Coefficient Related Functions) Many correlation coefficient related functions are offered, such as correlations, partial correlations and hypothesis testing using asymptotic tests and computer intensive methods (bootstrap and permutation). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press and Owen A. B. (2001). "Empirical likelihood". Chapman and Hall/CRC Press. ISBN: 9781584880714. 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Package: r-cran-corrtable Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-waldo, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corrtable_0.1.1-1.ca2404.1_all.deb Size: 17372 MD5sum: db13ed871cec5c8898ce3d2dbd2f5ed1 SHA1: ca83f14399f875e7ce38fa6167903de776a64983 SHA256: d9f3286a7a649616ee127b7b6dcada5cbfbcdadf103cd6fb64361cbf23b4eff5 SHA512: 4d93fe64d2a3c2b1dadcfb3f787a6bb0d273a488ec8a02abb1cf41f48e86ceb2ea44a463cde2733158dd0c5fff3ce722208a29dd258dcd71d3cc0e7b21edc947 Homepage: https://cran.r-project.org/package=corrtable Description: CRAN Package 'corrtable' (Creates and Saves Out a Correlation Table with SignificanceLevels Indicated) After using this, a publication-ready correlation table with p-values indicated will be created. The input can be a full data frame; any string and Boolean terms will be dropped as part of functionality. Correlations and p-values are calculated using the 'Hmisc' framework. Output of the correlation_matrix() function is a table of strings; this gets saved out to a '.csv2' with the save_correlation_matrix() function for easy insertion into a paper. For more details about the process, consult . Package: r-cran-corrtoolbox Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-binnonnor, r-cran-binordnonnor, r-cran-genord, r-cran-moments, r-cran-mvtnorm, r-cran-psych Filename: pool/dists/noble/main/r-cran-corrtoolbox_1.6.4-1.ca2404.1_all.deb Size: 93686 MD5sum: 982dabcdb724e7bf80763bb0d15110a1 SHA1: 3600148d4413f422753654bb99b54b3e7436aab9 SHA256: 3ded024dd19ad006c430ad0a663a8fd246fb36be81ffb984f56ebf0411f8b5b1 SHA512: cceb430fdfa7d200b3d064dff58629741b8434f16646115de4357a3af4b0dddd9b4b1c2363cb91afdab461c96222212d46928a62694464d09aebbefc71e07d4d Homepage: https://cran.r-project.org/package=CorrToolBox Description: CRAN Package 'CorrToolBox' (Modeling Correlational Magnitude Transformations inDiscretization Contexts) Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) . Package: r-cran-corrviz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-visnetwork, r-cran-plotly, r-cran-dendser, r-cran-gganimate, r-cran-igraph, r-cran-ggraph, r-cran-circlize, r-cran-ggally, r-cran-purrr, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-gifski Filename: pool/dists/noble/main/r-cran-corrviz_0.1.0-1.ca2404.1_all.deb Size: 2582696 MD5sum: 362dbf33e84726c048888791acb9b626 SHA1: 0fafdea29ba85f9856659602dca70d4685f478b9 SHA256: ca3b001bef3305ea4f9e46f475419bb043ee6c3bfdc5eba55ab4f915643db687 SHA512: 15b93c46e0b13226b9f08f599254cab7274bffe94f641b76693e5772e89fffd820d9a1825bb019cb8e5b88910dd8ec62312ebd2ae3de0ed08716362f4215b3bb Homepage: https://cran.r-project.org/package=corrViz Description: CRAN Package 'corrViz' (Visualise Correlations) An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results. Package: r-cran-corset Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-forecast, r-cran-hts, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corset_0.1-5-1.ca2404.1_all.deb Size: 27844 MD5sum: 06d416b2b083216dbd3caec4b7ef6d40 SHA1: 7fd11d6118d5d206f0a0a2fd4de4c1a64a32c897 SHA256: d2b2b88c5f67fbd95feeabd4961e8472b5e8e25b8c43e4caa87a9814330caf0a SHA512: f4ef02ede67b0f6999be01777fea90c4afbd6cd0f2d317673ecbd417e8908da2c93457722aac699d0b9dae2dd6f53d16157bcea3d981e8cdda0b64f0a5b5b424 Homepage: https://cran.r-project.org/package=corset Description: CRAN Package 'corset' (Arbitrary Bounding of Series and Time Series Objects) Set of methods to constrain numerical series and time series within arbitrary boundaries. Package: r-cran-corsym Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-corsym_1.0.1-1.ca2404.1_all.deb Size: 71554 MD5sum: a9ece35bbad125efff364248f5b8d77d SHA1: ad18faaa54acd090a1fe39e953bbbd9fc744063b SHA256: b54184d93cf369ae0967515d7669043e11486c00ec0d9670043018a7b8944d0f SHA512: c487160e642353b75bdafa2e56d62ea973386c58157ce869a3f83e9722b83ad866421f93b9916dd9c72841c043f9e6dd034dc14cbf00128daa879fa97ae3df86 Homepage: https://cran.r-project.org/package=corsym Description: CRAN Package 'corsym' (Correlation Estimation for Exchangeable/Symmetrical Variables) We implement a new correlation estimator, CorSym, designed for exchangeable variables, where the ordering of the two values in the pair is arbitrary. This kind of data arises frequently in the study of assortative pairing (for example, income in a couple). The standard Pearson estimator is sensitive to such ordering and can be highly biased when the order is biased (when the first value tends to have lower or higher values than the second value). CorSym gives the same estimate deterministically for all orders within each pair, and estimates the desired correlation without bias (variables must be exchangeable). The package also includes utilities to simulate biased orders and test for order bias. Described in Kennedy and Ochoa (2026) . Package: r-cran-cortest Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-igraph, r-cran-clustergeneration, r-cran-matrix, r-bioc-biobase, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cortest_1.0.7-1.ca2404.1_all.deb Size: 132740 MD5sum: 9a83a5ac9d629250402aafd41ed901bf SHA1: ced1536e0d8b894a8dcaebee55e6d39552e887d5 SHA256: 25cf1c3e87a9003e29aed1cf921b687487c6c971c9e966f070ef99b2610e1ad8 SHA512: 49fc705ce06aba7e1f66e30cffaf3ff3cab67a0039a222dce219ccd40504144a0207eafb42f396d306cb274fe325ba24d3ca41240eab1c726912d5eaa593072c Homepage: https://cran.r-project.org/package=corTest Description: CRAN Package 'corTest' (Robust Tests for Equal Correlation) There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports. Please see the reference: Yu et al. (2019) . Package: r-cran-corteza Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2214 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-codetools, r-cran-curl, r-cran-digest, r-cran-jsonlite, r-cran-llm.api, r-cran-printify, r-cran-processx, r-cran-saber Suggests: r-cran-clipr, r-cran-fortunes, r-cran-mx.api, r-cran-mx.client, r-cran-mx.crypto, r-cran-pensar, r-cran-rstudioapi, r-cran-simplermarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-corteza_0.7.1-1.ca2404.1_all.deb Size: 1177956 MD5sum: cf13637ef105218c53bdeb1008ac9990 SHA1: 2fcfe2579327b58088fc86e7547181ce93b9c4a8 SHA256: ef023b75bf07675e44d6c600c94027a2471f86e0a5d7517cec9821d72179b80a SHA512: 56c6708d1d25e51f4399ee48c59447357f870c88376c68d408ff682649db32f3911a984f6da3b49a10448dd3fd08f893fa315960dfd69398c50c800a2ee877ab Homepage: https://cran.r-project.org/package=corteza Description: CRAN Package 'corteza' (AI Agent Runtime) An agent runtime that gives Large Language Models (LLMs) from 'Anthropic' , 'OpenAI' , 'Moonshot' , and 'Ollama' direct access to a live R session with managed workspace state. Tools execute as R function calls with provenance tracking, and a deterministic retrieval system keeps relevant objects in context across turns. Three entry points: a shell command-line interface (CLI), a console read-eval-print-loop via chat(), and a Model Context Protocol (MCP) server via serve() for external clients. Package: r-cran-corto Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4016 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pbapply, r-cran-plotrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-corto_1.3.1-1.ca2404.1_all.deb Size: 3599144 MD5sum: 443258a4538122555c10e39f1582351f SHA1: 8cb0d6a82525a48924c52c6e79460ed2d87a3ecf SHA256: 8fbeb0cd718055c37716dfaec23a687591478e6230e17aa3020e7dff29d2f163 SHA512: f97900e6f4392133292749190f02afdc67d068a242a1191ec77e1319ae1fcc6d409673dcf36b1aa1493db05cf77261a13d9f6a798e6c8550538dccf7174f08ca Homepage: https://cran.r-project.org/package=corto Description: CRAN Package 'corto' (Inference of Gene Regulatory Networks) We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) . Package: r-cran-cortsinescore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-cortsinescore_0.1.0-1.ca2404.1_all.deb Size: 13672 MD5sum: d432d0a6c4cdff872f2db88602bf542b SHA1: facc8235eee475322269ebe0a5d34d92af7b04db SHA256: 6f2478e7f373664c7515390862f6bfe2559daae9a0ea0b0b0c2b0793efcfc06b SHA512: d5445a79f2db08bfc6a4d0cb796ad51a939300309fec2cc25633c34a7924959cd97943d28a09dae6a5bae1797af2b171a14cc7a9f5223396c71c9c8669e7c863 Homepage: https://cran.r-project.org/package=CortSineScore Description: CRAN Package 'CortSineScore' (Compute Cortisol Sine Score (CSS) for Diurnal Cortisol Analysis) Computes a single scalar metric for diurnal cortisol cycle analysis, the Cortisol Sine Score (CSS). The score is calculated as the sum over time points of concentration multiplied by sin(2 * pi * time / 24), giving positive weights to morning time points and negative weights to evening ones. The method is model-free, robust, and suitable for regression, classification, clustering, and biomarker research. Package: r-cran-corx Architecture: all Version: 1.0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggcorrplot, r-cran-glue, r-cran-clipr, r-cran-tidyselect, r-cran-rlang, r-cran-moments, r-cran-ggpubr, r-cran-ggplot2, r-cran-ppcor, r-cran-labelled Suggests: r-cran-covr, r-cran-papaja, r-cran-psych, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corx_1.0.7.3-1.ca2404.1_all.deb Size: 122430 MD5sum: 9f70eaa4b3cd3a4e5efaddfa002dd470 SHA1: 1c0696ead836616c94c04f06a5961e725dc2ea02 SHA256: 783a30d468d4cca81ba2829f8db8f02d42ed63ad9f536c10840888835607f5e6 SHA512: 7589d12e62eb4bb79b1231de1f0f686f5a9b07aa5f92a9d55ac810ac362266c9c5f63bf069dbf28078f5e69446005e98285e682e4bad3c0c4efbe35fc125eaf5 Homepage: https://cran.r-project.org/package=corx Description: CRAN Package 'corx' (Create and Format Correlation Matrices) Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'. Package: r-cran-cosa Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-msm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cosa_2.1.0-1.ca2404.1_all.deb Size: 375916 MD5sum: 1c2da2075cb45ad6eb766637dbd409ad SHA1: 12587f332959316f4819f5ae89c6904a1340c1f3 SHA256: b6495e5c5de96f93bb589494243a7478cb538ab699d2b15e1365097b9024ccdd SHA512: eccf048e251f2e6f3e533b19c0d93550f71d7a9a428201746433992a409fa80db742f30f9f24a25a9bb8a8576117c29f133347a565d201aeacc8a9d45f4675aa Homepage: https://cran.r-project.org/package=cosa Description: CRAN Package 'cosa' (Bound Constrained Optimal Sample Size Allocation) Implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) . Package: r-cran-coscorr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coscorr_1.0.0-1.ca2404.1_all.deb Size: 17218 MD5sum: 0542ea05c2a44ea6c594ed6869dcb8e7 SHA1: 34916c5a2e3de6c12f164075af0505b0ccf01c2c SHA256: 00054f2eb0d93ce3e7d47de0a501d21c74f000dc8b49e145c2da4c6b6b9cc11e SHA512: fd0eb86cda646bd468b8571c8e1136a8b2e1eb49e4e5c55d1ce984cafcfb62f8de74380c796bc4d86346471711c4da9d1e39069b9b360f731b06df3943e9355d Homepage: https://cran.r-project.org/package=cosCorr Description: CRAN Package 'cosCorr' (Cosine-Correlation Coefficient for Vector Variables) Computes the cosine-correlation coefficient for measuring the degree of linear dependence among variables in a multidimensional context. The package implements the generalized cosine-correlation theorem for p-1 variables, providing a quantitative assessment of interrelationships within experimental frameworks. This methodology extends classical correlation measures to higher-dimensional spaces using a dimensional exploration approach based on time scale calculus. Package: r-cran-cosinor2 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cosinor, r-cran-cowplot, r-cran-scales, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-matrixstats, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cosinor2_0.2.1-1.ca2404.1_all.deb Size: 162050 MD5sum: 4fb9e4e2c4a5089a425934ef982085eb SHA1: d8310cf49f9c5a95e627366aff83eb410d31cab9 SHA256: 3f4cc52f99cd19fc67219d8b71f99d5f6fd72f3f4e8763e37aacc4f68201fcb4 SHA512: 6c6494363111a2ab08b616ec0774277ce428b720e658fa87c2d595cdb99b99a9fbbda496805725e2342d14ecb98c20ccf5980692b982322c08f4d8d7811194d9 Homepage: https://cran.r-project.org/package=cosinor2 Description: CRAN Package 'cosinor2' (Extended Tools for Cosinor Analysis of Rhythms) Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). . Package: r-cran-cosinor Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-cosinor_1.2.3-1.ca2404.1_all.deb Size: 96020 MD5sum: d673b652d596240bfd007338346b1a37 SHA1: e7e7e27b8c3004ed900156a77d889dd1935de38b SHA256: edce801187e4fc992cf4f7fdf0f44b887c8d78d3a53d665110f12915b657e002 SHA512: 1cca463f97451f6b8ec27229106899ab78f50a4f5aa357d048d2462a58c8f467c723f92b406fedac9df001885f254b7bde9b8291b7cc7eeac00c69232f09543f Homepage: https://cran.r-project.org/package=cosinor Description: CRAN Package 'cosinor' (Tools for Estimating and Predicting the Cosinor Model) A set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients. Package: r-cran-cosmic Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-posterior Suggests: r-cran-arrangements, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-cosmic_0.5-1.ca2404.1_all.deb Size: 578436 MD5sum: baf27743b3bb3c64b7a8f5c515879606 SHA1: b0857b1c6071a821e6d6196265e56f225269022e SHA256: bb1c3fa0c82444edad9de04461d452525f6225ee1ca4d9f1fed1aa80cc2b5003 SHA512: 8d997aa3146ad21f9d31d0c915192ed321a657e42dc97c443c6cf1344ff526c265120dbba2ac459ad5818d8af20d6632950f457203b83493a265b444726d9b2f Homepage: https://cran.r-project.org/package=cosmic Description: CRAN Package 'cosmic' (Conditional Ordinal Stereotype Model for Incident-LevelComparison) Implements the Conditional Ordinal Stereotype Model for Incident-Level Comparison (COSMIC), a method for analyzing ordinal outcomes observed across multiple actors within shared events. The model uses a conditional likelihood to remove event-level confounding and estimate actor-specific propensities relative to their peers. Efficient computation is achieved via a dynamic programming algorithm for the Poisson-multinomial normalization term, enabling scalable estimation with Markov chain Monte Carlo. The package provides tools for data preparation, model fitting using Stan, and extraction of posterior summaries for comparative inference. Estimation of police officer propensity to escalate force is the primary motivation for the model. For more details see Ridgeway (2026) "A Conditional Ordinal Stereotype Model to Estimate Police Officers’ Propensity to Escalate Force" . Package: r-cran-cosmicsig Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2959 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-cosmicsig_1.3.1-1.ca2404.1_all.deb Size: 2940006 MD5sum: 3596ceb79bb1cf6d11e9a4e377abf792 SHA1: 11712ea048eb97a5ea620dd0f7a88a43057ba311 SHA256: b519e4daca333f81fb0e7d2fa269bba1c60a0d7b566cb3c4999dd694f57ffb4e SHA512: 36bf9c69e0fdeaf07c3c1c3bf3850c828f8d553c7387da86ce55b77371a16396248279b37942e5319b38d861eb57e5a1e5c821a8fc7c3ed2b5afc4336866bae9 Homepage: https://cran.r-project.org/package=cosmicsig Description: CRAN Package 'cosmicsig' (Mutational Signatures from COSMIC (Catalogue of SomaticMutations in Cancer)) A data package with 2 main package variables: 'signature' and 'etiology'. The 'signature' variable contains the latest mutational signature profiles released on COSMIC for 3 mutation types: * Single base substitutions in the context of preceding and following bases, * Doublet base substitutions, and * Small insertions and deletions. 'cosmicsig' stands for COSMIC signatures. Please run ?'cosmicsig' for more information. 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Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models. Package: r-cran-cost Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-cost_0.1.0-1.ca2404.1_all.deb Size: 278724 MD5sum: 5ff380bcb800b8d387de8378625a07f3 SHA1: 4df55ccf7bbfae112e50ad1c192557781797e6f2 SHA256: 02670c1e9ba828dd1fc33aca4e8903dedcc1e0833a72c9f5ac3f85821371dfeb SHA512: 7076831ff7ed9cbb537f6f8e3dd16f95806646d17174cae368460388f5ca00f7a738f177622e89fbb35ab65785be0b20fe368c11e959641ab2b4399e9bed1c9a Homepage: https://cran.r-project.org/package=COST Description: CRAN Package 'COST' (Copula-Based Semiparametric Models for Spatio-Temporal Data) Parameter estimation, one-step ahead forecast and new location prediction methods for spatio-temporal data. 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Numbered examples correspond to Feb 2011 preprint . Package: r-cran-count Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msme, r-cran-sandwich, r-cran-mass Filename: pool/dists/noble/main/r-cran-count_1.3.5-1.ca2404.1_all.deb Size: 381840 MD5sum: 14c6e33886f8059640f6bdb50135c55b SHA1: df169ff67ef5516b7869fc3c7b66e91d13bbc255 SHA256: 60a389db24abc5e7935ad0751673606a0d6a03cb32f6038e96d13e64eb4bcf8a SHA512: 190d2d3a191e5790a24b5d330f9f7959db6f3ea2ed17928273c06ba49bb9a6c186c81a149ee6da5ca934bd96e653ca51f190e8669b50160a9984e0ce2017d131 Homepage: https://cran.r-project.org/package=COUNT Description: CRAN Package 'COUNT' (Functions, Data and Code for Count Data) Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press). 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The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions. 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The following methods are currently implemented: Burghmans et al. (2022) , Dandl et al. (2020) and Wexler et al. (2019) . Optional extensions allow these methods to be applied to a variety of models and use cases. Once generated, the counterfactuals can be analyzed and visualized by provided functionalities. The package is described in detail in Dandl et al. (2025) . Package: r-cran-counternull Architecture: all Version: 0.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-effsize, r-cran-ggplot2, r-cran-randomizr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-counternull_0.2.12-1.ca2404.1_all.deb Size: 165268 MD5sum: e94729ce84b7e8bc850071c4ba486644 SHA1: 293db63b89757784636a9fa5cba229f4e0786dff SHA256: bd6fbb4ffa9b27f952108f6bc825b434353c0e28ddddc206062039d59faa638a SHA512: d39d46cf0631d10ece2b82a75c076c76b5a552ad449037fdb9ac47c4a9f5538c91d3d2d2011e8dc498664400d0b7393f0c61ed85971ce6254aaa173299c90235 Homepage: https://cran.r-project.org/package=Counternull Description: CRAN Package 'Counternull' (Randomization-Based Inference) Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. Retrieves counternull sets and generates counternull distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. Package includes visualization of randomization distributions and Fisher Intervals. Users can input custom test statistics and their own methods for randomization. Rosenthal and Rubin (1994) . Package: r-cran-countfitter Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-shiny, r-cran-pscl Suggests: r-cran-dplyr, r-cran-dt, r-cran-gridextra, r-cran-knitr, r-cran-pander, r-cran-reshape2, r-cran-rmarkdown, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-countfitter_1.5-1.ca2404.1_all.deb Size: 998332 MD5sum: 22a30b20523b6d37a588252dff745d4f SHA1: 1ba603a0509ac3b945a60a81839bd8397a656729 SHA256: 10204a6613183235b526a70e9eaf37b9c9ac16307dcd0dd237fa93d6eff99da1 SHA512: 9913c4a34b4c4e092ba39a86d76dec1da11342a1051a0a28d04ab0fedd5a88c3215ac7b821d7e28b74760ac7fcfc6eeb06c2b10af331e29ce38643a41a481896 Homepage: https://cran.r-project.org/package=countfitteR Description: CRAN Package 'countfitteR' (Comprehensive Automatized Evaluation of Distribution Models forCount Data) A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. ). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set. Package: r-cran-countgmifs Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-countgmifs_0.0.2-1.ca2404.1_all.deb Size: 280122 MD5sum: 61ffa94ea57aa786b123054acefa4295 SHA1: 7a703941b1d27090787d85c294ded2ad61274b85 SHA256: b467796bc55f18ecd687906bf0a48877e1e38e8e7482c7ae54b0d8cfa06c8dea SHA512: d7c97b96bdb5edcf2da19e5ae29b06adf78ffb5ce5464bbc6e6954d00c1ab706b51ca4b10ceb14f0059ed5e08582f7a50894e603d44aa39ff31c310cf0a1ae55 Homepage: https://cran.r-project.org/package=countgmifs Description: CRAN Package 'countgmifs' (Discrete Response Regression for High-Dimensional Data) Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. Package: r-cran-counthmm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-counthmm_0.1.0-1.ca2404.1_all.deb Size: 45694 MD5sum: 4478b1e99a380e81b7640d0696f7f3e1 SHA1: 650f1eebc23dae48c63bf9b0c90e3b2b26c23627 SHA256: 859d1ff33f0fe2bcbc58578f0e52cfe24def5a1146f4c44d718aed2c8af4e375 SHA512: 4d9ad23336356ec24f70c3b52b76a40e8a5fd40276f9ab9fde9fc8995fd29782e546260b6c57c453822aee62bd8bc40ff77bb3604a804a2807411e1a53d58c5b Homepage: https://cran.r-project.org/package=countHMM Description: CRAN Package 'countHMM' (Penalized Estimation of Flexible Hidden Markov Models for TimeSeries of Counts) Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. . Package: r-cran-countland Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-matrix, r-cran-ggplot2 Suggests: r-cran-tidyverse, r-cran-viridis, r-cran-gridextra, r-cran-igraph, r-cran-rspectra, r-cran-matrixtests, r-cran-rdist, r-cran-seurat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-countland_0.1.2-1.ca2404.1_all.deb Size: 222732 MD5sum: 332686c351a664d1cab9732858ea620c SHA1: 432e860d681fb510f0b52865092e71ba00c2fe35 SHA256: 3686994e00cff3ed092c61f07fd4fc998536d4d85cb1df6b4cc87abf0efc7351 SHA512: 90535ed61d40c76bd682da4a70f9b1a8c65886c903904d37398c8f0f469c0c5371540651fd5ba9f9085543d1061981a09feedd21dcd08cfa98f118be654d29f4 Homepage: https://cran.r-project.org/package=countland Description: CRAN Package 'countland' (Analysis of Biological Count Data, Especially from Single-CellRNA-Seq) A set of functions for applying a restricted linear algebra to the analysis of count-based data. See the accompanying preprint manuscript: "Normalizing need not be the norm: count-based math for analyzing single-cell data" Church et al (2022) This tool is specifically designed to analyze count matrices from single cell RNA sequencing assays. The tools implement several count-based approaches for standard steps in single-cell RNA-seq analysis, including scoring genes and cells, comparing cells and clustering, calculating differential gene expression, and several methods for rank reduction. There are many opportunities for further optimization that may prove useful in the analysis of other data. We provide the source code freely available at and encourage users and developers to fork the code for their own purposes. Package: r-cran-countmaskr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-countmaskr_0.1.1-1.ca2404.1_all.deb Size: 2124228 MD5sum: 6182ceff3ee79fc7edbc4c920123341d SHA1: dab6eeb19eccf0a3acb5cc533da93a1e1b68da4c SHA256: ef19421550f2c1d4d62b6192b131a2cf19acc50645178d492c9827584e84c0fa SHA512: 8fc41378a5c946ecf75a6efae811a4584d7d2b8612416d68ab1af7b5b4d77acb90cbb56b4b9412ec37398da5b00a0f0cc6417b15f77e349884cc0bbfb99d967f Homepage: https://cran.r-project.org/package=countmaskr Description: CRAN Package 'countmaskr' (Small Cell Masking Tool for One- & Two-Way Tabular Reports) Provides automated small-cell suppression for one- and two-way frequency tables. Cells falling below a user-defined frequency threshold are masked, with suppression propagated to secondary cells to prevent indirect disclosure. Designed for clinical and health administrative data, the package supports a range of tabular structures and fits into reproducible reporting pipelines, reducing manual review while applying consistent suppression rules across data sharing workflows. Package: r-cran-countprop Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-compositions, r-cran-zcompositions Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-countprop_1.1.1-1.ca2404.1_all.deb Size: 309822 MD5sum: a2dc269b708a5d080ad9c0ae437db0d2 SHA1: 9c8a75f4170c83480f467124b37c168d9d7bfeaa SHA256: fe24dbb63a635802fcc2e54de4d625b5097c6f547e4026bed2a2c2118de37913 SHA512: a7ddc0b7c28c5c5c387a4cf59e24ac6f569ca2cb80d01a8275fd059727cee0ae8408d7f39d67dab029095bdb5f2e8f25bed5c0d1f18fe2f681d0909de3a52114 Homepage: https://cran.r-project.org/package=countprop Description: CRAN Package 'countprop' (Calculate Model-Based Metrics of Proportionality on Count-BasedCompositional Data) Calculates metrics of proportionality using the logit-normal multinomial model. It can also provide empirical and plugin estimates of these metrics. Package: r-cran-countries Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringdist, r-cran-tidyr, r-cran-stringr, r-cran-dplyr, r-cran-knitr, r-cran-fastmatch, r-cran-lubridate, r-cran-httr, r-cran-jsonlite, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-data.table, r-cran-rmarkdown, r-cran-testthat, r-cran-curl, r-cran-rlang Filename: pool/dists/noble/main/r-cran-countries_1.2.4-1.ca2404.1_all.deb Size: 3196664 MD5sum: d1fbec47de00dd8f8e172e4676a90205 SHA1: 9a145ba35f9f2ea70cbce8c55a6b14bfd5a197db SHA256: 2bd69eadcdb96a1be3531fe374fcafd4b664f14273cb303dffe2d322ad8071a6 SHA512: aab8776fc38cbba93802c7f6ef36a18c3aba0f8cc74444a2f7c7a19dae7aab823efa9c79c0944840c0cdf53b40269e47a429a042a6b7890e3c01af6b6058f862 Homepage: https://cran.r-project.org/package=countries Description: CRAN Package 'countries' (Deal with Country Data in an Easy Way) Wrangle country data more effectively and quickly. This package contains functions to easily identify and convert country names, download country information, merge country data from different sources, and make quick world maps. Package: r-cran-countryatlas Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5036 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cachem, r-cran-cli, r-cran-countrycode, r-cran-dplyr, r-cran-ggplot2, r-cran-memoise, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-wdi Suggests: r-cran-biscale, r-cran-cartogram, r-cran-cartogramr, r-cran-classint, r-cran-comtradr, r-cran-covr, r-cran-cshapes, r-cran-dbi, r-cran-duckdb, r-cran-eurostat, r-cran-gganimate, r-cran-ggiraph, r-cran-ggpattern, r-cran-ggrepel, r-cran-ggsql, r-cran-gifski, r-cran-giscor, r-cran-gt, r-cran-knitr, r-cran-leaflet, r-cran-magick, r-cran-mapgl, r-cran-mapproj, r-cran-maps, r-cran-nanoarrow, r-cran-oecd, r-cran-owidr, r-cran-plotly, r-cran-regions, r-cran-rmapshaper, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-scales, r-cran-sf, r-cran-spdep, r-cran-stringdist, r-cran-testthat, r-cran-tmap, r-cran-units, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-countryatlas_3.0.0-1.ca2404.1_all.deb Size: 4053688 MD5sum: 3e2d7f285a4d7e6f7f9418154a69c11c SHA1: d39f34c22288ee9fc43fe957df300465fbc5d07b SHA256: 012353a9f1890f1be8fdd2594826ab0eb592df88930e567d5e39b2016c5f7c01 SHA512: d18e1f811b6082870de77e6689088a2de61d5a2ddea774f9e2ba476cc7131907e0c350841b0ace45ab11f4ce0c2300934eef463f9407fe5b70d082becd9481c9 Homepage: https://cran.r-project.org/package=countryatlas Description: CRAN Package 'countryatlas' (Join World Bank Data, Country Codes and Maps on the ISO Spine) A complete toolkit for getting country data onto honest maps. Country names rarely line up across data sources ("US", "U.S.", "United States", "United States of America" are one country, but a naive join treats them as four), so 'countryatlas' makes ISO codes the universal join key. It generalises a one-call, map-ready table that stitches together 'ggplot2' map geometry, 'WDI' World Bank indicators and the 'countrycode' Rosetta stone; exposes the join machinery for the user's own data; ships curated reference data (metadata, group memberships, an indicator catalogue, flags and currencies); adds analysis helpers (per-capita, regional roll-ups, ranking, inequality and convergence statistics); and turns one hand-drawn choropleth into a full vocabulary of projected, area-honest maps (binned and quantile choropleths, proportional-symbol, spike, bivariate, value-by-alpha, cartogram, tile-grid, flow, small-multiple, animated, globe and interactive), and can hand its curated, ISO-reconciled tables to 'ggsql' for database-side spatial rendering. Honesty is treated as a feature rather than a slogan: classification methods can be compared side by side, missing data can be hatched rather than greyed, coverage and provenance travel with the plot, and the distortion each projection introduces can be measured and drawn. Heavy spatial dependencies stay optional, and a bundled offline snapshot lets every example, test and vignette run without the network. Package: r-cran-countrycode Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-pkgsite, r-cran-eurostat, r-cran-testthat, r-cran-tibble, r-cran-yaml, r-cran-isocodes Filename: pool/dists/noble/main/r-cran-countrycode_1.9.0-1.ca2404.1_all.deb Size: 1281530 MD5sum: a26334625ddc2bd515925605967df9fe SHA1: c21f2fd242355d025d49aca70317dc417930393f SHA256: e812485a8b9440d275b34da97a5a018b36102d107774588bf722037c434ce06a SHA512: f3a5558af4d90c3b393e383ab707a1bf008b456347eafb943a7fca2e244b7bd3ea9ffee36f7bc3e38f4fa7b9f932cb6a0ff5f2c625257913b584dd32f355fc36 Homepage: https://cran.r-project.org/package=countrycode Description: CRAN Package 'countrycode' (Convert Country Names and Country Codes) Standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors. Package: r-cran-countryscales Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2443 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-rlang, r-cran-scales, r-cran-i18n Suggests: r-cran-covr, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-countryscales_0.3.0-1.ca2404.1_all.deb Size: 1927550 MD5sum: 81c8440da79b08a0587ffa4d89980428 SHA1: 15f853199e199478834c3d591bf1efdc7396aa6a SHA256: fe215e5aa3cdabaabbbfaafc9a982335b03c467952882215bbb43d115dabc5b4 SHA512: 6462620a39659dc86135ed83af3470630c3eb0837092f9ded1c6266cca2cf7096082b732776f0d8f0da189d81b0897c1d9838d87613639b7347067f9fbd93b08 Homepage: https://cran.r-project.org/package=countryscales Description: CRAN Package 'countryscales' (Country Scales) Format numbers, percentages and currencies, and label 'ggplot2' axes, using country- or locale-specific conventions such as the thousands separator, decimal mark, currency symbol placement, and sign placement. Locale data is sourced from the Unicode Common Locale Data Repository (CLDR, ) via the 'i18n' package, covering several hundred locales in addition to dedicated helpers for Germany, Switzerland and the United States. Package: r-cran-countseppm Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-expm, r-cran-numderiv, r-cran-lmtest Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-countseppm_3.1-1.ca2404.1_all.deb Size: 413606 MD5sum: d32a7fc20df1fe68c9e392ef562bd434 SHA1: 3bfc66681455d1055e6e272f842a188dd28345b0 SHA256: d829a3ef1ca0e5986a56351ba58dd6c0594994d30cd798d28ea8b8652b783ada SHA512: ca95945af9e75d6c3df8f32c0b92b946d1137cacbad92efd1d8a0568687f4f920971c0e54bf5b4d1cc4db5f53c5972dce9bdd08571c622148df070217078f90d Homepage: https://cran.r-project.org/package=CountsEPPM Description: CRAN Package 'CountsEPPM' (Mean and Variance Modeling of Count Data) Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) Package: r-cran-counttofpkm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1988 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-complexheatmap, r-cran-circlize Filename: pool/dists/noble/main/r-cran-counttofpkm_1.0-1.ca2404.1_all.deb Size: 548424 MD5sum: 0e7f3ec8019a5b52d4de978eac86522a SHA1: 655567c23c0b607035df3fa4fa95186d05a95e55 SHA256: e578411f836d47f414a75ee6a63d126b05c57d8d6d2af27740faec5374be7acb SHA512: 49f5a4705490124d670f2bd467919887585d2db72c1370af3cd75d31fbd16b98c3920f52ee0d9caa339e2aae32606604b7ce4de4c129c73c151acd0ef7d488c9 Homepage: https://cran.r-project.org/package=countToFPKM Description: CRAN Package 'countToFPKM' (Convert Counts to Fragments per Kilobase of Transcript perMillion (FPKM)) Implements the algorithm described in Trapnell,C. et al. (2010) . This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset. Package: r-cran-counttransformers Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-bioc-limma, r-cran-mass Filename: pool/dists/noble/main/r-cran-counttransformers_0.0.6-1.ca2404.1_all.deb Size: 84158 MD5sum: 06bb96113103bab330346e4ecc479c90 SHA1: 448ab57d0b93818c303eb9d6d588634b4588558a SHA256: 234051d694dc49e67afbdb65e06d16255bd65e9ecb7a57371e4c11e438ebd8ca SHA512: 4a462e2944a155ff22cb2e7ca2c57445287cc0dd2a48bd2436a2824e05c81938a2db3ec72635a83ac2e03e2ba3b60fcbe49e673503b6c65759f08aa03592a042 Homepage: https://cran.r-project.org/package=countTransformers Description: CRAN Package 'countTransformers' (Transform Counts in RNA-Seq Data Analysis) Provide data transformation functions to transform counts in RNA-seq data analysis. Please see the reference: Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. (2019) . Package: r-cran-countts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-fastdummies, r-cran-matrixstats, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-countts_0.1.0-1.ca2404.1_all.deb Size: 65980 MD5sum: b442985b183810a04bba6de53fecfb07 SHA1: d50ee748a88f0124a4113bda5d32cf017543c6d8 SHA256: 4f225af48453f9a15ed85ff9362fe001093fdda5717325569771ac41969f2a63 SHA512: acd3223b33df25f9d24e001950764b5d2fd48e72aca74dd16d7fa83720d7521444df9e613f83a610226692841c73b3be7f64adc91262d6aeb4d03dc00efcee36 Homepage: https://cran.r-project.org/package=countts Description: CRAN Package 'countts' (Thomson Sampling for Zero-Inflated Count Outcomes) A specialized tool is designed for assessing contextual bandit algorithms, particularly those aimed at handling overdispersed and zero-inflated count data. It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) . Package: r-cran-countyhealthr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-httr, r-cran-stringr, r-cran-jsonlite, r-cran-curl, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-countyhealthr_0.1.5-1.ca2404.1_all.deb Size: 75286 MD5sum: c2f94890dd9ac11b3559d4d14e20fa48 SHA1: bc0b654989a14dd100f2a9042ad8de477f8827bd SHA256: e9b8f9d2a3db20df6fd2905b79254700a7d7cdae80d36ab08ba0fc6efece89a1 SHA512: ef4f013cb5d85dae09bfddf5590f546e6a15b908c4ab351e010a854ae5cf70493f223f9325e7e9e3e2ff59fa829c1b581b0e155512308655ff20e0bbdfdf7cd6 Homepage: https://cran.r-project.org/package=countyhealthR Description: CRAN Package 'countyhealthR' (Programmatic Access to County Health Rankings & Roadmaps Data) Provides a simple interface to pull County Health Rankings & Roadmaps (CHR&R) county-level health data and metadata directly from 'Zenodo' . Users can retrieve data for CHR&R release years 2010 through 2025. CHR&R data support research and decision-making to promote health equity and policies that help all communities thrive. 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Provides find_routes() to discover R versions, manifest() to scan package libraries in a child R process, inventory() to compare two libraries, and ship() to install packages into a target R version using 'pak'. Includes a Shiny dashboard (open_hub()) for interactive source-to-target migration. Package: r-cran-coursekata Architecture: all Version: 0.21.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dslabs, r-cran-ggformula, r-cran-ggplot2, r-cran-glue, r-cran-lifecycle, r-cran-lsr, r-cran-metrics, r-cran-mosaic, r-cran-palmerpenguins, r-cran-purrr, r-cran-remotes, r-cran-rlang, r-cran-supernova, r-cran-vctrs, r-cran-viridislite Suggests: r-cran-fivethirtyeight, r-cran-knitr, r-cran-lubridate, r-cran-mass, r-cran-mockery, r-cran-mockr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-scales, r-cran-usethis, r-cran-simstudy, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-coursekata_0.21.0-1.ca2404.1_all.deb Size: 1035776 MD5sum: 5b168152ef7dd648d65c61ce9d7a12f3 SHA1: 81505a9f9b5f4e05ea896e3fce321148db7b4855 SHA256: 865c4c4706a6881d55065441790058166c7a5f9877d83399240054ded0b657c4 SHA512: a6cfce981f05021721bf060fd0c39db77f4276869ec48f1575ea5c436f0d99ba759acad0b9ef43dc2ebc0c577ccc8b03bd2cb659cfe62297059f065c0760f1d3 Homepage: https://cran.r-project.org/package=coursekata Description: CRAN Package 'coursekata' (Packages and Functions for 'CourseKata' Courses) Easily install and load all packages and functions used in 'CourseKata' courses. Aid teaching with helper functions and augment generic functions to provide cohesion between the network of packages. Learn more about 'CourseKata' at . Package: r-cran-covadap Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-covadap_1.0.1-1.ca2404.1_all.deb Size: 285600 MD5sum: 0402dff1131aba73e13102a5b938e1ba SHA1: fef7ec4c475df87f95cffdcc01d368437e85f07c SHA256: ff7386a922972f5d8b040445f30f1f3d1627bf13b72e1c08196b1067842e96f9 SHA512: ac347e7a3d0adef8f47e8997156535ce0fa37bcf3f002ed266aca65e408466ca55090813b9a75a8c9eb8b22edaf08390f6eb88e8c31baa606ede2b8d47323f57 Homepage: https://cran.r-project.org/package=covadap Description: CRAN Package 'covadap' (Implement Covariate-Adaptive Randomization) Implementing seven Covariate-Adaptive Randomization to assign patients to two treatments. Three of these procedures can also accommodate quantitative and mixed covariates. Given a set of covariates, the user can generate a single sequence of allocations or replicate the design multiple times by simulating the patients' covariate profiles. At the end, an extensive assessment of the performance of the randomization procedures is provided, calculating several imbalance measures. See Baldi Antognini A, Frieri R, Zagoraiou M and Novelli M (2022) for details. Package: r-cran-covalchemy Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-interp, r-cran-clue, r-cran-ggextra, r-cran-gridextra, r-cran-desctools, r-cran-mass Filename: pool/dists/noble/main/r-cran-covalchemy_1.0.0-1.ca2404.1_all.deb Size: 125498 MD5sum: c0e8de4c1822990aaebd73b610faeec2 SHA1: 3fbe4cd32d339e7da855a7a819d42dff7bd2414f SHA256: e0fa3880722dd2e1f746073a8c0f576c5d90b6026b2d7766716dadc6fe03ad58 SHA512: 9fd0c6c842e0e7d7c3cb60452c28cf202e95fde2bb35d6d738120d3747e93bab8f395a0725064b85d5c79ea126a9c175747d3d2c4a7b8bdfb00e03bdff3bbc3f Homepage: https://cran.r-project.org/package=covalchemy Description: CRAN Package 'covalchemy' (Constructing Joint Distributions with Control Over StatisticalProperties) Synthesizing joint distributions from marginal densities, focusing on controlling key statistical properties such as correlation for continuous data, mutual information for categorical data, and inducing Simpson's Paradox. Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox. Joe (1997) Sklar (1959) . Package: r-cran-covatest Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mathjaxr, r-cran-lubridate, r-cran-v8, r-cran-zoo, r-cran-gstat, r-cran-sp, r-cran-spacetime Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-covatest_1.2.5-1.ca2404.1_all.deb Size: 476848 MD5sum: f370fe8ef245ec4aa14e04e3a32f71de SHA1: fafd701d5a3c7ed5a4f4938fb170d3dab2bca036 SHA256: f4f16cc3cc88b31d3c7873fd112033045f7b4c37a261d4018a03bc0f14b80ee7 SHA512: 47cc7171e105fb914d35c00744137ce7f3da3e1227667231bfcd4b7ba3823ddfdbd0d69f2ea263196f44865ded1e6d96e1af1b194ed2baa740509e6f8274edd6 Homepage: https://cran.r-project.org/package=covatest Description: CRAN Package 'covatest' (Tests on Properties of Space-Time Covariance Functions) Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the papers of Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability and some classes of space-time covariance function models and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions . Package: r-cran-covcortest Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-manova.rm, r-cran-matrixcalc, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covcortest_1.2.0-1.ca2404.1_all.deb Size: 178568 MD5sum: f11e94267acbd926f6e2aa3390807200 SHA1: d013b0eebc9aea901f7fa4004155ee4e9231ffe8 SHA256: 890e294ab51b6f9d46a06ca054957c2386fdd3882d927c8544ab6cb24a58ecfc SHA512: af8cfb73175e7337972c742b65e6b067960d0b78da20dc883cccdb9e14f7b81e0c1f89f396788121722f8aff9796838b598a947ea7633baf19eb1eed94f265ca Homepage: https://cran.r-project.org/package=CovCorTest Description: CRAN Package 'CovCorTest' (Statistical Tests for Covariance and Correlation Matrices andtheir Structures) A compilation of tests for hypotheses regarding covariance and correlation matrices for one or more groups. The hypothesis can be specified through a corresponding hypothesis matrix and a vector or by choosing one of the basic hypotheses, while for the structure test, only the latter works. Thereby Monte-Carlo and Bootstrap-techniques are used, and the respective method must be chosen, and the functions provide p-values and mostly also estimators of calculated covariance matrices of test statistics. For more details on the methodology, see Sattler et al. (2022) , Sattler and Pauly (2024) , Sattler and Dobler (2026) , and Sattler and Jedhoff (2025) . 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Detailed description of the methods in Chianucci et al. (2022) . Package: r-cran-covests Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covests_1.1.0-1.ca2404.1_all.deb Size: 273874 MD5sum: 29912f16fe075a16f48d2946584e8244 SHA1: e4c3a90ce4cc5f20088abd5ebfb38394706323ca SHA256: 2123929d29890e99daf493f5c9e31e6358e771f61d9f9524baa661796cb6f1ab SHA512: aa3c5680104c95d264a7f24c7da858e532a1d93a530f9fda5d352c721a2c1e86a637850b437137ed4fc7ddf0cd4abca47085b4f288debd920baf602504ca4870 Homepage: https://cran.r-project.org/package=CovEsts Description: CRAN Package 'CovEsts' (Nonparametric Estimators for Covariance Functions) Several nonparametric estimators of autocovariance functions. Procedures for constructing their confidence regions by using bootstrap techniques. Methods to correct autocovariance estimators and several tools for analysing and comparing them. Supplementary functions, including kernel computations and discrete cosine Fourier transforms. For more details see Bilchouris and Olenko (2025) . 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Originally inspired by the infamous "covfefe" tweet of 2017. 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The datasets reporting the COVID-19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread. Package: r-cran-covid19 Architecture: all Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r.utils, r-cran-data.table Suggests: r-cran-rsqlite, r-cran-wbstats Filename: pool/dists/noble/main/r-cran-covid19_3.0.4-1.ca2404.1_all.deb Size: 36686 MD5sum: 540aee187beb9f78f1c4d9ce7210cccf SHA1: e35edcc6076a73aa4eeca47961404541acd8b0db SHA256: 58dfe475500b4ffc4403e07f70ec41a9d2d9e0260e6abd6afb51905181956e41 SHA512: dd133e30630023900451f16b2fc437b71c200aefef46f6b959dd9d1d8cc84873147d7b3d3e7c9670a39b95737ae1da11b4434bf8f5c5f150073506f955dadf31 Homepage: https://cran.r-project.org/package=COVID19 Description: CRAN Package 'COVID19' (R Interface to COVID-19 Data Hub) Provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. Includes policy measures, mobility data, and geospatial identifiers. Data source: COVID-19 Data Hub . Package: r-cran-covid19br Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-httr2, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-leaflet, r-cran-pracma, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-covid19br_1.0.0.1-1.ca2404.1_all.deb Size: 3273244 MD5sum: 2ff11c38e80ca91130329605c250f9ba SHA1: d6e461d1e761b7fef35815e81bad93094b78da21 SHA256: 60687a81d7f50928c9fdd23a1b334336f8d676bc3f4557cf1a320e3e9704fe41 SHA512: ed4850705d2d76cae5be9e6c1a8dda7044068f29f47ac43f560a4b0af286bd50c3e8d3f6e08222c225129dcdfce0c40700f71b6e1385c05e5b5195bfe99402f5 Homepage: https://cran.r-project.org/package=covid19br Description: CRAN Package 'covid19br' (Brazilian COVID-19 Pandemic Data) Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at , is available at the country, region, state, and city levels. The package also downloads world-level COVID-19 data from Johns Hopkins University's repository. COVID-19 data is available from the start of follow-up until to May 5, 2023, when the World Health Organization (WHO) declared an end to the Public Health Emergency of International Concern (PHEIC) for COVID-19. Package: r-cran-covid19brazil Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4814 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-dplyr Suggests: r-cran-remotes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19brazil_0.1.0-1.ca2404.1_all.deb Size: 4872594 MD5sum: 7f643389ae6f8a77cd99fdee7ee3caf9 SHA1: 043229dfd88d18cafc58def287282db416cb4c71 SHA256: 78fb46633e7991d867948bb47143c11826f1120c9b001c49ca457b0e657877ce SHA512: 5a3590a86303aaf994bbb6aeac538838080737498a88e76686fd285e60a1960baa08a1dd7b4ca636bb38072866f0a8caad8a4cc13cd83b795eef9388579412e2 Homepage: https://cran.r-project.org/package=covid19brazil Description: CRAN Package 'covid19brazil' (COVID-19 Dataset for Brazil) Dataset with strategic information about COVID-19 in Brazil. Data for municipalities, states, region and Brazil. Data source: Sistema Unico de Saude - SUS. 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It contains only information about COVID-19 possible treatment. Package: r-cran-covid19france Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19france_0.1.0-1.ca2404.1_all.deb Size: 19174 MD5sum: 09069665b5ff66b9af6524d08962e516 SHA1: 64216469ca913e9b916546e3d80f6bae2c117da0 SHA256: ed1e30fe7cb141ee44325d661e4dabd6b970c5c4774c697f331638360b77e514 SHA512: 469b05f2c078dea1bf4be159ad905900c495658ff1e6b8992b30449586ae6aae7d9c21593664e921162bba7d72b1c1b96b2e0c8fb723f1f511f3af8a6b424d46 Homepage: https://cran.r-project.org/package=covid19france Description: CRAN Package 'covid19france' (Cases of COVID-19 in France) Imports and cleans 'opencovid19-fr' data on COVID-19 in France. Package: r-cran-covid19india Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-epiestim, r-cran-cli, r-cran-gt, r-cran-httr, r-cran-glue, r-cran-janitor, r-cran-scales, r-cran-stringr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-covid19india_0.1.4-1.ca2404.1_all.deb Size: 84744 MD5sum: 437eb741e64af94d1e1a709829e4ba2c SHA1: 0a938ea0f1e34ba9aa380ce1c532e858c96fd8c3 SHA256: 5459f9b76f94e79ffe74280259bdfcdbf8139f60b1855a39fb24d0d2211df0ad SHA512: 0ba73dbc05880d73c90b4da40f234f68dc80501b6efb5a110566ef9f36976e21449770d25bf27493b5cfe34ba29844d20fe9cbc247b0614c22d4c632e43b1ec4 Homepage: https://cran.r-project.org/package=covid19india Description: CRAN Package 'covid19india' (Pulling Clean Data from Covid19india.org) Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org . Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19. Package: r-cran-covid19italy Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools Suggests: r-cran-knitr, r-cran-readr, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19italy_0.3.1-1.ca2404.1_all.deb Size: 2862746 MD5sum: 986ab5f426971b942aedf57c573ff709 SHA1: 507b56e451c4b970936717b5bc8e2f0a22084e93 SHA256: ae474eb1a81338aeff2c4895ca9276a068e193c09f402de91ae1394c8ef2348f SHA512: e12de65649e885c5410a47deca9a75631699c6a0fb4267411040240385b2ff64abfa152ecc66381ecce0cfae8583cd64648061f2743aa75f0ea585dbc08c65bb Homepage: https://cran.r-project.org/package=covid19italy Description: CRAN Package 'covid19italy' (The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset) Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile . Package: r-cran-covid19sf Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-mapview, r-cran-plotly, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-covid19sf_0.1.2-1.ca2404.1_all.deb Size: 2968382 MD5sum: 358fd9eb67f7e59c45849888b53d1300 SHA1: b29da25bfc2c1e292215083f90da9cd9da379ea4 SHA256: 84ee205584bdd0f73387c24ac30d0a8e90b6dae9bab991ff61c1cea42bf44c3f SHA512: 0b2d705fad2064bc7e499ca0352c2b3b6fe6822925587c033ce12c06b84424daaee77743ab834b978659296b0c93ef6e007bfb8bc6f4032c4c7fc2b9aac4eb2a Homepage: https://cran.r-project.org/package=covid19sf Description: CRAN Package 'covid19sf' (The Covid19 San Francisco Dataset) Provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division . Package: r-cran-covid19srilanka Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-covid19srilanka_1.1.0-1.ca2404.1_all.deb Size: 119164 MD5sum: e78b2a2323e30972ea320256285f45e6 SHA1: 53f6f8bdcc67b7958c15de05de9aae0f78d52d2f SHA256: 18893ff6f8ab57dfdead9d1041086ef2a02810446641080bb95e903728107ce7 SHA512: 5feb184a4f926bd84cb293ce66507136a447b326689a4fdc48e55f7d04dc7e82f1556069fde6598345c63c9a01e8ad012f14e16385e3b30a0156138d0968a787 Homepage: https://cran.r-project.org/package=covid19srilanka Description: CRAN Package 'covid19srilanka' (The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data in SriLanka) Provides a daily counts of the Coronavirus (COVID19) cases by districts and country. Data source: Epidemiological Unit, Ministry of Health, Sri Lanka . Package: r-cran-covid19swiss Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1293 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19swiss_0.1.0-1.ca2404.1_all.deb Size: 1218090 MD5sum: b3d15a1d811819c275a17172cd51c7c2 SHA1: 06048a65d755245b4d65bf7f6e79252a7c09ad22 SHA256: 676c89f10ea6566f998c9fd0fa81243acc5e7314b412814854a307610b1aba8a SHA512: b947850e0dd4c4aa24da7d6c31dc5253dac212daaec3719b5749983d02f4bee371e2ee2fa6f531ed33d4ac7a5c4c940e00d379ab1c5d2bc2eaac0613f5e7b6f9 Homepage: https://cran.r-project.org/package=covid19swiss Description: CRAN Package 'covid19swiss' (COVID-19 Cases in Switzerland and Principality of Liechtenstein) Provides a daily summary of the Coronavirus (COVID-19) cases in Switzerland cantons and Principality of Liechtenstein. Data source: Specialist Unit for Open Government Data Canton of Zurich . Package: r-cran-covid19tunisia Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19tunisia_0.1.0-1.ca2404.1_all.deb Size: 56190 MD5sum: 736ec228632a606d8cfde5b7d95db49a SHA1: a193af3b869bcefacf76cb7edf3fd0cc018421fd SHA256: 6d5a2b72de50d3855da54cf0c98f42c920a6ab5b05a252a948854ac5ce40308d SHA512: cb73b5c6918131f1bead8f9e51353fe4d3fc2aba6bdea7b3475fe457198fc6fc0b2da9bf7e1be08103d21c01829de8b42f8efbaf55ec98ab13f9c4699816950f Homepage: https://cran.r-project.org/package=covid19tunisia Description: CRAN Package 'covid19tunisia' (Cases of COVID-19 in Tunisia) Data personally collected about the spread of COVID-19 (SARS-COV-2) in Tunisia . Package: r-cran-covid19us Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-snakecase, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19us_0.1.9-1.ca2404.1_all.deb Size: 83438 MD5sum: 34f3a97eb7be0b55b56344c81cdfce90 SHA1: 8c3790af9244c80970af13a2dabef5c65fcfd234 SHA256: 01f004f41ebd448666ec57e5bf7cb156d24d001e216577f587e13ae43cfa7a3f SHA512: d973bbf5efae422c6011e2863c9ba9f59c3364591ea0fd7c09ed5ec77adad8721ecdfce603d06a8efbec0f60376765d93cd7dfd4884036bfe968f57821cdf487 Homepage: https://cran.r-project.org/package=covid19us Description: CRAN Package 'covid19us' (Cases of COVID-19 in the United States) A wrapper around the 'COVID Tracking Project API' providing data on cases of COVID-19 in the US. Package: r-cran-covidcast Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2723 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-mmwrweek, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-gridextra, r-cran-httptest, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-covidcast_0.5.3-1.ca2404.1_all.deb Size: 1930792 MD5sum: 9455eb50b905e7c2455e41668ab971c1 SHA1: 41bbb3983731c3626f6813eeb308a52e4bc5d0c5 SHA256: dd63d7846e6adfdc319e68e2b7589482ad6c4329610355dc0d5e360dd44936ca SHA512: c1b93c3fe78340f44089f651c39859c6c65660d9ab9fc4d4bf49b03455082d148fc9e153af96ddb689407fb9c0973f1fb8b5e99fb465225bfacb4df0a2ba86ca Homepage: https://cran.r-project.org/package=covidcast Description: CRAN Package 'covidcast' (Client for Delphi's 'COVIDcast Epidata' API) Tools for Delphi's 'COVIDcast Epidata' API: data access, maps and time series plotting, and basic signal processing. The API includes a collection of numerous indicators relevant to the COVID-19 pandemic in the United States, including official reports, de-identified aggregated medical claims data, large-scale surveys of symptoms and public behavior, and mobility data, typically updated daily and at the county level. All data sources are documented at . Package: r-cran-covidibge Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1088 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-survey, r-cran-tibble, r-cran-timedate Suggests: r-cran-convey, r-cran-sipdibge, r-cran-srvyr Filename: pool/dists/noble/main/r-cran-covidibge_0.2.2-1.ca2404.1_all.deb Size: 156162 MD5sum: 78c8521714a9c38b3fdf2545edd535b8 SHA1: 5fca783116e51954c6e791246b08a14436e4030c SHA256: 698a39f286b90da3217df09169faca60da7b073761fff97761b3286c5e669eb5 SHA512: 9cd5b326944c513465f8e8867ff6c5429d3cd5fd40b1a20351cea2787095e3801f3811ac17e168d053d080c7e45e1567d2bf08e36a6f09b0237e6cbe764245b7 Homepage: https://cran.r-project.org/package=COVIDIBGE Description: CRAN Package 'COVIDIBGE' (Downloading, Reading and Analyzing PNAD COVID19 Microdata) Provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-covidmutations Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4357 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-seqinr, r-cran-stringr, r-cran-ggpubr, r-cran-dplyr, r-cran-venndiagram Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covidmutations_0.1.3-1.ca2404.1_all.deb Size: 3124134 MD5sum: 588cfdb67d10ea604d90b6f284613802 SHA1: cab670f00813157a89c8001a91595f7b978b4fda SHA256: a6f2e21f76b8419b3a95bdccaadd825f36b2717e755e27ed38442326c52fa115 SHA512: f2ea30233796bacdde8c15dd8034b363d5dbc6e4c943d46cd207219b1c172497c4aac0d82edfc79d5dfe369b5fbad3c2c10b2fddf83074343fddae92d99b13c2 Homepage: https://cran.r-project.org/package=CovidMutations Description: CRAN Package 'CovidMutations' (Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease2019)) A feasible framework for mutation analysis and reverse transcription polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including mutation profile visualization, statistics and mutation ratio of each assay. The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020) . Package: r-cran-covidmx Architecture: all Version: 0.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-pins, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-cowplot, r-cran-crayon, r-cran-dbplyr, r-cran-epiestim, r-cran-ggformula, r-cran-ggplot2, r-cran-ggstream, r-cran-ggtext, r-cran-glue, r-cran-lubridate, r-cran-metbrewer, r-cran-remotes, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-scales, r-cran-sessioninfo, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-covidmx_0.7.7-1.ca2404.1_all.deb Size: 1310152 MD5sum: 7a7f82c06227b43677d6471a989c764a SHA1: bd557bb21670d3136898f9c842128eeb35063a6d SHA256: c2701731101474b747a6da7bcb0b5df8922d945de7f60c42e959386ea1c854ec SHA512: 1325735e26bb77f9451ffdc425c803647558773fb7aa212b9e094bf86d8c8d73a67211e8ca057faf3ceb2d9529f49b3ee925736a81d7acd179d6a4c9e07089bf Homepage: https://cran.r-project.org/package=covidmx Description: CRAN Package 'covidmx' (Descarga y analiza datos de COVID-19 en México) Herramientas para el análisis de datos de COVID-19 en México. Descarga y analiza los datos para COVID-19 de la Direccion General de Epidemiología de México (DGE) , la Red de Infecciones Respiratorias Agudas Graves (Red IRAG) y la Iniciativa Global para compartir todos los datos de influenza (GISAID) . English: Downloads and analyzes data of COVID-19 from the Mexican General Directorate of Epidemiology (DGE), the Network of Severe Acute Respiratory Infections (IRAG network),and the Global Initiative on Sharing All Influenza Data GISAID. Package: r-cran-covidnor Architecture: all Version: 2023.05.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-glue, r-cran-magrittr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-covidnor_2023.05.18-1.ca2404.1_all.deb Size: 1042198 MD5sum: 035ec1afc63e5eaadc044c56f4712e79 SHA1: 9bb8005edd00d684ff3b7250d934eb6362da4ec4 SHA256: 188a20f4cbbd1c8bfeed3e725446578c1eec653aa639e44935fec37e6bad5720 SHA512: 8bc2fa7b9eba539d518faed619998b92758c6c61d1aa3e04b518151436aa92481c663012afb6fcfffdf3173ec84f86b74094b4ac8da74a88d00ec38f2ef0b1b7 Homepage: https://cran.r-project.org/package=covidnor Description: CRAN Package 'covidnor' (Public COVID-19 Data for Norway) Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination. Package: r-cran-covidprobability Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covidprobability_0.1.0-1.ca2404.1_all.deb Size: 331228 MD5sum: 4fb29d8dc571904e7b661353e877c5fb SHA1: d6eb9ab62345460e782c727be1e131793c6d8129 SHA256: 4b0c559c84b51591a774c6d527bb34b0786c89fc4007c9606c6d7fd6b03f2604 SHA512: 972009d0557fa603958bcc7fe06ea199bc00cbf5e901b0c4988d163be1510050de0bac284ef06546d8826fc3c497293b156185a9e1738a1949beeaf416b8adf2 Homepage: https://cran.r-project.org/package=covidprobability Description: CRAN Package 'covidprobability' (Estimate the Unit-Wide Probability of COVID-19) We propose a method to estimate the probability of an undetected case of COVID-19 in a defined setting, when a given number of people have been exposed, with a given pretest probability of having COVID-19 as a result of that exposure. Since we are interested in undetected COVID-19, we assume no person has developed symptoms (which would warrant further investigation) and that everyone was tested on a given day, and all tested negative. 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The package allows easy access to summary statistics data from COVID Symptom Study Sweden. Package: r-cran-covkcd Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-covkcd_0.1-1.ca2404.1_all.deb Size: 33578 MD5sum: a8ae36bef231bf3957667722b0c583a7 SHA1: f26497e18fb1a70bf85f9b76a6e73e1d36277532 SHA256: 63727cd30a56ecaa5c3f91af3ef6aa8e0475f7a1b5804ffd51b62ebb0c87cc5f SHA512: cb98204dd7218c8288eadc4d27d3e43fe99a8e3a0350fd7d727e693fa50fe3012215362cb3ff7755fbaccb9692ebb3bad48e230d3996bac550471a18f384d89a Homepage: https://cran.r-project.org/package=covKCD Description: CRAN Package 'covKCD' (Covariance Estimation for Matrix Data with the Kronecker-CoreDecomposition) Matrix-variate covariance estimation via the Kronecker-core decomposition. 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This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise. 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A complete descriptions of the implemented tests can be found in the paper Aston et al. (2017) . 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Package: r-cran-covtracer Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-r6, r-cran-cli, r-cran-dplyr, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-covtracer_0.0.3-1.ca2404.1_all.deb Size: 273804 MD5sum: 4a5cd8dbd877653c0d60b246684263b2 SHA1: 8855ba110139fa681aa6867ea46a8d522cfd3515 SHA256: 88a9bf2540f81b4fe8dbca68d404ee7a499002beb9d59b6fb1779365f1869b5e SHA512: 5b63f9c1f87bb446f7d564b9776630fa30307a798710db2327e0cd6eedd87613035dc0fa639ce93381bcb75f28a5233177a334b25ac7f79e15d6563cf3b19f51 Homepage: https://cran.r-project.org/package=covtracer Description: CRAN Package 'covtracer' (Contextualizing Tests) Dissects a package environment or 'covr' coverage object in order to cross reference tested code with the lines that are evaluated, as well as linking those evaluated lines to the documentation that they are described within. 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Package: r-cran-cowfootr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1562 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-writexl Suggests: r-cran-testthat, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-plotly, r-cran-gt, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-cowfootr_0.1.3-1.ca2404.1_all.deb Size: 638682 MD5sum: f18e84cd2a8f36bef9df8756181cf072 SHA1: c0f1a666430596cf37948ce65e0476dd32622950 SHA256: 91956d318e310234f94a544f3c7cc58124dae52b21683d7b35c9b8d3088fe6e3 SHA512: b451dabe5b321f6b9ad12cfa0e25b81dd15f87fff73e0051101cfdb9190faa4936fb01ced04da832534c4d7ea63aa7311da7d52226a95115ce40deaa91db2a5c Homepage: https://cran.r-project.org/package=cowfootR Description: CRAN Package 'cowfootR' (Dairy Farm Carbon Footprint Assessment) Calculates the carbon footprint of dairy farms based on methodologies of the International Dairy Federation and the Intergovernmental Panel on Climate Change. 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Package: r-cran-coxaalencr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-coxaalencr_0.1.0-1.ca2404.1_all.deb Size: 172236 MD5sum: cd615e10e46c9e3cbb7eb4a9055b38c5 SHA1: 5c3685c5dd31188af8953e497d4b77c8ae76e234 SHA256: 3b977b163c83d53068e07c5594099cf95acd68a3d43d1ca4182288052a2148cd SHA512: d13b4c26567a38cf136315cc1827498a3d8ed5a2ee2e73f3f08bd0375c070c73e3629875280c4a4491366a09312a1305c88ff01513adf0a1e05a843f7cc39bd8 Homepage: https://cran.r-project.org/package=CoxAalenCR Description: CRAN Package 'CoxAalenCR' (Additive-Multiplicative Cox-Aalen Subdistribution Hazard Modelfor Competing Risks) Implements the flexible additive-multiplicative Cox-Aalen subdistribution hazard regression model for competing risks data as proposed by Li and Long (2019) . The framework accommodates both time-varying non-parametric additive covariate effects through an Aalen (1980) additive model and constant multiplicative effects via a Cox proportional hazards structure, generalizing Scheike and Zhang (2002) and Martinussen and Scheike (2002) . Includes inverse probability of censoring weighting (IPCW) with both Kaplan-Meier weights (Fine and Gray, 1999 ) and covariate-dependent Cox censoring weights (He et al., 2016 ; Li and Long, 2019 ). Provides simultaneous estimating equations based on Huffer and McKeague (1991) , asymptotic sandwich variance estimation with censoring-weight martingale corrections, cumulative incidence function (CIF) prediction with pointwise confidence intervals, supremum-type goodness-of-fit tests for time-varying covariate effects, and Monte Carlo competing risks data simulation. Package: r-cran-coxaipw Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-randomforestsrc, r-cran-polspline, r-cran-tidyr, r-cran-ranger, r-cran-pracma, r-cran-gbm Filename: pool/dists/noble/main/r-cran-coxaipw_0.0.3-1.ca2404.1_all.deb Size: 55858 MD5sum: 9ff68c683e6443913457800111f21516 SHA1: 5ca67d8edbe4f74a266229763004f31cf6968a83 SHA256: 2a313ae6c1aff0265df3d9f471c918fe2edf992791604eb8caa470a3dcb69138 SHA512: c4872b16146f56bb2b14b124eecc2e172cb4d5ed8aaf05e12ef3b269d994b8189579c455178973b69fac4d1294b6b0d2c15b0c04f5ec364ec7310b1810362560 Homepage: https://cran.r-project.org/package=CoxAIPW Description: CRAN Package 'CoxAIPW' (Doubly Robust Inference for Cox Marginal Structural Model withInformative Censoring) Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) ; Rava (2021) . Package: r-cran-coxbcv Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-survival Filename: pool/dists/noble/main/r-cran-coxbcv_0.0.1.0-1.ca2404.1_all.deb Size: 99690 MD5sum: e28fae0cd1f88a812e671a0c8d86d4bf SHA1: 1c99b983bafc3f4d70bdde8aa9ea213b3995f938 SHA256: 6e1b5900d31b533eb0ce86294b10cbc2a92271bf4332cbe82cc2de1092f27a5d SHA512: e4f791405d87aad56f0b37a9b48f21df5dfe2eb1706ad0b3fe09e8b360b13212200d54eb5a5e1b04cb556725dfbcf3876f58f67a0b3134bf005a3fd5c4ffccbe Homepage: https://cran.r-project.org/package=CoxBcv Description: CRAN Package 'CoxBcv' (Bias-Corrected Sandwich Variance Estimators for Marginal CoxAnalysis of Cluster Randomized Trials) The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review). Package: r-cran-coxed Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-survival, r-cran-mgcv, r-cran-permalgo, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-mediation Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bindrcpp Filename: pool/dists/noble/main/r-cran-coxed_0.3.3-1.ca2404.1_all.deb Size: 663538 MD5sum: 9a0f964844becea2ea832740e5b46ef6 SHA1: df9ee658e841d4b9737768499a5c47b24cc79da9 SHA256: 9366dc86391a2e93d10cc56a11992eadbd16cbc29b3b9e05137e8eef2333aa6a SHA512: 70f7a8ede28e071e517e8bab13e1b48933cbc922af979f2f5d555bd2f67b9267ac8ae0c73e0a9ae79d94c9516dd537105a9f57a5a62bcad4a0f592424ec88c66 Homepage: https://cran.r-project.org/package=coxed Description: CRAN Package 'coxed' (Duration-Based Quantities of Interest for the Cox ProportionalHazards Model) Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) and Harden and Kropko (2018) . Package: r-cran-coxicpen Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach Filename: pool/dists/noble/main/r-cran-coxicpen_1.1.0-1.ca2404.1_all.deb Size: 55506 MD5sum: d60483827247c23e2a5f461afb9f7b38 SHA1: 44c41548f93335dbc2d35778de269ed3131c6437 SHA256: 55b11ba571dda820ad1d86600d9f6aaa3991ff1aeea7968ebe033c50fc852628 SHA512: b1d15bf34a9a50dd77959ac1252773770e729e4b5efa18c9b46fbfa01cb150632b10d1d4ac758b55d93fbdce9f474e0a27d810fc1ff4a691ac7bff7a3c8d1bb4 Homepage: https://cran.r-project.org/package=CoxICPen Description: CRAN Package 'CoxICPen' (Variable Selection for Cox's Model with Interval-Censored Data) Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below. Package: r-cran-coxlikelihood Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-coxlikelihood_0.1.0-1.ca2404.1_all.deb Size: 17872 MD5sum: 368991b2f62ed5acd57ef5998a119630 SHA1: 58da430450c876eb1422bf3e81a849ad58c91022 SHA256: ca8d8db54b66d1651f8801f4014b780e260c7a27ba90ce31be8f1f3bb60dcc19 SHA512: 23093c784e52575707f913677e3d347d644f55665cf45609043935e75b96ca084cf20bd8484fecba9776f4811ec1fed38348ff2f9f7ba6b6eb9ec7abd95760e1 Homepage: https://cran.r-project.org/package=CoxLikelihood Description: CRAN Package 'CoxLikelihood' (Robust Likelihood Ratio Test and Confidence Intervals for theCox Model) Calculate the likelihood ratio test p-value and likelihood confidence intervals for misspecified Cox models, as described in Shao and Guo (2025) . Package: r-cran-coxmk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-survival, r-cran-irlba, r-bioc-gdsfmt, r-cran-bedmatrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-coxmk_0.1.1-1.ca2404.1_all.deb Size: 120268 MD5sum: ab18fd2ff45e4c08020592bb0b7d101e SHA1: 1a8e647c393f89b83ab24ac6047b5419b88b5d21 SHA256: caaf74214cb34d7b48afdad8bf1a7c513258d1ae05a64766576d365a32f009ad SHA512: 5956b8a983e88b55de263b2bc8342c74e463df1c1af79277121c644b37cfc93a113a6c220c2ce78f1502744e60d151f61ec5d5551dd3cdf1bba4eb1b6cc4a67f Homepage: https://cran.r-project.org/package=CoxMK Description: CRAN Package 'CoxMK' (A Model-X Knockoff Method for Genome-Wide Survival AssociationAnalysis) A genome-wide survival framework that integrates sequential conditional independent tuples and saddlepoint approximation method, to provide SNP-level false discovery rate control while improving power, particularly for biobank-scale survival analyses with low event rates. The method is based on model-X knockoffs as described in Barber and Candes (2015) and fast survival analysis methods from Bi et al. (2020) . A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the 'SPACox' package from GitHub which provides saddlepoint approximation methods for survival analysis. Package: r-cran-coxmnar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-coxmnar_0.1.0-1.ca2404.1_all.deb Size: 125964 MD5sum: 38fec28c747860bc91d78833c717d17f SHA1: 6bdf047694f07906283d03ed51cb76fa8eae8a84 SHA256: 6ed973c118ae9d515063bcb9cb4ee63614d6f5130c7a4e5973e9ef22e48ad139 SHA512: bd117011b45f33c3f2e3de847298874170c4d274fd5501079a355928e724fa08d54eeb7df87a8d4e47b5edcc187a97634001bb3e341a609d24866dde75826179 Homepage: https://cran.r-project.org/package=coxmnar Description: CRAN Package 'coxmnar' (Cox Regression with Missing not at Random Failure Indicators) Implements estimation for the Cox (1972, 1975) proportional hazards model when the failure indicator (cause of failure) is missing not at random (MNAR), following the two adjusted imputation-based estimating equations of Liu and Liu (2026) . Also provided for comparison are the full-data partial-likelihood estimator of Andersen and Gill (1982) , the complete-case estimator, and the missing-at-random imputation estimator of Liu and Wang (2010, Statistica Sinica, 20, 1125-1142). The probability models for the failure indicator and for the missingness mechanism are estimated jointly by maximum likelihood following Sun, Xie, and Liang (2013) , and a Nadaraya-Watson kernel-smoothed estimator of the missingness propensity is constructed following Qiu, Chen, and Zhou (2015) . Both an asymptotic (sandwich-type) variance estimator and a nonparametric bootstrap variance estimator are provided. When failure indicators are fully observed the estimators reduce algebraically to the classical Cox partial-likelihood estimator. Package: r-cran-coxphm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-coxphm_0.2.1-1.ca2404.1_all.deb Size: 26708 MD5sum: 34d08f69e5f71f092e7706a400d53e85 SHA1: 3c5347a40f682d7f11d71ed677c5a0b7267fb0e2 SHA256: d52a07246fadcda8b5a8d5ad993234f5158736e560ef2ec1f7ec751bae5de3dd SHA512: 4ca70fb2b476d3c9a6f530be5f62c7c9dded15dec83d6da9a1c7d717592abc7c166b97338e13a748f4aab62099cf1143890c280dc715e5bbc0c73e050f1a8965 Homepage: https://cran.r-project.org/package=coxphm Description: CRAN Package 'coxphm' (Time-to-Event Data Analysis with Missing Survival Times) Fits a pseudo Cox proprotional hazards model when survival times are missing for control groups. 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It supports gene- and transcript-level inferences, p-value aggregation for improved power, and both case-only and case-control designs. It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) . Package: r-cran-cpbayes Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-forestplot, r-cran-purrr, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cpbayes_1.1.0-1.ca2404.1_all.deb Size: 130166 MD5sum: c10b1aa208d387e66a0669c9629a6e9a SHA1: 04e2854f4c5aa15ffb3d22902a6c707f453aedbf SHA256: 5cd67046f7a93b8b8eaef50afe0cc11a7dffcc5b51067395c3b126409b664734 SHA512: fa0ea5940fc4a9b8e492b3589c833756c2e2433540f9bc9be9425d2773172753c34608a3d6bb33d78804b95dd217c8272b6c0e13fbe2b7984b5d3bd9b0198d2d Homepage: https://cran.r-project.org/package=CPBayes Description: CRAN Package 'CPBayes' (Bayesian Meta Analysis for Studying Cross-Phenotype GeneticAssociations) A Bayesian meta-analysis method for studying cross-phenotype genetic associations. It uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. CPBayes is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) . Package: r-cran-cpc Architecture: all Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dbscan, r-cran-rfast Filename: pool/dists/noble/main/r-cran-cpc_2.6.2-1.ca2404.1_all.deb Size: 82044 MD5sum: df3964937297e323f776e6edb1b109ee SHA1: a08c39dbe0b6431b0ac8b052f7690812bc7634f7 SHA256: f55f0146c00cbe58bc81d59bd04d3a5377e3e6d9ecab3f6812d96212bb645fb0 SHA512: 36c7b620863d1eca09ca847f78abaf2d624e6b2e6253f1946a000633503d01e5a4370293a1efd309b1651a1a2ef7aeb03babc4f10011fac3a5da6afd5651797c Homepage: https://cran.r-project.org/package=CPC Description: CRAN Package 'CPC' (Implementation of Cluster-Polarization Coefficient) Implements cluster-polarization coefficient for measuring distributional polarization in single or multiple dimensions, as well as associated functions. Contains support for hierarchical clustering, k-means, partitioning around medoids, density-based spatial clustering with noise, and manually imposed cluster membership. Mehlhaff (2024) . Package: r-cran-cpcat Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpcat_1.0.0-1.ca2404.1_all.deb Size: 35614 MD5sum: 279e2e5d1a2c896c1248746ed3c5b394 SHA1: c5c944c66a61de14c47c25baedea828a8ea715d7 SHA256: 4cb35e23cc8869a7bb83b33a58b974b8d92b910f17a61c51e431776a7611d62d SHA512: 92943405040ab31e77d748ef632e9d7bfb34c5428d617a6e8557b4fbf317bffa1a97045295e26c6db581714cd670eb015e59caf6d1c8ddff6e5663fb87997b25 Homepage: https://cran.r-project.org/package=CPCAT Description: CRAN Package 'CPCAT' (The Closure Principle Computational Approach Test) P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) ) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) ), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) ) and the combination of both, that is, the closure principle computational approach test. Package: r-cran-cpd Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hypergeo, r-cran-rdpack, r-cran-dgof Filename: pool/dists/noble/main/r-cran-cpd_0.3.3-1.ca2404.1_all.deb Size: 160220 MD5sum: ef3072086cb40dd7e9f15a8cb6f9c326 SHA1: e6929caf4ecbf212b403a42af673736b54a0fe66 SHA256: 14faff681164795b17c0d4c30eb5a87270af76e3455ca8be2175aa4b8e1efcf8 SHA512: 826832eca9a1104d0b3832c0c08cebe1cf685d43721318421aeeaececb2ca38831ae985e30933de2769f9335112fc5b58c6b0e54a83cac16ebba225cb429bfff Homepage: https://cran.r-project.org/package=cpd Description: CRAN Package 'cpd' (Complex Pearson Distributions) Probability mass function, distribution function, quantile function and random generation for the Complex Triparametric Pearson (CTP) and Complex Biparametric Pearson (CBP) distributions developed by Rodriguez-Avi et al (2003) , Rodriguez-Avi et al (2004) and Olmo-Jimenez et al (2018) . The package also contains maximum-likelihood fitting functions for these models. Package: r-cran-cpfa Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-multiway, r-cran-glmnet, r-cran-e1071, r-cran-randomforest, r-cran-nnet, r-cran-rda, r-cran-xgboost, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-clue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cpfa_1.3.3-1.ca2404.1_all.deb Size: 610868 MD5sum: 0513077597b4a2719d79fddee37a222b SHA1: 1db2a372acb93a6e6abe06a0d3db9daf16b06182 SHA256: 3852a4077a8b58cc9788d0f3547ee908763a98db96114dfebb10ded401b03918 SHA512: 0d9c32e613a9d27dcf4c1c1fe0370fdc0f7d59ab35d6d91bfdb98e0957f61fbc3e1c3ea29129b17ff53b3e4320b45ce630c8d61ed26933cd2466d50dcd2f04f6 Homepage: https://cran.r-project.org/package=cpfa Description: CRAN Package 'cpfa' (Classification with Parallel Factor Analysis) Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array. See Harshman and Lundy (1994): . Classification using principal component analysis (PCA) fit to a two-way data matrix is also supported. Uses component weights from one mode of a Parafac, Parafac2, or PCA model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Allows for constraints on different tensor modes. Allows for inclusion of additional features alongside features generated by the component model. Supports penalized logistic regression, support vector machine, random forest, feed-forward neural network, regularized discriminant analysis, and gradient boosting machine. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Uses the 'clue' package to align Parafac or Parafac2 models across data splits in the cross-validation procedure. Calculates classification importance of individual features using permutation feature importance. Implements parallel computing via the 'foreach', 'doParallel', and 'doRNG' packages. Package: r-cran-cpgassoc Architecture: all Version: 2.70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-cpgassoc_2.70-1.ca2404.1_all.deb Size: 1971086 MD5sum: 00da6a8a096bb1f559067397226b25e7 SHA1: 3e5289dd688298d2b423ddd48f362a9cd4d94f39 SHA256: 52f3ade9e460544b388aed5bf1ebf2fc1baa30b2d472a3edeb6ffb0fb6887751 SHA512: b640e286847e435fc5b151d08da9f17a3d2c5d39817e7ed805251824893bbe8bae22ceecd8c3bb44494ac76a148957bf27eb5a4f273966c5ea1e806eaa7d9f27 Homepage: https://cran.r-project.org/package=CpGassoc Description: CRAN Package 'CpGassoc' (Association Between Methylation and a Phenotype of Interest) Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites. Package: r-cran-cpge Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-quarto, r-cran-shiny, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpge_1.0.2-1.ca2404.1_all.deb Size: 320208 MD5sum: 98a49b947d5b25525f14fe0e8e927503 SHA1: b9ce729c0e24bf628ad350078677ec541443c3dd SHA256: 2d06ccd5302e9d7425db69c152f2890f9830e39aa37722c5ba436f975332eca9 SHA512: dd08595f61a05e551a89b6566648a8b7359a84f4b65487ad69e5903a291d5821ae5dc0a10bd81d6b3c9737fb66ad1626d29b183e173e50e5fabf6705e0c9f52e Homepage: https://cran.r-project.org/package=cpge Description: CRAN Package 'cpge' (Interactive Clustered Graph for French Scientific PreparatoryClasses) To help French students from scientific preparatory classes for the Grandes Ecoles (CPGE) in their choice of field of study and career options, this package provides an interactive tool and data visualization of a graph clustered by different competitive exams and sectors of activity for French selective engineering schools and selective higher education institutions like Ecoles Normales Superieures (ENS) or specialized university programs (magisteres). Besides, there are two drop-down menus to select on the graph many fields or more than 200 engineering schools or ENS or magisteres. It gives the opportunity to expand, collapse clusters of selective exams interactively too. For more information, see the demonstration video: . The data was collected via the official French website: . Package: r-cran-cpgfilter Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-cpgfilter_1.1-1.ca2404.1_all.deb Size: 19166 MD5sum: 3061d4a6a4d0c0918a2fd0a3422149d9 SHA1: f4bc58f768789b5d9298ac78cc66bab02d623b68 SHA256: a874f5fd5fa3710131690b3712fd72ceecb7ce846b556cb93708cc0008d10cfe SHA512: cc9272b496b7ac1cfb71d94b28c3be17a1e28cff1e010ad0fca21a3fa5938b60b53a52d21bded06aa55daf1b0cc7c5c28290d906e415bf11a690a5159b933733 Homepage: https://cran.r-project.org/package=CpGFilter Description: CRAN Package 'CpGFilter' (CpG Filtering Method Based on Intra-Class CorrelationCoefficients) Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design. Package: r-cran-cpgfr Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-osfr, r-cran-lubridate, r-cran-deflatebr, r-cran-curl Filename: pool/dists/noble/main/r-cran-cpgfr_0.0.1.0-1.ca2404.1_all.deb Size: 17696 MD5sum: 6743ef6d480cba244b1b6efb076a77bc SHA1: f28c8005c0b3520dc7c1d8cc57b399077e2a1fda SHA256: 52f26ca0556bd0c019e830fa93517dde9e4362931063c30865e89beb598ea519 SHA512: 79535684ee0d2f4f03b4236658f71eed3d4d12768b79cba60a21aba980af48fd8f8feb71c962206e3bee68171268da7c7932e1c3718e77930df3c45a17979f9e Homepage: https://cran.r-project.org/package=cpgfR Description: CRAN Package 'cpgfR' (Consolidates Information from the Federal Government PaymentCard) Provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal and a curated dataset hosted on the Open Science Framework . Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism. Package: r-cran-cphazard Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lamw, r-cran-survival, r-cran-envstats Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-cphazard_0.1.0-1.ca2404.1_all.deb Size: 131170 MD5sum: 39861e77402efe5584080c49fafcd03f SHA1: efde631e9427149b9c0294107fc19a372ac3847f SHA256: 65285ed5ad9ff18fbcc89b05b9ae5a364ae4f5b0ebe4fa9456da0bc54ab9b166 SHA512: ccb72618566af42b5b4f8960ffe87b70cf7dfcbb8b37091de9f4a62c603f7a0f10e8b452f2448dd5248888fe16ce2185d37784eb2aeedaa67a9f1adb8211c336 Homepage: https://cran.r-project.org/package=CPHazard Description: CRAN Package 'CPHazard' (Hazard Change Point Models for Different Lifetime Distributions) Estimates the parameters of models with a single change-point in the hazard rate for time-to-event data. Supported models include the exponential (Gijbels & Gürler (2003) , Matthews & Farewell (1982) ), Exponential-Lindley (Joshi & Rattihalli (2020) ), Lindley (Joshi, Jose, & Bhati (2016) ), log-logistic (Nadar, Upadhyay, & Joshi (2025) ), and Weibull (Williams & Kim (2013) ) hazard change-point models. Provides functions for generating random variates and evaluating the probability density function (PDF) and the cumulative distribution function (CDF) of the fitted change-point models. Includes Kaplan-Meier and Nelson-Aalen diagnostic plots, together with goodness-of-fit measures such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), distance metrics such as the L1-norm and L2-norm, and the Kolmogorov-Smirnov (K-S) statistic for model evaluation. Package: r-cran-cpi Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-mlr3, r-cran-lgr, r-cran-knockoff Suggests: r-cran-mlr3learners, r-cran-ranger, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-cpi_0.1.5-1.ca2404.1_all.deb Size: 47392 MD5sum: 8f25c2c00509d00a650ab2d92802c5d8 SHA1: d7320ff5713cc59f880798b0ad2bd97120c2ad32 SHA256: 289491adc6115e7a017b4c5aad54fa27cf8230fb5f21987ad68dcdd3713d2a8f SHA512: b7d154d9de3ce88684b316315b2f9746a862905b2dbe6c16baaf3c2250a3f439ec182d5a348bfad08ea3edd6cc9cbba9c8f23c015d0e38534acb948895195fc1 Homepage: https://cran.r-project.org/package=cpi Description: CRAN Package 'cpi' (Conditional Predictive Impact) A general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) . Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes. Package: r-cran-cplots Architecture: all Version: 0.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular Filename: pool/dists/noble/main/r-cran-cplots_0.5-0-1.ca2404.1_all.deb Size: 78006 MD5sum: 44502181b4774072bdf7000f3fb2fd4e SHA1: 750ed3f3725225972a9e26a4b1d5bac59f3104a5 SHA256: 12b83d54ed4db35b3d659f5a3a5e420149e88193e8eb6cfeb81f25dc05d4b8e8 SHA512: f42a731106b2ce20bd093ae8dfa3a4063a17cd3fd0d644c9e31c5ddeafb71a3114a1d9dd3e0f294027556a0c422f17922d1047dfe0d277609a1bfa8ce2e504c3 Homepage: https://cran.r-project.org/package=cplots Description: CRAN Package 'cplots' (Plots for Circular Data) Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include bar plots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms. Package: r-cran-cpmbigdata Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-hmisc, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-benchmarkme Filename: pool/dists/noble/main/r-cran-cpmbigdata_0.0.2-1.ca2404.1_all.deb Size: 37376 MD5sum: 8245d2cb9406934184687c2197a48a10 SHA1: 67d129f52203622c2f99bf97b895e94de04f3881 SHA256: 1c0797dd09df139391bfdf02aaf6aeb6af33c041a585be68b707a2ba01d491a3 SHA512: a9c82217adeec6bc1b6ea34858c8df59376c9771428863d93afe6663bed9ddb0782bfefe0535c371f9bf1fbfd563f8a96edcf15d6f7ac7ba0ed681071d008a72 Homepage: https://cran.r-project.org/package=cpmBigData Description: CRAN Package 'cpmBigData' (Fitting Semiparametric Cumulative Probability Models for BigData) A big data version for fitting cumulative probability models using the orm() function from the 'rms' package. See Liu et al. (2017) for details. Package: r-cran-cpmcglm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-plyr, r-cran-abind Filename: pool/dists/noble/main/r-cran-cpmcglm_1.2-1.ca2404.1_all.deb Size: 85152 MD5sum: b3e90dd48ed49a46113c6aa238fdc83a SHA1: 59970a79d18ab1419555a76dc592c8e0534158a0 SHA256: 1d0c4392c990d3d17a74bb15991d2ed6760c7c79f2066dfac779b3c0806db4a6 SHA512: e6f76bf17711eade1413b8e95be453753069c1c38830944c01fe4c34e2d6b76d0adbda0d91739513bf2faa382967c26f449a4d4a26b857878e72058fe2c28c96 Homepage: https://cran.r-project.org/package=CPMCGLM Description: CRAN Package 'CPMCGLM' (Correction of the P-Value after Multiple Coding in GeneralizedLinear Models) We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available. Package: r-cran-cpmerccutoff Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpmerccutoff_1.0.0-1.ca2404.1_all.deb Size: 80916 MD5sum: 85367c63d8afe6680f8725f7cc724fb9 SHA1: af467c485c086a5c572e5a5f5719d9e405c05714 SHA256: 9bf9f5853ab00b93b82fbf4c217794021691083caa351910f1e255807b6dbc7d SHA512: 17449090ae412f980ed9a0dad91032653fa8fde0dca2dc947a6f1ba729fed6ce96f39ea054ba4f8c077348896e179a13c9e06c58ae87d6c1512e71f20ad2bcb5 Homepage: https://cran.r-project.org/package=CpmERCCutoff Description: CRAN Package 'CpmERCCutoff' (Calculation of Log2 Counts per Million Cutoff from ERCC Controls) Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022). This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes. Package: r-cran-cpmr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-rfast, r-cran-tibble Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cpmr_0.1.1-1.ca2404.1_all.deb Size: 43018 MD5sum: 8d96680d29feb97459770f0ebeb58c38 SHA1: b4fabdb1a596c0e8ce4f4707a55b10bf930ef83b SHA256: f9029084df319d215ad202870fe09ea751101646b884cab63152f2b28222fc93 SHA512: 265e3809042d8e040e34868deccfdc8493d56d0d99fbf121ac2026e859b7660e353989491ae1534b5ea59b49489c4ba12cc47fb3bcd6072c8341d7ce70ffe1f2 Homepage: https://cran.r-project.org/package=cpmr Description: CRAN Package 'cpmr' (Connectome Predictive Modelling in R) Connectome Predictive Modelling (CPM) (Shen et al. (2017) ) is a method to predict individual differences in behaviour from brain functional connectivity. 'cpmr' provides a simple yet efficient implementation of this method. Package: r-cran-cpncoverageanalysis Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cpncoverageanalysis_1.1.0-1.ca2404.1_all.deb Size: 44566 MD5sum: 0cb168eae5c7392b470a3499b36dfca5 SHA1: a10410c03020f68c41af2db71379cb23707e31fe SHA256: c747286324d6a01e873af511b1cefafc2412b3b4f9c5bc698aaf784c130b9ed7 SHA512: 0f2af158b011ce918cb1d77d49df4456c07699929fae3b357df05ff71fb197adcd73d307bb9f30350116bb96a3c55d4023548182b0138501a09f1b0b5d1914cd Homepage: https://cran.r-project.org/package=CPNCoverageAnalysis Description: CRAN Package 'CPNCoverageAnalysis' (Conceptual Properties Norming Studies as Parameter Estimation) Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data. 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Package: r-cran-cpp4r Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-rbibutils, r-cran-litedown Filename: pool/dists/noble/main/r-cran-cpp4r_1.3.0-1.ca2404.1_all.deb Size: 165918 MD5sum: d1fae3ce4aafd5c150cca94c7c9e3543 SHA1: 230051ac9a2b8b2009f76015642b9e59fabe9c4b SHA256: 8f0f74fae652c5cbf3c6b45a0d4cf6a77c67eb63b7db29f599ee5ab6698d0c90 SHA512: d2ae43dfeda43348f3ab6fef13b6773ba648b3aa1193572f677efc247517b109c85c023d2084019b3efa6ae96d0bae6f6f2ccc6d8eafb4e9050e060c4328a02e Homepage: https://cran.r-project.org/package=cpp4r Description: CRAN Package 'cpp4r' (Header-Only 'C++' and 'R' Interface) Provides a header only, 'C++' interface to 'R' with enhancements over 'cpp11'. Enforces copy-on-write semantics consistent with 'R' behavior. Offers native support for ALTREP objects, 'UTF-8' string handling, modern 'C++' features and idioms, and reduced memory requirements. Allows for vendoring, making it useful for restricted environments. Compared to 'cpp11', it adds support for converting 'C++' maps to 'R' lists, 'Roxygen' documentation directly in 'C++' code, proper handling of matrix attributes, support for nullable external pointers, bidirectional copy of complex number types, flexibility in type conversions, use of nullable pointers, and various performance optimizations. Package: r-cran-cpp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq, r-cran-kappalab, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-cpp_0.1.0-1.ca2404.1_all.deb Size: 141698 MD5sum: a94f718e89fc023d8e8ada825de1baf1 SHA1: f2a73a581d04073909ae746a5bc608411a01dd9f SHA256: 20d6575aa427f3f7c186450cb49ba703220f51b0c0f965ec9465fdbfab15f35e SHA512: 8b8b7c30966a193fb76a101382e9c28cdadaed6cdc7eceafa7517695c9d2f0a639363a58329853479f1166a04419344348c5cf34c0603d946e5e2f9d70f11e80 Homepage: https://cran.r-project.org/package=CPP Description: CRAN Package 'CPP' (Composition of Probabilistic Preferences (CPP)) CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) . 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(in press), "Robust estimation of the effect of an exposure on the change in a continuous outcome", BMC Medical Research Methodology. Package: r-cran-cprr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cprr_0.2.0-1.ca2404.1_all.deb Size: 22514 MD5sum: 0ec148acf9a63b7cca6de2dd1dab2603 SHA1: f20c3b86e74f750d653f46a01772397b42b20316 SHA256: fd9d10b0cd9ce303296c028b7a69959bbe82af8020e55f2131deba3bd34b52b2 SHA512: c218159d9a5d7d1462ee3b9674d7dbc556947d4abe08400c33545c8ad0b7a42aac80bcd3607e3de2c2696ab7f4b085ebffb079c272e60b59b6dbecc636df54a1 Homepage: https://cran.r-project.org/package=cprr Description: CRAN Package 'cprr' (Functions for Working with Danish CPR Numbers) Calculate date of birth, age, and gender, and generate anonymous sequence numbers from CPR numbers. . 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If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) . The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen. Package: r-cran-cpsr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cpsr_1.0.0-1.ca2404.1_all.deb Size: 23178 MD5sum: 16ba552f6dccfc2f33540a689b2d81b8 SHA1: dbf308f54dc37fd31453a01002e09dd2ba3bef05 SHA256: 840a94124983ef4b1d03d2fe38d5c6a895dc58d82b8bfd5eb726eda07da53f41 SHA512: 3fab59ca8d6a71d6684b6397c65f646546e802073303d3e89668a865ba714b93362d75e95492071e058297f7c25370ad7abb866172dc03406532a2497170d42f Homepage: https://cran.r-project.org/package=cpsR Description: CRAN Package 'cpsR' (Load CPS Microdata into R Using the 'Census Bureau Data' API) Load Current Population Survey (CPS) microdata into R using the 'Census Bureau Data' API (), including basic monthly CPS and CPS ASEC microdata. 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The latter method takes advantage of the memoryless property of survival and simulates a separate distribution between change-points. We include two parametric distributions: exponential and Weibull. Inverse CDF method draws on the work of Rainer Walke (2010), . Package: r-cran-cpsvote Architecture: all Version: 0.2.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5807 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-readr, r-cran-dplyr, r-cran-stringr, r-cran-forcats, r-cran-rlang Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-rmarkdown, r-cran-survey, r-cran-srvyr, r-cran-here, r-cran-scales, r-cran-ggplot2, r-cran-usmap, r-cran-ggthemes, r-cran-tweenr Filename: pool/dists/noble/main/r-cran-cpsvote_0.2.0-1.ca2404.2_all.deb Size: 4813766 MD5sum: 39ebddd81f7c3e6fdddadfbbe78737a3 SHA1: 656307173c2f20f06337c0c182cdee53c4fc398e SHA256: 92bdff016070aca82d3bf2d0a41d10a4b866952d9031361bdc9a1cb2bc51031f SHA512: 7591b0106044a60bef2b29a52e2462a5d61bdbb4fc3dea0cf305329b217800ccd27d244dee21674a6cfed0504a5acaa8496a0108ac20d7fdd89935151292fdc3 Homepage: https://cran.r-project.org/package=cpsvote Description: CRAN Package 'cpsvote' (A Toolbox for Using the CPS’s Voting and Registration Supplement) Provides automated methods for downloading, recoding, and merging selected years of the Current Population Survey's Voting and Registration Supplement , a large N national survey about registration, voting, and non-voting in United States federal elections. Provides documentation for appropriate use of sample weights to generate statistical estimates, drawing from Hur & Achen (2013) and McDonald (2018) . Package: r-cran-cpt Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-randomforest, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-cpt_1.0.2-1.ca2404.1_all.deb Size: 33544 MD5sum: 5479e8e20023fb826f3f77d31db24037 SHA1: 8425444052e2e6483c227ca683e7d040dd96ec6d SHA256: 7faa39ad6a8d2457d70fa539a19439051874e994fdbdb9c0600d4208a851bedb SHA512: 28f0144e1492e02bb923e37070f3a303472bcbb275e94dcc5d90eb8e5ed22781552c9df72014cf429ee9c3c393298d6a60fd6eea79a078718cafce0cbc43ccaf Homepage: https://cran.r-project.org/package=cpt Description: CRAN Package 'cpt' (Classification Permutation Test) Non-parametric test for equality of multivariate distributions. Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected. Package: r-cran-cptcity Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1308 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cptcity_1.1.1-1.ca2404.1_all.deb Size: 985864 MD5sum: b1291322185fb1973330a8dd8823cb18 SHA1: 99bc33f62b0361d77926a3139540c96f234e35d1 SHA256: 9b526572efd910ce4a026c9d53b485ee38ee7f85548c339cdc42c5d1a5d87901 SHA512: 6dec8c19ad16f6c678ca5eeada4e193128f0998efd5cc40c7f3e0fff3de3c6bb93d0a57152be4e7820b22b69c5b11b4e4f17570cd7967a2c9d39e05b4b69d35f Homepage: https://cran.r-project.org/package=cptcity Description: CRAN Package 'cptcity' ('cpt-city' Colour Gradients) Incorporates colour gradients from the 'cpt-city' web archive available at . Package: r-cran-cquad Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-plm, r-cran-formula Filename: pool/dists/noble/main/r-cran-cquad_2.3-1.ca2404.1_all.deb Size: 198326 MD5sum: 8084bc141cc07f08b18ab0ee1c32d01c SHA1: 9f2775598a07d7b61426206656408b7da0b5c209 SHA256: 1708faa3dd24899d59b6bbfcc1a202098d7d069792effc3a23ff75b2da08ec97 SHA512: ad1dd056378469bc778083d1a1ce18f435c1f413fcc865c0368af12212e99a5622c40a7b0437a3b68ab6ed01c486e66163b174dfadca7086ef5626a2800849d6 Homepage: https://cran.r-project.org/package=cquad Description: CRAN Package 'cquad' (Conditional Maximum Likelihood for Quadratic Exponential Modelsfor Binary Panel Data) Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) . The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) . For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics . Package: r-cran-cr2 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-matrix, r-cran-generics, r-cran-magrittr, r-cran-broom, r-cran-dplyr, r-cran-performance, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cr2_0.2.1-1.ca2404.1_all.deb Size: 132244 MD5sum: e1a7105bfea8f28252ae8aa252fa3c3e SHA1: 6ad76e7ce6e039f46f5e2cff602b796a301ef995 SHA256: 803e47e5efe6a71eb7b6ff809e3e25f9e190a46af11e36d6e4992fc8754d1fd5 SHA512: 4b0adb2a9fb7c82e075baf963b94f800457fe98465d5696091717647f6b46275d395bfeef29d8d02382d5135c90de706471b949eec7ad086d0532662e3baf725 Homepage: https://cran.r-project.org/package=CR2 Description: CRAN Package 'CR2' (Compute Cluster Robust Standard Errors with Degrees of FreedomAdjustments) Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. 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Package: r-cran-craft Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-changepoint Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-craft_0.1.0-1.ca2404.1_all.deb Size: 230516 MD5sum: c5c53aaf88a9b0b36cfa830b1b31a1fd SHA1: 8a47fd230da6d033d279facb91fe8aaba6e2e1cb SHA256: 2876eba7fa74aed06e1cbf42a45adce7cf0da92d60daa831accadf60f3d6b8a8 SHA512: 17643297081f8158a133b48f360a74994520f8785ca3b65092d8a78a95d88fe9dd62805c5b47e0203e341c6006b9ea3af4b7007d3e8ad37f2aa7484bed9a1fe2 Homepage: https://cran.r-project.org/package=CRAFT Description: CRAN Package 'CRAFT' (Conditional Regime Analog Forecasting with Trajectories) Tools for Conditional Regime Analog Forecasting with Trajectories. The package builds lag and lead trajectory embeddings, detects regimes with singular value decomposition and changepoint analysis, estimates transition probabilities between regimes, samples future-trajectory analogues, and fits smooth empirical forecast distributions. 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In a single pass of batched data, the proposed method repeatedly trains a machine learning algorithm and tests its empirical performance. Because it utilizes the entire sample for both learning and evaluation, cramming is significantly more data-efficient than sample-splitting. Unlike cross-validation, Cram evaluates the final learned model directly, providing sharper inference aligned with real-world deployment. The method naturally applies to both policy learning and contextual bandits, where decisions are based on individual features to maximize outcomes. The package includes cram_policy() for learning and evaluating individualized binary treatment rules, cram_ml() to train and assess the population-level performance of machine learning models, and cram_bandit() for on-policy evaluation of contextual bandit algorithms. For all three functions, the package provides estimates of the average outcome that would result if the model were deployed, along with standard errors and confidence intervals for these estimates. Details of the method are described in Jia, Imai, and Li (2024) and Jia et al. (2025) . 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Package: r-cran-crayon Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mockery, r-cran-rstudioapi, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-crayon_1.5.3-1.ca2404.1_all.deb Size: 159302 MD5sum: 79349a485ae1c28fd6bfd9a65918df27 SHA1: be2560793bb037130f2535308dbbc32662ca6af6 SHA256: c6c9bac22a32835da50c7cd44ae7c3ae89db90ba5fcd4965811aff94b7a0347a SHA512: 14f80fff1e76b5e62fa51ca6dd2fb6509d9d19cc22a8cc7fc6fef5de3f62f6b974bb8d82cf79f1c1dde62a2e41d79a0ee9b35a51a89cb917151af1b8d83d70ec Homepage: https://cran.r-project.org/package=crayon Description: CRAN Package 'crayon' (Colored Terminal Output) The crayon package is now superseded. Please use the 'cli' package for new projects. Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 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Provides scales for 'ggplot2' for discrete coloring. 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The package supports prospective deterioration monitoring, uncertainty-aware risk assessment, intervention prioritisation, ablation analysis, and operational evaluation for healthcare performance management and health system resilience research. The methodological framework is informed by contemporary guidance on prediction model development and validation (Efthimiou et al., 2024 ), transparent reporting of prediction models (Collins et al., 2024 ), and decision-analytic model evaluation (Vickers and Elkin, 2006 ). 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Functions generate bullet and amortising debt schedules, compute credit metrics such as debt service coverage ratios (DSCR), debt yield ratios, and forward loan-to-value ratios (LTV), and expose an explicit property-level operating chain from gross effective income (GEI) to net operating income (NOI) and property before-tax cash flow (PBTCF). The toolkit supports end-to-end scenario execution from a YAML (YAML Ain't Markup Language) configuration file parsed with 'yaml', includes helpers for effective rent, constrained loan underwriting, and simplified SPV-level tax simulations, and ships reproducible vignettes for methodological and applied use cases. 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It relies on a two-stage pseudo-outcome regression, and it is supported by theoretical convergence guarantees. Bargagli-Stoffi, F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. arXiv preprint . Package: r-cran-cream Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cream_1.1.1-1.ca2404.1_all.deb Size: 42938 MD5sum: 0c1ce6bc01d4247d055d8fe3ff2ab980 SHA1: e210f79f1d57146a78efce7196f8d7fb0945e9cf SHA256: 7f76b4a629f6e0ce2fb3f1c8333890002437011fab6c6be2f6cb88a30ed6bfd5 SHA512: 20b257af9685ec8255d193820f06fb3c218051646ecde8e4e5ba439582fa2774833b2519dba1a6793826030a1ec76ea2f676d3da0128929e50088665abd1a674 Homepage: https://cran.r-project.org/package=CREAM Description: CRAN Package 'CREAM' (Clustering of Genomic Regions Analysis Method) Provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). 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Package: r-cran-creditas Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rvest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-creditas_0.3.0-1.ca2404.1_all.deb Size: 29248 MD5sum: a921441284dc3ae90a2b8ce232dced1f SHA1: 7f5260151cad09e83fc9dd680047909b9ae19c9e SHA256: 33224a1f29c0eb9cf941d3a5410444612b84ae2ede057d76a4af4196b8631597 SHA512: a8ad676e209cb5c1405e0527e48433689a37fe7bc3e732a49ba11ec154812147fdc1ea45f9a54b09c888a003f4c82e81d386ffd1992f7c59056e53ba80daea8d Homepage: https://cran.r-project.org/package=CRediTas Description: CRAN Package 'CRediTas' (Generate CRediT Author Statements) A tiny package to generate CRediT author statements (). It provides three functions: create a template, read it back and generate the CRediT author statement in a text file. Package: r-cran-creditmodel Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-doparallel, r-cran-glmnet, r-cran-rpart, r-cran-cli, r-cran-xgboost Suggests: r-cran-pdp, r-cran-pmml, r-cran-xml, r-cran-knitr, r-cran-gbm, r-cran-randomforest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-creditmodel_1.3.1-1.ca2404.1_all.deb Size: 4174690 MD5sum: 039fa6190559f62db25a860303ed2e8b SHA1: c1b0a2ec331bc4247095fdf4496173f69b47c5be SHA256: edf5b1b07d66d33be6d2bf25b9b0b649de458ac633ed23aa1d1bfe0a7a335f21 SHA512: 754f4896b0fba753e7c4622f4110a6216f84dd2ed548d3d5d88dba6ec0a5a4a798a91fe05bdc872d57eaf1049ff9a8a5217f54b3ab4ecf4f1ed7ae77cbf21bbc Homepage: https://cran.r-project.org/package=creditmodel Description: CRAN Package 'creditmodel' (Toolkit for Credit Modeling, Analysis and Visualization) Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),. Package: r-cran-creditrisk Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-creditrisk_0.1.7-1.ca2404.1_all.deb Size: 83516 MD5sum: 544397ab1f1701c03deeb359ccf8c9f2 SHA1: 4d7cc6bbb427214d7e06e49fbb2c6c0a3259eec2 SHA256: 94c15e4188b23db37a37ecacd0b216cdb67209ada3ad5cfc9cc990785b1cb0ec SHA512: 2b475b2b7ef1a51662360f592961766282e473b43d40db621aa22f91fa2a395033852b903296c041d12a01b777a752dbcd2d0a9cd38dc667722717f8bfcb755a Homepage: https://cran.r-project.org/package=CreditRisk Description: CRAN Package 'CreditRisk' (Evaluation of Credit Risk with Structural and Reduced FormModels) Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. References: Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013) . Print ISBN: 9780470748466, Online ISBN: 9781118818589. © 2013 John Wiley & Sons Ltd. Package: r-cran-creds Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-creds_0.1.0-1.ca2404.1_all.deb Size: 21048 MD5sum: 9936a64957738e0d26ed98b8fa888572 SHA1: 7ca1a6e180d0b6171a0a01c3d8dde792d59c11c0 SHA256: c08ef778f3b2ed160a012224c68c04bb43125af668c34bdaa9eb8ecf1cbbe735 SHA512: f79cfc392553e5f499cfd8def0e189f5328d7f6778e944362eea191dc0b21d76cfd5d85606f144748bf328add06556d1bfd9687ce1a928e90df1e98674055915 Homepage: https://cran.r-project.org/package=CREDS Description: CRAN Package 'CREDS' (Calibrated Ratio Estimator under Double Sampling Design) Population ratio estimator (calibrated) under two-phase random sampling design has gained enormous popularity in the recent time. This package provides functions for estimation population ratio (calibrated) under two phase sampling design, including the approximate variance of the ratio estimator. The improved ratio estimator can be applicable for both the case, when auxiliary data is available at unit level or aggregate level (eg., mean or total) for first phase sampled. Calibration weight of each unit of the second phase sample was calculated. Single and combined inclusion probabilities were also estimated for both phases under two phase random [simple random sampling without replacement (SRSWOR)] sampling. The improved ratio estimator's percentage coefficient of variation was also determined as a measure of accuracy. This package has been developed based on the theoretical development of Islam et al. (2021) and Ozgul (2020) . 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Package documentation includes the vignette included in this package, and the paper by Schnell, Fiecas, and Carlin (2020, ). Package: r-cran-creepyalien Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-cli, r-cran-glue Filename: pool/dists/noble/main/r-cran-creepyalien_1.0.0-1.ca2404.1_all.deb Size: 84226 MD5sum: d2dab780cca8c139bb5d2d58f2472ae4 SHA1: 47f22e24fd409ef78c2886dd299f986f820908b0 SHA256: 7c84bdcb17e86cf3cb5dfe34cbae9371e79a098ccf5c0b56a85af49afdbf84fc SHA512: 50b1822ff86f7472d4e034257a8f6a84cf8e43b4cffd853d63d3afab812615e93bc8b39b77feb91bc511bd73d26d4152ca0f39810c3ac9ad7b2e1bfc9239fe38 Homepage: https://cran.r-project.org/package=creepyalien Description: CRAN Package 'creepyalien' (Puzzle Game for the R Console) Puzzle game that can be played in the R console. Help the alien to find the ship. 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Package: r-cran-crew.cluster Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crew, r-cran-lifecycle, r-cran-nanonext, r-cran-ps, r-cran-r6, r-cran-rlang, r-cran-vctrs, r-cran-xml2, r-cran-yaml Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crew.cluster_0.4.0-1.ca2404.1_all.deb Size: 502970 MD5sum: 0ed6a0098f6a2f5cecb9be0e258021ca SHA1: 4dca2baab3c37d8a74e517eab0f0b3e89b1faae8 SHA256: 02925c18b29b9e4c7d4bdac67205757bd1e06f5aebaf728534d8749d72929c34 SHA512: 75a6d8143f094806e7c971f4bf025d3f19b611c1c52ae7f185ececbca3a34a3dfcdb523af039bbc3c73fe71abc51efa4d22c9e86717bd17920ed00e908a1be9d Homepage: https://cran.r-project.org/package=crew.cluster Description: CRAN Package 'crew.cluster' (Crew Launcher Plugins for Traditional High-Performance ComputingClusters) In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.cluster' package extends the 'mirai'-powered 'crew' package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages 'mirai' by Gao (2023) , 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) , 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). . Package: r-cran-crew Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-collections, r-cran-data.table, r-cran-later, r-cran-mirai, r-cran-nanonext, r-cran-processx, r-cran-promises, r-cran-ps, r-cran-r6, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-autometric, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crew_1.3.3-1.ca2404.1_all.deb Size: 1063608 MD5sum: a7710e6c56bfb911a8c65afbc7e085d3 SHA1: 0dccda58229e4cf8079d30b585f03e0b8c45da0b SHA256: 6c9edbea17eaf917d8efb6370e1f6169714b06c5ef71eae2b61fc86a8eb43403 SHA512: 9ce45da951e7f80d37b9ba359d1304556079cd34efe6dc37dc26de294c75b82b2c74201fbccf8d32afe939e7b09c50403eeb59a1cf194f590f1445bb00634067 Homepage: https://cran.r-project.org/package=crew Description: CRAN Package 'crew' (A Distributed Worker Launcher Framework) In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'NNG'-powered 'mirai' R package by Gao (2023) is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) , 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischel, and Surmann (2017) . Package: r-cran-cricketdata Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5415 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-lubridate, r-cran-readr, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-codetools, r-cran-gghighlight, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-here, r-cran-knitr, r-cran-paletteer, r-cran-patchwork, r-cran-rmarkdown, r-cran-r.rsp, r-cran-showtext Filename: pool/dists/noble/main/r-cran-cricketdata_0.3.0-1.ca2404.1_all.deb Size: 4542008 MD5sum: 51a9c19e18fdfae83b4db451d5089415 SHA1: 56a6b38138756b215cda3ce76b2f839e4956aa79 SHA256: 21c136dae10325ec9e5d05c53e55ea9d935dcbe18cfe64fd22aa4edb4de76911 SHA512: ae613b419d2d51ff9b66dd8ebb3b53652f3ab4a7fe6f623ce87cbc7e7781fdc135bd420337ab184460488007306e336a1e1abc5ab063b674de938b40cf99667e Homepage: https://cran.r-project.org/package=cricketdata Description: CRAN Package 'cricketdata' (International Cricket Data) Data on international and other major cricket matches from ESPNCricinfo and Cricsheet . This package provides some functions to download the data into tibbles ready for analysis. Package: r-cran-cricketr Architecture: all Version: 0.0.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-plotrix, r-cran-ggplot2, r-cran-scatterplot3d, r-cran-forecast, r-cran-lubridate, r-cran-xml, r-cran-httr Filename: pool/dists/noble/main/r-cran-cricketr_0.0.26-1.ca2404.1_all.deb Size: 383722 MD5sum: 2c2e7a981a0d96ca589c2cb8ba3c2b1b SHA1: 906f439fd20aba21fc020675e34a8dc345712892 SHA256: 952c71f6c834fc0a05977e9e48d6ce86fdf5a72f77a4d98877946f02be59069c SHA512: c3bcfe21d9bcdbd062f3a59bb487e46d73b046f69a88e449b13e9caabd000b8681a1ab2078bf55705ddf3450194ddfb6eb463b7711572bf54a7c543c9967c545 Homepage: https://cran.r-project.org/package=cricketr Description: CRAN Package 'cricketr' (Analyze Cricketers and Cricket Teams Based on ESPN CricinfoStatsguru) Tools for analyzing performances of cricketers based on stats in ESPN Cricinfo Statsguru. The toolset can be used for analysis of Tests,ODIs and Twenty20 matches of both batsmen and bowlers. The package can also be used to analyze team performances. Package: r-cran-crimedata Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-osfr, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crimedata_0.3.5-1.ca2404.1_all.deb Size: 1083858 MD5sum: cfb12294fe51c38134157a874c516c0e SHA1: 8ba31340891034037a60428c067ee33e50c4560f SHA256: 472f0032efdeaf610a873ac4f38ffa508aefc9a96ca11bb4c5d3c8ae1dcd780b SHA512: 2f85a04d0c0360fb2b2a1e07dc34bbf722937d68543f8dc1e6a6ddbcf3a67cb6d462a3d714b098754ed182bd53bde4489f957dc4624dee3313d897765917a1b6 Homepage: https://cran.r-project.org/package=crimedata Description: CRAN Package 'crimedata' (Access Crime Data from the Open Crime Database) Gives convenient access to publicly available police-recorded open crime data from large cities in the United States that are included in the Crime Open Database . Package: r-cran-crimedatasets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crimedatasets_0.1.0-1.ca2404.1_all.deb Size: 2067094 MD5sum: 39a24961fe2c0d4bcdc1fd1a98ccb98b SHA1: 973a579ce50763905d3f81e0db0b7773eb16ffa9 SHA256: 7544cf9e1afd58d1c169a0b836f4aefda77a5fbb093652cad46b66826e59c467 SHA512: c0f36428c18339725c3b893dacfa95b995201938574367ecd967f58d2c23eed651b141358a8cb744e1fbd62a7ec5857d87790fbe4bc8e1e36b725a991e1f3a93 Homepage: https://cran.r-project.org/package=crimedatasets Description: CRAN Package 'crimedatasets' (A Comprehensive Collection of Crime-Related Datasets) A comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, criminology, social and economic studies related to criminal behavior. Datasets span global and local contexts, with a mix of tabular and spatial data. Package: r-cran-crimeutils Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-ggplot2, r-cran-readr, r-cran-gridextra, r-cran-scales, r-cran-magrittr, r-cran-gt, r-cran-tidyr, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-crimeutils_0.5.1-1.ca2404.1_all.deb Size: 100498 MD5sum: 8c0629c2f685a47b25093c685b5eb19d SHA1: 49f29732af7b1a70a425aa480844f6267e471f96 SHA256: 7b006da920b70419cea4d7e6245381f33d632af4626ead68e874c65951cdd0c6 SHA512: 79fefa94f1ad784c822a962605d8b633aea65415537a6239b0156b737ccd508a9042dcc132deaaa17357e2b6d27b616d365d1bb7f3d9486c7b9ed1cb272b87a3 Homepage: https://cran.r-project.org/package=crimeutils Description: CRAN Package 'crimeutils' (A Comprehensive Set of Functions to Clean, Analyze, and PresentCrime Data) A collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data. Package: r-cran-crisp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-crisp_1.0.0-1.ca2404.1_all.deb Size: 97294 MD5sum: 2c49344a327738042ed8afe65f8c33d3 SHA1: c844eee7e52a3edc4fcee59d6fa784d621e829b0 SHA256: 8d6c5c5b026bc001f70b0a844f39b8e52b0eedc150992fbc711b6655a67510bc SHA512: 9d240085e20db8536de74e0e886f10fb594b364178141394aecd1716b6ba9063144d6847b1ba2b0d6d0aae1be478e12e955587aee7c2b5b824d7b9d128f3154d Homepage: https://cran.r-project.org/package=crisp Description: CRAN Package 'crisp' (Fits a Model that Partitions the Covariate Space into Blocks ina Data- Adaptive Way) Implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 . Package: r-cran-crisprdesignr Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2800 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-gbm, r-bioc-genomicranges, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-s4vectors, r-bioc-rtracklayer, r-cran-stringr, r-cran-vtreat, r-cran-shiny, r-cran-dt Suggests: r-bioc-bsgenome.scerevisiae.ucsc.saccer3 Filename: pool/dists/noble/main/r-cran-crisprdesignr_1.1.7-1.ca2404.1_all.deb Size: 2516566 MD5sum: e5a68e083497f2befd247ea8261221e3 SHA1: 589d7091d7313ab691e2ae84ebe9774edbb61e3a SHA256: 3520316c8999e241912213488a675157a60cef8a1e88d44b280fd27d2c90a841 SHA512: c998e142670a4e19256d33e7ce921f3439add1ace00d8ef512104ed74b77b04feb59e2080d041a4b410da7799e255da7fe9320fd6b4e375bfa0ea926930c0ed1 Homepage: https://cran.r-project.org/package=crispRdesignR Description: CRAN Package 'crispRdesignR' (Guide Sequence Design for CRISPR/Cas9) Designs guide sequences for CRISPR/Cas9 genome editing and provides information on sequence features pertinent to guide efficiency. Sequence features include annotated off-target predictions in a user-selected genome and a predicted efficiency score based on the model described in Doench et al. (2016) . Users are able to import additional genomes and genome annotation files to use when searching and annotating off-target hits. All guide sequences and off-target data can be generated through the 'R' console with sgRNA_Design() or through 'crispRdesignR's' user interface with crispRdesignRUI(). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and the associated protein Cas9 refer to a technique used in genome editing. Package: r-cran-criticality Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-bnlearn, r-cran-dplyr, r-cran-evd, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-keras, r-cran-magrittr, r-cran-reticulate, r-cran-scales Filename: pool/dists/noble/main/r-cran-criticality_0.9.3-1.ca2404.1_all.deb Size: 386474 MD5sum: 1a4cc686685a9ac3625330419faa5fb4 SHA1: cc9a9d918b661be72b98cceab9dca513da1fa633 SHA256: 324643e8c91ad0394bde9403df07ebd28b7adfc9d117cd96f9f5973108b47988 SHA512: 51ce88a4041451ee9297d77c8ec404cf154f2cd4e01ff0cf3913ec4896c397f642679dfef20ddaa23597c2eb6906fd4b1725a5aea7f0f22429a919a495cc51f8 Homepage: https://cran.r-project.org/package=criticality Description: CRAN Package 'criticality' (Modeling Fissile Material Operations in Nuclear Facilities) A collection of functions for modeling fissile material operations in nuclear facilities, based on Zywiec et al (2021) . Package: r-cran-criticalpath Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-magrittr, r-cran-r6, r-cran-stringr, r-cran-tibble Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-criticalpath_0.2.1-1.ca2404.1_all.deb Size: 283392 MD5sum: ac081e2701f430c8790610f44a04fd39 SHA1: 623b2c10568958bc0fb66b5a775adaa764735546 SHA256: 1201457a90b635f71f067f7a905ec818314c4f3ac2997a428c5ebc138781b3a6 SHA512: 99265b56da33580216162b96c35c0acf7fa2757cff290d09533ae14848b6971370bd3fd05b616a76b9168f317be93e47a1d642f479d0dab42ec8703377886c89 Homepage: https://cran.r-project.org/package=criticalpath Description: CRAN Package 'criticalpath' (An Implementation of the Critical Path Method) An R implementation of the Critical Path Method (CPM). CPM is a method used to estimate the minimum project duration and determine the amount of scheduling flexibility on the logical network paths within the schedule model. The flexibility is in terms of early start, early finish, late start, late finish, total float and free float. Beside, it permits to quantify the complexity of network diagram through the analysis of topological indicators. Finally, it permits to change the activities duration to perform what-if scenario analysis. The package was built based on following references: To make topological sorting and other graph operation, we use Csardi, G. & Nepusz, T. (2005) ; For schedule concept, the reference was Project Management Institute (2017) ; For standards terms, we use Project Management Institute (2017) ; For algorithms on Critical Path Method development, we use Vanhoucke, M. (2013) and Vanhoucke, M. (2014) ; And, finally, for topological definitions, we use Vanhoucke, M. (2009) . Package: r-cran-critpath Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-critpath_0.2.3-1.ca2404.1_all.deb Size: 633948 MD5sum: 830d34654eb242f53592a28b06c8fbba SHA1: 6fb94e9fca5ae133b88cd99ce94aa00662ba7380 SHA256: a433198e482b539f6ddd1e59dbc2baaf49bd3296d318cbcc6f4930278f096463 SHA512: 011491475970361d834026f521920e5415bceaade5d0df4dface391d83c5cb712bb8e38c5e7903c35eb043d7bdb9705716d44f01cb42ac1dc4eae7e800d706e0 Homepage: https://cran.r-project.org/package=critpath Description: CRAN Package 'critpath' (Setting the Critical Path in Project Management) Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation and Review Technique) and LESS (Least Cost Estimating and Scheduling) methods. The package sets the critical path, schedule and Gantt chart. In addition, it allows to draw a graph even with marked critical activities. For more information about project management see: Taha H. A. "Operations Research. An Introduction" (2017, ISBN:978-1-292-16554-7), Rama Murthy P. "Operations Research" (2007, ISBN:978-81-224-2944-2), Yuval Cohen & Arik Sadeh (2006) "A New Approach for Constructing and Generating AOA Networks", Journal of Engineering, Computing and Architecture 1. 1-13, Konarzewska I., Jewczak M., Kucharski A. (2020, ISBN:978-83-8220-112-3), Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9). Package: r-cran-crm12comb Architecture: all Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggforce Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crm12comb_0.1.12-1.ca2404.1_all.deb Size: 464398 MD5sum: 3c0599e454b4f07e511047aa9b3422b0 SHA1: e3d196e34f63851c99a4cf1972328e31bc5506a8 SHA256: 1e44c385f98091fa3894843eb9705b60e43511bb0c442774d19838d86941857d SHA512: e1b0a4cac3c4f193c08fdacb67aea90bd0da13d10a9cdd0f9d6cf8edc120878c2f2a9313f02987c3732b4ba5e71e377adfd0a9afb23f49f8b05beb88804322bd Homepage: https://cran.r-project.org/package=crm12Comb Description: CRAN Package 'crm12Comb' (Phase I/II CRM Based Drug Combination Design) Implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014) . Package: r-cran-crmetrics Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggpubr, r-cran-ggrepel, r-cran-magrittr, r-cran-matrix, r-cran-r6, r-cran-scales, r-cran-sccore, r-bioc-sparsematrixstats, r-cran-tibble, r-cran-tidyr Suggests: r-cran-conos, r-cran-data.table, r-cran-knitr, r-cran-markdown, r-cran-pagoda2, r-cran-reticulate, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-seurat, r-cran-soupx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crmetrics_0.3.3-1.ca2404.1_all.deb Size: 621770 MD5sum: 8ec9a02dc9aa6d7b8b6ef8170f601b32 SHA1: 4bee70b6fd0b788024af79b91b0b1df72b9a5257 SHA256: 65ebfe7dfeed2c57e081340d38319cc85c133f23bd470c967b2d24702841fed7 SHA512: 2a91e19be850ede670a49f95e7a32dd0d6f0b83482d817e64f812f53be35de76737204a9c7a7f9510a8e6a2165c07bb3577bc01b7626fe181c1249182ee4b129 Homepage: https://cran.r-project.org/package=CRMetrics Description: CRAN Package 'CRMetrics' (Cell Ranger Output Filtering and Metrics Visualization) Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) . 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) or 'CellBender' by Stephen J Fleming et al. (2022) . Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) or 'Seurat' by Yuhan Hao et al. (2021) followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) . Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) or 'DoubletDetection' by Gayoso et al. (2020) . In the end, cells are filtered based on user input for use in downstream applications. Package: r-cran-crmn Architecture: all Version: 0.0.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-pcamethods, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-crmn_0.0.22-1.ca2404.1_all.deb Size: 532774 MD5sum: e3025bb02f7076b5b625eb47f5460c08 SHA1: 1ff4840e116f733868f5a1fe41444a2baef1a9f9 SHA256: 25f1e092037cc1602d6af54f93b8f396a06cadeec9f61e4504febbe7a6e34a40 SHA512: c5081b620ccc7f960ef1761c498627f08e51241e7fee9a7ff46283e365f2a4f070188c38b1840d91e7aa7cd64be51de44d5081cf4b5b48172235e0628480efa8 Homepage: https://cran.r-project.org/package=crmn Description: CRAN Package 'crmn' (CCMN and Other Normalization Methods for Metabolomics Data) Implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry and other normalization algorithms. Package: r-cran-crmpack Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9148 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate, r-cran-dplyr, r-cran-futile.logger, r-cran-gensa, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-lifecycle, r-cran-magrittr, r-cran-mvtnorm, r-cran-parallelly, r-cran-rdpack, r-cran-rjags, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-bookdown, r-cran-broom, r-cran-covr, r-cran-data.tree, r-cran-diagrammer, r-cran-ggmcmc, r-cran-quarto, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-crmpack_2.2.1-1.ca2404.1_all.deb Size: 5592842 MD5sum: 8d0b09da1497d92739ffecf4525ef3f5 SHA1: 683d04373fc6aa5d7a6f5f627bf92191c2db0dd8 SHA256: 1c0369ad7a8098efaa0bf29a8879da2c357a1fe1d5f9bd9fc089154938cbd74b SHA512: 867645ed78e766ddd2412aed0ff3cbd0d64fa6776306eb74e9da16661160f7e389f3764e60f0496255fcebf2eaa2fbb7bda8f9aac78435bb5e965ccf63819974 Homepage: https://cran.r-project.org/package=crmPack Description: CRAN Package 'crmPack' (Object-Oriented Implementation of Dose Escalation Designs) Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanés Bové et al. (2019) . Package: r-cran-crmreg Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gplots, r-cran-pcapp, r-cran-plyr, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/noble/main/r-cran-crmreg_1.0.4-1.ca2404.1_all.deb Size: 92648 MD5sum: d3643f4bb7d6f0e70a4a49367c14fea0 SHA1: 431728f35a374b9608cb9521e32aef294323555e SHA256: 86a5e92550c688b86eeb1a25f8ffd622da3e3f4075003a885201747ba979f4d0 SHA512: 45c68f7dc8df50190e1578e5c25f3a67ce554762c8e2afd788bd013cda9dfc9f7c57aa9cee5bdb856692361d925b86c84d930c82b8e9c3d9698c49ac9d63c324 Homepage: https://cran.r-project.org/package=crmReg Description: CRAN Package 'crmReg' (Cellwise Robust M-Regression and SPADIMO) Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) ) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) ). Package: r-cran-crochet Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crochet_2.3.0-1.ca2404.1_all.deb Size: 54200 MD5sum: 3ba7e2e271e263258a9fba05b19d8e96 SHA1: 482c070a96c8320e091407c3105c8ef589ff320e SHA256: 6be48f8e3bc1aa0f9acea7d8df009559af88ce53ee187ffd74f5e6b61a300657 SHA512: 2f560de27bdd99b0e60bd3a04a2de3dba6a0e3f387a0bf21c27ae65eaa7b33fbd96005f883831f38f4205191633e7234e5eb0a4bccfa872ac62f6d688b9735a9 Homepage: https://cran.r-project.org/package=crochet Description: CRAN Package 'crochet' (Implementation Helper for '[' and '[<-' of Custom Matrix-LikeTypes) Functions to help implement the extraction / subsetting / indexing function '[' and replacement function '[<-' of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it). Package: r-cran-cromwelldashboard Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-stringr, r-cran-httr, r-cran-dplyr, r-cran-dt Filename: pool/dists/noble/main/r-cran-cromwelldashboard_0.5.1-1.ca2404.1_all.deb Size: 19396 MD5sum: 81f3700f172bf5ba3e0692ed0b966642 SHA1: e7b2ac511a241d04f11f7b3dafdd702abf746bfe SHA256: 0b50e2d4b528b1ac6620f61e8a5f6f7400163fc95ec8b89bcdd1ceee26e0cad7 SHA512: 12016515328e5f6d459050bd1567d2f67b097a8325faaf994d4f90858694952df68941d587cd45fd907e3ecde31b69e538ab77e623b98351409e0e447accfb3a Homepage: https://cran.r-project.org/package=cromwellDashboard Description: CRAN Package 'cromwellDashboard' (A Dashboard to Visualize Scientific Workflows in 'Cromwell') A dashboard supports the usage of 'cromwell'. 'Cromwell' is a scientific workflow engine for command line users. This package utilizes 'cromwell' REST APIs and provides these convenient functions: timing diagrams for running workflows, 'cromwell' engine status, a tabular workflow list. For more information about 'cromwell', visit . Package: r-cran-cronbach Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-cronbach_0.4-1.ca2404.1_all.deb Size: 25598 MD5sum: 634607b712851ba5e416093b349bdf5e SHA1: 24ed4d99c1f6c348a45fc2e8e48574662bfefc95 SHA256: d8dba032bf7ebf3359cb672a3dcc4ed1a1ee43a8b56a751c44f31e1e20471c6b SHA512: c94d03f64a91d21d2967b7d25a0f6ee005bdaa62b9afcd65fbf0e15fa781ca225978a40228baa4b1abe3f06557630308112bcc3dfd052044e3c882f8292c6866 Homepage: https://cran.r-project.org/package=Cronbach Description: CRAN Package 'Cronbach' (Cronbach's Alpha) Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece. Package: r-cran-crone Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1660 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-crone_0.1.1-1.ca2404.1_all.deb Size: 1289380 MD5sum: 84c86105623ffdff7256c26926b05ca8 SHA1: f91cabe2c6da63ec8d29f4ffa6804f1e975d6f95 SHA256: 6ea7168ee6302ce57b1f9a52a62db846c75d706f975784560f085871111ebb7b SHA512: 8cc6d5afad1fe7a4a6fa16fe2b8338bf521da98482603e191f8e834ec3dc6988fbe37b23ff465795a1b31d59bfb5f33c10eb897a27df266749fb313a29490c24 Homepage: https://cran.r-project.org/package=crone Description: CRAN Package 'crone' (Structural Crystallography in 1d) Functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) . Package: r-cran-cronologia Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-cronologia_0.2.0-1.ca2404.1_all.deb Size: 25554 MD5sum: ded225fa353a557f0962ab0bd8cb6489 SHA1: 8611b40dc05975f76d5a162e0429ce67ab435073 SHA256: 063442f8775e8e3b30fa3569af4a4a28fa1cf7ec346fa8506baa7473e139d75b SHA512: 1dde7b6164648d362797afdc036cea9cf0e9dc274c516e87bde1565d0016a2a0516ff1bcf4f5dcdfb4502f6450eaf64ad6ec6aa88deb184ce087c3f8ac3f67e5 Homepage: https://cran.r-project.org/package=cronologia Description: CRAN Package 'cronologia' (Create an HTML Vertical Timeline from a Data Frame in'rmarkdown' and 'shiny') Creates an HTML vertical timeline from a data frame as an input for 'rmarkdown' documents and 'shiny' applications. Package: r-cran-cronr Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Suggests: r-cran-miniui, r-cran-shiny, r-cran-shinyfiles, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-cronr_0.6.5-1.ca2404.1_all.deb Size: 79362 MD5sum: 7a334a453ae5b327795b618e4ddefa1d SHA1: 11a4afc37ffde49212cbdff3f029b0e921bc8dd3 SHA256: bd503045eda75acc966413acafe20cfc2d64ff6f550187659803bc6f6be22e89 SHA512: 53e37fc0b68953648ede4ecf4f3fd1dd0a24b837148fd15c0099724c0d79f2b44edefe28e54d7a52ca7e2508b42978086bd28d6642c5684db8d1000b3ee9cf7f Homepage: https://cran.r-project.org/package=cronR Description: CRAN Package 'cronR' (Schedule R Scripts and Processes with the 'cron' Job Scheduler) Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts. Package: r-cran-crookr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-lidr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crookr_0.1.0-1.ca2404.1_all.deb Size: 369966 MD5sum: e8937706374b03217cb87b0c7a4a661a SHA1: b5824d3dc288d1b679bca302ad49444139ad6f34 SHA256: 6ca56ca412ffb64c90c9526c363eaabf88fd5d08589eed0a052709974995e876 SHA512: 5c6226123411d4760e11268ec843dcbdbe8eaf0652477f20e862b0356f8e56a570cd6d6442611d974c0221a3f56d6be114e85f4599bc49c03aa2349cc86c54f7 Homepage: https://cran.r-project.org/package=crookR Description: CRAN Package 'crookR' (Synthetic Crook Deformations in Stem Point Clouds) Simulates parameterized single- and double-directional stem deformations in tree point clouds derived from terrestrial or mobile laser scanning, enabling the generation of realistic synthetic datasets for training and validating machine learning models in wood defect detection, quality assessment, and precision forestry. For more details see Pires (2025) . Package: r-cran-crop Architecture: all Version: 0.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crop_0.0-3-1.ca2404.1_all.deb Size: 14470 MD5sum: 58cbea2464d6160834d5d6b6a9c72545 SHA1: 9e9260dd2ca9e7e8af06982da595dcebd09a72b5 SHA256: 984132b9ec3a483d55a9e2e0cefe712d8b91d885ab00de574079dad802f6a617 SHA512: 590b3786cd5970152a95ee1fea122104534fb3d82344578d82664f42560481e6bb42cc0a72b67038588a02309756058f237e539571cdd251da9a24af74a3744e Homepage: https://cran.r-project.org/package=crop Description: CRAN Package 'crop' (Graphics Cropping Tool) A device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed. Package: r-cran-cropbreeding Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-metan, r-cran-rlang Filename: pool/dists/noble/main/r-cran-cropbreeding_0.1.0-1.ca2404.1_all.deb Size: 30136 MD5sum: 38fb84d7094b11a17c135c560d4c21cd SHA1: 763316d0c40fc775670fa3de526b4c043656ece7 SHA256: 35f0965420c1ce0bcd59a0aed9d7018ae311190f8b695c0d1594fa061bb15659 SHA512: 4aa0f9fdb902b78a7544f449a469f86d5ac745937a445bbfa242daa38f2d15c3206d11c71a5f340984ffbbe3130152e22081cdfa8b76ee9345f0d472dd3c53f4 Homepage: https://cran.r-project.org/package=CropBreeding Description: CRAN Package 'CropBreeding' (Stability Analysis in Crop Breeding) Provides tools for crop breeding analysis including Genetic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability, genetic advance calculations, stability analysis using the Eberhart-Russell model, two-way ANOVA for genotype-environment interactions, and Additive Main Effects and Multiplicative Interaction (AMMI) analysis. These tools are developed for crop breeding research and stability evaluation under various environmental conditions. The methods are based on established statistical and biometrical principles. Refer to Eberhart and Russell (1966) for stability parameters, Fisher (1935) "The Design of Experiments" , Falconer (1996) "Introduction to Quantitative Genetics" , and Singh and Chaudhary (1985) "Biometrical Methods in Quantitative Genetic Analysis" for foundational methodologies. Package: r-cran-cropcircles Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-magick, r-cran-purrr Filename: pool/dists/noble/main/r-cran-cropcircles_0.2.4-1.ca2404.1_all.deb Size: 62626 MD5sum: 8a05f850cf8d4e8dbbc8532b2e2f411d SHA1: 0cf0997d5adeea536a7bcfc017917cf7af12b9b3 SHA256: bc798c13f856fa89f2682b38f36ab9acd2609d553cf51d68fca0f08ed7f9e706 SHA512: dc9351fa5d277c82a3007ae06dc31513e73b6ba88005992b00029e0b57c23830f338eaa7bbcaac801175aa5ad94570cfaa7e86ae4285211a3a762cad2943beb2 Homepage: https://cran.r-project.org/package=cropcircles Description: CRAN Package 'cropcircles' (Crops an Image to a Circle) Images are cropped to a circle with a transparent background. The function takes a vector of images, either local or from a link, and circle crops the image. Paths to the cropped image are returned for plotting with 'ggplot2'. Also includes cropping to a hexagon, heart, parallelogram, and square. Package: r-cran-cropdatape Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cropdatape_1.0.0-1.ca2404.1_all.deb Size: 192502 MD5sum: 16b7d0e52326b136974aa153f5ffd31e SHA1: 28d1facdbe7f0ecf87c68c25df1d1ee8d8af7c54 SHA256: 880cf42e3b2e6cf7ade678f0eedfb32b63cfdbaf111058f853c38e8a727e9b44 SHA512: a5010b34b869742dbba7b7acf3d41ce6053eb3ee028410c54cc2a4c7eccb373e04e4e3a07dea832e25d75fe97062d04079c3d2496727890d58ac8c46e786824d Homepage: https://cran.r-project.org/package=cropdatape Description: CRAN Package 'cropdatape' (Open Data of Agricultural Production of Crops of Peru) Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014. Package: r-cran-cropdemand Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4483 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-terra, r-cran-sf, r-cran-tidyr, r-cran-ncdf4 Filename: pool/dists/noble/main/r-cran-cropdemand_1.0.3-1.ca2404.1_all.deb Size: 3261146 MD5sum: 87e3390e971d24f5833984e75cd0ca03 SHA1: d608772e96f87bc7079afc2d673ff4a65991a363 SHA256: 10e3ed44f387de423674127f530943b1fb3d830dc49ca658122fee8e7a3e05ed SHA512: c763a3243872d7fef326d41039a4c51c83d4cf77ef9b92295ae5cf8ee2aabe0710afde55df3834a93a379423ab8edfa2ad8c5c5c6f6785b44cf0ae48054d22d7 Homepage: https://cran.r-project.org/package=cropDemand Description: CRAN Package 'cropDemand' (Spatial Crop Water Demand for Brazil) Estimation of crop water demand can be processed via this package. As example, the data from 'TerraClimate' dataset () calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from 'TerraClimate' repository with the download.terraclimate function and access the original 'TerraClimate' products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with 'TerraClimate' dataset. Package: r-cran-cropgrowdays Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-purrrlyr, r-cran-httr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-furrr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cropgrowdays_0.2.2-1.ca2404.1_all.deb Size: 148426 MD5sum: 765be3de34a08f402365182d6da8a2a3 SHA1: a51eca26c23d9e1ee7b4452e57794a65fb00e551 SHA256: 754835baea4a6401aec581d12fd031e4c306a657723fe1d153d08637410eff61 SHA512: 1167876a4513d20756f2964a5c1a777965f64bfd60ced47b1a3b1bb61a6c25f0f123dfc1e91896aa2656dd7452a5080c2466e1e0664de0e29c839dac18a8ec0a Homepage: https://cran.r-project.org/package=cropgrowdays Description: CRAN Package 'cropgrowdays' (Crop Growing Degree Days and Agrometeorological Calculations) Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) ), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website . This data is freely available under the Creative Commons 4.0 licence. Package: r-cran-crops Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sets, r-cran-reshape, r-cran-tidyverse, r-cran-memoise, r-cran-ggplot2, r-cran-magrittr, r-cran-cowplot, r-cran-tibble, r-cran-rdpack Suggests: r-cran-fpop, r-cran-pacman Filename: pool/dists/noble/main/r-cran-crops_1.0.4-1.ca2404.1_all.deb Size: 52374 MD5sum: 2543749baccd6aab15ebb7839a82aee2 SHA1: d8c533863ba38c308bb2015e432d8fea2f3dd272 SHA256: c9059b4dfa51365faf9c7c0ac268304775a2a19ab87387c2088349b06a8ee4fd SHA512: 4a9064779168db30ee68394e4b319538b4dcd0504e1bf41e1bb70ea1b935b058fc9b52567fc5f828cbd8c5f3e039a48c061cde8cba2e5a87285044a3be8113f9 Homepage: https://cran.r-project.org/package=crops Description: CRAN Package 'crops' (Changepoints for a Range of Penalties (CROPS)) Implements the Changepoints for a Range of Penalties (CROPS) algorithm of Haynes et al. (2017) for finding all of the optimal segmentations for multiple penalty values over a continuous range. Package: r-cran-cropscaper Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-raster, r-cran-sf, r-cran-data.table, r-cran-httr, r-cran-rjsonio Filename: pool/dists/noble/main/r-cran-cropscaper_1.1.5-1.ca2404.1_all.deb Size: 93760 MD5sum: e0e4c2cb641ff47071ae63766eff690f SHA1: aec599d124f2b3d45454783b49b24b5f8fcdd5af SHA256: f5c042f691a32e01cf342f295ce1ad8f43a71223302d0b8f97e652faf9c944da SHA512: e78dcd41e99cd9bbd790b98e38d1d555a593277455d33cd8abd83c64fd5afa8412c7ea42ac5190c67d70bb8e50e1f0901e86d625dd862523a3eb60d7be0a59e0 Homepage: https://cran.r-project.org/package=CropScapeR Description: CRAN Package 'CropScapeR' (Access Cropland Data Layer Data via the 'CropScape' Web Service) Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' web service. Package: r-cran-cropwaterbalance Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-powersdi, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cropwaterbalance_0.2.0-1.ca2404.1_all.deb Size: 89810 MD5sum: 6b11f1f8c0d25a0ba458294ca362fd82 SHA1: c17574135794b56222b47a4cdf3c6b5e6c411808 SHA256: ad25114e0555e55e55facd93d0f8c2282fe7e2d13699a054579d8df875cd8459 SHA512: f9e98a0a2c3702b7095e73efa505044d72887ebef4abec62e9bb006425a20c646dd3e9fe5058090c39580c1e9f5d2e264ee10ac8632643462c2ec0d99a654063 Homepage: https://cran.r-project.org/package=CropWaterBalance Description: CRAN Package 'CropWaterBalance' (Climate Water Balance for Irrigation Purposes) Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) ; or Hargreaves and Samani (1985) . Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well. Package: r-cran-cropwatmul Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-openxlsx, r-cran-purrr, r-cran-readxl, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cropwatmul_0.1.0-1.ca2404.1_all.deb Size: 87102 MD5sum: 48cf396985dcaaeb7cbfa994f7eef0b1 SHA1: ba84009dcb6c074d3b312914d7841ee42081b15e SHA256: 0151c50c63c8e2a429ccd2a587e4e2d4df71d8b6ae6b2a4f88c1d60126a7d40b SHA512: d105484391e47236aa0f0cc5e10b7c07af5637d3a28fd6d7e8e7b42c5da3966cff4bc9e963450e7172ea792c14fd7182d9872c8965fae2b07136b384932c9217 Homepage: https://cran.r-project.org/package=cropwatMUL Description: CRAN Package 'cropwatMUL' (Crop Water Requirement and Irrigation Scheduling Across MultipleLocations) Estimates reference evapotranspiration, crop evapotranspiration, effective rainfall, crop water requirements, root-zone water balance, and irrigation schedules across multiple locations. The calculations use temperature-based procedures described in Food and Agriculture Organization Irrigation and Drainage Paper No. 56 and a workflow inspired by the 'CROPWAT' software for monthly-to-daily interpolation, aggregation into 10-day periods, and irrigation scheduling. Further details of the evapotranspiration calculations are provided by Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998, ISBN:92-5-104219-5) "Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements" . The package is an independent implementation and is not affiliated with or endorsed by the Food and Agriculture Organization of the United Nations. Package: r-cran-cropzoning Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-tidyr, r-cran-sf, r-cran-ncdf4, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cropzoning_1.0.3-1.ca2404.1_all.deb Size: 3204352 MD5sum: 3f28c299f96152ac134b39ce4f02cc4b SHA1: 7e6dacdf777529c1874deda154b92bd07f43bad1 SHA256: d22a86033d5b368eb6a57bbbc3373b8b39b1d17d58f4713016e62610e8aa71e6 SHA512: db5da95622fe34a2ef5f6e454ee635636247b0201c8be9d5bdbecb3a06adf14fbc1012953248f6dc4fec9d3b49b9f153e342fcd2fb7913ad1b96dcbdfb9f2954 Homepage: https://cran.r-project.org/package=cropZoning Description: CRAN Package 'cropZoning' (Climate Crop Zoning Based in Air Temperature for Brazil) Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from 'TerraClimate' dataset (), but, it have been calibrated with automatic weather stations of National Meteorological Institute of Brazil. The climate crop zoning of this package can be run for all the Brazilian territory. Package: r-cran-crosscarry Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gee, r-cran-ggplot2, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-crosscarry_1.2.0-1.ca2404.1_all.deb Size: 63136 MD5sum: a93dbf54d3b4a2d4c674df1ac07543d4 SHA1: aee8e4549631634a1427568a0c37237efda7898a SHA256: deedc0abdf07c1f1d9a7a0fb8bd313f1bf085a10cede52bc9951c8692b8c4137 SHA512: 008a94cb681d22a281f2a0738903599f441752cbfa8feda4d9503c037dba2d61eb63ec069262b97b0d801eedd708a44734fa9d8b65d3601ec5f78622a65d92df Homepage: https://cran.r-project.org/package=CrossCarry Description: CRAN Package 'CrossCarry' (Analysis of Data from a Crossover Design with GEE) Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. It contains functions for semiparametric estimates of carry-over effects in repeated measures and allows estimation of complex carry-over effects. Related work includes: a) Cruz N.A., Melo O.O., Martinez C.A. (2023). "CrossCarry: An R package for the analysis of data from a crossover design with GEE". . b) Cruz N.A., Melo O.O., Martinez C.A. (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures". and c) Cruz N.A., Melo O.O., Martinez C.A. (2023). "Semiparametric generalized estimating equations for repeated measurements in cross-over designs". . Package: r-cran-crossclustering Architecture: all Version: 4.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-cluster, r-cran-crayon, r-cran-dplyr, r-cran-flip, r-cran-mclust, r-cran-purrr Suggests: r-cran-covr, r-cran-devtools, r-cran-lintr, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-crossclustering_4.1.3-1.ca2404.1_all.deb Size: 77362 MD5sum: 998e96af07971d2e04ac51df0a0a1613 SHA1: 4bf7df53800cf0ecf02c3f41edec2027cb745b4f SHA256: fe327409399463f86f24eb44edef97013f52f9bf033b8d83ca2730f0508df9d2 SHA512: 58e507616959fac51391ca351b00acc02f4a0abbdaf6077c5431826db82f92991b7a1d91020820bb05db7189e6a691695fe3fae9d966c5edd41c304252bbdc64 Homepage: https://cran.r-project.org/package=CrossClustering Description: CRAN Package 'CrossClustering' (A Partial Clustering Algorithm) Provide the 'CrossClustering' algorithm (Tellaroli et al. (2016) ), which is a partial clustering algorithm that combines the Ward's minimum variance and Complete Linkage algorithms, providing automatic estimation of a suitable number of clusters and identification of outlier elements. Package: r-cran-crossdes Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-algdesign, r-cran-gtools Filename: pool/dists/noble/main/r-cran-crossdes_1.1-2-1.ca2404.1_all.deb Size: 84974 MD5sum: 5a78627b04cd4f61f1598fc41b72ec8d SHA1: 9024cfd4eceda549d439ab8d6785f355e3feb2d0 SHA256: 303692647b2b9121ce25700c9d158dcaec38b362bf0215a83170c51de161c43e SHA512: 79874b3ee2cde188f6b42ea3ae2e64aeb19eebcf2471164a3e0172f0ea75a31bf215139704b87e5606323621bf58f8f0959ccb53d1d3c3404839bb8517b75587 Homepage: https://cran.r-project.org/package=crossdes Description: CRAN Package 'crossdes' (Construction of Crossover Designs) Contains functions for the construction of carryover balanced crossover designs. In addition contains functions to check given designs for balance. Package: r-cran-crossdomainadjust Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crossdomainadjust_0.1.1-1.ca2404.1_all.deb Size: 31204 MD5sum: dd84d039cfdd306aa9a568f892600123 SHA1: ea6876e6a75a5b0441aa93d526ab98699d0da3ae SHA256: 34184cf991ea69fbdb8592b2ecabfc6c44b721ae0c4e7cdb18d140fad33e32eb SHA512: faddd15064fb44b6abade972abaa0936c4c43fd12cdc21976c3a365fd9f674d80bfc9e501ab90bfca3cc8cf28d17d946afc5d0a177248291cfa25c390c440e42 Homepage: https://cran.r-project.org/package=CrossDomainAdjust Description: CRAN Package 'CrossDomainAdjust' (Lambda-Controlled Cross-Domain Feature Adjustment) Provides cross-domain feature adjustment methods for biological and other tabular data. Domain labels define group centroids, and singular value decomposition of their offsets from a common anchor estimates a domain-shift subspace. An orthogonal projection removes a user-controlled fraction of each sample's component in that subspace. A correction strength of zero preserves the input; a strength of one removes the entire learned subspace component. Intermediate values provide partial correction. The fitted transformation can be applied to new samples without refitting. Package: r-cran-crossexpression Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2903 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast, r-cran-rann, r-cran-matrix, r-cran-ggplot2, r-cran-dplyr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-crossexpression_1.0.0-1.ca2404.1_all.deb Size: 2932744 MD5sum: 2e0628b8f2bf6fc53a6c338b315a5a22 SHA1: b66a9da1cb9aa818a918428979a85fbf55f4d0d1 SHA256: 472ccb867bf0c280d5e95c7008a6b58f650f8d23ff746a8cb130ca3fbba6eac2 SHA512: 65acd0cf945e77fd41752ce93672d301df1fe3e7ffe2d536540d1e322f25841320ac02181adaffac6fd4e0a2eec573c1201d7d7dcc76a2797931feedfc9105a2 Homepage: https://cran.r-project.org/package=CrossExpression Description: CRAN Package 'CrossExpression' (Cross-Expression Analysis of Spatial Transcriptomics Data) Analyzes spatial transcriptomic data using cells-by-genes and cell location matrices to find gene pairs that coordinate their expression between spatially adjacent cells. It enables quantitative analysis and graphical assessment of these cross-expression patterns. See Sarwar et al. (2025) and for more details. Package: r-cran-crossfit Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crossfit_0.1.4-1.ca2404.1_all.deb Size: 156276 MD5sum: 5d86c915881aca1f96d353ef83fc3756 SHA1: 3cc5859b38d6ab56c6015557dbbbbeeab7237b0c SHA256: 326a7ea96e92d2c473a3a7591c5f9f78be6f71ee2a0fe3903694485c2b9d61c4 SHA512: 7c8d5c6a7394553950a772b733e3f85f8831fe36f3e6ea4d86a1e1f0c47576635fb6e768bf34113f842f46e2be45badeca6fc430ed5aedd919346197d029f0e2 Homepage: https://cran.r-project.org/package=crossfit Description: CRAN Package 'crossfit' (A Graph-Based Cross-Fitting Engine in R) Provides a general cross-fitting engine for semiparametric estimation (e.g., double/debiased machine learning). 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Package: r-cran-crosshap Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1636 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-clustree, r-cran-data.table, r-cran-dbscan, r-cran-dplyr, r-cran-ggdist, r-cran-ggplot2, r-cran-ggpp, r-cran-gridextra, r-cran-gtable, r-cran-magrittr, r-cran-patchwork, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-umap, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-crosshap_1.4.0-1.ca2404.1_all.deb Size: 1604460 MD5sum: 391cc21c3c606ad11e14548532167e0a SHA1: d4f2aea5fe2f2ca5f1277c97de9d135612030730 SHA256: 8d4154917e34802a16989591d4eebe6490367ce110d90f5421e29ea8e5454c75 SHA512: 635999bbc63749295329dfe780f9d0281eaec9180f1d9493f8a25dd9bc85e26df0c3cd330607fa723d3aa51dab7f1cc78fbdb0a37354aa33869c6bae5fb45611 Homepage: https://cran.r-project.org/package=crosshap Description: CRAN Package 'crosshap' (Local Haplotype Clustering and Visualization) A local haplotyping visualization toolbox to capture major patterns of co-inheritance between clusters of linked variants, whilst connecting findings to phenotypic and demographic traits across individuals. 'crosshap' enables users to explore and understand genomic variation across a trait-associated region. For an example of successful local haplotype analysis, see Marsh et al. (2022) . Package: r-cran-crosslag Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamm4, r-cran-ggplot2, r-cran-lavaan, r-cran-mgcv, r-cran-rms, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-crosslag_0.1.0-1.ca2404.1_all.deb Size: 81546 MD5sum: 536d496a432bcd3a7c515c6ddb435302 SHA1: 6e01710a14c0b7cafa99ae63c0e1656da3b89bed SHA256: c183f4ff4fe26d1312150318011172f0a18dfe283f4df6cf48b2269494777da2 SHA512: 054a59814673110e5822888c3f5274418027b330db5fdf23ab11f8f1944d8e8d472ac0814166a542d09bbd69d773e3219c8fb25b6a38fcffff593e1022732dba Homepage: https://cran.r-project.org/package=crosslag Description: CRAN Package 'crosslag' (Perform Linear or Nonlinear Cross Lag Analysis) Linear or nonlinear cross-lagged panel model can be built from input data. Users can choose the appropriate method from three methods for constructing nonlinear cross lagged models. These three methods include polynomial regression, generalized additive model and generalized linear mixed model.In addition, a function for determining linear relationships is provided. Relevant knowledge of cross lagged models can be learned through the paper by Fredrik Falkenström (2024) and the paper by A Gasparrini (2010) . 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Methods are based on peer-reviewed literature in forest science. 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The usual methods 'select', 'filter', 'group_by', 'summarize', and 'collect' are implemented in such a way as to perform as much computation on the server and pull as little data locally as possible. Package: r-cran-crrcbcv Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crrsc, r-cran-abind, r-cran-pracma, r-cran-survival Filename: pool/dists/noble/main/r-cran-crrcbcv_1.0-1.ca2404.1_all.deb Size: 43782 MD5sum: 445c5d044babe54e5b5444382159bfc2 SHA1: b62482bc5a8f48262405f3d92a8843a51c478ee9 SHA256: b0dcb0acf8eab23440a333a18af320ff81af15072497df6ed7b240b5ac011804 SHA512: 14331f1c1f2764e0260059915dd95509953fa93c034377551c52491e58d62d86b567eb0423c6518e5b7905462aabc41c5a61debaf0b24c070a58c42de72c1a2f Homepage: https://cran.r-project.org/package=crrcbcv Description: CRAN Package 'crrcbcv' (Bias-Corrected Variance for Competing Risks Regression withClustered Data) A user friendly function 'crrcbcv' to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) , the KC-type bias correction by Kauermann and Carroll (2001) , the FG-type bias correction by Fay and Graubard (2001) , and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) . Package: r-cran-crrstep Architecture: all Version: 2025.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cmprsk Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crrstep_2025.1.1-1.ca2404.1_all.deb Size: 73736 MD5sum: 77ad7091548c41efdb17e5e3e1779692 SHA1: 0c72f51e67c1d215a16f264cd2cdb69fbec09ebf SHA256: 029157b5b7afd6358ceb74ab90a758fb5e5ccbb1ae2a68a6d340f8f01d4e724c SHA512: 33898a93ea0cf5a86c29f93b31390ca4450048770b645cd8b800c36c3adeb29bc3b4e6188d1115f85d3bc25a9bd4f9855d6cf82dc546cdd2b0171d81dee87a32 Homepage: https://cran.r-project.org/package=crrstep Description: CRAN Package 'crrstep' (Stepwise Covariate Selection for the Fine & Gray Competing RisksRegression Model) Performs forward and backward stepwise regression for the proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events. This version includes improved handling of factors, interactions, and polynomial terms. Package: r-cran-crseeventstudy Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-crseeventstudy_1.2.2-1.ca2404.1_all.deb Size: 74672 MD5sum: 2efb4004ad655dd7c3bfe39939b80761 SHA1: c9cc928b35d90af9bea5eb545446540218f91cf7 SHA256: 7062881f5735e34a28390cb6beefb23eb5b29d17e2dfc9e4ec179d2f5d656b24 SHA512: 51911a18fb63bda4171628cd571daa0d2df6a7a54257abf61e0efa863dc9efa5797985fc2b510bfd9d930639c58ab3f2398f7e600649cd0bde12886c7073c8e4 Homepage: https://cran.r-project.org/package=crseEventStudy Description: CRAN Package 'crseEventStudy' (A Robust and Powerful Test of Abnormal Stock Returns inLong-Horizon Event Studies) Based on Dutta et al. (2018) , this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) . Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss. Package: r-cran-crsmeta Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-crsmeta_0.3.0-1.ca2404.1_all.deb Size: 28708 MD5sum: 5ec2e24f5e4950dc01b9854a23087366 SHA1: 0119042846546a6105778437567b2659e0c51632 SHA256: dd9bf0b30032d3b60fc8b739d0438157796733577e439bf0af63f6181fd95fb7 SHA512: 705f6c5677829c9badbcc84b65aafb2c11ac70db93d753264f849e58a0c724f5afe6b41505380fb3bf4533c206674d5794c24bc7d3db5e0cf123959537c8cf0e Homepage: https://cran.r-project.org/package=crsmeta Description: CRAN Package 'crsmeta' (Extract Coordinate System Metadata) Obtain coordinate system metadata from various data formats. There are functions to extract a 'CRS' (coordinate reference system, ) in 'EPSG' (European Petroleum Survey Group, ), 'PROJ4' , or 'WKT2' (Well-Known Text 2, ) forms. This is purely for getting simple metadata from in-memory formats, please use other tools for out of memory data sources. Package: r-cran-crsnls Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crsnls_0.2-1.ca2404.1_all.deb Size: 43286 MD5sum: 93d175af14985c4f60662aa63f12305c SHA1: 67a7c346d9a63d14b28a8371cd9f4eb1dc620740 SHA256: de46bf63d2adf3aa7948fcdf5a11d1621344bc2b3b72d9bc05637d6049467e4c SHA512: ea542441c8174d14d916a2c48db45de66cdbc56a2eaccf57564d06ff9ae4d080388290c2caa44ecfd021ffa6869a48e13e7d05a114012ceae2c70d12cd4d6931 Homepage: https://cran.r-project.org/package=crsnls Description: CRAN Package 'crsnls' (Nonlinear Regression Parameters Estimation by 'CRS4HC' and'CRS4HCe') Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm. Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition. Package: r-cran-crso Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crso_0.1.1-1.ca2404.1_all.deb Size: 133620 MD5sum: 7e10df3980b3802aeeb07faa8d97cfac SHA1: d22d2e81169864e5dd6b35c3024884c019c7bd7f SHA256: bfc28c6dbe67f38780b787af63818ec33be068eb95cfec5d885ba252cc2cf40f SHA512: eaac3bf63159da7035250506ebb2ba1b2778533e4d37d3e031b7629054b2ac5a1386117d2ee8318f38c39d9e0db71348ff7295897bdbba8b76a1383cfd5308ec Homepage: https://cran.r-project.org/package=crso Description: CRAN Package 'crso' (Cancer Rule Set Optimization ('crso')) An algorithm for identifying candidate driver combinations in cancer. CRSO is based on a theoretical model of cancer in which a cancer rule is defined to be a collection of two or more events (i.e., alterations) that are minimally sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively are assumed to account for all of ways to cause cancer in the population. In CRSO every event is designated explicitly as a passenger or driver within each patient. Each event is associated with a patient-specific, event-specific passenger penalty, reflecting how unlikely the event would have happened by chance, i.e., as a passenger. CRSO evaluates each rule set by assigning all samples to a rule in the rule set, or to the null rule, and then calculating the total statistical penalty from all unassigned event. CRSO uses a three phase procedure find the best rule set of fixed size K for a range of Ks. A core rule set is then identified from among the best rule sets of size K as the rule set that best balances rule set size and statistical penalty. Users should consult the 'crso' vignette for an example walk through of a full CRSO run. The full description, of the CRSO algorithm is presented in: Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." BioRxiv 674234 [Preprint]. June 19, 2019. . Please cite this article if you use 'crso'. Package: r-cran-crsra Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2563 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-digest, r-cran-tidytext, r-cran-tibble, r-cran-rcorpora, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crsra_0.2.3-1.ca2404.1_all.deb Size: 2197602 MD5sum: 0f068d6517f4bbcc188fdf26bb4eea94 SHA1: 4d59cfd75b6a4bd8b9ba48ee4c1db3e34b2fbdb0 SHA256: 9511e4e59c0e55b211150488cf4b7f0889620a34735be1fd2b6a8238fdacadbc SHA512: 039a733c2e8934927bdaf70ef116debbfac9cba840896d99e435194f5119ae605b2c514a2cce9d268339ed7fbb7dfe7343768a2b144f8a2793388c10fa483a18 Homepage: https://cran.r-project.org/package=crsra Description: CRAN Package 'crsra' (Tidying and Analyzing 'Coursera' Research Export Data) Tidies and performs preliminary analysis of 'Coursera' research export data. 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Package: r-cran-crstools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-ggplot2, r-cran-terra, r-cran-cli, r-cran-jpeg, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pastclim, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-tidyterra, r-cran-svglite, r-cran-spelling Filename: pool/dists/noble/main/r-cran-crstools_1.0.0-1.ca2404.1_all.deb Size: 2674998 MD5sum: 77acac3b59f3c8bf92c0621682da582d SHA1: 1540436bff1be5a25ecffb54da2c8806efbf5df2 SHA256: 47a4515321c99d171251d609ee574d322d78a4e448260543ee00b5d3fcd91a8b SHA512: 15a9b4aa1c3112c924af6019981d723600fc9220758c1ae559c1cb63713734bec82e74e99db379249a1704650532674d2867dd2c30b5a5d4e695ce171e62c917 Homepage: https://cran.r-project.org/package=crstools Description: CRAN Package 'crstools' (Tools to Work with Projections) Choose the appropriate map projection for a given application, visualise the resulting distortion, and georeference data from unknown projections. The full functionalities of the package are described in Pozzi et al. (2026) (pre-print). 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Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) , Yang et al. (2022) , Pocock et al. (1987) , Vickerstaff et al. (2019) , and Li et al. (2020) . 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The estimator IPW-AUG-GEE is Doubly robust (DR). Package: r-cran-crtsize Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crtsize_1.2-1.ca2404.1_all.deb Size: 107084 MD5sum: e1a29c59cfb00116e6aeca4dbf7961f3 SHA1: 9c3fff2398a82277abe13d89a159a2453e113318 SHA256: 06d4d9f275019a556c05dd249af6c6fcdae739e8da79e2c06aa2213e4aae8182 SHA512: c1c84f2a465f2ccec909599fc3d8f13731f092a6c86276549e8dad430f455e28fd5eed26a07495064580a19346d6b31491eccb802c7ddad35da81990582f62a3 Homepage: https://cran.r-project.org/package=CRTSize Description: CRAN Package 'CRTSize' (Sample Size Estimation Functions for Cluster Randomized Trials) Sample size estimation in cluster (group) randomized trials. Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012). 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Package: r-cran-crul Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-r6, r-cran-urltools, r-cran-httpcode, r-cran-jsonlite, r-cran-mime, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-fauxpas, r-cran-webmockr, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crul_1.6.0-1.ca2404.1_all.deb Size: 627446 MD5sum: b1aa9eb347504ac5dc2e10880b2f1861 SHA1: 49a1e590a49f75bf15888ffb3acb49ba0554a048 SHA256: 59f8d5a8c1694480338d4468694280b7125122234d0f86c2ff2bf289272127ac SHA512: 04b5129e0803946fa5ff65be64b8ce6221bbc0829551b5729dfea736d7f5e918d4b594de601ad1febb60fb46ec9e7721238003bfa924b8a7b0e1f1b4f438dc20 Homepage: https://cran.r-project.org/package=crul Description: CRAN Package 'crul' (HTTP Client) A simple HTTP client, with tools for making HTTP requests, and mocking HTTP requests. 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Estimation makes use of recent advancements in Riesz-learning to estimate a set of required nuisance parameters with deep learning. The result is the capability to estimate mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning. 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Package: r-cran-cruts Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-raster, r-cran-stringr, r-cran-lubridate, r-cran-ncdf4 Filename: pool/dists/noble/main/r-cran-cruts_1.1-1.ca2404.1_all.deb Size: 29714 MD5sum: 4a04bcc99816b2e81e57f4b58ed8588b SHA1: 72d4610e7feb78a9585a969a57856fe1ebc79a04 SHA256: 3e10318498a9e04c71f8503c24cf068825aa6bbc6713d85b94f7b9b5ba4a3dde SHA512: 9e570535382104feb455c7282d7be5bf549193e39c78423e10b8976d503cdeff647f5b73ef9e9a7c5197ac0edac291fb02e53eb84baf3ad390461725e78099b0 Homepage: https://cran.r-project.org/package=cruts Description: CRAN Package 'cruts' (Interface to Climatic Research Unit Time-Series Version 3.21Data) Functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data. 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Package: r-cran-crwbmetareg Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmtest, r-cran-rfast2, r-cran-sandwich Suggests: r-cran-clusterses Filename: pool/dists/noble/main/r-cran-crwbmetareg_1.0-1.ca2404.1_all.deb Size: 44030 MD5sum: f3deef565a19b52c84e7ba0d151624c8 SHA1: fe5f38e7b688d308c55dbb7b2396be8f1d4b0a3c SHA256: 3ab3043b337a2e1fbdc1838bfe517800028ce4ef33a1bec8d2768d154cb2f132 SHA512: c72c53be6fb94cb9080552874810ddb1bcf055e7a0ca781ca7a94378e4e2dc57cbf6e64e42bda95065c3947ab34bec31abc4d00d10b361928fc0252bbf3c0aec Homepage: https://cran.r-project.org/package=crwbmetareg Description: CRAN Package 'crwbmetareg' (Cluster Robust Wild Bootstrap Meta Regression) In meta regression sometimes the studies have multiple effects that are correlated. For this reason cluster robust standard errors must be computed. However, since the clusters are unbalanced the wild bootstrap is suggested. See Oczkowski E. and Doucouliagos H. (2015). "Wine prices and quality ratings: a meta-regression analysis". American Journal of Agricultural Economics, 97(1): 103--121. and Cameron A. C., Gelbach J. B. and Miller D. L. (2008). "Bootstrap-based improvements for inference with clustered errors". The Review of Economics and Statistics, 90(3): 414--427. . Package: r-cran-crwrm Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crwrm_0.0.1-1.ca2404.1_all.deb Size: 15918 MD5sum: b3f1fb3cb0bfa0c7ff666c75fdcaf1de SHA1: bce2e57d0c3a96bf4e55c57d2907d42aa5fc4ba5 SHA256: c423e9ca751c6c73bd20d17230dd2847090dec7e76f8da6ea3cb5bc50f142842 SHA512: 11d829e2287c9241b5c675753c43e3225e8e170d67d1b295f4378e3721528c64224dc07ca354cb299ae2660e9191e7e049fa57c6d818b2838c6f1b0218b42f82 Homepage: https://cran.r-project.org/package=CRWRM Description: CRAN Package 'CRWRM' (Changing the Reference Group without Re-Running the Model) To re-calculate the coefficients and the standard deviation when changing the reference group. Package: r-cran-cry Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-cry_0.5.2-1.ca2404.1_all.deb Size: 1632982 MD5sum: cf54bfbdc8e5a47fa7216ef6945df33f SHA1: 792262b9591f97427c7553840806c0532939fab3 SHA256: 7df1d638c444384e3019ea3397a378516b3753fa90ca7e7ad1caec448781eab3 SHA512: 8691da518a65e2053310464d064ab58bda589812fcc4184d5aa82505c5f601c8c23642bfa1aad9a30ac707eb229acadf30b9e07c33161b005409261da23ce00f Homepage: https://cran.r-project.org/package=cry Description: CRAN Package 'cry' (Statistics for Structural Crystallography) Reading and writing of files in the most commonly used formats of structural crystallography. 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Historical data contains daily open, high, low and close values for all crypto currencies. The package draws on two complementary sources: 'CoinMarketCap' (primary, via the 'crypto_*' functions) and 'CoinGecko' (secondary, via the 'cg_*' functions). Both sources are queried without an 'API' key; the two function families return tibbles with identical column conventions so downstream pipelines work on either source. Package: r-cran-cryptography Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desctools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cryptography_1.0.0-1.ca2404.1_all.deb Size: 45862 MD5sum: 0f0480a7fa077fa15a772c10fd5eb61a SHA1: 55edb7c6ae61cfe3dd9a7ee802bb99fe94c89062 SHA256: 5d33c3dd503721f6d301880336c3e4efb95d8a5a5ab9343033e42aee4360924e SHA512: a072e8d497d09ba0f9bdd78a7e98fb7460b09b9c2f4acf38edd804205647dc4ef5cba56248f76f8b36cee3b490450d188435d9157f9e95ec93b33d5b6e120a03 Homepage: https://cran.r-project.org/package=cryptography Description: CRAN Package 'cryptography' (Encrypts and Decrypts Text Ciphers) Playfair, Four-Square, Scytale, Columnar Transposition and Autokey methods. Further explanation on methods of classical cryptography can be found at Wikipedia; (). Package: r-cran-cryptoquotes Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-jsonlite, r-cran-lifecycle, r-cran-plotly, r-cran-ttr, r-cran-xts, r-cran-zoo Suggests: r-cran-data.table, r-cran-knitr, r-cran-quantmod, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-cryptoquotes_1.3.4-1.ca2404.1_all.deb Size: 2148968 MD5sum: 305350ee18f2c6559b2a1d672f3ff093 SHA1: af978d0ce4882d42612024f98ebbfb221b6a4e28 SHA256: 18cdd7e8e07e590325f957f0f28ff78291f32da9dd29f3a276be3e5403ff17ad SHA512: a4712d145b3c1d7fdf046400d1d6ec394fcc18ecf6c9c8b2cd06422b7b789f01acbfd17306ea39da9210edb263895e20a2f234dc3b37ab228d0f89cb98f6d6e2 Homepage: https://cran.r-project.org/package=cryptoQuotes Description: CRAN Package 'cryptoQuotes' (Open Access to Cryptocurrency Market Data, Sentiment Indicatorsand Interactive Charts) This high-level API client provides open access to cryptocurrency market data, sentiment indicators, and interactive charting tools. The data is sourced from major cryptocurrency exchanges via 'curl' and returned in 'xts'-format. The data comes in open, high, low, and close (OHLC) format with flexible granularity, ranging from seconds to months. This flexibility makes it ideal for developing and backtesting trading strategies or conducting detailed market analysis. Package: r-cran-cryptotrackr Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringi, r-cran-openssl, r-cran-digest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cryptotrackr_1.3.3-1.ca2404.1_all.deb Size: 440756 MD5sum: 47342e8a179704732c27e573a88eb78e SHA1: 9d31997d271718517a41eac1c9101405f8ab926d SHA256: f81ab20ac90fb2518038b76e0cdd354cfca3236d764e937523aea938f27185a8 SHA512: 364866d6861805cc4ee9a1428c5df723453e2ddd9ad3eae4ecadf397236eac07394130fb36395d44439f9f530739d380170447507f97243ea2d3c32e7d56e5dd Homepage: https://cran.r-project.org/package=cryptotrackr Description: CRAN Package 'cryptotrackr' (An Interface to Crypto Data Sources) Allows you to connect to data sources across the crypto ecosystem. This data can enable a range of activity such as portfolio tracking, programmatic trading, or industry analysis. The package is described in French (2024) . Package: r-cran-cryptoverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cryptoverse_0.1.0-1.ca2404.1_all.deb Size: 1255012 MD5sum: f29ac678d6a679731bb4aaaa75d747dd SHA1: d1c62d344890e564f15aafca62e37a9c4bb7b95f SHA256: fbd7754ffcef56341f4a3ca1bc1dc6e9334b35bc61292538179538c00c50c25b SHA512: 658f499ec6eb5fe5f6db8b6eb7f51d0fe27e40c889876a01e5e1f96bfde8fb5fe4ce4690acb1dc06ec4f12099c97d8b2c6cbd3d5676b5dbf6fb0968a9bc3816d Homepage: https://cran.r-project.org/package=cryptoverse Description: CRAN Package 'cryptoverse' (Visualization and Analytics for the Cryptoverse) Providing data to quickly visualize and analyze data from several cryptocurrencies. Package: r-cran-cryptowatchr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-cryptowatchr_0.2.0-1.ca2404.1_all.deb Size: 61294 MD5sum: 7bb905b24adbff93a98c0a99c565f38a SHA1: 700bee6f3c3841bdc8cd61c3026abcda6b023267 SHA256: 928ee89c4366716f6f9d85e84c03c2dd1e84cca6805c520335c4e2cd20ad8f78 SHA512: b3ee033fd006882e4507eeb128f7d2e9c323deedb3f3cc48d048916c4645b41cb5373ef922e82701f430fa8f038d3dee51c721c967d8db2c7daa8a3bca1cb4c8 Homepage: https://cran.r-project.org/package=cryptowatchR Description: CRAN Package 'cryptowatchR' (An API Wrapper for 'Cryptowatch') An API wrapper for 'Cryptowatch' to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See for a detailed documentation. Package: r-cran-cryptrndtest Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ksamples, r-cran-sfsmisc, r-cran-rmpfr, r-cran-lambertw, r-cran-gmp, r-cran-tseries Suggests: r-cran-r.rsp, r-cran-bbotk Filename: pool/dists/noble/main/r-cran-cryptrndtest_1.2.7-1.ca2404.1_all.deb Size: 341610 MD5sum: 168d35d07ca9c4c5fb6c7d4b9545a6b2 SHA1: 4cd1e0f4c3dee2fe2eff3419188ef67776b13a95 SHA256: e95888a74989a5070e4f9513d6d1e76d56047466c8fdb6d1ce2b39e8b45ebb11 SHA512: 684ce7210163a86e9ec5c17442559d9b9c0869d8a00863b5e881609a21e779de53d012217945afdf9682cace8a4056eaa33ab39697889e3c9e6fa437a67854c8 Homepage: https://cran.r-project.org/package=CryptRndTest Description: CRAN Package 'CryptRndTest' (Statistical Tests for Cryptographic Randomness) Performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests (Ryabko and Monarev, 2005) . Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests. 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For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged. Package: r-cran-crystract Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1268 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-stringr, r-cran-future, r-cran-future.apply, r-cran-geometry Suggests: r-cran-dt, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crystract_1.0.2-1.ca2404.1_all.deb Size: 868136 MD5sum: 2c5fbf76e17cf776c874f4c4741dcb8e SHA1: 91152b44a562e9b6dca5827c47184df879de116c SHA256: 6d93b9c5501ea447e5ca31cb210a145b540ce8aa944db2b84368418742a8f862 SHA512: 2f81ca0362a9fd1a96556745bb8c06fd8e44970ad792db33e4ed1939793410e2e7299183a84e76cc43458f3fb1fab080b1b18a328f05ae6946d25cbf537e35ec Homepage: https://cran.r-project.org/package=crystract Description: CRAN Package 'crystract' (Crystallographic Information File (CIF) Data Processing Tools) Provides a suite of functions to parse Crystallographic Information Files (.cif), extracting essential data such as chemical formulas, unit cell parameters, atomic coordinates, and symmetry operations. It also includes tools to calculate interatomic distances, identify bonded pairs using various algorithms (minimum_distance, brunner_nn_reciprocal, econ_nn, crystal_nn), determine nearest neighbor counts, and calculate bond angles. The package is designed to facilitate the preparation of crystallographic data for further analysis, including machine learning applications in materials science. Methods are described in: Brunner (1977) ; Hoppe (1979) ; O'Keeffe (1979) ; Shannon (1976) ; Pan et al. (2021) ; Pauling (1960, ISBN:978-0801403330). 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Package: r-cran-csdm Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdpack, r-cran-generics, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-broom, r-cran-sandwich, r-cran-plm, r-cran-lmtest, r-cran-modelsummary Filename: pool/dists/noble/main/r-cran-csdm_2.0.0-1.ca2404.1_all.deb Size: 314140 MD5sum: e1d83e7d9ce02c3f8e97ef5693b925c1 SHA1: a3b2b971da76224e35f0935bcbba8e173bf25341 SHA256: 45e4b834df49a5aa9a809ae15e012ff3ee47f02b581b6ac16cd3c29845ea238f SHA512: 05212bef232b9fe7ad549ae20ed362a03a172eebfc70581eac3d2d8dfb251e8786398d3dcf4e7f4d4845d3cb8646369bb8d4108be7bea4229c753b163ed74eff Homepage: https://cran.r-project.org/package=csdm Description: CRAN Package 'csdm' (Cross-Sectional Dependence Models) Provides estimators and utilities for large panel-data models with cross-sectional dependence, including mean group (MG), common correlated effects (CCE) and dynamic CCE (DCCE) estimators, and cross-sectionally augmented ARDL (CS-ARDL) specifications, plus related inference and diagnostics. 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It includes implementations of commonly used methods such as Analogs, Linear Regression, Logistic Regression, and Bias Correction techniques, as well as interpolation functions for regridding and point-based applications. It facilitates the production of high-resolution and local-scale climate information from coarse-scale predictions, which is essential for impact analyses. The package can be applied in a wide range of sectors and studies, including agriculture, water management, energy, heatwaves, and other climate-sensitive applications. The package was developed within the framework of the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460), the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project BOREAS (PID2022-140673OA-I00). Implements the methods described in 'Ramon et al. (2021) ', 'Duzenli et al. (2024) ', 'Moreno-Montes et al. (2026) ', 'Duzenli et al. (2026) ', 'Duzenli et al. (2026) '. Package: r-cran-csem Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2606 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-alabama, r-cran-cli, r-cran-crayon, r-cran-expm, r-cran-future.apply, r-cran-future, r-cran-lifecycle, r-cran-lavaan, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-pbivnorm, r-cran-progressr, r-cran-psych, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-symmoments, r-cran-truncatednormal Suggests: r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-nnls, r-cran-prettydoc, r-cran-plotly, r-cran-rsvg, r-cran-rmarkdown, r-cran-rootsolve, r-cran-listviewer, r-cran-testthat, r-cran-ggplot2, r-cran-ga, r-cran-openxlsx, r-cran-spelling Filename: pool/dists/noble/main/r-cran-csem_0.7.1-1.ca2404.1_all.deb Size: 1992524 MD5sum: 2ac07341bdc94a820437a638be093843 SHA1: d0e0f1ec586a77163a8c9506ffe0f404005c2ce0 SHA256: 1e0212051bfb52c948b3cad1c967e1e2a962cece5588c41438418d024ac87ec5 SHA512: 74b6e6c55d993dda2a67d709c7420b8c15e7d9dac42353de4cea951fd2310cc49b9466a33eb32bbeaf847b7b1fc7eb3735ccde17eeb109c39639a7c9849c3174 Homepage: https://cran.r-project.org/package=cSEM Description: CRAN Package 'cSEM' (Composite-Based Structural Equation Modeling) Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.). Package: r-cran-csemgt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-boot, r-cran-mgcv, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-spelling, r-cran-mirt, r-cran-haven Filename: pool/dists/noble/main/r-cran-csemgt_1.0.0-1.ca2404.1_all.deb Size: 316474 MD5sum: 6a355a975478088750243859e6e06d0a SHA1: 137b91317b8921719c643899fe96562687c5109d SHA256: f27d001ae6b384ecdf4212a2fa5034d391fa970c01a9a5630d77797e5dad792b SHA512: b1dc7520eef70eec882c620a1567bc6dd0c283663fda441491d3c6b085873b4093b34a56fb8667f9609c4beeff516865c2a828820532ac86fb17d7f05e2eef09 Homepage: https://cran.r-project.org/package=csemGT Description: CRAN Package 'csemGT' (Conditional Standard Error of Measurement in GeneralizabilityTheory) Estimates the per-person conditional standard error of measurement (CSEM) under the persons-by-items single-facet crossed design of Generalizability Theory, following Brennan (1998) . Implements three estimators of the relative error variance (full, large_a, uncorrelated) and the closed-form absolute error variance, with both analytical and item-resampling bootstrap sampling variances, quadratic smoothing of CSEMs on observed score, D-study extrapolation, and base-graphics plotting. Package: r-cran-csemtools Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-ggplot2, r-cran-pbapply, r-cran-rlang, r-cran-patchwork Suggests: r-cran-psych, r-cran-psychtools, r-cran-mbess, r-cran-ksamples, r-cran-scam, r-cran-zoo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-csemtools_0.2.1-1.ca2404.1_all.deb Size: 272518 MD5sum: ba9e151a0adb84ad1b2b8cc73bc564a0 SHA1: de3bbacbf4302df88f74e88f3ff7312783201e74 SHA256: 5bada61f1264f9ea44dffbdbe7974bbd6970cb56cf5dd49e8517f378ebe41f1f SHA512: f2d745d4a2e85075e9f9f0977b44f938d6c61f0e6527427f279dfc6b18ed1387ef0aa7aa3441340af58f9aa9375b776a42cd922f202e0be3dc6a126172aa8246 Homepage: https://cran.r-project.org/package=csemTools Description: CRAN Package 'csemTools' (Conditional Standard Error of Measurement Tools for Test Scores) Compute and compare conditional standard errors of measurement (CSEM) across score distributions using methods from classical test theory. Includes approaches for smoothing, bootstrapped CSEM, standardized CSEM, CSEM for scale scores, and assessment of properties of split-half scores. Also supports comparison with global standard errors derived from reliability coefficients and graphical visualization of CSEM curves and relative precision across observed score ranges. Some of these implemented methods are based on work by Lord (1955) , Feldt and Qualls (1996) , McNeish and Dumas (2025) . Package: r-cran-cseqpat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm Filename: pool/dists/noble/main/r-cran-cseqpat_0.1.2-1.ca2404.1_all.deb Size: 16192 MD5sum: a948cf05b4ecd40380626aa3bdf72180 SHA1: 5674f647b1f1d36fd34a33db0568c1cfce6d7ae7 SHA256: 1c779ff331c381ac2c991bc769ea486195f562e3fe221539747f52ae53a28da2 SHA512: 31b7f5d0d1b7f07f2d4e1b0a9f566b800dd64bd43963455fbaf5dfc9626478706c1cea09286c81a2cfe4444a6938f8746abb1e0245dd658e3f96c050e28ca6f2 Homepage: https://cran.r-project.org/package=CSeqpat Description: CRAN Package 'CSeqpat' (Frequent Contiguous Sequential Pattern Mining of Text) Mines contiguous sequential patterns in text. Package: r-cran-csesa Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3069 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-csesa_1.2.0-1.ca2404.1_all.deb Size: 888554 MD5sum: 3bf517810beb3d5ad205993f310f510c SHA1: a94c88b17406c1dcd4a3e18922ba560cecbcddf0 SHA256: 1ee404f87338cb63e4f3d56d22f67d408f4069c860e4d1a7e4ebbd5d9c9d7b3d SHA512: b49571b4aac4128cda9166525b7a0fb05e31051d7c3c43022eaeed41c63f2cb13e6d478a538d971205324ee49ad2d396ab232e0b12b745873eff900f3f961757 Homepage: https://cran.r-project.org/package=CSESA Description: CRAN Package 'CSESA' (CRISPR-Based Salmonella Enterica Serotype Analyzer) Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping. Package: r-cran-csgo Architecture: all Version: 0.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzyjoin, r-cran-purrr, r-cran-httr, r-cran-stringr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-extrafont, r-cran-ggplot2, r-cran-future, r-cran-furrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-csgo_0.6.7-1.ca2404.1_all.deb Size: 415162 MD5sum: 9735cd8a64fed663c3f2e1652a60449a SHA1: 1b3c4be429c599dce37eabb097c90c4fd46ad657 SHA256: f8f9112051a9af1b1a69edfc78561b56e8703fb7b380377354d01b6866cf042a SHA512: 5301380f222715487341e3777e8b312998421733b28fac67427c23ed5da38e095d3e5587e6cb05d80eeb40a1fbb735993e402fa3582dfcf505d36b9114b339c1 Homepage: https://cran.r-project.org/package=CSGo Description: CRAN Package 'CSGo' (Collecting Counter Strike Global Offensive Data) An implementation of calls designed to collect and organize in an easy way the data from the Steam API specifically for the Counter-Strike Global Offensive Game (CS Go) . Package: r-cran-cshapes Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rmapshaper, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cshapes_2.0-1.ca2404.1_all.deb Size: 3698794 MD5sum: 1b79948fcc4db7bc394c09d2c17db1bd SHA1: f68cd41dacfb4babb456314a8c41b63cf578bfca SHA256: 83b383af8e60dcc2c79bf96a338783a03c6746877b17b8cc071b648817942bb2 SHA512: b9678e1eca2b753bb7bc4febc6ffd653ff1ff02001f75eb14535da8a5ad99ef25c94eab0ece01a77e19c88b93fae1f53285d6cff50e4e8e0d4a6fd8c14ae10d1 Homepage: https://cran.r-project.org/package=cshapes Description: CRAN Package 'cshapes' (The CShapes 2.0 Dataset and Utilities) Package for CShapes 2.0, a GIS dataset of country borders (1886-today). Includes functions for data extraction and the computation of distance matrices and -lists. Package: r-cran-cshshydrology Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1953 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circular, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-kendall, r-cran-lubridate, r-cran-plotrix, r-cran-mgbt, r-cran-outliers, r-cran-teachingdemos, r-cran-timedate, r-cran-stringr, r-cran-scales Suggests: r-cran-ggspatial, r-cran-httr2, r-cran-knitr, r-cran-readr, r-cran-rlang, r-cran-rmarkdown, r-cran-terra, r-cran-tidyhydat, r-cran-tidyterra, r-cran-testthat, r-cran-vctrs, r-cran-whitebox Filename: pool/dists/noble/main/r-cran-cshshydrology_1.6-1.ca2404.1_all.deb Size: 1255406 MD5sum: e29803c307eadb2504635a74b840cef8 SHA1: 47cbc3dc14b804c0028137a195d34262e855afd0 SHA256: d9d0e09a726fc441fd006e48b35ca814367b3e3eb46c8254eec7df0339fa5aa4 SHA512: f6751191021179eabd8d5e4d4e6125576853e9cbaca747223fd3757979a4b620f916cd3b73074bf35be767522205b1f289a180ffc2e84ce74ee78bba4e3f5787 Homepage: https://cran.r-project.org/package=CSHShydRology Description: CRAN Package 'CSHShydRology' (Canadian Hydrological Analyses) A collection of user-submitted functions to aid in the analysis of hydrological data, particularly for users in Canada. The functions focus on the use of Canadian data sets, and are suited to Canadian hydrology, such as the important cold region hydrological processes and will work with Canadian hydrological models. The functions are grouped into several themes, currently including Statistical hydrology, Basic data manipulations, Visualization, and Spatial hydrology. Functions developed by the Floodnet project are also included. CSHShydRology has been developed with the assistance of the Canadian Society for Hydrological Sciences (CSHS) which is an affiliated society of the Canadian Water Resources Association (CWRA). As of version 1.2.6, functions now fail gracefully when attempting to download data from a url which is unavailable. Package: r-cran-csindicators Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiapply, r-cran-climprojdiags, r-cran-cstools, r-cran-spei, r-cran-lmom, r-cran-lmomco, r-cran-zoo, r-cran-s2dv, r-cran-lubridate, r-cran-geosphere Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-csindicators_1.2.0-1.ca2404.1_all.deb Size: 2185102 MD5sum: 05203b4aea9594813140ec15da554d6d SHA1: b89d376d46895e3561a6f835e00dbf4eef4a9ca0 SHA256: 05dac2c35dc7f76c259dfe779b00c5ac783ca71c5667c3f3805ca7c9a5ee85bc SHA512: a23f85825ee6c200587dfb67757773ff2f82a3991e8d66e336cae7f91cb48161186418f0bb59fd8fc6a91a41911904a5450c6bc9f966d4a9f7556c1d558caf79 Homepage: https://cran.r-project.org/package=CSIndicators Description: CRAN Package 'CSIndicators' (Climate Services' Indicators Based on Sub-Seasonal to DecadalPredictions) Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, health...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'. This package is described in Pérez-Zanón et al. (2023) and was developed in the context of the H2020 projects MED-GOLD (776467) and S2S4E (776787) projects, as well as the Horizon Europe project MEDEWSA (101121192) and the national project BOREAS (PID2022-140673OA-I00). See Lledó et al. (2019) and Chou et al., 2023 for details. Package: r-cran-csmaps Architecture: all Version: 2025.8.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-ggrepel, r-cran-leaflet, r-cran-sf, r-cran-csdata Filename: pool/dists/noble/main/r-cran-csmaps_2025.8.21-1.ca2404.1_all.deb Size: 3845928 MD5sum: fe93a53ed91defc7063790207ee22fb4 SHA1: 0d2b8da46502bb86d414d040660cdd774367db41 SHA256: ede47421ce4a8abb933db903737c95735e510545fefdb6a73af72f39c25326a8 SHA512: b97c3dd8c5958c780820e5b5f6f35f5c9cddef19a692cf7e988e995ae9bf6f85e0ed9bc3b26a4f9b389045ee66856112fc52e20fdf55121d4119c403a510ddae Homepage: https://cran.r-project.org/package=csmaps Description: CRAN Package 'csmaps' (Preformatted Maps of Norway that Don't Need Geolibraries) Provides datasets containing preformatted maps of Norway at the county, municipality, and ward (Oslo only) level for redistricting in 2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, and with an insert for Oslo), allowing the user to rapidly create choropleth maps of Norway without any geolibraries. Package: r-cran-csmbuilder Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-rcpp, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-csmbuilder_0.1.1-1.ca2404.1_all.deb Size: 100516 MD5sum: f375c33385f8532f0be3a2ca938640da SHA1: 829e656632522a84c8cea9bdeab4664564ff73c9 SHA256: 242737a2e2d822c6974b9ab2c276f274cffbbdb190096026f625d947700d9141 SHA512: 7efaee30898e3f12cfa0f3421f6227984ddb79809740246a4bda793a6ef9b93563b5b93f666ad58331711fe6d49739dd8927ee3b31f7540678c3913774cc3316 Homepage: https://cran.r-project.org/package=csmbuilder Description: CRAN Package 'csmbuilder' (A Collection of Tools for Building Cropping System Models) A collection of tools for designing, implementing, testing, documenting and visualizing dynamic simulation cropping system models. Models are specified as a combination of state variables, parameters, intermediate factors and input data that define a system of ordinary differential equations. Specified models can be used to simulate dynamic processes using numerical integration algorithms. Package: r-cran-csmes Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mco, r-cran-rocr, r-cran-rpart, r-cran-zoo, r-cran-catools, r-cran-data.table Filename: pool/dists/noble/main/r-cran-csmes_1.0.1-1.ca2404.1_all.deb Size: 108728 MD5sum: 0f3b4dcb7d0c1602cdc2403367db7de4 SHA1: c3e00fb998e4812a3e0c39aa129d8a8e7b9ec692 SHA256: b0895459e9b0aa27591ad16b0d38dd5734fb66e5fcb1fd0477a63295dbacecfd SHA512: 3d5f56c81e2242d7b1eae5877f6a836ae1cbd4edde82046c8c131064c761775d0df4524a1b7dcaee56aefcf85fdef5b935146e70cc8716afdace773764a58cf7 Homepage: https://cran.r-project.org/package=CSMES Description: CRAN Package 'CSMES' (Cost-Sensitive Multi-Criteria Ensemble Selection for UncertainCost Conditions) Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) ) for cost-sensitive learning under unknown cost conditions. Package: r-cran-csmgmm Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-mvtnorm, r-cran-curl, r-cran-data.table, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-csmgmm_0.5.0-1.ca2404.1_all.deb Size: 149622 MD5sum: d48ff2c8f6d5005699e2b95c0f626ce4 SHA1: 165c725a405fc49c67f4d92c6a7e9865a321b753 SHA256: 9c5397162b0dc579803098dfd18a18451a3814c9e01e8ba69ecaf5a68624e05b SHA512: ffa1f5efec18ab1a055b805ca5f353d17289e38b4cd103925e49f0741d584f370927f57588ca900126ca5ef4f2cbc5c5c18f543b8febea294a6df39454436213 Homepage: https://cran.r-project.org/package=csmGmm Description: CRAN Package 'csmGmm' (Conditionally Symmetric Multidimensional Gaussian Mixture Model) Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (Journal of the American Statistical Association 2025, ). Package: r-cran-csmpv Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-glmnet, r-cran-hmisc, r-cran-rms, r-cran-forestmodel, r-cran-ggplot2, r-cran-ggpubr, r-cran-survminer, r-cran-xgboost, r-cran-scales, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-csmpv_1.0.5-1.ca2404.1_all.deb Size: 1021190 MD5sum: fd8ab98cad930a599135325d07e0e624 SHA1: f8f806f51047eafee98a6d256be23c0af25572bb SHA256: 66fef2d3e6792626ffb710039c97e69389b1e31401527dde85b8b760c9a48ee2 SHA512: 8b9064d0bd42037716b511e48728e8774432c53a9589633fe2138b099ca9ece2f0ff096bfc529bcee9027218496f20a90a268ac41737860fde2150a279c20321 Homepage: https://cran.r-project.org/package=csmpv Description: CRAN Package 'csmpv' (Biomarker Confirmation, Selection, Modelling, Prediction, andValidation) There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from 'stats', coxph() from 'survival', surv_fit(), and ggsurvplot() from 'survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the 'glmnet' R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs 'XGBoost' for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with 'XGBoost' for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) , - Friedman et al. (2010) , - Simon et al. (2011) , - Harrell (2023) , - Harrell (2023) , - Chen and Guestrin (2016) , - Aoki et al. (2023) . 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Package: r-cran-ctgdist Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mirt Filename: pool/dists/noble/main/r-cran-ctgdist_0.1.0-1.ca2404.1_all.deb Size: 20506 MD5sum: eadd23f0e6b5adf9d0af18c0eb7360bb SHA1: 816f01a492f1373ef6afaf71cfbc080b61186c1e SHA256: 1358db1422705e65334cfc5dc398a67b48344d232214ab383412fd35b32e7feb SHA512: 042df1b7b511612ffd076657862386dd06bb2bd43973c668d83c95a8ceb8eb0ecdb6e0cef4f8a5c08ef1502c275f93a712388e3c99eeb50ceee708a05ddc65f7 Homepage: https://cran.r-project.org/package=ctgdist Description: CRAN Package 'ctgdist' (Likert Category Distance Calculator) It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. 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Package: r-cran-ctmle Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-superlearner, r-cran-tmle, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ctmle_0.1.2-1.ca2404.1_all.deb Size: 166484 MD5sum: 6f1e08cd294157f488d37a5fe1ed426f SHA1: 474274dbe83cb088cf5a67310c85f62cd1bf1fb4 SHA256: 1360442020665cce9f113b9e7862f4fb2626d2058b98e608a1c7b55a86219e5c SHA512: 5628cbefb540c7c0134638455ddefceb92572ed7041467f493000d233cea5d6d100bc8928f3c1f4b9f4ccdaeff66a65d82d3b53f27372f8de23a7c3621f85181 Homepage: https://cran.r-project.org/package=ctmle Description: CRAN Package 'ctmle' (Collaborative Targeted Maximum Likelihood Estimation) Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) ) and the glmnet-C-TMLE algorithm (Ju et al. (2017) ). Package: r-cran-ctmm Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-data.table, r-cran-digest, r-cran-expm, r-cran-fasttime, r-cran-gmedian, r-cran-gsl, r-cran-manipulate, r-cran-mass, r-cran-numderiv, r-cran-parsedate, r-cran-pbivnorm, r-cran-pracma, r-cran-raster, r-cran-shape, r-cran-sf, r-cran-sp, r-cran-statmod, r-cran-terra Suggests: r-cran-animation, r-cran-bit64, r-cran-dplyr, r-cran-fftw, r-cran-knitr, r-cran-move, r-cran-quadprog, r-cran-rmarkdown, r-cran-suncalc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctmm_1.3.0-1.ca2404.1_all.deb Size: 3941984 MD5sum: a4ddd58b468b1884f33495331be7731b SHA1: 34e93cd5f3229e7251fef83420ff610d63bed37b SHA256: 3d673f8ef9705d50261b6ceaabed61755476420fb7942df1f459ced5cf992ada SHA512: c703495523d34d58e1189b0bc403ffbd560fb769a066bdfdda453be55ad803f742651e2e0f4dec0f2d420ef3d6b0fc3e94de2304ddddf72ca4af80c3c724bc3c Homepage: https://cran.r-project.org/package=ctmm Description: CRAN Package 'ctmm' (Continuous-Time Movement Modeling) Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic-process movement models to animal tracking data. The package is described in Calabrese et al (2016) , with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) , Fleming et al (2014) , Fleming et al (2015) , Fleming et al (2015) , Fleming et al (2016) , Péron & Fleming et al (2016) , Fleming & Calabrese (2017) , Péron et al (2017) , Fleming et al (2017) , Fleming et al (2018) , Winner & Noonan et al (2018) , Fleming et al (2019) , Noonan & Fleming et al (2019) , Fleming et al (2020) , Noonan et al (2021) , Fleming et al (2022) , Silva et al (2022) , Alston & Fleming et al (2023) . Package: r-cran-ctmva Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-polynom, r-cran-mass, r-cran-mgcv, r-cran-matrix, r-cran-ggplot2, r-cran-rlang, r-cran-vegan, r-cran-viridislite Suggests: r-cran-dplyr, r-cran-wbwdi Filename: pool/dists/noble/main/r-cran-ctmva_1.6.0-1.ca2404.1_all.deb Size: 146176 MD5sum: d998890530d2ca1ff7377e7413edbf70 SHA1: 2c53dea5e8872721d654dbd47a439a224792012e SHA256: d810d7b59b91d27f249b89775af9fcc012862cad77c7251af16e6f84955d9018 SHA512: 497b79538a7a0cc75a68623eec2325f00475e8c14eee8b5d64f987c19f372f84ff85a753339539249159c196ee83d1280b1a38daaabf0f9ad1fe97861872d4f9 Homepage: https://cran.r-project.org/package=ctmva Description: CRAN Package 'ctmva' (Continuous-Time Multivariate Analysis) Implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" . Package: r-cran-ctnote Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-ape, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-ctnote_0.1.0-1.ca2404.1_all.deb Size: 561022 MD5sum: 4cdee288762a1ccb224de0b16dff08ef SHA1: 08aec1878dacd4f0e706b106a2fad6abff86f89f SHA256: 075cc241692c52b8c2da98bf2c185655138b2ae1f661716469d0dca7a4e032c2 SHA512: 8f5b326c03d787a126dd6e3cac3eb3436e7558926d0cd2f4dc3624ab63949ce478e2f3865f92559e4c2349765af0d5a7bc96c12ce3791fae1ef387bcb8e575e6 Homepage: https://cran.r-project.org/package=CTNote Description: CRAN Package 'CTNote' (CTN Outcomes, Treatments, and Endpoints) The Clinical Trials Network (CTN) of the U.S. National Institute of Drug Abuse sponsored the CTN-0094 research team to harmonize data sets from three nationally-representative clinical trials for opioid use disorder (OUD). The CTN-0094 team herein provides a coded collection of trial outcomes and endpoints used in various OUD clinical trials over the past 50 years. These coded outcome functions are used to contrast and cluster different clinical outcome functions based on daily or weekly patient urine screenings. Note that we abbreviate urine drug screen as "UDS" and urine opioid screen as "UOS". For the example data sets (based on clinical trials data harmonized by the CTN-0094 research team), UDS and UOS are largely interchangeable. Package: r-cran-ctoclient Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-flextable, r-cran-httr2, r-cran-jsonlite, r-cran-officer, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctoclient_0.2.3-1.ca2404.1_all.deb Size: 246276 MD5sum: 2cc46296fa5e1e166c45a8cc9ee946f4 SHA1: 52bacc06e4a714643d03cfc42632796852d917f8 SHA256: 3b5091e6fecc5cfcb7140d036ca042e42a7ae7cef278b50844cd6bf2fb089c6c SHA512: 2dd0c51cc46c40a77d0e330be15c7a58e1344403051f949bda5582ac6ae7e45a730b445c7ccbb13f53c86ab9e3d671a0291423ea8dffa3e48b96c9da2faa25e6 Homepage: https://cran.r-project.org/package=ctoclient Description: CRAN Package 'ctoclient' (A Modern and Flexible Data Pipeline for 'SurveyCTO') A modern and flexible R client for the 'SurveyCTO', a mobile and offline data collection platform, providing a modern and consistent interface for programmatic access to server resources. Built on top of the 'httr2' package, it enables secure and efficient data retrieval and returns analysis-ready data through optional tidying. It includes functions to create, upload, and download server datasets, in addition to fetching form data, files, and submission attachments. Robust authentication and request handling make the package suitable for automated survey monitoring and downstream analysis. Package: r-cran-ctopendata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-ctopendata_0.1.1-1.ca2404.1_all.deb Size: 50306 MD5sum: 920085f2571d0cb1f53af3e72555712d SHA1: e4ed385255c3fc95b48d14d82b98af76c0bbae38 SHA256: 87543d7a27bfd95b43ea4321b85a1df8cef99e2f524c1416b1d0b9afb8e65a73 SHA512: d2eb15e554f1ddfc7f74c2681304097272650a21cf01a355738fe1236157c98144934a462de3f702c8d8fb72c41be328c6a1c3c0c5c52176f42c7dc23f738932 Homepage: https://cran.r-project.org/package=ctOpenData Description: CRAN Package 'ctOpenData' (A Lightweight Interface to Connecticut Open Data APIs) Provides a unified set of helper functions to access datasets from the Connecticut Open Data platform . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package provides a consistent interface for discovering and downloading datasets from the Connecticut Open Data Portal using human-readable dataset keys or official Socrata dataset identifiers. Package: r-cran-ctost Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2562 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-powertost, r-cran-cli, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-asciicast Filename: pool/dists/noble/main/r-cran-ctost_1.0.1-1.ca2404.1_all.deb Size: 1699764 MD5sum: 14684f19ad9caed92a57d66167db6880 SHA1: 401e40485fe5c84790b390b6d32a619486b8817a SHA256: 18c6783b9a8b02a71d6499242c427a64a21f9a1950fc982a01dc21b95a2b1d83 SHA512: ea88e4f1062341cd8c7b3036abcbbc48b265c65a7dce253665a053caf2ac3253af21c41a70a12fe2a40455a7bde466e349808b1ff481001c7d2a67f7b1d1771d Homepage: https://cran.r-project.org/package=cTOST Description: CRAN Package 'cTOST' (Finite Sample Correction of the Two One-Sided Tests in theUnivariate Framework) A system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) . Package: r-cran-ctpm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ctmm, r-cran-slouch, r-cran-clusterr Suggests: r-cran-phytools, r-cran-viridis, r-cran-knitr, r-cran-rmarkdown, r-cran-nlme, r-cran-progress Filename: pool/dists/noble/main/r-cran-ctpm_1.0.1-1.ca2404.1_all.deb Size: 268704 MD5sum: f193b2f460c33911ab740e71daf0b511 SHA1: 97fc33244b24031965f5e04aa3770eb26cd63e2b SHA256: a3e7a2eff92e0798f1708271ffae842e5153261bfd1384012b7e483135845313 SHA512: f65519e4507246ab705add44a48403f162e2f9f29d116b201f6e8cb8bd37ea48a1f0f211dbf8b7a14af9ae4303876a6c3404679d5a53993cee1384eedff367ff Homepage: https://cran.r-project.org/package=ctpm Description: CRAN Package 'ctpm' (Continuous-Time Phylogenetic Modeling) Functions for identifying, fitting, and applying continuous-time stochastic models to phylogenetic data. The package is based on methods introduced in Noonan et al. (2021) . Package: r-cran-ctqr Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pch Suggests: r-cran-car, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-ctqr_2.2-1.ca2404.1_all.deb Size: 99100 MD5sum: 32a8b2fb0745af923a00f20c962ccf99 SHA1: 3bba5aefde7bd098b0eca7494c71985557223c90 SHA256: 47cc53a8ffb77c971d45e4facf3c9ecd53751c09b481640f9673648c0e8e312e SHA512: 7b58c1f244738fc789a32f3b816582d3716c2d141c18f390ccb64c57ffb8551b64834fb1c8cc3898cbcddd6f31fa1ff31e870bdeda06f82e4dfcd559a86eaf6f Homepage: https://cran.r-project.org/package=ctqr Description: CRAN Package 'ctqr' (Censored and Truncated Quantile Regression) Estimation of quantile regression models for survival data. Package: r-cran-ctrdata Architecture: all Version: 1.26.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4941 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-nodbi, r-cran-rvest, r-cran-stringdist, r-cran-tidyr, r-cran-v8, r-cran-httr2, r-cran-stringi, r-cran-lubridate, r-cran-jqr, r-cran-dplyr, r-cran-zip, r-cran-readr, r-cran-rlang, r-cran-htmlwidgets Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-mongolite, r-cran-tinytest, r-cran-rpostgres, r-cran-duckdb, r-cran-httr, r-cran-tibble, r-cran-clipr, r-cran-chromote, r-cran-rmariadb Filename: pool/dists/noble/main/r-cran-ctrdata_1.26.3-1.ca2404.1_all.deb Size: 1848198 MD5sum: 7aa8aed37310426aed3573c46ed4185d SHA1: eea5f30116abfe04a786eba2529f5dca32ce9a5d SHA256: 53bbfb72c766004ee77a106d603f566d9cc8a5dc496a189c1e621274905b099b SHA512: 5a971c1d514b25ffc93a96beecb705fa16f09c5a8278fb5090375f0fe8793871b50f6771af7b9eebbe885b26d1974abd025ded1f9ff7a9859d7f90bf535f332a Homepage: https://cran.r-project.org/package=ctrdata Description: CRAN Package 'ctrdata' (Retrieve and Analyze Clinical Trials Data from Public Registers) A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', ), 'ClinicalTrials.gov' ( and also translating queries the retired classic interface), the 'ISRCTN' () and the 'European Union Clinical Trials Information System' ('CTIS', ). Trial information is downloaded, converted and stored as JSON in a database ('PostgreSQL', 'SQLite', 'DuckDB', 'MongoDB' or 'MariaDB'; via package 'nodbi'). Protocols, statistical analysis plans, informed consent sheets and other documents in registers associated with trials can also be downloaded. Other functions implement trial analysis concepts canonically across registers, identify deduplicated records across registers, easily find and extract variables (fields) of interest even from complex nested data as used by registers, merge variables and update queries. The package can be used for monitoring, meta- and trend-analysis of the design and conduct as well as of the results of clinical trials across registers. See overview in Herold, R. (2025) . Package: r-cran-ctreemi Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-partykit, r-cran-mice Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctreemi_1.1.0-1.ca2404.1_all.deb Size: 173052 MD5sum: b7ed20a9b235054fd420a0946aa64c66 SHA1: 0ed564d28abfc26636a1e584e5d45eb6e4c5dc54 SHA256: 282db5d047ea0942073f296b12d818663b51f9c6092d99d413eb6c3d00aede9f SHA512: 960b94f6058430af258c68e46ec839a5b9f2106e0ec53f18a2e7f0884debaa0e2e5337f1403576adc6bf83a87699279df8ab59988c44ff076c276fcaa1caab8d Homepage: https://cran.r-project.org/package=ctreeMI Description: CRAN Package 'ctreeMI' (Conditional Inference Trees with Stacked Multiple Imputation) Implements the stacked-imputation workflow for conditional inference trees ('ctree') described in Sherlock et al. (2026) . When data contain missing values, multiply imputed datasets (e.g., from 'mice') are stacked vertically and a single 'ctree' is fit on the combined data. To correct for the artificially inflated sample size introduced by stacking, every node-level test statistic is divided by the number of imputations M, the node-level p-values are recomputed from the chi-squared reference distribution 'ctree' uses (including its multiplicity adjustment across candidate splitting variables), and the tree is compressed bottom-up (the Stack/M correction). Degrees of freedom are derived for each node and each candidate variable, so univariate, bivariate and higher-dimensional outcomes are all handled, as are unordered factor predictors, whose degrees of freedom depend on how many levels remain in a node. The result is a single interpretable tree that incorporates imputation uncertainty without requiring pooling of structurally different trees. Also exports stack_imputations(), rescale_statistic(), prune_stackM(), node_table() and report_ctreeMI() as standalone utilities. The underlying 'ctree' algorithm is provided by 'partykit' (Hothorn & Zeileis, 2015; Hothorn, Hornik & Zeileis, 2006 ). Package: r-cran-ctrialsgov Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-dbi, r-cran-matrix Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-ctrialsgov_0.2.7-1.ca2404.1_all.deb Size: 3393546 MD5sum: 5de3a7144fa21fac3b80fe0ab4a58f2f SHA1: a29d066abec08b009a5bd59a015f0b8b3395ce55 SHA256: 0f93213a5521791fcae1f399a77e2983e2ab12d5d93cede873e640e3dcdf778b SHA512: 6a240fb14c067d5e8684162fae5d5ade308504f8600e7a95b7b83b946b1726025281063697241d5e8647845e5c34d905bf3dc7e0bab84ed5036f634c0caf3180 Homepage: https://cran.r-project.org/package=ctrialsgov Description: CRAN Package 'ctrialsgov' (Query Data from U.S. National Library of Medicine's ClinicalTrials Database) Tools to create and query database from the U.S. National Library of Medicine's Clinical Trials database . Functions provide access a variety of techniques for searching the data using range queries, categorical filtering, and by searching for full-text keywords. Package: r-cran-ctring Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xring, r-cran-functional, r-cran-oro.dicom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ctring_0.1.0-1.ca2404.1_all.deb Size: 358230 MD5sum: 982ad1962b4f37039347708d6f0aef29 SHA1: e8e5314325dea2263ec6a8680578b78a3f08887e SHA256: 3ca31d0fe8e23db6176ede4616e61e6b65fa4b70a303f92be401198f952c7183 SHA512: 7271722d261f6a17f30f870b296dae6ab9635a18a5db0f16652effde6dedc5c31a49170ed81088ab12c9446a439b1a17381ca92c8832703511c3d184eaf83c4b Homepage: https://cran.r-project.org/package=CTRing Description: CRAN Package 'CTRing' (Density Profiles of Wood from CT Scan Images) Computerized tomography (CT) can be used to assess certain wood properties when wood disks or logs are scanned. Wood density profiles (i.e. variations of wood density from pith to bark) can yield important information used for studies in forest resource assessment, wood quality and dendrochronology studies. The first step consists in transforming grey values from the scan images to density values. The packages then proposes a unique method to automatically locate the pith by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree ring profiles (average ring density, earlywood and latewood density, ring width and percent latewood for each ring) are then obtained. Package: r-cran-ctrlgene Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych Filename: pool/dists/noble/main/r-cran-ctrlgene_1.0.1-1.ca2404.1_all.deb Size: 45782 MD5sum: b790ae84c0a0f46660f45eabdcb2a3eb SHA1: 75dc0e1b16ef4dc1f35fef866adb5dd25c0ee080 SHA256: 7e2caa5199fc1974651f6f738337eef5b88deeb6fce6eb6df246e5f84702587a SHA512: ecb349c3d4e4db54b057f37ead517bc13746b5ae6596dec1e1fc66a6967cbc32cd05e1e1ae0b05959935ce7d878a35ae76d5da196a7f42e2d6f901b814cea883 Homepage: https://cran.r-project.org/package=ctrlGene Description: CRAN Package 'ctrlGene' (Assess the Stability of Candidate Housekeeping Genes) A simple way to assess the stability of candidate housekeeping genes is implemented in this package. 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Package: r-cran-cumseg Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars Filename: pool/dists/noble/main/r-cran-cumseg_1.3-1.ca2404.1_all.deb Size: 177536 MD5sum: ca20eba6f1f66e7317a1f716c0c24cb1 SHA1: bdf00bb2971c23463270467c35e1e1668faf75ea SHA256: 1becb8a1db1e8aeff5009ddbda275f6b271444ce896d06f71e65e90550962ac0 SHA512: d04dbe78a8e289141c3ff5b3aa5913e8c2640963b202222fb757fb14145d53fbe54d0b1fdc8cb3c49d9f866ab3def3aaae6360ffcbbf13fb8d189f0f8840af9d Homepage: https://cran.r-project.org/package=cumSeg Description: CRAN Package 'cumSeg' (Change Point Detection in Genomic Sequences) Estimation of number and location of change points in mean-shift (piecewise constant) models. Particularly useful (but not confined) to model genomic sequences of continuous measurements. 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This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Petkau (2024) . The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The package also implements non-parametric assessment of the calibration of individualized treatment effect (ITE) models using data from a randomized trial, via cumulcalibITE(), as described in Sadatsafavi et al. (2026) . The plot() and summary() methods are implemented for the results returned by cumulcalib() and cumulcalibITE(). Package: r-cran-cumulocityr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-cumulocityr_0.1.0-1.ca2404.1_all.deb Size: 262994 MD5sum: e7d1971f46723f1d1821fc3bd7389256 SHA1: 5d6f591e8ea0ff0f7debddd3e28e099feeb6e543 SHA256: adb9ca9dab2eb757c7076e53e960c782e029c145c05c68615e5ac907c022a093 SHA512: 5f945639c0b26145c33cdd7745aaea517bf3f8afd234c88771aa622e8ca767ceb379888568ed6f6b8a1568eee8b6e34039db4bc28ff41b214a0ad0dfb5568df4 Homepage: https://cran.r-project.org/package=cumulocityr Description: CRAN Package 'cumulocityr' (Client for the 'Cumulocity' API) Access the 'Cumulocity' API and retrieve data on devices, measurements, and events. Documentation for the API can be found at . 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Package: r-cran-cure Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rstpm2, r-cran-date, r-cran-numderiv, r-cran-statmod, r-cran-relsurv, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-cure_1.1.1-1.ca2404.1_all.deb Size: 990136 MD5sum: 8635602c43a1733acded33d4d11300ae SHA1: c9302c2e82dd94f56f62440f9a82b0cd93801331 SHA256: ecb280db98feec81838ea4349ea2ed79e4fc937dc5fdcce9017bc5f7c1938d16 SHA512: f5b689204122e7b26ee3c76959e073fc0121b4ae84e807863d0b08b1764a9568ee9882fce5b4b3eee6791a21960f216dc69c85fc0bf3e9abb247b46b4b9980d7 Homepage: https://cran.r-project.org/package=cuRe Description: CRAN Package 'cuRe' (Parametric Cure Model Estimation) Contains functions for estimating generalized parametric mixture and non-mixture cure models , loss of lifetime, mean residual lifetime, and crude event probabilities. 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Package: r-cran-cureauxsp Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-lars, r-cran-mvtnorm, r-bioc-tcgabiolinks Filename: pool/dists/noble/main/r-cran-cureauxsp_0.0.1-1.ca2404.1_all.deb Size: 61672 MD5sum: 536ab3cad0492374b2ad2b9067970c1e SHA1: 3bb2c5cb7d09692ef841b26d0a777c025a3c6797 SHA256: 2e96dff06950cbe7d3a6c2d4388ac4a2edbc4f62783b51fbb4cdade35a2dfa22 SHA512: a928d3b3d788a660450ba29f4309fd20aa0e0d152e35f7cc10a5c167f32a98eed5b74079051ede3daa5da6630621a5b1398af937e3ec189cb78badcd94501740 Homepage: https://cran.r-project.org/package=CureAuxSP Description: CRAN Package 'CureAuxSP' (Mixture Cure Models with Auxiliary Subgroup SurvivalProbabilities) Estimate mixture cure models with subgroup survival probabilities as auxiliary information. A reference of the underlying methods is Jie Ding, Jialiang Li and Xiaoguang Wang (2024) . Package: r-cran-curedepcens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dlm, r-cran-formula, r-cran-rootsolve, r-cran-survival, r-cran-matrixstats Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-curedepcens_0.1.0-1.ca2404.1_all.deb Size: 138854 MD5sum: dfcdb576b671590cd9ef434e93712363 SHA1: 0c4e71d49e2725e8b1f901f765a8df81e69013e0 SHA256: 172407d2be8134340026be22a035cf437125a3ea06566ba632302f30d434b943 SHA512: 36059c3bb58c1dd63c07017b1c3ee73d5f74c0030ffbed52681c3130011ea88c41627a2d58e0e4650d692310e92c54e7dd9ef4ebe1200825b6ff53c33bd26529 Homepage: https://cran.r-project.org/package=CureDepCens Description: CRAN Package 'CureDepCens' (Dependent Censoring Regression Models with Cure Fraction) Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) . Package: r-cran-curephem Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xfun, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-curephem_0.3.2-1.ca2404.1_all.deb Size: 146174 MD5sum: 4cd2f81eaefa4d957fb56393032ad67e SHA1: 1d94485098c738bbb787a7ee7a3cd068c23c2482 SHA256: b756892c981bb613f524bc5167ec9c7fa35dacba94952c8b9b090efa2f3a666a SHA512: 72c249b4d0d6c56fd154393c177b5ae3113862f8d7a7545f90cb24f0727c5de9e04fcb0afa8ce16b82ce927ea7fd977fafd6edc005fa7fb03638d406b737e2a9 Homepage: https://cran.r-project.org/package=curephEM Description: CRAN Package 'curephEM' (NPMLE for Logistic-Cox Cure-Rate Model) Expectation-Maximization (EM) algorithm for point estimation and variance estimation to the nonparametric maximum likelihood estimator (NPMLE) for logistic-Cox cure-rate model with left truncation and right- censoring. See Hou, Chambers and Xu (2017) . Package: r-cran-cureplots Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cureplots_1.1.1-1.ca2404.1_all.deb Size: 70390 MD5sum: d4c3a7a8587804a0c55206e672a1a92e SHA1: 13d22020638f47e4c9bb07b139cf161d83ab078d SHA256: faeff0c4c55d53db86f794c97629c57903cb43f84392a815bef56ed42441492e SHA512: 39590b2598a9dc1488bd067615cfc9d33bb36a2cd3735e7069aa8cdf4df66a8e875e2d06d38046932a493a8418380ef7dcec806ca84c5df4fd85868bf16ab2c8 Homepage: https://cran.r-project.org/package=cureplots Description: CRAN Package 'cureplots' (CURE (Cumulative Residual) Plots) Creates 'ggplot2' Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes. Package: r-cran-curesurv Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4088 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-survival, r-cran-numderiv, r-cran-randtoolbox, r-cran-bbmle, r-cran-optimx, r-cran-formula, r-cran-deriv, r-cran-statmod Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-xhaz, r-cran-survexp.fr Filename: pool/dists/noble/main/r-cran-curesurv_0.1.2-1.ca2404.1_all.deb Size: 1949484 MD5sum: 1977f4a2df6769af0ce1b8048f0c9858 SHA1: c147c45ca42bc07d64db3b8e4e6ec683a584f076 SHA256: 510d4a46414216e6394da95ca8fe555110c6faaf320f551d897177f6824b2ab1 SHA512: d67712ff52a78838097be3e13f73484c67f7da0900d2edab0dcbb8703413322a7e633144c3001c42631261ec143eaf90f51d1a6324c8620a4b21d16a0d8d14ec Homepage: https://cran.r-project.org/package=curesurv Description: CRAN Package 'curesurv' (Mixture and Non Mixture Parametric Cure Models to Estimate CureIndicators) Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) . Package: r-cran-currencyapi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-currencyapi_0.1.0-1.ca2404.1_all.deb Size: 33576 MD5sum: 03f7b940f4957a3b5ee79482bb574a59 SHA1: 28f6cdde62a4906adae50f180c60e74ca77fd811 SHA256: 54362d6ae3acf0102659db3659673ce865c1410f7eb2a16be12c9e59f284011e SHA512: 4d2069743834a58e47b9e3655cfee901cc1a45a7ace0a05a0ee378445c92381866dc2f783697596b1611f055e8fb0d5f9509a1d983c1d15b58f24bd73d7fdb5b Homepage: https://cran.r-project.org/package=currencyapi Description: CRAN Package 'currencyapi' (Client for the 'currencyapi.com' Currency Conversion API) An R client for the 'currencyapi.com' currency conversion API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-currentsurvival Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-cmprsk Filename: pool/dists/noble/main/r-cran-currentsurvival_1.1-1.ca2404.1_all.deb Size: 160268 MD5sum: 8d54bc51b1f0107d5334b96a958afe6c SHA1: 03e2af7d9f81df2eeeeb7acb556fbf8c353dca8b SHA256: db7977afe98d984507d75e9575d2148e0b230e45e914b876322dde6657951c3a SHA512: 30f5f1d761419230944362c9099e5cf2f8ea4d34300199994b95731bc57f8f6de2809c2b82c778f370dcfc2312847d3844f91dd64640b3c151982aa073163816 Homepage: https://cran.r-project.org/package=currentSurvival Description: CRAN Package 'currentSurvival' (Estimation of CCI and CLFS Functions) The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission. Package: r-cran-curricularanalytics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-curricularanalytics_1.0.0-1.ca2404.1_all.deb Size: 273040 MD5sum: 831e5b4afcb4fe17a87917fa45360e07 SHA1: cba2be8c09e4af4ca22a43659aff30bc2063144e SHA256: 88dc7e6ac894210a52abf2a3e20002783ed06b06b3bb40c06a51aa082b91d516 SHA512: 3fb71c1d7cad9a4cc9e2e0490f972ec753dbc884471ce5609a4e68010c364af738d3f0c83e48b84c573c11d742356ce2080a59fc418075cdcc280565b3e808d8 Homepage: https://cran.r-project.org/package=CurricularAnalytics Description: CRAN Package 'CurricularAnalytics' (Exploring and Analyzing Academic Curricula) Provides an implementation of ‘Curricular Analytics’, a framework for analyzing and quantifying the complexity of academic curricula. 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The original tool can be found at . Additional functions to explore curriculum complexity from the literature are also included. 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The contained functions let you start the evaluation of the iterations where you stopped (reading the already evaluated ones from cache), and work with the currently evaluated iterations while remaining ones are running in a background job. Parallel computing is also easier with the workers parameter. 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Designs can be single-stage or multi-stage. Non-stochastic curtailment is possible as a special case. Desired error-rates, maximum sample size and lower and upper anticipated response rates are inputted and suitable designs are returned with operating characteristics. Stopping boundaries and visualisations are also available. The package can find designs using other approaches, for example designs by Simon (1989) and Mander and Thompson (2010) . Other features: compare and visualise designs using a weighted sum of expected sample sizes under the null and alternative hypotheses and maximum sample size; visualise any binary outcome design. 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Package: r-cran-cusumcharter Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cusumcharter_0.1.0-1.ca2404.1_all.deb Size: 230318 MD5sum: f1c8bddede04deea0fc52d4c135ac38b SHA1: d87be4ffc9894d44e1e74389b718010b45049ef9 SHA256: 354401d457fbd0fd487c1911480295471f330ec2ad4a1cec1ddc2d2b371bf7d9 SHA512: 6e443cfe2be14938a4cfa877b1b8decd170fce697402a705eea1bc9ff139be79e54cff35db987f44dab7c46d65d6ea3a0d4ad0e857a5fdf5a266ffa78dfe6cbc Homepage: https://cran.r-project.org/package=cusumcharter Description: CRAN Package 'cusumcharter' (Easier CUSUM Control Charts) Create CUSUM (cumulative sum) statistics from a vector or dataframe. 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Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) . 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Some of the methods implemented in the package are novel, as described in Fox and Monette (2026) , and the package vignettes. For general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition". 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Package: r-cran-cvar Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gbutils, r-cran-rdpack Suggests: r-cran-testthat, r-cran-fgarch, r-cran-performanceanalytics Filename: pool/dists/noble/main/r-cran-cvar_0.6.1-1.ca2404.1_all.deb Size: 138696 MD5sum: d225e5d91af22e8a0aea253e5748e761 SHA1: eba1b0a8bddafba2ba75d470677fceda0ec33a6b SHA256: d3943e0538e5207206f18ef637c3e4343128a774c9b67bad0c325e469e7faac6 SHA512: 13fc708bdfdc2f20df314a471949441405cd14eb1e9fba398de8ac78766e1adc10e209c082076a3882b92e30db4a688fd2817ea5f3366ea0e14a02d837fa0abe Homepage: https://cran.r-project.org/package=cvar Description: CRAN Package 'cvar' (Compute Expected Shortfall and Value at Risk for ContinuousDistributions) Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator, probability density function, or data. 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Package: r-cran-cvauc Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-cvauc_1.1.4-1.ca2404.1_all.deb Size: 122878 MD5sum: 07c81b27853e24d857e52d51a6252000 SHA1: 1acd5ca1124cf9d3430742772c0a0f6fb19b2f16 SHA256: f4c0088144afbf2acc09dd4fec4b4ccf15706d70004a12d4d8d92e3f2ace593a SHA512: d6bba90e1454fce40ca57d98cdab5f4bc683460069581973dc2b6729eca4a444d18f4f3a65f3d24be6e333e442946f10b944abb257a99f2bc556000de9135bd3 Homepage: https://cran.r-project.org/package=cvAUC Description: CRAN Package 'cvAUC' (Cross-Validated Area Under the ROC Curve Confidence Intervals) Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package. Package: r-cran-cvcovest Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix, r-cran-origami, r-cran-coop, r-cran-rdpack, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-assertthat, r-cran-rspectra, r-cran-ggplot2, r-cran-ggpubr, r-cran-rcolorbrewer, r-cran-rmtstat Suggests: r-cran-future, r-cran-future.apply, r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-cvcovest_1.2.2-1.ca2404.1_all.deb Size: 624518 MD5sum: a9fea706f59aa910f279e14aaa2b4538 SHA1: cd0b7a7aa780bbe7b62316ed63ffaffaff6f5bfe SHA256: 071004bf9a5220c649d6d62443e60a1b7ea3fbc8ab30c9a40e9cdfadbf3c86f6 SHA512: 2a011e925a0bd56d6f0831b6a2e32c95917f3801b7f223760455fd79ab9a8cd4c3ac2f035e58a857fae17d2d2692fc1d9701527d8dce9674705873aabb3bfe02 Homepage: https://cran.r-project.org/package=cvCovEst Description: CRAN Package 'cvCovEst' (Cross-Validated Covariance Matrix Estimation) An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) to identify the optimal estimator from among a prespecified set of candidates. Package: r-cran-cvcqv Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-r6, r-cran-sciviews, r-cran-boot, r-cran-mbess Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-cvcqv_1.0.4-1.ca2404.1_all.deb Size: 349372 MD5sum: 332a26c330a0fff1522175a742e2d0fd SHA1: 371fa44c5cf8353834e00fb5500ae92cff94e708 SHA256: 0898e0628af7649c343ea984f8456413dd501bfabf8dc7f3784c296247d87bd7 SHA512: 6b5b426217362530868cac9ed6f67f3c7c73088d6e54a65c3a919db26170b3597640e560031fa7b224e64d6effaad63e99fcb8fba45db6076613da123dc4e9dd Homepage: https://cran.r-project.org/package=cvcqv Description: CRAN Package 'cvcqv' (Coefficient of Variation (CV) with Confidence Intervals (CI)) Provides some easy-to-use functions and classes to calculate variability measures such as coefficient of variation with confidence intervals provided with all available methods. References are 'Panichkitkosolkul' (2013) , 'Altunkaynak' & 'Gamgam' (2018) , 'Albatineh', 'Kibria', Wilcox & 'Zogheib' (2014) . Package: r-cran-cvcrand Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tableone Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cvcrand_0.1.1-1.ca2404.1_all.deb Size: 167124 MD5sum: d0fd9621278737bc439c79c899b3c625 SHA1: b17467e558188be71cdaa9a524552d0377fd4419 SHA256: 45493c21861d34f4613eae890070d6340f075700fb6821678b28328fed9c89fa SHA512: 42f376df5f978be93d8588dd743a99d628b58f8e34b336b76bba28c4c4b6d7c6a91fa814aaaf7302fa77b07d2cdaa6e319be38797d8b39f58a720e3ad576526a Homepage: https://cran.r-project.org/package=cvcrand Description: CRAN Package 'cvcrand' (Efficient Design and Analysis of Cluster Randomized Trials) Constrained randomization by Raab and Butcher (2001) is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) . Motivated from Li, et al. (2016) , the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials. Package: r-cran-cvd Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1771 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cvd_1.0.2-1.ca2404.1_all.deb Size: 1595888 MD5sum: 75b0bb38291622dc3c7b3fbca5eb0b16 SHA1: 1d7342511755a198e171fad766e593a89b973108 SHA256: 7eb60c692b99c169740612d97af27032583c8f5c0971d15234ddbf146f4db3a3 SHA512: 9454e21050c3121d32ab3e83d73f49a634fe14760cd8ebaa71eb8cc995eaf09c95789a2fb8af7c41758e26377b372842ee67408499af0822911fb17b3ebb75dd Homepage: https://cran.r-project.org/package=CVD Description: CRAN Package 'CVD' (Color Vision Deficiencies) Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation. Package: r-cran-cvdprevent Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-memoise, r-cran-purrr, r-cran-rappdirs, r-cran-tibble, r-cran-tidyr Suggests: r-cran-forcats, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-mockery, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-cran-cvdprevent_0.2.5-1.ca2404.1_all.deb Size: 456418 MD5sum: f612b446d2db4c6b8e41e0b519946025 SHA1: c4700631c9b5fb0f6bb2adf7676e720490686687 SHA256: adbafcc74b37d70fd670d3846f23ae72c8bc1dfbbc59dfcff85b6a4d7d844246 SHA512: 952ab082e76bad57c517b92ffbc11d414bb51a740d71fab5dccde22394e33b6cf04255fbb745bff4d763f639b3833fdc0242ca4a498c17e27789f76df1392120 Homepage: https://cran.r-project.org/package=cvdprevent Description: CRAN Package 'cvdprevent' (Access and Analyse Data from the 'CVD Prevent' API) Provides an R interface to the 'CVD Prevent' application programming interface (API), allowing users to retrieve and analyse cardiovascular disease prevention data from primary care records across England. 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Package: r-cran-cvgee Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-geepack, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-cvgee_0.3-0-1.ca2404.1_all.deb Size: 199638 MD5sum: 9ce4bc82fefb26c66b5d194bdf327473 SHA1: 825bc11375d2c57e468c806f4167a51a943deb26 SHA256: 4e6ae7fea92bece354c1ac3db4cd464c4106bc8934745aaf3a9addf21730c45b SHA512: b1c12f70a11336c3a487bd78fe15e81426f4dda6de25d1f0c9a8bf4ca590bab05f4b5f630dcee50f06bb14c0f9c581824080455a4cd7cd7ec833cf61908fe2da Homepage: https://cran.r-project.org/package=cvGEE Description: CRAN Package 'cvGEE' (Cross-Validated Predictions from GEE) Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) . 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This package is a simple wrapper around the popular 'glasso' package and extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations. See Friedman et al. (2008) for details regarding the estimation method. Package: r-cran-cvmaplfam Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-quadprog, r-cran-mgcv, r-cran-mass Filename: pool/dists/noble/main/r-cran-cvmaplfam_0.1.1-1.ca2404.1_all.deb Size: 58274 MD5sum: 896f5d6c714108eef99bbebcbdd0f8b8 SHA1: 253884d2bb3bcfe27744d066dd42fa5c27d93a32 SHA256: 26d6cbf81ace10e72da80e29610bf3ad6d2765a06b43ba0c5875dcccf8e47a59 SHA512: 9b0fc79d8f76d6fb2217472a11f826a4e0b59e27691734a82704eb736ddf8dd1a2f311465401f27f31901f0cf068b50567e8baf9d4b9fa49673c2844cf412f81 Homepage: https://cran.r-project.org/package=cvmaPLFAM Description: CRAN Package 'cvmaPLFAM' (Cross-Validation Model Averaging for Partial Linear FunctionalAdditive Models) Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, using multi-fold cross-validation criterion. More details can be referred to arXiv e-Prints via . Package: r-cran-cvmdisc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvmdisc_0.1.0-1.ca2404.1_all.deb Size: 51438 MD5sum: 497129ebd0a2c73406ff3614abc5e6b9 SHA1: 27519e4e52eed397fa70df7b190bc8b4ddadbfd6 SHA256: 98edafc2a290d820bc76319228f276e8944e175496ad73cd5148b07e663b344f SHA512: ff048d7b818d0e21846cae283427961884a6aae466da4621506fae79facc085b1140e054324a3f490d48b39e043b6021cca924245d99595099851dba3acad7e1 Homepage: https://cran.r-project.org/package=cvmdisc Description: CRAN Package 'cvmdisc' (Cramer von Mises Tests for Discrete or Grouped Distributions) Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) and Lockhart, Spinelli and Stephens (2007) (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) . Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values. Package: r-cran-cvmgof Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-cvmgof_1.0.3-1.ca2404.1_all.deb Size: 113748 MD5sum: 74752437a44215ec73ee628c2dd3dc35 SHA1: 15fbf2999a2097f01a403ae7ff243332516b211f SHA256: 4ae80ef6e650b8e805c137cc04fde72136cd6b1dd44207303ddab461b2715159 SHA512: 6a4878c99463503971aff7b525430f77dfce7cafdf5844b2c8a41347bab84d5eed918fd2da53d208a503fdfdd284d54d9be1a1e0f1f41cabc35ac2af4803ac4e Homepage: https://cran.r-project.org/package=cvmgof Description: CRAN Package 'cvmgof' (Cramer-von Mises Goodness-of-Fit Tests) It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model. Package: r-cran-cvmortalitymult Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stmomo, r-cran-forecast, r-cran-gnm, r-cran-tmap, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvmortalitymult_1.1.1-1.ca2404.1_all.deb Size: 1982824 MD5sum: 90e61006009cef31e4e2ba485b903619 SHA1: b5bc96657804b8210f5ffe75893703e181d0c676 SHA256: 5249f0ebd397ef952bfa8a346f05233e9aee8278ab8829dd6b2d81ca3c819cbc SHA512: 0d2cb3721dce64262e2652c1ef3d16bc56bfff1d391b0d8ab8afb09beb1568a364ecbd775e6c6d5ef5445006dcc02797b44f1bd7a259e2604a3a55e149bcbf9a Homepage: https://cran.r-project.org/package=CvmortalityMult Description: CRAN Package 'CvmortalityMult' (Cross-Validation for Multi-Population Mortality Models) Implementation of cross-validation method for testing the forecasting accuracy of several multi-population mortality models. The family of multi-population includes several multi-population mortality models proposed through the actuarial and demography literature. The package includes functions for fitting and forecast the mortality rates of several populations. Additionally, we include functions for testing the forecasting accuracy of different multi-population models. References, . Atance, D., Debon, A., and Navarro, E. (2020) . Bergmeir, C. & Benitez, J.M. (2012) . Debon, A., Montes, F., & Martinez-Ruiz, F. (2011) . Lee, R.D. & Carter, L.R. (1992) . Russolillo, M., Giordano, G., & Haberman, S. (2011) . Santolino, M. (2023) . Package: r-cran-cvms Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4860 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-groupdata2, r-cran-lifecycle, r-cran-lme4, r-cran-mumin, r-cran-parameters, r-cran-plyr, r-cran-proc, r-cran-purrr, r-cran-rearrr, r-cran-recipes, r-cran-reformulas, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-auc, r-cran-covr, r-cran-e1071, r-cran-furrr, r-cran-ggimage, r-cran-ggnewscale, r-cran-knitr, r-cran-merderiv, r-cran-nnet, r-cran-randomforest, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat, r-cran-xpectr Filename: pool/dists/noble/main/r-cran-cvms_2.0.1-1.ca2404.1_all.deb Size: 3548892 MD5sum: 5a411b230d157385d7a556d7b6c10729 SHA1: ee34dc71b4e1d07836b6049fecb544ec55c9ed83 SHA256: 72a7355d9bd075458b4562fbc02272e4ee0e29e0054f1cfe50e611739294e82d SHA512: 4a10bcf34d6c5bb265d3ee758fc6b153fd8f7074ca1b2969669d8a29a11b2b4ef0c1d6155f33c78377c3ca55d32bc6cb72c7b979070b5aaa0794914e313c1bd3 Homepage: https://cran.r-project.org/package=cvms Description: CRAN Package 'cvms' (Cross-Validation for Model Selection) Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134). Package: r-cran-cvrisk Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-preventr Suggests: r-cran-testthat, r-cran-covr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cvrisk_1.2.1-1.ca2404.1_all.deb Size: 475426 MD5sum: 3fd22790f9a3d432a5e8bd42b5f0ac3d SHA1: a4cb3f975bfe80f6c06ad38ec81b1cc427943b59 SHA256: d31084e65acff1740a66b4d3d0a6759b41625c3ec436bf37e76cfd2cf3dfb741 SHA512: e99ef392fafb09c332505165334721b2cce10a7b8c93c02178172d8111e398e8014eb6cca1bca060c2632ba10a1b00a3426986b3be601c9e6fa3f1ce01205e46 Homepage: https://cran.r-project.org/package=CVrisk Description: CRAN Package 'CVrisk' (Compute Risk Scores for Cardiovascular Diseases) Calculate various cardiovascular disease risk scores from the Framingham Heart Study (FHS), the American College of Cardiology (ACC), and the American Heart Association (AHA) as described in D’agostino, et al (2008) , Goff, et al (2013) , and Mclelland, et al (2015) , and Khan, et al (2024) . Package: r-cran-cvsem Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvsem_1.0.0-1.ca2404.1_all.deb Size: 39656 MD5sum: 1c3d9b74911e69c8d23276ef5844364a SHA1: 08750006db4e26bd9daee0cf9f6bfc0151473790 SHA256: fb411d5eaa8414423ae30cca44bf77286291bdd2534bd3e2b052cfbd820a03f0 SHA512: c0967dfeb7ea2484f99d2e6ca111c37012a255e63399cda6e68db30fca3b405a8d0b8defc23a3d6100a21372e59782550b8757459311947d11457abbf86b1e27 Homepage: https://cran.r-project.org/package=cvsem Description: CRAN Package 'cvsem' (SEM Model Comparison with K-Fold Cross-Validation) The goal of 'cvsem' is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using 'lavaan' syntax. 'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) . The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors. Package: r-cran-cvst Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernlab, r-cran-matrix Filename: pool/dists/noble/main/r-cran-cvst_0.2-3-1.ca2404.1_all.deb Size: 86926 MD5sum: 1de0d0ab177dc81f6472309b079afdc9 SHA1: 02caf8852ee936ee3ee6d0565ccda7cd4f62d1cd SHA256: b0515b7ebaf5e1a0734b75f725f9698a31ebfd016ee6719da916b7065cb3bb49 SHA512: ab21f166cce269b67a93582f5ca4d44b7b4cadf839c31e5c236f363ce3d74a3c8f74d429e846a817999c75dc2204e1cd8c6e60142d6fc756619709551a969f4a Homepage: https://cran.r-project.org/package=CVST Description: CRAN Package 'CVST' (Fast Cross-Validation via Sequential Testing) The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts. Package: r-cran-cvthresh Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh, r-cran-ebayesthresh Filename: pool/dists/noble/main/r-cran-cvthresh_1.1.2-1.ca2404.1_all.deb Size: 80114 MD5sum: 85f3a6ceb8ba7c1fe0d8224b0f3669da SHA1: abd88ec624cd8edb25df0c0d70a0a9412f1603dd SHA256: e5778022615b68bacdf6c0ac96817bb943fb640947fc9ece229f8bc6f093ff0e SHA512: a35c66d89c5dbf07a74ae0610806e38a9995bd82512e69e72fed34ae39e692a9e4bfe7e5c4ff42e3258ebc7f149dd88f6bafc34d49666628f78d86560ba6398e Homepage: https://cran.r-project.org/package=CVThresh Description: CRAN Package 'CVThresh' (Level-Dependent Cross-Validation Thresholding) The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors. Package: r-cran-cvtools Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-cvtools_0.3.3-1.ca2404.1_all.deb Size: 199752 MD5sum: d39dbbee9ff0c0a4fdf6ef8f1d617c2c SHA1: 3bf70282a631708c0d931db6caed18eda996b386 SHA256: 426789608c64ebb2ba76b74e5bd4a42b2ac1a696f476646fb75b1f8a441e6892 SHA512: 787b2b9496629a2687e3abb44012490b29888e50a8d436a64d13aa5e11270b4463c409b8f842cb2a521c55fda04445cc6967fec54735923de862a326dac5d795 Homepage: https://cran.r-project.org/package=cvTools Description: CRAN Package 'cvTools' (Cross-Validation Tools for Regression Models) Tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection. Package: r-cran-cvwrapr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-foreach Suggests: r-cran-doparallel, r-cran-gbm, r-cran-glmnet, r-cran-knitr, r-cran-matrix, r-cran-pls, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvwrapr_1.0-1.ca2404.1_all.deb Size: 392584 MD5sum: f0b727a1cadc07cdc928a390d2505d56 SHA1: d0bac0af160d38dd6d4aed9b68d5bc4685f4a657 SHA256: 9012d1d05691ce1f95686dae4991323e724565adc69d1bd6c021515e499b774c SHA512: d4559772e926342d152e9ace65b2091f2e53d8ba9ac55a930cdb54fbb5bfdbaf7bae4ccf4d6072ea58934388fa802a34666fb395548a59626b7453ca6bf8062a Homepage: https://cran.r-project.org/package=cvwrapr Description: CRAN Package 'cvwrapr' (Tools for Cross Validation) Tools for performing cross-validation (CV). The main function is a general purpose wrapper that performs k-fold CV for any tuning parameter in any supervised learning method. The package also has a function that computes the loss incurred by a set of predictions for a variety of loss functions and model families. Package: r-cran-cwad Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cwad_0.2.0-1.ca2404.1_all.deb Size: 63434 MD5sum: 119c7229258761263afd9845733520fd SHA1: fc88e2f2b9798b4ce406d806d90c9aa8cbf8cd41 SHA256: 2c69170740ac3cab62280a13ab005d1027d9b8344a219a793dc195beb131ba76 SHA512: 69111fc6fc2ee3ab2be9c9df768bb3e6e5aca1f4482745a05f284f46848fd4c253f3718ccf2399e1d7a228dec2af6faa4b5ef37a2c92529e9e43e83e8a88b337 Homepage: https://cran.r-project.org/package=cwad Description: CRAN Package 'cwad' (Connectivity-Weighted Allocation and Comparison of Field-PlotDesigns) A reproducible mixed-model toolkit for plant-breeding trial design. It evaluates any replication allocation under a known genetic relationship (kinship) matrix using one common linear-mixed-model engine on genotype means. Crucially, allocation and analysis model are crossed rather than confounded: every allocation can be scored both with and without kinship, so the precision gain attributable to a design can be separated from the gain attributable to the kinship-based analysis adopted alongside it. It computes A-optimal, connectivity-aware allocations via rank-1 Sherman-Morrison updates, and provides Monte-Carlo stress tests for outlier shrinkage and for an incorrectly specified kinship matrix, each with a matched control arm. 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The 'CWB' is memory efficient and its design makes running queries fast, see Evert (2011) . The 'cwbtools' package offers pure 'R' tools to create indexed corpus files as well as high-level wrappers for the original 'C' implementation of 'CWB' as exposed by the 'RcppCWB' package (). Additional functionality to add and modify annotations of corpora from within 'R' makes working with 'CWB' indexed corpora much more flexible and convenient. The 'cwbtools' package in combination with the 'R' packages 'RcppCWB' () and 'polmineR' () offers a lightweight infrastructure to support the combination of quantitative and qualitative approaches for working with textual data. Package: r-cran-cwise Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2230 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cwise_0.1.0-1.ca2404.1_all.deb Size: 2132734 MD5sum: b411f3ebfc9ca865e90479a467d40462 SHA1: e240139dc207f21c40d81a757e7c75da81b41f60 SHA256: 93db6780be56d99a4d3d6fef32d973e990ed22011942237a6a8b7ba833d29e1e SHA512: 38ccdc8bc472a688851f843e7d7340b5301809b38f223727dcd0d8a03f9d8759798870fe337808e6e676164180a1a4cb1a82b11ccb63f911a13489c4881bb4b7 Homepage: https://cran.r-project.org/package=cWise Description: CRAN Package 'cWise' (Crosswise Models for Sensitive Survey Questions) Implements a bias-corrected crosswise estimator and its extensions for sensitive survey questions. The methods are described in Atsusaka and Stevenson (2023). "A bias-corrected estimator for the crosswise model with inattentive respondents" . Package: r-cran-cwot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatest, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-cwot_0.1.0-1.ca2404.1_all.deb Size: 26652 MD5sum: 2e060f3f9a03e779b19732e90fb91451 SHA1: ce316b19a47229857d15c6006db371321ea9e70a SHA256: 54f28917ba362a1a097d475ecef763f97aafbf7a48297998bdef7c44bbd46172 SHA512: 0295a9331bc4d8b593da5f98f9325d48fa7aaf2210b09ce9325694179d0e3064465f2fe78b11fe31e1335cc0050ada65307ce805bf76d8e8c94e257fcabbe0b5 Homepage: https://cran.r-project.org/package=cwot Description: CRAN Package 'cwot' (Cauchy Weighted Joint Test for Pharmacogenetics Analysis) A flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) . Package: r-cran-cxr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2061 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-optimx Suggests: r-cran-bb, r-cran-deoptimr, r-cran-dfoptim, r-cran-dplyr, r-cran-gensa, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-minqa, r-cran-nloptr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-cxr_1.1.1-1.ca2404.1_all.deb Size: 1323012 MD5sum: eca21de01d69f03575cec5eeef8318e7 SHA1: 893bc1ab672d7a5ba88031b478b95c8ee5550ff1 SHA256: 1b13e233c58addb608737d66674162ab56776f57fdabf156b24dd7317332d34c SHA512: 35d8368a187a51ebe533d428557cd029248fe20e1f004cfe84847ac5d453c3eced3838296f6bd68a6b8be9ca2f11ecfaf3dd28a7d892a5ff6a21c2793e1853c5 Homepage: https://cran.r-project.org/package=cxr Description: CRAN Package 'cxr' (A Toolbox for Modelling Species Coexistence in R) Recent developments in modern coexistence theory have advanced our understanding on how species are able to persist and co-occur with other species at varying abundances. 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Package: r-cran-cxxfunplus Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-inline Suggests: r-cran-rcpp Filename: pool/dists/noble/main/r-cran-cxxfunplus_1.0.2-1.ca2404.1_all.deb Size: 151444 MD5sum: 5d015228e2f7011792be5f27fe463bf3 SHA1: b0bcda4cd01969af531919228ea11732745493cd SHA256: bea668fbf93ad09377413a389a4572cd7dcbcc0d1f3a37f641da5ab9a6a80197 SHA512: fac5540650fa9c896df1bc3f828154960c4e6b289e675a345ec7a506cadea801b2175c1795e02b5ee61657c8b6a0394ab5c9d2f5b650b4d3e0d5ad5cce6f4f39 Homepage: https://cran.r-project.org/package=cxxfunplus Description: CRAN Package 'cxxfunplus' (Extend 'cxxfunction' by Saving the Dynamic Shared Objects) Extend 'cxxfunction' by saving the dynamic shared objects for reusing across R sessions. Package: r-cran-cyclestreets Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-cran-dplyr, r-cran-data.table, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-progressr, r-cran-rcppsimdjson, r-cran-readr, r-cran-sf, r-cran-stringr, r-cran-stringi Suggests: r-cran-covr, r-cran-od, r-cran-stplanr Filename: pool/dists/noble/main/r-cran-cyclestreets_1.0.3-1.ca2404.1_all.deb Size: 1482422 MD5sum: 125221ed6db18990ecc430b37e9e4943 SHA1: 6bda1e8b4b353f28418ff1dde4f25b2cb32d7f80 SHA256: 5989af8d4fb1c4dc9648075044e9e38dcaa3b064972b59fdcd27e5fb702077e2 SHA512: a03c780c18149fce361a5ab76b3931f2aa9d106f5230547faaf74f687d201f4b873f07fa0ba88ae01e92a4c0cccff9da39c237d74de869c0aa014b631f62481d Homepage: https://cran.r-project.org/package=cyclestreets Description: CRAN Package 'cyclestreets' (Cycle Routing and Data for Cycling Advocacy) An interface to the cycle routing/data services provided by 'CycleStreets', a not-for-profit social enterprise and advocacy organisation. The application programming interfaces (APIs) provided by 'CycleStreets' are documented at (). The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist. An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles. These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively. Package: r-cran-cycletrendr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blocklength, r-cran-fancova, r-cran-ggplot2, r-cran-lomb, r-cran-changepoint, r-cran-mgcv, r-cran-nortest, r-cran-nlme, r-cran-tseries Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cycletrendr_0.3.0-1.ca2404.1_all.deb Size: 109646 MD5sum: f2155edf1b9ec9f6e6bf93348110ecac SHA1: 1ea02b6d58a52d5328307c79b80e4420992c5815 SHA256: 30078e9984e310e685511e6f704025bbe85c12f7445c9fe038ecb563c06673c6 SHA512: e81c5ffb651d2493cd09e326487706b09617c6cd5dc22ad0f09deb3169fdb4265d9582071de6a8737f57fb66f755dca2a62d634398bc1c9b519650a1ff1b737a Homepage: https://cran.r-project.org/package=cycleTrendR Description: CRAN Package 'cycleTrendR' (Adaptive Cycle and Trend Analysis for Irregular Time Series) Provides adaptive trend estimation, cycle detection, Fourier harmonic selection, bootstrap confidence intervals, change-point detection, and rolling-origin forecasting. Supports LOESS (Locally Estimated Scatterplot Smoothing), GAM (Generalized Additive Model), and GAMM (Generalized Additive Mixed Model), and automatically handles irregular sampling using the Lomb-Scargle periodogram. Methods implemented in this package are described in Cleveland et al. (1990) , Wood (2017) , and Scargle (1982) . Package: r-cran-cyclicwave Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbscan, r-cran-gsignal, r-cran-waveslim, r-cran-mass, r-cran-e1071, r-cran-ggplot2 Suggests: r-cran-fnn, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cyclicwave_0.1.0-1.ca2404.1_all.deb Size: 669614 MD5sum: 081cb1c495fc999171575984a63f8120 SHA1: 231d94e8e5076838d18737fad4b7448116dde145 SHA256: ce515cf5681044e1ca6ce8b5bf7aea930b075fa941c8439352835e80d2670854 SHA512: c3f243897ebff3d3adfeeee2dbbf838dfdc204a6fa4f0128ccbd0ae144cb88eb54eb52614623574681bf62721059e0403308c0229b995971a4d86c94a749fe24 Homepage: https://cran.r-project.org/package=cyclicwave Description: CRAN Package 'cyclicwave' (Cyclic Wave Analysis for Time-Series Clustering) A modular toolkit for feature extraction and density-based clustering of time-series data. It provides classical statistical, discrete wavelet, Hilbert-based phase, and circular statistical features. The Hilbert-based phase representation can support the analysis of periodic patterns, phase relationships, and circular behavior in time-series data. The package supports DBSCAN and OPTICS clustering, cluster evaluation, visualization, data preparation, and comparison of multiple feature extraction and clustering combinations. Methods are described in Karakaya and Purutcuoglu (2026) and Karakaya et al. (2026) . Package: r-cran-cyclocomp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-crayon, r-cran-desc, r-cran-remotes, r-cran-withr Suggests: r-cran-processx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cyclocomp_1.1.2-1.ca2404.1_all.deb Size: 33150 MD5sum: ce2f36d01debaffd66115ca59ffd822b SHA1: 94fe77aa1e463412f663e53e70816672b61e3bf7 SHA256: f87a093e9b2704735ffd0bf4ccd6f0a05d5790927a0022ec1cfc33d1c00508c4 SHA512: f8a36e0844c2dd503d252ff4bfc2bd611c56c22adec9c6d9b55b9f3aeb653586fe7b2d541eee0a707a050dd01524d10acc6f280fdfd7d0f5095fb295c349e165 Homepage: https://cran.r-project.org/package=cyclocomp Description: CRAN Package 'cyclocomp' (Cyclomatic Complexity of R Code) Cyclomatic complexity is a software metric (measurement), used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. It was developed by Thomas J. McCabe, Sr. in 1976. 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The cycloids can be visualised with any appropriate graphics function in R. Package: r-cran-cyclomort Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-lubridate, r-cran-magrittr, r-cran-mvtnorm, r-cran-plyr, r-cran-scales, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cyclomort_1.0.3-1.ca2404.1_all.deb Size: 247438 MD5sum: 2e54693b691fe71b974cbff603882b36 SHA1: 3649aae9f14049f8d955c7f945c6ab02740d5b31 SHA256: 0aeb58a2178781f27d42fb68e087ad9f6afadaadb0d5921855e9504af076f832 SHA512: 10847aad2a7c780c5b65cf0c8028e32c4222364ea926c772144f0fbeaa5b02edf189dcd091b0a09de9f2023befc691249aa25cc6433545e76853845bd221ac08 Homepage: https://cran.r-project.org/package=cyclomort Description: CRAN Package 'cyclomort' (Survival Modeling with a Periodic Hazard Function) Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period. The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) ). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations. Thus, the respective periods of higher mortality can be identified as "mortality seasons". Package: r-cran-cyclotomic Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-intmap, r-cran-gmp, r-cran-maybe, r-cran-memoise, r-cran-numbers, r-cran-verylargeintegers Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cyclotomic_1.3.0-1.ca2404.1_all.deb Size: 215842 MD5sum: 3d324ba3bfeae858984e3555fb0e07b4 SHA1: 6550bde47ec0d1c1758bf32afcc5d6aa5ab0baa4 SHA256: 2d152d4a28e8bb65febb6fc130adc610710dbb895768a4a91eeae27c4ca50253 SHA512: d1715494985d03901d42db2c967f8b679b55b1f65789ce44b282c43f781e6c2830ee4b9b2b39fbf8a16c29ee29749e5678c92d4c5fe098acec1d325dba8ea98d Homepage: https://cran.r-project.org/package=cyclotomic Description: CRAN Package 'cyclotomic' (The Field of Cyclotomic Numbers) The cyclotomic numbers are complex numbers that can be thought of as the rational numbers extended with the roots of unity. They are represented exactly, enabling exact computations. They contain the Gaussian rationals (complex numbers with rational real and imaginary parts) as well as the square roots of all rational numbers. They also contain the sine and cosine of all rational multiples of pi. The algorithms implemented in this package are taken from the 'Haskell' package 'cyclotomic', whose algorithms are adapted from code by Martin Schoenert and Thomas Breuer in the 'GAP' project (). Cyclotomic numbers have applications in number theory, algebraic geometry, algebraic number theory, coding theory, and in the theory of graphs and combinatorics. They have connections to the theory of modular functions and modular curves. Package: r-cran-cyjshiny Architecture: all Version: 1.0.42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5636 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-shiny, r-cran-jsonlite, r-bioc-graph, r-cran-base64enc Suggests: r-cran-runit, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-cran-cyjshiny_1.0.42-1.ca2404.1_all.deb Size: 2359202 MD5sum: 3cd5e214587932a6d0f6d35e12a15872 SHA1: 64d0eb85cef54c19747f4d4159262e5411b8b0c0 SHA256: a3ec6cfb8079c24c1f6fc14903f0b23ff5b12c31ca0b77be253d3e22eafecf18 SHA512: 15ec20e8244093d948cda4e9494714427a58c9d46c17c677b2c0f089323b0810132bf486eb9c2697a63a8405f4ad95ba1d1157bf60e9473d01de9d29753c4c33 Homepage: https://cran.r-project.org/package=cyjShiny Description: CRAN Package 'cyjShiny' (Cytoscape.js Shiny Widget (cyjShiny)) Wraps cytoscape.js as a shiny widget. cytoscape.js is a Javascript-based graph theory (network) library for visualization and analysis. 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Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. . The free-to-view PDF is located at . 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The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) and Liu & Wang (2021) . 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'DACT' is designed to serve a wide range of innovative statistical designs and analyses. The primary objective of the 'DACT' software is to promote the understanding and application of cutting-edge statistical solutions in clinical trials. For this reason, the software is free for non-commercial scientific research, including but not limited to academic researchers and research/teaching institutions. Computing codes are available upon request. For more details see P. Gao (2024) . Gao, P., Zhang, W. (2024) . P. Gao & Y. Li (2024) . P. Gao, Y. Li (2024) . Gao, P., L. Liu, and C. Mehta. (2013) . 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English: It provides a Portuguese translated version of the datasets listed above. 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Package: r-cran-dagassist Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-crayon, r-cran-dagitty, r-cran-magrittr, r-cran-writexl, r-cran-dplyr, r-cran-dotwhisker Suggests: r-cran-fixest, r-cran-ggplot2, r-cran-ggdag, r-cran-knitr, r-cran-modelsummary, r-cran-rmarkdown, r-cran-testthat, r-cran-marginaleffects, r-cran-weightit, r-cran-directeffects, r-cran-lme4, r-cran-readxl Filename: pool/dists/noble/main/r-cran-dagassist_0.3.0-1.ca2404.1_all.deb Size: 935772 MD5sum: e2f54bf65df230439be6c54ba4ad01e9 SHA1: b09753f29c92e7b706918ba4963d29bddb889ecf SHA256: 05bf3d86de515020620579881b7443844e713b2f178c7785ab51ce2cf6c3cb0b SHA512: 3fbeac2edf64c4b492c9144381ce1e63b88bf8733efe74060985afbab4d8b969f5f000cf28923086775a0fed8d24a7326c72d2d093431083cc21632d464eb131 Homepage: https://cran.r-project.org/package=DAGassist Description: CRAN Package 'DAGassist' (Test Robustness with Directed Acyclic Graphs) Provides robustness checks to align estimands with the identification that they require. Given a 'dagitty' object and a model specification, 'DAGassist' classifies variables by causal roles, recovers a target estimand, and generates a report comparing the original model with DAG-derived adjustment sets. Exports publication-grade reports in 'LaTeX', 'Word', 'Excel', 'dotwhisker', or plain text/'markdown'. 'DAGassist' is built on 'dagitty', an 'R' package that uses the 'DAGitty' web tool () for creating and analyzing DAGs. Methods draw on Pearl (2009) and Textor et al. (2016) . Package: r-cran-daghmm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-future, r-cran-matrixstats, r-cran-prroc, r-cran-bnlearn, r-cran-bnclassify Filename: pool/dists/noble/main/r-cran-daghmm_0.1.1-1.ca2404.1_all.deb Size: 75952 MD5sum: 98c3768be3f5a3f156290d8b6621a028 SHA1: a961335ac52203a31c1e5d6b4211dfa0826783ce SHA256: 382e2d5681f1681a595630deb3d2ca414f7cc846816f31e7c9ead34f125bb56d SHA512: f99887c73b94ce280921609fb5ab4b39e281e7642e98959e871d4f8bb2acff15c1e786820145636a5270f18a4c8116e3167a9c924bb87b0602d10fc37e59ab07 Homepage: https://cran.r-project.org/package=dagHMM Description: CRAN Package 'dagHMM' (Directed Acyclic Graph HMM with TAN Structured Emissions) Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path. Package: r-cran-dagirlite Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dagirlite_0.1.0-1.ca2404.1_all.deb Size: 4336832 MD5sum: a6a4d8e20c589e1a57f17443def99b83 SHA1: a65642825556f8f9c882ff2929cf049e73193628 SHA256: 2554789b03ae69b264fd4c27758c14a0ff97f1d1ced7c898443542abb8c5fc26 SHA512: 889d2d858fb3ea3711d8095ecc20d9af6e205a2c9463fe6c6e8757425199a784dd9a32859d4d23673f3070be25a2bef33e830e91b73ea11bdd9ac64fccf463c9 Homepage: https://cran.r-project.org/package=dagirlite Description: CRAN Package 'dagirlite' (Spatial Vector Data for Danmarks Administrative GeografiskeInddeling DAGI) Compressed spatial vector data originally from saved as Simple Features, SF, objects with data on population, age and gender from Statistics Denmark . Package: r-cran-dagitty Architecture: all Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-v8, r-cran-jsonlite, r-cran-boot, r-cran-mass Suggests: r-cran-igraph, r-cran-knitr, r-cran-base64enc, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown, r-cran-lavaan, r-cran-ccp, r-cran-fastdummies Filename: pool/dists/noble/main/r-cran-dagitty_0.3-4-1.ca2404.1_all.deb Size: 359936 MD5sum: 7f7880ee0bd76e82a0e5bfd27e49ee92 SHA1: be38e77c94bc5f378dda90ef96b0c48ce96abc49 SHA256: dc41ebf4625ba5be517d82900479cde472c3362d1f2bc23ff93e7454f2cbf2af SHA512: f34ac4651c2ed7728c949e76d9c8593a3fb7a77804b002cadc38d6cbbf925b535e2f99492933c2438e10075e41fe579c44504ea5640c707c3c8a9fe884a10994 Homepage: https://cran.r-project.org/package=dagitty Description: CRAN Package 'dagitty' (Graphical Analysis of Structural Causal Models) A port of the web-based software 'DAGitty', available at , for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation. Package: r-cran-dagr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dagitty Filename: pool/dists/noble/main/r-cran-dagr_1.2.1-1.ca2404.1_all.deb Size: 191938 MD5sum: e53a96ec3492ff32589f50aa3165c8cd SHA1: 83bd940f0da576599daf38793f328239d1428ee3 SHA256: 61ab2555c4d3c58bfd8c9015f61968faaafee392334ea3f2ff4cb933ae4c5439 SHA512: e7c5337af2009c74d76efcd6e48a2fa69e6fcf08248b6f42a4f66057bbb7b9f5dc7adc55a961c93b9a7d97386aa6937a50ade09e52d600fa9340727bb884bcd3 Homepage: https://cran.r-project.org/package=dagR Description: CRAN Package 'dagR' (Directed Acyclic Graphs: Analysis and Data Simulation) Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777. Package: r-cran-dagwood Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dagitty Suggests: r-cran-ggdag Filename: pool/dists/noble/main/r-cran-dagwood_0.1.4-1.ca2404.1_all.deb Size: 28634 MD5sum: 67d647ee8bddbf653bffd8562736829e SHA1: 2e582108e00db71836602a5c9b426010d2df8379 SHA256: c82d66ce527f5f0bcbbb0ab74a8bcf8c363bc7d25cec41e8a888c5c0c6898d2c SHA512: f67ea72c218ade58a9fdb6a16dcdb6fe6926a23052275bd23b7010179748cfc96be0a8d633eeded3a9cb15a6e467ad605c56f907ff9eef64bd1b18489b0a655a Homepage: https://cran.r-project.org/package=dagwood Description: CRAN Package 'dagwood' (DAGs with Omitted Objects Displayed (DAGWOOD)) DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in Haber et al (2022) . 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Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event). 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'Document AI' is a powerful server-based OCR service that extracts text and tables from images and PDF files with high accuracy. 'daiR' gives R users programmatic access to this service and additional tools to handle and visualize the output. See the package website for more information and examples. 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It uses phylogenetic and endemicity data to extract the separate island colonists and store them. 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Supports importing dlf output files, reshaping depth-dependent and time-series data, and creating static and interactive plots for exploratory analysis and comparison of simulation results. Package: r-cran-daks Architecture: all Version: 2.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-relations, r-cran-sets Filename: pool/dists/noble/main/r-cran-daks_2.1-3-1.ca2404.1_all.deb Size: 713166 MD5sum: 052bb7ccb2a6c536fb7190b08ac6ef7c SHA1: 684b06364f921535c07d34bab956274ae30f9193 SHA256: 93359055d134960c43b0dd5cdf1f2016e59c92aaa0b906998c327ac2196a34d6 SHA512: f856969ea648b9e736b0fc5f586c64cb6842c9985ace01b9f300726b2350a9e56d9c0d6cb08ff0bb1318dd31ca5cc3aa121bad7262daf02602a92e4fd1c7816c Homepage: https://cran.r-project.org/package=DAKS Description: CRAN Package 'DAKS' (Data Analysis and Knowledge Spaces) Functions and an example dataset for the psychometric theory of knowledge spaces. This package implements data analysis methods and procedures for simulating data and quasi orders and transforming different formulations in knowledge space theory. See package?DAKS for an overview. Package: r-cran-dalex Architecture: all Version: 2.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ibreakdown, r-cran-ingredients, r-cran-kernelshap Suggests: r-cran-gower, r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dalex_2.5.4-1.ca2404.1_all.deb Size: 1081432 MD5sum: b065401691d23818baaad1ef72a41615 SHA1: 4612ccc516ff7fc0078b4669d2f6864559ca0ea0 SHA256: 1dfc9d232a95134e5239d146b2c28e5af818c8861af7d61feaa542bee7f45940 SHA512: f9603e69886d22f155257bb57fbabec8f0a0acfbf8147efdfecaa8a92048615d28ddd6995acf66d2ab9316706cb077a00d84ef6873246623fd63325ad319078d Homepage: https://cran.r-project.org/package=DALEX Description: CRAN Package 'DALEX' (moDel Agnostic Language for Exploration and eXplanation) Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) . Package: r-cran-dalextra Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-ggplot2 Suggests: r-cran-auditor, r-cran-gbm, r-cran-ggrepel, r-cran-h2o, r-cran-iml, r-cran-ingredients, r-cran-lime, r-cran-localmodel, r-cran-mlr, r-cran-mlr3, r-cran-ranger, r-cran-recipes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rpart, r-cran-stacks, r-cran-xgboost, r-cran-testthat, r-cran-tidymodels Filename: pool/dists/noble/main/r-cran-dalextra_2.3.1-1.ca2404.1_all.deb Size: 366284 MD5sum: 9939d549eb71cf772f67b9a778e5b63a SHA1: bea4b0207103d0209b395e95333afae927d1bf54 SHA256: 747b68db80fb778f01635692a55138e062641bea17a567bd1e2b57d527d09762 SHA512: 14d3a4419ccf1d0a4609768b377db5e74ff5be4e108acffadbace1e853eb07f038c6d6481c368b4fa9c95a699decce3ca3a09531e638afeb236f967acbc7dc3e Homepage: https://cran.r-project.org/package=DALEXtra Description: CRAN Package 'DALEXtra' (Extension for 'DALEX' Package) Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot. Package: r-cran-dalsm Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubicbsplines, r-cran-mass, r-cran-plyr Filename: pool/dists/noble/main/r-cran-dalsm_0.9.1-1.ca2404.1_all.deb Size: 368692 MD5sum: fe3347896e9e9cca13f1f3edca03a25e SHA1: a6ad33305cac3d4edd33e16846639079ab661a13 SHA256: 27ce28eb00f8de17e0601c15ad1b51dd260fda227c469f0e3f54141364f026a5 SHA512: 40a2691e32a888a618b9977cbb9fe79ef86cff40efb8ab97fb6e59a74c68b411cb79bcd7bc6729cc0bd51c08a8c59e65e49345fb7e2d3499c46f05711d7167b0 Homepage: https://cran.r-project.org/package=DALSM Description: CRAN Package 'DALSM' (Nonparametric Double Additive Location-Scale Model (DALSM)) Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) . 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The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) . Package: r-cran-daltoolboxdp Architecture: all Version: 1.3.767-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tspredit, r-cran-daltoolbox, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-daltoolboxdp_1.3.767-1.ca2404.1_all.deb Size: 278328 MD5sum: 88686a5a00a08432471b5782165709d3 SHA1: e2b554cbed5cb957820d3be306742098f5b5ff41 SHA256: 21390b569bcaa5452b34d80947b761b40c96bf5ce1d29a394de44fcd25c5301b SHA512: 9a595b23f04bfefa5a91e066a7fa8494b01a2461f8c24b01f202790b0839c13a953c0a1b186c457e3dc24ad3324de63c82b8843f55258fa366e22c8d711bcb4b Homepage: https://cran.r-project.org/package=daltoolboxdp Description: CRAN Package 'daltoolboxdp' (Deep Python Extensions for 'daltoolbox') Extends 'daltoolbox' with Python-backed components for deep learning, scikit-learn classification, and time-series forecasting through 'reticulate'. 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Package: r-cran-dam Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dam_0.0.1-1.ca2404.1_all.deb Size: 9812 MD5sum: 8f8a948767ce966e6a1494b3e26326b6 SHA1: f2c492f21dbc8d58903c5e2e33d423723b7a3422 SHA256: b31011cf45404f035bf5e5acec476d03fc997ae045da19d680ae2fd5898fdf82 SHA512: fc8a6e78b40a64d0f63bc7a0235bacd9260a85620dd7ecf54dde909ce938128a360d3a2c2175b234903e968aec9b44a7f1f69c90c7b06f71522fb366ab781033 Homepage: https://cran.r-project.org/package=dam Description: CRAN Package 'dam' (Data Analysis Metabolomics) A collection of functions which aim to assist common computational workflow for analysis of matabolomic data.. 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Package: r-cran-damaoi Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1995 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fnn, r-cran-magrittr, r-cran-sf, r-cran-units, r-cran-smoothr, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-shiny, r-cran-leaflet, r-cran-shinydashboard Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-damaoi_0.1-1.ca2404.1_all.deb Size: 1753918 MD5sum: 07a70e134adfd300ae65e712388bf019 SHA1: 8ddc52f0e124721dda50a8fb8c5bcfc27c3a9bdd SHA256: 9b9c412e98d77b2562624adbd81b0ba42fbc110354de686b0c378ebacfea96cc SHA512: 245010191127a8f455dd52d5ebd281099d9a46c760c87a8ee70eec1176133be7672c15ef63b31ec4646bf992b3860bcd8b09e5268ae0ae214c4ac9bd674b3a9c Homepage: https://cran.r-project.org/package=damAOI Description: CRAN Package 'damAOI' (Create an 'Area of Interest' Around a Constructed Dam forComparative Impact Evaluations) Define a spatial 'Area of Interest' (AOI) around a constructed dam using hydrology data. 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Package: r-cran-damocles Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caper, r-cran-ape, r-cran-desolve, r-cran-matrixstats, r-cran-expm, r-cran-picante, r-cran-matrix, r-cran-ddd, r-cran-hmisc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-damocles_2.3-1.ca2404.1_all.deb Size: 150198 MD5sum: 304f7f337cb8438953c41936f19437b9 SHA1: 790207b79be03b505d2c4d14361e6d9a92094bdc SHA256: 70780a35c78338cf3cd79a8089ffbe93b35ef2b1364442b54e349937e9530a2d SHA512: ef8409ad2d69b05e5968f030ad8b38d11b1c0a4f688e49d8b00d284b100058b278611efd950d2edce0bc826630cb7f0effbe8d475d5c27d3d78756cd6455cc48 Homepage: https://cran.r-project.org/package=DAMOCLES Description: CRAN Package 'DAMOCLES' (Dynamic Assembly Model of Colonization, Local Extinction andSpeciation) Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history. 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This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of 'dampack''s functionality are detailed in Hunink et al. (2014) . Package: r-cran-damr Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 942 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-behavr, r-cran-data.table, r-cran-readr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-ggetho, r-cran-zeitgebr Filename: pool/dists/noble/main/r-cran-damr_0.3.8-1.ca2404.1_all.deb Size: 177956 MD5sum: e42236ff1506f9ec9283cf989bb2795d SHA1: 2813cc7f85336c124e24fabdc7a219e5c5480c99 SHA256: bf92018248e6fdfe29c19533586e197c50f1dc00f28f497c29f20e3c92e50389 SHA512: c165c7f7420d816315825c2da6b25e88628306d33255fe0fa0eea1f3bc7e5d034b936bdc3e56d9e4522faa3d78aaa92e843cfa0b21811446df9c05888144eeb5 Homepage: https://cran.r-project.org/package=damr Description: CRAN Package 'damr' (Interface to Drosophila Activity Monitor System Result Files) Loads behavioural data from the widely used Drosophila Activity Monitor System (DAMS, TriKinetics ) into the rethomics framework. 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Entire data from the NID cannot be obtained all at once and NID's website limits extraction of more than a couple of thousand records at a time. Moreover, selected data from the NID's user interface cannot not be saved to a file. In order to make the analysis of this data easier, all the data from NID was extracted manually. Subsequently, the raw data was checked for potential errors and cleaned. This package provides sample cleaned data from the NID and provides functionality to access the entire cleaned NID data. 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Part of the lexverse family of packages for legal and regulatory data. Package: r-cran-danstat Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-readr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-danstat_0.2.0-1.ca2404.1_all.deb Size: 112356 MD5sum: b1ad6b06a6b85f4bfa0e4ffb5949bfbe SHA1: 62a3ba4ff8c24d1738c0b781b3a82f44bca4c588 SHA256: ac61754da5bbeb989722c8426cf8b48a6fc0e0c329fb99e6838d7e00d12ce118 SHA512: 2d6433d9e6f34007d54d27a85f1b1d30967f7dfd5cc698b17bb805044e200d02c0e4d3d76ab879170eba9eab508e38701e06e1bb826db7a49c11521273e29f31 Homepage: https://cran.r-project.org/package=danstat Description: CRAN Package 'danstat' (R Client for the Statistics Denmark Databank API) The purpose of the package is to enable an R function interface into the Statistics Denmark Databank API mainly for research purposes. The Statistics Denmark Databank API has four endpoints, see here for more information and testing the API in their console: . This package mimics the structure of the API and provides four main functions to match the functionality of the API endpoints. Package: r-cran-daoh Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-irr Filename: pool/dists/noble/main/r-cran-daoh_0.2.6-1.ca2404.1_all.deb Size: 195020 MD5sum: f372dfa57e2098065cd35f7c796597f0 SHA1: 793583be8da86b504d8632a64e2a35ae50b9a20b SHA256: 8cbd753ac790964fe41affbcb152bf4b5a57aec33e81599a0006b61b81c9d713 SHA512: 802da23629bec180828abc675afa21630da417685e1449c549610bf6777dd1a993068a7b2cea5bab01eb049078e38db270e31094dcaa46ce8da8a009837a96fb Homepage: https://cran.r-project.org/package=daoh Description: CRAN Package 'daoh' (Days Alive and Out of Hospital (DAOH) Calculation) Calculates Days Alive and Out of Hospital (DAOH) from administrative admission/discharge/mortality data using three algorithms (nights, days, exact) and three death-handling approaches (midday, midnight, zero). Includes tools for comparing methods (Bland-Altman, ICC, reclassification), and plotting. Package: r-cran-dapper Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-furrr, r-cran-memoise, r-cran-posterior, r-cran-progressr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dapper_1.1.1-1.ca2404.1_all.deb Size: 286856 MD5sum: 73fab8c263716cb8a6e5196f2ab8a868 SHA1: 7282521b043cc4358419f72fffb795f3daa5a933 SHA256: b8b9ede428090645e98267eb9ca6a99f66b14f068d608fe08fd46bedfb7717de SHA512: 3114bc3ae92e59795a401ff24085adf53583b79e73fcbdd2d8fbcf314ba5ab5ccda659653e69939bb69c4c085cbd4749895ee21bc4114a4d33bfb0d35a66bc13 Homepage: https://cran.r-project.org/package=dapper Description: CRAN Package 'dapper' (Data Augmentation for Private Posterior Estimation) A data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the 'rstan' ecosystem. For methodological details on the sampler see Ju et al. (2022) , and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) . Package: r-cran-daqapo Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-stringdist, r-cran-stringr, r-cran-tidyr, r-cran-xesreadr, r-cran-rlang, r-cran-bupar, r-cran-readr, r-cran-edear, r-cran-magrittr, r-cran-purrr, r-cran-glue, r-cran-miniui, r-cran-shiny, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-daqapo_0.3.2-1.ca2404.1_all.deb Size: 187468 MD5sum: a4dca3cce60dd57cc7a4ff2508bbce8f SHA1: e83faac617a3486847a47e8fc52f127f891f0d50 SHA256: 282e8da3bda81cbf2c35c3136bce84789528518347df1c5140197eecffa3587c SHA512: 75fcb623396a7c5cdad5365885e984cd26da23a8521d3b9e25410727f51acc1da36405b77fe0a9ccdeefe16334bd3f94ea43ce882d6156021b5ecc011794b6a9 Homepage: https://cran.r-project.org/package=daqapo Description: CRAN Package 'daqapo' (Data Quality Assessment for Process-Oriented Data) Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...). Package: r-cran-darand Architecture: all Version: 0.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-darand_0.0.1.2-1.ca2404.1_all.deb Size: 30438 MD5sum: 0c3d21a89c79498005a3a3be921384fc SHA1: d482395a547d36c0f14e39b1046d5c4bbc42aef0 SHA256: b3b04d6e12192f482ffcb0f01170a977fe5db247066fbfac2fb7151270cf5b5f SHA512: fc7a7b0f514c61ca29cbd5492e39d469f8871a672d77bdbe1a144b1f68fdd72dfb0e32c2646793eb311d33825ed694a28aa017078b609decbd06305a21910c68 Homepage: https://cran.r-project.org/package=DArand Description: CRAN Package 'DArand' (Differential Analysis with Random Reference Genes) Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution. The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) ). Package: r-cran-dark Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dark_0.9.9-1.ca2404.1_all.deb Size: 399700 MD5sum: a27748b777876a37f0edb736c034d1b6 SHA1: 499315b9064bda95b48cc1f5e4959bccb68527de SHA256: 451f664ef74b1a2a8545e88b303e39d9e25b49e1144beac3ebc944cf30b28a9c SHA512: c0ae0bfe1f7d64f61140c51bc21b8423dbf100b8fb061b81058e0b05a5ebcecf3de77d4ee4bb0fd308af1d5cd7b943f15c7b1ab64c129786f128ad6d6b1a9f31 Homepage: https://cran.r-project.org/package=Dark Description: CRAN Package 'Dark' (The Analysis of Dark Adaptation Data) The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that 'best' describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a 'dark' list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed. Package: r-cran-darkdiv Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-darkdiv_0.3.0-1.ca2404.1_all.deb Size: 35582 MD5sum: e2524447ab759ac11cb914f4260049f3 SHA1: 2a90f9ee2a1bba5f1ac97758ed11f072ae01491f SHA256: fa55f4894767ca3c1482f8c42428af3918f1638f70281e97b6ce625fff7bc8e3 SHA512: 7d0fe2a6713718eecb1453b5df354a59f87b8aa8565ee79fd1988da3a31009f638e710aecfee20a566bac164d087d481ffe1f40134cee16fac9b26ca512c8d3a Homepage: https://cran.r-project.org/package=DarkDiv Description: CRAN Package 'DarkDiv' (Estimating Dark Diversity and Site-Specific Species Pools) Estimation of dark diversity and site-specific species pools using species co-occurrences. It includes implementations of probabilistic dark diversity based on the Hypergeometric distribution, as well as estimations based on the Beals index, which can be transformed to binary predictions using different thresholds, or transformed into a favorability index. All methods include the possibility of using a calibration dataset that is used to estimate the indication matrix between pairs of species, or to estimate dark diversity directly on a single dataset. See De Caceres and Legendre (2008) , Lewis et al. (2016) , Partel et al. (2011) , Real et al. (2017) for further information. Package: r-cran-darksky Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-gridextra, r-cran-gtable, r-cran-ggplot2, r-cran-plyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-darksky_1.3.0-1.ca2404.1_all.deb Size: 41356 MD5sum: 760e1e7f8f208fb545f416bad07a621a SHA1: ad6c2faf608b702d05ffd084af054b33c938556d SHA256: 2afeb2b50d7a7149baff1147f515ccf5a135f74323e8dce8cf5e9db97de20074 SHA512: ad4220df7d679a566bae0f9ac1f7abe81274ae70bc74bab17f9879fd762e4c53f7b48a6d71d58a87e8284584818860b7bbb186f731daf1d4a3e64a1b7753d3cd Homepage: https://cran.r-project.org/package=darksky Description: CRAN Package 'darksky' (Tools to Work with the 'Dark Sky' 'API') Provides programmatic access to the 'Dark Sky' 'API' , which provides current or historical global weather conditions. Package: r-cran-dartr.base Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-dartr.data, r-cran-adegenet, r-cran-ape, r-cran-crayon, r-cran-data.table, r-cran-foreach, r-cran-gridextra, r-cran-patchwork, r-cran-plyr, r-cran-reshape2, r-bioc-snprelate, r-cran-stampp, r-cran-stringr, r-cran-tidyr, r-cran-mass, r-cran-bigstatsr, r-cran-bigsnpr, r-bioc-snpstats, r-cran-gtools Suggests: r-cran-boot, r-cran-devtools, r-cran-directlabels, r-cran-dismo, r-cran-doparallel, r-cran-expm, r-cran-gdistance, r-cran-gganimate, r-cran-ggrepel, r-cran-gtable, r-cran-ggthemes, r-cran-gplots, r-cran-hardyweinberg, r-cran-hierfstat, r-cran-igraph, r-cran-iterpc, r-cran-knitr, r-cran-label.switching, r-cran-lattice, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-markdown, r-cran-mmod, r-cran-networkd3, r-cran-pegas, r-cran-pheatmap, r-cran-plotly, r-cran-poppr, r-cran-proxy, r-cran-purrr, r-bioc-qvalue, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rgl, r-cran-rmarkdown, r-cran-rrblup, r-cran-scales, r-cran-seqinr, r-cran-sf, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-siber, r-cran-stringi, r-cran-tibble, r-cran-vcfr, r-cran-zoo, r-cran-viridis, r-cran-fields, r-cran-testthat, r-cran-ggtern, r-cran-dendextend, r-cran-ggdendro, r-cran-terra, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-dartr.base_1.2.3-1.ca2404.1_all.deb Size: 3745724 MD5sum: 92a3121ddfcfc9d7cae86fd0650852a0 SHA1: d7ecef439b7a8d4a1436dea5b3b5c203ded50622 SHA256: b5bed0fa93da83aabbcf951e6c65ba59571985634eb806ab5f86a67fe9c90903 SHA512: 7213a4eaa0d11dc077139f6cc03233439d4b8612eb5bac7e15292978d23373364a96b00b3c0532adced908d3732d4af219d10a9766fe360566f8338e7d8e3744 Homepage: https://cran.r-project.org/package=dartR.base Description: CRAN Package 'dartR.base' (Analysing 'SNP' and 'Silicodart' Data - Basic Functions) Facilitates the import and analysis of 'SNP' (single nucleotide 'polymorphism') and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'callrate', 'heterozygosity', 'reproducibility', maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR.base' is the 'base' package of the 'dartRverse' suits of packages. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want to cite 'dartR', you find the information by typing citation('dartR.base') in the console. Package: r-cran-dartr.captive Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-dartr.sim, r-cran-adegenet, r-cran-crayon, r-cran-ggplot2, r-cran-patchwork, r-cran-stringr, r-cran-data.table, r-cran-gridextra, r-cran-magrittr, r-cran-reshape2, r-cran-tidyr, r-cran-digest Suggests: r-cran-siber, r-cran-gplots, r-cran-fields, r-cran-igraph, r-cran-rrblup, r-cran-scales, r-cran-spelling, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-dartr.captive_1.2.2-1.ca2404.1_all.deb Size: 1255156 MD5sum: 1531453f0203f3a019e837595537edf0 SHA1: 1cd9ff39d70e5708af9eb970727a0be1c3ee84c1 SHA256: 257183c5374787b80830c7782bd044a6ced97e464fe88fb05c07dd1dac774817 SHA512: ef804ff8426e875421980cf16e0f7d4bbd9302b1f5de6b4c73075e7934cfa1d7335b745e283832ca14146c9efcd945205756d3a01dff7133e79c05eceee5c791 Homepage: https://cran.r-project.org/package=dartR.captive Description: CRAN Package 'dartR.captive' (Analysing 'SNP' Data to Support Captive Breeding) Functions are provided that facilitate the analysis of SNP (single nucleotide polymorphism) data to answer questions regarding captive breeding and relatedness between individuals. 'dartR.captive' is part of the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) . Package: r-cran-dartr.data Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-adegenet Filename: pool/dists/noble/main/r-cran-dartr.data_1.2.2-1.ca2404.1_all.deb Size: 5370084 MD5sum: 39605e524b8a381d5ee06b3965c64b21 SHA1: 732e546bf4dfdcd6e0472a5c5310edd5292849ff SHA256: 7ab202d9943d0f2658372b55f4f4700198e03abb183b949a7b7769a852ed2d7f SHA512: e5b0fdab171d6fad14f4ec3e62a035da419b01e6706d8eb22190ec21e6239bd9a21b9b4a3266d66040cd673b737d7df491f5868ca5f2ef765e856b094f8cef33 Homepage: https://cran.r-project.org/package=dartR.data Description: CRAN Package 'dartR.data' (Auxiliary Data Package for Our Main Package 'dartR') Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions. Package: r-cran-dartr.popgen Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-mass, r-cran-dplyr, r-cran-patchwork, r-cran-crayon, r-cran-ggplot2, r-cran-data.table, r-cran-stringr, r-cran-furrr, r-cran-future, r-cran-ggdendro, r-bioc-lea, r-cran-pillar, r-cran-plyr, r-cran-terra, r-cran-purrr, r-cran-ggpmisc, r-cran-r.utils, r-cran-ape Suggests: r-cran-siber, r-cran-expm, r-cran-fields, r-cran-gplots, r-cran-gridextra, r-cran-igraph, r-cran-iterpc, r-cran-label.switching, r-cran-leaflet, r-cran-proxy, r-bioc-qvalue, r-cran-raster, r-cran-reshape2, r-cran-scales, r-bioc-snpstats, r-cran-tidyr, r-cran-viridis, r-cran-zoo, r-cran-gsubfn, r-cran-sp Filename: pool/dists/noble/main/r-cran-dartr.popgen_1.2.2-1.ca2404.1_all.deb Size: 1199292 MD5sum: 01648900f100f7a35f16fa5be86f7218 SHA1: c5fe99c69ab0a0b737a5139358dc69073e50760e SHA256: 9a623bb9a56def9b1753f4303b8ee7ec940c166d23c896d38b28dc1be24cda86 SHA512: be9dc56fb34d4e70bf7e63e9ac2c7f6249eb89bcac157e0ae64c7f712bd9ecd5bccbe3f597e24b82842ce7ff09d328800f8997170ad0cb139a0769a4ca0c34a1 Homepage: https://cran.r-project.org/package=dartR.popgen Description: CRAN Package 'dartR.popgen' (Analysing 'SNP' and 'Silicodart' Data Generated by Genome-WideRestriction Fragment Analysis) Facilitates the analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of functions to analyse such data in a population genetics context. It provides several functions to calculate population genetic metrics and to study population structure. Quite a few functions need additional software to be able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description in the help pages how to download and link the packages so the function can run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) . Package: r-cran-dartr.sexlinked Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-doparallel, r-cran-ggplot2, r-cran-foreach, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-dartr.sexlinked_1.2.2-1.ca2404.1_all.deb Size: 509678 MD5sum: 3c422ecffc504688d48cebb800c96cc7 SHA1: 0d3f242d8d06f64f0a649d3c5df41fa6b105354a SHA256: 2db858ecc4d6a268f60d9594dd9d21c07623b3b44397440d4e94c53d5f7a6bdd SHA512: 5d31028578f2972759f77c06c67da738a36dfc0e49c53ef232930abe7b657eba61a2979e73477343d8f67f4d73e2584c7142f0f87a9b0d589f85b92fae57f0af Homepage: https://cran.r-project.org/package=dartR.sexlinked Description: CRAN Package 'dartR.sexlinked' (Analysing SNP Data to Identify Sex-Linked Markers) Identifies, filters and exports sex linked markers using 'SNP' (single nucleotide polymorphism) data. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want understand the applied rational to identify sexlinked markers and/or want to cite 'dartR.sexlinked', you find the information by typing citation('dartR.sexlinked') in the console. Package: r-cran-dartr.sim Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-ggplot2, r-cran-dartr.popgen, r-cran-adegenet, r-cran-shiny, r-cran-fields, r-cran-stringi, r-cran-stringr, r-cran-data.table, r-cran-rcpp, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-hierfstat, r-cran-reshape2, r-cran-foreach, r-cran-ggrepel, r-cran-dplyr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-dartr.sim_1.2.2-1.ca2404.1_all.deb Size: 1391104 MD5sum: 0a20d4f53bf6ddd9928463967e686482 SHA1: 3cb718804ec12623c324ce6802eaaef230d42df0 SHA256: 8d52a9d6862836cb337226b2a523b2204c18fecc81de1a164df0ea7160c5574e SHA512: 6529cb01d149e5d1507cf7484afdeace3eea3deb12da39c49d15a14a91cb39919371846b26770032bafe8e086560922f4259733cd1d47149b7ed6c34bcc7b28a Homepage: https://cran.r-project.org/package=dartR.sim Description: CRAN Package 'dartR.sim' (Computer Simulations of 'SNP' Data) Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object. Package: r-cran-dartr.spatial Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-crayon, r-cran-stampp, r-cran-raster, r-cran-sp, r-cran-tidyr, r-cran-vegan, r-cran-mass, r-cran-ggplot2, r-cran-data.table, r-cran-dismo Suggests: r-cran-mmod, r-cran-gdistance, r-cran-gplots, r-cran-rrblup, r-cran-terra, r-cran-sf, r-cran-popgenreport Filename: pool/dists/noble/main/r-cran-dartr.spatial_1.2.2-1.ca2404.1_all.deb Size: 1314804 MD5sum: 3827f6fc35319cea17a2f46996a52c02 SHA1: e3f3361e4aad6e5c3efe803da2ac2d1db263cf99 SHA256: 9a0ae56e6f3749a1c86268f10e46038ce6f1aeb49aa4888f5a1e8cf44bcef9a4 SHA512: f6f7bdc9227a94c5a81309066d258758a2e42a2fdd53b3daeb0271b11f6dc61d652f1df50009345f47703f0ad6ca68fdfb8329da7dbea9028def3073619dc9aa Homepage: https://cran.r-project.org/package=dartR.spatial Description: CRAN Package 'dartR.spatial' (Applying Landscape Genomic Methods on 'SNP' and 'Silicodart'Data) Provides landscape genomic functions to analyse 'SNP' (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. 'dartR.spatial' is a package that belongs to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'. Package: r-cran-dartr Architecture: all Version: 2.9.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6054 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adegenet, r-cran-ggplot2, r-cran-dplyr, r-cran-dartr.data, r-cran-ape, r-cran-crayon, r-cran-data.table, r-cran-fields, r-cran-foreach, r-cran-gridextra, r-cran-mass, r-cran-patchwork, r-cran-plyr, r-cran-popgenreport, r-cran-raster, r-cran-reshape2, r-cran-shiny, r-bioc-snprelate, r-cran-sp, r-cran-stampp, r-cran-stringr, r-cran-tidyr, r-cran-gsubfn, r-cran-purrr Suggests: r-cran-boot, r-cran-devtools, r-cran-directlabels, r-cran-dismo, r-cran-doparallel, r-cran-expm, r-cran-gdistance, r-cran-ggtern, r-cran-gganimate, r-cran-ggrepel, r-cran-gtable, r-cran-ggthemes, r-cran-gplots, r-cran-hardyweinberg, r-cran-hierfstat, r-cran-igraph, r-cran-iterpc, r-cran-knitr, r-cran-label.switching, r-cran-lattice, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-markdown, r-cran-mmod, r-cran-networkd3, r-cran-pegas, r-cran-pheatmap, r-cran-plotly, r-cran-poppr, r-cran-proxy, r-bioc-qvalue, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rgl, r-cran-rmarkdown, r-cran-rrblup, r-cran-scales, r-cran-seqinr, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-siber, r-bioc-snpstats, r-cran-stringi, r-cran-terra, r-cran-tibble, r-cran-vcfr, r-cran-zoo, r-cran-viridis, r-cran-vegan Filename: pool/dists/noble/main/r-cran-dartr_2.9.9.5-1.ca2404.1_all.deb Size: 5259872 MD5sum: 067581a9da5131b80930605c8e0ae01c SHA1: 204820c7168cec7f811b659fddd07c3355fc3e56 SHA256: d8624881972219872010c98f6a6f896bb2dfb019e22fbc6fdc7f4165ea776dd7 SHA512: 4596403d3a239dcff249ff5e7bb990f8745bb3aa3b1c3ea0dc2503715cca648cbd4ea1a569d44cd303aa42bd3bdba706a3dbdfb6d5fff0fef4861a00691b5437 Homepage: https://cran.r-project.org/package=dartR Description: CRAN Package 'dartR' (Importing and Analysing 'SNP' and 'Silicodart' Data Generated byGenome-Wide Restriction Fragment Analysis) Functions are provided that facilitate the import and analysis of 'SNP' (single nucleotide polymorphism) and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR' supports also the analysis of 3rd party software package such as 'newhybrid', 'structure', 'NeEstimator' and 'blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate 'SNP' dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our 'github' repository: github.com/green-striped-gecko/dartR/. If you want to cite 'dartR', you find the information by typing citation('dartR') in the console. Package: r-cran-dartrverse Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-devtools, r-cran-rcurl, r-cran-httr Suggests: r-cran-dartr.base, r-cran-dartr.data, r-cran-dartr.sim, r-cran-dartr.captive, r-cran-dartr.popgen, r-cran-dartr.spatial Filename: pool/dists/noble/main/r-cran-dartrverse_1.0.6-1.ca2404.1_all.deb Size: 1679910 MD5sum: 1fa326829ab4e7d86671385df3ed970c SHA1: 3d2d0e53c6ea0cf7c43e7ae8922d47f7e3d0b7d0 SHA256: 6267a58a3dee3b78b72feb8a72cfa9ca2c2d5f1294db77313b3e2266ebc8111c SHA512: ff3f8afcc286fe722defd8b5f2bd9dcb619780308f2ebd8f976c4d3b4e7e4c3f6f481d6d3426e6c820f411e4d42b65e5ec092681dfd040c09a45d74cc0a50bdd Homepage: https://cran.r-project.org/package=dartRverse Description: CRAN Package 'dartRverse' (Install and Load the 'dartRverse' Suits of Packages) Provides a single function that supports the installation of all packages belonging to the 'dartRverse'. The 'dartRverse' is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar 'look and feel' and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the 'GitHub' pages . Package: r-cran-dasguptr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3979 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dasguptr_2.2.0-1.ca2404.1_all.deb Size: 570622 MD5sum: dacd9039a40178afc231e647b4fe29ee SHA1: f81a6e5bfc260e54cdc5893589f3be34d5aa6ac1 SHA256: 6b4b8120f422d747c22835eecf93c690fe1530cc0d642cf80713b4ba8ff75407 SHA512: 4d3ee962b505587921cdf82c069c05239bcf702fe5c61b9e199c88aa53c39ab14fcf1068403e77c20c9f834ad38781b9d6885507437a395563e0541bfdd67da2 Homepage: https://cran.r-project.org/package=DasGuptR Description: CRAN Package 'DasGuptR' (Das Gupta Standardisation and Decomposition) Implementation of Das Gupta's standardisation and decomposition of population rates, as set out "Standardization and decomposition of rates: A user’s manual", Das Gupta (1993) . 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Package: r-cran-dashboardapi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dashboardapi_0.1.0-1.ca2404.1_all.deb Size: 113764 MD5sum: d1f760a9cd66f108ff147be4590f1653 SHA1: fdf3558b10ffe26f3dfb9b56243ebbfe1446e04a SHA256: d52276b7485269357602ed6b40a24afcf0780f784916647f2fbd73cc5bbc9c59 SHA512: bfc0cb5b2ea6f7d08fef0d6db667ce574c1cc87f7b5e72fcc97c915e197afae626cd83def403e4670910e14a8482a73d2790457a039fc61b15f0d303075623ac Homepage: https://cran.r-project.org/package=dashboardapi Description: CRAN Package 'dashboardapi' (Access Japan's Statistics Dashboard API) An unofficial, tidy, rate-conscious interface to the Statistics Dashboard Web API provided by the Statistics Bureau of Japan. 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Generate good looking HTML report or print console output to display in logs of your data processing pipeline. Package: r-cran-data360r Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-reshape2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-data360r_1.0.9-1.ca2404.1_all.deb Size: 30284 MD5sum: aa8a23ea9be1f212bbc34173f7f519a8 SHA1: c77991c0cafc0170dae70b66d45412622f41e794 SHA256: 9174981166aa630c59738f40c2217be125f33545ba0f9a87554ee7b55bb6d633 SHA512: 6fcb1731fb0f5c527a4f13104b2e65b5390c5d5492fe1646e98f74bce9ac61253d2a8c8a6ceccce75a85f71171a2ba9a0c210b81a042d44a84b8e2f3f54848e3 Homepage: https://cran.r-project.org/package=data360r Description: CRAN Package 'data360r' (Wrapper for 'TCdata360' and 'Govdata360' API) Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdata360' platforms at and , respectively. These application program interfaces provide access to over 5000 trade, competitiveness, and governance indicator data, metadata, and related information from sources both inside and outside the World Bank Group. Package functions include easier download of data sets, metadata, and related information, as well as searching based on user-inputted query. Package: r-cran-dataaudit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dataaudit_0.1.0-1.ca2404.1_all.deb Size: 194520 MD5sum: 93268e6b056e13f7e1b80e9d39805762 SHA1: 4ec735b71fdef0ff13a93a17a9b317ab14d84133 SHA256: ea0924f43ef6ea2de308f847ebc2de4e914d95a4fd6bc3542ae8cdc6c2e7c39d SHA512: 7f7fe73e736ae261fd1d3cca8ae00170c83bb8455d616fcaf88cdc26c3940bb0d6c16d6d0c288562fb3d52e267296287c94b5f7569dfbcc19914f404409503b2 Homepage: https://cran.r-project.org/package=DataAudit Description: CRAN Package 'DataAudit' (Comprehensive Data Quality Auditing and Validation) Provides tools for systematic assessment, validation, and reporting of data quality. The package detects common data-quality problems including missing or blank values, duplicate observations, infinite values, constant and near-zero variance variables, outliers, invalid ranges and categories, type and pattern violations, problematic dates and identifiers, sequence errors, dependency violations, and cross-variable inconsistencies. It also supports reusable validation rules, integrated audit reports, and standardized data-quality scoring for reproducible data-quality assessment workflows. 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Supports both legacy CAGED (pre-2020), Novo CAGED (2020+), and adjustment files, enabling efficient local storage and analysis workflows. Data is sourced from the HuggingFace repository . 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'datacleanr' facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in 'R'. 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Users can adjust graphical elements to define data generation parameters, and the resulting datasets can be saved to temporary RDS files for further analysis or visualization. Methods are described in: Chang et al. (2024) ; Bostock et al. (2011) . 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The package allows for news searches using search phrases and date filters, and returns the results in a structured format, ready for analysis. Additionally, it includes functions to clean the extracted data, visualize it, and store it in databases. All of this can be done automatically, facilitating the collection and analysis of relevant information from Chilean media. Package: r-cran-datameta Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-datameta_0.1.1-1.ca2404.1_all.deb Size: 175150 MD5sum: cb32c62256837222cd68a96fccc9246a SHA1: db2ab12c4632bf753f98c82cd4bbec540911e0d7 SHA256: 37e6776203f50df22691ce4868a9383c98a48452ca780a72301f0c2c50a960f2 SHA512: ad9c97a03d381b6208334de250ac7e592b334736a197c00ebdff9e51a0cf001e0d2c473dab8e4c8b924ef6af132be50c808098651bfc29e5d95532bb1a53b9cc Homepage: https://cran.r-project.org/package=dataMeta Description: CRAN Package 'dataMeta' (Create and Append a Data Dictionary for an R Dataset) Designed to create a basic data dictionary and append to the original dataset's attributes list. 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The main types of data addressed are spatial (coordinates, longitude and latitude) and taxonomic data (ranking and nomenclature validity) with some additional options for user-determined dataset refinement. Combined or individual calls to the online repositories of the Global Biodiversity Information Facility (GBIF) via 'rgbif' and the Integrated Taxonomic Information System (ITIS) via 'taxize' enable built-in taxonomic checks. Package: r-cran-datana Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hmisc, r-cran-scales Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-datana_1.1.5-1.ca2404.1_all.deb Size: 5236548 MD5sum: a64bd40ee1a8c0d8be114c4a403f97cb SHA1: 9aad991686cc829ca0695e1a257914b680625ec3 SHA256: 299195fcbec51dcbac8eb9a7d0ae4273bcf0dc2fddf502e48a0dbab28ca73c1b SHA512: 3e68013bec4f9d6ff8d84a2618d737ac69f2a950ec8c5d5fa09b83d46238664a05078fe9f94a218c7618c28840bbf4c9966605e006123654f755dc5b94ce39e2 Homepage: https://cran.r-project.org/package=datana Description: CRAN Package 'datana' (Datasets and Functions to Accompany Analisis De Datos Con R) Datasets and functions to accompany the book 'Analisis de datos con el programa estadistico R: una introduccion aplicada' by Salas-Eljatib (2021, ISBN: 9789566086109). The package helps carry out data management, exploratory analyses, and model fitting. Package: r-cran-datanugget Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dosnow, r-cran-doparallel, r-cran-foreach, r-cran-rfast, r-cran-mgcv, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-datanugget_1.5.0-1.ca2404.1_all.deb Size: 82122 MD5sum: d187ef0bde151216fa2284631f3c0912 SHA1: 3cb97921eeb0ae1c0a88a631241791f1237eea28 SHA256: bd3c696b166fa7c30b65918b29a1f86d7f3d6c1b47fb3bc706dc182e268ef192 SHA512: c740a036146d0ad74ca8c8eccc2f3a0177c3ce7e935c7ceb5b8c5b7d60a2f3a6d05ea618511334fa289d815614c22ed06f881671e15ab6252f8431ef81c7b412 Homepage: https://cran.r-project.org/package=datanugget Description: CRAN Package 'datanugget' (Create, Optimize, and Refine Data Nuggets) Creating, optimizing and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are selected based on a space-filling maximum-entropy scheme. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget 'contains' these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. The optimal number of data nuggets is determined data-driven based on the relative second-order differences of propensity score indices. Data nuggets are refined by 'splitting' data nuggets which have high scales or elongated shapes (defined as the ratio of the two largest eigenvalues of the covariance matrix of the observations contained within the data nugget). 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Swap Execution Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are traded on or reported to those facilities (respectively). This package provides you the ability to get this data from supported sources. 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Each 'DataONE' repository implements a consistent repository application programming interface. Users call methods in R to access these remote repository functions, such as methods to query the metadata catalog, get access to metadata for particular data packages, and read the data objects from the data repository. Users can also insert and update data objects on repositories that support these methods. 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These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at . Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at . Package: r-cran-datapackage Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-jsonlite, r-cran-iso8601 Suggests: r-cran-simplermarkdown, r-cran-data.table, r-cran-codelist, r-cran-laf Filename: pool/dists/noble/main/r-cran-datapackage_0.2.3-1.ca2404.1_all.deb Size: 333296 MD5sum: 9e99c89ab7322366e08eeeb1ff832296 SHA1: 54fae1d0bf696872531bb6833f0a861f3f5e4d35 SHA256: 003937c4f96f7f384cd9464e932f024b190c6569341655a7e0b54fbdf52c2c5a SHA512: 87900f27cb080b2a5507000e114a3dec0f4038ede01342430af0ad0441eeee47d128bfe62cbabe3ecd919d0d59eb9de1d7f7faa009061c2383cca5213de5719e Homepage: https://cran.r-project.org/package=datapackage Description: CRAN Package 'datapackage' (Creating and Reading Data Packages) Open, read data from and modify Data Packages. Data Packages are an open standard for bundling and describing data sets (). When data is read from a Data Package care is taken to convert the data as much a possible to R appropriate data types. The package can be extended with plugins for additional data types. 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Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual 'R CMD build' process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled on 'GitHub', and used to share data for manuscripts, collaboration and reproducible research. 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The package code is under GPL-2; the data sets are covered by the terms in the 'LICENSE.note' file (the author's data sets under CC0 1.0, and the 'heartdisease' data under CC BY 4.0, see the 'heartdisease' help page). 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Package: r-cran-datasaurus Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1542 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-datasaurus_0.1.9-1.ca2404.1_all.deb Size: 865624 MD5sum: 2e0d7d410e2209c6de8ff7c303257d67 SHA1: 9652f3d74bf861ace923f78b341e1c803c5514d2 SHA256: 14d5e9f1e26a16a52049f76cc01084a2b030e71d911d2da7643c94dd416b7cb7 SHA512: de5144116b9a24afd1c6157fe81165f6519afb7f6b4784d301d8319f261bf1b0cd9607595597811eabe0319291d7b078b8bd43393ccb13cb3dfa9937bcdcb21c Homepage: https://cran.r-project.org/package=datasauRus Description: CRAN Package 'datasauRus' (Datasets from the Datasaurus Dozen) The Datasaurus Dozen is a set of datasets with the same summary statistics. They retain the same summary statistics despite having radically different distributions. The datasets represent a larger and quirkier object lesson that is typically taught via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet contains four very different distributions with the same summary statistics and as such highlights the value of visualisation in understanding data, over and above summary statistics. As well as being an engaging variant on the Quartet, the data is generated in a novel way. The simulated annealing process used to derive datasets from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" . Package: r-cran-datascan Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-pillar, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-datascan_0.1.1-1.ca2404.1_all.deb Size: 187198 MD5sum: 4b0930d6194fd8782806cea5d21bd688 SHA1: a0b35479ed6b3452d505cdfa49ec865c73714685 SHA256: e9523d4e08aa822172b066ca7f2c205bd7e735663d3b0cee88ebdf237e6203f2 SHA512: aec183c5cb61e9af2efd406015a549740a790c8be9b4092e3ed30d3e8152e82b36ab5814d66f8b091df95d6340f6e0a6ca671b4b7406f3bf905e4f814006d09c Homepage: https://cran.r-project.org/package=datascan Description: CRAN Package 'datascan' (Scan Data for Quick Structural Summaries and Checks) Scans data for checking columns that are constant, one-to-one, missing or all unique. 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Package: r-cran-datasetjson Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yyjsonr, r-cran-jsonvalidate, r-cran-hms Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-knitr, r-cran-haven, r-cran-rmarkdown, r-cran-withr, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-data.table Filename: pool/dists/noble/main/r-cran-datasetjson_0.3.0-1.ca2404.1_all.deb Size: 169728 MD5sum: 871005187fd6916ecd2be2daaf9070c7 SHA1: fe422d0ea24b83cfb24bf14d3b02a74866654cb2 SHA256: 82e35f411766a761afc2569dc40ae36f8dd7efeef0fefa0e613a2f63d4a83356 SHA512: c0464d45461ad410e1354036a7bcf13b81c5102b76da43e15eb7df227d6a3125fb17dacd5905dcea238ca265971b542ce60b69aa6e56195517e3019cb7722517 Homepage: https://cran.r-project.org/package=datasetjson Description: CRAN Package 'datasetjson' (Read and Write CDISC Dataset JSON Files) Read, construct and write CDISC (Clinical Data Interchange Standards Consortium) Dataset JSON (JavaScript Object Notation) files, while validating per the Dataset JSON schema file, as described in CDISC (2023) . Package: r-cran-datasets.load Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-dt Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-datasets.load_2.3.0-1.ca2404.1_all.deb Size: 33590 MD5sum: c1eb83c5d148ee5a131c5df5e1654c48 SHA1: 1214ad328c40693af3116a590cbf77da463cb122 SHA256: 5d2725bc5f48e0d989790abfe4e01482151b516ca07c928138eb45353b69bd6f SHA512: 432441afb2683f526d7965ca22a93efad34006d3efe6d3059bf542e5790f5c9f9e2239d72397aedeb40ab4669a308e1612a45edf05a2f217a0689a007a296714 Homepage: https://cran.r-project.org/package=datasets.load Description: CRAN Package 'datasets.load' (Graphical Interface for Loading Datasets) Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces. Package: r-cran-datasetsicr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-datasetsicr_1.0-1.ca2404.1_all.deb Size: 1062010 MD5sum: 3fddd6f8c59a19562db250880ff92e5b SHA1: 5a6039172bbf3c17f04a31c8b88787fe18cb35b8 SHA256: 3cb0f18372dc18a4cc379821ea6129fa1424362406c8b6c682bccd56dff7778f SHA512: ad3becafa87384f7504d8868c6522bdaa5373354cffaafb45846d6f37c5dfea6e9b205ea2994b9b43ff15a0d9de7c99f5f649011540a52103a6657298de9fcc2 Homepage: https://cran.r-project.org/package=datasetsICR Description: CRAN Package 'datasetsICR' (Datasets from the Book "An Introduction to Clustering with R") Companion to the book "An Introduction to Clustering with R" by P. Giordani, M.B. Ferraro and F. Martella (Springer, Singapore, 2020). The datasets are used in some case studies throughout the text. Package: r-cran-datasetsuni Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-datasetsuni_0.1-1.ca2404.1_all.deb Size: 245324 MD5sum: 221bcbff95f55a4ac4511ae1ebe00d46 SHA1: d0469aefaf1bdc63f2dcdc1024732916fc044283 SHA256: 4dc4f2963cfef328b0adb10234718b4bf47477ca583170e8b6b87d67b35ca620 SHA512: 3c695573867e3198384b45744b1433895f32e656be62b803c7780bac27e0b6457eef37611c7018293c4a371f424a7ba2c692caba1b0f762086778aed80be647c Homepage: https://cran.r-project.org/package=DataSetsUni Description: CRAN Package 'DataSetsUni' (A Collection of Univariate Data Sets) A collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses. Package: r-cran-datasetsverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timeseriesdatasets, r-cran-educationr, r-cran-crimedatasets, r-cran-meddatasets, r-cran-oncodatasets, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-datasetsverse_0.1.0-1.ca2404.1_all.deb Size: 455532 MD5sum: fbc2ee0bbc89e8f555858f4102e6a7c2 SHA1: fde4a696a4ff5e470ea1034828a6628f15d2f8c9 SHA256: 894690d32ed48ad0f3e3f0548403c84db26eeddfb93c5d113bcc245c2f68b543 SHA512: fa6dd03002fb24017de292fe43412d6e943097deaa26f78bcfced53663316edfbdfe98ce9a17ed455c18f5c9d7009894f7ac42def1abfcddcf27abe5b29f05b9 Homepage: https://cran.r-project.org/package=DataSetsVerse Description: CRAN Package 'DataSetsVerse' (A Metapackage for Thematic and Domain-Specific Datasets) A metapackage that brings together a curated collection of R packages containing domain-specific datasets. It includes time series data, educational metrics, crime records, medical datasets, and oncology research data. Designed to provide researchers, analysts, educators, and data scientists with centralized access to structured and well-documented datasets, this metapackage facilitates reproducible research, data exploration, and teaching applications across a wide range of domains. Included packages: - 'timeSeriesDataSets': Time series data from economics, finance, energy, and healthcare. - 'educationR': Datasets related to education, learning outcomes, and school metrics. - 'crimedatasets': Datasets on global and local crime and criminal behavior. - 'MedDataSets': Datasets related to medicine, public health, treatments, and clinical trials. - 'OncoDataSets': Datasets focused on cancer research, survival, genetics, and biomarkers. Package: r-cran-datasetviewer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84755 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-nanoparquet, r-cran-rlang Suggests: r-cran-artoo, r-cran-dplyr, r-cran-hms, r-cran-knitr, r-cran-quarto, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-datasetviewer_0.2.0-1.ca2404.1_all.deb Size: 13434316 MD5sum: 799fe78590c55df0912f787885c4ccf5 SHA1: d938f3819a83a4de3fd3b25a1ef57128285cc1e5 SHA256: 2e6360e7f9d9b8ef202486cb0ab449f1a71d8afff3b99e4ed8d55048ffe87085 SHA512: cdad6b278d3c352e2f1bb279da75423882fb7b8cca823d73a7c9f9a6d53248c1d6ec8193c6bcaaaf2a70c7bf5326757fa80a758c21a22ab62689b5b72c1394da Homepage: https://cran.r-project.org/package=datasetviewer Description: CRAN Package 'datasetviewer' ('SAS Studio'-Style Interactive Dataset Viewer) An interactive dataset viewer that renders a fast, scrollable grid with a column-selection panel, per-column property metadata, and a names-versus-labels header toggle, modelled on the 'SAS Studio' table viewer. 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Package: r-cran-datasimilarity Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot Suggests: r-cran-ade4, r-cran-approxot, r-cran-ball, r-cran-caret, r-cran-clue, r-cran-cramer, r-cran-crossmatch, r-cran-dbscan, r-cran-densratio, r-cran-dwdlarger, r-cran-e1071, r-cran-ecume, r-cran-energy, r-cran-expm, r-cran-fnn, r-cran-graphranktest, r-cran-gtests, r-cran-gtestsmulti, r-cran-hdlsskst, r-cran-hyporf, r-cran-kernlab, r-cran-kertests, r-cran-kmd, r-cran-knitr, r-cran-lpksample, r-cran-matrix, r-cran-mvtnorm, r-cran-nbpmatching, r-cran-proc, r-cran-purrr, r-cran-randtoolbox, r-cran-rlemon, r-cran-rpart, r-cran-rpart.plot, r-cran-testthat, r-cran-nnet, r-cran-synthpop, r-cran-igraph, r-cran-cluster Filename: pool/dists/noble/main/r-cran-datasimilarity_0.4.0-1.ca2404.1_all.deb Size: 1284664 MD5sum: dbe63087a9462cf627e8381d7e115704 SHA1: 7f2833ea76698cef985e7ab9ab93ce4447dfff10 SHA256: 35c7d47aaceac2294111880542f36c89fba25f3512009389a985d5932b6fb75d SHA512: a3aeaf2b77b175d09224b37970cd6d311dfc7926422f6e86dabbf148e7ebc78f0b99c6cfa362722ceaececebb76a9dbf21672a09df1ea1de15f7a323fa1c31cf Homepage: https://cran.r-project.org/package=DataSimilarity Description: CRAN Package 'DataSimilarity' (Quantifying Similarity of Datasets and Multivariate Two- Andk-Sample Testing) A collection of methods for quantifying the similarity of two or more datasets, many of which can be used for two- or k-sample testing. 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Includes panel identification algorithms for linking individuals across survey waves. 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We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic 'stats' functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package 'stats' de création et de gestion des séries temporelles (ts(), window()). Package: r-cran-dateback Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dateback_1.0.5-1.ca2404.1_all.deb Size: 25846 MD5sum: d137b5f661189d866ffa6d1d0c2d33e2 SHA1: b92b1fab043941c5c1ae1f3162caa8032ba8c2bb SHA256: 8b70c0746798a937231ecbc12cce952df085dcd6d1a1affc12acf36aa945c6a4 SHA512: 9ef93c85e6e0e2d80b6f3fdd3f321d81dd74b1155a4656735b6c7c2b9280d5c84cceaf239fff1bfdd825be02de165fb63186ec6a54015662e5432dfe58c53213 Homepage: https://cran.r-project.org/package=dateback Description: CRAN Package 'dateback' (Collect and Install R Packages on a Specified Date withDependencies) Works as a virtual CRAN snapshot for source packages. It automatically downloads and installs 'tar.gz' files with dependencies, all of which were available on a specific day. Package: r-cran-datelife Architecture: all Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4908 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-abind, r-cran-bold, r-cran-phangorn, r-cran-phytools, r-cran-ips, r-cran-cluster, r-cran-compare, r-cran-geiger, r-cran-stringr, r-cran-rotl, r-cran-paleotree, r-cran-knitcitations, r-cran-phylobase, r-cran-taxize, r-cran-treebase, r-cran-httr, r-cran-plyr, r-cran-phylocomr, r-cran-biocmanager, r-cran-data.table, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-devtools, r-cran-covr, r-bioc-msa, r-bioc-biostrings Filename: pool/dists/noble/main/r-cran-datelife_0.6.8-1.ca2404.1_all.deb Size: 4282756 MD5sum: 942d2b20719bd21c098fe71eeb3607d5 SHA1: 2ba308de92d3fa0bbad7dd26b0c920a40c45d8fe SHA256: dae16b6ca451f81548f8bb4da82a8c0dae5c4000c1a80e505a3a2b5fcfc61079 SHA512: dc8e710b0ab5046a084d7407e3b3818472142e14e750e2f6c9f1c2d42d6211a110ef9c31909bf469365664d1865b0eaf772d44f1666915c057e9b0687e289435 Homepage: https://cran.r-project.org/package=datelife Description: CRAN Package 'datelife' (Scientific Data on Time of Lineage Divergence for Your Taxa) Methods and workflows to get chronograms (i.e., phylogenetic trees with branch lengths proportional to time), using open, peer-reviewed, state-of-the-art scientific data on time of lineage divergence. This package constitutes the main underlying code of the DateLife web service at . To obtain a single summary chronogram from a group of relevant chronograms, we implement the Super Distance Matrix (SDM) method described in Criscuolo et al. (2006) . To find the grove of chronograms with a sufficiently overlapping set of taxa for summarizing, we implement theorem 1.1. from Ané et al. (2009) . A given phylogenetic tree can be dated using time of lineage divergence data as secondary calibrations (with caution, see Schenk (2016) ). To obtain and apply secondary calibrations, the package implements the congruification method described in Eastman et al. (2013) . Tree dating can be performed with different methods including BLADJ (Webb et al. (2008) ), PATHd8 (Britton et al. (2007) ), mrBayes (Huelsenbeck and Ronquist (2001) ), and treePL (Smith and O'Meara (2012) ). 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It wraps the JavaScript library 'daterangepicker' which is available at . Package: r-cran-datetime Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-chron Filename: pool/dists/noble/main/r-cran-datetime_0.1.4-1.ca2404.1_all.deb Size: 62568 MD5sum: d2eea8c95e8ea78bb7a6920bf332274f SHA1: 431ed60d4c9e6f07011a69afd31c3cfcb37e8621 SHA256: 65022829afc9e241fd6af9b6fb9d2e239cdef7e6063f14d0dcd453c8e87f5b7d SHA512: 2c50ec192723070cefe89337a6f7874d0c11dd5ab2b8220c647b36fa6d2f87e0ef8f5907ff2a58a4061637c4e4e3eb6729b89068a8b79e6009f44f3a69774046 Homepage: https://cran.r-project.org/package=datetime Description: CRAN Package 'datetime' (Nominal Dates, Times, and Durations) Provides methods for working with nominal dates, times, and durations. Base R has sophisticated facilities for handling time, but these can give unexpected results if, for example, timezone is not handled properly. This package provides a more casual approach to support cases which do not require rigorous treatment. It systematically deconstructs the concepts origin and timezone, and de-emphasizes the display of seconds. It also converts among nominal durations such as seconds, hours, days, and weeks. See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' . 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It creates a calendar allowing to select a start date and an end date as well as two fields allowing to select a start time and an end time. 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The package is lightweight (no dependencies, pure R implementations) and relies only on R's standard classes to represent dates and times ('Date' and 'POSIXt'); it aims to provide efficient implementations, through vectorisation and the use of R's native numeric representations of timestamps where possible. Package: r-cran-datetoiso Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-lubridate, r-cran-data.table, r-cran-dplyr, r-cran-purrr, r-cran-glue, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-datetoiso_1.2.1-1.ca2404.1_all.deb Size: 112452 MD5sum: 3f9466c6c2288ba47783c7d3ff055ac0 SHA1: de857ad4b4a9ee93471edf08e2c7bdcbbed9bb55 SHA256: ef25e471e7ac9290a0e60b1c196effe429f32019c5635d2f90a3224d9265a6f4 SHA512: c4cac5ee042e3f272fc62cca21cbc61ef0a1edd8022e26161bacf7042c7ea65b4866ea78a0f114907fc8f3dad3cfd1686b313f3c92449be1ff774b733859fb9c Homepage: https://cran.r-project.org/package=datetoiso Description: CRAN Package 'datetoiso' (Convert and Impute Dates to ISO 8601 Format and Reconcile DataSets) Provides tools for converting and imputing date values to the ISO 8601 standard format and for reconciling differences between two versions of a data set. The package automatically detects date patterns within data frame columns and converts them to consistent ISO-formatted dates, with optional imputation of missing day or month components based on user-defined rules. It also includes functionality to identify inserted, deleted, and updated records, as well as column- and value-level changes, when comparing old and new versions of a data frame. Only one date format may be applied within a single column. Package: r-cran-datom Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1887 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-digest, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-paws.storage, r-cran-purrr, r-cran-rlang, r-cran-yaml Suggests: r-cran-covr, r-cran-git2r, r-cran-knitr, r-cran-mockery, r-cran-rio, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-datom_0.2.0-1.ca2404.1_all.deb Size: 1297914 MD5sum: fc50033f34a6bff592dacfe6633a53e3 SHA1: 1f8e3f1ce881726c81a546e2167b88fb5f982827 SHA256: c49e9b258ae87d146b86872d0dc860faa37812461bb1b5948d00bb42e46a61c6 SHA512: 18ef05215b0039b3f25b726f61ac3064699c953bce04f90a040a8fcd67d2b1235cd2d3e28837d23a4e87df7aca9905e55ca587e1e27f415dd8016c93b418a80f Homepage: https://cran.r-project.org/package=datom Description: CRAN Package 'datom' (A Unified Framework for Versioned, Traceable Tabular Data) Provides versioned storage for tabular data without a database or a server. Each table is written as an immutable, content-addressed version -- identical content is detected and stored only once -- while its version history and metadata are kept as code in a 'git' repository and the data itself in a local filesystem or cloud object storage ('S3'). Any past version can be read back exactly by its identifier, and each table records the sources it was derived from, so a project carries full data lineage. A lightweight reader role retrieves current or historical data from storage alone, without 'git' or write access, giving downstream analyses and pipelines a single versioned source of truth. It targets analytical and scientific data management, such as preparing clinical study datasets, and is designed as a foundation for higher-level governance tooling. 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Package: r-cran-datos Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-babynames, r-cran-dplyr, r-cran-forcats, r-cran-fueleconomy, r-cran-gapminder, r-cran-ggplot2, r-cran-lahman, r-cran-nasaweather, r-cran-nycflights13, r-cran-palmerpenguins, r-cran-modeldata, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-datos_0.5.1-1.ca2404.1_all.deb Size: 160430 MD5sum: 590783e6a6dbff98ba7ff4b664ba611a SHA1: e36e848710c9690b4a8953c278932eeb19bf3761 SHA256: 4c31e4a15eea7d73ace397559ca44f01e72583bafa899b778a97ec1766ec9939 SHA512: 9af78742ad7a061033a96074a33edda845c85513843d807eaf50435d06761cdefd1d09edafdaf848cc3a6c58f814d1dad78f9a7d931c87494023e9351fa5ace8 Homepage: https://cran.r-project.org/package=datos Description: CRAN Package 'datos' (Traduce al Español Varios Conjuntos de Datos de Práctica) Provee una versión traducida de los siguientes conjuntos de datos: 'airlines', 'airports', 'AwardsManagers', 'babynames', 'Batting', 'credit_data', 'diamonds', 'faithful', 'fueleconomy', 'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers', 'mpg', 'mtcars', 'atmos', 'palmerpenguins', 'People, 'Pitching', 'planes', 'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b', 'table5', 'vehicles', 'weather', 'who'. English: It provides a Spanish translated version of the datasets listed above. 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Package: r-cran-datrprofile Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-odbc, r-cran-dplyr, r-cran-rsqlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-datrprofile_0.1.0-1.ca2404.1_all.deb Size: 71762 MD5sum: 86b1bc284f79eb8e9f0a1810c79d4b69 SHA1: 3191c3ffb2f702f0665246f523c3df61026c98a0 SHA256: c225a553ea25a996d03a5c6819a5584db20daad5386217cbf6835e5f45a2e1d6 SHA512: bc86fa9cada6c3a902a560bcd0da5aac91bd6b2965237dda6b6cfb417f56f7938291e8ee2ebee8f87dc6d5c3eeaedd75800d02b02ad6e86b25fe9e8a2b68af4e Homepage: https://cran.r-project.org/package=datrProfile Description: CRAN Package 'datrProfile' (Column Profile for Tables and Datasets) Profiles datasets (collecting statistics and informative summaries about that data) on data frames and 'ODBC' tables: maximum, minimum, mean, standard deviation, nulls, distinct values, data patterns, data/format frequencies. Package: r-cran-daur Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-daur_1.0-1.ca2404.1_all.deb Size: 33142 MD5sum: e97080acdc12ff101c8cbfe03acdc10b SHA1: b11ec37580913446943ef2afbb5bef41f4bd0030 SHA256: 3a68c3e6a0e43c3aa3771be9af62caeec0168ea15e69df0e092e8719d46e95d2 SHA512: 17134606869af4bf3e805bbf46f069ba82a24ea4c1e82eb892bff7f5b37d16cc9546cb74970f56b90e80bb8e818ab7d686af8ac4563b646e517f8a98d79839db Homepage: https://cran.r-project.org/package=dauR Description: CRAN Package 'dauR' (Datasets for "Sampling and Data Analysis Using R: Theory andPractice") Provides several datasets used throughout the book "Sampling and Data Analysis Using R: Theory and Practice" by Islam (2025, ISBN:978-984-35-8644-5). The datasets support teaching and learning of statistical concepts such as sampling methods, descriptive analysis, estimation and basic data handling. These curated data objects allow instructors, students and researchers to reproduce examples, practice data manipulation and perform hands-on analysis using R. Package: r-cran-davies Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1932 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-davies_1.2-1-1.ca2404.1_all.deb Size: 1935176 MD5sum: c72ae97f8396146a8f23965f79b5223b SHA1: 624a76ca89bfb17c8c424b39ffb617f8753c7c71 SHA256: 08b9d66c115c8e06f8ab401aefbb6c7b71af8834e843b205bbb8cda69aa959c6 SHA512: b9e2d54c17ff33bc76b3efdfce2be27d6e3f2d1d5c1d6119f6facdadd76f4162a558dd5417481af5acbb7fc9e041738863a874cee593cb885477c466e3034e73 Homepage: https://cran.r-project.org/package=Davies Description: CRAN Package 'Davies' (The Davies Quantile Function) Various utilities for the Davies distribution. 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The development of this package is completely independent from the government agency, Klimadatastyrelsen, who maintains the API. Package: r-cran-dawnn Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seurat, r-cran-reticulate, r-cran-keras, r-cran-withr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-callr, r-cran-dplyr, r-cran-pkgload, r-cran-viridis Filename: pool/dists/noble/main/r-cran-dawnn_2.2.0-1.ca2404.1_all.deb Size: 373930 MD5sum: f8ce17e91438f732da134057a6828927 SHA1: 9fc1df54c4c662eb61c7bf61d92de58eac9ef304 SHA256: 29b2fa6d85c5d4ba0371ba266b360251b9cde9e26bc49264ddfaafd81115f3c7 SHA512: c2cea0ed4580c6d2378260c56a86bc15687a990a9aeee1ba15db3c07ffef139acff8c7c2768a23947bc734599dd2e824d52ae385ff61eee6a1422231dae54a61 Homepage: https://cran.r-project.org/package=dawnn Description: CRAN Package 'dawnn' (Differential Abundance with Neural Networks) Detects regions of differential abundance in single-cell transcriptomic data by applying a pre-trained neural network model to the labels of each cell's nearest neighbours. Tests for both local and global differential abundance, controlling the false discovery rate with the Benjamini-Yekutieli procedure. The method is described in Hall and Castellano (2023) . Package: r-cran-daymetr Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-ncdf4, r-cran-httr, r-cran-tidyr, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-markdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-daymetr_1.7.1-1.ca2404.1_all.deb Size: 290952 MD5sum: 279a2c636dd497949acef5ba98f2f25f SHA1: 220d00e491992bd2696b1450e74affd4591dd02d SHA256: a6fd5c37caf66be5a674204386ab1cdfed57efe628949f4aa74186c035cfcd69 SHA512: e86e03c8bef7501a1a1b6bdb96fa38e83965da8eed5cda7932fc3256ad1def70173efe5ba3d7f15ea670e8c7ea46a5be3110ba559ffae0aed99bd53e4e84f2b9 Homepage: https://cran.r-project.org/package=daymetr Description: CRAN Package 'daymetr' (Interface to the 'Daymet' Web Services) Programmatic interface to the 'Daymet' web services (). Allows for easy downloads of 'Daymet' climate data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded (netCDF) data are provided. Package: r-cran-days2lessons Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-days2lessons_1.0.0-1.ca2404.1_all.deb Size: 48330 MD5sum: 6268cf1806eabdb3bd490c53db24a860 SHA1: acbdfb55a31daa3291e97ec55c18948f5d23bbb4 SHA256: ac7fb1f535da654d85f6acdb2cbb7ad265f3d7024e8a2b743e18002770e57c97 SHA512: 115954d035ecff57852c180f39d06744d53497a03b3751aa28696f127d41b7564a572919ff553d08fb5359a94a100c247de4848447905a88ea1040914fc4b950 Homepage: https://cran.r-project.org/package=days2lessons Description: CRAN Package 'days2lessons' (Distributes Teachers Lessons On Days in a Balanced Manner) The set of teacher/class lessons is completed with a column that allocates a day to each lesson, so that the distribution of lessons by day, by class, and by teacher is as uniform as possible. . Package: r-cran-daysupply Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lme4, r-cran-rlang, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-daysupply_0.1.0-1.ca2404.1_all.deb Size: 37096 MD5sum: f31c3496d556cfc01596ac1ec6929e14 SHA1: 2c6b200552bcc401cc2b54f99f2e196ecaed01c3 SHA256: 73cdc16447d90af60255f1d4231878a823acf6e57cb2e898bbb88c0400d23a3b SHA512: 319d86dd6d8b53b51dbe99c0a0646b25dc54a321f5482882fa5179f184e365eeb9b78e7491eaa8ebc955f77499041cb492a641169e2ef6f30e3b310c5bd8f21d Homepage: https://cran.r-project.org/package=daySupply Description: CRAN Package 'daySupply' (Calculating Days' Supply and Daily Dose of Prescriptions) Allows clinicians and researchers to compute daily dose (and subsequently days' supply) for prescription refills using the following methods: Fixed window, fixed tablet, defined daily dose (DDD), and Random Effects Warfarin Days' Supply (REWarDS). Daily dose is the computed dose that the patient takes every day. For medications with fixed dosing (e.g. direct oral anticoagulants) this is known and does not need to be estimated. For medications with varying dose such as warfarin, however, the daily dose should be assumed or estimated to allow measurement of drug exposure. Days’ supply is the number of days that patients’ supply of medication will last after each prescription fill. Estimating days’ supply is necessary to calculate drug exposure. The package computes days’ supply and daily dose at both the prescription and patient levels. Results at the prescription level are denoted with “-Rx-” and those at patient level are denoted with “-Pt-”. Package: r-cran-db2pq Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dbi, r-cran-keyring, r-cran-rpostgres, r-cran-tibble, r-cran-wrds Suggests: r-cran-adbcpostgresql, r-cran-adbcdrivermanager, r-cran-adbi, r-cran-duckdb, r-cran-dplyr, r-cran-dbplyr, r-cran-ggplot2, r-cran-knitr, r-cran-nanoarrow, r-cran-processx, r-cran-quarto, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-db2pq_0.0.4-1.ca2404.1_all.deb Size: 192010 MD5sum: 46b0f0544f1318fc93cb4d815a268908 SHA1: bed6e3c4bfb0bfbc1eec41ac684ae35d1f46346f SHA256: 378f8102d108a44716379228f6b27f1082b3821d5fd621caa9b328bf507195a2 SHA512: 7152b7ef8cc4c7e5a8215db32228a11d855fa9215468afe6d82656ee3028a1674b60b52211ac19f3d8135b0490cb16269c8ec8756e3ad092c7e5f3685d4de101 Homepage: https://cran.r-project.org/package=db2pq Description: CRAN Package 'db2pq' (Export Database Tables to 'Parquet') Tools for exporting 'PostgreSQL' tables to 'Parquet' files, with support for chunked writes, column type overrides, and timezone-aware timestamp handling. Includes functions for maintaining a local 'Parquet' data library sourced from 'WRDS' (Wharton Research Data Services), with update-checking based on table metadata, and archive management utilities for versioning local data files. See Gow and Ding (2024) "Empirical Research in Accounting: Tools and Methods" . Package: r-cran-dbacf Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-dbacf_0.2.8-1.ca2404.1_all.deb Size: 50078 MD5sum: 6327b34488b4dcf3c6a53649f548b782 SHA1: 5abbba2161d133b55250108b5ab580c9ccbfb343 SHA256: 52e662788d369eec23627dc8f4fba0d07dc09943bd16fbe53fe4c6088dfa8fa1 SHA512: 4df3a8e9afda462595b11cb02ccd1bf1602f9dda3c19652bee0cacd080d59c15b80f631030b8527f86c6ff8a2b5cefa77608f0db95a62870b3303d18190af789 Homepage: https://cran.r-project.org/package=dbacf Description: CRAN Package 'dbacf' (Autocovariance Estimation via Difference-Based Methods) Provides methods for (auto)covariance/correlation function estimation in change point regression with stationary errors circumventing the pre-estimation of the underlying signal of the observations. Generic, first-order, (m+1)-gapped, difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) . Bias-reducing, second-order, (m+1)-gapped, difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) . Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) . It also includes a general projection-based method for covariance matrix estimation. Package: r-cran-dbcsp Architecture: all Version: 0.0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-tsdist, r-cran-geigen, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-paralleldist, r-cran-plyr, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dbcsp_0.0.2.2-1.ca2404.1_all.deb Size: 3736354 MD5sum: f9b08dcacef75233919ff56c8fe467fe SHA1: 2080f0a96e7abe36af883e5cf9ddfc4e19e4c420 SHA256: ac834988dbf9d4e0cfc99b2eeae643e06e1a8a7ead5fdf2846f6d7622fcda7ca SHA512: a39d221e8a10aeaa4e1427cf6895a8fc0640cb5e4d8a32adbb2a5590c5307f3a671d0e01a0d4b609a7c5055f429df965d5d5755974f9cd2872bea725a2a4be93 Homepage: https://cran.r-project.org/package=dbcsp Description: CRAN Package 'dbcsp' (Distance-Based Common Spatial Patterns) A way to apply Distance-Based Common Spatial Patterns (DB-CSP) techniques in different fields, both classical Common Spatial Patterns (CSP) as well as DB-CSP. The method is composed of two phases: applying the DB-CSP algorithm and performing a classification. The main idea behind the CSP is to use a linear transform to project data into low-dimensional subspace with a projection matrix, in such a way that each row consists of weights for signals. This transformation maximizes the variance of two-class signal matrices.The dbcsp object is created to compute the projection vectors. For exploratory and descriptive purpose, plot and boxplot functions can be used. Functions train, predict and selectQ are implemented for the classification step. Package: r-cran-dbcvindex Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dbcvindex_1.6-1.ca2404.1_all.deb Size: 29548 MD5sum: 223bba3adddebc66cf49892bbb75fe6b SHA1: 9a58ed174a31d4d9aed333fbef4d7b7b87982d92 SHA256: 43496cf5d75fdf82066f8b4257aac227ec38a7b75067183f421a861f5c9ba8a5 SHA512: 09ef51e9d795b2a7f96aab770c94ac62c7f2e1e76702bf8989673392ab2a54adbcdc3fa1d72b8ea893a8282d09c39b0f0ac1d26b4ce2995735c7cbb893f88d21 Homepage: https://cran.r-project.org/package=DBCVindex Description: CRAN Package 'DBCVindex' (Calculates the Density-Based Clustering Validation (DBCV) Index) A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), . A more recent article on the 'DBCV' index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), . Package: r-cran-dbd Architecture: all Version: 0.0-22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hmm.discnp, r-cran-mass, r-cran-rmutil, r-cran-spcadjust Filename: pool/dists/noble/main/r-cran-dbd_0.0-22-1.ca2404.1_all.deb Size: 225408 MD5sum: 1e51c3c0ab7c2aaadda02c4f19972f66 SHA1: 9d7e49e4f929f7aaf28efa0001db98e1f3daabd9 SHA256: 34f14c0574896be1132fb4543641131754f60faab00c14d29849493cc0a25bf5 SHA512: 9ac88b54f3e3e6b85d83d2e0bc56afd0e7a2bbe31bfc2cbc30d4089c1b4a1ebf8c2f8de787cb39ac17881bf1f87cc54dd13b48b90118969030091db98e05396f Homepage: https://cran.r-project.org/package=dbd Description: CRAN Package 'dbd' (Discretised Beta Distribution) Tools for working with a new versatile discrete distribution, the db ("discretised Beta") distribution. This package provides density (probability), distribution, inverse distribution (quantile) and random data generation functions for the db family. It provides functions to effect conveniently maximum likelihood estimation of parameters, and a variety of useful plotting functions. It provides goodness of fit tests and functions to calculate the Fisher information, different estimates of the hessian of the log likelihood and Monte Carlo estimation of the covariance matrix of the maximum likelihood parameter estimates. In addition it provides analogous tools for working with the beta-binomial distribution which has been proposed as a competitor to the db distribution. Package: r-cran-dbest Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-dbest_1.8-1.ca2404.1_all.deb Size: 87130 MD5sum: 9eb71af98a0a100bfbf73953e32a7a18 SHA1: 3170b9fad4f53836b482f38ade61dfaab2054c09 SHA256: 0ed4f8a81f5ab0aedae16e803f60fabdd756daf97c6bde3ea00bb7fb562fef23 SHA512: 66acf849fe8ad3c0ae513ed262c550ca9e5497668d6043d529781f5a1ca394f63e59f3f5ccbd8d91cfeb53a1c33e75a34d8bebbcf382aa60636a256788448da9 Homepage: https://cran.r-project.org/package=DBEST Description: CRAN Package 'DBEST' (Detecting Breakpoints and Estimating Segments in Trend) A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage. 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Diagnostic models can be estimated with 'Stan'; however, the necessary scripts can be long and complicated. This package automates the creation of 'Stan' scripts for diagnostic classification models. Specify different types of diagnostic models, define prior distributions, and automatically generate the necessary 'Stan' code for estimating the model. Package: r-cran-dcode Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-dcode_1.0-1.ca2404.1_all.deb Size: 31842 MD5sum: d8356ae805b45459abf49623c74c9722 SHA1: 98a0f2d0021fe64cec6828d5b82913ab3940f226 SHA256: 69b62430303a69d4ab14d661f7dbea665012712c9d68b38ff2ae2a48055e25f0 SHA512: eb8255bcf7745e2515cb4a3cdbb427fc413ce6a49145871f36bcd591dbaa3e84ca630102d0a854e1b72472122d7616d04645b47003e8f028562883e78f39b83b Homepage: https://cran.r-project.org/package=DCODE Description: CRAN Package 'DCODE' (List Linear n-Peptide Constraints for Overlapping ProteinRegions) Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971). Package: r-cran-dcorvs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dcov, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-dcorvs_1.1-1.ca2404.1_all.deb Size: 31564 MD5sum: b762841b28411f6b78d274953b6f7020 SHA1: b0706de0d4d63dc8a24db427d4a09ba480cb8cfe SHA256: 458b2936640b0d8787e21f55822adfeb55cbb5b15cb78e5cdc8fbb8d75168f37 SHA512: d8e94ae861fcf4a9f4bd0bc0604c257e582e58fbbda303ca50c2d6aecfdc908e2a8fa5ba32aed8280caf0a8f80f63da90f55c9f7e2bf58444a124962812c5d4f Homepage: https://cran.r-project.org/package=dcorVS Description: CRAN Package 'dcorVS' (Variable Selection Algorithms Using the Distance Correlation) The 'FBED' and 'mmpc' variable selection algorithms have been implemented using the distance correlation. The references include: Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). "Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. . Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1--39. . Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435--447. . Package: r-cran-dcovts Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcov, r-cran-doparallel, r-cran-foreach, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-dcovts_1.5-1.ca2404.1_all.deb Size: 180316 MD5sum: 30c3f86c5396a50df7e18b18f69608c5 SHA1: a64ecc842ea24591616d1766d33e7b2a713a7256 SHA256: 80f71d3aa6be23336078aa0e4bb33767c5b06de6c573101bb260554c47f263db SHA512: 9bbfcc21a048a19035ffa6104717925c016eb6d20f3ee9fc73824ed850cbdc18d9ebbf16ccb1d7f1fa94d5c3efff7c5e2fb61a4e9b73b67120c8e41fdd1fc024 Homepage: https://cran.r-project.org/package=dCovTS Description: CRAN Package 'dCovTS' (Distance Covariance and Correlation for Time Series Analysis) Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). 'An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. . b) Fokianos Konstantinos and Pitsillou Maria (2018). 'Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. . c) Fokianos Konstantinos and Pitsillou Maria (2017). 'Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. . d) Pitsillou Maria and Fokianos Konstantinos (2016). 'dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. . Package: r-cran-dctensor Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fields, r-cran-rtensor, r-cran-nntensor Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dctensor_1.3.1-1.ca2404.1_all.deb Size: 1467042 MD5sum: 6437a7f5e9352208c1d48d5459312ca9 SHA1: 597a3e7ae4e7cf001bfb5f3f3cf681fb9c44abfb SHA256: 7dfe02d420b645891ede28955e97c600a471d8de1a7f0bdb6de1210d4bed58a7 SHA512: 3cc309005e82af2404c354444b15224595f9f27e9067a640baaddbb9081350d41cba63a8188c678f8a060d3f03406a1d123a26bbfb127e54cfc4b8bc01431670 Homepage: https://cran.r-project.org/package=dcTensor Description: CRAN Package 'dcTensor' (Discrete Matrix/Tensor Decomposition) Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md . Package: r-cran-dcur Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-mclust, r-cran-mass, r-cran-ppcor, r-cran-ggplot2, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-testthat, r-cran-snow Filename: pool/dists/noble/main/r-cran-dcur_1.0.2-1.ca2404.1_all.deb Size: 576908 MD5sum: 11022229fd51b0b6eeb8abded8222f2d SHA1: e4acb58ec300e52da4b05a2f69a6e4839f2cb0ea SHA256: bfb8fbd2ebb76ed13f8919347a5c11cf23217baead69d85f743151ffe93080d1 SHA512: 7743037111788ca4deb3c8a4e4889cd537bbc63c362d03450afafd8df44d984914835dcfa86c7696b10b9a6d87aac7d57012b181cf70b3e5f6233125256062bd Homepage: https://cran.r-project.org/package=dCUR Description: CRAN Package 'dCUR' (Dimension Reduction with Dynamic CUR) Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) ) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension. The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest. Package: r-cran-dcurves Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble Suggests: r-cran-broom.helpers, r-cran-covr, r-cran-gtsummary, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-dcurves_0.5.1-1.ca2404.1_all.deb Size: 366674 MD5sum: 63aaa5be85d49058f6ce7fa9e12741ac SHA1: c96765a31ce4322ad139aa640035c8d2342150a3 SHA256: 95360276d1221dc8f70cc4308f2c77e34bfdbabe38e6a6734e4bb045f616487d SHA512: 883dfab6ca7dd51d73fe14468ce59b995e1d22555438e88e7654f61dc4ee65fdc9886eee2299ea459a95a0c953705480f4e042cf305ce4d59ff742f891f1a05f Homepage: https://cran.r-project.org/package=dcurves Description: CRAN Package 'dcurves' (Decision Curve Analysis for Model Evaluation) Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) , Vickers (2008) , and Pfeiffer (2020) . Package: r-cran-dcvar Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstan, r-cran-posterior, r-cran-loo, r-cran-ggplot2, r-cran-patchwork, r-cran-bayesplot, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-sn Filename: pool/dists/noble/main/r-cran-dcvar_0.9.3-1.ca2404.1_all.deb Size: 885386 MD5sum: 4a6aa289e30329a73f01276136fed6b0 SHA1: 94436092bc3b21df8e0508594b10324123ccb86a SHA256: 70fcb13de94a09265630dac80f4964330dd29787a7e3f85bac45a4817fa7487c SHA512: 36b2228405c8eab6e9d63b3fba9ede4c65d09e9d01a028e9f994564b6fe70d77665e5e1a41ea3d27e15b4331cd6cb48257361b4acea289562762e7bd2504cfa9 Homepage: https://cran.r-project.org/package=dcvar Description: CRAN Package 'dcvar' (Dynamic Copula VAR Models for Time-Varying Dependence) Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with 'Stan' through 'rstan' or 'cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) and Vehtari, Gelman and Gabry (2017) . Package: r-cran-dda Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dhsic, r-cran-energy, r-cran-foreach Suggests: r-cran-boot, r-cran-doparallel, r-cran-devtools, r-cran-interactions, r-cran-iterators, r-cran-lmtest, r-cran-moments, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-dda_0.1.1-1.ca2404.1_all.deb Size: 158942 MD5sum: 4bc79d9a055ada1c76b7968ed00ded56 SHA1: 131976f0212217d9b203167484dff683a9a42a73 SHA256: 1fb9ffa0e7dfd42c7301912a0fbd64a90ec619edb1de530c74d368cc78c673e5 SHA512: 75adf17bb51bb4ab4f469d0672fcf3514d1df3758594c96b002eebacfe7d0c04433f5255ecf273e634255e6b0d688282a6b688d089b456926b851e6664d4a2a5 Homepage: https://cran.r-project.org/package=dda Description: CRAN Package 'dda' (Direction Dependence Analysis) A collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see . Package: r-cran-ddecompose Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rifreg, r-cran-formula, r-cran-hmisc, r-cran-pbapply, r-cran-sandwich, r-cran-ranger, r-cran-fastglm Suggests: r-cran-testthat, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ddecompose_1.0.0-1.ca2404.1_all.deb Size: 1342516 MD5sum: 7bb399fdabfa7ed5375c750decfded7c SHA1: a80f4b1b180799422b8b8af207846c58f4dd0331 SHA256: 28304927393e7d5577676acacb6aeba24ac8b04b732b8f2468bbecb8e46aee7b SHA512: 5e8e4f49bec9b21a9731a59f75d6e981d9ec7b4a5991c2ee88983f04654b57fc64e37e5501e3b95f1da5663740d9925f1f7790a13b8e3659b0fa0e74fd9f52a3 Homepage: https://cran.r-project.org/package=ddecompose Description: CRAN Package 'ddecompose' (Detailed Distributional Decomposition) Implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean. The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018). The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates. References: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) . "Decomposing Wage Distributions Using Recentered Influence Function Regressions." Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) . "Decomposition Methods in Economics." DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) . "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach." Oaxaca, Ronald. (1973) . "Male-Female Wage Differentials in Urban Labor Markets." Blinder, Alan S. (1973) . "Wage Discrimination: Reduced Form and Structural Estimates." Package: r-cran-ddesonn Architecture: all Version: 7.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-dplyr, r-cran-openxlsx, r-cran-tidyr, r-cran-proc, r-cran-prroc, r-cran-reshape2, r-cran-digest, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-quantmod, r-cran-randomforest, r-cran-reticulate, r-cran-zoo, r-cran-readxl, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ddesonn_7.1.11-1.ca2404.1_all.deb Size: 9713692 MD5sum: 499d5e85fc2336837e38f0856f331136 SHA1: 3a806e4c35baa91c8bd76ddb2d8d1b9c62ceea6e SHA256: 371c0c06bee12c61c48de271f483d89b969f4d7936dd2512e78e67d626c855af SHA512: 233bcfc04091f560ef4ce050565ef1a5727f4333a3a371a9c1376ea6870a00249bcfca8e10142b82188fef177ba9ac81ab135ef688064d90f30cb577cc52c60f Homepage: https://cran.r-project.org/package=DDESONN Description: CRAN Package 'DDESONN' (A Deep Dynamic Experimental Self-Organizing Neural NetworkFramework) Provides a fully native R deep learning framework for constructing, training, evaluating, and inspecting Deep Dynamic Ensemble Self Organizing Neural Networks at research scale. The core engine is an object oriented R6 class-based implementation with explicit control over layer layout, dimensional flow, forward propagation, back propagation, and transparent optimizer state updates. The framework does not rely on external deep learning back ends, enabling direct inspection of model state, reproducible numerical behavior, and fine grained architectural control without requiring compiled dependencies or graphics processing unit specific run times. Users can define dimension agnostic single layer or deep multi-layer networks without hard coded architecture limits, with per layer configuration vectors for activation functions, derivatives, dropout behavior, and initialization strategies automatically aligned to network depth through controlled replication or truncation. Reproducible workflows can be executed through high level helpers for fit, run, and predict across binary classification, multi-class classification, and regression modes. Training pipelines support optional self organization, adaptive learning rate behavior, and structured ensemble orchestration in which candidate models are evaluated under user specified performance metrics and selectively promoted or pruned to refine a primary ensemble, enabling controlled ensemble evolution over successive runs. Ensemble evaluation includes fused prediction strategies in which member outputs may be combined through weighted averaging, arithmetic averaging, or voting mechanisms to generate consolidated metrics for research level comparison and reproducible per-seed assessment. The framework supports multiple optimization approaches, including stochastic gradient descent, adaptive moment estimation, and look ahead methods, alongside configurable regularization controls such as L1, L2, and mixed penalties with separate weight and bias update logic. Evaluation features provide threshold tuning, relevance scoring, receiver operating characteristic and precision recall curve generation, area under curve computation, regression error diagnostics, and report ready metric outputs. The package also includes artifact path management, debug state utilities, structured run level metadata persistence capturing seeds, configuration states, thresholds, metrics, ensemble transitions, fused evaluation artifacts, and model identifiers, as well as reproducible scripts and vignettes documenting end to end experiments. Kingma and Ba (2015) "Adam: A Method for Stochastic Optimization". Hinton et al. (2012) "Neural Networks for Machine Learning (RMSprop lecture notes)". Duchi et al. (2011) "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization". Zeiler (2012) "ADADELTA: An Adaptive Learning Rate Method". Zhang et al. (2019) "Lookahead Optimizer: k steps forward, 1 step back". You et al. (2019) "Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (LAMB)". McMahan et al. (2013) "Ad Click Prediction: a View from the Trenches (FTRL-Proximal)". Klambauer et al. (2017) "Self-Normalizing Neural Networks (SELU)". Maas et al. (2013) "Rectifier Nonlinearities Improve Neural Network Acoustic Models (Leaky ReLU / rectifiers)". Package: r-cran-ddi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ddi_0.1.0-1.ca2404.1_all.deb Size: 19156 MD5sum: 7f792c63591b07ea7514ff2336d64665 SHA1: 134b1d980f5eac6c5450a140c1f9c069c9187a4a SHA256: cfffb5a6ee16904db5546ff61334d29c28339efb9fde666ecbb6d9bf3727d0ae SHA512: e8fcf19c6b5a25a55d04680324d2134841c6b5af004c098d13fed66cd6fda9f82fc11e8ca2edd6dc56710d8c00e6e1fa8c71eb0aaa2ad41e325167ca972e832c Homepage: https://cran.r-project.org/package=ddi Description: CRAN Package 'ddi' (The Data Defect Index for Samples that May not be IID) Implements Meng's data defect index (ddi), which represents the degree of sample bias relative to an iid sample. The data defect correlation (ddc) represents the correlation between the outcome of interest and the selection into the sample; when the sample selection is independent across the population, the ddc is zero. Details are in Meng (2018) , "Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election." Survey estimates from the Cooperative Congressional Election Study (CCES) is included to replicate the article's results. Package: r-cran-ddiv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-segmented, r-cran-qpdf Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ddiv_0.1.1-1.ca2404.1_all.deb Size: 405234 MD5sum: 9501aad02e0b2aeb5ae182a964224789 SHA1: 5f415643f4b8fb7e6f671b766fbf5ee9700b66e6 SHA256: a20cb9e4caa72fe5dc7ece9a281a9cdbbf07507dddc6de20b20ee0034ab747bb SHA512: 73c2d8e7e90083c40d33e70994b50fd87b0796a45ea9ff3cfaff55bb52608cdfc73706c2f7a3d78154db0cc7fcd657eee97a228b1d36b88046066ba1c7438f8e Homepage: https://cran.r-project.org/package=ddiv Description: CRAN Package 'ddiv' (Data Driven I-v Feature Extraction) The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. . 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Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) , Angiulli, F., & Pizzuti, C. (2002) , Hautamaki, V., & Ismo, K. (2004) and Zhang, K., Hutter, M. & Jin, H. (2009) . For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) , Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) , Schubert, E., Zimek, A. & Kriegel, H-P. (2014) , Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) , Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) , Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) , Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) , Zhu, Q., Feng, Ji. & Huang, J. (2016) , Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) , Tang, B. & Haibo, He. (2017) and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) . 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Provides radial (Charnes-Cooper-Rhodes and Banker-Charnes-Cooper) technical efficiency under constant, variable, non-increasing and non-decreasing returns to scale, the slacks-based measure of Tone (2001), the additive model of Charnes and others (1985), and the directional distance function of Chambers, Chung and Fare (1996), all through one interface and one result object. Efficiency estimates are accompanied by peers, slacks, returns-to-scale classification, scale efficiency and the optimal multipliers, and by bias-corrected estimates and confidence intervals from the smoothed homogeneous bootstrap of Simar and Wilson (1998). Where prices are known, cost, revenue and Nerlovian profit efficiency separate the technical component from the allocative one; where they are not, cross-efficiency with the secondary goals of Doyle and Green (1994) ranks units that a self-appraisal leaves tied. This package succeeds the archived 'DEA' package of Diaz-Martinez and Fernandez-Menendez (2008). 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Package: r-cran-dear Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-tidyr, r-cran-plotly, r-cran-igraph, r-cran-writexl, r-cran-dplyr, r-cran-gridextra, r-cran-optisolve Filename: pool/dists/noble/main/r-cran-dear_1.5.4-1.ca2404.1_all.deb Size: 788502 MD5sum: 59e1de82062d66984c893908f2df9d91 SHA1: fdf2bc5082b3907d3ccbec27e6ff154ed568fbbe SHA256: 7c2051bd3eff44db8ce5449c3419746c1250ef72225c9a999cff8892bde80ae6 SHA512: 5f5a44809e0d8f8a242e9c6912458adc9932c69948c2c394a53e95b44a686b3f48f8e4d96c31733b6dca83c23de575258184efa6c20a593b66ded175797fbaa4 Homepage: https://cran.r-project.org/package=deaR Description: CRAN Package 'deaR' (Conventional and Fuzzy Data Envelopment Analysis) Set of functions for Data Envelopment Analysis, including classical, fuzzy, cross-efficiency, bootstrapping, and Malmquist models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). , Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). . Package: r-cran-deaviz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1755 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-benchmarking, r-cran-lpsolve, r-cran-lpsolveapi, r-cran-kohonen, r-cran-smacof, r-cran-plotly, r-cran-ggrepel, r-cran-igraph, r-cran-graphlayouts, r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-deaviz_0.1.0-1.ca2404.1_all.deb Size: 1342544 MD5sum: 4cb5e79e8191860c265e78b6425490c1 SHA1: 7537a8a8d171bf0911e32ee6ed618bef9c4db618 SHA256: bdd8406d426c3488ef70378d938444681ba2036818e402f3b906e33e2fefc7e0 SHA512: 9aa68566c54892da86c103e69f5cb0400382af4ef487ea2d998991dd08c0e2bec02b6bcc5a51f5877c1b42adc23f691d1cdc948b7fa71fa73d8ef371b9e4e11d Homepage: https://cran.r-project.org/package=deaviz Description: CRAN Package 'deaviz' (Visualization of Data Envelopment Analysis Problems) High-dimensional visualization methods for data envelopment analysis (DEA), gathering in one place techniques that have appeared in the literature but remained scattered and largely unimplemented: cross-efficiency matrix unfolding, the Porembski network with lambda edges, principal component analysis biplots, multidimensional-scaling colour-plots, self-organizing maps, the Costa bi-dimensional efficient frontier, parallel coordinates, radar charts, panel-data trajectory biplots, peer and reference networks, and a set of descriptive plots. The package is built around a single validated dea_data() object and uses the 'Benchmarking' package as its DEA engine. The implemented methods draw on a body of literature; representative references include Doyle and Green (1994) , Porembski, Breitenstein and Alpar (2005) and Bana e Costa, Soares de Mello and Angulo Meza (2016) . Package: r-cran-debar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-aphid, r-cran-seqinr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-debar_0.1.1-1.ca2404.1_all.deb Size: 299678 MD5sum: fa16d9a47215bcac7453c3d55341a2ba SHA1: 2b8b047ca966317c3287153e11971ccb08d48af4 SHA256: 0a779bb3c18c484e2f72b88d4f1e8e7605af30ce033c3a5231eda52e05f786dc SHA512: cf54329e4412a2e24b403389a517b57195470e6b122526a656781c0faf065f4ae8704a8b30b976a9c12bc4253cfc9a2bcde96a8c77482f989bc9d3ce59519def Homepage: https://cran.r-project.org/package=debar Description: CRAN Package 'debar' (A Post-Clustering Denoiser for COI-5P Barcode Data) The 'debar' sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). 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Package: r-cran-debest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-flexsurv Filename: pool/dists/noble/main/r-cran-debest_0.1.0-1.ca2404.1_all.deb Size: 36084 MD5sum: e8f0d24f48ab012464c5d2604da6008c SHA1: 7b56b54f21949ac14b8ef8f556c2af8a4364b786 SHA256: 076c545962662bc6a20f6888b26ed63dd24122e3a213d9b2f870b545a9e6c921 SHA512: e3288a18fba61f99772f1fe33102e8199c85b8a89b22cf757aac73e112e59ac8307764a9f0c721d7e2a5ec61d2e64a23bb721a1ce81469c96e4a0a0419212ec9 Homepage: https://cran.r-project.org/package=debest Description: CRAN Package 'debest' (Duration Estimation for Biomarker Enrichment Studies and Trials) A general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. 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Package: r-cran-debiasedinference Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-debiasedinference_0.1.0-1.ca2404.1_all.deb Size: 77910 MD5sum: 6da79302d52ea005ab617b6d9934f7dc SHA1: 2c50e22520de334842716878acfd32a050b9e1fe SHA256: 425bb5daeea114213282c596ba09a30b082d203c65283131164bf62da3fde801 SHA512: f35e6978bd551e48ae7d1305bba5c2c30f6f39c387ea80a19cca2d25918db29d7a995377ee843c7fcf3d1c957db36a56fbc526c87796b8928c45058691178c89 Homepage: https://cran.r-project.org/package=debiasedInference Description: CRAN Package 'debiasedInference' (Bootstrap Inference with Debiased Nonparametric Estimators) Implements debiased kernel density and local-linear regression estimators with empirical-bootstrap simultaneous confidence bands, as proposed by Cheng and Chen (2019) . 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Package: r-cran-debiasinfer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-caret Suggests: r-cran-mass, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-debiasinfer_0.2.1-1.ca2404.1_all.deb Size: 36868 MD5sum: b9b6b75e496727766687317b91588c10 SHA1: 5b0b5f7015f4c206c81425b51757747d04d3b464 SHA256: 54024d6db613ec69961ad6e075e3ab39eba3e2a621b00c2b689ab55ad8c4a20f SHA512: 14846429d3df7d238cf2d0548e327827eb329f0f61c486b739ca9a09177f9cb7631dd2282f77bc8d410b42db43f154d27a89045c953116fe133b355123268802 Homepage: https://cran.r-project.org/package=DebiasInfer Description: CRAN Package 'DebiasInfer' (Efficient Inference on High-Dimensional Linear Model withMissing Outcomes) A statistically and computationally efficient debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. The reference paper is Zhang, Giessing, and Chen (2023) . Package: r-cran-debinfer Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1444 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-truncdist, r-cran-coda, r-cran-rcolorbrewer, r-cran-mass, r-cran-mvtnorm, r-cran-plyr, r-cran-pbsddesolve Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-r.rsp, r-cran-beanplot Filename: pool/dists/noble/main/r-cran-debinfer_0.4.4-1.ca2404.1_all.deb Size: 1239528 MD5sum: dff757cb4f78da70608ebce265e0d3a5 SHA1: f16e16fcff989deba01322f5093ff20b7355a47b SHA256: 150763974a91fea99f23af8ed29565414126a83cfc2ff0937042ec71c6e50fd5 SHA512: 5062e9ad71baa0d5068f447490fa78b9f50471217912cd86b113f8815b01a45da7b81a0232ea2fe5614613361cb5acb64cbba4560b7e46c3f7b9ed91a8b12e32 Homepage: https://cran.r-project.org/package=deBInfer Description: CRAN Package 'deBInfer' (Bayesian Inference for Differential Equations) A Bayesian framework for parameter inference in differential equations. 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Designed for terminal workflows and artificial intelligence (AI) agent consumption, offering views including hotspot analysis, call trees, source context, caller/callee relationships, and memory allocation breakdowns. Package: r-cran-debtkit Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-debtkit_0.1.3-1.ca2404.1_all.deb Size: 147994 MD5sum: 2d9d21ed4c7cc11f0cd28ac3aefd52cd SHA1: 95e3558373bb51c94ec55f25ea8be3712513307c SHA256: 2eb0800a6785eb419a892c12a5e011d4da1d34baa5cf69088e1a1d8507e5a274 SHA512: c7dda23ea5292711876369202159fed8d96eb27f9c4ab98cb67d9a82930a59d6917fe368142a70ac3b3fe1c5297c7331aad6ab8d9865c105d335e187b5359ceb Homepage: https://cran.r-project.org/package=debtkit Description: CRAN Package 'debtkit' (Debt Sustainability Analysis and Fiscal Risk Assessment) Analyses government debt sustainability using the standard debt dynamics framework from Blanchard (1990) and the IMF Debt Sustainability Analysis methodology (IMF, 2013) and the Sovereign Risk and Debt Sustainability Framework (IMF, 2022). Projects debt-to-GDP paths, decomposes historical debt changes into interest, growth, and primary balance contributions, and estimates fiscal reaction functions following Bohn (1998) . Produces stochastic fan charts via Monte Carlo simulation, standardised stress tests, and IMF- style heat map risk assessments. Computes S1/S2 sustainability gap indicators used by the European Commission. All methods are pure computation with no external dependencies beyond base R; works with fiscal data from any source. 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Debug messages only appear when a global option for debugging is set. This way, 'debugr' code can even remain in the debugged code for later use without any negative effects during normal runtime. Package: r-cran-decide Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-decide_1.3-1.ca2404.1_all.deb Size: 65044 MD5sum: 4070202f06715794d099e740a149acb3 SHA1: c639afaf4418481db522de6db5d4dbb688b99070 SHA256: 6c0814f44cb0602e180c4bfc82e698d89a98aa0c3a4b5f2d7fb76fad1c5c0b32 SHA512: a7b47e8571a293213915d61e1a943f49cfbebdada915b7feae0d72218e0ddc27f41166b3834fab6c766d19932786f438b60fdf54dea37663695bd7ef5ca4a3d9 Homepage: https://cran.r-project.org/package=DECIDE Description: CRAN Package 'DECIDE' (DEComposition of Indirect and Direct Effects) Calculates various estimates for measures of educational differentials, the relative importance of primary and secondary effects in the creation of such differentials and compares the estimates obtained from two datasets. Package: r-cran-decision Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-decision_0.1.0-1.ca2404.1_all.deb Size: 13796 MD5sum: e6b0f4b64a0d680731206c67bc4a4ede SHA1: 61ebfbe8ab42ed45cc3004459689b55164878448 SHA256: 57704f6a76520e949e9696f80b0ad53daec7ac24de5dd753f6d98302b68b8029 SHA512: a95fb2c760f7e1985d37ee9554267b7a8c2f36cfcbc3db7d8ecc18b02f60291da9f93a44071bf1917f2721947c77fd239676774a48a4e156b37228c035ccb081 Homepage: https://cran.r-project.org/package=decision Description: CRAN Package 'decision' (Statistical Decision Analysis) Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making. Package: r-cran-decisiondrift Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tibble Suggests: r-cran-decisionpaths, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-decisiondrift_0.1.0-1.ca2404.1_all.deb Size: 193838 MD5sum: 19e24d6bb2ef4ac34cf6d04e842f4d68 SHA1: e792e2ea21e6e96549d6e3d00e573e653f009b9a SHA256: e63de3b795f5d065b03ced8479548ac59c700d7ad53b8b5e4150a1e30d4177b8 SHA512: 5761f24cbf6fae501eaebe52eed97fe6cff580c21aabef394b8292ba8e9b609f2684dc914cf8b73a907295a5124cd33d9bdd3a1063e25b8285cfee4e123fa2a3 Homepage: https://cran.r-project.org/package=DecisionDrift Description: CRAN Package 'DecisionDrift' (Detecting, Decomposing, and Stress-Testing Temporal Change inRepeated Decision Systems) Tools for detecting, decomposing, and stress-testing temporal drift in repeated binary decision systems. Complements the 'decisionpaths' package by shifting focus from path construction to system-level change over time. Implements five core analytic modules: (1) prevalence drift — did the overall decision rate change over time?; (2) transition drift — did the probability of switching or persisting change?; (3) entropy and stability trends — did path complexity evolve?; (4) group-differential drift — did the system drift differently across subgroups?; (5) change-point and regime-shift detection — did the system change abruptly after a policy or model update? Additionally provides a robustness module for testing stability of drift conclusions across analytic choices, and a sensitivity module for probing vulnerability to data problems including missingness, miscoding, and threshold shifts. Defines four original drift indices: the Decision Drift Index (DDI), Transition Drift Index (TDI), Group Differential Drift (GDD), and Cumulative Drift Burden (CDB). Applications include algorithmic audit, AI governance, education, health, and organisational research. Package: r-cran-decisionfacets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tam, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-decisionfacets_0.1.0-1.ca2404.1_all.deb Size: 77238 MD5sum: debb9144bce08c21b060130f7fdbbe8c SHA1: a9cbeb67d579a02f3f7222c2be39f17e023171c1 SHA256: 12bd975016fdef15d26f971269a98e25769ac3d308ce9df0e69e8acc5b221cef SHA512: 9d0d90c682a9acebbd2d775f80937a9a5a11bc1eb5e950d9e166596c7ecb03158b1912a3f39dd2a8e87734239489cf69905d8ce1eb52a76c3d9007cae577b5ab Homepage: https://cran.r-project.org/package=decisionfacets Description: CRAN Package 'decisionfacets' (Decision Accuracy and Consistency for Rater-Mediated Exams) Answers "would this candidate have passed with a different set of raters?" for rater-mediated exams such as oral examinations, objective structured clinical examinations and essay scoring. Builds on the many-facet extension of the rating scale model (Andrich, 1978, ) to compute counterfactual pass probabilities under the observed, an average-severity and a random rater panel, under an explicit decision rule (raw total, fair average or measure), and splits expected misclassification into measurement error and rater assignment, extending item response theory classification accuracy (Lee, 2010, ) to rater effects. Models can be fitted with 'TAM' or a built-in joint maximum likelihood estimator. Package: r-cran-decisionpaths Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-decisionpaths_0.1.0-1.ca2404.1_all.deb Size: 85114 MD5sum: 187061a315c459ae1f8c8035beeb1f11 SHA1: fd104fcbe269249d15454aa31f17f93214998d89 SHA256: 833335c96ecb4235401826f2942541f31219c0393e2db84fa489dbc75b92c45a SHA512: 61e4a58fe7d92ef6ac8a1408bb59e4f17b4843a5a6e3d6a79557a9c524e50ee8968e524128488239301b4dca16ca809455c17f719c7c27b411bc9431b91cab1d Homepage: https://cran.r-project.org/package=decisionpaths Description: CRAN Package 'decisionpaths' (Construct and Audit Longitudinal Decision Paths) Tools for constructing and auditing longitudinal decision paths from panel data. Implements a decision infrastructure framework for representing institutional AI systems as generators of time-ordered binary decision sequences. Provides functions to build path objects from panel data, summarise per-unit descriptors (dosage, switching rate, onset, duration, longest run), compute the Decision Reliability Index (DRI) following Cronbach (1951) , estimate Shannon decision-path entropy following Shannon (1948) , classify systems by infrastructure type (static, periodic, continuous, human-in-the-loop), and evaluate subgroup disparities in decision exposure and stability. Applications include education, policy, health, and organisational research. Package: r-cran-decisionsupport Architecture: all Version: 1.115-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2013 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-chillr, r-cran-class, r-cran-dplyr, r-cran-fancova, r-cran-ggplot2, r-cran-magrittr, r-cran-msm, r-cran-mvtnorm, r-cran-nleqslv, r-cran-patchwork, r-cran-rriskdistributions, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-eha, r-cran-knitr, r-cran-mc2d, r-cran-pls, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-decisionsupport_1.115-1.ca2404.1_all.deb Size: 1426152 MD5sum: da6ae1b3b2d0f51c3a2ab400e36faead SHA1: 391ea53c5830d36ea7c24440b9f5bc9030b74ec1 SHA256: 18fc844ccd45c9b442ab84ce697918a372aed545e79e9af215753c5de29bad7e SHA512: f8bfd9d20c8e8aca20fec5669d5b53a0e48e7c6d9cfc8775572b904a211d73decfad84118a52c26cf754f7bfc46f420af424d463b7963c67ee6135af79dfeff7 Homepage: https://cran.r-project.org/package=decisionSupport Description: CRAN Package 'decisionSupport' (Quantitative Support of Decision Making under Uncertainty) Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own. Package: r-cran-deckgl Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-magrittr, r-cran-base64enc, r-cran-yaml, r-cran-jsonlite, r-cran-readr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rprojroot, r-cran-sf, r-cran-scales, r-cran-rcolorbrewer, r-cran-shiny Filename: pool/dists/noble/main/r-cran-deckgl_0.3.0-1.ca2404.1_all.deb Size: 1248776 MD5sum: c3d52b00d80588fdf3ae2d4653f60286 SHA1: d49471c8dfc2c803366920b025764566ed9067f9 SHA256: d9537a2b5554391a6de84af67660b9155dc9102fdce61621dfd5b82c651ea6ac SHA512: b6bc6079773b7ca40ec58ad2676fb80a1ac3a248ade406319b3d9ece7ca21d665fad0591fb50b7432bbc209d29cf559b887846cd84255346de5575a745ed7125 Homepage: https://cran.r-project.org/package=deckgl Description: CRAN Package 'deckgl' (An R Interface to 'deck.gl') Makes 'deck.gl' , a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the 'htmlwidgets' package. 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The heavy lifting is done on the 'JavaScript' side in the browser using 'deck.gl-geoarrow' (). Currently provides functions for adding Scatterplot (points), Path (lines) and Polygon (polygons) layers. Has support for data classes from R packages 'wk' and 'sf'. In addition, convenience functions for styling data, tooltips and popups, as well as layer management are provided. Furthermore, remotely hosted 'GeoParquet' and 'GeoArrow' files can be visualised directly in the browser, without the need to first read them into R memory. Only the styling instructions are prepared by the user in R and are then transferred to and applied in the browser as the data arrives. Package: r-cran-deckroadmap Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-revealjs, r-cran-quarto, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-deckroadmap_0.1.5-1.ca2404.1_all.deb Size: 866676 MD5sum: 34088e716b00f23230b5350c2c92fbe8 SHA1: 8a1c3600293b1d522e1b7d5413f662770af3103b SHA256: 6e3579219c783872cc479fb356e9db1e5a0d9b25204a5302646cb2dec8140e43 SHA512: 776d33965193e24ed566f83ae14f78cc65e21fcbf0ac67e9d4eb21fff90a1c13900f93907161e921fb3205759b35d9aab24cb2125e6b4d14f94bd80ed55ffa71 Homepage: https://cran.r-project.org/package=deckroadmap Description: CRAN Package 'deckroadmap' (Roadmap Footers for 'Reveal.js' Slides in 'Quarto' and 'RMarkdown') Adds section-aware roadmap footers to 'Reveal.js' slide decks created with 'Quarto' or 'R Markdown'. The footer highlights completed, current, and upcoming sections as slides advance. Supports multiple visual styles, inherited section tags, roadmap-free slides, and configurable colors, size, and positioning options. Package: r-cran-declaredesign Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomizr, r-cran-fabricatr, r-cran-estimatr, r-cran-rlang, r-cran-generics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-aer, r-cran-diffobj, r-cran-dplyr, r-cran-data.table, r-cran-tibble, r-cran-ggplot2, r-cran-future, r-cran-future.apply, r-cran-broom, r-cran-mass, r-cran-matching, r-cran-betareg, r-cran-biglm, r-cran-gam, r-cran-sf, r-cran-reshape2, r-cran-designlibrary, r-cran-coin, r-cran-marginaleffects, r-cran-psych, r-cran-causalqueries, r-cran-rdrobust, r-cran-rdss Filename: pool/dists/noble/main/r-cran-declaredesign_1.1.1-1.ca2404.1_all.deb Size: 331826 MD5sum: edafda4d61dd18076044f5a0b20edec6 SHA1: c6e7ec80eab775aa3948cd8c255cca9dbb685ec5 SHA256: 2a845cbb4c23ced54c1a43eb058e56c2ab37f7f1d8bb36d9c473ff1fae67b26b SHA512: a4f8655fd4a09daf68274e4b6bd0270574bec9b2652767cdacfc02718bb9260c855a10e0035f1a33b8d1b874d73d0ba07392d2f1ce13b79b756ca33ae45f461a Homepage: https://cran.r-project.org/package=DeclareDesign Description: CRAN Package 'DeclareDesign' (Declare and Diagnose Research Designs) Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued. Package: r-cran-decode Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5075 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-decode_1.2-1.ca2404.1_all.deb Size: 1587290 MD5sum: 57df7d642f7c267760708e3861681a91 SHA1: cb352754eac2b94549c843d45b3435417562e89b SHA256: 1a22aaf7480cbfd91d77eff6e5c0aa3eb7304287efa2e689bc9c2e0f66fc3cb7 SHA512: 7091d7695a9fafb6c4b8a8fe0f2ef2d5d1ea47276a55a2e21928edcb21ef7659b4f40a30b92fd201bf6b7e2385bd29c5a680521dd87eab0ed310697dfac2f099 Homepage: https://cran.r-project.org/package=decode Description: CRAN Package 'decode' (Differential Co-Expression and Differential Expression Analysis) Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm. Package: r-cran-decoder Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1870 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-dt, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-decoder_1.2.3-1.ca2404.1_all.deb Size: 1633442 MD5sum: e293bdbe95682a2d39e8083298c99a2d SHA1: 40e4397024d229fa1f80929a59b781561484049a SHA256: 03248da31060805fad7167156559872ea5a432eefb8ea547724c35d41a3a3a30 SHA512: 8c874103a2ddbc73713f5300f4996875f1e95adaf19b3718b8c26abd0f4fa74e05dda79b2768eb64ed3ba57c6c26860708e2df4924a83dbc7c40da0d45346b83 Homepage: https://cran.r-project.org/package=decoder Description: CRAN Package 'decoder' (Decode Coded Variables to Plain Text and the Other Way Around) Main function "decode" is used to decode coded key values to plain text. Function "code" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on 'keyvalue' objects used for translation. There are several 'keyvalue' objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets. Package: r-cran-decompdl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-reticulate, r-cran-tsutils, r-bioc-biocgenerics, r-cran-magrittr, r-cran-rlibeemd, r-cran-tsdeeplearning, r-cran-vmdecomp Filename: pool/dists/noble/main/r-cran-decompdl_0.1.0-1.ca2404.1_all.deb Size: 84252 MD5sum: 40d6c974f0a5dce16d832a1dbb5c0ef0 SHA1: 273644437214c3bac385dda3954b29442c1cbada SHA256: 35b4296f672fd8242b8aca02fac85c70dd186fe77708ee254169a4acd58a143a SHA512: 3426e8472ffaef6e04eeda6866c7b106b9a7bae70fdea67146519ffcefb946c9000217a8f3fb9ece7f19b6b5e31339dd68cb687f536cc36f97346c2e88b4b67e Homepage: https://cran.r-project.org/package=decompDL Description: CRAN Package 'decompDL' (Decomposition Based Deep Learning Models for Time SeriesForecasting) Hybrid model is the most promising forecasting method by combining decomposition and deep learning techniques to improve the accuracy of time series forecasting. Each decomposition technique decomposes a time series into a set of intrinsic mode functions (IMFs), and the obtained IMFs are modelled and forecasted separately using the deep learning models. Finally, the forecasts of all IMFs are combined to provide an ensemble output for the time series. The prediction ability of the developed models are calculated using international monthly price series of maize in terms of evaluation criteria like root mean squared error, mean absolute percentage error and, mean absolute error. For method details see Choudhary, K. et al. (2023). . Package: r-cran-decompml Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-nnfor, r-cran-rlibeemd, r-cran-vmdecomp Filename: pool/dists/noble/main/r-cran-decompml_0.1.1-1.ca2404.1_all.deb Size: 80656 MD5sum: 5b52fae7e5188124c984ad0ca5e51a3e SHA1: 288405336550ba5fc0fc065badf5b228c636d9bd SHA256: 3cd1a4a6d2832dc97a92e4aafe76230cfd83665b0ac5e4642810bd4ba0156a79 SHA512: e6f97c950ac62aa48f32dd03700dcba1e34c5253dc7f8924571f03c5f8e20e3e492410b0b1ae2047d78173c66b1c117987ec3374171d2351b3ff7ba787e4d73c Homepage: https://cran.r-project.org/package=decompML Description: CRAN Package 'decompML' (Decomposition Based Machine Learning Model) The hybrid model is a highly effective forecasting approach that integrates decomposition techniques with machine learning to enhance time series prediction accuracy. Each decomposition technique breaks down a time series into multiple intrinsic mode functions (IMFs), which are then individually modeled and forecasted using machine learning algorithms. The final forecast is obtained by aggregating the predictions of all IMFs, producing an ensemble output for the time series. The performance of the developed models is evaluated using international monthly maize price data, assessed through metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). For method details see Choudhary, K. et al. (2023). . 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Package: r-cran-decomposer Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-usethis, r-cran-tictoc, r-cran-stratigrapher, r-cran-hexbin, r-cran-colorramps, r-cran-dplyr Suggests: r-cran-emd, r-cran-rssa, r-cran-astrochron Filename: pool/dists/noble/main/r-cran-decomposer_1.0.7-1.ca2404.1_all.deb Size: 821508 MD5sum: 40ce1cedc021c80190e3ce912cf57a77 SHA1: 6d79cf0456e814126db4cf7dc636f444b64261fc SHA256: 70db30cd1418df5b548aa058e6a866f306deb796463b8ab7604fda224df9a20a SHA512: dc2fd9677022e88ec7aad03a162f35c363d532e2e11004efc247f01ec27bb9a96ed4bd7d5338d9150837eb0720eaf5b6276499d58248680b1cb3d6326a972a65 Homepage: https://cran.r-project.org/package=DecomposeR Description: CRAN Package 'DecomposeR' (Empirical Mode Decomposition for Cyclostratigraphy) Tools to apply Ensemble Empirical Mode Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new algorithm, extricate, that performs EEMD in seconds, a linear interpolation algorithm using the greatest rational common divisor of depth or time, different algorithms to compute instantaneous amplitude, frequency and ratios of frequencies, and functions to verify and visualise the outputs. The functions were developed during the CRASH project (Checking the Reproducibility of Astrochronology in the Hauterivian). When using for publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, "A decomposition approach to cyclostratigraphic signal processing". Earth-Science Reviews 225 (103894). . 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(2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141–172. In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions. Package: r-cran-deconstructsigs Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-genomeinfodb Suggests: r-bioc-variantannotation Filename: pool/dists/noble/main/r-cran-deconstructsigs_1.8.0-1.ca2404.1_all.deb Size: 269784 MD5sum: aba9bf3a65e554378ffa304a929d0915 SHA1: a066042a67f6e57215532ff844c454941c08b8c3 SHA256: 03e5177fd417ac8f0eab17e8d3428aaa6e33dfa1a1365f0302bf88aa0d50fbe9 SHA512: a857ee6e09ed549d2ec27a9772f1c91c55180aed47ac9330f6741300979777313e7e65f540d9e35b97f099db4da4cab8cf99d83dcb5c75f6c8ee33ff9ae53e3d Homepage: https://cran.r-project.org/package=deconstructSigs Description: CRAN Package 'deconstructSigs' (Identifies Signatures Present in a Tumor Sample) Takes sample information in the form of the fraction of mutations in each of 96 trinucleotide contexts and identifies the weighted combination of published signatures that, when summed, most closely reconstructs the mutational profile. 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An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, ). 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The method is as described in: Campos, D.F., (1984, ISBN:9686194444). 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Use classes like PerceptronLayer to create a layer of Percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set. 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The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way. It also adds some popularity measures for the packages e.g. in the form of download count through the 'cranlogs' package. Uses the CRAN metadata database and Bioconductor metadata . Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot. Package: r-cran-deepdive Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastdummies, r-cran-plyr, r-cran-rpart, r-cran-treeclust, r-cran-data.table, r-cran-stringr Filename: pool/dists/noble/main/r-cran-deepdive_1.0.4-1.ca2404.1_all.deb Size: 86670 MD5sum: e8a4448483ee16294fe0cafe0612401e SHA1: ba86900fc505f82c14916b6585fd017f1f6b7a25 SHA256: e8eebfdb2588e03890772e348a3b816e87f9f24f43384e9e610a4240740a8b74 SHA512: 2cdd8d8388144b3631565ae7b8506d488fbf43fc05005d83cba83275bdde123d1ba014dcab58e1209d9806966ac7e9c65794a73c31d13b66889d96c5263fd6e0 Homepage: https://cran.r-project.org/package=deepdive Description: CRAN Package 'deepdive' (Deep Learning for General Purpose) Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. 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Package: r-cran-deepgmm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor, r-cran-mclust Filename: pool/dists/noble/main/r-cran-deepgmm_0.2.1-1.ca2404.1_all.deb Size: 86980 MD5sum: 61ee61ae2d9d6ba6df781f68586d3988 SHA1: 2e48bc57f333f68a377a2407d7cdfd01fd1bc956 SHA256: d9f21f09c17395b2267fd4fc509d01347d03423672ec38166fbd2ad90fd7f98f SHA512: 85e2c94a35b5bc323fd23fc1988a0dce239d1e486803140b86f034f05fdbeb053bebcebf358bdecaf3934e7d16d143e658f903461020583dfe2ac4c9c8a41234 Homepage: https://cran.r-project.org/package=deepgmm Description: CRAN Package 'deepgmm' (Deep Gaussian Mixture Models) Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. 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Package: r-cran-deepimp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-torch, r-cran-luz, r-cran-vim, r-cran-robcompositions Suggests: r-cran-keras3, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-deepimp_1.1.0-1.ca2404.1_all.deb Size: 154576 MD5sum: 63142b4bd181da773b56c5d51e76251b SHA1: 40752932db8ed4b27ab9aba718206c81c60dd05d SHA256: 4d6bd0b4338ef9f431a04f1429639af729ec027cf9c9e0b7427327a97ed59b2e SHA512: 56ef3badf50f07baae194ec84db2fbbfad714a8c3c121b275008c3c495dab847f5d77c190db913d9207490807ca1d53af6cc3d57f99fe27a6de523992d226f00 Homepage: https://cran.r-project.org/package=deepImp Description: CRAN Package 'deepImp' (Imputation with Deep Learning Methods) Imputation of mixed-type and compositional data with neural networks. 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Package: r-cran-deeplearningcausal Architecture: all Version: 0.0.107-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr, r-cran-caret, r-cran-neuralnet, r-cran-superlearner, r-cran-ggplot2, r-cran-tidyr, r-cran-magrittr, r-cran-reticulate, r-cran-keras3, r-cran-hmisc Suggests: r-cran-testthat, r-cran-dplyr, r-cran-class, r-cran-xgboost, r-cran-randomforest, r-cran-glmnet, r-cran-ranger, r-cran-gam, r-cran-e1071, r-cran-gbm, r-cran-tensorflow Filename: pool/dists/noble/main/r-cran-deeplearningcausal_0.0.107-1.ca2404.1_all.deb Size: 342618 MD5sum: 50b9f7c9d77202dfb92d897d90980403 SHA1: 26f78ac373b12079586fe742cc260c6d0e3cdbe3 SHA256: 5596769d98a8bf12da926be1b8750bd92ae9c212ec9c30309fcf522f0514d35d SHA512: dfa6eeac4e482bdd333b93b74220fe4f65805537e1ddfb01ead9869f973dba8ea308ccf406f4b03fecc70b0375207a7d87ff8a5ff1c31e920469d38d02dd865f Homepage: https://cran.r-project.org/package=DeepLearningCausal Description: CRAN Package 'DeepLearningCausal' (Causal Inference with Super Learner and Deep Neural Networks) Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks. Package: r-cran-deeplr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-utf8, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-tokenizers, r-cran-jsonlite, r-cran-readr Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-deeplr_2.1.0-1.ca2404.1_all.deb Size: 165574 MD5sum: 43b812702df999d3451d31fe611c2647 SHA1: e120955732346ce83660459f54602cfc53d64e5c SHA256: 490144d6945076734fc983d70be22298531de776e8b2bab4f1ed4f41eae7e57a SHA512: 852f028f7c0738ea0b1e818c1d769ca4ed0b9abd7085e86c71f77cc8769021b9a4c695abe6e43e759be752174aea57c3524fdcf7acc919ca4620e6eeffb504bf Homepage: https://cran.r-project.org/package=deeplr Description: CRAN Package 'deeplr' (Interface to the 'DeepL' Translation API) A wrapper for the 'DeepL' API , a web service for translating texts between different languages. A DeepL API developer account is required to use the service (see ). Package: r-cran-deepmou Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-skmeans, r-cran-extradistr, r-cran-dplyr, r-cran-rfast, r-cran-entropy, r-cran-ggplot2, r-cran-mass Filename: pool/dists/noble/main/r-cran-deepmou_0.1.1-1.ca2404.1_all.deb Size: 126910 MD5sum: 6209552c09567b594d8f9ce5f1938b3c SHA1: 9f9e1365611493155631c5a665c0c70f03d61442 SHA256: e6c76ea37f53f0a5420ad8efdd6816bdb4c9e3391b0338130505baf1c5f7a744 SHA512: 6b503e51eee96cbab9aaaee2e559895cc773e9fce34944cd263546450cf3edf9d8f32f9b0f1f8e794250bcfee93fad434301ec8297985fb83f77062dc7001f6c Homepage: https://cran.r-project.org/package=deepMOU Description: CRAN Package 'deepMOU' (Clustering of Short Texts by Mixture of Unigrams and Its DeepExtensions) Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). ), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) ) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) ). There are also functions for graphical representation of clusters obtained. Package: r-cran-deepnet Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-deepnet_0.2.1-1.ca2404.1_all.deb Size: 60318 MD5sum: 9e1d3563c403b25900dcfa4cd8cdcda1 SHA1: 0971c25a892ae82698e68519662730ced85e9dec SHA256: 0f7d6099e2573eda5052d5e25b9dd79483c18123fb66077271583fc32c70ff25 SHA512: 9893fc087212bb645393553d9df0ef26ec4ed6327d3761f20b515c89ffcf1c39e2e68a89351fbfef85c6eae0fa410e43e6a2a146f85dd692caec08841a31de16 Homepage: https://cran.r-project.org/package=deepnet Description: CRAN Package 'deepnet' (Deep Learning Toolkit in R) Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on. Package: r-cran-deepnn Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-deepnn_1.2-1.ca2404.1_all.deb Size: 161674 MD5sum: 1ac99dbac6bcd34757af98aa4964f170 SHA1: 54599e4086c0671a2d4be838383df2eac0fcbae9 SHA256: 51d0efb16ce61e263afff1308379c7c70707f0ae8ccbe97b4acb12b576e425e2 SHA512: e0a2e326fae6b24d3e07383c9b030c7de8b5861e3f36fa539740d2a200e7abc828e9c6990f16f66d1cf62db06beb8f9402b23b6724fe730c7cf282a7732f2e2d Homepage: https://cran.r-project.org/package=deepNN Description: CRAN Package 'deepNN' (Deep Learning) Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) Neural Networks YouTube playlist. Michael A. Nielsen Neural Networks and Deep Learning. Package: r-cran-deepredeff Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-keras, r-cran-magrittr, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-seqinr, r-cran-tensorflow Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-deepredeff_0.1.1-1.ca2404.1_all.deb Size: 943938 MD5sum: a352a324875a4b33fa22fbaa4a01871e SHA1: b7423ba08f22c664eca30770394b8f3639e86fff SHA256: 690b436d69c5a43618f5ff7b4bcf16fa577f2da0878c2203456a75f9f206d49a SHA512: 93f68830b6b88993b9f711a1e193401b0fe856fdbc01af0b987c672b1a4b5b449d38306786f51e6ca6ad7dc0fb40b92da04b6f16584c0507b77e45fbb42ffaed Homepage: https://cran.r-project.org/package=deepredeff Description: CRAN Package 'deepredeff' (Deep Learning Prediction of Effectors) A tool that contains trained deep learning models for predicting effector proteins. 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Package: r-cran-deepregression Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-tfprobability, r-cran-keras, r-cran-mgcv, r-cran-dplyr, r-cran-r6, r-cran-reticulate, r-cran-matrix, r-cran-magrittr, r-cran-tfruns, r-cran-coro, r-cran-torchvision, r-cran-luz, r-cran-torch Suggests: r-cran-testthat, r-cran-knitr, r-cran-covr Filename: pool/dists/noble/main/r-cran-deepregression_2.3.2-1.ca2404.1_all.deb Size: 681724 MD5sum: a84db54785d30680830b13495dd78fdd SHA1: 3594c3b6d504f3fc1664d1a6ad7207cb5ea62b0b SHA256: 3ee63091a35f9a2574dbabc8fcd0e5d7735a862c36d8a044a9011da9a8cf095a SHA512: 73bf7721886220335640bac4d6bef61b7b8e6d41f1642b715a17b99b85cde061b57eb67d2e196b468c21b18ee703bfaf01e779b6938a8792e7af6bf828172a31 Homepage: https://cran.r-project.org/package=deepregression Description: CRAN Package 'deepregression' (Fitting Deep Distributional Regression) Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. 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Package: r-cran-deeprstudio Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-clipr, r-cran-rstudioapi, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-deeprstudio_0.0.9-1.ca2404.1_all.deb Size: 819052 MD5sum: 6e4cb210ec39bfccae1f2ca9685abf2a SHA1: ff8cf0a09d775991d58c02b5fd63487380161550 SHA256: 023a493059caef098d6a6c5468888d75cd31b332f134c5184396477ddb7b2403 SHA512: a4a3e0dd9cab7f16ef2aeb928b386c5d33a385aa5f5b977870a47ea3bd44b7caad58251f1f318a150f9b2eb2c6476db4aac3583565521580e6bdaefc402fc69b Homepage: https://cran.r-project.org/package=deepRstudio Description: CRAN Package 'deepRstudio' (Seamless Language Translation in 'RStudio' using 'DeepL' API and'Rstudioapi') Enhancing cross-language compatibility within the 'RStudio' environment and supporting seamless language understanding, the 'deepRstudio' package leverages the power of the 'DeepL' API (see ) to enable seamless, fast, accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating with international teams, simplifying the translation process and making code more accessible and comprehensible to people with diverse language backgrounds. This package uses the 'rstudioapi' package and 'DeepL' API, and is simply implemented, executed from addins or via shortcuts on 'RStudio'. With just a few steps, content can be translated between supported languages, promoting better collaboration and expanding the global reach of work. The functionality of this package works only on 'RStudio' using 'rstudioapi'. Package: r-cran-deepspat Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-matrix, r-cran-rlang, r-cran-reticulate, r-cran-keras, r-cran-tensorflow, r-cran-tfprobability, r-cran-evd, r-cran-fields Filename: pool/dists/noble/main/r-cran-deepspat_0.3.5-1.ca2404.1_all.deb Size: 553524 MD5sum: dbdc881850cb3a52f19131252807d29b SHA1: 3f4a88362629e63c53e4e09d3b4ef5aa53b1a2df SHA256: e3b32b303080e48d4792bf6c19794119bd2f33da1602705d552a9a0a9b88acc4 SHA512: a6b124b7830402358a18a18b12522466feb96e704632cff917ee32c8d6e9373efa8145df2039bb0c8043f595e9985cffcda348def6be40691f62d1a559708da0 Homepage: https://cran.r-project.org/package=deepspat Description: CRAN Package 'deepspat' (Deep Compositional Spatial Models) Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) , Vu et al. (2022) , Vu et al. (2023) , and Shao et al. (2025) . Package: r-cran-deeptime Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5278 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-deeptimedata, r-cran-ggplot2, r-cran-ggforce, r-cran-gridextra, r-cran-gtable, r-cran-lattice, r-cran-rlang, r-cran-scales, r-cran-ggfittext, r-cran-curl, r-cran-cli, r-cran-lifecycle, r-cran-grimport2, r-cran-ggh4x Suggests: r-cran-geomtextpath, r-cran-phytools, r-cran-dplyr, r-cran-divdyn, r-cran-gsloid, r-cran-ape, r-cran-palaeoverse, r-cran-paleotree, r-cran-disprity, r-bioc-ggtree, r-cran-tidytree, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-ggpattern, r-cran-ggrepel, r-cran-rmacrostrat, r-cran-svglite Filename: pool/dists/noble/main/r-cran-deeptime_2.4.0-1.ca2404.1_all.deb Size: 4060990 MD5sum: 65f38f1ee5c13cff4cc6ef4ae3049f74 SHA1: c838767b3498174189fed106fdf615202c11cb10 SHA256: 144b4d9950c2a7dff6bfb86944a6972c489fde866a66de2f112fc18a5254812f SHA512: 552a1b0eec717a935fb8b46c183ed2149c21d53f9a353d0d6a500f9b050f6b76455a97a1c9711ebb22214796238650c990dcbad76e548aa06e824ccc42ca702b Homepage: https://cran.r-project.org/package=deeptime Description: CRAN Package 'deeptime' (Plotting Tools for Anyone Working in Deep Time) Extends the functionality of other plotting packages (notably 'ggplot2') to help facilitate the plotting of data over long time intervals, including, but not limited to, geological, evolutionary, and ecological data. The primary goal of 'deeptime' is to enable users to add highly customizable timescales to their visualizations. Other functions are also included to assist with other areas of deep time visualization. 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Package: r-cran-deet Architecture: all Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-activepathways, r-cran-pbapply, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-ggrepel, r-cran-downloader Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-deet_1.0.12-1.ca2404.1_all.deb Size: 2093962 MD5sum: 2ad94ca5734c37ea390bab6612802351 SHA1: 4b2486ab46d678ae7f3a21c2ec2ce35d0070dc22 SHA256: 47dd6044a5aae346d415a5dc93e17ad03ade4249f43a87f35f8d6d82e618b529 SHA512: a45b0b6b90a576631298db087f82949a4a6874b85fd69925645853f61ebbfb7c2230cba94b9b969dd4c31be5d8864c2325459ec4c12147278700dcdb33b73ebb Homepage: https://cran.r-project.org/package=DEET Description: CRAN Package 'DEET' (Differential Expression Enrichment Tool) Abstract of Manuscript. Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user’s gene lists, aiding in hypothesis generation and data-driven literature review. Sokolowski, Dustin J., et al. "Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression." Nucleic Acids Research Genomics and Bioinformatics (2023). 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A high biological variability may impact the discovery of these genes once it may be divergent between the fixed effects. However, this variability can be covered by the random effects. 'DEGRE' was designed to identify the differentially expressed genes considering fixed and random effects on individuals. These effects are identified earlier in the experimental design matrix. 'DEGRE' has the implementation of preprocessing procedures to clean the near zero gene reads in the count matrix, normalize by 'RLE' published in the 'DESeq2' package, 'Love et al. (2014)' and it fits a regression for each gene using the Generalized Linear Mixed Model with the negative binomial distribution, followed by a Wald test to assess the regression coefficients. Package: r-cran-degreedaycalc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-degreedaycalc_0.1.1-1.ca2404.1_all.deb Size: 27700 MD5sum: b1e030f891d209d1b7296e85f306d69e SHA1: 15ee5c0ed225a4135aa5745cdc9ea41b748a2be9 SHA256: d4969eb3f2804f706fe22679e9f80ddb3ec60a44098c8f9d10d765c607cfa188 SHA512: 699b3324fad568a749e20e10da4f0653bbbc49b59cd725efee5d273300cb0e4cd20a6259655dd1e6234eca8360072d53cb2ad110a7afbdd47ddcf138b1eb16e7 Homepage: https://cran.r-project.org/package=DegreeDayCalc Description: CRAN Package 'DegreeDayCalc' (Degree-Day Phenology Calculator ('shiny' Application)) Provides a 'shiny' application to compute daily and cumulative degree-days from minimum and maximum temperatures using average, single triangle, and single sine methods, with optional upper temperature thresholds. The application maps cumulative thermal accumulation to user-defined developmental stage thresholds and supports exporting tabular and graphical outputs. The degree-day approach follows assumptions described by Higley et al. (1986) . 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Package: r-cran-delayed Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-igraph, r-cran-future, r-cran-rstackdeque, r-cran-rlang, r-cran-data.table, r-cran-visnetwork, r-cran-uuid, r-cran-bbmisc, r-cran-progress, r-cran-r.utils, r-cran-r.oo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-delayed_0.5.0-1.ca2404.1_all.deb Size: 323818 MD5sum: 1f04d0123169073aa1f443be3b419f9f SHA1: a26bbd19d54eae383a0da9bcb833d2758185371e SHA256: edf92d22569f7f1e539d568c37d5244ec83e507a488bc0ec47ba6b348bf79fe8 SHA512: d5978198b5da717b249536a4c71a6ed5b79e985ab79a00dae0880bc8b1720aed534f7706385ee0d9fd09c5cc05f97178097be4014660d147bbe0814b0d9ba2de Homepage: https://cran.r-project.org/package=delayed Description: CRAN Package 'delayed' (A Framework for Parallelizing Dependent Tasks) Mechanisms to parallelize dependent tasks in a manner that optimizes the compute resources available. 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Calculation of DRI, plot of intersubjective correlations (IC), generation of large-language model (LLM) survey data, and permutation tests are supported. Example datasets and a graphical user interface (GUI) are also available to support analysis. For more information, see Niemeyer and Veri (2022) . For an alternative version of this dataset, see Niemeyer et al. (2024) . 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The main issue with delta is that can not be computed by hand contrary to kappa. The current algorithm is based on the Version 5 of the delta windows program that can be found on . Package: r-cran-deltabreedquery Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1508 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-deltabreedquery_1.0.4-1.ca2404.1_all.deb Size: 458122 MD5sum: 2d5b58e3ac281ec6b4833d01c8693643 SHA1: 795000e95823d4ff594f639459c86480bd316b70 SHA256: 0a5d972113346a28c0404b7831f126b3de1fb37dbc7f9e8f2a01584d9b022631 SHA512: 6acde65cc38be2b2a244e278f6e589331879de76e1629d5f66a95bb1f2e49411760a6e14550e2640927d0e90afbab3c4c45088f04484dfaeafbed57ccbbeb587 Homepage: https://cran.r-project.org/package=deltabreedquery Description: CRAN Package 'deltabreedquery' (Fast, Simple API Tools for Retrieving Data from 'DeltaBreed') Simplified data retrieval from the 'DeltaBreed' breeding data management platform () via the 'BrAPI' open-source breeding data API (). 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See Shilts et al. (2018) . Package: r-cran-deltaman Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinymatrix, r-cran-xtable, r-cran-shinybs, r-cran-knitr Filename: pool/dists/noble/main/r-cran-deltaman_0.5.0-1.ca2404.1_all.deb Size: 114410 MD5sum: fff61da8cad43418a041428f7d774ae4 SHA1: b575049f329dd7b5e5f1d91824b73a98f38b9555 SHA256: c17e8c4b661fe559da6b5058279fb51c4ebbc27465d1c023a652ca68257b4bf8 SHA512: 80b97c54b1e1454438ec2fd0fa1a049ac89dcc4c6bf3fc8cb1f3cc9d40986102eae6eb10c516c62a004524264301d164c1fac2bf296348a4b79520ba151d602a Homepage: https://cran.r-project.org/package=DeltaMAN Description: CRAN Package 'DeltaMAN' (Delta Measurement of Agreement for Nominal Data) Analysis of agreement for nominal data between two raters using the Delta model. This model is proposed as an alternative to the widespread measure Cohen kappa coefficient, which performs poorly when the marginal distributions are very asymmetric (Martin-Andres and Femia-Marzo (2004), ; Martin-Andres and Femia-Marzo (2008) ). The package also contains a function to perform a massive analysis of multiple raters against a gold standard. A shiny app is also provided to obtain the measures of nominal agreement between two raters. Package: r-cran-deltapif Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-deriv, r-cran-s7, r-cran-scales Suggests: r-cran-evalue, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-deltapif_0.4.5-1.ca2404.1_all.deb Size: 379790 MD5sum: d7bf2b507eced32d5f3dfaf3bf888085 SHA1: 16614da3e5363f97652e42ccab1b684f9156b4a5 SHA256: 23cd287d2cc96aeccbb2e174f60cf00c33e64691da1a9231d7d99a06c765fd28 SHA512: 9cd974e1af3db5f1a1f79308d5d5d448f4ef5fcda37c5113b1090d41404e85237e6abed7ad5de9e491801e1757d34e38a43a2e0959245288ff38cb56a82b72c4 Homepage: https://cran.r-project.org/package=deltapif Description: CRAN Package 'deltapif' (Estimate Potential Impact and Population Attributable Fractionswith Aggregated Data) Uses the delta-method to estimate the Potential Impact Fraction (PIF) and the Population Attributable Fraction (PAF) from summary data. 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Item purification is supported (Magis and Facon (2014) ). 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The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model. Package: r-cran-demor Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-demor_1.0.10-1.ca2404.1_all.deb Size: 202178 MD5sum: 1848ab48e30523525f21b9d99f672197 SHA1: a9d238f67028c5b64d2d559b43a22d7e10c8400f SHA256: bfb9803ed3fb4d0809d4ac4bd5ca44554ace798da106472ad17f74a9fcdc107d SHA512: 70c2d09f0a0f1c45ce5856fb3f71e9347444a9cff8f8309502e2f214ccc82a47b070842399420a29aa973885062c63999999a91f32a7b837ca48155e28665bf6 Homepage: https://cran.r-project.org/package=demor Description: CRAN Package 'demor' (Methods for Demographic Analysis) Implements life tables, fertility and mortality indicators, decomposition methods, Lee-Carter mortality forecasting, Leslie matrices, and population pyramids for demographic analysis. Methods are described in Preston et al. (2001, ISBN:1557864519) and Ustyuzhanin (2025) . Package: r-cran-demoshiny Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-demoshiny_0.1-1.ca2404.1_all.deb Size: 241742 MD5sum: 6b1e047df1963f8c136936ec2eb97fa8 SHA1: 4e801a2a05fc70a49fe31301a2b4ad9792279e3f SHA256: 7caae4334c6cc35a9dfc6499d10a880a4c7af211e9cefd12a070c2b8cd0ed59b SHA512: 87d2c2c584b8b6b586044570377fbab355be49501497e58b478cd67b5bce47230add1eec9f85ba9c37dcdcc7e088686faab0a6b19cf6d45724af34bdcdb1986c Homepage: https://cran.r-project.org/package=demoShiny Description: CRAN Package 'demoShiny' (Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps) Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic). Package: r-cran-demova Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-demova_1.0-1.ca2404.1_all.deb Size: 57042 MD5sum: c2520040a952958eec4e234eab131279 SHA1: f86f81110e1ac23c54e46fe014b63ff74c78e6b8 SHA256: ec1b167ff4fcc8207100fe5580c9b91f1dc246ecf8621e93d6abbddc4a42ee5b SHA512: 1a4b593695b427390b02f949b05ca798a34d17646b3ca17233c550732fb6350188adaadd06f7ec8b52571e2095e6f6561d6e8ad045b141fc1627141d2d4c7938 Homepage: https://cran.r-project.org/package=DEMOVA Description: CRAN Package 'DEMOVA' (DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated usingTest Set) Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets. Package: r-cran-demovuln Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-demovuln_0.1.0-1.ca2404.1_all.deb Size: 295570 MD5sum: 065fc275b1d0b34fae41987c49639c20 SHA1: bc4e158c8af66d3024113054d64c3e6db8aa5317 SHA256: de00dc5c276b76d8f6a500d524da0afd846d15535965818929b6745cd0065b66 SHA512: bca275fc7219b8981c8656779d7c446cf71dcad42ef9b351db95b8484035a919526288e14a952bd37a9a06631538f316addf0532ef10b18089b533162af20ca4 Homepage: https://cran.r-project.org/package=demovuln Description: CRAN Package 'demovuln' (Demographic Vulnerability Metrics for Matrix Population Models) Simulates temporally structured perturbations in matrix population models and computes population reduction and integrated demographic vulnerability across perturbation regimes. Perturbations can be applied to adult survival, juvenile survival, fecundity, all demographic entries, or user-defined matrix elements. The package provides tools to simulate individual perturbation trajectories, evaluate perturbation grids, and summarize demographic vulnerability in structured populations. Package: r-cran-demulticoder Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-furrr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble Suggests: r-cran-biocmanager, r-bioc-biostrings, r-bioc-dada2, r-cran-metacoder, r-bioc-shortread, r-bioc-phyloseq, r-cran-rmarkdown, r-cran-rcppparallel, r-cran-testthat Filename: pool/dists/noble/main/r-cran-demulticoder_0.1.2-1.ca2404.1_all.deb Size: 1494526 MD5sum: 477b421eb0849488c0ac232d3f2e7951 SHA1: 4078dc93d35e836a686583f9feccfb8ea2842e24 SHA256: 56a611d1a512f74f4ff2bd47ce4b4d85b1f44817455b55deadeb5f805de51c93 SHA512: e6e27acc08e6cef10297290b8553373ac973d4d97047008e6a69a1540c12f1ccfcc130c19f95e565239c51170b058e03022d476dffb5614f07d5782b89fca1d5 Homepage: https://cran.r-project.org/package=demulticoder Description: CRAN Package 'demulticoder' (Simultaneous Analysis of Multiplexed Metabarcodes) A comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for 'Cutadapt' and 'DADA2' to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into 'phyloseq' and 'taxmap' objects. For more information on 'DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) . For more details on the demulticoder R package see Sudermann et al. (2025) . Package: r-cran-dendextend Architecture: all Version: 1.19.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7001 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-seriation, r-cran-colorspace, r-cran-ape, r-cran-microbenchmark, r-cran-gplots, r-cran-heatmaply, r-cran-dynamictreecut, r-cran-pvclust, r-cran-corrplot, r-cran-dendser, r-cran-mass, r-cran-cluster, r-cran-fpc, r-cran-circlize, r-cran-covr Filename: pool/dists/noble/main/r-cran-dendextend_1.19.1-1.ca2404.1_all.deb Size: 4813448 MD5sum: 62a21d55feca079cee2edba8c73801f2 SHA1: 457ee4c5aa3472134104a6a408656b9fb559cbc6 SHA256: bf391678dbf0c6ed4b1251d41573b693164ca90094ef5afabe8f239f28f5ba60 SHA512: 10421cc294c208e26347d07702bb48bf4d3f900b70a0c39da5bc97f7ebfa02653bc5c40b42c8ead86b70194c531d4848d566d4cb782773eed207a1cdd8a295e9 Homepage: https://cran.r-project.org/package=dendextend Description: CRAN Package 'dendextend' (Extending 'dendrogram' Functionality in R) Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another. Package: r-cran-dendroanalyst Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4146 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyverse, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-readxl, r-cran-tibble, r-cran-tidyr, r-cran-zoo, r-cran-forecast, r-cran-mgcv, r-cran-minpack.lm, r-cran-pspline, r-cran-moments, r-cran-signal, r-cran-readr, r-cran-boot, r-cran-rlang, r-cran-changepoint, r-cran-waveletcomp Suggests: r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-shinyfiles, r-cran-knitr, r-cran-rmarkdown, r-cran-writexl, r-cran-zip, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dendroanalyst_2.0.0-1.ca2404.1_all.deb Size: 3712522 MD5sum: df04a47a2fa53070ae5d016989c68ed8 SHA1: 25f827b0215152b5ee0db557e96f24542cd492e3 SHA256: 35125294fd7aa5ded67c15281305d915eb9f07c4bc842d685af650c3f8e0e061 SHA512: 93fc36613dc58daf8abb16a3f1b168060d04abae1c0ce2a0be1100cbddbb52862589297928b601d1372e3d34f9ba62ac8598771bc1425e5c55d488e013aafafa Homepage: https://cran.r-project.org/package=dendRoAnalyst Description: CRAN Package 'dendRoAnalyst' (A Tool for Processing and Analyzing Dendrometer Data) Tools for importing, cleaning, analyzing, and visualizing high-resolution dendrometer data and for linking them with climate data. Dendrometer and climate records can be imported with automatic date-time parsing (read.dendrometer(), read.climate()) and checked for a regular temporal resolution (reso_dm()). Preprocessing functions detect and correct artificial jumps with a threshold-based or an automatic changepoint method (jump.locator()), detect and fill gaps with spline, seasonal, or network interpolation (dm.na.interpolation(), network.interpolation()), and truncate or resample the series (dendro.truncate(), dendro.resample()). Daily statistics (daily.data()), the stem-cycle approach (phase.sc()), and the zero-growth approach (phase.zg()) separate radial growth from reversible stem shrinkage and swelling. The function phase.zg() also returns metrics of tree water deficit (TWD) phases, including the event-based ABr index, and the daily drought indices of Peters et al. (2025) . Climate data can be summarized at daily and sub-daily scales and attached to daily, phase-level, and point-level outputs (dm_add_climate()). Event-based climate analyses, superposed epoch analyses, and adverse-period analyses (dm_event_climate(), dm_epoch_test(), clim.twd()) relate tree responses to climate conditions. Seasonal growth can be fitted with Gompertz, logistic, Richards, generalized additive model, LOESS, and spline functions, detrended, and compared among methods (dm.growth.fit(), dm.detrend.fit(), dm.growth.evaluate()). Running correlations with climate (mov.cor.dm()) and wavelet power and coherence analyses based on 'WaveletComp' (dm_wavelet(), dm_wavelet_coherence()) are also provided. Most outputs have dedicated plot methods, and an optional 'shiny' application (dendroanalyst()) allows the complete workflow to be run without programming. The zero-growth approach follows Zweifel et al. (2016) , and the first version of the package is described in Aryal et al. (2020) . Package: r-cran-dendroextras Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dendroextras_0.2.4-1.ca2404.1_all.deb Size: 33862 MD5sum: 65dad709f6fb557ac4efb81e49979b86 SHA1: afddd5c1f0eda5bdd74cbc89dd0f6e17d48c1ca2 SHA256: 03dd61b592a33b97b4a07245a34809cc6ae51fb8317af7f627bae583d49cc72b SHA512: d9a1b932febfe61ea19eb407ba45aba5a8461e962104d07f4635010721320e9c702586ee1f084fb2aa39dda530e00b985c4b16f8c4aa82f9033bc8f114f58ed1 Homepage: https://cran.r-project.org/package=dendroextras Description: CRAN Package 'dendroextras' (Extra Functions to Cut, Label and Colour Dendrogram Clusters) Provides extra functions to manipulate dendrograms that build on the base functions provided by the 'stats' package. The main functionality it is designed to add is the ability to colour all the edges in an object of class 'dendrogram' according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut 'dendrogram' and 'hclust' objects and to set/get labels. Package: r-cran-dendroflux Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readxl, r-cran-ggplot2, r-cran-zoo, r-cran-forecast, r-cran-rlang Filename: pool/dists/noble/main/r-cran-dendroflux_1.0.3-1.ca2404.1_all.deb Size: 196060 MD5sum: 598819977af8bffe0b1ec02d1175a275 SHA1: a9b545cc4c26e022a38fd9385c6382ea2a2f06c6 SHA256: 21b828dd5a75c4f4f550d2d0297887f518c3550b5146d85824a63e608ecb28a0 SHA512: 8f846b11b99ac3bdf39a35ad20a3735c51951d65110ec127da1a5a9bcf9a3a1e6f122c3daa6836e38db3f4e87f92dacb5cba87b10fd46dc17097de796704b3b7 Homepage: https://cran.r-project.org/package=DendroFlux Description: CRAN Package 'DendroFlux' (Processing and Analyzing Dendrometer and Sap Flux Data) Data management and cleaning for dendrometer and sap flux data, including gap detection, NA identification, missing value interpolation, and date conversion. The package also calculates multiple growth metrics of tree radial change data, including the cumulative growth over the entire observation period, daily cumulative growth, and growth changes between adjacent time intervals. Various approaches can be applied to calculate the night delta-Tmax required for sap flow (Peters et al., 2018, ) and subsequently estimate sap flow density (Granier, 1987, ). In addition, it supports the creation of simple time‑series point plots to visually display the dynamic changes in tree growth status or sap flow density during that period. Package: r-cran-dendrometer Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-pspline, r-cran-zoo Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-dendrometer_1.1.1-1.ca2404.1_all.deb Size: 569934 MD5sum: 355f8d6460135737c2b9a6bcf76e8cde SHA1: 17be7a08bcc566aebd93a07870bf8d3de07b264e SHA256: 3eebc2145cbfbc44ff65bf53bdd9f8050d96430410f76c7f89dd228572854cd8 SHA512: 7b556529a3f0c3885bf31f931ff3a21ca44858f045bf1314ea3658ca9cd737e3e8f36321f78dea0ae2ccbfcd75007119e2f71c9d947a317fc95bb21a040b28cc Homepage: https://cran.r-project.org/package=dendrometeR Description: CRAN Package 'dendrometeR' (Analyzing Dendrometer Data) Various functions to import, verify, process and plot high-resolution dendrometer data using daily and stem-cycle approaches as described in Deslauriers et al, 2007 . For more details about the package please see: Van der Maaten et al. 2016 . Package: r-cran-dendrometry Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dendrometry_0.0.4-1.ca2404.1_all.deb Size: 525048 MD5sum: b6d459d37a11fb832f483217e35a94e0 SHA1: 63ff126b37f0079b6090ce254d05b2a1a674c0fa SHA256: 41619daf2fdbe6f2e11b352412e18e1611dd137e61a6194f130f496418411515 SHA512: d6ecd38b663e34df58722e0b0f72a643c4ba35f562fa78e072780bcabeb670d1e09209794089ee90422417491f0b9ffa1d031a61d1847287a60383c75c465ae2 Homepage: https://cran.r-project.org/package=dendrometry Description: CRAN Package 'dendrometry' (Forest Estimations and Dendrometric Computations) Computation of dendrometric and structural parameters from forest inventory data. The objective is to provide a user-friendly R package for researchers, ecologists, foresters, statisticians, loggers and other persons who deal with forest inventory data. The package includes advanced distribution fitting capabilities with multiple estimation methods (Maximum Likelihood, Maximum Product Spacing with ties correction methods following Cheng & Amin (1983), and Method of Moments) for probability distributions commonly used in forestry. Visualization tools with confidence bands using delta method and parametric bootstrap are provided for three-parameter Weibull distribution fitting to diameter data. Useful conversion of angle value from degree to radian, conversion from angle to slope (in percentage) and their reciprocals as well as principal angle determination are also included. Position and dispersion parameters usually found in forest studies are implemented. The package contains Fibonacci series, its extensions and the Golden Number computation. Useful references are Arcadius Y. J. Akossou, Soufianou Arzouma, Eloi Y. Attakpa, Noël H. Fonton and Kouami Kokou (2013) , W. Bonou, R. Glele Kakaï, A.E. Assogbadjo, H.N. Fonton, B. Sinsin (2009) , R. C. H. Cheng and N. A. K. Amin (1983) , and R. C. H. Cheng and M. A. Stephens (1989) . Package: r-cran-dendronetwork Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplr, r-cran-igraph, r-cran-stringr, r-cran-reshape2, r-bioc-rcy3, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-foreach, r-cran-lifecycle, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-dendronetwork_0.5.5-1.ca2404.1_all.deb Size: 1427810 MD5sum: daa87eb9079b4c0e04dd8352877ce93e SHA1: 31811838cd7156cb15e29ffe7fa6b70623a56aa1 SHA256: 3fd79023c5896ffb1f050b33ea70e2916f080d0a1d00041dd60b8eefef39546e SHA512: d89288e4607e50b18c73e7889c9b0d238f58cb6123ec25e12390eed89c97d5e52d9232df8996639007d11a1cda6a15b089d88771b57c5130efbfb9abafe790de Homepage: https://cran.r-project.org/package=dendroNetwork Description: CRAN Package 'dendroNetwork' (Create Networks of Dendrochronological Series using PairwiseSimilarity) Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the 'dplR' package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) or wood-use within a structure/context/site as described in Visser and Vorst (2022) . Package: r-cran-dendrosync Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-dendrosync_0.1.5-1.ca2404.1_all.deb Size: 128254 MD5sum: daa54afb28793cae1a72f33cd20cf5e1 SHA1: 82a9868f079378a84219253b83871f9148baa31b SHA256: 5f9456f76cc6111370000cf6fbeb468c76a2ae155dfbc480a1879a2c0f54b591 SHA512: 0f8be921a509fdbcd420f796049b6297ca71f1147bab18756804e07088852d942765612a3683f0432d9a1263143f72874c463377fcd28ecde010ba79d44af7aa Homepage: https://cran.r-project.org/package=DendroSync Description: CRAN Package 'DendroSync' (A Set of Tools for Calculating Spatial Synchrony BetweenTree-Ring Chronologies) Provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected. Package: r-cran-dendrotools Architecture: all Version: 1.2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3318 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-brnn, r-cran-reshape2, r-cran-scales, r-cran-oce, r-cran-mlmetrics, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-plotly, r-cran-randomforest, r-cran-cubist, r-cran-lubridate, r-cran-psych, r-cran-boot, r-cran-viridis, r-cran-dplr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dendrotools_1.2.16-1.ca2404.1_all.deb Size: 1553380 MD5sum: 4cae0be83b251c86419c44d75a3c4db1 SHA1: c486c9493b2e3d695c678d9d3f47cfad5bc817df SHA256: d8b2e46822daafd0adedc0bf7fa7a3e65cf1de1e3edd4c460c400515ca736e5b SHA512: 63aaa9b65c641acea64ead4409933dd81ae87eda8c91cb0120cc791e1b6b86b1219c00966a6427dd637a53a9e18d11d9a0907f9cb2719b5b70b958bc2f3ee29c Homepage: https://cran.r-project.org/package=dendroTools Description: CRAN Package 'dendroTools' (Linear and Nonlinear Methods for Analyzing Daily and MonthlyDendroclimatological Data) Provides novel dendroclimatological methods, primarily used by the Tree-ring research community. There are four core functions. The first one is daily_response(), which finds the optimal sequence of days that are related to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), which implements partial correlations in the analysis of daily response functions. For the enthusiast of monthly data, there is monthly_response() function. The last core function is compare_methods(), which effectively compares several linear and nonlinear regression algorithms on the task of climate reconstruction. 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A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 . 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This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), , and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution. 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Package: r-cran-densaftertransform Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-densaftertransform_0.1-1.ca2404.1_all.deb Size: 23984 MD5sum: 8c779327f7055ba0bd66ec92a3d295a5 SHA1: 5f8cbea0b021d508f19b9ca7eb7a97f7c6d81691 SHA256: 547b91f9f1e20fc8eb4429ba28687af0abe09d76f7ecf9223273782c4dd47b22 SHA512: ec8248f162491c3929fb264adf9e9264af7d6222baa6b6c1cd0d0cb15ebaebe35d3c9ee22077269b2cbb7cc3984130666dea1daffbd58a550508a7440ca0d658 Homepage: https://cran.r-project.org/package=DENSaftertransform Description: CRAN Package 'DENSaftertransform' (Estimating Density after Logarithmic or Power Transformation ofData) Functions for computing: (1) the adaptive normal PI estimate for data after the logarithmic transformation; (2) single-bandwidth PI density estimate for data after the logarithmic transformation; (3) single bandwidth PI estimate for data after the power transformation. 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(2011) ), RuLSIF (Yamada et al. (2011) ), and KLIEP (Sugiyama et al. (2007) ). Package: r-cran-denstest Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-denstest_1.0.2-1.ca2404.1_all.deb Size: 90302 MD5sum: 58656d22e804fe448d7c8524aa312b5f SHA1: ec89afb71123234b87cf2a221e2d5db4f9265350 SHA256: 55d549113447951f7ccbc1ad84f8782a420c8f47212c082973f5a0c3641d2bb1 SHA512: 7189480aad8c0a1c45f4433cfef231964b091b079dd8c02ee700feea4bdee2f1d83133a72452c1ace8d55cb1a409cd3bd0a6508c1eafa96b197336a735ad976c Homepage: https://cran.r-project.org/package=denstest Description: CRAN Package 'denstest' (Density Equality Testing) Methods for testing the equality between groups of estimated density functions. 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Package: r-cran-dentomedical Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-flextable, r-cran-tibble, r-cran-rlang, r-cran-fsa, r-cran-purrr, r-cran-broom, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dentomedical_0.2.0-1.ca2404.1_all.deb Size: 117616 MD5sum: bee17b69b863450698433d8d87e024fe SHA1: aa0c3649c24c1b5b9d6807fb1cfaba0ae2ea9324 SHA256: 1606f3cf83cde44fa9022ee0c183a07021de421a0ee42c493ea895c8f4032c92 SHA512: b36c16bbb01b482858baea6fd50c8bf407b5c50b5bcab7c642dc39aff8e9045e0a37572a69ff88dce6112ffbb608009974997b5d81540dd0dfa4425b49e042bb Homepage: https://cran.r-project.org/package=dentomedical Description: CRAN Package 'dentomedical' (Publication-Ready Descriptive, Bivariate, Regression,Correlation and Diagnostic Accuracy Tools for Medical andDental Data) The 'dentomedical' package provides a comprehensive suite of tools for medical and dental research. 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Package: r-cran-denvax Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-testthat, r-cran-usethis, r-cran-devtools, r-cran-roxygen2, r-cran-jsonlite, r-cran-ggplot2, r-cran-cowplot, r-cran-directlabels, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-denvax_0.1.2-1.ca2404.1_all.deb Size: 65776 MD5sum: 05bc87583916093fa104fa039c7b1be5 SHA1: be47e6ef416255c0bb363e5492cbf39fef3d4472 SHA256: 4fa509dec6b4abca230d2be589c66d723cd464c11155434ee8939d1b603e8792 SHA512: 2dd2cfc85aeeeeb589bbe89c223c1f4fe8292a13659956b30f912bdd032757f2f677fc275c7ff46e019be81f64f5f92b6e4bd3fe5898a78d688315a00188503d Homepage: https://cran.r-project.org/package=denvax Description: CRAN Package 'denvax' (Simple Dengue Test and Vaccinate Cost Thresholds) Provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds" in . Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis. Package: r-cran-deopendata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-deopendata_0.1.0-1.ca2404.1_all.deb Size: 47914 MD5sum: 60a1d0409fab2bd0b731cd3e32f5ed0c SHA1: 0795305078e7c7bafc29630738ff414177840bb2 SHA256: 56eb2ea71ff9f9026124859105ec6910606cd293413cc1252b837742418c00dc SHA512: 17bce373331b213ecd69a4fd796e21642dbdd175aa44e5fa43b0b3ca2fec438f238731b9247a67c3f18a82a64f26373556252711658e8682fd5682d8a6e215ea Homepage: https://cran.r-project.org/package=deOpenData Description: CRAN Package 'deOpenData' (A Lightweight Interface to Delaware Open Data APIs) Provides a unified set of helper functions to access datasets from the Delaware Open Data platform . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package provides a consistent interface for discovering and downloading datasets from the Delaware Open Data Portal using human-readable dataset keys or official Socrata dataset identifiers. Package: r-cran-deoptimr Architecture: all Version: 1.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mirai Filename: pool/dists/noble/main/r-cran-deoptimr_1.2-2-1.ca2404.1_all.deb Size: 106474 MD5sum: 702ba0d6d756f4f0978e4f0d83419dfa SHA1: e05653ad1a91561b3ff9e224bee4e32a51c23ed2 SHA256: 6d3398162eb483a6d99984dddc9879dad7ad79886548c4a969a8eeb726ec55d1 SHA512: 21f67371af46d87a68179b15f27e2a9a842089886d337aa0ac67db74218eff0d691d88130db8ff0a09f3d6a2cd3044263477f1a292d2068508f0c954f51d0fe0 Homepage: https://cran.r-project.org/package=DEoptimR Description: CRAN Package 'DEoptimR' (Differential Evolution Optimization in Pure R) Differential Evolution (DE) stochastic heuristic algorithms for global optimization of problems with and without general constraints. The aim is to curate a collection of its variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it provides implementations of the algorithms 'jDE' by Brest et al. (2006) for single-objective optimization and 'NCDE' by Qu et al. (2012) for multimodal optimization (single-objective problems with multiple solutions). Package: r-cran-depcens Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dlm, r-cran-formula, r-cran-rootsolve, r-cran-survival, r-cran-matrixstats Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-depcens_0.2.3-1.ca2404.1_all.deb Size: 110166 MD5sum: 6eb265e4ce4634cacf3fe348d8742d61 SHA1: d3348bcb728e3e79e09278ba9c2ba9918dce1425 SHA256: c1c20694b1875415583c5ec8aab3f020993197a56d2f0d00033fda22a9d7089e SHA512: 584f2bbe17a8f3ebb05b4f281a55c12edabcafa5d6bad7fc861e52903db97587947f1659ae37d6bace2cb03c9793e98a66fcaeb1865fd9b48839a8bfa73399a7 Homepage: https://cran.r-project.org/package=DepCens Description: CRAN Package 'DepCens' (Dependent Censoring Regression Models) Dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2019) . Package: r-cran-depcensoring Architecture: all Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-foreach, r-cran-doparallel, r-cran-pbivnorm, r-cran-mass, r-cran-nleqslv, r-cran-matrix, r-cran-envstats, r-cran-mvtnorm, r-cran-rvinecopulib, r-cran-nloptr, r-cran-numderiv, r-cran-copula, r-cran-r6, r-cran-lubridate, r-cran-splines2 Suggests: r-cran-testthat, r-cran-rkriging, r-cran-orthopolynom, r-cran-openmx, r-cran-stringr, r-cran-laplacesdemon, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-depcensoring_0.1.11-1.ca2404.1_all.deb Size: 803066 MD5sum: bcb5c505f376b01fe8ca7fd4017f429d SHA1: 21912b13604627112fe43c06520381011ed35f28 SHA256: 73428d69acd14805b62a2753a66df2356dfb0f46c43d82c29a6978c5b541370f SHA512: 424f77ec7098203291e3b6fc8ebeb99e68b6b42d6808b5f5d9fdf352050823ad71dcf9ed74df3d1267bafc0a4e236d8e57eca068323be06452e45b179b58aa63 Homepage: https://cran.r-project.org/package=depCensoring Description: CRAN Package 'depCensoring' (Statistical Methods for Survival Data with Dependent Censoring) Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) , Czado and Van Keilegom (2023) , Crommen et al. (2024) , Deresa and Van Keilegom (2024) , Willems et al. (2025) , Ding and Van Keilegom (2025) and D'Haen et al. (2025) . Package: r-cran-depdoubletruncks Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-depdoubletruncks_0.1.0-1.ca2404.1_all.deb Size: 98074 MD5sum: 98e34bc7f6fe67ba9d84ca42ec3d388c SHA1: 3cb3dafe5bfded863503df26539aed354dacaf2f SHA256: ac2ea4ef2076e57157492863621079b9fb37708b3bace70a9e85dc9f02f2f205 SHA512: 77dd19d60da6a47413fb743096002c72ec0f76e1a58e0c4f3139fc8e14a220f322ba6195420bccef4cd513520b6c52252723a5ed968cf393c61ad529d0af5db1 Homepage: https://cran.r-project.org/package=DepDoubleTruncKS Description: CRAN Package 'DepDoubleTruncKS' (Kolmogorov-Smirnov Test for Dependently Double-TruncatedDurations) Performs the Kolmogorov-Smirnov-type goodness-of-fit test for exponential duration models under independent or dependently double-truncated sampling scheme using Farlie-Gumbel-Morgenstern ('FGM') copulas, as proposed by Toparkus and Weissbach (2026) . Provides functions for profile maximum likelihood estimation / score equation solving, computation of the two-dimensional Kolmogorov-Smirnov test statistic over the double-truncation parallelogram, simulation of the asymptotic Gaussian process limit distribution for critical values and p-value calculation, and synthetic dataset generation. Package: r-cran-depend.truncation Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-depend.truncation_3.0-1.ca2404.1_all.deb Size: 235940 MD5sum: a7ffd751f62f14bababc95737a45b7f0 SHA1: 40d5806463c818368e6771df30f7b3fc50281aca SHA256: 36b691bc4af62d3bf1fad7c0234ad2666899d9715037c51c785f7e3caff7f9ab SHA512: 50aa640f03c22660aacff73cec12d7aac1b5470a43021fffef4af89eeade1bda3e9272f78ed333947fc795a063773e139ca8ad08a17c37051a9093b2518bf996 Homepage: https://cran.r-project.org/package=depend.truncation Description: CRAN Package 'depend.truncation' (Statistical Methods for the Analysis of Dependently TruncatedData) Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011), Emura & Wang (2012), and Emura & Murotani (2015). Parametric approaches are based on Emura & Konno (2012) and Emura & Pan (2017). A regression approach is based on Emura & Wang (2016). Quasi-independence tests are based on Emura & Wang (2010). Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015). Package: r-cran-dependentsimr Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-bioc-deseq2, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-mass, r-cran-corpcor, r-cran-testthat, r-cran-matrix, r-cran-sparsesvd, r-cran-knitr, r-cran-rmarkdown, r-cran-biocmanager, r-cran-remotes, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-dependentsimr_1.0.0.0-1.ca2404.1_all.deb Size: 103588 MD5sum: ae89413ca3eca10b8644730c31ec2870 SHA1: 2863e82d522749a425db5cc3e20cded10e3b2d17 SHA256: 85ea3eea634f4660254af10cd45b86ad9a297e0bec41f1a74e02168ddbddd1df SHA512: c60e7e4fb40379f1d39b0d5508a7ec3e238c5aa69c0faf97c34beb4b9d5e59151ace489f77df783fc84eddc447db376989422e7261e8695592315ec71e91149a Homepage: https://cran.r-project.org/package=dependentsimr Description: CRAN Package 'dependentsimr' (Simulate Omics-Scale Data with Dependency) Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and 'DESeq2' (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) . Package: r-cran-depguard Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-remotes, r-cran-pak, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-depguard_0.2.0-1.ca2404.1_all.deb Size: 110096 MD5sum: 3b6fff164cbbe642f15ed6c01b94107a SHA1: 2145f092a26b3d5ed6df633073e540486eb01756 SHA256: aefcb8eaff69ea7b06cd5d4938b72dc2f608526b6203cd8e9005e78a2858c897 SHA512: 6d5362b97e05c434d24e28cb5bfa7e5f98bcb85048e6f60e27fef52f26e197c12a86650b009da947eff8d5bb19357dca4c7c0db3b1e48d06b988a97a68684466 Homepage: https://cran.r-project.org/package=depguard Description: CRAN Package 'depguard' (Manifest-Based Dependency Conflict Detection for Sandboxed andDesktop R Sessions) Lightweight, offline-first checking of R package dependencies against the currently installed environment, without requiring a full project lockfile. Verifies a declared manifest of package versions, including version constraints declared by transitive dependencies, reports session-level snapshot differences (including stale versions still loaded in a running session), detects packages shadowed by another library, and offers single-package version rollback. Designed for hosted notebooks (e.g. Kaggle, Colab, Binder) where 'renv'-style lockfile ownership is impractical, and equally usable on a normal desktop. Package: r-cran-depictr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2042 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-scales, r-cran-patchwork, r-cran-rlang, r-cran-rdpack Suggests: r-cran-lme4, r-cran-lmertest, r-cran-broom, r-cran-simr, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-ggdist, r-cran-posterior, r-cran-boot, r-cran-cluster, r-cran-colorspace, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-depictr_0.3.0-1.ca2404.1_all.deb Size: 1491960 MD5sum: 84b4fcceb92cd2ab08878c59218efe34 SHA1: d18ec0f34daf5a74f09ed2141f823a188b222b6b SHA256: 6624608f2bf4e3584f7597075e9ffe10eadff9228a43bb9373c3ec5fa14bb784 SHA512: 4907f9d82c90cc71af8ccc5015cacd93c4dc441e2e8f1be16378ceb8d340b63b488a8f0dc0fb875487a1386f056b5da9bc326bbfbb58f3dac22ef5a529b0e7b3 Homepage: https://cran.r-project.org/package=depictr Description: CRAN Package 'depictr' (A Unified Toolkit for Visualising Statistical Models and Data) A cohesive, publication-ready toolkit of plots that span the whole analysis workflow with one consistent look. It covers exploratory data analysis (distributions, categorical summaries, bivariate plots, scatter-plot matrices, correlation heatmaps, missing-data maps, outliers, estimation statistics and descriptive tables); multivariate analysis, clustering with diagnostics and Kaplan-Meier survival curves; time series (trends, autocorrelation, decomposition, seasonality and forecasting); model estimates and inference (forest plots, model comparison, frequentist and Bayesian estimates, predicted values, interactions, random effects and optimiser checks); diagnostics and classification (residual panels, binned residuals, influence, quantile-quantile, receiver operating characteristic (ROC) curves, calibration, threshold tuning and confusion matrices); uncertainty and power; and reporting helpers (a shared theme, colourblind-aware palettes, plot composition and saving). Every plotting function returns a 'ggplot2' object (or a 'patchwork' object for composite panels), heavier modelling back-ends are optional, and the package ships with reproducibly simulated datasets so that every example and vignette runs without further setup. Package: r-cran-depigner Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desc, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-hmisc, r-cran-magrittr, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-rms, r-cran-rprojroot, r-cran-stringr, r-cran-telegram.bot, r-cran-tibble, r-cran-tidyr, r-cran-usethis Suggests: r-cran-covr, r-cran-spelling, r-cran-survival, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-depigner_0.9.1-1.ca2404.1_all.deb Size: 164180 MD5sum: e0f2e5bf426fc425948b9f1732880bb6 SHA1: 1cbd48bf976f521234c0cedc6cef1e1fb307eff2 SHA256: 75ee2aa968391e8980f7b87534724f3e73e45dd1a8519f6cb2f14738076767b7 SHA512: 5ca72140ab32aff8293f2593955589fd9fd34f24ce070e91adbc94544b3a9cd084be2183449333898b7f8f9d7dfd8dbb4f2d975d19d48faa472e467f5c99573c Homepage: https://cran.r-project.org/package=depigner Description: CRAN Package 'depigner' (A Utility Package to Help you Deal with "Pignas") Pigna [_pìn'n'a_] is the Italian word for pine cone. In jargon, it is used to identify a task which is boring, banal, annoying, painful, frustrating and maybe even with a not so beautiful or rewarding result, just like the obstinate act of trying to challenge yourself in extracting pine nuts from a pine cone, provided that, in the end, you will find at least one inside it. Here you can find a backpack of functions to be used to solve small everyday problems of coding or analyzing (clinical) data, which would be normally solved using quick-and-dirty patches. You will be able to convert 'Hmisc' and 'rms' summary()es into data.frames ready to be rendered by 'pander' and 'knitr'. You can access easy-to-use wrappers to activate essential but useful progress bars (from 'progress') into your loops or functionals. Easy setup and control Telegram's bots (from 'telegram.bot') to send messages or to divert error messages to a Telegram's chat. You also have some utilities helping you in the development of packages, like the activation of the same user interface of 'usethis' into your package, or call polite functions to ask a user to install other packages. Finally, you find a set of thematic sets of packages you may use to set up new environments quickly, installing them in a single call. Package: r-cran-deplogo Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7180 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-deplogo_1.2.2-1.ca2404.1_all.deb Size: 3556276 MD5sum: e39513a5b8021920f4822692dd29486c SHA1: ab9ba86656e10d54d00126e74e06971f1a179e37 SHA256: 26977f82181bf7b0c50aed115efb3d5f6614bda4a9906b98f29e6abb616e7ca1 SHA512: 55166d799c5797d501c0e6a458e92dc12246d977f0d52a50ec13dcbd08528e9e7c9171a1a85f418cb065fe22904a96f5c0bc632ce5964f937a24b213eca5f59b Homepage: https://cran.r-project.org/package=DepLogo Description: CRAN Package 'DepLogo' (Dependency Logo) Plots dependency logos from a set of aligned input sequences. 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Package: r-cran-depmod Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-diagrammer, r-cran-tidyverse, r-cran-bslib, r-cran-here Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-depmod_0.1.0-1.ca2404.1_all.deb Size: 410546 MD5sum: 9972e7d02f6ff7c61de3d456b03da436 SHA1: f91cf190b38c83a4d6548b0c1f9efd142368ee3f SHA256: 1a2ab0dc4c895fed03680490378c13973b1be8e8b267ed249a3312f22a4637bf SHA512: cc443ebf827a7544cdf66cbec28cc66761f7e543d6ba0a165994fee91797ba11cd6631c5ce3f282ae6f9d1d0cb920974f70f3e5c94b2cfb1c242b622f3ebe5c6 Homepage: https://cran.r-project.org/package=DepMod Description: CRAN Package 'DepMod' (Decision-Analytic Modelling for Depression Prevention andTreatment) Provides functions and example datasets to run a decision-analytic model for prevention and treatment strategies across depression severity states (sub-clinical, mild, moderate, severe, and recurrent). 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This package aims to help users identify novel targets within their data sets based on protein network interactions and publication precedence of target's association with research context based on literature precedence. Methods in this package are described in detail in: Douglas et al. (2025) . Key functionalities of this package also leverage methodologies from previous works, such as: - Szklarczyk et al. (2023) - Winter (2017) . 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Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface. 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Though there are other packages which does similar job but each of these are deficient in one form or other, in the measures generated, in treating numeric, character and date variables alike, no functionality to view these measures on a group level or the way the output is represented. Given the foremost role of the descriptive statistics in any of the exploratory data analysis or solution development, there is a need for a more constructive, structured and refined version over these packages. This is the idea behind the package and it brings together all the required descriptive measures to give an initial understanding of the data quality, distribution in a faster,easier and elaborative way.The function brings an additional capability to be able to generate these statistical measures on the entire dataset or at a group level. It calculates measures of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures are generated based on the data types as required by them,rather than applying numerical measures on character and data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a summary report for the data. 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Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface. For their installation, please follow the instructions at and . We have also included directions in the gurobi_installation file in the inst folder. 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M., Leinwand, B., Pipiras, V. (2021) "Two sample tests for high-dimensional auto-covariances" and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023) “Detecting functional connectivity changes in fMRI data” . 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The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, ) using 'ROI' or 'lpSolveAPI' . The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, ), divergence of those ratios indicates data separation. 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(2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission. Package: r-cran-detzrcr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-mass, r-cran-dt, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-detzrcr_0.3.3-1.ca2404.1_all.deb Size: 1439398 MD5sum: 7b89595502c0c9ce9d756aeca08637c7 SHA1: cb267e8328e479c034a8ad1259b2d569e2b4ef30 SHA256: e73d30f60c24364fac755d8d1c8e79fbbd993b456010fccb18f2582ab79e96fb SHA512: f67515989dcc0dc9ac748c53a2aa55643ffea60a52a6bc7cbe12fe5ba3625de345cf8cc18fcf8ac6bc7a316e5363018a61b8c1aaae99e6a03ef99b0b1014c341 Homepage: https://cran.r-project.org/package=detzrcr Description: CRAN Package 'detzrcr' (Compare Detrital Zircon Suites) Compare detrital zircon suites by uploading univariate, U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based user-interface. 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The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms. The methods and literature review are described in Rebaudo et al. (2018) , Rebaudo and Rabhi (2018) , and Regnier et al. (2021) . An example can be found in Rebaudo et al. (2017) . Package: r-cran-devtools Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-usethis, r-cran-cli, r-cran-desc, r-cran-ellipsis, r-cran-fs, r-cran-lifecycle, r-cran-memoise, r-cran-miniui, r-cran-pak, r-cran-pkgbuild, r-cran-pkgdown, r-cran-pkgload, r-cran-profvis, r-cran-rcmdcheck, r-cran-rlang, r-cran-roxygen2, r-cran-rversions, r-cran-sessioninfo, r-cran-testthat, r-cran-urlchecker, r-cran-withr Suggests: r-cran-biocmanager, r-cran-callr, r-cran-covr, r-cran-curl, r-cran-digest, r-cran-dt, r-cran-foghorn, r-cran-gh, r-cran-httr2, r-cran-knitr, r-cran-lintr, r-cran-quarto, r-cran-remotes, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-spelling, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-devtools_2.5.2-1.ca2404.1_all.deb Size: 463924 MD5sum: ce24d6bfed068e5a58269893d5796277 SHA1: 3abc014aec4aadaf4422c0e366629f9079a54526 SHA256: 1467e3a777c2dff52c40c0f779c1677b3ad0fa9dbc54ba8d25c148ec4d49b1f9 SHA512: 7c007ef9ab84f2b86b589919a8988e4aa3b8a1b99e2cfad49c87f3c0277b4d9e52a096efe07c61c9d833475b5f82625be88a52c1e70e0cb8fff9062337c591d0 Homepage: https://cran.r-project.org/package=devtools Description: CRAN Package 'devtools' (Tools to Make Developing R Packages Easier) Collection of package development tools. Package: r-cran-devtreatrules Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-dyntxregime, r-cran-modelobj Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-devtreatrules_1.1.0-1.ca2404.1_all.deb Size: 397530 MD5sum: 3fe81dfc04416e52f2b58812ecaba89e SHA1: 05b9ffee1ea2f4794583d7279282f3dace45d5e3 SHA256: 62883cc12102ae0a018b222db67875b316adcb11a65f048bc896190d919fcd9b SHA512: 08cc8e65481453a67aeacb1edcf774912af08173f9bfdaa145713e24146031338278119a43db3ae69885c4869ed88c74595d7e0955768cf0fe1fbac9f30299c2 Homepage: https://cran.r-project.org/package=DevTreatRules Description: CRAN Package 'DevTreatRules' (Develop Treatment Rules with Observational Data) Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) ; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) . Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample. Package: r-cran-dexir Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-stringr Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-ggally, r-cran-fmsb Filename: pool/dists/noble/main/r-cran-dexir_1.0.2-1.ca2404.1_all.deb Size: 556300 MD5sum: 764987be38f00cf8a53b7f349262955f SHA1: 17c61b74bf19c4a5d6c3dc48380f160834f361e5 SHA256: 3a3afc4de20cb77619f2884796a7a5d58296f88c4e425f93b6aba98408afa628 SHA512: 043660c4becb0ef98e0f0d2b82d6f31634a754f97e469bba1c3e727e79c8bcea7085a4d72415e9eff5bd99c674f7abc96b7484aa53727fe4781e557853fd97be Homepage: https://cran.r-project.org/package=DEXiR Description: CRAN Package 'DEXiR' ('DEXi' Library) A software package for using 'DEXi' models. 'DEXi' models are hierarchical qualitative multi-criteria decision models developed according to the method DEX (Decision EXpert, ), using the program 'DEXi' () or 'DEXiWin' (). 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Package: r-cran-dexisensitivity Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-algdesign, r-cran-plotrix, r-cran-genalg, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-xml2 Suggests: r-cran-sloop, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-dexisensitivity_1.0.4-1.ca2404.1_all.deb Size: 438642 MD5sum: 9718aaa6b3282722cd851d6e0ee14a20 SHA1: 8758fbd7dcdec0f1f50d2df36e7d1ce821627150 SHA256: 62fe6b4a943ee4bf85d124a77f7b06ea45c8e5d9af7248026668cad68f5ce8a2 SHA512: 111abd1c512cef8a01f8c991b84632436199299f1d52ede9bd08b4f71864c9a1911c34ae7068b13f0649c87cc3ea15c144ec85db73e144161e96ddd242991b12 Homepage: https://cran.r-project.org/package=dexisensitivity Description: CRAN Package 'dexisensitivity' ('DEXi' Decision Tree Analysis and Visualization) Provides a versatile toolkit for analyzing and visualizing 'DEXi' (Decision EXpert for education) decision trees, facilitating multi-criteria decision analysis directly within R. Users can read .dxi files, manipulate decision trees, and evaluate various scenarios. It supports sensitivity analysis through Monte Carlo simulations, one-at-a-time approaches, and variance-based methods, helping to discern the impact of input variations. Additionally, it includes functionalities for generating sampling plans and an array of visualization options for decision trees and analysis results. A distinctive feature is the synoptic table plot, aiding in the efficient comparison of scenarios. Whether for in-depth decision modeling or sensitivity analysis, this package stands as a comprehensive solution. Definition of sensitivity analyses available in Carpani, Bergez and Monod (2012) and detailed description of the package available in Alaphilippe et al. (2025) . 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Package: r-cran-dhs.rates Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape, r-cran-survey, r-cran-haven, r-cran-matrixstats, r-cran-dplyr, r-cran-rlang, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dhs.rates_0.9.2-1.ca2404.1_all.deb Size: 263218 MD5sum: f65caaf67f68ebd0f4d78d6c735293e3 SHA1: 5446189a86713205f3f29d9a9361dc37d82ffe4d SHA256: 8d60bc2b1e7ab134cb7b08dbfdec0e5e5003a54773551eb6cf2f90437ed6b656 SHA512: 1c1a419a2f7529930f06d70ddf4d3dbec34d23a5f891c3f09adb67eb21ae42281f1c6dea9700c9fe7f64f604dfc0fe667506bdd63cd35dd07de1d3bfbbc9ac9b Homepage: https://cran.r-project.org/package=DHS.rates Description: CRAN Package 'DHS.rates' (Calculates Demographic Indicators) Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR), and adult mortality indicators such as the Age Specific Mortality Rate (ASMR), Age Adjusted Mortality Rate (AAMR), Age Specific Maternal Mortality Rate (ASMMR), Age Adjusted Maternal Mortality Rate (AAMMR), Age Specific Pregnancy Related Mortality Rate (ASPRMR), Age Adjusted Pregnancy Related Mortality Rate (AAPRMR), Maternal Mortality Ratio (MMR) and Pregnancy Related Mortality Ratio (PRMR). In addition to the indicators, the 'DHS.rates' package estimates sampling errors indicators such as Standard Error (SE), Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). The package is developed according to the DHS methodology of calculating the fertility indicators and the childhood mortality rates outlined in the "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, ) and the DHS methodology of estimating the sampling errors indicators outlined in the "DHS Sampling and Household Listing Manual" (ICF International 2012, ). Package: r-cran-dhsage Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dhsage_0.1.0-1.ca2404.1_all.deb Size: 77424 MD5sum: 3d9dd17fe015b2090cb82f5a00854d64 SHA1: 65657648f69813b126eb252937cf8c6e6cd042af SHA256: c7f9008b42a3fcdfacd3d3379900ed7f299c51b1b7ce973e0c80ef009b2ba13d SHA512: f2ded80e9198b76372edfc577d9b1defb6351da135bc3cadf0c9ee503a29c549adccbaf469dd9913206d16b79788d050bfcae467aaa1cc21ca8af9ee98912de5 Homepage: https://cran.r-project.org/package=dhsage Description: CRAN Package 'dhsage' (Reproductive Age Female Data of Various Demographic HealthSurveys) We provide 70 data sets of females of reproductive age from 19 Asian countries, ranging in age from 15 to 49. The data sets are extracted from demographic and health surveys that were conducted over an extended period of time. Moreover, the functions also provide Whipple’s index as well as age reporting quality such as very rough, rough, approximate, accurate, and highly accurate. Package: r-cran-dhsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-sf, r-cran-spdep, r-cran-viridis, r-cran-nlme, r-cran-mumin, r-cran-tidyr Suggests: r-cran-knitr, r-cran-spdata, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dhsr_0.1.0-1.ca2404.1_all.deb Size: 67862 MD5sum: 9d8018dd9f16dc6c281fa759f31a6fe1 SHA1: 6ca9fbfb017d1f7b7f8dcf55b16e7f86462b6949 SHA256: b2b7a6fd099ef5b211a36ecbfd7d0062cbe6eb33f2b41179c1b8f8e750ef322a SHA512: ad461cda67b23a2381bc085f2e8a838bd33510e23dc364ad288f1fadc184f087d8f58b1300b0f22a43c5f09176695204d80ed2f110313965bfe96d206faf3bdb Homepage: https://cran.r-project.org/package=DHSr Description: CRAN Package 'DHSr' (Create Large Scale Repeated Regression Summary StatisticsDataset and Visualization Seamlessly) Mapping, spatial analysis, and statistical modeling of microdata from sources such as the Demographic and Health Surveys and Integrated Public Use Microdata Series . It can also be extended to other datasets. The package supports spatial correlation index construction and visualization, along with empirical Bayes approximation of regression coefficients in a multistage setup. The main functionality is repeated regression — for example, if we have to run regression for n groups, the group ID should be vertically composed into the variable for the parameter `location_var`. It can perform various kinds of regression, such as Generalized Regression Models, logit, probit, and more. Additionally, it can incorporate interaction effects. The key benefit of the package is its ability to store the regression results performed repeatedly on a dataset by the group ID, along with respective p-values and map those estimates. Package: r-cran-di Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-di_1.1.4-1.ca2404.1_all.deb Size: 283298 MD5sum: c44e97aa492c3426aa1affa31b0cec5a SHA1: 4e623b435564099452f7b850092d62b5ce52739a SHA256: 030f983ec4de8d5bdcf2f2edf57377435d823454d968d7aec6d5c605f99393c7 SHA512: d6a53e50830a7c6ddcff54c6f297f465759b49a3d7134bf2fa1bb06086dfacb41fc7fe6a61b050f42196573c1e15fb434b06d8a575dd30baa716e85717a006e5 Homepage: https://cran.r-project.org/package=di Description: CRAN Package 'di' (Deficit Index (DI)) A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)"Accumulation of Deficits as a Proxy Measure of Aging" by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, ; (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), . Package: r-cran-diagcounts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diagcounts_0.1.0-1.ca2404.1_all.deb Size: 20432 MD5sum: 7cce342b0b7261983c042880e22d626a SHA1: 3741aa1b18bd0412d687b47f1510368aaf1db4c7 SHA256: 80871473062124642776ada6530deef9e9da77bfeee23eb41463b41c0bf95ace SHA512: 783fb334f37025e205dbc202f9deb155207a4912989e24086f514530880cb87f3c3958113406a8dcc9817ff46ca7837d66ddbba593ccdc1f6173947eae8e2e37 Homepage: https://cran.r-project.org/package=diagcounts Description: CRAN Package 'diagcounts' (Recover Diagnostic Test Accuracy Counts from Reported AccuracyMeasures) Implements a system of linear equations to recover unreported diagnostic test accuracy cell counts from commonly reported measures such as sensitivity, specificity, predictive values, prevalence, and sample size. The package is intended for applied researchers who require complete 2x2 table counts for downstream analyses. 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Implements calibration, stability and tail diagnostics, including tail support, threshold elasticity, posterior error probability (PEP) reliability, and equal-chance checks. If you used this package in your research, please cite the associated preprint . Detailed examples of using this package can also be found on the GitHub repository (). Package: r-cran-diagl1 Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-greekletters, r-cran-conquer, r-cran-lawstat, r-cran-matrixmodels, r-cran-matrix, r-cran-mass, r-cran-cubature, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-diagl1_1.0.1-1.ca2404.1_all.deb Size: 167462 MD5sum: e946be2a42dc3d4e24b2afb37193b844 SHA1: 73829bbafb13cf69304f4c98d707e48873612541 SHA256: 7a5ed47fe61feba4cb3853d7f30d8545e01c976a7877655807a7439b97cb6638 SHA512: 1c1c83971f8fa3fe06fdaecdd2fc2cf67664b89b0985766bb2cb9bbf08ebee6576cab841373bf018bd1f3cca714ea3d825ca9659f71f95ab6ff4c1330093896e Homepage: https://cran.r-project.org/package=diagL1 Description: CRAN Package 'diagL1' (Routines for Fit, Inference and Diagnostics in Linear L1 and LADModels) Diagnostics for linear L1 regression (also known as LAD - Least Absolute Deviations), including: estimation, confidence intervals, tests of hypotheses, measures of leverage, methods of diagnostics for L1 regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005) , Elian et al. (2000) and Dodge (1997) . This package builds on the 'quantreg' package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) . Package: r-cran-diagmeta Architecture: all Version: 0.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-meta, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-diagmeta_0.5-1-1.ca2404.1_all.deb Size: 160822 MD5sum: 2a5441a9ea08ba42eb942201baa05301 SHA1: 4a3d77263b8bd55878a7041215b63608b11608ba SHA256: 4fca910601dce78f6a88d3299020efcc02d2d6f1537554fba574de68f04e25fe SHA512: 436a127fcaba574a3e097e8655a1ae6effc54299a683ba9b497db2871bc5c1c7d866cbf6b55c102945ad9df3faff945c70b89c5b1667974d19571e9e444b963c Homepage: https://cran.r-project.org/package=diagmeta Description: CRAN Package 'diagmeta' (Meta-Analysis of Diagnostic Accuracy Studies with SeveralCutpoints) Provides methods by Steinhauser et al. (2016) for meta-analysis of diagnostic accuracy studies with several cutpoints. Package: r-cran-diagnostysize Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-shinybs, r-cran-markdown, r-cran-dt, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diagnostysize_0.1.0-1.ca2404.1_all.deb Size: 263166 MD5sum: 19073c5efaca2fd0b934c8793b18f469 SHA1: 200002717bb05f789bff42b26dc37666f7c8db31 SHA256: aef0c85905fb5e6e43cced6e9e64b4865e309e1032e4e3c90b4b61e47b3d4e87 SHA512: b911da48a28dbb6dfcda6be43cffac3c7cdcb6e716f0001ca16309609ba853bf3727a616dc9e8dd82dcbaa761369c3b6c633879993fb5a622b0d0e3ce2aeea32 Homepage: https://cran.r-project.org/package=DiagnostySize Description: CRAN Package 'DiagnostySize' (App for Calculating the Sample Size in a Diagnostic Study) We developed a 'shiny' app for the optimal sample size calculations for diagnostic accuracy studies proposed in Stark and Zapf (2020) , and Stark et al. 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These tools can be used to define objects for creating, simulating, or validating values for such parameters. Package: r-cran-dialvalidator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-stringr Suggests: r-cran-xml2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dialvalidator_0.1.0-1.ca2404.1_all.deb Size: 159308 MD5sum: a3398ed4b3b1e8da59e2633c03485679 SHA1: f0fc5f85cc07fc5a080c49f464502b7e3ad12195 SHA256: 2f14c2cb3ccb33249a58c3150579c20f43a0a2747c2b234690ffa7577a37bc7d SHA512: df5bd1965dbda837a0a4cfe0bee3a09b387eb6d7d91be73c9cd023be1c404c9b73bdfaefffd367ca42aea47a4716a8ff67758d711f9a2374d8a221364baad7c3 Homepage: https://cran.r-project.org/package=dialvalidator Description: CRAN Package 'dialvalidator' (Phone Number Validation Using Google's 'libphonenumber' Metadata) Parses, validates, formats, and classifies phone numbers using Google's 'libphonenumber' metadata. Covers 240+ territories with support for mobile, landline, toll-free, and other number types. Unlike 'dialr', this package requires no Java runtime — metadata is parsed ahead of time from the upstream 'PhoneNumberMetadata.xml' and shipped as a bundled R object. Functions accept character vectors and return simple R types (logical, character, data.frame). Package: r-cran-diaplt Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-diaplt_1.4.0-1.ca2404.1_all.deb Size: 53450 MD5sum: 49cf33b7868930d4ad134f9d3345ff5d SHA1: c818868a761e1d564ac3f353b1f87c747834df8c SHA256: 719c44c77c4597b10614f0608096f2e6e16b49f08bc42c487106216a1e4447d5 SHA512: d9818f3fd92ba851a640e17373f6cb3d72f21c75edade1620bfb6239b9dd81193bd883b113707fddf8c3321d1f89f5895a53007cda39b60fb408f5d78446b8ca Homepage: https://cran.r-project.org/package=diaplt Description: CRAN Package 'diaplt' (Beads Summary Plot of Ranges) Visualize one-factor data frame. Beads plot consists of diamonds of each factor of each data series. A diamond indicates average and range. Look over a data frame with many numeric columns and a factor column. Package: r-cran-diario Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2406 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-tibble, r-cran-httr2, r-cran-keyring Suggests: r-cran-testthat, r-cran-httptest2, r-cran-withr Filename: pool/dists/noble/main/r-cran-diario_0.1.2-1.ca2404.1_all.deb Size: 157946 MD5sum: 924b7bfa1a800f26c82075d37e34cf0e SHA1: a0c0c907989722c80d51a49ad89cb6f966df20dc SHA256: efcd38ebb3c7947e90ba017e57e00fb1ab3ceed496cbd4dad286f155bba84ad4 SHA512: 1d3cfc951aadfab199fefcdb1266423a20f10c5b05c0bb7ecd8da5d1a4d84af1b6a59be2a9196aa6361a9cdb65d36be3b852764515a434c8b43255f4267f925f Homepage: https://cran.r-project.org/package=diario Description: CRAN Package 'diario' ('R' Interface to the 'Diariodeobras' Application) Provides a set of functions for securely storing 'API' tokens and interacting with the system. 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Package: r-cran-diathor Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2719 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringdist, r-cran-vegan, r-cran-ggplot2, r-cran-tidyr, r-cran-data.table, r-cran-purrr, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diathor_0.1.5-1.ca2404.1_all.deb Size: 2635102 MD5sum: 3bdb006dc5e3baa7199a61b943075ffe SHA1: a8a420ef7b6ba90467385d36101729c62726efd6 SHA256: 0c710de58ceee04fed83d58f7b9b3bf6b79b7fa2bafe244882b6c5add7cb8ef1 SHA512: c5b7ce98d205633ff5a21d260dd17f1190238c314061c5bbc8ae3a3225bb8585d849ce8fb109da1acea96808a994abaaaa11f0dd7c27d715f41462e425a191ca Homepage: https://cran.r-project.org/package=diathor Description: CRAN Package 'diathor' (Calculate Ecological Information and Diatom Based Indices) Calculate multiple biotic indices using diatoms from environmental samples. Diatom species are recognized by their species' name using a heuristic search, and their ecological data is retrieved from multiple sources. It includes number/shape of chloroplasts diversity indices, size classes, ecological guilds, and multiple biotic indices. It outputs both a dataframe with all the results and plots of all the obtained data in a defined output folder. - Sample data was taken from Nicolosi Gelis, Cochero & Gómez (2020, ). - The package uses the 'Diat.Barcode' database to calculate morphological and ecological information by Rimet & Couchez (2012, ),and the combined classification of guilds and size classes established by B-Béres et al. (2017, ). - Current diatom-based biotic indices include the DES index by Descy (1979) - EPID index by Dell'Uomo (1996, ISBN: 3950009002) - IDAP index by Prygiel & Coste (1993, ) - ID-CH index by Hürlimann & Niederhauser (2007) - IDP index by Gómez & Licursi (2001, ) - ILM index by Leclercq & Maquet (1987) - IPS index by Coste (1982) - LOBO index by Lobo, Callegaro, & Bender (2002, ISBN:9788585869908) - SLA by Sládeček (1986, ) - TDI index by Kelly, & Whitton (1995, ) - SPEAR(herbicide) index by Wood, Mitrovic, Lim, Warne, Dunlop, & Kefford (2019, ) - PBIDW index by Castro-Roa & Pinilla-Agudelo (2014) - DISP index by Stenger-Kovács et al. (2018, ) - EDI index by Chamorro et al. (2024, ) - DDI index by Álvarez-Blanco et al. (2013, ) - PDISE index by Kahlert et al. (2023, ). 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Package: r-cran-diegr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2211 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-gganimate, r-cran-plotly, r-cran-rgl, r-cran-sf, r-cran-scales, r-cran-purrr, r-cran-tidyr, r-cran-mass Suggests: r-cran-av, r-cran-gifski, r-cran-magick, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-dbi, r-cran-dbplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diegr_0.3.1-1.ca2404.1_all.deb Size: 2019274 MD5sum: a6ca6680b2c8ad30e9468f54bfdabab7 SHA1: 1789297f4b722bcad80deba82810c67cf5eae5f4 SHA256: b5ef422b6c9a3b5ca24e0226bd4d7b569594f83379325af148e185b8d001cd94 SHA512: c2ec66d902d536e338a0656ff96a966329436db21c1b1642d87e12465c02e99f4d88aadbbacc431f3f2ad74ce806d64dc84e5b767da13367b287d50d8e9e1d3f Homepage: https://cran.r-project.org/package=diegr Description: CRAN Package 'diegr' (Dynamic and Interactive EEG Graphics) Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023). Package: r-cran-diemr Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-vcfr, r-cran-data.table, r-cran-circlize Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diemr_1.5.5-1.ca2404.1_all.deb Size: 1307296 MD5sum: 48031c29c41656173577b14b09da3825 SHA1: d58f74a0d64c831f78ca44b83ecf849fb684a6ed SHA256: 577a8345f8f4319163bcec80918e8e639519c5938572cb0a0c37090661d00c97 SHA512: 34bae702f022f8677abb1f6e03aadb3eb1e3bcdee3a8642a3f684b52ff6a804ca6dc4562b4cffc3755afaca53149092e69e0e5127a88eeb5578196198902f979 Homepage: https://cran.r-project.org/package=diemr Description: CRAN Package 'diemr' (Genome Polarization via Diagnostic Index ExpectationMaximization) Implements a likelihood-based method for genome polarization, identifying which alleles of SNV markers belong to either side of a barrier to gene flow. The approach co-estimates individual assignment, barrier strength, and divergence between sides, with direct application to studies of hybridization. Includes VCF-to-diem conversion and input checks, support for mixed ploidy and parallelization, and tools for visualization and diagnostic outputs. Based on diagnostic index expectation maximization as described in Baird et al. (2023) . Package: r-cran-dietcost Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-xlsx, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-dietcost_1.0.0.0-1.ca2404.1_all.deb Size: 267984 MD5sum: b0ac0e01af2e41c7ac49184689d5bc1c SHA1: b7ae974c473e611a467196f2bddd0e43370e98ee SHA256: 7cf24427ddd45db045a1b8044d6efb80cc4bee56b697fa6e8789be2899aba197 SHA512: 34f3369e661b307490e3edbe50c3f4489ca9b2b7e06a32890b395701ddc3ed6e3023792425a3a8d27675672590e8f2e737d74b4f76114055ad2417e4e4ac950d Homepage: https://cran.r-project.org/package=DIETCOST Description: CRAN Package 'DIETCOST' (Calculate the Cost and Environmental Impact of a Ideal Diet) Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results — including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A 'standard version' of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the 'Python' software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) . Package: r-cran-dietr Architecture: all Version: 1.1.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 934 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfishbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dietr_1.1.6-1-1.ca2404.1_all.deb Size: 390326 MD5sum: 246c8ee57d7b2448db8c5fcbe2211d9f SHA1: 8aac55d32a6f5dbc39532e485e3652eb962460d5 SHA256: 3eb40d438d86781389f3bfd45425264c5c3b65fefad5c2a50b35c60d35037e69 SHA512: 09f7c69563967efcea3fd0aa354ebc3716d3e083c031b404e2931aa0fc50c78cc2811f41d688b6238837808329e8445bb60ba2bbb39752eac2f67822f59054a2 Homepage: https://cran.r-project.org/package=dietr Description: CRAN Package 'dietr' (Diet Estimated Trophic Levels) Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) . Package: r-cran-diezeit Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brew, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-diezeit_0.1-0-1.ca2404.1_all.deb Size: 130688 MD5sum: e1d2feb0b086fc9f09b60245fd169ca5 SHA1: 2c0e2ee5281911a29d8094b633ea10de56abdbae SHA256: 263d7ed0ac10e38973293f1de2e6a74de3aaf0de6b9e8b930e4439432c4a5a68 SHA512: bfb6a5fb69bdcdff0027460d26dfecf705503094ef76d298097e7c6602d9fbff21788d15d547ad11023a0fbdeb39bab65fb4eec6f115232b0221caa2abfafe32 Homepage: https://cran.r-project.org/package=diezeit Description: CRAN Package 'diezeit' (R Interface to the ZEIT ONLINE Content API) A wrapper for the ZEIT ONLINE Content API, available at . 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage. Package: r-cran-difboost Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-penalized, r-cran-stabs Filename: pool/dists/noble/main/r-cran-difboost_0.4-1.ca2404.1_all.deb Size: 30850 MD5sum: d3903f9c719dbfa0a900924a6eeaaf53 SHA1: d3e44bcd37df4bd46c8c81d6c052dca630bd6dfe SHA256: 5e50c10788b151f2b183643d3f0a9565b958aa339e1c525957c8f838874cbf15 SHA512: 4d4d74fb7d05e6e836c166fbfd26d3c80d867aea15a8d0c60c15011294c1e49d94eb34f2e380bb5e8bc5779a32d1a9b3eee38e89c55c09fbff0daedfd4a625d5 Homepage: https://cran.r-project.org/package=DIFboost Description: CRAN Package 'DIFboost' (Detection of Differential Item Functioning (DIF) in Rasch Modelsby Boosting Techniques) Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) . Package: r-cran-difconet Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gplots, r-cran-stringr, r-cran-data.table, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-difconet_1.0-4-1.ca2404.1_all.deb Size: 83132 MD5sum: 231be56d1943516d8d2399f197716dcf SHA1: 6c7c7ba3c48810f9a9484710486920035208322e SHA256: 7b2283bf16d8028b55f027101c3084a5e936c802e95ee7a1dcb896ef0324fb35 SHA512: 69ab48a4f9baae02f9e9b5656a4375fd5b51e41662db2299fb2555f31cee45039e7abf6ce4d1f85a7d950dc811f7c9b8753246661206ea403bb78b8ba1297ec3 Homepage: https://cran.r-project.org/package=difconet Description: CRAN Package 'difconet' (Differential Coexpressed Networks) Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see for further information. Package: r-cran-diffcor Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-diffcor_0.8.4-1.ca2404.1_all.deb Size: 68994 MD5sum: 66572c348198952ef0b2a9b23ec347e5 SHA1: 423079096b0b00ec64cfa7d4e733ea5dd7d9efeb SHA256: 3693808bb8799978dd6760319c50962a8302924b2564ad15a93f11f88667472c SHA512: 9cd074005eddd511f66793bb1b9e0fecd4aef8680e7216d3f7c3a9f447fd503691154e8ad993971135919ce1be92c929d1e9108879392079d878a440a958188e Homepage: https://cran.r-project.org/package=diffcor Description: CRAN Package 'diffcor' (Fisher's z-Tests Concerning Differences Between Correlations) Computations of Fisher's z-tests concerning different kinds of correlation differences. The 'diffpwr' family entails approaches to estimating statistical power via Monte Carlo simulations. Important to note, the Pearson correlation coefficient is sensitive to linear association, but also to a host of statistical issues such as univariate and bivariate outliers, range restrictions, and heteroscedasticity (e.g., Duncan & Layard, 1973 ; Wilcox, 2013 ). Thus, every power analysis requires that specific statistical prerequisites are fulfilled and can be invalid if the prerequisites do not hold. To this end, the 'bootcor' family provides bootstrapping confidence intervals for the incorporated correlation difference tests. Package: r-cran-diffcorr Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-igraph, r-bioc-multtest, r-bioc-pcamethods Suggests: r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diffcorr_0.4.5-1.ca2404.1_all.deb Size: 726728 MD5sum: c41522391396624dd5d2e9bdc35220e2 SHA1: af56fd79a9e57d87fdfc2b145640f931a44bbd3b SHA256: 5d2d92316c4e98818b57bec9cde5efc4a5019838c32d383dbe43c8d97af4cb34 SHA512: 0488441fba7e4480f1aca570569ed4ef84a81dea2755cb19a05c3be4b24f4ba5e30c6d7426c662bdb97dc2051b532413c30b0151095ee0b80b57bf491cb46300 Homepage: https://cran.r-project.org/package=DiffCorr Description: CRAN Package 'DiffCorr' (Analyzing and Visualizing Differential Correlation Networks inBiological Data) A method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson's correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions. Package: r-cran-diffdf Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-assertthat Suggests: r-cran-testthat, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-devtools, r-cran-covr, r-cran-bit64, r-cran-withr Filename: pool/dists/noble/main/r-cran-diffdf_1.1.2-1.ca2404.1_all.deb Size: 134924 MD5sum: c59627394cb310d2053e8107ad53fdf4 SHA1: 9ab3fa2b3532aa6f20e43e73af498a992fc2dc8f SHA256: d09a6953c99829f25703e73549a5a907762db2158d0e2f5daf8133d5f369c54d SHA512: a04769ae61862526ea3301b3366ebbc49b2d00afe2158b4cac1b49a2dcc9af63f1d412cd47ca8da306e796bb847f91fdd8dc3beab0f07a431028f67209ee4aa1 Homepage: https://cran.r-project.org/package=diffdf Description: CRAN Package 'diffdf' (Dataframe Difference Tool) Functions for comparing two data.frames against each other. The core functionality is to provide a detailed breakdown of any differences between two data.frames as well as providing utility functions to help narrow down the source of problems and differences. Package: r-cran-diffdfs Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-janitor, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-diffdfs_0.9.0-1.ca2404.1_all.deb Size: 15448 MD5sum: 80f1e80573304c88d8a2c70ed99662af SHA1: 24e5600b579a6eed8b4624aa9b415cba56f8e80a SHA256: dc90d7eaff375021a9203337416eec3804c533025e10f036f7bfc57277af0c9a SHA512: ed24c7010590679abc6a81bb307cd9590e35fa6eacfebc50573153d1a9504da7b6ad7847e5c116eb991e3ec4e89875220296a91db5d3139cedd426b116ec80ad Homepage: https://cran.r-project.org/package=diffdfs Description: CRAN Package 'diffdfs' (Compute the Difference Between Data Frames) Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data. Package: r-cran-diffdriver Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-data.table, r-cran-brglm, r-cran-fasttopics, r-cran-squarem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-logging, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diffdriver_0.1.7-1.ca2404.1_all.deb Size: 167622 MD5sum: cf7d7d9c335ad41de204251bd9076bbc SHA1: 2f0d90cdb58e530718158e0c9c155546dbcedde4 SHA256: 72aca4502e927a41563c344aacd587d914bbc783f02bcdbf0444ce3f64ce2be7 SHA512: 5b7d609ba41748fab2d161f948959d05974b2a6fc79cd79c1576e35079102c632d8d62dac45d52f475aa76d6b3831c9c2e3679d57406f0c5ff75991974527dc0 Homepage: https://cran.r-project.org/package=diffdriver Description: CRAN Package 'diffdriver' (Identify Differential Selection) Tests for context-dependent selection on cancer driver genes using somatic mutation data. The package implements the DiffDriver statistical framework to assess whether the strength of positive selection on mutations in a driver gene is associated with tumor- or individual-level context variables, such as clinical traits, genomic features, or immune microenvironment subtypes. DiffDriver estimates individual- and position-specific background mutation rates, models selection as a deviation from the background rate using functional annotations, and tests context effects through a latent-variable logistic model. It provides utilities for preparing mutation and annotation data, fitting differential-selection models, running gene-level association tests, summarizing candidate genes, and visualizing mutation patterns. The method is described in Zhou et al. (2026) "Detecting context-dependent selection on cancer driver genes with DiffDriver" . Package: r-cran-diffee Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4074 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-pcapp Filename: pool/dists/noble/main/r-cran-diffee_1.1.0-1.ca2404.1_all.deb Size: 4128276 MD5sum: 8156ad2549c6b4e65cfbb8b0ca253e45 SHA1: c0ce832ef47b86669fd534297ec43007448e294f SHA256: 4101bcf6640e648b45ef95df7351669ec78012f48cb6260b9b4ae0cdcb1730e4 SHA512: 39fdbc77845453990785cc56e63cc2cc7ab334eb7fc816f626c38fcbec4aa899509dfc5fbc3a7a4128c83d2d8988d6306b045da046767148241fcf3bdb8468e7 Homepage: https://cran.r-project.org/package=diffee Description: CRAN Package 'diffee' (Fast and Scalable Learning of Sparse Changes in High-DimensionalGaussian Graphical Model Structure) This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) . Package: r-cran-diffenrich Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-here, r-cran-rlang, r-cran-stringr, r-cran-reshape2, r-cran-ggnewscale Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-diagram Filename: pool/dists/noble/main/r-cran-diffenrich_0.1.2-1.ca2404.1_all.deb Size: 965276 MD5sum: d72e07e83ddd9427fda30bec1c0c8567 SHA1: 43234f2d6c21103117c86b1a41be14872809770a SHA256: d0999cbc63260f2ea955a2d112b4418031dc205394209478af2508d82daaf542 SHA512: bc25d8f5076141f5a4ee8424ebccccd6b75135886d0389ad3882b2d4d9e7df0925a3f4155e95b49ce0a97ba4be9b8b5b0673cda1a71a8454130a740a87b8016a Homepage: https://cran.r-project.org/package=diffEnrich Description: CRAN Package 'diffEnrich' (Given a List of Gene Symbols, Performs Differential EnrichmentAnalysis) Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) . Given a list of gene symbols, 'diffEnrich' will perform differential enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. This package provides a number of functions that are intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will download and clean species specific ENTREZ gene IDs and map them to their respective KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly enriched pathways from two gene sets, and finally, the user will identify pathways that are differentially enriched between the two gene sets. In addition to the analysis pipeline, this package also provides a plotting function. Package: r-cran-diffeqr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11068 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diffeqr_2.1.0-1.ca2404.1_all.deb Size: 521568 MD5sum: 59b59ed366d2e51a6cbeb9dbe1806c47 SHA1: d88ef6655a2a0cb3d55793e7779d80e8a60d3c5c SHA256: a4a6b8c031ba1233397be619a686c8338b392c0fca7c307808ec15bf18a5276c SHA512: 6e7c4ca49119655921cc8ba3323468c196d424acac200a5cd921c5bafacf9fe41be7dc74c73770d2cd12e3d00e536dde8171bfd424658ad946d28aac9426b4d4 Homepage: https://cran.r-project.org/package=diffeqr Description: CRAN Package 'diffeqr' (Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)) An interface to 'DifferentialEquations.jl' from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs). 'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information, see Rackauckas and Nie (2017) . Package: r-cran-differ Architecture: all Version: 0.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-ggplot2, r-cran-tidyr, r-cran-tidyselect, r-cran-rlang, r-cran-raster Filename: pool/dists/noble/main/r-cran-differ_0.0-8-1.ca2404.1_all.deb Size: 390634 MD5sum: e47199710b33c6a8ff072e07a81826b2 SHA1: 975cd43d41c9200a6309cbf422190ee892177055 SHA256: 73a453018a8fef67735df7abbdcf7032063cbbf5e17f8aee0fd225bf8a595311 SHA512: dc2fb4367236a87bf181da13aa3be7b135b4c8698b7736e4a811c19a25d9ec62eaa14c924ec0bd9d4d3f309034e395c14d2e739b0ca4235a9423bd154518ae94 Homepage: https://cran.r-project.org/package=diffeR Description: CRAN Package 'diffeR' (Metrics of Difference for Comparing Pairs of Maps or Pairs ofVariables) Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions. Package: r-cran-differentes Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boolnet, r-cran-dot, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-differentes_0.3.2-1.ca2404.1_all.deb Size: 42386 MD5sum: 8485e5e29eee3ff4fc300cc82ea8a484 SHA1: 0b7933946a57460df426276370f83e1eeec39926 SHA256: 8bd7481258af6d722ead21355a414387b2fda1c31fd24bbd87f8964c2573eb87 SHA512: 0b6504aef0112a2c5d41aa7f77aa47d2a8fec2583c4bc7472953b5fcd4084fcded25ae0541f3b794db433d5e25e936861ab8eeeb2d3d6377a0881a138ccd032f Homepage: https://cran.r-project.org/package=diffeRenTES Description: CRAN Package 'diffeRenTES' (Computation of TES-Based Cell Differentiation Trees) Computes the ATM (Attractor Transition Matrix) structure and the tree-like structure describing the cell differentiation process (based on the Threshold Ergodic Set concept introduced by Serra and Villani), starting from the Boolean networks with synchronous updating scheme of the 'BoolNet' R package. TESs (Threshold Ergodic Sets) are the mathematical abstractions that represent the different cell types arising during ontogenesis. TESs and the powerful model of biological differentiation based on Boolean networks to which it belongs have been firstly described in "A Dynamical Model of Genetic Networks for Cell Differentiation" Villani M, Barbieri A, Serra R (2011) A Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE 6(3): e17703. Package: r-cran-diffhts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyr, r-cran-rlang, r-cran-ggplot2 Suggests: r-bioc-complexheatmap, r-cran-circlize, r-cran-ggprism, r-cran-ggrepel, r-cran-readxl, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diffhts_0.1.0-1.ca2404.1_all.deb Size: 868438 MD5sum: c76b93cad303426007a8f5089670d1b2 SHA1: 825a9e93902d05676910f8f44d9a3f1497624f3e SHA256: 425008a89404c9065cad7a7ec0c96f47f83e075070cb3005f7526e23d24c9415 SHA512: a2a04292705a721d51a4c0353ab716de618f34a6eb297195cc87062e625ad0320cd1e6bcd0c13a62ffec8ae27d6249ac684d548984f8b357fff98f801b99b2a1 Homepage: https://cran.r-project.org/package=diffHTS Description: CRAN Package 'diffHTS' (Differential Drug Sensitivity Analysis for Two-ConditionHigh-Throughput Screens) A complete workflow for large-scale, two-condition high-throughput drug screening (HTS). 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Package: r-cran-diffr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-diffr_0.3.0-1.ca2404.1_all.deb Size: 74136 MD5sum: 02773b294adec7a7912669c7acaded3f SHA1: 699622da774f698a767ff24eafd472706aa712ca SHA256: 2662550a6ca26f5894d5a6ec3b6fb02f24bfd7ad526b2d5e60a7683cd740d2b7 SHA512: 74f4ad417ef0eb12d50de6cd256875cd8cac864be04354643ed34528b4c8306f465c16c8b8bed8c5ff25270767b8ea666aaeb92a2d8f59cb2eaaba788aa9c7c3 Homepage: https://cran.r-project.org/package=diffr Description: CRAN Package 'diffr' (Display Differences Between Two Files using Codediff Library) An R interface to the 'codediff' JavaScript library (a copy of which is included in the package, see for information). Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser. Package: r-cran-diffuser Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4126 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-torch, r-cran-jsonlite, r-cran-png, r-cran-jpeg Suggests: r-cran-av, r-cran-hfhub, r-cran-safetensors, r-cran-simplermarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-diffuser_0.2.2-1.ca2404.1_all.deb Size: 2411286 MD5sum: f7bcea19f39ab96cae8acfb4723d52dc SHA1: 4b2620951dcdfe696c4ef44826c614c754588ecd SHA256: ea7d68a4c30a03c3bc194073898f1fd9650b9b4ac8d8ad20da9e0c1d2d893980 SHA512: af6e0886ee439a5f3f1f5e08781e95f8055335ab893aec9653d8c7dee11e1f5b47bdd70a338888d7ce1f802afeb19cc09fc9f538f76eb26a5f8a26049e438eff Homepage: https://cran.r-project.org/package=diffuseR Description: CRAN Package 'diffuseR' (Functional Interface to Diffusion Models in R) A native R implementation of diffusion models providing a functional interface to state-of-the-art generative AI. Inspired by the 'Python' library 'diffusers' from 'Hugging Face' , 'diffuseR' generates and manipulates images from text prompts using models such as 'Stable Diffusion', with no 'Python' dependency. Supports multiple diffusion schedulers and device acceleration. 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Package: r-cran-diffusionmap Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scatterplot3d, r-cran-igraph, r-cran-matrix Filename: pool/dists/noble/main/r-cran-diffusionmap_1.2.0-1.ca2404.1_all.deb Size: 84832 MD5sum: beeda5da4b53256719fb82de76af82b2 SHA1: 07166ab11c2f57836b9368448e824a3c2c0a8a6c SHA256: b6f1b00e941aa5e7d782eec0e0e105c3fc2aeb8334b5f935ca72227931e857bb SHA512: f39d4886739d2d8c07392249bbc63c14b8f9d1df79bb7f16569e5250b7ac20274a10b82820fb1e3a0a3115e74c92b8bc78b0d9362ca77fdc6422f26cdf8f5f96 Homepage: https://cran.r-project.org/package=diffusionMap Description: CRAN Package 'diffusionMap' (Diffusion Map) Implements diffusion map method of data parametrization, including creation and visualization of diffusion map, clustering with diffusion K-means and regression using adaptive regression model. Richards (2009) . Package: r-cran-diffval Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-diffval_1.2.0-1.ca2404.1_all.deb Size: 165872 MD5sum: da60fa4f6133bcba6de23927f8d04586 SHA1: 6291ad40c99ebbac0e05567f6957b45d03d9e8bb SHA256: ba19e7ad97ab901cd7a1e7a2704d69e4d75ce6d8385aec85d006d386cbe7b67a SHA512: 6cb3232b9a9c64623fd0b47f0deade2c67f604f152a3c2605edc07a18aa92165191b7bae57dd8f263a68ae77aa06480c464a1c3ae0bebc8026651338a1e00016 Homepage: https://cran.r-project.org/package=diffval Description: CRAN Package 'diffval' (Vegetation Patterns) Find, visualize and explore patterns of differential taxa in vegetation data (namely in a phytosociological table), using the Differential Value (DiffVal). Patterns are searched through mathematical optimization algorithms. Ultimately, Total Differential Value (TDV) optimization aims at obtaining classifications of vegetation data based on differential taxa, as in the traditional geobotanical approach (Monteiro-Henriques 2025, ). The Gurobi optimizer, as well as the R package 'gurobi', can be installed from . The useful vignette Gurobi Installation Guide, from package 'prioritizr', can be found here: . 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Package: r-cran-diffwrap Architecture: all Version: 0.6-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-plyr, r-cran-ggplot2, r-bioc-edger, r-bioc-limma, r-cran-rcolorbrewer, r-cran-convertid, r-cran-pheatmap, r-cran-ggrepel, r-cran-data.table, r-cran-magrittr, r-cran-hmisc, r-cran-ltm, r-cran-openxlsx, r-cran-purrr, r-cran-dplyr, r-cran-venn, r-cran-venndiagram, r-cran-scales Suggests: r-cran-testthat, r-cran-withr, r-cran-futile.logger, r-cran-rappdirs, r-cran-knitr, r-cran-quarto, r-cran-dendextend, r-cran-rmarkdown, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-clusterprofiler, r-cran-gprofiler2, r-bioc-topgo, r-cran-igraph, r-cran-scatterplot3d, r-cran-readxl, r-cran-writexls, r-bioc-biomart Filename: pool/dists/noble/main/r-cran-diffwrap_0.6-3-1.ca2404.1_all.deb Size: 1041726 MD5sum: 124d61341cb71509d29738c6f5858a36 SHA1: 3c4a2309797c5048ac21fa47187dc257c10d4815 SHA256: 950da22cf010a2cddd053f88ca33f228943d656126c1fadb2c1f3d669cb47588 SHA512: 1f2748cba335c5c64b1758e79b9b719d193d6cb006a169f3495d199a58b649a23ef780a9d440136bf9432d17370d86a54865778cc8fc4ab44352b36729aae39b Homepage: https://cran.r-project.org/package=diffwrap Description: CRAN Package 'diffwrap' (Differential Expression Analysis of RNA-Seq Data) Functions for differential expression analysis of read counts from messenger RNA (mRNA) sequencing (RNA-Seq) data or micro RNA (miRNA) expression values generated by the Comprehensive Analysis Pipeline for microRNA Sequencing (CAP-miRSeq) 'expression_reports.sh' script. The workflow follows the 'edgeR'-'limma' expression data analysis pipeline providing options for different approaches, such as "pure" 'edgeR', voom or paired samples. The functions in the package generate text files with differential expression lists, optionally annotated with information from 'biomart', expression summary plots as well as several quality control (QC) plots. The main function, diffExpr(), is a convenience wrapper performing all steps automatically based on sensible defaults. Methods are described in Robinson, McCarthy and Smyth (2010) , Ritchie et al. (2015) , Law et al. (2014) and Sun et al. (2014) . 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Five tests are included: the comparative chi-squared test (Song et al. 2014) (Zhang et al. 2015) , the Sharma-Song test (Sharma et al. 2021) , the heterogeneity test, the marginal-change test (Sharma et al. 2020) , and the strength test (Sharma et al. 2020) . Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020) . They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (differential departure from independence). Second-order differences reveal more fundamental changes than first-order differences across heterogeneous patterns. 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This package allows users to implement extensions of the Mantel-Haenszel DIF detection procedures in the presence of multilevel data based on the work of Begg (1999) , Begg & Paykin (2001) , and French & Finch (2013) . 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This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7). 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Package: r-cran-dimodels Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hnp, r-cran-rootsolve, r-cran-multcomp, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-dimodels_1.3.3-1.ca2404.1_all.deb Size: 457310 MD5sum: 64e74806bab8e9ee14a767fcc0b7abda SHA1: 8a7a15c6c2937956e290b208b4ac5a85fd33cc58 SHA256: cfee72635967058d61375d549d650ab04d798b59654bd368dcd83f6357f6cfa0 SHA512: 31b8bde0f10197a2f0a0b1943e1d18a580baf6b19743d6ce219a8578d7b1f1f1f21efbdd38581e75647b4b94581fdb59ea996715431a9d74afdd1933b7f54d10 Homepage: https://cran.r-project.org/package=DImodels Description: CRAN Package 'DImodels' (Diversity-Interactions (DI) Models) The 'DImodels' package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) . Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function). Package: r-cran-dimodelsmulti Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dimodels, r-cran-plyr, r-cran-dplyr, r-cran-nlme, r-cran-reshape2, r-cran-knitr, r-cran-matrix, r-cran-shiny Suggests: r-cran-ggplot2, r-cran-dimodelsvis, r-cran-cli, r-cran-fansi, r-cran-crayon, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dimodelsmulti_1.2.1-1.ca2404.1_all.deb Size: 820256 MD5sum: b3b588d31c4f032081d4e9cd008dc16e SHA1: 4ddcf807f49a4c13c86c95b4eeb1dff73630c98d SHA256: 3930f4864d65876cbfcd5133e9f36c4f88ae0fd5a95c9fe5e052334a9f1a31eb SHA512: c5dc86e4ca1b77231e8b2f8e2f73d26b2ce6a3839f2122580ec881bc41a96bef92772f705def17c29fd80cd4639aa8377689063f6747d8160cc93bee0148e909 Homepage: https://cran.r-project.org/package=DImodelsMulti Description: CRAN Package 'DImodelsMulti' (Fit Multivariate Diversity-Interactions Models with RepeatedMeasures) An add-on package to 'DImodels' for the fitting of biodiversity and ecosystem function relationship study data with multiple ecosystem function responses and/or time points. This package uses the multivariate and repeated measures Diversity-Interactions (DI) methods developed by Kirwan et al. (2009) , Finn et al. (2013) , and Dooley et al. (2015) . Package: r-cran-dimodelsvis Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1743 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-colorspace, r-cran-dimodels, r-cran-dplyr, r-cran-forcats, r-cran-ggfortify, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-insight, r-cran-metr, r-cran-pieglyph, r-cran-plotwidgets, r-cran-rlang, r-cran-scales, r-cran-tidyr Suggests: r-cran-dimodelsmulti, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-plotly, r-cran-randomforest, r-cran-cowplot, r-cran-nnet, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-dimodelsvis_1.0.5-1.ca2404.1_all.deb Size: 1236288 MD5sum: bc99eb0dabc8763ff41b1f2374dc2311 SHA1: 3022b4d6a0497b2bf8ac713b67c7b74f8078d48a SHA256: be4bf7f9160a1fe3fd29d1892d775a5da3f9592f4a8f6b00aa23a535825a10f0 SHA512: d6c27cd854a0de1ad74b708fd341e692693e2951af50402ac68b9fc5b06181fd747e4662393eef29c5d23241d8c3a81af39e0a39730a1ef51308099962e30fc7 Homepage: https://cran.r-project.org/package=DImodelsVis Description: CRAN Package 'DImodelsVis' (Visualising and Interpreting Statistical Models Fit toCompositional Data) Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object. Package: r-cran-dimora Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-minpack.lm, r-cran-numderiv, r-cran-forecast, r-cran-reshape2, r-cran-desolve Filename: pool/dists/noble/main/r-cran-dimora_0.3.6-1.ca2404.1_all.deb Size: 119800 MD5sum: b6b779e5f3f97e65ffbaaf4a662ba075 SHA1: 10dd14aff7937264ff44728bd0172dd92e737d64 SHA256: 1d1f0fb7df3bc430f983c095c82a9fad9a42a221d404aa5acea978c31dfb1bd4 SHA512: 30f2884ee208da9f2f601f23396ec6f28a860e143f8d6b9b6d27ed3daaf0fcbbd42e49d98f04516c315eaa0a7cba532220a6bc5f8129bfdced0bd3b79231d525 Homepage: https://cran.r-project.org/package=DIMORA Description: CRAN Package 'DIMORA' (Diffusion Models R Analysis) The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition. Package: r-cran-dinamic.duo Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biomart, r-cran-dinamic, r-cran-plyr, r-cran-httr Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dinamic.duo_1.0.4-1.ca2404.1_all.deb Size: 1341882 MD5sum: b16bb451d452d3507e669d0e8633774d SHA1: 56af4ca0eb3622543f139bb31f504ba566a21abb SHA256: a47d4efaeb2db00399d085af2284ae8feec2eb864705fb75d68b940f514bc07d SHA512: 53ff5397ba06704d57028fe1de8d9ba5e5c4e46e39eb00bac223ea0c0b0ce71334212327d73ab97522f9cbc6d1e6833f8b8bbd400d9ca33ccb88acff89177fc0 Homepage: https://cran.r-project.org/package=DiNAMIC.Duo Description: CRAN Package 'DiNAMIC.Duo' (Finding Recurrent DNA Copy Number Alterations and Differences) In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) and leverages the theory developed in Walter et al. (2015) . An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at . Package: r-cran-dinamic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dinamic_1.0.1-1.ca2404.1_all.deb Size: 136576 MD5sum: 6a5392c6d58058e0451dcebffc6891ca SHA1: 217339b29938e31f1a46ac9c9fdf561a66cda1a8 SHA256: aa5719fbb6c15aa70fa30797554e82627b38213164b48ea8e030a9a809915758 SHA512: 7411ff1e06c41853ce2f774d44260749aa97e6340d1885e6488ff598fab892d6e2a455c12697410fe9f343bc7e0fb24fa7a0eeed6069594ff5cc07e56fc6cd23 Homepage: https://cran.r-project.org/package=dinamic Description: CRAN Package 'dinamic' (A Method to Analyze Recurrent DNA Copy Number Aberrations inTumors) In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. This package implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685). Package: r-cran-dineq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-dineq_0.1.0-1.ca2404.1_all.deb Size: 216636 MD5sum: c10afbc64cfe9ad1b291a593201c331b SHA1: 225980cdb294719b6276e76bf0db7888fa92e9e9 SHA256: dd063f05ad1affe084298ac05eab7817372e0818522f3a32b55ea5ad26035cd9 SHA512: 708dd8f9d727a14ca2cb4ef87e863f75edf804c64b3e2e693b76ac613b1b54c714dd291b6e674e929bd9a56e3fa6fb73a38c8e186fb00d9a43608d4183071fff Homepage: https://cran.r-project.org/package=dineq Description: CRAN Package 'dineq' (Decomposition of (Income) Inequality) Decomposition of (income) inequality by population sub groups. For a decomposition on a single variable the mean log deviation can be used (see Mookherjee Shorrocks (1982) ). For a decomposition on multiple variables a regression based technique can be used (see Fields (2003) ). Recentered influence function regression for marginal effects of the (income or wealth) distribution (see Firpo et al. (2009) ). Some extensions to inequality functions to handle weights and/or missings. Package: r-cran-diner Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-mass, r-cran-matrix, r-cran-progress Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diner_2.0.0-1.ca2404.1_all.deb Size: 78238 MD5sum: 53e49f5f6900489613f2ebbad6194fd6 SHA1: 7f59ffb4873623443a4460fbeade71aa9454e8e0 SHA256: eb3eac7b96bb7efd0f85dab6c2bf7e489cda4b314840ec9de0e9e49d1010cad0 SHA512: c20d844be59bbc4b4040bd1a2ba7c251b9f06dde4460fc9f9d2830955f4fd9a743ab759bbdccfaf986946a75e4f40e9d663ee7e910d88ba282299823e369c2b4 Homepage: https://cran.r-project.org/package=dineR Description: CRAN Package 'dineR' (Differential Network Estimation in R) An efficient and convenient set of functions to perform differential network estimation through the use of alternating direction method of multipliers optimization with a variety of different loss functions. Package: r-cran-dint Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-cran-dint_2.1.5-1.ca2404.1_all.deb Size: 263886 MD5sum: 9df509d2eee79addf861faab65599bb4 SHA1: a7d62d1ed8cfd9c40e4eebd25aaa7daf310a7943 SHA256: 82e1d0dbdc7882997f3c0a05de94dd59c63555e8deaf327d79af2d0499517046 SHA512: 103e2f858775b423b5dc5c8e3f4c016177f074d32d30f1f511a5e50a5d82e3bf3e3c8b6f5989ff431120c355440b62d8d86b18e4715ee28b37309d00706e6600 Homepage: https://cran.r-project.org/package=dint Description: CRAN Package 'dint' (A Toolkit for Year-Quarter, Year-Month and Year-Isoweek Dates) S3 classes and methods to create and work with year-quarter, year-month and year-isoweek vectors. Basic arithmetic operations (such as adding and subtracting) are supported, as well as formatting and converting to and from standard R date types. Package: r-cran-dipalm Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma, r-cran-wgcna, r-cran-ggplot2, r-bioc-pwalign, r-bioc-biostrings, r-cran-hashr Suggests: r-bioc-edger Filename: pool/dists/noble/main/r-cran-dipalm_1.3-1.ca2404.1_all.deb Size: 4250136 MD5sum: ecc4ca32d5f3de7763e732f0529fadd9 SHA1: 134812242d3a45b00014d811b9fe30e8254e205f SHA256: 137e6017aa70fe8e4158676f923e15d15bbcb997f26464a3690d1995d40a3e43 SHA512: e1999a23823ec79a58b2e440232d1d39ad47494fae6fb9a79617c8fcc128404261d48482a2e965673c8776233d6fb6a4b15e8bcd59e7e8cff29d511d997eb71a Homepage: https://cran.r-project.org/package=DiPALM Description: CRAN Package 'DiPALM' (Differential Pattern Analysis via Linear Modeling) Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA' package). Using the framework of linear model-based differential expression comparisons ('limma' package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). . Package: r-cran-diphiseq Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-diphiseq_0.2.0-1.ca2404.1_all.deb Size: 55674 MD5sum: 0b8be5127489177f1e6173abebdfefdf SHA1: 0d7297fc16fd45b91c61b085273368356be2b9a1 SHA256: c5659d9f26ea098b4b8f1ff34063ffd1cb30bfda97de2943eab84368359b89cf SHA512: b5960d09cc1909a55b6bbf2fca8ebcac85c39f3174704d7312242805b8e33ac576da7cbfd8f87924be5d5b0ec2930ab1be6b95a9edd2a6d5fdf23e1320468c35 Homepage: https://cran.r-project.org/package=DiPhiSeq Description: CRAN Package 'DiPhiSeq' (Robust Tests for Differential Dispersion and DifferentialExpression in RNA-Sequencing Data) Implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function. Package: r-cran-diproperm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-usethis, r-cran-ggplot2, r-cran-lemon, r-cran-gridextra, r-cran-dplyr, r-cran-dwdlarger, r-cran-e1071, r-cran-matrix, r-cran-sparsem, r-cran-sampling Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diproperm_0.2.0-1.ca2404.1_all.deb Size: 226786 MD5sum: 6797093b2764d0a556b4a551b4c65bad SHA1: 7f7f36c833cbb4b1160a9aec9aff62ecc0be9e1e SHA256: aa79c0ad1ec062086f1b64d53c6538706d90c701009ff1aa80d2201fe79383b9 SHA512: a3c026e32997bd5e031be5f21aef173280d2678b42aa2f8351ac708e022c06b94a33ec055740112eeb67b202665140561336f35b0f000117194efb1ebc1dde28 Homepage: https://cran.r-project.org/package=diproperm Description: CRAN Package 'diproperm' (Conduct Direction-Projection-Permutation Tests and Display Plots) Conducts a direction-projection-permutation test and displays diagnostic plots to facilitate the visual assessment of the test. See Wei et al. (2016) and Lam et al. (2018) for more details. Package: r-cran-dips Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-mvnfast, r-cran-rlemon Suggests: r-cran-optmatch Filename: pool/dists/noble/main/r-cran-dips_0.6.4-1.ca2404.1_all.deb Size: 125108 MD5sum: 3f227baf5d5b3cb00186ced0aaa23e0a SHA1: 6c970282dff63030cc2eceeda6f08d9dacc6d3be SHA256: f8d19313f05a2349ce355505b92164d0c94337e556d9b424118af61189185119 SHA512: ad26c38512b31593d6f536b7770a3871667b561d85ef4b8819a22a8d3f5cf59ced0c92fb2b6447860f00aa41f1f08e3dfe162716e5486dbb7907e44ead221746 Homepage: https://cran.r-project.org/package=DiPs Description: CRAN Package 'DiPs' (Directional Penalties for Optimal Matching in ObservationalStudies) Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. Yu and Rosenbaum (2019) . Package: r-cran-dipw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rmosek, r-cran-matrix Filename: pool/dists/noble/main/r-cran-dipw_0.1.0-1.ca2404.1_all.deb Size: 37704 MD5sum: f3180ba15202d7b9d6695dc4f6ebe76e SHA1: 3b81d585345efdf4a0791117c713a22c1d496436 SHA256: 2fdbbe5b2a886ad7f3ff7c060273668939b0273d168af05ca6e6163690d80ceb SHA512: 87d5c66f489d46ba7b96027df1a1cdc82e883962c7c215341d17efbd1ac3bf2d611f2c354698821cff603b25c0383bc79d131dcdf05a14ec2c394c4752f951bc Homepage: https://cran.r-project.org/package=dipw Description: CRAN Package 'dipw' (Debiased Inverse Propensity Score Weighting) Estimation of the average treatment effect when controlling for high-dimensional confounders using debiased inverse propensity score weighting (DIPW). DIPW relies on the propensity score following a sparse logistic regression model, but the regression curves are not required to be estimable. Despite this, our package also allows the users to estimate the regression curves and take the estimated curves as input to our methods. Details of the methodology can be found in Yuhao Wang and Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders" . The package relies on the optimisation software 'MOSEK' which must be installed separately; see the documentation for 'Rmosek'. 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Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include: a) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2018). "An elliptically symmetric angular Gaussian distribution". Statistics and Computing 28(3): 689-697. . b) Tsagris M. and Alenazi A. (2019). "Comparison of discriminant analysis methods on the sphere". Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467--491. . c) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2020). "Spherical regression models with general covariates and anisotropic errors". Statistics and Computing 30(1): 153--165. . d) Tsagris M. and Alenazi A. (2024). "An investigation of hypothesis testing procedures for circular and spherical mean vectors". Communications in Statistics-Simulation and Computation, 53(3): 1387--1408. . e) Yu Z. and Huang X. (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics, 18(1): 301--334. . f) Tsagris M. and Alzeley O. (2025). "Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling". Australian & New Zealand Journal of Statistics, 67(1): 77--103. . g) Tsagris M., Papastamoulis P. and Kato S. (2025). "Directional data analysis: spherical Cauchy or Poisson kernel-based distribution". Statistics and Computing, 35:51. . h) Alzeley O. and Tsagris (2026). "On the generalized circular projected Cauchy distribution". Mathematics, 14(11): 1934. . 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The aggregated sum of the resulting time series is strictly equal to the low-frequency time series within the benchmarking window. Typically, the low-frequency time series is an annual one, unknown for the last year, and the high frequency one is either quarterly or monthly. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8, ). Package: r-cran-disaggregatets Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-matrix, r-cran-lars, r-cran-zoo, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-corrplot, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-disaggregatets_3.0.1-1.ca2404.1_all.deb Size: 149208 MD5sum: 4219843c0f5e046d770c7c8852673d77 SHA1: 5d2ca418d6714dcfa965388ee384959ec33b4968 SHA256: 90c3e74ed679e33482c798241cdc769aa156f12a4d2d9d8f8763118c02d46c38 SHA512: 1eb1f3623511cd38b3ec558381de74b533a8b03487a985eee428e0586549c5cce78ff8a00145a325ce4e81fbc54b18b32e5bd34739d6ec384a7b2706253eca6d Homepage: https://cran.r-project.org/package=DisaggregateTS Description: CRAN Package 'DisaggregateTS' (High-Dimensional Temporal Disaggregation) Provides tools for temporal disaggregation, including: (1) High-dimensional and low-dimensional series generation for simulation studies; (2) A toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series as proposed by Dagum and Cholette (2006, ISBN:978-0-387-35439-2); (3) Novel techniques by Mosley, Gibberd, and Eckley (2022, ) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices. Package: r-cran-disagmethod Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-ltsa, r-cran-zoo, r-cran-xts, r-cran-tsbox, r-cran-tswge Filename: pool/dists/noble/main/r-cran-disagmethod_0.1.1-1.ca2404.1_all.deb Size: 62082 MD5sum: 62abb7dd5ca328c61d44bf1367c9bb7b SHA1: 87a2e09da484879269a7604d637bf93cfbbda3f0 SHA256: 2874c7866ab8514e17f4d1dae233994cb3d3e4571fe35de04c6bf407baae7324 SHA512: b1e12d7142a8943408bb1c4d323b5faec668105f1b94f0c5b9e97936016e047a0e0d5f0a97fe64e07a6915c7ed04ce6fab25793c341bac84e13c5e3c3d34901d Homepage: https://cran.r-project.org/package=disagmethod Description: CRAN Package 'disagmethod' (Autoregressive Integrated Moving Average (ARIMA) BasedDisaggregation Methods) We have the code for disaggregation as found in Wei and Stram (1990, ), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series" in Statistical Science I, Nova Publishing). 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Package: r-cran-disastr.api Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-disastr.api_1.0.6-1.ca2404.1_all.deb Size: 20856 MD5sum: b43adade1a02408a0a65f30a9bb7f0d5 SHA1: 93470f31379f09fe6a05a4b4adf0a48efd4d0405 SHA256: 2ddcb6ad2c27175f1bde83163187c2318fa65defdd5735a603022b18d6d53cef SHA512: b65e69f2d8847207da7bb0693955be8d0c8ebe1377aa62b328b3118318bd6112c5b2f60332b8dc13e4f3b2a3e97a932cd2dfe2d1935bea717ea3f4113158e71a Homepage: https://cran.r-project.org/package=disastr.api Description: CRAN Package 'disastr.api' (Wrapper for the UN OCHA ReliefWeb Disaster Events API) Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs' (OCHA) ReliefWeb disaster events at . The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow. Package: r-cran-discauc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-rlang, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-discauc_1.1.0-1.ca2404.1_all.deb Size: 79182 MD5sum: 3cc9f8ce86c3b35e57c21931ec131a1e SHA1: 99dd6cf85bb9815de37bb198d982bca85604bd76 SHA256: 7f58bf90be13d0854fbb4fe3ff3ae1ca1b060afc2fe872826c9a1b11c7adc82d SHA512: 4f6f36c20f97de8f8d3aa034ffbf78af1be5a44f07c0d7ae27f53b68dba8e8f55b0d7b5440bf65c69810af3bdfd0ba7e8249aaeee3631082fe618b292bae4031 Homepage: https://cran.r-project.org/package=discAUC Description: CRAN Package 'discAUC' (Linear and Non-Linear AUC for Discounting Data) Area under the curve (AUC; Myerson et al., 2001) is a popular measure used in discounting research. Although the calculation of AUC is standardized, there are differences in AUC based on some assumptions. For example, Myerson et al. (2001) assumed that (with delay discounting data) a researcher would impute an indifference point at zero delay equal to the value of the larger, later outcome. However, this practice is not clearly followed. This imputed zero-delay indifference point plays an important role in log and ordinal versions of AUC. Ordinal and log versions of AUC are described by Borges et al. (2016). The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)], impute indifference points when x = 0, calculate ordinal AUC in the case of Halton sampling of x-values, and account for probability discounting AUC. 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Package: r-cran-discfrail Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-discfrail_0.2-1.ca2404.1_all.deb Size: 392074 MD5sum: 58c9b7959df79a627fbf3e8e7c374e27 SHA1: bb79b58fc854f1fd7f7e05ea63a27235eda0b4f3 SHA256: 5f56456d362b383ab0467c76e2f4a49947aa17e1fdcaf3a7520b0e9d08a6782a SHA512: 9002a9f80c17eed7069a89ad437dd1e9f7e85a61061f0e895ad20a2a69578e9ce62c83a6f5f7e24678d965388686055af4af394e917dded626ad2d4515fa1aff Homepage: https://cran.r-project.org/package=discfrail Description: CRAN Package 'discfrail' (Cox Models for Time-to-Event Data with Nonparametric DiscreteGroup-Specific Frailties) Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) . There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function. Package: r-cran-discharge Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmom, r-cran-ggplot2, r-cran-circstats, r-cran-checkmate, r-cran-boot Filename: pool/dists/noble/main/r-cran-discharge_1.0.0-1.ca2404.1_all.deb Size: 276426 MD5sum: ef0be2a4c123f25704c72449ba63c360 SHA1: 6d1418fa944ca1f5bf4ecdf4608f1d94bb3dca3f SHA256: 2aa478fa6538e7ff3801ea3cad1d693e9897028591d924f12a7fa2e8e09d185c SHA512: c67642e4cfe787e43177819e0881b577cca5df8bfac83327804982f68cc8e5e35b46ea3446359c3d23087ca90f1cb72c6bbfa8f5ee682ed15ec4a4ab2ac3d242 Homepage: https://cran.r-project.org/package=discharge Description: CRAN Package 'discharge' (Fourier Analysis of Discharge Data) Computes discrete fast Fourier transform of river discharge data and the derived metrics. The methods are described in J. L. Sabo, D. M. Post (2008) and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) . Package: r-cran-disclap Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-disclap_1.5.1-1.ca2404.1_all.deb Size: 23414 MD5sum: 3e63d6bc2c347a8891f202da0abdefb5 SHA1: 1b1d7d2434d115dfd11fd5c1532afc71fdad6f28 SHA256: 5c20cf4a7fe1c196d1136a398ec73b6f49d36c89fe24529fedd21538e620399b SHA512: cb3fe7d7b6f348a476fc24b80417cfd95c2db094250c3608028fa437d4f389d9d75c6e1efae60b0f567c944a09ff379952b6cd2a61ffa8372346c244d311df8a Homepage: https://cran.r-project.org/package=disclap Description: CRAN Package 'disclap' (Discrete Laplace Exponential Family) The discrete Laplace exponential family for use in fitting generalized linear models. 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Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 ], [Sims, Trattner, and Garrison, 2024 ] for empirical examples, and [Garrison and colleagues for theoretical work ]. 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In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) , that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them. 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Takes the order of which individuals talk and converts it to a network edge and weight list. Returns the density, centrality, centralization, and subgroup information for each group. Based on the analytical framework laid out in Chai et al. (2019) . 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The function looks for terms in the title and abstract that also exist in other fields and highlights these as needing attention. suggest_keywords() - this function takes a full text document and produces a list of unigrams, bigrams and trigrams (1-, 2- or 2-word phrases) present in the full text after removing stop words (words with a low utility in natural language processing) that do not occur in the title or abstract that may be suitable candidates for keywords. suggest_title() - this function takes a full text document and produces a list of the most frequently used unigrams, bigrams and trigrams after removing stop words that do not occur in the abstract or keywords that may be suitable candidates for title words. check_title() - this function carries out a number of sub tasks: 1) it compares the length (number of words) of the title with the mean length of titles in major bibliographic databases to assess whether the title is likely to be too short; 2) it assesses the proportion of stop words in the title to highlight titles with low utility in search engines that strip out stop words; 3) it compares the title with a given sample of record titles from an .ris import and calculates a similarity score based on phrase overlap. This highlights the level of uniqueness of the title. This version of the package also contains functions currently in a non-CRAN package called 'litsearchr' . Package: r-cran-discovr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr, r-cran-ggplot2, r-cran-glue, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-discovr_1.0.0-1.ca2404.1_all.deb Size: 2482774 MD5sum: e50e529702d0e89f3688cedcdbd83ce8 SHA1: 98b9915ca9e2bc44fc6f7ded4866423edfea125b SHA256: 1e1a82a435799dcbfd72ca070f9c673c821d7d7f7233a3d9b15f7a0f2e687f7e SHA512: 7a959a15857d16666e5e8f5584a234e5c9825a06942e5bd15880e6e82ed74b2d4dfd409f7594194c4c9c9323bf07a056ec7bae7b19892d5704aa56c800706dea Homepage: https://cran.r-project.org/package=discovr Description: CRAN Package 'discovr' (Interactive Tutorials and Data for "Discovering Statistics UsingR and RStudio") Interactive 'R' tutorials and datasets for the textbook Field (2026), "Discovering Statistics Using R and RStudio", . Interactive tutorials cover general workflow in 'R' and 'RStudio', summarizing data, visualizing data, fitting models and bias, correlation, the general linear model (GLM), moderation, mediation, missing values, comparing means using the GLM (analysis of variance), comparing adjusted means (analysis of covariance), factorial designs, multilevel models, repeated measures designs, growth models, exploratory factor analysis (EFA), loglinear analysis, and logistic regression. There are no functions, only datasets and interactive tutorials. 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Package: r-cran-discretelaplace Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-discretelaplace_1.1.1-1.ca2404.1_all.deb Size: 57848 MD5sum: c503844a50641dc57fb2b2bc716a8201 SHA1: 7dcdfa3bbe0f21e77c94489a6390bf9b518fffff SHA256: 3ceaab9a1387eed8bba4b64ee10723846ad8a735bfc06ac411f0d240f55587b8 SHA512: 35d2054073890129f4f8cf098ca78be14d06313e46b423b65730759ef157835fbc521ab49ce3c4217b4c3c15daa4492c35c7f7070b6d8d2907d6641e3d3828bb Homepage: https://cran.r-project.org/package=DiscreteLaplace Description: CRAN Package 'DiscreteLaplace' (Discrete Laplace Distributions) Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions. Package: r-cran-discreteqvalue Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coin, r-cran-exactranktests Filename: pool/dists/noble/main/r-cran-discreteqvalue_1.1-1.ca2404.1_all.deb Size: 36340 MD5sum: 6c83c5bb931f3470689f2ca719f92b63 SHA1: 83f9b69c7d1ee2be3297551a666828dc73e95b8a SHA256: 228580ed1820aa50a12110f7da4ae9ee655cf554ded6faa330d4df94bdd8c416 SHA512: e18f235cbf00f0f51bc707908446a85bf2de2521314fedb2635022194e9239a85ffb190d7d4743debc993ba2490f71a18e4e3766c9a8d628432321707ff53a0a Homepage: https://cran.r-project.org/package=DiscreteQvalue Description: CRAN Package 'DiscreteQvalue' (Improved q-Values for Discrete Uniform and Homogeneous Tests) We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), . The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values. 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Exact and approximate computation methods are provided. For exact p-values, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) . 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Models include flavors of discriminant analysis, such as linear (Fisher (1936) ), regularized (Friedman (1989) ), and flexible (Hastie, Tibshirani, and Buja (1994) ), as well as naive Bayes classifiers (Hand and Yu (2007) ). 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Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics. Package: r-cran-ditwahlandslide Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-plotly, r-cran-stringr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-ditwahlandslide_1.2.0-1.ca2404.1_all.deb Size: 46520 MD5sum: b833dfaa196dd90edeb2bd1ef0a23af3 SHA1: 3b5b54541ea499515293ac9590f7525167171f06 SHA256: 0ba9a336c94b9894f2a8379ff8a32da9b4268ae422d0e3c66d8647cc587fdeef SHA512: 7ed5975b455ccc2b20a75374bcfe2d9e5a87af0f501be49f6b6914be978c0fdab1155d50626df778670cc318c9f8f5b1a15fa7a815c1cd369fb2a7226d858c97 Homepage: https://cran.r-project.org/package=ditwahLandslide Description: CRAN Package 'ditwahLandslide' (Early Warning Information on Landslides in Sri Lanka During theDitwah Storm) Provides curated early warning data on landslides in Sri Lanka during the Ditwah storm. It includes structured, machine-readable tidy dataset. This is developed for education and research purposes. 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It also provides methods to simulate the effect of bias, random team-data, etc. White paper: 'Philippe J.S. De Brouwer' (2021) . Book (chapter 36): 'Philippe J.S. De Brouwer' (2020, ISBN:978-1-119-63272-6) and 'Philippe J.S. De Brouwer' (2020) . Package: r-cran-dive Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-fme, r-cran-sp Filename: pool/dists/noble/main/r-cran-dive_1.3-1.ca2404.1_all.deb Size: 3226654 MD5sum: 6335140bf3284a2711c2233821b8452d SHA1: 397870cac6261e40f436f56b4f2d22a9ff461400 SHA256: 99b4034d0cae23e1e6f7886dc8aed87807725ffd0268f2ea5d204971c1867bb2 SHA512: c3014b9bf4dd41e8940400d01652aee6dac02426cf61c075f4e594c58b098a3036050de8dd1661df51b182b87b8a1f92b08128364482f41e20b46573072279b5 Homepage: https://cran.r-project.org/package=DivE Description: CRAN Package 'DivE' (Diversity Estimator) Contains functions for the 'DivE' estimator . The 'DivE' estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population. 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These tools work with and complement those of the 'tidyverse' suite, extending the grammar of 'ggplot2' to become a grammar of interactive graphics. The suite provides many visual tools designed for moderately (100s of variables) high dimensional data analysis, through 'zenplots' and novel tools in 'loon', and extends the 'ggplot2' grammar to provide parallel coordinates, Andrews plots, and arbitrary glyphs through 'ggmulti'. The 'diveR' package gathers together and installs all these related packages in a single step. Package: r-cran-diverge Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-diverge_2.0.6-1.ca2404.1_all.deb Size: 377776 MD5sum: b755a98ff4de10329e71f2ace89daa5a SHA1: e9625fa8f7317aa9e05f4ecc4ecd98fb741a2843 SHA256: 9edb03a5d07f068b23d023e8c29e74cd9c946020dbefd818aa1917602c5e3ac4 SHA512: bdf71520a7ea5457993facd0a6ffd24f0dc62adf2333def3531ada28f4725f8ee31e7c100e8a97930d63e49f670a35eb1e5f87de64299cc8776a0545f2fbf568 Homepage: https://cran.r-project.org/package=diverge Description: CRAN Package 'diverge' (Evolutionary Trait Divergence Between Sister Species and OtherPaired Lineages) Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework. Package: r-cran-diverse Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proxy, r-cran-reshape2, r-cran-foreign Suggests: r-cran-pheatmap, r-cran-igraph, r-cran-entropart Filename: pool/dists/noble/main/r-cran-diverse_0.1.5-1.ca2404.1_all.deb Size: 76416 MD5sum: b587400ca4c8b8ddd6877431666307b7 SHA1: 47fa24878c46a0eaf62e68e6a40bc4ddfc27a0c8 SHA256: 4ad2dc680a0d9a66e7ee2e9163fc4e7d6fac6239d28ef58a4f1bf39a37684017 SHA512: 6d30c8ad336e0b0e33577fef37ba658bc2df057e063cdf035b4925397068f2beb0e11ffa2e507c014bd90256d8e60b264a73870dd89c453c4b7276ed721402e6 Homepage: https://cran.r-project.org/package=diverse Description: CRAN Package 'diverse' (Diversity Measures for Complex Systems) Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research. Package: r-cran-diversificationr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-diversificationr_0.1.0-1.ca2404.1_all.deb Size: 40574 MD5sum: cffbbca357b0f0d788de4dabc1e9a973 SHA1: 47ccd73755ecdd6a94422a5940fba48712d38956 SHA256: cd91dd428640a556a94fe1ca60b4b3d62a60faa52dd185663d6318d87a227857 SHA512: db0f2dfbeb65e53e76de0485c4dc8b9c4b8d7a5b9c0a1998d3d1812f332f838b60b493e92cb570c847f2217b6de314a4131482d819563b8ec09bcd4a0bd95fc9 Homepage: https://cran.r-project.org/package=DiversificationR Description: CRAN Package 'DiversificationR' (Econometric Tools to Measure Portfolio Diversification) Diversification is one of the most important concepts in portfolio management. This framework offers scholars, practitioners and policymakers a useful toolbox to measure diversification. Specifically, this framework provides recent diversification measures from the recent literature. These diversification measures are based on the works of Rudin and Morgan (2006) , Choueifaty and Coignard (2008) , Vermorken et al. (2012) , Flores et al. (2017) , Calvet et al. (2007) , and Candelon, Fuerst and Hasse (2020). Package: r-cran-diversityarch Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-diversityarch_0.4.0-1.ca2404.1_all.deb Size: 67774 MD5sum: 223ca26ee8aea3ffb8bade922497a8ee SHA1: d17566ca309b79382d21ea91f880b05001402d59 SHA256: 4723d715ef697fde32ff5d963765d88116d52314556a24740be2c46f331c9b35 SHA512: 69bf7fbeaabb6a4b88d906eaa6dd8849e3bafef40fcf6bbe97298d081d22dde98bdd8fce204c59b760426df303deb3d673cac4c1fac42da6411071627a2fd394 Homepage: https://cran.r-project.org/package=diversityArch Description: CRAN Package 'diversityArch' (Computes Diversity Indices for Archaeological Data) Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, Rubén Llorente, Daniel Vivar-Ríos, Ella Assaf, Ran Barkai, Avi Gopher, & Jordi Rosell-Ardèvol (2026) . It computes Diversity Indices, decomposes several of them and computes bootstrap confidence intervals. Package: r-cran-diversitystats Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-mathjaxr, r-cran-multcompview, r-cran-rdpack, r-cran-tidyr Suggests: r-cran-cluster, r-cran-evaluatecore, r-cran-ggplot2, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-vegan Filename: pool/dists/noble/main/r-cran-diversitystats_0.1.0-1.ca2404.1_all.deb Size: 291384 MD5sum: 29bd9f941f68bd8a90eb21bbfab07e68 SHA1: 7f4ad4e29b4bc3765484a6c690b74d4ca5614136 SHA256: a37c5e561cd315b0752bf2f6eba758f7e9169d3016eb50405347c8ea231c34ef SHA512: 2efa00dd1fb31557806ff3afa05c737a22332103c27981efba44681dd929018f2652cca8b697f1b4be66ceac746137c98e70e315339c0ed55a2d84cee8bd65a9 Homepage: https://cran.r-project.org/package=DiversityStats Description: CRAN Package 'DiversityStats' (Diversity Indices with Statistical Inference) Provides a comprehensive framework for analyzing diversity from frequency/abundance count data. Implements a wide range of classical and entropy-based diversity indices, including Berger-Parker, Simpson (and related variants), Shannon, Brillouin, McIntosh, Margalef, Menhinick and Smith-Wilson. Supports permutation-based hypothesis tests for comparing groups with respect to diversity (global and pairwise comparisons), as well as confidence interval estimation using multiple bootstrap methods. Includes functionality for generating diversity profiles based on parametric families such as Hill numbers, Rényi entropy, and Tsallis entropy. The methods are applicable to ecological community data (species abundance counts) and genetic or phenotypic class frequency data. 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It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible. 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Spatial and temporal beta diversity can be partitioned into replacement and richness difference components. It also calculates standardized effect size for FD and PD alpha diversity and the average individual traits across multilayer rasters. The layers of the raster represent species, while the cells represent communities. Methods details can be found at Cardoso et al. 2022 and Heming et al. 2023 . 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This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) . Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a 'tidyselect' based workflow. 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Calculate common biodiversity and range-size metrics on subsampled data. Background theory and practical considerations for the methods are described in Antell and others (2024) . 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Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks. 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DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 ; Magrini et al., 2019 ). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available. 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It provides a computationally efficient way to update the prediction whenever new data becomes available. It allows for both time-varying and time-invariant coefficients, and use cubic smoothing splines to model varying coefficients. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at pre-specified time intervals. 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Multiple tables (data and metadata) are stored in a compound object, which can then be manipulated with a pipe-friendly syntax. Package: r-cran-dma Architecture: all Version: 1.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dma_1.4-2-1.ca2404.1_all.deb Size: 57864 MD5sum: 8c70f967f6e79591a78c5c7b39291a39 SHA1: d2ceef0aae50ad2a23cd81778a2d8937efae1a12 SHA256: 39df6bc2b8c4a4b7fe314ac3412a44430b8ad97145f53c0f5035686077ee975e SHA512: 979824943c4393563080517dc15a74216db5943629bde024b9e6ea9a9eb21553224fdf3f10de02dc9318344cd5e60a96edadffb99a7e83ee0b8d1ecf70e11428 Homepage: https://cran.r-project.org/package=dma Description: CRAN Package 'dma' (Dynamic Model Averaging) Dynamic model averaging for binary and continuous outcomes. Package: r-cran-dmai Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dmai_0.5.0-1.ca2404.1_all.deb Size: 21504 MD5sum: 1d48bfea8de0130eec171d76ffc9d79f SHA1: a7fb274e860452e50046b76021ef7919bc40a14a SHA256: 8470b2fe01fb5a1a37372a15f2acdbeab8af7f8cb87422811f87779cd797380f SHA512: 625c09cacc35b0f7b39c3781e1911b87b7090a89d481db891f23de16d063ca2885bdbaa09d93f09ee9def1a2fde29a5c0a33e807ee44cf47459e834d23d4484e Homepage: https://cran.r-project.org/package=dmai Description: CRAN Package 'dmai' (Divisia Monetary Aggregates Index) Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (). Package: r-cran-dmar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9417 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-generics, r-cran-withr Suggests: r-cran-boot, r-cran-car, r-cran-ggplot2, r-cran-ggrain, r-cran-knitr, r-cran-lavaan, r-cran-lme4, r-cran-lmertest, r-cran-mvtnorm, r-cran-nlme, r-cran-openmx, r-cran-patchwork, r-cran-reformulas, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dmar_1.0.0-1.ca2404.1_all.deb Size: 7258114 MD5sum: 78fc298fe0f5185ba886aa54d2101831 SHA1: d32941224f69339cef8bfa3e13b2656ffeae02f0 SHA256: 9921cfce8ea67ba23797abded170f86ebd7eef2388616ef54a4ac22e5ba0b272 SHA512: 73ed79000a04692c8d7a4f8cc5b894a3ddedd403a8236f532556368071c6b3a88e3ba44b355cb948b6f01cc42c7fe99e5f72d8915419d8e5eced0dc8633a2980 Homepage: https://cran.r-project.org/package=DMAR Description: CRAN Package 'DMAR' (Design, Measurement, and Analysis in R (DMAR)) Methods for design, measurement, and analysis, with the aim of being user friendly yet methodologically sound. 'DMAR' (pronounced "Dee-Mar") implements many advanced and nonstandard methods and makes them available for straightforward use, with interfaces, defaults, and documentation that are consistent across the package and grounded in the methodological literature, in support of sound and reproducible results. The package emphasizes effect size estimation with confidence intervals; sample size planning through accuracy in parameter estimation (AIPE) and power analysis (including composite power for designs whose conclusions require several results to hold at once), with minimum risk, sequential, and equivalence frameworks; reliability, agreement, and measurement more broadly, from coefficient omega with confidence intervals to measurement invariance; factor analysis and structural equation modeling, in which constructs, latent variables measured by multiple indicators, are modeled directly, with confirmatory factor analysis, convergent and discriminant validity, and sample size planning for structural equation models; mediation analysis, from the simple mediation model with bootstrap intervals to likelihood ratio tests of arbitrary indirect effects by model-based constrained optimization (MBCO), with multiple groups and the probing of moderated mediation; equivalence and noninferiority testing; meta-analysis; repeated measures, multivariate, ANOVA, and ANCOVA designs; and inference grounded in model comparison throughout. Measurement is approached from a psychometric perspective, and although many of the methods grew up in human-centered research, they apply broadly across the empirical sciences. Much of what is implemented traces to the author's methodological work, interests, and collaborations. 'DMAR' is a more modern, more general, and greatly expanded reimagining of the 'MBESS' package (Kelley, 2007a, ; 2007b, ), which has been on CRAN for more than two decades and remains available there in stable form. Most functions accept either raw data or the summary statistics typically reported in published articles, so an analysis can be reproduced from a paper without the original data, which is useful both for extending a published analysis and for meta-analytic work. The estimation, inference, and planning functions return one consistently formatted data frame per function that composes with the broader R ecosystem, and confidence intervals are reported alongside effect sizes throughout, as best practice recommends. Researchers who have data and a question but who are not R experts will find the package approachable, while methodologists gain access to advanced and nonstandard methods, including tables of critical values not available elsewhere. Package: r-cran-dmetatools Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mada Filename: pool/dists/noble/main/r-cran-dmetatools_1.1.1-1.ca2404.1_all.deb Size: 44770 MD5sum: 453cd5167bb865d27edc73e9966b5850 SHA1: 6d9539208a94f5d0aa9a044cac03c09c9b5b7f99 SHA256: af78a325cd47b00452b90d4aeb58309298e908a6505ca97a3634e2b9906865dc SHA512: 0344abfd58c6dd55ef323068f707498de699497d4218cbf2b6ee37b279ad927f81bb4dcb7b0c505c4566ca7c8ee65ce7d1d13e9e9c308401f2118e80cbc2f97d Homepage: https://cran.r-project.org/package=dmetatools Description: CRAN Package 'dmetatools' (Computational Tools for Meta-Analysis of Diagnostic AccuracyTest) Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) ). 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Package: r-cran-dmlalg Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-lme4, r-cran-matrixcalc, r-cran-randomforest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dmlalg_1.0.2-1.ca2404.1_all.deb Size: 236946 MD5sum: f09d68eda584898e46ce7a4d97785feb SHA1: c1590923651a22f92c94807187ad7d47b8715efa SHA256: 526c59a774c8d86adc17fdfa7e599c14962c04a82d8d5688726af1d51b9de946 SHA512: b123b516fa4e70644047e8d8e71416a19654e034f1b6422c8cfb99a2537ca45c2428ee57f02f989753a63825bf8fd51d979d487725b837929817aaf7a3cb241f Homepage: https://cran.r-project.org/package=dmlalg Description: CRAN Package 'dmlalg' (Double Machine Learning Algorithms) Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning in Partially Linear Endogenous Models" and Emmenegger and Buehlmann (2021) "Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements". First part: our goal is to perform inference for the linear parameter in partially linear models with confounding variables. The standard DML estimator of the linear parameter has a two-stage least squares interpretation, which can lead to a large variance and overwide confidence intervals. We apply regularization to reduce the variance of the estimator, which produces narrower confidence intervals that are approximately valid. Nuisance terms can be flexibly estimated with machine learning algorithms. Second part: our goal is to estimate and perform inference for the linear coefficient in a partially linear mixed-effects model with DML. Machine learning algorithms allows us to incorporate more complex interaction structures and high-dimensional variables. Package: r-cran-dmm Architecture: all Version: 3.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-robustbase, r-cran-pls, r-cran-nadiv Filename: pool/dists/noble/main/r-cran-dmm_3.2-2-1.ca2404.1_all.deb Size: 1358356 MD5sum: f946951cc535c61202476aefb22f0456 SHA1: cbbf09977ff1e97cedb013e08c87b894d8d0473b SHA256: 56797c8492350c3dbe28e1543170513b4d62d536e115ca1344255ac825b167c9 SHA512: c70d288c29c075e39d018e61fb4f728d199afb54dc08191ec64d2f8861b045d18a99eddd9c05c51ca6981c8295467c9cb78c4dd465f39c21f6e5b705572a176e Homepage: https://cran.r-project.org/package=dmm Description: CRAN Package 'dmm' (Dyadic Mixed Model for Pedigree Data) Mixed model analysis for quantitative genetics with multi-trait responses and pedigree-based partitioning of individual variation into a range of environmental and genetic variance components for individual and maternal effects. Method documented in dmmOverview.pdf; dmm is an implementation of dispersion mean model described by Searle et al. (1992) "Variance Components", Wiley, NY. Dmm() can do 'MINQUE', 'bias-corrected-ML', and 'REML' variance and covariance component estimates. Package: r-cran-dmod Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-code, r-cran-desolve, r-cran-rootsolve, r-cran-ggplot2, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-foreach, r-cran-doparallel Suggests: r-cran-mass, r-cran-reticulate, r-cran-pander, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dmod_1.0.2-1.ca2404.1_all.deb Size: 971338 MD5sum: efe574dc6dfaf73b952dc07679c8a2d6 SHA1: 2f0894ca998fa0e107c695251649a2be9f9ec03c SHA256: 0df32ec922a62590de93a7128ef194b9acab084b129b761507e76689e03b7cbd SHA512: fdbcd8ddc3a40aff9593af0dba653a084b55445bfea9846f65d7fa7e85c4e1d9f37f9489520384dd10db70fe3643cc7e77df975c9aed4383bcb3bdb9f06d1b2f Homepage: https://cran.r-project.org/package=dMod Description: CRAN Package 'dMod' (Dynamic Modeling and Parameter Estimation in ODE Models) The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives. The methods used in dMod were published in Kaschek et al, 2019, . Package: r-cran-dmrnet Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hclust1d, r-cran-glmnet, r-cran-grpreg Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dmrnet_0.4.1-1.ca2404.1_all.deb Size: 280798 MD5sum: ca9b7dc239faebf63ee755f6e4e070fd SHA1: 96cd5de5f93e27fd224acc51f15c48cbcd3f5590 SHA256: e13a27cd2b7550d5ed033aae9fa1bd2ac134597133db8f8ecd6a314c4f9639f8 SHA512: 3bb20bb03eea2cfc53774a0a8ae35cc5a9f9716c9f63129e99f13e145da611c553cf492cec61eac17ca3b3f47bbe28ce6404d4e803b86c3c91a30bff2174d06f Homepage: https://cran.r-project.org/package=DMRnet Description: CRAN Package 'DMRnet' (Delete or Merge Regressors Algorithms for Linear and LogisticModel Selection and High-Dimensional Data) Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and Agnieszka Sołtys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science – ICCS 2023. Lecture Notes in Computer Science, vol 14074, p. 455-470. Springer, Cham. . Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. . Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. . Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. . 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It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535. 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Package: r-cran-dnr Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-sna, r-cran-igraph, r-cran-arm, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dnr_0.3.6-1.ca2404.1_all.deb Size: 1442874 MD5sum: dfcbd8478bfebb28ecb260f5f6ab6fa0 SHA1: ae6ab691b0f93ea32d3eb4a8e385ef9acb0a6476 SHA256: 7c881ad8b8f6137d90b8db19fcfc05eff944ae6e5c3303891f93028dff21b4ac SHA512: 14f4cd9b04b15dee0a64b2d67d83e2f1bdeae26f5576edb92a120c49f3b8cb1356a5d62e2abb0dad348f3c00606979af24a4c68bb93d859ba74bf91809367b35 Homepage: https://cran.r-project.org/package=dnr Description: CRAN Package 'dnr' (Simulate Dynamic Networks using Exponential Random Graph Models(ERGM) Family) Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, . Package: r-cran-do3pca Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdimtools, r-cran-ape, r-cran-phytools, r-cran-matrixcalc, r-cran-mclust, r-cran-nloptr, r-cran-ratematrix Filename: pool/dists/noble/main/r-cran-do3pca_1.0.0-1.ca2404.1_all.deb Size: 54518 MD5sum: 2dcbb503bcc913d26b1187673ca17c06 SHA1: 9464de2f071929d65f91304287030cd95a528dab SHA256: 8998d29c554d1ab4b36b6058dc1b5ca871f5b11afa3d7b2f73980b1d0a4e5096 SHA512: e8a8b9bc174cb18d20dc4c7b7e273d2aac69b336ee67c6448e90c7f421470798810543911f58c158792814c59b5cebe1d14a4022ffa0d77a38bf74af125b8904 Homepage: https://cran.r-project.org/package=do3PCA Description: CRAN Package 'do3PCA' (Probabilistic Phylogenetic Principal Component Analysis) Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. 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Package: r-cran-dobin Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbscan, r-cran-ggplot2, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-outlierso3, r-cran-fnn Filename: pool/dists/noble/main/r-cran-dobin_1.0.5-1.ca2404.1_all.deb Size: 526498 MD5sum: 9180a3a3e37de53140b87f5e21f8d232 SHA1: 1ee908cedaa04133009741acaadd88e3ba8c7bda SHA256: e680de1f1d2a41b09f1713d7d8cb67b317409f074414de79909ee8626059bcf6 SHA512: 63962bc678e2825a1cd58e723697288e0d2c37a8eb077eae530c5a7e58f17077d480468b20df2440f726683a615573807dc6681291e63bdbd660468a5e411407 Homepage: https://cran.r-project.org/package=dobin Description: CRAN Package 'dobin' (Dimension Reduction for Outlier Detection) A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. 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The package implements clustering of barcode lineage trajectories, based on the assumption that similar temporal dynamics indicate comparable relative fitness. It also identifies persistent clonal lineages across time points. Input data can include lineage frequency tables derived from chromosomal barcoding, mutational libraries, or CRISPR/Cas screens. For more details, see Gagné-Leroux et al. (2024) . Package: r-cran-dobson Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-dobson_0.4.1-1.ca2404.1_all.deb Size: 88930 MD5sum: ce9e5a46b9e0cd4eb325be09819d506e SHA1: a6c0f939096417392c3830ad788ce8bb8122233d SHA256: d49efc745fcef0dbefcddfce0cf51fbfbfbe8bd91221f036eec6a1b15dfd8eb9 SHA512: 32b884a19fac79aeb2c5074256c6ade5768c8a27742a51f5a14578b952e85d122adf1ef20c19dfe86dec63081c9dff63c378f99b1604634c2e4fe651f37f5ede Homepage: https://cran.r-project.org/package=dobson Description: CRAN Package 'dobson' (Data from the GLM Book by Dobson and Barnett) Example datasets from the book "An Introduction to Generalised Linear Models" (4th edition) (Year: 2018, ) by Dobson and Barnett. 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General linear estimates. Data handling utilities. Functional programming, in particular restrict functions to a smaller domain. Miscellaneous functions for data handling. Model stability in connection with model selection. Miscellaneous other tools. Package: r-cran-doc2concrete Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2693 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tm, r-cran-quanteda, r-cran-glmnet, r-cran-stringr, r-cran-english, r-cran-textstem, r-cran-snowballc, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-doc2concrete_0.6.0-1.ca2404.1_all.deb Size: 2699328 MD5sum: f41fd2ba5a1573cb086ab236f0d638f9 SHA1: 87d7c948d690d12adbe4d229ce459caa59085962 SHA256: 2c8e9e4b25a713cdb8649a437ef6335a2e2e99170180e11aac8a035fadaa841d SHA512: 918f6f8c9de224b2732e9e20455bc44bba7307a0834f79af473e9f1f761beaf9bdf32900d7ad84c472e060a5d09ccc9decddd882f4c92897f45037083688d494 Homepage: https://cran.r-project.org/package=doc2concrete Description: CRAN Package 'doc2concrete' (Measuring Concreteness in Natural Language) Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) , which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) ) as well as two pre-trained models for the feedback and plan-making domains. Package: r-cran-dockerfiler Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-attempt, r-cran-cli, r-cran-desc, r-cran-fs, r-cran-glue, r-cran-jsonlite, r-cran-memoise, r-cran-pak, r-cran-pkgbuild, r-cran-purrr, r-cran-r6, r-cran-remotes, r-cran-usethis Suggests: r-cran-knitr, r-cran-renv, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-dockerfiler_1.0.0-1.ca2404.1_all.deb Size: 198634 MD5sum: abd9446cd0474ddf409b51fef97134c8 SHA1: 9fdc589fa19264fcbc2b57060cdbf514b41b2bfa SHA256: ff1567a06a06ab555db798ea8f7a21c6ea127a55e5ed5c537177b2302a6038df SHA512: efdb0e7598034e416d1d7ed21bde0e555fe2779f4bae14aec450beeba4405ca9ac1ca8c63b66371690769540b30f3079424af9f25676817a9e83083a4957d7f0 Homepage: https://cran.r-project.org/package=dockerfiler Description: CRAN Package 'dockerfiler' (Easy Dockerfile Creation from R) Build a Dockerfile straight from your R session. 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Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or 'Quarto' or in 'shiny' apps . In 'shiny' mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later. Package: r-cran-doclingr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate, r-cran-cli, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-doclingr_0.1.0-1.ca2404.1_all.deb Size: 231344 MD5sum: 32470b4069fc093321093acbcc065add SHA1: 6f4c91841cbf03b0e5056dcb7aaefd5f5142cdb4 SHA256: 13fa6e15f1e026d14ed783e48427d6976080cf679c0a6dd296578fbf7101059d SHA512: a7a89a2b556cd816b87c658996d787d5b3309b3b3c755b3f5636db5e240ac531633fb202a014675d42e997ad9623e729a5b87a3c1c853e6e62908f02478641e4 Homepage: https://cran.r-project.org/package=doclingr Description: CRAN Package 'doclingr' (Document Intelligence via 'Docling') An interface to 'Docling', a document-understanding library that converts 'PDF', 'DOCX', 'PPTX', 'HTML' and image documents into structured, AI-ready data. The package wraps the 'Docling' 'Python' package through 'reticulate' to extract layout-aware text, tables and metadata, export to 'Markdown' or 'JSON', and split documents into context-rich chunks suitable for retrieval-augmented generation (RAG) and embedding pipelines. Package: r-cran-doclisting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pkgload, r-cran-testthat Filename: pool/dists/noble/main/r-cran-doclisting_0.1.0-1.ca2404.1_all.deb Size: 24420 MD5sum: 9b36297117440bafadffdd2dfabda368 SHA1: a5993cd7995a625bff96896d59e1f5fbfaac56ae SHA256: 27e6d520f26bac1879de7e72e2c62e3be15c8d1cbc0f5feb113ce65181391e22 SHA512: 74e65426fe5eba50defdd227409d22b883a9c6ed6d09a6347c20f615bb3960b0985e745ab42774e024b1f799925dab5a11ca1d1c6becb3d81f95ae71718b2301 Homepage: https://cran.r-project.org/package=doclisting Description: CRAN Package 'doclisting' (List Functions in Documentation) Generate 'Rd' markup to list methods for a generic function. Makes it easier to document S3, S4, and S7 generics by automatically finding and linking to method documentation. 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Three main features are provided: generating document thumbnails, performing visual tests of documents, and updating fields and tables of contents of a 'Microsoft Word' or 'RTF' document. 'Microsoft Word' and/or 'LibreOffice' must be installed on the machine. If 'Microsoft Word' is available, it can produce PDF documents or images identical to the originals; otherwise 'LibreOffice' is used and the rendering may sometimes differ from the original documents. Package: r-cran-docopt Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-docopt_0.7.2-1.ca2404.1_all.deb Size: 242712 MD5sum: c202765813b635e032526bb2e3cc6b06 SHA1: 227d717b6bc48735b341c5f8d7f0e7100c0a30c1 SHA256: 1c82e2088caaeb18feda0d3827a12c0ab6fa71752da24b80fae17723ecf4d749 SHA512: 439cf4549e503db8bc8baef51392596d0f642246c5eade593aae7d8faaa68739e2a8106f58f8c34c6eb3f771cf70aa6bd07bfec2e81e3b424b75afac44d2864f Homepage: https://cran.r-project.org/package=docopt Description: CRAN Package 'docopt' (Command-Line Interface Specification Language) Define a command-line interface by just giving it a description in the specific format. Package: r-cran-docorator Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gt, r-cran-rmarkdown, r-cran-rlang, r-cran-cli, r-cran-dplyr, r-cran-rstudioapi, r-cran-purrr, r-cran-stringr, r-cran-stringi, r-cran-tidyr, r-cran-lifecycle, r-cran-png, r-cran-knitr, r-cran-withr, r-cran-quarto, r-cran-officer, r-cran-polish, r-cran-xml2 Suggests: r-cran-rprojroot, r-cran-testthat, r-cran-tfrmt, r-cran-pdftools, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-docorator_0.7.0-1.ca2404.1_all.deb Size: 238036 MD5sum: d53d73603b789cbbe023ddbd314511de SHA1: f9ef12dc5fcc3106581945acffc5541ee402849c SHA256: 95f2880963c2e30c81c37fb108073929b23b13dfd6ca5b11767879f25902c855 SHA512: 85c6c307188222c52b8e4bb67e2b74f7d36707138e2072be187412e998af8acbaaeffa86e77f84d514123946258eb5a46da931de6215d097b70a7c02cf7b98e6 Homepage: https://cran.r-project.org/package=docorator Description: CRAN Package 'docorator' (Docorate (Decorate + Output) Displays) A framework for creating production outputs. Users can frame a table, listing, or figure with headers and footers and save to an output file. Stores an intermediate 'docorator' object for reproducibility and rendering to multiple output types. 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Package: r-cran-docovt Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdpack, r-cran-rspectra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-docovt_0.6-1.ca2404.1_all.deb Size: 4200934 MD5sum: e4d8c6ef501130d78d36693e7a03ac8b SHA1: 4d6410994b3ff6d81b22dc44233ca73e791a9712 SHA256: c25829511af9d6c504c15bfe34b844d7fe75d4537706b3d0634a06d30c1c6e6a SHA512: 045a21fe0a926435ca00de69dd7e782f958f5c5b53907fa3a441df8076d12c91b505b48cbdb57d6474b3dbdcc875d94f9961f1f741e2fe8614a24584d82e67a1 Homepage: https://cran.r-project.org/package=Docovt Description: CRAN Package 'Docovt' (Distributed Online Covariance Matrix Tests) Distributed Online Covariance Matrix Tests 'Docovt' is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Docovt' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of 'Docovt' is described in Guo G.(2025) . Package: r-cran-docstring Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-devtools, r-cran-rstudioapi, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-docstring_1.0.0-1.ca2404.1_all.deb Size: 24924 MD5sum: 7752f1fdcad9d49bb978f09d040377ff SHA1: 730447b06bb25e17862d545e6b65f371147febe1 SHA256: bb50a91e42e2a73fc1efdb7c7cc0713894759bc6100da8e773d35047842de5a5 SHA512: 79835aa59f6c0cafde2bda732fc4970f4ede44456bcb14473033a48a1d3ab5ed26220d833e87ca9c37b8b982fce3c95b9281fb5c81cd74d2e19b29768eae30da Homepage: https://cran.r-project.org/package=docstring Description: CRAN Package 'docstring' (Provides Docstring Capabilities to R Functions) Provides the ability to display something analogous to Python's docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well. 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This is exactly what this package is about: running 'roxygen2' on (chunks of) a single code file. 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This task is made fast and reproducible using the functionality of 'documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion. Package: r-cran-docusignr Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-testthat, r-cran-covr, r-cran-drat Filename: pool/dists/noble/main/r-cran-docusignr_0.0.3-1.ca2404.1_all.deb Size: 35028 MD5sum: 107951ce8be10685ce50531c6b3b8511 SHA1: 0e1a70b75a5d609c8fc88acf066519c57b17505b SHA256: 8d5989a0eef10804920f211b894014b0b26ebe538cf3af69efa1f7580626f681 SHA512: e17adf9c49fda6cc4fde648b0f59aabeb59a35652073c47d24043c8cb332e940530d7d892d4fc00dd6368f7b7e96a57f3c617f09ec9e1e18a0e831a90ea4088a Homepage: https://cran.r-project.org/package=docuSignr Description: CRAN Package 'docuSignr' (Connect to 'DocuSign' API) Connect to the 'DocuSign' Rest API , which supports embedded signing, and sending of documents. Package: r-cran-docxtractr Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-purrr, r-cran-dplyr, r-cran-httr, r-cran-magrittr Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-docxtractr_0.6.5-1.ca2404.1_all.deb Size: 494972 MD5sum: 34df7d5983b8669659c091e48bac7a22 SHA1: 8c86748f14819b2922aff05368dce8950ea65384 SHA256: 6772143774c84be9e8d0b81532cabdb2b9869757830123f53b03e20a70bc1220 SHA512: deda2a6dcd53a8d63beef68f0e2202bee43bbb7c354108c91eb006da60ffdb28a3f7e011e35eeef4373f84842aa166375e8a5e835cfdcdf0fec2a297805fef4c Homepage: https://cran.r-project.org/package=docxtractr Description: CRAN Package 'docxtractr' (Extract Data Tables and Comments from 'Microsoft' 'Word'Documents) 'Microsoft Word' 'docx' files provide an 'XML' structure that is fairly straightforward to navigate, especially when it applies to 'Word' tables and comments. Tools are provided to determine table count/structure, comment count and also to extract/clean tables and comments from 'Microsoft Word' 'docx' documents. There is also nascent support for '.doc' and '.pptx' files. Package: r-cran-dodge Architecture: all Version: 0.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dodge_0.9-2-1.ca2404.1_all.deb Size: 60238 MD5sum: ee073fd642b870b9f1aadcad357d687b SHA1: bb41e96eb0e20c0acaacb326dfa15001f6f6323c SHA256: 6e6cbf45ba466479221d367894bce90fa35338d24468ab6e4becd71ed0b58194 SHA512: db19007e93432760fb1d6d1deb6151e90ef5429abf11a75c2ccd6adb4ea71df33e37cae07c47ef1f99baf3670bb3f52fe18be0503ff6e76140e0aa91d67a0d63 Homepage: https://cran.r-project.org/package=Dodge Description: CRAN Package 'Dodge' (Acceptance Sampling Ideas Originated by H.F. Dodge) A variety of sampling plans are able to be compared using evaluations of their operating characteristics (OC), average outgoing quality (OQ), average total inspection (ATI) etc. Package: r-cran-doe.base Architecture: all Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2231 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-conf.design, r-cran-vcd, r-cran-combinat, r-cran-mass, r-cran-lattice, r-cran-numbers, r-cran-partitions Suggests: r-cran-frf2, r-cran-doe.wrapper, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-doe.base_1.2-5-1.ca2404.1_all.deb Size: 1743702 MD5sum: ad2d1fa98c94c94448fdebdfdddaf9b0 SHA1: db12efc29abf10710b26b2b633143fee65353710 SHA256: d8b7a451e44c9d3c2ae3326429a4a5299f9a9407927452f98003c849c7fd21e5 SHA512: 3be6087a01d21fffbc75309af37b62aa034166e14d2ef807e0996cb8d007df09446063a70fd310249533b66d2185f346dd7e9128e0ceb9c5921b0d5b98dfe974 Homepage: https://cran.r-project.org/package=DoE.base Description: CRAN Package 'DoE.base' (Full Factorials, Orthogonal Arrays and Base Utilities for DoEPackages) Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. 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Package: r-cran-doe.wrapper Architecture: all Version: 0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-frf2, r-cran-doe.base, r-cran-rsm, r-cran-lhs, r-cran-dicedesign, r-cran-algdesign Suggests: r-cran-skpr Filename: pool/dists/noble/main/r-cran-doe.wrapper_0.13-1.ca2404.1_all.deb Size: 213098 MD5sum: e752a769f78fb4f5fc8f7aa34df99bab SHA1: 6dca89e8d121c70f845d34a7628603956809677c SHA256: 4db795d3d53503756e5bfd45d8fddc7de2b314dfb70452e823c44d68d23adbf3 SHA512: cf886a85e4bdc90aa3167e5ac52f4b03c8f8d6dbfe1df110d71c0e4151ce0b2b92b19ab3063773dbe5cf662e1e88e32a77ac557074ebc5b5930333e56d1e3bf8 Homepage: https://cran.r-project.org/package=DoE.wrapper Description: CRAN Package 'DoE.wrapper' (Wrapper Package for Design of Experiments Functionality) Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package 'rsm', Latin hypercube samples from packages 'lhs' and 'DiceDesign', and D-optimal designs from package 'AlgDesign' have been implemented. Package: r-cran-doebioresearch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-agricolae Filename: pool/dists/noble/main/r-cran-doebioresearch_0.1.0-1.ca2404.1_all.deb Size: 102756 MD5sum: 329355952bf39761c37e4580326794c8 SHA1: dd0dfa0289abe1e42b14e28dee13171b923a5e3d SHA256: de4827909ca63eab56e2ee59c6dc03f6c7714e3e1d9816dfa0a8511dd4256d88 SHA512: a17c4ee701912f10665678faf895c8bc766655f98335941a448194b626a7721ae985e45f1781ee5dcbf65e0973b4a294b9b374e6d98027e9d489691cef83a287 Homepage: https://cran.r-project.org/package=doebioresearch Description: CRAN Package 'doebioresearch' (Analysis of Design of Experiments for Biological Research) Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples. Package: r-cran-doem Architecture: all Version: 0.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-doem_0.0.0.1-1.ca2404.1_all.deb Size: 1342684 MD5sum: 5fddd87b045178b83639e6d93397573f SHA1: 4ca9dbf8cd9e6d0107d30a178c4e1fe54202d5b7 SHA256: c1e9468c87b5605b1636720c7468065bcb4c3f06ba95d562b90c30f01e173d7a SHA512: 09e241f82981601bff7b318409b869cd0ef42d3ca8c0c97960c3ee61232066c09d892b73b287096e446ffad1e9b2f19bd00c8c606c2c891eaf8d9d5def14e38b Homepage: https://cran.r-project.org/package=DOEM Description: CRAN Package 'DOEM' (The Distributed Online Expectation Maximization Algorithms toSolve Parameters of Poisson Mixture Models) The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) . Package: r-cran-doepro Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-pagedown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-doepro_2.0.1-1.ca2404.1_all.deb Size: 281384 MD5sum: 2e847d0f29b68e48ef70f2f4b878ea82 SHA1: 00b227efa3be1f6c2938e6286479f0e8019bd12c SHA256: 281237dddd1a0099811efa1e9ddad987b286c0d7cb93a66d178391af9ad442b7 SHA512: e33258bc7b22c60dc79abcd6809eee0d9e2b69cff79321a39f21bf8da8e5d57905e5ea363b2233336f1948bb46f5d4eab0548672bdba2d9728ce5cfd8166e3cb Homepage: https://cran.r-project.org/package=DOEpro Description: CRAN Package 'DOEpro' (Analysis of Designed Agricultural Experiments) A 'shiny' application and supporting functions for the analysis of designed agricultural experiments, following the procedures set out by Gomez and Gomez (1984, ISBN:9780471870920). Handles completely randomised, randomised complete block, Latin square, factorial (up to four factors), split-plot and strip-plot designs, and pooled (combined) analysis over environments including factorial treatments, after Yates and Cochran (1938) ; analyses several response variables simultaneously; recommends and applies variance-stabilising transformations using the profile likelihood of Box and Cox (1964) ; reports standard errors and critical differences for every legitimate comparison, including the four distinct comparisons of a split plot, the mixed ones using the approximation of Satterthwaite (1946) ; and produces publication-format tables of means, diagnostic plots and a written interpretation. Package: r-cran-doex Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-doex_1.2-1.ca2404.1_all.deb Size: 109200 MD5sum: 4d365195e71865e419c81b4c6e597628 SHA1: 90b50c469ac611caf6c23fc2837d9357f36cbe0d SHA256: 828f5b0e52156586b395ccacefbb83c76932b0c027eb6a09e424f15bf4ed232a SHA512: 0804f6d11d9c6a26084ef3524b25a0485bf6f6ab74b4cc4260ac8ea2ad50510894b59702448fc4d2ad7d370d3d7f28c62dba53c2d051cae475f6b0c5e61a5a9e Homepage: https://cran.r-project.org/package=doex Description: CRAN Package 'doex' (The One-Way Heteroscedastic ANOVA Tests) Contains the heteroscedastic ANOVA tests for normal and two-parameter exponential distributed populations. For normal distributions, Alexander-Govern test by Alexandern and Govern (1994) , Alvandi et al. Generalized F test by Alvandi et al. (2012) , Approximate F test by Asiribo and Gurland (1990) , Box F test by Box (1954) , Brown-Forsythe test by Brown and Forsythe (1974) , B2 test by Ozdemir and Kurt (2006) , Cochran F test by Cochran (1937) , Fiducial Approach test by Li et al. (2011) , Generalized F test by Weerahandi (1995) , Johansen F test by Johansen (1980) , Modified Brown-Forsythe test by Mehrotra (1997) , Modified Welch test by Hartung et al.(2002) , One-Stage test by Chen and Chen (1998) , One-Stage Range test by Chen and Chen (2000) , Parametric Bootstrap test by Krishnamoorhty et al.(2007) , Permutation F test by Berry and Mielke (2002) , Scott-Smith test by Scott and Smith (1971) , Welch test by Welch(1951) , and Welch-Aspin test by Aspin (1948) . These tests are used to test the equality of group means under unequal variance. Also, a modified version of Generalized F-test is improved to test the equality of non-normal group means under unequal variances and a revised version of Generalized F-test is given to test the equality of non-normal group means caused by skewness. Furthermore, it consists some procedures for testing equality of several two-parameter exponentially distributed population means under unequal scale parameters such as generalized p-value, parametric bootstrap and fiducial approach test by Malekzadeh and Jafari (2019) . There is also Hsieh test by Hsieh (1986) for testing equality of location parameters of two-parameter exponentially distributed populations under unequal scale parameters. Package: r-cran-dofuture Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-globals, r-cran-iterators Suggests: r-cran-dorng, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dofuture_1.4.0-1.ca2404.1_all.deb Size: 189880 MD5sum: b070cb58f37d2af82b23262602f0ef26 SHA1: 5ea3d18b883d74da620e7a216efb32c1ded7ee4f SHA256: c6b9ebe1af48343aad7819ef25c28188420864a69cf7b962147439c0d6339cc8 SHA512: 79fb10ded5a45cf8ba7824306f464187d11757e66a1429ff54d7a4fbf42a95cd24672c4bcddd60b890ef861e851a5e5d203210f033ba6488047dc7085f88fa5e Homepage: https://cran.r-project.org/package=doFuture Description: CRAN Package 'doFuture' (Use Foreach to Parallelize via the Future Framework) The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends . 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Package: r-cran-dogesr Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-igraph, r-cran-qpdf, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-devtools, r-cran-tidyr, r-cran-networkd3, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dogesr_0.5.2-1.ca2404.1_all.deb Size: 273428 MD5sum: 024b8e9452f7a009269ded7360accae6 SHA1: e889ee63780c468e419c7b5f2140f9f1643a0fc1 SHA256: 46785f526bb9fbc88e2c280fee54e7d42a1fcf2be39f7ffe57dbb752876f39c2 SHA512: 136b4004b5cc4950b1c4855eb1d6fd9a4b59b1d89d1c783cb0017cab962383e04c3f4b0ed5b59b626709b72fa6cf2cd85acd28e03b32cbcc9278b69362dd6059 Homepage: https://cran.r-project.org/package=dogesr Description: CRAN Package 'dogesr' (Work with the Doges/Dogaresse Dataset) Work with data on Venetian doges and dogaresse and the noble families of the Republic of Venice, and use it for social network analysis, as used in Merelo (2022) . 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Package: r-cran-doicreator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-officer, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-doicreator_0.1.0-1.ca2404.1_all.deb Size: 21360 MD5sum: 46582bd32f8fb4a9cf9a6ac2364d92c8 SHA1: b6f10eb99b8199c9376c0f708fefd8733ca1e612 SHA256: 40ceb1bc49613b3ff9c78f52584ea82f817afe1b4553e3012434d78a0a57457a SHA512: 63669e9586e28cd1712ce86722899ecb111833ce54c80645aa0a1af2cacee19ed51b78cc27de7126c6f0135bdb4a46b560ab51b4d60b7f056f905f0ce4f7cdbe Homepage: https://cran.r-project.org/package=DOIcreator Description: CRAN Package 'DOIcreator' (Append DOIs to References in Word Documents) Read 'Word' documents containing bibliographic references, search for corresponding DOIs using the 'Crossref' API, and append the retrieved DOIs directly to the references. 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Package: r-cran-dola Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-stringr, r-cran-knitr, r-cran-dplyr, r-cran-reshape2, r-cran-openxlsx, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dola_0.1.0-1.ca2404.1_all.deb Size: 3598014 MD5sum: be15797136e7f0786e622a0f301d118d SHA1: 62abe9903c7ee86c9a9fa6cc8bc6b4f761d9d8bc SHA256: 6b48bb01a22c42cf3fe9376b77507c2653a53ace6a3e0d0be792fcc429f71d81 SHA512: 91280ebc18b8a9ae73324fd16dc3743dcf3eca4af40cd85551855ef6e130b3c59dd035b9fc7c18be26408610923fa58642c2b4dc7e6f8df023b655cab6a4debb Homepage: https://cran.r-project.org/package=DoLa Description: CRAN Package 'DoLa' (Do Currículo Lattes Para o Programa de Pós-Graduação) Managing postgraduate programmes involves extracting information from Lattes CVs. This information can be used for strategic planning and self-evaluation, as well as for producing reports on the Sucupira Platform. Summary reports are produced for each period and course (specialisation, master's and doctorate), showing bibliographic production with and without student participation, as well as papers at events, technical or technological production, ongoing and completed supervision, research projects, exchanges (visiting professor, postdoctoral or short-term leave), awards and general activity indicators. Based on this information, a detailed report is then drawn up for each lecturer, taking into account their participation in exam boards, their research project contributions, their technical collaborations (e.g. advisory committee, editorial board) and the subjects they teach. For more details see Pagliosa and Nascimento (2021) . Package: r-cran-domc Architecture: all Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-domc_1.3.8-1.ca2404.1_all.deb Size: 166660 MD5sum: 421d9fba309aed48ce4982b2219cd76b SHA1: 02d72da6f3478002513d29ab3febdee1d0620d6c SHA256: 17540420be10b0c7885f2f07d36e630725940ec19dfc82fd39e9fd13603b3735 SHA512: 1d582e0e5378081bdad9e9a1ddf29aa362fb7810d02e801423766e88e3d58ea5401f2864f23a4790cc7c2fe6b378baed03cf2e917eef18413cdec2bb459d18b1 Homepage: https://cran.r-project.org/package=doMC Description: CRAN Package 'doMC' (Foreach Parallel Adaptor for 'parallel') Provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package. Package: r-cran-domean Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-domean_0.1-1.ca2404.1_all.deb Size: 41648 MD5sum: c595b83cdec51713f7028c3d5a5f0d43 SHA1: 060f2b494a02190b5c8c9cf6252683f8a11d98a5 SHA256: e1550aa1c2d72ed08ab331f91474af759b81ea5bd5e35a467bb44b8e0014c9ab SHA512: 047625e6863c846d7e3ff9c4436603a6f2469ce468a525075a321f39bc1cda93aad3677a81116f4316fc37ca853f36410ecdb07db12777f6ea1e4380b0a15515 Homepage: https://cran.r-project.org/package=Domean Description: CRAN Package 'Domean' (Distributed Online Mean Tests) Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. 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Package: r-cran-dominanceanalysis Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-lme4, r-cran-boot, r-cran-testthat, r-cran-car, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-pscl, r-cran-dynlm, r-cran-reshape2, r-cran-betareg, r-cran-performance Filename: pool/dists/noble/main/r-cran-dominanceanalysis_2.1.1-1.ca2404.1_all.deb Size: 327342 MD5sum: 26385f74760970c4e50301f154cd17f1 SHA1: ee1e59746a06a4a3a1bb3ba33ad07959a6f446e6 SHA256: 89b3addb7af8d5ac683d50d01a3787d6a15990a64bed855ffafaebad239eb25e SHA512: 09e612b6ef3db027665437f23c5b17409d7c885fcb37fa6da2586e6b639329f240e2107cd014450b80d1fc6d8ecca1b2e22a360ba997309fd5de5bf66bb123df Homepage: https://cran.r-project.org/package=dominanceanalysis Description: CRAN Package 'dominanceanalysis' (Dominance Analysis) Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models, hierarchical linear models, beta regression and dynamic linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) and Azen, R., & Budescu, D. V. (2003) for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) , Azen, R., & Traxel, N. (2009) and Luo, W., & Azen, R. (2013) , respectively. Package: r-cran-domino Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-domino_0.3.1-1.ca2404.1_all.deb Size: 46090 MD5sum: 505c37221c069249dd68c08e684b61c6 SHA1: 34e3141f86702c3f86a8afea062c2abef1782919 SHA256: 479a6f0ae9c5e26ce9aa20dd72dbdb1e4b8d6b9621897356c660cf3cbdeab495 SHA512: 44696e6d6cbfae7c645c4f70532e59b01e762f780a389e19fb049a6ef9cfe90cd94ea73265d8327fc08398081154077c729d8e312fdb010713a4e2536cf9bb8d Homepage: https://cran.r-project.org/package=domino Description: CRAN Package 'domino' (R Console Bindings for the 'Domino Command-Line Client') A wrapper on top of the 'Domino Command-Line Client'. It lets you run 'Domino' commands (e.g., "run", "upload", "download") directly from your R environment. 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Two of the functions require installation of the 'Gurobi' optimizer. Please see for guidance. 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The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample. Package: r-cran-doubleml Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-checkmate, r-cran-mlr3, r-cran-mlr3tuning, r-cran-mvtnorm, r-cran-clustergeneration, r-cran-readstata13, r-cran-mlr3learners, r-cran-mlr3misc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-patrick, r-cran-paradox, r-cran-dplyr, r-cran-glmnet, r-cran-lgr, r-cran-ranger, r-cran-sandwich, r-cran-aer, r-cran-rpart, r-cran-bbotk, r-cran-mlr3pipelines Filename: pool/dists/noble/main/r-cran-doubleml_1.0.2-1.ca2404.1_all.deb Size: 896710 MD5sum: 118f791f5fa3ba539e2eb5ba0f8a1866 SHA1: e40886bb55b9e6933a1d01131090c001b25a3dfe SHA256: 925f2ab50fc99275f071f176d704016f50eaa50682e362df01bca53b1751cc4f SHA512: ab78476adf66d2f970635c9edfe55e7841e8744d79db45577633b83043a9ff1d0552056d834ad9413738959ee2471c23d08e8fdfef37d5a626fa1a68d9fce2cd Homepage: https://cran.r-project.org/package=DoubleML Description: CRAN Package 'DoubleML' (Double Machine Learning in R) Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible. More information available in the publication in the Journal of Statistical Software: . Package: r-cran-doublin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-flexsurv, r-cran-ggplot2, r-cran-rjags, r-cran-magrittr, r-cran-tidyverse, r-cran-dt, r-cran-epicontacts, r-cran-lubridate, r-cran-mstats, r-cran-plotly, r-cran-shiny, r-cran-shinywidgets, r-cran-shinydashboard, r-cran-shinythemes, r-cran-visnetwork, r-cran-xtable, r-cran-dplyr Suggests: r-cran-testthat, r-cran-shinytest Filename: pool/dists/noble/main/r-cran-doublin_0.2.0-1.ca2404.1_all.deb Size: 74710 MD5sum: cae67dc07941530e635bd08d0213f4a7 SHA1: 07d85195467cd2e32bd9b09d213623d77ef4fd4c SHA256: cbd438076ec07c76987fb2407ec10e9020be6d01c02f70578e92c2a15ed6f315 SHA512: 1fc3f5ae5f37e0411da195ddf169254e7e32cecff9349c90503760de9f7fe08142aee8139693187b89ed90d14a160b2b9f8b2ca8c3aa0d1f785031c9e88a0e88 Homepage: https://cran.r-project.org/package=doublIn Description: CRAN Package 'doublIn' (Estimate Incubation or Latency Time using Doubly IntervalCensored Observations) Visualize contact tracing data using a 'shiny' app and estimate the incubation or latency time of an infectious disease respecting the following characteristics in the analysis; (i) doubly interval censoring with (partly) overlapping or distinct windows; (ii) an infection risk corresponding to exponential growth; (iii) right truncation allowing for individual truncation times; (iv) different choices concerning the family of the distribution. For our earlier work, we refer to Arntzen et al. (2023) . A paper describing our approach in detail will follow. Package: r-cran-doubt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-unglue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-doubt_0.1.0-1.ca2404.1_all.deb Size: 55044 MD5sum: b7a871eb56947dd229892fe454407ed1 SHA1: 3ed343c32c468f7fc3b72526220f29589e435a64 SHA256: 78353a18cb6ab99f01533e43502ef797994cd091e0238a16c14316b9723f7d33 SHA512: 6ff828c16b2dca213fb908b48fe113d333d9e13663aa5e383ffa3f852b9bbd016bbdbd29a2cb3e543c0806e788c6b7a459960ef5af3d2ef2173c7b609423a521 Homepage: https://cran.r-project.org/package=doubt Description: CRAN Package 'doubt' (Enable Operators Containing the '?' 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Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 . and ter Braak & van Rossum 2025 . Package: r-cran-doudpackage Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-kableextra, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-doudpackage_2.2.0-1.ca2404.1_all.deb Size: 229342 MD5sum: 216ae58d146110fab3cb21d14e4c4a04 SHA1: b060ff926431d1248287beb81bec5de4449d1263 SHA256: 7d6b34f6a848eb67e488d430909a8eecd6a045abd586723e378260c45443fd5e SHA512: 57d9ec2b2f964dacaa837d1aa3f3f153126df17050a6c9dd3d0fa8ca2ce623197379cf3132488a47496bf05ceed328f45caf6e7c0f1417f69fac8bcae9dfecd9 Homepage: https://cran.r-project.org/package=doudpackage Description: CRAN Package 'doudpackage' (Create Elegant Table 1 in HTML/'LaTeX' for Bio-Statistics) Creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate and bivariate analysis, and parse the result into an HTML or 'LaTeX' table ready to paste into a paper. 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Package: r-cran-dowd Architecture: all Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bootstrap, r-cran-mass, r-cran-forecast Suggests: r-cran-performanceanalytics, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dowd_0.12-1.ca2404.1_all.deb Size: 688846 MD5sum: 0020e35f333e1b4a2d1b3c39589eed50 SHA1: 1b1adf06c16dca7daa751c152781f2010754eac1 SHA256: 98c665b25437ccd834e7833019f016d7d3a3a25387a6cf135e5fec866e4d4534 SHA512: 670f22cbf93c4cd6628f250bb56c03257a6276450538db57780c97b4e03fb10a53d02ea511a0a2c014c73a53d9177ef5ac74d957b9a593c9c8400236f9f3c031 Homepage: https://cran.r-project.org/package=Dowd Description: CRAN Package 'Dowd' (Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd'sBook Measuring Market Risk) 'Kevin Dowd's' book Measuring Market Risk is a widely read book in the area of risk measurement by students and practitioners alike. As he claims, 'MATLAB' indeed might have been the most suitable language when he originally wrote the functions, but, with growing popularity of R it is not entirely valid. As 'Dowd's' code was not intended to be error free and were mainly for reference, some functions in this package have inherited those errors. An attempt will be made in future releases to identify and correct them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. It should be noted that 'Dowd' offers both 'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more recent version of 'MMR1' toolbox and they both have mostly similar function. The toolbox mainly contains different parametric and non parametric methods for measurement of market risk as well as backtesting risk measurement methods. Package: r-cran-downballotr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-reticulate, r-cran-rlang Suggests: r-cran-knitr, r-cran-dplyr, r-cran-pak, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-downballotr_0.1.0-1.ca2404.1_all.deb Size: 216448 MD5sum: 221985f181427c52a9c1c5ded7cc9cf8 SHA1: b046b3449c3f0c4b399a18cda8f226a367f753f0 SHA256: dafd8831e1434312e4f0b311f644fef3e2ea926da19378e61f3a6edde479f596 SHA512: eb284792727a6ce1d07ddaeb76bc31976bb3e72c58f362392d1307c7c55447a6fbf0d620ec7ede62f6d5a4adcd9d75e7bc76e40f1a95d108f0bf47f63b395a97 Homepage: https://cran.r-project.org/package=DownBallotR Description: CRAN Package 'DownBallotR' (Access Federal, State, and Local Election Data) Provides an 'R' interface for downloading and standardizing election data to support research workflows. Election results are published by states through heterogeneous and often dynamic web interfaces that are not consistently accessible through existing 'R' packages or APIs. To address this, the package wraps state-specific 'Python' web scrapers through the 'reticulate' package, enabling access to dynamic content while exposing consistent 'R' functions for querying election availability and results across jurisdictions. The package is intended for responsible use and relies on publicly accessible election result pages. 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Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) . 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Provides methods to 1) reconstruct unmutated ancestral sequences, 2) build B cell phylogenetic trees using multiple methods, 3) visualize trees with metadata at the tips, 4) reconstruct intermediate sequences, 5) detect biased ancestor-descendant relationships among metadata types Workflow examples available at documentation site (see URL). Citations: Hoehn et al (2022) , Hoehn et al (2021) . 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Package: r-cran-dpasurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 755 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-ggplot2, r-cran-survival, r-cran-timereg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dpasurv_0.1.0-1.ca2404.1_all.deb Size: 530958 MD5sum: b6e0badf8798da8c0123282616f6b7a4 SHA1: c0f5b886422f6ce842f48118448887996ce8b696 SHA256: 945986485615269ae9cc3febb1fe5194f9bab5a1a42b472e75427a0b32de8636 SHA512: 223332347be82a4e0e8eb00f5b998c8bc63ecd50075cbef2a97ba4aac8b5c12f695335ce526f9275da9078dac4ff40a3126780934a83a4f4ccf0f3dcc7effc56 Homepage: https://cran.r-project.org/package=dpasurv Description: CRAN Package 'dpasurv' (Dynamic Path Analysis of Survival Data via Aalen's AdditiveHazards Model) Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) . The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates. Package: r-cran-dpbbm Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmvtnorm, r-cran-vgam, r-cran-gplots, r-cran-ceoptim Filename: pool/dists/noble/main/r-cran-dpbbm_0.2.5-1.ca2404.1_all.deb Size: 434004 MD5sum: f81407614c20895e0085c3edb5a955cd SHA1: 3278079228d6dc7b61aaf446b1ea60bee8567c6b SHA256: 42a615807df9fc5cff44fb7fc19537b4e09edef29e0d16300c65153d871ccf08 SHA512: 31bdc7e94d1feb27327e39de85fd423fd2123779b55e87c658e77fa2979892350140f82cb608a793ce9cda5f5827c5f70e4b1b5f3ea77eb60639050978226463 Homepage: https://cran.r-project.org/package=DPBBM Description: CRAN Package 'DPBBM' (Dirichlet Process Beta-Binomial Mixture) Beta-binomial Mixture Model is used to infer the pattern from count data. It can be used for clustering of RNA methylation sequencing data. Package: r-cran-dpcd Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-ggplot2, r-cran-bayesplot, r-cran-mcclust, r-cran-cluster, r-cran-truncnorm Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-dpcd_0.0.1-1.ca2404.1_all.deb Size: 2004980 MD5sum: f0983abbf8f75be13c369946697e036d SHA1: cdfccc1c2a9d2f6aa0d88c43b5b62671b839295b SHA256: 25904625b921038db4734373a2c538b238266334984f6dab999995374082fce9 SHA512: 81c96aee45f138a01973ac7523a4677134b7bd296a6918d0cd3dd98410e7ad3c0167cbd13f0bfb6bf8c498c0f45f76e265828b61c3004d406fcf6bcacee642b3 Homepage: https://cran.r-project.org/package=DPCD Description: CRAN Package 'DPCD' (Dirichlet Process Clustering with Dissimilarities) A Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. 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Package: r-cran-dpcomb Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dpcomb_1.0.1-1.ca2404.1_all.deb Size: 268306 MD5sum: 82b6351050827c51c66a85f412257d8f SHA1: 7781e09683e8c92ac7c51db083900eba2912b55c SHA256: a80f0edfcf0d2b424df6207acc4ccfb307c6d928e2849925054f4fed498d1a4f SHA512: aa8f68232bad5826fb8c3088065021e0ea0c84ab0bda68167b2c085588a99d43b7bfec6421afac7609b651a22829597519fe740679b3208c9c2ad4c403605e99 Homepage: https://cran.r-project.org/package=DPComb Description: CRAN Package 'DPComb' (Discrete p-Value Combination Tests) Provides tools for performing p-value combination tests with discrete input p-values. These tests combine significance evidence derived from independent discrete statistics to test a global null hypothesis, which is defined by the specified null distribution(s) of these discrete statistics. The testing procedure involves two main steps: (1) Wasserstein Adjustment: Each component of the combination statistic is replaced by an adjusted Z statistic. This adjustment, based on the minimum Wasserstein distance, preserves the discrete nature of the original statistics while better aligning them with their counterparts under continuity. (2) Calculation of the Significance of the Combination Statistic: A continuous distribution that optimally matches the discrete distribution of the combination statistic is obtained, and the testing p-value for the global null hypothesis is computed. The first step is analogous to Lancaster's approach but is generalized based on Wasserstein optimization. The second step allows for asymptotic control of Type I error with higher statistical power. The package implements several p-value combination methods, including Fisher’s, Pearson’s, George’s, Stouffer’s, and Edgington’s methods. The individual tests to be combined can be right-sided, left-sided, or two-sided, and can be based on binomial, Poisson, hypergeometric, noncentral hypergeometric, negative binomial, or geometric distributions, or a mixture of them. The underlying methodology and its foundations are described in the following references: Contador, Gonzalo and Wu, Zheyang (2025). A minimum Wasserstein distance approach to Fisher's combination of independent, discrete p-values. Scandinavian Journal of Statistics, 52(3), 1281-1300. Contador, Gonzalo and Wu, Zheyang (2026). Optimal Adjustment and Combination of Independent Discrete p-Values. Under revision at the Journal of Computational and Graphical Statistics. Lancaster, HO (1949). The combination of probabilities arising from data in discrete distributions. Biometrika, 36(3/4), 370-382. . 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Package: r-cran-dqcheckrgui Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dqcheckr, r-cran-shiny, r-cran-bslib, r-cran-shinyvalidate, r-cran-shinyfiles, r-cran-shinyace, r-cran-reactable, r-cran-dt, r-cran-callr, r-cran-yaml, r-cran-readr, r-cran-stringi, r-cran-dbi, r-cran-rsqlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-withr, r-cran-quarto, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-dqcheckrgui_0.2.2-1.ca2404.1_all.deb Size: 106840 MD5sum: e3ffa69e82e3b5de109242f11211d51c SHA1: 4def1b5d76707e0ffca07fd9cbfb395be1f2128b SHA256: 253360e5cfb3689b4ab60cc2da44495670ae1b41fb78888cc9289ea7275cee34 SHA512: dc93e75e6a4aaac4faab70fe67ebbaad8463d59b9390b93d5a36e883ebe86ddc2732b6678aabd77dd0e5259dfb8f5bc876a43d99597f7d3ddfc153cc60353d76 Homepage: https://cran.r-project.org/package=dqcheckrGUI Description: CRAN Package 'dqcheckrGUI' (Point-and-Click GUI Client for 'dqcheckr') A graphical user interface for the 'dqcheckr' package. Provides a point-and-click 'shiny' application for configuring dataset quality checks, running them against recurring file deliveries, and browsing historical check results — without writing any R code. The package is feature-complete and is now maintained for corrections only; configuration features are developed in 'dqcheckr' itself, which offers a script-based workflow that does not need this interface. Package: r-cran-dqtg.seq Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-data.table, r-cran-doparallel, r-cran-openxlsx, r-cran-qtl, r-cran-stringr, r-cran-writexl, r-cran-vroom, r-cran-foreach Filename: pool/dists/noble/main/r-cran-dqtg.seq_1.0.2-1.ca2404.1_all.deb Size: 949766 MD5sum: 28bd8fde27443cf7ba92b2ec7c9d718c SHA1: ce530b383d674bc71e19600f78e80a4bcffcf811 SHA256: 210d5f2f8580970726c7e70fe4ec7391e4164ebc08f3f3067ae3be099b47af7f SHA512: 5810fb0c15a0232a40504b76689454cc888180407ed474c7a868df7bb2b89eaa1cc1dde354c6a1888b20660729e1b26dfce4b65f7b258e43ca8d46dfe2499a85 Homepage: https://cran.r-project.org/package=dQTG.seq Description: CRAN Package 'dQTG.seq' (A BSA Software for Detecting All Types of QTLs in BC, DH, RILand F2) The new (dQTG.seq1 and dQTG.seq2) and existing (SmoothLOD, G', deltaSNP and ED) bulked segregant analysis methods are used to identify various types of quantitative trait loci for complex traits via extreme phenotype individuals in bi-parental segregation populations (F2, backcross, doubled haploid and recombinant inbred line). The numbers of marker alleles in extreme low and high pools are used in existing methods to identify trait-related genes, while the numbers of marker alleles and genotypes in extreme low and high pools are used in the new methods to construct a new statistic Gw for identifying trait-related genes. dQTG-seq2 is feasible to identify extremely over-dominant and small-effect genes in F2. Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM (2022, ). Package: r-cran-dr4pl Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-tensor, r-cran-rdpack, r-cran-generics, r-cran-rlang, r-cran-glue Suggests: r-cran-drc, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dr4pl_2.0.0-1.ca2404.1_all.deb Size: 593006 MD5sum: 7622730678ab1e0531e97be34ff28f97 SHA1: d6be71b2b26141f898487bc0f32557e26da8d7ce SHA256: 495bd12c300fbcaef5121437650db1d3685ce9f0fb01d34104d1df764616435b SHA512: be2753112258cf1fcdcf4393e7b4e7b5a48484c3458d7d10e610695cc24438c82ae3d95c317a1ffcb4814f5fd19164b1d653cd18187695391408b24558547e71 Homepage: https://cran.r-project.org/package=dr4pl Description: CRAN Package 'dr4pl' (Dose Response Data Analysis using the 4 Parameter Logistic (4pl)Model) Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems. Gadagkar, S. R. and Call, G. B. (2015) Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) . Package: r-cran-dr Architecture: all Version: 3.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-dr_3.0.11-1.ca2404.1_all.deb Size: 440182 MD5sum: 01b7edd157035cadb56095e88e13110e SHA1: f6abc0060ce971be62047dc7cf5198db964cb3cb SHA256: 4117e49219c766deb3829c8e47dd51851ddac50be0247daca4c7b6c9db0718c5 SHA512: 2bfc1f6af601d14cd7e8a2f6a05442b5bcf132a4e5a5920738b0a01e1a09168d81e825c7af7a1ab60b381652803746bf8deca3c850c4fe1dc7d07db660c72545 Homepage: https://cran.r-project.org/package=dr Description: CRAN Package 'dr' (Methods for Dimension Reduction for Regression) Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified. Package: r-cran-dragmapr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-jsonlite, r-cran-rlang, r-cran-sf Suggests: r-cran-glasstabs, r-cran-knitr, r-cran-miniui, r-cran-pkgdown, r-cran-rmarkdown, r-cran-shiny, r-cran-shinywidgets, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dragmapr_0.2.0-1.ca2404.1_all.deb Size: 2198898 MD5sum: f87014d59edf264c36d4f4083f32f714 SHA1: 7e269c6d25156816bb522010cdd9e047b04cf284 SHA256: f09e6f26d3f924a1a066db5479fdb2f29f3e52df894ba5af9263874682cb6a50 SHA512: 963e9c5193a11578922c8266e12ca804faa15b83caee647812a2f1ca9bee5a044b1a9994f53cf94cd284fc3216516bc470f6604bfb0afa2f0d40d610622c16db Homepage: https://cran.r-project.org/package=dragmapr Description: CRAN Package 'dragmapr' (Create Draggable Plots from Projected Geometry) Creates interactive draggable plots from grouped projected 'sf' geometry. The primary deliverable is a browser-based 'D3' helper where regions and labels can be moved freely; users drag, then copy or download the resulting offset tables. Labels can be derived automatically with make_region_labels(), supplied directly with as_drag_labels(), and their moved positions saved and restored with read_label_state() and apply_label_state(). Hierarchical spatial datasets are supported via hierarchy detection, upload profiling, make_hierarchy_key(), and inherit_layout(), which recommend parent-child groupings and propagate parent-level drag offsets to finer child groupings. Automatic starting layouts are provided by suggest_offsets() using radial, grid, or directional algorithms. Spatial file diagnostics are available through dragmapr_diagnostics(). When a reproducible static image is also needed, render_dragged_map() reconstructs the layout as a 'ggplot2' plot from the source geometry plus the exported offset tables. Project bundles can be written with write_dragmapr_project() and rendered with render_dragmapr_project(). The interactive layer is built on the 'D3' library: Bostock, Ogievetsky and Heer (2011) . Spatial data handling uses the 'sf' package: Pebesma (2018) . Package: r-cran-dragonfarm Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1062 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-plotly, r-cran-processx, r-cran-ps, r-cran-reticulate, r-cran-rlang, r-cran-shiny, r-cran-sortable, r-cran-withr, r-cran-zip Suggests: r-cran-arrow, r-cran-ellmer, r-cran-httr2, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dragonfarm_0.3.3-1.ca2404.1_all.deb Size: 681612 MD5sum: e51de8eba819bf6c6d4a0a1487a9f4c4 SHA1: 251fabab8642660aca4e67bea1a96295d69124df SHA256: d66e0707fe494e24e7ef4f5cec30233f604a721138882891491ef6c7b2c54b59 SHA512: 27e8ed9e96ba48a60e9924ba23a6f60b588fd00f0481abdf40e3de530f4f860215e7f615595221a2c962431142d9964646bd6ec0e60a1e7fbcf38f31508da66e Homepage: https://cran.r-project.org/package=dragonfarm Description: CRAN Package 'dragonfarm' (Fine-Tune Small Language Models with LoRA from R) Fine-tune small (100M to 3B parameter) causal language models with LoRA (Low-Rank Adaptation) from R. Datasets are mapped to chat-format prompts and responses, training runs in a background 'Python' process built on Hugging Face 'transformers' and 'peft', and a 'shiny' app offers drag-and-drop dataset upload and column mapping. 'Python' dependencies are declared through 'reticulate' and resolved automatically on first use. Package: r-cran-dragonking Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dragonking_0.1.0-1.ca2404.1_all.deb Size: 29764 MD5sum: a4bf1451570fcec9d2804798a945aafd SHA1: 2402a1d7e5c8476f258334e4233087bf24525ed9 SHA256: dc280ccede0176f60c2023d4bb7252cb386d28383c9c3ee660c0cd8435c353bc SHA512: cc665a656b495c0a83316c209880a036dc0a3aa47caf53a244488a5f9645602d422a99d82edb099800b6090c5feeb27709232cacaf9336a3af8a4b01601629c9 Homepage: https://cran.r-project.org/package=dragonking Description: CRAN Package 'dragonking' (Statistical Tools to Identify Dragon Kings) Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) . Package: r-cran-dragracer Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dragracer_0.1.7-1.ca2404.1_all.deb Size: 232484 MD5sum: d88892974a02dc74b0b96e430dda29a0 SHA1: dc4cb170eba2ed6d6eda4682c5a3f9bc5b977639 SHA256: 667f2291d8762e3d5cad61180178eedaf9bdc33614dcc6eac73eaa5b42f8d76a SHA512: 72e14c6e86d4b2b8803989377f30ea4c4d2e523d3c3fca416078c198571233799832ed8bc4bbb5e9f1acff27398046c2c166af1c3b71ef36e57cc4fec9b84f2e Homepage: https://cran.r-project.org/package=dragracer Description: CRAN Package 'dragracer' (Data Sets for RuPaul's Drag Race) These are data sets for the hit TV show, RuPaul's Drag Race. Data right now include episode-level data, contestant-level data, and episode-contestant-level data. This is a work in progress, and a love letter of a kind to RuPaul's Drag Race and the performers that have appeared on the show. This may not be the most productive use of my time, but I have tenure and what are you going to do about it? I think there is at least some value in this package if it allows the show's fandom to learn more about the R programming language around its contents. Package: r-cran-dragular Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dragular_0.3.1-1.ca2404.1_all.deb Size: 27032 MD5sum: 23aa03ef91e56745c548d3b607151bdf SHA1: 38ecdff166b451ebad4a5039317aa8707bb12e5e SHA256: c5c6d79a0c621a32fea3cd46e19c90b11fb53cca3c90d246c7aacfb2890d8999 SHA512: bbd415f98da099b97f4be55da109467fc7ccc8eaf683be88a7ab39127dffa606dd963d09f034d86e9eed11eccf10684d75a019f4004a3739a7f5af3b2f886620 Homepage: https://cran.r-project.org/package=dragulaR Description: CRAN Package 'dragulaR' (Drag and Drop Elements in 'Shiny' using 'Dragula JavascriptLibrary') Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables. Package: r-cran-drake Architecture: all Version: 7.13.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2679 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64url, r-cran-digest, r-cran-igraph, r-cran-rlang, r-cran-storr, r-cran-tidyselect, r-cran-txtq, r-cran-vctrs Suggests: r-cran-abind, r-cran-bindr, r-cran-callr, r-cran-cli, r-cran-clustermq, r-cran-crayon, r-cran-curl, r-cran-data.table, r-cran-disk.frame, r-cran-downloader, r-cran-fst, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-keras, r-cran-knitr, r-cran-lubridate, r-cran-networkd3, r-cran-prettycode, r-cran-progress, r-cran-qs, r-cran-rcpp, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-styler, r-cran-testthat, r-cran-tibble, r-cran-txtplot, r-cran-usethis, r-cran-visnetwork, r-cran-webshot Filename: pool/dists/noble/main/r-cran-drake_7.13.11-1.ca2404.1_all.deb Size: 2274590 MD5sum: d484512c19791aa5f67530904dbdec75 SHA1: 175f4fe41b26cacc500e265f86fa3b7722317fe9 SHA256: ef4a16ed4ea84798097fa02adb1ee98526a56125d5a038a7328a78036de05ac3 SHA512: 42079ab118546d1d4485419cc5e72e24c2ebb0f9cdc9e5cd5a09191c25bc65e78fd627fc5ca424a2e26969a67206d572721c0e66b042f4a62268de3bd26ba649 Homepage: https://cran.r-project.org/package=drake Description: CRAN Package 'drake' (A Pipeline Toolkit for Reproducible Computation at Scale) A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website and the online manual . Package: r-cran-dramaanalysis Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-readr, r-cran-data.table, r-cran-httr, r-cran-git2r, r-cran-xml2, r-cran-tokenizers, r-cran-stringr Suggests: r-cran-testthat, r-cran-fmsb, r-cran-knitr, r-cran-magrittr, r-cran-highcharter, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/noble/main/r-cran-dramaanalysis_3.0.2-1.ca2404.1_all.deb Size: 752774 MD5sum: 51a4f21d6e130b4385da513d6a0f2544 SHA1: 0fecc21343746f2a47c300c8b3e7268407b2533c SHA256: 42ca9efbf05edbb4d0af439d06efa9d6359b8c03beab85bcdd325bb2c84e7afe SHA512: e03e7e03337dcb8a223aaa25c4037d6c94c1b809b7a941e8e159358953c9bbf32567580879e0eaaf1aec2ca020dbb1c4f712058dca07eae9641505fdd6265193 Homepage: https://cran.r-project.org/package=DramaAnalysis Description: CRAN Package 'DramaAnalysis' (Analysis of Dramatic Texts) Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) . Package: r-cran-drape Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-doubleml, r-cran-glmnet, r-cran-hdi, r-cran-knitr, r-cran-matrix, r-cran-mlr3, r-cran-paradox, r-cran-partykit, r-cran-rjson, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-drape_0.0.2-1.ca2404.1_all.deb Size: 112754 MD5sum: d3a10d858591c0da36780aae58f49648 SHA1: 3a241ea452dde567525ced3bbe345f32d910af25 SHA256: 8cc68063427933ed97b8aeab22eeb99eec4b70ed389b813a3e0099d3f7e67788 SHA512: 8bdf4fda319e5e3f1d22a0feb0ef2d7151c25b03181830e0da17cf1a5201708f1a4b84e0d701294e6746419fadd8f387e1bc52699f0016745fddba2d7751630c Homepage: https://cran.r-project.org/package=drape Description: CRAN Package 'drape' (Doubly Robust Average Partial Effects) Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) . 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Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template. Package: r-cran-draw Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-draw_1.0.0-1.ca2404.1_all.deb Size: 78474 MD5sum: a07b9cd955136d8dd7db051d96c1f18a SHA1: 9019d3e777ac76223fbb3a25252434b42064a08f SHA256: 4cad827ff3ecc8014f27b5f37e8f9539a3349455110f69d71564dd9b2529de62 SHA512: 6a239aa08240714c956a9a2c0523a67169e280dab1de2879a01f35588519ee4a377b9bbb7b73c13871cacc0b701088042ebb9b21642244d7d2f36769583272e0 Homepage: https://cran.r-project.org/package=draw Description: CRAN Package 'draw' (Wrapper Functions for Producing Graphics) A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R 'grid' package. Package: r-cran-drawer Architecture: all Version: 0.2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-magrittr, r-cran-glue, r-cran-bsplus, r-cran-shiny, r-cran-stringr Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-drawer_0.2.0.1-1.ca2404.1_all.deb Size: 191338 MD5sum: cc333a8842b5c2a195659253c3f67f25 SHA1: 1b04ac35154c24edaea2933429bc2371ed1ef492 SHA256: 605d86409e870f085ccb6cb6c6cb1fb029cf27cbed0b07c47c3bb7eceb3b7dcb SHA512: c0d9fb76ba0ed6715c0c7d97878aa5285acd8c071444164612e7b85e4c6b25f7b3be96567240387e12ca1d7309f7745fcba392e3f2cd101530ca3cd316b1590f Homepage: https://cran.r-project.org/package=drawer Description: CRAN Package 'drawer' (An Interactive HTML Image Editing Tool) An interactive image editing tool that can be added as part of the HTML in Shiny, R markdown or any type of HTML document. Often times, plots, photos are embedded in the web application/file. 'drawer' can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called 'canvas' to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure. Package: r-cran-drawr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rocr Filename: pool/dists/noble/main/r-cran-drawr_1.0.3-1.ca2404.1_all.deb Size: 46236 MD5sum: aed1ea8e7c7958ff7a521d17b3339f95 SHA1: 413bc62490e1c2f9e1ba5922b9a23d95fc3c2db0 SHA256: 57a064a67f45c1305bd92dd2f0cc47b2501ffc44c55430c7b5b18ff08ab818a5 SHA512: 60effa9649c44550f21bf856ed353c8c5247e9317184c64e330fc45fd4a6cd1b3bf55486dd9134e16828d6e0495c884f28e44565d4d0c5d151dc971ef9fd0fd8 Homepage: https://cran.r-project.org/package=DRaWR Description: CRAN Package 'DRaWR' (Discriminative Random Walk with Restart) We present DRaWR, a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types, preserving more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only the relevant properties. We then rerank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. Package: r-cran-drawsample Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lattice, r-cran-tibble, r-cran-psych, r-cran-moments, r-cran-readxl, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-xlsx Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drawsample_1.0.2-1.ca2404.1_all.deb Size: 249354 MD5sum: eda9fb521c64cc530e93bd0bb0b5151d SHA1: 29afb5c211cb10d30efed974d06aa1c08413427f SHA256: c68703ba05c219a9105cf9b240a96284f5998034132bfc1039208ce1c13056d9 SHA512: 1520a0136ee9d9e1a075c26f5d6f43579d6e2f7ef98b341bdc57c37ea14e9694034e9fef61ebf9d9b9337c4e9ee5230acfc221c093b8ac30906895bd8ea58599 Homepage: https://cran.r-project.org/package=drawsample Description: CRAN Package 'drawsample' (Draw Samples with the Desired Properties from a Data Set) A tool to sample data with the desired properties.Samples can be drawn by purposive sampling with determining distributional conditions, such as deviation from normality (skewness and kurtosis), and sample size in quantitative research studies. For purposive sampling, a researcher has something in mind and participants that fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) .Purposive sampling can be useful for answering many research questions (Klar & Leeper, 2019) . Package: r-cran-drayl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-rconics, r-cran-rmutil, r-cran-cubature Filename: pool/dists/noble/main/r-cran-drayl_1.0-1.ca2404.1_all.deb Size: 41806 MD5sum: b3b7440a4d95e256eca8763e888e57f5 SHA1: a8967b51c45d00c10458aebb989fe4a74758a7cb SHA256: a387027bc4cfffe383d4fecada9ecdfc1dd9d69ecbfeebca92adf621d69a515a SHA512: 8b199809c05867fc386f2ae05e529c8045d8cb159e0a482c0cbe5bffef8fd877c8a9988ab206ca945e981cb20ec4014860f790f0823e5e6e3798c07194cf8a5f Homepage: https://cran.r-project.org/package=DRAYL Description: CRAN Package 'DRAYL' (Computation of Rayleigh Densities of Arbitrary Dimension) We offer an implementation of the series representation put forth in "A series representation for multidimensional Rayleigh distributions" by Wiegand and Nadarajah . Furthermore we have implemented an integration approach proposed by Beaulieu et al. for 3 and 4-dimensional Rayleigh densities (Beaulieu, Zhang, "New simplest exact forms for the 3D and 4D multivariate Rayleigh PDFs with applications to antenna array geometrics", ). Package: r-cran-drbats Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ade4, r-cran-coda, r-cran-mass, r-cran-matrix, r-cran-sde Suggests: r-cran-fda, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drbats_0.1.6-1.ca2404.1_all.deb Size: 862156 MD5sum: 1cc1d6f2a5179af859ab2ddaab20d4fc SHA1: b68fbd89bddd85673da78bbaf49f9986af7ba8de SHA256: 89cc627c90ae83110d308c93772a5ad298d35c895c1f43823f1c5004c763c286 SHA512: 4e54725de2c54646ba025d44e54fe9892cd9c1997aecbad1c3bf4d0ad10dc04ebbfa85ecb081dfda2b65d99177983d17c9ae7039007fd031540726dc9753192f Homepage: https://cran.r-project.org/package=DrBats Description: CRAN Package 'DrBats' (Data Representation: Bayesian Approach That's Sparse) Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016. Package: r-cran-drc Architecture: all Version: 4.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 977 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-gtools, r-cran-multcomp, r-cran-plotrix, r-cran-sandwich, r-cran-scales Filename: pool/dists/noble/main/r-cran-drc_4.0-0-1.ca2404.1_all.deb Size: 882464 MD5sum: c0b6a84699a3772ef6328f7948086ada SHA1: 8118498dc805e323d508d06337f95a7016400c1d SHA256: e994a715792916e8431d86a8f78321447a3ab642e6c1d49f9ff1319c75b428ad SHA512: f5e8f73a903b5d029540b6a4fe4cc9245dc3d9324908d3c03f8fb72129da6a4fa3c9c5860f332f8345af16b56dac610109f5fa38f891303e41749dce6c3e7c10 Homepage: https://cran.r-project.org/package=drc Description: CRAN Package 'drc' (Dose-Response Analysis Using R) Analysis of various types of dose-response data is enabled through a suite of flexible and versatile model fitting and after-fitting functions. A wide range of dose-response models is available. Package: r-cran-drcarlate Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-mass, r-cran-stringr, r-cran-splus2r, r-cran-glmnet, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drcarlate_1.2.0-1.ca2404.1_all.deb Size: 196458 MD5sum: 9637c8f7c62f34a28e2dae4165d7b20d SHA1: fc415747edb359ef4c4ea5d77d358f8c1adb467f SHA256: 831ab794207e7a99e18474b49e56e4f09e3ad6cc9896892f88e52d4117cd4822 SHA512: 5d5b2125ced2e5b3d7a9343dde51416ae3b93364b10804e7bd85f64959c7b9e8dca9660d581ca6089bd71102733860f72d7cd54c3eed12a199b03727a7792412 Homepage: https://cran.r-project.org/package=drcarlate Description: CRAN Package 'drcarlate' (Improving Estimation Efficiency in CAR with Imperfect Compliance) We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) . Package: r-cran-drclass Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-drclass_0.1.0-1.ca2404.1_all.deb Size: 50022 MD5sum: c5550de8545041566154499e2cd828dd SHA1: c607cea31015b487196396b337c7340a0238331b SHA256: 4666081840132acc16ef0ae98c25a18343863bdad64420616dd4b6f79af56fa8 SHA512: 0e4854236bb0004567615e2ca3c279360babaf9229faddd4c61c676ed6f0b2557ae3292f9b733ca656f877b57ba2c5b06c94a4034a8a4539d5da5938dc877638 Homepage: https://cran.r-project.org/package=DRclass Description: CRAN Package 'DRclass' (Consider Ambiguity in Probabilistic Descriptions Using DensityRatio Classes) Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference. The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes. The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means. The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles. Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. . Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. . Package: r-cran-drcseedgerm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-drc, r-cran-drcte, r-cran-plyr, r-cran-dplyr, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/noble/main/r-cran-drcseedgerm_1.0.1-1.ca2404.1_all.deb Size: 372782 MD5sum: af49ce924044a33851cce9df0cd65300 SHA1: 59c72469bf531aa4bc9fcc4434352a9fcf1a821d SHA256: 647ce84e1f52d86c9103926ccef02e258c749db13657bb345476846b44b2e2e1 SHA512: e6b983ee6bf1bfa4f23b91d9caedc0d4d07cff82c6ba9188c9e622b9cb0ab102d772791ce613423d50442c1b2df083e14b3da0f80d70024e3abaafd2d5983c3d Homepage: https://cran.r-project.org/package=drcSeedGerm Description: CRAN Package 'drcSeedGerm' (Utilities for Data Analyses in Seed Germination/Emergence Assays) Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 . Contains several datasets for practicing. Package: r-cran-drcte Architecture: all Version: 1.0.65-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-plyr, r-cran-nor1mix, r-cran-mclust, r-cran-survival, r-cran-sandwich, r-cran-lmtest, r-cran-dplyr, r-cran-multcomp, r-cran-tidyr, r-cran-mass, r-cran-tibble, r-cran-car Filename: pool/dists/noble/main/r-cran-drcte_1.0.65-1.ca2404.1_all.deb Size: 493100 MD5sum: d5da90e829192f3e51f4dcecf70fe30b SHA1: fb57d4960cecd0f4da7aac59e3258be6a8b27162 SHA256: 7b0682cbbc6a4ffb205e803833a29c6eb9b5aeea793723770d7f840a3ef7e49f SHA512: 4a5b5bd450d731858da5db3d5790f5538dfde438f00a019187ebb201c6ca145119334eb916fea4f26538db2514dfdf02c1970b236930f8cfed1c5b60653e50c7 Homepage: https://cran.r-project.org/package=drcte Description: CRAN Package 'drcte' (Statistical Approaches for Time-to-Event Data in Agriculture) A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 . Package: r-cran-drda Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1196 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drda_2.0.5-1.ca2404.1_all.deb Size: 1065134 MD5sum: 591b24887c6437fdcde6f9c2d09c0050 SHA1: 3d45b1fbe59daf9b5715b374722afea8246d6522 SHA256: 30731c446b4311f15d6ae51a2b58d22573bbc5de8528fcba0c538579d1b8fa08 SHA512: 870048199ff270e4b960c82b33063797ae20d3d4bf0eacc0243950fbf0fbb6d312398587047c344e1f7bf94d67f0bbd16fae23f9c52329af2f4dd8c9b1f73954 Homepage: https://cran.r-project.org/package=drda Description: CRAN Package 'drda' (Dose-Response Data Analysis) Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) . Package: r-cran-drdata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny Suggests: r-cran-shinydashboard, r-cran-plotly, r-cran-dt, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-caret, r-cran-randomforest, r-cran-rpart, r-cran-rpart.plot, r-cran-e1071, r-cran-class, r-cran-nnet, r-cran-colourpicker, r-cran-glmnet, r-cran-cluster, r-cran-dbscan, r-cran-ggally, r-cran-gbm, r-cran-proc, r-cran-reshape2, r-cran-scales, r-cran-nortest, r-cran-tseries, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drdata_0.2.0-1.ca2404.1_all.deb Size: 66974 MD5sum: e67b08b2f9d562ed9ac9abee378b352f SHA1: 298fe051c0d6182f1e9f9cb1111547ce7e9dacb3 SHA256: b80f953a9f00955cd9ab481ccc0d45ac35bbc8887b4b6529c143afd8452d5400 SHA512: 1d1914a6a7df7f107fd3b0f8ea23e29100041f969d0bf650d9185078db060344a3b8430f1eb0b7acfa5f115668015bc579ffb9d37c5d647d68cae96311032fd0 Homepage: https://cran.r-project.org/package=DrData Description: CRAN Package 'DrData' (Interactive Statistical Analysis and Machine Learning Platform) A 'Shiny'-based interactive platform for end-to-end data science workflows. Provides modules for data import (CSV, 'Excel', RDS, TXT), data preprocessing (missing value imputation, encoding, scaling, outlier removal), exploratory data analysis with interactive plots and normality tests, supervised learning (regression and classification each with eight algorithms), and unsupervised learning (k-means, hierarchical clustering, density-based spatial clustering of applications with noise). Designed for students and practitioners in data science and artificial intelligence. Package: r-cran-drdimont Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1362 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-dplyr, r-cran-stringr, r-cran-wgcna, r-cran-rfast, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-rlang, r-cran-reticulate Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-drdimont_0.1.7-1.ca2404.1_all.deb Size: 1146606 MD5sum: ecf0bdcab1c0eb58a1159330643029c7 SHA1: 3bd335154b274072eb427ddac3f27ac1a715fca9 SHA256: 569bad78928a764ffa7fdc46b24094476e9b5be87c8098e21ad74704f3fffd80 SHA512: 3821c2262ae90a8025f3b71f60f8aac21714b4d26d1efe6b272314c5df9c87d9ca060e4d1149164e99275d93ccf0d03ea76ff5929a8f2d76d0a8cacc81164f17 Homepage: https://cran.r-project.org/package=DrDimont Description: CRAN Package 'DrDimont' (Drug Response Prediction from Differential Multi-Omics Networks) While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) . The package license applies only to the software and explicitly not to the included data. Package: r-cran-drdrtest Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-superlearner Filename: pool/dists/noble/main/r-cran-drdrtest_0.1-1.ca2404.1_all.deb Size: 53914 MD5sum: 0e0dbef57708c3fbe159153fdf0b1ae6 SHA1: 2a0a60021f0f239684f6638e84a31b2249c404d3 SHA256: 6b46fa994e92270c9d9e48e34a908f701ac20dbf15e2bc4c8ec1e02fa3986ecf SHA512: 53533f0a2889bc58d05e95c2451dd60551431fe6e29b105b7f784fbf787f1a6b843d3f35677cd491b463b6f3e7d3a049b08494048680e4ba12a005017691a7de Homepage: https://cran.r-project.org/package=DRDRtest Description: CRAN Package 'DRDRtest' (A Nonparametric Doubly Robust Test for Continuous TreatmentEffect) Implement the statistical test proposed in Weng et al. (2021) to test whether the average treatment effect curve is constant and whether a discrete covariate is a significant effect modifier. Package: r-cran-dreamer Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1362 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-ellipsis, r-cran-ggplot2, r-cran-purrr, r-cran-rootsolve, r-cran-rjags, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-fs, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-spelling Filename: pool/dists/noble/main/r-cran-dreamer_3.2.0-1.ca2404.1_all.deb Size: 984240 MD5sum: b8ed307e77e541ea5e3316488f9b7738 SHA1: 9bba63ad101c65fa4b3139725fe0443347180ace SHA256: 7880595aa44ffcd1eec6449720657c23ff29f6be2935e12e95a41d45246b4737 SHA512: 4816b7cb6f099131b632976543f7d0a6c2f302e3c510168228710700c963cdba5cdbc1b7111ea6d5412fde68bf1d28b3b43c6f50605fa5ed5b8ae1df07567303 Homepage: https://cran.r-project.org/package=dreamer Description: CRAN Package 'dreamer' (Dose Response Models for Bayesian Model Averaging) Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) . Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019). Package: r-cran-dreamerr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-stringmagic Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dreamerr_1.5.0-1.ca2404.1_all.deb Size: 949328 MD5sum: 4788f159240652dbb6a2fbd2e864c92b SHA1: 653adfa88b2fff7a769ed6b8453f8cdcb734efc9 SHA256: 7d6e0f7551f9f4123ce1ea1defa1097c3b1ab2745993944f14086c48e6e2c686 SHA512: b119d7e5bb347c58a16c19e4b0637ed2dc74cfbfdc0e5d9ca9720714967523b262ad4b5c91998bee83f566624087fe542efbf5bcecdbf987c4c8f4a075564977 Homepage: https://cran.r-project.org/package=dreamerr Description: CRAN Package 'dreamerr' (Error Handling Made Easy) Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging. Package: r-cran-dregar Architecture: all Version: 0.1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msgps Filename: pool/dists/noble/main/r-cran-dregar_0.1.4.0-1.ca2404.1_all.deb Size: 50336 MD5sum: 810d5d572d4aefe39442c4b5c16a70d0 SHA1: b371f259c47be5e89ebb11a0c48c023c35bf95b3 SHA256: ce165de63c2d380f67a3f75e84a0ad8848ac37ea9d83967661b13d3020972f9e SHA512: b10e6805cb0e607d23985dc62c399f0eec101e11132d11461f52967621685dc4057ebab6c9e2555fdb20fd9715faaa5ce4eb5c5aebac98b79031b4585dd56467 Homepage: https://cran.r-project.org/package=DREGAR Description: CRAN Package 'DREGAR' (Regularized Estimation of Dynamic Linear Regression in thePresence of Autocorrelated Residuals (DREGAR)) A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters. Package: r-cran-drglm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-speedglm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drglm_1.1-1.ca2404.1_all.deb Size: 57428 MD5sum: 595bbb0f40e62c3ac1a829f4479b6858 SHA1: 3b2f3d4a2ecfba15abfc4d38d607127309cf8a28 SHA256: f32a78ca2121861de5c741013c40a52fa864314853b854792aa22eb067acd771 SHA512: 443b01fdcda59eefa1f9f941ecc0e4e52c52a74eb785403933ccc561e070a92f62d792b347566817c957904faf6e8d54a483916fcee479d1b22f4def5784db78 Homepage: https://cran.r-project.org/package=drglm Description: CRAN Package 'drglm' (Fitting Linear and Generalized Linear Models in "Divide andRecombine" Approach to Large Data Sets) To overcome the memory limitations for fitting linear (LM) and Generalized Linear Models (GLMs) to large data sets, this package implements the Divide and Recombine (D&R) strategy. It basically divides the entire large data set into suitable subsets manageable in size and then fits model to each subset. Finally, results from each subset are aggregated to obtain the final estimate. This package also supports fitting GLMs to data sets that cannot fit into memory and provides methods for fitting GLMs under linear regression, binomial regression, Poisson regression, and multinomial logistic regression settings. Respective models are fitted using different D&R strategies as described by: Xi, Lin, and Chen (2009) , Xi, Lin and Chen (2006) , Zuo and Li (2018) , Karim, M.R., Islam, M.A. (2019) . Package: r-cran-drhotnet Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbsmapping, r-cran-raster, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drhotnet_2.3-1.ca2404.1_all.deb Size: 396882 MD5sum: 939d7ede3251b7fe1bfdee26d6f56f3a SHA1: d665aa78b6ae66f7c0a2c0e9c54b196c517277de SHA256: 7dcd8dbf9db1073ca9223aece7ca33b62839b0658bfe90d0ffa195218cd5d1d9 SHA512: f4920bd9a170d01c711c5ca2ec5c840e61ac5d2737355f4bb911b23cc101c967a2591d94b3713d28fefa9689d99485f0b3f83746b2d54ab38aea9c177c9e0cd1 Homepage: https://cran.r-project.org/package=DRHotNet Description: CRAN Package 'DRHotNet' (Differential Risk Hotspots in a Linear Network) Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) . The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented. Package: r-cran-drhur Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1356 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quarto Suggests: r-cran-servr, r-cran-rmarkdown, r-cran-scales, r-cran-modelsummary, r-cran-knitr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-drhur_2.0.1-1.ca2404.1_all.deb Size: 406624 MD5sum: f92bcf916b19f996fe1a55ac3ab5c561 SHA1: 926edff9eebfce004daeaec6f1fb9f2128991c28 SHA256: b4200b829ccdb536ade1ba7eb39d17fac4831a5846f7b251e6751e97722298f8 SHA512: f3e5736bc2ac1e578090752d90044bb20a5f1d0fa10283de8184b4a60b9ab683cce094d6dd0c7e0ad04f6862a01152fd2fa9cae0d5d74283475a4326dc5a9f47 Homepage: https://cran.r-project.org/package=drhur Description: CRAN Package 'drhur' (Learning R with Dr. Hu) Provides interactive workshops for learning R easily and happily. Each workshop is a self-contained 'Quarto' Live document whose code cells run in the browser via 'WebAssembly', so learners can read the instructions and run the exercises side by side without a local R setup. The materials accompany the "Learning R with Dr. Hu" workshop series. Package: r-cran-drhutools Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1865 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-htmltools, r-cran-sf, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-leaflet, r-cran-sp, r-cran-gganimate, r-cran-magick, r-cran-webshot, r-cran-animation, r-cran-png Suggests: r-cran-knitr, r-cran-remotes, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drhutools_1.1.1-1.ca2404.1_all.deb Size: 1649084 MD5sum: ffaea2442c8ffce4d41b9d17bf72116b SHA1: 6fe543f41b43746ebeb1a41945113107b1ddb859 SHA256: c9b26780a30e8daaa1c4198eda58109e565aa84210c15864228359eef2f825e5 SHA512: fa23a0917ef4b911683a1ef2b56f8547fa9ae4356298cfe2b93e668f37aad5f6905089254a76a29d2fdd29ebc89b15701a0a172aba2497c2db886d0a6415ad90 Homepage: https://cran.r-project.org/package=drhutools Description: CRAN Package 'drhutools' (Political Science Academic Research Gears) Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, set up color-blind palette, and test for Type I error (false positives) in Qualitative Comparative Analysis (QCA) for crisp-set, multi-value, and fuzzy-set variants. Package: r-cran-drifter Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dalex, r-cran-dplyr, r-cran-tidyr, r-cran-ingredients Suggests: r-cran-testthat, r-cran-ranger Filename: pool/dists/noble/main/r-cran-drifter_0.2.1-1.ca2404.1_all.deb Size: 38934 MD5sum: 2fd5d276bfcde21c2a5f0b7c8d2d42ab SHA1: d101cd3b6a61f4def5e22797f1db85b32d8d2b7b SHA256: 6020325c912fb45ad5679f3bd8f5c4fd5ddf3a43199f1b568c282e441149df83 SHA512: 4f17ea6bfd8406d7d24b2110f1b76b2ff28550935a3f58769519f5ff3918e91a76723d8015a3c143c435fa6733ede80a5c9ef723fd4fef2216554deddb791afe Homepage: https://cran.r-project.org/package=drifter Description: CRAN Package 'drifter' (Concept Drift and Concept Shift Detection for Predictive Models) Concept drift refers to the change in the data distribution or in the relationships between variables over time. 'drifter' calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. 'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) . Package: r-cran-driftwatch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-driftwatch_0.1.0-1.ca2404.1_all.deb Size: 61726 MD5sum: f690e58819c91131c82b8b1d2554a2eb SHA1: 0b7664adbde8c14d12b22d177c310f7cbd712e4f SHA256: 49e12626013424a7770341df08a9350a23478fd9a83f6d4846c92c9a786259bc SHA512: f2c2d00753afd16f5be71a78bb270b20da3774a2bbb8f344a2a45c627c732640f692c5d0852b5984f0516c659f1971602010264e1e421615564e4772ba16b736 Homepage: https://cran.r-project.org/package=driftwatch Description: CRAN Package 'driftwatch' (Sequential Monitoring of Item Parameter Drift) Ongoing surveillance of item parameter drift for continuous testing programs and pre-equated item banks. Estimates item difficulty in rolling calibration windows, runs sequential cumulative sum (CUSUM) detection (Page, 1954, ; applied to testing by Veerkamp and Glas, 2000, ) with change-point estimation, separates gradual drift from abrupt jumps, tunes alarm thresholds for a bank-wide false-alarm target by simulation on the program's own design, quantifies score and pass-rate impact, and records recommended actions in an audit log. Package: r-cran-drillr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-drillr_0.1-1.ca2404.1_all.deb Size: 43248 MD5sum: 1129a817d271675f87bda8b9a0756642 SHA1: 7a8b6b1500144ad8d294cd07ee46f5646cc3cc7c SHA256: 9bbad8da00123cf4e7e7b6ef79b33120a62f810d94bae478ce945046cc21734b SHA512: fdb254a3a755703b933f0405f33053927d83778074f35559d6d3e741db30954cef67824dd6f09d144240e04ff714a41d9f4d92f573486ee74086624df98de46f Homepage: https://cran.r-project.org/package=DrillR Description: CRAN Package 'DrillR' (R Driver for Apache Drill) Provides a R driver for Apache Drill, which could connect to the Apache Drill cluster or drillbit and get result(in data frame) from the SQL query and check the current configuration status. This link contains more information about Apache Drill. Package: r-cran-drimmr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seqinr, r-cran-ggplot2, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-rdpack, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drimmr_1.0.4-1.ca2404.1_all.deb Size: 388154 MD5sum: c8278beb06878ab1cea67769d1619678 SHA1: f8ea1c6c49c53ab5b742251ecb22a5dbc6e17f9c SHA256: 7476c3b4994dbb8cec11b7f82a52547b0182dbb9692cc18b2ada7612af23fdd6 SHA512: 1bc8366ea69781088fc7bc489ddb623b7c681fdfbc9894d7c9cdd33006385aa1eff302bb1e825cdf56556b203d0f6e4e754ab79acf69fa5789c46c7479eba803 Homepage: https://cran.r-project.org/package=drimmR Description: CRAN Package 'drimmR' (Estimation, Simulation and Reliability of Drifting Markov Models) Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), and Barbu, V.S., Vergne, N. (2019) . Package: r-cran-drisdiagnostics Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-readxl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drisdiagnostics_1.0.1-1.ca2404.1_all.deb Size: 122472 MD5sum: adeac7bf6c008c01f01f921d06bc6b4b SHA1: 9d5ecd6dece0d48894d4dc3bcaf8426849c132db SHA256: a061ac75d141964b072b2f8ea9e1c5674ecb1862b061787b5d94448abed4296a SHA512: 5641260d821fd8d6880667903d22814cae2d2047fd84c1dd422e626bc7b01e93feda20f28c676c69324d08224b1d5ce89e9c2c952010f3ec42f8dd3ce918a6fe Homepage: https://cran.r-project.org/package=drisdiagnostics Description: CRAN Package 'drisdiagnostics' (Diagnostic Systems for Plant Nutrient Analysis (DRIS, MDRIS,PASS)) Provides implementations of the Diagnosis and Recommendation Integrated System (DRIS), the Modified DRIS (MDRIS), and the Plant Analysis with Standardized Scores (PASS) approaches for nutrient diagnosis in crops. These methods allow quantitative evaluation of nutrient imbalances using ratio-based indices and standardized scores, supporting improved fertilizer use efficiency and crop management decisions. The DRIS method is described in Walworth, J.L. and Sumner, M.E. (1987) . The MDRIS approach is detailed in Beverly, R.B. (1987) . The PASS method combining DRIS and sufficiency ranges is presented in Baldock, J.O. and Schulte, E.E. (1996) . Package: r-cran-driveplotr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-leaflet, r-cran-plotly, r-cran-crosstalk, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-rlang, r-cran-viridislite Suggests: r-cran-testthat, r-cran-sf Filename: pool/dists/noble/main/r-cran-driveplotr_0.1.0-1.ca2404.1_all.deb Size: 1277954 MD5sum: 45c3e9893f86c475026d2c547651467e SHA1: 282b45b1f80621a8310250152a17dc86311d004c SHA256: edbc2554d3bfa5a415322000a03147c08de5fa65c1a5cb0356544e4d4d381a40 SHA512: d9a0fda8a5480f6e83bfc6833453a7e90252fdbc8b4f54cee8b85b8614f3eb6822bb92dcd50cd5560156698fa68a9d36f1ca3c5d45b00466f22b80e64e124d2c Homepage: https://cran.r-project.org/package=DrivePlotR Description: CRAN Package 'DrivePlotR' (Linked Plot Maps for Multivariate High-ResolutionSpatio-Temporal Data) Create interactive, linked plot maps for multivariate high-resolution spatio-temporal data, such as vehicle trajectories. You can explore the spatial, temporal, and multivariate aspects of the data simultaneously. Package: r-cran-driver Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-randomforest, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-s4vectors, r-cran-rlang Suggests: r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-driver_0.5.0-1.ca2404.1_all.deb Size: 2883606 MD5sum: 36e2bdddbc14e90b866898c68149438b SHA1: 5c81268c41d582fdd7665fe501a2064baf8ac2b5 SHA256: 40b0e0da53517f96715c93c915eac93e230a125b3518091c65cf1cb144820c46 SHA512: ed668d0bc01af3e5574ac783aa9447e5d23a29bd1d029f867134c428784e469696f05bc076069b6109212822396c517c71f2030f67248c23f3465830cb9976e0 Homepage: https://cran.r-project.org/package=driveR Description: CRAN Package 'driveR' (Prioritizing Cancer Driver Genes Using Genomics Data) Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics . Package: r-cran-drlate Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-haven Filename: pool/dists/noble/main/r-cran-drlate_0.3.1-1.ca2404.1_all.deb Size: 317906 MD5sum: ab6812d261361883b2e7efa230c37669 SHA1: 301ed1900a6be5c8f489b45a60c9486b22eb1ec9 SHA256: 650ab937f3c7fcdc07b82e3a8d0fdcabc0cf3579e9d304fea85ad0931f75b1a1 SHA512: 5a2b64fda9970b7c9b7d71327b0c5acadc9413ab3019ae339fa7a03dae3cce31f0f752fd547c8bed0502030b75b47d026ff0d4fa16861e937baf9846e6265f50 Homepage: https://cran.r-project.org/package=drlate Description: CRAN Package 'drlate' (Doubly Robust Estimation of Local Average Treatment Effects) Estimates the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) using observational data with a binary instrument, implementing the complete estimator suite of Sloczynski, Uysal, and Wooldridge: the doubly robust estimators of Sloczynski, Uysal, and Wooldridge (2022) -- inverse probability weighted regression adjustment (IPWRA), inverse probability weighting (IPW), augmented inverse probability weighting (AIPW), and regression adjustment (RA) -- and the Abadie-kappa weighting estimators of Sloczynski, Uysal, and Wooldridge (2025) . Supports linear, logistic, probit, Poisson, and fractional (fractional-logit and fractional-probit) outcome and treatment models, and instrument propensity scores estimated by maximum likelihood, covariate balancing (CBPS), or inverse probability tilting (IPT). Standard errors are computed jointly for all estimation stages by stacking the moment conditions of every model into a single M-estimation system; weak-instrument-robust Fieller confidence sets, cluster-aware bootstrap inference, design diagnostics, and a doubly robust Hausman-type test of unconfoundedness are included. Estimates and standard errors are validated against the authors' Stata commands 'drlate' (Statistical Software Components S459708) and 'kappalate' (S459257). 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'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. 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Package: r-cran-drugdevelopr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1647 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-mvtnorm, r-cran-cubature, r-cran-msm, r-cran-mass, r-cran-progressr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-kableextra, r-cran-magrittr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-drugdevelopr_1.0.2-1.ca2404.1_all.deb Size: 921254 MD5sum: 171b2157d9679ad77aa78e41f466cf63 SHA1: a3dccd122bb0de8fbb62dfe0c2c1d051a4ec7287 SHA256: dc2a9cd31895a2bd447833116236a0a55bdcbc53ee1d6170b1279f868604c7c6 SHA512: 9e2addcd92b857884b55f88abd8deaa3cd2c655a4ef901182ab028d0830a1cc1d508bc4adbfe8d1138cd7d8f8a1ac521f90ff6a9b0ff320eae8e371f7f1865e9 Homepage: https://cran.r-project.org/package=drugdevelopR Description: CRAN Package 'drugdevelopR' (Utility-Based Optimal Phase II/III Drug Development Planning) Plan optimal sample size allocation and go/no-go decision rules for phase II/III drug development programs with time-to-event, binary or normally distributed endpoints when assuming fixed treatment effects or a prior distribution for the treatment effect, using methods from Kirchner et al. (2016) and Preussler (2020). Optimal is in the sense of maximal expected utility, where the utility is a function taking into account the expected cost and benefit of the program. It is possible to extend to more complex settings with bias correction (Preussler S et al. (2020) ), multiple phase III trials (Preussler et al. (2019) ), multi-arm trials (Preussler et al. (2019) ), and multiple endpoints (Kieser et al. (2018) ). Package: r-cran-drugexposurediagnostics Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cdmconnector, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-checkmate, r-cran-glue, r-cran-drugutilisation, r-cran-omopgenerics, r-cran-r6 Suggests: r-cran-testthat, r-cran-duckdb, r-cran-odbc, r-cran-dbi, r-cran-knitr, r-cran-rmarkdown, r-cran-zip, r-cran-lubridate, r-cran-tibble, r-cran-dt, r-cran-sqlrender, r-cran-ggplot2, r-cran-plotly, r-cran-tictoc, r-cran-here, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-shinyjs, r-cran-shinytest2 Filename: pool/dists/noble/main/r-cran-drugexposurediagnostics_1.2.0-1.ca2404.1_all.deb Size: 982778 MD5sum: 7635f00491e9654a7e2c704425d2a264 SHA1: 39dd9624b110bfcfcaf2f764e7d974d4da34c703 SHA256: b2e56aa299a5dd184fc6b0e121719f66a1e215c777b667e00d34153d110c9e01 SHA512: b59c1a318ae5f275feba96faf0f1c168a05a39f60fd9643085a20173835da1b75ce497f149d444a3f9d5223872feb8f534d4cb64bdad0150ae58a7e240fa7ffa Homepage: https://cran.r-project.org/package=DrugExposureDiagnostics Description: CRAN Package 'DrugExposureDiagnostics' (Diagnostics for OMOP Common Data Model Drug Records) Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model. Package: r-cran-drugprepr Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-sqldf, r-cran-stringr, r-cran-purrr, r-cran-desctools, r-cran-doseminer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-drugprepr_0.0.7-1.ca2404.1_all.deb Size: 171082 MD5sum: 0baca30d6709932f6b07f5b94af83ea6 SHA1: 4f2c79e007241b676f024d3b77db43865f29191c SHA256: c9b95b040cf656e46ae429e906d7a5ead6174a5331cb9d212c52d9b535d6734b SHA512: 108c6570aef313490680fe3dc3fce2d7f8044ce1d0b2254c921ffdb1c3c6404ea6ea492447c9f2063513a8ed966da6fd12393bf996c1029c83630cf6e4dbb811 Homepage: https://cran.r-project.org/package=drugprepr Description: CRAN Package 'drugprepr' (Prepare Electronic Prescription Record Data to Estimate DrugExposure) Prepare prescription data (such as from the Clinical Practice Research Datalink) into an analysis-ready format, with start and stop dates for each patient's prescriptions. Based on Pye et al (2018) . Package: r-cran-drugsens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-knitr, r-cran-roxygen2, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drugsens_0.1.0-1.ca2404.1_all.deb Size: 133158 MD5sum: ab84570556445263ef2e320fec219a93 SHA1: 0da57f00fdeb31acbc2c38b073f18ca554c5c41d SHA256: 6f266af9b1b77f76066aba6dff5e2b63d28670bca9142b5c5280fb832d12fd1b SHA512: f1be4fa9d6769ba5f58e8d3b4e0ccb8eb6b62b79f764367a65df280ba26dc042a1de32acd632b7674d8d456d31bcf93933ce876e58c1fd1e1f54fdeda4317f9b Homepage: https://cran.r-project.org/package=drugsens Description: CRAN Package 'drugsens' (Automated Analysis of 'QuPath' Output Data and MetadataExtraction) A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. Provides functionality for automated data processing, metadata extraction, and statistical analysis of imaging results. The methodology implemented in this package is based on Labrosse et al. (2024) "Protocol for quantifying drug sensitivity in 3D patient-derived ovarian cancer models", which describes the complete workflow for drug sensitivity analysis in patient-derived cancer models. Package: r-cran-drugsim2dr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-pheatmap, r-cran-tidyr, r-cran-reshape2, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drugsim2dr_0.1.2-1.ca2404.1_all.deb Size: 2469258 MD5sum: a4ac5d8cc1200443f62968deb5f28e64 SHA1: e306578bae45c4c4c8009c064808aa41003f5c74 SHA256: 5c1c95bb6de74bad4223a58ddc540acbc87c977cf2ecdfaf3c35cd89606544a7 SHA512: 63521a0cb1782fd9b61b33480cb41b97985bf2fbe8e68fb6cf9f786dc8452a199aaadc1dcbbafe6400b34674ca4ed4b6189d2c2af0bb25a30cdbf1fe85a4bada Homepage: https://cran.r-project.org/package=DrugSim2DR Description: CRAN Package 'DrugSim2DR' (Predict Drug Functional Similarity to Drug Repurposing) A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs. Package: r-cran-drugutilisation Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-codelistgenerator, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-cohortconstructor, r-cran-cohortsurvival, r-cran-covr, r-cran-dbi, r-cran-downlit, r-cran-duckdb, r-cran-extrafont, r-cran-flextable, r-cran-ggplot2, r-cran-ggtext, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-plotly, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-visomopresults, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-drugutilisation_1.3.1-1.ca2404.1_all.deb Size: 888876 MD5sum: fa0c1bea845ef8ec0f0de209eed6842c SHA1: ccd031f8171abd4d6656c2fe115a889d69309912 SHA256: 2e3eed82ce7ca3118576a3eb7adb5be0a4161f924b5503bcb8bf9efcac8c71fb SHA512: 9a167a153611cda0bbd51fdc831e31f18d73b0c93d94122422f2c47e272ed5e77d36d970ce89cd3d6a0263ced81236dc812fb7f4260cd3472de34d0a1b48235d Homepage: https://cran.r-project.org/package=DrugUtilisation Description: CRAN Package 'DrugUtilisation' (Summarise Patient-Level Drug Utilisation in Data Mapped to theOMOP Common Data Model) Summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. New users and prevalent users cohorts can be generated and their characteristics, indication and drug use summarised. Package: r-cran-drumr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-stringr Filename: pool/dists/noble/main/r-cran-drumr_0.1.0-1.ca2404.1_all.deb Size: 1946640 MD5sum: 5ea320060ee6444ca0f16afb065f6993 SHA1: 0ceee2af61add1c5427b06b9e1a9c074f4d27922 SHA256: 9c45492cbd5f621e15fb20b55b934a9a6458828fecf723733f7920cbd3726ea4 SHA512: 03c3d6dfc84a8f0341af79345680fab927ad46a31dced0559391185da8f80921dbdae496b16c85e3fc99b681c25c118ad83f1aaea73a8c5e273ea84e27d815af Homepage: https://cran.r-project.org/package=drumr Description: CRAN Package 'drumr' (Turn R into a Drum Machine) Includes various functions for playing drum sounds. beat() plays a drum sound from one of the six included drum kits. tempo() sets spacing between calls to beat() in bpm. Together the two functions can be used to create many different drum patterns. Package: r-cran-drviaspcn Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4588 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gsva, r-bioc-clusterprofiler, r-cran-igraph, r-cran-pheatmap Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dt Filename: pool/dists/noble/main/r-cran-drviaspcn_0.1.5-1.ca2404.1_all.deb Size: 4506226 MD5sum: 86d9fa71bf576341b38494be60ae4eae SHA1: 087eb06633d7edf9a29e6d8bdb51db25a2ad2e2b SHA256: 8d103dae940f579ca0238348bef87678fba1d8444c805bfa7c75aa0a4c2396f8 SHA512: d71d26147031d7cf68bee2b8327a96bd406dc5cfe8e9a2fbb78145b711ab5174694d107e2a2683e6840d1fd739eb5f7894e90df90e9d673d00ace3adc3fa4c5e Homepage: https://cran.r-project.org/package=DRviaSPCN Description: CRAN Package 'DRviaSPCN' (Drug Repurposing in Cancer via a Subpathway Crosstalk Network) A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results. Package: r-cran-dryingkineticmodels Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readxl, r-cran-lmtest, r-cran-minpack.lm, r-cran-tseries, r-cran-officer, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dryingkineticmodels_1.0.0-1.ca2404.1_all.deb Size: 91288 MD5sum: e92db0e071db0e4e680b2307dafc8c83 SHA1: 38a2830cfcc022366c0267fb62db1c55795c8c37 SHA256: 3ceaceb3f683cd834f96d8a274c7456f11fa0140cfcf10b911b24f939ed8c057 SHA512: b71d1dd55aebff5c9739baed1a742965955945261dcc094e83821b2ed8469a2a8ca5a4600b6108e82d77533206b49260bc09b8edb7ec86be8f2d94ac554ecf8f Homepage: https://cran.r-project.org/package=dryingkineticmodels Description: CRAN Package 'dryingkineticmodels' (Drying Kinetic Models Comparison and Analysis) Fits multiple thin-layer drying kinetic models to experimental moisture ratio data, compares model performance using statistical criteria, performs residual diagnostics, identifies the best-fitting model, and exports results to Word documents. Twenty models from Ertekin and Firat (2017) are fitted using the Levenberg-Marquardt algorithm described in Marquardt (1963) . Package: r-cran-ds4psy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-unikn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ds4psy_1.3.0-1.ca2404.1_all.deb Size: 930430 MD5sum: d1bbb3aef94538dbc6e48c87ee1d01ac SHA1: c86c3e66e255e6563e7de93211f84fb51ab65732 SHA256: 680702202ffcfd569f12c995da9820047d4ab70e88119c1541423d5e3cb4d3da SHA512: 02b78edd6c9f5428889979161fe7a9588328abbeb712775acac76959a05503e3af72ad6cac066053c7adfbf5f0385537014e84824aa8e4fc0248f128068988ad Homepage: https://cran.r-project.org/package=ds4psy Description: CRAN Package 'ds4psy' (Data Science for Psychologists) All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2026, ), freely available at . The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than efficient or elegant. Package: r-cran-ds Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ds_4.0-1.ca2404.1_all.deb Size: 36770 MD5sum: 5571d4e09118fd7dca28d4c0b9dfb386 SHA1: 83f8966cafb5a6e6bc64a31c33ab0c621e148529 SHA256: 2691f026551286aa3fcb476699a7d43086478433d95976f656ab51d3bae47095 SHA512: 0b5c2e8b8fc93e0c0abe0baafa7579aa5121ca73b9138edd9f5a4dd95ae890e27eef829140c27c4ebee6f94f6e979f837dcf73dad39cabd38e479203a4915ddc Homepage: https://cran.r-project.org/package=ds Description: CRAN Package 'ds' (Descriptive Statistics) Performs various analyzes of descriptive statistics, including correlations, graphics and tables. 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Bundesbank Discussion Paper 41/2018. Package: r-cran-dsaide Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-adaptivetau, r-cran-desolve, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lhs, r-cran-nloptr, r-cran-plotly, r-cran-rlang, r-cran-xml Suggests: r-cran-covr, r-cran-devtools, r-cran-emoji, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsaide_1.0.0-1.ca2404.1_all.deb Size: 2637618 MD5sum: 76b1a45b9839ec282238221eb80fb6ba SHA1: b8520f6eef2dcf0991cf3684cfdc4b304740f0b7 SHA256: 048929da2d27d86e3a3315a20100595508167f8b5fb212ee7d8d16ee680038f2 SHA512: 38bab56231af44d2c1ba1addd80c80e5d20395f061d47422b50bdec54925013d654dde67a48ed69507ebbf4c71d8a584543eab54b132814879fe881a4eead635 Homepage: https://cran.r-project.org/package=DSAIDE Description: CRAN Package 'DSAIDE' (Dynamical Systems Approach to Infectious Disease Epidemiology(Ecology/Evolution)) Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models. Package: r-cran-dsairm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-adaptivetau, r-cran-boot, r-cran-desolve, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lhs, r-cran-nloptr, r-cran-plotly, r-cran-rlang, r-cran-xml Suggests: r-cran-covr, r-cran-devtools, r-cran-emoji, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsairm_1.0.0-1.ca2404.1_all.deb Size: 2373882 MD5sum: 177b3369dfed68a6996ab4b4f657ad4f SHA1: 061ed454d7e9029284d03c7b728a9e887f9f514a SHA256: f0cc09e7f54597ff6c661f432e0b0509bb37998a138afa46346a81777302bef3 SHA512: bab4cdc046c235a2ccdc58d0b90ec89bc5fd42475e914d7c3714a143a46de2409e3b4848cbada091a7a0c6206a9d59d3018bf62c95dccedd12a1b68d4a1ab32f Homepage: https://cran.r-project.org/package=DSAIRM Description: CRAN Package 'DSAIRM' (Dynamical Systems Approach to Immune Response Modeling) Simulation models (apps) of various within-host immune response scenarios. The purpose of the package is to help individuals learn about within-host infection and immune response modeling from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models. Package: r-cran-dsam Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-kohonen, r-cran-matrix, r-cran-proc, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-dsam_1.0.2-1.ca2404.1_all.deb Size: 111558 MD5sum: f90d4b3bfe80d5fc54292238eab41815 SHA1: 8081e72f62a3b507a31fddb88e9e11fee0f1d107 SHA256: 48232dc355e517a12e26bd1053ca8e6d02a709c82d4d98c81e00a6a59d9bc077 SHA512: 80a6c0d23421f17ef0df7e44b733adddc056cb5ee0f6102ece3886fdbc53582064f43d9aaa81ca6e1beccd176e4fdb6d43f8213512303fadc763e9c9f6024966 Homepage: https://cran.r-project.org/package=DSAM Description: CRAN Package 'DSAM' (Data Splitting Algorithms for Model Developments) Providing six different algorithms that can be used to split the available data into training, test and validation subsets with similar distribution for hydrological model developments. The dataSplit() function will help you divide the data according to specific requirements, and you can refer to the par.default() function to set the parameters for data splitting. The getAUC() function will help you measure the similarity of distribution features between the data subsets. For more information about the data splitting algorithms, please refer to: Chen et al. (2022) , Zheng et al. (2022) . Package: r-cran-dsample Architecture: all Version: 0.91.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mnormt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dsample_0.91.3.4-1.ca2404.1_all.deb Size: 51622 MD5sum: fc80e82863e2b1a23bb7eccdde87b0b0 SHA1: 7632a0167d13549e270fc7cdf9aa8895e50a9812 SHA256: 995ca04810da1d332c1569d912349516b0acf657f8bea71ec50798025a11352e SHA512: b5e2f033bf9d2e1ad0b768b02f0462349f86682e59d7a6c7dac7b0a1ba9ada6a151e626e88c41c32dcdfe0bab193e01bb87728ac33bca7a1f09c996415403441 Homepage: https://cran.r-project.org/package=dsample Description: CRAN Package 'dsample' (Discretization-Based Direct Random Sample Generation) Discretization-based random sampling algorithm that is useful for a complex model in high dimension is implemented. The normalizing constant of a target distribution is not needed. Posterior summaries are compared with those by 'OpenBUGS'. The method is described: Wang and Lee (2014) and exercised in Lee (2009) . Package: r-cran-dsb Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-bioc-limma, r-cran-mclust Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-cowplot, r-cran-spelling Filename: pool/dists/noble/main/r-cran-dsb_2.0.1-1.ca2404.1_all.deb Size: 3781412 MD5sum: 8eeb2b8289cb5c5b1d7befa6b4a7377c SHA1: 8ace5d5e5adfcb7259018cede235bfee4a811172 SHA256: 69468b6f3a9f10522bfff7e444dcb140780759cf59ca565479d465dcbdc65760 SHA512: 298fc8108601fd6ce281370e3213a719ce5a07b4a418f9039e3e2c521ae8b9e3d1a4a201846c0963e4b630897d9aa1d6eeaa0fc617bdf25ff415e5d67d70d86e Homepage: https://cran.r-project.org/package=dsb Description: CRAN Package 'dsb' (Normalize & Denoise Droplet Single Cell Protein Data (CITE-Seq)) This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 for more details on the method. Package: r-cran-dsbase Architecture: all Version: 6.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rann, r-cran-stringr, r-cran-lme4, r-cran-dplyr, r-cran-reshape2, r-cran-polycor, r-cran-gamlss, r-cran-gamlss.dist, r-cran-mice, r-cran-childsds Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsbase_6.3.5-1.ca2404.1_all.deb Size: 1233516 MD5sum: 4a90a2ae6a8082567cc599fdd870ff18 SHA1: 85eb181bacc925eb723c34efa6007bd8bafcfdf7 SHA256: 04f71f418a5506df38fc0efc1ff064c3fbca6c96f0fd8678dde0f2d744e4c695 SHA512: 018f7a7111db68c95bd318f8b640d7379dc948113f13e463281a33da24f75fba6c5357aca0a87eb94a4c9fee47cfa661c4e6cdf8db3b5c0a549cf4779d5a41f1 Homepage: https://cran.r-project.org/package=dsBase Description: CRAN Package 'dsBase' ('DataSHIELD' Server Side Base Functions) Base 'DataSHIELD' functions for the server side. 'DataSHIELD' is a software package which allows you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see 'citation("dsBase")'. Package: r-cran-dsbaseclient Architecture: all Version: 6.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dsi, r-cran-fields, r-cran-metafor, r-cran-meta, r-cran-ggplot2, r-cran-gridextra, r-cran-data.table, r-cran-dplyr Suggests: r-cran-lme4, r-cran-httr, r-cran-spelling, r-cran-tibble, r-cran-testthat, r-cran-e1071, r-cran-desctools, r-cran-dsopal, r-cran-dsmolgenisarmadillo, r-cran-dslite Filename: pool/dists/noble/main/r-cran-dsbaseclient_6.3.5-1.ca2404.1_all.deb Size: 1148184 MD5sum: 5aa168416f3ec13719fdae0dbff30416 SHA1: 514554dd5e3e0ff26582dc9d34417a953c1973f7 SHA256: 22baddd3e661f8b9c9758ac50b567b5910612e4c2d3b333c9a9de9863c359d69 SHA512: 97865a504c68ef66dec26c585df0eb9f11fe1ec6901d800c384abe26c2076996ef3c6a16327c1ac5fdebdfce7a20912195aa6f551a550a87600836861e110646 Homepage: https://cran.r-project.org/package=dsBaseClient Description: CRAN Package 'dsBaseClient' ('DataSHIELD' Client Side Base Functions) Base 'DataSHIELD' functions for the client side. 'DataSHIELD' is a software package which allows you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see citation('dsBaseClient'). Package: r-cran-dsbayes Architecture: all Version: 2023.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb Filename: pool/dists/noble/main/r-cran-dsbayes_2023.1.0-1.ca2404.1_all.deb Size: 75200 MD5sum: 59432a8766e575cd2ff7760a90bb90f2 SHA1: 4a40b30c7bac49881d86411db93d6d23ba19808d SHA256: e3d5b5da2188b060bd1e0d4b3f6d91d07879cb7735a2accdfcfd0f3e4fc2eac4 SHA512: 1c09536d53b0b98ea26a5ef09882a827751f9d7392b99063455cbf3e515e406f7aa08deb0bac0dbc379e5bee6dc97c0af921de2d808b3864c8d570f7e0434253 Homepage: https://cran.r-project.org/package=DSBayes Description: CRAN Package 'DSBayes' (Bayesian Subgroup Analysis in Clinical Trials) Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881. Package: r-cran-dscoremsm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-timeroc, r-cran-ggplot2, r-cran-survival, r-cran-mstate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dscoremsm_0.1.0-1.ca2404.1_all.deb Size: 269450 MD5sum: 743a98e2e48875674cf833bb2adaa9a2 SHA1: f71b35a250efdc0f4f867dadbbdb408a4a26a1d9 SHA256: a116d7668fb6977347ee420cb48752da564e1903d71f0364aad8e3ba84d4179e SHA512: e41bf621cdda1450a340f43517795c2720a09d338672a3d4056169cf8f4ef32b79e5643f330f094d291747864ee1353d26c71727f05921b80ca6e987050b1653 Homepage: https://cran.r-project.org/package=dscoreMSM Description: CRAN Package 'dscoreMSM' (Survival Proximity Score Matching in Multi-State Survival Model) Implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) . Package: r-cran-dscoretest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-grf, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-speff2trial, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dscoretest_1.0.0-1.ca2404.1_all.deb Size: 384902 MD5sum: 2166a7d1298acd51cecbe283ecf710a0 SHA1: fc36b591c8bbd3ae1119413fb84a231a26e52253 SHA256: c04d0718587b227c09c76ae6ec9c2f0687202b5a1104a01cd76e5d814c34f2a4 SHA512: f0077b5e1e79cdf965772a66dcca9dad0d668e8ae9a8247bfc8a50bdc5461f962adea43cb6d736dce88ffdd6bb16eb2f40917f2083cea014dc3f33bbc091b705 Homepage: https://cran.r-project.org/package=dScoreTest Description: CRAN Package 'dScoreTest' (Debiased Score Tests for Goodness of Fit and Model Comparison) Debiased (Neyman-orthogonalized) score tests for assessing whether a semiparametric or parametric regression model is well-specified and for comparing nested models. The test employs a hunt-and-test strategy: on a held-out hunt sample, it fits the null model and uses machine learning to find a direction in which the null model's score seems positive; on an independent test sample, it assesses the significance of the score in the hunted direction. The test employs orthogonalization to eliminate the bias from estimating the null model, yielding a test statistic that is asymptotically standard normal under the null without requiring a parametric form for the alternative. Methods are provided for 'glm', 'lm' and 'mgcv::gam' fits as well as for detecting heterogeneous treatment effects. The methodology is described in Dhawan, Guo and Shah (2026) . Package: r-cran-dsdrm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsdrm_0.1.3-1.ca2404.1_all.deb Size: 97102 MD5sum: dd53663ffe2327d3e17c301cd2af59f1 SHA1: 80854a9feba7d78ae07e893491082c87f71a333b SHA256: 862a11aa2016d6684c1c1dd6b132b5e6569987d462424a6c61f924ca9907b1d2 SHA512: 632ee46c72c4f54a4e6835e687efa25ea26773d55a1b85c506761b1f8aa1bba17f686705e338779d861fe2e26853bd390bf60608ab9caa064cc569f492d5b378 Homepage: https://cran.r-project.org/package=DSDRM Description: CRAN Package 'DSDRM' (Distributed Sampling for Dynamic Regression Models) A toolbox for distributed dynamic regression modeling, parallel estimation, multiple distributed sampling algorithms (Metropolis-Hastings, block bootstrap, adaptive, hypergeometric), sparse matrix optimization, model visualization, prediction and performance evaluation. The philosophy of the package is described in Guo (2025) . Package: r-cran-dsfm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-sn, r-cran-psych, r-cran-elasticnet, r-cran-sopc Suggests: r-cran-ggplot2, r-cran-cowplot, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsfm_1.0.1-1.ca2404.1_all.deb Size: 691844 MD5sum: b96ffac5bc263b41fae384015454f2c2 SHA1: c96454c056a26ba85657379e200f224cee2cb8f8 SHA256: b8e8bcc3ff252403b5425a40248c9fd46a4cc59fb420dcaf8df843b55b241d33 SHA512: 7a51b7465e5bdedd883f9f170690695aaed73fa5b2cc41b6fa7f229277992397099c383519371f3a8d7c86a885274b20c927e84423df01a5610b176ca618e95d Homepage: https://cran.r-project.org/package=DSFM Description: CRAN Package 'DSFM' (Distributed Skew Factor Model Estimation Methods) Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) . Package: r-cran-dsge Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1858 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-coda, r-cran-matrix, r-cran-r.matlab, r-cran-readxl, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dsge_1.2.0-1.ca2404.1_all.deb Size: 1623930 MD5sum: 027fa21ca4de4a263065917c463da3dc SHA1: 301d7f1552f42c6c543c6aa6facb4014d3ccf569 SHA256: 354fe75c913b3af22d973667331d067ffcce59da81404bd789b96534fc0de8ee SHA512: dd4ce996da2c3196535f4d1394720005b20caee8ed8e068809af5143fa14508f4b9d1b76c0af8151ef636e70e20e06a59196b3bd863a9d67a061557758d76cc4 Homepage: https://cran.r-project.org/package=dsge Description: CRAN Package 'dsge' (Dynamic Stochastic General Equilibrium Models) Specify, solve, and estimate dynamic stochastic general equilibrium (DSGE) models by maximum likelihood and Bayesian methods. Supports both linear models via an equation-based formula interface and nonlinear models via string-based equations with perturbation up to third order (Schmitt-Grohe and Uribe, 2004 ). Solution uses the method of undetermined coefficients (Klein, 2000 ). Likelihood evaluated via the Kalman filter or a bootstrap particle filter (Gordon et al., 1993). Bayesian estimation uses adaptive Random-Walk Metropolis-Hastings or Particle Marginal Metropolis-Hastings (Andrieu et al., 2010 ) with parallel chain support. Additional tools include Bayes factor model comparison with Kass-Raftery evidence scales, Ramsey optimal policy via linear-quadratic regulator, nonlinear perfect foresight via stacked-time Newton (Juillard et al., 1998), Kalman smoothing, historical shock decomposition, local identification diagnostics, parameter sensitivity analysis, occasionally binding constraints, impulse-response functions, forecasting, and robust standard errors. Package: r-cran-dsi Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress, r-cran-r6, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsi_1.8.0-1.ca2404.1_all.deb Size: 320020 MD5sum: a79b15146d143b69761d67f708a6a16f SHA1: 41ffe68dc5b2a1d152195c13bc133fb223de39fc SHA256: cbab6751e12bd14270ed352341e398d08930fd235047170999cdd62a70e25c06 SHA512: 0e29105f336a356194e46cb8b3008657435cb89ddf5f944d0bb622f8b4ca9c27e2758d221c7f5e7ba53b4b8fc8f6519bd2887e10ddc98c1425c851c26bed5b9c Homepage: https://cran.r-project.org/package=DSI Description: CRAN Package 'DSI' ('DataSHIELD' Interface) 'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package defines the API that is to be implemented by 'DataSHIELD' compliant data repositories. Package: r-cran-dsims Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dssd, r-cran-mrds, r-cran-distance, r-cran-sf, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-mgcv, r-cran-rstudioapi, r-cran-gridextra, r-cran-rlang Suggests: r-cran-testthat, r-cran-bookdown, r-cran-pbapply, r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dsims_1.0.6-1.ca2404.1_all.deb Size: 441874 MD5sum: 8735271b880bc0008cea1094132caef7 SHA1: 8f74e1ca1337ad8a7ea2e4c5e6fa930b7673290f SHA256: d99705163b571f3609571586e904988304235c32c2009453a2b18755eecf0014 SHA512: f8ad5caecf849dcd30c82ef14375e1f3ba2c17106e1c8236731ba5f1b111834eeabe8ad0495d06f91c81d86e57a426bef78e7d081ebb676d692cf24dff108166 Homepage: https://cran.r-project.org/package=dsims Description: CRAN Package 'dsims' (Distance Sampling Simulations) Performs distance sampling simulations. 'dsims' repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from 'dssd' are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at . 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Dataset containing information about job listings for data science job roles. 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Detection and remediation of bias in machine learning algorithms. 'Python' interfaces available. 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This 'DataSHIELD Interface' implementation is for analyzing datasets living in the current R session. The purpose of this is primarily for lightweight 'DataSHIELD' analysis package development. 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A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation. Package: r-cran-dsmmr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-discreteweibull Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsmmr_1.0.7-1.ca2404.1_all.deb Size: 232770 MD5sum: 2ca76ebbaa9ddbada3ddf44d21201468 SHA1: 55b1080526c576231eac4c45bd9447386db5453d SHA256: d19c828b4c54335cd569ae643b489bd0dfadf90c45201562bf3349572c2cb8a2 SHA512: 505d75c635d9d6d473c0502b1f0bdb85ec68360769927129d10e8e338b3c1b2ac472bd1d0fe0ec71c2e047ff05aa99f875846bdc157bacda88afd908839d5c25 Homepage: https://cran.r-project.org/package=dsmmR Description: CRAN Package 'dsmmR' (Estimation and Simulation of Drifting Semi-Markov Models) Performs parametric and non-parametric estimation and simulation of drifting semi-Markov processes. The definition of parametric and non-parametric model specifications is also possible. Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) . Drifting Markov models are described in: Vergne, N. (2008) . Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) . We acknowledge the DATALAB Project (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005). 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Package: r-cran-dtebop2 Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncdist, r-cran-doparallel, r-cran-foreach, r-cran-invgamma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survrm2adapt, r-cran-ggplot2, r-cran-reconstructkm, r-cran-survival, r-cran-survminer Filename: pool/dists/noble/main/r-cran-dtebop2_1.0.3-1.ca2404.1_all.deb Size: 248730 MD5sum: 0dd2f7ef403640de97b9a80c3e578d5b SHA1: 17b35849599aaf284ff924c7fcf49099cc491f29 SHA256: 769a33cd6f8a40986decb52096883152d87e2a6933565a101a757b566f88f0c6 SHA512: 853b39d823cd640c168e0a7c66f7177df96eb2e8fb6f71d269f14f62569a9e97af549bf8dcdea482aa478fde2994174595bf1e648547bf86b770fcedda8680d8 Homepage: https://cran.r-project.org/package=DTEBOP2 Description: CRAN Package 'DTEBOP2' (Bayesian Optimal Phase II Randomized Clinical Trial Design withDelayed Outcomes) Implements a Bayesian Optimal Phase II design (DTE-BOP2) for trials with delayed treatment effects, particularly relevant to immunotherapy studies where treatment benefits may emerge after a delay. 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'dtlog' redefines the subsetting method for data tables as well as several functions exported by 'data.table' so that each operation prints a short message describing what it did: how many rows were removed, which columns were added, updated or dropped, how many groups an aggregation produced, and so on. The operations themselves are left untouched, including modification by reference. It also provides dttable(), which describes the variables a single data table holds and passes every other call on to base::table() unchanged. Inspired by the 'tidylog' package. 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Package: r-cran-dtmapi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-testthat, r-cran-askpass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-dtmapi_0.1.0-1.ca2404.1_all.deb Size: 45398 MD5sum: 9d8d9190fb2e8cd396a55b4a810bf607 SHA1: 0bb4ba53f8383afaff3e90bf3123bf8e7ed48e1c SHA256: 1d9b2a48bcf8c5f0b06a2f9d8337575c5db429349d0851e422af7096cee6433b SHA512: c18889dbc1541e5960f611f0e0373faf9733248c563ab0d6702198cd8b7231f737c782b443606abb6d3b7a70935a5c0c055e28f3d95e2a3e9e831b8ea2223eb7 Homepage: https://cran.r-project.org/package=dtmapi Description: CRAN Package 'dtmapi' (Fetching Data from the 'Displacement Tracking Matrix') Allows humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by the 'Displacement Tracking Matrix' (), a unit in the International Organization for Migration. 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Package: r-cran-dtmcpack Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dtmcpack_0.1-3-1.ca2404.1_all.deb Size: 27124 MD5sum: d35ffbb408f4c2c2d25b61779bbf8a7b SHA1: d70e352501fff701d748f700a533ae3d123446ae SHA256: 0abc40e1392fed44aa9eb106a430889f772030f99499299e282cc6c8dee4cf71 SHA512: b92636e9d5f54ed15105a29bb191992cf0aff083d8a3e36a57dd85903373e8853fdb9cf1393fa35924bc3d94c802ef16b4b23f2a5b5219db9998522e4f977388 Homepage: https://cran.r-project.org/package=DTMCPack Description: CRAN Package 'DTMCPack' (Suite of Functions Related to Discrete-Time Discrete-StateMarkov Chains) A series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain. 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The package provides tools for processing data, several ways of estimating parametric and nonparametric multistate models, and an extensive set of Markov chain methods which use transition probabilities derived from the multistate model. Some of the implemented methods are described in Schneider et al. (2024) , Dudel (2021) , Dudel & Myrskylä (2020) , van den Hout (2017) . Package: r-cran-dtp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-gtools, r-cran-plyr Filename: pool/dists/noble/main/r-cran-dtp_0.1.0-1.ca2404.1_all.deb Size: 54706 MD5sum: 574c3024a0ebe993764bbf631e36d0c1 SHA1: 94eaeb2b30c84025d7c4a0ba863920394223722a SHA256: c61636e52d37e0f8e0ae5c366b0a8ed405d1a3385db395729c35208925b89c51 SHA512: 145ef12175bbee840f4d32827bc61f7870b546a58a6dad99edb4b7c89036f9a1f4ecb4dae48ed42f2fc7f5e1c2911122dcb81e6be9dd8e068e70a2c99476141c Homepage: https://cran.r-project.org/package=dtp Description: CRAN Package 'dtp' (Dynamic Panel Threshold Model) Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) ) in which they extended the (Hansen (1999) ) original static panel threshold estimation and the Caner and (Hansen (2004) ) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used. Package: r-cran-dtpcrm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-diagram, r-cran-dfcrm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dtpcrm_0.1.3-1.ca2404.1_all.deb Size: 194412 MD5sum: 52de5cc62e665b64d3440d0c70717ff2 SHA1: e85f74b738bc96cf9226db1348b5625398625729 SHA256: 7b8329f6726a426aa55752f1c73d61199f75ab8eab512867b5852fb458bd0755 SHA512: e97d1912dd1b41400d990c4b471a7fc1e50f9e6056ffdc5714d799a0e063eb4b3e8225ac1103866bb5cebb2d1eb4c9995d72b02ba02fcedff37dec3db5f8b459 Homepage: https://cran.r-project.org/package=dtpcrm Description: CRAN Package 'dtpcrm' (Dose Transition Pathways for Continual Reassessment Method) Provides the dose transition pathways (DTP) to project in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, deescalate or stop early) using all the accumulated toxicity information; See Yap et al (2017) . 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Inference via bootstrap and recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables. Wallace et al. (2017) ; Simoneau et al. (2020) . 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Built on 'React' (via 'reactR') and 'htmlwidgets', it offers column-type detection, multi-value checkbox filtering, sorting, column visibility toggling, virtual scrolling for large datasets, and a full-viewport modal. Includes 'dtsmartr_launch()' with an interactive, zero-code file upload wizard using 'datamods'. Widgets can be embedded in 'R Markdown' / 'Quarto' documents, 'Shiny' applications, or exported as standalone HTML files via 'save_dtsmartr()'. 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It is designed to work in distributed computing environments to handle large datasets efficiently. The philosophy of the package is described in Guo G. (2024) . Package: r-cran-dtt Architecture: all Version: 0.1-2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dtt_0.1-2.1-1.ca2404.1_all.deb Size: 21122 MD5sum: 9f30f71ff9b8c040f30577af5286030d SHA1: 243d50a6d82b81d9983746b9ee9d65d78f9d1bce SHA256: 6574fac4482423dd377ed8c639ff0a4acba21a9c289e2cfd71bbc7b23fc0b689 SHA512: 4378d521a917f09563fc8b3c4d27fe03d3b66d51b4aa4d2d0072aca2fd724359ab72be2ba04b8324c72f95ffae81dd2b2ec85706ccae4176addf3cad66ad87b5 Homepage: https://cran.r-project.org/package=dtt Description: CRAN Package 'dtt' (Discrete Trigonometric Transforms) Provides functions for 1D and 2D Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Hartley Transform (DHT). Package: r-cran-dttr2 Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chk, r-cran-hms, r-cran-lifecycle Suggests: r-cran-rlang, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dttr2_0.5.2-1.ca2404.1_all.deb Size: 255934 MD5sum: c6951f3a99dbade51d8c47f073919965 SHA1: 742cd0886f7b7ce3f755c2e19918d95c679750f7 SHA256: ce919e07a33c225b3bc8b289aa70497a7e29ff19d883846ccce50408f1998345 SHA512: 409ed49a7523973c1050b2e946817d3f648c0d444782c40ff57b6048d4a4d4373865e312f23355cf330c5ea3a44106bba3e3beceef5ac1264c29e8db5b0c6e34 Homepage: https://cran.r-project.org/package=dttr2 Description: CRAN Package 'dttr2' (Manipulate Date, POSIXct and hms Vectors) Manipulates date ('Date'), date time ('POSIXct') and time ('hms') vectors. Date/times are considered discrete and are floored whenever encountered. Times are wrapped and time zones are maintained unless explicitly altered by the user. Package: r-cran-dtwbi Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dtw, r-cran-rlist, r-cran-e1071, r-cran-entropy, r-cran-lsa Filename: pool/dists/noble/main/r-cran-dtwbi_1.2-1.ca2404.1_all.deb Size: 77208 MD5sum: d38335b8073dbd522b7461c576f441ca SHA1: 8f7341794350e5fab1110d6920b9dafc8139f4cb SHA256: d6b464cbc568fdb96dfd0ad91f0702a1261814987198636e2f6a4fd3e959e6e9 SHA512: eae5fd4254128b4021aacba319353d59f67eac9d33654db51bce66205314508d91d79397b946a78a7e5e7647ee2099aa6e45b1bc3fa3aad4bb1517bab618a10d Homepage: https://cran.r-project.org/package=DTWBI Description: CRAN Package 'DTWBI' (Imputation of Time Series Based on Dynamic Time Warping) Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), . Performance criteria are added to compare similarity between two signals (query and reference). 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These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data. Package: r-cran-dtwrappers Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dtwrappers_0.0.2-1.ca2404.1_all.deb Size: 89146 MD5sum: 318bf9e99cef97008e9ee33406083e46 SHA1: 9615b30a66490bb0f470e43821f0c718562beca2 SHA256: 75338538ec62018e4e68b7de6aa70e1265c6704933a3443eccb2007e1401a5ef SHA512: d559ffbf4c4782b472f2bd6296f9b23cf44219a96152c59ab5e90a2795e918b53926cb8bd3cccb12d5b034870b5b2d5dddc5599619058171a87f0c883641b63f Homepage: https://cran.r-project.org/package=DTwrappers Description: CRAN Package 'DTwrappers' (Simplified Data Analysis with Wrapper Functions for the'Data.Table' Package) Provides functionality for users who are learning R or the techniques of data analysis. 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Package: r-cran-dtwumi Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dtw, r-cran-rlist, r-cran-e1071, r-cran-entropy, r-cran-lsa, r-cran-dtwbi Filename: pool/dists/noble/main/r-cran-dtwumi_1.1-1.ca2404.1_all.deb Size: 80656 MD5sum: b754017bf1e2b42b7c9f03879459d699 SHA1: 7df8183594d5a6dcfa2eb9979e4929acf175de46 SHA256: 9c1cf44af3af2f0d333b60a57aca02bba202f67de6a3874b168e7f0cdeea99f3 SHA512: 31fdbd733e06c1294e3a9a8ab2aaed86ed158ec728a4d2775c869c65c476fac929978ff345f40441397bdddf8928beeb182779f92ae8cc92d1369ccb53f6d3d7 Homepage: https://cran.r-project.org/package=DTWUMI Description: CRAN Package 'DTWUMI' (Imputation of Multivariate Time Series Based on Dynamic TimeWarping) Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) : a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided. Package: r-cran-dual Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dual_0.0.6-1.ca2404.1_all.deb Size: 242686 MD5sum: cb5bc67ff95ecf9db65237d0312539a0 SHA1: 5156d7c1640b1992f160655ef15dcc879a7a2885 SHA256: b36cfee2f4a7cf9ccbdeed2d0f75ce2015746dfacca8d43572edb4a4ef4388ce SHA512: 054ed9aa7dab73b3f1c10304db893cf72920a63b5f53fa12d73b986ea3b91eace20cde86f4f4502622ecd9540f07a7cdb8a12b88ad96b81792326a75dc1ec78b Homepage: https://cran.r-project.org/package=dual Description: CRAN Package 'dual' (Automatic Differentiation with Dual Numbers) Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. 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Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets. Package: r-cran-duawranglr Architecture: all Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-haven, r-cran-readxl, r-cran-readr, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-duawranglr_0.6.8-1.ca2404.1_all.deb Size: 88382 MD5sum: 946c17a4f2307a456497d5a12e7dc3c0 SHA1: 8e7163037218a874413aacaa7f0146def5f139cc SHA256: 844d890cab177de6354f19a7718b67b37505f55398bbdd888fb76d9a5031e7d2 SHA512: b0cbf3724b117d0be8b5ccdaeef80d9fc29f7a368149f1e699553daf50feb97722d8f89ed4a9b49da69bce6ac483996890c8204b40b70259ea14ff423ffbe417 Homepage: https://cran.r-project.org/package=duawranglr Description: CRAN Package 'duawranglr' (Securely Wrangle Dataset According to Data Usage Agreement) Create shareable data sets from raw data files that contain protected elements. 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Package: r-cran-duckduckr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-duckduckr_1.0.0-1.ca2404.1_all.deb Size: 14892 MD5sum: c5dfc810d0154199086246e6163a11e4 SHA1: f8c7dd6354f26b383453379347483a7efb6e2fa1 SHA256: ce4e620b3036847793dd5120fe9231258823a75fc88c79e04995e65165fe2b6b SHA512: 016fa3022b9669ed15044fbb60d812264f83da990043fe706fd874b6612a11692d90d9d3c09556c4355bd778af7d95db37b5c7230a7df6efbf1e1b4129c975be Homepage: https://cran.r-project.org/package=duckduckr Description: CRAN Package 'duckduckr' (Simple Client for the DuckDuckGo Instant Answer API) Programmatic access to the DuckDuckGo Instant Answer API . Package: r-cran-duckh3 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-duckspatial, r-cran-glue Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-duckh3_0.1.0-1.ca2404.1_all.deb Size: 1727328 MD5sum: bc759ec0d0b727606ce7ebfe196ccee0 SHA1: 3f4c017bbd1bcb82e4ac453d44de72cbcd83f5ce SHA256: 266a38a29b729e448a278de473ea73399e42ee0879aa230b141e96e857e0bbdb SHA512: c84c14bebb534561cdb0b4ea72983e4dbe7b6bba42e552713f9eccd51b416471629359e8983a502bec2b314127f1043ba491718c99fb81af9c938e730c39045f Homepage: https://cran.r-project.org/package=duckh3 Description: CRAN Package 'duckh3' (H3 Extension of 'DuckDB') Fast & memory-efficient functions to analyze and manipulate large data sets. 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Package: r-cran-ducklake Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2417 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb Suggests: r-cran-admiral, r-cran-diagrammer, r-cran-fs, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-pharmaversesdtm, r-cran-purrr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ducklake_0.6.0-1.ca2404.1_all.deb Size: 967926 MD5sum: ba2b6423e32c4d4adcc77b9788ced557 SHA1: d4410fbb2494e3a523387465de33f2a047717307 SHA256: d4aa2145fa3af6ab25fc8377c3d20e5645914e068cdf6d17cb9840a97e3c4d9f SHA512: 0f3ecb7305ae6af7746589feb1db8fc5349ac7933d0a35bee6e021d1661ef92bf9f4b3193312c15efee7dcd1e12e7007a17fd9391d217c88ef6be29ace85cecf Homepage: https://cran.r-project.org/package=ducklake Description: CRAN Package 'ducklake' (Interact with 'DuckLake' from R) A 'tidyverse'-friendly interface to 'DuckLake' , the 'DuckDB' lakehouse format. Attach versioned data lakes from R and work with them using familiar 'dplyr' verbs, with support for ACID transactions, time travel queries, snapshot audit trails, data inlining, encrypted storage, multiple catalog backends ('DuckDB', 'PostgreSQL', 'SQLite', 'MySQL'), and remote access over the 'Quack' protocol from 'DuckDB'. 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Show DVH diagrams, check and visualize quality assurance constraints for the DVH. Includes web-based graphical user interface. Package: r-cran-dvir Architecture: all Version: 3.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pedtools, r-cran-forrel, r-cran-mirai, r-cran-pedfamilias, r-cran-pedprobr, r-cran-ribd, r-cran-verbalisr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dvir_3.4.2-1.ca2404.1_all.deb Size: 661338 MD5sum: 3e79d1129b6f785daa02a3787b8026e0 SHA1: 881334f38571d33ca0e9034044938ce68fcd7ef8 SHA256: 9b3a8ea0919ee4c4cf59cfaf239e6e9f89e938eb1922a35ac9f16560e70a1998 SHA512: 7904b6217bcbf892ecaee3d78f98f710b1b31e3e17a11b90067f5f76ed91bb1df655fad42163a90b46a5f27d9723501b0c4737af3f20645cc87736005885013e Homepage: https://cran.r-project.org/package=dvir Description: CRAN Package 'dvir' (Disaster Victim Identification) Joint DNA-based disaster victim identification (DVI), as described in Vigeland and Egeland (2021) . Identification is performed by optimising the joint likelihood of all victim samples and reference individuals. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. 'dvir' is part of the 'pedsuite' collection of packages for pedigree analysis. Package: r-cran-dvqcc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tsdyn Filename: pool/dists/noble/main/r-cran-dvqcc_0.1.0-1.ca2404.1_all.deb Size: 68922 MD5sum: 10eaa4bb150438ede3338efdd97b7439 SHA1: 15a0acf46db7eae7c1444b528883716ca75cc709 SHA256: 14a446b4fd4f8b66f5477bb09ad37c2e5b1f687e25000deb6ed25d74cf028ae4 SHA512: cea716627474f09d826c7e457b5de6e951b299f4c09681ddee3e0f13d1f292ae064c844cd8a4fb704d08938270f91a6d13228d372d16c614d818d78d87346c0b Homepage: https://cran.r-project.org/package=dvqcc Description: CRAN Package 'dvqcc' (Dynamic VAR - Based Control Charts for Batch Process Monitoring) A set of control charts for batch processes based on the VAR model. The package contains the implementation of T2.var and W.var control charts based on VAR model coefficients using the couple vectors theory. In each time-instant the VAR coefficients are estimated from a historical in-control dataset and a decision rule is made for online classifying of a new batch data. Those charts allow efficient online monitoring since the very first time-instant. The offline version is available too. In order to evaluate the chart's performance, this package contains functions to generate batch data for offline and online monitoring.See in Danilo Marcondes Filho and Marcio Valk (2020) . Package: r-cran-dvs Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-cmna Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dvs_0.0.1-1.ca2404.1_all.deb Size: 31236 MD5sum: 144446093f11c534e6ba0659f98a80cb SHA1: 1a65f6aecff542409375688909a92e6a0516c762 SHA256: 789fa7f8a0ec341b9f4e925aed0dcc6918657765847e236be580a4edcf907c9c SHA512: 5895ff13de6d0e261ad817be6f7ad6dbab236756c249495cefaeb30b84611c89f775eb1a21d150c81d068217871d565032cad5c243ed6870827bfaee3c256160 Homepage: https://cran.r-project.org/package=DVS Description: CRAN Package 'DVS' (Stability Selection with Lasso after Variable Decorrelation) Implements stability selection with Lasso after variable decorrelation for identifying relevant variables in high-dimensional data. The method applies Air-HOLP screening and Gram-Schmidt orthogonalization before Lasso-based stability selection. Package: r-cran-dwavenardl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nardl, r-cran-wavelets, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-dwavenardl_0.1.0-1.ca2404.1_all.deb Size: 17070 MD5sum: b8d7e6aff967522948e8d738408bdee4 SHA1: 436dc4f1bf09866be497717bbe1c0f7ec0f57767 SHA256: 03df16f8ec9bc85f3ad4604d9bd1984bd3420ce33848dbda36710f667ec66a0c SHA512: eb86a8556a9c84227c7f2b9c5c412dbf10c045cc2092c7db486e828c187875314a913e3789f71f6cf772a2b9a1425d36a48ca47e9fd0a89c9aeca1827b7091e7 Homepage: https://cran.r-project.org/package=DWaveNARDL Description: CRAN Package 'DWaveNARDL' (Dual Wavelet Based NARDL Model) Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. . Package: r-cran-dwbmodelun Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4564 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-dygraphs, r-cran-htmltools, r-cran-terra Suggests: r-cran-hydrogof, r-cran-knitr, r-cran-ncdf4, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dwbmodelun_2.0.1-1.ca2404.1_all.deb Size: 4052090 MD5sum: 4e47f570a6369a72618eb0505f364087 SHA1: 0c62c7c44851aee1814664f85aca47b5962e341d SHA256: 90b46c0a07ad86f4b77d3293a8613c95a4ecf12ff25af2aedb95f0218f072d04 SHA512: 06aa2f29b5434ce9b0a328286247ddb704fc37f0806e6dac0fb274f891906ce18d15ede81bcd8bd0d6c3fde2ee4ab1c42618ce89350c5be14f330df5646838fc Homepage: https://cran.r-project.org/package=DWBmodelUN Description: CRAN Package 'DWBmodelUN' (Dynamic Water Balance a Hydrological Model) A tool for hydrologic modelling using the Budyko framework and the Dynamic Water Balance model with Dynamical Dimension Search algorithm to calibrate the model and analyze the outputs from interactive graphics. It allows to calculate the water availability in basins and also some water fluxes represented by the structure of the model. See Zhang, L., N., Potter, K., Hickel, Y., Zhang, Q., Shao (2008) "Water balance modeling over variable time scales based on the Budyko framework - Model development and testing", Journal of Hydrology, 360, 117–131. See Tolson, B., C., Shoemaker (2007) "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration", Water Resources Research, 43, 1–16. Package: r-cran-dwctaxon Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-settings, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat, r-cran-mockery, r-cran-readr, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown, r-cran-patrick, r-cran-stringi, r-cran-english, r-cran-tidyr, r-cran-curl, r-cran-httr Filename: pool/dists/noble/main/r-cran-dwctaxon_2.0.4-1.ca2404.1_all.deb Size: 1087984 MD5sum: c9a085881a8ac56989f5f0f360d148b2 SHA1: 546f79a460e1511f1963158460af3a8ade16bdc8 SHA256: c0154da8c0dd5bda8302337544f76c441e1f8d021e391483f2ccad85d76054e5 SHA512: 64500ed0376e8707fe8523639eae54b79356011d7c0b5c1e1f8d65b9ed03bb40ab7b6570c6538fe403d9054f7fcb7b0d7cd49671700560cdf78127bfa4476964 Homepage: https://cran.r-project.org/package=dwctaxon Description: CRAN Package 'dwctaxon' (Edit and Validate Darwin Core Taxon Data) Edit and validate taxonomic data in compliance with Darwin Core standards (Darwin Core 'Taxon' class ). Package: r-cran-dwdlarger Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-dwdlarger_0.2-0-1.ca2404.1_all.deb Size: 109570 MD5sum: dd143e16042a4f3059e5c9c39e71310e SHA1: 5b0e674b5d8726be94d94505ef229ba9582baec3 SHA256: 35790693e08fecaa248476bf249eae1533de5ee6d1e2eb9d1bc3c7063f1f090e SHA512: d3b14dfba784ffac371e4aa6c2022e83d73d99aebf39cd2e66712b19d06b0e915490af1672449bbd7aefe6a65d640c1953e82bc21b85ceba42e3a2f59151cf8d Homepage: https://cran.r-project.org/package=DWDLargeR Description: CRAN Package 'DWDLargeR' (Fast Algorithms for Large Scale Generalized Distance WeightedDiscrimination) Solving large scale distance weighted discrimination. The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) for more details. Package: r-cran-dwlasso Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-hglasso Filename: pool/dists/noble/main/r-cran-dwlasso_1.1-1.ca2404.1_all.deb Size: 30106 MD5sum: 909880715ca320e43cfc361440a13ecc SHA1: 6d00139d47169cc924aaceaa524938fdebec727b SHA256: 1d402aa30d775ad1a922a468c45c36d9663ba288859ae984696cdc56218a82aa SHA512: 5a4c22a88ea27d8466120783c51a4930e022a33a8d8f913f340f3be1d98ca5ada187b3df7571a236b0b6b62fdedfd367ec2a812f06793677b2f8aefdd6395a07 Homepage: https://cran.r-project.org/package=DWLasso Description: CRAN Package 'DWLasso' (Degree Weighted Lasso) Infers networks with hubs using degree weighted Lasso method. Package: r-cran-dwlm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dwlm_0.1.0-1.ca2404.1_all.deb Size: 34134 MD5sum: 3f509b94d675f76bb71f15691ff094f9 SHA1: 0dc09eed84d96d106772fa6e52364b60c3ea22c9 SHA256: 53d4ff81a3741fe8f5d1913f7cea29f6ea95bae82ac119eedbd4f9947d4896c6 SHA512: 1cd626bfa16be4dcc85e01065c40ab67ef7aa050b975dd3f184e76c050eb2e8eb27245e4b3a649ebdca102cc29ee5d2eefe917ee10310fdad2a40aec83fc2f4c Homepage: https://cran.r-project.org/package=dwlm Description: CRAN Package 'dwlm' (Doubly Weighted Linear Model) This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) and Ripley & Thompson(1987) . Package: r-cran-dwls Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-reshape, r-cran-seurat, r-cran-rocr, r-cran-varhandle, r-cran-dplyr, r-cran-e1071, r-bioc-mast, r-bioc-summarizedexperiment Suggests: r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-dwls_0.1.0-1.ca2404.1_all.deb Size: 197078 MD5sum: 179fd3f3bb572d603a1ff24b439d4962 SHA1: 1e0ece09fd907515d3044bdea5553c1faf3c05fd SHA256: dfd2fad2ec0becfd3f0aef2442b16c20b8595fe56ee0cad317d763922faf537a SHA512: 714db8125f58842bdd778a969eb4fa9e59b3278cddebf83adc332ee489ca9cf9f9e488f5930980b56f38f63ab3b1fb338a0f0a77667b5ed8a64541b0b32a793a Homepage: https://cran.r-project.org/package=DWLS Description: CRAN Package 'DWLS' (Gene Expression Deconvolution Using Dampened Weighted LeastSquares) The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: for more information about the development of 'DWLS' and the methods behind our functions. Package: r-cran-dwmmlridge Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-styperidge.reg Suggests: r-cran-isdals, r-cran-mctest Filename: pool/dists/noble/main/r-cran-dwmmlridge_0.1.1-1.ca2404.1_all.deb Size: 30702 MD5sum: 97532c2349443313410c8175beb5104f SHA1: fb50e5343e5ffc24e11f01d422c19379be1f0346 SHA256: 200ec589d3f582f701872b3042f3e937e19af6395dcd0334dcd0a1a929db0865 SHA512: d8aa04682efab7c83562a6cf8d3fba17022e296838b09843b1192334b9e568cda6a4e8384241249572f15d514fc402f2596399adb45dd7c81666b274feec8c85 Homepage: https://cran.r-project.org/package=dwmmlRidge Description: CRAN Package 'dwmmlRidge' (Dynamically Weighted Modified Maximum Likelihood (DWMML) RidgeRegression) Implements the dynamically weighted modified maximum likelihood ridge (DWMMLR) regression estimator, a robust and multicollinearity-aware linear regression estimator that combines the DWMML3 weighting procedure of Sazak (2019) with ridge penalization to address both outlier sensitivity and variance inflation due to multicollinearity. The ridge parameter is selected automatically using the approach implemented in the 'ridgregextra' package (Karadag, Sazak, and Aydin, 2023) , described further in Karadag, Sazak, and Aydin (2026) , which targets a variance inflation factor (VIF) close to but not below 1, removing the need for manual tuning. Returns comprehensive outputs (coefficients, fitted values, residuals, mean squared error (MSE), standard errors, R-squared, and adjusted R-squared) through a simple x/y interface. Package: r-cran-dwp Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-gtools, r-cran-invgamma, r-cran-magrittr, r-cran-mass, r-cran-matrixstats, r-cran-mvtnorm, r-cran-plotrix, r-cran-pracma, r-cran-sf, r-cran-statmod, r-cran-vgam Filename: pool/dists/noble/main/r-cran-dwp_1.2-1.ca2404.1_all.deb Size: 420088 MD5sum: d636d5eefba8eb34d8537018ab19bb4f SHA1: 825ff85d2eeb957f131923589c3d308050b6ffcc SHA256: b8bfdc7fcb3773472bcc4f5b96f9bf20df90fb5c7455787f0f43f71d68ea0fbd SHA512: 119a72c9f05ef6a369198924d3ff3bc83f6b1894115bafd38aafc1b53fa52ea58629295f7ee111fed16535d98b6d2aa1513cc07f5faa0d0dbec3d6d60cb60cef Homepage: https://cran.r-project.org/package=dwp Description: CRAN Package 'dwp' (Density-Weighted Proportion) Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the 'GenEst' or 'eoa' package for estimating bird and bat mortality, following Dalthorp, et al. (2024) . Package: r-cran-dwreg Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-discreteweibull, r-cran-ecdat, r-cran-survival Filename: pool/dists/noble/main/r-cran-dwreg_3.0-1.ca2404.1_all.deb Size: 43636 MD5sum: a649f7da268e33dff32081687a18c039 SHA1: e3c6116e0a4e7beeaa93c98c5bf864ad0ae8b9ea SHA256: de07a88e5d09000b91bfffbd84250ef5e075a5927128e4fc8c39e42ca3ef6cb8 SHA512: 4987750b0bbb15135a3198054484d01886e8e0a4726e0a2180807f21710dc8c913432164f4cb7398aa0bfa2b5853921aa55dca686dec27efae1276a249269121 Homepage: https://cran.r-project.org/package=DWreg Description: CRAN Package 'DWreg' (Parametric Regression for Discrete Response) Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. 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The package supports empirical transition counts, maximum likelihood estimation of transition probabilities, and identification of univariate and bivariate patterns of interaction in dyadic sequences. Package: r-cran-dyadmlm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2412 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-pillar, r-cran-rlang, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-brms, r-cran-glmmtmb, r-cran-htmltools, r-cran-knitr, r-cran-lavaan, r-cran-marginaleffects, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dyadmlm_0.2.0-1.ca2404.1_all.deb Size: 768882 MD5sum: f416f4c199e400ab4a1f5f1df41a8897 SHA1: 1d74069e78c717ebf9b755d52f5dfc03972ecc3e SHA256: 66678d0767233e3ddcea7e98d467463d5470d4d4dd252200cef05297bf315101 SHA512: debc3aee16ce9c4b0c1f60f63c75b73ce4dbe0c0faaac2a88c30980ba11127954b003e8f6b49168cb8e5980b9a1d2245669bfb70a1ff27f5acf1cd41be58f9fe Homepage: https://cran.r-project.org/package=dyadMLM Description: CRAN Package 'dyadMLM' (Tools for Dyadic Multilevel Models) Provides tools for dyadic multilevel modeling with linear and generalized linear mixed-effects models. It validates and prepares long-format cross-sectional and intensive longitudinal data, including ecological momentary assessment designs, for distinguishable and exchangeable dyads. It also supports datasets containing multiple observed dyad compositions. It constructs composition-aware, model-ready variables for Actor-Partner Interdependence Models (APIMs), Dyadic Score Models (DSMs), and Dyad-Individual Models (DIMs). Prepared data can be used with model engines such as 'glmmTMB' and 'brms' for Gaussian and non-Gaussian outcomes, including counts, proportions, and skewed continuous responses. Post-estimation tools compare compatible fitted models and back-transform exchangeable sum-and-difference random-effect covariance structures into member-level quantities. The APIM and DSM specifications and their relationships follow Iida et al. (2018) ; the multilevel sum-and-difference random-effects implementation for exchangeable dyads adapts del Rosario and West (2025) . Package: r-cran-dyadratios Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-progress Suggests: r-cran-dplyr, r-cran-rio, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-dyadratios_1.4-1.ca2404.1_all.deb Size: 251258 MD5sum: 33540de29e95e63cde425e7179b5ffb0 SHA1: 645ee3bc6d165509db5ef47127697ba1dcf74604 SHA256: 52cfea99d86e58c182c3f50531b2aa9bfb9d09e013f1c1f4c0c0a020d6c5937f SHA512: 083d202e9b683378989667e38d2c7d82d1052d7af6605b990435822c9781953d09a11058f4af1cf3b59a100b68d312a91412abde79815c544968a6ad73e1f489 Homepage: https://cran.r-project.org/package=DyadRatios Description: CRAN Package 'DyadRatios' (Dyad Ratios Algorithm) Estimates the Dyad Ratios Algorithm for pooling and smoothing poll estimates. The Dyad Ratios Algorithm smooths both forward and backward in time over polling results allowing differences in both question type and polling house. The result is an estimate of a single latent variable that describes the systematic trend over time in the (noisy) polling results. See James A. Stimson (2018) and the package's vignette for more details. Package: r-cran-dyads Architecture: all Version: 1.2.22.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cholwishart, r-cran-mass, r-cran-rfast, r-cran-mvtnorm, r-cran-dplyr Suggests: r-cran-plyr Filename: pool/dists/noble/main/r-cran-dyads_1.2.22.3-1.ca2404.1_all.deb Size: 230306 MD5sum: 134ee2bce5520bb46352d4660e5a4092 SHA1: 92e6e40caf85be0f50428551315c825083dfb5c2 SHA256: 9fdcb2d68ad42da997a889ebe4c8f12516ce6d94700d249c85e17a5987945f65 SHA512: c12ae118ceb2b024989c57d63aee5f0a302e0a7775dc7f60d6d6e16705e3f0d3651422996e31dfe3668be51d1a60ee686012b7ebc48d1e97fe341356e77abe2f Homepage: https://cran.r-project.org/package=dyads Description: CRAN Package 'dyads' (Dyadic Network Analysis) Contains functions for the MCMC simulation of (multilevel) dyadic network models j2 (Zijlstra, 2017, ) and p2 (Van Duijn, Snijders & Zijlstra, 2004, ), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) ), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) , the (multilevel) b2 model. Package: r-cran-dycdtools Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ncdf4, r-cran-tidyr, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-lubridate, r-cran-r.utils Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dycdtools_0.4.4-1.ca2404.1_all.deb Size: 118634 MD5sum: 120b716dd6ab96da3521cc07df962339 SHA1: 029db5401fa298a04162d2bab1e19e90ca62d2c4 SHA256: ff44c5ece97fd252c1262d915e829c62bcb3c63cfb38046c8a5ac049e9ca5ba6 SHA512: de6ba38998ef5f770fdb9fdd98f2600acd389cd020e7f55b143e209dbe9b53ae7f3f18b4e201113d24ea8ae57cffbc39d1fab918906bda7bc7e9c3abd2b62b44 Homepage: https://cran.r-project.org/package=dycdtools Description: CRAN Package 'dycdtools' (Calibration Assistant and Post-Processing Tool for AquaticEcosystem Model DYRESM-CAEDYM) Dynamic Reservoir Simulation Model (DYRESM) and Computational Aquatic Ecosystem Dynamics Model (CAEDYM) model development, including assisting with calibrating selected model parameters and visualising model output through time series plot, profile plot, contour plot, and scatter plot. For more details, see Yu et al. (2023) . Package: r-cran-dydea Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chaos01 Filename: pool/dists/noble/main/r-cran-dydea_0.1.0-1.ca2404.1_all.deb Size: 31472 MD5sum: 0303a3d7d04a298166650af3a55161b6 SHA1: 3e8e338dfd6e9225ba6ea269d41058f87d1236a6 SHA256: 751d5d7cf82904ff1334380381ff7e9abda888bdf108d2f968a107f40e3a88fa SHA512: d1cc7a0821e44a27a737398aea212b15996c5d407b5ae105938b8291988ce7c7f9867af1a285cf7500e39e23e9b684ddfe64be9ba3c282ef58f6f20f291c2ddb Homepage: https://cran.r-project.org/package=dydea Description: CRAN Package 'dydea' (Detection of Chaotic and Regular Intervals in the Data) Finds regular and chaotic intervals in the data using the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) . Package: r-cran-dygraphs Architecture: all Version: 1.1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-htmlwidgets, r-cran-htmltools, r-cran-zoo, r-cran-xts Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dygraphs_1.1.1.6-1.ca2404.1_all.deb Size: 380868 MD5sum: 2d912465bb3cdc638ce0efa16bcaa8b3 SHA1: 44829dc7117578a931d37bbdfbe0acc6b6696d10 SHA256: ea9afc05ad2bef1d69e82df31b24af0479528bccb3d9886f6766504246ed5f60 SHA512: 3fa0031d98d0bccc1c290f464f5869602fd466e67e99f5dcd224cac32bcd92d3abdec39f228350f50f88b333dbb8a3181334351d0f12d9bb1e2401c47e11a554 Homepage: https://cran.r-project.org/package=dygraphs Description: CRAN Package 'dygraphs' (Interface to 'Dygraphs' Interactive Time Series Charting Library) An R interface to the 'dygraphs' JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting. Package: r-cran-dykstra Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dykstra_1.0-0-1.ca2404.1_all.deb Size: 22400 MD5sum: bcc4df8352325da5645b22ebea3c82f0 SHA1: c327f4898e51d5f9f3c8f77a3646d3d2a83fec5b SHA256: 01339e0d1f1ffa447f68c0da96c1d246115d64c4dcb1f7b9339e5805fc3826e0 SHA512: 0263767dce54df5df2bed5b11cd6ff843fea571943931a610902191a8708b40e36e1639f6d1da956f3a3d84ae823e635355bdddee5468f6f59ccf7199d10c06a Homepage: https://cran.r-project.org/package=Dykstra Description: CRAN Package 'Dykstra' (Quadratic Programming using Cyclic Projections) Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) for details. Package: r-cran-dym Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dym_0.2-1.ca2404.1_all.deb Size: 26362 MD5sum: 8d63d6a7d03d1f876a99209ae9d9eccf SHA1: 13417a1ab222161bf79798f8545ef5d70013b7b6 SHA256: 06b095f13e837189e818033520718102844a7a0689003b2f292ad498d098086d SHA512: f28f36de6a61dfadbef43843830abd6a4c08d64b5b1ec354d2a297448f90431487ddaf4852413f97509adfe74bd2e97ae2fbdbd67e47351414af6c44038b41ca Homepage: https://cran.r-project.org/package=DYM Description: CRAN Package 'DYM' (Did You Mean?) Add a "Did You Mean" feature to the R interactive. With this package, error messages for misspelled input of variable names or package names suggest what you really want to do in addition to notification of the mistake. 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Can be applied for remote sensing purposes, dynamically check the best subset of available covariates for the given dataset and crop. Package: r-cran-dymo Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dymo_2.0.0-1.ca2404.1_all.deb Size: 81314 MD5sum: a76e5f7a465d0f7a2812ed310f4b65d1 SHA1: e9d988bc460d0230509ffac8c954c7208e61f5a1 SHA256: fe3ab8ec48fb847aa9fd7bddd73c917fc10e97be629963b3f81e5cef5b817148 SHA512: 83c1726400846e36485e02046e2e97e2c541086e1cb3c13c49bb5b07a0891afdc4b0fccc405cdef98ae9cee313284142f7f42fda6dbf2f502a3df98916702c64 Homepage: https://cran.r-project.org/package=dymo Description: CRAN Package 'dymo' (Dynamic Mode Decomposition Forecasting with Conformal PredictiveSampling) The DYMO package provides tools for multi-feature time-series forecasting using a Dynamic Mode Decomposition (DMD) model combined with conformal predictive sampling for uncertainty quantification. Package: r-cran-dyn.log Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-glue, r-cran-r6, r-cran-rlang, r-cran-stringr, r-cran-yaml Suggests: r-cran-devtools, r-cran-usethis, r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-lintr, r-cran-remotes, r-cran-pkgdown, r-cran-prettydoc, r-cran-here, r-cran-fansi, r-cran-pander, r-cran-dt Filename: pool/dists/noble/main/r-cran-dyn.log_0.4.0-1.ca2404.1_all.deb Size: 321528 MD5sum: 92dcf2bd10c607fb4d8e6756580b0c29 SHA1: fe056a077e947a91a587c96a9887753c0c69830a SHA256: 77167a48ecbe2134f946f5eda8ebbfd7ced0b213104c657a0a49a9239d809b2d SHA512: 36aa136e67a8e9201a4094bfe38d2a9ba7af4c9d5e2a83999b33ed32868e50507d460e42af890895dbf6975c845e09456032e69d343c6af6d39c4090381178f8 Homepage: https://cran.r-project.org/package=dyn.log Description: CRAN Package 'dyn.log' (Dynamic Logging for R Inspired by Configuration DrivenDevelopment) A comprehensive and dynamic configuration driven logging package for R. While there are several excellent logging solutions already in the R ecosystem, I always feel constrained in some way by each of them. Every project is designed differently to solve it's domain specific problem, and ultimately the utility of a logging solution is its ability to adapt to this design. This is the raison d'être for 'dyn.log': to provide a modular design, template mechanics and a configuration-based integration model, so that the logger can integrate deeply into your design, even though it knows nothing about it. Package: r-cran-dyn4cast Architecture: all Version: 11.11.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10382 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-metrics, r-cran-tidyr, r-cran-ggplot2, r-cran-generics, r-cran-magrittr, r-cran-formattable, r-cran-zoo, r-cran-modelmetrics, r-cran-dplyr, r-cran-modelsummary, r-cran-corrplot, r-cran-marginaleffects, r-cran-tibble, r-cran-purrr, r-cran-lifecycle, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-tidyverse, r-cran-rmarkdown, r-cran-covr, r-cran-caret, r-cran-kableextra, r-cran-knitr, r-cran-spelling, r-cran-psych, r-cran-readr, r-cran-metbrewer, r-cran-data.table, r-cran-ggtext, r-cran-lubridate, r-cran-forecast, r-cran-mass, r-cran-mlogit, r-cran-nnet, r-cran-betareg, r-cran-mvprobit, r-cran-misctools Filename: pool/dists/noble/main/r-cran-dyn4cast_11.11.26-1.ca2404.1_all.deb Size: 2592180 MD5sum: 5a0e049cc03a82b1fb8c2bc95213e8fd SHA1: a795a39f3a3e79112615f04fcb1fbe4889aca082 SHA256: ad0a1a5e86f19ef6ab304a2e3cf878b0fce1c188ced3bda88767204ba43cc378 SHA512: 55f0011c9f8b784aaa1348707630cd5b6893a9e8ba7a7797e822c65d52ab22115aa2802e0f86a8af6a0a5e8f86091645ebe5eb0619a518eac39dd81e4af4d5d6 Homepage: https://cran.r-project.org/package=Dyn4cast Description: CRAN Package 'Dyn4cast' (Dynamic Modeling and Machine Learning Environment) Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving. The package is a variety but the following references are important guide to the major themes in the package (Hyndman & Athanasopoulos (2014 ISBN 978-0-9875071-0-5), Alkire & Santos (2014, doi.org/10.1016/j.worlddev.2014.01.026)). Package: r-cran-dyn Architecture: all Version: 0.2-11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zoo Suggests: r-cran-lattice, r-cran-mass, r-cran-mcmcpack, r-cran-quantreg, r-cran-randomforest, r-cran-sandwich, r-cran-tseries Filename: pool/dists/noble/main/r-cran-dyn_0.2-11.0-1.ca2404.1_all.deb Size: 52556 MD5sum: c1e78fd60a7e6388499c08752f1fce29 SHA1: fd08ffe85443c85dc73adac4930d450ca553e9e0 SHA256: 95d06e311c931d15b42ec91e7aa4c67da44c0cf017279d83f609b1fc3772f752 SHA512: a231fffcdf65946cb1c7d00aff22f2b0a187a4b2f2d9dcf3147f3cbe18df39de4c43f62f2318777954a3f602d88531dad0abd97890226d49b87697c889557b07 Homepage: https://cran.r-project.org/package=dyn Description: CRAN Package 'dyn' (Time Series Regression) Time series regression. The dyn class interfaces ts, irts(), zoo() and zooreg() time series classes to lm(), glm(), loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values. Package: r-cran-dynafluxr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bspline, r-cran-nlsic, r-cran-optparse, r-cran-qpdf, r-cran-arrapply, r-cran-slam, r-cran-gmresls, r-cran-shiny, r-cran-shinyjs, r-cran-shinyfiles Suggests: r-cran-runit, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dynafluxr_1.0.1-1.ca2404.1_all.deb Size: 266836 MD5sum: 1f6b6b1c71eefd6f00149e7255a23cc7 SHA1: 33647d47844c32125db4313c26b669379d6c09d5 SHA256: a047276b3213ceed5ea0d8cd85779caf798b842fa8bfc8c9ff45794227f70880 SHA512: 4ca43e6291ee43b8a097916adc6148e5b3c6ab8fd3f17f10afa47ad16b5f0e5584d1da4158bf597bc53e18aad50a8fa037006cfed511b67e34ac312919b99d76 Homepage: https://cran.r-project.org/package=dynafluxr Description: CRAN Package 'dynafluxr' (Retrieve Reaction Rate Dynamics from Metabolite ConcentrationTime Courses) Reaction rate dynamics can be retrieved from metabolite concentration time courses. User has to provide corresponding stoichiometric matrix but not a regulation model (Michaelis-Menten or similar). Instead of solving an ordinary differential equation (ODE) system describing the evolution of concentrations, we use B-splines to catch the concentration and rate dynamics then solve a least square problem on their coefficients with non-negativity (and optionally monotonicity) constraints. Constraints can be also set on initial values of concentration. The package 'dynafluxr' can be used as a library but also as an application with command line interface dynafluxr::cli("-h") or graphical user interface dynafluxr::gui(). Package: r-cran-dynamac Architecture: all Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lmtest Suggests: r-cran-urca, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dynamac_0.1.12-1.ca2404.1_all.deb Size: 487368 MD5sum: e9e15b129f36751be82915cdfac550ac SHA1: 44b7cdbda5af6123935870f18741cb8aeda6ee52 SHA256: a32ff4946298720428190c041fc35811cffbff286cd524b8dd21ca98d8fcf1ec SHA512: 9679a9c405dc7a94cf4b23363babafacd5c27f9800f69ffb0f298dcb500a2e82845b8bc7ffae103d8fef82bd835d7b5748fabeab917403f2307a7bf6651eda98 Homepage: https://cran.r-project.org/package=dynamac Description: CRAN Package 'dynamac' (Dynamic Simulation and Testing for Single-Equation ARDL Models) While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 ). 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Rationale and model structure are described here: Da Re et al. (2021) and Da Re et al. (2022) . Package: r-cran-dynamic Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-simstandard, r-cran-tidyr, r-cran-lavaan, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-patchwork, r-cran-stringr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dynamic_1.1.0-1.ca2404.1_all.deb Size: 341076 MD5sum: 8bcdd8f8955b305ea23847de4678687a SHA1: cdac590894d3f9327da2718bdb38f597258d0f1b SHA256: 7f194cbfa70a92f5169b39f093de8a1825edc3822e76f78799b6155d013c8448 SHA512: 99e66aa671adbeabd28a6b784494ddbf57213bfdaa67ac8a894a681fb3fce1c1bbb8b10618e238e82cdc9a8a790e0f0a93932054d98edcec98653755b20cb367 Homepage: https://cran.r-project.org/package=dynamic Description: CRAN Package 'dynamic' (DFI Cutoffs for Latent Variable Models) Returns dynamic fit index (DFI) cutoffs for latent variable models that are tailored to the user's model statement, model type, and sample size. This is the counterpart of the Shiny Application, . Package: r-cran-dynamicmultiplex Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-clue, r-cran-igraph, r-cran-rlang Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-gganimate, r-cran-gifski, r-cran-ggalluvial, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-tidyr, r-cran-peacesciencer Filename: pool/dists/noble/main/r-cran-dynamicmultiplex_1.3.1-1.ca2404.1_all.deb Size: 172924 MD5sum: 4db7e6dc280cde44462ac6fe8f6faab0 SHA1: 98cba5531f154b69ecbe2a296e313e3cecd62200 SHA256: f23a6b769fbe6c4214f52b8ae2dac2ba79ff596438d82c44c49322f03527db67 SHA512: f5d45249a1c58298cd7fed79ffeb23c06003938ec31176c4a5131a0ec2fb6fc78b706a6e4baadf6ae77b7a4263ecc52aedec3e4e4615118fd8bf2fba45f3fe90 Homepage: https://cran.r-project.org/package=dynamicmultiplex Description: CRAN Package 'dynamicmultiplex' (Community Detection for Evolving Multiplex Networks) Multiplex temporal community detection with customizable interlayer coupling. Runs Louvain or Leiden community detection on each network layer and constructs interlayer ties using Jaccard similarity, overlap coefficient, node-strength weighted variants, or direct node identity links, and also provides a two-stage snapshot-and-match tracker that aligns independently detected per-layer communities across time with the Hungarian assignment algorithm. Supports user-specified layer connectivity via the layer_links argument, enabling adjacent-only temporal coupling that avoids the long-range pooling problem in standard multislice approaches. Package: r-cran-dynamicpv Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 967 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-heemod, r-cran-readr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-flexsurv, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-dynamicpv_0.4.2-1.ca2404.1_all.deb Size: 611506 MD5sum: 4d2664776c4adf350e89fdd4346f2701 SHA1: fc3b3d8fd7b70ad6ab77cf81c90bbe652d2a1ff7 SHA256: ce72ee0fd6e6eebc006173e8c8d4840632384db7d29759e97c67ab7381695397 SHA512: f0fc03ce57422ff70d487beafb5f513d63a1a488dfe8e39344bb435a5a716741a9cec30735a0d757e2f597e240e5d695d9593bc4dbb7ded9e166c6f5b83f8723 Homepage: https://cran.r-project.org/package=dynamicpv Description: CRAN Package 'dynamicpv' (Evaluates Present Values and Health Economic Models with DynamicPricing and Uptake) The goal of 'dynamicpv' is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges. Package: r-cran-dynamicsdm Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1425 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-googledrive, r-cran-lubridate, r-cran-magrittr, r-cran-reticulate, r-cran-rgee, r-cran-terra, r-cran-tidyr, r-cran-sf, r-cran-readr Suggests: r-cran-ape, r-cran-coordinatecleaner, r-cran-covr, r-cran-gargle, r-cran-gbm, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-matrixstats, r-cran-rmarkdown, r-cran-spthin, r-cran-stars, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-dynamicsdm_1.3.4-1.ca2404.1_all.deb Size: 1150878 MD5sum: 0370a10ffe24ede637c998c471fefd28 SHA1: 08e3a06c4ab62a04b54190ee72e91ed22a4a49d5 SHA256: a192dd7d8cecef70912974dbcd9b5457d35d2504cf3aecd0c311a74e140c5f9e SHA512: 8999d00ae26607bbf1e3b5f294c2d5b7edf8e33bcffec774306b7fbdf2c2b82eedbbd12c7950b3ff11161b15d056543dc7820a137a36fae56ec21bd60b29aa0d Homepage: https://cran.r-project.org/package=dynamicSDM Description: CRAN Package 'dynamicSDM' (Species Distribution and Abundance Modelling at HighSpatio-Temporal Resolution) A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution. Package: r-cran-dynamictreecut Architecture: all Version: 1.63-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dynamictreecut_1.63-1-1.ca2404.1_all.deb Size: 90964 MD5sum: 69e6b9b5fa104dc907446237b981b097 SHA1: 5cfc08ea973b15651d5edbf7f93c5c9ee635fd28 SHA256: e38846123a85b74f6f9d2f068ac12dbaef4b670737c3c0041a6d8e0d3182d50e SHA512: 75f377a7b1a4c41a111e98441ece92a0105bf17e301552b982ac4d3b7241552807191e5cca40955ee1a603249de114230bba373d57ada5a47b3f832e51032d9d Homepage: https://cran.r-project.org/package=dynamicTreeCut Description: CRAN Package 'dynamicTreeCut' (Methods for Detection of Clusters in Hierarchical ClusteringDendrograms) Contains methods for detection of clusters in hierarchical clustering dendrograms. 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'Dynare' is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium ('DSGE') and overlapping generations ('OLG') models. This package does not only integrate R and Dynare but also serves as a 'Dynare' Knit-Engine for 'knitr' package. The package requires 'Dynare' () and 'Octave' (). Write all your 'Dynare' commands in R or R Markdown chunk. Package: r-cran-dynasim Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dynasim_1.1-1.ca2404.1_all.deb Size: 31662 MD5sum: fb0be003efcc40f908081426fd3ef62c SHA1: efa327702fa2e7069478c2e76034521f9e2c78fd SHA256: a1b3c495ff784edb343d98369c3b3a41591f8d34d87074fdfa212bca8c3bd024 SHA512: 154440eb58f42ceb2b071fce27fb964cacf6ea5caded35b58ce8b9821af4b8d2a800d0e7b4aff8e682bc7379a099f6dec6e65fc552474bb7815e574225de5615 Homepage: https://cran.r-project.org/package=dynasim Description: CRAN Package 'dynasim' (Dynamics Similarity Coefficient) Implements the quantile-graph based Dynamics Similarity Coefficient (DSC) for comparing intrinsic dynamics of time series. 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Package: r-cran-dynate Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyverse, r-cran-matrix, r-cran-reshape2, r-cran-tibble, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dynate_0.1-1.ca2404.1_all.deb Size: 452550 MD5sum: 794d83d8f5284eb5133b00026840cbe1 SHA1: 6798513495126be6cd9b773e408114aa69b846af SHA256: 6e04d17952ee4164690bbc08d50ce705be6e3c4ae7e2b8bfa495d537a5912f06 SHA512: 68b971dbc881737d5cc2d1cbc603fff8fff9cdcf587d7d1415ea30b88dc2fa247232e158ab5c59fd3ae5eb09c0771f31bedb54af099bd7c940b3aafca94f76fb Homepage: https://cran.r-project.org/package=DYNATE Description: CRAN Package 'DYNATE' (Dynamic Aggregation Testing) A multiple testing procedure aims to find the rare-variant association regions. When variants are rare, the single variant association test approach suffers from low power. To improve testing power, the procedure dynamically and hierarchically aggregates smaller genome regions to larger ones and performs multiple testing for disease associations with a controlled node-level false discovery rate. This method are members of the family of ancillary information assisted recursive testing introduced in Pura, Li, Chan and Xie (2021) and Li, Sung and Xie (2021) . Package: r-cran-dynatopgis Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-terra, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dynatopgis_0.2.5-1.ca2404.1_all.deb Size: 425798 MD5sum: 612085f888bc5f4d889a50e63ff297f7 SHA1: 247363fa4aa35231bf01f546239f859ced045469 SHA256: 6d185dd4ce1903b1cc94ff50e5e9dfd4ab204ca3e50b4d79019ce175282944ee SHA512: 1357875c25cb68d0054fc7cb3dca3b467716e50d941a5ef07228fab86c09014152c86d5b284bfd9abe651a636ad43f679e00a2565e70c4ae7e572933dca15813 Homepage: https://cran.r-project.org/package=dynatopGIS Description: CRAN Package 'dynatopGIS' (Algorithms for Helping Build Dynamic TOPMODEL Implementationsfrom Spatial Data) A set of algorithms based on Quinn et al. (1991) for processing river network and digital elevation data to build implementations of Dynamic TOPMODEL, a semi-distributed hydrological model proposed in Beven and Freer (2001) . The 'dynatop' package implements simulation code for Dynamic TOPMODEL based on the output of 'dynatopGIS'. Package: r-cran-dynbiplotgui Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tcltk2 Suggests: r-cran-tkrplot, r-cran-foreign, r-cran-readxl Filename: pool/dists/noble/main/r-cran-dynbiplotgui_1.1.6-1.ca2404.1_all.deb Size: 144962 MD5sum: 8c44fcf7f10687d3ee9fb5921cd1c1d7 SHA1: 40b87f4a4c49f2d7d0f66642ae4595946217903c SHA256: b896c9eb0ad57e2767110f3ac311ee1591c2f1e97a4cca720a36336f8643731c SHA512: 70fb931aeed8b0f5828e0b0d9e99f2b929295f8461fbe6f15d7c824d384e6c30413bd2f8a365c935e5206a01761cd8b6d3df0274462f028aefc1ac24a491a59c Homepage: https://cran.r-project.org/package=dynBiplotGUI Description: CRAN Package 'dynBiplotGUI' (Full Interactive GUI for Dynamic Biplot in R) A GUI to solve dynamic biplots and classical biplot. Try matrices of 2-way and 3-way. The GUI can be run in multiple languages. Package: r-cran-dynclust Architecture: all Version: 3.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dynclust_3.24-1.ca2404.1_all.deb Size: 615928 MD5sum: 3324646e5fe125cb959c2ee574218ecb SHA1: 850c199ca4d77993e1b9dd7b9ec2aab4f73bfe5b SHA256: c30258a52a95f1be0f7bdd697a8fa3125c2cf4e8e77e628685c1c3b8dac39eed SHA512: c656f82fd068bf93a823bc158f1225aceab06351d3e29da078261f12c8cc22d38d1d3c9bfa0bfa551b5d073b4a179943c0c15a675d814011172ea7ca2a1cacc9 Homepage: https://cran.r-project.org/package=DynClust Description: CRAN Package 'DynClust' (Denoising and Clustering for Dynamical Image Sequence (2D or3D)+t) A two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus. 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Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8. 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Package: r-cran-dyspiadata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3867 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dyspiadata_0.1.2-1.ca2404.1_all.deb Size: 3923722 MD5sum: c89cfccd0499950c223b12253f8e73a4 SHA1: d9092ca5cf8a3e53751ac5e9a31d3e03e4a6db7f SHA256: 567fd2526ecde8c565412051e99ce91865d7bc3bc31be757a714459a54cef5e0 SHA512: e6c6c6631a98c32a5d128c27a81733d8b83ccfbdd36b86c619d171250e47d7db0c2990591932b6ec824cab159d7e7b9f0c06e1d729483b925297bd445325be1b Homepage: https://cran.r-project.org/package=DysPIAData Description: CRAN Package 'DysPIAData' (Background and Pathway Data Used in 'DysPIA') This dataset includes Background and Pathway data used in package 'DysPIA'. 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Package: r-cran-e4tools Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5011 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-datacombine, r-cran-signal, r-cran-anytime, r-cran-chron, r-cran-data.table, r-cran-ggplot2, r-cran-scales, r-cran-accelerometry, r-cran-hms, r-cran-doparallel, r-cran-dosnow, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-e4tools_0.1.1-1.ca2404.1_all.deb Size: 4008308 MD5sum: 74f869c9b200689fdef5d9c248c8eeac SHA1: 4bd4c1073ba391a349df32a50a495976ee7cac96 SHA256: 38984745f281106687a1c0681518ff3d11793ff0641dfc23870363a46ffd7d8f SHA512: 5c75431091326438ef7471d75e170ee270decb0817533b7d48a489e3de36dd556a3556867e73fc5f68cd503a58273aa01cb9baf87d1c0ac6480bc2aa83180c61 Homepage: https://cran.r-project.org/package=E4tools Description: CRAN Package 'E4tools' (Management and Processing Tools for Data Produced by theEmpatica E4) Process and manage the data from the Empatica E4. 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This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) . 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Package: r-cran-earthdatalogin Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-openssl, r-cran-purrr, r-cran-base64enc, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-terra, r-cran-rsconnect, r-cran-testthat, r-cran-curl, r-cran-sf, r-cran-fs, r-cran-readr, r-cran-spelling, r-cran-gdalcubes, r-cran-rstac Filename: pool/dists/noble/main/r-cran-earthdatalogin_0.0.5-1.ca2404.1_all.deb Size: 1374648 MD5sum: c4a8c813caaf4bfd9919e03b9ac4c202 SHA1: 16428b2e6cd436946d378675b3534e05a4515c71 SHA256: 5cac35b24c5830a46c8afa87ac666842eed97b650a72f7284765b85736052087 SHA512: ad18e69668067f7faaa5cd55f07e029ffeda551db5106351190dc7a6512ba3a8c7fb06d8f23b3c67e4ff0f743afedeee20437152b9a0fa287a5acab8c7192cba Homepage: https://cran.r-project.org/package=earthdatalogin Description: CRAN Package 'earthdatalogin' (NASA 'EarthData' Access Utilities) Providing easy, portable access to NASA 'EarthData' products through the use of bearer tokens. Much of NASA's public data catalogs hosted and maintained by its 12 Distributed Active Archive Centers ('DAACs') are now made available on the Amazon Web Services 'S3' storage. However, accessing this data through the standard 'S3' API is restricted to only to compute resources running inside 'us-west-2' Data Center in Portland, Oregon, which allows NASA to avoid being charged data egress rates. This package provides public access to the data from any networked device by using the 'EarthData' login application programming interface (API), , providing convenient authentication and access to cloud-hosted NASA 'EarthData' products. This makes access to a wide range of earth observation data from any location straight forward and compatible with R packages that are widely used with cloud native earth observation data (such as 'terra', 'sf', etc.) Package: r-cran-earthtones Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maptiles, r-cran-terra, r-cran-sf Suggests: r-cran-testthat, r-cran-cluster, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-earthtones_0.2.0-1.ca2404.1_all.deb Size: 25386 MD5sum: b10893502dca7bf95b9179e98eea6364 SHA1: 65512012665b60054c31bcff6fbe7bb4163924c7 SHA256: 20564b9f7769bec4ca25d70929e41bcd7bef8ed4cdb65b2f06cfc9239936a8af SHA512: ccc69df02ace100d4d280a9548465fab3c659f43148448cad005e4807d1ad7cac989c728ec176a740c9d79017068acd2b6d3ab782d869fb818536df06f441af6 Homepage: https://cran.r-project.org/package=earthtones Description: CRAN Package 'earthtones' (Derive a Color Palette from a Particular Location on Earth) Downloads a satellite image via ESRI and maptiles (these are originally from a variety of aerial photography sources), translates the image into a perceptually uniform color space, runs one of a few different clustering algorithms on the colors in the image searching for a user-supplied number of colors, and returns the resulting color palette. 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Features include data import from CSV and 'Excel' files, automatic detection of categorical variables, interactive control of interaction terms via an allowed matrix, comprehensive model diagnostics with variable importance and partial dependence plots, and publication-quality report generation via 'Quarto'. 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(2022) , Palau et al. (2023) , Salazar de Pablo et al. (2025) . Package: r-cran-easy.utils Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fastmatch, r-cran-rlang, r-cran-scales Filename: pool/dists/noble/main/r-cran-easy.utils_0.1.0-1.ca2404.1_all.deb Size: 46634 MD5sum: 62096b277d9ade9756aa301079a44a54 SHA1: 5b95121dfd03983f87a1c5c932edeb87e0f46c23 SHA256: b3b5e9179478f02094a24a234ba43bb87210a9a7bed4f672fee1483347d92fad SHA512: d4915d301ef1bc5b271caac5d5b848cd8dc6dd811dedde323d82c285fc96d63f5afb6c998340704f2729152ae9d4c66eac0077e37c6ae12aa4c0e2d310f27901 Homepage: https://cran.r-project.org/package=easy.utils Description: CRAN Package 'easy.utils' (Frequently Used Functions for Easy R Programming) Some utility functions for validation and data manipulation. 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Package: r-cran-easyalluvial Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2399 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-purrr, r-cran-tidyr, r-cran-dplyr, r-cran-forcats, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggridges, r-cran-rcolorbrewer, r-cran-recipes, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-tibble, r-cran-gridextra, r-cran-randomforest, r-cran-progressr, r-cran-progress Suggests: r-cran-testthat, r-cran-covr, r-cran-islr, r-cran-nycflights13, r-cran-vdiffr, r-cran-pkgdown, r-cran-mlbench, r-cran-earth, r-cran-workflows, r-cran-future, r-cran-furrr, r-cran-e1071, r-cran-caret, r-cran-parsnip, r-cran-rpart, r-cran-glmnet, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-easyalluvial_0.4.1-1.ca2404.1_all.deb Size: 2387762 MD5sum: 3de71a981d07f863574c636385313275 SHA1: e7c6e28c5eba82cc23bc926f5ba77d280ae08c2d SHA256: cbccae6741f971c2f73ff106e10fbbdf68b65d7d484e1f0358167147dd676966 SHA512: dfc9b5530c259d77efb6285f42b310a9fbcbaab304e900fc89fe02c0c78af8623c525a5303990f2828e98efbbafb1f30f3c2ee016b52ce6530ddbd9492236422 Homepage: https://cran.r-project.org/package=easyalluvial Description: CRAN Package 'easyalluvial' (Generate Alluvial Plots with a Single Line of Code) Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. 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This package can perform descriptive statistics according to different data types. If the data is a continuous variable, the mean and standard deviation or median and quartiles are automatically output; if the data is a categorical variable, the number and percentage are automatically output. In addition, if you enter two variables in this package, the two variables will be described and their relationships will be tested automatically according to their data types. For example, if one of the two input variables is a categorical variable, another variable will be described hierarchically based on the categorical variable and the statistical differences between different groups will be compared using appropriate statistical methods. And for groups of more than two, the post hoc test will be applied. For more information on the methods we used, please see the following references: Libiseller, C. and Grimvall, A. (2002) , Patefield, W. M. (1981) , Hope, A. C. A. (1968) , Mehta, C. R. and Patel, N. R. (1983) , Mehta, C. R. and Patel, N. R. (1986) , Clarkson, D. B., Fan, Y. and Joe, H. (1993) , Cochran, W. G. (1954) , Armitage, P. (1955) , Szabo, A. (2016) , David, F. B. (1972) , Joanes, D. N. and Gill, C. A. (1998) , Dunn, O. J. (1964) , Copenhaver, M. D. and Holland, B. S. (1988) , Chambers, J. M., Freeny, A. and Heiberger, R. M. (1992) , Shaffer, J. P. (1995) , Myles, H. and Douglas, A. W. (1973) , Rahman, M. and Tiwari, R. (2012) , Thode, H. J. (2002) , Jonckheere, A. R. (1954) , Terpstra, T. J. (1952) . 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Compatible model types include lm(), rlm(), glm(), glm.nb(), betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least squares via gls(); and survival models via coxph() (from 'survival'). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() and betareg() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) . For beta regression using 'betareg', see Cribari-Neto and Zeileis (2010) . For mixed-effects models with 'lme4', see Bates et al. (2015) . For models using 'glmmTMB', see Brooks et al. (2017) . Methods for generalized additive models using 'mgcv' follow Wood (2017) . Package: r-cran-easywechat Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-easywechat_0.2.0-1.ca2404.1_all.deb Size: 12894 MD5sum: 9341aa34a85dde68692ba24ef5bea7af SHA1: d08a68fdb221cbcd058ca861456d19900c01f8ef SHA256: 16febf76022af42bda65e658ddb83e46cd24dd5ab99c0270e04cb2ed082ae436 SHA512: 315b41d173e6387b285a13fb35d2f24edde46cf16c84845b2b7c950716466719a9a0f0853e3e80cd74a0c55f2fccdeeb90069f1dce7da2bf4f6917e1c13db65a Homepage: https://cran.r-project.org/package=easyWechat Description: CRAN Package 'easyWechat' (A Notifier for R Users by 'WeChat') This is a 'WeChat' Notifier for R users to notice when script run complete. Package: r-cran-eat Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 832 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-conflicted, r-cran-ggplot2, r-cran-ggparty, r-cran-partykit, r-cran-ggrepel, r-cran-rdpack, r-cran-lpsolveapi, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-kableextra, r-cran-devtools Filename: pool/dists/noble/main/r-cran-eat_0.1.4-1.ca2404.1_all.deb Size: 522698 MD5sum: a14219394fcf2fc760dece0f5737fa6f SHA1: 7ce8a3c0bc1ab1fe90b64acb6ddc61e9c099a427 SHA256: 3b7907c9fec51ffd7008dfb5aa615c09d20a3824f7bcc9bcf13137581c5893be SHA512: f4ff32d0bf38d6e0636f0389108f9fb9605ac11553e3863e06af9a1b472037c8191cce00f1b55a6eb84d000dfd5b3359a23c147bc5db9d8cf3f83260cf3b1bec Homepage: https://cran.r-project.org/package=eat Description: CRAN Package 'eat' (Efficiency Analysis Trees) Functions are provided to determine production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package includes code for estimating radial input, output, directional and additive measures, plotting graphical representations of the scores and the production frontiers by means of trees, and determining rankings of importance of input variables in the analysis. Additionally, an adaptation of Random Forest by a set of individual Efficiency Analysis Trees for estimating technical efficiency is also included. More details in: . Package: r-cran-eatata Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rglpk, r-cran-mathjaxr, r-cran-lpsolve Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-rsymphony Filename: pool/dists/noble/main/r-cran-eatata_1.1.2-1.ca2404.1_all.deb Size: 236550 MD5sum: 1c22f528598a217b68c507399418b083 SHA1: 42c2da0fa8cecc25c3bc8747eb75115b1509a55a SHA256: ce3ca6921a2e65fef61c5592662b599a1380cbe24ae518076120c5133f599529 SHA512: fb0e93967a48de9bc8270ae5697f8e4ddbae3ca268c4fe969d927581d99170b9b8add76350ead39da16345949f27c958391f5b59c9a895c7f57104d159b76547 Homepage: https://cran.r-project.org/package=eatATA Description: CRAN Package 'eatATA' (Create Constraints for Small Test Assembly Problems) Provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, 'GLPK', 'lpSolve', 'Symphony', and 'Gurobi' are supported as solvers. The 'gurobi' package is not available from any mainstream repository; see . Package: r-cran-eatdb Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-eatdb_0.5.0-1.ca2404.1_all.deb Size: 54774 MD5sum: e7978c3fbe2ee27abd8fc8dc127e8c81 SHA1: 07007ff1bc7bf2a6896b3a5cce86de77935524a6 SHA256: 606aee4d0f0a1746aaab7d29187bb8b1c85afc4a842b28ec03dc36c93e974a3a SHA512: 05cfb4fd414b99e96d3ccc7180cc065b8a8f5632e2b73ea76c04ad143adcd89ba5786b2b0e8e275b25b36d97f2dd7406035176e74e5b24bec15c1c1e1bdd8104 Homepage: https://cran.r-project.org/package=eatDB Description: CRAN Package 'eatDB' (Spreadsheet Interface for Relational Databases) Use 'SQLite3' as a database system via a complete SQL free R interface, treating the data as if it was a single spreadsheet. Package: r-cran-eatgads Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2065 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eatdb, r-cran-haven, r-cran-plyr, r-cran-eattools, r-cran-tibble, r-cran-data.table, r-cran-hms, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-eatgads_1.2.0-1.ca2404.1_all.deb Size: 1106488 MD5sum: 222a21d974b145b1c3d240ce4186e188 SHA1: aaa8d75bc8cad07844fb56cd692c897518fd80c2 SHA256: 2c5ffe8861fb3d5f1514875ab1b3c651d0a4f53f4e2684303f35b23cd77b44b4 SHA512: 6d814135e7c451726272b8fd141f299a51e489efd0ee7a07ebc1fb3a321d2c7fbce8fc76c428e5aea30f512d6e81e54f373b215580b1452c5724241ba6570adc Homepage: https://cran.r-project.org/package=eatGADS Description: CRAN Package 'eatGADS' (Data Management of Large Hierarchical Data) Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases. Package: r-cran-eatme Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qcr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eatme_0.1.0-1.ca2404.1_all.deb Size: 85868 MD5sum: 40b0c5bfac720cb6bb31f5227e534c6e SHA1: db9fd5b4d155e8ea428acee2584124ecd5c56289 SHA256: bd00b162d84b146005825b2644015fac8beb0ed86562a54259b42f1564469551 SHA512: a7e35d002c356975515253eed11d8429414626e549de52e92f8433f978b8a8993648e85a84b890499708d64456ed8b2b13db43cc0cdad44dc432b1555896765e Homepage: https://cran.r-project.org/package=EATME Description: CRAN Package 'EATME' (Exponentially Weighted Moving Average with Adjustments toMeasurement Error) The univariate statistical quality control tool aims to address measurement error effects when constructing exponentially weighted moving average p control charts. The method primarily focuses on binary random variables, but it can be applied to any continuous random variables by using sign statistic to transform them to discrete ones. With the correction of measurement error effects, we can obtain the corrected control limits of exponentially weighted moving average p control chart and reasonably adjusted exponentially weighted moving average p control charts. The methods in this package can be found in some relevant references, such as Chen and Yang (2022) ; Yang et al. (2011) ; Yang and Arnold (2014) ; Yang (2016) and Yang and Arnold (2016) . Package: r-cran-eatrep Architecture: all Version: 0.15.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-bifiesurvey, r-cran-progress, r-cran-lavaan, r-cran-hmisc, r-cran-fmsb, r-cran-mice, r-cran-boot, r-cran-car, r-cran-reshape2, r-cran-plyr, r-cran-combinat, r-cran-miceadds, r-cran-tidyr, r-cran-effectliter, r-cran-estimatr, r-cran-eattools, r-cran-eatgads, r-cran-janitor, r-cran-msm, r-cran-checkmate, r-cran-lifecycle, r-cran-dplyr, r-cran-future, r-cran-reformulas, r-cran-stringr Suggests: r-cran-weights, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-zoo Filename: pool/dists/noble/main/r-cran-eatrep_0.15.3-1.ca2404.1_all.deb Size: 1223478 MD5sum: bea556e63f9c432089ca450be58aaad0 SHA1: 27ca6ff2e60306e565130775936e7b531b67befd SHA256: cf0992c94df51bd722859a22a1fa8f083320546dc7791d40bc4a135e92f74818 SHA512: fca5ee460563d2f4670a936a9e7ee7426d86b56f172e6fd125ed96275d5d846228c3bbfac21cc2397309f4bb1d7676d95b3c47a5c6ce7c6763b326ac4350d749 Homepage: https://cran.r-project.org/package=eatRep Description: CRAN Package 'eatRep' (Educational Assessment Tools for Replication Methods) Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references. Package: r-cran-eattools Architecture: all Version: 0.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-checkmate Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-eattools_0.7.10-1.ca2404.1_all.deb Size: 508672 MD5sum: b238bb50968bae93630effad0b25047f SHA1: e6ebf6aa91c33320fa7b249f069c4dd741060a66 SHA256: 7f3f447f8d112b860216c41bcc49e2190e4112085c21d2e57bd38a64b73772f1 SHA512: dfe764f9f27c5b509efaccdcab19215673dce4e679570f46f2367976796108944688744d7ed4b0f5ad7e8c2dae3e51fd985611feb1ba40eed6218ab993b94715 Homepage: https://cran.r-project.org/package=eatTools Description: CRAN Package 'eatTools' (Miscellaneous Functions for the Analysis of EducationalAssessments) Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep', 'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN'). The function for defining (weighted) contrasts in weighted effect coding refers to te Grotenhuis et al. (2017) . Functions for weighted statistics refer to Wolter (2007) . Package: r-cran-eava Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eava_1.0.0-1.ca2404.1_all.deb Size: 305494 MD5sum: bfae3d461c1ed241ca456cd069253836 SHA1: fc94a024ec1d245587e6dbd05d451b155216bcf0 SHA256: def95da810f75c3c1e5e645e34f7b5bf22975458cc763bc81222444a3a2dd644 SHA512: 22142ab852af979441d56d0dc95dd1dcd2d1c5834cfb642a7d11d88a74efa4a30b49495950a4af5f885ceb24648390f54e8cf41ec18a33dbb2fb18f882e400f8 Homepage: https://cran.r-project.org/package=EAVA Description: CRAN Package 'EAVA' (Deterministic Verbal Autopsy Coding with Expert Algorithm VerbalAutopsy) Expert Algorithm Verbal Autopsy assigns causes of death to 2016 WHO Verbal Autopsy Questionnaire data. odk2EAVA() converts data to a standard input format for cause of death determination building on the work of Thomas (2021) . codEAVA() uses the presence and absence of signs and symptoms reported in the Verbal Autopsy interview to diagnose common causes of death. A deterministic algorithm assigns a single cause of death to each Verbal Autopsy interview record using a hierarchy of all common causes for neonates or children 1 to 59 months of age. 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Useful for creating balanced samples in observational studies with a binary treatment where the control group is reweighted to match the covariate moments of the treatment group, and for reweighting a survey sample to known characteristics from a target population. 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A mass balance equation is used that provides estimates of parameters for gross primary production, respiration, and gas exchange. Methods adapted from Grace et al. (2015) and Wanninkhof (2014) . Details in Beck et al. (2024) . 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Package: r-cran-ebayesthresh Architecture: all Version: 1.4-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ebayesthresh_1.4-12-1.ca2404.1_all.deb Size: 772748 MD5sum: a202870b7dd67cfcae135de633ae1dc7 SHA1: 2d70c418f7fd03e022436536f4765bb18002de8d SHA256: 9e8a1abc5f14bfbe622a9c66a30a2460643659b576f4fa1c92420b7b6cbe85b7 SHA512: 6fc8223e4e4ccfd53ffcfbb6039cc43c371ef2db08b0c00c1011e95254fa52235efbbdc37afa56017f7ee65cf6a06d7751e1bebbfdb8a13f5f46d041fb9069f8 Homepage: https://cran.r-project.org/package=EbayesThresh Description: CRAN Package 'EbayesThresh' (Empirical Bayes Thresholding and Related Methods) Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package. Package: r-cran-ebchs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-fda Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ebchs_0.1.1-1.ca2404.1_all.deb Size: 92420 MD5sum: b2bd58d753e680aae69d4f61fe9e8ab3 SHA1: 92dbae02e66ba2e8f4338f1674dea216c773adc6 SHA256: b0dbe61b8e79772db769d1b29635850dd8b3ae37d82d9f4954a6ee3ed3db70f2 SHA512: 6c35618e1cd28551d34ea3d81372106ef28e4ecff5406f86e98aa7209020fe45d172bbf98d3ee50c773f08fc92888a3a0f3a1904fe102630a220f603fc702346 Homepage: https://cran.r-project.org/package=EBCHS Description: CRAN Package 'EBCHS' (An Empirical Bayes Method for Chi-Squared Data) We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2022) . Package: r-cran-ebci Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat, r-cran-lpsolve, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ebci_1.0.0-1.ca2404.1_all.deb Size: 195540 MD5sum: 3d54e1903af15012c228f96ff64374fa SHA1: 0f16f9c989d43de8867b15c274a75cf996685269 SHA256: 5df92fbb35636054f8d3779a90ce55b7d6a91d63c853b37ff7d444ed02ed5387 SHA512: 709aa59cd720dd0a4c636eb98566103bad8af4d504431d1ce3096ebcb96fb0a6e4df585b924cc5ff1328d857d90ed0d8a49aa8416a110c30b9d13f2754c8745b Homepage: https://cran.r-project.org/package=ebci Description: CRAN Package 'ebci' (Robust Empirical Bayes Confidence Intervals) Computes empirical Bayes confidence estimators and confidence intervals in a normal means model. The intervals are robust in the sense that they achieve correct coverage regardless of the distribution of the means. If the means are treated as fixed, the intervals have an average coverage guarantee. The implementation is based on Armstrong, Kolesár and Plagborg-Møller (2020) . Package: r-cran-ebcobart Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbarts, r-cran-loo, r-cran-posterior, r-cran-univariateml, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-ebcobart_1.1.2-1.ca2404.1_all.deb Size: 252764 MD5sum: 3c80df63d983547a53f2773c5d6ad0ee SHA1: 06f79bd401273e0493b3de1432ea2e663a2ce86e SHA256: f773b5c8fa93b50fab3c8fa70336750c073d186ec72c05b0590adfce84a0a513 SHA512: ab1e42073ccf698dd71c420b5868e30830490eef7a6119946cea0f54da29980044f900aa2e7630dd285a2ac2af9e3f2789f5a6ab34864743a6854384a33791f3 Homepage: https://cran.r-project.org/package=EBcoBART Description: CRAN Package 'EBcoBART' (Co-Data Learning for Bayesian Additive Regression Trees) Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) for details. Package: r-cran-ebdm Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ebdm_3.0.1-1.ca2404.1_all.deb Size: 57290 MD5sum: 069d61d27decc4d6ada6e1837d3a5210 SHA1: da5d5518cfccd81c2b83937734b5300b439b14a9 SHA256: 474fa603d854d277252628f95a33ac55f8e25cb77fbd2c683efb64a2dde23d47 SHA512: abb880c276d8dea17630e094617f4d6d955a2d9feffd99ea5b949d0c3db90eec6b9959f79795029a2aa2e00a21ab0648cadec3ba13073a02de32eaeb03bb17a4 Homepage: https://cran.r-project.org/package=ebdm Description: CRAN Package 'ebdm' (Estimating Bivariate Dependency from Marginal Data) Provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. The package supports three modules of bivariate joint distribution estimated from marginal summary data: (1) two binary, (2) two continuous, (3) one binary and one continuous These methods enable privacy-preserving joint estimation when individual-level data are unavailable. The approaches are detailed in Shang, Tsao, and Zhang (2025a) and Shang, Tsao, and Zhang (2025b) . Package: r-cran-ebdt Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ebdt_1.0.1-1.ca2404.1_all.deb Size: 125190 MD5sum: 14fcb2d0bcab025889549ac852219027 SHA1: b3dc446e0666d9befad7b5bd966b6a7062424d50 SHA256: d3ce8993305198c5d4348cfec72e3c74a6da9dafe4e4b1bd46cbd5f9ea3aeb52 SHA512: d19cdd388722787f349ed7af0c5bd80751d9414e179ecbb2a79e475fd9b67a05d37c9dcb3c8d6066c33e8a75c20822f31618a2d32e5fd4b0ab34597fa6cdbd29 Homepage: https://cran.r-project.org/package=ebdt Description: CRAN Package 'ebdt' (Evaluation of Binary Diagnostic Test) Calculate the point estimator and its confidence interval for the quality parameters of a binary diagnostic test, such as sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, weighted Kappa coefficient, a global diagnostic accuracy index, prevalence in a cross-sectional study, and sensitivity, specificity, positive and negative likelihood ratio, and a global diagnostic accuracy index in a retrospective study. Package: r-cran-ebgenotyping Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ebgenotyping_2.0.1-1.ca2404.1_all.deb Size: 31846 MD5sum: 4369eb0ac9685b66218f8ecfa9583fc2 SHA1: 557b57f289413fde987894692778618f3cb28536 SHA256: 00a3e076f3b2f9c1429aee3fc3d06c4223282047d9149bbaee7467cf57ae7d43 SHA512: 3c2df020f60b8df0452dae69427d302b39ee086fb61e43a81db7dad9da4e50eb67f4f8dd3e43d06312ffeee08ec9fb39c36072c52d5c6bd14d918ddb5d6edc79 Homepage: https://cran.r-project.org/package=ebGenotyping Description: CRAN Package 'ebGenotyping' (Genotyping and SNP Detection using Next Generation SequencingData) Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method. Package: r-cran-ebirdst Architecture: all Version: 4.2023.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-viridislite Suggests: r-cran-fields, r-cran-ggplot2, r-cran-lubridate, r-cran-presenceabsence, r-cran-rnaturalearth, r-cran-scico, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ebirdst_4.2023.0-1.ca2404.1_all.deb Size: 414440 MD5sum: ed30f622f304a27a4e24b565ef73c7b8 SHA1: ee620f0258485e70f891b656e489a999dfacce29 SHA256: d4354b999ac5ab6d39c12bd2164c3d77d8f39d005f16de348ea5f97b131f4894 SHA512: 95263bbfeebcec9cb7c401fdb5b81c10d57feaa363ef4ba8107cf28c68e178652649a183f7e0babf1a2756b227024fa90eab8c51027f8e8ffdb1a723bccc15dd Homepage: https://cran.r-project.org/package=ebirdst Description: CRAN Package 'ebirdst' (Access and Analyze eBird Status and Trends Data Products) Tools for accessing and analyzing eBird Status and Trends Data Products (). eBird () is a global database of bird observations collected by member of the public. eBird Status and Trends uses these data to model global bird distributions, abundances, and population trends at a high spatial and temporal resolution. Package: r-cran-ebm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-ggplot2, r-cran-lattice Suggests: r-cran-htmltools, r-cran-islr2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-ebm_0.1.0-1.ca2404.1_all.deb Size: 502410 MD5sum: 9fadcec822543345ba0365e4bd0b3937 SHA1: 9a9d1d517818682baf1239cec518ec5f485dddec SHA256: 76ca724a43bb26edea432ad84de7e290aaf8785fe85a9f8196507e221ab90917 SHA512: 5f4d8aaf942d54d9e498b97dc34ede0598440f356cef609b54918a1eaa6ac7d6648243e43ab364f9ed893c32269bdf542d09fa07d20d5037965f95eb064568a3 Homepage: https://cran.r-project.org/package=ebm Description: CRAN Package 'ebm' (Explainable Boosting Machines) An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable. Package: r-cran-ebmc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-rpart, r-cran-c50, r-cran-randomforest, r-cran-proc, r-cran-smotefamily Filename: pool/dists/noble/main/r-cran-ebmc_1.0.1-1.ca2404.1_all.deb Size: 70530 MD5sum: c1c268b1f597bd656f698f9b7e377c1d SHA1: d38808dd0acebdb370e2b6c0f2f1fb0a6c63ac41 SHA256: 52ff53a36c11aa0c46021ffd574a1c1f85437fa4ee858f5ccc55e5ee91eaffdd SHA512: ad1275b01829c39cc129154a48a1c21cd6dfe2eb0205d318a07a094e9593e1c398dee83c6cd5523b0127eedffb34b33609416ca4eb1f05a8ed8c2d269ade543d Homepage: https://cran.r-project.org/package=ebmc Description: CRAN Package 'ebmc' (Ensemble-Based Methods for Class Imbalance Problem) Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) , Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) , R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) , Shuo Wang and Xin Yao (2009) , Yoav Freund and Robert E. Schapire (1997) . Package: r-cran-ebnm Architecture: all Version: 1.1-42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ashr, r-cran-mixsqp, r-cran-truncnorm, r-cran-trust, r-cran-deconvolver, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-rebayes, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-mcmc, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-ebnm_1.1-42-1.ca2404.1_all.deb Size: 871034 MD5sum: 4a453ab64bbab3a03008a592413cf334 SHA1: 34c16d4d8bf2576700214b46b306835c0bfd71fe SHA256: 2af3bb2e7730c524e015d3985a02c92dccc06f1988d213e07ad0b50b02e2e4d2 SHA512: fc3bcb2e98a4b657709033bec6a034d8b7eea6a3699f1f796efcb0cae019b28f86b79e9b5aa54dfcf9f8d65bddb177f58e477a664492469b19f72bdbac4f98d6 Homepage: https://cran.r-project.org/package=ebnm Description: CRAN Package 'ebnm' (Solve the Empirical Bayes Normal Means Problem) Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) , provides a detailed introduction to the package. Package: r-cran-ebprs Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-bedmatrix, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ebprs_2.1.0-1.ca2404.1_all.deb Size: 55660 MD5sum: 4ded04b58b0fa49d79209e2c430e5850 SHA1: 628f65cfde3259137adda725b6f46bbb29a795e4 SHA256: 6083b4885f68b2f29c9613608ca30aff336bfc9aa298f88554adb09f53798779 SHA512: 439fabdf7b4fa4db3805f1de44bb60fd487110ff9229e08d51b8afe16e64a774aab9873b75f4f72ae0a37af9abff579d6a9e847db20d753fedeeb3a2254d3635 Homepage: https://cran.r-project.org/package=EBPRS Description: CRAN Package 'EBPRS' (Derive Polygenic Risk Score Based on Emprical Bayes Theory) EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) for a detailed presentation of the method. Package: r-cran-ebrahim.gof Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compquadform Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-resourceselection, r-cran-ggplot2, r-cran-statmod, r-cran-mgcv, r-cran-bagoft, r-cran-randomforest, r-cran-dcov, r-cran-givitir, r-cran-callr, r-cran-th.data Filename: pool/dists/noble/main/r-cran-ebrahim.gof_2.9.0-1.ca2404.1_all.deb Size: 1000622 MD5sum: ab2464fa7ca6955e120d6eb83ebf88f9 SHA1: 8c33392f6e05a616e6d523e176b3ae45fac65b60 SHA256: 9046aa0b1c8655dd6097e0d3fd307fc59d0c26bef064d8ce1348ca96dd53ce92 SHA512: 2b4721ed9243425ab967f44eff4f0c14e1582f04cda0f8320beb5ef0b71802f26e1507db1d55755c59e6ac7c8710ad219159f89877091dc1f8e2ff717ffefa95 Homepage: https://cran.r-project.org/package=ebrahim.gof Description: CRAN Package 'ebrahim.gof' (Goodness-of-Fit and Calibration Tests for Logistic Regression) Provides a unified battery of goodness-of-fit and calibration tests for binary logistic regression, runnable in a single call via 'run.all.gof()'. Around twenty-five tests spanning five decades of literature are aggregated and grouped by the departure each is built to detect: global and standardized statistics, partition tests such as Hosmer-Lemeshow, directed and covariate-space tests, smoothing and resampling tests, and calibration tests. Each is obtained from its own package where installed and attributed to its authors. The package also implements the author's own procedures for sparse data, where the Hosmer-Lemeshow test loses power: the omnibus Ebrahim-Farrington test 'ef.gof()', the directed 'edge.gof()' and its covariate-space variant 'cdef.gof()', the Cauchy-combination ensemble 'edges.gof()', 'DeepGOF-1' (a pretrained convolutional statistic whose level comes from the analyst's own parametric bootstrap rather than from the network), and 'legoft()' (a frozen-weight combination whose weights are fixed offline and ship frozen, so two analysts obtain the same p-value). For penalized (ridge) logistic regression, where shrinkage biases the fitted probabilities and invalidates the usual chi-squared references, the corrected statistics are referred either to a prepivoting bootstrap by 'shrink.gof()' or to a closed-form reference by 'calm.gof()', which needs a single fit and is validated for designs in which the number of predictors is a sizeable fraction of the sample size. For more details see Hosmer (1980) and Farrington (1996) . Package: r-cran-ebrank Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ebrank_1.0.0-1.ca2404.1_all.deb Size: 34464 MD5sum: 16ab253f3554d39b75aec73628cb631b SHA1: 04a7b8a62e1948a98eeee34a79873a6854711bcb SHA256: f266852f6972b02101a7a6093c4799766f275c5968104f5f4d434caf012f34a0 SHA512: 41c7a06e587bfb34eec533f278523679e99797fd840c0f2c3a3f893c866f9de447f8e5ff881b30db175f57bd7110bd44a95511fcc0854d64e64b96aebca628be Homepage: https://cran.r-project.org/package=EBrank Description: CRAN Package 'EBrank' (Empirical Bayes Ranking) Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given. Package: r-cran-ebreg Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars, r-cran-rdpack Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ebreg_0.1.3-1.ca2404.1_all.deb Size: 148510 MD5sum: fd718661d167920c871bbb7fe041058a SHA1: eb3ac06bcf31eb9f6f2c7651d185e3dec8b1bb60 SHA256: 89e83c461a5799a19c4715b1e15363f4740aecfb4dde4df4ccf821fb50cd978f SHA512: 38b7e2a074c10a638627a76cd0b0a0195d91f0e9905271b34c44b01345cef5a344981811c2fc0701fb1d39d216e9076d9f0277ec16a0bd5b1d72635d343a9030 Homepage: https://cran.r-project.org/package=ebreg Description: CRAN Package 'ebreg' (Implementation of the Empirical Bayes Method) Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) . More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) . Package: r-cran-ebvcube Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3024 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-bioc-delayedarray, r-cran-ggplot2, r-bioc-hdf5array, r-cran-httr, r-cran-jsonlite, r-cran-memuse, r-cran-ncdf4, r-cran-ncmeta, r-cran-reshape2, r-bioc-rhdf5, r-cran-stringr, r-cran-terra, r-cran-tidyterra, r-cran-withr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ebvcube_0.5.3-1.ca2404.1_all.deb Size: 2157314 MD5sum: 49238fd03dcf81532612acd2bdd0b6ab SHA1: 55259a92e1acb7f96ebe63f1dfe32552d00db8f4 SHA256: 28c81cfc6141a5349e5574c337b0fe849d83c5a7f7d07884bef02fdab04648d3 SHA512: c72cc42267b2a902366bc7c715198049e4f78f20e3865718a33973c5f8eb323737f7fcea311a7fe783d93febd5745e7b84ffc93c6a3cdffa7b8719d6fb92275d Homepage: https://cran.r-project.org/package=ebvcube Description: CRAN Package 'ebvcube' (Working with netCDF for Essential Biodiversity Variables) The concept of Essential Biodiversity Variables (EBV, ) comes with a data structure based on the Network Common Data Form (netCDF). The 'ebvcube' 'R' package provides functionality to easily create, access and visualise this data. The EBV netCDFs can be downloaded from the EBV Data Portal: Christian Langer/ iDiv (2020) . 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'echarty' users build R lists for 'ECharts' API. Lean set of powerful commands. Package: r-cran-echelon Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sf, r-cran-spdata Filename: pool/dists/noble/main/r-cran-echelon_0.2.0-1.ca2404.1_all.deb Size: 121728 MD5sum: a1a379776dfbbf6dea6d9381b5d85ea1 SHA1: dd15501cacd29973caf18e80e43713ec5051b214 SHA256: 7ee98c209ece3ae6863ca8308326b588f5eee3446000be767e50e2a5d7248e24 SHA512: b0f621a299a030a762187758a3c8633ef6b13e5b26aea2389f2045573e91459c697ff82b552cc51125251c76f77345d3f2306add3952cf6b96f7c512addb63a4 Homepage: https://cran.r-project.org/package=echelon Description: CRAN Package 'echelon' (The Echelon Analysis and the Detection of Spatial Clusters usingEchelon Scan Method) Functions for the echelon analysis proposed by Myers et al. 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Computes quantile treatment effects for every possible two-by-two combination in ecic(). Then, aggregating all bootstrap runs adds the standard errors in summary_ecic(). Results can be plotted with plot_ecic() aggregated over all cohort-group combinations or in an event-study style for either individual periods or individual quantiles. 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Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) . 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Allows for easy downloads of weather forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; ), Atmosphere Data Store (ADS; ) and Early Warning Data Store (CEMS; ). Package: r-cran-ecocbo Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-parabar, r-cran-parallelly, r-cran-vegan, r-cran-ssp, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecocbo_1.0.0-1.ca2404.1_all.deb Size: 2047132 MD5sum: de8aa38b91d5bbb6f4229c9898cada15 SHA1: cd192797eac66c5fbe11a9d0e2d266e3f3afcb0e SHA256: f90d648d6f47b2ea3ac6249c305d0fb17662dd486b1783ebfe5eed0be48d9a2a SHA512: 95a51eac671002b77db17020117d736a10ab2e898344babec6806d3f752543f75cdd2dad65e90ffbd7f2aaa97c31f92a371b0b87f6cb5e7e1d7385306021f862 Homepage: https://cran.r-project.org/package=ecocbo Description: CRAN Package 'ecocbo' (Calculating Optimum Sampling Effort in Community Ecology) A system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) prep_data() takes the original dataset and creates simulated sets that can be used as a basis for estimating statistical power and type II error. (2) sim_beta() is used to estimate the statistical power for the different sampling efforts specified by the user. (3) sim_cbo() calculates then the optimal sampling effort, based on the statistical power and the sampling costs. Additionally, (4) scompvar() calculates the variation components necessary for (5) Underwood_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() helps the user visualize the results of sim_beta(). Package: r-cran-ecochange Architecture: all Version: 2.9.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sf, r-cran-rastervis, r-cran-sp, r-cran-ggplot2, r-cran-landscapemetrics, r-cran-tibble, r-cran-httr, r-cran-getpass, r-cran-rlang, r-cran-lattice, r-cran-rasterdt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-curl, r-cran-xml2, r-cran-rvest, r-cran-viridis Filename: pool/dists/noble/main/r-cran-ecochange_2.9.3.3-1.ca2404.1_all.deb Size: 953882 MD5sum: 57d3b8fbf02b0088a2433c55e92f84a6 SHA1: a70fd9cf031a6c180a942be2d3cefd0220e4ecb2 SHA256: b5d79ec709e8086f42f355466e0b9b865b4f4eea85cec30585a1e9c273fc80ae SHA512: dd4967999196073114475f17576214bd715f7a0521223533931491fb8646ac1d44f9bdb5d786e4094c47c0823fc4f5df41e22d48ffcaddfeaf6c3c7ba8b47c46 Homepage: https://cran.r-project.org/package=ecochange Description: CRAN Package 'ecochange' (Integrating Ecosystem Remote Sensing Products to Derive EBVIndicators) Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) , including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) , Forest Change (Hansen et al., 2013) , and Continuous Tree Cover data (Sexton et al., 2013) . Package: r-cran-ecocleanr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geodata, r-cran-geosphere, r-cran-ggplot2, r-cran-mregions2, r-cran-patchwork, r-cran-rlang, r-cran-sdmpredictors, r-cran-sf, r-cran-worrms, r-cran-terra, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rgbif, r-cran-robis, r-cran-ridigbio, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecocleanr_1.0.3-1.ca2404.1_all.deb Size: 435114 MD5sum: 2660acc5c3825671998d59b896ce94d1 SHA1: 48e69e576e570d8ab2fe50fdadc86481fb50625c SHA256: 954f84e54d7fb74ea779e1979bcce83803a51dfafa9d35c0ec2019992d91420f SHA512: 5ec9892631244a0d87dc0f168e5335502d0c8a55a99fab3396de2e96d0e2f3e0842a14fc9e7d298215aae295b323657ef189177af704ac2c2973f3d8b3655b54 Homepage: https://cran.r-project.org/package=EcoCleanR Description: CRAN Package 'EcoCleanR' (Enhancing Data Quality of Biogeographic Ranges with Applicationfor Marine Invertebrates) Provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur. For methodological details see the associated publication.. 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'ecocomDP' is a flexible data model for harmonizing ecological community surveys, in a research question agnostic format, from source data published across repositories, and with methods that keep the derived data up-to-date as the underlying sources change. Described in O'Brien et al. (2021), . Package: r-cran-ecocopula Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-glasso, r-cran-plyr, r-cran-sna, r-cran-mass, r-cran-tweedie, r-cran-igraph, r-cran-betareg, r-cran-doparallel, r-cran-mgcv, r-cran-glm2, r-cran-ordinal, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-ggraph, r-cran-labdsv, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-ecocopula_1.0.6-1.ca2404.1_all.deb Size: 336630 MD5sum: 0958af32e32557d1ee32e128c76e6fb9 SHA1: a5fe0f3b857249f0707ec5411f86f31f134d298f SHA256: 023fe4895f96240389339c1e2b627ce7464ed2a279e0b8e8deb950f04d2cadf6 SHA512: cd072f7ef6f151cd802bf5adf473b804e03c52d979235519399b0c44ec8deb1cb1ee5310b67d133ac771c9fd2f6615e83141aff449e5c46f752005245488faf5 Homepage: https://cran.r-project.org/package=ecoCopula Description: CRAN Package 'ecoCopula' (Graphical Modelling and Ordination using Copulas) Creates 'graphs' of species associations (interactions) and ordination biplots from co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) . Package: r-cran-ecode Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-rlang, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ecode_0.1.0-1.ca2404.1_all.deb Size: 185790 MD5sum: a52d3f7bc109d4133ba16fbae17a9571 SHA1: c98b350dc6ffc9e6a54b8621670f0bfc5b93cc35 SHA256: 144b2515d726ad04caaa71029b05c53b183655c9bf01c89049d6dd80ede2bdb4 SHA512: 091a224a894888d017d710dbad8c63517763b789b919c1c8a84b27af86dfd28899a77f0d45527ca27775d5150f67c62bfa885f16ffa40fad074e3a35a16d1f15 Homepage: https://cran.r-project.org/package=ecode Description: CRAN Package 'ecode' (Ordinary Differential Equation Systems in Ecology) A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). 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Package: r-cran-ecoglmm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dharma, r-cran-ggeffects, r-cran-ggplot2, r-cran-glmmtmb, r-cran-mumin, r-cran-performance, r-cran-writexl Suggests: r-cran-knitr, r-cran-readxl, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecoglmm_0.1.3-1.ca2404.1_all.deb Size: 105386 MD5sum: 8c4a401592331ccf73a56d4c4a100893 SHA1: 29f6e742e5d921d12f541e1e7b3853f545c026bc SHA256: 1bfcf741f57677d1ea0a538f8ed8cb1e2ec75dc78ac19c4b63b5aa85e5bf312b SHA512: 819c147e9f455d72d1acf540f087933c29f7a56c22cde12d08c052e933cf5c27bfe8f5add42048ac49b7052c4aab4bcb542ab73b942f17d9174bfab317246e60 Homepage: https://cran.r-project.org/package=ecoGLMM Description: CRAN Package 'ecoGLMM' (Reproducible Ecological Generalized Linear Mixed Model Pipelines) Fits and compares generalized linear mixed models for multiple ecological responses and environmental predictors. The package supports additive and temporal-interaction candidate models, AICc model selection, likelihood-ratio tests, coefficient extraction, Nakagawa R-squared, simulation-based diagnostics, figures, and spreadsheet exports. Model selection follows Burnham and Anderson (2002, ISBN:9780387953649); marginal and conditional R-squared follow Nakagawa and Schielzeth (2013) . Package: r-cran-ecohydmod Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ecohydmod_1.0.0-1.ca2404.1_all.deb Size: 26766 MD5sum: 57fdba4a2d2011e7f2371e1684e736f6 SHA1: 951d014bbfa1a6b43e179f980600c741c2963078 SHA256: dc42917eda25ce51a190a82aa9ffee7f7844cf856e7d4700b1cf3df61b179214 SHA512: 4b9737b365c07b2830dbf47c3c4cf2be44711d9588e25b16cd16fdb5d5f5ce04e4c0141b7cfd8b7acd04462d6aaab60fa0462b18d69c5a2aef29e965f96db2e1 Homepage: https://cran.r-project.org/package=Ecohydmod Description: CRAN Package 'Ecohydmod' (Ecohydrological Modelling) Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) . 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Package: r-cran-ecol Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-e1071, r-cran-fnn, r-cran-igraph, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecol_0.4.4-1.ca2404.1_all.deb Size: 123730 MD5sum: 6c8e85cfc2eb2067dc3c2c77367af0f7 SHA1: 599183c73858d46e7b44e478209764d1b2997a5a SHA256: c91afad39b4a10ff25843504a5d468c2b8e4d42f8114f4b159f1f666a4893909 SHA512: 90fcf1cbb2eb00687c655316d5c1dbed799cf942bf653cdbd17b9fe0b492cceb9ab8945e25f208849a269002c4986a4725befd320523c8978c8e2121334d6421 Homepage: https://cran.r-project.org/package=ECoL Description: CRAN Package 'ECoL' (Complexity Measures for Supervised Problems) Provides measures to characterize the complexity of classification and regression problems based on aspects that quantify the linearity of the data, the presence of informative feature, the sparsity and dimensionality of the datasets. This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Lorena et al. (2019) and Lorena et al. (2018) . Package: r-cran-ecolottery Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abc, r-cran-ggplot2 Suggests: r-cran-ape, r-cran-knitr, r-cran-picante, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-ecolottery_1.0.2-1.ca2404.1_all.deb Size: 210908 MD5sum: e095801fcd8ff425e4d4bb8f69c6e9d6 SHA1: 47567207ba0a1628eb715d1a758f14c6a9c3e09c SHA256: 1569357e49284fd1ceea6ba4ac397780589685bfdd283fb580ffade19f60c80a SHA512: bc08bb31e7738272d80cee63e603006669a2f0740e28875bf5dfedebe9d95f77d65576ba8fd1279dd6be497ac43029a64bda710564bbda1b7cd5bcfa25e2eceb Homepage: https://cran.r-project.org/package=ecolottery Description: CRAN Package 'ecolottery' (Coalescent-Based Simulation of Ecological Communities) Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) . The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation. Package: r-cran-ecolrxc Architecture: all Version: 0.1.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-ecolrxc_0.1.1-10-1.ca2404.1_all.deb Size: 140958 MD5sum: 222733e7db970b6e3416f0701d3be098 SHA1: a914d83536ef620ac593074f7291c1c1bb99f8d4 SHA256: 2f4d28a545eb2c0804d5a223522b2022f1398d1bd8b392366f68bcd01501b58f SHA512: 6db7bc1666ea2dc06bcc72eafef60caeb8ca04141307d23777219cdcbf54890c8f81eab905ed84dad637904e276098168af60e996bd4205d4b943c9d45efc460 Homepage: https://cran.r-project.org/package=ecolRxC Description: CRAN Package 'ecolRxC' (Ecological Inference of RxC Tables by Latent StructureApproaches) Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches. References: Park, W.-H. (2008). ''Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. Thomsen, S.R. (1987, ISBN:87-7335-037-2). ''Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark. Package: r-cran-ecoltest Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ecoltest_0.0.1-1.ca2404.1_all.deb Size: 31198 MD5sum: c3ee8925470b1ffe66e9f3da81d42b02 SHA1: bf9b7b5d22df50499dda556e5a5acea5b88459eb SHA256: fd23a2e03756affa4223559787410cdb0b783f96b0b2dba5ee49aa7ea0165ac5 SHA512: de3a8411c1a8487a99b79bae74ba533c7058b69f4c485da6a4f4f0bc6c3dbccb9a28e14ae2b1cc81e1f7864fc3d5395963c8b8e7b5b9c7f5abfbfcacf33006d7 Homepage: https://cran.r-project.org/package=ecolTest Description: CRAN Package 'ecolTest' (Community Ecology Tests) Functions and data sets to perform and demonstrate community ecology statistical tests, including Hutcheson's t-test (Hutcheson (1970) , Zar (2010) ISBN:9780321656865). 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Multivariate equivalence testing by simulation from a Gaussian copula model. The package also provides functions for parameterising multivariate effect sizes and simulating multivariate abundance data jointly. The discrete Gaussian copula approach is described in Popovic et al. (2018) . 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Package: r-cran-ecoregime Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-ecotraj, r-cran-shape, r-cran-smacof, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-ecoregime_0.4.1-1.ca2404.1_all.deb Size: 2039542 MD5sum: 00d5c7ba29ba9183de41551ac91d1978 SHA1: 485bf938149c645a4b3fcb25295598d46972ac83 SHA256: 7a43e2fac2d6aeda33173411da9e7c9645e2185e029347a87a77049a51eeeaf1 SHA512: 861b6f704a1c2b24717fd5cdb5cddc5cdf7f21e3c19edf0c43ea676a0dfb892b43d3049b481932bd0a73e21bc9483db4fe89597164ad2032337d33c18c95c9f5 Homepage: https://cran.r-project.org/package=ecoregime Description: CRAN Package 'ecoregime' (Analysis of Ecological Dynamic Regimes) A toolbox for implementing the Ecological Dynamic Regime framework, including functions to characterize and compare groups of ecological trajectories (Sánchez-Pinillos et al., 2023 ); assess the ecological resilience of a disturbed system using a reference dynamic regime (Sánchez-Pinillos et al., 2024 ); and forecast ecological trajectories from a dynamic regime (Sánchez-Pinillos et al. 2026, ). Additional functions are also available for visualizing ecological dynamic regimes, their representative trajectories, as well as predicted trajectories in a multidimensional state space. Package: r-cran-ecorest Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-viridis Filename: pool/dists/noble/main/r-cran-ecorest_2.0.3-1.ca2404.1_all.deb Size: 202860 MD5sum: 70905445aac9dfb985b63df405428b4d SHA1: 8c7ab45af5a9444dbe2010ce3fc200124a38a42c SHA256: 03d3ba13d770a206f62b496030e5f4df04571891e04091f29ed5bfc0c058507f SHA512: 4732189795e4078a79393620cefcf74eb642274c34b001bfdc52be8bb67a9b0334e4324efc626074719d95bfe3768c5dbc76d59d77db5e73a6683b50f8613ef1 Homepage: https://cran.r-project.org/package=ecorest Description: CRAN Package 'ecorest' (Conducts Analyses Informing Ecosystem Restoration Decisions) Three sets of data and functions for informing ecosystem restoration decisions, particularly in the context of the U.S. Army Corps of Engineers. First, model parameters are compiled as a data set and associated metadata for over 300 habitat suitability models developed by the U.S. Fish and Wildlife Service (USFWS 1980, ). Second, functions for conducting habitat suitability analyses both for the models described above as well as generic user-specified model parameterizations. Third, a suite of decision support tools for conducting cost-effectiveness and incremental cost analyses (Robinson et al. 1995, IWR Report 95-R-1, U.S. Army Corps of Engineers). 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Package: r-cran-ecos Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ecos_0.1.7-1.ca2404.1_all.deb Size: 307104 MD5sum: 704c56dc54f6c90d63ea3dd7b5ebedae SHA1: 174eb107b212beaa7f61046bccbd3f905273ef87 SHA256: 2c035e0cb806e457af3f1417004db7f3bfafd02487ef6fa43992dee8811005c5 SHA512: b4eeee1f0c88981f4d3c51d5ea1fa0d822baef22962105de9354f2c2eeec37dc5cc90740964f7afeb9594b97f13c04ded389908fb6d5a2d8e9f95889af677758 Homepage: https://cran.r-project.org/package=ecos Description: CRAN Package 'ecos' (Economic Statistics System of the Bank of Korea) API wrapper to download statistical information from the Economic Statistics System (ECOS) of the Bank of Korea . Package: r-cran-ecosim Architecture: all Version: 1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-stoichcalc Filename: pool/dists/noble/main/r-cran-ecosim_1.3-5-1.ca2404.1_all.deb Size: 117140 MD5sum: 9b3e075b8871187183d01fdb087a47d1 SHA1: d0fb0b686c5cc9e27cfc3f35d92cfc91713fc083 SHA256: 1e2cc532147b20e36d4e1b563c2f572e49549aad03089494a877d428e459f01f SHA512: 0d54b213d80285dde18b7272576f4bfa28c98d268b7187bee6134f2763831ecfa1771b69405ad2a069d2eb7748a784f4403c7d1a4d5244512013facc029a6784 Homepage: https://cran.r-project.org/package=ecosim Description: CRAN Package 'ecosim' (Toolbox for Aquatic Ecosystem Modeling) Classes and methods for implementing aquatic ecosystem models, for running these models, and for visualizing their results. Package: r-cran-ecospace Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fd Suggests: r-cran-vegan, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ecospace_1.4.2-1.ca2404.1_all.deb Size: 212056 MD5sum: 2651df140298c9d526b3402e2af3104d SHA1: ed15eb99c1303b55bcb1129d1287106bfc48e7f9 SHA256: 6633938ce105f108d01cf5d9229c26eae462b24deae90ea16ea92bc9e38ec2ef SHA512: 640b6b8ff5e7e74b314f228ddbc421b1b7ff31d4530b8f9b93534fc934f2517dc07c65af1ed58309d40234fc9068250d428d0c0c4313d2167d199cf8fd6b2c93 Homepage: https://cran.r-project.org/package=ecospace Description: CRAN Package 'ecospace' (Simulating Community Assembly and Ecological DiversificationUsing Ecospace Frameworks) Implements stochastic simulations of community assembly (ecological diversification) using customizable ecospace frameworks (functional trait spaces). 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Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) for details. Package: r-cran-ecostate Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb, r-cran-tmb, r-cran-mass, r-cran-checkmate, r-cran-ggplot2, r-cran-ggnetwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ecostate_0.3.0-1.ca2404.1_all.deb Size: 200144 MD5sum: ed9bc5038d77fc79be7a7955a3e7b050 SHA1: 55b365eae9a56f47b9f37870fc2558c43ea22535 SHA256: 69ba53b9eb86d5b94ec060689812e5b1287b8980ae1d04793fa5b2de7d719bbc SHA512: 1a7afaa677a38b4462cffcb7b4f3801a3c4d5e154ea6660d421f7ad541affc10ee1f3f281302d00a201dda2c0277c4e4578d5f488461bb3b57a0c2769b1ce4d9 Homepage: https://cran.r-project.org/package=ecostate Description: CRAN Package 'ecostate' (State-Space Mass-Balance Model for Marine Ecosystems) Fits a state-space mass-balance model for marine ecosystems, which implements dynamics derived from 'Ecopath with Ecosim' ('EwE') while fitting to time-series of fishery catch, biomass indices, age-composition samples, and weight-at-age data. 'Ecostate' fits biological parameters (e.g., equilibrium mass) and measurement parameters (e.g., catchability coefficients) jointly with residual variation in process errors, and can include Bayesian priors for parameters. Package: r-cran-ecostats Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4734 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-ecocopula, r-cran-get, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ade4, r-cran-caper, r-cran-car, r-cran-corrplot, r-cran-daag, r-cran-dharma, r-cran-dplyr, r-cran-gclus, r-cran-ggally, r-cran-ggplot2, r-cran-ggthemes, r-cran-glmnet, r-cran-gllvm, r-cran-glmmtmb, r-cran-gparotation, r-cran-grplasso, r-cran-lattice, r-cran-leaps, r-cran-lme4, r-cran-mcmcglmm, r-cran-multcomp, r-cran-nlme, r-cran-ordinal, r-cran-permute, r-cran-pgirmess, r-cran-phylobase, r-cran-phylosignal, r-cran-psych, r-cran-reshape2, r-cran-smatr, r-cran-testthat, r-cran-vegan, r-cran-vgam, r-cran-covr Filename: pool/dists/noble/main/r-cran-ecostats_1.2.2-1.ca2404.1_all.deb Size: 2644358 MD5sum: 7e7b9a68946478328e7657d70c730efe SHA1: 7bf6070157ba849cd28d7c9d97cd34e96952aa15 SHA256: 5917dd259aae89192b2530ca5907ede0e1b16afcefe6b588953036c32e5680d5 SHA512: fe1c749630391daf6f2ee2b6392b2f4632102c919eb6ac708bfd38961ad432312fe44ace500b9a1f62bdd60a120f04f6c20dbb331cd49e4e7c4eb42dedf176ca Homepage: https://cran.r-project.org/package=ecostats Description: CRAN Package 'ecostats' (Code and Data Accompanying the Eco-Stats Text (Warton 2022)) Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises. Package: r-cran-ecostatscale Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-desolve Filename: pool/dists/noble/main/r-cran-ecostatscale_1.1-1.ca2404.1_all.deb Size: 49884 MD5sum: de6c8a8bc79d9696bdcdcca725e12878 SHA1: 1732b3de251684037a6e4c6d3d531cff3390e489 SHA256: 2e668fea74ecb2612c59a40bc9bb5905c6646111e3b47ab01d4ef8f84bec3bc2 SHA512: 1ab7d1c1d9301697bf1be427ed88824a315ffd673da01176e055f7e6505b91e9a806e9b18121b658ef7ab50a990048d4d96468028c1a17553d062acc082cb11c Homepage: https://cran.r-project.org/package=ecostatscale Description: CRAN Package 'ecostatscale' (Statistical Scaling Functions for Ecological Systems) Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters . Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details. Package: r-cran-ecoteach Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecoteach_0.1.0-1.ca2404.1_all.deb Size: 184228 MD5sum: d94e174b47054f74cef68e86f23d01c3 SHA1: b845e2baa83ed359e835406769811c49f594c745 SHA256: 42a0bbd2fe7e3ec4778668a47206c9f58380b2646e07bb7314796fc0d1a62029 SHA512: 1bba63411f4554209f34ed639dd478eddf1391bb6d3e7e1a66fb0564134d3bab1d1be384cd7e932ffce8359b099860d8b07c602fd154af49d45c62aa18177abc Homepage: https://cran.r-project.org/package=ecoteach Description: CRAN Package 'ecoteach' (Educational Datasets for Ecology and Agriculture) A collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses. Package: r-cran-ecotolerance Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-raster, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-terra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ecotolerance_0.1.0-1.ca2404.1_all.deb Size: 70756 MD5sum: 001813001bd33ee893b3243410d37ead SHA1: 831c0d4d1aabc9823026c8ae5a68c7fc65e458b9 SHA256: 3049764459dcccd088dec39999ba6f1792068fca02803094cc1e22c3d0aa728c SHA512: 580116806826d2245173c2c28e5e050c4c4a3271c004744b920dbc89ee4b29ea527c452cc96de1d881dcdda953f277a55473be8bedbfb0c6479778739dd1a40e Homepage: https://cran.r-project.org/package=ecoTolerance Description: CRAN Package 'ecoTolerance' (Ecological Tolerance Indices) Computes the Road Tolerance Index (RTI) and the Human Footprint Tolerance Index (HFTI) for species occurrence data. It automates data cleaning and integrates spatial data (roads and human footprint) to produce reproducible tolerance metrics for biodiversity and conservation research. The HFTI calculation is based on the global human footprint dataset by Mu et al. (2022) . This package is part of a PhD thesis focused on amphibian ecology in Brazil. Package: r-cran-ecotonefinder Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-colorspace, r-cran-corrplot, r-cran-e1071, r-cran-ggplot2, r-cran-qgraph, r-cran-igraph, r-cran-philentropy, r-cran-plyr, r-cran-purrr, r-cran-reshape, r-cran-rlang, r-cran-rmisc, r-cran-vegan, r-cran-vegclust, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-ecotonefinder_0.2.3-1.ca2404.1_all.deb Size: 214656 MD5sum: 74a7c9b2834883d4ff1bed4bed6cec87 SHA1: 3fb9925173a215fad3eb8fc9517bb3ce0d6bb373 SHA256: 0c80d4cb3e921bf5be4ef6e117830e4becbfcc22a43bd88abddffe3b2c49758f SHA512: 210578e77575c5b4cb9b7eb231cfda8b01e8a4dc901589cdd6c3eb7162e10cc23ba8a136fba18fc5583bb3cc0737d6c435b56dda8cf576421c0c258d3407e732 Homepage: https://cran.r-project.org/package=EcotoneFinder Description: CRAN Package 'EcotoneFinder' (Characterising and Locating Ecotones and Communities) Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) ), fuzzy clustering (De Cáceres et al. (2010) ), biodiversity indices (Jost (2006) ), and network analyses (Epskamp et al. (2012) ) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided. Package: r-cran-ecotourism Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6080 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-ggbeeswarm, r-cran-sf, r-cran-knitr, r-cran-lubridate, r-cran-quarto, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ecotourism_0.1.0-1.ca2404.1_all.deb Size: 4534954 MD5sum: 695dcfcf53544d5668c440a79fcc34b2 SHA1: 054ccc0ea83a77d96ea6eb1d04b586617d1f2694 SHA256: 2038e1b27072c4098fae45e5382247d23c78b1e230da0db6039d170111a38781 SHA512: 6b32b3a37714072f317d2c26641ffa3b03f994da85d6f2ccea8d040c53d6fb3ed62346e9f0072339b65a8df8fb9d9c4e0643577d20eee161572f5e10d5d376f4 Homepage: https://cran.r-project.org/package=ecotourism Description: CRAN Package 'ecotourism' (Collection of Data on Wildlife Sightings, Tourism Counts, andWeather from Australia) This is a collection of data files for exploring sightings of wild things, relative to weather and tourism patterns in Australia. Package: r-cran-ecotox Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-covr, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-hmisc, r-cran-openxlsx, r-cran-readr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ecotox_1.4.4-1.ca2404.1_all.deb Size: 93494 MD5sum: b4be180c5cf9ee0629388cce05fc380b SHA1: 5d9f1769634a302da459ded5aa0672b1be15da1d SHA256: efe6b5179c54e98e49387818af5ab8b03da85bd52611d014a2df6f1ad380e2d6 SHA512: 530345ee8d0d37bf4c4f72ab033594edcc93edcfd89f167261278f11eead34df222910234c9ee21309fd5291314613f6aab3c81fbcd9f5a71b9c9f7e50293197 Homepage: https://cran.r-project.org/package=ecotox Description: CRAN Package 'ecotox' (Analysis of Ecotoxicology) A simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of 'ecotox' comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) ; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312). Package: r-cran-ecotoxicology Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ecotoxicology_1.0.1-1.ca2404.1_all.deb Size: 213588 MD5sum: 83c325f6aa9af2a58b29073302d408ed SHA1: 687037bb1f51cf559512cd078e3ba4ec8922b84f SHA256: 6018d51481c414491e8ad4b711b9472ebc66366c80130b7ecd9b53e8803d3ec8 SHA512: d080b9b23934eb591885f891fa168cf394c3a43eff5561cb93ecddc4b5633d5cb5edf90d1ebf4ab403ca6fefc58c65144f713fe79cd7533ec833be1de3683a4d Homepage: https://cran.r-project.org/package=ecotoxicology Description: CRAN Package 'ecotoxicology' (Methods for Ecotoxicology) Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis. Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book. Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included. Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard. Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc. Package: r-cran-ecotoxr Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 902 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rsqlite, r-cran-bit64, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rappdirs, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-units Suggests: r-cran-dbi, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-standartox, r-cran-testthat, r-cran-webchem Filename: pool/dists/noble/main/r-cran-ecotoxr_1.2.5-1.ca2404.1_all.deb Size: 509684 MD5sum: 2c4521eacfb229fdbfcd84b2d25a66ae SHA1: c95f61a9413327b100123520277476fcb4bc7850 SHA256: bb9c7c665e208e2b57012c4654e8196463fd09754d50c38d5768ebc29c6ecc40 SHA512: 14594b1e80cc09818eb101d076f399fa614829c04117a534abe3601c3739f3fcb77646ed3388887bb52500a0ef9f8e7aecc5c6d3cebc640f2ca127cc57e8f586 Homepage: https://cran.r-project.org/package=ECOTOXr Description: CRAN Package 'ECOTOXr' (Download and Extract Data from US EPA's ECOTOX Database) The US EPA ECOTOX database is a freely available database with a treasure of aquatic and terrestrial ecotoxicological data. As the online search interface doesn't come with an API, this package provides the means to easily access and search the database in R. To this end, all raw tables are downloaded from the EPA website and stored in a local SQLite database . Package: r-cran-ecotrends Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1783 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzysim, r-cran-maxnet, r-cran-modeva, r-cran-terra, r-cran-trend Filename: pool/dists/noble/main/r-cran-ecotrends_1.2-1.ca2404.1_all.deb Size: 1577766 MD5sum: 133af1c98a911381551d2467c20e11a1 SHA1: 4abcf8723a6b22d1f931a23e4b49091d39be8fd3 SHA256: 9ad17265f8d50f94dafd9682d73951d46aba6c5f7364f3f445c45cba1a956600 SHA512: f8eef30a470087b6b68fc0049050f03a3cff146fae59077f0f8d6d32b66d08cf8be23539b83ffcfe823a33791992c9ef997f138c97653edebc772cc571e9442b Homepage: https://cran.r-project.org/package=ecotrends Description: CRAN Package 'ecotrends' (Temporal Trends in Ecological Niche Models) Computes temporal trends in environmental suitability obtained from ecological niche models, based on a set of species presence point coordinates and predictor variables. Package: r-cran-ecotroph Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml Filename: pool/dists/noble/main/r-cran-ecotroph_1.6.1-1.ca2404.1_all.deb Size: 155298 MD5sum: 8133f0341ae02cf5dc984bc2fc19d4b2 SHA1: bb60db0f11548c0dff0c1412ec34b91c4cb70b46 SHA256: be430169f9c1cf1ff15bb99b1ce717d734baf92fc17be2371f6c9566b72d0cf5 SHA512: 957ab77a3589523ce52b4ce09cd2e8272e1cc453b5eeca56ef1c7a2a2e6c756b71e21bc3228099666c4faaa00dd13b1d902d68910e883e1752ab646c28f6356a Homepage: https://cran.r-project.org/package=EcoTroph Description: CRAN Package 'EcoTroph' (An Implementation of the EcoTroph Ecosystem Modelling Approach) An approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions. 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Package: r-cran-ecpdist Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecpdist_0.2.1-1.ca2404.1_all.deb Size: 47510 MD5sum: 793a2c426e047a3e2e886e33874542be SHA1: e1f46f250c6dcd1d8e2c5f3b8d56a86a0852c78b SHA256: 41b26eba39177e12d398cbca14fcd394712b35b72e67af790f1bcc4f045dbac0 SHA512: a18f2f3182605b69c1fbc781bf6985fa449d0866524b735eeef7d369a9486fbcadfaa50837eb34229978bccebb4fe27411ed0ac13ff2b8b695f94e43170775e8 Homepage: https://cran.r-project.org/package=ecpdist Description: CRAN Package 'ecpdist' (Extended Chen-Poisson Lifetime Distribution) Computes the Extended Chen-Poisson (ecp) distribution, survival, density, hazard, cumulative hazard and quantile functions. It also allows to generate a pseudo-random sample from this distribution. The corresponding graphics are available. Functions to obtain measures of skewness and kurtosis, k-th raw moments, conditional k-th moments and mean residual life function were added. For details about ecp distribution, see Sousa-Ferreira, I., Abreu, A.M. & Rocha, C. (2023). . Package: r-cran-ecpromethee Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecpromethee_0.1.0-1.ca2404.1_all.deb Size: 44052 MD5sum: 93071df55c91961b0bed3fad480a2917 SHA1: a1e0f7564088118bfb0e057db46f530bc615a0bc SHA256: 5388d28c4cb9734dfe319d60d153bed736cd6050660e7a980726c5f6a85d871f SHA512: 93df4040528ba15e856059aef98ea35ffaa73e2488f219564b725cb2cd0aa66e30b3a9ceeef43d20084c65c06cc4cb016a3113aeee913c057321b49a36365c6c Homepage: https://cran.r-project.org/package=ecpromethee Description: CRAN Package 'ecpromethee' (EC-PROMETHEE Multi-Criteria Decision Method) Implements the EC-PROMETHEE multi-criteria decision method described by Basilio, Pereira and Yigit (2023) . 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Besides, the package also supports comparison between two estimation results. 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The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts. Package: r-cran-edfreader Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-edfreader_1.2.1-1.ca2404.1_all.deb Size: 382838 MD5sum: 44c384e0f4223396f0a0cf974fdcb9c8 SHA1: cacdd3f4e0a777e4f6d8291af629adcbd38b901f SHA256: e4b3fbd85b7d1caca1675523fd9d48e72db87baaa7050cb5b2e29184ae81067e SHA512: ff7a9f431d3b13e33adca0f9c625266d2118416db94de1eac8b1dc381583d9a7cda58be4934d0a390bdc074b9a9d5b713041a5d6da609d6deea9c2d19fe88cda Homepage: https://cran.r-project.org/package=edfReader Description: CRAN Package 'edfReader' (Reading EDF(+) and BDF(+) Files) Reads European Data Format files EDF and EDF+, see , BioSemi Data Format files BDF, see , and BDF+ files, see . The files are read in two steps: first the header is read and then the signals (using the header object as a parameter). Package: r-cran-edftest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-rmutil Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-edftest_0.1.0-1.ca2404.1_all.deb Size: 173448 MD5sum: e08468160b114d8d37516297241f6674 SHA1: 591828db84622d0be9aa2aeae326ca4638461500 SHA256: 86dcd698d363e642783a957105354d6071153ad3372c6367e8c32cd082a4be41 SHA512: b371ba24ed11b289b4a970d6ef9ba873f2718c545594963f2e47394472674d62c51715169d9d7a2f880e5f476c093e8d17006e6b49db6c6d6290123d125b38b1 Homepage: https://cran.r-project.org/package=EDFtest Description: CRAN Package 'EDFtest' (Goodness of Fit Based on Empirical Distribution Function) This repository contains software for the calculation of goodness-of-fit test statistics and their P-values. The three statistics computed are the Empirical Distribution function statistics called Cramer-von Mises, Anderson-Darling, and Watson statistics. The statistics and their P-values can be used to assess an assumed distribution.The following distributions are available: Uniform, Normal, Gamma, Logistic, Laplace, Weibull, Extreme Value, and Exponential. Package: r-cran-edfun Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-edfun_0.2.0-1.ca2404.1_all.deb Size: 301248 MD5sum: ff8240b0305059f2fa919c628a29404b SHA1: a57fd5a787cf35b67d7daa7f0e5f43d7076c5049 SHA256: 9fe41bae4e9674e9539cfaf6da64946a031b878a7af2d0a74e7157d1e749f442 SHA512: ae75d9458191ec497478ebecd2f45553f345e6f368ed1809d8863e4f39b255f22c05c3c513fef2810ac4a293367081341d04bcf582d0c34d5cd4ac40bb71b101 Homepage: https://cran.r-project.org/package=edfun Description: CRAN Package 'edfun' (Creating Empirical Distribution Functions) Easily creating empirical distribution functions from data: 'dfun', 'pfun', 'qfun' and 'rfun'. Package: r-cran-edgar Architecture: all Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-tm, r-cran-xml, r-cran-stringr, r-cran-stringi, r-cran-qdapregex, r-cran-httr Filename: pool/dists/noble/main/r-cran-edgar_2.0.8-1.ca2404.1_all.deb Size: 472904 MD5sum: add73c0fc5208f280ebd9de68f953e7f SHA1: 394cade84dddcccd9e938c269064d98249f077be SHA256: 38b0710cd649472ae979e74c7544457f73d6115755005470cd81a69ce947ac0b SHA512: a0ad3815a9301c94688aa0235029243b6dbaad8eb8803a21f46e50988a83409469262c3b682bb1d7e783aea9320faf175f7c13fb2b9d84e97f0702b074979047 Homepage: https://cran.r-project.org/package=edgar Description: CRAN Package 'edgar' (Tool for the U.S. SEC EDGAR Retrieval and Parsing of CorporateFilings) In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings. Package: r-cran-edgarfundamentals Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-rlang, r-cran-tidyquant Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-edgarfundamentals_0.1.2-1.ca2404.1_all.deb Size: 45460 MD5sum: a9ba1a73ef1287b6e4357190312763ab SHA1: 7223eb6e39542d54f28e2a42a6f8cce3d24deeb9 SHA256: 30c3c87f8dddfc12a634bdd364a8bf19ee11f35c542191293942c9cf0f77d69d SHA512: 5daaf3d048e408f9e90f04bdcf98e096fd3647e2caf9450093084ef5c75a4d4d248e897485e27bd1c74bcb80cf34817eaac89b39ecd7aca4311c6942ea995c9c Homepage: https://cran.r-project.org/package=edgarfundamentals Description: CRAN Package 'edgarfundamentals' (Retrieve Fundamental Financial Data from SEC 'EDGAR') Provides a simple, ticker-based interface for retrieving fundamental financial data from the United States Securities and Exchange Commission's 'EDGAR' 'XBRL' API . Functions return key financial ratios including earnings per share, return on equity, return on assets, debt-to-equity, current ratio, gross margin, operating margin, net margin, price-to-earnings, price-to-book, and dividend yield for any publicly traded U.S. company. Data is sourced directly from company 10-K annual filings, requiring no API key or paid subscription. Designed for use in quantitative finance courses and research workflows. 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Package: r-cran-edgebundler Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-rjson, r-cran-igraph, r-cran-shiny Suggests: r-cran-knitr, r-cran-huge Filename: pool/dists/noble/main/r-cran-edgebundler_0.1.4-1.ca2404.1_all.deb Size: 96754 MD5sum: b63def5299b2a4b4ab1cbab5fa644e46 SHA1: 4836573e9f47264657265c5e2a7b6d6c3f1c5467 SHA256: 19c0d4bd0bc129d6dde187107b19314a74134e1ee4b788adbdcbe5e39cccf2fd SHA512: a2713a5b7f9481e6e8977af4b7f6dc503c780fbea16734bca2cc099735e12245ca0effcbb001a9276929169e1281923f0a6bb8a67f9f48016e521396ab1f8caa Homepage: https://cran.r-project.org/package=edgebundleR Description: CRAN Package 'edgebundleR' (Circle Plot with Bundled Edges) Generates interactive circle plots with the nodes around the circumference and linkages between the connected nodes using hierarchical edge bundling via the D3 JavaScript library. See for more information on D3. Package: r-cran-edgecorr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-edgecorr_1.0-1.ca2404.1_all.deb Size: 28844 MD5sum: 7fd9b0007e6175192ffee06fa379662b SHA1: f57eaeb7e5e9b38714dc6729e1d1a49a466737ab SHA256: 2166860fbaadf6ab9193e9cdf1382edbc244ea7ae86b708fccee968b5d43f9a5 SHA512: a172ef17f770b348c269b4e552a38a7f24806da1591be7990e43863b03063c4d174c25d1784eb9f0385f0f3324fbb3079d3327dbb06e26e06c094b5ea1ad926b Homepage: https://cran.r-project.org/package=edgeCorr Description: CRAN Package 'edgeCorr' (Spatial Edge Correction) Facilitates basic spatial edge correction to point pattern data. Package: r-cran-edgedata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-edgedata_0.2.0-1.ca2404.1_all.deb Size: 193082 MD5sum: 79e4b3972f0a87e81776b475be3c1c8d SHA1: 5c595513ec2253cfd43b8eb1a5d70f8694e09f60 SHA256: b4469516e4b5a849aa13974f7c92154df370f4f5b43565e227a15a7b990fd3cc SHA512: 186b3df6140f8c4f9b0e3fa0cd916064cfd4d35f8b46eaf0611be4b11fa4080fe59b0fc002ec20881631e2cbe0ab5e760122054a1398d1c9e590b7b120f22f70 Homepage: https://cran.r-project.org/package=edgedata Description: CRAN Package 'edgedata' (Datasets that Support the EDGE Server DIY Logic) Datasets from most recent CCIIO DIY entry in a tidy format. These support the Centers for Medicare and Medicaid Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows health insurance issuers to calculate member risk profiles under the Health and Human Services-Hierarchical Condition Categories (HHS-HCC) regression model. This regression model is used to calculate risk adjustment transfers. Risk adjustment is a selection mitigation program implemented under the Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA. Under the ACA, health insurance issuers submit claims data to CMS in order for CMS to calculate a risk score under the HHS-HCC regression model. However, CMS does not inform issuers of their average risk score until after the data submission deadline. These data sets can be used by issuers to calculate their average risk score mid-year. More information about risk adjustment and the HHS-HCC model can be found here: . Package: r-cran-edibble Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-tibble, r-cran-cli, r-cran-pillar, r-cran-tidyselect, r-cran-nestr, r-cran-algdesign, r-cran-dae, r-cran-r6, r-cran-lifecycle, r-cran-dplyr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-openxlsx2, r-cran-visnetwork, r-cran-blocksdesign, r-cran-knitr, r-cran-scales, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-edibble_1.1.1-1.ca2404.1_all.deb Size: 3104598 MD5sum: d49f5d5d8029ebcb2da3e1aa6d751d61 SHA1: f8a6887cb39195dacaf739ec03d10cf6befc40a2 SHA256: 6e96ef4f371a0021688df5d2e55722e11584efb5e41a29d63f3c2ef12d0dff76 SHA512: c0aed114a331947a65257aea9ee9a3696f59acabd314db4f632ba2ceb98c26f590b7759d2549a2b4da1077cf671af7ecda2d29b7afa18f481d0ebfe000b812a7 Homepage: https://cran.r-project.org/package=edibble Description: CRAN Package 'edibble' (Encapsulating Elements of Experimental Design) A system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs . An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow. Package: r-cran-ediblecity Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-magrittr, r-cran-stars, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-ediblecity_0.2.2-1.ca2404.1_all.deb Size: 2190306 MD5sum: cc59463627b9debdb71de8b9c0f46ce6 SHA1: bd93b32ebb395ca06e325dbf7a71a463b2d14358 SHA256: 497e43e0164ecf3c676eae893a0199ee34737392150edfca4ba2bc6ca0956414 SHA512: a1f6ae5b0524b322d89ab8b24f6d7fdf7b9795ec884cfbf6efc3b11765f35160cd55982ce630a682d81ccb1006843901ba407a87d725d60da0d281b940923c29 Homepage: https://cran.r-project.org/package=ediblecity Description: CRAN Package 'ediblecity' (Modeling Urban Agriculture at City Scale) The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) . Package: r-cran-edison Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-edison_1.1.2-1.ca2404.1_all.deb Size: 257826 MD5sum: e7ab5227039d9949d8daad7389b9771b SHA1: 203803128e1a34c7cac5d75d72394f7f9ad5bb7c SHA256: df799a5f485236a524c95b8cefd3f49d991216dfc827b334bc591773d0069dbd SHA512: 6840224ba941a856b14e39392d2a70474da3b138ecbe09096df7eeda31d857b8ac11fce9c29993baafc9fe6d69c08f0bd0de35cb4dca40ac64b60e224ad516e0 Homepage: https://cran.r-project.org/package=EDISON Description: CRAN Package 'EDISON' (Network Reconstruction and Changepoint Detection) Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty. Package: r-cran-editbl Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1561 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-uuid, r-cran-fontawesome Suggests: r-cran-testthat, r-cran-dtplyr, r-cran-data.table, r-cran-vctrs, r-cran-rsqlite, r-cran-dbplyr, r-cran-glue, r-cran-dbi, r-cran-bit64, r-cran-knitr, r-cran-dm Filename: pool/dists/noble/main/r-cran-editbl_1.3.2-1.ca2404.1_all.deb Size: 661224 MD5sum: af28ae60e504973499fb43005fd61ce4 SHA1: 174cb3890c6766bb2cd2a14440211d9d17d731a2 SHA256: 5682e9c75d1cf3e36c86864b73a6db5beb9db55171a49468eb055987cedef2ee SHA512: 201feb444db7fe79b8af23fc13af9db47511a180fbf8ad28ca373abe5b135b01108a29ab717d9c231562c4a81d9b3b9d6da21c3ecb774479800134262c19cc9b Homepage: https://cran.r-project.org/package=editbl Description: CRAN Package 'editbl' ('DT' Extension for CRUD (Create, Read, Update, Delete)Applications in 'shiny') The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in 'shiny'. By the use of generic 'dplyr' methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case. Package: r-cran-editdata Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-dt, r-cran-tibble, r-cran-dplyr, r-cran-rio, r-cran-magrittr, r-cran-shinywidgets, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-editdata_0.1.8-1.ca2404.1_all.deb Size: 2112842 MD5sum: c906897090d3f3ae147d7f289adaa8c6 SHA1: 301f47a467100c73f8df5153c244792a3cb72965 SHA256: 8ac379023a22c7fc98f6b1e78977173f7d22d36c11a27946501e73d998ffd027 SHA512: ec8e40809a98434cb3ba4d0ff3426631a1e57ec37436ec816c1a956724a7277dcbd3550eee56f52629d497f3769dd36e934f8827c123756450f895338135a35b Homepage: https://cran.r-project.org/package=editData Description: CRAN Package 'editData' ('RStudio' Addin for Editing a 'data.frame') An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame' without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications. Package: r-cran-editrules Architecture: all Version: 2.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-lpsolveapi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-editrules_2.9.6-1.ca2404.1_all.deb Size: 519212 MD5sum: 4d494f3599c946fc196b2916d6493d51 SHA1: 79e7b472a593e6d3911ff4eac739ade0c25388c1 SHA256: 6bd179855b177fcd4ee6dd83c825a525f786755e8086a6ff1e1caf64ac43dfc5 SHA512: ac02556d9ca8d63658d8447a47a8f919e32153649ffb68a94ce435ba04d5707f76d8128e77b0df9775d5e47c743efe01f75fcc12c229f6a9f0d3aaaac6e7ca0d Homepage: https://cran.r-project.org/package=editrules Description: CRAN Package 'editrules' (Parsing, Applying, and Manipulating Data Cleaning Rules) Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package. Package: r-cran-ediutils Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-xml2 Suggests: r-cran-knitr, r-cran-readr, r-cran-vcr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ediutils_3.0.1-1.ca2404.1_all.deb Size: 359416 MD5sum: d021776c8060a9248a8eb96da3de8960 SHA1: 7eba2a4f39062b76a7e27d223e44cebd1616b71d SHA256: 7fd242031f91d15082a6453c0dab4515d06a769e9bed675f9d8fb4df18f3246a SHA512: 50a095a9a8d622d2e8633a6af7e38a89b2d0dc68fdee5cf6b024b11bb557f1bca59cc4dec5e1fc416788ddc391d7bf7144992f8c69ebcfe3abf3fd67bf8389dc Homepage: https://cran.r-project.org/package=EDIutils Description: CRAN Package 'EDIutils' (An API Client for the Environmental Data Initiative Repository) A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack and was developed in collaboration with the US 'LTER' Network . 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users. Package: r-cran-edl Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotfunctions, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-edl_1.1-1.ca2404.1_all.deb Size: 398000 MD5sum: c7e9a5110676dff290d1f2072c7a3411 SHA1: 40e658fb1c3bac05390a6c4b1c73d3a35fd47043 SHA256: ea27ea63bf7199f84a2017a6e4ccce3dfd4dcb14dbf5ec159347f0959aad99a2 SHA512: da68b10f4c09afaef7133f08f226ce666426d50a25f4bf75e3cff6d9e9b6a9902843ab4426dd98a182ef5cc60dc38e749ee777cbfada35927951947ea447ab01 Homepage: https://cran.r-project.org/package=edl Description: CRAN Package 'edl' (Toolbox for Error-Driven Learning Simulations with Two-LayerNetworks) Error-driven learning (based on the Widrow & Hoff (1960) learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, ) can be used to explain bottom-up human learning (Hoppe et al, ), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package 'ndl' (Baayen et al, 2011, ) is available on CRAN at . However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation 'pyndl' for that purpose.) Package: r-cran-edmdata Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-edmdata_1.3.0-1.ca2404.1_all.deb Size: 667226 MD5sum: 78fbd5860cb38812db8942f8458b3dba SHA1: 33ed8ad2973849b5ff2f21278ab8e3774b1c1580 SHA256: 01412e02ec02d76909c984407db6d5b41be1a15c7375f8d25cedb7f4287d30d0 SHA512: 46330ede40d12119e662e4d21b6e74f1ebab74b70e5650f052ec195ed2eea65ae6450bd428421a681c604383e44baedabf2526e4d15ee5a3f1828d50872e514f Homepage: https://cran.r-project.org/package=edmdata Description: CRAN Package 'edmdata' (Data Sets for Psychometric Modeling) Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) , Culpepper, S. A., & Balamuta, J. J. (2021) , Yinghan Chen et al. (2021) , Yinyin Chen et al. (2020) , Culpepper, S. A. (2019a) , Culpepper, S. A. (2019b) , Culpepper, S. A., & Chen, Y. (2019) , Culpepper, S. A., & Balamuta, J. J. (2017) , and Culpepper, S. A. (2015) . Package: r-cran-ednafuns Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-googlesheets4, r-bioc-phyloseq, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vegan, r-cran-ggplot2, r-cran-vroom, r-bioc-biostrings, r-cran-stringr, r-cran-tidyselect, r-cran-readr Suggests: r-cran-insect, r-cran-testthat, r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ednafuns_0.1.0-1.ca2404.1_all.deb Size: 140232 MD5sum: 061e6927cc87db94b02e45a8bf8fe906 SHA1: bb20f0449110afffa5b39e1d84d715b833e1a10d SHA256: 07bf66d3cb4bf5be9cbf9badee3031a9b907654b6ab8cfc8478e7d8261ccceac SHA512: 82efdeef9bc3c05531f1aa1b42bd7c1c9627a51dd188c0cd64960dc24c077f3dfe845d6f57e71b123ea317bcc0252e29b6c1b516e8b9168211f17c7d94c4d2eb Homepage: https://cran.r-project.org/package=eDNAfuns Description: CRAN Package 'eDNAfuns' (Working with Metabarcoding Data in a Tidy Format) A series of R functions that come in handy while working with metabarcoding data. The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way. In a way it is all about putting together the grammar of the 'tidyverse' from Wickham et al.(2019) with the functions we have used in community ecology compiled in packages like 'vegan' from Dixon (2003) and 'phyloseq' McMurdie & Holmes (2013) . The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader' and 'fastq_reader'), to process 'cutadapt' Martin (2011) 'info-file' output. When it comes to sequence counts across samples, the package works with the long format in mind (a three column 'tibble' with Sample, Sequence and counts ), with functions to move from there to the wider format. Package: r-cran-edne.eq Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-edne.eq_1.0-1.ca2404.1_all.deb Size: 36588 MD5sum: b93332d613d5bb5f384d941615a0654a SHA1: 7a972e4690c21a5f7e4cbb793156aa281d5f992f SHA256: b4d6eb002dfd8ff2a2a121d10a7042c7a3c66810bb1d1d97a2f2f33a0dc0a3af SHA512: e280d1da43406779f694afbde6e2a893f34879a9d2bca3d9f297a887459fd80068604ed0e630fa57eeef3557111664bc65705b32eac3ed960069cddcb27e2746 Homepage: https://cran.r-project.org/package=EDNE.EQ Description: CRAN Package 'EDNE.EQ' (Implements the EDNE-Test for Equivalence) Package implements the EDNE-test for equivalence according to Hoffelder et al. (2015) . "EDNE" abbreviates "Euclidean Distance between the Non-standardized Expected values". The EDNE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Euclidean distance. The test is an asymptotically valid test for the family of distributions fulfilling the assumptions of the multivariate central limit theorem (see Hoffelder et al.,2015). The function EDNE.EQ() implements the EDNE-test for equivalence according to Hoffelder et al. (2015). The function EDNE.EQ.dissolution.profiles() implements a variant of the EDNE-test for equivalence analyses of dissolution profiles (see Suarez-Sharp et al.,2020 ). EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The current regulatory standard approach for equivalence analyses of dissolution profiles is the similarity factor f2. The statistical hypotheses underlying EDNE.EQ.dissolution.profiles() coincide with the hypotheses for f2 (see Hoffelder et al.,2015, Suarez-Sharp et al., 2020). Package: r-cran-edoif Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-distr, r-cran-igraph, r-cran-ellipsis, r-cran-simpleboot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-edoif_0.1.4-1.ca2404.1_all.deb Size: 240738 MD5sum: c0e67e908c3bbce986151f020ad2230b SHA1: b8a3206eb4826907e7aa84ed9dcaecc867c710e4 SHA256: 9198035ad00a80ef9ff01ae4505b0d92db411dda6145c12fb4b4c1e3d7dd9c86 SHA512: 16b60b2794a7d22741c77109095e71a5ad6a2eb8e4a607bc7c5a0bf9dabd7f39464cdbe3441931462c96af4a7b9862c9def9e46bd6087775e4be1a07f453b29e Homepage: https://cran.r-project.org/package=EDOIF Description: CRAN Package 'EDOIF' (Empirical Distribution Ordering Inference Framework (EDOIF)) A non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) . Package: r-cran-edotrans Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abcanalysis, r-cran-opgmmassessment Filename: pool/dists/noble/main/r-cran-edotrans_0.2.5-1.ca2404.1_all.deb Size: 71460 MD5sum: eff0cdf7291c95dc8e9a20887626c4dd SHA1: adcbc75b9b1c1decebaef644daaab4eacc67348e SHA256: 15d79af3faf9b5dd534299666e52a47b5b1032fa630497ebf2d961b466171908 SHA512: b9acd20cfc567c8ea0453ce7d7308f996dd56de98180c464649dd7e925df57deb2355f51839a1f49c26d68aecf067555120a61b87df535dcecc5766e8dbc7011 Homepage: https://cran.r-project.org/package=EDOtrans Description: CRAN Package 'EDOtrans' (Euclidean Distance-Optimized Data Transformation) A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. Package: r-cran-edr4r Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-base64enc, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-leaflet, r-cran-rmarkdown, r-cran-sf, r-cran-svglite, r-cran-testthat, r-cran-webfakes Filename: pool/dists/noble/main/r-cran-edr4r_0.1.1-1.ca2404.1_all.deb Size: 563494 MD5sum: 6dea7d6930417fc9699a9268b49850ea SHA1: d160bd5736ca0ee14aaa606e3e20b01bfd1a8029 SHA256: 58646f46b35f4b3a0c7e01803d6fcd4f123a854f17289091faef4f21d40c3479 SHA512: 3dc76cee35cfa20780734ca12445b5d4c37e5816f00d14c498d16682192ec839e616cf5fc7d0dedc589d6f8b8ecc9289f6012196537509ce29c5e4f126e55e52 Homepage: https://cran.r-project.org/package=edr4r Description: CRAN Package 'edr4r' (Client for OGC API - Environmental Data Retrieval (EDR)) A tidy R client for services implementing the OGC API - Environmental Data Retrieval ('EDR') standard with JSON discovery and 'GeoJSON' or 'CoverageJSON' query responses. General purpose, but most of its real-world use is against in-situ monitoring networks (stream gauges, weather stations, snow and reservoir telemetry) that expose their stations and time series as EDR collections. Known working endpoints include the USGS waterdata OGC API and the Western Water Datahub. Provides discovery, query, and parsing helpers for the locations, items, position, area, cube, radius, trajectory, and corridor query types. Returns 'CoverageJSON' as tidy 'tibble' rows and 'GeoJSON' as 'sf' objects. Package: r-cran-edstan Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-edstan_1.1.0-1.ca2404.1_all.deb Size: 83020 MD5sum: b4f2a14594f3af4722cc3ce404e38b28 SHA1: 7350acfa89690abb51a80174d57e9f39cff1fa1f SHA256: 95c8634b9b3d96991bed86ba19d07f22d47756228677ca1ca5cca236b56a4cf8 SHA512: f09dc6f8b4f11a0741d1ff3b7085d4705d1c40a96d6b87f3de659af798dfc7c7693497d0b1eaf183ae3a93b1dd079838c5b4f857f7d2d6e0bef6da0483f92f62 Homepage: https://cran.r-project.org/package=edstan Description: CRAN Package 'edstan' (Stan Models for Item Response Theory) Streamlines the fitting of common Bayesian item response models using Stan. Package: r-cran-edsurvey Architecture: all Version: 4.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2946 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-lfactors, r-cran-dire, r-cran-data.table, r-cran-formula, r-cran-glm2, r-cran-haven, r-cran-laf, r-cran-lifecycle, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-naepprimer, r-cran-quantreg, r-cran-readxl, r-cran-tibble, r-cran-wcorr, r-cran-naepirtparams, r-cran-wemix, r-cran-xtable, r-cran-xml2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-withr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-edsurvey_4.0.7-1.ca2404.1_all.deb Size: 2397428 MD5sum: 72aa4cca4d0925bdc3c13e934ad4666a SHA1: d17fcda3021a510850d49cbeb0c5af8da5f55de9 SHA256: 27da9bfb689fb6eb507fa7fc07f94138a30915bec1c6ffbcb80c7b087d297713 SHA512: e93942b11f8217264b252950f386e0db2b5447fcc83124e3b0b37e207059f03d92552bc77f44c2caa2ee4c0241b4d1660378ed7ec0480373d0d7c0dc23c7223a Homepage: https://cran.r-project.org/package=EdSurvey Description: CRAN Package 'EdSurvey' (Analysis of NCES Education Survey and Assessment Data) Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) , including National Assessment of Educational Progress (NAEP) data and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) , including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) , including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd). Package: r-cran-educabr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6968 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-systemfonts, r-cran-testthat, r-cran-textshaping, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-educabr_1.2.0-1.ca2404.1_all.deb Size: 5272534 MD5sum: 84b9bb446a7bada19f4a106fa1dc95b2 SHA1: 2a4d615336233b007dea8a8c1dd8648f0dfde948 SHA256: 0eddb0796b8727aee486f2ed489be10c8d304500a12ae05a3f66b3b6ddcb045a SHA512: c728bd56a8d27c63ce608eece8136ab954d6d1bd53ffc26e8240a62a7bff66a235f6a15c57e35e2bcd6fc672cd1e1868bcc28e209c9f9ad5ff51dadd12dd47e2 Homepage: https://cran.r-project.org/package=educabR Description: CRAN Package 'educabR' (Download and Process Brazilian Education Data from INEP) Download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira). Provides functions to access microdata from the School Census (Censo Escolar), ENEM (Exame Nacional do Ensino Médio), SAEB (Sistema de Avaliação da Educação Básica), Higher Education Census (Censo da Educação Superior), ENADE (Exame Nacional de Desempenho dos Estudantes), ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos), IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado), CPC (Conceito Preliminar de Curso), IGC (Índice Geral de Cursos), CAPES graduate education data, FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), IDEB (Índice de Desenvolvimento da Educação Básica), and other educational datasets. Returns data in tidy format ready for analysis. Data source: INEP Open Data Portal . Package: r-cran-educationdata Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-educationdata_0.1.5-1.ca2404.1_all.deb Size: 346658 MD5sum: f319826cf5e24639f3af96a2ae7176ec SHA1: 7b0863aeb77eeb2923f7e1e4c414b29bbdda0b57 SHA256: 505976cdf3083442a84ede26a0377e6ab548f8ac0724bece33c2769f25e8aaa7 SHA512: 16d1cc313c3b0a8292cd7436705a19deb9f03bcf95abbf36e09709baad1f7c2eccbc6bf4a9e761fecd3fbbef1abd642e2cb95299209f18da6f2234d94b73f690 Homepage: https://cran.r-project.org/package=educationdata Description: CRAN Package 'educationdata' (Retrieve Records from the Urban Institute's Education DataPortal API) Allows R users to retrieve and parse data from the Urban Institute's Education Data API into a 'data.frame' for analysis. Package: r-cran-educationr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-educationr_0.1.0-1.ca2404.1_all.deb Size: 227148 MD5sum: fa674f4b45c3ff176dbf2a96115db14e SHA1: 5276a304b1229dffb8c40c9cdf2fe1755affd29d SHA256: 5dd938aae9fbd527f978ecbf4d5dae454d3f85e9829e450d3ebea2d522d90be9 SHA512: 979bf242d022a660d28ef1cbf51fae628101acd3da1a0edb6ea9558592efb90c79c3fda6836c338e803a7fcdad10f400a906db4def7821bc3c7dae43b218fa54 Homepage: https://cran.r-project.org/package=educationR Description: CRAN Package 'educationR' (A Comprehensive Collection of Educational Datasets) Provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education. Package: r-cran-educineq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq, r-cran-flexsurv Filename: pool/dists/noble/main/r-cran-educineq_0.1.0-1.ca2404.1_all.deb Size: 413658 MD5sum: 2ef3d32ebdfdabed505f670cd803499d SHA1: 0749f2c5fddc774ad9ba9696f2ba950476751c98 SHA256: 28a7568f5887966e8299bdad61d3d730596aac1e80bdf9460958e09aa5e8d97c SHA512: b2cde3f1df95919ad7fcd4e7a1859be93b4569983bbdb5ca3e846945ffa12b7fbc29266b3fd229db7af48d3f82c691efaff71a0a4cc5f90130d261a1b8e652ad Homepage: https://cran.r-project.org/package=educineq Description: CRAN Package 'educineq' (Compute and Decompose Inequality in Education) Easily compute education inequality measures and the distribution of educational attainments for any group of countries, using the data set developed in Jorda, V. and Alonso, JM. (2017) . The package offers the possibility to compute not only the Gini index, but also generalized entropy measures for different values of the sensitivity parameter. In particular, the package includes functions to compute the mean log deviation, which is more sensitive to the bottom part of the distribution; the Theil’s entropy measure, equally sensitive to all parts of the distribution; and finally, the GE measure when the sensitivity parameter is set equal to 2, which gives more weight to differences in higher education. The decomposition of these measures in the components between-country and within-country inequality is also provided. Two graphical tools are also provided, to analyse the evolution of the distribution of educational attainments: The cumulative distribution function and the Lorenz curve. Package: r-cran-eduresearchr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2632 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eduresearchr_0.1.0-1.ca2404.1_all.deb Size: 2516470 MD5sum: 8bc60962cb0ff6ab8401789f6bd83f85 SHA1: 73ab2aee255b15385b9a83fed6d9e4d5429463e6 SHA256: ee81fde84f04ef6168da75caa1ec8ce2cbe636fecf4924ebcd43100f1743e6b0 SHA512: 5d1da3d3766c7bddc393849542b193b90979702c18c2490386a2140ad73ee2a02d1e56eb3197f0c44b62687930c3abdf44f7ea51e91353030a40c4328a12f3b5 Homepage: https://cran.r-project.org/package=eduResearchR Description: CRAN Package 'eduResearchR' (A Collection of Educational Datasets for Research andStatistical Analysis) Curates a comprehensive and robust collection of 15 classic educational, economic, and social science datasets meticulously tailored for empirical research, project-based learning, and academic instruction in higher education. It streamlines exploratory data analysis, business intelligence modeling, regression techniques, and hypothesis testing by providing ready-to-use data structures sourced from prominent community packages (Kleiber and Zeileis (2008) ; Fox and Weisberg (2019) ). Package: r-cran-edwards97 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-tibble, r-cran-broom, r-cran-cli, r-cran-withr, r-cran-glue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-edwards97_0.1.1-1.ca2404.1_all.deb Size: 376944 MD5sum: eb98285665ce8d6bd957c8cf2daff5c2 SHA1: bc8f0e5b9607519bbb07baf45b7ebb95e79616d9 SHA256: eb3e73382cc0a4ff773c99ef1c4f26fdab69536890fb21daafd69a319d76edf9 SHA512: c60920845581f716114c8600834afbb39a76467c084c10daf5d9417c70ac03e65eec3ac7c4c47d56be26d2641d1f9271276b84dfa3779ed6d7ecd0991728b236 Homepage: https://cran.r-project.org/package=edwards97 Description: CRAN Package 'edwards97' (Langmuir Semi-Empirical Coagulation Model) Implements the Edwards (1997) Langmuir-based semi-empirical coagulation model, which predicts the concentration of organic carbon remaining in water after treatment with an Al- or Fe-based coagulant. Data and methods are provided to optimise empirical coefficients. Package: r-cran-ee.data Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7311 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-nnet, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-ee.data_0.2.0-1.ca2404.1_all.deb Size: 7448694 MD5sum: f88a3d7804803786ffd1fc00a0f744b5 SHA1: f7a5cde52a8d9133aa6298369266db7527482e20 SHA256: e27109cbcd1c38e3943ec7569c8c1777e796e0926a38d0dd7ee7cdee39ecde52 SHA512: 8ef1ce46d083e053f1cebad28584c309eaacbae02680d84258177494f0bfbd0680f1455913147d0635392a3db08b057b4e67c85d7b157bd29408de6984924d17 Homepage: https://cran.r-project.org/package=EE.Data Description: CRAN Package 'EE.Data' (Objects for Predicting Energy Expenditure) This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) and the models of Staudenmayer et al. (2015) , one a linear model and the other a random forest. The package is intended as a spoke for the hub-package 'accelEE', which brings together the above methods and others from packages such as 'Sojourn' and 'TwoRegression.' 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Package: r-cran-eefanalytics Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-ggplot2, r-cran-lme4, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcvis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eefanalytics_1.1.5-1.ca2404.1_all.deb Size: 260658 MD5sum: 174334c5d47583b667c1c84d9257d7fe SHA1: 0cac458c808a96b88f4467e332f40373fb6c4f42 SHA256: c93143c115e1e038c083ed34c7b16d6bdc916c698af5315f5577e5e3bf8ae5d3 SHA512: 55a56b90a6ba5b2fb2b68c114f51613fe5ddc378db10ffa66a04ee4ee6808bad5ea5a949020c3b682b52132a635595828f0f64ec8cd3bd406a6134a72b38c964 Homepage: https://cran.r-project.org/package=eefAnalytics Description: CRAN Package 'eefAnalytics' (Robust Analytical Methods for Evaluating EducationalInterventions using Randomised Controlled Trials Designs) Analysing data from evaluations of educational interventions using a randomised controlled trial design. 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Package: r-cran-eegkit Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eegkitdata, r-cran-bigsplines, r-cran-ica, r-cran-rgl, r-cran-signal Filename: pool/dists/noble/main/r-cran-eegkit_1.0-5-1.ca2404.1_all.deb Size: 560182 MD5sum: 1cdb67d6f3729ba3350980c70035c334 SHA1: d800385c76fcf562417cfc3abd1d3d975f9aac9e SHA256: 819883999f1f0ea92df9f18968c0b9b88717877ec2ee5c01400fbca2ea160458 SHA512: e2893c8213a7928aab2476d3e734402718f84306a42663b829aa05cc7f514ad106540010e5e21b9941b653aaf6ae17dd1241b703710af9d471d00cd12ad45b5f Homepage: https://cran.r-project.org/package=eegkit Description: CRAN Package 'eegkit' (Toolkit for Electroencephalography Data) Analysis and visualization tools for electroencephalography (EEG) data. 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The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) and Yeasin and Paul (2024) . 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Different methods are also provided to calculate common metrics such as humification index and fluorescence index. Package: r-cran-eeptools Architecture: all Version: 1.3.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1504 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-arm, r-cran-data.table, r-cran-vcd Suggests: r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-eeptools_1.3.0-1.ca2404.2_all.deb Size: 1424416 MD5sum: d0e825d68c8a7293018d149eef2af03e SHA1: ba9f78ff72d8b4b52bfe9d8de9981c1fdc4654a0 SHA256: 8695e963ec6dc7f6d65ba041549b82825fa31fbd2fe5daeadc1f5762e9e2dac7 SHA512: 653c7360bd126fdd69a6c4e1d0a056049f610403187fab2c80eead93af4eee711f24494a391e89e2184000f28a0643e7ac9505570714909dfa3503070cbc84de Homepage: https://cran.r-project.org/package=eeptools Description: CRAN Package 'eeptools' (Convenience Functions for Education Data) Collection of convenience functions to make working with administrative records easier and more consistent. 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Package: r-cran-eesim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-viridis Suggests: r-cran-dlnm, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-eesim_0.1.0-1.ca2404.1_all.deb Size: 1543540 MD5sum: 0c517da21a1e125d098d1692f0918a99 SHA1: 6d88daee1b5e8d1eb76d1cb0606bc29817668e10 SHA256: 45af3c23f1b5abfecd2fea2bec6a50a6db6f65a1322a7533fff45840ea70e348 SHA512: 037f07cfe81fcaf2203e79df89214265e9788360839386aa53893d320dd71b988927d36d0587c62597199083b4d0b04bc2c05a788a55fdc6d019e16af65a7a79 Homepage: https://cran.r-project.org/package=eesim Description: CRAN Package 'eesim' (Simulate and Evaluate Time Series for Environmental Epidemiology) Provides functions to create simulated time series of environmental exposures (e.g., temperature, air pollution) and health outcomes for use in power analysis and simulation studies in environmental epidemiology. This package also provides functions to evaluate the results of simulation studies based on these simulated time series. This work was supported by a grant from the National Institute of Environmental Health Sciences (R00ES022631) and a fellowship from the Colorado State University Programs for Research and Scholarly Excellence. Package: r-cran-eespca Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rifle, r-cran-mass, r-cran-pma Filename: pool/dists/noble/main/r-cran-eespca_0.8.0-1.ca2404.1_all.deb Size: 274866 MD5sum: f5ce6ccfb984e84b611538570ec842fc SHA1: 9f9d04a9dc14fca5ecdd1027692a8a37e5eacec6 SHA256: cc742c72184ac348e26d6b5e83923e6e770daeab1211d4a3d57c4ebc99296db6 SHA512: 0ae020d02853c721b97949e1c7bec8d57b53a485594c3708ad9bbd83cc90189770f93c25bce1c7b5f85937b5fe1fb72a94774e8ac83d84ec85b36940754216ab Homepage: https://cran.r-project.org/package=EESPCA Description: CRAN Package 'EESPCA' (Eigenvectors from Eigenvalues Sparse Principal ComponentAnalysis (EESPCA)) Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. 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Package: r-cran-efa.dimensions Architecture: all Version: 0.1.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-psych, r-cran-polycor, r-cran-efatools, r-cran-mirt, r-cran-gparotation, r-cran-lavaan, r-cran-semtools Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-efa.dimensions_0.1.9.2-1.ca2404.1_all.deb Size: 1218202 MD5sum: 7ae1635aae0568eabc0b82203c6f2aab SHA1: 0b45d7616b9173cae20ca35b175674d69b7b4176 SHA256: 15c35fc53befa3d9ba7f4af90ecce8b026f4249a32efb1217c0ee2810c3abcab SHA512: 6b38f121e7d3fc49a0f24022364aa8700307d324ba535fe54cae71c4c75d165bbcdbbc34c426e99d21f94246e792cbebc22f48b6bd6d2189578e2a8f84195e76 Homepage: https://cran.r-project.org/package=EFA.dimensions Description: CRAN Package 'EFA.dimensions' (Exploratory Factor Analysis Functions for AssessingDimensionality) Functions for an assortment of factor analysis-related procedures, including eleven procedures for determining the number of factors; for factor analysis with multiple options for methods of extraction and rotation; for bi-factor analysis; for extension factor analysis; options for running the analyses using either raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations; wrapper 'lavaan'-based functions for factorial invariance and exploratory structural equation modeling; functions for the factor-ability of a correlation matrix, for the congruence between factors from different datasets, for the assessment of local independence, for the assessment of factor solution complexity, for internal consistency, and for correcting Pearson correlation coefficients for attenuation due to unreliability. Auerswald & Moshagen (2019, ); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, ). Package: r-cran-efa.mrfa Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-scales, r-cran-pcovr, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-efa.mrfa_1.1.2-1.ca2404.1_all.deb Size: 98936 MD5sum: 3aa7f5737889813ea0c0467574fb79cf SHA1: 1ce4fb71bdeed41d59d77b62da71fbaf3f09c8d4 SHA256: a9bc631a90d18b01a91151a0eac602971754cedf27b59ffde30d9c2ff5eda0ec SHA512: 44570a4485a14f32ec1b8cce67c93d6064ffc30288d832eb19d31b913096aad86b08b186f0d3ec8ab29293b0343925d916ba5f98f033fb9cc7bf1fec50531c5e Homepage: https://cran.r-project.org/package=EFA.MRFA Description: CRAN Package 'EFA.MRFA' (Dimensionality Assessment Using Minimum Rank Factor Analysis) Performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 ) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 2011 ) for assessing the dimensionality of a set of variables using minimum rank factor analysis (see ten Berge & Kiers, 1991 for more information). 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Package: r-cran-efautilities Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gparotation, r-cran-plyr, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-efautilities_2.1.3-1.ca2404.1_all.deb Size: 324926 MD5sum: 088f98e46f67522911a71e777cc25c33 SHA1: 57bd2ad265b2774c81ca1b4ddfcaf34adb165e58 SHA256: dab06138b080c8997c1a96e0d484431b7ee4ce16f0438c3aa5eacdceb32f305c SHA512: 12cc9e7d0d5a17e745bdc16b1ccb6e47abfa32fa38e5e4d57338bf51dc8c826c0e0e28b04d1d36c7ab731eb0c3b950993e2a9ebeca0cb7972e2f2f3b4555919c Homepage: https://cran.r-project.org/package=EFAutilities Description: CRAN Package 'EFAutilities' (Utility Functions for Exploratory Factor Analysis) A number of utility function for exploratory factor analysis are included in this package. 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Package: r-cran-efdm Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-efdm_0.2.3-1.ca2404.1_all.deb Size: 265530 MD5sum: 65bf3d538487923802e752cc2fa2c647 SHA1: f97f9f74c27b6b437b1be9b13f29946373fb1cce SHA256: 7f4d882095449ea4c0c8f00521b12790216b8d0d75a00f3888cdea0748c7fb38 SHA512: 5a74ed370d119b912b1d0efe7a6d32d14e7de392269b34012b84ef645792faafaa93548cb2c0e1a3cc299f0fe6fa387b8079118cb51a6b314f5388ee28d67c6d Homepage: https://cran.r-project.org/package=efdm Description: CRAN Package 'efdm' (Simulate Forest Resources with the European Forestry DynamicsModel) An implementation of European Forestry Dynamics Model (EFDM) and an estimation algorithm for the transition probabilities. 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Package: r-cran-eff2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-bioc-rbgl, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-qgraph Filename: pool/dists/noble/main/r-cran-eff2_1.0.2-1.ca2404.1_all.deb Size: 195348 MD5sum: 07442ef7969eacf8b5eed713ed3aa046 SHA1: 1ee4c3945a7ff5eecf82528628d6d3141ef995f5 SHA256: ee42674d11d0baf305f156887f8f2f0049d7b40266887b60de1d87ff7a127db3 SHA512: 4131aa6d8051c443db4ea43925346461955d0aaf0721cf4631f27c8bdc696a6891be52e304e7dfc20545d815dc1fd1cf76fa6030fcad8b1555eeb4b1209b3e8a Homepage: https://cran.r-project.org/package=eff2 Description: CRAN Package 'eff2' (Efficient Least Squares for Total Causal Effects) Estimate a total causal effect from observational data under linearity and causal sufficiency. The observational data is supposed to be generated from a linear structural equation model (SEM) with independent and additive noise. The underlying causal DAG associated the SEM is required to be known up to a maximally oriented partially directed graph (MPDAG), which is a general class of graphs consisting of both directed and undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such graphs are usually obtained with structure learning algorithms with added background knowledge. The program is able to estimate every identified effect, including single and multiple treatment variables. Moreover, the resulting estimate has the minimal asymptotic covariance (and hence shortest confidence intervals) among all estimators that are based on the sample covariance. Package: r-cran-effclust Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fixest Suggests: r-cran-plm, r-cran-data.table Filename: pool/dists/noble/main/r-cran-effclust_0.8.0-1.ca2404.1_all.deb Size: 135238 MD5sum: 03cffb65ce4716f8d97913e4648eb3b9 SHA1: 25d0495a9f29cfa730253e751d5ad6b5710ffec5 SHA256: 73e3fcd1b9bdce61e4607a5784d2686f20d0b5b4873e709e13d601ed68f99d39 SHA512: 5efb7c124d4f139bc40f4cd649bb8663b94fe5344ca6e4d1efcee36ca37c78c5fc2cd20afd7211b770fd9d62af90ba5cba9ace012d94a4e400a8f19f8dca6312 Homepage: https://cran.r-project.org/package=effClust Description: CRAN Package 'effClust' (Calculate Effective Number of Clusters for a Linear Model) Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) . 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Parses test statistics, effect sizes, and confidence intervals across multiple citation styles including American Psychological Association (APA), Harvard, Frontiers, PLOS ONE, Scientific Reports, Nature Human Behaviour, PeerJ, eLife, PNAS, and others. Recomputes effect sizes using all plausible variants when design is ambiguous, and validates internal consistency. Supports t-tests, F-tests/ANOVA, correlations, chi-square, z-tests, regression, and nonparametric tests. Explicitly tracks all assumptions and uncertainty in output. Detects decision errors (significance reversals) similar to 'statcheck'. From v0.4.0 file extraction is no longer part of the package — pair with an external extractor (e.g., 'docpluck' at ) and pass the resulting text to check_text(). Note: this package is under active development and results should be independently verified. Use is at the sole responsibility of the user. Contributions and verification reports are welcome. 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Package: r-cran-egocor Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-rdpack, r-cran-shiny, r-cran-sp, r-cran-spatialtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-testthat Filename: pool/dists/noble/main/r-cran-egocor_1.3.4-1.ca2404.1_all.deb Size: 181460 MD5sum: 63864c9ce06f5375a661263f1684f1aa SHA1: 3fd2a26bb1749fda01e3ed85cec37ff7452eab73 SHA256: ae133b5e4077b7e23d2e2ebb4dee94a268428c6dc321212ead50f579448102ab SHA512: ffcc08027fb57e1227406c432bce556a4d76c1d0307fd72e24170574f494c7ac7b09e19678d0363ba8c74ee88d3ac51a8903d25be2d8bc8e452e3492bc681c92 Homepage: https://cran.r-project.org/package=EgoCor Description: CRAN Package 'EgoCor' (Simple Presentation of Estimated Exponential Semi-Variograms) User friendly interface based on the R package 'gstat' to fit exponential parametric models to empirical semi-variograms in order to model the spatial correlation structure of health data. Geo-located health outcomes of survey participants may be used to model spatial effects on health in an ego-centred approach. The package contains a range of functions to help explore the spatial structure of the data as well as visualize the fit of exponential models for various metaparameter combinations with respect to the number of lag intervals and maximal distance. Furthermore, the outcome of interest can be adjusted for covariates by fitting a linear regression in a preliminary step before the semi-variogram fitting process. 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Package: r-cran-egretci Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2728 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-egret, r-cran-binom, r-cran-foreach Suggests: r-cran-knitr, r-cran-testthat, r-cran-doparallel, r-cran-iterators, r-cran-rmarkdown, r-cran-pkgdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-egretci_2.0.5-1.ca2404.1_all.deb Size: 2568730 MD5sum: 144b357339523fedb7b9e0e054578056 SHA1: 897f3f8a54f8139c7762de0fc9bc7da9bd9093a9 SHA256: 26c66f1f5916cd50cd3458e9010b3e0558cdb7f485f537b9078a1001ebc593e8 SHA512: 6725cf3b49cc35da8ec9bd93b73ee710c2209bc657bb1b6703c051ea8a13c6118dc018afe75b57091f4c3d06a787ad324dfdc77cb8abad7a73529e49a77eaa09 Homepage: https://cran.r-project.org/package=EGRETci Description: CRAN Package 'EGRETci' (Exploration and Graphics for RivEr Trends Confidence Intervals) Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) , and expanded in Hirsch and De Cicco (2015) . The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) . Package: r-cran-egrni Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1382 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-cran-gdata, r-cran-mass, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-egrni_0.1.6-1.ca2404.1_all.deb Size: 770796 MD5sum: bf035d53042d9a8c4834e4b4ab3de41c SHA1: 6d86efe5c1d8f37bf979bd5a429c745d4e7ca88c SHA256: 7001cd43ec207a0b5ace61adb65a821ebe3dc32538270ee1b73c40b0c40d81e1 SHA512: 7a0e0097fa2b4865eece88404fa19b4fbec493236d4fa8dcb5da2ad04eae7c5c65b0a56df1f9f2bb6578d2cbaaa541fbd29ff7d1ce051439f3cb899fafce1bd9 Homepage: https://cran.r-project.org/package=EGRNi Description: CRAN Package 'EGRNi' (Ensemble Gene Regulatory Network Inference) Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. . Package: r-cran-egst Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-mvtnorm, r-cran-mass, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-egst_1.0.0-1.ca2404.1_all.deb Size: 196562 MD5sum: 501294ad7721c7581d0a2c1a66ef0050 SHA1: 5bd4706ba619799c5854814cdcc3a2cce787ba92 SHA256: 1649a27d849273b7d6e57edab0a45943958357af8c504a8793620ecd9229f2d5 SHA512: 0fafb2076dfddcc9f2b376d34045f9191b1b9c38eee2795bc8a1f9dfeedb25887aeb53da2a27f42d6399d841709b6bcdfff452f95f38f040e0105622313fa842 Homepage: https://cran.r-project.org/package=eGST Description: CRAN Package 'eGST' (Leveraging eQTLs to Identify Individual-Level Tissue of Interestfor a Complex Trait) Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) . Package: r-cran-ehagof Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ehagof_0.1.1-1.ca2404.1_all.deb Size: 67230 MD5sum: 656f7ac6f55b6d8eee8a064b9accafa7 SHA1: a92cf27fcc5d00e88309afbdddacc3eb6cd1ff13 SHA256: 5f0b5b458c75a089440751827c65f6f0f5b44056964bd544cc610745e5ba10cb SHA512: 7796cc455b6395a8641dc210eea316d0d9428e0a38473b4623277419e3665d702ebda3a0de0584db9d130c9dd309389e0accb1bc798f0a345befe58014fbfd9f Homepage: https://cran.r-project.org/package=ehaGoF Description: CRAN Package 'ehaGoF' (Calculates Goodness of Fit Statistics) Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE). 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(2023) ). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies. 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The 'EHR' package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) . Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) . Package: r-cran-ehrmuse Architecture: all Version: 0.0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mass, r-cran-nleqslv, r-cran-xgboost, r-cran-survey, r-cran-nnet, r-cran-simplexreg Filename: pool/dists/noble/main/r-cran-ehrmuse_0.0.2.1-1.ca2404.1_all.deb Size: 78742 MD5sum: c7d125e758860004e92ea9bfefe94aa8 SHA1: adca52e2a5049e40c0550308aaa69555ceafdf02 SHA256: 75a325415f4e9b95c10a79bed50aaed1e8a98173d99fec376a291f39988410db SHA512: b30a0792834202806cf0a29fa46130cde3b1f8500070cbaa9e2767b6fdd028b5c776809efd542b641324b9431d3e58dcf5d4666998769c6065873547d9dda54a Homepage: https://cran.r-project.org/package=EHRmuse Description: CRAN Package 'EHRmuse' (Multi-Cohort Selection Bias Correction using IPW and AIPWMethods) Comprehensive toolkit for addressing selection bias in binary disease models across diverse non-probability samples, each with unique selection mechanisms. It utilizes Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce selection bias effectively in multiple non-probability cohorts by integrating data from either individual-level or summary-level external sources. The package also provides a variety of variance estimation techniques. Please refer to Kundu et al. . Package: r-cran-ehrtemporalvariability Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plotly, r-cran-zoo, r-cran-xts, r-cran-lubridate, r-cran-rcolorbrewer, r-cran-viridis, r-cran-scales, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-dbscan, r-cran-webshot, r-cran-httr Filename: pool/dists/noble/main/r-cran-ehrtemporalvariability_1.2.2-1.ca2404.1_all.deb Size: 2755904 MD5sum: 12972bdacafc9d75fb6966209b23aa87 SHA1: b7479e1c04813fe7f0f4527fa38a185c029bb0c2 SHA256: 2f9264be80915c2a6e057e9d11bfbd7c4ebafc37698d5132b3375c685952104f SHA512: 9c89c4536dbe058987d4758ff8fce44f57ee5842ead3c3063e9f6e6d04f61c40ec0a1bd8a8cc9f0e2478f1f2b3b49f51f81514c19e322a6afb3fa91c5c20fd49 Homepage: https://cran.r-project.org/package=EHRtemporalVariability Description: CRAN Package 'EHRtemporalVariability' (Delineating Temporal Dataset Shifts in Electronic Health Records) Functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. 'EHRtemporalVariability' is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. 'EHRtemporalVariability' allows you to explore and identify dataset shifts through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding. (Sáez et al. 2020) . Package: r-cran-ehymet Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clustercrit, r-cran-kernlab, r-cran-tf Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ehymet_0.1.1-1.ca2404.1_all.deb Size: 199510 MD5sum: c7511e535a4c9aec00989d3ac7b6d073 SHA1: 7827fada7df4af8400add7aab6ceefdbe4703c69 SHA256: 87b01251740c096c7ef86051363a74fd03733fd7aa26854fc720e4c9306312a5 SHA512: a4546efb4c4f8f31e6161abb9fb471b0a2e40fec7d3cb1861fcdbba35b8a30456d8d9c6a6c431690d3b8838d57f629ab80eabcf102e7d6df23dfd8d12b786cf5 Homepage: https://cran.r-project.org/package=ehymet Description: CRAN Package 'ehymet' (Methodologies for Functional Data Based on the Epigraph andHypograph Indices) Implements methods for functional data analysis based on the epigraph and hypograph indices. These methods transform functional datasets, whether in one or multiple dimensions, into multivariate datasets. The transformation involves applying the epigraph, hypograph, and their modified versions to both the original curves and their first and second derivatives. The calculation of these indices is tailored to the dimensionality of the functional dataset, with special considerations for dependencies between dimensions in multidimensional cases. This approach extends traditional multivariate data analysis techniques to the functional data setting. A key application of this package is the EHyClus method, which enhances clustering analysis for functional data across one or multiple dimensions using the epigraph and hypograph indices. See Pulido et al. (2023) and Pulido et al. (2024) . Package: r-cran-ei.datasets Architecture: all Version: 0.0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4041 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ei.datasets_0.0.1-3-1.ca2404.1_all.deb Size: 4086402 MD5sum: 29d284ce9df3153209b2e48f0a5a8885 SHA1: b009eda6f04ffc4c0bd53ebe43bc4644cd2954aa SHA256: 1fe679234e49ad9cbd844fab008714efbf11cabd055eaa6481f43d73edbad638 SHA512: 2e46833e6e38c0e6bf6e21708dd96cb1c656ca8df90275cf7be717bf98d5f39e0aefb3769afb2cc2715cb0b855e6ec4fa061e45c0bfd1c1b0f96a58dcca906c9 Homepage: https://cran.r-project.org/package=ei.Datasets Description: CRAN Package 'ei.Datasets' (Real Datasets for Assessing Ecological Inference Algorithms) Provides more than 550 data sets of actual election results. Each of the data sets includes aggregate party and candidate outcomes at the voting unit (polling stations) level and two-way cross-tabulated results at the district level. These data sets can be used to assess ecological inference algorithms devised for estimating RxC (global) ecological contingency tables using exclusively aggregate results from voting units. Reference: Pavía (2022) . Package: r-cran-ei Architecture: all Version: 1.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eipack, r-cran-mvtnorm, r-cran-msm, r-cran-tmvtnorm, r-cran-ellipse, r-cran-plotrix, r-cran-mass, r-cran-ucminf, r-cran-cubature, r-cran-mnormt, r-cran-foreach, r-cran-sp Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-ei_1.3-3-1.ca2404.1_all.deb Size: 585444 MD5sum: 939f6e3a2c1bdeb9e0c0defb79446eba SHA1: a339adba606ba73830de44e4e40e0f89be2339a8 SHA256: cba07fac09281819d672ca0adef9612b1bd1039b5cc659f002a3baa1e3a406fe SHA512: 5c46eaa1280f832dff28924f9bf8a2dcd11cda0afcd45c7bca16296459994bac67391b619e23845c438c822ed9ab01207f9e669c9a8c487871579dc65ef4d968 Homepage: https://cran.r-project.org/package=ei Description: CRAN Package 'ei' (Ecological Inference) Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407. 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Package: r-cran-eiaapi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-knitr, r-cran-plotly, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eiaapi_0.2.0-1.ca2404.1_all.deb Size: 477584 MD5sum: 0c1e93224313e49a8197d6688a21e5c4 SHA1: 9a158aac9d71834da4927a41150f9c9cece05c3b SHA256: 4c7f96a1f184a5e3657daca661a610a6e068495c154851d58bbb2ad67e7c32c5 SHA512: fdbfa25f21ecfeff984dc73d77cf952bffb838df479aebc586fc94def8dc220e6461cb198d6d3777d06e3cc78137652203ad6263c2f9e59dbabbedcf21727d6a Homepage: https://cran.r-project.org/package=EIAapi Description: CRAN Package 'EIAapi' (Query Data from the 'EIA' API) Provides a function to query and extract data from the 'US Energy Information Administration' ('EIA') API V2 . The 'EIA' API provides a variety of information, in a time series format, about the energy sector in the US. The API is open, free, and requires an access key and registration at . Package: r-cran-eiballots Architecture: all Version: 0.1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-eiballots_0.1.0-1-1.ca2404.1_all.deb Size: 278814 MD5sum: 7cea416ff20a0fb6da6e9558022116d8 SHA1: 90f295b87b73cdb4eba93c8745ddcb0d40c2a861 SHA256: 1885fd9a42aaaf1bd8c7fb709e9fff075670b99dfff9aa6c870d606f121fd138 SHA512: 980c2f4bfd3a9717b702ee440b9070c6ea23dbc3cb6e019a4c7a7ba2d7986142c71eb1db6e05297fbe706fdf1ee721b027ca6178f5c032d4f9aeebb7cb62be75 Homepage: https://cran.r-project.org/package=eiballots Description: CRAN Package 'eiballots' (Ballot-Level Microdata and Summaries for Ecological Inference(Florida 2000)) Provides access to ballot-level electoral microdata from the Florida 2000 general election and tools for computing summaries suitable for ecological inference. Includes functions to load data by county or race (election), compute marginal distributions at the precinct level, and build joint contingency arrays across multiple races for use with ecological inference packages. Data files are stored in a remote repository and downloaded on demand; local copies are supported via the 'data_dir' option. Acknowledgements: We thank Jaime Ventura (ANES, University of Michigan) and Dan Keating (The Washington Post) for providing the raw data that serve as the starting point for the construction of this package. We also acknowledge funding from the Conselleria de Educación, Cultura y Universidades (grant CIACIO/2023/031). Package: r-cran-eicircles Architecture: all Version: 0.0.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlcoptim Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-eicircles_0.0.1-14-1.ca2404.1_all.deb Size: 256278 MD5sum: 9e29f327f5dab424dd8d7405679e1455 SHA1: 9c979dbf2b852a96d7bbc7bc12ae0da0ed3a4d08 SHA256: d6a7c82a9e7177dbdd95d64b3a3f31be7fb0b1da3a68be3517be439648fa878a SHA512: e63206f9b950b1300453ebd4ecb83f8d77e2b7777865d8f37531a6772e98d932112807f2bcbd619392cf5e9413c1009703a898bf86ac471df68d257faf0e7969 Homepage: https://cran.r-project.org/package=eiCircles Description: CRAN Package 'eiCircles' (Ecological Inference of RxC Tables by Overdispersed-MultinomialModels) Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates. References: Brown, P. and Payne, C. (1986). ''Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453–460. . Forcina, A., Gnaldi, M. and Bracalente, B. (2012). ''A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109–119. . Pavia, J.M, and Forcina, A. (2026). ''Simulating electoral behavior''. Modeling Decisions for Artificial Intelligence, MDAI 2025. Lecture Notes in Computer Science, vol 15957, Torra, V., Narukawa, Y., Domingo-Ferrer, J. (eds), Springer, Cham, pp. 54-65. . Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIAICO/2023/031) and MICIU/AEI/10.13039/501100011033/FEDER, EU (grant PID2021-128228NB-I00) for supporting this research. Package: r-cran-eider Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-stringr, r-cran-magrittr, r-cran-jsonlite, r-cran-logger, r-cran-purrr, r-cran-fs, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-eider_1.0.0-1.ca2404.1_all.deb Size: 244540 MD5sum: df7387c74db6002a7943d852f09cdfae SHA1: 04a969178f01ff16dff1f190d96f40bb5a56a9b5 SHA256: c6d03fc1d3a95fd8693d334d7118d14b81762b8da5efb8dfe04cc2bc099c637b SHA512: 890b472396d1e258deba57e30b82006e9bdda2bd18b2ad239c938572cc49ab8c7cd2b8a9424998b9c83070c55023411d4c4fb23bd4bade054b0076950e56da16 Homepage: https://cran.r-project.org/package=eider Description: CRAN Package 'eider' (Declarative Feature Extraction from Tabular Data Records) Extract features from tabular data in a declarative fashion, with a focus on processing medical records. 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Package: r-cran-eidosapi Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-fuzzyjoin, r-cran-httr Filename: pool/dists/noble/main/r-cran-eidosapi_1.2.1-1.ca2404.1_all.deb Size: 73772 MD5sum: 1a26eb04ff8db8c34fbe1a6defcfb2f0 SHA1: ceaa727599ae1a6f1455a5a7f690c32e23e6dba1 SHA256: 584d702b7035483a3c0f656a47fe5183a987ae9c7286dfd5675978a58b1c2d9a SHA512: 17e07fa28f054d9072292a4a99486805443aae4fac2112557df785aa90517f5e94b441c9f5d9201250ebad51e1b3f45ac3d270bb4351d9aa4b3501ffa3517e32 Homepage: https://cran.r-project.org/package=eidosapi Description: CRAN Package 'eidosapi' (Connect to the Taxonomic Services of the Spanish Inventory ofNatural Patrimony and Biodiversity) Provides access to 'EIDOS' , the taxonomic information service from the Spanish Inventory of Natural Patrimony and Biodiversity. This package includes a suite of functions that help retrieve species' taxonomic and conservation information from 'EIDOS' and match taxa names against the checklists available in the database. More information can be found at Miranda Cebrián, H. (2025) . 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These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Díaz-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." . Díaz-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." . 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The package implements methods described in Barreto, M., Collingwood, L., Garcia-Rios, S., & Oskooii, K. A. (2022). "Estimating Candidate Support in Voting Rights Act Cases: Comparing Iterative EI and EI-R×C Methods" . 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Package: r-cran-eiit Architecture: all Version: 0.0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-ggplot2, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eiit_0.0.2-1-1.ca2404.1_all.deb Size: 98078 MD5sum: 279edad7c3380aecabcadd68afe3f939 SHA1: 29e1248d1012a1695ce60b6644310c382d45ce96 SHA256: 55f5db3a81ca9e0c4ea344dd15836b599c950d656a9f673e121029cd2cb583f3 SHA512: d53988b14e00107fc21b4461df8874876bfef08b063ea2ab2c1254ad75672616ef226806a55429a74b8cc131e60286e31d9070a3cb12bce24608fb7a95a4f8ab Homepage: https://cran.r-project.org/package=eiIT Description: CRAN Package 'eiIT' (Ecological Inference via Information Theory) Estimates RxC transfer matrices from aggregated marginal data using a two-stage (GME+IPF; Generalized Maximum Entropy and Iterative Proportional Fitting) information-theoretic approach within a two-step (global+local) estimation procedure. The resulting matrices are consistent with observed row and column marginals across collections of subtables (e.g. precincts, polling stations, or districts). References: Golan, A., Judge, G., & Miller, D. (1996). Maximum Entropy Econometrics: Robust Estimation with Limited Data. Wiley. Judge, G., Miller, D.J., & Cho, W.K.T. (2004). "An information theoretic approach to ecological estimation and inference". In G. King, O. Rosen, & M. A. Tanner (Eds.), Ecological Inference: New Methodological Strategies (pp. 162–187). Cambridge University Press. Mittelhammer, R., Judge, G., & Miller, D. (2000). Econometric Foundations. Cambridge University Press. Pavia, J.M. (2023) Acknowledgements: The author wish to thank Conselleria de Economia, Hacienda y Administracion Publica (grant CIACIO/2023/031) for supporting this research. 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Package: r-cran-elcic Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-poisnor, r-cran-bindata, r-cran-geepack, r-cran-wgeesel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-elcic_0.2.1-1.ca2404.1_all.deb Size: 212574 MD5sum: f12fecd24503a516264489a8878afdfd SHA1: e94db45b29992642bd2caf9ee68fb81c949e0098 SHA256: e6a8778fd3aaf745a78cf169b88358f322e38b73d252393219d3e405b1c081ec SHA512: 3407b9d122e570042885ccb968b78ba98c1c7335ce9e844fc88977c0a2da20e85b09813ac3837cb046904d88d79eee9ec63fc874591f1f4591281534b414cd29 Homepage: https://cran.r-project.org/package=ELCIC Description: CRAN Package 'ELCIC' (The Empirical Likelihood-Based Consistent Information Criterion) We developed a consistent and robust information criterion to conduct model selection for semiparametric models. 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There are also functions to generate simulated voting data, including methods to simulation different types of voting errors which allow for simulations for checking the characteristics of these methods. Package: r-cran-elechemr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-elechemr_1.2.0-1.ca2404.1_all.deb Size: 186766 MD5sum: 32d83cf3160d00c0c8594e8f10bea240 SHA1: f51aeefeb03b39cd2304e077159314cf3a2258af SHA256: bfbfcb7f45a147dffdbc04cf432c38b80036bb590c73750814f6c5d367a0e6bd SHA512: 932219256dccc49509096e39025ab681a4a40dffe6a80e4645b7b13f57fead3ee89af53b08554260761100a3bb1b45b05e9388aa27d1654030cc420a7cfb2d12 Homepage: https://cran.r-project.org/package=EleChemr Description: CRAN Package 'EleChemr' (Electrochemical Reactions Simulation) Digital simulation of electrochemical processes. Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation. Bibliography regarding this methods can be found in the following texts. Dieter Britz, Jorg Strutwolf (2016) . Allen J. Bard, Larry R. Faulkner (2000) . 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References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). . Penades, A and Pavia, J.M. (2025) ''The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research. 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Package: r-cran-electivity Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-usethis Filename: pool/dists/noble/main/r-cran-electivity_1.0.2-1.ca2404.1_all.deb Size: 33574 MD5sum: ecf1ef4eb2af7d2450f1b66013d2738a SHA1: 3d0ca2bdc286fa8a4aeb0e36cd3436e39138c790 SHA256: 7cf92ff0dbf3e5ac71b53bb5807a47a0ce8e3728d2029cbabc58bf32b8536791 SHA512: 535cd95695ef506d55f4a3a317ca5aac1c2d7f47f2e7feabfe6886eacfdcde679bedc1a686ff49c68ca299536e38a776fe4280a6aa81071d083b28ad692a0a62 Homepage: https://cran.r-project.org/package=electivity Description: CRAN Package 'electivity' (Algorithms for Electivity Indices) Provides all electivity algorithms (including Vanderploeg and Scavia electivity) that were examined in Lechowicz (1982) , plus the example data that were provided for moth resource utilisation. 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Implemented highest averages allocating seats methods are D'Hondt, Webster, Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague, equal proportions and Adams. Implemented largest remainders allocating seats methods are Hare, Droop, Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders. The main advantage of this package is that ties are always reported and not incorrectly allocated. Party system scores provided are competitiveness, concentration, effective number of parties, party nationalization score, party system nationalization score and volatility. References: Gallagher (1991) . Norris (2004, ISBN:0-521-82977-1). Laakso & Taagepera (1979) . Jones & Mainwaring (2003) . Pedersen (1979) . Golosov (2010) . Golosov (2014) . Package: r-cran-electsys21 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-electsys21_0.1.0-1.ca2404.1_all.deb Size: 227936 MD5sum: 2199bdc221df99c32309da34773bd223 SHA1: e9387b143d01b895b6fe5a87bf233e80b4e7fd58 SHA256: f904059a7025cd3b44fc21e995fbf5fd7ab4d3fab57d522ec5d784ddfff7d806 SHA512: 6c4a307f75a0cce1aafeee051e7799880938549a324d211ce0d4bc7115031cedc4bef9ec043ea43880c08a084cda4091cfcd88fa0b67021350b8d22d4ab0cf14 Homepage: https://cran.r-project.org/package=electsys21 Description: CRAN Package 'electsys21' (Voting Methods for Ranked, Rated and Approval Ballots) Implements a range of voting methods and electoral systems for determining election winners, including the D21 method with and without minus votes (Janecek, ), first-past-the-post, two-round runoff, instant runoff, the Borda count, approval voting, majority judgement and the Condorcet method. 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This package provides access to many of those services and returns elevation data either as an 'sf' simple features object from point elevation services or as a 'raster' object from raster elevation services. In future versions, 'elevatr' will drop support for 'raster' and will instead return 'terra' objects. Currently, the package supports access to the Amazon Web Services Terrain Tiles , the Open Topography Global Datasets API , and the USGS Elevation Point Query Service . 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The default thresholds and the treeline definition is based on Paulsen and Körner (2014) , users are free to decide what climate layers they would like to use. Package: r-cran-elexr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-elexr_1.0-1.ca2404.1_all.deb Size: 11922 MD5sum: f51e247a93da735b9382fa2d6a645c98 SHA1: 584d971fdce910c4f080849ce756a27d725021ba SHA256: 17ec6ea91fb473d11c99d878858836ad984ea03f0cc7f080c1f65ed30cc3b39e SHA512: 8ddb25193e52a9c7cc17459eb3faa2ccd6cf1db0dcb60b6176edbdee3bc8a9ebb369a7d6da903b8b43b8d421438b4113a21ae5e14eaca7125854466607c9bcf1 Homepage: https://cran.r-project.org/package=elexr Description: CRAN Package 'elexr' (Load Associated Press Election Results with Elex) Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results. 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ELFs describe the relation between aquatic species richness (fish or benthic macroinvertebrates) and stream size characteristics (streamflow or drainage area). Journal publications are available outlining framework methodology (Kleiner et al. (2020) ) and application (Rapp et al. (2020) ). 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Drawing on characteristics of classical test theory, Exploratory Likert Scaling (ELiS) supports the user exploring multiple one-dimensional data structures. In common research practice, however, EFA remains the go-to method to uncover the (underlying) structure of a data set. Orthogonal dimensions and the potential of overextraction are often accepted as side effects. As described in Müller-Schneider (2001) ), ELiS confronts these problems. As a result, 'elisr' provides the platform to fully exploit the exploratory potential of the multiple scaling approach itself. 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This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2021) . Package: r-cran-ellmer Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3006 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-coro, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-later, r-cran-lifecycle, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-s7, r-cran-tibble, r-cran-vctrs Suggests: r-cran-connectcreds, r-cran-curl, r-cran-gargle, r-cran-jose, r-cran-knitr, r-cran-magick, r-cran-openssl, r-cran-otel, r-cran-otelsdk, r-cran-paws.common, r-cran-png, r-cran-rmarkdown, r-cran-shiny, r-cran-shinychat, r-cran-testthat, r-cran-vcr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ellmer_0.5.0-1.ca2404.1_all.deb Size: 2174704 MD5sum: 6b335b24b389e5753c2d599444e1eaac SHA1: f9ccabb331293b19e8ec3fb6def95a3504101b0c SHA256: 600df9d012628ab92476d2b1e4ef7c280f8666e67f8ff8e61ee06bfdf15c9ddd SHA512: 71ec6e4d029c590126e2537d029cbfe9aa7170e5bf239290baf7dcce526c939224608eb2ffb8223735c743b1415c499d38427c3c024e50f17d0a222b0762f336 Homepage: https://cran.r-project.org/package=ellmer Description: CRAN Package 'ellmer' (Chat with Large Language Models) Chat with large language models from a range of providers including 'Claude' , 'OpenAI' , and more. 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Package: r-cran-elmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-elmr_1.0-1.ca2404.1_all.deb Size: 33316 MD5sum: 6cfc0af4206b715a01d6601b38d9b5c7 SHA1: 43c8a929d4488d41ecbea27b85ac2c905861a5c7 SHA256: a9ac2d1f5d4d98950f39ae8a427893c5193643833a94b4b25620b29a42e3c95b SHA512: 4940d26eeb3f71dbaf349931a2b11b81ae2416a123c503ac8b03a350495de3cd92fd6dbddac843f479c0ede0bd91090350b300a1c61097954654c8aca201772d Homepage: https://cran.r-project.org/package=ELMR Description: CRAN Package 'ELMR' (Extreme Machine Learning (ELM)) Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. 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Package: r-cran-elmso Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-elmso_1.0.1-1.ca2404.1_all.deb Size: 27972 MD5sum: 9ab699b9a2b4fbd43fa4bde8a5c56105 SHA1: 8e10203d3f07f8b4049a31ce942aa84c94604378 SHA256: 92e546c875c92d8823c78697eba15c16fce459c47dac9c2755e9d389aea8fced SHA512: fe0fd6c3696dd8b0cf451c0732bb22b23f584f56f50e80477a0cca8d19c67d533a69c09f8b9d7451874a86cace76e61a7b86676edd80b9ada95bee9364b353d2 Homepage: https://cran.r-project.org/package=ELMSO Description: CRAN Package 'ELMSO' (Implementation of the Efficient Large-Scale Online DisplayAdvertising Algorithm) An implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and for a full-text version of the paper). 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Package: r-cran-eltr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eltr_0.1.0-1.ca2404.1_all.deb Size: 58494 MD5sum: df4ee55ba600628fc9198063d68f09e8 SHA1: 396d6d8cf23e9953c25e365211562fb08fec1e40 SHA256: d9e3cbe53df1f7ed8fb720627dd99d539edc794e52c504f97474f175caa57e65 SHA512: 21282617245b01067753765d18636642938e730d6e5dd104d8e535046e595dbc2ca89e2759b98aa150cbb5bf60b258e680299ef7bc67bb88248a2576eee4eee3 Homepage: https://cran.r-project.org/package=eltr Description: CRAN Package 'eltr' (Utilise Catastrophe Model Event Loss Table Outputs) Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution. Package: r-cran-em.fuzzy Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-distrib Filename: pool/dists/noble/main/r-cran-em.fuzzy_1.0-1.ca2404.1_all.deb Size: 67532 MD5sum: 40f352e3eab97acb44fc386dedadc42d SHA1: a21e9d7cd224918c1423352e0355e8e33971a523 SHA256: 10606f91178383421ea60dc2134d6c6bdf63ca9a2ab49438362affc4adf88951 SHA512: d31833bfc23e1900e1ef7d2f8be9e68ec321610521e1d79c9235a50e763b9e4c95c0321aaa6de775e0a8a21659bbc470292abab1df34252ccb7e47a469db7139 Homepage: https://cran.r-project.org/package=EM.Fuzzy Description: CRAN Package 'EM.Fuzzy' (EM Algorithm for Maximum Likelihood Estimation by Non-PreciseInformation) The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) . Package: r-cran-emailjsr Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shiny.i18n, r-cran-httr, r-cran-shinybrowser Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-emailjsr_0.0.2-1.ca2404.1_all.deb Size: 147142 MD5sum: 8f0115a9fca260645593e677a7b6c8ad SHA1: ceba74fa423cd0cecf6a8ee003629ac2144e67b3 SHA256: eeccf4bdae76a408d0ba6880828b94b66668c8a4aed6a4013d709fbdd3527ebc SHA512: 31fff435c50ad75d88a629b4cfd20087d2309599e1917e219271f771048d159af5189a9a4fd32265d52b3fbc4a14214d0b01fa2a322c0d746cb3aca9b106c80a Homepage: https://cran.r-project.org/package=emailjsr Description: CRAN Package 'emailjsr' ('emailjs' Support) Use 'emailjs' API easily in 'R'. This package is not official. . You can send e-mail with 'emailjs' with function, based on 'httr'. You can also make a 'shiny' ui and server function. It can be used for making feedback form, inquiry, and so on. Package: r-cran-emailvalidation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-emailvalidation_0.1.0-1.ca2404.1_all.deb Size: 20194 MD5sum: 969e233dcb466a5ec3472852d057e268 SHA1: da7d8712f1ffece470d75ec6eaffa344cfa2130c SHA256: 79147d27d35be265f45fd3afcbfc0eff490ada63cdbe2e2457ffbb7fa5f9fe3b SHA512: 2f76b374a87b4c7040e0f2c5de345c68b5b92b58b9499f478416f486df91ae4239f48899b2b989e6b8f392fc078e9aed7dbc256f97758c47a68f5bf2dbbbe511 Homepage: https://cran.r-project.org/package=emailvalidation Description: CRAN Package 'emailvalidation' (Client for the 'emailalvalidation.io' E-Mail Validation API) An R client for the 'emailvalidation.io' e-mail verification API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-emar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emar_1.0.0-1.ca2404.1_all.deb Size: 19644 MD5sum: 879901d04408812d2d9b9eba07dc89e8 SHA1: 89b2e30ef51c02b8549f60a6e05fa25baee44924 SHA256: 628209bfce8d4a34ae991dcd4b62daea4ed44952f804be16205ee4c35f004dde SHA512: a1edc80e2a812efb65d46f80664997e4f565d63d026740de6f3aaf555313dd94026ce3f930d8f6a6983f272a0bd388a22b98f0f77cd3b5691054ff5bd4b75464 Homepage: https://cran.r-project.org/package=EMAR Description: CRAN Package 'EMAR' (Empirical Model Assessment) A tool that allows users to generate various indices for evaluating statistical models. The fitstat() function computes indices based on the fitting data. The valstat() function computes indices based on the validation data set. Both fitstat() and valstat() will return 16 indices SSR: residual sum of squares, TRE: total relative error, Bias: mean bias, MRB: mean relative bias, MAB: mean absolute bias, MAPE: mean absolute percentage error, MSE: mean squared error, RMSE: root mean square error, Percent.RMSE: percentage root mean squared error, R2: coefficient of determination, R2adj: adjusted coefficient of determination, APC: Amemiya's prediction criterion, logL: Log-likelihood, AIC: Akaike information criterion, AICc: corrected Akaike information criterion, BIC: Bayesian information criterion, HQC: Hannan-Quin information criterion. The lower the better for the SSR, TRE, Bias, MRB, MAB, MAPE, MSE, RMSE, Percent.RMSE, APC, AIC, AICc, BIC and HQC indices. The higher the better for R2 and R2adj indices. Petre Stoica, P., Selén, Y. (2004) \n Zhou et al. (2023) \n Ogana, F.N., Ercanli, I. (2021) \n Musabbikhah et al. (2019) . Package: r-cran-emas Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mediation, r-cran-multilevel, r-cran-ggplot2, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-emas_0.2.4-1.ca2404.1_all.deb Size: 231268 MD5sum: 2168b800e7ebf5c7c4a01df6edff0cb7 SHA1: 8bf856c00226c9d651b25bac1176a83bf253b1a8 SHA256: 435d2cb522a2a7584742f869825797142599e780f142f8e5401864e87dbd6318 SHA512: 23a2ed9ba906ca0d4dde209a087c63f7370e8422408ec02a89ca63f096f00eb68a382eea8209a98adea9d9d8cb1a661ea50991d5b996bc65738bdf4901881e21 Homepage: https://cran.r-project.org/package=EMAS Description: CRAN Package 'EMAS' (Epigenome-Wide Mediation Analysis Study) DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) , Turner, S. D. (2018) , Rosseel, D. (2012) . Package: r-cran-ematools Architecture: all Version: 0.1.6-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-lmertest, r-cran-sjstats, r-cran-anytime, r-cran-plyr Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-ematools_0.1.6-1.ca2404.2_all.deb Size: 40102 MD5sum: dff7c7c8fea72df78d2db6c31ecd3220 SHA1: 8c69c5126527225b4e3ebfa6c8ba6aeb1c918b92 SHA256: 3b1c593131605f2976014111928418219ac0a3e1c5ef3ab5cd9cd01498b9aae2 SHA512: e483544b4432aa28f316aeee9501169ed08d13948fa21ee998107bacaf4a9d67553d49948b56a64fb20f6a30c9f83f53bc927da168b8f90f8fc99337734ae43b Homepage: https://cran.r-project.org/package=EMAtools Description: CRAN Package 'EMAtools' (Data Management Tools for Real-Time Monitoring/EcologicalMomentary Assessment Data) Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. For background on this data type see Shiffman, Stone and Hufford (2008) , and on the centering methods see Enders and Tofighi (2007) . This is VERY early release software, and more features will be added over time. 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Package: r-cran-embryogrowth Architecture: all Version: 2026.8.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3507 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desolve, r-cran-optimx, r-cran-numderiv, r-cran-ggplot2, r-cran-helpersmg, r-cran-rdpack Suggests: r-cran-entropy, r-cran-shiny, r-cran-coda, r-cran-polynom, r-cran-car, r-cran-gam, r-cran-pbapply, r-cran-cranlogs, r-cran-mapdata Filename: pool/dists/noble/main/r-cran-embryogrowth_2026.8.24-1.ca2404.1_all.deb Size: 3317676 MD5sum: 82266941b95d1a30cfb10f4a0a6402e9 SHA1: 47fc5a86296faf9cb5bb30e402ba7a0385bc1dba SHA256: f378a287be6879e9efaceb17694b2ccf2a6851ca8e31743fe83791a9fdda0f9a SHA512: af1738bed968183973ea8aef478e3069cee5d63deee1cab8f78037bfa5164412efa6bca4c4b76e5378a79e2482fa9aa7a072098640ea8cf672bca02e11bafea1 Homepage: https://cran.r-project.org/package=embryogrowth Description: CRAN Package 'embryogrowth' (Tools to Analyze the Thermal Reaction Norm of Embryo Growth) Tools to analyze the embryo growth and the sexualisation thermal reaction norms. 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So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) ), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) ) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R". 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Package: r-cran-emgcr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-formula, r-cran-actuar, r-cran-flexsurv, r-cran-tibble, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-emgcr_0.3.0-1.ca2404.1_all.deb Size: 121352 MD5sum: a61441c3069a69981c3b6ba4a3ec4e25 SHA1: b9fec3075cb7e6f4e7493d263c52906acccbac5c SHA256: 639fd550a636c00ea06324ea7981307c6f626f03a4e5e55dae33dc01d8dc0c51 SHA512: 6d04452f0bba7f933634d8da8a67052f7424c3953f4c552944721439efaaefbf7c1d4b255967efca5aea8983d4a4078e073c159a7f54f6dfa7bcbb12ac4a4d48 Homepage: https://cran.r-project.org/package=EMGCR Description: CRAN Package 'EMGCR' (Mixture Cure Rate Models with Flexible Link Functions via the EMAlgorithm) Fits mixture cure rate models by the Expectation-Maximization (EM) algorithm. The incidence component (the probability of being uncured) accepts the logit, probit, cauchit, power logit and reversed power logit link functions, and the latency component accepts the exponential, Rayleigh, Weibull, log-normal, log-logistic and inverse Gaussian distributions. The package provides parameter estimates with standard errors, simulation of data from the model, and diagnostic tools based on residuals and simulated envelopes. The methods build on Berkson and Gage (1952) , Dempster, Laird and Rubin (1977) and Bazán, Torres-Avilés, Suzuki and Louzada (2017) . Package: r-cran-emhawkes Architecture: all Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-misctools Filename: pool/dists/noble/main/r-cran-emhawkes_0.9.8-1.ca2404.1_all.deb Size: 267578 MD5sum: 3ffe33d4398d8f7be7bb576c00a8d6a9 SHA1: 6f73b5a9aaee9097836ad905fdea18dfeca40200 SHA256: 74b20c81c0a9e6de6bf1daf5d720b87a7ba9ec503e030f21aa4a4e9ea2d18d02 SHA512: d0cb1c8a1c89ad8a2317a15493b7d5907c2668e2334d4f70cad5a89efde53cfc15fcebfeff0802fead6381a6c41517b3c4b126fb586a972f4f0f2c93ada9d1d6 Homepage: https://cran.r-project.org/package=emhawkes Description: CRAN Package 'emhawkes' (Exponential Multivariate Hawkes Model) Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) , with an exponential kernel and fits the parameters from the data. Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) , with 'maxLik' package. Package: r-cran-emissv Architecture: all Version: 0.666.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3005 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncdf4, r-cran-units, r-cran-raster, r-cran-sf, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-lwgeom Filename: pool/dists/noble/main/r-cran-emissv_0.666.0.0-1.ca2404.1_all.deb Size: 1103568 MD5sum: c35ed705187b0e13577814303144e55d SHA1: e01e3f36ca026845d0589ba84fc04def78821a4b SHA256: edcfdca3cbd2f3d6b550918050a0a0ca445d45e6c5539ffcf6b90b268605e363 SHA512: 93d689a1d740b7164cc5bc3788e5794e22d72a463af5e27bdf9d2d0aff9d1bc7330d09e8c9eedd3d08094787fe60516ce77b7dddbd565614aa389a108c837860 Homepage: https://cran.r-project.org/package=EmissV Description: CRAN Package 'EmissV' (Tools for Create Emissions for Air Quality Models) Processing tools to create emissions for use in numerical air quality models. Emissions can be calculated both using emission factors and activity data (Schuch et al 2018) or using pollutant inventories (Schuch et al., 2018) . Functions to process individual point emissions, line emissions and area emissions of pollutants are available as well as methods to incorporate alternative data for Spatial distribution of emissions such as satellite images (Gavidia-Calderon et. al, 2018) or openstreetmap data (Andrade et al, 2015) . Package: r-cran-emistatr Architecture: all Version: 1.2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-foreach, r-cran-lattice, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-emistatr_1.2.3.0-1.ca2404.1_all.deb Size: 165020 MD5sum: 656c48600ed150e709464182e001f6a0 SHA1: a3338de2b3e7628cb591ed2a9193915096480d96 SHA256: 430ed0dc573148813b796672bcd46900eeb5daf78ba37fe2cdf860a92a859c0a SHA512: 2f5f4c6ad290a3807cfbf513b04aa313d079c36236376f6ca020955a801d30ac360de1d159dc50fcd5b4fecb7f9c8ec1cdfdc022d6938085b86abd6b4658e493 Homepage: https://cran.r-project.org/package=EmiStatR Description: CRAN Package 'EmiStatR' (Emissions and Statistics in R for Wastewater and Pollutants inCombined Sewer Systems) Provides a fast and parallelised calculator to estimate combined wastewater emissions. It supports the planning and design of urban drainage systems, without the requirement of extensive simulation tools. The 'EmiStatR' package implements modular R methods. This enables to add new functionalities through the R framework. Package: r-cran-emjmcmc Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-glmnet, r-cran-biglm, r-cran-hash, r-cran-bas, r-cran-stringi, r-cran-speedglm, r-cran-withr Suggests: r-cran-testthat, r-cran-bindata, r-cran-clustergeneration, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-emjmcmc_1.5.0-1.ca2404.1_all.deb Size: 357964 MD5sum: 4b17645e813ed85d65bad14c53fc5480 SHA1: 3ac7f3b0012b7a7defb5919403346c69aac36657 SHA256: 53b292f85e23d9cd6f8c37491e537c76ca4687b23a3263e9272f5700b257ebb3 SHA512: 461e9d54e9d6a780ac9a638c28257d3e53accec4e8bbebad6d8e6a6ca624a8fa070781bad8d2d28bf59d214ffe7250326b86780a2c9b931ef913f212672b7c69 Homepage: https://cran.r-project.org/package=EMJMCMC Description: CRAN Package 'EMJMCMC' (Evolutionary Mode Jumping Markov Chain Monte Carlo ExpertToolbox) Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) , Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) , Hubin, A., Storvik, G., & Frommlet, F. (2021) , and Hubin, A., Heinze, G., & De Bin, R. (2023) , and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) , which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models. Package: r-cran-eml Architecture: all Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-digest, r-cran-emld, r-cran-jqr, r-cran-jsonlite, r-cran-uuid, r-cran-rmarkdown, r-cran-dplyr Suggests: r-cran-knitr, r-cran-taxadb, r-cran-tibble, r-cran-testthat, r-cran-covr, r-cran-units, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinyjs, r-cran-spelling Filename: pool/dists/noble/main/r-cran-eml_2.0.7-1.ca2404.1_all.deb Size: 779894 MD5sum: feba488c67f68f6535ee9fb07999b525 SHA1: fadd417cbc75382ab8f5534434172183ba1ed7e4 SHA256: 2f9e7497c59de9bccaa9cf0e723388ff24ded36545081bce56f339b76190c4af SHA512: bd8e5163ac11d91ab8bc381f93690d26167dae57c626f9d71482bf3796d5694831b74362a44023ebd2bd21266c8a4eb4ab599f2ed3d0ab54951994e4d9c8d575 Homepage: https://cran.r-project.org/package=EML Description: CRAN Package 'EML' (Read and Write Ecological Metadata Language Files) Work with Ecological Metadata Language ('EML') files. 'EML' is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), . Package: r-cran-emld Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-jsonlite, r-cran-jsonld, r-cran-yaml Suggests: r-cran-spelling, r-cran-testthat, r-cran-magrittr, r-cran-rmarkdown, r-cran-covr, r-cran-knitr, r-cran-rdflib, r-cran-jqr Filename: pool/dists/noble/main/r-cran-emld_0.5.3-1.ca2404.1_all.deb Size: 329752 MD5sum: 4a0ebdd31d489f99b20df2c0f82b4459 SHA1: b1b23b1f5bae447cd5ab66f066461aa93d8168ed SHA256: eccfdc0c50f09f84db831445e01c0f6021305ca17597052a9db119a75550c4d6 SHA512: 2c026a321d24a9cb50c33b066c1ed89247533971d27a221fa47dd7dfec86ba97d3b083b7cbc2f8974a65b262fc728772130e83aca99917464f72f4a1731b9fd6 Homepage: https://cran.r-project.org/package=emld Description: CRAN Package 'emld' (Ecological Metadata as Linked Data) This is a utility for transforming Ecological Metadata Language ('EML') files into 'JSON-LD' and back into 'EML.' Doing so creates a list-based representation of 'EML' in R, so that 'EML' data can easily be manipulated using standard 'R' tools. This makes this package an effective backend for other 'R'-based tools working with 'EML.' By abstracting away the complexity of 'XML' Schema, developers can build around native 'R' list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the 'JSON-LD' representation enables the use of developer-friendly 'JSON' parsing and serialization that may facilitate the use of 'EML' in contexts outside of 'R,' as well as the informatics-friendly serializations such as 'RDF' and 'SPARQL' queries. Package: r-cran-emli Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emli_0.3.0-1.ca2404.1_all.deb Size: 56394 MD5sum: 54c1c303f90ec107c4533eb83e91d706 SHA1: dd6514c2d22a5f3459f6ff4763fa194125ed6c06 SHA256: 51b514da459ec681bbb26135b496b59ff5f49983ea6c3c2b8ff831285b88b62c SHA512: b97fac63241731ed6871d1fd7c943b5cc949870ed205fad4caf663972b154be8ac0b526937aa69e085cfe6396c10f6eb386e68f8e7e26f6dd40ec1a426b1f15a Homepage: https://cran.r-project.org/package=EMLI Description: CRAN Package 'EMLI' (Computationally Efficient Maximum Likelihood Identification ofLinear Dynamical Systems) Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms. Package: r-cran-emln Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2226 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-igraph, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-matrix, r-cran-dt, r-cran-hablar, r-cran-rlang Suggests: r-cran-bipartite, r-cran-httpuv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-emln_1.2.0-1.ca2404.1_all.deb Size: 1502098 MD5sum: fb54914c66be9777250c7501adf63c17 SHA1: 13a4dfc4c4b13cd3129d2d765d6ca1249e555231 SHA256: f9cfc2c0c42d74486eab8857d066de556d87fb0abca47cc767088937c864e96e SHA512: cf67eaebd074549df0ca4b3ae38444493d2944495abe6a76ffd1361f04ec18f81d51240e094bd041142ada9df4f059b974add8e7d0f67fd9419770b9d9ff88f1 Homepage: https://cran.r-project.org/package=emln Description: CRAN Package 'emln' (Organize, Handle, and Explore Ecological Multilayer Networks) Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) . Package: r-cran-emmageo Architecture: all Version: 0.9.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gparotation, r-cran-nnls, r-cran-catools, r-cran-shiny Filename: pool/dists/noble/main/r-cran-emmageo_0.9.10-1.ca2404.1_all.deb Size: 591430 MD5sum: a3cc98055433ff49f91922d99930bf7f SHA1: 22a0d17c7007b680dacdc890442ded4899f072cb SHA256: e53fbf29887966965a1b12054205eb3713dd28d8c42431a2d87519f53936bf79 SHA512: 212b7b74abdb0dbebf74f219d9663ef1164139fba5f28844313b93525d7a1fbd36ca46632e84f9c2ced96d8c78f22aa2f3344950d7b4919df4fef830d2d1518f Homepage: https://cran.r-project.org/package=EMMAgeo Description: CRAN Package 'EMMAgeo' (End-Member Modelling of Grain-Size Data) End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose. Package: r-cran-emmeans Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3474 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-estimability, r-cran-mvtnorm, r-cran-numderiv, r-cran-rlang Suggests: r-cran-bayesplot, r-cran-bayestestr, r-cran-biglm, r-cran-brms, r-cran-car, r-cran-coda, r-cran-compositions, r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-mass, r-cran-mediation, r-cran-mgcv, r-cran-multcomp, r-cran-multcompview, r-cran-mumin, r-cran-nlme, r-cran-ordinal, r-cran-pbkrtest, r-cran-rmarkdown, r-cran-robmixglm, r-cran-rsm, r-cran-sandwich, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-xtable Filename: pool/dists/noble/main/r-cran-emmeans_2.0.4-1.ca2404.1_all.deb Size: 2334746 MD5sum: 4b2a74b6731d8c8f21218add99aefdb7 SHA1: a518dee993779a1ddf0c4248644769e8a7712f4f SHA256: ee943c89e94ffaa70089b8e1b45593eb73a3f7cce2e493a59a1bfae5e7225b64 SHA512: b6ca07ec753ff112e3948699997bd62e8647d99580dddd147b015dfd29556af6ba3892bd6e1283a51daa03eaf8f2e034a520eb2a18c20caa9353ccd116c46f1b Homepage: https://cran.r-project.org/package=emmeans Description: CRAN Package 'emmeans' (Estimated Marginal Means, aka Least-Squares Means) Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 . Package: r-cran-emmixssl Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-emmixssl_1.1.1-1.ca2404.1_all.deb Size: 296704 MD5sum: 794f0ab5ce1c9e609b61a333d6875b61 SHA1: 72e657e54dc9cd5a2e2c3f0c235712da6d2143de SHA256: 03edb8add0828e06c948aaa84859fc8e8d30ed1cef96cd86e56da20aabfb99b0 SHA512: b1324a4044e9f63c7bd70b647cacd02c1c1554b6133e5c639c4ba882ef992905ff10c9e86c1df5b3ada2c9e8248c38a6ce7006a125ac07daac2fca35b047d36d Homepage: https://cran.r-project.org/package=EMMIXSSL Description: CRAN Package 'EMMIXSSL' (Semi-Supervised Gaussian Mixture Model with a Missing-DataMechanism) The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule. Package: r-cran-emmli Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-emmli_0.0.3-1.ca2404.1_all.deb Size: 75694 MD5sum: b30c46a32d3df8633a144cd8a6a64e27 SHA1: f059c93452c6329574579c732f62d6e092322928 SHA256: 5a92e2eaecbf704622ddfeb3c4ce78f1525bc44c5aa6237ad7211f4f853f77bb SHA512: 834cbd1ad0f80890d573c11fb7d7789da5dc3745fce09ad4bd58384333eb2dd774f3548065784ce6172d8dedb85c68869d7de9755de81216a43cd755a1344339 Homepage: https://cran.r-project.org/package=EMMLi Description: CRAN Package 'EMMLi' (A Maximum Likelihood Approach to the Analysis of Modularity) Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module. Package: r-cran-emmreml Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-emmreml_3.1-1.ca2404.1_all.deb Size: 47286 MD5sum: 8ca7859094c1c53796698529222896d2 SHA1: 1ba4dfc841b0c7a4ec54a5d068307d7ef1b2dcf8 SHA256: f47fa41b8c5b7b669e4dd04c471f8c53ae31fb7dc4dab154367c9ffff4091615 SHA512: 350745f0e7589a652329ae533f4108cc199f9b6a6b139ae2b1b0732216ce92a6b70ea88e82129754e641526c2962e468e51727ed2d249483817a856ed4f39e50 Homepage: https://cran.r-project.org/package=EMMREML Description: CRAN Package 'EMMREML' (Fitting Mixed Models with Known Covariance Structures) The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm. 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The use of ensemble modeling, which consists of combining predictions from multiple models, has the potential to yields more accurate and robust estimates of lactation patterns than relying solely on single model estimates. The package EMOTIONS fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion (AIC), Bayesian information criterion (BIC), root mean square percentage error (RMSPE) and mean squared error (MAE), variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average (BMA). The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. The package allows the graphical visualization of the model ranks and the predicted lactation curves. Additionally, the packages allows the user to detect milk loss events and estimate residual-based resilience indicators. Package: r-cran-emov Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emov_0.1.1-1.ca2404.1_all.deb Size: 68990 MD5sum: c301595824fe1a2aeb0746f97a8b7af7 SHA1: 8d67d29fed6ca65a18d33403cf0deda74b2ae03a SHA256: 2facb071caf41deb2646de10a89f6082b3b4567d3e78e1b3e7e044e31f8c3651 SHA512: a85b3c04ceefc167ca6a8adaa8cd7537841fbee58931cc6ea484bd7cba7b53fff190a893ac84110251e343ad8897ac478a1421e5786dc0e0116f956b1fdd4b19 Homepage: https://cran.r-project.org/package=emov Description: CRAN Package 'emov' (Eye Movement Analysis Package for Fixation and Saccade Detection) Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position. Package: r-cran-emp Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr Filename: pool/dists/noble/main/r-cran-emp_2.0.6-1.ca2404.1_all.deb Size: 25190 MD5sum: 12b8e65e2b8e6925a1e87d5154dbec0a SHA1: fd0ea6a0077b6ca5ff60de6bb1588840751b786a SHA256: ea3a6bb2b4b2cfe2b1e852f55ac4ffe4c78ee505e4d7cfa9e3dfc9d2a96ae981 SHA512: 27d32f10e9c803c92f91e1921dc4ee083a4890ec817a8d5615c3433a33c9a3557e5b7b2857e9b5662f95a0f7bc743ff16c5f1568b16d9d051b5eef72c568d79d Homepage: https://cran.r-project.org/package=EMP Description: CRAN Package 'EMP' (Expected Maximum Profit Classification Performance Measure) Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) , . Package: r-cran-empeaksr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-empeaksr_0.3.1-1.ca2404.1_all.deb Size: 99598 MD5sum: 471cf2051e790c1281eb7130e5f60d34 SHA1: 587d6286e5bbf8eeef21217e48ac38f9c85649ac SHA256: 1eae171b21a0b27ac49391eb0f1e6b611c4a8ec25ba0a2b7ffc8c922f7776186 SHA512: 8fc44e47d32bc7078a9b3d7417e8d32d4ad649d24f27ce4d6c236ec0e29b1b1832d8fe22e8a98c874d89b2555e2c1b6d7dc3f60becc4a479dfa71afec84eb428 Homepage: https://cran.r-project.org/package=EMpeaksR Description: CRAN Package 'EMpeaksR' (Conducting the Peak Fitting Based on the EM Algorithm) The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) , . The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed. 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The package implements a complete workflow from data preprocessing (including Total Variation Regularized differentiation for noisy economic data), visual exploration of dynamical structure, and symbolic equation discovery via genetic algorithms. It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide industrial-grade robustness, physics-informed constraints, and rigorous out-of-sample validation. Designed for economists, physicists, and researchers studying dynamical systems from observational data. 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The method used is based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in . 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When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS. 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Package: r-cran-emsnm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emsnm_1.0-1.ca2404.1_all.deb Size: 66842 MD5sum: 2481253af66280ab1d72640e3b45ecf1 SHA1: 467d148ceab1997f6ed3971f2698ea40ceaef8ec SHA256: c4ba024fc0fda22d3cd6bf1d97e9c5c8d3911cbdbf477c7996c670a47c447d1d SHA512: eead9952f8c263c037adcfc18a63ac68277466443082e49cd6bcdf3fbf68f24d57efccca901e4e73004a6d891456648d571813bf3eb627b93f790d03a5fbad3a Homepage: https://cran.r-project.org/package=EMSNM Description: CRAN Package 'EMSNM' (EM Algorithm for Sigmoid Normal Model) It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure. Package: r-cran-emss Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampleselection, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-emss_1.1.1-1.ca2404.1_all.deb Size: 79794 MD5sum: 11e651679a829b20ffe837d3d3a0e1f3 SHA1: de96747f3672589bdeb54300713bde86401bb9c9 SHA256: c9c3c8afb2cd5fdcf60667f851fea2936d059b288c4bb3a182542de3bcc37ebb SHA512: 2e42e169d1b92d9fd32193713d66903aa280c9fb63ae3de306fd5dbd1a52a287d72060d3739835cd6e863f5d83c342b00f349c052d7aeb77fcbc45ad937bba8c Homepage: https://cran.r-project.org/package=EMSS Description: CRAN Package 'EMSS' (Some EM-Type Estimation Methods for the Heckman Selection Model) Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) . Package: r-cran-emstreer Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlpack, r-cran-scatterplot3d, r-cran-ggplot2, r-cran-sf Filename: pool/dists/noble/main/r-cran-emstreer_3.2.0-1.ca2404.1_all.deb Size: 53914 MD5sum: b3db2740783ab5f4949b753bde625aa1 SHA1: 9a593f47b9822af63a3f932e01255ea6c812f723 SHA256: 5deba9aa41309e4a345e796d2062ebd412cce5f021daeba3851b7ca4ad4f8c50 SHA512: b9611641556676595b58fd4c153afceccf96e456eeb09f2e29104b22297389c8f9a7a66e97ba9bbee89ec4ef20606271267e4eb3edeff17151d3b6ec2e332172 Homepage: https://cran.r-project.org/package=emstreeR Description: CRAN Package 'emstreeR' (Tools for Fast Computing and Visualizing Euclidean MinimumSpanning Trees) Fast and easy computation of Euclidean Minimum Spanning Trees (EMST) from data, relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). 'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, ), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles. Package: r-cran-emt Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emt_1.3.2-1.ca2404.1_all.deb Size: 38628 MD5sum: 2304dc393ab58a207fb5c8e8450dcd56 SHA1: 2534d92896f63ad9c01f050742b6d4bf70472bb8 SHA256: 556e34b6cda42cd6e2a07fa5965b33f24474bb8546daed1492df6b904ca93a02 SHA512: f3ede09a967a15b3e41303199d0c8dd892fc594ef4a9b64a2724794ffae2e3e3a8196992833575ddbe3298ffc14b0a3a75889f41563175cc81a17d97af2bd78f Homepage: https://cran.r-project.org/package=EMT Description: CRAN Package 'EMT' (Exact Multinomial Test: Goodness-of-Fit Test for DiscreteMultivariate Data) Goodness-of-fit tests for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. Monte Carlo methods are provided to make the package capable of solving high-dimensional problems. Package: r-cran-emtscore Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-bioc-aucell, r-bioc-gsva, r-cran-ggpubr, r-bioc-complexheatmap, r-cran-circlize, r-cran-gridextra, r-cran-magrittr, r-cran-gsa, r-cran-nsprcomp, r-cran-stringr, r-cran-foreach, r-cran-doparallel, r-cran-seurat, r-cran-pheatmap, r-cran-paletteer, r-cran-ggthemes, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-emtscore_0.1.1-1.ca2404.1_all.deb Size: 58122 MD5sum: 5d093f0ff110b78649472b10dcf50aca SHA1: 50f5bcf0092d4f217bee9de8cec33c5900ae25e5 SHA256: 2baa87a80ebff17484a64bf73717bc6771d54526b8b8e35f632d818a666d0aa4 SHA512: 73fea5e244425f8d52ee5db62cbb48a53bdb583408bee91e30621657227f19165795cd3c182d07dacdbdeb9d138407d5574753efb8db8c76147fdb60e843b3c2 Homepage: https://cran.r-project.org/package=EMTscore Description: CRAN Package 'EMTscore' (Calculate 'EMT' Scores Based on 'Omics' Data) Epithelial-Mesenchymal transition ('EMT') is an important form of cellular plasticity that is fully or partially activated in several biological scenarios including development and disease progression. 'EMT' involves altered expression of hundreds of protein-coding and non-protein-coding genes. Recent studies showed the prevalence of partial 'EMT' in multiple processes such as various cancers and organ fibrosis, which necessitates rigorous quantification of the degree of 'EMT'. While traditional gene set scoring methods such as gene set variation analysis have been used to generate 'EMT' scores from omics data, multiple 'EMT' scoring algorithms and 'EMT' gene sets have been used by different groups without standardization. Furthermore, comparisons of 'EMT' scores computed from different methods and/or different EMT gene sets are generally difficult due to both the context dependent nature of 'EMT' and the lack of tools that comprehensively integrate varying components for 'EMT' scoring. To address this problem, we have built a toolbox named 'EMTscore' that enables users to select scoring methods from a list of previously used algorithms and 'EMT' gene sets from a list of gene sets produced from different experiments. We provided several visualization methods for making publication quality plots of 'EMT' scores from 'omics' data. Furthermore, we showed a unique utility of a method based on principal component analysis for scoring divergent 'EMT' processes from a single dataset. Overall, 'EMTscore' provides an integrated solution for assessing the degree and complexity of 'EMT' from 'omics' data, and it paves the way for standardizing the comparison of EMT programs across multiple contexts. Package: r-cran-emulator Architecture: all Version: 1.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-quadform Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-emulator_1.3-0-1.ca2404.1_all.deb Size: 316580 MD5sum: db784f632de6964c6a82c85df86d440b SHA1: b94cec74cdfa3001e15b7e25c376634d8f040947 SHA256: 1d8a3ac5ed36d0a5a653876840dbc37e22b16d7bcd4c3e08ecad9b537d12d6f3 SHA512: ead9ef8f9331c770bcd63615b0405f451bf68d460c2a46778442e47a7fa4676613fe51a8b06326f8580c338fd95ee8356b3f2aae6164d01de2f3804364cd445d Homepage: https://cran.r-project.org/package=emulator Description: CRAN Package 'emulator' (Bayesian Emulation of Computer Programs) Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently. Package: r-cran-emur Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-stringr, r-cran-uuid, r-cran-base64enc, r-cran-shiny, r-cran-wrassp, r-cran-jsonlite, r-cran-rsqlite, r-cran-dbi, r-cran-httpuv, r-cran-dplyr, r-cran-readr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-mime, r-cran-rstudioapi, r-cran-httr, r-cran-v8, r-cran-cli, r-cran-fs, r-cran-tidyselect Suggests: r-cran-mass, r-cran-ggplot2, r-cran-testthat, r-cran-compare, r-cran-knitr, r-cran-rmarkdown, r-cran-matlabr, r-cran-r.matlab Filename: pool/dists/noble/main/r-cran-emur_2.6.0-1.ca2404.1_all.deb Size: 2792844 MD5sum: 88c44c7e9f7d8de61d99754c0f1cce4d SHA1: b1c170af02da420f7f455637ca8bcc32900a3ec8 SHA256: e1e437d1e46dc7e42d495967da057999a384037bc9ec6a9abe0258c832bdf9a7 SHA512: 5e4cb909725fcb7a9fde3cf173c624aa5b5d8d4cbddbaa76844138f85b51747ff1e594c7dc8fb50587a5f7812bb7bb45c0c2f9f4b85a9f05f1723d3677c7432c Homepage: https://cran.r-project.org/package=emuR Description: CRAN Package 'emuR' (Main Package of the EMU Speech Database Management System) Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See for more details. Package: r-cran-enaho Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-enaho_0.2.5-1.ca2404.1_all.deb Size: 238170 MD5sum: 98511c41524790e588a86b3dd65c1b89 SHA1: 8b1097051000e7195be30f67e24296f9fb5638ca SHA256: 52e3177f0d0e213ad06750d5ed7319aa4fae7e69141b78bc67080e0e76fc3e83 SHA512: 079530337022d53af9d0143f556824f51c53fcecdc03f59a261fdd930883aa54ca749756c4bbb5c2effeb25843903a252e59500c90af31b775b03312101b06b8 Homepage: https://cran.r-project.org/package=enaho Description: CRAN Package 'enaho' (Encuesta Nacional de Hogares (Peruvian Home National Survey)) Descarga, lee y analiza bases de la Encuesta Nacional de Hogares (ENAHO) y otras encuestas del Instituto Nacional de Estadística e Informática (INEI) del Perú. (Downloads, reads, and combines data from the Peruvian Home National Survey and other surveys from the National Institute for Statistics (INEI).) Package: r-cran-encdna Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/noble/main/r-cran-encdna_1.0.2-1.ca2404.1_all.deb Size: 166136 MD5sum: 5ca5255549ee92f97ec62c0578908fa5 SHA1: c320dd327ee95524a8ad8d9302d36e4c2ad699b8 SHA256: 79eab02d11b6b7c155c4649a0b30d3afa59249fae8e5d5e70138b4ed266a4ca9 SHA512: 789da0b31609af40b908318ebcfbe691b2ab078c7b317d3744e7df2c92860f4f250c4a3f08957babd1155d597dd1e5309ec12853eccc56b5943bcbc8d019ae72 Homepage: https://cran.r-project.org/package=EncDNA Description: CRAN Package 'EncDNA' (Encoding of Nucleotide Sequences into Numeric Feature Vectors) We describe fifteen different splice site sequence encoding schemes that have been used in earlier studies for mapping of splice site sequences into numeric feature vectors. These encoding schemes will also be helpful for transforming other nucleotide sequences into numeric forms, provided they are of equal length. These encoding schemes will help the computational biologist working in the field of classification (binary or multiclass) or prediction involving nucleic acid sequences of equal length. Package: r-cran-encode Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-encode_0.3.7-1.ca2404.1_all.deb Size: 47188 MD5sum: 7e32bbd1d9c9b68ee506804fba80da97 SHA1: 55ea040521659a02bc9f34d049d84f99763112e6 SHA256: 14d2e6fd83a0b93e7378967f44be90b924c2d2aec640e2a2e78afded0423f714 SHA512: 9c861b65bf9a402dc443c6f9252f26dee52828ec58a14b3cb495afc3434a689ede08a1fbdd810cc36210b0108bd31e462e331b30117c65f343b7d11b6c5ba7ed Homepage: https://cran.r-project.org/package=encode Description: CRAN Package 'encode' (Represent Ordered Lists and Pairs as Strings) Interconverts between ordered lists and compact string notation. Useful for capturing code lists, and pair-wise codes and decodes, for text storage. Analogous to factor levels and labels. Generics encode() and decode() perform interconversion, while codes() and decodes() extract components of an encoding. The function encoded() checks whether something is interpretable as an encoding. If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor. Package: r-cran-encompasstest Architecture: all Version: 0.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-encompasstest_0.22-1.ca2404.1_all.deb Size: 31392 MD5sum: 5aeed477e21a8ae5df815a1ba8dc5578 SHA1: a3820159a5a0573af9d6b405f8a2c81e9c0fe6b8 SHA256: 9b68155bee9ae4f543ecc7799d8393f11ab634f5e77cb6652446a69b32276180 SHA512: 33bcb35c03778110899d25a26ed77f15172d705d83898794c8f886ffde2e8c8962503c7ceccd21908d016d60ca4fe0371f756211087c0423d7cf3ca5dbd3118d Homepage: https://cran.r-project.org/package=EncompassTest Description: CRAN Package 'EncompassTest' (Direct Multi-Step Forecast Based Comparison of Nested Models viaan Encompassing Test) The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) . The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output. Package: r-cran-encryptedrmd Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sodium, r-cran-readr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-encryptedrmd_0.2.1-1.ca2404.1_all.deb Size: 38346 MD5sum: d10bc3599b037b0d0df5b1edd9757b62 SHA1: 321de78cf72758d58aa99738092e087e874ea979 SHA256: 506e9f1163eb0776fa1cd3c2c28ab48beb362bec043583ba400d845e3dc60656 SHA512: 7b3f7213c58cdf7499b829cb75ced95af447e3a77e12627300f4be2ca99a6800aa728819e248580d859d6dde4111bf576791b2f339d204d301af1337788873aa Homepage: https://cran.r-project.org/package=encryptedRmd Description: CRAN Package 'encryptedRmd' (Encrypt Html Reports Using 'Libsodium') Create encrypted html files that are fully self contained and do not require any additional software. Using the package you can encrypt arbitrary html files and also directly create encrypted 'rmarkdown' html reports. Package: r-cran-encryptr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-openssl, r-cran-purrr, r-cran-readr, r-cran-rlang Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-encryptr_0.1.4-1.ca2404.1_all.deb Size: 93792 MD5sum: 9e598dcf7c50891b145a6a94ce79a0b3 SHA1: 57d4ddb42fbe1f0df0d74ae895c1d30c12e14a57 SHA256: 67fa92a2ed35b582c3a69add83276ec5e90d3ff83a044eb6d3a242dc45aa2559 SHA512: 6c9cf7603f537354ac4a22314e6bc514ff8a5f4bfc5249f1512b5e1b5ce4644008673a5b783381e1a86296b9c58c8ed670f072c2a53e73037383093ab03ff32e Homepage: https://cran.r-project.org/package=encryptr Description: CRAN Package 'encryptr' (Easily Encrypt and Decrypt Data Frame/Tibble Columns or Filesusing RSA Public/Private Keys) It is important to ensure that sensitive data is protected. This straightforward package is aimed at the end-user. Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns. A public key can be shared to allow others to encrypt data to be sent to you. This is particularly aimed a healthcare settings so patient data can be pseudonymised. Package: r-cran-enderecobr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-enderecobr_0.4.1-1.ca2404.1_all.deb Size: 179532 MD5sum: a22158b846564696c686ec4d92e5d4a9 SHA1: b4c24dcecd341df7bfb3de855a3abc622d9905a1 SHA256: 7d2b2f53308f181e1858a8527cc1a960dcb0ee78de648a64ae227eb1ef857d11 SHA512: 08065233954e3e6b55323b2b224fc0eea196451f181d9ee2fb9af7fc0d69b0fded14df419c77f417823ba3d5cee4a5b0fcee332b883ad8a920a6ab846183e8e2 Homepage: https://cran.r-project.org/package=enderecobr Description: CRAN Package 'enderecobr' (Padronizador de Endereços Brasileiros (Brazilian AddressesStandardizer)) Padroniza endereços brasileiros a partir de diferentes critérios. Os métodos de padronização incluem apenas manipulações básicas de strings, não oferecendo suporte a correspondências probabilísticas entre strings. (Standardizes brazilian addresses using different criteria. Standardization methods include only basic string manipulation, not supporting probabilistic matches between strings.) Package: r-cran-endogenous Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-endogenous_1.0-1.ca2404.1_all.deb Size: 56604 MD5sum: 629f116ee0217a8ffa67c3b86e569e7d SHA1: 67183b1c608893abf38458de0469d96638db1289 SHA256: 131eb59d258a444cd7106a83b0f62deb2c2c5c281fde71efbe50e8be40c5abec SHA512: a43846c9cda3c1a04942ce5c0ba970e9b642d726220ed14ce24833a47e9661d53a693b2cc75f3bdd13945156bdbf19997b8fb395217e98d4938e0077c96ca1c1 Homepage: https://cran.r-project.org/package=endogenous Description: CRAN Package 'endogenous' (Classical Simultaneous Equation Models) Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification. Package: r-cran-endoswitch Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-maxlik, r-cran-msm Filename: pool/dists/noble/main/r-cran-endoswitch_1.0.0-1.ca2404.1_all.deb Size: 67448 MD5sum: 0f6ebde2db92d31af5f34c47ecbab12a SHA1: 37e98e9a97c70f80434927d43900f04da332609d SHA256: 8c8c7e58e5227a70cc6835a67dffbfbc752f5e295e9899889d178a4deaa7db1d SHA512: fd6191f903a36d2e15b29a2e3db62eee10fb456e6063963dc67076f2d717d1a9d324254236a55831d059ed8954df17bc0d0e05d7f827767ddbd813da49077b8b Homepage: https://cran.r-project.org/package=endoSwitch Description: CRAN Package 'endoSwitch' (Endogenous Switching Regression Models) Maximum likelihood estimation of endogenous switching regression models from Heckman (1979) and estimation of treatment effects. 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It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities. Package: r-cran-energygof Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-energy, r-cran-gsl, r-cran-boot, r-cran-fitdistrplus, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-energygof_0.1-1.ca2404.1_all.deb Size: 185414 MD5sum: eb6f8c5e044eb2316ecf8c8c13b0e406 SHA1: a57d40b7884c8bd08fe36741ae1501721f37ddd9 SHA256: d980f5a131605c29e0a79a39c795a23fbccb4c2a3c5518d9cced5a3ca4b13381 SHA512: 9fc56948475a7ce80469f486618e1d6984fb2760a77a2ed73e3b2b5f11b24dfc70f036101857eba5758d22646a7eee43a571a76c8773ef795ec5ae7f58e2b21d Homepage: https://cran.r-project.org/package=energyGOF Description: CRAN Package 'energyGOF' (Goodness-of-Fit Tests for Univariate Data via Energy) Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap. Package: r-cran-energyonlinecpm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-energy, r-cran-mass Filename: pool/dists/noble/main/r-cran-energyonlinecpm_1.0-1.ca2404.1_all.deb Size: 26190 MD5sum: e65be7f3ede03c1b077b096ba21483c5 SHA1: 97fd94b17993577a18c818fb1f7cc0d46f8f30b4 SHA256: 96d2009b662f07a503237a5b781ec18c47dc1ca707238ceb8f5cdf942220ced5 SHA512: 21af002c70f8549359f10c905d179d22b4deeadb76dc5e11de81dab7de74f777ac810ef317bffe7401f8e62108c817b336bbdab4fecae1223d8ff963d2b688a2 Homepage: https://cran.r-project.org/package=EnergyOnlineCPM Description: CRAN Package 'EnergyOnlineCPM' (Distribution Free Multivariate Control Chart Based on EnergyTest) Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) . Package: r-cran-energyr Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-here, r-cran-tidygeocoder, r-cran-usethis, r-cran-testthat Filename: pool/dists/noble/main/r-cran-energyr_0.4-1.ca2404.1_all.deb Size: 241330 MD5sum: 6b77ad656be03801ab5e3eb7ce6f2b6a SHA1: 3c01289fac8f931a9348b4ae15132ca0a8c07d82 SHA256: 3e38cc880257d7e3182f6fd697a19e2544a8b662cd32834a8b95437af0719d90 SHA512: 605f067646bb9bbd4a1cffddbb1e62669f70f1ba240872cf930a87dd4530b9f54b5654fa4e9d0ff7ff893f1d3cec6544efdacc6e69dea7122e35ac69023170a5 Homepage: https://cran.r-project.org/package=energyr Description: CRAN Package 'energyr' (Data Published by the United States Federal Energy RegulatoryCommission) Data published by the United States Federal Energy Regulatory Commission including electric company financial data, natural gas company financial data, hydropower plant data, liquified natural gas plant data, oil company financial data natural gas company financial data, and natural gas storage field data. 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This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) “Engineering Economic Analysis”. Package: r-cran-engression Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch Filename: pool/dists/noble/main/r-cran-engression_0.1.6-1.ca2404.1_all.deb Size: 48894 MD5sum: cf5d8d9db68a628e8d7dd0a4c1aeaef1 SHA1: 0d3392f3e07d55b2232db291de596ed42c86f5a1 SHA256: 901f6c9f5f3d55d64548832dd6faa0b127e1cbee2601c52a56c188661ab4d975 SHA512: e7518a86e6d5eb7cc4f6ee7a7ab550f12d8f7797299e2b0689ce6e60c6d214a9ca8c4ded3dd1aea7a02642f8251e74fa555bf5e8fc166b4f3dbd1709cf453819 Homepage: https://cran.r-project.org/package=engression Description: CRAN Package 'engression' (Engression Modelling) Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). . Package: r-cran-engrexpt Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-engrexpt_0.1-8-1.ca2404.1_all.deb Size: 215822 MD5sum: e15545102eee4ea65c9aaaf476551236 SHA1: dc1fd928d2c118f2f03579d546e1849a80f4cbc8 SHA256: 0c5a8c080d60269433951aed1a405cd1da68784b4c3bd4099a32965cfef14099 SHA512: c977bffc24f337b5af8dc17319392b3413cf803c4bda7045c69a6c6aec5b4a19178916053d552bca0c8977103100f36356697acbcaf10e2916b36f7f44a5d61d Homepage: https://cran.r-project.org/package=EngrExpt Description: CRAN Package 'EngrExpt' (Data sets from "Introductory Statistics for EngineeringExperimentation") Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code. Package: r-cran-enhancer Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-crayon Filename: pool/dists/noble/main/r-cran-enhancer_1.1.2-1.ca2404.1_all.deb Size: 4127888 MD5sum: 42fe07d65067d3f6822720ac2eedf403 SHA1: 794d0da2adba3dcdc49df84e9aee105dec022b4c SHA256: 26dd8e1275b4a6c0c7b10d35a4fc264ae9ce7382fe0e4fc629542e7f525e6207 SHA512: b2051977cd2c5997ced2063a5ac8fda0c02f885d32950f096b5591946113936109f01191d5d2993c6a285c4b17213099cadeed5d240e556535b22cf30ee31b3f Homepage: https://cran.r-project.org/package=enhancer Description: CRAN Package 'enhancer' (Mixed-Effects Models Enhancing Functions) Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) . Package: r-cran-enmeval Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6412 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-maxnet, r-cran-predicts, r-cran-patchwork, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-glmnet, r-cran-rangemodelmetadata, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-rjava, r-cran-spocc, r-cran-rcolorbrewer, r-cran-sf, r-cran-blockcv, r-cran-devtools, r-cran-tibble, r-cran-ecospat, r-cran-geodata, r-cran-usdm Filename: pool/dists/noble/main/r-cran-enmeval_2.0.6-1.ca2404.1_all.deb Size: 3892008 MD5sum: a8d04283fed16660f868bb4bd04cdcdb SHA1: 1bdda0fa586debcecd1ee83daef5b94ac759e74f SHA256: 263870bc1ec8b317df58c73177ea560380640694678ff5e53d4a62138776f652 SHA512: 253d874fb545435e94e0698d0a916a2fd8ee9647eb56489b0dc5c9901f75aaacd7495ec04d0f7d253345c0bbd29bbf4df9105efb3f5bde071bd00b98bec07a52 Homepage: https://cran.r-project.org/package=ENMeval Description: CRAN Package 'ENMeval' (Automated Tuning and Evaluations of Ecological Niche Models) Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: . Package: r-cran-enmsdmx Architecture: all Version: 1.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aiccmodavg, r-cran-boot, r-cran-cowplot, r-cran-data.table, r-cran-doparallel, r-cran-dt, r-cran-foreach, r-cran-gbm, r-cran-ggplot2, r-cran-ks, r-cran-maxnet, r-cran-mgcv, r-cran-omnibus, r-cran-predicts, r-cran-ranger, r-cran-rjava, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-sp, r-cran-statisfactory, r-cran-terra Filename: pool/dists/noble/main/r-cran-enmsdmx_1.2.12-1.ca2404.1_all.deb Size: 1990404 MD5sum: 0ca2cc12ad53546288d4d531bfb6b873 SHA1: 14d629936e664cdaff46b0af7abc2c99afdf27e3 SHA256: a08e1288df34227ddaee90b1f336960ecc303f362f1a3cfebb6d29b1e4fdacf5 SHA512: fb43039581ee3c128f46462db51ddd51f4b898a0b3fa34b7015ce1b45785c0b9634f9eb4df9b69f3a13879c032022698d04368700ef53933520b74ef5cafa121 Homepage: https://cran.r-project.org/package=enmSdmX Description: CRAN Package 'enmSdmX' (Species Distribution Modeling and Ecological Niche Modeling) Implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with 'PROJ6' geodetic objects and coordinate reference systems. Package: r-cran-enmtools Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dismo, r-cran-knitr, r-cran-ggplot2, r-cran-gridextra, r-cran-lhs, r-cran-magrittr, r-cran-enmeval, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-ggpubr, r-cran-forcats, r-cran-terra, r-cran-raster Suggests: r-cran-testthat, r-cran-hypervolume, r-cran-leaflet, r-cran-mgcv, r-cran-ecospat, r-cran-randomforest, r-cran-ranger, r-cran-caret, r-cran-calibratr, r-cran-ape, r-cran-resourceselection, r-cran-reshape2, r-cran-vip, r-cran-pdp, r-cran-fastshap, r-cran-viridis, r-cran-progress, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-enmtools_1.1.5-1.ca2404.1_all.deb Size: 1660176 MD5sum: c55e74a580c23a0227467d70c69ad8f9 SHA1: a64da6d799b889b249816f4581ffa2e4fbdf3c4d SHA256: 588ebe6bb4cfc6d740352aad6d44de89c4887e678fa656ffe2c62d3244521652 SHA512: 021c2aa5be2fd3f51df67682486f87396d3b20ac0cd2276a285dca60821d2d5529c633776fb0ccdef30d309e9f126c32d29acdc7d662a8462162eeb0614fb909 Homepage: https://cran.r-project.org/package=ENMTools Description: CRAN Package 'ENMTools' (Analysis of Niche Evolution using Niche and Distribution Models) Constructing niche models and analyzing patterns of niche evolution. Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography. Warren, D.L., R.E. Glor, and M. Turelli (2008) Glor, R.E., and D.L. Warren (2011) Warren, D.L., R.E. Glor, and M. Turelli (2010) Cardillo, M., and D.L. Warren (2016) D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) . Package: r-cran-enpls Architecture: all Version: 6.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-spls, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-reshape2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-enpls_6.1.1-1.ca2404.1_all.deb Size: 1808910 MD5sum: 179fb4175d6ec4800c4a0ffa7325c364 SHA1: 0dc6b2523ad9f8a314eef8cd3cc1262818c15479 SHA256: 68a29d4dd3e4448a18396186956e529db6b5e355cf76379e1d2455abe7319088 SHA512: c60a31626a11f915573c7a9c43058cd215e0866caa8e4441e6eb905a0093326f6abb6e5506b4a29eed01c0e237cafa3af9d79ff194800f10cf9b10c3bf20082d Homepage: https://cran.r-project.org/package=enpls Description: CRAN Package 'enpls' (Ensemble Partial Least Squares Regression) An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions. 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This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data. Package: r-cran-enrichintersect Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-networkd3, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-webshot2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-enrichintersect_0.7-1.ca2404.1_all.deb Size: 1108194 MD5sum: 5ac8b131d4fd66036f3ab54b94c96035 SHA1: adec5b72a7d2f636db71ccbeec42585f3be0a30f SHA256: 4b3c702d45b11bfd6dbff7db9d6347977e68383176e9d63f739a5620ab9dd236 SHA512: 5452996e7ec7a599eaf96826fa15155cdfdb3f7d8988a38101c114d017dbdc9a1380e401870f992650c2d1357596acf4b73f4330ca8689873a2c639b4d07c0a8 Homepage: https://cran.r-project.org/package=EnrichIntersect Description: CRAN Package 'EnrichIntersect' (Enrichment Analysis and Intersecting Sankey Diagram) A flexible tool for enrichment analysis based on user-defined sets. 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Package: r-cran-enrichr Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-curl, r-cran-rjson, r-cran-ggplot2, r-cran-writexls Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-enrichr_3.4-1.ca2404.1_all.deb Size: 256512 MD5sum: 8c743cba34accdbabf3459038eeea106 SHA1: 0c64d88a439d3110248d2191206cc9a5f45e33c8 SHA256: 62f7b6d1874ac1d5e6a73134fbf7905b04ddac14240769ef9c179236270d9808 SHA512: 06d6f6ed12dbf2b229f6817715560993349d34978bce529c098fe61166906c4ff39222019d3d7bd69e4f837dcafe5e52ba758968f0bd3d458dd9c879b8631cb0 Homepage: https://cran.r-project.org/package=enrichR Description: CRAN Package 'enrichR' (Provides an R Interface to 'Enrichr') Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See for further details. 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All algorithms share a consistent API: em_fit(), em_predict(), em_evaluate(), and em_tune(). Includes built-in cross-validation, feature importance, calibration diagnostics, partial dependence plots, and model comparison utilities. Methods: Breiman (2001) ; Chen and Guestrin (2016) ; Freund and Schapire (1997) ; Breiman (1996) . 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Package: r-cran-ensemblepp Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ensemblebma, r-cran-crch, r-cran-gamlss, r-cran-ensemblemos, r-cran-specsverification, r-cran-scoringrules, r-cran-glmx, r-cran-ordinal, r-cran-proc, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ensemblepp_1.0-0-1.ca2404.1_all.deb Size: 252810 MD5sum: 482682799bcf89747b75793f841a414f SHA1: 6a3d976b0fe39824bd9b7b02827a0fa94461c7d6 SHA256: d2925fd4d604c4d78b443db4981b7ba333450dfb3077dd5e31cca6c6bc10daae SHA512: 9321ac1cc393aac86b79684d537acab645c5962bb19ee8d78c975d1c4f5d55abb4581975f0e2bd6c090bbc49aca80e1f4522c18479031855f082186d1d43f87a Homepage: https://cran.r-project.org/package=ensemblepp Description: CRAN Package 'ensemblepp' (Ensemble Postprocessing Data Sets) Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided. 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Package: r-cran-enviropra2 Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ksamples, r-cran-fitdistrplus, r-cran-truncdist Filename: pool/dists/noble/main/r-cran-enviropra2_1.0.1-1.ca2404.1_all.deb Size: 85460 MD5sum: 7fde1d774b3109ebfd49ea51108b5bef SHA1: bfa3f94f8ebd69c170436ed17a309be84b455317 SHA256: 146d3b991cb69fbd1e2bdb3878e518e2c6435408220dc40a228bde8d78110319 SHA512: c3d011b7fedff40d165e1d6ff688270570c31eb6dcaa5997a9ee5d362102c9f76b515a24412b079c1c50d5e8868bf17f1e4e4538711fcaa671042bcc5363e1f0 Homepage: https://cran.r-project.org/package=EnviroPRA2 Description: CRAN Package 'EnviroPRA2' (Environmental Probabilistic Risk Assessment Tools) It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" . 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Package: r-cran-envoutliers Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-changepoint, r-cran-ecp, r-cran-ismev, r-cran-lokern, r-cran-robustbase Suggests: r-cran-openair Filename: pool/dists/noble/main/r-cran-envoutliers_1.1.0-1.ca2404.1_all.deb Size: 213176 MD5sum: b4687b22779f8a03a469469a4eb22d4a SHA1: 3971428e87923af6e6fa5b4e0929f9f143eab705 SHA256: 017409dd30d60507f5e8e64170bca91e63f7f5d7c1e1ca9795a1989410100369 SHA512: ed9e072b3e8ac0ddaf9c81e15463f30ae97e66a0c57af883377bb6ad712e1efa09f064b2ff1b5d2497e8a8cfc7b15f4ccb606b5936163b5c2523c4f36d050016 Homepage: https://cran.r-project.org/package=envoutliers Description: CRAN Package 'envoutliers' (Methods for Identification of Outliers in Environmental Data) Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. 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This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package. Package: r-cran-epanetreader Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-epanet2toolkit, r-cran-data.table Filename: pool/dists/noble/main/r-cran-epanetreader_1.0.0-1.ca2404.1_all.deb Size: 171678 MD5sum: 72e3341abad9aebb7fcdc16067c3f33d SHA1: 7b440e21d772a0e440d36dfb612cb66427c04662 SHA256: 377f6df4fcf297595a6d235325d9359e5c22183512b8b8cbc7216a8e548b51a4 SHA512: 62f113dfb85cb246434ecebff72c128f5a4e37e56c58e14bbe1c1a55ee9032b2cdd83904d587d264cfb83b9313c8ef91369360fbb483544558d0802a28995635 Homepage: https://cran.r-project.org/package=epanetReader Description: CRAN Package 'epanetReader' (Read Epanet Files into R) Reads water network simulation data in 'Epanet' text-based '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems. Package: r-cran-epca Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-irlba, r-cran-matrix, r-cran-gparotation Suggests: r-cran-elasticnet, r-cran-ggcorrplot, r-cran-tidyverse, r-cran-rmarkdown, r-cran-reshape2, r-cran-markdown, r-cran-rspectra, r-cran-matlabr, r-cran-knitr, r-cran-pma, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epca_1.1.0-1.ca2404.1_all.deb Size: 137522 MD5sum: 1cd41e99aa1f21a469327ff26ae7be98 SHA1: b5e3b4d182e7fee8b87bc47e905bbce56255b190 SHA256: cf13c45197b59f443f12143f0361e4db9088b55e851d84e966e4d6f412fd50e2 SHA512: 94ceb0eb2842072c40c3df6927cce431eff195a317a6db163f1e39d6d531c0edbf7874485536e97752eea41d4421e66f25a8fbce50394f88571e506fe6ce5785 Homepage: https://cran.r-project.org/package=epca Description: CRAN Package 'epca' (Exploratory Principal Component Analysis) Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation. Package: r-cran-epcr Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4771 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-hamlet, r-cran-survival, r-cran-timeroc, r-cran-pracma, r-cran-bolstad2, r-bioc-impute Suggests: r-cran-mass, r-cran-rocr, r-cran-c060, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epcr_0.11.0-1.ca2404.1_all.deb Size: 4697298 MD5sum: a20a3a37374f0b66ed860019040fcb5b SHA1: 600433c51519144d1c4352124a86a3095999e5e5 SHA256: 0f468eef6d4e6fc768d7cb410d677e1c08a2427d71ad9d351ff517d11c1fb32d SHA512: ce1711d9852180d4bed98b215daacca6735d463bef2b3f4abe52e27eab9c1714e91806f6527d8bd4c26772a6f91e89fcd6fa05f3c2bfdfdd5ae918d5a652ce01 Homepage: https://cran.r-project.org/package=ePCR Description: CRAN Package 'ePCR' (Ensemble Penalized Cox Regression for Survival Prediction) The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge presented in Guinney J, Wang T, Laajala TD, et al. (2017) is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects. Package: r-cran-epe4md Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4674 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-scales, r-cran-purrr, r-cran-tidyr, r-cran-readxl, r-cran-jrvfinance, r-cran-janitor, r-cran-furrr, r-cran-zoo, r-cran-lubridate, r-cran-tsibble, r-cran-stringr, r-cran-fabletools, r-cran-feasts, r-cran-tibble, r-cran-assertthat, r-cran-forcats, r-cran-future, r-cran-magrittr, r-cran-readr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-epe4md_0.1.4-1.ca2404.1_all.deb Size: 3866114 MD5sum: 951fc3b7bf72f601cedbbf49ce7962ee SHA1: df7cfe5d2fa292d3e6aba1e08760a5391afb475b SHA256: 62b0d9690c9beca05f18089bd9f509b85f6e735957df803b64d866e147e675d6 SHA512: 3969cc09ab86e82dff23937d4d458728a4f7824eaac1c4b9a2fe915e7bff9f910690271bbe1ea3b6b4e90c71119d7ca6284cb77f89452ea5fd458bc51b04808d Homepage: https://cran.r-project.org/package=epe4md Description: CRAN Package 'epe4md' (EPE's 4MD Model to Forecast the Adoption of DistributedGeneration) EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuída - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. . 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The implemented methods are based on INDEC (2016) . As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish. Package: r-cran-epibasix Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-epibasix_1.5-1.ca2404.1_all.deb Size: 92428 MD5sum: debbc9e77c95daeb1d2338768dea65d9 SHA1: 996e4c879c749474bcecce1cdbccbf7227552cb3 SHA256: 9c841b3b0794a3996d0b24f8e3dbb2e72a6c7465c0961152a998a215dc314a3a SHA512: e9650937539b4e72c6af99a83fe50e38832c31ba6e2d588337239265c78cb29e67946965f4cc9834d5c5d0e42ec0c507e1d109e6ad1ec3dde9c3f0aa90ad3734 Homepage: https://cran.r-project.org/package=epibasix Description: CRAN Package 'epibasix' (Elementary Epidemiological Functions for Epidemiology andBiostatistics) Contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). 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Intended for teaching, for checking hand calculations, and for generating worked solutions in course materials. Scope is deliberately limited to methods a student can compute by hand on paper. Methods follow Mantel and Haenszel (1959) , Greenland and Robins (1985, Biometrics 41, 55-68), Robins, Breslow and Greenland (1986, Biometrics 42, 311-323), and Breslow and Day (1980, IARC Scientific Publications No. 32). Package: r-cran-epicasting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-metrics, r-cran-wavelets Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-epicasting_0.1.0-1.ca2404.1_all.deb Size: 21316 MD5sum: bcc20389e32368e97fd0d59397f24a8d SHA1: 0dbf1e4ef7c5e7d29d47c7178c940fce3e3450ec SHA256: 8c017237e7de22712df01b961236244d2bcb36494c7efad50ebd49ef03ea3d72 SHA512: c4482526b58dca7634fbbf31d2e042b1052c54850355ed08a5f476457cf5ffa483ead614b21b80861c1c9de4ed6436724ce22a226411ed849a507f8120b22a44 Homepage: https://cran.r-project.org/package=epicasting Description: CRAN Package 'epicasting' (Ewnet: An Ensemble Wavelet Neural Network for Forecasting andEpicasting) Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) . 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Package: r-cran-epidata Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-rvest, r-cran-xml2, r-cran-tidyr, r-cran-readr, r-cran-stringi, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-epidata_0.4.0-1.ca2404.1_all.deb Size: 506236 MD5sum: 75f1dfbf93f4645559eece619dbbf6cc SHA1: c3e5bde1d3d11d077b70acf352c08464a34fb7ce SHA256: aa8d39ae1649617b26a37eb0debd1e0af77fe09fff53342d0418f819813de20d SHA512: 2346fac7a02f82deb5f2d2187f4a25df1b95f58a34993c78e6c7140b38e04be06bc105fc505b1e2f0f8179b6b9c2c454e15a0cd575af7dc91a689764d796fbfc Homepage: https://cran.r-project.org/package=epidata Description: CRAN Package 'epidata' (Tools to Retrieve Economic Policy Institute Data LibraryExtracts) The Economic Policy Institute () provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. 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It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. . Package: r-cran-epideaths Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-epideaths_1.1.8-1.ca2404.1_all.deb Size: 45920 MD5sum: be4aa378df7d2ba3591b8f5f97b4621b SHA1: 56b114b79bab38b1d8e58cb5e17fecbe27c1ec3e SHA256: 08aff9bafe163d99469f45988db4bb02fb5fc9efbfcd5e9bdaafe355ae5c4349 SHA512: 4d9cc64a8f73153ca468cc4748190f0ac3506fd549fc0aff00af11adec4ee983cb2461a69ac7a3d47b374ef3a981cbeaa692d2bb02ca5f12c0f3e61384a132a6 Homepage: https://cran.r-project.org/package=epiDeaths Description: CRAN Package 'epiDeaths' (Functions for Calculating Mortality Indicators) Provides functions for calculating mortality indicators. These include geometric interpolation between two periods and projections for future years, as described in the textbook by Laurenti, Mello Jorge, Lebrão and Gotlieb (2005, ISBN:9788512408309), the standardised mortality ratio (Bruce, Pope and Stanistreet, 2018, ISBN:9781118665411), the age-adjusted mortality rate (direct standardisation), years of potential life lost (Gardner and Sanborn, 1990, ; Ma, Ward, Siegel and Jemal, 2015, ), and age-standardised years of potential life lost (Silva Filho et al., 2024 ). Confidence intervals for the standardised mortality ratio are obtained according to Vandenbroucke (1982) and Ulm (1990) . The function also includes a function that produces a graph similar to an age pyramid. Package: r-cran-epidesc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-nseq Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epidesc_0.1.0-1.ca2404.1_all.deb Size: 113688 MD5sum: 8c91ba364a861a09e29cad8f60f4001d SHA1: 8b44d0d4f1002360328aaa982a206d33a3379771 SHA256: a3674344ec57f60b8152aff122c45553fa381e50188b69fe1b5c5e959c578dac SHA512: dd162d49c4aeb469f7dcc779e5404039551e0a9806db2450d36aaa8227d0c91f31752453869ec602796a3f824633b191be25b3055a69ccb70393ba6fe2160f5f Homepage: https://cran.r-project.org/package=epidesc Description: CRAN Package 'epidesc' (Calculation of Epidemiological Descriptors) Provides tools to easily compute a series of epidemiological indicators to characterise different transmission profiles of infectious diseases with a simple pipeline: format the dates in epiyearweek format, choose the descriptors and their parameters, and compute them. The package is based on the publication 'How heterogeneous is the dengue transmission profile in Brazil? A study in six Brazilian states' . 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Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) and Wickham et al. (2019, ISBN:9781492052040>. 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Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using 'grf'. Package: r-cran-epigraphdb Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3083 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tibble, r-cran-httr, r-cran-glue, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-devtools, r-cran-usethis, r-cran-pkgdown, r-cran-styler, r-cran-lintr, r-cran-covr, r-cran-igraph, r-cran-gtools, r-cran-stringr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-epigraphdb_0.2.3-1.ca2404.1_all.deb Size: 1124386 MD5sum: 018a23a427bc9cb77d27c5c56189eaa6 SHA1: 691a53077e8947fe332cedf029bbb01f892d4993 SHA256: 8af7ff2793f50828efc757f1cbd904290a4b08de81f289ca5f93115f4bede5f7 SHA512: 0e942bba8611f743cb87ba05df5256ce0ca051a113bc76a77513901283005085e145ed837a807f7b8fc889741651b8a18d0df7f9c35173c7a7e9ce2dc3b61603 Homepage: https://cran.r-project.org/package=epigraphdb Description: CRAN Package 'epigraphdb' (Interface Package for the 'EpiGraphDB' Platform) The interface package to access data from the 'EpiGraphDB' platform. It provides easy access to the 'EpiGraphDB' platform with functions that query the corresponding REST endpoints on the API and return the response data in the 'tibble' data frame format. Package: r-cran-epikit Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-dplyr, r-cran-rlang, r-cran-forcats, r-cran-tidyr, r-cran-tibble, r-cran-glue, r-cran-tidyselect, r-cran-ggplot2, r-cran-sf Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epikit_0.2.0-1.ca2404.1_all.deb Size: 177902 MD5sum: 020763146d11d645cfb6547f078608b3 SHA1: d800712577a7d149284dd44a1696026d03358c72 SHA256: 95d624b140c532881eec06eba974d7f51cea694496c17ee4bfc8cf6734cfece9 SHA512: 1eeea0f1693cf1038ca4bcccfbd8d94ab9573773f929a2b933918ebd7b0f06aa25622329c697e2c0ffb167d5c3d8061dd661f14a4d8c3c1fc9d5beb5bcffe984 Homepage: https://cran.r-project.org/package=epikit Description: CRAN Package 'epikit' (Miscellaneous Helper Tools for Epidemiologists) Contains tools for formatting inline code, renaming redundant columns, aggregating age categories, adding survey weights, finding the earliest date of an event, plotting z-curves, generating population counts and formatting proportions with confidence intervals. This is part of the 'R4Epis' project . Package: r-cran-epilogi Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-epilogi_1.2-1.ca2404.1_all.deb Size: 22874 MD5sum: 7a43bf47b8c64b5822e785631c5fbe05 SHA1: f8dd26f5579912097fca126afd53b1ca051cd814 SHA256: 1bb1c23c23ca160aeac0d123281e9787a2e14e0f6f78ce8ea19589863e7ded86 SHA512: 2b435c2826f962d9a79d2bb4f578e78dc0125082be571f79a3a6f20422d60a2beaa86f9c5f577af7b708d31418c662f2a41ea4218332db44183aa69068dfe5a5 Homepage: https://cran.r-project.org/package=epilogi Description: CRAN Package 'epilogi' (The 'epilogi' Variable Selection Algorithm for Continuous Data) The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. . Package: r-cran-epilossr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epilossr_0.1.0-1.ca2404.1_all.deb Size: 37678 MD5sum: a3ad0c19952771feb127bf497781885c SHA1: 1b68a88a7f6838649a8cd2fc475318ec91171cd8 SHA256: ef7eb2205348409b73e61a3bcd5a2ab723a4b5e191d596df9e7e00c9638f9e24 SHA512: 592cfadc3d7187e2fef17195c5e2a37b84bdc40b819ea79315bd3edca356d670241c24b3f57094435974c2f21ff8a6a9b153407980d74762036164d30865b54b Homepage: https://cran.r-project.org/package=EpiLossR Description: CRAN Package 'EpiLossR' (Economic Loss Estimation for Animal Disease Mortality andMorbidity) Provides standardized tools for estimating direct economic losses associated with mortality and morbidity in animal diseases. The package implements reproducible methods for calculating mortality- and morbidity-related losses using a common S3 object framework with methods for summarization, visualization, and data export. It is intended for veterinary epidemiologists, animal health economists, veterinarians, researchers, and students. The methods are informed by Rushton (2009, ISBN:9781845936934) and Bennett (2003, ISBN:9780851996224). Package: r-cran-epimdr2 Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-desolve, r-cran-plotly, r-cran-polspline, r-cran-ggplot2 Suggests: r-cran-ade4, r-cran-bbmle, r-cran-fields, r-cran-forecast, r-cran-imputets, r-cran-lme4, r-cran-ncf, r-cran-nleqslv, r-cran-nlme, r-cran-nlts, r-cran-plotrix, r-cran-pomp, r-cran-rootsolve, r-cran-rwave, r-cran-rworldmap, r-cran-statnet, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-epimdr2_1.1-1-1.ca2404.1_all.deb Size: 2367426 MD5sum: 964a6df79ea7a0dbfee62e3374dad8d8 SHA1: b6f5826a25341cd88880766944cddb1f9c5cac33 SHA256: 13a8178211572fb40dabb8eadaab434fd57a59091ac53f703799b6c6af134381 SHA512: f2d3e8b3a59d12f3cf90a227747801ee262a1cd4dd4d42c462cbbebef79002931cd27d1bda1d64bc685b9e8555c70e2214220df898d31933983f62416e46b743 Homepage: https://cran.r-project.org/package=epimdr2 Description: CRAN Package 'epimdr2' (Functions and Data for "Epidemics: Models and Data in R (2ndEdition)") Functions, data sets and shiny apps for "Epidemics: Models and Data in R (2nd edition)" by Ottar N. Bjornstad (2022, ISBN: 978-3-031-12055-8) . The package contains functions to study the Susceptible-Exposed-Infected-Removed SEIR model, spatial and age-structured Susceptible-Infected-Removed SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. Package: r-cran-epimdr Architecture: all Version: 0.6-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-desolve, r-cran-polspline Suggests: r-cran-ade4, r-cran-bbmle, r-cran-fields, r-cran-forecast, r-cran-imputets, r-cran-lme4, r-cran-ncf, r-cran-nleqslv, r-cran-nlme, r-cran-nlts, r-cran-plotrix, r-cran-pomp, r-cran-rootsolve, r-cran-rwave, r-cran-statnet Filename: pool/dists/noble/main/r-cran-epimdr_0.6-5-1.ca2404.1_all.deb Size: 1257062 MD5sum: ae1c52746fc40c68d61e5f1ae849bad9 SHA1: 3b3997c0c437760d98cdd40dbfa315034efcd1f7 SHA256: 22c5ec8066f3593240fd99a387bac3a7b8409929e6f673b6a3795d384a54462c SHA512: 27acf277e248f8af7f4e7cf8c88cd27be39d1e95e43371859df692694a881fa13313af4ba7597ecfab2dd292f10fcbc9dd89cfda94a1740cd552a8d7f8af240e Homepage: https://cran.r-project.org/package=epimdr Description: CRAN Package 'epimdr' (Functions and Data for "Epidemics: Models and Data in R") Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) . The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course . Package: r-cran-epinova Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales Suggests: r-cran-deoptim, r-cran-mass, r-cran-numderiv, r-cran-rstan, r-cran-tmb, r-cran-epiestim, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggpubr, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-epinova_0.1.0-1.ca2404.1_all.deb Size: 157946 MD5sum: de57502f8bfb65a0b22f743ab2629ea0 SHA1: 8b64a9710759eb66442f48e21129dcc18926f90b SHA256: 5cefd64d5ad4b7e270bbc0b74a5663db4768868b8ac4b6136952935c962823ea SHA512: d4fa620ceaf41d2259f41b65a7242bb55d81e37f90edb526a93f5a891232eb83fc4c48b9a2ad3977e90b91f67f76c8d0f4b9bbcdea135b9d4a5e965a50524e2d Homepage: https://cran.r-project.org/package=EpiNova Description: CRAN Package 'EpiNova' (Flexible Extended State-Space Epidemiological Models with ModernInference) An extended epidemiological modelling framework that goes beyond the classical SIR (Susceptible-Infectious-Recovered) model. Supports SEIR (Susceptible-Exposed-Infectious-Recovered), SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased), SVEIRD (Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased), and age-stratified compartmental models with flexible intervention functions (spline-based, Gaussian process, or user-defined). Inference is available via maximum likelihood or sequential Monte Carlo (SMC, also known as particle filtering) with no external binary dependencies. Includes a dependency-free real-time effective reproduction number (Rt) estimator, spatial multi-patch models with gravity-model mobility, ensemble forecasting via Bayesian model averaging (BMA), and proper scoring rules including CRPS (Continuous Ranked Probability Score), coverage, and MAE (Mean Absolute Error) for forecast evaluation. Methods follow Anderson and May (1991, ISBN:9780198545996), Doucet, de Freitas, and Gordon (2001) , Cori et al. (2013) , and Gneiting and Raftery (2007) . Package: r-cran-epiomics Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-qgcomp, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epiomics_1.2.0-1.ca2404.1_all.deb Size: 393518 MD5sum: 2754e698be8353a733d7a58b9c75d3c7 SHA1: ff6f75aa9f8aaf844ffaa01c8ea0e915668c5cbd SHA256: 3c6cf12776d29d59af69b3344e1b97feb26994faafeb7b023145dbefc9820d92 SHA512: 3119266d7918d290a9aea4c45c3d7c9616c8f043d0f6447c0290d83b12ea488ec9d486edc8c229effb802897b277ede6b9ba8b78b9c515370d04b8cccd564dbc Homepage: https://cran.r-project.org/package=epiomics Description: CRAN Package 'epiomics' (Analysis of Omics Data in Observational Studies) A collection of fast and flexible functions for analyzing omics data in observational studies. Multiple different approaches for integrating multiple environmental/genetic factors, omics data, and/or phenotype data are implemented. This includes functions for performing omics wide association studies with one or more variables of interest as the exposure or outcome; a function for performing a meet in the middle analysis for linking exposures, omics, and outcomes (as described by Chadeau-Hyam et al., (2010) ); and a function for performing a mixtures analysis across all omics features using quantile-based g-Computation (as described by Keil et al., (2019) ). Package: r-cran-epiparameter Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cachem, r-cran-checkmate, r-cran-cli, r-cran-distcrete, r-cran-distributional, r-cran-epiparameterdb, r-cran-lifecycle, r-cran-pillar, r-cran-rlang Suggests: r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-epiparameter_0.4.1-1.ca2404.1_all.deb Size: 835396 MD5sum: 3194f6c2fed43fd56e4c482584999b68 SHA1: bb33b413c2488d1a1607567a8f718ad4d15931b2 SHA256: 6923e202936dfbdfd21ff71563ca2226e3da940411cf93a4e577eef547d5cca4 SHA512: 86f3789109417ef5f61d5c6a6c1ea7a7521b8e250ccca0a023a0cdc0575163eb2eddb74ff5e774732af6ccb683c37540585c5b8f816eb72329fb9f7d1fe58d03 Homepage: https://cran.r-project.org/package=epiparameter Description: CRAN Package 'epiparameter' (Classes and Helper Functions for Working with EpidemiologicalParameters) Classes and helper functions for loading, extracting, converting, manipulating, plotting and aggregating epidemiological parameters for infectious diseases. Epidemiological parameters extracted from the literature are loaded from the 'epiparameterDB' R package. Package: r-cran-epiparameterdb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dt, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epiparameterdb_0.1.0-1.ca2404.1_all.deb Size: 185356 MD5sum: ec309ab8dd1cd5296eff465ac659ae01 SHA1: 5d31b0a1e5a45147ff4801630484a4de9859b225 SHA256: 9bf0b6f87e8b0f749348cd023cf4119305b64c1fcc1db6941298dca34a285985 SHA512: 986cb7df783da4f27968d9167ce6c6e8330edb4b492007bff95b21c5f0e8e0b3dbbe211738a72ff58f2a7106a4fc92148b6007d722783dcf9b024bc52d4d3c88 Homepage: https://cran.r-project.org/package=epiparameterDB Description: CRAN Package 'epiparameterDB' (Database of Epidemiological Parameters) A data package containing a database of epidemiological parameters. It stores the data for the 'epiparameter' R package. Epidemiological parameter estimates are extracted from the literature. Package: r-cran-epiquestionr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-lavaan, r-cran-psych, r-cran-generics, r-cran-withr, r-cran-tibble Suggests: r-cran-broom, r-cran-broom.helpers, r-cran-car, r-cran-corrplot, r-cran-covr, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-forcats, r-cran-gt, r-cran-gtsummary, r-cran-haven, r-cran-janitor, r-cran-knitr, r-cran-leaflet, r-cran-ltm, r-cran-mass, r-cran-mirt, r-cran-mbess, r-cran-nnet, r-cran-officer, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-semplot, r-cran-sf, r-cran-shiny, r-cran-shinydashboard, r-cran-spdep, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-tmap, r-cran-e1071, r-cran-jsonlite, r-cran-openxlsx, r-cran-reshape2, r-cran-scales, r-cran-yaml Filename: pool/dists/noble/main/r-cran-epiquestionr_0.1.1-1.ca2404.1_all.deb Size: 659194 MD5sum: cf6ded29c78d57bee90faad9e9689d13 SHA1: 633a45b9245ac1a98461eb263ede6b44cf911b27 SHA256: 4133270d0664a12fff9f7ae9c0cca29d5e7b2e9cf8e8dcb904521535751e68b9 SHA512: 2698967f8ca5439b91b14bb83062d14e0a4b7649495c06db2b0006d42559b964e96397c66180c6804af27207c8e7f9ff7fcef3cb1ca57ce81c740c3c0e83d933 Homepage: https://cran.r-project.org/package=EpiQuestionR Description: CRAN Package 'EpiQuestionR' (Questionnaire Analysis for Epidemiology and One Health Research) Provides tools for the analysis of questionnaire and survey data in epidemiological and One Health research. The package supports data preparation, reliability assessment, exploratory factor analysis, Kaiser-Meyer-Olkin assessment, parallel analysis, visualization, reporting, and export of results using a consistent analysis workflow. The methods are based on established approaches to psychometric and multivariate analysis; see Kaiser (1974) , Horn (1965) , and Tabachnick and Fidell (2019, ISBN:9780134790541). Package: r-cran-epir Architecture: all Version: 2.0.99-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5524 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-biasedurn, r-cran-pander, r-cran-sf, r-cran-lubridate, r-cran-zoo, r-cran-flextable, r-cran-officer, r-cran-dfba Suggests: r-cran-mass, r-cran-dplyr, r-cran-foreign, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-plyr, r-cran-rcolorbrewer, r-cran-scales, r-cran-sp, r-cran-spdata, r-cran-spatstat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-epir_2.0.99-1.ca2404.1_all.deb Size: 1828636 MD5sum: 5eb782bf04b55f170a1b99ed611973de SHA1: 12f70df17a9e6693c1940c7e362bae8e580445c1 SHA256: 6e96d0d3b91e762b0fe52a9b900d258e7d88015380afa7f0fb79ec9a47243a8d SHA512: 4327f1ea018c068f0de44a575246d63e188786f8aac72bfc3082b93ac775a25f0b9a908ad8772bb30abbf7dbe4e36cd792e8230fdb81dbdc75f36d77c74378b4 Homepage: https://cran.r-project.org/package=epiR Description: CRAN Package 'epiR' (Tools for the Analysis of Epidemiological Data) Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses. Package: r-cran-epireport Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-officer, r-cran-flextable, r-cran-zoo, r-cran-png, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epireport_1.0.4-1.ca2404.1_all.deb Size: 2609286 MD5sum: baf11533b502a97c51ef73b942ff2f62 SHA1: 35eb3fa7516ffeaafcb5f318a053b0c0f78ebc98 SHA256: bfd8982ccbddedb00089384bc6ed1f44df9564713dc10776ec3eab08d15e489e SHA512: 4476961033ae5f0452d6f0a09aaf6a8a82944cf1df904f76d1e11c45522b62849010f5682db3e320830ebc56e4315bae7ecc511945657a246936092804cc87e3 Homepage: https://cran.r-project.org/package=EpiReport Description: CRAN Package 'EpiReport' (Epidemiological Report) Drafting an epidemiological report in 'Microsoft Word' format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control. Through standalone functions, it is specifically designed to generate each disease specific output presented in these reports and includes: - Table with the distribution of cases by Member State over the last five years; - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, by month, over the past five years; - Trend plot with the trend and number of cases at the European Union / European Economic Area level, by month, over the past five years; - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level. Two types of datasets can be used: - The default dataset of dengue 2015-2019 data; - Any dataset specified as described in the vignette. Package: r-cran-episcan Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-episcan_0.0.1-1.ca2404.1_all.deb Size: 69392 MD5sum: 8ae1f092c5087b1b79f238393c49e160 SHA1: 72d49d1b7cccc5ccf12b31ef15bc1769cb803cb6 SHA256: 7aafe7f30080eea8f0f9f1bb838498ed31bef25d2f4e3509ac6b6aba8d45015a SHA512: 68c28e9740d1c29364cb889c0da67e63ccc87a0829ff823b56ea079e24f80f5464e3b26c8f8aa8acefa5e85ef60cfbe79f5109b3cec4cd38b06362baed5c51ad Homepage: https://cran.r-project.org/package=episcan Description: CRAN Package 'episcan' (Scan Pairwise Epistasis) Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). . 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011). .) Package: r-cran-episcopek Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-episcopek_0.1.1-1.ca2404.1_all.deb Size: 140132 MD5sum: b94c8035395aa840037999c310f2f035 SHA1: 4530aa13b539a9e5a0b7823958fb806ee985fae8 SHA256: 94534a21a9df5604539e853b08bdb5fac689c1beff6b24eee01dca3fcca5b496 SHA512: 5962b46c797844d96857ec08122e520e90715480fa8e778aebe2b4da5348d7488841f7b2bef88a0be08618cec6804a8124a2e4983841d1176432af550cf7e86d Homepage: https://cran.r-project.org/package=EpiScopeK Description: CRAN Package 'EpiScopeK' (Comprehensive Epidemiological Analysis Toolkit) Provides a unified framework for epidemiological data analysis and disease surveillance. The package supports descriptive epidemiology, incidence, prevalence and mortality estimation, age standardization, trend analysis, geographic summaries, disease risk prediction, and automated analytical workflows. Designed for researchers and public health professionals, it facilitates reproducible analyses of epidemiological datasets using established statistical and predictive modeling techniques. Methods are informed by standard epidemiological references including Rothman et al. (2008, ISBN:9780781755641) and Gordis (2014, ISBN:9781455737338). Package: r-cran-episemble Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-bioc-biostrings, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-stringr, r-cran-tibble, r-cran-doparallel, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-foreach, r-cran-ftrcool, r-cran-iterators Filename: pool/dists/noble/main/r-cran-episemble_0.1.1-1.ca2404.1_all.deb Size: 980992 MD5sum: 4927991de3eae484e7199f8cfc7ce923 SHA1: 2e4e5046bf27b9cc2bf4d41d0841eef91fc07389 SHA256: bd3c7edb9e084c2dbe1659da0448bdbc14e67e13b9cf8f5981941a3074e9d229 SHA512: 2f2bb05baf5f8199bfa555952dbaaf75e3f3e5f9cde02474e35a232d62871f208b1447b5637e6375e387a99a377deaff8c3ee79e87c97b9f7e79ef5291acd4f0 Homepage: https://cran.r-project.org/package=EpiSemble Description: CRAN Package 'EpiSemble' (Ensemble Based Machine Learning Approach for PredictingMethylation States) DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) . Package: r-cran-episensr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-actuar, r-cran-boot, r-cran-cli, r-cran-dagitty, r-cran-forcats, r-cran-ggdag, r-cran-trapezoid, r-cran-triangle, r-cran-truncnorm, r-cran-mass, r-cran-lifecycle, r-cran-magrittr Suggests: r-cran-aplore3, r-cran-covr, r-cran-directlabels, r-cran-knitr, r-cran-lattice, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-episensr_2.2.0-1.ca2404.1_all.deb Size: 842872 MD5sum: f89edfcb49f2b1e5ceb4744c4b276272 SHA1: 63530471d06ffe35144100676e658705292a4ef9 SHA256: 1fa0c8baa05df23a762ba70c63a909a976717f583d572571d6c84ace4f7beb4d SHA512: 87d3d0484f6a944ec07ba11e942128cac1d187ef8cafc8e40ac6ca0e624ebbf2bb757cae449b8fd6bbdd19a5db52a266ba11f8480443f3aa57dfab1bba206b33 Homepage: https://cran.r-project.org/package=episensr Description: CRAN Package 'episensr' (Basic Sensitivity Analysis of Epidemiological Results) Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021). Package: r-cran-episignaldetection Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-isoweek, r-cran-rmarkdown, r-cran-shiny, r-cran-surveillance Suggests: r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-pander Filename: pool/dists/noble/main/r-cran-episignaldetection_0.1.3-1.ca2404.1_all.deb Size: 498748 MD5sum: c7b793c59f1ed605fb2dfb6962a5805a SHA1: c0b08fe5e6383cf2481fc09c8ffb35531e86f71b SHA256: e2678accc2ca437b5f387a7a1d2a15e4058c70e99d5fdae98c110e07b0d75a8a SHA512: d480ce743386fee19d48bc12a930eae7b8d6608cc5fdb3a4690e96ea5a82fc258c29a5fe72afc692a9d7c15280b38bef0c40f2aadadb2b25e7444c7c3b29076e Homepage: https://cran.r-project.org/package=EpiSignalDetection Description: CRAN Package 'EpiSignalDetection' (Signal Detection Analysis) Exploring time series for signal detection. It is specifically designed to detect possible outbreaks using infectious disease surveillance data at the European Union / European Economic Area or country level. Automatic detection tools used are presented in the paper "Monitoring count time series in R: aberration detection in public health surveillance", by Salmon (2016) . The package includes: - Signal Detection tool, an interactive 'shiny' application in which the user can import external data and perform basic signal detection analyses; - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs. This report can also be stratified by population characteristics (see 'Population' variable). This project was funded by the European Centre for Disease Prevention and Control. Package: r-cran-episimr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-desolve, r-cran-openxlsx, r-cran-dplyr, r-cran-dt, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-episimr_1.1-1.ca2404.1_all.deb Size: 24854 MD5sum: 842b405738f201741b8775b0bf8c87ec SHA1: e151df347327d1b4fd7ad0835c2830742f0d6fce SHA256: 57271ca4679e068b56ec6237fb874a8801fe51b3b4e0178919a8409df59ffbb0 SHA512: 70b419f6f48d6e7b428d749b33ac63e85acaab390b6301a8671c0f0c097862f366f0b21a9916f2619e47afc3e7a5e43c949921a3c7ad9cd0be55a2dfd241b785 Homepage: https://cran.r-project.org/package=EpiSimR Description: CRAN Package 'EpiSimR' (A 'Shiny' App to Simulate the Dynamics of Epidemic and EndemicDiseases Spread) The 'EpiSimR' package provides an interactive 'shiny' app based on deterministic compartmental mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. It allows users to explore various intervention strategies, including vaccination and isolation, by adjusting key epidemiological parameters. The methodology follows the approach described by Brauer (2008) . Thanks to 'shiny' package. Package: r-cran-episomer Architecture: all Version: 3.0.35-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-dt, r-cran-emayili, r-cran-future, r-cran-httr, r-cran-htmltools, r-cran-jsonlite, r-cran-keyring, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-plotly, r-cran-processx, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-openxlsx, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-xtable, r-cran-httr2, r-cran-lubridate, r-cran-sf, r-cran-cli Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-episomer_3.0.35-1.ca2404.1_all.deb Size: 3213220 MD5sum: 291c5e0b33d35c40b1f733e9eb90a963 SHA1: 6e694746e8002e3bba985631f15183f5d8170467 SHA256: 26931885bbf3a43e38ace563a384b3dbeffe06950f16d62cd9ab4d5a96a06992 SHA512: 72a0d3e009adf47d5fe402f9130ce9f3472ca68a6a68382432b5073c8fa7accc82e90ccb907f50f54f2c59eb1b01db288344251c34a17e8b71495c9ee27115e0 Homepage: https://cran.r-project.org/package=episomer Description: CRAN Package 'episomer' (Early Detection of Public Health Threats from Social Media Data) It allows you to automatically monitor trends of social media messages by time, place and topic aiming at detecting public health threats early through the detection of signals (i.e., an unusual increase in the number of messages per time, topic and location). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information on the original package 'epitweetr' is available in the peer-review publication Espinosa et al. (2022) . Package: r-cran-epistandard Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dbi, r-cran-duckdb, r-cran-incidenceprevalence, r-cran-omopgenerics Filename: pool/dists/noble/main/r-cran-epistandard_0.2.0-1.ca2404.1_all.deb Size: 851024 MD5sum: 3bb1cf5973e306620fec0ea8e81ba19e SHA1: d54f7aa40c52f0b374867793be0b0d6e7725dfd6 SHA256: 0f61da66f7c838b9a8694091547664608f50888ea15d66298537f73e8dbaa57a SHA512: dc36bbaf74052c00255f6adf6d5457ab8b03a278f4adc9f4d556ffebcb8e700ca61b7b8992f8db5d7d4ace4b432f319682754b0581b450f0074e1d810e4448f0 Homepage: https://cran.r-project.org/package=EpiStandard Description: CRAN Package 'EpiStandard' (Directly Standardise Rates by Age) Provides functions for age standardisation of epidemiological measures such as incidence and prevalence rates. It allows users to apply standard population structures to observed age-specific estimates in order to obtain comparable summary measures across populations or time periods. Functions support calculation of standardised rates, outcome counts, and corresponding confidence intervals. The tools are designed to facilitate reproducible and transparent adjustment for differences in age distributions in epidemiological and public health research. Package: r-cran-epistandardiser Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-epistandardiser_0.0.2-1.ca2404.1_all.deb Size: 44038 MD5sum: 6d1b0cdd26d1cf4b3369589b8bb0f3f9 SHA1: ae9e7cbe148c614dd4bf579e27b7c62ff8e63c72 SHA256: f72bfe738ecc06d375e05f44982791ea4a16301da4d8bf2a4b678d9e7d654a03 SHA512: 44e77cf155197a6e81f7bb437379c3cae28b2c68fc4d41491bd07d52d88aba5e9bcf0b68f29e1cf89a8f4e5e7b0e1612d47bc2eeb7bb027e33fa356d1d2bd65d Homepage: https://cran.r-project.org/package=epistandardiseR Description: CRAN Package 'epistandardiseR' (Tools for Direct Standardisation with Confidence Intervals) Provides tools to compute directly standardised rates using the 2013 European Standard Population for age and deprivation-standardised rates using a 10% per decile assumption. Sex standardisation uses an assumed equal proportion of both males and females. The package Includes variance estimation and 95% confidence intervals for population health applications. Functions support flexible grouping variables and age bands, enabling reproducible and automated analyses. Package: r-cran-epistats Architecture: all Version: 1.6-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-epir, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epistats_1.6-2-1.ca2404.1_all.deb Size: 401726 MD5sum: decc3085c858a296789677092ea763c7 SHA1: fc5d2041288a7c34c418838b2dfd5ae1ebe0933c SHA256: 7919669b96fb8c08e99b4104420928b0923f6bc6a5f3057aaf65956033db882a SHA512: 2c57cfd7440b3328ce83df9eca73d5b15b64d340638491a06db9bc876be53881131504021e1ddc4a5a410ff48eba932aad4406901dacf4e29ec9ea346e9bd45f Homepage: https://cran.r-project.org/package=EpiStats Description: CRAN Package 'EpiStats' (Tools for Epidemiologists) Provides set of functions aimed at epidemiologists. The package includes commands for measures of association and impact for case control studies and cohort studies. It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands. References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press. Woolf, B. 1955. On estimating the relation between blood group disease. Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. Newton Lower Falls, MA Epidemiology Resources. Gilles Desve & Peter Makary, 2007. 'CSTABLE Stata module to calculate summary table for cohort study' Statistical Software Components S456879, Boston College Department of Economics. Gilles Desve & Peter Makary, 2007. 'CCTABLE Stata module to calculate summary table for case-control study' Statistical Software Components S456878, Boston College Department of Economics. Package: r-cran-epistemicgametheory Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epistemicgametheory_0.1.2-1.ca2404.1_all.deb Size: 33400 MD5sum: b61e32fc2beefaaca8375ed25406dad6 SHA1: f907f4c85f8a2e89a26568cd95bde0e93984f5f2 SHA256: c39aabc3b10b88f8cf6febdf0e672bce20d1869f23b324e06e7e580efdfd30eb SHA512: 7a710d30eba9c513c99c4533b065512062d3654d1b146e1fad2b7001b523f7e7c988d4466648ddbfd07f83f1bbffbc09c28b38e1120e161dff13f9d1afffa6a0 Homepage: https://cran.r-project.org/package=EpistemicGameTheory Description: CRAN Package 'EpistemicGameTheory' (Constructing an Epistemic Model for the Games with Two Players) Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality. Package: r-cran-epitab Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-survival, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epitab_0.2.2-1.ca2404.1_all.deb Size: 154954 MD5sum: 13400d32acc9486d11454ac31ebe2107 SHA1: d876aec27630ab8aec9044b339c1c2bb8aa86bc9 SHA256: 7e5531f6bb65d1d270e3cd79074ba6c862ab1a3d9e78735c3465d5758f3e9290 SHA512: 5855a978a0a9b2232eed28ad0c1d23e386da6f29d01344ee1bdac98e2014723ba506e3dc2c91eb4d568baae4f608db0fa92bb01e29fb4c2569f86e9291449b81 Homepage: https://cran.r-project.org/package=epitab Description: CRAN Package 'epitab' (Flexible Contingency Tables for Epidemiology) Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: 'HTML', 'LaTeX', and 'Excel'. Package: r-cran-epitabulate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binom, r-cran-dplyr, r-cran-epikit, r-cran-forcats, r-cran-glue, r-cran-gtsummary, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-broom.helpers, r-cran-cardx, r-cran-covr, r-cran-matchmaker, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epitabulate_0.1.0-1.ca2404.1_all.deb Size: 81744 MD5sum: 14cf8f83283e6dfc548277f145b10cee SHA1: 0a1eb69459fb221ab5863c32738c08a2d674ceb3 SHA256: 2c078beed8bee7320672ffa62bbd8fcf09dfe223d3776b70f5fe583ddf60d8dc SHA512: 792b4965710288ced3a951d8622da14416e33c39d48cc96dd8e2c2e715d0577d8fb3f598f999045458408199a9ea76fc60bdffb4efbd3f811318904e5a8270eb Homepage: https://cran.r-project.org/package=epitabulate Description: CRAN Package 'epitabulate' (Tables for Epidemiological Analysis) Produces tables for descriptive epidemiological analysis. These tables include attack rates, case fatality ratios, and mortality rates (with appropriate confidence intervals), with additional functionality to calculate Mantel-Haenszel odds, risk, and incidence rate ratios. The methods implemented follow standard epidemiological approaches described in Rothman et al. (2008, ISBN:978-0-19-513554-2). This package is part of the 'R4EPIs' project . Package: r-cran-epitest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-mm4lmm, r-cran-purrr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-epitest_1.0.0-1.ca2404.1_all.deb Size: 262604 MD5sum: b6b1805c2c05aa5182e76cc9df51aac4 SHA1: 114df7782fece111910b8b1d93671145a753c8fb SHA256: ad8d8cee53ed5de32540818bd7e2ad111d2354625e6ebdfd335be658476b4fae SHA512: 8a2a544e89183e31e3e6beca987632fa3ce82a4ec00a10146f79c080eb4ed4b15d912c774363bad28b79295bdabca4fc69bb92c9ab277ffb3ea10e6094b0f43f Homepage: https://cran.r-project.org/package=EpiTest Description: CRAN Package 'EpiTest' (Test for Gene x Gene Interactions in Bi-Parental Populations) Provides functions to test for gene x gene interactions in a bi-parental population of inbred lines. The data are fitted with the mixed linear model described in Rio et al. (2022) , that accounts for gene x gene interactions at both the fixed effect and variance levels. The package also provides graphical tools to display the gene x gene interaction trend at the mean level and the variance component analysis. Package: r-cran-epitools Architecture: all Version: 0.5-10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-epitools_0.5-10.1-1.ca2404.1_all.deb Size: 319824 MD5sum: 6667ff41f3551fc33243eaa7d49ffe85 SHA1: b34988329097f9cf0f5628391230f3d8cb4223a0 SHA256: a9473a6b6e4478a40875ae7a8db4786e75c16e6538ba767ab4bc5a5ab8bc95ad SHA512: d2c3191e64e09b291ed2b79faff2254ce34e35fe5f460c0bad4c25a9e1a57fd48594bad37e7ebd4c5c334eb0805901d7528e5e0e73a5c3fe932e87acdfbc8a52 Homepage: https://cran.r-project.org/package=epitools Description: CRAN Package 'epitools' (Epidemiology Tools) Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables. 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Package: r-cran-epiworldrshiny Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2, r-cran-epiworldr, r-cran-plotly, r-cran-bslib Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-epiworldrshiny_0.2.3-1.ca2404.1_all.deb Size: 283080 MD5sum: 61523194db85f1f080999470f836dd22 SHA1: 6e0050483e64d9f8dc564d7fb841a8b412b0aa4f SHA256: 8e653dac8a7b156e312d46194074c810799d4bcb237a8c2abb2611ace2e523cc SHA512: fb7b72f2db5274553fa0493e0ed5a7fbf9b36941ddab38e327e7d25a50a470e1077beffdb943bcce88f678c6bfb7fcecfde66a30a6fb992b84bab1c79e6ff62a Homepage: https://cran.r-project.org/package=epiworldRShiny Description: CRAN Package 'epiworldRShiny' (A 'shiny' Wrapper of the R Package 'epiworldR') R 'shiny' web apps for epidemiological Agent-Based Models. 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Package: r-cran-epos Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 662 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hash, r-cran-ggplot2, r-cran-testthat, r-cran-gridextra, r-cran-topklists, r-cran-stringr, r-cran-xtable, r-cran-mongolite, r-cran-venndiagram, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epos_1.2-1.ca2404.1_all.deb Size: 443888 MD5sum: b04a0e3b78e6b1816183b71db3e02a78 SHA1: e748854d64e287c18b9f0ec1da6d90076de0b97e SHA256: 7564d8492102998c389b25850c0787caa07109550d1f601110994b2864701c05 SHA512: 89eb76c1236ac03191659bdd3d58657dee33fa6bcfecad28858a97c193cac3279efb0226edd46bd41103b31cbdad366b93fdfa46c8f3a818d846eeabd4b6ce0f Homepage: https://cran.r-project.org/package=epos Description: CRAN Package 'epos' (Epilepsy Ontologies' Similarities) Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) , Mueller, Bernd et al. (2017) , Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) , and Mueller, Bernd et al. (2022) . 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Package: r-cran-eppofinder Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-checkmate, r-cran-tibble Suggests: r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-eppofinder_2.0.0-1.ca2404.1_all.deb Size: 345426 MD5sum: 5dd509b80cd4fe452a9f6867f413d3fd SHA1: 038ac085958f2d22a25a22f43d3c42094f100036 SHA256: 70b8ca2a0d56f7579222225a505ce27bae87863928f6ebd589ae6e4ea0285fdb SHA512: aef8cb20dbe11e131f34da1c1722f6d58a329743ebd1e9a02ab967190f049fd0e60488e1c1424fc50e19493ddb2ab2aa5d102f03fd676c2d4e5c6b8bc8aa0799 Homepage: https://cran.r-project.org/package=eppoFindeR Description: CRAN Package 'eppoFindeR' (Interface to the EPPO Database and Public APIs) Provides an interface to the public APIs of the European and Mediterranean Plant Protection Organization (EPPO) database. 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Package: r-cran-epsiwal Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epsiwal_0.2.0-1.ca2404.1_all.deb Size: 57144 MD5sum: 637d7bfa31ec61da8a4e31af4bc1e228 SHA1: 1f3e56d4002353d62d311e824830e86d27a13e32 SHA256: 962a8b04a686daa6f4a941452951ca2117dac10e01438125a8262b1224120798 SHA512: af9f2ff4e0241b173b7ff67147ed338e432f821195ab6867e0f2e596c63bc001aaa1c0cab222195d89000ab1040acbae95e72a2580d0ed9485f9f6a7c6620ae6 Homepage: https://cran.r-project.org/package=epsiwal Description: CRAN Package 'epsiwal' (Exact Post Selection Inference with Applications to the Lasso) Implements the conditional estimation procedure of Lee, Sun, Sun and Taylor (2016) . This procedure allows hypothesis testing on the mean of a normal random vector subject to linear constraints. Also supports computation of the MLE of the mean subject to the same constraints. Package: r-cran-ept Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ept_0.7.6-1.ca2404.1_all.deb Size: 157686 MD5sum: 618bff6e9d20ab6f7ab909b6cae78401 SHA1: e9f690bcf5c5bbf758f37a12616864aae0512702 SHA256: c15fb5f5978ad34d37137bab7b4196381a553f060db90367a420d6f7bde085b3 SHA512: 03af9ed00eae7d6ace2e1e4141757956749d3fb6ee7b3e997e350ab6d640b86df4ad36d1d16d7fbc9176cce465b20c49b4d6c3c41db78c7729300a2db79c14f9 Homepage: https://cran.r-project.org/package=EPT Description: CRAN Package 'EPT' (Ensemble Patch Transform, Visualization and Decomposition) For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Kim et al. (2020), and Oh and Kim (2020). D. Kim, G. Choi, H.-S. Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 . H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 . Package: r-cran-epts Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eefanalytics, r-cran-lme4, r-cran-ggplot2, r-cran-ggpubr, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcvis, r-cran-dplyr, r-cran-magrittr, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epts_1.2.2-1.ca2404.1_all.deb Size: 192294 MD5sum: b431c68a321b0bba0286b8dc79992ad3 SHA1: d74544d0adabb614645eeec742563de1611d4ad1 SHA256: a280452eafee86eac05b9c42f34a9ac08819021bfcf3e0bc77f3604f91ee41a6 SHA512: bd8f38b5352d2c14c5803939ce6f3c50e5ca07598e82e9cf074c25496d422843081e4f75daf48781f37e3cb9147a2f531b69a0ba28a97c238aedd974e01c8fa2 Homepage: https://cran.r-project.org/package=epts Description: CRAN Package 'epts' (Educational Platform Trials Simulator) Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. 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The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'. Package: r-cran-epwshiftr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-eplusr, r-cran-fst, r-cran-future.apply, r-cran-jsonlite, r-cran-pcict, r-cran-progressr, r-cran-psychrolib, r-cran-r6, r-cran-rappdirs, r-cran-rnetcdf, r-cran-units Suggests: r-cran-testthat, r-cran-curl, r-cran-mockery, r-cran-withr, r-cran-pingr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epwshiftr_0.1.4-1.ca2404.1_all.deb Size: 476722 MD5sum: 52c435be7489bbe7e5215fd637f30cd2 SHA1: 408bbdcb02fba33a452df0b11f3812ab573252e8 SHA256: 26dd20c38002eea18ad70552154041998deecdc5aac50640c42d3de53412454c SHA512: b2f32155f3ff19b6cd0dcc9b97210d09db38097ab44f2181ff7d7297be0d7c39b9e5d0eec9f1f22f0dffc8c57579aa99fe2a7beb6d8ceef7f84a08fea9a171eb Homepage: https://cran.r-project.org/package=epwshiftr Description: CRAN Package 'epwshiftr' (Create Future 'EnergyPlus' Weather Files using 'CMIP6' Data) Query, download climate change projection data from the 'CMIP6' (Coupled Model Intercomparison Project Phase 6) project in the 'ESGF' (Earth System Grid Federation) platform , and create future 'EnergyPlus' Weather ('EPW') files adjusted from climate changes using data from Global Climate Models ('GCM'). Package: r-cran-epxtor Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-httr Filename: pool/dists/noble/main/r-cran-epxtor_0.4-1-1.ca2404.1_all.deb Size: 35260 MD5sum: 7138e9b01202cf8d8d8bcca64b800cd8 SHA1: 0d62e136cb54491ca91b67ca1c3aaf79fc95cf67 SHA256: 41463fd46a8094df16b7daebb2486ebde18c9e1fc64ea0c71017bdff6f1e253b SHA512: 9a05928b5b69f2648983724980508e305d09e7f8d6d8e08ee45f9210a78723c7cba6c04308fd7a360ea11ab72a29848d5c3abfddc47cbd0783917817ce167a81 Homepage: https://cran.r-project.org/package=epxToR Description: CRAN Package 'epxToR' (Import 'Epidata' XML Files '.epx') Import data from 'Epidata' XML files '.epx' and convert it to R data structures. Package: r-cran-eq5d Architecture: all Version: 0.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6475 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-testthat, r-cran-shiny, r-cran-dt, r-cran-mime, r-cran-readxl, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggiraphextra, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-fsa, r-cran-pmcmrplus, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-eq5d_0.17.0-1.ca2404.1_all.deb Size: 6247106 MD5sum: 94387313eecdf037b2ec9821e7a59f74 SHA1: b8b62b7d0862e765ed4cc22f355a453d9116f313 SHA256: 3bc37b152f7c8adbdd62ce3fa5db2cb3905ef6b5dd5ae7f734200ea6ba53ead9 SHA512: d0cbbf2f8a73f3086741461c2b36c6fce6b86f7390cd746833ff6779d268c1dec3d8e71cb64f2c481666d593adc3c04bfee29c4ea1520669a792b926378136be Homepage: https://cran.r-project.org/package=eq5d Description: CRAN Package 'eq5d' (Methods for Analysing 'EQ-5D' Data and Calculating 'EQ-5D' IndexScores) EQ-5D is a widely used health-related quality-of-life instrument developed by the EuroQol Group and used in the clinical and economic evaluation of health care. Health is described using five dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) rated on either a three-level (EQ-5D-3L and EQ-5D-Y-3L) or five-level (EQ-5D-5L) scale. Responses can be reported as EQ-5D health states or converted to utility index scores using country-specific value sets. The package provides methods for the valuation, reporting and analysis of EQ-5D data. Utility index scores can be calculated for EQ-5D-3L, EQ-5D-5L and EQ-5D-Y-3L data using a wide range of value sets and mapping approaches. Functionality is also provided for descriptive-system reporting, severity and distributional summaries, informativity measures, health-state distribution analysis, longitudinal change analysis, probability of superiority analysis and Health Profile Grid visualisation. Methods described in Devlin et al. (2020) are implemented where appropriate. A companion 'Shiny' application is included for interactive analysis and visualisation of EQ-5D datasets. Package: r-cran-eq5dsuite Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2201 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-ggplot2, r-cran-moments, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-rappdirs Suggests: r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-readxl, r-cran-spelling, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eq5dsuite_2.0.0-1.ca2404.1_all.deb Size: 1643068 MD5sum: 6c16e03122e2a488ed183993d81ac808 SHA1: ed912cdf7a8988ee8f8a8092b25ff88c1513e4e0 SHA256: d0b4410b1ad150fce4c55db00bea465cd36bdf17b6f40c0b6bea7fbfb74c0d8c SHA512: 235f34b0fa03a89682e51a2a06a6da95c0c0c08da7642f7494e29a76da26942b5709238033844f9d23691288161773f71b9f54e3349d2bc84101c313cdbbe86f Homepage: https://cran.r-project.org/package=eq5dsuite Description: CRAN Package 'eq5dsuite' (Handling and Analysing EQ-5d Data) The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), developed by the EuroQol group . It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) (mapping EQ-5D-3L responses to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. Additionally, the package provides tools to analyze EQ-5D data according to the recommended guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) . Package: r-cran-eql Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ttutils, r-cran-lattice Filename: pool/dists/noble/main/r-cran-eql_1.0-1-1.ca2404.1_all.deb Size: 112302 MD5sum: 2e9dd58064333096c88bfa2f1dfd9b2f SHA1: 01d76f0bd932d5887c5926082d3f6eebdeeaa4f1 SHA256: 862fa16f61c844f49c1404ce1fdd99dd66df61002d9f59a775c7407dad3a3246 SHA512: a2ea58b431910719ef932306c2098376e52f50a13368a91585ce07d6109628d18684ebb7a10dceb33e5785ef5c3b7198fb122793eb2a47a8c89e8678f7131ebb Homepage: https://cran.r-project.org/package=EQL Description: CRAN Package 'EQL' (Extended-Quasi-Likelihood-Function (EQL)) Computation of the EQL for a given family of variance functions, Saddlepoint-approximations and related auxiliary functions (e.g. Hermite polynomials). Package: r-cran-eqrn Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-dofuture, r-cran-evd, r-cran-foreach, r-cran-future, r-cran-ismev, r-cran-magrittr, r-cran-torch Filename: pool/dists/noble/main/r-cran-eqrn_0.1.2-1.ca2404.1_all.deb Size: 427370 MD5sum: 98deed6cb6b4d0248ca8b6dc071ce23a SHA1: fce6af49ab855532177fd06a57b0109deedcf693 SHA256: c898ffc1f16b6e6a57859eccd5727a98a860d5332847c6acf3b2afcb9da79cb1 SHA512: d59b406a83cbb0d3f656fae004290f77ba88341a3299e911956b611b605d4982a314832c9b82cb9546b434d7355dfb1ccc91348112235f41cf7132606eecbaad Homepage: https://cran.r-project.org/package=EQRN Description: CRAN Package 'EQRN' (Extreme Quantile Regression Neural Networks for Risk Forecasting) This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) (also available in preprint: Pasche and Engelke (2022) ). 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For details, see Goeman, Solari, & Stijnen (2010) and Isager & Fitzgerald (2024) . Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) . 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Gurusamy,K (2025). 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Gurusamy,K (2025) and Gurusamy,K (2025). Package: r-cran-equalprognosis Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-base64enc, r-cran-calibrationcurves, r-cran-mime, r-cran-predtools, r-cran-proc, r-cran-stringr Filename: pool/dists/noble/main/r-cran-equalprognosis_0.1.3-1.ca2404.1_all.deb Size: 257992 MD5sum: aaeae7434809a0ed273dc93e664e5583 SHA1: 1af91c9194679cdff0b2e641dcccb0ebb839f801 SHA256: 85912f654ca1572f94cc9c62d7b2d5a8d2e88c449f4e0bfb093d2bd532ffd5a9 SHA512: c5b0936cb639d0da3889fd5359a52120c67ff2a25bc72bd0071c32d609705ed767ab5a99192fb0d50ce286706e8655212406611529235cd76a7698273727b7bc Homepage: https://cran.r-project.org/package=EQUALPrognosis Description: CRAN Package 'EQUALPrognosis' (Analysing Prognostic Studies) Functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. This minor update fixes issues related to memory requirements with large number of simulations and deals with situations when there is overfitting of data. Gurusamy, K (2026). 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'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024). 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'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024). Package: r-cran-equaltestmi Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-semtools, r-cran-printr Filename: pool/dists/noble/main/r-cran-equaltestmi_0.6.1-1.ca2404.1_all.deb Size: 323016 MD5sum: 6da605c16a4c1a2ff72f46403af9b7b6 SHA1: 964c3566b224d832c33ad2af84d9df2b60e2e946 SHA256: 3072eb048e39306b4c2ebfe44681a1a6f568eda2828b165dbbe38c17e9a5626c SHA512: 84e7fbce95bbcc9d772458a6514471bfd2a35adc17dff998f50e20c66ccb6d1760812853835b39bace2acdc8dac6ac8a1bb60fa716df4e7db79997e51e4df37c Homepage: https://cran.r-project.org/package=equaltestMI Description: CRAN Package 'equaltestMI' (Examine Measurement Invariance via Equivalence Testing andProjection Method) Functions for examining measurement invariance via equivalence testing are included in this package. The traditionally used RMSEA (Root Mean Square Error of Approximation) cutoff values are adjusted based on simulation results. In addition, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts. For more information, see Yuan, K. H., & Chan, W. (2016) , Deng, L., & Yuan, K. H. (2016) , and Jiang, G., Mai, Y., & Yuan, K. H. (2017) . 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The 'XML' result can then be included in 'HTML', 'Microsoft Word' documents or 'Microsoft PowerPoint' presentations by using a 'Markdown' document or the R package 'officer'. 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Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided. 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Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) ). The package includes tests to assess the stability of the equating transformations (Battauz(2022) ). 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The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model. Package: r-cran-equibspd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-equibspd_0.1.0-1.ca2404.1_all.deb Size: 22012 MD5sum: 1a6037c025deabf41c568970d8cfbedb SHA1: 8be0469cae76879e1e2f0a1bdc48bfffb0459605 SHA256: 8951e71da772e3110c7a259c66e530c4f7984d23aa369b0dfad8cae62331dedc SHA512: a52b825d780351921cda60abcaa7cf0aa86e8071b89f36cc6f59a9cf7de47fd720c905758d0b1934300640cc76b4d830c0dbb43bb1d7f674f01a921e441ddcbc Homepage: https://cran.r-project.org/package=equiBSPD Description: CRAN Package 'equiBSPD' (Equivalent Estimation Balanced Split Plot Designs) In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) .Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD(). Package: r-cran-equil2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-units Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-equil2_1.1.0-1.ca2404.1_all.deb Size: 112728 MD5sum: c43c43cdafeb9a16389a516e8cc8a4a5 SHA1: 4c7ff63b38b11e19addb6f89ea7b01a7ec56a327 SHA256: 22bd849b5300ddba0887a62f38631de2d068a2457f849bb671d4f064f7a1e008 SHA512: f6b4c4ca285968ee3338bc3ea339bc7230cf67b92d4ea776ace7e106efcefdee35f2ccd242c3ed83406d661e01d2c1e20bbf407b005cbcbcc8868a93d42c733c Homepage: https://cran.r-project.org/package=equil2 Description: CRAN Package 'equil2' (Calculate Urinary Saturation with the EQUIL2 Algorithm) Saturation of ionic substances in urine is calculated based on sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate, oxalate, citrate, ph, and urate. This program is intended for research use, only. The code within is translated from EQUIL2 Visual Basic code based on Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation of urinary saturation" to R. The Visual Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic. Package: r-cran-equisurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-eha Filename: pool/dists/noble/main/r-cran-equisurv_0.1.0-1.ca2404.1_all.deb Size: 54962 MD5sum: e448ece8500b5b57f2e2369202ba70ca SHA1: 104e26144ddc3d41b09c740ce73f62f23bccec7a SHA256: 1b5dd1cc7d52c89873c8d87eb3eb9ee9e1b65432570fe87a6e927cc7ad420f13 SHA512: 4d77f5163db696022c1a61932d9a0d851b61b73632902d020b97f4f78fc36812efc1aaa701b785d25d5be996f9f1bfa0a813c925580591c92c9a76dcc8198ca1 Homepage: https://cran.r-project.org/package=EquiSurv Description: CRAN Package 'EquiSurv' (Modeling, Confidence Intervals and Equivalence of SurvivalCurves) We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data . 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The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results. 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Basically a variant of a t-test with (relaxed) null and alternative hypotheses exchanged. Package: r-cran-er Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3081 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggplot2, r-cran-scales, r-cran-gridextra, r-cran-glmnet, r-cran-pls, r-cran-plsvarsel Filename: pool/dists/noble/main/r-cran-er_1.1.2-1.ca2404.1_all.deb Size: 3091792 MD5sum: ff1d096beb7903c40ed3e1335c2fb0d4 SHA1: 73df491caf349a7872de8887a75027fe2c60a59a SHA256: 55972598b83d8b6c7cd497bb8bd85e71f74391257aa0bef30614c56760f02bad SHA512: c69bd99bb5617d64df47d9530cabb29d87e634cc92d3666a627f55027012b5b63b7f6de2126e4f814c3720f172aca325a15270601f9816e46bbc93e2913350f7 Homepage: https://cran.r-project.org/package=ER Description: CRAN Package 'ER' (Effect + Residual Modelling) Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) and EF Mosleth et al. 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Includes functions to conduct mediation and moderation analyses and to diagnose multicollinearity. URL: . BugReports: . Duxbury, Scott W (2021) . Long, J. Scott, and Sarah Mustillo (2018) . Mize, Trenton D. (2019) . Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) . Duxbury, Scott W (2018) . Duxbury, Scott W, Jenna Wertsching (2023) . Huang, Peng, Carter Butts (2023) . 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Package: r-cran-erp Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 942 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-irlba, r-cran-fdrtool, r-cran-mnormt, r-cran-pacman Suggests: r-cran-prettydoc, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-erp_2.2-1.ca2404.1_all.deb Size: 724390 MD5sum: 72f91402f356a76b97f41d07152b26fd SHA1: cbc886e1e7d4a70d32f90692137c7e774ea839f8 SHA256: 7ada95eca96b6436b669971cd53ff2cad8c1e640da581d1567519034c635ff4c SHA512: 5cecc71b55e452d2fcf5bc48cee9187f7fa99d4fc33eca8286fd1c2b270c2112c81fe9619bc4b01974c9e9f523244c66f0590edd26d976a8e19bb7ff0e004ffc Homepage: https://cran.r-project.org/package=ERP Description: CRAN Package 'ERP' (Significance Analysis of Event-Related Potentials Data) Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, ). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 ), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, ). 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Designed for ecological and genomic forecasting with climate or environmental covariates. Methods build on Bürkner (2017) for Bayesian regression via 'Stan', Friedman, Hastie, and Tibshirani (2010) for elastic net regularization, Wright and Ziegler (2017) for random forests, and Vehtari, Gelman, and Gabry (2017) for leave-one-out cross-validation. 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Package: r-cran-ertg3d Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1377 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circstats, r-cran-ggplot2, r-cran-pbapply, r-cran-plotly, r-cran-raster, r-cran-rastervis, r-cran-tiff Suggests: r-cran-knitr, r-cran-pander, r-cran-gridextra, r-cran-plyr, r-cran-rmarkdown, r-cran-sf, r-cran-sp, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-ertg3d_0.7.0-1.ca2404.1_all.deb Size: 1137046 MD5sum: 8b63fb83bc73299c4196d42e5c7a5e33 SHA1: e187d6754ac33c00c33479d26a68b8410f97fb9b SHA256: a639d97f901cea2ad8f6ca78c4f97cab47f7363a8baa9fb0a5065232179f7022 SHA512: ac93186d295d096fd1abfa2de731e7c967f7fb8fece2278e1c4ba568fd9a1704192bb498a71cbb897876d0eb0f2adfd1db87bee6d2b53832fc42038e48bd2fb8 Homepage: https://cran.r-project.org/package=eRTG3D Description: CRAN Package 'eRTG3D' (Empirically Informed Random Trajectory Generation in 3-D) Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories. Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. . Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. . Package: r-cran-ervissexplore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ervissexplore_0.1.0-1.ca2404.1_all.deb Size: 156356 MD5sum: b6a255330fec3cc1db9cfec068c153bd SHA1: 9d1b7616943bac48c29500782bfd04cbfd846b2a SHA256: d46bf2b8c1e6c71bcb687858fb54c7fe62cf2666c17bc139400caeb7c503ebe6 SHA512: ddcffbba15dd30b717ef5ff7380791b71c2f95e70f30ed1fb9b8c442716cc874a5b3d0d0b940628b1205f60bf88e9552af0056d1a19975a076de6c133ce252df Homepage: https://cran.r-project.org/package=ervissexplore Description: CRAN Package 'ervissexplore' (Retrieve and Explore ERVISS Respiratory Virus Surveillance Data) Provides easy access to ERVISS (European Respiratory Virus Surveillance Summary) data from the EU-ECDC . Enables retrieval, filtering, and optional visualization of data across European countries. Data is fetched directly from the EU-ECDC Respiratory Viruses Weekly Data repository, with support for both latest data and historical snapshots for reproducible analyses. Package: r-cran-es.dif Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-es.dif_1.0.2-1.ca2404.1_all.deb Size: 37750 MD5sum: 4a949a6f89f7f6ac9d01b0f1bdc9ce31 SHA1: fdafc0c34dc1731a7369f0145620350581429d1a SHA256: 183cb267546918fb35a7a5ceb1c3759624407f80aee74892952b5e715d2ea9fa SHA512: 7c2048bc801ccc09dde8f06718d38c88acc4453e6d11c564d55b4224cb9fc9024489ce12ddc0b5b01aaa3ff5ba85cee4a6b82c9ce2838e9ac3bb6c6f5eccc1fb Homepage: https://cran.r-project.org/package=es.dif Description: CRAN Package 'es.dif' (Compute Effect Sizes of the Difference) Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality). Package: r-cran-es Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-es_1.1-1.ca2404.1_all.deb Size: 40748 MD5sum: 121641cd64dc8bd29c21a8dae6b17074 SHA1: 2ed12db05f61f06a203fabb92f04427c3d308b12 SHA256: 21ea0c13ab6b7906f892c235d5e9d0fdccf6d04faeb1f2818ba86e2fcfa4ca7e SHA512: fd74d189e517e945af7260e87494ff89664f23a6c7ab01221d2ae202a27bd3cbde3e2248ed2de0833469104b2b2f2ddb48a929ec442a486431379cfa1ff676e2 Homepage: https://cran.r-project.org/package=ES Description: CRAN Package 'ES' (Edge Selection) Implementation of the Edge Selection Algorithm for undirected graph selection. The least angle regression-based algorithm selects edges of an undirected graph based on the projection of the current residuals on the two dimensional edge-planes. The algorithm selects symmetric adjacency matrix, which many other regression-based undirected graph selection procedures cannot do. Package: r-cran-esabcv Architecture: all Version: 1.2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-svd Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-esabcv_1.2.1.1-1.ca2404.1_all.deb Size: 1674032 MD5sum: 084babe9e8072da54c57ebc1398cf0e5 SHA1: 643108c6daeacc525d6cec920b9e8564028222bb SHA256: afa4e49557cf7cf8b3c06dce0feca8dc517da48abfcae4e178f3cbdb39947960 SHA512: 47ce5b83e9cf23ff498bb298419350c5d327fd3c2e0ddcd38a88d310f19d485d5f46053f7522cf5f7f131db66faa3207f0ae840044b12232a1fdc2f1f4b59d60 Homepage: https://cran.r-project.org/package=esaBcv Description: CRAN Package 'esaBcv' (Estimate Number of Latent Factors and Factor Matrix for FactorAnalysis) These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (). Package: r-cran-esaps Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-plyr, r-cran-readods, r-cran-readxl Filename: pool/dists/noble/main/r-cran-esaps_0.2.2-1.ca2404.1_all.deb Size: 255822 MD5sum: 8290f103d3f6afba8111b20d17cd2fab SHA1: d72c7ddd52d77a436091e9396b62bd8a6e6012a0 SHA256: 6f9dbda08c8f7cb005ebd5c2222b40b6e27cafe488cba7015538c1552c67474b SHA512: aad2897b565290d72c7281a6c8aeda0f761d7177b2dfbb4ce9922606fa8ae84ade7908cd5acd779c8c4e5344fd164a36aaf119554287ee451aeb40a6734e4ab7 Homepage: https://cran.r-project.org/package=esaps Description: CRAN Package 'esaps' (Indicators of Electoral Systems and Party Systems) It allows structuring electoral data of different size and structure to calculate various indicators frequently used in the studies of electoral systems and party systems. Indicators of electoral volatility, electoral disproportionality, party nationalization and the effective number of parties are included. Package: r-cran-esback Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-esreg Filename: pool/dists/noble/main/r-cran-esback_0.3.1-1.ca2404.1_all.deb Size: 110062 MD5sum: 66fb3f773c8b110790a66e7866eb4b3d SHA1: b5a1299592ebd341d0711bad8dbc89cbe7983f25 SHA256: ee6f7397a94978d6071b5d06e53929ac1144e697d5d3234a117094ccbb1a7245 SHA512: 5ef0cbb2f697564a5b2bfb5967f88eee81def1d7b53f26410fd52d59a59109ad028cbf23254b7d94003676c9ae002b90c07986033c9041ba406c4fef5c184e23 Homepage: https://cran.r-project.org/package=esback Description: CRAN Package 'esback' (Expected Shortfall Backtesting) Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk of investments. A special feature of the backtests of Bayer and Dimitriadis (2020) is that they only require forecasts of the expected shortfall, which is in striking contrast to all other existing backtests, making them particularly attractive for practitioners. Package: r-cran-esc Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-esc_0.5.1-1.ca2404.1_all.deb Size: 171782 MD5sum: 9d7fa8da98d05af3666259f1d990564f SHA1: dea33f9a1080d8916962e1b75a8dad9869dd2fad SHA256: 12c970929ee99190b2178b8a34b8870908c2b395b442a77b49f0843b0eea4af7 SHA512: 55562aebd50c660bccdb92a34de16a86d55b40449340f2dbfa0bb7172609d919d26f0ce5cafcf5490b3d69977d031d64f2256339925dd3337a8b8255a74b80f9 Homepage: https://cran.r-project.org/package=esc Description: CRAN Package 'esc' (Effect Size Computation for Meta Analysis) Implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson () in R. Based on the input, the effect size can be returned as standardized mean difference, Cohen's f, Hedges' g, Pearson's r or Fisher's transformation z, odds ratio or log odds, or eta squared effect size. Package: r-cran-escalation Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17068 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-gtools, r-cran-dfcrm, r-cran-boin, r-cran-trialr, r-cran-diagrammer, r-cran-rcolorbrewer, r-cran-viridis, r-cran-binom, r-cran-r6, r-cran-mvtnorm, r-cran-iso, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-escalation_0.2.3-1.ca2404.1_all.deb Size: 1706898 MD5sum: 47f8881a2eda0b8d45d0b2d2d6d987ee SHA1: cd10222f5c9dfcbeaf96a75889aa8410f01850d6 SHA256: 6741ab2c8754c4038cc665e0df288dc97fda6c153064deb38917e781792d66ea SHA512: 484f2b7896ac7a71704ca8001bec957a8b5cfa125839dfacabe6fa8a2c95f08a08caed6f1071ebe530de8abdc715230951057f504c25a6a00bb8790e9fe3a2e7 Homepage: https://cran.r-project.org/package=escalation Description: CRAN Package 'escalation' (A Modular Approach to Dose-Finding Clinical Trials) Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) , the toxicity probability interval (TPI) design by Ji et al. (2007) , the modified TPI (mTPI) design by Ji et al. (2010) , the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) , EffTox by Thall & Cook (2004) ; the design of Wages & Tait (2015) , and the 3+3 described by Korn et al. (1994) . All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients. Package: r-cran-escaper Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-escaper_0.1.0-1.ca2404.1_all.deb Size: 203882 MD5sum: af83f722d75f03791188653f7c719718 SHA1: 53b383a0fccfb696b1a74406968d1f8d5ae7f97f SHA256: eb4196756b3441c647971aecab60f0783d41e3d0f85022aae258a835650cd52a SHA512: 3e7429deeb4a8b8ccfafdee6678c875c8c04ceb7e67a85462b659b88e7ef600b12a47d75b8367cb0b6f2692246e0fbc38873ec3b381daa99c6ebc20f839f2eed Homepage: https://cran.r-project.org/package=escapeR Description: CRAN Package 'escapeR' (Escape Room Adventures for Learning R in Ecological Statistics) A lightweight classroom game where students learn R by solving ecological-statistics puzzles inside a virtual escape room. The package remembers each player's progress, offers hints, and uses tasks inspired by introductory R teaching material, numerical ecology, ecological modelling, and distance sampling. Package: r-cran-esci Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggbeeswarm, r-cran-ggdist, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-jmvcore, r-cran-legendry, r-cran-metafor, r-cran-mathjaxr, r-cran-multcomp, r-cran-r6, r-cran-rdpack, r-cran-rlang, r-cran-sadists, r-cran-statpsych, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-esci_1.0.10-1.ca2404.1_all.deb Size: 3042850 MD5sum: dc52a69d40fe55efcc4705670346636a SHA1: f4ffc794f27775e4dd9967fd60cae1bed605a072 SHA256: 2758d0e3508c7ff4771607afb3d765da55a63d886e57e44616fa32376d74b8d5 SHA512: a8fbde4b3bccb41025e2762389c3da22aed4ded5c1316c1b14a297b8ff632074a4136b5161046c72317f15cbb3fa32e1d9bfc835711bf09fb8e079d7a6a49837 Homepage: https://cran.r-project.org/package=esci Description: CRAN Package 'esci' (Estimation Statistics with Confidence Intervals) A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'. This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions. Data sets are included for all examples from Cumming & Calin-Jageman (2024) . Package: r-cran-escvtmle Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-superlearner, r-cran-origami, r-cran-dplyr, r-cran-tidyselect, r-cran-mass, r-cran-stringr, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-escvtmle_0.0.2-1.ca2404.1_all.deb Size: 123154 MD5sum: e2238ba94a0bc410bdeecf0df4065e12 SHA1: 2f90f669136dd2ce1b3f09a1c83b280adc019dda SHA256: dd634d683698fb6be97fff5fd1f26e34f13db706ebfb63d0c5d4983958b68658 SHA512: f2d1db6fb13e8955818e299097db4c4c660cf065921264ea6acd140b821c51784f100e32cd112b982ec5d57358657dcba06793779f0b66303bb8430146241a85 Homepage: https://cran.r-project.org/package=EScvtmle Description: CRAN Package 'EScvtmle' (Experiment-Selector CV-TMLE for Integration of Observational andRCT Data) The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) . Package: r-cran-esdesign Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-esdesign_1.0.3-1.ca2404.1_all.deb Size: 124434 MD5sum: a808811eb199d7e09eecda66621230f2 SHA1: 0209e028c0a6cfd3dabe491583b6f9a740495830 SHA256: 6b74217031c29a936cc2a34c460911f17c82eb24e8e00df28f51d6d5e224a993 SHA512: 13f6891b829f88883b304f03d35a6a31fc9aa8d904d43c5cf6d4c365af1e3aa862729660fdf3a97c994d487550d94301b1dc566632a5c2cb7698c3d02761bd1f Homepage: https://cran.r-project.org/package=esDesign Description: CRAN Package 'esDesign' (Adaptive Enrichment Designs with Sample Size Re-Estimation) Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation presented in Lin et al. (2021) . In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) , the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR. Package: r-cran-esdm Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rocr, r-cran-sf, r-cran-shiny, r-cran-units Suggests: r-cran-colorramps, r-cran-colourpicker, r-cran-dichromat, r-cran-dt, r-cran-knitr, r-cran-leafem, r-cran-leaflet, r-cran-maps, r-cran-raster, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinyjs, r-cran-testthat, r-cran-tmap, r-cran-viridis, r-cran-zip Filename: pool/dists/noble/main/r-cran-esdm_0.4.4-1.ca2404.1_all.deb Size: 3687736 MD5sum: 7998f4c443391c83f3f892b4d7597b10 SHA1: b9fb03c54c55c6b2413dedd5a862de035e063517 SHA256: 752bca6c80961b1c3d33b2987cd14795a47aa27e847351d76965caaa7d7393ec SHA512: cd744147590ab3c1cf1d9330252e384b234945c738ad24f504110f01838c01843c38444312a4273855b058db9b0f3d780829078f843061c1a1dd035a81d414bc Homepage: https://cran.r-project.org/package=eSDM Description: CRAN Package 'eSDM' (Ensemble Tool for Predictions from Species Distribution Models) A tool which allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions. The package is further described in Woodman et al (2019) . Package: r-cran-eselect Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-comparedesign Filename: pool/dists/noble/main/r-cran-eselect_1.1-1.ca2404.1_all.deb Size: 67704 MD5sum: 8011af9e6331257e543da58e333d6d41 SHA1: 7d60db8938d001551a8f82f70ebcaa5e4991d6b7 SHA256: 539d6459bfd81de24bacc2de557baaa796ce4bfa31a785a0b833f6599871db95 SHA512: 94fd251069868503d81c2ec34ef4bbfcb7911661aeb1a62831663d950db366bb94da1f48f6ebcbe8ac7c9f1a8e83bc754b950fc8073f93acc7c331cb680df47b Homepage: https://cran.r-project.org/package=eselect Description: CRAN Package 'eselect' (Adaptive Clinical Trial Designs with Endpoint Selection andSample Size Reassessment) Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). . Package: r-cran-esem Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gparotation, r-cran-lavaan, r-cran-magrittr, r-cran-psych, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-esem_2.0.0-1.ca2404.1_all.deb Size: 111616 MD5sum: e59e7443468edc7f084008ab6504dfa0 SHA1: c724c81645c57336d40c07770b784c77a33bae68 SHA256: e43596c7735549e3b8af6fc13513f175aadebe6c245464c3b02d64d5b97ac489 SHA512: e866d1123fd068e06ba14d92388612e33374ee28b4b66351557d5d096147ff5dba1e095f13ecacf63b8eb1c990a17b36b9ecaa271b72605fc2cf107935a4a310 Homepage: https://cran.r-project.org/package=esem Description: CRAN Package 'esem' (Exploratory Structural Equation Modeling ESEM) A collection of functions developed to support the tutorial on using Exploratory Structural Equiation Modeling (ESEM) (Asparouhov & Muthén, 2009) ) with Longitudinal Study of Australian Children (LSAC) dataset (Mohal et al., 2023) . The package uses 'tidyverse','psych', 'lavaan','semPlot' and provides additional functions to conduct ESEM. The package provides general functions to complete ESEM, including esem_c(), creation of target matrix (if it is used) make_target(), generation of the Confirmatory Factor Analysis (CFA) model syntax esem_cfa_syntax(). A sample data is provided - the package includes a sample data of the Strengths and Difficulties Questionnaire of the Longitudinal Study of Australian Children (SDQ LSAC) in sdq_lsac(). 'ESEM' package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data. Package: r-cran-esg Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-esg_1.3-1.ca2404.1_all.deb Size: 180236 MD5sum: 8ca88fffde81f8cb498a7c0916010904 SHA1: ee0c174c1f2715371bd4d3518c18f4cc10179ec9 SHA256: b9b293b7dce86c1484517a1c1cd4324ad18c9d3e9d8ae0758d2ce75c3a687cb3 SHA512: 7d6f43c45f21f088491207f435ce9a126106a48bdba6b58c051e87579564fb7a2f538e38f8f48e19940abc0bce4a7358beba90cecfb9afab942bbf75d411d47e Homepage: https://cran.r-project.org/package=ESG Description: CRAN Package 'ESG' (A Package for Asset Projection) Presents a "Scenarios" class containing general parameters, risk parameters and projection results. Risk parameters are gathered together into a ParamsScenarios sub-object. The general process for using this package is to set all needed parameters in a Scenarios object, use the customPathsGeneration method to proceed to the projection, then use xxx_PriceDistribution() methods to get asset prices. Package: r-cran-eshrink Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-eshrink_0.2.0-1.ca2404.1_all.deb Size: 66732 MD5sum: 9df5bb94d89a5f24279af5d4d29e4b43 SHA1: eb0862d43f7f8b7bf1e8db8b9f72a1209dc29a58 SHA256: 7196a3130c5d92f009434971a7ebc5ee4fd101e7859e4bf4a9d39fd55fd98676 SHA512: 542fa43014b8a3e8c2d6034bec71fe19288ba77259e472e2d228ef7f026d7d6b09db0a3a0df2588e122d68f847b9bb88b539e6ec2634a34b31a085049f27803f Homepage: https://cran.r-project.org/package=eshrink Description: CRAN Package 'eshrink' (Shrinkage for Effect Estimation) Computes shrinkage estimators for regression problems. Selects penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) for more details. 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There are several functions available by 1) including a time-varying transmission modifier, 2) adding a time-dependent quarantine compartment, 3) adding a time-dependent antibody-immunization compartment. Wang L. (2020) . Package: r-cran-esmisc Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-ggplot2, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-esmisc_0.0.3-1.ca2404.1_all.deb Size: 416742 MD5sum: 318f0e402bf90babd5417d993e740361 SHA1: 2ee87eb9ba217ead27cd8549e4c6cfe1fdd94297 SHA256: 1773428dd6024c49905d36c317a7f0d72ae72d966e2484220c860a53a842b728 SHA512: 92f1b75a880d7ece1fbfc22eab6cc702f6263b2237fefd9352aeb66ca9fb86ca7d5a0f62e135586de4a73b53a4c28d4051c65fe2d63e3d241192aa445c844c66 Homepage: https://cran.r-project.org/package=esmisc Description: CRAN Package 'esmisc' (Misc Functions of Eduard Szöcs) Misc functions programmed by Eduard Szöcs. Provides read_regnie() to read gridded precipitation data from German Weather Service (DWD, see for more information). 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The package is intended to support textbook examples by distributing data in a form that is easy for students and instructors to access within R. Current functionality includes packaged datasets and convenience wrappers for functions from 'ez', 'pwr', and 'WebPower' for analysis of variance and statistical power calculations. Package: r-cran-essurvey Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-haven, r-cran-rvest, r-cran-tibble Suggests: r-cran-foreign, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-essurvey_1.0.8-1.ca2404.1_all.deb Size: 126682 MD5sum: 217bd27883b34f99151c4d21c6822bce SHA1: 2a3cf51465a35bba31907f22160a7e2d6c1fe43f SHA256: c1ae51d02c462d9c67ade9713cd5437d8ed508bb42f51d56ae9a512426957cd3 SHA512: 8d6eeebd383a5b6a47332d610d7392e329c723fa0dd788a649dfe931d1d7d4f46b448ccf4a09b67a528b210f6e8a360adbacb15b9e866d621bca50a33324e8b3 Homepage: https://cran.r-project.org/package=essurvey Description: CRAN Package 'essurvey' (Download Data from the European Social Survey on the Fly) Download data from the European Social Survey directly from their website . 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Package: r-cran-estadistica Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2039 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-plotly, r-cran-ggplot2, r-cran-rio, r-cran-shiny, r-cran-shinydashboard, r-cran-knitr, r-cran-forecast, r-cran-openxlsx, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-estadistica_1.2.2-1.ca2404.1_all.deb Size: 1243632 MD5sum: f8e7bc2450eaefa856edf596d420bcdc SHA1: 55dd498864c2732f0a54bf8e2b107b7f9a0ef1d1 SHA256: 882926900485d2ccaef83091ecd7e4409e622acb47bb2bb969cfe169976a00fc SHA512: e2c953d4bfe23420d4d1006cc5d8b2ace600009020aa8bab7f8947b3a83408456172a8030e827954acb6eac6a59064ff0da3e117ece2d1ea8f47289279ee70d1 Homepage: https://cran.r-project.org/package=estadistica Description: CRAN Package 'estadistica' (Fundamentos de estadística descriptiva e inferencial) Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadística descriptiva e inferencial. Las funciones contenidas en el paquete 'estadistica' cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package 'estadistica' cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) . 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There are two sets of functions. The first set corresponds to functions that measure stability at any level of organisation, from individual to community and can be applied to a time series of a system’s state variables (e.g., body mass, population abundance, or species diversity). The properties included in this set are: invariability, resistance, extent and rate of recovery, persistence, and overall ecological vulnerability. The second set of functions can be applied to Jacobian matrices. The functions in this set measure the stability of a community at short and long time scales. In the short term, the community’s response is measured by maximal amplification, reactivity and initial resilience (i.e. initial rate of return to equilibrium). In the long term, stability can be measured as asymptotic resilience and intrinsic stochastic invariability. Figueiredo et al. (2025) . Package: r-cran-estatapi Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-purrr, r-cran-readr, r-cran-dplyr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-keyring Filename: pool/dists/noble/main/r-cran-estatapi_0.4.0-1.ca2404.1_all.deb Size: 43246 MD5sum: 246416ea13496f89722d67a2a25f15d9 SHA1: da5d3db3cf5e615d63007f3a06c811bae2c1a413 SHA256: 1a0c062f82dbee179ff0e763b3814b524d49058ba188702296107b93e463f24d SHA512: d51e4943435cb2dc452f3953c091a2dbc59299ab9fb6b0a088c6ed49f283751798c94c178acdde85feba6fbacdebcf137168fab04b7cc165a4ebcdcc3e976c14 Homepage: https://cran.r-project.org/package=estatapi Description: CRAN Package 'estatapi' (R Interface to e-Stat API) Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government. 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Tsagris M. and Papadakis M. (2025). . Package: r-cran-estbanr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-estbanr_0.1.1-1.ca2404.1_all.deb Size: 92062 MD5sum: 543bea291081544099b12c10d36a6b1e SHA1: d8bacbacc77b417c8795261beed736dfe0f87fcb SHA256: de2c672a09680ec009e58df6c87fb05a3fbcb862b6c855af01301f0a59558ec2 SHA512: 039aaf113671bf5eb84ab37264dea18af65b866503df989f4fe25f43fc4b15c4a19bd264215e40af15dd8b7a1753a84d8e9b00ab838d99e0dc9ca6010a9c141c Homepage: https://cran.r-project.org/package=estbanr Description: CRAN Package 'estbanr' (Brazilian Monthly Banking Statistics by Municipality (ESTBAN)) Download, read and tidy the ESTBAN (Estatistica Bancaria Mensal por Municipio, Monthly Banking Statistics by Municipality) files published by the Brazilian Central Bank (Banco Central do Brasil) for every bank branch and municipality in Brazil. Each file reports balance-sheet accounts of the COSIF (Plano Contabil das Instituicoes do Sistema Financeiro Nacional, the chart of accounts of the Brazilian financial system) such as credit operations, deposits and savings. Files are fetched from the official site with an idempotent local cache, read from their Latin-1 encoded CSV (comma-separated values) layout into tibbles, optionally filtered by state, and aggregated by municipality. Includes tools to detect and impute institution-month non-reports (an institution present in the file with every account equal to zero), which would otherwise be mistaken for zero balances. 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Package: r-cran-estempmm Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4265 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-mass, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-estempmm_0.5.0-1.ca2404.1_all.deb Size: 2701236 MD5sum: 690b0f7b2410730bddb77359fe1ca029 SHA1: 241d9debd71654af9f1dab8adc71e87040a59bab SHA256: 9345d4a80169592d1bc499ab8ada2e315a1996b9e3f53c7b4e01239270c226cf SHA512: ec1ac19e8dac8ae8b94684861bd00e9cf8ca3d5af36cc153212288198f0ef2ad970b5a9a6c033509599e9e0af8cf87b9ba3c2402a919f8a17b0ab5cd0a7ef451 Homepage: https://cran.r-project.org/package=EstemPMM Description: CRAN Package 'EstemPMM' (Polynomial Maximization Method for Non-Gaussian Regression) Implements the Polynomial Maximization Method ('PMM') for parameter estimation in linear and time series models when error distributions deviate from normality. The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary least squares ('OLS') when errors exhibit significant skewness. The 'PMM3' variant (S=3) targets symmetric platykurtic error distributions, reducing variance when excess kurtosis is negative. Includes automatic method selection ('pmm_dispatch'), linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference. Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) , Zabolotnii, Tkachenko, and Warsza (2022) , and Zabolotnii, Tkachenko, and Warsza (2023) , and Zabolotnii (2025) . Package: r-cran-ester Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2328 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-lme4, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ester_0.2.0-1.ca2404.1_all.deb Size: 1107524 MD5sum: c5cbce2808715610a4724f559a75cecd SHA1: c14d5e4fca516540e3914560a37400246293f264 SHA256: 8d960b323327e1d58671c88d4c0f9fd9ecb0a30fde3bcc18ecde2cf5ff96add9 SHA512: 1b4f3ec9d10593af5f0b0655bb98ad304053b2becddecf4afe5dea5e2d14cb5b773f238f77cebb5777bf20bfaf787dcc7707acfdba2bef0fda52edc41329c718 Homepage: https://cran.r-project.org/package=ESTER Description: CRAN Package 'ESTER' (Efficient Sequential Testing with Evidence Ratios) An implementation of sequential testing that uses evidence ratios computed from the weights of a set of models. These weights correspond either to Akaike weights computed from the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) and following Burnham & Anderson (2004, ) recommendations, or to pseudo-BMA weights computed from the WAIC or the LOO-IC of models fitted with 'brms' and following Yao et al. (2017, ). Package: r-cran-esthtseed Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-esthtseed_0.1.0-1.ca2404.1_all.deb Size: 29524 MD5sum: f0742fe5c161b76317457bb36b75308e SHA1: 570fd7eaf00224b6996a138031f94937bf2a76ab SHA256: e03ec90d4fb32d2f529c0aa37d5c8173a514350bb44775b09597c55c38911c75 SHA512: 5764dd4f97632be24cf0e8e778e31fbc855c43b5011543ea6cf55de04549f1ccf9e6398caec7580639ca14540fa8066c9bdbd5add36ef6bd4be77002d29c801d Homepage: https://cran.r-project.org/package=EstHTSeed Description: CRAN Package 'EstHTSeed' (Hydro Time Analysis of Seed Germination) Dry seed germinates by imbibing water from soil where the physiological process of germination starts after sufficient water has been imbibed by the seed. The germination time of the seed is inversely proportion to the difference between soil water potential and the base seed water potential which is described by hydro time model (Bradford, 2002 ). The parameters of the model like speed of germination, stress tolerance, uniformity of germination are unknown fixed values (Ghosh et al., 2026 ) which are to be estimated using statistical regression model where the validity of the adopted statistical model has been established theoretically. The package will help to estimate the tuning parameter for proportion of viable seeds along with standard error and p- values for inference. Package: r-cran-estimability Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-estimability_2.0.0-1.ca2404.1_all.deb Size: 326424 MD5sum: 53b971d28b43528ea3c70a1dc9c8c7ae SHA1: 183d19ea25f4c4773ab098a0fdcb5423658c8240 SHA256: 391569d876398724ec1758942632ace6e70871496ea72f8ee1befa8f9cc296d0 SHA512: 454a7144c5bb9dfd4cdc1e0317c820a3317c4d225cc5fad0a6a1417e75962bc2b9db41dd0c646d30c84ef01be7251fd1193aa9f38da1eaa94982bbb682a3ed81 Homepage: https://cran.r-project.org/package=estimability Description: CRAN Package 'estimability' (Tools for Assessing Estimability of Linear Predictions) Provides tools for determining estimability of linear functions of regression coefficients, and 'epredict' methods that handle non-estimable cases correctly. Estimability theory is discussed in many linear-models textbooks including Chapter 3 of Monahan, JF (2008), "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4). Package: r-cran-estimatebreed Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-broom, r-cran-purrr, r-cran-ggrepel, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-nasapower, r-cran-tidyr, r-cran-viridis, r-cran-cowplot, r-cran-sommer, r-cran-lme4, r-cran-minque, r-cran-car, r-cran-lmtest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-dt Filename: pool/dists/noble/main/r-cran-estimatebreed_1.0.2-1.ca2404.1_all.deb Size: 342656 MD5sum: 58440ae184dd31472e0e85846374d94e SHA1: d88fc78205d9f1c29ac4162154760a8449575082 SHA256: cb7976c448b0b09d156cf91a3dc294ec3865679c9e03a7c2b890cf397a9d8201 SHA512: 6b2a20d268e363a34befe7ed3dc0d42559cf69e3bfac7b3faf163b96e5a8bc81ee6ff95cf9c6895269f7de21fbd987b67aa2ea2b03803252159ac21127dfc5d4 Homepage: https://cran.r-project.org/package=EstimateBreed Description: CRAN Package 'EstimateBreed' (Estimation of Environmental Variables and Genetic Parameters) Performs analyzes and estimates of environmental covariates and genetic parameters related to selection strategies and development of superior genotypes. It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) , Stress indices for genotype selection referenced by Ghazvini et al. (2024) , the Environmental Stress Index described by Tazzo et al. (2024) , industrial quality indices of wheat genotypes (Szareski et al., 2019), , Ear Indexes estimation (Rigotti et al., 2024), , Selection index for protein and grain yield (de Pelegrin et al., 2017), , Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) , estimation of Leaf Area Index (Meira et al., 2015) , Restriction of control variability (Carvalho et al., 2023) , Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) . Package: r-cran-estimategroupnetwork Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-qgraph, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-mvtnorm, r-cran-jgl, r-cran-psych Filename: pool/dists/noble/main/r-cran-estimategroupnetwork_0.3.1-1.ca2404.1_all.deb Size: 103262 MD5sum: df7a2c503d82d2c16f7be964689b1f8d SHA1: a712411ef5137ef6489159e1fc82801ebb237768 SHA256: 664f7dd0a799d0536ea6fd7c0ecb3d7b3d44c4ee9a99961b74ecd2093260d836 SHA512: 520a4f440917cfa04b10e0196f9f6fc2c6dc2bb9ce753dee70e47dcb51f25d83ac241ca59318cdfe6fd6fe230e077488427172116f9a53500a2aecf4c4ac291d Homepage: https://cran.r-project.org/package=EstimateGroupNetwork Description: CRAN Package 'EstimateGroupNetwork' (Perform the Joint Graphical Lasso and Selects Tuning Parameters) Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation. Package: r-cran-estimatew Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-plot.matrix, r-cran-r6 Filename: pool/dists/noble/main/r-cran-estimatew_0.2.0-1.ca2404.1_all.deb Size: 331956 MD5sum: c69fd59e2519195f6746c1c04635ecbf SHA1: f01ac90bd05caf71478192ffd84f7f12ab7ddd78 SHA256: 20a7240db2dce43635bee44f6a13a6233d3972ef38c382bba9753ab4f59af08f SHA512: 61750069e6ed1a3d8bd0f7de2e37163a77248c5925253082954f3b04cf56d92d289f30cdd0cbcfa043b439d77dfa9d02c3fc7b640ada3e3468238815639fa4c2 Homepage: https://cran.r-project.org/package=estimateW Description: CRAN Package 'estimateW' (Estimation of Spatial Weight Matrices) Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) . Package: r-cran-estimationtools Architecture: all Version: 4.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-deoptim, r-cran-bbmisc, r-cran-ga, r-cran-rdpack, r-cran-numderiv, r-cran-boot, r-cran-autoimage, r-cran-stringr, r-cran-gaussquad, r-cran-car Suggests: r-cran-rmpfr, r-cran-gamlss.dist, r-cran-knitr, r-cran-rmarkdown, r-cran-adequacymodel, r-cran-readr, r-cran-covr, r-cran-testthat, r-cran-vdiffr, r-cran-spelling, r-cran-lifecycle, r-cran-matrixstats, r-cran-lintr, r-cran-v8 Filename: pool/dists/noble/main/r-cran-estimationtools_4.3.1-1.ca2404.1_all.deb Size: 378676 MD5sum: 61096c18b81d6eb5307f06c2d732f8ea SHA1: c1443af365db4cd9daba0fd0813b8c4bbeb39f16 SHA256: 3429ca2f2c24da58273ec605131a3841bfd795ea69ae09584c9184e734119ec1 SHA512: 99294ea81161e8a6f2d133b4c89281f2c02673d644f414ab20dbca24a61460eed1ecc89d3f08f5c986728b8338d00648b8120e09d9caa1398c1bc725914e74ff Homepage: https://cran.r-project.org/package=EstimationTools Description: CRAN Package 'EstimationTools' (Maximum Likelihood Estimation for Probability Functions fromData Sets) Total Time on Test plot and routines for parameter estimation of any lifetime distribution implemented in R via maximum likelihood (ML) given a data set. It is implemented thinking on parametric survival analysis, but it feasible to use in parameter estimation of probability density or mass functions in any field. The main routines 'maxlogL' and 'maxlogLreg' are wrapper functions specifically developed for ML estimation. There are included optimization procedures such as 'nlminb' and 'optim' from base package, and 'DEoptim' Mullen (2011) . Standard errors are estimated with 'numDeriv' Gilbert (2011) or the option 'Hessian = TRUE' of 'optim' function. Package: r-cran-estimators Architecture: all Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-ggh4x, r-cran-ggplot2, r-cran-matrix, r-cran-progress Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-estimators_0.8.5-1.ca2404.1_all.deb Size: 572254 MD5sum: 32255ff155832c3f2b4b0974c5a65ed8 SHA1: 93b02de318c160eac5327d73c8030c3336f69233 SHA256: 651c686a327914db5ac81d30ab96757094053e567a7b1f4d85aa94b8f06e1f1c SHA512: 5c4786b8a03fca3dd1715d29827be0620436098df3eb8edad1ec8047de91e4d77638e2f2c1b22f3e025c66d9ddcd63d1615c4e88eea30ec7793d4c2a2bd487c5 Homepage: https://cran.r-project.org/package=estimators Description: CRAN Package 'estimators' (Parameter Estimation) Implements estimation methods for parameters of common distribution families. The common d, p, q, r function family for each distribution is enriched with the ll, e, and v counterparts, computing the log-likelihood, performing estimation, and calculating the asymptotic variance - covariance matrix, respectively. Parameter estimation is performed analytically whenever possible. Package: r-cran-estimdiagnostics Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-reshape2, r-cran-ggplot2, r-cran-goftest, r-cran-testthat, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-estimdiagnostics_0.0.3-1.ca2404.1_all.deb Size: 99200 MD5sum: 3ff1a3872a1e01e395fd9ad4a0182bd5 SHA1: 84afd85dc0ae66538fce02623cd6d566511e0284 SHA256: b6b2db3324b874e02f0b8081700d179f32d7c22600adafb9244673c9044f7d3d SHA512: 732acdab395ee20041d8cfad66df4fd9c6c38a8498a2812e22863a5f459d099ee65afef6c0eccdc23101ad77d86705bdfabd5fb636df5d46cd441da2d26e39ca Homepage: https://cran.r-project.org/package=EstimDiagnostics Description: CRAN Package 'EstimDiagnostics' (Diagnostic Tools and Unit Tests for Statistical Estimators) Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance. Package: r-cran-estimraw Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-estimraw_1.0.0-1.ca2404.1_all.deb Size: 21536 MD5sum: 3db1368d4345da1a3c3b9b31329e6174 SHA1: 338cc38dc7d981aa97a8dfd2bb32e0b0b5bf6150 SHA256: cf009df6e7fd37d0400702a18de4c2928718839c92f612033d049a5c1c992854 SHA512: 1969bd167c1580c8ec2bea0819c71857a00491749d654caf068ccd838dc1012d3cde4ef84132cbddd62bf4d4f46b0f2774b7ee48ced633b8387be5c97ee437f9 Homepage: https://cran.r-project.org/package=estimraw Description: CRAN Package 'estimraw' (Estimation of Four-Fold Table Cell Frequencies (Raw Data) fromEffect Size Measures) Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) . Package: r-cran-estmeansd Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metablue Filename: pool/dists/noble/main/r-cran-estmeansd_1.0.1-1.ca2404.1_all.deb Size: 109560 MD5sum: c69bb458e8968f363da81f91fd4eef7e SHA1: 326a9dc3ca02344a21b6684bc065253a6f9c3a18 SHA256: 2d583f3a040bc7f52b9b45044f0639c44d39e636c77f73110e7846946ee560ba SHA512: a6cf521f26e38e8eb497ed74a246e412362dba552112d8ce09ac3e8ac5ae087629d4022d26ff3bf0f41d4c0c8083e14cd38b6bc4c6585393611fc4a5f7b89e7a Homepage: https://cran.r-project.org/package=estmeansd Description: CRAN Package 'estmeansd' (Estimating the Sample Mean and Standard Deviation from CommonlyReported Quantiles in Meta-Analysis) Implements the methods of McGrath et al. (2020) and Cai et al. (2021) for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. These methods can be applied to studies that report the sample median, sample size, and one or both of (i) the sample minimum and maximum values and (ii) the first and third quartiles. The corresponding standard error estimators described by McGrath et al. (2023) are also included. Package: r-cran-estprod Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lazyeval, r-cran-boot, r-cran-minpack.lm, r-cran-formula, r-cran-gmm Filename: pool/dists/noble/main/r-cran-estprod_1.2-1.ca2404.1_all.deb Size: 124198 MD5sum: 8f04afdd09c8b38f3d5faf14b299c108 SHA1: 37039409369b10629211f69a1da94063f7906ef9 SHA256: a70a2cee09912b2eac292ba2920cb05ee91ad29a16df811ca46ac158da6bc511 SHA512: 4a01b717cd1b0c2389bf1c9e48d2d9d9b8f8825dee4778d363bdafb23e152e23c5e451fab7b1d7f22e5d7918bace252bf367a8738406c37631943a93a23e3ab7 Homepage: https://cran.r-project.org/package=estprod Description: CRAN Package 'estprod' (Estimation of Production Functions) Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. The package aims to reproduce the results obtained with the Stata's user written opreg and levpet commands. The first was originally proposed by Olley, G.S. and Pakes, A. (1996) . The second by Levinsohn, J. and Petrin, A. (2003) . And the third by Wooldridge (2009) . Package: r-cran-esvis Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-hmisc, r-cran-tibble Suggests: r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-esvis_0.3.1-1.ca2404.1_all.deb Size: 1196328 MD5sum: 291dcd9ae7b63580c6fd4c14bdbb54d7 SHA1: a9121d1619d971b461131ea27be28621b48ae388 SHA256: bd95c536431987f99a2d6df4a46cfb8a7f886a7006bebcdb1516e0b845a396cc SHA512: 9d645be5218bc4b29d5aa3d2dcbaa2ddceea424ae550ff836b2c9e9864584a6dedaab87491f5eaeb47ffae02bcbecd9eaae832b63fd5a1f56295e94eb37d637e Homepage: https://cran.r-project.org/package=esvis Description: CRAN Package 'esvis' (Visualization and Estimation of Effect Sizes) A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; ), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; ), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified. Package: r-cran-esviz Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maps, r-cran-climprojdiags, r-cran-sf, r-cran-ggplot2, r-cran-rnaturalearth, r-cran-cowplot, r-cran-s2dv, r-cran-rcolorbrewer, r-cran-cstools, r-cran-easyncdf, r-cran-dplyr, r-cran-plyr, r-cran-data.table, r-cran-reshape2, r-cran-scales, r-cran-foreach, r-cran-rlang, r-cran-kableextra, r-cran-gridextra, r-cran-gtable, r-cran-doparallel, r-cran-mapproj, r-cran-webshot2, r-cran-jsonlite, r-cran-sp, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rnaturalearthdata Filename: pool/dists/noble/main/r-cran-esviz_0.0.4-1.ca2404.1_all.deb Size: 704034 MD5sum: afdbe117ef2e8290b53f1c5acd76cb0f SHA1: 93ce4abbea75ecf73e9fce51082c20eac6547456 SHA256: 5b4bd0a85bdfaafd4d27f1ffab186b63730627e8262a993d14a51a3ec9000584 SHA512: 0d581612fbed4546ba980015da492b235bfdac2199acc1307d4bceda3e9222c34e3591ac8f54dea1a4354aa5cdda23046ebbc390f7c3fbb1bb213b7c0f8db072 Homepage: https://cran.r-project.org/package=esviz Description: CRAN Package 'esviz' (Plotting Functions for Climate Science and Services) A plotting package for climate science and services. Provides a set of functions for visualizing climate data, including maps, time series, scorecards and other diagnostics. Some functions are adapted and extended from the 's2dv' and 'CSTools' packages (Manubens et al. (2018) ; Pérez-Zanón et al. (2022) ), with more consistent and integrated functionalities. Package: r-cran-et.nwfva Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-et.nwfva_0.2.0-1.ca2404.1_all.deb Size: 1235068 MD5sum: b9280dacfb28bdf074740c50385facd2 SHA1: f5889a97214b740c22fc4974c579cf1f344339c1 SHA256: 1bfe0201b7d5632f5de590a9bb55c7f0e2b097661b12fa3f085637da6d908e52 SHA512: ec1ac038ff06aefe9de7566ea08569fb7546d520fde6fcfb2d2d026532f4e04130e9bd9039a56cb64693a45419f3e8bf7a032cf1fd5da023c598a30702870be5 Homepage: https://cran.r-project.org/package=et.nwfva Description: CRAN Package 'et.nwfva' (Forest Yield Tables for Northwest Germany and their Application) The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach. Package: r-cran-et0tempmodels Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-et0tempmodels_1.0.0-1.ca2404.1_all.deb Size: 165690 MD5sum: dde7822e1c58aa165ee5734819b636ea SHA1: 2c2f89d10a23f318352fdb9ee2919516d84465a9 SHA256: 848624b22411fda1205a1b66e9c6d9697956a799a1dbec24fe4e7aadf6e84346 SHA512: 3b793a1eef57ed678485817136053ddcdadab19511b3ea385ad1a0b176473e89961fcfa02a70ec08675e5b72a4daea803184cc9d9114227e9cf04c71a1f020e6 Homepage: https://cran.r-project.org/package=ET0TempModels Description: CRAN Package 'ET0TempModels' (Evapotranspiration Estimation Using Temperature-Based Models) Provides functions to estimate daily reference evapotranspiration (ET0) using 10 temperature-based empirical models, with the Food and Agriculture Organization (FAO) Penman-Monteith method included as the standard reference for model comparison. Includes statistical evaluation metrics, such as Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE), and visualization tools (scatter plots and Taylor diagrams). Based on Singh et al. (2026) . 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Package: r-cran-etasbootstrap Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-etas, r-cran-mass, r-cran-spatstat.geom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etasbootstrap_0.2.1-1.ca2404.1_all.deb Size: 400702 MD5sum: 303e3b7d87d6c88629cea2d53317fcc8 SHA1: 7696a9464d50d8c9f9ebbc3c42028af4c2db9e98 SHA256: 46478421d6ea3a53aa2864de3ba331f7285db305ccc66c3a2a861a357b77cc2d SHA512: 0ece5423c05276a15a8628d4a564df63bd4e33dfc20da63fcffbb169e59c15ec69b7280bf61cdbedeac835eeae9d614d2970e29c203428f913a1fa41af34a68f Homepage: https://cran.r-project.org/package=ETASbootstrap Description: CRAN Package 'ETASbootstrap' (Bootstrap Confidence Interval Estimation for 'ETAS' ModelParameters) The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is widely used to 'decluster' earthquake data catalogs. Usually, the calculation of standard errors of the 'ETAS' model parameter estimates is based on the Hessian matrix derived from the log-likelihood function of the fitted model. However, when an 'ETAS' model is fitted to a local data set over a time period that is limited or short, the standard errors based on the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals for parameters may not always be reliable. As an alternative, this package allows for the construction of bootstrap confidence intervals based on empirical quantiles for the parameters of the 2-D spatial and temporal 'ETAS' model. This version improves on Version 0.1.0 of the package by enabling the study space window (renamed 'study region') to be polygonal rather than merely rectangular. A Japan earthquake data catalog is used in a second example to illustrate this new feature. Package: r-cran-etc Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-simcomp, r-cran-multcomp, r-cran-mratios Filename: pool/dists/noble/main/r-cran-etc_1.5-1.ca2404.1_all.deb Size: 55902 MD5sum: 124c0c771bffb128e0cfc537645c8167 SHA1: 9ca9b9ed3b9220683dec2675f963926fa5afaa77 SHA256: 2d59ca4fca0a81910b4668b5adbb9c5f24f6b9757300cd7870703535e877b003 SHA512: d21cae5a00f42d839d0e5ee8f446e44928df43a97d1ad50e9e3b57f26052d81587b34b2585ccad6224c5ea061f1a8886ef731fa55cd74e1a92766b6f8805fa34 Homepage: https://cran.r-project.org/package=ETC Description: CRAN Package 'ETC' (Equivalence to Control) Treatments of a one-way layout, being equivalent to a control, can be selected with this package. Bonferroni adjusted "two one-sided t-tests" (TOST) and related simultaneous confidence intervals are given for both differences or ratios of means of normally distributed data. For the case of equal variances and balanced sample sizes for the treatment groups, the single-step procedure of Bofinger and Bofinger (1995) can be chosen. For non-normal data, the Wilcoxon test is applied. Package: r-cran-etdqualitizer Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etdqualitizer_1.1.0-1.ca2404.1_all.deb Size: 146360 MD5sum: a84737e4123b543a8741a007c18d8242 SHA1: 7acab0addd02f5aa0f7a9680823aec1f722543b8 SHA256: d41100f528bf33ece7f40d54e7c9dc66f04082cf003e42d43abb08eb3d26c980 SHA512: 8d4970678a4d308e203d3c2d65b540fe0eb644e38805ad7426bd4dcd819d9812e02fa4fcac40b338afca4f0ddf222e329d567b00c5811e7be58a3d0a033a057f Homepage: https://cran.r-project.org/package=ETDQualitizer Description: CRAN Package 'ETDQualitizer' (Automated Eye Tracking Data Quality Determination forScreen-Based Eye Trackers) Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. The package supports gaze input in screen pixels or degrees and reports angular measures in degrees where appropriate. If you use this package, please cite Niehorster, D.C., Nyström, M., Hessels, R.S., Benjamins, J.S., Andersson, R., and Hooge, I.T.C. (2026). The fundamentals of eye tracking, Part 7: Determining data quality. Behavior Research Methods. . Package: r-cran-ethnobotanyr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-cowplot, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2, r-cran-magrittr Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-vegan, r-cran-bnlearn, r-cran-causaleffect, r-cran-grain, r-cran-pbapply, r-cran-tidyselect, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-ethnobotanyr_0.2.0-1.ca2404.1_all.deb Size: 2500586 MD5sum: 110b9e8e92f0a0a5dc07ea7e5a03474b SHA1: 52950e8739ed350bf56b3bf554eff1ce92259d39 SHA256: 9e8024ea00ff6033456c623f70e549312c459f24c7e6d0d335d4525eac073d4c SHA512: da3b3132ef7bd8e7fba61bc9ce8db3a1609b9308b8e6e01ea656b88b6a46abc5d408ac9035e41df298ef32b3054bb6bc95b8b74529f4ace94225e9732eeadb3d Homepage: https://cran.r-project.org/package=ethnobotanyR Description: CRAN Package 'ethnobotanyR' (Ethnobotanical Analysis, Decision-Framing, and TEK Modeling) Tools for quantifying Traditional Ecological Knowledge (TEK), modeling TEK in decision frameworks, and designing structured decision-framing exercises in conservation and development contexts. The package implements quantitative ethnobotany indices (Use Value, Relative Frequency of Citation, etc.) but positions them within a larger framework of Bayesian modeling and participatory decision analysis. Includes critical assessment of indices' limitations and case studies of participatory workshops. Package: r-cran-etl Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-downloader, r-cran-fs, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-rlang, r-cran-rvest, r-cran-tibble, r-cran-usethis, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rsqlite, r-cran-rpostgres, r-cran-rmariadb, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-etl_0.4.3-1.ca2404.1_all.deb Size: 127314 MD5sum: e72b0489eed7b60ba6769cd1125b075a SHA1: 24f1c143360cbeafeaa6f7702cf0fd0393d8b31c SHA256: 3b43a0c5846dd955f264c629afb4d975c5aa34184200062e6ede2f69f4b4fa2d SHA512: bacf97e3352bdb178de86ba7818a7a3c7ea1fd6612f842c2bdc538596f47ecf45565f090b4fb56c56e448b00f6135dc14ec44d3d8ac170e6144d014d27b6fc50 Homepage: https://cran.r-project.org/package=etl Description: CRAN Package 'etl' (Extract-Transform-Load Framework for Medium Data) A predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system. Package: r-cran-etlutils Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff, r-cran-bit Suggests: r-cran-rsqlite, r-cran-zoo, r-cran-dbi, r-cran-rodbc, r-cran-rjdbc Filename: pool/dists/noble/main/r-cran-etlutils_1.6-1.ca2404.1_all.deb Size: 102310 MD5sum: 9010dc409a315590c32ec79c53842389 SHA1: 1dbe2e3410cc064b8f15baf6f818de885ac4fbbd SHA256: 49f84583c4eddba9304abff61b33283f9fc759f816af1832798101eee2485a99 SHA512: 6ede3a6b4335266354bfef849454485c0ba35aa4f0ecf9275ccbd355b171e890c1e1c6760618eefd7b38b6a77841c7057aa9a9133e96639535cc34fbd9f09072 Homepage: https://cran.r-project.org/package=ETLUtils Description: CRAN Package 'ETLUtils' (Utility Functions to Execute Standard Extract/Transform/LoadOperations (using Package 'ff') on Large Data) Provides functions to facilitate the use of the 'ff' package in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to do fast left outer join merging based on 'match', factorisation of data and a basic function for re-coding vectors. Package: r-cran-etrader Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-urltools, r-cran-dplyr, r-cran-rvest, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etrader_0.1.5-1.ca2404.1_all.deb Size: 113612 MD5sum: 89d2bf0dfb41d6efeb4dc0dc6a96a733 SHA1: bddafdcf7d7dabd317e0e2200eb77821c58810e2 SHA256: f4c79ec6844a400c5e92d49de6931cebe5d97cc5da5a76237903f89d7c1bf825 SHA512: 0d13c179c71431db87ecd5423407de22e59f47a26cf2ff491b8ab0f6160becae11880489165ab336da4b10340f4c2ad17d7321a378e59b5ef590a343dec8223d Homepage: https://cran.r-project.org/package=etrader Description: CRAN Package 'etrader' ('ETRADE' API Interface for R) Use R to interface with the 'ETRADE' API . Functions include authentication, trading, quote requests, account information, and option chains. A user will need an ETRADE brokerage account and 'ETRADE' API approval. See README for authentication process and examples. Package: r-cran-etree Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-braingraph, r-cran-cluster, r-cran-energy, r-cran-fda.usc, r-cran-igraph, r-cran-networkdistance, r-cran-partykit, r-cran-survival, r-cran-tda, r-cran-usedist Suggests: r-cran-knitr, r-cran-mlmetrics, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-etree_0.1.0-1.ca2404.1_all.deb Size: 3120708 MD5sum: aa7d8dfe927756169f6826bafdc80abc SHA1: cd2e45ccd549bd30173099ad872b1518d76db812 SHA256: 90ebc7db28a54fe0c7b98a97195231932f39ec970819bb6a579b5afe0672bddf SHA512: 5e2c4dec1c207f6de827606fbd9e135a85d3fa6f7043ebbde3460f52d714ae9804a0324043cf4c7327f23bd1df216a8cdd464d460b538303d0563de52d1970b3 Homepage: https://cran.r-project.org/package=etree Description: CRAN Package 'etree' (Classification and Regression with Structured and Mixed-TypeData) Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) . Package: r-cran-etrep Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1919 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-shapes, r-cran-morpho, r-cran-matlib, r-cran-rspincalc, r-cran-rotations, r-cran-rvcg, r-cran-fields, r-cran-truncnorm, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-etrep_1.2.1-1.ca2404.1_all.deb Size: 1902034 MD5sum: 0473b141f855e0cbf26c04b62e1f29fb SHA1: d9dd7334809d85a76b00a1146a5a115553c7def7 SHA256: 0c6a60050a983a16fdbb02f81f54756fb4ad64be8ab62b5665da6e9c016ef8c3 SHA512: 8860b8afd7f857bb481fb18da32f32fdf8ac152775a5e8c5c80d5c4b1c24b3b2f5b228ed321b8fb1d27325b75e8ce60060ea3ef6f4e32c4ae71ecf1df1fbfdde Homepage: https://cran.r-project.org/package=ETRep Description: CRAN Package 'ETRep' (Analysis of Elliptical Tubes Under the Relative CurvatureCondition) Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." Journal of Computational and Graphical Statistics and arXiv . Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway . Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC. Package: r-cran-etrm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-etrm_1.0.1-1.ca2404.1_all.deb Size: 722304 MD5sum: 63ac1cf596144035455133dbc0a57f0d SHA1: 4bcb548692b7806a2a73a67f09a26ba63c815317 SHA256: 1a9f7661a6015836ba0f8c2d47a0adf22e7d6316dc521e586903b7ad09718520 SHA512: ccbe573eff8393580113ffaea9983fcbcf4b1efbe77432426d66bd442c8e4ba591f4c23e58ee967da3f1a318bb980e19ddef81873dce9935c1e0317997ead4ef Homepage: https://cran.r-project.org/package=etrm Description: CRAN Package 'etrm' (Energy Trading and Risk Management) Provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) . Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) , T. Bjork (2009) , F. Black and R. W. Jones (1987) and H. E. Leland (1980) . Package: r-cran-etrunct Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etrunct_0.1-1.ca2404.1_all.deb Size: 12146 MD5sum: b8285cc7f6078a691ef8832d280a875e SHA1: 722889d7acd4c4301c1227b16278adba3c9415cf SHA256: 6e06b0c05181b96db124511e4145949f92d3661139a479ad3b89c625b1c6cc51 SHA512: 931c3003deffd82a4848bff6f4cfc5a82ea70e8f6b4b103b759450cf1e06c529341f5ff567527b75b0961373a924a7c964ff9e1daadde84ac8568c35e8b738ac Homepage: https://cran.r-project.org/package=etrunct Description: CRAN Package 'etrunct' (Computes Moments of Univariate Truncated t Distribution) Computes moments of univariate truncated t distribution. There is only one exported function, e_trunct(), which should be seen for details. Package: r-cran-etsi Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hetsurr Filename: pool/dists/noble/main/r-cran-etsi_1.0-1.ca2404.1_all.deb Size: 106698 MD5sum: 0ed82672eb25d6175e161ee7b61f94b7 SHA1: 2300ad6d7084d19bcbfb51f7f00e622bca7c4ff3 SHA256: b875917fde13df90a01c1deb5c55147220517d6be63046c12d979864f30ba876 SHA512: e5b30e674a92f1627ba72cefa41a10697808588b59d2eaf77d8f266d97b74fdb6c979593fe00388653d82b6d366cb3fd0ba1a21b788e85b4b795299ef9033a95 Homepage: https://cran.r-project.org/package=etsi Description: CRAN Package 'etsi' (Efficient Testing Using Surrogate Information) Provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, . A tutorial for this package can be found at . 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This package helps a user guess four things (mean, MD, scaled MSD, and RMSD) before they get the SD. 1) The package displays the Empirical Cumulative Distribution Function (ECDF) of the given data. The user must choose the value of the mean by equating the areas of two colored (blue and green) regions. The package gives feedback to improve the choice until it is correct. Alternatively, the reader may continue with a different guess for the center (not necessarily the mean). 2) The user chooses the values of the Mean Deviation (MD) based on the ECDF of the deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 3) The user chooses the Scaled Mean Squared Deviation (MSD) based on the ECDF of the scaled square deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 4) The user chooses the Root Mean Squared Deviation (RMSD) by ensuring that its intersection with the ECDF of the deviations is at the same height as the intersection between the scaled MSD and the ECDF of the scaled squared deviations. 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Package: r-cran-euronext Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-magrittr, r-cran-stringr, r-cran-jsonlite, r-cran-rlang, r-cran-rvest, r-cran-httr2, r-cran-xts, r-cran-dplyr, r-cran-flextable, r-cran-highcharter Suggests: r-cran-lubridate Filename: pool/dists/noble/main/r-cran-euronext_2.0.2-1.ca2404.1_all.deb Size: 260192 MD5sum: 6d1d96e479ef523069895c3e6cd33a2b SHA1: 87b1b591ee0ae82ff8204f3aa5523d8fc4a82a17 SHA256: 3eba60600fe8581899aa2815ed88ff8add470847302e2291a8680f1ad2a13908 SHA512: 44feeac58cee318d968962b7d0de20371f99b1afb636dde43a793beb8fcae9bd89ac245d3c3f16b8846e6cbaaeb4d2f706f944c4e51fb92ae6c69f7b40ecf8df Homepage: https://cran.r-project.org/package=Euronext Description: CRAN Package 'Euronext' (Retrieve Historical Data of Companies Listed on the 'Euronext'Stock Exchange) Provides seamless access to historical data of companies listed on the 'Euronext' Stock Exchange (), enabling users to retrieve real-time information directly within the R environment. With functions tailored for data retrieval and manipulation, users can effortlessly access a wide range of financial data, including stock prices, trading volumes, and more. Leveraging the power of R, this package facilitates efficient analysis and visualization of stock market trends, aiding investors, analysts, and researchers in making informed decisions. By combining ease of use with comprehensive data access, 'Euronext' empowers R users to delve deep into the dynamics of European financial markets, offering valuable insights for various financial applications. Package: r-cran-europeanar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rdpack Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-europeanar_0.1.0-1.ca2404.1_all.deb Size: 54604 MD5sum: f1d8d5a1b660d251cb9700ce8b6a59ca SHA1: 8a0a618b3488ebb77119eadb0374be624a3557f8 SHA256: 6c1853589097973ba61cf446489b7fc8b4145756d4a5b9daee078be78244f3e8 SHA512: 8338f88b67ef1075c97b88e403a612a29c6705c796f2e2aa712f6f48ebecd0631927d63f6dee5232eec0385e4489900ae353b645766839322f95f3afed264c1a Homepage: https://cran.r-project.org/package=europeanaR Description: CRAN Package 'europeanaR' (Interact with Metadata Records and Media on the EuropeanaRepository) Interact with the Europeana Data Model via a variety of API endpoints that contains digital collections from thousands of institutions around Europe. This translates to millions of Cultural Heritage Objects in the form of image, text, video, sound and 3D, accompanied by rich metadata. The Data Model design principles are based on the core principles and best practices of the Semantic Web and Linked Data efforts to which Europeana contributes (see, e.g., Doerr, Martin, et al. The europeana data model (edm). World Library and Information Congress: 76th IFLA general conference and assembly. Vol. 10. 2010.). The package also provides methods for bulk downloads of specific subsets of items, including both their metadata and their associated media files. Package: r-cran-europepmc Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-plyr, r-cran-dplyr, r-cran-progress, r-cran-urltools, r-cran-purrr, r-cran-xml2, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-europepmc_0.4.3-1.ca2404.1_all.deb Size: 431826 MD5sum: 513ce0649e43d96ee39f808bf8169f7f SHA1: 303d5f825528d639786ebb294956554d6e8992f4 SHA256: adac47c92dc864b809bf90d6eb8c01d401c0143ca14a376fd7d7d0bce74d9246 SHA512: 6685f3fcc4354d86973a6a50ab1417183017fade65d79fd72b6dbf9e4ba0cf7a2f50b1dc2397a5cfcfec06fc2820848e0cb4e32f362474668e9d77e40447c5b3 Homepage: https://cran.r-project.org/package=europepmc Description: CRAN Package 'europepmc' (R Interface to the Europe PubMed Central RESTful Web Service) An R Client for the Europe PubMed Central RESTful Web Service (see for more information). It gives access to both metadata on life science literature and open access full texts. Europe PMC indexes all PubMed content and other literature sources including Agricola, a bibliographic database of citations to the agricultural literature, or Biological Patents. In addition to bibliographic metadata, the client allows users to fetch citations and reference lists. Links between life-science literature and other EBI databases, including ENA, PDB or ChEMBL are also accessible. No registration or API key is required. See the vignettes for usage examples. Package: r-cran-europop Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-readr Filename: pool/dists/noble/main/r-cran-europop_0.3.1-1.ca2404.1_all.deb Size: 26328 MD5sum: 47189e42a4527b230397cbabb556709f SHA1: 9832ecbbccc3821bb02242fcce0e7de800b1e503 SHA256: 84c3e64ede059f3fe6d748fab2fc2f211a307480b8026af57b4f2c41b4670157 SHA512: e28dfab44a5a656e4c3abf5113ed695cc8899c59d9de325864a0e55eb2c60e7a1ce6e7c5a50c3202ab834f72c42c03fc94c7e468d24be3e1e217fa417ddaa87e Homepage: https://cran.r-project.org/package=europop Description: CRAN Package 'europop' (Historical Populations of European Cities, 1500-1800) This dataset contains population estimates of all European cities with at least 10,000 inhabitants during the period 1500-1800. These data are adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984). Package: r-cran-eurosarcbayes Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-vgam, r-cran-data.table, r-cran-plyr, r-cran-clinfun Filename: pool/dists/noble/main/r-cran-eurosarcbayes_1.1-1.ca2404.1_all.deb Size: 357806 MD5sum: 6a37b8ef6304e3dc080f050b321e63c6 SHA1: c2d902b3a525144f6f388ba935641d9b1e9e40a4 SHA256: ad892bb3f70025bf33c2e098fb813e5e59d549b3af2ebfe2309f7c72cd26822e SHA512: 5604f7d4d9110acd256298853cb741957c4bd9fa050c6e93cf21bd975490b9661d138007b6a745a77ca883908de01f51c514a376b6bdec043131426903e10fb5 Homepage: https://cran.r-project.org/package=EurosarcBayes Description: CRAN Package 'EurosarcBayes' (Bayesian Single Arm Sample Size Calculation Software) Bayesian sample size calculation software and examples for EuroSARC clinical trials which utilise Bayesian methodology. These trials rely on binomial based endpoints so the majority of programs found here relate to this sort of endpoint. Developed as part of the EuroSARC FP7 grant. 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Package: r-cran-eva3dm Architecture: all Version: 1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-ncdf4 Suggests: r-cran-knitr, r-cran-riem, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eva3dm_1.20-1.ca2404.1_all.deb Size: 3263060 MD5sum: b6c6ae019d9160cad3e2fe641ffb7313 SHA1: e8627bc61a08376b998ae8ab91c097ba380f3e58 SHA256: 93471d8c8fe480e6987a50dbf4bd7554286c0584a31fd187e9373d5486e03cda SHA512: 21578f154664317a4090a8edff74fdc135fcbcb5927c19e64086e34c3a08a555e822b0615eb7e42b85586fa20f742e3aadc52dcbaf255593378079084c844e70 Homepage: https://cran.r-project.org/package=eva3dm Description: CRAN Package 'eva3dm' (Evaluation of 3D Meteorological and Air Quality Models) Provides tools for post-process, evaluate and visualize results from 3d Meteorological and Air Quality models against point observations (i.e. surface stations) and grid (i.e. satellite) observations. Package: r-cran-eva Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2754 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spatialextremes Filename: pool/dists/noble/main/r-cran-eva_0.2.7-1.ca2404.1_all.deb Size: 2278236 MD5sum: 8dbd09e461a822221b5cc8f786bc107c SHA1: 1c6ea0890c5741854a3082f77c9d3179e8109f78 SHA256: 3af6d403441e36f368361656a45ec5517754f09d99d3f5f760b7a3fcb0ff7f74 SHA512: b976f468642ad6778f57a6f63d10936b1c496c52fe3afa985adae1b9cae7efae2c781ae3b91c65e3fbe7cefa808a840c2b62d7db4f134455339cd325c53f9921 Homepage: https://cran.r-project.org/package=eva Description: CRAN Package 'eva' (Extreme Value Analysis with Goodness-of-Fit Testing) Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD. Bader, B., Yan, J. & Zhang, X. (2016) . Bader, B., Yan, J. & Zhang, X. (2018) . Package: r-cran-evabic Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evabic_0.1.4-1.ca2404.1_all.deb Size: 147156 MD5sum: 81eb9d07db0cd7aadccfc1f01b3c3079 SHA1: 71e8fd88ce5aac625a0870221d2959338a4c09eb SHA256: 0e6d4c80e439904146aefeda11bcd4b453d9f44e23d0bc8b01805da751cff875 SHA512: 3689e370f8adc09b2ddc2f7f912e648bd8da452839491bf550757d21d30e3274279b9f74d7d045b50a805bba5f2559a8349748dab74b72c127f7438fe766866f Homepage: https://cran.r-project.org/package=evabic Description: CRAN Package 'evabic' (Evaluation of Binary Classifiers) Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes. Package: r-cran-evacluster Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mlbench, r-cran-emcluster, r-cran-inaparc, r-cran-ppclust, r-cran-fresa.cad, r-cran-mass, r-cran-class, r-cran-nmf, r-cran-cluster, r-cran-rtsne, r-cran-proxy, r-cran-uwot, r-cran-mclust Filename: pool/dists/noble/main/r-cran-evacluster_0.1.0-1.ca2404.1_all.deb Size: 67112 MD5sum: d24bee06fa29e71b641abb95fa355ef6 SHA1: 3950b0dcb653410eaf0b7654093c8ba8255d1717 SHA256: 5be2c12464b6c69a08a608a3a191cd381b453340918626ae978527db271cd5e4 SHA512: a3a95de237eec1385d48387bfbd501a518bd604304d1b4ebde5cb9af0f6a2edffbc1cacc8182d2a216e93b7f39c07032a9314a65dede461d849691c966cda476 Homepage: https://cran.r-project.org/package=Evacluster Description: CRAN Package 'Evacluster' (Evaluation Clustering Methods for Disease Subtypes Diagnosis) Contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021), and Fahimeh Nezhadmoghadam, et al.(2021). Package: r-cran-evacpath Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-leastcostpath Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-fields Filename: pool/dists/noble/main/r-cran-evacpath_0.1.0-1.ca2404.1_all.deb Size: 2936840 MD5sum: bfedde89859d34a423f93e31303cbb40 SHA1: c467a876b0f71ac741578418a093b0a80edfe777 SHA256: b32a0c2393ee2592ee25a57d8e32353c9331ddcb7bd010294b9b7ee9696392f3 SHA512: af035fef1eb1da8ff41abd071f88b47c5829e21f214fb6ccbcab9385b40706148fe0060aff8490c527c80f123dfedbacd321abc2d33776c5eb50b0e7b07c27ce Homepage: https://cran.r-project.org/package=evacpath Description: CRAN Package 'evacpath' (Least-Cost Pedestrian Evacuation Modeling) Tools for road-constrained, least-cost pedestrian evacuation modeling. The package provides reusable functions for preparing hazard zones, generating road-based evacuation origin points, identifying escape/safety points, creating slope-based conductance surfaces, calculating least-cost distance to safety, and converting distance outputs into evacuation-time polygons. It is designed to support workflows like tsunami evacuation modeling while remaining adaptable to other regions and hazards. Tsunami-specific helpers support separate land-only hazard zones, water-combined escape zones, road-aware escape boundaries, and study-area inset cropping for quality assurance and quality control. Methods build on Cordero et al. (2025) , Lewis (2021) , and Joseph Lewis's 'leastcostpath' package (2023) . 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The package includes tools to estimate uniform confidence bands for estimation of the group average treatment effect sorted by generic machine learning algorithms (GATES). It also provides the tools to identify a subgroup of individuals who are likely to benefit from a treatment the most "exceptional responders" or those who are harmed by it. Detailed reference in Imai and Li (2023) . 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The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2023) . 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(2015) . Package: r-cran-evaluatellm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-evaluatellm_0.1.0-1.ca2404.1_all.deb Size: 246812 MD5sum: 4c3478c6e8d7d42c501e910f21afb5ce SHA1: dc49c2989f3f83fbeafc948bf7895d664802e744 SHA256: 692527a2d21ce4a4c9ec7ed275d73bd92f580aab6b74636b9dc8e807d0bcf5c8 SHA512: bc16332476bff6683be0c8c57a0c79703f4bfad64f43a95e9d5d62d8c3d5c1acaa93b15287789039bebaa975008a2b007adc6ed4e97df6c6b6b09a2c4357d0ba Homepage: https://cran.r-project.org/package=evaluatellm Description: CRAN Package 'evaluatellm' (Statistical Inference for Language Model Evaluations) Treats language model evaluations as statistical experiments and supplies the inference they require. Provides central limit theorem and cluster-robust standard errors for evaluation scores, paired and unpaired model comparisons, variance decomposition when several responses are drawn per question, control-variate variance reduction, multiplicity adjustment across benchmark suites, and power and minimum detectable effect calculations for planning evaluations, following Miller (2024) . For evaluations scored by a model judge, implements agreement statistics against a human gold standard and prediction-powered inference (Angelopoulos et al. 2023) with the power-tuned estimator of Angelopoulos, Bates and Jordan (2023) , so a small set of human labels debiases a large set of judge scores. Leaderboards are supported through bootstrap rank intervals and Bradley-Terry ratings (Bradley and Terry 1952) . Accepts scores from any evaluation harness. Package: r-cran-evaluationmeasures Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-evaluationmeasures_1.1.0-1.ca2404.1_all.deb Size: 56876 MD5sum: 90d37974804e2acce7d2587c243ded97 SHA1: e14431a81642d41d4f7016bb94907d08824948c0 SHA256: 3969bd06da73869053c6553bca9a0b3c5fe9232aa2f1b77357b4373ac756f4be SHA512: e767ed83a459c08c859b130b1b89492358271a43ed556150c6a4b6d86555ea4feded7dbfcf6aa95d7e5597d775577d8b8ce7faaec70caf87d8a592252968e416 Homepage: https://cran.r-project.org/package=EvaluationMeasures Description: CRAN Package 'EvaluationMeasures' (Collection of Model Evaluation Measure Functions) Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned. Package: r-cran-evalue Architecture: all Version: 4.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-metafor, r-cran-boot, r-cran-metautility, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-metadat Filename: pool/dists/noble/main/r-cran-evalue_4.1.4-1.ca2404.1_all.deb Size: 828304 MD5sum: 1c32edd1f62c209dfb0caf8f1866db38 SHA1: 4a1a175f108bb50c5bbef071fb21d61ab2fb7c19 SHA256: d80bcf06cdb0dd690ca43def9b8da692a9c87c74a5232d61b3bf17aebff6d1a5 SHA512: 84adece96077f839cbca73274ce82b5a519cbeb7736f79b640815592ee2b5d60e7d3f174b90dae22283093668c1f3f5ff8dddf6d6f8f33b89d71c2cddc956f23 Homepage: https://cran.r-project.org/package=EValue Description: CRAN Package 'EValue' (Sensitivity Analyses for Unmeasured Confounding and Other Biasesin Observational Studies and Meta-Analyses) Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error (individually or in combination; VanderWeele & Ding (2017) ; Smith & VanderWeele (2019) ; VanderWeele & Li (2019) ; Smith, Mathur, & VanderWeele (2021) ). Also conducts sensitivity analyses for unmeasured confounding in meta-analyses (Mathur & VanderWeele (2020a) ; Mathur & VanderWeele (2020b) ) and for additive measures of effect modification (Mathur et al., ). 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Provides ~50 functions covering package management, color palette management, data visualization (bar, pie, density, Venn, forest plots), statistical testing (t-test, ANOVA, chi-square, correlation, power analysis), bioinformatics utilities (gene ID conversion, GMT parsing, GEO access), and custom infix operators. Implementation follows tidyverse principles (Wickham et al. (2019) ). Package: r-cran-evapore Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3492 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-lubridate, r-cran-raster, r-cran-twc Suggests: r-cran-rmarkdown, r-cran-ggpubr, r-cran-knitr, r-cran-spelling, r-cran-kableextra, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evapore_1.0.1-1.ca2404.1_all.deb Size: 2450884 MD5sum: 37dfb308fc52eef3121e9ef500115f66 SHA1: b7d35ed4a874cf89ae4cbc3acc944037f8184163 SHA256: 743e618f009ae865eedff89958a5ca1c3a8146583b5cbdbacd9a13bf735eefc0 SHA512: 85ec0941e3ce9d53fcd7931d72763fa65e8f220c2f8b6b3ce75cf50bde9f655cd2fa18421c58b702d26420092e6e0917e65528e07fc8b4fa2515361075d27b28 Homepage: https://cran.r-project.org/package=evapoRe Description: CRAN Package 'evapoRe' (Evapotranspiration R Recipes) An R-based application for exploratory data analysis of global EvapoTranspiration (ET) datasets. 'evapoRe' enables users to download, validate, visualize, and analyze multi-source ET data across various spatio-temporal scales. Also, the package offers calculation methods for estimating potential ET (PET), including temperature-based, combined type, and radiation-based approaches described in : Oudin et al., (2005) . 'evapoRe' supports hydrological modeling, climate studies, agricultural research, and other data-driven fields by facilitating access to ET data and offering powerful analysis capabilities. Users can seamlessly integrate the package into their research applications and explore diverse ET data at different resolutions. Package: r-cran-evapotranspiration Architecture: all Version: 1.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-evapotranspiration_1.16-1.ca2404.1_all.deb Size: 595556 MD5sum: 5fc742b7bd92119020547b2818226536 SHA1: a27780eb5c93a44a0acb2363aaec891dfe85dda5 SHA256: e5440454a3c890673826b821de06b79587c325b544e0a41e4dc2816b725c1682 SHA512: 3b7dcfd703ea79cac409845a8184b5c91711d348bd0df80df090a3712a69f48424370c56a08b87b2552e35e51f434caa5c8392e516a3c5b1b34bcc82cb069ace Homepage: https://cran.r-project.org/package=Evapotranspiration Description: CRAN Package 'Evapotranspiration' (Modelling Actual, Potential and Reference CropEvapotranspiration) Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations. Package: r-cran-evbsreg Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatialextremes, r-cran-ggplot2 Suggests: r-cran-gamlss, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evbsreg_1.2.0-1.ca2404.1_all.deb Size: 340116 MD5sum: 9caa34678696fc3c9afc8e0eec04d63b SHA1: 2fa419bc1ade6409d63b802b7819067220976ad4 SHA256: 279575fa386eea822ea88f07c4dad8ddad150fd193c64c9350348995e3d3fd21 SHA512: 84064c50a61a3db9d8a5e0dc33d3fdb4c45b4872f7218aea2aa06277a485a04552a99960e91c2719096999da3bc4b422d591e035aa5ef7cb67a0fee7c29d2cd1 Homepage: https://cran.r-project.org/package=evbsreg Description: CRAN Package 'evbsreg' (Local Influence Diagnostics for the Extreme-ValueBirnbaum-Saunders Regression Model) Implements local influence diagnostics for the Extreme-Value Birnbaum-Saunders (EVBS) regression model: joint maximum likelihood estimation, conformal normal curvature diagnostics under three perturbation schemes (case-weight, response variable, and explanatory variable), randomized quantile residuals with simulation envelope, Monte Carlo simulation utilities, and publication-quality density and diagnostic plots. Version 1.1.0 adds the density, distribution and quantile functions, the finite upper endpoint, return levels and expected shortfall, block bootstrap standard errors for serially dependent series, local influence diagnostics for the generalized extreme-value regression model, and a GAMLSS family allowing the tail-shape parameter to depend on covariates. Version 1.2.0 adds a prospective control chart for endpoint identifiability. The methods are described in Ospina, Lima, Barros, and Macedo (2026, submitted) and are applied to monthly maximum wind gust data from Itajai, Brazil. 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The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations. 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It is well known that the charging function of a EV is a concave function that can be approximated by a piece-wise linear function, so bigger the state of charge, slower the charging process is. Moreover, the other important function is the one that gives the electricity price. This function is usually step-wise, since depending on the time of the day, the price of the electricity is different. Then, the problem of charging an EV to a certain state of charge is not trivial. This library implements an algorithm to compute the optimal charging cost function, that is, it plots for a given state of charge r (between 0 and 1) the minimum cost we need to pay in order to charge the EV to that state of charge r. The details of the algorithm are described in González-Rodríguez et at (2023) . Package: r-cran-evclass Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ibelief, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nnet Filename: pool/dists/noble/main/r-cran-evclass_2.0.2-1.ca2404.1_all.deb Size: 322530 MD5sum: 54e604a49750aec413e3bac578f029a7 SHA1: 5901eb4c4bf1e02df431061a5c9e2e02b901f7ed SHA256: 6e2f3aecbe963e3f67eeac6c8c9588577b88eed0b31801e5f0ab614020c4d68d SHA512: ccb63caa40cbe9a0ef9c8d4c3fcdcf6b7297a43df719a71a4ba13e70af375cf8645d5ae7cf359ed44cb0b3528d4de95e4f4d72a82e62a63cdf40565739308935 Homepage: https://cran.r-project.org/package=evclass Description: CRAN Package 'evclass' (Evidential Distance-Based Classification) Different evidential classifiers, which provide outputs in the form of Dempster-Shafer mass functions. The methods are: the evidential K-nearest neighbor rule, the evidential neural network, radial basis function neural networks, logistic regression, feed-forward neural networks. Package: r-cran-evclust Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-r.utils, r-cran-limsolve, r-cran-matrix, r-cran-mclust, r-cran-quadprog, r-cran-plyr Suggests: r-cran-kernlab, r-cran-mass Filename: pool/dists/noble/main/r-cran-evclust_2.0.3-1.ca2404.1_all.deb Size: 2958628 MD5sum: 8aba4876a667fc47e8fe1471e50ec2b7 SHA1: c9fd8359aacd29633bea67a316f6832baba9632b SHA256: d48534956eb6c80df546644c527e00dcc5e79fea24b9972e60e4b2a8f17826cb SHA512: ad38b830bd2c4fbb7c9d39e097d0278c0d1c5ca1015bffaf35edcc6f981237ccc94f6c71883564178ca4b437edda1c742058134efd57c733e72968258e614453 Homepage: https://cran.r-project.org/package=evclust Description: CRAN Package 'evclust' (Evidential Clustering) Various clustering algorithms that produce a credal partition, i.e., a set of Dempster-Shafer mass functions representing the membership of objects to clusters. The mass functions quantify the cluster-membership uncertainty of the objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based Evidential Clustering. 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Package: r-cran-eve Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-eve_1.1-1.ca2404.1_all.deb Size: 60186 MD5sum: d8e7033eb64a6b87ef314d92102abdb9 SHA1: 6ce1bae05e71fb360d5d3306cce5f99f5487a576 SHA256: 18b4c19521b3fdc4300fa634ebbbd634a9d724108dbf880cb2d212bdbdca6dfb SHA512: 3633eeaf6a137b04c8ec3c436a0593f201a5e6e0fd4cac8e827d2f3aa47dfe3449a0959acc859095ba9c5f0754bf08aaabd58db4e93afbd5ce5b10c251fc5cb7 Homepage: https://cran.r-project.org/package=eve Description: CRAN Package 'eve' (The Eigenvalues Entropy as a Classifier Evaluation Measure) The confusion matrix (CM) is used to get a classifier's evaluation measure in order to select a method among many. A stochastic matrix and its transformation are computed from the CM. The eigenvalues of the transformed symmetric matrix are used to get an entropy which appears to be a good evaluation measure. Many other measures, commonly used, are provided for comparison purpose. Package: r-cran-evenbreak Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evenbreak_1.0-1.ca2404.1_all.deb Size: 268518 MD5sum: f791c4e24718d5b5706704db7f4a50e3 SHA1: 6e4ef72aecff26fed2cf3026bfe196d0ce9aa6f9 SHA256: 6be869ba385bf987d7b10ed6959c243c23fefdc2529c4300e4e0798c1dd1fbab SHA512: 62014d6c5a694eec0d10d1e2cf090d00dba9d9c4121b75bfbaa56fa7319c437d17b7f54b19825e370045a3000737bc7c1d8d4fdab2a8ca46dd89545f9643a24f Homepage: https://cran.r-project.org/package=evenBreak Description: CRAN Package 'evenBreak' (A Posteriori Probs of Suits Breaking Evenly Across Four Hands) We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson , Gregory Stoll , and details of performing the probability calculations from Jeremy L. Martin , Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5) "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" Ken Monzingo (2005) "Hand and Suit Patterns" . Package: r-cran-eventdatar Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2278 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eventdatar_0.3.1-1.ca2404.1_all.deb Size: 1381894 MD5sum: 4721746fcfc43d4e0f6d5bf0560e3316 SHA1: 2beaf841e70d62ff804e6f780a53da6d3c1c3b86 SHA256: 2f19b73fa0b4bfb3b501973f0aa4015a9d2d4689965fd9e93710e3c5e83feaf0 SHA512: c500d029155fb528ea9c29668625a954dcf64555c29c5b52d5b04269edc3b5ed605fa141aea3e9e9c976ce2bfad52029502c7f7673a21ddb59538c11b997e05c Homepage: https://cran.r-project.org/package=eventdataR Description: CRAN Package 'eventdataR' (Event Data Repository) Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages. Janssenswillen et al. (2020) . Package: r-cran-eventdetectgui Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eventdetectr, r-cran-shinybs, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-xml, r-cran-plotly, r-cran-dt, r-cran-ggplot2 Suggests: r-cran-roxygen2, r-cran-shinytest, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-eventdetectgui_0.3.0-1.ca2404.1_all.deb Size: 1453022 MD5sum: 5de55889d9f4617c5cae87d7c8631afa SHA1: 75ddf4e43b1ddffc9bdcac09eeb67e362b7a8b63 SHA256: fea8d43613723db89c90641dac461574d269c84ae75484ba102778d256aa0bba SHA512: d2d9bd84a27ac93515a2a755407c40f8328d3be1a1d2664e5be03b1320961670f5e0d0c753a8f344403ee89f0b8263159fc5eaee90f6b8371c603b5de0728957 Homepage: https://cran.r-project.org/package=EventDetectGUI Description: CRAN Package 'EventDetectGUI' (Graphical User Interface for the 'EventDetectR' Package) A graphical user interface for open source event detection. Package: r-cran-eventdetectr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2062 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-imputets, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-neuralnet Suggests: r-cran-testthat, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-eventdetectr_0.3.5-1.ca2404.1_all.deb Size: 1428300 MD5sum: 44e56dd039c845bb2512c8870e661010 SHA1: 75338f0df5995c6a076ea2e2132c4586eb6c394a SHA256: bb7831487a7c8d7d5503e2051f88a724754d5447187d47164e2bf5c2f8d0f973 SHA512: 14078ff5f5496faefe2ee068607bf721846bc86c16779016be8129314ecfd8f134ea3edb3adcf920391a3e049cf26165748c3ef976d888aee25d8bbc3f9cd224 Homepage: https://cran.r-project.org/package=EventDetectR Description: CRAN Package 'EventDetectR' (Event Detection Framework) Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection. Package: r-cran-eventinterval Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-eventinterval_1.3-1.ca2404.1_all.deb Size: 37468 MD5sum: 1cf276ab1f237fceb5aac5fd8ca481c8 SHA1: 9b99c6e4b10c6dc775a092eae7118e3c181630a2 SHA256: 1f76a24ce415cb8ae650b3acd14ed2d02ea49d7ac3a017e659293f4b2c77a8bb SHA512: a2b01479594d591f8aefac30c823270eb895ff44537c1ad0e2b14501f83a411adbc90737d1709450901b2c8e4d2f0cbabc205d3145414c2ad0735d4659390258 Homepage: https://cran.r-project.org/package=eventInterval Description: CRAN Package 'eventInterval' (Sequential Event Interval Analysis) Functions for analysis of rate changes in sequential events. Package: r-cran-eventpred Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 925 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-ggplot2, r-cran-plotly, r-cran-survival, r-cran-matrix, r-cran-mvtnorm, r-cran-rstpm2, r-cran-numderiv, r-cran-purrr, r-cran-flexsurv, r-cran-erify, r-cran-shiny, r-cran-rlang, r-cran-lrstat Suggests: r-cran-dt, r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-prompter, r-cran-rmarkdown, r-cran-readxl, r-cran-shinybusy, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinymatrix, r-cran-testthat, r-cran-pkgdown, r-cran-writexl Filename: pool/dists/noble/main/r-cran-eventpred_0.3.1-1.ca2404.1_all.deb Size: 664196 MD5sum: 7dfd4d5779d036c55152dae8dbd28a26 SHA1: fba20488bf9d9e076ab7648758564118eb301282 SHA256: bd84f34f1777158e4087c2db8293dc90363698a49cbe5d0693fe16cf2247f4a9 SHA512: a94d372aa24d5687b71f85ae22220161fa7de46b274fbf3540cffc6d687b896a858a3da645a0e23b7b46391697d0c023a87bf65a1f9c477b670cd59d63c77688 Homepage: https://cran.r-project.org/package=eventPred Description: CRAN Package 'eventPred' (Event Prediction) Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations. Package: r-cran-eventpredincure Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-perm, r-cran-msm, r-cran-mlecens, r-cran-kmsurv, r-cran-dplyr, r-cran-rlang, r-cran-plotly, r-cran-survival, r-cran-matrix, r-cran-mvtnorm, r-cran-rstpm2, r-cran-numderiv, r-cran-tmvtnsim, r-cran-erify, r-cran-lubridate, r-cran-flexsurv, r-cran-mass Filename: pool/dists/noble/main/r-cran-eventpredincure_1.0-1.ca2404.1_all.deb Size: 365906 MD5sum: 2703ffbb75075e78440c7b0dab87b274 SHA1: 9cb1e2124d2d9c8a7665f931c42caabc412bc69e SHA256: cacac284b0c589a7c00f86aaf8a32ac35b9ec24d9e808260f99d6ef89ccdbe28 SHA512: c8a12e4b7b741b8212487db5ae42d670d1296bf307c8b04482eca0b67742f64a2b24309977e1348c1149cff5129e0f6b7faf053bf0d6d4bfd2154a0d82a17df0 Homepage: https://cran.r-project.org/package=EventPredInCure Description: CRAN Package 'EventPredInCure' (Event Prediction Including Cured Population) Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) . Package: r-cran-eventreport Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-scales, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-tidyverse, r-cran-tinytable Filename: pool/dists/noble/main/r-cran-eventreport_0.1.2-1.ca2404.1_all.deb Size: 619006 MD5sum: 864d95d9b68b574ca2ecda263d8a40e5 SHA1: 17f72c56e35aee60da8affa55508ca6f10fd8610 SHA256: 9ffa57a310722b7f46adab4c06ff69d82b34c741c45308219fde28c480c4a620 SHA512: 6e754c1a48b1bdf8a7312fff95800f2947cbc91b942670a267ca273df3b6d87d0faa24273d88a09a84f5f4d29732967e5ed38a7556f1438261dee99c7b27028a Homepage: https://cran.r-project.org/package=eventreport Description: CRAN Package 'eventreport' (Diagnose, Visualize, and Aggregate Event Report Level Data) Diagnose, visualize, and aggregate event report level data to the event level. Users provide an event report level dataset, specify their aggregation rules, and the package produces a dataset aggregated at the event level. Also includes the Modes and Agents of Election-Related Violence in Côte d'Ivoire and Kenya (MAVERICK) dataset, an event report level dataset that records all documented instances of electoral violence from the first multiparty election to 2022 in Côte d'Ivoire (1995-2022) and Kenya (1992-2022). For more details see van Baalen and Höglund (2026) . Users of the enclosed MAVERICK dataset should also cite van Baalen and Höglund (2026) . Package: r-cran-eventstream Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4608 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-tensora, r-cran-glmnet, r-cran-dbscan, r-cran-mass, r-cran-changepoint, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eventstream_0.1.1-1.ca2404.1_all.deb Size: 4596594 MD5sum: 4fc542822d09aef4689f248d1e8d241c SHA1: bdcd30196e0ab53376cb00a195aa89f4ad7e320a SHA256: bcf4a6574c87fceee2dc42778531f704eb243b1b6d4db0250ab4d26784a7e935 SHA512: 0013b1e98595f0670be6817d40edd233128484aa035b3f72a4fc45abacf90a7047dafa5c046d6ef839f36e414ba97463a2f9016870f4085045a702ac1731a2cf Homepage: https://cran.r-project.org/package=eventstream Description: CRAN Package 'eventstream' (Streaming Events and their Early Classification) Implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) . Package: r-cran-eventstudyr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-data.table, r-cran-dplyr, r-cran-estimatr, r-cran-fixest, r-cran-ggplot2, r-cran-mass, r-cran-rlang, r-cran-pracma, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eventstudyr_1.2.1-1.ca2404.1_all.deb Size: 243800 MD5sum: 640289a20a8160d3d1282235c58f8dc1 SHA1: 9f8d15249b15407546b64f2c13cc528ca5196b3b SHA256: ac065789a3dc3ddaeb6c7e38c86d86d5172ca29f4d897d1e5518602df1bdbaf0 SHA512: 906483d785b90465d837f0a006cecbd36779e5f4fde1362281258d660e9de7bd47f5900a5e8833a80b86f12c2c75fbdaefbccda18a4748f9b33a8d9a03c73976 Homepage: https://cran.r-project.org/package=eventstudyr Description: CRAN Package 'eventstudyr' (Estimation and Visualization of Linear Panel Event Studies) Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) . Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) . Package: r-cran-eventtrack Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-muhaz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rpact, r-cran-fitdistrplus, r-cran-gestate Filename: pool/dists/noble/main/r-cran-eventtrack_1.0.4-1.ca2404.1_all.deb Size: 122856 MD5sum: beca698b2cac00fb72ba2273817c5be2 SHA1: 665be2a744c7ca53c5dd9c725cde43d2ce318615 SHA256: 13b851a4e38f0021b337ffc74e8c55b990dd03832e6d5a7d738c2a4b54370ca6 SHA512: b74aa4766d3890dff8ea002996dfe6a115030744788551fdf774c7c3aafde36e83af55853b26b88ebb42c808a40e1f38f0d174160a35924480fc72f19614b071 Homepage: https://cran.r-project.org/package=eventTrack Description: CRAN Package 'eventTrack' (Event Prediction for Time-to-Event Endpoints) Implements the hybrid framework for event prediction described in Fang & Zheng (2011, ). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential. Package: r-cran-eventwinratios Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-eventwinratios_1.0.0-1.ca2404.1_all.deb Size: 101622 MD5sum: 6f55670e130364a158983521e70a0cb0 SHA1: 3be5db4aee19fd5e2a1fb35fa4d20e6fd3747d18 SHA256: 069763d08cfe52205d8fe975fcf17a302fbfe80ef501a4ab1f71bed012f2bd53 SHA512: 793fc960ba6b5a3ce343fcbe7fdd853a4dd64f563e5698313299b4da65a025e8407456a7dbc877f67e76be3504005d2b29cf563e0b1d885d8113462949f29522 Homepage: https://cran.r-project.org/package=EventWinRatios Description: CRAN Package 'EventWinRatios' (Event-Specific Win Ratios for Terminal and Non-Terminal Events) Provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets. Package: r-cran-evi Architecture: all Version: 0.2.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evi_0.2.0-0-1.ca2404.1_all.deb Size: 91112 MD5sum: 341b36f0863b4d7ca514d66c386ccafe SHA1: c6a4009c168d827ff427d046f5bca8b3371ccddf SHA256: 5a0e66ce9eecf19151b71250060740cf5176e81ba62c55dec03a222eb337e937 SHA512: 7459e398c58c00d072b149ec53bc19a099b6e62bb7f435b5f49542f874201c7225968690d8bdecce6deff6efe0a0399feb61d3af2afda0d1fb029e54002c1298 Homepage: https://cran.r-project.org/package=EVI Description: CRAN Package 'EVI' (Epidemic Volatility Index as an Early-Warning Tool) This is an R package implementing the epidemic volatility index (EVI), as discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Package: r-cran-evian Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-profilelikelihood, r-cran-sandwich, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-evian_2.1.0-1.ca2404.1_all.deb Size: 120894 MD5sum: 7e909ea1833458917cd91f3e69680642 SHA1: 9316f02394df5a02b2d2062b0e632dcefb3e2197 SHA256: ea3fba41ec448c65b8ea9dfdb6dd76cea363fdf4e4bd9455617ff803a2c1a2d4 SHA512: 0a8db8605011c4fc175b98151104520f106fdf6bd4631aba1cb84c1f4ff8a991dcd4dfb994f67479aa086ce7163dfe0fee84c568e05d23609954fb9456d20056 Homepage: https://cran.r-project.org/package=evian Description: CRAN Package 'evian' (Evidential Analysis of Genetic Association Data) Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package: Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) . Strug, L. J., & Hodge, S. E. (2006) . Royall, R. (1997) . Package: r-cran-evidence Architecture: all Version: 0.8.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rstanarm, r-cran-loo, r-cran-lattice, r-cran-learnbayes, r-cran-laplacesdemon Filename: pool/dists/noble/main/r-cran-evidence_0.8.10-1.ca2404.1_all.deb Size: 235766 MD5sum: 7a3d13de7d1e3ffed0d73b05c1b13458 SHA1: 821e7d8ba02f3f8bd578f6061c2f19a322cd3edc SHA256: aea76fb4078cc97d4ef820621f72518e3ba646b615728332e3e5d988e2a48245 SHA512: ed14b9759b4cc47aadbcc1f15eba04ef8b4f760a919f33721868b8a9d1abfe75117cd84be3210608234b5158ffb84af07b0fa426b23d8bdcfee7d6c76df8ca74 Homepage: https://cran.r-project.org/package=evidence Description: CRAN Package 'evidence' (Analysis of Scientific Evidence Using Bayesian and LikelihoodMethods) Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach. The functions in the package also make extensive use of graphical displays for data exploration and model comparison. Package: r-cran-evidencefactors Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sensitivitymv Filename: pool/dists/noble/main/r-cran-evidencefactors_1.8-1.ca2404.1_all.deb Size: 47156 MD5sum: 80592c70b753a151e323fee37b1e7ef8 SHA1: 79a1fcdb3303d0eb0e9a3783c8256589c13b1e96 SHA256: 5566e534e15b4fdba1a24d8f587a5c1536fc769acac0ad9c6aab15cf914c4ac4 SHA512: f150ee143844561a2ff1255fbff56e72cc9b5650e08f2e251d2a19011126f74dc03690ff10bbd382a0bd483a947f95fb909f99011f55386b25d72216932f92db Homepage: https://cran.r-project.org/package=evidenceFactors Description: CRAN Package 'evidenceFactors' (Reporting Tools for Sensitivity Analysis of Evidence Factors inObservational Studies) Provides tools for integrated sensitivity analysis of evidence factors in observational studies. When an observational study allows for multiple independent or nearly independent inferences which, if vulnerable, are vulnerable to different biases, we have multiple evidence factors. This package provides methods that respect type I error rate control. Examples are provided of integrated evidence factors analysis in a longitudinal study with continuous outcome and in a case-control study. Karmakar, B., French, B., and Small, D. S. (2019). Package: r-cran-evidenceratio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-waldo, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-evidenceratio_0.1.0-1.ca2404.1_all.deb Size: 76000 MD5sum: 03a36b16b98e65f12c13eca3c7ff58c6 SHA1: f3aeff0ef304fc1fecf21321cfbe2354471ec379 SHA256: c1642e0a051f421df94a653c30f2c37572ad0c42804eedc732f943ff5380d471 SHA512: 633f1b9c6dc3b6d504c93dc1822e1dbf25066596b3fb8ef6d91304fca7c2c3b74afd32171d40d47800007706970a992a5c0ab505db2264c00aed158b35f40d43 Homepage: https://cran.r-project.org/package=evidenceratio Description: CRAN Package 'evidenceratio' (Likelihood-Based Evidence Ratios for Classical Statistical Tests) Implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) . Package: r-cran-evidencesynthesis Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-ggplot2, r-cran-ggdist, r-cran-gridextra, r-cran-meta, r-cran-empiricalcalibration, r-cran-rjava, r-cran-beastjar, r-cran-cyclops, r-cran-hdinterval, r-cran-coda, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-sn, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-evidencesynthesis_1.1.0-1.ca2404.1_all.deb Size: 2182284 MD5sum: 2002aa224b90aacd7f7b9b35d8cf0cfe SHA1: 014bf236434cdc136f3abe87ce30fe2705a9230b SHA256: da065994e88412f8b3b441f5963dce593aa8b04fd27c7cee3a32537ecb49e6e6 SHA512: dbd4febf03503998e044f624457aaf48460c0e436371761a21a42a1213d3173683acc778867f3fb6d0e64ccdb1a6a5a2932b7fe38c6fc64b24aa6b11c9a9b775 Homepage: https://cran.r-project.org/package=EvidenceSynthesis Description: CRAN Package 'EvidenceSynthesis' (Synthesizing Causal Evidence in a Distributed Research Network) Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter. Package: r-cran-evident Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-sensitivity2x2xk, r-cran-sensitivitymult, r-cran-sensitivitymv, r-cran-senstrat, r-cran-dos2 Filename: pool/dists/noble/main/r-cran-evident_1.0.4-1.ca2404.1_all.deb Size: 110138 MD5sum: 53005f8a673c8439742a208181280b30 SHA1: 42070bd626f08feee8971b9de6bfd37e18c645bd SHA256: 902e716bdcd6314c068e40e2fd53a4e6af04ee1dbcb350b9349bd1c818105bc6 SHA512: a1fd59637ad2eb146917cb6bd8e7b24c4c589b769e17a80b1ef90912391c8e3ebcf17a11b8e9bda8e6c72913913edb012f11c172626829a5b67208dc3fbbad09 Homepage: https://cran.r-project.org/package=evident Description: CRAN Package 'evident' (Evidence Factors in Observational Studies) Contains a collection of examples of evidence factors in observational studies from the book Replication and Evidence Factors in Observational Studies by Paul R. Rosenbaum (2021) . Package: r-cran-eviewsr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-magrittr, r-cran-xts, r-cran-zoo, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-eviewsr_0.1.7-1.ca2404.1_all.deb Size: 2267038 MD5sum: 18044d1e2e9a7512b5bf07b72b0cb3c1 SHA1: 91bd6b112f893f44394709482ec48d82b66fbad0 SHA256: 940b11d6edabaebe09e545e4af096057f50e3c4bc629f12152a00a1fbd2d8e63 SHA512: 38abdff2a6eb80905aa71931e2dd665a40c5b41557e006f19d9fa52fb3c9fb9da4697256fc21c88b58d71ce77afcd34e5f35672ae4fa4ea02462f595397b64b2 Homepage: https://cran.r-project.org/package=EviewsR Description: CRAN Package 'EviewsR' (A Seamless Integration of 'EViews' and R) It allows running 'EViews' () program from R, R Markdown and Quarto documents. 'EViews' (Econometric Views) is a statistical software for Econometric analysis. This package integrates 'EViews' and R and also serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R, R Markdown or Quarto documents. For details, please consult our peer-review article Mati S., Civcir I. and Abba S.I (2023) . Package: r-cran-evildice Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-evildice_1.0-1.ca2404.1_all.deb Size: 16742 MD5sum: 4d1a26f3b4df77976678d5cc325f38ca SHA1: 07b3655e10021f839034c8651397621af1535011 SHA256: 0dea15987bd72931985a32ac5935505251a5ca51f81d252650b09d849d2837ad SHA512: 37df998a2e15e930bbab026358afac2ed7a77a313794c3f148b3ddff8a6111c2379a6a1b5fc7b90f10e9af05c4daf3ff5c178b326545cd538a3080c41a514b3b Homepage: https://cran.r-project.org/package=evilDice Description: CRAN Package 'evilDice' (Test Dice Sets for Intransitive Properties) Checks to see whether a supplied set of dice (their face values) are transitive, returning pair-win and group-roll win probabilities. Expected returns (mean magnitude of win/loss) are presented as well. Package: r-cran-evir Architecture: all Version: 1.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-evir_1.7-4-1.ca2404.1_all.deb Size: 464976 MD5sum: 1b3d7d73f002f0fcebd9f05248879807 SHA1: 86a427ead6ef1047cc269ce8b9d5ef2d6b72f565 SHA256: 4946eea0b76667439c0cf0e676b2cd91e1e5d19d1c18aaf3b9694f9bb636d073 SHA512: 607e8c7f0a3b56109190ec76a179a2f14a81a556a447d7b4ce529cf802ccf9f5e84224785088dfb1778d0b4904a4ca97c47d32973505b7f2b9eb98aa71efd130 Homepage: https://cran.r-project.org/package=evir Description: CRAN Package 'evir' (Extreme Values in R) Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions. Package: r-cran-evmissing Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlssx, r-cran-itp, r-cran-nieve, r-cran-revdbayes, r-cran-rust Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evmissing_1.0.2-1.ca2404.1_all.deb Size: 335668 MD5sum: 953766993823b7bcc66d46bdcbd6985a SHA1: 6ac36609e59efce301814184da68a3b58a94e6c7 SHA256: d6cfa35a7d08d54a843115ec06d542c61ae286ee2532ff7e8f369c782a0f1def SHA512: c1a683ce129d56c12378fcb87b79c202631af806f6a8f53388326aa3a954170b8cb2b1f076c2b773dd51ca3f9b6d137b20040cb4b7d9c311baff3abd49bae150 Homepage: https://cran.r-project.org/package=evmissing Description: CRAN Package 'evmissing' (Extreme Value Analyses with Missing Data) Performs likelihood-based extreme value inferences with adjustment for the presence of missing values based on Simpson and Northrop (2026) . A Generalised Extreme Value distribution is fitted to block maxima using maximum likelihood estimation, with the location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing. Example datasets containing missing values are provided. Package: r-cran-evmix Architecture: all Version: 2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5042 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-gsl, r-cran-sparsem Filename: pool/dists/noble/main/r-cran-evmix_2.12-1.ca2404.1_all.deb Size: 4890694 MD5sum: cc6c27a48edbc0d031788ea8d83e6079 SHA1: 07380a03d03f09adb76ae8dec978a407697069a5 SHA256: ecf84b1a8981202e4bb1c81e178095467365cadc52619814f68b1b1b1c47281c SHA512: 900fe35eb60cce2e118a039151cfdbff7f4e55676bf4603e3171e93397b66351a0aa5a64bc197602878da7fdb782792f04045a77c2774180aab47695ab71554a Homepage: https://cran.r-project.org/package=evmix Description: CRAN Package 'evmix' (Extreme Value Mixture Modelling, Threshold Estimation andBoundary Corrected Kernel Density Estimation) The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code. Package: r-cran-evmr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-eva, r-cran-lmomco, r-cran-numderiv, r-cran-rsolnp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evmr_0.2.0-1.ca2404.1_all.deb Size: 355530 MD5sum: 40e5e5b580f3437d6102c977c9123956 SHA1: 60c60d16623c51e231f3b18359db0a39dea98d9b SHA256: fa5861459698ea096163154576e46368398a088b7c95523bd1123e5a114618a2 SHA512: cbdac3dc14982eb0ca2269169eb6def8b00b36c2330628e172f9fd429fb9d3579f72a4f0170ab53ca7745b6672b3e3d286f85bfefed4e0c2b1e8ae9ccd234940 Homepage: https://cran.r-project.org/package=evmr Description: CRAN Package 'evmr' (Extreme Value Modeling for r-Largest Order Statistics) Tools for extreme value modeling based on the r-largest order statistics framework. The package provides functions for parameter estimation via maximum likelihood, return level estimation with standard errors, profile likelihood-based confidence intervals, random sample generation, and entropy difference tests for selecting the number of order statistics r. Several r-largest order statistics models are implemented, including the four-parameter kappa (rK4D), generalized logistic (rGLO), generalized Gumbel (rGGD), logistic (rLD), and Gumbel (rGD) distributions. The rK4D methodology is described in Shin et al. (2022) , the rGLO model in Shin and Park (2024) , and the rGGD model in Shin and Park (2025) . The underlying distributions are related to the kappa distribution of Hosking (1994) , the generalized logistic distribution discussed by Ahmad et al. (1988) , and the generalized Gumbel distribution of Jeong et al. (2014) . Penalized likelihood approaches for extreme value estimation follow Martins and Stedinger (2000) and Coles and Dixon (1999) . Selection of r is supported using methods discussed in Bader et al. (2017) . The package is intended for hydrological, climatological, and environmental extreme value analysis. Package: r-cran-evobir Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr, r-cran-ape, r-cran-geiger, r-cran-shiny, r-cran-phytools Filename: pool/dists/noble/main/r-cran-evobir_1.1-1.ca2404.1_all.deb Size: 166552 MD5sum: 4e6b16219619a3d0a0839bafca862991 SHA1: 833dfae18679183a9037da5e5ef542f5bd68446a SHA256: cd1e5cfefcac262e850d94065f926d2bca110247e5ba9aede0422369b791cfcd SHA512: fff3c8616bf2c662685dc401a6bb5e4933142638ae0abf46073f7b2fa71651b54566aed49079a18a6c6d1a819393a04ec86166fadf00de3694ad4adf06f5e8b7 Homepage: https://cran.r-project.org/package=evobiR Description: CRAN Package 'evobiR' (Comparative and Population Genetic Analyses) Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses. Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching. Package: r-cran-evofe Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-lightgbm, r-cran-xgboost, r-cran-digest, r-cran-uwot, r-cran-quitefastmst, r-cran-genieclust, r-cran-paradox, r-cran-bbotk, r-cran-mlr3mbo, r-cran-lhs Suggests: r-cran-glmnet, r-cran-rhpcblasctl, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lumbermark, r-cran-deadwood, r-cran-keras3, r-cran-httpuv, r-cran-jsonlite, r-cran-farff, r-cran-ranger, r-cran-dicekriging, r-cran-rgenoud, r-cran-mlr3learners Filename: pool/dists/noble/main/r-cran-evofe_1.0.0-1.ca2404.1_all.deb Size: 665424 MD5sum: 49f137d325dd005c1c65da86d9c12a92 SHA1: 5f177f7fa7dd379eae47c242496e272672497293 SHA256: 9b304e8eb628e095b6cfcc3f7268645a6c15c956f5ad41ba13c98196eeb800b7 SHA512: cbbee2d15ed7de026f1d34354ce16b39ced4a78c062421d53ea68fad81784bc1f8a18ebf20aedb817b981dcba5f8a3ddeb685cfd28c271dfcd30bcf126acb735 Homepage: https://cran.r-project.org/package=evoFE Description: CRAN Package 'evoFE' (Evolutionary Feature Engineering) Automates feature engineering using evolutionary algorithms inspired by genetic programming. Starting from raw input features, the package evolves candidate transformation recipes through selection, crossover, and mutation, evaluating fitness via cross-validation or train/validation splits with gradient-boosted tree models ('LightGBM' or 'XGBoost'). Built-in transformers include arithmetic, logarithmic, and power operations, interaction terms, target encoding, quantile and log-based binning, principal component analysis, truncated singular value decomposition, Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, and minimum spanning tree (MST) graph-based clustering. The evolutionary search yields an optimised feature recipe that can be applied to new data for prediction. Methods are described in McInnes et al. (2018) , Ke et al. (2017) , Chen and Guestrin (2016) , Gagolewski (2021) , Gagolewski (2026) , and Gagolewski (2026) . Package: r-cran-evola Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-alphasimr, r-cran-matrix, r-cran-crayon, r-cran-enhancer Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-evola_1.0.9-1.ca2404.1_all.deb Size: 191172 MD5sum: 148ad248fec9ae77771117407a675e35 SHA1: 9cb61380950ab686aa5e8c6d1189633e06f1e3b6 SHA256: 60cf4c042f49bdfb20a3042bd51e33ceedac93dcb4423e0750762ab0dfa120c3 SHA512: 1672dcb3dfa9fe378ade873a167a5c32a597db3262d0f167a52977686feab4c060ea192118ae1f926bfed62892ea95fe057460474ef7970d2d8dce92ca82883b Homepage: https://cran.r-project.org/package=evola Description: CRAN Package 'evola' (Evolutionary Algorithm) Runs an evolutionary algorithm using the 'AlphaSimR' machinery . Package: r-cran-evolmap Architecture: all Version: 1.3.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-curl, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-evolmap_1.3.14-1.ca2404.1_all.deb Size: 2809276 MD5sum: f4d9f218fcb030b19c87997b6670b237 SHA1: a0729fcbf50c9afa74e579d7606983b478257b2e SHA256: 0d590df44ca29ff889634247f3960c98e36162cdc44ec3cb058ae695996773d0 SHA512: d59297508411969c739064410053157c66518a1442cce5c2c8d5fbe2b1b851c14a6eb712e4e9fd8927c93abdbd2d6b3682514b08cdcedb7b2efc6bc7ea555ce5 Homepage: https://cran.r-project.org/package=evolMap Description: CRAN Package 'evolMap' (Dynamic and Interactive Maps) Dynamic and Interactive Maps with R, powered by 'leaflet' . 'evolMap' generates a web page with interactive and dynamic maps to which you can add geometric entities (points, lines or colored geographic areas), and/or markers with optional links between them. The dynamic ability of these maps allows their components to evolve over a continuous period of time or by periods. Package: r-cran-evolution Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-cli, r-cran-jsonlite, r-cran-base64enc Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-evolution_0.1.1-1.ca2404.1_all.deb Size: 113700 MD5sum: 610ddc7bae54befbe8ac55d7e3b2db49 SHA1: edae3e1fc40d3ac0da9007713a26fd6ad058f53c SHA256: ed16ff145a507636a5593fb5dda608aaaaecf0271197e221d5a015ba3600d557 SHA512: e916daced0f3c4d82188a1c6f4f17ef75979b12afb39c5362ef08522263290e8192bb3990d8ee595a126a41a9d6e9975143595c04b863b68e591d0813bfb024d Homepage: https://cran.r-project.org/package=evolution Description: CRAN Package 'evolution' (A Client for 'Evolution Cloud API') Provides an 'R' interface to the 'Evolution API' , enabling sending and receiving 'WhatsApp' messages directly from 'R'. Functions include sending text, media (image/video/document), audio, stickers, geographic locations, contacts, polls, interactive lists and button messages. Also includes number verification and structured CLI logging for debugging. Package: r-cran-evolutionarygames Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-geometry, r-cran-ggplot2, r-cran-interp, r-cran-mass, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/noble/main/r-cran-evolutionarygames_0.1.2-1.ca2404.1_all.deb Size: 2968228 MD5sum: 0bd43f4b63de1aa2664f9abf1149e6d0 SHA1: 52d4b3d69905638e87770d24d1aa80a8fd398732 SHA256: e264f066fcdb1e38859809099d15f4a666f4adf17d8335f6eff12cf9be4ccae6 SHA512: 3460007a083b9424d2fcbc4923d13e3d6a435633eb3e511a8f8964ab89a3d122fdadf51446c5c511c7da03527f5277090333344b99859ee44d1ca4191701ca2a Homepage: https://cran.r-project.org/package=EvolutionaryGames Description: CRAN Package 'EvolutionaryGames' (Important Concepts of Evolutionary Game Theory) Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided. Package: r-cran-evolvability Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-matrix, r-cran-ape, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-evolvability_2.0.1-1.ca2404.1_all.deb Size: 309232 MD5sum: 044bbbc66930e789464752ed87ed2f25 SHA1: a44a11f8d9231a4a64486c91a7417bd254be5fb0 SHA256: c080d8b2cad2448881591140d858784f909e8561c6d385aedf867654d49364fa SHA512: 8beaa373a6efaf2b877f80ad37a3e6d268839d9de904a03fd8090cf89ca5f06be3049d90dd52dcfe7a1ffbcc51a08b4a8f318c25d9ffdb7969b66243f108664f Homepage: https://cran.r-project.org/package=evolvability Description: CRAN Package 'evolvability' (Calculation of Evolvability Parameters) Provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology ). The package was released with Bolstad et al. (2014) , which contains some examples of use. 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In particular, the tools provided include functions that simulate evolutionary processes (e.g., genetic drift, natural selection within a single locus) or concepts (e.g. Hardy-Weinberg equilibrium, phylogenetic distribution of traits). More than only simulating, the package also provides tools for students to analyze (e.g., measuring, testing, visualizing) datasets with characteristics that are common to many fields related to evolutionary biology. Importantly, the package is heavily oriented towards providing tools for inquiry-based learning - where students follow scientific practices to actively construct knowledge. For additional details, see package's vignettes. Package: r-cran-evomorph Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-geomorph, r-cran-stringr Filename: pool/dists/noble/main/r-cran-evomorph_0.9-1.ca2404.1_all.deb Size: 417574 MD5sum: 3b69e900053162833fffb392bf3ab12f SHA1: 82b2590e82e4dde2257d53286701f4879e5060f4 SHA256: ba724d2568957fc99a2944669168d47e961256dd10f3bc2a48c204649f8e5dea SHA512: 760f5d0de4795c16ac119c183f465395bd3a769cbead77b854d7362044ed9e320e3161d5263a2ec2b98f6b31526e1a8462f488d3e24d0aa396c30d9786cca2b2 Homepage: https://cran.r-project.org/package=Evomorph Description: CRAN Package 'Evomorph' (Evolutionary Morphometric Simulation) Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions. Package: r-cran-evoper Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rrepast, r-cran-logger, r-cran-boot, r-cran-reshape, r-cran-ggplot2, r-cran-desolve, r-cran-plot3d, r-cran-plyr, r-cran-data.table, r-cran-rnetlogo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evoper_0.7.0-1.ca2404.1_all.deb Size: 521636 MD5sum: c8d3abdef48f7d556417b6caf3b6a696 SHA1: 40d421b43973aa25a05048cdb76abac03cbc97e4 SHA256: 02520d514a27ec843c2e49e66025b81b8bd3023976744e93672d4412e2557e34 SHA512: 66295163bad8825fffb563c471fd47b807fda4ea152a09bb00893106dcd540a52a1b58d2d6b02a38ed542aa438fffc51b36fb35e37bae53b6bff84f8dd6aefc7 Homepage: https://cran.r-project.org/package=evoper Description: CRAN Package 'evoper' (Evolutionary Parameter Estimation for 'Repast Simphony' Models) The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework () and with NetLogo () which are the most used frameworks for Agent-based modeling. 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Please see for information about the package and the implemented models. 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The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) . Package: r-cran-ewascaller Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-digest, r-cran-ggplot2, r-cran-ggwordcloud Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ewascaller_0.1.0-1.ca2404.1_all.deb Size: 98086 MD5sum: 456d492c298442f1bd3564d419166972 SHA1: 47151f0a42cb222f6a925d354719ade79f3d1679 SHA256: ab22b1ceafd2d9d97b82ca2ae0dfdeda73f1d1f719398f542d677d364aa40435 SHA512: 8fa852823a4d35bf905f9dc90be19a5503088bb21b2a9731f166357ac89b54eca6e2921caaec5cfec68d3bbbb7f1ca5c32914c948963e3a17b153db6cb50c694 Homepage: https://cran.r-project.org/package=EWAScaller Description: CRAN Package 'EWAScaller' (Query and Analyse the 'EWAS Atlas' Database) Provides a client for the 'EWAS Atlas' web services (; Li et al. (2019) ), allowing users to query epigenome-wide association study (EWAS) data by CpG (cytosine-phosphate-guanine) probe identifier, gene symbol, or genomic region, and to run trait, Gene Ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, and genomic location enrichment analyses on a set of CpG probes. Query functions support concurrent, rate-limited requests to the remote service. Results are returned as tidy data frames with dedicated summary and plotting methods, including word clouds of enriched 'EWAS Atlas' trait terms. 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It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics. 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It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics. Package: r-cran-ewr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ewr_1.4-1.ca2404.1_all.deb Size: 40320 MD5sum: 8ee7e2e407e25c3f9cfa46a5216fe6bf SHA1: 03fa35e07af68d6bb04cac275b6846a9edda530a SHA256: c0ad41b20b9f3a3a22249ff6f96b432c7807b40e9aeba1472ff644c3330c949e SHA512: d4d57f06e917516131bfea37c0c8d1915b1087504d0be38f9c87e40ba6ff729e542da6ba212cbfdca3519101cd5082af3d476e286c0ff546e722d75e53d0a173 Homepage: https://cran.r-project.org/package=EwR Description: CRAN Package 'EwR' (Econometrics with R) Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. It is possible to makes Durbin two stage method for autocorrelation, generalized differencing method for correction autocorrelation, Hausman Test for identification and computes LM, LR and Wald test statistics for redundant variable by using the functions written in this package. Package: r-cran-ews Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-ews_0.2.0-1.ca2404.1_all.deb Size: 122824 MD5sum: 5a3c7c821e587ffeba152cbdb0919d24 SHA1: 4a0baf247e499d8e1d3e40cfb699fafcaa8236cf SHA256: b0cbbcf54350c72eb1bc92ef4974e0d5ac8cd6bdbba93682b64948205e45bddf SHA512: af7522cd179e35b441682ff7f4a66a5a01a6d2652c0621357f238109aa72a5de6d9e09df3dea71598001b76d8f40498ec355ba4fd2c949bbfceba533518c69d5 Homepage: https://cran.r-project.org/package=EWS Description: CRAN Package 'EWS' (Early Warning System) The purpose of Early Warning Systems (EWS) is to detect accurately the occurrence of a crisis, which is represented by a binary variable which takes the value of one when the event occurs, and the value of zero otherwise. EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) . Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) . This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers. Package: r-cran-ewsmethods Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-egg, r-cran-ggplot2, r-cran-gtools, r-cran-forecast, r-cran-foreach, r-cran-infotheo, r-cran-mar, r-cran-moments, r-cran-redm, r-cran-reticulate, r-cran-scales Suggests: r-cran-data.table, r-cran-devtools, r-cran-doparallel, r-cran-knitr, r-cran-fs, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ewsmethods_1.3.3-1.ca2404.1_all.deb Size: 3043156 MD5sum: fc5ec3d9fb76e7dadb7ae2c6bf339d9b SHA1: e5828f418911b4180faf7eb7a7a7041436c4360c SHA256: d78ce5f7c8a2b5ab2afb02175a5646e58ab08e18a84cc97b1ec32caa942358a8 SHA512: 025f5c620712a2831219db61212739242368535e927ce5e274f8d75a460eb7b2e1d244f4558dc635360a4dde6f48eae699c6bbabc9de02e2dc4230d22335c1db Homepage: https://cran.r-project.org/package=EWSmethods Description: CRAN Package 'EWSmethods' (Forecasting Tipping Points at the Community Level) Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) , Deb et al. (2022) , Drake and Griffen (2010) , Ushio et al. (2018) and Weinans et al. (2021) for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials. Package: r-cran-exact.n Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4750 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-exact.n_1.1.1-1.ca2404.1_all.deb Size: 4343938 MD5sum: b05a040ea4472198dce5626e6569466e SHA1: a0094d7475f710a6872055fa9278b7564f89047e SHA256: 7c884619dce68f6e40fb7e288978d6419409a94e931664d661931a1da3b75b80 SHA512: 9b99d451d4f2528c0ba161e4fc72103f9302516a601e081b24858a918767c27af5446fa7ea6c8e6cefca2228f7f75db299fe813801f31a4a61e381b3761fc9ed Homepage: https://cran.r-project.org/package=exact.n Description: CRAN Package 'exact.n' (Exact Samples Sizes and Inference for Clinical Trials withBinary Endpoint) Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see “Exact samples sizes for clinical trials subject to size and power constraints” by Lloyd, C.J. (2022) Preprint . Package: r-cran-exact2x2 Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exactci, r-cran-ssanv Suggests: r-cran-testthat, r-cran-exact, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-exact2x2_1.7.0-1.ca2404.1_all.deb Size: 931330 MD5sum: dc3cad39ad40e68360551f0daef8e0c1 SHA1: 55802b5e8588e2920b758c830b3b88daaf581cb4 SHA256: 1b8ed2a7ee65e5b9f6aa4e9ed9e457aa8bafa7a1b85a156e79ce37de68b2ef1c SHA512: 1306c0065c0fc556cbe471764e1fe0b3cfe9f984db1480038b0d6703e963e824314dcfd26348fbba444de2100f3b3e8e989c36bb96c9498feb77b71a4a4c6d9a Homepage: https://cran.r-project.org/package=exact2x2 Description: CRAN Package 'exact2x2' (Exact Tests and Confidence Intervals for 2x2 Tables) Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, ). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, ), an unconditional exact test for the situation where the controls are all expected to fail. Gives confidence intervals compatible with exact McNemar's or sign tests (Fay and Lumbard, 2021, ). For review of these kinds of exact tests see Fay and Hunsberger (2021, ). Package: r-cran-exact Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-exact_3.3-1.ca2404.1_all.deb Size: 204234 MD5sum: 2a4ace04740549178b028eae2ae0eeab SHA1: cc90198e1d73c0e2e40e7de6fd8b1d283b80d59f SHA256: 86686192024c251614237e91d9d79a8abc061e936aa48bed985c31c3c2aceec1 SHA512: 9028190f55fec9d4c84cbb127786f340e06e26b2842ee1472f08e5fa76a7b262c72503a967fa55a557ebce7e005a18dc4eedea5046dc31d50779e96611019584 Homepage: https://cran.r-project.org/package=Exact Description: CRAN Package 'Exact' (Unconditional Exact Test) Performs unconditional exact tests and power calculations for 2x2 contingency tables. For comparing two independent proportions, performs Barnard's test (1945) using the original CSM test (Barnard, 1947 ), using Fisher's p-value referred to as Boschloo's test (1970) , or using a Z-statistic (Suissa and Shuster, 1985, ). For comparing two binary proportions, performs unconditional exact test using McNemar's Z-statistic (Berger and Sidik, 2003, ), using McNemar's conditional p-value, using McNemar's Z-statistic with continuity correction, or using CSM test. Calculates confidence intervals for the difference in proportion. This package interacts with pre-computed data available through the ExactData R package, which is available in a 'drat' repository. Install the ExactData R package from GitHub at . The ExactData R package is approximately 85 MB. Package: r-cran-exactamente Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-exactamente_0.1.1-1.ca2404.1_all.deb Size: 74792 MD5sum: 978f3560d536e5becc75afa9d7c89bbd SHA1: ec62b5a5cf02b531cabc1b00d36aa3101005c668 SHA256: cdc188c3036a6dd5fb7df24fb1f91bbbb55f6d2565ce973ca068909e76912412 SHA512: 537d0329a344c1d70e280bafbc65ba00eee15ec40201a7fd724f9816bf38de753ce4edde129e2a17b865f68b2516532334c9ec3c5ca562e6eb0aea58f3de701f Homepage: https://cran.r-project.org/package=exactamente Description: CRAN Package 'exactamente' (Explore the Exact Bootstrap Method) Researchers often use the bootstrap to understand a sample drawn from a population with unknown distribution. The exact bootstrap method is a practical tool for exploring the distribution of small sample size data. For a sample of size n, the exact bootstrap method generates the entire space of n to the power of n resamples and calculates all realizations of the selected statistic. The 'exactamente' package includes functions for implementing two bootstrap methods, the exact bootstrap and the regular bootstrap. The exact_bootstrap() function applies the exact bootstrap method following methodologies outlined in Kisielinska (2013) . The regular_bootstrap() function offers a more traditional bootstrap approach, where users can determine the number of resamples. The e_vs_r() function allows users to directly compare results from these bootstrap methods. To augment user experience, 'exactamente' includes the function exactamente_app() which launches an interactive 'shiny' web application. This application facilitates exploration and comparison of the bootstrap methods, providing options for modifying various parameters and visualizing results. 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Package: r-cran-exactcione Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-exactcione_1.0.5-1.ca2404.1_all.deb Size: 100916 MD5sum: 42e11e2726bbfffb38c8515f708ae173 SHA1: c9013f3a142994c390612600379b39892c91e87a SHA256: 341aee8950869be0690f0270d6d8006c89d8c3cecefc3f561d7e9d229b0b358c SHA512: 0cd4073b2381e95d7977b509c8ae65dbf056befd1cccd920df086a5f2cfed6ff39ed217cdc966bc062670a3f1eb4889ab5b54fab2f85bc51112e2436c66ce396 Homepage: https://cran.r-project.org/package=ExactCIone Description: CRAN Package 'ExactCIone' (Admissible Exact Intervals for One-Dimensional DiscreteDistributions) Construct the admissible exact intervals for the binomial proportion, the Poisson mean and the total number of subjects with a certain attribute or the total number of the subjects for the hypergeometric distribution. Both one-sided and two-sided intervals are of interest. This package can be used to calculate the intervals constructed methods developed by Wang (2014) and Wang (2015) . Package: r-cran-exactcox Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/noble/main/r-cran-exactcox_0.1.0-1.ca2404.1_all.deb Size: 20882 MD5sum: ee203c8787cf291ac176d27a290e21b3 SHA1: 3bcb902eba67f113af677118dede2ba2d5c9633a SHA256: 5912f4d4cafdda81974a4ca6530cb6348290fb7c2414e97e17c7a8c6695761d1 SHA512: cc1b13bd56bf5884ee5d1b4e50a11e191a4429b3e7f55bb2b632d281dd16c3d0ccc84ba589b87fb9dc2091bf9a4f56a3b802f36ed72a5eb07a3a87a082e7170c Homepage: https://cran.r-project.org/package=ExactCox Description: CRAN Package 'ExactCox' (Exact Test and Exact Confidence Interval for the Cox Model) Performs the exact test on whether there is a difference between two survival curves. Exact confidence interval for the hazard ratio can also be generated for the Cox model. Package: r-cran-exactgmh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-shiny Filename: pool/dists/noble/main/r-cran-exactgmh_0.1.0-1.ca2404.1_all.deb Size: 29214 MD5sum: b9db726454bbf26c1488acdf1be7593d SHA1: 62250a2b628fd66fd8dc24f8a06e02acbda36015 SHA256: 45091b3bf990d6fc3b17c2bc1bf727b1a2971a83531c451bed0bff98714e299d SHA512: 102a7c16f73592248cd7b01f1228a0443413a0221dfe490393c407a29ad9e38274298ab27113b88fb3ddecaba5230994279a8ad4a4a95c2ffb1edea0f028a085 Homepage: https://cran.r-project.org/package=exactGMH Description: CRAN Package 'exactGMH' (Exact and Permutation-Based Mantel Tests for Differential ItemFunctioning in Dichotomous and Polytomous Items) Screens dichotomous and polytomous test items for Differential Item Functioning (DIF) using an extension of the Mantel (1963) and generalized Mantel-Haenszel statistic, with statistical significance computed via permutation rather than the conventional asymptotic chi-square approximation. Following Hemerik and Goeman (2018) , the permutation p-value is exact at the nominal level rather than an approximation, even for a finite number of permutations. This makes the test valid for small samples (fewer than 200 examinees per group), a condition common in classroom-, program-, and institution-level assessment where existing exact-inference options in other software are restricted to dichotomous items only. An optional Benjamini-Hochberg or Bonferroni correction addresses multiple comparisons when screening many items at once. Package: r-cran-exactltre Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-popdemo Filename: pool/dists/noble/main/r-cran-exactltre_0.1.2-1.ca2404.1_all.deb Size: 3257812 MD5sum: efc9d93bb3be85b6a962484a2343c958 SHA1: 00da3399213c45754d29153fc0038ee596d6d825 SHA256: 05f2ed5bb0d8e6220bf5fc7ed77fc150139408202407a112f08f2af22558bf6b SHA512: e5bdf2f5aaa4fdfd0f356b922f2121f490a2db8043e86659fd582c9c51b128fea566dbe835c0da722dca4bb918189ed1257b416cf834579d609ce9aa3911398a Homepage: https://cran.r-project.org/package=exactLTRE Description: CRAN Package 'exactLTRE' (An Exact Method for Life Table Response Experiment (LTRE)Analysis) Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) . We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) . For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) . 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Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research. 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It is designed for multiple-choice, true-false, and open-ended questions. The toolkit is usable with datasets in 1-0 or other formats. Key analyses include difficulty, discrimination, response-option analysis, and reports. The classical test theory methods used are described in Ebel and Frisbie (1991, ISBN:978-0132892314). 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Sort of like python 'doctests' for R. Package: r-cran-exams.forge.data Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exams, r-cran-exams.forge Suggests: r-cran-aer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-exams.forge.data_0.1.3-1.ca2404.1_all.deb Size: 435682 MD5sum: 2f9bddcfcc7149af714846dd9c367cec SHA1: 7d00e2f90a0f710da7f4760312fc475669a4401e SHA256: fc5bc546c7fd4b8b11e0fbb53fb98f5389febe197cc3b07ffa9872ef37b20270 SHA512: 3d8a360fd9357fe5d818d2dcc3378865d3364cfb15f63b88f17052d1a18d8a706310b3194cd4b4028bc171a6f89a49eb94726424da7d1f93fe393acb152d1c4e Homepage: https://cran.r-project.org/package=exams.forge.data Description: CRAN Package 'exams.forge.data' (Sample and Precomputed Data for Use with 'exams.forge') Provides a small collection of datasets supporting Pearson correlation and linear regression analysis. 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(2009) . Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'. 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Package: r-cran-exeval Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mapbayr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-mrgsolve, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-exeval_0.0.1-1.ca2404.1_all.deb Size: 3210730 MD5sum: cff4e9849d81cb9b78b77be58e0675a4 SHA1: d0b9d93a702db69219297d8a969a8bd567e45d0e SHA256: f44c9716fbf7e87ac8883f2e68a81d3211e22db71c4e0bd0f71385b5f8d72422 SHA512: a00c93ac25506e48842b5ee5326aadd1987198322d988ca2b19a61a83389569501d4e1a7e867def3c9fb3c4332eefdfffde5d87be04c6d47d382297240f70817 Homepage: https://cran.r-project.org/package=exeval Description: CRAN Package 'exeval' (External Evaluation of PopulationPharmacokinetic-Pharmacodynamic (popPKPD) Models) Provides tools to automate external pharmacokinetic model evaluation workflows, including Bayesian forecasting, predictive performance metrics, diagnostic plotting, and automated reporting. Package: r-cran-exgaussestim Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-nloptr, r-cran-invgamma, r-cran-dlm, r-cran-fitdistrplus, r-cran-gamlss.dist Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-exgaussestim_0.1.2-1.ca2404.1_all.deb Size: 34270 MD5sum: f8efd68abacdc7b4b0605b4aa99444d8 SHA1: 079365fe106ab2dab18403733e49bff3a06acb3e SHA256: d53d3e1e55548bd4dea5bbe81bafc869c7354e2afded1f683804f28cbf53a382 SHA512: 5588b6b6fbf0f3e896c98799e0e2c88dcc60c77456eb33647b4859dda06d2103b58ad9f44731468f2f10b7328be7a985b109a1a7bfe81898c983f804b90cc63b Homepage: https://cran.r-project.org/package=ExGaussEstim Description: CRAN Package 'ExGaussEstim' (Quantile Maximization Likelihood Estimation and BayesianEx-Gaussian Estimation) Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) ; McCormack, P. D., & Wright, N. M. (1964) ; Van Zandt, T. (2000) ; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) ; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) . Package: r-cran-exhaustiverasch Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-erm, r-cran-psychotree, r-cran-psych, r-cran-tictoc, r-cran-psychotools, r-cran-pairwise, r-cran-arrangements, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-exhaustiverasch_0.3.7-1.ca2404.1_all.deb Size: 256028 MD5sum: c7921133aafaf72e7e13b8446c1a99e2 SHA1: 6b0dbf09629d8a75bc58b20c0e44769edd259c70 SHA256: bf003751fcc6abf5ebf57cce9abdfd5fe7b8b13be1afb00eb28204d8d0aa0d9d SHA512: f13132cd576cdcb10d5c422768f3c0c5ee2c38bde177a13c2f7eac217cc0e692a6a6e3fcc366b42ee2d105ca5e3fdce5cd08319a0007eeb439e5ef6a4986b7a2 Homepage: https://cran.r-project.org/package=exhaustiveRasch Description: CRAN Package 'exhaustiveRasch' (Item Selection and Exhaustive Search for Rasch Models) Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used. 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ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, Lyrics3, as well as the maker notes of many digital cameras by Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony. Package: r-cran-exmort Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8563 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-forecast, r-cran-ggplot2, r-cran-ggrepel, r-cran-htmltools, r-cran-isoweek, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-mgcv, r-cran-openxlsx, r-cran-plotly, r-cran-reactable, r-cran-readxl, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-zoo Suggests: r-cran-dsir, r-cran-officedown, r-cran-testthat, r-cran-tinytex Filename: pool/dists/noble/main/r-cran-exmort_0.1.1-1.ca2404.1_all.deb Size: 3394036 MD5sum: 2b157852418907f254770525a166870c SHA1: 6d7c285ffd4a49f36cae9452906be3b9316af6db SHA256: 141f440b7f3b8bb4a600872c869b09f4cc5f5c284124244aa441754342849bfe SHA512: ac6968e8ce3239ac652090a59f472c7153ac412b65f2bfe32b0674f9c415aca5212a3310a874cd75b8b0f6e919ba7b70b8ee640cd66b45df08143734096205a8 Homepage: https://cran.r-project.org/package=exmort Description: CRAN Package 'exmort' (All-Cause and Excess Mortality Calculator) An interactive 'shiny' application that estimates all-cause mortality and excess mortality from country-level weekly or monthly death counts. Users supply observed deaths and an event calendar (for example COVID-19 waves or typhoons); the app fits one or more statistical baseline models (historical average, negative binomial regression, quasi-Poisson regression, zero-inflated Poisson regression, ARIMA (autoregressive integrated moving average) and SARIMA (seasonal ARIMA) models, GAM (generalized additive model) splines, and the model of Karlinsky and Kobak (2021) ) using periods outside the supplied events, estimates expected deaths across the observed series, and reports excess deaths, P-scores (excess deaths as a percentage of expected deaths) and confidence limits with tables, plots and downloadable reports. Launch the application with run_app(). Package: r-cran-exnruleensemble Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn Filename: pool/dists/noble/main/r-cran-exnruleensemble_0.1.1-1.ca2404.1_all.deb Size: 26370 MD5sum: 4a930f24ed5285cc5a2768850e81707f SHA1: 3f444426a4435405988a5d1a9878ae2c3e877d88 SHA256: 04f24e3b8fff412cf1cabdfa880882479ca65aebb84dfb71ba65b89f0ec3df55 SHA512: 0ed570e0d8ecbd6a3adb6efd1a9c66e4773b0a52e8e932e38218a6e96cf316403acd060c44bd775de97e88d2d4b3fd8368764279a466c1674be442264c8dc73d Homepage: https://cran.r-project.org/package=ExNRuleEnsemble Description: CRAN Package 'ExNRuleEnsemble' (A k Nearest Neibour Ensemble Based on Extended NeighbourhoodRule) The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) . 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'EDGAR' was originally developed as a suite of 'Excel' workbooks by the Biometrics team at Rothamsted Research . The algorithms were subsequently re-implemented in the open-source 'Python' project 'rotsl/edgar' , distributed as the 'edgar-design' package on 'PyPI' . This R package is a native R port of that 'Python' implementation: it does not require 'Python', 'reticulate' , or any external service at runtime, and provides deterministic, reproducible randomisation for nine experimental designs including alpha designs (Patterson and Williams, 1976) . Cross-language reproducibility with the 'Python' implementation is achieved by porting the Mersenne Twister seeding implementation from 'CPython' and the Fisher-Yates shuffle to native R. Package: r-cran-experimentr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-experimentr_0.1.0-1.ca2404.1_all.deb Size: 64492 MD5sum: 81adb7e73721c3c0b0d3d442be37a360 SHA1: 19fcb6e110e395db2ac20ed95c2ea5bd7d9df1ed SHA256: 287e91d6b54233761e0edb6900e67047a7de22379a395a1107d5b69445ae120f SHA512: 0bc64fa4e0972ad66458cb1fd10dee2317f59914b4b60155d1168359f625cfd23fc19765f856b27cdebd7b2b191a097748dbf213e81604b9531bdc0989cd8aeb Homepage: https://cran.r-project.org/package=experimentr Description: CRAN Package 'experimentr' (Datasets Used in Social Science Experiments: A Hands-onIntroduction) Contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at . Package: r-cran-expertchoice Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-far, r-cran-dplyr, r-cran-doe.base, r-cran-rlist, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-expertchoice_0.2.0-1.ca2404.1_all.deb Size: 314692 MD5sum: c228f3d2f35d4fbb777b17788828fd09 SHA1: ced3a9ac7465044a7ff9447f8a38b1a7b53017c3 SHA256: a7256a576771b0ec85a0f11655da822e0b5fb921f0dd3bd393032a2c3cbe1c10 SHA512: cbcd87da3b0b66753a488d58a2f6ed92633883904cad496b84cc4458b2d3e1f4e7c79b91111ab35bbc71099639fa6e7448075979458139472017fe79e457e561 Homepage: https://cran.r-project.org/package=ExpertChoice Description: CRAN Package 'ExpertChoice' (Design of Discrete Choice and Conjoint Analysis) Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) . Package: r-cran-expgenetic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-deseq2, r-cran-futile.logger, r-cran-ggplot2, r-cran-ggsci, r-cran-plyr, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-expgenetic_0.1.0-1.ca2404.1_all.deb Size: 138400 MD5sum: 2489d775d97941b2511f11d267fc3ea3 SHA1: 4ed71433ebdc78d55ec65f6203c988e3ff734779 SHA256: de86e11407da97eda76d8e4bd94223f7cc8300ccdb9c74bffda251c749c8cdc9 SHA512: fd50441d2c3aaef9e3c588bab51bf3d8b9ea5d7316a8539e764d6d122308dd8f808aceed0f84090b67e2cf89696b727fa0c8bf6a0a68e6c4663a042c40f2c721 Homepage: https://cran.r-project.org/package=ExpGenetic Description: CRAN Package 'ExpGenetic' (Non-Additive Expression Analysis of Hybrid Offspring) Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being. Package: r-cran-expimage Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4398 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-rstudioapi, r-cran-progress, r-cran-randomforest, r-cran-ggplot2, r-cran-crayon, r-cran-doparallel, r-cran-foreach, r-cran-schemr Suggests: r-cran-biocmanager, r-bioc-ebimage, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-expimage_0.10.1-1.ca2404.1_all.deb Size: 3300660 MD5sum: 11acf140b1e5c969451ce70eaf85e166 SHA1: 1dc9fc16a7bab91f366e6972dab52df3570fb261 SHA256: 6653f168a39c09300c93feddfe543487c27aca0a6d09cbadca6ec17a9905ba91 SHA512: 8608f2886944ef2deabbce9e83bf17fe300afb506ae8b8eb233323c838040f1ee8393de0664d60f0518dc17931e21645388f1332bd94cea7f1a54af0b37b8d32 Homepage: https://cran.r-project.org/package=ExpImage Description: CRAN Package 'ExpImage' (Analysis of Images in Experiments) Tools created for image analysis in researches. There are functions associated with image editing, segmentation, and obtaining biometric measurements (Este pacote foi idealizado para para a analise de imagens em pesquisas. Ha funcoes associadas a edicao de imagens, segmentacao, e obtencao de medidas biometricas) . Package: r-cran-expirest Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-expirest_0.1.7-1.ca2404.1_all.deb Size: 334074 MD5sum: 7fa1edbaf560dd0e5c8927f90bdde630 SHA1: c63202bfa638569a40c1bd56977a37fd32b41ad7 SHA256: a33316a51a6e7156caa0517e2863b527dfe474dddc31f053c91e269a9b6f0c5c SHA512: 5128cd3403073cc36658e57664afc8533ecfd0cf688df409f4f060130f57c71542fa9185f291d2a1fcdf9e4664be3cd415cc51f1f1564c1c5cec78d7f16b899c Homepage: https://cran.r-project.org/package=expirest Description: CRAN Package 'expirest' (Expiry Estimation Procedures) The Australian Regulatory Guidelines for Prescription Medicines (ARGPM), guidance on "Stability testing for prescription medicines", recommends to predict the shelf life of chemically derived medicines from stability data by taking the worst case situation at batch release into account. Consequently, if a change over time is observed, a release limit needs to be specified. Finding a release limit and the associated shelf life is supported, as well as the standard approach that is recommended by guidance Q1E "Evaluation of stability data" from the International Council for Harmonisation (ICH). Package: r-cran-explainer Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cvms, r-cran-data.table, r-cran-dplyr, r-cran-egg, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggpubr, r-cran-magrittr, r-cran-plotly, r-cran-tibble, r-cran-tidyr, r-cran-writexl, r-cran-gridextra, r-cran-scales Suggests: r-cran-cowplot, r-cran-mlr3, r-cran-mlr3learners, r-cran-knitr, r-cran-broom, r-cran-iml, r-cran-forcats, r-cran-mlr3viz, r-cran-plotroc, r-cran-psych, r-cran-reshape2, r-cran-remotes, r-cran-mlbench, r-cran-ranger, r-cran-precrec, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-explainer_1.0.2-1.ca2404.1_all.deb Size: 164260 MD5sum: c16ce1da5e09692db7aca718a80a653c SHA1: 4d315dde58ea9e75a7d8d4000f87863223d0a599 SHA256: 048024b39c80e5c4ca1324c55edf76d9c7e3f9c5abbb0307ae54325e3d223ed5 SHA512: c88696d1e5dd45b465e40d4cd4c784845da8d316265bb3e6cb885b8b281f8cd4687565219be60902f91b34b64d42b09a984229f7a822d6a06642aa4de60938d7 Homepage: https://cran.r-project.org/package=explainer Description: CRAN Package 'explainer' (Machine Learning Model Explainer) It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements. Package: r-cran-explainprediction Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corelearn, r-cran-semiartificial Suggests: r-cran-nnet, r-cran-e1071, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-explainprediction_1.3.0-1.ca2404.1_all.deb Size: 79946 MD5sum: 0b1668801557cc3fb0e7391ae32b1e3a SHA1: dc05306dde6e92bd9ccb17554dd088d5fa87544a SHA256: 8fcc5dd53511727078df5522a42c3832031b4cb8e45ebe2795383468a6af408e SHA512: 69a1e12c29c2560cf6e336486c1ce7c2bf1b3bb3bb727ab66fbe00d215b28b2103ab32c3048362d9208528a956385c67f09ca214423d4eb53d80f4d0809a55ef Homepage: https://cran.r-project.org/package=ExplainPrediction Description: CRAN Package 'ExplainPrediction' (Explanation of Predictions for Classification and RegressionModels) Generates explanations for classification and regression models and visualizes them. Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly in the model. The IME method is slower as it samples from all feature subsets. For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) , and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18). All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'. Package: r-cran-explodelayout Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-explodelayout_0.1.3-1.ca2404.1_all.deb Size: 72470 MD5sum: 98e886063416352c3a61d46f50b37349 SHA1: 06d2f4afbc3bafbdb8b527621bfa4117dcb0c2b6 SHA256: 34ea5c20c94ffdc4b69994a2e7fca63a1c9ad977a998f7245ee2122a80bdea03 SHA512: cf585982860be45bd7a553cd39cc56aec4e8f9dc635df4d3ab6f1850b5cc181eb59e22e1202f01ade441abdc2fc3d620a71646b6c57dfaa7633571c653c4b759 Homepage: https://cran.r-project.org/package=ExplodeLayout Description: CRAN Package 'ExplodeLayout' (Calculate Exploded Coordinates Based on Original NodeCoordinates and Node Clustering Membership) Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual “hairball” that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be “exploded” to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which “explodes” the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl] that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) ). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks. Package: r-cran-explodemap Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-purrr, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tigris Filename: pool/dists/noble/main/r-cran-explodemap_0.2.0-1.ca2404.1_all.deb Size: 370652 MD5sum: 37a2b865cb2b592a8495b40c4193f8ee SHA1: 082d9401aee15846281af1e46d105793dac49e03 SHA256: 0c441fa37e8e86b2745a734712488efda45994204647714a835788c45c873c14 SHA512: 67a32817f65b0235928ab04d1a2fa8a8f9b5d2da3a726312bc154d5157ba62b6d183dcf378f434002aaa413156c6910f6c49cf793b24f82eba22f26877c0e051 Homepage: https://cran.r-project.org/package=explodemap Description: CRAN Package 'explodemap' (Hierarchical Exploded-View Cartography) Tools for generating hierarchical exploded-view maps from dense administrative boundary data. The package applies rigid-body translations to polygon geometries using a centroid-driven vector field, preserving the internal geometry of each feature while separating units within and across regions. Parameters can be derived analytically from dataset geometry using closed-form models for regional separation and local expansion. The package also includes grouped layouts, optional bounded collision refinement, and an interactive focus-map widget for selected-area inspection in 'htmlwidgets' and 'Shiny'. It implements the methodology described in George Arthur (2026) "A Hierarchical Vector-Based Framework for Multi-Scale Exploded-View Cartography". Package: r-cran-explor Architecture: all Version: 0.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-highr, r-cran-formatr, r-cran-scatterd3, r-cran-rcolorbrewer Suggests: r-cran-factominer, r-cran-ade4, r-cran-gdatools, r-cran-mass, r-cran-quanteda, r-cran-quanteda.textmodels, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-explor_0.3.11-1.ca2404.1_all.deb Size: 484144 MD5sum: 6ad8ab24333a9f557aca8b34e3c357c2 SHA1: 41a7acc93e3d9f1e53a450a2faecd2ed497be6ed SHA256: 2e68bb616db78e5374c75a8d3a08e55d231faf1f6fd2996ae04148600fefcd89 SHA512: 58a75e4899ceaad9a9c51babcae2a64c7fe6c9523bac9adc5980d6820c8deb42aeafb492d5eec5d8dfd99aef2e37617296191a032ede329868e1532d64114b8f Homepage: https://cran.r-project.org/package=explor Description: CRAN Package 'explor' (Interactive Interfaces for Results Exploration) Shiny interfaces and graphical functions for multivariate analysis results exploration. Package: r-cran-exploratory Architecture: all Version: 0.3.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggridges, r-cran-lemon, r-cran-lm.beta, r-cran-mediation, r-cran-remotes, r-cran-shiny, r-cran-shinydashboard, r-cran-weights Suggests: r-cran-moments Filename: pool/dists/noble/main/r-cran-exploratory_0.3.31-1.ca2404.1_all.deb Size: 196254 MD5sum: b1e922d64b8af38d7ac3ca180034645f SHA1: 0d79233c3c47d6e08339d5f21fa383f9513fc727 SHA256: f62d252e2c76beded21e41503e0d257cd44db09822d11d63cdc93da340fff538 SHA512: 67381c3fbb47a8dd468c71dae08a90935caa206feda2f7872db506e43ba6b20945cf60ad982555e786448dc0c16fd8dcbaaa304eb70ba742db3dd17dfb9e2487 Homepage: https://cran.r-project.org/package=exploratory Description: CRAN Package 'exploratory' (A Tool for Large-Scale Exploratory Analyses) Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages 'kim' and 'ezr'. Selected References: Chang et al. (2021) . Dowle et al. (2021) . Kim (2023) . Kim (2021) . Kim (2020) . Simmons et al. (2011) Tingley et al. (2019) . Wickham et al. (2020) . 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Package: r-cran-explorer Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-explorer_0.1-1.ca2404.1_all.deb Size: 39232 MD5sum: f2c44050107abba699fcf6b632c82b53 SHA1: 224e00580f5a1130df7ee417ab1c9acbcf667fe4 SHA256: 056519a506c5c4ae5eef5308d2e94e14d7445d5dee731a88fa49b4f9233e2ebf SHA512: 0ac200b3ec11e4be4b75fe29d68a1f5f7468b5d97198354b0a08e4ef796ca669cae8cf353f2d6c1e8a31c6a676cccd3c7b9d0b7d9dd35f888e98c1bac7faeedf Homepage: https://cran.r-project.org/package=exploreR Description: CRAN Package 'exploreR' (Tools for Quickly Exploring Data) Simplifies some complicated and labor intensive processes involved in exploring and explaining data. Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data. Also includes some convenience features designed to remove as much redundant typing as possible. Package: r-cran-explorethedata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-propcis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-explorethedata_0.1.0-1.ca2404.1_all.deb Size: 79162 MD5sum: 796682cf6b1b798e39be51f017a717d5 SHA1: 45e8ddb8b835e34ab5893c7b8662cfb2da3c02a8 SHA256: 52a1e1a2a3855176eb1e04091ccbce362d1cbb4c6da794779e8f77f01d0c5e6a SHA512: 5d4687278ffe0859af476ece3403388d3965ba42bde43d53f1daebf13d5c9f66c3c07b9e9c73fffcd0a1a6d5874e20f21aa3d3b283385dbd4b992fec3d89790c Homepage: https://cran.r-project.org/package=ExploreTheData Description: CRAN Package 'ExploreTheData' (A Set of Tools for Exploratory Data Analysis) Functions to profile a dataset, identify anomalies (special values, outliers, and inliers, defined as data values that are repeated unusually often), and compare data subsets with respect to either numerical or categorical variable distributions. Package: r-cran-expoquimr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dt, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-expoquimr_0.1.0-1.ca2404.1_all.deb Size: 1303356 MD5sum: 6f2756e8341e8a2b083f5cb2c69c5956 SHA1: 498e9d1ea99b75c09cbbc0d389b6eff5b440a479 SHA256: 1c02e5709854c766042d8fc9985c6b75c2f81267398efc70a4dd62ddaaf4e4d8 SHA512: 9b718c648ae760e55b552ec7f97255afb0c99e94cd27dbd0fd5d9cc02d0c3a8b0095796b09b7399ab0e56f468cc0e315e5036fb93dbd8f6f719c3bb4f75a7cdb Homepage: https://cran.r-project.org/package=expoquimR Description: CRAN Package 'expoquimR' (Qualitative and Quantitative Assessment of Occupational ChemicalExposure Risk) Provides a unified toolkit for occupational chemical exposure risk assessment, implementing three internationally recognised methods end to end: the qualitative control-banding methods COSHH Essentials (UK Health and Safety Executive) and the method of the French National Research and Safety Institute (INRS), together with the quantitative statistical procedure of the UNE-EN 689 standard for comparing measured exposure levels against occupational exposure limits. Every step of each method, from hazard banding and exposure scoring to lognormal or normal distribution fitting, one-sided tolerance limits, and monitoring-interval recommendations, is implemented as a small, independently callable, and unit-tested function, so assessments are reproducible and auditable without depending on any graphical interface. Optional 'shiny' applications provide a guided, interactive workflow for occupational hygienists and health and safety practitioners who prefer not to write code. References: UK Health and Safety Executive (2003) ; Mallet, Pilorget and Berne (2013, ISBN:978-2-7389-2166-2) "Evaluation du risque chimique" INRS ED 6084; European Committee for Standardisation (2018) . 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Output to 'Microsoft Office' is in editable 'DrawingML' vector format for graphs, and can use corporate template documents for styling. This enables the production of standardized reports and also allows for manual tidy-up of the layout of 'R' graphs in 'Powerpoint' before final publication. Export of graphs is flexible, and functions enable the currently showing R graph or the currently showing 'R' stats object to be exported, but also allow the graphical or tabular output to be passed as objects. The package relies on package 'officer' for export to 'Office' documents,and output files are also fully compatible with 'LibreOffice'. Base 'R', 'ggplot2' and 'lattice' plots are supported, as well as a wide variety of 'R' stats objects, via wrappers to xtable(), broom::tidy() and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as well as matrices and data frames and many more... 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The geospatial features can be of any type of geospatial data, including point, polygon or line data. Package: r-cran-extrc Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-extrc_1.3-1.ca2404.1_all.deb Size: 65178 MD5sum: cc1d271dc1367c59535f4bf5bfc287ba SHA1: 7ba1bf4669ae26ac739107ad0fd8d02f32d185b1 SHA256: 57e57b381be95ce52030e6b60a663ca4a66ffa666d395946a7440a27a27a7de9 SHA512: 768ac1a9533c95608a5a3e619cc87793411dfa54df4b1246675c57ddebc8c92a0765f6436de0bfc5699f958626cd131358c4dfa1c8c82ea1b735acccb1bdd89d Homepage: https://cran.r-project.org/package=extRC Description: CRAN Package 'extRC' (Extended RC Models for Contingency Tables) Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) . These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures. Package: r-cran-extremebounds Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula Filename: pool/dists/noble/main/r-cran-extremebounds_0.1.7-1.ca2404.1_all.deb Size: 350078 MD5sum: 3daa8ccd315b7f05ab6b86d840b691cf SHA1: 71cdecb6c29c0b3b273ff27f1e9511aab12f5b80 SHA256: fe5995e7bdf66ea1195de96a79f0e22810872b8cb54e22de20a2fec083d84963 SHA512: 79ccda965ab47cf490d481832a3ed7dc124bbc7d973b1ea98b731bc339859a78559c4c751e194318fe5d37f943fd6718fce98a6bc02e5613e299c9a6a38a37ad Homepage: https://cran.r-project.org/package=ExtremeBounds Description: CRAN Package 'ExtremeBounds' (Extreme Bounds Analysis (EBA)) An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis. Package: r-cran-extremeci Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dofuture, r-cran-dplyr, r-cran-evd, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-extremeci_0.2.1-1.ca2404.1_all.deb Size: 225688 MD5sum: 56923ad0bb5bd30d9dbdc4f508b9b304 SHA1: a34c960281ae585d3da96a887a15c9f53099355a SHA256: 72bb33da8999acfcedd914c60231af8c8a23dd54b3d4c8197bcb7df0b61f9d37 SHA512: 9e429c587590c3a868223258a5a871d57dd3c729c8db279513acc9f47c38219076f9aee920d378abb830d584c94f9433e746699f9cf06dcfddab31a88830c6b4 Homepage: https://cran.r-project.org/package=ExtremeCI Description: CRAN Package 'ExtremeCI' (Realistic Confidence Intervals for Non-Stationary Extreme ValueStatistics) This framework provides versatile algorithms to efficiently infer confidence intervals for extreme value statistics, such as extreme quantiles and return levels, that are representative of the asymmetric uncertainty spread, using extreme value theory extrapolation and the profile likelihood (see e.g., Coles (2001) ). Unlike existing algorithms, the CI endpoints are found without the need for a strict prespecified range, can be covariate-dependent, and can be based on weighted samples. This package is motivated by Zeder et al. (2023) and by Pasche et al. (2026) . Package: r-cran-extremeconformal Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-extremeci, r-cran-extremes, r-cran-ismev Filename: pool/dists/noble/main/r-cran-extremeconformal_0.2.2-1.ca2404.1_all.deb Size: 72680 MD5sum: e845497321d158f6ac4e6241c622fe06 SHA1: 0bbc671a5ac8bd8d5c4a011910318be54a8de2e1 SHA256: 7367f07f69b629d0855c0ed6cedbbbc7a54c89a78310dbe362bf00fb0970afd0 SHA512: 223ba8a4e09a629d4882ccc4ea9be9ad37eef972dc47a88cf39646cdf647ecb7dda4919ec90ba6f16856677babfa6c039322d1349ba7eb396d80f9f406aebd11 Homepage: https://cran.r-project.org/package=ExtremeConformal Description: CRAN Package 'ExtremeConformal' (Extreme Conformal Prediction Intervals) This new extreme conformal prediction framework provides informative prediction intervals at the high-confidence levels for which classical conformal methods fail. In applications with potentially high-impact events, a very high level of confidence is often required for predictions. If that level is too large relative to the amount of data used for calibration, classical conformal methods provide infinitely wide, thus, uninformative prediction intervals. Our extreme conformal procedure bridges extreme value statistics and conformal prediction to provide reliable and informative prediction intervals with high-confidence coverage, which can be constructed using any black-box extreme quantile regression method. A weighted version of the approach can account for nonstationary data. The methodology was introduced in Pasche, Lam, and Engelke (2026) . 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This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) . 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In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) and for bootstrapping, please see Gilleland (2020) . 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Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417. Package: r-cran-extremevalues Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-extremevalues_2.4.1-1.ca2404.1_all.deb Size: 405088 MD5sum: 22656a5b9f190164b21c46b77e63c939 SHA1: dd27868eee8e8c90a2208602743b814c7bf76a70 SHA256: 7e17177326f018e4ad87ab0ab1e7830726b0aa4e4548e927360ea9b1721975ce SHA512: 4cadf4a3e7761a2a6d48a46bc2bc2e9f610c2a3a1d926b71cccacbcf768b0d130382ebd6f484c66de25070f5e608e5b3b76743f09e697e7385ef13d1e515a653 Homepage: https://cran.r-project.org/package=extremevalues Description: CRAN Package 'extremevalues' (Univariate Outlier Detection) Detect outliers in one-dimensional data. 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The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms. 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Different notations, untidy entries. This shall now be a matter of the past. Eye makes it as easy as pie to work with VA data - easy cleaning, easy conversion between Snellen, logMAR, ETDRS letters, and qualitative visual acuity shall never pester you again. The eye package automates the pesky task to count number of patients and eyes, and can help to clean data with easy re-coding for right and left eyes. It also contains functions to help reshaping eye side specific variables between wide and long format. Visual acuity conversion is based on Schulze-Bonsel et al. (2006) , Gregori et al. (2010) , Beck et al. (2003) and Bach (2007) . 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This is real life data of people with intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF), due to age-related macular degeneration or diabetic macular edema. Associated publications of the data sets: Fu et al. (2020) , Moraes et al (2020) , Fasler et al. (2019) , Arpa et al. (2020) , Kern et al. 2020, . Package: r-cran-eyelinker Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-readr, r-cran-intervals Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-covr Filename: pool/dists/noble/main/r-cran-eyelinker_0.2.2-1.ca2404.1_all.deb Size: 1616036 MD5sum: 2a4ea1172f97ce23f8483c1e79ce3a8b SHA1: 852cc0e9799f33d619d93c90891a585b7429e3d8 SHA256: a8c78085c4420e5ccdf1f5ab9fa294c666958d8cdb3373ba1dc300605a492943 SHA512: 8c8e4e90972a1e8ba05a1c3401d724545a7a58e163f45c16ca7ae193bf665f16fceb8f7badecef26e2ff9e5102764d2d75cf06e0f99fb8cfb20915f0357070b7 Homepage: https://cran.r-project.org/package=eyelinker Description: CRAN Package 'eyelinker' (Import ASC Files from EyeLink Eye Trackers) Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization. Package: r-cran-eyeprocess Architecture: all Version: 0.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9456 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-withr Suggests: r-cran-brms, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-lme4, r-cran-lnirt, r-cran-mirt, r-cran-rmarkdown, r-cran-tam, r-cran-arrow, r-cran-diffirt, r-cran-gdina, r-cran-openmx, r-cran-traminer, r-cran-testthat, r-cran-callr, r-cran-eyetrackingr, r-cran-future, r-cran-future.apply, r-cran-openssl, r-cran-pupillometryr, r-cran-rtdists, r-cran-seqhmm, r-cran-lsmjml, r-cran-mass, r-cran-mgcv, r-cran-nnet, r-cran-survival, r-cran-erm, r-cran-factominer, r-cran-mice, r-cran-missforest, r-cran-plm, r-cran-psychotree, r-cran-ranger, r-cran-robfilter, r-cran-tidylpa, r-cran-loo, r-cran-posterior, r-cran-targets, r-cran-equateirt, r-cran-catr, r-cran-mirtcat Filename: pool/dists/noble/main/r-cran-eyeprocess_0.11.1-1.ca2404.1_all.deb Size: 5844494 MD5sum: f96a6cebdcbfb551b1cba01efc708d92 SHA1: 3693ac2bc663a86a0fb91d784e97bcf8bb58a3c2 SHA256: e61bea047b846978e3dfef894762c93b7499defa59562d3c18c63651ce190a45 SHA512: f4043c67d6bfff29aa854fa3f4c23ae72aab0ca36f09171065695da8f76ba50057eab64146606247fddf4c50e47fd6f09d1d243cd7811ec59fd79545b440bb9e Homepage: https://cran.r-project.org/package=eyeprocess Description: CRAN Package 'eyeprocess' (Harmonize Eye-Tracking, Pupillometry, Biometrics, andPsychometric Process Data) Provides an extensible, vendor-neutral framework for importing, validating, harmonizing, transforming, visualizing, and modelling eye-tracking, pupillometry, behavioural, and biometric process data. The package uses explicit timebase and coordinate-space registries, preserves native fields and provenance, and offers first-class adapters for Gazepoint Analysis and Gazepoint Biometrics exports alongside generic and vendor-specific importers. Downstream tools support trial and area of interest reconstruction, signal-quality auditing, feature derivation, scanpath analysis, response-time and item-response workflows, and optional psychometric modelling engines. An integrated Gazepoint workflow produces quality-control evidence, media-trial reconstruction, plots, analysis-ready process tables, item response theory (IRT)-ready response structures, and reproducible reports. Brain Imaging Data Structure (BIDS) interoperability for eye-tracking and validation-release infrastructure support disk-backed storage, independent multi-vendor evidence, grouped validation, simulation calibration, model-equivalence audits, and explicitly experimental advanced psychometric process models. Research-scale infrastructure adds deterministic resumable Monte Carlo execution, atomic validation checkpoints, explicit advanced-model promotion gates, independent multi-vendor evidence registries, stable object contracts, partitioned disk-backed storage, optional probabilistic engines, and a fully synthetic multimodal benchmark for reproducibility testing. The measurement-intelligence programme adds probabilistic and compositional area of interest (AOI) analysis, measurement-uncertainty propagation, calibration and device-transportability audits, process reliability, phase-amplitude pupil registration, informative-missingness sensitivity, temporal and spatial process models, item-bank decision optimization, fairness monitoring, conditional process reference distributions, and evidence-provenance graphs. Package: r-cran-eyeread Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-data.table, r-cran-tidyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-eyeread_0.0.4-1.ca2404.1_all.deb Size: 53370 MD5sum: 0b894c1c2c467ebbbc6d6a50d0bfd26d SHA1: b53181d780be4f05254ff74f4cae8057664234c9 SHA256: c5aa10bc1883d29b85f1b85374c8b09c1d25702908e61f3f7fd0f3666ce238ee SHA512: c7f738b710b284cb339a9ebcc890a86078ed832bc198575b581f12a65c1a2bd289775d9d245d2ed8023bd88c5f1694005ee81390d0d06bb81cec836118e18e37 Homepage: https://cran.r-project.org/package=eyeRead Description: CRAN Package 'eyeRead' (Prepare/Analyse Eye Tracking Data for Reading) Functions to prepare and analyse eye tracking data of reading exercises. The functions allow some basic data preparations and code fixations as first and second pass. First passes can be further devided into forward and reading. The package further allows for aggregating fixation times per AOI or per AOI and per type of pass (first forward, first rereading, second). These methods are based on Hyönä, Lorch, and Rinck (2003) and Hyönä, and Lorch (2004) . It is also possible to convert between metric length and visual degrees. Package: r-cran-eyeris Architecture: all Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6403 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-eyelinker, r-cran-dplyr, r-cran-gsignal, r-cran-purrr, r-cran-zoo, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-progress, r-cran-data.table, r-cran-withr, r-cran-lifecycle, r-cran-mass, r-cran-viridis, r-cran-jsonlite, r-cran-rmarkdown, r-cran-dbi, r-cran-glue, r-cran-base64enc, r-cran-reaborn, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-arrow, r-cran-duckdb, r-cran-knitr, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-eyeris_3.3.0-1.ca2404.1_all.deb Size: 3943768 MD5sum: 94ba877720471ad7ef93dc28cfa1e359 SHA1: 2d588ae884825d19f0286b63428f3f48462d4719 SHA256: a43bd38bec726bc35c5225e417945b978aef26814dd414d37fffd07e58872d75 SHA512: 14ae3cd74f15571028c0ca42b4f32cf9f8ee1e45c985dbd1f06f74f70ae334c8549635bc0de998582ccc58705245efc78dc29116211100eb50910e7f388665e5 Homepage: https://cran.r-project.org/package=eyeris Description: CRAN Package 'eyeris' (Flexible, Extensible, & Reproducible Pupillometry Preprocessing) Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) ; Kret & Sjak-Shie (2018) ; Strauch (2024) ]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) ]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) ; Gorgolewski et al. (2016) ]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology. Package: r-cran-eyetools Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4136 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-viridis, r-cran-glue, r-cran-hdf5r, r-cran-lifecycle, r-cran-magick, r-cran-pbapply, r-cran-rlang, r-cran-zoo, r-cran-png, r-cran-ggrepel, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eyetools_0.10.0-1.ca2404.1_all.deb Size: 3747026 MD5sum: 6d31bd0fa18754b58d62bef844bfb934 SHA1: e5321d9ae5cc13e92ac94774ae99f11501f7e662 SHA256: 47a3692935756a79fdad2602b208426a2eb3acaddd6d09454b74b59b02f854e1 SHA512: 98dae24edad67d1f7f965ccd4c79837e63af432db0e70ad3bbc92ca7fdb68b9b4be1edcb3be2529ebcedce39548b76f85c767df23aa6fed6eb9068dcadb2bd24 Homepage: https://cran.r-project.org/package=eyetools Description: CRAN Package 'eyetools' (Analyse Eye Data) Enables the automation of actions across the pipeline, including initial steps of transforming binocular data and gap repair to event-based processing such as fixations, saccades, and entry/duration in Areas of Interest (AOIs). It also offers visualisation of eye movement and AOI entries. These tools take relatively raw (trial, time, x, and y form) data and can be used to return fixations, saccades, and AOI entries and time spent in AOIs. As the tools rely on this basic data format, the functions can work with data from any eye tracking device. Implements fixation and saccade detection using methods proposed by Salvucci and Goldberg (2000) . Package: r-cran-eyetrackingr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1662 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-broom, r-cran-broom.mixed, r-cran-ggplot2, r-cran-lazyeval, r-cran-rlang, r-cran-zoo, r-cran-tidyr, r-cran-purrr Suggests: r-cran-pbapply, r-cran-knitr, r-cran-lme4, r-cran-glmmtmb, r-cran-mass, r-cran-matrix, r-cran-testthat, r-cran-rmarkdown, r-cran-domc, r-cran-foreach Filename: pool/dists/noble/main/r-cran-eyetrackingr_0.2.2-1.ca2404.1_all.deb Size: 897204 MD5sum: 4d305f63174d202dd8db3dbc51c6c678 SHA1: c522349251e971bce43b339f5ba8d13f359f1a61 SHA256: 9b18eb5c5f5431b4f5f4acd3b019a0f93a93c413d52b690086c5cdbbaf3ead6f SHA512: dc77582da2378082ad03625b253a262c8a2dfef8482369fec31db08a94eebd30f1b4311fc4ad4b2b4b884d6d789efcbedc0b4fd7e286215f56a10eed66a140c9 Homepage: https://cran.r-project.org/package=eyetrackingR Description: CRAN Package 'eyetrackingR' (Eye-Tracking Data Analysis) Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) , and Barr (2008) . Package: r-cran-eyetrackr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2908 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eyetrackr_1.0.1-1.ca2404.1_all.deb Size: 2937640 MD5sum: bea50befef246f9951b12831d6fe906a SHA1: f273e924767b016d538aeac4ae4b078460c2e772 SHA256: 7f095a21ee5a5e7e29623c9566c46269e14620ae531922ba554ff0d7d37c4060 SHA512: 6cf35e84566bb9d2883380fc3ffbc3ba6de4d93fc4765cd284e368a60481533f7458dd0e93f5301b5eb70c46b9bb2ba6562589e89d647bf4b9093f89640b3d87 Homepage: https://cran.r-project.org/package=eyeTrackR Description: CRAN Package 'eyeTrackR' (Organising and Analysing Eye-Tracking Data) A set of functions for organising and analysing datasets from experiments run using 'Eyelink' eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as 'mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary. Package: r-cran-ez.combat Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ez.combat_1.0.0-1.ca2404.1_all.deb Size: 29004 MD5sum: 7246df4934e6b8d28ff384a17d87828d SHA1: 12cb73362bce149f6cdce61d308d123a1e05ea0a SHA256: 656993ec9b93095c52709540aae527b4c5509862907814df53e0cc9801997ecd SHA512: d042b8b740b0faaf5096f5781d6cf95c419c4906c262a72c9fb94ff333ebb0d7e421b68b54f7ebbc4224eea5df4e1191194bb005cb639fac5d303f3ef3e69794 Homepage: https://cran.r-project.org/package=ez.combat Description: CRAN Package 'ez.combat' (Easy ComBat Harmonization) A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Johnson WE & Li C (2007) "Adjusting batch effects in microarray expression data using empirical Bayes methods." Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) "Harmonization of cortical thickness measurements across scanners and sites." Fortin J-P, Parker D, Tun B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) "Harmonization of multi-site diffusion tensor imaging data." Package: r-cran-ez Architecture: all Version: 4.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-plyr, r-cran-reshape2, r-cran-scales, r-cran-stringr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ez_4.5-0-1.ca2404.1_all.deb Size: 334990 MD5sum: c6c3ebcdf603d3ea33083839b7a95c3c SHA1: 91d35bd2febc9bc89837c84c73e8d59417c9a66e SHA256: 78f1b934892764c954e2ba2d2cb9d7af1f0cd02b77075564dd4370f786d4cccd SHA512: 3fc82c5de446704f9a2be46915811e0616ce4e79cdbca9517331ca57d84cce51653021830417a9666c5c5ead58a0befef17ef9dc7fbe3d5b606414a2fab16c99 Homepage: https://cran.r-project.org/package=ez Description: CRAN Package 'ez' (Easy Analysis and Visualization of Factorial Experiments) Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design. Package: r-cran-ezbakr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-bioc-tximport Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezbakr_0.1.0-1.ca2404.1_all.deb Size: 867786 MD5sum: 4f7ad5d8d8ccae3997d9c9bfc15267cd SHA1: 77cfaf17aa408b2fce8e1f691605c93078e61b7c SHA256: 079e0416f5fc82b903727bdb423d89ffc4f5e175438dfb6107ab872fa602b85b SHA512: e4daabd3bebb9f3f33fe082b137c0c890b207af5ca3423ff720be36f766d412e1b6637b01bec6a0a2373848eeaf465a4c35f3177c7abda9ac282243412286452 Homepage: https://cran.r-project.org/package=EZbakR Description: CRAN Package 'EZbakR' (Analyze and Integrate Any Type of Nucleotide Recoding RNA-SeqData) A complete rewrite and reimagining of 'bakR' (see 'Vock et al.' (2025) ). Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq), TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. 'EZbakR' extends standard NR-seq standard NR-seq mutational modeling to support multi-label analyses (e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. 'EZbakR' also generalized dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, 'EZbakR' implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. Package: r-cran-ezcox Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forestmodel, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble, r-cran-utf8 Suggests: r-cran-covr, r-cran-furrr, r-cran-future, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezcox_1.0.4-1.ca2404.1_all.deb Size: 623290 MD5sum: 12071a456bf17142060d3646128eabcb SHA1: 4fa8f71367317c2cfe68df59618ec0006ac43b7d SHA256: 3f004a4586e02c1d0def3af783b84e4c5874c3caa06a158ecf742bc03b9c65b7 SHA512: e40da4f60ae9d2a54dca63b7005a41a5a103f573265df599cfdeb173006b7bcb18ba4b874d2026c0e84f2f16d6e398251b050ecc218af08ae2bbdf0f9bacedac Homepage: https://cran.r-project.org/package=ezcox Description: CRAN Package 'ezcox' (Easily Process a Batch of Cox Models) A tool to operate a batch of univariate or multivariate Cox models and return tidy result. Package: r-cran-ezcutoffs Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-lavaan, r-cran-moments, r-cran-progress Suggests: r-cran-boot Filename: pool/dists/noble/main/r-cran-ezcutoffs_1.0.2-1.ca2404.1_all.deb Size: 52776 MD5sum: 7d48e1dc8fb107eaf52216f37c88ef41 SHA1: e1d34ecb0c3b15716cdf6c3c2f0fad4b3b984dca SHA256: fd90c888dbaf66de26292b4759c95a82bfb56ffc00a3019aa1f4606868871644 SHA512: ee17c0768b9e6a9f4f57d439d12b54571f007c5f2c6ca5a3415acdaa62013983592cefde760c774a64f4cae097466fa811789d547c74ed47e8f486747537c60d Homepage: https://cran.r-project.org/package=ezCutoffs Description: CRAN Package 'ezCutoffs' (Fit Measure Cutoffs in SEM) Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 ) with the same parameters (population model, number of observations, etc.) as the model under consideration. Package: r-cran-ezec Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-dplyr Suggests: r-cran-testthat, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ezec_1.0.2-1.ca2404.1_all.deb Size: 93708 MD5sum: 33de3bba25c5ef6f44d18d6d3059b468 SHA1: ff005ea534c0919d385dda9bd8dea2e8b20562e5 SHA256: 320c6b65b4a15030dc67bcb715d7e2923f529f11a7d15a416a81df18b086da94 SHA512: 88ba7e9b9c08206ba14193617a7e7bd00bcc602fb45b49b3e46ce37848492ac3c54070c98182b5905a87d679ef46c44e3052543a2e0b6f238a019760f3b3123e Homepage: https://cran.r-project.org/package=ezec Description: CRAN Package 'ezec' (Easy Interface to Effective Concentration Calculations) Because fungicide resistance is an important phenotypic trait for fungi and oomycetes, it is necessary to have a standardized method of statistically analyzing the Effective Concentration (EC) values. This package is designed for those who are not terribly familiar with R to be able to analyze and plot an entire set of isolates using the 'drc' package. Package: r-cran-ezecm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ellipse, r-cran-klar, r-cran-lhs, r-cran-mcmcpack, r-cran-rdpack, r-cran-mvnfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-laplacesdemon Filename: pool/dists/noble/main/r-cran-ezecm_1.0.0-1.ca2404.1_all.deb Size: 333832 MD5sum: f3659a7d565e9710643a9c0fc8660777 SHA1: be92ae4c233a07901ae15cee5f31d57b5fe8cb14 SHA256: 7c7ebbe85c2fbcb396a19978de5985af5159739d910ce3cdc766514b1a2c957a SHA512: b3be260cad4802619a931268ecd7aadab7a24cda18cd91980018c04621efd857645b82510dddde579e4f1b371e303b7f5943e22199f2bf697db1157596d676a6 Homepage: https://cran.r-project.org/package=ezECM Description: CRAN Package 'ezECM' (Event Categorization Matrix Classification for NuclearDetonations) Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at . Package: r-cran-ezeda Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-ggally, r-cran-scales, r-cran-magrittr, r-cran-purrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ezeda_0.1.1-1.ca2404.1_all.deb Size: 542274 MD5sum: b7a510ae224ae6dc112b79d2ec58dae4 SHA1: cd54c47cce81b30eb73967f94f6f46e40fc816e6 SHA256: cbf7099c89407c0e8e4950a0ecb8932c6571f1a82303f22eb701454c47ff8d3c SHA512: 8d2e4e291f496d132737c2692908b7e0e45bfc2ff5728b79d36c85b031ea2cc786a9ace1ffe6c93cc74d297b3430d22a99217d8d0845795875ee39ce5a00ce17 Homepage: https://cran.r-project.org/package=ezEDA Description: CRAN Package 'ezEDA' (Task Oriented Interface for Exploratory Data Analysis) Enables users to create visualizations using functions based on the data analysis task rather than on plotting mechanics. It hides the details of the individual 'ggplot2' function calls and allows the user to focus on the end goal. Useful for quick preliminary explorations. Provides functions for common exploration patterns. Some of the ideas in this package are motivated by Fox (2015, ISBN:1938377052). Package: r-cran-ezfragility Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epoch, r-cran-ggplot2, r-cran-viridis, r-cran-ggtext, r-cran-glue, r-cran-rlang, r-cran-foreach, r-cran-progress, r-cran-ramify, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dosnow, r-cran-gsignal Filename: pool/dists/noble/main/r-cran-ezfragility_2.1.1-1.ca2404.1_all.deb Size: 2677482 MD5sum: 358468d37e439041927e34db21143258 SHA1: c056ff58057004cf8cc11e040d6b0f42c006ad71 SHA256: 471a8e0f2fe619850c51a8a82feb1e7c7f1fbac00e8d1caecd1546d77473e478 SHA512: 9a0c7444607bf8a4b706b523c29c75de47a3c7685dd39d63b91807fbbce47a3be3c73c5f7f1765989acfa43338f267f195782f09321c6e83d632ac001e0dc8d8 Homepage: https://cran.r-project.org/package=EZFragility Description: CRAN Package 'EZFragility' (Compute Neural Fragility for Ictal iEEG Time Series) Provides tools to compute the neural fragility matrix from intracranial electrocorticographic (iEEG) recordings, enabling the analysis of brain dynamics during seizures. The package implements the method described by Li et al. (2017) and includes functions for data preprocessing ('Epoch'), fragility computation ('calcAdjFrag'), and visualization. Package: r-cran-ezgp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezgp_0.1.0-1.ca2404.1_all.deb Size: 798234 MD5sum: e7c426317e60082bb1cdb513ea761a87 SHA1: d888bbedfc494878b7e6125666fe2b5c615c3837 SHA256: 7910133e34c7ac4ebcbd784b6ae19a55367664a8d29da7c33c7e156bf42684f6 SHA512: b0bff3e3508e78b6663535e41576ca20a5265b8db7dd609a11ce3ecfd844516e561fab438cb39841b3c66326be3aa5255ca714d1b259f1712b15e7fd467002c8 Homepage: https://cran.r-project.org/package=EzGP Description: CRAN Package 'EzGP' (Easy-to-Interpret Gaussian Process Models for ComputerExperiments) Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs. The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()). The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors" by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) . Package: r-cran-ezknitr Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-markdown, r-cran-r.utils Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ezknitr_0.6.3-1.ca2404.1_all.deb Size: 44336 MD5sum: 01b7773e090782c0218095f721dd4746 SHA1: b19e9e19a0060226aa656e0703cc6ed081dfbf6d SHA256: 9bfc2a4129e1a1b7dc02538b77ada0cb20a86e6eb107482e67617c51bc0c45c3 SHA512: 2a1c46032fcd7c443cdf8c88cf64e2eee4b58207307cb56909138fab86c45f7492404d50249d6ebabc70a4ff32bde611ec5b88dab57cce5c2dcc6edaeb0b43fb Homepage: https://cran.r-project.org/package=ezknitr Description: CRAN Package 'ezknitr' (Avoid the Typical Working Directory Pain When Using 'knitr') An extension of 'knitr' that adds flexibility in several ways. One common source of frustration with 'knitr' is that it assumes the directory where the source file lives should be the working directory, which is often not true. 'ezknitr' addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier. Package: r-cran-ezmmek Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-assertable, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-nls2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezmmek_0.2.4-1.ca2404.1_all.deb Size: 465526 MD5sum: 676714e7a75247744af90355466e283a SHA1: 4072f1f0fdd4fdd1e9b91c58e34fc21665b0a957 SHA256: 945d56e941ddf91c5a18836e720692eb0ecada7691c8f060b1f1350406a236e9 SHA512: 6f3f5adbba4fff9a6c8ae822f55dec4fb46bd9e25f8103a8b63f00a789cc8ef87e5c9ba43cc0e94b79835df84e0d2cb97554d1d0fb4d9729d55ccdb2da917853 Homepage: https://cran.r-project.org/package=ezmmek Description: CRAN Package 'ezmmek' (Easy Michaelis-Menten Enzyme Kinetics) Serves as a platform for published fluorometric enzyme assay protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they relate to the transformation of synthetic substrates. At present, 'ezmmek' implements two common protocols found in the literature, and is modular to accommodate additional protocols. Here, these protocols are referred to as the In-Sample Calibration (Hoppe, 1983; ) and In-Buffer Calibration (German et al., 2011; ). protocols. By containing multiple protocols, 'ezmmek' aims to stimulate discussion about how to best optimize fluorometric enzyme assays. A standardized approach would make studies more comparable and reproducible. Package: r-cran-ezplot Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lubridate, r-cran-rlang Suggests: r-cran-covr, r-cran-dt, r-cran-e1071, r-cran-ggrepel, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rocr, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-tsibbledata Filename: pool/dists/noble/main/r-cran-ezplot_0.8.2-1.ca2404.1_all.deb Size: 696854 MD5sum: dadbec3119d07235f46b8e9ab3482cb5 SHA1: 2c5cefb35ba852a7e24b361b70cff5a05fe24f58 SHA256: 76e6c93792976ecc9164bc9588ae645c9243dbf40276c1135ab43e5ed04f1ff8 SHA512: 496f1e1915181ddd87504cc91323a494518642ea96d7303828a6ea350d287216e2adc3f8dbcbdae3e7827bb82c6df272a62f97fd110b501f6015e995e605cb34 Homepage: https://cran.r-project.org/package=ezplot Description: CRAN Package 'ezplot' (Functions for Common Chart Types) Wrapper for the 'ggplot2' package that creates a variety of common charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce typing. Package: r-cran-ezr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggridges, r-cran-moments, r-cran-shiny, r-cran-shinydashboard, r-cran-weights Filename: pool/dists/noble/main/r-cran-ezr_0.1.5-1.ca2404.1_all.deb Size: 77450 MD5sum: 6f09741f7b5cc3daafa705b3add426e9 SHA1: 70091e8942c8ad0f61dd3b10c8cfb763a6ad1ae2 SHA256: ad945e2f707845b73247157d48683334dae8ed8b69d328aecaf8b10a296674cc SHA512: d3cd9cfb2a89cb67870afacfbc75838cd096c86fdad1cdfec1ecf9b7a4f34d569d5284d9e065b1cedf52bf319d8271211e871c62936d7ac2e3758239549c182e Homepage: https://cran.r-project.org/package=ezr Description: CRAN Package 'ezr' (Easy Use of R via Shiny App for Basic Analyses of ExperimentalData) Runs a Shiny App in the local machine for basic statistical and graphical analyses. The point-and-click interface of Shiny App enables obtaining the same analysis outputs (e.g., plots and tables) more quickly, as compared with typing the required code in R, especially for users without much experience or expertise with coding. Examples of possible analyses include tabulating descriptive statistics for a variable, creating histograms by experimental groups, and creating a scatter plot and calculating the correlation between two variables. Package: r-cran-ezrshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-colourpicker, r-cran-dt, r-cran-ggiraph, r-cran-here, r-cran-plotly, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-ggplot2, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-ezrshiny_0.1.0-1.ca2404.1_all.deb Size: 106154 MD5sum: 06c2aef8e92714aed06602514fc2892b SHA1: 568faeb1c26ad9770bbe19db029d44b6c7722ad3 SHA256: e6997bc7c70d491d32b053b8afbeda8ea18d0f8e65f3e49742d839ec774b9992 SHA512: 4a5060c717799d531f8d431bdcecdfdb1397133231d837c9a34520d0adeb88e3e716dc968a0696211ca3b80b5917238efa11188f4354867c420c40f2b0992b52 Homepage: https://cran.r-project.org/package=EZRShiny Description: CRAN Package 'EZRShiny' (Build Consistent 'shiny' App Layouts with Less Code) Short, consistent wrappers around 'shiny', 'bslib' and 'shinyjs' for building multi-page 'shiny' apps. One function call builds the page with a navigation bar, logo, title and dark mode switch. Tab functions nest pages up to three levels deep, with optional sidebars. Input functions create full-width inputs with optional information tooltips, and helpers switch buttons on and off and show or hide tabs from the server. Includes a starter app template and an example app that explores the Titanic passenger data. Package: r-cran-eztrack Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-adehabitathr, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-htmltools, r-cran-leaflet, r-cran-magrittr, r-cran-readxl, r-cran-sf, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-eztrack_0.1.1-1.ca2404.1_all.deb Size: 415354 MD5sum: 9912c77b5fb88a6cb73bdd2598e18fea SHA1: 5dd7c88cf20b102acbcc8e00ab9c8ed8d3ef74e8 SHA256: 3ffbea015ab29328d96a0ee0adf1c2701761cd7ab03acef00b4f5b4622e7fd97 SHA512: f86b228560299a48652208b6ec36abf14aae9cb51242b92b55120f9b51ba04162f1afb6ca64cea32d2ed4f35aaa3001230cf7328f08782ee111b234bb4f12361 Homepage: https://cran.r-project.org/package=ezTrack Description: CRAN Package 'ezTrack' (Tools for Exploring Animal Movement Data) Provides tools for exploring animal tracking data, from raw telemetry points to summaries, interactive maps, and home range estimates. 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Package: r-cran-eztune Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2057 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ada, r-cran-e1071, r-cran-ga, r-cran-gbm, r-cran-optimx, r-cran-rpart, r-cran-glmnet, r-cran-rocr, r-bioc-biocstyle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mlbench, r-cran-doparallel, r-cran-dplyr, r-cran-yardstick, r-cran-rsample Filename: pool/dists/noble/main/r-cran-eztune_3.1.1-1.ca2404.1_all.deb Size: 1492942 MD5sum: aeeccd77d127b09f230ac5282c612d4b SHA1: f3b61fe7662588dd41dd72be40688553ed3ba8c9 SHA256: 501ae87a1127394056ff27c235e6e0d74bc778aa735bd8621752346645fd75a4 SHA512: eefa9ab29c1abbcf8662d460f2c442587f66a4538b861d039416de81bd7b78840a8760349767068f60c305218cc45f50174583cb528343a3546991b8ef81e4e9 Homepage: https://cran.r-project.org/package=EZtune Description: CRAN Package 'EZtune' (Tunes AdaBoost, Elastic Net, Support Vector Machines, andGradient Boosting Machines) Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune.cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading. Package: r-cran-f1datar Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3774 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-glue, r-cran-magrittr, r-cran-tibble, r-cran-jsonlite, r-cran-httr2, r-cran-memoise, r-cran-janitor, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-lifecycle, r-cran-cli, r-cran-rappdirs, r-cran-cachem, r-cran-withr Suggests: r-cran-ggplot2, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-f1datar_2.0.1-1.ca2404.1_all.deb Size: 2613768 MD5sum: cd226c8c5927ef0decfce6221f8a79af SHA1: b39508726056012193a3a9d7b0d9f551b072c9c0 SHA256: 4b8ac9e1bfc5705175a55d963252d362ce12a9747776760bf62f7412918897e6 SHA512: a6f8469c74c19bd8182cdaecc4be0851d321aa87b0ed20472ec697bd29b8579484d0967b09f8bbe1c51d3501c45afc82a9cbe5ae548d122761f3780648b8d916 Homepage: https://cran.r-project.org/package=f1dataR Description: CRAN Package 'f1dataR' (Access Formula 1 Data) Obtain Formula 1 data via the 'Jolpica API' and the unofficial API via the 'fastf1' 'Python' library . Package: r-cran-f1pits Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-tibble, r-cran-httr, r-cran-jsonlite, r-cran-f1datar Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-f1pits_1.3.2-1.ca2404.1_all.deb Size: 80302 MD5sum: 09bf74b2b6ed22cffc9e20aa81704c0f SHA1: 3db6d18531395938e38e75ad73c7217bc6acedaa SHA256: 4c6f35fc749ab4cc5ec1e461e062956e7861b6e45162b51d3140ab1fe28e94fe SHA512: 01cbb9f468ad3e4b34e7e182d3ea2777987fb57da7680a45ceae5ba44c1f662fd6fe3a588646c49012e8362da4c5db8e9d500543bc395170b03836fd246b917b Homepage: https://cran.r-project.org/package=f1pits Description: CRAN Package 'f1pits' (F1 Pit Stop Datasets) Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2018 or higher). 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Package: r-cran-faasr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-cli, r-cran-jsonvalidate, r-cran-uuid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-faasr_2.0.0-1.ca2404.1_all.deb Size: 140014 MD5sum: b70f70ffa0464044f423e649227db108 SHA1: 3a96476899402c34d98dce8c8c704cec09ce68ae SHA256: 255f35fa6d9486e54ef742f3e0666763ad20f788addd506f42091c603eaf9339 SHA512: abd91d119352ed9437c12a3c70b95e6a70cb706aa1b8c0da2f08d8b3ed1bc2a52321a2d5e0981e95e189847165a2bc242022a0ecb3289c352ed8393a78183eb2 Homepage: https://cran.r-project.org/package=FaaSr Description: CRAN Package 'FaaSr' ('FaaSr' Local Test Development Package) Provides a local execution environment for testing and developing the 'FaaSr' workflows without requiring cloud infrastructure. The 'FaaSr' package enables R developers to validate and test workflows locally before deploying to Function-as-a-Service (FaaS) platforms. Key features include: 1) Parsing and validating JSON workflow configurations compliant with the 'FaaSr' schema 2) Simulated S3 storage operations using local file system with local logging 3) Support for conditional branching 4) Support for parallel rank functions execution 5) Workflow cycle detection and validation 6) No cloud credentials or infrastructure required for testing This package is designed for development and testing purposes. For production deployment to cloud FaaS platforms, use the main 'FaaSr' package available at . Package: r-cran-fabci Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-fabci_0.3-1.ca2404.1_all.deb Size: 70698 MD5sum: e4ae773149177476f10e3fa321c6b3ad SHA1: 4c17f262e03f0cf3d899425e63ca9e9dae4a6c3b SHA256: 18a4b41564e1ec4ca9432c22bb914aacd380f80b80b699d8b39b9af29da78dba SHA512: 2a208392fa6a87bdea993c55a4d6605569c07605a94fa17742d02862b2a017884908109db9f07170c32378bf881076b8b25bfe56d968a1926e11577836d38036 Homepage: https://cran.r-project.org/package=fabCI Description: CRAN Package 'fabCI' (FAB Confidence Intervals) Frequentist assisted by Bayes (FAB) confidence interval construction. See 'Adaptive multigroup confidence intervals with constant coverage' by Yu and Hoff and 'Exact adaptive confidence intervals for linear regression coefficients' by Hoff and Yu . Package: r-cran-fabinference Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-fabinference_0.1-1.ca2404.1_all.deb Size: 67818 MD5sum: 1e00c88b3b4bad53c85f2044fee3c323 SHA1: 21cddcbe9c25aa2d904a372447ffb2ffb7933f09 SHA256: afe92310da389a55bc33d35680c9589ab34e79537befa888e5bb77781d29db52 SHA512: e005fc2ff538dda0eb10ba5e34b0627c3c2267a8edf4337bd639999ea24c107a8e37edc5e58d831d4e854684542d50482ed2c9871be4c1c2b5721092823f9a40 Homepage: https://cran.r-project.org/package=FABInference Description: CRAN Package 'FABInference' (FAB p-Values and Confidence Intervals) Frequentist assisted by Bayes (FAB) p-values and confidence interval construction. See Hoff (2019) "Smaller p-values via indirect information", Hoff and Yu (2019) "Exact adaptive confidence intervals for linear regression coefficients", and Yu and Hoff (2018) "Adaptive multigroup confidence intervals with constant coverage". Package: r-cran-fabisearch Architecture: all Version: 0.0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nmf, r-cran-rgl, r-cran-reshape2, r-cran-foreach, r-cran-doparallel, r-cran-dorng Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fabisearch_0.0.4.5-1.ca2404.1_all.deb Size: 4449890 MD5sum: 09e5b79e298b343dee8c16171c3e5079 SHA1: d1ec8c5f95057cb64a4c456572c4b01fd0f1d92d SHA256: f84261fd43ae30242a6587bec1cd1c5ab8c44cbd484953541e5755d82e2dd04e SHA512: 28932bc656411fb8b19471b9cb25a34804324c28e3c073bb25a561f181cc5afe966dd7d7d959235da1a697283d2b09f6860acdb2e6ff5872d5b2186d158259c9 Homepage: https://cran.r-project.org/package=fabisearch Description: CRAN Package 'fabisearch' (Change Point Detection in High-Dimensional Time Series Networks) Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) . For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint. Package: r-cran-fable.ata Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fabletools, r-cran-ataforecasting, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tsibble, r-cran-distributional, r-cran-tsbox, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-fable.ata_0.0.6-1.ca2404.1_all.deb Size: 64456 MD5sum: 005ccf67799676b1c6dec3dd0cb89cef SHA1: 26782e5bdffdfcca186c4ad040a999ac8db355f3 SHA256: 493e2c8ff783c7572f087391763be3515fc110d2778e674a1a26116d462ef4da SHA512: 020e899be1ad0d903a517851eb72ff94b921013a2391f7d6ab4efffaa71e3a79074a9451bf1e5870abcbc84121c5dc2242bf693194f5271e74a5de70133a1874 Homepage: https://cran.r-project.org/package=fable.ata Description: CRAN Package 'fable.ata' ('ATAforecasting' Modelling Interface for 'fable' Framework) Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of 'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). Package: r-cran-fable.bayesrecon Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayesrecon, r-cran-fabletools, r-cran-purrr, r-cran-vctrs, r-cran-distributional, r-cran-dplyr, r-cran-tsibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-cli, r-cran-fable, r-cran-fable.intermittent, r-cran-ggtime, r-cran-tsibbledata, r-cran-tibble, r-cran-ggplot2, r-cran-scales, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fable.bayesrecon_0.2.1-1.ca2404.1_all.deb Size: 1991584 MD5sum: 2ee725bd1450188a6d78fa18a31ab65e SHA1: b57723a9dedc506a1e90b156b79d2cfae1b7ea95 SHA256: b5460d2da64677765bc41c6951e45bfed5e5cfd02774cc174917a4d511d07440 SHA512: 130dcaaad4c6557c31a3d23469ce23f22904004509be47cfe83c02aa3b903c7b0bf25bb7737edd3809552b5d0c00cae4be14b03e4eb9792b40dbd1cf8750491f Homepage: https://cran.r-project.org/package=fable.bayesRecon Description: CRAN Package 'fable.bayesRecon' (Bayesian Reconciliation in the 'fable' Framework) Implements the 'bayesRecon' probabilistic reconciliation methods within the 'fable' framework for hierarchical time series forecasting. Bayesian reconciliation (bayesRecon) methods are accessed via the 'reconcile' verb, following 'fable' conventions. For methodological background, see Corani et al. (2021) , Zambon et al. (2024a) , Zambon et al. (2024b) , and Carrara et al. (2026) . Package: r-cran-fable.prophet Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-fabletools, r-cran-rlang, r-cran-tsibble, r-cran-lubridate, r-cran-prophet, r-cran-dplyr, r-cran-distributional Suggests: r-cran-tsibbledata, r-cran-testthat, r-cran-ggplot2, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fable.prophet_0.1.1-1.ca2404.1_all.deb Size: 539068 MD5sum: 366fb4dfe3bfa83e79d1830776c44332 SHA1: e9b6f5fd2ee9b5623c93fc1e461059a04e2023e2 SHA256: b9c98525bf4c6b00a0599cf98b520a64bfee3543fd52680c24e845ce467e50fb SHA512: 0a0d392abf36aa3daf3e92d3c2f981eaab316d419f1e88329c03389ae3b33500c670c3c7961775164cdc1591337cf7334d7b0620a933fbba125f26fd0f497b74 Homepage: https://cran.r-project.org/package=fable.prophet Description: CRAN Package 'fable.prophet' (Prophet Modelling Interface for 'fable') Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. Package: r-cran-fablecount Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fabletools, r-cran-tscount, r-cran-glarma, r-cran-fable, r-cran-dplyr, r-cran-tsibble, r-cran-tibble, r-cran-tidyr, r-cran-distributional, r-cran-lubridate, r-cran-stringr, r-cran-tsibbledata Suggests: r-cran-rcpp, r-cran-rlang, r-cran-covr, r-cran-feasts, r-cran-forecast, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fablecount_0.1.0-1.ca2404.1_all.deb Size: 96768 MD5sum: 03e8f10d6531e74d7542234d8d99381e SHA1: 9912e8c59ee64bd90d68b75474b9671f9dffd7e0 SHA256: 867ea1b40d500d54387580669795a220d7714a619c1aebcabb2c4190804cc241 SHA512: 8d5f4bc1024b2a9705c3b40d52b8352954f66de9e45a31caa1eb30182a28c524b483a836fbff638be09f3a59fa45a78429c1d8deda31588593367a4a3e2dd987 Homepage: https://cran.r-project.org/package=fableCount Description: CRAN Package 'fableCount' (INGARCH and GLARMA Models for Count Time Series in FableFramework) Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood. Package: r-cran-fabletools Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tsibble, r-cran-tibble, r-cran-ggplot2, r-cran-tidyselect, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-generics, r-cran-r6, r-cran-vctrs, r-cran-distributional, r-cran-progressr, r-cran-lifecycle, r-cran-ggdist, r-cran-scales, r-cran-cli Suggests: r-cran-covr, r-cran-crayon, r-cran-fable, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-pillar, r-cran-feasts, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-lubridate, r-cran-urca, r-cran-mvtnorm, r-cran-matrix, r-cran-ggtime Filename: pool/dists/noble/main/r-cran-fabletools_0.8.0-1.ca2404.1_all.deb Size: 695006 MD5sum: 59c67d7e85cf228b703a6b3b625d326e SHA1: 7ac4327cf41d2cf3a95fe855c69646d5b9f67133 SHA256: 8ae0b7daef6ee754c105d351a9355f73439ebbf270768482ad0f6d97e216b3f1 SHA512: 1b2ff25f6edf449e226c32b70490233b7231617b7730ad9215d63b9ee7597cf335d15b1b04e27b4b9eb3d3eb16b8c1048f9e480ab50dfa3316448cdd83552c8d Homepage: https://cran.r-project.org/package=fabletools Description: CRAN Package 'fabletools' (Core Tools for Packages in the 'fable' Framework) Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis. Package: r-cran-fabr Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-usethis, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-janitor, r-cran-fs, r-cran-readr, r-cran-readxl, r-cran-writexl, r-cran-haven, r-cran-lubridate, r-cran-bookdown, r-cran-xfun, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fabr_2.1.1-1.ca2404.1_all.deb Size: 140104 MD5sum: 57841051421afb0577698ae9e597f4d8 SHA1: 36a18103f36c687c8359545e16a50eeca522944f SHA256: a7634eedb6cd015efdd96bff38afcbfa3df0c3c788d4c7015fb1c80d8676ab4e SHA512: 030837eadf782f37427df30e7f06d5aa4f575569fa451b616cff6020acb7969d87e48201eb3dad02a01ef53ed9b070eeb1d811559f52d7fcea03f7baa6b40849 Homepage: https://cran.r-project.org/package=fabR Description: CRAN Package 'fabR' (Wrapper Functions Collection Used in Data Pipelines) The goal of this package is to provide wrapper functions in the data cleaning and cleansing processes. These function helps in messages and interaction with the user, keep track of information in pipelines, help in the wrangling, munging, assessment and visualization of data frame-like material. Package: r-cran-fabricatr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-data.table, r-cran-mvnfast, r-cran-mass, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-fabricatr_1.0.2-1.ca2404.1_all.deb Size: 146932 MD5sum: cd9c3b79a81c62f71a9041959c9c58d5 SHA1: 79010f37113e4be32738d968d7a06680b3a8449f SHA256: d9845b6a8ebdab058b9d7072bf8270bff10126ee17cdd5b2392b66d7dea22b76 SHA512: c7b646d4c064ef1db93d307d2416c386b72cede6bba5d5b76aea71ae09f7be481c372dd70506b08fbb9086ba049e3dd0375006c9e8c9d15cc3517d05a9d8ad52 Homepage: https://cran.r-project.org/package=fabricatr Description: CRAN Package 'fabricatr' (Imagine Your Data Before You Collect It) Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources. This package is faster with 'data.table' and 'mvnfast' installed. Package: r-cran-fabricerin Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1201 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue Filename: pool/dists/noble/main/r-cran-fabricerin_0.1.2-1.ca2404.1_all.deb Size: 888994 MD5sum: 9f653121ad179309db06e5dadb177830 SHA1: cae003998ebbf3336ef303f2c33ca143640037b0 SHA256: 60f338bab4dfbaba5e763aa25eb1b2f538eaff716efa77098e5e29009244683e SHA512: e4cf832d3020deede15c2cf31361dc26ee4eab0302a5694a1dec314e7847a3d0b1901e979b73faeee5a0425ec729c1ff46184788930636c2bda68a293a1d7baf Homepage: https://cran.r-project.org/package=fabricerin Description: CRAN Package 'fabricerin' (Create Easily Canvas in 'shiny' and 'RMarkdown' Documents) Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. 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Package: r-cran-fabricqueryr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2625 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-azureauth, r-cran-bit64, r-cran-httr2, r-cran-r6, r-cran-vctrs, r-cran-tibble, r-cran-jsonlite, r-cran-cli, r-cran-nanoarrow, r-cran-reticulate, r-cran-rlang Suggests: r-cran-adbcdrivermanager, r-cran-adbi, r-cran-arrow, r-cran-dbi, r-cran-dplyr, r-cran-knitr, r-cran-lifecycle, r-cran-odbc, r-cran-processx, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-waldo, r-cran-webfakes, r-cran-withr Filename: pool/dists/noble/main/r-cran-fabricqueryr_1.0.1-1.ca2404.1_all.deb Size: 2058282 MD5sum: fe63044e281b9f02b9361cf3f7280833 SHA1: 6c584a78b18954a13ca9488c93aa3e24c6f6b454 SHA256: 1063dfe8cea04f860f95fd140d8b0371fde6715cd46b4fbac330c6e4ccc6dede SHA512: a295ad3c3a7989231453edaeb6e020b6164c6eb51457b5b54e4b84e4ce6865cd35867f7c4f8d5b07c25fa3f48189bcd42ef8d005ae1648664fc9a63df4ad4c08 Homepage: https://cran.r-project.org/package=fabricQueryR Description: CRAN Package 'fabricQueryR' (Access and Manage 'Microsoft Fabric') Access 'Microsoft Fabric' workspaces, items, and workload endpoints through its web application programming interfaces (APIs). Connect to data in 'OneLake', 'Lakehouse', 'Warehouse', semantic model, and 'Eventhouse' items, with support for 'DBI', 'Arrow', 'GraphQL', and 'Spark'. Manage files, tables, refreshes, jobs, schedules, ingestion, and long-running operations. 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For more details on the underlying measurement framework, see Linacre (1994, ISBN:0-941938-02-6) and Linacre (2023) . 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Input rates of transition between states of individuals with and without the disease-causing organism, distributions of states at facility admission, relative infectivity of transmissible states, and the facility length of stay distribution. Calculate the model equilibrium and the basic facility reproduction number, as described in Toth et al. (2025) . Package: r-cran-facmodcs Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-xts, r-cran-zoo, r-cran-performanceanalytics, r-cran-lattice, r-cran-sn, r-cran-tseries, r-cran-robustbase, r-cran-robstattm Suggests: r-cran-pcra, r-cran-corrplot, r-cran-lmtest, r-cran-rugarch, r-cran-hh Filename: pool/dists/noble/main/r-cran-facmodcs_1.0-1.ca2404.1_all.deb Size: 427672 MD5sum: e02e4a86e4d071da7d4debe673ef684c SHA1: 5563aae24ef3aa345252a72f27bda11521c12789 SHA256: 3e9d7a4c3c6b0b0fb8571dd97cb5b03ab62ff3ea8b17f2ee8f974b8f60b26722 SHA512: 7aba6a99ccbf86366ec50f19e8f4fe0200e6900a5624a8a070d7e4e26ea4f3ee81dd6b0a13c91b394fec5079acc5b2594cfc3ee29b34867480bfaaf2572be6b7 Homepage: https://cran.r-project.org/package=facmodCS Description: CRAN Package 'facmodCS' (Cross-Section Factor Models) Linear cross-section factor model fitting with least-squares and robust fitting the 'lmrobdetMM()' function from 'RobStatTM'; related volatility, Value at Risk and Expected Shortfall risk and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo. The package authors would like to thank Chicago Research on Security Prices,LLC for the cross-section of about 300 CRSP stocks data (in the data.table object 'stocksCRSP', and S&P GLOBAL MARKET INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and "factor exposures") fundamental data on the 300 companies in the data.table object 'factorSPGMI'. The 'stocksCRSP' and 'factorsSPGMI' data are not covered by the GPL-2 license, are not provided as open source of any kind, and they are not to be redistributed in any form. Package: r-cran-facmodts Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-lars, r-cran-lattice, r-cran-leaps, r-cran-performanceanalytics, r-cran-portfolioanalytics, r-cran-r.cache, r-cran-corpcor, r-cran-quadprog, r-cran-robstattm, r-cran-robustbase, r-cran-sandwich, r-cran-sn, r-cran-xts, r-cran-zoo Suggests: r-cran-corrplot, r-cran-hh, r-cran-lmtest, r-cran-r.rsp, r-cran-rugarch, r-cran-strucchange, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-facmodts_1.0-1.ca2404.1_all.deb Size: 250294 MD5sum: 2720fd29b2e16f0ee97fd58f3f3120cf SHA1: e5c09bf200a193e507da8c7241fb93af559bf023 SHA256: 11e27a8ab8362216ff16446e5024676c9290d0031e64bebfb7d2eb25585a02d0 SHA512: 32330c68044855cfd8b828b6513272d4c63c545e8614914e2e5f1a06bca75e6c02aa7a90f33086d35566ae73b9429d5816d3f8df3cfdd6dc6cff98351615060e Homepage: https://cran.r-project.org/package=facmodTS Description: CRAN Package 'facmodTS' (Time Series Factor Models for Asset Returns) Supports teaching methods of estimating and testing time series factor models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) . Package: r-cran-facomplex Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-psych, r-cran-lavaan, r-cran-testthat Filename: pool/dists/noble/main/r-cran-facomplex_1.0.0-1.ca2404.1_all.deb Size: 102226 MD5sum: 09912d78901760bed707da4cad2a72ec SHA1: 5bc576e622af94f4def84d46f4a87a6aa4d3c897 SHA256: a4ebb2ef92cdda7cda7dad9993c2715aea0dcabfaedd0966c4cbebe0a3b75d0b SHA512: d84f17db659eb96acf609485777a127a24076fb27c2680d2c18c901ba4d769d9f74dac09856e9f29cb2d9cee27fbaff55c6edfd670883649aec3285cda55acb9 Homepage: https://cran.r-project.org/package=facomplex Description: CRAN Package 'facomplex' (Methods for Assessing Factor Complexity and Simplicity in FactorAnalysis Solutions) Provides methods for estimating factor complexity coefficients in exploratory and confirmatory factor analysis (EFA/CFA) results. Included indices are the Hofman coefficient, Fleming's approach for factor simplicity, and others. Additional outputs include descriptive statistics (minimum, maximum, and mean) for target and non-target loadings, and visualization of results. References: Fleming, J.S. (2003) ; Hofmann, R.J. (1978) ; Kaiser, H.F. (1974) ; Bentler, P.M. (1977) ; Lorenzo-Seva, U. (2003) . 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The package is capable of re-assigning instances to clusters (algorithm agnostic), preserves the integrity of the data and does not introduce additional models. 'FACT' is inspired by the principles of model-agnostic interpretation in supervised learning. Therefore, some of the methods presented are based on 'iml', a R Package for Interpretable Machine Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018) . Package: r-cran-factchar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-factchar_1.0-1.ca2404.1_all.deb Size: 69774 MD5sum: 888d11a279460bc39b42b467c1495195 SHA1: 543a3d6d14d45107a9337f9fcea867d03c74d204 SHA256: 3f6e1d6256fd8ae9cefdec4d771901549c2318416fc88f06a240488076264980 SHA512: b61a1a05595381ca5b6a5f04c01fee8c7fa7f016b3f519ad4f62861dfd1d0442099df8f4c6bb607bfc17c01fd3ad1aaa8669e5c42fd03e050026f2f7e4fc1969 Homepage: https://cran.r-project.org/package=FactChar Description: CRAN Package 'FactChar' (Characterization and Diagnostic Tools for Factorial BlockDesigns) Description: Provides comprehensive tools for analysing and characterizing mixed-level factorial designs arranged in blocks. Includes construction and validation of incidence structures, computation of C-matrices, evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal factorial structure (OFS), diagnostics based on Hamming distance, discrepancy measures, B-criterion, Es^2 statistics, J2-distance and J2-efficiency, Phi-p optimality, and symmetry conditions for universal optimality. The methodological framework follows foundational work on factorial and mixed-level design assessment by Xu and Wu (2001) , and Gupta (1983) . These methods assist in selecting, comparing, and studying factorial block designs across a range of experimental situations. 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This package includes tools to check whether a design has orthogonal factorial structure (OFS) with balance or not and is able to find the orthogonality deviation value if not having OFS. This package includes function to evaluate efficiency factor of all factorial effects in two situations, in the first situation if the design is verified with OFS and balance then calculate the efficiencies of all factorial effects using a specific analytical procedure and in the second situation if the design is verified with non-OFS and balance then a new general method has been developed and used to calculate efficiencies under the condition that the design should be proper and equi-replicated, See Gupta, S.C. and Mukerjee, R. (1987): "A Calculus for factorial arrangements". Lecture Notes in Statistics. No. 59, Springer-Verlag, Berlin, New York, . For the easy use of package, 'shiny' app is used for giving inputs and inputs validation. 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Package: r-cran-factmle Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rarpack Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-factmle_1.1-1.ca2404.1_all.deb Size: 21832 MD5sum: 8044383b5990cfdb55bb3b2ff2c68c33 SHA1: d39a30781213a2baf8c87da0d7605ee32e566599 SHA256: 344ae0d4103aca7d9c7a502e09d02529355a4018720ec4a6d1a3f182153f3ee3 SHA512: 270ea762224b38a83342b3884c3f4244b4befda4539c5c95aab67e33051a2ff2f3f37fddbe2c9a7d18335b7ce2b36d9a622e5d8919e4f9b7c0ae4dd5b7585d7a Homepage: https://cran.r-project.org/package=FACTMLE Description: CRAN Package 'FACTMLE' (Maximum Likelihood Factor Analysis) Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix. 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It standardizes coordinates, squared cosines, contributions, and eigenvalues from several analysis backends and produces 'ggplot2'-based factor maps, scree plots, contribution plots, clustering diagnostics, dendrograms, and silhouette plots. It also visualizes two-dimensional 'UMAP' and t-SNE embeddings and adapts precomputed or 'tidymodels' principal-component results for the same plotting interface. 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'FAfA' (Factor Analysis for All) guides users through data uploading, assumption checking (descriptive statistics, collinearity, multivariate normality, outliers), data wrangling (variable exclusion, data splitting), exploratory factor analysis (EFA) with various rotation and extraction methods, confirmatory factor analysis (CFA), reliability analysis (e.g., Cronbach's Alpha, McDonald's Omega), and measurement invariance testing across groups. Factor retention methods include parallel analysis following Horn (1965) , optimized parallel analysis following Timmerman and Lorenzo-Seva (2011) , permutation parallel analysis for categorical variables following Lubbe (2019) , the Hull method following Lorenzo-Seva et al. (2011) , minimum average partial criteria following Velicer (1976) and O'Connor (2000) , and the empirical Kaiser criterion following Braeken and van Assen (2017) . 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The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2025) . Package: r-cran-fairml Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-lattice, r-cran-gridextra, r-cran-cccp, r-cran-cvxr, r-cran-survival Filename: pool/dists/noble/main/r-cran-fairml_0.9.1-1.ca2404.1_all.deb Size: 2850068 MD5sum: ea05c272a61ec714c7cb8c03a6fb0dae SHA1: 4325f7d376c7df2a072f786eadaae20d12cd1fbb SHA256: e95d4254dcfc68eb3f7514080043597b77c20be0a42eb049a7d39c2dad6a11b1 SHA512: 81083f3dd6ec7d6a4e3d3fb17691371d688870f1adf52508e26b736d912915c8e9038083afa7d18954108ed56370a435bee8477c3f84bfc5f8f63f4121413c37 Homepage: https://cran.r-project.org/package=fairml Description: CRAN Package 'fairml' (Fair Models in Machine Learning) Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) , Zafar et al. (2019) and my own approach from Scutari, Panero and Proissl (2022) that uses ridge regression to enforce fairness. 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Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) . 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Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) , Chouldechova (2017) , Feldman et al. (2015) , Friedler et al. (2018) and Zafar et al. (2017) . The package also offers convenient visualizations to help understand fairness metrics. 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Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) ). 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Appropriate ascertainment correction is utilized to estimate age-dependent penetrance functions either parametrically from the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm can infer missing genotypes and carrier probabilities estimated from family's genotype and phenotype information or from fitted models. Plot functions include pedigrees of simulated families and predicted penetrance curves based on specified parameter values. For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs, Journal of Statistical Software 97 (7), 1-30. 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The app integrates Mendelian error analysis, parentage assignment and genetic composition/ancestry methods to help researchers evaluate genomic relationships through an accessible, web-based interface without requiring command-line tools. Pedigree validation, Mendelian error analysis and parentage assignment build on the 'BIGpopA' package () and support diploid and polyploid data. Ancestry estimation uses the sparse non-negative matrix factorization method of Frichot et al. (2014) as implemented in the 'LEA' package by Frichot and Francois (2015) . Line and breed composition are estimated using the breed composition regression method of Funkhouser et al. (2017) , extended to polyploid species by Sandercock et al. (2025) . Package: r-cran-familial Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-depthproc, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-familial_1.0.7-1.ca2404.1_all.deb Size: 77138 MD5sum: 2c41252983f19906fc795f044765d391 SHA1: c2c791db1712c70c0ac7dbb6e45e8362fa3c1c37 SHA256: 15729abe945c122e9652071d5530fe0d8326261764feaade7b3b5bc8790e7c1a SHA512: bcd4acad4542f640b8c95c92780c700379610d468859269108d8eb6be0b8da3d28c61463ed731845ebb697525162dd0cd7f0b1db24a55a5f412dbb40b789f107 Homepage: https://cran.r-project.org/package=familial Description: CRAN Package 'familial' (Statistical Tests of Familial Hypotheses) Provides functionality for testing familial hypotheses. Supports testing centers belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and two-sample tests are supported, as are directional tests. Methods for visualizing output are provided. Package: r-cran-familiar Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6635 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rlang, r-cran-rstream, r-cran-survival Suggests: r-cran-bart, r-cran-callr, r-cran-cluster, r-cran-coro, r-cran-dynamictreecut, r-cran-e1071, r-cran-fastcluster, r-cran-fastglm, r-cran-ggplot2, r-cran-glmnet, r-cran-gtable, r-cran-harmonicmeanp, r-cran-isotree, r-cran-knitr, r-cran-labeling, r-cran-lagp, r-cran-maxstat, r-cran-microbenchmark, r-cran-nnet, r-cran-paletteer, r-cran-power.transform, r-cran-praznik, r-cran-proxy, r-cran-randomforestsrc, r-cran-ranger, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-xml2, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-familiar_2.0.3-1.ca2404.1_all.deb Size: 4583308 MD5sum: 72c207698e4517821d016889bb5658e4 SHA1: f2f6302e66a41e44cb29ee308ea1614205cba58b SHA256: d70923956eb11ce92c1aea635653143d22dfb6ea7a62d8f35d6b06a3e0082604 SHA512: 97032bb33221b1749bf3b2dfc127420fbb340685feb50bc92467f79aa681a4b74a0fb052f5f3f7735dc9aae7a762d83279a1399e466aed6d5f8bf84c71c1d823 Homepage: https://cran.r-project.org/package=familiar Description: CRAN Package 'familiar' (End-to-End Automated Machine Learning and Model Evaluation) Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals. Package: r-cran-families Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msm, r-cran-reshape, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate, r-cran-xml2, r-cran-plyr, r-cran-virtualpop Filename: pool/dists/noble/main/r-cran-families_2.0.2-1.ca2404.1_all.deb Size: 479338 MD5sum: 1dd07c2db1bf7a9e0ea13d5e0d592b4c SHA1: 902da7026a768b4d4cc0c63ff8199a176ee57c5a SHA256: 38a5437791890f8d7421259463a8509f3c1893dd758a2994159c20865c1d84b5 SHA512: c0156b598bdc8ea05f190f0b49a1522cc15ba5fca605d69f969ee74d09e4608ed25c3c22bf7c82afb2a990eed36035ceedad13d60f87b60fe351e649e134416c Homepage: https://cran.r-project.org/package=Families Description: CRAN Package 'Families' (Kinship Ties in (Virtual) Multi-Generation Populations) Tools to study lineages, grandparenthood, loss of close relatives, kinship networks and other topics in multi-generation populations. Package: r-cran-famish Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-distionary, r-cran-fitdistrplus, r-cran-ismev, r-cran-lmom, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-famish_0.2.1-1.ca2404.1_all.deb Size: 1845632 MD5sum: a1da287088b89b7e1ba7e6fceffc7770 SHA1: a42e9eda62fe923be4ecfc5bff5cb51a86712487 SHA256: f48ff2a93f052ef0a7a813ff4683324f337a7e0baf8d18483fbb6e51a3152023 SHA512: ecee95f3fd82070e54504e1c8a2ab96a65956215d0009465b10342bc367edbce7aa56a321801baee731d6a6ee3b0a4586b21670045441a1da9e21e77c6f98e8e Homepage: https://cran.r-project.org/package=famish Description: CRAN Package 'famish' (Flexibly Tune Families of Probability Distributions) Fits probability distributions to data and plugs into the 'probaverse' suite of R packages so distribution objects are ready for further manipulation and evaluation. Supports methods such as maximum likelihood and L-moments, and provides diagnostics including empirical ranking and quantile score. Package: r-cran-famnesia Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dt, r-cran-bslib, r-cran-htmltools, r-cran-pedfamilias, r-cran-pedmut, r-cran-pedprobr, r-cran-pedtools, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-famnesia_0.1.1-1.ca2404.1_all.deb Size: 28630 MD5sum: f99c11f0dc62a3a7212bb106787e9f05 SHA1: 563a70b73a0234b46ae35287a4419cc17778e87c SHA256: f89db715753d1d1ff3c11af9acfac0bb052dd925fd7b600b320e19f6bf876173 SHA512: 7db793683c27a2b69eb3b7848b0a8d2d2995b2183d2cb72509bb6cf8fa6e16c376b9d6f187d66058368cca17da6c1f23ef8524c231973b22bfa8e279e82082b7 Homepage: https://cran.r-project.org/package=famnesia Description: CRAN Package 'famnesia' (Anonymising Familias Files) A 'shiny' application for anonymising files exported from the 'Familias' software for forensic kinship analysis (Egeland et al. (2000) ). Pedigrees, marker data, allele frequencies and mutation models can be masked or modified, with options for preserving likelihood ratios exactly. The application is built on the 'pedsuite' packages for pedigree analysis. Package: r-cran-famos Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-r.utils Suggests: r-cran-future.batchtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-famos_0.3.1-1.ca2404.1_all.deb Size: 152804 MD5sum: b775a6c3e79d0c62ce7c9dcae3731124 SHA1: 0195076803b1192c483e0f53ab803e7606b1fb41 SHA256: e21c72813015bd58dbeed9126b10d26cfb778e0a684e08e1e1302b01b6aa0606 SHA512: 351bd88736f93a6cda036bcbf7a7ef92fd446147659bc74a17181025f4da4a1d6e046519843d7471632a730457b8f3e06a6997875865983fedc018e7977d539d Homepage: https://cran.r-project.org/package=FAMoS Description: CRAN Package 'FAMoS' (A Flexible Algorithm for Model Selection) Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) . Package: r-cran-famskatrc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-kinship2, r-cran-coxme, r-cran-bdsmatrix Filename: pool/dists/noble/main/r-cran-famskatrc_1.1.0-1.ca2404.1_all.deb Size: 41292 MD5sum: 4765f1751b9d724db6114f460b232ae3 SHA1: 966a584e8b8f7562b989b812e2df6163e713ef3f SHA256: dc98e9307a3f62d31eab96c9b3e2c3745ef1a8548ee1f054591241d287398ba2 SHA512: 2393a86483121a643b29098aa0bd382ed718ba89275d2529a390840528910a206edaa297a7cb2fb5fc18652f1f76ea3a35b6a68cb44bb73ae9da4894159dc59f Homepage: https://cran.r-project.org/package=famSKATRC Description: CRAN Package 'famSKATRC' (Family Sequence Kernel Association Test for Rare and CommonVariants) FamSKAT-RC is a family-based association kernel test for both rare and common variants. This test is general and several special cases are known as other methods: famSKAT, which only focuses on rare variants in family-based data, SKAT, which focuses on rare variants in population-based data (unrelated individuals), and SKAT-RC, which focuses on both rare and common variants in population-based data. When one applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one applies famSKAT-RC and set the kinship matrix (fullkins) to the identity matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also include a small sample synthetic pedigree to demonstrate the method with. For more details see Saad M and Wijsman EM (2014) . Package: r-cran-famt Architecture: all Version: 2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-bioc-impute Filename: pool/dists/noble/main/r-cran-famt_2.6-1.ca2404.1_all.deb Size: 3442792 MD5sum: 5c0cfb2019427838a9519274c3414620 SHA1: 072a55c72938efbe110371271933302f8befc4e8 SHA256: 957e42fddc4fcf8d260e396988870d2cfba5a3dcd8c62a63495d25d9e7bd516f SHA512: b7b95e025c7814e63a2e7844e5dc525bb55baa0b591fb4b29ec322da2838260c046f1700f6e53327c08854a7aaa9be9c03c09fb7beedf8220bc6dc062aad1f6d Homepage: https://cran.r-project.org/package=FAMT Description: CRAN Package 'FAMT' (Factor Analysis for Multiple Testing (FAMT) : Simultaneous Testsunder Dependence in High-Dimensional Data) The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors. 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The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) . 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For example, by translating error messages and descriptive analysis results into a language familiar to the user, it enables a better understanding of the information, thereby reducing the barriers caused by language. It offers several helper functions to query gene information to help interpretation of interested genes (e.g., marker genes, differential expression genes), and provides utilities to translate 'ggplot' graphics. This package is not affiliated with any of the online translators. The developers do not take responsibility for the invoice it incurs when using this package, especially for exceeding the free quota. 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(1998, ISBN:92-5-104219-5) "Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56". 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A list of functions to download statistics from FAOSTAT (database of the FAO ) and WDI (database of the World Bank ), and to perform some harmonization operations. Package: r-cran-faoutlier Architecture: all Version: 0.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sem, r-cran-mvtnorm, r-cran-lattice, r-cran-lavaan, r-cran-mirt, r-cran-mass, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-faoutlier_0.7.7-1.ca2404.1_all.deb Size: 142988 MD5sum: ead1f97ad2e6242c40d1ce6569842335 SHA1: c155586dd6468ecf422f33f8ca00658f0e59ee1f SHA256: f107d17d063ae3d69a633bbac6aacc711cf9e09e06764963c836b80179401090 SHA512: 123c0768ca7dad74466756a9b69d2ad872aef4f7c6a1378c32b4310d784c30b97384735a02d81232795d39f4b449ddb6d23a9323614bfb3778734bd484713538 Homepage: https://cran.r-project.org/package=faoutlier Description: CRAN Package 'faoutlier' (Influential Case Detection Methods for Factor Analysis andStructural Equation Models) Tools for detecting and summarize influential cases that can affect exploratory and confirmatory factor analysis models as well as structural equation models more generally (Chalmers, 2015, ; Flora, D. 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Package: r-cran-fapa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fapa_0.1.1-1.ca2404.1_all.deb Size: 107102 MD5sum: f9b4ab0ca46c9b32527e163797ac2cd7 SHA1: 4913eaf9abd523d484568ff6f78528d5e9b77815 SHA256: 5765d49a3f004ab70d818b508e3255f738efbeeafacd646b0a115dab21e369c1 SHA512: cb1a0f6ccc971a71219dce97964863d17599802e2b80356aaf627ed8ee3910abc8b9fe39fbbc0bc0e98e5480577d063e6f3d910734fb4927f34b8a9d26dc4045 Homepage: https://cran.r-project.org/package=FAPA Description: CRAN Package 'FAPA' (Factor Analytic Profile Analysis of Ipsatized Data) Implements Factor Analytic Profile Analysis of Ipsatized Data ('FAPA'), a metric inferential framework for pattern detection and person-level reconstruction in multivariate profile data. After row-centering (ipsatization) to remove profile elevation, 'FAPA' applies singular value decomposition ('SVD') to recover shared core profiles and individual pattern weights. Dimensionality is determined by a variance-matched Horn's parallel analysis. A three-stage bootstrap verification framework assesses (1) dimensionality via parallel analysis, (2) subspace stability via Procrustes principal angles, and (3) profile replicability via Tucker's congruence coefficients. BCa bootstrap confidence intervals for core-profile coordinates are computed via the canonical 'boot' package implementation of Davison and Hinkley (1997) . 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Package: r-cran-faraway Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme Suggests: r-cran-leaps Filename: pool/dists/noble/main/r-cran-faraway_1.0.9-1.ca2404.1_all.deb Size: 768662 MD5sum: fd57234b466c4f0df2e281b2b1693190 SHA1: 9cc41a1f04a7dd1e73e63a62768ffa0fe68b3372 SHA256: af85be35e8f712dce545e91c40f93910846a9a038142fa44521569e83bd656e4 SHA512: b4647144761b1edefaf1f9b4e77f17652ab915ad9503470e224800161921fe965112635fe1b3408bd17b9ac4c4fdd2b3d7c5f0011d9315db51dd00e143b7deae Homepage: https://cran.r-project.org/package=faraway Description: CRAN Package 'faraway' (Datasets and Functions for Books by Julian Faraway) Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated). 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Chapman and Hall/CRC. and . Package: r-cran-farrell Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-data.table, r-cran-magrittr, r-cran-shinywidgets, r-cran-benchmarking, r-cran-shinycssloaders, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-farrell_0.2.1-1.ca2404.1_all.deb Size: 642542 MD5sum: 2c24e589672c349e7dcd73da42810df8 SHA1: 61b5154796b988247d494123680a61b96a9f39da SHA256: 7a65234f92ad2ed57caffac99bb0cc37caee481499dfffc85dce6683f02493dd SHA512: 94058f835463103dd45adf9e128522d2a25f04617018491a591372dded3823a50ea73959b401b8ebca2682e80947a6bdccabcaabe5f44615f5d725bef74f25a0 Homepage: https://cran.r-project.org/package=farrell Description: CRAN Package 'farrell' (Interactive Interface to Data Envelopment Analysis Modeling) Allows the user to execute interactively radial data envelopment analysis models. 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Package: r-cran-fars Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-magrittr, r-cran-ggplot2, r-cran-plotly, r-cran-sn, r-cran-nloptr, r-cran-ellipse, r-cran-syscselection, r-cran-quantreg, r-cran-tidyr, r-cran-dplyr, r-cran-forcats, r-cran-mass, r-cran-reshape2, r-cran-stringr Suggests: r-cran-r.rsp, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-openxlsx, r-cran-readxl, r-cran-zoo Filename: pool/dists/noble/main/r-cran-fars_0.8.0-1.ca2404.1_all.deb Size: 1768352 MD5sum: d0165eae24c8fe129c1733fbb5e8c75e SHA1: 3f8ab2c8324855bbedcd3a9440645331e98b6da5 SHA256: f5b95a897ae0617da256b44511680fa27a1b56ee2c6d2b51e23b45725d6d3a67 SHA512: a21a771f735fbf846c23422a37a2e8f43f8e903be7b432ea6ada861ab4b9316f71654eb9c473ff66f01fae7b9366189f39123d5982511b5bd4356a746d7fdf6d Homepage: https://cran.r-project.org/package=FARS Description: CRAN Package 'FARS' (Factor-Augmented Regression Scenarios) Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed. Package: r-cran-fase Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-rtensor, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-fase_1.0.1-1.ca2404.1_all.deb Size: 139270 MD5sum: efbef0094b8e74dc011b83fc9beb6836 SHA1: 4f311e6fb18e442020240c9a5f586656b018ee8c SHA256: f27ccf43a7d5ae72732efa9ff89f00867b219cc78dface3154ab05b83fa2c72c SHA512: fac582389ce4f5cd409b924864835efef28d17947da48942fe219f72163cb7cb847cfa949482b57be99d9ff41ffc7dd0bc0f2cb9f169d88fba2e6b5c2f167fe5 Homepage: https://cran.r-project.org/package=fase Description: CRAN Package 'fase' (Functional Adjacency Spectral Embedding) Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. 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Package: r-cran-fasjem Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-fasjem_1.1.2-1.ca2404.1_all.deb Size: 359924 MD5sum: 2e67e4f122b5ef5f28a0e7f4e680df98 SHA1: a11f127e4ad6d5b0d384688d5b57a361c75ab167 SHA256: ae73b6d8b7d13f2ce00817157a2593b1a7bcbba0be667b0a81e9efe490558793 SHA512: 74645ba277c4784e40fce9dd8c5623c05d563110f1b17b924d8370723df41d733fbbc5bae8440aab4d27b805e34562dd431bd21b1642e954d17ebfe9000438a7 Homepage: https://cran.r-project.org/package=fasjem Description: CRAN Package 'fasjem' (A Fast and Scalable Joint Estimator for Learning MultipleRelated Sparse Gaussian Graphical Models) This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). 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Package: r-cran-fasta Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fasta_0.1.0-1.ca2404.1_all.deb Size: 17518 MD5sum: 6bb21cac98b5b11181caa74b9434d497 SHA1: fe43a7621f044fc97ad33fa1d969e251fd648312 SHA256: 9376abb8d60286eb23a40b046f52d323550b77176c6d6d870204299d0d39e1b6 SHA512: 7017c65f7b12863a61b947f1783e59407fd5ceae98912e93e1211d6e79085d21e02e37749c0c3b266a1a5f6bbbfe44932ea4ac00ae4bb5e81130b040d09559bd Homepage: https://cran.r-project.org/package=fasta Description: CRAN Package 'fasta' (Fast Adaptive Shrinkage/Thresholding Algorithm) A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) . 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Package: r-cran-fastbackward Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastbackward_1.0.1-1.ca2404.1_all.deb Size: 27490 MD5sum: 81fdde4b118328e3f2df7b4275999f9b SHA1: ec4dc3c97683ec35d98bb7252bcf3877948dbade SHA256: caf1b687197d902a73b1194d990fc6c09b1f2b8d3900a9677c5320575067f4d8 SHA512: afdb75bd9a7c1c1aa4cdf1227a730f6106dd010534d275141585b6aa4da9275eaab81a872e4c3df715855c3c28b8bbc27a0414ff70500e6039d326aa9dd41956 Homepage: https://cran.r-project.org/package=fastbackward Description: CRAN Package 'fastbackward' (Fast Backward Elimination Based on Information Criterion) Performs backward elimination with similar syntax to the stepAIC() function from the 'MASS' package. A bounding algorithm is used to avoid fitting unnecessary models, making it much faster. 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The package overcomes computational bottlenecks by automatically switching between an in-memory framework using the 'terra' package to maximize speed for smaller datasets, and an on-disk tiling framework for rasters that exceed available RAM, leveraging 'exactextractr' and 'Rfast' to process data in chunks. The core functions, derive_bioclim() and derive_statistics(), offer a unified interface with flexibility for custom time periods beyond standard quarters and the use of fixed temporal indices, facilitating the creation of temporally-matched environmental variables for ecological and biogeographical modeling. Visit the package website to find tutorials in English and Spanish. Package: r-cran-fastcub Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-cub Filename: pool/dists/noble/main/r-cran-fastcub_0.0.4-1.ca2404.1_all.deb Size: 271138 MD5sum: ed0a3cb6ae7f44740f7bb43d1cdf60c6 SHA1: c5f28eebd0de9b87fd430fa89605501963db2c4d SHA256: c60421c73676e1f5ee0c49c13b4cdd0ed01559d84d34421148363addf49ffb93 SHA512: f18e3552bc70928b01ca8e30b255d34c7ee51ba949e5b6b90c61ede1f2642f641ae6573f13b6208079283b620f71c7b4aa018017ef167386496d49329c2ec404 Homepage: https://cran.r-project.org/package=FastCUB Description: CRAN Package 'FastCUB' (Fast Estimation of CUB Models via Louis' Identity) For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). 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Package: r-cran-fastdid Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-bmisc, r-cran-collapse, r-cran-dreamerr, r-cran-ggplot2 Suggests: r-cran-did, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-fastdid_1.0.6-1.ca2404.1_all.deb Size: 358894 MD5sum: 8a6f8394e0caf1a246613c6dd3ff3d1c SHA1: 15cfa5ce113b90357b97f2a42cf2b30f5122f6dc SHA256: dc6de581e2a0bfd47fa5ee78e914f4293642eda4e562184d0b8e0178b2326a00 SHA512: 1f1ae4518e9e26c7cbf77270e92a92ffb193d3d48e38bf9da8769b943630c1678e1136d8be559c75c372d76347b3d8e0ac8653816bdcb991789b23c59c116463 Homepage: https://cran.r-project.org/package=fastdid Description: CRAN Package 'fastdid' (Fast Staggered Difference-in-Difference Estimators) A fast and flexible implementation of Callaway and Sant'Anna's (2021) staggered Difference-in-Differences (DiD) estimators, 'fastdid' reduces the computation time from hours to seconds, and incorporates extensions such as time-varying covariates and multiple events. Package: r-cran-fastdummies Architecture: all Version: 1.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tibble, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fastdummies_1.7.6-1.ca2404.1_all.deb Size: 42088 MD5sum: 5f94c79c0e95a716eb58b54514bf8164 SHA1: c2b106c3b6d543a1236d3f16f69921133ae4d972 SHA256: 94f0e8217c958f7a23ce6a2467fd1a8f9fcb7d76a24ce36f9809d2ff2831315a SHA512: 8cf464313d323b1bf41106818557d72b8411b1a356b76468996cf8e43aea704e53732b468de1af5c5fb0c45598bc6d0a9f01846b0bd2f3682cf2e1c1f47058b1 Homepage: https://cran.r-project.org/package=fastDummies Description: CRAN Package 'fastDummies' (Fast Creation of Dummy (Binary) Columns and Rows fromCategorical Variables) Creates dummy columns from columns that have categorical variables (character or factor types). 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Package: r-cran-fasterraster Architecture: all Version: 8.4.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-omnibus, r-cran-rgrass, r-cran-sf, r-cran-shiny, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fasterraster_8.4.1.2-1.ca2404.1_all.deb Size: 4212326 MD5sum: 5f177996c9df18070a3ef576570d396f SHA1: 3a5591f71f9b9cbdde23eaca2f44d479c8be2cd4 SHA256: 6ec37d5d52222a3af42d16d27cdef6b9cffabcdd9006615ef71cacdfcbc74bc5 SHA512: d3420596d587999377263e42e8938adb8b3726cb4c0b2032c859ac354c1898b5fc900f80e8a79522bcc23504bb6e6dbec5bbea012796fae095e0a43e6585cb4a Homepage: https://cran.r-project.org/package=fasterRaster Description: CRAN Package 'fasterRaster' (Faster Raster and Spatial Vector Processing Using 'GRASS') Processing of large-in-memory/large-on disk rasters and spatial vectors using 'GRASS' . Most functions in the 'terra' package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using 'terra' or 'sf', but for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster. To use most of the functions, you must have the stand-alone version (not the 'OSGeoW4' installer version) of 'GRASS' 8.0 or higher. 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Package: r-cran-fastgraph Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-jmuoutlier Filename: pool/dists/noble/main/r-cran-fastgraph_2.1-1.ca2404.1_all.deb Size: 87162 MD5sum: 31065fb99f4f5793558a43be7fe75562 SHA1: 68d926b22a2b78ba840ee695283164e76a904602 SHA256: bdeff11a5415623221ada90a47b8ab2decd0d1a0cdb539bd8471fb696ebf3c58 SHA512: 66412c8eb8b5812edd7614583e6de6296644282c3fdd85516e9e1ece95560025e177aabb1da6d40f022156d5ce1b749304a26e81a075e10fa73ec2daaf29cc47 Homepage: https://cran.r-project.org/package=fastGraph Description: CRAN Package 'fastGraph' (Fast Drawing and Shading of Graphs of Statistical Distributions) Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title. Package: r-cran-fastimputation Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-fastimputation_2.2.1-1.ca2404.1_all.deb Size: 623840 MD5sum: f468aef01e9ce775e5f2a3245516df02 SHA1: aaf683460aaa856445d06e99ca814e68e8167459 SHA256: a2501d39349e15353c3ce38e8e7cb05cbe5cfcb2dc1ccd977141cc2dc5e44bd4 SHA512: 67fbba987c032dbeee1a328e23ea06fb8159b9a50c0ab9b8951b305942371a5595a1dca64270823605a1d9b3cb8ca5d401ee9106cc760d1e8cdbca42bba32fca Homepage: https://cran.r-project.org/package=FastImputation Description: CRAN Package 'FastImputation' (Learn from Training Data then Quickly Fill in Missing Data) TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class 'FastImputationPatterns'. 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Package: r-cran-fastkm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rarpack Suggests: r-cran-skat, r-cran-survival Filename: pool/dists/noble/main/r-cran-fastkm_1.2-1.ca2404.1_all.deb Size: 88200 MD5sum: 9cadd0f93fbb6aee2da5165f2212defc SHA1: 8cc948709a1a781fce41dbb38b68a05adf51f0dc SHA256: ffba75646bd4b642516640e96a8664aecdb83d42a4ebb4cb3d32ad09ce78659b SHA512: daad7f7fced9e42a86659551db0c3703ea89234ab94d7159f3942d6f92acf407718f6b763c2ad1e47675cac3e35a4a952d9ee768f27c8cf53de86316ab340b18 Homepage: https://cran.r-project.org/package=FastKM Description: CRAN Package 'FastKM' (A Fast Multiple-Kernel Method Based on a Low-Rank Approximation) A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from . Package: r-cran-fastknn Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdist, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-fastknn_0.0.1-1.ca2404.1_all.deb Size: 19612 MD5sum: f980fa38163b340c497a1456e1e35582 SHA1: e40ceb9584ea05b82b2b62474a4fa6f92c0854b7 SHA256: 91023f309cbf1ef8885ac8898850f4175b44a21c47f931341b385ec7742d358e SHA512: 20192d36c8a6b8fdc86a31618aa8721c333f4997f6ac48395d05b54522fdeb6d06e1ff6c7423e249e1d441495af74b1ea7d3d9e8dc05a3ce075b7e3eb316b963 Homepage: https://cran.r-project.org/package=FastKNN Description: CRAN Package 'FastKNN' (Fast k-Nearest Neighbors) Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time. Package: r-cran-fastlaplace Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-bbmle, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-mgcv, r-cran-ngspatial, r-cran-mass Filename: pool/dists/noble/main/r-cran-fastlaplace_0.0.2-1.ca2404.1_all.deb Size: 72390 MD5sum: 0c630fd47516b251a9a3a615b096be35 SHA1: 5a188b937586eeb27c843f72f50d16042298ceb1 SHA256: ff9a114d9389c9cec8566cc6638340fbe989c89e6305aaf2a452ea04f20b3f7a SHA512: 0f379673b75cec3d869ea6ccfafca85f8f6a7eb0505d9a2b03ca1d8bb319411c45189b94b3853fe77c832a73322b9434a4cf0d03799771cc3a6dfcce934a9351 Homepage: https://cran.r-project.org/package=fastLaplace Description: CRAN Package 'fastLaplace' (A Fast Laplace Method for Spatial Generalized Linear Mixed Model) Fitting a fast Laplace approximation for Spatial Generalized Linear Mixed Model as described in Park and Lee (2021) . Package: r-cran-fastlogitme Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fastlogitme_0.1.0-1.ca2404.1_all.deb Size: 24966 MD5sum: 0bc498860e9a1bd65a552ecdcf46c727 SHA1: 03f17981a5fea1979051a49393fb26ade6ff2132 SHA256: 2f5b7b67e56f9820b7ef9dc6b576993165b6c65d27f2b8e7d7f279a382a551a0 SHA512: bf61c12ec212472e8f033cca02eb4a4b9a6b3a7fe9f66ffa740779f172b78919fd914554656d4618ddec10198b54e2e01210a599e0006399ad267b2ca5210abf Homepage: https://cran.r-project.org/package=fastlogitME Description: CRAN Package 'fastlogitME' (Basic Marginal Effects for Logit Models) Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power. Package: r-cran-fastml Architecture: all Version: 0.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1224 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-recipes, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rsample, r-cran-parsnip, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-tibble, r-cran-rlang, r-cran-dials, r-cran-rcolorbrewer, r-cran-dofuture, r-cran-foreach, r-cran-finetune, r-cran-future, r-cran-viridislite, r-cran-magrittr, r-cran-proc, r-cran-janitor, r-cran-stringr, r-cran-broom, r-cran-tidyr, r-cran-purrr, r-cran-survival, r-cran-xgboost Suggests: r-cran-testthat, r-cran-withr, r-cran-baguette, r-cran-c50, r-cran-dalex, r-cran-discrim, r-cran-fairmodels, r-cran-flexsurv, r-cran-ibreakdown, r-cran-iml, r-cran-lime, r-cran-modelstudio, r-cran-pdp, r-cran-plsmod, r-cran-probably, r-cran-ranger, r-cran-rstpm2, r-cran-survrm2, r-cran-aorsf, r-cran-censored, r-cran-crayon, r-cran-ceterisparibus, r-cran-kernlab, r-cran-klar, r-cran-kknn, r-cran-keras, r-cran-lightgbm, r-cran-bonsai, r-cran-lme4, r-cran-rstanarm, r-cran-patchwork, r-cran-ggally, r-cran-glmnet, r-cran-themis, r-cran-dt, r-cran-upsetr, r-cran-vim, r-cran-dbscan, r-cran-gridextra, r-cran-htmlwidgets, r-cran-kableextra, r-cran-modeldata, r-cran-moments, r-cran-naniar, r-cran-nnet, r-cran-plotly, r-cran-rpart, r-cran-scales, r-cran-skimr, r-cran-sparsediscrim, r-cran-knitr, r-cran-rmarkdown, r-cran-pec Filename: pool/dists/noble/main/r-cran-fastml_0.7.10-1.ca2404.1_all.deb Size: 1170602 MD5sum: bbf5eab7eeb0401e178beaf635c454e9 SHA1: a791e9803b7e3827d61ec5ecf6c9601d6830f0a4 SHA256: 31edf397f31712e7b528d84c896581acfc0bd736020f9996d31722b63a0f774c SHA512: 80092b9e0229e4457f52a7379e3e5139d218f6da6ce2e0b3992550affa4f64dbc49f2d349a34a14d5fbc9c18b781435057960f6eadbe0a14f46e810921691428 Homepage: https://cran.r-project.org/package=fastml Description: CRAN Package 'fastml' (Guarded Resampling Workflows for Leakage-Aware Machine Learningin R) Provides a guarded resampling workflow for training and evaluating machine-learning models. When the guarded resampling path is used, preprocessing and model fitting are re-estimated within each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight AutoML-style workflow by automating model training, resampling, and tuning across multiple algorithms, while keeping evaluation design explicit and user-controlled. Package: r-cran-fastnaivebayes Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastnaivebayes_2.2.1-1.ca2404.1_all.deb Size: 1214420 MD5sum: 36f113d5e00a6302ccd1e2b6b3214f95 SHA1: 96f81e058ab7e680952d832cd8b09c7391f5515e SHA256: d49510bdfdbc91671011730333ee7960fd87502e82a9068ebada77f59f77b7d4 SHA512: 3991f7106791a8f7e9e622c9f7ad1006fc5b66a4e4d297737cca9db0b09343d0c6058663cd763b19de479b32c7a60ee65e1151c7ba80c6475fd828d0620d1bf5 Homepage: https://cran.r-project.org/package=fastNaiveBayes Description: CRAN Package 'fastNaiveBayes' (Extremely Fast Implementation of a Naive Bayes Classifier) This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) . Any issues can be submitted to: . Package: r-cran-fastnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-fastnet_1.0.0-1.ca2404.1_all.deb Size: 180508 MD5sum: 590a957490d44742cf883d5705d30875 SHA1: 5df81b53bc25b5f64b6151324f47c6057ed85a40 SHA256: 5f8b5412ec6356a307e295a47cbafdb4ec2eae3f2c15ff5196d17bc5832ea942 SHA512: b36fbf7793edbfd731e5663be5259b2482dcf62301c4ced79c78c5bde959253944115a164e8f92c91c6ee19dab6f15fa3e568a68fdec93bb9fcb61ae1a9d5ec7 Homepage: https://cran.r-project.org/package=fastnet Description: CRAN Package 'fastnet' (Large-Scale Social Network Analysis) We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, . Package: r-cran-fastonlinecpt Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-fastonlinecpt_1.0-1.ca2404.1_all.deb Size: 134098 MD5sum: 8a8e24a47736b19bb03b2ce7a3fcd745 SHA1: b10562d09604a9fe17c871b61179536680623480 SHA256: e13ac58c5ee3f3f87697f884211dd04b192fa3bd7ed9b742424d86540bfca523 SHA512: b6e7a6e88e0ec09745350ad5daec020a668a18c37befd7d4d5663b788ef1d992021e287f180a0f2ec8cf54b3b5c07e86c85e585f826c7cd7251b9e8cdd74f881 Homepage: https://cran.r-project.org/package=fastOnlineCpt Description: CRAN Package 'fastOnlineCpt' (Online Multivariate Changepoint Detection) Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay. Package: r-cran-fastqcr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gridextra, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-rmarkdown, r-cran-rvest, r-cran-tibble, r-cran-tidyr, r-cran-scales, r-cran-xml2, r-cran-rlang Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fastqcr_0.1.3-1.ca2404.1_all.deb Size: 1282122 MD5sum: 7f3a19d459e331383053627314299423 SHA1: ea9b575e0f78564453be57e6ca5b416ecd15ba46 SHA256: 4b4feb37e8615e2e9541863da3be2d87cbd1a3326a92138c430e652d77bb7a02 SHA512: a068da012365c9f697fd83f81d3a89ed0eba44eb4b4884fb923938eea98761d82b3cb25f1a04e824a6a90cce6f229358959b427401db149c1f5491ed28a1f08d Homepage: https://cran.r-project.org/package=fastqcr Description: CRAN Package 'fastqcr' (Quality Control of Sequencing Data) 'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data. It produces, for each sample, an html report and a compressed file containing the raw data. If you have hundreds of samples, you are not going to open up each 'HTML' page. You need some way of looking at these data in aggregate. 'fastqcr' Provides helper functions to easily parse, aggregate and analyze 'FastQC' reports for large numbers of samples. It provides a convenient solution for building a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations. Package: r-cran-fastqrs Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-copula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sampleselection, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-fastqrs_1.0.0-1.ca2404.1_all.deb Size: 264488 MD5sum: 4f85062ceae69e8abcd5adb27ea0bd16 SHA1: 8021ab409460ed52b2a54853d1b5f6f9a67fdd47 SHA256: 0283d43898fe60265b5d4e1cd18bbb5e68512c934822e57fcc9fc39ee4aeb9aa SHA512: bff65f4e316fc7d90db484b5de94df62b522bc65fd1604e5c12db35abc652f56a519fe5a2a90bffb95c9f5265ff4730a37a521a575a2159afab25cef1bb7a4f3 Homepage: https://cran.r-project.org/package=fastqrs Description: CRAN Package 'fastqrs' (Fast Algorithms for Quantile Regression with Selection) Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. This estimator can be used to estimate models that feature sample selection and heterogeneous effects in cross-sectional data. For more details, see Arellano and Bonhomme (2017) and Pereda-Fernández (2024) . Package: r-cran-fastr2 Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2086 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mosaic, r-cran-maxlik, r-cran-numderiv, r-cran-dplyr, r-cran-ggplot2, r-cran-lattice, r-cran-misctools Suggests: r-cran-ggformula, r-cran-mosaiccalc, r-cran-tidyr, r-cran-readr, r-cran-mass, r-cran-faraway, r-cran-hmisc, r-cran-daag, r-cran-multcomp, r-cran-vcd, r-cran-car, r-cran-alr4, r-cran-corrgram, r-cran-bradleyterry2, r-cran-cubature, r-cran-knitr, r-cran-mosaicdata, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fastr2_1.2.5-1.ca2404.1_all.deb Size: 1154056 MD5sum: a7a228c869a0446cb17a99f8ee381b46 SHA1: 4bdc159992b8f715b3c773c90d16c7a0e3ff4161 SHA256: 9aca2b1a1178a9e4fe86c74bf6a4fdcb401d936c3e2d591f8a1719683f00989a SHA512: 04cb8810ba4b5490129c57ba3450e04189028134dacbb69c1cd4f0c5bb310e9fc399533cbf4dd3c7b4c1497d9fdc8a91b8ffc8e738d46b6dd71d4a2e3daef4f7 Homepage: https://cran.r-project.org/package=fastR2 Description: CRAN Package 'fastR2' (Foundations and Applications of Statistics Using R (2nd Edition)) Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet() function. Package: r-cran-fastreg Architecture: all Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2997 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-haven, r-cran-osdc, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-uuid Suggests: r-cran-crew, r-cran-tidyr, r-cran-dbplyr, r-cran-devtools, r-cran-duckdb, r-cran-knitr, r-cran-qs2, r-cran-quarto, r-cran-targets, r-cran-testthat, r-cran-tidyselect, r-cran-withr, r-cran-tarchetypes Filename: pool/dists/noble/main/r-cran-fastreg_0.14.1-1.ca2404.1_all.deb Size: 761346 MD5sum: dd20fc1f698032ee9641bf852c13f683 SHA1: 37188d6077475c5a904decae098695d7f0c02437 SHA256: 7e9869790037595f71702b61428cde6ee42679a84757db945d1a7a8965660f53 SHA512: edd2d80ba9ea1e18793ad0297e43bb2750a6751c1a12b0177ff30bcbb53adcaa3faab0c76e9338815f174e0b9c58afc75213177ca543f0c6cc8e366479f8155d Homepage: https://cran.r-project.org/package=fastreg Description: CRAN Package 'fastreg' (Fast Conversion and Querying of Danish Registers with 'Parquet') Converts large Danish register files ('sas7bdat') into 'Parquet' format with year-based 'Hive' partitioning and chunked reading for larger-than-memory files. Supports parallel conversion with a 'targets' pipeline and reading those registers into 'DuckDB' tables for faster querying and analyses. Package: r-cran-fastrep Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-janitor, r-cran-kableextra, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastrep_0.7-1.ca2404.1_all.deb Size: 118020 MD5sum: d49cb2b0ba6068a7009d0facdb95558f SHA1: c34f23622da9bbfc63a9706d4498e6f4a23c98f8 SHA256: 84ef70b7becb849687359107ebfbb748124f08fe26710bc09a22be285100af4d SHA512: bb0dca5115ccf9f34f807c16998bb2decbb023ed582e12c04ca59fdfcdb62ccc438e0c2de4cb9fea4caed1439528229f0205d6846e1af4a3db959ef9d237f97c Homepage: https://cran.r-project.org/package=fastrep Description: CRAN Package 'fastrep' (Time-Saving Package for Creating Reports) Provides templates for reports in 'rmarkdown' and functions to create tables and summaries of data. Package: r-cran-fastrerandomize Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fastrerandomize_0.3-1.ca2404.1_all.deb Size: 2084070 MD5sum: fa1fcb8e483a34c6ae08a3efe73ada97 SHA1: 869c329b6895ec84db23e0c5f923c23c32ea8b8f SHA256: bc6eefecdbb472ee64c47937e0514b7879132dce2f8f804e1777e1ed6d50c013 SHA512: 9adc801c455c32fba7f26049f7b74493e461f42ea961c7fd8b4bbede179a589036d21af5bb2e896674f4b12969bcca079df12e185d3ec07cdc9b7c2d61330087 Homepage: https://cran.r-project.org/package=fastrerandomize Description: CRAN Package 'fastrerandomize' (Hardware-Accelerated Rerandomization for Improved Balance) Provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a 'JAX' backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. The package supports various hardware acceleration frameworks including 'CPU', 'CUDA', and 'METAL', making it versatile across accelerated computing environments. This allows researchers to efficiently implement stringent rerandomization designs and conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) . Package: r-cran-fastret Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-cluster, r-cran-data.table, r-cran-dt, r-cran-future, r-cran-ggplot2, r-cran-glmnet, r-cran-htmltools, r-cran-openxlsx, r-cran-promises, r-cran-rcdk, r-cran-rlang, r-cran-shiny, r-cran-shinyhelper, r-cran-shinyjs, r-cran-withr, r-cran-xgboost Suggests: r-cran-callr, r-cran-caret, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-languageserver, r-cran-lintr, r-cran-pkgdown, r-cran-pkgbuild, r-cran-pkgload, r-cran-rmarkdown, r-cran-servr, r-cran-tibble, r-cran-testthat, r-cran-toscutil, r-cran-usethis Filename: pool/dists/noble/main/r-cran-fastret_1.3.0-1.ca2404.1_all.deb Size: 1496498 MD5sum: 0781fe5854768cc851ffc968d6691eaa SHA1: 32a7851b46de05f61966e54714c5124030b16e3e SHA256: e838554691265f9cd832ead030f0d8c498cce9bc65f39addd51e25f123fdc82c SHA512: 921123eb53f47757300f3e91aeab2ae85856451859f23b798043235339b0994439ba61ee898a4a3f4eaf607789f3c76e979dc7e42d569b93d39e7f6a003ec644 Homepage: https://cran.r-project.org/package=FastRet Description: CRAN Package 'FastRet' (Retention Time Prediction in Liquid Chromatography) A framework for predicting retention times in liquid chromatography. Users can train custom models for specific chromatography columns, predict retention times using existing models, or adjust existing models to account for altered experimental conditions. The provided functionalities can be accessed either via the R console or via a graphical user interface. Related work: Bonini et al. (2020) . Package: r-cran-fastrg Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-igraph, r-cran-rlang, r-cran-rspectra, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr Suggests: r-cran-covr, r-cran-irlba, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastrg_0.4.0-1.ca2404.1_all.deb Size: 498200 MD5sum: 6df7a4469e8cd9bb50e0bc8bb7c1b4c5 SHA1: 4bc2d9ef91ed377c35e35e530e3b1075922e4584 SHA256: e9d02608eefcb3395bd779a1435aa89fff2542165028ef942e3447d8540d6924 SHA512: 626789d6707380a9ab9bb5dac1290c286b0fb1fd0fc2276064c5feecf40bf8ea3ba40a7e850d73478905d4a6af37d3538088ca52c772370deb7dec8e220d5d61 Homepage: https://cran.r-project.org/package=fastRG Description: CRAN Package 'fastRG' (Sample Generalized Random Dot Product Graphs in Linear Time) Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) . The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes. Package: r-cran-fastrhockey Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3659 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-progressr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-testthat, r-cran-usethis, r-cran-withr, r-cran-xgboost, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-fastrhockey_1.0.0-1.ca2404.1_all.deb Size: 3379670 MD5sum: 4072a074938d6b7db497f686bcf726b5 SHA1: 58e7fff027ebe7da9d91ca6b0b0943ad225960f6 SHA256: b7b57cdb6cd8389aed7d77a4b34218144191f2656a5211049e39a97aa258136d SHA512: 39c704a585ddba90a055de8e72e703b4c3809f320edaec5289389ace28457fab6576d1256104789a5c5b0cf8543eb0d9e7614a900853e33aaf7c1cb95dd62599 Homepage: https://cran.r-project.org/package=fastRhockey Description: CRAN Package 'fastRhockey' (Functions to Access Professional Women's Hockey League andNational Hockey League Play by Play Data) A utility to scrape and load play-by-play data and statistics from the Professional Women's Hockey League , formerly known as the Premier Hockey Federation (PHF) or National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's stats API . Package: r-cran-fastrmodels Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastrmodels_2.1.0-1.ca2404.1_all.deb Size: 16886244 MD5sum: b261e52dc84ebdd5d6d0e5a6dd75c332 SHA1: b195b7fe2a7abdbb0dfbf5b778e67db4daa6ef52 SHA256: 56b38ae0cf78efb1bec9171f4a510bf4b2610b3c3c83cc6c599ed4040dbe5b9b SHA512: 9e1b5df8db5faadce0f778f63eea05525f46be739e46d881fe7fcccbab5a1b07d7e0418e57ef4f3a60eda85b667d20d2f80a5d98cee4409dc69c66928da418f7 Homepage: https://cran.r-project.org/package=fastrmodels Description: CRAN Package 'fastrmodels' (Models for the 'nflfastR' Package) A data package that hosts all models for the 'nflfastR' package. Package: r-cran-fastsegmentation Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-ebimage, r-cran-magick, r-cran-pracma, r-cran-robustgasp Suggests: r-cran-plot3d Filename: pool/dists/noble/main/r-cran-fastsegmentation_0.0.1-1.ca2404.1_all.deb Size: 408428 MD5sum: b759adbd5d0c26bb05b0af4478373247 SHA1: 57f97596471bab097cc5ce30df862588587901c3 SHA256: 5736bc1f749d7d4667c82b74c548ad2d60c1dfff77f610464970fa5596bc0f31 SHA512: 703c0ecded72eca353a61969b6a7a373942145f31445d4ad36875002cdbe0c339db77487f89be03f030cb4c3a8c60e3efa803ff6b7d94b255aff4432118e034b Homepage: https://cran.r-project.org/package=FastSegmentation Description: CRAN Package 'FastSegmentation' (Unsupervised Cell Segmentation by Fast Gaussian Processes) Performs fast Gaussian process-based segmentation of microscopy images using spatial smoothing and data-driven thresholding. Code based on Baracaldo, L., King, B., Yan, H., Lin, Y., Miolane, N., & Gu, M. (2025). "Unsupervised cell segmentation by fast Gaussian processes." arXiv preprint . Package: r-cran-faststepgraph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-faststepgraph_0.1.1-1.ca2404.1_all.deb Size: 327360 MD5sum: 4bd9b28a5ed2a50c59937eea39f09aab SHA1: 29e510664903eb51ae0d5c6e5390e41561e170d2 SHA256: 419eecaa5e543df3a4c96aeafaab86874cae0019c7e97942a099e65680b66ca9 SHA512: 35f4e65aebfa5a7e1343ce92275a4455fd86c0a0537d75639980809c8eee5f02fc36f2a15795e97c99853fcc2284fdb0a8c8f001b20b439832d1814afb603762 Homepage: https://cran.r-project.org/package=FastStepGraph Description: CRAN Package 'FastStepGraph' (A Fast Algorithm for Sparse Precision Matrix Estimation) It implements an improved and computationally faster version of the original Stepwise Gaussian Graphical Algorithm for estimating the Omega precision matrix from high-dimensional data. Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) . Package: r-cran-fastts Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ncvreg, r-cran-rcpproll, r-cran-rlang, r-cran-yardstick Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-fastts_1.0.3-1.ca2404.1_all.deb Size: 395660 MD5sum: 967ad8eba5045ca7c0a52beed7462194 SHA1: a8c2f64cfb609c9d2f57562430d5349cb1db3018 SHA256: 70b028c2afa33fec63b57aa061d07d6f00c310e54560ee9c9dedb8ce7caf0d0b SHA512: ed09cf03dd2fbc1ad4a6398495c437220d98a37ad26599af842bc7d8f5883a789907c862f2ebd19485ed71d77e9f61957566b7785592374b4b75e5f62a30bac4 Homepage: https://cran.r-project.org/package=fastTS Description: CRAN Package 'fastTS' (Fast Time Series Modeling for Seasonal Series with ExogenousVariables) An implementation of sparsity-ranked lasso and related methods for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2024) , which also describes this package in greater detail. The sparsity-ranked penalization methods for time series implemented in 'fastTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The method is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature. Package: r-cran-fastverse Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-collapse, r-cran-kit, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fastverse_0.3.4-1.ca2404.1_all.deb Size: 107358 MD5sum: e08030e12beddf002b02c2569c910206 SHA1: 714cc94a93a7fee9c9695343c2963b4ad46994d6 SHA256: 6e257be47472609d98a64c9b2b3522e20d2c386c44bf5686da7ee7dca997889e SHA512: dfe7f2cc37410796551a84a5b5f051b6787b40a24723df6f8677b7716d0f6a464a26e84f9b2350b0933fcc3b705647ebf539238755ca96351dd21428817286e5 Homepage: https://cran.r-project.org/package=fastverse Description: CRAN Package 'fastverse' (A Suite of High-Performance Packages for Statistics and DataManipulation) Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 'kit' and 'magrittr', that jointly only depend on 'Rcpp'. The 'fastverse' can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: . Package: r-cran-fat2lpoly Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kinship2, r-cran-multgee Filename: pool/dists/noble/main/r-cran-fat2lpoly_1.2.6-1.ca2404.1_all.deb Size: 421886 MD5sum: d7ffc3956bc2bbea685d4cea09bb7ac1 SHA1: 70c15c73c9c73b5e0984840b736c3fda7fefc517 SHA256: 807903128ee789b3e93ca06e777a6d7d1f2d0e21c5885d46c2f56ace4e3dcbc1 SHA512: 7273b3c1a240bdd2c47fc42b6f23ff38f3069d930d59ab4480a42ae2d8dd812f46a6a24b45621b9538e671da09d51aab50b3f3d00947ed65ffe156c2f239a468 Homepage: https://cran.r-project.org/package=fat2Lpoly Description: CRAN Package 'fat2Lpoly' (Two-Locus Family-Based Association Test with Polytomous Outcome) Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models defined by some design matrix. Package: r-cran-fateid Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locfit, r-cran-matrixstats, r-cran-pheatmap, r-cran-princurve, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-som, r-cran-umap Suggests: r-bioc-deseq2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fateid_0.2.2-1.ca2404.1_all.deb Size: 2951438 MD5sum: d05f379af064b9fc662bab841f54245d SHA1: 5a5133baa5773d394c9f576e5e17c2424ae9cc2b SHA256: a3fd462fd6830d4cc8716e4a5c3a4bcfc84e810db047b34c913451a39f2991e7 SHA512: 2fb759d8c6375f2b1c708ab18bd548bd3f41df3d9b842849fb4536f0d17d2278a794c74b23756c27b907df905d6ce2da5b855ec01815e6699edbbf6c3f19327f Homepage: https://cran.r-project.org/package=FateID Description: CRAN Package 'FateID' (Quantification of Fate Bias in Multipotent Progenitors) Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) . Package: r-cran-fattailsr Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-timeseries Suggests: r-cran-zoo, r-cran-xts Filename: pool/dists/noble/main/r-cran-fattailsr_2.0.1-1.ca2404.1_all.deb Size: 2217400 MD5sum: 004ea16e121d8c6af72b849d72ea95df SHA1: 8436148e43b6d6589df4d8961d9e696059032554 SHA256: e68ebfff94335599be698dae0418186ee8510473aa86e06ecfada689456056f6 SHA512: 0295997b7d87b32d15dd88e4162a23f43c55d11c92af38e32e79d0fdeb9d9004f7a091b2c08782c3d10981abdbb066e5a77d615dac2e04bc04a501ec5e96e6e0 Homepage: https://cran.r-project.org/package=FatTailsR Description: CRAN Package 'FatTailsR' (Kiener Distributions and Fat Tails in Finance and Neuroscience) Kiener distributions K1, K2, K3, K4 and K7 to characterize distributions with left and right, symmetric or asymmetric fat tails in finance, neuroscience and other disciplines. Two algorithms to estimate the distribution parameters, quantiles, value-at-risk and expected shortfall. IMPORTANT: Standardization has been changed in versions >= 2.0.0 to get sd = 1 when kappa = Inf rather than 2*pi/sqrt(3) in versions <= 1.8.6. This affects parameter g (other parameters stay unchanged). Do not update if you need consistent comparisons with previous results for the g parameter. Package: r-cran-faunabr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4581 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-faunabr_1.1.2-1.ca2404.1_all.deb Size: 3090170 MD5sum: 0fe3e7f868f1d22c6c82b8742eee4966 SHA1: d65f80c1809de6acef029fbeeb9ce3644a85d0f7 SHA256: 680fe688a2112d6725863229fecf74530f7107b7e0ed3a8d0d4f13c97cf787db SHA512: 4c99c15faef78d754b9e18963b82d439dd19775f53273bc8bc6c4065cb05bdcf7aa61b71c0df5aeada00d77352853e1a76fd67607ac603864dd3dd0938317b90 Homepage: https://cran.r-project.org/package=faunabr Description: CRAN Package 'faunabr' (Explore Catálogo Taxônomico da Fauna do Brasil Database) A collection of functions designed to retrieve, filter and spatialize data from the Catálogo Taxônomico da Fauna do Brasil. For more information about the dataset, please visit . 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Training-time 'terms', 'xlevels', and 'contrasts' are stored on the fit object and reused at predict time, so the design matrix is reconstructed consistently across sessions. Complete-case bookkeeping is exposed via 'nobs_info', and linearly dependent columns are detected by a QR pivot and reported as 'NA' in 'coef()' and 'summary()' (the 'stats::glm()' convention), distinguishing "not identifiable" from "shrunk to zero by the penalty". Novel factor levels at predict time raise the same error 'stats::predict.glm()' does by default, with 'on_new_levels = "na"' as a production-style opt-in. Accepts character family strings ('gaussian', 'binomial', 'poisson', 'cox', 'multinomial', 'mgaussian') and any 'glm' family object the underlying 'glmnet' itself accepts, including 'Gamma' and fixed-theta negative binomial via 'MASS::negative.binomial'. Package: r-cran-fbst Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2805 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-ks, r-cran-viridis, r-cran-rstanarm, r-cran-bayestestr Suggests: r-cran-bayesfactor, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fbst_2.2-1.ca2404.1_all.deb Size: 1558128 MD5sum: 6815d5834f48a288bb72bcc22ae13be4 SHA1: b5aec629c46150f84d164c7515a34c0bd9ebcad3 SHA256: b9209a2315995cb4ce95e87d5451930502ea206490196bbf44497cb3f91869cd SHA512: 0f53c16392df4fa5d16ab528851d241fce4b42578f07d60d5003ffb2e808755133a023661691b3ee0d417e955db8c58b419a1132861716799f152c8410a02aec Homepage: https://cran.r-project.org/package=fbst Description: CRAN Package 'fbst' (The Full Bayesian Evidence Test, Full Bayesian Significance Testand the e-Value) Provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available. Package: r-cran-fc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-codetools Suggests: r-cran-magrittr, r-cran-purrr Filename: pool/dists/noble/main/r-cran-fc_0.1.0-1.ca2404.1_all.deb Size: 24126 MD5sum: 337711ae8d2c89973fd017965bfdf60d SHA1: ccee765b1210a99d5bf3f6d463f1e1237a561b19 SHA256: 8d835b7c33ef60f9147be31fe6571b0aeebde047b3210e65b13bb18750a29611 SHA512: 5f799f11c9efef4163a43076a627d82ded226be0b9de47a6200e43c4b53615ccb20a0cd41d7aea9133193838cf27d14c7a2abdea585e2875c8a8619eb610f5b0 Homepage: https://cran.r-project.org/package=fc Description: CRAN Package 'fc' (Standard Evaluation-Based Multivariate Function Composition) Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%. Package: r-cran-fca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fca_0.1.0-1.ca2404.1_all.deb Size: 28986 MD5sum: 98fe1ad9e0b4437f2cffcca431ea9efb SHA1: 93071f9b4cf3b58d6968ca5fd712eee74206c35e SHA256: 680139071fc39b3193862d2b86e9713cb85039843e389bb742829fa424a1a5f7 SHA512: 13ce253d6fec5e4216a70d6ca80a92ab4d6c850d66d2d0ea869006cdc6eb19079532e435f1447eeae66b21f7ec6a67714888659f4a6653ca0b455ca59291b396 Homepage: https://cran.r-project.org/package=fca Description: CRAN Package 'fca' (Floating Catchment Area (FCA) Methods to Calculate SpatialAccessibility) Perform various floating catchment area methods to calculate a spatial accessibility index (SPAI) for demand point data. The distance matrix used for weighting is normalized in a preprocessing step using common functions (gaussian, gravity, exponential or logistic). Package: r-cran-fcall Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-waldo Suggests: r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fcall_0.1.7-1.ca2404.1_all.deb Size: 461418 MD5sum: 52018ad95720c518a094c1c046b82ef5 SHA1: 9e7d42cb2780d2920e0eee91984c64e8b1eb2491 SHA256: 8fe3450e400e437c97a38997dafd83dc27950289c125a944d9f75a07ec4fb7f8 SHA512: a1ba3271d27690309e09da7f5e0cf0d45bd61a8b4e0e71d89fef186a63a1b3faaec6f152e05e65fb03020ab56e5043c07520ce65a0ff218b7497d5961592273f Homepage: https://cran.r-project.org/package=fcall Description: CRAN Package 'fcall' (Parse Farm Credit Administration Call Report Data into Tidy DataFrames) Parses financial condition and performance data (Call Reports) for institutions in the United States Farm Credit System. Contains functions for downloading files from the Farm Credit Administration (FCA) Call Report archive website and reading the files into tidy data frame format. The archive website can be found at . Package: r-cran-fcfdr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locfdr, r-cran-mass, r-cran-ggplot2, r-cran-cowplot, r-cran-fields, r-cran-dplyr, r-cran-spatstat.geom, r-cran-polycub, r-cran-hexbin, r-cran-bigsplines, r-cran-data.table, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-digest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fcfdr_1.0.0-1.ca2404.1_all.deb Size: 3096166 MD5sum: 629ba2ae6713773f6872eac0967ac45c SHA1: d0937365ab5dbd5db91c0b5f3b8ae2fbe2446e70 SHA256: 8adc1010f6d3370fda33ae488ff94fd88eaf9b9dde2bc4a0575004659c07498a SHA512: 6e238e6e85777b685a7224ccf1e09775465e2ca3fcffd402ebb8095d33b007e2b7632fcd24368cc381c2fdd1da1e0560597e4cae517de627b3e3a66383e5ca58 Homepage: https://cran.r-project.org/package=fcfdr Description: CRAN Package 'fcfdr' (Flexible cFDR) Provides functions to implement the Flexible cFDR (Hutchinson et al. (2021) ) and Binary cFDR (Hutchinson et al. (2021) ) methodologies to leverage auxiliary data from arbitrary distributions, for example functional genomic data, with GWAS p-values to generate re-weighted p-values. Package: r-cran-fcgr Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kerdiest, r-cran-kernsmooth, r-cran-nlme, r-cran-mass, r-cran-mgcv, r-cran-pspline, r-cran-sfsmisc Filename: pool/dists/noble/main/r-cran-fcgr_1.2-0-1.ca2404.1_all.deb Size: 63164 MD5sum: 6039b3aa2c3401320f05d088a32ddcc2 SHA1: 704bae6866f58510125b117e585198436167db78 SHA256: f22961836c2b410acdc93517ed88cd76041c449dc9be7c3d8c2d9180b38efeeb SHA512: 61a68e4000ab05ba7a3bc7f3e71884aec5a950f5a227ee4ab828d34cd2e1bd44b735f320815b37878c06fd6f919ae25f6a9de7d8e36b660589f5792ff72e2688 Homepage: https://cran.r-project.org/package=FCGR Description: CRAN Package 'FCGR' (Fatigue Crack Growth in Reliability) Fatigue Crack Growth in Reliability estimates the distribution of material lifetime due to mechanical fatigue efforts. The 'FCGR' package provides simultaneous crack growth curves fitting to different specimens in materials under mechanical stress efforts. Linear mixed-effects models with smoothing B-Splines and the linearized Paris-Erdogan law are applied. Once defined the fail for a determined crack length, the distribution function of failure times to fatigue is obtained. The density function is estimated by applying nonparametric binned kernel density estimate ('bkde') and the kernel estimator of the distribution function ('kde'). The results of Pinheiro and Bates method based on nonlinear mixed-effects regression ('nlme') can be also retrieved. The package contains the crack.growth(), PLOT.cg(), IB.F(), and Alea.A (database) functions. Package: r-cran-fcm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fcm_0.1.3-1.ca2404.1_all.deb Size: 47338 MD5sum: f7bf434a9c2032b669f208de5dbe00d2 SHA1: 9441ccd8df27824370a4b644179d4c79fd0ee080 SHA256: f5bb0a182ef757e66582cef3a22669ccd16d38403f1b1e083d76a303ea94440a SHA512: 0b0e9b7796f85c1681d8f87d7ab450c661c2cb9175962a44b080b223cc2e60387c901a19bd2f0681b3be303dd7583f19fc30c59fe2ad6f04b2275c221518671f Homepage: https://cran.r-project.org/package=fcm Description: CRAN Package 'fcm' (Inference of Fuzzy Cognitive Maps (FCMs)) Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) providing ideal causal cognition tools for modeling and simulating dynamic systems. Package: r-cran-fcmapper Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-fcmapper_1.1-1.ca2404.1_all.deb Size: 45756 MD5sum: f66690467a7681a951bb41b529214aa0 SHA1: 08ee12fb97f3978b07ddcb9311066f28bffb02a8 SHA256: 8e4cf39b611d587a117b14fa99af24c304cb00b3d0f9742cec30eab20389791c SHA512: 0b64098ba4196d9e6d5a3b08dd9ec142e9d7ca6d55754b5f973f34d1be7b2e3b03b4b2c98acd35de92215a7cec24835767a95d9e5ab2e058dc1b012e5e8ba76b Homepage: https://cran.r-project.org/package=FCMapper Description: CRAN Package 'FCMapper' (Fuzzy Cognitive Mapping) Provides several functions to create and manipulate fuzzy cognitive maps. It is based on 'FCMapper' for Excel, distributed at , developed by Michael Bachhofer and Martin Wildenberg. Maps are inputted as adjacency matrices. Attributes of the maps and the equilibrium values of the concepts (including with user-defined constrained values) can be calculated. The maps can be graphed with a function that calls 'igraph'. Multiple maps with shared concepts can be aggregated. Package: r-cran-fcmfd Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fcmfd_0.1.2-1.ca2404.1_all.deb Size: 122286 MD5sum: 3dfc4cde1a1749a1f0973f96d82387be SHA1: b920cf9ce70510e99ab4a0b05923fced8c0db172 SHA256: e10afdf875bbb633b629b44da09e55708d69d7716379e4096776a37bb79e8354 SHA512: 71dbc372144e11ae171fe9f3730deae753887135ad31e1c3707f038ca96fc5bb9f94418fdb46bb9acbd5d93bbbf003b33d8471383c0488a0282101fe5ed5a76a Homepage: https://cran.r-project.org/package=fcmfd Description: CRAN Package 'fcmfd' (Fuzzy C-Means for Fuzzy Data) Implements a fuzzy clustering approach for ordinal Likert-type data using triangular fuzzy numbers (TFNs). The package extends the classical fuzzy C-means algorithm to better handle uncertainty in ordinal scales and includes automatic selection of the number of clusters using the Xie-Beni validity index. References: Coppi, R., D'Urso, P., and Giordani, P. (2012), "Fuzzy and possibilistic clustering for fuzzy data", . Xie, X. L. and Beni, G. (1991), "A validity measure for fuzzy clustering", . Package: r-cran-fco Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cutpointr, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-lavaan, r-cran-overlapping, r-cran-poisbinordnor, r-cran-psych, r-cran-rcompanion, r-cran-simstandard, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fco_2.0.2-1.ca2404.1_all.deb Size: 271598 MD5sum: 54ff62da6bbe5060625420846466d2a0 SHA1: 575a41bf392588f49818b099a778af76f56b2b26 SHA256: d04e08d360cc91e168374efb692f6c42a1ff7c58c10bb18dedab048c5388627f SHA512: 50c1ca5eed7eedaf85423ec8cbad82ca8fc07e016dc8a60fd951fe72ae37f8ff92d7985903b0a4b13a3e2f92bebd51228f2303150e001c4abdd4bac07f5b5b41 Homepage: https://cran.r-project.org/package=FCO Description: CRAN Package 'FCO' (Flexible Cutoffs for Model Fit Evaluation in Covariance-BasedStructural Models) A toolbox to derive flexible cutoffs for fit indices in 'Covariance-based Structural Equation Modeling' based on the paper by 'Niemand & Mai (2018)' . Flexible cutoffs are an alternative to fixed cutoffs - rules-of-thumb - regarding an appropriate cutoff for fit indices such as 'CFI' or 'SRMR'. It has been demonstrated that these flexible cutoffs perform better than fixed cutoffs in grey areas where misspecification is not easy to detect. The package provides an alternative to the tool at as it allows to tailor flexible cutoffs to a given dataset and model, which is so far not available in the tool. The package simulates fit indices based on a given dataset and model and then estimates the flexible cutoffs. Some useful functions, e.g., to determine the 'GoF-' or 'BoF-nature' of a fit index, are provided. So far, additional options for a relative use (is a model better than another?) are provided in an exploratory manner. Package: r-cran-fcopulae Architecture: all Version: 4052.86-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-fbasics, r-cran-fmultivar Suggests: r-cran-runit, r-cran-mvtnorm, r-cran-sn Filename: pool/dists/noble/main/r-cran-fcopulae_4052.86-1.ca2404.1_all.deb Size: 592222 MD5sum: 3be7e1da936442e86de24378d528cdf6 SHA1: c9822e3f717159aceb11e0cf589dc67265e58512 SHA256: 6f2703f5258f4987aea0be3df7d9c41842f2f3a36206624c0d8a39bf5ccbbcc4 SHA512: 351ac09d8b4e4bdaaa5c7bb9e28899e44be57cf563afb552f424993cc253a851d839ff34244edc6a48cf6616ea8fc4f22bca36e95a4c543823f98b74b000d355 Homepage: https://cran.r-project.org/package=fCopulae Description: CRAN Package 'fCopulae' (Rmetrics - Bivariate Dependence Structures with Copulae) Provides a collection of functions to manage, to investigate and to analyze bivariate financial returns by Copulae. Included are the families of Archemedean, Elliptical, Extreme Value, and Empirical Copulae. Package: r-cran-fcp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fcp_0.1.0-1.ca2404.1_all.deb Size: 15354 MD5sum: 8a5501d7cb534a9cb5207e074ed4d542 SHA1: f7be05a18ca8df355a0e02ee35ccd863f4e2937f SHA256: c07236430e2df703106a5edf011c8d32e2b15d05c42ea8b33ac23d813223d8ec SHA512: 5b64c3eba558d6528ddc0f351d36ee87c7f5a6df91bbe3b47f21239616662159860be035af82e9c67277e0a9e53fa6ecc48298d08e64c62cdeb658f7b5eb4d73 Homepage: https://cran.r-project.org/package=fcp Description: CRAN Package 'fcp' (Function Composition) A function composition operator to chain a series of calls into a single function, mimicking the math notion of (f o g o h)(x) = h(g(f(x))). Inspired by 'pipeOp' ('|>') since R4.1 and 'magrittr pipe' ('%>%'), the operator build a pipe without putting data through, which is best for anonymous function accepted by utilities such as apply() and lapply(). Package: r-cran-fcps Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7189 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mclust, r-cran-ggplot2, r-cran-datavisualizations Suggests: r-cran-mlpack, r-cran-kernlab, r-cran-cclust, r-cran-dbscan, r-cran-kohonen, r-cran-mcl, r-cran-cluster, r-cran-databionicswarm, r-cran-orclus, r-cran-flexclust, r-cran-abcanalysis, r-cran-apcluster, r-cran-pracma, r-cran-emcluster, r-cran-pdfcluster, r-cran-paralleldist, r-cran-plotly, r-cran-projectionbasedclustering, r-cran-generalizedumatrix, r-cran-mstknnclust, r-cran-densityclust, r-cran-energy, r-cran-r.utils, r-cran-tclust, r-cran-spectrum, r-cran-genie, r-cran-protoclust, r-cran-fastcluster, r-cran-clusterability, r-cran-signal, r-cran-reshape2, r-cran-ppci, r-cran-clustrd, r-cran-smacof, r-cran-rgl, r-cran-dendextend, r-cran-moments, r-cran-prabclus, r-cran-varsellcm, r-cran-sparcl, r-cran-mixtools, r-cran-hdclassif, r-cran-clustvarsel, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-leiden, r-cran-clustmixtype, r-cran-clustersim, r-cran-networktoolbox, r-cran-clusterr, r-cran-partitioncomparison, r-cran-aricode, r-cran-pfclust, r-bioc-consensusclusterplus, r-cran-clue Filename: pool/dists/noble/main/r-cran-fcps_1.4.3-1.ca2404.1_all.deb Size: 4117158 MD5sum: 38b1d73e86ad3c2278c2a27ec1e0be6e SHA1: 3156e2cbad31e33303e5a736494a31046c21614b SHA256: c4ccef311b2ce1afb20e35a1e7234a90492b39d144ae10a94709326e25b9c19f SHA512: ef14f45618ea7997ac3c8e4f6ed0411d850279ba2b1b81bc3833e7681e2124f1a6a7d4919bc58b56310670a8ef48be3deb8b7eb177a88d7324de072fab9091be Homepage: https://cran.r-project.org/package=FCPS Description: CRAN Package 'FCPS' (Fundamental Clustering Problems Suite) Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, . Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, . Package: r-cran-fcr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-face, r-cran-mgcv, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fcr_1.0-1.ca2404.1_all.deb Size: 534160 MD5sum: 1bea0fd05eecd6d809aed4b91b352fd7 SHA1: 7d4658bbc7c76f21891ab2bec58300d74e28b99d SHA256: 6b06638feb3679fb9264db45915c92876cb54c5e2ab518daf7e04da97e56c562 SHA512: e03c38ac34d99269edc5f30e39cb4549b8722d49e163e32aaaf63f5971227b29a2d7bb8ea870b89dcf529a471efd4f39594b0bf5f7cf2a0b2a48669123347211 Homepage: https://cran.r-project.org/package=fcr Description: CRAN Package 'fcr' (Functional Concurrent Regression for Sparse Data) Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured. Leroux et al. (2017), Statistics in Medicine, . 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Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from . 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Package: r-cran-fdapoifd Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-magrittr, r-cran-reshape2, r-cran-patchwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdapoifd_2.0.1-1.ca2404.1_all.deb Size: 437864 MD5sum: e974636d3f8b39cb9ce60eaa939255e5 SHA1: 7724ad718ccf2ca2f60ecd2a8cdd6a2eb75db102 SHA256: f5742f7d179ed93eccf5684e3e50ac2c54add717ea266448194a8c1f56db8269 SHA512: b121a6b9c8bd9e4ddcf94ae38f7e3bfa5c96c552cc1026dceebfd5d0a6e16d328706c15fad4c668a2f68fa687be46bb46d2c373b14f562d4481ea1283a7dc226 Homepage: https://cran.r-project.org/package=fdaPOIFD Description: CRAN Package 'fdaPOIFD' (Partially Observed Integrated Functional Depth) Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elías, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, . Elías, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, . Elías, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, . Package: r-cran-fdatest Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda Filename: pool/dists/noble/main/r-cran-fdatest_2.1.1-1.ca2404.1_all.deb Size: 251226 MD5sum: 6fa244fafe534d0f91138adf2cddc4d2 SHA1: 36668152b744232eaa3d72d2f5d7a22f69ca6fc4 SHA256: 8d688d5d5bb2835e6eb5ed54bc6022feef67452593ca981ce448c809223a7f5a SHA512: 3bfafb3805cbf85caa400d07d812b82c17ad4e2b78a83621a26f37153501e514259d2cefd597c8ce3d479e4f075a2c6810f24151805dd01000c0ee932d53c9b3 Homepage: https://cran.r-project.org/package=fdatest Description: CRAN Package 'fdatest' (Interval Testing Procedure for Functional Data) Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data. Package: r-cran-fdb Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdb_0.2.2-1.ca2404.1_all.deb Size: 205640 MD5sum: 9de88474d1eae565c1cc6a059421e687 SHA1: 949fee1ff4e14ea17093cd308a0e10c87cad77ad SHA256: 51f6e17c190c9cdf8364ac8ed207950006a4c12c24dc251faef10650fe47d932 SHA512: 8510aadd21ee57a1a7f7cbd93666943d8c670cc94f6a9de01f1037feab0836624c7391b20e6b38c1242402e722cad0967af50f92d163cccbc3b48055201d21ad Homepage: https://cran.r-project.org/package=fdb Description: CRAN Package 'fdb' (Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials) Implements a class of likelihood-informed frequentist dynamic borrowing methods for hybrid-control survival trials based on penalized Cox partial likelihood estimation. Implements four likelihood-informed penalty structures (precision-weighted L1, smoothed integrated-gate, information-adaptive minimax concave penalty (MCP), and likelihood-ratio-weighted L1), together with the adaptive lasso borrowing approach of Li et al. (2023, ). Provides conditional model-based standard errors and local plug-in sandwich variance approximations, with smoothed penalties. Tools for design-stage lambda calibration via simulation, including a two-stage coarse-fine grid search, drift-level early stopping, and per-method tuning under both inference types, are also provided. A simulation harness for evaluating type I error and statistical power across population drift scenarios is included. Package: r-cran-fdboost Architecture: all Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3766 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-gamboostlss, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-stabs, r-cran-zoo Suggests: r-cran-fda, r-cran-fields, r-cran-ggplot2, r-cran-knitr, r-cran-mapdata, r-cran-maps, r-cran-refund, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdboost_1.1-4-1.ca2404.1_all.deb Size: 3308640 MD5sum: 8ddb3803c4a7b6a8b2e25c99971df204 SHA1: d222724644592db60d8525354b21e6592e6f98c9 SHA256: 7e2d88d8d4b93c0a64cb23031eb7f5a259f8766038a14f9ce66f0166ec752d82 SHA512: f0085372f9f72c0deef271d8ad5dc65a7dd29f499cce98e44d502f4ecb66357104432030251ed0394198502e978cc05107a277c8f844b949dc730cf737a4c28d Homepage: https://cran.r-project.org/package=FDboost Description: CRAN Package 'FDboost' (Boosting Functional Regression Models) Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) . Package: r-cran-fdclassify Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-modeest Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdclassify_0.1.0-1.ca2404.1_all.deb Size: 90822 MD5sum: bb487c1c116965b5aa6082961eb6de7b SHA1: b9bd2457edc500e690851955254e98330be9ed3b SHA256: 93c57e7d3790ebdf8469fb8f15aaffcb50a5d476fa6bc1e9ed98cbce753dbf51 SHA512: 99e81badca009c52596c539c307b7c4f58d7ce723b93b6a2a1e9e9093efd12e879fc66e7d7f5cc4fed8eda34720c7aada34b6abb1eebb8e81b6060d0b0a7ff1a Homepage: https://cran.r-project.org/package=fdclassify Description: CRAN Package 'fdclassify' (Supervised Classification for Functional Data via Signed Depth) Provides a suite of supervised classifiers for functional data based on the concept of signed depth. The core pipeline computes Fraiman-Muniz (FM) functional depth in either its Tukey or Simplicial variant, derives a signed depth by comparing each curve to a reference median curve via the signed distance integral, and feeds the resulting scalar summary into several classifiers: the k-Ranked Nearest Neighbour (k-RNN) rule, a moving-average smoother, a kernel-density Bayes rule, logistic regression on signed depth and distance to the mode, and a generalised additive model (GAM) classifier. Cross-validation routines for tuning the neighbourhood size k and parametric bootstrap confidence intervals are also included. Package: r-cran-fdic Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fdic_0.1.2-1.ca2404.1_all.deb Size: 850552 MD5sum: 57c99f833b05fbc9988eca4fac59ad00 SHA1: 32a0716e7568b487c448f63c415f583ba1b64300 SHA256: 816f2fe3ea7b1218dd8edf0febf8b3150fb3ce329531e15b7ef5ee73bf0475b0 SHA512: bf3ea0bc5fe8bf62f42bccb1f1182ed10381fd4f3717df3214bfa81526235e357fda0915e5af46348a09254122fac1802e4ce707ba38f7ac45eb9cc3907e08a3 Homepage: https://cran.r-project.org/package=fdic Description: CRAN Package 'fdic' (Interface to 'BankFind Suite API') Provides a convenient interface to the 'BankFind Suite API' made available by the Federal Deposit Insurance Corporation (FDIC). Contains functions to retrieve data related to qualitative institution information, branch and office locations, Summary of Deposit reporting, financial information, failed financial institutions, structural change events, historic aggregate industry data, and demographics information. See for the official 'BankFind Suite API' documentation published by the FDIC. Package: r-cran-fdicdata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdicdata_0.1.2-1.ca2404.1_all.deb Size: 54172 MD5sum: 239efef4c373fec4961a2c915fd6b611 SHA1: 2511a1757f1ba52352530296df1c2dc2032809e7 SHA256: e6b193054639dc303f768b933efe51401f5cf4c56cbd0834881e614f3a47a14f SHA512: baa3db64851d7be8a8495857f8295bf19b571aadbd7aa23cb69696d6d71b1fd1d37db7b951f27b6d4f992bb2c6083f28ce66dd4a78d539ad2bdad009b56343cd Homepage: https://cran.r-project.org/package=fdicdata Description: CRAN Package 'fdicdata' (Accessing FDIC Bank Data) A system provides a set of functions for working with data from the Federal Deposit Insurance Corporation (FDIC), including retrieving financial data for FDIC-insured institutions and accessing the data taxonomy. Package: r-cran-fdid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-estimatr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect, r-cran-rcolorbrewer, r-cran-foreach, r-cran-dofuture, r-cran-future, r-cran-ebal, r-cran-grf, r-cran-car, r-cran-sandwich Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdid_1.0.2-1.ca2404.1_all.deb Size: 217670 MD5sum: 238759e5ccba9e6ee357f5a2e2d9c710 SHA1: 35c6bda1abd676f06882058ac97dddc496f3cb2c SHA256: eb715d7cf910bffe18f71f21e40b1795c33b9da14c37a44ecb50abc0611ceaa6 SHA512: 8697f808599916a4dd10d2179db5bf0c98fa6161214e2adb83f879fecf1da67a461a919d73ea134cbb89cb43355bba1a0d193361e30dd9ca6c3e519334fca858 Homepage: https://cran.r-project.org/package=fdid Description: CRAN Package 'fdid' (Factorial Difference-in-Differences) Implements the factorial difference-in-differences (FDID) framework for panel data settings where all units are exposed to a universal event but vary in a baseline factor G. Provides support for various estimators; supports robust, bootstrap, and jackknife variance; returns dynamic, pre/event/post aggregates and raw means; and includes helpers for data preparation and plotting. Methodology follows Xu, Zhao and Ding (2026) . Package: r-cran-fdm2id Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3904 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arules, r-cran-arulesviz, r-cran-factominer, r-cran-nnet, r-cran-matrix, r-cran-mclust, r-cran-pls Suggests: r-cran-car, r-cran-caret, r-cran-class, r-cran-cluster, r-cran-e1071, r-cran-fds, r-cran-flexclust, r-cran-fpc, r-cran-glmnet, r-cran-ibr, r-cran-irr, r-cran-knitr, r-cran-kohonen, r-cran-leaps, r-cran-mass, r-cran-mda, r-cran-meanshiftr, r-cran-mlbench, r-cran-questionr, r-cran-randomforest, r-cran-rmarkdown, r-cran-rspectra, r-cran-rocr, r-cran-rpart, r-cran-rpart.plot, r-cran-rtsne, r-cran-snowballc, r-cran-stopwords, r-cran-testthat, r-cran-text2vec, r-cran-wordcloud, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-fdm2id_1.0.2-1.ca2404.1_all.deb Size: 3156778 MD5sum: 8b2d6cfa3a2fb95fec6741f7b773c585 SHA1: 32deeef2dd91c4cfb2ca6f98e31949ae4be33cb1 SHA256: 11eab6908e827b2a18d40c96434f712b293b0d1b3005393833f53730411e6b8f SHA512: 30d71578134f9d380b5f721bb0cb4a8d502bfa2ec780f14f38197c4ef8c40e6e232411310388edf4adac19210dfeeb34a9802ed9fd59cae8306b669c37dc8d80 Homepage: https://cran.r-project.org/package=fdm2id Description: CRAN Package 'fdm2id' (Data Mining and R Programming for Beginners) Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle". Package: r-cran-fdp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-fdp_1.0.0-1.ca2404.1_all.deb Size: 285054 MD5sum: 4a00ed90acd527dc6c3290ab54584e14 SHA1: d01dd5feeb1f0b9f25a74b5bef8cf44e73b4d60a SHA256: 968539efcb87cde28a5c96566d2f29bba4861dfc51ba294dc5a27e6ad5920f68 SHA512: 0bc47a3a450517b28912fb96078ae5b38bcd36b1250be310c26a0e4eb150bfb86102210ab5dab5b8a37f878c59437aeada6ceeaabfcd185ac87b0c0abc506db1 Homepage: https://cran.r-project.org/package=fdp Description: CRAN Package 'fdp' (f-Differential Privacy and Gaussian Differential Privacy) Constructs and visualises trade-off functions for f-differential privacy (f-DP) as introduced by Dong et al. (2022) . 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Package: r-cran-fdq Architecture: all Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fgmutils, r-cran-data.table, r-cran-sqldf, r-cran-randomcolor, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-fdq_0.12-1.ca2404.1_all.deb Size: 9798 MD5sum: 045bbe76a157c747fa23673689eaa4e3 SHA1: f6ca293c0e5b27f56b1eecfc5d73bbcdf3108203 SHA256: 918f71a19a0d522c78438146f716a7f980957883c74d1e13dceb44b3a3bd83c0 SHA512: e69a054e7e98f332b806505fda6c2c8a5c136d81051839a32ebb79e2f028658d8a6055d179332a53a5e288f40815ca1467bb4c59f7dfb5f413fe524e36b98820 Homepage: https://cran.r-project.org/package=fdq Description: CRAN Package 'fdq' (Forest Data Quality) Forest data quality is a package containing nine methods of analysis for forest databases, from databases containing inventory data and growth models, the focus of the analyzes is related to the quality of the data present in the database with a focus on consistency , punctuality and completeness of data. Package: r-cran-fdrci Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-geoquery, r-cran-dplyr, r-cran-foreach Filename: pool/dists/noble/main/r-cran-fdrci_2.4-1.ca2404.1_all.deb Size: 237926 MD5sum: a5bcda03fc6f5655855f8deae7979996 SHA1: 297f722d044a6deb704aedb67a61c5e01dcdc850 SHA256: 3264f677776cd3bb644ac00e43dd446c7c97f73cfb80de7513940d1e141f1b09 SHA512: e6f5586aad4b895194ea5059d7225233039c586b7d63c89b223a50b616bc4b0b657fc4808668d97aa8ec70c9ec73e443d7c0279b195c6d3195f5357aa3b6fe65 Homepage: https://cran.r-project.org/package=fdrci Description: CRAN Package 'fdrci' (Permutation-Based FDR Point and Confidence Interval Estimation) FDR functions for permutation-based estimators, including pi0 as well as FDR confidence intervals. The confidence intervals account for dependencies between tests by the incorporation of an overdispersion parameter, which is estimated from the permuted data. Also included are options for an analog parametric approach. Package: r-cran-fdrdiscretenull Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-bioc-qvalue Filename: pool/dists/noble/main/r-cran-fdrdiscretenull_1.4-1.ca2404.1_all.deb Size: 120240 MD5sum: d2fa7776a81ae5c7d014dbb90b5cf977 SHA1: 248114be51bdc0955134f577cbfeea281a9f7c87 SHA256: 0283f89edd44071c06de62580b28d1d23eeb27808fec360499d87ca00f08600f SHA512: 14d87af2b3dc5a6a324b88f5d056a2bda00f072616865d6d88b9cb7132fa88d7fc37752106a6fa58f2ca3b198046664e494d353d47aa68e0c4da9bb3b5b0e282 Homepage: https://cran.r-project.org/package=fdrDiscreteNull Description: CRAN Package 'fdrDiscreteNull' (False Discovery Rate Procedures Under Discrete and HeterogeneousNull Distributions) It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success. Package: r-cran-fdrestimation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-fdrestimation_1.0.1-1.ca2404.1_all.deb Size: 63422 MD5sum: 7186d0f70cf2fa8fc169c4c434907392 SHA1: d442a3396bef53170ef102cb46e681effb2ff236 SHA256: 23b7c5b8e1a3c41dfc424d3484cb1c39472448df80ec3e096a8b12c1e642559c SHA512: 1b9f4f9257dbf78aadd77d05d2361dedfe7e8e6c318af319da014943c6b357a54ca1384806deaa833734f1193bd8f7f6487cadc329fed7e97f13b7f8395541d6 Homepage: https://cran.r-project.org/package=FDRestimation Description: CRAN Package 'FDRestimation' (Estimate, Plot, and Summarize False Discovery Rates) The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines. Package: r-cran-fdrsamplesize2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdrsamplesize2_0.2.0-1.ca2404.1_all.deb Size: 116998 MD5sum: 7486bab3cb37901e4de5eb680b105ee0 SHA1: d2452b5bcad4791f6451807b7d6b5b3086c13403 SHA256: fb8c8093cf2af4967d7e8e9a41accbb6088e431ef69fcd8940d50ac0331c63f4 SHA512: 7d8dff1c3af9d1c979bf0f5d0375faf94f8a31105fff930aaed5bed7dc2ea21fce97bfde7ad94a7ebfca3dd7114b615350983fe60df0e12f0c78590048ca2799 Homepage: https://cran.r-project.org/package=FDRsamplesize2 Description: CRAN Package 'FDRsamplesize2' (Computing Power and Sample Size for the False Discovery Rate inMultiple Applications) Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review. 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Package: r-cran-featureterminator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-caret, r-cran-tibble, r-cran-dplyr, r-cran-lattice, r-cran-e1071, r-cran-randomforest Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-featureterminator_1.0.0-1.ca2404.1_all.deb Size: 126170 MD5sum: 93ca60ff547fd370bbf8eca4a088057f SHA1: 181ddaa4584532fc2de313a9f025b988f442e4c3 SHA256: 2085677dcea2cca126690fbde674572e69c0b990e550df71a4632fe7a75c2395 SHA512: 13a0b1a1e5247dbd3daf87f6f172c507c6df13b54744c4b2fbd7e94aa07260a356a36639e791086b53322b02f0ab4954062d5a0954df9a61740758151af961d0 Homepage: https://cran.r-project.org/package=FeatureTerminatoR Description: CRAN Package 'FeatureTerminatoR' (Feature Selection Engine to Remove Features with MinimalPredictive Power) The aim is to take in data.frame inputs and utilises methods, such as recursive feature engineering, to enable the features to be removed. What this does differently from the other packages, is that it gives you the choice to remove the variables manually, or it automated this process. Feature selection is a concept in machine learning, and statistical pipelines, whereby unimportant, or less predictive variables are eliminated from the analysis, see Boughaci (2018) . Package: r-cran-featurizer Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-featurizer_0.2-1.ca2404.1_all.deb Size: 36154 MD5sum: 9da69560f487ab90f858d506b546e8c2 SHA1: ba3abde1c17166142d30e27eb28fb674d7310c47 SHA256: 41463969d03c45fe53af82872c6ac07a1da62b00b5c29cd61fda87028c81acd6 SHA512: 8cbf49a9027d795e006e6fa97f72d3a0b908bb454f1c084a383aeabac78c6b910b30e34650a449d2bfabc890ba5eac985a8a91e492901e6e4d1ebd492c87972b Homepage: https://cran.r-project.org/package=featurizer Description: CRAN Package 'featurizer' (Some Helper Functions that Help Create Features from Data) A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down. Package: r-cran-fec16 Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-vroom, r-cran-usethis Suggests: r-cran-testthat, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-fs, r-cran-lubridate, r-cran-ggplot2, r-cran-stringr, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-fec16_0.1.6-1.ca2404.1_all.deb Size: 2169540 MD5sum: 6c4978f58c38227b1fb78754fdd7d242 SHA1: 2c3552dbb43b2116567111e8e32c281ff8821384 SHA256: 737312205639df91544c2872dc97ff7ce7c265ab934646d4b7f9f3d8c73bcd14 SHA512: 191ed6c4b726a5694d78f152202599d340d4e61b4fb30785eb6daf75580b975c4fa35930a3675c9dc0b0ee37bb201f18b226427230f4d28dbaa26de7c0fcb4c1 Homepage: https://cran.r-project.org/package=fec16 Description: CRAN Package 'fec16' (Data Package for the 2016 United States Federal Elections) Easily analyze relational data from the United States 2016 federal election cycle as reported by the Federal Election Commission. This package contains data about candidates, committees, and a variety of different financial expenditures. Data is from . 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Currently, the package enables extraction from nine datasets: The National Elevation Dataset digital elevation models ( 1 and 1/3 arc-second; USGS); The National Hydrography Dataset (; USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (; NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (; GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 4, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (; DAAC); the International Tree Ring Data Bank; the National Land Cover Database (; NLCD); the Cropland Data Layer from the National Agricultural Statistics Service (; NASS); and the PAD-US dataset of protected area boundaries (; USGS). Package: r-cran-federalregister Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-federalregister_0.2.0-1.ca2404.1_all.deb Size: 44844 MD5sum: cc1a57de5198c7d9b4a090dd0e888cb4 SHA1: 930ae22883339063efafe0b8e9a6ee5af9f9d38e SHA256: ca37ebdccae7e12c23bb6e032d951fbff798b09dcf044cf6e6967432c7491d66 SHA512: 163a293ffb56a113c3b4ee470e3d225670213ac5635ea6da0539e08f7aa472017ab3d8af1fd4341ce1b22b5b5ff0ac0759aee1f86648e5b357fe76333d0063e2 Homepage: https://cran.r-project.org/package=federalregister Description: CRAN Package 'federalregister' (Client Package for the U.S. Federal Register API) Access data from the Federal Register API . Package: r-cran-fedirt Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-pracma, r-cran-shiny, r-cran-httr, r-cran-callr, r-cran-dt, r-cran-ggplot2, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fedirt_1.1.0-1.ca2404.1_all.deb Size: 147156 MD5sum: c6157ab65f8e7c473877d5fee0e3170b SHA1: 00d032e7fe5781922dd66a84e2bd2073b656a834 SHA256: ebb386754a2f5322a98384af3d262f5195b6f810b97a520d5f39403e4120e9dc SHA512: 380edc9c69f17e90592678725c1c85d25af013d888fa31d193178958af74104407489b747910df3ee91604155158734c81489de3e1bdfac22ed24be54f351a94 Homepage: https://cran.r-project.org/package=FedIRT Description: CRAN Package 'FedIRT' (Federated Item Response Theory Models) Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included. 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Simply, the 'fedregs' package facilitates word processing and sentiment analysis of the CFR using tidy principles. Note: According to the Code of Federal Regulations XML Rendition User Guide Document: "In general, there are no restrictions on re-use of information in Code of Federal Regulations material because U.S. Government works are not subject to copyright. OFR and GPO do not restrict downstream uses of Code of Federal Regulations data, except that independent providers should be aware that only the OFR and GPO are entitled to represent that they are the providers of the official versions of the Code of Federal Regulations and related Federal Register publications." Package: r-cran-fedstatapir Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml2, r-cran-readsdmx, r-cran-magrittr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-fedstatapir_1.1.0-1.ca2404.1_all.deb Size: 223614 MD5sum: 49c71ed655719fd19195a6272d6bd961 SHA1: a526bd6ea3d5ad64561f1e5995f1c57b63e51fa3 SHA256: 34df2704c8333097fd5131fae53f88257efd792f639a708f4e1416f948ca560c SHA512: 41fc8bb7400f1803a654681942ac768b683ea75e80ae63844b5c3bee4e61e395e26ee400029da37c7de9fe50d4187c61c4065d675696ce5530da4553e32db9d5 Homepage: https://cran.r-project.org/package=fedstatAPIr Description: CRAN Package 'fedstatAPIr' (Unofficial API for Fedstat (Rosstat EMISS System) for Automaticand Efficient Data Queries) An API for automatic data queries to the fedstat , using a small set of functions with a common interface. Package: r-cran-fedz1 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fedz1_0.1.0-1.ca2404.1_all.deb Size: 256234 MD5sum: d91efbaac7ef34be41e2a5c8588dd18e SHA1: a2051848cf9b6b5a88fc2e13b6fd24028495595d SHA256: c40870aa78bb0cbaff7cb74342fb3b1566ec6b6fe130224e3ea584ba358ae1c4 SHA512: bad7c4a0ba3b1f9bce0d56935fc5c2deccbd3be278d89c994fcb6c6a71a9a62925658db53c49a45b72ca2eddb972139818aaebab6c5b50da6fd7eba0d7f492b6 Homepage: https://cran.r-project.org/package=fedz1 Description: CRAN Package 'fedz1' (An Easier Access to Financial Accounts of the United States(Z.1)) Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all datasets , tables and descriptions with functions to understand series and explore them. Package: r-cran-fee Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oneinfl Filename: pool/dists/noble/main/r-cran-fee_1.0.0-1.ca2404.1_all.deb Size: 52740 MD5sum: 837bc44504403df0e13942744a55b2ca SHA1: 4224a94818a8f7fce7e1c169c907846ddd82fc39 SHA256: 05c4128107b31055e855cfda46500c37bdcdc1c4070d219df62c7bbed62baab7 SHA512: bc4faf576570ebbc0b8031f20a987db83832b8347072e84bd0699ccea7b9c3070254d8b7feee8cb5a0c1345e9c26687889e0db72c446bfc29fb0907ddda43d74 Homepage: https://cran.r-project.org/package=fee Description: CRAN Package 'fee' (Estimate the First-Exposure Effect (FEE) using Count Data Models) Estimates the first-exposure effect (FEE) using a one-inflated positive Poisson model, or a one-inflated zero-truncated negative binomial model. In addition, estimates the marginal FEE, and standard errors for the FEE and marginal FEE. Package: r-cran-feedbackts Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-mapdata, r-cran-proj4, r-cran-sp, r-cran-gstat, r-cran-automap Filename: pool/dists/noble/main/r-cran-feedbackts_1.5-1.ca2404.1_all.deb Size: 330454 MD5sum: 34475478ee54222a91b1b5de787e0236 SHA1: 97eff3f83c380d7ccdbb3e538a3d5afd3a7a3384 SHA256: aecebb970ec60243f3ad6a79b4cbb681165cd0dad6054948c42569d9ea342011 SHA512: d3985673565259defc9cbf02b7bf3e0869b9d5334a7aa56c1bf1506f55ed4c1742cd4eb38c8ce8eb2721b0b80ea01c87550f7fc8907403f12aa317205aa41921 Homepage: https://cran.r-project.org/package=FeedbackTS Description: CRAN Package 'FeedbackTS' (Analysis of Feedback in Time Series) Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain. Package: r-cran-feisr Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-plm, r-cran-rdpack, r-cran-dplyr Suggests: r-cran-texreg, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-feisr_1.3.1-1.ca2404.1_all.deb Size: 288798 MD5sum: 1e5692af14e2b541a7f293d17f7fe2f0 SHA1: 9bd281ad07a8f4bfa361bc1716af1f2a70e6b93b SHA256: 61dc1dd48fe5d78b0bffe805ebf6924b7336ae70ce069705b9da863a6e71d47c SHA512: 75656ddcc8a34de80f7431bd0153d0a6bff397b64a1a1cb10ccaa05bbd8204171c3da30bb146d55cc0e286d94ad322f598af8cff6c65777b652bcdd5f301185d Homepage: https://cran.r-project.org/package=feisr Description: CRAN Package 'feisr' (Estimating Fixed Effects Individual Slope Models) Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) . In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) ; Polachek and Kim (1994) ; Ruettenauer and Ludwig (2020) ; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test. Package: r-cran-fejiv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-haven Filename: pool/dists/noble/main/r-cran-fejiv_0.1.1-1.ca2404.1_all.deb Size: 22856 MD5sum: 33762b76e91816849089202f20b6311e SHA1: 4e0a48e44bbee959f96d256bf1f1cf835ab92f48 SHA256: d95297bfbe76162ea69754b4610fe6dfc08a0598a694cbaa80d0dafe9e1c05a1 SHA512: 13a321435127710e5f5c4db87e14f4e469a99d988b94b6472aeef19054cd2a6bd7fa4b6b8706b816fab18fcdb2d7f3fadff753e1d58b4539a3d62c801ea9f25d Homepage: https://cran.r-project.org/package=fejiv Description: CRAN Package 'fejiv' (Fixed Effect Jackknife Instrumental Variables Estimation) Implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) , allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by Słoczyński (2024) as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) . 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The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects. The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo). Package: r-cran-feltr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-httptest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-feltr_0.1.0-1.ca2404.1_all.deb Size: 192080 MD5sum: b810e2b7c69bff5e8465e7705cb8021d SHA1: 1d6752fb04987211af0c6d2190295c8df60111be SHA256: f70642b75e6eb59e26280ceaa81b2f12b162f4b9e9f6b4de8568061e79910725 SHA512: d3c90bda63ffd1d03c1c10d7d786d2c1e4d974c76f3cd47fe9a1082407edc3bcf6fd04d30dfe00953b7068a87692b4f16c31cd2860374e936908ac8b03115727 Homepage: https://cran.r-project.org/package=feltr Description: CRAN Package 'feltr' (Access the Felt API) Upload, download, and edit internet maps with the Felt API (). Allows users to create new maps, edit existing maps, and extract data. Provides tools for working with layers, which represent geographic data, and elements, which are interactive annotations. Spatial data accessed from the API is transformed to work with 'sf'. Package: r-cran-fence Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lme4, r-cran-ggplot2, r-cran-sae, r-cran-fields, r-cran-snowfall, r-cran-snow Suggests: r-cran-pscl Filename: pool/dists/noble/main/r-cran-fence_1.0-1.ca2404.1_all.deb Size: 152104 MD5sum: 348fd709636fe474875ed40234266f35 SHA1: 04dde99ef4c1e42c6d029c760e47238fba64735a SHA256: e29e61783a04178abc2f804ed4c5e9831ca6a1e8f45370d1a9aa88649b978c90 SHA512: 48f7486137f6c9b4e50a893edeadb397835641cd23619e9f57ba3cf90a8bcd9aff57394166f817a421a5108e9c740679af4c89d349f707851967c8bbda025b14 Homepage: https://cran.r-project.org/package=fence Description: CRAN Package 'fence' (Using Fence Methods for Model Selection) This method is a new class of model selection strategies, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. References: 1. Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692. . 2. Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629. 3. Jiang J., Nguyen T., Rao J.S. (2010), Fence Method for Nonparametric Small Area Estimation. Survey Methodology, 36(1), 3-11. . 4. Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415. . 5. Thuan Nguyen & Jiming Jiang (2012), Restricted fence method for covariate selection in longitudinal data analysis. Biostatistics, 13(2), 303-314. . 6. Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662. . 7. Jiang, J. (2014), The fence methods, in Advances in Statistics, Hindawi Publishing Corp., Cairo. . 8. Jiming Jiang and Thuan Nguyen (2015), The Fence Methods, World Scientific, Singapore. . 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Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) . Package: r-cran-fer Architecture: all Version: 0.94-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fer_0.94-1.ca2404.1_all.deb Size: 61356 MD5sum: 7084f0f9aa99049320ebf604a80162f2 SHA1: 1570dd039781080e028feba8271bfc73a163c75d SHA256: 89a9c127cd5ed3a62beefaf7b682ff07b8e7b03e30cacd40e6ff75ae418b7b70 SHA512: 9aa8a9af665e48d66f655c7d3e489e64084fd463b434628a3ca2630ca4841e28441e5943ca326798d2656ee7c5814303d7bbfce85948dca1b087da79488234af Homepage: https://cran.r-project.org/package=FER Description: CRAN Package 'FER' (Financial Engineering in R) R implementations of standard financial engineering codes; vanilla option pricing models such as Black-Scholes, Bachelier, CEV, and SABR. 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Package: r-cran-fes Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bayesfactor, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fes_1.0-1.ca2404.1_all.deb Size: 67986 MD5sum: 263bb7436a62aca666c2529e3a6aa323 SHA1: ba9b0f559aab1d454631f932914677ce25af689b SHA256: 73e138c246c322c262957372619b976cd278684dd96d17ccead2c4e28415345d SHA512: 4ba14ec142687ef821a0234a64be0b0c7e6692537aeb0cb185c6040fa0108dfb7b18f53896acf94ab913cfcdbe253e3535f5acc3f01bba5f911020157ee8946a Homepage: https://cran.r-project.org/package=FES Description: CRAN Package 'FES' (Fisher Exact Scanning for Dependency) Implements Fisher exact scanning (FES), a multiscale test of dependence for continuous or discrete bivariate data. 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Package: r-cran-fextremes Architecture: all Version: 4032.84-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fbasics, r-cran-fgarch, r-cran-timedate, r-cran-timeseries Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-fextremes_4032.84-1.ca2404.1_all.deb Size: 558152 MD5sum: 49a03d0eaf7b78fbdf0e473b17d6e5f6 SHA1: f89930c759e23318d0d088d2e6c2b2eb232dcc55 SHA256: c7aa0a1f0eac6f19015905bc85c41f43ad7c7de47e6b8f62e471181f22dad76e SHA512: 063bdd347ec33f6688d3428cf95236f7e18c8cf21a38e41baa2cd772913525c4e2e231270eb010d6bb9f376aee0dbaf5c69aedb433876d2d6fcff1189657d51f Homepage: https://cran.r-project.org/package=fExtremes Description: CRAN Package 'fExtremes' (Rmetrics - Modelling Extreme Events in Finance) Provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index. 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This package implements the FFA framework proposed by Vidrio- Sahagún et al. (2024) , originally developed in 'MATLAB', now adapted for the 'R' environment. This work was funded by the Flood Hazard Identification and Mapping Program of Environment and Climate Change Canada, as well as the Canada Research Chair (Tier 1) awarded to Dr. Pietroniro. Package: r-cran-ffd Architecture: all Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1584 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2html Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-ffd_1.0-9-1.ca2404.1_all.deb Size: 1073228 MD5sum: 1ae5ff0ea8d55ee83b0da93c19737687 SHA1: e56fb6449fc1d8b29b8b10590fbfc6658c0db5d1 SHA256: 79ebbc82d6f8dc598b5951c50ee77def24bfb6ea60efbc63721582d37acf2b6e SHA512: 0537847c6aad477f9a6ed744693913501fa4f853b5f7fc33e4d3473fd0161edd2d02f34b1acae2246c34e2e3423516d32016383b2243fcc16946de3a2a96b180 Homepage: https://cran.r-project.org/package=FFD Description: CRAN Package 'FFD' (Freedom from Disease) Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, ). 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Rotation testing, described by Langsrud (2005) , is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) . Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) . Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) . This means that the method is invariant to scale changes and that common pitfalls are avoided. 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FFTs are simple and transparent decision trees for solving binary classification problems. FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting. 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Package: r-cran-fgdir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-matrix, r-cran-magrittr, r-cran-refund Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggpubr, r-cran-gridextra, r-cran-forcats Filename: pool/dists/noble/main/r-cran-fgdir_0.1.0-1.ca2404.1_all.deb Size: 1678912 MD5sum: ebc02e854b859703b78157cd84e71a90 SHA1: 9e2d07c1bdbbec65ab6c5e327fbd2797885eb3cc SHA256: cdde7dbae05271e1e9a512f130db20abcf183b83d38666e394be4723af0edd78 SHA512: c17d6c862228c0bb6b561268e4e3d3f5f9287a9b8af3077efec46e57b5d6b13c8a56e51432b31c4d3a5e3c21f96bda9337c33bc20bf6d34cfdc1cbf04e74869d Homepage: https://cran.r-project.org/package=fgdiR Description: CRAN Package 'fgdiR' (Functional Gait Deviation Index) A typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject’s gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. This R package provides tools for computing the Functional Gait Deviation Index, a novel index for quantifying gait pathology using multivariate functional principal component analysis. The package supports analysis at the level of both legs combined, individual legs, and individual joints/planes. It includes functions for functional data preprocessing, multivariate functional principal component decomposition, FGDI computation, and visualisation of gait abnormality scores. Further details can be found in Minhas, S. K., Sangeux, M., Polak, J., & Carey, M. (2025). The Functional Gait Deviation Index. Journal of Applied Statistics . 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Interactive time series plots include multiple options for incorporating external data such as forecasts and events. Other static plots useful for time series data include an intuitive and generic scatter plotter, a boxplot generator suitable for multiple time series, and event study plotters for time series analysis around sets of dates. 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Package: r-cran-finbipartite Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spectralgraphtopology, r-cran-quadprog, r-cran-mass, r-cran-progress, r-cran-mvtnorm, r-cran-cvxr Suggests: r-cran-testthat, r-cran-igraph Filename: pool/dists/noble/main/r-cran-finbipartite_0.1.0-1.ca2404.1_all.deb Size: 155616 MD5sum: dbe54372943203f465f78f57863476fa SHA1: 44b14e7e51e67945e7d803685cb2bdc869b10a14 SHA256: c3b2e0be0d3d3e2282e6b78543e96bf4400ae7ec3b2fcc4da74f86ad0745b839 SHA512: fd2e658e5e94cfe906fab5f2ca3049d164c21ddd696829c97a31e66f437f67e5f3a655291bfb44a2d86105ffa1fecdbf3b3fe5907fee37adec4618f26b91bf3b Homepage: https://cran.r-project.org/package=finbipartite Description: CRAN Package 'finbipartite' (Learning Bipartite Graphs: Heavy Tails and Multiple Components) Learning bipartite and k-component bipartite graphs from financial datasets. This package contains implementations of the algorithms described in the paper: Cardoso JVM, Ying J, and Palomar DP (2022). "Learning bipartite graphs: heavy tails and multiple components, Advances in Neural Informations Processing Systems" (NeurIPS). 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Package: r-cran-find Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-gtable, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-find_0.1.1-1.ca2404.1_all.deb Size: 199588 MD5sum: 19a759f8aaef92740dc3dd505ae35253 SHA1: 999b726a3169d52cb61ea79b1c787ae5089d7bd6 SHA256: 727cdb384e7a2d09d6814377814a75cf27e3d9c78cd9ade801c890c52153ab4e SHA512: ec43b1090906c3dddb944e86c9440bad4f048c7b711956d2e54c112b954e976f946fae2421ee53e027e2d93418f10a85f6a1e3f54be23a1bf0dbafe7426c0e30 Homepage: https://cran.r-project.org/package=FIND Description: CRAN Package 'FIND' (Objective Comparison of Phase I Dose-Finding Designs) Generate decision tables and simulate operating characteristics for phase I dose-finding designs to enable objective comparison across methods. Supported designs include the traditional 3+3, Bayesian Optimal Interval (BOIN) (Liu and Yuan (2015) ), modified Toxicity Probability Interval-2 (mTPI-2) (Guo et al. (2017) ), interval 3+3 (i3+3) (Liu et al. (2020) ), and Generalized 3+3 (G3). Provides visualization tools for comparing decision rules and operating characteristics across multiple designs simultaneously. Package: r-cran-findgsep Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-pracma, r-cran-scales, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-findgsep_1.2.0-1.ca2404.1_all.deb Size: 196146 MD5sum: 54175d33f5e1441299dce046282f4ad4 SHA1: bf1cdf75cfbaec17e368051fbc5c6681cd1643c8 SHA256: f2f71e3fd7b9162b4965422db44527eb8cfc3b461bf07018ac0a63b105e4ff4c SHA512: 8895cef2d0b5b2d97167bcc64ba210cb00c6a692ef162b264e256a0eb673fbe495bb58fc9beb149882ce5b36438b56d17b8b0c871f9d900f13bff585f3fac003 Homepage: https://cran.r-project.org/package=findGSEP Description: CRAN Package 'findGSEP' (Estimate Genome Size of Polyploid Species Using k-MerFrequencies) Provides tools to estimate the genome size of polyploid species using k-mer frequencies. This package includes functions to process k-mer frequency data and perform genome size estimation by fitting k-mer frequencies with a normal distribution model. It supports handling of complex polyploid genomes and offers various options for customizing the estimation process. The basic method 'findGSE' is detailed in Sun, Hequan, et al. (2018) . Package: r-cran-findinfiles Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-htmlwidgets, r-cran-shiny, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-fs, r-cran-shinyace, r-cran-shinyfiles, r-cran-shinyjqui, r-cran-shinyvalidate, r-cran-shinywidgets Filename: pool/dists/noble/main/r-cran-findinfiles_0.5.0-1.ca2404.1_all.deb Size: 66844 MD5sum: 4cb2dc767a31203e1a3e59692a9d340a SHA1: 5aa88ea0e9c41d16fdf86cb65dcccc68784a2024 SHA256: cc74d1ee36b9dcc60950b3fc35e930b872707fa99a4490620ed212dc17db4ba4 SHA512: 1aee85999dd134e0a9e6d1a1341fdfd0056498d4cd123cde3b1202343768fe9ed98981b4f7bdd633433aaf26122e5db425f87f36ca3aed7bd957c456736b6477 Homepage: https://cran.r-project.org/package=findInFiles Description: CRAN Package 'findInFiles' (Find Pattern in Files) Creates a HTML widget which displays the results of searching for a pattern in files in a given folder. The results can be viewed in the 'RStudio' viewer pane, included in a 'R Markdown' document or in a 'Shiny' application. Also provides a 'Shiny' application allowing to run this widget and to navigate in the files found by the search. Instead of creating a HTML widget, it is also possible to get the results of the search in a 'tibble'. The search is performed by the 'grep' command-line utility. Package: r-cran-findingit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-crayon, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-findingit_0.1.1-1.ca2404.1_all.deb Size: 54318 MD5sum: 4e3a060d3467fee723e9944705388511 SHA1: 1170ebf9b9c6d6bff2941ee65dbd55c30737f5db SHA256: e952fc422aaa4453e7eadc30055dd0c5d60e44f6a799c4f9257b138d5eea1c2f SHA512: feedc563490bf4eed88a1481a626e57edd6e3bc5d450c7540f53ad1433778dcc6c90f2ff779205855a951f4afe132db0cce13b7051a5cc215d4836e0a61ec010 Homepage: https://cran.r-project.org/package=findInGit Description: CRAN Package 'findInGit' (Find Pattern in Files of All Branches of a 'git' Repository) Creates a HTML widget which displays the results of searching for a pattern in files in a given 'git' repository, including all its branches. The results can also be returned in a dataframe. Package: r-cran-findit Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-glmnet, r-cran-lars, r-cran-matrix, r-cran-quadprog, r-cran-glinternet, r-cran-igraph, r-cran-sandwich, r-cran-lmtest, r-cran-limsolve Filename: pool/dists/noble/main/r-cran-findit_1.3.0-1.ca2404.1_all.deb Size: 427660 MD5sum: 0f8d614ddd956dd5d8cef4e0f26015b5 SHA1: d58f1582ea484cb205ee0c1a5ca5f0bd4cff9816 SHA256: 562f86f8eae4c62b89a28638bc75cc5f3d9e74d407847210a6e488926b276711 SHA512: d5686c97f52b8c4da261a7f01393f9bc7f4d60e06ccef3c0d921363c9ffea1604160b3da6248c5e9f21441d9ddddda6ef73f426f516172b60fee557d870fbf5d Homepage: https://cran.r-project.org/package=FindIt Description: CRAN Package 'FindIt' (Finding Heterogeneous Treatment Effects) The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013). The proposed method is applicable, for example, when selecting a small number of most (or least) efficacious treatments from a large number of alternative treatments as well as when identifying subsets of the population who benefit (or are harmed by) a treatment of interest. The method adapts the Support Vector Machine classifier by placing separate LASSO constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. This allows for the qualitative distinction between causal and other parameters, thereby making the variable selection suitable for the exploration of causal heterogeneity. The package also contains a class of functions, CausalANOVA, which estimates the average marginal interaction effects (AMIEs) by a regularized ANOVA as proposed by Egami and Imai (2019). It contains a variety of regularization techniques to facilitate analysis of large factorial experiments. Package: r-cran-findn Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-scales Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-findn_0.2.0-1.ca2404.1_all.deb Size: 46386 MD5sum: 187ece906b18a3054558b95a0d8b6b35 SHA1: 41ff7d2fd91ae9ced3c4e3302131ab7775fa549f SHA256: d50c94a476a9dcaba71f4dde3136f82d2c7d250ea8821d9bac3a053c343717f7 SHA512: 010600fa1de84bf9b39560ccc4d6fb097b35e10269bcb475f5171fd3857755adaf0a257721b55bc243e50b5e57fb8e516c8a66786e2e605cdf7695ed03b3fedd Homepage: https://cran.r-project.org/package=findn Description: CRAN Package 'findn' (Simulation Based Sample Size Estimation) Estimates the sample size for a test or a trial based on repeated simulation using a model based approach. Implements a method by Maruo et al. (2018) and an extension. Package: r-cran-findpackage Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-findpackage_0.2.0-1.ca2404.1_all.deb Size: 15554 MD5sum: a42fc592df8f25896eecab31e6e9fab6 SHA1: dca37b393f5949a236c0404cb373928d85ee5f2e SHA256: ce57e0f4195d1f5334e53c0335919f0e34738ff0d30577fd10d295a8713a3144 SHA512: fbfba16db7ae9912cc074c9bd6341e0fe7eef518255dc10df724d97fa3d639d4d7c882c91a904f9646b013aab9ec03bf6d6bd871cef483778fa0b6541778f85c Homepage: https://cran.r-project.org/package=findPackage Description: CRAN Package 'findPackage' (Find 'CRAN' Package by Topic) Finds 'CRAN' packages by the topic requested. The topic can be given as a character string or as a regular expression and will help users to locate 'CRAN' packages matching their specified requirement. findPackage() returns a data frame of packages with description containing the input string. Package: r-cran-findpython Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-reticulate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-findpython_1.0.9-1.ca2404.1_all.deb Size: 23238 MD5sum: b70521f007713c815e6b7bbada94e7d4 SHA1: 7edc7ffd0ae7e7f5214b955e75268ec88b51c301 SHA256: 0363a0c2042bde937bfe70371a6b00b628f05b09666cfc2d435b4e0cfebf6c98 SHA512: 0ac6363f94b28f26f0b21a7409a12d8bbe997f649b1522e18a6b078720a1a2c5073e740e919a3dcfd36bc7d1e3237b91ce657625012dabfdb4448e8bead10e60 Homepage: https://cran.r-project.org/package=findpython Description: CRAN Package 'findpython' (Functions to Find an Acceptable Python Binary) Package designed to find an acceptable python binary. Package: r-cran-findr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdftools, r-cran-stringr Filename: pool/dists/noble/main/r-cran-findr_0.2.1-1.ca2404.1_all.deb Size: 477096 MD5sum: 5591dd173efd0f00830854ded5476ff0 SHA1: defdb75bb771714f8f8b5478ab379e856b81c9f9 SHA256: 553bb9931275446d79c961f9119ec70035fde43fe79d55e3e0b4f2d5074a5d06 SHA512: bac5e857534d2ae85bbebb4f963f100f02122192abd50608bc7fa87caf26ba0036174cf3de4997827b6a38228a0bee6ccd1004d4824fe67e8d81ed0ad9b6b0f0 Homepage: https://cran.r-project.org/package=findR Description: CRAN Package 'findR' (Find Code Snippets, R Scripts, R Markdown, PDF and Text Fileswith Pattern Matching) Scans all directories and subdirectories of a path for code snippets, R scripts, R Markdown, PDF or text files containing a specific pattern. Files found can be copied to a new folder. Package: r-cran-findsvi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2752 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tidycensus, r-cran-tidyr, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-htmltools, r-cran-glue, r-cran-sf, r-cran-ggplot2, r-cran-reactable, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-findsvi_0.2.0-1.ca2404.1_all.deb Size: 2217882 MD5sum: 503ef280474035615277c99953f53a66 SHA1: 278b79da061286de10aa62383fe1a4ca3450758a SHA256: 5b09b751904906e7bdcd8eaccf06e45da6314c9334d84bf2d57d9f1580fade84 SHA512: d551ffc5bf7fa7328631039af8204e0ecf30ccfcb36398cf56083e1b3adbff4f836001922e17ae024ba37679c146f7c380b2560f3dfca0b5a455618f56cae942 Homepage: https://cran.r-project.org/package=findSVI Description: CRAN Package 'findSVI' (Calculate Social Vulnerability Index for Communities) Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, 'findSVI' retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) . Package: r-cran-findviews Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-findviews_0.1.3-1.ca2404.1_all.deb Size: 145130 MD5sum: 5ef1a7666fbc4f451bdbeb14e00adcbf SHA1: 115d143e7f60b23f6028ab4198606077727d4ef4 SHA256: cd4856f7332e42acd6b98bd2c7df51404b239e8633260a483b030c364993da05 SHA512: 6fb32117fc5d086cdef947bc2446d739b8af2b140922f2084a41edf19b14d92b505529360dbbc2e804d9c75515697ccd45cb122b4d5329600731c6d415a57257 Homepage: https://cran.r-project.org/package=findviews Description: CRAN Package 'findviews' (A View Generator for Multidimensional Data) A tool to explore wide data sets, by detecting, ranking and plotting groups of statistically dependent columns. Package: r-cran-finepop2 Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-finepop2_0.6-1.ca2404.1_all.deb Size: 117900 MD5sum: 9f5298bc628df397d3bb897fc4dcf402 SHA1: b5a5a72c66f814945f776ea5245916cb557dd75e SHA256: 13e5d88717e3df853d3a865b343e4cba8d9d68bf8470ced01039abb83615a6d6 SHA512: 812a5167096e7dd7c2c36c2dd4db453e6f9de753a9dcb117f42393d4cbe64a2df8cd8e6d84835b7d67002953154f4ba6a4bee9520bdaf2ea56ed81a6ed8f6d7c Homepage: https://cran.r-project.org/package=FinePop2 Description: CRAN Package 'FinePop2' (Fine-Scale Population Analysis (Rewrite forGene-Trait-Environment Interaction Analysis)) Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) . Package: r-cran-finepop Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ape Filename: pool/dists/noble/main/r-cran-finepop_1.5.2-1.ca2404.1_all.deb Size: 429378 MD5sum: 0cbdca2866b5c4e3ba7f52ba8b6ddc48 SHA1: d0f3201286f7c9fcc0434375b31b95c4dcf62c83 SHA256: 16c26ef58c0732d00d5f954a631b10e47ae1f2792cfb8fb90ed112e5f385af6a SHA512: 6c3368bdd06b68cea46b2de48730b333dbd01c6897290a777433fca6168e7789a5978e8b98789b24060a4bb5120038f79f302b4696f065dcf4c21e4468210083 Homepage: https://cran.r-project.org/package=FinePop Description: CRAN Package 'FinePop' (Fine-Scale Population Analysis) Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files. Package: r-cran-finetune Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tune, r-cran-cli, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-parsnip, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-workflows Suggests: r-cran-bradleyterry2, r-cran-covr, r-cran-discrim, r-cran-kknn, r-cran-klar, r-cran-lme4, r-cran-modeldata, r-cran-ranger, r-cran-recipes, r-cran-rpart, r-cran-rsample, r-cran-spelling, r-cran-testthat, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-finetune_1.3.0-1.ca2404.1_all.deb Size: 221218 MD5sum: 11324ea5154f204511917e4e0bff37b9 SHA1: ead7453891ed4a904b68e51af618856227a18e3f SHA256: 629ea048bdc09cfd9aa07bb57fdd3eb1b1db876a2b5955433956ad550cf816a2 SHA512: 28fbc3b16d169ab806cac2e34cc49fe935ae6e259e12244cd06baa315aa3251f8b0e01b6cdaaa3c1f559e5a997c4af977ee158cbb9a03b83c546a35e5985e6c7 Homepage: https://cran.r-project.org/package=finetune Description: CRAN Package 'finetune' (Additional Functions for Model Tuning) The ability to tune models is important. 'finetune' enhances the 'tune' package by providing more specialized methods for finding reasonable values of model tuning parameters. Two racing methods described by Kuhn (2014) are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) is also included. Package: r-cran-fingraph Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spectralgraphtopology, r-cran-mass, r-cran-progress, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fingraph_0.1.0-1.ca2404.1_all.deb Size: 494198 MD5sum: 8f97e4d41c7df59b3f819cd3b2d6c6cc SHA1: 8f7889e91a366a6140c5562fc271d318b1f27704 SHA256: 744451935e4eaa6d5c7b9aaf974877ea6f86dafba1756f7e05d0b824943a2259 SHA512: e0625a9a7d75ebe3d047e33cad5b98b0ffe0af5b365d7272f41cee17b51f5a61e736dd6974756c946391634baa48f4d4055bddffbb6793251aa3757233896a14 Homepage: https://cran.r-project.org/package=fingraph Description: CRAN Package 'fingraph' (Learning Graphs for Financial Markets) Learning graphs for financial markets with optimization algorithms. This package contains implementations of the algorithms described in the paper: Cardoso JVM, Ying J, and Palomar DP (2021) "Learning graphs in heavy-tailed markets", Advances in Neural Informations Processing Systems (NeurIPS). Package: r-cran-finiteruinprob Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sdprisk, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-finiteruinprob_0.6-1.ca2404.1_all.deb Size: 134430 MD5sum: 8c452cd1dcb66f87cb725131c691ea42 SHA1: 410fd436e72a7e0dc0f36f07f3fc73de7a56bcb3 SHA256: f084320ea7cb96e653ebfc44631de3030f8527c4479c2901dfe4748d23bea74a SHA512: c977b926b4d1040522b15f94e67fc455a451c4729126c91d873e521433fea15818b3dbd9aa30e9805691e7a0b6cc9928481fd42c7ed96d9d5e6fa4369589c9cc Homepage: https://cran.r-project.org/package=finiteruinprob Description: CRAN Package 'finiteruinprob' (Computation of the Probability of Ruin Within a Finite TimeHorizon) In the Cramér–Lundberg risk process perturbed by a Wiener process, this packages provides approximations to the probability of ruin within a finite time horizon. Currently, there are three methods implemented: The first one uses saddlepoint approximation (two variants are provided), the second one uses importance sampling and the third one is based on the simulation of a dual process. This last method is not very accurate and only given here for completeness. Package: r-cran-finlabr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-ggplot2, r-cran-performanceanalytics, r-cran-zoo, r-cran-class, r-cran-quantmod, r-cran-reshape2, r-cran-mclust, r-cran-shiny Suggests: r-cran-bslib, r-cran-ttr, r-cran-dt, r-cran-xts, r-cran-yfr, r-cran-cryptoquotes, r-cran-tidyquant, r-cran-rtsne, r-cran-umap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-finlabr_1.0.0-1.ca2404.1_all.deb Size: 490918 MD5sum: 96d24b9302cbd330abe9e1bd8ec43af4 SHA1: 75b847b15117166c457ebf9cf5f624f9e5a7c996 SHA256: dd35b417ce1310d12526a09a79592400ec76b3c2439f4a21c0a54bfec26c7015 SHA512: 2a272593b7a7b36a9ada49ab6e895e046053394cf01a352538546e87848fbbdf48bcc277237685a96b07642864470dc187185c1e200cc5688a72db517744d5c4 Homepage: https://cran.r-project.org/package=finlabR Description: CRAN Package 'finlabR' (Portfolio Analytics and Simulation Toolkit) Tools for portfolio construction and risk analytics, including mean-variance optimization, conditional value at risk (expected shortfall) minimization, risk parity, regime clustering, correlation analysis, Monte Carlo simulation, and option pricing. Includes utilities for portfolio evaluation, clustering, and risk reporting. Methods are based in part on Markowitz (1952) , Rockafellar and Uryasev (2000) , Maillard et al. (2010) , Black and Scholes (1973) , and Cox et al. (1979) . Package: r-cran-finlex Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-tibble, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-finlex_0.2.1-1.ca2404.1_all.deb Size: 153964 MD5sum: 5e49a1353c933956f3fb5f0738853424 SHA1: 0a354f48947f040ed0e9ff725d322c64f1d72a4f SHA256: 2ae2bec53fde9bd14bdd53c3798bd0b45e6248a02879e0a9aaaf731b54db31e6 SHA512: 97205f9f7f6d12ff7efbfdda0b1ac69c411a9425e1f0b09832b4f0937097f730a4d9a7ab77ab63813943d483ab381c2baf5531f834960514e45ccad0fcdafb64 Homepage: https://cran.r-project.org/package=finlex Description: CRAN Package 'finlex' (Access Open Data from the Finnish 'Finlex' Legislative Database) Provides functions to retrieve and structure Finnish legislative data made available through the 'Finlex' Open Data API (). Functions cover retrieval of statute catalogues, statute titles, structured statute metadata, and cross-references between amending and amended statutes, returned as tidy tibbles for further analysis. 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Package: r-cran-finnishgrid Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-finnishgrid_0.2.0-1.ca2404.1_all.deb Size: 485266 MD5sum: 6dddd37e5cd98a583a7bd687c69ce4eb SHA1: 1edc166332a43fc53c1581f6c2b2f9bc3a61c1dc SHA256: ce16955163407e4abd4378ac67f6ed82fc118fac2b90005b68dd3295315ce5dd SHA512: a05e4e7e18e4e4824ca9f05f245494fbd5d259e90cc15a4b65fb6579038b51db82d5930a6d46e60d1f19ae0a6e38a191d0813b27a9487580042bf34371900a8f Homepage: https://cran.r-project.org/package=finnishgrid Description: CRAN Package 'finnishgrid' ('Fingrid Open Data API' R Client) R API client package for 'Fingrid Open Data' on the electricity market and the power system. get_data() function holds the main application logic to retrieve time-series data. API calls require free user account registration. Data is made available by Fingrid Oyj and distributed under Creative Commons 4.0 . Package: r-cran-finnsurveytext Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-stopwords, r-cran-stringr, r-cran-textrank, r-cran-tibble, r-cran-tidyr, r-cran-udpipe, r-cran-wordcloud Suggests: r-cran-dt, r-cran-htmlwidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-survey Filename: pool/dists/noble/main/r-cran-finnsurveytext_2.1.1-1.ca2404.1_all.deb Size: 657532 MD5sum: 3bc6551f3729e8feecfe7d93ccab7cdd SHA1: 186bb2f54620703f4469c1b05f5c9903eda5b67c SHA256: 78cc7b8ba0c188260ac37c3bb8e9a5384c355a3ccad3c970ffa9e8187848a392 SHA512: a6d099d6cdc98dd5a384df7697f0c56d8b144be6a4789da5df93cbe4bfe37d4757b7d9566d43c70136e24126407e39594cbf7c70859094b8d8aa68abc0ca0a05 Homepage: https://cran.r-project.org/package=finnsurveytext Description: CRAN Package 'finnsurveytext' (Analyse Open-Ended Survey Responses in Finnish) Annotates Finnish textual survey responses into CoNLL-U format using Finnish treebanks from using UDPipe as described in Straka and Straková (2017) . Formatted data is then analysed using single or comparison n-gram plots, wordclouds, summary tables and Concept Network plots. The Concept Network plots use the TextRank algorithm as outlined in Mihalcea, Rada & Tarau, Paul (2004) . Package: r-cran-finnts Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2475 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-modeltime, r-cran-callr, r-cran-cli, r-cran-cubist, r-cran-dials, r-cran-digest, r-cran-doparallel, r-cran-dplyr, r-cran-earth, r-cran-feasts, r-cran-foreach, r-cran-forecast, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-glmnet, r-cran-gtools, r-cran-httr, r-cran-hts, r-cran-jsonlite, r-cran-kernlab, r-cran-lubridate, r-cran-magrittr, r-cran-parsnip, r-cran-plyr, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rules, r-cran-snakecase, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timetk, r-cran-tune, r-cran-vroom, r-cran-workflows Suggests: r-cran-arrow, r-cran-azurestor, r-cran-boruta, r-cran-caret, r-cran-corrr, r-cran-ellmer, r-cran-energy, r-cran-knitr, r-cran-microsoft365r, r-cran-nixtlar, r-cran-notebookutils, r-cran-qs2, r-cran-ranger, r-cran-reactable, r-cran-rmarkdown, r-cran-sparklyr, r-cran-testthat, r-cran-tseries, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-finnts_0.7.0-1.ca2404.1_all.deb Size: 1712602 MD5sum: 0b7b0a23183dae1ff14a5adaad205cb7 SHA1: c5e14d4389ce76bc62c2295489621085cd1dedfd SHA256: 3a3f169b81a5f4467687db874dc9ef1808de0595756acd83072c14b98e36dd0b SHA512: abdc46eff2c5abef63f83f0c363e93c40e53c4e91f1814478d25135447a1c1e60d6fed032cfc4c7f3d8d5ba7c644dddbea4b3d321e1328f125eba856889e82b0 Homepage: https://cran.r-project.org/package=finnts Description: CRAN Package 'finnts' (Microsoft Finance Time Series Forecasting Framework) Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting! Package: r-cran-finreportr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-httr, r-cran-rvest, r-cran-xbrl, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-finreportr_1.0.4-1.ca2404.1_all.deb Size: 49402 MD5sum: 7a27ae31e2318652bcdd6ff9024886c4 SHA1: d6de914328442a6ecb1d66a0e71c3ad4925a1cc5 SHA256: 9dc86e73def3a187ad80df651b9178bdf035802841197021a9176b14d25f34c2 SHA512: 0cbcf182a22dbeceb821a7a0663edf4e1ad7145c6dd0ef17e4d087da65dd10f2c320e3ef568089e2e31cee1f513f86e25079e0da6ddde21697d3d8053e5b845c Homepage: https://cran.r-project.org/package=finreportr Description: CRAN Package 'finreportr' (Financial Data from U.S. Securities and Exchange Commission) Download and display company financial data from the U.S. Securities and Exchange Commission's EDGAR database. It contains a suite of functions with web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR in an automated and scalable manner. See for more information. Package: r-cran-finto Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-httr, r-cran-dplyr, r-cran-jsonlite, r-cran-isocodes, r-cran-tidyr, r-cran-stringr, r-cran-rlang, r-cran-purrr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-finto_0.1.2-1.ca2404.1_all.deb Size: 146660 MD5sum: 52ba678159e41b1688cca81294810ba9 SHA1: cacc45ff3f5656253788f8654e4ff1598ef7835a SHA256: 26bc184517039174b9c10d6bc99f6fab3ad11034df460ef116d6f141b3b165d7 SHA512: 00f8c848e589388886854ad25adb439ec53699ef05c9fb8360d4964d09eeccd9f7d395e5278823c5327c1dadee53302c273987479ef072ef1068aa6d509261cc Homepage: https://cran.r-project.org/package=finto Description: CRAN Package 'finto' (Access the 'Finto' API) Access and retrieve vocabulary data 'Finto' API , which is a centralized service for interoperable thesauri, ontology and classification schemes for different subject areas. Package: r-cran-fints Architecture: all Version: 0.4-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4583 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Suggests: r-cran-moments, r-cran-tseries, r-cran-urca, r-cran-lmtest, r-cran-sandwich, r-cran-psych, r-cran-gparotation, r-cran-chron, r-cran-polynom, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-fints_0.4-9-1.ca2404.1_all.deb Size: 4417140 MD5sum: aae9ed95901c45c9653c3dd4bda2481a SHA1: 865e157e006cb66580802348a9837add80666729 SHA256: 6a41a816c1e8478300dc0f9643650a423a73c467b60ebe1ddce93d8de1501b88 SHA512: 37679f232895cb3c11f15bb2df4980c1ca26f4e5dc3eefc8884baa71062e34c81e827cc11eacc9cb4d9d30a9820259744f2a423d668b723ffd003b015276f6f8 Homepage: https://cran.r-project.org/package=FinTS Description: CRAN Package 'FinTS' (Companion to Tsay (2005) Analysis of Financial Time Series) R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11. Package: r-cran-fiodata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7000 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fio Filename: pool/dists/noble/main/r-cran-fiodata_0.2.0-1.ca2404.1_all.deb Size: 7093016 MD5sum: 87a14cb33fec1d7be18bdb7e2a91466e SHA1: a02f016661bc6fae35a1b63afe46c6f3b01a5423 SHA256: c8937c7ca1c4dc419ff5e5900ae3e37828c232c2adaee762faceecb0727d3813 SHA512: a8df54507fdb3a8bd4612d42686e6cd1563f14091d3f0aa4f6594a5ea7a76b6eb03f377066a68719494bb1056917c913499b071681abd86156974bf98bd69d71 Homepage: https://cran.r-project.org/package=fiodata Description: CRAN Package 'fiodata' (Regional and Multi-Regional Input-Output Data) Provides Regional (Brazil, 2020) and Multi-Regional (World, 2000) input-output matrices for R. This package serves as a data-only companion to the 'fio' package, facilitating input-output analysis by providing standardized R6 data objects. 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'FIORA' allows to predict Mass-Spectra based on the SMILES code of chemical compounds. It is described in the Nature Communications article by Nowatzky (2025) . 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Package: r-cran-firebase.auth.rest Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2 Filename: pool/dists/noble/main/r-cran-firebase.auth.rest_1.0.1-1.ca2404.1_all.deb Size: 47634 MD5sum: 2254dfd2be387b2d7b70d1482055cf51 SHA1: 003f05dee284102c86207199bd1fddde5442a1ac SHA256: bde93187a4c90510b0224c2de01c3a6d4ce7703e6c0055f611c3f5d7368b8be6 SHA512: 07cbf307cf10ed2d3419401e185f5b593e1f2eed429c82866cfc549cedfade5cb9283823233392a956b76b8809fad3552ab10bc4f380b6dfe23defcb94c15a7b Homepage: https://cran.r-project.org/package=firebase.auth.rest Description: CRAN Package 'firebase.auth.rest' (R Wrapper for 'Firebase Authentication REST API') A convenient and user-friendly interface to interact with the 'Firebase Authentication REST API': . 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Package: r-cran-firebehavior Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-truncnorm, r-cran-xtable Filename: pool/dists/noble/main/r-cran-firebehavior_0.1.2-1.ca2404.1_all.deb Size: 459170 MD5sum: e69771e676663aa885a23870a72f1ecf SHA1: a4ca611d3228b48b4546be8f0a02c5eb2711f00b SHA256: 9e53af3181ef8339d7448107b4ffc2d970555e837bfb041e8738f309cba6c527 SHA512: 04b8560ef1b5e352ed58461dfa8bf811f425a9fa0945b2d2caf33b5bdeb628c6fdda8b65cd9949d1622fdda958d150c4a27f6f3c90236320fcaa490ebbe22db9 Homepage: https://cran.r-project.org/package=firebehavioR Description: CRAN Package 'firebehavioR' (Prediction of Wildland Fire Behavior and Hazard) Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System and Alexander et al.'s (2006) Crown Fire Initiation & Spread model . Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard. Package: r-cran-firedata Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-curl, r-cran-r6, r-cran-openssl, r-cran-yaml Suggests: r-cran-testthat, r-cran-shiny, r-cran-shinyjs, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-firedata_2.0.2-1.ca2404.1_all.deb Size: 464036 MD5sum: fd8e09816f98e317f9150966fea6e533 SHA1: a81d7b6ffce5dff1aad372c824aa9701827cbb53 SHA256: d24609f57a25ddfd8ddd2a793988ffaddcea5acdb8bc6a7564d43ea14599feca SHA512: 49fc9442506955e6e260e9eda1c06715af126dda33ec68f39380215deb76f9077d1ba21ade2ec1a19c727ae2f531ff0eb6493b0a26aa8e1238b6e465f7313e90 Homepage: https://cran.r-project.org/package=fireData Description: CRAN Package 'fireData' (Connect to 'Google Firebase') Provides an interface to 'Google Firebase' services , including 'Firebase Realtime Database', 'Cloud Firestore', 'Firebase Authentication', and 'Cloud Storage for Firebase'. Supports interactive use and 'shiny' applications as well as automated server-side workflows. Data frames and objects can be stored and retrieved, users can be authenticated, and files can be managed through the services' application programming interfaces. Package: r-cran-fireexposur Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-ggspatial, r-cran-magrittr, r-cran-maptiles, r-cran-multiscaledtm, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-tidyterra, r-cran-tmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fireexposur_1.2.0-1.ca2404.1_all.deb Size: 3900526 MD5sum: 7ddd7f0ba53270eb0138547e5c40c821 SHA1: 5c8e89dd4528c183f318f5f098c831a2bfcbf6b3 SHA256: 3fb0cdb1b0a52223a3f1d72e918386d53c64a7fafb5856529e4f54fe78229cf3 SHA512: acb94f144d56e1eebf92a7f91e75e5cfe03bf8ad1e7059ae0bbf2efb0901080383bc3317f266bd855fa69dda1665b3f8789ea503c6292bb94c8cafc37cb0d189 Homepage: https://cran.r-project.org/package=fireexposuR Description: CRAN Package 'fireexposuR' (Compute and Visualize Wildfire Exposure) Methods for computing and visualizing wildfire ignition exposure and directional vulnerability that are published in a series of scientific publications are automated by the functions in this package. See Beverly et al. (2010) , Beverly et al. (2021) , and Beverly and Forbes (2023) for background and methodology. Package: r-cran-fireproof Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-curl, r-cran-fiery, r-cran-firesale, r-cran-jose, r-cran-jsonlite, r-cran-r6, r-cran-reqres, r-cran-rlang, r-cran-routr, r-cran-sodium, r-cran-urltools Suggests: r-cran-callr, r-cran-dplyr, r-cran-quarto, r-cran-rmarkdown, r-cran-storr, r-cran-testthat, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-fireproof_0.1.0-1.ca2404.1_all.deb Size: 463458 MD5sum: 65fc2d7c5963efd21855995ec66c692f SHA1: 060d901c944e3cda87422998c71f5dc5f31d716a SHA256: 156b7c17fab02595e91a77bae5613ee93eb245a39a4d9e89b966f66ceaee2724 SHA512: 1f7952b8496efa37697b25cb42221354f20f57c13bf4a9b356a8a7bdbdb72a56a0642ada6b2425770e36bcf7efdeb91bccea1ee318a937afbf3db3ecc2c403cf Homepage: https://cran.r-project.org/package=fireproof Description: CRAN Package 'fireproof' (Authentication and Authorization for 'fiery' Servers) Provides a plugin for 'fiery' that supports various forms of authorization and authentication schemes. 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Package: r-cran-firesafety Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang, r-cran-routr Suggests: r-cran-fiery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-firesafety_0.1.0-1.ca2404.1_all.deb Size: 197546 MD5sum: b203061c18891f1c5cd32e68911ea873 SHA1: 20986136b78d13da5e49720f1b605179f850ad45 SHA256: 1d9e9d9678c195a7284b11d656ab0491e8a70b9989bbb76941da96399890d37c SHA512: 069ce3c1e23984a992bca20cbec4db7e9137b29de001eb1056c6b661178700ee657877b853c58b5952add1d4fc144fcef807c0b3b89c7275eb628a9a823edc40 Homepage: https://cran.r-project.org/package=firesafety Description: CRAN Package 'firesafety' (A Collection of Security Related Plugins for 'fiery') Provide a range of plugins for 'fiery' web servers that handle different aspects of server-side web security. Be aware that security cannot be handled blindly, and even though these plugins will raise the security of your server you should not build critical infrastructure without the aid of a security expert. Package: r-cran-firesale Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-reqres, r-cran-rlang, r-cran-storr Suggests: r-cran-fiery, r-cran-later, r-cran-testthat Filename: pool/dists/noble/main/r-cran-firesale_0.1.1-1.ca2404.1_all.deb Size: 88730 MD5sum: d362af781cef781055cdc6ac49538b12 SHA1: 6ca23b7d8cd9215177edd6d4106147899c2d42fa SHA256: cfb404ac0b2f59a3df1fbf8afc78f7edeed87c0479218d0a933a103eae262226 SHA512: 974888b8d4949f7b27d9c76eb9edbce11e422eb4b6c12e326798df7b83a435539aa8033ec69c33b2b0781f2a901d86c44c056714c0da14b274ef6105c4061629 Homepage: https://cran.r-project.org/package=firesale Description: CRAN Package 'firesale' (Datastore for 'fiery' Web Servers) Provides a persistent datastore for 'fiery' apps. The datastore is build on top of the 'storr' package and can thus be based on a variety of backends. The datastore contains both a global and session-scoped section. Package: r-cran-firestorm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang, r-cran-websocket Suggests: r-cran-fiery, r-cran-httpuv, r-cran-later, r-cran-reqres, r-cran-testthat Filename: pool/dists/noble/main/r-cran-firestorm_0.1.0-1.ca2404.1_all.deb Size: 56764 MD5sum: ae4923be913537c6102a2cc1bd1c7d71 SHA1: 2346bf907b4e430cb478569d42c62894a7f35dac SHA256: 9f43dc9302342d144adefb8fee30bbcfd9f98589a2dfce59feb133308e7eff7f SHA512: b369a09b24294072a7f359bf81807e73274c2d984c3378af2bac8b9a5cc85e51fad19cd91c345d67982908995c31895a36d51f438a39e157afe41bc87b4be857 Homepage: https://cran.r-project.org/package=firestorm Description: CRAN Package 'firestorm' (Reverse Proxy and Load Balancing for 'fiery') Provides plugins for setting up 'fiery' apps as a reverse proxy. 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Package: r-cran-firmmatchr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-stringdist, r-cran-zoomerjoin, r-cran-dbi, r-cran-rsqlite, r-cran-cli, r-cran-progressr, r-cran-httr, r-cran-jsonlite, r-cran-glue, r-cran-purrr, r-cran-readr, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-firmmatchr_0.2.0-1.ca2404.1_all.deb Size: 93512 MD5sum: d7cf4a6269f24695b7c42bb704979b4b SHA1: 805a2af16fc53112350bb4fc7fda937a8239f499 SHA256: b6b093b0334066e3a40472443deaea848bcb1eb9c00d3f19a880b32975f4a77e SHA512: 9b4a00d745be0e80e41330c7a3df345abd9ec761164a7f0e5338d7154b5dd51bff3b990eda507a20018d89216c1d7c613b84eb95997548f095e4d67187f51fdf Homepage: https://cran.r-project.org/package=firmmatchr Description: CRAN Package 'firmmatchr' (Robust Probabilistic Matching of Company Names) A pipeline for matching messy company name strings against a clean dictionary (e.g., 'Orbis'). Implements a cascading strategy: Exact -> Fuzzy ('zoomerjoin') -> 'FTS5' ('SQLite') -> Rarity Weighted. Name normalization covers German, French, Italian and English legal forms and conventions, which suits multilingual registers such as the Swiss one. Normalization discards detail, so several dictionary entries can collapse onto one string; these groups are matched once and a crosswalk back to every original entry is retained. References: Beniamino Green (2025) ; . Package: r-cran-first Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-twinning Filename: pool/dists/noble/main/r-cran-first_2.1-1.ca2404.1_all.deb Size: 60174 MD5sum: 8629054d9d2a927fa7895848d1112d64 SHA1: cc3a66719ac3c90fc09216f114c3ecf9019edd7e SHA256: 8014191474fca54d434bbe7fb2fb10b26713e842c46563ab08cd1f51090366e2 SHA512: e4b0694aee019ca5384aa166b952a564783d2eca05706dac6deca8825ccc2e41d482e4f8e6ee092285acab5c5cc22a78cf6033f71b2e45aee3970d5462f21822 Homepage: https://cran.r-project.org/package=first Description: CRAN Package 'first' (Factor Importance Ranking and Selection using Total Indices) A model-independent factor importance ranking and selection procedure based on total Sobol' indices. Please see Huang and Joseph (2025) . This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873. Package: r-cran-fiscal Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fiscal_1.0.2-1.ca2404.1_all.deb Size: 140806 MD5sum: c915047a82fb573a305cd898ae0c801f SHA1: ebc846d7d57179c310a09e1a0b6600ef6982307a SHA256: 89c9332f98990dda6358c7a7eb55e1fa73cbb0e7adf36431a8193bb27e2958b7 SHA512: c9d2fcfda869ebbf506c134f7734a235527f3a16cbdc0468721f8a2e71ecdf5196aa929dd8d449de81717d9066b1bd725e3764463d1b36c8bdbd71283568b2f1 Homepage: https://cran.r-project.org/package=Fiscal Description: CRAN Package 'Fiscal' (Income Tax Calculations (UK)) Income tax calculations for England, Northern Ireland and Wales. Estimate annual income tax within the different taxation bands at specified levels of both taxable income and the Personal Allowance, emulating the results obtained at . Calculate the standard Personal Allowance at various levels of taxable income. Estimate the personal allowance required to recoup a specified amount of income tax. Package: r-cran-fish Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda.usc, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-fish_1.1-1.ca2404.1_all.deb Size: 23406 MD5sum: ac34559b69cdbd68e0b68065a62d5c91 SHA1: f19070e244e8a700da5a0d972d517bfca6ac0d98 SHA256: b304ec678a7d19b3acd39fe679a822b6471b75e117176443fc357356ad6b01ce SHA512: 7ff6e157e92b56ea30ff065bad6e99a8f39f991a3c615f8d4a46add76d2326c222177b47981a800f92adf3ff227ece740e6a9bfa36c724868f5d65bb33a1deef Homepage: https://cran.r-project.org/package=FiSh Description: CRAN Package 'FiSh' (Fisher-Shannon Method) Proposes non-parametric estimates of the Fisher information measure and the Shannon entropy power. More theoretical and implementation details can be found in Guignard et al. . A 'python' version of this work is available on 'github' and 'PyPi' ('FiShPy'). Package: r-cran-fishbc Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-chk, r-cran-covr, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-fishbc_0.2.2-1.ca2404.1_all.deb Size: 61034 MD5sum: 19015eb3c3d14c68013da4408b1aa197 SHA1: 72e4951fc20fce9e1e757ebf49c8a15c44417ccb SHA256: 820369a3ae46dca999b368a3470b1fd5902260f4cebe80842a80b76457c0d858 SHA512: 65a0ed5b54c9f5f7c8badf61006159bf50fd5002aea780477488afb96c12e31d60a4a0a81d4f2edce346176251a64921d63a3afd90296b5501511c73f0f882b5 Homepage: https://cran.r-project.org/package=fishbc Description: CRAN Package 'fishbc' (Fishes of British Columbia) Provides raw and curated data on the codes, classification and conservation status of freshwater fishes in British Columbia. Marine fishes will be added in a future release. Package: r-cran-fishboot Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-ks, r-cran-tropfishr, r-cran-fishmethods, r-cran-cli Filename: pool/dists/noble/main/r-cran-fishboot_1.0.4-1.ca2404.1_all.deb Size: 218158 MD5sum: c56a0fdd0b57b0e8500dd7b094b3b894 SHA1: bebbc0d025af8a8840f627252176c446642c8703 SHA256: 7a3a5e909c6d6b95a99b00b28a95081e85fa64a171a136c3c57b35b8c2022d21 SHA512: 5fcd8220415da913ebc054689e29e258cfd70aee98d613df38a94c598fd96ba41518e0c3b68ff4933edc88b497ab236edee8bceb83360e5dd7e12ae5343e4777 Homepage: https://cran.r-project.org/package=fishboot Description: CRAN Package 'fishboot' (Bootstrap-Based Methods for the Study of Fish Stocks and AquaticPopulations) A suite of bootstrap-based models and tools for analyzing fish stocks and aquatic populations. Designed for ecologists and fisheries scientists, it supports data from length-frequency distributions, tag-and-recapture studies, and hard structure readings (e.g., otoliths). See Schwamborn et al., 2019 for background. The package includes functions for bootstrapped fitting of growth curves and plotting. Package: r-cran-fishdata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3660 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-magrittr, r-cran-dm, r-cran-ggplot2, r-cran-tidyr, r-cran-diagrammersvg, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-fishdata_1.0.1-1.ca2404.1_all.deb Size: 1404976 MD5sum: 7369c14bd05d499745bfbe5a869324af SHA1: 537ecda0e2d203489af8194f8346e863dbd3b772 SHA256: f220daecc920766d4f617c36c71d78aa498fb934038b85376fb7ce5cd2b91c1f SHA512: 2fe7aca3d8731ea9d887e3074a3cac51672d353b1dbc100a785733855654ed83fc5ff041db31c5ea92d41895a9234a5c5123fa0aa0945df1941f9411a939784b Homepage: https://cran.r-project.org/package=fishdata Description: CRAN Package 'fishdata' (A Small Collection of Fish Population Datasets) A collection of four datasets based around the population dynamics of migratory fish. 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Package: r-cran-fishdiver Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-factominer, r-cran-geometry, r-cran-ggplot2, r-cran-gridextra, r-cran-lubridate, r-cran-moments, r-cran-patchwork, r-cran-colorspace, r-cran-rgl, r-cran-rfast, r-cran-rlang, r-cran-scales, r-cran-suncalc, r-cran-tidyr, r-cran-waveletcomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishdiver_1.1.0-1.ca2404.1_all.deb Size: 5401312 MD5sum: 8697db21b06e79cde8bf189aa3b5dc78 SHA1: 505cd0c485c39f8ddc2c5e6e9ba1f831e873e124 SHA256: 3390a6ed517fad6e4fa06d0bf85390e02951e78b368b35bd59643cfee8bf9969 SHA512: 8b64f1af31f8f58fe528ff29f05ebe3cdd152a24c1e446530ed5f39026162569d18b6ef516519a8ac6aa0ee7c06b063e994b2eb019a5fbf46d8ea0838caea57d Homepage: https://cran.r-project.org/package=FishDiveR Description: CRAN Package 'FishDiveR' (Classify Aquatic Animal Behaviours from Vertical Movement Data) Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering. Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics. Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster. Based on principals of combining CWT with k-means first developed by Sakamoto (2009) and redeveloped by Beale (2026) . Package: r-cran-fisherem Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-elasticnet, r-cran-ggplot2, r-cran-ellipse, r-cran-plyr Suggests: r-cran-testthat, r-cran-aricode Filename: pool/dists/noble/main/r-cran-fisherem_1.6-1.ca2404.1_all.deb Size: 225572 MD5sum: e2c56c273a46fc8a03532c80a420d511 SHA1: 0bcf2ee248023d6c9ee86173cf90573621bef89f SHA256: 25b8ce7629ad45f77d7eaa8ad216aa16998495a940c65e700beef8daf9833ebe SHA512: 0ab33d2c068ba68fa9a693d16e34350bdd8c1b52dd23f4727f84f7d00a3a2bd0b77ce9c3eba40ca1b02d5ec4a2d67735a11bbc8e867fc81d48ded94a9b894c6f Homepage: https://cran.r-project.org/package=FisherEM Description: CRAN Package 'FisherEM' (The FisherEM Algorithm to Simultaneously Cluster and VisualizeHigh-Dimensional Data) The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) , is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided. Package: r-cran-fisheye Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-fisheye_0.2.0-1.ca2404.1_all.deb Size: 16574 MD5sum: 3f7b1987af6e843a01fb0efe3441d529 SHA1: 178eec1c67bbe505b34d616de694d199f2add241 SHA256: ff494236d6dc62b37f48360574c21d8be6a60a736d1fcbc7f88edce189d841c6 SHA512: a6c2db495ecfc35971a0c0704080f91b4ce20cb73175f2ee41d5bf6f983d65c231c0ffdfd424aed7d2cfe0c1f8deb1676e59e5c22b743546626d4bb67515fbc8 Homepage: https://cran.r-project.org/package=fisheye Description: CRAN Package 'fisheye' (Transform Base Maps Using Log-Azimuthal Projection) Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation. Package: r-cran-fishgrowth Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb Suggests: r-cran-areaplot Filename: pool/dists/noble/main/r-cran-fishgrowth_1.0.4-1.ca2404.1_all.deb Size: 226560 MD5sum: 59b7896bac60e0e9b4411025a6229f9e SHA1: fb0a0de73b7d00aa7ef70a18cdc25383571f6c10 SHA256: ae961c8eb8ef49a367bf27be34377006029017cb7af4f7873bc8eeab29fbc67d SHA512: 9756683b77f09262aec15d6510036e1a459b89694f5818b198be8461fc49b711ec89908976337b744febf58ba519c087c67d0bcfea9d13ad21b2e02c3e45798b Homepage: https://cran.r-project.org/package=fishgrowth Description: CRAN Package 'fishgrowth' (Fit Growth Curves to Fish Data) Fit growth models to otoliths and/or tagging data, using the 'RTMB' package and maximum likelihood. The otoliths (or similar measurements of age) provide direct observed coordinates of age and length. The tagging data provide information about the observed length at release and length at recapture at a later time, where the age at release is unknown and estimated as a vector of parameters. The growth models provided by this package can be fitted to otoliths only, tagging data only, or a combination of the two. Growth variability can be modelled as constant or increasing with length. Package: r-cran-fishkirkko2015 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fishkirkko2015_1.0.0-1.ca2404.1_all.deb Size: 14636 MD5sum: 1bea32e7123325cd8c8e55fd229bdd32 SHA1: ea736dcfb0b4a86a92c733a3cadada0b0266b644 SHA256: 9393209695e6058e6cec2e14d60801a83330efa22c794374e484c329b7a4459b SHA512: dc6027aea448195e58ae9b1a20aab7a12501af4bd44e25680e71dd890d4b1b61b2d54d7711255c5a9050eb34190a6f7cbba4f0a89e687812eb88389889a2086b Homepage: https://cran.r-project.org/package=fishkirkko2015 Description: CRAN Package 'fishkirkko2015' (Dataset of Measurements of Fish Species at Kirkkojarvi Lake,Finland) Dataset of 302 measurements of 11 fish species to accompany the manuscript "Length-weight relationships of six freshwater fish species from lake Kirkkojarvi, Finland". Package: r-cran-fishmechr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-gsignal, r-cran-pracma, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishmechr_1.0.3-1.ca2404.1_all.deb Size: 1169038 MD5sum: 4065016fbdd8cd35a5c8a09cc6558c36 SHA1: a5152c0665d7ce20435ecf779b6b7a8704d9d1e0 SHA256: 6621692a46179c47dcf55be912aa49aa854354c2a1eb5f85ecc1a4c5b57c051c SHA512: 604ecc3ee083602848142eacce73346c05903836f5c42a5fbf9911a40aec9ace2e9c92a4039996cf1f36315468e4d75c36caae9f3b6500b00cf4f4cee17a690a Homepage: https://cran.r-project.org/package=fishmechr Description: CRAN Package 'fishmechr' (Computes Kinematic Parameters for Swimming) Processes tracked points on a fish's body and uses them to estimate standard kinematic parameters such as tail beat amplitude and frequency, body wavelength and wavespeed. As part of this, it also estimates the location of the center of mass and the principal swimming axis. The techniques are described in detail in the main vignette and are published in the book chapter Hawkins, O.H., Di Santo, V., Tytell, Eric.D., 2025. "Biomechanics and energetics of swimming", in: Higham, T.E., Lauder, G.V. (Eds.), Integrative Fish Biomechanics, Fish Physiology. Academic Press. . Package: r-cran-fishphylomaker Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-fishtree, r-cran-geiger, r-cran-knitr, r-cran-phytools, r-cran-progress, r-cran-rfishbase, r-cran-rmarkdown Suggests: r-cran-markdown, r-cran-gh Filename: pool/dists/noble/main/r-cran-fishphylomaker_0.2.0-1.ca2404.1_all.deb Size: 703146 MD5sum: c3f4e8ec6122f88eab68e7c107dc689e SHA1: 5bb17427981d0eeb5aea8e1e5f6a8f6d41765b80 SHA256: 2421e76b7074cefd630e7daba04de4adf160c2462220fe0dd52b9e560f30c00a SHA512: de735aba38d56f1dd65d7f578aa02080509bb50026cbcc52657816cd41ffef0bde7617ca9dcd77e078cd1d7dd52d3cfd4e92459ed703032d68bbef3e856d7014 Homepage: https://cran.r-project.org/package=FishPhyloMaker Description: CRAN Package 'FishPhyloMaker' (Phylogenies for a List of Finned-Ray Fishes) Provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) . Package: r-cran-fishproxcompanalyzer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fishproxcompanalyzer_0.1.0-1.ca2404.1_all.deb Size: 22044 MD5sum: 2cdd6d3a39fff198fde4df0ba799d067 SHA1: fc31679b02c2565f615e126735571e7d6e520a35 SHA256: 95fe52c692beb0b97a94806bc03f430fdeb78eeede90e2dd5d5e44596abdb017 SHA512: 6fc3a4a69f0e7571f5206fc819dbc4329ca535c2f5267d5c213defa71f523f0e9ec32ece00bcd510eff0b24ae4a38c0409180c11e3039eee12a6466980eb92ff Homepage: https://cran.r-project.org/package=FishProxCompAnalyzer Description: CRAN Package 'FishProxCompAnalyzer' (Proximate Composition Analysis of Fish and Feed Ingredients) The proximate composition analysis is the quantification of main components that constitutes nutritional profile of any food and food products including fish, shellfish, fish feed and their ingredients. Understanding this composition is essential for evaluating their nutritional value and for making informed dietary choices. The primary components typically analyzed include; moisture/ water in foods, crude protein, crude fat/ lipid, total ash, fiber and carbohydrates AOAC(2005,ISBN:0-935584-77-3). In case of fish, shellfish and its products, the proximate composition consists of four primary constituents - water, protein, fat, and ash (mostly minerals). Fish exhibit significant variation in their chemical makeup based on age, sex, environment, and season, both within the same species and between individual fish. There is minimal fluctuation in the content of ash and protein. The lipid concentration varies remarkably and is inversely correlated with the water content. In case of fish, carbohydrates are present in minor quantity so that are quantified by subtracting total of other components from 100 to get percentage of carbohydrates. Package: r-cran-fishresp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5503 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-lattice, r-cran-mclust, r-cran-rmr, r-cran-respirometry Filename: pool/dists/noble/main/r-cran-fishresp_1.1.2-1.ca2404.1_all.deb Size: 3770798 MD5sum: 081b89f2e095c315d28ff8957d885d74 SHA1: 4d06d39afe2f5c49b8b9bd08622665348d0d5b03 SHA256: 80336ea3450e08c35d42994f895b9b633dc1fed73dbcaf82206ba40af1f87f4e SHA512: 783503e5d11b7665b0bea6e6f551efd2a9e35127ae82f5be96a7306461a4f97a58493431660ed9061083e39409bdbcfebbf8a39256551ca6a65c208ffeccb82f Homepage: https://cran.r-project.org/package=FishResp Description: CRAN Package 'FishResp' (Analytical Tool for Aquatic Respirometry) Calculates metabolic rate of fish and other aquatic organisms measured using an intermittent-flow respirometry approach. The tool is used to run a set of graphical QC tests of raw respirometry data, correct it for background respiration and chamber effect, filter and extract target values of absolute and mass-specific metabolic rate. Experimental design should include background respiration tests and measuring of one or more metabolic rate traits. The R package is ideally integrated with the pump controller 'PumpResp' and the DO meter 'SensResp' (open-source hardware by FishResp). Raw respirometry data can be also imported from 'AquaResp' (free software), 'AutoResp' ('LoligoSystems'), 'OxyView' ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and 'Q-box Aqua' ('QubitSystems'). More information about the R package 'FishResp'is available in the publication by Morozov et al. (2019) . Package: r-cran-fishrman Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-countrycode, r-cran-dplyr, r-cran-ggplot2, r-cran-golem, r-cran-httr, r-cran-jsonlite, r-cran-maps, r-cran-sf, r-cran-shiny, r-cran-shinybs, r-cran-shinyjs, r-cran-shinywidgets, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishrman_1.2.3-1.ca2404.1_all.deb Size: 596712 MD5sum: ce52c4ca00a30d769e0f84bed9a6b26b SHA1: 1685da595df678b2ba0a3fcb5e345ddbcc1859ee SHA256: 8eb4f8dc637357777a67fd24f45a1c934b05aeeef40bfb677215ef0e3508bacb SHA512: 9d105a55bb7d32fc88f5f1787ec265e5881be3499e77fbcb86159cd1bbe204cf62f9380bcbe64d726f76b71373b4fc1432a698eb5b73833fc458a2575ab310c5 Homepage: https://cran.r-project.org/package=fishRman Description: CRAN Package 'fishRman' (The Fisheries Scientist's Toolbox) A bundle of analytics tools for fisheries scientists. A 'shiny' R App is included for a 'no-code' solution for retrieval, analysis, and visualization. Package: r-cran-fishstat Architecture: all Version: 2026.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4848 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-areaplot Filename: pool/dists/noble/main/r-cran-fishstat_2026.1.0.0-1.ca2404.1_all.deb Size: 4758340 MD5sum: d76056e5aaa1ff4cdf77d83d11f68331 SHA1: bc73a0caed372196b013e10f10b80de4e045ef36 SHA256: 446dd0a24840fa0c40307bc9762b671d990cbfd48157365f65972a13257cb8e5 SHA512: 12b74d0854b08d29c79790aac97690aa4f017a7638ec39baca9413f06cc703667d68446fe3be1bb3e38281d6d72dfaf9642ae777762b914a021f191a4d8738e9 Homepage: https://cran.r-project.org/package=fishstat Description: CRAN Package 'fishstat' (Global Fishery and Aquaculture Statistics) The Food and Agriculture Organization of the United Nations (FAO) FishStat database is the leading source of global fishery and aquaculture statistics and provides unique information for sector analysis and monitoring. This package provides the global production data from all fisheries and aquaculture in R format, ready for analysis. Package: r-cran-fishtree Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-jsonlite, r-cran-memoise, r-cran-rlang Suggests: r-cran-diversitree, r-cran-geiger, r-cran-hisse, r-cran-knitr, r-cran-markdown, r-cran-phytools, r-cran-picante, r-cran-rfishbase, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishtree_0.3.4-1.ca2404.1_all.deb Size: 393272 MD5sum: ef37ad9e81b3a9558e249e19d4596e78 SHA1: 907c1f4afa92526e6ef1edd94ff44bf0c5698e25 SHA256: 1eaab3ef22ee5d61e1d1bb3ac98969301723c3c215b7df8607ac75c2150c252d SHA512: f27d4d017dcd74022eb74a932e345a450b4da27b3408e1d2171460252407e269edf959cee3edabf40f83c4969f113e47c1306b45820fefe1eb2810ae4eb7604a Homepage: https://cran.r-project.org/package=fishtree Description: CRAN Package 'fishtree' (Interface to the Fish Tree of Life API) An interface to the Fish Tree of Life API to download taxonomies, phylogenies, fossil calibrations, and diversification rate information for ray-finned fishes. Package: r-cran-fishualize Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1455 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-png, r-cran-downloader, r-cran-httr, r-cran-magrittr, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-rlang, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rfishbase, r-cran-rnaturalearth, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-fishualize_0.2.3-1.ca2404.1_all.deb Size: 1020240 MD5sum: 68af71be7161990bbf26479ebe469340 SHA1: 74870779c20783682593970ed7447a18d036ade2 SHA256: 6ac6d952ad94305061b4355eedcd9d3b32f7e58d7d72a55ff8ec8ca0804c3a97 SHA512: f375bc7dd9a562493591d8cebc725d6257cfa7a4c692116d0e7f4390f4437784775b757f202c354c9132dc3abffdc2d64833d7ac104acbd02c36a1f550d50cbf Homepage: https://cran.r-project.org/package=fishualize Description: CRAN Package 'fishualize' (Color Palettes Based on Fish Species) Implementation of color palettes based on fish species. Package: r-cran-fit.models Architecture: all Version: 0.64-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-fit.models_0.64-1.ca2404.1_all.deb Size: 134948 MD5sum: 2b47d0a29f30f530fc7b4a5e920d29ab SHA1: 5479ed4b29c0e5d80d695213abe7d0d4d54560e9 SHA256: 8b5742049d4517c099abcf8af014706773c9544fb62886f1cf9426578f8d31d2 SHA512: 3352f7113996043687f89ca2a45ebb6d54d1701384a3c7baa340adfafada26262b365b24e7b8f6b97e1fefe42ae6d32d33f350fe532fcbc4374b61916d5d8ef2 Homepage: https://cran.r-project.org/package=fit.models Description: CRAN Package 'fit.models' (Compare Fitted Models) The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002) 'Robust Library User's Guide' ). The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally. Package: r-cran-fitbitr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-checkmate, r-cran-devtools, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitbitr_0.3.0-1.ca2404.1_all.deb Size: 446398 MD5sum: d5b8c5d2c63ce4e6244f9ff2274ba201 SHA1: 395409cfb24a66621ed801082f79842cc8847adc SHA256: c1b8b6c3344c6ec6e18748d93159ca0de2ca6afe8db6c8e67702aa6a989bd3a7 SHA512: 07eca2643eb4b53084831348833be6984e27b4ea189ca956468418173fa3c794d6e232fc2837aa386e6cd24c009d487ca51b39052c9db5d437cf265ad3342105 Homepage: https://cran.r-project.org/package=fitbitr Description: CRAN Package 'fitbitr' (Interface with the 'Fitbit' API) Many 'Fitbit' users, and R-friendly 'Fitbit' users especially, have found themselves curious about their 'Fitbit' data. 'Fitbit' aggregates a large amount of personal data, much of which is interesting for personal research and to satisfy curiosity, and is even potentially useful in medical settings. The goal of 'fitbitr' is to make interfacing with the 'Fitbit' API as streamlined as possible, to make it simple for R users of all backgrounds and comfort levels to analyze their 'Fitbit' data and do whatever they want with it! Currently, 'fitbitr' includes methods for pulling data on activity, sleep, and heart rate, but this list is likely to grow in the future as the package gains more traction and more requests for new methods to be implemented come in. You can find details on the 'Fitbit' API at . Package: r-cran-fitbitscraper Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-fitbitscraper_0.1.8-1.ca2404.1_all.deb Size: 58820 MD5sum: d4bed0b228ef3802398266d48eb46b34 SHA1: e59da19a500f852697d2b4728a44340893f50bc6 SHA256: ebeaebf40a56c19e11ef5cfef9bbd2614b2668805d5a2f46b32cd990391e8a6d SHA512: 20c3bef9793352d7a14bf0a8a67306001ee72760eadb6a291dc8eea5c865dbfccee2284a87c11b1462b4b6e19cc17e242bd63f7cdf4e31ce97091a0a8233bf5e Homepage: https://cran.r-project.org/package=fitbitScraper Description: CRAN Package 'fitbitScraper' (Scrapes Data from Fitbit) Scrapes data from Fitbit . This does not use the official API, but instead uses the API that the web dashboard uses to generate the graphs displayed on the dashboard after login at . Package: r-cran-fitbitviz Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-ggplot2, r-cran-lubridate, r-cran-patchwork, r-cran-data.table, r-cran-viridis, r-cran-scales, r-cran-ggthemes, r-cran-paletteer, r-cran-xml, r-cran-hms, r-cran-leaflet, r-cran-sf, r-cran-rstudioapi, r-cran-leafgl, r-cran-raster, r-cran-terra, r-cran-magrittr, r-cran-lifecycle, r-cran-reshape2 Suggests: r-cran-copernicusdem, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-rgl, r-cran-rayshader, r-cran-magick Filename: pool/dists/noble/main/r-cran-fitbitviz_1.0.8-1.ca2404.1_all.deb Size: 2154904 MD5sum: 6cec7ba8eab64ba2da7958194f9ea7e4 SHA1: a0d687bc755d9b6a5c072b8319bed4124d197d29 SHA256: 9d6ad55e1c7e8a298ed92c14afed1d022f2e5a7c17197cfc15f4ddb6d3069ffc SHA512: 3df0a68a120c4c0e040bbc4aeb1a3d52942715ec27fb0d45f342dbc1668296dc75b1c4b1bf132cc5b15d14031c6694c71e69b909742aec64d21e043c0a45158f Homepage: https://cran.r-project.org/package=fitbitViz Description: CRAN Package 'fitbitViz' ('Fitbit' Visualizations) Visualization of pre-downloaded 'Fitbit' personal health data using 'ggplot2' Visualizations, 'Leaflet' and 3-dimensional 'Rayshader' Maps. The 3-dimensional 'Rayshader' Map requires the installation of the 'CopernicusDEM' R package which includes the 30- and 90-meter elevation data. Package: r-cran-fitclust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-transport, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fitclust_1.0.0-1.ca2404.1_all.deb Size: 281500 MD5sum: d5f475768f230bc6c664366bd82fcb23 SHA1: bd867c329af901cf81b5bb9e27a5772182270060 SHA256: 83a6c4b1478c77fbfcca543cc27470a4b4b50d3b4aaae179fc751d5778fa7d91 SHA512: 6bca36975444ff6cc0ab71fbd2449c1c0d5f75530d35c805ceef4449fbf4095491dc99040cc4f424b15a06f23df98bfd36ccd80ff8fb23d6c5d0f1b040c6756c Homepage: https://cran.r-project.org/package=FITclust Description: CRAN Package 'FITclust' (Fair Interpolated Transport for Group-Fair Clustering) Implementation of Fair Interpolated Transport (FIT), an algorithm-agnostic preprocessing framework for group-fair clustering. Group-conditional empirical distributions are moved along Wasserstein-2 geodesics toward a shared barycenter at a tunable transport intensity, and the smallest intensity meeting a soft-fairness tolerance is selected. Three soft clustering families are provided, centroid based, graph based, and model based. Package: r-cran-fitconic Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-fitconic_1.2.1-1.ca2404.1_all.deb Size: 92620 MD5sum: 3ed7cd9d34f2a40bd97e9ff1cb9836e3 SHA1: 34fde0bacc5150478434c6ae9b8e57e5c26396bb SHA256: f4987a8fed29fefe1e05516330656588409fc10ab40a1be074d8422c16450b96 SHA512: fe124222af5ea374e8689fd8c397df1c5385eb4522fcaf3c570e8bbf04e71a9b312fb1aaccdfee660c0764c9dc20de283f9c6ddd563f68605a9859f82ca173e9 Homepage: https://cran.r-project.org/package=fitConic Description: CRAN Package 'fitConic' (Fit Data to Any Conic Section) Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an arbitrary angle and the fit will still succeed. Helper functions are provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by least squares" ; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI, Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302. Package: r-cran-fitdistcp Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mev, r-cran-extradistr, r-cran-gnorm, r-cran-fdrtool, r-cran-pracma, r-cran-rust, r-cran-actuar, r-cran-fextremes Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fitdistcp_0.2.3-1.ca2404.1_all.deb Size: 3549302 MD5sum: 76c2a58d29eb59d58a3f67ff8d7003b1 SHA1: 8712138ef1485c7b3d22b6eb6a6796d1ca590d19 SHA256: 2abf6ed7c99aaa1e0ae54a06d9e0d18e38cd210c39c979d2bc461cbe8479c696 SHA512: f311ca94d65095395d68960298c3d40b1609dd6fce1ec0337d6bc53d3f1cab5ce48ffcb21c960e4a6aa36e750f3775227fab268ddb88a0b5a423184c45e87dd2 Homepage: https://cran.r-project.org/package=fitdistcp Description: CRAN Package 'fitdistcp' (Distribution Fitting with Calibrating Priors for Commonly UsedDistributions) Generates predictive distributions based on calibrating priors for various commonly used statistical models, including models with predictors. Routines for densities, probabilities, quantiles, random deviates and the parameter posterior are provided. The predictions are generated from the Bayesian prediction integral, with priors chosen to give good reliability (also known as calibration). For homogeneous models, the prior is set to the right Haar prior, giving predictions which are exactly reliable. As a result, in repeated testing, the frequencies of out-of-sample outcomes and the probabilities from the predictions agree. For other models, the prior is chosen to give good reliability. Where possible, the Bayesian prediction integral is solved exactly. Where exact solutions are not possible, the Bayesian prediction integral is solved using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion. Optionally, the prediction integral can also be solved using posterior samples generated using Paul Northrop's ratio of uniforms sampling package ('rust'). Results are also generated based on maximum likelihood, for comparison purposes. Various model selection diagnostics and testing routines are included. Based on "Reducing reliability bias in assessments of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T. and Jewson, L. (2024); . Package: r-cran-fitdistrbayes Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-posterior, r-cran-loo Filename: pool/dists/noble/main/r-cran-fitdistrbayes_0.5.1-1.ca2404.1_all.deb Size: 466374 MD5sum: 106bd553419c669984013b1153b17403 SHA1: da32e6ab3f22057678b61e345376332536d3e039 SHA256: ecadd2acf7bd5e0b026e7b0f5d7a7ce48294e1466206075dc6f214c2e94516ff SHA512: bf59ac37e7684d73d6fe3ae3eed278db00040c197293f4e51a5edeca16e603892112506bec8ba8a308ce3b3d5e98899780d2bf017a9543da381720a9aa7f55ee Homepage: https://cran.r-project.org/package=fitdistrBayes Description: CRAN Package 'fitdistrBayes' (Objective Bayesian Distribution Fitting) Fits common univariate distributions using registered objective Bayesian priors, including Jeffreys, reference, and maximal data information priors, and supports user-defined distributions and priors through an extensible model specification. Model-specific posterior propriety and moment conditions are checked before computation when registered or supplied. Exact simulation, marginalization, slice sampling, adaptive Metropolis, and user-supplied posterior samplers share a common interface for summaries, diagnostics, prediction, and pointwise log-likelihood evaluation. A separate interface fits independently right-censored observations using the registered complete-data priors, observed-data likelihood sampling or data augmentation, with sufficient posterior-propriety checks. Optional post-processing provides WAIC, PSIS-LOO, and DIC for observed-data likelihoods. The reference-prior framework follows Bernardo (1979) . Package: r-cran-fitdistrplus Architecture: all Version: 1.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4003 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-rlang Suggests: r-cran-actuar, r-cran-rgenoud, r-cran-mc2d, r-cran-gamlss.dist, r-cran-knitr, r-cran-ggplot2, r-cran-generalizedhyperbolic, r-cran-rmarkdown, r-cran-hmisc, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-fitdistrplus_1.2-6-1.ca2404.1_all.deb Size: 2866746 MD5sum: c917b08890bb12e5d1bebf678dc69217 SHA1: f38fa24438ecae8657476de11dc64f3c5dcacb5b SHA256: 0a997fd9b1420920df53b501058903d9b0a4617b51ece8ada8f0e35a6aae3776 SHA512: 4bb5ca76504c2091b77f11f8198ea4c5d82bb8204b9b2511337e5cf8cf9f44ce7723e5274a24ff713f0e9caa39c7b07d9e60cf76a9a3d6cd113f95858ab96ca3 Homepage: https://cran.r-project.org/package=fitdistrplus Description: CRAN Package 'fitdistrplus' (Help to Fit of a Parametric Distribution to Non-Censored orCensored Data) Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation. Package: r-cran-fitdynmix Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evir, r-cran-mass, r-cran-pracma, r-cran-rdpack, r-cran-ks Filename: pool/dists/noble/main/r-cran-fitdynmix_1.0.2-1.ca2404.1_all.deb Size: 107576 MD5sum: def0d924d44d91138f4d18072fd068d9 SHA1: 43301b8a27dacac19c177b9588ce7658d9b75be3 SHA256: 4c98842527644cb331f2b88a6e173a9869a2cf89048d64999a6175bfc4593c02 SHA512: ae6ba0d88c35d1bccef4bca470f59f320a16bb96316e076ee7130eaa7c8c0f310d2762a4bb2e49028154148c23dcf2ca68cfef4f82f4e8b4756693ce1b29261c Homepage: https://cran.r-project.org/package=FitDynMix Description: CRAN Package 'FitDynMix' (Estimation of Dynamic Mixtures) Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) . Package: r-cran-fitheavytail Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-icsnp, r-cran-mvtnorm, r-cran-ghyp, r-cran-numderiv Suggests: r-cran-ggplot2, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitheavytail_0.2.0-1.ca2404.1_all.deb Size: 744190 MD5sum: 7683239aa41e9c3fa108e8ae4cdbf2f6 SHA1: e7e90e4fd96fdad3b11b3585944bebbb0e967828 SHA256: 501df099cdb9ea1716692bfabb5aabe882b82eb11fa81777f3f26cce133eb401 SHA512: 87d0d498c39576b1b59270a99753ba0f423c154c321c8d0a19450d3599e4cc2250dc74b1c6324c703c69e7bf8129d39e19af038bdf8ebcf7ba0e08fe1a8a9284 Homepage: https://cran.r-project.org/package=fitHeavyTail Description: CRAN Package 'fitHeavyTail' (Mean and Covariance Matrix Estimation under Heavy Tails) Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021). Package: r-cran-fitlandr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-future.apply, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-mass, r-cran-numderiv, r-cran-plotly, r-cran-purrr, r-cran-r.utils, r-cran-rfast, r-cran-rlang, r-cran-rootsolve, r-cran-simlandr, r-cran-sparsevfc, r-cran-tidyr Suggests: r-cran-akima, r-cran-colorramps, r-cran-future, r-cran-knitr Filename: pool/dists/noble/main/r-cran-fitlandr_0.1.1-1.ca2404.1_all.deb Size: 289186 MD5sum: 6587fb7af9f15cfead5ac2694ee859f8 SHA1: dd6937099e37a6145b9d7e9d190c60d14c41b157 SHA256: e00e9184cde97afdfdafba2363d14190ee7272a4d55b3aa96d4746494560f186 SHA512: da2c7a9a7d7decd0eea4026338c29a477c80acf01835faec14a1841fb7a9f9810d4b45222461bedabaa6968f2a9e69f573e80a308640e4d1d0dd2ad9bdbf42b1 Homepage: https://cran.r-project.org/package=fitlandr Description: CRAN Package 'fitlandr' (Fit Vector Fields and Potential Landscapes from IntensiveLongitudinal Data) A toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) . The potential landscapes can be constructed with a simulation-based approach with the 'simlandr' package (Cui et al., 2021) , or the Bhattacharya et al. (2011) method for path integration . Package: r-cran-fitmix Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-fitmix_0.1.1-1.ca2404.1_all.deb Size: 43936 MD5sum: dd6983e2463e056942751a3ce53f3e04 SHA1: f3b591b75cbb3dd2454de4a5aecc96dfeb7eb9bb SHA256: d99b8b3d1b208359908f48a2f35ef063d7622d322a519137cde7651b453063f8 SHA512: 1eb73acd154b2e10b79ee594ae5900cffaf13277de728746b5c490de2b4375511314915e0cf090b84ff546e670539619ab1a26e0965c6b33d924411b70da9b90 Homepage: https://cran.r-project.org/package=fitmix Description: CRAN Package 'fitmix' (Finite Mixture Model Fitting of Lifespan Datasets) Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell et al. (1982) and Al-Hussaini et al. (2000) . Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering. Package: r-cran-fitnmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-abind, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-gslnls Filename: pool/dists/noble/main/r-cran-fitnmr_1.0-1.ca2404.1_all.deb Size: 7029326 MD5sum: a92a2bf7d84b712c421c7a7d6d86e384 SHA1: 660caf5ad43e318e5c3f07cace9808cdcae9e513 SHA256: da3fd6390148831a0c7c6297bdccc8d313d426ece3bf680a2b41c815c7b03b66 SHA512: 9e8ca1b56ffdfa0f1e62ef9663e87c4aa495240f2f3db834cb3465b4d0bbde74bec0c55222538604c8b2a7f442fe45662af882b70ee90207aa83d0904e056d71 Homepage: https://cran.r-project.org/package=fitnmr Description: CRAN Package 'fitnmr' (Multidimensional Nuclear Magnetic Resonance Peak Fitting andAnalysis) Tools for fitting and analyzing 1D-4D nuclear magnetic resonance spectra with analytical models of peak shapes and peak groups. The package reads spectra in 'NMRPipe' format, builds constrained parameter structures for chemical shifts, line widths, scalar couplings, volumes, and phases, and performs nonlinear least-squares optimization for iterative peak discovery or simultaneous fits across multiple spectra. It also provides methods for visualization, preprocessing, and kinetic analysis of 1D time-series data, including automated phase optimization, solvent suppression, time-domain correction for frequency shifts and line broadening, modeling spectra as linear combinations of two component spectra, and exponential rate fitting. Package: r-cran-fitodbod Architecture: all Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bbmle, r-cran-hypergeo, r-cran-mass, r-cran-mvtnorm, r-cran-rdpack Suggests: r-cran-flextable, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-viridis Filename: pool/dists/noble/main/r-cran-fitodbod_1.5.6-1.ca2404.1_all.deb Size: 729764 MD5sum: 53ab608f2923602a122bd5499fe003db SHA1: 749d3f49250fe4d6a1b4a7d01ede437cd81b600f SHA256: 8d81f5e68cc296b468db86bf75cd2337b485611e496fea2122aa5bc28e13eece SHA512: cfb8fb08d98614cd584cb79727dee50457b7a221d2aab27348b6101b0c1615a6406b8449b9dc3dcc21b4855c0b88e4c4bb29f492847257c2a6893daa93b2d406 Homepage: https://cran.r-project.org/package=fitODBOD Description: CRAN Package 'fitODBOD' (Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD) Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) ) and Alternate Binomial Distributions (ABD) (Paul (1985) ), also Journal article to use the package(). Package: r-cran-fitodbodrshiny Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5558 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-config, r-cran-flextable, r-cran-ggplot2, r-cran-golem, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyscreenshot Filename: pool/dists/noble/main/r-cran-fitodbodrshiny_1.0.2-1.ca2404.1_all.deb Size: 2805728 MD5sum: 19802b8ccced76dbc3b3184b2155fbe9 SHA1: a42948afbd8df7a754c1d7d83927952e77640082 SHA256: 07217d5b1bca79ed486f0d3b607a32ce533c465599ec27301f7a4294ae1a87b2 SHA512: ecfb565fae4205140c62677d2dbdf9b2348d2e9b8771d5d5d5f28313e68622d305b45eca0a82d86aad0031a4673a3386224e668d09d5849b923529bb15725f7e Homepage: https://cran.r-project.org/package=fitODBODRshiny Description: CRAN Package 'fitODBODRshiny' ('Shiny' Application for R Package 'fitODBOD') For binomial outcome data Alternate Binomial Distributions and Binomial Mixture Distributions are fitted when overdispersion is available. Package: r-cran-fitode Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle, r-cran-desolve, r-cran-deriv, r-cran-mass, r-cran-numderiv, r-cran-mvtnorm, r-cran-coda Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitode_0.1.1-1.ca2404.1_all.deb Size: 690408 MD5sum: f965f036968093e267dced92940e133b SHA1: fb05a584105501331afcc254fc54ae65b5951ac5 SHA256: 8aec3980cc949eaf5a2a7ac19db3d6f6d4d7870ba995b39b6eb070e6c6d802aa SHA512: 4350d73733c77f9804064e7a543d500c9a90c255b263b4e1945eee6b238761f7c6a4f9ecf9b267b1543bb2b3a450117ba9825d3cbf72beea00bd5c20b6fd132f Homepage: https://cran.r-project.org/package=fitode Description: CRAN Package 'fitode' (Tools for Ordinary Differential Equations Model Fitting) Methods and functions for fitting ordinary differential equations (ODE) model in 'R'. Sensitivity equations are used to compute the gradients of ODE trajectories with respect to underlying parameters, which in turn allows for more stable fitting. Other fitting methods, such as MCMC (Markov chain Monte Carlo), are also available. Package: r-cran-fitplc Architecture: all Version: 1.2-3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-car Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitplc_1.2-3.1-1.ca2404.1_all.deb Size: 89214 MD5sum: e7390741fde847e459eb0e578fd0cd5a SHA1: 63cec9b3225d6168efc6939f515123eae4bb11f0 SHA256: 7822a56e67f487c9c92f52219a7d6f88ad5f17143fd1d817e961e7f878e75385 SHA512: bcd6d7b6085b91630d5e80ff092931414d9fab9b01c0b6d06dc4c2624ccb499f42b054bdebb60722a77e1499183975baafb187ea6edebab0174c2a9c09f9b20e Homepage: https://cran.r-project.org/package=fitplc Description: CRAN Package 'fitplc' (Fit Hydraulic Vulnerability Curves) Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods. Package: r-cran-fitplotr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fitplotr_0.1.0-1.ca2404.1_all.deb Size: 57290 MD5sum: 22e72798223378db0c76be788f79b4ed SHA1: 7d92ad6e68fea1fc11166c9f75389d12dda39d92 SHA256: 56cc035dc3e65239b027bce5773443a4d26adf343b5f029cf01186deb23da681 SHA512: 8619c73d72c0e5e64d6c03395562550e7810aadf3e6f5b163b286dfc28ea966dfdc489e61f7f38b0da8fad0f31004ae57ccc0dd36add5d8958b513656d9efafe Homepage: https://cran.r-project.org/package=fitPlotR Description: CRAN Package 'fitPlotR' (Plotting Probability Distributions) Provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) . Package: r-cran-fitpoly Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-devemf, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fitpoly_4.0.0-1.ca2404.1_all.deb Size: 1862464 MD5sum: 520f36adb64edfc7b549f2d35b7ada79 SHA1: a5013713a6a3b79be8ae64c79a0dceeb9d73ebee SHA256: 6324ed81fd54b3414f33ffa76261c67906bbccdf51c9e9f56fe4c6c938d5cb16 SHA512: 0616bb37fc4e34f3cd94a0a0ed2a116471cf58d247023e49278a5fd9ed30a360cce6fd0f28fed31784a71782a22688af065bf305022f1e35d222958f59f4cab8 Homepage: https://cran.r-project.org/package=fitPoly Description: CRAN Package 'fitPoly' (Genotype Calling for Bi-Allelic Marker Assays) Genotyping assays for bi-allelic markers (e.g. SNPs) produce signal intensities for the two alleles. 'fitPoly' assigns genotypes (allele dosages) to a collection of polyploid samples based on these signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011) . New functions added on conversion of data from SNP array software formats, drawing of XY-scatterplots with or without genotype colors, checking against expected F1 segregation patterns, comparing results from two different assays (probes) for the same SNP, recovery from a saveMarkerModels() crash. Package: r-cran-fitps Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cubature, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-hmisc, r-cran-iterators, r-cran-knitr, r-cran-ks, r-cran-pbapply, r-cran-rdpack, r-cran-readxl, r-cran-vgam Suggests: r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-fitps_1.1.4-1.ca2404.1_all.deb Size: 1319222 MD5sum: 53217ab9c96cf82352054658032e2a3d SHA1: cccc33ed00543137a8e7a2142854096ca440596c SHA256: 9aae18cc0d5f89780796311e26a7bcfbac8679d1d0284bc4d52d34f2ac8b535d SHA512: 43461286eea640112134d2275e183601066c0f90356b4529db8ccf73584d5cf84ceafd25df63a8a2903ff60fca885d288ce9c18fc9b9ca9504ebe66a88b95eeb Homepage: https://cran.r-project.org/package=fitPS Description: CRAN Package 'fitPS' (Fit Probability Models to Forensic Survey Data) Fits probability models to P- and S-type count data arising from forensic surveys of clothing for the background presence of glass, paint, and related trace material. Built-in models include zeta, zero-inflated zeta, and logarithmic distributions, with a public extension interface for additional models. Inference is available by maximum likelihood, parametric Bayesian methods, the ordinary nonparametric bootstrap, and Rubin's Bayesian Bootstrap. The clothing-survey setting is described by Coulson, Buckleton, Gummer, and Triggs (2001) . Package: r-cran-fitscape Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitscape_0.1.0-1.ca2404.1_all.deb Size: 25088 MD5sum: 7d64b6cf3f20b2fc32116f29562447e7 SHA1: c8d811346cc2dcee2a83ef13bfe95ea3fe91888f SHA256: b730146a878b3e9319e3d2a2363cb41f5c336a44e75cba30067727d6757c276a SHA512: d806e214cbe14aaa6113e3b8fb2364f82b25d242a361e3298f6db3c9ab100b0d8126210bbbf0452d25f3a962d252e967f56f48fd8dcce6b92ff2fba2a4fa1009 Homepage: https://cran.r-project.org/package=fitscape Description: CRAN Package 'fitscape' (Classes for Fitness Landscapes and Seascapes) Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend. Package: r-cran-fitsio Architecture: all Version: 2.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fitsio_2.1-6-1.ca2404.1_all.deb Size: 140146 MD5sum: a5cfa04e22bbd5ab5a826c2e8c6c2b58 SHA1: aa1cf0543961e1bbd76921e81d8cc4e65efce707 SHA256: 88cac86da2b9d1ac692242f476a172bdbbd5b745550308781f1feae89e33fc71 SHA512: 90a7406cfd99f9d52c3b7aad14c0a1a4a7fb1b21055cd617b6a94fbd332d2cc4f4ac3d35089e0e69ba885f0aa99b689220057e2175d6227283553449a2ecc14d Homepage: https://cran.r-project.org/package=FITSio Description: CRAN Package 'FITSio' (FITS (Flexible Image Transport System) Utilities) Utilities to read and write files in the FITS (Flexible Image Transport System) format, a standard format in astronomy (see e.g. for more information). Present low-level routines allow: reading, parsing, and modifying FITS headers; reading FITS images (multi-dimensional arrays); reading FITS binary and ASCII tables; and writing FITS images (multi-dimensional arrays). Higher-level functions allow: reading files composed of one or more headers and a single (perhaps multidimensional) image or single table; reading tables into data frames; generating vectors for image array axes; scaling and writing images as 16-bit integers. Known incompletenesses are reading random group extensions, as well as complex and array descriptor data types in binary tables. Package: r-cran-fitter Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-shiny, r-cran-dplyr, r-cran-maxlik, r-cran-r.utils Suggests: r-cran-actuar, r-cran-ald, r-cran-benchden, r-cran-biasedurn, r-cran-bridgedist, r-cran-davies, r-cran-discreteinverseweibull, r-cran-discretelaplace, r-cran-discreteweibull, r-cran-emdbook, r-cran-emg, r-cran-envstats, r-cran-evd, r-cran-evir, r-cran-extdist, r-cran-extremefit, r-cran-fadist, r-cran-fattailsr, r-cran-fbasics, r-cran-fextremes, r-cran-flexsurv, r-cran-gambin, r-cran-gb, r-cran-genbinomapps, r-cran-generalizedhyperbolic, r-cran-gld, r-cran-gldex, r-cran-glogis, r-cran-gsm, r-cran-hermite, r-cran-hyperbolicdist, r-cran-kscorrect, r-cran-loglognorm, r-cran-marg, r-cran-mc2d, r-cran-minimax, r-cran-msm, r-cran-normallaplace, r-cran-normalp, r-cran-paretoposstable, r-cran-pearsonds, r-cran-poistweedie, r-cran-polyaaeppli, r-cran-qmap, r-cran-qrm, r-cran-reins, r-cran-renext, r-cran-revdbayes, r-cran-rmkdiscrete, r-cran-rmtstat, r-cran-sadists, r-cran-skellam, r-cran-skewhyperbolic, r-cran-skewt, r-cran-smr, r-cran-sn, r-cran-stabledist, r-cran-statmod, r-cran-trapezoid, r-cran-triangle, r-cran-truncnorm, r-cran-variancegamma Filename: pool/dists/noble/main/r-cran-fitter_0.2.0-1.ca2404.1_all.deb Size: 73062 MD5sum: c3fa91cec880d3fda62ce9931b1b8254 SHA1: a31f9b4ef7c3b5495b141da002094493295be7d0 SHA256: d36f08182e961e77aff7e817c0ed346e7b9f3391fc3be03ca3f57b3ea5d8e368 SHA512: 4933202f44a59b70da2e9101a9515c7e89a92bf7087e5d03a8d498a82910fbea8c4d487a2f68082064da81ba29ba02326dce18d371f78b617bce158d13cae909 Homepage: https://cran.r-project.org/package=fitteR Description: CRAN Package 'fitteR' (Fit Hundreds of Theoretical Distributions to Empirical Data) Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test. Package: r-cran-fitultd Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-adgoftest, r-cran-fitdistrplus, r-cran-assertthat, r-cran-mass, r-cran-purrr, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-fitultd_3.1.0-1.ca2404.1_all.deb Size: 60960 MD5sum: a7366352192b9e7eb159cfabd4b43fd0 SHA1: 7e0c6159b9e18f979f9e9e00d6771142fa673be9 SHA256: da67aa6779815956d25a1d0db6141356e28f8ae14ba369ff3c2547677615d9ab SHA512: 5b1edd200d84734822b6542c0a0bd6c3cf861211179ed792830474432bae16a6df6c7b4a806547fe204b39d26e0bcfcb1735ea851f2dd2660330434d2fed066a Homepage: https://cran.r-project.org/package=FitUltD Description: CRAN Package 'FitUltD' (Fit Univariate Mixed and Usual Distributions) Extends the fitdist() (from 'fitdistrplus') adding the Anderson-Darling ad.test() (from 'ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from 'stats' package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from 'mclust'). Package: r-cran-fitur Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-actuar, r-cran-e1071, r-cran-ggplot2, r-cran-goftest, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fitur_0.6.2-1.ca2404.1_all.deb Size: 125564 MD5sum: 3f766de165540337a763e75641f4e62a SHA1: b3774362419b73723c72d5ba4bb8e31011262292 SHA256: 4c3b9b3272909e8e2fabf4c9a18c33f12c52c3334062bb8cfda6b93528012c82 SHA512: 1c662ec98e74deac29bc50cedec5882898453efddc87d4f3832f4428f3f219a43b1efab9142e0754d1087f9a54531846f7ef60282e22d9e8a348480a090d2868 Homepage: https://cran.r-project.org/package=fitur Description: CRAN Package 'fitur' (Fit Univariate Distributions) Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages. Package: r-cran-fitvarmxid Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-openmx, r-cran-simstatespace Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitvarmxid_1.0.6-1.ca2404.1_all.deb Size: 234406 MD5sum: b08d08fcfbc6ebf6155e68718f86421c SHA1: bb87d01b4751c02961b240a64906db74700bc774 SHA256: 053334d9dcf584b338d1d6c146ecaf08dcd6e450694fccfc2d151ffc97776e3f SHA512: 9465415b6258569bce2cecbfe04044de0098a23241fa90cebb27b840ab2d8e622bb085827381d7c9039d4dd608b88b5daf256c6909e22c8e0d5b3ef44015cb62 Homepage: https://cran.r-project.org/package=fitVARMxID Description: CRAN Package 'fitVARMxID' (Fit the Vector Autoregressive Model for Multiple Individuals) Fit the vector autoregressive model for multiple individuals using the 'OpenMx' package (Hunter, 2017 ). Package: r-cran-fitverse Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2485 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-actuar, r-cran-mc2d, r-cran-evd, r-cran-sn, r-cran-fitdistrplus, r-cran-lmomco, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra Suggests: r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-plotly, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-jsonlite, r-cran-base64enc, r-cran-mass, r-cran-generalizedhyperbolic, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-fitverse_1.0-2-1.ca2404.1_all.deb Size: 1612948 MD5sum: b90e5756127f17ea0549fd2c84cd5631 SHA1: f84c982cde4ac7290ca8062de6b083f167589456 SHA256: c09d02709db7e9429d1ac395b3b000ad57809c6f9fb870dd15e657085f424cee SHA512: 9d92d844bfb69976eef1f56b35576043043c7acd67aee927ec7d32ddc5fcb66f31f3deaf8d654f75b04219534edd6fdeed3024753731bf2f6f13148e88c62f04 Homepage: https://cran.r-project.org/package=FitVerse Description: CRAN Package 'FitVerse' (Parametric Distribution Fitting and Analysis) Provides a unified, user-friendly interface for fitting parametric probability distributions to continuous univariate data. 'FitVerse' supports 52 distribution families spanning symmetric, right-skewed, heavy-tailed, bounded, and extreme-value shapes, and three estimation methods: Maximum Likelihood Estimation (MLE), Method of Moments (MOM), and L-Moments (L-MOM). Automatic best-fit selection is performed using AIC, BIC, and goodness-of-fit tests (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises (CvM)). Every fitted model produces a publication-quality diagnostic plot: a histogram overlaid with the fitted density curve and the estimated PDF formula annotated directly on the figure. An optional interactive version is produced via 'plotly'. Additional tools include bootstrap confidence intervals for parameter estimates and return levels, batch fitting across multiple columns for automated workflows and web-upload use cases, JSON serialisation for integration with 'Shiny' web applications, and automated HTML/PDF report generation. 'FitVerse' is designed to support data characterisation in survey sampling, hydrology, and actuarial workflows, where identifying the underlying distribution of a variable is a prerequisite for downstream modelling and inference. L-moment estimation follows Hosking (1990) and Hosking and Wallis (1997, ISBN:9780521430456). Model selection via AIC follows Akaike (1974) and via BIC follows Schwarz (1978) . Bootstrap confidence intervals follow Efron and Hastie (2016, ISBN:9781107149892). Package: r-cran-fitzroy Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2061 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2, r-cran-tibble, r-cran-glue, r-cran-cli, r-cran-lifecycle, r-cran-httr2, r-cran-janitor, r-cran-nanoparquet Suggests: r-cran-covr, r-cran-elo, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-spelling, r-cran-curl Filename: pool/dists/noble/main/r-cran-fitzroy_1.8.0-1.ca2404.1_all.deb Size: 1726282 MD5sum: da8a399f88e8dc73530e76a0a1393e01 SHA1: cbba4099a1558caabbea4c303b7ad0228fcde86b SHA256: d12174701beb4cf2bbe55ab9b675c599af5c10c21577f592174365239360bd6a SHA512: 99596d8d8a59d9e280149c2dddf3584282db1ba2326b250d7ea504e4ab4af93331c7c3741f5981f67c1c098fee3e86b8a85773bcc118d9e39090879ed50d50ac Homepage: https://cran.r-project.org/package=fitzRoy Description: CRAN Package 'fitzRoy' (Easily Scrape and Process AFL Data) An easy package for scraping and processing Australia Rules Football (AFL) data. 'fitzRoy' provides a range of functions for accessing publicly available data from 'AFL Tables' , 'Footy Wire' and 'The Squiggle' . Further functions allow for easy processing, cleaning and transformation of this data into formats that can be used for analysis. Package: r-cran-fivethirtyeight Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4995 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-tibble, r-cran-stringr, r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-fivethirtyeight_0.6.2-1.ca2404.1_all.deb Size: 4542586 MD5sum: 2c8b9524fcc14a05bb137816e84cf1d5 SHA1: 28795abea4e8609ce5ff29ad7e6c761bac79c791 SHA256: e39f45c06004cc307b6666b5b518d4b492374d15ad0d3b5ceb30d72d353b30be SHA512: 53ce92f6bbc4da4619822e05c91346d868d8df6a7f67e40ee25f7e6e5413c4e99afdb72d7cc51607b477b2bfc1c04079288859ba186bb4423f7a12b802fe49a2 Homepage: https://cran.r-project.org/package=fivethirtyeight Description: CRAN Package 'fivethirtyeight' (Data and Code Behind the Stories and Interactives at'FiveThirtyEight') Datasets and code published by the data journalism website 'FiveThirtyEight' available at . 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Package: r-cran-fixedcv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-atsa, r-cran-tseries, r-cran-dplyr, r-cran-lubridate, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-fixedcv_0.1.0-1.ca2404.1_all.deb Size: 746404 MD5sum: af8c630d36cae7db508c6e0fc09ecbdc SHA1: 510089090a18e156d103645ff927e16bf3a240ea SHA256: cc7850555bc7ca186193c246173d6a10c63f79c60287265f5a1a8806cc725d23 SHA512: aa563e0cfc03475e77cb5705ab1f242da616a6c36df4cd32fac391d3eba75ee3e558749dbba73893c266b02804ab77c5d5be5b54d3c109cbb33015742fd0238b Homepage: https://cran.r-project.org/package=fixedCV Description: CRAN Package 'fixedCV' (Fixed-b Critical Values for Robust Inference with Time SeriesData) Provides functions for computing fixed-b critical values and conducting robust inference procedures for time series data with unknown correlation structures. Implements long-run variance estimators using various kernel functions and lugsail transformations for improved finite-sample properties as described by Kurtz-Garcia and Flegal (2026) . 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A rich set of functions that helps with calculations of interest rates and fixed income. It has objects that abstract interest rates, compounding factors, day count rules, forward rates and term structure of interest rates. Many interpolation methods and parametric curve models commonly used by practitioners are implemented. 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This package was originally developed in the context of detecting mixture of cognitive processing strategies, based on observed response time distributions. The method is explain in more detail by Van Maanen, De Jong, Van Rijn (2014) and Van Maanen, Couto, Lebreton, (2016) . 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Maleyeff, L., Golchi, S., Moodie, E. E. M., & Hudson, M. (2024) "An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables" . 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It implements two main methods: MFKmL (Multidimensional Fréchet distance-based K-means for Longitudinal data), an extension of the K-means algorithm using the Fréchet distance originally developed in the 'kmlShape' package, adapted for multidimensional trajectories; and SFKmL (Sparse multidimensional Fréchet distance-based K-medoids for Longitudinal data), a K-medoids-based clustering algorithm that incorporates variable selection. These tools are designed to enhance clustering performance in high-dimensional longitudinal data settings, particularly those with time delays, variations in trajectory speed, irregular sampling intervals, and noise. This package implements methods derived from Kang et al. (2023) . 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The package's name derives from a play on the fact that lipid scrambling is also sometimes referred to as 'flipping'. The package is originally published as Cotton, R.J., Ploier, B., Goren, M.A., Menon, A.K., and Graumann, J. (2017). "flippant–An R package for the automated analysis of fluorescence-based scramblase assays." BMC Bioinformatics 18, 146. . 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The full variety of 'brms' formula-based effects structures are available to use in multiple classes of occupancy model, including single-season models, models with data augmentation for never-observed species, dynamic (multiseason) models with explicit colonization and extinction processes, and dynamic models with autologistic occupancy dynamics. Formulas can be specified for all relevant distributional terms, including detection and one or more of occupancy, colonization, extinction, and autologistic depending on the model type. Several important forms of model post-processing are provided. References: Bürkner (2017) ; Carpenter et al. (2017) ; Socolar & Mills (2023) . 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Package: r-cran-flowchart Architecture: all Version: 1.0.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gmisc, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-rlang, r-cran-cli Suggests: r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-flowchart_1.0.1-1.ca2404.2_all.deb Size: 220492 MD5sum: 2b937b21880e920ef7d6c780f625cd94 SHA1: 280ffbde0c91aa8aa97bf18520c6a591e5904bc9 SHA256: a53adf6ade9d188097f330c27ca9145170be921ca3d1afc21ef90a6b1533c3d5 SHA512: badb13d0dfc1ed1482ea2a0b708d771dfd8576c4f94d35e2eef99e200ad0cc1cf23560a6686d7d163c092a72e2fde542c16e4d4a18dde9cea5ca8edec006c7ee Homepage: https://cran.r-project.org/package=flowchart Description: CRAN Package 'flowchart' (Tidy Flowchart Generator) Creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way. Package: r-cran-flowcluster Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dbscan, r-cran-dplyr, r-cran-glue, r-cran-lwgeom, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-units, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-flowcluster_0.2.1-1.ca2404.1_all.deb Size: 1900414 MD5sum: 75fc454d3716e82505b23e8d49d4606b SHA1: 01a5675964aa9f5300bb46f1a5de5e68ebeec629 SHA256: 4a5f36f2152065c2c6e09a7f3ae230788a894ca9fc6a9078ddb6fc9a9a8a0872 SHA512: c91c1d47516426ff4ec6b47b5fab2f020ea48460baecb843384c3cae864344acef35d3c949d1f7f9382a35e148a48b36e7c1fb6439b27367f82b0db193d8440f Homepage: https://cran.r-project.org/package=flowcluster Description: CRAN Package 'flowcluster' (Cluster Origin-Destination Flow Data) Provides functionality for clustering origin-destination (OD) pairs, representing desire lines (or flows). This includes creating distance matrices between OD pairs and passing distance matrices to a clustering algorithm. See the academic paper Tao and Thill (2016) for more details on spatial clustering of flows. See the paper on delineating demand-responsive operating areas by Mahfouz et al. (2025) for an example of how this package can be used to cluster flows for applied transportation research. Package: r-cran-flowermate Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flowermate_1.1-1.ca2404.1_all.deb Size: 144170 MD5sum: 40875c2b9664a9a6caece055900bd817 SHA1: fdd3825e91e8f03f11e39e7dee6b4af56c1f7f0e SHA256: 55e2bb0b508aa91d37dba4e3be534d47880843b8201ca271f3d72a82414b6be3 SHA512: e9352f97d96024eb81a361ce54f4bffd6c510a2dc8bc0666369a2f0aa3078097d8b50d6c146135aa8056f5e17560c5b369d6a68233328e4ec4f91bd09c759027 Homepage: https://cran.r-project.org/package=FlowerMate Description: CRAN Package 'FlowerMate' (Reciprocity Indices for Style-Polymorphic Plants) Computes unidimensional and multidimensional Reciprocity and Inaccuracy indices. These indices are applicable to common heterostylous populations and to any other type of stylar dimorphic and trimorphic populations, such as in enantiostylous and three-dimensional heterostylous plants. Simón-Porcar, V., A. J. Muñoz-Pajares, J. Arroyo, and S. D. Johnson. (in press) "FlowerMate: multidimensional reciprocity and inaccuracy indices for style-polymorphic plant populations." Package: r-cran-flowmapblue Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5094 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-quarto Filename: pool/dists/noble/main/r-cran-flowmapblue_0.0.2-1.ca2404.1_all.deb Size: 1681924 MD5sum: 8b5a67a5c6e032ac3f1c213f3c7086cb SHA1: 3de43a4d4b89ae171d0307f0334b54ba861f629d SHA256: 78e30bd254a13037b4443eafe05b403263c94f7c5a980f1935cb482652c26c6c SHA512: 9eada416f8dafe990492aa528c6b5955fdcf0f54c2c4a0d412a81fbd437e92acc2611ae79c35be396c81add0460a562c8d77c7386aea458c835bc169a0a856e4 Homepage: https://cran.r-project.org/package=flowmapblue Description: CRAN Package 'flowmapblue' (Flow Map Rendering) Create interactive flow maps using 'FlowmapBlue' 'TypeScript' library , which is a free tool for representing aggregated numbers of movements between geographic locations as flow maps. It is used to visualize urban mobility, commuting behavior, bus, subway and air travels, bicycle sharing, human and bird migration, refugee flows, freight transportation, trade, supply chains, scientific collaboration, epidemiological and historical data and many other topics. The package allows to either create standalone flow maps in form of 'htmlwidgets' and save them in 'HTML' files, or integrate flow maps into 'Shiny' applications. Package: r-cran-flowmapper Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-forcats, r-cran-scales, r-cran-purrr, r-cran-sfheaders, r-cran-sf, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-flowmapper_0.1.4-1.ca2404.1_all.deb Size: 953146 MD5sum: 4ea79fa54c562ba76711ac3c21df27b9 SHA1: 310878abcba8ef1264f6d4ad808fc376e2d724bc SHA256: dbf1a90b5b23bf5b86494b9ef3c8571e03df12416d75e92fd83bbb204e062050 SHA512: 35f594d17478dd3497f88a65862d6ac1a0ec8f27adfe54c79a5089d8f36c3a10c12069ac2e6594476fe005174a87042b6fd7fa9a250a620fc8fe31f24f39204f Homepage: https://cran.r-project.org/package=flowmapper Description: CRAN Package 'flowmapper' (Draw Flows (Migration, Goods, Money, Information) on 'ggplot2'Plots) Adds flow maps to 'ggplot2' plots. The flow maps consist of 'ggplot2' layers which visualize the nodes as circles and the bilateral flows between the nodes as bidirectional half-arrows. Package: r-cran-flowml Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abcanalysis, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-fastshap, r-cran-furrr, r-cran-future, r-cran-magrittr, r-cran-optparse, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rjson, r-cran-rlang, r-cran-rsample, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vip Suggests: r-cran-ada, r-cran-adabag, r-cran-arm, r-cran-bartmachine, r-cran-bst, r-cran-c50, r-cran-catools, r-cran-class, r-cran-cubist, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-evtree, r-cran-fastica, r-cran-foreach, r-cran-frbs, r-cran-gam, r-cran-gbm, r-cran-ggplot2, r-cran-glmnet, r-cran-h2o, r-cran-hda, r-cran-ipred, r-cran-keras, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-knitr, r-cran-kohonen, r-cran-lars, r-cran-leaps, r-cran-liblinear, r-cran-logicreg, r-cran-mass, r-cran-matrix, r-cran-mboost, r-cran-mda, r-cran-mgcv, r-cran-monomvn, r-cran-neuralnet, r-cran-nnet, r-cran-nnls, r-cran-pamr, r-cran-partdsa, r-cran-party, r-cran-partykit, r-cran-penalized, r-cran-pls, r-cran-plyr, r-cran-proxy, r-cran-quantregforest, r-cran-randomforest, r-cran-ranger, r-cran-rferns, r-cran-rmarkdown, r-cran-rpart, r-cran-rrcov, r-cran-rrcovhd, r-cran-rsnns, r-cran-rweka, r-cran-sda, r-cran-shapviz, r-cran-spls, r-cran-superpc, r-cran-vgam, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-flowml_0.1.3-1.ca2404.1_all.deb Size: 308030 MD5sum: c0b2b3d3324e4d8fb27cc720ae7386d3 SHA1: 40060912c93f0e3b36643df90e5f6bf9f8ceb200 SHA256: 56fceaf9ac19f4c13f6a67f5be9f5c2d6cff8accf99ce5d689bb7dce89bd9a90 SHA512: 7bfbb4981e8101fe483a9916074a9bd34f0fd754eae1a4d8843c26be91fa08d4cde650fc18efe2178e38d0a7762c14517cedfaae62c63e2e3abc04dbfd02aecf Homepage: https://cran.r-project.org/package=flowml Description: CRAN Package 'flowml' (A Backend for a 'nextflow' Pipeline that PerformsMachine-Learning-Based Modeling of Biomedical Data) Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' ) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning. Package: r-cran-flowr Architecture: all Version: 0.9.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2877 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-params, r-cran-diagram, r-cran-whisker, r-cran-readr Suggests: r-cran-reshape2, r-cran-knitr, r-cran-testthat, r-cran-funr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flowr_0.9.11-1.ca2404.1_all.deb Size: 639788 MD5sum: b56dcd3dfe941c41a947f310f0ed50e2 SHA1: d02bcab79d6dc7863d376e502330d4364a4e908e SHA256: af6a897fcd2efab943e5f65674e41e075cf2702281775a791539b9924bdf85c6 SHA512: 444dd3a01bd614030b1f6980d547fcd04e491a1e9e65a4cd3052f2c7e2b3b14f9e3e959a557e895209fd31e76f341a56e004091782be7f3e78c7de7d97ad3f1a Homepage: https://cran.r-project.org/package=flowr Description: CRAN Package 'flowr' (Streamlining Design and Deployment of Complex Workflows) This framework allows you to design and implement complex pipelines, and deploy them on your institution's computing cluster. This has been built keeping in mind the needs of bioinformatics workflows. However, it is easily extendable to any field where a series of steps (shell commands) are to be executed in a (work)flow. Package: r-cran-flowregenvcost Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-flowregenvcost_0.1.2-1.ca2404.1_all.deb Size: 120636 MD5sum: 5e6aed97abf4cfa119c0938466f1807a SHA1: 0655ab174daba44473d66c794604e25c2c1ed35a SHA256: 079308f1d8021c52c9560d9c8c4c901f278f7083be904384914435b890762fbe SHA512: 77dcf43dce18fbab13b8f76a161ec44ba211417caf5487b26a97f6e0857abc0b4ab47930c95120395d33c95ede2ee65231ba774dc79d6f8243e85a620c4038c6 Homepage: https://cran.r-project.org/package=FlowRegEnvCost Description: CRAN Package 'FlowRegEnvCost' (The Environmental Costs of Flow Regulation) An application to calculate the daily environmental costs of river flow regulation by dams based on García de Jalón et al. (2017) . Package: r-cran-flowscreen Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zyp, r-cran-changepoint, r-cran-evir Filename: pool/dists/noble/main/r-cran-flowscreen_2.1-1.ca2404.1_all.deb Size: 1624456 MD5sum: 5e78528949d001b99edfb77308db1914 SHA1: 44fde259931e3f8bb080f7eec0c352115ed7112a SHA256: bfa16dcba62d2d743c1ee9806492fb28692ef5d824edd5f5428bf2ddef479869 SHA512: beaa394bbfdf7dc086b5b4dc5d6c6ca0da5f1e45e67ec45926124d2727caf20bcd172beb1952b5866dead7f8e97969e9c003dda7b5ebde47de3b61af6becfc43 Homepage: https://cran.r-project.org/package=FlowScreen Description: CRAN Package 'FlowScreen' (Daily Streamflow Trend and Change Point Screening) Screens daily streamflow time series for temporal trends and change-points. This package has been primarily developed for assessing the quality of daily streamflow time series. It also contains tools for plotting and calculating many different streamflow metrics. The package can be used to produce summary screening plots showing change-points and significant temporal trends for high flow, low flow, and/or baseflow statistics, or it can be used to perform more detailed hydrological time series analyses. The package was designed for screening daily streamflow time series from Water Survey Canada and the United States Geological Survey but will also work with streamflow time series from many other agencies. Package update to version 2.0 made updates to read.flows function to allow loading of GRDC and ROBIN streamflow record formats. This package uses the `changepoint` package for change point detection. For more information on change point methods, see the changepoint package at . Package: r-cran-flowtracer Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-comprehenr, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-data.table, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-flowtracer_0.1.1-1.ca2404.1_all.deb Size: 236676 MD5sum: f2d4612ad056b2149516de8be4b1cd22 SHA1: 08ca50af1605951206c6e6f9c33e3538fc858d94 SHA256: ad68df0cd6d9696cb1326a4d03ee80fe521b199df58cfa95f0817b895d9ebc75 SHA512: d44736ad22f2bc61eafae29620302a4a0f54f0679c9b5938cfdaef9c30ae3499fd176b1c52826e1a8c2019cb92a36201dd3fa0d9a8423ca714129a68dd225f07 Homepage: https://cran.r-project.org/package=flowTraceR Description: CRAN Package 'flowTraceR' (Tracing Information Flow for Inter-Software Comparisons in MassSpectrometry-Based Bottom-Up Proteomics) Useful functions to standardize software outputs from ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant on precursor, modified peptide and proteingroup level and to trace software differences for identifications such as varying proteingroup denotations for common precursor. Package: r-cran-flps Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-mirt, r-cran-mass, r-cran-mvtnorm, r-cran-ggplot2, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan, r-cran-data.table Filename: pool/dists/noble/main/r-cran-flps_1.1.0-1.ca2404.1_all.deb Size: 819396 MD5sum: 848badd4fe8e02cae4bd67bc4fa1c310 SHA1: 5531ccd4092858c260e15c943ed6cc47fbc599db SHA256: f05f5b290f72e4e2817f247fbb5efcb1b9a4654fae5fb40a5977d2844a5c8069 SHA512: 2997a09daa513a03af0f7799174f180e9ee075c8cd3ba7dd74e8c5caa6c2faad7202c6e4f1015c43479a22f49d2eee0242d2d99c27745b742217ba4cf48486d6 Homepage: https://cran.r-project.org/package=flps Description: CRAN Package 'flps' (Fully-Latent Principal Stratification) Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). . This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036. Package: r-cran-flr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat Filename: pool/dists/noble/main/r-cran-flr_1.0-1.ca2404.1_all.deb Size: 71852 MD5sum: 8faaadac7705d839c5be6fb397c99fde SHA1: 125e3cb059ed8dcd8a09ee99f30fc23fbb800151 SHA256: 1337ee4aee5ecd1c255cd4236cc752955f91bf5738616538a56279e32a8f3684 SHA512: b8deac0973e11a0381b8654d4582337eafc1fed90bc100f1c416bc9a06eed7a6c1bc82c34ff1992bbfc2ca80305effa4289ede7cd67c5f064bad263df1d65fa2 Homepage: https://cran.r-project.org/package=FLR Description: CRAN Package 'FLR' (Fuzzy Logic Rule Classifier) FLR algorithm for classification Package: r-cran-fluffy Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7 Suggests: r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-lobstr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-fluffy_1.0.1-1.ca2404.1_all.deb Size: 261868 MD5sum: 9aa936e9b5dc5c08cca50c50ec90308a SHA1: 6646fe502f121e69e694786bafe713f33c834daf SHA256: 9827a0559617b70e18efef744c70d01f3f1c98894d6cee60cfca8134fd86ceb9 SHA512: 497a4d7b54a6299bd58f1c01b3a8c6a9a12ae24134639523f07ffe081f8944c3478148daf77dbb86f5252ecbdf4d6a6e2248e9796b8ac7698d961912be67e720 Homepage: https://cran.r-project.org/package=fluffy Description: CRAN Package 'fluffy' (Schema-Based Validation of 'R' Objects with User-Defined Rules) A schema-based validation framework for 'R' objects using user-defined rules. Provides three 'S7' classes 'Registry', 'Schema', and 'Validator' to manage rules, define list-based schemas, and validate data in a flexible and extensible manner. Package: r-cran-fluidigm Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-reshape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-digest Filename: pool/dists/noble/main/r-cran-fluidigm_0.2-1.ca2404.1_all.deb Size: 428044 MD5sum: 84c6f071d757abc5d16842867cadfcc8 SHA1: 2d1bec07782b27b6bbac20fb94fca17bdf59f1de SHA256: 427ee9339c84cd28a29de8ce8833d9df4653404e92d06ddcb80c600d9d60c08d SHA512: 7825087770028aab77607bb2c81d4ea44c0da599894361bf18c6ef7edf0a3426044ce366f496f110f15b40ad3865e9804615f3e01d938a37eb4de5efed5476b4 Homepage: https://cran.r-project.org/package=Fluidigm Description: CRAN Package 'Fluidigm' (Handling Fluidigm Data) Designed to streamline the process of analyzing genotyping data from Fluidigm machines, this package offers a suite of tools for data handling and analysis. It includes functions for converting Fluidigm data to format used by 'PLINK', estimating errors, calculating pairwise similarities, determining pairwise similarity loci, and generating a similarity matrix. Package: r-cran-flumodl Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dlnm, r-cran-mvmeta, r-cran-tsmodel Filename: pool/dists/noble/main/r-cran-flumodl_0.0.3-1.ca2404.1_all.deb Size: 185846 MD5sum: 3792ff5eaf0702326932bc93960cb9a2 SHA1: e82449900d048ea7d0585d66bc0c2036f6b1a0a2 SHA256: 05b8f34ce45c64e81070a2f8e24536337665b315209257f00fef787daa2c3117 SHA512: 246dc1eccd99e0a19369e700c8299843d9809316788490310a95bad712b5c8ba526c7380d47b14eb615a04cca3d15612109632235aeffc4747f90629c0024b4c Homepage: https://cran.r-project.org/package=FluMoDL Description: CRAN Package 'FluMoDL' (Influenza-Attributable Mortality with Distributed-Lag Models) Functions to estimate the mortality attributable to influenza and temperature, using distributed-lag nonlinear models (DLNMs), as first implemented in Lytras et al. (2019) . Full descriptions of underlying DLNM methodology in Gasparrini et al. (DLNMs), (attributable risk from DLNMs) and (multivariate meta-analysis). Package: r-cran-fluorojip Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluorojip_0.1.1-1.ca2404.1_all.deb Size: 498378 MD5sum: e2dd043bfa3ea8cff12fb05fcf6b1f86 SHA1: b5669765ff743a0d9446c49affa7ba344b660c1f SHA256: 58a4af0bbdb4c0d31230e5843401c82bbf33ecdd9eb7f17cb6a3104bbfdbcd7b SHA512: 4c3268f2f3cbd2534b8d5df4d47ee352b86da611377b873418c029b479c269f31ddaa172abac6814b864c3925e0ba2a68a8e69c5be7c5d33f22144e898636688 Homepage: https://cran.r-project.org/package=fluorojip Description: CRAN Package 'fluorojip' (Analysis of Chlorophyll a Fluorescence Transient Parameters) Computes chlorophyll a fluorescence transient parameters from fluorescence summary data, including minimum and maximum fluorescence, selected transient steps, and area. Provides standard photosynthetic performance indices and fluxes per reaction center and per cross section. Includes helpers to read exported trace tables in supported 'csv' formats and validation workflows based on bundled example files, as well as visualization tools and an interactive 'shiny' interface. The implemented calculations are based on Strasser et al. (2004) and Stirbet and Govindjee (2011) . Package: r-cran-fluosurv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1644 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-fluosurv_1.0.0-1.ca2404.1_all.deb Size: 982952 MD5sum: 74cbff159a6d5d524709afe74ac26c5a SHA1: be84d8b84bcd36fe2f7a3edf1d7a491fbbc3e45f SHA256: 40dcde52af758c7f933b8e9a5d2fa44db7f582e79545fcea5f64de8f71dade88 SHA512: 6126ffa4c8615990f572dda6908dc3287e9f44a6489097b61841877b3474c5939d5d890a0d08d26fb21e93a9f7f0f0a0b419b89fe5fd2d8a67258be4875c96a5 Homepage: https://cran.r-project.org/package=fluoSurv Description: CRAN Package 'fluoSurv' (Estimate Insect Survival from Fluorescence Data) Use spectrophotometry measurements performed on insects as a way to infer pathogens virulence. Insect movements cause fluctuations in fluorescence signal, and functions are provided to estimate when the insect has died as the moment when variance in autofluorescence signal drops to zero. The package provides functions to obtain this estimate together with functions to import spectrophotometry data from a Biotek microplate reader. Details of the method are given in Parthuisot et al. (2018) . Package: r-cran-fluspect Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-fluspect_1.0.0-1.ca2404.1_all.deb Size: 54430 MD5sum: 15d280a3574f823a4fd82cc56074586f SHA1: 293390b2058a900c7422a24d215f440891471010 SHA256: bbb07e91385031dae96db2755a9c2c8cddeb11cecf8987964490fd6aa0f6efe4 SHA512: 5535a473ec071f78aace47ef79dcefb7d3a9edf74c3fbabc0df8343f941304d967a83ebcfe597d2f48768fd743f8cf8e7bda23ad7443e3e352c6dc7b6b690a1f Homepage: https://cran.r-project.org/package=fluspect Description: CRAN Package 'fluspect' (Fluspect-B) A model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) . Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II. Package: r-cran-flux Architecture: all Version: 0.3-0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-catools Filename: pool/dists/noble/main/r-cran-flux_0.3-0.1-1.ca2404.1_all.deb Size: 603770 MD5sum: 8190f13c3c481d9850429805cadc9c67 SHA1: eebf2b6ee91635277866ce8528ac518dcb85fa40 SHA256: 4552d67b9b9fc37cd026b3d7c2f77eaf0ab879be57788cafdb2d8992e0feff85 SHA512: 6b6b56724a4df850204bd18791bc83a99008743f3494bcf5d4d34076065c1890efb1b988be0ecdd1b0fbabf5aa6d9603cafc489869726deed82bf15e384c75bb Homepage: https://cran.r-project.org/package=flux Description: CRAN Package 'flux' (Flux Rate Calculation from Dynamic Closed Chamber Measurements) Functions for the calculation of greenhouse gas flux rates from closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control in details is wanted. Additionally plot and preparation functions as well as functions for modelling gpp and reco are provided. Package: r-cran-fluxcore Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-future, r-cran-future.apply, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxcore_2.1.0-1.ca2404.1_all.deb Size: 488522 MD5sum: b4d72c329a5b0efced86142151dc656f SHA1: 8ac8a60163a517416fc964730f7d68a912af1d7f SHA256: 757be17a3fcbb7ca22c9f2bdb051e252140c3a5ce7b04086fed7b0cb5f751c90 SHA512: 117e92def0aeaa3b3556727c19d0c13aee68646c7a863bfe299bc5c9dc1b43a3fc1303350d1332130228c7432bf4652df7ce1d70675a782edc04fa540b8cb09b Homepage: https://cran.r-project.org/package=fluxCore Description: CRAN Package 'fluxCore' (Probabilistic Simulation of Single-Entity Systems in IrregularTime) A foundation for probabilistic simulation of single-entity systems in which events occur at irregular times and each event updates only a small, sparse subset of the entity's state. Models are assembled from a declared schema and a bundle of callback functions for event proposal, state transition, and stopping, with their contracts validated before simulation. Supports competing event processes, schema-declared decision points with user-supplied policies, typed parameter draws for representing uncertainty, and optional trajectory recording for auditing simulated decisions. Designed to be domain agnostic: this package contains no model of any particular system, only the scaffolding for building one. Package: r-cran-fluxfinder Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-jsonlite, r-cran-lubridate, r-cran-mass Suggests: r-cran-gasfluxes, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fluxfinder_1.2.2-1.ca2404.1_all.deb Size: 194066 MD5sum: 6e46dfb82b6057f9a37abae15eb83b04 SHA1: 0f652456eca3e940c5e5c82f50af7cebe0ced17c SHA256: 31298e1059bfb60cd53d26722f14ffb517aa7e06982071325eb0ee1c61c4d2df SHA512: 29b2cf8d62ef16954d655802dcda1b3969ed4af37b6ced64414a8b43f9650c8e33d1569c58d0b384fc27861d064d428c3432c5abed05bdf9f08ba42a8b8f4339 Homepage: https://cran.r-project.org/package=fluxfinder Description: CRAN Package 'fluxfinder' (Parsing, Computation, and Diagnostics for Greenhouse GasMeasurements) Parse static-chamber greenhouse gas measurement files generated by a variety of instruments; compute flux rates using multi-observation metadata; and generate diagnostic metrics and plots. Designed to be easy to integrate into reproducible scientific workflows. Package: r-cran-fluxfixer Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gsignal, r-cran-lubridate, r-cran-magrittr, r-cran-ranger, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxfixer_1.1.0-1.ca2404.1_all.deb Size: 1497290 MD5sum: d6f0823443d627d5599ff61b864c1e70 SHA1: 48fa22c9c5e79153b8651fe3a1399935b32f9f5d SHA256: f072fef78b5dda33c38adb1d7352519ba03c16cab37093c18875489d80bc9bed SHA512: c2c05dcc221080a5932126035c84cdfcb122de16ce70349d674b77054ca09b101591a621375fb13947f5692a7dfed338ac534a3b49868cb868b1a3366f515f96 Homepage: https://cran.r-project.org/package=fluxfixer Description: CRAN Package 'fluxfixer' (Advanced Framework for Sap Flow Data Post-Process) Provides a flexible framework for post-processing thermal dissipation sap flow data using statistical methods and machine learning. This framework includes anomaly correction, outlier removal, gap-filling, trend removal, signal damping correction, and sap flux density calculation. The functions in this package can also apply to other time series with various artifacts. Package: r-cran-fluxible Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3526 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-haven, r-cran-lubridate, r-cran-rlang, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-zoo, r-cran-progress, r-cran-purrrlyr, r-cran-tidyselect, r-cran-lifecycle, r-cran-forcats, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-tidyverse, r-cran-fs, r-cran-licoread, r-cran-readr Filename: pool/dists/noble/main/r-cran-fluxible_1.4.0-1.ca2404.1_all.deb Size: 2561078 MD5sum: 6a42430357785e45d093f83068ff116e SHA1: 1f9f327c4f707e557ccb033999301ce83a37fb6d SHA256: 744fa703f90eb83cb8081c12b73271f5ef9aa9bee8e0e4313a2d79149c95130e SHA512: f3730b58f493da626badb10eaba8f81b748cd4badbf2fac5757c4964ef59761f2ae4cc47d7d45c27ea7c66212a20e989ea443a58a812a7e478878f229ce15b51 Homepage: https://cran.r-project.org/package=fluxible Description: CRAN Package 'fluxible' (Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup) Toolbox to process raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Organized with one function per step, maximizing user flexibility and backwards compatibility. Different models to estimate the fluxes from the raw data are available: exponential as described in Zhao et al (2018) , exponential as described in Hutchinson and Mosier (1981) , quadratic, and linear. Other functions include quality assessment, plotting for visual check, calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...), gross primary production and transpiration rate calculation, and light response curves. Package: r-cran-fluxpoint Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmatrix, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-nnls, r-cran-pracma, r-cran-simdesign Filename: pool/dists/noble/main/r-cran-fluxpoint_0.1.2-1.ca2404.1_all.deb Size: 133852 MD5sum: 1b6f6ce6bf7ce56ab2306b517f1799eb SHA1: 8e2fcebf22a4d554faa4e35110ac794f465a0330 SHA256: a136aaa1d2b0f46d05e31003fb3ea916c38a2e20bffd32d5a147c4862611b1ae SHA512: 957ed035ccb0a8da05417ccf66b0797d446d2f5177886bdffc828dac241b4d5682cb5625277c80b3e14ab54b1af0465e55186503c5de00f76f6a04df98387eb6 Homepage: https://cran.r-project.org/package=FluxPoint Description: CRAN Package 'FluxPoint' (Change Point Detection for Non-Stationary and Cross-CorrelatedTime Series) Implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. The methodology is based on a model in which each component has a fluctuating mean represented by a random walk with occasional abrupt shifts, combined with a stationary vector autoregressive structure to capture temporal and cross-sectional dependence. The framework is broadly applicable to correlated multivariate sequences in which large, sudden shifts occur in all or subsets of components and are the primary targets of interest, whereas small, smooth fluctuations are not. Although random walks are used as a modeling device, they provide a flexible approximation for a wide class of slowly varying or locally smooth dynamics, enabling robust performance beyond the strict random walk setting. Package: r-cran-fluxseparator Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggpubr, r-cran-hmr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-shiny, r-cran-shinydashboard, r-cran-stringr, r-cran-tidyr, r-cran-ttr, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxseparator_2.0.0-1.ca2404.1_all.deb Size: 171300 MD5sum: 5f9edda13d36e39166e6d172d3b369a2 SHA1: 47a22514dab0551792a3fbbcfa90ee7d5b1dc8fb SHA256: 265317ef90c4dbf608707706f613c21f94254d4573a40383bd88b210296a8534 SHA512: ed72e4ae3a272caf65fbb3ddb93bfa82e2355decd63e57f99c5018d788b0f9b1a837cb28cd09d896c57684dbf2445d6a19586e34536d9910555277434f5b6b31 Homepage: https://cran.r-project.org/package=FluxSeparator Description: CRAN Package 'FluxSeparator' (Separation of Diffusive and Ebullitive Fluxes) Separates diffusive and ebullitive (bubble) fluxes from continuous concentration measurements using a running variance approach. Ebullitive events are identified when the running variance exceeds a user-set threshold. Diffusive fluxes are calculated via linear regression on the non-ebullitive portion of the data. See Sø et al. (2024) for details. Package: r-cran-fluxtools Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plotly, r-cran-readr, r-cran-shiny, r-cran-tibble Suggests: r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-shinywidgets, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxtools_0.7.1-1.ca2404.1_all.deb Size: 271132 MD5sum: 43fe2b805394d9c03910706934f157da SHA1: d3709b6a0e286a75457eec8c968defd41f12fed9 SHA256: 197d181e7f576a487c70cbe6575bf9ebb57b5002cf32cdc6051de948faf99c36 SHA512: 30f173dad395e32efcc41d3a160152560068e0ef301d89dd774a9606bbd6b442bba4696d64b5b6a18f3c210a816e4e1c889db00c998723b028fd84fecccfa5db Homepage: https://cran.r-project.org/package=fluxtools Description: CRAN Package 'fluxtools' (A 'shiny' App for Reproducible QA/QC of Eddy Covariance Data) An interactive 'shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) . Package: r-cran-fluxweb Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fluxweb_2.0.1-1.ca2404.1_all.deb Size: 265472 MD5sum: e75b58a6d44036d904bbe513684c71e1 SHA1: 0af319805132446ad08399a486a6f884c4a5d4be SHA256: d7b6f2d87927f4997fefd318fcb94ee8b35f33d86419240310a5adb6956f8409 SHA512: 10b03e75697d7f21a21d066503a450a27e90b474e3ba9172a58a190e49ee37dc9b9637bfef1902c5e59f7c524516b233af6386a9f191ca0fb7a64db82a13bff2 Homepage: https://cran.r-project.org/package=fluxweb Description: CRAN Package 'fluxweb' (Estimate Energy Fluxes in Food Webs) Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) to calculate energy fluxes, which are also used to calculate equilibrium stability. Package: r-cran-fma Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fma_2.5-1.ca2404.1_all.deb Size: 476538 MD5sum: 021966d28d099c40867eba1eb7a5ff34 SHA1: 7a5e19a733d9cac3a80793a651523550844d0616 SHA256: d9764f9f125df1cbbaa91fda99b83f273e36b9f2ed6255a88a47dc1d34c9b717 SHA512: cbd827fcf0885b575deea7d7ca99e27c5e68e46c319e48a01562162e7551e251bbddded928c08a498c16ff65855fa32492394b6f4cf2c8b6b133ace35636db55 Homepage: https://cran.r-project.org/package=fma Description: CRAN Package 'fma' (Data Sets from "Forecasting: Methods and Applications" byMakridakis, Wheelwright & Hyndman (1998)) All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) . Package: r-cran-fmadist Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-actuar, r-cran-envstats, r-cran-extradistr, r-cran-mass, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-fmadist_0.1.2-1.ca2404.1_all.deb Size: 40938 MD5sum: c44cb05a48d48027af09cb32aaa1a4a1 SHA1: 9e606044e680396d8ed3c40b1832f5e86aae836a SHA256: b1ab411e284c83d5d7938f1a7eeb8779bf2baf2b3a1e265e32085c907d15f715 SHA512: ed285a291b5d881f0a575480d06719bfe82b33ee1dab53c538b58bf2df0f473909903832f943657c38d8fdb16e826c9e216114038088948c7977cd8663a52f75 Homepage: https://cran.r-project.org/package=FMAdist Description: CRAN Package 'FMAdist' (Frequentist Model Averaging Distribution) Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586. Package: r-cran-fmat Architecture: all Version: 2026.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-data.table, r-cran-stringr, r-cran-forcats, r-cran-rvest, r-cran-psych, r-cran-irr, r-cran-glue, r-cran-crayon, r-cran-cli, r-cran-purrr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-brucer, r-cran-psychwordvec, r-cran-text, r-cran-sweater, r-cran-nlme Filename: pool/dists/noble/main/r-cran-fmat_2026.1-1.ca2404.1_all.deb Size: 116424 MD5sum: a7e1fbfdd653b778bf5e856af377fd9a SHA1: fa3f475915a2a82f178a23ef292acb431381c84f SHA256: 573f24a35636f36e5566072a63b9d48cabf6b012c881e6626dd8859bfddf95ed SHA512: 2f7a14cb8d026a3bcc8cc837140a471ef468ccd95fd08f69c2fc2606a6dc61050c32456b2ae6eb51f3cad76a572273b1e070e390a2718b37676746cc140201c8 Homepage: https://cran.r-project.org/package=FMAT Description: CRAN Package 'FMAT' (The Fill-Mask Association Test) The Fill-Mask Association Test ('FMAT') is an integrative, probability-based social computing method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositional semantic representations in natural language. Supported language models include 'BERT' and its variants available at 'Hugging Face' . Methodological references and installation guidance are provided at . 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This tools contain functions that facilitate analysis in atmospheric chemistry (especially in ozone pollution). Some functions of time series are also applicable to other fields. For detail please view homepage. Scientific Reference: 1. The Hydroxyl Radical (OH) Reactivity: Roger Atkinson and Janet Arey (2003) . 2. Ozone Formation Potential (OFP): , Zhang et al.(2021) . 3. Aerosol Formation Potential (AFP): Wenjing Wu et al. (2016) . 4. TUV model: . 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Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), snaive() and arfima() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) ), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy. Package: r-cran-forecastingensembles Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distributional, r-cran-doparallel, r-cran-dplyr, r-cran-fable, r-cran-fabletools, r-cran-fable.prophet, r-cran-feasts, r-cran-fracdiff, r-cran-ggplot2, r-cran-gt, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-urca Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-forecastingensembles_0.5.1-1.ca2404.1_all.deb Size: 2007186 MD5sum: e3375ba8b00db53d95a4ef6c46ca6f28 SHA1: 4b5ddc78d7b2301516774b93679003409d583bce SHA256: 02ab679c49d8b7a5262f2b915fa6a9ae99e265bd59a92162e6db95f91a3ff673 SHA512: 6435962f70e578075ae0e0996f5a1f553cd90f782cd24062b2f5cfd9c68ef897a5866c0feb8c1292bee88d8f8bba9cb5d72a13fb2f2045a849d4045645f2edd3 Homepage: https://cran.r-project.org/package=ForecastingEnsembles Description: CRAN Package 'ForecastingEnsembles' (Time Series Forecasting Using 23 Individual Models) Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data. For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon. Package: r-cran-forecastlsw Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-locits, r-cran-wavethresh, r-cran-lpacf, r-cran-forecast Filename: pool/dists/noble/main/r-cran-forecastlsw_1.1.1-1.ca2404.1_all.deb Size: 119574 MD5sum: dfd0d52ed11618f5495929b35b1411b1 SHA1: f78e59bda61a58d74a333908fb3eafcfe7554fcd SHA256: ab3b462238b65b0c59e707f7bf01a29e871167a72479dc1696b1eea0fc803c79 SHA512: 36d12e3ca7720c6d063edc461c2b7bf07b1637a43baaff39283984de0ff97174d0c361987c068dab387b6b1a3d9304366fde8ef0ba69492a0add8970f3ee18bf Homepage: https://cran.r-project.org/package=forecastLSW Description: CRAN Package 'forecastLSW' (Forecasting Routines for Locally Stationary Wavelet Processes) Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series. Package: r-cran-forecastml Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3089 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-lubridate, r-cran-ggplot2, r-cran-future.apply, r-cran-purrr, r-cran-data.table, r-cran-dtplyr, r-cran-tibble Suggests: r-cran-glmnet, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-xgboost, r-cran-randomforest, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-forecastml_0.9.0-1.ca2404.1_all.deb Size: 1332938 MD5sum: b70c5bed19a2490b7088af26f483e7be SHA1: e7dc05f7c3355ee489833c367444a3056fe5b642 SHA256: f449b868bf6640d5e3054f359f54f2ffdd2ec2664cce9eb4865558db3049c966 SHA512: d49c994712a78fb7dcf38d06af438b3cf6f4c28aa781f8e0f745d73d0a2cbdbeaf4a8fdbff5469cf028d54ce249333e2665696c064af28cb89d342e45c067abd Homepage: https://cran.r-project.org/package=forecastML Description: CRAN Package 'forecastML' (Time Series Forecasting with Machine Learning Methods) The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" . 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Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) , and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) , alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) theoretically characterize the size-power frontier for the Gaussian multivariate location model. 'ForeComp' computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice. 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Scene points are mapped from field measurements (baseline, triangulation, polar). Shooting incidents are reconstructed from bullet defects with explicit uncertainty, following the methods described in Haag and Haag (2020, ISBN:9780128193976) and Hueske (2015, ISBN:9781498707664). Evidence and custody are logged in a structured form, and reproducible scene reports are rendered to Word, PDF or HTML. 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This packages offers a user-friendly solution to quickly obtain datasets such as forest height, forest types, tree species under various climate change scenarios, or land use data among others. Package: r-cran-forestdisc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-nloptr, r-cran-moments Filename: pool/dists/noble/main/r-cran-forestdisc_0.1.0-1.ca2404.1_all.deb Size: 36362 MD5sum: 4b8c572ddeeb014128f606e94770150b SHA1: 1f72a832bd200a6053b4d0170aab1bc69ce9a2fc SHA256: 9c34de4a65afec904efdc9d03d34d8ea2c9867d26de05261f5e7195e3f1bcfe4 SHA512: b6517878cae8046c1134d8e42927f7998c6fce39aad9c4e476e50fc11e6837c955b99bb138e6262b735a04b329b3f21cd1f92549d83f9c193ee5b65093297146 Homepage: https://cran.r-project.org/package=ForestDisc Description: CRAN Package 'ForestDisc' (Forest Discretization) Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) , and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) . Package: r-cran-forestdynr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biomass Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestdynr_0.0.1-1.ca2404.1_all.deb Size: 47986 MD5sum: 59cbe94d374276769cf6008f6b0cce52 SHA1: 9d03f1440301896f9455557e1494c2e2969499b8 SHA256: d17fbf2e2013ca44c4e74d5a0528e80385beb4c57790fe167290f7d00d5b93df SHA512: 0fa1e625b8d711dc871ffecd2588ad669b9960675574b214c6a5f3a5d996bf44b3e8cf3dc9edbffe751463de3f893ebd3e636186334b9cbfad195db6126daa73 Homepage: https://cran.r-project.org/package=forestdynR Description: CRAN Package 'forestdynR' (Calculate Forest Dynamics) Determines the dynamics of tree species communities (mortality rates, recruitment, loss and gain in basal area, net changes and turnover). Important notes are a) The 'forest_df' argument (data) must contain the columns 'plot' (plot identification), 'spp' (species identification), DBH_1 (Diameter at breast height in first year of measure) and DBH_2 (Diameter at breast height in second year of measure). DBH_1 and DBH_2 must be numeric values; b) example input file in 'data(forest_df_example)'; c) The argument 'inv_time' represents the time between inventories, in years; d) The 'coord' argument must be of the type 'c(longitude, latitude)', with decimal degree values; e) Argument 'add_wd' represents a dataframe with wood density values (g cm-3) format with three columns ('genus', 'species', 'wd'). This argument is set to NULL by default, and if isn't provided, the wood density will be estimated with the getWoodDensity() function from the 'BIOMASS' package. Package: r-cran-forestecology Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1671 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-glue, r-cran-ggplot2, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggridges, r-cran-mvnfast, r-cran-sf, r-cran-sfheaders, r-cran-snakecase, r-cran-tibble, r-cran-yardstick, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-patchwork, r-cran-blockcv Filename: pool/dists/noble/main/r-cran-forestecology_0.2.3-1.ca2404.1_all.deb Size: 1392300 MD5sum: 016d3266149f534189f551fcf1b068f1 SHA1: 9445e04d1d74ff3b96aaced159211e4ddd1ed667 SHA256: edd2ae09dee9ac593a7cca2d903c5b701f915b97899ff30d44643aa967f69797 SHA512: b94cdf6f3e1c46e889d771eae9869ff14190843077c438b79668a19a2f2c2f80866fe2039c42c3a0963b890f0327832bde4c80c800974ed584f8d42a557be777 Homepage: https://cran.r-project.org/package=forestecology Description: CRAN Package 'forestecology' (Fitting and Assessing Neighborhood Models of the Effect ofInterspecific Competition on the Growth of Trees) Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation. Package: r-cran-forested Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-forested_0.2.0-1.ca2404.1_all.deb Size: 852992 MD5sum: cef8452adf876b37d6155387e9acca7d SHA1: 57d98bbc62fb4327acdddc17a0bddcb4e14fcab8 SHA256: 039f94554da5ffd8b277e11605c8f8ca4896c724a596600204825a214d4ada89 SHA512: 956fe695d7b57f82b84ccbb30f3b7e37e7e060391551b14501400c06d5f9e47e39251c47bd63226460cf13ccbc829200ff431a60318f8158fe35723c67d85532 Homepage: https://cran.r-project.org/package=forested Description: CRAN Package 'forested' (Forest Attributes in U.S. States) A small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The 'forested' package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia. Package: r-cran-forestelementsr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4229 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-rdpack, r-cran-tidyr, r-cran-vctrs, r-cran-stringr, r-cran-tidyselect, r-cran-doby Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestelementsr_3.0.0-1.ca2404.1_all.deb Size: 1380286 MD5sum: 5e0065f6203a12623a607e18a0321ee2 SHA1: f247af547ba2363e7c8e8a7c2347659ab42be52a SHA256: a3da8580d4f27b42863db3541bf3df4ab6362ab353145c22c1cf9827e2e1313e SHA512: 6c9d02af51dbc599084891aec69bfe81b1d2606e6361fb006aec52b7c73ddb7d2ddf04679b5df620449af0944059bbb824f2dd858e027012fb68f76d954d450c Homepage: https://cran.r-project.org/package=ForestElementsR Description: CRAN Package 'ForestElementsR' (Data Structures and Functions for Working with Forest Data) Provides generic data structures and algorithms for use with forest mensuration data in a consistent framework. The functions and objects included are a collection of broadly applicable tools. More specialized applications should be implemented in separate packages that build on this foundation. Documentation about 'ForestElementsR' is provided by three vignettes included in this package. For an introduction to the field of forest mensuration, refer to the textbooks by Kershaw et al. (2017) , and van Laar and Akca (2007) . Package: r-cran-foresterror Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-purrr Suggests: r-cran-randomforest Filename: pool/dists/noble/main/r-cran-foresterror_1.1.0-1.ca2404.1_all.deb Size: 54532 MD5sum: b1e130daee50b8845db269cb56901853 SHA1: 4727ef8747f007dfb1ef006d087ae632c3a1d347 SHA256: ceea9b60c6d9ce6cea4a319899026ca666bfe6c3c16d92e257eb04af5bf7b13e SHA512: 4f9938aa2bcfa15df00928be68b9477bda1fb167e4bdf34c2667568a99ed72255c3c45c89e618a874e4b7b790fe6f8def0b43d3bf5c3d8fde9065c5d218f2893 Homepage: https://cran.r-project.org/package=forestError Description: CRAN Package 'forestError' (A Unified Framework for Random Forest Prediction ErrorEstimation) Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R. Package: r-cran-forestfit Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-pracma Filename: pool/dists/noble/main/r-cran-forestfit_2.4.3-1.ca2404.1_all.deb Size: 453582 MD5sum: 1606719bd1279b393504d378f326c4cd SHA1: 3832ab9df98ad79e40e97b91ebc9612c8211fb11 SHA256: 7c4499de2a35076f8a28cfce804584bd8791320888117e77be5b5585cb6a8482 SHA512: 939bc4f6ffbe5017f9aa8d80f2c84618e9b472618d8ef0dfc47da720d82a824be1030a6eb33751339a486f261cfa030d9a7c1f7eee78ec409a01b2ab8a787c42 Homepage: https://cran.r-project.org/package=ForestFit Description: CRAN Package 'ForestFit' (Statistical Modelling for Plant Size Distributions) Developed for the following tasks. 1 ) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models. 2 ) Point estimation of the parameters of two - parameter Weibull distribution using twelve methods and three - parameter Weibull distribution using nine methods. 3 ) The Bayesian inference for the three - parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. 5 ) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve fitted to the height - diameter observation, 7 ) Estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) , 8 ) The Bayesian inference, computing probability density function, cumulative distribution function, and generating realizations from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear regression analysis when error term follows skewed t distribution, 10 ) Estimating parameters of a given distribution fitted to grouped data using method of maximum likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through the Bayesian, method of moment, conditional maximum likelihood, and two - percentile method. Package: r-cran-forestgapr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-powerlaw, r-cran-raster, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-vgam, r-cran-viridis Filename: pool/dists/noble/main/r-cran-forestgapr_0.1.7-1.ca2404.1_all.deb Size: 526526 MD5sum: 25ec78c21dc586ac18cb6f10fd091cfa SHA1: 946d4926a9571a41d43839c1e9b247c9645f778c SHA256: 36e025b9cf5db8988c04897224ef32f49e03ec6088889c4288cf48af7e2e2215 SHA512: 1f9aa47d1c555de668c79d2ff4de6f457e3df6e3e174fbb5f1228bbee752413e634df8c0feb725c0eacb079a059eefef481d057b2510af36f6fb24a9c5c8b07b Homepage: https://cran.r-project.org/package=ForestGapR Description: CRAN Package 'ForestGapR' (Tropical Forest Canopy Gaps Analysis) Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) . Package: r-cran-forestgym Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-forestgym_1.0.0-1.ca2404.1_all.deb Size: 44970 MD5sum: 63af5740d10d1aabdf8fceb50cbb5bd0 SHA1: 590d1e7d9b93fa0174563d7040fabf72fec00b1d SHA256: e8babc613d1fa7db3ea5b2fe48e9d7f731d505cb7e9165bd415ab6300283e761 SHA512: 038d8d783096f36a72bd979039ef426960ef28114bce3d7a90e70310afc7321e5f765e55fa21be778c2f30db97197fc02c267c1dabe28dfa9122203c37a7f1e6 Homepage: https://cran.r-project.org/package=forestGYM Description: CRAN Package 'forestGYM' (Forest Growth and Yield Model Based on Clutter Model) The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021). Guera O, Silva J, Ferreira R, et al(2019). Package: r-cran-foresthes Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-foresthes_2.0.1-1.ca2404.1_all.deb Size: 68724 MD5sum: 684c6cebf016332210d465a4c06b67a2 SHA1: 0cfdbf57f917cc4f06bfdb463d5fdf42be06e641 SHA256: 58705f5a2ca477fa11c5e27c290e4e03803e56a0d36446fde79171ce0953a9c2 SHA512: 71addd706e581d9ecaa876276279f8e3226a7c6d4929be1662f608345aa55b46f0af4613471d1eaa7e9e61847fe61185ed344e152a242b918b0f0666db0c8cb5 Homepage: https://cran.r-project.org/package=forestHES Description: CRAN Package 'forestHES' (Forest Health Evaluation System at the Forest Stand Level) Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources. Package: r-cran-forestinventory Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-forestinventory_1.0.0-1.ca2404.1_all.deb Size: 1351078 MD5sum: 887ac21ca163536c434fd0ddf5f3e998 SHA1: 80a4895ffeaf056ffaee9e46a6d21ce86a17040d SHA256: e92919dc7f8ebefd8884399c17bd6e6b9df4657a5460318105e5b129b63c9262 SHA512: 5b1a214a2a5650552d2d861ec0242512de0661e69a469907b057844e40ef753835d8c02069a100cf942b5834e212aecd11922e654c5846047f2f5da4256c5165 Homepage: https://cran.r-project.org/package=forestinventory Description: CRAN Package 'forestinventory' (Design-Based Global and Small-Area Estimations for MultiphaseForest Inventories) Extensive global and small-area estimation procedures for multiphase forest inventories under the design-based Monte-Carlo approach are provided. The implementation has been published in the Journal of Statistical Software () and includes estimators for simple and cluster sampling published by Daniel Mandallaz in 2007 (), 2013 (, , , ) and 2016 (). It provides point estimates, their external- and design-based variances and confidence intervals, as well as a set of functions to analyze and visualize the produced estimates. The procedures have also been optimized for the use of remote sensing data as auxiliary information, as demonstrated in 2018 by Hill et al. (). Package: r-cran-forestly Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12465 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brew, r-cran-crosstalk, r-cran-glue, r-cran-htmltools, r-cran-metalite, r-cran-metalite.ae, r-cran-reactable, r-cran-reactr, r-cran-ggplot2, r-cran-uuid Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-r2rtf, r-cran-rmarkdown, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestly_0.1.6-1.ca2404.1_all.deb Size: 1427838 MD5sum: e15134e89b0fc30d5cbd497541ae5424 SHA1: 86b7073d1019a966c91f8be3e039b4e6204705e7 SHA256: 1ab06dbecada827f6cb93be3e9dec4453271b049225edca1ce124debe8c4261d SHA512: b2f8eaac140df29b7dd5bc88e658181b996a007496885598ef8a96f088a8a19e52382e6486f218e4bc1a078517a5cba2f2797efab9597dcbbc01c9f2a00b663d Homepage: https://cran.r-project.org/package=forestly Description: CRAN Package 'forestly' (Interactive Forest Plot) Interactive forest plot for clinical trial safety analysis using 'metalite', 'reactable', 'plotly', and Analysis Data Model (ADaM) datasets. Includes functionality for adverse event filtering, incidence-based group filtering, hover-over reveals, and search and sort operations. The workflow allows for metadata construction, data preparation, output formatting, and interactive plot generation. Package: r-cran-forestmangr Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-tidyr, r-cran-broom, r-cran-purrr, r-cran-plyr, r-cran-tibble, r-cran-systemfit, r-cran-ggpmisc, r-cran-rlang, r-cran-car, r-cran-magrittr, r-cran-minpack.lm, r-cran-fincal, r-cran-scales, r-cran-ggdendro, r-cran-gridextra, r-cran-shiny, r-cran-miniui Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyselect, r-cran-formattable Filename: pool/dists/noble/main/r-cran-forestmangr_0.9.9-1.ca2404.1_all.deb Size: 781448 MD5sum: 76a6bcaa792e3f4160e5b8e0c01fa830 SHA1: 41be319db010f08443de9073ddaa11b8aa38e1cb SHA256: f7e92cbac9c5373bfa78d7fe7832158be780f6d91b98d70bd359b7b74c7b4f8b SHA512: 3c161320cf49cf5b0448c7329e62e9bfd8c171b6b8680e49b78bf9e71dec25bea06f68033ef846022736a85c14a4529a64d52211151957a2d50fca7dd79d6a29 Homepage: https://cran.r-project.org/package=forestmangr Description: CRAN Package 'forestmangr' (Forest Mensuration and Management) Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). . Package: r-cran-forestmodel Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-broom, r-cran-rlang, r-cran-tibble Suggests: r-cran-survival, r-cran-metafor, r-cran-labelled Filename: pool/dists/noble/main/r-cran-forestmodel_0.6.2-1.ca2404.1_all.deb Size: 116368 MD5sum: 4b52bc424186800bfa0143159bbcaa2c SHA1: 7f18196a50fca72a6a1ff043de475520b88d402d SHA256: 5e2882f8ec23968ffa241e1bfd31f764395ee381990040d9ce70e22d7fa44ee9 SHA512: 8534c50dd2cd58c451cfe6c41c39f82e1135bb8395b3ab630f2ba4384aa7200c5a943fc5d2adbd308c00377973bd976904bee7dd3708a7ba36978ec447ab9ff4 Homepage: https://cran.r-project.org/package=forestmodel Description: CRAN Package 'forestmodel' (Forest Plots from Regression Models) Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph(). Package: r-cran-forestplot Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-abind Suggests: r-cran-dplyr, r-cran-gmisc, r-cran-greg, r-cran-knitr, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-rmeta, r-cran-rms, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-forestplot_3.2.0-1.ca2404.1_all.deb Size: 1086794 MD5sum: 28a14336e383cb8cf589e76bb1fe4537 SHA1: 2084d0013b25e14b0b501d954f2a48e01d44949e SHA256: 92192b5897f59fad866ed6c67f5f43fd6918bac8918edbda72a5546d806c42fa SHA512: ac146ac77da51543c51fcdbfab5f96b7475d5da72ea3e3d848582812fa1941d0a4aad4f4d1b5d0608a4126bf5e7de051647374d08ee59b559f6c7f6c76cf4120 Homepage: https://cran.r-project.org/package=forestplot Description: CRAN Package 'forestplot' (Advanced Forest Plot Using 'grid' Graphics) Allows the creation of forest plots with advanced features, such as multiple confidence intervals per row, customizable fonts for individual text elements, and flexible confidence interval drawing. It also supports mixing text with mathematical expressions. The package extends the application of forest plots beyond traditional meta-analyses, offering a more general version of the original 'rmeta' package’s forestplot() function. It relies heavily on the 'grid' package for rendering the plots. Package: r-cran-forestploter Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2392 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gridextra, r-cran-gtable Suggests: r-cran-gridmicrotex, r-cran-rmarkdown, r-cran-knitr, r-cran-vdiffr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-forestploter_1.2.0-1.ca2404.1_all.deb Size: 1669258 MD5sum: 5439f477928993edeccc4001e6cffb79 SHA1: 65f7f62c1bb19935a6a2c6f5be39746b404f70b1 SHA256: 2f85368c7494205563704898b07dbea91169acdb8cc13270dadd0594d7efdb62 SHA512: 73e5ed6be116e4f662322566cb6db3e431f090972162a8512cb9fdc8c0daf2830b3e2b68bcee5f755e64fa4bd0715a569ce0eb63b40a276e327c344d2bfc29e4 Homepage: https://cran.r-project.org/package=forestploter Description: CRAN Package 'forestploter' (Create a Flexible Forest Plot) Create a forest plot based on the layout of the data. Confidence intervals in multiple columns by groups can be done easily. The plot is built step by step with the pipe, adding the axis, the labels and a style, editing the plot, inserting/adding text, and much more. Package: r-cran-forestpsd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-ttr, r-cran-modelr, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-forestpsd_1.0.0-1.ca2404.1_all.deb Size: 30420 MD5sum: aeeb62d1792566f4b26161795750b065 SHA1: 5836bcf18c4fe3ffb859bfe0dcaa30cd7a1c015f SHA256: 8d5927a7b9e088acbb2347bd514c225c16871f3266f84efac99953cc566dca81 SHA512: 8c3124fe12a51e01bab774949cc36a3721654bb87bebd212ed4986900d1a7ddd21fe86c39e60a0b243a32d406fad2ba3001deabdb28a845f0b083a652f7b2e09 Homepage: https://cran.r-project.org/package=forestPSD Description: CRAN Package 'forestPSD' (Forest Population Structure and Numeric Dynamics) Analysis of forest population structure and quantitative dynamics is the research and evaluation of the composition, distribution, age structure and changes in quantity over time of various populations in the forest. By deeply understanding these characteristics of forest populations, scientific basis can be provided for the management, protection and sustainable utilization of forest resources. This R package conducts a systematic analysis of forest population structure and quantitative dynamics through analyzing age structure, compiling life tables, population quantitative dynamic change indices and time series models, in order to provide support for forest population protection and sustainable management. References: Zhang Y, Wang J, Wang X, et al(2024). Yuan G, Guo Q, Xie N, et al(2023). Package: r-cran-forestr Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-viridis, r-cran-tidyr, r-cran-moments, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-forestr_2.0.2-1.ca2404.1_all.deb Size: 1163752 MD5sum: 5391adca3c8748550c360b8f6057d09e SHA1: 1b04a149ac75b960836745c0dde78ede7c6c49a0 SHA256: c8dbf6576d154181b3147c99a8908ce4bce044a00e65f9e0b904be57c0e5e448 SHA512: d6151220174538b2d82a135f238a87a35c1698de1f2d7cbdd34979cded5354b833aacbd03dd27cd2fff21e4cef997db052e7b6a0d74904161d8a325be9c76422 Homepage: https://cran.r-project.org/package=forestr Description: CRAN Package 'forestr' (Ecosystem and Canopy Structural Complexity Metrics from LiDAR) Provides a toolkit for calculating forest and canopy structural complexity metrics from terrestrial LiDAR (light detection and ranging). References: Atkins et al. 2018 ; Hardiman et al. 2013 ; Parker et al. 2004 . Package: r-cran-forestrk Architecture: all Version: 0.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2, r-cran-rapportools, r-cran-partykit, r-cran-pkgkitten, r-cran-knitr, r-cran-mlbench Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-forestrk_0.0-5-1.ca2404.1_all.deb Size: 380252 MD5sum: a503a4fc7f6bd2a8f8aa43c2276edb72 SHA1: 2903baa9608b0771a535bb8fcdb9d76c1eea5892 SHA256: 2a4a73539cdcf54073d2c2d339c557d8a1f323fc28f155d485ff045cae8d12be SHA512: ed7fdde7f37da47f63dae8326a6fe31531afb83974cdbe451918ae22b66f0e26437804ed8e940cce8228686fb2aa0b14b5da1da38f43f6890c9d8d20819925f3 Homepage: https://cran.r-project.org/package=forestRK Description: CRAN Package 'forestRK' (Implements the Forest-R.K. Algorithm for Classification Problems) Provides functions that calculates common types of splitting criteria used in random forests for classification problems, as well as functions that make predictions based on a single tree or a Forest-R.K. model; the package also provides functions to generate importance plot for a Forest-R.K. model, as well as the 2D multidimensional-scaling plot of data points that are colour coded by their predicted class types by the Forest-R.K. model. This package is based on: Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) "Forest-R.K.: A New Random Forest Induction Method", Fourth International Conference on Intelligent Computing, September 2008, Shanghai, China, pp.430-437. Package: r-cran-forestry Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.tree Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-forestry_0.1.1-1.ca2404.1_all.deb Size: 46410 MD5sum: de9b8a6dbaebcb6ee81495daa325f630 SHA1: 871eb8c9f9103a67cec3079e4c41ef14f3e16ecc SHA256: d81ee07482b5e8260c5100a060f9b678ec34e5a0a89005750875cbd0688a5040 SHA512: 0f3525a7cdaf590c5a45e1c6e396ae263cbb1439454634a9c5b0eedc3d4c47fd3041b5cd41858d6db7c7d2d15f1fc3217012036665c403922066a5fc660c66ca Homepage: https://cran.r-project.org/package=forestry Description: CRAN Package 'forestry' (Reshape Data Tree) A series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree. Package: r-cran-forestsas Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggimage, r-cran-reshape2, r-cran-spatstat.geom, r-cran-spatstat.random Suggests: r-cran-ggplot2, r-cran-shiny, r-cran-spatstat, r-cran-spatstat.data Filename: pool/dists/noble/main/r-cran-forestsas_2.0.5-1.ca2404.1_all.deb Size: 2315376 MD5sum: 315a23dce5e32810546a58c2e01589ee SHA1: d5f008f9766762e95ec78470e29c8cd39352057a SHA256: c13179e1258fee8d7dacfe35bb46a91ae265b55fba0001d9e40fdca7f86981fa SHA512: 3d986bba5f20a7cfd71261710d0ac8d01b173a65d05279ac6605cfb95b1f41f62e0f72f64463b5be266b75cbda84c4102ecd5fc4341eea131ace5cef4a660b81 Homepage: https://cran.r-project.org/package=forestSAS Description: CRAN Package 'forestSAS' (Forest Spatial Structure Analysis Systems) Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) . Package: r-cran-forestsearch Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8673 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dofuture, r-cran-dplyr, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-future.callr, r-cran-ggplot2, r-cran-glmnet, r-cran-grf, r-cran-gt, r-cran-matrix, r-cran-mvtnorm, r-cran-patchwork, r-cran-policytree, r-cran-progressr, r-cran-randomforest, r-cran-rlang, r-cran-survival, r-cran-weightedsurv Suggests: r-cran-diagrammer, r-cran-dorng, r-cran-htmltools, r-cran-mass, r-cran-sandwich, r-cran-tidyr, r-cran-forestploter, r-cran-cubature, r-cran-svglite, r-cran-knitr, r-cran-rmarkdown, r-cran-katex, r-cran-latentcor, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestsearch_0.4.0-1.ca2404.1_all.deb Size: 5514344 MD5sum: d73e64422092a1801cfe67366fe25913 SHA1: 2ca3e486f946262439dba9a3d25edb7af24a92d9 SHA256: 2eca254e19f6e055fc161b30c448ee875039c4c363e0fced89949baa9e93e004 SHA512: cd17cf67ef83f303f76384a4c4c9957184f22ab7b8dd39cd1bfef2506f647aa94a53e17ae0f78fe60c618c3d538ba7132afb5bd6599f9f85d5b2602319bb72e7 Homepage: https://cran.r-project.org/package=forestsearch Description: CRAN Package 'forestsearch' (Exploratory Subgroup Identification in Survival and GLM Outcomes) Implements statistical methods for exploratory subgroup identification in clinical trials. Provides tools for identifying patient subgroups with differential treatment effects using machine learning approaches including Generalized Random Forests (GRF), LASSO regularization, and exhaustive combinatorial search algorithms. Supports survival endpoints (Cox proportional hazards), binary outcomes (log odds ratio, log relative risk, risk difference), continuous outcomes (mean difference), and count / rate outcomes (log incidence rate ratio via Poisson, quasi-Poisson, or negative-binomial GLMs with optional person-time offset). Features bootstrap bias correction using infinitesimal jackknife methods to address selection bias in post-hoc analyses. Designed for clinical researchers conducting exploratory subgroup analyses in randomized controlled trials, particularly for multi-regional clinical trials (MRCT) requiring regional consistency evaluation. Methods are described in Leon et al. (2024) . Post-selection inference for the identified subgroup follows León and Anderson (2026) . Package: r-cran-foresttools Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3013 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-matrix, r-cran-imager, r-cran-glcmtextures Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-foresttools_1.0.3-1.ca2404.1_all.deb Size: 2955482 MD5sum: dfb4c05be719e355393083e2c0c29111 SHA1: 61467e6b7d2a7a0900747ea24221ee4733bfda2b SHA256: 82fc66aeadcf36ed0590e911db66eade55f8f65b10d0e33676bfc9e6625ae995 SHA512: 9fa2384f9aabe3a5c5abb2c1454bac66c71a10af6847faf08220432aa5910639b34fcb680ba2278afdfb4bd3ff16ad0bf445fbd2363a1fd97a972c3b1f1fd92d Homepage: https://cran.r-project.org/package=ForestTools Description: CRAN Package 'ForestTools' (Tools for Analyzing Remote Sensing Forest Data) Tools for analyzing remote sensing forest data, including functions for detecting treetops from canopy models, outlining tree crowns, and calculating textural metrics. Package: r-cran-foresty Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1356 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-checkmate, r-cran-ggplot2, r-cran-patchwork, r-cran-scales Suggests: r-cran-base64enc, r-cran-broom, r-cran-bslib, r-cran-data.table, r-cran-geepack, r-cran-gt, r-cran-hmisc, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-nnet, r-cran-ragg, r-cran-rmarkdown, r-cran-rms, r-cran-sandwich, r-cran-shiny, r-cran-survey, r-cran-survival, r-cran-svglite, r-cran-tibble, r-cran-testthat, r-cran-zip Filename: pool/dists/noble/main/r-cran-foresty_0.2.0-1.ca2404.1_all.deb Size: 1247742 MD5sum: b48469b8c22d4eb230560578fbf1a625 SHA1: 7a77ccedee13d5418666604b9283292357e8ed32 SHA256: 1d4af3cb435f05618606e115c5f0ffb351a2d39ee11ee87c5354987ea2a2d391 SHA512: d1ddada5d479109a072a5e6ff819acbba674a27f12ede39acc39c94e1f903d9aa59f52343d80d9a470289e61cdb9295e3f548dcf6f66732b13aea33c686aee80 Homepage: https://cran.r-project.org/package=foresty Description: CRAN Package 'foresty' (Forest Plots and Subgroup Effects from Fitted Regression Models) Draws forest plots of exposure effects from fitted regression models. Name an exposure and 'foresty' plots its effect. Name an effect modifier as well and it refits the model with the interaction term, estimates the exposure effect within each level of the modifier as a linear combination of the coefficients, and reports the joint interaction test beside those estimates. It takes one exposure and one modifier at a time, so the interaction is always a two-way one. Rows of the plot and of the table beside it share one scale, in a layout that can follow a journal's house style. The same results go to a self-contained HTML page holding the subgroup estimates, the joint test and the coefficient table. The 'car' package computes the linear combinations and their tests. Models fitted by stats::glm(), stats::lm(), the 'survival' package, the 'lme4' package, the 'geepack' package and the 'survey' package are supported, as is any fit supplying coef() and vcov(). Ordinal outcomes are supported through the 'MASS' package and nominal ones through the 'nnet' package, where the figure carries one row per level of the outcome and the interaction is tested jointly across the equations. Fits from the 'rms' package are refused, naming the function that fits the same model in their place. The estimation of an exposure effect within a level of a modifier, and the test of the difference between such estimates, follow Altman and Bland (2003) and VanderWeele and Knol (2014) ; the reporting of subgroup effects beside the interaction test follows Wang et al. (2007) , and the figure itself the forest plot described by Lewis and Clarke (2001) . Package: r-cran-foretell Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-foretell_0.2.0-1.ca2404.1_all.deb Size: 577192 MD5sum: dfc9fb4b32bdae928858f0fde1f625c3 SHA1: 29a356b4727366d3dbcec770e2fb4e846951419e SHA256: f057b7d796b5b2296221fb8660db1169a0e6dab10db26d0251fa24ffefb0cee5 SHA512: 763c15cbd3fb73999fcf1766fa8c2a3ce3ee3580975a81c52f1da9f3864cb8cd627a93115799f503d0bbd0ef3ec6bef9b8b4e6d2e2e6e390efb2c54d9e01d159 Homepage: https://cran.r-project.org/package=foretell Description: CRAN Package 'foretell' (Projecting Customer Retention Based on Fader and HardieProbability Models) Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class Discrete Weibull Models.This package is based on Fader and Hardie (2007) and Fader and Hardie et al. (2018) . Package: r-cran-forge Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-magrittr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-forge_0.2.1-1.ca2404.1_all.deb Size: 41694 MD5sum: b4fd463d4b5acd7882a52c33f19d77aa SHA1: 1f3ec1febe6ec4d862834133e49e35d8578792a5 SHA256: 49b7ef64166be6c5ab518b43b8cb7dbd0682c0cf674801b4fd4c22e3602a4b2e SHA512: b91f542aa6950c81dfa72fd38b0df347176ac21b6adda10cd08d5907a101e1b8313676a9a35c2255f5f3913ab577e5d658554998af643c9059ac695a693b5446 Homepage: https://cran.r-project.org/package=forge Description: CRAN Package 'forge' (Casting Values into Shape) Helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking. 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Package: r-cran-forimage Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-xml2, r-cran-raster, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forimage_0.1.0-1.ca2404.1_all.deb Size: 1552590 MD5sum: 8b0eccc5df4634a9aac4dbe71560f111 SHA1: 86fe89860efa3ae89ff7890a68b1eaa8afd9124b SHA256: 6263a75206b5b647af5caaddda8928e771c7fcae4edad183d4564fd36f53e3dc SHA512: dc851d9e253cb7cf2a1cd01038b4c7b198ed6db584012134efecdc82381f35d7fb3c4934385e421d552fbda7afc98ce306849402a972fddd30bf5e3406dd5d80 Homepage: https://cran.r-project.org/package=forImage Description: CRAN Package 'forImage' (Foraminiferal Image Analysis and Test Measurement) The goal of this collection of functions is to provide an easy to use tool for the measurement of foraminifera and other unicellulars organisms size. With functions developed to guide foraminiferal test biovolume calculations and cell biomass estimations. The volume function includes several microalgae models geometric adaptations based on Hillebrand et al. (1999) , Sun and Liu (2003) and Vadrucci, Cabrini and Basset (2007) . 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(2011) published a very detailed study producing a uniform system of functions to estimate tree volume and phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory () are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment. Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species identification is now based on EPPO species codes (). 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Package: r-cran-forrest Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5792 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tinyplot Suggests: r-cran-broom, r-cran-covr, r-cran-data.table, r-cran-knitr, r-cran-marginaleffects, r-cran-matchit, r-cran-quarto, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forrest_0.3.0-1.ca2404.1_all.deb Size: 3277786 MD5sum: be6edb17fd3a84f1b8f928376dd19392 SHA1: 5086ab772447acdbb33247b80d86dda4de792bd0 SHA256: 94d1c3e151084ebc24fae418dca66409553710b74d87af57d05d6efda9712dc1 SHA512: 2f3de1452b5f6a9882fc695ba7caa96cb5611bc1f3c9c105faa467f9b6882d0a65d7327d579d3b1dd7f8e8df9a7a581e34e633d33679491c9eb56a03fb31f5d1 Homepage: https://cran.r-project.org/package=forrest Description: CRAN Package 'forrest' (Publication-Ready Forest Plots) Creates publication-ready forest plots from any tabular data containing point estimates and confidence intervals. 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Package: r-cran-foundry Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-jsonlite, r-cran-httr, r-cran-r6, r-cran-yaml Suggests: r-cran-lintr, r-cran-httptest, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-foundry_0.13.0-1.ca2404.1_all.deb Size: 105812 MD5sum: e20838399e1bb5c65b06409dbe09b79f SHA1: aae885c48feb49582318ca932dfe0a7b926b2ea9 SHA256: f3edd5f8eeec035a14339844844161da7319286bead82f946e017548f194f29b SHA512: 0bd0c407acb638c7d9db6acd9184be6ff83a45c367bc3a906e5cb8ea31973565b72c6eda088a74b79d132961d2788d45e8ccbdc77f2985b90dd352680ffe0255 Homepage: https://cran.r-project.org/package=foundry Description: CRAN Package 'foundry' ('Palantir Foundry' Software Development Kit) Interface to 'Palantir Foundry', including reading and writing structured or unstructured datasets, and more . 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Provides data-frame-returning helpers for 'Azure AI Content Safety', 'Azure OpenAI' Responses API calls, strict structured extraction, vector representations, files, batch jobs, audio, media, and chat completions. Supports research annotation, safety gates, semantic search, and 'tidymodels' recipes. Helps teams keep model workflows inside their 'Azure' environment while preserving analyzable outputs. See the Microsoft Foundry REST API documentation and Azure AI Content Safety documentation . 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Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility. 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It contains a collection of built-in functions that play the game at various skill levels, for users to test their own functions against. Package: r-cran-fourscores Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fourscores_1.5.1-1.ca2404.1_all.deb Size: 56006 MD5sum: c6a63e58b1c599c89836c8789bf856f9 SHA1: 7ae2c6b49cf26956604c0a35e2dd75fec18c0ea4 SHA256: 8388727cd78847a66ac0afb2b8b352858ccd46317980520d9e2841208503c19b SHA512: 758adff34b2d526501d3e48b3612b837d8b279246675ed6f9646ac8b0367d049f9c50feef66d1836f405fc907a7ffe7beb1131c185abed174a16bff38ea5e4c7 Homepage: https://cran.r-project.org/package=FourScores Description: CRAN Package 'FourScores' (A Game for Human vs. Human or Human vs. AI) A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. 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(2020) . The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models. 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Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc. Package: r-cran-fpca3d Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fpca3d_1.0-1.ca2404.1_all.deb Size: 23070 MD5sum: 6ced8f7e26d62625cc936469128ea4b5 SHA1: 04b40b38431f2572f68ffda1e897877b39277ae6 SHA256: 64808b0a9f733928de50ad6f4c3372a33b7707f6e61632680aa194009aef868b SHA512: 86e1ca062f8c5cae2af4c35e1dba2c65642a524ecf97a7cf14cacc5c0aaee62b39e4a45c325f6bcd8b016a6ff9765f64f83d416eef9741a705b5ceee44de9ac5 Homepage: https://cran.r-project.org/package=FPCA3D Description: CRAN Package 'FPCA3D' (Three Dimensional Functional Component Analysis) Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) . 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PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets. 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All packages required to run the examples are also loaded. 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All packages required to run the examples are also loaded. Additional data sets not used in the book are also included. 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Includes univariate one-part, two-part, and double-inflated three-part fractional models. Further incorporates estimators for panel data settings and addresses unobserved heterogeneity and endogeneity via correlated random effects and control function approaches. Extends fractional methodology to multivariate data via fractional multinomial logit models and handles high-dimensional multicollinear data via fractional ridge regression. Calculates analytical partial effects across all model types and includes generalised goodness-of-functional-form (GGOFF) and Regression Equation Specification Error Test (RESET) hypothesis tests. Methods are described in Papke and Wooldridge (1996) , Papke and Wooldridge (2008) , Buis (2008) , Ramalho, Ramalho and Murteira (2011) , Fang and Ma (2013) , Mullahy (2015) , Murteira and Ramalho (2016) , and Rokem and Kay (2020) . 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Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) . Package: r-cran-fractd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-imager, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fractd_0.1.0-1.ca2404.1_all.deb Size: 204278 MD5sum: 7c5a1134fa068249a41ad26ba69e4304 SHA1: 8be589c08cac08c1e1f6b4e48a7bd6a8844b42c1 SHA256: c8abeeb44c5d7ccfe54e5132ea104e7676bd1272db29dbf47fb2e2ec5e958ac9 SHA512: 4d4e7470a8a4590ce68402835c5f0706a9e711b44a77cfb1ec3acd2a13e4cf5286082cfe5c39bb481dede4a30322ba954ed7543a7379e237e66168bb6b97c095 Homepage: https://cran.r-project.org/package=fractD Description: CRAN Package 'fractD' (Estimation of Fractal Dimension of a Black Area in 2D and 3D(Slices) Images) Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) . 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Package: r-cran-fragilitidy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fragilitidy_0.1.0-1.ca2404.1_all.deb Size: 67782 MD5sum: 890a69d895650879539828f08043434b SHA1: 20085fe0d1bfd742601b688ede4feaf9782531ce SHA256: 5621e0ea59fed1cbad9874aff346098ba9b25a13f939d9b26455cf82eb0dedee SHA512: 3bd95c4b9871fbf91250306c36a2f2393e55159b3b063b342ad7462ad936d4a6e803b4068096b6058c4a3b3f4772979c14fdd5fd9f5e69ac63d054eb30fc3658 Homepage: https://cran.r-project.org/package=FragiliTidy Description: CRAN Package 'FragiliTidy' (Tidyverse-Compatible Fragility Index Calculations) Provides optimized, Tidyverse-compatible functions for calculating the Fragility Index and Reverse Fragility Index for 2x2 contingency tables from clinical trials. 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Package: r-cran-fragility Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-meta, r-cran-metafor, r-cran-netmeta, r-cran-plotrix, r-cran-survival Filename: pool/dists/noble/main/r-cran-fragility_2.0-1.ca2404.1_all.deb Size: 433630 MD5sum: 861d857d77b6dfd051a4b4006cd8fb06 SHA1: 49870ba01266126bf7d69c2bb90fd63dafed8d70 SHA256: 95b10af07936244921258ebc4c2d73881c260927eff9848cf5aefdc8062833cb SHA512: ecf49f6fa1065f1ef13fcf027898734b6e7ee339eb94e35c21397e5314f6bb7ef1a99a761207843adf9a6f157ce6608d8adf510a00462135aa31c7ff736d5494 Homepage: https://cran.r-project.org/package=fragility Description: CRAN Package 'fragility' (Assessing and Visualizing Fragility of Clinical Results) A collection of user-friendly functions for assessing fragility of clinical results with binary and survival outcomes. For binary outcomes, the package assesses and visualizes fragility of individual studies (Walsh et al., 2014 ; Lin, 2021 ), conventional pairwise meta-analyses (Atal et al., 2019 ), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 ). The functions for binary outcomes are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. For survival outcomes, the package implements the event status modification method based on the log-rank test described by Xing et al. (2026 ). It calculates the fragility index and fragility quotient for two-group studies with right-censored data, modifying event status in one or both groups while preserving follow-up times and group assignments. Results include the sequence of modifications, corresponding p-values, and an S3 print method. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 ) and detailed in Lin and Chu (2022 ). Package: r-cran-fragman Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2622 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fragman_1.1.0-1.ca2404.1_all.deb Size: 2643846 MD5sum: 500c624c1f30370d7448943367875f7a SHA1: 74110c2667d5fafa5599739dc3de9f5e169f9287 SHA256: 37aaa1d0928b00d535bf102680db8611265637f19194b77dddfb1f59a34c53e0 SHA512: 6c3c19b52db5cc1d908d801772be443acf46671d24cba409d615b7aa6af36e4580facf1d8b2e87a4696e497fedc7b317b402845beecca6c3b654d276b22ccf14 Homepage: https://cran.r-project.org/package=Fragman Description: CRAN Package 'Fragman' (Fragment Analysis in R) Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. 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Package: r-cran-frailtycomprisk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-tibble, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-frailtycomprisk_0.1.1-1.ca2404.1_all.deb Size: 91194 MD5sum: 4d0f90778fdd5cc74d739fd7c4100661 SHA1: a99ddcf5277582f23b4a5fa60763370dcec7ac08 SHA256: 7d510355c32f9dd5dba8ca864fe2e8bd38905e42a63bec66a3e3c481f809adab SHA512: e01443030973626065da93eee9ea9105bbe2d5d0a11df597746aff4243c1d0141188458d8e323b442d619c59cff9a540867a01e46a2c37545ced2d15e3a9416d Homepage: https://cran.r-project.org/package=FrailtyCompRisk Description: CRAN Package 'FrailtyCompRisk' (Competing Risks Models for Multi-Center Survival Data withFrailty) Implements methods for analyzing competing risks data in multi-center survival studies using frailty models. The approach relies on a mixed proportional hazards model for the sub-distribution, allowing for cluster-specific random effects. The package provides tools for model estimation with or without frailty using Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML). It supports flexible modeling of between-center heterogeneity and is particularly suited for multi-center clinical trials or registries. Core features include data simulation, likelihood computation, cluster-dependent censoring options, and testing of frailty effects. For methodological details, see Katsahian et al. (2006) . 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See Beyene and Chen (2024) . Package: r-cran-frair Architecture: all Version: 0.5.203-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle, r-cran-lamw, r-cran-boot, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-frair_0.5.203-1.ca2404.1_all.deb Size: 203082 MD5sum: ac58e1d72cfefa13b1bcd60b0ff74aba SHA1: 404bca7ed0fd4571c2834d7f7c72e04654975e06 SHA256: 4683b48ef6e2c138ded103078687f840a68956f7ddb72ece887c9fcc3d5975fc SHA512: 860f9b9fdd17296156e5a6a8d88526e7d336f0654d4a27b621e8e59592383ce29cd423a403a19b3e47cf45559342b07077dc4c5a0f19827c7a8d1d46c897ba5d Homepage: https://cran.r-project.org/package=frair Description: CRAN Package 'frair' (Tools for Functional Response Analysis) Tools to support sensible statistics for functional response analysis. 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In contrast to classic sampling theory, where only one sampling frame is considered, dual frame methodology assumes that there are two frames available for sampling and that, overall, they cover the entire target population. Then, two probability samples (one from each frame) are drawn and information collected is suitably combined to get estimators of the parameter of interest. 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'FRApp' is not limited to the analysis of FRAP experiments only. Any nonlinear mixed-effects models with an asymptotic exponential functional relationship to hierarchical data in various domains can be fitted. The analysis of data available in the package is presented in Di Credico, G., Pelucchi, S., Pauli, F. et al. (2025) . 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(2004) ; Day, Charles A. (2012) . Package: r-cran-frb Architecture: all Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrcov, r-cran-corpcor Suggests: r-cran-robustbase Filename: pool/dists/noble/main/r-cran-frb_2.0-1-1.ca2404.1_all.deb Size: 624036 MD5sum: 59efc3c121f0a996160c699aef782931 SHA1: 53091b3017930f651c2eba7ac70a7af7a3747f0f SHA256: 4184829cd1a293e44b18760a547473a916802b58ba2747523bd5bf21fbb027e8 SHA512: 1dc954d7b8844a697ab014c3c9e7baa506d3cabc340e48bf720f911cad3ace1af7a422a610e1c500cee2fdd85cf53cbec843568a143aa220bbbfb8c23e683d74 Homepage: https://cran.r-project.org/package=FRB Description: CRAN Package 'FRB' (Fast and Robust Bootstrap) Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) ). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap. 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Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community. 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Please see the following for details: Raul Cruz-Cano, Mei-Ling Ting Lee, Fast regularized canonical correlation analysis, Computational Statistics & Data Analysis, Volume 70, 2014, Pages 88-100, ISSN 0167-9473 . 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Compute and plot the fuzzy membership functions of the methods, and the expected length compared with the infimum. 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The core of this package is Fréchet regression for random objects with Euclidean predictors, which allows one to perform regression analysis for non-Euclidean responses under some mild conditions. Examples include distributions in 2-Wasserstein space, covariance matrices endowed with power metric (with Frobenius metric as a special case), Cholesky and log-Cholesky metrics, spherical data. References: Petersen, A., & Müller, H.-G. (2019) . 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Package: r-cran-frogger Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-here, r-cran-rappdirs, r-cran-rlang, r-cran-rstudioapi, r-cran-usethis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-frogger_1.0.1-1.ca2404.1_all.deb Size: 4779866 MD5sum: 165c7bd3f8a9ab54cd889bcd9a3849a4 SHA1: 495b967781b92692737282375d8c67a038289fb2 SHA256: 638d2a5a1672fc490f65e7988f27847990956381bab90f78a25c87baba087dd6 SHA512: 9bac921b6adc7724e24edd2dca84dc98015e4ee323ceb459d4fed582c05184505c84b1e91c4c4719d1e25723700498f24275149f8de91c87be24cd65424e8d8a Homepage: https://cran.r-project.org/package=froggeR Description: CRAN Package 'froggeR' (Project Scaffolding for R and 'Quarto') Creates structured R and 'Quarto' projects with a consistent directory layout: scripts in R/, analysis documents in analysis/, and web assets in www/. 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Procedures in the package allow to i) downscale daily meteorological variables to hourly values (Forster et al (2016) ), ii) estimate chilling and forcing heat accumulation (Miranda et al (2019) ), iii) estimate plant phenology (Schwartz (2012) ), iv) calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr et al (2017) ), v) estimate the incidence of weather-related disorders in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi) estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)). 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Application to Reverse-Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) gene-expression profiling and classification is illustrated in Mareschal, Ruminy et al (2015) . Gene-fusion detection and Sanger sequencing are illustrated in Mareschal, Palau et al (2021) . Examples are provided for genotyping applications as well. 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This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012. Package: r-cran-fscontext Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3448 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-fs, r-cran-progress, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-dataset, r-cran-glue, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-stringr, r-cran-labelled, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fscontext_0.2.1-1.ca2404.1_all.deb Size: 3047370 MD5sum: 3286d8a7d99bf90212306a78cf46b361 SHA1: 6db01879fe8ba3aafece1fe5cffefd00904bfe58 SHA256: db4061338fb680abf30060c889e6162ed4d7ea2747b2a7b8aeaa0fb2986669b4 SHA512: 93d21d0140e9cbda7b77656da00c87c26859ce6b0f73d30dd4b01533fd7a667e8030cb0b93080507858e2014016643719ad2d50551ca42cb76ae09434ef197d7 Homepage: https://cran.r-project.org/package=fscontext Description: CRAN Package 'fscontext' (File System Contextualisation and Record Set Reconstruction) Provides a provenance-aware framework for contextual reconstruction from file systems and related digital resource collections. The package creates reproducible snapshots of file-level metadata, paths, repository context, and optional content signatures. It supports contextual grouping, structural abstraction, temporal analysis, semantic stabilization, duplicate and reuse detection, and lightweight workflow reconstruction from file system observations. The framework deliberately separates observational evidence, contextual abstraction, semantic interpretation, and analytical reconstruction, enabling reproducible workflows that can be inspected by reviewers. It is designed to support future alignment with archival and contextual knowledge representation models, including the World Wide Web Consortium Provenance Ontology (PROV-O): Lebo et al. (2013) and Records in Contexts developed by the International Council on Archives Expert Group on Archival Description (EGAD) . Package: r-cran-fsdam Architecture: all Version: 2024.7-30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil, r-cran-reticulate Suggests: r-cran-r.rsp, r-cran-runit Filename: pool/dists/noble/main/r-cran-fsdam_2024.7-30-1.ca2404.1_all.deb Size: 69278 MD5sum: 80b212d9abbc88c933b55f65e2ff822f SHA1: 0abc56551003d892fa02cd175fa7bd8af99c2b18 SHA256: 93dd1590d12f0d9bcb2a7833b3fdc79017ae4896b40b986da88cad5c1805ea37 SHA512: e0951b1367ee8fd5bbfc2ee91a03a87280d698ef65b61286414523458efc837dec64a73de9c81d694dc357e90d218ceefef1c3c17ca74e71ce583ee6983fe5b1 Homepage: https://cran.r-project.org/package=FSDAM Description: CRAN Package 'FSDAM' (Forward Stepwise Deep Autoencoder-Based Monotone NLDR) FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints. Package: r-cran-fsdar Architecture: all Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3083 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rjava, r-cran-ggplot2 Suggests: r-cran-robustbase, r-cran-rrcov, r-cran-mass Filename: pool/dists/noble/main/r-cran-fsdar_0.9-1-1.ca2404.1_all.deb Size: 2292344 MD5sum: e6e61f2daf48425ab2cba807a1e3f16c SHA1: 51160455f0109731fc5cf4c09c83e0bf7248ce3f SHA256: b9cabbcc6a1146c0796c27e911638329361f27475727d766bbb4a50dc2f24f23 SHA512: ce2001520afbf8afe79ddb3f1259a135754f1d5baa02ed574bebececed4a33d80eb575f2f39b36ba75dade4418090d7470b78432a4880aa95febb0beb799c1ec Homepage: https://cran.r-project.org/package=fsdaR Description: CRAN Package 'fsdaR' (Robust Data Analysis Through Monitoring and DynamicVisualization) Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis (FSDA)' which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis. Package: r-cran-fselector Architecture: all Version: 0.34-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-entropy, r-cran-randomforest, r-cran-rweka Suggests: r-cran-mlbench, r-cran-rpart Filename: pool/dists/noble/main/r-cran-fselector_0.34-1.ca2404.1_all.deb Size: 93996 MD5sum: f30d62678aafd737fc7458249814387f SHA1: e4e59d29db3d9bdcbeddf6ec92cb9d5019eb532a SHA256: d809bfb0a13bc84c8670f73edc8e7bccfff8890c08f2de88c4ed82ebc49fc6aa SHA512: b2cf35b8f4e1fc0baacbff687700a3754bfbb95a38829f68fdb2cc91d672b0d978636f937bff4d77ddd3c60a6b73bc1ca85ce23d6d3ed4c3d88dc37f9d698774 Homepage: https://cran.r-project.org/package=FSelector Description: CRAN Package 'FSelector' (Selecting Attributes) Functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. Package: r-cran-fsemipar Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-dicekriging, r-cran-gtools, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-fsemipar_1.1.1-1.ca2404.1_all.deb Size: 1693788 MD5sum: bb4c270324e10bc98d50b1ef87650ea6 SHA1: 857228c9873a4f74fff3ece886373e541498526f SHA256: dd0ec82d2b6a3fe7ac31796a80428bf5bc3b5b1160d18eb72b5ced39249e23ac SHA512: af0f8baffdd0ea058b9a15d2159c26491fa9fc3787e05bb55cca5e75f7275b0c92e67dab68b6914c233c41ea3109d89a137ff3deadeb00f46f16ba2b418d0c9a Homepage: https://cran.r-project.org/package=fsemipar Description: CRAN Package 'fsemipar' (Estimation, Variable Selection and Prediction for FunctionalSemiparametric Models) Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. These models include the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. Additionally, the package offers algorithms for handling scalar covariates with linear effects that originate from the discretization of a curve. This functionality is applicable in the context of the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model. Package: r-cran-fshybridpls Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fda Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fshybridpls_0.1.0-1.ca2404.1_all.deb Size: 127134 MD5sum: d9c78113d595074673096c2263f1c84e SHA1: 7672a54564a4f5b0c801f76952c19b1f7dc58e85 SHA256: 8476cb8cf15ada3505eb7270acfdcbde8ae5f8ec50165dde64abc95e7141b2fa SHA512: 47ca3d040ca3f4eead42ab15ed53df6984f1173429e80ba7bef16c32d148f668ba5dc1089be42d50345c3a2e889d0f2de175880e75d3c3e6609f69ef6855815f Homepage: https://cran.r-project.org/package=FSHybridPLS Description: CRAN Package 'FSHybridPLS' (Hybrid Penalized Partial Least Squares for Mixed Data) Fits Penalized Partial Least Squares (PLS) regression when predictors are hybrid objects that combine functional curves (infinite-dimensional 'fda' objects) and scalar covariates (finite-dimensional numeric matrices). The package treats a hybrid predictor as an element of a product Hilbert space formed by the functional and Euclidean components, and implements the arithmetic (addition, scalar multiplication, and inner products, including roughness-penalized inner products) needed to run penalized PLS directly in that space. The algorithm extracts latent components that maximize covariance with a scalar response while penalizing roughness of the estimated functional coefficient curves. Helpers are included for constructing hybrid predictors, two-step within- and between-modality normalization, train/test splitting, synthetic data generation, cross-validated component selection, and prediction. The method is described in Mun and Jang (2026) . Package: r-cran-fsia Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fsia_1.1.2-1.ca2404.1_all.deb Size: 555942 MD5sum: a7a5c09c6f3ed232ecb2c3d0d6b4e183 SHA1: 615940df1cd7938c6c99b49f0554bd48f1809975 SHA256: 251bdd3e0708ebdf92166f9bd207eb1d473ccb345e8e05ded403de610aa8bd5c SHA512: 5a740334d79c07559cb3eec9d63011f6c589be58d2af581f3bf9a5f1168d1b0fd8f16f31cd22e0e5fb291928f1c4c90ac88852b6d0024a5eb3028b9752f8975f Homepage: https://cran.r-project.org/package=fsia Description: CRAN Package 'fsia' (Import and Analysis of OMR Data from FormScanner) Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from . The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects. Package: r-cran-fsinr Architecture: all Version: 2.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-caret, r-cran-rdpack, r-cran-ga, r-cran-dplyr, r-cran-tidyr, r-cran-prodlim, r-cran-rlang, r-cran-purrr, r-cran-e1071 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rsnns, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-fsinr_2.0.10-1.ca2404.1_all.deb Size: 412418 MD5sum: 3e9b9685d14617542ed00d4a2da28182 SHA1: 4440485f1ce9d79561b7ad371d8826ae2cb0e346 SHA256: fa7502358de9d456a10c9fe377b7f03c35f705b5a469c6137ef1f97a70ebdd5f SHA512: 6d1b9c4e96e7e0780990abcf914e3ce79dcba104798cd6741ec91b3341c2b0ff49ba11370045e0fa7e9430ce6517cf1e559131acf04336e806f1041ccfb0898d Homepage: https://cran.r-project.org/package=FSinR Description: CRAN Package 'FSinR' (Feature Selection in R) Feature subset selection algorithms modularized in search algorithms and measure utilities. Package: r-cran-fsk2r Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2890 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-readxl, r-cran-readtext, r-cran-xml2, r-cran-jsonlite, r-cran-shiny, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fsk2r_0.2.0-1.ca2404.1_all.deb Size: 1301046 MD5sum: 15d56dfd578eda949daa635563b183c6 SHA1: 8adc0f6359684a01d72541177dcc58f3551cfa5c SHA256: d1e9265838e46267f56bea3ed2aeeba733dd58d6975958dfd9936b6256fa534e SHA512: 9200143b4b9a190adcfa7dee9b2a9035c036fe22c7ab2e8d79a41733967392f7bf76b517a2bc8559e6c3597c59528c9ad1d2d40d188d2f13abd6f0ad0bc55092 Homepage: https://cran.r-project.org/package=FSK2R Description: CRAN Package 'FSK2R' (An Interface Between the 'FSKX' Standard and 'R') Functions for importing, creating, editing and exporting 'FSK' files using the 'R' programming environment. 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Package: r-cran-fslr Architecture: all Version: 2.27.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.nifti, r-cran-neurobase, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-fslr_2.27.0-1.ca2404.1_all.deb Size: 756090 MD5sum: d508b6aca8362a751e19cd6c042585ef SHA1: 8f812da51161901efa0b3bdb3047d2d4d6717bc1 SHA256: 78d9e1d4c1a7ced637e2013452180432ec8f356e4b7009dee2dc4fd61b5976c4 SHA512: 57b1503c102accc219dbf094680bceb064aaab4754f332ae375921bcd845bce1277e00e093b31fcd6b258bddf4e8f66a54d1f57fc5524abb2d8f3e67a100b6cd Homepage: https://cran.r-project.org/package=fslr Description: CRAN Package 'fslr' (Wrapper Functions for 'FSL' ('FMRIB' Software Library) fromFunctional MRI of the Brain ('FMRIB')) Wrapper functions that interface with 'FSL' , a powerful and commonly-used 'neuroimaging' software, using system commands. The goal is to be able to interface with 'FSL' completely in R, where you pass R objects of class 'nifti', implemented by package 'oro.nifti', and the function executes an 'FSL' command and returns an R object of class 'nifti' if desired. Package: r-cran-fsm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fsm_1.0.0-1.ca2404.1_all.deb Size: 99814 MD5sum: b99e907f5b7d087e190fbd2da761affa SHA1: bae67c63429d6d1eb9e07bbb264aa33eb5c558c3 SHA256: 5220b78185403abf3ceff969a2e82a5f970d2c411a4a191d31623e8f42a12ccd SHA512: 502a5792e66c95b8079a62a56577f25f003b77108f26e78473e0cc9f66288268206225d01ee3b6486da39f6dfeab4f526f0361a5fcab5e09e33665829060d574 Homepage: https://cran.r-project.org/package=FSM Description: CRAN Package 'FSM' (Finite Selection Model) Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) for details on the original version of the FSM. Package: r-cran-fsn Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-fsn_0.4-1.ca2404.1_all.deb Size: 41270 MD5sum: eed7374189ff74192f028c5cc496347a SHA1: b95ed6b0468b1d6568b5ed8282fb971e66e9b013 SHA256: 189f3c63a120da9e19d0d2760ae60e8d98fbb1b18f5154692d18bb84339f726c SHA512: 08dea9b9c10fff20bd16cbe5bb61c40e29e871b83e76bd036de0a599cb48960bb786de459338c8607199ec072651832d86a59c3e605ac2d804f9edb089d7cf20 Homepage: https://cran.r-project.org/package=fsn Description: CRAN Package 'fsn' (Rosenthal's Fail Safe Number and Related Functions) Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. . Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. . Package: r-cran-fspe Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-fspe_0.1.2-1.ca2404.1_all.deb Size: 31482 MD5sum: 5a5de4e96a36d884898ca938e33001c6 SHA1: 214ce8ec20e44f0c9c5ca37fec1ca9ea9ac0aac6 SHA256: 23191d3bcb22918170566572911a793b6d846216590526a4d507d2215b42a6cc SHA512: b4f6a13bf5db0321b3c3ee4fc84d03e3fa6e8cc8176dc20535553899c7d0df252ccc3dfe696177186d068ed6e201ba8d3465cfa1b6534724a7220b787ec5453b Homepage: https://cran.r-project.org/package=fspe Description: CRAN Package 'fspe' (Estimating the Number of Factors in EFA with Out-of-SamplePrediction Errors) Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) . Package: r-cran-fspls2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-jsonlite, r-cran-matrix, r-cran-glmnet, r-cran-tidyr, r-cran-mass, r-cran-ggplot2, r-cran-proc, r-cran-binom, r-cran-confintr, r-cran-dbi, r-cran-rsqlite, r-cran-tibble, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fspls2_0.1.0-1.ca2404.1_all.deb Size: 1997416 MD5sum: ab7143e65103f8cd9a9437a22788ce5c SHA1: a71bfc88289ab99c74cf6b6a17883a863dcb5a64 SHA256: 0a6f83ad80e92303f2ef746e39086be0d666ba076bd81c4d317ef944f8e70eb9 SHA512: 89bbe4b0f3b6597c974549f2a761ccd86e5ad37a8567047f764a0a84c5329ca21d7a659e981d849eaeed9ab9a8d22599183b4650965768d45907f3cb8c637db4 Homepage: https://cran.r-project.org/package=fspls2 Description: CRAN Package 'fspls2' (A Package to Find Minimal Transcriptional Signatures) Identifies minimal biomarker signatures for predicting phenotypes from multiomic data. Integrates multiple omics layers, supports internal cross-validation. Supports missing values in predictors and outcomes. Enables model based imputation of uncertain values. Supports continuous, binary, multi-class and ordinal outcomes. Package: r-cran-fsr Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-sf, r-cran-dplyr, r-cran-ggplot2, r-cran-stringr, r-cran-tibble, r-cran-pso, r-cran-e1071 Suggests: r-cran-lwgeom Filename: pool/dists/noble/main/r-cran-fsr_2.0.1-1.ca2404.1_all.deb Size: 437450 MD5sum: e99044b75a1f0ab874f39a2d5e513034 SHA1: dfc9ab80eede11e43b84335ef27156a5f38512db SHA256: f560e949c420f775dbdbf4b2064f0c99f0ee84401a08fc7ee63fade518abcd7d SHA512: f121d5cd70de1f9b6101023fc201891c6dfefb5437e0f5de4e157cb51a893b9c499062d14e20a357ac9bc04247ab9d163ac94a819ecee5dfa2b4706544cbf6ab Homepage: https://cran.r-project.org/package=fsr Description: CRAN Package 'fsr' (Handling Fuzzy Spatial Data) Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. It mainly implements underlying concepts defined in the following research papers: (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" ; (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" ; (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" ; (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" ; (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" . 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Features include spanning header, grouping rows, parsing markdown and so on. Package: r-cran-ftrading Architecture: all Version: 3042.79-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-fbasics Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-ftrading_3042.79-1.ca2404.1_all.deb Size: 97430 MD5sum: 5054fe691145f12419d0b550cc212501 SHA1: 7c38f227f6d3202d71594cffe4adb9ef33a07d0e SHA256: b936accde6b9e435d2cde364db989b0785cc9f16079d029b9d509c62f7fa1bd9 SHA512: 1a961d758b771ed4094ce339b19cff5f9e8a574ade9ef6beb5c765be91858afba399c5270746880b39a8f88eb86d53a6e1e9681d6140783d7a582502a46ca1be Homepage: https://cran.r-project.org/package=fTrading Description: CRAN Package 'fTrading' (Rmetrics - Trading and Rebalancing Financial Instruments) A collection of functions for trading and rebalancing financial instruments. It implements various technical indicators to analyse time series such as moving averages or stochastic oscillators. Package: r-cran-ftrcool Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2784 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ftrcool_2.0.0-1.ca2404.1_all.deb Size: 1469228 MD5sum: 33746eb4a05cb35ca6d44cea3bcfe9bb SHA1: 16eda249e7569a0ac00442a9c71475422037f72b SHA256: f435270fe1d24b0e70faf0986182dd846594687e8353b918d74a443a7174dc18 SHA512: 89391136ca44f4e26bc58fe3568081cfc5ed0c578eef3ef7a71d3c7a94bba2e2f3098e971c3f0f15d2869e09ac7b803126054571198265fb72af348cdbe956a9 Homepage: https://cran.r-project.org/package=ftrCOOL Description: CRAN Package 'ftrCOOL' (Feature Extraction from Biological Sequences) Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) . 'iFeature' 'Z. Chen et al.' (2018) . . 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) . 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) . 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) . 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) . 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These methods are described in Kokoszka, Rice, and Shang (2017) , Yeh, Rice, and Dubin (2023) , Kim, Kokoszka, and Rice (2023) , and Rice, Wirjanto, and Zhao (2020) . 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Package: r-cran-fuel Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fuel_1.2.0-1.ca2404.1_all.deb Size: 80688 MD5sum: 40b311d999219cf26694a7771810efbe SHA1: f927ba639744ae865ad2db4e68c098e64531cf74 SHA256: d7fc379e5f5b0b376ee52cc64a36882e10123bf0a7e30e3adff5885639147137 SHA512: 3df8adaa8a08992b4c1eb2ff12161fc224642d816603398c8d00a0cb59ab774b9981b544fce94e5d2b60dfe164b1843e91233a6fac0ad03742e07d8f4c9fad68 Homepage: https://cran.r-project.org/package=fuel Description: CRAN Package 'fuel' (Framework for Unified Estimation in Lognormal Models) Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package 'fuel' implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report. Package: r-cran-fueldeep3d Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-viridislite, r-cran-rlang Suggests: r-cran-lidr, r-cran-reticulate, r-cran-dbscan, r-cran-ggplot2, r-cran-rgl, r-cran-rcsf, r-cran-scales Filename: pool/dists/noble/main/r-cran-fueldeep3d_0.1.1-1.ca2404.1_all.deb Size: 6214370 MD5sum: 29810ac623d8eb541f18b6e34b8e0b4d SHA1: d9afe96c42a9347b45436bf39c7f3039e89f07f0 SHA256: eff3116a8ea3bc2030479960187e92dac80d05d000b1bc061e6a65a2c0929c72 SHA512: 26ef80fce90973cab581bd2dadeabd5ac17aea3a2417ea3a1ad1f25d0d28643fb17be9d6f7d12a30db5506b4c2d3efec3a97c3848906637c8efb09743f457237 Homepage: https://cran.r-project.org/package=FuelDeep3D Description: CRAN Package 'FuelDeep3D' (3D Fuel Segmentation Using Terrestrial Laser Scanning and DeepLearning) Provides tools for preprocessing, feature extraction, and segmentation of three-dimensional forest point clouds derived from terrestrial laser scanning. Functions support creating height-above-ground (HAG) metrics, tiling, and sampling point clouds, generating training datasets, applying trained models to new point clouds, and producing per-point fuel classes such as stems, branches, foliage, and surface fuels. These tools support workflows for forest structure analysis, wildfire behavior modeling, and fuel complexity assessment. Deep learning segmentation relies on the PointNeXt architecture described by Qian et al. (2022) , while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang et al. (2016) . Package: r-cran-fueleconomy Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fueleconomy_1.0.0-1.ca2404.1_all.deb Size: 241994 MD5sum: 4822e91dc20203f4cbc839eefc474840 SHA1: 7602c679a973b5888fd7e2636124c72f8dc7cb07 SHA256: 44e97db9908d83d234c3a87cd81c38d8bbcb4f4c3f3ec32e6cce444a0381b0b5 SHA512: 052bab68c011701b8134aa718dbf86cef0b2553fd799a48bf629b306e1aa6a0b0cae8f69b55c0b290fd772ec9104230896090789da831168c1357fe7d4564c50 Homepage: https://cran.r-project.org/package=fueleconomy Description: CRAN Package 'fueleconomy' (EPA Fuel Economy Data) Fuel economy data from the EPA, 1985-2015, conveniently packaged for consumption by R users. Package: r-cran-fugue Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fugue_0.1.7-1.ca2404.1_all.deb Size: 69276 MD5sum: 3f30faf0fd1ce4b5b9fed99c327067df SHA1: 054979d6020ceec08b28d8e99e5cfa7366821939 SHA256: bfa1683289401d83a8fd3efe9a4bcb20029961c8f1ce63c58008bc2b22c9b397 SHA512: c9a96fcb629e7f2e95d3334de0980218ee67f4619ea596ac9536b647f52b495860a867d43beecdbe9378c2014795f45571f24d8852e4df3149e2428b776cadea Homepage: https://cran.r-project.org/package=fugue Description: CRAN Package 'fugue' (Sensitivity Analysis Optimized for Matched Sets of Varied Sizes) As in music, a fugue statistic repeats a theme in small variations. Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes. The main functions in the package are sen() and senCI(). For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 . Package: r-cran-fuj Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuj_0.3.0-1.ca2404.1_all.deb Size: 283602 MD5sum: a6cc9c24bb62a525f73a6c708b045bfb SHA1: 4cd646f3000614af6c460f301bdd2eca6201fd84 SHA256: d4a4da9d73688bafd677c6004526d3403f08ca8019ddf2d934a59886f80070f5 SHA512: db10f5c6b06979d44424912ce82cfffeab470d4d6ee2c319486ca2c38fb37e69ae3e7c19fb0cadc29bc4ad670158e77bef2e365c434d2ec161f9c66c774d76b9 Homepage: https://cran.r-project.org/package=fuj Description: CRAN Package 'fuj' (Functions and Utilities for Jordan) Provides core functions and utilities for packages and other code developed by Jordan Mark Barbone. 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Package: r-cran-fullrankmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1788 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat, r-cran-weightit, r-cran-caret, r-cran-plm, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fullrankmatrix_0.1.0-1.ca2404.1_all.deb Size: 1032840 MD5sum: 5abe1630dc5adb457fdf2728e0ebf33b SHA1: 3d4519827365a8e1565442b2170553ade8076e71 SHA256: 552e7d6b26f1c946abd88206de3ac7b7e159e8fe15799330bc60dbc361f444a6 SHA512: 5381fae1eec321fc30d0bd088486a3b5448d7bc7e731f9b25693005462b673d11b37379e1559c4af895a35b74b9dfb651405878599cce687acb3e601a91e31ad Homepage: https://cran.r-project.org/package=fullRankMatrix Description: CRAN Package 'fullRankMatrix' (Generation of Full Rank Design Matrix) Creates a full rank matrix out of a given matrix. The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, 'fullRankMatrix' can be applied to any matrix to make it full rank. It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications. This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) . Package: r-cran-fullroc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fullroc_0.1.0-1.ca2404.1_all.deb Size: 46878 MD5sum: 2939179247592b9a941354a42f614a25 SHA1: eb68b5b3155584902a23f78d15d87d1b93429ab1 SHA256: 4e459f7662b2a50690bdb7f61ce519d839f02c32318d8c97820872705a1da9bc SHA512: ec23c73d4a6eae19b6a334e75997b9a41ab0f32c2e84e2f6b2469eb78a25f3359c553947f8afa88641394cd78100e926b72b1ad9edd4fc6d402bb6a9bfff35af Homepage: https://cran.r-project.org/package=fullROC Description: CRAN Package 'fullROC' (Plot Full ROC Curves using Eyewitness Lineup Data) Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". , Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. . 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Package: r-cran-funcc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-narray, r-cran-biclust, r-cran-reshape, r-cran-rcolorbrewer, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-funcc_1.0-1.ca2404.1_all.deb Size: 498114 MD5sum: ac5de2b98110c249266578e54c9ea9c4 SHA1: fdc54152538cc9edd9670690b071a20c82d1c7e0 SHA256: 9a7a40c84fedff483ef8135b14fcd9e376bba403677f33b0919627d498ae72e6 SHA512: 319dcdd61df76fcd721c6656807735dbe1c2e72aa69c1e3104cd1a35dbd6ab0980b6cea27418f9bbf146c439ee2d6695198e111a1bea01ce922f3105d6da6dce Homepage: https://cran.r-project.org/package=FunCC Description: CRAN Package 'FunCC' (Functional Cheng and Church Bi-Clustering) The FunCC algorithm allows to apply the FunCC algorithm to simultaneously cluster the rows and the columns of a data matrix whose inputs are functions. 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Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank. FuncMap should be useful for comparing packages at a high level for their overall design. Plus, it's just plain fun. The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package. Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots. 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The package uses standard formulas for model specification and provides stable S3 interfaces for fitting, evaluation, tuning, interpretation, and causal estimation across a learner registry with multiple backend engines. Implemented interpretation methods build on established approaches such as permutation-based variable importance, partial dependence, individual conditional expectation, accumulated local effects, SHAP, and LIME; see Friedman (2001) , Goldstein et al. (2015) , Apley and Zhu (2020) , Lundberg and Lee (2017) , and Ribeiro et al. (2016) . The framework is intentionally opinionated: preprocessing is expected to occur outside the modeling step, and the API emphasizes explicit inputs, consistent object contracts, and compact interfaces rather than feature-by-feature competition with larger machine learning ecosystems. Plug-in g-computation follows Naimi, Cole, and Kennedy (2016) . 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In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights. 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Mills, Alexander F. Wilson, Joan E. Bailey-Wilson, and Momiao Xiong (2013) ). 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Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, . Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, . Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, . Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. . 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The model is similar to that of Lindquist (2012) although allowing a binary outcome as an alternative to a numerical outcome. The current version is a minor bug fix in the vignette. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Package: r-cran-funmodeling Architecture: all Version: 1.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-rocr, r-cran-ggplot2, r-cran-gridextra, r-cran-pander, r-cran-reshape2, r-cran-scales, r-cran-dplyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-moments, r-cran-entropy, r-cran-cli, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-funmodeling_1.9.6-1.ca2404.1_all.deb Size: 1000448 MD5sum: 40fdaa3999049cbadb9f8775867f3f86 SHA1: 1c713de357da6b77bb5bb2c9cdbe7047eecae594 SHA256: 1cea3f2ceee93aaa913a71f3adf01c6d28e1c0d77b06e60b568b5d82809381e8 SHA512: 2d551db3cb2bed10c39a083752df6f6376559012a64559918df38a862f88fb8fbbb9d9f9cbb29f01169b7dc24179b23dca1e693fd6f70dec327664e3467c2a48 Homepage: https://cran.r-project.org/package=funModeling Description: CRAN Package 'funModeling' (Exploratory Data Analysis and Data Preparation Tool-Box) Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book () are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions. 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Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored. 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Package: r-cran-funprog Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-purrr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-funprog_0.3.0-1.ca2404.1_all.deb Size: 34092 MD5sum: 0fef0fa9dfeaa1057d0cfcb94a96a709 SHA1: cafc6d7e94580d61ba9bb1580ee3c8f7d47e7148 SHA256: 6a6469edce923778b796553803e605f83c83d76f0b475db3584ad403451ab169 SHA512: 8c7b20b29a664a4b0ae6beca3ea21ecf4ebd8affef1e4565714c30209493efd15e0945a4074fee578a891cdccbd636f6ce636c56a23576dc298529b4aa99cf14 Homepage: https://cran.r-project.org/package=funprog Description: CRAN Package 'funprog' (Functional Programming) High-order functions for data manipulation : sort or group data, given one or more auxiliary functions. Functions are inspired by other pure functional programming languages ('Haskell' mainly). The package also provides built-in function operators for creating compact anonymous functions, as well as the possibility to use the 'purrr' package syntax. Package: r-cran-funr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-funr_0.3.2-1.ca2404.1_all.deb Size: 30360 MD5sum: c857d25ae364c964a771a7114767fadf SHA1: 8eda3539bf6f55be2248a2e63bc14d1f2afe5ffe SHA256: 8e93df9dfcd87162ce632373c22d8652c0561f7807f5dbed98c06bcfc275caca SHA512: d47c5b92c92cb9c14776b4456669e21aaaa9bd412b68bb997b1da4692a547d3b42f023c5ffa064ae5f06f32bba5a85fbc28f04a6307f8cf793e767f6b9666cc9 Homepage: https://cran.r-project.org/package=funr Description: CRAN Package 'funr' (Simple Utility Providing Terminal Access to all R Functions) A small utility which wraps Rscript and provides access to all R functions from the shell. Package: r-cran-funrar Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-matrix Suggests: r-cran-ade4, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidytext Filename: pool/dists/noble/main/r-cran-funrar_1.5.0-1.ca2404.1_all.deb Size: 540670 MD5sum: 8b9564d0058eec15acce5097e4d6e543 SHA1: ad6bf9f84de8cc924c46b03da18c101ddf06acff SHA256: 1cc90ae12fc80734d159f35daf17900570208eed3c3cc8ed0f50c8ed47053e38 SHA512: e1daa462b99786c0c7f3feb2202ad5a36f8d22337c960293838a7133a459306604b98468ae67c0b251575e51c039a7bfd4a053bdc9a2625ec743a63c1487fbbc Homepage: https://cran.r-project.org/package=funrar Description: CRAN Package 'funrar' (Functional Rarity Indices Computation) Computes functional rarity indices as proposed by Violle et al. (2017) . Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Grenié et al. (2017) . Package: r-cran-funreg Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-funreg_1.2.2-1.ca2404.1_all.deb Size: 410886 MD5sum: cb9a105c0a415267e440b1585719f891 SHA1: eb2d67d7d08b472b846f766748e9e6af10a0380e SHA256: 1027e8654528121a4d70f5242fc1239f0996068ff48be8ca3cd003fd1e411b7d SHA512: 2071ba86ab79af4bdb04535689d1d88ff664de9f2a9b57747b3cab89ca56681e3e9841835cfe7edb591825f3046096bdfba18a841473f5731a228fd3de89896a Homepage: https://cran.r-project.org/package=funreg Description: CRAN Package 'funreg' (Functional Regression for Irregularly Timed Data) Performs functional regression, and some related approaches, for intensive longitudinal data (see the book by Walls & Schafer, 2006, Models for Intensive Longitudinal Data, Oxford) when such data is not necessarily observed on an equally spaced grid of times. The approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu, Caffo, and Reich (2011) and the approach taken in their sample code, but with some modifications to make it more feasible to use with long rather than wide, non-rectangular longitudinal datasets with unequal and potentially random measurement times. It also allows easy plotting of the correlation between the smoothed covariate and the outcome as a function of time, which can add additional insights on how to interpret a functional regression. Additionally, it also provides several permutation tests for the significance of the functional predictor. The heuristic interpretation of ``time'' is used to describe the index of the functional predictor, but the same methods can equally be used for another unidimensional continuous index, such as space along a north-south axis. Note that most of the functionality of this package has been superseded by added features after 2016 in the 'pfr' function by Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the 'refund' package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel. The development of the funreg package in 2015 and 2016 was part of a research project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, or the National Institutes of Health. Package: r-cran-funresmech Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-deoptim, r-cran-dplyr, r-cran-future, r-cran-ggplot2, r-cran-magrittr, r-cran-plotly, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybs, r-cran-shinythemes Suggests: r-cran-kableextra, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-funresmech_1.0.4-1.ca2404.1_all.deb Size: 74696 MD5sum: 78fa7e62a1d8fcd661767a5837314cbc SHA1: b3164e475231b58dd0dba16a7195afaedfec0437 SHA256: df06d1900203084cd85ed0aed0425b0c5b8524f84f85333ed0406cb9893cad30 SHA512: 7207619e6d8c19ece3a1c5f979924a7678353c1d957d23ab0d2bcb3972a3eff44b6880bf888a565cc17e11f1009056735fb23cb5ac22f06e63fd1033096f0fab Homepage: https://cran.r-project.org/package=funresMech Description: CRAN Package 'funresMech' (Mechanistic Functional Response Analysis) Implements the mechanistic functional response model proposed by Okuyama (2012) for host-parasitoid systems. 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Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) , Horvath et al (2013) or Cuevas et al (2004) are also included in the package, as well as procedures to run test result comparisons and power analysis using simulations. 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Package: r-cran-futile.logger Architecture: all Version: 1.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambda.r, r-cran-futile.options Suggests: r-cran-testit, r-cran-jsonlite, r-cran-httr, r-cran-crayon, r-cran-rsyslog, r-cran-glue Filename: pool/dists/noble/main/r-cran-futile.logger_1.4.9-1.ca2404.1_all.deb Size: 114280 MD5sum: 73075a150a3b2eeb105a298315363bdb SHA1: 5594a22589c27a99ee9d4eb6188fcf855a27b381 SHA256: 8d974e67194c26b080aebd35a4e9c5402bcd74561d839ed554cb7830c0b585f8 SHA512: dfd47d36077421c8a460dabc553bc3b3becb31bb2a9e6fa3fe060e36e69a08b09210f7e140739c50dfdf7d074643743f122b793bedf6e6ac0dce84511daf7b3e Homepage: https://cran.r-project.org/package=futile.logger Description: CRAN Package 'futile.logger' (A Logging Utility for R) Provides a simple yet powerful logging utility. Based loosely on log4j, futile.logger takes advantage of R idioms to make logging a convenient and easy to use replacement for cat and print statements. Package: r-cran-futile.options Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-futile.options_1.0.1-1.ca2404.1_all.deb Size: 19772 MD5sum: 958c598db3fc80c45ef94d210cf54f4c SHA1: add93f41ea6cc07d98f4fc838797dda8a0f5178a SHA256: f7b23993bc54f8311e9dd73190dcf9feeaebd7419221e0d0ef4197709bba801b SHA512: e06de4643939522e6b859ce1971f4d17f7ba806203ba7ff4d0271d9036482910c5cea7ccdb5228345286b82ccd1060a29e666ded30534e7fe646ef934286131d Homepage: https://cran.r-project.org/package=futile.options Description: CRAN Package 'futile.options' (Futile Options Management) A scoped options management framework. Used in other packages. Package: r-cran-futility Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-futility_0.5-1.ca2404.1_all.deb Size: 274508 MD5sum: 94e1b99f7abd2c7fd804b1f21944ce29 SHA1: 1b04146cafc3b73dcd54432d76eace293253c84c SHA256: b0e7d4939df2a02b829cf746bc6726fae54fba7fd49672ad6cb48d8d62344d72 SHA512: 23f3f41b92b616a09ae975acd4185d812f2a7d73b3f32bbc2a75cb84e14ee4cd726f0d0182d7084ced802e2e075fddaad2ec670d2754bb46fd478a707253a467 Homepage: https://cran.r-project.org/package=futility Description: CRAN Package 'futility' (Interim Analysis of Operational Futility in Randomized Trialswith Time-to-Event Endpoints and Fixed Follow-Up) Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results. Package: r-cran-future.apply Architecture: all Version: 1.20.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-globals Suggests: r-cran-listenv, r-cran-r.rsp, r-cran-markdown Filename: pool/dists/noble/main/r-cran-future.apply_1.20.2-1.ca2404.1_all.deb Size: 190756 MD5sum: 365446d11ce21ca88fb3278af5c51573 SHA1: de7cfd1cec67eb72b7918b2e188393f671ea78e4 SHA256: adbb057167d1dcb6c18f0d0ce505c32025546f16d42df87be0a551fd1bec1684 SHA512: edcdc4f1c0ba00a65e0dfd15bf58b88b554562b4b62647422ef62afac44e1b8b66ba75a2fb19ecbd3d19ad6efb09f578a1a61eb94b8e64934ec537745b645cec Homepage: https://cran.r-project.org/package=future.apply Description: CRAN Package 'future.apply' (Apply Function to Elements in Parallel using Futures) Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework . Package: r-cran-future.batchtools Architecture: all Version: 0.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-parallelly, r-cran-batchtools, r-cran-checkmate, r-cran-stringi Suggests: r-cran-globals, r-cran-future.apply, r-cran-listenv, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-future.batchtools_0.22.0-1.ca2404.1_all.deb Size: 290564 MD5sum: 87cc2e36c134f7f1660c00d89ec52fb8 SHA1: ba94c4a4c67ee5a143c09d6a9b58d86fe21c1a89 SHA256: 5e6c22e3c27e9a5f3412fe9b1c894cddc33657bb0db6389183223e64e0a68d10 SHA512: ba962ab83b51af8fb293a6129261bff87fb739765c8e5b1135f670eab1ba3d0e2385be41b3a01ca422df6467eeac95c172d27c1079ba79efbf80d6e4b578e610 Homepage: https://cran.r-project.org/package=future.batchtools Description: CRAN Package 'future.batchtools' (A Future API for Parallel and Distributed Processing using'batchtools') Implementation of the Future API on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'. Package: r-cran-future.callr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-parallelly, r-cran-callr Suggests: r-cran-globals, r-cran-future.apply, r-cran-listenv, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-future.callr_1.0.0-1.ca2404.1_all.deb Size: 86294 MD5sum: b72ce26fe72dddf8f5f686ca3d7065b6 SHA1: 4fdd3942f811d5bb62dc5d5a75a5e7e48dc413bc SHA256: be0877579d01a0e3c437269395b844e29f379bf13780c569f337b328c2e8b2ee SHA512: 57aeaa01ea2252779bc29819e26263d1c7ccfb3a00906890963047371c499ac6d739ef7fff42879d88eb18ad21cd95e0cfe5501ac5e9fafee640d7cfd84b2e21 Homepage: https://cran.r-project.org/package=future.callr Description: CRAN Package 'future.callr' (A Future API for Parallel Processing using 'callr') Implementation of the Future API on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes. Package: r-cran-future.mirai Architecture: all Version: 1.0.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-mirai, r-cran-parallelly Suggests: r-cran-future.tests, r-cran-future.apply, r-cran-listenv Filename: pool/dists/noble/main/r-cran-future.mirai_1.0.0-1.ca2404.2_all.deb Size: 89256 MD5sum: 191fdd05fe56e7a0f769f518d64e54c6 SHA1: a18564220a44d519d0c579b9f8784f77e3acd202 SHA256: 5ca1f73b0a451580af6cb7450bef29d96d1bb9344263157ecaf9d4f217aeb3b1 SHA512: 314c78dbfb6b20081925ac902537a0f5a3b830f271f31e47346d018c21144b50b9ef0ff1eb70d9f873b857241b4b7505ba997ad8676c165435bc22f9a79f3cc6 Homepage: https://cran.r-project.org/package=future.mirai Description: CRAN Package 'future.mirai' (A 'Future' API for Parallel Processing using 'mirai') Implementation of the 'Future' API on top of the 'mirai' package . By using this package, you get to take advantage of the benefits of 'mirai' plus everything else that 'future' and the 'Futureverse' adds on top of it. It allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local machine or across remote machines. Contrary to back-ends relying on the 'parallel' package (e.g. 'multisession') and socket connections, 'mirai_cluster' and 'mirai_multisession', provided here, can run more than 125 parallel R processes. As a reminder, regardless which future backend is used by the user, the code does not have to change, it gives identical results, and behaves exactly the same. Package: r-cran-future.tests Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-cli, r-cran-crayon, r-cran-prettyunits, r-cran-sessioninfo Suggests: r-cran-commonmark, r-cran-base64enc, r-cran-data.table, r-cran-ff Filename: pool/dists/noble/main/r-cran-future.tests_1.0.0-1.ca2404.1_all.deb Size: 205614 MD5sum: 38b021f35ce0e553d0c6f603a1f869e1 SHA1: 3c7196fb009ff0ce352e7e628fb92bc05147084d SHA256: 0942489eecc30516e9560175d3a9672941f8faf5bff073913ad2d4df6ebfbbc9 SHA512: 305d799533b6385b107b0230cb66490e7c3625b342a823232a994a512045588161aae743eb0aab7bc3fb7415b0e5525afa8420406d64948c2c3daea1cf0133ea Homepage: https://cran.r-project.org/package=future.tests Description: CRAN Package 'future.tests' (Test Suite for 'Future API' Backends) Backends implementing the 'Future' API , as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines. Package: r-cran-future Architecture: all Version: 1.76.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1799 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-globals, r-cran-listenv, r-cran-parallelly Suggests: r-cran-rhpcblasctl, r-cran-r.rsp, r-cran-markdown Filename: pool/dists/noble/main/r-cran-future_1.76.0-1.ca2404.1_all.deb Size: 961210 MD5sum: d7a852d5beef2d04de820f2ba5dcbab5 SHA1: c30e854a15d8d0b007651037563d0cb837667993 SHA256: 55ca6f0bbd453450ea587ed672c18ea5544e9d8c2887d72b2838506aacd4ae9e SHA512: 4a7527a90d81738e01cfe6ebeca8dbe4919eb8a61cba88152922b5b05a2d63c0cf1ce5be599a422f613b155c51ec696d3cf5cd5404df6d100426569982beee05 Homepage: https://cran.r-project.org/package=future Description: CRAN Package 'future' (Unified Parallel and Distributed Processing in R for Everyone) The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multisession)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures. Package: r-cran-futureverse Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-futurize, r-cran-future.apply, r-cran-furrr, r-cran-dofuture, r-cran-progressify, r-cran-progressr Suggests: r-cran-future.batchtools, r-cran-future.callr, r-cran-future.mirai, r-cran-rhpcblasctl Filename: pool/dists/noble/main/r-cran-futureverse_0.3.0-1.ca2404.1_all.deb Size: 38552 MD5sum: fa55a9a67a0b7c74107da6374b7bb85b SHA1: 46314e2f2a33250d31f4792e9166a36007470144 SHA256: b915b9c5f30c7f95075a2e32af902dda62085f84e2d6fc9e47598cd5353e62cc SHA512: 7e39b20cb5671926e2201a1a4ab8562cc1c769d4e30e3a6ebe0a4e5ef8436f1088dd70dbfae9b8451efa1c3ea3562390b6b81c75b9f6248c685fc9b8ba3b7885 Homepage: https://cran.r-project.org/package=futureverse Description: CRAN Package 'futureverse' (Install 'Futureverse' in One Go) The 'Futureverse' is a set of packages for parallel and distributed processing with the 'future' package at its core (Bengtsson, 2021, ). Another notable component is the 'futurize' package (Bengtsson, 2026, ) for turning common sequential calls into parallel ones via a single function futurize(). Similarly, the progressify() of the 'progressify' package makes common calls to report of progress. This package is designed to make it easy to install common 'Futureverse' packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each 'Futureverse' package as a dependency as needed. Package: r-cran-futurize Architecture: all Version: 1.0.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2000 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future Suggests: r-cran-future.apply, r-cran-foreach, r-cran-dofuture, r-cran-purrr, r-cran-furrr, r-cran-crossmap, r-cran-plyr, r-cran-pbapply, r-bioc-biocparallel, r-cran-boot, r-cran-survival, r-cran-caret, r-cran-randomforest, r-bioc-deseq2, r-cran-dicekriging, r-cran-ez, r-bioc-fgsea, r-bioc-genomicalignments, r-bioc-rsamtools, r-cran-fwb, r-cran-gamlss, r-cran-glmmtmb, r-cran-glmnet, r-bioc-gsva, r-cran-kernelshap, r-cran-lme4, r-cran-metafor, r-cran-mgcv, r-cran-modelsummary, r-cran-parameters, r-cran-partykit, r-cran-pls, r-cran-pvclust, r-cran-riskregression, r-cran-rugarch, r-cran-sandwich, r-bioc-scater, r-bioc-scuttle, r-cran-seriation, r-cran-shapr, r-cran-sim.diffproc, r-cran-simdesign, r-bioc-singlecellexperiment, r-cran-stars, r-cran-strucchange, r-cran-superlearner, r-bioc-sva, r-cran-tm, r-cran-vegan, r-cran-commonmark, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-futurize_1.0.0-1.ca2404.2_all.deb Size: 502518 MD5sum: cad6a56c27d1a5a36eadd8f2691cab89 SHA1: 7f1bb12d3245dc855ca58fa063b8bbe411398f30 SHA256: a05f217232ee7f6508df1d040244c37e3c83e01839cd0d3c28dfaff72ab12af8 SHA512: 24666e8e27419b56657af7c6e36234365fb8124c70ba73165f648441dc21a4eeeea38b771d9112903cd0e251e2106d7bfd3c1869d046f9d5f93e47a10180bf4c Homepage: https://cran.r-project.org/package=futurize Description: CRAN Package 'futurize' (Parallelize Common Functions via One Magic Function) The futurize() function turns sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem . By combining this transpiler function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |> futurize()', 'purrr::map(xs, fcn) |> futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |> futurize()'. Other map-reduce packages that can be "futurized" are 'BiocParallel', 'plyr', 'crossmap', 'pbapply' packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. 'boot', 'caret', 'DiceKriging', 'ez', 'fgsea', 'fwb', 'gamlss', 'glmmTMB', 'glmnet', 'kernelshap', 'lme4', 'metafor', 'mgcv', 'modelsummary', 'parameters', 'partykit', 'pls', 'pvclust', 'riskRegression', 'rugarch', 'sandwich', 'seriation', 'shapr', 'Sim.DiffProc', 'SimDesign', 'stars', 'strucchange', 'SuperLearner', 'tm', 'TSP', and 'vegan') and on Bioconductor (e.g. 'DESeq2', 'GenomicAlignments', 'GSVA', 'Rsamtools', 'scater', 'scuttle', 'SingleCellExperiment', and 'sva'). Package: r-cran-fuzzr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-progress, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzr_0.2.2-1.ca2404.1_all.deb Size: 49894 MD5sum: e842926542ae62fc979c99cbf23369b7 SHA1: 063aebe3c0e6cc48b7458af54f311cb6d44c1bb0 SHA256: 09ba070b4e074f7f7792ef4db5ac209b559f7ba85179977daaddb4361e730580 SHA512: 3d2d6f4ed4adf0fae92e6e1e12c48697ce055ea527f6b8570957b88de2c2bab8be1203e6e04c4703aefcb4eb3c152dc5c5cac7378e8da8010a6ce93e61ea92db Homepage: https://cran.r-project.org/package=fuzzr Description: CRAN Package 'fuzzr' (Fuzz-Test R Functions) Test function arguments with a wide array of inputs, and produce reports summarizing messages, warnings, errors, and returned values. Package: r-cran-fuzzy.p.value Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers Filename: pool/dists/noble/main/r-cran-fuzzy.p.value_1.1-1.ca2404.1_all.deb Size: 51062 MD5sum: 17ce071c0344c6b6b3cd0c1567fa0242 SHA1: fee890fca3232052d1c437b01fcc14727ef6e757 SHA256: 004a6d48768d794522bc5d2c4b05b6ab229363a7a98bab8961e31123ec1a60e7 SHA512: 242ee0605186ddcc958b7897d1f181036ae22be5e9ac9fa0e815cdcf5d63e0614f8c70c8fca4bae6becd36817ba68fe50c83bfb84ee6bf9e1e7df8d779d78046 Homepage: https://cran.r-project.org/package=Fuzzy.p.value Description: CRAN Package 'Fuzzy.p.value' (Computing Fuzzy p-Value) The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package. Package: r-cran-fuzzyahp Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyahp_0.9.5-1.ca2404.1_all.deb Size: 187176 MD5sum: 9c16e047ba4aef379bfed6fdb3e8d0ca SHA1: f418bb2581b83146aaabf4115548b485cc7fd695 SHA256: 272dd4e488420d6133693a543d97ec8cb712afca7e31d7df9985fdab66d49b98 SHA512: 051622e5a2dfb1137713de8e9f65a38fa6f83f5f9cadab235ef24410bbe438ffea3c5e5d760c8ff581696e0a57336eb7548cd6a14a4cf8b0b2461e6842e57659 Homepage: https://cran.r-project.org/package=FuzzyAHP Description: CRAN Package 'FuzzyAHP' ((Fuzzy) AHP Calculation) Calculation of AHP (Analytic Hierarchy Process - ) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights. Package: r-cran-fuzzyclass Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-catools, r-cran-e1071, r-cran-envstats, r-cran-mass, r-cran-mvtnorm, r-cran-rdpack, r-cran-rootsolve, r-cran-trapezoid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-maxlik Filename: pool/dists/noble/main/r-cran-fuzzyclass_0.2.0-1.ca2404.1_all.deb Size: 564868 MD5sum: dcd6ad58cb0d507fa62ce2fadea22516 SHA1: f8487daf86e935b32202175575b133ed1d46cd69 SHA256: e82609f29775f6ccbdd9bcb5e86cd072b0c4892bdff5e0c450675ef7bd0068b6 SHA512: 3b7755a4724b3229a03dc5aed81b20da08108300ab1ed8f25eb4ac8fa27fc7a9f213b607ed14845485fbd1e7b542e5a0ab649d0f84365d3fd769c14cfd4571ae Homepage: https://cran.r-project.org/package=FuzzyClass Description: CRAN Package 'FuzzyClass' (Fuzzy and Non-Fuzzy Classifiers) It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) . Package: r-cran-fuzzydbscan Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-r6, r-cran-data.table, r-cran-dbscan, r-cran-checkmate Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-factoextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fuzzydbscan_0.0.3-1.ca2404.1_all.deb Size: 289148 MD5sum: 37ee635632489b9b1a357d86084a3502 SHA1: 8336ed26b8aff204ec927f4f7d1b20c0afa451f6 SHA256: f548022a24da02a195de3b5609d161b39bd94cdbd0175ffc0796d625fa918079 SHA512: 9df914a736317442c2354533573fca474db7e51472c2f8390ea71af4ce0cda75ed6ccff8404bb4d89cde8c53283951de90c0cfa8245d746732b6f749f5cdacd1 Homepage: https://cran.r-project.org/package=FuzzyDBScan Description: CRAN Package 'FuzzyDBScan' (Run and Predict a Fuzzy DBScan) An interface for training Fuzzy DBScan with both Fuzzy Core and Fuzzy Border. Therefore, the package provides a method to initialize and run the algorithm and a function to predict new data w.t.h. of 'R6'. The package is build upon the paper "Fuzzy Extensions of the DBScan algorithm" from Ienco and Bordogna (2018) . A predict function assigns new data according to the same criteria as the algorithm itself. However, the prediction function freezes the algorithm to preserve the trained cluster structure and treats each new prediction object individually. Package: r-cran-fuzzyforest Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 857 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-wgcna, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyforest_1.0.8-1.ca2404.1_all.deb Size: 825876 MD5sum: 05d2d02bb31c55f039b3539cdb0105e0 SHA1: 685417d0492f868d594d342aaa4b3f7d1f142513 SHA256: 50bd433a2f6579d4c75213c72c583681cbbda5d2ea526cdfc4ace5e1a3351e8f SHA512: 2c22e647bf2d06314d364a11cd71e0a721aae1c8deb51458ea3519d96c70d6ef742a5c21efe9510870d5e1dd5fe60e248b4d882048e971f5de80e84eec2a035f Homepage: https://cran.r-project.org/package=fuzzyforest Description: CRAN Package 'fuzzyforest' (Fuzzy Forests) Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) for further details. Package: r-cran-fuzzyimputationtest Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fuzzysimres, r-cran-fuzzynumbers, r-cran-missforest, r-cran-miceranger, r-cran-vim, r-cran-fuzzyresampling, r-cran-mice Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzyimputationtest_0.5.5-1.ca2404.1_all.deb Size: 350388 MD5sum: 956c142ddf0b601a1ed08266b6f52e7f SHA1: ef7316cd5b534ced69092ac2a926edcbd5637f7b SHA256: 945efe09c7bdf83e2d8a3a783eadd502058a773278d1a3c5be0f0aeb989dcb95 SHA512: 3bfa06faf860b765c961c2cb63f0ceb5affd72091cee5c42564aacf63ce268888e1cf204db532ca7a0ca309d9d771bae5bd3c84753757c333e01c3131c110e0f Homepage: https://cran.r-project.org/package=FuzzyImputationTest Description: CRAN Package 'FuzzyImputationTest' (Imputation Procedures and Quality Tests for Fuzzy Data) Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets. Package: r-cran-fuzzyjoin Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringdist, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-geosphere, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-qdapdictionaries, r-cran-readr, r-cran-rvest, r-cran-rmarkdown, r-cran-maps, r-bioc-iranges, r-cran-covr Filename: pool/dists/noble/main/r-cran-fuzzyjoin_0.1.8-1.ca2404.1_all.deb Size: 129232 MD5sum: cf3c2354dcce220340988a7209088152 SHA1: 03ec551528e8d920585f6a0af5bac49a64e18824 SHA256: 15b65c2ed73476d8d36ccebae58d99a88bc227004612dd78c3e4639a0f101534 SHA512: 6a19b0eb67c1e2cda3bf6e384db716270e09f919d056cf5523cc53cf318e3da7f4741e53c09731d22e9047ed3c6b4e8cd958484310249a8233e779f9c9a7d746 Homepage: https://cran.r-project.org/package=fuzzyjoin Description: CRAN Package 'fuzzyjoin' (Join Tables Together on Inexact Matching) Join tables together based not on whether columns match exactly, but whether they are similar by some comparison. Implementations include string distance and regular expression matching. Package: r-cran-fuzzylink Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rfast, r-cran-reshape2, r-cran-stringdist, r-cran-stringr, r-cran-httr, r-cran-jsonlite, r-cran-httr2, r-cran-ranger, r-cran-ellmer Filename: pool/dists/noble/main/r-cran-fuzzylink_0.4.1-1.ca2404.1_all.deb Size: 63244 MD5sum: c0c90711571c6228d4744d5343080df5 SHA1: 36195bd25b3904147cff4161d653811ea425ad49 SHA256: 4ec1c87cb7ca3aaede3cea9f25c068e002c72a7036d13d051044dfc2e3776ae3 SHA512: 7531d5d8f66df1348317b8f9311408e42f9e1bfabd1d691eedbeb6d0791c583a6b79cdd24619e020806a78baed8a9dfe277767520201fc4f38200fd01c945d1f Homepage: https://cran.r-project.org/package=fuzzylink Description: CRAN Package 'fuzzylink' (Probabilistic Record Linkage Using Pretrained Text Embeddings) Links datasets through fuzzy string matching using pretrained text embeddings. Produces more accurate record linkage when lexical string distance metrics are a poor guide to match quality (e.g., "Patricia" is more lexically similar to "Patrick" than it is to "Trish"). Capable of performing multilingual record linkage. Methods are described in Ornstein (2025) . Package: r-cran-fuzzylogit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzylogit_0.1.1-1.ca2404.1_all.deb Size: 157266 MD5sum: dd389c418af3e1aeba4a9059645d7fc1 SHA1: ab136bf3f486aa2c34a4cdea604c567d7512efa9 SHA256: 0b1c70a507200cb6038fad7da9ff07c3f4b30bf2c73c519f222b5b3e2a95f540 SHA512: ef9e17fb37a637328bc67ab7174285d146b86595324259d49c982fd5842ec9d4565dcb674aa8f4d12cef6fa8ed23a01e22b052b6a02c643a7e8bfb52fcb75773 Homepage: https://cran.r-project.org/package=FuzzyLogit Description: CRAN Package 'FuzzyLogit' (Fuzzy Logistic Regression) Fits logistic regression models in which the binary response is represented by a triangular fuzzy number rather than an exact crisp label, allowing uncertainty in class membership to be encoded directly in the outcome. Model parameters are estimated using the Fuzzy Least Squares approach of Diamond (1988) , following the integrated fuzzy logistic regression method of Yapici Pehlivan and Sahin (2018) . Provides fitting, prediction, classification, cross-validation, and diagnostic plotting methods, along with tools for comparing model behaviour across different assumed levels of label uncertainty. Package: r-cran-fuzzylp Architecture: all Version: 0.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roi, r-cran-fuzzynumbers, r-cran-roi.plugin.glpk Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzylp_0.1-7-1.ca2404.1_all.deb Size: 421764 MD5sum: 7b190a6280751ab7118972fb246156e5 SHA1: 5f47da3e19b64e98518ac2156387a6b738cfe3fe SHA256: 770b3795ca49c7759c13ede27835ccac155622f4f7f851ef7db6362beed94e14 SHA512: bed0b19ea9208a2aed05f7594c884f2d42cfcaf3619229380f6288a524937203525a575e094e509643a415c22c899534f30f1650abec3e01c9ec36a72df66663 Homepage: https://cran.r-project.org/package=FuzzyLP Description: CRAN Package 'FuzzyLP' (Fuzzy Linear Programming) Provides methods to solve Fuzzy Linear Programming Problems with fuzzy constraints (following different approaches proposed by Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix. Package: r-cran-fuzzym Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzym_0.1.0-1.ca2404.1_all.deb Size: 652468 MD5sum: a10fa1fdb6269d42a6215dff360b30ff SHA1: a0446a3a8a06649bb07d6196f8d50e5e4a7a61f4 SHA256: 3f88fee613b6d0dc38ee3f01f2aecb33f821918e02ead621810d15dfb32359d4 SHA512: ac32ee1f31731b0bcd4b05c2399831039948bbbc7081865d6c72b32fba14e5aaef67fc883924d44b8b85c66ff7ba907364d5f66b2e11c0893329764de94e8086 Homepage: https://cran.r-project.org/package=FuzzyM Description: CRAN Package 'FuzzyM' (Fuzzy Cognitive Maps Operations) Contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) . System indicators are described by Cox, Earl D. (1995) . Executable examples are provided in the "inst/examples" folder. Package: r-cran-fuzzynumbers.ext.2 Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers Filename: pool/dists/noble/main/r-cran-fuzzynumbers.ext.2_3.2-1.ca2404.1_all.deb Size: 49012 MD5sum: 05794c774325a0ef753ea5c843134d26 SHA1: e5fa74077ac41ab782137fc7e5e098e6e782f32f SHA256: 260d9c6aa02a0b79112411ad0233fe25d7fa58c88f592c7952fa0cf518f74fb3 SHA512: 430fd69153173d57bd2fa3e8ea80e58e561f497aeced0ea8b0660fa6c013200570689a48fb46d5f56e2321fdea8156dc694cdc6c8fb73a29e3f401c700ae8374 Homepage: https://cran.r-project.org/package=FuzzyNumbers.Ext.2 Description: CRAN Package 'FuzzyNumbers.Ext.2' (Apply Two Fuzzy Numbers on a Monotone Function) One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result. Package: r-cran-fuzzynumbers Architecture: all Version: 0.4-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-digest Filename: pool/dists/noble/main/r-cran-fuzzynumbers_0.4-7-1.ca2404.1_all.deb Size: 785548 MD5sum: b75bf4c96c0b567d0d379aca4ea18c48 SHA1: 5d9c9d2f0ae5d4415c5cb58230418fb60563d04a SHA256: 30d48254325f9e499c14491c5480e36aa0f18f69b334f7e1d30f922a2b81089b SHA512: 45f9907d7c6050b18ac54b2ec4ceae4274e7844fe1f05b48475803471e4951b7a4e7e9a806ebfa78c524a6f4162cabe575112d48d4ba44eb94eaa6d020e64801 Homepage: https://cran.r-project.org/package=FuzzyNumbers Description: CRAN Package 'FuzzyNumbers' (Tools to Deal with Fuzzy Numbers) S4 classes and methods to deal with fuzzy numbers. They allow for computing any arithmetic operations (e.g., by using the Zadeh extension principle), performing approximation of arbitrary fuzzy numbers by trapezoidal and piecewise linear ones, preparing plots for publications, computing possibility and necessity values for comparisons, etc. Package: r-cran-fuzzypovertyr Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-sampling, r-cran-ecp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fuzzypovertyr_3.0.2-1.ca2404.1_all.deb Size: 643770 MD5sum: 5e72af75d90b80b29ef54a0dd9afc134 SHA1: 0b30a75690123b43fb054184a8b483b494f31064 SHA256: c8756dfce6d137a517a5606d750d17dbcda4448cc44fae66e5bcb048a819094b SHA512: cad26ea7fb028698d2570ca9b597509e43e85dbb7f27ad8a4e67d9ef13f0e22b161c04441573ca2d20b0b489de94f8711870a2747a60823a9095897994a394dd Homepage: https://cran.r-project.org/package=FuzzyPovertyR Description: CRAN Package 'FuzzyPovertyR' (Estimation of Fuzzy Poverty Measures) Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications. Package: r-cran-fuzzyq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Filename: pool/dists/noble/main/r-cran-fuzzyq_0.1.0-1.ca2404.1_all.deb Size: 58878 MD5sum: 6989bd2b2e1030cb1dfc9fb33f000e8a SHA1: 2a061ed4b8facfa1b305691fcc8cda0b18674b11 SHA256: 8b112fe77b8e07c4008f95293aed18292f4a927a191b6aaa784b8d4683a4b914 SHA512: 3208790260c62ef35906c043c474634a2f384b74736d9f5a3887f332eaf4eddaafc221943477159c50ca516ca1b4fdec7214106405804f8ee69525221a4b1b63 Homepage: https://cran.r-project.org/package=FuzzyQ Description: CRAN Package 'FuzzyQ' (Fuzzy Quantification of Common and Rare Species) Fuzzy clustering of species in an ecological community as common or rare based on their abundance and occupancy. It also includes functions to compute confidence intervals of classification metrics and plot results. See Balbuena et al. (2020, ). Package: r-cran-fuzzyr Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-plyr Filename: pool/dists/noble/main/r-cran-fuzzyr_2.3.2-1.ca2404.1_all.deb Size: 323242 MD5sum: eef839917fbe20245e34e3cfbe5db6fc SHA1: 7923e7ac20c68e7d7e7da11261760910cf587e16 SHA256: e443e1a5f378da9255b5998028f5e4590a35e6670a1dce7b0fe626967d249c24 SHA512: f9ebc9f5902cb98e8fed8ceb9e87ab107ad0a6c1f970063778d1d8ced5abd2254282d291512497b0b350e7bcf8e35e23350fc781ae4acbe2049091eb3c06d039 Homepage: https://cran.r-project.org/package=FuzzyR Description: CRAN Package 'FuzzyR' (Fuzzy Logic Toolkit for R) Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic. This toolkit includes with graphical user interface (GUI) and an adaptive neuro- fuzzy inference system (ANFIS). This toolkit is a continuation from the previous package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA) and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. A big thank you to the many people who have contributed to the development/evaluation of the toolbox. Please cite the toolbox and the corresponding paper when using it. More related papers can be found in the NEWS. Package: r-cran-fuzzyreg Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-limsolve, r-cran-quadprog Suggests: r-cran-testthat, r-cran-fuzzynumbers, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-fuzzyreg_0.6.2-1.ca2404.1_all.deb Size: 345168 MD5sum: d16a3eab4f66d139681656953124fb2f SHA1: b2a544f6c90c9ed5cb8b267394d513ac82568be3 SHA256: d8e61a250d2b07881e6604807c74943f83b6873c7b7ce1ae734d2d1a046fbfa1 SHA512: dfd124d45ab0512aa2d06ea52aa164508c9a3c66add09cbb94ab9cd667aeafbd7f636cb62177e2eff15975f5a788c77de0707e53bc429cfa883a83c92c2c69c0 Homepage: https://cran.r-project.org/package=fuzzyreg Description: CRAN Package 'fuzzyreg' (Fuzzy Linear Regression) Estimators for fuzzy linear regression. The functions estimate parameters of fuzzy linear regression models with crisp or fuzzy independent variables (triangular fuzzy numbers are supported). Implements multiple methods for parameter estimation and algebraic operations with triangular fuzzy numbers. Includes functions for summarising, printing and plotting the model fit. Calculates predictions from the model and total error of fit. Individual methods are described in Diamond (1988) , Hung & Yang (2006) , Lee & Tanaka (1999) , Nasrabadi, Nasrabadi & Nasrabady (2005) , Skrabanek, Marek & Pozdilkova (2021) , Tanaka, Hayashi & Watada (1989) , Zeng, Feng & Li (2017) . Package: r-cran-fuzzyresampling Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzyresampling_0.6.4-1.ca2404.1_all.deb Size: 416372 MD5sum: 57382a933e157bbec91e355432083816 SHA1: 6d480ae605998c54b34f2dbeeabf6ec25f826c80 SHA256: 3eabf59ecd42a62e17477850e43d8b8be4e7f9d112e287334c980e1e98844313 SHA512: f27d1e9f3bdc1accb1522df51c2da34514f0537424972ac873141c866c493696e5b383662d53e6fff368c1898e89681605118e70b7a41556a4707a01f8aba5f4 Homepage: https://cran.r-project.org/package=FuzzyResampling Description: CRAN Package 'FuzzyResampling' (Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers) The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317) "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data. The main aim of the algorithms implemented in this package is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample. To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. , Grzegorzewski et al. (2020) , Grzegorzewski et al. (2020) , Grzegorzewski and Romaniuk (2022) , Romaniuk and Hryniewicz (2019) ). Some additional procedures related to these resampling methods are also provided, like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers") and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, )). Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions. Package: r-cran-fuzzysim Architecture: all Version: 4.60-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-modeva, r-cran-stringi Suggests: r-cran-aod, r-cran-geodist, r-cran-phylolm, r-cran-raster, r-cran-terra Filename: pool/dists/noble/main/r-cran-fuzzysim_4.60-1.ca2404.1_all.deb Size: 462188 MD5sum: b90c48aaebd56e9d673a722a0c091b6e SHA1: de144c1a923a892f6ef6042126cc6ca4142aab79 SHA256: a790a36065810cafdcbff313518ca7cb952f0ec6ff1334f853621476d82496af SHA512: 0bd5e16e9a25d4a61cb7f24187b6bd3ad36062031b992b7de036432e40e14b3ced7d50d456fae859a847576f832623dc101b0e5e6748c560cdc9c2f5e4561106 Homepage: https://cran.r-project.org/package=fuzzySim Description: CRAN Package 'fuzzySim' (Fuzzy Similarity in Species Distributions) Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) . Package: r-cran-fuzzyspec Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-thresher, r-cran-fclust, r-cran-mclust, r-cran-mvtnorm, r-cran-np, r-cran-ggplot2, r-cran-viridislite Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-patchwork, r-cran-devtools, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fuzzyspec_1.0.0-1.ca2404.1_all.deb Size: 225994 MD5sum: 3160b28e6b53a367aa740d29c49d8c4a SHA1: 687d326f59e6485e285ef9a91e3f77319aeaeb1e SHA256: b347c6133557204efe98e7114ea51cb37701f21ccf9be2971553053af14760b9 SHA512: 7e0580eea73759d3d33076ce859530d0e9ed89f525eaaebf11272f52c5cd57ea1b412114e5085dade74e41274d6eaa1d7ff0b90d39fae4327def9dc26b816c73 Homepage: https://cran.r-project.org/package=FuzzySpec Description: CRAN Package 'FuzzySpec' (Fuzzy Spectral Clustering with Variable-Weighted AdjacencyMatrices) Implementation of the FVIBES, the Fuzzy Variable-Importance Based Eigenspace Separation algorithm as described in the paper by Ghashti, J.S., Hare, W., and J.R.J. Thompson (2025). Variable-Weighted Adjacency Constructions for Fuzzy Spectral Clustering. Submitted. Package: r-cran-fuzzystatprob Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multinomialci, r-cran-fuzzynumbers, r-cran-deoptim Suggests: r-cran-markovchain, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzystatprob_2.0.4-1.ca2404.1_all.deb Size: 709468 MD5sum: 2a6dc47e6013da0e467831015f18a92c SHA1: fb3aae8ed43030fb682ab7a823be15db16a244dc SHA256: 579afcb54ff81b79a1484fca43e74f10c52f9b6b63a1bdac1ac72eff19c7a991 SHA512: eb6f39f7807d222da613e1bb285c0ccece8fc1f0747896c61b72ca2ce617ab5baa379d6adeac0ee88e3a7ad6e4d5d4611e8d98857278abd3b8082f49acde6d7e Homepage: https://cran.r-project.org/package=FuzzyStatProb Description: CRAN Package 'FuzzyStatProb' (Fuzzy Stationary Probabilities from a Sequence of Observationsof an Unknown Markov Chain) An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain, from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities. Package: r-cran-fuzzystattra Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fuzzystattra_1.0-1.ca2404.1_all.deb Size: 165506 MD5sum: 587addcf7ed0df8bdee2eaaa91770382 SHA1: 0bbdac6e23deb9f943497ec1c7b42573c9242721 SHA256: f57c8bb2410b54d44b8493f1617a18232dbc5835a5ad6d8952fdff86dfab2155 SHA512: 2084aa90daa1796e22aae338761f309d083e99169bd5eafc46325a7d82a58ef71a6823124952446e55348aa859dde31f6a7d8fab2e2248aed8be5eaa0b7f87bc Homepage: https://cran.r-project.org/package=FuzzyStatTra Description: CRAN Package 'FuzzyStatTra' (Statistical Methods for Trapezoidal Fuzzy Numbers) The aim of the package is to provide some basic functions for doing statistics with trapezoidal fuzzy numbers. In particular, the package contains several functions for simulating trapezoidal fuzzy numbers, as well as for calculating some central tendency measures (mean and two types of median), some scale measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one diversity index and one inequality index. Moreover, functions for calculating the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev distance between fuzzy numbers are included, and a function to calculate the value phi-wabl given a sample of trapezoidal fuzzy numbers. Package: r-cran-fuzzystattraeoo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-fuzzystattraeoo_1.0-1.ca2404.1_all.deb Size: 381310 MD5sum: 0b72ca6940f215881fc6a09f60552977 SHA1: a25bd00dc246a13c64717732068d32167d0ea3e0 SHA256: c698a95df6f6e1f5cfbb7c745df9dc7eb30f99ed629ed6870ff16c81570da0c3 SHA512: 0c1659dbde8af46b706f74ff7955ad67d38c9b7f3ba6429334b2a1578bd5326c6250a4945e51527b64f25babca085a6ab82541a673def26c1af4d66ba5931bef Homepage: https://cran.r-project.org/package=FuzzyStatTraEOO Description: CRAN Package 'FuzzyStatTraEOO' (Package 'FuzzyStatTra' in Encapsulated Object OrientedProgramming) The aim of the package is to contain the package 'FuzzyStatTra' in Encapsulated Object Oriented Programming using R6. 'FuzzyStatTra' contains Statistical Methods for Trapezoidal Fuzzy Numbers, whose aim is to provide some basic functions for doing statistical analysis with trapezoidal fuzzy numbers. For more details, you can visit the website of the SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and Comparison of Distributions of Random Elements) Research Group (). The most related paper can be found in References. Now, those functions are organized in specific classes and methods. This object-based approach is an important step in making statistical computing more accessible to users. Package: r-cran-fuzzysts Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-polynom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fuzzysts_0.5-1.ca2404.1_all.deb Size: 884192 MD5sum: 19cade07ec2ea5b13813cf9bf6ad18ec SHA1: 2979b77164301fe0a5e415c84a6a0f00edc4cd41 SHA256: 62263c8ff9ae59f07fa1a392622576bfb88738c19b1753becb956561f18d1e11 SHA512: 53d8836b44d00f4be476ce62502bb5f692d39c701af690b57989af8e963427476609dcdf0ae209cc7b5dab67ad0518eec60878cdef3d0cb0d4d014f0a88993a4 Homepage: https://cran.r-project.org/package=FuzzySTs Description: CRAN Package 'FuzzySTs' (Fuzzy Statistical Tools) The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the book entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" . For the theoretical approaches, see Berkachy R. and Donze L. (2019) . For the empirical approaches, see Berkachy R. and Donze L. (2016) ). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance with all its properties. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen. Package: r-cran-fuzzyurn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyurn_0.1.0-1.ca2404.1_all.deb Size: 24920 MD5sum: 4973fae5d504bb08fbd7b17916002911 SHA1: b4370a8ea72245f49d186b35cdae53ddcc44f355 SHA256: 72c713e82b1fecae6af2be5b657cd4bec4fc18df4f6015464d209217dcf62ddb SHA512: c318dcbabb68c0234b9817f18cb0189b47b1ee6ba808840ab57e420dd59e342b9a219e77b0d126a89e26ec8ca498fb17fe096ce82bcc946756809f08d92df32c Homepage: https://cran.r-project.org/package=fuzzyurn Description: CRAN Package 'fuzzyurn' (Generalized Non-Central Fuzzy Dynamic Hypergeometric Processes) Implements Generalized Non-Central Fuzzy Dynamic Hypergeometric Processes. Includes discrete Markov chain sampling under dynamic weight decay and continuous fuzzy membership maps, infinitesimal generator evaluation, weak convergence to Itô diffusion SDEs, numerical solutions for Fokker-Planck PDEs, stationary Gibbs-Boltzmann densities, and Azuma-Hoeffding concentration bounds. Package: r-cran-fuzzywuzzyr Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fuzzywuzzyr_1.0.6-1.ca2404.1_all.deb Size: 191686 MD5sum: 4fbd227175be79e8e2879cae14463dad SHA1: a7c0e5e269f13a54167096c2aa79d96376c04b91 SHA256: 8f46821f5edb55c153c07f6ef21733b003b0da87e050e74ac37cb373557cfae1 SHA512: 06ddafcaeb235d51a8d518d0fd9b112d5c81191ff0f244ea64362deee881219da51202be4c862fe5ce12f960c5e09cebe4158f0cce6fcedc78a151cd5c44f186 Homepage: https://cran.r-project.org/package=fuzzywuzzyR Description: CRAN Package 'fuzzywuzzyR' (Fuzzy String Matching) Fuzzy string matching implementation of the 'fuzzywuzzy' 'python' package. It uses the Levenshtein Distance to calculate the differences between sequences. Package: r-cran-fwb Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arg, r-cran-rlang, r-cran-pbapply, r-cran-generics Suggests: r-cran-survival, r-cran-cobalt, r-cran-boot, r-cran-mvtnorm, r-cran-sandwich, r-cran-ggdist, r-cran-lmtest, r-cran-nnet, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-waldo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fwb_0.6.0-1.ca2404.1_all.deb Size: 282282 MD5sum: 7e64d83eaa555fffaad9e189a19755d4 SHA1: 5b2adf7dc136b7785f0b2a857affab76b648e29b SHA256: 8dd3ccef60ecabf1bf56514b8d7a7d76d57ea6a1296099a27c69c9783e6a8c4e SHA512: 82d2d094abfe5b1c1b53f6db6aef8bcf8251e5c2839ed90ae4a5a56be4fe2d81136d9fd1f048166858ab37e67e3fb44b6613f02c21780818522939547d9f9a9b Homepage: https://cran.r-project.org/package=fwb Description: CRAN Package 'fwb' (Fractional Weighted Bootstrap) An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the 'boot' package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) for details. Package: r-cran-fwdselect Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvtools, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-fwdselect_2.1.1-1.ca2404.1_all.deb Size: 178628 MD5sum: 2653ea3270be434cf2fd8425b5488eaa SHA1: d165859da774dcd074e182d24f92c8555c2e8a11 SHA256: b7d72e91ae9a8ec821538c2f84734769e7cbc4cdc915018d965bc70fd0411c2d SHA512: f4214198ccb1c78018638a3914718a53be06abadeb578242f079a1bb3fbe325ecd7d9bb7982f9f83408f7a3aa7fc9f90c9bc983b141ce5df9493e573d55a2895 Homepage: https://cran.r-project.org/package=FWDselect Description: CRAN Package 'FWDselect' (Selecting Variables in Regression Models) A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). Implemented methodology described in: M. Sestelo, N. M. Villanueva, L. Meira-Machado and J. Roca-Pardiñas (2016). FWDselect: an R package for variable selection in regression models. The R Journal, 8 (1), 132-148. . Package: r-cran-fwlplot Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-fixest, r-cran-tinyplot Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-fwlplot_0.3.0-1.ca2404.1_all.deb Size: 1729912 MD5sum: 2d2e45406dcf5976e206c06ada06b842 SHA1: 69a827760adc0a466864de2651a345dba81f6dd5 SHA256: 47854f508d77d69e8b2f5730fa6e8c21415d516b4e1f762ea77830a8db41c622 SHA512: 96753277f6b129193e2b053f154c90605dc0f30b416802ab25fd529e239a97966a9f18efae9fad94c8a2dddb5a8efb6f999ee51fa0726827f54380ba386effbc Homepage: https://cran.r-project.org/package=fwlplot Description: CRAN Package 'fwlplot' (Scatter Plot After Residualizing Using 'fixest' Package) Creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the 'fixest' package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) . Package: r-cran-fwrgb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-imager, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-fwrgb_0.1.0-1.ca2404.1_all.deb Size: 254564 MD5sum: 0ec132f752ff9104c23137f438119fdf SHA1: 6f4ffe96cd160e910845276734920acb3d3128db SHA256: d3b227aeb28fad605257741f15fb95355ff37d25b07c16e4ce0275bd22cc686e SHA512: b423a94e67ce625cf9dfcd031787889072e2ee95983e682909900503df01ec3f10bc694f642be89e91ad76c0bf41097f1ca52ef1e42016eb80e709232b9d1aa5 Homepage: https://cran.r-project.org/package=FWRGB Description: CRAN Package 'FWRGB' (Fresh Weight Determination from Visual Image of the Plant) Fresh biomass determination is the key to evaluating crop genotypes' response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight. Package: r-cran-fwtraits Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rstudioapi, r-cran-r.cache Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-vcr, r-cran-dplyr, r-cran-tidytext, r-cran-testthat, r-cran-fd, r-cran-tidyr, r-cran-tibble, r-cran-cluster Filename: pool/dists/noble/main/r-cran-fwtraits_1.0.0-1.ca2404.1_all.deb Size: 1472588 MD5sum: a30347d0337f840a29496d76d06f0d5d SHA1: b3253a776238b5eed3cbe6dec424b18afdcc32bd SHA256: 9faead20503769de6e6ea55762e3661a1612fec81c22b5ca813b88ee9dbf06f1 SHA512: a2f41a905cc0d643493348781276342f1983a2df0a55cfe57726488cbb1cc2f66e7de0363c57521d9482857d56b19ec2c8f85ceea09941e60312484219698648 Homepage: https://cran.r-project.org/package=fwtraits Description: CRAN Package 'fwtraits' (Extract Species Ecological Parameters fromWww.freshwaterecology.info) Support the extraction and seamless integration of species ecological traits or preferences from the www.freshwaterecology.info into several ecological model workflows. During data extraction, different taxonomic levels are acceptable, including species, genus, and family, based on the availability of data in the database. The data is cached after the first search and can be accessed during and after online interactions. Only scientific names are acceptable in the search; local or English names are not allowed. A user API key is required to start using the package. Package: r-cran-fxl Architecture: all Version: 1.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-here, r-cran-tidyverse, r-cran-scales, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fxl_1.7.3-1.ca2404.1_all.deb Size: 4204940 MD5sum: 97ec773867e360c71e154a7dbf2c6aa6 SHA1: 397ae8fc0ac11dbb89f74d9f87c6dbe69b43129b SHA256: ae2c3d35287cd77390b93f6ea599a877173214249f91d3df6c938c7aeda0d5af SHA512: 6a0b50d6d9555c7af8febf50deeea53b89160b9804a763b96fce9edbb6bd8e468a5020a5612a6a0637dc7ba356b546a9aa68a1d574e2419a6a3df094508f1862 Homepage: https://cran.r-project.org/package=fxl Description: CRAN Package 'fxl' ('fxl' Single Case Design Charting Package) The 'fxl' Charting package is used to prepare and design single case design figures that are typically prepared in spreadsheet software. With 'fxl', there is no need to leave the R environment to prepare these works and many of the more unique conventions in single case experimental designs can be performed without the need for physically constructing features of plots (e.g., drawing annotations across plots). Support is provided for various different plotting arrangements (e.g., multiple baseline), annotations (e.g., brackets, arrows), and output formats (e.g., svg, rasters). Package: r-cran-fxregime Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2003 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zoo, r-cran-strucchange, r-cran-car, r-cran-sandwich Suggests: r-cran-lmtest, r-cran-foreach, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fxregime_1.0-5-1.ca2404.1_all.deb Size: 1794904 MD5sum: 7ca205ccd016995ffb72e728cec9aa93 SHA1: 6bb02a9d6a120896f1c85ff3b8e3539f04e0116b SHA256: ae17e1862f38c29cc98cca875068265a7c817f1eda25170f22d4160642167f22 SHA512: 4020b7b0c00324d28c7a24ad731369ddb797bd549cccbbc0d9313801dc59491c5b40e41132b89688b09cbec067300b74cf39a67c6d6d777ebdfa9848e3f49754 Homepage: https://cran.r-project.org/package=fxregime Description: CRAN Package 'fxregime' (Exchange Rate Regime Analysis) Exchange rate regression and structural change tools for estimating, testing, dating, and monitoring (de facto) exchange rate regimes. Package: r-cran-fxtwapls Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-geosphere, r-cran-ggplot2, r-cran-jops, r-cran-mass, r-cran-progressr Suggests: r-cran-magrittr, r-cran-progress, r-cran-scales, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-fxtwapls_0.1.3-1.ca2404.1_all.deb Size: 122936 MD5sum: 3baac9a7d062846c91af512ebe2ce9de SHA1: 90846b6d8a852e1df17adfcad7afa41bf5a342ba SHA256: 0ffe30b3a65827bf4608972132e9fb6b5c66cbd716051d4a4afb04b612e25a9f SHA512: b4816d204fde5e7fec291a5aa900af5484cda7cd5afe7944730fe0ac4ca5cfbd7587418f9e2cc8563360e0f2d09cf42efc4d1ce910ba2935bcc74a8aa5d3de9f Homepage: https://cran.r-project.org/package=fxTWAPLS Description: CRAN Package 'fxTWAPLS' (An Improved Version of WA-PLS) The goal of this package is to provide an improved version of WA-PLS (Weighted Averaging Partial Least Squares) by including the tolerances of taxa and the frequency of the sampled climate variable. 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Package: r-cran-fy Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatch, r-cran-data.table, r-cran-hutils Suggests: r-cran-testthat, r-cran-withr, r-cran-rlang, r-cran-zoo, r-cran-covr Filename: pool/dists/noble/main/r-cran-fy_0.4.2-1.ca2404.1_all.deb Size: 58052 MD5sum: 79a378100bf6cb411eccd0a89f642220 SHA1: 3f740b76061ca4ca85a78217797fe627eb572fbc SHA256: 9a31345356f0f1fa3d31651619afb2d2a9be2e1948c021f36a6f57d8b64f2c53 SHA512: 30cda1f98a9b93d45de0a0be7cf829d19b0ae322143170baa6eb0a00709d7761be28bd34139c6ab4d9e7f17e0efc52f1f696f8676d3cd1eb62bd0f115a3f24ab Homepage: https://cran.r-project.org/package=fy Description: CRAN Package 'fy' (Utilities for Financial Years) In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June of the following calendar year. As such, many databases need to represent and validate financial years efficiently. While the use of integer years with a convention that they represent the year ending is common, it may lead to ambiguity with calendar years. On the other hand, string representations may be too inefficient and do not easily admit arithmetic operations. This package tries to make validation of financial years quicker while retaining clarity. Package: r-cran-fz Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fz_1.2.0-1.ca2404.1_all.deb Size: 86982 MD5sum: 03c43d9d76c877bfc05b75550fd4f8b1 SHA1: c1d28f5630af4933894e1fcc41d9bcb59b7d5058 SHA256: c4f3da1850c2b45b76e32a8ff7e1544c8bce5c7fef71e2c35eb5ac838d1cbfd8 SHA512: df3f4938faf45e1c09464e2501eeb71527f94837fd85002024c8acd490cb475a125c21b20311f2bf9c531ffbd9bcce369bd673d47a18196e211f49270b4fd061 Homepage: https://cran.r-project.org/package=fz Description: CRAN Package 'fz' (R Wrapper for the 'funz-fz' Parametric Simulation Framework) Provides R bindings to the 'funz-fz' Python package using 'reticulate'. The 'fz' framework wraps arbitrary simulation codes to run parameter sweeps, design-of-experiments studies, and iterative algorithm-driven analyses by substituting variable placeholders in text input files and collecting outputs into data frames. Calculators can run locally (shell), over SSH, or on 'SLURM' clusters. See for the underlying framework. Package: r-cran-g.data Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-g.data_2.4.1-1.ca2404.1_all.deb Size: 130350 MD5sum: 6345d6996ac4453e1973a2330ad7dbab SHA1: 23933c14c3a667a5cf22b7b4c75002752375677b SHA256: 48e66cec71c028e011b8e78aace36e96c0b7660646879b664b10aedc90571232 SHA512: 05417f493cb060e79a6c536e0ba828d4df20d694b015718c2d751a8dc99cb45e225a9d0b57b1d72f7d8e315aa5017b014774e2d9a2071d1d241e4f64111f4bfa Homepage: https://cran.r-project.org/package=g.data Description: CRAN Package 'g.data' (Delayed-Data Packages) Create and maintain delayed-data packages (ddp's). Data stored in a ddp are available on demand, but do not take up memory until requested. You attach a ddp with g.data.attach(), then read from it and assign to it in a manner similar to S-PLUS, except that you must run g.data.save() to actually commit to disk. Package: r-cran-g.ridge Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-g.ridge_1.0-1.ca2404.1_all.deb Size: 21628 MD5sum: dc6db9391d0395346c700426637e7c36 SHA1: 769287df83ac262d38689a249bbd751d1a7075a2 SHA256: e24700f7fa89882177d6aaf2a1a987fcd61db0d230b006035db9cbd10ac9121d SHA512: 3dfa8075b946cc5135818bad689077757b7217acc5c27bbba742decaf338c4aa506661f26e4d5bbf9c86a87287570a0687502f4f80750633117a361c9ca47522 Homepage: https://cran.r-project.org/package=g.ridge Description: CRAN Package 'g.ridge' (Generalized Ridge Regression for Linear Models) Ridge regression due to Hoerl and Kennard (1970) and generalized ridge regression due to Yang and Emura (2017) with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. 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Package: r-cran-g2sd Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-viridis, r-cran-patchwork, r-cran-scales, r-cran-shiny, r-cran-bslib, r-cran-shinywidgets, r-cran-plotly Filename: pool/dists/noble/main/r-cran-g2sd_2.2-1.ca2404.1_all.deb Size: 223672 MD5sum: 5987d11c90dcf91cb19f2b1cbe53d216 SHA1: 82dad0c8c0ba030c12693c1d738321579501d7a4 SHA256: 5be07a14138de9948d8687fb0049e2184fb0c678f0c5e141564fc3db5a30470c SHA512: 52f39e7c254daceaa51aaa9e4fae8fa1ab1c0a79b962ea0769893e9aa429b0e59c3052c7cd00e5041bd7d928f3d8bee7a147b1ef5f420bbd41b1340813de3e44 Homepage: https://cran.r-project.org/package=G2Sd Description: CRAN Package 'G2Sd' (Grain-Size Statistics and Description of Sediment) Full descriptive statistics, physical description of sediment, metric or phi sieves. Includes a Shiny web application for interactive grain size analysis and visualization. Package: r-cran-g3viz Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-stringr, r-cran-httr2, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-g3viz_1.2.0-1.ca2404.1_all.deb Size: 837024 MD5sum: 7f605fcc2ce909e2fdfab9c13f49ba93 SHA1: 21d5bd123898a82349e8a353b6b04dbc9455998c SHA256: aededb96a9f61b3fe48ff05ef8600d36b601d602d178e456a46dc1c81610d539 SHA512: 125e099e7499ba3ee6b596d278817401935a2132f6d3f549921773666051d9e6070d87bfac53b693c41f7fa68812c43cb37ba31e479506bdcc9769cb8e60f7cb Homepage: https://cran.r-project.org/package=g3viz Description: CRAN Package 'g3viz' (Interactively Visualize Genetic Mutation Data using aLollipop-Diagram) Interface for 'g3-lollipop' 'JavaScript' library. Visualize genetic mutation data using an interactive lollipop diagram in 'RStudio' or your web browser. Package: r-cran-g6r Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4017 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-igraph, r-cran-roxy.shinylive, r-cran-testthat, r-cran-withr, r-cran-stringr, r-cran-htmltools, r-cran-bslib Filename: pool/dists/noble/main/r-cran-g6r_0.6.5-1.ca2404.1_all.deb Size: 1152932 MD5sum: bbe51bdc3ef89dd28bd181075222dfb0 SHA1: 0d5ccb349036625962bcc94528d09c0ca49425e7 SHA256: 9e0ff8406de03536206412a95a2886ed0b98b64bb13e9de947ef72b26e176c56 SHA512: 6f03bf6e4311f7638f2909b30034bc00bb820d702261630443fdecf8552ef4645ed733f45ae3508a57800091b78d8313f03828fc2224b2de3b3a73e660284bc2 Homepage: https://cran.r-project.org/package=g6R Description: CRAN Package 'g6R' (Graph Visualisation Engine Widget for R and 'shiny' Apps) Create stunning network experiences powered by the 'G6' graph visualisation engine 'JavaScript' library . 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Package: r-cran-gaawr2 Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-gap, r-cran-gap.datasets, r-cran-ggplot2, r-cran-survival, r-cran-rdpack Suggests: r-cran-blr, r-cran-bglr, r-bioc-biomart, r-cran-bookdown, r-cran-cairo, r-bioc-ensdb.hsapiens.v75, r-bioc-ensembldb, r-cran-gmmat, r-cran-hardyweinberg, r-cran-haplo.stats, r-cran-htmltools, r-cran-httr, r-cran-httpuv, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-mcmcglmm, r-cran-plumber, r-cran-powereqtl, r-cran-r2jags, r-cran-regress, r-bioc-rsamtools, r-cran-snpassoc, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-gaawr2_0.0.8-1.ca2404.1_all.deb Size: 112070 MD5sum: 51877adb6a5c8ea9966501cbb8e3769a SHA1: 69a5d0d3f70f29dabbf3094057a47dd74dfd511c SHA256: 4364ee19cda4a443e2ff3665ae0417418b6c329bd824f7721459f42b12ba3c98 SHA512: c711f8c84337103a56e4e8772d03790a7c7509c1523560f54d1d0d03cc693de7f9235ee6802ad673dc8a7694a8821a521c631d365a006b370f2b03d1400080b4 Homepage: https://cran.r-project.org/package=gaawr2 Description: CRAN Package 'gaawr2' (Genetic Association Analysis) This is a companion to Henry-Stewart talk by Zhao (2026, ), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, ) provides a brief introduction to these topics. Package: r-cran-gabb Architecture: all Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-egg, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggplotify, r-cran-ggpubr, r-cran-tidyr, r-cran-hotelling, r-cran-pheatmap, r-cran-vegan Suggests: r-cran-factominer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gabb_0.3.10-1.ca2404.1_all.deb Size: 85876 MD5sum: cb2431966e3450e1f66d8727defb09ec SHA1: fc64b8ccae7654e1c37b27eae4cb4b8a05c7011e SHA256: fbe9c35e8e2c1f042b136e5d735f19c851059546229a7db2334600c7b1eea2be SHA512: afb776a19fdb6b56be762eed15744a48eaabee9f33633375ba5b8c338e5e896df34ceaab945d25cc1a163d50023a70c9e11d49762cd542ef2565dd9d9363cb30 Homepage: https://cran.r-project.org/package=GABB Description: CRAN Package 'GABB' (Facilitation of Data Preparation and Plotting Procedures for RDAand PCA Analyses) Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects. Inputs are : data frame, RDA (package 'vegan') and PCA (package 'FactoMineR') objects. Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters. Package: r-cran-gace Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-forecast Filename: pool/dists/noble/main/r-cran-gace_1.0.0-1.ca2404.1_all.deb Size: 44904 MD5sum: 135cd5eae17c1560581a8b12740d6dce SHA1: d6e8bf328bb5b78d75102e111fa4dafef56a8409 SHA256: 89cc7c10d0f8ed1d22919756cfdfabd3b44559bec22fac69bcfd73a0ba4f4f3f SHA512: a4a2f915284d665a3513b687874710a73c6301abe63db09131e07493539cac858867de4286a532413e1dd18a84c3af257b442ba120576d158c9205f60427ed0a Homepage: https://cran.r-project.org/package=GACE Description: CRAN Package 'GACE' (Generalized Adaptive Capped Estimator for Time SeriesForecasting) Provides deterministic forecasting for weekly, monthly, quarterly, and yearly time series using the Generalized Adaptive Capped Estimator. The method includes preprocessing for missing and extreme values, extraction of multiple growth components (including long-term, short-term, rolling, and drift-based signals), volatility-aware asymmetric capping, optional seasonal adjustment via damped and normalized seasonal factors, and a recursive forecast formulation with moderated growth. The package includes a user-facing forecasting interface and a plotting helper for visualization. Related forecasting background is discussed in Hyndman and Athanasopoulos (2021) and Hyndman and Khandakar (2008) . The method extends classical extrapolative forecasting approaches and is suited for operational and business planning contexts where stability and interpretability are important. Package: r-cran-gacff Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gacff_1.0-1.ca2404.1_all.deb Size: 79158 MD5sum: a58013d8662030c156cdf9c1f4926b15 SHA1: 17bb72fd3284221820b4dea904e86f4d82f62234 SHA256: 523302f46a0ab91665bc199df0cd998da5dc343d6b457eb1b3f4a7b1ed5e07a9 SHA512: 586f15aa078d5c7331947c75260eb0dbaafca7e933aba2d2ca45e2f8408e416010d42b2e62adcdee3403313a30322ba3e667f6510f57d8eb53bae6df50438fa2 Homepage: https://cran.r-project.org/package=GACFF Description: CRAN Package 'GACFF' (Genetic Similarity in User-Based Collaborative Filtering) The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method. By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed. This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others. First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user. By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions. The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem. Package: r-cran-gad Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-gad_2.0-1.ca2404.1_all.deb Size: 125864 MD5sum: 2cbfc333d03c708db671defcfb7baa5f SHA1: 432c1496d661269c121a1d5a4f242b3825bec064 SHA256: 380b9ef85b64307fe73a5c3c9fe6bb7f517624b56bd92dc881a5720f54d5abb6 SHA512: 473525265cc1f03acda577f3c3ab5232a9f6e9b31ddb78d643451a4a5e7fcf285471b8bb686cefbb902b9cf92b2c281454319ad5f2aaf6e11461fa397107291d Homepage: https://cran.r-project.org/package=GAD Description: CRAN Package 'GAD' (Analysis of Variance from General Principles) Analysis of complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. Homogeneity of variances is checked using Cochran's C test and 'a posteriori' comparisons of means are done using Student-Newman-Keuls (SNK) procedure. For those terms with no denominator in the F-ratio calculation, pooled mean squares and quasi F-ratios are provided. Magnitute of effects are assessed by components of variation. Package: r-cran-gadget3 Architecture: all Version: 0.15-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-rlang, r-cran-tmb Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-unittest Filename: pool/dists/noble/main/r-cran-gadget3_0.15-1-1.ca2404.1_all.deb Size: 1294982 MD5sum: 4ee67009cd98acf74fa71465450046ef SHA1: ed9d2263b7154f33cd9fe1405efea568f6082e32 SHA256: 7931cac291136afbc543fb501d4e124b258dccafd28321d10f1401f576cd87fa SHA512: 2b399b007d11e109c046af4b585b267fec366a1862bc15d5f665839bfd31ff7a6952a0c79dbe6a853dc6c3cb30e753376990e883d6780d692288b29e24c24cd2 Homepage: https://cran.r-project.org/package=gadget3 Description: CRAN Package 'gadget3' (Globally-Applicable Area Disaggregated General Ecosystem ToolboxV3) A framework to assist creation of marine ecosystem models, generating either 'R' or 'C++' code which can then be optimised using the 'TMB' package and standard 'R' tools. Principally designed to reproduce gadget2 models in 'TMB', but can be extended beyond gadget2's capabilities. Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) "TMB: Automatic Differentiation and Laplace Approximation.". Begley, J., & Howell, D. (2004) "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.". Package: r-cran-gagblup Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ga, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-gagblup_1.0-1.ca2404.1_all.deb Size: 1580770 MD5sum: 77b419fee9ea4443dad5e9ef4e53e097 SHA1: 9ee70461ad94faf33cd1724be4edb4f95b96a565 SHA256: c3d649538ccd2fcbc9ddfa1104dc2c746eafaac399aa6a57e1c60379bda27b3d SHA512: 54b014ba95e4f52cfdc3a05761c0a0ff24075f31bbd139d9e97e19bfb904e05896f654078d4ca38558d7dbfa23417a34ed9c6ccd3e5f77de0cea06b3c2e307d7 Homepage: https://cran.r-project.org/package=GAGBLUP Description: CRAN Package 'GAGBLUP' (Genetic Algorithm Assisted Genomic Best Liner UnbiasedPrediction) Performs genetic algorithm (Scrucca, L (2013) ) assisted genomic best liner unbiased prediction for genomic selection. 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Package: r-cran-gagerr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readxl, r-cran-shiny Filename: pool/dists/noble/main/r-cran-gagerr_0.1.0-1.ca2404.1_all.deb Size: 92416 MD5sum: 77aa9f1c2b081dadde57b2d485aae5c4 SHA1: 47d245bdcac8c535aee17f9f07f1adcf4e5ea82d SHA256: 9f827c69e1815c3b6895c5c14265538baec6f3681364ecbf7df4b0a12a9f40e5 SHA512: 78f55f4d8a8e1188bd0ba282900fef5685bdcf3e1be268baed2410547beb1fb270d26e4d01edd9167d9e7510756fa301eb74807317a5919cf39af5251c020b49 Homepage: https://cran.r-project.org/package=gageRR Description: CRAN Package 'gageRR' (Calculate Gauge Repeatability and Reproducibility) Procedures for calculating variance components, study variation, percent study variation, and percent tolerance for gauge repeatability and reproducibility study. 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Ding, and D. Cabezon (2019) . Package: r-cran-gains Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1983 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gains_1.2-1.ca2404.1_all.deb Size: 1901032 MD5sum: b33e505d5ff0c96f4d893ecd1bf251f9 SHA1: a0bfe987c74108fafe28ed9b3cc9f785b4eec258 SHA256: ee844fc7d1a0bb1cc7fe5d21928cc4fb93c7f9b34ce010f4f4314c7fdc4a2b0a SHA512: 12f133429f43246b62d9dded46ca4fe96693d2a73cf2cf415bf45ee18f30a519ba3213b44ec71df630ffcd9cbcdfc49aa81ddb25aeab01eb140502345638b58b Homepage: https://cran.r-project.org/package=gains Description: CRAN Package 'gains' (Lift (Gains) Tables and Charts) Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications. The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11. Package: r-cran-gaipe Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gaipe_1.1-1.ca2404.1_all.deb Size: 38442 MD5sum: 3a3388976763b89467f3dec52b689655 SHA1: 5370512a0e148cab694bc239c74453ad5400c001 SHA256: 8b99998ef29e87c44275b14a41942cec975553bd194c41d46672ff178a441bcf SHA512: f71290c2d6102eb0ab0f661842892f6df2e6928542e565b931c513efbef83c765ad611279144f408637e8d9066a295a821a6b4e8511c4c24aebc1c95efea0557 Homepage: https://cran.r-project.org/package=GAIPE Description: CRAN Package 'GAIPE' (Graphical Extension with Accuracy in Parameter Estimation(GAIPE)) Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) . And, it can also implement AIPE on RMSEA and power analysis on RMSEA. 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Package: r-cran-galahad Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-galahad_2.0.0-1.ca2404.1_all.deb Size: 63834 MD5sum: 6b948875f2ef162180b20f45b2d592bd SHA1: 3fa2aeebff804beb8e9d74b5d58cf9ae5018e756 SHA256: 877a5213f7c754243c381bb2f599fbb548ac5d8c6c860e5574dac260cce77e65 SHA512: 36de401924265e96e87caa8d6cb0d17ff5e9b76a58606343d78ca9d4aa1576080a2f6ea326cff0b2f92b03862c0ef794e939ae82f27ca7db504060b44212cec1 Homepage: https://cran.r-project.org/package=GALAHAD Description: CRAN Package 'GALAHAD' (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer withSoftplus Reparameterization and Trust-Region Control) Implements the GALAHAD algorithm (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer), updated in version 2 to replace the hard-clamp positivity constraint of v1 with a numerically smooth softplus reparameterization, add rho-based trust-region adaptation (actual vs. predicted objective reduction), extend convergence detection to include both absolute and relative function-stall criteria, and enrich the per-iteration history with Armijo backtrack counts and trust-region quality ratios. Parameters constrained to be positive (rates, concentrations, scale parameters) are handled in a transformed z-space via the softplus map so that gradients remain well-defined at the constraint boundary. A two-partition API (positive / euclidean) replaces the three-way T/P/E partition of v1; the legacy form is still accepted for backwards compatibility. Designed for biological modeling problems (germination, dose-response, prion RT-QuIC, survival) where rates, concentrations, and unconstrained coefficients coexist. Developed at the Minnesota Center for Prion Research and Outreach (MNPRO), University of Minnesota. Based on Conn et al. (2000) , Barzilai and Borwein (1988) , Xu and An (2024) , Polyak (1969) , Nocedal and Wright (2006, ISBN:978-0-387-30303-1), and Dugas et al. (2009) . Package: r-cran-galaxias Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corella, r-cran-cli, r-cran-delma, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-usethis, r-cran-withr, r-cran-zip Suggests: r-cran-gt, r-cran-here, r-cran-janitor, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-r.utils, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-galaxias_0.1.2-1.ca2404.1_all.deb Size: 424724 MD5sum: 21d5f6b5a5df77a159f009df4c39b3b8 SHA1: fff6fae0213e4319dc1c98b7727dba7413a02daf SHA256: ffe8d6b596458b07d6f14c1fa6343795b945db2b9be46134cae3aea667cc0af5 SHA512: 8cdcec0be37d3fa792bf14f7019a555a32526de9c3f9a62c04e2ab669d20c1390fd932055f5c14309fb3a9239a2d8cccf3c6ee1a323c7c983709e5bc38336d30 Homepage: https://cran.r-project.org/package=galaxias Description: CRAN Package 'galaxias' (Describe, Package, and Share Biodiversity Data) The Darwin Core data standard is widely used to share biodiversity information, most notably by the Global Biodiversity Information Facility and its partner nodes; but converting data to this standard can be tricky. 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Package: r-cran-galigor Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rstudioapi, r-cran-tibble, r-cran-gargle, r-cran-ryandexdirect, r-cran-rfacebookstat, r-cran-rvkstat, r-cran-rmytarget, r-cran-rym, r-cran-getproxy, r-cran-rgoogleads, r-cran-rappsflyer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-galigor_0.2.5-1.ca2404.1_all.deb Size: 73376 MD5sum: 9f33ede59c18f1b1ab295d53285f77b3 SHA1: 1fe64915cd5bfa2313fd49063f7a473c6301d7ff SHA256: f05da5785d3ef4e7633f26e33177771a03ebf94a2bcddf3fc41d265cbbe2248b SHA512: 2fb115f20d48f3dc1c61fac4ab72b37425cf23233ec11ed3505777ba476a5e723b1221faabd506d36451c56950623d88863bfda410ac8387996274340a2f2954 Homepage: https://cran.r-project.org/package=galigor Description: CRAN Package 'galigor' (Collection of Packages for Internet Marketing) Collection of packages for work with API 'Google Ads' , 'Yandex Direct' , 'Yandex Metrica' , 'MyTarget' , 'Vkontakte' , 'Facebook' and 'AppsFlyer' . This packages allows you loading data from ads account and manage your ads materials. Package: r-cran-galisats Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Filename: pool/dists/noble/main/r-cran-galisats_2.2.0-1.ca2404.1_all.deb Size: 191730 MD5sum: 18d6482b2eac75472c5c69ac0ac22068 SHA1: 60519280a6782a3e9d2790db2ff004415ae57bb9 SHA256: dadaf7de6b223010c43785ae13ed886ab441fa8b46dbee5f84509239c3500cb7 SHA512: 6a408d5fcd4517727e74e3e1625b8b11ca9cb5fde92771e8df96bcae89b28908e31eea2881f001c1c9a72fb61d5ba76acfb00f6d770693d704f883e962f832a3 Homepage: https://cran.r-project.org/package=galisats Description: CRAN Package 'galisats' (Configuration of Jupiter's Four Largest Satellites) Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellites’ positions. x – the apparent rectangular coordinate of the satellite with respect to the center of Jupiter’s disk in the equatorial plane in the units of Jupiter’s equatorial radius; X is positive toward the west, y – the apparent rectangular coordinate of the satellite with respect to the center of Jupiter’s disk from the equatorial plane in the units of Jupiter’s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites' positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds. Package: r-cran-gallery Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gallery_1.0.0-1.ca2404.1_all.deb Size: 172230 MD5sum: d72a122d41c8e2eb6a4266edc5a381fb SHA1: 3da1e0acceef68e7cb44fdd743128f1240f49539 SHA256: da296612eb836bb5c69b87003a3d6dd6950465c6d476660c0b929359624ac1f9 SHA512: 730efc375880ffe35e9dfd282b4dbc7177f1b1421d3559d38d1b4bff8905e83283410e6edca9bc2de191b6702e3c9b256658da7aebf99ab49fa46f275051a690 Homepage: https://cran.r-project.org/package=gallery Description: CRAN Package 'gallery' (Generate Test Matrices for Numerical Experiments) Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. 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The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others. Package: r-cran-galts Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-genalg, r-cran-deoptim Filename: pool/dists/noble/main/r-cran-galts_1.3.2-1.ca2404.1_all.deb Size: 26652 MD5sum: ffbdecff88828dc004169b4814993342 SHA1: ea42351259d001945c7738a097f9ae503d9ea00b SHA256: 166ab1beab42bb059344ebd11dab0841d1a20710dc73fc8b4947009ef9a69a22 SHA512: fdcd877ca9fa9a534b3f0b1a6922dfe2748fdcbf0d49e07425848f5d26b3e981d7dca6d70432f6471c690fe3812b870d00a78d3a06283d32aed162c8b53b3bd3 Homepage: https://cran.r-project.org/package=galts Description: CRAN Package 'galts' (Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)Estimation) Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression. 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It is an R package designed for the calculation of biological diversity using sequence data. It simplifies the process by requiring only sample IDs and accession numbers. Whether you're analyzing genetic or microbial diversity, It provides efficient tools for diversity analysis. Serially one should go for the functions as presented here expand_accession_ranges(), get_sequence_information(), preprocess_for_alignment(), write_fasta(), SampleID_vs_NumSequences(), data_sampling(), alignment_info(), compute_average_similarity_matrix(), generate_heatmaps(), clustering_average_similarity(), clustering_percent_similarity(), bubble_plot_count(), bubble_plot_percentage(), tree_average_similarity(), tree_percent_similarity(). Till date there are total 15 functions. More details can be found in Faith (1992) . 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Rigby, Stasinopoulos, Heller, De Bastiani (2019). Distributions for Modeling Location, Scale, and Shape Using GAMLSS in R, . Stasinopoulos, Kneib, Klein, Mayr, Heller (2024). Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications, . 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It accommodates both uncensored and right-censored reliability observations in the presence of observed and unobserved covariates. Provides Phase I maximum likelihood estimation of Weibull 'AFT' gamma frailty model parameters, and Phase II monitoring procedures including probability-limits-based control charts, exponentially weighted moving average ('EWMA') charts with conditional expected values, and likelihood-ratio cumulative sum ('CUSUM') control charts. Competing 'CUSUM' schemes (ignoring unobserved frailty or both covariates) and Average Run Length ('ARL') simulation utilities are also provided. The statistical methodology is based on Asadzadeh (2022) . 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Package: r-cran-gaqsar Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ga, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales, r-cran-prospectr, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qsardata Filename: pool/dists/noble/main/r-cran-gaqsar_1.2.3-1.ca2404.1_all.deb Size: 210006 MD5sum: d77b108d8aef26f478d2d60877c78856 SHA1: 1ca6efc09bd19202cb6d14d022aee445f670404d SHA256: 67a2571444fb7bc75f034d080dc677626fdf3d80ed994015e79509071a40fa99 SHA512: 8283d0dae93cd4892496ff12d518f1a17d0a1440fdea375d1e0799d08ecdf3e5ab8e56ea907110377ea2934796b0f6652e414ac0bcc0f8a9b103e1bebf724380 Homepage: https://cran.r-project.org/package=gaQSAR Description: CRAN Package 'gaQSAR' (QSAR Modelling Using Genetic Algorithm Based Variable Selection) Implements genetic algorithm-based variable selection for building quantitative structure-activity relationship (QSAR) models. The package provides a workflow for selecting optimal predictor subsets from large descriptor spaces using leave-one-out cross-validation (LOOCV) with Q2 as the fitness criterion. Features include automatic handling of multicollinearity via variance inflation factor (VIF) thresholding, customizable genetic algorithm operators, and diagnostic tools for model evaluation. Supports both training set optimization and external validation, plus nested (double) cross-validation for unbiased performance estimation and predictor stability diagnostics. Built-in visualization functions include Q2 curves and Williams plots to assess model applicability domain. The method is demonstrated in papers predicting antibacterial activity by Araya-Cloutier et al. (2018) and Kalli et al. (2021) . Package: r-cran-garch.x Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nnls, r-cran-ga, r-cran-gensa, r-cran-pso, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-garch.x_3.0-1.ca2404.1_all.deb Size: 165394 MD5sum: a8bebe8939e064d361b51c9e9ea62811 SHA1: 9222908b841038261eab7d2399134ce7daed6ab4 SHA256: c8dbe7c6669484949745f239f133af12403520399eac35a476b72d3b8c760f15 SHA512: 33c4b6b0a89e52f7459005e0d7c0a9ce19da8bdca405b56ae51edf706fdaa2a88ff0bca1de332c0c7f56d185fab25d027c1e4e2c6c36139eac30ef201154628a Homepage: https://cran.r-project.org/package=GARCH.X Description: CRAN Package 'GARCH.X' (Estimation and Exogenous Covariate Selection for ARCH-m(X),Additive ARCH-m(x), and GARCH-X Models) Estimates the parameters and nonparametric functions of an ARCH-m(X) model with exogenous covariates, estimates the parameters and nonparametric functions of an Additive ARCH-m(X) model with exogenous covariates, estimates the parameters of a GARCH-X model with exogenous covariates, performs hypothesis tests for the covariates returning the p-values, and performs stepwise variable selection on the exogenous covariates, and uses False Discovery Rate p-value corrections to select the exogenous variables. Package: r-cran-garchinfolstm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-torch, r-cran-rugarch, r-cran-ggplot2, r-cran-cli, r-cran-coro Filename: pool/dists/noble/main/r-cran-garchinfolstm_0.1.0-1.ca2404.1_all.deb Size: 105166 MD5sum: 7ee8c4091479f1abe0e4f919e1ab1346 SHA1: 6efde2042f70b71e438d997a22cd9db9e984bb15 SHA256: 3d96519c1c6adf3837e4a90361f4c8262aaaf4974aacec04ac7b28ceb75eb67f SHA512: 3fe147c30e427f3dd80fafb9f4dbd404b050e2d33c6108a583a22ba5a8dc7e36d67b9bf78bb597b423469632ebf202d991c69733b8a8be4057fc0c99e43a2352 Homepage: https://cran.r-project.org/package=GARCHInfoLSTM Description: CRAN Package 'GARCHInfoLSTM' (GARCH-Informed LSTM Model for Volatility Forecasting) The proposed Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-informed Long Short-Term Memory (LSTM) model follows the concept of physics-informed machine learning (PIML) by integrating established econometric knowledge of price volatility into a data-driven forecasting framework. In the model, conditional volatility estimated from the GARCH process is incorporated as an additional explanatory signal or volatility-based weighting component within the LSTM architecture. This enables the LSTM to learn nonlinear temporal dependencies while remaining informed by the underlying characteristics of agricultural price series, including volatility clustering, heteroscedasticity and market uncertainty. The optimized weighting parameter, lambda, controls the contribution of the GARCH-derived volatility information to the final prediction. Thus, the model combines the statistical interpretability of GARCH with the nonlinear learning capability of LSTM, producing a hybrid PIML framework that is more responsive to both normal price movements and periods of extreme market volatility. The methodology is motivated by hybrid forecasting framework proposed by Yeasin and Paul (2024) . Package: r-cran-garchito Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-garchito_0.1.0-1.ca2404.1_all.deb Size: 142820 MD5sum: b26955756d07dff4dc224127448ed342 SHA1: 0fc0e702e28ded7e119ecaf96e8cdda5269043ca SHA256: 21fcda83edb08fc99c7f12e84245e3e3da134ba85f0ae3ea521bf03ee3be997b SHA512: 439e10f803cd3b5afa8795f50e176be91528672c160a6bdd164525c6e3354337d6751b466bd5e83c79253bf7813bc6ac04ef9fd68cc6bd88f1c57f9d62035c0a Homepage: https://cran.r-project.org/package=GARCHIto Description: CRAN Package 'GARCHIto' (Class of GARCH-Ito Models) Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) ] and Realized GARCH-Ito [Song et. al. (2020) ] models. Optimization is done using augmented Lagrange multiplier method. Package: r-cran-garchsk Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-garchsk_0.1.0-1.ca2404.1_all.deb Size: 76618 MD5sum: 346400e5086c102df64eb44d8e50f5f2 SHA1: fe1dc8695f98dcdf16b48c420c4a33afc6983b8e SHA256: ff9f81b7416dada162cb5861b9ead574fb9adde7bca18df5d118cf6a88330397 SHA512: 28ce4412e73317a8279acf354db96a9324a79867eca5ab2198b8b69a3b28e8f7dceaef19f7d45eba45e52cc145abca9deae2146e66dc794fae81b61fffa28d7c Homepage: https://cran.r-project.org/package=GARCHSK Description: CRAN Package 'GARCHSK' (Estimating a GARCHSK Model and GJRSK Model) Functions for estimating a GARCHSK model and GJRSK model based on a publication by Leon et,al (2005) and Nakagawa and Uchiyama (2020). These are a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. Package: r-cran-garcom Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-vcfr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-garcom_1.2.2-1.ca2404.1_all.deb Size: 59068 MD5sum: 37c10fc48a3c890b55ed34ad1d49e52d SHA1: ea1056e70b07f6afece9e86385ddbe8a29b4816e SHA256: 1d798cf5989bb996d7800ae8112ef8678a626e4dafa52ceb99b4f9ade995bf4c SHA512: d4b005c4d4270a71725d8ff8862b3be3be504e3d0213dd2ac7db80860925c7c0aa5fe272f45d50052f7140f01e964906e8439c5e99da067fad483bd73cd3bb34 Homepage: https://cran.r-project.org/package=GARCOM Description: CRAN Package 'GARCOM' (Gene and Region Counting of Mutations ("GARCOM")) Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. 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Package: r-cran-gareg Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepointga, r-cran-ga Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gareg_0.1.2-1.ca2404.1_all.deb Size: 342688 MD5sum: 66d3c30c5ea469dddffbe1f8d6b15a8f SHA1: 269e0f8b44166c3df1baa273a14aef09fd4d978d SHA256: 539547ac383300256ade776fa8cbb204e958bbb622a74ed9c6014e1af10e99d2 SHA512: 3415906880740a7490b05c3e2a98bacebdcc7ccc47afec78f4f597d13924b33fc6e4ca35e15e0d337496a07b7d997762ebe6476f1f3e2a7032a3b7dc49e08981 Homepage: https://cran.r-project.org/package=GAReg Description: CRAN Package 'GAReg' (Genetic Algorithms in Regression) Provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, ). The computation is built on the 'GA' engine of Scrucca (2017, ) and 'changepointGA' engine from Li and Lu (2024, ). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified. Package: r-cran-gargle Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-openssl, r-cran-rappdirs, r-cran-rlang, r-cran-withr Suggests: r-cran-aws.ec2metadata, r-cran-aws.signature, r-cran-covr, r-cran-httpuv, r-cran-knitr, r-cran-rmarkdown, r-cran-sodium, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gargle_1.6.1-1.ca2404.1_all.deb Size: 708046 MD5sum: 8f2148cfdc1abb676f196e84f264d688 SHA1: 9b7ebdbcf743af972ddebadf836ca4f6e4680a57 SHA256: 06ce1f930d18a7a358300e36de546902538b80b456412e9445f7885f73b918ba SHA512: 89f8121cc2dd77ea751886c4dcff0849504b06ef45272e9e635d40935cef92d8d3aa67e67388989d8ea5488a88d408eec741d7076cca8ffcdc15367ea96517de Homepage: https://cran.r-project.org/package=gargle Description: CRAN Package 'gargle' (Utilities for Working with Google APIs) Provides utilities for working with Google APIs . 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Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method. Package: r-cran-gatepoints Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gatepoints_0.1.5-1.ca2404.1_all.deb Size: 296790 MD5sum: 030d861397d960418d041712ec947cc7 SHA1: ffe455d45dd3f41a5d82c2e7b2f847ef3d4093ec SHA256: 1632e10290a05a804802f302a17dbfe1efda420fe67dd2d2d71223f8b64e6cec SHA512: 4c6ad26aae14a3d86d4af95d4f916fc427187d0150473e4768218e2fcc73e101b27640decebc8817d77eb0d6142badd8654908666d06957eb64ab994002ea29f Homepage: https://cran.r-project.org/package=gatepoints Description: CRAN Package 'gatepoints' (Easily Gate or Select Points on a Scatter Plot) Allows user to choose/gate a region on the plot and returns points within it. Package: r-cran-gater Architecture: all Version: 0.1.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-lifecycle, r-cran-rlang, r-cran-sparr, r-cran-spatialpack, r-cran-spatstat.geom, r-cran-terra, r-cran-tibble Suggests: r-cran-dplyr, r-cran-r.rsp, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-gater_0.1.16-1.ca2404.1_all.deb Size: 1000874 MD5sum: 4e8cc357a732532c3bd66bdcdab7780f SHA1: 19b3a761a751aee814e3e54344f54151d5abd024 SHA256: 1ca61841542ca729c88f0ef09c3c96894030895eecfd5e547bb313463520e575 SHA512: be6308b40b6e3173a225a89b59a1d21d925ed3bc78df0134a80d951e09f05f8facd33ca618018e21de1da5f04a7cd50311f5f6bb57394342b59e3b3ddd1758b6 Homepage: https://cran.r-project.org/package=gateR Description: CRAN Package 'gateR' (Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation) Estimates statistically significant marker combination values within which one immunologically distinctive group (i.e., disease case) is more associated than another group (i.e., healthy control), successively, using various combinations (i.e., "gates") of markers to examine features of cells that may be different between groups. For a two-group comparison, the 'gateR' package uses the spatial relative risk function estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) . Details about kernel density estimation can be found in J. F. Bithell (1990) . More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) . 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Package: r-cran-gauser Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gauser_1.3-1.ca2404.1_all.deb Size: 290764 MD5sum: 22d9088f88c3010fdf711eee10c39f40 SHA1: 8dae083f72ee926af4a06f2dcffc2bdc033d9af2 SHA256: 167c40e82967ba5f9bb6a696a3ca43cff40cbfd4aa0980366cb5a6f14053b14d SHA512: 7ae18f87a687a9d747ce52341d39ee0cd686bb78cf1dceb696dff4b6d6b72d52e7268088ef9768603b7030b0482904e2f77b253224ddd98c2cbbed34fcfadb4f Homepage: https://cran.r-project.org/package=gauseR Description: CRAN Package 'gauseR' (Lotka-Volterra Models for Gause's 'Struggle for Existence') A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at , and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution. Package: r-cran-gaussdiff Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gaussdiff_1.1.1-1.ca2404.1_all.deb Size: 21244 MD5sum: c05b4cb970bc7bb94abc844595fd0b26 SHA1: c8fb82100af68ba2a45dba3f3add17e7f5ea680b SHA256: cab59264826ac537635252fcf0f20d92d0b461a82c918401c1815841e7427369 SHA512: 56af9c9c9126129e8ec44030a0444c6c788623b8cf3565c5d9320792775d0ec14c440b130133e491ede11245af0529373b4da805861d91c39aefb862a68b1143 Homepage: https://cran.r-project.org/package=gaussDiff Description: CRAN Package 'gaussDiff' (Difference Measures for Multivariate Gaussian ProbabilityDensity Functions) A collection difference measures for multivariate Gaussian probability density functions, such as the Euclidea mean, the Mahalanobis distance, the Kullback-Leibler divergence, the J-Coefficient, the Minkowski L2-distance, the Chi-square divergence and the Hellinger Coefficient. 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Package: r-cran-gaussratiovegind Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gaussratiovegind_3.0.0-1.ca2404.1_all.deb Size: 73282 MD5sum: 87ffead408e417a30973f8c40815a066 SHA1: 529aa86c282b817989462bd76082ec5c5d72cffa SHA256: b1f570d68beb1b522c4f55f7d932db4f249791929ea2e5c3ef1d6d9a66f3e204 SHA512: 326f7baa865ddd8f635eebf2fe7dc8f235cce652c363daa535fe5019d8cb3acf829d89811cda579e3b2fe7e699c03daf99190f4da40ab9d68ded3151f13061d4 Homepage: https://cran.r-project.org/package=gaussratiovegind Description: CRAN Package 'gaussratiovegind' (Distribution of Gaussian Ratios) It is well known that the distribution of a Gaussian ratio does not follow a Gaussian distribution. The lack of awareness among users of vegetation indices about this non-Gaussian nature could lead to incorrect statistical modeling and interpretation. This package provides tools to accurately handle and analyse such ratios: density function, parameter estimation, simulation. An example on the study of chlorophyll fluorescence can be found in A. El Ghaziri et al. (2023) and another method for parameter estimation is given in Bouhlel et al. (2023) . Package: r-cran-gausssuppression Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ssbtools, r-cran-regsdc, r-cran-matrix, r-cran-ellipsis, r-cran-rlang Suggests: r-cran-formattable, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lpsolve, r-cran-rsymphony, r-cran-rglpk, r-cran-slam, r-cran-highs, r-cran-data.table Filename: pool/dists/noble/main/r-cran-gausssuppression_1.3.0-1.ca2404.1_all.deb Size: 590422 MD5sum: c1e142d32d7b2dedff1deee3fe4b442a SHA1: 34685bf19e9938d453d884fd9bb0b60d4494f220 SHA256: 91df5b2c3e482f1be325caa4015b51e3fa9ab5ef3c54c1f07de7f8df7570db5b SHA512: 4b09901f13828a53b1ad4bf6dbe09008e6aa57f78c3c3ba56674658e05eb72ff0b0585c5ce94907066b8b3743baab08892d0c19d853c4ae47a8ccee1c7e6ac8c Homepage: https://cran.r-project.org/package=GaussSuppression Description: CRAN Package 'GaussSuppression' (Tabular Data Suppression using Gaussian Elimination) A statistical disclosure control tool to protect tables by suppression using the Gaussian elimination secondary suppression algorithm (Langsrud, 2024) . A suggestion is to start by working with functions SuppressSmallCounts() and SuppressDominantCells(). These functions use primary suppression functions for the minimum frequency rule and the dominance rule, respectively. Novel functionality for suppression of disclosive cells is also included. General primary suppression functions can be supplied as input to the general working horse function, GaussSuppressionFromData(). Suppressed frequencies can be replaced by synthetic decimal numbers as described in Langsrud (2019) . Package: r-cran-gawdis Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fd, r-cran-ga Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gawdis_0.1.5-1.ca2404.1_all.deb Size: 95126 MD5sum: 8b48fcca1e7fe75d94104021c2fe16da SHA1: f4970f255beb1af5c002af0ef88f05a745824053 SHA256: 662010abc7baef6fc2f05d9ce510a5af228abf3dcfd711f6808e5e560a490183 SHA512: 56d173136ac8b07f273208bf82941da549613e923a512a706c58ca191f7eead191d2773800707c094bc0409337f1631339732387c7db85f55db08d5f58a90299 Homepage: https://cran.r-project.org/package=gawdis Description: CRAN Package 'gawdis' (Multi-Trait Dissimilarity with more Uniform Contributions) R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function. Package: r-cran-gazepath Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sp, r-cran-jpeg, r-cran-zoo, r-cran-scales, r-cran-shiny Filename: pool/dists/noble/main/r-cran-gazepath_1.4-1.ca2404.1_all.deb Size: 151340 MD5sum: 37d50dae36ce9113f1b752d6cfaf4147 SHA1: 0826a3ffad09dc9f0671077ba821247abd6d17dd SHA256: 55813313572ab92d1aba341df176bae5163f883c88339eb999662c2f51aa6abc SHA512: 3021511eb5a1069010ab652f805ea65ae7db738ef9412f39166fd142ad382db5bac92bb604c401edb66135c9350789f8d2f40ddd93961c3bce77495635ce9293 Homepage: https://cran.r-project.org/package=gazepath Description: CRAN Package 'gazepath' (Parse Eye-Tracking Data into Fixations) Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality. The method is described in van Renswoude et al. (2018) . Package: r-cran-gb2 Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-hypergeo, r-cran-laeken, r-cran-numderiv, r-cran-survey Suggests: r-cran-simframe Filename: pool/dists/noble/main/r-cran-gb2_2.1.2-1.ca2404.1_all.deb Size: 246252 MD5sum: 6611f1988755785fc63d9d019ff90b8c SHA1: 471886f50d6adee65823e31fe3a7d5f0f273380a SHA256: c085b1805e1eb15033e13b02ffcab2b253165908df21686e5a3e91096b763bad SHA512: 984cdfe371492423426f75501f62a4bf0d983ebfed327eeadcef2abb7cff4175e1a9623776cfca4a33e6d7ca4120d80b64fbfb41b91851b8e79c76cec2a8871a Homepage: https://cran.r-project.org/package=GB2 Description: CRAN Package 'GB2' (Generalized Beta Distribution of the Second Kind: Properties,Likelihood, Estimation) The GB2 package explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distribution are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots. Package: r-cran-gb2group Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gb2, r-cran-minpack.lm, r-cran-ineq, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-gb2group_0.3.0-1.ca2404.1_all.deb Size: 186584 MD5sum: 182658d58d794e3b5df0ea5d547fb180 SHA1: d0ec5efd84a55c636acb4ad75edbd1fb1f8b81ed SHA256: 3547130b7ad2efeb1a045752c224558fd3d5f29ed42c5b9a52720c48f5fba710 SHA512: dd7cb7d9942daec62a6d37ee756320f41ed7a09aa12196a67f79269b68399bc730331aed80880e15b1bcf588bee748af4f96cf21fa7e172436714782a56d0af7 Homepage: https://cran.r-project.org/package=GB2group Description: CRAN Package 'GB2group' (Estimation of the Generalised Beta Distribution of the SecondKind from Grouped Data) Estimation of the generalized beta distribution of the second kind (GB2) and related models using grouped data in form of income shares. The GB2 family is a general class of distributions that provides an accurate fit to income data. 'GB2group' includes functions to estimate the GB2, the Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 'GB2group' deploys two different econometric strategies to estimate these parametric distributions, the equally weighted minimum distance (EWMD) estimator and the optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the OMD estimates. Standard errors of the EWMD estimates are obtained by Monte Carlo simulation. See Jorda et al. (2018) for a detailed description of the estimation procedure. Package: r-cran-gb5mcpred Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4599 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-bioc-biostrings, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-stringr, r-cran-tibble, r-cran-doparallel, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-foreach, r-cran-ftrcool, r-cran-iterators Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gb5mcpred_0.1.0-1.ca2404.1_all.deb Size: 2523348 MD5sum: fa0cbb6efffeb4af0b633916118cc387 SHA1: 3270826e0b766af2d62442aee6c693a0968099fa SHA256: 57c4b8359c2433d71dba8c17dfa8f928c3139e87d9270462eed08f431420b069 SHA512: 0ccf104f2f3f5b253d157af5b062fbcc2e84931ebd89aa72dfa3d937e98794c0eb27788b6b3518a908eed81365c22f9a110922c6fb38f1c1ecde97ca54013116 Homepage: https://cran.r-project.org/package=GB5mcPred Description: CRAN Package 'GB5mcPred' (Gradient Boosting Algorithm for Predicting Methylation States) DNA methylation of 5-methylcytosine (5mC) is the result of a multi-step, enzyme-dependent process. Predicting these sites in-vitro is laborious, time consuming as well as costly. This ' Gb5mC-Pred ' package is an in-silico pipeline for predicting DNA sequences containing the 5mC sites. It uses a machine learning approach which uses Stochastic Gradient Boosting approach for prediction of the sequences with 5mC sites. This package has been developed by using the concept of Navarez and Roxas (2022) . Package: r-cran-gbass Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-gigrvg, r-cran-bass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lhs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gbass_2.0.1-1.ca2404.1_all.deb Size: 200872 MD5sum: ca4d2e4e01d8ef017f75a5db4a740020 SHA1: 196567f815d9d55134841244e87cbfb464997503 SHA256: a0a1d903851f3a0cf28611a61c29562134cae69894596ff898533e46a8d76cb0 SHA512: ee0523521b5ee07bd8b6176a90342ee88446dffed3f3273d24e5fd0d649dbff82c888d6b3240c71ba842b346a68ede21acd84f5b152d9bead974c36b624f30e8 Homepage: https://cran.r-project.org/package=GBASS Description: CRAN Package 'GBASS' (Generalized Bayesian Adaptive Smoothing Splines) Bayesian nonlinear regression under a range of likelihood models using generalized Bayesian adaptive smoothing splines. Robust regression with Student's t likelihoods, quantile regression, and related latent-scale models are included as special cases. Package: r-cran-gbcrosswalk Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gbcrosswalk_0.0.5-1.ca2404.1_all.deb Size: 265074 MD5sum: f1ec0043ebb89e3a5ab3bcbb95e6c2e1 SHA1: 2c2715684fa80900394dc3bbd8fb8377e50eaa0a SHA256: 5052b52f735a4fdb19009fa5b335f289c6e4ae0600cb3899dc01ce4786e829da SHA512: 7661db345a43f0cab902531340df6801993e73e0c7001115330ff4a0eba1c658230d113140cfc663d3d13439993683b980023d268dcf37ef738d4749975b4436 Homepage: https://cran.r-project.org/package=gbcrosswalk Description: CRAN Package 'gbcrosswalk' (Crosswalk Chinese GB Industry Classifications Across Years) Tools for cleaning, building, and composing crosswalks between Chinese GB/T 4754 industry classification vintages. The package starts with the historical workflow for 1986, 1994, 2002, 2011, and 2017 and is designed so later PDF-derived vintages can be added as additional adjacent pairwise crosswalks. 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Package: r-cran-gbif.range Architecture: all Version: 1.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5603 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-rgbif, r-cran-coordinatecleaner, r-cran-sf, r-cran-clusterr, r-cran-fnn, r-cran-geometry, r-cran-cluster, r-cran-mclust, r-cran-zip, r-cran-class, r-cran-nmof Suggests: r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gbif.range_1.9.2-1.ca2404.1_all.deb Size: 3650564 MD5sum: 7d5aa01e8c10d7c89f49361b25dce7f1 SHA1: b6b85d1d3f068f924c03c0073b32508c86e61c64 SHA256: a3df2f4c644008983016d658ab3ddc58f9f81fe6e43004bd6da7d33b09e30c17 SHA512: acc9423fa9c5280577e1ab9a9d0488b93f1b4cba613e6473334e50879b02619977b5185b0c0bac2cc9ea5e2406d2e1506a696e2493eb2e227a8691858cab5731 Homepage: https://cran.r-project.org/package=gbif.range Description: CRAN Package 'gbif.range' (Species Range Mapping from GBIF Using Ecoregion Constraints) A user-friendly, end-to-end workflow to generate ecologically informed species range maps from sparse observations using environmental clustering and convex hulls. Serves as a standalone framework or complementary approach to Species Distribution Models (SDMs). By constraining estimated ranges within authoritative or custom ecoregion boundaries, the approach prevents spurious range over-prediction common in geometric hull methods. The package automates data acquisition via 'GBIF' synonym-aware, tiled downloads; curates records using 13 configurable filters; and supports multi-scale analysis by integrating global or user-provided spatial layers. Also includes disk-based batch processing for large-scale studies and built-in tools for cross-validation and expert-derived evaluations. Package: r-cran-gbifdb Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-duckdbfs Suggests: r-cran-spelling, r-cran-dbplyr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-minioclient Filename: pool/dists/noble/main/r-cran-gbifdb_1.0.0-1.ca2404.1_all.deb Size: 293898 MD5sum: 4c84780693df9a2ab671badf6bb4d009 SHA1: dfe8da87c7d4e2b9df663e4919b2d1e337c706d3 SHA256: 3eeac72fd7e07d6db1e6708afd788219b5e98ad54dffdad021870834eed96060 SHA512: 608cb83c84f56b97095089e3f6e83e17fd3859642e4cd0365f49e3b530d5492e78ebfa8e32c48f02f4b928c7d952d8430cbd9df87eb6838a3fc1b55dfd75b6c8 Homepage: https://cran.r-project.org/package=gbifdb Description: CRAN Package 'gbifdb' (High Performance Interface to 'GBIF') A high performance interface to the Global Biodiversity Information Facility, 'GBIF'. In contrast to 'rgbif', which can access small subsets of 'GBIF' data through web-based queries to a central server, 'gbifdb' provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary 'SQL' or 'dplyr' operations on the complete 'GBIF' data tables (now over 1 billion records, and over a terabyte in size). 'gbifdb' accesses a copy of the 'GBIF' data in 'parquet' format, which is already readily available in commercial computing clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer, or can be accessed directly without downloading, or downloaded to any server with suitable bandwidth and storage space. The high-performance techniques for local and remote access are described in and respectively. Package: r-cran-gbm.auto Architecture: all Version: 2024.10.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-dismo, r-cran-dplyr, r-cran-gbm, r-cran-ggmap, r-cran-ggplot2, r-cran-ggspatial, r-cran-lifecycle, r-cran-lubridate, r-cran-mapplots, r-cran-metrics, r-cran-readr, r-cran-sf, r-cran-stars, r-cran-starsextra, r-cran-stringi, r-cran-tidyselect, r-cran-viridis Filename: pool/dists/noble/main/r-cran-gbm.auto_2024.10.01-1.ca2404.1_all.deb Size: 3852258 MD5sum: a3e00ac6b4ca3dd9a23a3c2f637b3c78 SHA1: 1859d4593c46df2754269139e87aea4ebef84e87 SHA256: e79aeb976fd567ba0915c33cf1c7bc0a9116b0390c4deb72bc6299249d5c7143 SHA512: 1987e7f5aea9ecffd6711d756bf4eccfbe220259d9b828cdf62615a9203868b96f864a3cef4bc671b3ae98f9d0586e1eff3093a2e49ac11551e13737d08c2808 Homepage: https://cran.r-project.org/package=gbm.auto Description: CRAN Package 'gbm.auto' (Automated Boosted Regression Tree Modelling and Mapping Suite) Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around 'gbm' (gradient boosting machine) functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 'Working Guide' ; workflow follows Appendix S3. See for published guides and papers using this package. 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Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages. Package: r-cran-gcalcium Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-catools, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gcalcium_1.0.0-1.ca2404.1_all.deb Size: 95582 MD5sum: e68140a38a598adfba3f22f11d824623 SHA1: 708e655f715417b99cbaa42c761b91789a326d51 SHA256: 3612a5a5ca87a536f42c0c017db0df7a582e3946c8d4498c716c7ae5e464b74c SHA512: 2a28b8c82a86f2cf5170a73cfda779cab145b03059250d18c36a8835b2b4a203048431d0efb94df8ae28296c297f523753f9a264c8a0bc682b7ba375787a6a49 Homepage: https://cran.r-project.org/package=GCalcium Description: CRAN Package 'GCalcium' (A Data Manipulation and Analysis Package for Calcium IndicatorData) Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users. 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The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 . 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Package: r-cran-gccfactor Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-sandwich, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-plm Filename: pool/dists/noble/main/r-cran-gccfactor_1.2.1-1.ca2404.1_all.deb Size: 1138412 MD5sum: 6785496d5b9fd702e632a697713d66a0 SHA1: d55cf672143b64afb0cea551ccbaa5d09bc2cd39 SHA256: c45def603177e271851719f3aef889ae4304b424fc0db5b7c8867a91ed76e050 SHA512: ba03d6c17935061d8128efc88d4db8c266bf4b9880a6d89e8019ff27acc73d1713ce1e2d64050e28fb664f3f9159c27ae33c748c524674ed8132eadb9dc8e177 Homepage: https://cran.r-project.org/package=GCCfactor Description: CRAN Package 'GCCfactor' (GCC Estimation of the Multilevel Factor Model) Provides methods for model selection, estimation, inference, and simulation for the multilevel factor model, based on the principal component estimation and generalised canonical correlation approach. 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Data include charcoal series (age, depth, charcoal quantity, associated units and methods) and information on sedimentary sites (localisation, depositional environment, biome, etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at . Package: r-cran-gcemod Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-cmprsk, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gcemod_0.3.0-1.ca2404.1_all.deb Size: 272122 MD5sum: ad3761970126e7360f0c408768473044 SHA1: 835a6a906933aac164cd27f0f5a5a4db1350fa5f SHA256: 347f1c7e7786f5286d47476eb84449f457008deeafa181501442f7d79c987ae3 SHA512: ed44bb63e1324f4a8896a77edccc9b8d5ebe336ef2c881ebbf183858d6040daa876442e183383596e1e3d183073a859606b8d8f35a1eb690ccf2cc8d36ca1de8 Homepage: https://cran.r-project.org/package=gcemod Description: CRAN Package 'gcemod' (Generalized Competing Event Models with Lunn-McNeil Testing) Fits generalized competing event (GCE) models and estimates covariate effects on omega-plus, the ratio of the hazard for an event of interest to the hazard for a competing event, on both the cause-specific (Cox) and subdistribution (Fine-Gray) hazard scales. Confidence intervals and p-values are obtained from the Lunn-McNeil (1995) stacked (augmented) data approach. The package builds GCE risk scores from the model linear predictor, identifies risk-score cutpoints that maximize the separation in omega-plus between groups, and produces cumulative incidence ("alligator") plots by risk group and calibration plots of predicted versus observed omega-plus. It also compares covariate effects across the primary, competing, and total (composite) events, and estimates covariate effects on the ratio of cumulative incidence functions (a cumulative-incidence-scale GCE model). Methods follow Carmona et al. (2014) , Mell et al. (2024) , and Lunn and McNeil (1995) . Package: r-cran-gcerisk Architecture: all Version: 19.05.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-cmprsk, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-gcerisk_19.05.24-1.ca2404.1_all.deb Size: 64148 MD5sum: c60bcad332e90169b20c1402c7123898 SHA1: bad6ada09afef5b0238e02ae1ef580a91940f699 SHA256: e2ace70c653e8bcbafbc47512ff1147c595020ca3cb0d171dfa03209a1d7cea4 SHA512: 968cfe70f4cb746477ae44fd33f863908e0ac9a0febd85dd024dc436a7192f2c925a9e04d412e1c3a526a2cdd176750495d92bc3586955f2e7da501fed865769 Homepage: https://cran.r-project.org/package=gcerisk Description: CRAN Package 'gcerisk' (Generalized Competing Event Model) Generalized competing event model based on Cox PH model and Fine-Gray model. This function is designed to develop optimized risk-stratification methods for competing risks data, such as described in: 1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F, and Mell LK (2014) . 2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) . 3. Lunn, Mary, and Don McNeil (1995) . Package: r-cran-gcestim Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3644 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zoo, r-cran-downlit, r-cran-data.table, r-cran-rlang, r-cran-lbfgs, r-cran-lbfgsb3c, r-cran-meboot, r-cran-optimparallel, r-cran-optimx, r-cran-rstudioapi, r-cran-clustergeneration, r-cran-simstudy, r-cran-pracma, r-cran-pathviewr, r-cran-rsolnp, r-cran-bayestestr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggdist, r-cran-latex2exp, r-cran-plotly, r-cran-viridis, r-cran-hdrcde, r-cran-shiny, r-cran-miniui, r-cran-shinywidgets, r-cran-shinydashboardplus, r-cran-readxl, r-cran-dt, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-nlcoptim Filename: pool/dists/noble/main/r-cran-gcestim_1.1.0-1.ca2404.1_all.deb Size: 1870382 MD5sum: af805084324113fda7d3d4ba9990f11d SHA1: 8b9b38b680215cbc32f13515c03a15e8c5aba84b SHA256: d2b987ed5d8ad4774e50ed9763bfd245921029f6e30ffa85fb60f15bc107ce77 SHA512: 47021c30dcb06c639c9de4bbe25606c8694b3d1e906e626b56ab1fdad1942708f35156c74da2ad5c636cf66f348b3ee741bc16f146e77da74b6f45ad1271c7a1 Homepage: https://cran.r-project.org/package=GCEstim Description: CRAN Package 'GCEstim' (Regression Coefficients Estimation Using the Generalized CrossEntropy) Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) ). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) ) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints. 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For each input covariate the method builds spatial-pattern features (local indicator of spatial association, local Geary's c, log local variance, rank quantile entropy, geocomplexity, log scale variance, local variogram exponent, and signed z-score and median absolute deviation outlier strengths over a series of buffer radii) and neighbourhood-distribution features (buffer-wise quantiles of the covariate values surrounding each location), reduces the buffer and quantile sweeps to a compact set of interpretable functional summaries, and selects variables by random forest importance combined with spatial-block stability resampling and group voting. The GCF method is positioned as prediction-oriented feature construction: its output feeds any downstream regression learner. Methods are described in Song (2026) . 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Tools for reshaping common plate reader outputs into 'tidy' formats and merging them with design information, making data easy to work with using 'gcplyr' and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at . Package: r-cran-gctensor Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rtensor, r-cran-einsum Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gctensor_1.0.1-1.ca2404.1_all.deb Size: 49120 MD5sum: ea63f98850a9face4cd37bb78530ce69 SHA1: 4d62e423ff526912fabf14e4c86ceb9cc3a4c53c SHA256: 8d49df2c0288b31f5d8d847e2c71d3fb388eabb495cdd5f5d01ea00a10137c1c SHA512: 8d6b28a2e895ff36b49f625375ebe9a33f483821f6f087b7860a0432aa160dec886df0808891552905bd7efdeb478820aeb8a5b1627ae728dbb5c7f077ea36ad Homepage: https://cran.r-project.org/package=gcTensor Description: CRAN Package 'gcTensor' (Generalized Coupled Tensor Factorization) Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md , for details of the method. Package: r-cran-gcuber Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-gcuber_0.1.3-1.ca2404.1_all.deb Size: 2579664 MD5sum: 3d4aebabfa3a0a3615efb79c0e87bb59 SHA1: 594e2c4482ab5a0cbf2e9da51f7bd6239ce06da7 SHA256: 4e3753c6ed13f3978f877722a4e3df58cde84b51b2d59285b87f008a237b67c2 SHA512: 0216ca4dc477eee044c06c277a4756e862a725b4304940ae57168c511bc1f76cc2894ed5e718acf6dd1f44f26586628556fcbd0002a725600a7b3a76b91fa695 Homepage: https://cran.r-project.org/package=GCubeR Description: CRAN Package 'GCubeR' (Estimation of Forest Volume, Biomass, and Carbon) Provides tools for estimating forest metrics such as stem volume, biomass, and carbon using regional allometric equations. The package implements widely used models including Dagnelie P., Rondeux J. & Palm R. (2013, ISBN:9782870161258) "Cubage des arbres et des peuplements forestiers - Tables et equations" , Vallet P., Dhote J.-F., Le Moguedec G., Ravart M. & Pignard G. (2006) "Development of total aboveground volume equations for seven important forest tree species in France" , Pauwels D. & Rondeux J. (1999, ISSN:07779992) "Tarifs de cubage pour les petits bois de meleze (Larix sp.) en Ardenne" , Massenet J.-Y. (2006) "Chapitre IV: Estimation du volume" , France Valley (2025) "Bilan Carbone Forestier - Methodologie" . Its modular structure allows transparent integration of bibliographic or user-defined allometric relationships. 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Package: r-cran-gd Architecture: all Version: 10.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bammtools Suggests: r-cran-gdverse, r-cran-knitr, r-cran-rmarkdown, r-cran-spedm Filename: pool/dists/noble/main/r-cran-gd_10.9-1.ca2404.1_all.deb Size: 2711670 MD5sum: 59be64380d3871e774b6ffff4bb3fb0a SHA1: 1bbdf2c29555d1661aa66d109af915aed1426334 SHA256: 71065fcaa8d7d869bf63b0f4542e795b91465e2a0ba9d9d9c444fd1f4470ab4a SHA512: 5f15f91fbf3ce171e30b47300770f4dc4afc2975f566ec360346f66b45610c86a1d8e4cdd4c1da393e15b979e748b3d199e231915c073a9521e2599d6a63fe2b Homepage: https://cran.r-project.org/package=GD Description: CRAN Package 'GD' (Geographical Detectors for Assessing Spatial Factors) Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) and Jinfeng Wang (2016) . 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Package: r-cran-gdadata Architecture: all Version: 0.93-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-gdadata_0.93-1.ca2404.1_all.deb Size: 416330 MD5sum: 5be3a51668fd4d9a4a4293b1fa70ed37 SHA1: dfe765337e0ba0c9e80ce108bf0ae00d399923fe SHA256: 72e17cab050184c33f86e2314ba93b07b2d083463b89727dc459de14165ebfa9 SHA512: 990817fd63d75cafac7f462fce4fca221df7d42165b7b67b4d3cf9519bb3303e2f1841df96f370f6b5cf4792b1693095d26ffa2fd7f2cbeeb433deb22973f5b4 Homepage: https://cran.r-project.org/package=GDAdata Description: CRAN Package 'GDAdata' (Datasets for the Book Graphical Data Analysis with R) Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015). 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Package: r-cran-gdatools Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-descriptio, r-cran-factominer, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang Suggests: r-cran-traminer, r-cran-sf, r-cran-shiny, r-cran-miniui, r-cran-esquisse, r-cran-rclipboard, r-cran-factoextra, r-cran-ade4 Filename: pool/dists/noble/main/r-cran-gdatools_2.3.1-1.ca2404.1_all.deb Size: 1029546 MD5sum: 5d94e4e4485f2bc61650e6af59230195 SHA1: 7d72c0fb2e4ceeeaed21928c8b5bd6890a4f5104 SHA256: 561c859c73071fc0992dc551ae9ca9fffb85b6b9fc1edc1e31c0e1bdbeedbeb2 SHA512: 66d1b58762e2c6f91c60409ddaa3041f193845eb32be42558c5c14f8aea2e9f2f3ebaae790c6720f4f1f6f8e99e48b2ba86408a2d968eeaaadffe0acc05b42cb Homepage: https://cran.r-project.org/package=GDAtools Description: CRAN Package 'GDAtools' (Geometric Data Analysis) Many tools for Geometric Data Analysis (Le Roux & Rouanet (2005) ), such as MCA variants (Specific Multiple Correspondence Analysis, Class Specific Analysis), many graphical and statistical aids to interpretation (structuring factors, concentration ellipses, inductive tests, bootstrap validation, etc.) and multiple-table analysis (Multiple Factor Analysis, between- and inter-class analysis, Principal Component Analysis and Correspondence Analysis with Instrumental Variables, etc.). 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Package: r-cran-gdilm.seirs Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-ngspatial Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdilm.seirs_0.0.7-1.ca2404.1_all.deb Size: 134002 MD5sum: 8e995246701921fbadb2a90326c8aabf SHA1: 1b6d41e39bb0ab5947330ff63ecea5c84baacbf7 SHA256: 8bc050e35f05506d48576f0ae1f6eab2e843f5680ea0469d96b15a1bb154dfa4 SHA512: 58049e5a2afcf1cc6de9d578b217af81638165f6fb194cd7154276c58d46d809788c3c3109820bc952840447d1db7a069285bd2f748919741ad7458b541e6e10 Homepage: https://cran.r-project.org/package=GDILM.SEIRS Description: CRAN Package 'GDILM.SEIRS' (Spatial Modeling of Infectious Disease with Reinfection) Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings. The methods are described in Abed, Torabi and Mashreghi (2025) . Package: r-cran-gdilm.sir Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3449 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-psych, r-cran-mass, r-cran-numderiv, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdilm.sir_1.2.1-1.ca2404.1_all.deb Size: 1055594 MD5sum: 96495577b0d404f6a8ba335c6fb1e89e SHA1: 9eea5b58c2b81693312f2b754bc52772a494e7b0 SHA256: b89997beb5170c56cb56b38a2ba47b35e288b96b76b1e8ab8c8334e9b29b9d0c SHA512: fec53c10b79cfeb172d25bb2fc9754af013b7595b9adff92b11ce5c896684c500f2fa83811926c1029e2de9402467266ab70492cec23f88e1843b6aa7616ea5e Homepage: https://cran.r-project.org/package=GDILM.SIR Description: CRAN Package 'GDILM.SIR' (Inference for Infectious Disease Transmission in SIR Framework) Model and estimate the model parameters for the spatial model of individual-level infectious disease transmission in Susceptible-Infected-Recovered (SIR) framework. Package: r-cran-gdim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dplyr, r-cran-ggplot2, r-cran-irlba, r-cran-progress, r-cran-rlang, r-cran-tibble Suggests: r-cran-epca, r-cran-fastrg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdim_0.1.1-1.ca2404.1_all.deb Size: 50738 MD5sum: e1dcb3c885d30eb6476856cf24082c00 SHA1: 4329e7e177d671b0e6df1e02d89344390978b290 SHA256: 4335d4566cd086e44c6809f787c3208bd9e01a42cb6100bbe8aff19386ef4889 SHA512: f41bb028969ff4eede5fac1481ef55e74eea8aeeff16769a30cb3c3d91acfe6df920a69d9e954465be785dc735432efcf4611f6b7c5d255a2c2085df657b7c1a Homepage: https://cran.r-project.org/package=gdim Description: CRAN Package 'gdim' (Estimate Graph Dimension using Cross-Validated Eigenvalues) Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. 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The package implements measures to model dispersal histories first presented by van Etten and Hijmans (2010) . Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. The distances implemented in the package are used in geographical genetics, accessibility indicators, and may also have applications in other fields of geospatial analysis. 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Package: r-cran-geecrt Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rootsolve, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geecrt_1.1.5-1.ca2404.1_all.deb Size: 325776 MD5sum: c481f43f66ff896da29e9cb44aeb63f2 SHA1: 3da8e7c1ac4bb0f2bcfc49b396591be6ea8dbf70 SHA256: 735b422b3c9542513673af09ef143d0d462f74162fb7e212496cec8f296737f9 SHA512: 58bcaaece7247637cc73ed710fd5aaa23322eeed93cf99590f383d01a69489a4e30fbb84c454d53acf4cd14bb2c0ce3c3df0218de0b1cab35fa5d364a4630e8c Homepage: https://cran.r-project.org/package=geeCRT Description: CRAN Package 'geeCRT' (Bias-Corrected GEE for Cluster Randomized Trials) Population-averaged models have been increasingly used in the design and analysis of cluster randomized trials (CRTs). To facilitate the applications of population-averaged models in CRTs, the package implements the generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by Li et al. (2022) specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives a simple and efficient estimating equations approach based on the cluster-period means to estimate the intervention effects as well as correlation parameters. In addition, the package also provides functions for generating correlated binary data with specific mean vector and correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) or the conditional linear family method in Qaqish (2003) . Package: r-cran-geecure Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-cran-mass, r-cran-geepack Filename: pool/dists/noble/main/r-cran-geecure_1.0-6-1.ca2404.1_all.deb Size: 165538 MD5sum: 9f137f85e3e502eca1bbbed498375592 SHA1: 52231c0a27274c5b1159e353eb054fa9c88b8eba SHA256: 84ff20b0eff1d77c487fe478d615b1027b050155cd5c47e65e1f7597c9c3d52f SHA512: fe9fa9d24636da2cf433fe76f47626bbc8f4ce3c7f6b7606234098504ee7316672753fe07c0ccc1cd2a99093a4f6ed0704e9cc696c864d1dd2d5217356a0e0d2 Homepage: https://cran.r-project.org/package=geecure Description: CRAN Package 'geecure' (Marginal Proportional Hazards Mixture Cure Models withGeneralized Estimating Equations) Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) . Package: r-cran-geelite Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rnaturalearthdata, r-cran-rnaturalearth, r-cran-googledrive, r-cran-data.table, r-cran-reticulate, r-cran-rstudioapi, r-cran-geojsonio, r-cran-lubridate, r-cran-jsonlite, r-cran-magrittr, r-cran-progress, r-cran-reshape2, r-cran-rsqlite, r-cran-stringr, r-cran-crayon, r-cran-dplyr, r-cran-h3jsr, r-cran-knitr, r-cran-purrr, r-cran-tidyr, r-cran-rgee, r-cran-cli, r-cran-sf Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-optparse, r-cran-leaflet, r-cran-withr Filename: pool/dists/noble/main/r-cran-geelite_1.0.6-1.ca2404.1_all.deb Size: 408814 MD5sum: 7213d3cfe5e179fd3c7dcc78a9433346 SHA1: d19acf23e31ddb745c2a40d5519e6fe5a8efb834 SHA256: f132a77034338fcc19240d31ad9639a471f45fed092f06dc189e01b6710ec518 SHA512: 555eb4b56c36f4364a19c9b32f44e7c5fc2f9c4be579a55049a7f0b6ca4d37021e70735c4e43fdcb5d6ce76e640502c7b307ca6b7dadd9df2cc8b5c4e33e08c8 Homepage: https://cran.r-project.org/package=geeLite Description: CRAN Package 'geeLite' (Building and Managing Local Databases from 'Google Earth Engine') Simplifies the creation, management, and updating of local databases using data extracted from 'Google Earth Engine' ('GEE'). It integrates with 'GEE' to store, aggregate, and process spatio-temporal data, leveraging 'SQLite' for efficient, serverless storage. The 'geeLite' package provides utilities for data transformation and supports real-time monitoring and analysis of geospatial features, making it suitable for researchers and practitioners in geospatial science. For details, see Kurbucz and Andrée (2025) "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package" . Package: r-cran-geem Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-geepack, r-cran-testthat, r-cran-mumin Filename: pool/dists/noble/main/r-cran-geem_0.10.1-1.ca2404.1_all.deb Size: 81054 MD5sum: 0f5c6e31183ccf9d49489783996faa80 SHA1: 3bf343136ac7b99e48096d653d2b2bbdc2086038 SHA256: f5cdc219d561c84c668940bdfbcb1f9f44a92acdd5fed1f82f20d9d077d89eb5 SHA512: cb5b57ea6acffd623d18b9f4b24130ae47f94b5bef6ade012c4c83e971b176a24c0bcae994788d93044930ea12a3617e99731fd71b8935a81c2872c2c244ce8a Homepage: https://cran.r-project.org/package=geeM Description: CRAN Package 'geeM' (Solve Generalized Estimating Equations) GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting. The 'M' in the name 'geeM' is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R. Package: r-cran-geemediate Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee Filename: pool/dists/noble/main/r-cran-geemediate_1.1.4-1.ca2404.1_all.deb Size: 30266 MD5sum: 33fd3c08aafe57ad6aa2d18111fb7bea SHA1: 269a22b8482843147cdc0b59dd2961741f3c2ad4 SHA256: 4b0abdce0dff6185e0814526816f1e68508f1424764cef7bc94372e245de3701 SHA512: 83fef9c648989d79e8ccd7e037b614d0ee74c0990e8c45930ecbd7fdd445b704b004ed47ea60895942e1403a1d9aab307bc09e62be8b3d6c7de0a96cca6f50e8 Homepage: https://cran.r-project.org/package=GEEmediate Description: CRAN Package 'GEEmediate' (Mediation Analysis for Generalized Linear Models Using theDifference Method) Causal mediation analysis for a single exposure/treatment and a single mediator, both allowed to be either continuous or binary. The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) . Package: r-cran-geesmv Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-gee, r-cran-matrixcalc, r-cran-mass Filename: pool/dists/noble/main/r-cran-geesmv_1.3-1.ca2404.1_all.deb Size: 139854 MD5sum: 413a677d12e7b24a0a4e953f355907ab SHA1: bcfa667ce4a9350ba5cf37c57a98a8aea8ce26b5 SHA256: f09cef15667989fefc41092f835b46af8202034b22ebe9448909a4db921023c0 SHA512: b07653388027183334e5a449735c7c2b9e73284c2f896fbc82dcbdc3608898f64816c8c38cfbaee9f85850a91ea3121fa74180bd986542e8b4e3d963c483b68d Homepage: https://cran.r-project.org/package=geesmv Description: CRAN Package 'geesmv' (Modified Variance Estimators for Generalized EstimatingEquations) Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance. Package: r-cran-geess Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geess_1.0.2-1.ca2404.1_all.deb Size: 61988 MD5sum: 00b8e7847976d87163634caf036f2587 SHA1: 6b3c692052127b58de33e07dbda1845e087afea9 SHA256: 79bcd052110fc239092a23b06b9fd17742ca6c510e84aa59b3214f50d6178509 SHA512: 655812df63af994b00162ca2595574c6df4f6cc808e1a6fc26bc058ed29c803bef0fa62ab7b61cd45bbd49695f14d89105cdea085ebd9663cb4e4374479ce0e8 Homepage: https://cran.r-project.org/package=geess Description: CRAN Package 'geess' (Modified Generalized Estimating Equations for Small-Sample Data) Analyze small-sample clustered or longitudinal data using modified generalized estimating equations with bias-adjusted covariance estimator. The package provides any combination of three modified generalized estimating equations and 11 bias-adjusted covariance estimators. Package: r-cran-geessbin Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geessbin_1.0.2-1.ca2404.1_all.deb Size: 72302 MD5sum: ece77d0877d8b6bc481dd0b0649276c4 SHA1: 0e334553f415cc4d52ca5e164684e5a5999139f1 SHA256: a991e1c5567d398d15d021b6e317c3b5068b7cf764a2697167046d0595ba4137 SHA512: 32fde77129e919403492399dc376ca69d08374a941aed1f329b140f76e594d6426ba499a10f71b9ce28cd2b51c47e9ad5d23b91e85fb22dee54d832b55f2532a Homepage: https://cran.r-project.org/package=geessbin Description: CRAN Package 'geessbin' (Modified Generalized Estimating Equations for Binary Outcome) Analyze small-sample clustered or longitudinal data with binary outcome using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. The package provides any combination of three GEE methods and 12 covariance estimators. Package: r-cran-geex Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rootsolve, r-cran-numderiv, r-cran-lme4 Suggests: r-cran-testthat, r-cran-knitr, r-cran-dplyr, r-cran-moments, r-cran-sandwich, r-cran-inferference, r-cran-xtable, r-cran-aer, r-cran-icsnp, r-cran-mass, r-cran-gee, r-cran-saws, r-cran-rmarkdown, r-cran-geepack, r-cran-covr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-geex_1.1.1-1.ca2404.1_all.deb Size: 854748 MD5sum: 40ff2887f1fee126754f88fa716017f1 SHA1: d231593b416cc10f9f8f0aa8a192227a470c68ec SHA256: 6eb78102fcc897e05d28f4d075bbedb231ed7b44a8d7ee2a9a826d9f0b11f2db SHA512: be0f57f5f82008c5a06d9fa51ec73952a30a4fa29ebeb969ae75b7f530d07d3b996cea6214fa43c3b3347a178df6107a606e1c9b0ef1de8b53c03383e597b9df Homepage: https://cran.r-project.org/package=geex Description: CRAN Package 'geex' (An API for M-Estimation) Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) ). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) . Also provides an API to compute finite-sample variance corrections. Package: r-cran-geinfo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-rvest, r-cran-dplyr, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-geinfo_1.0-1.ca2404.1_all.deb Size: 108814 MD5sum: 686a81ee969263a4f962929f58d9d8dd SHA1: c0fa3a8deef980d2ef353c5805153a6ae4fab117 SHA256: fc763c8b038fa389f1b6e16e0aee93f65be0b0982556cbbafd8b6731d6e63c99 SHA512: 286e45994374fdc6ed17eb1dd3c92da2a3a1c3f6e259b927f516f3011442660668a06e0dfc0c5951c75b3ef5f6a0a0a2cad8af43b0a7723320b2c794e2f8d766 Homepage: https://cran.r-project.org/package=GEInfo Description: CRAN Package 'GEInfo' (Gene-Environment Interaction Analysis Incorporating PriorInformation) Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions. Package: r-cran-geint Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-bindata, r-cran-nleqslv, r-cran-pracma, r-cran-speedglm, r-cran-rje, r-cran-geepack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geint_1.1-1.ca2404.1_all.deb Size: 141810 MD5sum: a8d91fef59227c4582dc2de44dc8325a SHA1: b3f90971bb629ea7cc63d2104f25a090da390eef SHA256: bbfdd752335036faa3c6d0a432b2850844ab1921d048598bed0ad697c7b17640 SHA512: 8ea829029fcc416095707ee7ef3c627d81113d250e57d40e845e34cf8bb7d9759fbc25790ffda266092a6b17cabfea0ec2b3d9d86ae45bb9e57567246acd3241 Homepage: https://cran.r-project.org/package=GEint Description: CRAN Package 'GEint' (Misspecified Models for Gene-Environment Interaction) The first major functionality is to compute the bias in regression coefficients of misspecified linear gene-environment interaction models. The most generalized function for this objective is GE_bias(). However GE_bias() requires specification of many higher order moments of covariates in the model. If users are unsure about how to calculate/estimate these higher order moments, it may be easier to use GE_bias_normal_squaredmis(). This function places many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate normal distribution) and is thus able to automatically calculate many of the higher order moments automatically, necessitating only that the user specify some covariances. There are also functions to solve for the bias through simulation and non-linear equation solvers; these can be used to check your work. Second major functionality is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample performance than other inference procedures for testing GxE interaction. More details on these functions are available in Sun, Carroll, Christiani, and Lin (2018) . Package: r-cran-geinter Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pcapp, r-cran-hmisc, r-cran-survival, r-cran-quantreg, r-cran-reshape2, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-geinter_0.3.2-1.ca2404.1_all.deb Size: 3120174 MD5sum: 007ec48ccf54571531240bac175ea2e7 SHA1: 5eebfdb628f81274005a7772f2416539cf1579de SHA256: d64213546b8daa54f9b62c86823c81b61cc94a887a7bea42aed3e915ce97d675 SHA512: d47e2f176aaaea82d3cba4f8e902a9cf0c323f7651ba43fa627f68446b575246138eb1e0edc6733cc585f9e065b587cde24668cdbe054a02924f050613de0b5e Homepage: https://cran.r-project.org/package=GEInter Description: CRAN Package 'GEInter' (Robust Gene-Environment Interaction Analysis) Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), ; Mengyun Wu et al (2017), ; Yaqing Xu et al (2018), ; Yaqing Xu et al (2019), ; Mengyun Wu et al (2021), ). Package: r-cran-gellipsoid Architecture: all Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-gellipsoid_0.7.3-1.ca2404.1_all.deb Size: 279482 MD5sum: a93b6c3ea447c2a5b6c1a23e39ffd44b SHA1: a6aa058098efe2448cf13de9cbdb06231f3d4148 SHA256: e30d71d5c3d6e3b22f2756e54b7e120070790453c3c672841c7e79f5b8d0dd85 SHA512: 16c98ec268bb0129c2b5e4afe7852c2daa71faf313ee27f64f5d9f52ebb29b5b068755fefd11606369d0b3ced011ac664cde84b8856b8216e9b8289b27cab649 Homepage: https://cran.r-project.org/package=gellipsoid Description: CRAN Package 'gellipsoid' (Generalized Ellipsoids) Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting. Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc. This permits exploration of a variety to statistical issues that can be visualized using ellipsoids as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods Through Elliptical Geometry . Package: r-cran-gemetrics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bglr Filename: pool/dists/noble/main/r-cran-gemetrics_1.0.0-1.ca2404.1_all.deb Size: 38766 MD5sum: 7e9decc27c56e80d555987d12b40d7fd SHA1: 5314a28d45911bd9ba71a07521bf2476c064e4b4 SHA256: 6889ec26929dab9cfe87e2a61b58eec6ba481bf6ccd7f29201c607cc0f6d232b SHA512: 6d074722a61795c2ff26af71c907f06c3d82ba0c6345d06c298ad330df32c83907c5063b397b309d4a332dc38ddd9e4d2c0aecee762b5dde3fd91f74ce25f345 Homepage: https://cran.r-project.org/package=GEmetrics Description: CRAN Package 'GEmetrics' (Best Linear Unbiased Prediction of Genotype-by-EnvironmentMetrics) Provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model. Package: r-cran-gemini.r Architecture: all Version: 0.17.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-knitr, r-cran-rstudioapi Suggests: r-cran-quarto, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gemini.r_0.17.2-1.ca2404.1_all.deb Size: 4382476 MD5sum: e35bc76fbfd46c7cd61b4cfb19f479c2 SHA1: f00cb3fbd3036dba50f9bc14e1735ee21a0ad962 SHA256: 849ad347519585aa59a765d323ab69841ef962cc4cee98a9c82f43b043a66a9c SHA512: 053456774e6780ab51eb139bfdfe0316a34b8a70cc6036beb7e88e469080eb9b9024bc78fcb0235e5911bc032457bdce2bf429ae598169fd380d442b39e3030d Homepage: https://cran.r-project.org/package=gemini.R Description: CRAN Package 'gemini.R' (Interface for 'Google Gemini' API) Provides a comprehensive interface for Google Gemini API, enabling users to access and utilize Gemini Large Language Model (LLM) functionalities directly from R. This package facilitates seamless integration with Google Gemini, allowing for advanced language processing, text generation, and other AI-driven capabilities within the R environment. For more information, please visit . Package: r-cran-gemma2 Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4467 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/noble/main/r-cran-gemma2_0.1.3-1.ca2404.1_all.deb Size: 399792 MD5sum: 6e2ff4bd3d563e82b7e0a37ade896131 SHA1: d361882434943f45baf84c1e9b50168b6941e770 SHA256: 85b4ae9dc8fdc30d9fbd1c1e2c25c5e1aec177227a7a0f6372438595ce75b704 SHA512: fcbe327d1e0b3fac232cbe732ad911493fe42e9d2b158acf5a4d972806c6191e686c89b5305f43eb6b916457eeb22e46ebb546939b4686bc522b9cf706b3de8c Homepage: https://cran.r-project.org/package=gemma2 Description: CRAN Package 'gemma2' (GEMMA Multivariate Linear Mixed Model) Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package 'gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies. 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Provides an interface to the 'GenderAPI.io' V2 web service that infers gender from personal names, email addresses and usernames, runs batches of up to 50 items, reads credit usage and validates phone numbers. Responses are returned as parsed lists with all fields kept, including unknown results, confidence metadata, billing status and batch summaries; errors are raised as structured conditions. Requests are never retried and redirects are never followed. Results are inferences, not verified identity, and can be unknown. 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Package: r-cran-gendercoder Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bs4dash, r-cran-haven, r-cran-shiny Filename: pool/dists/noble/main/r-cran-gendercoder_0.1.1-1.ca2404.1_all.deb Size: 147544 MD5sum: 5a9d138df9070aedc3ef07d296354548 SHA1: 2fb98a77104f435df3fd0f332cc9ba1b16075202 SHA256: 86425bbc90927f2f197ca74b7a90816f1ddf4fd616305f25e5420cb7aef6e01b SHA512: 9e2cf5f85f2180f55dd20ff14a82e293274e88511e2ee09c343515270131621d1abf4c69f0fae0b1a2f7593345c622994955cd0aebf5272baee086b7d84da19a Homepage: https://cran.r-project.org/package=gendercoder Description: CRAN Package 'gendercoder' (Recodes Sex/Gender Descriptions into a Standard Set) Provides dictionary-based tools for recoding free-text gender responses into consistent categories while preserving gender diversity where possible. The package standardises spelling, capitalization, whitespace, and common variants through curated named character-vector dictionaries, supports either detailed or collapsed output categories, and can retain original unmatched responses for manual review. It also includes helpers for creating custom dictionaries from approximate string matches and a local interactive application for recoding uploaded data files. Package: r-cran-genderinfer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-binom Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-genderinfer_0.1.0-1.ca2404.1_all.deb Size: 482474 MD5sum: a2e3a8ed05b729f0e248931df8b11e2a SHA1: a1c3d47d59eecbfadb020c21ca482abc2445aafc SHA256: 41b7f5b377542e1387bc945ac17fe463a111637fe1633dcf6db753750b25d271 SHA512: 152380b8cb2136370082104563acc50afaa358f5393734d3fab814e5d9e6b408efd955c157b1088c521ebf01e6baf0013d1af5e0ca28fc747d671ad46acb5ea2 Homepage: https://cran.r-project.org/package=GenderInfer Description: CRAN Package 'GenderInfer' (This is a Collection of Functions to Analyse Gender Differences) Implementation of functions, which combines binomial calculation and data visualisation, to analyse the differences in publishing authorship by gender described in Day et al. (2020) . It should only be used when self-reported gender is unavailable. Package: r-cran-genderstat Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genderstat_0.1.5-1.ca2404.1_all.deb Size: 66692 MD5sum: adf509219fd48f6b1b8429eaa82262a1 SHA1: 881f8d6da056db7f53ced26b27297e049ef41efd SHA256: 512551d95c10fd96a6c7947ecf5067d0e1cd3218fb4803c62f1fcbda24291056 SHA512: 933cd2910d361603a4daea31e4f6cc5eb9c6699c11cf0cf8d31fe35f9c4823d95c1fece2a4180e8b21f57f9eac435710d9b57f2d9811a668efb2b12d4463c402 Homepage: https://cran.r-project.org/package=genderstat Description: CRAN Package 'genderstat' (Quantitative Analysis Tools for Gender Studies) Provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on . References: Miller, Kevin; Vagins, Deborah J. (2021) . Jacques Charmes & Saskia Wieringa (2003) . Gaëlle Ferrant (2010) . Package: r-cran-gendertext Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-readtext, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gendertext_0.1.0-1.ca2404.1_all.deb Size: 67790 MD5sum: 08603264824842e8f3168cb93da44412 SHA1: 0e158fb79bce65cfc27919dd86eeb675668ab1aa SHA256: e7e7ef763aefe02fd2a2ac4607b327996af40b5804f5b758e74a0a1a32f4c628 SHA512: 5aead49d546581596a9fbbb46d114668d39d930055b99a0a5f77c0e7e0c4b0591ba75dd5e99333660895bc5df8af2267b973569f38adc696f4c8298920ebc7b1 Homepage: https://cran.r-project.org/package=gendertext Description: CRAN Package 'gendertext' (Detect Gendered Words in Text and Suggest Neutral Alternatives) Identifies gendered words and phrases in text using a built in dictionary of more than two hundred gendered terms paired with gender neutral alternatives. Reports the share of gendered language in a text, lists every gendered term found together with its suggested neutral replacement, and can rewrite a text in gender neutral form. Plain text files are read with base R, while other document formats such as PDF and Word are supported through the optional 'readtext' package. The dictionary is informed by published guidance on gender inclusive language, including the United Nations guidelines and the European Parliament guidance on gender neutral language. Package: r-cran-gendist Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gendist_2.0-1.ca2404.1_all.deb Size: 86682 MD5sum: cb141acd9da4dd1f27b5c4ad1c0c5a65 SHA1: 7e5df2ba218ad334ffd7a16d71ad9e6d195debec SHA256: b411b90a1e6da3edf3b046c5cef0124e141be917e52e96dc73f55c972c9064b4 SHA512: 1f48bc2dc2aaaabb9d7f7729c257b3ccd94820421f7e41a29f1059965aa1eff4c8e1fd3b2d72b8ef5f517aebd1c5a8241a05c3523945407ebf6c85a53ff1887e Homepage: https://cran.r-project.org/package=gendist Description: CRAN Package 'gendist' (Generated Probability Distribution Models) Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models. 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For more details on the 'GENEActiv' device, see . Package: r-cran-genecycle Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-longitudinal, r-cran-fdrtool Filename: pool/dists/noble/main/r-cran-genecycle_1.1.6-1.ca2404.1_all.deb Size: 187916 MD5sum: 6b8b539595d5a64e47b4dc25ea62b8e5 SHA1: 3161f68933383ac3c1219f4052eac24d57ac1f3c SHA256: 96317cda84d5826a3228ea63f5ac44978247c10ab7a1f9bfe2d0d5c9b6f165c2 SHA512: f6563b5d862610c1f9b6b5a9ae37f8f9a1e6517e842723829805731dc154816476d0ca1b8aa0923c237f36d2b1febfe1b664de5eefe9dd64447c4499a82ccf2f Homepage: https://cran.r-project.org/package=GeneCycle Description: CRAN Package 'GeneCycle' (Identification of Periodically Expressed Genes) The GeneCycle package implements the approaches of Wichert et al. (2004) , Ahdesmaki et al. (2005) and Ahdesmaki et al. (2007) for detecting periodically expressed genes from gene expression time series data. Package: r-cran-geneexpressionfromgeo Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-bioc-biobase, r-bioc-annotate, r-bioc-geoquery Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geneexpressionfromgeo_1.3-1.ca2404.1_all.deb Size: 30276 MD5sum: 8bc2f1c7d3be91686e9ee317b9a7a6ad SHA1: 93599dae5ab49e8b192802abe587327780bf159e SHA256: 679b6cb29ef7a7054cfd32dcb1dc8ecbfc859b2084adb5b3baa98cf1adefd0ed SHA512: bbbd2fa3965ba6be42831e2e1caf86f69a4dceaf3c9c0125a6f4c2e609a0f6bde50c29a623124bcff8cb7f1ed05ca9f637e4bccb22ce7723435f1164fad5e115 Homepage: https://cran.r-project.org/package=geneExpressionFromGEO Description: CRAN Package 'geneExpressionFromGEO' (Easily Downloads a Gene Expression Dataset from a GEO Code andRetrieves the Gene Symbols of Its Probesets) A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL . More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, . Package: r-cran-genef Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genef_1.0.1-1.ca2404.1_all.deb Size: 34358 MD5sum: f4f13d3299c8cc6b79fbb065c71c9add SHA1: 167cc7f24b2d1115c23fb7dc9dd2d176d9d8507d SHA256: 07b2190456f3c6988a5e16f399648b73df357f92920dcdb94f011c7e8fe4fcfd SHA512: f1b11ee15a9a80efd2f9c25e290675ee7ec06f47e1735bd7806c2ee7ebb075fa63f4c68717cafd2f2c38fdb5484ed7e7dbd7b4678a6991b09e34f7993f9fe918 Homepage: https://cran.r-project.org/package=GeneF Description: CRAN Package 'GeneF' (Package for Generalized F-Statistics) Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) . Package: r-cran-genefindr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-gtexr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-bioc-deseq2, r-bioc-s4vectors, r-cran-testthat Filename: pool/dists/noble/main/r-cran-genefindr_1.1.0-1.ca2404.1_all.deb Size: 143084 MD5sum: 6c732d4d294495c792e3da869fdefaf7 SHA1: 59bf33ac361540db3d6be452a48e3ced1c4595d1 SHA256: 23b6a937ed8582964cb5f3fb502fa663c3b31bcb30a600042c80569b09c131ae SHA512: 952e5b23e92cd9520d79dca6f98154c727d217447ae37fb87ad607bf046f886a9360c3a674cedd6bbe536745ca88579d0473f8e612a4c08b4cebf8a568de00c0 Homepage: https://cran.r-project.org/package=genefindr Description: CRAN Package 'genefindr' (Rapid Gene Characterization Using Public Genomic Databases) A user-friendly interface for characterizing gene function by disease type and tissue site, integrating curated data from publicly available genomic and proteomic databases to support candidate gene prioritization in experimental workflows. 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Package: r-cran-genehummus Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-stringr, r-cran-dplyr, r-cran-httr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genehummus_1.0.11-1.ca2404.1_all.deb Size: 89868 MD5sum: 25e36aee80d82ebb4ac014f06e988f5d SHA1: 239bb0e2c0ed54c3d845f473c502025b00db7055 SHA256: e43224829348dab4a36683ff7116f3d9125fea886da1e5277f1dd14eaca8d848 SHA512: fd41f2e3456cd90d0374bb7c23f28de8196d449c889daca5d1671365195ffdd572503dd7e552b459d1c1b2ea929b27c19562326a0dc48a8561533f9c077ffa37 Homepage: https://cran.r-project.org/package=geneHummus Description: CRAN Package 'geneHummus' (A Pipeline to Define Gene Families in Legumes and Beyond) A pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) for more information and examples. Package: r-cran-genekitr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2848 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-clusterprofiler, r-cran-dplyr, r-cran-europepmc, r-cran-fst, r-cran-geneset, r-cran-ggplot2, r-cran-ggraph, r-cran-ggvenn, r-cran-igraph, r-cran-magrittr, r-cran-openxlsx, r-cran-stringr, r-cran-stringi, r-cran-tidyr, r-cran-rlang Suggests: r-bioc-annotationdbi, r-cran-cowplot, r-cran-complexupset, r-cran-forcats, r-bioc-fgsea, r-cran-ggplotify, r-cran-ggsci, r-cran-ggrepel, r-cran-ggridges, r-cran-ggnewscale, r-cran-goplot, r-bioc-gosemsim, r-cran-labeling, r-cran-pheatmap, r-cran-tm, r-cran-treemap, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rio, r-bioc-rrvgo, r-cran-wordcloud Filename: pool/dists/noble/main/r-cran-genekitr_1.3.0-1.ca2404.1_all.deb Size: 2814040 MD5sum: 761f3e465014a7c9057075ddb0e4554d SHA1: 86301fe406b160df5cb5ed9429f863553c697e95 SHA256: eace89a5ca1326e73eb40925c08c6375d87b9b90f3c7976b35615b8746612695 SHA512: 2552a96ca26adf2d9eb9ec7126731eda76e35bb277892e3b3dae2e2f2128481ca2ea39d8766bac8ec91ff52545f8a69a6d2ca27ca2ed4a242b7312f6a276ce60 Homepage: https://cran.r-project.org/package=genekitr Description: CRAN Package 'genekitr' (Gene Analysis Toolkit) Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication. 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Package: r-cran-genenet Architecture: all Version: 1.2.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-longitudinal, r-cran-fdrtool Suggests: r-bioc-graph, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-genenet_1.2.17-1.ca2404.1_all.deb Size: 264982 MD5sum: 8f7f38626e75c00f57dbd62ff38bec99 SHA1: 5fc9272b1b87122e3dc8bc1afa1303aac0768350 SHA256: 98893b48289002975fbc3ccec7659fed415ce863b3f16e9617e37294f83a4888 SHA512: c7ddb5f7c47fda934763512bd74e72e7cf786d819332639fd25fecd6946e1269304e4fc9e7d5d367c464b7a7039f582c5be73a2e0c0c7c0d156c660cc242cc0c Homepage: https://cran.r-project.org/package=GeneNet Description: CRAN Package 'GeneNet' (Modeling and Inferring Gene Networks) Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions). Package: r-cran-genenmf Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppml, r-cran-matrix, r-cran-seurat, r-cran-cluster, r-cran-lsa, r-cran-irlba, r-cran-pheatmap, r-cran-dendextend, r-cran-viridis, r-cran-colorspace Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-fgsea, r-cran-msigdbr Filename: pool/dists/noble/main/r-cran-genenmf_0.9.2-1.ca2404.1_all.deb Size: 817630 MD5sum: 0b7548dedb8d542cd35296b42b751f0b SHA1: a86098049f8f123371bdcf3d5cfab35a15a729ba SHA256: 280beb2bb805f94671a7d45e2c442452a5465cee1d08f2b8357b3a1910a88582 SHA512: d2ec5fa86023d325d99a2341f1a9f04babc02edff5e0235868278e0980d9967505808b13375c12406e27d09948a9fc7b5eda50b5ea647ea45c181d3825a28cfc Homepage: https://cran.r-project.org/package=GeneNMF Description: CRAN Package 'GeneNMF' (Non-Negative Matrix Factorization for Single-Cell Omics) A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures. Package: r-cran-genenr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readr, r-cran-stringr, r-cran-httr, r-cran-rvest, r-cran-xml2, r-cran-writexl, r-cran-vcfr, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-genenr_3.0.0-1.ca2404.1_all.deb Size: 122836 MD5sum: 373d50ee96cb30a1248a7b71ad5b77e9 SHA1: a47dc97c0cab1691454c5e89838c63448bbdee80 SHA256: b8b62bfbe0a36d0b5ee9912d565e057e70d7195cfc4fb637a8a899f1a858d428 SHA512: d7deda6474cc9f36d751bb0213c30ef4aeeaef3015e1298608f941a549f2ad91bf9288d6b5fd5ae6dd8a5ba632ee1eb9875d93b108e2dbb4d4230c2533b3aebe Homepage: https://cran.r-project.org/package=geneNR Description: CRAN Package 'geneNR' (Automated Gene Identification for Post-GWAS and QTL Analysis) Facilitates the post-Genome Wide Association Studies (GWAS) and Quantitative Trait Loci (QTL) analysis of identifying candidate genes within user-defined search window, based on the identified Single Nucleotide Polymorphisms (SNPs) as given by Mazumder AK (2024) . It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output. Package: r-cran-genepopstats Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcfr Filename: pool/dists/noble/main/r-cran-genepopstats_0.1.0-1.ca2404.1_all.deb Size: 182256 MD5sum: 0678f357ce641124bc9586a030115638 SHA1: 2c48d9e0ed97aef0357255c6cde38bc3a39539b5 SHA256: a4dd5c17653c7b7240fbbe42b22ded66c505728490d9d49f4f2266aebc9769af SHA512: 93cf974b82de9cd46fb180274273d192c734e0abffb548d6821382b02b822d59757965ca83faf3dc791be1a3276ee4fbbbd6a5de0e343f0c7dfca9f05f05712d Homepage: https://cran.r-project.org/package=GenePopStats Description: CRAN Package 'GenePopStats' (Population Genetics Statistics for Selective Sweep) Selective Sweep can be calculated by five significant Population Genetics Statistics such as "Pi", "Wattersons_theta", "Tajima_D", "Kelly_ZnS" and "Omega" Statistics in specified chromosomal region. It has been developed by using the concept of "Kern" and "Schrider" (2018). Package: r-cran-generalcorr Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3039 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-xtable, r-cran-meboot, r-cran-psych, r-cran-lattice Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-generalcorr_1.2.6-1.ca2404.1_all.deb Size: 2641436 MD5sum: 000dfc4d708ef643a12895d3863451dd SHA1: 0334f2e32f3ed097065d206425692d1646b6b2a6 SHA256: 5e194396a42f9c06f12ce4ef0da3eea70e911bb3ea874fb34ffbed20bad69ed4 SHA512: 1a2723d132fdc460bbaf3d9d7c31e4b12119492337e44441b6d806adeea07bc4334f04476fa235513344363b1f145029d48bc77049869df7291a1b4994f895dd Homepage: https://cran.r-project.org/package=generalCorr Description: CRAN Package 'generalCorr' (Generalized Correlations, Causal Paths and Portfolio Selection) Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}. Package: r-cran-generalhoslem Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape, r-cran-mass Suggests: r-cran-nnet, r-cran-mlogit, r-cran-ordinal Filename: pool/dists/noble/main/r-cran-generalhoslem_1.3.4-1.ca2404.1_all.deb Size: 54450 MD5sum: c5568a3f3c415e9cdf18ce839ab34778 SHA1: c6371448b2fea7175d6cb1090634a97ae767402d SHA256: 515e74003baa19715ad2dcfffcbc9e6ce31244d46ba396ff5099a6836919cfc2 SHA512: bdca142fb5249faf142db8038a3d01cc7a1d1225044938075bf193d5dad4032c2b9f87bada857bd4fa4348e2e7ca64f3189a457efca48c6cac780d361c66b3ea Homepage: https://cran.r-project.org/package=generalhoslem Description: CRAN Package 'generalhoslem' (Goodness of Fit Tests for Logistic Regression Models) Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal). Package: r-cran-generalisedcovariancemeasure Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cvst, r-cran-kernlab, r-cran-mgcv, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-generalisedcovariancemeasure_0.2.0-1.ca2404.1_all.deb Size: 36452 MD5sum: 13ab43f8a8314fba6aab2b2532bf9302 SHA1: f373d457747a169928373cd07dedd8946610a2aa SHA256: 4b198d1e9c8692cc0430e0c0c30358e6980add1a0eafe8d85cc3da20aa2c1319 SHA512: b42754a91838af4fc835f77b20a34ed013d5158fa70551dd4f6d719610a56c7738fd3a7697a70163b8f3987bfbab61aaec7495d8b73e4beaf5c1eaa0f854d6e8 Homepage: https://cran.r-project.org/package=GeneralisedCovarianceMeasure Description: CRAN Package 'GeneralisedCovarianceMeasure' (Test for Conditional Independence Based on the GeneralizedCovariance Measure (GCM)) A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters: The Hardness of Conditional Independence Testing and the Generalised Covariance Measure, Annals of Statistics 48(3), 1514--1538, 2020. Package: r-cran-generalizedhyperbolic Architecture: all Version: 0.8-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distributionutils, r-cran-mass Suggests: r-cran-variancegamma, r-cran-actuar, r-cran-skewhyperbolic, r-cran-runit Filename: pool/dists/noble/main/r-cran-generalizedhyperbolic_0.8-7-1.ca2404.1_all.deb Size: 587024 MD5sum: 107e63b1da45b14d89eec330a80f0429 SHA1: 37b1d20723d9ea044118a837f378d7e949be408f SHA256: 3f8db3b926a5411faaef1baf01e255d17bd35196f20b0554e713d20ed7c3302d SHA512: 77b1c9cd8f49f93d5f439ab3f39d642284357c35bb8cf814caa362c76c2213ff15bbcb8b87287e3b45172e31296b893e4941132446373ba4a3a6b8ecd6c11d1c Homepage: https://cran.r-project.org/package=GeneralizedHyperbolic Description: CRAN Package 'GeneralizedHyperbolic' (The Generalized Hyperbolic Distribution) Functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit. 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Package: r-cran-generalrss Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emplik, r-cran-rootsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-generalrss_0.1.3-1.ca2404.1_all.deb Size: 100706 MD5sum: 8f19d009a9f2d83858252c74d0fcdf50 SHA1: a9baefd956b17d47e27b6749552e78f3a02c6f2a SHA256: bed70aed54efe209a0618a6e1338716cb4519717dd09b40d705ea4df7244a79f SHA512: b3c6c1173847b9f2b5c53bcef4502dc131cd08f80847466fb52c996e1a6537956a2f7871019e0d925b7ad0afc64391f7d0339527a635c912ff7d3a2dd4ade576 Homepage: https://cran.r-project.org/package=generalRSS Description: CRAN Package 'generalRSS' (Statistical Tools for Balanced and Unbalanced Ranked SetSampling) Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). 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This package's workhorse is the 'mlr3' framework of Lang et al. (2019) , which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported. Package: r-cran-generics Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-pkgload, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-generics_0.1.4-1.ca2404.1_all.deb Size: 79790 MD5sum: 1183b21cef21a200b8d6748479037e5f SHA1: b7c6aca86ef93fcc824598ac6ec525876a07b7d4 SHA256: 360b92799a375f37fdf6e85a483a0df95c338700781a0d52c4d51353ee9188e8 SHA512: 8747e95e292a4ebf2a2c5271b698e35d3d2c9d821ed73c19ad53755129efb257dd2d650249e00515d43cc56ea35776fc7616dd69e6ac53e6218608817e8c7f13 Homepage: https://cran.r-project.org/package=generics Description: CRAN Package 'generics' (Common S3 Generics not Provided by Base R Methods Related toModel Fitting) In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics. 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This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) ). 'GeneScape' incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes. Package: r-cran-genescorer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genescorer_0.2.0-1.ca2404.1_all.deb Size: 19928 MD5sum: 642a1b718644f52900e263960b2555ff SHA1: fcc1c09646bb3fc3ed0a4ca68c61a15a63513531 SHA256: bcb7897f73e9351690583a681ebb6deb2df76e6b1b83c9f9d97b5034a8e83537 SHA512: 9cb4ac078e18aba1fb5fba789602ffbf4e1cae9b396871cc24772ee4b16cab73bb94f10579601fb09295699e101d3a263c48c3a37431da7469f85ae6624ffe18 Homepage: https://cran.r-project.org/package=GeneScoreR Description: CRAN Package 'GeneScoreR' (Gene Scoring from Count Tables) Provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) . Package: r-cran-geneselectr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tmod Suggests: r-bioc-clusterprofiler, r-bioc-go.db, r-cran-knitr, r-cran-rmarkdown, r-cran-biocmanager, r-cran-upsetr, r-bioc-annotationhub, r-bioc-ensembldb, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-geneselectr_1.0.1-1.ca2404.1_all.deb Size: 464836 MD5sum: ddd99f7e7aa665ddfa864c104506adce SHA1: 1a352233287253720f3e6ba7e5b278bd5ec959e1 SHA256: cf27e732824a82d22cda6f88536f6ccff93fb3c1df9dec606cb032bba9e5c554 SHA512: 971d8e8d1fbb09ddabc6405689e241d2eccc363aab781967bf998354f5022c6fddbdfb9fe29890967e360c9366dc0d2e54408c07b176e4434e338b8b209f1052 Homepage: https://cran.r-project.org/package=GeneSelectR Description: CRAN Package 'GeneSelectR' ('GeneSelectR' - Comprehensive Feature Selection Workflow forBulk RNAseq Datasets) The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' () machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) , using 'clusterProfiler' (Wu et al., 2021) and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) . This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets. Package: r-cran-geneset Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rcurl, r-cran-fst, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geneset_0.2.8-1.ca2404.1_all.deb Size: 374420 MD5sum: 71315929eb5e21e1bd1e2398e0634fa8 SHA1: 4a41c5056d37fc666762b8ba5b363469b6709c40 SHA256: 89ff9c9898b420bfc3e87a883082dde63890f6e08e2aff6f4e6c56aece01bf7f SHA512: d52e2e12710883de98090643a4aa9553c0bcbf692403fed975a8c5cea59d5b56b29389074e596d2397b2bb39a3b536256a3e94e236a758a628d42e0ddc444ec4 Homepage: https://cran.r-project.org/package=geneset Description: CRAN Package 'geneset' (Get Gene Sets for Gene Enrichment Analysis) Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) ), 'KEGG' (Minoru et al. (2000) ), 'WikiPathway' (Marvin et al. (2020) ), 'MsigDb' (Arthur et al. (2015) ), 'Reactome' (David et al. (2011) ), 'MeSH' (Ish et al. (2014) ), 'DisGeNET' (Janet et al. (2017) ), 'Disease Ontology' (Lynn et al. (2011) ), 'Network of Cancer Genes' (Dimitra et al. (2019) ) and 'COVID-19' (Maxim et al. (2020) ). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description. Package: r-cran-geneslope Architecture: all Version: 0.38.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-slope, r-cran-ggplot2, r-cran-bigmemory Suggests: r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geneslope_0.38.3-1.ca2404.1_all.deb Size: 420276 MD5sum: 9fbb61e459d88ba846a0eac481518377 SHA1: e4bc0c2d137033c6c384552906e3edc51315c4e1 SHA256: 7ace869dd47d471a277300e5ad19426768c0be2b87d95875bbdbae6ed981704c SHA512: deabfd824bec89aae298320f768df59c43c9720ed817f10d92603059cb13f07a5f1c8107f28b9ee5dad94fbd2dcc980a7db7ac7926a49ee7668df4dfe827b468 Homepage: https://cran.r-project.org/package=geneSLOPE Description: CRAN Package 'geneSLOPE' (Genome-Wide Association Study with SLOPE) Genome-wide association study (GWAS) performed with SLOPE, short for Sorted L-One Penalized Estimation, a method for estimating the vector of coefficients in linear model. In the first step of GWAS, SNPs are clumped according to their correlations and distances. Then, SLOPE is performed on data where each clump has one representative. Package: r-cran-genesysr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-readr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genesysr_2.2.0-1.ca2404.1_all.deb Size: 106094 MD5sum: 606b588fd84da688bfd3706d35cab303 SHA1: ad0eb033465520159cce22439445d515ed5f963a SHA256: 77ea0215621cbaeace677fc18bdc57f01ba087e9fc4b085cf54079ba9d9ed673 SHA512: 82d8223da78aa3711369e8e5c77b142b4c1799cc97f5982a3891f53b67793ea8f77bc63cdf1c32917914d97a29438a0988f149d78de78292fdb5483ee5e98505 Homepage: https://cran.r-project.org/package=genesysr Description: CRAN Package 'genesysr' (Genesys PGR Client) Access data on plant genetic resources from genebanks around the world published on Genesys (). Your use of data is subject to terms and conditions available at . 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Package: r-cran-geneticae Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-scales, r-cran-mass, r-bioc-pcamethods, r-cran-rrcov, r-cran-dplyr, r-cran-missmda, r-cran-tidyr, r-cran-rlang, r-cran-corpcor Suggests: r-cran-agridat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geneticae_1.0.1-1.ca2404.1_all.deb Size: 2678764 MD5sum: 30d07a288b7b67d08d59a13037157ece SHA1: 48d7810188a0553d16a198e6a5ee30a3257c1825 SHA256: 7412cb50b2f6e96d736225389ca547d056aa6a9c962fbf5bc973358d61a35fd0 SHA512: 72bb43e01d7532b50ffd67f7bea6b17d16e70b3dc378543a2e74dfc1cd0ee5766b9ee006a37285b58478999cf1f5b3733b9dd34c204ed67f6ff5e62d15ee2393 Homepage: https://cran.r-project.org/package=geneticae Description: CRAN Package 'geneticae' (Statistical Tools for the Analysis of Multi EnvironmentAgronomic Trials) Provides tools for the analysis of multi-environment agronomic trials, with a specific focus on plant breeding experiments. Implements the Additive Main effects and Multiplicative Interaction (AMMI) model (Gauch, 1992, ISBN:9780444892409) and the Site Regression (SREG) model (Cornelius, 1996, ). To ensure reliable results even with outliers or missing data, it includes robust versions of AMMI (Rodrigues et al., 2016, ) and SREG (Angelini et al., 2022, ). Furthermore, the package offers advanced imputation techniques for multi-environment data, covering classical methodologies (Arciniegas-Alarcón et al., 2014, ) and recently published imputation methods for MET data (Angelini et al., 2024, ). 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Function include allele frequencies, flagging homo/heterozygotes, flagging carriers of certain alleles, estimating and testing for Hardy-Weinberg disequilibrium, estimating and testing for linkage disequilibrium, ... Package: r-cran-geneticsubsetter Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geneticsubsetter_0.8-1.ca2404.1_all.deb Size: 80102 MD5sum: ef93e6b87f7dd955a2ee496c6c544f2b SHA1: 6cf53c4b8e199301c05deb935a935e311ed84dc4 SHA256: 8db8636d6333d8af556b634f6b6ec94b1b5b21e69c266c11b4da1ee15043eba1 SHA512: aeea8330c445306a7b5588d35d5b1fb723204342fe7e0195a4b532ccc90b04747c5569c0b36530fc89f0d2b82c68e7cd14043cc499baa35aee5877e940930332 Homepage: https://cran.r-project.org/package=GeneticSubsetter Description: CRAN Package 'GeneticSubsetter' (Identify Favorable Subsets of Germplasm Collections) Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively. 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It performs BLASTP and MUMmer alignments [Altschul et al. (1990) ; Delcher et al. (1999) ] and displays results on gene arrow maps. Extensive customization options are available, including legends, labels, annotations, scales, colors, tooltips, and more. 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The genomic and clinical data are provided in multiple releases (separate releases for each cancer cohort with updates following data corrections), which are stored on the data sharing platform 'Synapse' . The 'genieBPC' package provides a seamless way to obtain the data corresponding to each release from 'Synapse' and to prepare datasets for analysis. 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Search hosted content, extract associated metadata and retrieve lyrics with ease. Package: r-cran-genlogis Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-foreach, r-cran-distr, r-cran-manipulate, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-genlogis_1.0.2-1.ca2404.1_all.deb Size: 84670 MD5sum: 37cc2a3ecf7122a014f35dc21e91235f SHA1: 83efa33faaa98250e7884d117b3daddff137d8e5 SHA256: bfd2ca37359cb9da26b5d17d4564f9a801999ec9aa02e5d6deb71d5932434e85 SHA512: 353c572746d56836130ee60055e322d80701d5e431539f22140644cc37d1e6780430f2783a8399d004be6c69a2a103ec7610de50e705474cbb50ca0884447cfe Homepage: https://cran.r-project.org/package=genlogis Description: CRAN Package 'genlogis' (Generalized Logistic Distribution) Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) . 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Methods outlined in a forthcoming paper. 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It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) .The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1). 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Implements the methods of Tvedebrink et al (2018) . Package: r-cran-genomic.autocorr Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-reshape Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-genomic.autocorr_1.0-1-1.ca2404.1_all.deb Size: 24636 MD5sum: 1827e4f5f8dd2464ea38bdf9ca8fb957 SHA1: 0e6034272f57f1cc633f73fd180058fb0c571585 SHA256: cb421adb305de602e3cfa8ce2403deec37a29b01018e898da1f1c9548b32e3fe SHA512: 21e14f3e5a1fa7fd22e0e0ff95fb652d40221717859f7484bd53f088648e0d8188702d7253a2d2c42d74f7e00059b9e77fa4c5b625de75a19a809ec7417ff7ec Homepage: https://cran.r-project.org/package=genomic.autocorr Description: CRAN Package 'genomic.autocorr' (Models Dealing with Spatial Dependency in Genomic Data) Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function. Package: r-cran-genomicper Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genomicper_1.8-1.ca2404.1_all.deb Size: 170116 MD5sum: 759cb7d4e59e7d36819c57fa33ca21ed SHA1: 6fb60f95c9ade744146f81892142fa5ba8554301 SHA256: 0b1f8bf562630667bee83d140ac921ef435553e964f53ca2683042d3ffbb81ff SHA512: 585193cd65d476fa4f7f085b87fef9d6fbc06b44251632f0a2d89749349257d1828a76f6beb9ee869943c763fe1c4d85ed9915c7bc2d6ee802ba3816af6de528 Homepage: https://cran.r-project.org/package=genomicper Description: CRAN Package 'genomicper' (Circular Genomic Permutation using Genome Wide Associationp-Values) Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user. Cabrera et al (2012) . Package: r-cran-genomicsig Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kaos, r-bioc-biostrings, r-cran-entropy, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-genomicsig_0.1.0-1.ca2404.1_all.deb Size: 49836 MD5sum: e52652bbb2f1b7431d9c5d3e5ff6e8bd SHA1: b3b6d4854163dfdb69c7be510a99ee29ce1ed3f6 SHA256: 402a4aa599bab36e3ebeb766533af69cf223adac62eec5154cded599d4c21529 SHA512: 257f8b1ddbf14f63b6d0dcdcf24cfdc4abcd09f1715ac06e1780220b3386f749c4c89e04721970bf4ef27b82d05164c6492fe965f8251b340281af3921668b7d Homepage: https://cran.r-project.org/package=GenomicSig Description: CRAN Package 'GenomicSig' (Computation of Genomic Signatures) Genomic signatures represent unique features within a species' DNA, enabling the differentiation of species and offering broad applications across various fields. This package provides essential tools for calculating these specific signatures, streamlining the process for researchers and offering a comprehensive and time-saving solution for genomic analysis.The amino acid contents are identified based on the work published by Sandberg et al. (2003) and Xiao et al. (2015) . The Average Mutual Information Profiles (AMIP) values are calculated based on the work of Bauer et al. (2008) . The Chaos Game Representation (CGR) plot visualization was done based on the work of Deschavanne et al. (1999) and Jeffrey et al. (1990) . The GC content is calculated based on the work published by Nakabachi et al. (2006) and Barbu et al. (1956) . The Oligonucleotide Frequency Derived Error Gradient (OFDEG) values are computed based on the work published by Saeed et al. (2009) . The Relative Synonymous Codon Usage (RSCU) values are calculated based on the work published by Elek (2018) . 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It includes several functions to calculate commonly used population genomic metrics and a method for reference panel free genotype imputation, which is described in the preprint Gurke & Mayer (2024) . 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Package: r-cran-genoscan Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat, r-cran-matrix, r-cran-mass, r-cran-seqminer, r-cran-data.table Filename: pool/dists/noble/main/r-cran-genoscan_0.1-1.ca2404.1_all.deb Size: 108298 MD5sum: e6497918ab562d6c2c4699cb8baf7511 SHA1: cf8185aa8ba0fa642b2b756c1af6c2f744f668cd SHA256: f5342609d28608f2aa31254dc7fda51cd4203057766e8dc17e2b9a8eff09d236 SHA512: 42496e46b3b102d4bef8964fdd8723ba3d055f07fca9bc2539072e1b53da788e456194f435c7d717e53ab91ffc29404b4f2c1a0782f3b5992a1d5027c5e478fd Homepage: https://cran.r-project.org/package=GenoScan Description: CRAN Package 'GenoScan' (A Genome-Wide Scan Statistic Framework for Whole-Genome SequenceData Analysis) Functions for whole-genome sequencing studies, including genome-wide scan, candidate region scan and single window test. Package: r-cran-genotriplo Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shinybs, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-htmltools, r-cran-processx, r-cran-rlang, r-cran-rmixmod, r-cran-shiny, r-cran-shinythemes, r-cran-data.table, r-cran-shinyfiles, r-cran-bslib, r-cran-stringr Filename: pool/dists/noble/main/r-cran-genotriplo_2.2.1-1.ca2404.1_all.deb Size: 381356 MD5sum: 8e47ae916cba56d94099f41e883957e2 SHA1: 29877fedc4303644b37a23f9979ab85cb632a12c SHA256: 4ceac1d6cb639593cfa54b04e9560a1c64f1dba412b581d9dd0906923cf3424c SHA512: d41d29a50653eee00af22b56453976f516bc7e2a7e2a4779c4af2820aee66dcc325243406cf94b3a73f89365b8bfb6b477ee55fae9b3e4914dbe0649d3240009 Homepage: https://cran.r-project.org/package=GenoTriplo Description: CRAN Package 'GenoTriplo' (Genotyping Triploids/Tetraploids (or Diploids) from LuminescenceData) Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids and tetraploids. Three main functions: Create_Dataset_from_file() to build dataset ; Clustering_parallele_from_dir() that regroups individuals with a same genotype based on proximity and Genotyping_parallele_from_dir() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny(). Package: r-cran-genou Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-genou_0.2.1-1.ca2404.1_all.deb Size: 60482 MD5sum: 112145b2ece8c70e6041f2aa0e0c10e1 SHA1: ba2b25c47425d8099ec5a407bb3cd48a2d61d7e0 SHA256: 11e8f5409cdba24f70c3d403162233c837d49d43e0f9c7e5dfdd6f7bd4dedf83 SHA512: faf5e3404c0355824f0ef5a2d9306b559e9872da0e6c69c46955152b16f4c15b29074fd2c75b5f05590d88b762ac08be198d7aac5fde4933782b5020935a0eec Homepage: https://cran.r-project.org/package=GenOU Description: CRAN Package 'GenOU' (Sequential Change-Point Tests for Generalized Ornstein-UhlenbeckProcesses) Sequential change-point tests, parameters estimation, and goodness-of-fit tests for generalized Ornstein-Uhlenbeck processes. Package: r-cran-genpathmox Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-csem, r-cran-diagram, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-genpathmox_1.1-1.ca2404.1_all.deb Size: 243984 MD5sum: e82beef0d3697415136c2a49be89f362 SHA1: 64c921a662e7c2b2c716f95158d68eb51d126f84 SHA256: a68dd6445ee90322b001f184584ef07f5ec53b08d00d7a5222396b35429b2123 SHA512: cab3b02522e588a24e1a028a3626b4dec0ab3cdf23a91fa77d93f99cb86171d026e973f3f7be85e56605a4f0530d99ff581a26d6ffc6c5465d715f29db7488e8 Homepage: https://cran.r-project.org/package=genpathmox Description: CRAN Package 'genpathmox' (Pathmox Approach Segmentation Tree Analysis) It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) ). Package: r-cran-genproc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-progressr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-genproc_0.2.0-1.ca2404.1_all.deb Size: 186254 MD5sum: 45651ad910f9aee6da02c40fdb9e1218 SHA1: 762443b87baa126295930116a89b811d1ed2b0ca SHA256: dd283993f5e2f64806612dc01004ab343cbaf21f180fcf0a4890a5038f99f829 SHA512: 749adadbd31db7c62af08b9fc84dcaf87a778394ea40e25926e60d358f27e1c53d0a85c16dc66b61737471bcc185ee87d7b0201c3e1dfe061ba0e631192918dc Homepage: https://cran.r-project.org/package=genproc Description: CRAN Package 'genproc' (Robust, Logged and Reproducible Iteration at OrganizationalScale) Turns one-off iterative R procedures (such as for loops, lapply() or pmap() from 'purrr') into production-grade workflows by wrapping them with orthogonal, composable execution layers. Two layers are always active: structured logging with real traceback and per-case timing; and reproducibility capture, which records the R version, loaded package versions, execution environment, the exact iteration mask, and a stat-based fingerprint of every input file referenced in the mask (with a diff_inputs() helper to detect silent drift between runs). Parallel execution (built on the 'future' framework, Bengtsson (2021) ), non-blocking background jobs, and opt-in progress reporting (via 'progressr') are implemented as optional, composable layers. Further layers (error replay, content-hash input fingerprinting, content-based case identifiers) are planned and will remain composable with the default layers. Package: r-cran-genpwr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-nleqslv, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genpwr_1.0.4-1.ca2404.1_all.deb Size: 508058 MD5sum: b37a50d002b00ff645663b6da07075f3 SHA1: 1d0ec8ab5937e8e0aa8cc76fa538a687deaa1edb SHA256: a7656a696febd9f6434ffb41271575165b002c29df1e93eaae42728b1477a6d7 SHA512: 180f025dd82f79670d5e1c970fa2ab99200a236405e29ee26ba068f3dc1aa1c080bfa52eade01870539b6f08737276cea280e74a97582b1dc25fb4af631bf032 Homepage: https://cran.r-project.org/package=genpwr Description: CRAN Package 'genpwr' (Power Calculations Under Genetic Model Misspecification) Power and sample size calculations for genetic association studies allowing for misspecification of the model of genetic susceptibility. "Hum Hered. 2019;84(6):256-271.. Epub 2020 Jul 28." Power and/or sample size can be calculated for logistic (case/control study design) and linear (continuous phenotype) regression models, using additive, dominant, recessive or degree of freedom coding of the genetic covariate while assuming a true dominant, recessive or additive genetic effect. In addition, power and sample size calculations can be performed for gene by environment interactions. These methods are extensions of Gauderman (2002) and Gauderman (2002) and are described in: Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification. American Society of Human Genetics. October 2018, San Diego. Package: r-cran-genridge Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-rgl, r-cran-colorspace Suggests: r-cran-mass, r-cran-bestglm, r-cran-vcdextra Filename: pool/dists/noble/main/r-cran-genridge_0.8.0-1.ca2404.1_all.deb Size: 334014 MD5sum: 8cc5932ce06153e5a6cf013543d7b33a SHA1: a62ff3b057538725222813f259a3767c219e8526 SHA256: a195dc4ea77086d43acfd88bfe3de322bad2a4b3f8b92b942fa62869a3c97a6c SHA512: 198322688240fd9c92dc36706ecdd4453b5fb7aa123cb0d7a3567471df96a5e77d7ff9040e59ff96beb46bcb478a9f0930e13df09b38a6e0943538a2d0498187 Homepage: https://cran.r-project.org/package=genridge Description: CRAN Package 'genridge' (Generalized Ridge Trace Plots for Ridge Regression) The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors. Package: r-cran-genseir Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-minpack.lm, r-cran-nlsr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-genseir_0.1.1-1.ca2404.1_all.deb Size: 99796 MD5sum: 76f8ba7d683200943f705a0b1b624c01 SHA1: 5ae46a14d7d7465a320ee5f4f89a23413da6abc0 SHA256: 2090eee90c9438c14846348a82ed0ab6c128f3e6bfae0a3173afc2e665078cab SHA512: c8d37ad3c4cdcb01e335582638e491e41f14ed3977678f3d17f056ad5f38e0577ecd175104635b334a4af92e0243f3e2e5a4264cb21a0ff5fa1f035f086b21d4 Homepage: https://cran.r-project.org/package=genSEIR Description: CRAN Package 'genSEIR' (Predict Epidemic Curves with Generalized SEIR Modeling) Performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) . Package: r-cran-genset Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genset_0.1.1-1.ca2404.1_all.deb Size: 54156 MD5sum: 158b44cb5296133be96543f2bd33ad11 SHA1: 280ccc16ee68c08d6db10f690e310f65faa1add2 SHA256: 8c39cb8da709e6025015f70a6cb12ff4a0c3f1c557fd48a0f0cc96f5b9eb57f0 SHA512: 8cc4f19cc848963a3512accd4657593ae33f6d699120d1a0d1c362f74196345c9e63db536be0105d15d504d471407dc3270c7594b5e3f735ada29cceabecfb13 Homepage: https://cran.r-project.org/package=genset Description: CRAN Package 'genset' (Generates Multiple Data Sets) Generate multiple data sets for educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results. Package: r-cran-gensphere Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvmesh, r-cran-geometry, r-cran-sphericalcubature, r-cran-rgl, r-cran-simplicialcubature Filename: pool/dists/noble/main/r-cran-gensphere_1.3-1.ca2404.1_all.deb Size: 123846 MD5sum: fa4251960ecaeb965f20f60bf4f66333 SHA1: 2a03803282755af740bcd1252ea31cdecc532877 SHA256: f328f11f543ca5afa317dcec4e3a1bdccd9b53175eeb278ab7775bd3f2a4f227 SHA512: e0e55b1a713eebba2db02a2e44bbe012358a62f9770dce81b16c0095974418dad6d3365c231549aa740af44df776dc6dbf0673a4990c59ece343c91db24608b6 Homepage: https://cran.r-project.org/package=gensphere Description: CRAN Package 'gensphere' (Generalized Spherical Distributions) Define and compute with generalized spherical distributions - multivariate probability laws that are specified by a star shaped contour (directional behavior) and a radial component. The methods are described in Nolan (2016) . Package: r-cran-genstab Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genstab_1.0.0-1.ca2404.1_all.deb Size: 46716 MD5sum: c7462476abf3bc005adc81f83a160a37 SHA1: e73a2d942219660af7d3d9bf805242b69c1fb008 SHA256: 8e7f13c577ff4e16bf6fac7514243811f76f6bd94377aacc052946e021005ea0 SHA512: 99cba4c25cb53da03795422a8a072cd64f9a93796149c7f982fb834d4598a374ed18f13e4b075f2ac79d2485c56d786ff28af242e929a8f999e6505251e9af92 Homepage: https://cran.r-project.org/package=genstab Description: CRAN Package 'genstab' (Resampling Based Yield Stability Analyses) Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated. Package: r-cran-gentag Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gentag_1.0-1.ca2404.1_all.deb Size: 70458 MD5sum: 5a144728811e256a2a35dcc5660e4c2c SHA1: 7bda489dec93cad0bdcd5000f101a9c174a8a774 SHA256: 53a9adeb79a8b804052fea227a1d1a3741befdcc9e2a954c50787c7df27daa72 SHA512: 928528e8e1dd019c23f3b5ef2f3055a088aa9a8fe0520180a2a381730ab53c33484cde28e6b316a4056ba07852babf89a7a500155c1bdce620283f8bc9ac25aa Homepage: https://cran.r-project.org/package=GenTag Description: CRAN Package 'GenTag' (Generate Color Tag Sequences) Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications. Package: r-cran-gentransmuted Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-vgam Filename: pool/dists/noble/main/r-cran-gentransmuted_1.0-1.ca2404.1_all.deb Size: 74560 MD5sum: 5a4709d08aff115030c57e7de828c36e SHA1: f2bf69323c7e14cddd2bf45328a9ac05b55a503b SHA256: d8bb0ab4689b72d5aedb5026984729541cbd57cc2fc386bbb100262f066bf4cc SHA512: b1b92f08791de36b698ecea9b566718e859fce824a7bf97fa1a0fe1908a2742b764572558763d851265c238d76282a8ffbbbfb7dbbd963417149e3049cc3457c Homepage: https://cran.r-project.org/package=gentransmuted Description: CRAN Package 'gentransmuted' (Estimation and Other Tools for Generalized Transmuted Models) Provide estimation and data generation tools for a generalization of the transmuted distributions discussed in Shaw and Buckley (2007). See for more information. Package: r-cran-gents Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gents_0.1.4-1.ca2404.1_all.deb Size: 1468534 MD5sum: 117f28aa35456c0fadeb48cdffe2341f SHA1: c44d21ab21cec5a89756d98a84723aeb80a624ce SHA256: 22bdd05989827f09ba29fcae0913960f31a9bb99b67dc7c757104ee6abda6124 SHA512: 552a43fe508d9532ac9cc2ddfae703ac5f80fa96a64ac8f34aa94b217c4686c3cbf2271ddd54e7214995dbdf6901917e6dc3d49229be80e60792a771eb724ddc Homepage: https://cran.r-project.org/package=genTS Description: CRAN Package 'genTS' (R Shiny App for Creating Simplified Trial Summary (TS) Domain) Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide . Package: r-cran-gentwoarmstrialsize Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-trialsize, r-cran-dplyr, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gentwoarmstrialsize_0.0.5-1.ca2404.1_all.deb Size: 236162 MD5sum: 73b0e133dd60d628e9b362b6bf859282 SHA1: 9dc94db621fcf07ea42b4d333392d9f154f46e39 SHA256: 421d703cb39c96db1f9c68996257d52ac47ba2e2a3563c4466aa45964ac62e26 SHA512: 91fc819dcc07ebb6ab06e1e931fc1bd8e3c643d975e13fb310c7657a9d51a63adee89b1875e412a06973c1bc39c9c99cd53bf42744f82693d84babaeb75d143b Homepage: https://cran.r-project.org/package=GenTwoArmsTrialSize Description: CRAN Package 'GenTwoArmsTrialSize' (Generalized Two Arms Clinical Trial Sample Size Calculation) Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) , Wittes, J (2002) , Sato, T (2000) , Lachin J M, Foulkes, M A (1986) , Whitehead J(1993) , Julious SA (2023) . Package: r-cran-genular Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-plyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-genular_1.0.1-1.ca2404.1_all.deb Size: 77694 MD5sum: de804f6bbf8fc734aa5198141c5619a4 SHA1: 07cbd07edb6ebd3fce32452829d3fc29616346d8 SHA256: 7f9174329199bf0f8d8b42e210d509b4dcbe4c6bb0e02fd25337e86f4957d444 SHA512: c1ff5c0473fdf05e5a24e44e6bfef7836b25e7d39687290676001ac9c2274c62ab5c17aa24afffaf7c61095e0fc1610d216f851fd8853b68a03d8205afc1245e Homepage: https://cran.r-project.org/package=genular Description: CRAN Package 'genular' ('Genular' Database API) Provides an interface to the 'Genular' database API (), allowing efficient retrieval and integration of genomic, proteomic, and single-cell data. It supports operations like fetching gene annotations, cell expression profiles, and other information as defined in the 'Genular' database, enabling seamless incorporation of biological data into R workflows. With functions tailored for bioinformatics and machine learning, the package facilitates exploration of cellular heterogeneity, gene-disease relationships, and pathway analysis, streamlining multi-omics data analysis. Package: r-cran-genwin Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pspline Filename: pool/dists/noble/main/r-cran-genwin_1.0-1.ca2404.1_all.deb Size: 983648 MD5sum: 07c088644ff6acf09dd12481cdb914a9 SHA1: 57f8de4cff8819fdca12dc9d0fb0265621e08963 SHA256: 0c70276901553c229d9a2d40082c3bed5a81fb4aabac10b1b2a1ce697112a2f4 SHA512: bd8de6763b0b7832725ef524c1bd599dbb35722ade7953276c3d2f3c13b11755815fb2fff9af5c70787a49fdd2fdf2112daef59521e8d82809361ad0b1a64f5b Homepage: https://cran.r-project.org/package=GenWin Description: CRAN Package 'GenWin' (Spline Based Window Boundaries for Genomic Analyses) Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. Along with defining boundaries, a technique to evaluate results obtained from unequally-sized windows is provided. Applications are particularly pertinent for, though not limited to, genome scans for selection based on variability between populations (e.g. using Wright's fixations index, Fst, which measures variability in subpopulations relative to the total population). Package: r-cran-geoaddsae2 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-sae Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoaddsae2_0.1.1-1.ca2404.1_all.deb Size: 66516 MD5sum: 3ebe3bc30bf2333b6c6e05c69de8160d SHA1: d9fedc02f0800f7973e537ac3e8c51202b323c9b SHA256: 844e58d191e7ba9b0474bc2946133e170ed7da90aaa725666c7a6d0d3b2f946b SHA512: a4aa8192c5b6570f552b93d5ae702fa5f43e1a99b5fb761d956ccedbfb86b35026b6ea95d055538e678f7d40d6fb15933f69cc074353c2a1010393017669bce1 Homepage: https://cran.r-project.org/package=geoaddSAE2 Description: CRAN Package 'geoaddSAE2' (Geoadditive Small Area Estimation for Area-Level Model) Fits area-level geoadditive small area estimation (SAE) models by extending the Fay-Herriot area-level model with linear, nonlinear, and spatial effects. The Fay-Herriot model is described by Fay and Herriot (1979) . Geoadditive models combine nonlinear covariate effects and spatial variation as described by Kammann and Wand (2003) , while their application to small area estimation is discussed by Pusponegoro et al. (2019) . Nonlinear covariate effects are represented using penalized splines, while spatial effects are represented using a smooth function of geographic coordinates. Models are estimated using restricted maximum likelihood (REML), and mean squared error (MSE) is estimated using a parametric bootstrap. The package also provides comparisons with the Fay-Herriot and spatial Fay-Herriot (SFH) models. Package: r-cran-geoar Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-httr2, r-cran-promises, r-cran-assertthat, r-cran-attempt, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-curl, r-cran-glue, r-cran-leaflet, r-cran-jsonlite, r-cran-purrr Suggests: r-cran-testthat, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-geofacet, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-geoar_1.2.2-1.ca2404.1_all.deb Size: 658700 MD5sum: 61115360fb7905a280d20dde929f247b SHA1: 2ae89d600feaaf90969e70cc714bc1e84b2502fd SHA256: 18e3690c7085b5159b467f8bedaf1169f13ae653bf1cc1e18789232b8816eb44 SHA512: 2b0dc413d2cc1a40ae7a298545f50139168188ad2979faec6e5389a01fab13ee3e06b9b600170bf150a461fa0419c4db8528085411a70ec2f5f8fd8ef08a3f5f Homepage: https://cran.r-project.org/package=geoAr Description: CRAN Package 'geoAr' (Argentina's Spatial Data Toolbox) Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English. Package: r-cran-geoarrowwidget Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2789 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-listviewer, r-cran-nanoarrow Suggests: r-cran-geoarrow, r-cran-quarto, r-cran-tinytest, r-cran-wk Filename: pool/dists/noble/main/r-cran-geoarrowwidget_0.1.0-1.ca2404.1_all.deb Size: 1089712 MD5sum: 0630563175950796daa3164c5a6a1dae SHA1: a4b2cfc06b34b50f80afe262b628326cf22403f3 SHA256: d11ea05b23503c0ad21c2192be6973aff1130c6c28abd48acb9e0e063934d134 SHA512: 7eab1070467e1178858fa036323bf71ed23afa5b53cfd478c3b95897e36b7f822f8afb94c007ea7f6040cff51cb63b94a3a87cd4f0db0bc11a1ad4e20a681cd8 Homepage: https://cran.r-project.org/package=geoarrowWidget Description: CRAN Package 'geoarrowWidget' (Attach '(Geo)Arrow' and/or '(Geo)Parquet' Data to a Widget) Provides functions and necessary 'JavaScript' bindings to quickly transfer spatial data from R memory or remote URLs to the browser for use in interactive 'HTML' widgets created with the 'htmlwidgets' R package. Leverages 'GeoArrow' () data representation for data stored in local R memory which is generally faster than traditional 'GeoJSON' by minimising the amount of copy, serialization and deserialization steps necessary for the data transfer. Furthermore, provides functionality and 'JavaScript' bindings to consume 'GeoParquet' () files from remote URLs in the browser. Package: r-cran-geobounds Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-countrycode, r-cran-dplyr, r-cran-httr2, r-cran-sf Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-geobounds_1.0.1-1.ca2404.1_all.deb Size: 429082 MD5sum: 79d90ead814e9b03c502a904d139f149 SHA1: b602ec2c6acb74256fea34c51f3288758898996f SHA256: f1f00e1a7894685c946c54296e31c288436c4b8e57d5f5c475280ee7a3335b90 SHA512: 23757937169ed15f6bdca7b44d415a56c1aac409cf1a6619bd81f5e747d9316531bad8a1e5794f7835e4c535eb821a8cb81a922247751dbcb8f8281e706e6824 Homepage: https://cran.r-project.org/package=geobounds Description: CRAN Package 'geobounds' (Download Administrative Boundary Data from 'geoBoundaries') Provides tools to download individual country boundaries and global composite boundaries from 'geoBoundaries' across multiple administrative ('ADM') levels. Returns boundaries as 'sf' objects for mapping and spatial analysis. Runfola et al. (2020) describe the underlying database. Package: r-cran-geobr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1985 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-dbi, r-cran-duckdb, r-cran-duckspatial, r-cran-fs, r-cran-httr2, r-cran-nanoarrow, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-stringr Suggests: r-cran-censobr, r-cran-covr, r-cran-data.table, r-cran-geoarrow, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geobr_2.1.0-1.ca2404.1_all.deb Size: 1152188 MD5sum: e6b351cd9a7ceb52e183388b6a6f7c36 SHA1: a057b2a8cfda41527648b4d910e9ae3b21d649ec SHA256: 082277955eed9c5968a2f6cb3d330b3e54ecbdc187889666d1138b0f8b9c0d94 SHA512: ccdcad2077a39c9fddbadf0297e0831f81cb0babe1b057df49a5aed60963b98f0e3049001953ff11cfc54f412c60a63f8dc1674d8e85fa3441542b277dd7c859 Homepage: https://cran.r-project.org/package=geobr Description: CRAN Package 'geobr' (Download Official Spatial Data Sets of Brazil) Easy access to official spatial data sets of Brazil. The package offers a wide range of spatial data sets available at various geographic scales and for various years with harmonized attributes, projection and fixed topology. All functions allow for seamless integration sf, DuckDB and Arrow. Package: r-cran-geocacher Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-tibble, r-cran-threewords Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geocacher_0.1.0-1.ca2404.1_all.deb Size: 42768 MD5sum: e645fc6ac9ac2b6bbc5404039c046cfa SHA1: b5f842c03a92ff343e92b5249e647366dedf600c SHA256: 64c256cad5c877a18a2c077aec333511a8b0d1e113bbde83e86e2f30e030b3e4 SHA512: 412772a12a4d8f46b4de0cd31a39cf96c70746b97b9f4995b3eb9106d97885e4f77c4369531b5b9f62e7f325fd60165999530506067f5a878c8d7763e6e817c0 Homepage: https://cran.r-project.org/package=geocacheR Description: CRAN Package 'geocacheR' (Tools for Geocaching) Tools for solving common geocaching puzzle types, and other Geocaching-related tasks. Package: r-cran-geocausal Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crsuggest, r-cran-ggthemes, r-cran-data.table, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-ggpubr, r-cran-mclust, r-cran-progressr, r-cran-purrr, r-cran-rglpk, r-cran-sf, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-spatstat.univar, r-cran-spatstat.random, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tidyterra Suggests: r-cran-elevatr, r-cran-gridextra, r-cran-knitr, r-cran-readr, r-cran-gridgraphics Filename: pool/dists/noble/main/r-cran-geocausal_0.4.2-1.ca2404.1_all.deb Size: 1315444 MD5sum: bfb40ed494bf8223c5f8fdacf4220de8 SHA1: a75e779eb42a458cf5671c9301c44d9a862b1310 SHA256: 0799b22de2454ecd488701128bc48d30c734a57bfe0f620604f3d0446672e461 SHA512: cb2c9fcb3eaad252f6107accf954530227d631c28e7f9482f13d76f6d039eb89e8935d4ae298a94814e2fb86484f0cee2f86f280122f9388b1cf56de7e861e86 Homepage: https://cran.r-project.org/package=geocausal Description: CRAN Package 'geocausal' (Causal Inference with Spatio-Temporal Data) Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) and Mukaigawara, et al. (2024) . Package: r-cran-geocodebr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2773 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-callr, r-cran-cli, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-duckdb, r-cran-duckspatial, r-cran-enderecobr, r-cran-fs, r-cran-glue, r-cran-h3r, r-cran-httr2, r-cran-nanoarrow, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-sfheaders Suggests: r-cran-covr, r-cran-dbplyr, r-cran-geobr, r-cran-ggplot2, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geocodebr_0.7.0-1.ca2404.1_all.deb Size: 2531060 MD5sum: 66a289326f629e2982118c53a31fb55b SHA1: d44dbc2399f3b6ccd427f3b0709782f362314081 SHA256: fcbba7298577aa571ebbc130bcf5ee5b97cb6ae7b14ff92e495f85f70e2272b0 SHA512: 555c690db990f3a44a5aebb5849fb86205350dd3fd5ecf8847dd7507111df40cd127ee112183a7a3bbf5e4a9a09ad38a99f957a912958315cffe61f74fcd9b00 Homepage: https://cran.r-project.org/package=geocodebr Description: CRAN Package 'geocodebr' (Geolocalização De Endereços Brasileiros (Geocoding BrazilianAddresses)) Método simples e eficiente de geolocalizar dados no Brasil. O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatística (IBGE), Brazil's official statistics and geography agency.) Package: r-cran-geodadata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3348 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-geodadata_0.1.0-1.ca2404.1_all.deb Size: 3375018 MD5sum: 6c1cff1949fa5409ede92d44d0428122 SHA1: da58e6f66d6dacd5543b91a3a1c8a75c98c99126 SHA256: f58c0e939f45962bd42749055a59c482a071a3e89096664ba3e0aa74948230f7 SHA512: c90a480a7fa6c8590a89710c894ddc4c9daf0aa1be95b77e9828dedca841d56ca4cde36a5aafa94dd1ea34fa7120afaa0bc65c6f48ed14514cb3e309c6ba48c7 Homepage: https://cran.r-project.org/package=geodaData Description: CRAN Package 'geodaData' (Spatial Analysis Datasets for Teaching) Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the 'GeoDa' software workbook and data site developed by Luc Anselin and team at the University of Chicago. Datasets are stored as 'sf' objects. Package: r-cran-geodata Architecture: all Version: 0.6-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rappdirs Suggests: r-cran-jsonlite, r-cran-r.utils, r-cran-httr, r-cran-archive Filename: pool/dists/noble/main/r-cran-geodata_0.6-9-1.ca2404.1_all.deb Size: 268422 MD5sum: 8f7484887c180d90fb14231eb74640d7 SHA1: a01f3f6357e1fce3f0f890ac25de2f927ee5b06c SHA256: f947ae5a496c9076e47985dcf6f84982ad1512500d3949dc85fed0202174affb SHA512: 1a12b564a40d8a72500f62698406051e508d6199b5d1e9f0eb11a31b409d2f2d858a868f49bdc37184529f49beef2751d1b03a58cce8bfc5b2972cf0691c0998 Homepage: https://cran.r-project.org/package=geodata Description: CRAN Package 'geodata' (Access Geographic Data) Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data. Package: r-cran-geodeltaaudit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geodeltaaudit_0.1.0-1.ca2404.1_all.deb Size: 1213198 MD5sum: 351aab2250490033a38261de1091ff4f SHA1: 0166221fc2381c35bd39587d8f5f61c3459e4dcf SHA256: 2247d4eaeb3c25443e96ee9b9e5b5fdba368ed5364e34f27f5155c7aaef49915 SHA512: df79f419ae4b7038900750e9083dcf4cbfa412dfaedb98de63d07cf251e3b46efb47bf8d646dd55b402361795afdf48d86ead6b563d1abae9263df3c27d75f79 Homepage: https://cran.r-project.org/package=geoDeltaAudit Description: CRAN Package 'geoDeltaAudit' (Quantifying Variable Change Induced by Administrative BoundaryTransformations) Tools for auditing how analytic variables change when data are transformed across administrative boundary systems. The package is agnostic to data source, variable type, and administrative geography, and is designed to quantify transformation-induced change without attributing blame to any specific boundary definition or allocation scheme. Package: r-cran-geodensityr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geodensityr_0.2.0-1.ca2404.1_all.deb Size: 16204 MD5sum: dfdb7411309fa25ecabd6a5710cb60d4 SHA1: 6b6c980a79e83fd8d6412a953b8d328f12560f0a SHA256: 6ab18ffdfe3f36f4f8c5d3ecb77da6d026d58f5838b8e1c7df3c1d4c0f52ff3a SHA512: 79a8832902ca4cc4a296c51ca1497f5652ac3a184805314b449f87df0bfc6bb1cea92d955150af320f2a39b011ba21609cb2e85de2b4968974a7be1c0d70cdfc Homepage: https://cran.r-project.org/package=GeoDensityR Description: CRAN Package 'GeoDensityR' (Generate Density Rasters from Polygon and Census Data) Creates continuous density raster surfaces by joining vector spatial layers with tabular data frameworks, normalizing values by ellipsoidal polygon area calculations. Reconciles discrete boundary-constrained census or survey counts with uniform grids. Accepts spatial objects in-memory or via file paths. Methods are based on spatial rasterization workflows implemented in the 'terra' package Hijmans (2025) . Package: r-cran-geodetector Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2655 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geodetector_1.0-5-1.ca2404.1_all.deb Size: 1683042 MD5sum: 5036ca24679e4918ba2b4e38781b183c SHA1: 745c745c9dd14aab0cf8ca9698288f05688b6be8 SHA256: d6e6ca0fd3a3fc13f0500e1f9c2b45f4aaacc2ab0806e2dcec6fb85b73763bfd SHA512: 928b5f649640a1111b1c0322e41f990f04977760cf05b88a632805699234b4e6ef38c6004279a9c316b4a4a801c8dce24518cb639940fd4f3402bff87a1ebc4f Homepage: https://cran.r-project.org/package=geodetector Description: CRAN Package 'geodetector' (Stratified Heterogeneity Measure, Dominant Driving ForceDetection, Interaction Relationship Investigation) Spatial stratified heterogeneity (SSH), referring to the within strata are more similar than the between strata, a model with global parameters would be confounded if input data is SSH. Note that the "spatial" here can be either geospatial or the space in mathematical meaning. Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test the power of determinant X of a dependent variable Y according to the consistency between their spatial distributions; and (3) investigate the interaction between two explanatory variables X1 and X2 to a dependent variable Y (Wang et al 2014 , Wang, Zhang, and Fu 2016 ). Package: r-cran-geodimension Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3079 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-snakecase, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geodimension_2.0.0-1.ca2404.1_all.deb Size: 3030064 MD5sum: 2b3310e9b6a800c28b560110eb1f6a61 SHA1: 43be015cfa4ee5d6a8a09cff79e7a7007e91c755 SHA256: 56f0c86229455f20663439a909a022eb8cddabc65079433250c6b2f4dccba322 SHA512: e6d72a342fe34cf502cdf788f04f2f66ffc30136dbc75fa5abe564ea2232a146ea7ff9dcbb7945b410c11ce36371c9bf1ec5fec317b2f4491b9302bfd0096442 Homepage: https://cran.r-project.org/package=geodimension Description: CRAN Package 'geodimension' (Definition of Geographic Dimensions) The geographic dimension plays a fundamental role in multidimensional systems. To define a geographic dimension in a star schema, we need a table with attributes corresponding to the levels of the dimension. Additionally, we will also need one or more geographic layers to represent the data using this dimension. The goal of this package is to support the definition of geographic dimensions from layers of geographic information related to each other. It makes it easy to define relationships between layers and obtain the necessary data from them. Package: r-cran-geodl Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-luz, r-cran-torchvision, r-cran-dplyr, r-cran-terra, r-cran-multiscaledtm, r-cran-psych, r-cran-coro, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geodl_0.3.1-1.ca2404.1_all.deb Size: 642614 MD5sum: c781c443db6902997deebace78270d23 SHA1: 1b0d3bd257db1f27811698b88bf4adfa5685e33b SHA256: b21322fc4f6cf216dbdf43d7e25ab1e4bc8e017342c23555d4e66c8f1dff8e15 SHA512: dc04220b9669e99d9adc8700961c6de5f3115ffea3d154a4b6e9d4c5058188d8262ca119e1ae708331210f74b903241b2c0ca8c466f1540f0ec453d56ec815ea Homepage: https://cran.r-project.org/package=geodl Description: CRAN Package 'geodl' (Geospatial Semantic Segmentation with Torch and Terra) Provides tools for semantic segmentation of geospatial data using convolutional neural network-based deep learning. Utility functions allow for creating masks, image chips, data frames listing image chips in a directory, and DataSets for use within DataLoaders. Additional functions are provided to serve as checks during the data preparation and training process. A UNet architecture can be defined with 4 blocks in the encoder, a bottleneck block, and 4 blocks in the decoder. The UNet can accept a variable number of input channels, and the user can define the number of feature maps produced in each encoder and decoder block and the bottleneck. Users can also choose to (1) replace all rectified linear unit (ReLU) activation functions with leaky ReLU or swish, (2) implement attention gates along the skip connections, (3) implement squeeze and excitation modules within the encoder blocks, (4) add residual connections within all blocks, (5) replace the bottleneck with a modified atrous spatial pyramid pooling (ASPP) module, and/or (6) implement deep supervision using predictions generated at each stage in the decoder. A unified focal loss framework is implemented after Yeung et al. (2022) . We have also implemented assessment metrics using the 'luz' package including F1-score, recall, and precision. Trained models can be used to predict to spatial data without the need to generate chips from larger spatial extents. Functions are available for performing accuracy assessment. The package relies on 'torch' for implementing deep learning, which does not require the installation of a 'Python' environment. Raster geospatial data are handled with 'terra'. Models can be trained using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU); however, multi-GPU training is not supported by 'torch' in 'R'. Package: r-cran-geodrawr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-leaflet, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-geodrawr_2.0.0-1.ca2404.1_all.deb Size: 29952 MD5sum: 2fec267952dfe55b1f15e66daa403afa SHA1: cc47f2cbe4b2ae2f2bf5afc254aee5e91e21ba86 SHA256: 9d4319eae11af3140eea01c2b2019fff7be672d682a57176e34c4d30af2102cd SHA512: 8c47e4a37a85245bac5adce1bac63d26750705c37f3c7652b32ac4059db49b5c03a5ba4f922fff127eda58e323c2c0e7754f7d6af74a2ed13bbc11667abc7f5d Homepage: https://cran.r-project.org/package=geodrawr Description: CRAN Package 'geodrawr' (Making Geospatial Objects) Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects. Package: r-cran-geodregr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-zipfr Filename: pool/dists/noble/main/r-cran-geodregr_0.2.0-1.ca2404.1_all.deb Size: 146480 MD5sum: 76efe84f758fe1a980431b3516088d81 SHA1: d9a73d564cb32f7b8aeb59fa52c699e2a97c1cd8 SHA256: 5e4fec2686f568450675dc59afa0f002b8dbfe2f8c9740774f052e8462fe514f SHA512: cc2be8ef3efac678d519eedb20e7b335578bfdd2432268497a13e5f21fc7601be221723205f07f9c553840ee6449fe95e9e2a23bd5b1b969766887a3ebc10d30 Homepage: https://cran.r-project.org/package=GeodRegr Description: CRAN Package 'GeodRegr' (Geodesic Regression) Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) . Also see Fletcher, P. T. (2013) . Package: r-cran-geoelectrics Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-rgl, r-cran-fields Suggests: r-cran-testthat, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-geoelectrics_0.2.2-1.ca2404.1_all.deb Size: 899056 MD5sum: fc4d7f638e60d804a9e341ab5b4caad2 SHA1: 35f116fb2db3ea464198a2408ae8dccf3ae177b7 SHA256: 58b7d969732b878268a050a52514b322f2c11822c5fb14ccdd7903be6ed181e4 SHA512: ac7f0b52b7f14063101f9a1474798bd09b9a75b351601dd662767abd04e1adf25e90f17d5195195a41724aa28976e503370496d234b58fd5df4db8b2feb9ed72 Homepage: https://cran.r-project.org/package=geoelectrics Description: CRAN Package 'geoelectrics' (3D-Visualization of Geoelectric Resistivity Measurement Profiles) Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions. Package: r-cran-geofacet Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1805 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-rnaturalearth, r-cran-sp, r-cran-ggrepel, r-cran-gridextra, r-cran-geogrid, r-cran-rlang, r-cran-httr2 Suggests: r-cran-sf, r-cran-testthat, r-cran-covr, r-cran-lintr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geofacet_0.2.4-1.ca2404.1_all.deb Size: 1196538 MD5sum: f3fe30b472fabf91b97645119ea98d4e SHA1: 3aed4308dda3f82bb44530009ba868edf0ce5b41 SHA256: a07d95b8c76c53a57b99fc03fcbc4985532956d76f7eb58519fd1e6bd436e9c3 SHA512: 7e1ba0266a8eae01b5146e6da005535a99cd8c45e1579c2a8dec1d210706dfb98e8d8233bb2cc4babf648a03dc7b67b38d5601ad5f281ba76250e7eef55666a8 Homepage: https://cran.r-project.org/package=geofacet Description: CRAN Package 'geofacet' ('ggplot2' Faceting Utilities for Geographical Data) Provides geographical faceting functionality for 'ggplot2'. Geographical faceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation. Package: r-cran-geofi Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4865 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httpcache, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-xml2, r-cran-yaml Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggrepel, r-cran-geofacet, r-cran-htmltools, r-cran-httptest, r-cran-janitor, r-cran-knitr, r-cran-leaflet, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown, r-cran-sotkanet, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-geofi_1.2.1-1.ca2404.1_all.deb Size: 3321372 MD5sum: 34284aa0258c428bc097a1c4cb9a6732 SHA1: 3bea164ab023547216b0bc80039900abc50b8d62 SHA256: 1a24a1ab6fcce487e648a654d0aa9860db1939cd6d376f2462a27b9e70f567a8 SHA512: afbd6e164a2f89ab1cd44841795b259deb2d0bb41c2f4e351edc6a933e83fa8c05eee8683b087873d4bc0901a883e7f6048c8eee9a3f630a8bc872c2d6d67205 Homepage: https://cran.r-project.org/package=geofi Description: CRAN Package 'geofi' (Access Finnish Geospatial Data) Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API , the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations. Package: r-cran-geoflow Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2773 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-dotenv, r-cran-benchmarkme, r-cran-httr, r-cran-mime, r-cran-jsonlite, r-cran-yaml, r-cran-xml, r-cran-xml2, r-cran-rdflib, r-cran-curl, r-cran-whisker, r-cran-digest, r-cran-dplyr, r-cran-readr, r-cran-arrow, r-cran-zip, r-cran-png, r-cran-uuid, r-cran-sf, r-cran-sfarrow, r-cran-lwgeom, r-cran-smoothr, r-cran-terra, r-cran-geometa, r-cran-geosapi, r-cran-geonapi, r-cran-geonode4r, r-cran-ows4r Suggests: r-cran-testthat, r-cran-readxl, r-cran-gsheet, r-cran-googledrive, r-cran-dbi, r-cran-rapiclient, r-cran-rmariadb, r-cran-rpostgres, r-cran-rpostgresql, r-cran-rsqlite, r-cran-ncdf4, r-cran-thredds, r-cran-eml, r-cran-emld, r-cran-datapack, r-cran-dataone, r-cran-rgbif, r-cran-ocs4r, r-cran-zen4r, r-cran-atom4r, r-cran-d4storagehub4r, r-cran-rmarkdown, r-cran-dataverse, r-cran-blastula, r-cran-waldo Filename: pool/dists/noble/main/r-cran-geoflow_1.4.0-1.ca2404.1_all.deb Size: 1808972 MD5sum: 3c5d4b5786c6f0bb34dba4cd7b98d88b SHA1: 941e9d8db00d01bc3b517ea2cb525dd1cdc692f2 SHA256: e86b5e79c2421d36554372c050d6a50236956db66973e90129fd46bb1778a6c0 SHA512: c5021ad628f758adcc4b258c751ef03d20222588e3320305051bf58e53ae092f0bac7454cfd13b07048421b9d17d91e07c2279bf9d664d5d95c01b0fc80b2b82 Homepage: https://cran.r-project.org/package=geoflow Description: CRAN Package 'geoflow' (Orchestrate Geospatial (Meta)Data Management Workflows andManage FAIR Services) An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with 'FAIR' (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the 'DublinCore' standard (), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with 'ISO/TC211' () and 'OGC' () geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. 'INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. 'PostGIS'), metadata catalogues (eg. 'GeoNetwork', 'CSW' servers), data servers (eg. 'GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of 'EML' metadata, and their management with 'DataOne' servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of 'CF' conventions, 'NetCDF' data format and 'OPeNDAP' access protocol, and their management with 'Thredds' servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', 'DataCite'), to facilitate the publication of data within (meta)data repositories such as 'Zenodo' () or DataVerse (). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference 'Zenodo' 'DOI' in 'GeoNetwork'/'GeoServer' metadata, or vice versa reference 'GeoNetwork'/'GeoServer' links in 'Zenodo' or 'EML' metadata). The use of standardized configuration files ('JSON' or 'YAML' formats) allow fully reproducible workflows to facilitate the work of data and information managers. Package: r-cran-geogam Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-mgcv, r-cran-grpreg, r-cran-mass Suggests: r-cran-raster, r-cran-sp Filename: pool/dists/noble/main/r-cran-geogam_0.1-4-1.ca2404.1_all.deb Size: 4815150 MD5sum: d548d00cc4dd8ebec8bc361043dae111 SHA1: 362263e9275834dde8a5fe65fcbdf2a145cffc49 SHA256: 067f5d2c2147bb8e3040231e4dfb0592ecf4bb907a7101286fc88d66f525aa04 SHA512: 637148b2b83ba9e04b786d0ca4e2c5f3928f1d64724a48190d5a86b95f5259dd8f0c5a6da137a86809d2d6d5e70a5a78ce3f35cdf1f3c58ce3396a13fd0cd579 Homepage: https://cran.r-project.org/package=geoGAM Description: CRAN Package 'geoGAM' (Select Sparse Geoadditive Models for Spatial Prediction) A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) . Package: r-cran-geogenr Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6856 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geomultistar, r-cran-httr, r-cran-readr, r-cran-rolap, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dm, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-rsqlite, r-cran-snakecase, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geogenr_2.0.1-1.ca2404.1_all.deb Size: 4856148 MD5sum: 0406628f6a25504734054867dc5cf412 SHA1: 7b86ad2132dde7cc8dfae2876addc8d0a5ee11e0 SHA256: 27329b85dd92e2119df1286bbf98a7ca9e07f435ae2b721f5471a9bb213cf924 SHA512: a40d7c936c964cb58de11458bcfb8cfac312c1d8e98a27c339901f217996cc04d0c84a77082a5576c369c6e0de3f35cf90cef11dd13f4b65385818cfc462e2da Homepage: https://cran.r-project.org/package=geogenr Description: CRAN Package 'geogenr' (Generator from American Community Survey Geodatabases) The American Community Survey (ACS) offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in 'GeoPackage' format or in multidimensional 'ROLAP' (Relational On-Line Analytical Processing) star format. Package: r-cran-geohabnet Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-config, r-cran-geosphere, r-cran-igraph, r-cran-terra, r-cran-yaml, r-cran-stringr, r-cran-memoise, r-cran-viridislite, r-cran-beepr, r-cran-rnaturalearth, r-cran-future.apply, r-cran-future, r-cran-magrittr, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-devtools, r-cran-knitr, r-cran-lintr, r-cran-mockthat, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geohabnet_2.3-1.ca2404.1_all.deb Size: 788204 MD5sum: 2e16fc004fbc9f4f08ed8e084ef7fcfd SHA1: 7c1b83105ab4b565d8feae4b64477f6e4c0d6ef2 SHA256: 99167a40b39249423ace2843043f9de4b6542b4f1241512d45eab67321e56b80 SHA512: e09ec636a5f6d4d38f14747f7ce9ec3b190bb055ca518daddc8a9ea6b8c026de45d898b0f51b4f8445905208fcb9e1cd2c35f07e8cef85bbe90d25bf93091a57 Homepage: https://cran.r-project.org/package=geohabnet Description: CRAN Package 'geohabnet' (Geographical Risk Analysis Based on Habitat Connectivity) The 'geohabnet' package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in 'geohabnet' were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of 'geohabnet' are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in 'geohabnet' to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the 'geohabnet' package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the 'geohabnet' package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the 'geohabnet' package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The 'geohabnet' package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The 'geohabnet' package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary functions are msean() and sensitivity analysis(). Most functions in 'geohabnet' provide three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as 'final' outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at to see examples of each function and how to access each of these outcome types. To change parameter values, the file called 'parameters.yaml' stores the parameters and their values, can be accessed using 'get_parameters()' and set new parameter values with 'set_parameters()'. Users can modify up to ten parameters. Package: r-cran-geoheatmap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4943 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geofacet, r-cran-statebins, r-cran-ggplot2, r-cran-plotly, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridislite, r-cran-testthat, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-geoheatmap_0.1.0-1.ca2404.1_all.deb Size: 1807196 MD5sum: b84570933df1ac56519b22e54b77e865 SHA1: e06d2420bda4fb36d7a7426516cadce9a621761a SHA256: dc1fbf1ca1400c38d5c0b7f1b874851a5f0a200820d8e8d9f81b657471f89234 SHA512: fb15cd45c8818d281aa0c511c0f1c8fce93464c70e742a1a8ebe839725cebdde1814a0975b86a4286ec72e0545349894ef3edaa6f01a99a5dfc1e4af22478ac1 Homepage: https://cran.r-project.org/package=geoheatmap Description: CRAN Package 'geoheatmap' (Create Geospatial Cartogram Heatmaps) The functionality provided by this package is an expansion of the code of the 'statebins' package, created by B. Rudis (2022), . It allows for the creation of square choropleths for the entire world, provided an appropriate specified grid is supplied. Package: r-cran-geoidep Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-archive, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-rvest, r-cran-sf, r-cran-tidyr, r-cran-terra, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mapgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoidep_0.5.0-1.ca2404.1_all.deb Size: 2736434 MD5sum: e10ee16d250a2e737d050b9b48b36ece SHA1: 458f2cc4384fb504ad0af3309b65c42579ec264d SHA256: 296d4460aaa6138e898a146dfd8c3e4dece7365efbc4dd66d8a11071ccbefae3 SHA512: a3d77495fa56006deeed8a009ec29d3a295c28b64e531a967ed0d7f2fa3a90ba964c73a1e11fa0727aca5af069345f190fc0c75b3682c4d952cacb800c215d83 Homepage: https://cran.r-project.org/package=geoidep Description: CRAN Package 'geoidep' (Download Geographic Data on Various Topics Provided and Managedby the Spatial Data Infrastructure of Peru) Provides R users with easy access to official cartographic data from Peru across a range of topics, including society, transport, environment, agriculture, climate, and more. It also includes data from regional government entities and technical-scientific institutions, all managed by Peru's Spatial Data Infrastructure. For more information, please visit: . Package: r-cran-geoindexr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoindexr_0.1.0-1.ca2404.1_all.deb Size: 189288 MD5sum: f221be5bcd5e3aaa57a17ad62cc38408 SHA1: 4992c3f0ec44419f47474b48c0acae48b2d9d272 SHA256: 2a46ae945d887b4c289e590fc2931d4d5206a385bcca4c15cbeedd35ab735766 SHA512: ef853484a1b7508f3fddba95c2508729639ec7b7cf449a1d4c58e4713cdbdbfc315a9c81e5f42ec5d2667a6986801024a85d901589503422b8b9f1e8427dd2e2 Homepage: https://cran.r-project.org/package=GeoIndexR Description: CRAN Package 'GeoIndexR' (Computation of Spectral and Geospatial Indices fromMultispectral Raster Data) A unified, fast, and extensible framework for calculating spectral and geospatial indices from multispectral raster datasets (e.g., NDVI - Normalized Difference Vegetation Index, NDWI - Normalized Difference Water Index, MNDWI - Modified Normalized Difference Water Index, NDBI - Normalized Difference Built-up Index, SAVI - Soil Adjusted Vegetation Index, EVI - Enhanced Vegetation Index, GNDVI - Green Normalized Difference Vegetation Index, NDMI - Normalized Difference Moisture Index, BSI - Bare Soil Index). Designed around 'terra' 'SpatRaster' objects, it supports multi-band rasters, automatic band resolution, sensor presets (Sentinel-2, Landsat-8/9), customizable index parameters, vectorized computations, and comprehensive validation. Methods based on Rouse et al. (1973) , McFeeters (1996) , Xu (2006) , Zha et al. (2003) , Huete (1988) , Gitelson et al. (1996) , Wilson and Sader (2002) , and Rikimaru et al. (2002). Package: r-cran-geojson Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-jsonlite, r-cran-protolite, r-cran-jqr, r-cran-magrittr, r-cran-lazyeval Suggests: r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-stringi, r-cran-covr Filename: pool/dists/noble/main/r-cran-geojson_0.3.5-1.ca2404.1_all.deb Size: 1029408 MD5sum: 6aefc9877bc8b40fa4e3fbbf9ab30894 SHA1: 6f253fe3999874ef93cf856fb1c2368aaf9633cc SHA256: 4abedbc33eb373367ed146c225cf3e0dd81794ff461e4e939a5d98fa0c146e2d SHA512: 47022da93d8decc7b36e366f5f2a5cdb914a7be1827d9749a3b43afd4ae1288ab4cd2ec084841459b8b66f30de3ab3e1a9d28f62ee5226e8b27418730f854ca6 Homepage: https://cran.r-project.org/package=geojson Description: CRAN Package 'geojson' (Classes for 'GeoJSON') Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' format. Package: r-cran-geojsonio Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-geojson, r-cran-geojsonsf, r-cran-jqr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-sf, r-cran-sp, r-cran-v8 Suggests: r-cran-covr, r-cran-dbi, r-cran-gistr, r-cran-leaflet, r-cran-maps, r-cran-rpostgres, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-geojsonio_0.11.3-1.ca2404.1_all.deb Size: 717924 MD5sum: d04da7d46bc1f48ab359636bb01c78f3 SHA1: 7ff8d024346594b5e5a384d9562fcc81e2a2273b SHA256: 3ed4344c347c63ed54d3ae66bcdfdba21a3d18840d284206f247aa5996f042df SHA512: f8110c038bedf819167da0f559c1840e4c129575cb25b98d081c9104ac59e2ca22bb3b2d6e5bd7ff938a09a5d57daeae0dd45e34558fe6777a9db502efdba370 Homepage: https://cran.r-project.org/package=geojsonio Description: CRAN Package 'geojsonio' (Convert Data from and to 'GeoJSON' or 'TopoJSON') Convert data to 'GeoJSON' or 'TopoJSON' from various R classes, including vectors, lists, data frames, shape files, and spatial classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'. Package: r-cran-geolibre Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Suggests: r-cran-sf, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geolibre_0.2.0-1.ca2404.1_all.deb Size: 352796 MD5sum: dffa93f4766779e70d0f6142deada221 SHA1: 82e36a7503e6d98f7cbca29c5a4b62e63ee2bf7e SHA256: 0968155e6fef56e985830392fe671094655a5b1dcd0eeff18f3e176bc4ee4c61 SHA512: 1fc5d77e80fc6193d70a4904c01e08fdcaa721212868e0d8fae05ebf4eb860339caf7e994e2e62f7a45fec1caf1576e47f8daa973a0600e0097ed573c5954dc1 Homepage: https://cran.r-project.org/package=geolibre Description: CRAN Package 'geolibre' (Interactive GIS with 'GeoLibre') Embeds the full 'GeoLibre' geographic information system in 'R Markdown', 'Quarto', 'Shiny', and the 'RStudio' Viewer. Create maps from 'GeoJSON' and 'sf' objects, markers, heatmaps, and tabular coordinates; add 'Cloud Optimized GeoTIFF', 'XYZ', 'WMS', 'WMTS', 'WFS', 'PMTiles', vector tile, '3D Tiles', and video sources; classify choropleths, arrange layers, and add legends, colorbars, and split-map comparisons. Control the camera, export standalone 'HTML', and read and write '.geolibre.json' project files. The underlying application is described in Wu (2026) . Package: r-cran-geomapdata Architecture: all Version: 2.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1277 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-geomap Filename: pool/dists/noble/main/r-cran-geomapdata_2.0-2-1.ca2404.1_all.deb Size: 1269500 MD5sum: fd7349c503db4ca6534e852c249780d1 SHA1: d63c24c440bdd2f2b7943b85e6a0c42101782970 SHA256: 4604b07376ae7c4fdd4681090b1b8cfae8023f798b8e76c3e5d2f05a23c9192d SHA512: bb9c24151786b020b8c5d58469ff63e27780ce595a3413ede121b5dd24e48961468aeee63b9d1a373533a84b8197b4a67766386d637943b583bfcd3b2fe46aa3 Homepage: https://cran.r-project.org/package=geomapdata Description: CRAN Package 'geomapdata' (Data for Topographic and Geologic Mapping) Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map. Package: r-cran-geomarchetypal Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-geometry, r-cran-archetypal, r-cran-doparallel, r-cran-plot3d, r-cran-distances, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-mirai, r-cran-abind, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geomarchetypal_1.0.3-1.ca2404.1_all.deb Size: 572538 MD5sum: ec1b74e98ad90abbc1ca3c00e10fb000 SHA1: 52492dbfd726b1566e5cd43923ac995d7e4117e7 SHA256: 8817b6ed0a1e134705c97cfe082b73059c8576b1927f3c4adb67cb0004f17c96 SHA512: 0991b3fff09904822162d1306859c3cd305cf1b985593d4dff26f8ec13e0275558113f7861030789a49ec59b72e420e6103d4691ebbfaf884d1cb4fbf4d64337 Homepage: https://cran.r-project.org/package=GeomArchetypal Description: CRAN Package 'GeomArchetypal' (Finds the Geometrical Archetypal Analysis of a Data Frame) Performs Geometrical Archetypal Analysis after creating Grid Archetypes which are the Cartesian Product of all minimum, maximum variable values. Since the archetypes are fixed now, we have the ability to compute the convex composition coefficients for all our available data points much faster by using the half part of Principal Convex Hull Archetypal method. Additionally we can decide to keep as archetypes the closer to the Grid Archetypes ones. Finally the number of archetypes is always 2 to the power of the dimension of our data points if we consider them as a vector space. Cutler, A., Breiman, L. (1994) . Morup, M., Hansen, LK. (2012) . Christopoulos, DT. (2024) . Package: r-cran-geomaroc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geomaroc_0.1.1-1.ca2404.1_all.deb Size: 38422 MD5sum: af26a38bf83ea2f98de6142292744a6f SHA1: bec633bc7001bf4b6644fb7e6bcedc4726d59775 SHA256: 7f323ef494afc81225ef66a3697aad098f3af189e3262b2a90571959f729c09c SHA512: 33d1299e356c9e5a3b7bc27b47bdc2decdb68c80cd37425d6cd1e87cda952c990dea9b86da6cdc5f7d5197925bda5299f336e6a2daa63aa16b2de4703d4664c9 Homepage: https://cran.r-project.org/package=geomaroc Description: CRAN Package 'geomaroc' (Easily Visualize Geographic Data of Morocco) Tools to easily visualize geographic data of Morocco. This package interacts with data available through the 'geomarocdata' package, which is available in a 'drat' repository. 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Package: r-cran-geomcomb Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-forecastcombinations, r-cran-ggplot2, r-cran-matrix, r-cran-mtsdi, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geomcomb_1.0-1.ca2404.1_all.deb Size: 168946 MD5sum: b031de149a2bcd35b4a9c1dc3e1f19e7 SHA1: 08709543f2827f9e8f2aefc2d276072fe8b8e550 SHA256: 0b1e6414fc1fa6df0a95049d3706ffb2166972f717c3a9703f74a2a1ce66818d SHA512: 9e8c30a7faf86087728674f397b6a846b4dafdb186156574b202528aeb042fc4482e0bb1c518f8194adee83292445b6aa9f4f61219877548bacb180eafd7481b Homepage: https://cran.r-project.org/package=GeomComb Description: CRAN Package 'GeomComb' ((Geometric) Forecast Combination Methods) Provides eigenvector-based (geometric) forecast combination methods; also includes simple approaches (simple average, median, trimmed and winsorized mean, inverse rank method) and regression-based combination. Tools for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity). Package: r-cran-geomerge Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-spdep, r-cran-raster, r-cran-terra, r-cran-sf, r-cran-geosphere, r-cran-lubridate, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Filename: pool/dists/noble/main/r-cran-geomerge_0.3.4-1.ca2404.1_all.deb Size: 1636072 MD5sum: 8d67c9fff8272eb72860f11129afbf8a SHA1: ed031c38f1ca576ec4421862f591c693362877f6 SHA256: 7e3b3bea09f63252c33adb8b8937b535c4ffe7b0dfe5167b26818d71ccde6257 SHA512: 4bd9f21e2405480164709aeb51b9261b4880e9bce43a9c3880db484fcd70d27e5a6b76541e2eb9ca544ea10de7eea4da418712fb6a2be24fddfdbf3a566e0a1a Homepage: https://cran.r-project.org/package=geomerge Description: CRAN Package 'geomerge' (Geospatial Data Integration) Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution. Package: r-cran-geometa Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22064 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-xml, r-cran-httr, r-cran-jsonlite, r-cran-keyring, r-cran-readr, r-cran-crayon, r-cran-digest Suggests: r-cran-sf, r-cran-ncdf4, r-cran-eml, r-cran-emld, r-cran-units, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-geometa_0.10.1-1.ca2404.1_all.deb Size: 9499456 MD5sum: a025ee8c24d3a396e60d4c3018b26bea SHA1: 75045cbc8094b94e2af039e25da73962e8a34ca5 SHA256: 5694d006c93de10a118435f393dbb7ed06e0ddab7d2103106996c377639342db SHA512: e52ead7ca4efa13ef57d37170e0944cde78507982978539337bf85aa15b2a7daae7312cd3284d43ee5d51029b610e8e16d3f976247f1084ab21a6d98384bcbdc Homepage: https://cran.r-project.org/package=geometa Description: CRAN Package 'geometa' (Tools for Reading and Writing ISO/OGC Geographic Metadata) Provides facilities to read, write and validate geographic metadata defined with ISO TC211 / OGC ISO geographic information metadata standards, and encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) and 19136 (GML). Other interoperable schemas from the OGC are progressively supported as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID). Package: r-cran-geometricmorphometricsmix Architecture: all Version: 0.6.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-mclust Suggests: r-cran-ape, r-cran-morpho, r-cran-geometry, r-cran-nlshrink, r-cran-lmf, r-cran-mass, r-cran-clustergeneration, r-cran-ggplot2, r-cran-rmpfr, r-cran-knitr, r-cran-rmarkdown, r-cran-phytools, r-cran-mvmorph, r-cran-testthat, r-cran-future, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-geometricmorphometricsmix_0.6.1.1-1.ca2404.1_all.deb Size: 273830 MD5sum: 29160e8c0d69944f0eddd189c53f34f1 SHA1: a2466408b063aa81a2730ad4723e6ffd22a5ac43 SHA256: e4a91d7b25707e252722d04e8cfccc0fb9e2c19562dacdcf01625766742ec246 SHA512: 5abc3f8ba05424a2bc733f2f866c2ee8618bee76b85f39474cc029e82b1490b5b293eb777769d881882e89902b884969d614f17d6cac23f8160d1328398200f3 Homepage: https://cran.r-project.org/package=GeometricMorphometricsMix Description: CRAN Package 'GeometricMorphometricsMix' (Heterogeneous Methods for Shape and Other Multidimensional Data) Tools for geometric morphometric analyses and multidimensional data. Implements methods for morphological disparity analysis using bootstrap and rarefaction, as reviewed in Foote (1997) . Includes integration and modularity testing, following Fruciano et al. (2013) , using Escoufier's RV coefficient as test statistic as well as two-block partial least squares - PLS, Rohlf and Corti (2000) . Also includes vector angle comparisons, orthogonal projection for data correction (Burnaby (1966) ; Fruciano (2016) ), and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992) ). Package: r-cran-geomod Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-caret, r-cran-rastervis, r-cran-randomforest, r-cran-arm, r-cran-kernlab, r-cran-e1071, r-cran-cubist, r-cran-rpart, r-cran-ranger, r-cran-qrnn, r-cran-quantregforest, r-cran-nnet Filename: pool/dists/noble/main/r-cran-geomod_0.1.0-1.ca2404.1_all.deb Size: 112402 MD5sum: dce1ed228c6b7490ed61b11b26d9fea3 SHA1: 2d717fbe1aa7f15e72488a48bd36a3f636385879 SHA256: 64485fa0063741848e07d89ebbc0ec4ea7aa7b4f729bdae4dcfe7dc9cd997fba SHA512: c56f18ac63ee06307915d1d00e8c6c66a43c58010654c7874a74824fd1c879583469a025a6be943b834c65b7bcb5648cf4bfaee7bb17f740660e4d581d7d3c58 Homepage: https://cran.r-project.org/package=geomod Description: CRAN Package 'geomod' (A Computer Program for Geotechnical Investigations) The 'geomod' does spatial prediction of the Geotechnical soil properties. It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a map or a series of maps. It uses the interpolation technique where a single or statistically “best” estimate of spatial occurrence soil property is determined. The interpolation is based on both the sampled data and a variogram model for the spatial correlation of the sampled data. The single estimate is produced by a Kriging technique. Package: r-cran-geomongo Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-r6, r-cran-geojsonr, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geomongo_1.0.3-1.ca2404.1_all.deb Size: 302366 MD5sum: 5fb197dabf69fd666b41c47915108ca8 SHA1: 78090d6dee76b01d0c9601638c427add245816fe SHA256: 3a52b56023dfe13588d65ea884d8b900ad0d1e6d378b55c397500aa5afed96ac SHA512: a60467a1a4c5533618f24deb5e726ef9419a3d47d167588ca66af79a33e3566e657c3996fc00b48c415b0f22c25b9fac064c8e19e4d6464c466604fdec7f1d21 Homepage: https://cran.r-project.org/package=GeoMongo Description: CRAN Package 'GeoMongo' (Geospatial Queries Using 'PyMongo') Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions. 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Package: r-cran-geomorphr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-progressr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geomorphr_0.1.0-1.ca2404.1_all.deb Size: 104154 MD5sum: 2202dc925056c016e2f2786e8aa0cc97 SHA1: e62c394ecc967b7708a647dd5833148b3e665d22 SHA256: 7872a0fc57dc99310e6665f8bdf8fc1c257377b3d48ff1ae9e722e23655c95db SHA512: 0307f25646e8ce2b4f8b847a39a3beac90560ea9df94d1cc5025c5a748703435500f9ed048e8b3e7a1426075c0ddc48d727b5995eec8d211aac7895c51a530a6 Homepage: https://cran.r-project.org/package=geomorphR Description: CRAN Package 'geomorphR' (Geometric Features for Building Footprints in Sf Objects) Extracts reproducible geometric and urban-morphology descriptors from polygon and multipart polygon building footprints stored as sf objects. 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Package: r-cran-geonames Architecture: all Version: 0.999-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjson Filename: pool/dists/noble/main/r-cran-geonames_0.999-1.ca2404.1_all.deb Size: 65250 MD5sum: b5bb1aba926148ed0bfb0e4fb8928c99 SHA1: 4136c4803555beb97c85268c41ee56f43e13b53d SHA256: 5e0485c638047cf41e14444925d9424837a00e88266eab70adf34a2d0310f501 SHA512: 0101f38421e9a7d722811e7373860e4b7c921d4c96b6820fb8e66bb2b2023e69f274c3516211c1b62faeae790f492330a0c1acffc4333c5ae5de7f47e4e61e58 Homepage: https://cran.r-project.org/package=geonames Description: CRAN Package 'geonames' (Interface to the "Geonames" Spatial Query Web Service) The web service at provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist. 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Package: r-cran-geonuts Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eurostat, r-cran-ggplot2, r-cran-giscor, r-cran-units, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geonuts_1.0.1-1.ca2404.1_all.deb Size: 197064 MD5sum: 1d127e168a04c19c8bd343d5c2ec939a SHA1: 340ffa0cb2ec30af4a4b8abef93c3f266cb995b2 SHA256: ce37c91b82c251bdabf34950759d35466a554eb0ff813319b21a56e3d04e9435 SHA512: 83805c35826702498030704c99cd4a3f18cb77985c29db037c868796002c79ca08e78e6464578491a6b7332f1762c9dd2325415786534522cfc0d2e202e590b2 Homepage: https://cran.r-project.org/package=geonuts Description: CRAN Package 'geonuts' (Identification and Visualisation of European NUTS Regions fromGeolocations) Provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. 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Package: r-cran-geoperu Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoperu_0.0.1-1.ca2404.1_all.deb Size: 452918 MD5sum: 145cf6f98a3f3164868a99bc53f1e535 SHA1: 79e02269e6f361e790c6151caed3a29a24bbac23 SHA256: 5005118a57070805500c6100c95471353a591b2d5d015d8678e12210886d40f7 SHA512: 547a47a103006f69720ab0059ad1b72a832b4679c563e5320e1ef9ee4bdfe46344fabb21f06ba96439fc6118ba9d68641550ce35afd42481b2aa19d7e98c8065 Homepage: https://cran.r-project.org/package=geoperu Description: CRAN Package 'geoperu' (Download Spatial Datasets of Peru) Provides access to official spatial datasets of Peru as 'sf' objects in 'R'. 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Method are based on coordinate rotation algorithm by Schaeben et al. (2024) . Package: r-cran-geopsych Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-geopsych_0.1.0-1.ca2404.1_all.deb Size: 47244 MD5sum: 27837d25574842e8e6945e61e231995f SHA1: 7dcf357c8e1bff4f0359eb12ba27f43f0d0ff321 SHA256: 613b804945103423bf1acb30e951132436b24740074e3fa5741699c1ed3c2cd8 SHA512: d0a5424fe3f2876c64f68683eed1c467eec908bc7c9aa0b2e5276038afbe4e82730a4873cb4d4a6edd05a105abb907a5076cd41fb55d23ebe80b4c3c5b597d07 Homepage: https://cran.r-project.org/package=geopsych Description: CRAN Package 'geopsych' (Methods of Applied Psychology and Psychometrics in GeographicalAnalysis) Integrating applied psychological and psychometric methods into geographical analysis. 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Package: r-cran-georefar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr Suggests: r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-georefar_0.1.0-1.ca2404.1_all.deb Size: 102894 MD5sum: 520ac7904ea61927dda0e8dd2d6a7c1f SHA1: c0cf1b66b7f281fde158b9d592ec37e2a1f79c57 SHA256: e91d3e9ef76ebd0c58a524c05cde52d5f7ad785b11d32238414847a4fa3f6756 SHA512: dddc3128121522fd95e787727d3099ca29183f335f5a17a23d802fd3a1d18bbec57f0cea8b9b6eaa2a7f24c801a468f8fa811781de269c3ae9dea5b92db1252c Homepage: https://cran.r-project.org/package=georefar Description: CRAN Package 'georefar' (Wrapper for the Argentine 'Georef' Geocoding API) Query the Argentine national geographic normalization service 'georef' from R, searching provinces, departments, municipalities, localities, settlements, census units, streets and educational institutions, normalizing addresses and performing reverse geocoding. Results are returned as data frames, and batch queries can be sent in a single request. Package: r-cran-georefdatar Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-spelling, r-cran-testthat, r-cran-readxl, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-georefdatar_0.6.5-1.ca2404.1_all.deb Size: 230218 MD5sum: dc7ac26c32ffad363ed3da44faa7cd37 SHA1: 43debf182f0ca7527fc0ec9cf83184a1fc07b92a SHA256: 943071558039980de975baeb547a8b9afe8768dc08cad2bbe63411b8f1e0eccb SHA512: 72be862313c4fb918d4fd79e477edd3f443ecf8ac9aa3438c4d1b07f44971a5f60886f80d567eb79375dfdde4be158741d641c4318028277d30aea5180b30523 Homepage: https://cran.r-project.org/package=georefdatar Description: CRAN Package 'georefdatar' (Geosciences Reference Datasets) Reference datasets commonly used in the geosciences. 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Package: r-cran-georob Architecture: all Version: 0.3-23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-abind, r-cran-constrainedkriging, r-cran-fields, r-cran-lmtest, r-cran-nlme, r-cran-nleqslv, r-cran-quantreg, r-cran-robustbase, r-cran-snowfall Suggests: r-cran-gstat, r-cran-multcomp, r-cran-lattice Filename: pool/dists/noble/main/r-cran-georob_0.3-23-1.ca2404.1_all.deb Size: 2203606 MD5sum: 3a0c397f9dd91022a38676e76132835e SHA1: 69b76902e17d7b98f037843caa20b3667beb393b SHA256: 55759492411d6e8c2d172ad7cb415a0eb56ec6269baa31f195c45774214d393d SHA512: 8a079d849a78e75251d8f1b5a09a287330e2459a9f9fec6fc84bbac3ce25d6b6ce30f8dc0bddf21b59cfbee5b089c7c72fe0b875d18fd87c5f882d1abe74dabe Homepage: https://cran.r-project.org/package=georob Description: CRAN Package 'georob' (Robust Geostatistical Analysis of Spatial Data) Provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) ) and Gaussian (Harville (1977) ) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) ), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) ), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) ). Package: r-cran-geosae Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme Filename: pool/dists/noble/main/r-cran-geosae_0.1.0-1.ca2404.1_all.deb Size: 37948 MD5sum: 30110129f7d7359800de54a8c38642d3 SHA1: c15e6854b1b52ecfb40e342afcd904ce18238995 SHA256: dc5697bfad5680399370a61a76e3065f4618c96ccc5fbf7b53367be01d85d055 SHA512: c141d29413d92e2335deeb4f80c54fc1aaa363e001b56ca5ccb9fd9e7d88afe2019b350a466129ed8ef24db6caab0fceb46637023f7b2a1ededed7de2a00ed76 Homepage: https://cran.r-project.org/package=geoSAE Description: CRAN Package 'geoSAE' (Geoadditive Small Area Model) This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016), and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018). Package: r-cran-geosapi Architecture: all Version: 0.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2587 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-openssl, r-cran-httr, r-cran-xml2, r-cran-magrittr, r-cran-keyring, r-cran-readr Suggests: r-cran-testthat, r-cran-waldo, r-cran-roxygen2, r-cran-shiny, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-geosapi_0.8-1-1.ca2404.1_all.deb Size: 1937214 MD5sum: 7e2a56e5bc8fbe7880cd0489b36b8180 SHA1: a235e8070cfa6a919cde7801747e94b7b9629074 SHA256: c4c862b36a31a41d4682de5f738e81f954c6fc75727e9d1755fae2b75bb72dd2 SHA512: d01b7bd298b80fed63056e36db93cd80564c4194bf3fa8d84df08e1b094e481041315ad593b5106eaf8bb142f3b035e20fc9c133df7eb04deb2d157e1fe863cf Homepage: https://cran.r-project.org/package=geosapi Description: CRAN Package 'geosapi' (GeoServer REST API R Interface) Provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see . Package: r-cran-geoscale Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-geoscale_2.0.1-1.ca2404.1_all.deb Size: 95156 MD5sum: 3e3e6b8efcdb862323ef848a3d79a463 SHA1: d705494f9047fec935a37e82a815ddb3def9b117 SHA256: 13a41a4fa2cd899d063c9610bfe5fd6067cc759945a0d3515b4f70708e83cde0 SHA512: 592a0dd9ee681157feb1e727d3446e929e0163200046d1747ec5b63036075659969025ead24f9b9843aadc6cd3a192ee0fc65de9776728b333772e48f50d0d0c Homepage: https://cran.r-project.org/package=geoscale Description: CRAN Package 'geoscale' (Geological Time Scale Plotting) Functionality for adding the geological timescale to bivariate plots. Package: r-cran-geosed Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sp, r-cran-mapview Filename: pool/dists/noble/main/r-cran-geosed_0.1.1-1.ca2404.1_all.deb Size: 46436 MD5sum: f64d33af2603bfca753671edef9a5838 SHA1: 40b4db6eacc1d0bf928b894e581dbd836eb57d60 SHA256: 97237a14df0734139312f69aead4ee051013d8f86f45078392e72fd0ed831a6b SHA512: fbc6f864a7b6aee0a31c174ce8eca30e752334328227b054282955198543b09f0772628d8fec1d91ee26a03abb6f60b6b5f22742fcd54cef6c65fceb9eb5cd0d Homepage: https://cran.r-project.org/package=geosed Description: CRAN Package 'geosed' (Smallest Enclosing Disc for Latitude and Longitude Points) Find the smallest circle that contains all longitude and latitude input points. From the generated center and radius, variable side polygons can be created, navigation based on bearing and distance can be applied, and more. Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from . Package: r-cran-geosimilarity Architecture: all Version: 3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-magrittr, r-cran-ggrepel Suggests: r-cran-cowplot, r-cran-viridis, r-cran-car, r-cran-desctools, r-cran-performanceanalytics, r-cran-testthat, r-cran-sdsfun, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-geosimilarity_3.9-1.ca2404.1_all.deb Size: 1471044 MD5sum: 3ceffad0b1153e9d9ebd3b584a076659 SHA1: 37a4c0269f82239e27fe637680920726a843e713 SHA256: 51d9440c184f01489f60b7abd29017d8f602db0348daf336ec02b6b1381dd225 SHA512: aab62278bc89db79c746bd3ae5ca8d15f909a01502e26dcd52ca32e51d42c0f10f42a79c82691138598243c3d101e8d14a78200c3cff06fe14e08ff4e36c61d1 Homepage: https://cran.r-project.org/package=geosimilarity Description: CRAN Package 'geosimilarity' (Geographically Optimal Similarity) Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) . GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) . GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty. Package: r-cran-geospark Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr, r-cran-dplyr, r-cran-dbplyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-geospark_0.3.1-1.ca2404.1_all.deb Size: 20322 MD5sum: 6c0e848068bb4e7308611c4c9af69da2 SHA1: 171ce958914f9aae07419012580c3cf578a0be03 SHA256: 9c877e00b5e563c6882ed682ca8f458b51649cb7b28bd4f98058c73b59dc18da SHA512: eacd021d30d918e5252a965cf3873aa3a76f0f02a2e2a8c8270d34bd9cc400849d63155045b8c7be743d3ede0ae16e9a5d4d5436353ff173219ca9bb00ae81bf Homepage: https://cran.r-project.org/package=geospark Description: CRAN Package 'geospark' (Bring Local Sf to Spark) R binds 'GeoSpark' extending 'sparklyr' R package to make distributed 'geocomputing' easier. Sf is a package that provides [simple features] access for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring the same simple features access like sf but running on Spark distributed system. Package: r-cran-geospatialsuite Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2322 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-leaflet, r-cran-magrittr, r-cran-mice, r-cran-rcolorbrewer, r-cran-rnaturalearth, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-tigris, r-cran-viridis Suggests: r-cran-knitr, r-cran-pkgnet, r-cran-rmarkdown, r-cran-kableextra, r-cran-nhdplustools, r-cran-zipcoder, r-cran-tidygeocoder, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geospatialsuite_0.2.0-1.ca2404.1_all.deb Size: 1081822 MD5sum: 71aeb5b1f3ff77972b2146c694ac5c55 SHA1: b198171d27750c8f424f37f7fd0763881112ea76 SHA256: 1ec7f5ff407ad8fc115ab846fb63a2221068e60f5274030023d48704f2c8aaeb SHA512: 849ba4301efaea41c1d720f4643627155ec4a01ae7b804d0ee9a8ffd6db497de67176ee57c23496ba7849ad579fd05b24f3cdfb8145201c63259d1a67f4d7711 Homepage: https://cran.r-project.org/package=geospatialsuite Description: CRAN Package 'geospatialsuite' (Comprehensive Geospatiotemporal Analysis and MultimodalIntegration Toolkit) A comprehensive toolkit for geospatiotemporal analysis featuring 60+ vegetation indices, advanced raster visualization, universal spatial mapping, water quality analysis, CDL crop analysis, spatial interpolation, temporal analysis, and terrain analysis. Designed for agricultural research, environmental monitoring, remote sensing applications, and publication-quality mapping with support for any geographic region and robust error handling. Methods include vegetation indices calculations (Rouse et al. 1974), NDVI and enhanced vegetation indices (Huete et al. 1997) , (Akanbi et al. 2024) , spatial interpolation techniques (Cressie 1993, ISBN:9780471002556), water quality indices (McFeeters 1996) , and crop data layer analysis (USDA NASS 2024) . Funding: This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. Package: r-cran-geospt Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-genalg, r-cran-mass, r-cran-sp, r-cran-minqa, r-cran-fields, r-cran-gsl, r-cran-plyr, r-cran-teachingdemos, r-cran-sgeostat Filename: pool/dists/noble/main/r-cran-geospt_1.0-6-1.ca2404.1_all.deb Size: 540276 MD5sum: 80310c8182a0f7aa059deb7925a803c3 SHA1: 19ad7af0fbeb300142728b3b2a501bdcb0e21c49 SHA256: 92af594e6694a522de70cbc6c218fadf23abac7e22c614fe7cc642d3fb134f7e SHA512: 5a6efb0e403181cfa79398bceee1fb651dd7eda8d1faac109a366345f7a6708037e7a3fd840682e3b2a81d44a5e20dec6f33a8026e138af082f42c1fa7738cca Homepage: https://cran.r-project.org/package=geospt Description: CRAN Package 'geospt' (Geostatistical Analysis and Design of Optimal Spatial SamplingNetworks) Estimation of the variogram through trimmed mean, radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, pocket plot, and design of optimal sampling networks through sequential and simultaneous points methods. Package: r-cran-geosptdb Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmatch, r-cran-fields, r-cran-sp, r-cran-mass, r-cran-minqa, r-cran-gsl, r-cran-geospt Filename: pool/dists/noble/main/r-cran-geosptdb_1.0-3-1.ca2404.1_all.deb Size: 468186 MD5sum: 12a3b0bc9d4514ab109853ec050ad842 SHA1: a055a94dbace8bfb7321b9652277b8c05c20ea06 SHA256: a28e9fc31ef3e798ea40a187350fd26d5cc0cc0eee1702afd99b524aaf6c754b SHA512: c7563801c63f25c14b9d3b1bbf924de90199d249ff6a0ac3989b26ca6e5e4382fca264952a92fa643a970ec521910a7debf21a5d1d3ab2d42ba72dc9d6e47a9c Homepage: https://cran.r-project.org/package=geosptdb Description: CRAN Package 'geosptdb' (Spatio-Temporal Radial Basis Functions with Distance-BasedMethods (Optimization, Prediction and Cross Validation)) Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance. Package: r-cran-geostats Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-geostats_1.6-1.ca2404.1_all.deb Size: 900768 MD5sum: 614e9ac40138cc7cd4e4518634af8cb0 SHA1: a6bdb5e0f35bc9ed877f6ed7742d9f7fe6051f90 SHA256: ad0ab66034a5f77ed97a93403e2357f1aa393e058e22fdd201f385eea8de9a88 SHA512: 5f09ac3f7c61c57bfd0c7cba33eb665fb1896e17d52de0c57af421a7c78d64c8e309bdc078b9a86a3f5d1ecaaade56aa8586577e2327ba1644163b874bb53244 Homepage: https://cran.r-project.org/package=geostats Description: CRAN Package 'geostats' (An Introduction to Statistics for Geoscientists) A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at . Package: r-cran-geotargets Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-targets, r-cran-rlang, r-cran-cli, r-cran-terra, r-cran-withr, r-cran-zip, r-cran-lifecycle, r-cran-gdalraster Suggests: r-cran-crew, r-cran-knitr, r-cran-ncmeta, r-cran-rmarkdown, r-cran-sf, r-cran-stars, r-cran-testthat, r-cran-fs, r-cran-spelling Filename: pool/dists/noble/main/r-cran-geotargets_0.3.1-1.ca2404.1_all.deb Size: 599740 MD5sum: 136b0f55b71cfbc50483aad9ffb8b6ae SHA1: c40295e4b532326a51b7c4cd9368ebfd5f771042 SHA256: f072bf50207890d8bc27c246b5db5581faf3e2fd89975929d092be14c809c1fa SHA512: 11e4c8e4ec3355f2c2533aa720030d4c9d2c4107bd3a38ad3c7b8c618b4f8c14fa7f5b9abc85746e178578d2545c35828e68768d8bf22ec51b76a2f00af50508 Homepage: https://cran.r-project.org/package=geotargets Description: CRAN Package 'geotargets' ('targets' Extensions for Geographic Spatial Formats) Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions. Package: r-cran-geothinner Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2220 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-matrixstats, r-cran-nabor, r-cran-sf, r-cran-terra Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-s2, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-geothinner_2.1.2-1.ca2404.1_all.deb Size: 1844180 MD5sum: acd9c63b1a145caec71b71e148a66032 SHA1: 722e5554e59f4e68643a3a706cb10ee0d38e72cb SHA256: ec84057dc94b83300090ad0785e551f29be95297f841927cb49d8bbc68507d61 SHA512: 7464efab292bb8df305cf57a0d80d845316b769e1a283c764699c9bd9f0108ba726eb92551ac29d94758f729658e1fa810d00e05d494c94772b226dba334e214 Homepage: https://cran.r-project.org/package=GeoThinneR Description: CRAN Package 'GeoThinneR' (Efficient Spatial Thinning of Species Occurrences) Provides efficient geospatial thinning algorithms to reduce the density of coordinate data while maintaining spatial relationships. Implements K-D Tree and brute-force distance-based thinning, as well as grid-based and precision-based thinning methods. For more information on the methods, see Elseberg et al. (2012) . Package: r-cran-geotools Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-geotools_0.1-1.ca2404.1_all.deb Size: 1549088 MD5sum: 4784c27fac4a35cfea361ed5a1a90900 SHA1: 85863330b61aa7b1138acb51e03f872883a3ce72 SHA256: 7ea8da14170416d6ae39ddb92d08bfe7a2ee3bdb51a5d0ab896cb9b60d879f58 SHA512: a81a9b2bdc53f0a58e5c4a89c422e0d5826489ca0967edae0705a952e2ad354d1f2509d633466a82efae546853e2ea6b56f1c312e88c675563e4f57ae40219d5 Homepage: https://cran.r-project.org/package=geotools Description: CRAN Package 'geotools' (Geo tools) Tools Package: r-cran-geotoolsr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-geotoolsr_1.2.1-1.ca2404.1_all.deb Size: 75414 MD5sum: 32f38053c597db30ff4b78c0c6d010b0 SHA1: 0e6e66cecb48ac454c989596c351f9d906b7a210 SHA256: af5f38399c82254238f94506ffa0af45d9fd559e3487b1a2a8aba16a0f14fd32 SHA512: eb667c35d6b05e1069b40778b36686a1df7708a683ccd626ca09ceac98e69b94ed75b0a98d8f6e3fdf9158f8836b27c6f676d45c7bf7b6d23fb68c943a6073e8 Homepage: https://cran.r-project.org/package=geotoolsR Description: CRAN Package 'geotoolsR' (Tools to Improve the Use of Geostatistic) The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms. Package: r-cran-geotopbricks Architecture: all Version: 1.5.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3879 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-stringr, r-cran-zoo, r-cran-sf, r-cran-terra Suggests: r-cran-soilwater Filename: pool/dists/noble/main/r-cran-geotopbricks_1.5.9.1-1.ca2404.1_all.deb Size: 3254616 MD5sum: 606b2ddc7da52a6a84d12cbf93bb6e70 SHA1: c3ed47150078bc1d126b4eaed42d1ff2d2c46fee SHA256: f4f284108dd8b5436845cdbe1fc37cb5d699e11766b17336674eb7bbc20eaa19 SHA512: 9cf2c615a333dde9d39cd676e8ad24be20eadca60fb1ba90fdbe2c1d3d3d29d7356f5893b24151a43bcb4793478f297b24e5c357bc97b8635a2b491fb30198d9 Homepage: https://cran.r-project.org/package=geotopbricks Description: CRAN Package 'geotopbricks' (An R Plug-in for the Distributed Hydrological Model GEOtop) It analyzes raster maps and other information as input/output files from the Hydrological Distributed Model GEOtop. It contains functions and methods to import maps and other keywords from geotop.inpts file. Some examples with simulation cases of GEOtop 2.x/3.x are presented in the package. Any information about the GEOtop Distributed Hydrological Model can be found in the provided documentation. Package: r-cran-geotox Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-duckplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-truncnorm Suggests: r-cran-blob, r-cran-ggplot2, r-cran-ggridges, r-cran-httk, r-cran-httr2, r-cran-knitr, r-cran-quarto, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-sf, r-cran-stringr, r-cran-testthat, r-cran-withr, r-cran-zip Filename: pool/dists/noble/main/r-cran-geotox_1.0.1-1.ca2404.1_all.deb Size: 2767980 MD5sum: d51e7b67b8128ca41e7ee59b7704f0ce SHA1: a6718aa383db60ed8806d34fe84396aa3a3ae549 SHA256: ea96128cbf93afb1ee74e542fbbd17f099652e013ff2de7931edb085547b07e5 SHA512: 1c21e35e0351e911effcf24ee07067d313800b492323f4438c4a86bbf4ee7ef6d49e480aa6655a1d18b6bab17f168b71714fa8dfa1bd6e153b66dcad74b3c650 Homepage: https://cran.r-project.org/package=GeoTox Description: CRAN Package 'GeoTox' (Spatiotemporal Mixture Risk Assessment) Connecting spatiotemporal exposure to individual and population-level risk via source-to-outcome continuum modeling. The package, methods, and case-studies are described in Messier, Reif, and Marvel (2025) and Eccles et al. (2023) . Package: r-cran-geots Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-doparallel, r-cran-ff, r-cran-foreach, r-cran-robustbase, r-cran-sp Filename: pool/dists/noble/main/r-cran-geots_0.1.10-1.ca2404.1_all.deb Size: 784254 MD5sum: 256af224c2f26864b5db9c1805da6b03 SHA1: 1502eae251d55c1af8c27a500abe3063920e716d SHA256: 1c34f7507325db11844c19ef443065d6eae06cfe79ae47a3b01807378dc2c515 SHA512: e9b93c3b5256471ff6ceb0384cc4142de106df51659ad17a417af7140355a4b12216714fb24c38f4059564e406cba9a1da3200c3286c6652ae33d14ea0135932 Homepage: https://cran.r-project.org/package=geoTS Description: CRAN Package 'geoTS' (Methods for Handling and Analyzing Time Series of SatelliteImages) Provides functions and methods for: splitting large raster objects into smaller chunks, transferring images from a binary format into raster layers, transferring raster layers into an 'RData' file, calculating the maximum gap (amount of consecutive missing values) of a numeric vector, and fitting harmonic regression models to periodic time series. The homoscedastic harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) . Package: r-cran-geouy Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-ggspatial, r-cran-ggthemes, r-cran-glue, r-cran-magrittr, r-cran-raster, r-cran-rjson, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stringr, r-cran-tidyselect, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geouy_0.2.8-1.ca2404.1_all.deb Size: 320674 MD5sum: 9357a8a11c5d95752555185167a978f4 SHA1: f26ad1dcf809c31fbf1c8126c97e44a892b71658 SHA256: c37863afd5564f38bcf4f01c5cbaab516a4055258db47e5427335af9f495bf8d SHA512: fe92f302651b956a18980feabdff656aac360aa2caa7c3e8dde4731ccc52ab1c7c267a3273a4b0cb2c7d3428e327e46937cf61d513acbb19d002fbfcab6dbbcb Homepage: https://cran.r-project.org/package=geouy Description: CRAN Package 'geouy' (Geographic Information of Uruguay) The toolbox have functions to load and process geographic information for Uruguay. And extra-function to get address coordinates and orthophotos through the uruguayan 'IDE' API . Package: r-cran-geoversa Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger, r-cran-caret, r-cran-withr Suggests: r-cran-cubist, r-cran-gstat, r-cran-sp, r-cran-nnet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoversa_0.3.0-1.ca2404.1_all.deb Size: 126412 MD5sum: 7ae572bf7c1664d822bd8a9b582544ef SHA1: 97096055d8f8002159da77236f9a3e8302c161b0 SHA256: fabad7e4e8df98695ef929486ea8671161ee6aaa4bce7ade211465af4389e505 SHA512: 1e102e6f1658b7d6ac8a97432399dabc40f3eecbad99bb8210ecc7d8d3cf43db0ea0f5c10f7d1a94720ed1349c0efcffccf50904afe30212449db31f98ffb64e Homepage: https://cran.r-project.org/package=GeoVersa Description: CRAN Package 'GeoVersa' (Design-Based Residual-Correction Forests for Digital SoilMapping) Implements DB-TARF (Design-Based Targeted Adaptive Residual Forest) for large-scale digital soil and ecological mapping evaluated under the design-based paradigm of Wadoux et al. (2021) . A random forest is augmented by a cross-fitted, out-of-fold-selected residual correction (residual forests, ordinary kriging, recalibration), together with design-based conformal prediction intervals. Package: r-cran-geovizr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5523 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-geojsonsf, r-cran-jsonlite, r-cran-sf Filename: pool/dists/noble/main/r-cran-geovizr_1.0.0-1.ca2404.1_all.deb Size: 2262148 MD5sum: 8377da6a1d8d9a925622ff34063d786c SHA1: b2438b5f5a4b11942109002687d9700f58f9122c SHA256: d99e065da33fff4adf5c6cb2c3ab1c41f2a56a0ec8173251c2e946c781a8aa84 SHA512: 1f18886f587bf9846c8b715ae410a733ca5de67bd70665536b7cd3479774c2ed93ebb9ff510fb7599aed7b8461c3235189944491472649fb642c73b0fa03486a Homepage: https://cran.r-project.org/package=geovizr Description: CRAN Package 'geovizr' (Interactive Cartography) Create a wide range of interactive, zoomable vector maps. This package is an 'R' binding for the 'geoviz' 'JavaScript' library , itself based on the 'd3.js' ecosystem . 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Package: r-cran-geovol Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-geovol_1.1-1.ca2404.1_all.deb Size: 44620 MD5sum: bac773165802212be82548ae8232aa6a SHA1: 20be540f5014094511aa6f0a2f137131a7c00477 SHA256: 1540c5a455b2532b1f3e68bcb4d0fea3fc1fd0e7097ec2d50cb4336228b662b7 SHA512: f4b89ccef3ac87458fface30152923f18a6c6d47b71a6a8ad836bc1977eb331e719e8b6d6a7771534cc5fb74245dbac67d9b549a555e70a5cc5be65b032d479c Homepage: https://cran.r-project.org/package=geovol Description: CRAN Package 'geovol' (Geopolitical Volatility (GEOVOL) Modelling) Simulation, estimation and testing for geopolitical volatility (GEOVOL) based on the global common volatility model of Engle and Campos-Martins (2023) . GEOVOL is modelled as a latent multiplicative volatility factor with heterogeneous factor loadings. 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Package: r-cran-geoweightedmodel Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1766 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-cartography, r-cran-dplyr, r-cran-dt, r-cran-gwmodel, r-cran-raster, r-cran-readxl, r-cran-shiny, r-cran-shinyalert, r-cran-shinybs, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sp, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geoweightedmodel_1.0.3-1.ca2404.1_all.deb Size: 879314 MD5sum: 3e0e3e13e3c421319dc4db6f846f26d5 SHA1: 62dee46365fa8893de845a88075b9aa5e307af9c SHA256: 219c0b522e1d801ac6ac4ea23d746fd73bca272c6bfd55c0c861956c1dedcb16 SHA512: 95dec57048f77fcdcb124b2c718d8c616b9e88c63bb2f59a54c6b8b301f0109d8028d757e2b3782b0fc77d69801dd598faff6113c823d4436f1f8c17d762273d Homepage: https://cran.r-project.org/package=GeoWeightedModel Description: CRAN Package 'GeoWeightedModel' (User-Friendly Interface for Geographically-Weighted Models) Contains the development of a tool that provides a web-based graphical user interface (GUI) to perform Techniques from a subset of spatial statistics known as geographically weighted (GW) models. Contains methods described by Brunsdon et al., 1996 , Brunsdon et al., 2002 , Harris et al., 2011 , Brunsdon et al., 2007 . Package: r-cran-geozarr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zarr, r-cran-cftime, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geozarr_0.2.0-1.ca2404.1_all.deb Size: 552934 MD5sum: 0e78cde25517a09e7a271e7727faf984 SHA1: 1b86035d3d27acf2199eef957d042df796f4af89 SHA256: 861c46368c5f91301b61651ba0dec985a4c40462e3d5d7812a3e62254be09538 SHA512: 28dba3e3f4bab4c29052928bf70ba84a5b4e776da1fb786333700de2d873885a35934773d78b195dce14bad67215079e3687e021d3d1dfc2b5403b3a74a1626a Homepage: https://cran.r-project.org/package=geozarr Description: CRAN Package 'geozarr' (GeoZarr Conventions for Geospatial Data in Zarr Stores) Large-scale gridded data stores are increasingly using the Zarr format. GeoZarr is defined in terms of a number of community conventions built on top of the Zarr specification. These conventions specify how Zarr metadata is to be interpreted to attach semantic meaning to the data in the Zarr array providing the metadata. This package implements a number of community conventions on top of the 'zarr' package, enabling the R community to use geospatial data stored in Zarr. Package: r-cran-geozoo Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops Suggests: r-cran-tourr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geozoo_0.5.1-1.ca2404.1_all.deb Size: 93974 MD5sum: ed853762de652bca4e5abbcec4100fbf SHA1: fef8a97b4328b1184e29d88928759b666ed6780f SHA256: df289cb561832e3d4b7a2e34fd3ff9458c305cf15b024eccda70def6dc718a3c SHA512: 38c0b57c84790c0490a7ee9fc61723e01cd6e853cdb9b641219fa9bd53a5e8bbc9cd94fb72fc97975ab4985da640dada33fc686d8ad34fa29dd2a6de293daf7d Homepage: https://cran.r-project.org/package=geozoo Description: CRAN Package 'geozoo' (Zoo of Geometric Objects) Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'. Package: r-cran-gepaf Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-gepaf_0.2.0-1.ca2404.1_all.deb Size: 17924 MD5sum: 004c95089bd6f33759dbc65832717a94 SHA1: 1f1cdc286037b316ff68a858923e686e939cafb9 SHA256: 0f1c063055cacbb0fa1dd703161fef1236c54a13aa19ef38afc7eb115b319729 SHA512: d920ec3b98d7cdda854089475f0289e7a2b365384ce258f6cee6daa05a69bfcb81ccfdcdf202924cbba517e4e15a1e6440cf10ebbbfa540fd461943b625a740f Homepage: https://cran.r-project.org/package=gepaf Description: CRAN Package 'gepaf' (Google Encoded Polyline Algorithm Format) Encode and decode the Google Encoded Polyline Algorithm Format. See for more information. Package: r-cran-gephiforr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-gephiforr_0.1.1-1.ca2404.1_all.deb Size: 49050 MD5sum: 8c0e047a07dcbf5d142df976a1482ada SHA1: 3e21ac168b88076b3a187a423e34ecbd13d5aea1 SHA256: bc60b55184ae9d4c4d34e29c099628dfeea1d497bf37ccb8ee7e30c3ae6180ce SHA512: 4cb36f9ca7ca1c0bc1451baf65a61c773632d09a90ea6a3048638c5201bf7c08f0487a6b665fa8b49725efc37a0f397359d7b31f65649c733cb405219db9b611 Homepage: https://cran.r-project.org/package=GephiForR Description: CRAN Package 'GephiForR' ('Gephi' Network Visualization) Implements key features of 'Gephi' for network visualization, including 'ForceAtlas2' (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating 'Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014) and Noack, A. (2009) . Package: r-cran-geppe Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rangen Suggests: r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-geppe_1.1-1.ca2404.1_all.deb Size: 37708 MD5sum: 0b59eab04acd2c313f76d9103c1711b4 SHA1: cb54e49d8520bafcbe3a8cbac0a5ad21a91c2fef SHA256: 53e8f0ffd3d36ea5e8a11713d80a8ddaf661afc8f880cb37a9fcca391516ff48 SHA512: f99ffa47ef14a4df9f2f6db0584be30accf908e2edf4d362540ebe1b7593831ba792202ca2595d0a44bbb1aef5cd21914ea8fdd3a9866096a98954956db1233a Homepage: https://cran.r-project.org/package=geppe Description: CRAN Package 'geppe' (Generalised Exponential Poisson and Poisson ExponentialDistributions) Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. . Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. . Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. . Package: r-cran-gerbil Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desctools, r-cran-lattice, r-cran-mass, r-cran-mvtnorm, r-cran-openxlsx, r-cran-pbapply, r-cran-truncnorm Suggests: r-cran-dplyr, r-cran-knitr, r-cran-mice, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gerbil_0.1.9-1.ca2404.1_all.deb Size: 1187836 MD5sum: 4fa0dae4d55f32c218a0441f264ccf70 SHA1: 1f6153a67df052f2429feccf12b265a0a481a770 SHA256: 7175dd4ac79b833b2d83587c58cdc6a113e4d61aaf0a4b9bb12771dc0889336d SHA512: 3afaf2980766d10873f5c7d47bb97a3fb8a3d76265e6da853fc68039909d823fbfa15c6fee552e5ad2bab5f65ab55bae80c65370170abcf0bb5d5099a2599a14 Homepage: https://cran.r-project.org/package=gerbil Description: CRAN Package 'gerbil' (Generalized Efficient Regression-Based Imputation with LatentProcesses) Implements a new multiple imputation method that draws imputations from a latent joint multivariate normal model which underpins generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. See Robbins (2021) . Package: r-cran-gerda Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringdist, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gerda_0.8.1-1.ca2404.1_all.deb Size: 1009288 MD5sum: 74645d4ff1fe3372bb0bb255ab5d3425 SHA1: cc2263580da209318775e6412335d8c1230d47d1 SHA256: df5769dba63f17ad0e3cb2bdfdb5ab5a4a998156b050ff50a4a870e902fe5a02 SHA512: 481e8d690fb8dd594c9d3f1f3fd77637cd7d88a7a6cbf3f9e84ac5116b650ea6eacec29bb807877441ec00e9fd9417a6cbcb409490782a8aa38070e1a6b32d3f Homepage: https://cran.r-project.org/package=gerda Description: CRAN Package 'gerda' (German Election Database (GERDA)) Provides tools to download datasets of German elections covering local, state, federal, mayoral, European Parliament, and county (Kreistag) elections, with federal county-level coverage from 1953 and other families extending through 2025. The package supplies turnout, vote shares, and derived indicators at the municipal and county level, including geographically harmonized datasets that account for changes in municipal boundaries over time and incorporate mail-in voting districts. Bundled data includes county-level INKAR covariates (1995-2022) and municipality-level Zensus 2022 indicators. Data is sourced from . Package: r-cran-gerefer Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bibliorefer Filename: pool/dists/noble/main/r-cran-gerefer_0.1.4-1.ca2404.1_all.deb Size: 757024 MD5sum: e0d992f239f1bdfb94a8244257244928 SHA1: 66ed818565b2315d892352d00c90e25f47a41a79 SHA256: a7041e67f2fe80af9be97322950e1ed6d63a99c27a9f5580e8624633ab1fc23d SHA512: d081f75b81bb1bcc15ca63a5048a66296645537c3889dcecf0d501b08db0cb13b4526ec31ce568a1dd1d0e898eb17748b3a148fdc91f1028e9a86c66ddbdc272 Homepage: https://cran.r-project.org/package=gerefer Description: CRAN Package 'gerefer' (Preparer of Main Scientific References for Automatic Insertionin Academic Papers) Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package 'bibliorefer'. The generated file contains the list of metadata of the principal references in 'BibTex' format. Massimo Aria, Corrado Cuccurullo. (2017) . Caibo Zhou, Wenyan Song. (2021) . Hamid Derviş. (2019) . Package: r-cran-germinar Architecture: all Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-agricolae, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-dt Suggests: r-cran-gsheet, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-inti Filename: pool/dists/noble/main/r-cran-germinar_2.1.7-1.ca2404.1_all.deb Size: 865908 MD5sum: 367fc73e7f50de17ed685c46961295d1 SHA1: 29fad1dc4f5a54a74d9262edee9ebf804f633229 SHA256: 531f089e2ecfae5a6091dc441a816ad7110516232acbbf6bffa2e55c8bc66861 SHA512: 68d046d4636dfa911748045cff7f130c6edc85858b34b6936d5678ecadc1a19ad9c89bf4d05da168fb1ed4c832a2667b0ec15dcfc6e68ded913b00427c39e9e4 Homepage: https://cran.r-project.org/package=GerminaR Description: CRAN Package 'GerminaR' (Indices and Graphics for Assess Seed Germination Process) A collection of different indices and visualization techniques for evaluate the seed germination process in ecophysiological studies (Lozano-Isla et al. 2019) . Package: r-cran-germinationmetrics Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gslnls, r-cran-mathjaxr, r-cran-plyr, r-cran-rdpack, r-cran-rlang Suggests: r-cran-httr, r-cran-knitr, r-cran-pander, r-cran-curl, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-xml Filename: pool/dists/noble/main/r-cran-germinationmetrics_0.1.10-1.ca2404.1_all.deb Size: 905366 MD5sum: 92ce3493655d4467093fc44d116286bd SHA1: 4c2fd75dc9fc1a0217ccc78c78dcd3eede36d60b SHA256: 3210c4f7236abac1498db4928ba6f78fbd093f45431fa0b95dc30b822be5cfa5 SHA512: ef177d54aeb29f97b1c6b48d9d725c5bd1570fd9b024f9757be9ca268fed96cb1535cc3749d1fdf742863e10c7d78cbe31ad138253b321e129cafbf475b954b4 Homepage: https://cran.r-project.org/package=germinationmetrics Description: CRAN Package 'germinationmetrics' (Seed Germination Indices and Curve Fitting) Provides functions to compute various germination indices such as germinability, median germination time, mean germination time, mean germination rate, speed of germination, Timson's index, germination value, coefficient of uniformity of germination, uncertainty of germination process, synchrony of germination etc. from germination count data. Includes functions for fitting cumulative seed germination curves using four-parameter hill function and computation of associated parameters. See the vignette for more, including full list of citations for the methods implemented. Package: r-cran-gernika Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-purrr, r-cran-reshape2, r-cran-diagrammer, r-cran-colorspace, r-cran-dplyr, r-cran-vctrs, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-knitcitations, r-cran-ggpubr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-gernika_1.2.0-1.ca2404.1_all.deb Size: 2338712 MD5sum: b3b94f9db25ca35f1ca3ab430c9aeb34 SHA1: fd65c8d35abb7fe80768b193e058d43a4e553d13 SHA256: 5728bf740b9aed52526d8c6fdfcc5361d7fff0ce8e5562e84247bddc2fd1efea SHA512: 49ed8ddae05aeff8fdcc6847e86ce855b4f32207e1153ab71cb09d1d1f4d87c6753b7397ec1335ea62183b3321df8fb3926bf2a4aa0bc150815214693a08fde5 Homepage: https://cran.r-project.org/package=GeRnika Description: CRAN Package 'GeRnika' (Simulation, Visualization and Comparison of Tumor Evolution Data) Simulating, visualizing and comparing tumor clonal data by using simple commands. This aims at providing a tool to help researchers to easily simulate tumor data and analyze the results of their approaches for studying the composition and the evolutionary history of tumors. Package: r-cran-gesca Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gesca_1.0.5-1.ca2404.1_all.deb Size: 148794 MD5sum: fb657e742692624731ad96b7feda0973 SHA1: 724b1756e4cd591bb43879979dc544f99a5af1d3 SHA256: 5b5e5aae51ac25033a9f5901bce5d280d95200d9d741012299f44c87f80d7dea SHA512: b58be17c60baa4f54f0a237914946015c521dd2be206cab605a0150e2a42f5faf2b6a11b3565f6af361f51b28b1847a7f3a5a648cad0f020b908b7776ae18d2c Homepage: https://cran.r-project.org/package=gesca Description: CRAN Package 'gesca' (Generalized Structured Component Analysis Structural EquationModeling) Implementing generalized structured component analysis (GSCA) and its basic extensions, including constrained single and multiple group analysis, and second order latent variable modeling. For a comprehensive overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754). Package: r-cran-gesisdata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-foreign, r-cran-magrittr, r-cran-netstat, r-cran-rio, r-cran-rselenium, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gesisdata_0.1.2-1.ca2404.1_all.deb Size: 681556 MD5sum: 3742a7d3a8becb215a3f8c469bc8fd77 SHA1: 1716f04574ccf3e5e09b2d1921809a00ea540414 SHA256: 46392c79d02836502c2e4135a5d4cec0397200aa1519dc79e6a14ec936a2e226 SHA512: 75d60b788c7cc8871fa2569cbecfb7c1ae01bfc7c64cd3e58465e4b576c739a31139ca894d185051beb40130de74e5784f7b2b427adafa61f0566bf2b09c8274 Homepage: https://cran.r-project.org/package=gesisdata Description: CRAN Package 'gesisdata' (Reproducible Data Retrieval from the GESIS Data Archive) Reproducible, programmatic retrieval of datasets from the GESIS Data Archive. The GESIS Data Archive makes available thousands of invaluable datasets, but researchers using these datasets are caught in a bind. The archive's terms and conditions bar dissemination of downloaded datasets to third parties, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one has employed. The 'gesisdata' package cuts this knot by providing registered users with programmatic, reproducible access to GESIS datasets from within 'R'. Package: r-cran-geslar Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-cli Suggests: r-cran-bslib, r-cran-dt, r-cran-knitr, r-cran-leaflet, r-cran-leafpop, r-cran-lubridate, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyalert, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-geslar_1.0-3-1.ca2404.1_all.deb Size: 188118 MD5sum: 4b7f3095ab78bfe62e27d1f6b6c0bb2c SHA1: a0ee53535854384c9e7a838f8e7ed36eab17792c SHA256: 47c2ab044b16c0943f8d72c0dddd4695807373d976851cbda446c2cb8948d119 SHA512: 32c1b4ffcf8c787a22933e96776e98376c5b7e6312e91699e07e8945de7427c5becb674c695852d0415db3e684c2470251caa69d9ca56114c6de8518f5460719 Homepage: https://cran.r-project.org/package=geslaR Description: CRAN Package 'geslaR' (Get and Manipulate the GESLA Dataset) Promote access to the GESLA (Global Extreme Sea Level Analysis) dataset, a higher-frequency sea-level record data from all over the world. It provides functions to download it entirely, or query subsets directly into R, without the need of downloading the full dataset. Also, it provides a built-in web-application, so that users can apply basic filters to select the data of interest, generating informative plots, and showing the selected sites. Package: r-cran-gestalt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-magrittr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gestalt_0.2.0-1.ca2404.1_all.deb Size: 195328 MD5sum: 660a425331e1000d7964c0d2a32ac12f SHA1: bec4e1c9aff2c5a430ae10b213156a1c748dc128 SHA256: 6cc5ec9f2b6c02aae0fafa800e88c0cd5653805f3c3fefb62f32cc6b2cfa6e21 SHA512: 897975cb5e86a8d4381b69c141c1971bd92b1dc389d8391b3fc2fb68c6e3e7b0445379b32ce7599dcba75b7d28b27cc05fd099e1e51f50390bd07521d2b733fb Homepage: https://cran.r-project.org/package=gestalt Description: CRAN Package 'gestalt' (Tools for Making and Combining Functions) Provides a suite of function-building tools centered around a (forward) composition operator, %>>>%, which extends the semantics of the 'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables you to construct composite functions that can be inspected and transformed as list-like objects. In conjunction with %>>>%, a compact function constructor, fn(), and a partial-application constructor, partial(), are also provided; both support quasiquotation. Package: r-cran-gestate Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-shiny, r-cran-shinythemes, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gestate_1.6.0-1.ca2404.1_all.deb Size: 714100 MD5sum: f0fddccb6b7d428d18286a9046dc831d SHA1: 349105fe3068e1de7514ff1dabafd395ddca82ea SHA256: 1baee110d59ee096e7c8d9d1bbc42afefbe6fb40851b069f61e6430f8fe67a66 SHA512: b37c1474df8dd100c12cff0f47dc7ff25f14233f5d9fc8cb1e6d8e5864a047c3c9a878406f537bf5b8da2715c33c7a8ef439249c62b0e93a0418721a363f0f72 Homepage: https://cran.r-project.org/package=gestate Description: CRAN Package 'gestate' (Generalised Survival Trial Assessment Tool Environment) Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. 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Package: r-cran-gesttools Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datacombine, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect, r-cran-geem, r-cran-rsample, r-cran-nnet, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gesttools_1.3.0-1.ca2404.1_all.deb Size: 95122 MD5sum: dd7b1ae21e7f623e814b7442e449571b SHA1: 475e049f54aab3dc308501ff3516f4f463678d83 SHA256: 568af2e0e4221cb5e5c35cdeb53cf9648f5cd0f947a6c434a064eb352ade16dc SHA512: 3beabfa7f159d48bf3a5a1a5177fc41dfc37cfa39ed136c05a711075ec826177f1b899c9e169ef975a2d575cdb366278814a823a440c93e95a90a933071e0e86 Homepage: https://cran.r-project.org/package=gesttools Description: CRAN Package 'gesttools' (General Purpose G-Estimation for End of Study or Time-VaryingOutcomes) Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) and Dukes and Vansteelandt . The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable. 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A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2024) , Myllymäki et al. (2017) , Mrkvička and Myllymäki (2023) , Mrkvička et al. (2016) , Mrkvička et al. (2017) , Mrkvička et al. (2020) , Mrkvička et al. (2021) , Myllymäki et al. (2021) , Mrkvička et al. (2022) , Dai et al. (2022) , Dvořák and Mrkvička (2022) , Mrkvička et al. (2023) , and Konstantinou et al. (2024) . Package: r-cran-getable Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr Suggests: r-cran-jsonlite, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-getable_1.0.3-1.ca2404.1_all.deb Size: 24196 MD5sum: 4d5df281181407d3fa5eeaf22def656a SHA1: 3e7788a87478dd22b9eafbc0e64cc85c6463b5e2 SHA256: 463f3e1022a926472347bd051db7701dcdc14acdcdcdf9a83368534cd79f58a1 SHA512: 53eaa8e386ece2bf6fbc9a7d79450b908a017fc9cb1558c6c3c2dbebb6a2fa6e41543f15674a695e1b98333a9ddb925377d216f36062e63abf86115c10a95e53 Homepage: https://cran.r-project.org/package=getable Description: CRAN Package 'getable' (Fetching Tabular Data "Onload" in Compiled R Markdown HTMLDocuments) Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents. Package: r-cran-getbcbdata Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-future, r-cran-furrr, r-cran-jsonlite, r-cran-memoise, r-cran-purrr, r-cran-cli, r-cran-parallelly, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-getbcbdata_0.9.5-1.ca2404.1_all.deb Size: 42362 MD5sum: d32bb1c16679c9e7d9040738f8916f68 SHA1: e25af9dd010f588d69e2fdce3ddf07b66690128b SHA256: 0c198f09fe76fa44220a220b5014671ab43ff688e6bc7addd0190c8c2cf14d28 SHA512: d0bab215574792c57de2596c3a64ca54621af12b79bc19f19cc84e4571ed90acdaf1bf13880181c1e1e2cc0b2f7b7d44c87f76c3579f353b574a8a9eb6ad859c Homepage: https://cran.r-project.org/package=GetBCBData Description: CRAN Package 'GetBCBData' (Imports Datasets from BCB (Central Bank of Brazil) using ItsOfficial API) Downloads and organizes datasets using BCB's API . Offers options for caching with the 'memoise' package and , multicore/multisession with 'furrr' and format of output data (long/wide). Package: r-cran-getcrucldata Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-httr2, r-cran-rlang, r-cran-terra Suggests: r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-roxygen2, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-getcrucldata_2.0.0-1.ca2404.1_all.deb Size: 589352 MD5sum: 4a86e54ed247cbb6bc082c2501ab5185 SHA1: 05e5cb703d4f69254db3cb1ad27b49057c515fac SHA256: 28590f64f46745e80526e0919b705e2fd22e28a91fe983c5905dd2034a8941f4 SHA512: 330e226fdf0f79b562f8a546f08404426557f836c5200f0d125661a0379138de80d82aea42189a54ea626e9a9f821cdd19a23c6fe6f3af2764dd37bdefbbbb8a Homepage: https://cran.r-project.org/package=getCRUCLdata Description: CRAN Package 'getCRUCLdata' ('CRU' 'CL' v. 2.0 Climatology Client) Provides functions that automate downloading and importing University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a data.table object or a 'terra' 'SpatRaster' object. 'CRU' 'CL' v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arc minutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, . Package: r-cran-getdesigns Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-getdesigns_1.2.0-1.ca2404.1_all.deb Size: 21074 MD5sum: 0f6ba2e23623d45872e7395c9bc8817a SHA1: 6cba482ec048615aca8d5915502010fdf8625fea SHA256: 45930098b24d12bc924b44fb39ec7e31c5655eade139b70b214f178c6adb2165 SHA512: 3ee8f2a3e0bb8977eb4bc8fad8395a41e9137c1932153dd7c07f96e92a11be62d9f8d0dc2a4b20c7d561c9f36d642f9dace659e6517af169ca5332fb4cfd534c Homepage: https://cran.r-project.org/package=GETdesigns Description: CRAN Package 'GETdesigns' (Generalized Extended Triangular Designs ('GETdesigns')) Since their introduction by Bose and Nair (1939) , partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs. 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Package: r-cran-getdteval Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-formulaic, r-cran-microbenchmark Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-covr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-getdteval_0.0.2-1.ca2404.1_all.deb Size: 260438 MD5sum: 3cf3e472930c5d18954b4be7533dd752 SHA1: ad57563290a2fb28c63f005209a3ca30ddffb86b SHA256: 307bed61a837ea35c6aeb417316e9d6dcff265ed066139f46282d65cecdca690 SHA512: b93a847fae9c6b484166cd5f7ba4035d886a0e3331dec6650cadb386d7c25be7618d3cfe99b4cb2075d8b84854fb35d76f6bc38edf944e5ee912421e00894681 Homepage: https://cran.r-project.org/package=getDTeval Description: CRAN Package 'getDTeval' (Translating Coding Statements using get() and eval() forImproved Run-Time Coding Efficiency) The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. 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All data is downloaded and imported from the ftp site . Package: r-cran-getlattes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-tibble, r-cran-janitor, r-cran-purrr Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-getlattes_1.0.0-1.ca2404.1_all.deb Size: 4184252 MD5sum: 8c8ff6dc0a1248048d56882425eb6f23 SHA1: 9964e471b9303ec4ef185f22d9196a45787b03d6 SHA256: 95e5fd504a91baed63e45ed2be77b1c88d9199229a76decf30814b9ce6a896f4 SHA512: e67db8fa33e0be03850a3f99d1d567f21d18f77d82a595029f91a520f9389c0f7678fa7b8d2824c2a0c4c7d61c5b58bc9406b8cb48e0391d8595c20d9c278b7e Homepage: https://cran.r-project.org/package=getLattes Description: CRAN Package 'getLattes' (Import and Process Data from the 'Lattes' Curriculum Platform) Tool for import and process data from 'Lattes' curriculum platform (). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format. 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The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See for statistical statistical theory, documentation and examples. Package: r-cran-getopt Architecture: all Version: 1.21.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-getopt_1.21.1-1.ca2404.1_all.deb Size: 53346 MD5sum: fcc373558f51b58e5c61965b823b721e SHA1: eb60c89ec6e2f1863a59ba99154373de0bd347fa SHA256: 0dc2e8c9a3a50d12b8b2bf2b1537b44de2fa4ece51148f90e794e31e02ba0284 SHA512: 51315a1e39c105385f47e9c21dee3b391b90ec8262abdf5155a1d89eca68dcb1067fe3348bba606664ceb78bcc46d865905927458665f518ddf07a153e7edec4 Homepage: https://cran.r-project.org/package=getopt Description: CRAN Package 'getopt' (C-Like 'getopt' Behavior) Package designed to be used with Rscript to write '#!' shebang scripts that accept short and long flags/options. 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Package: r-cran-getquandldata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-memoise, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-fs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-getquandldata_1.0.0-1.ca2404.1_all.deb Size: 34478 MD5sum: e396b9eff4f54bf943be4744e851ca59 SHA1: e4debaed6803ac4bde70add21ae0eaee81ebbf9e SHA256: 7ea9127db98e769f3ab37444742685ab101ce7f7a43eb8033eaebfaabf051f32 SHA512: 18c71dafda62588e1dace7bd2f7d6aa9d015646febfbd0ad4acc3b215553a17975cca20416b70524132ce2b32911bc9c7024df5bd798c3068f54a6c77afe7e02 Homepage: https://cran.r-project.org/package=GetQuandlData Description: CRAN Package 'GetQuandlData' (Fast and Cached Import of Data from 'Quandl' Using the 'jsonAPI') Imports time series data from the 'Quandl' database . 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Package: r-cran-ggallin Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggallin_0.1.2-1.ca2404.1_all.deb Size: 694146 MD5sum: e07d2893c2c757384812a4a2b0bdef12 SHA1: 9e3857bca554bcee27d0ecc48aa6d4ece563f3d8 SHA256: 995317294f8e457ed4ab375805bf89786443187eb05d382102b1a559417e745c SHA512: 666bc5f6b3ef8b726252c1d626534b4ab539fa415f171c3229b2e4dde000bce1bc2db8b2a657bad9b38c22bd997df22d4533deb6d4d50e33620b1e2166ab3ca9 Homepage: https://cran.r-project.org/package=ggallin Description: CRAN Package 'ggallin' (Grab Bag of 'ggplot2' Functions) Extra geoms and scales for 'ggplot2', including geom_cloud(), a Normal density cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans, which are loglike but appropriate for negative data; interp_trans() and warp_trans() which provide scale transforms based on interpolation; and an infix compose operator for scale transforms. Package: r-cran-ggalluvial Architecture: all Version: 0.12.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-lazyeval, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-alluvial, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-babynames, r-cran-sessioninfo, r-cran-ggrepel, r-cran-shiny, r-cran-htmltools, r-cran-sp, r-cran-ggfittext, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggalluvial_0.12.6-1.ca2404.1_all.deb Size: 1538898 MD5sum: 5b03af3ac5a58c64a13c20ebb7a31219 SHA1: 8193811fdf014b2961f6b8e82f344431e9fbab37 SHA256: 34370c8f3f61e103904ab10533af397053594313bdae7e0edd078cc355e6b27b SHA512: 0af0981945693929c01b98a46626e5535eca8fa002ecf92c884540c91592f7827377b1b8db153d9e816ea1527121fd7e36e4993cbf74fe4e5df553a58c08c4e7 Homepage: https://cran.r-project.org/package=ggalluvial Description: CRAN Package 'ggalluvial' (Alluvial Plots in 'ggplot2') Alluvial plots use variable-width ribbons and stacked bar plots to represent multi-dimensional or repeated-measures data with categorical or ordinal variables; see Riehmann, Hanfler, and Froehlich (2005) and Rosvall and Bergstrom (2010) . 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Package: r-cran-ggally Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2083 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-dplyr, r-cran-ggstats, r-cran-gtable, r-cran-lifecycle, r-cran-magrittr, r-cran-progress, r-cran-rcolorbrewer, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-tidyr Suggests: r-cran-airports, r-cran-broom, r-cran-broom.helpers, r-cran-chemometrics, r-cran-crosstalk, r-cran-emmeans, r-cran-geosphere, r-cran-ggforce, r-cran-hmisc, r-cran-igraph, r-cran-intergraph, r-cran-knitr, r-cran-labelled, r-cran-mapproj, r-cran-maps, r-cran-network, r-cran-nnet, r-cran-rmarkdown, r-cran-scagnostics, r-cran-sna, r-cran-spelling, r-cran-survival, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggally_2.4.0-1.ca2404.1_all.deb Size: 1993234 MD5sum: bbe3f3bd110d1fdb3790596f490a7abb SHA1: ab426c882b64cff5f43f693e3aae960d69dce545 SHA256: 2cd0ad231d11c52f43859f2096d97eb9d7ba7633a207e74f67175e55e81f99e6 SHA512: 9ab5f02b1244381fee88b8ad7dc2b8f0d45925ec09be752e3b83398383243490ef21227f0fde6e7f4e49e489211753402803da4621f94dd86680c5774ff1d356 Homepage: https://cran.r-project.org/package=GGally Description: CRAN Package 'GGally' (Extension to 'ggplot2') The R package 'ggplot2' is a plotting system based on the grammar of graphics. 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Package: r-cran-ggarchery Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-purrr, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-glue, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ggarchery_0.4.4-1.ca2404.1_all.deb Size: 744458 MD5sum: 8228d0549dc7a1ffb51e31e8412868c9 SHA1: 0ecb6f9ed7954b5af43bd6351751c303aa4343ec SHA256: 799386007de454a215e7897dc7117a44292a7b238536b7d2cc35ca22d5365cc9 SHA512: 284043fdecf832888d9899d20ac637a59feadea2734e0290171d901816b768efc11de7696dcf0882b7ae6326b5bc9318960bc66a1bbf159b827b4452d8a4dc64 Homepage: https://cran.r-project.org/package=ggarchery Description: CRAN Package 'ggarchery' (Flexible Segment Geoms with Arrows for 'ggplot2') Geoms for placing arrowheads at multiple points along a segment, not just at the end; position function to shift starts and ends of arrows to avoid exactly intersecting points. 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Provides the 'ggcpt' S3 result class with 'broom'-style tidy/glance/augment methods, 'autoplot()' (with confidence intervals, significance regions, detector statistics, solution paths, scale space, and multivariate facets), composable geoms ('geom_changepoint()', 'geom_cpt_segment()', 'geom_cpt_ci()', 'geom_cpt_region()', 'geom_cpt_label()', 'geom_cpt_event()', 'stat_changepoint()'), and a 'cpt_detect()' dispatcher covering fifty methods with introspection via 'cpt_methods()': penalised/optimal partitioning, multiscale and search methods, nonparametric and kernel methods, Bayesian methods, high-dimensional, functional, covariance and network methods, regression breaks, seasonal-trend decomposition, the classical single-change tests, and robust detection under drift and autocorrelation. Adds inference (Narrowest Significance Pursuit regions, a unified 'cpt_confint()', post-detection tests), selection of the number of changes, influence and sensitivity diagnostics, supervised detection with learned penalties, consensus and method recommendation, event annotation and reproducible reports, benchmarks against the Turing Change Point Dataset, sequential monitoring with detection-delay accounting, power and study design, and an extension mechanism ('as_ggcpt()', 'cpt_register_method()') that brings external and non-CRAN detectors into the same grammar. 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Package: r-cran-ggchinaflag Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-showtext, r-cran-ggforce Suggests: r-cran-sysfonts Filename: pool/dists/noble/main/r-cran-ggchinaflag_1.0.0-1.ca2404.1_all.deb Size: 243960 MD5sum: 698df5b32feb2a2e29e7c8125929a96e SHA1: 233eca676c6faef94459263d659c9dd562140332 SHA256: f3cf5f0256376acbd5af144804d0dd697e9a05d5888ca8bbe68b6450227260c5 SHA512: 749c99e92377b6cc2d8dc25307e9886a62ae2d389eb68622c71dae92c8ca1173d704f209a11673025835346145d5efd3a4ac8a9c844dad796aa99a879cb93140 Homepage: https://cran.r-project.org/package=ggChinaFlag Description: CRAN Package 'ggChinaFlag' (Drawing Chinese National and Historical Flags with 'ggplot2') Provides programmatic implementations for drawing Chinese national and historical flags using analytic geometry and 'ggplot2'-based vector graphics. 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Package: r-cran-ggchord2 Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-geomtextpath, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggchord2_0.0.1-1.ca2404.1_all.deb Size: 296734 MD5sum: 439984ddf92688150fc395e8121af88e SHA1: 4261c3f30acf03c8ceb3488e4b5708aaea18eef1 SHA256: 13f29658706b732b463c221141b854705f9d7cc1f049e6b88060577ca1af4967 SHA512: e35035109e38eb538d6acd8c1a069cadb82ab22956c29f53b2252affe591a624cd4cbb3ef2b6d7b013d2ffe40091f172476c0a5efc49e5f49f2e10f231b24cb3 Homepage: https://cran.r-project.org/package=ggchord2 Description: CRAN Package 'ggchord2' (Chord Diagrams with 'ggplot2') A 'ggplot2' extension that provides functions for drawing chord diagrams for visualising flows between categories. The package extends 'ggplot2' by adding geoms and stats for drawing chord sectors, arcs, and labels. Package: r-cran-ggchord Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6342 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-dplyr, r-cran-plotly, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggchord_0.8.0-1.ca2404.1_all.deb Size: 3685054 MD5sum: a8e591e6c34cc56bc65dc27025304b4a SHA1: b1f8405cf7912ef23bc67ed00499f22940674469 SHA256: d2962b1fd78f3d32666375395736d7e025efef7496bfbada323bab4c9f046968 SHA512: bd6b4643100fa48bf53daafbf5bcba7ed8d67a21ceb7e35bbd3f31cc0104decfe21f2b730269f2e33ee8c8fdc96d34e41316900bb467be120ccac671c86b1f38 Homepage: https://cran.r-project.org/package=ggchord Description: CRAN Package 'ggchord' (Multi-Sequence Alignment Chord Diagram Visualization Tool) A 'ggplot2'-based R package that visualizes multi-sequence alignment results as chord diagrams using layered grammar of graphics. 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Package: r-cran-ggcircular Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2669 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-vctrs Suggests: r-cran-circular, r-cran-knitr, r-cran-momentuhmm, r-cran-posterior, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggcircular_0.1.0-1.ca2404.1_all.deb Size: 2005324 MD5sum: 6a3a48d3a51f50f9586b11350b279e22 SHA1: 3ce9418ffaf3ed14918ddd2c9688ae67af512c94 SHA256: 9ad9fa04bafdfbc52bb66b02eb4be9c3e0cdc8b34c6d3c90332859d95aacde1b SHA512: 890c6e2d33dd2060d603ff5cd098b2a8622dc35ac0c088d0d56fd44caf377b9c04a678041df99a90a29d8ccbccfa4f044240c10efa821fc3e85e9e0921ed3e5b Homepage: https://cran.r-project.org/package=ggcircular Description: CRAN Package 'ggcircular' (A 'ggplot2' Extension for Circular and Directional Data) Provides a 'ggplot2' grammar for circular, axial and directional data, including rose diagrams, circular densities, mean directions, confidence arcs, theoretical circular distributions and movement data visualizations. 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Cleveland's book 'Visualizing Data' is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the 'ggplot2' versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis. Package: r-cran-ggcompare Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ggcompare_0.0.6-1.ca2404.1_all.deb Size: 141494 MD5sum: 66fd569384231b22125b82a32533d7cd SHA1: 01041af3a26228743011d8408382d96acb9932f9 SHA256: f45d1baccf3e42f2a99b23d624ce33540c03fa2c7a4c6eba6d00858708bbb2f2 SHA512: f177d2a68b5336d37a52d1cc5fe940eb602cf892bfdb7b7f849ff98e67e05bcf585b4a8778a7f163ce6e5a7eb5e49bef388fc6f65820601c0cf79f95bc259bb2 Homepage: https://cran.r-project.org/package=ggcompare Description: CRAN Package 'ggcompare' (Mean Comparison in 'ggplot2') Add mean comparison annotations to a 'ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric). Package: r-cran-ggcorrheatmap Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-dplyr, r-cran-dendextend, r-cran-ggnewscale, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggcorrheatmap_0.3.0-1.ca2404.1_all.deb Size: 744012 MD5sum: b2f6d83a0b3a2d95bec7b7aebb8926f9 SHA1: d9c1d2d36699b989363ce0e035f8ff3c73bac54b SHA256: 4a175bc76da7e81023a33f9ae2e26f9acd2022facd081ec7a173cfe8823d2511 SHA512: 789ca3658907df33131fa3f559b99ad70d4f4a8d951420717a070fa06304c3b768e296e46b43446f5db58f27dc5485a6bde4b550970ac21caf158fe2f4fb707b Homepage: https://cran.r-project.org/package=ggcorrheatmap Description: CRAN Package 'ggcorrheatmap' (Make Flexible 'ggplot2' Correlation Heatmaps) Create correlation heatmaps with 'ggplot2' and customise them with flexible annotation and clustering. 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Package: r-cran-ggcorset Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3696 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gghalves Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-viridis, r-cran-metbrewer Filename: pool/dists/noble/main/r-cran-ggcorset_0.5.0-1.ca2404.1_all.deb Size: 2781378 MD5sum: 045b32a5df0416e34c51e03cb8f0d1eb SHA1: 755360e61e6b12e349de9ef04acb40580c2ec546 SHA256: bd8c2e35649add06fcdd3760123ce9510d1565e366ad21c890b8a94002df7d0d SHA512: d437952892f07cbeef02ced132231ed7c47ecf11eb2ea9238b655fd2dc66d15bd020ec012b1af5b075da9f0036c2b7a8df63d1b6286bc889e9cc232b03276da0 Homepage: https://cran.r-project.org/package=ggcorset Description: CRAN Package 'ggcorset' (The Corset Plot) Corset plots are a visualization technique used strictly to visualize repeat measures at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at each time point, whilst the trajectories of individual change are visualized by connecting the pre- and post- values linearly. These lines can be coloured to represent the magnitude of change, or other user-defined value. This method of visualization is ideal for showing the heterogeneity of data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy integration so that users can customize their visualizations as required. Users can create corset plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively. Package: r-cran-ggcube Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5126 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-scales, r-cran-rlang, r-cran-lifecycle, r-cran-labeling, r-cran-purrr, r-cran-magrittr, r-cran-polyclip, r-cran-isoband, r-cran-systemfonts, r-cran-htmlwidgets Suggests: r-cran-alphashape3d, r-cran-av, r-cran-base64enc, r-cran-geometry, r-cran-gifski, r-cran-gtable, r-cran-knitr, r-cran-mass, r-cran-mgcv, r-cran-patchwork, r-cran-progress, r-cran-ragg, r-cran-rjson, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggcube_0.3.0-1.ca2404.1_all.deb Size: 4369988 MD5sum: 20c4b4ff5d5b91c73221c9037e35d4fd SHA1: 550d34ccd034fd79f746f844c264e7166985fc4d SHA256: ba1b3c49983c27635cfbf9e0e236b3f764cc3be22b4a8c281cb5e8d9fa3432c9 SHA512: 5c6e86828c55dfff7c64ec36e79f0ad9b7a319a7693d52a72aec683b2d2b06b4e1255fcc276a12e6f778fd3abe3b7e6f786622da5580ef9a731d0fedd333bb3e Homepage: https://cran.r-project.org/package=ggcube Description: CRAN Package 'ggcube' (3D Plotting with 'ggplot2') A 'ggplot2' extension for creating 3D figures. 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Package: r-cran-ggdark Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1596 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggdark_0.2.1-1.ca2404.1_all.deb Size: 1513256 MD5sum: feb467eab41a466f6e0f1aa46d427d73 SHA1: f25935e2bd62021504d84b26bcde5544c35dbcb4 SHA256: 8ca13fc7793f673da9f7f5645438f647edfa4118c746d82ae0f397641218efb2 SHA512: d1e53bdfa3310200cae0f6d203312f4b944d97da100b2452e95fd0f990b614786357bb1e09b7abfdfb36c466d9af23f66b87935a6711b97b8c638f5449d395b2 Homepage: https://cran.r-project.org/package=ggdark Description: CRAN Package 'ggdark' (Dark Mode for 'ggplot2' Themes) Activate dark mode on your favorite 'ggplot2' theme with dark_mode() or use the dark versions of 'ggplot2' themes, including dark_theme_gray(), dark_theme_minimal(), and others. 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Package: r-cran-ggdemetra Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4087 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rjdemetra, r-cran-ggrepel, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggdemetra_0.2.9-1.ca2404.1_all.deb Size: 2426148 MD5sum: 0211538e9fd1b07b08f6c0481ab3362d SHA1: 10d6f7af6f0308114fda11106914871a05bdb173 SHA256: 0a8702e756a1a91e914f258440e76d0f7e366c986350af73f932e23831a0896d SHA512: dc4cb108bd5e8bb16ede9f47adae2e37f1a3d0f542f16db0e4c4cf00801495ee15f718bafc00a548e867b7a8e8dd63a75f7476da9c621eba788516fc54882d99 Homepage: https://cran.r-project.org/package=ggdemetra Description: CRAN Package 'ggdemetra' ('ggplot2' Extension for Seasonal and Trading Day Adjustment with'RJDemetra') Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (), the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Package: r-cran-ggdendro Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-rpart, r-cran-tree, r-cran-testthat, r-cran-knitr, r-cran-cluster, r-cran-scales, r-cran-spelling, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggdendro_0.2.0-1.ca2404.1_all.deb Size: 201154 MD5sum: 964eb2c7130f634643bfe63c98bafcd8 SHA1: 0396e1d04b9a62d3e4829f883848315514bed77e SHA256: 7bb60f1fa180899577575854683138da7787b7e37eb76917dcdc8c476bd1de6d SHA512: 1af7ece23d7c9cb0d23cdabd4f10b4ff3607d9e0f340ba91a5234b288304d4e68d273d4609f9cfbebba0b9de1291ebc756891f6ecba37a390950410f600ee358 Homepage: https://cran.r-project.org/package=ggdendro Description: CRAN Package 'ggdendro' (Create Dendrograms and Tree Diagrams Using 'ggplot2') This is a set of tools for dendrograms and tree plots using 'ggplot2'. The 'ggplot2' philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device without exposing the data. The 'ggdendro' package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for 'tree', 'rpart', as well as diana and agnes (from 'cluster') diagrams. Package: r-cran-ggdensity Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-isoband, r-cran-vctrs, r-cran-tibble, r-cran-mass, r-cran-scales Suggests: r-cran-vdiffr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggdensity_1.0.1-1.ca2404.1_all.deb Size: 370184 MD5sum: 47c5867c9181d7c9997aa2b44c2659a4 SHA1: a4c89fafb9bb1711eb893e1ee3be8e57e514fb35 SHA256: 0a31546b01ae2d7916801ec26b65b285e2fd12a839a330501d6ba460d9235fbf SHA512: a5a87a6b48950d34e2bcd6e709d99664eb38a3935be0c53a8699096b5edf39ce61a07fa31ea42ad6dda09943ae2d5480b9107fe587277c352b13dc066aa3b7aa Homepage: https://cran.r-project.org/package=ggdensity Description: CRAN Package 'ggdensity' (Interpretable Bivariate Density Visualization with 'ggplot2') The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface. Package: r-cran-ggdiagram Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11369 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrowheadr, r-cran-bezier, r-cran-cli, r-cran-dplyr, r-cran-farver, r-cran-geomtextpath, r-cran-ggarrow, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext, r-cran-janitor, r-cran-lavaan, r-cran-magick, r-cran-magrittr, r-cran-pdftools, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-signs, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tinter, r-cran-tinytex, r-cran-vctrs Suggests: r-cran-knitr, r-cran-marquee, r-cran-quarto, r-cran-rmarkdown, r-cran-simstandard, r-cran-spelling, r-cran-svglite, r-cran-testthat, r-cran-tidyverse, r-cran-viridis, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggdiagram_0.2.0-1.ca2404.1_all.deb Size: 7997960 MD5sum: d2d401c0cb1a8db2f0c655906d6d00f6 SHA1: 423fc147d8845f6cd8699ac6cef5528489dbbe19 SHA256: 69560c986fb16afd4dbcbd92bc4344eb07ea0f9dd9567e7c40c7d1c927cad746 SHA512: 6e790f11e28b8520cca19a5d0196295ea5c54c41d32da0ab671e28becbae11efd3a35d2e6d388e9217a4d0de1b3f2ad5ebb818cf1d90f20ea8611881730e22f2 Homepage: https://cran.r-project.org/package=ggdiagram Description: CRAN Package 'ggdiagram' (Object-Oriented Diagram Plots with 'ggplot2') Creates diagrams with an object-oriented approach. Geometric objects have computed properties with information about themselves (e.g., their area) or about their relationships with other objects (e.g, the distance between their edges). The objects have methods to convert them to geoms that can be plotted in 'ggplot2'. Package: r-cran-ggdibbler Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6450 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-distributional, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-lifecycle, r-cran-scales, r-cran-tidyr, r-cran-tibble, r-cran-cli, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-mgcv, r-cran-fable, r-cran-urca, r-cran-gganimate, r-cran-tidyverse, r-cran-tidygraph, r-cran-ggthemes, r-cran-gifski, r-cran-ggridges, r-cran-quantreg, r-cran-ggdist, r-cran-ggraph, r-cran-feasts, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-ggdibbler_0.6.5-1.ca2404.1_all.deb Size: 5585092 MD5sum: 18ed37405da31fe671e5aaacd60cd524 SHA1: 3b71acf6b4467687a6f1e4beca01aecb9d29911c SHA256: 8029330029414b8f97f7266134ca67914168d27836fa88b0f7b7acd7a5ab55e9 SHA512: d5c3d319050f1bff66a00dfd5019ff1ef8c341a583f6ee4e2c0faa235eab112f52feae0c66e9ed5b1efe735cac42b1bfbfd2f4819d56e662b281098653281753 Homepage: https://cran.r-project.org/package=ggdibbler Description: CRAN Package 'ggdibbler' (Add Uncertainty to Data Visualisations) A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart. Package: r-cran-ggdiceplot Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-legendry, r-cran-scales, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ggdiceplot_1.2.0-1.ca2404.1_all.deb Size: 78730 MD5sum: 53c9a70b1bfc139b682129c62bd53e6a SHA1: 8bb6591bac12d914c10d94df2c719ec95eddcea3 SHA256: 3b93d8cc2fcdc6293c226b543670675e938f33693e6fe277dbc2574901cff59c SHA512: 8be6553798c2c4cb0e2e70f0f96bc922c6196814a9b85c7d397b78d2cf897b2403719eb7b393987a4a766c359964b3c744d7607264793cdc671f94be247d8f39 Homepage: https://cran.r-project.org/package=ggdiceplot Description: CRAN Package 'ggdiceplot' (DicePlot Visualization for 'ggplot2') Provides 'ggplot2' extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes 'geom_dice()' for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously. Package: r-cran-ggdnavis Architecture: all Version: 1.0.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1403 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggnewscale, r-cran-ggplot2, r-cran-png, r-cran-ragg, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bslib, r-cran-colourpicker, r-cran-jsonlite, r-cran-knitr, r-cran-magick, r-cran-markdown, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggdnavis_1.0.1-1.ca2404.2_all.deb Size: 752392 MD5sum: 6a5c2baecf5844c1e10c70a6ef2f6427 SHA1: 6fa846cf506ef66810ae3e2b929190b97a98c7e6 SHA256: 4c932de1776fd770dd49820ac659288639f8e3a736aab69ef1315b5deb5d6715 SHA512: d4ca24851752fe7d1820ea0893ac3dd55a95040feda13ca225ecdae6032d604d10aacdc9f7c69614a7f4f60d6ff61caa5d66437e6e0e40df769e4041d56892bc Homepage: https://cran.r-project.org/package=ggDNAvis Description: CRAN Package 'ggDNAvis' ('ggplot2'-Based Tools for Visualising DNA Sequences andModifications) Uses 'ggplot2' to visualise either (a) a single DNA/RNA sequence split across multiple lines, (b) multiple DNA/RNA sequences, each occupying a whole line, or (c) base modifications such as DNA methylation called by modified bases models in Dorado or Guppy. Functions starting with visualise_<>() are the main plotting functions, and functions starting with extract_and_sort_<>() are key helper functions for reading files and reformatting data. Source code is available at , a full non-expert user guide is available at , and an interactive web-app version of the software is available at . Package: r-cran-ggdoe Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-insight Suggests: r-cran-roxygen2, r-cran-tibble, r-cran-mass, r-cran-viridislite, r-cran-ggrepel, r-cran-patchwork, r-cran-unrepx, r-cran-geomtextpath, r-cran-rsm, r-cran-lhs, r-cran-doe.base Filename: pool/dists/noble/main/r-cran-ggdoe_0.8-1.ca2404.1_all.deb Size: 814634 MD5sum: ab5aad5f07eef8d3e560f25486b0c9fd SHA1: d4a0857c75b0de4970bffebee50299360730a1a3 SHA256: ae0951aa72d31235cde21a1c0f42964b2f895b5f6c4e5d1d56d44531edd7acd4 SHA512: 9892187d818cae9c7fb4f289a8e24ba640826530e83d29322752c666cd1d0e7b4a45949fc9d389b3245d14a172e0751aaf86e1ade895ca595e5713dff884eaf2 Homepage: https://cran.r-project.org/package=ggDoE Description: CRAN Package 'ggDoE' (Modern Graphs for Design of Experiments with 'ggplot2') Generate commonly used plots in the field of design of experiments using 'ggplot2'. 'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design. Package: r-cran-ggdoubleheat Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-ggnewscale Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dplyr, r-cran-scales, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ggdoubleheat_0.1.3-1.ca2404.1_all.deb Size: 206280 MD5sum: 44661e50fbb498b62c098d655d545280 SHA1: 010b6da1647508087e8b505871b2222981675d4a SHA256: 21e1eaa8f09434849a1fbfad2e22c69ea828d29f72002d15fe5b5a6311ea0ca4 SHA512: 3db74101a6b69046652a98d7408951d653ae3042a7d0bf9d3749501fc3d876bcf604f104e4e7a42f66ecd9d15a0d235bf6b04af2c86c1c357d7044793a4f70c4 Homepage: https://cran.r-project.org/package=ggDoubleHeat Description: CRAN Package 'ggDoubleHeat' (A Heatmap-Like Visualization Tool) A data visualization design that provides comparison between two (Double) data sources (usually on a par with each other) on one reformed heatmap, while inheriting 'ggplot2' features. Package: r-cran-gge Architecture: all Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nipals, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-agridat, r-cran-knitr, r-cran-lattice, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gge_1.11-1.ca2404.1_all.deb Size: 159700 MD5sum: 511108cfa2f4549e8ce2d60b4076d1c8 SHA1: 0e7ea21a63ee68b6bc21788218c9ec95686a0e2d SHA256: 4b94844cd9117159d00e76ce7837085aa7fe43629b814835062e113fa85bcff1 SHA512: 84dc1beed2699eca40f6e4f038dcdb7d18324853b2cb57c9276df1ad2dbee650ac3b03d30e088a07604d7c30338b32ddedd2d2fb3c36e0c5ca75d406ebc35c00 Homepage: https://cran.r-project.org/package=gge Description: CRAN Package 'gge' (Genotype Plus Genotype-by-Environment Biplots) Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) . Package: r-cran-ggeasy Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-clipr, r-cran-labelled, r-cran-patchwork, r-cran-vdiffr, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggeasy_0.1.6-1.ca2404.1_all.deb Size: 3692150 MD5sum: 80e0d3a65088b7e350104c7d05f64d69 SHA1: 34da689f189ffc924c8712ec149086806e3bc8ee SHA256: ead8a509778d7691feb68623aed1541b1d105e8f63a59fc99334218370423454 SHA512: bd41492c6431ee273fc65dc81e036e927125af672dc9d1c92d6c72fdb8d05a4e2752ac67653ed3189e89656675ae331baab29c16d6c2e44a21457e7008fbe2e1 Homepage: https://cran.r-project.org/package=ggeasy Description: CRAN Package 'ggeasy' (Easy Access to 'ggplot2' Commands) Provides a series of aliases to commonly used but difficult to remember 'ggplot2' sequences. Package: r-cran-ggebiplots Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-scales Filename: pool/dists/noble/main/r-cran-ggebiplots_0.1.3-1.ca2404.1_all.deb Size: 71170 MD5sum: ae9642f6c556aaad84b129097e6cbf41 SHA1: 3596437870b7d2416d8e61680ce7c6db0ad99980 SHA256: 80fac9efdb23cd8fcc51aff9eab18d37cfe6f33c5245a43abd597fd3f049184b SHA512: 612fd8d230855ef46c528650e16019a5553292ffbd09c0db10c466ab60333dfb1aa12c3c4f4abd87bf74e2f7a56bfad9e0d2232d7f68d205d5d43e558db5ceaf Homepage: https://cran.r-project.org/package=GGEBiplots Description: CRAN Package 'GGEBiplots' (GGE Biplots with 'ggplot2') Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within the archived package 'GGEBiplotGUI'. Package: r-cran-ggeda Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertions, r-cran-cli, r-cran-ggiraph, r-cran-ggplot2, r-cran-ggtext, r-cran-patchwork, r-cran-rank, r-cran-rlang, r-cran-scales Suggests: r-cran-covr, r-cran-infotheo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tsp Filename: pool/dists/noble/main/r-cran-ggeda_0.3.0-1.ca2404.1_all.deb Size: 2524772 MD5sum: c42f6dda31e6def0dcb82ffff00abcba SHA1: 60457dec5bdd51b87e8b595e9777d25c0b923671 SHA256: e6e62bc3642d73d5cd7b76e7b8b8d0895fe06bd42aa358981df4f1a7bcdc3af3 SHA512: 9a09f3aa4906cf6dc289ed5fef19119a05e3616be08b1a23ddc5ec62b4523f294ac07ac5d0dcfa8abee2701cfb0dc36f700e1e8418315183819bbdc4de7dfd99 Homepage: https://cran.r-project.org/package=ggEDA Description: CRAN Package 'ggEDA' (Turnkey Visualisations for Exploratory Data Analysis) Provides interactive visualisations for exploratory data analysis of high-dimensional datasets. Includes parallel coordinate plots for exploring large datasets with mostly quantitative features, but also stacked one-dimensional visualisations that more effectively show missingness and complex categorical relationships in smaller datasets. 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Checks for color accessibility concerns including colorblind-unfriendly palettes, misleading scale manipulations such as truncated axes and dual y-axes, text readability issues like small fonts and overlapping labels, and general accessibility barriers. Provides comprehensive audit reports with actionable suggestions for improvement. Color vision deficiency simulation uses methods from the 'colorspace' package Zeileis et al. (2020) . Contrast calculations follow WCAG 2.1 guidelines (W3C 2018 ). 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This package is part of the 'rethomics' framework . 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Supports a wide range of psychometric models including Item Response Theory, Latent Class Analysis, Latent Rank Analysis, Biclustering (binary, ordinal, and nominal), Bayesian Network Models, and related network models. All plot functions return 'ggplot2' objects that can be further customized by the user. Package: r-cran-ggextra Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colourpicker, r-cran-ggplot2, r-cran-gtable, r-cran-miniui, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat, r-cran-vdiffr, r-cran-fontquiver, r-cran-svglite, r-cran-withr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-ggextra_0.11.0-1.ca2404.1_all.deb Size: 349672 MD5sum: af3f4e9c60324bd7790338b5e6a1040f SHA1: 463b1f2d18542794d1e9d483f6187ee4f68d9d82 SHA256: ad167fe0b5447d1c16e7b30778a3575107e9ef07a1d31a1c13ea02d66780c5c8 SHA512: d9c8a069956cb5395e4db6a7d17d247e73887ddddc1d604a27a1752cbe32612d9cd5e375e2f513ed8d71d24c3e2896f3a9fe9f1ce3a7326edd3e29f9a8e944c3 Homepage: https://cran.r-project.org/package=ggExtra Description: CRAN Package 'ggExtra' (Add Marginal Histograms to 'ggplot2', and More 'ggplot2'Enhancements) Collection of functions and layers to enhance 'ggplot2'. 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The bar chart race interpolates values on a uniform time grid, ranks every frame on its own values and eases bars into new positions, so a reordering field stays readable. Frames are ordinary 'ggplot2' objects laid out on a fixed pixel grid, which keeps the axis and label column from drifting between them, and are encoded to GIF or MP4. Circular images, such as the bundled country flags, can be placed at the end of each bar. Causal diagrams are drawn as directed acyclic graphs whose nodes and arrows each carry a rationale and references, shown on hover and opened with clickable links on click, through 'ggiraph'; the same diagram is also available as a static 'ggplot2' object. Network plots for network meta-analysis are drawn from arm level data, with the baseline characteristics and outcomes of every arm shown side by side on click. Forest plots for 'metafor' and 'meta' fits carry each study's record and risk of bias traffic lights, and replay a cumulative meta-analysis as an animation; funnel plots shade where each study would be significant and carry the pooled estimate without it, trim and fill and the tests for small-study effects; league tables for 'netmeta' fits show the direct and indirect evidence behind every estimate. Kaplan-Meier plots read survival and the hazard ratio at any time under the pointer, beside a risk table and proportional hazards tests, and swimmer plots give each patient a lane with their responses, progression and death. Nomograms of regression models, from linear and generalized linear models to mixed, Cox, parametric survival, ordinal and multinomial models, have a handle per predictor and compute each prediction with its confidence interval in the page. Choropleth maps of the world or of any 'sf' map step or play through the years, with several measures side by side for the same year. Causal diagrams can also show which paths between an exposure and an outcome an adjustment set leaves open, by the backdoor criterion; Kaplan-Meier plots give the restricted mean survival time up to a horizon the reader can move; league tables and network plots show where each network estimate's evidence comes from; and swimmer plots can carry a waterfall of best change and each patient's course beside the lanes. Four explorers put a threshold or an assumption in the reader's hands: the cutoff of a diagnostic test, with what it means for 1,000 people at any prevalence; the strength of unmeasured confounding, with E-values; the choices of a multiverse of analyses; and the threshold that defines a responder. Plots after CINeMA judge the confidence in each estimate of a network meta-analysis in six domains, with every judgment's reason and source, and show what lies behind them: each study's contribution, the estimates against a movable range of little difference, direct against indirect evidence, and a league table and a network marked with the judgments. 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Ideal for visualising wind speeds, water currents, electric/magnetic fields, etc. Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars) objects as input. Vector fields are depicted as arrows starting at specified locations, and with specified angles and radii. 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Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) , and the minimum spanning trees used by Jolliffe (2002) to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) and Gower & Harding (1988) for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) for foundations. 'gggda' extends Wickham's (2010) layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data. 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Package: r-cran-gginnards Architecture: all Version: 0.2.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-s7, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-lobstr Filename: pool/dists/noble/main/r-cran-gginnards_0.2.0-2-1.ca2404.1_all.deb Size: 284842 MD5sum: fd681578165ca81f126f9592933eb385 SHA1: ffdf91024a6f7d0b75f0c18961346add959821f5 SHA256: 025590c7550ce1165583a0a826cee17d1d022ab00c18f1674519651349fcd03a SHA512: da342553b3d7f3a1a73db69674c281c27bd74b5d9f105f6de7689ebfac0572bc6310c0f109b7b6da70fa7a15e6708655f32d3aeb1165ebd4b9015c122c66b97b Homepage: https://cran.r-project.org/package=gginnards Description: CRAN Package 'gginnards' (Explore the Innards of 'ggplot2' Objects) Extensions to 'ggplot2' providing low-level debug tools: statistics and geometries echoing their data argument. 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Package: r-cran-ggir Architecture: all Version: 3.3-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10561 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-doparallel, r-cran-signal, r-cran-zoo, r-cran-unisensr, r-cran-ineq, r-cran-psych, r-cran-irr, r-cran-lubridate, r-cran-ggirread, r-cran-actcr, r-cran-read.gt3x, r-cran-arrow Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-actilifecounts, r-cran-readxl Filename: pool/dists/noble/main/r-cran-ggir_3.3-9-1.ca2404.1_all.deb Size: 3226036 MD5sum: c8a5a899facfd4394f166117114e4240 SHA1: 33ba9ba19ba5c18ef20263a6e1c75ff202bc139a SHA256: 714acde83a96044f884e312ef0f84be94b27793f0b7a3f5a03d46d7d7475fa8f SHA512: 192c91fa8e41bcaf596f96c0c5693891cffd00f9769045513168fdb698bb094e060267710572933137f490992319bc103ff725a4e661f7d4c528e47e10e99667 Homepage: https://cran.r-project.org/package=GGIR Description: CRAN Package 'GGIR' (Raw Accelerometer Data Analysis) A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' , binary (.gt3x) and .csv-export data from 'Actigraph' devices, and binary (.cwa) and .csv-export data from 'Axivity' . These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format. Also the package allows for external function embedding. 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Package: r-cran-ggmice Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mice, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-knitr, r-cran-lifecycle, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggmice_0.1.1-1.ca2404.1_all.deb Size: 3531120 MD5sum: f7deb8d7fa7a6b4c1d39180e90bbd8a8 SHA1: 82b8367837c9d9c96e00aab6443385c17388697d SHA256: 3c8c9004a99ae6f08377969494bb9ca46e0b8252b2df0a2283b022cd18e32729 SHA512: d1e3412035f609b02b1ffbbdb8b3ae89c2e266754a0c3a8eb249a93d5b13f88a45e62065847b0cd2b258d831a92eec56623684d2986d31a8f16e0f95750db685 Homepage: https://cran.r-project.org/package=ggmice Description: CRAN Package 'ggmice' (Visualizations for 'mice' with 'ggplot2') Enhance a 'mice' imputation workflow with visualizations for incomplete and/or imputed data. The plotting functions produce 'ggplot' objects which may be easily manipulated or extended. Use 'ggmice' to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data. Package: r-cran-ggmix Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4400 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-matrix Suggests: r-cran-rspectra, r-cran-popkin, r-cran-bnpsd, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggmix_0.0.2-1.ca2404.1_all.deb Size: 1254582 MD5sum: 2ea1daa24946a19068629554570a5fb7 SHA1: 34b1eeffb484c5b2968346ebc56447a8add07825 SHA256: dc60996368d912be66f17d491e31a64fcf9e3ef290b0dd889598b03217264f57 SHA512: fb248748138c9eed30614ebb04144ef3f93fa7509c6a14c5ef0f559d8815507d7da1e818b544c9981f4b920430c66708184521898990ce8218d982959431621b Homepage: https://cran.r-project.org/package=ggmix Description: CRAN Package 'ggmix' (Variable Selection in Linear Mixed Models for SNP Data) Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, . Package: r-cran-ggmnonreg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-bestglm, r-cran-ggally, r-cran-network, r-cran-sna, r-cran-matrix, r-cran-poibin, r-cran-doparallel, r-cran-foreach, r-cran-corpcor, r-cran-psych, r-cran-mass, r-cran-ggplot2, r-cran-ggmncv Suggests: r-cran-qgraph Filename: pool/dists/noble/main/r-cran-ggmnonreg_1.0.0-1.ca2404.1_all.deb Size: 546302 MD5sum: 1ec2eec986deb59b5e6d3a537be0c996 SHA1: f4deb2a98072bd089990a00d9cba91e0f8c1056f SHA256: c1c32d772542a04dba9068d98f5c8404f9193a968352ca2611420b82d6d5baf8 SHA512: fa3033bec60debf534bcef1c058ffe6209ae771a4ca60de2c6ec7ee204960f38a58e2be655074be2e8a1d405467cdb962ea07c8ebe0ffa7b8a16ddf85a76593c Homepage: https://cran.r-project.org/package=GGMnonreg Description: CRAN Package 'GGMnonreg' (Non-Regularized Gaussian Graphical Models) Estimate non-regularized Gaussian graphical models, Ising models, and mixed graphical models. 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Package: r-cran-ggmosaic2 Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3208 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-productplots, r-cran-dplyr, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-ggrepel, r-cran-scales, r-cran-withr Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-patchwork, r-cran-vcdextra Filename: pool/dists/noble/main/r-cran-ggmosaic2_0.5.1-1.ca2404.1_all.deb Size: 2047032 MD5sum: 57f32f56aac36d04b7c7103e1ebbb0ed SHA1: 29cecd7a811b8c08d4cdb319e2d374898be354e9 SHA256: c9059e4ef1ec64e28ca08212e321af7b8478d1e785cacc0238aaee8867eaa218 SHA512: 588196ae499d7e15c56c6758072bfefc25e6e0fbe041b1928d41f5d1c76a270acc0cb7be00d40b9d3f5c814842c32209c581790912ead4fb5705d46c957900dd Homepage: https://cran.r-project.org/package=ggmosaic2 Description: CRAN Package 'ggmosaic2' (Mosaic Plots in the 'ggplot2' Framework, Extended) Mosaic plots in the 'ggplot2' framework. 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Layout and estimation algorithms follow their published descriptions, including Bruls, Huizing and van Wijk (2000) for squarified treemaps, Fruchterman and Reingold (1991) for force-directed graphs, and Kaplan and Meier (1958) for survival curves. 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This Grammar of Graphics implementation also incorporates categorical variables into the plots in a principled manner. By separating the data managing part from the visual rendering, we give full access to the users while keeping the number of parameters manageably low. Package: r-cran-ggpedigree Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3848 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bgmisc, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-plotly, r-cran-scales, r-cran-tidyr Suggests: r-cran-selectr, r-cran-svglite, r-cran-kinship2, r-cran-quadprog, r-cran-ggrepel, r-cran-paletteer, r-cran-mockery, r-cran-patchwork, r-cran-viridis, r-cran-knitr, r-cran-tidyverse, r-cran-purrr, r-cran-data.table, r-cran-discord, r-cran-openmx, r-cran-nlsylinks, r-cran-rmarkdown, r-cran-tibble, r-cran-corrplot, r-cran-showtext, r-cran-sysfonts, r-cran-withr, r-cran-htmlwidgets, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpedigree_1.2.0-1.ca2404.1_all.deb Size: 1121742 MD5sum: 216b3565ef002520f0f49637fcc51994 SHA1: 129adbae60a39f03fd8c339a89cbee97e363c173 SHA256: 9aa4cee3d12fb6800a1e9c5c466ffc1202bc2d5dceb64a758fd379ba1bfdebc7 SHA512: c5ea3acbb634a1a3c58a19d70ed0091de79954a5cb0ea7f9a4a360e3d4628d26bf0fe43782f150f1c3b74347408742c60e3efc875458058a7592945230b4489f Homepage: https://cran.r-project.org/package=ggpedigree Description: CRAN Package 'ggpedigree' (Visualizing Pedigrees with 'ggplot2' and 'plotly') Provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package 'BGmisc' [Garrison et al. (2024) ] by rendering pedigrees using the 'ggplot2' framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from 'kinship2' [Sinnwell et al. (2014) ]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work. Package: r-cran-ggperiodic Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sticky, r-cran-tidyselect, r-cran-data.table Suggests: r-cran-covr, r-cran-knitr, r-cran-maps, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggperiodic_1.0.4-1.ca2404.1_all.deb Size: 343100 MD5sum: 5478f3abbff2f3b9e3d2a6a27b585bda SHA1: 3682eb9e5ec4ba572d72736f8b80b1324d874b68 SHA256: dc5c476e465c6c0de73c5a917846ead14cb3da74e74ec90561a1bf6b45a12706 SHA512: b22d32f6f7f9e75aec91c235e96ae4e83bb81f5e25d7954cd548ef13ae9b2cd4e5cf3ae3aa682db5c44756792d3dc24077b76507b51a868be2dd74d2a46b230b Homepage: https://cran.r-project.org/package=ggperiodic Description: CRAN Package 'ggperiodic' (Easy Plotting of Periodic Data with 'ggplot2') Implements methods to plot periodic data in any arbitrary range on the fly. Package: r-cran-ggpicrust2 Architecture: all Version: 2.5.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5990 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggh4x, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-ggprism, r-cran-patchwork, r-cran-ggplotify, r-cran-magrittr, r-cran-progress, r-cran-tidygraph, r-cran-ggraph Suggests: r-bioc-biobase, r-cran-ggdendro, r-bioc-keggrest, r-bioc-complexheatmap, r-bioc-biocgenerics, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-aldex2, r-bioc-deseq2, r-bioc-edger, r-cran-ggally, r-bioc-limma, r-cran-microbiomestat, r-bioc-summarizedexperiment, r-cran-circlize, r-bioc-lefser, r-bioc-maaslin2, r-bioc-metagenomeseq, r-bioc-fgsea, r-bioc-clusterprofiler, r-bioc-enrichplot, r-bioc-dose, r-cran-ggvenndiagram, r-cran-upsetr, r-cran-igraph, r-cran-ggridges, r-cran-ggrepel, r-cran-logging Filename: pool/dists/noble/main/r-cran-ggpicrust2_2.5.19-1.ca2404.1_all.deb Size: 4694528 MD5sum: ede5a3a1134757de781a34315abdf779 SHA1: 3301914eb5bbf9a63f3da2622d6375fa8edf4e54 SHA256: b17687a00f724c8f3395487959a5932e2e9df45549a107da394f65ced7a49976 SHA512: 4e089c5d8cdafc8816e122eddc1e7c40a5b6a886b87fffcc1697315eeaf9f310e9a7cdf57a3c15d84d443f97cee2ee73eaf68d789450417d6d9194948924c83c Homepage: https://cran.r-project.org/package=ggpicrust2 Description: CRAN Package 'ggpicrust2' (Make 'PICRUSt2' Output Analysis and Visualization Easier) Provides a convenient way to analyze and visualize 'PICRUSt2' output with pre-defined plots and functions. Allows for generating statistical plots about microbiome functional predictions and offers customization options. Features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. Streamlines the 'PICRUSt2' analysis and visualization process. For more details, see Yang et al. (2023) . 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Package: r-cran-ggplate Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-farver Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggplate_0.4.0-1.ca2404.1_all.deb Size: 5805818 MD5sum: c65da2fd7a7165a68b72f3af1e13cf7d SHA1: 7b929c81a95133766d0f3e202cbbe1e1ce477099 SHA256: c1f8e273cd7343447dedd1ccd319475583331cdf4178600fcd069ac590184333 SHA512: 39488ea5cfc1fbbff15c08042f8ad9e4a249fca8a8d04a7f6fc91c45855701b8546499908d250a51a64051a14450f4b76612a381ad6be808fbfbdf13f0984c87 Homepage: https://cran.r-project.org/package=ggplate Description: CRAN Package 'ggplate' (Create Layout Plots of Biological Culture Plates and Microplates) Enables users to create simple plots of biological culture plates as well as microplates. 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Package: r-cran-ggplot.multistats Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-scales, r-cran-hexbin, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ggplot.multistats_1.0.1-1.ca2404.1_all.deb Size: 32986 MD5sum: 095584197a1c3fa4aa36e7cba59f76c2 SHA1: fcd39d3757d741cc35b245ee7e8c12617c5b3386 SHA256: 062594678ed95faaaf30f3a1cac73659b8a9e6ae157529c768844401d34cceaf SHA512: 77afcae9a0b6edb2395bd022aeb125c0aafe9c3b91fe2cf86f68a4b4adcf7092340fe53729ba51d360d6c1811e22476f96bf1324d922f8e07487e52021c5c67b Homepage: https://cran.r-project.org/package=ggplot.multistats Description: CRAN Package 'ggplot.multistats' (Multiple Summary Statistics for Binned Stats/Geometries) Provides the ggplot binning layer stat_summaries_hex(), which functions similar to its singular form, but allows the use of multiple statistics per bin. 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This package imports functions from 'EnvStats' by Millard (2013), 'ggpp' by Aphalo et al. (2023) and 'ggstats' by Larmarange (2023), and then exports them. This package also contains modified code from 'ggquickeda' by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably. Package: r-cran-ggplot2 Architecture: all Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-gtable, r-cran-isoband, r-cran-lifecycle, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-vctrs, r-cran-withr Suggests: r-cran-broom, r-cran-covr, r-cran-dplyr, r-cran-ggplot2movies, r-cran-hexbin, r-cran-hmisc, r-cran-hms, r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-mass, r-cran-mgcv, r-cran-multcomp, r-cran-munsell, r-cran-nlme, r-cran-profvis, r-cran-quantreg, r-cran-quarto, r-cran-ragg, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-rpart, r-cran-sf, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-ggplot2_4.0.3-1.ca2404.1_all.deb Size: 7780086 MD5sum: c0da7805177f637c0967e40e3ed6217c SHA1: 98c81c893d6d185b6aeabd349369762bf687fa7f SHA256: 34106918ea5fcf995d7a656b6a1310d711d313124cc47eb5f29cfb8b72bb7688 SHA512: 87c14416f8858de2ae6d2a769dc587142f8f1de2f584d84473c03e9acc68b72a6f8e56748ad2fb2bc5d06753542355043b4fce31f8552d5ab6d91aaef3ca5174 Homepage: https://cran.r-project.org/package=ggplot2 Description: CRAN Package 'ggplot2' (Create Elegant Data Visualisations Using the Grammar of Graphics) A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. Package: r-cran-ggplot2movies Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1280 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ggplot2movies_0.0.1-1.ca2404.1_all.deb Size: 1267886 MD5sum: a7f65a60b247dc86911ffdd1645d5c4e SHA1: 40c61501ebe857382ef67e7477fa7194fe2b4f6e SHA256: bfb1d238a3942f9938b6bcb51abe295c397fe067df76ac654ff0c2225a411a12 SHA512: 690fb1f6a200a7261a46337192680f36cca0a5e9bbcf5c0fa0aff6b4653f6704ecf5e9d163eb2ef5cdecc13e79f346604e9a446fa6f46a0b154b65f3514cc976 Homepage: https://cran.r-project.org/package=ggplot2movies Description: CRAN Package 'ggplot2movies' (Movies Data) A dataset about movies. This was previously contained in ggplot2, but has been moved its own package to reduce the download size of ggplot2. Package: r-cran-ggplotassist Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1418 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-shinywidgets, r-cran-shinyace, r-cran-stringr, r-cran-tidyverse, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-scales, r-cran-ggthemes, r-cran-gcookbook, r-cran-moonbook, r-cran-editdata Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-ggplotassist_0.1.3-1.ca2404.1_all.deb Size: 756340 MD5sum: 89221e5b5c6161c365916abc16a934b8 SHA1: 80485eb1697399b3900a6c0b6820b87b5a57bd4d SHA256: 799ba530a03d6f16289b82eda3e2fb14f852aca35102e032af25f9fcf699cc14 SHA512: abae08e07910972cdc58046c472790679f5e02fa231c5a48447ed0692dbfba1b6a07f4e7a033b541dd8a97be7ee5419fd0e078692a63cdf249e876b729568e94 Homepage: https://cran.r-project.org/package=ggplotAssist Description: CRAN Package 'ggplotAssist' ('RStudio' Addin for Teaching and Learning 'ggplot2') An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot. Package: r-cran-ggplotgui Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-shiny, r-cran-ggplot2, r-cran-stringr, r-cran-readr, r-cran-haven, r-cran-readxl, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-ggplotgui_1.0.0-1.ca2404.1_all.deb Size: 624780 MD5sum: b355973c36a97a740e3a02b3d3fc9a28 SHA1: 842527306e74b01d0bb889228b6d48f945b4810f SHA256: f37ff092e97db878510f0b9507e3357589baad200ccb3eea0947c793c973fb82 SHA512: 0cbb8c68fa644a2db8a4bb1e2b57dff435d14b9fcf5786d503f1cda1daf09a0d8e4a6ff079361853bb14d116f7b388ea1a95a49963a3cced7fc80728fb0a11a0 Homepage: https://cran.r-project.org/package=ggplotgui Description: CRAN Package 'ggplotgui' (Create Ggplots via a Graphical User Interface) Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided. 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The package ease the process of making a 'plotly' figures generated from 'ggplot2' object more aesthetic in terms of labels and customizability. Package: r-cran-ggplotplus Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-s7, r-cran-gtable, r-cran-scales, r-cran-viridislite, r-cran-polyclip, r-cran-ggrepel, r-cran-dplyr Suggests: r-cran-testthat, r-cran-ragg, r-cran-patchwork, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-ggplotplus_0.5.7-1.ca2404.1_all.deb Size: 504622 MD5sum: 1fd71738387e9ed97e64b9f4e3e395b0 SHA1: 4a995f6724e3fbae05f1ff4d1df87383f4930cf8 SHA256: 10daec09e3c676926dba68923cd3da76d037f44a085a0bf4f0bf4e005e7b16a6 SHA512: 120dc35b37d40eac904d8b00acf123e1f3189fb512f1ebaaec668abac1bf8c7ab96d9302fbf7e845415111f3bfe78e44100379e474d44ba390aac1919cd3734b Homepage: https://cran.r-project.org/package=ggplotplus Description: CRAN Package 'ggplotplus' (Universal Design-Oriented Enhancements for 'ggplot2') A collection of enhancements to 'ggplot2', with a focus on creating Universally Designed, accessible graphs easily and quickly. Package: r-cran-ggpmisc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5412 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggpp, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-generics, r-cran-polynom, r-cran-confintr, r-cran-splus2r, r-cran-tibble, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-catools, r-cran-quantreg Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-robustbase, r-cran-mass, r-cran-lmodel2, r-cran-nlme, r-cran-smatr, r-cran-lspline, r-cran-multcomp, r-cran-multcompview, r-cran-mixtools, r-cran-segmented, r-cran-onls, r-cran-gginnards, r-cran-ggrepel, r-cran-ggtext, r-cran-xdvir, r-cran-marquee, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpmisc_1.0.0-1.ca2404.1_all.deb Size: 3795874 MD5sum: edfe9ee93bb5b8070301301328f49a08 SHA1: 21a29728f0d077840dc8cbcd37fdd5171471db35 SHA256: fe4d7afa22b83e9ea5b5c79075d92d9dd63b77676e63c2f0cf0761b8685fc584 SHA512: 930de4a5daebf24c7203887aed49b73f541132300ae2a93f5f15ce9d5a2750054a4bde640608270299151c0eb0f64ecd0fbfb310030167b8204770485c68294a Homepage: https://cran.r-project.org/package=ggpmisc Description: CRAN Package 'ggpmisc' (Miscellaneous Extensions to 'ggplot2') Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics to locate and tag peaks and valleys and to label plots with the equation of a fitted polynomial model by ordinary least squares, major axis, quantile and robust and resistant regression approaches. Line and model equation for Normal mixture models. Labels for P-value, R^2 or adjusted R^2 or information criteria for fitted models; parametric and non-parametric correlation; ANOVA table or summary table for fitted models as plot insets; annotations for multiple pairwise comparisons with adjusted P-values. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported as well as user-defined wrappers on model fit functions, allowing model selection and conditional labelling. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates. Package: r-cran-ggpmx Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6843 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-yaml, r-cran-r6, r-cran-gtable, r-cran-ggplot2, r-cran-ggforce, r-cran-magrittr, r-cran-stringr, r-cran-assertthat, r-cran-ggally, r-cran-zoo, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-checkmate, r-cran-scales Suggests: r-cran-testthat, r-cran-xtable, r-cran-vdiffr, r-cran-rxode2, r-cran-nlmixr2est, r-cran-nlmixr2data, r-cran-nlme, r-cran-xgxr, r-cran-withr, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-ggpmx_1.3.2-1.ca2404.1_all.deb Size: 3978610 MD5sum: 5bb83616d9908736115882a6a3b7f319 SHA1: 3daa4e56a63ed523699f9fccb80d434bda9568a7 SHA256: 6ca755ec187ff923474186463c1890fa6b75981587c8e810730350fa3094645f SHA512: 3cb8210dbe98f66479118b3da5972d437dc95fdf91e60c6a12ce4385e6c842c7ac42f62f57c50ccc59c5addbe67bfd541087670f0e5a8cc069bb836d2da0ce49 Homepage: https://cran.r-project.org/package=ggPMX Description: CRAN Package 'ggPMX' ('ggplot2' Based Tool to Facilitate Diagnostic Plots for NLMEModels) At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP . While not required, you can get/install the 'R' 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required Chart Data instead of exporting it from the 'Monolix' gui. Package: r-cran-ggpointless Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2275 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-farver, r-cran-scales, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat, r-cran-ragg, r-cran-vdiffr, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggpointless_0.3.0-1.ca2404.1_all.deb Size: 2123266 MD5sum: 37a1a223ed96903899ad941fa1eec257 SHA1: 557869e74cb45c8363708e4231b403aa4c75038f SHA256: 8f4896c4a64ff170d59c98abdf465c865bb87ec25429cdfb2995755bb61f7fe5 SHA512: e3f5db2c11d2fcfb3f0ffe80f65b9f9c08b84788e8a318c1c55ee43c8c60b8592ca69c88a3b5f4bfd83d85a808a1fb75512f05341551111334f44307135ec5ec Homepage: https://cran.r-project.org/package=ggpointless Description: CRAN Package 'ggpointless' (Extra Geometries and Stats for 'ggplot2') A collection of layers for 'ggplot2'. Provides geoms built on linear and radial gradients from the 'grid' package, giving areas, bars, paths, rectangles, and ridgelines a fading or glowing visual effect. Also includes mathematically driven layers — catenary curves, Chaikin's corner-cutting smoothing (Chaikin, 1974, ), and Fourier-series reconstruction — plus Lexis diagrams, isotype bar charts. Package: r-cran-ggpol Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-plyr, r-cran-rlang, r-cran-dplyr, r-cran-tibble, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggpol_0.0.7-1.ca2404.1_all.deb Size: 269022 MD5sum: 5aae833d473645bbb8622572886f7204 SHA1: b7dce7ea93c4807ae8ff4e1d10781e7dc22a99b9 SHA256: 00011c08e338039585dd632b1f500fb407154657cdb9d659a19dde96ac912ad1 SHA512: c660950dbf7ed298930fc2b81c872f596408460437b34162043f56a5b7ef049311f877ba2903d173773e4b275228b148072463e742e798f8133e99bca4772eee Homepage: https://cran.r-project.org/package=ggpol Description: CRAN Package 'ggpol' (Visualizing Social Science Data with 'ggplot2') A 'ggplot2' extension for implementing parliament charts and several other useful visualizations. 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Package: r-cran-ggpolypath Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ggpolypath_0.4.0-1.ca2404.1_all.deb Size: 272096 MD5sum: 55b9fee0bd1c0fca9423ad3933de3cf0 SHA1: 6176974ff0c892b9d2011ca208cfe91c3fefea7c SHA256: 305810d722d6f261285614628163ddc5fb57080148f34734ad82eacb2eba47f8 SHA512: 96b36dc8ef2948585252c645288860cfe8080da5d3151b894220a1f074cb2c0d02f960b2a27e8e0581ccac99384be61674383878314ee9b8279be9e9f6213e8f Homepage: https://cran.r-project.org/package=ggpolypath Description: CRAN Package 'ggpolypath' (Polygons with Holes for the Grammar of Graphics) Tools for working with polygons with holes in 'ggplot2', with a new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding' rules. Package: r-cran-ggpop Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-ggimage, r-cran-magick, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-fontawesome, r-cran-rsvg, r-cran-cli, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-ggforce, r-cran-gganimate, r-cran-ggrepel, r-cran-ggtext, r-cran-scales, r-cran-reactable, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-ggpop_1.9.0-1.ca2404.1_all.deb Size: 1258478 MD5sum: e42d25cb64a283647c75c4d47ec3409a SHA1: fba29722cedd844ed6b4655351b6756c5916aacf SHA256: fe63bb6c23774fee595247b591fcd650326e19f4ef07180ea93bb60c44c20dfe SHA512: 837d7d03f4681ec5e8630e33a15a490f310d71ba468d63a014e7e7984df8cda029ac87f707468ea0847e0e821400e7b62cc2779f913671ba65eabd2e7ba0a49e Homepage: https://cran.r-project.org/package=ggpop Description: CRAN Package 'ggpop' (Icon-Based Population Charts and Plots for 'ggplot2') Create engaging population charts and point plots in R. 'ggpop' allows users to represent population data and points proportionally using customizable icons, facilitating the creation of circular representative population charts as well as any point-plots. Package: r-cran-ggpower Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bs4dash, r-cran-config, r-cran-ggplot2, r-cran-golem, r-cran-shiny Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpower_0.1.2-1.ca2404.1_all.deb Size: 270712 MD5sum: 3bb57059ea4aa42d476a3987e6d0daee SHA1: 45f99058877c8e20e346ee1e3f02dde028e61fb5 SHA256: 160cd19e2d8bbffd46e1eea81a808279b51d1188e30516fba597d587a2b9101d SHA512: e6088521098fe934ce52a94d4563c87a3b06634475912680e78a51455d815b9db5be54baa33ef4b6310beae6023c044b9c0da09fab67a51eec53aecff032295c Homepage: https://cran.r-project.org/package=ggpower Description: CRAN Package 'ggpower' (Publication-Ready Power Analysis and Visualization) Provides statistical power analysis and sample size calculations for t-tests, ANOVA, regression, chi-square, proportion, correlation, nonparametric, biomarker, and clinical trial designs. 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Package: r-cran-ggpp Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2396 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-vctrs, r-cran-glue, r-cran-gridextra, r-cran-scales, r-cran-tibble, r-cran-dplyr, r-cran-xts, r-cran-zoo, r-cran-mass, r-cran-polynom, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-gginnards, r-cran-magick, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpp_0.6.1-1.ca2404.1_all.deb Size: 1707984 MD5sum: cca2dec15cd4ada9516357823604d3c8 SHA1: 48886c15e903ee2adfa685fd83f2c35fc3aeef9b SHA256: d4d3d115230b08f6d8aa407c6276f697ea515884a48b4a990bf53c8fb640d2eb SHA512: 91f9414a09deb92fb6be031f4b89b6775236a5c8bf1401765bf5d7e7b98de09ab87209e0c3a09970353646816bcc8a065977110e91ab660f03deab9f4eabe373 Homepage: https://cran.r-project.org/package=ggpp Description: CRAN Package 'ggpp' (Grammar Extensions to 'ggplot2') Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to plots using native data coordinates, while geom_table_npc(), geom_plot_npc() and geom_grob_npc() do the same using "npc" coordinates through new aesthetics "npcx" and "npcy". Statistics: select observations based on 2D density. Positions: radial nudging away from a center point and nudging away from a line or curve; combined stacking and nudging; combined dodging and nudging. Package: r-cran-ggprism Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-ggplot2, r-cran-glue, r-cran-gtable, r-cran-rlang, r-cran-scales, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggnewscale, r-cran-knitr, r-cran-magrittr, r-cran-patchwork, r-cran-rmarkdown, r-cran-rstatix, r-cran-tidyr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-ggprism_1.0.7-1.ca2404.1_all.deb Size: 2736870 MD5sum: fe3c4a41b1627d464eec223b1093cb07 SHA1: 64b60953c09e71b9e0dc200554d830b15ece2971 SHA256: a2da395ee81af13e535975fc98d562df271e160b47b7ad5c7e103ce94de8ecb9 SHA512: 651b5806311174659ead4ef199f1bafed190f3c946fcb6ca5b42e7bf2cc86ad7ed25ba2e2d26df5a3ac51ffce99217318169f5152c1fe2460f4bd11683d4b874 Homepage: https://cran.r-project.org/package=ggprism Description: CRAN Package 'ggprism' (A 'ggplot2' Extension Inspired by 'GraphPad Prism') Provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in 'GraphPad Prism'. The 'Prism'-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue(). Package: r-cran-ggpubr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2412 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-tidyr, r-cran-purrr, r-cran-dplyr, r-cran-cowplot, r-cran-ggsignif, r-cran-scales, r-cran-gridextra, r-cran-glue, r-cran-polynom, r-cran-rlang, r-cran-rstatix, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-gtable, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpubr_1.0.0-1.ca2404.1_all.deb Size: 2327146 MD5sum: 2a27684698a991b7407841553ac3e905 SHA1: 489211edaefc1162c5e789053e188f58af96c448 SHA256: 8f62d708982553767dc030bd4fea8bd7a6fe973942ddf714d19bdacda5c217ec SHA512: c33fce3b51956e6bd47ad4a5bea82e1f1776e90d527f5ed23bb53ebcec893336ff6de07d42b76c6316309dd5e818d59ef23962024d64b21dd1883397fe703781 Homepage: https://cran.r-project.org/package=ggpubr Description: CRAN Package 'ggpubr' ('ggplot2' Based Publication Ready Plots) The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots require some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'-based publication ready plots. This version includes modern R ecosystem compatibility updates and customizable p-value formatting presets (APA, AMA, NEJM, Lancet, GraphPad, and scientific notation) for publication workflows, plus robust sparse-subset handling in statistical annotation layers such as 'stat_compare_means()' and 'geom_pwc()', with informative per-group skip diagnostics for non-comparable subsets. Package: r-cran-ggpval Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpval_0.2.5-1.ca2404.1_all.deb Size: 1107040 MD5sum: 12607d2ba76001757db6e4fb2773f9b8 SHA1: 20be32fbb99dc09d925e5041c196b71d2f3fef48 SHA256: e163dac08e05a28b8029e1462a83fdbd413b60abd24f0104d1ca89e1920f3a45 SHA512: 01b75481a5a2c7ed00e37b4404d7309557c2f4d424b1f1b5996196752e1e873fcdec3d231de15b9267bfb6cc045fbbc22f8a1d0528e192de6a97274c9a62bc72 Homepage: https://cran.r-project.org/package=ggpval Description: CRAN Package 'ggpval' (Annotate Statistical Tests for 'ggplot2') Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on 'ggplot2' objects, performs the desired statistical test between groups of interest and annotates the test results on the plot. 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Package: r-cran-ggreveal Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-ggplotify, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-patchwork Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggreveal_0.2.0-1.ca2404.1_all.deb Size: 100678 MD5sum: c939d3954d6e3ef70edec34b64f3c1ff SHA1: 421bbf6d0cbad10f739d79551646664fc13f5137 SHA256: 6fb7983487552b55c40ad98f958467d68104254409adcabaada47582d4b18de2 SHA512: 07124f8ad697d77815a51fae23d96c3b080e9cca1e5464641156b722d4acfb2860e9cec08ec0cafa8671735a6cfc32856697210153d92f3b7c98b49c041e2256 Homepage: https://cran.r-project.org/package=ggreveal Description: CRAN Package 'ggreveal' (Reveal a 'ggplot' Incrementally) Provides functions that make it easy to reveal 'ggplot2' graphs incrementally. 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Package: r-cran-ggridge Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grbase, r-cran-cvglasso, r-cran-mass Filename: pool/dists/noble/main/r-cran-ggridge_1.1.0-1.ca2404.1_all.deb Size: 51396 MD5sum: c11b1ddda5b860a594d2f2bb0d0370f5 SHA1: e45616bc1957b0fd5259dacf04e4527e8fc8f1ff SHA256: 7cfa8c32f6d7ac85a58a2ab0ec31ab0725def3e1b4937a128e9c7f5222cb017f SHA512: 8c01fdc2803a1c89abcff1a9f205e94f4406e816dd84fd01a473cb23ae2735845ee2d125302835e964fd90920f6ace81e5ef9376dac255510984fbab86b134f3 Homepage: https://cran.r-project.org/package=GGRidge Description: CRAN Package 'GGRidge' (Graphical Group Ridge) The Graphical Group Ridge 'GGRidge' package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and Zoubeidi, T. (2020) . Package: r-cran-ggridges Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3007 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-withr Suggests: r-cran-covr, r-cran-dplyr, r-cran-patchwork, r-cran-ggplot2movies, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggridges_0.5.7-1.ca2404.1_all.deb Size: 2148880 MD5sum: 5e36374d74fc8093877fbd8fbc9f2ae3 SHA1: 3fe31fbb3c8c8650598c2c808041158f92899404 SHA256: fb1b3a7dc79f6fb7543d89edf8638b0351611b794d809ebbbf5e280b16d87476 SHA512: 680382ed87bc0b85d4aed60686939f6f00ccac10f8aa94a8eb2e542f4790152c329be32863dc6b7e591e6c723abfb42cb90809d531899bba2a2fcdf7c6816029 Homepage: https://cran.r-project.org/package=ggridges Description: CRAN Package 'ggridges' (Ridgeline Plots in 'ggplot2') Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'. Package: r-cran-ggrisk Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-egg, r-cran-do, r-cran-set, r-cran-cutoff, r-cran-rms, r-cran-nomogramformula, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-ggrisk_1.3-1.ca2404.1_all.deb Size: 60556 MD5sum: 6b9301d28847ad86ec7bc8aa7918955b SHA1: e180824549967866a3b19e1b3244cdb831e02c4c SHA256: 591f9866291289be733d8b34c4855f1b535f752e763171cb161ef7d2ea2c5a1e SHA512: 53e7c13d655b121a346f3104d9ff87eea2bfb35176039855d79348fe540d93a5aa68cc40592b9bb5e90895c6d1bb3d77dc70cb5ec05e6ffcab046305ca0c8d34 Homepage: https://cran.r-project.org/package=ggrisk Description: CRAN Package 'ggrisk' (Risk Score Plot for Cox Regression) The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome. 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Also, there are some examples of how to draw sectors in 'ComplexHeatmap'. 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Ships four bundled atlases ('Desikan-Killiany', 'FreeSurfer' 'aseg', 'TRACULA', 'SUIT') and functions for querying, subsetting, renaming, and enriching atlas objects. Also includes readers for 'FreeSurfer' statistics files. Package: r-cran-ggseg.meshes Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-freesurfer, r-cran-freesurferformats, r-cran-gifti, r-cran-ggseg.formats, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggseg.meshes_0.0.1-1.ca2404.1_all.deb Size: 2925746 MD5sum: 79080fd9f1246f3c3a8f5961c8089847 SHA1: 5a11bc3e065152443c7586a8687f7ba659df1c47 SHA256: 2dd27318b728f78beb74d702752e06c4b4fc9271ec3538e8ec452aa2ee958110 SHA512: 7bdb6b1c57e4162d8f538077340cb4ae8fc6a4e730ff7daee0268536a2d51f17a52bc0f5fb26638c05bfe37bd0d95f05828584d3dc4dd347685f886ed0285c75 Homepage: https://cran.r-project.org/package=ggseg.meshes Description: CRAN Package 'ggseg.meshes' (Additional Brain Surface Meshes for the 'ggsegverse' Ecosystem) Provides additional brain surface meshes for cortical and cerebellar visualisation in the 'ggsegverse' ecosystem. 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Package: r-cran-ggshadow Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-glue, r-cran-vctrs, r-cran-cli Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ggshadow_0.0.5-1.ca2404.1_all.deb Size: 579690 MD5sum: f865fdad2e760f10387da4f069367af2 SHA1: a880a83ec5aed9d45f43b1fcb3e3a0de7b9c7cc7 SHA256: b6ffd776a97a02671dcb59404eadef6e976a9a1cc6c54a44f05fd69eef621fbe SHA512: a1c1812f84ee2d92dfe7796b26b4d5799295b55908945b9847e8dda2c0d1819f0667f2b65f2a568a7979f49cfda16ef5b57ffea16c6343f2887fb6e707cff871 Homepage: https://cran.r-project.org/package=ggshadow Description: CRAN Package 'ggshadow' (Shadow and Glow Geoms for 'ggplot2') A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style. 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Package: r-cran-ggsignif Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1056 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggsignif_0.6.4-1.ca2404.1_all.deb Size: 563624 MD5sum: a661f850b98ddc807670aa8a8d8012f5 SHA1: 641b8b2df3c98548f46d553d3cbea6163d59a5c7 SHA256: 1b1bb4365b98fcdd0432992e3a45d1a9df192fb87087ccc52de3d47ad744671d SHA512: ed676c2e34d3c7ffb52a169dacf93fb16f9069e85b05ede0bad24f03f4fafb3fc14fcdf96b625a1129e1196e09a83594560bc8c326a59a19d893402e2df5e3e7 Homepage: https://cran.r-project.org/package=ggsignif Description: CRAN Package 'ggsignif' (Significance Brackets for 'ggplot2') Enrich your 'ggplots' with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. 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Package: r-cran-ggsketch Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4163 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang, r-cran-scales, r-cran-withr Suggests: r-cran-gganimate, r-cran-gifski, r-cran-hexbin, r-cran-igraph, r-cran-knitr, r-cran-magick, r-cran-mass, r-cran-patchwork, r-cran-quantreg, r-cran-ragg, r-cran-rmarkdown, r-cran-sf, r-cran-svglite, r-cran-systemfonts, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggsketch_2.0.0-1.ca2404.1_all.deb Size: 4015910 MD5sum: 609621c64f41eca177622667807c5150 SHA1: d50d792f9933a17b3814453e805e1cedb25da527 SHA256: b8cf572cd852135110adc9e3a139ab1aa9bf9af8ed7f9991d289204cbc1d9d95 SHA512: 16ba11ae7da379fa53cf3d05a64b90c98d17375e36c5758791e358945afdc952fdc21e545df1656d98982db527c159c9eafc40af200e2e5d0774f8ff096eab61 Homepage: https://cran.r-project.org/package=ggsketch Description: CRAN Package 'ggsketch' (Grammar-Native Hand-Drawn Geoms for 'ggplot2') Provides 'ggplot2' geoms that render with a hand-drawn, sketchy aesthetic: roughened strokes, double-pass lines, and hachure, cross-hatch, zigzag, and dots fill patterns. 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Package: r-cran-ggskewboxplots Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-waldo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggskewboxplots_1.0.0-1.ca2404.1_all.deb Size: 90714 MD5sum: 8bf71cf797b09707839b70c6c15abaee SHA1: 35e945f8d9575122d383396498ffb59e92c76ff1 SHA256: 8fd0ec6b47d03204b741d748483402d168104f75316e41535bb184bde133a908 SHA512: b89e52266b5d189f94e63eebed39658cef2893a9985cea2e3a4108962c26d81b5d73db8adb8727728f812a74a47e59e10d20803feeae39340efb7eaf91fa41a6 Homepage: https://cran.r-project.org/package=ggskewboxplots Description: CRAN Package 'ggskewboxplots' (Skew Boxplot Geoms for 'ggplot2') Provides 'ggplot2' extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 ; Hubert and Vandervieren, 2008 ; Adil et al., 2015 ; Babura et al., 2017 ; Walker et al., 2018 ). 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Package: r-cran-ggsmc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-poorman, r-cran-ggplot2, r-cran-gganimate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggsmc_0.2.0-1.ca2404.1_all.deb Size: 1124312 MD5sum: 66b089f278c86ec2ed72d78904859781 SHA1: 1c79658e0ad8d8a1e70ef926d41339aafa6bab68 SHA256: 163f88a5eaaaf01fe4f8da8341b97e7ce2d27eae2fd53668eeddfe1378c9007f SHA512: c01c7b9bf7326ab8f7f321956bc1236ea0b649d29b2bafe70a31bbb52aa0cfa9ecd30f117de8863671cf103a8917149cc3121ea6a15ab981350685c0fa373e9f Homepage: https://cran.r-project.org/package=ggsmc Description: CRAN Package 'ggsmc' (Visualising Output from Sequential Monte Carlo andEnsemble-Based Methods) Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions. Package: r-cran-ggsnap Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-png, r-cran-rlang, r-cran-glue, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ggsnap_0.1.2-1.ca2404.1_all.deb Size: 17346 MD5sum: ce5a76cf37c992c240c142525b602bbf SHA1: 59cc9a532964bf7b89706da79e199de2860d9082 SHA256: 90862d0ea2fa7ac4fbb4cd1b2ef967c0e376ce74e7cdc18c0c2d935a6fb2a867 SHA512: 771ef8b3a63afa041d9d3f660d7bd3e199fa3b293ddfe329cfe444235899c4c930f2a5d2c2a56137ec5ce185c7ed61418c6f0829206af8929b668e293816c19a Homepage: https://cran.r-project.org/package=ggsnap Description: CRAN Package 'ggsnap' (Save a 'ggplot2' Plot using '+' Operator) Provides ggsnap(), which saves a 'ggplot2' plot to disk in a '+' chain, acting as a thin, chainable wrapper around ggplot2::ggsave(). Can be called multiple times in a chain to save different snapshots. Package: r-cran-ggsoccer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-ggsoccer_0.2.0-1.ca2404.1_all.deb Size: 269968 MD5sum: 86da990d4cdf5c30d2dbba9ff594312d SHA1: dae6e4fbbf80ff6a5ba0708928f9412db09a56ee SHA256: 88bea4d463a358a7c8dddf49703a5d857677c9abf327abe80993bae731757a94 SHA512: 09bef6b9897e649aa7fc068bfed05fd5e443150910699ddde7f129839b02169905738eb83e9fe202634f3306bb26525a811e3d611fbce8ed9e0a1533daeb2e9a Homepage: https://cran.r-project.org/package=ggsoccer Description: CRAN Package 'ggsoccer' (Plot Soccer Event Data) The 'ggplot2' package provides a powerful set of tools for visualising and investigating data. 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Package: r-cran-ggsolvencyii Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-covr, r-cran-ggmap, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggsolvencyii_0.1.2-1.ca2404.1_all.deb Size: 310348 MD5sum: 45e60b45f65e394e2371a9a07f2c9949 SHA1: 5ca0815afababb725e1a3322c0e40fa117e94843 SHA256: 93643163a38020749f6a679d3c5281d3870a582a23028eb115a45a0ae226da4e SHA512: d91c6cb72dd3d2cfebdfd52953d5f838ecc82e247e045e400319ebb12521dfa8d91525c066bcd1d00560e446da03f2109eb7da26a7b42b3b4b320a5cbe4e0019 Homepage: https://cran.r-project.org/package=ggsolvencyii Description: CRAN Package 'ggsolvencyii' (A 'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM) An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014. . Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided. The structure files can be adopted for localization or for insurance companies who use Internal Models (IM). Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts. With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment). Package: r-cran-ggsom Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-kohonen, r-cran-assertthat, r-cran-data.table, r-cran-entropy, r-cran-tibble Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggsom_0.4.0-1.ca2404.1_all.deb Size: 38132 MD5sum: 7f68a876f64184074c52b539032c1e5a SHA1: 5acc8091f59874673158cfb850c73901159ed931 SHA256: d92290c1d0dce7137597222830267856d93f753275ff1416c130ade157a06871 SHA512: 939b73435edc2480146cbe7e3ee50d547f55e46fcbcadaeae4c694bbde4ee41dfe6fe9c3701576271edff395fb9c9173dc00182daed4bb9a1db270bc2f7a24fb Homepage: https://cran.r-project.org/package=ggsom Description: CRAN Package 'ggsom' (New Data Visualisations for SOMs Networks) The aim of this package is to offer more variability of graphics based on the self-organizing maps. 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Package: r-cran-ggspec Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-tibble, r-cran-dplyr Suggests: r-cran-learnr, r-cran-palmerpenguins, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggspec_0.1.0-1.ca2404.1_all.deb Size: 262516 MD5sum: 9b1fd0c7751aa182b155a6d7cf605d0f SHA1: dc8d5b2d3a1f763dfa525d57e684c5bd6ce7f5c3 SHA256: 4cda5ab827f66e47a03e4b6c50540dcf43ef2f880b7b9550b69fbca07b809f04 SHA512: f5462153a86de7b8b767faa96b9cc6ca9169946876c2bdb1168fcfb74dcbcf85e62f772bebe37562e8bf26f038edcd332afaaa211e081c4dc6bdf2e8b88061e9 Homepage: https://cran.r-project.org/package=ggspec Description: CRAN Package 'ggspec' (Extract and Compare 'ggplot2' Plot Specifications as Tidy DataFrames) Inspects 'ggplot' objects by extracting their full declarative specification - layers, aesthetic mappings, scales, facets, coordinate systems, and labels - as tidy data frames. 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Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-ggstackplot Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4607 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-lifecycle, r-cran-tidyselect, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-cowplot, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-scales, r-cran-pangaear Filename: pool/dists/noble/main/r-cran-ggstackplot_0.4.1-1.ca2404.1_all.deb Size: 3433478 MD5sum: f939c82c5acfb5f5d40130b426d90fca SHA1: 1ef395456cd616d58a1ed4d6a3fb641457058992 SHA256: 75c62fd6eeee6f7c3c078ee2514260058044fc486e4e95d202f4130acf91c30a SHA512: 942c4ee854eec41fec832f42370c1fe2efe4617dad665ca300283da497aac8ea059de41e4ca9abaa1a29b26e57cadb8518d2132c3652112823c5a7ec109a4d85 Homepage: https://cran.r-project.org/package=ggstackplot Description: CRAN Package 'ggstackplot' (Create Overlapping Stacked Plots) Easily create overlapping grammar of graphics plots for scientific data visualization. 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Package: r-cran-ggstats Architecture: all Version: 0.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1951 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lifecycle, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr Suggests: r-cran-betareg, r-cran-broom, r-cran-broom.helpers, r-cran-emmeans, r-cran-glue, r-cran-gtsummary, r-cran-knitr, r-cran-labelled, r-cran-reshape, r-cran-rmarkdown, r-cran-nnet, r-cran-parameters, r-cran-pscl, r-cran-testthat, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggstats_0.14.0-1.ca2404.1_all.deb Size: 1261904 MD5sum: 2296fe7a817fae1315bcfa6dd8351a36 SHA1: b7e79bfbd155adb717f5b3277136c96dabe17377 SHA256: 1e56d6dd7a59a4100fd4ea293cefc7e92e45f8d585f5d884ff4208fb57007626 SHA512: a6240afd463be3d7e3cfd46bd86c9fd3d3956c05a7d9764e02bbe80a9c10ea9b900c7894ffdaefdbcb6d727195f378d4b4743e9cbede510f43b4d24b94e2c90e Homepage: https://cran.r-project.org/package=ggstats Description: CRAN Package 'ggstats' (Extension to 'ggplot2' for Plotting Stats) Provides new statistics, new geometries and new positions for 'ggplot2' and a suite of functions to facilitate the creation of statistical plots. Package: r-cran-ggstatsplot Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3859 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-correlation, r-cran-datawizard, r-cran-dplyr, r-cran-forcats, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggside, r-cran-ggsignif, r-cran-glue, r-cran-insight, r-cran-paletteer, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-purrr, r-cran-rlang, r-cran-statsexpressions, r-cran-tidyr Suggests: r-cran-afex, r-cran-bayesfactor, r-cran-bayestestr, r-cran-gapminder, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-metabma, r-cran-metafor, r-cran-metaplus, r-cran-patrick, r-cran-psych, r-cran-rmarkdown, r-cran-rstantools, r-cran-survival, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-withr, r-cran-wrs2 Filename: pool/dists/noble/main/r-cran-ggstatsplot_1.1.2-1.ca2404.1_all.deb Size: 3261234 MD5sum: 34a5dcd46b2c9418e2515c30f8c61736 SHA1: 4ebb70cca614211a17d41751389e66123daf1445 SHA256: 0320711b11d8fe38fa5f490eebb6ecb785e1224c632f6aa9b0dc32d41884e5ee SHA512: 15794718fdcf559281a594ddfd02d5ffb93101cdbf5014665adf402a3e5f323c12b120b4900e711e75650a7de72c07692b51a173755ba24064f1d9d2dde1d64d Homepage: https://cran.r-project.org/package=ggstatsplot Description: CRAN Package 'ggstatsplot' ('ggplot2' Based Plots with Statistical Details) Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) . Package: r-cran-ggstratify Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-patchwork, r-cran-ragg, r-cran-shiny, r-cran-survey, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-svglite, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ggstratify_0.2.0-1.ca2404.1_all.deb Size: 571942 MD5sum: f382b470d1aa4300cc4d87679c975f5c SHA1: ebccb0066648b6c1684b5118849f0652ec930239 SHA256: ca78f2640292d5dc777c70ade8d63f3ae3e68cc23ca023deadbfcc04d71cee6c SHA512: 73f45e6cd07e0555988c4abfeeeadd3133bfddd3b2bf04db33b2f443218c6f6dcd319a4533469860a9921516287562b76a6c6b20ed0c28a1aaafb09595efd918 Homepage: https://cran.r-project.org/package=ggstratify Description: CRAN Package 'ggstratify' (Fast Stratified Descriptive Figures with a Point-and-Click GUI) A point-and-click 'shiny' interface for the descriptive analysis that comes before any model is chosen. Pass a data frame, pick the variable to describe, and add the layers you want to see it within: a second variable becomes the panels of a 'ggplot2' facet_wrap(), and further variables become separate figures, one file each, taken either one variable at a time or crossed. Every stratum is reported with the number of observations behind it, on the figure and on each of its panels; strata that contain none are listed rather than dropped, and rows with a missing value in a layer variable are excluded and counted. A continuous variable can be categorized into quantile groups, equal-width bins or user-supplied cut points, a variable of any type can be turned into whether it is missing or observed, so that the rows a layer would exclude become a stratum of their own, and a date or date-time variable can be read at a chosen resolution, either as a calendar period or as a position in the yearly cycle such as the month or the season; any of them can then be used as a layer. The figure types follow those offered by the 'ggplotgui' package and add the line plot for change over time, an optional LOWESS smoother, and the Kaplan-Meier curve estimated by 'survival', with an optional number-at-risk table. An error bar can show a standard error or a confidence interval, the latter from the t distribution for a mean and from the Clopper-Pearson or Wilson method for a proportion, and the points can be joined by a line computed from the same summary, which is how a trend over time is read. A survey weight can be set: the figure is then drawn from the weighted data, every count is reported both as rows and as the sum of the weights, and error bars and confidence bands are design-based estimates from the 'survey' package. Columns are described as they are typed, so convert each to the type you mean first. Figures are written as PNG or SVG, and the application prints the 'ggplot2' code behind the figure on screen, so that a description can be repeated, shared or accounted for later. Everything runs locally, with no network access and no AI involved. Package: r-cran-ggstream Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-forcats Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggstream_0.1.0-1.ca2404.1_all.deb Size: 51590 MD5sum: ca9b55b23d1aee963f18d852c96838ee SHA1: 4b1b19a42c4750ef3d8c93fee045f17b8a2a1c5a SHA256: 0431f4362fbc0fb8a9ea914e8b26382503609f398d7cc296fb6cfa4d3a3145d0 SHA512: 96b50dbf9e7984dbe24701793931c1d9c3330291e035b1e2c85cdb79313fdd848f8de2551e402b6357cf1df81a7f22ea6c84fc8b3488091456ff920c58d6c5b8 Homepage: https://cran.r-project.org/package=ggstream Description: CRAN Package 'ggstream' (Create Streamplots in 'ggplot2') Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time. Package: r-cran-ggstudent Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-scales Filename: pool/dists/noble/main/r-cran-ggstudent_0.1.2-1.ca2404.1_all.deb Size: 54834 MD5sum: 770d2f530361fc46d3240fc584e24042 SHA1: a39799907a956ccdc66d572871a44f19a58f6f57 SHA256: 4561f81b7e8b50ac1989f47b6d2505b31d98e3ce114ae8819750b9dde329e7fc SHA512: 0169e675d7fc86325fd639d6c4ad9d20cac6020dbdd120faf187d91ed706dfda1f6e0ca6005e26f9885fea495afa4b4b9d93be9376bf2d2a9800628d01064ab0 Homepage: https://cran.r-project.org/package=ggstudent Description: CRAN Package 'ggstudent' (Continuous Confidence Interval Plots using t-Distribution) Provides an extension to 'ggplot2' (Wickham, 2016, ) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) . Package: r-cran-ggsurveillance Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-isoweek, r-cran-legendry, r-cran-lubridate, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggrepel, r-cran-hmisc, r-cran-knitr, r-cran-outbreaks, r-cran-plotly, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggsurveillance_0.5.2-1.ca2404.1_all.deb Size: 963880 MD5sum: 9ad6561c2b3e865e982bf83bf7ee36a0 SHA1: 511cb6e67f79a51e75a0f77d01649d78c206dff5 SHA256: e3e9931403b1b8c86cc96062c2d957e9457f490d2e466da6532c0e0b70edd5a4 SHA512: 0946d2a24d8d3f27bb044b86e1d3706a8945c0cc8e46e6996b821fd250a29565f3a4b904a10e2e0845aefe36be7cd821b1f667cafaee352ebf98d87cad962704 Homepage: https://cran.r-project.org/package=ggsurveillance Description: CRAN Package 'ggsurveillance' (Tools for Outbreak Investigation/Infectious Disease Surveillance) Create epicurves, epigantt charts, and diverging bar charts using 'ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation (time series data). Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the 'ggplot2', (D) labelling of the last value of a time-series. An introduction on how to use epicurves can be found on the US CDC website (2012, ). Package: r-cran-ggsurvey Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survey, r-cran-hexbin, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ggsurvey_1.0.0-1.ca2404.1_all.deb Size: 72974 MD5sum: 5ed132114059e3dff3ff4af81d139f45 SHA1: 8b749c1a707c59365d0b0956be5a78bdd85faf6c SHA256: 7e2e63308e61f926ee54a3b055aa3a3ccfd5fd690a231e67cafa8160cdbf2232 SHA512: f69544563cc7f3e8ebb8a5a19005dfead99c8c2370794fa2f6f6a87836642c7dba6e7233ff0b51d6b9a3af6fe3395116ceda94ffd729d525953846af474bbe43 Homepage: https://cran.r-project.org/package=ggsurvey Description: CRAN Package 'ggsurvey' (Simplifying 'ggplot2' for Survey Data) Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data. Package: r-cran-ggsurvfit Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-gtable, r-cran-patchwork, r-cran-rlang, r-cran-survival, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tidycmprsk, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggsurvfit_1.2.1-1.ca2404.1_all.deb Size: 408614 MD5sum: a242719b12af68255c08e8371294c451 SHA1: 214fdb0eb70dee191de1a2773d9ac803924a0cc6 SHA256: 93838f3847c4e91de4310e66c6cc7eb77d1d8db51ce0398805602b4759e953ac SHA512: 3363587ef425c32e3af999ab3eac506688f5d1cfd384680df77427e4c62f89f51e9cdb57df36e14882d1489d364696f5cf9ca924bc76975ae333629cabf2da5c Homepage: https://cran.r-project.org/package=ggsurvfit Description: CRAN Package 'ggsurvfit' (Flexible Time-to-Event Figures) Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages. Package: r-cran-ggswissmaps Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-sf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggswissmaps_0.1.2-1.ca2404.1_all.deb Size: 1666052 MD5sum: f6f95366dfe696ae946df373eeea5ea9 SHA1: 184d6aa9d373b6123e753eea384ee523d8ed1a1f SHA256: 31dae509d00b9a13f333076c1846233510aae1c1155a0a4e904ad3846e533d96 SHA512: 45010de258e4455af904107acc8c02e2c7013e816c56a490f7951f0c44189f8574614ddf70e44e1e20699938a63c1949c78bde58883ed0dd9e3a3e226af9431f Homepage: https://cran.r-project.org/package=ggswissmaps Description: CRAN Package 'ggswissmaps' (Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects) Offers various swiss maps as data frames and 'ggplot2' objects and gives the possibility to add layers of data on the maps. Data are publicly available from the swiss federal statistical office. In addition to the \code{maps2} object (a list of 8 swiss maps, at various levels), there are the data frames with the boundaries used to produce these maps (\code{shp_df}, a list with 8 data frames). Package: r-cran-ggtaichi Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2318 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-farver, r-cran-ggplot2, r-cran-ggnewscale Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-gganimate, r-cran-gifski, r-cran-ggiraph, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-ggtaichi_0.3.0-1.ca2404.1_all.deb Size: 1717262 MD5sum: 7dec5b98efad215ba3f443330a124648 SHA1: bd8695c6faad13167915e970c67fc4908b13b549 SHA256: 99ac949c7c8126c3b7b0c15675b8ddf43bd153bc017b3492cd736d28e0e6291d SHA512: 54c4a716dd4df38aff376eb3eb794bcb5c27a41f0792532dd725b3f209046e46a2fed42560ba75dd6e32dfd09ff6bb0e2da8d491a660f1efec6fe25caedd075f Homepage: https://cran.r-project.org/package=ggtaichi Description: CRAN Package 'ggtaichi' (Taichi-Diagram Visualization for Two Data Sources) A data visualization design that compares two (usually on a par with each other) data sources on one grid of taichi (yin-yang) diagrams, where the two interlocking fish of every symbol are filled by the two sources, while inheriting 'ggplot2' features. Package: r-cran-ggtangle Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3510 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggfun, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-rlang, r-cran-yulab.utils Suggests: r-cran-aplot, r-cran-cli, r-cran-ggiraph, r-cran-ggnewscale, r-bioc-ggtree, r-cran-quarto, r-cran-scatterpie Filename: pool/dists/noble/main/r-cran-ggtangle_0.1.4-1.ca2404.1_all.deb Size: 2523532 MD5sum: b8597f6ed5bb3d71709eaf1f1b8093e7 SHA1: bc98a6526ea346e80fa769cfe845ad34ffcde000 SHA256: a0084bce4888735664c77a2857f2e808b5f5b40646e117a5978a674a0813afec SHA512: 4a3aecfc56097ecec176fcf634c3ed606ac912f0fcbf6922352ec3934955311a9b8a5db24b22816fe714b62eb41fc2ede88756339f774aa7d820c94aab3f7c69 Homepage: https://cran.r-project.org/package=ggtangle Description: CRAN Package 'ggtangle' (Draw Network with Data) Extends the 'ggplot2' plotting system to support network visualization. Inspired by the 'Method 1' in 'ggtree' (G Yu (2018) ), 'ggtangle' is designed to work with network associated data. Package: r-cran-ggtaxplot Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse, r-cran-scales, r-cran-rcolorbrewer, r-cran-cluster, r-cran-vegan, r-cran-ggalluvial, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-ggtaxplot_0.0.1-1.ca2404.1_all.deb Size: 56854 MD5sum: 9584aecada6afb7d2afbd9777008bc2c SHA1: c78b6dbaa434ebfe83f662a3761334b6e0456cc2 SHA256: dbe47e2c1d7d6edf93e3af856d17cf683f0887503910a5f5bddfdaefd3a317a0 SHA512: 62e953b42e938c80ac934f03085a8f8ba3a7cd9bf20a126d46056d32e3f6edf850792e23edf29ba1af688178663bf7153807285f49c0d2a90a56c933ba9e8991 Homepage: https://cran.r-project.org/package=ggtaxplot Description: CRAN Package 'ggtaxplot' (Create Plots to Visualize Taxonomy) Provides a comprehensive suite of functions for processing and visualizing taxonomic data. It includes functionality to clean and transform taxonomic data, categorize it into hierarchical ranks (such as Phylum, Class, Order, Family, and Genus), and calculate the relative abundance of each category. The package also generates a color palette for visual representation of the taxonomic data, allowing users to easily identify and differentiate between various taxonomic groups. Additionally, it features a river plot visualization to effectively display the distribution of individuals across different taxonomic ranks, facilitating insights into taxonomic visualization. Package: r-cran-ggtea Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ggtea_0.1.1-1.ca2404.1_all.deb Size: 32258 MD5sum: 717371e9a32f60243387852c9721690a SHA1: 554d40a33491d17ed700f4b9075dc2d25be2b878 SHA256: 8511dd5be0a57244320eea612a10aef9f4ffd17e3e3ba4a40cebbebde18425a8 SHA512: 2dd509ea3ec1e63dcd2b13a48bed7bf40d9852f334b948dc08c9770aa8f340793e902cd8302ddecd7252e2d3c152e8dc1038ef8bfd86b6dc8b8d8fd5e9c0e057 Homepage: https://cran.r-project.org/package=ggtea Description: CRAN Package 'ggtea' (Palettes and Themes for 'ggplot2') A collection of palettes and themes for 'ggplot2', offering a light, pastel aesthetic. Syntax follows the 'viridis' package. Package: r-cran-ggtern Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-compositions, r-cran-gridextra, r-cran-gtable, r-cran-latex2exp, r-cran-mass, r-cran-plyr, r-cran-scales, r-cran-proto, r-cran-lattice, r-cran-hexbin, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ggtern_4.0.0-1.ca2404.1_all.deb Size: 1515204 MD5sum: 3e22eae61bfdb0ffb53bbb934ef639f6 SHA1: 82e3dbfe0925c1647a224d10cd218471a59ad077 SHA256: 7cfd396965016f801caec4cc17d89ae83bd326633b05b6b1d62aaa10f3719fea SHA512: b91184f4e985f7c35d25657df3e6acbf3932701e4daea7bbd0dcc9cc8e3b32bdd23c30b05a01dafd672de0040c20049ad579740ccfed4f80b59d5cbb275aa07f Homepage: https://cran.r-project.org/package=ggtern Description: CRAN Package 'ggtern' (An Extension to 'ggplot2', for the Creation of Ternary Diagrams) Extends the functionality of 'ggplot2', providing the capability to plot ternary diagrams for (subset of) the 'ggplot2' geometries. 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Package: r-cran-ggvegan Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-dplyr, r-cran-tibble, r-cran-vctrs, r-cran-ggplot2, r-cran-tidyr, r-cran-ggrepel, r-cran-generics Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-permute, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggvegan_0.2.1-1.ca2404.1_all.deb Size: 229832 MD5sum: ebf0d1f0dbe5695047d7b4c08a969dd2 SHA1: f518e9a3105f17049ff7e00bc24be561d0155d70 SHA256: 4e77746152fbf17997b3fa98e4925de5cfba250dbf8e9fe3166870d49feadd62 SHA512: dd35f4234a54f86c38a0eae38668dd3e613a0ecced37395acd83ad28bfef041646d28fc219651340df29eb8c5d29745985ac9830e84e35ed7f32a9146b544583 Homepage: https://cran.r-project.org/package=ggvegan Description: CRAN Package 'ggvegan' ('ggplot2' Plots for the 'vegan' Package) Functions to produce 'ggplot2'-based plots of objects produced by functions in the 'vegan' package. Provides 'fortify()', 'autoplot()', and 'tidy()' methods for many of 'vegan''s functions. The aim of 'ggvegan' is to make it easier to work within the 'tidyverse' with 'vegan'. Package: r-cran-ggvenn Architecture: all Version: 0.1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggvenn_0.1.19-1.ca2404.1_all.deb Size: 77764 MD5sum: ff416d135a9a42a392607532036d9bbe SHA1: 4edd53f6101ba8c2655940c333cdbcbf02cce7a2 SHA256: b0b33acd7f7a06c5a591c85ee7e5b977d8d511a27034008ce87112176b5ac0fc SHA512: 74629ea30618c8e42c2942024fe586541b3ead3d9a53440d084649b1ae777b236182198cdd0622680a6d84d3faf5489911d1c6ebef49cd8e1e1c7216d8878677 Homepage: https://cran.r-project.org/package=ggvenn Description: CRAN Package 'ggvenn' (Draw Venn Diagram by 'ggplot2') An easy-to-use way to draw pretty Venn diagrams using 'ggplot2'. This package provides functions to create Venn diagrams with customizable colors, labels, and styling options. Package: r-cran-ggvenndiagram Architecture: all Version: 1.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-aplot, r-cran-venn, r-cran-yulab.utils, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-shiny, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ggvenndiagram_1.5.7-1.ca2404.1_all.deb Size: 3832180 MD5sum: 16212976ca0056850ca92a2dcfcb06b3 SHA1: e8d4f66802780f6444a5ae6e5ec743f877850135 SHA256: 1e5e63711f1c55eba640af988e8f8e3f62f15b534b7850412471176f0275980e SHA512: 26cb6d464ad3a174f7a0e84201d6de61def28d01b6bc751a2678387ad1d2d1c7643b7ff0d686cd265355b4383b33638268699e1783023af3676a06401580469e Homepage: https://cran.r-project.org/package=ggVennDiagram Description: CRAN Package 'ggVennDiagram' (A 'ggplot2' Implement of Venn Diagram) Easy-to-use functions to generate 2-7 sets Venn or upset plot in publication quality. 'ggVennDiagram' plot Venn or upset using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in four components, set edges/labels, and region edges/labels. From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar. From version 1.4.4, it supports unlimited number of sets, as it can draw a plain upset plot automatically when number of sets is more than 7. Package: r-cran-ggversa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-ggversa_0.1.1-1.ca2404.1_all.deb Size: 196260 MD5sum: f8315bddb3304c43c7d3b1be34f4eb7c SHA1: f79be420c8bb3a31f57fbd4b1be80fc7f11a2a0a SHA256: dafa084519219246b505d82726cfa2518341dfad5d55a7718eb9cbe65b5ce51d SHA512: c36f2759294c390b3417620d90b100c5c48f4f3c7c72d9da06f2138b20ff4e1c195046f76ca9ceb93ee027dab47f76648d1af75fc8f0cf5e1c029a93424db07f Homepage: https://cran.r-project.org/package=ggversa Description: CRAN Package 'ggversa' (Conjuntos de Datos para 'Graficas Versatiles con ggplot2') Una coleccion de conjuntos de datos para el libro "Graficas versatiles con ggplot: Analisis visuales de datos", por Raymond L. Tremblay y Julian Hernandez-Serrano. Incluye datos de ecologia, salud publica, educacion, economia y biodiversidad para la ensenanza de visualizacion de datos con 'ggplot2'. Package: r-cran-ggvfields Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2113 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-farver, r-cran-tibble, r-cran-numderiv, r-cran-desolve, r-cran-scales, r-cran-sp, r-cran-gstat, r-cran-cli, r-cran-mgcv Suggests: r-cran-ggdensity, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggvfields_1.0.1-1.ca2404.1_all.deb Size: 1754556 MD5sum: f053cde0b9470481c9d227e5a9327898 SHA1: c78e368580506e80afbc230d5a2f84fe32fe620a SHA256: 964c13264839440001745069241d2935d3d04ef6a0c7050d9327c26e6a2056b2 SHA512: b2c8d15355f61b51c3ca3e4f46296e90d26d8125179bcf7632cfc6679441b57f151e2ad2ab46777e546ee2a78c7ac21b0c77b33be910d6dfa2e2f90122ce7591 Homepage: https://cran.r-project.org/package=ggvfields Description: CRAN Package 'ggvfields' (Vector Field Visualizations with 'ggplot2') A 'ggplot2' extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns. Package: r-cran-ggview Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rstudioapi Suggests: r-cran-testthat, r-cran-png Filename: pool/dists/noble/main/r-cran-ggview_0.2.2-1.ca2404.1_all.deb Size: 28362 MD5sum: 4c8274a8abe37fa2e2e821be1e4bf453 SHA1: 5c054aee995f46275042d8434194c598928ee26b SHA256: 0c0b82bfeb9f2953e5357ef31166867e6b97d80cf0293d41918d7a66266f947e SHA512: a980afdaf99e653013adcef464640fc09da312e9802712b2a87e8d005a2783a0f7f50235a191214a1e05432149b42906b10d06e44cec6ad0c4d315b17c928a3a Homepage: https://cran.r-project.org/package=ggview Description: CRAN Package 'ggview' ('ggplot2' Picture Previewer) Preview what a 'ggplot2' plot would look like if you save it to a file. Attach picture dimensions as a canvas() element and get an instant preview. These dimensions will then be used when you save the plot. Package: r-cran-ggvis Architecture: all Version: 0.4.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2830 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-shiny Suggests: r-cran-knitr, r-cran-lubridate, r-cran-mass, r-cran-mgcv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggvis_0.4.10-1.ca2404.1_all.deb Size: 1023428 MD5sum: 68f0226325cca2f4914ea597bd959503 SHA1: 6cdfadf632538818e48617e534f3c6d47b7575d2 SHA256: d0ee57c80e022447444f123b29421bb6eeb523f37aaa126b953c86b499877a7f SHA512: cda10d2ce5599897df7395a4c735892458da68e770507194c81c440418fe99c2b93a857d2aba4d3a601d254a55d9ff4d28b05f5afeeccb02e37f5d40485b9fce Homepage: https://cran.r-project.org/package=ggvis Description: CRAN Package 'ggvis' (Interactive Grammar of Graphics) An implementation of an interactive grammar of graphics, taking the best parts of 'ggplot2', combining them with the reactive framework of 'shiny' and drawing web graphics using 'vega'. Package: r-cran-ggvolc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtext, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-ggvolc_0.1.0-1.ca2404.1_all.deb Size: 258978 MD5sum: bede7e7c611cd66b09a29374d473aacd SHA1: c8f76fda98d64bed2972c494567a13fbde33f511 SHA256: 05d6ab8ac83b7d126a9aa9d4bf884cff995e1447e10531d09b501d21102959dd SHA512: 96c0ff6a608b9bb001d99934cb7a9ebc9683b8f9a8b26a7ec2affc016c35d501f0ee4108bed60ad15ffd179f5e3e981880970ef34cc5c96af7aed5df10dec9a0 Homepage: https://cran.r-project.org/package=ggvolc Description: CRAN Package 'ggvolc' (Create Volcano Plots for Differential Gene Expression Data) Provides functionality to create customizable volcano plots for visualizing differential gene expression analysis results. The package offers options to highlight genes of interest, adjust significance thresholds, customize colors, and add informative labels. Designed specifically for RNA-seq data analysis workflows. Package: r-cran-ggvolcano Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-ggplot2, r-cran-ggrepel, r-cran-golem, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggvolcano_0.1.4-1.ca2404.1_all.deb Size: 304508 MD5sum: 1896a423f6475ecb7cdd4bbf6e02fc2f SHA1: 1da0cce8edf070cd238a58ec6c021c31c18a605a SHA256: be1097317ba3f228cf86884159887916a67a8a2569c47a4ffdcea78ae9d45c2f SHA512: d23ffb63f65e24429b32bd0cf3cfb0d49070d5e0fda8a45eb48abce5aaef8c214e80d1b124e47a871484a890cf373cec104182ac4459c695a406c40aeec1ec7b Homepage: https://cran.r-project.org/package=ggvolcano Description: CRAN Package 'ggvolcano' (Publication-Ready Volcano Plots) Provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the 'ggplot2' framework Wickham (2016) . Package: r-cran-ggwebgl Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5905 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-boids4r, r-cran-chromote, r-cran-knitr, r-cran-magick, r-cran-mass, r-cran-pkgdown, r-cran-pkgload, r-cran-plotly, r-cran-processx, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-xgeortr Filename: pool/dists/noble/main/r-cran-ggwebgl_0.8.0-1.ca2404.1_all.deb Size: 3223550 MD5sum: 6b7b2f1f73facf2a057a9bcf99160d63 SHA1: 05e046a43eaf404ae135b92c3835a42cae9209af SHA256: dbcc5344d867ddccfceefa51cb62ffaced1628f091467dadc1a0ec055a90561e SHA512: 1b35d15d4c45c4219f15eb9cc3ba8fc5175b9594bf0051847b3b851a0bd08486e653a90caf3e84ed34ad4e635c6bc396e1555c02ca00deaf1121c5a8150c4c15 Homepage: https://cran.r-project.org/package=ggWebGL Description: CRAN Package 'ggWebGL' (Browser-Native 'WebGL' Rendering for R Graphics) Provides browser-native 'WebGL' rendering for R graphics through 'htmlwidgets'. The package supports grammar-style graphics workflows and renderer-ready specifications for dense analytical and scientific scenes, including point, line, trajectory, raster, vector, mesh, and surface layers, shader-driven display modes, timeline controls, structured views, selection metadata, and publication-oriented static export helpers. Rendering stays in the browser, and the core package remains cross-platform without requiring 'CUDA', 'Metal', or 'OpenCL' toolchains. Package: r-cran-ggwidth Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-dplyr, r-cran-forcats, r-cran-patchwork, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggwidth_0.2.0-1.ca2404.1_all.deb Size: 187026 MD5sum: 2e214f8071b1702fe0b95c780fbb1fd9 SHA1: 9c37cd5ccedbf149f7bf7b618c437ceab0f9c1f7 SHA256: 66f927199b25e20d4f35752a57bf4d1b085231331286105add1d2599449d9462 SHA512: 202a93905acbfeeecb433e1bf925dd88c96c1c6ce218fca868053f4165bac3fbe20b53e853836f8351459a7d6a2747311828518a3e859e68421c405cc40f71e3 Homepage: https://cran.r-project.org/package=ggwidth Description: CRAN Package 'ggwidth' (Publication-Quality 'ggplot2' Geom Width) Width helper functions for publication-quality 'ggplot2' visualisation. These functions make it easier to create geoms such as bars that have a consistent width appearance across plots. Package: r-cran-ggx Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sets, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-ggx_0.1.1-1.ca2404.1_all.deb Size: 375550 MD5sum: 755cc08b38466ae71f62994ce1e721d6 SHA1: 874db7e0bae031bfa4694a6bafed748ff967f8a2 SHA256: c085da08e4907982d383d14508f8d923b7dcfbf9b026edebe6c31e1a25005c9c SHA512: 003b84d2dfe7f371c99179401b47f80d5e790ca80416b1147ad2bcf9a53055304a27e8d4159ac60f755e7e4308bacf4c0b9d70c2eb48b0baefaf76e831cf3c05 Homepage: https://cran.r-project.org/package=ggx Description: CRAN Package 'ggx' (A Natural Language Interface to 'ggplot2') The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands. Package: r-cran-gh Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-gitcreds, r-cran-glue, r-cran-httr2, r-cran-ini, r-cran-jsonlite, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-connectcreds, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rprojroot, r-cran-spelling, r-cran-testthat, r-cran-vctrs, r-cran-webfakes, r-cran-withr Filename: pool/dists/noble/main/r-cran-gh_1.6.1-1.ca2404.1_all.deb Size: 170108 MD5sum: b7c82b0a508a22e64c8ce39dd43075ae SHA1: c1e82f93d718dbfd7f89fe39a1aa3d000a7cca1d SHA256: 32abe62c9bbf8313999eedb0e93dca22653c96fa087f995710c8aa685bb28060 SHA512: 6ca3ef547c1c0cbaa71595ec65375a774a69e3fd766b3744f1a6eeb19d7544bdced055ecea0cbcebb3ef638f2a0005c06958cd9fdacbff7d403db58ddf17cc4a Homepage: https://cran.r-project.org/package=gh Description: CRAN Package 'gh' ('GitHub' 'API') Minimal client to access the 'GitHub' 'API'. Package: r-cran-ghap Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-pedigreemm, r-cran-sparseinv, r-cran-e1071, r-cran-class, r-cran-data.table, r-cran-stringi Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ghap_3.0.0-1.ca2404.1_all.deb Size: 1013786 MD5sum: 628d3cf91ab463da9e2808f190e421e8 SHA1: 119835890e34b8b8cfdea91867a81127c20c3703 SHA256: b9df03c5994beefc0846299e964c6e87e2fa329ce7121193c96d64bdf3541a6d SHA512: 47952d98844b821454e1861117248f3757076034e51abec03a13dc765b07fcbb69212bb9b2700cdd4fcb7d815ce8b9e3fb7d6511ea3ad123cfceb7b67a26d702 Homepage: https://cran.r-project.org/package=GHap Description: CRAN Package 'GHap' (Genome-Wide Haplotyping) Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, ). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, ). The source code for v3 was modified for improved performance and inclusion of new functionality, including analysis of unphased data, runs of homozygosity, sampling methods for virtual gamete mating, mixed model fitting and GWAS. Package: r-cran-ghapps Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gh, r-cran-jose, r-cran-openssl Filename: pool/dists/noble/main/r-cran-ghapps_1.1.1-1.ca2404.1_all.deb Size: 22590 MD5sum: ba42b6847c72a51de922f4f3caa113ac SHA1: a0c729bb340ca832b902c98164322c301557b419 SHA256: 2e3f7dc31326df2cc74827213cf97fe3e4ef452b5440dcdfe8d77e536cdae102 SHA512: a4bcbd04f9617bcc2178e1a94e4d43568a4e2a370e81a53ed3b850a499491dcd5f5522e7ab643e5c4f77135bf406a6d8b1650158eded8a7daa706904e2a8e391 Homepage: https://cran.r-project.org/package=ghapps Description: CRAN Package 'ghapps' (Authenticate as a 'GitHub' App) 'GitHub' apps provide a powerful way to manage fine grained programmatic access to specific 'git' repositories, without having to create dummy users, and which are safer than a personal access token for automated tasks. This package extends the 'gh' package to let you authenticate and interact with 'GitHub' in 'R' as an app. Package: r-cran-ghat Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2007 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrblup Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ghat_0.2.0-1.ca2404.1_all.deb Size: 1979148 MD5sum: e00f979f72380fb8b123a8bbdf7dedf1 SHA1: 009990117dd14076d36dff129b9b506698d5df91 SHA256: c8cb646b4900daaf6e64edc1ecf3346cb4338f19c361c735c1e21131fb27c472 SHA512: aa06fe67ef5579f494655a056918834c40b84ef10b3b25e2864f7c35d3923011ba72b40922b19fb68e62c6c71943a45f55857f01e030174eddbc998c81645a43 Homepage: https://cran.r-project.org/package=Ghat Description: CRAN Package 'Ghat' (Quantifying Evolution and Selection on Complex Traits) Functions are provided for quantifying evolution and selection on complex traits. The package implements effective handling and analysis algorithms scaled for genome-wide data and calculates a composite statistic, denoted Ghat, which is used to test for selection on a trait. The package provides a number of simple examples for handling and analysing the genome data and visualising the output and results. Beissinger et al., (2018) . Package: r-cran-ghcclm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ghcclm_0.1.0-1.ca2404.1_all.deb Size: 18558 MD5sum: 92530e9f0eba845c78e429e82d39f12f SHA1: fd12a9fa3ec592e18dc750d3ea2dcd537f45047b SHA256: 71caf7a39a6d4df19e11cb9588c19c651d1e237f255162326008adc0041a1a78 SHA512: 2ec03927f6042c5c6aa5df60627b73a4dbeb311323a7b53494e128dccc2ee165aca925a8ae73df3e7062d29c67cda7a1a255fe99901ae96a0a5df76f0c74ed43 Homepage: https://cran.r-project.org/package=ghcclm Description: CRAN Package 'ghcclm' (Generalized Hybrid Contrast Coding in Linear Models) Implements generalized hybrid contrast coding methods for K-level categorical predictors in linear models as established by Obulezi (2026)[cite: 3]. The package automates design matrix construction mixing dummy indicators and composite group contrasts, checks rank constraints via singular value decomposition, computes closed-form OLS parameter mappings, and estimates robust heteroscedastic covariance matrices[cite: 3]. Package: r-cran-ghclass Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1857 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-fs, r-cran-gh, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-whisker, r-cran-withr, r-cran-dplyr, r-cran-cli, r-cran-lifecycle, r-cran-readr, r-cran-tidyselect Suggests: r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-sodium, r-cran-styler, r-cran-testthat, r-cran-usethis, r-cran-gert, r-cran-gitcreds Filename: pool/dists/noble/main/r-cran-ghclass_0.4.2-1.ca2404.1_all.deb Size: 1197856 MD5sum: d5bbd79f96f79dbe0335b15c4559eda6 SHA1: f9d5e0c946be4b0d9f147c0a315b022c27ead107 SHA256: aedeb41eb2638bf7635535e6dfae9f558dc1749b6a1661ce7eda4de06bfbaf8a SHA512: 49cd001a11e3c0ba75a48cf1e966385b2fc82dee9ea00faea8580740bccb0c31d828373f7117c4775ad34a91686390f7e229bd1d33aaa6f5193eb7e0c21348ba Homepage: https://cran.r-project.org/package=ghclass Description: CRAN Package 'ghclass' (Tools for Managing Classes on GitHub) Interface for the GitHub API that enables efficient management of courses on GitHub. 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Package: r-cran-ghibli Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prismatic Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-crayon, r-cran-cowplot, r-cran-codemeta Filename: pool/dists/noble/main/r-cran-ghibli_0.3.4-1.ca2404.1_all.deb Size: 762254 MD5sum: 92834c3306f68e36572cbe336aed6f76 SHA1: 1a6b33e108c9a30b8676aace3a752fe46d052369 SHA256: bce8dc73d6988d5107227cf3c5cdf8e0afc520a66560174547d82719793403cd SHA512: 71286ae528d8c5ab14d2ed58afb7b78e17c45beeb6d0c5a71e62b6daf4808fbe1e6af6d43aeede992a2ad47612962b7daac3844f35f3b28ded6096cf213e4a74 Homepage: https://cran.r-project.org/package=ghibli Description: CRAN Package 'ghibli' (Studio Ghibli Colour Palettes) Colour palettes inspired by Studio Ghibli films, ported to R for your enjoyment. 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Package: r-cran-ghostknockoff Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-cvxr, r-cran-rdsdp, r-cran-gtools, r-cran-seqminer, r-cran-rspectra, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-ghostknockoff_0.1.0-1.ca2404.1_all.deb Size: 51718 MD5sum: 9214ab46b39e154ee2529415c4fde134 SHA1: aeb256cc74d2a54fa6e6b85ca0a8f14bde1526c9 SHA256: 9eb8f9da20ffdccb61c597e187adf6b38795e1373845120a15c6fe4b3743fbb5 SHA512: f6a9e3a32bffb34679f2e489331611418a81e84c1078c62fffc90b445a53a24929b8c3d732d10b9b889de700d6b1b22cb8ccd9d26c880532f95bcf922c392eb3 Homepage: https://cran.r-project.org/package=GhostKnockoff Description: CRAN Package 'GhostKnockoff' (The Knockoff Inference Using Summary Statistics) Functions for multiple knockoff inference using summary statistics, e.g. Z-scores. The knockoff inference is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. This package provides a procedure which performs knockoff inference without ever constructing individual knockoffs (GhostKnockoff). It additionally supports multiple knockoff inference for improved stability and reproducibility. Moreover, it supports meta-analysis of multiple overlapping studies. 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Package: r-cran-ghrmodel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6527 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dlnm, r-cran-dplyr, r-cran-ggplot2, r-cran-ghrexplore, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-sf, r-cran-sn, r-cran-rcolorbrewer, r-cran-colorspace, r-cran-testthat, r-cran-spdep, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ghrmodel_0.1.1-1.ca2404.1_all.deb Size: 5468060 MD5sum: b0148b73c39d6a6f29138aa84de0086d SHA1: 7e19d14c7401b09ced34083e02721c163678c1f3 SHA256: 4c5bfe29c9ccc2877d641188ab8a43a5f05e09e2c87c12dc6fd09e4e83cc2b4c SHA512: 82ca5dae1c6f1e79c7321dd203bfed58bef4e8f8d59f14aec0fefaa3215b8e0e17b3d987d00312f33ee632e5c879881ab2cb3ba1c3ad8395752d86b96e1f0d6e Homepage: https://cran.r-project.org/package=GHRmodel Description: CRAN Package 'GHRmodel' (Bayesian Hierarchical Modelling of Spatio-Temporal Health Data) Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the 'INLA' framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) . Package: r-cran-ghs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ghs_0.1-1.ca2404.1_all.deb Size: 1061668 MD5sum: f8beba8d3da3f82c5504b163b6f4abbe SHA1: 08e84ef51642aed1dc97161abc7107d8fe981ff7 SHA256: 8a40fd4db71be442cf7b62dde478dc043eeec57a6b6c058d41a5203c65b00ea8 SHA512: 5b31550f806a4045b3ea0959033d86f3305b02cd590978adcadaf402b9d4e8f7862c3783d25f3a7c53f9a4d540be7121c69eb39ccd2b9c3b9032401ddfa2269a Homepage: https://cran.r-project.org/package=GHS Description: CRAN Package 'GHS' (Graphical Horseshoe MCMC Sampler Using Data Augmented BlockGibbs Sampler) Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) . The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) , and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) . Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) . Package: r-cran-ghypernet Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbmcapply, r-cran-plyr, r-cran-numbers, r-cran-purrr, r-cran-dplyr, r-cran-rlang, r-cran-reshape2, r-cran-rootsolve, r-cran-texreg Suggests: r-cran-biasedurn, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggraph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ghypernet_1.1.2-1.ca2404.1_all.deb Size: 897238 MD5sum: 62fc1ec8b65ede1b450d05b2b054db18 SHA1: 3924828b387f840ad4aa369ffa95069a96159467 SHA256: 08839fb54315ce84a44d00a7e184b8eaaf78f1bf7ee0641a5bc3c260b5bda8b9 SHA512: b7aa9eb8ca55cd7395b11d8aef386e9e9acc8f0607b94a1fefdc001fd86e6b7f6c2df4b9435a477907b2366297208771480a25c3dcb06192ffbc6daba453b125 Homepage: https://cran.r-project.org/package=ghypernet Description: CRAN Package 'ghypernet' (Fit and Simulate Generalised Hypergeometric Ensembles of Graphs) Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) . Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) . Casiraghi, G., (2017) . Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) . Casiraghi, G. (2019) . Casiraghi, G., Nanumyan, V. (2021) . Casiraghi, G. (2021) . Package: r-cran-giacr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18415 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chromote, r-cran-jsonlite, r-cran-pingr, r-cran-processx, r-cran-r6 Filename: pool/dists/noble/main/r-cran-giacr_1.0.1-1.ca2404.1_all.deb Size: 4832992 MD5sum: 20f05068be513dd328dce1abb97ba763 SHA1: cbc72866534a55dd8b282240c63357d9c1190995 SHA256: 9c7151c963f8e3e6e9d5321855f14e3d27615e3965994e3262c7d39fa25d9bfe SHA512: 086acd64b488298b83d9f8f262a0537e3ecfe2d8509c532c70d826e718f9fd2bc1e9b1bc8f963a6c347224aedb3cacd080c5c6cd063f49c7d2f66b12e7f6ebd1 Homepage: https://cran.r-project.org/package=giacR Description: CRAN Package 'giacR' (Interface to the Computer Algebra System 'Giac') 'Giac' is a general purpose symbolic algebra software. It powers the graphical interface 'Xcas'. This package allows to execute 'Giac' commands in 'R'. 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Translation and restructuring operations for planar shapes and other hierarchical types require a data model with a record of the underlying relationships between elements. The gibble() function creates a geometry map, a simple record of the underlying structure in path-based hierarchical types. There are methods for the planar shape types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages. 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'GIFTr' takes dataframe of questions of four types: multiple choices, numerical, true or false and short answer questions, and exports a text file formatted in 'MOODLE' GIFT format. You can prepare a spreadsheet in any software and import it into R to generate any number of questions with 'HTML', 'markdown' and 'LaTeX' support. Package: r-cran-gillespiessa Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1931 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gillespiessa_0.6.2-1.ca2404.1_all.deb Size: 1261782 MD5sum: a73cc70b59b345c3dc47d1b03ca1faaf SHA1: 791dac205a9c0288c60ac21faadd267a06ccb51c SHA256: 703d86424f2216a70bad1a848c4d7f56bdea6a18e0e7fcd9df4ab2bba51dc140 SHA512: 5d956b810335c2ac959fec11ceace289c99a7e194c118fd0f2f65d8945bb2f62742ff4266ab85a66cb3af6e534a939e0fd928bd62756ba86973d53ad37bac8fc Homepage: https://cran.r-project.org/package=GillespieSSA Description: CRAN Package 'GillespieSSA' (Gillespie's Stochastic Simulation Algorithm (SSA)) Provides a simple to use, intuitive, and extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous-time model. Currently it implements Gillespie's exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap). The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, 'Decaying-Dimerization' reaction set, linear chain system, logistic growth model, 'Lotka' predator-prey model, Rosenzweig-MacArthur predator-prey model, 'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model. Pineda-Krch et al. (2008) . Package: r-cran-gilmour Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gilmour_0.1.1-1.ca2404.1_all.deb Size: 645908 MD5sum: 1b2c679401925f1439797d308756e30a SHA1: e76d30484eb835d6f5ac51caf42a9c2e10564a92 SHA256: c865703548bdd7e4644ef190ad1f4cdc99301ae78a2f39c53f142f970881729a SHA512: db78154d322b9cb6cc75917d75b33499e00ad461a19b13a98a97a0473575e0b8d78a2e95ed0689fbed2158f728256baca0027cb68bdb3f638166fd9f77b3f8ba Homepage: https://cran.r-project.org/package=gilmour Description: CRAN Package 'gilmour' (The Interpretation of Adjusted Cp Statistic) Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression. One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study. Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated. To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper. For more details see the help of the function final_model() and the original study (1996) . Package: r-cran-gim Architecture: all Version: 0.33.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gim_0.33.1-1.ca2404.1_all.deb Size: 372160 MD5sum: 8c113d9db3867b67c954c6698c4e1d73 SHA1: 9d63014c99a929cc92ebeaa554ce5b8c09dcb544 SHA256: 7ad8b796dcec007dfd6c80fe1419bd03a761026bf3d2a561d2f6a8463fcf9c31 SHA512: e3b68795eb7dd9c978657f977b769f04c3564da0e5d85cb3597aa2d03cd08817ec0f81afbe015b18abdfaf65c44cf1f3e311fb188231e5f5f4160c01a8837b3d Homepage: https://cran.r-project.org/package=gim Description: CRAN Package 'gim' (Generalized Integration Model) Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. 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Package: r-cran-gimap Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14057 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readr, r-cran-dplyr, r-cran-tidyr, r-cran-rmarkdown, r-cran-vroom, r-cran-ggplot2, r-cran-magrittr, r-cran-pheatmap, r-cran-purrr, r-cran-janitor, r-cran-stringr, r-cran-httr, r-cran-jsonlite, r-cran-openssl Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-kableextra, r-cran-knitr Filename: pool/dists/noble/main/r-cran-gimap_1.1.3-1.ca2404.1_all.deb Size: 3073170 MD5sum: 5d7d31c9ae0e8d1c2a156b72aa1358a2 SHA1: c51756116a14cf3416b051e1eff7e2998dafd16a SHA256: 118c8572b1cbce5598edf088daef9bdbae2be43bf85e93749f0ba94051d09c94 SHA512: 58df84ed57577003e121ab7261643dd0a8e69c062f8eef5f00d4cb935ee45ca1e883ef085a4252c7e0c06593430225e6b61a60da7a7a0babadbbd8b8f4fd9d04 Homepage: https://cran.r-project.org/package=gimap Description: CRAN Package 'gimap' (Calculate Genetic Interactions for Paired CRISPR Targets) Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) . 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The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) . Package: r-cran-gimmegvar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-graphicalvar, r-cran-here, r-cran-qgraph, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gimmegvar_0.1.0-1.ca2404.1_all.deb Size: 199354 MD5sum: 454978e2e7784f6d5d86f8609a7f1dce SHA1: 985a9d8be5c92c6f49f13427a13a33629f23a177 SHA256: b396e39978b971732a4bbbc01c049ad78a3b465daa7a0e080d5d0723133a4793 SHA512: 9c64a1250a25961eaeb4ca802caa237495bdf55ff01859012f1578c65cbcb24d5b32f6df89bc9e0d208dc1937a710baca6490c3fa47cfbfcdc245a61397f53b5 Homepage: https://cran.r-project.org/package=GIMMEgVAR Description: CRAN Package 'GIMMEgVAR' (Group Iterative Multiple Model Estimation with 'graphicalVAR') Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. 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As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation. 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It supports the identification of credible sets of genetic variants. Package: r-cran-gini Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gini_0.1.0-1.ca2404.1_all.deb Size: 27992 MD5sum: b5135b18c8109544fdba19ba33c381f3 SHA1: 2938ed6610f15cf20df70a58e9917308f4be1636 SHA256: 4605ccf63838f9e04519a811ea06d52f46d53b338c5b1a3eb977977e2e003978 SHA512: a3bd1e339e0a4fa5107d58ec7578234af6137f2758f840c9984f8a8894e54b50d12588761ba2af2b0e61f95662a2150f98adb245335086184d7966023c3508c9 Homepage: https://cran.r-project.org/package=Gini Description: CRAN Package 'Gini' (Gini Coefficient) Providing various equations to calculate Gini coefficients. The methods used in this package can be referenced from Brown MC (1994) . 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This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework. References: Yu, C., N. T. Penna, and Z. Li (2017) . Yu, C., Li, Z., & Penna, N. T. (2017) . Yu, C., Penna, N. T., and Li, Z. (2017) . 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Methods based on the 'gips' framework identify and impose permutation structures that regularize covariance estimates and improve stability and interpretability for symmetric or exchangeable features. The package provides pooled and class-specific covariance models, including multi-class variants with shared or independently estimated symmetry structures. The underlying methodology is described by Graczyk et al. (2022) and Chojecki, Morgen, and Kołodziejek (2025) . 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Package: r-cran-gitcreds Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-codetools, r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-oskeyring, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gitcreds_0.1.2-1.ca2404.1_all.deb Size: 87546 MD5sum: f36686c7e7c6f34e6d82f6793fdb6328 SHA1: 43e75a556ad05a3957c061d3dece0384717d7fe6 SHA256: d4f353f0f1e9c0e9f5450a98509eeeb4e210c141b15bd6e9f4d2f4e5cf6e1e55 SHA512: f40aa8695f17b142a781ba26c635e1e99122495e443a07cfce96f1feed644f2e68c6a706d9d0151a5e7d222e2f0435e8bf0733f232d5b1af79d96483a8874a32 Homepage: https://cran.r-project.org/package=gitcreds Description: CRAN Package 'gitcreds' (Query 'git' Credentials from 'R') Query, set, delete credentials from the 'git' credential store. Manage 'GitHub' tokens and other 'git' credentials. 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Package: r-cran-gitear Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-mockery, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-gitear_1.0.0-1.ca2404.1_all.deb Size: 200800 MD5sum: c20adfc09e544087d6e49f6d3f3e1021 SHA1: 2373142a92f132c811bb413d58212a3ff292c952 SHA256: 30f7b55ca44f06c72164f8b3367d13f4d5ff7ffb698e4b5e681ccea62982eafa SHA512: 1c59ee7fef51b7fca6c69295ee6e60c4625042f6913b4cdb8a92a098cb4b49bd51df27dbd18a08787c1220600a404bacc7d64f38149fcd8c2cd2fa3c87eabf2f Homepage: https://cran.r-project.org/package=gitear Description: CRAN Package 'gitear' (Client to the 'gitea' API) 'Gitea' is a community managed, lightweight code hosting solution were projects and their respective git repositories can be managed . 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Package: r-cran-gitgadget Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-curl, r-cran-jsonlite, r-cran-dplyr, r-cran-shinyfiles, r-cran-callr, r-cran-usethis, r-cran-markdown Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gitgadget_0.8.2-1.ca2404.1_all.deb Size: 130024 MD5sum: 1600db7b1e9e434038cf7239d27d649d SHA1: 5ba568cf6d33f6cc2759e8f2735fae79c8e4df1c SHA256: d899d55e5bcd3e47aed04726efc4c36a47b03a48de291dcfcff0718fef82c87c SHA512: 3e6f4fd490198b772a443fee4cc683e408ce421315a5ef9e29ccf59162147dd0311f94014380407d27eef0cb22254314f752d225cce55067a012898073650be6 Homepage: https://cran.r-project.org/package=gitgadget Description: CRAN Package 'gitgadget' ('Rstudio' Addin for Version Control and Assignment Managementusing Git) An 'Rstudio' addin for version control that allows users to clone repositories, create and delete branches, and sync forks on GitHub, GitLab, etc. Furthermore, the addin uses the GitLab API to allow instructors to create forks and merge requests for all students/teams with one click of a button. Package: r-cran-gitgpt Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-gitgpt_0.1.3-1.ca2404.1_all.deb Size: 25354 MD5sum: e193111d778739a24d772340ef2f50d7 SHA1: 912b8e90228ef6b11baaf2038322be296247bd17 SHA256: b9a40c4475872ee98852ca7225cf739375bbe36ef9a14ecf7ae2e8591387a488 SHA512: a98e113c1508ead45be9019a4a1213949d8642c5080713bfbed90e02e420dbf8501edccb7feed68a99bf503bf9bf68522b3a01d269eed6773c32254d528df94e Homepage: https://cran.r-project.org/package=gitGPT Description: CRAN Package 'gitGPT' (Automated Git Commit Messages using the 'OpenAI' 'GPT' Model) Automates the process of adding, committing, and pushing changes to a 'git' repository using commit messages generated by passing the git diff output to the 'OpenAI' 'GPT-3.5 Turbo' model (). Package: r-cran-githubinstall Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-devtools, r-cran-httr, r-cran-jsonlite, r-cran-mockery Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-githubinstall_0.2.2-1.ca2404.1_all.deb Size: 63456 MD5sum: 9f88838f7b157b2e95310e5950c51c79 SHA1: 005d8035def30666e20d6265cf4498bc1eb88cac SHA256: 0a0944a5ac715092ea961db4780ca978ec00a05bc3d120bf589de5e6dbe826e5 SHA512: 4c23f11dc7144ea81c60d331021ccced2c659517b7dd9f71e5412a48a581029a9e882ceafe4878eb4df4cc0c7edb0307ec9b0aef0ececaf53a99c689d24a427f Homepage: https://cran.r-project.org/package=githubinstall Description: CRAN Package 'githubinstall' (A Helpful Way to Install R Packages Hosted on GitHub) Provides an helpful way to install packages hosted on GitHub. Package: r-cran-githubr Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gitcreds, r-cran-dplyr, r-cran-gh, r-cran-magrittr, r-cran-httr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-githubr_0.9.1-1.ca2404.1_all.deb Size: 28750 MD5sum: 0c1d77eb11f598f780daced3ce240db9 SHA1: c55c820f62b28eeeb47eb62a2f098796908742c8 SHA256: 4d698ac78bb7f9fd8fe66643b055da543980c3af679dbd345c4b18f62484fdae SHA512: d82e5bbf47ba450eeadc87a88d222084f61d21e284161824515059cc63d2313eedd29577caf602d11a39553bfb6a12e8948e41759a298d205c2ebadc6b9a87ef Homepage: https://cran.r-project.org/package=githubr Description: CRAN Package 'githubr' (Easier to Use API Wrapper for 'GitHub') This is a 'GitHub' API wrapper for R. It uses the 'gh' package but has things wrapped up for convenient use cases. 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Package: r-cran-gitstats Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-lubridate, r-cran-mirai, r-cran-rlang, r-cran-r6, r-cran-purrr, r-cran-stringr Suggests: r-cran-dbi, r-cran-jsonlite, r-cran-rpostgres, r-cran-rsqlite, r-cran-spelling, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gitstats_2.5.2-1.ca2404.1_all.deb Size: 928274 MD5sum: 848f86655cd22c0289316cdcc3b3f932 SHA1: c9b19e83a70e9987f6f8f854e6eb7b184918c353 SHA256: f0144d068ea1d461c02c12f41e21c776e3578fa7c5c5b001c45c8e5f8746b282 SHA512: a787769d2403b400501de58332a1100790f11ccda09405297b6d25eb1e46365c66d656f1f08ebe522865be0562556503e60646cf4d7c6fbec380b72bc5d4273a Homepage: https://cran.r-project.org/package=GitStats Description: CRAN Package 'GitStats' (Standardized Git Repository Data) Obtain standardized data from multiple 'Git' services, including 'GitHub' and 'GitLab'. 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Package: r-cran-gittargets Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-data.table, r-cran-gert, r-cran-processx, r-cran-targets, r-cran-tibble, r-cran-uuid Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gittargets_0.0.7-1.ca2404.1_all.deb Size: 279750 MD5sum: 51b0a4503cce0440756f35a25c5fddcc SHA1: 5b763aaeb494d2702812fb83724bab7834665cf9 SHA256: 09c8530e0ab5ba9f7e69504fb7c0fd4829fe8f9850466fd8a5ad53c6aa1e41e9 SHA512: d2526f82a57f055f2daaa92033a356d98b746b8206c5c497b97f7ee08095fc31f9d1b528b300142adda80987f874b6317f6f52805b9d8725754cd6c9fbbfa199 Homepage: https://cran.r-project.org/package=gittargets Description: CRAN Package 'gittargets' (Data Version Control for the Targets Package) In computationally demanding data analysis pipelines, the 'targets' R package (2021, ) maintains an up-to-date set of results while skipping tasks that do not need to rerun. 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The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model. 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The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; ). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; ) and scale (Deng et al., 2019; ). Package: r-cran-gjrm.data Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gjrm Filename: pool/dists/noble/main/r-cran-gjrm.data_0.1-1-1.ca2404.1_all.deb Size: 789768 MD5sum: 8d952458743fd3c906c16082ccca9a74 SHA1: cd66636bc1cb51cba230db4604c5ec5f2be1cec8 SHA256: c20db46589a551f0b52e0053aab01056746d267541eb29a2919a5f53009030b3 SHA512: e5f59ec2b93311046648e18655d83e2e982cf331f42b59ba8c6a78b0ae1cf038cedc374287703ec78e91297adb8e84f27c2467447df31c2a5b91f150f2d79de5 Homepage: https://cran.r-project.org/package=GJRM.data Description: CRAN Package 'GJRM.data' (Data Sets for Copula Additive Distributional Regression Using R) Data sets used in the book Marra and Radice (2025, ISBN:9781032973111) "Copula Additive Distributional Regression Using R", for illustrating the fitting of various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association. 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Package: r-cran-gkrreg Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sm Suggests: r-cran-robustbase, r-cran-quantreg, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gkrreg_0.4.0-1.ca2404.1_all.deb Size: 311026 MD5sum: e3c0d6bcb13ae03a410e14c62633afa8 SHA1: 17751ba77cd6a1c51e5b1ac77df970e4486bc10b SHA256: e37ea987e3565e0fd9b9471fbff847feb9991132f6481e5bb0b61f54d9c3b004 SHA512: 6973993489df86fe60c0f8c2231da7e9e1d598dfada129ebe931bf40549e3b470264c40856de021d7fe1acc21dcefda6824ae39f81614d13b44a8f2194d0e73e Homepage: https://cran.r-project.org/package=gkrreg Description: CRAN Package 'gkrreg' (Gaussian Kernel Robust Regression (GKRReg)) Implements the Gaussian Kernel Robust Regression (GKRReg / GKRR) method proposed by De Carvalho, Lima Neto and Ferreira (2017) . The method re-weights observations iteratively using the Gaussian kernel so that poorly-fitted observations (outliers, leverage points) receive small weights, yielding resistance to Y-space outliers, X-space outliers and leverage points. Convergence is guaranteed by Propositions 4.1 and 4.2 of the original paper. Three estimators for the kernel width hyper-parameter are provided (S1: Caputo, S2: pairwise median, S3: residual variance). Inference is provided via an analytic sandwich variance estimator (default) or via bootstrap (percentile, normal and BCa intervals with p-values) through gkrr_boot(). Six real datasets from the robust regression literature are included to facilitate reproducible comparisons. Package: r-cran-glam Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glam_1.0.2-1.ca2404.1_all.deb Size: 54776 MD5sum: a126df29720e0721dbc636424e70071c SHA1: 6d0da227b45296cd4ba91fc4bb5e4e937aeb74b5 SHA256: 5c54c4b4cc98a44d5256e7ab2b6860ca1af44d60a1d6e5eec80609ef8a405916 SHA512: e9c097f818b4915734370d626d9bcb864b154d0c6aadc570851b1e8c2f3fbb01cff422b7948133f61f120ea9162114063991974a81bff06d9ee06767383b469e Homepage: https://cran.r-project.org/package=glam Description: CRAN Package 'glam' (Generalized Additive and Linear Models (GLAM)) Contains methods for fitting Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). Generalized regression models are common methods for handling data for which assuming Gaussian-distributed errors is not appropriate. For instance, if the response of interest is binary, count, or proportion data, one can instead model the expectation of the response based on an appropriate data-generating distribution. This package provides methods for fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma regression, and Binomial regression (currently GLM only) settings. Models are fit using local scoring algorithms described in Hastie and Tibshirani (1990) . 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Several standard data sets are included in the package. Package: r-cran-glarmavarsel Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-markdown, r-cran-formatr, r-cran-domc Filename: pool/dists/noble/main/r-cran-glarmavarsel_1.0-1.ca2404.1_all.deb Size: 56746 MD5sum: 8115043f25dadd9600c87077e464ac13 SHA1: 3fb6fefe89a3093d6fc0ef55f716fd68260ef06b SHA256: f692b92405068bcf88e0a94217291b22137aa044710d8af54d636d66ea0bb1cc SHA512: 0c6bc165d2e476cbd4c829aabd6f60cff06942cf3fe2da29e6495126e29807c418cd52603e6dd60e72a30844bc1a702e871e0473e8203939ba4d334bca1b154c Homepage: https://cran.r-project.org/package=GlarmaVarSel Description: CRAN Package 'GlarmaVarSel' (Variable Selection in Sparse GLARMA Models) Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), . Package: r-cran-glassdoor Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glassdoor_0.9.0-1.ca2404.1_all.deb Size: 85916 MD5sum: 31aa3951b634e6eda2316f5306f2788f SHA1: fe5dcf403c914d96a50b91372c21919ec10eb92c SHA256: 92d85f8990ea8388b9dc860df3b4c1f630a4e369cc9e5f3bc71c22e13d67532c SHA512: 5195159a507e6ae578862c3ea782d02ed600c64901d1d1f15bd8df4c59c029f1e76e6f0fe1546b036c76b5946f084836a53a768e16d882b67d551edc9ca56575 Homepage: https://cran.r-project.org/package=glassdoor Description: CRAN Package 'glassdoor' (Interface to 'Glassdoor' API) Interacts with the 'Glassdoor' API . 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For full documentation and examples see Arthur (2026) . Package: r-cran-glba Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glba_0.2.1-1.ca2404.1_all.deb Size: 76300 MD5sum: f61f350fe84b4b87dcee4296500e5e45 SHA1: 7c2d8e167164364709e94ee684ed0795a850cee9 SHA256: 2d5320df15a05ebfb4bff819194ea68ee884b47502628b1578f48cffc014aa64 SHA512: 2d2de6aeb1b68b7a12d080ce1b1efa26ac131c60c858c64b2df4ca700891d8a90940ac6b7517f01244063153b8590f4eb317c2c8eb793ed5afd7ac7a95877bfa Homepage: https://cran.r-project.org/package=glba Description: CRAN Package 'glba' (General Linear Ballistic Accumulator Models) Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). 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The package provides pointwise and grid-based estimation workflows, sparse-prefix grid-count computation, plotting helpers, and plug-in bandwidth selection. Methodological background follows Scott (1992) , Terrell and Scott (1985) , and Carbon and Duchesne (2024) . Package: r-cran-glcdp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5854 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-readr, r-cran-tibble Suggests: r-cran-bslib, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glcdp_1.1.0-1.ca2404.1_all.deb Size: 4393314 MD5sum: 59fecde9b2826389ca41eb680656980e SHA1: 91f26bfe81b2606d3f894edd2044854de41d061b SHA256: 70ee141a3c1312d7b2cff7521ceb42af14d40576fd4c8dd4e5f0b7dd239e4545 SHA512: c7a31c805fcd5a74186bdf33f6f31997a71deb7f8d9250c05f86727031275d9889fcbb57490a9c0c28f3d2c1d531545d7437ed07ab726e7e41bdf15e87e3bcea Homepage: https://cran.r-project.org/package=glcdp Description: CRAN Package 'glcdp' (Discover, Access, and Import Global Light Commons Data Packages) Discovers Global Light Commons data packages through their registry, opens immutable passing revisions, and provides searchable inventories of package metadata. Selected metadata and measurement files can be downloaded or imported with metadata-defined columns, types, factor levels, date-time values, and time zones. 'Git Large File Storage' objects are resolved without requiring an external 'Git LFS' installation, and imported file groups can be explicitly collected into data suitable for personal light exposure analysis workflows. An included 'shiny' application supports interactive discovery, inspection, selection, preview, and reproducible handoff to 'R'. Package: r-cran-gldreg Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gldex, r-cran-ddst Suggests: r-cran-mass, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-gldreg_1.1.2-1.ca2404.1_all.deb Size: 154976 MD5sum: 18965de7615ad3e5ee05326058fa06cb SHA1: 659fdf39395fa6e5ca2ce9b6ccdc77b2e40bde2d SHA256: 7da3ad649a4441bd78e48f3e32186718cbed7592c6c3d61056edccddd7c6cbb4 SHA512: ffe3f6ef3bcb091341015162934d7ecb73f57597e90a1ada27e25de7055bb5425816cdf17bbb52f1977100eb25584cb09cd5cc6acaaf3051fecc012c2272e5c2 Homepage: https://cran.r-project.org/package=GLDreg Description: CRAN Package 'GLDreg' (Fit GLD Regression/Quantile/AFT Model to Data) Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model". Package: r-cran-gldrm Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gldrm_1.6-1.ca2404.1_all.deb Size: 116706 MD5sum: c7082e14aaf5de790b2210c5c72ad0f6 SHA1: 67a03a23e0d8511f5e3a204e8bddd4c9a4d03134 SHA256: a709a401cdd281eec8f838130b1b4b68cfc7ca132df301093df1c3139b7d2bd1 SHA512: cd36bf5e72575c3036c41a4bbd55f75b1c54a79050414143bb9ea249f5228afd4025c0a7b6bae00dd03fd0517be8bcb2f5a5f922a33b3274c84a8dafec85de5f Homepage: https://cran.r-project.org/package=gldrm Description: CRAN Package 'gldrm' (Generalized Linear Density Ratio Models) Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) . Huang and Rathouz (2012) . Rathouz and Gao (2008) . Package: r-cran-gleam Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gleam_0.8.0-1.ca2404.1_all.deb Size: 2829130 MD5sum: 8bf8c488a4eb66909e554f9125143359 SHA1: 2f8856878d8dd9cd6966f2f3cc6e8322dd8c7ad7 SHA256: 88604d666d14878ebefd57524745850e5139d45d6dc80cbaceedb9140cfdbb74 SHA512: 99f9d93b97e3a441bfc85a79895799851f539e74fec45d0e52bf55c9fe67b6ed03640bf49091952a2b867bd721a64f0ce5f826c994fff99c5986c9d0805f7180 Homepage: https://cran.r-project.org/package=gleam Description: CRAN Package 'gleam' (Global Livestock Environmental Assessment Model (GLEAM-X)) The official implementation of the Global Livestock Environmental Assessment Model (GLEAM) of the Food and Agriculture Organization of the United Nations (FAO) in R. GLEAM-X provides a modular, transparent framework for simulating livestock production systems and quantifying their environmental impacts. Methodological background: MacLeod et al. (2017) "Invited review: A position on the Global Livestock Environmental Assessment Model (GLEAM)" . Further information: . Package: r-cran-gleifr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-xml2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gleifr_0.3.0-1.ca2404.1_all.deb Size: 89206 MD5sum: 4fc32beef1d665ab6a703a8a50115076 SHA1: dd902abae4ee8c58d7c5fc2c90daf64816196059 SHA256: 210c53b4803fdacf6228d6146c28fa31be67b24480131043784e7154b9499f74 SHA512: ec452c3a59ede2cd29629ce539b8f64e81182819104fbf2e05c1ba20458ae2399053d5d801a9ad52ad218d34b6d1bf66dea8a5d02bc8501842063d1781a4333f Homepage: https://cran.r-project.org/package=gleifr Description: CRAN Package 'gleifr' (Client for the 'GLEIF' API) Download legal entity reference data from the 'Global Legal Entity Identifier Foundation' ('GLEIF') API. Retrieve Legal Entity Identifier ('LEI') records, their direct and ultimate parent and child relationships, accredited issuers ('Local Operating Units'), and mappings from 'LEI' codes to other identifiers such as 'ISIN', 'BIC', and 'MIC'. See for further details. Package: r-cran-glhd Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glhd_1.1-1-1.ca2404.1_all.deb Size: 24494 MD5sum: c70dc6d2b537b02c54e28cb593b2134b SHA1: 8d55495d83be0c8d42e2ae432789d6dd13af6d87 SHA256: 1deb637162031b67a372da3593a1f3a6904ff84f43cd339db916151a21d69b0c SHA512: 45d698edef837f8817d92e7d97a9d14d36cbe94e22556a1e273bb4837ac380568a81a41e7fd2927192a07b6c93382e8a0a0abdb845ef791b7135005013122db7 Homepage: https://cran.r-project.org/package=GLHD Description: CRAN Package 'GLHD' (Grouped Latin Hypercube Designs with Controlled Correlations) We provide a method of constructing grouped Latin hypercube designs by controlling correlations. Details of the algorithm can be found in Wenlong Li, Jian-Feng Yang and Peter Chien (2026). Grouped Latin hypercube designs with controlled correlations. Technometrics, published online. Important function in this package is "GLHD_CC". Package: r-cran-glioblastomaehrsdata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dataexplorer, r-cran-flextable, r-cran-ggplot2, r-cran-rmarkdown, r-cran-summarytools, r-cran-table1, r-cran-tinytex Suggests: r-cran-ragg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glioblastomaehrsdata_1.1.0-1.ca2404.1_all.deb Size: 78166 MD5sum: b924b884473867d91c42438d3853de9f SHA1: 974909a76e2dbf8ec87d5713347dab801557a592 SHA256: 602d27f8003774104767e40c147c7a273db90feb6712d6d82b8ee4c29582c84e SHA512: 4b86ba3996a8033a72cbaf5bff6b2d9a3880c58b1857f205e30ef5008e1832f1771ee1e523a7fbfc41c0f69b1d05b516f0b2724f0e154c87ba7bf529e72f8fba Homepage: https://cran.r-project.org/package=glioblastomaEHRsData Description: CRAN Package 'glioblastomaEHRsData' (Descriptive Analysis on Three Glioblastoma EHRs Datasets) Provides functions to load and analyze three open Electronic Health Records (EHRs) datasets of patients diagnosed with glioblastoma, previously released under the Creative Common Attribution 4.0 International (CC BY 4.0) license. Users can generate basic descriptive statistics, frequency tables and save descriptive summary tables, as well as create and export univariate or bivariate plots. The package is designed to work with the included datasets and to facilitate quick exploratory data analysis and reporting. More information about these three datasets of EHRs of patients with glioblastoma can be found in this article: Gabriel Cerono, Ombretta Melaiu, and Davide Chicco, 'Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March 2024). . Package: r-cran-glm.predict Architecture: all Version: 4.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-aer, r-cran-survival, r-cran-mass, r-cran-mlogit, r-cran-dfidx, r-cran-survey, r-cran-lme4, r-cran-vgam Suggests: r-cran-ggplot2, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glm.predict_4.3-2-1.ca2404.1_all.deb Size: 309542 MD5sum: 756582d239bdaec203522903a3be81a1 SHA1: e368aaaf3c80c6649ead3b5620dc302de15a8df5 SHA256: 29006d9198fe01f484b1e97f47f131b3e0b5b829fc7c60643352589f760c5e5c SHA512: d4c524026605f611d62d0d46abad7da63fb15f23170374aa16bb5ed1d43ef71fdefa4b120c254fd430dc3abc824df7afcc5703efd4ae334c31d4504eb7380abb Homepage: https://cran.r-project.org/package=glm.predict Description: CRAN Package 'glm.predict' (Predicted Values and Discrete Changes for Regression Models) Functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], vglm() [generalized ordinal logistic model], multinom() [multinomial model], tobit() [tobit model], svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) . Package: r-cran-glm2 Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glm2_1.2.1-1.ca2404.1_all.deb Size: 50046 MD5sum: 7f3ed7dd5a8a2dd800f7c0f4715ce07c SHA1: a6cc40e683c06d372d7a9b7763305e5cfffd60dc SHA256: ffb9e5cfeeacedb8da7f81757745b3c772548a44bf1f79ad387d8d21a68ca64f SHA512: 744b27fb3c8e77b2d4f7525dbfe1417e40a5f2d32659b2fe71799f34d45de86a0eabdf891a4bfa9e829b9b67c8b6c4cd4046c8fd3c3d889e96c9aa784c428272 Homepage: https://cran.r-project.org/package=glm2 Description: CRAN Package 'glm2' (Fitting Generalized Linear Models) Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm. Package: r-cran-glm4 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixmodels Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glm4_0.1.0-1.ca2404.1_all.deb Size: 255408 MD5sum: 4eedbb5857a304e40adeddd9ed13a3a8 SHA1: 4b0b1761db5ebce2c3999569a4ef7ffad1d65b6c SHA256: 63ead2226ff2821961d520348dba2bfe8e009d943291287e22f8f8c21b475b41 SHA512: bf1b6c4beed747b29ef2cfb54d7f8693e92ba6b55dc494a0357150a821995b9bc17cc1ab270337ce6567c4a86d150e1a41aab95cf17d8bb56e86fce131981b75 Homepage: https://cran.r-project.org/package=glm4 Description: CRAN Package 'glm4' (Fitting Generalized Linear Models Using Sparse Matrices) Fits Generalised Linear Models (GLMs) with sparse and dense 'Matrix' matrices for memory efficiency. Acts as a wrapper for the glm4() function in the 'MatrixModels' package , but adds convenient model methods and functions designed to mimic those associated with the glm() function from the 'stats' package. Package: r-cran-glmbasedraschestimation Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-dt, r-cran-haven, r-cran-readxl, r-cran-shiny Filename: pool/dists/noble/main/r-cran-glmbasedraschestimation_0.2.0-1.ca2404.1_all.deb Size: 36620 MD5sum: c280ae162e94a110fbe456fb0a08dcae SHA1: eb70f37c371f942ea3e0eb9cd75f8c50c7140224 SHA256: 013fc4fe89a77ff8bde2946fb3ebc5375859cabf3ce42bda025d4fb9448d1646 SHA512: e5190e27008e1b3423aa6043c31f26e3c3a42ce5b888ef6b2e0cdea05a55009fbe34bfa55c6d4dd89a810ebe6073ec9b7c691e84df0f282f1251eed448ab34cf Homepage: https://cran.r-project.org/package=GLMBasedRaschEstimation Description: CRAN Package 'GLMBasedRaschEstimation' (GLM-Based Estimation for Rasch Model Parameters) Provides functions for estimating Rasch model parameters using the Generalized Linear Model (GLM) framework. The methods implemented are based on Brown (2018, ISBN:978-3-319-93547-8) and Debelak et al. (2022, ISBN:978-1-138-71046-7) . 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The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; ). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; ). Package: r-cran-glmfitmiss Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-abind, r-cran-mass, r-cran-brglm2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-glmfitmiss_2.1.0-1.ca2404.1_all.deb Size: 173678 MD5sum: f476524e9e8d1480c116da3c415b9b42 SHA1: 276e2ee7a8b1bae07f54d1b0bd3d82b89c71d5ad SHA256: 85dc962a17e1364a469d393ecbb9a59c13728f825a98b3cdeba7dd2449d7f493 SHA512: 57c1dd17c1402c6dfd891e58a3112e30e3e78bcef8feda22ba78a995cc4f659558127af940e20132830aeef7143230d0aa197e57321e535920c0d5d0c969a257 Homepage: https://cran.r-project.org/package=glmfitmiss Description: CRAN Package 'glmfitmiss' (Fitting GLMs with Missing Data in Both Responses and Covariates) Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details. Package: r-cran-glmglrt Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-parameters, r-cran-mass Suggests: r-cran-testthat, r-cran-lme4, r-cran-nlme, r-cran-nnet, r-cran-survival, r-cran-lmertest, r-cran-mgcv, r-cran-gam, r-cran-multcomp Filename: pool/dists/noble/main/r-cran-glmglrt_0.2.2-1.ca2404.1_all.deb Size: 135848 MD5sum: 390feea2c7255f1df81b608d29977961 SHA1: 4717fc6da194ff0915eb1fea9d472a2c0fd0e0c3 SHA256: 452c1ffcc7a9636fe6d7afecf868a006474551747ef817cdfe901978c3f59b37 SHA512: a618a9804b495e1f20a194a52655f88e09bee339195b7eee2fb61a82de5c28c6b5cf4cac07d7c8d03604487b763c48970a07df257ac9d6d8cf286bdceab076c7 Homepage: https://cran.r-project.org/package=glmglrt Description: CRAN Package 'glmglrt' (GLRT P-Values in Generalized Linear Models) Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values, less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint(). See Wilks (1938) for the LRT chi-square approximation. See Rao (1948) for Rao's score test. See Wald (1943) for Wald's test. Package: r-cran-glmm.hp Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mumin, r-cran-ggplot2, r-cran-vegan, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-glmm.hp_1.0-0-1.ca2404.1_all.deb Size: 59318 MD5sum: 4954d499be344d3f2a15976a0f6b8e9c SHA1: 752eecc8f6cd631d3bf85366eae254feb124dbff SHA256: 97a0f9d02380924af70b4563207dbd6d3c5e300ffc19b97441e2dccac14cf533 SHA512: 9a527ca583ecccba8cc723ec319ba3e66249e40fbbeb06226b62d18201f3bd46f400286f82e9359fd3003478bab1e9b0075079ac5b44c0be472908436c84b105 Homepage: https://cran.r-project.org/package=glmm.hp Description: CRAN Package 'glmm.hp' (Hierarchical Partitioning of Marginal R2 for GeneralizedMixed-Effect Models) Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307. Package: r-cran-glmmadaptive Architecture: all Version: 0.9-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-matrixstats Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-multcomp, r-cran-emmeans, r-cran-estimability, r-cran-effects, r-cran-dharma, r-cran-optimparallel Filename: pool/dists/noble/main/r-cran-glmmadaptive_0.9-7-1.ca2404.1_all.deb Size: 375424 MD5sum: 41b83e8d49828d663ea2d22733469a1b SHA1: 9308ff9f7aa295bc52e02d928e0d4e48be8029dd SHA256: f02c7473f814064a6a3447b75c0b95c0a581f32aeec5fe294c80d8a323dd1d0a SHA512: b2ee46702a52e5af961f20a9f1a1d672c407d52de9c1dedbe812703b19a62bf0762f1991b2df2f8443106b49f5488fbf02632e5c10aace5e79f7c74599b08fe6 Homepage: https://cran.r-project.org/package=GLMMadaptive Description: CRAN Package 'GLMMadaptive' (Generalized Linear Mixed Models using Adaptive GaussianQuadrature) Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) . Package: r-cran-glmmcosinor Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-cowplot, r-cran-ggforce, r-cran-ggplot2, r-cran-glmmtmb, r-cran-reformulas, r-cran-rlang, r-cran-scales Suggests: r-cran-cosinor, r-cran-covr, r-cran-dharma, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ftextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-glmmcosinor_0.2.2-1.ca2404.1_all.deb Size: 1164816 MD5sum: b6e5e12e64a498769d9f4cf4e82c83d3 SHA1: 86dbcce294e8900b3a48e1de44e803634e02f21a SHA256: fcbf23cb833036e85807766ad3b84d68324c8a0e137157fd178db67f285c5846 SHA512: c83f32cef5b07b95aff7fc663fcf7f64ec9fffdcc0dbc76499f19c64a3a50e9415029184bc4e625cd804e740bc3f8a6452a38d3f8f853190818b4a8a23fafa0f Homepage: https://cran.r-project.org/package=GLMMcosinor Description: CRAN Package 'GLMMcosinor' (Fit a Cosinor Model Using a Generalized Mixed Modeling Framework) Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modeling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) . Package: r-cran-glmmfel Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat, r-cran-lme4, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-glmmfel_1.0.6-1.ca2404.1_all.deb Size: 246050 MD5sum: b0b8623e0de73c5f29d22ad373e6b807 SHA1: b043c7185c0be0bd11963225588d266bc0f34091 SHA256: 1dd455c654a8e181bd59a9f6f8b74815f63fb5981571dd7534ed1e36e245db00 SHA512: fa6215528e3ee2fccd7580af301dbd03aad600d72bdcdf0a0eca9038e216ce30572473423ba2b60a695a6f1e1e02d42b1a7a9eeee95c2f9dd59ec72961779ce3 Homepage: https://cran.r-project.org/package=glmmFEL Description: CRAN Package 'glmmFEL' (Generalized Linear Mixed Models via Fully Exponential Laplace inEM) Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and variance-diagonal corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) , and is adapted from the implementation used in the 'RealVAMS' package (Broatch, Green, and Karl (2018)) . The FEL implementation follows Karl, Yang, and Lohr (2014) and related work (e.g., Tierney, Kass, and Kadane (1989) ; Rizopoulos, Verbeke, and Lesaffre (2009) ; Steele (1996) ). Package code was drafted with assistance from generative AI tools. Package: r-cran-glmmisrep Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-poisson.glm.mix Filename: pool/dists/noble/main/r-cran-glmmisrep_0.1.3-1.ca2404.1_all.deb Size: 264210 MD5sum: e3d2fbbdba7d4adc8645d39b46219e15 SHA1: 7b2b44218b544a576159034cee63eef664f6e9f9 SHA256: 9194121d375367fe975ef308029f447579e7be30bfa31acf8ceed577ec3ec376 SHA512: d9273932aa3977fb5f282cf1e1fe52bb44db0b6feb8eac71be448464c9d474d4c9a2fe0097e5269d1d372ba87baab59dec5270c4fbc1320c489c2f3b92ec6cca Homepage: https://cran.r-project.org/package=glmMisrep Description: CRAN Package 'glmMisrep' (Generalized Linear Models Adjusting for Misrepresentation) Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) . Package: r-cran-glmmrr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lattice, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-glmmrr_0.6.0-1.ca2404.1_all.deb Size: 633464 MD5sum: b37ff754493523dd8a68996d85a24fda SHA1: 44eb711d7a1136b829cb5c2071021626bad8acd3 SHA256: 7d1b74a3a87e647ec50d18419e14fdfcc70ea29a903e4e95b248bc5c641344d7 SHA512: 82b86d6979aef2948a577ef16f24699f24ff233720acd7505de45b6c291d0ad601020f7a32ae6a1fd7900e8bb78f9c3bc0b2e1ce5a3dd2cc55d314440aa3c212 Homepage: https://cran.r-project.org/package=GLMMRR Description: CRAN Package 'GLMMRR' (Generalized Linear Mixed Model (GLMM) for Binary RandomizedResponse Data) Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. . Package: r-cran-glmmselect Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glmmselect_1.2.0-1.ca2404.1_all.deb Size: 109304 MD5sum: 699bbc46389b278091b7f2bd9dacb806 SHA1: 487f35b7c5b53a316cd22aae64d8155879a38093 SHA256: 240a183fcb6d318e6436eb3bed6df6e08523052d71e08e0233f1fb6d792edaf3 SHA512: 21d98fe96fe0637208c45495cf6a97fc6c2a21bce070a6a4c81b295bf223bc66688a16f1d680a05eb29d8c40f4d2d391e1d232569e3c96af7973d761d7305859 Homepage: https://cran.r-project.org/package=GLMMselect Description: CRAN Package 'GLMMselect' (Bayesian Model Selection for Generalized Linear Mixed Models) A Bayesian model selection approach for generalized linear mixed models. Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. 'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. 'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review. Package: r-cran-glmmseq Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2973 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-emmeans, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmmtmb, r-cran-mass, r-cran-lme4, r-cran-lmertest, r-cran-kableextra, r-cran-mcprogress, r-cran-plotly, r-bioc-qvalue, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-deseq2, r-bioc-edger Filename: pool/dists/noble/main/r-cran-glmmseq_0.5.7-1.ca2404.1_all.deb Size: 1656780 MD5sum: 0fbdb371190ccae6d87b40bdbf59596e SHA1: 6700639f4e8914e85e351520261d3d0498a9ba1c SHA256: 2c5ed714fbbd60184cf12d1047011f11422c3c16c67fdb16583727dddf746f9a SHA512: e4eff717fc6a4b4ef3c2b39cb5ef0d0f69cbb689ee36851a4be8ce2a94d7e37773f9e7297b97b408af8970d43699879e1432273ed4eda7fac02688797ba747e9 Homepage: https://cran.r-project.org/package=glmmSeq Description: CRAN Package 'glmmSeq' (General Linear Mixed Models for Gene-Level DifferentialExpression) Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine . Package: r-cran-glmnetcr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-glmnetcr_1.0.7-1.ca2404.1_all.deb Size: 807732 MD5sum: 20d6afd759a485a676817085e2b6cf95 SHA1: 534174f4beb976c7a34f4b558ca45851c2c3c758 SHA256: 7a666bacc131e51d796dd5810cfc02977bb057560da26e98c27406774a4de183 SHA512: f7ad2254e7b07b1b03806cfbee8c48259f860ca5eed4294f4d7d7235cad76f2ab5efbbd755c3252829e8c36f1057ab8c53ca079e3460fcd70e280f822eae0b92 Homepage: https://cran.r-project.org/package=glmnetcr Description: CRAN Package 'glmnetcr' (Fit a Penalized Constrained Continuation Ratio Model forPredicting an Ordinal Response) Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring. Package: r-cran-glmnetr Architecture: all Version: 0.6-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3287 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-matrix, r-cran-xgboost, r-cran-lgr, r-cran-paradox, r-cran-bbotk, r-cran-mlr3mbo, r-cran-dicekriging, r-cran-proc, r-cran-randomforestsrc, r-cran-rpart, r-cran-torch, r-cran-aorsf Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-glmnetr_0.6-4-1.ca2404.1_all.deb Size: 2863092 MD5sum: aa40f25c6fd5169e8efee08f6f49923b SHA1: 7c3291f27dca323fa772035955919f47fd24888d SHA256: b6050b00c9a3e3d72a0184ae3b60e1e8ead6486414ed7be675233aa1b37680d5 SHA512: f10a6998def1c7f997be61af8696d8577a2cc4733651174ebf2f17e80c9bb4a80f3c8b79571a585088e219a315f63811bc083038aea01528dadbc19e5a1756f8 Homepage: https://cran.r-project.org/package=glmnetr Description: CRAN Package 'glmnetr' (Nested Cross Validation for the Relaxed Lasso and Other MachineLearning Models) Cross validation informed Relaxed LASSO (or more generally elastic net), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Artificial Neural Network (ANN), Recursive Partitioning ('RPART') or step wise regression models are fit. Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means. Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples. Note, at the time of this writing, in order to fit gradient boosting machine models one must install the packages 'DiceKriging' and 'rgenoud' using the install.packages() function. For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet(). This may be remedied by using the 'path=TRUE' option when calling glmnet() and cv.glmnet(). Within the 'glmnetr' package the approach of path=TRUE is taken by default. other packages doing similar include 'nestedcv' , 'glmnetSE' which may provide different functionality when performing a nested CV. Use of the 'glmnetr' has many similarities to the 'glmnet' package and it could be helpful for the user of 'glmnetr' also become familiar with the 'glmnet' package , with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially useful in this regard. Package: r-cran-glmnetse Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-glmnetse_0.0.1-1.ca2404.1_all.deb Size: 44984 MD5sum: e21181847f6a8a451dfda3f12f37f638 SHA1: a775073d82373677970cd1222b6f8d391f7afb6a SHA256: c44d356e71f4facf5016beec9ddbf9dc77f34caaafd1ce594c741f41347aa6f1 SHA512: d3545985961f1e2b72ff09d69547755c101b618796c86a816b0380a9b1428e76cec7d246c94dbac431fa016b26b4e66187c409629bb37bf96db16f0de33082c8 Homepage: https://cran.r-project.org/package=glmnetSE Description: CRAN Package 'glmnetSE' (Add Nonparametric Bootstrap SE to 'glmnet' for SelectedCoefficients (No Shrinkage)) Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or 'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) and Simon et al. (2011) . 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Package: r-cran-glmom Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lmomco, r-cran-nleqslv, r-cran-robustbase, r-cran-ismev, r-cran-rsolnp, r-cran-zoo Filename: pool/dists/noble/main/r-cran-glmom_2.0.1-1.ca2404.1_all.deb Size: 397752 MD5sum: c77f2d56168f7756d569bb692b1c7bca SHA1: 2e27b498f3dbec54b044fd0e3834b338c37092d8 SHA256: 20dbd106c32bea461766aa166e7d9f19cb06b0373d3cbc187aedb8a0e018d8f1 SHA512: 58ea0fb1f5360da80f38afad2d9dfa0cf8f753b6ba3fffe7efd00e49175f2ece8f57a061514a9abdaf72f7b854d2900563e941a75ab61a5c1e922f7d27b447f3 Homepage: https://cran.r-project.org/package=GLmom Description: CRAN Package 'GLmom' (Generalized L-Moments Estimation for Extreme Value Distributions) Provides generalized L-moments estimation methods for the generalized extreme value ('GEV') distribution. 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The package downloads these search volumes provided by 'Google Trends' and uses them to measure and analyze the distribution of search scores across countries or within countries. The package allows researchers and analysts to use these search scores to investigate global trends based on patterns within these scores. This offers insights such as degree of internationalization of firms and organizations or dissemination of political, social, or technological trends across the globe or within single countries. An outline of the package's methodological foundations and potential applications is available as a working paper: . 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For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), . 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Given a variable of interest measured in two groups with scaled survey weights so that their hypothetical populations are of equal size, tlorenz() computes the proportion of members of the group with smaller values (ordered from smallest to largest) needed for their sum to match the sum of the top qth percentile of the group with higher values. rlorenz() shows the fraction of the total value of the group with larger values held by the pth percentile of those in the group with smaller values. Fd() is a survey weighted cumulative distribution function and Eps() is a survey weighted inverse cdf used in rlorenz(). Ramos, Graubard, and Gastwirth (2025) . 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GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) ) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales. Optionally, pseudo-absences can be generated within the environmental space using the external package 'flexsdm' (not on CRAN), which can be downloaded from ; this functionality is used conditionally when available and all core features work without it. 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Package: r-cran-gluedo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue Filename: pool/dists/noble/main/r-cran-gluedo_0.1.0-1.ca2404.1_all.deb Size: 15232 MD5sum: f86ac2705decd87b638ebeb71a34a3a6 SHA1: aaa6a65cd4d3035a6cee660d1788fc93a289f651 SHA256: 6359411f9a2308bad37b1122493a4c3abf141231a46915ede90fbb555709fe82 SHA512: 253ad82dc81b1cdd896df670992b812fa2b3b6279b7c9d2472ba8fad097819b5d630a60357a552352a0f7e1724a5a16f39acd1e413f0e453483ae1b599e84952 Homepage: https://cran.r-project.org/package=glueDo Description: CRAN Package 'glueDo' (Wrapper Functions for the 'glue' Library) Provides convenient wrapper functions around the 'glue' library for common string interpolation tasks. The package simplifies the process of combining 'glue' string templating with common R functions like message(), warning(), stop(), print(), cat(), and file writing operations. Instead of manually calling glue() and then passing the result to these functions, 'glueDo' provides direct wrapper functions that handle both steps in a single call. This is particularly useful for logging, error handling, and formatted output in R scripts and packages. The main reference for the underlying 'glue' package is Hester and Bryan (2022) . Package: r-cran-gluedown Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-magrittr Suggests: r-cran-covr, r-cran-dplyr, r-cran-httr, r-cran-knitr, r-cran-markdown, r-cran-mockr, r-cran-rmarkdown, r-cran-rvest, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-gluedown_1.0.9-1.ca2404.1_all.deb Size: 274610 MD5sum: 6bd93a6529c75273e74c578adc1ab052 SHA1: 2ba32ee3b299e78203f6eee19e877d8966371717 SHA256: 8440d7f266e9eeca8b6acc8bbf694ffabc2d69af1277404aa71e44741bfacdb8 SHA512: a1a70d8c3227cceca2b9ce4c393a4d4ddd0757f97f12a26ad202d392b6de571bb63960b35c52b7b9bbf42a0f827d28ec9e395f4d53789fe44830f80b885fda7e Homepage: https://cran.r-project.org/package=gluedown Description: CRAN Package 'gluedown' (Wrap Vectors in Markdown Formatting) Ease the transition between R vectors and markdown text. With 'gluedown' and 'rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) . Package: r-cran-gluvarpro Architecture: all Version: 7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma, r-cran-scales, r-cran-tidyr, r-cran-zoo Filename: pool/dists/noble/main/r-cran-gluvarpro_7.0-1.ca2404.1_all.deb Size: 305012 MD5sum: 17a8b76414a24b1ec80f47c62d7dd4b3 SHA1: cfdc00e83a43024729773f8400a69d0b0d3e7d9a SHA256: 0dbe3cb222207abb359fa7e9403caa621d4dcb399eeef1bf761a1d73d276bf5d SHA512: 65fcc8365498e79ab49fa9e4530f3141f2413add573bcb2475ca77e8dce8ad4cd906d7c692c58f3758e8aaeb40060102827e48d27413ffd80e7ed37fd6b044e7 Homepage: https://cran.r-project.org/package=gluvarpro Description: CRAN Package 'gluvarpro' (Glucose Variability Measures from Continuous Glucose MonitoringData) Calculate different glucose variability measures, including average measures of glycemia, measures of glycemic variability and measures of glycemic risk, from continuous glucose monitoring data. Boris P. Kovatchev, Erik Otto, Daniel Cox, Linda Gonder-Frederick, and William Clarke (2006) . Jean-Pierre Le Floch, Philippe Escuyer, Eric Baudin, Dominique Baudon, and Leon Perlemuter (1990) . C.M. McDonnell, S.M. Donath, S.I. Vidmar, G.A. Werther, and F.J. Cameron (2005) . Everitt, Brian (1998) . Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) . Dougherty, R. L., Edelman, A. and Hyman, J. M. (1989) . Tukey, J. W. (1977) . F. John Service (2013) . Edmond A. Ryan, Tami Shandro, Kristy Green, Breay W. Paty, Peter A. Senior, David Bigam, A.M. James Shapiro, and Marie-Christine Vantyghem (2004) . F. John Service, George D. Molnar, John W. Rosevear, Eugene Ackerman, Leal C. Gatewood, William F. Taylor (1970) . Sarah E. Siegelaar, Frits Holleman, Joost B. L. Hoekstra, and J. Hans DeVries (2010) . Gabor Marics, Zsofia Lendvai, Csaba Lodi, Levente Koncz, David Zakarias, Gyorgy Schuster, Borbala Mikos, Csaba Hermann, Attila J. Szabo, and Peter Toth-Heyn (2015) . Thomas Danne, Revital Nimri, Tadej Battelino, Richard M. Bergenstal, Kelly L. Close, J. Hans DeVries, SatishGarg, Lutz Heinemann, Irl Hirsch, Stephanie A. Amiel, Roy Beck, Emanuele Bosi, Bruce Buckingham, ClaudioCobelli, Eyal Dassau, Francis J. Doyle, Simon Heller, Roman Hovorka, Weiping Jia, Tim Jones, Olga Kordonouri,Boris Kovatchev, Aaron Kowalski, Lori Laffel, David Maahs, Helen R. Murphy, Kirsten Nørgaard, Christopher G.Parkin, Eric Renard, Banshi Saboo, Mauro Scharf, William V. Tamborlane, Stuart A. Weinzimer, and Moshe Phillip.International consensus on use of continuous glucose monitoring.Diabetes Care, 2017 . Package: r-cran-glvmfit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glvmfit_0.1.0-1.ca2404.1_all.deb Size: 137026 MD5sum: 97e27a970d9ff601f74dcb1598a924c7 SHA1: 7a9c3a2f3e09ba0fea74634d42413c94abb4a86c SHA256: 7b0bcd554405145583a927ac43f9c494de0ff4ee25430df16155f05e8429e736 SHA512: 0da10369470b5ff4b567d2684783d502dfc869c444480f00eb36eb4f3964c3c38b5c8fae4cb52504ab54ef1c3fa177fa6b124fce2546b721b330cbf64c56dc12 Homepage: https://cran.r-project.org/package=glvmfit Description: CRAN Package 'glvmfit' (Methods to Assess Generalized Latent Variable Model Fit) Provides residual global fit indices for generalized latent variable models. Package: r-cran-glycanr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-coin Suggests: r-cran-knitr, r-cran-markdown, r-bioc-preprocesscore, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glycanr_0.4.0-1.ca2404.1_all.deb Size: 731864 MD5sum: 809d3661244ba775e0c04a8e19556b25 SHA1: 3d559d05596fbdf36a050aa428c25e3733f3d3c3 SHA256: d0b3e2411c6d7bd9b86484d4848b227f91307b119f5cdbe3f13d24cbb4a44841 SHA512: f124f81500438d0f8462ca98318e1a50c7233b4be4e08272a94de1cadf98cd53366be949cc4d6f6a75dd48443d8a5b4e8c3e3731648f355006b8797f710b78a0 Homepage: https://cran.r-project.org/package=glycanr Description: CRAN Package 'glycanr' (Tools for Analysing N-Glycan Data) Useful utilities in N-glycan data analysis. This package tries to fill the gap in N-glycan data analysis by providing easy to use functions for basic operations on data (see for more details on Glycomics). At the moment 'glycanr' is mostly oriented to data obtained by UPLC (Ultra Performance Liquid Chromatography) and LCMS (Liquid chromatography–mass spectrometry) analysis of Plasma and IgG glycome. Package: r-cran-glydraw Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2581 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glyrepr, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-purrr, r-cran-png, r-cran-glyparse, r-cran-cli, r-cran-checkmate, r-cran-scales, r-cran-fs Suggests: r-bioc-complexheatmap, r-cran-ggraph, r-cran-ggsketch, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-systemfonts, r-cran-testthat, r-cran-tibble, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-glydraw_0.9.0-1.ca2404.1_all.deb Size: 2090378 MD5sum: 1961d9f9645f70bb786b806e23bcb5f6 SHA1: d538add8207f7941a7075f74900c57afa84deb17 SHA256: 3cf7fbf10be823e4407bc5d01640eaf35b6adfb60a28ea538e0e6270155e2bc4 SHA512: 0afcd235e77cc60e12311a9e469641630d81ab77efd86432c2325a25b8d49d6cbe70e24769caaa0d59b96fc57916fdff8d8821b6ee55e5206afc48f15f16fc96 Homepage: https://cran.r-project.org/package=glydraw Description: CRAN Package 'glydraw' (Draw Beautiful Symbol Nomenclature for Glycans) A 'ggplot2'-native plotting engine for drawing reproducible beautiful Symbol Nomenclature for Glycans (SNFG) glycan cartoons from glycan structure objects or text notations, with support for batch export, structural highlighting, and deep appearance customization. It follows the SNFG specification described at . Package: r-cran-glyparse Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glyrepr, r-cran-igraph, r-cran-purrr, r-cran-rlang, r-cran-rstackdeque, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-glue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glyparse_0.8.1-1.ca2404.1_all.deb Size: 340260 MD5sum: 14edba5198201630dc03379d348b7664 SHA1: f5b34a10e9f9a21cedb148c254d1bf7d498c1d94 SHA256: 27841d59526c86ca273ff863c6c5541ba1f68578372474719f53282ffd6f8cb3 SHA512: 4cfe9f282abc90c7dfa70e68fa777ba513fc077b83ce0a1064562b7239094317009c450d85ea73d4487d7eef3313e3e4ea5aa448b06eecab58f07e749411b915 Homepage: https://cran.r-project.org/package=glyparse Description: CRAN Package 'glyparse' (Parsing Glycan Structure Text Representations) Provides functions to parse glycan structure text representations into 'glyrepr' glycan structures. Currently, it supports StrucGP-style, pGlyco-style, IUPAC-condensed, IUPAC-extended, IUPAC-short, IUPAC-compact, WURCS, LINUCS, Linear Code, GlycoCT, KCF, and GlycoWorkbench formats. It also provides an automatic parser to detect the format and parse the structure string. Package: r-cran-glyph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glyph_0.1.1-1.ca2404.1_all.deb Size: 218080 MD5sum: ba5ad930cb1f9d5689707956c3e5c88c SHA1: 1e930a936b60c86c3a22b69dcf7995910d538929 SHA256: 10175ce51520da71f4b76a9671b79bed0615a0f48055b9613bbad8975d966d2f SHA512: d1baed2cf24439545c38f2caa435d4c2470163740fa30fb982391fdb8bc6f375aa24630ca040966a3ef9e3b3d3c85b36dc8dbbe21a7d1da264a126587d66c79e Homepage: https://cran.r-project.org/package=glyph Description: CRAN Package 'glyph' (A Next-Generation Grammar of Interactive Graphics) A modern visualization grammar that treats interactivity, animation, and composable layouts as first-class concepts rather than afterthoughts. 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Package: r-cran-glyrepr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-igraph, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-rstackdeque, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-testthat, r-cran-patrick, r-cran-knitr, r-cran-rmarkdown, r-cran-tictoc, r-cran-lobstr Filename: pool/dists/noble/main/r-cran-glyrepr_1.0.0-1.ca2404.1_all.deb Size: 589488 MD5sum: 129bda5c35f4db7f424104c7d2e7c63d SHA1: 02c71acb20b8c6dce22494422a6bec48a94d5929 SHA256: fb1750a235861dad31f8e7b57139390a5fe221251ee866b2f54d7aa712a04a40 SHA512: 2ab1ab6f39c2f2f8a1ed3e11ce1c9deff9e8bcb42912c0b04cd95bb74b038ae5ef9006a8f041289ec2a5a7ace3fee824c8e41406e38d92a7899b5f1d82232859 Homepage: https://cran.r-project.org/package=glyrepr Description: CRAN Package 'glyrepr' (Representation for Glycan Compositions and Structures) Computational representations of glycan compositions and structures, including details such as linkages, anomers, and substituents. Supports varying levels of monosaccharide specificity (e.g., "Hex" or "Gal") and ambiguous linkages. Provides robust parsing and generation of IUPAC-condensed structure strings. Optimized for vectorized operations on glycan structures, with efficient handling of duplications. As the cornerstone of the glycoverse ecosystem, this package delivers the foundational data structures that power glycomics and glycoproteomics analysis workflows. Package: r-cran-gm Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3947 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-erify, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-gm_2.0.0-1.ca2404.1_all.deb Size: 2840826 MD5sum: 194119538a62d696311d2b5094e2d8a0 SHA1: d81ba14c3408580f8411bd4d371c7db0312a0027 SHA256: a43fc8aa013a40b2e0d74a394203472a9cc8b630670eb1ecc14096ece2824e17 SHA512: 55ddefff26a3f2d4846a42be3ce9d6a10d800cd9fa9fc1ee3925642d451ead5aead674c68c9f72beb8fa1c5e4c3c1780b432ae27a927c1f6bf5ea0de66b73b9b Homepage: https://cran.r-project.org/package=gm Description: CRAN Package 'gm' (Create Music with Ease) Provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in 'RStudio', 'R Markdown' documents, and R 'Jupyter Notebooks'. Internally, uses 'MusicXML' to represent music, and 'MuseScore' to convert 'MusicXML'. Package: r-cran-gma Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-car Filename: pool/dists/noble/main/r-cran-gma_1.0-1.ca2404.1_all.deb Size: 280432 MD5sum: 54022d6211ca279d87675abe1ad4748e SHA1: 71fde50aa473fb0d3a1907ed57e0934b57e3aecd SHA256: 8cd78799eefd0b96fc1a0dc3d7aeb316389864ea36298d67ca012316cf27c4a9 SHA512: e931aa1ee992d2ff0df346e7cd5463a618ca1cf7b1d991a56c62c0d668bf4189fa171a151cb5bbd3227d0972e7a4bfaccb46d634e3a070088e60059b4e84b948 Homepage: https://cran.r-project.org/package=gma Description: CRAN Package 'gma' (Granger Mediation Analysis) Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, for details. Package: r-cran-gmac Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gmac_3.2-1.ca2404.1_all.deb Size: 300676 MD5sum: 2e5fccbc3787df531e7554d11429fc88 SHA1: 1594d476106816fc4f123d2e35c6d4b1b90fede6 SHA256: b7008e9212941d3e7a3377e131601261593d4f4b41812bf98693d2d217e5dada SHA512: de932bed2080ea48ca19630d4142a8221736f497b10da3b6b93ee8e9adc8ace94b7a9a8734b1876dc81c89f1b5cbef1db74935326d2cbd77cd628ada44a1c1e2 Homepage: https://cran.r-project.org/package=GMAC Description: CRAN Package 'GMAC' (Genomic Mediation Analysis with Adaptive Confounding Adjustment) Performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) . It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment). Package: r-cran-gmailr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-crayon, r-cran-gargle, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-mime, r-cran-rappdirs, r-cran-rematch2, r-cran-rlang Suggests: r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gmailr_3.0.0-1.ca2404.1_all.deb Size: 269508 MD5sum: 3a0ba527cf778d20675cc5dcc56b6972 SHA1: b3e9d9d0f51fea486d4eb695e97e7b7a6d169299 SHA256: 7aefe62f22c907c1d7d4e0c3ea1a0d9c9c193215d3676c08332a35de8ee77158 SHA512: f0a9053b40b252c429d18755045fc02334f4f7a377376f9e1f8409b929afcf01b21ec2a13d71d3bdd3cdff3130fd97e8e9e82eeb665e87cf750cc6696a0ff4a6 Homepage: https://cran.r-project.org/package=gmailr Description: CRAN Package 'gmailr' (Access the 'Gmail' 'RESTful' API) An interface to the 'Gmail' 'RESTful' API. 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Four possible modes of transportation (bicycling, walking, driving and public transportation). 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Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons. 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An optional indexing layer parses provider files in formats including 'PEER' 'NGA-West2' 'AT2', 'CESMD' 'V2'/'V2c', 'NWZ' 'V2A', Geological Survey of Canada 'TR', 'IGP'/'UCR' 'AC' variants, and generic two-column ASCII text, normalises components, writes per-record CSV (comma-separated values) and JSON (JavaScript Object Notation) pairs, and computes per-record intensity tables. 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Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) , Savi Virolainen (2025) , Savi Virolainen (in press) . 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Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) . 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Package: r-cran-gofedf Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-glm2, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gofedf_1.1.0-1.ca2404.1_all.deb Size: 141634 MD5sum: 9d9321453ab653f46caf0e6c56579137 SHA1: aa2025e6ac168d09704275eb6c05a6e4abb42090 SHA256: 1ce0d80fadb277b07969d315d93e595f683c12ee3ccabe012d2958b0aa359e22 SHA512: 243ab01d003482cbb7d20e5e6d77d035fa499f5c2a81dc50dc68b4ab38b8e1ff760517a379d99abade9f7e37a9527e1598236f3ad30d5d34555efd59085fc9e7 Homepage: https://cran.r-project.org/package=gofedf Description: CRAN Package 'gofedf' (Goodness of Fit Tests Based on Empirical Distribution Functions) Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. 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For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) . The recent procedures presented in Henze, Meintanis & Ebner (2012) and Betsch & Ebner (2019) are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) . Package: r-cran-gofig Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-rmutil Filename: pool/dists/noble/main/r-cran-gofig_1.0-1.ca2404.1_all.deb Size: 84394 MD5sum: 4a0a9b39e0df3911ef6772643469effd SHA1: 4c8711e431362f32286195a3ca7d52c4c4c653b8 SHA256: 815e6b661911f37ba216931a4596f120c48e597703584bb6a74492639d93f2fe SHA512: 8c977defd8047244f28644abea32bc487d8cc805e8f10d6894b98d56e84acc85cc98ef8d1419045f0c899ec1aa251bec0f3b8dfb1eb65f0ea7d2a02c1ac269a4 Homepage: https://cran.r-project.org/package=gofIG Description: CRAN Package 'gofIG' (Goodness-of-Fit Tests for the Inverse Gaussian Distribution) We implement various tests for the composite hypothesis of testing the fit to the family of inverse Gaussian distributions. Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) , as well as two tests from Henze and Klar (2002) . Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) . For each test a parametric bootstrap procedure is implemented. 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Supports both 'knitr' and interactive execution within 'RStudio'. Package: r-cran-gofkernel Architecture: all Version: 2.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-gofkernel_2.1-3-1.ca2404.1_all.deb Size: 62444 MD5sum: 78166f0729fcb8bc7de4c681d8bf9b1b SHA1: 4e56be40ffcc51d2284b64d5182ccad93da40748 SHA256: 7f27bba932dc81825615c7605aed3058275d53a13e794246eb112489f298389e SHA512: ce22cd44a96129382db136cf079970d106cb315e09c970fa3459d73dee19785b67c00539995aef24192b380f133e8bfb2ce29de55304661a0f4f75963107a7d6 Homepage: https://cran.r-project.org/package=GoFKernel Description: CRAN Package 'GoFKernel' (Testing Goodness-of-Fit with the Kernel Density Estimator) Tests of goodness-of-fit based on a kernel smoothing of the data. References: Pavía (2015) . Package: r-cran-goflorenz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-goflorenz_0.1.0-1.ca2404.1_all.deb Size: 81016 MD5sum: c251333d3ad466b2cf8192597161e248 SHA1: 8e932689c19909c4c924a442b034289d02c978e5 SHA256: c302babde6f0d06930a6f0594c967924938ae8a922844e35f3dcbe36747c8a81 SHA512: ac23582d27b29593ab519ba61d84d5492066322e97eefe559ff2685de14897058e4227f4684aceb3762b8d9df269db310736914d27c5fb60bc85412c923215b3 Homepage: https://cran.r-project.org/package=gofLorenz Description: CRAN Package 'gofLorenz' (Goodness-of-Fit Tests for Location-Scale Distributions viaLorenz Curve) Implements goodness-of-fit test statistics and graphical methods for symmetric and asymmetric location-scale distributions under progressive Type-II censoring using the modified Lorenz curve and ratio modified sample Lorenz curve, as proposed by Lee (2024) . Also provides order statistics distance test statistics based on Pakyari and Balakrishnan (2013) . Supports calculation of test statistics, Monte Carlo p-values, critical values, and L-plot visual diagnostics for complete and progressively Type-II censored data. Package: r-cran-gofmalm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gofmalm_0.1.0-1.ca2404.1_all.deb Size: 74386 MD5sum: 261fff58bf5c958daafd6770014d164e SHA1: 3064f25cbed9a43f957ef3e9d44b3b317839fe20 SHA256: 93e777babd1147c058a6c1f26faaba2c459aae5afcac9eb1008975041d14daf2 SHA512: 0e5e52165c67af1cc7559133c7cb2ae87f289f170fa15d04f23532a6b5c089932628e69e096ab8bc32b11402356a38cfdafc5f83f152ebdddb0902ac1fd0b95b Homepage: https://cran.r-project.org/package=gofmalm Description: CRAN Package 'gofmalm' (Goodness-of-Fit Tests for Type-II Censored Samples via theMalmquist Transformation) Goodness-of-fit tests for an arbitrary user-specified continuous distribution under Type-II right- or left-censoring. Implements the transformation-based method of Lin, Huang and Balakrishnan (2008) , which uses a property of order statistics due to Malmquist (1950) to convert an r-out-of-n Type-II censored uniform sample into a complete sample of size r, alongside the earlier transformation of Michael and Schucany (1979) . Also implements the direct (untransformed) censored-sample statistics of Barr and Davidson (1973) and Pettitt and Stephens (1976) , and the modified-statistic maximum-likelihood procedure of Chen and Balakrishnan (1995) for testing composite hypotheses. General background on empirical-distribution-function goodness-of-fit methods follows D'Agostino and Stephens (1986, ISBN:982-0-8247-7487-5). Package: r-cran-gofphcs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-gofphcs_0.1.0-1.ca2404.1_all.deb Size: 85162 MD5sum: 8f17393509619a4d4b75cce3e0e03585 SHA1: 6585c3f0f50e9c45ebfdfd3bf2d66e1bb2dcdecd SHA256: 3a47ed2ae8f1917daba3ba5a4f326e5acfaaaa89ee7c1040a8c401e7d60d3862 SHA512: e9fd5fd9fdd3fa981f964ef3f53db16671eee9ff55624b48a0c68a2d2296e33e6ffb3a97c31ab0746827b813c985447685ed20bbcfa1e83c61c5a1db54cfefe6 Homepage: https://cran.r-project.org/package=gofPHCS Description: CRAN Package 'gofPHCS' (Goodness-of-Fit Tests for Complete, Progressively Type-II,Type-I Hybrid, and Type-II Hybrid Censored Data) Provides goodness-of-fit tests for lifetime data collected under complete sampling, progressive Type-II censoring, and Type-I/Type-II hybrid censoring schemes. Users supply the observed (censored) data and the assumed probability density/mass function, cumulative distribution function, or survival function of the target model, and the package returns the corresponding test statistic together with an asymptotic or Monte Carlo p-value. Implements the spacings-based exponentiality test of Balakrishnan, Ng and Kannan (2002, in "Goodness-of-Fit Tests and Model Validity", Birkhauser, pp. 89-111) and its location-scale generalization Balakrishnan, Ng and Kannan (2004) , the power comparison and Kaplan-Meier based tests of Doering and Cramer (2019) , the Kolmogorov-Smirnov type tests for hybrid censored data of Banerjee and Pradhan (2018) , and follows the unified treatment of hybrid censoring schemes reviewed in Balakrishnan and Kundu (2013) and in Cramer and Balakrishnan (2023, "Hybrid Censoring Know-How", Chapter 11) . Package: r-cran-gofpt2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gofpt2_0.1.0-1.ca2404.1_all.deb Size: 64846 MD5sum: 893f91c2804d33b5c2137c11b6615ac1 SHA1: 0e94af0b9b3a41c276b1c52b43ecbbdc79bd63c5 SHA256: 6094a1e8e39331a34138d16744665a370efff325f8ebe9c86d85261f5ef7668a SHA512: 4367523bbb39d58d08c3b9435287b3d9ea030e76445011008a2f4f4a8ccdc1c23d596a189c26d81b24cd4d784c6602b544a7f8c6a12c06461c6b70e00854d1da Homepage: https://cran.r-project.org/package=Gofpt2 Description: CRAN Package 'Gofpt2' (Generalized Goodness-of-Fit Test for Progressive Type-IICensored Data) Implements a generalized goodness-of-fit test based on spacings for general progressive Type-II censored data. The test statistic is based on the work of Qin et al. (2022) and extends the methodology of Balakrishnan et al. (2003) . Users can test data against any distribution by providing custom pdf, cdf, and survival functions. The package supports both normal approximation and Monte Carlo simulation approaches for computing p-values and critical values. 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Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) , Bierens & Wang (2012) , Dikta & Scheer (2021) and Kremling & Dikta (2024) . As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method. Package: r-cran-gofshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-rhandsontable Filename: pool/dists/noble/main/r-cran-gofshiny_0.1.0-1.ca2404.1_all.deb Size: 69878 MD5sum: bbcc4156e670efa160e3c3701c387774 SHA1: 1318c3e0dc1debb78a97b237f298594bb98a3c9e SHA256: ecc61139939c5f4088d084e5505f0e9ea198dd28b33f187af9835232f2177e6d SHA512: 1e179752ffc30f810bfe58923f2994d185ccf506760f362d786023c5914ce57b5ff1115606f38f29cfd73e23e4469ade9366b0ed53857edd6157952492b50b6f Homepage: https://cran.r-project.org/package=GOFShiny Description: CRAN Package 'GOFShiny' (Interactive Document for Working with Goodness of Fit Analysis) An interactive document on the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . 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The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. . Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. . 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The package focuses on Gazepoint-specific biometric channels such as GSR/EDA, heart rate, interbeat intervals, pulse signals, engagement dial, TTL markers, and synchronisation fields that can be combined with Gazepoint GP3 and GP3 HD eye-tracking workflows. Package: r-cran-gpbstat Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpbstat_0.4.4-1.ca2404.1_all.deb Size: 180724 MD5sum: c0360c30c0850e6eb0e846af2641574b SHA1: 59b79d344146605c3c6c85d2a5d56d90215e8567 SHA256: d18864ddc96f5a2c2d920250036f22f508a12a5819d3e60bd339215cc1a9872c SHA512: 57fb659b4138049ad4672905abaacaba1e747bc96176856b8b950a8f3bfea3f1857c9774bf54d88e65291be7099bd06fdc2b08f99962444eb45bc8aae5927bf6 Homepage: https://cran.r-project.org/package=gpbStat Description: CRAN Package 'gpbStat' (Comprehensive Statistical Analysis of Plant Breeding Experiments) Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) and Diallel analysis as per Griffing, B. (1956) . Package: r-cran-gpci Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-numderiv, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpci_0.1.0-1.ca2404.1_all.deb Size: 237616 MD5sum: 58a693da5a3793143d14f5456a202591 SHA1: 4952aa6d120db4bb3ee3f0af34c075d71785df96 SHA256: 61a287c3ecfa444a3827d2c5501c3974669f57b2748457b99c1d37aaef2669dc SHA512: cef757c849bbb8ac03d7cccb55ffbe1dbe9bf516beff0353a991ceef3dc659faa6e1764783592ade6fb00cb5c69d085c698e11455150481ae686ff2d571fac29 Homepage: https://cran.r-project.org/package=gpci Description: CRAN Package 'gpci' (Generalized Process Capability Indices and Bootstrap ConfidenceIntervals) A comprehensive, generalized framework for computing, estimating, and validating Generalized Process Capability Indices (GPCIs). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions with uncensored data parameter estimation via Maximum Likelihood Estimation (MLE). Provides classical and non-normal capability indices, including Cpy (Maiti, Saha and Nanda, 2010) , Spmk (Dey and Saha, 2019) , CpTk (Saha, Dey and Maiti, 2019) , Cpc (Saha, Dey and Nadarajah, 2022) , CNpmc (Alotaibi, Dey and Saha, 2022) , CNpmkc (Saha, Tripathi and Dey, 2024) , CNpk (Saha, Dey and Maiti, 2018) , and Vannman capability indices. Computes parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% confidence levels using percentile, normal, basic, BCa, BCp, and studentized bootstrap methods. Evaluates Highest Posterior Density (HPD) intervals and Heidelberger-Welch convergence diagnostics. References: Maiti, Saha and Nanda (2010) , Saha, Dey and Maiti (2018) , Dey and Saha (2019) , Saha, Dey and Maiti (2019) , Alotaibi, Dey and Saha (2022) , Saha, Dey and Nadarajah (2022) , Saha, Tripathi and Dey (2024) . Package: r-cran-gpciemprogii Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-unicensorem Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpciemprogii_0.1.0-1.ca2404.1_all.deb Size: 99682 MD5sum: d5e823802377bd12f8ba2df4323c63a2 SHA1: 8cb184e1a91cba2b1a574df9430aa27e624201a3 SHA256: 14c0c44a569e91c4e040b56e17712e74498d92251109cb2e8136ab0cf1b3f6df SHA512: 426002991a9607087543f66889bdeba3dc708db359717bc4542bb21235c364e79c527e8710cb451903fe00bf208526c7a61938caa1aee69a42ab379d19bf0380 Homepage: https://cran.r-project.org/package=gpciEMprogII Description: CRAN Package 'gpciEMprogII' (Generalized Process Capability Indices via EM Algorithm underProgressive Type-II Censoring) Implements the Expectation-Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) for parameter estimation under progressive Type-II censored data (Balakrishnan and Aggarwala (2000) ) and computes Generalized Process Capability Indices (GPCIs). Uses the 'UniCensorEM' package for EM estimation. Supports classical and generalized capability indices including Cpy (Maiti et al. (2010) ), Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk (Dey and Saha (2019) ), CpTk (Saha et al. (2018) ), Cpc, CNpmc (Alotaibi et al. (2022) ), CNpmkc (Saha et al. (2024) ), and CNpk (Saha et al. (2022) ). Computes point estimates, bias, mean squared error, risk, Heidelberger and Welch convergence diagnostics, convergence probability, and bootstrap confidence intervals at 90 percent, 95 percent, and 99 percent levels. Accommodates user-defined probability density or mass functions, cumulative distribution functions, and survival functions. Package: r-cran-gpcihybridii Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-mlecensor, r-cran-gofphcs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpcihybridii_0.1.0-1.ca2404.1_all.deb Size: 164642 MD5sum: 2bbbe90a5596239278b4bf01854c7878 SHA1: 5aee6442b0e9398179106ae917d1c363327562f8 SHA256: a2f76c8db7d8b75d2cb483428bcd351af4bd99ddfade7cbe8c441f14b3eed281 SHA512: 55e684646d95db273a5600a57c7877dbe4718fbc87e597c18a095f597f18df8d8ba2122ea594a96fe8ec3d724de84f79f577eb349a014d7f7b292d2789827da9 Homepage: https://cran.r-project.org/package=gpcihybridII Description: CRAN Package 'gpcihybridII' (Generalized Process Capability Indices under Hybrid Type-IICensoring) A comprehensive, generalized framework for computing, estimating, and validating Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data. Supports user-supplied probability density or mass functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions. Parameter estimation under Hybrid Type-II censoring is performed via Maximum Likelihood Estimation using the 'MleCensoR' package (Childs et al., 2003 ; Balakrishnan & Kundu, 2013 ). Computes classical and non-normal capability indices, including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family. Evaluates parametric and non-parametric bootstrap confidence intervals at 90 percent, 95 percent, and 99 percent levels of significance using percentile, normal, basic, BCa, BCp, and studentized bootstrap methods. Computes standard errors, mean squared errors, and coverage probabilities for both distribution parameters and capability indices. Integrates goodness-of-fit testing for Hybrid Type-II censored data via the 'gofPHCS' package. Package: r-cran-gpcihybridiiem Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-unicensorem, r-cran-gofphcs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpcihybridiiem_0.1.0-1.ca2404.1_all.deb Size: 184710 MD5sum: b169c24ee6710eb64c6c4f50faffb6c3 SHA1: fc04a47749870761762b91222e86261379af6484 SHA256: e0ac1dfec0dffbbb5ee661fe6f789ef1e4e93f33050e1ba6a292845da800ff91 SHA512: 39014e8a50802f6b9673086e39d4f2ebf315be4ff43433b4f3b59776b31e6d988bc47bc82c1f2e4d4a969c9de1e300b653dc56725cff47c16b44a8ec989f9a79 Homepage: https://cran.r-project.org/package=gpcihybridIIEM Description: CRAN Package 'gpcihybridIIEM' (Generalized Process Capability Indices via EM for Hybrid Type-IIData) Implements the Expectation-Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) for parameter estimation under Hybrid Type-II censored data (Childs et al. (2003) ; Balakrishnan and Kundu (2013) ) using the 'UniCensorEM' package and computes Generalized Process Capability Indices (GPCIs). Supports classical and generalized capability indices including Cpy (Maiti et al. (2010) ), Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk (Dey and Saha (2019) ), CpTk (Saha et al. (2018) ), Cpc, CNpmc (Alotaibi et al. (2022) ), CNpmkc (Saha et al. (2024) ), and CNpk (Saha et al. (2022) ). Computes point estimates, bias, mean squared error, risk, Heidelberger and Welch convergence diagnostics, convergence probability, Bayesian MCMC sampling chains, goodness-of-fit testing via 'gofPHCS', and bootstrap confidence intervals at 90 percent, 95 percent, and 99 percent levels. Accommodates user-defined probability density or mass functions, cumulative distribution functions, and survival functions. Package: r-cran-gpcihybridiiimpsam Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda Suggests: r-cran-gofphcs, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpcihybridiiimpsam_0.1.0-1.ca2404.1_all.deb Size: 168016 MD5sum: 3b4b52a26568c9ccd75bd1e61bd2b08f SHA1: 8cc6db87ddbfa4597b04a75cc556a7a0b750ec17 SHA256: 4f52ae75a2f4f93df88935c52e3589cd317165bd5600bb340d72083ca991a916 SHA512: 1d5f9b669f8185e06e0ad70f0d3657cdcc252e7ca29e586aebef3fec029cea4b8e38f9e719c2140b6b08b3d94b6c5e6f945a23265f425e0914046caeebc9171e Homepage: https://cran.r-project.org/package=gpcihybridIIImpSam Description: CRAN Package 'gpcihybridIIImpSam' (Process Capability Indices for Hybrid Type-II Data viaImportance Sampling) Evaluates Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data using Importance Sampling (Sampling Importance Resampling, SIR). Implements Bayesian parameter estimation and evaluates classical and generalized capability indices including Cpy, Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk, CpTk, Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc, CNpmkc, and Vannman's Cp(u,v) family. Computes initial maximum likelihood estimates under Hybrid Type-II censoring, parameter MCMC chains, GPCI posterior chains, posterior point estimates, bias, mean squared error (MSE), Bayes risk, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, Heidelberger and Welch's MCMC convergence diagnostics, and convergence probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Goodness-of-fit testing for Hybrid Type-II censored data is supported via 'gofPHCS'. Methods are based on Childs et al. (2003) , Kundu and Pradhan (2009) , Maiti et al. (2010) , Dey and Saha (2019) , Alotaibi et al. (2022) , Saha et al. (2022) , and Saha et al. (2024) . Package: r-cran-gpcihybridiilinapp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-mlecensor, r-cran-gofphcs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpcihybridiilinapp_0.1.0-1.ca2404.1_all.deb Size: 201516 MD5sum: fd77bfcbdfb31c96b5255b0809d55340 SHA1: ea26a81b22b53c13e0c782525a083af02f082461 SHA256: c656d2bb1d3a0ea101a54c0519a56761299c440469aacfbe62ccb0033f545548 SHA512: 6d7e5cb66cab05ee93321adbb50084956ef808e04766a318ae2b911756b15e9282b82128f5ce5192654718b9db07242f87ef020b117e754d0b5765825eea0fd1 Homepage: https://cran.r-project.org/package=gpcihybridIILinApp Description: CRAN Package 'gpcihybridIILinApp' (Lindley Approximation for Capability Indices under HybridCensoring) Provides a comprehensive framework for estimating Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data using Lindley's 3rd-order approximation method (Lindley, 1980 ). Supports user-supplied probability density/mass functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions. Computes Maximum Likelihood Estimates (MLE) using the 'MleCensoR' package (Childs et al., 2003 ; Balakrishnan & Kundu, 2013 ) and Bayesian posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family. Generates posterior parameter and GPCI chains via sampling with burn-in and thinning, calculating Bias, Mean Squared Error (MSE), Bayes Risk (SEL and Linex), Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, and Heidelberger and Welch's MCMC Convergence Diagnostics (Heidelberger & Welch, 1983 ) with convergence probabilities. Evaluates parametric and non-parametric bootstrap confidence intervals (Percentile, Normal, Basic, BCp, BCa) at 90%, 95%, and 99% levels of significance. Integrates goodness-of-fit testing for Hybrid Type-II censored data via the 'gofPHCS' package. Package: r-cran-gpcihybridiimcmc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda Suggests: r-cran-gofphcs, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpcihybridiimcmc_0.1.0-1.ca2404.1_all.deb Size: 151196 MD5sum: 0cab306fee983cd88eb516ab68ab0174 SHA1: ea64f72aacdc53d03b17ef3422675a9537d232d9 SHA256: e4f8df230d97323821e555c00235f800399f89ca1ae00a9c9d365bbb06e49dc4 SHA512: eb90b8388a028d0027716765d5c5168fcee4d30046f34ffc8243c1d958569e34412692b47bbf6257dfe586c622ef60d05ae5861c4bc833cc992a05237c1d651c Homepage: https://cran.r-project.org/package=gpcihybridIImcmc Description: CRAN Package 'gpcihybridIImcmc' (Generalized Process Capability Indices for Hybrid Type-IICensored Data using MCMC) Implements Bayesian Markov Chain Monte Carlo (MCMC) estimation using Metropolis-Hastings within Gibbs sampler for Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data. Supports classical and generalized capability indices including Cpy, Cp, Cpk, Cpm, Cpmk, Spmk, CpTk, Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc, and CNpmkc. Calculates posterior point estimates, bias, mean squared error (MSE), Bayes risk, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, Heidelberger and Welch's MCMC convergence diagnostics, and coverage probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Based on methods described in Childs et al. (2003) , Kundu and Pradhan (2009) , Saha and Dey (2019) , Alotaibi et al. (2022) , Dey et al. (2017) , and Wu et al. (2021) . Package: r-cran-gpciimpsam Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpciimpsam_0.1.0-1.ca2404.1_all.deb Size: 104978 MD5sum: 660f0a990e475fe8534c9b984c33b9d9 SHA1: 45e07a51cf2f227485fe4f8c941422792b332ddb SHA256: 7197c639768c6fec31fdb9c612228b5e0be5905cb1b142e3b541d5bc11e5b807 SHA512: c64c25051753578a8cf1f6f71d7ff06d507c4abe5f356d291b0e11538826202ee21963a73a0570cb7688503d3a42c279788ffba8f8767d977c00610bcdffb9a3 Homepage: https://cran.r-project.org/package=gpciImpSam Description: CRAN Package 'gpciImpSam' (Importance Sampling Estimation of Generalized Process CapabilityIndices) Provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under uncensored data using Importance Sampling (ImpSam). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Computes classical and generalized capability indices including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family. Generates parameter and GPCI MCMC chains via Sampling Importance Resampling (SIR) after burn-in and thinning. Provides point estimates, bias, MSE, risk values, Highest Posterior Density (HPD) intervals at 90, 95, and 99 percent levels of significance, Heidelberger and Welch MCMC convergence diagnostic, and convergence probability. Package: r-cran-gpciintcensor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-boot, r-cran-mlecensor Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpciintcensor_0.1.0-1.ca2404.1_all.deb Size: 129488 MD5sum: 353e0e76ae4a6a603bc18fbf6fba2b1d SHA1: cad0289f82c1a151077cbb80bc851a454a042d09 SHA256: 9316cadecf042cdb90522cdd550ad83fa5c0ac981f4ff3788c692b447bff74c5 SHA512: d59266014db6734b0a287c0c7ff66c00de3a6187616929827f98f130583111918c5af3a839e126d4b722ad8946d5b96b75b57c0244a2682c7c459fdc60d7682c Homepage: https://cran.r-project.org/package=gpciIntCensor Description: CRAN Package 'gpciIntCensor' (Generalized Process Capability Indices for Interval-CensoredData) A comprehensive framework for computing, estimating, and validating Generalized Process Capability Indices (GPCIs) under interval-censored data. Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Parameter estimation is performed using Maximum Likelihood Estimation for interval-censored data via the MleCensoR package. Computes classical and generalized capability indices including Cpy (Maiti et al., 2010), Spmk (Dey & Saha, 2019), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022), CNpmc (Alotaibi et al., 2022), CNpmkc (Saha et al., 2024), CNpk (Saha et al., 2018), and Vannman's Cp(u,v) family. Provides parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% confidence levels using percentile, normal, basic, BCa, BCp, and studentized bootstrap methods. Computes standard errors, mean squared errors, and coverage probabilities for both distribution parameters and capability indices. References: Maiti, Saha & Nanda (2010) , Saha, Dey & Maiti (2018) , Dey & Saha (2019) , Saha, Dey & Maiti (2019) , Alotaibi, Dey & Saha (2022) , Saha, Dey & Nadarajah (2022) , Saha, Tripathi & Dey (2024) . Package: r-cran-gpcilindapproxprogii Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-numderiv, r-cran-boot, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpcilindapproxprogii_0.1.1-1.ca2404.1_all.deb Size: 155470 MD5sum: 042f5466e3f35df294a783f2ca9d2ae8 SHA1: 37696f8e69d1ad620eb8157bcc623ee7e9c47cda SHA256: 69de16943f11b942ea9ddc86d7c2abb764481f376b8f89c2ea3f2dd5f403ee0c SHA512: f7a469f3e20dcc082be12195b4827261d879b37f95f2ce5bedd2f1d1d158b000318beeecee9f427bc71469883299b6036e8e7e068509c8aee1a39d869c3bd418 Homepage: https://cran.r-project.org/package=gpciLindApproxProgII Description: CRAN Package 'gpciLindApproxProgII' (Lindley Approximation for Capability Indices under ProgressiveCensoring) Implements Bayesian parameter and Generalized Process Capability Indices (GPCIs) estimation using the Lindley approximation method (Lindley, 1980 ) under progressive Type-II censored data (Balakrishnan & Aggarwala, 2000 ). Evaluates point estimates and posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019 ), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family (Vannman, 1995 ). Calculates point estimates, bias, mean squared error (MSE), Bayes risk under Linex and squared error loss, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, and Heidelberger and Welch's MCMC convergence diagnostics (Heidelberger & Welch, 1983 ) with convergence probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Supports progressive parametric and non-parametric bootstrap confidence intervals (Efron, 1987 ) at 90%, 95%, and 99% significance levels. Package: r-cran-gpcilindleyapprox Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-numderiv, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpcilindleyapprox_0.1.0-1.ca2404.1_all.deb Size: 110796 MD5sum: 8a2ffbfbce27973d533a20770405a589 SHA1: 528c7bb6eebb4251c92003ba897ebe74cf97b846 SHA256: b5ec00b4b4493dc65b9a26510ddb84dac8c241b4ac33c292dbe87d8b7fda4a75 SHA512: b7d78058fd373e2d241c18d4eb3b6f8cfcf9bb6d9a77719182b8fd7deeac7c107e20ed3478b8e7b1071e7efb4504afcd7f59fc3461a0ba93e5a7c42c3a7a6193 Homepage: https://cran.r-project.org/package=gpciLindleyApprox Description: CRAN Package 'gpciLindleyApprox' (Lindley Approximation Method for Generalized Process CapabilityIndices) Provides a comprehensive framework for estimating Generalized Process Capability Indices (GPCIs) using the Lindley approximation method for uncensored data under Bayesian inference. Evaluates point estimates and posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010), Spmk (Dey & Saha, 2019), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022), CNpmc (Alotaibi et al., 2022), CNpmkc (Saha et al., 2024), CNpk (Saha et al., 2018), and Vannman's Cp(u,v) family. Computes parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% levels of significance. Supports MCMC chain generation with burn-in and thinning, Highest Posterior Density (HPD) intervals, Bias, MSE, Risk values, and Heidelberger and Welch's MCMC Convergence Diagnostic with convergence probabilities. References: Lindley (1980) , Maiti, Saha & Nanda (2010) , Saha, Dey & Maiti (2018) , Dey & Saha (2019) , Saha, Dey & Maiti (2019), Alotaibi, Dey & Saha (2022) , Saha, Dey & Nadarajah (2022) , Saha, Tripathi & Dey (2024) . Package: r-cran-gpciprogtyii Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-boot, r-cran-mlecensor Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpciprogtyii_0.1.0-1.ca2404.1_all.deb Size: 92042 MD5sum: 27ca782426d672b396c9819ab636408c SHA1: 746a64dc47e5d5bb0cd89879812cca8a380d33aa SHA256: 497b5bc62c42e2bd23558ae31dafb697a721ca79d0ed51a8d471581bd299f849 SHA512: bf180ceefeb20fc3da82f48dd93ab3c87630475e040c87610093bf35b9fdb149601c8fe06772be9bb8c87399bb3567b6936ed0c858c60c1603a8ff770bbcd4f2 Homepage: https://cran.r-project.org/package=gpciProgTyII Description: CRAN Package 'gpciProgTyII' (Generalized Process Capability Indices under Progressive Type-IICensoring) Provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under Progressive Type-II censored data using the MleCensoR package. Accepts user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Computes classical and generalized capability indices including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family. Evaluates parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% levels of significance. Computes Standard Errors, Mean Squared Error (MSE), Bias, and Coverage Probabilities for model parameters and capability indices. References: Balakrishnan & Aggarwala (2000) , Maiti, Saha & Nanda (2010) , Saha, Dey & Maiti (2018) , Dey & Saha (2019) , Saha, Dey & Maiti (2019), Alotaibi, Dey & Saha (2022) , Saha, Dey & Nadarajah (2022) , Saha, Tripathi & Dey (2024) . Package: r-cran-gpciprogtyiiimpsam Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpciprogtyiiimpsam_0.1.0-1.ca2404.1_all.deb Size: 97960 MD5sum: 321263944c2a9d21e42d3018bd016a5e SHA1: f007e22d5a5fb44f1ad08191f302b4440d23964b SHA256: f212ad5ffefef36a70e2ee9c4317a21c8f45ea1658bbfb81d249a91139188ebd SHA512: 7d5082854cd3d615a638de585f86629826c79dd6384a890f944ec6b1e42ee01aed551c0fdd5340aab78bdc265347f9ede4ff3bf0927fd3c05995a1fa254af92c Homepage: https://cran.r-project.org/package=gpciProgTyIIImpSam Description: CRAN Package 'gpciProgTyIIImpSam' (Generalized Process Capability Indices for Progressive Type-IICensored Data using Importance Sampling) Implements Importance Sampling (Sampling Importance Resampling, SIR) for Bayesian parameter estimation and Generalized Process Capability Indices (GPCIs) under progressive Type-II censored data. Evaluates classical and generalized capability indices including Cpy, Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk, CpTk, Cpc, CNp, CNpk, CNpm, CNpmk, CNpmc, CNpmkc, and Vannman's Cp(u,v) family. Computes initial uncensored estimates, parameter MCMC chains, GPCI posterior chains, point estimates, posterior means, bias, mean squared error (MSE), Bayes risk under loss functions, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, Heidelberger and Welch's MCMC convergence diagnostics, and convergence probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Methods based on Balakrishnan and Aggarwala (2000) , Maiti et al. (2010) , Dey and Saha (2019) , Alotaibi et al. (2022) , Saha et al. (2022) , and Saha et al. (2024) . Package: r-cran-gpcsign Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dicekriging, r-cran-tmvtnorm, r-cran-truncatednormal, r-cran-future.apply, r-cran-future Suggests: r-cran-dicedesign, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpcsign_0.1.1-1.ca2404.1_all.deb Size: 192110 MD5sum: 8a6bb917712310a7147c2f32d0a120f4 SHA1: 65137d7c4ee122575afd6fa9ccec5d2ce3ecad93 SHA256: 125a6cfd97d257997456845777d7cef345d74f9a608bed1a4e50f5f3575743d3 SHA512: 407fcb2fe120b37a6208b7c4a5ed45bbe8df42cf2ba8e7874f07a228645acd68b59f5e86350aebacdfd7cd1f3c725b5d16483202d0498a9794084637d8afdd98 Homepage: https://cran.r-project.org/package=GPCsign Description: CRAN Package 'GPCsign' (Gaussian Process Classification as Described in Bachoc et al.(2020)) Parameter estimation and prediction of Gaussian Process Classifier models as described in Bachoc et al. (2020) . Important functions : gpcm(), predict.gpcm(), update.gpcm(). Package: r-cran-gpemr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1001 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpemr_0.1.0-1.ca2404.1_all.deb Size: 315724 MD5sum: 511ae240052a9a3bf3ff2f89e6324f39 SHA1: 9ae4b661bf6ddcc8259b125011d3f29ee0108977 SHA256: 944a0304a43ec1076e220edd04ae567314da6653c9ba5a2d887b837c03af61a6 SHA512: 6762bd6a230dc5fc2e57e79e397f34d670f56daca23b7d2a1ce0c241ec5c977f367b0026ddbba3152b9b1a75f542ab835965e37723f453d163f09d71f6ae7f8a Homepage: https://cran.r-project.org/package=GPEMR Description: CRAN Package 'GPEMR' (Growth Parameter Estimation Method) Provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (). An interactive web application is also available at [GPEMR Web App](). 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Supports interactive and static plots tailored for presentations and publications, with customizable features like colors, themes, and annotations to align with specific analytical and presentation goals. Package: r-cran-gpic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gpic_0.1.0-1.ca2404.1_all.deb Size: 92692 MD5sum: 54b33bd1cab26a97503cbbda194ec391 SHA1: 940dce79a199da9c6fd701eec0a30a16aa3df68a SHA256: 027e05d58528c091aaf99608d8cddc6dfad620558b8f6fef51c41b660671267b SHA512: 4bad1963b7002d95ee65fefcef59678e271eff228d5795fbad2a82cb5a6cee0caa98e1344ebebefbc2b65cb2c75d70f221ede87585abd004bf108fab08066460 Homepage: https://cran.r-project.org/package=GPIC Description: CRAN Package 'GPIC' (Quantifying Group Performance in Individual Competitions) Compute the GPIC index as described in Pham (2020) . Package: r-cran-gpindex Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpindex_0.6.3-1.ca2404.1_all.deb Size: 267828 MD5sum: a19b4e72f21a939df75822d9220db356 SHA1: e6e5982cdce688b29ae70afcbc1335a9d0e6b603 SHA256: 37d7eac141c26919dfa2f2cb4b1062722563413158d80aee861bf429254a5025 SHA512: 45e82286c2661082b1417cdc0129fac2251055e8e6f2f755722f8973227dba22a29c66452d5465609f7bfc5efc160135b905b51f32ad170d4400aba5dbb4cb7d Homepage: https://cran.r-project.org/package=gpindex Description: CRAN Package 'gpindex' (Generalized Price and Quantity Indexes) Tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008, ), von der Lippe (2007, ), and the CPI manual (2020, ). Package: r-cran-gpk Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gpk_1.0-1.ca2404.1_all.deb Size: 290056 MD5sum: 379b444bf9ca362bc1745e33034d6304 SHA1: c7a2b0d773be0e1c572b3abbb48626a9154fdf4f SHA256: cb577edb0cd3a17ac929049fbaa7509cf4a93fcdc6acfacada0ffabd29ab335a SHA512: 888bfbabbe2833eb0d2aafe9aa2164ce7a9035f129d1c72a4f5bd27ca52c910b8a92284665cb0c2ef6f1c190c9626821d0a09d5fb6495e7e7e3815c9ac94a87b Homepage: https://cran.r-project.org/package=gpk Description: CRAN Package 'gpk' (100 Data Sets for Statistics Education) Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner. 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(2002) at for more details. Package: r-cran-gpltr Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gpltr_1.5-1.ca2404.1_all.deb Size: 522302 MD5sum: 8e268393f86bf4bce969eafe8c6090fb SHA1: de9175ab0406dcb2f8095344def10ad061adb16e SHA256: b3f70cc5ec1b745b58435044f1503111f6453227ed257ef60269c5d105b5b8a6 SHA512: 6a6dd6365411f2954479bd07113e4d0856a0e261d86daf8b70d89d12a707e2e11bfba88ddaad917506720e011f6d488e9c3ca4e9586817276f15888495c06482 Homepage: https://cran.r-project.org/package=GPLTR Description: CRAN Package 'GPLTR' (Generalized Partially Linear Tree-Based Regression Model) Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See 'Cyprien Mbogning et al.'(2014) and 'Cyprien Mbogning et al.'(2015) for an overview of all the methods implemented in this package. 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The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 . 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It can be applied to single or multiple time series under various conditions of noise, time series lengths, sampling, etc. This platform is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects Crops'IChaos and Musc & SlowFast). The method is described in the article : Mangiarotti S. and Huc M. (2019) . Package: r-cran-gpp Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan Filename: pool/dists/noble/main/r-cran-gpp_0.1-1.ca2404.1_all.deb Size: 77530 MD5sum: 91d32df13678cc4f5d02cb85e2ab2c14 SHA1: 2589416bf63920b531ba5717629419ffa30175e2 SHA256: 357fcbf5e515f5252507df36414a61688536f5ebca2264996c5b8cf2f0281d2d SHA512: 641bdcddfcd31344edd1891985ad3f34ef6fe07f10728d51c6aa40c04b16a95314a2c9ab61f649725fb4a5503572958d8c10eb9c49e521f68ba181f7644c3e2a Homepage: https://cran.r-project.org/package=GPP Description: CRAN Package 'GPP' (Gaussian Process Projection) Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot. 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The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. 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Formats the data into data frames where each sentence is an observation. Paragraph-level and document-level predictions are organized to align with the sentences. 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Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the 'GPUmatrix' package (available on CRAN). 'GPUmatrix' mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. 'GPUmatrix' relies on either the 'Torch' or 'Tensorflow' R packages to perform the GPU operations. We have demonstrated its usefulness for several statistical applications and machine learning applications: non-negative matrix factorization, logistic regression and general linear models. We have also included a comparison of GPU and CPU performance on different matrix operations. 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Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included. 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This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), ). 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This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially. 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Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage . Citation: . 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GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function. 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Package: r-cran-grand Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-grand_0.9.1-1.ca2404.1_all.deb Size: 105426 MD5sum: f72b30088edec9481094642989405f87 SHA1: 1510b99c39eabf37601de237879d2861549380e0 SHA256: ecd29baefe54baef73f2c6de58f4c65dda1aff6d31f0563d5760b377660827ad SHA512: 4d143e53c86cc837735db59300c352e8bb835771bcb2be06e450811f5303c9bc50f54eb06da617192c21e5854a554a135dd712214d0cdbac166bf02a67741c05 Homepage: https://cran.r-project.org/package=grand Description: CRAN Package 'grand' (Guidelines for Reporting About Network Data) Interactively applies the Guidelines for Reporting About Network Data (GRAND) to an 'igraph' object, and generates a uniform narrative or tabular description of the object. Package: r-cran-grandpriv Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-envstats, r-cran-rmutil, r-cran-rspectra, r-cran-diffpriv, r-cran-truncnorm, r-cran-randnet, r-cran-igraph, r-cran-hcd, r-cran-transport Filename: pool/dists/noble/main/r-cran-grandpriv_0.1.3-1.ca2404.1_all.deb Size: 56076 MD5sum: 42ee3f9464eeb0367d12d50e1451415d SHA1: 55a00a160f8081cd76a67bae634853ac130e11e9 SHA256: acaed39eb0885e15ad808738813ec1f6237bcbd60ae0eecbff9320f70847bc47 SHA512: 77704c7cef1e2e5df1c74ff5e8b2b02c3b115ba962e8637fac30e01b96989e504195e300d7ee72af8c0d87beb0e146233f7d5ab7c2fe150510ff832ec71cd5a3 Homepage: https://cran.r-project.org/package=GRANDpriv Description: CRAN Package 'GRANDpriv' (Graph Release with Assured Node Differential Privacy) Implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the 'randnet' package . For more details, see the "proposed method" section of Liu, Bi, and Li (2025) . Package: r-cran-grangerrkhs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-grangerrkhs_0.1.0-1.ca2404.1_all.deb Size: 43290 MD5sum: 7afacd4dbc86c8cbbcb402f4a1648713 SHA1: 20f3079a075af91b0378d9c81fd2a6e0337848e8 SHA256: bc2d7da7825910fbe1395138173bf854a4892bdc4eeb2aa349bec94afa0ebfc1 SHA512: 6b6a1b842f1ca53d163e11e17b6c53c4316c8ba72b2f7d8b387b18d81c4934d4e8e5e0ca36bd634e943475f16c5f2a09d50c8370260eed457f6eaf72be6cbc31 Homepage: https://cran.r-project.org/package=GrangerRKHS Description: CRAN Package 'GrangerRKHS' (RKHS-Based Nonlinear Granger Causality Testing via ConditionalCentering) Provides methods for nonlinear Granger causality testing in reproducing kernel Hilbert space (RKHS), using kernel ridge regression for conditional mean estimation and conditional centering for construction of the test statistic. Package: r-cran-grangers Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vars, r-cran-tseries Filename: pool/dists/noble/main/r-cran-grangers_0.1.1-1.ca2404.1_all.deb Size: 114870 MD5sum: 315b0da7c77eba342d2a0cb1455bc093 SHA1: ea499ffffd39b227152f183babf80caba82bf2a3 SHA256: fbc536b84b49367e8f3e92053b729ed656bf3b7915dbd4120260e39dea6e9e5d SHA512: 13cdf04536a37a00d3aa15e30f9f81d0af9a7f667156929da1119ed46b8859371ce71b1b3f8ffd257ab5ab88f66187a3a1ad5c90d037e311821708c3f12ce83d Homepage: https://cran.r-project.org/package=grangers Description: CRAN Package 'grangers' (Inference on Granger-Causality in the Frequency Domain) Implements unconditional and conditional Granger-causality spectra in the frequency domain, bootstrap inference for both spectra and their difference, and the Breitung-Candelon parametric tests. The bootstrap procedures follow Farnè and Montanari (2022) . Package: r-cran-grangersearch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vars, r-cran-rlang, r-cran-tibble, r-cran-generics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-grangersearch_0.1.0-1.ca2404.1_all.deb Size: 112000 MD5sum: fce1de6eb3e3e09ab744d58f51a8026a SHA1: d832aaee98098c5f1629e642968c68f0c5c53f7d SHA256: 18ad0fefef5188e412a4c98576ad664e6458d6082055b3d44841b8a39ad3e6b1 SHA512: af0e3e5e7e3fac1e22de4ca6f987b89a73234ad9c15be417810cb59baecfae66d736922eb3a443eedb78c67e239461eb373a78869e0ccbc554277780d9f2eeff Homepage: https://cran.r-project.org/package=grangersearch Description: CRAN Package 'grangersearch' (Granger Causality Testing for Time Series) Performs Granger causality tests on pairs of time series to determine causal relationships. Uses Vector Autoregressive (VAR) models to test whether one time series helps predict another beyond what the series' own past values provide. Returns structured results including p-values, test statistics, and causality conclusions for both directions. Package: r-cran-granova Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Suggests: r-cran-mgcv, r-cran-rgl, r-cran-mass, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-granova_2.3-1.ca2404.1_all.deb Size: 83862 MD5sum: 66f461169be84d678212a09af04b7c1a SHA1: e29848cb452c0c5b7878b0e3db50f83990c49ccd SHA256: ea68797950b85052d53f5218a6c69fb49a7887fd7676678b77a5e015f446c2fc SHA512: b85a47acb42b8f37d32b601d76eb452416c1fe4a06d390811f659eafe3432fb521e95eace0a096e12dd4e2c30a547247a0f93c4a831e28e921e701f0e1ad7ed2 Homepage: https://cran.r-project.org/package=granova Description: CRAN Package 'granova' (Graphical Analysis of Variance) This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) , Robert M. Pruzek and James E. Helmreich . The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. Package: r-cran-granovagg Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-granovagg_1.4.1-1.ca2404.1_all.deb Size: 142566 MD5sum: a914055759e1ef10a06fa19a89a95c40 SHA1: 59d1ab68ae101039b8310c9f69f5cd5d7c977c5b SHA256: 2a1bd817d867a72c66793f2a8fdc11c665e4b129747bef5b720001681a1d132e SHA512: 7e2e04184f60ff2f33d1543b4e68ec8430c7a6fb54d30cdf89bf9088a58e43b0567fe518de07d02f754cf083e4b951a2b4ced879d2d910df1847c815884615dd Homepage: https://cran.r-project.org/package=granovaGG Description: CRAN Package 'granovaGG' (Graphical Analysis of Variance Using ggplot2) Create what we call Elemental Graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use 'ggplot2', Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w() (a graphic for one way ANOVA); two other functions (granovagg.ds() and granovagg.contr()) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w(), which entails dynamic displays of data, is not currently part of 'granovaGG'.) The 'granovaGG' functions are to display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w() a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds() and granovagg.contr() provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. The graphics based on these functions can be especially helpful for learning how the respective methods work to answer the basic question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But these methods can be of assistance for work-a-day applications of many kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. In the case of granovagg.1w() and granovagg.ds() several arguments are provided to facilitate flexibility in the construction of graphics that accommodate diverse features of data, according to their corresponding display requirements. See the help files for individual functions. 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The Grantham distance attempts to provide a proxy for the evolutionary distance between two amino acids based on three key chemical properties: composition, polarity and molecular volume. In turn, evolutionary distance is used as a proxy for the impact of missense mutations. The higher the distance, the more deleterious the substitution is expected to be. Package: r-cran-grape Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-grape_0.1.1-1.ca2404.1_all.deb Size: 39420 MD5sum: d6830d8384c73d77da03d2b382aac6f3 SHA1: 53131830952552a756b0a0e2196b4487b8b23859 SHA256: 354781d05def1412ed55a0d76aa0d30cf4dc0401d295474f26ed9e9b6f33cbdc SHA512: 36009e1c3d196c9d3953b897879669f59c336d5df29c2052d32023949b554f8a5c211b686e5a82c4c104ced50b599b86f77dfffa45da6478791165c12fdd0fa3 Homepage: https://cran.r-project.org/package=GRAPE Description: CRAN Package 'GRAPE' (Gene-Ranking Analysis of Pathway Expression) Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017). Package: r-cran-grapes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grapes_1.0.0-1.ca2404.1_all.deb Size: 83500 MD5sum: 826c8551f79fa91a3924fb9be03f3800 SHA1: 96db1426284f8c792931c1d38c00317f0291be21 SHA256: 619be97cbe28ca27f59def9359d572f192c653a1bd418470a06f0d18a89dbfb3 SHA512: c93bb13349162a31cf87c833f2657e9cf1276fcfdff4dacc1bebd134c664ccd706e8050b7319798bf329193e303368a0a2a1e67c53c37ee65bc3f3d68943e85f Homepage: https://cran.r-project.org/package=grapes Description: CRAN Package 'grapes' (Make Binary Operators) Turn arbitrary functions into binary operators. Package: r-cran-grapesagri1 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinywidgets, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-magrittr, r-cran-summarytools, r-cran-dplyr, r-cran-pastecs, r-cran-ggpubr, r-cran-hmisc, r-cran-corrplot, r-cran-ggplot2, r-cran-reshape2, r-cran-gridgraphics, r-cran-rcolorbrewer, r-cran-desplot, r-cran-agricolae, r-cran-paireddata, r-cran-gtools, r-cran-rdpack Suggests: r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grapesagri1_1.1.0-1.ca2404.1_all.deb Size: 3504660 MD5sum: d7d82554e6095321e76f2dc486cddddc SHA1: 738457357e702a0592a47d175db3fb3ac51ffffa SHA256: 8dd63505ca5e3e38512e3a394908b1ae5affccb26f0a6f533445fb0e772d7e04 SHA512: 9fe64e245c823056601c02f60da887819c7ee118f1c6c016c40505f6126c84c078f8c153cbe6600011cf3e01f26f95d7b3f2529cffd4e387bc0d41ac8ae00b5b Homepage: https://cran.r-project.org/package=grapesAgri1 Description: CRAN Package 'grapesAgri1' (Collection of Shiny Apps for Agricultural Research Data Analysis) Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format. Package: r-cran-graph3d Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-lazyeval Suggests: r-cran-shiny, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-graph3d_0.2.0-1.ca2404.1_all.deb Size: 105008 MD5sum: d40e72150e7a20cb1825dab0c96a7956 SHA1: 0c081ea78e6c200e82cd8bf3469d1b061075c26c SHA256: 0aaab42694825e0a75f46ff0caa57b7c87e23301a0f25a32ab4913a38166a0fb SHA512: 03d2df009a970f8691b172748e8aa4227d6c5fc24aa800e8293e96b03dbc772cf5de007bc2afe31671af99e308500284789efe5a87d932c8aedf10b40ce5fd9e Homepage: https://cran.r-project.org/package=graph3d Description: CRAN Package 'graph3d' (A Wrapper of the JavaScript Library 'vis-graph3d') Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. Based on the 'vis.js' Graph3d module and the 'htmlwidgets' R package. Package: r-cran-graph4lg Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9702 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-adegenet, r-cran-ggplot2, r-cran-stringr, r-cran-igraph, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-matrix, r-cran-vegan, r-cran-pegas, r-cran-mass, r-cran-tidyr, r-cran-sf, r-cran-hierfstat, r-cran-rappdirs, r-cran-gdistance, r-cran-raster, r-cran-terra, r-cran-ecodist, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-graph4lg_2.0.0-1.ca2404.1_all.deb Size: 3241514 MD5sum: d0fdd3249ff2dd19f13b7f674adaec0b SHA1: c8b011116a55b20cb3ebcf0be63d1878f0122980 SHA256: df263403370f917a166f3406b475790fcb6ef5ce9e6fffba190b54955ccdfbb6 SHA512: 437238ee6e92dbdc8ba8e3a35a8a4abbde6bc847669fe7f2f113903f19033a0cbd09d896b47599571bb95ad35b3ca55d12f4528e1565da95f35bf6a42e8e091f Homepage: https://cran.r-project.org/package=graph4lg Description: CRAN Package 'graph4lg' (Build Graphs for Landscape Genetics Analysis) Build graphs for landscape genetics analysis. This set of functions can be used to import and convert spatial and genetic data initially in different formats, import landscape graphs created with 'Graphab' software (Foltete et al., 2021) , make diagnosis plots of isolation by distance relationships in order to choose how to build genetic graphs, create graphs with a large range of pruning methods, weight their links with several genetic distances, plot and analyse graphs, compare them with other graphs. It uses functions from other packages such as 'adegenet' (Jombart, 2008) and 'igraph' (Csardi et Nepusz, 2006) . It also implements methods commonly used in landscape genetics to create graphs, described by Dyer et Nason (2004) and Greenbaum et Fefferman (2017) , and to analyse distance data (van Strien et al., 2015) . Recent updates introduce multiple habitat graph functionalities (as described by Savary et al., 2024) . Package: r-cran-graphclust Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockmodels, r-cran-igraph, r-cran-sclust Filename: pool/dists/noble/main/r-cran-graphclust_1.3-1.ca2404.1_all.deb Size: 116332 MD5sum: b61e27fa15f84471320770fe28f8892c SHA1: 096dd27707c25af4f783d3f7f720b44f6f41e4a2 SHA256: 681b65f219852db264c8fa85eda12fdf3c181a8279256a1224f9d6c6f39546ea SHA512: ee8b83c11e7edd8327b3808f1d4fe7610e40e22c8767fa4924b47859e8fccd1faa0723625dde4ba6fb797b588056e28112002dfcb3b8227d16f883f607e22646 Homepage: https://cran.r-project.org/package=graphclust Description: CRAN Package 'graphclust' (Hierarchical Graph Clustering for a Collection of Networks) Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) . Package: r-cran-grapher Architecture: all Version: 1.9-86-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-grapher_1.9-86-5-1.ca2404.1_all.deb Size: 694912 MD5sum: c25540dc8a01d3bddfa2a288df18a3fa SHA1: 09b50f8948fab857ade6afe268a889d67816dac5 SHA256: 63b20d956077a2da70f3f01592275d51d76ffb412a9f098e4e68b804fab72650 SHA512: d97919611b3c0d094314c1bcef43b0f9445075e1812596a0465af2467e36b74596f00daa0f7982c8961e9c5332ad08ecffda2c589385e7e52c5cce44804b9748 Homepage: https://cran.r-project.org/package=GrapheR Description: CRAN Package 'GrapheR' (A Multi-Platform GUI for Drawing Customizable Graphs in R) A multi-platform user interface for drawing highly customizable graphs in R. 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Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses. 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Node identity is positional rather than value-based, so isolated nodes and repeated values are represented without special handling. Three complementary data structures are provided, each an ordinary vector that stays a column in a data frame and slices consistently with it: 'node_vec' is vectorised along the nodes of a graph, 'edge_vec' is vectorised along its edges, and 'agg_vec' (with the tabular 'agg_df') represents the aggregation structure common in data analysis, such as a total row over a set of categories. This makes graph relationships a native part of tidy rectangular data analysis workflows, alongside tools such as those in 'dplyr'. Each of these can also be converted to 'igraph' objects for further analysis. 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This package includes statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design. See Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report. 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Generates a Report Card for rater reliability on binary outcomes from an N x k subject-by-rater rating matrix, on both the inter-rater and intra-rater axes. Each panel coefficient is positioned on a data-generating-process-calibrated reference surface conditioned on the study's rater count, sample size, and prevalence, yielding a pooled percentile (the coefficient's position within the design's achievable agreement range) together with a consistency band on panel quality: the quality levels whose sampling distributions are consistent with the observed value at that design. The panel coefficients are the prevalence-adjusted bias-adjusted kappa (PABAK) of Byrt, Bishop, and Carlin (1993) , the first-order agreement coefficient (AC1) of Gwet (2008) , the multi-rater kappa of Fleiss (1971) , and the observed intraclass correlation. A cross-coefficient discordance diagnostic (delta-hat) reports the spread of the coefficients' implied panel qualities and flags panels for which no single coefficient is a stable summary by the spread's percentile on a matched null distribution; for such divergent panels the report routes to a pairwise PABAK matrix and per-rater sensitivity and specificity recovered from the latent-class model of Dawid and Skene (1979) , with the two-rater bounds of Hui and Walter (1980) . 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The original research files are not bundled in this R package; they remain hosted by BioStudies. The manifest can be used in image-analysis and plant-pathology workflows, including workflows based on the 'grayleafspotr' software. Related research outputs are documented using their persistent identifiers. 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Intended to be used for analyzing grouped and right-censored data, which is widely applied in many branches of social sciences. The algorithm implemented is described in Fu et al., (2021) . Package: r-cran-greatr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-neldermead, r-cran-optimization, r-cran-patchwork, r-cran-scales Suggests: r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-greatr_2.1.0-1.ca2404.1_all.deb Size: 802336 MD5sum: eb309e1639f3183783e075f895198316 SHA1: 84d223dc1fe93b4569904f336720bd704a6f14fa SHA256: c9507c3e43fd1b39c4db521f7eb43e6023603bf51a91e8251cbca0f0f2e6786d SHA512: 045cc4c7c37e58f1ccc28f4fb787436c6a12dfc6eb370edf6f0cc179c8010e4c183dbfbb924a0d0f00d99c2350d7cf16a7f1023c05f3e81edd18adbc72264e97 Homepage: https://cran.r-project.org/package=greatR Description: CRAN Package 'greatR' (Gene Registration from Expression and Time-Courses in R) A tool for registering (aligning) gene expression profiles between reference and query data. Package: r-cran-grec Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3545 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-imagine, r-cran-raster, r-cran-terra, r-cran-abind, r-cran-lifecycle, r-cran-cli Filename: pool/dists/noble/main/r-cran-grec_1.6.4-1.ca2404.1_all.deb Size: 3523150 MD5sum: 7243a5c79e482011287143ec373f2097 SHA1: 5f2c962a8b65de867093755d8a6b8cc12603b4d6 SHA256: b81256817cd08fed362885bbf382ae53affed882b0d250e7f0e2ea358ce1c3ff SHA512: aa735553afcabb90c26b096dc6b719d779d16b1f97b9042967469d5e0557f005f7096734de5e6defd5e3be5b271d9f190b725e16e1bac73e66e6e33c2566a8bc Homepage: https://cran.r-project.org/package=grec Description: CRAN Package 'grec' (Gradient-Based Recognition of Spatial Patterns in EnvironmentalData) Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition. Package: r-cran-greedyexperimentaldesignjars Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-greedyexperimentaldesignjars_1.0-1.ca2404.1_all.deb Size: 597356 MD5sum: 49433c3eb180d5e5897d2b3e4457373f SHA1: 22de5b6785fa3a7577633362f9ed132b4b4b4916 SHA256: 693b681f292dc9eeafaa0f94c4caf74778258ef1fb0511b86b9889a2192105a3 SHA512: 1acd2b8219ecc28bf8af0c03ab060eb4dc8f6199fbc65554b325aa37772514c1024541af13d88e18a9f68a72d1a48982a9f650ee298e1bd3f0efacde0e98c292 Homepage: https://cran.r-project.org/package=GreedyExperimentalDesignJARs Description: CRAN Package 'GreedyExperimentalDesignJARs' (GreedyExperimentalDesign JARs) These are GreedyExperimentalDesign Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without GreedyExperimentalDesign. Package: r-cran-greekletters Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-assertthat Suggests: r-cran-clisymbols, r-cran-swirlify, r-cran-swirl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-greekletters_1.0.4-1.ca2404.1_all.deb Size: 33106 MD5sum: 81704e0793ef59b39fa216fabd3165d6 SHA1: 3bf8717be1728ba8acd10a1360d7e7f88822a1a3 SHA256: 1675807851b13d7fd1de2683be0817d3395be0b974073bc562faa6a3ccada33b SHA512: 3105218161a98f66421fe8d9bcfa9494388b8421ae0a71b8c70ecfb25584fb8cd69f31e55fa4aa7ee59ce751f4abcc31f77493419fdf812526269b320c5732ec Homepage: https://cran.r-project.org/package=greekLetters Description: CRAN Package 'greekLetters' (Routines for Writing Greek Letters and Mathematical Symbols onthe 'RStudio' and 'RGui') An implementation of functions to display Greek letters on the 'RStudio' (include subscript and superscript indexes) and 'RGui' (without subscripts and only with superscript 1, 2 or 3; because 'RGui' doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the 'swirlify' package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the 'clisymbols' package. Package: r-cran-greenbook Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-inflater, r-cran-openxlsx, r-cran-officer, r-cran-flextable Filename: pool/dists/noble/main/r-cran-greenbook_0.1.1-1.ca2404.1_all.deb Size: 197624 MD5sum: e3300179ed495f6e8793bf17f43c1830 SHA1: cfcde7b19b5fc6896a0239c75bcdf6da4c7f5709 SHA256: 5e031017f0778234931e6bc0629b06f8953960dd6a6352cdd761192bfd14bc6b SHA512: 24c7da88a940be05a057f166b7c41f3fa1fdf1890ea56bc9a8c58d2ae5013dc31b3b8de87ee71dfc8f1f6275bc8cb4457fc5f948a6522efda2b0816cfde81650 Homepage: https://cran.r-project.org/package=greenbook Description: CRAN Package 'greenbook' (HM Treasury Green Book Cost-Benefit Analysis Primitives) Implements cost-benefit analysis primitives from HM Treasury Green Book guidance (HM Treasury, 2022, 2026): the kinked Social Time Preference Rate (STPR), discount factors, net present value (NPV), equivalent annual cost, and real-terms rebasing using the GDP deflator. 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Package: r-cran-greenclust Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-greenclust_1.1.1-1.ca2404.1_all.deb Size: 159622 MD5sum: 7c16aa4e30f4522f8898154bd433ef13 SHA1: 350f57a5d07ab54b2115f8ea27a1461f3744c02a SHA256: 4c730bc52f07dd6271c1ce287f2b427166435232316070632dcc3339edfe6d49 SHA512: c354ce0db4fb79369735ece88ee36f2160043df2d1cb900a1dbf1961d7b7410c5b3d29b331decc13b4e90136ef56673e9a4cfebff1ef890d9c5218b5ca092a3b Homepage: https://cran.r-project.org/package=greenclust Description: CRAN Package 'greenclust' (Combine Categories Using Greenacre's Method) Implements a method of iteratively collapsing the rows of a contingency table, two at a time, by selecting the pair of categories whose combination yields a new table with the smallest loss of chi-squared, as described by Greenacre, M.J. 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Designed to support teaching at the Universidad Autónoma Chapingo, it facilitates results interpretation and assumption validation through automatic graphical diagnostics. Developed as part of an undergraduate thesis at the Universidad Autónoma Chapingo, under the supervision of Dr. Julio César Buendía Espinoza (thesis advisor), with the participation of the thesis committee: Diego Ernesto Lira González (secretary), Israel Lerma Serna (member), Juan Uriel Avelar Roblero (alternate), and Elisa del Carmen Martínez Ochoa (alternate). Methods for regression and time series are based on Montgomery et al. (2021, ISBN:978-1119570141) and Box & Jenkins (1970, ISBN:978-0816211043). 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Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich' package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for time-splitting a dataset when modeling non-proportional hazards in Cox regressions. Package: r-cran-gregoryquadrature Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gregoryquadrature_1.0.0-1.ca2404.1_all.deb Size: 17474 MD5sum: eb5c95a2dbaeeb3c613b4e382814648e SHA1: 2848717de6d4d8903665d27a3aada2c42b7a9d09 SHA256: 3ddecca374432a2f3be1a80079269e5fd1089ef767527c8a553c5c1143b87aa9 SHA512: 39ede814f5ed7f0f985ae11da2dd2c6d1fd7cad07c10e274804003a552fbb0e9cac906013b6c78d7bde211f659080427d72df51b1a084982b03491dc8eeab6a1 Homepage: https://cran.r-project.org/package=GregoryQuadrature Description: CRAN Package 'GregoryQuadrature' (Gregory Weights for Function Integration) Computes Gregory weights for a given number nodes and function order. Anthony Ralston and Philip Rabinowitz (2001) . 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The functions are designed to work well within a forestry context, and estimate multiple estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when describing the linear model. 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It covers several state-of-the-art univariate and multivariate grey models. A user friendly interface allows users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within user chosen confidence intervals. Chang, C. (2019) , Li, K., Zhang, T. (2019) , Ou, S. (2012) , Li, S., Zhou, M., Meng, W., Zhou, W. (2019) , Xie, N., Liu, S. (2009) , Shao, Y., Su, H. (2012) , Xie, N., Liu, S., Yang, Y., Yuan, C. (2013) , Li, S., Miao, Y., Li, G., Ikram, M. (2020) , Che, X., Luo, Y., He, Z. (2013) , Zhu, J., Xu, Y., Leng, H., Tang, H., Gong, H., Zhang, Z. (2016) , Luo, Y., Liao, D. (2012) , Bilgil, H. (2020) , Li, D., Chang, C., Chen, W., Chen, C. (2011) , Chen, C. (2008) , Zhou, W., Pei, L. (2020) , Xiao, X., Duan, H. (2020) , Xu, N., Dang, Y. (2015) , Chen, P., Yu, H.(2014) , Zeng, B., Li, S., Meng, W., Zhang, D. (2019) , Liu, L., Wu, L. (2021) , Hu, Y. (2020) , Zhou, P., Ang, B., Poh, K. (2006) , Cheng, M., Li, J., Liu, Y., Liu, B. (2020) , Wang, H., Wang, P., Senel, M., Li, T. (2019) , Ding, S., Li, R. (2020) , Zeng, B., Li, C. (2018) , Xie, N., Liu, S. (2015) , Zeng, X., Yan, S., He, F., Shi, Y. (2019) . 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Details of the method can be found in Dufey (2017) . 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Create balanced partitions and cross-validation folds. Perform time series windowing and general grouping and splitting of data. Balance existing groups with up- and downsampling or collapse them to fewer groups. 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The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. 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The difference analysis is based on the 'limma' package, which can cover gene and protein expression profiles (Reference: Matthew E Ritchie , Belinda Phipson , Di Wu , Yifang Hu , Charity W Law , Wei Shi , Gordon K Smyth (2015) ). The GO enrichment analysis is based on the 'clusterProfiler' package and supports three common species: human, mouse, and yeast (Reference: Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He (2012) ). The results of batch difference analysis and enrichment analysis are output in separate folders for easy viewing and further visualization of the results during the process. The results returned a heatmap in R and exported to 3 folders named DEG, go, and merge. 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The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website . 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The package also handles delayed and missing responses, which broadens its use in real-world trials. It offers functions for simulating a variety of response-adaptive randomization procedures, to help guide the choice of design for a clinical trial, including the doubly adaptive biased coin design and the multi-arm efficient randomized adaptive design (ERADE), k-arm optimal target allocations, group sequential monitoring, and a function that computes allocation probabilities for an ongoing trial. For details of the methods and algorithms, see the following references: Wei, L. J. (1979) ; Wei, L. J. and Durham, S. (1978) ; Durham, S. D., Flournoy, N. and Li, W. (1998) ; Ivanova, A., Rosenberger, W. F., Durham, S. D. and Flournoy, N. (2000) ; Bai, Z. D., Hu, F. and Shen, L. (2002) ; Ivanova, A. (2003) ; Hu, F. and Zhang, L. X. (2004) ; Hu, F. and Rosenberger, W. F. (2006, ISBN:978-0-471-65396-7); Zhang, L. X., Chan, W. S., Cheung, S. H. and Hu, F. (2007) ; Zhang, L. and Rosenberger, W. F. (2006) ; Hu, F., Zhang, L. X., Cheung, S. H. and Chan, W. S. (2008) ; Tymofyeyev, Y., Rosenberger, W. F. and Hu, F. (2007) ; Hu, F., Zhang, L. X. and He, X. (2009) ; Zhu, H. and Hu, F. (2010) ; Zhai, G., Li, Y., Zhang, L. and Hu, F. (2024) ; Alkhnefr, N., Hu, F. and Zhai, G. (2025) . 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Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) ). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance. 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The package allows users to fit a variety of growth models, including linear, exponential, logistic, and 'Gompertz' functions. For non-linear models, starting values are automatically calculated using initial least-squares estimates. The package includes functions for summarizing models, visualizing data and results, calculating doubling time and other key statistics, and generating model diagnostic plots and residual summary statistics. It also provides functions for generating publication-ready summary tables for reports. Additionally, users can fit linear and non-linear least-squares regression models if clustering is not applicable. The mixed-effects modeling methods in this package are based on Comets, Lavenu, and Lavielle (2017) as implemented in the 'saemix' package. Please contact us at models@dfci.harvard.edu with any questions. 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(2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see for more information). The package 'growthPheno' can also be installed from . 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Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017". Package: r-cran-gspcr Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2853 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-factominer, r-cran-ggplot2, r-cran-mass, r-cran-mlmetrics, r-cran-nnet, r-cran-pcamixdata, r-cran-reshape2, r-cran-rlang Suggests: r-cran-knitr, r-cran-lmtest, r-cran-patchwork, r-cran-rmarkdown, r-cran-superpc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gspcr_0.9.5-1.ca2404.1_all.deb Size: 2535006 MD5sum: 2543997d5622a92f366f86a60e7059e4 SHA1: ac3368dbe4da19b13cc2159a7bce1e672b43ce48 SHA256: 5111de31c5b660c83608ed251853226be68f9880c95452e61ba204fb9da00f78 SHA512: f0102c2afd5ab07632dbc4f50ed73313c626f0caa17717327d49cab3d559ddabf3ec47ace990a11ab116339c08f77ef31c572f683ada5cbdf80de19f65dcdb1b Homepage: https://cran.r-project.org/package=gspcr Description: CRAN Package 'gspcr' (Generalized Supervised Principal Component Regression) Generalization of supervised principal component regression (SPCR; Bair et al., 2006, ) to support continuous, binary, and discrete variables as outcomes and predictors (inspired by the 'superpc' R package ). Package: r-cran-gsrs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-gsrs_0.1.1-1.ca2404.1_all.deb Size: 280492 MD5sum: 0f431e36bb6cec8168be742e88140b27 SHA1: 552a11cae0a878ae539c2b90fce47f173b1b7309 SHA256: 1a7f972b3b2cdaff6914fed6cb8264397515040c119bb7af8a8e10da09ec750d SHA512: a877bf04bef6ffc60724d35f1db1a9e6248d4dabd6c469c5262206bffd7c7d9a1f53acd0eea5b933c6263eb68a645e3234c8cb60b18a88f07df0f9a0e2cf335f Homepage: https://cran.r-project.org/package=gsrs Description: CRAN Package 'gsrs' (A Group-Specific Recommendation System) A group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System for the details. Package: r-cran-gsrsb Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ldbounds, r-cran-xtable Filename: pool/dists/noble/main/r-cran-gsrsb_1.2.1-1.ca2404.1_all.deb Size: 90694 MD5sum: eed679df40896f6c00e3015cb08462d6 SHA1: 214ea028be3f4de5ac6fd851f48284460ce83fab SHA256: e3c064d9ee694f8e86b53b26395e2caaffe3b6f3bfeafcbd95b38d266fcb3882 SHA512: dd57b985cbc31372e2f4bbc58305374802f9a2a12f7d29a0212a2e4d6abb37a7c4485991763ba398598955bce2cb8a65e7971ce7262888bb715ac919083257e3 Homepage: https://cran.r-project.org/package=gsrsb Description: CRAN Package 'gsrsb' (Group Sequential Refined Secondary Boundary) A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48. Package: r-cran-gsse Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-zoo Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-gsse_0.1-1.ca2404.1_all.deb Size: 69932 MD5sum: 28ec213610ef8fe46d40a46e0c919650 SHA1: 746ebd4168e065c039ca7fab14b1238607bf1a5f SHA256: 07fe8e871e14f90a198846f0097ef1c1b6d6bcbd8eb062677b75e9c1c78a4f96 SHA512: d9d09efea4a5388d6099fc747b4a56029efea6ef64805d7c55902a0802937005e4951dcbb6d54a240f2ead80c5cd131ac94f73659c1db3be8109ce34f44d368b Homepage: https://cran.r-project.org/package=GSSE Description: CRAN Package 'GSSE' (Genotype-Specific Survival Estimation) We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees. We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. The estimators are calculated via an expectation-maximization algorithm. Package: r-cran-gsstda Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4964 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-survival, r-cran-visnetwork, r-bioc-complexheatmap, r-cran-circlize, r-cran-devtools Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gsstda_1.0.0-1.ca2404.1_all.deb Size: 4053730 MD5sum: f3454c143c33529eaf310461fb56bc37 SHA1: c2ba2897b037b101b543aef6ee5168d9cb612199 SHA256: 16591e7da4ba893af97405759bd115a3c225010f45b8bb491b7dd98d213a464c SHA512: a73ba16b70c67db8f87a9fd1b200aeb6629d532885187e8c58df51ffce27724333e20e4d9571feb8f4a36b0702ebebff81ef8442c74bcee9504a62b6cf2f4057 Homepage: https://cran.r-project.org/package=GSSTDA Description: CRAN Package 'GSSTDA' (Progression Analysis of Disease with Survival using TopologicalData Analysis) Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" . Package: r-cran-gstar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-xts, r-cran-zoo, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gstar_0.1.0-1.ca2404.1_all.deb Size: 52540 MD5sum: 6dab449d8e20a7fc9857564f0bd74c6c SHA1: ddda1edab902bf8433e9a97ebc99720d523b651d SHA256: 22e3e70699605864479cf5b286e71402c6f158c87d06678b0cd726c520b2323f SHA512: 8e37badfb01f2cec87d9004cce7d33237a66fbe30d5c82066df967b587b0edfa9ce48ddf32817cb361957c97e6f56aeb0213e1a626963f036714bd6717054eb4 Homepage: https://cran.r-project.org/package=gstar Description: CRAN Package 'gstar' (Generalized Space-Time Autoregressive Model) Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) . Package: r-cran-gstream Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gstream_0.2.0-1.ca2404.1_all.deb Size: 149844 MD5sum: 75e2e20b6a0bd90e746635a8360b71b3 SHA1: ada33d6153c2289b227a60c1bf85f5a7b6cc6d3f SHA256: 50f259a30be5870f01ad11f84bab34bf298375d6577399c5ef3364b693c0a92a SHA512: 26a1ceec2c2fda6f4e18cc150165d7ea437096a7884d0a3a363b69d4db81172fb6ab4990dd7e31e15ffdf3576e3290386957a42b8fdc1ee639364798f3f70766 Homepage: https://cran.r-project.org/package=gStream Description: CRAN Package 'gStream' (Graph-Based Sequential Change-Point Detection for Streaming Data) Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available. See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data . Package: r-cran-gstsm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gstsm_1.0.0-1.ca2404.1_all.deb Size: 65746 MD5sum: 9c0478339bde2cde61a30facbd8e6c1f SHA1: dc9b46639c8523b570ad30cb3209fea346d95fcb SHA256: bbaadaf1464801affb743d8be5f0b9e715d1237f6f9d0b5f44508958637244b6 SHA512: 66d4e943474e4250e34c0bf8cedb98685929e6ba97dd4907a1f7cd9d1e88137948feaea27cf68ac4d58d84467681a08b5f515db30fc9c03fa99b91aafd5891cc Homepage: https://cran.r-project.org/package=gstsm Description: CRAN Package 'gstsm' (Generalized Spatial-Time Sequence Miner) Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 ). 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Package: r-cran-gsympoint Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm, r-cran-rsolnp, r-cran-rocr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gsympoint_1.1.2-1.ca2404.1_all.deb Size: 131032 MD5sum: 6ccf976c70f09bc5db7bfdc89f972e82 SHA1: 8b9eba78ca1d91718b13b0c5f166dea20a253acb SHA256: d44b23d7ca083835b82773a26a07ebd6d37cd196b374286f8e9afc99b8ada1b2 SHA512: 53c9570d6ce94d76da48ca6ad9a418b4b798c6b628738e3d15b91ea6667d767db197654d12d6a33eaeee0728a5868482e79fb482943c179ced1beee3473c2373 Homepage: https://cran.r-project.org/package=GsymPoint Description: CRAN Package 'GsymPoint' (Estimation of the Generalized Symmetry Point, an OptimalCutpoint in Continuous Diagnostic Tests) Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. 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Package: r-cran-gsynth Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-fect, r-cran-panelview Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gsynth_1.4.0-1.ca2404.1_all.deb Size: 105250 MD5sum: ba50481f57fdf46ea75e84075b80fd06 SHA1: aa2f81a27b7c7e4b6aa5973d0ce5b90e97000b89 SHA256: 185ebcfeb7367dea04c76889ffabea52b2a520a39e1ed3b4c1a5708c76b9b77d SHA512: 12258cc012f7c1689efeb02ce788fd30cd30a561456876b28cc31e59c4c8768aba9f0dcdb80c273e5d9aff96b50a7cbbf05ee7039d4e5aa9748c2d62fad6cb9d Homepage: https://cran.r-project.org/package=gsynth Description: CRAN Package 'gsynth' (Generalized Synthetic Control Method) Conducts causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. See Xu (2017) for details. Package: r-cran-gt4ireval Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gt4ireval_2.0-1.ca2404.1_all.deb Size: 59720 MD5sum: 63e198bd0ae24e60ae416b6b32d4081c SHA1: 02c7657be2b714f648f757e2bb33ff24394e3690 SHA256: 12ba5f130ba767d18acc6e51c26a47b7a260ceb10649e2dcbb4953ff68b2c457 SHA512: e649af86825baa5566e4a01a3d0a6253ed0c5e6c7fae9c886a44e8a66c422b80c104623b0158b5d43f146ff1edd3e3eae6f605273a4af192e40e131eb62abe26 Homepage: https://cran.r-project.org/package=gt4ireval Description: CRAN Package 'gt4ireval' (Generalizability Theory for Information Retrieval Evaluation) Provides tools to measure the reliability of an Information Retrieval test collection. 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Package: r-cran-gt Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-bigd, r-cran-bitops, r-cran-cli, r-cran-commonmark, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-htmltools, r-cran-htmlwidgets, r-cran-juicyjuice, r-cran-magrittr, r-cran-markdown, r-cran-reactable, r-cran-rlang, r-cran-sass, r-cran-scales, r-cran-tidyselect, r-cran-vctrs, r-cran-xml2 Suggests: r-cran-bit64, r-cran-farver, r-cran-fontawesome, r-cran-ggplot2, r-cran-gtable, r-cran-katex, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-paletteer, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rsvg, r-cran-rvest, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-webshot2, r-cran-withr Filename: pool/dists/noble/main/r-cran-gt_1.3.0-1.ca2404.1_all.deb Size: 6255630 MD5sum: a63ee14d6578fe89313fdef99f515438 SHA1: 1e73f96cfa7f506364a803a27cdf9a6fd6f3c9c8 SHA256: 464ee0e57c93d31dddf1d107ab242797846befb603ee7dedb718ccaefbe2bb01 SHA512: b075877fa73928e5e80d486b27a7a0e35f4e35c2e684c3bca959ccc4c64536059a4735215486ab5680d6a4a5f7879e3d124c0eb275b397c89cb9415c0dbb4c9a Homepage: https://cran.r-project.org/package=gt Description: CRAN Package 'gt' (Easily Create Presentation-Ready Display Tables) Build display tables from tabular data with an easy-to-use set of functions. 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Package: r-cran-gtakeout Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-here, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-zip Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtakeout_0.1.0-1.ca2404.1_all.deb Size: 18158 MD5sum: 5a68a9f49db16dc3f955928ae3b647ce SHA1: 7aa80516f7f15345f66d8175a2d29071d2539c20 SHA256: 13ce15c6b4f48c238e6fc60116ad62fc8efa2bdd3176c9a05029549ac2168b1d SHA512: 1ff221f310f2ac60e13e05d241d9e20698aeebb2123aaa938065b77b7fcc511322139e1e0d04ed519c5fe4157167184b6ac672a5dc3bed4355cfee07f0522619 Homepage: https://cran.r-project.org/package=gtakeout Description: CRAN Package 'gtakeout' (Extract Data from Google Takeout) Provides functions to analyze data exported from 'Google Takeout'. The package supports unzipping archives and extracting user review data from Google Business Profile exports into tidy data frames for further analysis. 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Package: r-cran-gtbasedim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3646 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coopgame, r-cran-rweka Filename: pool/dists/noble/main/r-cran-gtbasedim_1.0.0-1.ca2404.1_all.deb Size: 633620 MD5sum: dd7637706a5b32714f81b270878c0610 SHA1: 6d08517559e711ea308113650f67c8ad3405cf9a SHA256: 62806921ec332c0346ccaa1bf91abbea3c882351d7f8f5dff4e26efd678badd8 SHA512: a7bd46dd4b80d43be90f73a54bb7d213d0532ae3d721e1e4eeece7d929d873a0183fdc623a309c99a2b039415da9bff4254e32749f1c6a5a397a0e6f1db8d284 Homepage: https://cran.r-project.org/package=GTbasedIM Description: CRAN Package 'GTbasedIM' (Game Theory-Based Influence Measures) Understanding how features influence a specific response variable becomes crucial in classification problems, with applications ranging from medical diagnosis to customer behavior analysis. 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Package: r-cran-gtdesign Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cvxr, r-cran-tibble, r-cran-mass Suggests: r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-doparallel, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-gtdesign_0.1.1-1.ca2404.1_all.deb Size: 507838 MD5sum: 82208ef62739a09f463502c8256f44e3 SHA1: 1c1d87ae69a1aa5d1436960a339fb710d76f593e SHA256: f9c7e7791460bcd34378fa0fff8dfb0d5d0c11afcb49a2e298eecc7a90c6e07d SHA512: e9531ef052229603f8d82d8ffa33b7685274c175e559d67b3aa48d3ed57a8e2715c502ed32894a4ab3dd66a9383f0d5f2186ab34a4d81a09ab03013454e3b07f Homepage: https://cran.r-project.org/package=gtDesign Description: CRAN Package 'gtDesign' (Convex Optimal Designs for Group Testing Experiments) Finite candidate-set approximate optimal designs for group testing and related experiments, using convex optimization and equivalence checks. 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The GTEx project is a comprehensive public resource for studying tissue-specific gene expression and regulation in human tissues. Through systematic analysis of RNA sequencing data from 54 non-diseased tissue sites across nearly 1000 individuals, GTEx provides crucial insights into the relationship between genetic variation and gene expression. This data is accessible through the GTEx Portal API enabling programmatic access to human gene expression data. For more information on the API, see . Package: r-cran-gtextras Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gt, r-cran-commonmark, r-cran-dplyr, r-cran-fontawesome, r-cran-ggplot2, r-cran-glue, r-cran-htmltools, r-cran-paletteer, r-cran-rlang, r-cran-scales, r-cran-knitr, r-cran-cli Suggests: r-cran-base64enc, r-cran-bitops, r-cran-covr, r-cran-fs, r-cran-hms, r-cran-magrittr, r-cran-rvest, r-cran-sass, r-cran-stringr, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-webshot2, r-cran-xml2, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-gtextras_0.6.2-1.ca2404.1_all.deb Size: 3999816 MD5sum: f5c0ee12b3bba4425a9c41047d8fd825 SHA1: a38f3c5af598a512fc472d73f380f74ef47bf2f7 SHA256: 0dcdfaa22880b51ee8d1fa3d430e531dece69e75fcba0f0bdf9de46c881e957c SHA512: 428f173e3ce013a0bb77917030c5b97c4b374547eaafc0c9c917e3e3983114512af41ad96754962041de1e057513505d57aaa19cd522f4bb7b36aa04374f3568 Homepage: https://cran.r-project.org/package=gtExtras Description: CRAN Package 'gtExtras' (Extending 'gt' for Beautiful HTML Tables) Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions. Package: r-cran-gtexture Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dlookr, r-cran-dplyr, r-cran-fitscape, r-cran-igraph, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtexture_1.0.1-1.ca2404.1_all.deb Size: 107246 MD5sum: 149144cf466f04e8a4a5948b9e5330e0 SHA1: 7b743be52c7ec0774544c537426a97d7757f77d2 SHA256: 578bf1daf169084d5412c7531396c2a0991a6593e43e71877a8e7ad579341a2d SHA512: 6cd92b545b495c19fea3aa22480c2780acb6b1a817d061bbd6aae0da8ae573c5143b3568b405591daf4f3b0a8a9e9744e4c1ec865e9d2ca2fda1da9be843ac7f Homepage: https://cran.r-project.org/package=gtexture Description: CRAN Package 'gtexture' (Generalized Application of Co-Occurrence Matrices and HaralickTexture) Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) . A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) . Package: r-cran-gtfs2emis Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-gtfs2gps, r-cran-sf, r-cran-sfheaders, r-cran-terra, r-cran-units, r-cran-parallelly Suggests: r-cran-gtfstools, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtfs2emis_0.1.2-1.ca2404.1_all.deb Size: 2490160 MD5sum: c5e3ca68ea7a896b2731120dc661d4fa SHA1: 3548c9755e0fe15b66611e84853b0a2bcca5904d SHA256: 0e8ad8095b8c4b33b96c87b25de5528ef3afe5a2bf6ce964d281cbf5c5cbb7f4 SHA512: 5241637a26795313dcc8331f35559a029ab07f5ee334e05112a0ff43aa966cab6a69e28455cab917257575e9e31687ca690f68642c9ad7822183901de1c89856 Homepage: https://cran.r-project.org/package=gtfs2emis Description: CRAN Package 'gtfs2emis' (Estimating Public Transport Emissions from General Transit FeedSpecification (GTFS) Data) A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) . Package: r-cran-gtfshift Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6028 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidytransit, r-cran-gtfstools, r-cran-sf, r-cran-tidyselect, r-cran-tidyr, r-cran-lubridate, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-osmdata, r-cran-stringr, r-cran-callr, r-cran-purrr, r-cran-rlang, r-cran-xml2, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mapview, r-cran-osmextract, r-cran-rosmium, r-cran-testthat, r-cran-zip, r-cran-gtfsrouter, r-cran-reticulate, r-cran-rprotobuf, r-cran-stplanr, r-cran-lwgeom, r-cran-progress, r-cran-stringi, r-cran-spelling Filename: pool/dists/noble/main/r-cran-gtfshift_1.1.0-1.ca2404.1_all.deb Size: 5134846 MD5sum: 187130d2de7c3200a375fec06e27c99d SHA1: 2dce389270332067d3c1b1216a1cb2f1bfe7126a SHA256: eedc9c0d074731754696e013b282763e3057af6c29486e0e3c0fbf19c7ca1990 SHA512: fc6a5ec3b9bf3abc414b03d468ac2e4ed4850ce4195c6a3f916d6bd422e43dc0b1bf3e48f0c44457bee541479ae7d42f14743360542ecc24b1b324bcf021cada Homepage: https://cran.r-project.org/package=GTFShift Description: CRAN Package 'GTFShift' (Explore and Analyse General Transit Feed Specification (GTFS)Files with a Focus on Urban Mobility) A bundle of methods to harmonize GTFS and OSM data, enabling the integration and exploration of different layers of transit data, starting with the planned operations (GTFS), but also the infrastructure topology (OSM) and real-time information (GTFS-RT). Package: r-cran-gtfsio Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-fs, r-cran-zip, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-gtfsio_1.2.2-1.ca2404.1_all.deb Size: 360214 MD5sum: a6d2b5864bdece0cc7d32dd6fd6d57e6 SHA1: 44fc62f108baee65bf88d7a28150365f0468a12b SHA256: b65be6c0217904764629b4b18d498c0d3fd5aae154ffb86446703b26d35b995d SHA512: 90bef9d648dc9027c248ee3ab0cd1ef16f0582642ed3c8eedcfd0ff83bc98f12bdd354ffa5fa7d49cd4e74281a1f2aedc46a6ecb31411f44fbdd8c4b3d49ae8d Homepage: https://cran.r-project.org/package=gtfsio Description: CRAN Package 'gtfsio' (Read and Write General Transit Feed Specification (GTFS) Files) Tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic 'gtfs' class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects. Package: r-cran-gtfswizard Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4751 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-gtfsio, r-cran-rlang, r-cran-tibble Suggests: r-cran-data.table, r-cran-gtfstools, r-cran-hms, r-cran-knitr, r-cran-leaflet, r-cran-plotly, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat, r-cran-tidytransit Filename: pool/dists/noble/main/r-cran-gtfswizard_1.2.1-1.ca2404.1_all.deb Size: 4613526 MD5sum: 022136457b7f94e7bc09fa6edbdd932b SHA1: 1fa56c6160e2b21957f64f25fbc80028eabfe659 SHA256: c0dca6920d4f590b6a018538b59d2396af05edcc087aeb5cf8509d59b715bbad SHA512: ca9b5a362fcb157a2f90a64c821c8ec92a556c75a29e78fb45801ff7329ab29ddd3a8f01178459179144194ae72c72b27d7beb2e1dad1c23f4ac987eb87eed45 Homepage: https://cran.r-project.org/package=GTFSwizard Description: CRAN Package 'GTFSwizard' (Creating, Exploring, and Manipulating GTFS Files) Creating, exploring, analyzing, and manipulating General Transit Feed Specification (GTFS) files, which represent public transportation schedules and geographic data. The package allows users to filter data by routes, trips, stops, service dates, and time, generate spatial visualizations, and perform detailed analyses of transit networks, including headway, dwell times, route frequencies, service span, scheduled vehicle-hours, and trip duration. Methods follow common public transport planning and operation concepts described in Ceder (2007, ISBN:978-0-7506-6166-6), Vuchic (2005, ISBN:978-0-471-63265-8), Vuchic (2007, ISBN:978-0-471-75823-5), Cascetta (2009) , and Gentile and Noekel (2016) . 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Specifies evaluator, prompt, generation, and repeated-run facets through crossed or explicitly nested random sources with configurable item interactions. Fits univariate models, joint Gaussian models, and joint discrete models for binary, ordinal, and unordered categorical outcomes, with source-specific covariance. Gaussian models use exact balanced likelihood; discrete models use a dense first-order Laplace approximation with Gaussian latent random effects. Supported balanced decision studies compare evaluator, prompt, and replication allocations using observed Gaussian or explicitly requested latent binary and ordinal reliability, with random or fixed facets after Brennan (2001). Gaussian fits report asymptotic Wald standard errors for their variance components and delta-method intervals for the coefficients; discrete fits report point estimates only. Scalar nominal reliability and joint Gaussian-discrete fitting are not implemented. Includes three publicly archived LLM annotation datasets covering hate-speech, mental-health, and drug-review tasks. Discrete fitting is limited to small models; the preflight report describes supported designs and computational limits. Generalizability coefficients follow the variance-decomposition framework of Brennan (2001) . 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Implements standard guessing correction methods and latent class models that leverage informative pre-post test transitions to account for guessing behavior. The package helps researchers obtain more accurate estimates of actual learning when respondents may guess on closed-ended knowledge items. For theoretical background and empirical validation, see Cor and Sood (2016) . 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Package: r-cran-gwrf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger, r-cran-tibble, r-cran-dplyr, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gwrf_0.1.1-1.ca2404.1_all.deb Size: 51330 MD5sum: 6ee1e77c524e57121ff4dd7dcf8137a2 SHA1: a4c3e4858dd5b3e430de38cf7b7980852583489c SHA256: 2f5eb369b3107d6ed7218c43b60e82a6ca5af2a35acd315b374b5b0768e13782 SHA512: 6c64e5404c07926270555d00d95a02297f43d007c89f8c6ee332cf06b3be773b7497509cf55a51b81c54f325167e68f6303dee38bf3571cacfcf78ecb8713e43 Homepage: https://cran.r-project.org/package=gwrf Description: CRAN Package 'gwrf' (Geographically Weighted Random Forests) Fits geographically weighted random forest models using spatially localized training neighborhoods and 'ranger' as the random forest engine. Supports fixed-distance and adaptive neighborhoods defined by observation rows or unique spatial locations, including repeated observations at the same location. Provides local predictions and permutation-based variable importance for examining spatial variation in predictive relationships. The geographical random forest approach is described by Georganos et al. (2021) , and the 'ranger' engine by Wright and Ziegler (2017) . Package: r-cran-gwrlasso Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-glmnet, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gwrlasso_0.1.0-1.ca2404.1_all.deb Size: 37932 MD5sum: 57df024c71e0a438765c407d3a58e13a SHA1: 6e397f9516ccf0cf6275a5cedee710db9bf4b720 SHA256: a50e946c217fed36e1a0316547b828a07e90da47bd5daa9ce1baf4baa76a479c SHA512: c6cb365d443400c8e9b411e0c6e90daf19d42a89bd534a0e7356ec815ef8a185aa64858912aa9c576c37361f32e93141abaee7972bdaea9a5f0c9657a165d9d3 Homepage: https://cran.r-project.org/package=GWRLASSO Description: CRAN Package 'GWRLASSO' (A Hybrid Model for Spatial Prediction Through Local Regression) It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).. The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex spatial patterns and large datasets with multiple predictor variables. Package: r-cran-gwrm Architecture: all Version: 2.1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-gwrm_2.1.0.4-1.ca2404.1_all.deb Size: 92652 MD5sum: c4756c7f092d4129d32c17c51626f41f SHA1: 054f42f94c7f2ec5eb2df2217f75fa04ca137552 SHA256: 6d16fb1e7ccfb50f33ba74e88f464cec5df77e3e13e99b5b170605960f44ae85 SHA512: e309328dada0539732ef0aab90616c5fb810b3a26faeaa3908d13904d3917be844b6cad574fefddafa43ebe754acd39fa5fd7192c13c997cf0b420c0c2320be2 Homepage: https://cran.r-project.org/package=GWRM Description: CRAN Package 'GWRM' (Generalized Waring Regression Model for Count Data) Statistical functions to fit, validate and describe a Generalized Waring Regression Model (GWRM). Package: r-cran-gwrpvr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gwrpvr_1.0-1.ca2404.1_all.deb Size: 48414 MD5sum: b433265cc4c779a563a65b6a214fd72f SHA1: 853fe5fd339b3cc6577a6c74d9e25e26374403fb SHA256: cd15ff5c70ee934f2276966f743cdcb8891922fc6259442cc216df64c276258e SHA512: fc2f5d82587abcbf301138d9a357d91b7d796ef4e6f7b358b550d4c64dfe0fb170652793dbfd6ae8a4a2b926282ba75cc47c54b3c522bf578d89ed283d27201f Homepage: https://cran.r-project.org/package=gwrpvr Description: CRAN Package 'gwrpvr' (Genome-Wide Regression P-Value (Gwrpv)) Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution. Finite-sample genome-wide regression p-values (Gwrpv) with a non-normally distributed phenotype (Gregory Connor and Michael O'Neill, bioRxiv 204727 ). Package: r-cran-gwrr Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-lars Filename: pool/dists/noble/main/r-cran-gwrr_0.2-2-1.ca2404.1_all.deb Size: 68890 MD5sum: 2cfb806deaad01110b730fa7145fc824 SHA1: 5160d48a279d31a5382c968d9ce4b6c4fe18a128 SHA256: 5f15d12f95e8ce731558bb4323b6a3cee8300f6df8fcfb260fea2b870ed74089 SHA512: 1967267d796e0ade7dee591f4273a896d7b00657ba0cf92e2f6cdd7bceb755a5da9c0df26230a3ec6c3b8f326a937b8c7d2b84b0fb38020612b3346f4948c0f4 Homepage: https://cran.r-project.org/package=gwrr Description: CRAN Package 'gwrr' (Fits Geographically Weighted Regression Models with DiagnosticTools) Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) and Wheeler (2007) for more details. Package: r-cran-gwsdat Architecture: all Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3774 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deldir, r-cran-digest, r-cran-geometry, r-cran-kendall, r-cran-lattice, r-cran-lubridate, r-cran-mass, r-cran-matrix, r-cran-officer, r-cran-raster, r-cran-readxl, r-cran-rhandsontable, r-cran-sf, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinyjs, r-cran-sm, r-cran-sp, r-cran-splancs, r-cran-zoo Suggests: r-cran-dbi, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-gwsdat_3.3.0-1.ca2404.1_all.deb Size: 2466074 MD5sum: b763b804001548dfa9411c6319d89e46 SHA1: 56267497ab865a16eebdf63728cba57b51814571 SHA256: beae41f84c4efcd4b7ae519688c5b9e9e1177a4d4fe998e8053ef6ad8db30fee SHA512: 47323235503b55709a4714bd98b673430c9ab72cb6d519a1ba2636cc75fb1fa70db4d526aee81cb89e257fe27e139e3d0356c408147bfdd801915943ce30e218 Homepage: https://cran.r-project.org/package=GWSDAT Description: CRAN Package 'GWSDAT' (GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)) Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format. Package: r-cran-gwsignif Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gwsignif_1.2.1-1.ca2404.1_all.deb Size: 22264 MD5sum: 3a4d7160071ab3b2539e53a232e5378b SHA1: a636d78365ec16c9c4679c3561f44a101b0484ae SHA256: 04c9bdde2a661f97ff3f23ff5fb5465b7b5b9d14cd7ff99feb3f0995cafc2a0c SHA512: aa215af76b47261301b61197b014f3f12fb3b7bdc2a84c762184a57bd25fcc4495ae7429a7fcc3338c46920f78137a8cc564e9d9a73c9c238304c9ab61936871 Homepage: https://cran.r-project.org/package=GWsignif Description: CRAN Package 'GWsignif' (Estimating Genome-Wide Significance for Whole Genome SequencingStudies, Either Single SNP Tests or Region-Based Tests) The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome. Package: r-cran-gwzinbr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-gwzinbr_0.1.0-1.ca2404.1_all.deb Size: 157964 MD5sum: 57264cc6cd63f730a2c5c200ed3bf23b SHA1: 9cfd798ae261f60672358a310b40758dec76670a SHA256: 3570933196d730d06f532f0fe89c44d9032b9b91a6423aeb02e771bc6d99d5ab SHA512: 3655c73e49f2f4761e6ac99db187842905ef6b8023a825a4983591073179303be241d1c4ae467eb5aeab160199836f302decb3c333ea0b558ad1e17811ac3256 Homepage: https://cran.r-project.org/package=gwzinbr Description: CRAN Package 'gwzinbr' (Geographically Weighted Zero Inflated Negative BinomialRegression) Fits a geographically weighted regression model using zero inflated probability distributions. Has the zero inflated negative binomial distribution (zinb) as default, but also accepts the zero inflated Poisson (zip), negative binomial (negbin) and Poisson distributions. Can also fit the global versions of each regression model. Da Silva, A. R. & De Sousa, M. D. R. (2023). "Geographically weighted zero-inflated negative binomial regression: A general case for count data", Spatial Statistics . Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). "Geographically weighted regression: a method for exploring spatial nonstationarity", Geographical Analysis, . Yau, K. K. W., Wang, K., & Lee, A. H. (2003). "Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros", Biometrical Journal, . Package: r-cran-gxeprs Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gxeprs_1.3-1.ca2404.1_all.deb Size: 1134470 MD5sum: ef6556c6dd349033c080f58d50ebb2e2 SHA1: 89ba1a5da9dda4e714e33564c6e7f892d332bd47 SHA256: d555d12447ee6e43f715d226a08ffc249960f298f2c259333f109b5af08800b4 SHA512: e85a7cde4ceeb0c9f4a6723e0c310f94630c533b49e9e5022df637a60129cbeece95acee1fd8db2a4495d0196a3ad7d0a041e657e3a71ab64b58f0e4721cbf03 Homepage: https://cran.r-project.org/package=GxEprs Description: CRAN Package 'GxEprs' (Genotype-by-Environment Interaction in Polygenic Score Models) A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) , (Pandis et al., 2013) , (Peyrot et al., 2018) and (Song et al., 2022) , as well as newly proposed models for genomic risk prediction (refer to the URL for more details). Package: r-cran-gxescanr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lsreg, r-cran-binarydosage Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gxescanr_3.0.0-1.ca2404.1_all.deb Size: 142682 MD5sum: 548500300c9a0ea7c022322a43642c52 SHA1: c1fc9551c7fd943814d2a9b54d388367022e135f SHA256: 50f2c10b6ac3722daccb7ef10e8d5d21a854e151bec7e936ca9b1c88be5d3462 SHA512: dc6128bb74c5090d707de7870afa4643e2940c7a93816a42ada96ebb62d0d3a27efcb36cbc5db371f407888cd63720b0757e5546e468c3601537cbfcfff3d26b Homepage: https://cran.r-project.org/package=GxEScanR Description: CRAN Package 'GxEScanR' (Run GWAS/GWEIS Scans Using Binary Dosage Files) Tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. The user provides a data frame with the subject's covariate data and the information about the binary dosage file returned by the BinaryDosage::getbdinfo() routine. Package: r-cran-gym Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gym_0.1.0-1.ca2404.1_all.deb Size: 50106 MD5sum: 32ab8977908e9845d7befffdc3514567 SHA1: a1bd74e885879feb5a0b108761c91b7e6802c9e1 SHA256: a533944a9611710b0011c8f72b43762f453ac50c3fb4fa20d452f42fe30ca78d SHA512: c5fe5033d8689abbe36ed2aa7acead45ca95c33754cfdf6c44d1e73f9355a39f746d32f9b72f25f816a6b41174857a72d377b6e59b990b0423881aac522553ec Homepage: https://cran.r-project.org/package=gym Description: CRAN Package 'gym' (Provides Access to the OpenAI Gym API) OpenAI Gym is a open-source Python toolkit for developing and comparing reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API, and enables access to an ever-growing variety of environments. For more details on OpenAI Gym, please see here: . For more details on the OpenAI Gym API specification, please see here: . 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Package: r-cran-h2otools Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-h2o, r-cran-curl, r-cran-boot Filename: pool/dists/noble/main/r-cran-h2otools_0.4-1.ca2404.1_all.deb Size: 51916 MD5sum: a6dae21a96ef2be38dea2c091396e484 SHA1: 3199fb427400d1ebc1e15d0c130daa55d8efe157 SHA256: 8b6e18a31c7a8a7b2a179f86ca4d271149ccc8b4e2101375054897caff0b31f9 SHA512: 4a59b58f5220d5a1520ef6ded84e9670adc01e800bed7aa2551181b47d01d53e42e0a655a75f2a8063664482fa5e409f302a3a3d41fe4ca9c97d3144dfebd1d8 Homepage: https://cran.r-project.org/package=h2otools Description: CRAN Package 'h2otools' (Machine Learning Model Evaluation for 'h2o' Package) Enhances the H2O platform by providing tools for detailed evaluation of machine learning models. 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Package: r-cran-h3sdm Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2415 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-terra, r-cran-spatialsample, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-ecospat, r-cran-dalex, r-cran-stacks, r-cran-h3jsr, r-cran-landscapemetrics, r-cran-rbiodatacr, r-cran-spocc, r-cran-exactextractr, r-cran-tidyr, r-cran-cli Suggests: r-cran-ggplot2, r-cran-paisaje, r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-themis, r-cran-dalextra, r-cran-ingredients, r-cran-tidyterra, r-cran-tidymodels, r-cran-workflowsets, r-cran-ranger, r-cran-xgboost, r-cran-ggbrick, r-cran-parsnip, r-cran-tidyverse, r-cran-geodata Filename: pool/dists/noble/main/r-cran-h3sdm_0.1.7-1.ca2404.1_all.deb Size: 2155902 MD5sum: ac085bc85481e68da482072afcb64d92 SHA1: 7672a8c0c121e2999393fd3e3e461b836b190fff SHA256: 27a3c151206be0ffb98fda16f13dd28b4d39dc426ef001405fad35eb4e55541c SHA512: 4563d619d4f83db7955ce244a0458a7b9ead4f60e7d0ad8386964dc6e9191e871eb0729101c4a317dc6d88a92f70f09214b807b1167de19ea43a03fb1d23441d Homepage: https://cran.r-project.org/package=h3sdm Description: CRAN Package 'h3sdm' (Species Distribution Modeling with H3 Grids) Provides tools for species distribution modeling using H3 hexagonal grids (Uber Technologies Inc., 2022, ). 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Package: r-cran-haarfisz Architecture: all Version: 4.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/noble/main/r-cran-haarfisz_4.5.4-1.ca2404.1_all.deb Size: 54478 MD5sum: 1cde8712b152c27009e5482dc9f0d029 SHA1: 3c25a259b0908a1f55b713f1ced0fd5a101e5a15 SHA256: 70ca7248987507c582b47f91ac0948814d48e698ade2213dbe859f6c3957cde2 SHA512: ae7eceb02c115862fd273a7b02275125df1430b187367b993095b1430a913761ceccdfd6a0a182e2abdb1f8a4a7002ec178503fe2563f2c7efb99861ee1dab75 Homepage: https://cran.r-project.org/package=haarfisz Description: CRAN Package 'haarfisz' (Software to Perform Haar Fisz Transforms) A Haar-Fisz algorithm for Poisson intensity estimation. Will denoise Poisson distributed sequences where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the forward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. Main reference: Fryzlewicz, P. and Nason, G.P. (2004) "A Haar-Fisz algorithm for Poisson intensity estimation." Journal of Computational and Graphical Statistics, 13, 621-638. . 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Easily cast variables to different data types. Keep rows with NAs. Shift row values. Package: r-cran-hacksig Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-future.apply, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-future, r-cran-ggplot2, r-cran-knitr, r-cran-msigdbr, r-cran-purrr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hacksig_0.1.2-1.ca2404.1_all.deb Size: 1083478 MD5sum: d09bcfdb71c7c0fe54be68e09310b61e SHA1: 9ee846c870f036dde40b4e4e47da2f439d3b382b SHA256: df19497c2ebdb8cf624a25f85d47aff93fdb52528c94f0a6aea92fc387107aad SHA512: d8a04a4dd7c50671a913a25bc2746e285b4bb6e03e19184e9944988100b974145f2aa4d40111b914371d422b35e41cbab97e8f46c36fb1948d843abee49b8336 Homepage: https://cran.r-project.org/package=hacksig Description: CRAN Package 'hacksig' (A Tidy Framework to Hack Gene Expression Signatures) A collection of cancer transcriptomics gene signatures as well as a simple and tidy interface to compute single sample enrichment scores either with the original procedure or with three alternatives: the "combined z-score" of Lee et al. (2008) , the "single sample GSEA" of Barbie et al. (2009) and the "singscore" of Foroutan et al. (2018) . The 'get_sig_info()' function can be used to retrieve information about each signature implemented. Package: r-cran-hadamardr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-hadamardr_1.0.0-1.ca2404.1_all.deb Size: 235778 MD5sum: a5ed1126f1c834d38ad24e581d672e59 SHA1: bb991c76796a7c38d743d8ee07a4eb73149d0246 SHA256: eb519c0234db482e20b612e19fe6271685c76b7166c6f6f2333a426f9936f663 SHA512: ebd735a6da102d5dfc287e8d77169b9caf4002e58be07640abcf8160e807a1354462b617c15dc5108492d790ed3fbf8008922826f6da7305f17e68f8397c40ad Homepage: https://cran.r-project.org/package=HadamardR Description: CRAN Package 'HadamardR' (Hadamard Matrix Generation) Generates Hadamard matrices using different construction methods. For those who want to generate Hadamard matrix, a generic function, Hadamard_matrix() is provided. For those who want to generate Hadamard matrix using a particular method, separate functions are available. See Horadam (2007, ISBN:9780691119212) Hadamard Matrices and their applications, Princeton University Press for more information on Hadamard Matrices. Package: r-cran-hadex2 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-gridextra, r-cran-magick, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-r3dmol, r-cran-remotes, r-cran-stringi, r-cran-tidyr, r-cran-ggiraph Suggests: r-cran-bookdown, r-cran-digest, r-cran-knitr, r-cran-magrittr, r-cran-pander, r-cran-renv, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-vdiffr, r-cran-scales, r-cran-shiny, r-cran-spelling Filename: pool/dists/noble/main/r-cran-hadex2_1.0.0-1.ca2404.1_all.deb Size: 3970740 MD5sum: 61e9c26a1dfa0b5eb3592c73bbb2673e SHA1: e5cd6b657c6a8dd5111bbf9803d563cee3172443 SHA256: f37e5b9483bbdcd30ace7fbb76f7e8c0a042699b0a4e7bb8b03acf23d58b9eee SHA512: 18f8b3eec6dadd48ccc157fc05d21c1ee83dc637e990015effaa4b5072a5dcc571952e6ecc6d246d230ce8c5edc0f1ef2229d1aaae3fddba41d045c6681bcd94 Homepage: https://cran.r-project.org/package=HaDeX2 Description: CRAN Package 'HaDeX2' (Analysis and Visualisation of Hydrogen/Deuterium Exchange MassSpectrometry Data) Processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS). 'HaDeX2' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure. Additionally, it covers data exploration, quality control and generation of publication-quality figures. 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'HaDeX' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the in-built 'Shiny' app. Package: r-cran-hadibds Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hadibds_1.0.1-1.ca2404.1_all.deb Size: 19036 MD5sum: 8ff2499853e492df01c2982450a8d00c SHA1: 1905f01fcd31d435e9ba5785cae540124fc931c6 SHA256: 012b276655dabd56e37e7bc0ab77df043953da4f8d6ef3e2d8f875f7a01f05b7 SHA512: 42f27c431bb1d4699df75de27b13424dbecb42badc76c86ac2330d109fd2be24368f0251f0bc6856dceecb8fbaaf9cfaabc03a5d551e77864dff2c76b5d76bcf Homepage: https://cran.r-project.org/package=HadIBDs Description: CRAN Package 'HadIBDs' (Incomplete Block Designs using Hadamard Matrix (HadIBDs)) Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices. Package: r-cran-hagis Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-pander Suggests: r-cran-ape, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-vdiffr, r-cran-vegan Filename: pool/dists/noble/main/r-cran-hagis_4.0.0-1.ca2404.1_all.deb Size: 726438 MD5sum: 3ee8dab679bb34a4c340c470951ffa86 SHA1: 455a2569ab546ddd65a81fb306433603fa75337d SHA256: e10ad8f67e0357135597760a5b187c582706d7a6fb5b1d6589c5e947422b851d SHA512: ef3f933b4b7c60dec86d0524149a8f7f92c0d0266c343eee07c1325f6a39adf76206f1549fe5733f654cfad100b72e47edb85ffebf2051c1abde2206647da9a9 Homepage: https://cran.r-project.org/package=hagis Description: CRAN Package 'hagis' (Analysis of Plant Pathogen Pathotype Complexities, Distributionsand Diversity) Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) Habgood-Gilmour Spreadsheet, 'HaGiS', previously used for pathotype analysis. Package: r-cran-hakaiapi Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-r6, r-cran-readr, r-cran-tibble Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hakaiapi_1.0.5-1.ca2404.1_all.deb Size: 69436 MD5sum: 5b25eb3fb670533660dfd35eac91e842 SHA1: add1fda2e855eed7d26c2dc7479cada46d7efca4 SHA256: 6a57ba78dd61281f3c32ab9f2b32fa4ac7ad4893e9662e8b43a288523c2767b4 SHA512: 88975a6e647de8b7b29fabc06daddd94cb09cce51ed4f08cfc099714b4ac7766edf35f6b6b1afad04f966086f8966e387f9282d869aa9c1a18fef5028c65027f Homepage: https://cran.r-project.org/package=hakaiApi Description: CRAN Package 'hakaiApi' (Authenticated HTTP Request Client for the 'Hakai' API) Initializes a class that obtains API credentials and provides a method to use those credentials to make GET requests to the 'Hakai' API server. Usage instructions are documented at . 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Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Conducts Bayesian analysis summaries and graphs, decision curve analysis, and produces some Shewhart control charts. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Cohen (1988, ISBN:0-8058-0283-5), Gebski (2012) , Gelman & Goodrich (2019) , Gelman & Hill (2007, ISBN:978-0-521-68689-1), Harrell (2015, ISBN:978-3-319-19424-0), Imbens & Rubin (2015, ISBN:978-0-521-88588-1), Kline (1999, ISBN:9780415211581), Kruschke (2014, ISBN:9780124058880), Linden (2015) , Merlo (2006) , Muthen & Satorra (1995) , Pitman (1993, ISBN:978-0-387-97974-8), Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5), Rosenbaum (2010, ISBN:978-1-4419-1212-1), Ryan (2011, ISBN:978-0-470-59074-4), and Vickers & Elkin (2006) . 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Package: r-cran-hansard Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-lubridate, r-cran-tidyr, r-cran-snakecase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-hansard_0.8.0-1.ca2404.1_all.deb Size: 380330 MD5sum: 9dd7a8cc51fdba48cf57b5ad8898b4a8 SHA1: 1d5b0d7de299688399e89cc27c066118a4034990 SHA256: 7ace22ebdb4786a416f2d4749b26cf0c1b550bc34adee77e4432bdbdcf2ba5e7 SHA512: f9ded2a29ad73238bcb162fa084194297a972c8698a638ffa9917ad4cd1eecf7b689bfe2304e032510869c10e7626788315e71d3da116b7ae6b33cc3ca62409e Homepage: https://cran.r-project.org/package=hansard Description: CRAN Package 'hansard' (Provides Easy Downloading Capabilities for the UK Parliament API) Provides functions to download data from the APIs. Because of the structure of the API, there is a named function for each type of available data for ease of use, as well as some functions designed to retrieve specific pieces of commonly used data. Functions for each new API will be added as and when they become available. Package: r-cran-hanstat Architecture: all Version: 0.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-car, r-cran-crayon, r-cran-ggplot2, r-cran-lmtest, r-cran-olsrr, r-cran-ggpubr, r-cran-devtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hanstat_0.90.0-1.ca2404.1_all.deb Size: 31846 MD5sum: 67864522c025332986c10d692ae22874 SHA1: c11bbc59d83f681f09d6664235f69cf56a1b9a5b SHA256: 11562b06e4164edf5bedb5c2191ed5ee9b6a1f7582a7cab8a779718220c109fe SHA512: c9b577da92d10060e5bc816963c68d98b9aea087cdc3a3acf281d3a188194b89314ba1dcc79cafea0049adee16e3762e7b8206db35a033ad208018ee736c42b2 Homepage: https://cran.r-project.org/package=HanStat Description: CRAN Package 'HanStat' (Package for Easy Interpretation of Statistical Methods) A simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements. Package: r-cran-hanyupinyin Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hanyupinyin_0.1.3-1.ca2404.1_all.deb Size: 1130610 MD5sum: 2a49a7d9b1bcbae4ed1f8b54da231035 SHA1: be89503ababcd2caaf1362467e285692ea6d2318 SHA256: 5a4ce006b3ec6b9b6c0d742e43c36c5862d3037358b9acd714f4e13174176f07 SHA512: 151d1f61c76bbd8eba2365587761f7d911fb7df8268c152afa6340c18643255912d642089adb597aad3899fdda7b5f05836d4c40ab9d8433f3d917f9a3018620 Homepage: https://cran.r-project.org/package=hanyupinyin Description: CRAN Package 'hanyupinyin' (Convert Chinese Characters into Hanyu Pinyin) Convert Chinese characters into Hanyu Pinyin (the official romanization system for Standard Chinese) with support for tones, toneless output, initials, URL slugs, and valid R variable names. The package was inspired by the now-orphaned CRAN package 'pinyin' (archived in April 2026 after the maintainer became unreachable). 'hanyupinyin' is a ground-up rewrite using the authoritative Unicode Unihan database, a vectorized engine, and modern R practices. Dictionary data are derived from the Unicode Unihan Database (Unicode Consortium, 2025) . Package: r-cran-hapi Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmm, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hapi_0.0.3-1.ca2404.1_all.deb Size: 730982 MD5sum: 943cbfb330f72f01e73b79022658e264 SHA1: 5ddd0cf963b91f525f68ef25bd74642e2f7c322b SHA256: d81b3c66fbe9374b5e86cf069ca9762b62cf6c09487260cd48d24ae9e3b643f2 SHA512: b48867ad8accf5e0e27aaa582279e5a249a7dedd05fc0b0a5c724d9b49055e480116eb75a1c5701d5bd164157b160afb3e71c7d79e6b92bcbe28f049954a7207 Homepage: https://cran.r-project.org/package=Hapi Description: CRAN Package 'Hapi' (Inference of Chromosome-Length Haplotypes Using Genomic Data ofSingle Gamete Cells) Inference of chromosome-length haplotypes using a few haploid gametes of an individual. The gamete genotype data may be generated from various platforms including genotyping arrays and sequencing even with low-coverage. Hapi simply takes genotype data of known hetSNPs in single gamete cells as input and report the high-resolution haplotypes as well as confidence of each phased hetSNPs. The package also includes a module allowing downstream analyses and visualization of identified crossovers in the gametes. Package: r-cran-haplo.ccs Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haplo.stats, r-cran-survival Filename: pool/dists/noble/main/r-cran-haplo.ccs_1.3.3-1.ca2404.1_all.deb Size: 67690 MD5sum: 4a6eb8841dc1066c6748651219677441 SHA1: 203839d8d258c6776def3fc3a5e685fc49a9e745 SHA256: 8487f60a02eb4896470c23c65d3f4467c37f1011ffe68aa141d1f0765e2149aa SHA512: b48efcd6c241533fe0946729cd4cf725eabca5dbf05dabb7e286566f4f16e42ebf08e1bbac650794e7c868ea94c8e695de466d67d61915498bfb08b0516188d9 Homepage: https://cran.r-project.org/package=haplo.ccs Description: CRAN Package 'haplo.ccs' (Estimate Haplotype Relative Risks in Case-Control Data) Haplotype and covariate relative risks in case-control data are estimated by weighted logistic regression. Diplotype probabilities, which are estimated by EM computation with progressive insertion of loci, are utilized as weights. French et al. (2006) . Package: r-cran-haplocatcher Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-randomforest Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-haplocatcher_2.0.1-1.ca2404.1_all.deb Size: 1243300 MD5sum: dcab25c95f645dc8b4257c7840841670 SHA1: 26488bc3370951debe2fdbf6ca680e5fb70ffa80 SHA256: 67ce195ba180897d5d981f014dde2f6eb4bc4285a97292a08a4f9e4642b6458c SHA512: 82a90f07a32715d7822654cab1c92079ce890a14da7d0e425095b98ce3133b1cbe0849268751a115f998f96a51df75606cefb8034eebd8fe088403591377ae1f Homepage: https://cran.r-project.org/package=HaploCatcher Description: CRAN Package 'HaploCatcher' (A Predictive Haplotyping Package) Used for predicting a genotype's allelic state at a specific locus/QTL/gene. This is accomplished by using both a genotype matrix and a separate file which has categorizations about loci/QTL/genes of interest for the individuals in the genotypic matrix. A training population can be created from a panel of individuals who have been previously screened for specific loci/QTL/genes, and this previous screening could be summarized into a category. Using the categorization of individuals which have been genotyped using a genome wide marker platform, a model can be trained to predict what category (haplotype) an individual belongs in based on their genetic sequence in the region associated with the locus/QTL/gene. These trained models can then be used to predict the haplotype of a locus/QTL/gene for individuals which have been genotyped with a genome wide platform yet not genotyped for the specific locus/QTL/gene. This package is based off work done by Winn et al 2021. For more specific information on this method, refer to . Package: r-cran-haplodiploidequilibrium Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-vcfr, r-cran-data.table, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-haplodiploidequilibrium_0.1.0-1.ca2404.1_all.deb Size: 73446 MD5sum: af8ac3e1882cf934990497ca2a8f305f SHA1: a43a4f1420025023ade8461819974ed0a85d8c5f SHA256: fe8b2b08f6c769096a12c445df96a4c0ea55bac20dac3f2cbcc23fede0f261de SHA512: c8a54d4f13b1b1a60460051e4dafaba25689c03803ab9ac8cb16adc9e569363441e0592f119f3385a3e688420cc2f94a02041d4db97c6f11b1e44fba6e61d36d Homepage: https://cran.r-project.org/package=HaploDiploidEquilibrium Description: CRAN Package 'HaploDiploidEquilibrium' (Calculate F Statistics Using Mixed Haploid and Diploid OrganismData) Provides functions to estimate population genetics summary statistics from haplo-diploid systems, where one sex is haploid and the other diploid (e.g. Hymenoptera insects). It implements a theoretical model assuming equal sex ratio, random mating, no selection, no mutation, and no gene flow, deriving expected genotype frequencies for both sexes under these equilibrium conditions. The package includes windowed calculations (operating over genomic sliding windows from VCF input) for allele and genotype frequencies, the inbreeding coefficient (Fis), pairwise Fst, Nei's H (gene diversity), Watterson's Theta, and sex-specific reference allele frequencies. Most statistics are agnostic to ploidy, allowing the package to be applied to both strictly haplo-diploid and fully diploid systems. Package: r-cran-haplor Architecture: all Version: 4.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml, r-cran-tibble, r-cran-runit, r-cran-plyr, r-cran-dt, r-cran-rcurl, r-cran-rjsonio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-haplor_4.0.7-1.ca2404.1_all.deb Size: 302874 MD5sum: 9c34ae236149c61a67ddb8419d8f316b SHA1: 232cfba58bf8e67aa0a8d8a74fbfd5642344dc16 SHA256: 7e930967aacb0f360575d9b4664295b6fcae4a1b5832efa154406d81851c1d3e SHA512: 3efe694685bfe80de13a4f0fb00d326ac777d5194ea2de515fd8af800bd609555f54a312392b8e71310decbf6c1660853da46db9b6dbc91c753c4881fcdc928e Homepage: https://cran.r-project.org/package=haploR Description: CRAN Package 'haploR' (Query 'HaploReg', 'RegulomeDB') A set of utilities for querying 'HaploReg' , 'RegulomeDB' web-based tools. The package connects to 'HaploReg', 'RegulomeDB' searches and downloads results, without opening web pages, directly from R environment. Results are stored in a data frame that can be directly used in various kinds of downstream analyses. Package: r-cran-haplosim Architecture: all Version: 1.8.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pedigree Filename: pool/dists/noble/main/r-cran-haplosim_1.8.4.2-1.ca2404.1_all.deb Size: 203816 MD5sum: 63588191427c2739d76953571525ea2e SHA1: f777c9c7a318ca7f26a48fa9eaf96e175e7f4bfa SHA256: 35a461eb444998ece7b7a12a1be69366cc2d7cfeb3508f4bb1a50150908a845d SHA512: fc801054900d7b3a5c6d51830e2dae192de0b8f7e62f277076e2770d7ed12c1f1827acb458578058b1886e25a92f65c689945763cca32cdd79f05e1ca133cc13 Homepage: https://cran.r-project.org/package=HaploSim Description: CRAN Package 'HaploSim' (Functions to Simulate Haplotypes) Simulate haplotypes through meioses. Allows specification of population parameters. Package: r-cran-haplotyper Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-haplotyper_0.1-1.ca2404.1_all.deb Size: 46016 MD5sum: fc313fa964c736f14c28847c030e63e0 SHA1: ebba8fd11cb9b4a4973d8d19592defd73a3559f9 SHA256: 9dda2ffeba890691ef1aa9c058a2db6e6197716d5ccdcca6ed9535e697f27e38 SHA512: 72c32fadcdcb2437f232c87b31dde3e0527fe92a6eec656e69560cd507bcb39eb04fad93accf3f3be0b31f76165b0779a017cd5730eb440162872fbd3e68a48e Homepage: https://cran.r-project.org/package=haplotyper Description: CRAN Package 'haplotyper' (Tool for Clustering Genotypes in Haplotypes) Function to identify haplotypes within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL. This function groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, haplotyper groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, haplotyper calculates such probability from relative frequencies. Package: r-cran-haplotypes Architecture: all Version: 1.1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-sna, r-cran-ape, r-cran-phangorn, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-haplotypes_1.1.3.2-1.ca2404.1_all.deb Size: 577806 MD5sum: 4d3e222929130307c754307dd0174795 SHA1: ee5f0d9aba27002052a3c65efcdb6043a91b046d SHA256: 92d7a19644aaa5eccef15a45c0387d22631e4e54c15875744ec43bf035a28b29 SHA512: 6e307e8fdd73789ff14dc11390e46463026f2a343a0390a2117fe1879f5b5737aa32133fe65c84d19a0b5f3335c4a5b7894dd605fde14d659da99dc6b69ebfdf Homepage: https://cran.r-project.org/package=haplotypes Description: CRAN Package 'haplotypes' (Manipulating DNA Sequences and Estimating Unambiguous HaplotypeNetwork with Statistical Parsimony) Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel-coding method (only simple indel-coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapsing identical DNA sequences into haplotypes or inferring haplotypes using user-provided absolute pairwise character difference matrix. This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks. Package: r-cran-haplovar Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-dbscan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-haplovar_0.1.2-1.ca2404.1_all.deb Size: 935910 MD5sum: 9b293a32982dc44c5f90bbf7e2024b64 SHA1: ae35df8ee997e99c4293d9c0f47b1b1a7a2693b0 SHA256: 1c5237374c9668181d6914f565fc2ec82378c31bc496bd59e1d61721315157ed SHA512: 2ff26fe145febaa5d85e2ce129822191cb11294b4c04f4da45ce61508e91957580d9acceebcc90d3c1663e0b158f202eb044a7a474ab8511bdc348bdfbb85ad1 Homepage: https://cran.r-project.org/package=HaploVar Description: CRAN Package 'HaploVar' (Defining Local Haplotype Variants for Use in Trait Associationand Trait Prediction Analyses) A local haplotyping tool for use in trait association and trait prediction analyses pipelines. 'HaploVar' enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN) (Ester et al., 1996) . Package: r-cran-happign Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2, r-cran-sf, r-cran-terra, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-httptest2, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-happign_0.3.8-1.ca2404.1_all.deb Size: 1538392 MD5sum: b49946bbc40ad8bbbb4c3df9b559e575 SHA1: 810fe1814afcda2aa306765ab77ddb16ccefec6b SHA256: b3ad0b1f5cfea3af18aaa82ad1efa16bbb1e1499ce1ae69fb3e26c08782c0dcf SHA512: 34133c796775a9189ab0405005b408db723734b38ee205145371936a4d48b944fcfab5de254c3c202fb54c364a920cffefd0bf54048ad6f3814b2d06b1c9a7bc Homepage: https://cran.r-project.org/package=happign Description: CRAN Package 'happign' (R Interface to 'IGN' Web Services) Automatic open data acquisition from resources of IGN ('Institut National de Information Geographique et forestiere') (). Available datasets include various types of raster and vector data, such as digital elevation models, state borders, spatial databases, cadastral parcels, and more. 'happign' also provide access to API Carto (). Package: r-cran-happytime Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-happytime_0.1.0-1.ca2404.1_all.deb Size: 28476 MD5sum: 0fd3822651cb4626ff0de4aa6b03ee3b SHA1: 16398d6900104b0db20b6fcaa85af5229f509f48 SHA256: 1688c62b77723546bfdde878eb3b3cbac1cf1b48c8647e5f6de9a05cb9ba57b3 SHA512: 765766cffe8a55e58d5a8f9cd701b3e6a9b259b27bda615f733b373db4bab29cc7ffe0fe18bc8f1c3d27e883ad2b586f3ae9c4fcb3fc72c0e8c2a0caa597da30 Homepage: https://cran.r-project.org/package=happytime Description: CRAN Package 'happytime' (Two Games to Relieve the Boredom) There are two interesting games in this package, one is 2048 games(for windows), using up and down to control the direction until there is a 2048 figure. And the other is 'what to eat today',preparing for people who choose difficulties, including most of the delicious Cantonese cuisine. Package: r-cran-harbinger Architecture: all Version: 2.1.707-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tspredit, r-cran-changepoint, r-cran-daltoolbox, r-cran-forecast, r-cran-ggplot2, r-cran-hht, r-cran-rcpphungarian, r-cran-dplyr, r-cran-dtwclust, r-cran-rugarch, r-cran-patchwork, r-cran-stringr, r-cran-strucchange, r-cran-tsmp, r-cran-wavelets, r-cran-zoo Suggests: r-cran-ocp Filename: pool/dists/noble/main/r-cran-harbinger_2.1.707-1.ca2404.1_all.deb Size: 657100 MD5sum: b503696f9cb90acfd865a150ec90fbc1 SHA1: 77225a6f9efe51f7d7dc3677438a60a292441695 SHA256: 91710e4fbed58389b1ef19dada5cbfb84ce9fc3b8950f60de1cae8767b36dc74 SHA512: 8a38219b7dbd99bc849a1614d0f447231a1db6ed1e38387123a7ddd1c5118fdff528dcbd9ba353495e1d3deb374c651bd5b64ed21d07bad53577923cfdeb8679 Homepage: https://cran.r-project.org/package=harbinger Description: CRAN Package 'harbinger' (A Unified Time Series Event Detection Framework) By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) . Package: r-cran-hardhat Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-sparsevctrs, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-crayon, r-cran-devtools, r-cran-knitr, r-cran-matrix, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-hardhat_1.4.3-1.ca2404.1_all.deb Size: 854190 MD5sum: c038822cbdff1971ad6af0f424c08618 SHA1: e145b75e9795a71094eeb3698da817f965c898ea SHA256: ec2967e23a31131802f7f2fbd131daadb6dcfc0ce1fb5f9d17b4538c4f5ab789 SHA512: ada9de6acf300c0f7d2b3096e614b2ff8ee8eed49047f42722ecf81799de6a94f031443c541d58d3ea02f5760fa800108d608a7db4b114c9fd8ced31ad0c7738 Homepage: https://cran.r-project.org/package=hardhat Description: CRAN Package 'hardhat' (Construct Modeling Packages) Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input. Package: r-cran-harf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arf, r-cran-data.table, r-cran-clusterr, r-cran-matrixstats, r-cran-pracma, r-cran-pls, r-cran-fastpls, r-cran-rgcca, r-cran-ranger, r-cran-rsvd, r-cran-foreach Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-checkmate, r-cran-rtsne, r-bioc-singlecellexperiment, r-cran-corrplot, r-bioc-scater, r-cran-cowplot, r-cran-ggplot2, r-cran-doparallel, r-cran-proc, r-cran-caret Filename: pool/dists/noble/main/r-cran-harf_0.1.0-1.ca2404.1_all.deb Size: 638022 MD5sum: 8997943214c87e9bad9a136ea040c4ca SHA1: 63fc55dbdbd512cd81007770270e74c35b7444ea SHA256: e09edbfbf38ef48ea450f95d3ab24f46bfbc60f1dc7b25b64520eb3027ac8fd5 SHA512: 33f89c4f604efbfeb70cef9cb246a88ae5e3f88cefb7546599c2ec7e70788255f38702ede2bd52a32ef0183e8616faf68f5344b77a6b0a7b6790fa164b269d3b Homepage: https://cran.r-project.org/package=harf Description: CRAN Package 'harf' (Adversarial Random Forests for Omics Synthesis) We extend Adversarial Random Forests to a high-dimensional framework. The method partitions the feature space into regions where the assumption of feature independence within tree leaves is more likely to hold. Region-specific adversarial random forest models are trained to capture local dependence structures, while an additional adversarial random forest is fitted to a meta-space representation to model dependencies between regions. New observations are generated by first sampling from the meta-space model and then conditionally sampling from each region-specific model. The proposed methodology is described in Fouodo et al. (2026) . Package: r-cran-harmonicmeanp Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmstable Suggests: r-cran-knitr, r-cran-ape, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-harmonicmeanp_3.0.1-1.ca2404.1_all.deb Size: 533302 MD5sum: 84e7d35a9005d4b13795622b31dd1856 SHA1: 2d89135acab4c6b588e00eae1aa21bafc830bb5a SHA256: 123e6795574120c8517d0434f93f32f513a828ecc2cc18b5bcfe45d5d1dd1dd8 SHA512: 3d9cacc5acca7717dbce1dcb64dc0eca19aff03aa00a0ecdd4d6aad49a3b4a735d6eae81e36be3dad53726e4e2d3a7f0b2937036a6f249a3a822b4545527799c Homepage: https://cran.r-project.org/package=harmonicmeanp Description: CRAN Package 'harmonicmeanp' (Harmonic Mean p-Values and Model Averaging by Mean MaximumLikelihood) The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended. Package: r-cran-harmonizer Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3326 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-harmonizer_0.3.2-1.ca2404.1_all.deb Size: 1692378 MD5sum: b0b6400eaf573e64ff8a91a7c50ae944 SHA1: 69906f47b1dd8982ca922b47d82adebfdfc99996 SHA256: 17e32b3ca6ba93c51c1b254415123d58823223d742da5022d53829c389b72032 SHA512: 292ce89fd6a83ddcbc52587fdd216b7fc92ba317a5046ebc6a4b6c31b7736badf95071ab1c01d675e09618419c629b3bdd302315a5e2b1db239b74a911705cac Homepage: https://cran.r-project.org/package=harmonizer Description: CRAN Package 'harmonizer' (Harmonizing CN8 and PC8 Product Codes) Several functions are provided to harmonize CN8 (Combined Nomenclature 8 digits) and PC8 (Production Communautaire 8 digits) product codes over time and the classification systems HS6 and BEC. Harmonization of CN8 codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 2021, respectively. Package: r-cran-harmonydata Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-uuid, r-cran-base64enc, r-cran-jsonlite, r-cran-assertthat, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-harmonydata_0.3.2-1.ca2404.1_all.deb Size: 38092 MD5sum: cf29db9d3826dd383b4c2e6425b8640f SHA1: d49fe2f216c0a43968712885e51b275a76edf052 SHA256: 1283d038b04e39139e612b8c806fba7de77d9d1afb8695aa64393e9e2d9fcf1a SHA512: 0d81a591faa4afd17860a38d6e1c2c5fadb168825d2a43e80b1ebbe8d76c90897b7b9195351da28a1f1caadea5e010b4e3754b89c2b62c4b05381652651c3b37 Homepage: https://cran.r-project.org/package=harmonydata Description: CRAN Package 'harmonydata' (R Library for 'Harmony') 'Harmony' is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try 'Harmony' at and you can read our blog at or at . Documentation at . Package: r-cran-harness Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-yaml Suggests: r-cran-rstudioapi, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-harness_0.2.0-1.ca2404.1_all.deb Size: 115598 MD5sum: 49d9f9f3be27a04fd3ec8d1e547ef735 SHA1: b3760c0941c4450424eb9291a3cfb995f78234dd SHA256: a200fc81c022d386a5a36bf8999edb2c4a1a0319d8c0d47f92bf784e589d9afd SHA512: 0eed6c90b40ffb21745adead0d8ed97822a0fdbda80333391721214d9b41bc82bbbc8ec1bfb5f1ee592a9039ffb6704e343f938394e5a5f07dd74e2c4ce38004 Homepage: https://cran.r-project.org/package=harness Description: CRAN Package 'harness' (Curated Agentic Harnesses for R Professional Roles) A bootstrapper that launches a command-line coding agent of the user's choice in a terminal tab pre-configured for a professional R role. Each role is described by a curated harness: a subset of community skills, a system prompt, a folder layout, and quality gates. The package does not run an agent loop and does not call a language model; it discovers the chosen coder binary, generates its configuration, links the curated skills, and opens the terminal. Code written by the agent is run manually by the user, by design, so that every generated script passes through a human audit gate before execution. Package: r-cran-harplus Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-openxlsx, r-cran-haven, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-harplus_1.2.0-1.ca2404.1_all.deb Size: 730932 MD5sum: feded0d4c55ea5744caec1a8d689e332 SHA1: d689576f614c8ac3d4937b2b0ad949bd16096728 SHA256: 2b6b4bb5cbf249a9b2c252c62617c0f740214f9c75047d346ab60229d356fc64 SHA512: 8750b46b7c221b8d19fcb650b5c5d5768020a7efd48dc3ecfd6659145992be70d8c3be6c4a9f16ce0e7b99c83db062316dd02fe6704167a4584e9c61871eef61 Homepage: https://cran.r-project.org/package=HARplus Description: CRAN Package 'HARplus' (Enhanced R Package for 'GEMPACK' .har and .sl4 Files) Provides tools for processing and analyzing .har and .sl4 files, making it easier for 'GEMPACK' users and 'GTAP' researchers to handle large economic datasets. It simplifies the management of multiple experiment results, enabling faster and more efficient comparisons without complexity. Users can extract, restructure, and merge data seamlessly, ensuring compatibility across different tools. The processed data can be exported and used in 'R', 'Stata', 'Python', 'Julia', or any software that supports Text, CSV, or 'Excel' formats. Package: r-cran-harr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Filename: pool/dists/noble/main/r-cran-harr_1.1.0-1.ca2404.1_all.deb Size: 57736 MD5sum: fac6852f3d4c3b25105461cd5b694805 SHA1: 34675514b75a133e95ff30fce902faf68958df70 SHA256: 4c7448076fcd25a2917dfa9126aa4f810211c6ecfa0f83a49073feedb635c7a1 SHA512: 66be9b126f1c2fae5fce91063da3bcba9c0e665357e3b8896ebd90c6c2b8067abe72c7a5185eae4ef6eba23e29e08856833bcfe8793f8b84c4851d81656ad192 Homepage: https://cran.r-project.org/package=HARr Description: CRAN Package 'HARr' ('HAR' ('GEMPACK') File Read/Write Utility) HAR files are generated and consumed by 'GEMPACK' applications. This package reads/writes 'HAR' files (and 'SL4' files) directly using basic R functions. Package: r-cran-harrietr Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-bioc-ggtree, r-cran-magrittr, r-cran-lazyeval, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-harrietr_0.2.3-1.ca2404.1_all.deb Size: 368686 MD5sum: 3d4d7e4140787080beaff88147de2410 SHA1: 88fc626a0a4926e2f5dba0d1236bf7efafda728b SHA256: 821c266ae952e16c17747d67073404f6d20339df2d3ea2c865b5a4c851b80691 SHA512: 0910d964f8c88061f71eec84375f439b5f9638c2e95c7d6348cc68ce90c998c8e3732fdcfb6686965505176cb62510b3e26572bdfee3effcbc90f86c05311d2c Homepage: https://cran.r-project.org/package=harrietr Description: CRAN Package 'harrietr' (Wrangle Phylogenetic Distance Matrices and Other Utilities) Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr' implements some function to manipulate distance matrices and phylogenetic trees to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse' tools. Package: r-cran-harrypotter Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-hexbin, r-cran-testthat Filename: pool/dists/noble/main/r-cran-harrypotter_2.1.1-1.ca2404.1_all.deb Size: 229526 MD5sum: aa470b3f2c7590401fc17042522a5f2c SHA1: b298d7c5f8f6f21f1fe5c5ee1829b05cd6bd3649 SHA256: 33a16c84eba68aac9d4617b0faec55d51032c62d33c9853e7cb3147bcd08e1ba SHA512: 6b581a5b9aa44d3e5c81e35d0c15cf413fab567bbbfc62e8af7888e817b59ebba33049247ea118565ceccaa95a36c0d00c94810ef3d824bc36c95e48ae4f7915 Homepage: https://cran.r-project.org/package=harrypotter Description: CRAN Package 'harrypotter' (Palettes Generated from All "Harry Potter" Movies) Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise. Package: r-cran-harvest.tree Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-harvest.tree_1.1-1.ca2404.1_all.deb Size: 64384 MD5sum: da2e50fac50be2a45a18faa31563c49b SHA1: ee0679847b6f3c2983ebb61082237b8b30c7478e SHA256: c88b37edeca8185ed0b04b8af8bbac5865c79d137734edd382a74bc1a830fe6d SHA512: 37082ffc689cd896672bd42bb1e92e51ccd6526661005202d57349d83b22666ad6d74d8dce7c585e7fd1cc1e3ece4bcafb95f1ba80d4cd72e4e7fc541d80605f Homepage: https://cran.r-project.org/package=Harvest.Tree Description: CRAN Package 'Harvest.Tree' (Harvest the Classification Tree) Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information. Package: r-cran-hash Architecture: all Version: 2.2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-hash_2.2.6.4-1.ca2404.1_all.deb Size: 189150 MD5sum: c9953aaae7e00329fd53c440cd3efb12 SHA1: 9c18734862e46bc4b7645709a4912b76d960b975 SHA256: fc0cf210aba240e3a0ccced5c27e4138cbabfdf4c801166840c2ac2716fa89fe SHA512: 130dc370b9b0d06d765ab2fc46baa74826457ca0920b66c06bcc21ce8e8dda271835e9d79e529829dc10d7e1a90254a647da4005d551bca59ee007daf1bfd8f8 Homepage: https://cran.r-project.org/package=hash Description: CRAN Package 'hash' (Full Featured Implementation of Hash Tables/AssociativeArrays/Dictionaries) Implements a data structure similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. For objects of appreciable size, access using hashes outperforms native named lists and vectors. Package: r-cran-hashids Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hashids_0.9.0-1.ca2404.1_all.deb Size: 44804 MD5sum: e052d5c985371018dfaa57ee5bb6a4db SHA1: 2f721fc8cc5f705e0ff4494d6ada88b6a07fbc77 SHA256: e58b6012e87170f359b47b8bb5b2d14a05ecb3d63bd1a946be0478ca19ab9f5e SHA512: 008dc8b82a76b877fa5d0136c4173268242554e02ae9d2a9e2c7e5d423cd60a02edeb04dc4e3f50310abd43c2c695debee5ca0f3487fe7276e1d512f3492901c Homepage: https://cran.r-project.org/package=hashids Description: CRAN Package 'hashids' (Generate Short Unique YouTube-Like IDs (Hashes) from Integers) An R port of the hashids library. hashids generates YouTube-like hashes from integers or vector of integers. Hashes generated from integers are relatively short, unique and non-seqential. hashids can be used to generate unique ids for URLs and hide database row numbers from the user. By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other. hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer. Package: r-cran-hassani.sacf Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hassani.sacf_2.0-1.ca2404.1_all.deb Size: 16062 MD5sum: 1eec337d5cfe6f231745c2b3e10f7c83 SHA1: 7c7481dc6d50e5b8caba2f2bd7bbb12d4b0ba124 SHA256: e4ca1785758e70a6a62b1b9960e3b407c4185a923a5b3111c905c5eb95dbd117 SHA512: c448e39edaeea36fac196592525f703d1c92776793b53a63c412e593ec1fdfd75256dfd403bdba7bcc8a6f10cc9bf3544985f9e13392a6119def5b88704f8875 Homepage: https://cran.r-project.org/package=Hassani.SACF Description: CRAN Package 'Hassani.SACF' (Computing Lower Bound of Ljung-Box Test) The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) . Package: r-cran-hassani.silva Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hassani.silva_1.0-1.ca2404.1_all.deb Size: 17154 MD5sum: 352d775f01f9d9d696bdc18e47c3a829 SHA1: c4fa44aff9f0f47b0db8598bbcd70bacee1c1949 SHA256: 70228e9f7cac7caf4f3f246ae94523c138df794063423571aee65847188eca1d SHA512: 98f4edf7571395f80829beab677919faf3f011939c18839d51701362e2daa91708e4e24e8eb71cd83d601a7b7dbd5a2b55b952314a0cb8aa5eccd5b2cf0d5895 Homepage: https://cran.r-project.org/package=Hassani.Silva Description: CRAN Package 'Hassani.Silva' (A Test for Comparing the Predictive Accuracy of Two Sets ofForecasts) A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. KSPA test has been described in : Hassani and Silva (2015) . 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The methodological foundation follows the standard area-level Small Area Estimation literature, primarily Rao and Molina (2015, ISBN: 9781118735787) , while computational implementation is adapted to the parameterisation and prior-specification conventions of the 'brms' package , which targets the Stan back-end. Supports a principled Bayesian workflow , with prior predictive checks, convergence diagnostics, model comparison, spatial random effects, custom distributions, missing-data handling, and a bilingual 'shiny' application for non-programmer analysts. 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Package: r-cran-hcandersenr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4368 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hcandersenr_0.2.0-1.ca2404.1_all.deb Size: 4317824 MD5sum: 2112cbcde7a3db1ff51ebfd499e1a0df SHA1: 288422fee74f860f6bb53a66ee4baebfef28b153 SHA256: 5af2a7cbbd22ff76a8c6ce1267b05c79a7c04301c2298dc8a4c50d9148264bc2 SHA512: 8da6ad280db238755257b221ab5d2926227edbd326bd32d455c3fbc8c2449370dc65a591200e11cb56dfcee4e3dd91e86d2640db083e38780a42ef91916db2f8 Homepage: https://cran.r-project.org/package=hcandersenr Description: CRAN Package 'hcandersenr' (H.C. Andersens Fairy Tales) Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French. Package: r-cran-hcci Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hcci_1.2.0-1.ca2404.1_all.deb Size: 50084 MD5sum: 073d8249af487762f792389581a3f8b7 SHA1: 2b9600ce56bbb1da4d92755401c150ee30c12b37 SHA256: 1b96017db0b98ba712effbbb2acbb5401faf84925bfc9fda392329879bafa38c SHA512: 192b379c087a8747e4202a1265d74854ce9bc46fcd67c66b6e0e8ed33601ecc0839964e0242f0bf824c88d953a056cc51f144b89aaf16c86d4b351864e87b4d4 Homepage: https://cran.r-project.org/package=hcci Description: CRAN Package 'hcci' (Interval Estimation of Linear Models with Heteroskedasticity) Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at . The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) , while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) . The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) . This bootstrap scheme is known as weighted or wild bootstrap. Package: r-cran-hcd Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-randnet, r-cran-rspectra, r-cran-irlba, r-cran-data.tree, r-cran-data.table, r-cran-stringr, r-cran-dendextend Filename: pool/dists/noble/main/r-cran-hcd_1.0-1.ca2404.1_all.deb Size: 53190 MD5sum: df491a9d154e0247b09d5ee32a78f316 SHA1: 986691627851b5c3e9a646195ba1ee3fc5d2cf54 SHA256: 80ecb0a59d490b8f649b24301098918e140f7da3de55d906408c7694da428d40 SHA512: f3ef283f4d7f4e173a9c0bcbdb1af1435bc11ad5cc4165d648f73f83734d1b3699a1cb35c02af653fa7c5594c8e4e0e65be7990603a0843fe86a065a17df783a Homepage: https://cran.r-project.org/package=HCD Description: CRAN Package 'HCD' (Hierarchical Community Detection by Recursive Partitioning) Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) . The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities. Package: r-cran-hce Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6166 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hce_0.9.4-1.ca2404.1_all.deb Size: 1143626 MD5sum: 80df44ed4f903380f9866bc3e728e44f SHA1: ca256ca068fdf46a5535efe404fb6d68db21a84f SHA256: 84016c57108d4bd957a0ff8b5463bc90dfee2154ba5eb5c8df436b3fb8f8068c SHA512: b33cb7b1ffe8764c5d0a22f660e13f9acea1e4b8a71f1bf75405a1cdcf769d234787c5320941fc6ee9c19bac02725076528117b017c3fad2585591ff7634748a Homepage: https://cran.r-project.org/package=hce Description: CRAN Package 'hce' (Design and Analysis of Hierarchical Composite Endpoints) Simulate and analyze hierarchical composite endpoints with univariate distributions by Gasparyan, Koch, Brunner in (2025) in “The Univariate Distribution of Hierarchical Composite Endpoints and the Condorcet Non-transitivity Paradox.” (Biometrical Journal 68 (3), ). Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) “Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression” (Journal of the American Society of Nephrology 34 (2): 2025–2038, ). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method, but other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in “An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties” (Statistical Papers 66 (1): 20, ). Includes implementation of a new Wilson-type, compatible confidence interval for the win odds, as proposed by Schüürhuis, Konietschke, Brunner (2025) in “A new approach to the nonparametric Behrens–Fisher problem with compatible confidence intervals.” (Biometrical Journal 67 (6), ). Stratification and covariate adjustment are performed based on the methodology presented by Koch GG et al. in “Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them” (Statistics in Medicine 17 (15-16): 1863–92). For a review, see Gasparyan SB et al (2021) “Adjusted win ratio with stratification: Calculation methods and interpretation” (Statistical Methods in Medical Research 30 (2): 580–611, ). Package: r-cran-hchinamap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-dplyr, r-cran-shiny, r-cran-colourpicker Filename: pool/dists/noble/main/r-cran-hchinamap_0.1.0-1.ca2404.1_all.deb Size: 577454 MD5sum: a2c105f6b206682c8394bc114c08cbf2 SHA1: 6026073daaaea480705c4d644256588387900d86 SHA256: 7843c50acb51138ee5326ecbb5cfb37f15f1c7b218dad80c10ab65ae3f57cf27 SHA512: d181509e87ee277aaf1e08c6ac01aad0bd3a0002cbf53c83efeb31d733205b31735ee54d46376848d981fe1dc89c4e749a63d74bb646b0de6c77579108e8f34c Homepage: https://cran.r-project.org/package=hchinamap Description: CRAN Package 'hchinamap' (Mapping China and Its Provinces) By binding R functions and the 'Highmaps' chart library, 'hchinamap' package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan. Package: r-cran-hcidata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1015 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-hcidata_0.1.0-1.ca2404.1_all.deb Size: 990854 MD5sum: 009b06f8ed9f8c1f5dcf730301cdd950 SHA1: b9b1e363f22f4579e2fc013f0dd6699fbb0e4b24 SHA256: ad0874d1025717de07226020c3d41e01a7b36e7a24341f1be83f6d197005abed SHA512: 70fc1d74f998569253ecdb492e2f9acd0cd68a0b17ef60a0c28286d619eb71d705d1902af7333448df1840cc6c3495f8d902753abb139d3e359160bb39902fe2 Homepage: https://cran.r-project.org/package=hcidata Description: CRAN Package 'hcidata' (HCI Datasets) A collection of datasets of human-computer interaction (HCI) experiments. 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Package: r-cran-hclusteasy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clustersim, r-cran-factoextra, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hclusteasy_0.1.0-1.ca2404.1_all.deb Size: 111944 MD5sum: 5938167c64e80cb94c63c7cefd244cf3 SHA1: ad3a80019544d1ca56045b0313592bab5ecec1e5 SHA256: 402713c6f81dc7691810f576d90e4ef9f795ecbdca9e5a8076ebb6bc4d21feeb SHA512: 6a34bd668114548a5c25fadd09220b00b76cf76fc98e6711abf0c99c9a4c1e125d93a1936f3403f0ce3d8d1c248b27d19e83755a469a26a79b2c6afd0d7f0370 Homepage: https://cran.r-project.org/package=hclusteasy Description: CRAN Package 'hclusteasy' (Determining Hierarchical Clustering Easily) Facilitates hierarchical clustering analysis with functions to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization techniques to the dataset, perform hierarchical clustering and construct scatter plot from principal component analysis to evaluate the groups obtained. 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The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test. 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Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) for a description of some of our methods. 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Package: r-cran-hctdesign Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-diversitree, r-cran-mvtnorm, r-cran-flexsurv, r-cran-survival, r-cran-crayon Filename: pool/dists/noble/main/r-cran-hctdesign_0.7.4-1.ca2404.1_all.deb Size: 126134 MD5sum: 30015f041f04ca1964a488f8764dad4d SHA1: b1c26c01196723bfa92b9214f51d81ad6ccc3a13 SHA256: 002c3295df3336c17247046763545cb442ae8483180d5b9a65ce4007fc142aa9 SHA512: e314b04fd061827e7e2e815611fe3a9d30d45de495909ab7e3e50dd1b2c204152b3ff83f70a6f06d3f1ee38ef03de69949c708a50ddd533826f5378957fb77b2 Homepage: https://cran.r-project.org/package=HCTDesign Description: CRAN Package 'HCTDesign' (Group Sequential Design for Historical Control Trial withSurvival Outcome) It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) and Jianrong Wu, Yimei Li (2020) . Package: r-cran-hctr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-harmonicmeanp, r-cran-mass, r-cran-ncvreg, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-hctr_0.1.1-1.ca2404.1_all.deb Size: 48564 MD5sum: f5406cefec778743d1e585305b19898f SHA1: 1aeb4f3c87282335565713ff54db463983c94716 SHA256: ac95b5651639a6d8b9f9b510f810c88b6315a963d1793f86246af23cd2408c1b SHA512: 4026b696ca55bf7ada37894059ff5435360e043b2aac821e3382d9217d945a69c250e13a8076895dbe525b26a10aa41e0d86ff31634e018758fa6894eb99b32c Homepage: https://cran.r-project.org/package=HCTR Description: CRAN Package 'HCTR' (Higher Criticism Tuned Regression) A novel searching scheme for tuning parameter in high-dimensional penalized regression. We propose a new estimate of the regularization parameter based on an estimated lower bound of the proportion of false null hypotheses (Meinshausen and Rice (2006) ). The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance testing, which is constructed by dependent p-values from a multi-split regression and aggregation method (Jeng, Zhang and Tzeng (2019) ). An estimate of tuning parameter in penalized regression is decided corresponding to the lower bound of the proportion of false null hypotheses. Different penalized regression methods are provided in the multi-split algorithm. Package: r-cran-hctrial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clinfun, r-cran-genbinomapps Filename: pool/dists/noble/main/r-cran-hctrial_0.1.0-1.ca2404.1_all.deb Size: 43182 MD5sum: 8b0a0939358545f0f58a61e5d12d644f SHA1: 43952e05b473632aa37e3ec86fbc441a4ea8152c SHA256: deb55e1bf9a1efdca3cded53b6b67edcdbd16034b558037aa624f6201175833a SHA512: 90c7c2c94ff093dd832c723e3f1df78151b15a39a8e61f8801c0fd897beedf83db7e1f0c20cf35d5f54c097ad648c7e6bcbdc121ef32a93c5a92220e306edc79 Homepage: https://cran.r-project.org/package=hctrial Description: CRAN Package 'hctrial' (Using Historical Controls for Designing Phase II Clinical Trials) Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls. 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The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see . 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Package: r-cran-hdanova Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1772 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-lme4, r-cran-mass, r-cran-mixlm, r-cran-pls, r-cran-pracma, r-cran-progress, r-cran-rspectra Suggests: r-cran-knitr, r-cran-vegan Filename: pool/dists/noble/main/r-cran-hdanova_0.8.4-1.ca2404.1_all.deb Size: 1121458 MD5sum: b0247623e857ea0880f18825b2bf8891 SHA1: 693d3c292d8994e11cb5842524f06b02d15a8b81 SHA256: d84fe779fd15f6b834312709574a6dc14c6dc7c98ea1c65624ac6b0cc1e651e6 SHA512: 955f49050bcec958b155ad5868f82388b167fe4ef0e4a85223c39a72ba23cb3a1c947404d1d604fe92bb59d7f2de12d73dae436356607274b47da125b35e6d51 Homepage: https://cran.r-project.org/package=HDANOVA Description: CRAN Package 'HDANOVA' (High-Dimensional Analysis of Variance) Functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". This implements and imports a collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting functions, multiple real data sets and four vignettes covering a range different applications. Package: r-cran-hdar Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr, r-cran-htmltools, r-cran-stringr, r-cran-scales, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdar_1.0.7-1.ca2404.1_all.deb Size: 1794062 MD5sum: a4c06eb1f26cd16590b9cd4290b2476b SHA1: 238618e2be7101695e2466ca6788da2f153170a6 SHA256: 14f2c4a2f2f2fa2191bdacfc75cdc991d3d4bcbaf648caf96d106ddf75423611 SHA512: 5e55b7db08260dd0ffef42a8b078ad4b5c9b4df899c29d7866eeb4498afec516424eb2068ebbbe75e8f3495f7b8d555746626e1483b9f079e4a34ef72ea8002b Homepage: https://cran.r-project.org/package=hdar Description: CRAN Package 'hdar' ('REST' API Client for Accessing Data on 'WEkEO HDA V2') Provides seamless access to the WEkEO Harmonised Data Access (HDA) API, enabling users to query, download, and process data efficiently from the HDA platform. With 'hdar', researchers and data scientists can integrate the extensive HDA datasets into their R workflows, enhancing their data analysis capabilities. Comprehensive information on the API functionality and usage is available at . Package: r-cran-hdbma Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-gplots, r-cran-mass, r-cran-survival, r-cran-lattice Filename: pool/dists/noble/main/r-cran-hdbma_1.0-1.ca2404.1_all.deb Size: 181580 MD5sum: 195501953651cba759e65f75aa7b427d SHA1: 163d18b1f7fc29db51b5839c3e55e72d13fc1a8f SHA256: 7285986e1df3bd25120f47c621e299e719cc0cabd8dba1a3896d50695acebc8c SHA512: b6f53c39a797bb650b8c919b759804fcc22886b0c587c1957c305d294b2c95ff61b3865c961d7df385f617aa50f3e2a24179b67ccf3f621a52a1c53e805e02eb Homepage: https://cran.r-project.org/package=hdbma Description: CRAN Package 'hdbma' (Bayesian Mediation Analysis with High-Dimensional Data) Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators. Package: r-cran-hdbrr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3008 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-bigparallelr, r-cran-bigstatsr Filename: pool/dists/noble/main/r-cran-hdbrr_1.1.5-1.ca2404.1_all.deb Size: 2617280 MD5sum: 306da12d548a1e9cb3475c695e4ee4d3 SHA1: b5bf5fd5d86e9b588cc0f15e2a21a4d747f274cd SHA256: 59ffa7b97397779c34d098eb9c8ce1d33f459e35402096615bd3767877464f4e SHA512: c64ce89c517364b80a977ff0b9d74236bda15d1c94e477c9f82cf7585c77301a30673b7e97369f2490a8658faeb17f37d31e5eea888d7e113df3ba576d2dad48 Homepage: https://cran.r-project.org/package=HDBRR Description: CRAN Package 'HDBRR' (High Dimensional Bayesian Ridge Regression without MCMC) Implements Bayesian ridge regression for high-dimensional data without using Markov chain Monte Carlo (MCMC). Posterior computations are performed using singular value decomposition (SVD) or QR decomposition. The package also provides variable selection and prediction methods. 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In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the “double robustness” property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics . Package: r-cran-hdcce Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdcce_0.1.0-1.ca2404.1_all.deb Size: 322638 MD5sum: 2fcdff820efc8ca1dcade4ff805fbba3 SHA1: 3489baf3caca3699fe97bf41c2e56c1543fbb6d0 SHA256: 49f428df8cf6c78ae21a82c3ba9630724d32b87da5b91fd3a0c63db1dd5a6bda SHA512: 0e5e6b4b90d9a2e77ab87a4d6286a8dfbfed978fb7815b48723488e7d48691c907900bd0e93227f922f295f219d44326d0503454364c5d07b9be2b3a38bbb841 Homepage: https://cran.r-project.org/package=hdcce Description: CRAN Package 'hdcce' (Estimation and Inference for High-Dimensional Panel Data Modelswith Interactive Fixed Effects) Estimation and inference for panel data models with interactive fixed effects. The methods cover i) the linear specification of Ruecker, M., Vogt, M., Linton, O. and Walsh, C. (2025) "Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects" , and ii) the dictionary design of Ruecker, M., Vogt, M. and Linton, O. (2026) "High-Dimensional Panel Data Models with Interactive Fixed Effects: Beyond the Linear Case" . Package: r-cran-hdci Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-slam, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-lattice, r-cran-matrix, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-hdci_1.0-2-1.ca2404.1_all.deb Size: 106790 MD5sum: b928ab4922dbfa2f493a45e12cfef39f SHA1: e3a723dca06e71fe3c5d2a438b7e6abd19297ae0 SHA256: b9c4d47a01b750fadd8c4077f48257743eedea685225483e95c03127a7c1caa3 SHA512: 2ffb99d5883676a5ca8886a05fd2d4bcf14c9d69d304535a84bb1323e630dd474389ba45a8bc1fdf5e129f4ae49778850ab184aed27036d3c29318cc9613789a Homepage: https://cran.r-project.org/package=HDCI Description: CRAN Package 'HDCI' (High Dimensional Confidence Interval Based on Lasso andBootstrap) Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge. 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Package: r-cran-hdcuremodels Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2079 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-flexsurv, r-cran-flexsurvcure, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-knockoff, r-cran-mvnfast, r-cran-plyr, r-cran-survival, r-cran-withr Suggests: r-cran-knitr, r-cran-rdsdp, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hdcuremodels_0.0.8-1.ca2404.1_all.deb Size: 1849890 MD5sum: 6f8e031f31d9906eb981576ca76cc90a SHA1: c8e5e0793689d1c2ddb1127d2fc813b8593f29ae SHA256: 33572e90234743f4ad0d18f41685c3ffbb7f491540f4ce366789282fc7bca85c SHA512: 2986f76fb9541c4254ea459b1a43d9b60757ae63aea65070f82edb290db5b9c0fb5e639842854e4c9321bed1b102c242bd03dfaa54edd88d384715154afbb16d Homepage: https://cran.r-project.org/package=hdcuremodels Description: CRAN Package 'hdcuremodels' (High-Dimensional Cure Models) Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022) and Archer et al (2024). False discovery rate controlled variable selection is provided using model-X knock-offs. 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The data sets are located on disk but look like in-memory, the syntax for manipulation is similar to 'data.table'. Operations are performed "chunk-wise" behind the scene. See for more information. Package: r-cran-hddplot Architecture: all Version: 0.59-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-bioc-multtest Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-hddplot_0.59-2-1.ca2404.1_all.deb Size: 2953518 MD5sum: 477219c18758f200cf61c171a590d099 SHA1: 3aefa6cda0c7c2c295cd0d78d0ef0a73983d1269 SHA256: 14ff519edd672d921ec7ef76dfb50332aa1ea5337a8ad4d9c87c2654a01ee7b5 SHA512: 3a5745d375c0292792b52d2c2e088bdcb7fd049dbc3f5a3aa814a0464ec9bbe578ccb81909a575e7bde537210a5995896db9ed87e15ddff351599646ecb3a9f3 Homepage: https://cran.r-project.org/package=hddplot Description: CRAN Package 'hddplot' (Use Known Groups in High-Dimensional Data to Derive Scores forPlots) Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included that are intended for didactic use. The package implements, and extends, methods described in J.H. Maindonald and C.J. Burden (2005) . 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Jimenez-Varon, Ying Sun and Han Lin Shang (2024, Journal of Computational and Graphical Statistics). Package: r-cran-hdi Architecture: all Version: 0.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2391 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scalreg, r-cran-mass, r-cran-glmnet, r-cran-linprog Suggests: r-cran-matrix Filename: pool/dists/noble/main/r-cran-hdi_0.1-10-1.ca2404.1_all.deb Size: 2408828 MD5sum: 713a1d9cba9d044708b6a5a5b7a42c11 SHA1: bbf787731dec60291ff9543acce6f3d4622d1f2b SHA256: 5286c91820a7187e7f463fc9822e61b9563e9c92a0dcc410485e39e91efdc57a SHA512: fa9f475f6d9eba780a42b8877966ac70b2a45d2464219360fd6881380c11ad0c9caa28fb2e9441ed76466415c9c58e9841b30bbcd29cbb17e300d53ca93113c4 Homepage: https://cran.r-project.org/package=hdi Description: CRAN Package 'hdi' (High-Dimensional Inference) Implementation of multiple approaches to perform inference in high-dimensional models. 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See Waggoner (2023) for more on 'hdImpute', Stekhoven and Bühlmann (2012) for more on 'missForest', and Mayer (2022) for more on 'missRanger'. 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Package: r-cran-hdir Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-npcirc, r-cran-circular, r-cran-rgl, r-cran-directional, r-cran-movmf Suggests: r-cran-ggplot2, r-cran-maps, r-cran-mapproj, r-cran-dirstats Filename: pool/dists/noble/main/r-cran-hdir_1.1.3-1.ca2404.1_all.deb Size: 360874 MD5sum: 05591ad2aef1778e363faea643745f8f SHA1: 67977c2009a788f10cdb118e28ae7f22daa1bafe SHA256: 3222faa3b95649bcc299c688776213b54ae52a92e8e39065903c65197b7fd8cf SHA512: 9056140830b494d2544ca9cf392b2e19621731aac847006a69a031e532e881438a7813157e4ba34a275234c7f512b3ee8ef55a7188c778b8345c712f46862a5e Homepage: https://cran.r-project.org/package=HDiR Description: CRAN Package 'HDiR' (Directional Highest Density Regions) We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) . This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences. Package: r-cran-hdivar Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdivar_1.0.2-1.ca2404.1_all.deb Size: 68526 MD5sum: a33a30e75aec14a36acfb38295f13537 SHA1: 5f5514fa8633cbb56b6341201b58ad7e520b4023 SHA256: 7ea16a0abd5bdf30260ce0c6d998dc58f2cf4f95282a6ec567056e6fcacfb7d2 SHA512: d007964c8156b60d7e55d62b06dc56975d81ceb91c0870cbd7bc28fe68c5b6bb021d0a2f6f4e1cbec08cefcff8aed1a8747e369c488ca249ecd56f2e3f0335c2 Homepage: https://cran.r-project.org/package=hdiVAR Description: CRAN Package 'hdiVAR' (Statistical Inference for Noisy Vector Autoregression) The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica. Package: r-cran-hdm Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-ggplot2, r-cran-checkmate, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-xtable, r-cran-mvtnorm, r-cran-markdown Filename: pool/dists/noble/main/r-cran-hdm_0.3.2-1.ca2404.1_all.deb Size: 1649800 MD5sum: dfabe9980ef1bb8bf3306697087e1e52 SHA1: 1b786cd333d44e441c7c830237e5af687619e9c8 SHA256: f5b8b56e863d0a566b7f7e4570a35d8c4633ec0dc17d91bfd5fde3ab3d4169e2 SHA512: b2cdf63e785c48704d08ccc823115a27397bd370c1c1d2c0cb37b467297ea1b7c7d4c47ff2a66c54abe7c4564512808c14df9283610f95cfacfd7742b6343841 Homepage: https://cran.r-project.org/package=hdm Description: CRAN Package 'hdm' (High-Dimensional Metrics) Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) . Package: r-cran-hdmed Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bama, r-cran-foreach, r-cran-freebird, r-cran-gcdnet, r-cran-genlasso, r-cran-hdi, r-cran-iterators, r-cran-mass, r-cran-mediation, r-cran-ncvreg Filename: pool/dists/noble/main/r-cran-hdmed_1.0.1-1.ca2404.1_all.deb Size: 229790 MD5sum: 5af5235a21bee07d8eb8c53475e3855c SHA1: fee849343d20feda60ff7edb12795c6091e79a7a SHA256: c74b6d9c48fc017088ab34d0b226110fcbc4a4e425a9a20a25475841811a59ae SHA512: 2f3a0459f31d951ee7fc344653a1d8826f782fe28af21e184b62cf5ee22e00eb895d467405588ce23da813b79cfa275393f4f841d058d9b9bcd3351fac80887d Homepage: https://cran.r-project.org/package=hdmed Description: CRAN Package 'hdmed' (Methods for Mediation Analysis with High-Dimensional Mediators) A suite of functions for performing mediation analysis with high-dimensional mediators. In addition to centralizing code from several existing packages for high-dimensional mediation analysis, we provide organized, well-documented functions for a handle of methods which, though programmed their original authors, have not previously been formalized into R packages or been made presentable for public use. The methods we include cover a broad array of approaches and objectives, and are described in detail by both our companion manuscript---"Methods for Mediation Analysis with High-Dimensional DNA Methylation Data: Possible Choices and Comparison"---and the original publications that proposed them. The specific methods offered by our package include the Bayesian sparse linear mixed model (BSLMM) by Song et al. (2019); high-dimensional mediation analysis (HDMA) by Gao et al. (2019); high-dimensional multivariate mediation (HDMM) by Chén et al. (2018); high-dimensional linear mediation analysis (HILMA) by Zhou et al. (2020); high-dimensional mediation analysis (HIMA) by Zhang et al. (2016); latent variable mediation analysis (LVMA) by Derkach et al. (2019); mediation by fixed-effect model (MedFix) by Zhang (2021); pathway LASSO by Zhao & Luo (2022); principal component mediation analysis (PCMA) by Huang & Pan (2016); and sparse principal component mediation analysis (SPCMA) by Zhao et al. (2020). Citations for the corresponding papers can be found in their respective functions. Package: r-cran-hdmfa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-hdmfa_0.1.1-1.ca2404.1_all.deb Size: 70990 MD5sum: d35330fcd53eb2c3bfe626ec2d85f63d SHA1: f237882f04cdbcb946e0216aa5d06fa1bc4b622a SHA256: f0b772636cac9812e9043d9b30b1e69eaf2afc5bf1697deb385a9ba1b22a2006 SHA512: 3af04a2869dcee51d990b2c786d6a525d0ec4c55a5bef2f710039b9111f65337d5a94d169ee9e456e60b510684f32374e99a80845331652bdcc5ab048f0a4887 Homepage: https://cran.r-project.org/package=HDMFA Description: CRAN Package 'HDMFA' (High-Dimensional Matrix Factor Analysis) High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023). The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) . In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) , the Projected estimation (PE) method by Yu et al. (2022). In addition, the methods for determining the pair of factor numbers are also given. Package: r-cran-hdmt Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1886 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-bioc-qvalue Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hdmt_1.0.5-1.ca2404.1_all.deb Size: 1858486 MD5sum: b8946810f5979655552413f0575958ac SHA1: c36c2bb39f67e28ae45fba6aea1209af2e9f56a1 SHA256: 028d5aa7496fd55575161a9f4109754efbb3b5c9c54aa21c64be070da587faa4 SHA512: 3938da95062af5bc4509d78a92cdaa5c3937ac192922bd5e2eb4ac84bee922349a5cc18b1a0e6ece24f1a9a095bb35acf788db95699c13cef1cd96d77a6ca395 Homepage: https://cran.r-project.org/package=HDMT Description: CRAN Package 'HDMT' (A Multiple Testing Procedure for High-Dimensional MediationHypotheses) A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) . Package: r-cran-hdmtd Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-igraph Suggests: r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-hdmtd_0.1.5-1.ca2404.1_all.deb Size: 644174 MD5sum: bb5ddbbd7cb3cd49b77290d60c211e62 SHA1: fbcabb0236c32976967cd793ed4848092f5afca0 SHA256: 17264208dc17d2cdc25050da5df6673de351facbf000521c14ee24fe6a72c789 SHA512: 10daa5c3ca8a8bbc79d033caac505263c497015546bb235e55b424d2737815eb3a6bcf666f04d6de9262b1d8a7031cc87cc96492b67561271d641c42442673b1 Homepage: https://cran.r-project.org/package=hdMTD Description: CRAN Package 'hdMTD' (Inference for High-Dimensional Mixture Transition DistributionModels) Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) . Package: r-cran-hdoutliers Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3532 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-factominer, r-cran-mclust Filename: pool/dists/noble/main/r-cran-hdoutliers_1.0.4-1.ca2404.1_all.deb Size: 2511408 MD5sum: 1b448e4ad404e36261cd948ff5189b46 SHA1: 5ac71c043609d025fecc67a4d001cb98978855b2 SHA256: bbcd7b51696ba2f175a3c039a3a9cc29d8965b0715217aec9a6752f9869d6970 SHA512: 87f7bbe5614f41c58a48aea43328d6e6e6cb815f0af7123a5f6d4911fae627b27cc7876c793b8a9741b85088a6e5166e40d8da2a812458c0226535fc087295e0 Homepage: https://cran.r-project.org/package=HDoutliers Description: CRAN Package 'HDoutliers' (Leland Wilkinson's Algorithm for Detecting MultidimensionalOutliers) An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. 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Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) . 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Package: r-cran-healthfinance Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-healthfinance_0.1.0-1.ca2404.1_all.deb Size: 30568 MD5sum: eefcc3954130fa6f1623e064f9e0ac92 SHA1: ad28ebca9abd9409b88465cc8b3fca7b9329ec70 SHA256: 2e7d15fd70e50108af7f9fbf58371807332491114ee9c691cb06b5b2cec2780f SHA512: 6f400f616bb254836ba7d55f02d38720440d7a0dfcc96447604686c567bee809ce65cffc3f1b030b0a43419f1b960b7202a69b220d9f4c9388cf87188e9e2445 Homepage: https://cran.r-project.org/package=healthfinance Description: CRAN Package 'healthfinance' (Financial Projections and Planning for Health Care Practices) Provides a shiny interface for a free, open-source managerial accounting-like system for health care practices. This package allows health care administrators to project revenue with monthly adjustments and procedure-specific boosts up to a 3-year period. Granular data (patient-level) to aggregated data (department- or hospital-level) can all be used as valid inputs provided historical volume and revenue data is available. For more details on managerial accounting techniques, see Brewer et al. (2015, ISBN:9780078025792). Package: r-cran-healthiar Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rdpack Suggests: r-cran-sf, r-cran-terra, r-cran-exactextractr, r-cran-testthat, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-healthiar_0.2.6-1.ca2404.1_all.deb Size: 1419524 MD5sum: 2cf80241a47c5caca19e9f7989dd1c47 SHA1: ba958c1a58a448f4b13203e8865fa6c75848ce0a SHA256: 2a91555da39f41ac004625d91dac3c193205d5a993cb85c4b230af1d5978d2aa SHA512: 24bad29bc7a472dedf6e262286665d91ccacef3c1013a1e4f74ecabe637d9e86168c8dd6a6d9e7272c616dec3f59091ca26fa92c44c33f1b221f78c86b6eea4f Homepage: https://cran.r-project.org/package=healthiar Description: CRAN Package 'healthiar' (Quantifying and Monetizing Health Impacts Attributable toExposure) This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. Selection of relevant references (in chronological order): WHO (2003) , Murray et al. (2003) , Miller & Hurley (2003) , Steenland & Armstrong (2006) , WHO (2011) , GBD 2019 Risk Factors Collaborators (2020) . Package: r-cran-healthmarkers Architecture: all Version: 0.1.4-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pkgload, r-cran-cvrisk, r-cran-di, r-cran-rspiro, r-cran-pooledcohort, r-cran-qrisk3, r-cran-riskscorescvd, r-cran-usethis, r-cran-withr, r-cran-mice, r-cran-missforest Filename: pool/dists/noble/main/r-cran-healthmarkers_0.1.4-1.ca2404.2_all.deb Size: 1186372 MD5sum: 23d291abd00eb05c39a4b9de84daea4e SHA1: e531d47f1f9e42950561c027275e64733aa6da14 SHA256: 9de5f7b75378b54b2dc8a4fbb8b5a4e0f0b5e9e80f3c2d5c63576ba4eedfefe9 SHA512: 887f2294bb304f474e13deb6d6c709d010051cbd6003700e16ae81e5ec4131a65051b8f4d9ad2e2a01c164aab1253715e4cc192908ef237ca1b82c2b74ea02a0 Homepage: https://cran.r-project.org/package=HealthMarkers Description: CRAN Package 'HealthMarkers' (Clinical and Metabolic Biomarker Calculation Toolkit) Computes specialist biomarker indices and risk scores for metabolic, cardiovascular, renal, hepatic, inflammatory, frailty, and psychiatric health assessment. Includes fasting and OGTT insulin sensitivity/resistance indices, ASCVD/QRISK3/KFRE risk equations, liver and kidney markers, frailty and comorbidity indices, biofluid marker panels, and utilities for column mapping, normalization, imputation, and combined marker dispatch. Package: r-cran-healthmotionr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15700 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-healthmotionr_0.2.0-1.ca2404.1_all.deb Size: 15599250 MD5sum: ea00a064519c6ca0a149f8d6fb622db1 SHA1: 67798b8e5303cbfbb1039d61e6cba2b87571f557 SHA256: 53dd543ce4b8d3c1abd614590e6e18c2db00e33e2e48109858a0a0d931525d6d SHA512: 1b8d110d2e18dc763003d2c92cc3e7fc1754fa2717309ff7eb5adae482454663c697356fe4e7960f1f606896395ef837f64a456cd9ebb78c3395de515bb6098d Homepage: https://cran.r-project.org/package=healthmotionR Description: CRAN Package 'healthmotionR' (A Comprehensive Collection of Health and Human Motion Datasets) Provides a broad collection of datasets focused on health, biomechanics, and human motion. It includes clinical, physiological, and kinematic information from diverse sources, covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis, accelerometry, gait analysis, motion sensing, and biomechanics experiments. Designed for researchers, analysts, and students, the package facilitates exploration and analysis of data related to health monitoring, physical activity, and rehabilitation. Package: r-cran-healthyr.ai Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-yardstick, r-cran-broom, r-cran-ggrepel, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-forcats, r-cran-recipes, r-cran-purrr, r-cran-dials, r-cran-parsnip, r-cran-tune, r-cran-workflows Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-healthyr.data, r-cran-scales, r-cran-tidyselect, r-cran-janitor, r-cran-timetk, r-cran-plotly, r-cran-rsample, r-cran-kknn, r-cran-hardhat, r-cran-uwot, r-cran-stringr, r-cran-parallelly, r-cran-doparallel, r-cran-h2o Filename: pool/dists/noble/main/r-cran-healthyr.ai_0.1.2-1.ca2404.1_all.deb Size: 597492 MD5sum: 26657051bf2d6409f7e1aa8bf8a4f8d1 SHA1: 35a87bd158ebb910e4a349f27142882bd99193ab SHA256: 1fd2b76673b6ecde6552714218a0fb924f2ca980b12ccbfa40f140b24ff97eee SHA512: 6d46daf2440f66adfde78c8aa00daff72766ea0bfd1dff54cac3edcd4b0c167d6a103c2489a22dd5db28e51b688e093a9254032ad09c88260b150a212557d780 Homepage: https://cran.r-project.org/package=healthyR.ai Description: CRAN Package 'healthyR.ai' (The Machine Learning and AI Modeling Companion to 'healthyR') Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. Package: r-cran-healthyr.data Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4265 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-janitor, r-cran-dplyr, r-cran-httr2, r-cran-stringr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-healthyr.data_1.2.1-1.ca2404.1_all.deb Size: 4307404 MD5sum: 3ea4f5caf811e7e6f6e048a5230ae0ea SHA1: e38fd7601a7786dc2b58a4a778e2e433737a1656 SHA256: b2de3a92864fdd6f7c47e7b9417f2e612eaab4676f66162503d918bd86833099 SHA512: 8e6df981a3ce2134272b928d401f743c881695a6e8dc202cb26a40eda3dec08ad2fd1c4b0cb912a73c1435c4e27028fbe5295e1b13505e4f49cc7d426ef91dac Homepage: https://cran.r-project.org/package=healthyR.data Description: CRAN Package 'healthyR.data' (Data Only Package to 'healthyR') Provides data for functions typically used in the 'healthyR' package. Package: r-cran-healthyr.ts Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-timetk, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-lubridate, r-cran-plotly, r-cran-recipes, r-cran-modeltime, r-cran-cowplot, r-cran-forcats, r-cran-stringi, r-cran-parsnip, r-cran-workflowsets, r-cran-hardhat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-rsample, r-cran-healthyr.ai, r-cran-stringr, r-cran-forecast, r-cran-tidymodels, r-cran-glue, r-cran-xts, r-cran-zoo, r-cran-tsa, r-cran-tune, r-cran-dials, r-cran-workflows, r-cran-tidyselect, r-cran-glmnet, r-cran-earth, r-cran-smooth, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-healthyr.ts_0.3.2-1.ca2404.1_all.deb Size: 2234794 MD5sum: 20d0b7cf500006087c846b605de1375b SHA1: 25162df64ada17605db789713a42baf1d2c073d3 SHA256: 1382e43a0a7c55c15e57f654d3a65d21113f7279fbd2131c83b896bdbef0d571 SHA512: 5c3727936ae4274ca2bec0383f3b3d97104c35092ac6453e6c1ee03242ef572636f538ce09c993cc595cd65044289d999fbbf92c2d3a1ca315e9982f279371ad Homepage: https://cran.r-project.org/package=healthyR.ts Description: CRAN Package 'healthyR.ts' (The Time Series Modeling Companion to 'healthyR') Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. Package: r-cran-healthyr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5525 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-timetk, r-cran-ggplot2, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-stringr, r-cran-writexl, r-cran-cowplot, r-cran-scales, r-cran-sqldf, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pacman, r-cran-healthyr.data, r-cran-broom, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-healthyr_0.2.2-1.ca2404.1_all.deb Size: 2445958 MD5sum: b16233214071aa1b84d818dfcd607f62 SHA1: ba180aa430656f2c6dea64626c6bf31deb4bacac SHA256: dc1c331279d79bfd112bb8397f2e5b344bff559c921e1a1602ebfadacbdd7045 SHA512: 76249c684df19c90dfe92b8c53ed89e2bc551b4b612a6b22f3a15bf3bdd967c778db68b61519e2202b72de6067f85219f1876165e31f02e0380e0793c45ff81e Homepage: https://cran.r-project.org/package=healthyR Description: CRAN Package 'healthyR' (Hospital Data Analysis Workflow Tools) Hospital data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, readmission rates, average net pay amounts by service lines just to name a few. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. 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Package: r-cran-heaping Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fitdistrplus, r-cran-envstats Suggests: r-cran-vim, r-cran-ranger, r-cran-data.table, r-cran-ggplot2, r-cran-simpop, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heaping_0.2.0-1.ca2404.1_all.deb Size: 543094 MD5sum: aa839e056a94dd341335e83879d8a7b2 SHA1: eaf7bc344d1166c80876da5634bb0c6520999d15 SHA256: 853f567de0a01d8fac10ca4726d4fd939da41dd9d1a8d2bf94d4e7fc34f1ebac SHA512: 4b5706006522935f7f57ddfa51a769d506ff8c5d61b632b37ef2ac6f8bbbbc2be39bf1642fa1e4bfa69e32dc92b779ba4d70d20f8b9f99303972f6a88be2dbc7 Homepage: https://cran.r-project.org/package=heaping Description: CRAN Package 'heaping' (Correction of Heaping on Individual Level) Provides methods for correcting heaping (digit preference) in survey data at the individual record level. Age heaping, where respondents disproportionately report ages ending in 0 or 5, is a common phenomenon that can distort demographic analyses. Unlike traditional smoothing methods that only correct aggregated statistics, this package corrects individual values by replacing a calculated proportion of heaped observations with draws from fitted truncated distributions (log-normal, normal, or uniform). Supports 5-year and 10-year heaping patterns, single heap correction, survey weights, and optional covariate-conditional (model-based) correction via quantile regression forests or linear models to preserve relationships. A multiple-imputation wrapper repeats the correction to propagate the added uncertainty into downstream inference. Package: r-cran-heapsofpapers Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aws.s3, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-magrittr, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-heapsofpapers_0.1.0-1.ca2404.1_all.deb Size: 2059638 MD5sum: cc210f1fae84d98e224284eb98aaa523 SHA1: cf4dcb01e7cd5a8242e87e6fb166022093b9788e SHA256: 12d0cdb83cd21060c50618bf38ad4e33d32902f9e793646455c9394a192d20a1 SHA512: ce2446f4de800de075d2634fad256d4d08c3d71b02fd8d587ab4372c43116b7380cb2b254d64f0e893390ba1937a4d7684dc003a4489309dfbf81963118f5ebb Homepage: https://cran.r-project.org/package=heapsofpapers Description: CRAN Package 'heapsofpapers' (Easily Download Heaps of PDF and CSV Files) Makes it easy to download a large number of files such as PDF files and CSV files, while automatically slowing down requests, letting you know where it is up to, and adjusting for files that have already been downloaded. Package: r-cran-heartbeatr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-transformr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heartbeatr_1.0.0-1.ca2404.1_all.deb Size: 2150284 MD5sum: 67aa68c9c6db9ad50771760877310274 SHA1: f128955c1a14b03636b8e493957cf24cdd780008 SHA256: ea1cd82df7e60ad6a920125e4f56ab69eece5c17d5b7d6603e5e8c94344eea4b SHA512: 927687511495fdc0301481d936856f716b9de49366ab7d24129e3afb4dd624eeb4efc0fc0a7d8d5f5fd4c136a15d18490d57427e87680dc676bd9ccc5b57c2a9 Homepage: https://cran.r-project.org/package=heartbeatr Description: CRAN Package 'heartbeatr' (A Workflow to Process Data Collected with PULSE Systems) Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system () from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting. Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) . Check original code at . ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (). 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The program enables heat exchange calculations for a range of environmental conditions when wearing various clothing ensembles. Package: r-cran-heatmap3 Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastcluster Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-heatmap3_1.1.9-1.ca2404.1_all.deb Size: 174344 MD5sum: 1206c60695ac4d33190a895dc82c71f0 SHA1: 9416cc2bbde06e6d6588e5bb003a7a7bedac7ebe SHA256: 4d3a1dcdfad5b5ae74f938519fc30351dd97f3133f2ed5a73f40804ec3260f72 SHA512: aa7b2926aae49a60ac296b65918536dd9ad250416c30832e49d46da0c669fae548304496c4c374a71d09b298b9b120fcb36fb2739f475b56afe12d2aed091cbd Homepage: https://cran.r-project.org/package=heatmap3 Description: CRAN Package 'heatmap3' (An Improved Heatmap Package) An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features. 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Package: r-cran-heatmapfit Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-heatmapfit_2.0.4-1.ca2404.1_all.deb Size: 39030 MD5sum: 7655d6bc66517b3076cb3875c22a7c81 SHA1: 0923bdbec95c9ba9b62e9276056bad8c1dc350dc SHA256: 31a828fa5f588458dcb37df9723677083b32459972d1c15f18e4df69dbe4f1ce SHA512: 1b62a9ab96153c180d42a1db53ca9e790d26189810785a35c88c301304fa19e489fe4f2a5c665b835f9f7b91c85d3c48c6822956f9aaebd8659d69a9b32a5630 Homepage: https://cran.r-project.org/package=heatmapFit Description: CRAN Package 'heatmapFit' (Fit Statistic for Binary Dependent Variable Models) Generates a fit plot for diagnosing misspecification in models of binary dependent variables, and calculates the related heatmap fit statistic described in Esarey and Pierce (2012) . Package: r-cran-heatmapflex Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-bioc-heatplus, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-heatmapflex_0.1.2-1.ca2404.1_all.deb Size: 1017156 MD5sum: 1000181f7e8f4b4b9c4dd2792802fb77 SHA1: 45c2e0dcd9be8665deebd9a86a10efec4631c44c SHA256: 504ba3b4dfa9343bbc00d7d64246fd9bd0c6abb60ab792132a6448c363d556f6 SHA512: bfd8c890e0bae0d41d53653c69cc84ea89931be2fc1b81eda80c2d68b2b9b4b63840e7960a15066b55bbea4b7bcd2e3481ecbe95bddfbd979fab509c21402037 Homepage: https://cran.r-project.org/package=heatmapFlex Description: CRAN Package 'heatmapFlex' (Tools to Generate Flexible Heatmaps) A set of tools supporting more flexible heatmaps. The graphics is grid-like using the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set, e.g., clustered and ordered by a supervised clustering method. Package: r-cran-heatmaply Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-viridis, r-cran-ggplot2, r-cran-dendextend, r-cran-magrittr, r-cran-reshape2, r-cran-scales, r-cran-seriation, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-htmlwidgets, r-cran-webshot, r-cran-assertthat, r-cran-egg Suggests: r-cran-knitr, r-cran-covr, r-cran-gplots, r-cran-tidyselect, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heatmaply_1.6.0-1.ca2404.1_all.deb Size: 1668798 MD5sum: 7a967e46b8a1b92bfd2b9feee968c850 SHA1: 0af0b7c71fb74dd026a8cbae440107e5c1f9c2da SHA256: 3cdbc715e6f3ef82cc16d5873f8e84acc48787fa6a67915193a90ab1a912b852 SHA512: 5eb4e730ea3dc4258b007413499f3c59541ea1029c2793a7aaa37492ec6f2b5a05cf2d91fd0a3ef77e6e3cb7f2faa2bf943ff80dc2f7b79a029227881f249139 Homepage: https://cran.r-project.org/package=heatmaply Description: CRAN Package 'heatmaply' (Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2') Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' to 'heatmap.2' with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'. Package: r-cran-heatmapr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heatmapr_1.1.0-1.ca2404.1_all.deb Size: 615952 MD5sum: 652816b676e30b935008aace6709e5e2 SHA1: acb5731a49c3ebd8e812976c75dbbdb099d87c13 SHA256: f909eb5f72de7d928c7f48291adb837d0274da3353025164a3184009715b8ef2 SHA512: 9b07ca89bd5bc8b553e315f747f0ebda1bbd3f08e47bca03c98372de40afc182a4c693031805a86d24633596de2338ae5420859a98bc98ee1bb010649c9c5ee2 Homepage: https://cran.r-project.org/package=HeatmapR Description: CRAN Package 'HeatmapR' (Create Heatmaps Using Base Graphics) Provides a lightweight framework for creating high quality, complex heatmaps using base graphics. Supports hierarchical clustering with dendrograms, column and row scaling, cluster sub-divisions, customizable cell colours, shapes and sizes, legends, and flexible layouts for arranging multiple heatmaps. Designed to return plot objects that can be easily arranged with other plots without sacrificing resolution. Methods for hierarchical clustering and distance computations are described in Murtagh and Contreras (2012) . Dendrogram visualisation methods are based on the 'ggdendro' package by de Vries and Ripley (2020) . Package: r-cran-heatstressr Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat Suggests: r-cran-pkgload, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heatstressr_2.4.0-1.ca2404.1_all.deb Size: 219844 MD5sum: 8cf09d11ce3ca9644a3c916bc9a93629 SHA1: 119c5ce5aec3fdc1ef54b1175d41c4cfbf536467 SHA256: f892d85e541a353992617e0d52f81a56fefaf51c0af53040f48bf3827d9ba1fc SHA512: b927440d438b653604ff5a318ee0ea72acae16368394f485a8b9d32012d883f3955c91a63f07a2880d1a71dcc420ffd07e1e535f8609817cfe526a0f3d81dcec Homepage: https://cran.r-project.org/package=HeatStressR Description: CRAN Package 'HeatStressR' (Calculate Heat Stress Indices) Calculates heat-stress indices from meteorological observations, including the physically based wet-bulb globe temperature model described by Liljegren et al. (2008) . 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Package: r-cran-heckmange Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glm2, r-cran-maxlik, r-cran-misctools, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-heckmange_1.0.0-1.ca2404.1_all.deb Size: 4171768 MD5sum: 03a4a9c3b0eae74253d3e24e489b6033 SHA1: e1998268450b0c309542833be106f75ed7103a1d SHA256: 8618e5d0edea773d28f42123700ef4688360d63d6043712ab253b35dcf3f58dc SHA512: 8ed777f3ebd86d673863bab10a461b51e7658ee35de76046d28e5099ff158c93da9896b399e9aecd13874791ed29ad0a1fef4dbf84d4d7589bf743cb2d5c82c1 Homepage: https://cran.r-project.org/package=heckmanGE Description: CRAN Package 'heckmanGE' (Estimation and Inference for Heckman Selection Models withCluster-Robust Variance) Tools for the estimation of Heckman selection models with robust variance-covariance matrices. 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Package: r-cran-heckmanstan Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-mvtnorm, r-cran-loo Filename: pool/dists/noble/main/r-cran-heckmanstan_1.0.0-1.ca2404.1_all.deb Size: 131364 MD5sum: f2f2bd5bab488d44d1c3f799525a5a72 SHA1: 1e80feef63b6079e04fd9091bf8b9f8e0542a988 SHA256: e7f60cbdc03c01615093506b8cb94881a4ed9f55f45afc15fc23343ea27e955e SHA512: 2531786f7dc0fb2f69e8ecd3ec5332c450522d3d199846b65b1624b55af57b9e16a219d3bbead12452e7aa2883c05e76fda3ba7475591776226c07d83dc179b1 Homepage: https://cran.r-project.org/package=HeckmanStan Description: CRAN Package 'HeckmanStan' (Heckman Selection Models Based on Bayesian Analysis) Implements Heckman selection models using a Bayesian approach via 'Stan' and compares the performance of normal, Student’s t, and contaminated normal distributions in addressing complexities and selection bias (Heeju Lim, Victor E. 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Package: r-cran-henna Architecture: all Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abdiv, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggeasy, r-cran-ggforce, r-cran-ggraph, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrepel, r-cran-liver, r-cran-paletteer, r-cran-reshape2, r-cran-rlang, r-cran-tidygraph, r-cran-viridis, r-cran-withr Suggests: r-bioc-enhancedvolcano, r-cran-qs2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-henna_0.8.5-1.ca2404.1_all.deb Size: 2377704 MD5sum: 2555f4ac1775469e33287b183ea2dbdc SHA1: aed1dcc0f7ce91174a310a91af1fde2c1378ca46 SHA256: 29381dbcccf28a524690a15ccbbaadbdcb9eada54cf99ea8708c5b354759a416 SHA512: aac404fb84ef6309d02e217e28821c9883ecb3574014ebfe7136c960d86785e15ef04efbe6d1bbd174b3ecf91eb6a205c45cb98416b085c76789cf721a579227 Homepage: https://cran.r-project.org/package=henna Description: CRAN Package 'henna' (A Versatile Visualization Suite) A visualization suite primarily designed for single-cell RNA-sequencing data analysis applications but well-suited for other purposes as well. It introduces novel plots to represent two-variable and frequency data and optimizes some commonly used plotting options (e.g., correlation, network, density, alluvial and volcano plots) for ease of usage and flexibility. Package: r-cran-heplots Architecture: all Version: 1.8.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5716 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-generics, r-cran-mass, r-cran-magrittr, r-cran-purrr, r-cran-rgl, r-cran-tibble, r-cran-boot, r-cran-glue Suggests: r-cran-broom, r-cran-candisc, r-cran-cardata, r-cran-lmtest, r-cran-effects, r-cran-reshape, r-cran-gplots, r-cran-nlme, r-cran-lattice, r-cran-reshape2, r-cran-corrgram, r-cran-animation, r-cran-mvinfluence, r-cran-knitr, r-cran-litedown, r-cran-rmarkdown, r-cran-markdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-bookdown, r-cran-patchwork, r-cran-tinytable, r-cran-here, r-cran-htmltools, r-cran-sleuth2, r-cran-rrcov, r-cran-archdata, r-cran-qqtest, r-cran-ggbiplot, r-cran-vcdextra, r-cran-kernsmooth, r-cran-aplpack, r-cran-foreign, r-cran-robustbase, r-cran-effectsize, r-cran-parameters Filename: pool/dists/noble/main/r-cran-heplots_1.8.6-1.ca2404.1_all.deb Size: 2844872 MD5sum: 45661c85ca929931fc50f267170c0ae9 SHA1: 6b3c91bc705a2a56c2e41d2d3d3cb6f65c40ad88 SHA256: bbc04e4d0ee22117d2b7064f93853aab7d29d704e682ed7a3464c361afacabdc SHA512: 2a93e14fc1e90e26a53c6be44138aab3b49a05513eb056ea55a9bff7ae04bc882928616a082b647dc115045810dc06b785d9c1da792bb33727473eeb8bd47dab Homepage: https://cran.r-project.org/package=heplots Description: CRAN Package 'heplots' (Visualizing Hypothesis Tests in Multivariate Linear Models) Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables. Package: r-cran-hera Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-evaluate, r-cran-glue, r-cran-irdisplay, r-cran-jsonlite, r-cran-r6, r-cran-repr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-hera_0.1.1-1.ca2404.1_all.deb Size: 164976 MD5sum: 7bba5b850ddbb1a22f33cff8ff6366aa SHA1: 19469e19c6f8a2b5876d06d77152db77000691c9 SHA256: 2bf780c03e57be11230f433b0f7725801dd7e475436218ce4d156a86cf4584b8 SHA512: fe2f7cc07a5164d217cf2cc20bbd076b61342bdeae7848daaf8637381c202044dcf85a2949d4bb51355230df6af9d4893ab5f879bb849a73241a23326eb6267f Homepage: https://cran.r-project.org/package=hera Description: CRAN Package 'hera' (Companion to the 'xeus-r' 'jupyter' Kernel) Set of R functions to be coupled with the 'xeus-r' 'jupyter' kernel in order to drive execution of code in notebook input cells, how R objects are to be displayed in output cells, and handle two way communication with the front end through comms. Package: r-cran-here Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rprojroot Suggests: r-cran-conflicted, r-cran-covr, r-cran-fs, r-cran-knitr, r-cran-palmerpenguins, r-cran-plyr, r-cran-readr, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid, r-cran-withr Filename: pool/dists/noble/main/r-cran-here_1.0.2-1.ca2404.1_all.deb Size: 48444 MD5sum: cbf94b188b621945b23ba0444206cb3f SHA1: ef5eb2ab5abf0ccf844c7cb75b0d1e484d257947 SHA256: 136a7925b43f15f23b8b9c362792aa68f2a281365a3f7294d764dbc08859b48d SHA512: b03495e8a1d8a727f93ba979ac918a77a0b85aab13621f767a0d2e3091e2998c0cb993ceab56fcfd3e56d90bf6539e2886911785b467c0d71bdb3ffd430233d5 Homepage: https://cran.r-project.org/package=here Description: CRAN Package 'here' (A Simpler Way to Find Your Files) Constructs paths to your project's files. 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Package: r-cran-herer Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-curl, r-cran-data.table, r-cran-flexpolyline, r-cran-jsonlite, r-cran-sf, r-cran-stringr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-leafpop, r-cran-lwgeom, r-cran-mapview, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-herer_1.1.0-1.ca2404.1_all.deb Size: 840708 MD5sum: 5b7dfedbb9fcdf398e45c43bc7e3ee67 SHA1: 57ea6d2ee329befd78d2374123652f8785cdbbe3 SHA256: b7d66aa02961073bd4e0e292d52b88b809841e96e6640ead28adc1c18faa8a0d SHA512: 7e99ee9b26bd922d9233f98b36444ef74a9f55bd8202cd00527895c95c5d6d36eaa9b1fdb7eef7a257799624ad19a23caf42958aa3d6fb52018dd7c14739b3de Homepage: https://cran.r-project.org/package=hereR Description: CRAN Package 'hereR' ('sf'-Based Interface to the 'HERE' REST APIs) Interface to the 'HERE' REST APIs : (1) geocode and autosuggest addresses or reverse geocode POIs using the 'Geocoder' API; (2) route directions, travel distance or time matrices and isolines using the 'Routing', 'Matrix Routing' and 'Isoline Routing' APIs; (3) request real-time traffic flow and incident information from the 'Traffic' API; (4) find request public transport connections and nearby stations from the 'Public Transit' API; (5) request intermodal routes using the 'Intermodal Routing' API; (6) get weather forecasts, reports on current weather conditions, astronomical information and alerts at a specific location from the 'Destination Weather' API. Locations, routes and isolines are returned as 'sf' objects. Package: r-cran-heritability Architecture: all Version: 1.5-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1384 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heritability_1.5-1.ca2404.2_all.deb Size: 1372436 MD5sum: e9ed20ab35485d2d106348651d5bd7c0 SHA1: edd3845f225b8e215606ce3ea10a60de0b0c5bec SHA256: 7807448572b054d2fe93b51175072c43ad53d9d44bd5de5072ebda32b58bd410 SHA512: 06733e2551f772e84e12f339d21cbb61440dc260a45ce8a9c48341e6dc55e842aedad204d8f36a9f102b13d41c9f10f987b167bc4cedf57f9a296524ad8c7b63 Homepage: https://cran.r-project.org/package=heritability Description: CRAN Package 'heritability' (Marker-Based Estimation of Heritability Using Individual Plantor Plot Data) Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana. Package: r-cran-heritable Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-emmeans, r-cran-matrix, r-cran-stringr, r-cran-vctrs Suggests: r-cran-testthat, r-cran-agridat, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-pbkrtest, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-here Filename: pool/dists/noble/main/r-cran-heritable_0.1.0-1.ca2404.1_all.deb Size: 175332 MD5sum: 75770e14460f3e179f4973ae210305b0 SHA1: f9ccf35d2fd2af36c6ef517fba5f89c7042a1eca SHA256: 5ef5a9bfb7a53502d8596151bfd3fd400d3fc5c1477d2d10a3fd34ba8c928135 SHA512: f138a321fef73f400530f52a9b9dfcbaf79cbb72ca49f33fce806767c67e2fb4120a8bce6dbbb9751e982748e662b46730ed7db18c7c3e4caa801e52dfd4a163 Homepage: https://cran.r-project.org/package=heritable Description: CRAN Package 'heritable' (Heritability Estimation from Mixed Models) Reporting heritability estimates is an important to quantitative genetics studies and breeding experiments. Here we provide functions to calculate various broad-sense heritabilities from 'asreml' and 'lme4' model objects. All methods we have implemented in this package have extensively discussed in the article by Schmidt et al. (2019) . Package: r-cran-heritseq Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cplm, r-bioc-deseq2, r-cran-lme4, r-cran-pbapply, r-cran-tweedie, r-cran-mass, r-bioc-summarizedexperiment Filename: pool/dists/noble/main/r-cran-heritseq_1.0.2-1.ca2404.1_all.deb Size: 2734116 MD5sum: 8404e676ff9529ed41d3e4b0de5f2b5f SHA1: 55425f0f60f21507bd3270f2529a600359b94a5d SHA256: 6e4951b32d462ff174ee4b0b19f00db28309064a303a7ce6ab534f9c9877099b SHA512: 03544a8c2a6af387956ee89a087aa64903ee2cbf69efd0769b2af05a42a47f0fcc8faf76686fc6077fc2a2fc6d51adb4eed401f40cdf5b9eab64ecc87f480e75 Homepage: https://cran.r-project.org/package=HeritSeq Description: CRAN Package 'HeritSeq' (Heritability of Gene Expression for Next-Generation Sequencing) Statistical framework to analyze heritability of gene expression based on next-generation sequencing data and simulating sequencing reads. Variance partition coefficients (VPC) are computed using linear mixed effects and generalized linear mixed effects models. Compound Poisson and negative binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143. Package: r-cran-hermite Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maxlik Filename: pool/dists/noble/main/r-cran-hermite_1.2.1-1.ca2404.1_all.deb Size: 73534 MD5sum: 350d46fe57ea50aaea4b2a2fb160ac16 SHA1: 27dbad67ae71990e1f4b69a1544c26763f415ed3 SHA256: 5145489560dc843c117219371440f9f390494ad98489b6f0e8fcc2f5bb17c8f8 SHA512: e16345313cdbcdf21329bd7106ed21454bd201fafbdb17642bed41be9d0b97aeb9db92ed2df737ddb37f6897277ba93ca6b26d7f8c9351229728170c4268035f Homepage: https://cran.r-project.org/package=hermite Description: CRAN Package 'hermite' (Generalized Hermite Distribution) Probability functions and other utilities for the generalized Hermite distribution. Package: r-cran-hero Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-optimx, r-cran-pbapply, r-cran-sf, r-cran-sp, r-cran-fields Suggests: r-cran-autoimage, r-cran-devtools, r-cran-fda, r-cran-igraph, r-cran-testthat, r-cran-future.apply, r-cran-rmpi Filename: pool/dists/noble/main/r-cran-hero_0.6-1.ca2404.1_all.deb Size: 2542876 MD5sum: 634091ece2bed91337c7eaccc207dac6 SHA1: 63c916eeee1f850935be099b7f9358d4bc896297 SHA256: 4194f413191bfff5b93c001448282a87c466c96efaafd05a0a10cf14d1971a77 SHA512: 36a20dd53d58871036e5a0d43a56302ae14986f01d9981bf6c892daa605b872f73ece1cff056261e24506bab2bee7826936b3b54e54d6c72709d54b4c773fb95 Homepage: https://cran.r-project.org/package=hero Description: CRAN Package 'hero' (Spatio-Temporal (Hero) Sandwich Smoother) An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) . A hero is a specific type of sandwich. Dictionary.com (2018) describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) . Package: r-cran-hessrna Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-bioc-deseq2, r-cran-ssizerna, r-cran-rdpack Suggests: r-cran-car, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hessrna_1.0.1-1.ca2404.1_all.deb Size: 38998 MD5sum: 20692d69565bf64b56b853e2ea5e48a4 SHA1: 05b43f90bcc4a1c29e92ad0d6b75b4b6d0278356 SHA256: 89111354c462d8c9f85a949c21444ba108f0a06d9a8e86e0c9358d95ea920576 SHA512: fc61f8c02867d790503e3b37d51a4352196adb123ebc751face3fdc87ad9b2c597ddbf4a9d1400145de4904959eb9e68c80e42517e6e5fc6b62e4ed5080598d1 Homepage: https://cran.r-project.org/package=HEssRNA Description: CRAN Package 'HEssRNA' (Heritability-Based Estimation of Sample Size for RNA-Seq Data) Provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation. Package: r-cran-hetcorfs Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polycor, r-cran-dplyr, r-cran-cluster, r-cran-psych Filename: pool/dists/noble/main/r-cran-hetcorfs_1.0.1-1.ca2404.1_all.deb Size: 52688 MD5sum: 17722afc691bd1924b5191080246598f SHA1: 8faa02cb85d03ca1d56fdee02dbe307e6f43a34e SHA256: 9177853ea73af4aad4e4cd184e2c7737816664481123a482c41a8878e07404fc SHA512: bada86d12cbd21a1792eda433e31f04ce3827a474ee67eaab01f8fd1a683e0f822d4d451d1cc306267081055d2a01adbdedba3f402a37f16a285b1695f8aeb38 Homepage: https://cran.r-project.org/package=hetcorFS Description: CRAN Package 'hetcorFS' (Unsupervised Feature Selection using the HeterogeneousCorrelation Matrix) Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features, and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review . This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of ‘The SMILE Project: Statistical Modelling and Inference to Live the Environment’, funded by the European Union – Next Generation EU (Punzo). Package: r-cran-heterfunctionaldata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-heterfunctionaldata_0.1.0-1.ca2404.1_all.deb Size: 63282 MD5sum: 467b4dfe47dd7a1f55ae31e4627f294e SHA1: 4aa3317f42a05636544f7c14e7427e81e11238fe SHA256: 314715c577b93c48c648ccdcfc9613e50f4a8510e9e5c2aa036353a971f11bd0 SHA512: 0436428bac0888c5e8719793bc7b94ada36c87efe0dd50030e65e0a3e1e12067827f26d11a1f23c0bc40743acf57ee31d9c55a6bca83fdbf446b9e485e759a47 Homepage: https://cran.r-project.org/package=HeterFunctionalData Description: CRAN Package 'HeterFunctionalData' (Test of No Main and/or Interaction Effects in Functional Data) Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) , no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) and tests based on ranks given in Wang and Akritas (2010b) . Package: r-cran-heterocop Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-igraph, r-cran-matrixcalc, r-cran-foreach, r-cran-stringr, r-cran-dosnow, r-cran-huge Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-heterocop_1.0.1-1.ca2404.1_all.deb Size: 103966 MD5sum: 5ae6f98c8b0129fbf618b1944f872d35 SHA1: ecc956b1b5c9ab5e54ae9343e811a346eeaa5235 SHA256: b744074b63d4cd7525a4d1508eb6a3690a279f80c118795ee841319b9f21e378 SHA512: cea424d628529df614639f63581dc0f91dd885de947aeaf7e20f5a086aad5477974325742769916112185fe0f9c75a868c73ee299d982e5e74d64dff0cc2e925 Homepage: https://cran.r-project.org/package=heterocop Description: CRAN Package 'heterocop' (Semi-Parametric Estimation with Gaussian Copula) A method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula. Package: r-cran-heterogeneouspeereffects Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-flextable, r-cran-caret, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heterogeneouspeereffects_0.1.0-1.ca2404.1_all.deb Size: 125970 MD5sum: 4ba33f96757aedde805ea090d72b80a6 SHA1: 2f8d8850c4dacf266ce33dadbf30acf9edad0940 SHA256: 1e998b8dc0de22e0b5ef7060fa8f7eedd7d18be12ff55fcbe72aabc355c79e24 SHA512: f44148515a92aa0e5a440536e99db426846174f130019fb4d0f3e678db44e4ad7282fcc6fade5e16fca9ef575161ba54510ee709cdcdcf543ca3cf1da8694c19 Homepage: https://cran.r-project.org/package=heterogeneouspeereffects Description: CRAN Package 'heterogeneouspeereffects' (Heterogeneous Peer Effect) Heterogeneous Peer Effect Package provides two-step Generalized Method of Moments (GMM) estimators for heterogeneous peer effects in group-level treatment models developed by Pasquier, Rossi and Wang (2026) . The package separates the direct effect of treatment from within-group and between-group spillover effects, using a cross-fitted, semiparametric approach that leaves the propensity score unspecified and estimates it nonparametrically. Two identification settings are implemented: one in which eligibility for treatment coincides with group identity, and one in which identity is orthogonal to eligibility, allowing peer effects to differ across subgroups (e.g. by gender). Point estimates, standard errors, and test statistics are returned for the direct effect and for each within- and between-group peer effect. Package: r-cran-heteroggm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-mass, r-cran-huge Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heteroggm_1.0.1-1.ca2404.1_all.deb Size: 311010 MD5sum: 2558458e98759ea70f244f6ff2fc20d8 SHA1: 3df83109afa97f4f009820a398bf798d2d3bc406 SHA256: 547c0c466250f43c76c2232e68e3621c7548e62ef7f7dcb52b46c2a8880e9d35 SHA512: 61873a06956ab86f0c4ac2f7c28a6997b39b9f4d06d2ac2c57e806e0faf1bd64f29eecac3ef79a4e0687744185f443a73ad1b763380c65966e1cdda8b17bd6a6 Homepage: https://cran.r-project.org/package=HeteroGGM Description: CRAN Package 'HeteroGGM' (Gaussian Graphical Model-Based Heterogeneity Analysis) The goal of this package is to user-friendly realizing Gaussian graphical model-based heterogeneity analysis. Recently, several Gaussian graphical model-based heterogeneity analysis techniques have been developed. A common methodological limitation is that the number of subgroups is assumed to be known a priori, which is not realistic. In a very recent study (Ren et al., 2022), a novel approach based on the penalized fusion technique is developed to fully data-dependently determine the number and structure of subgroups in Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing the Gaussian graphical model technique in more practical settings. Beyond Ren et al. (2022), more estimations and functions are added, so that the package is self-contained and more comprehensive and can provide ``more direct'' insights to practitioners (with the visualization function). Reference: Ren, M., Zhang S., Zhang Q. and Ma S. (2022). Gaussian Graphical Model-based Heterogeneity Analysis via Penalized Fusion. Biometrics, 78 (2), 524-535. Package: r-cran-heterometa Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-mathjaxr Filename: pool/dists/noble/main/r-cran-heterometa_0.5-1.ca2404.1_all.deb Size: 31018 MD5sum: da05e70098da4507a94e62ae3dbd6fae SHA1: b31e45149b9b52b476d97751e3d136a10d446018 SHA256: f5357efe408c5440e5644e5e28fcce98246300d9c48c70194c846cbce603456c SHA512: 43a64ea09038cdffa065694f1b31187858414fec08ba0e0be9e7da57ea0ac31e2971fa39ab48d8aa5bd98b3439b76d50e2c21f93e163bfba7d689e2e9c80c5cb Homepage: https://cran.r-project.org/package=heterometa Description: CRAN Package 'heterometa' (Convert Various Meta-Analysis Heterogeneity Measures) Published meta-analyses routinely present one of the measures of heterogeneity introduced in Higgins and Thompson (2002) . For critiquing articles it is often better to convert to another of those measures. Some conversions are provided here and confidence intervals are also available. Package: r-cran-heteromixgm Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-tmvtnorm, r-cran-glasso, r-cran-bdgraph, r-cran-mass Filename: pool/dists/noble/main/r-cran-heteromixgm_2.0.2-1.ca2404.1_all.deb Size: 110148 MD5sum: 55a4037834f0f6271988298c4d02cc68 SHA1: 3483558c59492ca97e690cd7f240c70962718170 SHA256: 9c86c534a215491576ac46cc7a35436600307f7b362dae73e8b0e7a775e73d73 SHA512: 0b4e5f65da5fd633f0f8b1eb33ce21704b699815bc8dfe074a9ea773115d8025857d4d34d507ce36cef8d0dead9816e0d0060b58ba2e35180a8e271969a0b877 Homepage: https://cran.r-project.org/package=heteromixgm Description: CRAN Package 'heteromixgm' (Copula Graphical Models for Heterogeneous Mixed Data) A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) . Package: r-cran-heterotests Architecture: all Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-curl, r-cran-generics, r-cran-scales, r-cran-r6, r-cran-suppdists Suggests: r-cran-quickcheck, r-cran-testthat, r-cran-styler, r-cran-lintr, r-cran-digest, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-shiny, r-cran-dt, r-cran-plotly, r-cran-htmlwidgets, r-cran-readxl, r-cran-matrix, r-cran-bench, r-cran-lmtest, r-cran-plm, r-cran-withr, r-cran-car, r-cran-vartest, r-cran-mgcv, r-cran-quantreg, r-cran-sandwich, r-cran-spdep, r-cran-broom, r-cran-workflows, r-cran-parsnip, r-cran-recipes, r-cran-survey, r-cran-data.table, r-cran-dtplyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-heterotests_0.11.2-1.ca2404.1_all.deb Size: 1660836 MD5sum: bb8b33697219e7a9621fb109d12532c4 SHA1: 04926b3130b3f9b128cad608024dee9a4d6f9f3a SHA256: 53508ad330cf782635b484b22af0ab874752600104e63458c96e18d115d0347c SHA512: 10def134c582c96b5a8a8526e5a31bbc582ab8bb6a94f010749bf8ffb730e3e491b3c11c9784876ab192cac086ec7518c06615f220ea074b76b47ef7f2c9adc9 Homepage: https://cran.r-project.org/package=heteroTests Description: CRAN Package 'heteroTests' (Heteroscedasticity Diagnostics for Linear Models) Provides a unified set of heteroscedasticity diagnostics for linear-model workflows. It implements classical auxiliary-regression tests, including those of White (1980) , Breusch and Pagan (1979) , Koenker (1981) , Goldfeld and Quandt (1965) and Harvey (1976) ; the score test of Cook and Weisberg (1983) ; the ARCH test of Engle (1982) ; and group-wise tests of equal variance, including those of Bartlett (1937) , Brown and Forsythe (1974) and Hartley (1950) . Resampling and scalable variants, simulation utilities, diagnostic visualisation and remediation helpers share a consistent interface designed for reproducible statistical workflows and integration with common modelling tools. Package: r-cran-hetools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hetools_1.0.0-1.ca2404.1_all.deb Size: 16662 MD5sum: 8ec49fb0c198e3420927d03203477447 SHA1: e5828365c6ff2561c154fe34eccefe563b398373 SHA256: 4161970fe086db8c0ee1b9c65797951808001123c2a879db84b698572d6ed85e SHA512: d751b82d0e0b7752101a9584e045fd77c3ce4464fcbf8097c4463445174c4f70935f25e37591839b2127ee81db200e5da0b799aaa17cb1fb69c25643b3f162c4 Homepage: https://cran.r-project.org/package=HEtools Description: CRAN Package 'HEtools' (Homomorphic Encryption Polynomials) Homomorphic encryption (Brakerski and Vaikuntanathan (2014) ) using Ring Learning with Errors (Lyubashevsky et al. (2012) ) is a form of Learning with Errors (Regev (2005) ) using polynomial rings over finite fields. Functions to generate the required polynomials (using 'polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided. Package: r-cran-hetop Architecture: all Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags Filename: pool/dists/noble/main/r-cran-hetop_0.2-6-1.ca2404.1_all.deb Size: 74518 MD5sum: 7ea261b9da548858ea65b29e3e2fd009 SHA1: 08988cc21f2e3932a1e37d411f15cafce18c3f63 SHA256: 8fef0afc6030a948fb3329f6057432e289e3f522ff87f519ed5ee8b8f792c83c SHA512: ad6745b6da6149732da0118d40360d1d66eab5e045cbc33d302910bdc73ac5285ea7ac87ef26817f352d3adf51d680fcf39ce5acb62e96fd72fe6aa81e6a7885 Homepage: https://cran.r-project.org/package=HETOP Description: CRAN Package 'HETOP' (MLE and Bayesian Estimation of Heteroskedastic Ordered Probit(HETOP) Model) Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) and Reardon, Shear, Castellano and Ho (2017) . It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) . 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Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see 'Berg et al' ). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results. Package: r-cran-hetsurr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsurrogate Filename: pool/dists/noble/main/r-cran-hetsurr_1.0-1.ca2404.1_all.deb Size: 119316 MD5sum: 258a11029701c74b7333e10a228bfa0b SHA1: 12db5d21c85e5f6672bc0cdceaefef6a5faba14e SHA256: ee17e46c1c4ad58d70c3fe5120feb823b33b33720ce1a9e2b63509e5e012cb98 SHA512: b55e6e123732920500a719eb07640fd167458012612b1f9512a2d132b925023e38dbbfa7b1724bb3d323ed7313318494d17cdc7fc682f2b03f73f5f52ede0ec3 Homepage: https://cran.r-project.org/package=hetsurr Description: CRAN Package 'hetsurr' (Assessing Heterogeneity in the Utility of a Surrogate Marker) Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press. 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More details are available in Parast et al (2024) . Package: r-cran-hett Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-hett_0.3-3-1.ca2404.1_all.deb Size: 66728 MD5sum: 2ed1d9ce2c6870cf35fccc8d26012551 SHA1: 17b7a61e1102fe35f6f68b7f8fd29f626145f0a8 SHA256: 224fd0758a0013c99df248be991adbe8d1f3505965a54a307a4cc983e8808188 SHA512: 0bbda3b3345f23426b60b688e95a85ed323b2543bef6d4567b694fc5c056978bf76dda600d213dc9c1e941282bad37e35ef8cdc06c405c56d896df9e6969a9a3 Homepage: https://cran.r-project.org/package=hett Description: CRAN Package 'hett' (Heteroscedastic t-Regression) Functions for the fitting and summarizing of heteroscedastic t-regression. Package: r-cran-hettest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hettest_1.0-1.ca2404.1_all.deb Size: 75516 MD5sum: 17b393a0ba0c741d627938d812a6c539 SHA1: 425c92dc5e8b90626d77c3a6c7cac74932f99c80 SHA256: f53ef2cc1185029f8933f2aecdca7167c215bbcfdf651596b20d160b0c77dd9e SHA512: 555d31f02f834e6bdece387c70931058816ecf0faf83f17db894fed61af31972ef3f1607a19f2fe59787a6cfd8c648b43cfbdffed96c519fe867611529e3308c Homepage: https://cran.r-project.org/package=hettest Description: CRAN Package 'hettest' (Testing for a Treatment Effect Using a Heterogeneous SurrogateMarker) Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) . Package: r-cran-hettreatreg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hettreatreg_0.1.0-1.ca2404.1_all.deb Size: 221470 MD5sum: 75dabbcd29d64315cc5c36c65dd92b75 SHA1: 87914542b5195b5483ebc52de789b441cad56b0e SHA256: 71b60ebd5668d0e39d418933f18f0a9b6749578842b79d62e2d2f0baa88e6df8 SHA512: 77ff1e6b4ad549a5d35bc6758bdf565071b092a50f6ae0014c8bf2eddd76fbeb7ab4ba15478b5c19dfe4470c050106b48e00823bb59c201ff5fa2391e552d20f Homepage: https://cran.r-project.org/package=hettreatreg Description: CRAN Package 'hettreatreg' (Heterogeneous Treatment Effects in Regression Analysis) Computes diagnostics for linear regression when treatment effects are heterogeneous. The output of 'hettreatreg' represents ordinary least squares (OLS) estimates of the effect of a binary treatment as a weighted average of the average treatment effect on the treated (ATT) and the average treatment effect on the untreated (ATU). The program estimates the OLS weights on these parameters, computes the associated model diagnostics, and reports the implicit OLS estimate of the average treatment effect (ATE). See Sloczynski (2019), . Package: r-cran-hettx Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-quantreg, r-cran-mvtnorm, r-cran-mass, r-cran-foreach, r-cran-doparallel, r-cran-moments, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr Filename: pool/dists/noble/main/r-cran-hettx_1.0.1-1.ca2404.1_all.deb Size: 592558 MD5sum: 20898c93d8d59687b8743104d0607ac4 SHA1: 03d33bfab12c0ca1f7c5826150083054f24a2d93 SHA256: d63c567df454619bd2c0ed53f5c786fcca11953bbdc2a19dc953d614374710d1 SHA512: 02f38914f2fdd01295871380e91472aee0b07ef60c08b41257f54106a0856d7b519ca08572b43503cddb13925dca6e14e16a2d1f0e01bd69f0cbfb91c5da52df Homepage: https://cran.r-project.org/package=hettx Description: CRAN Package 'hettx' (Fisherian and Neymanian Methods for Detecting and MeasuringTreatment Effect Variation) Implements methods developed by Ding, Feller, and Miratrix (2016) , and Ding, Feller, and Miratrix (2018) for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test. 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It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) . We provide an implementation (Ronnegard, Alam and Shen 2010) following Lee, Nelder and Pawitan (2006) with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) . 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The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster. 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The HIDECAN plot is presented in Angelin-Bonnet et al. (2023) (currently in review). 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Package: r-cran-hierbipartite Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1934 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-irlba Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hierbipartite_0.0.2-1.ca2404.1_all.deb Size: 1845538 MD5sum: 800031931081ed87897ec1af7adce33c SHA1: c71749c770d7e9d111da0331153ccdf5df961ba6 SHA256: 70fcc96f368d093f06750ac3e6165116ed5591aa5384740845222718a8fd93f2 SHA512: 5bfcb1c4935e5ff70b475108efe3915f4e66362fd58e9b96aa3571560aab47276df2767d809617a5ea14ef60bcea0712f639b1050019d40c71125384d793b117 Homepage: https://cran.r-project.org/package=hierBipartite Description: CRAN Package 'hierBipartite' (Bipartite Graph-Based Hierarchical Clustering) Bipartite graph-based hierarchical clustering, developed for pharmacogenomic datasets and datasets sharing the same data structure. 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Package: r-cran-hierfstat Architecture: all Version: 0.5-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-adegenet, r-cran-gaston, r-cran-gtools Suggests: r-cran-ape, r-cran-pegas, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hierfstat_0.5-11-1.ca2404.1_all.deb Size: 444162 MD5sum: 04648deb4b82ab1994961ea8a8a0225c SHA1: 23935279364c5fc62e219e854ad80d8c292794d6 SHA256: dc651766b941378c4a109a04189984d8e56777f9df8496f94ff61eb5da99409b SHA512: 2723ac7c313909f4716a642dad37f32f55bababc1663620a060693772a954363a74f4619955051b4f424ade3767c8b8133d822f2c9c8515655c39eaa2b88d8d3 Homepage: https://cran.r-project.org/package=hierfstat Description: CRAN Package 'hierfstat' (Estimation and Tests of Hierarchical F-Statistics) Estimates hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution(1998), 52:950). 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Provides multiple machine learning approaches (Cox elastic net, random survival forest, accelerated oblique random survival forest, gradient-boosted Cox, stability selection, classical univariate Cox screening, pseudo- observation bridging to arbitrary regression learners, and Fine-Gray competing risks selection) under a single interface. Adds causal survival forest estimation of heterogeneous treatment effects on survival (experimental), conformal survival prediction with finite- sample coverage guarantees, and time-dependent 'SHAP' explanations via 'SurvSHAP(t)'. Methodology is based on regularised Cox regression (2011) , random survival forests (2008) , oblique random survival forests (2024) , stability selection (2010) , causal survival forests (2023) , time-dependent survival explanations (2023) , conformal survival prediction (2023) , the Fine-Gray model for competing risks (1999) , and pseudo-observation regression (2010) . Package: r-cran-highr Architecture: all Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xfun Suggests: r-cran-knitr, r-cran-markdown, r-cran-testit Filename: pool/dists/noble/main/r-cran-highr_0.12-1.ca2404.1_all.deb Size: 38582 MD5sum: 15b47b868423608f7cef7f90cf3ab1e3 SHA1: aaef686b29f0e61496d180d753255fe76152c73e SHA256: f31aa06f5c40bf1690484dd6d4510fef0b748297d735caa252efe1b5701acfff SHA512: dcc00ea6105112e1d1bab8023a94beb0b7f5703d81540ef6edd0615dca04926d90cd8519bed60e9dc062ac8baaede0f427ab5dbb4edd77debbd9ee3f38079f3f Homepage: https://cran.r-project.org/package=highr Description: CRAN Package 'highr' (Syntax Highlighting for R Source Code) Provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. Source code of other languages is supported via Andre Simon's highlight package (). 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'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for diet reconstruction, microbial community profiling or more general ecosystem characterisation analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity partitioning and (dis)similarity measurement. All of these grounded in abundance-based and incidence-based Hill numbers. The statistical framework developed around Hill numbers encompasses many of the most broadly employed diversity (e.g. richness, Shannon index, Simpson index), phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most common analyses of diversity to be performed while grounded in a single statistical framework. The methods are described in Jost et al. (2007) , Chao et al. (2010) and Chiu et al. (2014) ; and reviewed in the framework of molecularly characterised biological systems in Alberdi & Gilbert (2019) . Package: r-cran-hillr Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fd, r-cran-plyr, r-cran-ade4, r-cran-ape, r-cran-tibble, r-cran-geiger Suggests: r-cran-testthat, r-cran-covr, r-cran-betapart Filename: pool/dists/noble/main/r-cran-hillr_0.5.2-1.ca2404.1_all.deb Size: 87304 MD5sum: 078a6db6d6e088123703cc87a26c7a7b SHA1: fa97a1cc4274431d4acc62127cbff0d570157086 SHA256: 03c1a676c8b51e02540ca0c3b7bcd64d70571642bfb2f2b0dc751b9dea1904fb SHA512: 5c308ee2f99075f239de2c3bd526885330c49a215f81332b81c4281d5846a5ca411294dadf6d9664035500eff457a6da40c04c33eb851c2d78f0449725a8c2af Homepage: https://cran.r-project.org/package=hillR Description: CRAN Package 'hillR' (Diversity Through Hill Numbers) Calculate taxonomic, functional and phylogenetic diversity measures through Hill Numbers proposed by Chao, Chiu and Jost (2014) . 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Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format. Package: r-cran-hipecr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-shiny, r-cran-shinywidgets, r-cran-formatr, r-cran-dlookr, r-cran-ggpubr, r-cran-stringr, r-cran-ggplot2, r-cran-forcats, r-cran-survminer, r-cran-survival, r-cran-labelled, r-cran-gganimate, r-cran-magick, r-cran-tidyr, r-cran-rlang, r-cran-curl, r-cran-htmltools, r-cran-webshot2 Suggests: r-cran-diagrammer, r-cran-gifski, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hipecr_2.0.0-1.ca2404.1_all.deb Size: 4641004 MD5sum: 513e847ad28822765bd19a8304904cfe SHA1: 06744d794e24e01c5b0baef324fec9be01c98fe6 SHA256: 1e428b580fc9fc1f37234d3ea348a0d1fd2a2941c23729c404fea1af9fabe35f SHA512: 65829be6aa7095552cfebe38a25f58056c4a7464e2fafa74e40260020f53b8047d44a663bf8d8485042f2fb54dd352078bace66df7a440f5f5e7e365b5acc21c Homepage: https://cran.r-project.org/package=hipecR Description: CRAN Package 'hipecR' (Tools for Analysing HIPEC Patient Data) Provides helper functions for analysing patient data in hyperthermic intraperitoneal chemotherapy (HIPEC) workflows. Includes functions to estimate peritoneal surface area (PSA), summarise registry data, and produce reporting graphics. Body surface area calculations are based on Du Bois and Du Bois (1916) . Package: r-cran-hiphop Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hiphop_0.0.1-1.ca2404.1_all.deb Size: 891536 MD5sum: a3e69403847398f2df2383981fa237a6 SHA1: e94ce64267eb397c9974c942cb039539b43c620f SHA256: aed2bb20de322712646b85df08c01628637cd1f31caef631a5c2545552664c75 SHA512: d37f57893f5ce93d122f4c61dfa8c51d9808f87b8380dcbad789f33b6f88986a88fddb66b525c2da8f82947aaf07ef2a9e7eaaa501aeefc90dbb9c12e58853a3 Homepage: https://cran.r-project.org/package=hiphop Description: CRAN Package 'hiphop' (Parentage Assignment using Bi-Allelic Genetic Markers) Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 ) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package 'hiphop' can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance. 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It maps 'PLINK' alleles to the webtool's required 'rsID_Allele' columns (0/1/2/NA). No external tools (e.g., 'PLINK CLI') are required. Package: r-cran-hirt Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rms, r-cran-ltm, r-cran-matrix Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-hirt_0.4.0-1.ca2404.1_all.deb Size: 238632 MD5sum: c87b576a5c6e7cb9663fabf3b113d20d SHA1: 3b31e940df427b911fe0e07e0fa6e0fbd4e898ee SHA256: f1ee614c2edfa68c54dc71703bb16b8bbde7adc71ba84e20093801274ea507bc SHA512: f12ef3137ee761988df45ef541ac33f3371aa988c0ffd1d28c1ee0aa8ce0164ca8c46e46bf3e1f84d6e74704894e7e461912d721dd0012263585d05feb53efc8 Homepage: https://cran.r-project.org/package=hIRT Description: CRAN Package 'hIRT' (Hierarchical Item Response Theory Models) Implementation of a class of hierarchical item response theory (IRT) models where both the mean and the variance of latent preferences (ability parameters) may depend on observed covariates. The current implementation includes both the two-parameter latent trait model for binary data and the graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM) algorithm. Asymptotic standard errors are derived from the observed information matrix. See Zhou (2019) for details. 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Package: r-cran-historicalborrow Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-posterior, r-cran-rjags, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-historicalborrow_1.1.1-1.ca2404.1_all.deb Size: 215534 MD5sum: 1e02145f75b535043ca54a820145a305 SHA1: 68503ae57802d13a9b31e61f692361a43763455c SHA256: ca33fd3bf5631e18ff197561b214f6b73c7730c974b336016f39458bf517b31e SHA512: b57a10edace14033665cad051e191f50457ecf9811828c98d83b321eb76170b8bc6e257b4858df8d4dfb839740f790da47857040f7b6f4d71a027452c826e82c Homepage: https://cran.r-project.org/package=historicalborrow Description: CRAN Package 'historicalborrow' (Non-Longitudinal Bayesian Historical Borrowing Models) Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a hierarchical model and a mixture model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through benchmark models (independent and pooled). Some of the methods are discussed by Viele et al. (2013) . 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Package: r-cran-hivdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hivdata_0.1.0-1.ca2404.1_all.deb Size: 24832 MD5sum: c364afbe28bd516a56cb11950560446a SHA1: 510bb3719561a41f66ae58bbd6ff87450ccfcf3c SHA256: 3a933d3252e7b42e7b448719f2aafb9d1446693c0e091f014da7f68075bf4cf4 SHA512: 2f420f3eea05bbb0c405cc04d00749752e181961118882ffc1ee919f84980b3fd720cace87c2f6e2a825062a3e7cfa8f6aca57042ad3cc5749a9f8b3c489f1de Homepage: https://cran.r-project.org/package=hivdata Description: CRAN Package 'hivdata' (Six-Year Chronological Data of HIV and ART Cases in Pakistan) We provide the monthly number of HIV and antiretroviral therapy (ART) cases of male, female, children and transgender as well as for the whole of Pakistan reported at various treatment centers in Pakistan from January 2016 to December 2021. Related works include: a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. . Package: r-cran-hive Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-xml Filename: pool/dists/noble/main/r-cran-hive_0.2-2-1.ca2404.1_all.deb Size: 94796 MD5sum: a6827615168ed2c9f03cf127d4967a36 SHA1: 0ef543286b102e46257a5b6fe7556ea3feed7c13 SHA256: aa17184987412d6f2eb76b49bf4d74bd972f672a4c504be5bd16048ad1deda90 SHA512: dc36b528da6e4a5cc4690b162ae242e91a28c29307ed116a8cd8f434f9182a2da65704d2678b6d2e07f053e2aa85f70f72f17bc089239b137fb425b43a148d12 Homepage: https://cran.r-project.org/package=hive Description: CRAN Package 'hive' (Hadoop InteractiVE) Hadoop InteractiVE facilitates distributed computing via the MapReduce paradigm through R and Hadoop. An easy to use interface to Hadoop, the Hadoop Distributed File System (HDFS), and Hadoop Streaming is provided. Package: r-cran-hiver Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-jpeg, r-cran-png, r-cran-rcolorbrewer, r-cran-rgl, r-cran-xtable Suggests: r-cran-bipartite Filename: pool/dists/noble/main/r-cran-hiver_0.4.0-1.ca2404.1_all.deb Size: 678224 MD5sum: 56d99624983d19837909b865996fa94f SHA1: 2d05906d391d34beaf50950a305fcc832252f42b SHA256: 1c46a5e49a5efd9b364e4fd4d83c13995b58f0bcd4ea18d51919847903024ec7 SHA512: 0adb35c20b5f025a6e63c6782df092ebc03798657591ecdca3997b748fed32b0704e34744fffc7f3cd184319b176022f5463bbe535dc420d79989e325619e122 Homepage: https://cran.r-project.org/package=HiveR Description: CRAN Package 'HiveR' (2D and 3D Hive Plots for R) Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics. Package: r-cran-hiviz Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-readxl, r-cran-haven, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-wordcloud, r-cran-ggrepel, r-cran-paletteer, r-cran-shinywidgets Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hiviz_0.1.2-1.ca2404.1_all.deb Size: 130160 MD5sum: db59f443088f85a86713eaa610bf267f SHA1: b4db96f53b9cb21b678e011e9170e3553ed2b6fc SHA256: e1c3ba60b463de18a78e54c7995228e33c5247988241329165048132ccb6695c SHA512: 608aa42d53f23d6d77361560858a189a971363be7daef9db4093fd4a21b31250518de9a459aac90a19b917edd8301b9c6ec2a8fbab70984a1ff6e3043401a93b Homepage: https://cran.r-project.org/package=HIViz Description: CRAN Package 'HIViz' (Interactive Dashboard for 'HIV' Data Visualization) An interactive 'Shiny' dashboard for visualizing and exploring key metrics related to HIV/AIDS, including prevalence, incidence, mortality, and treatment coverage. The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15–24, 25–34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly. Package: r-cran-hjam Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-dplyr, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hjam_1.0.0-1.ca2404.1_all.deb Size: 714798 MD5sum: a7f0c3366ab2a3f1ad535568d28eb12f SHA1: 1b21a228bb35eeffbcdda0ffe67c2f44ff69bee3 SHA256: 9f467ce7e303e270287399a861f15ef02dbe8ad3d003ae856447cd2aef5eef03 SHA512: 84dfdc6a6b3f53c973cb5a2b4b48d6eaa06eb5a31399920b1ec22809e13b10067ccf92ee5d6dbe5484e6443eebd8ee17f077da8db04c9d7dfb5f1fa37d452a69 Homepage: https://cran.r-project.org/package=hJAM Description: CRAN Package 'hJAM' (Hierarchical Joint Analysis of Marginal Summary Statistics) Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." . Package: r-cran-hk80 Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hk80_0.0.2-1.ca2404.1_all.deb Size: 80914 MD5sum: b8bacfa53e7d6ca83e7d4ff1d35703ea SHA1: e3d268b1c0cc145440a8e564c325b45d23982dc6 SHA256: e60579c55ebce2f4c26cebaf4a5f50b395676135d2349174e135227b80e6269e SHA512: fc5c818c39b00ad6db47aa89080516715d3b20646d28dc1e8ba95950af756684a9e7816cd106994833e66402e5884882ee449161561da8c72b83c1527b560db4 Homepage: https://cran.r-project.org/package=HK80 Description: CRAN Package 'HK80' (Conversion Tools for HK80 Geographical Coordinate System) This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995). Package: r-cran-hkdatasets Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fst Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-here Filename: pool/dists/noble/main/r-cran-hkdatasets_1.0.0-1.ca2404.1_all.deb Size: 359030 MD5sum: ff0c989704731346d175990164ef69b3 SHA1: 450c9be516c08a05fb6ce052a792b000f6c13666 SHA256: 5ff7587dd3f39df0578f0922d48a5d24e71eaa3b3d67e65689038ec1b8e0296f SHA512: e283e115d11e6b35709439f0e6c38da46c6021b6280d90bed38b0ff900634de423b1a527af6da6140393ca91f33ea22ca084dd744f0d96cb9ee8f5622098b172 Homepage: https://cran.r-project.org/package=hkdatasets Description: CRAN Package 'hkdatasets' (Datasets Related to Hong Kong) Datasets related to Hong Kong, including information on the 2019 elected District Councillors ( and ) and traffic collision data from the Hong Kong Department of Transport (). All of the data in this package is available in the public domain. Package: r-cran-hkrbook Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1673 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinydashboardplus, r-cran-dt, r-cran-highlight, r-cran-formatr, r-cran-scatterplot3d Suggests: r-cran-cluster, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hkrbook_0.1.3-1.ca2404.1_all.deb Size: 841890 MD5sum: c122e765eb94d6e1e5d7d3dcd17a2053 SHA1: 1e883072b474e9e8081f0959fc940c6bfb0761f0 SHA256: 79bf19e42dfa0a9ee92c8df0184926e453cc9eae223081b0558a51c7bb64de43 SHA512: c9782e530fbfaf97bc940999fe43ecd6d0632bfc8048989476387634f4715f3a42317617152c80af76821799277d12004b0e716475a1a7bcf4ac0b93315b9f6e Homepage: https://cran.r-project.org/package=HKRbook Description: CRAN Package 'HKRbook' (Apps and Data for the Book "Introduction to Statistics") Functions, Shiny apps and data for the book "Introduction to Statistics" by Wolfgang Karl Härdle, Sigbert Klinke, and Bernd Rönz (2015) . Package: r-cran-hlar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-dplyr, r-cran-reshape2, r-cran-schoolmath, r-cran-tibble, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-readr, r-cran-janitor Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hlar_1.0.0-1.ca2404.1_all.deb Size: 189476 MD5sum: 141f4fcb657884d15fae95b4a721ed2d SHA1: b71165957a224a61636920b9855a55cf4c738e1b SHA256: 4d3aede2f9b0d22d466ec83fd94570417254d2d8f05235c526ba70d657c16109 SHA512: aaf874666250b3949a3a98bc670da40e0b9f5ac26d5b763cbaf62afeba05cb0b3946d14815659a19328921c0f60a89ac1bc21ce114dd3b50c50d5e11ede793f4 Homepage: https://cran.r-project.org/package=hlaR Description: CRAN Package 'hlaR' (Tools for HLA Data) A streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database . High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting 'HLAMatchMaker' versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality. Package: r-cran-hlatools Architecture: all Version: 1.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desctools, r-cran-dplyr, r-cran-fmsb, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-xfun Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hlatools_1.11.0-1.ca2404.1_all.deb Size: 602256 MD5sum: 62c330f7172fe557a265dd8abf5ded14 SHA1: e7018aa14c1e738ce7ae6aff4055f7f02ace864d SHA256: 447197d33f33d2ce948855374988b151ae22a041f6359cdda70cdb24123c7adb SHA512: 091e3fb0c2a9a8b965cc1ed5ede7eded44425dfbf7886caf6ec8f8efc4dcfa9165c071259568496d8ff29c49004eb296de11eb25449a60423b023fe1b1ae05ce Homepage: https://cran.r-project.org/package=HLAtools Description: CRAN Package 'HLAtools' (Toolkit for HLA Immunogenomics) A toolkit for the analysis and management of data for genes in the so-called "Human Leukocyte Antigen" (HLA) region. Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA 'GitHub' repository (ANHIG/IMGTHLA) , validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods. Tran et al., (2025) . Package: r-cran-hlctools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hlctools_0.1.0-1.ca2404.1_all.deb Size: 60882 MD5sum: 07d1908b5528ea76cfc93f0a6a936d2e SHA1: 6f55d2786558836affa5fcf9c71433781729f2e3 SHA256: 6848f5a2e9837af15ca661968851f0980648a15145a60976ab80809e893432f0 SHA512: 3719639ce7f2d3257b57cd48246018b2b840e050aa2ad25712f68c416bd4c46f2c80a52ec2d96c87e9891003d7873e2cc0231f2eadbfe62ef451db789cb087f6 Homepage: https://cran.r-project.org/package=HLCtools Description: CRAN Package 'HLCtools' (Calculate Herd Lying Concordance Metrics) Calculates Herd Lying Concordance (HLC) metrics from individual animal lying-behaviour data. HLC is a continuous framework for quantifying group-level behavioural cohesion from between-animal dispersion within observation intervals. The package implements standard deviation, mean absolute deviation, interquartile range, and entropy formulations, lying-weighted extensions, threshold-based synchrony comparisons, temporal summaries, and descriptive method-ranking tools. The HLC formulations are introduced in this package; related approaches to cattle behavioural synchrony include Raussi et al. (2011) , Kok et al. (2023) , and the activity metric framework of van Dixhoorn et al. (2024) . Package: r-cran-hlidacr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-curl, r-cran-usethis, r-cran-urltools Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-vcr Filename: pool/dists/noble/main/r-cran-hlidacr_0.2.0-1.ca2404.1_all.deb Size: 71166 MD5sum: e58a488e9acc2e1e1d098748c05a142c SHA1: 3d32d6898a92f01df8a0521d1444ea7c65953ca5 SHA256: 166fe33d75b4a4b52237a968fb355e0a3d6b88bb3d7a6765ba29ae7b87ac3e82 SHA512: c1b6497ebb78e7bf2ec38a3073f5e66732b1fcd20ba74491abc37a26cce958d7a70e754a87f8ef3f7150fbc6406a09f91e95a3825c2674ee80c1e88adaf3d90c Homepage: https://cran.r-project.org/package=hlidacr Description: CRAN Package 'hlidacr' (Access Data from the 'Hlídač Státu' API) Provides access to datasets published by 'Hlídač státu' , a Czech watchdog, via their API. Package: r-cran-hlmlab Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-scales Suggests: r-cran-shiny, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hlmlab_0.2.0-1.ca2404.1_all.deb Size: 112314 MD5sum: f9645ea662ba63e5e7ed4998c2cfd64b SHA1: 9b3a176cb789a213c2a3f7e9156b9751688e7edc SHA256: 9d0c3279d8f55fdcd0719cc6b2142768c6b7d2f1be8bc3320bb7af939da483e5 SHA512: 04e7b1ca78c1ec14502ada90bb72a6f5fed48e23d6973dc0f7f0050593a669574c1d64a24b5b4a0de5512b9203b985529bc5fab0cb56bb3763b73002d5ea965e Homepage: https://cran.r-project.org/package=hlmLab Description: CRAN Package 'hlmLab' (Hierarchical Linear Modeling with Visualization andDecomposition) Provides functions for visualization and decomposition in hierarchical linear models (HLM) for applications in education, psychology, and the social sciences. Includes variance decomposition for two-level and three-level data structures following Snijders and Bosker (2012, ISBN:9781849202015), intraclass correlation (ICC) estimation and design effect computation as described in Shrout and Fleiss (1979) , and contextual effect decomposition via the Mundlak (1978) specification distinguishing within- and between-cluster components, with the uncertainty of the contextual contrast obtained from the full fixed-effect covariance matrix. Teaching displays cover simulated intraclass correlations, partial pooling of cluster means, random-slope heterogeneity, and cross-level interaction with an observed Level-2 moderator, following Hofmann and Gavin (1998) and Hamaker and Muthen (2020) . Multilevel models are estimated using 'lme4' (Bates et al., 2015 ). An optional 'Shiny' application enables interactive exploration of model components and parameter variation. The implementation follows the multilevel modeling framework of Raudenbush and Bryk (2002, ISBN:9780761919049). Package: r-cran-hlrhotrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hlrhotrix_0.1.0-1.ca2404.1_all.deb Size: 213120 MD5sum: 9b2aa99c126595d68e717dac15348ea1 SHA1: ced079421e26cd301e4edca002dd2e708180d523 SHA256: bb29133dea5024be4b144c8241958334a241138a69312344abc1958c1a4f0618 SHA512: 31e7ad8e87fa50ae1ef7c6457ec97c02f739125d57feb1f8a80c7692ea9795144015c1f1ee0dca89e7182a90c114e14bbd7ff2687e5265c3fb0e5a96063d832d Homepage: https://cran.r-project.org/package=hlrhotrix Description: CRAN Package 'hlrhotrix' (Algebraic Operations and Visualisation for Hl-Rhotrices) Provides constructors for hl-rhotrices of dimension 2, 4 and 6, together with computation of the determinant, adjoint, inverse and eigenvalues under the Robust Multiplication Method (RMM). A 'ggplot2'-based function visualises the rhomboidal layout and the decomposition into principal minors. Package: r-cran-hmc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-irlba, r-cran-pma, r-cran-mass, r-cran-grpreg Filename: pool/dists/noble/main/r-cran-hmc_1.2-1.ca2404.1_all.deb Size: 120724 MD5sum: 3c7a674f1ee1f1e9d3721cab169d9935 SHA1: c6db81c8d62f306464344fca4d52270f02b4c067 SHA256: dfc004102b317afcb064436278fefd88f0ac3afa64fbc7e9b8fb695f66db9931 SHA512: 2e6c02e20b8526ca4a8e32ba57264b135a84864563fed831ac28a7378039bfc1f1e6e990c12395db10be2a830e575babab00f642f5040c10d52dc5163619a2c9 Homepage: https://cran.r-project.org/package=HMC Description: CRAN Package 'HMC' (High-Dimensional Mean Comparison with Projection andCross-Fitting) Provides interpretable high-dimensional mean comparison methods (HMC). For example, users can apply these methods to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, the methods focus on the interplay between features and identify those that are predictive of the group label. The tests are valid frequentist procedures and yield sparse estimates indicating which features contribute to the group differences. Package: r-cran-hmclearn Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-cran-lme4, r-cran-cardata, r-cran-mlbench, r-cran-ggplot2, r-cran-mlmrev, r-cran-testthat, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-hmclearn_0.0.5-1.ca2404.1_all.deb Size: 1068324 MD5sum: 6831ec4ce1698579182b5256a92c540c SHA1: 5b4e8c10ad41a9da7582ac255aaeb32672289f94 SHA256: 5ea900b021f3a70fe96f1f8fb1c01ca1e4739c7864afddd78eb35e3493ddf9e9 SHA512: 76f017a4a5fa82c729ebe0d1c8a35df12a0a2f6aa254d357f23396d8bcd22ab9959efe0cf0351ef691e4bae59e92e9ed015f875a661177a2e54bc8e1e9d33177 Homepage: https://cran.r-project.org/package=hmclearn Description: CRAN Package 'hmclearn' (Fit Statistical Models Using Hamiltonian Monte Carlo) Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" , Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" , Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Package: r-cran-hmda Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-h2o, r-cran-shapley, r-cran-autoensemble, r-cran-h2otools, r-cran-splittools, r-cran-psych, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-hmda_0.4.1-1.ca2404.1_all.deb Size: 1212576 MD5sum: 4a7b27c5e9050d843179cfbd3b6efff8 SHA1: 8d9c677464750fff77ccb6bc38ccfee8c51cb01b SHA256: 80546cf059dc1e60f0f7ca947ae53e06980a62b83ec62531409511b5626b43ad SHA512: 517675656891a5a09bccae324e331e6cb94d88f3bc8b259d62c91b5682a87ad58dba69ae2ed6dcda4348a75cfd3afe226850e09453eab182bbe484099263b5bb Homepage: https://cran.r-project.org/package=HMDA Description: CRAN Package 'HMDA' (Holistic Multimodel Domain Analysis for Exploratory MachineLearning) Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) . Package: r-cran-hmdhfdplus Architecture: all Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rvest, r-cran-dplyr, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-rcurl Filename: pool/dists/noble/main/r-cran-hmdhfdplus_2.0.8-1.ca2404.1_all.deb Size: 80120 MD5sum: 528f00234f1f09cd6bddd7fb32b7bd30 SHA1: c474fab4eff2d2ca531eec73e48b9d848539b10b SHA256: 45fe906f4a7d13caff10a803c5bdc8068918239447448b691d255e93d5ac8d0f SHA512: 9ee2ad45e63cd9d9f327da5910fc346684888699ad65c1b5a304ac13b23c7cf99137a1db849e8cc7dd3aab1425be02f4c61a153663a02c787652b2f6d3fa1303 Homepage: https://cran.r-project.org/package=HMDHFDplus Description: CRAN Package 'HMDHFDplus' (Read Human Mortality Database and Human Fertility Database Datafrom the Web) Utilities for reading data from the Human Mortality Database (), Human Fertility Database (), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (), The Japanese Mortality Database (), and the Canadian Human Mortality Database (). Arguments and data are standardized. Package: r-cran-hmeasure Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-class, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hmeasure_1.0-2-1.ca2404.1_all.deb Size: 379506 MD5sum: e032f4081b62be21700bca26bc640aa3 SHA1: 2134bf266e03b17093e028b79936cad4506c973b SHA256: 7701da9ccae5a48ce2a55878f0107d365e03b127616db7736f98bd8bfadf7918 SHA512: 0741a4385468983939a91af95ae07c5a3e3cffab3f1b4eb95d6b6a79792d79ede320bdf9fe01dbe0b25607969f6fa036be79e060b148b30d72d89644d7a463a6 Homepage: https://cran.r-project.org/package=hmeasure Description: CRAN Package 'hmeasure' (The H-Measure and Other Scalar Classification PerformanceMetrics) Classification performance metrics that are derived from the ROC curve of a classifier. 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Package: r-cran-hmer Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-lhs, r-cran-mass, r-cran-r6, r-cran-viridis, r-cran-mvtnorm, r-cran-ggally, r-cran-rlang, r-cran-isoband, r-cran-cluster, r-cran-pdist, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-desolve, r-cran-testthat, r-cran-covr, r-cran-progressr Filename: pool/dists/noble/main/r-cran-hmer_1.6.2-1.ca2404.1_all.deb Size: 3190066 MD5sum: 382afebc9bd97e8b6506afdbea969318 SHA1: 90a6bb6307298528b91f052e63c49ad9ee565bb1 SHA256: fa50c1e7938fa656f6fb1e2bdf2c9459a1ede4d5f86d369afadfdf4f327703fb SHA512: 7778cc4866e8890b4f08a6b8236301d23a5a9aa81ddec85230f9cc3f0fa283705b61783076fa71920a784fb3bb057340f2596b6ccdcd6afb35511dd4fddc7da5 Homepage: https://cran.r-project.org/package=hmer Description: CRAN Package 'hmer' (History Matching and Emulation Package) A set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating 'waves' of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) . Bower, R.G., Goldstein, M., and Vernon, I. (2010) . Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) . Package: r-cran-hmetad Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1266 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-abind, r-cran-dplyr, r-cran-glue, r-cran-posterior, r-cran-rlang, r-cran-stringr, r-cran-tidybayes, r-cran-tidyr Suggests: r-cran-colorspace, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-hmetad_0.2.0-1.ca2404.1_all.deb Size: 712724 MD5sum: a16d7c17d82d364f5bf97fd6a85d7d96 SHA1: a0e069a49c8bf8f0e796e258259e96da65258336 SHA256: a49ad648bbfbd4155381cff54f567a6d64c72ef637506247c7d06360bf32e49c SHA512: 15bcb423dec5015da2b2d16c50a1039eb405f21d12f8e69893ed515e5bc9045996e6b63345f6f752bd85f6831043b25a9a94a52372ba7b6f63c3b34e841475a4 Homepage: https://cran.r-project.org/package=hmetad Description: CRAN Package 'hmetad' (Fit the Meta-D' Model of Confidence Ratings Using 'brms') Implementation of Bayesian regressions over the meta-d' model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau (2012) . The package is a front-end to the 'brms' package, which facilitates a wide range of regression designs, as well as tools for efficiently extracting posterior estimates, plotting, and significance testing. Package: r-cran-hmix Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-normalp, r-cran-glogis, r-cran-gld, r-cran-purrr, r-cran-hmm, r-cran-mc2d, r-cran-cubature, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hmix_1.0.3-1.ca2404.1_all.deb Size: 55570 MD5sum: 6a8cf5422ba09ca4b75f534e71381ead SHA1: 2ccff3f0c8276abae2d50070863fe519ca0cd616 SHA256: 2d09c80397c160e8809f10353bef7794f0c0a0dd4231ebc4bf52467cdd5ed928 SHA512: 8d4be8f2a2d160e5a211aba797bb7594a8eb13ff78503619b8c3b3923959869990f317f0f0e89e617e4552ec664f40d279054dc71714b60a1be67b7bc517d0ee Homepage: https://cran.r-project.org/package=hmix Description: CRAN Package 'hmix' (Hidden Markov Model for Predicting Time Sequences with MixtureSampling) An algorithm for time series analysis that leverages hidden Markov models, cluster analysis, and mixture distributions to segment data, detect patterns and predict future sequences. Package: r-cran-hmm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hmm_1.0.2-1.ca2404.1_all.deb Size: 58852 MD5sum: 28d6ea0fa013ecfd6177d47c4067eb1c SHA1: 40c0213827c0a62329fd083fdd2340ac00c6f659 SHA256: 3725b4f3a35ca2421346f78b4cc668a19c1f406e2dc0f72346e5218f88de4fbd SHA512: f123be9ef1b9c220e7793cda7c6298984ea63333607d51b1c5b7e160aafbefc8d1e636310d1b93f346b77bfc2bea1cba8b6275d5dc1ccd846fd0e9fb9363c0ad Homepage: https://cran.r-project.org/package=HMM Description: CRAN Package 'HMM' (Hidden Markov Models) Easy to use library to setup, apply and make inference with discrete time and discrete space Hidden Markov Models. Package: r-cran-hmmcopula Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-foreach, r-cran-doparallel, r-cran-copula Filename: pool/dists/noble/main/r-cran-hmmcopula_1.1.0-1.ca2404.1_all.deb Size: 114710 MD5sum: 47918ff4d42589fc56b91d550628b59b SHA1: 09ae4072d2f136da6d7fe2f9a56464934e6c40e4 SHA256: d2a2e7563144cbb451a32758fddce48d47e52b314f20704604efa73552cdd6d1 SHA512: a24243171ca4f77a2e411395596569f91e5d35f5941e295f0de55c9f83313ff401dd15d1ff1ed7f7c892ccd1076cdc58551dda34928327656b8874b330b5ea2e Homepage: https://cran.r-project.org/package=HMMcopula Description: CRAN Package 'HMMcopula' (Markov Regime Switching Copula Models Estimation andGoodness-of-Fit) Estimation procedures and goodness-of-fit test for several Markov regime switching models and mixtures of bivariate copula models. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) . Package: r-cran-hmmm Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-mass, r-cran-mvtnorm, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-hmmm_1.0-5-1.ca2404.1_all.deb Size: 642434 MD5sum: f652a58ba9d7e11ebffd5d23b80cd1ee SHA1: 9ca22bd50d4a84edb9d9c2b4bf5140f14557fbc7 SHA256: c4b50a242ff631c376a991eaa3573fea100f7ddc897001b41bba8167592560d7 SHA512: c190aec774be01ab3bb4f06749d1dbb7b4f88fdd5ef76c1181cf07c8e66925dded139353b7105af843c5b4810c4cb79c1112e11f8bb9e62bd31c2b9266907c18 Homepage: https://cran.r-project.org/package=hmmm Description: CRAN Package 'hmmm' (Hierarchical Multinomial Marginal Models) Functions for specifying and fitting marginal models for contingency tables proposed by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci, Colombi and Forcina (2007) ; multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency tables proposed by Lang (2004) and Lang (2005) . Inequality constraints on the parameters are allowed and can be tested. Package: r-cran-hmmpa Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hmmpa_1.0.2-1.ca2404.1_all.deb Size: 195194 MD5sum: 05c84704d78a09cbf8881d59756f3f59 SHA1: ed97d864f05f6a634ee2a2916402ad1868b572ab SHA256: 141392b78203183612fe17bf92cbd30675e521c6b0e0a9881950ab7e6efe2154 SHA512: f4c6d226d21c4275e8eec5fb08a5346ac3c1369f0206506dc136f7b9b045143a9e6123df7f9f1c88c080402c0a7c31e27c9c41e414cd3ee7f79c81da0bf960a4 Homepage: https://cran.r-project.org/package=HMMpa Description: CRAN Package 'HMMpa' (Analysing Accelerometer Data Using Hidden Markov Models) Analysing time-series accelerometer data to quantify length and intensity of physical activity using hidden Markov models. It also contains the traditional cut-off point method. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014). . Package: r-cran-hmmr Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-depmixs4, r-cran-boot Filename: pool/dists/noble/main/r-cran-hmmr_1.0-1-1.ca2404.1_all.deb Size: 706918 MD5sum: 1e3a4397648c0847921bd82c6450877c SHA1: b5a17b75ab3eafe14911b2b1c37860413b97948c SHA256: a5bc939b83b3b28d0c940c3d2d9d1a4c290ad4c6559650f71849c7c858e93aa2 SHA512: a4b8d3fe4ee841158d1da561f83ed39a9e8fc20c23c69aa19d5ebf66678620a7c6c9ba470682f815920f2974ee68d322248a6bf38d379ba7d2500c38673b6a6f Homepage: https://cran.r-project.org/package=hmmr Description: CRAN Package 'hmmr' (Mixture and Hidden Markov Models with R: Datasets and ExampleCode) Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", . Package: r-cran-hmmrel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hmmrel_0.1.1-1.ca2404.1_all.deb Size: 76672 MD5sum: 9c689f27c29e021bd7f69c534834eb9f SHA1: b1665ee4ed8919f43d6a5993ee2652acd26c4ce7 SHA256: b1c127dadce377e0642a6d9109bf05589f694c75e55f339f524e6fa561b52ce2 SHA512: 4178e3e6278ad67653c4225ef02dde1ed20340303d501924cf60565f0322a14bc63156dcb7a3c4a9aaa8fdf7c93e6db444a1d98b6c92c7292ec272fb9fc13a8f Homepage: https://cran.r-project.org/package=HMMRel Description: CRAN Package 'HMMRel' (Hidden Markov Models for Reliability and Maintenance) Reliability Analysis and Maintenance Optimization using Hidden Markov Models (HMM). The use of HMMs to model the state of a system which is not directly observable and instead certain indicators (signals) of the true situation are provided via a control system. A hidden model can provide key information about the system dependability, such as the reliability of the system and related measures. An estimation procedure is implemented based on the Baum-Welch algorithm. Classical structures such as K-out-of-N systems and Shock models are illustrated. Finally, the maintenance of the system is considered in the HMM context and two functions for new preventive maintenance strategies are considered. Maintenance efficiency is measured in terms of expected cost. Methods are described in Gamiz, Limnios, and Segovia-Garcia (2023) . Package: r-cran-hmmtensor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rtensor, r-cran-symtensor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hmmtensor_0.1.0-1.ca2404.1_all.deb Size: 52008 MD5sum: 3e1b0c2c32b6a0a0c5d34ebea669f9f0 SHA1: 35f1b46f341a781c580456b54a649e300c710d56 SHA256: 4acaefb50af9f933c8f6fcc921d39220ed7589e5b95c241f7d2b527afe9ee2f3 SHA512: 79eeff3bc0bf92d25894990973588c0245feea2da6f8f361529ce643438e66d2d9c80533d414c368f253c5e1eed7747d61399a48a14c81708f2e3b34c2cf4678 Homepage: https://cran.r-project.org/package=hmmTensor Description: CRAN Package 'hmmTensor' (Hidden Markov Model by Matrix and Tensor Decomposition) Solves Hidden Markov Models (HMMs) via matrix and tensor decomposition. Converts observation sequences to co-occurrence matrices/tensors and applies Symmetric Non-negative Matrix Factorization (symNMF), Singular Value Decomposition (SVD), CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT) decomposition to recover HMM parameters. Also provides standard HMM algorithms (Forward, Backward, Viterbi, Baum-Welch) for comparison. The spectral learning approach for HMMs is based on Hsu, Kakade, and Zhang (2012) . The symNMF method is described in Kuang, Yun, and Park (2015) . The Tensor-Train decomposition is described in Oseledets (2011) . 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Returns annotated 'hmrc_tbl' data frames with provenance metadata (source URL, fetch time, vintage, cell methods) for reproducible fiscal research. Covers monthly tax receipts (41 tax heads from 2008), 'VAT' (from 1973), fuel duties (from 1990), tobacco duties (from 1991), annual 'Corporation Tax' receipts, stamp duty, research and development tax credit statistics (from 2000), tax gap estimates, 'Income Tax' liabilities by income range, and monthly property transaction counts. File URLs are resolved at runtime via the 'GOV.UK' Content API , so data is always current without hardcoded URLs. Files are cached locally between sessions. 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Package: r-cran-hmsr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ga, r-cran-msm, r-cran-uuid Suggests: r-cran-testthat, r-cran-ecr, r-cran-filelock, r-cran-doparallel, r-cran-smoof Filename: pool/dists/noble/main/r-cran-hmsr_1.0.1-1.ca2404.1_all.deb Size: 231564 MD5sum: 17baab7f583c9debedc708878fe82075 SHA1: ba95975f5ba0d7f7de36cbe2b3c6101a8622e389 SHA256: de9ba7367f63d6a757028866006c0a909ebecd44d9356dc57e3da0e78c1303f7 SHA512: 5531aec689eecbb005033b09b6a6b42403ccbf8148fe2a07e79a5ca13c409589590d0038239ccf2c08cff4be141792303770fdc52be46d351186ba0ba74b1b4b Homepage: https://cran.r-project.org/package=hmsr Description: CRAN Package 'hmsr' (Multipopulation Evolutionary Strategy HMS) The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. 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References, Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2017) Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2021) Arcagni, A., Cerqueti, R., & Grassi, R. (2023) . 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Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code. Package: r-cran-hoif Architecture: all Version: 0.2.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-smut, r-cran-ustats Suggests: r-cran-mass, r-cran-testthat, r-cran-reticulate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hoif_0.2.0-1.ca2404.2_all.deb Size: 326336 MD5sum: 53406f9f1e1bd8861d6ae53e0e82e33d SHA1: d25e62b63d5ff93fb47aeb21f2d63cba1d3ec929 SHA256: 378d37e00e8e5df0943fa1993c285e4ac79d15f2bc27365c3bd1243cd8769b92 SHA512: b3fac136a30a59a7c9d258819add0fe878cbb5f376e38c353b0f00895d7e664ffda34071165e9b081e8c459f978631a3b1117d825eb84cad7fc1435463aff585 Homepage: https://cran.r-project.org/package=HOIF Description: CRAN Package 'HOIF' (Higher-Order Influence Function Estimators for the AverageTreatment Effect) Implements Higher-Order Influence Function (HOIF) estimators of the Average Treatment Effect (ATE), following Robins et al. (2008) , Liu et al. (2017) and Liu and Li (2023) . Estimators of any order are supported, with optional covariate basis transformations (B-splines, Fourier) and optional K-fold sample splitting (cross-fitting) for improved finite-sample performance. The core higher-order U-statistics are computed exactly via the 'ustats' package, an R interface to the 'Python' package 'u-stats'; the underlying algorithm and its computational complexity are analyzed in Chen, Zhang and Liu (2025) . A pure R implementation (up to order 6) is also provided as a fallback that does not require 'Python'. Package: r-cran-hoifcar Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brglm2, r-cran-bb, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-hoifcar_1.1.1-1.ca2404.1_all.deb Size: 178470 MD5sum: 7437b0b67a62e36ad687e63407825c7c SHA1: 68120eb32206637bfa4562d0461f232c47c678e6 SHA256: 03c5c13409a30c9c9a2d5cf36a39fb2da6f2a3847f92288294b9a73f83ebe5ce SHA512: 66a303dfb29a99e7718d88417f5d88720d566d34aa6bc98413ce1ad5403c43662c53bce51ac12f4b2cd76d0bafd951b4aee9cd79a778fa9a8953f9dc48e55267 Homepage: https://cran.r-project.org/package=HOIFCar Description: CRAN Package 'HOIFCar' (Covariate Adjustment in RCT by Higher-Order Influence Functions) Estimates treatment effects using covariate adjustment methods in Randomized Clinical Trials (RCT) motivated by higher-order influence functions (HOIF). Provides point estimates, oracle bias, variance, and approximate variance for HOIF-adjusted estimators. For methodology details, see Zhao et al. (2024) and Gu et al. (2025) . Package: r-cran-holi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-ggplot2, r-cran-likelihoodasy, r-cran-mass, r-cran-pool, r-cran-rpostgres, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-holi_0.1.1-1.ca2404.1_all.deb Size: 72702 MD5sum: fcc914b64c97258a2ae9d24512eb5fcd SHA1: cfc485e38e30ebacda850fea82d87fb1cedb2d38 SHA256: 9df35864a881afe65e8de4b579fa0705ce4cf1b6958a0220f0128d973ba40d40 SHA512: 3809d1c3d606b621b0b5f6bd0aa52a1fb46046db51169b214577c041e039a75ce40a1ec733bff83b3d5cdb1a196df15834a4119aabe594ff9eb7fcd064ecff2d Homepage: https://cran.r-project.org/package=holi Description: CRAN Package 'holi' (Higher Order Likelihood Inference Web Applications) Higher order likelihood inference is a promising approach for analyzing small sample size data. The 'holi' package provides web applications for higher order likelihood inference. It currently supports linear, logistic, and Poisson generalized linear models through the rstar_glm() function, based on Pierce and Bellio (2017) and 'likelihoodAsy'. The package offers two main features: LA_rstar(), which launches an interactive 'shiny' application allowing users to fit models with rstar_glm() through their web browser, and sim_rstar_glm_pgsql(), which streamlines the process of launching a web-based 'shiny' simulation application that saves results to a user-created 'PostgreSQL' database. 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Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment. 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Package: r-cran-holobiont Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-bioc-phyloseq, r-cran-phytools, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-castor, r-cran-vegan, r-cran-data.table Filename: pool/dists/noble/main/r-cran-holobiont_0.1.3-1.ca2404.1_all.deb Size: 165178 MD5sum: 37f67009bb1c398fe16852f66bb3aba2 SHA1: b81d2612891107b86a26ec0c76077bd2c99066a3 SHA256: 23b528c892295f4f85613a99e3bd4f89a13a1966140fc5a69c9425ed4049a87d SHA512: 9ed2ed4d1f4477de1b1e5438ae3af65e9f3d7e6d4804ea0e37694409b940d809cc501693454d4366957670849627845aa953fcb578b9f2c88b36a04822eab89d Homepage: https://cran.r-project.org/package=holobiont Description: CRAN Package 'holobiont' (Microbiome Analysis Tools) We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faith’s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. 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After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended 'Holomics' workflow. Methods used in the package were implemented in the package 'mixomics' by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) and are described there in further detail. 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Based on inputs of mean body size, taxonomic class, and optional classifications of environment and trophic level or foraging mode, 'HomeRangeR' predicts home-range size using the most appropriate model for the species selected from a collection of empirically derived vertebrate home-range allometries. Package: r-cran-homeric Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-homeric_0.1-3-1.ca2404.1_all.deb Size: 17470 MD5sum: 4256130d92a9c0a26e4127c4d989bcb0 SHA1: 4ccc00d62cb2a1c332b0fd43319ecd6b9a10e701 SHA256: 64d7ce52493a6508da4ef3de0925eeafa5323a9b3a24cb5c57e9c82fd1f01275 SHA512: ee76191b19a1c519f5ad2323fded071d913a849a1d8e4c92926ead6747569cb1b6b7b442178f8cfa01a0f9694d3e913033c91676838e37e30ae1b8798be59fef Homepage: https://cran.r-project.org/package=Homeric Description: CRAN Package 'Homeric' (Doughnut Plots) A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts. 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Package: r-cran-homologene Architecture: all Version: 1.4.68.19.3.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4085 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-r.utils Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-homologene_1.4.68.19.3.27-1.ca2404.1_all.deb Size: 3751574 MD5sum: 870dc7b258e1bacdd1827bb1ce241b85 SHA1: 33b91211405d460b462a118b637c42b806e084b8 SHA256: cc8d6a99253ddb7f5dff95aca9eafdb1cb74b670f7154d917a22047f925569b1 SHA512: 575298a96ab60aeb366f9d1c3148789390ef44eb4df1436ce844d122cf2a06b5713ba2f57e750f2d220217bf2cc23239f12e3f9d419eb7b7e0efe2b33731c99b Homepage: https://cran.r-project.org/package=homologene Description: CRAN Package 'homologene' (Quick Access to Homologene and Gene Annotation Updates) A wrapper for the homologene database by the National Center for Biotechnology Information ('NCBI'). It allows searching for gene homologs across species. Data in this package can be found at . The package also includes an updated version of the homologene database where gene identifiers and symbols are replaced with their latest (at the time of submission) version and functions to fetch latest annotation data to keep updated. Package: r-cran-homomorpher Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12862 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-cli, r-cran-gmp, r-cran-openfhe.r, r-cran-rlang, r-cran-sodium Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-survival, r-cran-cvxr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-homomorpher_1.0-1.ca2404.1_all.deb Size: 4231402 MD5sum: 5e765f3b4d25053c33e59047415657ad SHA1: c1dbd842015ccb08c373b1ffb8324c5fb652f52a SHA256: 44ac63250adf88e09dbf853e9e3f9525d34da085052674cdd24b8a706d2ac528 SHA512: d1d5df542380397ee3a83e0dbda53817e0fe95f549792575fb2644cb0d4dfb35edf6e7eb451db495ba4be70b6dd2a038ed25e07fc6d9f86cf4ac2b3ed845cf71 Homepage: https://cran.r-project.org/package=homomorpheR Description: CRAN Package 'homomorpheR' (Homomorphic Computations in R) Privacy-preserving statistics across sites that never share their data, using fully homomorphic encryption through the 'openfhe.R' interface to OpenFHE (CKKS, BFV, BGV), with n-of-n threshold key generation so that no single party can decrypt. Ships master/worker primitives that let ordinary R modeling code run across sites, and a frozen implementation of the Paillier additive scheme kept for backward compatibility. Package: r-cran-homomorphicencryption Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-hetools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-homomorphicencryption_0.9.0-1.ca2404.1_all.deb Size: 69302 MD5sum: ce3831f4f39c5b1ff363b3f3b61f6ba7 SHA1: 0435e0d80b6842dfd6b6142902e76aa5c89e8cec SHA256: d5b15a70af4a695f2d49bd84aba86a89ef4a0bf3c636d769dae62d0a9ee97148 SHA512: 88230b70555eb8671785e716fc3739591e312357b49ca2969f3207f45f385f0faf68a50fb72c56504eacd8fdf795ff1d712f7df7e537c13f7924b5314ea580de Homepage: https://cran.r-project.org/package=HomomorphicEncryption Description: CRAN Package 'HomomorphicEncryption' (BFV, BGV, CKKS Schema for Fully Homomorphic Encryption) Implements the Brakerski-Fan-Vercauteren (BFV, 2012) , Brakerski-Gentry-Vaikuntanathan (BGV, 2014) , and Cheon-Kim-Kim-Song (CKKS, 2016) schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures. Package: r-cran-honestdid Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-foreach, r-cran-matrixstats, r-cran-cvxr, r-cran-ecosolver, r-cran-latex2exp, r-cran-lpsolveapi, r-cran-matrix, r-cran-pracma, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-rglpk, r-cran-mvtnorm, r-cran-truncatednormal Suggests: r-cran-knitr, r-cran-testthat, r-cran-haven, r-cran-lfe, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-honestdid_0.2.8-1.ca2404.1_all.deb Size: 810026 MD5sum: 7f903db31b1a63440c5e415c4743ec35 SHA1: 909cbbb1b3f460a61cf68edc2c993056b0f7969b SHA256: 1212ac2a3a183e31ea58a6bf017e93a9f5b41df6448916a11be3d2398e66e1fa SHA512: 96c9aa9dd85af37607b5f0c6638f5bd31f09744d1d7d37ad5e7f2af594bc16564d8f47afc4e3a2167ed860356fdd527f5c1a67316cedfc5247b97a433b840ddb Homepage: https://cran.r-project.org/package=HonestDiD Description: CRAN Package 'HonestDiD' (Robust Inference in Difference-in-Differences and Event StudyDesigns) Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) , "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets. Package: r-cran-hoopr Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4423 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glmnet, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-progressr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringdist, r-cran-stringi, r-cran-stringr, r-cran-tidyr Suggests: r-cran-arrow, r-cran-chromote, r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-rmarkdown, r-cran-rsqlite, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-xml2, r-cran-yaml Filename: pool/dists/noble/main/r-cran-hoopr_3.1.0-1.ca2404.1_all.deb Size: 4096782 MD5sum: ee9089e60fa41ddfc68e2f37154cfd24 SHA1: babde70dab549e9ab6477c642538cb267535a025 SHA256: 2a3152caeecc041a1646c29dbf6e65b4ac0659675d7ab92fc2a77f59d4c84b86 SHA512: bd8ab937807b299dfbcef5151f9075f860692246da28b8cfd4cb35cf609767f9ebe283e2863ff148fc81d99832f8889bcffef643287f7c904ddbbb58a2f39e52 Homepage: https://cran.r-project.org/package=hoopR Description: CRAN Package 'hoopR' (Access Men's Basketball Play by Play Data) A utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN with shot locations when available. It is also a full NBA Stats API wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves. Package: r-cran-hopbyhop Architecture: all Version: 3.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pastecs, r-cran-ggplot2 Suggests: r-cran-endtoend, r-cran-opportunistic Filename: pool/dists/noble/main/r-cran-hopbyhop_3.41-1.ca2404.1_all.deb Size: 40850 MD5sum: 17431db1294c9f6275abc870328b6961 SHA1: 12de2a221173e35de187c2258032c33609829d13 SHA256: d4f528d9e79d7cb75dd97e10d5a462581b16997a0b141b86baec181f4eb9b517 SHA512: c944b3fd49f25db0a381c49c4122388606b7372819bb824103dbe4de6a9df68814675ccff165c32c7973916b51c2f22faea327cf5b4fecccba873336df2d5dcc Homepage: https://cran.r-project.org/package=hopbyhop Description: CRAN Package 'hopbyhop' (Transmissions and Receptions in a Hop by Hop Network) Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities. Package: r-cran-hopkins Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-donut, r-cran-pdist, r-cran-rann Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spatstat.data, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hopkins_1.2-1.ca2404.1_all.deb Size: 80038 MD5sum: 00efab058a31774fd9bc3b6f7f5dca95 SHA1: a7cdfb2d1ecee64dcb46927008c8df81e6dc7c5e SHA256: fcca4d716b223f16df81fc3ca595dc2b3b8f870c7316435b5852d263fdf0d1ad SHA512: f7e295912d337d08e43f0fd73bf90b32c9bf04d1d85d6786ce59f5db5f8fca1214fb7aa0746350dbe49ba29d10078fe79527d38e693ead76db0fb93768081d4a Homepage: https://cran.r-project.org/package=hopkins Description: CRAN Package 'hopkins' (Calculate Hopkins Statistic for Clustering) Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) . Package: r-cran-horm Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-orthopolynom, r-cran-quantmod, r-cran-rsm Filename: pool/dists/noble/main/r-cran-horm_0.1.4-1.ca2404.1_all.deb Size: 164870 MD5sum: 6092a4264a24f29e2b58dfa6925d5e27 SHA1: ae427a71874c1d5f3f159616284f9c7b76ba7491 SHA256: 644e9f2ad7fddfe27fee6cfc52b6d7e865fe7fb3fb135e7926301d402c4e8a03 SHA512: 6bfba4cf0cbbe03c45cd24d7bd3e6200b25055b19e97e23b148e6a16d731f73d3350793006c558ab24206364e1b090be5f990fa1d4149798961779239560257a Homepage: https://cran.r-project.org/package=HoRM Description: CRAN Package 'HoRM' (Supplemental Functions and Datasets for "Handbook of RegressionMethods") Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares. Package: r-cran-hornpa Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hornpa_1.1.1-1.ca2404.1_all.deb Size: 15038 MD5sum: d50fb6026e68761e3fdf0272a8b3f768 SHA1: 57315fd07ba73272140b8e67af48cd545e52aa38 SHA256: 60db4e420d5d278125fa7025aef1ddc86050aabaf0f78fa3bc9cf67269d89c41 SHA512: 0e4d07cd012a00b669507e327cdfffbd10d7c054e8b489059f121dc8820fdd6ac4522d10b5cb35d73fe043acc02d8b1f3ea1e7afaa03b9416ec49740227c1445 Homepage: https://cran.r-project.org/package=hornpa Description: CRAN Package 'hornpa' (Horn's (1965) Test to Determine the Number of Components/Factors) A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis ). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185. Package: r-cran-horsekicks Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-horsekicks_1.0.2-1.ca2404.1_all.deb Size: 49514 MD5sum: 0db15c66942a7a73e682df789cb24c91 SHA1: 99581cf43fd3ea7c255a907f52c0297299e8577e SHA256: 52d6427081d1f1be02fa270228a35e0cbcdc5de6e92296b5948cec8e82891c78 SHA512: 3198ec037ebda18e34c720f8c19421db3181c2fd97e0f99e7ddb57e8a002405d09a4b5dd041128e834ed92c6f2ebb23904ac29f8bcaa9b068b38fee8dd3846a4 Homepage: https://cran.r-project.org/package=Horsekicks Description: CRAN Package 'Horsekicks' (Provide Extensions to the Prussian Army Death by Horsekick Data) We provide extensions to the classical dataset "Example 4: Death by the kick of a horse in the Prussian Army" first used by Ladislaus von Bortkeiwicz in his treatise on the Poisson distribution "Das Gesetz der kleinen Zahlen", . As well as an extended time series for the horse-kick death data, we also provide, in parallel, deaths by falling from a horse and by drowning. Package: r-cran-horseshoe Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hmisc, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-horseshoe_0.2.0-1.ca2404.1_all.deb Size: 355774 MD5sum: 498717ccb7803ba505614923be4c6a41 SHA1: 217ccfb2eaabde6b2fad012fb70707a9a5aeb4d3 SHA256: a90b42ba70e59f3e532f26510ab212d7bbf6beab3d8cc6a5ccdb8f29e001ec4f SHA512: bf5677e7a79259b58a4ac551313b283f44aeb37bdde2227e82d90a77e0e326d1984ed38901100e08d1f32fefd4c9c5afe949bcb8d47b537322f4cb1b02af93bf Homepage: https://cran.r-project.org/package=horseshoe Description: CRAN Package 'horseshoe' (Implementation of the Horseshoe Prior) Contains functions for applying the horseshoe prior to high- dimensional linear regression, yielding the posterior mean and credible intervals, amongst other things. The key parameter tau can be equipped with a prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe, is for linear regression. In addition, there are functions specifically for the sparse normal means problem, allowing for faster computation of for example the posterior mean and posterior variance. Finally, there is a function available to perform variable selection, using either a form of thresholding, or credible intervals. Package: r-cran-horseshoenlm Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-msm Suggests: r-cran-boot, r-cran-pgdraw, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-horseshoenlm_0.0.6-1.ca2404.1_all.deb Size: 71440 MD5sum: 5000c535976ace213aa1681f617ca1c2 SHA1: d29725c774b4b1d542f6e8fe0dae6d42be2f69c6 SHA256: f20966254806ee0804152becabb72738fce29b503ba4c6aea594fc66ecc2c227 SHA512: 1457b5f3ac91d31d71437b6c2a1477443f601119ecf069634396a9a71f372557548559f9dd6402b530fe9bf1f88ba81d28f6dbfced84223b0594a3cbeea789fa Homepage: https://cran.r-project.org/package=horseshoenlm Description: CRAN Package 'horseshoenlm' (Nonlinear Regression using Horseshoe Prior) Provides the posterior estimates of the regression coefficients when horseshoe prior is specified. The regression models considered here are logistic model for binary response and log normal accelerated failure time model for right censored survival response. The linear model analysis is also available for completeness. All models provide deviance information criterion and widely applicable information criterion. See Maity et. al. (2019) Maity et. al. (2020). Package: r-cran-horsey Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tibble, r-cran-dplyr, r-cran-stringr, r-cran-cli, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-horsey_1.0.1-1.ca2404.1_all.deb Size: 42396 MD5sum: 4ba1cb04cebabe879558716592199d46 SHA1: f7f3b34ddabf00553ef9e50ded30508ba7278a58 SHA256: 453082109fd602b9f76ca6ca0313bb9961132124bc1638f2fc506d6fec4562b4 SHA512: 13e5f630f3864a33442ab809c7bd580d46b1f401780caf21f8c091cfaae9c75c3436406c3cdeac822e1780b2b935507c96dc44cd543b7f261e4564636d245e27 Homepage: https://cran.r-project.org/package=horsey Description: CRAN Package 'horsey' (Access to the 'Lichess' API) Package that accesses the 'Lichess' API (). Supports both authenticated and unauthenticated requests. Basic functionality for game and player analysis. Package: r-cran-hosm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-sf, r-cran-tidyverse, r-cran-units, r-cran-tibble, r-cran-readxl Filename: pool/dists/noble/main/r-cran-hosm_0.1.0-1.ca2404.1_all.deb Size: 23546 MD5sum: 3a0da59645a0003e25e418430f94bccc SHA1: 17f538c771acc74ce0bc22da2b19e1b7cc0f58c7 SHA256: 2901229068de01e6302aabae4ff87d748fbb55f3527699c786654580ff363040 SHA512: 649d48ea08e1b0d0c08ec9414f94a971d71e2171944cfbe0a603a518464f0dc9b8f323b70818375e7ec7f578e7a0cce616337641c2202828686cb25b8a0b363c Homepage: https://cran.r-project.org/package=hosm Description: CRAN Package 'hosm' (High Order Spatial Matrix) Automatically displays the order and spatial weighting matrix of the distance between locations. This concept was derived from the research of Mubarak, Aslanargun, and Siklar (2021) and Mubarak, Aslanargun, and Siklar (2022) . Distance data between locations can be imported from 'Ms. Excel', 'maps' package or created in 'R' programming directly. This package also provides 5 simulations of distances between locations derived from fictitious data, the 'maps' package, and from research by Mubarak, Aslanargun, and Siklar (2022) . 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Methods provided have been used in Donker T, Wallinga J, Grundmann H. (2010) , and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) . Package: r-cran-hospitals Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hospitals_0.1.0-1.ca2404.1_all.deb Size: 66952 MD5sum: 8e4bdfd492725c795621903477982a8a SHA1: 2b7c3840c4602bc03926bcc16baf6f892a75130f SHA256: 8745575281a4e8f173fab7f72fcc54b12f74d39b2dd6d4fe5530b4f8a95c9f7e SHA512: fc3843cdbb0e5572eede6caffc3cbb695eeebe7d5c1200092057a60fcdc0c10a99d37576944b77ac2a24a518dc4f65dfd9823a8ace19a4b2f5c55848df107d44 Homepage: https://cran.r-project.org/package=hospitals Description: CRAN Package 'hospitals' (Portuguese 'NHS' Hospitals) A data set of the Portuguese 'NHS' hospitals. 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Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2006). The first chapter of the book, which is entitled ''An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available. Package: r-cran-hscovar Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-rlist, r-cran-pwr Filename: pool/dists/noble/main/r-cran-hscovar_0.4.2-1.ca2404.1_all.deb Size: 309112 MD5sum: 44da7a9dceee4905765dd9e22fce2f68 SHA1: 90bd3f0a94478d0cea061ceb91dbd77404b288bd SHA256: 0c71ca16260bb592a794d0896b48d8dbefcc132ae538d56e5e3b7666028a9a25 SHA512: 18e1f9dea472540d911ae13e36c6798f7bdff257b7a8472acc0fc9f0253f1b9faca797f98b30eab3297ac1d3c3c6c3cd873cd0b96a90e5e2662aa503c4a81c2e Homepage: https://cran.r-project.org/package=hscovar Description: CRAN Package 'hscovar' (Calculation of Covariance Between Markers for Half-Sib Families) The theoretical covariance between pairs of markers is calculated from either paternal haplotypes and maternal linkage disequilibrium (LD) or vise versa. A genetic map is required. Grouping of markers is based on the correlation matrix and a representative marker is suggested for each group. Employing the correlation matrix, optimal sample size can be derived for association studies based on a SNP-BLUP approach. The implementation relies on paternal half-sib families and biallelic markers. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations" . Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium" . Package: r-cran-hsdic Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncvreg, r-cran-glmnet, r-cran-quadprog, r-cran-matrix Filename: pool/dists/noble/main/r-cran-hsdic_0.1-1.ca2404.1_all.deb Size: 27424 MD5sum: c913f3f3920b30dcfa196b77eb6e7b08 SHA1: efbfd0ae32767caeec30ac4ea9846f285ac3cace SHA256: ef909f06cb81b2ddbc503e5dddf37ab449692f139c336b29fe738c59b7aa6014 SHA512: 9ccf8d3678455d55edaec01d2a9966e4d79e58e3ae9f3a3da21accc3ab90f4d65c0dd29a0bbd48478aa0f6413bf34e6beea31031e59b7e5f71b662f0e8dd1b95 Homepage: https://cran.r-project.org/package=HSDiC Description: CRAN Package 'HSDiC' (Homogeneity and Sparsity Detection Incorporating PriorConstraint Information) We explore sparsity and homogeneity of regression coefficients incorporating prior constraint information. A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators. Package: r-cran-hse Architecture: all Version: 0.0-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hse_0.0-28-1.ca2404.1_all.deb Size: 6930 MD5sum: 533069c1b535d238f5943553fc0db28a SHA1: ef1a1ab580d6cb1e9db7782b6887d8fd4ec5a6ae SHA256: 9be50887f09626a52aa83578ad8015283860f2cb7f757bc8f6d6f8aff00cac17 SHA512: f57195c36a37a2e0c6556654a53777e390290c8af7867ebafa83bc561910afe3ee61bb4f86ddab699b9eb361e7962442fb759d70534cff2e91407ff5dd850c45 Homepage: https://cran.r-project.org/package=hse Description: CRAN Package 'hse' (The hse Distribution) Deprecated. Package: r-cran-hsem Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-hsem_1.0-1.ca2404.1_all.deb Size: 342768 MD5sum: 9a3c601be5719d5c808653b7222f403c SHA1: b0f2b9acb624738d37ab1220a95605e13c13ba7f SHA256: ca40dec012b0d26d11769ceabcc6f5892ab808c14503299e2e6fe0ad7485be39 SHA512: 44b9af00e854374ed5c1493046142a772c82dd088b1ebdc78556209ec71b01c134ebea44f858ec54f69d7ecd9f8f7ae0bc0484c7b607864f068c75afd4c5cd7b Homepage: https://cran.r-project.org/package=hsem Description: CRAN Package 'hsem' (Hierarchical Structural Equation Model) We present this package for fitting structural equation models using the hierarchical likelihood method. This package allows extended structural equation model, including dynamic structural equation model. We illustrate the use of our packages with well-known data sets. Therefore, this package are able to handle two serious problems inadmissible solution and factor indeterminacy . Package: r-cran-hset Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hash Filename: pool/dists/noble/main/r-cran-hset_0.1.1-1.ca2404.1_all.deb Size: 179548 MD5sum: f3f1296be1b82dd534ff640830d0907c SHA1: 98bceb3d363f151a410c52ae48941c29cba99c6a SHA256: 574cba9c00abf07c1317bfa9f37b7c07878fab19bd4558357442efd552832bd1 SHA512: d74dfe1ac6f2106c15cc09676d09105833ff4833c1d240b65a353fe50ab666f5a2ff87d097afa3f12c2ed6db1e095471ac492c9509e6147a2abb01f2278de0e4 Homepage: https://cran.r-project.org/package=hset Description: CRAN Package 'hset' (Sets of Numbers Implemented with Hash Tables) Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package 'hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) . Package: r-cran-hsetest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hsetest_0.1.0-1.ca2404.1_all.deb Size: 11338 MD5sum: c8145faa1e354d9ad5138395fdd32cd4 SHA1: 2f38e80eca39aa36623eeb50ca057d889aa42f25 SHA256: 0cb0ac6f95566462b0ca1445c074182cbf5c8a844a7ed48f316e6f1bfcf4f7e0 SHA512: 76ed1bd5ea65ef140ad472fe700a29f3ad6a240bdc9eb0db58092f7fcba87e050bedc56233ff85517bc705fea832511024a2cc2d0480b03c73ab61d6b06c2a08 Homepage: https://cran.r-project.org/package=HSEtest Description: CRAN Package 'HSEtest' (Homogeneity of Stratum Effects Test) To test the homogeneity of stratum effects in stratified paired binary data. Package: r-cran-hspm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-sphet, r-cran-spdep, r-cran-matrix Suggests: r-cran-splm Filename: pool/dists/noble/main/r-cran-hspm_1.1-1.ca2404.1_all.deb Size: 1349994 MD5sum: 349512e251d7565ffb1bc316a687b64b SHA1: 71abcdf3b025a97599d01ed028cc31b6f1636237 SHA256: 8bfe20ba6bca8163591951190baac39c3324c7ba94adf20dd034983e4afd480b SHA512: 396dfa037bebfcf24077c171d4e6606274fbb36e3b2890e3d128ed9b1580155c96f20f43f00f8ee3c9e3f3b3a8b1b37afb7539706688d9f9e1576cda5259ede4 Homepage: https://cran.r-project.org/package=hspm Description: CRAN Package 'hspm' (Heterogeneous Spatial Models) Spatial heterogeneity can be specified in various ways. 'hspm' is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of 'hspm' deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) , Bivand and Piras, (2015) , Drukker et al., (2013) , Kelejian and Prucha, (2010) ). Package: r-cran-hspor Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-npregfast Filename: pool/dists/noble/main/r-cran-hspor_1.1.9-1.ca2404.1_all.deb Size: 166246 MD5sum: 900523c9de8d287d5efd255d3a637c2d SHA1: 328689b5e57f277451c7216cca0a44a6da1ee5bb SHA256: 7cc1ccc6a1b0c0cda34ef65e4df4838e6a1a4c4974703da6caae33e7714dd8a8 SHA512: f9de87e70cd7a2e3b2a5755fa414b988a12e43763ea81e2fc969b3f28c5a537463c50a88e093b4c6056ad286e4b0524a36ad19b96d2abe6aab8ba3a78b758e93 Homepage: https://cran.r-project.org/package=HSPOR Description: CRAN Package 'HSPOR' (Hidden Smooth Polynomial Regression for Rupture Detection) Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available. Package: r-cran-hstats Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hstats_1.2.2-1.ca2404.1_all.deb Size: 347898 MD5sum: 4cdf94eb53953a96c55d561cbd6a6941 SHA1: d873a1892219012c27a6ff88fb0d5d30243e996b SHA256: 18cbf8feebf7659ff90f3677800fb9d19679a9debe1d8369d4c86523b9ff3afb SHA512: 8fd96769ce07599941874fbba55d9b77be363c6d2bbb00302e8eeb58c5dcfcf2b22d9a2a9e860c989565e4a1e8f587ae5b977f254ac1400b152f757f38a69686 Homepage: https://cran.r-project.org/package=hstats Description: CRAN Package 'hstats' (Interaction Statistics) Fast, model-agnostic implementation of different H-statistics introduced by Jerome H. Friedman and Bogdan E. Popescu (2008) . These statistics quantify interaction strength per feature, feature pair, and feature triple. The package supports multi-output predictions and can account for case weights. In addition, several variants of the original statistics are provided. The shape of the interactions can be explored through partial dependence plots or individual conditional expectation plots. 'DALEX' explainers, meta learners ('mlr3', 'tidymodels', 'caret') and most other models work out-of-the-box. Package: r-cran-htabim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-cli, r-cran-checkmate, r-cran-glue, r-cran-purrr, r-cran-tibble, r-cran-scales, r-cran-flextable, r-cran-officer, r-cran-shiny, r-cran-bslib, r-cran-bsicons, r-cran-dt, r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-covr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-htabim_0.1.0-1.ca2404.1_all.deb Size: 287592 MD5sum: 80735751e7607642ad09f56240cc7614 SHA1: 4d10ebc78d38867b3fb25b634f496d34aed711cd SHA256: 8cc71aa5d513c8a77cc6252c8098ad68d4e457f980a914b2142f482862497149 SHA512: 60675ef88a08d20dc0f97c5e972d22adfea4dc73b5d33ab50c75bdff6ec630ac38c6f2d9fd768c5415f5cdd914129043a25b3b7e32c62aee75addb82360d519f Homepage: https://cran.r-project.org/package=htaBIM Description: CRAN Package 'htaBIM' (Budget Impact Modelling for Health Technology Assessment) Implements a structured, reproducible framework for budget impact modelling (BIM) in health technology assessment (HTA), following the ISPOR Task Force guidelines (Sullivan et al. (2014) and Mauskopf et al. (2007) ). Provides functions for epidemiology-driven population estimation, market share modelling with flexible uptake dynamics, per-patient cost calculation across multiple cost categories, multi-year budget projections, payer perspective analysis, deterministic sensitivity analysis (DSA), and probabilistic sensitivity analysis (PSA) via Monte Carlo simulation. Produces submission-quality outputs including ISPOR-aligned summary tables, scenario comparison tables, per-patient cost breakdowns, tornado diagrams, PSA histograms, and text and HTML reports compatible with NICE, CADTH, and EU-HTA dossier formats. Ships with an interactive 'shiny' dashboard built on 'bslib' for point-and-click model building and exploration. Package: r-cran-htdv Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan Suggests: r-cran-bayesplot, r-cran-bridgesampling, r-cran-knitr, r-cran-loo, r-cran-posterior, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htdv_0.2.0-1.ca2404.1_all.deb Size: 245110 MD5sum: 953809403569e3da72df2a24d65978d6 SHA1: 474c8523893e02f64aa77ffa0d01f234994dad49 SHA256: 3ad4e4644a4d7d64555489c25cd8a33d9ef9ace229f1250c3e8cf48a71c07bba SHA512: 7f81863e7433633cd5ba1d283f666824d44dfce05c0795eb7d32920d97ea64a6729afb9dff2249a49485574c89120ce8193fd77f9221d9e95322b33b4dee35be Homepage: https://cran.r-project.org/package=HTDV Description: CRAN Package 'HTDV' (Hypothesis Testing for Dependent Variables with Unbalanced Data) Implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) , Kiefer and Vogelsang (2005) , Patton, Politis and White (2009) , Vehtari, Gelman and Gabry (2017) , Kruschke (2018) , and Gibbs and Candes (2021) . Package: r-cran-htestclust Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bootstrap, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-htestclust_0.2.2-1.ca2404.1_all.deb Size: 235328 MD5sum: 729c542b195649d69a48d17949d11bb3 SHA1: 2e03d8917f119a9a7559f1472476d1cd6829ae3f SHA256: 785f076f71b7078c0bdb2020c53b0b82695e8931a7bac7ffa76163fb51461bdc SHA512: 035a42e2fcabe3ce0a5b0b2b0f58d2c91397dbc37c5408b8bc12320f685f93179b82e800c712e3f1f628b3626eb4ea02b34176756744849569e85ba48dee087f Homepage: https://cran.r-project.org/package=htestClust Description: CRAN Package 'htestClust' (Reweighted Marginal Hypothesis Tests for Clustered Data) A collection of reweighted marginal hypothesis tests for clustered data, based on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) . The tests in this collection are clustered analogs to well-known hypothesis tests in the classical setting, and are appropriate for data with cluster- and/or group-size informativeness. The syntax and output of functions are modeled after common, recognizable functions native to R. Methods used in the package refer to Gregg, M., Datta, S., and Lorenz, D. (2020) , Nevalainen, J., Oja, H., and Datta, S. (2017) Dutta, S. and Datta, S. (2015) , Lorenz, D., Datta, S., and Harkema, S. (2011) , Datta, S. and Satten, G. (2008) , Datta, S. and Satten, G. (2005) . Package: r-cran-htgm2d Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-gplots, r-cran-jaccard, r-cran-vprint, r-cran-randomgodb, r-cran-hgnchelper Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm2d_1.1.1-1.ca2404.1_all.deb Size: 944006 MD5sum: e967ab66777b4bdfacb36e0ad88bc29e SHA1: dea611f222ce00b709936de46a422d200168cd23 SHA256: 72f974ad3fa5b3306d382c00263bdcdb261f8d9fa4d13f79d7198ae816f77859 SHA512: ab5ce926fdd4597e947ecdef7541e44bfa632fef49af05891ed6bd70064ebc56906ee0f9928c146d52a4b41e1813ec0eaea7d4db434e8ea017151474cee71823 Homepage: https://cran.r-project.org/package=HTGM2D Description: CRAN Package 'HTGM2D' (Two Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I developed a two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair. Package: r-cran-htgm3d Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-htgm2d, r-cran-r2html, r-cran-rgl, r-cran-vprint, r-cran-randomgodb, r-cran-stringr, r-cran-salesforcer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm3d_1.0.3-1.ca2404.1_all.deb Size: 1318972 MD5sum: 4f8c24920845fa5ff0cd557553854c19 SHA1: 00a99aaa4b7166fd1d157dacc2c15ce26f0fc9c2 SHA256: 4de99ed8aa6b8eef5a4b6dc908e60e2c920bae0ae2bb25e6d0395208b6bf788b SHA512: 4301d2fe5e0b8f13045490234e60765149606532e8e917a5a8416c7956651ca1ad21f21b249e264061c98d38661e0e32271b1bffcbd4e706c760c855b17e8056 Homepage: https://cran.r-project.org/package=HTGM3D Description: CRAN Package 'HTGM3D' (Three Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed 'HTGM3D', a three-dimensional version of 'GoMiner'. Package: r-cran-htgm4d Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-htgm2d, r-cran-gplots, r-cran-vprint, r-cran-randomgodb, r-cran-hgnchelper, r-cran-png, r-cran-magick, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm4d_1.0-1.ca2404.1_all.deb Size: 2152202 MD5sum: c2a27e7090ea5857e27e607649dc40fb SHA1: 25bb252010d41eb94d68609363224cb1b877461c SHA256: 9e3aa63ec5f0bd055d7de8950fa7665c8fdbfbdeb56f978766a8837c91b86988 SHA512: 64e3f71fc8c8a79b830909492d9db96364a2c287f778642445d71f014c58fdc8763aac8a42f2de8bb007b915c681cd662d31e99c64e8e950b6cc4ba610abaf61 Homepage: https://cran.r-project.org/package=HTGM4D Description: CRAN Package 'HTGM4D' (Four Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I now present an enhancement of the existing two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'), which is called 'HTGM4D'. The original 'HTGM2D' is augmented by adding two instances of the original 'GoMiner' genes versus categories heatmaps, aligned with the categories axes of the 'HTGM2D' heatmap. Package: r-cran-htgm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-gplots, r-cran-vprint Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm_1.2-1.ca2404.1_all.deb Size: 847250 MD5sum: b2a2164ddc14742fc29fbdb7ff79348a SHA1: 0b50898579a22638b8374e4a5f430c27264a6d64 SHA256: 37b509e0ecf06726438aac8ef778aba93596589ede48bf4d127b65e719d7dbaf SHA512: c279079fcf5f1d72b0602c35b8dfbcf3143875aded021215acf50deb8909a3d9026622d616df787a51773b48221b1e8232c652dfa700405fb01f97f7495f91a8 Homepage: https://cran.r-project.org/package=HTGM Description: CRAN Package 'HTGM' (High Throughput 'GoMiner') Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005) ) implement 'GoMiner' and 'High Throughput GoMiner' ('HTGM') to map lists of genes to the Gene Ontology (GO) . Until recently, these were hosted on a server at The National Cancer Institute (NCI). In order to continue providing these services to the bio-medical community, I have developed stand-alone versions. The current package 'HTGM' builds upon my recent package 'GoMiner'. The output of 'GoMiner' is a heatmap showing the relationship of a single list of genes and the significant categories into which they map. 'High Throughput GoMiner' ('HTGM') integrates the results of the individual 'GoMiner' analyses. The output of 'HTGM' is a heatmap showing the relationship of the significant categories derived from each gene list. The heatmap has only 2 axes, so the identity of the genes are unfortunately "integrated out of the equation." Because the graphic for the heatmap is implemented in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each picture element to the relevant list of genes. By clicking on the desired picture element, the user can recover the "lost" genes. Package: r-cran-htm2txt Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-htm2txt_2.2.2-1.ca2404.1_all.deb Size: 54850 MD5sum: 7419f1b220a9aa0386167bda3ae18df9 SHA1: 244c03f19e74eefbee6a3df94fd925fb4aac0893 SHA256: e3c19a29400f2fe9e08bebb2cd8dd8aaf05ebb5fcb50a8d5a588384a2212f50b SHA512: a5436193856e6f58aabc08e8f8a2bbba8f993b6fe1fd516968585484bcf319cee5c1dbf8be11e1e08fbaa718cb02e6424a67bf9e3f4b241f1c38aa9f7fbae8cd Homepage: https://cran.r-project.org/package=htm2txt Description: CRAN Package 'htm2txt' (Convert Html into Text) Convert a html document to plain texts by stripping off all html tags. 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Package: r-cran-html2r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-shiny, r-cran-shinyace, r-cran-shinythemes, r-cran-shinyjqui Filename: pool/dists/noble/main/r-cran-html2r_0.1.0-1.ca2404.1_all.deb Size: 62502 MD5sum: dbeaa0b4c3efcec529d55aaefdf5a374 SHA1: 890530bfe43d1c44a3272ee97554f951de71f4f5 SHA256: a6276d2747e98c64055cb93596b6cc87366a36227d29f4ae0e96e0d0845d6733 SHA512: 1598b262fd3b91da877676ef5e2f9702ae0b74985e250df5db3b4f8583455aa5b0ad589005369aa2af1c3cef04b42a3a8c10a3e17298d5a3db6ed0e6abcd9dd9 Homepage: https://cran.r-project.org/package=html2R Description: CRAN Package 'html2R' (Convert 'HTML' to 'R' with a 'Shiny' App) Provides a 'Shiny' app allowing to convert 'HTML' code to 'R' code (e.g. 'Hello' to 'tags$span("Hello")'), for usage in a 'Shiny' UI. 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Tools help with extraction of page titles, links, images, rss feeds, social media handles and page metadata. 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Based on , its sister project. 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While allowing advanced layout, the underlying css-structure is simple in order to maximize compatibility with common word processors. The package also contains a few text formatting functions that help outputting text compatible with HTML/LaTeX. 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Package: r-cran-htna Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nestimate, r-cran-cograph, r-cran-igraph Suggests: r-cran-codyna, r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-testthat, r-cran-tna Filename: pool/dists/noble/main/r-cran-htna_0.3.1-1.ca2404.1_all.deb Size: 3715550 MD5sum: 83695401a02b19a1f5ea9c8954f747e0 SHA1: 9b74fe4bf895355a100f9b7270a351aa949401b6 SHA256: f574c6963ecafcf5080a5496dfa28e95ee083d435e1bc0817437ab0da0e0dc1a SHA512: d97ef121179826cdc5a77286076228bbc8b6ef28fc45ba0c2fea806180de2798bc2eb1696c83e0a4f9ff04a503ebbf160a95f180d8569d53ec381326cdeaf944 Homepage: https://cran.r-project.org/package=htna Description: CRAN Package 'htna' (Heterogeneous Transition Network Analysis) Implements the Heterogeneous Transition Network Analysis (HTNA) method described by López-Pernas et al. (2026) . The method is an extension of transition network analysis (TNA) where actions or events belong to two or more distinct actor types (e.g. Human and AI), preserving the actor type partition on the resulting network. Provides a thin, focused API on top of the 'Nestimate' estimation engine and the 'cograph' rendering engine, so downstream bootstrap, permutation, reliability, centrality, and plotting functions treat each actor's codes as a distinct node group. Package: r-cran-htrspranalysis Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-readxl, r-cran-openxlsx, r-cran-minpack.lm, r-cran-zoo, r-cran-gridextra, r-cran-readr, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-ggplot2, r-cran-purrr, r-cran-forcats, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-htrspranalysis_0.1.3-1.ca2404.1_all.deb Size: 1254858 MD5sum: 87d1dabbfbe314291e276256a2b24d37 SHA1: 1d037bc78f46babf54441b7b5ad824f67667f325 SHA256: 5dc2f5e9608ff68ff6abe30f04890734206699c1446ba81abdf76d406bf7a668 SHA512: 1b723dbf7a5a834368d60ad7923eed7f001d25ccdb59e55cbe56e02e376e3950e09ed548d1ada219d25683a2d5ee767ee6dc56afde3fb9d71526eebb251c5998 Homepage: https://cran.r-project.org/package=htrSPRanalysis Description: CRAN Package 'htrSPRanalysis' (Analysis of Surface Plasmon Resonance Data) Analysis of Surface Plasmon Resonance (SPR) and Biolayer Interferometry data, with automations for high-throughput SPR. This version of the package fits the 1: 1 binding model, with and without bulkshift. It offers optional local or global Rmax fitting. The user must provide a sample sheet and a Carterra output file in Carterra's current format. There is a utility function to convert from Carterra's old output format. The user may run a custom pipeline or use the provided 'Runscript', which will produce a pdf file containing fitted Rmax, ka, kd and standard errors, a plot of the sensorgram and fits, and a plot of residuals. The script will also produce a .csv file with all of the relevant parameters for each spot on the SPR chip. Package: r-cran-htrx Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastglm, r-cran-caret, r-cran-glmnet, r-cran-tune, r-cran-recipes Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-htrx_1.2.4-1.ca2404.1_all.deb Size: 829332 MD5sum: ce136c51309dec4d286b39c7b0540947 SHA1: 660afb4d4d8a9d3a02adfc1559a4a4909ef4e31e SHA256: 42063be5528f93f5f9ff6c7b5c1cf3ca098cdfe3c8e10e00f65b23dd9a05f50e SHA512: 8875ccf4e525ae93415197a4fc8581ede2df232c68c8c2da0d59e2676ccfc534ca07566b2b0c5f1ca9e73340a2add4d05474ad7991cdb0e46882a1c898a1b18c Homepage: https://cran.r-project.org/package=HTRX Description: CRAN Package 'HTRX' (Haplotype Trend Regression with eXtra Flexibility (HTRX)) Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) and Barrie W, Yang Y, Irving-Pease E.K, et al (2024) . Package: r-cran-htscluster Architecture: all Version: 2.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-edger, r-cran-plotrix, r-cran-capushe Suggests: r-bioc-htsfilter, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-htscluster_2.0.11-1.ca2404.1_all.deb Size: 511628 MD5sum: d7b201d53d1dbab7973e80c6f788c20b SHA1: bcee626591c38e4448e3e9d538fc04500f78f19f SHA256: de8ea7e9076beb353358f46985d0108e87900979519e6770d827fb5e92871306 SHA512: d8f34de733caa3d447140c833b18fc94e4526a36405d88f7b0129d7622cd7cf1a1c93589d95e1d68603b7aee89bcc7f66e98ddf332c34f663905d2ffbfa92072 Homepage: https://cran.r-project.org/package=HTSCluster Description: CRAN Package 'HTSCluster' (Clustering High-Throughput Transcriptome Sequencing (HTS) Data) A Poisson mixture model is implemented to cluster genes from high- throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters). Package: r-cran-htsdegenerater Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparsem Suggests: r-cran-forecast, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-htsdegenerater_0.1.0-1.ca2404.1_all.deb Size: 36450 MD5sum: fc7fe297ab3971920e6953464750930a SHA1: 258a796319843bdef4bfa45eefd9ff08b7e60002 SHA256: 201b5e0aa9975c088446608b1eb58a669656bd83ac4516d9d39fbb798eea7623 SHA512: 13da6a2df218dc56874296da814a2129f80e91ae378bba57bf93580b4749c95f75f3802586a957b37ec04ef830f8967d6eee7a9ff906052de9f1a240265b2693 Homepage: https://cran.r-project.org/package=htsDegenerateR Description: CRAN Package 'htsDegenerateR' (Degenerate Hierarchical Time Series Reconciliation) Takes the MinT implementation of the 'hts' package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024). The MinT algorithm is based on Wickramasuriya et al. (2019). Package: r-cran-htseed Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-htseed_0.1.0-1.ca2404.1_all.deb Size: 27716 MD5sum: 1cc6b23fea6b5dde0c53dded35f5efcd SHA1: f1c193306d022d0afb2f6185f89dd3f7a6cb96ef SHA256: 7b48c506d66da23d678f8f32e3975b9d32e6bb3d29ee18f52090a42dff4b6ab4 SHA512: 19a7d375162d4c008a1df55de265304057c0c8356b68a5a2d5f3bea4249a44a38744cf99d628877e350308591ef73f1de680fd860cf134419641656893941a70 Homepage: https://cran.r-project.org/package=HTSeed Description: CRAN Package 'HTSeed' (Fitting of Hydrotime Model for Seed Germination Time Course) The seed germination process starts with water uptake by the seed and ends with the protrusion of radicle and plumule under varying temperatures and soil water potential. Hydrotime is a way to describe the relationship between water potential and seed germination rates at germination percentages. One important quantity before applying hydrotime modeling of germination percentages is to consider the proportion of viable seeds that could germinate under saturated conditions. This package can be used to apply correction factors at various water potentials before estimating parameters like stress tolerance, and uniformity of the hydrotime model. Three different distributions namely, Gaussian, Logistic, and Extreme value distributions have been considered to fit the model to the seed germination time course. Details can be found in Bradford (2002) , and Bradford and Still(2004) . Package: r-cran-htseedglm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-htseedglm_0.1.0-1.ca2404.1_all.deb Size: 19738 MD5sum: b29e0f9fb670839007da94c9e5061c4d SHA1: 4ec60173f2409ce11fd566f7857f86990a2926b4 SHA256: 3f11a930413dae29f0a9a7968ab503fe602d28e76e09c930ac6a98c65efe0dab SHA512: 7b07da895a97e41257e7c947343c8bf5b25bc3db87e2ae294572ce01777a62ca31dbb4984bcc60d945a81c935d1fc55eccc82d14ad2307ca4034cea5b26ea73f Homepage: https://cran.r-project.org/package=HTSeedGLM Description: CRAN Package 'HTSeedGLM' (Hydro Thermal Time Analysis of Seed Germination UsingGeneralised Linear Model) Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) and Bradford (2002) . Package: r-cran-htssip Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-ape, r-cran-magrittr, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-vegan, r-bioc-deseq2, r-bioc-phyloseq, r-cran-coenocliner, r-cran-lazyeval Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-htssip_1.4.1-1.ca2404.1_all.deb Size: 1531592 MD5sum: 05604aad8c9923735c78881e61e66b1e SHA1: 7c751f10bef4a47662bfdc21dca81e1c2e3d04b2 SHA256: 5876ed9e60f8faed928ad20fcc2e234a29dae55f8676a36bab47c1021c317c12 SHA512: 53a5dbbb5de44f07ad4a54e65e4ffa187e3d51f1ba4ef8ca6d6d1fb93ee876367921ace40cb90bc6c1727ff4f958c9f3a4cf8ec43720c12f64e8714031b5f34c Homepage: https://cran.r-project.org/package=HTSSIP Description: CRAN Package 'HTSSIP' (High Throughput Sequencing of Stable Isotope Probing DataAnalysis) Functions for analyzing high throughput sequencing stable isotope probing (HTS-SIP) data. Analyses include high resolution stable isotope probing (HR-SIP), multi-window high resolution stable isotope probing (MW-HR-SIP), and quantitative stable isotope probing (q-SIP). Package: r-cran-httkexamples Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4907 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httk, r-cran-rmarkdown, r-cran-knitr, r-cran-rdpack Suggests: r-cran-dplyr, r-cran-tidyverse, r-cran-xlsx, r-cran-metrics, r-cran-ggplot2, r-cran-ggforce, r-cran-ggpubr, r-cran-ggrepel, r-cran-viridis, r-cran-ggh4x, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-pracma, r-cran-cgwtools, r-cran-openxlsx, r-cran-ggstar, r-cran-latex2exp, r-cran-smatr, r-cran-reshape, r-cran-gdata, r-cran-censreg, r-cran-gmodels, r-cran-gplots, r-cran-scales, r-cran-colorspace, r-cran-gridextra, r-cran-rvcheck Filename: pool/dists/noble/main/r-cran-httkexamples_0.0.1-1.ca2404.1_all.deb Size: 1837112 MD5sum: c9bc42248f1485d1a40f2b0fc6a62f2f SHA1: 7250b143416df2fe15925ef85f6fb76f57bf27e5 SHA256: 3c3d3f52e70e061c840f27ed67985399035b5ba409ffd6bc5461083cab207402 SHA512: 7548e3e8cba071c5633dbfe1fb671fbd71b8080c1e44740ece27491c474a1c2fdb52211bbc056d691990269a0893d3cf304d17cc874238dc86751e8068471a18 Homepage: https://cran.r-project.org/package=httkexamples Description: CRAN Package 'httkexamples' (High-Throughput Toxicokinetics Examples) High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) () and Breen et al. (2021) (). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte. Package: r-cran-httpcache Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-httr Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-httpcache_1.2.0-1.ca2404.1_all.deb Size: 62324 MD5sum: d1276882ba7c5df85b62ffff00d7ffb7 SHA1: 5c6735a8db1fc01a5dd64546550d786e9415191b SHA256: e62329eccf10eed7b12a421b4ffe20b16b13992cf75a8a0f35e090c2455fbb84 SHA512: ab288bac6186779abd1704a1e02de6eb90f764b87bfa8216b072d80621ffad776c6c3fee99b8305a117d8c92a1efe38eaf8095be7604dacb887e26e35c321e00 Homepage: https://cran.r-project.org/package=httpcache Description: CRAN Package 'httpcache' (Query Cache for HTTP Clients) In order to improve performance for HTTP API clients, 'httpcache' provides simple tools for caching and invalidating cache. It includes the HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in replacements for those in the 'httr' package. These functions are cache-aware and provide default settings for cache invalidation suitable for RESTful APIs; the package also enables custom cache-management strategies. Finally, 'httpcache' includes a basic logging framework to facilitate the measurement of HTTP request time and cache performance. Package: r-cran-httpcode Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-httpcode_0.3.0-1.ca2404.1_all.deb Size: 33856 MD5sum: fae16348bdb989247864d54b3a40c381 SHA1: 7f590c5c7179342a25947fe1c5058cd9dbc66098 SHA256: a22ca4318a9b9718a0493b4d8b0a088071c8ab9397af707c2b90c86c50e43a10 SHA512: 4c3554c85a087e1367fc4b6c372cd9834630ae2e0d1d38f7949d19c857189c4809c0660f33785e9539321eb38645088a3b5c477cc756185891c8c5e5665afd33 Homepage: https://cran.r-project.org/package=httpcode Description: CRAN Package 'httpcode' ('HTTP' Status Code Helper) Find and explain the meaning of 'HTTP' status codes. Functions included for searching for codes by full or partial number, by message, and get appropriate dog and cat images for many status codes. Package: r-cran-httping Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-httpcode, r-cran-lobstr Suggests: r-cran-testthat, r-cran-webfakes Filename: pool/dists/noble/main/r-cran-httping_0.3.0-1.ca2404.1_all.deb Size: 24522 MD5sum: eaa4cd3a916e52170cce23169dfc7d13 SHA1: 8446ec7af18a47c4a33e47a8741f72f4b0ea9a6f SHA256: 1a785a6a1b1e02e418ccdb2054e805326dedc99c8128072c72f45500fef50e0f SHA512: 46d991344fa1146bd7faffa8103effccfe418532473cbfc1b00198d48b82fd9024edb298940bd17590659169e2106bfdaaac86adae3eff67329571d6c335bd0a Homepage: https://cran.r-project.org/package=httping Description: CRAN Package 'httping' ('Ping' 'URLs' to Time 'Requests') A suite of functions to ping 'URLs' and to time 'HTTP' 'requests'. Designed to work with 'httr'. Package: r-cran-httpproblems Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-httpproblems_1.0.1-1.ca2404.1_all.deb Size: 22366 MD5sum: 4a39b00424a29290c9c7848de87023f3 SHA1: b880e4686c6962324ddc72806637a3560b66b85e SHA256: 3c1062c61d7de239a1499c0b62902d632ddb04ee23e362b4e9d338ac6eaa3a5b SHA512: fac84d13535b9fbba3fd029a0f6e327d8850c076b943d822d4df68f47996e7394740232d15151bfdb484605a040973c0bf5dc6cf87fdba028cf7b992683b34ba Homepage: https://cran.r-project.org/package=httpproblems Description: CRAN Package 'httpproblems' (Report Errors in Web Applications with 'Problem Details' (RFC7807)) Tools for emitting the 'Problem Details' structure defined in 'RFC' 7807 for reporting errors from 'HTTP' servers in a standard way. Package: r-cran-httprequest Architecture: all Version: 0.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-httprequest_0.0.11-1.ca2404.1_all.deb Size: 27724 MD5sum: c3d1e93d27469dcf6f8f85a30911cd37 SHA1: a5e4811b7104ef84fb027371cd4b25ae09b23eef SHA256: 9c00a3dd82db0c5dfd6b5574ccaa5300d478bc258210df9852bc096e756804fa SHA512: 7c0d579606c417a33eed721776f8fa61773d4fcb93eab8f5514607c995d2a4fabb69ec05f8051a5e0f7c4a8390bc5906941499e6286f96226febc8c379d49fcb Homepage: https://cran.r-project.org/package=httpRequest Description: CRAN Package 'httpRequest' (Basic HTTP Request) HTTP Request protocols. Implements the GET, POST and multipart POST request. Package: r-cran-httptest2 Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-testthat Suggests: r-cran-curl, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httptest2_1.2.2-1.ca2404.1_all.deb Size: 133994 MD5sum: 73b52f993d38686532dbe462a450e59d SHA1: 287fbb5a7b4c32f1a38d53e7a6d0b7abc16ea0ce SHA256: 5b4b9918749f762f1312c4bb80ea078661685720ceb3978a64278fe424fc2c01 SHA512: e2dd71dde9952c5bbe3aa2b96f882043ec540e7d6948622cadcb183e04f9579414e87a1fbc463d927a07bb4b221a7d02a1e6ec56e847499aa311185be48b17f7 Homepage: https://cran.r-project.org/package=httptest2 Description: CRAN Package 'httptest2' (Test Helpers for 'httr2') Testing and documenting code that communicates with remote servers can be painful. This package helps with writing tests for packages that use 'httr2'. It enables testing all of the logic on the R sides of the API without requiring access to the remote service, and it also allows recording real API responses to use as test fixtures. The ability to save responses and load them offline also enables writing vignettes and other dynamic documents that can be distributed without access to a live server. Package: r-cran-httptest Architecture: all Version: 4.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-testthat, r-cran-curl, r-cran-digest, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httptest_4.2.4-1.ca2404.1_all.deb Size: 167264 MD5sum: 3dbb2b5cefadf09e9d837a40dcb4c139 SHA1: 54ac5421e4ac3467e51eaa5a11893aa24b583aff SHA256: 45df1904c122f11b1bede96c4aefa2e37fbef46e76cd602825a6d7e2819fde55 SHA512: 008dc9901238cf667a0b5b07b9845a9e9c1e2193bcbcee0797f69f4066f1e4e3750c4d68f02d588dd05487d0c621c58d4592e659a9d5f09c467bf2be9d735853 Homepage: https://cran.r-project.org/package=httptest Description: CRAN Package 'httptest' (A Test Environment for HTTP Requests) Testing and documenting code that communicates with remote servers can be painful. Dealing with authentication, server state, and other complications can make testing seem too costly to bother with. But it doesn't need to be that hard. This package enables one to test all of the logic on the R sides of the API in your package without requiring access to the remote service. Importantly, it provides three contexts that mock the network connection in different ways, as well as testing functions to assert that HTTP requests were---or were not---made. It also allows one to safely record real API responses to use as test fixtures. The ability to save responses and load them offline also enables one to write vignettes and other dynamic documents that can be distributed without access to a live server. Package: r-cran-httr2 Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-openssl, r-cran-r6, r-cran-rlang, r-cran-vctrs, r-cran-withr Suggests: r-cran-askpass, r-cran-bench, r-cran-clipr, r-cran-covr, r-cran-digest, r-cran-docopt, r-cran-httpuv, r-cran-jose, r-cran-jsonlite, r-cran-knitr, r-cran-later, r-cran-nanonext, r-cran-otel, r-cran-otelsdk, r-cran-paws.common, r-cran-promises, r-cran-rappdirs, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httr2_1.3.0-1.ca2404.1_all.deb Size: 923794 MD5sum: e2048ea304fafbe56acd97659acd83c2 SHA1: 225b7b8671634e8c0619a914ab63e5dfd719177b SHA256: c3329b663280882f4530ec4fac4a90be0cd144c122144988ae80424b862251a5 SHA512: cc3338f516dcf67344ef36a8ada538599455f2d0dd36a97b4be73ee897011293aceaaa35288e4403c5940d64ee480bc40a5329c91314d28ad1c82a38f2a9d62f Homepage: https://cran.r-project.org/package=httr2 Description: CRAN Package 'httr2' (Perform HTTP Requests and Process the Responses) Tools for creating and modifying HTTP requests, then performing them and processing the results. 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Package: r-cran-hubensembles Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-distfromq, r-cran-dplyr, r-cran-hubutils, r-cran-lifecycle, r-cran-matrixstats, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hubensembles_1.0.0-1.ca2404.1_all.deb Size: 112310 MD5sum: 4fc3efa01adfd91dfe962fdb6d37b399 SHA1: 7a48a0f89cab9df3ff55479c5667ade30fcb1cd9 SHA256: 79382d743948c420149606295fcdd30edf11cbcf0f9f548edd534fed8e884fd1 SHA512: 79ae432ac4747ba64f5ede4dce451fb23374d28c9134c5bcf87fb3416bbedd6ba52b310c9ace1377221475ae81ec6a453a16fc31300dd9e524c46f09b7153c1d Homepage: https://cran.r-project.org/package=hubEnsembles Description: CRAN Package 'hubEnsembles' (Ensemble Methods for Combining Hub Model Outputs) Functions for combining model outputs (e.g. predictions or estimates) from multiple models into an aggregated ensemble model output. 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Run text classification, embeddings, chat, translation, image, audio, and other tasks from tidy 'R' workflows without installing 'Python' by default. Results are returned as data frames or simple 'R' objects so they can be composed with 'dplyr', 'tidyr', and related tooling. Helpers also support Hub search, file download, provider discovery, and guarded uploads for authenticated workflows. Optional local embeddings and text classification use 'Python' through 'reticulate'. 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This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided. 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Package: r-cran-hurdlr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hurdlr_0.1-1.ca2404.1_all.deb Size: 98874 MD5sum: 58a4e51921c82a07d79b1a2f9a0d77d7 SHA1: 2cc57ffa84fc5b7ea17ac10307db2f9ca70eaa00 SHA256: c3676622be8521dfbfebad703d444968c97fc62f842c76074bbdc4457ad2cf70 SHA512: adcbd9fdfd0fd99edec3c28b0e45a4f82452b73d024f92e53281a1d6eb43d3ac8ee0b21e714d05e88810b2e7abf61f8d45068e7e013f661136253e7c256ee176 Homepage: https://cran.r-project.org/package=hurdlr Description: CRAN Package 'hurdlr' (Zero-Inflated and Hurdle Modelling Using Bayesian Inference) When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed. 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Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. For details on the original version of the model written in Borland Pascal, see: Boose, Chamberlin, and Foster (2001) and Boose, Serrano, and Foster (2004) . 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Package: r-cran-hybridehr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-lubridate, r-cran-magrittr, r-cran-jsonlite, r-cran-openxlsx, r-cran-dbi, r-cran-rsqlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hybridehr_0.2.0-1.ca2404.1_all.deb Size: 70852 MD5sum: e63577ed74487f1a3e19d879d0f4cc24 SHA1: 6a227b87403a73234193d3375c961a97b2dc147c SHA256: f1ab6098f3bc0b0c09817f8cd3e53e610ffa85d68d84d8e5191190ae0f7bf974 SHA512: 41d07f3f2b2c95215edb765b98704aef94c8174d186f7f3862bca66c3474f98dfd4d52bbf4b239600414e20c845873b89126e2a6aef7ba894e76603910987c06 Homepage: https://cran.r-project.org/package=hybridEHR Description: CRAN Package 'hybridEHR' (Synthetic Hybrid Electronic Health Record Generation forSARS-Related Research and CT Views) Generates synthetic electronic health record data, including patients, encounters, vitals, laboratory results, medications, procedures, and allergies. The package supports optional SARS-focused and computed tomography (CT) research views and export to CSV, SQLite, and Excel formats for research and development workflows. Package: r-cran-hybridmicrobiomes Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-compositions, r-cran-rlang, r-cran-permanova, r-cran-vegan, r-cran-ks, r-cran-ape, r-cran-geometry, r-cran-stereomorph, r-bioc-phyloseq Filename: pool/dists/noble/main/r-cran-hybridmicrobiomes_0.1.1-1.ca2404.1_all.deb Size: 124536 MD5sum: 87a3fed39376c924e4b3b12820bca9ef SHA1: 5bd4b4c8ff2a541dd26eac7851f1dfa7fce6945d SHA256: fbe81962f61dc759b56bd82cb3d9401b425dd189870dfc81754a64da77b49ed3 SHA512: 5b5dcb42202fe0bc8683cdab382a5768edaa69b9916092ef42cee46454f8e5dabfaf517186f2d1576d45aef8ed4ef4d7d2816c4fa6ff362753f8f8e1e4d59158 Homepage: https://cran.r-project.org/package=HybridMicrobiomes Description: CRAN Package 'HybridMicrobiomes' (Analysis of Host-Associated Microbiomes from Hybrid Organisms) A set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz"). Package: r-cran-hybridmodels Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-ggplot2, r-cran-gillespiessa, r-cran-reshape2, r-cran-stringr Filename: pool/dists/noble/main/r-cran-hybridmodels_0.3.8-1.ca2404.1_all.deb Size: 139814 MD5sum: 9121a10f2580d5f662007e9f45a0285e SHA1: 7b33386f33197a72d8db8a7b1214e6407512afee SHA256: e799a6714c984ec66bd36b371c05197e6ac268f22df0308bb780273936e5d5e1 SHA512: e6be13da2fe738893b9c83d06d31987d1140fc1a035ea335e52886848d9d4beda68b410a5c8bd1e5e5b1446cb6e21c4e4062815a03e8642881aa988b88bc9586 Homepage: https://cran.r-project.org/package=hybridModels Description: CRAN Package 'hybridModels' (An R Package for the Stochastic Simulation of Disease Spreadingin Dynamic Networks) Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm () and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) . Package: r-cran-hybridogram Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hybridogram_0.3.2-1.ca2404.1_all.deb Size: 17296 MD5sum: 519f50a96a195e7541999f647705c6ac SHA1: fc9a072b14ca48989f970cf010501c9d0b23c1d0 SHA256: af8a456bbfc7601ba1d3822e80d09a3649d1ef4c829618793a557884e757ae1a SHA512: 5929df4f991b2565fe5b933641273cc57e37f4edf62a26d26f57c53cb7ca11838bfe4d906b8268dff47430d1ff7006459b30817dbdb588346befa2441d079aaf Homepage: https://cran.r-project.org/package=hybridogram Description: CRAN Package 'hybridogram' (Function that Creates a Heat Map from Hybridization Data) Using hybrid data, this package created a vividly colored hybrid heat map. The input is two files which are auto-selected. The first file has three columns, the first two for pairs of species, with the third column for the hybrid experiment code (an integer). The second file is a list of code and their descriptions in two columns. The output is a figure showing the hybrid heat map with a color legend. Package: r-cran-hybridts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-nnfor, r-cran-waveletarima, r-cran-metrics Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-hybridts_0.1.0-1.ca2404.1_all.deb Size: 50682 MD5sum: 10827843a302d4e789bf88dabc42e4d3 SHA1: 4a392c712b3b754c699f8729bd333491b2ccafe2 SHA256: 5497224c395f14ccd588e5dd7baa827dfd8c26bcf7adb6777576927f79b1b333 SHA512: 8d6821e0c5ef79a39658da794aa7907f9c0286d6e372bf837877f2eade11f217063f50382af67442bbe590574400d458076e441c2708c060fb60530028246182 Homepage: https://cran.r-project.org/package=hybridts Description: CRAN Package 'hybridts' (Hybrid Time Series Forecasting Using Error Remodeling Approach) Method and tool for generating hybrid time series forecasts using an error remodeling approach. These forecasting approaches utilize a recursive technique for modeling the linearity of the series using a linear method (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, and finally, the forecasts from both the steps are combined to produce the final output. This method additionally provides the confidence intervals as needed. Ten different models can be implemented using this package. This package generates different types of hybrid error correction models for time series forecasting based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022) . Package: r-cran-hyd1d Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2723 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdpack, r-cran-httr2, r-cran-curl Suggests: r-cran-dbi, r-cran-rpostgresql, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-devtools, r-cran-pak, r-cran-pkgdown, r-cran-roxygen2, r-cran-revealjs, r-cran-shiny, r-cran-shiny.i18n, r-cran-shinytime, r-cran-lubridate, r-cran-usethis, r-cran-yaml, r-cran-desc, r-cran-rsconnect Filename: pool/dists/noble/main/r-cran-hyd1d_0.5.5-1.ca2404.1_all.deb Size: 1809584 MD5sum: dea1d131c75bd4c985566b84cfe51b87 SHA1: c2b6658f4b8e92589da713b4745fd3b0dd901f8d SHA256: 5123670b1eda3926960310169e51f6b0978a03d12714f1b24898d525e446c8a4 SHA512: 8eddd8a03bcddf2bb90dc09dabade0f5b0f347c9bf26022effc62f5f5ff2388bca598c12aec522ef46965ed3806ed0521247b388e92a4fa0e517736060c37fb1 Homepage: https://cran.r-project.org/package=hyd1d Description: CRAN Package 'hyd1d' (1d Water Level Interpolation along the Rivers Elbe and Rhine) An S4 class and several functions which utilize internally stored datasets and gauging data enable 1d water level interpolation. The S4 class (WaterLevelDataFrame) structures the computation and visualisation of 1d water level information along the German federal waterways Elbe and Rhine. 'hyd1d' delivers 1d water level data - extracted from the 'FLYS' database - and validated gauging data - extracted from the hydrological database 'WISKI7' - package-internally. For computations near real time gauging data are queried externally from the 'PEGELONLINE REST API' . Package: r-cran-hydflood Architecture: all Version: 0.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-raster, r-cran-hyd1d, r-cran-rdpack, r-cran-httr2, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-pkgdown, r-cran-roxygen2, r-cran-testthat, r-cran-plot3d, r-cran-shiny, r-cran-shinyjs, r-cran-shiny.i18n, r-cran-leaflet, r-cran-leaflet.extras, r-cran-leaflet.esri, r-cran-pangaear, r-cran-rgrass, r-cran-stringr Filename: pool/dists/noble/main/r-cran-hydflood_0.5.10-1.ca2404.1_all.deb Size: 1793096 MD5sum: 557c224f209d5a1040f0f87888fb55d5 SHA1: 77bfbfcb9cb8e6d573edeef1604dd67ac6ce7f4c SHA256: 82283cb56d7a54e6327508fbab26dfb4c022d7720ae9c7e4cd875c988aac2ab8 SHA512: 53cead6e84c99086e408a11ee132e2d567087ec176d52cdfef9f8a84f0d8ab5b8c4b131c89d2e8e1cd27515a43299fc059b1ece0ee5eddd0bf45363e417e16cc Homepage: https://cran.r-project.org/package=hydflood Description: CRAN Package 'hydflood' (Flood Extents and Duration along the Rivers Elbe and Rhine) Raster based flood modelling internally using 'hyd1d', an R package to interpolate 1d water level and gauging data. The package computes flood extent and duration through strategies originally developed for 'INFORM', an 'ArcGIS'-based hydro-ecological modelling framework. It does not provide a full, physical hydraulic modelling algorithm, but a simplified, near real time 'GIS' approach for flood extent and duration modelling. Computationally demanding annual flood durations have been computed already and data products were published by Weber (2022) . Package: r-cran-hydra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-igraphdata, r-cran-matrix, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-hydra_0.1.0-1.ca2404.1_all.deb Size: 49636 MD5sum: 9f8e67dd663b864ffc856012a2cef12c SHA1: 873198e306fee91e56153da2c73eccb52d0ad3f7 SHA256: 6d9f42274b1870cb61e95bf8ecaba98dcb3b89aeddaa81bc362913de2cc71ebe SHA512: 8ab9df8a1128f3cb152694dd1f64c6173950d18896453aa55873f48c06a294dec903b54ac726c8e07fc90b4f8f161b632cf7704281a19df5cd55ef8618ab3c20 Homepage: https://cran.r-project.org/package=hydra Description: CRAN Package 'hydra' (Hyperbolic Embedding) Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see . Package: r-cran-hydraulics Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtools, r-cran-pracma, r-cran-purrr, r-cran-reshape2, r-cran-tibble, r-cran-units Suggests: r-cran-formatdown, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hydraulics_0.7.2-1.ca2404.1_all.deb Size: 823666 MD5sum: 9899c4d60051be7776bdf6a156122a5d SHA1: 71e74277172bf400196fb9d5048beb412599f642 SHA256: a0004e61ebd93f4851f1365cb7f5bb3f0f921f13a7863584bdca57faa0312d9c SHA512: 024f584cfd6d2eb746b4da2ae3c58f61c6aeb8e66ee2e44bcb7452490711deb5bf23b83146b08ed2b2d338870627cc83c398b86cd803dda33cb0e8ef1e0b7c48 Homepage: https://cran.r-project.org/package=hydraulics Description: CRAN Package 'hydraulics' (Basic Pipe and Open Channel Hydraulics) Functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, plotting a Moody diagram, matching a pump characteristic curve to a system curve, and solving for flows in a pipe network using the Hardy-Cross method. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) . For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Maurer (2024, ISBN:978-1-264-78729-6). Some gradually- and rapidly-varied flow functions are included. For the Manning equation solutions, this package uses modifications of original code from the 'iemisc' package by Irucka Embry. Package: r-cran-hydreng Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydreng_1.0.0-1.ca2404.1_all.deb Size: 216938 MD5sum: fb142bacfdbd9b02a50f9add06cf1320 SHA1: fcbfead7db7865e747a7ec971734cf0ec6831c3e SHA256: d6462ebaa848e8a297be48879bb3c5c14a77b77193c5ce1c127d7f7121c60aa4 SHA512: 3aac74c6f06fac2951129a56563ae35cc421946cfe5158e6c60936faaca337213474fe17d99c05fdedd1263f7a8f07dae5ab404356b7742685915a8b655f9b73 Homepage: https://cran.r-project.org/package=hydReng Description: CRAN Package 'hydReng' (Hydraulic Engineering Tools) The 'hydReng' package provides a set of functions for hydraulic engineering tasks and natural hazard assessments. It includes basic hydraulics (wetted area, wetted perimeter, flow, flow velocity, flow depth, and maximum flow) for open channels with arbitrary geometry under uniform flow conditions. For structures such as circular pipes, weirs, and gates, the package includes calculations for pressure flow, backwater depth, and overflow over a weir crest. Additionally, it provides formulas for calculating bedload transport. The formulas used can be found in standard literature on hydraulics, such as Bollrich (2019, ISBN:978-3-410-29169-5) or Hager (2011, ISBN:978-3-642-77430-0). Package: r-cran-hydrocal Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hydrocal_1.0.0-1.ca2404.1_all.deb Size: 66672 MD5sum: 3e9a0c4520189c52caa6b21a6f82075a SHA1: b08a5a6230e9010efe485fbe3d657b7412c196c1 SHA256: 25c5f1b5c0d2fcda8ce97e2b5677c3ebd982a8657014beeaad312fa08f545a90 SHA512: 8b3ee379faaa9a76d0700fce8b20e8e4b627dbf7a138c822d8dd5a24b3b84a5e755c262d6e58fe85d848d2143c56234c137835c3606b1d952a7293a6106442b5 Homepage: https://cran.r-project.org/package=HYDROCAL Description: CRAN Package 'HYDROCAL' (Hydraulic Roughness Calculator) Estimates frictional constants for hydraulic analysis of rivers. This HYDRaulic ROughness CALculator (HYDROCAL) was previously developed as a spreadsheet tool and accompanying documentation by McKay and Fischenich (2011, ). Package: r-cran-hydrocan Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-hydrocan_0.0.1-1.ca2404.1_all.deb Size: 493898 MD5sum: 76a4cab1569ca01e38bd85d93885006c SHA1: a6493cc0a0c4a0a5a2d464e3589d70dd36a444ca SHA256: ea4a9d375ae47af86c7c035f59bd3389752a66dd1bffa49a84eb7b704d669448 SHA512: a4d9716fa3ff1a8504053969aab3da05409c9820042199190e78d9ab1c4b99a380461e149f6fd6711bf8f0cdd3b72c6e257705a02ab242780414aa925c2b36f7 Homepage: https://cran.r-project.org/package=hydrocan Description: CRAN Package 'hydrocan' (Unified Access to Canadian Hydrometric Data) Provides a unified interface for accessing diverse web-published hydrometric data sources across Canada, presenting users with consistent tabular output regardless of the underlying data source. Package: r-cran-hydrochem Architecture: all Version: 0.1.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cowplot, r-cran-data.table, r-cran-ggplot2, r-cran-scales Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-hydrochem_0.1.0-1.ca2404.2_all.deb Size: 302270 MD5sum: 98334a9aefccf5dd946954f479ef1a12 SHA1: 6489fb40a0399e59b3a94e5a998db4e4e1837430 SHA256: 5155d20d58444454bbee409f08a3452cd93be529f6a28c5f15f3038307f0d839 SHA512: 7b2f391bbbfed8d392f4149fe043c77c4910429e71052dde1b1469d098ab69d2aec08943ba1a6e532d51f69ed530db79996c050306b58aeec26e17d4a32aa2ed Homepage: https://cran.r-project.org/package=hydrochem Description: CRAN Package 'hydrochem' (Analysis and Visualization Tools for Hydrochemical Analysis) Provides a comprehensive suite of tools for processing, analyzing, and visualizing hydrochemical data in a reproducible and programmatic workflow. Implements unit conversion, censored data management, ionic balance calculation, water type classification, and a range of hydrochemical indices relevant to water quality assessment. Offers advanced visualization functions for generating Piper (Piper, 1944, ), Durov (Durov, 1948), Stiff (Stiff, 1951, ), Collins (Collins, 1923, ), Schoeller (Schoeller, 1935), Gibbs (Gibbs, 1970, ), and ternary diagrams — the standard graphical tools used in hydrochemical interpretation. Unlike existing solutions that rely on spreadsheets or proprietary graphical software, 'hydrochem' is designed for scalability and reproducibility, lowering the technical barrier for hydrochemists working with datasets of varying complexity. It integrates seamlessly into modern R workflows and supports rigorous water resource management and research. Package: r-cran-hydrocode Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-hydrocode_1.0.3-1.ca2404.1_all.deb Size: 4813658 MD5sum: 00407f6562c8e0bbec394b1d39d46cf7 SHA1: 9eaf30e9b4f80f739c13b65a7bd50ce51bb5ed04 SHA256: be1549722000df5cf6a476d4dec06e2df58a4a9738476f8d2afcd184b6b7b0ae SHA512: 62962c5fe9a455368d35787f7ddd35c6a96cf8ee233f16132f018e48da305d0a9f8fae0f51034cc04068cea5fd6750031cc7190c7407257578079dc51aea5b53 Homepage: https://cran.r-project.org/package=HydroCode Description: CRAN Package 'HydroCode' (Hydrological Codes) Pfafstetter Hydrological Codes as cited in Verdin and Verdin (1999) are decoded for upstream or downstream queries. Package: r-cran-hydrodcindex Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hydrodcindex_1.0.0-1.ca2404.1_all.deb Size: 143900 MD5sum: 2fc626fa6c721f71a9d601a0972388a3 SHA1: 493a42739a77fb96d5ae1629dfa3872f9e4b870b SHA256: 7d0bf2fc9b6d550a7a0912c398f0335afc7d57381ff916f981472329eb2c1aae SHA512: 58cdb88edd50ab0aacbf76302cc05c1dbcd6d65f597d008049d563153a4a129b416dedc9672557f9afe54f185135a2e6048ae417b577233a8f4ff60de8b0e78a Homepage: https://cran.r-project.org/package=hydroDCindex Description: CRAN Package 'hydroDCindex' (Duration Curve Hydrological Model Indexes) Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) . Package: r-cran-hydrodownloadr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dataretrieval, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-pdftools, r-cran-progress, r-cran-rappdirs, r-cran-ratelimitr, r-cran-rlang, r-cran-rsqlite, r-cran-sf, r-cran-magrittr, r-cran-tibble, r-cran-cellranger, r-cran-stringi, r-cran-stringr Suggests: r-cran-odbc, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rvest, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2, r-cran-cachem, r-cran-curl, r-cran-memoise, r-cran-testthat, r-cran-writexl Filename: pool/dists/noble/main/r-cran-hydrodownloadr_0.1.5-1.ca2404.1_all.deb Size: 1256616 MD5sum: b1ed029ee67a0d77278745ec75e31f90 SHA1: 5e5de22b73446463394fd14cc412be09072c037f SHA256: aaf6c0d8c3e67b3f39456ed13862c1aaf0f6b92d64f1adca57d148ae3479fef6 SHA512: 26340172949482f81a711f33842cd3be9ef073f3db69b1d89c32362d9513f7966a107565cdfd122cd81e74203f437433c89c9feb7bafde39ceca9a515ac382d7 Homepage: https://cran.r-project.org/package=hydrodownloadR Description: CRAN Package 'hydrodownloadR' (Hydrologic Station Catalogs and Time Series from Public APIs) Provides a unified, extensible interface for discovering hydrological stations and downloading hydrological daily time series (e.g., water discharge, water level, water temperature) and discrete water-quality observations from national and regional public APIs. Water-quality observations are retained at their original sampling timestamps. Includes a provider registry, S3 generics 'stations' and 'timeseries', licensing metadata, date-range and 'complete history' modes, rate limiting and retries, optional authentication via environment variables, tidy outputs, UTF-8 to ASCII transliteration, and WGS84 coordinates. Designed for reproducible workflows and straightforward addition of new providers. Background and use cases are described in Farber et al. (2025) and Farber et al. (2023) . Package: r-cran-hydroeval Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydroeval_0.1.0-1.ca2404.1_all.deb Size: 164434 MD5sum: 3f8fa4944e28e7f76876be7dd753febe SHA1: ac70d09d14ad7118a2f11259d716b17e3b6eb414 SHA256: 4bc0bf40c6e8beb47a3a5ff7386e0c0ab79b71421e2ab4bdd6bbaf580d10045e SHA512: a6245428c5b8acad5228923ca26c1210f02559d07fe3de2e4ca20f8441eff0f85665aeccb8ee198896700fed255d80b2baed2fc7421b3e835e8965b00d444f62 Homepage: https://cran.r-project.org/package=hydroeval Description: CRAN Package 'hydroeval' (Hydrological Evaluation Metrics and Goodness-of-Fit Summaries) Computes scalar performance metrics and goodness-of-fit summaries for comparing simulated and observed hydrological or regression values. Provides error metrics, percentage bias, Nash-Sutcliffe efficiency, Kling-Gupta efficiency variants, correlation measures, agreement indices, and comparison tables via gof() and gof_compare(). Metric definitions are registry-governed with shared validation, provenance-aware wrappers, and explicit handling of undefined or degenerate cases. Methods include Nash and Sutcliffe (1970) , Gupta et al. (2009) , Kling et al. (2012) , and Willmott et al. (2012) . Package: r-cran-hydroevents Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3415 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hydroevents_0.13.0-1.ca2404.1_all.deb Size: 3168116 MD5sum: e632beb76a89522bbb95be0ba2e637f4 SHA1: b7f66c5bfae82dfe1be9ee1a9c2c8b5c4e3e3aab SHA256: fffc5f0bb750128fd613863bb048cdbcdfff14cc55ab72d07fc2c4d3bd7a339a SHA512: ee83b86503a859c4c396166a24c7dd39481ece38117b2974538b59daf99a3c2cf19ad9263bb132b9ad473a6f3aee625b6937c1d687168a7f8858ecc6de96c585 Homepage: https://cran.r-project.org/package=hydroEvents Description: CRAN Package 'hydroEvents' (Extract Event Statistics in Hydrologic Time Series) Events from individual hydrologic time series are extracted, and events are matched across multiple time series. The package has been applied in studies such as Wasko and Guo (2022) and Mohammadpour Khoie, Guo and Wasko (2025) . Package: r-cran-hydrogeo Architecture: all Version: 0.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydrogeo_0.6-1-1.ca2404.1_all.deb Size: 142270 MD5sum: 4de47dcc4dd72ddd624a3b3fd0495a76 SHA1: 0780e5688ddf8a89614273f7ba0e9a235c4cd6e3 SHA256: 72859464cf167c961e08fd8da6107407cbdecbd2e147646db8a67627a6b4f58b SHA512: b6ad270015599792e33b4f287c3948f8c7dff5d02427a3b01fb2557cd8e3375e6f1197457df766b0564e2e10c3445380c197351bcfbac13fdd0564864216b337 Homepage: https://cran.r-project.org/package=hydrogeo Description: CRAN Package 'hydrogeo' (Groundwater Data Presentation and Interpretation) Contains one function for drawing Piper diagrams (also called Piper-Hill diagrams) of water analyses for major ions. Package: r-cran-hydrogeofetch Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1743 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-hydroloom, r-cran-dataretrieval, r-cran-dplyr, r-cran-sf, r-cran-units, r-cran-jsonlite, r-cran-httr2, r-cran-xml2, r-cran-data.table, r-cran-arrow, r-cran-zip, r-cran-memoise, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggmap, r-cran-ggplot2, r-cran-lwgeom, r-cran-gifski, r-cran-leaflet, r-cran-httptest2, r-cran-streamcattools, r-cran-terra, r-cran-maptiles, r-cran-mapsf Filename: pool/dists/noble/main/r-cran-hydrogeofetch_2.0.4-1.ca2404.1_all.deb Size: 1612190 MD5sum: 88e1278a6c6c811d1e82011a71d583f8 SHA1: c447f5e1735af6d88254347ff690a404674bc37f SHA256: d904a2e957b5b5e560e29087854721cf27c417fcbc3aa9c3ae1b610a9e53af2a SHA512: 85a1a52a1b90785d1a693b6c1dffd558580278bbd07c54460bae322e600ba77acfc994c688403775dfdd7c2f5a65e839c8b483a7e8a664ddad2616c34482609f Homepage: https://cran.r-project.org/package=hydrogeofetch Description: CRAN Package 'hydrogeofetch' (Hydrologic Geospatial Fabric Extraction Tool Chain) Traverses and works with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'hydrogeofetch' are available in the NHDPlus documentation available from the US Environmental Protection Agency . Previously published as 'nhdplusTools'. Package: r-cran-hydrogof Architecture: all Version: 0.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-hydrotsm, r-cran-xts Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydrogof_0.7-0-1.ca2404.1_all.deb Size: 2932556 MD5sum: ce30f1fadecdd5ab35d82aba9374f4b9 SHA1: 2083865682fd129268ebef5b98fc4737ab267d4d SHA256: f83fcf096e3ce51ea0ca24c6ccef0cb80e28ddcf5efc096ada105e35275f6e9e SHA512: ac174e694005e5bbce0cd2d615a1496a52f5b055dd6265a9b27ccdca487394b29feb08a655d2377f90a9ecbfcb5ba51bddcddc5110c514ddc2a4910c841dc18f Homepage: https://cran.r-project.org/package=hydroGOF Description: CRAN Package 'hydroGOF' (Goodness-of-Fit Functions for Comparison of Simulated andObserved Hydrological Time Series) S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed. Package: r-cran-hydroloom Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1896 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-sf, r-cran-units, r-cran-pbapply, r-cran-tidyr, r-cran-rann, r-cran-rlang, r-cran-fastmap Suggests: r-cran-testthat, r-cran-hydrogeofetch, r-cran-future, r-cran-lwgeom, r-cran-future.apply, r-cran-knitr, r-cran-rmarkdown, r-cran-geos Filename: pool/dists/noble/main/r-cran-hydroloom_1.2.2-1.ca2404.1_all.deb Size: 912386 MD5sum: b10dfb0091f84fca3dfcfc3e380634ca SHA1: 609b0bb90214ff6f4ed85daeede8300ff1746e4d SHA256: 86d4dce1af2f029081a15cdcd6d05f865a4a08e7f90c6610cfe7c8adabcb1907 SHA512: 5fc2b7f1df995c1e820936b5393c700ff866a1e61d7f3b02a3105983f470f72acd0a8bebe07388de78bdc869ff94f0743f5fdcc48b93eb5429b36c2f6b27601e Homepage: https://cran.r-project.org/package=hydroloom Description: CRAN Package 'hydroloom' (Utilities to Weave Hydrologic Fabrics) A collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) , Cormen and Leiserson (2022) and Verdin and Verdin (1999) . Package: r-cran-hydrome Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-minpack.lm, r-cran-nlme Filename: pool/dists/noble/main/r-cran-hydrome_2.1.2-1.ca2404.1_all.deb Size: 96134 MD5sum: 78c6b96de9065d4003a57f5d7d325dba SHA1: 2de7b6cb09fdac0be9d58edee67cec17507abd4a SHA256: 86b36ffce4d5b51a61e4a418ff9f8722c316037f2de2fc7f778fc43d61b21adf SHA512: 052683f9d263f6eb2140d41815a1fbe96b0153de6ad071472b819f204d1bfd0432296efa5aa55ae0dd8588deaef082e7af461b7055c7322b21572c6e235080fc Homepage: https://cran.r-project.org/package=HydroMe Description: CRAN Package 'HydroMe' (Estimating Water Retention and Infiltration Model Parametersusing Experimental Data) Estimates the parameters of infiltration and water retention models using the curve-fitting methods as shown in Omuto and Gumbe (2009) . The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves. Package: r-cran-hydromeso Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydromeso_0.1.0-1.ca2404.1_all.deb Size: 627470 MD5sum: 7ac21e971d61dd4ef3d947a173e9024c SHA1: 0d4220e1e9c50b5590d553b8edd6864fb49907f1 SHA256: 93c8951c120207420c0acef4025d81d9d60580ebaf28052110fad81d0c9ac5e5 SHA512: 65b40ef5b465fbe6615e97c39149ca39a21c730b985f4c362b1f19156a68b808dc9e7f37783d47d8ff39f343eae3154c511a84309adaeb718bc220de6fd6f30e Homepage: https://cran.r-project.org/package=hydromeso Description: CRAN Package 'hydromeso' (Classify Fluvial Mesohabitats from Depth and Velocity) Classifies hydraulic conditions into nominal fluvial mesohabitat categories using water depth and velocity. It implements the eight-class scheme in the preprint by Cordero and Harris , supports validated rectangular custom schemes, and works with tabular and spatial data through 'terra'. Outputs describe hydraulic classes and do not by themselves establish biological habitat quality or species occurrence. Package: r-cran-hydromopso Architecture: all Version: 0.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-randtoolbox, r-cran-lhs, r-cran-hydrotsm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-smoof, r-cran-hydrogof, r-cran-airgr, r-cran-tuwmodel Filename: pool/dists/noble/main/r-cran-hydromopso_0.1-14-1.ca2404.1_all.deb Size: 693020 MD5sum: 8649f73450102e204c7296f42ae8b0cd SHA1: d94849015b0fa0c0221a6694a34f7f4633c0bbf8 SHA256: 2af8dd096159ef6a03e3187f46e6dfa69b2f10076522fb39973516f73234a9af SHA512: 564985ce1c03c1e4e261caebf193743785abe3a259e6550541a4436ca9b6fef4bfee91d48f35fa6287f59f72f80022d874110561a964a4db7002e3e08eaf6a85 Homepage: https://cran.r-project.org/package=hydroMOPSO Description: CRAN Package 'hydroMOPSO' (Multi-Objective Optimisation with Focus on Environmental Models) State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) . This package is inspired by and closely follows the philosophy of the single objective 'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) ), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of 'hydroMOPSO' is optimising environmental and other real-world models that need to be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, 'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time. Package: r-cran-hydropeak Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-data.table Filename: pool/dists/noble/main/r-cran-hydropeak_0.1.3-1.ca2404.1_all.deb Size: 103358 MD5sum: 9d7a5c8ccb399f564cd16e465a66b911 SHA1: 8af7f476115d2c345423461ab487ea06e5871696 SHA256: cb69917e5d227606b00b1ef1c8f24657d9ccc6f39c4de19f44d2fba553c2f0af SHA512: 7c08b0d4278828ae23ccbfe2381418f01ef80c70fddc1a3d39c6a30b3a5b96925f741ae5c69790830fecd06d42fdd118e6b9b714e7b2e3b9ac7216cc9c726f03 Homepage: https://cran.r-project.org/package=hydropeak Description: CRAN Package 'hydropeak' (Detect and Characterize Sub-Daily Flow Fluctuations) An important environmental impact on running water ecosystems is caused by hydropeaking - the discontinuous release of turbine water because of peaks of energy demand. An event-based algorithm is implemented to detect flow fluctuations referring to increase events (IC) and decrease events (DC). For each event, a set of parameters related to the fluctuation intensity is calculated. The framework is introduced in Greimel et al. (2016) "A method to detect and characterize sub-daily flow fluctuations" and can be used to identify different fluctuation types according to the potential source: e.g., sub-daily flow fluctuations caused by hydropeaking, rainfall, or snow and glacier melt. This is a companion to the package 'hydroroute', which is used to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path as proposed and validated in Greimel et al. (2022) . Package: r-cran-hydroponicsk Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-hydroponicsk_1.0.3-1.ca2404.1_all.deb Size: 38082 MD5sum: 2d72c28068616cc7cd6221fbbeac9495 SHA1: ad1a51ad7e406a729f58b297df2c2c42d3a9884c SHA256: 9691b6e7e227769329c4d10309a15211c85955d4ca13b0f943c924f240308009 SHA512: 4601ea849249b8db2c710f5e8cbc33066ad6260d05d39b3f896898fc49936d528d81b9fd503a7d2a410899ed893a17a5343362e04a4b5cccdbe906ad9633b567 Homepage: https://cran.r-project.org/package=HydroPonicsK Description: CRAN Package 'HydroPonicsK' (Hydroponic Data Analysis Tools) Provides statistical and graphical tools for the analysis of hydroponic crop production data. The package includes functions for descriptive statistical analysis, data visualization, correlation analysis, heatmap generation, and graphical summaries of plant growth and nutrient-related variables. These tools support researchers, students, and practitioners in evaluating crop performance and environmental conditions in hydroponic cultivation systems. The package utilizes standard statistical methods implemented in R for data exploration and visualization. Methods are described in James et al. (2021, ISBN:9781071614172) and Wickham (2016, ISBN:9783319242750). Package: r-cran-hydroportailstats Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd, r-cran-mvtnorm, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hydroportailstats_1.1.0-1.ca2404.1_all.deb Size: 206790 MD5sum: de7db876b6e021912b5574f3914027d7 SHA1: b2b9d2e261521cc61a6444af3d2d8f381310cd56 SHA256: 6492bb1f6cf32434e3178c698ce136a20fcae16b4f1c81e931f89fabf5b2699e SHA512: ac50a1369d7b307a3845c3b794ae13432cd4253d383349d8591dda193b4dfce18db261241f304d09b3e17d1c635d26492c3f55b90ae364cbecac91289cec2068 Homepage: https://cran.r-project.org/package=HydroPortailStats Description: CRAN Package 'HydroPortailStats' ('HydroPortail' Statistical Functions) Statistical functions used in the French 'HydroPortail' . This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) . Package: r-cran-hydroroute Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggpmisc, r-cran-ggplot2, r-cran-gridextra, r-cran-hydropeak, r-cran-lubridate, r-cran-reshape2, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hydroroute_0.1.2-1.ca2404.1_all.deb Size: 819236 MD5sum: 108e529ce5e1bfd3032f2b8b6def3758 SHA1: 706d0e365e03f674830f8d8e7f6aa4a0ae8a6988 SHA256: b6745f8c2142e15ee524860e72c273765d9d79323eefd14b8f3be0792536ea8a SHA512: 4227202e5b311280fb559b425909b068d0d7594612e1958e8c92ea413a0b514845f70412834179bd72587e158102f92a4eeddfcf08517763d9dca956ab20249c Homepage: https://cran.r-project.org/package=hydroroute Description: CRAN Package 'hydroroute' (Trace Longitudinal Hydropeaking Waves) Implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) . The identification of associated events is based on the event detection implemented in 'hydropeak'. Package: r-cran-hydrostate Architecture: all Version: 0.2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3857 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-sn, r-cran-truncnorm, r-cran-diagram, r-cran-padr, r-cran-zoo, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-hydrostate_0.2.0.0-1.ca2404.1_all.deb Size: 3086714 MD5sum: cb2a325e5e9d6ba08a4aa5e6b36ee2f5 SHA1: dedcb45b48e7a51d7b02d26908fd0bb9f1f07344 SHA256: dbad0e97f5678e5cc7976751a3d9901ea656b8e55fdb359749fae53d06fb33a6 SHA512: 348788c6e9d2cd6bb32604804d5a77f010c8b2a9b12b9dfb8ba0d5d6cbfb6b2ff8ec72bf9eac0d972b7c9c2ba16f1702ccce6442de4a4ccd0715f92016f1c85a Homepage: https://cran.r-project.org/package=hydroState Description: CRAN Package 'hydroState' (Hidden Markov Modelling of Hydrological State Change) Identifies regime changes in streamflow runoff not explained by variations in precipitation. The package builds a flexible set of Hidden Markov Models of annual, seasonal or monthly streamflow runoff with precipitation as a predictor. Suites of models can be built for a single site, ranging from one to three states and each with differing combinations of error models and auto-correlation terms. The most parsimonious model is easily identified by AIC, and useful for understanding catchment drought non-recovery: Peterson TJ, Saft M, Peel MC & John A (2021) . 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Package: r-cran-hydrotsm Architecture: all Version: 0.8-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-e1071, r-cran-lattice, r-cran-classint, r-cran-timechange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-hydrotsm_0.8-6-1.ca2404.1_all.deb Size: 4078002 MD5sum: eda66c8ebda7fe0f6c7b759ef855343b SHA1: 1edffecae8c85292660cb8693d4b1284e89e1e0f SHA256: 358b7f3471cb0c5451e8a48b60df9f2a112e609dc6cbe90b86f272719d02ea79 SHA512: 66f921a4c8c0c54025d8cb183269e7f4c5726b668279764c9e43f5402168018e130c47c1b08a4b650ffb5428dfd98e7c8b12f1e2ae1772d8b4c23fa5cc71ddf9 Homepage: https://cran.r-project.org/package=hydroTSM Description: CRAN Package 'hydroTSM' (Time Series Management and Analysis for Hydrological Modelling) S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. 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'HyperbolicDist' provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data. 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A useful applications is also considered in this package for the construction of attribute sampling plans which is an important field of statistical quality control. The quantile, and the confidence limit for the attribute sampling plan are also implemented in this package. The hypergeometric distribution can be represented in terms of Chebyshev polynomials. This representation is particularly useful in the calculation of exact values of hypergeometric variables. 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The families of distributions are Bernoulli, Exponential, Geometric, Inverse Normal, Normal, Gamma, Gumbel, Lognormal, Poisson, and Weibull. This package is an ideal resource to help with the teaching of Statistics. The main references for this package are Casella G. and Berger R. (2003,ISBN:0-534-24312-6 , "Statistical Inference. Second Edition", Duxbury Press) and Hogg, R., McKean, J., and Craig, A. (2019,ISBN:013468699, "Introduction to Mathematical Statistic. Eighth edition", Pearson). 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Several functions are provided for extracting key elements from the tabular datasets. 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Package: r-cran-iadapt Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-iadapt_2.0.1-1.ca2404.1_all.deb Size: 222580 MD5sum: 46c8bd682777dcee5cb1af83b0f6bead SHA1: 474e66ac8e917c406c7a363ddec6fa2b45724c30 SHA256: 3d75241868e338179a3f5117280d6f976cb5827e38ba95df2aeb9f72e05370af SHA512: 94ccfd5b50d4211c4f039835037289f1b4c77a46aa3e968bab40a9642dcf96d10e21895100bec0b03f8faab9944c6447e87de4174fba38c611169497dbd3f6a6 Homepage: https://cran.r-project.org/package=iAdapt Description: CRAN Package 'iAdapt' (Two-Stage Adaptive Dose-Finding Clinical Trial Design) Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading . 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Package: r-cran-iadt Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmpfr, r-cran-mgcv, r-cran-rdpack, r-cran-mvnfast Filename: pool/dists/noble/main/r-cran-iadt_1.2.1-1.ca2404.1_all.deb Size: 31592 MD5sum: 4fd36509aa8fb87e1ceb1b7fa0fc9b1d SHA1: 1158d29d437cd79d6b4a2a4da190e3b466df6a5e SHA256: a86572018cdbdc411b904343f9338a277f67b38c2a02ace93fc1accfdaedb0ed SHA512: afabcedd074acd6bfa1f4ec65f38cc49d4ac0f497c35294197b1a7006d5e75aa2ae3897e9c4b1b8038a4e27ad3f1c3144aecbc4af07ccb8159b1970cd0821809 Homepage: https://cran.r-project.org/package=IADT Description: CRAN Package 'IADT' (Interaction Difference Test for Prediction Models) Provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) and Welchowski (2022) ) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models. Package: r-cran-iai Architecture: all Version: 1.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall, r-cran-stringr, r-cran-rlang, r-cran-lifecycle, r-cran-rappdirs, r-cran-ggplot2, r-cran-cowplot, r-cran-rjson Suggests: r-cran-testthat, r-cran-covr, r-cran-xml2, r-cran-withr Filename: pool/dists/noble/main/r-cran-iai_1.10.2-1.ca2404.1_all.deb Size: 520902 MD5sum: 8c02a7b428418afbc394ae529002a8d1 SHA1: 16db81114d92a31f6ec8d4ca9caf595f5312bf67 SHA256: 2b81eba5e498f85b4363fc571bbeaf06d9886b360ff3b6ee057c86ce2b884245 SHA512: 8f35736883a10fbd777af6384f37027bfe0c817c8e69aa8ed94de61722870a51dd221cc2189b54b8d19d88380135012558498569107cb5d0379fa5a6741fc538 Homepage: https://cran.r-project.org/package=iai Description: CRAN Package 'iai' (Interface to 'Interpretable AI' Modules) An interface to the algorithms of 'Interpretable AI' from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available. Package: r-cran-ialiquor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4063 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ialiquor_0.1.0-1.ca2404.1_all.deb Size: 3900646 MD5sum: 35db960491e387cc9d71215ffa64fcea SHA1: 8cf8d0eba8b21f60581a951beef006e8f795a46d SHA256: 8dc9536b0e523d80d4643ce5abccfc17db73926355d6628515c964cff61c738e SHA512: f16b53d03ab6c9c5988cc3af7c46403839805e6aff93a54fdfb87a397adfb02175008ecc3acf12d06d49df85e056e0a0ed313510f4ef574615e3a66b29dea89f Homepage: https://cran.r-project.org/package=ialiquor Description: CRAN Package 'ialiquor' (Monthly Iowa Liquor Sales Summary) Provides a monthly summary of Iowa liquor (class E) sales from January 2015 to October 2020. See the package website for more information, documentation and examples. Data source: Iowa Data portal . Package: r-cran-ials Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-pracma, r-cran-hdmfa Filename: pool/dists/noble/main/r-cran-ials_0.1.3-1.ca2404.1_all.deb Size: 29834 MD5sum: e609195c12dcff095ebbca584a3d5ccb SHA1: 36cbb32a88f0d24112fafeb6fc5d073cc000e7b4 SHA256: 41d2e0d5eb6d6e7f769cc8151694663e7c2d4a990e2bd9274189ec5571f4e7dc SHA512: 2ba59bb6ab1bcd1cc143b674ad043b07630fad174d7cb7d8c5b7acc74c4edae7cf1e4ebdbec7e29199062aea8fc360e497d5cf16ac81647ebfef32aa4881b736 Homepage: https://cran.r-project.org/package=IALS Description: CRAN Package 'IALS' (Iterative Alternating Least Square Estimation forLarge-Dimensional Matrix Factor Model) The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) . Package: r-cran-iarm Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-erm, r-cran-ggplot2, r-cran-gridextra, r-cran-hmisc, r-cran-psychotools, r-cran-vcdextra Filename: pool/dists/noble/main/r-cran-iarm_0.4.3-1.ca2404.1_all.deb Size: 170924 MD5sum: 270c81dbdc0caa6bf6428ab65ffe9b3e SHA1: 1df6e7173a37398949e10f21da2554a8f42244a5 SHA256: b66683e4597a575ca6fac5d41f1d5d72eaad2af91ac9d1426809aad02391608e SHA512: 1c99e2ff55cd15e09cb8e1e3f02420fab663e4b862cb1fa3c519c7f1fd85398675cda26297a5c0032ef0626ef48b2c8a44e2eefa05531d41ff393e7137b660da Homepage: https://cran.r-project.org/package=iarm Description: CRAN Package 'iarm' (Item Analysis in Rasch Models) Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0). Package: r-cran-iasd Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iasd_1.1.1-1.ca2404.1_all.deb Size: 33640 MD5sum: 88ce9666e9e11a4870d110bcb39c87f3 SHA1: 1e36ef5575ac34a03f89bf96ff27447a0dca52e2 SHA256: caa7ec7232729db52657cb05897dd9207226282823deb8d1c924b4c2f27547ef SHA512: 113b6ebad5516a96b7ece4f20ed472479ed6bda0133e9ab7f153964665b9841f034d7172d3612505d5b0602bc48e8e38167309e913fe8ffe5d46c2178e934fd8 Homepage: https://cran.r-project.org/package=IASD Description: CRAN Package 'IASD' (Model Selection for Index of Asymmetry Distribution) Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal. Package: r-cran-iat Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lazyeval Filename: pool/dists/noble/main/r-cran-iat_0.3-1.ca2404.1_all.deb Size: 79334 MD5sum: d1566727b034002e3d950de0a055a780 SHA1: ed2fd390241beba6c79050c3743e40c0807c9dac SHA256: 305b1c4cf41eed50e480de4a149650fbc7c0c1fa80adc31ca6234994171c4be4 SHA512: 9898777b2bf2d89029aba6671c297e9b6bc2f262fef70267e55c1a6872689a913ea09c6fac17fcc2eea55e1896fe93aab3b9396e284d702132f8e2e338a6722f Homepage: https://cran.r-project.org/package=IAT Description: CRAN Package 'IAT' (Cleaning and Visualizing Implicit Association Test (IAT) Data) Implements the standard D-Scoring algorithm (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT) data and includes plotting capabilities for exploring raw IAT data. Package: r-cran-iatanalytics Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iatanalytics_0.2.0-1.ca2404.1_all.deb Size: 25122 MD5sum: 8fee1c123bfef681ba989436d23a045e SHA1: 90806551e80b6f15bf3ba0bd43626d896b5f8165 SHA256: 9a2e7f8e2aeff9eff24d1f35a5476de2157404cd149e9574b08bd8ab2cc0860f SHA512: 2ce56aa273a2276e52ba32e2e1dcfe7ee445b50c7dfd21d89af0ba4842875283f2589fa46f25945f371b603d2f5f52ca25f874cbaecdb930d13b337bd2094fd2 Homepage: https://cran.r-project.org/package=IATanalytics Description: CRAN Package 'IATanalytics' (Compute Effect Sizes and Reliability for Implicit AssociationTest (IAT) Data) Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) . IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) . The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). A sample dataset (titled 'sampledata') is included in this package to practice with. Package: r-cran-iatscore Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iatscore_0.2.0-1.ca2404.1_all.deb Size: 24682 MD5sum: 111f983b3b240e32c6c0fb89857098f3 SHA1: a530a8a82b249cfd0a43aa5e17e87cb3fc2b3b44 SHA256: b46819a9ed03292324a8b7930f58be5fa8b37b5ca75ca4fb4aa677a7af6517a9 SHA512: bbc5c9756d2ec0d3e36816a9881f62719293714da585094117b5fb73d7c899aa0bb39ab3db2ba999d82281bc31a09d3aad83f934d45bdc9c1f8e1b248b51db30 Homepage: https://cran.r-project.org/package=IATScore Description: CRAN Package 'IATScore' (Scoring Algorithm for the Implicit Association Test (IAT)) This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) . IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) . Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with. Package: r-cran-iatscores Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-reshape2, r-cran-qgraph Suggests: r-cran-nparcomp Filename: pool/dists/noble/main/r-cran-iatscores_0.2.8-1.ca2404.1_all.deb Size: 107036 MD5sum: d02bf4e5eb0e2945e5b321f70b0c4c40 SHA1: 9b9dc6a82108bc3dd7c72debf0c7ae0b25725182 SHA256: 6dfc74857b9b245e25391fcd2a10240598b37ece7f4ea32814e12d1d9fa631cc SHA512: 0386fad3eccf3935ae58418e545c7f292160b1d25bd3c6d5931be6640762fde0d22969c4a477b4e5c28b4df8759b286157898442d4de3a4233de25b6c76ca435 Homepage: https://cran.r-project.org/package=IATscores Description: CRAN Package 'IATscores' (Implicit Association Test Scores Using Robust Statistics) Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, ) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, ). Package: r-cran-ib Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-lme4, r-cran-formula, r-cran-mass, r-cran-matrix, r-cran-rdpack, r-cran-vgam Suggests: r-cran-testthat, r-cran-knitr, r-cran-nlraa, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ib_0.2.1-1.ca2404.1_all.deb Size: 591244 MD5sum: c4b772d3db2a0cb6b1b2542e1dc0c89b SHA1: acbe5fff52956b651ed2d412e38a9f64114b158b SHA256: 2f3546026b77942d87ce30a9aaa20667c71e358735fe3d28c3cefb15735c191b SHA512: 14bab03e633f304cfd43dcd136e948d0c48cc01a5d717606b3545543b87b491ba54e96d64cd075305f58bdfecf88fbd5932765c2eb3a0e0d9dbc8a5fca5b87b9 Homepage: https://cran.r-project.org/package=ib Description: CRAN Package 'ib' (Bias Correction via Iterative Bootstrap) An implementation of the iterative bootstrap procedure of Kuk (1995) to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique. 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It finds the symbolic formula of the regression function y=f(x) as described in Ye, Senftle, and Li (2023) . 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Package: r-cran-ibb Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-rlang, r-cran-magrittr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-covr, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ibb_0.0.2-1.ca2404.1_all.deb Size: 84902 MD5sum: b1f4f87e12ae64db10c719d7bc4ec381 SHA1: 08e0f39babf630287eff5a38ef4e9caf1e22a6c9 SHA256: d910ccead8de99999f59537509b4bc8f382131e25a25f8cb4514c507e6b6d4a0 SHA512: 1f8209434b1f4fae4e588040ce25a70f82a8bdf582626b82c2992488e62666dd8efc93db5ad04af3f2d2e4ab78502373aff95719fda5f6047f4391fa327b4e95 Homepage: https://cran.r-project.org/package=ibb Description: CRAN Package 'ibb' (R Wrapper for Istanbul Municipality Open Data Portal) Call wrappers for Istanbul Metropolitan Municipality's Open Data Portal (Turkish: İstanbul Büyükşehir Belediyesi Açık Veri Portalı) at . Package: r-cran-ibcf.mtme Architecture: all Version: 1.6-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lsa, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ibcf.mtme_1.6-0-1.ca2404.1_all.deb Size: 90824 MD5sum: 9c598f91ce4086f10534adfc1671da8a SHA1: 2ff0980f3b69cf0c1192303254c85ec438f581a4 SHA256: c065b9ee597bf60b82a8f154d4606f6f8f0c9ec97c7bc8ae49c23d425819bc3d SHA512: ea620b8c851b5ea43d18579d439c30c9cd32d8e278808037035b2e7571c5f8821fabfefce4893652ff70d78f224da268362f7612c654aca852c60afce231cc46 Homepage: https://cran.r-project.org/package=IBCF.MTME Description: CRAN Package 'IBCF.MTME' (Item Based Collaborative Filtering for Multi-Trait andMulti-Environment Data) Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) . Package: r-cran-ibd Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-car, r-cran-emmeans, r-cran-multcomp Suggests: r-cran-multcompview Filename: pool/dists/noble/main/r-cran-ibd_1.6-1.ca2404.1_all.deb Size: 125050 MD5sum: 1cd4ae30d41e5866caa5e5aa33410aca SHA1: 06d8d06e17c82401db97dbb4dcd095c8c51cff9a SHA256: 3d1c6bab226f4a06a22b8df22622ebdfeea1914caef69f4ab798bba0b76871b1 SHA512: c6e2099be9242ed66a365b4d4eb376d342fee69064bc76b1b6458373139397f1bb60192411ccfb97b777bad4c8a62d88fd49eaddb3ed69324e42691b2b565d85 Homepage: https://cran.r-project.org/package=ibd Description: CRAN Package 'ibd' (Incomplete Block Designs) A collection of several utility functions related to binary incomplete block designs. Contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. Contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported. Package: r-cran-ibdfindr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forrel, r-cran-ggplot2, r-cran-ibdsim2, r-cran-pedtools, r-cran-ribd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibdfindr_0.5.0-1.ca2404.1_all.deb Size: 892478 MD5sum: 3c4d9ca48b07029f67cee7f92d0661cf SHA1: f904e4b96e70f4874c84cae94f31c9014614d928 SHA256: c8b01ee3d428516e13b8848e5263b0f2709b9f40e16599adbd3c996393505684 SHA512: 41e06bd4a6453e7b3d9a0bbaababe5e3825ab2473f5e823b97e374bf23c942ecc549c1b7416f3b78fa7368e173c53750d78a1378a389d20b87daa6af41c0f543 Homepage: https://cran.r-project.org/package=ibdfindr Description: CRAN Package 'ibdfindr' (HMM Toolkit for Inferring IBD Segments from SNP Data) Implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals. Supports two- and three-state models using single-nucleotide polymorphism (SNP) genotypes or genotype likelihoods. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data. The methodology and package are described in Vigeland et al. (2026) . Package: r-cran-ibdinfer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crossdes, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ibdinfer_0.1.0-1.ca2404.1_all.deb Size: 35082 MD5sum: 4b4041711eb3e8e54a537fc36e59e8f8 SHA1: 8fd3bd74ba3e360fea06f4b1d80368dfb90a9c65 SHA256: 838c58cced9ba37ea57852248d8008ac678faf848c2b6b24b77b4a2b31400402 SHA512: 64367402412bf09ba4852cf8e838fb69fea3ae977ad7711181bd52baa6c0e9f61227f60e330c96e8a55e1dcf5262a2e4f90740c03193beb41a1eb8c9750ab8cc Homepage: https://cran.r-project.org/package=IBDInfer Description: CRAN Package 'IBDInfer' (Design-Based Causal Inference Method for Incomplete BlockDesigns) This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on 'Koo and Pashley' (2026) , it includes block-level estimators and extends to unit-level effects using 'Horvitz-Thompson' and 'Hájek' estimators. The package also provides asymptotic confidence intervals to support valid statistical inference. Package: r-cran-ibelief Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ibelief_1.3.1-1.ca2404.1_all.deb Size: 87272 MD5sum: 4278f49b27a6d3ec15e5e617d17ca161 SHA1: 1dc6e2ed2d3472c898f8180fcb2a942ba8c0a28b SHA256: 5a85528646500511c3bd0552f93e47703db628a855320ad6e8f824c200841235 SHA512: 4f37e7013b9073f3d9ea683b2933797749d0cb279cb6b685120a1b1731b0bc09097185cf8088c2c4e4abefdc36aca5ac4917a0cac9e83d599f578af27a3f6001 Homepage: https://cran.r-project.org/package=ibelief Description: CRAN Package 'ibelief' (Belief Function Implementation) Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets , evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management. Package: r-cran-ibfs Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ibfs_1.0.0-1.ca2404.1_all.deb Size: 31902 MD5sum: a418989f864ff0da2af68ed8846ab823 SHA1: 83629a66f1cf0f864903566f4d23d13f2c6d1786 SHA256: 8a84f865b9516c901d54bf2ff14b621e3731bd4ace89b8dbe46c8af68e56ed0f SHA512: 0083d91e99bc791eaf1df1eda06df2841077a35b6b8b790f78633b12e283d234655137c64d0f71f00dadf05c0c6f4a2595e54a96dbb6cd269e85b977b7b90923 Homepage: https://cran.r-project.org/package=IBFS Description: CRAN Package 'IBFS' (Initial Basic Feasible Solution for Transportation Problem) The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. This initial basic feasible solution can be obtained by using any of the following methods. a) North West Corner Method. b) Least Cost Method. c) Row Minimum Method. d) Column Minimum Method. e) Vogel's Approximation Method. etc. For more technical details about the algorithms please refer below URLs. . . . . Package: r-cran-ibger Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-dplyr, r-cran-rlang, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-tidyr, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-bsicons, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibger_0.2.0-1.ca2404.1_all.deb Size: 180114 MD5sum: 24111a606b6ca14ef2a1f92f59606472 SHA1: 31665ed0036d2dfeaf0a093e1917d253a34a838a SHA256: c8a8faac7d6bcefe1fcbf65e7767ea2567f32591fa85e23684b728f10322346d SHA512: e218468f321623031713a838f46e4d17e2d83695e03b377aae6bfe0b8987e8318427b6621a3f3ff9037890c02af8e9c07d5641e6d4d1ef99ae4366d9dd65867e Homepage: https://cran.r-project.org/package=ibger Description: CRAN Package 'ibger' (Access the 'IBGE' Aggregate Data API from 'R') 'Tidyverse'-friendly interface to the Brazilian Institute of Geography and Statistics ('IBGE') aggregate data 'API' . Query aggregates, variables, localities, periods, and metadata from surveys and censuses conducted by 'IBGE'. Package: r-cran-ibkrcp Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibkrcp_0.1.2-1.ca2404.1_all.deb Size: 55310 MD5sum: 809d9ba0861a2eff44274599d039af89 SHA1: f0c93114fe571eaa78b6927f2cd515c7efbc7e73 SHA256: adfeec3fd5fa85bc8d17d4cd871ec0688e3cc2fdb15e3ac2b774ca114dc002f3 SHA512: 3ee27760ef0d12118a32c095516091080533bed6fa7a2e66b5525be5383b99c27edaac67de4e017b7183bf37a4c7c45ce3f9b8583ba4373dfec49c1cb5c591d4 Homepage: https://cran.r-project.org/package=ibkrcp Description: CRAN Package 'ibkrcp' (R Client for the Interactive Brokers Client Portal API) Provides a lightweight R interface to the Interactive Brokers (IBKR) Client Portal REST API. Functions cover session management, account and portfolio queries, market data retrieval, and order placement and cancellation. Requires a locally running IBKR Client Portal Gateway. 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These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) . Package: r-cran-ibmacousticr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-xml Filename: pool/dists/noble/main/r-cran-ibmacousticr_0.2.1-1.ca2404.1_all.deb Size: 39244 MD5sum: 0f3a35f58386520afe72f478f8abd9c0 SHA1: a7c402dfe418bc095cc18d18e47f4624b36c94a2 SHA256: 2f72929cbc01cbd1277c68b75ad486519989f1ab2dea4a32b2a5163443a10d21 SHA512: c8f198e361dc23a137d33bce236d8dc6a31ffd473b52b3fc9d0bcf14dacafafde575b6b437ce44b9a1925d45d36c9ba4077721f9688eccbecc2700f8de4060ca Homepage: https://cran.r-project.org/package=ibmAcousticR Description: CRAN Package 'ibmAcousticR' (Connect to Your 'IBM Acoustic' Data) Authentication can be the most difficult part about working with a new API. 'ibmAcousticR' facilitates making a connection to the 'IBM Acoustic' email campaign management API and executing various queries. The 'IBM Acoustic' API documentation is available at . This package is not supported by 'IBM'. Package: r-cran-ibmdbr Architecture: all Version: 1.51.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rodbc, r-cran-matrix, r-cran-arules, r-cran-mass, r-cran-rpart, r-cran-rpart.plot Suggests: r-cran-ggplot2, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ibmdbr_1.51.0-1.ca2404.1_all.deb Size: 1083202 MD5sum: e324a34cec8b49eb2041529b8b2e1547 SHA1: 803f311263e252e808d609dcaccb33303e9b9c59 SHA256: 2031fdff7ce7e23328832acfcdf81c974fb176572ffafd39fc8460a1a6dfa8ce SHA512: b01563f5534be4d8b42bcdb360f861057d76ff7a32d6f159753bb1f4083ded791bcf9347ae46a0a46fd0903a53b504c2844db07d07977e805209de0014c610cc Homepage: https://cran.r-project.org/package=ibmdbR Description: CRAN Package 'ibmdbR' (IBM in-Database Analytics for R) Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (). The optional 'ggplot2' package is needed for the plot.idaLm() function only. Package: r-cran-ibmsunburst Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibmsunburst_0.1.4-1.ca2404.1_all.deb Size: 328248 MD5sum: 81f521441b168b76b4fd4d8b07646914 SHA1: c0a9968f650eec9a2c547fe85df186fe16001675 SHA256: 475a65cb6e43dcb37c1e9e047dc7c5c814561692c0eef87808be0f4656bd2801 SHA512: 56532849b061ed73a2159164e2c1b441e6fcf9360431017419f91efce77ee4478ade8eb3821f55fd1e22a403c2a5a99cf9ede7a5abe5b9fce9a22edb54968a37 Homepage: https://cran.r-project.org/package=ibmsunburst Description: CRAN Package 'ibmsunburst' (Generate Personality Insights Sunburst Diagrams) Generates Personality Insights sunburst diagrams based on 'IBM Watson' Personality Insights service output. Package: r-cran-ibreakdown Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dalex, r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-e1071, r-cran-ranger, r-cran-nnet, r-cran-testthat, r-cran-r2d3, r-cran-jsonlite, r-cran-covr Filename: pool/dists/noble/main/r-cran-ibreakdown_2.1.2-1.ca2404.1_all.deb Size: 440888 MD5sum: aff5c2a97a95d889f7bc723670640c9c SHA1: 5ca0c2759262f17dc9b82ca968cf861f20ea304d SHA256: 8d039057294afcbfe74e763862b94ba6a92ce98b12e81a25f59e9d49c5ea52d2 SHA512: fe969644e6fb566f2c3c5ae6da597407ad859abfd4b4f7e71d4c4592d0fb9c52d3162fb2a2c40e5b659c1361c0fd4fa6fae70bf3f8f8d168fe1aa3a7326fa576 Homepage: https://cran.r-project.org/package=iBreakDown Description: CRAN Package 'iBreakDown' (Model Agnostic Instance Level Variable Attributions) Model agnostic tool for decomposition of predictions from black boxes. Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) , with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) This package is a part of the 'DrWhy.AI' universe (Biecek 2018) . Package: r-cran-ibrokers Architecture: all Version: 0.10-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-ibrokers_0.10-2-1.ca2404.1_all.deb Size: 507334 MD5sum: c8404b101cc443675bc445d633841e47 SHA1: bc6b85fd1176261b4c2991467fd0a1173ae37237 SHA256: 95f67fcb1511ecfcae5920e7517a153b1623a8c42803e6b68566f4a3138c7c4f SHA512: 8b83df7c9ad527c901cce1ff5a9c88d0cf16f8c4c8918e451b8fd76ababc96fe5d7a4c4b25cd9bc01847e005c7b61fd571f618d9bbedf60bd90aa9c1cde43b9d Homepage: https://cran.r-project.org/package=IBrokers Description: CRAN Package 'IBrokers' (R API to Interactive Brokers Trader Workstation) Provides native R access to Interactive Brokers Trader Workstation API. Package: r-cran-ibrtools Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-data.table, r-cran-gtools, r-cran-tidyr, r-cran-fmsb, r-cran-binhf, r-cran-tidyselect Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibrtools_0.1.3-1.ca2404.1_all.deb Size: 57872 MD5sum: bb8d7139dc62d59c721e7217b71b7f0d SHA1: bc8ddb65aefb9c71934fdb74437ba62f219178a9 SHA256: dea037258bb419ef8997cd3a40a2140e083d20b183af46c8c4932054e9f11b58 SHA512: cfedff62ea14b3bfe1e4b5af5a28898affdacf66f29718d8a20f07eccf89470ab7c1e88a5952daae0c3fd41147c2dcb50cd02d788bb2d6c6a7859c866df4a328 Homepage: https://cran.r-project.org/package=IBRtools Description: CRAN Package 'IBRtools' (Integrating Biomarker-Based Assessments and Radarchart Creation) Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). . Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). . Minato N. (2022). . Package: r-cran-ic.infer Architecture: all Version: 1.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog, r-cran-mvtnorm, r-cran-boot, r-cran-kappalab Suggests: r-cran-relaimpo Filename: pool/dists/noble/main/r-cran-ic.infer_1.1-8-1.ca2404.1_all.deb Size: 312332 MD5sum: 6bf7bfce1ce59cd183d0c309b223c02e SHA1: c521e19175e6262fc0b6d53c8738c439dc64c718 SHA256: ca783329c2f9e084b066b520e4ea6a645e4ce1ecbf597040cdcee14f9da4d769 SHA512: 9a26c346b680540edb6ce4c0a743a78e0c2d64cf817559c265c72fcea0b8e5796f0562899a5758943a6988739662a15b22033041fb0b87bae7de3b22d085799b Homepage: https://cran.r-project.org/package=ic.infer Description: CRAN Package 'ic.infer' (Inequality Constrained Inference in Linear Normal Situations) Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included. Package: r-cran-ic10 Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pamr, r-bioc-impute, r-cran-ic10trainingdata Filename: pool/dists/noble/main/r-cran-ic10_2.0.3-1.ca2404.1_all.deb Size: 79600 MD5sum: 8c7d018596217d55a825bcf069bd3c5d SHA1: f79d7265cce3922f9250dea4bd3063ecdf41cfd0 SHA256: 108f6117f60f207b65b5e50690f1affc62eadf79042b6896903f189064a200a8 SHA512: 6023e60c7231a40b6bb5dac124fabe494c682566d6a612ee36ed2375209c214263f1c0279700efb667453fecb707d679d8ca31a93cbb64b2fd27b6eb4e582964 Homepage: https://cran.r-project.org/package=iC10 Description: CRAN Package 'iC10' (A Copy Number and Expression-Based Classifier for Breast Tumours) Implementation of the classifier described in the paper Ali HR et al (2014) . It uses copy number and/or expression form breast cancer data, trains a Tibshirani's 'pamr' classifier with the features available and predicts the iC10 group. Package: r-cran-ic10trainingdata Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5765 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ic10trainingdata_2.0.1-1.ca2404.1_all.deb Size: 5856210 MD5sum: 0f19168b0fececd19135def8f648bdbc SHA1: 2fa0972a26e9e8a54a393bfa14cf3f443207529c SHA256: 1b5f444eb339c3a37f31e53b2ea34879970ef180786edc06c47da41458ad80a6 SHA512: 5554821457f64b4fe58899c41eaacc76ceee6245cad57f541978577f29eea401e1d97da9b9e4359854c12f74b81872b34f0ca7b64b95cfd84dbdaa01c44574ed Homepage: https://cran.r-project.org/package=iC10TrainingData Description: CRAN Package 'iC10TrainingData' (Training Datasets for iC10 Package) Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package. Package: r-cran-ica Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ica_1.0-3-1.ca2404.1_all.deb Size: 86440 MD5sum: 3b7694b6c1583f7b0a1de8ff61368da6 SHA1: d0c3243e2eb1c50d7ea33a6cfaca36b6f58ad7f4 SHA256: 263bc0c3b3385de29dc01d1cdb30cebece4e101a7c45d321959a71711a9249c4 SHA512: 58d236a4c0ef994f02e29882dc5cfe5d8cf2db08321efbbe3c57b36393b6c28bfac2f9aea809c98b72296a2f15b51939d4e2d72eae77c522c4e931a4f7619147 Homepage: https://cran.r-project.org/package=ica Description: CRAN Package 'ica' (Independent Component Analysis) Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE). Package: r-cran-ical Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-v8 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-ical_0.1.6-1.ca2404.1_all.deb Size: 68380 MD5sum: 8dbecc2c9f346d688d6fc43ee8bd208f SHA1: 896874cdbe574ed8f13b420fb6c9179b687080f3 SHA256: 07b407d0ef67dbf4fd11e7c6aa72af913652a6796dba230a605900f982203a29 SHA512: bce93b9a2765e1b62a78ff6e83a6aebea4d84e3a36d7e85569c8efb154f2e520e7035998665247a4eb9c38eed756a6cf9d61d495d1bc61b15e16dc9647830d42 Homepage: https://cran.r-project.org/package=ical Description: CRAN Package 'ical' ('iCalendar' Parsing) A simple wrapper around the 'ical.js' library executing 'Javascript' code via 'V8' (the 'Javascript' engine driving the 'Chrome' browser and 'Node.js' and accessible via the 'V8' R package). This package enables users to parse 'iCalendar' files ('.ics', '.ifb', '.iCal', '.iFBf') into lists and 'data.frames' to ultimately do statistics on events, meetings, schedules, birthdays, and the like. Package: r-cran-icamp Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vegan, r-cran-permute, r-cran-ape, r-cran-bigmemory, r-cran-nortest, r-cran-minpack.lm, r-cran-hmisc, r-cran-dirichletreg, r-cran-data.table Filename: pool/dists/noble/main/r-cran-icamp_1.9.1-1.ca2404.1_all.deb Size: 1502872 MD5sum: 118766c2fffd4f6a7b7dfa3044183a36 SHA1: 9ef46c10094e373d44cee8e3abb5b40ec4262ddb SHA256: 73fceee8bb0e46a4d3e6f80a7e234da63087f3f31d5dfe507f17212fe0780f62 SHA512: 9b504ec29a8e82b023ce7cf9f1c63bb1d67cbebfd1a2af749936b20876968eacc01f5d2ef7134166d73ee98ca0df91247af52a2817c1859452feffd88844f8a8 Homepage: https://cran.r-project.org/package=iCAMP Description: CRAN Package 'iCAMP' (Infer Community Assembly Mechanisms by Phylogenetic-Bin-BasedNull Model Analysis) To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) . It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) . It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa). Package: r-cran-icams Architecture: all Version: 3.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-bioc-bsgenome, r-cran-data.table, r-cran-dplyr, r-cran-fuzzyjoin, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-cran-lifecycle, r-cran-rcolorbrewer, r-cran-stringi, r-cran-zip Suggests: r-bioc-bsgenome.hsapiens.1000genomes.hs37d5, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icams_3.0.11-1.ca2404.1_all.deb Size: 2787178 MD5sum: d65759d98826c8b6c0c7fde707843ebf SHA1: 438f67f963402b02032da2ca870383d6c264f3c7 SHA256: 549198c7cd1668dcb744e63fafcd533ccc3093b535133444c60cbf3de6a31679 SHA512: 01d8b963e09d4219fcb81a87a812b9924fee7935b0d23eb9ee2a016044011a449de87c77a341d1e6d343f3747f3533956b63eac99c86fd64fbc52bab01bd0337 Homepage: https://cran.r-project.org/package=ICAMS Description: CRAN Package 'ICAMS' (In-Depth Characterization and Analysis of Mutational Signatures('ICAMS')) Analysis and visualization of experimentally elucidated mutational signatures -- the kind of analysis and visualization in Boot et al., "In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors", Genome Research 2018, and "Characterization of colibactin-associated mutational signature in an Asian oral squamous cell carcinoma and in other mucosal tumor types", Genome Research 2020 . 'ICAMS' stands for In-depth Characterization and Analysis of Mutational Signatures. 'ICAMS' has functions to read in variant call files (VCFs) and to collate the corresponding catalogs of mutational spectra and to analyze and plot catalogs of mutational spectra and signatures. Handles both "counts-based" and "density-based" (i.e. representation as mutations per megabase) mutational spectra or signatures. Package: r-cran-icardafigsr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-httr, r-cran-magrittr, r-cran-plotroc, r-cran-plyr, r-cran-raster, r-cran-reshape2, r-cran-sp, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-icardafigsr_1.0.2-1.ca2404.1_all.deb Size: 978120 MD5sum: 111e4e9613036f4177d15a8ce2d17470 SHA1: 44f99028c964591b5b3930d776d16bc3c4041a6d SHA256: 83d8654d7d404937b57935daa2ca8426f6ba50e6535cbcd42b74cfc28032ce76 SHA512: 5b71d304e6d41a712bf7927a0dcea3ce2f7538e4ecfa1f06623cf46de9bc93db0385e1f8b9ab9fc1a7fc164d29e27708e7e11d9de4adb83edafb6a3354c489b5 Homepage: https://cran.r-project.org/package=icardaFIGSr Description: CRAN Package 'icardaFIGSr' (Subsetting using Focused Identification of the GermplasmStrategy (FIGS)) Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection. Package: r-cran-icarh Architecture: all Version: 2.0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-mass, r-cran-ggplot2, r-cran-glue, r-cran-rcurl, r-bioc-kegggraph, r-cran-igraph, r-cran-reshape2, r-cran-mc2d, r-cran-abind, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icarh_2.0.2.1-1.ca2404.1_all.deb Size: 124810 MD5sum: 2f36e545fef8c3db1c50289c15724914 SHA1: bea4942a2f40dbf7455f4c27bd2de2f19509d8ed SHA256: 4d5374e869bc44c4b75fb5eeaf093ac5274b1005d9b23a7c3a1dbf5f81fe6c72 SHA512: 64638cc9e18b2dd671c29273666c33c269783d4d1fba5aa756ecfce6646926a05ca074b87d17db9095c9dbc977dc1bea4f522b8d716a86c92a1766530f5f253f Homepage: https://cran.r-project.org/package=iCARH Description: CRAN Package 'iCARH' (Integrative Conditional Autoregressive Horseshoe Model) Implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) . The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters. Package: r-cran-icarm Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rpart, r-cran-class, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-jsonlite, r-cran-digest Suggests: r-cran-randomforest, r-cran-xgboost, r-cran-e1071, r-cran-mgcv, r-cran-glmnet, r-cran-nnet, r-cran-dalex, r-cran-proc, r-cran-lightgbm, r-cran-ipred, r-cran-mass, r-cran-ggiraph, r-cran-rmarkdown, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-icarm_0.3.0-1.ca2404.1_all.deb Size: 248740 MD5sum: b222b641138156aa21eb80ae64013c59 SHA1: f8f5842a46bf4fde5d7f5cbaf14a7a6a2880912f SHA256: 25fe1b6641ddefb29bce96d94b1244c3e25a66d872292672ab83f3ee0c9ab05f SHA512: a354b6f1f2990cf66bb21bca23d9b03a653f6ad004f230ca1ed27a280537520975c33727c86277ba2fdb7a42333f5bdfd5326e47e9f52108614ab94a1ca9adb5 Homepage: https://cran.r-project.org/package=icarm Description: CRAN Package 'icarm' (Interpretable Contextual-Accountable and Responsible MachineLearning) A general-purpose framework for Interpretable Contextual-Accountable and Responsible Machine Learning (ICARM) that works with any clean tabular data across any application domain including healthcare, finance, social science, business, and education. Automatically detects whether a prediction task is binary classification, multi-class classification, or regression from the target variable type. Provides a unified entry point icarm_fit() supporting both interpretable learners (Classification and Regression Trees (CART), logistic regression, linear regression, Generalized Additive Models (GAM)) and extended learners (random forest, 'XGBoost', Support Vector Machines (SVM)) with consistent interfaces for global and local model explanation including approximate SHapley Additive exPlanations (SHAP) values and Partial Dependence Profiles (PDPs), learning curve diagnostics, group-level fairness auditing across protected attributes, probability calibration, threshold analysis, multi-model comparison, reproducible JavaScript Object Notation (JSON) audit trails, and accountability scorecards. The contextual accountability framing emphasises that algorithmic fairness and interpretability requirements depend on the deployment domain and must be evaluated accordingly. Extends the 'civic.icarm' framework (Awe 2025) to general-purpose applications beyond civic and political education. Package: r-cran-icarus Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-rglpk, r-cran-slam, r-cran-xtable, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-icarus_0.3.3-1.ca2404.1_all.deb Size: 3981210 MD5sum: ce336f093d3abcba81cca08e09317a31 SHA1: bdb7a2dadfc514f4d3fb5fbd4f3be89917e308af SHA256: 9ea1996f00069c156ad22de375cc220b6c640fd6a6976b1df60240b56720485b SHA512: 729f50d4a5f794544b54a343a4c3073a5839a5c44a87b6b81657d0af1943cb9ea81c75ba63382c236810b4b1d8cb3330046687f87ea93750cc99d4655c5fc141 Homepage: https://cran.r-project.org/package=icarus Description: CRAN Package 'icarus' (Calibrates and Reweights Units in Samples) Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration. Package: r-cran-icbiomark Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3896 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-dplyr, r-cran-purrr, r-cran-latex2exp, r-cran-matrixstats, r-cran-ggplot2, r-cran-gglasso, r-cran-prroc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icbiomark_0.1.4-1.ca2404.1_all.deb Size: 3878626 MD5sum: 9c7c4cbc93bc02505e092e5e64b23964 SHA1: a01cf5cf0ce7f3ece3acef666a05a9162dbdf2d6 SHA256: 910e9566f6274aaee992deeebb72247ffd2ae71a7a080b8849edea9e8489fad7 SHA512: 95769f56a5c88d2c363775a742b1d47e01466b00411dcb4a08b5e92f8c41352a4d4a49b02c99cc8c59f0c1fe9dd045369c9d91dca2755640e11f487fa1802931 Homepage: https://cran.r-project.org/package=ICBioMark Description: CRAN Package 'ICBioMark' (Data-Driven Design of Targeted Gene Panels for EstimatingImmunotherapy Biomarkers) Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) . This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models. Package: r-cran-icc.sample.size Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-icc.sample.size_1.1-1.ca2404.1_all.deb Size: 29608 MD5sum: abfac719df0c844579d3a90b29206440 SHA1: 78869dca0494ad3e4de66ff797b16aad998c2762 SHA256: 947b78e21b130300ffece50bb04ce618177ea4b7306ecd9f05f2d7f2a15589c1 SHA512: 4cfbb32e20c10cf00e869c49bb6c04fc4b9de38be5bc3741a9e66d9b054e073f7dec74f8ab7974a56909908044df515742795b3e7a007cf60e2f09b03a2630ca Homepage: https://cran.r-project.org/package=ICC.Sample.Size Description: CRAN Package 'ICC.Sample.Size' (Calculation of Sample Size and Power for ICC) Provides functions to calculate the requisite sample size for studies where ICC is the primary outcome. Can also be used for calculation of power. In both cases it allows the user to test the impact of changing input variables by calculating the outcome for several different values of input variables. Based off the work of Zou. Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in medicine, 31(29), 3972-3981. Package: r-cran-icc Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-icc_2.4.0-1.ca2404.1_all.deb Size: 47116 MD5sum: bd9ac410bc66b3c5c16b1d213a94b878 SHA1: 1113f8811f4aa0351cb56521e6a390a4223802c9 SHA256: fef44475300269274cc86435e937c4ac776c9ece4885c1ea09c4d1323383b65a SHA512: 478cf020bbc1b57459d01ed7c3e1a552a7765cce9f8a0e11cf64e81f904906bc482cc409f187371c75e974be8f5689cd27dbdb404b9a1f526ed8947547d429f2 Homepage: https://cran.r-project.org/package=ICC Description: CRAN Package 'ICC' (Facilitating Estimation of the Intraclass CorrelationCoefficient) Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width. Package: r-cran-iccbin Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-iccbin_1.2.0-1.ca2404.1_all.deb Size: 58688 MD5sum: c7d8c290a84c6758f0be9f4ffd1f8b50 SHA1: 6cbcd6f5537701c6f3fe09b185fd0cbc421344b8 SHA256: 40e5ec80a647aba1b95ba4d3eb18234401e7be0c90af14a912d38f4c3b36bb36 SHA512: 6a06f1f192c13ca0cba3997543d7cd00bc654a6c8e773ddbd119d629f5709568f2fa77d0412c3bc3a2ef53ff02b155755b047bd1783daf04ba0b919afd23108a Homepage: https://cran.r-project.org/package=ICCbin Description: CRAN Package 'ICCbin' (Facilitates Clustered Binary Data Generation, and Estimation ofIntracluster Correlation Coefficient (ICC) for Binary Data) Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals. Package: r-cran-icccompare Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-dplyr, r-cran-deriv, r-cran-mass, r-cran-furrr, r-cran-future, r-cran-progressr, r-cran-parallelly, r-cran-bbmle, r-cran-mvtnorm Suggests: r-cran-cccrm Filename: pool/dists/noble/main/r-cran-icccompare_1.1.0-1.ca2404.1_all.deb Size: 75000 MD5sum: 40f611946f6312688341f400730e947c SHA1: 471844523766775ff2cd5769e48deb1a3ae036f1 SHA256: fac4db3a997e3da323cc95dff1e04991f01e2bf076d5f9adb66707269df03ac5 SHA512: 0b6cd1bdc637a0afd0ec7d8047b0521bfa578694d162e9fe363ba35f86c05632bf767c88cabd94519b1ddf2ae67a2e76653521460ece5dcc1c3bdce3b266cbb7 Homepage: https://cran.r-project.org/package=iccCompare Description: CRAN Package 'iccCompare' (Comparison of Dependent Intraclass Correlation Coefficients) Provides methods for testing the equality of dependent intraclass correlation coefficients (ICCs) estimated using linear mixed-effects models. Several of the implemented approaches are based on the work of Donner and Zou (2002) . Package: r-cran-icccounts Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmmtmb, r-cran-ggplot2, r-cran-deriv, r-cran-gridextra, r-cran-vgam, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icccounts_1.1.4-1.ca2404.1_all.deb Size: 282274 MD5sum: 0d08258d76064937c939cb37a210e2e5 SHA1: 6206332f7da00115ca899d9bddb397e1fc1b70fa SHA256: a582bbc9dc4f52f19c5843872a560d3b7feac6f20f9cf15973aaea4eed92ca85 SHA512: 54015f281a0e2400b8c7b323eb2f540f66b082546e68e8f7948b569f81d96646c5d63893a244373bc4854456471f5bc426f92ece23b06a3da44b6da0876c803b Homepage: https://cran.r-project.org/package=iccCounts Description: CRAN Package 'iccCounts' (Intraclass Correlation Coefficient for Count Data) Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) . Package: r-cran-iccde Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iccde_0.3.9-1.ca2404.1_all.deb Size: 27574 MD5sum: b1f607c09ddf2728e2cdaba278ddca8f SHA1: ce76b2fc449630b503abd9f54bed6a4f8d5ca805 SHA256: 7a8430fd33cd9addfd0ed5b34fdd3ee2b9415aaed978db14eb1553d014304aa2 SHA512: 1269dd2827c9cb271a1f61a4cfacbec727cb8742437347f85a70dd9f82e259e01a0806e9315d0e1195a9f982c52844a43ac2363e614768abe049730c3c722d30 Homepage: https://cran.r-project.org/package=iccde Description: CRAN Package 'iccde' (Computation of the Double-Entry Intraclass Correlation) The functions compute the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). Profiles comprising correlations are automatically transformed according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) or McCrae (2008) for details. Package: r-cran-iccdesign Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-iccdesign_0.1.1-1.ca2404.1_all.deb Size: 157806 MD5sum: 0d21899597c1fe4fe4edb6cc85436dbf SHA1: 964e02a28df1212612056a80e535dfd0a0fc0824 SHA256: cb3f5214d635a2fef6b3178eb329acfe5345fefb75f834cd2814bb432120d650 SHA512: 64aaa29e48d6358b0cbd103f8c923c7ed1096ca7f1b864443051aec65e357e58d17122ee5f271ade679f333dd2cd905e54bc37364bc1091b2d8d904d5896cb31 Homepage: https://cran.r-project.org/package=ICCDesign Description: CRAN Package 'ICCDesign' (Intraclass Correlation Coefficient (ICC) Design, Calculation andInteractive 'shiny' Toolkit) A comprehensive toolkit for intraclass correlation coefficient (ICC) analysis, integrating three core functionalities: (1) Closed-form sample size calculation for ICC estimation with assurance probability, based on Zou (2012) ; (2) Full implementation of all 10 ICC types (6 common + 4 supplementary) for point estimation, exact confidence interval calculation, and formal hypothesis testing, following the methods of McGraw & Wong (1996) and the standard decision framework; (3) An interactive 'shiny' application that guides users through ICC type selection, performs calculations, and provides reliability evaluation based on the Koo & Li (2016) criteria. Compared to existing packages, it provides a unified decision workflow and supports all less common ICC variants. Package: r-cran-iccforest Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-survival, r-cran-icenreg, r-cran-ipred Suggests: r-cran-ltrctrees, r-cran-inum Filename: pool/dists/noble/main/r-cran-iccforest_0.5.1-1.ca2404.1_all.deb Size: 59780 MD5sum: b7a24ab6fbcc0dbf1cefc0d1369e952f SHA1: 390bc58b99d0ae87c278920e0214e3cd8ac9dd99 SHA256: 2df4a50830791850b1f4e5462b794409963a40c427ce14d8e072542a928354fb SHA512: a489d1d14f2df8c35748bb75d504c2a519746763ec18dc334a4ca529168b49b77c1047dead25c57768c19dd65985b75b5a848e4d51c2d7e0a08ba542c634b82d Homepage: https://cran.r-project.org/package=ICcforest Description: CRAN Package 'ICcforest' (An Ensemble Method for Interval-Censored Survival Data) Implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) . 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The distances implemented are the extended Hausdorff distances (Min et al. 2007) and the discrete Fréchet distance (Magdy et al. 2015) . Package: r-cran-icd.data Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5004 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icd.data_1.0-1.ca2404.1_all.deb Size: 4408776 MD5sum: 05cd2ee95a3ecc918044ce1d29c80c04 SHA1: 6429458f0af67d6ab47382060da507d35e6dfc40 SHA256: acf25cce52aced2e6628249d37efb65f988389be65b9fdc4823b91d58ff1c0b3 SHA512: b79439c40995c24eef38f912bb6dba03849aa6dc282d4885902652c7fd94dc70afd3ff4f485f6c46101204742729a4cd1b567dd5cfd2315d2d7879c3756f9e3a Homepage: https://cran.r-project.org/package=icd.data Description: CRAN Package 'icd.data' (International Classifcation of Diseases (ICD) Data) Data from the United States Center for Medicare and Medicaid Services (CMS) is included in this package. There are ICD-9 and ICD-10 diagnostic and procedure codes, and lists of the chapter and sub-chapter headings and the ranges of ICD codes they encompass. There are also two sample datasets. These data are used by the 'icd' package for finding comorbidities. Package: r-cran-icd10gm Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringi, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rvest Filename: pool/dists/noble/main/r-cran-icd10gm_1.2.5-1.ca2404.1_all.deb Size: 1281842 MD5sum: 47f65f5b9bfb40c4f55f0103d1ae6038 SHA1: 1ad3cd315551842b4835ebf053b60cab10e83e5c SHA256: d72f378bf7d7c81e4591845c6ddc4f8ff28a36aa0f0ded56c6a81e77ad1ea2b3 SHA512: f58b4f405f300b8ed4bdf18f37f89cd9398ae78bb9d4c00999d858d36ef125de6aafbd84fda8e2692a0bd7293273a17ac90dd21bf8e8721b071a1281e060ce2b Homepage: https://cran.r-project.org/package=ICD10gm Description: CRAN Package 'ICD10gm' (Metadata Processing for the German Modification of the ICD-10Coding System) Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information , and are redistributed in accordance with their license. Package: r-cran-icdcomorbid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icdcomorbid_1.0.0-1.ca2404.1_all.deb Size: 49112 MD5sum: df99939e57740e1d7d56a77e30562286 SHA1: 99e3ef55f4bb401dafafb8f2227250ee855277e8 SHA256: 73e8c4d98dd53d23c51cae904448f67c32da412a02832a474145b482b68c760b SHA512: 1104d6b409be390ad207c2a7b0a61a664c18ec26bd38811a6719f784f3dec86341996bb6690d017063ea000163c3404a1a736937fd4d7285c56e3b0311611ef1 Homepage: https://cran.r-project.org/package=icdcomorbid Description: CRAN Package 'icdcomorbid' (Mapping ICD Codes to Comorbidity) Provides tools for mapping International Classification of Diseases codes to comorbidity, enabling the identification and analysis of various medical conditions within healthcare data. Package: r-cran-icdglm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-icdglm_1.0.2-1.ca2404.1_all.deb Size: 51150 MD5sum: 48779707573b23be3eb1b3c9d3558b14 SHA1: d6ce54820b62241bf2bfdad6aa56df8e674675dd SHA256: d0a34e932dbf90908b8dd304a6584131476ef34f7febcebe3c55b835773dfcfb SHA512: bd713d1853294c3551e0a54591a40e0f1f22122633a12de4ef571954e1f273cf0f0b686712a876f8c89f722dd4693b9c3e5bdc667de9b382e016c95b0eab36d0 Homepage: https://cran.r-project.org/package=icdGLM Description: CRAN Package 'icdGLM' (EM by the Method of Weights for Incomplete Categorical Data inGenerlized Linear Models) Provides an estimator for generalized linear models with incomplete data for discrete covariates. The estimation is based on the EM algorithm by the method of weights by Ibrahim (1990) . Package: r-cran-icdhelper Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3582 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readxl, r-cran-arules Filename: pool/dists/noble/main/r-cran-icdhelper_0.1.2-1.ca2404.1_all.deb Size: 2457464 MD5sum: d20364857ed2a12058403944e369d61b SHA1: 4b80b4bc9eba4601d2359ba3d9f0e93777cf9167 SHA256: 44ee6edef96015da1084e314bc2c4b7f98b5779985bb6b118be1dce93a174b25 SHA512: 79fbe41342426f58b0ba7d4fdde87dafde05564048ef28ec7f2e7bc2b0ecacd66fcbfee1df56297308e305e9638dc31d42777c96fa02259b24eb47a94e1343e1 Homepage: https://cran.r-project.org/package=icdhelper Description: CRAN Package 'icdhelper' (Simple 'ICD-10' Code Descriptions) Provides utilities to retrieve and manage 'ICD-10' code descriptions from 'Excel' files. Supports vectors, data frame columns, and association rule outputs (e.g., 'arules' objects) that require mapping to 'ICD-10' descriptions. Designed to simplify processing of healthcare datasets. Package: r-cran-icdpicr2 Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icdpicr2_2.1.0-1.ca2404.1_all.deb Size: 689220 MD5sum: 644f8941c73e959241cc1a8ab1f2733c SHA1: 7842326155117a020770d0251d2807914c89b65e SHA256: a51b5d3478458e375019438cc3b828f87e1d8288f46c2c22c3c4040fafc8a7cc SHA512: d44844a7aab081eb25028ae66a0d40f45f1b3554a20f879e5d22b85f653b9a5f0742fefe21b9bb13be68e7af3d1c64b9083cf2e748d851f7bfbf0c9096cd5198 Homepage: https://cran.r-project.org/package=icdpicr2 Description: CRAN Package 'icdpicr2' (Categorize Injury Diagnosis Codes) Functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject. They return original data with injury categorizations and severity scores added. Package: r-cran-icdpicr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-rcmdcheck Filename: pool/dists/noble/main/r-cran-icdpicr_1.0.1-1.ca2404.1_all.deb Size: 1423102 MD5sum: cdc54806ad71aff94418600a7b975804 SHA1: ceb65f3b30446119074bd7d62aa1911be2a17686 SHA256: 82c7f7c7daa6192bea52c17d36fbe8d722983bf744737e2b718c02bac5df72ac SHA512: 32f2841c0055f6b77e56b6a90332aaae0f030aec717fd577fa7bf2c7887df6b4f3180730be7eea9c09b57dfb25c4aea9860e02fb2d3afe213354737a00298738 Homepage: https://cran.r-project.org/package=icdpicr Description: CRAN Package 'icdpicr' ('ICD' Programs for Injury Categorization in R) Categorization and scoring of injury severity typically involves trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS') from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update that accepts both 'ICD-9' and 'ICD-10' codes. Package: r-cran-icds Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-graphite, r-cran-metap, r-bioc-org.hs.eg.db Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icds_0.1.3-1.ca2404.1_all.deb Size: 608842 MD5sum: d4cd3b37d13bf3767cab8ac479219eed SHA1: 9c9390ed2c2392daf2daf6781e7175c09961ab82 SHA256: 27834ea4727dd24587e8c78c61c14e8c28efce1975c66a1d6322ca1cc42d77de SHA512: cbe623dc40e083bc75de548af13749cdd9c7e79a4f7b22d19529ad6702dafdd52709850e87434117bfc0f3fbfe7af44db74fa593cf514c854bf08560d21c21be Homepage: https://cran.r-project.org/package=ICDS Description: CRAN Package 'ICDS' (Identification of Cancer Dysfunctional Subpathway with OmicsData) Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways. Package: r-cran-icebox Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc Suggests: r-cran-randomforest, r-cran-mass Filename: pool/dists/noble/main/r-cran-icebox_1.1.5-1.ca2404.1_all.deb Size: 168370 MD5sum: 732f9832ade2b88085aa206cc34244ca SHA1: 96739c95b8f24fb5b9df8a0aafc1b98747fbd294 SHA256: 90c8ec9fae655fff9c9a596a38e025a2ed095d09983a2a14ba32fe19599595af SHA512: b43d377930d2f744ebd49025c2439096be3825b66f49717b9d5e8e33e415e6cc04ec6ec31bfb8a220c931ab40ef4a924c9500ddbf002445853252ae5483aae2e Homepage: https://cran.r-project.org/package=ICEbox Description: CRAN Package 'ICEbox' (Individual Conditional Expectation Plot Toolbox) Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist. Package: r-cran-icecdr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-glue, r-cran-httr2, r-cran-rlang Suggests: r-cran-here, r-cran-ncdf4, r-cran-rcdo, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-icecdr_1.2.0-1.ca2404.1_all.deb Size: 153716 MD5sum: 61073f0de65d26bdc330de883034a75e SHA1: 867b3f14734185f2473778a0596baf9f71f14c51 SHA256: 87d0af0ffb1a58213cb3f2fa64390726157aa15f885cf13b0b8a57236de82f94 SHA512: df6ae7206f1e269352b5875a615b519ac6be8f2464c5d799c3030d209dcd67aa13611a5dc8dd27e67e4d3340fab86f5b29fe0d886829834f8110c448ff49f713 Homepage: https://cran.r-project.org/package=icecdr Description: CRAN Package 'icecdr' (Download Sea Ice Concentration Data from the NSIDC Climate DataRecord) Programmatic access to NSIDC's sea ice concentration CDR via ERDAPP server and Sea Ice index . Supports caching results and optional fixes for some inconsistencies of the raw files. Package: r-cran-icecream Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-pillar, r-cran-purrr, r-cran-rlang Suggests: r-cran-checkmate, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-icecream_0.2.2-1.ca2404.1_all.deb Size: 42350 MD5sum: 9da88504766a03292a9b62b129409061 SHA1: ec898fd1ed8ea13ee81f7739043ee86cdde337cb SHA256: 50da1d13cc8c2579dd07172362cdb5ab0864331474d10b65074b104573d82550 SHA512: dd12f592f91e32f5c862a8c7ceeb706ed7f8f7a2db49fdc0735bfc0cb76f8c624718f578248e9edca11c52dc9eb75d5dbf155d3bcb199a24c63a86967ff85db0 Homepage: https://cran.r-project.org/package=icecream Description: CRAN Package 'icecream' (Print Debugging Made Sweeter) Provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code. Package: r-cran-iced Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-knitr, r-cran-lavaan, r-cran-mass, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iced_0.0.1-1.ca2404.1_all.deb Size: 772334 MD5sum: 4d724fb5b378d82de269455de29e60e1 SHA1: e295568d060e40be97c49351845e1c7a14bdb7e4 SHA256: 2fd3b2b61381f8a5367f9ac0c6c984bdb2f2e5b14ace92883b6de2324b6aaedf SHA512: 327c215a101aa6cba9b2427e54a7dd55d4afd35035213209b2945485b3bf6d8d4206fda1f57a100cd0b26e90dcf22708e201ae1ca168dbbad1449953061afac7 Homepage: https://cran.r-project.org/package=ICED Description: CRAN Package 'ICED' (IntraClass Effect Decomposition) Estimate test-retest reliability for complex sampling strategies and extract variances using IntraClass Effect Decomposition. Developed by Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" Also includes functions to simulate data based on sampling strategy. Unofficial version release name: "Good work squirrels". Package: r-cran-icehmeasures Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-survey, r-cran-rlang, r-cran-car, r-cran-tidyselect Suggests: r-cran-viridislite Filename: pool/dists/noble/main/r-cran-icehmeasures_2.1.0-1.ca2404.1_all.deb Size: 71872 MD5sum: 65352982dcddbe7720f2f913067551c4 SHA1: 892798065badf66fa38ffc634f5cfc379570004a SHA256: 35b413c20fd7d147b6447fb5e5ff5d33cf0d7f638f68eef43d9086c51f925c90 SHA512: bd212878d5c11cc4e477d90cd8e69157119810659a9170a70ff52d23047a9ec9df5f0fe2a1037db1e4a297da019103c2b19efd8b28244d3b79123d626ec57ef2 Homepage: https://cran.r-project.org/package=ICEHmeasures Description: CRAN Package 'ICEHmeasures' (The Equiplot Graph and Complex Inequality Measures) Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) . Package: r-cran-iceinfer Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-iceinfer_1.3-1.ca2404.1_all.deb Size: 1146626 MD5sum: 3990c4419ad0271e515d640f78c6a15e SHA1: 8e1db96beb64b4721dba51178efcb6183123f39c SHA256: b12a78422177e1095481f7455bde10eb71d3684de45345ff9c3d5ff60b906982 SHA512: 6611311e476aa4991268ac681aa8e53f8451e217daf4d412b9af40b5530aa1e283cf956bd58fb1d0059b88864e61283183704844eabdcdabae05eadf92c9fa2a Homepage: https://cran.r-project.org/package=ICEinfer Description: CRAN Package 'ICEinfer' (Incremental Cost-Effectiveness Inference using Two UnbiasedSamples) Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters. Package: r-cran-icensbkl Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass, r-bioc-icens, r-cran-teachingdemos, r-cran-mvtnorm, r-cran-smoothsurv, r-cran-gtools Suggests: r-cran-interval, r-cran-logspline, r-cran-mixak, r-cran-bayessurv, r-cran-fhtest, r-cran-icenreg, r-cran-mlecens, r-cran-cubature, r-cran-runjags, r-cran-coda, r-cran-dynsurv Filename: pool/dists/noble/main/r-cran-icensbkl_1.5-1.ca2404.1_all.deb Size: 355238 MD5sum: 18bc11a1ddafc9f8c9497421870e1f82 SHA1: bdef942b159da2831b12c787d0c60d549e710c09 SHA256: 9ea2bd17c4615d0568cb2f855e2d8c3e683960e41399b26ec8a9f82b44add48d SHA512: ba4c04bd707384f95da63addcee53b433ed4e566d4f75809e8ba87b5af22d8add453cfc33fa91dc7edf0207d7cee7983e50601e7b818984a1a6fcba21d65cd52 Homepage: https://cran.r-project.org/package=icensBKL Description: CRAN Package 'icensBKL' (Accompanion to the Book on Interval Censoring by Bogaerts,Komarek, and Lesaffre) Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" . Full R code related to the examples presented in the book can be found at . Packages mentioned in the "Suggests" section are used in those examples. Package: r-cran-icertool Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-purrr, r-cran-dt, r-cran-tidyverse, r-cran-readxl, r-cran-ggplot2, r-cran-ggrepel, r-cran-digest, r-cran-shinyhelper Filename: pool/dists/noble/main/r-cran-icertool_0.0.3-1.ca2404.1_all.deb Size: 29964 MD5sum: 075b4e83d36e4fdd1e60407c5364d581 SHA1: b93e79aef05c4972976f4c02fa8699d911e47a22 SHA256: 87083d064d2cfd498104ec8eb4dc18a0f6ee34206a216f9d400cf79a405026eb SHA512: 88398b6184b4c9954365eaa9b6c0c66ff48a0453ea14199bbfd40d33094ef19e1f8077a4cbf714122b1708c0fac38aeb1c0ebafebdb743eaf8ae05738f4674ab Homepage: https://cran.r-project.org/package=icertool Description: CRAN Package 'icertool' (Calculate and Plot ICER) The app will calculate the ICER (incremental cost-effectiveness ratio) Rawlins (2012) from the mean costs and quality-adjusted life years (QALY) Torrance and Feeny (2009) for a set of treatment options, and draw the efficiency frontier in the costs-effectiveness plane. The app automatically identifies and excludes dominated and extended-dominated options from the ICER calculation. Package: r-cran-icesadvice Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-icesadvice_2.1.2-1.ca2404.1_all.deb Size: 61626 MD5sum: 5e1032c610182bbca6fd0c88d6b61395 SHA1: fa0e950cb11654bf21aa3981e27bb3fcf72c67b2 SHA256: 2fbcd252f9ba5c43162701904b05c0ad861bda86c6c899150973bf632a0bf1f0 SHA512: 4beecb2fbb9f76f012033e9f414d34dd6451df6fccb66daf07e565ac903ca1ffc6d6f47a73d71f68140f8d56fb8e2f5dd24e4da39ac459eb3ba9c2cad99c5bab Homepage: https://cran.r-project.org/package=icesAdvice Description: CRAN Package 'icesAdvice' (Functions Related to ICES Advice) A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics. Package: r-cran-icesat2r Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3636 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glue, r-cran-sf, r-cran-lwgeom, r-cran-units, r-cran-data.table, r-cran-httr, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-leaflet, r-cran-leafgl, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leafsync, r-cran-miniui, r-cran-shiny, r-cran-rnaturalearth, r-cran-lubridate, r-cran-rvest, r-cran-withr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dt, r-cran-mapview, r-cran-stargazer, r-cran-reshape2, r-cran-plotly, r-cran-geodist, r-cran-copernicusdem, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icesat2r_1.1.0-1.ca2404.1_all.deb Size: 3336286 MD5sum: faccc0ca295f208d44b43bc74e80c5af SHA1: e9d5a16b0baed1c2f12f892e42bf963f0dc85e3a SHA256: 0a438615be694b1b33d6f5e00b95043571313ca2b66d1fdd92ae7d443ecee1ae SHA512: 3cab1893a69a9351e85115446e5969bf61615316adc985c4f0aa871b127511c1fbcdc44ef48066a904a539ac57f169dde0ed9dba2f59e636be07d94ba2ac6408 Homepage: https://cran.r-project.org/package=IceSat2R Description: CRAN Package 'IceSat2R' ('ICESat-2' Altimeter Data using R) Programmatic connection to the 'OpenAltimetry' API to download and process 'ATL03' (Global 'Geolocated' Photon Data), 'ATL06' (Land Ice Height), 'ATL07' (Sea Ice Height), 'ATL08' (Land and Vegetation Height), 'ATL10' (Sea Ice 'Freeboard'), 'ATL12' (Ocean Surface Height) and 'ATL13' (Inland Water Surface Height) 'ICESat-2' Altimeter Data. The user has the option to download the data by selecting a bounding box from a 1- or 5-degree grid globally utilizing a shiny application. The 'ICESat-2' mission collects 'altimetry' data of the Earth's surface. The sole instrument on 'ICESat-2' is the Advanced Topographic Laser Altimeter System (ATLAS) instrument that measures ice sheet elevation change and sea ice thickness, while also generating an estimate of global vegetation biomass. 'ICESat-2' continues the important observations of ice-sheet elevation change, sea-ice 'freeboard', and vegetation canopy height begun by 'ICESat' in 2003. Package: r-cran-icesconnect Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-base64enc, r-cran-jsonlite, r-cran-askpass Filename: pool/dists/noble/main/r-cran-icesconnect_1.1.4-1.ca2404.1_all.deb Size: 33538 MD5sum: 3d4af475f6fa7cdc400209ddb3c83dbf SHA1: 3aa5fd34087bd7607f5b1052793610d230cce43b SHA256: 180f5e41284de8f2fd8e09d0ceb88c51e4da06c74d4e991f49123dd1adbc525a SHA512: d4670a010a7fa876405632946d5027fa6690f8e456e5fb742db101d091abf2227d282f4159f9926db752b4bd506b7f11f6026cbbcd8f00befaeca575df298067 Homepage: https://cran.r-project.org/package=icesConnect Description: CRAN Package 'icesConnect' (Provides User Tokens for Access to ICES Web Services) Provides user tokens for ICES web services that require authentication and authorization. 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ICES is an organization facilitating international collaboration in marine science. 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The method combines information from base forecasts constructed using different information sets while ensuring coherence. It is implemented using a penalized regression-based framework. 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However, there are some major disadvantages of training such networks via the widely accepted 'gradient-based backpropagation' algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) , wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) , namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) , i.e., the PCA based ELM model given by Castano et al. (2013) , from which the implemented ICA based algorithm is greatly inspired. 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Package: r-cran-idconverter Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4744 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idconverter_0.4.0-1.ca2404.1_all.deb Size: 4792310 MD5sum: c5b62780402814fc31633e1f8c31af2b SHA1: 889ab9e6eadf7d33bead15ab21e21164e7839bbf SHA256: ed5942dad3600ea52f07104f77a56a1b2659030b62e40e6718c1eb3fa4ce6cac SHA512: 6cb6793c036c54097e109c81d46c7105ec84d60c61bd47ccc6c8970f872148d8bd9ddb4b2278431f66d51e6b850e6c1f460a59ca0dd98a23f08d0777e3121038 Homepage: https://cran.r-project.org/package=IDConverter Description: CRAN Package 'IDConverter' (Convert Identifiers in Biological Databases) Identifiers in biological databases connect different levels of metadata, phenotype data or genotype data. This tool is designed to easily convert identifiers within or between different biological databases (Wang, Shixiang, et al. (2021) ). Package: r-cran-ide Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-sp, r-cran-spacetime, r-cran-dplyr, r-cran-tidyr, r-cran-frk, r-cran-deoptim, r-cran-sparseinv Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-ide_0.3.1-1.ca2404.1_all.deb Size: 264220 MD5sum: ee4e772f6f39402b4a785637b6c60f6a SHA1: 96cf7bf1153d5e0632d15e6066e15a2b93f08d52 SHA256: 7d23327962c13950af4aade853020390717683b161c4f5e9825def3547c90709 SHA512: 437be6c0419efef7cba6587771d52c97ae62f735736d2a6c48d03e777454973b899232c60fafb57d7f1b4421136e4d49710edc0e4c34e5e2b1a64754c2662aef Homepage: https://cran.r-project.org/package=IDE Description: CRAN Package 'IDE' (Integro-Difference Equation Spatio-Temporal Models) The Integro-Difference Equation model is a linear, dynamical model used to model phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011, ISBN:978-0-471-69274-4) or Dewar et al. (2009) . At the heart of the model is the kernel, which dictates how the process evolves from one time point to the next. Both process and parameter reduction are used to facilitate computation, and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted by maximum likelihood, and estimation is carried out using an evolution algorithm. Package: r-cran-ideafilter Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggplot2, r-cran-pillar, r-cran-purrr, r-cran-rcolorbrewer, r-cran-shiny, r-cran-shinytime Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest, r-cran-shinytest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ideafilter_0.2.1-1.ca2404.1_all.deb Size: 206524 MD5sum: dd0020a7d84527e742cf52995904b0b0 SHA1: 8938720e35d839e0feed3b24431d28697cd5a2c9 SHA256: 95fdfc88acca86f24225b3b32bd7ed38f380dca68a7830872d011eb81374b417 SHA512: 59795183308a8a371d91278e0d885a7d68236c2f36c76ebc1dacdc91761d951846d2cd15e721c8418e371379355cefc78ae7f4aa0ced9e9e4654a97f730c3a8b Homepage: https://cran.r-project.org/package=IDEAFilter Description: CRAN Package 'IDEAFilter' (Agnostic, Idiomatic Data Filter Module for Shiny) When added to an existing shiny app, users may subset any developer-chosen R data.frame on the fly. That is, users are empowered to slice & dice data by applying multiple (order specific) filters using the AND (&) operator between each, and getting real-time updates on the number of rows effected/available along the way. Thus, any downstream processes that leverage this data source (like tables, plots, or statistical procedures) will re-render after new filters are applied. The shiny module’s user interface has a 'minimalist' aesthetic so that the focus can be on the data & other visuals. In addition to returning a reactive (filtered) data.frame, 'IDEAFilter' as also returns 'dplyr' filter statements used to actually slice the data. Package: r-cran-idealstan Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-svdialogs, r-cran-tidyr, r-cran-stringr, r-cran-bayesplot, r-cran-posterior, r-cran-gghighlight, r-cran-ggrepel, r-cran-ggplot2, r-cran-tidybayes, r-cran-lazyeval, r-cran-rlang, r-cran-ggthemes, r-cran-forcats, r-cran-ordbetareg, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-quarto, r-cran-knitr, r-cran-rmarkdown, r-cran-loo, r-cran-lubridate, r-cran-rstan, r-cran-shinystan, r-cran-pscl, r-cran-tinytable, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idealstan_1.0-1.ca2404.1_all.deb Size: 659242 MD5sum: b27ab7ea07db9dbc168687204fde0ce6 SHA1: eb6bbd9ed4e41bc866c59938d39d264718d388b1 SHA256: 3fb94e69cf7405869b1aece767fe8198492504ac83555d27e1bc218159ad673f SHA512: 93b5c53fc19051e00fb55e1dc33beef1841cfeaee10865518cab4ae07a171abb742edaf03363eae08febc2c9420abb11a978bea54efaea44aa9bdeb40a25a4a5 Homepage: https://cran.r-project.org/package=idealstan Description: CRAN Package 'idealstan' (Robust Measurement with 'Stan') Offers item-response theory (IRT) ideal-point measurement modeling for diverse distributions, missing data, and over-time variation. Full and approximate Bayesian sampling with 'Stan' (). Package: r-cran-ideamdb Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ideamdb_0.0.9-1.ca2404.1_all.deb Size: 401084 MD5sum: 551d006a818658e6a454995c49b18088 SHA1: 8b113c8ee8221c46f8e752f67215be753b9f5da7 SHA256: 05a797812eb964456ba29cce9425e30555d27ee2cd3859e514ed425ed19ff5a6 SHA512: 58200e5e8ef582ae26c097ba78f7567f9018662b407c9251ce676b327ec8ab8608c6e7432a3a4f0fefaf1fee590a7e082967ef744043e605cecc731bab2e45f0 Homepage: https://cran.r-project.org/package=ideamdb Description: CRAN Package 'ideamdb' (Easy Manipulation of IDEAM's Climatological Data) Time series plain text conversion and data visualization. It allows to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales) daily series from plain text to CSV files or data frames in R. Additionally, it is possible to obtain exploratory graphs from times series. IDEAM’s data is freely delivered under formal request through the official web page . Package: r-cran-ideanet Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraphdata, r-cran-cliquepercolation, r-cran-cluster, r-cran-colorspace, r-cran-concorr, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridgraphics, r-cran-igraph, r-cran-intergraph, r-cran-jsonlite, r-cran-magrittr, r-cran-matrix, r-cran-moments, r-cran-network, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-rspectra, r-cran-shiny, r-cran-sna, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dt, r-cran-devtools, r-cran-egor, r-cran-ergm, r-cran-shinythemes, r-cran-shinywidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-shinycssloaders, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-ideanet_1.1.1-1.ca2404.1_all.deb Size: 2706474 MD5sum: 119b514d1339e36d389e6cf28192456e SHA1: a16d2b6b7980ef3b09d4ee5169a629b51c08c9bd SHA256: 3e1bffbfda5755415153c7538c9380c0e174b4e7ac61d18f3f58f569ec404512 SHA512: 0269db63647602053819b15f8284eb70e421531661f9f44a3214265b3a145cf097a1c764d5674413cee0451a0a2df230a81455dc55c80eb66c39c23d8f6cb778 Homepage: https://cran.r-project.org/package=ideanet Description: CRAN Package 'ideanet' (Integrating Data Exchange and Analysis for Networks ('ideanet')) A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding. Package: r-cran-ideatools Architecture: all Version: 3.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-readxl, r-cran-tibble, r-cran-stringi, r-cran-ggimage, r-cran-pdftools, r-cran-ggpubr, r-cran-ggplot2, r-cran-rlang, r-cran-ggtext, r-cran-rmarkdown, r-cran-shiny, r-cran-openxlsx Suggests: r-cran-covr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ideatools_3.5.2-1.ca2404.1_all.deb Size: 2334864 MD5sum: 2272be480c31cfedbc65f4bfad6b3511 SHA1: 394247ec56d99c82f05596312cfbe2860759ecbd SHA256: 4d0f940a5697de2f41b347c536f30604242ec6ae52af1da412352669925091a9 SHA512: 1812e4e72c7afdd0308e2b48ab4f56dff1bdf2a150a7fe81024dbc6a2ae5502c6f4d899c7290c6795763e8f240a19c0d8169cd61ec73ec1c2b42bfc37eaed9da Homepage: https://cran.r-project.org/package=IDEATools Description: CRAN Package 'IDEATools' (Individual and Group Farm Sustainability Assessments using theIDEA4 Method) Collection of tools to automate the processing of data collected though the IDEA4 method (see Zahm et al. (2018) ). Starting from the original data collecting files this packages provides functions to compute IDEA indicators, draw modern and aesthetic plots, and produce a wide range of reporting materials. Package: r-cran-idendr0 Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tkrplot Suggests: r-cran-dendser, r-cran-cluster, r-cran-rcolorbrewer, r-cran-loon, r-cran-mass, r-bioc-flowstats, r-cran-hyperspec Filename: pool/dists/noble/main/r-cran-idendr0_1.5.4-1.ca2404.1_all.deb Size: 1555988 MD5sum: 3e4df104d7e0e104c12a8358d0c68091 SHA1: 44318b705161daab0140aaf72b64e278aa9366be SHA256: 22e6d5b822074455feaef349ff9560f572c35f7cb325b1a9cc700b3e605817c1 SHA512: 835aac1eeb277c78af4e3dfcd8dda9f0c88ba360003794eaea61ced9a40c68ad18d341d06647abb0dabd28f928f16be658912e3a05122e58128b8e87913a345c Homepage: https://cran.r-project.org/package=idendr0 Description: CRAN Package 'idendr0' (Interactive Dendrograms) Interactive dendrogram that enables the user to select and color clusters, to zoom and pan the dendrogram, and to visualize the clustered data not only in a built-in heat map, but also in 'GGobi' interactive plots and user-supplied plots. This is a backport of Qt-based 'idendro' () to base R graphics and Tcl/Tk GUI. Package: r-cran-ider Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-glm2 Filename: pool/dists/noble/main/r-cran-ider_0.1.1-1.ca2404.1_all.deb Size: 933830 MD5sum: 9b8586855e031d3a59d00a7cb269eabe SHA1: 4861ac7f3adb0dff2e2897c4f0f1f8c85fd14e15 SHA256: 21d1614685bc2a9be88328e7417cd42b5d61d4fb34fe5da184724766528b5867 SHA512: 671cae0d78f3c6fcd6948d0d4713d06fe7978231cfa1216bf2b320364fe33052e9a583a93546eb54340f55150438360da41f26bb11856f597779e6fd74cadf45 Homepage: https://cran.r-project.org/package=ider Description: CRAN Package 'ider' (Various Methods for Estimating Intrinsic Dimension) An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension. Package: r-cran-idetect Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-idetect_0.1.1-1.ca2404.1_all.deb Size: 155878 MD5sum: d8a33e3b7c9bf7252817d2f0f700f1a9 SHA1: a1570a8411761cb5c049ce650ddeed66f5bd7d11 SHA256: 9e5e3a295e6b011113a9570df324dd2f78996b62d648671d5fc54beceff5f17d SHA512: bb08471f5aafc20888c9064f55da901dfcd3d712027dba0605015104d605cd3b147ce74656ff5cd9e1856f8d10d61e4606f667afae050554622732e06cf2c9ae Homepage: https://cran.r-project.org/package=IDetect Description: CRAN Package 'IDetect' (Isolate-Detect Method for Multiple Change-Point Detection) The IDetect provides efficient implementation of the ID methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the `deterministic + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, continuous piecewise-linear signal, continuous piecewise-linear signal with a heavy-tailed noise. Package: r-cran-idf Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd, r-cran-ismev, r-cran-rcpproll, r-cran-pbapply, r-cran-fastmatch Filename: pool/dists/noble/main/r-cran-idf_2.1.3-1.ca2404.1_all.deb Size: 284730 MD5sum: 002bacd5837f8a7221c288bd6bf5329d SHA1: 51f49a579828ed1daa0a1882635d6d452ad8ddb7 SHA256: 6ffea39458e9fdf154542d15a85fb34787b71ed66a708b771f1feaf471ad7c2e SHA512: 42037220155548994f700f6aabf5a0469adcc3d977283b766af50e1b162a78d1862bfed8939f576db98820fce57aeae0679c13924ff292ae761be70692b80f96 Homepage: https://cran.r-project.org/package=IDF Description: CRAN Package 'IDF' (Estimation and Plotting of IDF Curves) Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, ]. The package 'IDF' provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, ]. Package: r-cran-idingo Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mvtnorm, r-cran-glasso, r-cran-ggmridge, r-cran-visnetwork, r-cran-scales Filename: pool/dists/noble/main/r-cran-idingo_1.0.4-1.ca2404.1_all.deb Size: 305884 MD5sum: 8905b186d091915523f721c0b79820ff SHA1: 9580af8a625a7617a6c2998b151e6e2f4a647443 SHA256: 598c37706848a9c0a4ee3eb3b4d95b1a644afd4ee562c99382150cf89f5aa83f SHA512: d7da9458d629fe54dc3c1b9f35b0718a115907b97a94ee870dc8ca2909e3367634ef166b7aad1959c3f12d2fe9ac180de4e086e123dc36ff467187231872c18a Homepage: https://cran.r-project.org/package=iDINGO Description: CRAN Package 'iDINGO' (Integrative Differential Network Analysis in Genomics) Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. 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Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) , karyotype indices of Watanabe et al. (1999) and Romero-Zarco (1986) and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964). Package: r-cran-idiographic Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2835 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-lme4, r-cran-lavaan, r-cran-cograph, r-cran-mlvar, r-cran-mplusautomation, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-idiographic_0.3.4-1.ca2404.1_all.deb Size: 2013446 MD5sum: 37b69d8c2153f4cdb3ddba871f61c9c9 SHA1: 5bd0e3a7e28738dfdeae7447b0fb5a116a8af90d SHA256: 85b39ac2348a515d731538c0f177df6ed1975536d32b3f4a6cb828a1229efaa8 SHA512: 8f5f3992b4532c344efd8726965900aef69e1697f562f1603e6e272425f844688c4d80dac72aa24a76ec89c9a7aad1975c67861917b6d30c0cf1f48b629b7659 Homepage: https://cran.r-project.org/package=idiographic Description: CRAN Package 'idiographic' (Person-Specific (Idiographic) and Heterogeneous Complex Networks) Person-specific and within-person network estimation from intensive longitudinal and panel data. Estimators include ordinary vector autoregression (VAR), graphical vector autoregression (graphical VAR), multilevel vector autoregression (mlVAR), rolling ordinary and graphical VAR, native Bayesian VAR and multilevel Bayesian VAR, unified Structural Equation Modeling (uSEM), and Group Iterative Multiple Model Estimation (GIMME). All estimators are native clean-room implementations. All functions are validated against authoritative literature. Also provides preprocessing audits, edge-stability diagnostics, model-comparison reports, and rolling forecast validation. Methods are described in and . Package: r-cran-idiolect Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quanteda, r-cran-caret, r-cran-dplyr, r-cran-fdrtool, r-cran-ggplot2, r-cran-kgrams, r-cran-pbapply, r-cran-proc, r-cran-proxy, r-cran-quanteda.textstats, r-cran-spacyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-readtext, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idiolect_1.2.0-1.ca2404.1_all.deb Size: 771574 MD5sum: 7dd40031b9cd3e9752bc27ad320347e9 SHA1: 22b0c52104451d56a047a900e19b4134855a5ee3 SHA256: 9f612f3cb75367a579ead8c28b803f449d7bace8a22ca93d345a87e4502ce6c6 SHA512: e919b0630df992c7e98eb48b87fd45952d8cda594cef4249b397e696ad1706000706b0426b3db9918faae80ba56be3726de3607b6f76ec32a7a68d8659d566da Homepage: https://cran.r-project.org/package=idiolect Description: CRAN Package 'idiolect' (Forensic Authorship Analysis) Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta or Koppel and Winter's (2014) Impostors Method , as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios. Package: r-cran-idionomics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-forcats, r-cran-forecast, r-cran-ggplot2, r-cran-metafor, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idionomics_0.1.0-1.ca2404.1_all.deb Size: 182534 MD5sum: ec3ec90f0dcdf0d7191550e025f7b4bf SHA1: cd5cff1293cb5eb4b073f96c2baba49bf68f4020 SHA256: 4680a41dd4373fed2e06c15f8e9f4b8200a172c37e93a359d586b4c005a64b39 SHA512: d9fa6324db021b13db4e59e4a8214531aaf8ec34566376d84beba9df9802d08ae4bd8ce472e03fbb6827c7aa48f40b43b15ba9b08b10d37baed76de99c52466d Homepage: https://cran.r-project.org/package=idionomics Description: CRAN Package 'idionomics' (Conduct Idionomic Analyses for Time Series Modeling) A toolkit for idionomic science, a research philosophy that places the unit of the ensemble (individual/couple/group) at the center of analysis. Rather than assuming a common distribution, a similar enough process for each unit, and fitting a single model to the whole ensemble, idionomic methods model each unit separately, then aggregate upward if sensible. The group-level picture emerges from individual results, not the other way around, while explicitly evaluating whether aggregation is reasonable given the measured level of heterogeneity of effects. The package is built around intensive longitudinal data where each participant contributes a time series. It provides a pipeline from preprocessing through modeling to group-level summaries. Current functions: data quality screening (i_screener()), within-person standardization (pmstandardize()), linear detrending (i_detrender()), per-subject ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs) modeling and meta-analysis (iarimax()), individual p-values (i_pval()), Sign Divergence and Equisyncratic Null tests (sden_test()), and directed loop detection (looping_machine()). Methods are described in Hernandez et al. (2024) , Ciarrochi et al. (2024) , and Sahdra et al. (2024) . Package: r-cran-idlfm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-sparsearray Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-idlfm_1.0.0-1.ca2404.1_all.deb Size: 30148 MD5sum: 281fa733b70db20289a123d7305c4548 SHA1: 5723bb2a3f8eceb5718b591f4b62c022acb2a157 SHA256: 2bc1fab29a0d0e366c37efb4272abfef7e9450c13f815950de62239b5430bef0 SHA512: 3f1a5f40890f6305df7f94fbd7f3d52e474462bae2db8012fefa106a166da8fe2b3a8af10fbd50a6e3021550c056accaf845e6fd6e0142fb5163a56f1147b681 Homepage: https://cran.r-project.org/package=IDLFM Description: CRAN Package 'IDLFM' (Individual Dynamic Latent Factor Model) A personalized dynamic latent factor model (Zhang et al. (2024) ) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series. Package: r-cran-idm Architecture: all Version: 1.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-animation, r-cran-corpcor, r-cran-ca, r-cran-ggrepel Suggests: r-cran-caret Filename: pool/dists/noble/main/r-cran-idm_1.8.3-1.ca2404.1_all.deb Size: 514536 MD5sum: f48b33d8cfc0b6be9822286469d3c4f2 SHA1: 107ce23e267acd74819862f253e0b4fbfb17efe7 SHA256: b1159da91f3d31a92186ed9d8464a22105e4d807933ec415b87f47b0b0c76ab1 SHA512: 5836cba6c8a03416bf0c3e3fb15ff17757b5332d5da1c93b576702dcdea4022a96b446f9482ae86bee67eef8b0c2af7e4f9d4daabea079eda3321b47ea6b0edd Homepage: https://cran.r-project.org/package=idm Description: CRAN Package 'idm' (Incremental Decomposition Methods) Incremental Multiple Correspondence Analysis and Principal Component Analysis. Package: r-cran-idmact Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idmact_1.0.1-1.ca2404.1_all.deb Size: 42586 MD5sum: 75e8f9ca5f69dfcaba40afff32c8140f SHA1: 5f1122f4aec26d3c04d0d6e770c598e0ee00bdc2 SHA256: 3960bd712b4f051eed98a41bc1ca5d1f7635dac44bd8321aa7bc7de63bbb785d SHA512: 5f98e3755044899de225117539e2fe40da25ccc5d9f2b338e6313e30cdc197287a09101c349dfddbc9803e29945534d0a766c933702d51e5d5d455d1c92569d6 Homepage: https://cran.r-project.org/package=idmact Description: CRAN Package 'idmact' (Interpreting Differences Between Mean ACT Scores) Interpreting the differences between mean scale scores across various forms of an assessment can be challenging. This difficulty arises from different mappings between raw scores and scale scores, complex mathematical relationships, adjustments based on judgmental procedures, and diverse equating functions applied to different assessment forms. An alternative method involves running simulations to explore the effect of incrementing raw scores on mean scale scores. The 'idmact' package provides an implementation of this approach based on the algorithm detailed in Schiel (1998) which was developed to help interpret differences between mean scale scores on the American College Testing (ACT) assessment. The function idmact_subj() within the package offers a framework for running simulations on subject-level scores. In contrast, the idmact_comp() function provides a framework for conducting simulations on composite scores. Package: r-cran-idmc Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-magrittr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-usethis Filename: pool/dists/noble/main/r-cran-idmc_0.4.1-1.ca2404.1_all.deb Size: 51480 MD5sum: 60f0a3e23787a47550deadf71c598c40 SHA1: 8f331df7a6b35645bb4f3b8bdac8db44c23215c7 SHA256: bfbbe950fee71118851fcd1969ab78da9dbb3f8972cf28b013e63f99b3f744f6 SHA512: cdb259654d408387bd0027f16162fa674f15e3dfc515c6f8733aae492c86f979bdd18358b795cb69cb3749eab3327b27c49405791eeb3addf2e40abbe44336ef Homepage: https://cran.r-project.org/package=idmc Description: CRAN Package 'idmc' (Load and Wrangle IDMC Displacement Data) Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates. Package: r-cran-idmeasurer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-infotheo, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-idmeasurer_1.0.0-1.ca2404.1_all.deb Size: 917806 MD5sum: 37f8a485ead5033d7164732f6f9261c2 SHA1: 83a08c0a1251ce9b2236cc755bc32371bf1c2356 SHA256: 65d4f8c8bd7628d325a6b6222c90f33d1832a0f4f15869ed714e9499431a7f72 SHA512: b21d64cc2750b64e77c9f0deeda8193dc02b6e05e4aa7fc4738160fdf66097f4873fb578329a0b448c19a67cc3efd640e7c137fcb2357d82bd20a06f4e39ebf6 Homepage: https://cran.r-project.org/package=IDmeasurer Description: CRAN Package 'IDmeasurer' (Assessment of Individual Identity in Animal Signals) Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) : "Measuring individual identity information in animal signals: Overview and performance of available identity metrics". 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And the Morisita estimator is used for the ID estimation, but other tools are included as well. Package: r-cran-idmir Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3550 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-egg, r-cran-fastmatch, r-cran-forestplot, r-cran-ggplot2, r-cran-igraph, r-cran-pheatmap, r-cran-survival, r-cran-survminer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-idmir_0.1.1-1.ca2404.1_all.deb Size: 3513668 MD5sum: 26e1526f95cd791293528f197d5c1f34 SHA1: 61bbdad7264d305bf4698d954f0ff8f7e0e4feff SHA256: 8119aca9b7419eec5df452bc27754d6b327abc2cc81d405972993ca10efe469a SHA512: 9099031a1610dffa6848f8f867ec4cbc676366daebac340e4d7732edab9b5256deef525cae29fdbd3b481578fa1e41e9d0a74bd630246de9a9e1bf1398e9e955 Homepage: https://cran.r-project.org/package=IDMIR Description: CRAN Package 'IDMIR' (Identification of Dysregulated MiRNAs Based on MiRNA-MiRNAInteraction Network) A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs. Package: r-cran-idmodelr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-viridis, r-cran-magrittr, r-cran-purrr, r-cran-future, r-cran-furrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-desolve Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-pkgnet, r-cran-dt, r-cran-vdiffr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-idmodelr_0.4.0-1.ca2404.1_all.deb Size: 988632 MD5sum: 19dc5474daf33439529706c141b8525a SHA1: 5488de241d983d4a71c5ad6858ebd5e9c96420b0 SHA256: 2bd24d307cb0bb5c4b7a4c057f22730acfe41bdddb2756ecbd7b9bcb06cf733e SHA512: 23054ad650aa1d59d9deba622f518464f843474e685de33a9d8cc7693ff89f3c4f460952da00061f48b54f1e9c0954a27337d2acc6571f0940b5c80d703ff57d Homepage: https://cran.r-project.org/package=idmodelr Description: CRAN Package 'idmodelr' (Infectious Disease Model Library and Utilities) Explore a range of infectious disease models in a consistent framework. The primary aim of 'idmodelr' is to provide a library of infectious disease models for researchers, students, and other interested individuals. These models can be used to understand the underlying dynamics and as a reference point when developing models for research. 'idmodelr' also provides a range of utilities. These include: plotting functionality; a simulation wrapper; scenario analysis tooling; an interactive dashboard; tools for handling mult-dimensional models; and both model and parameter look up tables. Unlike other modelling packages such as 'pomp' (), 'libbi' () and 'EpiModel' (), 'idmodelr' serves primarily as an educational resource. It is most comparable to epirecipes () but provides a more consistent framework, an R based workflow, and additional utility tooling. After users have explored model dynamics with 'idmodelr' they may then implement their model using one of these packages in order to utilise the model fitting tools they provide. For newer modellers, this package reduces the barrier to entry by containing multiple infectious disease models, providing a consistent framework for simulation and visualisation, and signposting towards other, more research focussed, resources. Package: r-cran-idopnetwork Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1876 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-orthopolynom, r-cran-desolve, r-cran-ggplot2, r-cran-reshape2, r-cran-glmnet, r-cran-igraph, r-cran-scales, r-cran-patchwork Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-idopnetwork_0.1.2-1.ca2404.1_all.deb Size: 1676864 MD5sum: 5f449fd3b78d44b5f24f80220a992e33 SHA1: 9e81a5b7e92d0501a7dd00a9b8f8d2ee2e7f31f3 SHA256: 27533a4cb28ab8912c02d4a1a6767db5f4b45aa780c4f25ee2a66a785d697c57 SHA512: 62f4693957a2df2d05e9933f7597575ad48f28cf78cccf34a0566a67a589a16cc8ce1bbd38a47893dbdf8d911826674d1ecc21391b6f03f55c6b170d263df3dd Homepage: https://cran.r-project.org/package=idopNetwork Description: CRAN Package 'idopNetwork' (A Network Tool to Dissect Spatial Community Ecology) Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their 'dynamic' form. 'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) , Wang et al. (2011) . For more information on our model, see Chen et al. (2019) , and Cao et al. (2022) . 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Package: r-cran-idr Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-idr_1.3-1.ca2404.1_all.deb Size: 71502 MD5sum: 9bd0d5202348e5d05c8fd3b54511a63d SHA1: a4823553aa9702e0485f2236a47a0c174e5d41e8 SHA256: fa98da7374008b0ab5ec78e84d685c391d2940d2a0b58ada4035ef872e735e7a SHA512: e1b766baabf2ea1f5ffc684d75db040e66a24f637fccbd09ea9eceeb01f93847dede4dfde0d4ae49b3bbce117b7c01f19c5fd82ae64fe198729ee0c58148b718 Homepage: https://cran.r-project.org/package=idr Description: CRAN Package 'idr' (Irreproducible Discovery Rate) This is a package for estimating the copula mixture model and plotting correspondence curves. Details are in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel. 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Package: r-cran-idsa Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gd, r-cran-ggplot2, r-cran-reshape2, r-cran-kableextra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-idsa_2.1-1.ca2404.1_all.deb Size: 314092 MD5sum: 70d903887c3e8ee786cac483d66bc667 SHA1: 377d74855656c24fb7279f501a7dbbb460db3b8b SHA256: b48c87e9e96a6985df9fdc79166a4f4e112bd55dee3d45a0a7f132c7f39c943b SHA512: 87007cd509b3529eda1cc20a84854c4b8bc3100d86a10ba8e42a4dae8f79efc84bd234035faacf49fef587e5af396a92501d2abbd5ef0fa24435d36fb4b4a501 Homepage: https://cran.r-project.org/package=IDSA Description: CRAN Package 'IDSA' (An Interactive Detector for Spatial Associations) Method of interactive detector for spatial associations (IDSA) as described in Yongze Song (2021) . 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'IDSL.FSA' also provides a number of modules to convert and manipulate .msp and .mgf files. The 'IDSL.FSA' workflow was integrated in the 'IDSL.CSA' and 'IDSL.NPA' packages introduced in . 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Irucka created these data sets while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee. 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Please refer to the COPYRIGHTS file and the text_citation.cff file for the reference copyright information and for the complete citations of the reference sources, respectively. 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The item-focused tree model combines logistic regression with recursive partitioning to detect Differential Item Functioning in dichotomous items. The model applies partitioning rules to the data, splitting it into homogeneous subgroups, and uses logistic regression within each subgroup to explain the data. Differential Item Functioning detection is achieved by examining potential group differences in item response patterns. This method is useful for understanding how different predictors, such as demographic or psychological factors, influence item responses across subgroups. 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For reference, see below: 1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) 2. Wu, S., Wu, H. (2013) 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) . 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Package: r-cran-ilsamerge Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-haven, r-cran-httr2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ilsamerge_1.4.2-1.ca2404.1_all.deb Size: 253890 MD5sum: 294dad5f5ad35d0d6121ae5f2bd38411 SHA1: 56844fa7826b7a40fe5f4af1aa0f3569d21da8b7 SHA256: 52f1589dfaed0736d7d995af56f5bdad6b984341e75432bb20820a9e3da8f938 SHA512: c1351431b2d61adff45c9ce4994fd0f7105b3469c5896577bbc48e2ef6dbd0c85c68ca3e19a4c5771f7038f69c0f299ecac3d17b306210ad5d0a72e8df737619 Homepage: https://cran.r-project.org/package=ILSAmerge Description: CRAN Package 'ILSAmerge' (Merge and Download International Large-Scale Assessments (ILSA)Data) Merges and downloads 'SPSS' data from different International Large-Scale Assessments (ILSA), including: Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and other studies from . 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The method begins with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution of model parameters. Accepted points result in model predictions that are within the initially specified tolerance intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) . 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The functions provide scores for several basic aesthetic principles that facilitate fluent cognitive processing of images: contrast, complexity / simplicity, self-similarity, symmetry, and typicality. See Mayer & Landwehr (2018) and Mayer & Landwehr (2018) for the theoretical background of the methods. 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The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity. However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting. 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(2015) and the U-Net++ architecture by Zhou et al. (2018) . We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation. 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Package: r-cran-imix Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-mvtnorm, r-cran-mixtools, r-cran-mclust, r-cran-ggplot2, r-cran-mass Filename: pool/dists/noble/main/r-cran-imix_1.1.5-1.ca2404.1_all.deb Size: 152690 MD5sum: 911d70a76f9b60d18133f3ffa651e9ca SHA1: f1b0212e79bc8f2a946f68a4de3a3475a56526a1 SHA256: ff09d06e4ecc2c36881b0517fd6889ce23bfedac77ae780eb3c30fad59ae4328 SHA512: 78ecdf0fefd6d0dc8fc59b8ac54d2b58a7482d119176d3c80cfdb877cd14f348032b841871edcd88d4fe2cf5d764e4ef94a0dd4ab56b5582de4359f7e8a22caf Homepage: https://cran.r-project.org/package=IMIX Description: CRAN Package 'IMIX' (Gaussian Mixture Model for Multi-Omics Data Integration) A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) . Package: r-cran-iml Architecture: all Version: 0.11.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-formula, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-metrics, r-cran-r6 Suggests: r-cran-aleplot, r-cran-bench, r-cran-bit64, r-cran-caret, r-cran-covr, r-cran-e1071, r-cran-future.callr, r-cran-glmnet, r-cran-gower, r-cran-h2o, r-cran-keras, r-cran-knitr, r-cran-mass, r-cran-mlr, r-cran-mlr3, r-cran-party, r-cran-partykit, r-cran-patchwork, r-cran-randomforest, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat, r-cran-yaimpute Filename: pool/dists/noble/main/r-cran-iml_0.11.4-1.ca2404.1_all.deb Size: 660836 MD5sum: 2970d7cb302e7f3a5d9202f0c09b962e SHA1: 8ab117cc46ffe2fad9bebca9ca383a3f91fe542c SHA256: 4e974f688ff94b0a733da0766e6f7849961acac0cdb33cff0abd5ca82d322a9b SHA512: 4cc4912c1f4b39c6cacfbc563a27cdb3df914f415dc69112b8d655126fed3da3595debb47bfc2073cf2c5c00c8fe6784f40fa012f1073534b71eb82065a0bf29 Homepage: https://cran.r-project.org/package=iml Description: CRAN Package 'iml' (Interpretable Machine Learning) Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) , accumulated local effects plots described by Apley (2018) , partial dependence plots described by Friedman (2001) , individual conditional expectation ('ice') plots described by Goldstein et al. (2013) , local models (variant of 'lime') described by Ribeiro et. al (2016) , the Shapley Value described by Strumbelj et. al (2014) , feature interactions described by Friedman et. al and tree surrogate models. Package: r-cran-immailgun Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-immailgun_0.1.2-1.ca2404.1_all.deb Size: 44448 MD5sum: df44a42c7c4ae1542de538d465bba4f5 SHA1: 9f22c195ac696704b351bcc1b790d38f9c3f9c79 SHA256: 03ae7f0b494000e8fd2ecb44470da48ba3ffa990c39fd2be85db3b7752a80698 SHA512: 88340c8262b1d8f91b8fef1a7d16645185569f4364ffe199483da1369dc9bd1a7d91a748b243690646584ddc5bbb0085d44430edff122951f51019898ee0ad20 Homepage: https://cran.r-project.org/package=IMmailgun Description: CRAN Package 'IMmailgun' (Send Emails using 'Mailgun') Send emails using the 'mailgun' api. To use this package you will need an account from . 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Based on the biological descriptors and drug-disease interaction networks, it can analyze the potential poly-pharmacological mechanisms of Traditional Chinese Medicine and be used for drug-repositioning in Traditional Chinese Medicine. Package: r-cran-imml Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rpart, r-cran-caret, r-cran-randomforest, r-cran-e1071, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-imml_0.1.5-1.ca2404.1_all.deb Size: 24124 MD5sum: c0d2f2e3383ccc4c3411f5e47a6c6114 SHA1: d248da04fd565767479bf039bb1ca2736463d437 SHA256: 36698f71579e0451c54825e2539705824085de3f51a1878b69f6fb4695ef6415 SHA512: f262caf5e84dce951d8dde69c7cf0ecfb5f5bfb9b96e399e6be1ad3db7b0e93e290e179ad4fb1c7ef5eefa94e68bd89befe8932e59d7e76fea5efe74c86ca067 Homepage: https://cran.r-project.org/package=ImML Description: CRAN Package 'ImML' (Machine Learning Algorithms Fitting and Validation for Forestry) Fitting and validation of machine learning algorithms for volume prediction of trees, currently for conifer trees based on diameter at breast height and height as explanatory variables. Package: r-cran-immunaut Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-plyr, r-cran-dplyr, r-cran-caret, r-cran-proc, r-cran-prroc, r-cran-rlang, r-cran-rtsne, r-cran-dbscan, r-cran-fnn, r-cran-igraph, r-cran-fpc, r-cran-mclust, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-clustersim, r-cran-doparallel, r-cran-mlmetrics Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-immunaut_1.0.3-1.ca2404.1_all.deb Size: 195672 MD5sum: e943ea77d841f5d5dc3de61560e2bd5e SHA1: bc4b4f665a7d320b5448d538f20f93198eb2afb6 SHA256: 1a41c211c317c71f52fce0bbb736e1c1c7c981cd76e07548f8c41ed3fec57e9b SHA512: 83e5d5f50e1d476a88d90982fda7d9c0472f64d81fb169de2477eed91605994b80dd614b232b7b17a89f469328830b8fa1465972b99fe6b046214922aaea37f7 Homepage: https://cran.r-project.org/package=immunaut Description: CRAN Package 'immunaut' (Machine Learning Immunogenicity and Vaccine Response Analysis) Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations. Package: r-cran-immundata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5042 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-duckplyr, r-cran-checkmate, r-cran-cli, r-cran-dbplyr, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-tibble Suggests: r-cran-duckdb, r-cran-rmarkdown, r-cran-testthat, r-cran-seurat Filename: pool/dists/noble/main/r-cran-immundata_0.1.0-1.ca2404.1_all.deb Size: 3861456 MD5sum: 0a079f59d6df0a1d3c7db2623a11bc51 SHA1: c9fde2dd1a2f28122ea46184398b8f5f364c8c00 SHA256: 6ffd4cb3225beb1c83f08359021056e05fa598e8f57e33ba9cce8d4ff5d36a18 SHA512: b2b8d96e7a73be85c3a3b10300aaeaa549cc16d978d312c2b735133dd3d3fe89fd94e7ae8ccc41076c1c0a90aeb2a6e0200cea0bb5ef6aa8e74907501ecb9e3a Homepage: https://cran.r-project.org/package=immundata Description: CRAN Package 'immundata' (A Unified Data Layer for Large-Scale Single-Cell, Spatial andBulk Immunomics) Provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data. 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Users can search immune cell signatures, retrieve marker lists, export GMT files, create custom marker sets, and score gene-by-cell expression matrices with dependency-free rank-based or mean-expression methods. Cell subpopulations are distinguished by their source PMIDs. For the core curation of the lung cell atlas, see Travaglini et al. (2020) . For the pan-cancer B cell signatures, see Fitzsimons et al. (2024) . Package: r-cran-immunesim Architecture: all Version: 0.8.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4313 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-powerlaw, r-cran-stringdist, r-bioc-biostrings, r-cran-igraph, r-cran-stringr, r-cran-data.table, r-cran-plyr, r-cran-reshape2, r-cran-ggplot2, r-cran-ggthemes, r-cran-rcolorbrewer, r-cran-metrics, r-cran-repmis Filename: pool/dists/noble/main/r-cran-immunesim_0.8.7-1.ca2404.1_all.deb Size: 4382522 MD5sum: 92240b59235e840c65171966823b1edd SHA1: e2c068456e21c2b9c37c0648f7c9c8f22d7f4a5a SHA256: 2975f4b0c0ed8c10d27acd80085f48886d4faae85f395e6ea11d5bf9c889d787 SHA512: e306ec87f896b89c65fa76ce9c131035f40db161c4f7608f9e1cc1e379b6f79ddcca241a59de716a4b769671b8501db73c32496b83210e1e66d383822c20b431 Homepage: https://cran.r-project.org/package=immuneSIM Description: CRAN Package 'immuneSIM' (Tunable Simulation of B- And T-Cell Receptor Repertoires) Simulate full B-cell and T-cell receptor repertoires using an in silico recombination process that includes a wide variety of tunable parameters to introduce noise and biases. Additional post-simulation modification functions allow the user to implant motifs or codon biases as well as remodeling sequence similarity architecture. The output repertoires contain records of all relevant repertoire dimensions and can be analyzed using provided repertoire analysis functions. Preprint is available at bioRxiv (Weber et al., 2019 ). Package: r-cran-immunogenetr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2957 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-immunogenetr_1.5.0-1.ca2404.1_all.deb Size: 347110 MD5sum: 8417b4c856bbf6e509981009a6297b4a SHA1: b00996077f79d23ad577d2d44a25286662c01b0e SHA256: 798ea7bcf58b2216c86a0ec4d3b65ce5b6769572742f6584b8d8bbd95825fb8f SHA512: 4121a82df2219241dc90c893bb9464e3be6dd301215b1147c892907cba9430c5426f2df7ce3a7fc06bb9aa8601af7a4dab9b25e7f328aaf8683ef585427cd684 Homepage: https://cran.r-project.org/package=immunogenetr Description: CRAN Package 'immunogenetr' (A Comprehensive Toolkit for Clinical HLA Informatics) A comprehensive toolkit for clinical Human Leukocyte Antigen (HLA) informatics, built on 'tidyverse' principles and making use of Genotype List String (GL String, Mack et al. (2023) ) for storing and computing HLA genotype data. Specific functionalities include: coercion of HLA data in tabular format to and from GL String; calculation of matching and mismatching in all directions, with multiple output formats; automatic formatting of HLA data for searching within a GL String; truncation of molecular HLA data to a specific number of fields; and reading HLA genotypes in HML files and extracting the GL String. This library is intended for research use. Any application making use of this package in a clinical setting will need to be independently validated according to local regulations. Package: r-cran-imneuron Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlmetrics, r-cran-ggplot2, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-imneuron_0.1.0-1.ca2404.1_all.deb Size: 22978 MD5sum: 514b6235888017d113675cfdbf59f613 SHA1: 7d817a707c766f290e81b395b7d7c23f5755f231 SHA256: c4d71f068c99ba9fb2429482ff413cbad4ca317d6fa8c56d7d7f0d3f869f3c9c SHA512: da97f02434111e687f351f8c10445b76eed7a2c9894fb6882afce8656f2ee7ff93f7a1ec47671885859249a706a90d131ec2156bcb7c7079fd8f3730d9f1458b Homepage: https://cran.r-project.org/package=Imneuron Description: CRAN Package 'Imneuron' (AI Powered Neural Network Solutions for Regression Tasks) It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. 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Package: r-cran-imnn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlmetrics, r-cran-ggplot2, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-imnn_0.1.0-1.ca2404.1_all.deb Size: 21694 MD5sum: 90a5d2e65ad3fb5dff915df9758ebbaf SHA1: 05e0bf9d468bfad65fa8af941e2ce2dfeb2ce870 SHA256: 2a9ee7ac9a9ef4e72bcc182039f7b614373330877aa445fd131effa0ac964d73 SHA512: 367db446ba866169ad890aa89c2f26657da87b061072dec5e69655011ba2b393be0e18e3b3dbba99f6b72cf72a578023613bb87ab4c4069c1379f0b671ff37b4 Homepage: https://cran.r-project.org/package=ImNN Description: CRAN Package 'ImNN' (Neural Networks for Predicting Volume of Forest Trees) Neural network has potential in forestry modelling. 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Package: r-cran-impact Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-impact_0.1.1-1.ca2404.1_all.deb Size: 15932 MD5sum: d63cf574cfa8dbaf22d9712952ad3854 SHA1: 06ba929976a8335dcc44596de9f07ba9341a7cd9 SHA256: f5fb43bf532638a4aa97c7c1d6e6b997649cca7e494118ea42f801564300acdf SHA512: 400d0d0fad3e1b4aba0918887f64ce00882b4800336e885895e94385ae4850f43f43c3dd810009428da0a77cd642334b6e4040046cbbff83870ded1eb492b528 Homepage: https://cran.r-project.org/package=IMPACT Description: CRAN Package 'IMPACT' (The Impact of Items) Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items. Package: r-cran-impadapttype2censor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-impadapttype2censor_0.1.0-1.ca2404.1_all.deb Size: 97656 MD5sum: c6a00d355c32f20ad610ff9b902d131b SHA1: 109068a8c7225b836d5ea5a16f58b104e4b517f8 SHA256: f41822e51c78ec694f11242e01c6d5ea5d888b48946bff25da049665348bcc68 SHA512: cb2358e107fa89c6683fff3e17b464263affdbd3787674350954740eb3e98f533f23976092fa957670b2ccb6b2da39459ab809e30c83112bc276d552ed7ea1f9 Homepage: https://cran.r-project.org/package=ImpAdaptType2Censor Description: CRAN Package 'ImpAdaptType2Censor' (Data Generation and Statistical Inference for Improved AdaptiveType-II Progressive Censoring Schemes) Comprehensive computational routines for random data generation, Maximum Likelihood Estimation (MLE), Maximum Product of Spacings Estimation (MPSE), and MCMC Bayesian estimation under the Improved Adaptive Type-II Progressive Censoring Scheme (IAT-II PCS). Users can supply custom probability density functions (PDF), cumulative distribution functions (CDF), survival functions, parameter ranges, and progressive censoring plans for any continuous univariate lifetime distribution, or rely on built-in parametric models (e.g., Generalized Exponential). Point estimation methods include MLE via optimization algorithms (Broyden-Fletcher-Goldfarb-Shanno (BFGS), Newton-Raphson (NR), Nelder-Mead (NM), Conjugate Gradients (CG), L-BFGS-B, Simulated Annealing (SANN), and Berndt-Hall-Hall-Hausman (BHHH)) and MPSE. Bayesian inference utilizes Metropolis-Hastings within Gibbs sampling under Squared Error Loss (SEL) and LINEX Loss (LL) functions to compute point estimates and Highest Posterior Density (HPD) credible intervals. Asymptotic confidence intervals for parameters, reliability, and hazard rate functions are constructed using asymptotic normality and delta method. Methods are based on Dev and Chacko (2026, Journal of the Iranian Statistical Society, 25, 1-29), Yan, Zhang, and Dong (2021, Journal of Computational and Applied Mathematics, 381, 113022, ), Ng, Kundu, and Chan (2004, Naval Research Logistics, 51, 1145-1168, ), Cheng and Amin (1983, Journal of the Royal Statistical Society Series B, 45, 394-403, ), Kundu and Gupta (1999, Australian & New Zealand Journal of Statistics, 41, 173-188, ), and Berndt, Hall, Hall, and Hausman (1974, Annals of Economic and Social Measurement, 3, 653-665). 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This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) . 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See . Package: r-cran-impower133 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1893 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-survival, r-cran-survminer, r-cran-gt, r-cran-forestplot, r-cran-ipdfromkm, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-impower133_1.0.0-1.ca2404.1_all.deb Size: 1478366 MD5sum: f02401aa81b063bbb4482583793b12eb SHA1: f9cf401adf399204a0aa46a989895aaa4cd72198 SHA256: 2e4b29f2b56b9b83826c6c19a31ee64ea43a8da8fcb3a5b57a7cdf9f73252e8d SHA512: d7cc18332286ad25ba7de0f0be58cb3a83ada02ba0fa80decbab1f97c95bdf439280250be69f39030f371b49e2b09cafe18fc4e00b3ed549f5099f9f7887a15a Homepage: https://cran.r-project.org/package=impower133 Description: CRAN Package 'impower133' (Reproduce IMpower133 Clinical Trial Results) Provides functions to simulate baseline characteristics, reconstruct overall survival data from published Kaplan-Meier curves, and generate publication-ready tables and forest plots reproducing the IMpower133 clinical trial results (Horn et al., 2018, ). The IPD reconstruction method is based on Liu et al.(2021, ). Package: r-cran-imprecise101 Architecture: all Version: 0.2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tolerance, r-cran-pscl Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-imprecise101_0.2.2.4-1.ca2404.1_all.deb Size: 60476 MD5sum: f29bc46af6788ea233f5ce93b3a7cc85 SHA1: a28001650d5d0defca42a7d115c0fc023ec30fe1 SHA256: a5267beda68e1672089c66594cd44d94cb5c001b9e83c5c5d62e349aa1375dff SHA512: 058b775aaefad184244bb6a630f5bcc140d8953d4485f803705b0ad09bfb215e0cafb1f0255c909fae079f17641090b55b74b57c46b917062d0a55f99f9d7bad Homepage: https://cran.r-project.org/package=imprecise101 Description: CRAN Package 'imprecise101' (Introduction to Imprecise Probabilities) An imprecise inference presented in the study of Walley (1996) is one of the statistical reasoning methods when prior information is unavailable. Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated. Package: r-cran-impressionist.colors Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3565 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-impressionist.colors_1.0-1.ca2404.1_all.deb Size: 3618926 MD5sum: f77166c4b6a27e4751ba4367213f9278 SHA1: 1686b1400f61b55fdeb4e3464a477c203289dbe8 SHA256: c8ee8b27b8ac676b0ada2c384b6a6aa40f2b37e5042529442f52cb02d1288c10 SHA512: 6cbc4e7f6ebae135466077a4b7cd9164b8098e724a5b07801973945d7f2e1b2820a8289ea89bcdbb0a16b1b342640450e2e6068729e027934b4e21112ffd79b4 Homepage: https://cran.r-project.org/package=impressionist.colors Description: CRAN Package 'impressionist.colors' (Impressionism's Color Palettes) Provides color palettes from Impressionism and post-Impressionism artworks. This package allows to select colors combinations while looking at the original paintings where colors were sampled from. Package: r-cran-imprintcapasm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-readxl, r-cran-writexl, r-cran-vcfr, r-cran-ggplot2, r-bioc-rsamtools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-imprintcapasm_0.1.1-1.ca2404.1_all.deb Size: 1322234 MD5sum: 010d02ee2ac75f7649b8758535a04afb SHA1: 88bb05a3f89ad5cee89581ba0f9fc9b8805d6ba8 SHA256: 126f41aca858f26ea7e9ababbd50623be21d0072fe51e778a25cfd50f686db9f SHA512: 8c5b23673981993d0e9777c16411ef67b0afd8ee9ec3f19c37aaa59f1ebe3d5e73789157b002631d328d1344437aabc1339ce1a57a75b936a595e28a04bea9f4 Homepage: https://cran.r-project.org/package=ImprintCapASM Description: CRAN Package 'ImprintCapASM' (Allele-Specific Methylation Analysis for Imprinted DMRDiagnostics) Provides functions for SNP-phased allele-specific methylation (ASM) analysis across the 41 canonical human imprinted differentially methylated regions (DMRs). Reads are assigned to REF or ALT alleles based on bisulfite-aware SNP detection, enabling diagnosis of imprinting disorders from whole-genome bisulfite sequencing data. . Package: r-cran-imprinting Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-cowplot, r-cran-ggplot2, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-imprinting_0.1.1-1.ca2404.1_all.deb Size: 400880 MD5sum: 86a5f20b61940104b8c47e57e4cb88a6 SHA1: 1a409aeb7118143cc31ee020affd15cf867fded7 SHA256: 0fb205c8d99a53953d40b2f605a396e67f8e8e88d339f6759d5a6c8bcc83e9af SHA512: 35c97cfa425d531019cb59180197eb0d09e920d92a24270db3b6707f02eace5f63ff2b6435a7c6c1a1db2d2b1b17aef3491c8c5dbbd2c52c79220ad210002f2e Homepage: https://cran.r-project.org/package=imprinting Description: CRAN Package 'imprinting' (Calculate Birth Year-Specific Probabilities of Immune Imprintingto Influenza) Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) . Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc. Package: r-cran-impshrinkage Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-impshrinkage_1.0.0-1.ca2404.1_all.deb Size: 112148 MD5sum: afca8b63d27b16f9643fc286e3874b29 SHA1: d38604524c7c88ad922942a46afb207e386903b2 SHA256: 42f79432aadbba33e2667eb877303f1c9aaaa2fa13fd34d06aece20d9e8688c1 SHA512: 2b9552134591a8b161ca7dcff93d2e7de7aa59957acc420aa9716a81513a3bb5b723a23dded45246e979abdc7abff1ac6d709ddfb972f191c7ce625134842134 Homepage: https://cran.r-project.org/package=ImpShrinkage Description: CRAN Package 'ImpShrinkage' (Improved Shrinkage Estimations for Multiple Linear Regression) A variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) . Package: r-cran-imputecgm Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mice, r-cran-fnn, r-cran-ranger, r-cran-data.table, r-cran-xgboost, r-cran-lightgbm, r-cran-forecast, r-cran-cgmanalyzer, r-cran-lifecycle, r-cran-reticulate, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-imputecgm_0.0.3-1.ca2404.1_all.deb Size: 184962 MD5sum: 32fb352a35504295f8ee3491018e4f43 SHA1: 1d09c76f7bfd28421f5866ff6c54cb68b62cc295 SHA256: 6656a8655a7b56fa899ed707e79724532de59a01b5995cc843aa2c3eb6fc70df SHA512: 974ef995950c7a424cce3128d4e9971f8ed704398bcb42c8be0762b37e7a802b7800f3c54c5a3a3c5af3c78c1e60832bccea23312b6d31fd62c07e8416ab6903 Homepage: https://cran.r-project.org/package=imputeCGM Description: CRAN Package 'imputeCGM' (Impute Missing Glucose Values in CGM Data) Imputes missing glucose values in repeated-measures continuous glucose monitoring (CGM) data. Workflows create time-series features from raw timestamps, support model selection, and return the user's original columns plus an imputed glucose column. Methods include multiple imputation by chained equations using 'mice' (Azur et al. (2011) ), Random Forest regression using 'ranger' (Breiman (2001) ), k-nearest-neighbor regression using 'FNN' (Zhang (2016) ), 'XGBoost' using 'xgboost' (Chen and Guestrin (2016) ), 'LightGBM' using 'lightgbm' (Ke et al. (2017) ), and ARIMA forecasting using 'forecast' (Hyndman and Khandakar (2008) ). A 'Python'-compatible backend uses 'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', 'xgboost', and optional 'lightgbm'. Package: r-cran-imputefin Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1474 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-zoo, r-cran-mvtnorm, r-cran-magrittr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-xts Filename: pool/dists/noble/main/r-cran-imputefin_0.1.2-1.ca2404.1_all.deb Size: 1075726 MD5sum: 3f54d98329087b1a0834395ec7496a71 SHA1: c7e6cbf54e6e8a685a17e367ef3a561c548b8a73 SHA256: f75a6a4fd8972424f174a33a4b5e6a89065e7168f61f4b743d8f4ba480ad1fe1 SHA512: dc9847cf79c31382aa89be6bc38e6793fe7e4278a7d52665e5cc0858c70177a40f93c85dfc67f9acd065132c8beaa8ecd8c347ad16b73b9d21124da4b9d6c7df Homepage: https://cran.r-project.org/package=imputeFin Description: CRAN Package 'imputeFin' (Imputation of Financial Time Series with Missing Values and/orOutliers) Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. . Package: r-cran-imputegeneric Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gower, r-cran-parsnip Suggests: r-cran-missmethods, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-imputegeneric_0.1.0-1.ca2404.1_all.deb Size: 58908 MD5sum: ff99e991580ee6d9551fe1d9e9acc995 SHA1: 2a4b090a265059401b487cd600a5b9c9eee2d869 SHA256: 2a118690bd14be6f7c33d4fd74c5b5b9a6de70610d1622d6f38877da3bd59d91 SHA512: 07c010e0c3497981e62caa3d12b7dd9e92ecbe3fdcf0971dd2867bcf81f2b8c40e14dc8ea3ae9b9fed1aaf30d5a937137b54332c141250c8420a30a0c83e2573 Homepage: https://cran.r-project.org/package=imputeGeneric Description: CRAN Package 'imputeGeneric' (Ease the Implementation of Imputation Methods) The general workflow of most imputation methods is quite similar. The aim of this package is to provide parts of this general workflow to make the implementation of imputation methods easier. The heart of an imputation method is normally the used model. These models can be defined using the 'parsnip' package or customized specifications. The rest of an imputation method are more technical specification e.g. which columns and rows should be used for imputation and in which order. These technical specifications can be set inside the imputation functions. Package: r-cran-imputelcmd Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmvtnorm, r-cran-norm, r-bioc-pcamethods, r-bioc-impute Filename: pool/dists/noble/main/r-cran-imputelcmd_2.1-1.ca2404.1_all.deb Size: 635782 MD5sum: bf187a09e3f64139379521334b6c4815 SHA1: d6603805f28376e1b0429fd9a029b940d1d22f45 SHA256: 60b7ed5f644ff49832d281bd5a9446ad3a2523e8c145e66964767b62795c44db SHA512: f50fa613cddbf70e980f84ceddef5a1afa14f625e082997c8e4bca9181ba25940ff9621bc4e4d337da7da37a762e7a2dc133ef1e4301cc656c09798ac4ba2b90 Homepage: https://cran.r-project.org/package=imputeLCMD Description: CRAN Package 'imputeLCMD' (A Collection of Methods for Left-Censored Missing DataImputation) A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR) mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR. Package: r-cran-imputelongicovs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-imputelongicovs_0.1.0-1.ca2404.1_all.deb Size: 410048 MD5sum: 8311a1cc686f02a7ecc9dcdf65ec766e SHA1: cca88addbf99fd99a090348fb4a164cb8b22a4d5 SHA256: bf3210739ee83989f4db487d1eb11ca6836ce35528ece8b4d973cc36682cb327 SHA512: b49053c8c9346fd7bbf118674d275efc7794e36a453b54b32ce1273f0ef8b636559de3a2f1435ec1ca8fccfd2f6cc7ec6f3ee6f3eb02412bada6872a4b45d4ce Homepage: https://cran.r-project.org/package=ImputeLongiCovs Description: CRAN Package 'ImputeLongiCovs' (Longitudinal Imputation of Categorical Variables via a JointTransition Model) Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) for an overview. Package: r-cran-imputemissings Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-imputemissings_0.0.4-1.ca2404.1_all.deb Size: 22406 MD5sum: 9ce484e0825a38067c66f2d595be9ea5 SHA1: 042c4813a48758c003b37ff0f17305d4c06b195a SHA256: 1232c52bfc77f292080aa0dc31bc69dc4482b0f108b97cd96eca982d7562544a SHA512: 1227c87ff9051b8e5acc1894ef1eea1ce2562324a7b1ec2348331e6aed28e84030ad1269b818d212b1d7d6b57c483253742209a5430c898291d5615d92d281c1 Homepage: https://cran.r-project.org/package=imputeMissings Description: CRAN Package 'imputeMissings' (Impute Missing Values in a Predictive Context) Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest. 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These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS. 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(2019) . Package: r-cran-imputerobust Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-mice, r-cran-purrr, r-cran-extremevalues, r-cran-gamlss.dist, r-cran-lattice Filename: pool/dists/noble/main/r-cran-imputerobust_1.3-1-1.ca2404.1_all.deb Size: 55164 MD5sum: 906be9f91c0625d33e76d764757a8694 SHA1: 65baa6baaf04a3fd03341cb3a92e1c1b8deaa05d SHA256: 22f3ced0060c9ce8554fbdd8895f52f73ce90f6c607df757206eea4d7ab2bef4 SHA512: 38a0fc986165217f1edecf8e39603a0815b06eab26fba906b66dd0de58c387538c5cc546e8a38ad20fa78c85d6938b59275fca76b37e417de94c2744f0846fbb Homepage: https://cran.r-project.org/package=ImputeRobust Description: CRAN Package 'ImputeRobust' (Robust Multiple Imputation with Generalized Additive Models forLocation Scale and Shape) Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess . Package: r-cran-imputetestbench Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-imputets, r-cran-reshape2, r-cran-tidyr, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-imputetestbench_3.0.3-1.ca2404.1_all.deb Size: 53254 MD5sum: bd8ef3a692437ac4e6593847be064c7f SHA1: e6524227b2f2364c8248f605807f527e9d789696 SHA256: 6ad1f779beffe8194ee1e7e4fe19488277e4c508eb8408f7c667f4fa2ea12fff SHA512: d6bd27af9fe31d6589e95274c07ab53f546985b1134b535a9f992c4975c5eb27e632c4d66d729746e377fad5e72dad0af82946664674da6931adee8efb33d230 Homepage: https://cran.r-project.org/package=imputeTestbench Description: CRAN Package 'imputeTestbench' (Test Bench for the Comparison of Imputation Methods) Provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used. Package: r-cran-imrmc Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-imrmc_2.1.0-1.ca2404.1_all.deb Size: 4982176 MD5sum: f05cfe403ef2773cc22f54aed11ad127 SHA1: b981c8df61bae827618ec115ac127cac896860a3 SHA256: 58eb5b6f591d8d4f127f3539d1d5ca3bffb241c0e154bbd86209b2852ef4cdc9 SHA512: a43fdfc18105139e547cf9e885e0405f5bd27ae479cff45b04f7015c8c0566c66dfc566b03479297dd75147c1630d0a098e24f4d805c5c2815e9590f8a3f1cf3 Homepage: https://cran.r-project.org/package=iMRMC Description: CRAN Package 'iMRMC' (Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, andOther Metrics)) This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: . Package: r-cran-imsig Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hiclimr, r-cran-rcolorbrewer, r-cran-igraph, r-cran-ggplot2, r-cran-gridextra, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-imsig_1.1.3-1.ca2404.1_all.deb Size: 307362 MD5sum: cf81c1dd4b5ffccde8e4b0fcae2f60f4 SHA1: 0f43798c26bd5316ce23609344542211a6b83485 SHA256: cb52d2044ddedfd5e2898036a531bba719745860002b9ea7622b870cc344a371 SHA512: 13b845162925eb36b905bb090e7f5a8afde6aee37ed2fe63957413636d70cb3da788e80bc5f3a13395d0a69b7e4782c2423b8686386e9a46d792d48cd2899917 Homepage: https://cran.r-project.org/package=imsig Description: CRAN Package 'imsig' (Immune Cell Gene Signatures for Profiling the Microenvironmentof Solid Tumours) Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data. Package: r-cran-imtest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ltm, r-cran-mass, r-cran-lme4, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-imtest_1.0.0-1.ca2404.1_all.deb Size: 124706 MD5sum: b8bf75de2c80540bbcd9a5067af979d3 SHA1: 637542a75ae2260a32a3f00ac324ef8bf50501fe SHA256: 648353444f345303e25176d8f7db3e7af953aa84bf4f57e055f781916bd48ec7 SHA512: ff32df22947f778f7eb1ff797a0a2e33ae8934ae5702fea8d1d8fa93d5f3fa75acd67834454023d67a075ddbe03972197e598903156bfd18db378fd3b28067cb Homepage: https://cran.r-project.org/package=IMTest Description: CRAN Package 'IMTest' (Information Matrix Test for Generalized Partial Credit Models) Implementation of the information matrix test for generalized partial credit models. Package: r-cran-imv Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lme4, r-cran-mirt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-imv_0.3-1.ca2404.1_all.deb Size: 59748 MD5sum: 16866c36bfd9e5027927ddb2fd9e34e4 SHA1: 2d00e9c6a90acfc526d0e633cce57355c39555f9 SHA256: 35b5a4871494a95380ccc9bc2e9b78526d9a78dfdba7cfca5096b6d4a913a47d SHA512: c0d6f6ec22565acee1bcaa52a9f9da3e79f2d00b09845330e93ffe04056507e9203452edffdd6c7d234eebf13ac5e43d05a39a18c96ee8705f610beb758eb3bb Homepage: https://cran.r-project.org/package=imv Description: CRAN Package 'imv' (Model Comparison via the 'InterModel Vigorish' ('IMV')) Computes the 'InterModel Vigorish' ('IMV'), a metric for comparing the predictive accuracy of two models for binary outcomes. The 'IMV' is derived from the expected value of a bettor using one model's predicted probabilities against those of a competing model, and is estimated via k-fold cross-validation. Methods are provided for generalized linear models, mixed-effects models ('lme4'), and item response theory models ('mirt'). See . Package: r-cran-imvol Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyverse, r-cran-nls2, r-cran-caret, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-imvol_0.1.0-1.ca2404.1_all.deb Size: 29272 MD5sum: ae09fa66eeadcede025ac2e83798fd47 SHA1: 542eb2847f3514ea82206cdd0268408aa3340f37 SHA256: 5bdafe6628813d1126a808986d77a7ab65b5f76573a1d1b1f3985fcb4f7f7e86 SHA512: 802cd7a0408a0af8408e00393e0bd9afd143cfddc98ecbd024507e3f51cc327454f31f78d2d48495c4c85041ad8a97022c0178fac83c4a460f71df5fa65cb19f Homepage: https://cran.r-project.org/package=ImVol Description: CRAN Package 'ImVol' (Volume Prediction of Trees Using Linear and Nonlinear AllometricEquations) Volume prediction is one of challenging task in forestry research. This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) . 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Package: r-cran-inaparc Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kpeaks, r-cran-lhs Suggests: r-cran-ggally, r-cran-nbclust, r-cran-cluster, r-cran-factoextra, r-cran-vegclust Filename: pool/dists/noble/main/r-cran-inaparc_1.2.1-1.ca2404.1_all.deb Size: 270884 MD5sum: d51d5134465ccfe77ce1b05c185e45ea SHA1: 1c99e7dd09cd84f227170d7e071f4df5b0f65394 SHA256: 4aaaecef9809ddb7c42e0213f2b62083a6b0b2dce2b69ca5613ba8bca1d754f3 SHA512: f714a6b983d4174b32a8bc0c95cd00b2d5210ed7062808d63f93b7eade706ccc11a1229c8dd204f24d5258da6f034ae98833a967537f5cc27de4604d604f2028 Homepage: https://cran.r-project.org/package=inaparc Description: CRAN Package 'inaparc' (Initialization Algorithms for Partitioning Cluster Analysis) Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. 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Package: r-cran-inatpick Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httptest2 Filename: pool/dists/noble/main/r-cran-inatpick_0.2.2-1.ca2404.1_all.deb Size: 52960 MD5sum: 41fc94fd3967531860db504e756d4db0 SHA1: a42ad22de7e04d4bc1833568007b72413b2842f7 SHA256: cc5667ab99e10f41510be71b3696ece9067daa56989686edc15178935461425b SHA512: 59467f84776ae6bc29385b82a327166c90b63bcaa4150789c34072a7f1d8767d32ad7ea94515b7f1fbbcbb7f521d83a26408ca0457820e63205601e4adfa8eb5 Homepage: https://cran.r-project.org/package=inatpick Description: CRAN Package 'inatpick' (Download Photos and Metadata from 'iNaturalist') A lightweight interface to the 'iNaturalist' API () for downloading observation photos and exporting metadata to CSV. Supports filtering by taxon, place, user, and annotation. Note that downloaded photos retain their original licenses as set by 'iNaturalist' observers; users are responsible for respecting these licenses. 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Package: r-cran-incaam Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-posterior, r-cran-minpack.lm, r-cran-brms Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-incaam_1.8-1.ca2404.1_all.deb Size: 53692 MD5sum: 65d4defc9ce5315b36e2d2ae1f770df7 SHA1: ef8ddcff0c210a956387fd3345ca9dcc635c1c74 SHA256: 91ade2a639102c467d3f1cdcb13f6cb94fcba8bb0a55bca6e56a4149e0bba3ed SHA512: 7659b3b1c47b1eb9140e9861647ad6f64c838ecc4d34211be422e4d7a1318b35eb1c30f87805c826fe0a8e3cf26072b5ea4109ba7484f58254234268dc28aeff Homepage: https://cran.r-project.org/package=IncAAM Description: CRAN Package 'IncAAM' (Bayesian Age-at-Maturity Analysis from Increment-Width GrowthSeries) Bayesian hierarchical nonlinear mixed-effects analysis for estimating age at sexual maturity from annual increment-width growth series. The method estimates age at maturity using tangent-ratio and jerk approaches and provides a posterior threshold-crossing ogive with an estimated A50. Methodological details and applications are described in Campana, Smoliński, Morrongiello and Black (2026, in press). Package: r-cran-incase Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-plu, r-cran-rlang Suggests: r-cran-dplyr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-incase_0.4.0-1.ca2404.1_all.deb Size: 200186 MD5sum: 9cc8d2630fcc6848e43e501309fdca1f SHA1: b1001d0180e01f94abf6af73298416ce411c489f SHA256: 4e403202aeba8001aa1aea70841e4ec8a94d6ae73d8455090d518e7937a99215 SHA512: 214e4c7f6255d5e03aad7d64d201385b63f14abac4d2c4027a746c120d4d2dd6bcaaaa1535c36fdcaa302bcee4c29e9d8efbc39cd1c1e64816ce4ebc14756e32 Homepage: https://cran.r-project.org/package=incase Description: CRAN Package 'incase' (Pipe-Friendly Vector Replacement with Case Statements) Offers a pipe-friendly alternative to the 'dplyr' functions case_when() and if_else(), as well as a number of user-friendly simplifications for common use cases. These functions accept a vector as an optional first argument, allowing conditional statements to be built using the 'magrittr' dot operator. The functions also coerce all outputs to the same type, meaning you no longer have to worry about using specific typed variants of NA or explicitly declaring integer outputs, and evaluate outputs somewhat lazily, so you don't waste time on long operations that won't be used. Package: r-cran-incatome Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1776 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-limma, r-bioc-siggenes, r-bioc-multtest, r-bioc-genefilter, r-bioc-rankprod Filename: pool/dists/noble/main/r-cran-incatome_1.0-1.ca2404.1_all.deb Size: 1722102 MD5sum: e12295d30ad6c63b5c9a363b78777af0 SHA1: 4daa9b52b68c3e0e3273202d83c6a7b848db6eb4 SHA256: 6be9c9ceb29494fc45f3d21ef99aee52c71e599fd0661b7d9e4ff8e8830b864c SHA512: cf2875b5fd621014420d36d7beaee8b2434d1b991a981b304ede338258a2833ed63c3833a3659d995b7812a71bcf441ef8d94c2f81662bb548d6ec1627e05671 Homepage: https://cran.r-project.org/package=INCATome Description: CRAN Package 'INCATome' (Internal Control Analysis of Translatome Studies by Microarrays) Data analysis, normalisation and differential expression for Translatome studies by microarrays (T Sbarrato et al. RNA. 2017 Aug 25; ). Package: r-cran-incidence2 Architecture: all Version: 2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grates, r-cran-data.table, r-cran-pillar, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-vctrs, r-cran-ympes Suggests: r-cran-outbreaks, r-cran-ggplot2, r-cran-scales, r-cran-litedown, r-cran-testthat, r-cran-citools, r-cran-withr Filename: pool/dists/noble/main/r-cran-incidence2_2.6.4-1.ca2404.1_all.deb Size: 618608 MD5sum: cc2a4253a3b40877a10c730c6a8b0cdd SHA1: a9212367495e934cb4ee836dcbd4c2bc636fed14 SHA256: eccdf8b64e72d4bb34c4b3c38c9dd0f504ea305f03ac8061ccb0cc7db7ffc2f4 SHA512: bebe851a02637d06ce62b5737d96a3414e0edb73d05e0bed82400821b121f3583e03ec06bc19d89120ebc12d5a3f70432fcc97aae20b021425e7f8d6f25699e8 Homepage: https://cran.r-project.org/package=incidence2 Description: CRAN Package 'incidence2' (Compute, Handle and Plot Incidence of Dated Events) Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence2' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. Package: r-cran-incidence Architecture: all Version: 1.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-aweek Suggests: r-cran-magrittr, r-cran-outbreaks, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-incidence_1.7.6-1.ca2404.1_all.deb Size: 1385538 MD5sum: cf3f809a3d20b58c52b5824489e863ae SHA1: 55e313bac971a80b8b2e4d703a3e5e45d5af7975 SHA256: 920c22bc341e7c1ecdeb282981f429bdb09fe6ff4c69cf1d0416ffe521504162 SHA512: cc74bbef3432314e7d91206d09915371419ee0ea2630c06e22fb116f3f956e5271e31b29984ef266e611b4bfaa18c4b412139e331ac67a4147ae678ecbc1f3a3 Homepage: https://cran.r-project.org/package=incidence Description: CRAN Package 'incidence' (Compute, Handle, Plot and Model Incidence of Dated Events) Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON () toolkit for outbreak analysis. Package: r-cran-incidenceprevalence Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cdmconnector, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-magrittr, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dbplyr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-duckdb, r-cran-dbi, r-cran-odbc, r-cran-here, r-cran-hmisc, r-cran-epitools, r-cran-tictoc, r-cran-testthat, r-cran-spelling, r-cran-gt, r-cran-flextable, r-cran-ggplot2, r-cran-scales, r-cran-visomopresults, r-cran-patchwork, r-cran-binom Filename: pool/dists/noble/main/r-cran-incidenceprevalence_1.2.1-1.ca2404.1_all.deb Size: 804758 MD5sum: df63f24b32d8d327870beef1767a9d0e SHA1: 0368a8f2254be7c6b50cf61194d7ca48c26f6d70 SHA256: 9499a25342b62b1f690ceb1d8299fbdab38f7f5c75f447c9a7d626b2af8b2c4d SHA512: 76ce22515851d47ac750e0f7bae39bbf52b20fcbba6da6f32d60c4698391751988171cdf0e442428ba7399743a61fa071ad9f419e4efaf18ef27b5fd8f70c7f9 Homepage: https://cran.r-project.org/package=IncidencePrevalence Description: CRAN Package 'IncidencePrevalence' (Estimate Incidence and Prevalence using the OMOP Common DataModel) Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort. Package: r-cran-incidental Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-matrixstats, r-cran-numderiv, r-cran-dlnm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-incidental_0.1-1.ca2404.1_all.deb Size: 460618 MD5sum: ea232fa6eb1d8439d66ef15cd4cd7fcb SHA1: 1b880c05ab55e69394f7994905cdf2ade69a3803 SHA256: 126c5929127d23a155eafa22e5eee854f70dfce6546c8d80763136c4588bf6cb SHA512: ba16b7cac1425de5a7af8e3ef6e38c9deca77e2d09ed2bbc03c80adfb91450fa6d59cf311c2cc2899e98c213c2fbfbbb11c108f387907e3db86829229b921101 Homepage: https://cran.r-project.org/package=incidental Description: CRAN Package 'incidental' (Implements Empirical Bayes Incidence Curves) Make empirical Bayes incidence curves from reported case data using a specified delay distribution. Package: r-cran-incidentally Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-xml2 Suggests: r-cran-backbone, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-incidentally_1.0.4-1.ca2404.1_all.deb Size: 540346 MD5sum: fb83e5a0a25c14bb27a9ec194d37282f SHA1: b41e3505b49c99e4c6baa99931f35786ddd74bc9 SHA256: d52b07062106ea56c1ba5ffa5598248f95e74dce984802921998464dd25064c4 SHA512: b5bca270a4f76115b7542388aef342c305663105c6cf07774f3d8fba61f47c84230492a6624ab818a0a0ec84773f8a50a5d73eb63358b187d377d5625594e98b Homepage: https://cran.r-project.org/package=incidentally Description: CRAN Package 'incidentally' (Generates Incidence Matrices and Bipartite Graphs) Functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress . It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation , or examine the space of binary matrices with fixed marginals. Package: r-cran-incompair Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-incompair_0.1.0-1.ca2404.1_all.deb Size: 53704 MD5sum: 0055f6e5c844d4ea7c29b94b10867489 SHA1: 0dd08f11aff18dd3d15338a1be5696cfdef6496e SHA256: b88e6aed67c2eb1f999a56e12bef3ca1df9ddf538b6c81e1b444535134efdd14 SHA512: 2de51bf59660adb04c362b2ac15fb3eae537d20852cc71252dac8d3bee014123d0f5050e99500f1b1458365b6c092e443cbeeef71933435ae39134fd008be8f0 Homepage: https://cran.r-project.org/package=IncomPair Description: CRAN Package 'IncomPair' (Comparison of Means for the Incomplete Paired Data) Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values. Bhoj, D. S. (1991) "Testing equality of means in the presence of correlation and missing data". Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) "Rank-based procedures for mixed paired and two-sample designs". Einsporn, R. L. and Habtzghi, D. (2013) "Combining paired and two-sample data using a permutation test". Ekbohm, G. (1976) "On comparing means in the paired case with incomplete data on both responses". Lin, P. E. and Stivers, L. E. (1974) On difference of means with incomplete data". Maritz, J. S. (1995) "A permutation paired test allowing for missing values". Package: r-cran-incr Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-suncalc, r-cran-lubridate Suggests: r-cran-codetools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-incr_2.1.1-1.ca2404.1_all.deb Size: 180592 MD5sum: d0dca39500f77c8c3c852d31eb851018 SHA1: e3d95b6ed9b90a3eb1a2b66605dc05bf5e276c00 SHA256: 9f53c8ba492c470badf6245758f792a21e522600301b4756aa5903ac8c98125b SHA512: 4fa422634b009c6841ee80f836283077060c6f5198c2432181b671b2619b164dd39027292e83cdeebbcf17e97f7b0f2c6f425a3172a9d1d75cb457ae809ca5f0 Homepage: https://cran.r-project.org/package=incR Description: CRAN Package 'incR' (Analysis of Incubation Data) Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds . Package: r-cran-incrementalitytest Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-boot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-incrementalitytest_0.1.1-1.ca2404.1_all.deb Size: 55474 MD5sum: e7545035f7cd698e47ae253a8b6c2d8c SHA1: 3c8bea56e1b56e9de029ef065ee4134d84ec6cc9 SHA256: 4e5adb5feb233b64083ec2970cc638cca6dfae30b48adaaeb251a272e5dac4b1 SHA512: d3b80112150f68f9a1c5c95406d46a4eb254dba0ac52e48fdfb7cf99b97c41e4154fd5e4128ac6073a169d4bb8f366a799cad00cf1811ed4f358fa44ce5c795d Homepage: https://cran.r-project.org/package=IncrementalityTEST Description: CRAN Package 'IncrementalityTEST' (Analyze Incrementality Experiments) Tools for calculating commerce metrics, pairing treatment and control results from A/B testing experiments, estimating incremental effects, and quantifying uncertainty with Student's t and nonparametric bootstrap confidence intervals. Includes validation helpers, a high-level analysis workflow, and compatibility functions for the original package interface. Package: r-cran-inctools Architecture: all Version: 1.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-tibble, r-cran-cubature, r-cran-tmvtnorm, r-cran-ggplot2, r-cran-glm2, r-cran-plyr, r-cran-pracma, r-cran-foreach, r-cran-doparallel, r-cran-binom Suggests: r-cran-survey, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-inctools_1.0.15-1.ca2404.1_all.deb Size: 228404 MD5sum: 8fb2368beacf3681386615ff39cd7772 SHA1: 2d139a7161003bd9df36087dd8ec0caec8bdcabe SHA256: a9d7ef92425240202dbfaa350c015d6fd32a41020444cfc67127beae2d59230f SHA512: bbd7ff91f7525bc2847bf4e10425808afb9a6390451a87f8244b50f31678fca5f8e20fb9f22eda295f4e3aca7d99ee68884942d708f7973cbd1fe4ecc7812f93 Homepage: https://cran.r-project.org/package=inctools Description: CRAN Package 'inctools' (Incidence Estimation Tools) Tools for estimating incidence from biomarker data in cross- sectional surveys, and for calibrating tests for recent infection. Implements and extends the method of Kassanjee et al. (2012) . Package: r-cran-incvcommunitydetection Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra, r-cran-clusterr, r-cran-irlba, r-cran-cluster, r-cran-rfast, r-cran-data.table, r-cran-imifa Suggests: r-cran-latentnet, r-cran-network, r-cran-rdist, r-cran-igraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-incvcommunitydetection_0.1.0-1.ca2404.1_all.deb Size: 288594 MD5sum: 6974fb56a27215393d86c3ed56c2e81d SHA1: aaa3592f088297c0b8475d074ac82e802d277c98 SHA256: bcb957ce52b80bdfd8e94a8b8e648ae14b1f73c48251ac8808e83276178217fe SHA512: f1729367b5bf7444a43928728306c5dc0d645076655c3a85aeb285b33a7e39ce8c68463e014ca5f9778a1585e6f962c38e34ad6aec9689ace7f671284416f1d9 Homepage: https://cran.r-project.org/package=INCVCommunityDetection Description: CRAN Package 'INCVCommunityDetection' (Inductive Node-Splitting Cross-Validation for CommunityDetection) Implements Inductive Node-Splitting Cross-Validation (INCV) for selecting the number of communities in stochastic block models. Provides f-fold and random-split node-level cross-validation, along with competing methods including CROISSANT, Edge Cross-Validation (ECV), and Node Cross-Validation (NCV). Supports both SBM and Degree-Corrected Block Models (DCBM), with multiple loss functions (L2, binomial deviance, AUC). Includes network simulation utilities for SBM, RDPG, and latent space models. Package: r-cran-indago Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1841 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-callr, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-ggrepel, r-cran-heatmaply, r-cran-hmisc, r-cran-htmltools, r-cran-magrittr, r-cran-matrixstats, r-cran-memuse, r-cran-paletteer, r-cran-pheatmap, r-cran-plotly, r-cran-r.devices, r-cran-readr, r-cran-reshape2, r-cran-rintrojs, r-cran-seqinr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spscomps, r-cran-svglite, r-cran-tibble, r-cran-tidyr, r-cran-upsetjs, r-cran-upsetr Suggests: r-bioc-biocgenerics, r-cran-biocmanager, r-bioc-biostrings, r-bioc-edger, r-cran-harrypotter, r-bioc-htsfilter, r-bioc-limma, r-cran-oompabase, r-cran-palr, r-bioc-rsamtools, r-bioc-rsubread, r-bioc-rtracklayer, r-bioc-s4vectors, r-bioc-shortread, r-cran-testthat, r-bioc-xvector Filename: pool/dists/noble/main/r-cran-indago_1.0.4-1.ca2404.1_all.deb Size: 852962 MD5sum: c788d8f08207117620545d5630cf00d4 SHA1: ab296096ad3965f2c00d8c5b3d22794543579cf1 SHA256: d4a7b865d6f6e9b293206c98f2275e2c30f5d3773cbc3f758b4f649748ae6c7d SHA512: 4be1a1d082bf9eb1893465c4e0380cd8bfd01260135a47708c6eaaf20d7148bf25a5172cf6662aee8a4036993255f4963e2d1d324e962cdda72791d982f5fd27 Homepage: https://cran.r-project.org/package=inDAGO Description: CRAN Package 'inDAGO' (A GUI for Dual and Bulk RNA-Sequencing Analysis) A 'shiny' app that supports both dual and bulk RNA-seq, with the dual RNA-seq functionality offering the flexibility to perform either a sequential approach (where reads are mapped separately to each genome) or a combined approach (where reads are aligned to a single merged genome). The user-friendly interface automates the analysis process, providing step-by-step guidance, making it easy for users to navigate between different analysis steps, and download intermediate results and publication-ready plots. 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Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors. Package: r-cran-index0 Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-index0_0.0.1-1.ca2404.1_all.deb Size: 16940 MD5sum: 1c2a783ab1683efc07bfa82ec6bba6eb SHA1: c6e8d311f7ca158d961021e1b9e6dad9ca1be0e1 SHA256: 89eb29b5cd53d5ad9346be4016e04f4951dfd58763fb6072843cec49b2ce3514 SHA512: 8cfc7e5288b24f655b36cfe3380c25e0864d36ccb355043981261975c187b5ffc404268529ee38f44d28c04ea2f0acdf9066b61b4de910d8b9fa909621e0a29f Homepage: https://cran.r-project.org/package=index0 Description: CRAN Package 'index0' (Zero-Based Indexing in R) Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages. 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Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) . 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The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) . For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited. 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Provides data sets and functions to complete the case studies and contains the book original Rmd files and tutorials. Package: r-cran-ineapir Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1027 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ineapir_0.2.6-1.ca2404.1_all.deb Size: 920132 MD5sum: 37c2e9a45aef1b35e4e7c0cd1bff6ab0 SHA1: 8094e9fe5bc840d07fb85cdc48bb31480179ecf4 SHA256: 6d15fe1710d6456682c6fe8febc8d102fd3f9acb3e30504383a200a7ab01e449 SHA512: 4391d82def4c6996f387e831b986ea40893d2f7cd37934f8cda0505f7ea9d07c4a71b5f4249dc6a17cbc29df4bb37cdafbf4f1de2936b39d3f332962223c3868 Homepage: https://cran.r-project.org/package=ineapir Description: CRAN Package 'ineapir' (Obtaining Data Published by the National Statistics Institute) Get open statistical data and metadata disseminated by the National Statistics Institute of Spain (INE). The functions return data frames with the requested information thanks to calls to the 'INE' API . 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Decomposition of the Theil index is based on Giammatteo, M. (2007) . Decomposition of the squared coefficient of variation is based on Garcia-Penalosa, C., & Orgiazzi, E. (2013) . Package: r-cran-ineq Architecture: all Version: 0.2-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ineq_0.2-13-1.ca2404.1_all.deb Size: 88698 MD5sum: 8d64510f79f62df5fef845cf15a616b7 SHA1: d16cfc2f765dfc01c5ef35a4c1af48de49ca7c73 SHA256: 7c709c9ae225dc8d45463fb8fdc144b363fe07addf5dd7b931bac1dfcbdbefa9 SHA512: ac444029b537ebabee2d5321f8ed5219d9d1abc3d513e0b659e6261eba5b506fc21b64eb250f4da38247ade58852206206edd04ddc90e1171e845855eb5bdf98 Homepage: https://cran.r-project.org/package=ineq Description: CRAN Package 'ineq' (Measuring Inequality, Concentration, and Poverty) Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical). Package: r-cran-ineqjd Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ineqjd_1.0-1.ca2404.1_all.deb Size: 67654 MD5sum: 22de2b6b275e3c3dac418e1309e2a60d SHA1: 466b61bba9d054c6436b16c1335fa6783ccdb7a0 SHA256: 522b18f64132a885a5755cd47b851cac0da2120823128a309b291732ebe493aa SHA512: ff26f716d79b705d4d5a65443a05b73a7bfc105ff215835e7f89120ff265e2be70751863b0cbdd15bd2771d796f60abee7eddfeaaca28ee922cc0f406b47deec Homepage: https://cran.r-project.org/package=ineqJD Description: CRAN Package 'ineqJD' (Inequality Joint Decomposition) Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) Zenga M. (2015) Zenga M., Valli I. (2017) Zenga M., Valli I. (2018) . 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Computes the Gini coefficient with bootstrap or asymptotic confidence intervals following Davidson (2009) , the extended S-Gini family, Theil T and L indices (generalised entropy family), the Atkinson index, the Kolm absolute inequality index, Palma ratio, Hoover index, percentile ratios, and Lorenz curves. Supports between-within group decomposition following Bourguignon (1979) , income share tabulation, concentration indices for health inequality with Erreygers (2009) correction, Kakwani tax progressivity and Reynolds-Smolensky redistribution indices, Foster-Greer-Thorbecke poverty measures including the Sen index, growth incidence curves following Ravallion and Chen (2003) , and Wolfson polarisation. All functions accept optional survey weights and work with data from any source. 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Influence functions are provided for linearization and variance estimation, along with a rescaled bootstrap for complex sampling designs. Estimation from grouped data is also supported. See Scarpa et al. (2025) for details. 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(2024) "INet for network integration" . 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The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) for statistical background. 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Package: r-cran-infectiousr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-infectiousr_0.1.1-1.ca2404.1_all.deb Size: 424680 MD5sum: a0374c17532c3eeef8b0fe00a934387e SHA1: d91f4a043148d1710797c86dedd4f8669bbe8158 SHA256: 957137535b1e8d4ffe0524577baa993428e73a3ab103f4e43c76e5a0460c29ef SHA512: ea2ccf3dee2588bb0cfffa3c1137e7caf5f2d3fe2049ee753428725936b92ce2f4ad4479d8d69b4cece41685fd611fca2fb78cbba8a333ec0147650e242ab93b Homepage: https://cran.r-project.org/package=infectiousR Description: CRAN Package 'infectiousR' (Access Infectious and Epidemiological Data via 'disease.sh API') Provides functions to access real-time infectious disease data from the 'disease.sh API', including COVID-19 global, US states, continent, and country statistics, vaccination coverage, influenza-like illness data from the Centers for Disease Control and Prevention (CDC), and more. 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Supports British pounds (GBP), Australian dollars (AUD), US dollars (USD), Euro (EUR), Canadian dollars (CAD), Japanese yen (JPY), Chinese yuan (CNY), Swiss francs (CHF), New Zealand dollars (NZD), Indian rupees (INR), South Korean won (KRW), Brazilian reais (BRL), and Norwegian krone (NOK). Currency codes and country names are both accepted as input. Package: r-cran-inflation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seasonal Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-inflation_0.1.0-1.ca2404.1_all.deb Size: 268792 MD5sum: 2712649db8940497db54ae4398430d0a SHA1: ee3b783b07aa24f687bc17f411430c93aa678c36 SHA256: 7e7a0bfdbcb7a4f7ad2e34ae50a2ff8f2c9ebaed59f2fed39b39f6748e88dde4 SHA512: 17220d4cea14a534d8ddc8f29cbb9ede24a075beaf22aa8fa5cd07c9f88531342b45aa3e76b47d7b096401a696147050890f31c8dff46cd5f086f7d01886ee2f Homepage: https://cran.r-project.org/package=Inflation Description: CRAN Package 'Inflation' (Core Inflation) Provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) . Package: r-cran-inflationkit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-inflationkit_0.1.0-1.ca2404.1_all.deb Size: 127128 MD5sum: 967655601af21bd9a278836cd265c8ce SHA1: 640a5ba60f87e1c3d305324a8e8dfe7b8bdb4cdf SHA256: 5a67d7667fb9a95294e13610b26cc00a7369a1332f0d9ab61e2209aeda0690c3 SHA512: 276f133a139939b9c371a7dbd707ff34306318b426d30e2461688c366f45450f2a0c63602d652ce5705308a327a30cf6535b9fe0eebf55710b41faa50f76d743 Homepage: https://cran.r-project.org/package=inflationkit Description: CRAN Package 'inflationkit' (Inflation Decomposition, Core Measures, and Trend Estimation) Tools for analysing inflation dynamics. Computes weighted contributions of price index components, core inflation measures (trimmed mean, weighted median, exclusion-based) following Bryan and Cecchetti (1994) , inflation persistence via sum-of-AR-coefficients, diffusion indices, Phillips curve estimation, breakeven inflation, and trend inflation using the Beveridge-Nelson decomposition and Hodrick-Prescott filter. All functions are pure computation and work with price data from any source. Package: r-cran-inflection Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-inflection_1.3.7-1.ca2404.1_all.deb Size: 279678 MD5sum: a5597f7f290881244adfcb2b9cd8a02a SHA1: 8e0c71b8e3f8671aef9f9d6eeb19688ec8950b6b SHA256: 32c0bffb1c9af508d259040c585c8bc0382d5d4e382575a024ad33e06e1f8d6a SHA512: 45ffbed52487f441e6d35b89d44f56569a73706e39f5a911783e958dbf22f02c0e3398f0201ece68e9b81aa4ab54b655eeda1c36884c296c54854ad727b45d7c Homepage: https://cran.r-project.org/package=inflection Description: CRAN Package 'inflection' (Finds the Inflection Point of a Curve) Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . Christopoulos, DT (2014) . Christopoulos, DT (2016) . Christopoulos, DT (2016) . Package: r-cran-inflectssp Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-data.table, r-cran-plotrix, r-cran-tidyr, r-cran-ggplot2, r-cran-xlsx, r-cran-httr, r-cran-jsonlite, r-cran-ggally, r-cran-network, r-cran-rcolorbrewer, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-inflectssp_1.6-1.ca2404.1_all.deb Size: 83464 MD5sum: 68d2c19f29c4e7039ca9c7f51fd07cb1 SHA1: 445f6ea52404706789f261988695f09c9e90197e SHA256: 6f9a007304f6bb17dcc4edb017d76861075c8f82eb48249dacceecde74d1ccdf SHA512: 22f7fb71e7c6e7e23b139348813e1d59719708f209a0986a9c1400beaf99ca42b9c42ad670b6bff3f84582cb95fd3dbc6a9f852f864615274aa64ec3649ddd47 Homepage: https://cran.r-project.org/package=InflectSSP Description: CRAN Package 'InflectSSP' (Melt Curve Fitting and Melt Shift Analysis) Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. McCracken (2022) . Package: r-cran-inflongitudinal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-mice Filename: pool/dists/noble/main/r-cran-inflongitudinal_0.1.0-1.ca2404.1_all.deb Size: 812152 MD5sum: d3b6f587bdbab523e13e677b518a08c1 SHA1: 72ee9527cbf6216889e6c876ed3584d5ef54959a SHA256: 5c89c90ac5fb34bc97b7534d1976fe32f75411d3a54db89acc614a588c509c68 SHA512: 3d29d164d4f573e1017e41e83b96519f2a601a825f8f5ff6bff8e0973e0e58058efb4de5bc3224741f626606f8471ed4ac22766e0fcd621893ff431eada57d9a Homepage: https://cran.r-project.org/package=Inflongitudinal Description: CRAN Package 'Inflongitudinal' (Detecting Influential Subjects in Longitudinal Data) Provides methods for detecting influential subjects in longitudinal data, particularly when observations are collected at irregular time points. The package identifies subjects whose response trajectories deviate substantially from population-level patterns, helping to diagnose anomalies and undue influence on model estimates. Package: r-cran-influence.me Architecture: all Version: 0.9-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-lattice Filename: pool/dists/noble/main/r-cran-influence.me_0.9-10-1.ca2404.1_all.deb Size: 84278 MD5sum: 2897802149e25bf20dbdb817f780294b SHA1: 79b8e01b5c29b55a26e00d4ecef690837b21b31a SHA256: 200bcb7004a72ee3a0e75c58193a2223b0b369a162a98e318b26b2776bed8ebd SHA512: 345966fea37e87a7b47138295ac263c7713c31518cb7f99ea8939347860b8255c796f19dea095d13de582df1749a43bcf007b153a44e3ee0d4b72bd201f5fd78 Homepage: https://cran.r-project.org/package=influence.ME Description: CRAN Package 'influence.ME' (Tools for Detecting Influential Data in Mixed Effects Models) Provides a collection of tools for detecting influential cases in generalized mixed effects models. It analyses models that were estimated using 'lme4'. The basic rationale behind identifying influential data is that when single units are omitted from the data, models based on these data should not produce substantially different estimates. To standardize the assessment of how influential a (single group of) observation(s) is, several measures of influence are common practice, such as Cook's Distance. In addition, we provide a measure of percentage change of the fixed point estimates and a simple procedure to detect changing levels of significance. Package: r-cran-influence.sem Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-influence.sem_2.4-1.ca2404.1_all.deb Size: 78882 MD5sum: bc299e1a69f317e53b6a3535c7693bfd SHA1: 0d2915ecdb1b3cc226147dddf211c7a9f02200ce SHA256: 34ba091788f0aeb984453feb039617ea2fdea5574b8cd3ed61ac7ffe0dc670d1 SHA512: e759e03d8dbcbd03c40f16e08b32a872b3806ea1cc21d9bb68f8670a57951d65e3e8fc6dfdf6d407facf22fea3cdd08c03548ca938c4795a33a47c3812b2d818 Homepage: https://cran.r-project.org/package=influence.SEM Description: CRAN Package 'influence.SEM' (Case Influence in Structural Equation Models) A set of tools for evaluating several measures of case influence for structural equation models. Package: r-cran-influenceauc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geigen, r-cran-ggplot2, r-cran-ggrepel, r-cran-rocr Filename: pool/dists/noble/main/r-cran-influenceauc_0.1.2-1.ca2404.1_all.deb Size: 64184 MD5sum: b4e3f321d0b9e4a0f46b51daa46abbe5 SHA1: 1289759a47976d578feb834307fe317aabfa810b SHA256: 5fe9152d5065e3a2afd8b3be8d8acc69292a443178f0ef5361311413d7d800c1 SHA512: 6b736d94f4c86309aef5b1839f3ae3168878aca097bfd23c7232c60729d500a2950e068cddbb575e84aeb1aa433135f425570a762029c4c49eb960d65ca2d317 Homepage: https://cran.r-project.org/package=influenceAUC Description: CRAN Package 'influenceAUC' (Identify Influential Observations in Binary Classification) Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization. Package: r-cran-influenceborrowing Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-krls Filename: pool/dists/noble/main/r-cran-influenceborrowing_0.1.0-1.ca2404.1_all.deb Size: 59310 MD5sum: 34edc6115abf1461719f6ae03b418f4b SHA1: c70677a9ee3cdb35289af4258787e82e2b9981e1 SHA256: b66122513903c68cf9ecbbe11b6fd8f9d8f62f6b447a097d8589bae5316d66f4 SHA512: b0e6de85128b922ae7acb8ae5353940370653c2d3ab409ec18bac919b8c309ab969a04172b6dd9a284b7aff09ce1f40803d21bb2111d1fe926bb8bd805deee6c Homepage: https://cran.r-project.org/package=InfluenceBorrowing Description: CRAN Package 'InfluenceBorrowing' (Adaptive Influence-Based Borrowing for Hybrid Control Trials) Implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression. Package: r-cran-influential Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-janitor, r-cran-ranger, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-hmisc, r-cran-mgcv, r-cran-nortest, r-cran-nns, r-cran-biocmanager, r-cran-readr, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-colourpicker, r-cran-magrittr, r-cran-dt, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-influential_2.3.0-1.ca2404.1_all.deb Size: 1084954 MD5sum: 8e3f1fbee06319bf142f6578c1a9b04f SHA1: f3a65db5b40387ff745fc7e194abb36151fb4971 SHA256: 40871012d1e17bf678266a2b6f1a440bb7853cf83fd7b5733f432c63bfabcc6f SHA512: 8d9366542b535c36b3b5bc93f83a91d042d7d1ca4c3ef22d0b11ecf814c7bf2f8c9f34f6d7ee6469d2fca055cc1d5655a61f67b7e5c922c529f094119d90ba63 Homepage: https://cran.r-project.org/package=influential Description: CRAN Package 'influential' (Identification and Classification of the Most Influential Nodes) Provides functions for classification and ranking of candidate features, reconstruction of networks from adjacency matrices and data frames, topological analysis, and calculation of centrality measures. The package includes the SIRIR model, which combines leave-one-out cross-validation with the conventional SIR model to rank vertex influence in an unsupervised manner. Additional functions support assessment of dependence and correlation between network centrality measures, as well as estimation of conditional probabilities of deviation from their corresponding means in opposite directions. Package: r-cran-influxdbclient Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-bit64, r-cran-nanotime, r-cran-plyr Suggests: r-cran-testthat, r-cran-httptest Filename: pool/dists/noble/main/r-cran-influxdbclient_0.1.2-1.ca2404.1_all.deb Size: 372528 MD5sum: 6423e2dbb6844caf1d3c40d29af6e11e SHA1: 6b6dc8092e4be5a2c435ef2d4091653faa6362f4 SHA256: 24994ba5fe141b72334ba23ee768b88c9b105e5934f9308141703fc291fcb287 SHA512: d17e0fce37c21633f7029d5e347d55eb3e7d06fb28526ce80658e08a965957f46055204c32cf55ad8e55888e3f717f641e9148076faffac85f39f319f1e70ed5 Homepage: https://cran.r-project.org/package=influxdbclient Description: CRAN Package 'influxdbclient' ('InfluxDB' 2.x Client) 'InfluxDB' 2.x time-series database client. Supports both 'InfluxDB' OSS () and Cloud () version. 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This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided. Package: r-cran-infodecompute Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-infodecompute_0.6.2-1.ca2404.1_all.deb Size: 178472 MD5sum: 1e36641ba837c24cb77f16a728bb726f SHA1: 3bcf3a3e26b7ab9efdda4deb7492f440ac00886e SHA256: 86ebfd607507ba8b06776e887e9aefd7276f797e7509a78cf0287f5757b88779 SHA512: b3c83cf4ac23ba5b02125775600f1ca44247bf06e70c40ba397b7a3a5d6cb6436a4b275047fd2617a44fcd666c42cd9e905692db316b64a88365238dc9d3c31a Homepage: https://cran.r-project.org/package=infoDecompuTE Description: CRAN Package 'infoDecompuTE' (Information Decomposition of Two-Phase Experiments) The main purpose of this package is to generate the structure of the analysis of variance (ANOVA) table of the two-phase experiments. The user only need to input the design and the relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, this package can then quickly generate the structure of the ANOVA table with the coefficients of the variance components for the expected mean squares. Thus, the balanced incomplete block design and provides the efficiency factors of the fixed effects can also be studied and compared much easily. Package: r-cran-infoelectoral Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1776 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-httr Suggests: r-cran-ggplot2, r-cran-mapspain, r-cran-patchwork, r-cran-purrr, r-cran-tidyr, r-cran-knitr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-infoelectoral_1.0.3-1.ca2404.1_all.deb Size: 638338 MD5sum: e5c7010756ba19d9f76d7b60f6ee09df SHA1: 4dc27611d386db9c991485c7928b10fd2c1d0271 SHA256: fa389d6d05832d16b916bd8e9de77f134541dcdf9b4eb80cad5321aec6d4b4e9 SHA512: 9d7c7ff186f00a61a1b175bad5b634209f64a2bcb8f437d48498c31cb145eb0f00c9388ffd5e303941f9346e1715c7d45b3bf2dfc22625499bb7a5c0fd4ff574 Homepage: https://cran.r-project.org/package=infoelectoral Description: CRAN Package 'infoelectoral' (Download Spanish Election Results) Download official election results for Spain at polling station, municipality and province level, format them and import them to the R environment. Data are provided by the Spanish Ministry of the Interior (). Package: r-cran-infometrics Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-infometrics_0.3.0-1.ca2404.1_all.deb Size: 468162 MD5sum: 29d120f2eb461c8d9da2d0b8b84d06ab SHA1: 640ae98984512639343cd28395c314803b6f9f5f SHA256: 5b2bf613ce3ab863db42016549febceebbcd2922f0fdee97f440b7068db2e135 SHA512: 545c8248ba97e02f7d82158b6bcceefb156997e8f68b7ecd68977f9fbaa773d45d02c29f491663d696534940011ee5cb96aef87cddecc36c9687da953ab54b9e Homepage: https://cran.r-project.org/package=infometrics Description: CRAN Package 'infometrics' (Information-Theoretic Methods for Econometric Estimation) Implements the class of Information-Theoretic (IT) estimators for econometric models, following the unified framework of Golan (2008) . Provides Generalized Maximum Entropy (GME) and Generalized Cross-Entropy (GCE) estimators for linear regression, instrumental variables, one-way error-component panel data, multinomial response, matrix balancing, and first-order Markov transition matrices, together with pure and noisy inverse-problem solvers. All estimators use the concentrated (dual) formulation for computational efficiency and report normalized-entropy, entropy-ratio, and Fano-bound diagnostics. Package: r-cran-information Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-plyr, r-cran-iterators, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-reshape2, r-cran-clustofvar, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-information_0.0.9-1.ca2404.1_all.deb Size: 1103120 MD5sum: 3540189e7805bdc108350a3f321fd1f1 SHA1: b436b941c910ffab2d61d31c51c8161d4e063adf SHA256: 1b7c73bf28d21e95c189a1b3260c44ba4b08216a50619e425e858ab9bd9e7cb8 SHA512: 6840fe7a4e2fae44fe48738360eb69fd324be5e0516a195857548e668102e954db7aab9a14179d84b2ee9f96569d47bf3c25b5e49db16b517b254297342fcb1b Homepage: https://cran.r-project.org/package=Information Description: CRAN Package 'Information' (Data Exploration with Information Theory (Weight-of-Evidence andInformation Value)) Performs exploratory data analysis and variable screening for binary classification models using weight-of-evidence (WOE) and information value (IV). In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV). 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Gamma Imputation described in and Risk Score Imputation described in . Package: r-cran-informativesci Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmcp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-informativesci_1.0.4-1.ca2404.1_all.deb Size: 109786 MD5sum: 0830a7bff1ac80fce91a4a9defe2ccfa SHA1: aee7a10e591efd6adc8bb9d1f80c394cf3b43ef1 SHA256: d2dc2c3ecaeaabb10c568207a19235f0029e8478dbe0689bf9b4863b52027317 SHA512: acd4a734fc85c4a5e2a9d193e14e0bd7d4d6f945d227773ec7715ffc559c8daaf450c3fb0486bbb1160507f66595a75c8fbc0ba8b735afbccfdf571436e8b154 Homepage: https://cran.r-project.org/package=informativeSCI Description: CRAN Package 'informativeSCI' (Informative Simultaneous Confidence Intervals) Calculation of informative simultaneous confidence intervals for graphical described multiple test procedures and given information weights. Bretz et al. (2009) and Brannath et al. (2024) . Furthermore, exploration of the behavior of the informative bounds in dependence of the information weights. Comparisons with compatible bounds are possible. Strassburger and Bretz (2008) . 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The package uses the optimization software gurobi obtainable from , together with its associated R package, also called gurobi; see: . The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 . Package: r-cran-infoset Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-colorspace, r-cran-dendextend, r-cran-quadprog, r-cran-mixtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-infoset_4.1.1-1.ca2404.1_all.deb Size: 1479374 MD5sum: 0952e26d494134c0879a23510425cc83 SHA1: bda35a7e1c040f9a0defc84fff7447e2d7bdcd9f SHA256: f6bf46acbd3e345f4f1e4b307d7bfe4fec1a7aea5213a45227a0d6159e375d11 SHA512: f8c399cce7d775b8f63363c2f20b1eefb7f1e321fcbd7a744955a9783bcf88471b2eae5ecad33e344a3bb47e91f17b0580dfec99f763a78c1659ff138dbf98d2 Homepage: https://cran.r-project.org/package=INFOSET Description: CRAN Package 'INFOSET' (Computing a New Informative Distribution Set of Asset Returns) Estimation of the most-left informative set of gross returns (i.e., the informative set). The procedure to compute the informative set adjusts the method proposed by Mariani et al. (2022a) and Mariani et al. (2022b) to gross returns of financial assets. This is accomplished through an adaptive algorithm that identifies sub-groups of gross returns in each iteration by approximating their distribution with a sequence of two-component log-normal mixtures. These sub-groups emerge when a significant change in the distribution occurs below the median of the financial returns, with their boundary termed as the “change point" of the mixture. The process concludes when no further change points are detected. The outcome encompasses parameters of the leftmost mixture distributions and change points of the analyzed financial time series. The functionalities of the INFOSET package include: (i) modelling asset distribution detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function) (iv) portfolio construction (ptf_construction function). The package also provide a specific function to construct rolling windows of different length size and overlapping time. Package: r-cran-infotest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-infotest_0.1.0-1.ca2404.1_all.deb Size: 20042 MD5sum: ccede39273237096c6553da7958f2c4b SHA1: 83d59c8226e017abac586652147b6fa3d01a7a7c SHA256: f447a18e1d02fe72fe3f4f6ee7201db003013d9bbb9e82c39600912850d0bb10 SHA512: 5a44f971f35f9ea5d1b47f00277718730bc65d315615a5e4cc4ed16c5cfe133f5500fc499bc7ae33db29e5c30449a03a76254451416e4575d52c2348a513f425 Homepage: https://cran.r-project.org/package=infotest Description: CRAN Package 'infotest' (Information Matrix Test for Regression Models) Implements the Information Matrix test for regression models following Cameron, A. C., & Trivedi, P. K. (1990) Decomposes the test into components for heteroscedasticity, skewness, and kurtosis to diagnose specific forms of misspecification. Provides both overall and component-wise statistics for model assessment. Package: r-cran-infotrad Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-infotrad_1.2-1.ca2404.1_all.deb Size: 50448 MD5sum: b7c4ffaf73cf454f25fc7e95fe7c54c7 SHA1: 8dbb3e83f87feb85fade802a9057f2828d00fc57 SHA256: 82471d366cb7693c2c1e9cdf7858f1787f3c71d10be1e18cfd7befd94441006c SHA512: 6b44f9e11a3f3151b9abfeb74fa2e8f58b5b745a31bc15210f75e9af7efc9c3e02b1e31a12466c9cab90c327efe6d8361f9f21d3b4595dcf1483f174fd754104 Homepage: https://cran.r-project.org/package=InfoTrad Description: CRAN Package 'InfoTrad' (Calculates the Probability of Informed Trading (PIN)) Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) . Contribution of the package is that it uses likelihood factorizations of Easley et. al. 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Key functions are: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the what-if plots, partial_dependence() for partial dependence plots, conditional_dependence() for conditional dependence plots, accumulated_dependence() for accumulated local effects plots, aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive, D3 based explanations, and generic describe() for explanations in natural language. The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) . 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Package: r-cran-injector Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-injector_0.2.4-1.ca2404.1_all.deb Size: 28482 MD5sum: 5dced8ed0c35d5c2a2e3a3f861314e54 SHA1: f53a943c71f3e1d95b8352d0ac8ef13a96cf08ea SHA256: a7f562ee627f6ed71b96484adea071147fc3baac894d3c8b249f07de7e7059ed SHA512: 486d51a459db60756409658d7a675279870604c6bc8f59435291c939814ac87980547a45cd5b5bc7bdd573327269aed9cc3a4a9ef556c91e995620fb824bf376 Homepage: https://cran.r-project.org/package=injectoR Description: CRAN Package 'injectoR' (R Dependency Injection) R dependency injection framework. Dependency injection allows a program design to follow the dependency inversion principle. The user delegates to external code (the injector) the responsibility of providing its dependencies. This separates the responsibilities of use and construction. Package: r-cran-injuryseverityscore Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-injuryseverityscore_0.0.0.2-1.ca2404.1_all.deb Size: 36034 MD5sum: a9ea765dfcf14c5b7ee7a3e59dc94472 SHA1: fd736d43b9c86ce4d4fbb349f3aa12ee9f5471ab SHA256: 496b58ed3f6c220a4942e084bc78dbb466db7a6e58ec516e9783de8f5e84937f SHA512: 1bde38e9fc95753f815271dbcaa0b0e849919c44e1a128d0cfa872328d45049a568be5fbbfe7f28dd6febf16107333a7c991f42762db8cc3e726edb8ed8ffbcd Homepage: https://cran.r-project.org/package=InjurySeverityScore Description: CRAN Package 'InjurySeverityScore' (Translate ICD-9 into Injury Severity Score) Calculate the injury severity score (ISS) based on the dictionary in 'ICDPIC' from . The original code was written in 'STATA 11'. The original 'STATA' code was written by David Clark, Turner Osler and David Hahn. I implement the same logic for easier access. Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. "ICDPIC: Stata module to provide methods for translating International Classification of Diseases (Ninth Revision) diagnosis codes into standard injury categories and/or scores," Statistical Software Components S457028, Boston College Department of Economics, revised 29 Oct 2010. Package: r-cran-injurytools Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lubridate, r-cran-metr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-coxme, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-pscl, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-spelling, r-cran-survival, r-cran-survminer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-injurytools_2.0.1-1.ca2404.1_all.deb Size: 1257956 MD5sum: 634eb5fe991a93ded94912b9924e4fea SHA1: ec6af571e2774378eb082d7fbeacada4d009fc69 SHA256: e3e997f9e4fd5f4c28bcad8a0d7c536b4da7bfbf8ce990346b7363c7dd46dbdf SHA512: cc923c8dbd7b0c0e65f4dc04717725c2ba13ee6389486ba24d364b7e924d9b4f13af63e32eaa212613a16ad705702cbf186fde06c9336231fa00306cc9aeddc0 Homepage: https://cran.r-project.org/package=injurytools Description: CRAN Package 'injurytools' (A Toolkit for Sports Injury and Illness Data Analysis) Sports Injury Data analysis aims to identify and describe the magnitude of the injury problem, and to gain more insights (e.g. determine potential risk factors) by statistical modelling approaches. The 'injurytools' package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses. 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Features a bilingual interactive wizard, fuzzy and normalized indicator search, multi-indicator downloads with automatic tidy merging (long/wide), guaranteed consistent output schema, robust disk caching with automatic retry, and 'ggplot2' themes for regional mapping. Package: r-cran-inlabma Architecture: all Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-matrix, r-cran-spdep Filename: pool/dists/noble/main/r-cran-inlabma_0.1-12-1.ca2404.1_all.deb Size: 85552 MD5sum: faa43a6492efa9b3cc92cfa10c9bc2bf SHA1: d4cd42a49bc3a2ae4cd224b0ba74ddbd17cfbdf5 SHA256: 5ca71a121e0f16e5c45170344a5ef36208c5e2a2cdc6dc5cd87520ddc9bc2384 SHA512: 429d7108430afec3fde9f0ac13fd69ffa3ffd6a744cc3c3103a4b68f93d2293ad938aad994e39de5a5af616e1dd3083b39479bcec21314365cf9249e8f8affac Homepage: https://cran.r-project.org/package=INLABMA Description: CRAN Package 'INLABMA' (Bayesian Model Averaging with INLA) Fit Spatial Econometrics models using Bayesian model averaging on models fitted with INLA. The INLA package can be obtained from . Package: r-cran-inlabru Architecture: all Version: 2.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3771 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmesher, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-matrixmodels, r-cran-matrix, r-cran-plyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-withr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-mgcv, r-cran-patchwork, r-cran-raster, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-scales, r-cran-scoringrules, r-cran-shiny, r-cran-sn, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.data, r-cran-sphereplot, r-cran-splancs, r-cran-terra, r-cran-tidyterra, r-cran-testthat, r-cran-tidyr, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-inlabru_2.13.0-1.ca2404.1_all.deb Size: 3162212 MD5sum: a5dca8bff95c45e57192f30238753873 SHA1: 598b40f8891292fd8956f46f46d88797e9ceca0c SHA256: 4e40231983570a5d99a267d599a7743f82fb86ce5f41add68ba201d2b04e1315 SHA512: 67d8ea58e4f771440d3b3ada20239423d56be44c2bce207852110a4071b08ab92346b22df28c7f6cca90e17b01ecba9931182cbe7e247402aebb06832788b487 Homepage: https://cran.r-project.org/package=inlabru Description: CRAN Package 'inlabru' (Bayesian Latent Gaussian Modelling using INLA and Extensions) Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) . Package: r-cran-inlajoint Architecture: all Version: 26.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-reformulas, r-cran-ggplot2, r-cran-matrix, r-cran-nlme, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-survival, r-cran-jmbayes2, r-cran-rstanarm, r-cran-frailtypack, r-cran-smcure, r-cran-fmesher, r-cran-mass, r-cran-sn Filename: pool/dists/noble/main/r-cran-inlajoint_26.6.2-1.ca2404.1_all.deb Size: 1070716 MD5sum: 8497b2999d38cc84f493c486192abe3b SHA1: c6abe6a7bcd26ccbf544b537dc35590f9ee67c90 SHA256: 26927534bbbaf129bbdce0d57df1b0c84b7ed1a990e3a260952608afeee2af61 SHA512: 1fbd216c94acc90abd752e05b852672dfadb6dcda5c1d75e415961fcb259190455c80064aac934914744866cc02cd2f033747553fbc1185ec60fedc67d26794f Homepage: https://cran.r-project.org/package=INLAjoint Description: CRAN Package 'INLAjoint' (Multivariate Joint Modeling for Longitudinal and Time-to-EventOutcomes with 'INLA') Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console). Package: r-cran-inlamemi Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat, r-cran-tibble, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-inlamemi_1.1.0-1.ca2404.1_all.deb Size: 918486 MD5sum: 6ece40ab548ca207f721f029fcaae198 SHA1: 5f8191cfa56e6b268c0f3c331a2be7ac254f7971 SHA256: 84cebe84ec76a50489d24f35b3cd43fb0824db71597b8356bc90827a977f337f SHA512: 594690bcd3eb0c6963421b941a8cc213ef287479ffd54ece159082b6075be3eb043ec95de89831660eec549c76d791fbc4f9212065fef68fe9eff49e41344422 Homepage: https://cran.r-project.org/package=inlamemi Description: CRAN Package 'inlamemi' (Missing Data and Measurement Error Modelling in INLA) Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) . See Skarstein and Muff (2024) for details on using the package. Package: r-cran-inlavaan Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lavaan Suggests: r-cran-blavaan, r-cran-ggplot2, r-cran-knitr, r-cran-lme4, r-cran-numderiv, r-cran-qrng, r-cran-quarto, r-cran-sn, r-cran-testthat, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-inlavaan_0.2.4-1.ca2404.1_all.deb Size: 2259148 MD5sum: 221cf6faec48fcb11a2ba00b72e9788d SHA1: ed4933417d90197e341094a5406486f4a19b9c2e SHA256: 2498ac930eb491a3119e5d10da59ece8c41a6f3da8ebcc711c789d54f5fac5aa SHA512: 798e5b3c993f66b77328660f5e450e9538f5a8437ba5ffc16c0081798f4880b8354c1b41d0620c05f3c501fd3808b712ebf9c6d739f376643ad27a8ca2d629a7 Homepage: https://cran.r-project.org/package=INLAvaan Description: CRAN Package 'INLAvaan' (Approximate Bayesian Latent Variable Analysis) Implements approximate Bayesian inference for Structural Equation Models (SEM) using a custom adaptation of the Integrated Nested Laplace Approximation (Rue et al., 2009) as described in Jamil and Rue (2026a) . 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The data collected from wells and surface-water stations at the Idaho National Laboratory and surrounding areas have been used to describe the effects of waste disposal on water contained in the eastern Snake River Plain aquifer, located in the southeastern part of Idaho, and the availability of water for long-term consumptive and industrial use. The package includes long-term monitoring records dating back to measurements from 1922. Geospatial data describing the areas from which samples were collected or observations were made are also included in the package. Bundling this data into a single package significantly reduces the magnitude of data processing for researchers and provides a way to distribute the data along with its documentation in a standard format. Geospatial datasets are made available in a common projection and datum, and geohydrologic data have been structured to facilitate analysis. 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Package: r-cran-inough Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-ggplot2, r-cran-patchwork, r-cran-rlang, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-inough_0.1.0-1.ca2404.1_all.deb Size: 137732 MD5sum: e237450a65a2cdc10acf62b91348d046 SHA1: 884d687d4eed256a0f20c78111d6b08c39514ba2 SHA256: 625e6e95667c426c199b4365917cd410c7d91b624d944d68a8946847ba35065d SHA512: f0118b0953474668ed01f8a4debbc770f9e3787fe9009eb0ae353c0b71be3abf59ed6c88e09f3bb24ce625308be1e88376b696b04913399c844412d8d45e3607 Homepage: https://cran.r-project.org/package=inough Description: CRAN Package 'inough' (Inattention Detection Pipeline for Psychophysical Tasks) Three-stage pipeline for detecting inattention episodes in long psychophysical tasks (200+ trials). Uses accuracy residuals and response pattern signals to locate, sharpen, and formally test candidate inattention regions at trial-level precision. Package: r-cran-inpdfr Architecture: all Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wordcloud, r-cran-rcolorbrewer, r-cran-tm, r-cran-snowballc, r-cran-cluster, r-cran-entropart, r-cran-metacom, r-cran-stringi, r-cran-r.devices Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-inpdfr_0.1.12-1.ca2404.1_all.deb Size: 148986 MD5sum: 10a9b92df83891c8d3f6727ec5ac66a8 SHA1: 6f611cf720b813f2cd679ac4f57a3c173abe6511 SHA256: 5a5dc98a204c4c2f531c1ff6151469f79a513ad6b2926f68c6b0e7d42a24aa22 SHA512: 1c6b099bc2729f8935d5648e64ce686373395fb5b33b7e25be1c1841cb53b42f13d8fd6d4321f05165334dd1cf63f18d8e0466f221682d04557c0fa9f49155ee Homepage: https://cran.r-project.org/package=inpdfr Description: CRAN Package 'inpdfr' (Analyse Text Documents Using Ecological Tools) A set of functions to analyse and compare texts, using classical text mining functions, as well as those from theoretical ecology. Package: r-cran-inphr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fdatest, r-cran-flipr, r-cran-phutil, r-cran-rlang, r-cran-tdavec Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-inphr_0.0.1-1.ca2404.1_all.deb Size: 316952 MD5sum: 64ef90a569f0b4eec0e0aa74558ff1c4 SHA1: a06d5ed2745957c11587a24f137921a72748ca49 SHA256: 752d81850b55eb6b307b38bb3e7a0d0c5701d596aedf25ca3d93c8ddd9ae5da5 SHA512: 7c6245ea0e3b486a4a98632b3a637927a8c46a5792c4915e54fac9c1717a5679373f076082ced773760b49fc876f5825e361d6511a2b1e7d670c277f12a9570b Homepage: https://cran.r-project.org/package=inphr Description: CRAN Package 'inphr' (Statistical Inference for Persistence Homology Data) A set of functions for performing null hypothesis testing on samples of persistence diagrams using the theory of permutations. Currently, only two-sample testing is implemented. Inputs can be either samples of persistence diagrams themselves or vectorizations. In the former case, they are embedded in a metric space using either the Bottleneck or Wasserstein distance. In the former case, persistence data becomes functional data and inference is performed using tools available in the 'fdatest' package. Main reference for the interval-wise testing method: Pini A., Vantini S. (2017) "Interval-wise testing for functional data" . Main reference for inference on populations of networks: Lovato, I., Pini, A., Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued data" . Package: r-cran-inposition Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prettygraphs, r-cran-exposition Filename: pool/dists/noble/main/r-cran-inposition_1.0.0-1.ca2404.1_all.deb Size: 103890 MD5sum: b5364cf6dbaa68b36bc91560e0a07ad6 SHA1: 244d4d3fb01586273111f50ad1a9a514b95f932c SHA256: 3d7324e66e4aec7233094bf2e271eb62fe8f563d53cd9d177f4f7af94b1e8dfd SHA512: 6e123ffb892f8525e11972659f9bc664aa12a3e5fb75fca37b64151a0b5cd5ef8e3705e094537f9fc29df1b598d8aa5ebe11317ad6c18e6a5f0c415e545c2a47 Homepage: https://cran.r-project.org/package=InPosition Description: CRAN Package 'InPosition' (Inference Tests for ExPosition) Non-parametric resampling-based inference tests for ExPosition. Package: r-cran-inqc Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd, r-cran-gdata, r-cran-suncalc Suggests: r-cran-readr Filename: pool/dists/noble/main/r-cran-inqc_2.0.5-1.ca2404.1_all.deb Size: 291398 MD5sum: d7921a3ea4e36b3b4990a1c1bd8ee0b1 SHA1: c9e47dce43f954ea3ce03a75692ad94564b501d4 SHA256: 6c9bab5271f5db5d86dfa4cd9b48fd4d156bd8f8edf0d9a87043149f1176e107 SHA512: 33fdc80c1e01660340ff015b4c63a4c064d5f2999d231678d88a6a868eac95271c6a552a3e0e091177de628420eaff19f37af609b91c846b3e4564c215416b1f Homepage: https://cran.r-project.org/package=INQC Description: CRAN Package 'INQC' (Quality Control of Climatological Daily Time Series) Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data). Package: r-cran-inquilab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-inquilab_0.1.0-1.ca2404.1_all.deb Size: 19214 MD5sum: 94c45804dde497f8b883e0b2dc8c77cd SHA1: ec46db78e80c5594a3484d238decfba0d36ffc86 SHA256: 531df3ffb81b4b793311a577d4e108e752b443acc8fa20b8079671f31f3341d8 SHA512: 16cab5dcce12ef31c2f238b79f305cc55a6d82cf8697132132a417388b8612b5a7ca4b8899d32ad2e2118d88ed1c48d173fc8047cc6decee6bfce3cf633ad66d Homepage: https://cran.r-project.org/package=Inquilab Description: CRAN Package 'Inquilab' (Dissipation Kinetics Analysis, Half Life Period, Rate Constant,Plots) For environmental chemists, ecologists, researchers and agricultural scientists to understand the dissipation kinetics, calculate the half-life periods and rate constants of compounds, pesticides, contaminants in different matrices. Package: r-cran-insane Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-patchwork, r-cran-purrr, r-cran-readxl, r-cran-shiny, r-cran-tidyr Suggests: r-cran-covr, r-cran-roxygen2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-insane_1.0.3-1.ca2404.1_all.deb Size: 2576302 MD5sum: a94f008af67d751de17c37741a45f337 SHA1: 660aff27c669f250fedcdbb5c767b080b609e295 SHA256: 7bba2b114a2c9d15dd500c0ea974d7e6bcbd192609259a8b70c47cc70857a46f SHA512: d5af0454ed51ab4a0f885db093619f74578e21919fc9c23490a0015e14d050dbd971563f849b1ba63a0742b93ea01c315df38ce45dd31574c8afbe2cf9e582d0 Homepage: https://cran.r-project.org/package=insane Description: CRAN Package 'insane' (INsulin Secretion ANalysEr) A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets. The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician. Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface. The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (). Package: r-cran-insect Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-aphid, r-cran-kmer, r-cran-openssl, r-cran-phylogram, r-cran-rann, r-cran-seqinr, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-insect_1.4.4-1.ca2404.1_all.deb Size: 551000 MD5sum: 482f0edeb3e5623c5ff3807c358029c7 SHA1: 6cf47823edabda04114fec87004eeae7dc072eef SHA256: 9accacf9ff4474ee6fb71178a024b67c775a1585177532982bb51dd09bf17acd SHA512: f56b6c0e7c2261637544c3cedfb3e5ba407d2b6b8957b273eda6289bca4f05c7b156e3f7db4f43e935bf94cf0110c1c2e03cd44cafb6201a5dd1504cfbd7d6c4 Homepage: https://cran.r-project.org/package=insect Description: CRAN Package 'insect' (Informatic Sequence Classification Trees) Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) . Package: r-cran-insectdisease Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-taxize Suggests: r-cran-corrplot, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-insectdisease_1.2.2-1.ca2404.1_all.deb Size: 1502114 MD5sum: dcc0fe6a562648852ea8dcb9a2d3268f SHA1: 8c8bb4cbf302df05c23f2b25224b7ee97d8cda08 SHA256: c5420ce9b63e4a9b1ae3e594b82f54d7040b80e686b669de4ff6270526ebba1d SHA512: 98174b708a67f7c383eee1379b0279aa92efc6c5f0d93ab7c12a9ac7c52ff9f09954d48c5b6f2a8a525c4c2cd53527305b6cbc88a0e5144bd900bb4a58331112 Homepage: https://cran.r-project.org/package=insectDisease Description: CRAN Package 'insectDisease' (Ecological Database of the World's Insect Pathogens) David Onstad provided us with this insect disease database, sometimes referred to as the 'Ecological Database of the Worlds Insect Pathogens' or EDWIP. Files have been converted from 'SQL' to csv, and ported into 'R' for easy exploration and analysis. Thanks to the Macroecology of Infectious Disease Research Coordination Network (RCN) for funding and support. Data are also served online in a static format at . Package: r-cran-insectecol Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2175 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-openxlsx, r-cran-ragg, r-cran-scales, r-cran-sysfonts, r-cran-systemfonts, r-cran-readr, r-cran-showtext, r-cran-tidyr Suggests: r-cran-readxl, r-cran-testthat, r-cran-writexl Filename: pool/dists/noble/main/r-cran-insectecol_1.1.2-1.ca2404.1_all.deb Size: 1756198 MD5sum: 9addf0c2738c58cdaafffa6837120226 SHA1: 09e6c8904b690cfa07a4c8b5b3cb2cab23eb2344 SHA256: f3895f1449cc0d376387f26e888fa153784307055f2e7558cb4867cf56d2a8b6 SHA512: 451c7416397008d7688b8d9d61a172382449f0ce5c91ea9b1615b934bd8fce08fbc0e5ef47ec796af33b54a220dcd3ca8b3d6ed916cb94828ef400d7b49246f5 Homepage: https://cran.r-project.org/package=insectecol Description: CRAN Package 'insectecol' (Insect Ecology Data Analysis Toolkit) A collection of analytical tools for insect ecology research, currently covering age-stage, two-sex life table analysis, dose-response bioassays, temperature-dependent development and insect phenology prediction. The life table module follows the age-stage, two-sex life table theory of Chi (1988) and Chi et al. (2020) . It supports fast batch processing of multi-group datasets, validates raw 'csv' data, computes cohort size, mean fecundity, age-stage survival rates, age-specific survival, age-specific fecundity, life expectancy, and derived population parameters (net reproductive rate, intrinsic and finite rates of increase, mean generation time), simultaneously generates age-stage survival curves for all groups, and exports all tabular results and plots to 'Excel' in a single run. The bioassay module estimates lethal concentrations by the traditional and the weighted (improved) linear regression methods and by probit analysis (Bliss, 1934) , with the control-mortality correction of Abbott (1925) , 95% confidence intervals and chi-square goodness-of-fit tests; the lethal proportion can be set freely (e.g., 25%, 50%, 70% or 90%), so any LC value such as the LC25, LC70 or LC90 can be computed, not only the LC50. The regression plots and tables are exported to 'Excel'. The degree-day module estimates the developmental threshold temperature and the effective accumulated temperature by the linear degree-day method (Campbell et al., 1974) , and fits the common nonlinear temperature- dependent development models following Logan et al. (1976) (Logan-6), Lactin et al. (1995) and Briere et al. (1999) , plus a 7-parameter Wang model; it selects the best model by AICc, predicts durations and accumulates field degree-days. The emergence module applies the stage-grading method to a single survey of the population stage structure, i.e. to stage-frequency data (Kiritani and Nakasuji, 1967) and Manly (1974) , and projects the beginning, peak and end of the adult emergence period (the 16%, 50% and 84% quantiles), optionally shifting the eclosion dates by the pre-oviposition period and the egg duration to forecast larval hatch. Further extensions, such as median lethal temperature/time (LT50), are planned. Package: r-cran-insectlabelr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-shiny Suggests: r-cran-testthat, r-cran-shinyjs, r-cran-readxl, r-cran-readr, r-cran-readods, r-cran-dt, r-cran-editbl Filename: pool/dists/noble/main/r-cran-insectlabelr_1.0.4-1.ca2404.1_all.deb Size: 90218 MD5sum: 51315f71017fbd7669a9a6a7a1cc72c1 SHA1: 6e5a0b58a11e7992195cf7af7e92d2781fe033f4 SHA256: 3391b0eab7a381e126e6d642fc32f11741281ba703e6d733a55532d6fc6c479c SHA512: 0fc068a5e4274855b88145578d48ae876de1b4ebc77c7b9b9886c8cb5c7bf73e6e5c185bba036cbaf65772c20a21925b80292ed6547051c177809ec70c1d8fe2 Homepage: https://cran.r-project.org/package=InsectLabelR Description: CRAN Package 'InsectLabelR' (Create Labels for Insect in Collection) Streamlines the creation of high-quality labels for insect pinning. By taking a dataset as input, the package allow to generate printable labels in 'LaTeX' and PDF format, helping researchers and entomologists maintain accurate and standardized specimen records. Requires a compatible installation of 'pdflatex' (e.g. ). For enhanced accessibility, the package includes a user-friendly 'shiny' application (accessible online ), which provides a graphical interface for generating labels without requiring programming expertise. 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Package: r-cran-insetplot Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-insetplot_1.4.0-1.ca2404.1_all.deb Size: 180992 MD5sum: c22eef9772bff03168b2dceb126a4666 SHA1: a0e84528fb5c5bb02d09baedc982c01abe263a8d SHA256: 007869ad6cc626338bbeb3c0980337ecb30e4624acb8232c56a04f980696028e SHA512: cea0200e800d097b71f3aa8387e59aaf3d9a67348781a370aa38ecafb152400900864d39a26defdc1b7692ce005b885df3e09025172d731758d80295017756bb Homepage: https://cran.r-project.org/package=insetplot Description: CRAN Package 'insetplot' (Inset Plots for Spatial Data Visualization) Tools for easily and flexibly creating 'ggplot2' maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code. 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R is great for installing software. Through the 'installr' package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation. 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Package: r-cran-insurancedata Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-insurancedata_1.0-1.ca2404.1_all.deb Size: 626520 MD5sum: 786766b395a8e6aad07a0decbb18e344 SHA1: 39e09fa4be2147f5d1b4d298af9b64fa5ac1e978 SHA256: 2cef14c9bed75a6312d5bf83c1549e7e06ae1998eee466753b5773b82bd5e3d9 SHA512: 8240087eb1a9bb99cb38296b82d1b3df9cd50639f406a04648347b6adb8e2b55d3257c69fb9786a9790d9ec71954d84ec083a8ffdeebb5aa0501d2e8f07c9b12 Homepage: https://cran.r-project.org/package=insuranceData Description: CRAN Package 'insuranceData' (A Collection of Insurance Datasets Useful in Risk Classificationin Non-life Insurance) Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center. Package: r-cran-insurancerating Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2410 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-citools, r-cran-data.table, r-cran-dharma, r-cran-dplyr, r-cran-evtree, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-mgcv, r-cran-patchwork, r-cran-partykit, r-cran-rlang, r-cran-scales, r-cran-scam, r-cran-stringr, r-cran-tibble Suggests: r-cran-classint, r-cran-dbi, r-cran-dbplyr, r-cran-duckdb, r-cran-ggbeeswarm, r-cran-ggrepel, r-cran-gt, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-insurancerating_0.8.2-1.ca2404.1_all.deb Size: 1844028 MD5sum: f4e74425639b4fde66d16f91c8c8ab59 SHA1: 2d357f437a8dc8034efe50d7ccde12e956260bbc SHA256: d164ce7f5718fd71dcc410ae12c53e5297d1c1cc5a4db75bb712d061ffe201a6 SHA512: cf166d2132a400f34f48dfae4af44a30cfe3e72b005446d3a3e3145968d661695651eb656bbed0eb678ca36013fae0bd6005df3c276fd8185405c28c2b7f2ab4 Homepage: https://cran.r-project.org/package=insurancerating Description: CRAN Package 'insurancerating' (Actuarial Tools for Insurance Pricing Models) Provides actuarial tools and building blocks for analysing, modelling, refining, and validating insurance rating models. Designed to support common GLM-based pricing tasks and the translation of statistical model output into practical tariff structures. The package supports the construction of insurance tariff classes using a data-driven approach, based on the methodology of Antonio and Valdez (2012) . Package: r-cran-insusenscalc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-insusenscalc_0.1.0-1.ca2404.1_all.deb Size: 46536 MD5sum: 1b2b52dd819798a5ac5d902f55a550b1 SHA1: f914a4c0bc96bb565eb5712d1538018f6adf629d SHA256: d8fe786ff3318d04b2f8fcc1772498857f55339bc0fd8de0d02618737f764569 SHA512: 6d49090e66ea3b87f6486b0f0105973f147a97ebc9e4a1364e1db265ba0b8d11e7f017c8cb0fe0559245252ee5ff9df3c1d941a8f79af690e30182c17425362b Homepage: https://cran.r-project.org/package=InsuSensCalc Description: CRAN Package 'InsuSensCalc' (Insulin Sensitivity Indices Calculator) Facilitates the calculation of 40 different insulin sensitivity indices based on fasting, oral glucose tolerance test (OGTT), lipid (adipose), tracer (palmitate and glycerol rate), and DXA (fat mass) measurement values. Enables easy and accurate assessment of insulin sensitivity, critical for understanding and managing metabolic disorders like diabetes and obesity. Indices calculated are described in Gastaldelli (2022) , Suleman (2024) , and Lorenzo (2010) . Package: r-cran-int3ract Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpattern, r-cran-patchwork Suggests: r-cran-knitr, r-cran-lme4, r-cran-mcmcpack, r-cran-rmarkdown, r-cran-rsiena, r-cran-testthat Filename: pool/dists/noble/main/r-cran-int3ract_2.0.0-1.ca2404.1_all.deb Size: 266220 MD5sum: 93576a66bffb8b4155ef32ecacb2a2a3 SHA1: dda091c11b3414a500915c1f1db37ad7a7ad45d3 SHA256: 5cc6d791f085121bf36c60fa7fa14e5ca738d5a394f1659d029f356e58c92bdf SHA512: 4cb2c7649113801fb53f3673c70df064e0f9192632a2534eb13165153fc8aeff3540b7f6e75f19785672cac641a32055b5574793a6b8f7775135d3fc65fb16ff Homepage: https://cran.r-project.org/package=int3ract Description: CRAN Package 'int3ract' (Johnson-Neyman Analysis of Two- and Three-Way Interactions) Reports and plots the conditional effect of each variable involved in a multiplicative interaction across the range of its moderators, together with the region over which that effect is distinguishable from zero. Extends the classic framework of Johnson and Neyman (1936) and Johnson and Fay (1950) to three-way interactions and to Bayesian models. The single entry point JN() dispatches on the fitted object, with methods for lm()/glm() models, 'lme4' models, 'RSiena' and 'multiSiena' results, and matrices of posterior draws; support for further model classes is added by writing one jn_input() method. Results are classed objects with print(), summary() and plot() methods, and the figures carry data-density panels showing how much empirical support each part of the moderator range has. A detailed introduction can be found in Krause (2026) . Package: r-cran-intamap Architecture: all Version: 1.5-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-gstat, r-cran-sf, r-cran-automap, r-cran-mba, r-cran-mvtnorm, r-cran-mass, r-cran-evd, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rworldmap, r-cran-psgp Filename: pool/dists/noble/main/r-cran-intamap_1.5-11-1.ca2404.1_all.deb Size: 539700 MD5sum: 48347aaa0e4e75f46235eecabd89d436 SHA1: a8a48c6bccd88651660ac6826a5f3be8c04a2056 SHA256: 67871c711e35c446ed7414b3b618fa5d8635f1f087b8da4beacf5f460696b807 SHA512: 3744bd85698c68022269514f604c3e480929c2f4a4d25e351299bc825ed9892f95efcd1f956041bbb0127f0f2c2f1d6cfb9b97fbc0b9e475c45d355644a75b10 Homepage: https://cran.r-project.org/package=intamap Description: CRAN Package 'intamap' (Procedures for Automated Interpolation) Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. 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Pebesma et al (2010) gives an overview of the methods behind and possible usage . Package: r-cran-intcal Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1779 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-intcal_0.3.1-1.ca2404.1_all.deb Size: 597994 MD5sum: ac2e08c59b243c6b67f7dd6a03bddcbd SHA1: bdb47478b977870c1fae316e96b4be3c9bc121d9 SHA256: 57816fc0caaf3200f32c6c0237bc4fcb207fc8e8eb4e64ed4e593081bd33aa1f SHA512: cf49c01f33facc30536e76b8cc14fdb303da5418a9003458a7abf5bf4a5c5d146d4bbbea5705cdaa812df3f98735372457aebc53f228aabe245a90fe89c95eda Homepage: https://cran.r-project.org/package=IntCal Description: CRAN Package 'IntCal' (Radiocarbon Calibration Curves) The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 ) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates. Package: r-cran-intccr Architecture: all Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-alabama, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-splines2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-intccr_3.0.4-1.ca2404.1_all.deb Size: 1971502 MD5sum: 7d3739c0a03c068a67d56ca39013b330 SHA1: 03af75a293765cb236f73ec74f9e31905cbef51b SHA256: 22ed69643a420c3da9596214ebb3672ed31d3dcced8ed4d9230011419cfecccc SHA512: a04e056b6b1b6c29bdd5450f0e6a6fc2f87f0b061474b49a5b0e9aaf2b9608c3daaaef97de0a079acc1535c36df2110c98a5b254735b0fd96ff3f1e3c40ab012 Homepage: https://cran.r-project.org/package=intccr Description: CRAN Package 'intccr' (Semiparametric Competing Risks Regression under IntervalCensoring) Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models. Package: r-cran-integirty Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1498 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ltm, r-cran-foreach, r-cran-doparallel, r-cran-mclust, r-cran-mass, r-cran-abind Suggests: r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-integirty_1.0.9-1.ca2404.1_all.deb Size: 1341202 MD5sum: 2108eef7f83b0a5796f990699d9a0b13 SHA1: 62c910e4c3a69ce41ba380f1262824ffd1802b3b SHA256: 78916119ee6e532abe4e3c81c78a6f33d919a0bcbb279c1e7903147ec11549d0 SHA512: 4ec133f5d3b257682f7e41164f45ead51ef86a2b365891e5aa5733ba174d68a8d936b7d60f1e839d4bd4e925394214bf00f112f3f6cf4ff70b6554612e28a55a Homepage: https://cran.r-project.org/package=integIRTy Description: CRAN Package 'integIRTy' (Integrating Multiple Modalities of High Throughput Assays UsingItem Response Theory) Provides a systematic framework for integrating multiple modalities of assays profiled on the same set of samples. The goal is to identify genes that are altered in cancer either marginally or consistently across different assays. The heterogeneity among different platforms and different samples are automatically adjusted so that the overall alteration magnitude can be accurately inferred. See Tong and Coombes (2012) . Package: r-cran-integr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rsvg, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-integr_1.0.0-1.ca2404.1_all.deb Size: 571176 MD5sum: f55d221b874e12d0a45a614341e6c213 SHA1: d05744b6e17c40d819dc8ac1665da8c251cffe5c SHA256: 0b9ef2472e4ff42276d032ba77b1305feeda52433e34ad683de264dc07ba8cda SHA512: 9c58db95f180b63b8cd25e09a3a00b049d46d5a646f190bd8a66c6b90e33fde5814c5b25abe8fd3893e4e10c386dd83256fabe4e08d13248d99595bead40c818 Homepage: https://cran.r-project.org/package=integr Description: CRAN Package 'integr' (An Implementation of Interaction Graphs of Aleks Jakulin) Generates a 'Graphviz' graph of the most significant 3-way interaction gains (i.e. conditional information gains) based on a provided discrete data frame. Various output formats are supported ('Graphviz', SVG, PNG, PDF, PS). For references, see the webpage of Aleks Jakulin . Package: r-cran-integratebs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-integratebs_0.1.0-1.ca2404.1_all.deb Size: 10908 MD5sum: 3f9c85beb3c5bdfacda6c264f6d9f5a5 SHA1: 2cc95dfd65f3c7182436568fbbe0a1c23d3b6887 SHA256: f8aea26b0fd5ce2dfdf96668cacf0a07735c790baf30eb31f2be6c8f8c6518e5 SHA512: d1020bba555c341cf18223a2782b1f889272e24583e15e92dfaf11df88ef218ede31a6cb28967836f18d7fdc0cf7d5925554c618fdf7d6158b290a7ed6e9f9a8 Homepage: https://cran.r-project.org/package=IntegrateBs Description: CRAN Package 'IntegrateBs' (Integration for B-Spline) Integrated B-spline function. Package: r-cran-integratedjm Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-nlme, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-integratedjm_1.6-1.ca2404.1_all.deb Size: 130608 MD5sum: 43edbf23b9033bd8c4753154d43797d7 SHA1: 1fd9aee336958e37739bc90d0b3b96cf2835ff0d SHA256: 2adc1e8cb1792d2467df75b6e4d3dba3a26e768a8884f3d60eecb97b9552cad7 SHA512: c1138475ff6b636941f3b3402b98027cb99bd63b3df4f6904334a110ab21b26bf0d92a41aea3420e57b08a1d11788cd74334d7b38d8fd36fdd65c24f56a3f5ae Homepage: https://cran.r-project.org/package=IntegratedJM Description: CRAN Package 'IntegratedJM' (Joint Modeling of the Gene-Expression and Bioassay Data, TakingCare of the Effect Due to a Fingerprint Feature) Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis. 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This package enables to access and query data provided by the Carbon Intensity API (). National Grid’s Carbon Intensity API provides an indicative trend of regional carbon intensity of the electricity system in Great Britain. 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This package is based on the following research: Eckardt and Mateu (2018) . Eckardt and Mateu (2021) . Package: r-cran-inteq Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-inteq_1.1-1.ca2404.1_all.deb Size: 436210 MD5sum: a46a60fb5923577231d450fa9b6b0c10 SHA1: 039bad251a403eb0ba47baf1660b1336f0604117 SHA256: 83000939af177f3dd2695eda10934b6cc514d0de6d449ed6979771c0964a097d SHA512: 77a38fb33e03d9d0ad7bc437b758c83c44a263187f60b5762c4038bb3f302325a0977d1191a07910d4ecf846b43b7e13185b1f7f86b710555763559bd6023580 Homepage: https://cran.r-project.org/package=inteq Description: CRAN Package 'inteq' (Numerical Solution of Integral Equations) An R implementation of Matthew Thomas's 'Python' library 'inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) . Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) . Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) . Package: r-cran-interactionpower Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-polynom, r-cran-chngpt, r-cran-rlang, r-cran-tidyr, r-cran-ggbeeswarm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-interactionpower_0.2.4-1.ca2404.1_all.deb Size: 440380 MD5sum: f614c30b7ba19ac0d737381d8981eeb7 SHA1: ee4ae69c51077943be0c3188494bcf46755c40c3 SHA256: 46ef95cca3c84e1fa7419ecf75522ac33bd141ae67e0b2c9878b89270fb47b50 SHA512: 6fc9a3465fce9ea5069aa1161f6399cdb8c1302fda9243b7d9cda3b0e5d9d1d131714fc26080423fe85d1755326b996d77c9e0ead9e0cfeb9e1f8b85fbc8b5c3 Homepage: https://cran.r-project.org/package=InteractionPoweR Description: CRAN Package 'InteractionPoweR' (Power Analyses for Interaction Effects in Cross-SectionalRegressions) Power analysis for regression models which test the interaction of two or three independent variables on a single dependent variable. Includes options for correlated interacting variables and specifying variable reliability. Two-way interactions can include continuous, binary, or ordinal variables. Power analyses can be done either analytically or via simulation. Includes tools for simulating single data sets and visualizing power analysis results. The primary functions are power_interaction_r2() and power_interaction() for two-way interactions, and power_interaction_3way_r2() for three-way interactions. The function run_pos_power_search() provides a stability analysis for two-way interactions. Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2023). "Tutorial: Power analyses for interaction effects in cross-sectional regressions." . If you use the stability analyses, please cite: Castillo A, Miller JD, Vize C, Baranger DAA, Lynam DR. "When Do Interaction/Moderation Effects Stabilize in Linear Regression?". Package: r-cran-interactionr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msm, r-cran-car, r-cran-officer, r-cran-flextable Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-interactionr_0.1.7-1.ca2404.1_all.deb Size: 134298 MD5sum: a75821f2dd9efaf8456c5d58ddbb9b04 SHA1: c30b7e55b90592061ab197713974db767b7a08c5 SHA256: 4365d216431eeb1f039f6063770f57bd0c5a8fd2e0b91cc57fa870ec5a0b00bf SHA512: 8556cba5c65eba90ded94b0b05d871b26f39fd6a1fa3c7fa6fbfcef04b2dc8ca22dee5805e963cee5afa53d27a42b91a74e33bbcaeef757f7f25800a70546b7f Homepage: https://cran.r-project.org/package=interactionR Description: CRAN Package 'interactionR' (Full Reporting of Interaction Analyses) Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) []. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), []), variance recovery method (see Zou (2008), []), or percentile bootstrapping (see Assmann et al. (1996), []). 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Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) ; Greenland S. (1995) . 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This package is able to effectively integrate deconvolution results from multiple scRNA-seq datasets and calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Moreover, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system. 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Currently supported classes are those defined in packages: network and igraph. Package: r-cran-interim Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-interim_0.8.0-1.ca2404.1_all.deb Size: 58670 MD5sum: c97deef698a55a8c8934a69b8880c388 SHA1: 113528a5bb1261c691b629b5c87a5fa6f7be03a1 SHA256: 668a87043a487dcca12e764f56c31b51c23acea25db9791c0469a96f6229a8e8 SHA512: e90520686b6f4403fa87baaf08aaa91ee506aebc11c6d84b2eb53bdce628474da078d4a661d05fa0d596357bbcc38153085ffda5261a48e087f869b413f581cb Homepage: https://cran.r-project.org/package=interim Description: CRAN Package 'interim' (Scheduling Interim Analyses in Clinical Trials) Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed. 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Package: r-cran-interlinear Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-reshape2 Suggests: r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-interlinear_1.0-1.ca2404.1_all.deb Size: 360046 MD5sum: eabd552f0acbe013cdf9c49af5d5335c SHA1: 437b4dfbff3cf512befacfde689fd05ac38baa60 SHA256: b339a75204ba8750336324eabb9f668713a0bcf1c9c6c68efc7c076417928aef SHA512: 8b3d753025c857312e9491424a15a12e50d729739a9af510be8e5604fe111492d1aa0d54d5275b6a0579e778c1dc6eb1760b3fcc4efa904c4a3a6d9421a55754 Homepage: https://cran.r-project.org/package=interlineaR Description: CRAN Package 'interlineaR' (Importing Interlinearized Corpora and Dictionaries as Producedby Descriptive Linguistics Software) Interlinearized glossed texts (IGT) are used in descriptive linguistics for representing a morphological analysis of a text through a morpheme-by-morpheme gloss. 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'InterlineaR' provide a function for turning the LIFT XML dictionary format into a set of data frames following a relational model in order to represent the dictionary entries, the sense(s) attached to the entries, the example(s) attached to senses, etc. Package: r-cran-internl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-mlmetrics Filename: pool/dists/noble/main/r-cran-internl_0.1.0-1.ca2404.1_all.deb Size: 17930 MD5sum: ed3e95dbd8888db31d02f8d408ba05ed SHA1: 7b495f4f4cae6e64594fce5bc05961ac3ed34f3c SHA256: b43ef5a71cf29e10ba234bef530f57b69dc327413dea60fb82b95304dd9c3984 SHA512: 450ab4bfc5ee36e072e72a7134ac48804d7408aa2b89b8d6292fbe962b522274735b87badf0d8925b7f6feb2804f345a752c75b430f4b0fc53a0876507107174 Homepage: https://cran.r-project.org/package=InterNL Description: CRAN Package 'InterNL' (Time Series Intervention Model Using Non-Linear Function) Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. and Paul and Yeasin . 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Package: r-cran-inters Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fixest, r-cran-glmnet Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-lmtest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-inters_0.2.0-1.ca2404.1_all.deb Size: 152762 MD5sum: f1deab39d8664e64938d2de2b8a140aa SHA1: b403443826ab53a41f538821363de4d971f59568 SHA256: ccfc0be9a2b4b4721862c1c4452dd4316209081ad12ce582046e413c85a38ce6 SHA512: ddc4190ad6579fd0519323a92e19cf79c5b108fc55f825a75d52b753e09def55758f9d1d33abd2750e88e1860a0a6ea50fb0a8faa7efc63845450ba333f2da7c Homepage: https://cran.r-project.org/package=inters Description: CRAN Package 'inters' (Flexible Tools for Estimating Interactions) A set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) . 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Complex-survey microdata supply outcomes and association information, while one or more aggregate census tables supply overlapping population margins. A generalized iterative proportional fitting engine reconstructs a coherent latent population table, and an augmented model-assisted estimator produces domain means, proportions, and totals for sampled and unsampled intersections. Tools diagnose non-identification, compute linear-programming sensitivity bounds, propagate sampling and model uncertainty with replicate-weight or multiplier bootstrap procedures, enforce structural zeros, and benchmark estimates to official totals. The framework extends calibration ideas from Deville and Sarndal (1992) and small area estimation ideas from Fay and Herriot (1979) . 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Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset. We therefore introduce the package, 'intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online. An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) . 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Package: r-cran-invasible Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-caper, r-cran-ggplot2, r-cran-phylolm, r-cran-phyr, r-cran-phytools, r-cran-proc, r-cran-rotl Filename: pool/dists/noble/main/r-cran-invasible_1.0.0-1.ca2404.1_all.deb Size: 43718 MD5sum: 10f923c38f72439e16047c743513129f SHA1: 2d6392a220d27d320c41c82be9a88b45cf780b9c SHA256: 055f4accc78c78723b305eef4c3e303c213dc393f8812e9b7fd5c2cfe810e49f SHA512: f4edd045423afe09b9cb84a1572c897a3764b80167f806d461035d05c2d2c90d1f87cda3bdc4b19c4324bd00cc66a04b132a0afdbd59db7fc68899368f42a2b2 Homepage: https://cran.r-project.org/package=invasible Description: CRAN Package 'invasible' (Predicting Invasion Probabilities from Phylogenetic Data andSpecies Traits) A phylogenetic modelling approach for predicting species invasion risk, out of a given pool of local species where a subset is known to be invasive elsewhere. The package uses phylogenetic signal estimation and phylogenetic linear and logistic models to estimate probabilities of being invasive based on phylogeny and any set of additional predictors. A ranking method is implemented to evaluate prioritisation strategies. A manuscript describing these methods, by Shahar Dubiner and Tamar Guy-Haim, is in preparation. 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The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates. Package: r-cran-invctr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-plyr Suggests: r-cran-knitr, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-invctr_0.2.0-1.ca2404.1_all.deb Size: 119516 MD5sum: df8835f9289d13829b3742a9f5bf8e0e SHA1: cadd22e5dc5e69232b52248066d4e5d4addc4f81 SHA256: 0e5f3851e13d59654c4e7a578e419e4967bc3596d351e76f7cfeb50b75b10440 SHA512: 0ea6b4ed3306a7e8391837fa17ef4e522eeb4d26a8d81156a668802f4338f8f4f4b7defbc4dc5b833804d4cf70b0629e312a9d33dee0767cd8833f6d3d864a88 Homepage: https://cran.r-project.org/package=invctr Description: CRAN Package 'invctr' (Infix Functions For Vector Operations) Vector operations between grapes: An infix-only package! 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The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations. The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization. The functions are referenced from : 1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. 3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 4-Ballou, R.H. Business Logistics Management. 5-MIT Micromasters Program. 6- Columbia University course for supply and demand analysis. 8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W For further details or correspondence :, . 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A generic function is also provided for plotting fitted regression models with or without confidence/prediction bands that may be of use to the general user. For a general overview of these methods, see Greenwell and Schubert Kabban (2014) . 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Package: r-cran-invgauss Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-optimx Filename: pool/dists/noble/main/r-cran-invgauss_1.2-1.ca2404.1_all.deb Size: 60814 MD5sum: 0b11f57f2f89b0c4dac21c27fbd1fbb4 SHA1: c5cc66b5988f1447dd0af08df6aea8a85ef83f87 SHA256: 3f5e2a5b468dffde82338cb00b9fdd0239d38b7e3e5b2a8f116f0a74839e9654 SHA512: 2f5746089274eb150334dc32634162668cad2d4bbb89445c7d0c42d322f4ecf8d2bc93bb1337b5212b5fb408f23a83406f9051fc11b2d76020663c7d52c30a1e Homepage: https://cran.r-project.org/package=invGauss Description: CRAN Package 'invGauss' (Threshold Regression that Fits the (Randomized Drift) InverseGaussian Distribution to Survival Data) Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions. Package: r-cran-invitrotkdata Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1511 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-invitrotkdata_0.0.2-1.ca2404.1_all.deb Size: 1425746 MD5sum: 425cee113abd675addd383e68b77404b SHA1: 7db7e2f2ee24b62703f552550cc2f2dc13018fe6 SHA256: 7be120c894d32637c2036563cecac28d526c060f3aaafa4799f749b47d9a41f1 SHA512: f577b3ce70ee5d23a3713dc8d83375596f4244715192c0d81b5cfdb8642458471abfd6e6628cf9d8db66b0a3e80b3f623ef13583f3ebd3264f840bd6bc22283e Homepage: https://cran.r-project.org/package=invitroTKdata Description: CRAN Package 'invitroTKdata' (In Vitro Toxicokinetic Data Processed with the 'invitroTKstats'Pipeline) A collection of datasets containing a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The datasets provided by the package were processed and analyzed with the companion 'invitroTKstats' package. 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The package was developed to perform frequentist and Bayesian estimation on a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The methods provided by the package were described in Wambaugh et al. (2019) . 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These methods are described in detail in "Informatics for Toxicokinetics" (2025). Package: r-cran-invstableprior Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-cran-nimble Filename: pool/dists/noble/main/r-cran-invstableprior_0.1.1-1.ca2404.1_all.deb Size: 36322 MD5sum: 14f4281ee4ed9959f5dd567201c8eb89 SHA1: b4da21a98430ccff979efc95db46efbd0e319db1 SHA256: a7b5b383e7a9686d06ea834868ea8e9de0f7fade22b2bd75b1fb59e561e2be9a SHA512: d24442fb01b2af3193de9f7a2bc28a1733f9ee7cc5c59057e7e863ccb78b472902b8dea7fd7d73efdb7a6bf2a95cfbf01a0e2f0827696acba026f888907a846a Homepage: https://cran.r-project.org/package=InvStablePrior Description: CRAN Package 'InvStablePrior' (Inverse Stable Prior for Widely-Used Exponential Models) Contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019) . The package can also be used as a teaching demo for introductory Bayesian courses. 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Package: r-cran-inzightplots Architecture: all Version: 2.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-chron, r-cran-colorspace, r-cran-dichromat, r-cran-dplyr, r-cran-emmeans, r-cran-expss, r-cran-hexbin, r-cran-hms, r-cran-inzightmr, r-cran-inzighttools, r-cran-lubridate, r-cran-magrittr, r-cran-quantreg, r-cran-rlang, r-cran-s20x, r-cran-scales, r-cran-stringr, r-cran-units, r-cran-survey Suggests: r-cran-covr, r-cran-forcats, r-cran-dbi, r-cran-dbplyr, r-cran-ggbeeswarm, r-cran-ggmosaic, r-cran-ggplot2, r-cran-ggridges, r-cran-ggtext, r-cran-ggthemes, r-cran-gridsvg, r-cran-hextri, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rsqlite, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-viridis, r-cran-waffle Filename: pool/dists/noble/main/r-cran-inzightplots_2.16.0-1.ca2404.1_all.deb Size: 1285622 MD5sum: 01caf99735768fd4a07bc1397c10b85f SHA1: 5aa65be45d9edd36fb81e47d88ba2372aca30f1e SHA256: 327f9043aac6495a3a694a67996bde409aa5091e4573d6f1f28e53dc0cb4709e SHA512: afb6dcd34927bd1f7e2fc85713e636145b9b8ede4d26271a7a4a43b6e4e0cde32326daaadb4cde87c104c761fcf45f71358d8f03caa597e645ac66d5a1b8c9f4 Homepage: https://cran.r-project.org/package=iNZightPlots Description: CRAN Package 'iNZightPlots' (Graphical Tools for Exploring Data with 'iNZight') Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. The goal is to make it easy for beginners to start exploring a dataset through simple R function calls, as well as provide a similar interface to summary statistics and inference information. Includes functionality to generate interactive HTML-driven graphs. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. 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Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding. 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Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) , Winters (1960) , Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess". Package: r-cran-io Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-filenamer, r-cran-stringr Suggests: r-cran-xml, r-bioc-rhdf5, r-cran-yaml, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-io_0.3.2-1.ca2404.1_all.deb Size: 98002 MD5sum: cbed1a541536826a73e71bd231c2c964 SHA1: d837dc40eb17a64d9c341697407654959cdb6623 SHA256: 7d35e27cf8a795a11d59d3c99c6320184d9b825f880815de6f033cc7bd40e112 SHA512: def2588bbe1ae8470d3022bd432f95f75c81f8d2c97e80360b152589621cd40ad5ae3ca69a38a55bc8789539e50eaa90fc9560eec2fb945b0abf91b40e61dd3c Homepage: https://cran.r-project.org/package=io Description: CRAN Package 'io' (A Unified Framework for Input-Output Operations in R) One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation. Package: r-cran-ioanalysis Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plot3d, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-ioanalysis_0.3.4-1.ca2404.1_all.deb Size: 255068 MD5sum: afe240d2bdc83fd701f9193e2d5b53bc SHA1: ba0fec3701c947620fb324f327eb8d86a4ee0c28 SHA256: 9d9bf908a7d4322e375fe089bef51f7df5b9aaa0785356d04d6af66dce4342b3 SHA512: 012792341fd5d3ed0350bb369e2bd24e90ef90f1f23af45578afed4d8a45959b096f37b9106e555eb69d7523fea01964ef217de12afd0a13a4f418a2f2a12570 Homepage: https://cran.r-project.org/package=ioanalysis Description: CRAN Package 'ioanalysis' (Input Output Analysis) Calculates fundamental IO matrices (Leontief, Wassily W. (1951) ); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) , Blair and Miller (2009) , Antras et al (2012) , Hummels, Ishii, and Yi (2001) ); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) , Sonis, Hewings, and Guo (2006) ); and a variety of table operators. Package: r-cran-iobr Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4230 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-bioc-gsva, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr Suggests: r-cran-biocmanager, r-bioc-biocparallel, r-bioc-biomart, r-cran-circlize, r-bioc-clusterprofiler, r-bioc-complexheatmap, r-cran-corrplot, r-bioc-deseq2, r-cran-doparallel, r-bioc-dose, r-cran-e1071, r-bioc-easier, r-bioc-enrichplot, r-cran-factoextra, r-cran-factominer, r-cran-foreach, r-cran-ggdensity, r-cran-ggpp, r-cran-ggpubr, r-cran-ggsci, r-cran-gridextra, r-cran-hmisc, r-cran-knitr, r-bioc-limma, r-cran-limsolve, r-bioc-maftools, r-cran-mass, r-cran-matrix, r-cran-msigdbr, r-cran-nbclust, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-patchwork, r-cran-pmcmrplus, r-cran-pracma, r-bioc-preprocesscore, r-cran-prettydoc, r-cran-proc, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-rocr, r-cran-sampling, r-cran-scales, r-cran-seurat, r-cran-seuratobject, r-bioc-sva, r-cran-testthat, r-cran-tidyheatmap, r-cran-timeroc, r-cran-webr, r-cran-wgcna Filename: pool/dists/noble/main/r-cran-iobr_2.2.3-1.ca2404.1_all.deb Size: 3556088 MD5sum: 8adb837201584d2855d5a44560ccf161 SHA1: d66dbd43a32eedb3dcfa4742f7692dd49ed32b8e SHA256: 55af1781429829b23ab964e70ee3c92fb1e476b83f852295c41f748cdfd512a8 SHA512: 25f6ae0c105b8d575fec48f81dbc54fb7daaebf54316b7efe2ddbf04a5a784807967561c0a2fe90a45a7acd02a07f268ad1941bced9207249e35fcf26a85a6e1 Homepage: https://cran.r-project.org/package=IOBR Description: CRAN Package 'IOBR' (Immune Oncology Biological Research) Provides six modules for tumor microenvironment (TME) analysis based on multi-omics data. These modules cover data preprocessing, TME estimation, TME infiltrating patterns, cellular interactions, genome and TME interaction, and visualization for TME relevant features, as well as modelling based on key features. It integrates multiple microenvironmental analysis algorithms and signature estimation methods, simplifying the analysis and downstream visualization of the TME. In addition to providing a quick and easy way to construct gene signatures from single-cell RNA-seq data, it also provides a way to construct a reference matrix for TME deconvolution from single-cell RNA-seq data. The analysis pipeline and feature visualization are user-friendly and provide a comprehensive description of the complex TME, offering insights into tumour-immune interactions (Zeng D, et al. (2024) . Fang Y, et al. (2025) ). Package: r-cran-iod25 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4722 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-tibble Filename: pool/dists/noble/main/r-cran-iod25_1.0.0-1.ca2404.1_all.deb Size: 4319376 MD5sum: 969853db5c27559fb037101fd76d3ea3 SHA1: 9c4f9f8dee56c73edee9bda62096ff029593e4d0 SHA256: 8b31fbbb3ff5a3fdc79c1cce770da1673217fc809dc456cd892a81fe52c53a8e SHA512: 6d4a3c154c90b2db99117aa1ea08054a9f823c8ba9e2e9206e0e9b21d88a21d5ed33142a95b172c802e31ca8b6d7b1f064fe0dd9711a2a75a426d3c122023761 Homepage: https://cran.r-project.org/package=iod25 Description: CRAN Package 'iod25' (English Indices of Deprivation (IoD25)) Set of relative measures of deprivation for small areas (Lower-layer Super Output Areas) across England. Package: r-cran-iols Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-matlib, r-cran-boot, r-cran-randomcolor, r-cran-stringr Filename: pool/dists/noble/main/r-cran-iols_0.1.4-1.ca2404.1_all.deb Size: 63462 MD5sum: 596dc87d4c53d10d902f783b385fd619 SHA1: 3e19dc843eb9f599d5ede0a3eac7c6e290680254 SHA256: 964ec4254d09b41de6c99644ae8d41b97fd8711e9a88192d3ee9657f184158e8 SHA512: e3cabc8544580d8aedb2198d0e9a7407c87040d876860d25ddd7bf233bf41b873a682fba3f608d5c5d46def97535911927245b238721154409d89993233591cf Homepage: https://cran.r-project.org/package=IOLS Description: CRAN Package 'IOLS' (Iterated Ordinary Least Squares Regression) Addresses the 'log of zero' by developing a new family of estimators called iterated Ordinary Least Squares. 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The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) . Package: r-cran-ionr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gplots, r-cran-psych Suggests: r-cran-lavaan Filename: pool/dists/noble/main/r-cran-ionr_0.3.0-1.ca2404.1_all.deb Size: 76260 MD5sum: d41dd99450798c6e5881b71225a4106e SHA1: 4037d3f5d43cc044c3028469bff8d3451cddad39 SHA256: 219823bf150626e685f17f98949e0d1b0f44d185031794781c8981c5cd8b2abc SHA512: 406de11ebe8125b54b96dbe2afc945109a7315420d3feb94bdac3258ff87b0068a9e70bd7519779d53ae62a616bc2a28cfa63532e5f2234a8b3e64ad2eb31b11 Homepage: https://cran.r-project.org/package=ionr Description: CRAN Package 'ionr' (Test for Indifference of Indicator) Provides item exclusion procedure, which is a formal method to test 'Indifference Of iNdicator' (ION). When a latent personality trait-outcome association is assumed, then the association strength should not depend on which subset of indicators (i.e. items) has been chosen to reflect the trait. Personality traits are often measured (reflected) by a sum-score of a certain set of indicators. Item exclusion procedure randomly excludes items from a sum-score and tests, whether the sum-score - outcome correlation changes. ION has been achieved, when any item can be excluded from the sum-score without the sum-score - outcome correlation substantially changing . For more details, see Vainik, Mottus et. al, (2015) "Are Trait-Outcome Associations Caused by Scales or Particular Items? Example Analysis of Personality Facets and BMI",European Journal of Personality DOI: <10.1002/per.2009> . Package: r-cran-iopspackage Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-economiccomplexity, r-cran-readxl, r-cran-tidyr, r-cran-openxlsx, r-cran-usethis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-iopspackage_2.1.0-1.ca2404.1_all.deb Size: 600046 MD5sum: 5a77c5ea0d3493e90ea27287e2cf96ac SHA1: 86a583d07fd8f151d6e4bdfccdaa21769ca3a0f4 SHA256: 35779f1b68b419d5512f6f21f4746d28d1772444a53df1cb4abac5752079122a SHA512: 9961eefe7b4bc6d72b9215b966ff04a159ea48458b47069b00192c73dd250b48cf56d7eb002a16adee73dfd5dec86d67061ef7579fb8bbaff736dd5dc01841f1 Homepage: https://cran.r-project.org/package=iopspackage Description: CRAN Package 'iopspackage' (IO-PS Framework Package) A developmental R tool related to the input-output product space (IO-PS). 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Part of rOpenGov for open source open government initiatives. Package: r-cran-ip2location.io Architecture: all Version: 0.0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-mockery Filename: pool/dists/noble/main/r-cran-ip2location.io_0.0.0-2-1.ca2404.1_all.deb Size: 21666 MD5sum: 570d609a3b9c4f6b94580e4e91e3ef58 SHA1: 0648a48b64fb6bc63d9271ce6e48f61bb23fccf4 SHA256: 868502aee7af31cfe29e8521ff78cb3ca4cdb292d31d6d5eea57d8e13d457aca SHA512: d1147bc33329c426e8bdf7fde83028e0237ce52935486d1bd24282d711f715410aea12466755d325ebd3543b58bdcf3edb19d835cbbfba7a2eda412703743d00 Homepage: https://cran.r-project.org/package=ip2location.io Description: CRAN Package 'ip2location.io' (Batch IP Data Retrieval and Storage Using 'IP2Location.io') A system for submitting multiple IP information queries to 'IP2Location.io'’s IP Geolocation API and storing the resulting data in a dataframe. You provide a vector of IP addresses and your 'IP2Location.io' API key. The package returns a dataframe with one row per IP address and a column for each available data field (data fields not included in your API plan will contain NAs). This is the second submission of the package to CRAN. Package: r-cran-ip2location Architecture: all Version: 8.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite, r-cran-ggplot2, r-cran-maps, r-cran-scales Filename: pool/dists/noble/main/r-cran-ip2location_8.1.4-1.ca2404.1_all.deb Size: 31076 MD5sum: c7f1b3bf6be0d582c512fc073b1a8f92 SHA1: 883272fa206097566020c715e320058e4a51fe45 SHA256: 9d5a5c2988202f3a8ff9a9c57d141b10dfe33c23f65e82ca6521adaec2b6991f SHA512: 1b5d459a3062642ef802a8fabeaa2d6ff643fa33f01d27e0713e482db6525c29329a62cd2cb55273d6bcc53a0a9cde8eb0e3eaace6e919b651fafc0cbf86aed9 Homepage: https://cran.r-project.org/package=ip2location Description: CRAN Package 'ip2location' (Lookup for IP Address Information) Enables the user to find the country, region, district, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather, Mobile Country Codes (MCC), Mobile Network Code (MNC), mobile brand name, elevation, usage type, address type, IAB category and Autonomous system number (ASN) that any IP address or hostname originates from. Supported IPv4 and IPv6. Please visit to learn more. You may also want to visit for free database download. This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module. Package: r-cran-ip2locationio Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-ip2locationio_1.1.1-1.ca2404.1_all.deb Size: 23210 MD5sum: f878574ba516eda4876bf97c8d96557f SHA1: 05f5f51516f9210463b64c502b1a931743cc1810 SHA256: 1b4de9ff5a73533ea72f2a83337dde8f3a5200528939fe28aefbcf72f0e87d0e SHA512: e7ca05ff0eda058cdf96b9d2e69dcd010dba52163e080f5050e2dabe1504a0da855ff7816167bb26b07548d538755d03ffe182d1e5d4da3a19fd2e7037396aa7 Homepage: https://cran.r-project.org/package=ip2locationio Description: CRAN Package 'ip2locationio' (Lookup Geolocation and Proxy Information using 'IP2Location.io'API) Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the 'IP2Location.io' API to query this data. To get started with a free API key, sign up here . Package: r-cran-ip2proxy Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate, r-cran-ggplot2, r-cran-maps, r-cran-scales Filename: pool/dists/noble/main/r-cran-ip2proxy_1.2.2-1.ca2404.1_all.deb Size: 33612 MD5sum: cd962c1710cbd006b52c9acfdc9ca7e0 SHA1: 486eb766d4ae1c5728dbae52153b03c940676397 SHA256: e5f974d14537e80663033ef2abfef5207fe13f9ebc084a3de2fbb1cd068f46e0 SHA512: 7cd09c0936905527b4c2dce01ccdc6422f7d7759be0dfe90fb288b360bd6299b431cf6a67e603f455b779d409a4666396dfe8ef53d504bdc9c7065724a1b67a9 Homepage: https://cran.r-project.org/package=ip2proxy Description: CRAN Package 'ip2proxy' (Lookup for IP Address Proxy Information) A R package to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits. The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit for free database download. Package: r-cran-ip2whois Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-ip2whois_1.0.1-1.ca2404.1_all.deb Size: 29200 MD5sum: 95b82f8529f6e0d2359c9c15b2b1767e SHA1: 6e48ee0570f28523fd82d2aef434c0c01508c68c SHA256: b143b5f5d98eff277bc7a4b06f122b97ae023b7eee6a920c8384548205b6f074 SHA512: 870da5f31dec7d572ee63d83c325d2d4be08d9273c01f3ede4202625a74cbff6b25602518515470f4cdc5edf30cf75d0a5751060c6941c914df4afae383ae9b0 Homepage: https://cran.r-project.org/package=ip2whois Description: CRAN Package 'ip2whois' (Lookup 'WHOIS' Information for a Particular Domain) Easily implement the checking of 'WHOIS' information for a particular domain. 'IP2WHOIS' supports the query for 1113 Top-level Domains(TLDs) and 634 Country Code Top-level Domains(ccTLDs). To get started with a free API key, you may sign up at here . Package: r-cran-ipa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipa_0.1.0-1.ca2404.1_all.deb Size: 72464 MD5sum: 846fb5582cd601822988e190badc7dec SHA1: ab1131e86831aaac91e624c3bd63384c405ef27e SHA256: 1df4c4517f37add52248449aac9b2748c3a8d52eca0deb2046d1839fd8c4b6ee SHA512: c4feaf6cd096120fb20a21e1e10831804a4ade900f4218a5ba7f0207afeda4e503d121d82532741ab585dd7ba85314f1bc6596b7bea1649da9cc93943b48c896 Homepage: https://cran.r-project.org/package=ipa Description: CRAN Package 'ipa' (Convert Between Phonetic Alphabets) Converts character vectors between phonetic representations. Supports IPA (International Phonetic Alphabet), X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and ARPABET (used by the CMU Pronouncing Dictionary). Package: r-cran-ipadmixture Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treemap, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ipadmixture_0.1.2-1.ca2404.1_all.deb Size: 1008170 MD5sum: ac2aa083d798435d31a50a0c0b9767b3 SHA1: 32b1931acf366a68d381831ee59551dbedfcf735 SHA256: 38b86b70e12d01fcbdc26e8630c9c14353cc42e545a110a6483c903494841d1f SHA512: 0df6f1e08d99fc8fee6fef2410db4d39e4fb3ffd3d518f8298e532e6b77644673cc239d378a9e5b9cdacc4a2a392484819f8977c8d8f0a80507751d6b2c11490 Homepage: https://cran.r-project.org/package=ipADMIXTURE Description: CRAN Package 'ipADMIXTURE' (Iterative Pruning Population Admixture Inference Framework) A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) . Package: r-cran-ipag Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ipag_0.1.0-1.ca2404.1_all.deb Size: 133978 MD5sum: 2fbd6d4508919027a1bfce2183015580 SHA1: 9b35bea882e51f784bad1962aab1d38134e18acd SHA256: 4b22b6c2a57ada64277d1bb2ece094e194f3de5d6f164b01ae0ebec1d93e4563 SHA512: e6918dfda6fa03620452036d9d8392e5b87472a03655d4c7618e1b3b74e6148943ff63ca5684e4de85d70cb2972781e9cda9dadcb0db25a645d79b7492579cc3 Homepage: https://cran.r-project.org/package=IPAG Description: CRAN Package 'IPAG' (Tools for IPAG Courses) Provides a collection of intuitive and user-friendly functions for computing confidence intervals for common statistical tasks, including means, differences in means, proportions, and odds ratios. The package also includes tools for linear regression analysis and several real-world datasets intended for teaching and applied statistical inference. Package: r-cran-ipanema Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rmysql Filename: pool/dists/noble/main/r-cran-ipanema_1.2.0-1.ca2404.1_all.deb Size: 43874 MD5sum: d490b119b607ee267e08d7e15ac7a386 SHA1: dbe81c2d3a6368ff9b1473ca70f9123a525e1a54 SHA256: 85b7d214981947d1874847f014e8f21efb63dc05df779af38ba0fa3651fe2db0 SHA512: b5e5731468d84de786473db5e36eee7ec66e922c5db258b54c0584c0de8ccb7fc10ec434ca6dfc8a00dd154cd72e1e23fe4943ec788d434f13dc58ef3940ebe1 Homepage: https://cran.r-project.org/package=ipanema Description: CRAN Package 'ipanema' (Read Data from 'LimeSurvey') Read data from 'LimeSurvey' () in a comfortable way. Heavily inspired by 'limer' (), which lacked a few comfort features for me. Package: r-cran-ipbase Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipbase_0.1.1-1.ca2404.1_all.deb Size: 25958 MD5sum: 2373924bd151355a96482f7acbc1e556 SHA1: 5f0075eecce9a1d76d6c158dd77bbb0ee27ce0c1 SHA256: 6026d0554f1fd162235461daffcb191ed7b5c1fbf58da7b2aeb4a25f1b0f58e6 SHA512: a19f9a616e91bb86ada633e8313e5faee21cbc1d5e6f8053bbbebefea0de1eedda951689b435b7c51be2b59f8932c4a18d5c66953e9effbc57e493ea4d8ffa05 Homepage: https://cran.r-project.org/package=ipbase Description: CRAN Package 'ipbase' (Client for the 'ipbase.com' IP Geolocation API) An R client for the 'ipbase.com' IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-ipc Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-shiny, r-cran-txtq Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-future, r-cran-promises, r-cran-redux Filename: pool/dists/noble/main/r-cran-ipc_0.1.4-1.ca2404.1_all.deb Size: 352388 MD5sum: be87430487d5b1e09bd8364d1897829c SHA1: 1c921fadb39cf39922a1bbed2393d06a164a7c8a SHA256: 3aca8b2e2216049b6ca660d5f41718b2ecc5d4f054e17305f75a30a3705c60f8 SHA512: 956ec7ab5c34f0c206274e1253961291fb19298e1299d29169b97e64d59ad8cc273d6fdf4cdb315c1d7e2ec8f53b38ea56891baf9dfb65685e566579f28e231e Homepage: https://cran.r-project.org/package=ipc Description: CRAN Package 'ipc' (Tools for Message Passing Between Processes) Provides tools for passing messages between R processes. Shiny examples are provided showing how to perform useful tasks such as: updating reactive values from within a future, progress bars for long running async tasks, and interrupting async tasks based on user input. Package: r-cran-ipcaps Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-expm, r-cran-kris, r-cran-fpc, r-cran-lpcm, r-cran-apcluster, r-cran-rmixmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipcaps_1.1.8-1.ca2404.1_all.deb Size: 472132 MD5sum: cd3fdc83e67c4da5eaec72f391287b64 SHA1: 8092802f9e6377051ba720bad6e93457707d3aa0 SHA256: 89d7d3f0288e8f4343b41e4c6fdef2c09766195e302a3281194c5fa52f4c2396 SHA512: 1ac19761820532a9da3fd75b0f99662569fcb693747e861f18aaab490d982f88c14b427ab78c52c36ee2091f15541dc85a477e1f24c7ee183584a1d9b43c7562 Homepage: https://cran.r-project.org/package=IPCAPS Description: CRAN Package 'IPCAPS' (Iterative Pruning to Capture Population Structure) An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) . The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) . In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'. Package: r-cran-ipcwk Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-survival Filename: pool/dists/noble/main/r-cran-ipcwk_1.0-1.ca2404.1_all.deb Size: 25184 MD5sum: c13afd93d2d4991eb477988aa67c230a SHA1: caad303e03400de9b015b8f57d8932c775b2c364 SHA256: 98adf613c7d36e596536d3ffa87d43a686527de08505e579be6a2d1ec2f745f2 SHA512: 9733512efbeb3a0010d31f2ef70d27292220e50a4bb6c28e12e543a116d655b08e84b2ac6095653efcdb270e419b19c8fa76d9d23f726f1f2dc4bc5db3c1cca7 Homepage: https://cran.r-project.org/package=IPCWK Description: CRAN Package 'IPCWK' (Kendall's Tau Partial Corr. for Survival Trait and Biomarkers) We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) . Package: r-cran-ipcwswitch Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-ipcwswitch_1.0.4-1.ca2404.1_all.deb Size: 86480 MD5sum: 1aef7f11f4df59fb4490fbb823820e2d SHA1: bbe5277ff46b707fdc0d0837b4fabc7e40be3478 SHA256: 14e2a922e0aa4c36324d080ec397af8d4c483d005c16de771a103bccabe614b2 SHA512: a442afdda42bc65fe1f86b4b21b269c983363f0b3d6d4cbd6e245049fb9151c6467302800b2ca2180cd1b83b4247e8dacee9bfafab5be7a59f18a3d86e658e2d Homepage: https://cran.r-project.org/package=ipcwswitch Description: CRAN Package 'ipcwswitch' (Inverse Probability of Censoring Weights to Deal with TreatmentSwitch in Randomized Clinical Trials) Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials. Package: r-cran-ipd Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3093 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-caret, r-cran-gam, r-cran-ranger, r-cran-mass, r-cran-randomforest, r-cran-tibble Suggests: r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyverse, r-cran-xfun, r-bioc-biocstyle, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-ipd_0.4.1-1.ca2404.1_all.deb Size: 1580828 MD5sum: a46beed74a7e94e093eb1d987cec285f SHA1: 3e42bfa769f88946afb7f8251c57bad097448b02 SHA256: 45ef5a8c0f6eb530736f3d4564e22ffdcdc4aa8c2e2fe5a2f112b7cb4796ec92 SHA512: ac876d8ae744c86b042768e1bce03cdeb868e2a12fa58701319ee91b1c2cc6e0c219161b491b6ae148b55a08aa68d21ea87b5a73cb23c35fc03c2c619d3d5ae9 Homepage: https://cran.r-project.org/package=ipd Description: CRAN Package 'ipd' (Inference on Predicted Data) Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. 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Package: r-cran-ipdfilecheck Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-testthat, r-cran-lubridate, r-cran-eeptools, r-cran-hash, r-cran-kableextra, r-cran-gtsummary, r-cran-effsize, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ipdfilecheck_0.8.1-1.ca2404.1_all.deb Size: 134362 MD5sum: 689a9d77fb4ba2ccfc27aa4aed65ff41 SHA1: eb9809143b0f92301373a87782224e61d02d0fa3 SHA256: c57d2d167bbfc8c9f027e9980b043875bb266c821f3d878ff1d793a7ac83d7a0 SHA512: 5c1456545d6f551253770b9f6a9fb1d9628a0bf2a3e442b093387f84cc0bc94bcdf4a6d53447ef101f419e19e2ab69f055c54c2bfcb9e589832fde0c1a00fb42 Homepage: https://cran.r-project.org/package=IPDFileCheck Description: CRAN Package 'IPDFileCheck' (Basic Functions to Check Readability, Consistency, and Contentof an Individual Participant Data File) Basic checks needed with an individual level participant data from randomised controlled trial. This checks files for existence, read access and individual columns for formats. The checks on format is currently implemented for gender and age formats. Package: r-cran-ipdfromkm Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-survival, r-cran-gridextra, r-cran-readbitmap Filename: pool/dists/noble/main/r-cran-ipdfromkm_0.1.10-1.ca2404.1_all.deb Size: 119808 MD5sum: 2be9e02d72d45a6e1eb654ff459c2342 SHA1: f7534aba1efc3a453e7c06d340700f426e773aea SHA256: b6a6bdec665748c69dd8e4fd28118d0b3e0938323b589e585718cc5698f7d85f SHA512: d053e506aaa9176ccb6b9b9095a8436f72d27083ee50ba6183363e73b9f1ff87e8140f20d44da38eaa1f1b4aafa31f0784e2b0543364400700d2bc1a65fb69a3 Homepage: https://cran.r-project.org/package=IPDfromKM Description: CRAN Package 'IPDfromKM' (Map Digitized Survival Curves Back to Individual Patient Data) An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011) . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) with some modifications. 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Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances. 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The layer models each subject's biomarker history with a random intercept (and an optional random slope) and autocorrelated, gap-scaled residuals, so that prediction uncertainty grows with the time between visits and per-visit specificity is preserved under irregular sampling. Marker weights are learned to optimize a user-selected clinical objective -- maximizing sensitivity at a fixed specificity, extending detection lead time, or a combined objective -- with optional feature selection and a choice of scalar or multivariate combiner. Functions for fitting, prediction, and evaluation (sensitivity, lead time, and specificity at chosen operating points) are provided. A manuscript describing the method is in preparation. 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See Bates and Watts (1980) and Ratkowsky and Reddy (2017) for details. 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Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms. Package: r-cran-iprsue Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-bigstatsr, r-cran-logistf Filename: pool/dists/noble/main/r-cran-iprsue_1.0.0-1.ca2404.1_all.deb Size: 187142 MD5sum: 7fe7b243e51c936e1419e83dc75ad4a5 SHA1: bf4951e7d633c8163c7b0c20a660426e15275ce8 SHA256: d003eb62af32e5bc036b096b8fac08b47697b82106a5d57f61bf01e1a8a67a53 SHA512: 410f809163875c5fb0ca787eb4426244aabf4f54fe2453531cbe38c8a1bb041a0c11e72c0ccf5893fce5a30c0c4c102d9854522e4babe1ba90238234fd2546b7 Homepage: https://cran.r-project.org/package=iPRSue Description: CRAN Package 'iPRSue' (Individual Polygenic Risk Score Uncertainty Estimation) Provides tools for estimating uncertainty in individual polygenic risk scores (PRSs) using both sampling-based and analytical methods, as well as the Best Linear Unbiased Estimator (BLUE). These methods quantify variability in PRS estimates for both binary and quantitative traits. See Henderson (1975) for more details. Package: r-cran-ips Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-dplyr, r-cran-phangorn, r-cran-plyr, r-cran-rlang, r-cran-tibble, r-cran-xml Filename: pool/dists/noble/main/r-cran-ips_0.1.1-1.ca2404.1_all.deb Size: 405836 MD5sum: b45950a1292a93dc36a844b3c213bd31 SHA1: 94f36a05d2b65d243f2746d7eeb304f74167fbcb SHA256: ff326db87f24265cf42679f54ad6db2e98eef8861a2c6d2fc16b88d758bc3fcb SHA512: 80cc6c12ee8af1de7f7fc7444939117e20f4785b33d437433d6bf7147e36c271739999c47858c116cd536363d5fec9ecc56b8d5bc83a8a6d76b15c1626c0064e Homepage: https://cran.r-project.org/package=ips Description: CRAN Package 'ips' (Interfaces to Phylogenetic Software in R) Functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states. Package: r-cran-ipsfs Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ipsfs_1.0.0-1.ca2404.1_all.deb Size: 223176 MD5sum: 759d35c3a516357be73a7ad284202289 SHA1: fb348a156b74ce53bdb22d75f1f2f35a1db347a7 SHA256: 457e4f2b2f174a02b12cb27037f9b31d410503ead38009db470188d4b5f47f4f SHA512: 81b98236ed6f935e2ff7ea5ffddb247894546e2f85955eb7969b91bcfabf4a84110844c7a94acfd3e3f021cdb2ac3be0ab74aa9ba03e8262f7b82f2a1ad0d86a Homepage: https://cran.r-project.org/package=ipsfs Description: CRAN Package 'ipsfs' (Intuitionistic, Pythagorean, and Spherical Fuzzy SimilarityMeasure) Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets. Package: r-cran-ipsrdbs Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5719 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-extradistr Suggests: r-cran-xtable, r-cran-ggally, r-cran-magick, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-huxtable, r-cran-rcolorbrewer, r-cran-markdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-cran-ipsrdbs_1.0.0-1.ca2404.1_all.deb Size: 3739782 MD5sum: 87d7e3d900da2d9da294bcab7a49e21f SHA1: 005d47b9427ceced898b588c3511a7ec86ffeb7e SHA256: bcc04b51bf268f903a4379072c815b052995074d790f41e70bba7e863aa25d46 SHA512: 5de09eacb47deeee2bae14c7009adfd319a431becf961fb08e31b51561fb0c964a9746ccb2d0aec7a2029f671c10219ec1a4f73cd1c9e626d8b55ae86546cfa4 Homepage: https://cran.r-project.org/package=ipsRdbs Description: CRAN Package 'ipsRdbs' (Introduction to Probability, Statistics and R for Data-BasedSciences) Contains data sets, programmes and illustrations discussed in the book, "Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences." Sahu (2024, isbn:9783031378645) describes the methods in detail. Package: r-cran-ipumsr Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3672 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-hipread, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-xml2, r-cran-zeallot Suggests: r-cran-biglm, r-cran-covr, r-cran-crayon, r-cran-dbi, r-cran-dbplyr, r-cran-dt, r-cran-ggplot2, r-cran-htmltools, r-cran-knitr, r-cran-rmapshaper, r-cran-rmarkdown, r-cran-rsqlite, r-cran-rstudioapi, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ipumsr_0.10.0-1.ca2404.1_all.deb Size: 2684730 MD5sum: 7ee3dd50646f44e9a64df04ae8b62a1f SHA1: d63eb910859cd66318c29a02aa36c21d08688475 SHA256: e0e54a20c61b736cf445c4b5dab988acf0f4ec2f9c8bd97a41258a5c53af8217 SHA512: deab4b5790b8ba4c3fefe804aa1ce68c5600254906b21b10dced2ec63899c4f8618a5f711e812b1bc1744babe47de523c39f046f05dc06cdb9ea2e792259e025 Homepage: https://cran.r-project.org/package=ipumsr Description: CRAN Package 'ipumsr' (An R Interface for Downloading, Reading, and Handling IPUMS Data) An easy way to work with census, survey, and geographic data provided by IPUMS in R. Generate and download data through the IPUMS API and load IPUMS files into R with their associated metadata to make analysis easier. IPUMS data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from the IPUMS website . 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Package: r-cran-ipw Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-survival, r-cran-geepack Suggests: r-cran-nlme, r-cran-survey, r-cran-boot, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipw_1.3.0-1.ca2404.1_all.deb Size: 221526 MD5sum: 4fdcec54d69f9a17c727b150a97336b6 SHA1: 77b9b8cd8f6761cd90f0aedeeda2a4d679f199f4 SHA256: 8a3fd78b94c9fb22113289aef11d768e5b326713eef8fc35734943c8f918495a SHA512: 9b21cb30e82cd94885e024b42bbd8758cb8fe737e4b15a991fa1e2245ecbcdad0a32e4912d65e117ddd62c189311de2083da0e68070da9bb0ba0c65e20ddb2aa Homepage: https://cran.r-project.org/package=ipw Description: CRAN Package 'ipw' (Estimate Inverse Probability Weights) Functions to estimate the probability to receive the observed treatment, based on individual characteristics. The inverse of these probabilities can be used as weights when estimating causal effects from observational data via marginal structural models. Both point treatment situations and longitudinal studies can be analysed. The same functions can be used to correct for informative censoring. 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The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) ", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method. 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Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals. 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The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data. 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This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) . Package: r-cran-irccheck Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-rdpack, r-cran-ggmncv, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-irccheck_1.0.0-1.ca2404.1_all.deb Size: 148646 MD5sum: 2b926677593276ced689db38c2a4fd5c SHA1: 87245120744a8b51b28f856f19331528bc68480e SHA256: 608b1f94a42fa3bae5961b7691141d0165e346cc42d73fb4f29c5df39dfd9d4d SHA512: d7aa34444b7a9f4558ea74151ee0691d1ecb399d7d96a9890abf71c82094193c936ceece51bafcbd01c4b4f1fb6dea513b304e6e8fddb5595938e80f9df95bfa Homepage: https://cran.r-project.org/package=IRCcheck Description: CRAN Package 'IRCcheck' (Irrepresentable Condition Check) Check the irrepresentable condition (IRC) in both L1-regularized regression and Gaussian graphical models. The IRC requires that the important and unimportant variables are not correlated, at least not all that much, and it is necessary for consistent model selection. Exploring the IRC as a function of the number of variables, assumed sparsity, and effect size can provide valuable insights into the model selection properties of L1-regularization. Package: r-cran-ircor Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ircor_1.0-1.ca2404.1_all.deb Size: 26014 MD5sum: 023b479253230a1434a9f167b0bd468b SHA1: 76e4a2679d4ee3dc13d3aa09bedadacb1b4bc812 SHA256: b4b78aea2e95f91271a4aaeec39d748c1a28d411add0330702efe048319c7d02 SHA512: 730de30737cfa4f5d8821a615da99314f0f1cb6bc00bed06d5b0ef664ca288625d9e6e7545369f92ee69c00ca2fe7daf751ff8c9683ca05f3009ba42dfb124f7 Homepage: https://cran.r-project.org/package=ircor Description: CRAN Package 'ircor' (Correlation Coefficients for Information Retrieval) Provides implementation of various correlation coefficients of common use in Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008) tauAP correlation coefficient, and versions tauAP_a and tauAP_b developed by Urbano and Marrero (2017) to cope with ties. Package: r-cran-irdisplay Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-repr Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-irdisplay_1.1-1.ca2404.1_all.deb Size: 33262 MD5sum: f30f8684ec9582fb82f6f3987b1dd32d SHA1: d790bc84797924b1e144dbb5bf68a878eec8f413 SHA256: 9bd204798ef76a63892e3cfbcd597c2e3578d1cc08f3d977b1b751aab7c3b710 SHA512: 01739396b4b3f535e087e56dfe5232a64ceede20306d56f968733bd9b8b9558cbbe1e5d5439860434d0ee75beb1a476f14bb0e3c85f1832ab6c0a30f837a9534 Homepage: https://cran.r-project.org/package=IRdisplay Description: CRAN Package 'IRdisplay' ('Jupyter' Display Machinery) An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') . Designed to be used from a running 'IRkernel' session . Package: r-cran-iregression Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-iregression_1.2.1-1.ca2404.1_all.deb Size: 127624 MD5sum: 1bfed66cbbb6651c1ef16da1688e912a SHA1: ce293a04fbf09838e1d31b0f0775bd90b2bba0f8 SHA256: 8345b7cf07b3cb27e6b5ace3f265cc415931680f4acf3f3ff19b5d9d4b5e1b23 SHA512: 0e4151e5367e0b45363f0ae4c52005c36d7fe1cabb935ac6256e423cf33eeb41f8894541ac2702eb0e8606140e6bcd7c272ccbff56e52771f89204f51d0d991d Homepage: https://cran.r-project.org/package=iRegression Description: CRAN Package 'iRegression' (Regression Methods for Interval-Valued Variables) Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures. Package: r-cran-irelink Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-duckdb, r-cran-ggplot2, r-cran-glue, r-cran-rlang, r-cran-stringdist, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-dbplyr, r-cran-dplyr, r-cran-igraph, r-cran-jsonlite, r-cran-knitr, r-cran-nanoparquet, r-cran-rmarkdown, r-cran-rsqlite, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-irelink_0.0.1-1.ca2404.1_all.deb Size: 1389096 MD5sum: 5ec40bf95f39ffb6779fffe5032ab21e SHA1: a8e9cab930190333c529965df7d74dd1f167508d SHA256: be7c02dcbfdb2e0e95727eb28161cf4983614b05e063f4fc235b4a31e714dea6 SHA512: cb59b198ef8064f6b0c5fe3ee42c8e17e60e00ce75a7248559cd29775534863ab34ada5ae86df999078bf3d2eeace57d4a23d15e01fcc661d227c260c8b63531 Homepage: https://cran.r-project.org/package=irelink Description: CRAN Package 'irelink' (Fast Probabilistic Record Linkage) Performs fast, scalable probabilistic record linkage and deduplication using the Fellegi-Sunter model. Records lacking a shared unique identifier are compared across configurable dimensions using exact, fuzzy, and distance-based comparisons, with model parameters estimated via unsupervised Expectation-Maximization. Multiple SQL backends are supported through 'DBI', including execution via 'DuckDB'. This package is a translation of the Python 'splink' library by Linacre et al. (2022) into idiomatic R. Package: r-cran-irepro Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-irepro_1.2-1.ca2404.1_all.deb Size: 89126 MD5sum: e005592ec0288b9519fc349a402e7978 SHA1: fe7112d50a662ed23bdbff55a556c61239a876bf SHA256: 7e65bc0891ce722ae01be184995b50863721bfe0c5357ef244edb85cbe7493ea SHA512: b109fd17f0f4e19b200f51cd2a600692374c6c9facc8a56e12d09cda76f38e5dc6dec3cd69891b23d3ff7300775b90024609918efdc6ed4f6ddaba6f4865a78d Homepage: https://cran.r-project.org/package=iRepro Description: CRAN Package 'iRepro' (Reproducibility for Interval-Censored Data) Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) ). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided. Package: r-cran-irescale Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-sp, r-cran-e1071, r-cran-rdpack, r-cran-fbasics, r-cran-imager, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-irescale_2.3.0-1.ca2404.1_all.deb Size: 297440 MD5sum: ac97c77ce34e68954786e1d54ab5c90f SHA1: 41ea13cb1c0a0bac7b99f22cd8e1be336b5ede1e SHA256: 9fba8fac780ec60ea62bd3d67aace0f0cf37e1c6d8bf02b71aeb8b8e267469b4 SHA512: 0a388ca8fa7f728771797954421120c634be4b6b834d2353665416788659f57c224db9942f75bf9939a64142215ecf5ef661d11cd9ae1e45c7e7079f944aefc5 Homepage: https://cran.r-project.org/package=Irescale Description: CRAN Package 'Irescale' (Calculate and Rectify Moran's I) Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) . 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Package: r-cran-irfcb Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4497 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-png, r-cran-readr, r-cran-reticulate, r-cran-sf, r-cran-shiny, r-cran-stringr, r-cran-tidyr, r-cran-worrms, r-cran-zip, r-cran-jsonlite Suggests: r-cran-hdf5r, r-cran-knitr, r-cran-mockery, r-cran-r.matlab, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-irfcb_0.10.0-1.ca2404.1_all.deb Size: 4263522 MD5sum: e32d8c583523f34f69cf30bfecbbcc61 SHA1: 96f036263d75caaa6918fe3b0e88cc9f32e8240f SHA256: ba848aa4b413a93a3f84fa9eefd7f16279ba7c6e24858b84637dd54c5b6f48a7 SHA512: 5e57c354baeacf29419326d15124d6f613f896043449077e7cd348414a9901741b665938c2b3afb07fb0563039ac434d4b8e282e5a71f86448038e35abb88cf5 Homepage: https://cran.r-project.org/package=iRfcb Description: CRAN Package 'iRfcb' (Tools for Managing Imaging FlowCytobot (IFCB) Data) A comprehensive suite of tools for managing, processing, and analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports quality control, geospatial analysis, and preparation of IFCB data for publication in databases like , , , , and . The package integrates with the MATLAB 'ifcb-analysis' tool, which is described in Sosik and Olson (2007) , and provides features for working with raw, manually classified, and machine learning–classified image datasets. Key functionalities include image extraction, particle size distribution analysis, taxonomic data handling, and biomass concentration calculations, essential for plankton research. Package: r-cran-irg Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpproll, r-cran-chk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammer, r-cran-ggplot2, r-cran-curl, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-irg_0.1.6-1.ca2404.1_all.deb Size: 849856 MD5sum: 2b9fbffadd69ee917be33a6c624cc2b5 SHA1: cf15929a5bbf4077338f83bf79a5c1dd58742e98 SHA256: a66988acf02ca513b7eb1501c9bf0df3b7cef431227b6cbf1b4d27d5474a06a4 SHA512: 93f7a282eebf397ebae2d7744934947b2a7d10c2ff5451359c3113908e2ba14510ed6552c336c674672197554538b2b7f6f62ec327be2f4f5b6330355cad8013 Homepage: https://cran.r-project.org/package=irg Description: CRAN Package 'irg' (Instantaneous Rate of Green Up) Fits a double logistic function to NDVI time series and calculates instantaneous rate of green (IRG) according to methods described in Bischoff et al. (2012) . 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The functionality in this package builds upon the base classes of the 'IRISSeismic' package. Metrics include basic statistics as well as higher level 'health' metrics that can help identify problematic seismometers. 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Includes a data dictionary and categorized metadata for several sections such as assets, rights, debts, and income brackets. More information about the data source can be found at . 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Package: r-cran-irrcac Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-irrcac_1.4-1.ca2404.1_all.deb Size: 909426 MD5sum: 4391aa876e8946be0e73aea2064bae82 SHA1: 95dfcc99873a669bb44e76e2c12d956d2d891c43 SHA256: 6aff6e7abef2c8777c3feb060fdd4c4efc4d0a860565f1f2c6f4e8ddf56cd084 SHA512: 1fd0c7b8ea33c808db061911e03be93128452e96478cd91442deea81d3d2e7370bfb1fbf5ebb4fdc0c2dfcb8971f4d5a350d295f4e9699e4b787f5185f4b7c28 Homepage: https://cran.r-project.org/package=irrCAC Description: CRAN Package 'irrCAC' (Computing the Extent of Agreement among Raters withChance-Corrected Agreement Coefficient (CAC)) Contains a series of R functions for calculating various chance-corrected agreement coefficients (CAC) among 2 or more raters. Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. Also included in this package is Bangdiwala's B coefficient. 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Package: r-cran-irtawsi Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-mirt, r-cran-psych, r-cran-readxl, r-cran-shiny, r-cran-shinywidgets, r-cran-shinycssloaders, r-cran-rmarkdown, r-cran-bs4dash, r-cran-gt, r-cran-diagram, r-cran-writexl, r-cran-mirtcat, r-cran-wrightmap Filename: pool/dists/noble/main/r-cran-irtawsi_0.4.1-1.ca2404.1_all.deb Size: 47310 MD5sum: 5acf64e99be57b10cd93a3350da6610f SHA1: e4002be9f01113510225ef9ee69b6d34e0b67e08 SHA256: 6948b74966c67df46e61a8ce8bc5dc048f19c0a93ef2c2c34bb8a6dc27b382c4 SHA512: 72877d4c6647db30f6ea5fca8b39ea4867b887e2d8f2f469ef68d8190b79f993524753f50b3eca32a921d15c6ed2c9ca61818ecae997664ddaa297aae0520ccc Homepage: https://cran.r-project.org/package=irtawsi Description: CRAN Package 'irtawsi' (Items Response Theory Analysis with Steps and Interpretation) Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) ) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory. Package: r-cran-irtbem2pl Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-irtbemm, r-cran-statmod Filename: pool/dists/noble/main/r-cran-irtbem2pl_1.0.1-1.ca2404.1_all.deb Size: 74684 MD5sum: 1eabd90443721792b2792c1439091dab SHA1: f3a0587fa4b43f3a97622620c336cbd18a62c371 SHA256: bec0bd791c4797c2ff8022bbd5598a22957cfb12e7c01d903a960c414d5421f9 SHA512: 2b53343ec03f206d2f2160cb1c6f6e845adfef3391fade11d6ec52e95da56444e1be8e0e11e1fe9d44c00172b6d6ce3bcc3aaabf7e974ca6aa9b327734dcd8af Homepage: https://cran.r-project.org/package=irtbem2pl Description: CRAN Package 'irtbem2pl' (Marginalized Bayesian Item Parameter Estimation, 2pl Model IRT) Estimates item parameters of the two-parameter logistic (2PL) model in Item Response Theory (IRT) using the marginal Bayesian modal estimation via the Expectation-Maximization (EM) algorithm. The package calibrates item discrimination and difficulty parameters, yielding results comparable to software like 'BILOG-MG'. Package: r-cran-irtbemm Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-irtbemm_1.0.8-1.ca2404.1_all.deb Size: 202952 MD5sum: 7081a139ab3f026c3f01d90a5784cf07 SHA1: f38c350397d90c39c1c0a226e685551099d1a2b5 SHA256: c786c920868a1240d3611386722e0c65983a52a67a360d500eca1dee8d4ccb70 SHA512: 7dc0c797a38330bd3806d62ebe14f3e244f52ca5616384f3fbdf87c8666af1bc8fdc46a154af081ce5850fafd0b883228852dfed54b6db0896fddb3e5334a55b Homepage: https://cran.r-project.org/package=IRTBEMM Description: CRAN Package 'IRTBEMM' (Family of Bayesian EMM Algorithm for Item Response Models) Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) ; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) . The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) ; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) ; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) ; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) . Thus, both Bayesian modal estimates and maximum likelihood estimates are available. Package: r-cran-irtdemo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-fgarch Filename: pool/dists/noble/main/r-cran-irtdemo_0.1.5-1.ca2404.1_all.deb Size: 24260 MD5sum: aac7e843e9a524ab1ea6254d7977a9c3 SHA1: ec0cf450df170eedbea95b613be60b272c54fa0e SHA256: 0e30de568c6239696ab081c50acfb25c8101411e7fcc58fc61d78ad0906f4a28 SHA512: 547527faa93c74c9ba9f44e408eddf67f732df8e2223a9de57d61041a6e81c9828b793a8dfc3dcf8d367aa3f71e5a13a672832be31f5265920f4532ab3d67d8e Homepage: https://cran.r-project.org/package=irtDemo Description: CRAN Package 'irtDemo' (Item Response Theory Demo Collection) Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models. Package: r-cran-irtest Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betafunctions, r-cran-dcurver, r-cran-ggplot2, r-cran-usethis, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-irtest_2.2.0-1.ca2404.1_all.deb Size: 583274 MD5sum: 4c488d86a426d6bdb0a5f31e871f98e4 SHA1: 24921f72064a69e56bd31fae7f09812016b6bc8d SHA256: 559229e4b65ee1724770d411734bbc09b5ef15103086dccd5497a5cc50705c5a SHA512: 049f5300c6f521db0c4fd1c3633eb30099d9a28d5ecc97d41c74ae070156fab50d548ca108938a6f0a057dc704774caa3f9ab94dd68c340bea8914deb617aa30 Homepage: https://cran.r-project.org/package=IRTest Description: CRAN Package 'IRTest' (Parameter Estimation of Item Response Theory with Estimation ofLatent Distribution) Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm (Bock \& Aitkin, 1981 ). Within parameter estimation algorithm, several methods for latent distribution estimation are available. Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution. Package: r-cran-irtgui Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-shiny, r-cran-shinydashboard, r-cran-shinycssloaders, r-cran-readxl, r-cran-mirt, r-cran-psych, r-cran-wrightmap, r-cran-writexl, r-cran-irtoys Filename: pool/dists/noble/main/r-cran-irtgui_0.2-1.ca2404.1_all.deb Size: 53966 MD5sum: f4d1cf44fc2490491ef7c8395301de7e SHA1: dd22e9daf9d90cd211243f22d68e679a6bef2d14 SHA256: bcc1b9c9b0deebbf66cbbe7118368949a3db7917aae603f961208d98c82b3d22 SHA512: ff067b70571d29d7243b4af683c99c2467fa333a6f557e7909c412d5ce9785d92ee2891bb305577a1a364d607d61d63b00461b35f25a76770c505576ab163dfb Homepage: https://cran.r-project.org/package=irtGUI Description: CRAN Package 'irtGUI' (Item Response Theory Analysis with a Graphic User Interface) Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface. Package: r-cran-irtpwr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mirt, r-cran-deriv, r-cran-digest, r-cran-spatstat.random, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-irtpwr_1.0.3-1.ca2404.1_all.deb Size: 173168 MD5sum: 351fd2ec5554614dcdad2e6323ed4832 SHA1: ca21bd7b78fb3a6c39069afe0de33b30cd512898 SHA256: d7858b71a6c1c07cf82774414b1d5d77683a66306b2143f83393f1704555b949 SHA512: 4ca4eb0cd60ed66cc6d9c11e97adcb87fcba64c75010ec9395bda6df0c408d553b8c586268741b3fe2577745b44ba006751b17587c5d91a61ef45a616eb05e54 Homepage: https://cran.r-project.org/package=irtpwr Description: CRAN Package 'irtpwr' (Power Analysis for IRT Models Using the Wald, LR, Score, andGradient Statistics) Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) ). Package: r-cran-irtq Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4844 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-rlang, r-cran-reshape2, r-cran-janitor, r-cran-ggplot2, r-cran-gridextra, r-cran-matrix, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pkgdown, r-cran-rhpcblasctl, r-cran-mirt Filename: pool/dists/noble/main/r-cran-irtq_1.3.1-1.ca2404.1_all.deb Size: 4494560 MD5sum: 82dcbdde43864d6e3746b83de610a47b SHA1: 4f4c856a886afa0116617aa3ab1a54b76e8a2996 SHA256: 15292644b4a1a9b418c1471ea4bda6d2670d7c28d851a9b75016b85d2a47c776 SHA512: f10b7fbca82cb73f88eccd9b9e06de4512a17ba8c5e1c2dcfeaac76e343fd33d095564fab337f7c3caab9e3014067b4c3c2910b64fa3088cffc134943afdbb54 Homepage: https://cran.r-project.org/package=irtQ Description: CRAN Package 'irtQ' (Unidimensional Item Response Theory Modeling) Fit unidimensional item response theory (IRT) models to test data, which includes both dichotomous and polytomous items, calibrate pretest item parameters, estimate examinees' abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as differential item functioning analysis. In addition, the package provides a set of classical test theory functions for computing item- and test-level statistics (e.g., item difficulty, item-total correlation, and coefficient alpha) and for scoring and analyzing selected-response item data. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2020) ). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) ). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (Gonzalez (2014) ). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the catR package (Magis & Barrada (2017) ). Package: r-cran-irtrees Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-tidyr Suggests: r-cran-lme4, r-cran-mirt, r-cran-knitr, r-cran-flextable, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-irtrees_1.0.0-1.ca2404.1_all.deb Size: 1096048 MD5sum: fb83ebec5cd8fb6cd741bead51429383 SHA1: f2bd92b67fe4b1e0eb7aa19eb8001ca21cbf1e1b SHA256: aa73e2dc689a78a87c0d4fd70c0e92db10d29e10482b56c44c5a56a12fb220c1 SHA512: 13f40da7e83a56d68b39ed367bb90c117b70acc166b13fd03a91b03efa8d1eb7061e7d4c3dd43e6cdde756801c67538f5676d1b86ae92b4cda9d4a478c2ca16e Homepage: https://cran.r-project.org/package=irtrees Description: CRAN Package 'irtrees' (Estimation of Tree-Based Item Response Models) Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software. Package: r-cran-irtreliability Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ltm, r-cran-mirt, r-cran-fastghquad Filename: pool/dists/noble/main/r-cran-irtreliability_0.1-1-1.ca2404.1_all.deb Size: 95298 MD5sum: 232a95f6bef293c2bc2e557fb1f3ac19 SHA1: c3ef269fff8410ef90dcc240afeaacc279cd283c SHA256: 8092ceedb5929ef04f98c0e6fc9e24b7145f2658a97d4249bb7e2fa2ae076573 SHA512: 3de407184faa1e871e9a15ca58a703bde91679429c458d743a82414f2822296fdcbd7b852b77fdef1b6f7fb591f584d980eea3ccb16d54889368e25e154b5bff Homepage: https://cran.r-project.org/package=irtreliability Description: CRAN Package 'irtreliability' (Item Response Theory Reliability) Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) and Kim, S. and Feldt, L. S. (2010) . The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) . 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Implements the 10-decision framework from Schroeders and Gnambs (2025) as a three-step workflow: specify the data-generating model with irt_design(), add study conditions with irt_study(), and run simulations with irt_simulate(). Supports one-parameter logistic (1PL), two-parameter logistic (2PL), three-parameter logistic (3PL), graded response (GRM), partial credit (PCM), and generalized partial credit (GPCM) models with missing-completely-at-random (MCAR), missing-at-random (MAR), booklet, and linking missingness mechanisms. Results include mean squared error (MSE), bias, root mean squared error (RMSE), standard error (SE), and coverage criteria with summary and plot methods. 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Package: r-cran-iscam Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-usethis, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-iscam_1.2.0-1.ca2404.1_all.deb Size: 497760 MD5sum: 39af02db0fb61313ffa98248db3a65c2 SHA1: 8f86cf17305e360331887d0bd26fb29bd9566b93 SHA256: 74a71d76b0bb65194c75b40f933b90909f50ff941cbc06568d1b5294941a0b9e SHA512: 2d0b47411294efcfccfd709ac32fda80b5f90f0165f384c37833407a313a128d47151c7ca53207f801fa1cf7434677bddf547b2b91a2166ea36526d31453e2d5 Homepage: https://cran.r-project.org/package=ISCAM Description: CRAN Package 'ISCAM' (Companion to the Book "Investigating Statistical Concepts,Applications, and Methods") Introductory statistics methods to accompany "Investigating Statistical Concepts, Applications, and Methods" (ISCAM) by Beth Chance & Allan Rossman (2024) . Tools to introduce statistical concepts with a focus on simulation approaches. Functions are verbose, designed to provide ample output for students to understand what each function does. Additionally, most functions are accompanied with plots. The package is designed to be used in an educational setting alongside the ISCAM textbook. Package: r-cran-isco08conversions Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-isco08conversions_0.2.0-1.ca2404.1_all.deb Size: 88064 MD5sum: 4a89276020d17b45ec6ba8451e0b7893 SHA1: 4f401e53cca50117594b61febd52cbe355a9d2de SHA256: 3f6181d0493ad99fb154d01930fa9094f3b7282fec12f7bc5a5e30f4a2bd21c0 SHA512: bbfb624db24b4ffef06a68f10cc134781548d7959c81c330ccea00cb3adb5ab10d9c994488315a969104282d17173dcef33965dbe7ded971282f60177b7ba191 Homepage: https://cran.r-project.org/package=ISCO08ConveRsions Description: CRAN Package 'ISCO08ConveRsions' (Converts ISCO-08 to Job Prestige Scores, ISCO-88 and Job Name) Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. 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The software package provides the integrative analysis methods including integrative sparse principal component analysis (Fang et al., 2018), integrative sparse partial least squares (Liang et al., 2021) and integrative sparse canonical correlation analysis, as well as corresponding individual analysis and meta-analysis versions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). Integrative sparse principal component analysis. Journal of Multivariate Analysis, . (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). Integrative sparse partial least squares. Statistics in Medicine, . 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EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data. Package: r-cran-isinglandr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gganimate, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinythemes, r-cran-simlandr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gifski, r-cran-isingfit, r-cran-isingsampler, r-cran-transformr Filename: pool/dists/noble/main/r-cran-isinglandr_0.1.1-1.ca2404.1_all.deb Size: 158248 MD5sum: aca9c30fa17d8ae6d88415c0758ae84c SHA1: 5d4d59715df74face31267bfe963b60d6220e3be SHA256: 10b28a1a2657749709bcd5b6734a281f176b9a479cdb19631b164082bdb65bbf SHA512: 74cbd63291292d0cfb9b899cfcebe914d00f1af082499a6722f2ce3e8469aa96776d63485217fc08a4cae820543630b93f98880c57776222bea3a2587886e98d Homepage: https://cran.r-project.org/package=Isinglandr Description: CRAN Package 'Isinglandr' (Landscape Construction and Simulation for Ising Networks) A toolbox for constructing potential landscapes for Ising networks. The parameters of the networks can be directly supplied by users or estimated by the 'IsingFit' package by van Borkulo and Epskamp (2016) from empirical data. The Ising model's Boltzmann distribution is preserved for the potential landscape function. The landscape functions can be used for quantifying and visualizing the stability of network states, as well as visualizing the simulation process. Package: r-cran-islandcodes Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-countrycode Suggests: r-cran-dplyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-islandcodes_0.2.0-1.ca2404.1_all.deb Size: 74706 MD5sum: 273654d7b816a1389732747cf86809d5 SHA1: 104a0559dca66702eda7131336915a63cb405df1 SHA256: f8c6d9c0aa3632cb0d16fee76b325a18d28dd817fa32afc86ba72db51c918f26 SHA512: 22456ad17a4d332fcff52b726315495d2df885db60047e8016d68e9106ccd6d5592da20be6b61ac2005b2cb537bd9f02d6363fc321fe15544715ddb7ed181ace Homepage: https://cran.r-project.org/package=islandcodes Description: CRAN Package 'islandcodes' (Reference Data and Helpers for Small Island States andTerritories) A curated reference list of countries and territories with classifications for Small Island Developing States (SIDS), sub-national island jurisdictions (SNIJ), World Bank region and income group, and political association. 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Package: r-cran-isletcalc Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-isletcalc_0.0.1-1.ca2404.1_all.deb Size: 40352 MD5sum: 4e5519ca15b6cd73c9c1196be83da6b8 SHA1: 83c80a317174e807ef67ac9872a985b7f54473a7 SHA256: f8e327346d3bc0fe37ca526a039a08539a64c0a59cd00f9b41efeb3626d7c47b SHA512: 9074e377c27a968b6cdb7e634f80cee351998de15417c37eaabe1ba672b9be2c127d4855f65289a24da93dfa5ca043512dfd7d3f653997f8d9ddf7a5f7c7e246 Homepage: https://cran.r-project.org/package=IsletCalc Description: CRAN Package 'IsletCalc' (Calculators for Insulin and Glucagon Release Indices) Facilitates the calculation of validated pancreatic islet hormone-release indices from fasting and oral glucose tolerance test (OGTT) measurements. Provides beta-cell insulin release indices (including HOMA-beta, corrected insulin response, Stumvoll first-phase index, BIGTT-AIR, and disposition indices) as described in Madsen (2024) , alongside alpha-cell glucagon release and glucagon resistance indices derived from the glucagon-suppression and liver-alpha-cell-axis literature. Enables reproducible assessment of beta-cell and alpha-cell function for metabolic and endocrine research. Package: r-cran-islr2 Architecture: all Version: 1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4582 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-islr2_1.3-2-1.ca2404.1_all.deb Size: 4128634 MD5sum: 5796cba8d1983ca4f19660a508382d2b SHA1: 6d5cb8502c939e38c91560dd84c428e1e3810f37 SHA256: 85adce033244ad020220d7ae32aed6eaae812f1b623df0e765181e6220f1322d SHA512: fda7bbef80ccca24e30ba6f656234152ecedf5e4efffded23bbbf00f86732c408169ed173846def7f0dbe5d4d0cbef591a4ca7f482c018edb56d050ce57730d1 Homepage: https://cran.r-project.org/package=ISLR2 Description: CRAN Package 'ISLR2' (Introduction to Statistical Learning, Second Edition) We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets. Package: r-cran-islr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2936 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-islr_1.4-1.ca2404.1_all.deb Size: 2909740 MD5sum: 4afb26d749487db78487aa1c99ed93b6 SHA1: 0745a1e1a5bd38a86c3228bef221b7ca636f68f1 SHA256: e88f0ef06e07d719e25cee674126f9bbdfb2707dc5e84bd2795284417836e103 SHA512: 5cda138d49c553ee0badc229e79da54026a228a51d6648316661723e73274a6101dc31d17ab5c8ad01b68f7cceac4c072b231ebcad1816bde69cfd04caff5338 Homepage: https://cran.r-project.org/package=ISLR Description: CRAN Package 'ISLR' (Data for an Introduction to Statistical Learning withApplications in R) We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'. Package: r-cran-ism Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xlsx, r-cran-rjava, r-cran-xlsxjars Filename: pool/dists/noble/main/r-cran-ism_0.1.0-1.ca2404.1_all.deb Size: 260380 MD5sum: 421bb9d7d170b9a26cca91929e16e6a9 SHA1: e1a02bbad9e5c09dae2c6d7b4dba26fe59986334 SHA256: 4ebc8bb61933f89e7710e8d1db9db8f1f9b9306e7393a62e5444880ae63c6cd7 SHA512: eef535e261d2a65b78817f400b31bc1e2795366fd5c2e53b369fa94815bf615d1652b22b566ea152acf07737f78e9fc357c527f9e59a328e1894758efc84f051 Homepage: https://cran.r-project.org/package=ISM Description: CRAN Package 'ISM' (Interpretive Structural Modelling (ISM)) The development of ISM was made by Warfield in 1974. ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure. To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's. Warfield,J.N. (1974) Warfield,J.N. (1974, E-ISSN:2168-2909). Package: r-cran-ismev Architecture: all Version: 1.43-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-ismev_1.43-1.ca2404.1_all.deb Size: 266912 MD5sum: dccef4a87fbe16949153e33ce7d03b0e SHA1: 6e432d696546abc1c726cec83fa41ac08057286d SHA256: 10583b9feb187d9c775ff11cebe7def9f6fc24931819f5e4410e8be44294e4eb SHA512: 60051a15afe342ca4fdeaa6e0d33394dad6a933778d40954d9d751cfb5809fd021fa3838b54e7dafd03f60ba813bd99d71df47666beac21839a39cb54aad8ed1 Homepage: https://cran.r-project.org/package=ismev Description: CRAN Package 'ismev' (An Introduction to Statistical Modeling of Extreme Values) Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes. Package: r-cran-ismtchile Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ismtchile_2.1.5-1.ca2404.1_all.deb Size: 209788 MD5sum: d74e9b735692038293e686d606bb45cd SHA1: 7d1ea4184338ec059519594941783501892d1a8f SHA256: 90c906b463acdf2a14d9023287855e2c433c610d6b9305197c5e2909b1128076 SHA512: 7735d5de26a76478404ee1ef8b66a4b5a6218ab66dfb418aa87b6e87c5e8804a4b67991b51c5418a3e32713ce6e3959309a3546b48222334564486e7c63d3b57 Homepage: https://cran.r-project.org/package=ismtchile Description: CRAN Package 'ismtchile' (Calculating Socio Material Territorial Index) Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" () [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" () [Observatorio de Ciudades UC (2019)]. Package: r-cran-ismtools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-visnetwork, r-cran-viridis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ismtools_0.1.0-1.ca2404.1_all.deb Size: 121766 MD5sum: 0e8b9d6bc669254acc8724c7411cf60c SHA1: ae359b6d9b82afaecbe3c4d930d6d4f3e84e4411 SHA256: 131129997e700fab036ffb7770436e736218166267acb235d974d226e07d045d SHA512: 78d29e8491f2799c7f53c6f404f6ba63a590d22cfddd5bd8877e4d6de58dbe40aed3bb23c8704d3d0dcde2ceaea7850b4d336ffa18fddaa3f34a1bf4c0675795 Homepage: https://cran.r-project.org/package=ISMtools Description: CRAN Package 'ISMtools' (Interpretive Structural Modelling Analysis Tools) A comprehensive toolkit for Interpretive Structural Modelling (ISM) analysis. Provides functions for creating adjacency matrices from various input formats including SSIM (Structural Self-Interaction Matrix), computing reachability matrices using Warshall's algorithm, performing hierarchical level partitioning, MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis, and visualizing ISM structures through both static and interactive diagrams. ISM is a methodology for identifying and summarizing relationships among specific elements which define an issue or problem, as described in Warfield (1974) . Package: r-cran-isni Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-mvtnorm, r-cran-nnet, r-cran-matrixcalc, r-cran-formula, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-isni_1.3-1.ca2404.1_all.deb Size: 201838 MD5sum: ef77f38d73e201d69f005b7927b6ecee SHA1: bee0c28f0aea0301f1c2e209487a336a92b0b98b SHA256: 71b9fa3f4e36866e915e887f74237b69cc01db10d143426f37c3b6b0b200a9a3 SHA512: 014b70706e9272a614786412ed9590e1524908288429c00781b3f5747b81018d0905faf8dad62d4a5e7edf83105ce8f440144c1d08d0b2c7a7dba418b79a7b76 Homepage: https://cran.r-project.org/package=isni Description: CRAN Package 'isni' (Index of Local Sensitivity to Nonignorability) The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random is with respect to the assumption of ignorability. For a tutorial, download at . For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) and Ma Troxel and Heitjan (2005) and Xie (2008) and Xie (2012) and Xie and Qian (2012) . Package: r-cran-iso11784tools Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iso11784tools_1.2.0-1.ca2404.1_all.deb Size: 62088 MD5sum: 5f49af23b08599a697ab420e503c5ea1 SHA1: 5699aca474b231ad03eeed81525f105e4887bc18 SHA256: af725c576a481a4bfcb1b776f08129b29929e6c32989381c1959b8c2ca9b9a08 SHA512: 77fe7c28cea6f4475e4c032cea536ca280013a4aa439cc655237e2abc936af4b33aa561a271554e3c55e7f7dce93e3f1bc3e599b2cf369dc244865f148a45f39 Homepage: https://cran.r-project.org/package=ISO11784Tools Description: CRAN Package 'ISO11784Tools' (ISO11784 PIT Tag ID Format Converters) Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers. The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal 'dot' format, e.g., 3E7.1CBE991A14. These are referred to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary structure (see ). There are two 'flavours' of this format, a left and a right variation. Which flavour a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically. Package: r-cran-isoboost Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-isotone, r-cran-rpart Filename: pool/dists/noble/main/r-cran-isoboost_1.0.2-1.ca2404.1_all.deb Size: 102056 MD5sum: e7102402795cdc618453fee0fc902886 SHA1: 290e423b04fd0b11cc335ea3320849ce119cc426 SHA256: 2ecb3619a0df2085510967f8581f945faf28a5a9bd06545d3a4068cce4b253d1 SHA512: 18b0eae300af3d5fd99489b8568d52d571f40fdad92b376204f9f27a3652ac899d04594b2c5af172639fc8646da63bdebd0fb872d53ba5e0181cf21ea5390291 Homepage: https://cran.r-project.org/package=isoboost Description: CRAN Package 'isoboost' (Isotonic Boosting Classification Rules) In classification problems a monotone relation between some predictors and the classes may be assumed. In this package 'isoboost' we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. 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The package also allows the sweeping of many parameters in both static and dynamic conditions. The mathematical models used in this package are derived from Albarede, 1995, Introduction to Geochemical Modelling, Cambridge University Press, Cambridge . Package: r-cran-isocalcr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-isocalcr_0.1.1-1.ca2404.1_all.deb Size: 288334 MD5sum: c1e401f02860c8ead0de0df61854e561 SHA1: 4b63cea3400921a1eb250409fe304651f19146c5 SHA256: 5711f052fe6e5d755c146be27e8f06ecefb4883e80bac40dc0357eaa136c95f2 SHA512: 2c48515d2e044b2deeb86ab310fa9b35cff099b114f70d91ffb62780037dc923dbb1eea56c74a2b9311635d1399807edf4e75a7e15866d37b9ad722ada50bac4 Homepage: https://cran.r-project.org/package=isocalcR Description: CRAN Package 'isocalcR' (Isotope Calculations in R) Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology . Package: r-cran-isocat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1682 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-plyr, r-cran-magrittr, r-cran-foreach Suggests: r-cran-dendextend, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-pvclust, r-cran-rmarkdown, r-cran-raster, r-cran-rastervis, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-isocat_1.0.1-1.ca2404.1_all.deb Size: 1365740 MD5sum: 3a3c7d7759b8541cd8a7aa50b08870cb SHA1: 10a13bb888b8dbf28f64e55d729a01832ec83b12 SHA256: 2b1dbb25c5a83516a7830c1ab7687b54a76017577136a23f502a957522d69de8 SHA512: ba66913c8f94c552d9f1442fae5d1ff78dd18b06cc0bca178faf02e86073d7c9af525ac7d7a48fc8e43a3638a156a21c929046ebf64288a592e740b607197e48 Homepage: https://cran.r-project.org/package=isocat Description: CRAN Package 'isocat' (Isotope Origin Clustering and Assignment Tools) This resource provides tools to create, compare, and post-process spatial isotope assignment models of animal origin. It generates probability-of-origin maps for individuals based on user-provided tissue and environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] ) using the framework established in Bowen et al. (2010) ). The package 'isocat' can then quantitatively compare and cluster these maps to group individuals by similar origin. It also includes techniques for applying four approaches (cumulative sum, odds ratio, quantile only, and quantile simulation) with which users can summarize geographic origins and probable distance traveled by individuals. Campbell et al. [2020] establishes several of the functions included in this package . Package: r-cran-isocheck Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-dplyr, r-cran-plyr Filename: pool/dists/noble/main/r-cran-isocheck_0.1.0-1.ca2404.1_all.deb Size: 103378 MD5sum: 8dccde859e200486d940aad2df98fbc1 SHA1: 196674838cc7f65141a49663991e5d7f5dd3559e SHA256: 85cbf003c9e77c695976b3fee9aeb911f183449ac66b83ecc17c81adf43383ce SHA512: 55dc889d4245d39f10feda4b66869307618e1a750c24d5929789746609564243d0081cbf15f5a829bba192c75018583b6ead9d2c94975fed85df17b049daa71a Homepage: https://cran.r-project.org/package=IsoCheck Description: CRAN Package 'IsoCheck' (Isomorphism Check for Multi-Stage Factorial Designs withRandomization Restrictions) Contains functions to check the isomorphism of multi-stage factorial designs with randomisation restrictions based on balanced spreads and balanced covering stars of PG(n-1,2) as described in Spencer, Ranjan and Mendivil (2019) . Package: r-cran-isocodes Architecture: all Version: 2026.03.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-isocodes_2026.03.28-1.ca2404.1_all.deb Size: 215796 MD5sum: e211101a8c92801b12d6dea4b19a0962 SHA1: 53950dcd5d62139ddebd47b31eb21b2b7672544a SHA256: 5841236c93b0b7cd2095c3333bd5699db32201f5d9efee77669fc1513f8849cb SHA512: 655b3cca594b59267e073d8c72b7a7e45246345527f82336dc0681bf5a79e6de5568c6a6eb64730ada8d1de8ec9179065e0e432042f643176f5fa2f407bcd0bd Homepage: https://cran.r-project.org/package=ISOcodes Description: CRAN Package 'ISOcodes' (Selected ISO Codes) ISO language, territory, currency, script and character codes. Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes. Package: r-cran-isocor Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-config, r-cran-dt, r-cran-golem, r-cran-maldiquant, r-cran-markdown, r-cran-plyr, r-cran-shiny, r-cran-shinyalert, r-cran-shinyjs Suggests: r-cran-shinytest2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-isocor_0.2.8-1.ca2404.1_all.deb Size: 3564984 MD5sum: 07b51d946d0eda7a08a2bd57a7287a86 SHA1: 1cb049a672779680945fb8e721249fb612f079a1 SHA256: 876b96c9f32152159e2221121a14ad241e06eeb26b84ad2b677fc65d3ac4e4a6 SHA512: abc44fb1082984acf69a3c4cbc5590535a6e028cf2d6ca86ceb9ee9d7fe8307ba33730dfd7abf7db9440b31ead2ee1938df9c0caf60990765a3be87aa460251e Homepage: https://cran.r-project.org/package=IsoCor Description: CRAN Package 'IsoCor' (Analyze Isotope Ratios in a 'Shiny'-App) Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) measurement data to evaluate isotope ratios (IRs) is a complex process. The 'IsoCor' package facilitates this process and renders it reproducible by providing a function to run a 'Shiny'-App locally in any web browser. In this App the user can upload data files of various formats, select ion traces, apply peak detection and perform calculation of IRs and delta values. Results are provided as figures and tables and can be exported. The App, therefore, facilitates data processing of ICP-MS experiments to quickly obtain optimal processing parameters compared to traditional 'Excel' worksheet based approaches. A more detailed description can be found in the corresponding article . The most recent version of 'IsoCor' can be tested online at . Package: r-cran-isocountry Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-isocountry_0.8.0-1.ca2404.1_all.deb Size: 27970 MD5sum: 3ba19e3887dd986ee77e048d7993d12f SHA1: a98efc0583efcf30259cb22ce9fee0237a5406e7 SHA256: 38e032be868dd50d8649f9fcc05798c048cf5e615a838891cccfbd45c89ddcee SHA512: 9df9f6f0032a03d1218b00470d782bc78d4be817a49911c32f8373dc81241b2671876cecaa0a07f0b49f620bb3a031f20cda7c1bc5c54616104a86f7030d6732 Homepage: https://cran.r-project.org/package=isocountry Description: CRAN Package 'isocountry' (ISO 3166-1 Country Codes) ISO 3166-1 country codes and ISO 4217 currency codes provided by the International Organization for Standardization. Package: r-cran-isoexplorer Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-htmltools, r-cran-shiny, r-cran-bslib, r-cran-shinyjs, r-cran-shinyace, r-cran-shinycssloaders, r-cran-rlog, r-cran-dt, r-cran-ggplot2, r-cran-isoreader2, r-cran-callr, r-cran-httpuv, r-cran-htmlwidgets, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-testthat, r-cran-pkgload Filename: pool/dists/noble/main/r-cran-isoexplorer_0.5.0-1.ca2404.1_all.deb Size: 460936 MD5sum: 361abbe0bf2bb9ec234e9505e4b5c732 SHA1: cd1c4d4a89c2d187baf22a4165172ea27c52dd42 SHA256: d4ebe68fe17c8e6c484857cc575f24614789ca40f6d021c20630d84ade81ee2a SHA512: d1124b30b454b0189825213823d2b101899e771815307f92e40c668024a9d8ef0da0e4f62b54f04fbe6c523bcb31258f25d47934da5a0c897922569733d22202 Homepage: https://cran.r-project.org/package=isoexplorer Description: CRAN Package 'isoexplorer' (GUI Components to Explore Stable Isotope Data Files) Provides graphical user interface components to explore stable isotope data files using the 'isoreader2' package, including browsing, visualizing, and exporting isotope data. Package: r-cran-isogeochem Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-shades, r-cran-viridislite, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-isogeochem_1.1.1-1.ca2404.1_all.deb Size: 624606 MD5sum: a1619d66536b77dc9555099b1c8c93cc SHA1: c80aaa33b64db6fd9ef9407ebe2abe0ea6d17b67 SHA256: cd4c854165c129895323899d2b431f918387b30ef5a2fa4da469e2fcfcd03071 SHA512: ef13a8fbac2ffde66318e52f659962088bcd85094da76f2df896d1c6cc7eec5a51251d2ca1c8fc9e6f261c934220d87df316e39251617d9d3bf4c1f28b30f983 Homepage: https://cran.r-project.org/package=isogeochem Description: CRAN Package 'isogeochem' (Tools for Stable Isotope Geochemistry) This toolbox makes working with oxygen, carbon, and clumped isotope data reproducible and straightforward. Use it to quickly calculate isotope fractionation factors, and apply paleothermometry equations. Package: r-cran-isokernel Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-matrix Filename: pool/dists/noble/main/r-cran-isokernel_0.1.0-1.ca2404.1_all.deb Size: 15204 MD5sum: 996ff0ebba228c85d5cdf4f12a693b06 SHA1: 8bae14622379f5e32a39e62405e15f975342e155 SHA256: 1b1149e9b8fb7558fdf9fdc11a16aa293a1c5fd74fec280e5ab223ad028e1063 SHA512: fff446fe9737b6fedf73d4c18f0472943934f9b2686bf28943dfeba12183cc8be7fea25718564ab55e058ffe2ca866ad935ea96cdcc93c7fed260c9174fb21f6 Homepage: https://cran.r-project.org/package=isokernel Description: CRAN Package 'isokernel' (Isolation Kernel) Implementation of Isolation kernel (Qin et al. (2019) ). Package: r-cran-isomemo Architecture: all Version: 23.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-modules Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-isomemo_23.10.1-1.ca2404.1_all.deb Size: 43706 MD5sum: e9ff56a15824e2ce30adf8379f6dab71 SHA1: a11306e83951776cfb7334b92a721913315fc933 SHA256: 234af9c060ea0d1ea5eb276a78e185f6653e803104439ea7323ab397f7adc37a SHA512: e39c944c62ac58f2e6291f69de2434de5786e6cc051f5ec126f0e48efab9c56a67123353afcde78f28d3199c8a9b4f603aaadb6d655fd1cb1b372914042da957 Homepage: https://cran.r-project.org/package=IsoMemo Description: CRAN Package 'IsoMemo' (Retrieve Data using the 'IsoMemo' API) API wrapper that contains functions to retrieve data from the 'IsoMemo' partnership databases. Web services for API: . Package: r-cran-isoniche Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-isoniche_1.0.0-1.ca2404.1_all.deb Size: 52332 MD5sum: 22ea60599b4ac6bb9b089aa8ef465bd5 SHA1: 1223f5d69e07e49e576acb18bb9fe3253311a6d0 SHA256: 85a9ba89e347551dfacc3096f993d173a7e1fb19f76ab2ae1a7ee9e04b2c32f3 SHA512: 2e9f68e283d82b30b64020ed1f34109a500f4a41055b851968cacf7008b250785a6c673cab95698dcb6b242b2cdbc6f90f1383b9f2323a468a798bd5fc2d8f16 Homepage: https://cran.r-project.org/package=isoniche Description: CRAN Package 'isoniche' (Calculating Density-Independent Niche Breadth Indices fromAbundance Data) Deriving density-independent and density-dependent niche breadth indices from abundance data of two or more habitats, including both the pairwise and n-dimensional methods to calculate isodar-adjusted inequality. Methods are described in Granot, Dubiner & Belmaker (in revision), "Why abundance-based indices of niche breadth are biased, and what can be done to improve them", Ecology Letters. Package: r-cran-isoorbi Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-withr, r-cran-lifecycle, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-readr, r-cran-readxl, r-cran-openxlsx, r-cran-purrr, r-cran-prettyunits, r-cran-arrow, r-cran-knitr Suggests: r-cran-devtools, r-cran-fansi, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-forcats Filename: pool/dists/noble/main/r-cran-isoorbi_1.5.2-1.ca2404.1_all.deb Size: 2247236 MD5sum: 66d3908878dfc21a289df5029cb6b3e6 SHA1: 2affe10527434bcd19872fd47d7272755c2cc5b4 SHA256: 51d94eb017b3875f68cb7e61683a4df5fe6f61e145e03257744a8ff94592918a SHA512: 9a4478a81298748abd6073d173012c6f19ee690611ddc569ad2cb06576c9571524f654b75f88e3ad432d477e7e3a0e242f4b2dc645e3d749724ea1bc47e1e72f Homepage: https://cran.r-project.org/package=isoorbi Description: CRAN Package 'isoorbi' (Process Orbitrap Isotopocule Data) Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Citation: Kantnerova et al. (Nature Protocols, 2024). Package: r-cran-isopam Architecture: all Version: 3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vegan, r-cran-cluster, r-cran-fastkmedoids, r-cran-future, r-cran-future.apply, r-cran-igraph, r-cran-ps, r-cran-rspectra, r-cran-proxy, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-isopam_3.7-1.ca2404.1_all.deb Size: 130694 MD5sum: aaa58daede4a512daef0a13744dfd6b7 SHA1: 55dadc46753650cf17ed697e389d969451f3e4cb SHA256: 935385a5cd8a569b8b4988831bc10f5573e11b90b8303ceab6ca4e975832624f SHA512: 583dbb5ce15e352b129eae76f6909221549903dcd1eac43b6e9b7219ef1e3371e0aba8ea1f6742b86c29a1f5e1e513933b2c7aebd9b8ef291c7479d852c34f5b Homepage: https://cran.r-project.org/package=isopam Description: CRAN Package 'isopam' (Clustering of Sites with Species Data) Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of diagnostic species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids. Package: r-cran-isopleuros Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-interp, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-isopleuros_1.4.0-1.ca2404.1_all.deb Size: 531392 MD5sum: 93747dd2f8af002736fe39fbec1a7b84 SHA1: 9956d6b2ac37679812a3dc5c74fe006588de830c SHA256: 4969198d6891c7577cba85a8d29b71b54a45d1e7072bbce96c88eb253529cf1b SHA512: f405bb57cccaa3e24cce675e4bdd3e9c9f0216996b6f6fe43c51348a8a06be2ae98247654550f346059fe0554cd015ed875a1d53c36c57345188dcef73107074 Homepage: https://cran.r-project.org/package=isopleuros Description: CRAN Package 'isopleuros' (Ternary Plots) Ternary plots made simple. This package allows to create ternary plots using 'graphics'. It provides functions to display the data in the ternary space, to add or tune graphical elements and to display statistical summaries. It also includes common ternary diagrams which are useful for the archaeologist (e.g. soil texture charts, ceramic phase diagram). Package: r-cran-isoplotr Architecture: all Version: 7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1596 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-isoplotr_7.0-1.ca2404.1_all.deb Size: 1456008 MD5sum: a0e8f19edb68cdb3d2e2ccafc6c0b116 SHA1: fe22918733e851297c1b4add04c44ee7f8542c65 SHA256: 1f587afa429c6c119e39f6d4b6df57e3e9aa2b2dd9cb1ed9999beb84512dcdc8 SHA512: 87e002408d0fb00b9bad1b601321d5e46a5ed3939546677fe6bc20252e596614e2652e5036c2593703724baa972e16bebecbdc98354802777efb37fb24bbe5cf Homepage: https://cran.r-project.org/package=IsoplotR Description: CRAN Package 'IsoplotR' (Statistical Toolbox for Radiometric Geochronology) Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots. Package: r-cran-isoplotrgui Architecture: all Version: 7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3725 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-isoplotr, r-cran-shinylight Filename: pool/dists/noble/main/r-cran-isoplotrgui_7.0-1.ca2404.1_all.deb Size: 1480018 MD5sum: 2177880a17c98e86996919c788508495 SHA1: 491e39c7e2e0f3c446cb45eb83287a17a271503b SHA256: 85ad773236267894fdba5a0c200ec06e04b41e3be2e253a596abdb72f1adc832 SHA512: 3ad52dd4d28eb6963bf21281a7de9cd4623dc927ebce29de9c71a16d35bbfe35f77ea3af9c58ae3fdf0c012938bef433fc519a6cf2fb817633205f7310d85533 Homepage: https://cran.r-project.org/package=IsoplotRgui Description: CRAN Package 'IsoplotRgui' (Web Interface to 'IsoplotR') Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox. Package: r-cran-isoreader2 Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7986 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-cli, r-cran-lifecycle, r-cran-processx, r-cran-readr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-purrr, r-cran-withr, r-cran-rcppsimdjson, r-cran-scales, r-cran-fansi, r-cran-knitr Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-arrow, r-cran-openxlsx, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-isoreader2_0.7.0-1.ca2404.1_all.deb Size: 3507238 MD5sum: e775f3ece5ba6528287aecb01663eba9 SHA1: 0a675eedc0909fd03b6f189723ec6f065794238a SHA256: 11afbca22fc9aca7eff73ac91f9c4c003cd8b1c8a36b86c1bc0cea3efcf4aa9f SHA512: f4607119bd091660cd8aaeb2a928ba1b986aaf5a75e5c54616117bbe853406622a7cf6ddf19a4bdf826720c5ff3560359f8a64b2fbbdd846f2d77d8a162af39b Homepage: https://cran.r-project.org/package=isoreader2 Description: CRAN Package 'isoreader2' (Read Stable Isotope Data Files) Interface to the raw data and metadata stored in the file formats commonly encountered in scientific disciplines that make use of stable isotopes. Supports Isodat (.dxf, .cf, .did, .caf, .scn), IonOS (.iarc), LyticOS (.larc), Callisto (.bch), and Qtegra (.imexp) file formats. Provides a consistent data structure together with tools to aggregate, convert signal units, filter, and visualize the extracted data. The approach is described in Kopf et al. (2021) . 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It includes functions for storing, prioritizing (sorting), displaying, adding, deleting, and selecting (filtering) issues based on qualitative and quantitative information. Issues (labels and milestones) are written in lists and categorized into the S3 class to be easily manipulated as datasets in R. 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The 'itol.toolkit' package can support all types of annotation templates. Package: r-cran-itop Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-corpcor Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nmf, r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-itop_1.0.2-1.ca2404.1_all.deb Size: 91346 MD5sum: 593068ec90462c17e7582d8757a8780f SHA1: 448098b8aa070db3de00d8eac0801b9430df9268 SHA256: d07545682eb079bbf120735387ffb98cf06c1ad9965fd2bcdcd7d55d6a4a8045 SHA512: f9d0abcfddb850bb76b720b8ec993c11b1911376f3776f595c5448d7f12c7404be9f1af354f34087e929ad92b196a64177ab8609c43caed5f40271e25692b4f5 Homepage: https://cran.r-project.org/package=iTOP Description: CRAN Package 'iTOP' (Inferring the Topology of Omics Data) Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, ), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, ). Package: r-cran-itos Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcbalance, r-cran-biasedurn, r-cran-xtable Suggests: r-cran-weightedrank Filename: pool/dists/noble/main/r-cran-itos_1.0.3-1.ca2404.1_all.deb Size: 270496 MD5sum: 3b3eef17a54f331532cad50718827a9c SHA1: 901a299aa18ab1de885aeeaf9f3214cf6160fca3 SHA256: 1a5516fb540f5d0e9d3bc43e424792eaa16267356e7e72c06fbdbb33469cdda6 SHA512: 5a360cb75f48d2cc422cda46a8fe813302804dcdf6bca1119a04ba30782a3a6c9ab6655677e7e7dec4c3ec605c9bce6127ebda365c364133ce73ce125bb41824 Homepage: https://cran.r-project.org/package=iTOS Description: CRAN Package 'iTOS' (Methods and Examples from Introduction to the Theory ofObservational Studies) Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) and 'bingeM' from Rosenbaum (2023b) . The function makematch() uses two-criteria matching from Zhang et al. (2023) to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) ) and matching with fine or near-fine balance (Rosenbaum et al. (2007) and Yang et al (2012) ). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'. Package: r-cran-itraxr Architecture: all Version: 1.13.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-compositions, r-cran-readr, r-cran-tiff, r-cran-janitor, r-cran-ggcorrplot, r-cran-rlang, r-cran-tidyr, r-cran-broom, r-cran-tibble, r-cran-stringr, r-cran-munsellinterpol, r-cran-plyr, r-cran-tidyselect Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-itraxr_1.13.2-1.ca2404.1_all.deb Size: 2381012 MD5sum: 664ff9f9cfbd2610f31df89f3320f75a SHA1: a5678f42faeb095e6b3cea9f4ea1d18417415982 SHA256: 712a505eab3de65d312faf58bb03f541057037c8941cfdf5b7fc6b45277199d7 SHA512: 5e27a8f7759711929f05681e105da10cb4299e817dca550b132f67598e0a40354ef90f641e085271f047698e81b1d3a72498d3717be0086b5162c8702cf4f066 Homepage: https://cran.r-project.org/package=itraxR Description: CRAN Package 'itraxR' (Itrax Data Analysis Tools) Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. Multivariate methods are implemented with appropriate data transformation. Package: r-cran-itrimhoch Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-itrimhoch_1.0.0-1.ca2404.1_all.deb Size: 109026 MD5sum: 9282a77f5e0e696d1676bf4ea3e1ee24 SHA1: 68c7606b28564744d5c55e99d4e5b887e181709f SHA256: e52b99d436bfa91571537895899b0bc55970520228314f42dd363d14e9e91b35 SHA512: 582aa92d9d5311ba3a953b78fa022a6267f6c5668072acbe1b824ca792e732550d0c00e5aa53504405a0ed2e8ff804ffa487570ac260caaf0c042946c7da0184 Homepage: https://cran.r-project.org/package=itrimhoch Description: CRAN Package 'itrimhoch' (Improved Trimmed Weighted Hochberg Procedures and Sample SizeOptimization) The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods. See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report. Package: r-cran-its.analysis Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-car, r-cran-forecast, r-cran-boot, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-its.analysis_1.6.0-1.ca2404.1_all.deb Size: 37002 MD5sum: 1cab4d5ea5e7902754a32e75b58b0d3c SHA1: 57e50f5a9fd0af3b15e81919cb92e5249eee5699 SHA256: f8af98ca1144e3461039201763f360762d6084e13cb8916f461cc32b062699ef SHA512: 90db57d18c953803ac691e84e1c3b4db5f6dd86baa24afbf2076f015854eeaa5ad448385a05f2cebdd74b46b9e2a92f7914713a584e2e8e1b421ed8cb4a8d3e6 Homepage: https://cran.r-project.org/package=its.analysis Description: CRAN Package 'its.analysis' (Running Interrupted Time Series Analysis) Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' for an overview of the method. Package: r-cran-itsadug Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-plotfunctions Suggests: r-cran-knitr, r-cran-xtable, r-cran-sp, r-cran-data.table Filename: pool/dists/noble/main/r-cran-itsadug_2.5-1.ca2404.1_all.deb Size: 3301338 MD5sum: e4d7463c0199a42af10a06203feb2992 SHA1: fcb0032259ce8678d15109db883c719f966310e4 SHA256: 748c19ced03cfb94c3591650e12f73ec66982dd5c0ed67499f1d14499f98e03b SHA512: d39534d77b15ed35eb27cb815ce6b45a0d6bfada9ec70ee9fcb908d150d386ec8f6d152726b9dac007025750d374eaca7c9fd1d6d75a67dd564a410e592f2fdb Homepage: https://cran.r-project.org/package=itsadug Description: CRAN Package 'itsadug' (Interpreting Time Series and Autocorrelated Data Using GAMMs) GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. 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Package: r-cran-itscalledsoccer Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httpcache, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-r6, r-cran-tidyr, r-cran-stringi, r-cran-rlang, r-cran-data.table, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-itscalledsoccer_0.3.2-1.ca2404.1_all.deb Size: 552896 MD5sum: 33b8e691c5e2866f55dd21b3839fb73c SHA1: 7aab0c404a57701cb0cedf385738c8eecf517473 SHA256: 04e719eeb5c7035fcf3f7760b1040dcedb4a3f44ded8483dc6e3d2eba59758af SHA512: 3c473c37a4e345317305e3e6cccbea9c51584e4d9018356c2fda4dd2229791639f53378a682e2ab0fe0604fdcab260bdce6ba6f859e548f100be486e95db102a Homepage: https://cran.r-project.org/package=itscalledsoccer Description: CRAN Package 'itscalledsoccer' (American Soccer Analysis API Client) Provides a wrapper around the same API that powers the American Soccer Analysis app. Package: r-cran-itsdm Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-fastshap, r-cran-ggplot2, r-cran-isotree, r-cran-mgcv, r-cran-ncdf4, r-cran-outliertree, r-cran-patchwork, r-cran-raster, r-cran-rlang, r-cran-rocit, r-cran-sf, r-cran-stars, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-itsdm_0.2.2-1.ca2404.1_all.deb Size: 1459210 MD5sum: 39016bb607153703d69ffcc02b550f44 SHA1: 86a8d4b7194b72bbc230560fd76239d9c2f4290a SHA256: de52e9a46cee91d1a767ccab9c00ce74cb9a662cbf5ae9c1c9b1d33963109ce7 SHA512: da171bc11e3dda8cff1ac93cd01e62c0d0a3be625b6abc9f050ea75f806de6e37128e96dca9396a2cd4137d20ae81849a31b89f8f3ba4afcf5cf14fed9f3b692 Homepage: https://cran.r-project.org/package=itsdm Description: CRAN Package 'itsdm' (Isolation Forest-Based Presence-Only Species DistributionModeling) Collection of R functions to do purely presence-only species distribution modeling with isolation forest (iForest) and its variations such as Extended isolation forest and SCiForest. See the details of these methods in references: Liu, F.T., Ting, K.M. and Zhou, Z.H. (2008) , Hariri, S., Kind, M.C. and Brunner, R.J. (2019) , Liu, F.T., Ting, K.M. and Zhou, Z.H. (2010) , Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016) , Cortes, D. (2021) . Additionally, Shapley values are used to explain model inputs and outputs. See details in references: Shapley, L.S. (1953) , Lundberg, S.M. and Lee, S.I. (2017) , Molnar, C. (2020) , Štrumbelj, E. and Kononenko, I. (2014) . itsdm also provides functions to diagnose variable response, analyze variable importance, draw spatial dependence of variables and examine variable contribution. As utilities, the package includes a few functions to download bioclimatic variables including 'WorldClim' version 2.0 (see Fick, S.E. and Hijmans, R.J. (2017) ) and 'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020) . Package: r-cran-itsmr Architecture: all Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-itsmr_1.11-1.ca2404.1_all.deb Size: 171914 MD5sum: 82faaa991014eb689275f0396a5e2a2e SHA1: 6a6d10dac74407a57b89c6c3424201e6bb07c485 SHA256: 64aae8778cc0f771ac51ccdbe61a130879a3e56284b66a5858d0e0676bccfd9e SHA512: 0b9426394cc9f0122d24caf57d1cf6fa52915edc086d05b83a816648d34bbbc8ef7c987b31744c0e84236446f045802986265bffe3b98222c960c653cfadcdf2 Homepage: https://cran.r-project.org/package=itsmr Description: CRAN Package 'itsmr' (Time Series Analysis Using the Innovations Algorithm) Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. Package: r-cran-iucnr Architecture: all Version: 0.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-stringr Suggests: r-cran-rlang, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iucnr_0.0.0.1-1.ca2404.1_all.deb Size: 21636 MD5sum: 84878d2262dcb9e52cbfdba8e6d4d9e8 SHA1: 59757cf31cbb6b138836b389b9c04f6ab6c5fb60 SHA256: 9d92128e10dbffac3c4266902757987b2195ef2bd396a9685dc73fa4c1b0a3d8 SHA512: 5eb8c27f3fed580fd745102635cf7b14c31e6fd5f38d7f2fcf89f1cdf03aebe9ce7e3ad77990c75b4422cc450b4d459ff36b908cc7c0fad48ae43b52a3987b70 Homepage: https://cran.r-project.org/package=iucnr Description: CRAN Package 'iucnr' (IUCN Red List Data) Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the 'iucnrdata' package, available on GitHub . To install the data package, use pak::pak('PaulESantos/iucnrdata'). 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Covers Kitagawa (2015) , Mourifie and Wan (2017) , and Frandsen, Lefgren, and Leslie (2023) . Includes a one-shot wrapper that runs all applicable tests on a fitted instrumental variable model. Dispatches on 'fixest' and 'ivreg' model objects. Package: r-cran-ivcor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-bwquant, r-cran-quantdr Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivcor_0.1.0-1.ca2404.1_all.deb Size: 59144 MD5sum: e9905f9186fd3a05cac067ed2f5d4489 SHA1: 840c9e71cacddfb9ade3b62766924025b6b64f7b SHA256: f48c987c06684265d46b83937cc4e4789a775301fda76c2549a7058f407e2279 SHA512: a678a1f1450dfcac18173314e63c193ad409ece5dbc6efdbe24cf6cdb90708d29510e280aa60efbcf1ad6a313f954eadae6d7c935b36cce5d52860fe093e75be Homepage: https://cran.r-project.org/package=IVCor Description: CRAN Package 'IVCor' (A Robust Integrated Variance Correlation) A integrated variance correlation is proposed to measure the dependence between a categorical or continuous random variable and a continuous random variable or vector. This package is designed to estimate the new correlation coefficient with parametric and nonparametric approaches. Test of independence for different problems can also be implemented via the new correlation coefficient with this package. Package: r-cran-ivd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-ggplot2, r-cran-patchwork, r-cran-future, r-cran-future.apply, r-cran-rstan, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivd_1.0.0-1.ca2404.1_all.deb Size: 322200 MD5sum: 3842ddcadbc1bc8690e8fdf8cb5717b8 SHA1: 1e64c0f61454f63c6577d7d71200b8f5dbf50683 SHA256: 3a21b69c5ffabea5686d75f22f5e03728425deb32a00418d5c7ec2a9ca1a71e0 SHA512: 39a03e6fd4e46cc4776d4265e59464cf7ac290d3297ebb9a24e77dfb1b2591abedf420c14ca62ab30c8630d29ae96f7eb1c24bd27986bd2e73268d0d824a1c47 Homepage: https://cran.r-project.org/package=ivd Description: CRAN Package 'ivd' (Individual Variance Detection) Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) . 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This package implements the method described in Marbach and Hangartner (2020) and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) . 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(2023) , including bootstrapped confidence intervals, effective F-statistic, Anderson-Rubin test, valid-t ratio test, and local-to-zero tests. Package: r-cran-ivdml Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-ranger, r-cran-xgboost Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivdml_1.0.2-1.ca2404.1_all.deb Size: 103790 MD5sum: d741dd278a7988fef45330da4fd2625a SHA1: 9513aa26dc625a3cc04d7a6ccf1b00de70ab8f97 SHA256: 8d2618fce1c9b3db80ba61d0d6c8d1cdb9135aec62025f9490203e3b532a0600 SHA512: 9e62269cdf74d5d35b019007edcacf7448f975df0052e1b1856432ab2aba8f4d23616e737ca70e187b7b887c6940632009f6155aa08ead495574b2038e2a07f9 Homepage: https://cran.r-project.org/package=IVDML Description: CRAN Package 'IVDML' (Double Machine Learning with Instrumental Variables andHeterogeneity) Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. 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Facilities include method comparison, commutability assessment, Bland-Altman and receiver operating characteristic analysis, qualitative agreement, C5 and C95 estimation, precision and variance-component analysis, linearity, interference, dilution and spiking studies, high-dose hook assessment, measurement uncertainty, reference-material bias, reference intervals, stability studies, quality-control charts, curve fitting, analytical sensitivity, outlier and normality assessment, and sample-size calculations. For methodological details, see Bland and Altman (1986) , Passing and Bablok (1983) , Linnet (1993) , Hawkins and Kraker (2026) , Hanley and McNeil (1982) , Horn et al. (1998) , Westgard et al. (1981) , and Lu et al. (2016) . 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Furthermore, indicator saturation methods can be used to detect outliers and structural breaks in the sample. Package: r-cran-ivgls Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ivgls_0.1.0-1.ca2404.1_all.deb Size: 60408 MD5sum: 41b51940afc6e4986e4dc4fed94cc3b9 SHA1: fa2de578d8e60966c69390d7478685c9a5a5c9bf SHA256: d7678a26c920e5740526544cf11ceab498271ff6c087cbcfe8f105d1f77fe84d SHA512: 8d732b50f7f8fdc1e3dcb2dee7783e1188dc55379cb2eb245e53dd09d621e69843defb24e9008ffae43ae62d28cc051dc73e4014aa45b0c88fc72e2c8e56c606 Homepage: https://cran.r-project.org/package=ivgls Description: CRAN Package 'ivgls' (Network-Aware IV Regression with Graph-Fused Lasso) Implements network-aware instrumental variable regression for causal node discovery in high-dimensional settings with graph-structured exposures. Provides IVGL and IVGL-S estimators combining graph-Laplacian penalization with IV-based identification, including correction for invalid instruments via a sisVIVE-style update. Methods are described in Pal and Ghosh (2026) . The 'glmgraph' package, required for the main estimators, is available at the additional repository . Package: r-cran-ivitr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-randomforest, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ivitr_0.1.0-1.ca2404.1_all.deb Size: 61062 MD5sum: 0bdc21f3ce5b4ac9cb4d8af17b36a396 SHA1: b70ab1ca8dc2e7a8c99b65eafeda5f3710bded44 SHA256: 4d979d2f7719ddf5744f589865a72acc8ff56884ff26a33c338fe452635ce6d4 SHA512: f49d1c950671c212f886294e4eb1230b5ae876bd79730b87a0b1dfc82a78bbd7798b24360abd95e20715327ab6ae7f0b369ca6d504cd00abc5879050ffbced16 Homepage: https://cran.r-project.org/package=ivitr Description: CRAN Package 'ivitr' (Estimate IV-Optimal Individualized Treatment Rules) A method that estimates an IV-optimal individualized treatment rule. An individualized treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. Please refer to for more details on the methodology and some theory underpinning the method. Function IV-PILE() uses functions in the package 'locClass'. Package 'locClass' can be accessed and installed from the 'R-Forge' repository via the following link: . Alternatively, one can install the package by entering the following in R: 'install.packages("locClass", repos="")'. Package: r-cran-ivmodel Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-formula, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivmodel_1.9.1-1.ca2404.1_all.deb Size: 417316 MD5sum: 640c60e5ab17eecb6134852bb8924623 SHA1: 69fc140057b43bd4182288abda60ab73e13379d0 SHA256: 344edef3b14655b55d9f67965795b7ee8483166e82e9ef4f086e2cddce76d61e SHA512: 32e5fa4ad1cd1979b130baeb3ed2b0ddeebe9919aea9150427bbba8c0632386dcb0c3ea836ae5af780ada38273b94198d852be81893523f99d73fc3838851807 Homepage: https://cran.r-project.org/package=ivmodel Description: CRAN Package 'ivmodel' (Statistical Inference and Sensitivity Analysis for InstrumentalVariables Model) Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2020) for details. Package: r-cran-ivmte Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula Suggests: r-cran-slam, r-cran-lpsolveapi, r-cran-rmosek, r-cran-testthat, r-cran-data.table, r-cran-splines2, r-cran-future.apply, r-cran-future, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-aer, r-cran-lsei, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-ivmte_1.4.0-1.ca2404.1_all.deb Size: 1501414 MD5sum: 7b77e4e3adb90218c0de6721427c5a7f SHA1: b86f5698a7e5a96fa728222ede36f7e305ec36c7 SHA256: 52fa448fb33111ac21e409f76717458b3832d37ed2389420eea9e073eb3ba0fd SHA512: 9ad47865abbf0ff63d2f51644638addd706c418702724e8c8d400de647eb384691901969bed6b29855d613cc9498925b629cc2dd82d75e5c1e716245be153634 Homepage: https://cran.r-project.org/package=ivmte Description: CRAN Package 'ivmte' (Instrumental Variables: Extrapolation by Marginal TreatmentEffects) The marginal treatment effect was introduced by Heckman and Vytlacil (2005) to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) . This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) , and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from . CPLEX can be obtained from . CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from . The lp_solve library is freely available from , and is included when installing its API 'lpSolveAPI', which is available from CRAN. Package: r-cran-ivo.table Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-flextable, r-cran-checkmate, r-cran-gt, r-cran-officer, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-palmerpenguins Filename: pool/dists/noble/main/r-cran-ivo.table_0.7.1-1.ca2404.1_all.deb Size: 630624 MD5sum: 13a418665e36bc9a71d2595db89ef498 SHA1: 17e8a750540babc5a2a3e0869c19b0aa81333c2b SHA256: 5b8d2c794e49526ee6878fd58870ae25c3d47e14e47798b3e9965e4b0c4d831c SHA512: 3a9ddcf717815c760851ee8d8adad081afadec74470cd11df21327f41b44f5cfe0a06fa7ff46053ee107d8d075d43b39c9f529b6acda9b8e7e19b96cd0f6ff43 Homepage: https://cran.r-project.org/package=ivo.table Description: CRAN Package 'ivo.table' (Nicely Formatted Contingency Tables and Frequency Tables) Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or 'Office' documents. Designed to work with pipes. Package: r-cran-ivolcano Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37021 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggrepel, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-patchwork, r-cran-rlang Suggests: r-bioc-clusterprofiler, r-bioc-enrichplot, r-cran-fanyi, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-yulab.utils Filename: pool/dists/noble/main/r-cran-ivolcano_0.0.6-1.ca2404.1_all.deb Size: 7826340 MD5sum: 321ebea438773abfd77049aea9ea9088 SHA1: ea11365b74964a8d6512f54a36c3373bf1f11a62 SHA256: 203fe45cbf86bd968b9e0b18717cd20a567efdbcdf3e343e396a67b5927c8eb6 SHA512: df6fb8592c2678bd6fbb03d6dd70d2514407c4ead9a03f719110a1b882df77531a4f82abeb38bf661545703788fad61952f67a0a8f84d8142e7345c2f4670a68 Homepage: https://cran.r-project.org/package=ivolcano Description: CRAN Package 'ivolcano' (Interactive Volcano Plot) Generate interactive volcano plots for exploring gene expression data. Built with 'ggplot2', the plots are rendered interactive using 'ggiraph', enabling users to hover over points to display detailed information or click to trigger custom actions. Package: r-cran-ivpp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bootnet, r-cran-clustergeneration, r-cran-dplyr, r-cran-mvtnorm, r-cran-psychonetrics, r-cran-graphicalvar, r-cran-lifecycle, r-cran-future.apply, r-cran-future, r-cran-networktools, r-cran-qgraph, r-cran-fmsb Filename: pool/dists/noble/main/r-cran-ivpp_1.1.2-1.ca2404.1_all.deb Size: 102814 MD5sum: d817ad0f3d6e795c9884471603b1e7a5 SHA1: 86f57913ee3993df5cd06fc67797ec0a807b0cad SHA256: 078cbd26774ea7cb4bc9d6cf7a8cb226cf04cd16d540e7c4c5b81e23b47db328 SHA512: c8b545f9603ef83375bad797e5c4ed1920acaf53b079f39ae44cc3f7d32875ee8e9b5875eb501ce258906fd25f6c71633f82764a9d1966a440a2575178846131 Homepage: https://cran.r-project.org/package=IVPP Description: CRAN Package 'IVPP' (Invariance Partial Pruning Test) An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning. The package also allows you to compute centrality measures and use radar chart to plot. Analysis of bridge centralities by community pairs is also possible (e.g., the bridge strength from depression to anxiety, and from depression to panic disorder). Package: r-cran-ivreg2r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1781 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-generics, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ivreg, r-cran-knitr, r-cran-modelsummary, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ivreg2r_0.1.0-1.ca2404.1_all.deb Size: 1275804 MD5sum: ba00b0c96989d4d4e076af74cf83aacf SHA1: 3ab5a2266d0c4606365832e38e362aa27b499777 SHA256: d7d6f05791797b5c10bf11da608789a4f65b858209452214e0abe638e78c4b88 SHA512: 29284199d3e996f02b62183a6cf926ab97938f452be50cc9b54cbfaa34852f233b685df323cbab3571afcd5205a2441232ac3b263d42c58ec4254b66219947cc Homepage: https://cran.r-project.org/package=ivreg2r Description: CRAN Package 'ivreg2r' (Extended Instrumental Variables Estimation with Diagnostics) Comprehensive instrumental variables and GMM estimation with automatic diagnostics, inspired by the 'Stata' command 'ivreg2' of Baum, Schaffer, and Stillman (2003) and Baum, Schaffer, and Stillman (2007) . Supports 2SLS, LIML, Fuller, k-class, two-step efficient GMM, and continuously-updated (CUE) estimators. Provides classical, robust, cluster-robust, HAC, and Driscoll-Kraay standard errors. Reports weak identification, underidentification, overidentification, and endogeneity tests at estimation time. All outputs are verified against 'Stata' within tight numerical tolerances. Package: r-cran-ivreg Architecture: all Version: 0.6-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-formula, r-cran-lmtest, r-cran-mass Suggests: r-cran-aer, r-cran-effects, r-cran-knitr, r-cran-insight, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat, r-cran-modelsummary, r-cran-gt, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ivreg_0.6-8-1.ca2404.1_all.deb Size: 908676 MD5sum: b70c4d27028f03c53e252f9f04abcce9 SHA1: cbe535b6f5dc45a48e8acac2c21119528be7e8ee SHA256: 96f903943d0a70d376be94835280ea941f9e7a32239b14b8bc43982588233782 SHA512: 3f639258ec51008959b85871d10f6142828c29e8a6e433cceb5f9b4754606196d34d60e5364131723ebb8a094e2fa2ba9a93737f42ffb97481ee768fa2b278b6 Homepage: https://cran.r-project.org/package=ivreg Description: CRAN Package 'ivreg' (Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM',with Diagnostics) Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). 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Package: r-cran-izmir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rvest, r-cran-stringr, r-cran-jsonlite, r-cran-readr, r-cran-openxlsx, r-cran-magrittr Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-covr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-izmir_0.1.0-1.ca2404.1_all.deb Size: 18018 MD5sum: b1b172f7408884e98ef16787ec633c0a SHA1: 1637170833943be37e004732afde3465f9b7af65 SHA256: 8abaaa257cb4694168ca29fbc290b2e313dad58f19a4cf64ee793904cac96b0b SHA512: 8e533b6395e28cd6f29f819d920c69e98bcf1b74baeb1af10194babfa1e3523b73e2131bba3891d57bbfc7bba313ee2cf36f70050caffe07a11c27e5b9b4d890 Homepage: https://cran.r-project.org/package=izmir Description: CRAN Package 'izmir' (R Wrapper for Izmir Municipality Open Data Portal) Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. 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Package: r-cran-jaatha Architecture: all Version: 3.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-r6 Suggests: r-cran-boot, r-cran-coala, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jaatha_3.2.5-1.ca2404.1_all.deb Size: 289504 MD5sum: f36960491e6cee708c1be0a5ceefdd25 SHA1: d2ca7c329869b2abbd904792d7775156a9d483c7 SHA256: 99990ae9d78714a8183ba4d7716fd9c7a150a9523254e5f93197af904dbd44ab SHA512: 9fcff94fbeac71edbc91262ef48207e82cb07ccf91227b563c886bc748d2a8f10ad64374ab58b54c84cb6a42142e89fca923e274c04f958be790124db6f117da Homepage: https://cran.r-project.org/package=jaatha Description: CRAN Package 'jaatha' (Simulation-Based Maximum Likelihood Parameter Estimation) An estimation method that can use computer simulations to approximate maximum-likelihood estimates even when the likelihood function can not be evaluated directly. It can be applied whenever it is feasible to conduct many simulations, but works best when the data is approximately Poisson distributed. It was originally designed for demographic inference in evolutionary biology (Naduvilezhath et al., 2011 , Mathew et al., 2013 ). It has optional support for conducting coalescent simulation using the 'coala' package. Package: r-cran-jab.adverse.reactions Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tm, r-cran-stringi, r-cran-bracer, r-cran-mgsub, r-cran-qdapregex, r-cran-stringr, r-cran-data.table, r-cran-xmlconvert, r-cran-jsonlite, r-cran-anytime, r-cran-cffr, r-cran-rbibutils, r-cran-install.load Filename: pool/dists/noble/main/r-cran-jab.adverse.reactions_1.0.3-1.ca2404.1_all.deb Size: 35996 MD5sum: 231d1d1c694fa5efe48c7dbca1c21cd6 SHA1: 597d47678ba019b22df32b9a4ebe3f54da39f738 SHA256: 0de9735896fd57f9ffb795204b3b30c37d39a9634bf29c056096532ea58c2864 SHA512: 690afc5b70113b80dc195c500b326da895ed8fbebb6306319d8884be5d1146e008546aee51ea3e41f69eca7ebe672745f1af62b2c6bc6c50741106c1c1e9ac4a Homepage: https://cran.r-project.org/package=jab.adverse.reactions Description: CRAN Package 'jab.adverse.reactions' (Possible Adverse Events/Reactions from theVaccinations/Experimental Gene Therapies) Provides data about the possible adverse events/reactions resulting from being injected with a vaccine/experimental gene therapy. Currently, this data set only includes information from six reference sources. Refer to the CITATION.cff file for the complete citations of the reference sources. For information about vaccination$/immunization$ hazards, visit , , , and . Package: r-cran-jackknifer Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-future.apply Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-jackknifer_2.0.0-1.ca2404.1_all.deb Size: 34526 MD5sum: 5af316afccd4a03eb2f2ff55b88558a8 SHA1: 83a1ac677a7ff9d8377a0ee549a77f2061fadb56 SHA256: 1e6f63ac10e5c8004241564efb1d7e752ffb584346be12522b01579003c0f428 SHA512: 91ff6d18f231a6d9518d0e84bde4886fe20788a19a138cfb8a1a47139c66e456018380107d9e36e0908b40c1e9a65c8b128f54e3ddd47581644aca081aafafd1 Homepage: https://cran.r-project.org/package=jackknifeR Description: CRAN Package 'jackknifeR' (Delete-d Jackknife for Point and Interval Estimation) Implements delete-d jackknife resampling for robust statistical estimation. The package provides both weighted (HC3-adjusted) and unweighted versions of jackknife estimation, with parallel computation support. Suitable for biomedical research and other fields requiring robust variance estimation. Package: r-cran-jackstrap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fbasics, r-cran-benchmarking, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-doparallel, r-cran-reshape, r-cran-tidyr, r-cran-scales, r-cran-plyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jackstrap_0.1.0-1.ca2404.1_all.deb Size: 118696 MD5sum: f691ca73cc2170a3c7d574f2817eacfa SHA1: 90eb215c165a4938a1d8306ef5d2c907418efd66 SHA256: 504ca9c87f88efc18fc93b83ea693accfba7d83cba640c9499c0ff28819c0acd SHA512: 568bec37821d48c89f1e39e5aca033642e1840ae1b77821b0b3aef0a8c4b53679f0d8fb94d7c50c56ae4d995f597634f9771bbb6c389592c3fdc0aee335cf03f Homepage: https://cran.r-project.org/package=jackstrap Description: CRAN Package 'jackstrap' (Correcting Nonparametric Frontier Measurements for Outliers) Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." . Package: r-cran-jackstraw Architecture: all Version: 1.3.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-irlba, r-cran-rsvd, r-cran-clusterr, r-cran-cluster, r-cran-bedmatrix, r-cran-genio Suggests: r-bioc-qvalue, r-bioc-lfa, r-bioc-gcatest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jackstraw_1.3.21-1.ca2404.1_all.deb Size: 433124 MD5sum: 88e3597e4ef0693de32aab0aae55d1f0 SHA1: 554584381f0bb3f84fd68ab16a5f4ded6c1bf002 SHA256: 6f6fc3558bf4d5bd2f4afa2c26b157507202a02a09d01e75b33177a658716cf6 SHA512: 069e3979eadb096c6c276078d2508318ae68dea2dec3d933b76826092e18f92b624d54256abade9b2041c1fda943f39e9913016df08ae33d53a35a24f3a1fb4d Homepage: https://cran.r-project.org/package=jackstraw Description: CRAN Package 'jackstraw' (Statistical Inference for Unsupervised Learning) Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq). Package: r-cran-jacpop Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jacpop_0.6-1.ca2404.1_all.deb Size: 14980 MD5sum: b397706e1462ea105c231fc3458b1329 SHA1: 9de814d95ced9bdabc52450346c9fdb4155234f2 SHA256: 2ca2c307b082bc5fd7f18efcf8c13fb9e2c72bc66f845eceab1660092af37dc5 SHA512: 20bbc1240057f82fb6bd82d702106b52c465706701d62d9b2151557e9134bcd37c0da02f8f6a0222a662852b8cf2098b2c251ebb3477fe33c0b1d2776134e693 Homepage: https://cran.r-project.org/package=jacpop Description: CRAN Package 'jacpop' (Jaccard Index for Population Structure Identification) Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis. Package: r-cran-jacquard Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jacquard_1.0.2-1.ca2404.1_all.deb Size: 923682 MD5sum: 28c2e78995c04e782f163db3ed5fb475 SHA1: 71c0cf19f99e074c217c605770d28e5999e9773c SHA256: 9b1843bbb32f4cee98584045209e18657563d593fd6960d728c7d571294d444e SHA512: b0501830f064f5b99d41bcf45467db5c325f4c6bf500e3fea5e9cfbe72ccff0f9f39188eb6a5af111acc91f2475995a3d86c2eb21e3b782bae800b81fd4390cd Homepage: https://cran.r-project.org/package=Jacquard Description: CRAN Package 'Jacquard' (Estimation of Jacquard's Genetic Identity Coefficients) Contains procedures to estimate the nine condensed Jacquard genetic identity coefficients (Jacquard, 1974) by constrained least squares (Graffelman et al., 2024) and by the method of moments (Csuros, 2014) . These procedures require previous estimation of the allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard coefficients, such as individual inbreeding coefficients and kinship coefficients. Package: r-cran-jadelizardoptions Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-jadelizardoptions_1.0.1-1.ca2404.1_all.deb Size: 24968 MD5sum: e840ac2d80f1ed1338c213731ef957a6 SHA1: 8d8e8eb93683bb5bed283b832f6dafb34028f853 SHA256: 3f329d055752c70b9566288237c3e4244bdc35523fd5ad400f4e264ce05b8edf SHA512: b515ec0380a29ffec1822d36ce6d15c657ac8dd81b71269e0b147b4681baff399edb99bfef5cfd9343be7d8b93381101193368a99415ea6a58fbebfb08909432 Homepage: https://cran.r-project.org/package=jadeLizardOptions Description: CRAN Package 'jadeLizardOptions' (Trading Jade Lizard Option Strategies) Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (“The option strategy desk reference: an essential reference for option traders (First edition.)”, 2019, ISBN: 9781949443912). Package: r-cran-jage Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-data.table, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-jage_0.1.0-1.ca2404.1_all.deb Size: 63970 MD5sum: 5ea4697080c0ebbb24ba4b4b954027ab SHA1: e33163de7638a442547943fa333f14e121e5b664 SHA256: f931fdbcbf3a660eeab3e6bc899778d7a5866f08f6790fa7fe147e3fb1c0a917 SHA512: bfc32ac953f67c59c83c49e86b4cd21d8294c2f21113a0d37b23a3a0479871873c9958152204c39f1f7a3b5a4fc8dbe062edb8ee103460677a34f9d05b4efbbf Homepage: https://cran.r-project.org/package=jage Description: CRAN Package 'jage' (Estimation of Developmental Age) Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) . For details on the conditional correlation correction, see Sgheiza (2022) . Dental scoring is based on Moorrees, Fanning, and Hunt (1963) . Package: r-cran-jaggr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formatr, r-cran-glue Filename: pool/dists/noble/main/r-cran-jaggr_0.1.1-1.ca2404.1_all.deb Size: 71678 MD5sum: 1151a60224daab84f80020b287b15854 SHA1: 3f921d32cac37f01873725cbe1be01956afa1c7f SHA256: 0320ca667dd6204f10161f0fed2028b7a048dfdfad17cd9d8ac57e135295b2a2 SHA512: f3b84baf1f9dc615ff1766ca411136c093bfcf8a827d2aa0d68847218c99952288c4abad7057066a6e8fa83118f68ff475cb2a152e7c7d6e4b74d443b9a9e4cd Homepage: https://cran.r-project.org/package=jaggR Description: CRAN Package 'jaggR' (Supporting Files and Functions for the Book Bayesian Modellingwith 'JAGS') All the data and functions used to produce the book. We do not expect most people to use the package for any other reason than to get simple access to the 'JAGS' model files, the data, and perhaps run some of the simple examples. The authors of the book are David Lucy (now sadly deceased) and James Curran. It is anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with bibliographic details to follow at that point. Until such time, further information can be obtained by emailing James Curran. Package: r-cran-jagshelper Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4022 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jagsui, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jagshelper_0.4.3-1.ca2404.1_all.deb Size: 3870700 MD5sum: 02b667243481d0c151ccb1061ab260a2 SHA1: 07a49c19fdeac767dd981108bef73f0d69542d07 SHA256: 493bd043643c76f51de7a808c2eb338ff0981ae80a79ab35fce3dbb6e62f6334 SHA512: 843358ec0ae4f00e5ad1ca8051a77050d1a70adcebe0f671b9a590224d7eb6e418b32b90d78cbeb88b653ea00cbfe370ebee1952a2bf3ddddf8110803684b3d4 Homepage: https://cran.r-project.org/package=jagshelper Description: CRAN Package 'jagshelper' (Extracting and Visualizing Output from 'jagsUI') Tools are provided to streamline Bayesian analyses in 'JAGS' using the 'jagsUI' package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running 'JAGS' from 'R'. Referenced materials can be found at . Package: r-cran-jagstargets Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-fst, r-cran-posterior, r-cran-purrr, r-cran-qs2, r-cran-r2jags, r-cran-rjags, r-cran-rlang, r-cran-secretbase, r-cran-targets, r-cran-tarchetypes, r-cran-tibble, r-cran-tidyselect, r-cran-withr Suggests: r-cran-dplyr, r-cran-fs, r-cran-knitr, r-cran-qs, r-cran-r.utils, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-jagstargets_1.2.2-1.ca2404.1_all.deb Size: 385616 MD5sum: 1f623e992a530bc37227ba01f280e758 SHA1: 389082e6e88d647d2593eada9a26a1f65109838e SHA256: 3c458fc79119e59bef85ff64870bde1cac2bdc06640cb2de943b7a67ff0b36c6 SHA512: 12d596bf6ead8cb9249291dbae0a60541c4e6b5648480afd5afd80ebdd491cd44357988b8ad562b011f3ce93cd32d4cb9d9d23dd41a4e604d83c28590832b9d6 Homepage: https://cran.r-project.org/package=jagstargets Description: CRAN Package 'jagstargets' (Targets for JAGS Pipelines) Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' and 'JAGS' (Plummer 2003) . Package: r-cran-jagstree Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.tree, r-cran-gtools, r-cran-mcmcplots, r-cran-r2jags, r-cran-tidyverse, r-cran-diagrammer, r-cran-autowmm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jagstree_1.0.1-1.ca2404.1_all.deb Size: 516802 MD5sum: 99499b22ac54ed8f209bd48d1e22838a SHA1: 1b2d29845c6c817f44beb104fb21ce63aa43de9a SHA256: b30850468e97df18dc360e5406505d2ad99911dd52a1ae5829dfa4b0959e1ff2 SHA512: 90119dff091b0fb808af87a7d386acacd859cb57ea09de93980592b67ceff354cdbcfda796297b51a3aaf814a2e2957686b95fcdbb664867ef54ec41ea84b5cc Homepage: https://cran.r-project.org/package=JAGStree Description: CRAN Package 'JAGStree' (Automatically Write 'JAGS' Code for Hierarchical Bayesian Modelson Trees) When relationships between sources of data can be represented by a tree, the generation of appropriate Markov Chain Monte Carlo modeling code to be used with 'JAGS' to run a Bayesian hierarchical model can be automatically generated by this package. Any admissible tree-structured data can be used, under the assumption that node counts are multinomial and branching probabilities are Dirichlet among sibling groups. The methodological basis used to create this package can be found in Flynn (2023) . Package: r-cran-jagsui Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-markdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-jagsui_1.6.3-1.ca2404.1_all.deb Size: 1527108 MD5sum: 0a4e9034cb91c9b683f03958d955bff3 SHA1: 57f7aaf59ab25c6c909fbc1d5713873aa9ecaecd SHA256: 46035358875be5953fee2f49e24158fe3e0bb15940c55d8d0cfb777f3bbca669 SHA512: 99a4ffe5678b9703d2ea03aac71ab9b8330a941257f4ead2aed1c03294658bf8a7bb0fff3e17fee79efafcc9fb380062f9117ed5cd19746fe35222e37cf51df1 Homepage: https://cran.r-project.org/package=jagsUI Description: CRAN Package 'jagsUI' (A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses) A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers. Package: r-cran-jalcal Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jalcal_0.3.0-1.ca2404.1_all.deb Size: 24028 MD5sum: 3fd5d2ef971550ea8748170f1712ea9c SHA1: e77416938cbee04ef8b6438757a639f72dcaa7c9 SHA256: 03847cd809742e25167ca404c8b0eab1ec45bedc732e6871a02055ca9e88a0f0 SHA512: 754b23910e722e256d67b4b4f8586c00cd588427a6b6c3c13b5624a8d2c32c565d0849df80694d5a919ccdbbd14cbf473ee96d15579ec9a0186e58c744f21596 Homepage: https://cran.r-project.org/package=jalcal Description: CRAN Package 'jalcal' (Convert Between Jalaali (Persian or Solar Hijri) and GregorianCalendar Dates) The Jalaali calendar, also known as the Persian or Solar Hijri calendar, is the official calendar of Iran and Afghanistan. It starts on Nowruz, the spring equinox, and follows an astronomical system for determining leap years. Each year consists of 365 or 366 days, divided into 12 months. This package provides functions for converting dates between the Jalaali and Gregorian calendars. The conversion calculations are based on the work of Kazimierz M. Borkowski (1996) (), who used an analytical model of Earth's motion to compute equinoxes from AD 550 to 3800 and determine leap years based on Tehran time. Package: r-cran-jamba Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2208 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-kernsmooth, r-cran-withr Suggests: r-cran-crayon, r-cran-farver, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jamba_1.0.5-1.ca2404.1_all.deb Size: 1838548 MD5sum: 79a3f3b76e599c209db70e5b572e936f SHA1: 3a7da9960300a61f1bf095b870ea2e3308486a89 SHA256: eb14c87e2482f0c8217a99b898dc6f9cd01cefcfd709f885435dbcfa26b3db0e SHA512: f9b650dfee24b9117268c21eef6dc4bcd6255239cd9dad60732484432588b443c6581f491e4134347300df8df4f69b46d620f09cc49e8032780caa3427370fc5 Homepage: https://cran.r-project.org/package=jamba Description: CRAN Package 'jamba' (Just Analysis Methods Base) Just analysis methods ('jam') base functions focused on bioinformatics. Version- and gene-centric alphanumeric sort, unique name and version assignment, colorized console and 'HTML' output, color ramp and palette manipulation, 'Rmarkdown' cache import, styled 'Excel' worksheet import and export, interpolated raster output from smooth scatter and image plots, list to delimited vector, efficient list tools. Package: r-cran-jamendor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-jamendor_0.1.1-1.ca2404.1_all.deb Size: 132344 MD5sum: 3575070519413c1166358b96a38010aa SHA1: c2c7938e2084b9f77051b87a7ad3593528f958a9 SHA256: fd529882d5150cf7090ef3751f83cab287afe929dfacc74c47231202dc008461 SHA512: 2f377eedacb9d51f197280788362c933fd449450765cd4c1dd42f9b7f8b00ca7714e760332455866df24b8684cef0deaf238b82d0488c3718a8076646254d49a Homepage: https://cran.r-project.org/package=JamendoR Description: CRAN Package 'JamendoR' (Access to 'Jamendo' API) Provides an interface to 'Jamendo' API . Pull audio, features and other information for a given 'Jamendo' user (including yourself!) or enter an artist's -, album's -, or track's name and retrieve the available information in seconds. Package: r-cran-janeaustenr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-janeaustenr_1.0.0-1.ca2404.1_all.deb Size: 1620086 MD5sum: 5f5b329a897b8810ca8d99ab5e20937e SHA1: 8b8c3b0011a576193d7f524912cfbc95a5625001 SHA256: 33ff2cafa5780d4edc2636073334378c2eb991bbe014a39c696a72fa81a6e6f5 SHA512: 628eaf1124811098a01fa9e08d357806611d19ffc28e233afd61bbcf1d131c896efffc18186a1e47b960cf090fa681d69f202fdca8298e0ea09b2edf487c3074 Homepage: https://cran.r-project.org/package=janeaustenr Description: CRAN Package 'janeaustenr' (Jane Austen's Complete Novels) Full texts for Jane Austen's 6 completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion". Package: r-cran-janitor Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-snakecase, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-dbplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-janitor_2.2.1-1.ca2404.1_all.deb Size: 270160 MD5sum: 553405bac3cf292f35b09da205ecaf9b SHA1: 2fac862b548d4bd7139d263600aaf95643ee0caa SHA256: 773815cc4ff1115811bd6ebcb2eca4111ac8b00af400e5f00896309a26dffcc2 SHA512: 99c7ee6e90aea57fa1902cb056bd6009349ad8e3893ee562e3dcb627f009cf9c2ebfc655f5e8b262968d8ce1a634f72a72d7c0a8dc629f9abb0823868e7fe792 Homepage: https://cran.r-project.org/package=janitor Description: CRAN Package 'janitor' (Simple Tools for Examining and Cleaning Dirty Data) The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the "tidyverse" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness. Package: r-cran-janus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-dplyr, r-cran-purrr, r-cran-forcats, r-cran-tictoc, r-cran-readr, r-cran-ggplot2, r-cran-narray, r-cran-lubridate, r-cran-rcppalgos, r-cran-rmpfr, r-cran-metrics, r-cran-statrank, r-cran-hash, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-janus_1.0.0-1.ca2404.1_all.deb Size: 55098 MD5sum: c52ecac968b54f681829c2c3a56adbaa SHA1: d5d3d0ccc4dea356711da544ec7779b435a32458 SHA256: 0adb8d8eef825e8950f3bbf4456127371edefab305e11479e443a5275cdac97d SHA512: f0ceff8aa0ebac49e60f31631c38dc6d0a5d6b4e3e5f24d05ae6f5987c187336e4631aaa9c2bfbac239cca02cd059f8ec26b68210b4a694b2fc473978e7a5314 Homepage: https://cran.r-project.org/package=janus Description: CRAN Package 'janus' (Optimized Recommending System Based on 'tensorflow') Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'. Package: r-cran-janusplot Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3472 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2, r-cran-patchwork, r-cran-cli, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-agridat, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-mass, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-janusplot_0.1.1-1.ca2404.1_all.deb Size: 2501232 MD5sum: 846b93456a3664cce07a98323d5362b4 SHA1: cbdf9f75aeee9785575f5851db3e1b7284d8b8e8 SHA256: c815760edef0b42eea6c6d82dae2f8b0411bbab0752fe8e95daabf7f5fff779a SHA512: 01240d617ec22b81756ca02e4df315b765eeafa9da014628162701ca30233829412d0c989ca10a7e245cdf88a5a98904ea091acd310f628a045f67cf12c53c97 Homepage: https://cran.r-project.org/package=janusplot Description: CRAN Package 'janusplot' (Asymmetric Smoothed-Association Matrices via GAM Fits) Render a pairwise, asymmetric smoothed-association matrix of continuous variables. Each cell shows the fitted spline from an 'mgcv' generalised additive model, with the upper triangle displaying 'gam(x_j ~ s(x_i))' and the lower triangle 'gam(x_i ~ s(x_j))'. Unlike Pearson's correlation matrix, the visualisation is intentionally asymmetric, revealing heteroscedasticity, leverage, and directional non-linearity that a single scalar correlation hides. An asymmetry index and a 24-category shape taxonomy quantify the directional difference and qualitative form of each fitted smooth. Package: r-cran-japanapis Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-japanapis_0.1.1-1.ca2404.1_all.deb Size: 664230 MD5sum: b8ffa72603b727dfb119c18238148a7c SHA1: 2ee7e5dfd460b50899530886cc8b1ff98476bc77 SHA256: d37c754d441503c3d48eda4a1e68fa52532017dfc2a6cbbd67a5bb2801b6f9c0 SHA512: 1e6112329170d863347e8e8918575a452ecc4fcf40ac8f5fcd75dc66d08fd88ff48825492b4fe1920a8954e0d6ff5f78856af50a179fd4bfd691020ceb8e658f Homepage: https://cran.r-project.org/package=JapanAPIs Description: CRAN Package 'JapanAPIs' (Access Japanese Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to Japan, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on Japan, covering topics such as natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-japanstat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr, r-cran-pillar, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-keyring, r-cran-testthat Filename: pool/dists/noble/main/r-cran-japanstat_0.1.0-1.ca2404.1_all.deb Size: 64672 MD5sum: 556964c799177795b53d979225d87fb0 SHA1: c8eeb406bf3e90b5737ea7aff48540d78e48811b SHA256: 40b1a44cff5ef855b7c5f40b295ddd7d727c8ea669dee868701e69cc39767aef SHA512: 62c95ab02d6cdcb05b33e5d922c74ea9b91d290bf61a84a9f4098f0219f97e2681144b2f63ecb2e6ecdf98f564e0147fd08ceb6d87f57b2d3ad7a3a6ab640422 Homepage: https://cran.r-project.org/package=japanstat Description: CRAN Package 'japanstat' (Tools for Easy Use of 'e-Stat' API) Provides tools for using the API of 'e-Stat' (), a portal site for Japanese government statistics. Includes functions for automatic query generation, data collection and formatting. Package: r-cran-jarbes Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-ggplot2, r-cran-ggextra, r-cran-mass, r-cran-gridextra, r-cran-bookdown, r-cran-tidyr, r-cran-kableextra, r-cran-ggally, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-jarbes_2.5.0-1.ca2404.1_all.deb Size: 606258 MD5sum: 0e93df1c4c12120c18834dfe8ceec920 SHA1: f22eae5e1f1940af5bb2bdf0142b8bb17dffdce8 SHA256: 178ece3898a9c0b21088dab6bff5618dcef24cf31ddaa29046d1d6aa2c292825 SHA512: 7b028ce1811726bb874260694349fa6552efacb7c387be5da2db853898550f25a242af84b4a8a9c5bcca525aeed84b7d1b9b12df13d43874255ddff10dc65494 Homepage: https://cran.r-project.org/package=jarbes Description: CRAN Package 'jarbes' (Just a Rather Bayesian Evidence Synthesis) Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). Package: r-cran-jatsdecoder Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-opennlp Filename: pool/dists/noble/main/r-cran-jatsdecoder_1.3.1-1.ca2404.1_all.deb Size: 576114 MD5sum: 0b31ce88f966245a3e5569fc5fbef8ea SHA1: f992d2c24dc2d2787ee64c3b0d9d2b76af40bcc3 SHA256: 122988808927463f5f1ef6028c2e6feb42f69c195a757bf63f00e1de29728098 SHA512: 3c946de45f25257a7eae7068e0db47cd21e2d8fec2773aaae3ecbe9ddf20f6d668567576b3890fd3a4a8f563cbfc2d70717b6806035ea370f841258b8c6df1f3 Homepage: https://cran.r-project.org/package=JATSdecoder Description: CRAN Package 'JATSdecoder' (A Metadata and Text Extraction and Manipulation Tool Set) Provides a function collection to extract metadata, sectioned text and study characteristics from scientific articles in 'NISO-JATS' format. Articles in PDF format can be converted to 'NISO-JATS' with the 'Content ExtRactor and MINEr' ('CERMINE', ). For convenience, two functions bundle the extraction heuristics: JATSdecoder() converts 'NISO-JATS'-tagged XML files to a structured list with elements title, author, journal, history, 'DOI', abstract, sectioned text and reference list. study.character() extracts multiple study characteristics like number of included studies, statistical methods used, alpha error, power, statistical results, correction method for multiple testing, software used. The function get.stats() extracts all statistical results from text and recomputes p-values for many standard test statistics. It performs a consistency check of the reported with the recalculated p-values. An estimation of the involved sample size is performed based on textual reports within the abstract and the reported degrees of freedom within statistical results. In addition, the package contains some useful functions to process text (text2sentences(), text2num(), ngram(), strsplit2(), grep2()). See Böschen, I. (2021) Böschen, I. (2021) , Böschen, I. (2023) , and Böschen, I. (2024) . Package: r-cran-javateak Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-javateak_1.0-1.ca2404.1_all.deb Size: 17646 MD5sum: 6bc14747fc7d2c0d72583110d617a7b4 SHA1: 734ecc1e548b969b518c11cb5b2ad5e720762839 SHA256: 971c8344944ceb65056ea7d4c0167e36328e01dc41c42e0b540f0f70d18dd092 SHA512: 2ccfd5888018193323598c29f9d481ba4052ba90d721908edae42d4bca95cb469e165cc6fa0f36ef4b65b7be38304222c4e02ca5a190fe9c1f595d5f6c3bb2ec Homepage: https://cran.r-project.org/package=javateak Description: CRAN Package 'javateak' (Javanese Teak Above Ground Biomass Estimation) Simplifies the process of estimating above ground biomass components for teak trees using a few basic inputs, based on the equations taken from the journal "Allometric equations for estimating above ground biomass and leaf area of planted teak (Tectona grandis) forests under agroforestry management in East Java, Indonesia" (Purwanto & Shiba, 2006) . This function is most reliable when applied to trees from the same region where the equations were developed, specifically East Java, Indonesia. This function help to estimate the stem diameter at the lowest major living branch (DB) using the stem diameter at breast height with R^2 = 0.969. Estimate the branch dry weight (WB) using the stem diameter at breast height and tree height (R^2 = 0.979). Estimate the stem weight (WS) using the stem diameter at breast height and tree height (R^2 = 0.997. Also estimate the leaf dry weight (WL) using the stem diameter at the lowest major living branch (R^2 = 0.996). Package: r-cran-jaya Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-evaluate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jaya_1.0.3-1.ca2404.1_all.deb Size: 62894 MD5sum: 34d003b5c5a77fe2a5af1aad669891fc SHA1: 0c79f6c04ead383521ef3a5cc0e0460dc894056f SHA256: dc72a46f8c39a61a8e94f78c63577e0eaea3b2cd1f3f0b1f28575c731e67f155 SHA512: dca390bbf0121cb0889e794e59998439f336ac02629e4e6b4912a1f1a8831091cdc30f33e54d2e8ff146127f85b7ff75fe48551d98aeca9ea53ad33c185e1f66 Homepage: https://cran.r-project.org/package=Jaya Description: CRAN Package 'Jaya' (Gradient-Free Optimization Algorithm for Single andMulti-Objective Problems) An implementation of the Jaya optimization algorithm for both single-objective and multi-objective problems. Jaya is a population-based, gradient-free optimization algorithm capable of solving constrained and unconstrained optimization problems without hyperparameters. This package includes features such as multi-objective Pareto optimization, adaptive population adjustment, and early stopping. For further details, see R.V. Rao (2016) . Package: r-cran-jbrowser Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-stringr, r-cran-magrittr, r-cran-readr, r-cran-jsonlite, r-cran-httpuv, r-cran-mime, r-cran-cli, r-cran-ids, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jbrowser_0.10.2-1.ca2404.1_all.deb Size: 1024386 MD5sum: 50ddcfb8daa33909330c99c29b79bd6c SHA1: 0d456d3fc70dc63e02d98800ac5887fe5bb03096 SHA256: 303641604f26f9a9e1380a2dab871daaa62494f5f23d6e40745bc91dbeefc6b2 SHA512: be4f942705c20493d4437cb822b9af0ab240cea82896e688219358b32d77d2b7581b64d3bb821890b0cc12999dc29cc03903c49ba697d7a4cc5eb84e71bee2df Homepage: https://cran.r-project.org/package=JBrowseR Description: CRAN Package 'JBrowseR' (An R Interface to the JBrowse 2 Genome Browser) Provides an R interface to the JBrowse 2 genome browser. 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Package: r-cran-jcalendar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jcalendar_0.1.0-1.ca2404.1_all.deb Size: 153190 MD5sum: ed3b871f7ebd74094f071efefbeb81e7 SHA1: b122629fda97c2c15032bcb89b725c3240557308 SHA256: 91172825c6062514b67d7c43bddb046ccccd34d522bdcd255e30ecf1b4cb255a SHA512: 34ec0d86c6e5f8f253d44d098668e3c96b74ef8a06ae76632b7dfa231bcb804edd8390b6c9cf4d312bbb77fcf9f98f334681810db07b5a7e98d11dc4bdc70020 Homepage: https://cran.r-project.org/package=jcalendaR Description: CRAN Package 'jcalendaR' (Interconversion Between the Japanese Calendar System and theWestern Calendar) This is a set of simple utility functions to perform mutual conversion between the current Japanese calendar system that Wareki, the old Japanese calendar system that the Kyureki calendar and the Julian and Gregorian calendar. To calculate each calendar method, it converts to the Julian Day Number. Package: r-cran-jcext Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-rcolorbrewer, r-cran-maps, r-cran-rworldmap, r-cran-ggplot2, r-cran-sp Suggests: r-cran-ncdf4 Filename: pool/dists/noble/main/r-cran-jcext_0.1.1-1.ca2404.1_all.deb Size: 2232456 MD5sum: 326a1e97290fa65c96f36a9e4fcc38ad SHA1: 16da1545cab3672fde4ee86b16c03455aadcfe26 SHA256: a1b53f5536d0dd0cbec72b0bed594449b6a2ae21e4f635fc19823f1819ac674c SHA512: 3c0dde2110eef33a2a50f228af21ac55d4519e47e7e9a4fb704ca98c5c248171b94e57b5fbc966c24c50ee7e40dec02ed5c3c648a0e669c1922a7c7e9fbf77e2 Homepage: https://cran.r-project.org/package=jcext Description: CRAN Package 'jcext' (Extended Classification of Weather Types) Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) for more information. 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Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, . 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The process of stock prices are represented as Geometric Brownian motion [Black (1973) ] or jump diffusion processes [Kou (2002) ]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other. 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Package: r-cran-jeek Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-pcapp, r-cran-igraph Filename: pool/dists/noble/main/r-cran-jeek_1.1.1-1.ca2404.1_all.deb Size: 448080 MD5sum: bf892a8d86a7e1dd40b85639f0ae4730 SHA1: 1e4fd3f379aac9fb11a9f02466e6133ea9c8a2c6 SHA256: 983951b65d6cac112fe8d8760104140a468078d7224a52917871e47c7d20016b SHA512: b9e83272446821eb2ee6c097c1e321c858c5079a5c6cadb52e848a157734296cb8d0a925a8f4a2c2deaf74f250ab5e829b4e9aaffff50850317932db44a28d67 Homepage: https://cran.r-project.org/package=jeek Description: CRAN Package 'jeek' (A Fast and Scalable Joint Estimator for Integrating AdditionalKnowledge in Learning Multiple Related Sparse GaussianGraphical Models) Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) . 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This approach provides an intuitive way to analyze tumor heterogeneity and evolution over time and across anatomical locations. The Jellyfish plot visualization design was first introduced by Lahtinen, Lavikka, et al. (2023, ). This package also supports visualizing ClonEvol results, a tool developed by Dang, et al. (2017, ), for analyzing clonal evolution from multi-sample sequencing data. The 'clonevol' package is not available on CRAN but can be installed from its GitHub repository (). 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Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) . 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Authenticate once, query issues with the Jira Query Language (JQL), and retrieve projects, fields, dashboards and more as tibbles. Built on 'httr2' with automatic pagination, informative errors and support for API tokens and personal access tokens. 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Package: r-cran-jlme Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics, r-cran-juliaconnector, r-cran-juliaformulae, r-cran-mass Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-lme4, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jlme_0.4.1-1.ca2404.1_all.deb Size: 88272 MD5sum: 237d1e88993dcf03e9f018be1e4aba86 SHA1: bc6cabeb844335a8e525c4ce524ec66f66407241 SHA256: e8e7dc54dae613a1f3a703ba36467e3837df8dc247cc4378317279599d35aae6 SHA512: b2fb74e0251b4450b64d1a8c5d97792558224db9dd8fbc339eb27f0c01c5594f0f09a241308eb33cae36e871a1e91968f92a4a1a2027673ab4c3e8f6d2b5babb Homepage: https://cran.r-project.org/package=jlme Description: CRAN Package 'jlme' (Regression Modelling with 'GLM.jl' and 'MixedModels.jl' in'Julia') Bindings to 'Julia' packages 'GLM.jl' and 'MixedModels.jl' , powered by 'JuliaConnectoR'. Fits (generalized) linear (mixed-effects) regression models in 'Julia' using familiar model fitting syntax from R. Offers 'broom'-style data frame summary functionalities for 'Julia' regression models. 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It integrates robust phenotype summarization, computes genotype probabilities, and imputes missing markers for association and linkage mapping. Empirical significance thresholds are estimated via permutation testing coupled with stepwise regression. The framework enables genome-wide scans under both univariate and multivariate trait models, streamlining the discovery of complex genetic architectures. 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Useful for handling complex survival and longitudinal data in clinical research. Package: r-cran-jmbig Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jmbayes2, r-cran-joinerml, r-cran-rstanarm, r-cran-fastjm, r-cran-dplyr, r-cran-nlme, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-jmbig_0.1.3-1.ca2404.1_all.deb Size: 197744 MD5sum: 7ffa905d19f47588007bedd4b15e9618 SHA1: 98083ec86bcceb5c7a7ec6eb8a12290491ee1f3a SHA256: c0971d16660a0b50af548e9794ae38a182e18725ae9201e73e28ba5690b673ef SHA512: 196d5552f3b2f54e7d878eb56d7617b5a0c0c5f9b27db1ef1b27cadecf8f33a133d1671d6566efee2beaf62e8f7987e36a2d5769aa953ff073a012a5ae73e1a2 Homepage: https://cran.r-project.org/package=jmBIG Description: CRAN Package 'jmBIG' (Joint Longitudinal and Survival Model for Big Data) Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences. 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Package: r-cran-joinery Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1767 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-rlang, r-cran-data.table, r-cran-igraph, r-cran-stringi, r-cran-phonics, r-cran-lubridate, r-cran-cli, r-cran-glue, r-cran-tinyplot Suggests: r-cran-duckdb, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-tidyllm, r-cran-tibble, r-cran-stringdist, r-cran-generics, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-yardstick, r-cran-probably, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-joinery_1.0.1-1.ca2404.1_all.deb Size: 1528728 MD5sum: bdf4041a4a27c8ea3e0d2b17212bf386 SHA1: e087a3b3e6e2a61bcedbca8c636b2991ee03d6a7 SHA256: 2b6a1d756302baa8b7bd9c30aba9e0c7e2656c679be81fb461ad166637f5b841 SHA512: bf2ac605fc80949155ac631f836ad49380132a3afcfc3ee51991a65933ac59541fa2f3705f001279053dc0fbd49afca69b61aa2a896d690397b0f930732a0f0e Homepage: https://cran.r-project.org/package=joinery Description: CRAN Package 'joinery' (Heuristic Index-Based Record Linkage) Links records that refer to the same entity across sources that share no common key, such as people, firms, or addresses with spelling variation, abbreviations, or reordered words. Linkage is described declaratively as a strategy that normalises, tokenises, phonetically encodes, weights, and blocks each field; candidate pairs are then scored by the rarity-weighted overlap of their tokens and every score is attributed back to individual tokens for explainability. Strategies compose into staged pipelines of exact, fuzzy, and optional embedding-based matching that carry unmatched records forward and resolve entities as connected components. The same strategy runs on an in-memory 'data.table' backend or an out-of-core 'DuckDB' backend, and diagnostic and calibration tools help tune a strategy and filter false positives. The token-retrieval heuristic follows Doherr (2023) . Package: r-cran-joinet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-palasso, r-cran-cornet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-mice, r-cran-earth, r-cran-spls, r-cran-mrce, r-cran-multivariaterandomforest, r-cran-sier, r-cran-mcen, r-cran-gpm, r-cran-rmtl, r-cran-mtps Filename: pool/dists/noble/main/r-cran-joinet_1.0.0-1.ca2404.1_all.deb Size: 98132 MD5sum: 15ec6c24b8e50ef1747c913822b808f1 SHA1: b528b5e69b4b1c93c5a03b447b70432082024758 SHA256: 769a3ec71ed0153acfdb4c12c7f0fcba59eab0534233c025cca8a9ea39ee785c SHA512: 607002f8169f7fb4146cab321b1853a6592f47c5120f83815bff9f7e356b7eddca21f923002740af4d9a0438beea049cf1e6f31791747872338debbcaf3c5fca Homepage: https://cran.r-project.org/package=joinet Description: CRAN Package 'joinet' (Penalised Multivariate Regression ('Multi-Target Learning')) Implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. For optional comparisons, install 'remMap' from GitHub (). Package: r-cran-joinless Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-joinless_0.0.1-1.ca2404.1_all.deb Size: 33228 MD5sum: 7f992791d175bcdca17cd4de10b94aa4 SHA1: 94bea94dc736e8aec5a219a530159765394867e3 SHA256: b5b27a6aeadd7765f4009a3ad35664d0e32f305123ae886a7b91d391ab5b5cc2 SHA512: cd24cb21adfcaca544121e099f9ab6bb3fdacb3835d879a0147b7d93bbc3a61959f5903887fccbe779489da304816ad34a0ec1a865db0a2f51843dfc8c7d1050 Homepage: https://cran.r-project.org/package=joinless Description: CRAN Package 'joinless' (Exploratory Analysis of Relationships Between Variables) Provides tools to explore and summarize relationship patterns between variables across one or multiple datasets. The package relies on efficient sampling strategies to estimate pairwise associations and supports quick exploratory data analysis for large or heterogeneous data sources. Package: r-cran-joinpointr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 924 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-flextable, r-cran-ggplot2, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-joinpointr_2.0.0-1.ca2404.1_all.deb Size: 637284 MD5sum: 06165d16b8e529ea92a32d3b492f2db9 SHA1: e04cf3dad34171972acc77c8339124567c6f638f SHA256: ef7bd8ddc92e9bd13efd240ccde4300c642d6009404fae03e81dba14e8f01711 SHA512: 4e903c827b58e92f2d3f161b396dbb6524a5f366c4529eb80355ee595da28e364ccd431897d9e1dc5e790624b24c16fa321e91f4381abe8579ca9d6e0c3ddb51 Homepage: https://cran.r-project.org/package=joinpointR Description: CRAN Package 'joinpointR' (Tidy Tools for Joinpoint Regression Models) Provides tools to fit joinpoint regression models with a log-linear specification by levels of one or two categorical variable(s) using the grid-search method. It includes functions to estimate the Annual Percent Change (APC) and the Average Annual Percent Change (AAPC), along with their 95% confidence intervals, and to generate formatted summary tables and plots of results. Package: r-cran-joinspy Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-dplyr, r-cran-data.table, r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-miniui Filename: pool/dists/noble/main/r-cran-joinspy_0.8.3-1.ca2404.1_all.deb Size: 303382 MD5sum: 031152db3a1f87b80f8d4c205eb7bc72 SHA1: 610c86429d97351e7fe1497c8643e22fabdba5eb SHA256: 1bdefc8a420f8ff6c2129254fe38c039fd89fb83b1294011735f72270890235f SHA512: ea0fb60a12f36d440e44650fc084fd4d2330d6dbbdac4dc039d3eb8b1d5d8fdb4a53624b57a8e781e2fd3e5fa169663950e1435eacb252fb77fedf9a2efae5fd Homepage: https://cran.r-project.org/package=joinspy Description: CRAN Package 'joinspy' (Diagnostic Tools for Data Frame Joins) Provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in 'Wickham' (2014) . Package: r-cran-joint.cox Architecture: all Version: 3.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-joint.cox_3.16-1.ca2404.1_all.deb Size: 2234964 MD5sum: cec11f0ac84b260b2556bc22464567c3 SHA1: d0aa4a380039dbbc3f208e704a0c73d86e3a2028 SHA256: 0e2e5c0e5d193ea537412567876fc7acf4b83c4ba5c978ab474865e5278f9699 SHA512: b3fe72cd523d2a98b2617b3f83664ec2a04913aaf8296a0736571f9b65c0787fe8dbc44e539da14b218aea09f5dfa8b301e062302109217dbf192c8bfa1fe6b0 Homepage: https://cran.r-project.org/package=joint.Cox Description: CRAN Package 'joint.Cox' (Joint Frailty-Copula Models for Tumour Progression and Death inMeta-Analysis) Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) , Emura et al. (2018) , Emura et al. (2020) , Shinohara et al. (2020) , Wu et al. (2020) , and Emura et al. (2021) . See also the book of Emura et al. (2019) . Survival data from ovarian cancer patients are also available. Package: r-cran-jointai Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-mcmcse, r-cran-coda, r-cran-rlang, r-cran-future, r-cran-mathjaxr, r-cran-survival, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-foreign, r-cran-ggplot2, r-cran-corrplot, r-cran-ggpubr, r-cran-svglite, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-jointai_1.1.0-1.ca2404.1_all.deb Size: 2388952 MD5sum: c7b369bcbc59c13c769aa159ef9e2705 SHA1: 60f286bfad51c5dba0086a380de583c6f1c6f8ff SHA256: 431540f051f8ccd7d4607f8453dd37dad2b11c5811d24a719a2c436e8f173b9b SHA512: 75b044fa0d6e106de60fac6fe5ff414680465359dd10c622ea4b904649647b3008210585c444624f0a4977a4fedc74ad9a1c6e9459b8d4f50c5ecf70371ce761 Homepage: https://cran.r-project.org/package=JointAI Description: CRAN Package 'JointAI' (Joint Analysis and Imputation of Incomplete Data) Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) . Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' with the help of the package 'rjags'. Package: r-cran-jointcalib Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-laeken, r-cran-sampling, r-cran-mathjaxr, r-cran-survey, r-cran-mass, r-cran-ebal Filename: pool/dists/noble/main/r-cran-jointcalib_0.1.0-1.ca2404.1_all.deb Size: 46298 MD5sum: 00b43d30e36124c5efd9ccde92b0abce SHA1: 4aa516ed017eba5ab9db2a918debf94514c86787 SHA256: de84f7c214eacc2cd86c88751cc1e2d1128a098dd116394c5b8689aa0a4efb0c SHA512: 346239b243ddc893cb876fc215eb7b7e9dcd9bf5a89a22814582aec271429a3e4fdbd370ed7c06118be34f829cdee21e9cae1c18cd26407efd4dcd9f55d66876 Homepage: https://cran.r-project.org/package=jointCalib Description: CRAN Package 'jointCalib' (A Joint Calibration of Totals and Quantiles) A small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) , the calibration for quantiles is based on Harms and Duchesne (2006) . The package uses standard calibration via the 'survey', 'sampling' or 'laeken' packages. In addition, entropy balancing via the 'ebal' package and empirical likelihood based on codes from Wu (2005) can be used. See the paper by Beręsewicz and Szymkowiak (2023) for details . Package: r-cran-jointcomprisk Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jointcomprisk_0.1.2-1.ca2404.1_all.deb Size: 86280 MD5sum: 60cd2f172bf59f634777911e3014ec53 SHA1: 883d9324e8dba10c1c5708d0cfc0e62251792c18 SHA256: e3276363d9d8334c1ce0ee57b751dfb5bdb0b2c7e027a9602cc58f3b59c1afdb SHA512: 43a129f3de3d6d9b411589845eecf4154633988aeb19129d5caad7b31543dbbe8ae7504b106c1d6e9ea1cff42b91c1493b139a52fcecbf059ef6c330cc95f64f Homepage: https://cran.r-project.org/package=jointCompRisk Description: CRAN Package 'jointCompRisk' (Joint Inference for Competing Risks Data Using MultipleEndpoints) Tools for competing risks trials that allow simultaneous inference on recovery and mortality endpoints. Provides data preparation helpers, standard cumulative incidence estimators (restricted mean time gained/lost), and severity weighted extensions that integrate longitudinal ordinal outcomes to summarise treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645 . Package: r-cran-jointest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flipscores, r-cran-flip Suggests: r-cran-knitr, r-cran-logistf, r-cran-rmarkdown, r-cran-ggplot2, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-jointest_1.0-1.ca2404.1_all.deb Size: 97082 MD5sum: 241a2b617cdcb715c90e60a826f0d67f SHA1: e644ad39c262d048c2880039d15c53fc9b0f36d7 SHA256: e92531c262d4772c0a22708309ebf52367e9947cdc603cf31db8b13561aeb736 SHA512: fca130239eaa97e571c17965a0a0facca22bb3690e0be1a31c65ed5a0f35992d654e9471141204fa9246b35ee9f4528c8ddb178878b8d20366b0796bd14c97d8 Homepage: https://cran.r-project.org/package=jointest Description: CRAN Package 'jointest' (Multivariate Testing Through Joint Resampling-Based Tests) Runs resampling-based tests jointly, e.g., sign-flip score tests from Hemerik et al., (2020) , to allow for multivariate testing, i.e., weak and strong control of the Familywise Error Rate or True Discovery Proportion. Package: r-cran-jointfpm Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstpm2, r-cran-survival, r-cran-data.table, r-cran-rlang, r-cran-lifecycle, r-cran-rmutil, r-cran-cli, r-cran-matrixstats, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-jointfpm_1.3.0-1.ca2404.1_all.deb Size: 66536 MD5sum: c6d1bf28247cd86bc7f96c4205585d59 SHA1: ddda6fc167eb38780616d800dbd090edd892cc25 SHA256: 141967ab344bd9823a2e199da243c1eebdc5bd75ea8572acfd8112a5b2002e79 SHA512: 9e169f469d93037b9277df6f2351d1c888aeb983a818a64e4f07ab708fdaeeff9969703f9503f0a8b2b94c5ed07f70af186a500962dd7fba6bbf9cff9029ca08 Homepage: https://cran.r-project.org/package=JointFPM Description: CRAN Package 'JointFPM' (A Parametric Model for Estimating the Mean Number of Events) Implementation of a parametric joint model for modelling recurrent and competing event processes using generalised survival models as described in Entrop et al., (2025) . The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available. Package: r-cran-jointmeancov Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jointmeancov_0.1.0-1.ca2404.1_all.deb Size: 66426 MD5sum: e6f15609b80df663da8d32f14abaddc8 SHA1: 49eced95accba44f99a3d8bbf01c317783a5743c SHA256: 7eb444d143d849649394eaf93dc521448ccbaaeb72a5d728e5a2eef0bbd99328 SHA512: e599e81c8c193945f114a68d3d71a317c28c689d448eb4e3850a4be781a292b3ff64f3fe00985bab351e88490a1ab92b8a075355c323ac670cc8c984f8d4f4bb Homepage: https://cran.r-project.org/package=jointMeanCov Description: CRAN Package 'jointMeanCov' (Joint Mean and Covariance Estimation for Matrix-Variate Data) Jointly estimates two-group means and covariances for matrix-variate data and calculates test statistics. This package implements the algorithms defined in Hornstein, Fan, Shedden, and Zhou (2018) . Package: r-cran-jointnmix Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jointnmix_1.0-1-1.ca2404.1_all.deb Size: 95416 MD5sum: 7b5232b4117d08e479584019ccea1949 SHA1: d920a922712f6ff00f30d618938ea6ecdb6dfc12 SHA256: 0ae48ec45403b070bfc0a96ff9e967bfb5f8837a8ba575d590cb1e7905fc89e5 SHA512: ac3401a820ab9406d998c33a9d05390dae5a8d4c9340372b0979c6e61f1aaffb4164cfbf1cb15c89e3e830bf0ade377aba4b03d76d51d0bf02205cdd142f8794 Homepage: https://cran.r-project.org/package=jointNmix Description: CRAN Package 'jointNmix' (Joint N-Mixture Models for Site-Associated Species) Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods. Package: r-cran-jointpm Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jointpm_2.3.2-1.ca2404.1_all.deb Size: 47076 MD5sum: e0296fa097d2ebe79c75672efda5cdcb SHA1: 50e294dbab8d890dde6d4296e5cdb2940c23ca82 SHA256: 9e99d1a588c3a75dfe601a9d2be239bf172a6494ddf983019ce4f55ff6b60564 SHA512: 41451b7d21a25b371a3ce17ae614326e38b43b15ace4742a757f8e99d3b07bd64d7cfa83f6204e9c015dfdfce7e2f28bd381a3032d8336842894c2d4d89fdfd0 Homepage: https://cran.r-project.org/package=jointPm Description: CRAN Package 'jointPm' (Risk Estimation Using the Joint Probability Method) Estimate risk caused by two extreme and dependent forcing variables using bivariate extreme value models as described in Zheng, Westra, and Sisson (2013) ; Zheng, Westra and Leonard (2014) ; Zheng, Leonard and Westra (2015) . Package: r-cran-jointvip Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggrepel, r-cran-ggplot2 Suggests: r-cran-causaldata, r-cran-devtools, r-cran-knitr, r-cran-matchit, r-cran-weightit, r-cran-optmatch, r-cran-optweight, r-cran-rmarkdown, r-cran-testthat, r-cran-stringr Filename: pool/dists/noble/main/r-cran-jointvip_1.0.1-1.ca2404.1_all.deb Size: 1905692 MD5sum: 5e3e671e78302ea5ef18f5f5ebfe6646 SHA1: 5d334c5b985ef74e3285e74ae6725a4cfd600872 SHA256: bc6d246d07eafc7d7b0e252efad2f6936ffe09e69f413e44527221b44c0cb5a2 SHA512: 3ec7faec1fd2eb04d504a20316962ee7eebada7dc68c91fda4f6afb0d6c4401fe009634821254cf8e7443b87eb97d104d1dc841cc23594926b76c993c9dde224 Homepage: https://cran.r-project.org/package=jointVIP Description: CRAN Package 'jointVIP' (Prioritize Variables with Joint Variable Importance Plot inObservational Study Design) In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) . See the package paper by Liao and Pimentel (2024) for a beginner friendly user introduction. 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The plotting workflow mirrors the 'usmap' package and includes a transform that moves Okinawa and Ogasawara into visible inset locations. Boundary helpers build local 'GeoPackage' files from Japan's official MLIT N03 administrative area data . Package: r-cran-jpmapdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 23465 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-jpmapdata_0.1.0-1.ca2404.1_all.deb Size: 11821220 MD5sum: 165f629c08dfd1e55b786e87d92d5100 SHA1: 177871e3c2cd17315f093bbfd67fad227b47094e SHA256: 41645b0b957d74509bfe55b9d8f4cf759ba0e4416dea48363f842643a6e8f29d SHA512: e96be1f6352cb98bf103926ffbf729fe366bf83adb9684b1beff9c31ba05852478d9cd29bc2f1090707c3aea2205d244611effceedd24c4fbbafd9688b307ae2 Homepage: https://cran.r-project.org/package=jpmapdata Description: CRAN Package 'jpmapdata' (Boundary Data for Japan Maps) Provides boundary 'GeoPackage' files used by the 'jpmap' package, including Japan prefecture example boundaries and official MLIT N03 administrative area data converted for 'jpmap'. Keeping these data in a separate package lets 'jpmap' update its functionality without repeatedly redistributing large boundary files on CRAN mirrors. Package: r-cran-jpmesh Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-memoise, r-cran-miniui, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-tibble, r-cran-units, r-cran-magrittr, r-cran-vctrs Suggests: r-cran-knitr, r-cran-lintr, r-cran-lwgeom, r-cran-testthat, r-cran-rmarkdown, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-jpmesh_2.1.0-1.ca2404.1_all.deb Size: 1151110 MD5sum: a2049ca0aad10e03972cdf065c2a52a6 SHA1: f945ffbec0da7613f445e370c2f4671378a1023e SHA256: 09c6e673ebbe9a3c4b32bbbe254577c909c37d395f36cb8a6fff828212f155bf SHA512: e860d9cc52c245774bf67853d4db81a4f57922777ea335fc0de0e3714042085195ad63528ad7cba4cfa193a45c3eb5c5318e4b7bf0acbcdfb319d060ba387b57 Homepage: https://cran.r-project.org/package=jpmesh Description: CRAN Package 'jpmesh' (Utilities for Japanese Mesh Code) Helpful functions for using mesh code (80km to 100m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc. Package: r-cran-jpstat Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs, r-cran-navigatr, r-cran-lifecycle, r-cran-stickyr, r-cran-httr2, r-cran-cli Suggests: r-cran-keyring, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jpstat_0.5.0-1.ca2404.1_all.deb Size: 90096 MD5sum: b2b7ef62a8d81bf751e63de0ab9d167b SHA1: 84cb5c7b08373e47c0dee8da0d6aa14685ccc3e1 SHA256: 0d43ed5c0c29374e56f2ce0389393b833fd64e082df024f703afa33c066263e5 SHA512: c234d2976407883a562a408590a3ce8b0296d19292d9a8e95090dfafa5da3abd3011518ba554d05de7e29e1ff7a6ffb8c273a2bb7dff70e58d4e53521b31797a Homepage: https://cran.r-project.org/package=jpstat Description: CRAN Package 'jpstat' (Tools for Easy Use of the 'e-Stat' API) Provides tools to use the 'e-Stat' API (), the portal site for Japanese government statistics. Package: r-cran-jpsurv Architecture: all Version: 3.0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales Filename: pool/dists/noble/main/r-cran-jpsurv_3.0.20-1.ca2404.1_all.deb Size: 1303938 MD5sum: be1cd4eb1f0757fbfbfa5de34eaa0b23 SHA1: 28944ee388ade4f327e39e14e89bb65e99285772 SHA256: a13229d4ecd7452c6b55e9ed5a8d60c4532b16fc6ee73bea97986c17d2626e53 SHA512: 6705dba518bbc49db6c471afc38ac01565846be3c1d50f7351c98cd4daf1f0a3caf2708ab1aec56c74cd83020115b3ffd897cda54d672f004656868be862c363 Homepage: https://cran.r-project.org/package=JPSurv Description: CRAN Package 'JPSurv' (Joinpoint Model for Relative and Cause-Specific Survival) Contains functions for fitting a joinpoint proportional hazards model to relative survival or cause-specific survival data, including estimates of joinpoint years at which survival trends have changed and trend measures in the hazard and cumulative survival scale. See Yu et al.(2009) . Package: r-cran-jqbr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-bslib, r-cran-packer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jqbr_1.0.4-1.ca2404.1_all.deb Size: 70060 MD5sum: a5d940f16cbabd5e9e0ee6685b1eed58 SHA1: ed1f95f8760b2ba89bcef1cef95155b6c50443d5 SHA256: bbd5b1fb9aa9071b07532fa4d69fd52ecd7c0cc4251a1b8b4dd3a7937685f53d SHA512: 76ea55b0ef1c05618f432e48dd91f0e45beaace16cd9ced15719e752c9d7e429663e1475c755ad84e65f3191478a49628410749011312d0dd409d9bdb59e8112 Homepage: https://cran.r-project.org/package=jqbr Description: CRAN Package 'jqbr' ('jQuery QueryBuilder' Input for 'Shiny') A highly configurable 'jQuery' plugin offering a simple interface to create complex queries/filters in 'Shiny'. The outputted rules can easily be parsed into a set of 'R' and/or 'SQL' queries and used to filter data. Custom parsing of the rules is also supported. For more information about 'jQuery QueryBuilder' see . Package: r-cran-jql Architecture: all Version: 3.6.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-pdist, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-jql_3.6.9-1.ca2404.1_all.deb Size: 50574 MD5sum: f0b48d09036d176e870d570650b4585b SHA1: f364a441abe7f2de156bd1eeb4b3b83cceb33879 SHA256: 613a2d410c8d737cb9ccc8854cd89edc6c525bb379b9aa8731909ce0bc173f9e SHA512: 0885e7cb3dfa9558a676fc282f6a77131e25c6fa8407caaf5ff8ee296221c55dbb85dbe39cb523e778af7a520e58ad7bfd0e26dcd1d8c0dea5faaf598b9c2ad8 Homepage: https://cran.r-project.org/package=JQL Description: CRAN Package 'JQL' (Jump Q-Learning for Individualized Interval-Valued Dose Rule) We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap. Package: r-cran-jrc Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httpuv, r-cran-jsonlite, r-cran-stringr, r-cran-stringi, r-cran-mime, r-cran-r6, r-cran-r.utils Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-jrc_0.6.0-1.ca2404.1_all.deb Size: 178092 MD5sum: 6dd1430036c965c670aa50ef873b3e41 SHA1: 05d2d75cf6b72b801979f570fea62aa6fdb9cbd3 SHA256: 1f018c8b39ba59236bd3caa758f446c1c8b7c1223f2a2e0714b88800f6ec7003 SHA512: b25d5e7600fcec48dac22653ca4815e76417c2f17463405d658cc023901f6adc6208b2ceeda5230efbfb1dee30f0ff322ebedf9daa19535101d542d1997ce0b0 Homepage: https://cran.r-project.org/package=jrc Description: CRAN Package 'jrc' (Exchange Commands Between R and 'JavaScript') An 'httpuv' based bridge between R and 'JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session. Package: r-cran-jrich Architecture: all Version: 0.60-35-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/noble/main/r-cran-jrich_0.60-35-1.ca2404.1_all.deb Size: 617324 MD5sum: fb7240006798dc3aaea7579896867e51 SHA1: 02f62b6a48a88b851a2489987f0abb9d028da2e0 SHA256: 2fb84dc9a5299fee45ad7fa6c3663789d350c770384df9656c1b0f77dc4c8b9b SHA512: be4a02627eb4e1e6a4c595d9ca1dd523d5e5ad4d63617608cfe421e7d53dc9f40b452d1243db9c730ce5f3a479202cb98d471db234bd617e6368928394e931a6 Homepage: https://cran.r-project.org/package=jrich Description: CRAN Package 'jrich' (Jack-Knife Support for Evolutionary Distinctiveness Indices Iand W) These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) . 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A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions. 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This includes finding the IRR and NPV of regularly spaced cash flows and annuities. Bond pricing and YTM calculations are included. In addition, Black Scholes option pricing and Greeks are also provided. 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The details of J-score is described in Ahmadinejad and Liu. (2021) . Package: r-cran-jsdne Architecture: all Version: 4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-molar, r-cran-nnet, r-cran-rvcg Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jsdne_4.6-1.ca2404.1_all.deb Size: 1022232 MD5sum: b12145a4b8af6c036f743242c56fe407 SHA1: a24468b02d06158c15bcf6ceb12936f5a1015968 SHA256: 8d3b1038ce5a753d88da1096be6453507c9087a54100c313fef06c1a7d480181 SHA512: 7624eae9926aa8375c01ee54922a096ddbff8a44bf2cdef4e5978444cd0f75f96fe2ef10d49bbf79b94c7a0d14aa8884b10207048c2972a5257f84dcb0493bd7 Homepage: https://cran.r-project.org/package=JSDNE Description: CRAN Package 'JSDNE' (Estimating the Age using Auricular Surface by DNE) The age is estimated by calculating the Dirichlet Normal Energy (DNE) on the whole auricular surface and the apex of the auricular surface. It involves three estimation methods: principal component discriminant analysis (PCQDA), and principal component logistic regression analysis (PCLR) methods, principal component regression analysis with Southeast Asian (A_PCR), and principal component regression analysis with multipopulation (M_PCR). The package is created with the data from the Louis Lopes Collection in Lisbon, the 21st Century Identified Human Remains Collection in Coimbra, and the CAL Milano Cemetery Skeletal Collection in Milan, and the skeletal collection at Khon Kaen University (KKU) Human Skeletal Research Centre (HSRC), housed in the Department of Anatomy in the Faculty of Medicine at KKU in Khon Kaen. Package: r-cran-jsdtools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jsdtools_0.1.0-1.ca2404.1_all.deb Size: 72412 MD5sum: 1774acb2bd7fb8bed0b8de5289a46f03 SHA1: eabfe56885cad2fef56bfc82ac7dff3520858702 SHA256: 5ea67af5dc3bbf085edf1cb6b1b827416b922a38a0725ae52737b824bb00ad10 SHA512: 62d8412cb527ab7aadc8483fca816f51c77c125b7d4378359c4c0d306e5cd5ae71eb62ee01e869aa3a945da0ebe60973769a31b3c93b51db4136429415913a21 Homepage: https://cran.r-project.org/package=jsdtools Description: CRAN Package 'jsdtools' (Jensen-Shannon Divergence Estimation, Confidence Intervals, andDistribution Plots) Estimates Jensen-Shannon divergence (JSD) for quantifying distributional differences between two groups on a given variable. Supports both continuous and discrete variables, with tools for point estimation, bootstrap confidence intervals, and visualization of raw group-specific distributions. 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Package: r-cran-just.install Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-remotes, r-cran-dplyr Suggests: r-cran-knitr, r-cran-box, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-just.install_1.0.2-1.ca2404.1_all.deb Size: 20264 MD5sum: ad2b1dac866874294701c9425964497d SHA1: ed99b8723f6defecd761eb8d8f27f82b7e109699 SHA256: 63b85ea805da4aea6bffbc845e80a626f8517eb45804acceb355b05dd20a9cd8 SHA512: 90630d6491aba660c91fb50f0b53d8659f524ba134c93ca3add3dcf1f7ef6364bfb3a296a6aa74e391c25199d98087390c9132d8f7ca048aa331f9655ceee118 Homepage: https://cran.r-project.org/package=just.install Description: CRAN Package 'just.install' (Very Simple Function to Install Packages without Attaching) Install packages without attaching them. If a package it is already installed, it will be skipped. 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Package: r-cran-k4rumah Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-k4rumah_0.1.0-1.ca2404.1_all.deb Size: 18686 MD5sum: 247c1df59cbb0b6bb1d5696fa115c9ce SHA1: ef9448ff5526d7127140e2e69579fa687f68b51b SHA256: 5b3a9c48bb94aab1c4c9c7a4d2e228b8ab744165def6f3089d238b9b9fcec123 SHA512: 4a018de6846681c090ad24c26e121b74e77ea186ecf0ea1996709bec6ecd1b888dd1954f58ff1869f6906fc92c1d3ffe52aaaf51c4eb667501420fc003deb2de Homepage: https://cran.r-project.org/package=K4Rumah Description: CRAN Package 'K4Rumah' (Home Context Data Files for TIMSS 2023 Grade 4) The official Trends in International Mathematics and Science Study (TIMSS) 2023 website provides Home Context Data Files for Grade 4 in RData format. However, the available data are presented solely as numerical values. 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This package transforms the numerical data into categorical variables, enabling clearer interpretation and reducing ambiguity in statistical analysis. The category labels are provided in Bahasa Indonesia. This initiative contributes to promoting the use of Bahasa Indonesia in programming, in line with its designation as one of the official languages of the United Nations. For more details see . 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Package: r-cran-kaphom Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kaphom_0.3-1.ca2404.1_all.deb Size: 36210 MD5sum: 644d0606b59aab6da3ec362dfc90e21b SHA1: 2d9f04aa123d915c5c567ebf03f2a498ecc7f6d3 SHA256: 03c240908c9b8dbff56e7e8a2a3fa8f56bd8e6b4c303dbb2bd7b8acef4d356e5 SHA512: d503378a676a4dfc5d6994963ca7b8f669700224ac38a0a084e6808e7636fead5a6f4e39e0f3b840f3409eb392f14f9c7918d240945182f663c5f4b8ea27104d Homepage: https://cran.r-project.org/package=kaphom Description: CRAN Package 'kaphom' (Test the Homogeneity of Kappa Statistics) Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. 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The package supports simultaneous modeling of symmetric and asymmetric regressors, flexible treatment of short-run and long-run asymmetries, and automated equation handling. It includes several cointegration testing approaches such as the Pesaran-Shin-Smith F and t bounds tests, and narayan test. Methodological foundations are provided in Pesaran, Shin, and Smith (2001) and Shin, Yu, and Greenwood-Nimmo (2014, ISBN:9780123855079). Package: r-cran-karel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-gganimate, r-cran-gifski Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-karel_0.1.1-1.ca2404.1_all.deb Size: 1420142 MD5sum: 19732305284dba3e65be6df97f0158c7 SHA1: 0b5cbaebf50e1c0278a0c542253832c92dbd62c0 SHA256: e63dee29195530f1219fd34dd07720c550d93d2b5d5b85e6c429fa3a42a4723c SHA512: cbd8d09e63079874137bee2a2cfc6a56f27a414ea07ea5597857a753bff59c2b4fefaba7b2341893bea0f6a8a00c77d29a4e013757767d200798e7a087783ee8 Homepage: https://cran.r-project.org/package=karel Description: CRAN Package 'karel' (Learning programming with Karel the robot) This is the R implementation of Karel the robot, a programming language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is an useful tool to teach introductory concepts about general programming, such as algorithmic decomposition, conditional statements, loops, etc., in an interactive and fun way, by writing programs to make Karel the robot achieve certain tasks in the world she lives in. Originally based on Pascal, Karel was implemented in many languages through these decades, including 'Java', 'C++', 'Ruby' and 'Python'. This is the first package implementing Karel in R. Package: r-cran-karen Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-gaussquad, r-cran-scales, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-mass, r-cran-igraph, r-cran-xtable, r-cran-stringr, r-cran-abind, r-cran-expm Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-karen_1.0-1.ca2404.1_all.deb Size: 667866 MD5sum: 74068eab4ddc4df410459a1dad2f4bed SHA1: f6fc61e45bf35ceb559035926c965a2929304286 SHA256: 5c6c124e6501a61c35cda07b291de2700ed78bfc5a9775f2d2a2fd6f7c9e1b24 SHA512: 7991d10411ee341a58d51f5f88acca6bdf7d7a6dcdb75bab6103c33ff1d7eae30be458240f16a8cb5d143d52ce0c5a9217a572a22482a011b2527002ed791ee7 Homepage: https://cran.r-project.org/package=Karen Description: CRAN Package 'Karen' (Kalman Reaction Networks) This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) . Package: r-cran-karlen Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2889 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-karlen_0.0.2-1.ca2404.1_all.deb Size: 2846576 MD5sum: b41255650918e6fec92d77e43dfa66d6 SHA1: 230da7f3829eabba13bd4e80f5a23c973ed962c3 SHA256: e0cd90b5fcbfbc78f358422d93120519308838cb4be9ab821016d88440dff427 SHA512: 5127afe9d28ffae0d058e4a86de74c165738ebea28d2f3d57db3b7e0b6d3d850491a4d1a30023d1dea585a9742f2ad94b959b33699f6675bbcccce4355b1c5f1 Homepage: https://cran.r-project.org/package=karlen Description: CRAN Package 'karlen' (Real-Time PCR Data Sets by Karlen et al. (2007)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Karlen et al. (2007) . Provides one single tabular tidy data set in long format, encompassing 32 dilution series, for seven PCR targets and four biological samples. The targeted amplicons are within the murine genes: Cav1, Ccn2, Eln, Fn1, Rpl27, Hspg2, and Serpine1, respectively. Dilution series: scheme 1 (Cav1, Eln, Hspg2, Serpine1): 1-fold, 10-fold, 50-fold, and 100-fold; scheme 2 (Ccn2, Rpl27, Fn1): 1-fold, 10-fold, 50-fold, 100-fold and 1000-fold. For each concentration there are five replicates, except for the 1000-fold concentration, where only two replicates were performed. Each amplification curve is 40 cycles long. Original raw data file is Additional file 2 from "Statistical significance of quantitative PCR" by Y. Karlen, A. McNair, S. Perseguers, C. Mazza, and N. Mermod (2007) . Package: r-cran-karsts Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-mvn, r-cran-tcltk2, r-cran-tserieschaos, r-cran-stlplus, r-cran-tseries, r-cran-forecast, r-cran-stinepack, r-cran-missforest, r-cran-nonlineartseries, r-cran-zoo, r-cran-rgl, r-cran-mgcv, r-cran-infotheo, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-karsts_2.4.1-1.ca2404.1_all.deb Size: 3060746 MD5sum: d9c42444942d2f079245d12714b21020 SHA1: 8560a5717efe565c5e251ee31a57c9b97bf83c85 SHA256: b8079480aa83083a48f490a91df4648e2e9e783b2605d42df41de15f2936f483 SHA512: 43ea7fd0ddacdc7ea3315d55e86bea657ea2f57ee3c0fa7ff6170d6dce91cd9cf9c0992ec544b65c37c604bfd7800e01ddb167dea48b327b43a8df305b732cda Homepage: https://cran.r-project.org/package=KarsTS Description: CRAN Package 'KarsTS' (An Interface for Microclimate Time Series Analysis) An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) ) and filling methods (Moffat et al. (2007) ), as well as tools to manipulate easily time series and gap sets. Package: r-cran-karyotapr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-circlize, r-cran-cli, r-bioc-complexheatmap, r-cran-dbscan, r-cran-dplyr, r-cran-fitdistrplus, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-gtools, r-bioc-iranges, r-cran-magrittr, r-cran-purrr, r-bioc-rhdf5, r-cran-rlang, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr, r-cran-umap, r-cran-viridislite Suggests: r-bioc-biostrings, r-cran-knitr, r-cran-rmarkdown, r-bioc-rsamtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-karyotapr_1.0.2-1.ca2404.1_all.deb Size: 1431272 MD5sum: da8fabbec8a1a5a00d711a3004dd4240 SHA1: 61016d39b95745c5f393881a1b3593fcaaca5a70 SHA256: 8c8f3a57100336c3962bd3f23c5da1fc4d0b6ee176e08733530a9627ca5daf30 SHA512: f1a29d0f215ec03262942f742d03e468dc7383c87587c26838105e414fdc4a91b629a03744e91876a44c8ccf0ed8178a31b8ffbf89da08d8e3ead77cb3e2f307 Homepage: https://cran.r-project.org/package=karyotapR Description: CRAN Package 'karyotapR' (DNA Copy Number Analysis for Genome-Wide Tapestri Panels) Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated 'Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs. Package: r-cran-katex Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-v8 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-katex_1.5.0-1.ca2404.1_all.deb Size: 187990 MD5sum: 12ec475e2da28bf2d288cc0fcf7f85f6 SHA1: 96bb2ce52f718a2061aa5998057e07d00e485fa9 SHA256: f3e6b14a1da6cd9846f59e217cebb4596681019be1c1efdd49fd9d9b71202e3f SHA512: cc3065fe7c3bbae0ba0c738998602ce42935e8b208f8bb83dcc7b97e6efe58a3f8a4c8c4273bdefa2b14273ab82ebcb7e2d868cf061a6934ff5bf66ffb17d541 Homepage: https://cran.r-project.org/package=katex Description: CRAN Package 'katex' (Rendering Math to HTML, 'MathML', or R-Documentation Format) Convert latex math expressions to HTML and 'MathML' for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the 'MathJax' library into your web pages. In addition a 'math-to-rd' wrapper is provided to automatically render beautiful math in R documentation files. Package: r-cran-kayadata Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-forcats, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-scales, r-cran-purrr Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-kayadata_1.4.0-1.ca2404.1_all.deb Size: 700360 MD5sum: d60afe65938ace10155aa97f9dc200b1 SHA1: c5274a56cb84829b33dc9d1e1fbd31191bff472f SHA256: 522dad12f0ffdd0dc73fa63707216a274cac9ae0266eff683d38a8cf6bc841f3 SHA512: d4c5349dfa2d33aee6f3ddbdd84ce1fee1068d61ccc307bf41d3e1aa5a55e6b64d667b7a68f49e42b1497035c9db637ae0e8a576a63909920d9b5a6e8c09d36c Homepage: https://cran.r-project.org/package=kayadata Description: CRAN Package 'kayadata' (Kaya Identity Data for Nations and Regions) Provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions) for the world and for individual nations, and utility functions for looking up data, plotting trends of Kaya variables, and plotting the fuel mix for a given country or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, "Environment, Energy, and Economy: Strategies for Sustainability" (United Nations University Press, 1998) and ) expresses a nation's or region's greenhouse gas emissions in terms of its population, per-capita Gross Domestic Product, the energy intensity of its economy, and the carbon-intensity of its energy supply. Package: r-cran-kbmvtskew Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kbmvtskew_1.1.0-1.ca2404.1_all.deb Size: 41200 MD5sum: 9500ca8ee714e2a32db836b6641c3921 SHA1: 0d245a4e69f41d0e0a655afb939d0dbf796aa78b SHA256: 4e024a6086adedcf17de1b984d0f33615f66bb1456ed6dd716ecd4776cfcb3e0 SHA512: 1b962072c03b9d1b0b74b2ef4a9188c5942e8c1e11a0cd69308c574e1cb32f113d26610f580c21e0b01778e7c65d278802ab7a6d13a365542dbcda09c72f6669 Homepage: https://cran.r-project.org/package=KbMvtSkew Description: CRAN Package 'KbMvtSkew' (Khattree-Bahuguna's Univariate and Multivariate Skewness) Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearson’s coefficient of skewness, Bowley’s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) . Package: r-cran-kcmeans Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kcmeans_0.1.0-1.ca2404.1_all.deb Size: 26846 MD5sum: a67b8e3aef09a418cddc318af6cea389 SHA1: eb40121598e885e29f1ee0fc685e0ebf2022e37f SHA256: 84a45cfd734559a802a0fd437356134f9b625429aed8f74da35a2eec339fae84 SHA512: 95843d12f4fd56a1890ad01c6599d6eee4c9c4cc0316409734ba6a3d590ca0cbd74be150653bf484c4268d432f73e0300a37ac18de73de1e2475b2d024cb45cb Homepage: https://cran.r-project.org/package=kcmeans Description: CRAN Package 'kcmeans' (Conditional Expectation Function Estimation withK-Conditional-Means) Implementation of the KCMeans regression estimator studied by Wiemann (2023) for expectation function estimation conditional on categorical variables. 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Package: r-cran-kcop Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-dplyr, r-cran-gtools, r-cran-orthopolynom Filename: pool/dists/noble/main/r-cran-kcop_1.0.0-1.ca2404.1_all.deb Size: 47088 MD5sum: d60e3f1b66f386a37d1de4515b235f8d SHA1: 7781dd197607d4e9dee88830bb4086ff067757b2 SHA256: db4d27e8e1f742b7689aa1fb48f82d3d41b8ad4831d99e867dc7ffba8d293b83 SHA512: 58e96ab4ffbecf7ec2f174936021f307484d50be9c1cf1c9aff9ffc5647ad83a8a980a211c1a40ba4c4c77f24dbe2bd76f6dea51b6cd05fd645fb774e708f1ac Homepage: https://cran.r-project.org/package=Kcop Description: CRAN Package 'Kcop' (Smooth Test for Equality of Copulas and Clustering Multivariate) Implements approaches of non-parametric smooth test to compare simultaneously K(K>1) copulas and non-parametric clustering of multivariate populations with arbitrary sizes. See Yves I. Ngounou Bakam and Denys Pommeret (2022) and Yves I. Ngounou Bakam and Denys Pommeret (2022) . Package: r-cran-kcsknnshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rhandsontable, r-cran-dplyr, r-cran-caret, r-cran-fnn Filename: pool/dists/noble/main/r-cran-kcsknnshiny_0.1.0-1.ca2404.1_all.deb Size: 21912 MD5sum: 508ba020a35632570628b5634d5f0c6f SHA1: 0f0c8eaa090fc02ca63dea6d8cf8ab280d406589 SHA256: 5765778858cae43b190ac89f74bdc55de4932304f53f0fc44c917c0fdf7cf973 SHA512: 7b7d17c197d60be122f0705e67c23fdcd62696b189ff3a090d2757a4ffe7375dacea44a38fc74c131400d6e8d1130c1a66fc0f72e527e7b1b7f71dd7c017bdf9 Homepage: https://cran.r-project.org/package=KCSKNNShiny Description: CRAN Package 'KCSKNNShiny' (K-Nearest Neighbour Classifier) It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given. The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute). This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values. Package: r-cran-kcsnbshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rhandsontable, r-cran-dplyr, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-kcsnbshiny_0.1.0-1.ca2404.1_all.deb Size: 20886 MD5sum: bf60be4170a798fc42dc9e03286dfbb4 SHA1: 8f6660bfadd86141dc7765dcc72cf4c39b0db57f SHA256: 9f0299273c614f8648f8b0b7d90af9b437a50cfada8ab7290a2bcbc32a417e30 SHA512: fddc651e2e5e4f58dfc08fdb2de67af86902b78efd1e108d4284f7b360041c229ee7f755c016d458d2b5cb7c169d81b0960675d703d53e3ac6861dff573289fa Homepage: https://cran.r-project.org/package=KCSNBShiny Description: CRAN Package 'KCSNBShiny' (Naive Bayes Classifier) Predicts any variable in any categorical dataset for given values of predictor variables. If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. The user can upload their own datasets and select what variable they want to predict. A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown. Package: r-cran-kdensity Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-univariateml Suggests: r-cran-extradistr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kdensity_1.2.0-1.ca2404.1_all.deb Size: 317152 MD5sum: 3be595d6eae9e9a447d676565be5753c SHA1: 62e8609876a682d26f004521c684fc705fde1215 SHA256: d7965282cdb75ac7dcab16ae4dc63974e2aeeff4d6f6582006b399c236a6918b SHA512: 2f336424671f82cf381b305dd5fa2f4be4f1fc61abb5026ed8f60cce5627532aff7d4a25c2f29fbf2b4abd8b02759611b59b2572885c5813a63e70db3fd768f0 Homepage: https://cran.r-project.org/package=kdensity Description: CRAN Package 'kdensity' (Kernel Density Estimation with Parametric Starts and AsymmetricKernels) Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) . Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) , the beta kernel of Chen (1999) , and the copula kernel of Jones & Henderson (2007) . User-supplied kernels, parametric starts, and bandwidths are supported. Package: r-cran-kdglm Architecture: all Version: 1.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5664 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-extradistr, r-cran-rfast, r-cran-generics, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/noble/main/r-cran-kdglm_1.2.15-1.ca2404.1_all.deb Size: 1983450 MD5sum: c12012a26bfceb7c4a2e29fc18aeef1f SHA1: 7d65c7801646606a78be19fc0212a4e627efd2d6 SHA256: 82d7c9b4ae8ba632b593a0cd901a6977b48b61514ca0cdd8e147e36b52047752 SHA512: 7045fc72c8889a6abb7c9500821aa67b595821d9a3c2e4f4699572c8feeec99ee6eb36a06c60425b6ccf1e3137247faebcf6ed8d138d78f26a0cc4d75cbd1d03 Homepage: https://cran.r-project.org/package=kDGLM Description: CRAN Package 'kDGLM' (Bayesian Analysis of Dynamic Generalized Linear Models) Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024) and dos Santos Jr. et al. (2024). Package: r-cran-kdist Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kdist_0.2-1.ca2404.1_all.deb Size: 25934 MD5sum: 7f9b3cf3b75beab8fb4f988a28386bfb SHA1: 87ff29491b5768fb4a2a9aac3de50b9f00a664a7 SHA256: 5720bbbd95bf26ebc8faedb5f15ea13b4f3d8d275da36ae47abbba47df01ffd2 SHA512: 33487e2eb2756059159bc39b4bcb2a39ea791071d57405025510bfa6b2ab139ab683f4219b5860056538d5a5e38f79423ceaf9cde161251363cf75c3ba2a5d4f Homepage: https://cran.r-project.org/package=kdist Description: CRAN Package 'kdist' (K-Distribution and Weibull Paper) Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc. Package: r-cran-kdml Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-mass, r-cran-markdown Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kdml_1.1.1-1.ca2404.1_all.deb Size: 156214 MD5sum: 363cb2139e89e6d3015b4547d595d2ae SHA1: 04d7b0bcbcbc2d94244ecd9306dbdd6d074679ed SHA256: 00b4cd05eed684b3af806958ff4f82576e42cab4a6ed22dfc4094aabc01a11bb SHA512: 0dec8d5f23a41bdf691522449b3a19e2d7e6e900b75a79e32003f35e2b841cc6d591c1ccffa60e66addcc1fb0f01bf43f8b95e7d2d2f23d2bbbafa85bb5b8303 Homepage: https://cran.r-project.org/package=kdml Description: CRAN Package 'kdml' (Kernel Distance Metric Learning for Mixed-Type Data) Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) for dkps() methodology, and Ghashti (2024) for dkss() methodology. Package: r-cran-kdps Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-progress, r-cran-tibble Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kdps_1.0.0-1.ca2404.1_all.deb Size: 174992 MD5sum: 44d566b988ad84b03e0d1930e024c250 SHA1: 93f5d387e9ae755a93036e70ac1b85ddbd4c5a40 SHA256: 2c39303bb17413e741c62c0d156a9edfee68f4de6216f5b386dd592e5bbad98a SHA512: 74c4e17c41e9b402ad870e4dd639f5baa8cbd36d985e7fc4d7133927170785fdc3ed6be1fa1260247049ee20869cfc072ed2df5ca587a14b6a699a42068d02e8 Homepage: https://cran.r-project.org/package=kdps Description: CRAN Package 'kdps' (Kinship Decouple and Phenotype Selection (KDPS)) A phenotype-aware algorithm for resolving cryptic relatedness in genetic studies. It removes related individuals based on kinship or identity-by-descent (IBD) scores while prioritizing subjects with phenotypes of interest. This approach helps maximize the retention of informative subjects, particularly for rare or valuable traits, and improves statistical power in genetic and epidemiological studies. KDPS supports both categorical and quantitative phenotypes, composite scoring, and customizable pruning strategies using a fuzziness parameter. Benchmark results show improved phenotype retention and high computational efficiency on large-scale datasets like the UK Biobank. Methods used include Manichaikul et al. (2010) for kinship estimation, Purcell et al. (2007) for IBD estimation, and Bycroft et al. (2018) for UK Biobank data reference. 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Package: r-cran-kendallrandomwalks Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-actuar Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-kendallrandomwalks_0.9.4-1.ca2404.1_all.deb Size: 217742 MD5sum: 0523c053f7c1e9d39a11148896a1622e SHA1: dd95ed4041d171f3924243e9a8559a32f6554bc9 SHA256: 3299b9403ac0be71dc899c906b0def42df27f99eb1c0d3c65d787de279bc5a13 SHA512: fa075d59136bae6beeb7d56f73b3c753d672e6e518ff3503e94e6e53468b7f34354fccf72fc8180ebd6878c720b9b829d14a1ac9e6dc6a3aa2783d0d07071e91 Homepage: https://cran.r-project.org/package=kendallRandomWalks Description: CRAN Package 'kendallRandomWalks' (Simulate and Visualize Kendall Random Walks and RelatedDistributions) Kendall random walks are a continuous-space Markov chains generated by the Kendall generalized convolution. This package provides tools for simulating these random walks and studying distributions related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) . Package: r-cran-keng Architecture: all Version: 2026.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-effectsize, r-cran-tidyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-keng_2026.9.0-1.ca2404.1_all.deb Size: 363628 MD5sum: 3d5c8b04ab8507985219dc53cbf89efe SHA1: 32e5d4edb8a3611981927cddb615fbda848767bd SHA256: 057d95fb87ce56d6caee16d9140c11378abc883edebbbe390cdfbdc770465872 SHA512: 6ce9a9c97ef23717565bf0ad77bbaec2c9adfa092eb0dd3e171c8bfe3e05d35313a81de9cea8fb6e18a14d96e5ffcaf6cb5876b4cd6aea1a7586ca1cef7f508a Homepage: https://cran.r-project.org/package=Keng Description: CRAN Package 'Keng' (Knock Errors Off Nice Guesses) Miscellaneous functions and data used in psychological research and teaching. Keng currently has four built-in datasets, and could (1) scale a vector; (2) divide a vector into three groups, (3) compute the cut-off values of Pearson's r with known sample size; (4) test the significance and compute the post-hoc power for Pearson's r with known sample size; (5) conduct a priori power analysis and plan the sample size for Pearson's r; (6) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or partial Eta-squared); (7) calculate PRE from partial correlation, Cohen's f, or f_squared; (8) conduct a priori power analysis and plan the sample size for one or a set of predictors in regression analysis; (9) conduct post-hoc power analysis for one or a set of predictors in regression analysis with known sample size; (10) randomly pick numbers for Chinese Super Lotto and Double Color Balls; (11) assess course objective achievement in Outcome-Based Education. Package: r-cran-kensyn Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-lme4, r-cran-metafor Filename: pool/dists/noble/main/r-cran-kensyn_0.3-1.ca2404.1_all.deb Size: 71674 MD5sum: ce66d04d32d9c64700bf4f4f8fe3e37f SHA1: 68fb6050448b34fe661c7de2430e6f8b7821b876 SHA256: a64bc3cc4c353db58134a8255bb7f06e0e3aa4aa3832fc888656725ab81391d8 SHA512: 2ec95666ecd39878f34436f278a25f8e28aaa695bf529eafb4670eab9befc3f2002cfaa4cc5fbd5722db80dbf50d1e6647935b09f044c1da9d592dce6ec52cc3 Homepage: https://cran.r-project.org/package=KenSyn Description: CRAN Package 'KenSyn' (Knowledge Synthesis in Agriculture - From Experimental Networkto Meta-Analysis) Demo and dataset accompaying the books : De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun A full description of all the material is in both books. ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" () have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France. Package: r-cran-kepted Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm, r-cran-compquadform, r-cran-cubature Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kepted_0.2.0-1.ca2404.1_all.deb Size: 59046 MD5sum: f2c2f13f21e54dbf25a748504199c328 SHA1: 923ab6ae8d0ce4bf782070141e0a934fe940d2c6 SHA256: e91ddbb9995dec2c33910282106b124f22b3ead3d96ed85af5f177126b274085 SHA512: 7fb9e9c31795a9d3947a1cd6755107434b35833681c80089a196fd8f221f70998d7864f3f787204c104dd044ccf9d9a12e9eb4fcda9bf8a789f3dd3256865842 Homepage: https://cran.r-project.org/package=KEPTED Description: CRAN Package 'KEPTED' (Kernel-Embedding-of-Probability Test for Elliptical Distribution) Provides an implementation of a kernel-embedding of probability test for elliptical distribution. This is an asymptotic test for elliptical distribution under general alternatives, and the location and shape parameters are assumed to be unknown. Some side-products are posted, including the transformation between rectangular and polar coordinates and two product-type kernel functions. See Tang and Li (2024) for details. 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It aims at making the life of AI practitioners, hypertuner algorithm creators and model designers as simple as possible by providing them with a clean and easy to use API for hypertuning. 'Keras Tuner' makes moving from a base model to a hypertuned one quick and easy by only requiring you to change a few lines of code. Package: r-cran-kerdaa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-kerdaa_0.1.1-1.ca2404.1_all.deb Size: 20800 MD5sum: 3a131a5e34f23848d1a08077716d1204 SHA1: 0bbdc93a97d1c2383dc2e826ce53a0a988d83a1e SHA256: 65086ccf17acd062cceac011738b2cf0349a5b82f2f9c47a2b93db53c7306e7e SHA512: ad096a663842ae32007bb0b6a04013a43620055740895c31172a4fe4a8d6ab70420bbaf228953bae4295b2c6be812c661772b403e914e4968b6225d8d4d56362 Homepage: https://cran.r-project.org/package=kerDAA Description: CRAN Package 'kerDAA' (New Kernel-Based Test for Differential Association Analysis) A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions. Song, H. and Wu, M.C. (2023) . Package: r-cran-kerdiest Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-chron, r-cran-date, r-cran-evir Filename: pool/dists/noble/main/r-cran-kerdiest_1.3-1-1.ca2404.1_all.deb Size: 99122 MD5sum: c65a1e6018df5a84b0fd90a5d0d0272c SHA1: 24e7dacc9d9853305d5053e842bfdb6ad5c94234 SHA256: c7e0f72acb7357491069d1a45126752bf76d9864209c60ee4ae8338482e4e338 SHA512: 26b919f7914b36258f21fb5d58b8f6190dcebed2c7142ad781bf5918401ab4eb76e775967d2f2f05cb7fa92be66ca47a46b0b9f25993a5e54c8362b03e166e6a Homepage: https://cran.r-project.org/package=kerdiest Description: CRAN Package 'kerdiest' (Nonparametric Kernel Estimation of Distribution Function) Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Río and Estévez-Pérez (2012) . Package: r-cran-kernelfactory Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-auc, r-cran-genalg, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-kernelfactory_0.3.0-1.ca2404.1_all.deb Size: 54854 MD5sum: 31954f8791d6c506f16bad916723fab6 SHA1: d54b2c5780a1b7aca845c5c9434852525c1499a8 SHA256: 1e8223eedbe7d9923bc42dffbcfb6331984bbf49bd1f947ff6e3488918d3ed74 SHA512: 6b49c049874b50c418bac7236106bf4c2f4a39997ce5edabd17c76afd31af5674ac3c7b9da3687f4fef8f0c14ae3e4f15c8ae376bec10c64f29e3e92edccfc08 Homepage: https://cran.r-project.org/package=kernelFactory Description: CRAN Package 'kernelFactory' (Kernel Factory: An Ensemble of Kernel Machines) Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data. Package: r-cran-kernelheaping Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ks, r-cran-sparr, r-cran-sp, r-cran-plyr, r-cran-dplyr, r-cran-fastmatch, r-cran-fitdistrplus, r-cran-gb2, r-cran-magrittr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-kernelheaping_2.3.0-1.ca2404.1_all.deb Size: 170964 MD5sum: eac4b70f21be5882a076d3f6d37e1f2f SHA1: 7531223d343fed9506c8a73ef21fb16327dd898f SHA256: d32a44e7e44b2775922a69a103aa691e047cbc9f52f3ebdc5e5d9a7a5c5e9e35 SHA512: 4f6c10a27055006d38bbed387a5e34af2a7d7b2ba8f9c76af8ae9f9b965c379fb0d9530eb5e1c1235fdea56ef9343e47272c09a45b199dfee11e0bf667260711 Homepage: https://cran.r-project.org/package=Kernelheaping Description: CRAN Package 'Kernelheaping' (Kernel Density Estimation for Heaped and Rounded Data) In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly different degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (). Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (), as well as data aggregated on areas is supported. Package: r-cran-kernelphil Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-benchmarkme, r-cran-directlabels, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hmisc, r-cran-pbapply, r-cran-reshape2, r-cran-rlang, r-cran-terra, r-cran-wordspace Filename: pool/dists/noble/main/r-cran-kernelphil_0.2-1.ca2404.1_all.deb Size: 125114 MD5sum: 5a6eed0fbe32018b3e9e88c1142fbf76 SHA1: ce051c2888f754b6d810f5ca24902cf1f9c1e08e SHA256: ff9ff5eb415580de054998c74ee41417a4a0a037ecdd6518976069adca7b1dc8 SHA512: 342260ae80ca089e9a1cad1a34f83f841d0c2e8da51f42f549f1e7a0c22dd10e60e7ce0c1f5ac699072771679f522235a34054e1943866b09befd227e20aef59 Homepage: https://cran.r-project.org/package=kernelPhil Description: CRAN Package 'kernelPhil' (Kernel Smoothing Tools for Philology and Historical Dialectology) Contains kernel smoothing tools designed for use by historical dialectologists and philologists for exploring spatial and temporal patterns in noisy historical language data, such as that obtained from historical texts. The main way in which these might differ from other implementations of kernel smoothing is that they assume that the function (linguistic variable) being explored has the form of the relative frequency of a series of discrete possibilities (linguistic variants). This package also offers a way of exploring distributions in 2-dimensional space and in time with separate kernels, and tools for identifying appropriate bandwidths for these. Package: r-cran-kernelshap Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kernelshap_0.9.1-1.ca2404.1_all.deb Size: 255298 MD5sum: 01a739a9c27cf118f01e63c1fd3aeb84 SHA1: ec2d6f6d87d2652d003b066fcca325c7b681d4d0 SHA256: 4119ca437eeb223a9ee2af3be2eea08c8389a1d2b952bd2b42c88d608ff56114 SHA512: 7415f82f9e501920ae34d5247f7d1381b4598e41a78a9dddd393f7466d380f3cb7067780008b594979f4d39f8dd6d0718d683e9b79b63eee774e817c0e91f11e Homepage: https://cran.r-project.org/package=kernelshap Description: CRAN Package 'kernelshap' (Kernel SHAP) Efficient implementation of Kernel SHAP (Lundberg and Lee, 2017, ) permutation SHAP, and additive SHAP for model interpretability. For Kernel SHAP and permutation SHAP, if the number of features is too large for exact calculations, the algorithms iterate until the SHAP values are sufficiently precise in terms of their standard errors. The package integrates smoothly with meta-learning packages such as 'tidymodels', 'caret' or 'mlr3'. It supports multi-output models, case weights, and parallel computations. Visualizations can be done using the R package 'shapviz'. Package: r-cran-kernhaz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-foreach, r-cran-doparallel, r-cran-ga Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-kernhaz_0.1.0-1.ca2404.1_all.deb Size: 101636 MD5sum: 45c38c7af5e061a357b623b3ca6b9804 SHA1: 3026d11d864ce8b62e56b811a633b1dca487593f SHA256: 18758b3887c94b0025239189be34835ef408fcdcc4c9ed62466b6bd83e5cf4c2 SHA512: 825019167dc52fa203d6b597bc1abd472ca0e54d1abcde150fceeefade17d930d9bf5ce08693d55a8a955b6f77ce2bc0d95b35f60da2769be7544859fa7eac73 Homepage: https://cran.r-project.org/package=kernhaz Description: CRAN Package 'kernhaz' (Kernel Estimation of Hazard Function in Survival Analysis) Producing kernel estimates of the unconditional and conditional hazard function for right-censored data including methods of bandwidth selection. Package: r-cran-kernopt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kernopt_1.0.0-1.ca2404.1_all.deb Size: 165594 MD5sum: 3987ad3298bdd99bc51c789cddc11585 SHA1: 105c7c13a6a1f5a2a5403da67ed70d1cdb19046f SHA256: 33d4c7f93772fac9e4d5bf901f8adddd39f1b257c72c503033e416fa1fef1185 SHA512: 60be9adba398620379425277c56f62e01b02c599a7bf109de82448c0dcb4a33f6e436f0ad865bc344d4bf88701466ac3db4f40f670c00c88e94c844373c64b97 Homepage: https://cran.r-project.org/package=kernopt Description: CRAN Package 'kernopt' (Estimating Count Data Distributions with Discrete OptimalSymmetric Kernel) Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) .The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels. Package: r-cran-kernplus Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-kernsmooth, r-cran-mixtools Filename: pool/dists/noble/main/r-cran-kernplus_0.1.2-1.ca2404.1_all.deb Size: 67652 MD5sum: 033f51e85c0d30ea7501554a8e7ddd04 SHA1: 95bed8228c3c9833893369ab5ba7ac5943d0dea3 SHA256: 40a425da82e4431ac10a68f04ca3563c733dd9b1da7ab195fb2e3a500c9dd573 SHA512: 7974da27690a3954193f456de8379c7129dc6b6bf5a8b4410f23d10171a4541873fa36706664c39a8e4a0b72e7d8c42f01092a8b2707a9884358acd653688189 Homepage: https://cran.r-project.org/package=kernplus Description: CRAN Package 'kernplus' (A Kernel Regression-Based Multidimensional Wind Turbine PowerCurve) Provides wind energy practitioners with an effective machine learning-based tool that estimates a multivariate power curve and predicts the wind power output for a specific environmental condition. Package: r-cran-kernscr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-readxl, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-kernscr_1.0.7-1.ca2404.1_all.deb Size: 102002 MD5sum: 73f33562291092d0d31c5ac26a5f45ba SHA1: 786a40a8d584c6702dd7b499d9416302c0c6d578 SHA256: 1a1ac9ac628c947222fedc861fbf4bf9f10fb462007d226462c95feff92f07d8 SHA512: 8b95c2121eb2c368542bff01e06ab965380ccfc547214e89a20e0fbdef9b726fa6968e8d312cb62296ee493727b49df1916218ec30fc832eb713d0667d24e753 Homepage: https://cran.r-project.org/package=kernscr Description: CRAN Package 'kernscr' (Kernel Machine Score Test for Semi-Competing Risks) Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) . Package: r-cran-kernstadapt Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-misc3d, r-cran-sparr, r-cran-spatstat.explore, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.utils, r-cran-spatstat.linnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-kernstadapt_0.4.0-1.ca2404.1_all.deb Size: 1570040 MD5sum: dfd52723e181660b47788956793d958b SHA1: 635d4b549ac5b75a556623c3e05e6ba5417a7955 SHA256: dffc872530147b2cac166250a6a6f34843d5ac0a9ee2bfddfdee1514aa0ba4f8 SHA512: 736b6d48859f80540feafe5f446e3bacb9f20bf4191bace3230aedcf8221422eb58a499821a5191d620c2ccc9cf63aa060e1977b16747722be2bcacd6cb5c7e5 Homepage: https://cran.r-project.org/package=kernstadapt Description: CRAN Package 'kernstadapt' (Adaptive Kernel Estimators for Point Process Intensities onLinear Networks) Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). . Package: r-cran-kerntools Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1823 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-kernlab, r-cran-reshape2, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kerntools_1.2.1-1.ca2404.1_all.deb Size: 1213922 MD5sum: 2fb238ad1329aee7060faa20bae0cdd3 SHA1: cb75b588d24658a598a29e024cf7797beceeed29 SHA256: 5a0ea58e15eaf437861020b21ece24b9c6f5593bec9c7f7d1474271539a7544a SHA512: 37c9ef177c18fb56e1504c50a665fb8baae6913da435c46a90040f2577acd5718e82497685483d5e4ff8db77df31f684b45ebe6d4dabda7f9433aa3b04a7248c Homepage: https://cran.r-project.org/package=kerntools Description: CRAN Package 'kerntools' (Kernel Functions and Tools for Machine Learning Applications) Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features' importance for Support Vector Machines (SVMs), which expand other 'R' packages like 'kernlab'. Package: r-cran-kertests Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kertests_0.1.4-1.ca2404.1_all.deb Size: 24768 MD5sum: f7f515adbde6f0c2236b65c2fdc636f8 SHA1: 730ebb0ef06663d64220c2c1c138966f775b26ea SHA256: 7055eb91c4d1789d237d4a954fbd942e955d29242174682aaf09e8f8908dd2e4 SHA512: c222ecc851b004f3a5fec3ec569ba2786dec3c7bb5360904a7402448ce14524cab3c86831fb1ca89bb980630a8c75c56503b7e98c38a843580c25aacab530a76 Homepage: https://cran.r-project.org/package=kerTests Description: CRAN Package 'kerTests' (Generalized Kernel Two-Sample Tests) New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2023) . Package: r-cran-kesernetwork Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-htmltools, r-cran-config, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-plotly, r-cran-reactable, r-cran-rintrojs, r-cran-rlang, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyhelper, r-cran-shinywidgets, r-cran-stringr, r-cran-visnetwork, r-cran-yaml Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kesernetwork_0.1.0-1.ca2404.1_all.deb Size: 699800 MD5sum: 29995069e3a6c9546df51097c40aa89a SHA1: 3f1c581982ab571c91271d31c81f975cabf20511 SHA256: 00a17525d00d595948d8802d1a4b74eb99add161fe26a859ad04f72a3ab55990 SHA512: 95d6072f17a8c915fa5acdb3737b03967f19f427cb66a2480e0ad8595e1e74aa3d614cf9513171cb95c7e791a5e7bbd3a40a2f1416ab413c6247bda94bb960ff Homepage: https://cran.r-project.org/package=kesernetwork Description: CRAN Package 'kesernetwork' (Visualization of the KESER Network) A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) . Package: r-cran-keyboard Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-iso, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-keyboard_0.1.3-1.ca2404.1_all.deb Size: 269936 MD5sum: 47b916e01a53c2a4f953906680d9e67e SHA1: aafa3cc47af99d78474d6de59bf215d9e51a7b36 SHA256: 3dfccfcbe7ac444a35a63dcc242b8f6d4aef561feb4089d67696a2174a9c4cf4 SHA512: 744af301f17251c05dcd3b7366dca1fcf48a7a7906dd708083e5ae2b0ec9f794f99076efc15541e0f5787c294339c70ba461478b0ab6668fc4b01c5121f957c4 Homepage: https://cran.r-project.org/package=Keyboard Description: CRAN Package 'Keyboard' (Bayesian Designs for Early Phase Clinical Trials) We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) , Li DH, Whitmore JB, Guo W, Ji Y. (2017) , Liu S, Johnson VE (2016) , Zhou Y, Lee JJ, Yuan Y (2019) , Pan H, Lin R, Yuan Y (2020) ). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication. Package: r-cran-keyed Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-digest, r-cran-lifecycle, r-cran-pillar, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-joinspy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid Filename: pool/dists/noble/main/r-cran-keyed_0.2.0-1.ca2404.1_all.deb Size: 164624 MD5sum: ae4d49d6813864a093ab79e2e16839f5 SHA1: 1e081758a6ce89061978503d2a2521cc5538de44 SHA256: 031925e2f564a6a9a78457198040769a5ed6566c737b1cece56f7d0b99766315 SHA512: 3c16efb720db9ffe3ecc59b8a12864d33b543f0ddf638d20c4e720d623059183b41479527536bfbfa1311df0b527610fe19c4350728303614e34ca3656f823fc Homepage: https://cran.r-project.org/package=keyed Description: CRAN Package 'keyed' (Explicit Key Assumptions for Flat-File Data) Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and automatic drift detection via watched data frames that snapshot before each transformation and report cell-level changes. 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This is done by creating special attribute "keys" which is updated after every change in rows (subsetting, ordering, etc.). This package is designed to work tightly with 'dplyr' package. 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Package: r-cran-keytoenglish Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openssl, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-keytoenglish_0.2.1-1.ca2404.1_all.deb Size: 230782 MD5sum: acbe4693cfb4689bbc2487ce3e2a2852 SHA1: f13fd65d5785b9389bffad722ff4f2ad9d5c3beb SHA256: a8df5a85698fcc16f9b620206c953cacb0e09e82e529d48f5dd981b5a8c1a3e1 SHA512: 91cbc154ac0e450a1d10525678dcf102f827700e2fd9ef9b8db9762434a4c325be28cd8fba778c57babd65e10a0c514af38273d43f7a2b0089763c686d62b9b6 Homepage: https://cran.r-project.org/package=keyToEnglish Description: CRAN Package 'keyToEnglish' (Convert Data to Memorable Phrases) Convert keys and other values to memorable phrases. Includes some methods to build lists of words. Package: r-cran-kfa Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-doparallel, r-cran-flextable, r-cran-foreach, r-cran-gparotation, r-cran-knitr, r-cran-lavaan, r-cran-officer, r-cran-rmarkdown, r-cran-semtools, r-cran-simstandard Suggests: r-cran-semplot Filename: pool/dists/noble/main/r-cran-kfa_0.2.2-1.ca2404.1_all.deb Size: 3379518 MD5sum: e3cbc49055782badacb3950457218729 SHA1: ae84a7c457deaa719c301e1f682b680b5a0dfd5f SHA256: 97e49f576a96585a65b0d403f1355a1fa838c5cb3a1bb852693f8f958a9a3cc4 SHA512: c9ee93d60bcf101dc4c501e705e4b05c30d24fd920c66e1306ef19422c14efe53f5b775c969496b6ce230bd0fd07ec0f3f339b7207bab208232f2267c2d16a79 Homepage: https://cran.r-project.org/package=kfa Description: CRAN Package 'kfa' (K-Fold Cross Validation for Factor Analysis) Provides functions to identify plausible and replicable factor structures for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development (Flora & Flake, 2017) with a cross validation technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) . Also available are functions to determine k by drawing on power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) , generate model syntax, and summarize results in a report. Package: r-cran-kfda Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kernlab, r-cran-mass Filename: pool/dists/noble/main/r-cran-kfda_1.0.1-1.ca2404.1_all.deb Size: 20218 MD5sum: 65fcefcb0bc82ce2d0c5b6cfac1e8372 SHA1: 49cb3b097c38ac1e9b852f9e99e389d4c20820b7 SHA256: e5d245a2ae44cd8b914a2799569fc5df5489d8f98029ab75c0695174330412c5 SHA512: 0f831f6a4b00c158137b799a62f5e5ef031d20dbe1b08c91e319cfc041aa1203c1eeccd7f2c87ee566141e5d5e5aaa83a0fed8d6b34859075e84b1de41d88877 Homepage: https://cran.r-project.org/package=kfda Description: CRAN Package 'kfda' (Kernel Fisher Discriminant Analysis) Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA). There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions. In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly. This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) . Package: r-cran-kfigr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr Suggests: r-cran-ggplot2, r-cran-markdown Filename: pool/dists/noble/main/r-cran-kfigr_1.2.1-1.ca2404.1_all.deb Size: 44632 MD5sum: 2ca82338429f0852c506ebc090aea5db SHA1: 093177460d45dc2758e9d1c31d01c4e90a09b3f0 SHA256: ee45cb5e7975544916bcbbe7798fc92e2fce9d7f02f107bdf87b5963e46e15d1 SHA512: 1cb94eb83d531be6a314cf45d00b85657dbbef05e16f84c7776753346b26def71fa1b4e37078137ca40760787e136a83c4d7c0867a49efb870e1be5d1511a7ca Homepage: https://cran.r-project.org/package=kfigr Description: CRAN Package 'kfigr' (Integrated Code Chunk Anchoring and Referencing for R MarkdownDocuments) A streamlined cross-referencing system for R Markdown documents generated with 'knitr'. R Markdown is an authoring format for generating dynamic content from R. 'kfigr' provides a hook for anchoring code chunks and a function to cross-reference document elements generated from said chunks, e.g. figures and tables. Package: r-cran-kfino Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-kfino_1.0.0-1.ca2404.1_all.deb Size: 875654 MD5sum: 39395163fac3ce4672a542b7d026f342 SHA1: 67e94d8d706bd1d44728c13f21ffe106c0790561 SHA256: cbec4ef07efa291df6975b2e44b22f6d925f5554b78c069d62ab9dc473f3ae4e SHA512: 26ba65687f2c2e99bdd8ac71260817f8fd191092b8d2951f3d281fb09fc7b49f4ffe7db3052cc5d430c4d92d00dc2250298ac97e44ca76b586c418738e3a1ba0 Homepage: https://cran.r-project.org/package=kfino Description: CRAN Package 'kfino' (Kalman Filter for Impulse Noised Outliers) A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: . Package: r-cran-kfpca Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kader, r-cran-pracma, r-cran-fdapace, r-cran-fda Filename: pool/dists/noble/main/r-cran-kfpca_2.0-1.ca2404.1_all.deb Size: 87644 MD5sum: 5801a711c676ba7a6bb894d6c06093f3 SHA1: 7f154a5d3edac2234a339ab53939a7e27918d5c7 SHA256: fec65d9dce4177b83b0fd7dd4ea094e969e526c26232343a67de0c1e69ea48a6 SHA512: 624369ea55c809f8147a7ef022190473b161b7b856a3455f6c3d213b731a82bd953010ccd1c541421e6139569f7d8eb988ea7c018d2e9b44a35e41e64e02ba13 Homepage: https://cran.r-project.org/package=KFPCA Description: CRAN Package 'KFPCA' (Kendall Functional Principal Component Analysis) Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) . Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) . Package: r-cran-kfpls Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda Filename: pool/dists/noble/main/r-cran-kfpls_1.0-1.ca2404.1_all.deb Size: 26778 MD5sum: 7fcf6f59de6667d6d84cdc23ab7655a4 SHA1: d0d3bc5d9dd2387a01c57b9dc2c85a1cc6bf82f4 SHA256: 36a72f773e2649bdfcbf96fdc0343f7f0b164dc8fbc432dc058e1f128ac5fcf0 SHA512: fc75c23fc57ce900c0300c9411286a9251a15c88094076d88accc8f6e6673f95434fde121b9449e190518c50d7dbc6adb0b9cedc109146fb4fb33c2c2f1bd915 Homepage: https://cran.r-project.org/package=KFPLS Description: CRAN Package 'KFPLS' (Kernel Functional Partial Least Squares) Implementation for kernel functional partial least squares (KFPLS) method. KFPLS method is developed for functional nonlinear models, and the method does not require strict constraints for the nonlinear structures. The crucial function of this package is KFPLS(). Package: r-cran-kfre Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-r6 Suggests: r-cran-devtools, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-rprojroot, r-cran-spelling, r-cran-svglite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kfre_0.0.2-1.ca2404.1_all.deb Size: 114136 MD5sum: 1302a0b92d5253cc041ed4b530d4448d SHA1: 39f7018fb3aec3072aa6d00fbba0da934fd9f7ea SHA256: 31bc41e46788b3d7f7581471c0101c2fd0c77859bd35705cb01e4011419fe390 SHA512: 81bba9adfc05b48c7df1c312a86bd69ceeb26c0d47e3d549f05f3b59d26d062b5fcbdff23101bf5b2b127766fd60a3588e3267148866f6c6cbec3bef2df55538 Homepage: https://cran.r-project.org/package=kfre Description: CRAN Package 'kfre' (Kidney Failure Risk Equation (KFRE) Tools) Implements the Kidney Failure Risk Equation (KFRE; Tangri and colleagues (2011) ; Tangri and colleagues (2016) ) to compute 2- and 5-year kidney failure risk using 4-, 6-, and 8-variable models. Includes helpers to append risk columns to data frames, classify chronic kidney disease (CKD) stages and end-stage renal disease (ESRD) outcomes, and evaluate and plot model performance. Package: r-cran-kgc Architecture: all Version: 1.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3595 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgc_1.0.0.2-1.ca2404.1_all.deb Size: 2646720 MD5sum: 7372de1e17a08edfe2609f45100385e6 SHA1: 70a32bca0ff114b9f5e582b10d9ffe5d4297c6e1 SHA256: 608d172e7b0c68b1d4f1449041bcdae9d32897661c1a510b45751ccec48d1d69 SHA512: 5de0e5ecfc4fd4a5302be5da5db8601186a3ed2f13c762500b85690c00b617e90bdf4e7bf781fc8b76288300118137e602e4c28cd6363b4aee226ef63b856c66 Homepage: https://cran.r-project.org/package=kgc Description: CRAN Package 'kgc' (Koeppen-Geiger Climatic Zones) Aids in identifying the Koeppen-Geiger (KG) climatic zone for a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system to classify regions of the earth by their relative heat and humidity through the year, for the benefit of human health, plant and agriculture and other human activity [1]. This climate zone classification system, applicable to all of the earths surface, has continued to be developed by scientists up to the present day. Recently one of use (FZ) has published updated, higher accuracy KG climate zone definitions [2]. In this package we use these updated high-resolution maps as the data source [3]. We provide functions that return the KG climate zone for a given longitude and lattitude, or for a given United States zip code. In addition the CZUncertainty() function will check climate zones nearby to check if the given location is near a climate zone boundary. In addition an interactive shiny app is provided to define the KG climate zone for a given longitude and lattitude, or United States zip code. Digital data, as well as animated maps, showing the shift of the climate zones are provided on the following website . This work was supported by the DOE-EERE SunShot award DE-EE-0007140. [1] W. Koeppen, (2011) . [2] F. Rubel and M. Kottek, (2010) . [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) . Package: r-cran-kgen Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-reticulate, r-cran-checkmate, r-cran-data.table Suggests: r-cran-future, r-cran-progressr, r-cran-future.apply, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgen_1.1.1-1.ca2404.1_all.deb Size: 72022 MD5sum: 800ccc2c5251bb862ca2d36ae1869a42 SHA1: e36ae15bfa3b63382ab5f81354310ba1905e2ef1 SHA256: 157c37215aee70a108268a5ecf902c6cf362c3ed0e86a6344290b26ebdd210d9 SHA512: 0f452951ea4e406bbd434816b0e2e279cc9779b2a647d47730f785afd0f1497f1a82a1e056ef4466a1c2df76cc334f84a39ef40eae516283ee0e962a7fde9681 Homepage: https://cran.r-project.org/package=kgen Description: CRAN Package 'kgen' (A Tool for Calculating Stoichiometric Equilibrium Constants (Ks)for Seawater) A unified software package simultaneously implemented in 'Python', 'R', and 'Matlab' providing a uniform and internally-consistent way of calculating stoichiometric equilibrium constants in modern and palaeo seawater as a function of temperature, salinity, pressure and the concentration of magnesium, calcium, sulphate, and fluorine. Package: r-cran-kgode Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-pracma, r-cran-pspline, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-kgode_1.0.5-1.ca2404.1_all.deb Size: 364702 MD5sum: 6e0e6687f503c52a48382b4937667874 SHA1: 4b860c9832df1b8f262fd94f25f9d264508fc642 SHA256: 4558174b542ac170b47ea4ba389cb8e667c626511c81bc6533c92511eb4ceedc SHA512: 7243885844f720391d37d9717f5e0f43ade6a30cbb9cbcf6d4cf0ecfbdfb2d4cf106baf19e5bbeda7885616294246933427aae042b9b8b1aeab0b80b2a00778f Homepage: https://cran.r-project.org/package=KGode Description: CRAN Package 'KGode' (Kernel Based Gradient Matching for Parameter Inference inOrdinary Differential Equations) The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) and the warping algorithm proposed in Niu et al. (2017) are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping. Package: r-cran-kgp Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-kgp_1.1.1-1.ca2404.1_all.deb Size: 306422 MD5sum: 1b0a19bcc9e133dbc88f9546ba58c421 SHA1: cdefe3a9b6cc8dd727a86432990d294b969abdaa SHA256: f82e53a802a75b041a6a103ce26a2a1beaf14605e9be0b224dc4ab85b11ef627 SHA512: 20e8e5d5f50b3f95155d7c46e4e5502f2960f23bcfd785301e5ba48edad12fad2eaf5988a2268f4863f1ae1b385b92f2197a666ff847d049081a6a5e132a7b7e Homepage: https://cran.r-project.org/package=kgp Description: CRAN Package 'kgp' (1000 Genomes Project Metadata) Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) and Byrska-Bishop et al. (2022) , and raw data is available at . See Turner (2022) for more details. Package: r-cran-kgraph Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1669 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-igraph, r-cran-magrittr, r-cran-opticskxi, r-cran-plyr, r-cran-proc, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-sgraph, r-cran-shiny Suggests: r-cran-bslib, r-cran-data.table, r-cran-dt, r-cran-knitr, r-cran-nlpembeds, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgraph_1.2.0-1.ca2404.1_all.deb Size: 1179132 MD5sum: 35cffe3d52861437b978f0eabc75ff09 SHA1: e0923cbdc185cb0c5b6671659e74db5b4b408bb0 SHA256: 6ba89b96358132d26e5ad0fcdae89c801ae3bb906ceeeed6229a0943f976adf5 SHA512: 5ec1891e4a4c2618677dc0e842eef9870ab43e79c67f79127f6be5f01a23ebd70e75284a7c1e70c2a4d13496315cc54d171be0b135afeb36160f21a32cfdba94 Homepage: https://cran.r-project.org/package=kgraph Description: CRAN Package 'kgraph' (Knowledge Graphs Constructions and Visualizations) Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The 'kgraph' package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The 'kgraph' package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live 'shiny' applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application based on Hong C. (2021) . Package: r-cran-kgschart Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-deepnet, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-matrixstats, r-cran-nnet, r-cran-png, r-cran-shiny, r-cran-stringr Suggests: r-cran-jsonlite, r-cran-shinyjs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgschart_1.3.5-1.ca2404.1_all.deb Size: 388484 MD5sum: 91efa33c57ec0efe1da370b3ef7499a9 SHA1: 4e94ac839404b0f9e29b704c835624fd5788f183 SHA256: e310e64d8d7abf8f4a0341c2a652abe2d68401b73d22ad484c703b2246ad8008 SHA512: 309a28e6b330c5ba28ce06b39f24971faee493c1aee3f21ed61749eeb76cbb98dcff3dafe116e07eac2b34c18bcf30e79540e8dd3a0a58e17051d21dbbafe92a Homepage: https://cran.r-project.org/package=kgschart Description: CRAN Package 'kgschart' (KGS Rank Graph Parser) Restore underlining numeric data from rating history graph of KGS (an online platform of the game of go, ). A shiny application is also provided. Package: r-cran-khaos Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lhs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-khaos_2.1.1-1.ca2404.1_all.deb Size: 211772 MD5sum: 634b268a26c157454f54002006d4c13b SHA1: cebb54be482a2c6e1e809ed8493a2b5e77f49118 SHA256: 4ea4f6e768a6b019f18a3cef0a67c686b78d34e4d8c5f32c0d440ad5db2cb76c SHA512: bad93f8d45704d480ac3482b2f15f7805015c518a830c69f1fca01e371cc00d77cb7d5e837e640569734ccebc8a75f02d77f0ab1c761a79151d790dad0396bce Homepage: https://cran.r-project.org/package=khaos Description: CRAN Package 'khaos' (Bayesian Sparse Polynomial Chaos Expansion) Implements Bayesian polynomial chaos expansion methods for surrogate modeling, uncertainty quantification, and sensitivity analysis. Includes sparse regression-based approaches and adaptive Bayesian models based on reversible-jump Markov chain Monte Carlo. Optional screening and basis-enrichment strategies are provided to improve scalability in moderate to high dimensions. Package: r-cran-khisr Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-khisr_1.0.8-1.ca2404.1_all.deb Size: 359786 MD5sum: 1cac2c6857c12947322d5fd989d344a6 SHA1: 26eabb37eb1454436a024286d1a6d3fecb0e8895 SHA256: 1968cf1b401c1fb33d4c381764fc3219ee1a1361ab03514ba8ba5bc57928ae0e SHA512: 3e577982f3cc0148d1842c4ded3cac1c2e6ae93055cb26539414970f1d71266c825f0a7dd0949f55f16b6d62c386186987f74fe7cc8d3972850ae9f2264577e3 Homepage: https://cran.r-project.org/package=khisr Description: CRAN Package 'khisr' (An R Client to Retrieve Data from DHIS2) Provides a user-friendly interface for interacting with the District Health Information Software 2 ('DHIS2', ) instance. It streamlines data retrieval, empowering researchers, analysts, and healthcare professionals to obtain and utilize data efficiently. Package: r-cran-khq Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-khq_0.2.0-1.ca2404.1_all.deb Size: 95980 MD5sum: 23b04d02bb6cb575a0e756189a795093 SHA1: b3daa439d0ed921628c024815288c38d0da6a781 SHA256: e2ebc517dba21e933afb611ab226b37e494a673fba9f590ef5f548f29b24c51f SHA512: 5af8f184a062c63f5e9aa6cd34558f42690fea832840bf1eda9eeb77c8f0e6c900516a3d8c6cb177557e3decf18d4b8e0112189b792a5f61226dae01b55442bc Homepage: https://cran.r-project.org/package=KHQ Description: CRAN Package 'KHQ' (Methods for Calculating 'KHQ' Scores and 'KHQ5D' Utility IndexScores) The King's Health Questionnaire (KHQ) is a disease-specific, self-administered questionnaire designed specific to assess the impact of Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed by Kelleher and collaborators (1997) . It is a simple, acceptable and reliable measure to use in the clinical setting and a research tool that is useful in evaluating UI treatment outcomes. The KHQ five dimensions (KHQ5D) is a condition-specific preference-based measure developed by Brazier and collaborators (2008) . Although not as popular as the SF6D and EQ-5D , the KHQ5D measures health-related quality of life (HRQoL) specifically for UI, not general conditions like the others two instruments mentioned. The KHQ5D ca be used in the clinical and economic evaluation of health care. The subject self-rates their health in terms of five dimensions: Role Limitation (RL), Physical Limitations (PL), Social Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. This package provides methods to calculate scores for each dimension of the KHQ; converts KHQ item scores to KHQ5D scores; and also calculates the utility index of the KHQ5D. Package: r-cran-khroma Architecture: all Version: 1.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4891 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fontquiver, r-cran-ggplot2, r-cran-ggraph, r-cran-knitr, r-cran-markdown, r-cran-rsvg, r-cran-scales, r-cran-spacesxyz, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-khroma_1.17.0-1.ca2404.1_all.deb Size: 3305294 MD5sum: 95664257063afe74c9aa4b1e6b5122b8 SHA1: 13ed487374594e34d8218487f7ecd301bc3692ef SHA256: 6c297ccd2bfe90ccec45fb6fc2873aee37e255359c6dff50d7416203f285aea6 SHA512: 91ce13df12bd6bc2f6e1a2344c9aab9af556cb85c65d1647ad0c2a3020e2d27bc03c41cac1b544eaddc5ba36b98a6cb50a88faf8148edd304c26429dcd651d12 Homepage: https://cran.r-project.org/package=khroma Description: CRAN Package 'khroma' (Colour Schemes for Scientific Data Visualization) Color schemes ready for each type of data (qualitative, diverging or sequential), with colors that are distinct for all people, including color-blind readers. This package provides an implementation of Paul Tol (2018) and Fabio Crameri (2018) color schemes for use with 'graphics' or 'ggplot2'. It provides tools to simulate color-blindness and to test how well the colors of any palette are identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are also implemented. Package: r-cran-kibior Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4983 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-stringr, r-cran-purrr, r-cran-jsonlite, r-cran-rio, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-elastic, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-rtracklayer Suggests: r-cran-ggplot2, r-cran-readr, r-cran-xml2, r-cran-yaml, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-kibior_0.1.1-1.ca2404.1_all.deb Size: 1761342 MD5sum: 840df921c245cc37c7015c476bd0180c SHA1: c9c29572d443e01201fecdec38f4617f040d1c6c SHA256: e91a48e5ef9f9d245ef78c611b1f8ffe59fcc4c8f366091f33930ab39d5a8f02 SHA512: 66042ce4cbc0c6d16ae7f5d411b309590a42c311643eba64f1e8be7e86223c1943fecd9f3ae17c627c9a6f05885268496aa4a117278fe9c4106766ea8a1f2b42 Homepage: https://cran.r-project.org/package=kibior Description: CRAN Package 'kibior' (A Simple Data Management and Sharing Tool) An interface to store, retrieve, search, join and share datasets, based on Elasticsearch (ES) API. As a decentralized, FAIR and collaborative search engine and database effort, it proposes a simple push/pull/search mechanism only based on ES, a tool which can be deployed on nearly any hardware. It is a high-level R-ES binding to ease data usage using 'elastic' package (S. Chamberlain (2020)) , extends joins from 'dplyr' package (H. Wickham et al. (2020)) and integrates specific biological format importation with Bioconductor packages such as 'rtracklayer' (M. Lawrence and al. (2009) ) , 'Biostrings' (H. Pagès and al. (2020) ) , and 'Rsamtools' (M. Morgan and al. (2020) ) , but also a long list of more common ones with 'rio' (C-h. Chan and al. (2018)) . Package: r-cran-kidney.epi Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kidney.epi_1.4.0-1.ca2404.1_all.deb Size: 540220 MD5sum: 425db16a338b5bbead98036ec0abd16d SHA1: 45e9bcb968cb7ab97607e24217224c7518425881 SHA256: a01c0e1661d17aaaec4e285e5ff30728cf3889040a2a66f917285e34d7b9ed28 SHA512: c46edc68f345f5764a44d5574905536df56a2b323fe6724923dbf11b6ce8c03e16666320788338d43a9f13959c472bb2c37ba10da086f3d9bb27ab18dd201d73 Homepage: https://cran.r-project.org/package=kidney.epi Description: CRAN Package 'kidney.epi' (Kidney-Related Functions for Clinical and EpidemiologicalResearch) Contains kidney care oriented functions. Current version contains functions for calculation of: - Estimated glomerular filtration rate by CKD-EPI (2021 and 2009), MDRD, CKiD, FAS, EKFC, etc. - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors. - Citation: Bikbov B. kidney.epi: Kidney-Related Functions for Clinical and Epidemiological Research. Scientific-Tools.Org, . . Package: r-cran-kidsides Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-r.utils Suggests: r-cran-ggplot2, r-cran-dbplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggthemes, r-cran-rlang, r-cran-ggrepel, r-cran-scales, r-cran-pacman, r-cran-dplyr, r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-dt, r-cran-gt, r-cran-lobstr, r-cran-prettyunits, r-cran-purrr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-kidsides_0.5.0-1.ca2404.1_all.deb Size: 24972 MD5sum: a9fc62452af96e5c74411d7f2bdd320c SHA1: 6ab30c632deeb402265af3b5d9d4785fd29e517e SHA256: d9220f1c67bb6c5e3f7b650945f05c60c10552dd5a09242c8819aa2fb18a4b1e SHA512: 38f9674b7412433c383a408c52aa1644c827b1ffb012345cb545b9b55d8c6e97aa926fa9dc6af0502609d2c9b301834d44240e3bbb846207c6a4a71b033fa779 Homepage: https://cran.r-project.org/package=kidsides Description: CRAN Package 'kidsides' (Download, Cache, and Connect to KidSIDES) Caches and then connects to a 'sqlite' database containing half a million pediatric drug safety signals. The database is part of a family of resources catalogued at . The database contains 17 tables where the description table provides a map between the fields the field's details. The database was created by Nicholas Giangreco during his PhD thesis which you can read in Giangreco (2022) . The observations are from the Food and Drug Administration's Adverse Event Reporting System. Generalized additive models estimated drug effects across child development stages for the occurrence of an adverse event when exposed to a drug compared to other drugs. Read more at the methods detailed in Giangreco (2022) . Package: r-cran-kifidi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kifidi_0.1.0-1.ca2404.1_all.deb Size: 22020 MD5sum: 12a9d2ceba346598b793c80795db0f50 SHA1: 086635bd6be617f5f2b66e2ca97e25f49f875ba1 SHA256: 65bd487799ddb7526053df4d1dcbe8ad9d2255f3b77a50d2d3aed7e0ff4efd54 SHA512: e7547efb50624c9e65d39e24b29c87d829f6925b1c43f75985fd05b0dd7211ec7d31897b4b8217dc7484c480fdb96e774ee948ca83d92526e182077f6fc7b2f3 Homepage: https://cran.r-project.org/package=Kifidi Description: CRAN Package 'Kifidi' (Summary Table and Means Plots) Optimized for handling complex datasets in environmental and ecological research, this package offers functionality that is not fully met by general-purpose packages. It provides two key functions, 'summarize_data()', which summarizes datasets, and 'plot_means()', which creates plots with error bars. The 'plot_means()' function incorporates error bars by default, allowing quick visualization of uncertainties, crucial in ecological studies. It also streamlines workflows for grouped datasets (e.g., by species or treatment), making it particularly user-friendly and reducing the complexity and time required for data summarization and visualization. Package: r-cran-kim Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-remotes Suggests: r-cran-boot, r-cran-ggplot2, r-cran-moments, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kim_0.6.4-1.ca2404.1_all.deb Size: 962392 MD5sum: f6fc28a7461373c8bd708a4727981a5a SHA1: 36144e303a21735be135814dd895de6114261862 SHA256: 4684eafae177beb1a73cc92da2fe11b4cf5a35c2073e66f6ec0370cd3387bdfe SHA512: 006dad0c48067184f245fdc8c2673227233d96fc91545347247d3bd2da3fc041cc9c098a88436ef12a219f4bbc96e7a1d121b3b71d9d72051c5d622e8c998883 Homepage: https://cran.r-project.org/package=kim Description: CRAN Package 'kim' (A Toolkit for Behavioral Scientists) A collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) . Wickham et al. (2021) . Package: r-cran-kimisc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-memoise, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kimisc_1.0.1-1.ca2404.1_all.deb Size: 119984 MD5sum: 08727a4f096924ad5532919caefbf0b0 SHA1: c3e13ae9dad30895120a80ca6cfdc7d9d9fbd06b SHA256: 5cfd1a8a78ca00881c756d1962702002e5ce429e5ac02e44a1526955578d2714 SHA512: be15aa4e3c9bce4098042f9ae205f20a6cfbe09d6fdeee3717a10425618418d2769a6e528775a4d33a1f08953e3ab1f25ca1e149444136a85d69899bfdf163d8 Homepage: https://cran.r-project.org/package=kimisc Description: CRAN Package 'kimisc' (Kirill's Miscellaneous Functions) A collection of useful functions not found anywhere else, mainly for programming: Pretty intervals, generalized lagged differences, checking containment in an interval, and an alternative interface to assign(). Package: r-cran-kin.cohort Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-kin.cohort_0.7-1.ca2404.1_all.deb Size: 158386 MD5sum: 6121c29f48c56c39aab5b632ab101502 SHA1: 1e96725f7ebbb9c9fbd8523d59b99aa9010c4bdc SHA256: 87ede0bd78c41a42839e15eb3c677a1d8c8eafc3573426b4e6293eeeabe9ce48 SHA512: a5fd23ea9d6b01bbbccc0e1d7e67a5c632e1f18c945736d6d779d02e18a49142774bf5322511f0cc5b2fb5a47d875474db258e6026c9bb4dd988e5ec50ab1e52 Homepage: https://cran.r-project.org/package=kin.cohort Description: CRAN Package 'kin.cohort' (Analysis of Kin-Cohort Studies) Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are retrospectively built from relatives of probands for whom the genotype is known. Currently the method of moments and marginal maximum likelihood are implemented. Confidence intervals are calculated from bootstrap samples. Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee. Package: r-cran-kindisperse Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-readr, r-cran-shiny, r-cran-shinythemes, r-cran-ggrepel, r-cran-fitdistrplus, r-cran-laplacesdemon, r-cran-here, r-cran-tibble, r-cran-magrittr, r-cran-plotly, r-cran-dplyr, r-cran-rlang, r-cran-tidyselect, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kindisperse_0.10.2-1.ca2404.1_all.deb Size: 870632 MD5sum: 392c4e91580b80c9ad2d679d4a169826 SHA1: 3ad51c6000177242ce5c14e60f5b9f87085d384c SHA256: bdd552621da1f1752cc4a2199c5db381477555d5787704084f4219f740c2b686 SHA512: 073464ca890cd01f7f0ecfdbf25f9f22280320dd102f02ada75c3e0fc7a7ec1c46c2d7c48ff968e9f3334f5b8eddcb59ec39a5edfe4f7d93d2322bdcdd36f2eb Homepage: https://cran.r-project.org/package=kindisperse Description: CRAN Package 'kindisperse' (Simulate and Estimate Close-Kin Dispersal Kernels) Functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included. Package: r-cran-kindling Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-purrr, r-cran-torch, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-vctrs, r-cran-parsnip, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-neuralnettools, r-cran-ggplot2, r-cran-tune, r-cran-dials, r-cran-hardhat, r-cran-lifecycle, r-cran-coro Suggests: r-cran-testthat, r-cran-magrittr, r-cran-box, r-cran-recipes, r-cran-workflows, r-cran-rsample, r-cran-yardstick, r-cran-mlbench, r-cran-modeldata, r-cran-knitr, r-cran-rmarkdown, r-cran-dicedesign, r-cran-lhs, r-cran-sfd, r-cran-covr, r-cran-fansi Filename: pool/dists/noble/main/r-cran-kindling_0.3.2-1.ca2404.1_all.deb Size: 597536 MD5sum: af9e5faa0481fc917c964762d9e5a2ab SHA1: e09cd92dc19b477b5e182942a0da9c311331fe3d SHA256: 553c518d8569d3972d4c58788c99f636a98e93ee1d14adb6764cd8d91ab9244d SHA512: a8a30deb47b9dd9fbdf929b8ba2a1fb522fa63e9189c93c727058c0d7ae77c3afd46d76142e9209be41bf6d9cef1b5f27f60c5eb7b52a2a8da42aa2eae5f8b39 Homepage: https://cran.r-project.org/package=kindling Description: CRAN Package 'kindling' (Higher-Level Interface of 'torch' Package to Auto-Train NeuralNetworks) Provides a higher-level interface to the 'torch' package for defining, training, and fine-tuning neural networks through code generation. The package supports several architectures, including feedforward (multi-layer perceptron) and recurrent neural networks (RNN, LSTM, GRU), while reducing boilerplate 'torch' code. Model training methods also bridge to machine learning frameworks in R, particularly the 'tidymodels' ecosystem, including 'parsnip' model specifications, workflows, recipes, and tuning tools. Package: r-cran-kinematics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-kinematics_1.0.0-1.ca2404.1_all.deb Size: 672444 MD5sum: 9ab995d9076e30eae8c33422eb656e95 SHA1: 3504984f8bca0f36b9ff674629eeb2d9762f0a5b SHA256: 5874892f9c466316ddd0f0fae019089f854fca4dde3033f2b135db1d21dd4ace SHA512: b32d5878f20a8acd0c53bbebc41c0044209a825a5f3043bf961c62dbe889875ff3b2b64c2e8c7d2e3e4e204b73dae34f9d51367a1b394c982da134d911ad31a3 Homepage: https://cran.r-project.org/package=kinematics Description: CRAN Package 'kinematics' (Studying Sampled Trajectories) Allows analyzing time series representing two-dimensional movements. It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns, and returns several dynamical properties such as speed, acceleration or curvature. Package: r-cran-kinesis Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aion, r-cran-ananke, r-cran-arkhe, r-cran-bslib, r-cran-config, r-cran-dimensio, r-cran-gt, r-cran-isopleuros, r-cran-kairos, r-cran-khroma, r-cran-mirai, r-cran-nexus, r-cran-sass, r-cran-shiny, r-cran-tabula Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-kinesis_0.5.0-1.ca2404.1_all.deb Size: 641000 MD5sum: bb59b5f71d3573960fbf1c3eb13fca24 SHA1: b0d7e4739f0c5a0212a327c7ea78b3c568055604 SHA256: ca4a15ba503fb53380dee36fa90a852ed073b71c34d2d94d6ca248506f7cd467 SHA512: 6dbb4309c0b9d4b680e67aa9aace046eb6fd843b9f2de981ca6c5a448f967bdca45965d2f6bc33ce86aa44c53d8aabb588170b5247db72ca91e2cf15dd42f923 Homepage: https://cran.r-project.org/package=kinesis Description: CRAN Package 'kinesis' ('shiny' Applications for the 'tesselle' Packages) A collection of 'shiny' applications for the 'tesselle' packages . This package provides applications for archaeological data analysis and visualization. These mainly, but not exclusively, include applications for chronological modelling (e.g. matrix seriation, aoristic analysis) and count data analysis (e.g. diversity measures, compositional data analysis). Package: r-cran-kinformr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kinformr_0.1.2-1.ca2404.1_all.deb Size: 457134 MD5sum: b76585c568a950f42251c1667a75667e SHA1: 7c47e1f7295fcc15f7706b30a4435a83c339773d SHA256: cc63064605da28628713eb684898295266fa2627d3e29d2f17154aaec2e98612 SHA512: b6c952df5344aea9702b68916b8bd0ccb65d21f6b3e92c28d87b22891ccab99fc3992e90a6adfb4ffcdfc8807000fec879dd6e9fd32a18735f9d5f62f6315827 Homepage: https://cran.r-project.org/package=KinformR Description: CRAN Package 'KinformR' (Relationship-Informed Pedigree and Variant Scoring) Comparative evaluation of families and candidate variants in rare-variant association studies. The package can be used for two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic evaluation, evaluation of relative detection power of pedigrees, can direct recruitment efforts by showing which individuals not yet sampled would be the most meaningful additions to a study. Second, after sequencing and analysis, variants based on association with disease status and familial relationships of individuals, aids in variant prioritization. Methodology is described in Nugent (2025) . Package: r-cran-kingcountyhouses Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-kingcountyhouses_0.1.0-1.ca2404.1_all.deb Size: 611584 MD5sum: 77a6beae20f702bb331fb5543ffa08a7 SHA1: 5271a98669fc3d50112cefc64eb6900ff7e2b63e SHA256: 0889750f1ac774530c092ace7b9796b10011a6020291461cc17641eb3a1144b7 SHA512: aa4a9c07ff182dc8ff108284743f7f1114d8b44c41b091c044141ed7881f3deab64267c66ec46e230c9e8101840f0e1d7be65ead4a66f06a29df6a4070b33c5c Homepage: https://cran.r-project.org/package=KingCountyHouses Description: CRAN Package 'KingCountyHouses' (Data on House Sales in King County WA) Data on houses in and around Seattle WA are included. Basic characteristics are given along with sale prices. Package: r-cran-kinmixlite Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dnamixtureslite, r-cran-graven, r-cran-statnet.common, r-cran-grbase, r-cran-rsolnp, r-cran-numderiv, r-cran-matrix, r-cran-ribd, r-cran-pedtools Filename: pool/dists/noble/main/r-cran-kinmixlite_2.2.1-1.ca2404.1_all.deb Size: 267672 MD5sum: 086ced445885e32f3df94586197dfcdb SHA1: f600aa38e33178167fde1bdf69a3a9137ca884e4 SHA256: 06916d11ce0502c2f5102bbda7a33e98ba2f2633cd17641b255e21b4ee1be59e SHA512: dcc9a09353907820be69c36976620cd63c64e9749d4c0330f0d63d61a12ce1f67398ff234511f744939ecfb9f82e155e9e709283267dbddedf42bcde4d407973 Homepage: https://cran.r-project.org/package=KinMixLite Description: CRAN Package 'KinMixLite' (Inference About Relationships from DNA Mixtures) Methods for inference about/under complex relationships using peak height data from DNA mixtures: the most basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) and Green, P. J. and Mortera, J. (2021) . Package: r-cran-kinship2 Architecture: all Version: 1.9.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-quadprog, r-cran-knitr Filename: pool/dists/noble/main/r-cran-kinship2_1.9.6.2-1.ca2404.1_all.deb Size: 505204 MD5sum: b0e0d5d696925ba9668f271a76fd6c3b SHA1: 81ecf823cccedcda0e0e7f48c7e7ce5d127f3f16 SHA256: 4b89c412f7b6a6a2afa3ba08ee35941456f3648a95d9b56763bba834130108af SHA512: b1725ac697bea9418118c2696a030663880422fb08cad2e74f2d1a0716f9f9fb48e5d74902a328e34623ade01b7ca321cf9001a0d42814976ce550d4f6af0bcb Homepage: https://cran.r-project.org/package=kinship2 Description: CRAN Package 'kinship2' (Pedigree Functions) Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the pedigree object with various criteria, and kinship for the X chromosome. Package: r-cran-kinsimu Architecture: all Version: 0.1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kinsimu_0.1.3-2-1.ca2404.1_all.deb Size: 127706 MD5sum: a5d638508a0b5cf8fc295516ef710bd1 SHA1: 6922a114f728001908c4633a5c7f69d5a159b3b5 SHA256: f4861f78e30df5938bdb3ed34749b018aff84434f44d32842770873e85888b38 SHA512: 1b719b67a508d57e3c0aa07133779ee392658a0100f895d84bab233a9acc8ea4636446c543a0673d93fdd493326ab78da8112972269880d3d7231edf73f863a6 Homepage: https://cran.r-project.org/package=KINSIMU Description: CRAN Package 'KINSIMU' (Panel Evaluation in Forensic Kinship Analysis) Evaluate specific panels in different aspects: i) Simulation tools related to pedigree researches; ii) calculation for systemic effectiveness indicators, such as probability of exclusion (PE). Package: r-cran-kirby21.base Architecture: all Version: 1.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-git2r Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kirby21.base_1.7.3-1.ca2404.1_all.deb Size: 44408 MD5sum: e90f59b8a4c78447b3133c27a97689ae SHA1: 86b65aee3ba3e48f54fe0d6b13ed859af4559951 SHA256: f46895ed708ce298e6b322148812070adc9cc958ec55ed2d59d88e644b1f89c4 SHA512: b8b171f5880374bdfcd84904a78a066f7d1b8d0fd43b8a064dc5ec7e6065898bf0390830f133d57c5037b87d3be7f977b5d367ebd77691f8e9a6aeed9fdd346f Homepage: https://cran.r-project.org/package=kirby21.base Description: CRAN Package 'kirby21.base' (Example Data from the Multi-Modal MRI 'Reproducibility' Resource) Multi-modal magnetic resonance imaging ('MRI') data from the 'Kirby21' 'reproducibility' study , including functional and structural imaging. Package: r-cran-kirby21.fmri Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kirby21.base Suggests: r-cran-kirby21.t1 Filename: pool/dists/noble/main/r-cran-kirby21.fmri_1.8.0-1.ca2404.1_all.deb Size: 14042 MD5sum: 4194636d735827c565c256f049ee3d42 SHA1: ba67791a37d5dd00ae8b9a41c918cedd78680ce1 SHA256: 8e19f3bcded07267522ecfda9f42ceec05fae8972b4298594520fd5bb368d882 SHA512: 42f446527e4602bdc39e44b6f5d21099b3b65a21b804c44cc7331a7d7461d325265e4fe0f0770ba9acd5d04c91bb29c98e16f78680d7ea42a047845e292f6bba Homepage: https://cran.r-project.org/package=kirby21.fmri Description: CRAN Package 'kirby21.fmri' (Example Functional Imaging Data from the Multi-Modal MRI'Reproducibility' Resource) Functional magnetic resonance imaging ('fMRI') data from the 'Kirby21' 'reproducibility' study . Package: r-cran-kirby21.t1 Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kirby21.base Filename: pool/dists/noble/main/r-cran-kirby21.t1_1.8.0-1.ca2404.1_all.deb Size: 14922 MD5sum: 13a4bf4d4d7667e5c337ae887af70178 SHA1: 610334b32cf8ce13e22f4834a32b9ff14256c9cd SHA256: ec34dbeeb9573f75f3c15640880650c9101f0f17e97320f05983b142d6650c26 SHA512: dc3271b125c785f77c61b83f0c4f1358891e18cab552ea305cc8318a41c8da2edccdbc33014b90ea8ce68ed0e24403955fcebd0ffc908342fed5428a1cfcc1fe Homepage: https://cran.r-project.org/package=kirby21.t1 Description: CRAN Package 'kirby21.t1' (Example T1 Structural Data from the Multi-Modal MRI'Reproducibility' Resource) Structural T1 magnetic resonance imaging ('MRI') data from the 'Kirby21' 'reproducibility' study . Package: r-cran-kisopenapi Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-httr2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-kisopenapi_0.0.2-1.ca2404.1_all.deb Size: 75732 MD5sum: 05ca3149056db5e02603c7fb8fb4be6f SHA1: 5c1867a391dd935deef776e7ef2ffcbb703c4bca SHA256: 709d66dbd8600c607039ccaae45479d31fa83d8307781c09a78e1e00323a7671 SHA512: 28f314e54b14b4dedfdd6d87aea86a98bd7ae9a623442ea90bd501d184d0bc2acd5a2a900044fab3c45fc1b0caf8d3fb7fc205f6ae1c7f7b1201b93cbb5de8b8 Homepage: https://cran.r-project.org/package=kisopenapi Description: CRAN Package 'kisopenapi' (Korea Investment & Securities (KIS) Open Trading API) API Wrapper to use Korea Investment & Securities (KIS) trading system that provides various financial services like stock price check, orders and balance check . Package: r-cran-kitagawa Architecture: all Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-kelvin, r-cran-psd Suggests: r-cran-dplyr, r-cran-tibble, r-cran-rcolorbrewer, r-cran-signal, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-covr Filename: pool/dists/noble/main/r-cran-kitagawa_3.1.3-1.ca2404.1_all.deb Size: 563974 MD5sum: ad7e52ff825626683fb574cfe6d5f8bc SHA1: 7882db03a6cc57f58a1365a7a0e5aba9eb32f260 SHA256: 31d8f8b0b3d4338f55223ea84e0d0e0e9492c0903337ebb6f3ccb284f3844083 SHA512: 88738f762665edccd4fd901ba6535056d74412c494c24f376ca0d788670bb15f55599fbf170ad734b7461601e90a85afd944e471127fdd51837924cfa63627bb Homepage: https://cran.r-project.org/package=kitagawa Description: CRAN Package 'kitagawa' (Spectral Response of Water Wells to Harmonic Strain and PressureSignals) Provides tools to calculate the theoretical hydrodynamic response of an aquifer undergoing harmonic straining or pressurization, or analyze measured responses. There are two classes of models here, designed for use with confined aquifers: (1) for sealed wells, based on the model of Kitagawa et al (2011, ), and (2) for open wells, based on the models of Cooper et al (1965, ), Hsieh et al (1987, ), Rojstaczer (1988, ), Liu et al (1989, ), and Wang et al (2018, ). Wang's solution is a special exception which allows for leakage out of the aquifer (semi-confined); it is equivalent to Hsieh's model when there is no leakage (the confined case). These models treat strain (or aquifer head) as an input to the physical system, and fluid-pressure (or water height) as the output. The applicable frequency band of these models is characteristic of seismic waves, atmospheric pressure fluctuations, and solid earth tides. Package: r-cran-kitesquare Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggh4x, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-kitesquare_0.0.2-1.ca2404.1_all.deb Size: 138092 MD5sum: 949de6bc4e8de89542634f9847bf239d SHA1: ec375184262f3c94e4380237c063aa044fae00c4 SHA256: 19833ca8508df4ec2f0fd0d0dee4f4551d26bf7b15b9ac8574767f7bd5dbacd7 SHA512: 542917e55ebed07736af956536fe391d45f2d02f3b8c5b1bd8e92375df27a6a0642fe71fb1ad68f2c5f42acf933bf30c0a44c053eec19f25f0939902b3384d25 Homepage: https://cran.r-project.org/package=kitesquare Description: CRAN Package 'kitesquare' (Visualize Contingency Tables Using Kite-Square Plots) Create a kite-square plot for contingency tables using 'ggplot2', to display their relevant quantities in a single figure (marginal, conditional, expected, observed, chi-squared). The plot resembles a flying kite inside a square if the variables are independent, and deviates from this the more dependence exists. Package: r-cran-kiwisr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-lubridate, r-cran-dplyr, r-cran-purrr, r-cran-httr2 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kiwisr_0.2.4-1.ca2404.1_all.deb Size: 36472 MD5sum: 73d2bf92a7d4130ce1c8aa5d1367fbe2 SHA1: a414ef3142ac142099226f1e02ccbbcd8d10c622 SHA256: 12983522d6f23d499f03faede2a29e1d5a74d34c041b5275c594de90a30ce804 SHA512: 3e2081b783d7a9616a962231835aa9c8e4e291be1e24fe002d537f50e4b1b2159268ce6a85b177e0472b0807be858dd5d5eb0b2f8831546dc111900d913702ac Homepage: https://cran.r-project.org/package=kiwisR Description: CRAN Package 'kiwisR' (A Wrapper for Querying KISTERS 'WISKI' Databases via the 'KiWIS'API) A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides access to 'WISKI' databases via HTTP requests (). Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL. Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles. Package: r-cran-kko Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-knockoff, r-cran-doparallel, r-cran-foreach, r-cran-extdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-kko_1.0.1-1.ca2404.1_all.deb Size: 69924 MD5sum: 2cd556dba1f58770ad0bf9c534037a04 SHA1: 448b0dc489af283c2ca13f249e1ed68d1ee64026 SHA256: f787efa6199581bc131db465104c144348661491655ad5021ff9f1420e61d79c SHA512: 37921060721a6b8444181bfedc2b73a9de87772b1eb8bf2e3cd3ddc704515ff33848dd8beec27cc886e0db0038b328f4e28d2c1bbc9173f13510b8f5884515f5 Homepage: https://cran.r-project.org/package=kko Description: CRAN Package 'kko' (Kernel Knockoffs Selection for Nonparametric Additive Models) A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint . Package: r-cran-klar Architecture: all Version: 1.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-combinat, r-cran-questionr Suggests: r-cran-scatterplot3d, r-cran-som, r-cran-mlbench, r-cran-rpart, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-klar_1.7-4-1.ca2404.1_all.deb Size: 566346 MD5sum: fb7c10da19da46c1add0b794948015c2 SHA1: c68fd2a32e982c2480dd1bc4a6eb6d9c0751d476 SHA256: d7eae6a23961f44f67eaa1314a339b3c4e49b046ffc2999a2d892e4a1d29ffc2 SHA512: 2b157f7d9656479466f7d5bf1a550b9208f48fc8562f6898cb638f68a7e8e005724da38c355c52169a3eff3632809bf53359f4548e61d3022ac5142f1f1795c2 Homepage: https://cran.r-project.org/package=klaR Description: CRAN Package 'klaR' (Classification and Visualization) Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing. Package: r-cran-klassr Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tm, r-cran-httr, r-cran-jsonlite, r-cran-igraph, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-kableextra, r-cran-magrittr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-klassr_1.0.8-1.ca2404.1_all.deb Size: 323168 MD5sum: dc0b04a0c546a4bb9b321f27f6e72f79 SHA1: 219d2c4730d39639a9566d92316d2f5b099b545c SHA256: 8378d4e4c26644519690cfba93cbcb1cfe18c402a0e71bd95e56f2adbc36608a SHA512: 524c2979f5eee2c46f7940977639ea4a5ee105e4ce983026b4147a6fbb081d723a65b6df9efce77083fad9d0f14164f11d24d2153f3e8e41d9bad6204be5d2a1 Homepage: https://cran.r-project.org/package=klassR Description: CRAN Package 'klassR' (Classifications for Statistics Norway) Functions to search, retrieve, apply and update classification standards and code lists using Statistics Norway's API from the system 'Klass'. Retrieves classifications by date with options to choose language, hierarchical level and formatting. Package: r-cran-klausur Architecture: all Version: 0.12-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xtable, r-cran-psych Filename: pool/dists/noble/main/r-cran-klausur_0.12-14-1.ca2404.1_all.deb Size: 386186 MD5sum: 6048355a57886d29aba6ed3d67a26506 SHA1: 8608ad74c35079dd16d8379eb8fa170f19373c64 SHA256: 6847c41a34a4aff636eaa033e9f57f038f87c255603734844616fb508b868989 SHA512: 15b635f088ebee9bc2c209fbb92bc860b6f63fb44f95503ae060c190c503d9c06dc1de90e2257060d23c59abc332443e62d5a6706a8a777106590eb977e57820 Homepage: https://cran.r-project.org/package=klausuR Description: CRAN Package 'klausuR' (Multiple Choice Test Evaluation) A set of functions designed to quickly generate results of a multiple choice test. Generates detailed global results, lists for anonymous feedback and personalised result feedback (in LaTeX and/or PDF format), as well as item statistics like Cronbach's alpha or disciminatory power. 'klausuR' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage. Package: r-cran-kldest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kernsmooth, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kldest_1.0.0-1.ca2404.1_all.deb Size: 110926 MD5sum: 27bc2d58cf2f137e3ad20f0d6a113855 SHA1: 0314f4b23bb677155762a8883c9527bd1e547bb2 SHA256: ab1ee240c4be7dd9c620632d1cd465980cd25d7c6f63b7339de72837c7e656bd SHA512: 5eb50bcd48dd71f958c239d89a636997ad89f18604462309f7940eff2a533bf0c86bb7d8e4b9945a667e6474f11de6a7e235765e2a8680d3f6463c4dbd6e3eef Homepage: https://cran.r-project.org/package=kldest Description: CRAN Package 'kldest' (Sample-Based Estimation of Kullback-Leibler Divergence) Estimation algorithms for Kullback-Leibler divergence between two probability distributions, based on one or two samples, and including uncertainty quantification. Distributions can be uni- or multivariate and continuous, discrete or mixed. Package: r-cran-kldtools Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kldtools_1.2-1.ca2404.1_all.deb Size: 23824 MD5sum: 7bc5fb7ecf260155db4b63daab4a2076 SHA1: 78f4eec98708bd4f10ebe2f4b964ddeb0db63164 SHA256: ff825e7e203fb432d71736b82fc46541cbdcf181977dc6ed57d55e8b25b0e9a2 SHA512: 0309777ca039b3daa869ad3c7426aea501476af3dade29ee78e948085d7fd3f0361a09306eb9ebd908ba3cc19e1423d58a7be0147636348945cb97f58290f611 Homepage: https://cran.r-project.org/package=kldtools Description: CRAN Package 'kldtools' (Kullback-Leibler Divergence and Other Tools to AnalyzeFrequencies) Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator and their confidence intervals. Package: r-cran-klerrss Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-readxl, r-cran-rlang Filename: pool/dists/noble/main/r-cran-klerrss_0.2.4-1.ca2404.1_all.deb Size: 33742 MD5sum: 230d8e11862d2532b9e10aa0369363b6 SHA1: 4fcdb9d990170b9ebeec0745eaf2581df9c3da7d SHA256: 94629f4a02f99c4ec35f4c2050d498b3feb3f0f69c84e998bb013fc5976922bc SHA512: 52bac952d5e2e61cf0fbccb21e85400c1cba5f68f624fd4d2a860881546346818bf60bb512844643caf03b55b04a83a85472add92d0c5b75e4a979b5893713a9 Homepage: https://cran.r-project.org/package=KlerRSS Description: CRAN Package 'KlerRSS' (Intelligent Ranked Set Sampling with 'Excel' Integration and SRSComparison) Provides tools for Ranked Set Sampling (RSS) analysis, data import, statistical estimation, and comparison with Simple Random Sampling (SRS). The package offers a complete workflow from 'Excel' and CSV data import and cleaning to RSS implementation, efficiency evaluation, visualization, and automated reporting. Intelligent ranking procedures based on correlation analysis, regression models, and machine learning methods are included to address imperfect ranking commonly encountered in practical RSS applications. Monte Carlo simulation tools are provided for evaluating estimator performance under different sampling scenarios. Ranked Set Sampling was originally introduced by McIntyre (1952) as an efficient alternative to simple random sampling when ranking information is available at low cost. The package supports researchers, statisticians, and practitioners working in agricultural, environmental, biological, and other applied sciences. Package: r-cran-klexdatr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1910 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-chk, r-cran-covr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-klexdatr_0.1.2-1.ca2404.1_all.deb Size: 1901130 MD5sum: c4adc5025b6752f0f5d3187c7a6edbea SHA1: f2858147b9660bc3d395a747e1a3d3ac83197574 SHA256: 10de32db25c6f1ba6a617646c03b600f7c50a7e9180bd9b1143dbb6cd04f4974 SHA512: 8a2a8fa6a83b2f755dbf892c25fa6ed3febb3880040e30600698bafcb0e9e77853d14c41be8920d0a44661e1786fb40030186551a084d6f5f6c544436f08302b Homepage: https://cran.r-project.org/package=klexdatr Description: CRAN Package 'klexdatr' (Kootenay Lake Exploitation Study Data) Six relational 'tibbles' from the Kootenay Lake Large Trout Exploitation study. The study which ran from 2008 to 2014 caught, tagged and released large Rainbow Trout and Bull Trout in Kootenay Lake by boat angling. The fish were tagged with internal acoustic tags and/or high reward external tags and subsequently detected by an acoustic receiver array as well as reported by anglers. The data are analysed by Thorley and Andrusak (1994) to estimate the natural and fishing mortality of both species. Package: r-cran-klexp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-klexp_1.0.0-1.ca2404.1_all.deb Size: 22380 MD5sum: a4c10420b0d6a0609911075991a3aaaa SHA1: f59fd8d1f9135d4b76189f0d690afb8360f656b8 SHA256: 95b62653f40b9d18d98defe613416d9924846c3535012786de44aa757601f20e SHA512: 9c38d14b81b5e30a7ff3d7370c19f98f94bcdb4e641407f4388b7eab5f332c1c3e409be734315887b20c4255ad512a35c16da76f19a34927ce9b85bac9e7cb09 Homepage: https://cran.r-project.org/package=KLexp Description: CRAN Package 'KLexp' (Kernel_lasso Expansion) Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) . Package: r-cran-klic Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1926 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-cluster, r-cran-coca, r-cran-rcolorbrewer, r-cran-pheatmap Suggests: r-cran-rmosek, r-cran-tikzdevice, r-cran-mclust, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-klic_1.0.4-1.ca2404.1_all.deb Size: 738176 MD5sum: 7991393e086f42263bcdd58fbf3d1bea SHA1: f015a0141bac85ae2a0224314f74c1c3c42af21e SHA256: 76c8ec7a7aafe626188ae52a0c2d28f10f615b4e741b8a6a199b60b2c529127a SHA512: ae3d9e05068028e1ebbdc73fc41038ab341215be99cf448c0ee376d4c40c84fe6d30ecffcfdc6c1e743f9fe35ac5c0d9492ac442a1b37f387a74a766a55e4a60 Homepage: https://cran.r-project.org/package=klic Description: CRAN Package 'klic' (Kernel Learning Integrative Clustering) Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) . Package: r-cran-kliner Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kliner_0.1.1-1.ca2404.1_all.deb Size: 131276 MD5sum: 89add60b45e06c126550753b8aed18a7 SHA1: d11ced556c7917bb6b352e67e35c03965456a429 SHA256: d35ea44daa1526daa7a295f4c0f67a08e50d43b3492f522035f75f4580c6e402 SHA512: 7f88614d089572898f1424bf3848bc537194cedda0e99bc52574fd419ec441ea183ce1303f79b32eebb42c0c81c4590670dfe10e0ee53e5e6e10c5916651b8ca Homepage: https://cran.r-project.org/package=klineR Description: CRAN Package 'klineR' (Candlestick Pattern Detection and Stock Screening) Detects classical candlestick patterns and structure-based chart patterns from open, high, low, close, and volume (OHLCV) time series and provides reusable stock-screening workflows. Built-in detectors include single- and multi-candle patterns, trend structures such as double bottoms and ascending triangles, and a configurable "golden pit" recovery setup. Includes a unified API to run pattern scans across one or many symbols. Methods are informed by Nison (2001, ISBN:9780735201811) "Japanese Candlestick Charting Techniques" and Bulkowski (2021, ISBN:9781119739685) "Encyclopedia of Chart Patterns". Package: r-cran-klink Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forrel, r-cran-gt, r-cran-norstr, r-cran-openxlsx, r-cran-pedfamilias, r-cran-pedmut, r-cran-pedprobr, r-cran-pedtools, r-cran-scales, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-verbalisr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-klink_1.2.2-1.ca2404.1_all.deb Size: 705054 MD5sum: 7afb14d2c335046e598030607079ed3a SHA1: 211afa9442d4e104967178f9acde525c43493cf7 SHA256: 7303282823050df3f0c59234fcb6fcdb297eebe1b5fe9bc1e9616e58ae3db472 SHA512: 1415297569a8ea8d45c43f779377e5941a1835e97061ea2f8c3cd491cf2cd1353ca3ad4de829c020464bfc4853cc40053e2e033f471821ad1aaf75a261de367e Homepage: https://cran.r-project.org/package=KLINK Description: CRAN Package 'KLINK' (Kinship Analysis with Linked Markers) A 'shiny' application for forensic kinship testing, based on the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R) software 'Familias' and 'FamLink', but offers several unique features, including visualisations and automated report generation. The calculation of likelihood ratios supports pairs of linked markers, and all common mutation models. The program is described in Vigeland and Gilfillan (2026) . Package: r-cran-klovan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-fields, r-cran-ggally, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-gstat, r-cran-metr, r-cran-pracma, r-cran-sp, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-sf, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-klovan_0.1.0-1.ca2404.1_all.deb Size: 1408852 MD5sum: e5bb153f6aec23bcd8b0bd7646d1c752 SHA1: 978ffec9c15b9330a870ce9c4a03105fb91f5191 SHA256: f0fe8993a1ebf2ad57d63672571f5411a8bb3fecea1f60214b7d2552bb6bd51b SHA512: f03cd5a2cdc4e02a254c9d81c5463b1dbfd40a4fe59013a155025dda51ac0f1f896151bcf226a5cf98da2dc624819d4e206e2f28bdfaaaac39c32d95d07f9e7c Homepage: https://cran.r-project.org/package=klovan Description: CRAN Package 'klovan' (Geostatistics Methods and Klovan Data) A comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book 'Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the 'klovan' datasets provide a valuable and illustrative resource. Note that some methods require the 'RGeostats' package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104. Package: r-cran-klsh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blink, r-cran-plyr, r-cran-rcpp, r-cran-stringi, r-cran-snowballc Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-klsh_0.1.0-1.ca2404.1_all.deb Size: 99902 MD5sum: 513343aa082e150db544c5871ce826a9 SHA1: 90fb032347ee48071b8db978bc99a144e895471f SHA256: 36020b2a934899e658d290b74fe5aaed0b13d7626eca392844faa0aa9888fd2e SHA512: 4545dd28117e7c51638b2a1e980f88c7e2515567e7935e6bf256a1177b93ddbe6d5285bcc6a4f5b349baaf6631f3cd59c64fb198edf55e3a17a195290877daf7 Homepage: https://cran.r-project.org/package=klsh Description: CRAN Package 'klsh' (Blocking for Record Linkage) An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) , which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette. Package: r-cran-klustr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-klustr_0.1.0-1.ca2404.1_all.deb Size: 204226 MD5sum: 3dd9f76e35dff53d4eb15966517d8874 SHA1: e165f22d5fe8abf9924774e4046dcbfbb88a8471 SHA256: 352946b996a6063b255abf3f3942ce7294690a567462d509892713988c1b5bfd SHA512: 0104015c5f38cdf2a79bf8fdf65b5049471cdf64ef4c603390235f23f573653709668a7c0433279334305a8266e52198307894ae0dd78fba66fede12c8226371 Homepage: https://cran.r-project.org/package=klustR Description: CRAN Package 'klustR' (D3 Dynamic Cluster Visualizations) Used to create dynamic, interactive 'D3.js' based parallel coordinates and principal component plots in 'R'. The plots make visualizing k-means or other clusters simple and informative. Package: r-cran-km.ci Architecture: all Version: 0.5-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-km.ci_0.5-6-1.ca2404.1_all.deb Size: 67876 MD5sum: e9ef88a0117c6c835c07fc4ce3c11e41 SHA1: ab632e6a81e1caa7c978b96207e9aff997e87c68 SHA256: 6ca61dc21019bb33ef5742166033f8cb34e4805da76bd1eed42d08391961edff SHA512: f057982f26c53a83fd8c2633cb17c890aa322c9b447e2594b60e98fd3a9a9bdd91b4ca3d0581022873188aa37567056ff616e9fb94e48776844d18bb9b29a8a5 Homepage: https://cran.r-project.org/package=km.ci Description: CRAN Package 'km.ci' (Confidence Intervals for the Kaplan-Meier Estimator) Computes various confidence intervals for the Kaplan-Meier estimator, namely: Peto's CI, Rothman CI, CI's based on Greenwood's variance, Thomas and Grunkemeier CI and the simultaneous confidence bands by Nair and Hall and Wellner. Package: r-cran-kmd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rann, r-cran-proxy, r-cran-mlpack, r-cran-boot, r-cran-igraph Filename: pool/dists/noble/main/r-cran-kmd_0.1.1-1.ca2404.1_all.deb Size: 46160 MD5sum: bb8e16c6fe0781afc928382ae2839cc2 SHA1: 43f50e91134213830cb2ae615a82f58fb822701e SHA256: 76c5d7e03815c3c2033460fb9092f447b7f3482e117d3a767d84ebba1647c6ba SHA512: df97f72024c15f0a9bf5346de4466129272fad4ccf5b34619038fd28c3244f2f34191d5e29073598042d7025ac369b7b86df52bdc949af7c55098667a2bf5f62 Homepage: https://cran.r-project.org/package=KMD Description: CRAN Package 'KMD' (Kernel Measure of Multi-Sample Dissimilarity) Implementations of the kernel measure of multi-sample dissimilarity (KMD) between several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD measures the dissimilarity between multiple samples, based on the observations from them. It converges to the population quantity (depending on the kernel) which is between 0 and 1. A small value indicates the multiple samples are from the same distribution, and a large value indicates the corresponding distributions are different. The population quantity is 0 if and only if all distributions are the same, and 1 if and only if all distributions are mutually singular. The package also implements the tests based on KMD for H0: the M distributions are equal against H1: not all the distributions are equal. Both permutation test and asymptotic test are available. These tests are consistent against all alternatives where at least two samples have different distributions. For more details on KMD and the associated tests, see Huang, Z. and B. Sen (2024) . Package: r-cran-kmeans.knn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factoextra, r-cran-cluster, r-cran-ggplot2, r-cran-assertthat, r-cran-class, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmeans.knn_0.1.0-1.ca2404.1_all.deb Size: 40428 MD5sum: 7ee4c6ab7a63710640cf13227ffe0706 SHA1: c8b56f9cbccd275c0720c140f277b44c19d68635 SHA256: 6774dab56ef4a9cd617b7c52a1f18e0a2a9d6c5932ca1d734b3aa75ebb9feac0 SHA512: 90e5cc10884a33c0ffa983dcc4c211a467a8bc04cf7b3324bd1e08026002b4ae7edffef6b5d51fe0188c63c77918ac764576018b5ee51c46bdd8db07c7247229 Homepage: https://cran.r-project.org/package=KMEANS.KNN Description: CRAN Package 'KMEANS.KNN' (KMeans and KNN Clustering Package) Implementation of Kmeans clustering algorithm and a supervised KNN (K Nearest Neighbors) learning method. It allows users to perform unsupervised clustering and supervised classification on their datasets. Additional features include data normalization, imputation of missing values, and the choice of distance metric. The package also provides functions to determine the optimal number of clusters for Kmeans and the best k-value for KNN: knn_Function(), find_Knn_best_k(), KMEANS_FUNCTION(), and find_Kmeans_best_k(). Package: r-cran-kmed Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kmed_0.4.2-1.ca2404.1_all.deb Size: 268764 MD5sum: 85a9703b005593a588c9bbe829828f32 SHA1: d8b5706a32a209c60947dd1dc96e8761bd13e116 SHA256: 870786a77f150d870a98cec2bbc4940d8cd2bf52880870c216ec2f9c0051f5cf SHA512: 3060678ee3d25dee1a22da7ae84d521979ad8052ec2d3081fcfc6090f74d5c9ad3cc65e9b4513c0a74e0d9db7314cc162bfe7ec2c280912761cfc1b128528a63 Homepage: https://cran.r-project.org/package=kmed Description: CRAN Package 'kmed' (Distance-Based k-Medoids) Algorithms of distance-based k-medoids clustering: simple and fast k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. Calculate distances for mixed variable data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and relative criteria. The internal criteria includes silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages. The cluster result can be plotted in a marked barplot or pca biplot. Package: r-cran-kmedians Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-genieclust, r-cran-gmedian, r-cran-mvtnorm, r-cran-capushe, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-kmedians_2.2.0-1.ca2404.1_all.deb Size: 61176 MD5sum: 61ea3dc596e2ba87e72798973eecb61e SHA1: c91606c41fafd03ed71ab3cea585c125bb2866bf SHA256: fd8dcdb47c6ab89a82597ef32e5160f7cdc0de5208f8f86fffadda496a123f46 SHA512: 2295e7082769bde0019a48ba76b215d270426462943f488a05a8dce711514967f5744ebcbffb2cdbcdc7b6493a8da38af534e01bc81ac29ca513ddb7ea0da9f0 Homepage: https://cran.r-project.org/package=Kmedians Description: CRAN Package 'Kmedians' (K-Medians) Online, Semi-online, and Offline K-medians algorithms are given. For both methods, the algorithms can be initialized randomly or with the help of a robust hierarchical clustering. The number of clusters can be selected with the help of a penalized criterion. We provide functions to provide robust clustering. Function gen_K() enables to generate a sample of data following a contaminated Gaussian mixture. Functions Kmedians() and Kmeans() consists in a K-median and a K-means algorithms while Kplot() enables to produce graph for both methods. Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. . Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 . Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians" Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. . Package: r-cran-kmers Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-bioc-biocgenerics, r-bioc-pwalign Suggests: r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown, r-cran-unittest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmers_2.1.0-1.ca2404.1_all.deb Size: 62874 MD5sum: af45ea2af42609137a3473255ecc5d92 SHA1: 72133faaa6730a3ab33e46cae98ae3e9f6ca98c8 SHA256: 1e432308393d2aef10534cc2a86b9a3a9549399859270ba50c3b17d8cd459672 SHA512: 628eeec5e81567af2176267916469a0fe5734dda578ba7e572c44b186ca8712118cc4d1f004ed457b54968c2903fd6e0dcc5341860fa2a0b22f3b1076938c73c Homepage: https://cran.r-project.org/package=kmeRs Description: CRAN Package 'kmeRs' (K-Mers Similarity Score Matrix and HeatMap) Similarity Score Matrix and HeatMap for nucleic and amino acid k-mers. Similarity score is evaluated by Point Accepted Mutation (PAM) and BLOcks SUbstitution Matrix (BLOSUM). The 30, 40, 70, 120, 250 and 62, 45, 50, 62, 80, 100 matrix versions are available for PAM and BLOSUM, respectively. Alignment is evaluated by local and global alignment. Package: r-cran-kmi Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mitools, r-cran-survival Filename: pool/dists/noble/main/r-cran-kmi_0.5.5-1.ca2404.1_all.deb Size: 73920 MD5sum: 05f32f88b07dd84a5097bd5300b51826 SHA1: c3a722ad0d674a05a3dfe48c73240390fd0c816d SHA256: f9adf0474a9c40fa4cc6ec4b1c070e8e75fc62459bd90292946af502f97840a3 SHA512: 71492eda505f48f354a0b0811a064206b242695690b538a8b88f10905f72273b4562e88d94e70895fa16e0989414e6fbf88acc28df32d3292564700289b4a997 Homepage: https://cran.r-project.org/package=kmi Description: CRAN Package 'kmi' (Kaplan-Meier Multiple Imputation for the Analysis of CumulativeIncidence Functions in the Competing Risks Setting) Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 ). Package: r-cran-kml3d Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-rgl, r-cran-misc3d, r-cran-longitudinaldata, r-cran-kml Filename: pool/dists/noble/main/r-cran-kml3d_2.5.0-1.ca2404.1_all.deb Size: 297722 MD5sum: ae0778ec93ef51cfbd56dfcee4ad7b40 SHA1: be0f7f11ff06e41259e7534e206d2bd3a10fb3b7 SHA256: 82a8d8756549f984d0479a77a320e808dc6878ea9164f6622dca79411af79fc9 SHA512: 3f77cbc06ecd4952e67fd90000ccfb96e9e61a3f4532e1264f2b2741893a3d14dbdafdd885ac537b7d1167c9f4158121320ef9a2237c8a62da88c9ea754c33f8 Homepage: https://cran.r-project.org/package=kml3d Description: CRAN Package 'kml3d' (K-Means for Joint Longitudinal Data) An implementation of k-means specifically design to cluster joint trajectories (longitudinal data on several variable-trajectories). Like 'kml', it provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical interface for choosing the 'best' number of clusters. In addition, the 3D graph representing the mean joint-trajectories of each cluster can be exported through LaTeX in a 3D dynamic rotating PDF graph. Package: r-cran-kmltoshape Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-stringr, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmltoshape_0.1.0-1.ca2404.1_all.deb Size: 24802 MD5sum: 50866cf4def912d343840d9067f28122 SHA1: 9b48b2a9e20a92da238a70494d67bd0f4d016fdb SHA256: e4b07b3cfc19bc7c46ad73a9f68121014b3f21465f632b417a880c910af60934 SHA512: e2ca0359cb22b49c12740631a84ff37c9ee0df08188c9910a968e2873131fd4d2f863e95c7438b85b75731bcd77a91df722f0b47c3add66befc1165652c51c11 Homepage: https://cran.r-project.org/package=KMLtoSHAPE Description: CRAN Package 'KMLtoSHAPE' (Preserving Attribute Values: Converting KML to Shapefile) The developed function is designed to facilitate the seamless conversion of KML (Keyhole Markup Language) files to Shapefiles while preserving attribute values. It provides a straightforward interface for users to effortlessly import KML data, extract relevant attributes, and export them into the widely compatible Shapefile format. The package ensures accurate representation of spatial data while maintaining the integrity of associated attribute information. For details see, Flores, G. (2021). . Whether for spatial analysis, visualization, or data interoperability, it simplifies the conversion process and empowers users to seamlessly work with geospatial datasets. Package: r-cran-kmodr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmodr_0.2.0-1.ca2404.1_all.deb Size: 24508 MD5sum: c38ea78377b9717446925bf6eb816a90 SHA1: e88eeea7305589c6d18374e47198ec2b84350b57 SHA256: e167aca3f371995da8007d545d1ca3cc43471876748080a71205b24a10df8fe0 SHA512: 551b1f13ef94de1d79ce4a23e0334b7d500912d2edc23c9d485197248c7ef6fb13c88290e676b16a1f9953afdfeb0e0f58c090203b178e030a81edc5b0bc1cc2 Homepage: https://cran.r-project.org/package=kmodR Description: CRAN Package 'kmodR' (K-Means with Simultaneous Outlier Detection) An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space. Package: r-cran-kmsurv Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kmsurv_0.1-6-1.ca2404.1_all.deb Size: 126500 MD5sum: 61f52aa5453ed96ff69e81a6362fb07e SHA1: 23645ca35c5e0db7633fa92fd84f5ecf398cf451 SHA256: 492e4f274407e52503301b9665b693a666c1ec60cb56a38d6cf2d5fe303d8a39 SHA512: e554cace3b09b4149dd824c2f5da19be7085b205803e3b3b584ef221a81b169cc0d475ddfba0fcaad9a65635453fe3d9eedcdcaf9e870cb9482c28701b475401 Homepage: https://cran.r-project.org/package=KMsurv Description: CRAN Package 'KMsurv' (Datasets from Klein and Moeschberger (1997), Survival Analysis) Datasets and functions for Klein and Moeschberger (1997), "Survival Analysis, Techniques for Censored and Truncated Data", Springer. Package: r-cran-kmunicate Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-checkmate, r-cran-cowplot, r-cran-ggplot2, r-cran-pammtools, r-cran-tidyr Suggests: r-cran-broom, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-simsurv, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-kmunicate_0.2.5-1.ca2404.1_all.deb Size: 2101202 MD5sum: d7b523dc46ec0b2192fc050428e336fe SHA1: 4f7268288355b472c871342698049fe241c77a7f SHA256: cf457204bb47afe9e584ccccc63c9249bec8f703c100931d6dea3b2c2e3480ae SHA512: 2618fc231c218f1f0ade8f37a442895a85842e529aa316461e652a703ef64ba31e11390a59cbad5741bd712021dd8b3bbcef545d813cd655ad8377c63fb17e1e Homepage: https://cran.r-project.org/package=KMunicate Description: CRAN Package 'KMunicate' (KMunicate-Style Kaplan–Meier Plots) Produce Kaplan–Meier plots in the style recommended following the KMunicate study by Morris et al. 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Package: r-cran-knapsacksampling Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-knapsacksampling_0.1.1-1.ca2404.1_all.deb Size: 27636 MD5sum: 6d167cbecd14462e059655cee7daf7c3 SHA1: 7ec7d8fe7eec8927d470eac2915c55f6a4c149e2 SHA256: fe2417681685cfaa4adbeb8cec09fb2f4336c9eb787319670fc8c5d45e112e92 SHA512: eee8ea3f1a0d10feb25b58f29fd1d51750b8814788fd605929a67ee5ede45be41c9233709f42b5a9877f4803ad838d4f61ef192141015792697c60eb8eaaed39 Homepage: https://cran.r-project.org/package=KnapsackSampling Description: CRAN Package 'KnapsackSampling' (Generate Feasible Samples of a Knapsack Problem) The sampl.mcmc function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints. Package: r-cran-kneearrower Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kneearrower_1.0.0-1.ca2404.1_all.deb Size: 51210 MD5sum: c5ba9a8ace24239247ab765e721e3455 SHA1: f9251958c08db2add4872064827375c913e9ee19 SHA256: 9eb4167f6cf2960655ac0af7e2e4f2f7011232ac476174928a3c265a09b01400 SHA512: 73959333f6382545701d91166bb7d42535545bfe9ca4d7b4c6805aaf13a95a1dc057937b115f4371071b0fb3117c2aef48de70514f8bae48d46861f928734d1d Homepage: https://cran.r-project.org/package=KneeArrower Description: CRAN Package 'KneeArrower' (Finds Cutoff Points on Knee Curves) Given a set of points around a knee curve, analyzes first and second derivatives to find knee points. 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In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function. 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The package is primarily aimed at authoring in the R 'markdown' format, and can provide outputs for web-based authoring such as linked text for inline citations. Cite using a 'DOI', URL, or 'bibtex' file key. See the package URL for details. 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One of them is predicting values with k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm. These are carried out in parallel using multiple threads. 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For more information on the formulation of this similarity metric please see Trupiano (2021) . 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For more details see Schaefer (1954) , Pella and Tomlinson (1969) and MacCall (2002) . 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For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577. Package: r-cran-knockoffhybrid Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-spatest Filename: pool/dists/noble/main/r-cran-knockoffhybrid_1.0.1-1.ca2404.1_all.deb Size: 62866 MD5sum: 813a83d80332027ea2132d5e8059d5af SHA1: 8e1d58e827de3c9f005c5d37df06246ab3ea5c23 SHA256: f808f78f15cfa6542653ec663e5e47c96f9ce33d58c832144d37ee59ba89a865 SHA512: cd50fae53d984bc873ae6d8df5ec2f60b8e38749cefc7439ae6c4de44061415b364ece5c654ce06edfb2eab24f78b26ccce974b290f7b6c7c9ddf021e8aac0c3 Homepage: https://cran.r-project.org/package=KnockoffHybrid Description: CRAN Package 'KnockoffHybrid' (Hybrid Analysis of Population and Trio Data with KnockoffStatistics for FDR Control) Identification of putative causal variants in genome-wide association studies using hybrid analysis of both the trio and population designs. The package implements the method in the paper: Yang, Y., Wang, Q., Wang, C., Buxbaum, J., & Ionita-Laza, I. (2024). KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. The American Journal of Human Genetics, in press. Package: r-cran-knockoffscreen Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-seqminer, r-cran-bigmemory, r-cran-compquadform, r-cran-data.table, r-cran-spatest, r-cran-irlba Filename: pool/dists/noble/main/r-cran-knockoffscreen_0.3.0-1.ca2404.1_all.deb Size: 84146 MD5sum: 2f5822a817d2346c0ff8bcc97f790596 SHA1: ad6767f181fd0e4e7888b9c8515a268d2d58c300 SHA256: c246fb5f70b0ee8a8795427d0c16cd3d4ccb13a3b8ca6cde4b812f12d0951c46 SHA512: c4c508bd2297bdca1707757128bd44f0fba261d24a3fc1e1e41c5cceed4177b106c96e15da7da4a02da964b4e81524e8567b30fe37ed24875102be0b4188dc41 Homepage: https://cran.r-project.org/package=KnockoffScreen Description: CRAN Package 'KnockoffScreen' (Whole-Genome Sequencing Data Analysis via Knockoff Statistics) Functions for identification of putative causal loci in whole-genome sequencing data. The functions allow genome-wide association scan. It also includes an efficient knockoff generator for genetic data. Package: r-cran-knockofftrio Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-knockofftrio_1.1.0-1.ca2404.1_all.deb Size: 75928 MD5sum: df6666faed6c1d3d215b8f9a5008f7a7 SHA1: ecc146c410d981695c134aa3368cd07cf272cb5e SHA256: 1d47fdc93922901225fbe745d4909cd6752278fd2971e94697114824019b4788 SHA512: 029ed497dbebe40a517cf6fe8ff960132097f4a4f099b88d5c18f50ab919c15193a8d5ac8a0631d6dc8d396d6bae242d13baa5d2653ec940ae2a9e036c695990 Homepage: https://cran.r-project.org/package=KnockoffTrio Description: CRAN Package 'KnockoffTrio' (GWAS with Trio and Duo Data using Knockoff Statistics for FDRControl) Identification of putative causal variants in genome-wide association studies with trio and duo families. The package calculates the W feature statistics from KnockoffTrio and p-values from the family-based association test (FBAT) using trio and/or duo data. Compared to previous versions, a significant improvement has been made in Version 1.1.0 to allow the package to be applied not only to trio families but also to duo families. The package implements the methods in the paper: "Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776." Package: r-cran-knotr Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2747 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-knotr_1.0-4-1.ca2404.1_all.deb Size: 1740500 MD5sum: 6d4ba606b46844e41668de7955690f8f SHA1: 93e3348c6364e7fb7b36cc8d698110f5bddda466 SHA256: 56e1526304dac13b52de6d7971d5012665b935389b66c7da9b354661bee1d299 SHA512: 7244cf9eda308c61fbba50a6839b073b1d6c641c7e0ae915bda61dc88550e5d7a7073af10b32691f972e309cae19a198bfa68a316b9e220a8dfd0bf454d5ffbb Homepage: https://cran.r-project.org/package=knotR Description: CRAN Package 'knotR' (Knot Diagrams using Bezier Curves) Makes visually pleasing diagrams of knot projections using optimized Bezier curves. Package: r-cran-knowbr Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4366 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fossil, r-cran-mgcv, r-cran-plotrix, r-cran-sp, r-cran-vegan Suggests: r-cran-raster, r-cran-rgbif, r-cran-usdm, r-cran-car, r-cran-idpmisc, r-cran-psych, r-cran-candisc Filename: pool/dists/noble/main/r-cran-knowbr_2.2-1.ca2404.1_all.deb Size: 3585936 MD5sum: 9ec27c3069e8193ba6225248494cf13f SHA1: 68ecc2afd8949fee0431cea9e2c374cf584cb22e SHA256: 40180be5a0ecbb8a204f7c3bc4e6f2d0882faf8a35785e2d9ad63d8054bf9a37 SHA512: d851c877199625d510a98c16e25a684d3904154aa15303d91465b49e2e91b320a66c00e9b6d0e06bb41a542e665e7d52dc76d1051d5cb2733b42796e6c7f232f Homepage: https://cran.r-project.org/package=KnowBR Description: CRAN Package 'KnowBR' (Discriminating Well Surveyed Spatial Units from ExhaustiveBiodiversity Databases) It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. 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Package: r-cran-kntnr Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-rstudioapi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kntnr_0.4.4-1.ca2404.1_all.deb Size: 50576 MD5sum: d752bd50e601947685d56a85bd0d661a SHA1: 2d5dd60b14b69cbf53555924add68f94201cfce8 SHA256: 1ad02ae448d97c865bb3006a888d63eac6436260ee04cf7fe59c26c73cd96bcd SHA512: ac3b9a42f4cc15031f86dce19284fd050a58e8a22352127873ff564feb81008c6b76fed789dd369405726074ee78f2990af4da7ce5771c5b88a54faba8669fe0 Homepage: https://cran.r-project.org/package=kntnr Description: CRAN Package 'kntnr' (R Client for 'kintone' API) Retrieve data from 'kintone' () via its API. 'kintone' is an enterprise application platform. Package: r-cran-koboconnectr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-curl, r-cran-jsonlite, r-cran-mime, r-cran-openssl, r-cran-r6, r-cran-dplyr, r-cran-readxl, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-koboconnectr_2.0.0-1.ca2404.1_all.deb Size: 79672 MD5sum: afaca6f4a34e20b08fec6acf2887e64a SHA1: f263abdc2105e0e1af7babd4375f42a91a4f1288 SHA256: ad2e63736455be3fed42abc008d5dd342330a7b0c82193f80ae51b1d55816556 SHA512: abfbf93d1a39c956e31b01d59c5042c9cb5c7208484f6b12c3ab1e8a2a06d3dbc0d1136f7935c2366a51a5ad6d1710ddbd57baced40225cb184702820cd5335d Homepage: https://cran.r-project.org/package=KoboconnectR Description: CRAN Package 'KoboconnectR' (Download Data from Kobotoolbox to R) Wrapper for 'Kobotoolbox' APIs ver 2 mentioned at , to download data from 'Kobotoolbox' to R. Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'. Package: r-cran-kofdata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-xts, r-cran-zoo Suggests: r-cran-tstools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kofdata_0.2.1-1.ca2404.1_all.deb Size: 48346 MD5sum: 52ec233cf68ac48943d7d8a5b70f515b SHA1: 6f4416a4c8a4e5e87f9a723966b3adef20ea7ab4 SHA256: fea461990d9775e3ac2205d5de79d29cdf705bf7e793c8e8dbf5c567221eee2b SHA512: 6821656d7ff8a35bcd13fe68c7247fb5578f6591ddc3840c11c5123a3782fb30f991357e7bf5e2df665d1c74ab41e422979cfe961b93a33a048bfd7cdac1ed35 Homepage: https://cran.r-project.org/package=kofdata Description: CRAN Package 'kofdata' (Get Data from the 'KOF Datenservice' API) Read Swiss time series data from the 'KOF Data' API, . The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token. Package: r-cran-kofm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensormiss, r-cran-mefm, r-cran-rspectra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kofm_1.1.1-1.ca2404.1_all.deb Size: 53652 MD5sum: b99f75f29fe009e86e5676f2da53866f SHA1: 3619c2537a1cfecff9e8dbb960c0079c8bdcaf60 SHA256: f604689134dffba1dc39944b45355d9ac67dd24fc79c2bb1c6d1d9741d97a68a SHA512: 27ad0f0a1a30f1ed9ec7a0e60eee59b52330dfa7a394d50381fe435d2b5d327292cd9bf63795304e09e06b197bcb79be69cf04ba12b23abe6950d880b12c8584 Homepage: https://cran.r-project.org/package=KOFM Description: CRAN Package 'KOFM' (Test the Kronecker Product Structure in Tensor Factor Models) To test if a tensor time series following a Tucker-decomposition factor model has a Kronecker product structure. Supplementary functions for tensor reshape and its reversal are also included. Package: r-cran-kofn Architecture: all Version: 0.4.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-likelihood.model, r-cran-compositional.mle, r-cran-generics, r-cran-dist.structure, r-cran-algebraic.dist, r-cran-flexhaz Suggests: r-cran-algebraic.mle, r-cran-maskedcauses, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kofn_0.4.0-1.ca2404.2_all.deb Size: 1066372 MD5sum: 41819e49591d2e125d1b52b41d9968ad SHA1: 2ff5516daae61269f05d54406543b914e596f296 SHA256: b98a70a6e0c5ce19bc1859e133c075e190843cbeb6e44fc8e9904034d85ce316 SHA512: bac72c9e1c557197c58add6ea15e0723972171e02f3c03773bda5f412736ff9b072d13afeeba76c1bf8b168439b02b0c29cf21a68e8a2288bed12d4b75bd46ce Homepage: https://cran.r-project.org/package=kofn Description: CRAN Package 'kofn' (Maximum Likelihood Estimation for k-Out-of-n System Data) Maximum likelihood estimation of component lifetime parameters from system-level observations of k-out-of-n systems. Supports exponential and Weibull component distributions under multiple observation schemes: Scheme 0 (system lifetime only), Scheme 1 (periodic inspection), and Scheme 2 (complete monitoring). Provides an EM algorithm for Weibull parallel systems and Fisher information comparison across schemes. The k-out-of-n framework unifies series (k=1) and parallel (k=m) systems as a censoring problem on component lifetimes. Conforms to the 'likelihood.model' generics and returns fitted objects compatible with 'algebraic.mle'. The data-generating process and topology infrastructure (system survival, density, signature, structure function, importance measures) are delegated to the 'dist.structure' package; 'kofn' focuses exclusively on inference for the k-out-of-n family. Package: r-cran-kofnga Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory Filename: pool/dists/noble/main/r-cran-kofnga_1.3-1.ca2404.1_all.deb Size: 45696 MD5sum: d485d997bd99b76e00966aca4862b4e1 SHA1: c4cd0fbd1bd235e80d0982e8208af71055c91ef2 SHA256: fc1b808c94ef0dc797aed6e18f2731ccc2162b91b52cf65fd9e72fdd1f4cbada SHA512: d6659298f94f264b49dbf4ea815d149e237723aee76f6cddd98d55d34d1836a9f8c2cabc7b63639cdaee61dd2557a3176031ba08beee0814064433b79d9d27bb Homepage: https://cran.r-project.org/package=kofnGA Description: CRAN Package 'kofnGA' (A Genetic Algorithm for Fixed-Size Subset Selection) Provides a function that uses a genetic algorithm to search for a subset of size k from the integers 1:n, such that a user-supplied objective function is minimized at that subset. The selection step is done by tournament selection based on ranks, and elitism may be used to retain a portion of the best solutions from one generation to the next. Population objective function values may optionally be evaluated in parallel. 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The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed. Package: r-cran-kokudosuuchi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kokudosuuchi_1.0.0-1.ca2404.1_all.deb Size: 377584 MD5sum: b0997c3d172547a2ac9e9f9035bfb5c8 SHA1: 307f4ceb128a5d07a1a49e9e1f4dad1c947ec43c SHA256: 10ea27ef4584da0b5c239908eddb8c20fc69165c54cd4a3d4ac1866e37ba1740 SHA512: 9ed26fdab4589f9575c7f58c0b7b09e2f0f448186278e3749c5d10c8b38a47fa22b674b87f70f34eefb8df11545e6cb61f2aefbac7b672b0c5b14814467fcdd3 Homepage: https://cran.r-project.org/package=kokudosuuchi Description: CRAN Package 'kokudosuuchi' (Utilities for 'Kokudo Suuchi') Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government. See for more information. Package: r-cran-kolaide Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kolaide_0.0.1-1.ca2404.1_all.deb Size: 97328 MD5sum: ef27221106aaf82f8daf85f874df7b88 SHA1: 6bc792f27d834264950e59a0c7f81f5b9bf61acf SHA256: dfb7ecbd40a09ebacb109848d41c8a8340bcf3dc0a9028da461f37e6328772fb SHA512: b433fd097e204b7d6cf53b80dbec5ae689f8f2b100eb67fe761114b090ea85e2d61aa6f0c56e0810e46cc02c06c71eafe3dd8fa998e1782e2dca9b8cc9c1c087 Homepage: https://cran.r-project.org/package=KOLaide Description: CRAN Package 'KOLaide' (Pick and Plot Key Opinion Leaders from a Network GivenConstraints) Assists researchers in choosing Key Opinion Leaders (KOLs) in a network to help disseminate or encourage adoption of an innovation by other network members. 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The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000) . Two-means clustering is described in Hessels et al. (2017), . The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010),. A documentation of the 'kollaR' can be found in Kleberg et al (2026) . Cite this paper when using 'kollaR' See a demonstration in the URL. 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Provides tools to specify systems of behavioral equations and accounting identities, transform and manage time series, simulate from the posterior using a Metropolis-within-Gibbs sampler, and generate unconditional and conditional forecasts with user-defined priors and restrictions. Methods are described in Rathke A. and Sarferaz S. (forthcoming) "Bayesian Estimation of Simultaneous Equations Model". Package: r-cran-komaletter Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-komaletter_0.5.0-1.ca2404.1_all.deb Size: 1211548 MD5sum: af3fd846d12db56cb8dc6f8e67eb6355 SHA1: 7dc786068cb3036b7cdc63e221c0ecdfd30527fa SHA256: fc19d2fd3228da1f6f927cd6cad53f392de3ccb42075023a4686b9585e18b3fb SHA512: fdf055a1534eecb07c88056fc15208f6aa0256f32de2d827a23cc8107ec0e098b2024e01d7b4616dd70dad1f4fea2ef3af89200397b94a6bab7a4b9d9da0d714 Homepage: https://cran.r-project.org/package=komaletter Description: CRAN Package 'komaletter' (Simply Beautiful PDF Letters from Markdown) Write beautiful yet customizable letters in R Markdown and directly obtain the finished PDF. Smooth generation of PDFs is realized by 'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class. 'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes with emphasis on typography and versatility. 'KOMA-Script' is particularly useful for international writers as it handles various paper formats well, provides layouts for many common window envelope types (e.g. German, US, French, Japanese) and lets you define your own layouts. The package comes with a default letter layout based on 'DIN 5008B'. Package: r-cran-konfound Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-lavaan, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-lme4, r-cran-tibble, r-cran-ggrepel, r-cran-pbkrtest, r-cran-ppcor Suggests: r-cran-covr, r-cran-devtools, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-roxygen2, r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-konfound_1.0.3-1.ca2404.1_all.deb Size: 292596 MD5sum: a9a89332170fa9b888ffa530404eee30 SHA1: cdc6db0c7582b1ab2990174b4730b812b2738dca SHA256: 3b5e960d488ea0e50ef7eecfafc80739ef9acd70910facd52f115e30db4d6c5a SHA512: 1c83124813b29b2ee97e6dcd0218af7609c6f74e1453ac23ddf06ff77e99cde955a3ef4bef6d3c960c2c884a5dbc3f1b1b38befaa395904765104ea585f42bf3 Homepage: https://cran.r-project.org/package=konfound Description: CRAN Package 'konfound' (Quantify the Robustness of Causal Inferences) Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) and Frank et al. (2013) extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively. Package: r-cran-kor.addrlink Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringdist, r-cran-stringi Filename: pool/dists/noble/main/r-cran-kor.addrlink_1.0.1-1.ca2404.1_all.deb Size: 1340522 MD5sum: 5f265e36dc7a8595296db1abf5933de8 SHA1: d704dc3ce3a9eff82e64d572946775e7415b8430 SHA256: 85ef262794fdf937f580777da9b916c4381cf8b725321f08d7b2f5733a24342e SHA512: 9586843f7100050f81498f93881ee1265c85ee6ce118b8dec330f1bc35ba88bf2594c43b47d867d4e134abe3c52ca0caf8cb4dd7f23f53c133980765740ed453 Homepage: https://cran.r-project.org/package=KOR.addrlink Description: CRAN Package 'KOR.addrlink' (Matching Address Data to Reference Index) Matches a data set with semi-structured address data, e.g., street and house number as a concatenated string, wrongly spelled street names or non-existing house numbers to a reference index. The methods are specifically designed for German municipalities ('KOR'-community) and German address schemes. Package: r-cran-korpus.lang.en Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-korpus, r-cran-sylly.en Filename: pool/dists/noble/main/r-cran-korpus.lang.en_0.1-4-1.ca2404.1_all.deb Size: 21092 MD5sum: 1817a928564f199b518825c6e1d776c4 SHA1: c0c3e534909b73bcb8d2c176b38521de815f3741 SHA256: 8955c7ea4448899e2203bfcfd9dbfacbabcf5f377cb53c48af1080e8958e6bc1 SHA512: 467d8334fbfde38e2be91cf1edf8748c052042e455d9a4c37f6e0866097d3492e06f66799c80cf9ec3a4d420b5e8f236b4775f9e2d56a2af22bf1011b96f8873 Homepage: https://cran.r-project.org/package=koRpus.lang.en Description: CRAN Package 'koRpus.lang.en' (Language Support for 'koRpus' Package: English) Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (). Package: r-cran-korpus Architecture: all Version: 0.13-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sylly, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat, r-cran-tm, r-cran-snowballc, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-korpus.lang.en Filename: pool/dists/noble/main/r-cran-korpus_0.13-9-1.ca2404.1_all.deb Size: 1278540 MD5sum: add1d159b0b0f3e11ad4db27fbb3b2c5 SHA1: e0f87fcff1e9a28c566676159c76d1b7c7b73af0 SHA256: b38a8f2a17524c12ee0979402de36483bb2a4b1b6dcdb4ad5d4fccd1f5d480e7 SHA512: 4264272d9333c16d80bb1696161738732b456f843b8d0a954b7e4b56bf4e0e2ecc136ae0b3ac4d0e0dadd55f31192ec77611f139d3046afa597bb1ddf89c1ddf Homepage: https://cran.r-project.org/package=koRpus Description: CRAN Package 'koRpus' (Text Analysis with Emphasis on POS Tagging, Readability, andLexical Diversity) A set of tools to analyze texts. Includes, amongst others, functions for automatic language detection, hyphenation, several indices of lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch, SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also provided, to enable frequency analyses (supports Celex and Leipzig Corpora Collection file formats) and measures like tf-idf. Note: For full functionality a local installation of TreeTagger is recommended. It is also recommended to not load this package directly, but by loading one of the available language support packages from the 'l10n' repository . 'koRpus' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (). Package: r-cran-kosel Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ordinalnet Filename: pool/dists/noble/main/r-cran-kosel_0.0.1-1.ca2404.1_all.deb Size: 36386 MD5sum: 987d643fc4ef7aa1954161a032f14f15 SHA1: 0376c661d4c3adca31e357d0de299abad1466408 SHA256: 65fc166e8846506c0cd99ef1c2d047f6881357a4843c39261bb6baac4900faa3 SHA512: c017396db38aa6fd0e493a1868ac107118cc7fdddf41859fee866a6a952fe61b6d5447a454463256bb79e9026dd5f4b40526f8915d7ed0ddcf4e264f1fb2cbf5 Homepage: https://cran.r-project.org/package=kosel Description: CRAN Package 'kosel' (Variable Selection by Revisited Knockoffs Procedures) Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) . Package: r-cran-kosis Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-kosis_0.0.1-1.ca2404.1_all.deb Size: 44376 MD5sum: 613d80401e334cfe5742ce916f75b275 SHA1: fee87d3930b99bb991692aa9cddfd17e18c7f45d SHA256: 344886120351a791a37e41865c1f09910ee001b704d8b2429c4009c235660f47 SHA512: bf0ba465e3d176c9c003cd307575481d51d2d5affc0b963924940d9f1e345a64ef7667c6da6493de8106ed05087ccd395b42b0ddbdc0d6aeea6fe12b54be2ce9 Homepage: https://cran.r-project.org/package=kosis Description: CRAN Package 'kosis' (Korean Statistical Information Service (KOSIS)) API wrapper to download statistical information from the Korean Statistical Information Service (KOSIS) . Package: r-cran-kotory Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-robustbase Suggests: r-cran-lmtest, r-cran-skedastic, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kotory_0.1.0-1.ca2404.1_all.deb Size: 84690 MD5sum: c5cfb71dd66498b5d78befc5b6e83ffd SHA1: f3da14d2303925565a66b00bf4716e5b7451f3cc SHA256: 4f97128c5550b350da271ddb7f9b2c7d365a1d1d9d52c26757b18d28981ca139 SHA512: 2c78bb489fed2e000a9f6e69389d1150d0229f2030659b54b2a8a0f2aa5f2c9c566f058d192c232a292fb15ac83a1615c47932dd594fae32a84289e7e3a5e3b8 Homepage: https://cran.r-project.org/package=KOTORY Description: CRAN Package 'KOTORY' (Robust Three-Group Tests for Heteroscedasticity in LinearRegression) Tests for heteroscedasticity in the linear regression model that sort the data by a regressor, split them into three equal parts and compare the error scale of the parts. The ordinary least squares version 'kah3.test()' refers the ratio of the largest to the smallest residual mean square to its exact null distribution, Hartley's maximum F-ratio with three groups; the robust version 'kah.robust.test()' replaces the mean squares by least trimmed squares scales, so that outliers neither create nor hide heteroscedasticity, and refers the ratio to a maximum F-ratio with simulated effective degrees of freedom, to a Monte Carlo reference or to a residual bootstrap. The distribution, density, quantile and random generation functions of the maximum F-ratio are provided, together with 'run.all.het()', which runs the proposed tests next to the Goldfeld-Quandt, Breusch-Pagan, White and robust modified Goldfeld-Quandt tests in one call. For more details see Hartley (1950) , Goldfeld and Quandt (1965) and Rousseeuw (1984) . Package: r-cran-kpart Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps Filename: pool/dists/noble/main/r-cran-kpart_1.2.2-1.ca2404.1_all.deb Size: 16900 MD5sum: a8d3a3b636db6b6a478e60d560c2d8ee SHA1: 51344966f585dde009836f647ed37f58e0f121e4 SHA256: 293abd556d8c53b9469486130ad53cf0547a570b135fab4b2674c9f31fb5048c SHA512: aa3f1bde59b6aa25a951b21b9561cc20a6f1e32dc1b02aae809a9d29c5378d8d04f740b56b4ae27bf7726d0a2ae9435844c6fc3b588e47bdc4a4c89e2366ffde Homepage: https://cran.r-project.org/package=Kpart Description: CRAN Package 'Kpart' (Cubic Spline Fitting with Knot Selection) Cubic spline fitting along with knot selection, includes support for additional variables. Package: r-cran-kpc Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kernlab, r-cran-data.table, r-cran-rann, r-cran-proxy, r-cran-mlpack Filename: pool/dists/noble/main/r-cran-kpc_0.1.4-1.ca2404.1_all.deb Size: 94446 MD5sum: f1d3a1c130bb4972b36181d86a8f3e4d SHA1: 7cb98eb115fb7546e16a726fe02c0f880a14a2f9 SHA256: ee8610808e8995d5f822b651495959d8babb79f0142e20a4d10af85357937f98 SHA512: 687e188f95708f0c24284ca94ccb97f9a40897e6b2e0433ff8547a656a1d0d25eb4a37b464b6883064777366255fac91a8b299f2456f4d1e347fcf65893e0e38 Homepage: https://cran.r-project.org/package=KPC Description: CRAN Package 'KPC' (Kernel Partial Correlation Coefficient) Implementations of two empirical versions the kernel partial correlation (KPC) coefficient and the associated variable selection algorithms. KPC is a measure of the strength of conditional association between Y and Z given X, with X, Y, Z being random variables taking values in general topological spaces. As the name suggests, KPC is defined in terms of kernels on reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is consistent under very weak conditions. The other empirical estimator, defined using conditional mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm based on the RKHS based estimator. For more details on KPC, its empirical estimators and its application on variable selection, see Huang, Z., N. Deb, and B. Sen (2022). “Kernel partial correlation coefficient – a measure of conditional dependence” (URL listed below). When X is empty, KPC measures the unconditional dependence between Y and Z, which has been described in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using kernels and geometric graphs” , and it is implemented in the functions KMAc() and Klin() in this package. The latter can be computed in near linear time. Package: r-cran-kpcaig Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-kernlab, r-cran-ggplot2, r-cran-progress, r-cran-viridis, r-cran-wallomicsdata Filename: pool/dists/noble/main/r-cran-kpcaig_1.0.1-1.ca2404.1_all.deb Size: 45878 MD5sum: cc17e92468425919e8a7db522486b82e SHA1: 9d5201a046592476b1c48476c30881b3acf33c54 SHA256: d76b1f2abb7a2e4e0628d2cb757848afeb4ff7c5185037b163bf846235e5611b SHA512: a014014019c6768ddeb65344152ca157beaf5afd0cb0b5dfb8fa9155ae243f1ef354ef77f86f0a99120096db8cf58b800fd12ef4c6c09609dd9c8d8467702d80 Homepage: https://cran.r-project.org/package=kpcaIG Description: CRAN Package 'kpcaIG' (Variables Interpretability with Kernel PCA) The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. 'kpcaIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) . It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones. Package: r-cran-kpcalg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-cran-energy, r-cran-kernlab, r-cran-mgcv, r-cran-rspectra, r-bioc-graph Suggests: r-bioc-rgraphviz, r-cran-knitr Filename: pool/dists/noble/main/r-cran-kpcalg_1.0.1-1.ca2404.1_all.deb Size: 238824 MD5sum: 9019bb9b035a092ab971b6c99fd1bd37 SHA1: 5929570fc5e03860d22490c462dd00885818a086 SHA256: 9f3ac99fa60a0df7c539f5aee028e49e2051e1c86d9e6f4e7c26886ca3127be0 SHA512: 0402bf40429fa16a3c59c01a1185f0d1657d4a27238f0b4bbca708c555df362abf14841129d349185dc4de4d79c6213ff1664d9d2c71d3c149014caae56e5c2a Homepage: https://cran.r-project.org/package=kpcalg Description: CRAN Package 'kpcalg' (Kernel PC Algorithm for Causal Structure Detection) Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise. Package: r-cran-kpeaks Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kpeaks_1.1.0-1.ca2404.1_all.deb Size: 79408 MD5sum: 8944a0689ce1194952f22cf376dc6606 SHA1: 6968bafe3770f526ff0326cf0a8a45c69536fd14 SHA256: a625e550cce99515521cb3b821f85ab3cb51317d84eceb18478c10176c90a994 SHA512: d26d25610111240a32aaccc377d3100571691d40ccfa9d93c84ef2657bd1151d6debcd0e2a9e774d60d80fd4fb19d810bd305afca9bf1a07f6db3d117268eaf2 Homepage: https://cran.r-project.org/package=kpeaks Description: CRAN Package 'kpeaks' (Determination of K Using Peak Counts of Features for Clustering) The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set. Package: r-cran-kpiwidget Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crosstalk, r-cran-htmlwidgets Suggests: r-cran-dplyr, r-cran-dt, r-cran-flexdashboard, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kpiwidget_0.1.1-1.ca2404.1_all.deb Size: 130696 MD5sum: de3a3514bf6d29441bf45545465f5f8e SHA1: d10d34fc72e619038597b173b6652c28a4361771 SHA256: 6d18f552cdc7b025605777c9d91553df9eccff798f2bf297fa473c64561e38c4 SHA512: 880bd9a64b67caf008e040c37b06127cca85169b5d16b6d5e737c4c4f25a09e6126e8019bdbf67cafe3a05d784f5e7c69f09f9105c0c7209b773ea96f25b3ea3 Homepage: https://cran.r-project.org/package=kpiwidget Description: CRAN Package 'kpiwidget' (KPI Widgets for Quarto Dashboards with Crosstalk) Provides an easy way to create interactive KPI (key performance indicator) widgets for 'Quarto' dashboards using 'Crosstalk'. The package enables visualization of key metrics in a structured format, supporting interactive filtering and linking with other 'Crosstalk'-enabled components. Designed for use in 'Quarto' Dashboards. Package: r-cran-kpmt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-kpmt_0.1.0-1.ca2404.1_all.deb Size: 1184194 MD5sum: d9c0c823f2acd72a927af3b78acc28aa SHA1: 881303652936c05008e1699e575dc37d01c6c0c8 SHA256: b77b6c401542ec8aa66047c3e2dd9356c3e3ce655be96c280bffb2529d894c6f SHA512: a3d8185640cd3f56c49e876c8d84b129caedada921e0694b395b51df1fc11c702d84cc0f9fe168e46d43750217dec7a43e854c4c6e5bc3c5604b3bb70e316b9e Homepage: https://cran.r-project.org/package=kpmt Description: CRAN Package 'kpmt' (Known Population Median Test) Functions that implement the known population median test. Package: r-cran-kpodclustr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kpodclustr_1.1-1.ca2404.1_all.deb Size: 26412 MD5sum: 38c174a7bcc33e97d9eb5c67a07d8cf6 SHA1: 9c3b60dc4ba6a8b203875ef9546b5ee718351623 SHA256: bf294349ee8f3aa069a2bd2b3c61ea46bd7b08d31e99c3e9d6c59f51fd08bbb5 SHA512: fc5617766de4d2159835fd870de9da04f8b2728e318d4ae01661d99476e1fb46a94e4b5820ae88da5d31b48d5c2a1cbe081f2fe6947cc030fffbdb8558029faf Homepage: https://cran.r-project.org/package=kpodclustr Description: CRAN Package 'kpodclustr' (Method for Clustering Partially Observed Data) Software for k-means clustering of partially observed data from Chi, Chi, and Baraniuk (2016) . Package: r-cran-kpp2019 Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-readxl, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-tidyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kpp2019_0.0.1-1.ca2404.1_all.deb Size: 486300 MD5sum: 4cc5aebe69568ba02630ebb9ffd23d30 SHA1: 2fc9e47d6535154491f436cfbf0fe171826562ea SHA256: 6e03815218625b84027ba3a441e67440f44a4ab39aa75bab0c0f98752311f1c7 SHA512: 00452c2b660d694c6c43837ad11c4a4c40a6a536251a25aa1579bff40c56042c6693bfbe1744b06ed432e70a4294048a0a39081f6b3762af52420d11c658adc0 Homepage: https://cran.r-project.org/package=kpp2019 Description: CRAN Package 'kpp2019' (Kenya Population Projections 2019) Provides population projection data for Kenya and its 47 counties from 2020 to 2045, derived from the 2019 Kenya Population and Housing Census and subsequent projections published by the Kenya National Bureau of Statistics (KNBS). 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KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014, ). 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The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. . Package: r-cran-kro.inv.test Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmtstat, r-cran-rspectra, r-cran-covkcd, r-cran-pracma Filename: pool/dists/noble/main/r-cran-kro.inv.test_0.1.3-1.ca2404.1_all.deb Size: 117160 MD5sum: e36fcd77551618e1438ad206024f059f SHA1: b70ee69e5aef031df0508c9b7ed6dae48e52d008 SHA256: 84b9292b87410f67a9ab8d3970599d15396fc6a96081b42f3506dd1126932c64 SHA512: c49756b66ca6b72f39b98207280324a2a949ae7814a048a1201c6164df1e6c482aa631594b5c50bb8c44060c5aeb67178bec3314bb179a8dd652cf697981f522 Homepage: https://cran.r-project.org/package=kro.inv.test Description: CRAN Package 'kro.inv.test' (Kronecker-Invariant Tests for High-Dimensional SeparabilityTesting) Kronecker-invariant tests for high-dimensional separability testing of matrix-variate data, focusing on Gaussian populations as benchmark cases. 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A KRT lists the resources used and generated in a study (antibodies, cell lines, organisms, chemicals, software, datasets, protocols, and more), each paired with a persistent identifier such as a Research Resource Identifier (RRID), a Digital Object Identifier (DOI), a repository accession, or a catalog number, so that resources are unambiguously identifiable and machine-actionable. The package models resources as typed, validated records around a neutral core schema and maps them to journal or funder output profiles, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles of Wilkinson et al. (2016) . It normalizes and optionally resolves identifiers against public registries, extracts resources from manuscripts, and renders tables both in the STAR (Structured, Transparent, Accessible Reporting) Methods style used by Cell Press journals and in the style required by ASAP (Aligning Science Across Parkinson's), with an emphasis on transparency, reproducibility, and correct per-component licensing. 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Tools for generating univariate and multivariate Faa di Bruno's formula and related polynomials, such as Bell polynomials, generalized complete Bell polynomials, partition polynomials and generalized partition polynomials. For more details see Di Nardo E., Guarino G., Senato D. (2009) , . 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Package: r-cran-ktsolve Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-nleqslv, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-ktsolve_1.4-1.ca2404.1_all.deb Size: 21572 MD5sum: 1d1874c705a618178c1887f54025b016 SHA1: 2079624cfcff662136ea08c8793347e6af255bda SHA256: 46ccf3c1f01ec8175191993828752139ebc29cd04943e89bb753333105c725ec SHA512: eb95d7b7241be1ddce6b80464f6bd753b603716eeb0ff46416b3a3bba7cfabc3d44057d7ed1d5fd918525af65a148de334ef5e499d8d2740040c84cc9a475fb9 Homepage: https://cran.r-project.org/package=ktsolve Description: CRAN Package 'ktsolve' (Configurable Function for Solving Families of NonlinearEquations) This is designed for use with an arbitrary set of equations with an arbitrary set of unknowns. The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems () was the inspiration for this function. Package: r-cran-kuenm2 Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-enmpa, r-cran-foreach, r-cran-fproc, r-cran-glmnet, r-cran-mgcv, r-cran-mop, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-kuenm2_0.1.3-1.ca2404.1_all.deb Size: 3132708 MD5sum: 3e9d407a78c0688025433c07613742e5 SHA1: 14873c503fa896063a3bfd386ea18e600c1ed453 SHA256: 08e45a10f6ea3f425f36656e8991f0c42f5d55894d000c5d5bcb9a4e832da416 SHA512: 6096165b4e40b83100e322f3576ce98ffdfbddef8cb85e7a297c24d49f1a252dff561a9df1f4c559afd4e8acb6485a4a7fd2b008a931a3e6595e388d50550de4 Homepage: https://cran.r-project.org/package=kuenm2 Description: CRAN Package 'kuenm2' (Detailed Development of Ecological Niche Models) A new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) , Radosavljevic and Anderson (2014) , Peterson et al. (2018) , Cobos et al. (2019) , Alkishe et al. (2020) , Machado-Stredel et al. (2021) , Arias-Giraldo and Cobos (2024) , Cobos et al. (2024) . Package: r-cran-kuiper.2samp Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kuiper.2samp_1.0-1.ca2404.1_all.deb Size: 16110 MD5sum: 9b044bb52051ef475a2ce469cdf3f653 SHA1: 35bf2838751d3fa129cecfc69c5fff5d872ad8ca SHA256: 07404a23afa35d8f9d08ac4e4e0cfca614e7c6bcd36d6da2a8cbbe12f21da87f SHA512: 7d3bb19206dad27f04ff67b370afe721a07d9b8cc3bee1c3f8ad898af94bb36a778f1bf2868254c1b5b9dece45b1529d43fadf9ae318c443e76551580d7370f6 Homepage: https://cran.r-project.org/package=kuiper.2samp Description: CRAN Package 'kuiper.2samp' (Two-Sample Kuiper Test) This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). and (2). Paltani, S. (2004). . Package: r-cran-kumquat Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bundle, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-glue, r-cran-logger, r-cran-rlang, r-cran-tidyr Suggests: r-cran-colorspace, r-cran-knitr, r-cran-patchwork, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-kumquat_1.1.0-1.ca2404.1_all.deb Size: 1168528 MD5sum: 3641e82767f5a8e204f56b233f13e759 SHA1: 41e057ca447d69931a7ff52c8f6fcca808fb00e7 SHA256: 094b55c23c43a306cf01f833bd244ac03df885c35186c2b8a4c2dfa7be6dd07a SHA512: 44f6158e0e037813c0aec3dbd1633b9a3ea545ad0e8a895a2f89940206b23073347ea2860b386a91d6d9df47781f6fd6e737c447ddfb4783d52af2b69926a997 Homepage: https://cran.r-project.org/package=kumquat Description: CRAN Package 'kumquat' (A Smaller Version of LIME) The existing implementation of 'lime' can be quite limiting in understanding the underlying components that make Local Local interpretable model-agnostic explanations (LIME) work. 'kumquat' is a simpler implementation of 'lime' that is easier to understand and is more transparent on the pieces that come together to make LIME work. For more details on LIME, see Ribeiro, Singh, and Guestrin (2016) . Package: r-cran-kurt Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-expm Filename: pool/dists/noble/main/r-cran-kurt_1.1-1.ca2404.1_all.deb Size: 55114 MD5sum: e05f16da043858567d964f209b4de119 SHA1: 009c613320a5326d4b6e5e3800a6836d0af5b1de SHA256: aa8ef915e1d684bd9e78467fc52fdae9ba114d929befe07e62dbd4fcfa8e4729 SHA512: b2a586a5226f0b87e12c1fae57153c22a1c831f3ec54f102455e4d0e643f5b72514fe1d11ff37ac0332ae5b4cb1824298ca4ba6c527d98872a78134643c13178 Homepage: https://cran.r-project.org/package=Kurt Description: CRAN Package 'Kurt' (Performs Kurtosis-Based Statistical Analyses) Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit. Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis". Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications". Package: r-cran-kutils Architecture: all Version: 1.73-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-xtable, r-cran-plyr, r-cran-openxlsx, r-cran-runit Suggests: r-cran-rockchalk Filename: pool/dists/noble/main/r-cran-kutils_1.73-1.ca2404.1_all.deb Size: 727120 MD5sum: ff66f92046cf61f4fb70578a6f4fe019 SHA1: 14336f94ad351d33e7ad270c1691b8bbf5039bec SHA256: 25d0f07bea77bc2983c60c9ec1c1416898c9901c79916be035bc7a3de53f2b15 SHA512: ee8b545f8f1aa58fd176c44dbcffb063ae5d90915f02803733564cdba65ccd66626ee50bbf58b48f27ce046fb4d6dea608d0e1597ed71c2c30645eb521f4ccf0 Homepage: https://cran.r-project.org/package=kutils Description: CRAN Package 'kutils' (Project Management Tools) Tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding. We are eager to have community feedback about the variable key and the vignette about it. In version 1.7, the function 'semTable' is removed. It was deprecated since 1.67. That is provided in a separate package, 'semTable'. Package: r-cran-kuzco Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ellmer, r-cran-gt, r-cran-gtextras, r-cran-imager, r-cran-jsonlite, r-cran-magick, r-cran-ollamar, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-quarto, r-cran-shiny, r-cran-bslib, r-cran-rmarkdown, r-cran-purrr, r-cran-mirai, r-cran-tibble, r-cran-tictoc, r-cran-httr Filename: pool/dists/noble/main/r-cran-kuzco_0.1.0-1.ca2404.1_all.deb Size: 991520 MD5sum: 9e30d2f11940159a4357b6d8777540e9 SHA1: d49338f78149cabaea951a4382b9cc69fb662c9d SHA256: 49fa6a1bd26b21f2e66ec7e3935f21b07c23f22972e345bbbdfe1873f3dd1a70 SHA512: 575278b401e1dd8336b72e217b40582c423711d1291f149797920d59d9eb512343f4d2093090a73b71d86696cfa9826f49f7ee8c25a9d75360e17ad98667569b Homepage: https://cran.r-project.org/package=kuzco Description: CRAN Package 'kuzco' (Computer Vision with Large Language Models) Make computer vision tasks approachable in R by leveraging Large Language Models. Providing fine-tuned prompts, boilerplate functions, and input/output helpers for common computer vision workflows, such as classifying and describing images. Functions are designed to take images as input and return structured data, helping users build practical applications with minimal code. Package: r-cran-kuzur Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-igraph, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-g6r, r-cran-jsonlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-arrow Filename: pool/dists/noble/main/r-cran-kuzur_0.2.3-1.ca2404.1_all.deb Size: 1741182 MD5sum: 173cf433c54acb9b123208263be3c9aa SHA1: d110a84b0f183bf4b8dabfc2124fb619bc292cf8 SHA256: 8256500bda81b9af0e04221d5de54c2e0fe3af3dcbae61edb669371d0afb1822 SHA512: 42b1a7c3a36b1aa7079827b0ee67cec2e60dfd55dee1edc8620126c80824eda4e5635a6befc39cc5ef66635acf430277d4eb5e724ec19c0be0fcea02e210c86f Homepage: https://cran.r-project.org/package=kuzuR Description: CRAN Package 'kuzuR' (Interface to 'kuzu' Graph Database) Provides a high-performance 'R' interface to the 'kuzu' graph database. It uses the 'reticulate' package to wrap the official 'Python' client ('kuzu', 'pandas', and 'networkx'), allowing users to interact with 'kuzu' seamlessly from within 'R'. Key features include managing database connections, executing 'Cypher' queries, and efficiently loading data from 'R' data frames. It also provides seamless integration with the 'R' ecosystem by converting query results directly into popular 'R' data structures, including 'tibble', 'igraph', 'tidygraph', and 'g6R' objects, making 'kuzu's powerful graph computation capabilities readily available for data analysis and visualization workflows in 'R'. The 'kuzu' documentation can be found at . Package: r-cran-kvkapir Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-kvkapir_0.1.2-1.ca2404.1_all.deb Size: 116040 MD5sum: 014740fe9639721a876d582d023ee44f SHA1: 88ae8db0429343494c89738d95ae82a0862e88e9 SHA256: dd288916f8a24d5b86e00fec816e131b2b55c2388f6c3ab7c328e082bd0d2d05 SHA512: b3eddfdfe9e1fcef29503dfa41c05faaa4409ab8bd687a4b9e6fc2567fc6e568fde718e1a2e0525ffec4d12def8e73d84ee1974cc760e8d5bbe8ab4174e497ea Homepage: https://cran.r-project.org/package=kvkapiR Description: CRAN Package 'kvkapiR' (Interface to the Dutch Chamber of Commerce (KvK) API) Access business registration data from the Dutch Chamber of Commerce (Kamer van Koophandel, KvK) through their official API . Search for companies by name, location, or registration number. Retrieve detailed business profiles, establishment information, and company name histories. Built on 'httr2' for robust API interaction with automatic pagination, error handling, and usage tracking. Package: r-cran-kvr2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-insight, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kvr2_0.2.0-1.ca2404.1_all.deb Size: 187156 MD5sum: 0948d244bf94bc794bff67e8612589ec SHA1: 52a87aa51673752a30c80d7aac024975ce4e5a03 SHA256: 1b02f05eabfd1cc2cdcb0775211eb808c4ad62e0b1916899d795b4f1aaf3b9ea SHA512: ac42dae2df36ddd1ec4758065585ed3939e90d1ba1f5155cd023f2453937b3d97a48c9927bb39fbed73f9785ffa88b084a943e853b2a71acde175a1dafec7980 Homepage: https://cran.r-project.org/package=kvr2 Description: CRAN Package 'kvr2' (Calculate and Compare Multiple Definitions of Coefficient ofDetermination) Calculate nine types of coefficients of determination (R-squared) based on the classification by Kvalseth (1985) . This package is designed for educational purposes to demonstrate how R-squared values can fluctuate depending on the choice of formula, particularly in power regression models or linear models without an intercept. By providing a comprehensive list of definitions, it helps users understand the mathematical sensitivity of goodness-of-fit indices. Package: r-cran-kwela Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kwela_1.0.0-1.ca2404.1_all.deb Size: 139378 MD5sum: 12d5d93dcc7f64192ffcbcaa66ddb733 SHA1: bc57a699de702fbe16cdb7e0686536ffbef6fc69 SHA256: 73e3b64a87124843dee095af8ca9febde7f912f14b2cea9639965008fb0f9570 SHA512: ea8ba58634e93e4e3e134acbb12400cd4b8f1acf5325f0e7b37b6dac1eefc5540b5cce33fda4abef57dcc52e8ccc36493ceae36a138df711ff12275f7f642481 Homepage: https://cran.r-project.org/package=KWELA Description: CRAN Package 'KWELA' (Hierarchical Adaptive 'RT-QuIC' Classification for ComplexMatrices) Extends 'RT-QuIC' (Real-Time Quaking-Induced Conversion) statistical analysis to complex environmental matrices through hierarchical adaptive classification. 'KWELA' is named after a deity of the Fore people of Papua New Guinea, among whom Kuru, a notable human prion disease, was identified. Implements a 6-layer architecture: hard gate biological constraints, per-well adaptive scoring, separation-aware combination, Youden-optimized cutoffs, replicate consensus, and matrix instability detection. Features dual-mode operation (diagnostic/research), auto-profile selection (Standard/Sensitive/Matrix-Robust), RAF integration for artifact detection, matrix-aware baseline correction, and multiple consensus rules. Methods include energy distance (Szekely and Rizzo (2013) ), CRPS (Gneiting and Raftery (2007) ), SSMD (Zhang (2007) ), and Jensen-Shannon divergence (Lin (1991) ). This package implements methodology currently under peer review; please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems ('Anthropic Claude' and 'OpenAI GPT') served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies. Package: r-cran-kzs Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-kzs_1.4.1-1.ca2404.1_all.deb Size: 3491780 MD5sum: c041b89e0305c0388d70669390ac0299 SHA1: abda2bdd24d86e8635e3670ba15475c8901ae949 SHA256: c79b942b2805d51a612055f4067a2b65d68111d0c213de187947aef6ccb86d60 SHA512: b3b0fc472844c5f0e83e1089072f2776914d4133e2d088276e9334f06ac1e91d76cf0dfeebc2342537fa915144f0d041bdcd87d92d675d3dc9aeca7b5af02e3d Homepage: https://cran.r-project.org/package=kzs Description: CRAN Package 'kzs' (Kolmogorov-Zurbenko Spatial Smoothing and Applications) A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise. Package: r-cran-l0cpt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-l0cpt_0.2.0-1.ca2404.1_all.deb Size: 60686 MD5sum: eb1a83afc34a1cc501e4f65a14353c31 SHA1: 52a507f725a2f616d6338a2f15b4099b01bf876a SHA256: d1ed4e5938e0a4e1aaa9bb6b1f025e425a8dbe851cfbffbe0c97a0bc4b3aa117 SHA512: 0d5549ef6a31fec8a27150a5f5e166c2bed3db89d91a57ca7c609921989b2b774788ea0fff5b0568f747853446730eb264e97db8613cefdc344e364ae6f97025 Homepage: https://cran.r-project.org/package=L0cpt Description: CRAN Package 'L0cpt' (Change Point Detection with L0 Penalty) Under an L0 penalty framework, a computationally efficient implementation of change point detection is developed. By integrating active set algorithms with warm start initialization, the package achieves linear-time complexity for solving change point detection problems. References: Wen et al. (2020) ; Zhu et al. (2020). Package: r-cran-l0tfinv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-l0tfinv_0.1.0-1.ca2404.1_all.deb Size: 463126 MD5sum: f0c4b028ffbd5b7a36e45e491e0468d6 SHA1: 2c297c449f2d657182f66c59b7a332436905ce27 SHA256: 8bff3b7429f2424fe38db57f01273fb489c2eaa6e248de4d0a03bd1b4bb31d15 SHA512: cee63cfd1137e5c60558afac813a1828c786deee308a4957ad6d64813bab6a29a1175a0d03ec1c506dd91f9dfa7f0cb092f84dd39fe39e3da197077e4c7230ac Homepage: https://cran.r-project.org/package=L0TFinv Description: CRAN Package 'L0TFinv' (A Splicing Approach to the Inverse Problem of L0 Trend Filtering) Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) ; Zhu et al. (2020); Wen et al. (2023) . Package: r-cran-l1ball Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam Filename: pool/dists/noble/main/r-cran-l1ball_0.1.0-1.ca2404.1_all.deb Size: 33708 MD5sum: 6a43236845397061154bdc75b246ea15 SHA1: 54d2f3aa2396ec15a0decdd9daa56ce702a2c407 SHA256: 596baf4ee7d40b9df1d43ff27015d989bf477b1c5d7ada282dce305e4714f839 SHA512: e855a8e7c5dda2bb63104707c868c9c52f94c47d7c5aab717f6d16e66cdac5aa5ee7c24d3204091fd9f842bbc8cd2534e77e6ca851990a3d71e7f78e2fd9597d Homepage: https://cran.r-project.org/package=l1ball Description: CRAN Package 'l1ball' (L1-Ball Prior for Sparse Regression) Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) . Package: r-cran-l1kdeconv Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixtools, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-l1kdeconv_1.2.0-1.ca2404.1_all.deb Size: 40056 MD5sum: 81b0a25a3223e5febb00e580b61b113b SHA1: 32e866797f8c088b6ac308d2e30f3afdf77acb1a SHA256: 43f117c30e7205b7a6a7b9ba4e287dfdf0ae7974f77e8e82b4be8e92a0c783e9 SHA512: 82e6e94fb03bdeec7cf1c9194b37f1d348ab2132cab34c5878565cbfc5f8cf72fec96dcb17747cefc1041dddc73cd72734439c73604394eb19be29d920d0ee61 Homepage: https://cran.r-project.org/package=l1kdeconv Description: CRAN Package 'l1kdeconv' (Deconvolution for LINCS L1000 Data) LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution. Package: r-cran-l1rotation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-matrixstats, r-cran-pracma, r-cran-scales Suggests: r-cran-knitr, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-l1rotation_1.0.1-1.ca2404.1_all.deb Size: 467444 MD5sum: 8b6002c2599f5a7f3f397c75309be705 SHA1: ae958f4dabf428e140cf7670ad2259cf216942da SHA256: 1e666e89bfc23c9ad9a0fb743fdaee523115353296626fe6a43f9dd8d160f3ae SHA512: cd98bf1397b407702f48eff59baf3fc71fc823d44528fdb525c8dda5e099aa784c6ed7c4f8ab71b8744bf3212bef9d516caa516aed8f45a542f38943a51a1de1 Homepage: https://cran.r-project.org/package=l1rotation Description: CRAN Package 'l1rotation' (Identify Loading Vectors under Sparsity in Factor Models) Simplify the loading matrix in factor models using the l1 criterion as proposed in Freyaldenhoven (2025) . Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors(). Package: r-cran-l2boost Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-l2boost_1.0.3-1.ca2404.1_all.deb Size: 293000 MD5sum: 3b2e81a02a49624a45594a140018ee72 SHA1: 7e6058e2d80bb47f836e238b499bfad1299dc796 SHA256: dea73ce0d155887ea5fc2c7cd0889de5eff8597114dfc5c6a5444e607d2719b4 SHA512: e52e12ffec1edbbd1f5fb528f93ba56f0178945c552464e7a6e2efbdea45b19ef41dcb15f6b9c1ac10ce6d66401762583ed3457ecb654707b2acba7cde380f0e Homepage: https://cran.r-project.org/package=l2boost Description: CRAN Package 'l2boost' (Exploring Friedman's Boosting Algorithm for Regularized LinearRegression) Efficient implementation of Friedman's boosting algorithm with l2-loss function and coordinate direction (design matrix columns) basis functions. Package: r-cran-l2densitygoftest Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fgarch, r-cran-nor1mix, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-l2densitygoftest_0.6.0-1.ca2404.1_all.deb Size: 59202 MD5sum: 1ebd8377d013cc7f6890a1ef58cd2dc9 SHA1: 0c840b2e8665b9949078a265dac3b1bf57716130 SHA256: a6668c979c26ef75913802586ec30dc35a7ced40fe28b9e7ea425d663b14ed57 SHA512: d44c2caab7d2c569d2c62bbfcadef243cc2853d37c72cce10f1780e3483c8fb3c3b556d79cf532d6dc97ce6a39c37a52c09bcfc941a6eb57823703db4123ef50 Homepage: https://cran.r-project.org/package=L2DensityGoFtest Description: CRAN Package 'L2DensityGoFtest' (Density Goodness-of-Fit Test) Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance. Package: r-cran-l2e Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 470 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-osqp, r-cran-isotone, r-cran-cobs, r-cran-ncvreg, r-cran-matrix, r-cran-signal, r-cran-robustbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-latex2exp Filename: pool/dists/noble/main/r-cran-l2e_2.0-1.ca2404.1_all.deb Size: 403008 MD5sum: 3de30f271237e77b73dda5675385bec5 SHA1: 6432f8700f9f15504c8a7c74a24dacc16d7a16d5 SHA256: 8b21b046e1d18cb2811d8478da18e99ad249720938f45409761d762ec30950d4 SHA512: cb284e7ca930189f0ae36c1b15f93abcde0e3eb951fd9b6f37105ea00a700263c632f3a6d0d0c6f23940d546bb9cf0aa2af9f342dc3d418123d0e26baf7abd2b Homepage: https://cran.r-project.org/package=L2E Description: CRAN Package 'L2E' (Robust Structured Regression via the L2 Criterion) An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0. Package: r-cran-l2hdchange Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-l2hdchange_1.0-1.ca2404.1_all.deb Size: 387154 MD5sum: 8aa2a24aa67962743a61c790af1be258 SHA1: c169e9e0e9364db114ee87ca4894d097b42ebe24 SHA256: a63ec7e2a47341ff5705809ca00aa66751ea19fbf3c41cf5ecd8203b8d07dad2 SHA512: 491f530338d82a9db92612e1680941b576ffb854ac43d2e84d05d1c70432cb17bebac0cfe099b255c2dd027ba1d1c79f6e626b5d402fe3adfeeb55a659f00075 Homepage: https://cran.r-project.org/package=L2hdchange Description: CRAN Package 'L2hdchange' (L2 Inference for Change Points in High-Dimensional Time Series) Provides a method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. See Li et al. (2023) "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". arXiv preprint . Package: r-cran-lab2clean Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-kableextra, r-cran-printr Filename: pool/dists/noble/main/r-cran-lab2clean_2.0.0-1.ca2404.1_all.deb Size: 1231520 MD5sum: 56ef04ca57aff08d4411767c90615751 SHA1: 69dbb1e15f8db3d63d843e4b6bd6fde1e9684d78 SHA256: f031ceba00de53f96d66d489aab5df4c1e2aaf37318d230025f3a7ef6f9e168e SHA512: 836774b37db71a9b7be1564fd523dc679580413091a2bb21aa8ab1b044fa3dc63f885eeabf2f6c2e565f2c4cfe9cd9bbc524e7ece676be659e58d55cb2dbe43e Homepage: https://cran.r-project.org/package=lab2clean Description: CRAN Package 'lab2clean' (Automation and Standardization of Cleaning Clinical LaboratoryData) Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm 'lab2clean' as an open-source R-package. 'lab2clean' package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in 'Zayed et al. (2024)' . Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in 'Zayed et al. (2025)' . Package: r-cran-labapplstat Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emmeans, r-cran-ggplot2, r-cran-ggraph, r-cran-vctrs Suggests: r-cran-isdals, r-cran-estimability, r-cran-dobson, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-labapplstat_1.4.4-1.ca2404.1_all.deb Size: 91934 MD5sum: 950dcd1f334af7d8ef48566b90f775f2 SHA1: 478223e352f864afb739d098a5cf286c877a8dbe SHA256: 67dcd127b406bd0477e6a52565bd623bd6ef830be40102ac4a99e2c34b15f52b SHA512: 1b29025a63978777c8002321321ac70c840c5f8fea7080f5d39440d8d87082c1ee1a1bb108110f80a15bb115027b058b6d767028ff5909f0ab419bb575ed8cf7 Homepage: https://cran.r-project.org/package=LabApplStat Description: CRAN Package 'LabApplStat' (Miscellaneous Scripts from the Data Science Laboratory (UCPH)) Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design. Package: r-cran-label.switching Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-label.switching_1.8-1.ca2404.1_all.deb Size: 156596 MD5sum: 9e322dacd054c9f9cb270716c4019b08 SHA1: 28d627aff5f05733d3b7c3649f73e92e0171e501 SHA256: 1698afc50a13fd0a5288393909b0b60e24647d3c0a83c6a997179f9f81cd391b SHA512: 8afd08bca36f24981179088cb088745c3bbf32336ef897632f29d908f2764151ee8d62abef8eedc97e4b5fd23af2165ceeda2b88d5b1855632f291925f0f1657 Homepage: https://cran.r-project.org/package=label.switching Description: CRAN Package 'label.switching' (Relabelling MCMC Outputs of Mixture Models) The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms. Package: r-cran-labeler Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4887 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-blastula, r-cran-keyring, r-cran-testthat Filename: pool/dists/noble/main/r-cran-labeler_0.4.0-1.ca2404.1_all.deb Size: 4490176 MD5sum: 9dc0a63507d0cdcb5a8e25fd6015ed8b SHA1: b6dbdb6015e5c5e770120b0e2829444617e4047d SHA256: da8f5944af60428510e0065d70be3c1f89ff4d5550355e30cf75853b3388d3a8 SHA512: 5d4906300f8bd4de4dad5ebbef7fb7bd07423c35430becab13d78cc789674a916e572c80a201ff62eadba5124e614bfacb0122bcd3aa28755de0d2543a9b192f Homepage: https://cran.r-project.org/package=labeleR Description: CRAN Package 'labeleR' (Automate the Production of Custom Labels, Badges, Certificates,and Other Documents) Create custom labels, badges, certificates and other documents. Automate the production of potentially large numbers of herbarium and collection labels, accreditation badges, attendance and participation certificates, etc, and deliver them automatically. Documents are generated in PDF format, which requires a working installation of 'LaTeX', such as 'TinyTeX'. Package: r-cran-labeling Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-labeling_0.4.3-1.ca2404.1_all.deb Size: 61974 MD5sum: 778191194956cb9bec142f9e426b6632 SHA1: e445196153dcafed7623e4c4c7446f49507f8f65 SHA256: 325fb207fa3d3eee02d5207f6e3761dc63e9be9bc4f74d46d0d25092e37088e3 SHA512: bbe89dfc01edcd2d65d6050de47cc27ba47c94002be6ea21e2d3afa7571b0e483764834e065b94dc8eaa68edca5bec1ca3cde9017561c52659436dc992254282 Homepage: https://cran.r-project.org/package=labeling Description: CRAN Package 'labeling' (Axis Labeling) Functions which provide a range of axis labeling algorithms. 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This package provides useful functions to deal with "haven_labelled" and "haven_labelled_spss" classes introduced by 'haven' package. 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'labelmachine' manages your label assignment rules in 'yaml' files and makes it easy to use the same labels in multiple projects. Package: r-cran-labelr Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-nycflights13, r-cran-collapse, r-cran-tibble, r-cran-haven, r-cran-dplyr, r-cran-modelr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-labelr_0.1.9-1.ca2404.1_all.deb Size: 605550 MD5sum: 45ea8c81ad34f8d50777a56606294aac SHA1: 66707708233a77d3e67c47b30b7bc8dd1aae801e SHA256: 85559e78e0c396a81b24fc02ba4f1e0549b0278fb11471ea681c5c397e17b183 SHA512: cd0de77cba81e96066e15a4a7b2c9fe741727087bbafa772eea42b8d8ad060cdf539ed5cefc47ba815b35efe2fb34f2e345e13e485f5b91e8b910b94fd4df01a Homepage: https://cran.r-project.org/package=labelr Description: CRAN Package 'labelr' (Label Data Frames, Variables, and Values) Create and use data frame labels for data frame objects (frame labels), their columns (name labels), and individual values of a column (value labels). Value labels include one-to-one and many-to-one labels for nominal and ordinal variables, as well as numerical range-based value labels for continuous variables. Convert value-labeled variables so each value is replaced by its corresponding value label. Add values-converted-to-labels columns to a value-labeled data frame while preserving parent columns. Filter and subset a value-labeled data frame using labels, while returning results in terms of values. Overlay labels in place of values in common R commands to increase interpretability. Generate tables of value frequencies, with categories expressed as raw values or as labels. Access data frames that show value-to-label mappings for easy reference. 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While variable names are succinct, quick to type, and follow a language's naming conventions, labels may be more illustrative and may use plain text and spaces. R does not provide native support for labels. Some packages, however, have made this feature available. Most notably, the 'Hmisc' package provides labelling methods for a number of different object. Due to design decisions, these methods are not all exported, and so are unavailable for use in package development. The 'labelVector' package supports labels for atomic vectors in a light-weight design that is suitable for use in other packages. Package: r-cran-lablaster Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-smooth, r-cran-scales, r-cran-rlang Filename: pool/dists/noble/main/r-cran-lablaster_1.0.1-1.ca2404.1_all.deb Size: 45606 MD5sum: d922c3a4ec2fd8440d684bca4fdd8eea SHA1: 97cf898d35ace97fdde31e6654f53061574d4bc3 SHA256: 95db1f50005a89ef4cf02fe7077edc106f7b003fe48fb1172f82df16d4c84036 SHA512: d78373d8a6510d7f878fcead1271d5fbab8ae3344c93d1811138e20e36a05ca4e8c08b1c8c74058a2c25c6ce9e59780c29d777a99c0ff544054e4e7738695b75 Homepage: https://cran.r-project.org/package=lablaster Description: CRAN Package 'lablaster' (Laser Ablation Blast Through Endpoint Detection) Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time. Package: r-cran-labnorm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rappdirs, r-cran-scales, r-cran-tibble, r-cran-withr, r-cran-yesno Suggests: r-cran-covr, r-cran-mockery, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-labnorm_1.0.1-1.ca2404.1_all.deb Size: 3722740 MD5sum: 0e1b3fec833530c47602a8206d66b22a SHA1: 894461351e097b20ea6aef6b2bb75c38f30f19cf SHA256: 164e35ac27cd0db9d0174801b2c22541bd691fabf210e520e744a4f734f6444c SHA512: 97d4b6f2f90762228456f794612b01a6ef2b694d26149e820bf1cb966e8acaac4d97e159db39adf9dc2c5e944b5973008d88a874073dde0963875c9d6cafcf88 Homepage: https://cran.r-project.org/package=labNorm Description: CRAN Package 'labNorm' (Normalize Laboratory Measurements by Age and Sex) Provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, ). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at . Package: r-cran-labourmarketareas Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2098 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-sf, r-cran-data.table, r-cran-spdep, r-cran-tmap Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-labourmarketareas_3.4-1.ca2404.1_all.deb Size: 1207146 MD5sum: 5bc0e6e455d4d0e70b1860051507f9a9 SHA1: 356881f6b2da34cfc0c55abe54d48f10d3c7b2f7 SHA256: e6ea5f827d7aa71831604a68b55b726c74ca3005e766459c245c5f34c1b3cf7d SHA512: 2bf663650a350dcb4aad2b6351b3f2ef4d7e536a274f091b468d59b04fa676507f3d8af95c4e16c6616a773fd29e121d8d5f9790028236d3f0058bd7859a2f67 Homepage: https://cran.r-project.org/package=LabourMarketAreas Description: CRAN Package 'LabourMarketAreas' (Identification, Tuning, Visualisation and Analysis of LabourMarket Areas) Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography. Package: r-cran-labourr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cld2, r-cran-magrittr, r-cran-stopwords, r-cran-stringdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-labourr_1.0.0-1.ca2404.1_all.deb Size: 2905962 MD5sum: afefaf1c6aaeaa162990017a9f956729 SHA1: df1d4df413506cfe05ca7600e154aeded40db629 SHA256: c446780f32d4d460fe9d37e48adbea955ffda9baa443ea301f0e58293ba57b13 SHA512: 5c34e8c7e1a70dd99c978918c743971bb5ff4e335cc603228972f87c22cbb62481716bf571d59e7c6c5bd1db1a1f921edbb05f7d8c7b3dffeeb30b3ba1ba211c Homepage: https://cran.r-project.org/package=labourR Description: CRAN Package 'labourR' (Classify Multilingual Labour Market Free-Text to StandardizedHierarchical Occupations) Allows the user to map multilingual free-text of occupations to a broad range of standardized classifications. The package facilitates automatic occupation coding (see, e.g., Gweon et al. (2017) and Turrell et al. (2019) ), where the ISCO to ESCO mapping is exploited to extend the occupations hierarchy, Le Vrang et al. (2014) . Document vectorization is performed using the multilingual ESCO corpus. A method based on the nearest neighbor search is used to suggest the closest ISCO occupation. Package: r-cran-labrs Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-labrs_0.2.1-1.ca2404.1_all.deb Size: 351740 MD5sum: 00a5fbc5e7d6e995808af4e5c06bb132 SHA1: 73f092d91796317ff400dd99d3481b57117339de SHA256: 23947f42b368edd5cd1a75e35b22248c846c676c17a5e5570b9ddab91413f896 SHA512: 7ca3e5f37ded442eb090c37e69f74a8244d330824f6eeea4eb5ff88a19ea3fed29bad7320b71054408c56123bd1334c44bbc2dd08f118647ff48f0e5437d63df Homepage: https://cran.r-project.org/package=LabRS Description: CRAN Package 'LabRS' (Laboratorio di Ricerca Sociale con R) Libreria di dati, scripts e funzioni che accompagna il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale". Package: r-cran-labsimplex Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3853 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scatterplot3d, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-frf2 Filename: pool/dists/noble/main/r-cran-labsimplex_0.1.2-1.ca2404.1_all.deb Size: 2827288 MD5sum: e222718ca33581f15ad0ab70d4c73d26 SHA1: 1b132ec7563f8aa85e7af49cb3481e6e2ee3173e SHA256: 264baf5aab092a2a32295238a2b94de6132e850e8b8ea6df2c66eff2153db203 SHA512: f46d5ef0b723841917ac2815e1b8840aa285ea6bf26fe6ffef8ad8ae5a0005ec796f681f5fb597a5c72564be3ea82e1c06e83efd9081ab3c4c40b454365de848 Homepage: https://cran.r-project.org/package=labsimplex Description: CRAN Package 'labsimplex' (Simplex Optimization Algorithms for Laboratory and ManufacturingProcesses) Simplex optimization algorithms as firstly proposed by Spendley et al. (1962) and later modified by Nelder and Mead (1965) for laboratory and manufacturing processes. The package also provides tools for graphical representation of the simplexes and some example response surfaces that are useful in illustrating the optimization process. Package: r-cran-labstatr Architecture: all Version: 1.0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-labstatr_1.0.14-1.ca2404.1_all.deb Size: 187202 MD5sum: 112b6dfea27b0e87fd0b359ec4d5012a SHA1: 333b5a309e8a525ae463edc6595bd2088f1fbb5b SHA256: d9f04c38b1c7c2896eb918ff6a26805a31450c36bf41439d817d70ea760eb9e7 SHA512: b50f9c46d7954ea424482a2e8461a5703e6eb12b652bce25c74e23ce6a5f7889aa5294743e68cb13d633f07bf935beaabf14dc371a05334cf3fe1eb82290521e Homepage: https://cran.r-project.org/package=labstatR Description: CRAN Package 'labstatR' (Libreria Del Laboratorio Di Statistica Con R) Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well. Package: r-cran-labstats Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-labstats_1.0.1-1.ca2404.1_all.deb Size: 83500 MD5sum: 3f4faed79efc68b337955363dcbd3529 SHA1: f00cb8f166d249e26c140b6c74b4f7df5cbba92f SHA256: 338a8372243b8d785a89d8059d993a3829bdffa19b9b44b0cd6318f9b5842d6f SHA512: 71dd741cc0d33e0a09ba2e81e65ff3735b699384346be065574d49e58e15dd82bbd611b1e441341382903f8ac0091ee9317f0ad67fdda8d8dfe621bf006be1bc Homepage: https://cran.r-project.org/package=labstats Description: CRAN Package 'labstats' (Data Sets for the Book "Experimental Design for LaboratoryBiologists") Contains data sets to accompany the book: Lazic SE (2016). "Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility". Cambridge University Press. Package: r-cran-labtnscpss Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-checkmate, r-cran-data.table, r-cran-stringi, r-cran-ggplot2, r-cran-lubridate, r-cran-purrr Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-devtools Filename: pool/dists/noble/main/r-cran-labtnscpss_1.0-1.ca2404.1_all.deb Size: 2906768 MD5sum: 34f1bf34ca0b02ba14e9b81e275f24fb SHA1: c0f2937078f1a18cf7fd471552d13db6f5323172 SHA256: ca915ba7ee14d25220095b96f750f310ef7b857261a3b1496d20d3e247183096 SHA512: 37691d5c28fbe8a9029ce6910aabcdebf7da87d0a335e3a79d317fbcc3836493a5a383eb25e1144ef3e3e7067f757151c9a5666a0b8b048ca7cba13f69884aab Homepage: https://cran.r-project.org/package=LABTNSCPSS Description: CRAN Package 'LABTNSCPSS' (Calculation of Comorbidity and Frailty Scores) Computes comorbidity indices and combined frailty scores for multiple ICD coding systems, including ICD-10-CA, ICD-10-CM, and ICD-11. The package provides tools to preprocess episode data, map diagnosis codes to chronic categories, propagate conditions across episodes, and generate comorbidity and frailty measures. The methods implemented are original to this package and were developed by the authors for research applications; a manuscript describing the methodology is currently in preparation. Package: r-cran-lacrmr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-janitor, r-cran-sjmisc, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-httptest, r-cran-mockery Filename: pool/dists/noble/main/r-cran-lacrmr_1.0.5-1.ca2404.1_all.deb Size: 230714 MD5sum: c16885f9a4626e0764732ee0674ebb5e SHA1: 91dce2c23104ab636fd575df0c5e9b19abe5ec0b SHA256: 17349a6ad27717908b00d0dd76ef587837b453e0be812f993d8b1672e768f527 SHA512: 941be77f05284af71bf2da949b9e443f11f3c961d6f4c5f7568b4012f1a866174eebfe8854e0e00058acc197260e5c3dd393545730a628da21c9c6a16143a1b6 Homepage: https://cran.r-project.org/package=lacrmr Description: CRAN Package 'lacrmr' (Connect to the 'Less Annoying CRM' API) Connect to the 'Less Annoying CRM' API with ease to get your crm data in a clean and tidy format. 'Less Annoying CRM' is a simple CRM built for small businesses, more information is available on their website . Package: r-cran-lactater Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-patchwork, r-cran-lubridate, r-cran-minpack.lm, r-cran-pracma, r-cran-rlang, r-cran-segmented, r-cran-stringr, r-cran-tidyr, r-cran-forcats Suggests: r-cran-bsplus, r-cran-covr, r-cran-datapasta, r-cran-glue, r-cran-knitr, r-cran-miniui, r-cran-rhandsontable, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lactater_0.2.0-1.ca2404.1_all.deb Size: 270896 MD5sum: 00898d3b24ae3fc8731bfde0bd5ab80a SHA1: 4d563175483044924554cec1c4313cca14fc971e SHA256: 5c0a0876562f20b051f98f6c346a82e973081524f2915f9dcef909557b800d9f SHA512: 9ce4596c0222761c07f1bb0ae6a85706893ea22bb701e8a011967e8ccf50a0e2e3610e095dbd2806f0e9ae63061db7eb61c0b825a70d275272b9b4081f78b506 Homepage: https://cran.r-project.org/package=lactater Description: CRAN Package 'lactater' (Tools for Analyzing Lactate Thresholds) Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP), and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming. Beaver WL, Wasserman K, Whipp BJ (1985) . Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) . Kindermann W, Simon G, Keul J (1979) . Skinner JS, Mclellan TH (1980) . Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) PMID 2408033. Zoladz JA, Rademaker AC, Sargeant AJ (1995) . Cheng B, Kuipers H, Snyder A, Keizer H, Jeukendrup A, Hesselink M (1992) . Bishop D, Jenkins DG, Mackinnon LT (1998) . Hughson RL, Weisiger KH, Swanson GD (1987) . Jamnick NA, Botella J, Pyne DB, Bishop DJ (2018) . Hofmann P, Tschakert G (2017) . Hofmann P, Pokan R, von Duvillard SP, Seibert FJ, Zweiker R, Schmid P (1997) . Pokan R, Hofmann P, Von Duvillard SP, et al. (1997) . Dickhuth H-H, Yin L, Niess A, et al. (1999) . Package: r-cran-lactcurvemodels Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-lactcurvemodels_0.1.5-1.ca2404.1_all.deb Size: 137528 MD5sum: f7c9f57a7c76fb2fd0bad4c7a5919170 SHA1: ccf4b1c8d514c98407f92bd8dbc540d3081aaf2a SHA256: cfa7ebaef09248a7f4a742e2c06cf9922014c18adc0c9b15c0ff930161eef4ba SHA512: 3c0c88d5df0afb29555d62b4685a6293e9c77b9e7dd616ebe439f522858a05cd7a3f587448cf7dfa18c122467052679c378bfbb23750997257c299b67147fea9 Homepage: https://cran.r-project.org/package=LactCurveModels Description: CRAN Package 'LactCurveModels' (Lactation Curve Model Fitting for Dairy Animals) Fits up to 20 nonlinear lactation curve models to dairy animal milk yield data. Models fitted include exponential, polynomial, mixed logarithmic, inverse polynomial, and sigmoid families published between 1923 and 2000. Supports batch processing of multiple animals from a single CSV file, with flexible selection of animals and models. Produces per-animal parameter tables, goodness-of-fit metrics including R-squared (R2), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and a serial autocorrelation statistic, 15 diagnostic figures, and combined cross-animal comparison outputs. References: , , , , . Package: r-cran-lactcurves Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-orthopolynom Filename: pool/dists/noble/main/r-cran-lactcurves_1.1.0-1.ca2404.1_all.deb Size: 155982 MD5sum: 0157c1197b4e69d0cdcb6d3fbd9e41b8 SHA1: cd529733ae07c10468e14a07d7134ea412fef292 SHA256: 0f948fda5895a63c8dda6ae825f7c1f3ea7da221018464d4f6fcb88af57fc736 SHA512: a569fddd6666f0b90000d84c951e9c3ab7fc19049bebc1a440911177f3f20d617d904b8c96af2df95a24f4a2fad9f62fea1efcfc00f3e8fa3e7871abcb3843a0 Homepage: https://cran.r-project.org/package=lactcurves Description: CRAN Package 'lactcurves' (Lactation Curve Parameter Estimation) AllCurves() runs multiple lactation curve models and extracts selection criteria for each model. This package summarises the most common lactation curve models from the last century and provides a tool for researchers to quickly decide on which model fits their data best to proceed with their analysis. Start parameters were optimized based on a dataset with 1.7 million Holstein-Friesian cows. If convergence fails, the start parameters need to be manually adjusted. The models included in the package are taken from: (1) Michaelis-Menten: Michaelis, L. and M.L. Menten (1913). (1a) Michaelis-Menten (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). (1b) Michaelis-Menten + exponential (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). (2) Brody (1923): Brody, S., A.C. Ragsdale, and C.W. Turner (1923). (3) Brody (1924): Brody, S., C.W. Tuner, and A.C. Ragsdale (1924). (4) Schumacher: Schumacher, F.X. (1939) in Thornley, J.H.M. and J. France (2007). (4a) Schumacher (Lopez et al. 2015): Lopez, S. J. France, N.E. Odongo, R.A. McBride, E. Kebreab, O. AlZahal, B.W. McBride, and J. Dijkstra (2015). (5) Parabolic exponential (Adediran): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (6) Wood: Wood, P.D.P. (1967). (6a) Wood reparameterized (Dhanoa): Dhanoa, M.S. (1981). (6b) Wood non-linear (Cappio-Borlino): Cappio-Borlino, A., G. Pulina, and G. Rossi (1995). (7) Quadratic Polynomial (Dave): Dave, B.K. (1971) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (8) Cobby and Le Du (Vargas): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). (9) Papajcsik and Bodero 1: Papajcsik, I.A. and J. Bodero (1988). (10) Papajcsik and Bodero 2: Papajcsik, I.A. and J. Bodero (1988). (11) Papajcsik and Bodero 3: Papajcsik, I.A. and J. Bodero (1988). (12) Papajcsik and Bodero 4: Papajcsik, I.A. and J. Bodero (1988). (13) Papajcsik and Bodero 6: Papajcsik, I.A. and J. Bodero (1988). (14) Mixed log model 1 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). (15) Mixed log model 3 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). (16) Log-quadratic (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (17) Wilmink: J.B.M. Wilmink (1987). (17a) modified Wilmink (Jakobsen): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). (17b) modified Wilmink (Laurenson & Strucken): Strucken E.M., Brockmann G.A., and Y.C.S.M. Laurenson (2019). (18) Bicompartemental (Ferguson and Boston 1993): Ferguson, J.D., and R. Boston (1993) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (19) Dijkstra: Dijkstra, J., J. France, M.S. Dhanoa, J.A. Maas, M.D. Hanigan, A.J. Rook, and D.E. Beever (1997). (20) Morant and Gnanasakthy (Pollott et al 2000): Pollott, G.E. and E. Gootwine (2000). (21) Morant and Gnanasakthy (Vargas et al 2000): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). (22) Morant and Gnanasakthy (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (23) Khandekar (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). (24) Ali and Schaeffer: Ali, T.E. and L.R. Schaeffer (1987). (25) Fractional Polynomial (Elvira et al. 2013): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). (26) Pollott multiplicative (Elvira): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). (27) Pollott modified: Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (28) Monophasic Grossman: Grossman, M. and W.J. Koops (1988). (29) Monophasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). (30) Diphasic (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). (31) Diphasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). (32) Legendre Polynomial (3th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). (33) Legendre Polynomial (4th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). (34) Legendre + Wilmink (Lidauer): Lidauer, M. and E.A. Mantysaari (1999). (35) Natural Cubic Spline (3 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). (36) Natural Cubic Spline (4 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). (37) Natural Cubic Spline (5 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999) (38) Natural Cubic Spline (defined knots according to Harrell 2001): Jr. Harrell, F.E. (2001). The selection criteria measure the goodness of fit of the model and include: Residual standard error (RSE), R-square (R2), log likelihood, Akaike information criterion (AIC), Akaike information criterion corrected (AICC), Bayesian Information Criterion (BIC), Durbin Watson coefficient (DW). The following model parameters are included: Residual sum of squares (RSS), Residual standard deviation (RSD), F-value (F) based on F-ratio test. Package: r-cran-lacunarity Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zoo, r-cran-plyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lacunarity_0.1.0-1.ca2404.1_all.deb Size: 59364 MD5sum: d8061abfaed54bbaeaa8c0254abb4835 SHA1: 0705fd867c02a6eafdef7680cbe28bfbd84c2eaf SHA256: 00e99f6de7e07c820809118c170cda4dbec79def18e7895858dfd63f0e5a79e6 SHA512: 61ce69085eed7fe1b53e38f1ef0cb39638196390b7bb1aad35362d491463e6d32c6c969d25d08c9281dc8f3f99662b3b3902e6b1a7d06107756eaa6a200e06f2 Homepage: https://cran.r-project.org/package=lacunarity Description: CRAN Package 'lacunarity' (Standard and Generalized Lacunarity for Binary Time Series) Estimates lacunarity and generalized lacunarity for unidimensional binary time series. The lacunarity index summarizes the similarity of parts from different regions of a series at a given scale by averaging the behavior of variable size structures of zeros and ones. The generalized lacunarity concept provides an enhanced measure of the organization of the gaps over all measured scales and over the different arrangements of smaller and larger gaps in the series. Package: r-cran-lacunaritycovariance Architecture: all Version: 1.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-rcpproll, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random Suggests: r-cran-cubature, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lacunaritycovariance_1.1-9-1.ca2404.1_all.deb Size: 440516 MD5sum: 7daaffc44ce30dd6b55aeae317cd745c SHA1: 1ccfebad22b42af57009031871493eb40562c65f SHA256: 1eb938bfef3a7c622c1faee4dde0481bc86befe80a313581c766174c1591eee3 SHA512: 114d3a0c5c4279653af34f951710f2857955bcce71efa7d2d2204c3bf3cb56fa89a1662d45bec8093622bb14b9eb1c056466eea84ae1112a71b74d0f6b4bd12a Homepage: https://cran.r-project.org/package=lacunaritycovariance Description: CRAN Package 'lacunaritycovariance' (Gliding Box Lacunarity and Other Metrics for 2D Random ClosedSets) Functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) and balanced estimators, proposed by Picka (2000) , for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies. Package: r-cran-lad Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-lad_0.1.0-1.ca2404.1_all.deb Size: 1793926 MD5sum: d0c7fd3fc9698a69d0eff7ec225764c9 SHA1: 97b778df9a749972788c08366b05c1078974e3a2 SHA256: 5dcc62f18e5d8461c90f50d4313cc81b3ae6089fedbb90c40a4b5b6b57f5fbd9 SHA512: 2f5e2395159960f6ca658104f71412582422ec748d6e0be0df821306e585988d2332c1298c48ec47d036bc37466ad5001d422fca8b87c01d15e5a960bcec2127 Homepage: https://cran.r-project.org/package=LAD Description: CRAN Package 'LAD' (Derive Leaf Angle Distribution (LAD) from Measured LeafInclination Angles) Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) . Package: r-cran-ladder Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-flextable, r-cran-gargle, r-cran-httpuv, r-cran-httr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ladder_0.0.3-1.ca2404.1_all.deb Size: 234900 MD5sum: 1a1081dd2e3970f9d49433270d80d8a9 SHA1: fd79434abf6c3d63a41f1efd3b534cb0599b2fb0 SHA256: 68bb7e5b5b87a16c22cfc8753b9cf94de56a438143cf6a6159ee6155ba2a1f26 SHA512: 7ffc834df53fd400028e1dbc0acec0bbd7e820bd08861e7ac2a477f2874cd2bed0a88500317820a75e79be615dc263f61654fd7965b154027f3a40899723bd37 Homepage: https://cran.r-project.org/package=ladder Description: CRAN Package 'ladder' (Get on to the Slides) Create tables from within R directly on Google Slides presentations. Currently supports matrix, data.frame and 'flextable' objects. Package: r-cran-ladderfuelsr Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gdata, r-cran-ggplot2, r-cran-magrittr, r-cran-segmented, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ladderfuelsr_0.0.7-1.ca2404.1_all.deb Size: 720960 MD5sum: f92d21271fdbc17b97ca96df650ef540 SHA1: ca93c503338361b4263b0a4ed5603c6978baa425 SHA256: 6c2e179281e35d26e8896d0c1339bea87978b1ad8837a3b760942e3062edb4b5 SHA512: 974d5f2dae7d499baaac48cfe1951951826bae5cece1692f25f3ed4d584557e2479a501a18769ab4e7d733eb9ec800c281887a8558ec02b2c50df659064291c5 Homepage: https://cran.r-project.org/package=LadderFuelsR Description: CRAN Package 'LadderFuelsR' (Automated Tool for Vertical Fuel Continuity Analysis usingAirborne Laser Scanning Data) Set of tools for analyzing vertical fuel continuity at the tree level using Airborne Laser Scanning data. The workflow consisted of: 1) calculating the vertical height profiles of each segmented tree; 2) identifying gaps and fuel layers; 3) estimating the distance between fuel layers; and 4) retrieving the fuel layers base height and depth. Additionally, other functions recalculate previous metrics after considering distances greater than certain threshold. Moreover, the package calculates: i) the percentage of Leaf Area Density comprised in each fuel layer, ii) remove fuel layers with Leaf Area Density (LAD) percentage less than 10, and iii) recalculate the distances among the reminder ones. On the other hand, it identifies the crown base height (CBH) based on different criteria: the fuel layer with the highest LAD percentage and the fuel layers located at the largest- and at the last-distance. When there is only one fuel layer, it also identifies the CBH performing a segmented linear regression (breaking points) on the cumulative sum of LAD as a function of height. Finally, a collection of plotting functions is developed to represent: i) the initial gaps and fuel layers; ii) the fuels base height, depths and gaps with distances greater than certain threshold and, iii) the CBH based on different criteria. The methods implemented in this package are original and have not been published elsewhere. Package: r-cran-laeken Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-mass Filename: pool/dists/noble/main/r-cran-laeken_0.5.3-1.ca2404.1_all.deb Size: 3103676 MD5sum: ba5f23af4ab40be9b17177f4a4cc42ee SHA1: e0234422474ebc224d51066491516d211d048149 SHA256: f69a015f102e9425a14a17a69f545d4ed52b459cd80975e3110e0d87655c95c8 SHA512: 3c091f23e8c755c1efbe2cee1a0909ad44b2f97376ca7f93e214a848fd5e07e49a8132a6bc166303e28c43c18cda1db3abe5ca9313df83489b85939f462662cb Homepage: https://cran.r-project.org/package=laeken Description: CRAN Package 'laeken' (Estimation of Indicators on Social Exclusion and Poverty) Estimation of indicators on social exclusion and poverty, as well as Pareto tail modeling for empirical income distributions. Package: r-cran-lagdynamics Architecture: all Version: 0.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6650 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cograph, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-nestimate, r-cran-tna, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lagdynamics_0.32-1.ca2404.1_all.deb Size: 4090836 MD5sum: 96072fe37ee3ebe3df329b0139f2d32c SHA1: 1696d241d2be727b7cb2f4c83be178b4129d5d59 SHA256: 7f590250c83afb4a40b13eb9a988582eb68520a697aa4d409364826c65a43c1e SHA512: e34f8668b3a71f6052f65591eac253292b077670640e509b7672839002d0299b2df3bcfb6d5dc2513f4c797193bbe4deed7786b44da68eb97b44ba3caeb3cefd Homepage: https://cran.r-project.org/package=lagdynamics Description: CRAN Package 'lagdynamics' (Lag Sequential Analysis, Dynamics, and Lag Transition Networks) A modern, tidy toolkit for lag sequential analysis and lag transition networks of categorical event and sequence data. It provides an accessible, unified workflow for fitting, inspecting, visualising, and comparing lagged transition patterns, with tidy outputs throughout. Includes confirmatory tools for uncertainty, robustness, and group differences, including bootstrap intervals, analytic certainty, split-half reliability, case-drop stability, permutation tests, and Bayesian group comparisons. Supports long-format event-log import, import from common sequence and state-sequence objects, multi-lag analysis, structural-zero constraints, transition and initial probabilities, plotting of transition structures, and a directed transfer-entropy measure. The lag sequential analysis framework follows Sackett and others (1979) . Package: r-cran-lagged Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lagged_0.3.2-1.ca2404.1_all.deb Size: 401944 MD5sum: eeeb8da3ed0ae9968446af0dd337ea7b SHA1: b8da9948ff4af6163a537ecca8a4c2facca7d8e7 SHA256: 7bc122999f4978b488e3886f8b754cbc53eccede13df4edcd55eb3ede0fd1998 SHA512: fa98715579f687b6c2465784044354fb91784162555e7c62512ac238587b4080055a8889dae1e33310f373cde08f8eca138c1e1019c5149f73f055125f545f7a Homepage: https://cran.r-project.org/package=lagged Description: CRAN Package 'lagged' (Classes and Methods for Lagged Objects) Provides classes and methods for objects, whose indexing naturally starts from zero. Subsetting, indexing and mathematical operations are defined naturally between lagged objects and lagged and base R objects. Recycling is not used, except for singletons. The single bracket operator doesn't drop dimensions by default. Package: r-cran-lagosne Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1864 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rappdirs, r-cran-purrr, r-cran-magrittr, r-cran-sf, r-cran-curl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-memoise, r-cran-rlang, r-cran-progress, r-cran-qs2, r-cran-xml2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-r.rsp, r-cran-printr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lagosne_2.0.5-1.ca2404.1_all.deb Size: 569186 MD5sum: eafe3d0b9077af0b9cf3653762ad869c SHA1: 4f37fe134282e2b0533d224d7cfc5f30d8154a6e SHA256: 5c6b04d8f78ca48aec378e74c93dcc1f0b95aa51cb7495d23c22f23053a5ba52 SHA512: d05831dfe7839ff231cb24d27583fe13ab2629920f2c1b5998023eecebf41db845352d5aed0cb98234f746dad4e0edc045bc8bb3de7e81053215d67915f14a8d Homepage: https://cran.r-project.org/package=LAGOSNE Description: CRAN Package 'LAGOSNE' (Interface to the Lake Multi-Scaled Geospatial and TemporalDatabase) Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database , with functions for accessing lake water quality and ecological context data for the US. Package: r-cran-lagsarlmtree Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-spatialreg, r-cran-formula Suggests: r-cran-strucchange Filename: pool/dists/noble/main/r-cran-lagsarlmtree_1.0-2-1.ca2404.1_all.deb Size: 8655798 MD5sum: ed0bdbbfd568d644120931ded0ba9e70 SHA1: b18c839661cc3067130b66299df03a5aef48b8d9 SHA256: b894dd0037678173ca04db77f538cddc3db4a9f3b4183bb4dcfd22435be48b64 SHA512: 858ccb2f9697a49f0dda5cfc5800e14f4de983ca285d90d90c62f14ddfaa08846238939e7131ce5fae7899f44f031cf6bfbcc2b75a37f6dba3f037e9b6d9b352 Homepage: https://cran.r-project.org/package=lagsarlmtree Description: CRAN Package 'lagsarlmtree' (Spatial Lag Model Trees) Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) . Package: r-cran-lagsequential Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lagsequential_0.1.1-1.ca2404.1_all.deb Size: 151988 MD5sum: af05d3ea60008bc77e0336600648a4f7 SHA1: eda02886a5bcce1081a661b23d6c8ecf3e1994ca SHA256: acedcc68779f72f08b32cc5d545f5a62c1d6b3b9560569e00a343a041724687e SHA512: 3973593f7d7431c2a986774aca374d50a7aac8d03a5b3f9fd9f4d68661780cfca149d3273cc37e466b91bb1cd8ede656156dbb897467fbec441b83db270ab3b0 Homepage: https://cran.r-project.org/package=LagSequential Description: CRAN Package 'LagSequential' (Lag-Sequential Categorical Data Analysis) Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) , O'Connor (1999) , Wampold & Margolin (1982) , and Wampold (1995, ISBN:0-89391-919-5). Package: r-cran-lahman Architecture: all Version: 14.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-lattice, r-cran-ggplot2, r-cran-googlevis, r-cran-data.table, r-cran-vcd, r-cran-reshape2, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-plyr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-lahman_14.0-0-1.ca2404.1_all.deb Size: 6852454 MD5sum: 85ded382a04d1f529bc9555bf4bc2e13 SHA1: 218eacecb93311c93e82cfb8b24d9fcb2a91ae7f SHA256: 6c3c9b0b9dd47f7c4ef619b39f8d36182077fcb7899161731ac11bf7b7ccc1c8 SHA512: 066b43b03264fa42b776674069625682ed563ab30663945626db99f85f4878fe1ce407747c5fb2abe06bff25e4b0e89d7bf711b348560d7ae417bb3e3c125211 Homepage: https://cran.r-project.org/package=Lahman Description: CRAN Package 'Lahman' (Sean 'Lahman' Baseball Database) Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2024, as recorded in the 2025 version of the database. Documentation examples show how many baseball questions can be investigated. Package: r-cran-lair Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-terra Filename: pool/dists/noble/main/r-cran-lair_0.3.0-1.ca2404.1_all.deb Size: 647890 MD5sum: 0845b5c2474673b8dba5556935252637 SHA1: e74c2a58ee775a089bee49aaba12b6acb9cc79bd SHA256: c8892edc01fe5949a9b8131e7adb09e597f19265ef2b702ce4ae969f69341b66 SHA512: e93bf84bec382b94d865fefb308fcb80e079e98611eb67ee6f158adc5386b12a30448fef1a8d10334bc1a2bdd1b98b89eb1f9bf70fbccdf5865421c76dfe7303 Homepage: https://cran.r-project.org/package=LAIr Description: CRAN Package 'LAIr' (Converting NDVI to LAI of Field, Proximal and Satellite Data) Convert Leaf Area Index (LAI) from the Normalized Difference Vegetation Index (NDVI) using available equations from literature. Detailed description of conversion equations in Bajocco et al. 2022 . Package: r-cran-lakefetch Architecture: all Version: 0.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-osmdata, r-cran-ggplot2 Suggests: r-cran-hydrogeofetch, r-cran-jsonlite, r-cran-shiny, r-cran-leaflet, r-cran-base64enc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lakefetch_0.1.14-1.ca2404.1_all.deb Size: 356252 MD5sum: 7c080c5b272b162abbb8c380173808a6 SHA1: 4ea1d52c28520471ec0e428d032a6195d7bdc368 SHA256: e7325695da7aa1e75bbd73a04bae7f77d0524abc0a1422f11c1a031b63b1b6ea SHA512: 08245c33da727b5e637a4ab7f923526ef302dfeb257f47010ec5d6855b06018b31bd04e7dfc70b327e306425ac186f3aba6f22186626a28f93254d73a3e77668 Homepage: https://cran.r-project.org/package=lakefetch Description: CRAN Package 'lakefetch' (Calculate Fetch and Wave Exposure for Lake Sampling Points) Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from 'OpenStreetMap', calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset ('NHD') data for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive 'shiny' application for visualization. Package: r-cran-lakemorpho Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-geosphere, r-cran-cluster Filename: pool/dists/noble/main/r-cran-lakemorpho_1.3.2-1.ca2404.1_all.deb Size: 421790 MD5sum: 035d3fed99168ad781c675183b4824ea SHA1: 17dd1ee11f67fc54f13f1a792e64de5d53dcd20e SHA256: f02f1e7fb79fc5829806e1f5833e2f043a1af8e837bbb8b964ab28153f850452 SHA512: c614205dc6813b63d8ed88ae9c11778eb01bb402c83dfd04a5c3af8c6e14be3a4e63e08d1e7854bb221190d82789b916c041476f206addb0b105008a8c8e6897 Homepage: https://cran.r-project.org/package=lakemorpho Description: CRAN Package 'lakemorpho' (Lake Morphometry Metrics) Lake morphometry metrics are used by limnologists to understand, among other things, the ecological processes in a lake. Traditionally, these metrics are calculated by hand, with planimeters, and increasingly with commercial GIS products. All of these methods work; however, they are either outdated, difficult to reproduce, or require expensive licenses to use. The 'lakemorpho' package provides the tools to calculate a typical suite of these metrics from an input elevation model and lake polygon. The metrics currently supported are: fetch, major axis, minor axis, major/minor axis ratio, maximum length, maximum width, mean width, maximum depth, mean depth, shoreline development, shoreline length, surface area, and volume. Package: r-cran-laketemps Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-laketemps_0.5.1-1.ca2404.1_all.deb Size: 584926 MD5sum: cd52cfb089729afe4544ddf1ce996b58 SHA1: aea191fb2e0283374f77bebed46adfb4594789aa SHA256: 0ccc984cd60c39bd3399ba292ff45cbb2e87aac5f8bc57bb9d949c4a67321896 SHA512: 575d9d5342e62dc75f7dbbe4a784519abcc5930969bc0c07575c4c5212564907530355947fe425519a8fcb6f5d5fb5b7f4cfacfe406fffaf7db2469daa41189d Homepage: https://cran.r-project.org/package=laketemps Description: CRAN Package 'laketemps' (Lake Temperatures Collected by Situ and Satellite Methods from1985-2009) Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution. Package: r-cran-lakhesis Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-ca, r-cran-ggplot2, r-cran-rdpack, r-cran-shiny, r-cran-shinydashboard, r-cran-bslib Filename: pool/dists/noble/main/r-cran-lakhesis_0.0.1-1.ca2404.1_all.deb Size: 127288 MD5sum: 18654fc559b3f3c1edf78bbc7573968a SHA1: 56e2d289e4ba0ba98052f9c3b56a911f81686cae SHA256: ccc8547ad0c22e16a3ae790ba63ecc18aeccc7d26ccd36c4a2ad2ddcddec4bdd SHA512: 4ad7c55d596ceaed039861116beb5823c5c51a563be2afe0415695515500c2a56f08776cc9532244abd96330c4b534667d4e5a599a9bb95e8b005608e49e11c8 Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) . Package: r-cran-lambda.r Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formatr Suggests: r-cran-testit Filename: pool/dists/noble/main/r-cran-lambda.r_1.2.4-1.ca2404.1_all.deb Size: 111356 MD5sum: 830bc7ec8cd97cd630bd203b634b9092 SHA1: db604a185b3e9db237d0a453a7a3160149dc2874 SHA256: 5186e2bc4a338982a27659a671441579e36374797b52dcb0af3d1538f767bf9b SHA512: f6cf66fc07ff1489d3db076f99bfedc4e04c9a7427fbebc1512861cb8074ad4913e353b7843ae7511a185d2669813784462c37fb739a32f1d33a6c4dcfbd88a2 Homepage: https://cran.r-project.org/package=lambda.r Description: CRAN Package 'lambda.r' (Modeling Data with Functional Programming) A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety. Package: r-cran-lambdastar Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot Filename: pool/dists/noble/main/r-cran-lambdastar_0.8.1-1.ca2404.1_all.deb Size: 890764 MD5sum: 9a3dc68b5311bffeac118a2d4ed95cea SHA1: ce7aa2abdffd976a957d0585c2447f333350d557 SHA256: 7ddf547474ad7b88ddea9c6cb24a0bb1da1ece877e851de6c28f0b48af58fcdc SHA512: ed06747a8dda33c608cdd2a7a4ca20e42b46cc9cfb6bc57d013460da7ba66a60aaef754f102c0dc86a9145d351552a1ac756e76e65e2240c8742b5b17eb0e672 Homepage: https://cran.r-project.org/package=lambdastar Description: CRAN Package 'lambdastar' (Measurement and Linear Hypothesis Models for Lambda Star) Estimates intrinsic and captured noncentrality from parallel measurements and evaluates the numerical parsimony functional for an explicitly encoded hypothesis matrix, formula, or compatible linear model. Includes common-case model comparisons, quantized entropy capacity, controlled temperature integration, and explicit singularity diagnostics. Uses manuscript projection estimates by default, with an explicit alternative population estimator. Supports common nuisance adjustment and ordinary case or cluster percentile bootstrap intervals, with diagnostics for undefined estimates and preserved model coding. Provides reusable row-bound design specifications and explicit conditional term blocks, named linear restrictions, explicit predictor-grid contrasts, and fixed or reevaluated basis recipes under a homogeneous isotropic measurement-fluctuation assumption. Supports explicit known-reference mean hypotheses and paired differences from parallel measurement pairs. Encodes fixed person-by-occasion models with parallel indicators, implicit person adjustment and whole-person bootstrap with distinct sampled copies. The underlying method is described in Hammes (2026) . Package: r-cran-lambdats Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-torch Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lambdats_2.0.0-1.ca2404.1_all.deb Size: 238696 MD5sum: af12607a8b46b8e1a9f8e0224c4fe9b9 SHA1: d39d12a204dae9ce796c1ebe159fa67f3b22bd97 SHA256: bd4d9496e564e0cb182ce8ff65a40282fdce8dc3c1afc7ba62743c88faf42d2f SHA512: 7ed0521cf39551aea14bc7b601b7b854901a79c3239eaac2e6ce4c120793cd31b8ef4fece002d0a9baa9fc5910e50cd431f2b9beef98b6cca368a85920c5b74c Homepage: https://cran.r-project.org/package=lambdaTS Description: CRAN Package 'lambdaTS' (Variational Seq2Seq Model with Lambda Transformer for TimeSeries Analysis) Probabilistic multivariate time series forecasting using a variational sequence-to-sequence model with Lambda-style temporal aggregation. Provides transformations, uncertainty estimates, diagnostics, and publication-ready plots. Package: r-cran-lambdr Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-logger Suggests: r-cran-withr, r-cran-testthat, r-cran-webmockr, r-cran-knitr, r-cran-rmarkdown, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-lambdr_1.2.5-1.ca2404.1_all.deb Size: 320430 MD5sum: 67b2f270fcd77bad2e162cbf4e88cb2d SHA1: d254c5edf7844af6aa37353c222a10ecc0615a0d SHA256: 704dae8303e018d1c4d4114475e384151b0b081c0d67830f9a239d0f9fcd7214 SHA512: 413cbca5bafd5c3557d5ff4bbb977b9d688bebdfa8c241061c6c6cf62c059acfcf73afa958a82af842ce9c8a549a71e24898b85cda87fd6ee093e4767b32a2b5 Homepage: https://cran.r-project.org/package=lambdr Description: CRAN Package 'lambdr' (Create a Runtime for Serving Containerised R Functions on 'AWSLambda') Runtime for serving containers that can execute R code on the 'AWS Lambda' serverless compute service . 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Package: r-cran-laminr Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-lifecycle, r-cran-pkgload, r-cran-purrr, r-cran-r.utils, r-cran-r6, r-cran-reticulate, r-cran-rlang, r-cran-sessioninfo, r-cran-withr Suggests: r-cran-anndata, r-cran-arrow, r-cran-irkernel, r-cran-jsonlite, r-cran-knitr, r-cran-magick, r-cran-readr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rsvg, r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-laminr_1.3.1-1.ca2404.1_all.deb Size: 510576 MD5sum: 2fe8acdf130d0a08f5ed5bb9e08f7d81 SHA1: bc1db1465d344a2bf1638e416d46217d5e649828 SHA256: 7758d23126301152420e5f28420f7c92471531e56ceb876a28d5e473e9c5b015 SHA512: 6839f5e7fc94f21e3f180aa23c5990958e06af4be185e4349377b86eb71ecd4a1056ec1c4fd026241ef412cfd489a8c7bd8295fe14387230e4b8768d8e0a3a80 Homepage: https://cran.r-project.org/package=laminr Description: CRAN Package 'laminr' (Client for 'LaminDB') Interact with 'LaminDB'. 'LaminDB' is an open-source data framework for biology. This package allows you to query and download data from 'LaminDB' instances. Package: r-cran-lamme Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lamme_0.0.1-1.ca2404.1_all.deb Size: 280732 MD5sum: 6dbf20da55cf48a6666a082b3a1ced59 SHA1: 64b3a5624e163c2d945ef0b0ad96492c00b1e506 SHA256: d45f843d43645978b6aaf8e9fa5f9c203550d95a2492414985a948d0f3b337b7 SHA512: eaccfa120957e504d283f87fabfbf8bec65b7caa1fdb6fc1da42d804da3e58883c9aab86280016ef7caba12db6fae30e90f27c7d7361820ea1c69170747241cc Homepage: https://cran.r-project.org/package=lamme Description: CRAN Package 'lamme' (Log-Analytic Methods for Multiplicative Effects) Log-analytic methods intended for testing multiplicative effects. Package: r-cran-lancor Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-boot, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lancor_0.1.3-1.ca2404.1_all.deb Size: 50840 MD5sum: 5afc6f45c2977e544430ee1a74663e9c SHA1: 045565b878e918c2eb02a5d04461bbab8b40b286 SHA256: 927b05c2960fd00e728a76252d091bb3f9963ea6001de8dd96a34bc2adee7db1 SHA512: 05e1f6f7eed287f3e17b3ee2f9fc10b99184299c5fa813acb33740b3fc81e7c57e658a1b14ee27a376d34ac063be06532c072d32a1933a6ced1c27cce9d12e1d Homepage: https://cran.r-project.org/package=lancor Description: CRAN Package 'lancor' (Statistical Inference via Lancaster Correlation) Implementation of the methods described in Holzmann, Klar (2024) . Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics. Package: r-cran-land4health Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2401 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-tidyr, r-cran-dplyr, r-cran-rgee, r-cran-sf, r-cran-httr2, r-cran-ows4r, r-cran-reticulate, r-cran-rappdirs, r-cran-tibble, r-cran-terra Suggests: r-cran-geojsonio, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-rstudioapi, r-cran-geoidep, r-cran-withr Filename: pool/dists/noble/main/r-cran-land4health_0.3.0-1.ca2404.1_all.deb Size: 1889114 MD5sum: ba160324f04c1fb4121e57c6e5d0afde SHA1: 248fe2e5b6910781ce3cc1becc25274cdbcdb7df SHA256: a6131852c33a44d80d170efbab91080f0630daf4feed3b115ba7426c366f8ef0 SHA512: 72e2aaf8671bd079b51eaa844b3253edcba69c57486d5371e2cddb0cd6223563ba5718091d6383cf7b197ec726a279e5d30fdd4f00aaa1c3dada9107bd320c06 Homepage: https://cran.r-project.org/package=land4health Description: CRAN Package 'land4health' (Remote Sensing Metrics for Spatial Health Analysis) Calculate and extract remote sensing metrics for spatial analysis in the field of health. The package offers R users a quick and straightforward way to obtain areal or zonal statistics of key environmental indicators, covariates, and vector-borne disease data ideal for modeling infectious diseases within the framework of spatial epidemiology. Package: r-cran-landcomp Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-landcomp_0.0.5-1.ca2404.1_all.deb Size: 761512 MD5sum: 7cc72bd8fc93e5ca4a0eb2c60eca9c0d SHA1: 363a912d2052d38f8f3f5eb03c86edb11f3aedb4 SHA256: feb2af470b44a44c0e66cead2f7ac7ab6d5c06bf399a6a032fe9849c1b7bca86 SHA512: 8a94fda2c90f47a5d16871cd1cc85ef66c1325202e2111806603b8ac25b50a325b97544bd8b493989f019f641c462b53f244766992480c6b80f7efab439e3998 Homepage: https://cran.r-project.org/package=LandComp Description: CRAN Package 'LandComp' (Analysing Landscape Composition and Structure at Multiple Scales) Changes of landscape diversity and structure can be detected soon if relying on landscape class combinations and analysing patterns at multiple scales. 'LandComp' provides such an opportunity, based on Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983 ). Functions can handle multilayered data. Requirements of the input: binary data contained by a regular square or hexagonal grid, and the grid should have projected coordinates. Package: r-cran-landest Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-landest_1.2-1.ca2404.1_all.deb Size: 274640 MD5sum: 0cdb9b84a3c7b1455d659290c4b15fb3 SHA1: 2ea692c45952634ca4c7ae4adf9d12403e203eb8 SHA256: 638a55658473c93578042f2a4d314ec12e87912e750d86d7d5a4b174720354be SHA512: a278404a6b71567dd7818c7df5b87edaec933b2e5901f285bdbceae276102dae6d0fca27918afe674ca4b4e0df43caebf4abb7b50afd2b020edbc2e14a30e37c Homepage: https://cran.r-project.org/package=landest Description: CRAN Package 'landest' (Landmark Estimation of Survival and Treatment Effect) Provides functions to estimate survival and a treatment effect using a landmark estimation approach. Package: r-cran-landform Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-landform_0.2-1.ca2404.1_all.deb Size: 22320 MD5sum: b0d8967676c84e7b67e6980e695b0bd0 SHA1: 1cb2de8928389a90cc96064cfc1d6e8a5794c078 SHA256: 8ff9e3c442e8e7da36f65e72dc3e3f63f920a3b404543da8881329f09c330fba SHA512: c1f6cee23d9da7508cd4b78f09e92cf00f6f48c311ff8b419cdfa9c10f31f5c5d14e1e5df822068a880d6845f90244178d0128126d605575ee160700ebe0be03 Homepage: https://cran.r-project.org/package=landform Description: CRAN Package 'landform' (Topographic Position Index-Based Landform Classification) Provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types. Package: r-cran-landgraph Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2003 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-terra, r-cran-deldir, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-landgraph_0.0.3-1.ca2404.1_all.deb Size: 1908312 MD5sum: dd28ed215cbe1c127581ed43cde23435 SHA1: be0503ba8eba53a1b3e07c0a368a4e0213363f05 SHA256: 451ecc8810115ca2b1075bbed9359dac7b7a385ece41bcfa4f20c544915acc76 SHA512: c461ce6b946d8c4a1b79217f53c83f4ad1554f1b84a17223f93cc4cd6ce720e935b87ecc8c4cdef02772e582dafcf1dfecd46cff1028bc2b70c236f32a2afd02 Homepage: https://cran.r-project.org/package=landgraph Description: CRAN Package 'landgraph' (Graphs and Covariance for Landscape Genetics) Shared, dependency-light primitives for landscape-genetic network methods: a lightweight deme/landscape graph (vertex coordinates and an undirected edge list) with constructors from coordinates; genetic covariance and distance from biallelic or multiallelic data (the Yang-style normalized-dosage covariance and the Dyer-style multivariate covariance); and antisymmetric per-edge directional covariate builders (the gradient of a scalar potential, and the projection of a vector flow field). Used by 'terradish' (symmetric resistance). No compiled code. Package: r-cran-landmark Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1004 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-matrix, r-cran-pec, r-cran-riskregression, r-cran-rlang, r-cran-lcmm, r-cran-doparallel, r-cran-foreach, r-cran-prodlim Suggests: r-cran-jmbayes2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2, r-cran-tidyverse, r-cran-spelling, r-cran-joiner, r-cran-survminer, r-cran-withr Filename: pool/dists/noble/main/r-cran-landmark_0.1.3-1.ca2404.1_all.deb Size: 595808 MD5sum: fbc32bdd9d5adede472ae9605b6c2113 SHA1: 4265e149b2a2f1333557de81178c0c13c27c59ca SHA256: 3d0e1edf42f0c7965516dc354f684eec34469933d56c42673adc0a00c63e659a SHA512: 75efb123a65dc669e71cf56b68443e2a64f72f80f9ec3661b2a4cc93092acecd2ff26a57c14d3a25cadfb1e44222506cedde798b00b190bae31cc74ca52880af Homepage: https://cran.r-project.org/package=landmaRk Description: CRAN Package 'landmaRk' (Time-to-Event Landmark Analysis using an Array of Longitudinaland Survival Sub-Models) Provides a modular end-to-end framework for dynamic risk prediction based on time-to-event and longitudinal data. This allows flexible specifications for the longitudinal and survival sub-models. The 'landmaRk' package enables reproducible benchmarks of different model choices, including cross-validation to assess out-of-sample predictive performance. Methods are described in Velasco-Pardo, Constantine-Cooke, Lees and Vallejos (2026, manuscript under preparation) 'Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories'. Package: r-cran-landmarked Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-survival, r-cran-survminer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-landmarked_0.2.0-1.ca2404.1_all.deb Size: 39414 MD5sum: 36d8c9a5819feadd0ad68c97f8b7faab SHA1: 8e7920d229326dfd5bf3351f8ada67393221eec6 SHA256: 1645f8e6a883b480acd6cd3d797570e6e3fdb5641728b7de9dcc17ac89edd00c SHA512: 9c09c151e6f549756685b8399ecab81881e3e616fa02cbaf6824cfe53cfd77363965e00997998eaeea1cad7df59b8a7280883c008d5c2b25cbe722ed46bd7e93 Homepage: https://cran.r-project.org/package=landmarked Description: CRAN Package 'landmarked' (Plot Adjusted Kaplan-Meier Estimates from a Landmark Time) Plots contextual landmark Kaplan-Meier curves. An extension of the 'survminer::ggsurvplot()' function that allows the specification of a landmark time and an optional label. The period before the landmark is displayed as a pooled survival curve, while curves beyond the landmark are presented according to the groups defined in the supplied 'survival::survfit' object. 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The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see . Package: r-cran-landmix Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-landmix_1.0-1.ca2404.1_all.deb Size: 68980 MD5sum: 78c608ccd3252f83458bd7c62c7bda29 SHA1: 11a883c37ceae811ad05e895e6a0bf1388047479 SHA256: 3d80ff2b9a4dbc345dd31f5f7f28321876a8992bce8fa4e23cf9e4d32e4b7b59 SHA512: 0f70d3d366d85fcf3c61fa1281f6b886f875e4f310c6695f2f598748728cb0f6a8463f242d639b435f0c5d3bbceb2812c5af73f942888c7e0b180315b3f94ea1 Homepage: https://cran.r-project.org/package=landmix Description: CRAN Package 'landmix' (Landmark Prediction for Mixture Data) Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) . Package: r-cran-landmulti Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-landpred, r-cran-nmof, r-cran-emdbook, r-cran-snow Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-landmulti_0.5.0-1.ca2404.1_all.deb Size: 181322 MD5sum: b3895b6b836577d97d2eb03cd307f143 SHA1: 313bfb59db782640b57768c80db3c6e147f30929 SHA256: c408f3997615e1b3b2d21cd8182f7764dd78c6a7e1ce4e7845d4a4c8f8f6118b SHA512: e485c9acd67dfbeedffbbde3626880467e012b66d7ee3cc1d3e7534cf2902e6bcd26e359b47ca0394b1a9ef4ae59ed8f9c7bdf2b7497fa451c0363d44a89cec6 Homepage: https://cran.r-project.org/package=landmulti Description: CRAN Package 'landmulti' (Landmark Prediction with Multiple Short-Term Events) Contains functions for a flexible varying-coefficient landmark model by incorporating multiple short-term events into the prediction of long-term survival probability. For more information about landmark prediction please see Li, W., Ning, J., Zhang, J., Li, Z., Savitz, S.I., Tahanan, A., Rahbar.M.H., (2023+). "Enhancing Long-term Survival Prediction with Multiple Short-term Events: Landmarking with A Flexible Varying Coefficient Model". 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Package: r-cran-larf Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula Filename: pool/dists/noble/main/r-cran-larf_1.4-1.ca2404.1_all.deb Size: 203046 MD5sum: 673e85f701a45a76567c1f0df5869f56 SHA1: 9c0ed4cc21d9faa529c5886d03722b807112f0bd SHA256: 28785f30c072b04c0ed0185b68d9c2b0aad09d0cbb01c449646295b0f154d55f SHA512: 6e1dae3e4712083d64759e30ac9181d026b2598608994490ca2e3999b2a14fb03b5821c2e454d2f540929af6a67def451f1a166175a29af5aa458e8eaf95b05f Homepage: https://cran.r-project.org/package=LARF Description: CRAN Package 'LARF' (Local Average Response Functions for Instrumental VariableEstimation of Treatment Effects) Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes. Package: r-cran-largevars Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-tibble, r-cran-data.table, r-cran-readr Filename: pool/dists/noble/main/r-cran-largevars_1.0.3-1.ca2404.1_all.deb Size: 447378 MD5sum: b3c724e195f729bd6ef69a7a008c9213 SHA1: 232ed71efcc3b0ff91394d36fab255dcb2a98b8c SHA256: b272f4a7c3543411c60d72f30df39292bb3967b32c3ef3328cbaaa6f1e15bbf6 SHA512: 98cc207fbf61e6e418341355849b4d8ec889451815d277ecbdfa43f07794b55d9729c9daef29702f6d1fe7e7d66d99e9822059c575fbad003cb5d0fa2d56c967 Homepage: https://cran.r-project.org/package=Largevars Description: CRAN Package 'Largevars' (Testing Large VARs for the Presence of Cointegration) Conducts a cointegration test for high-dimensional vector autoregressions (VARs) of order k based on the large N,T asymptotics of Bykhovskaya and Gorin, 2022 (). 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Package: r-cran-lasars Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2305 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-purrr, r-cran-pmwg Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lasars_0.1.1-1.ca2404.1_all.deb Size: 2190650 MD5sum: 84996af3683441f1ec441d5609db9181 SHA1: b25bf3553a83360c75238ecd2f3400eb8e8c1c77 SHA256: acac8053f623471df88c8bd9ba871c630514a59dab76bbfce6bd72015694add2 SHA512: 4b3eae4484f26c5e042b96acfdefea53b2fc9cb3ca7ec42a2796e05ce5d72f91b3a33b63405954b714d6de5f7c3ab91a19c1fd8295cab9c1121b08d9179e476e Homepage: https://cran.r-project.org/package=lasars Description: CRAN Package 'lasars' (Explore Response Style in Survey Responding) Provides tools to fit the latent state and response style ('lasars') model to survey data using either Particle Metropolis within Gibbs ('pmwg') or maximum likelihood estimation. The package facilitates estimation of less-biased latent state and psychologically interpretable response style parameters. 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This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values. 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Includes real-time and historical data through public 'RESTful' APIs ('Nager.Date', World Bank API, REST Countries API, and country-specific APIs) and extensive curated collections of open datasets covering economics, demographics, public health, environmental data, political indicators, social metrics, and cultural information. Designed to provide researchers, analysts, educators, and data scientists with centralized access to Latin American data sources, facilitating reproducible research, comparative analysis, and teaching applications focused on these five major Latin American countries. Included packages: - 'ArgentinAPI': API functions and curated datasets for Argentina covering exchange rates, inflation, political figures, national holidays and more. - 'BrazilDataAPI': API functions and curated datasets for Brazil covering postal codes, banks, economic indicators, holidays, company registrations and more. - 'ChileDataAPI': API functions and curated datasets for Chile covering financial indicators ('UF', UTM, Dollar, Euro, Yen, Copper, Bitcoin, 'IPSA' index), holidays and more. - 'ColombiAPI': API functions and curated datasets for Colombia covering geographic locations, cultural attractions, economic indicators, demographic data, national holidays and more. - 'PeruAPIs': API functions and curated datasets for Peru covering economic indicators, demographics, national holidays, administrative divisions, electoral data, biodiversity and more. For more information on the APIs, see: 'Nager.Date' , World Bank API , REST Countries API , 'ArgentinaDatos' API , 'BrasilAPI' , 'FINDIC' , and API-Colombia . 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The main functionality of this package is to calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is bounded between +1 (100% probability to exclusively record latitudinal shifts, i.e., range shifts data sampled along a perfectly South-North oriented straight line) and -1 (100% probability to exclusively record longitudinal shifts, i.e., range shifts data sampled along a perfectly East-West oriented straight line). 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Package: r-cran-latentfactor Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-car, r-cran-eganet, r-cran-fspe, r-cran-googledrive, r-cran-ineq, r-cran-lavaan, r-cran-matrix, r-cran-mlr, r-cran-mvtnorm, r-cran-psych, r-cran-rstudioapi, r-cran-xgboost Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-latentfactor_0.0.7-1.ca2404.1_all.deb Size: 279660 MD5sum: f2c67cc3562088e49029ba1f2e0a03db SHA1: a3ce9fc12e6a1667bd2e4905197b87a31c5dca0a SHA256: 26667e71adcc9b5cd82862171248f377b0417d1e971ddd070ba603a0a9db1fe9 SHA512: abe6c410c4daa505f5ac53a56b8c4cca2520ecabda6959e30a57f048a7bed4c447e109269b1231c9c746a7119635d86ec156396e7d91add761d38df72ac2a035 Homepage: https://cran.r-project.org/package=latentFactoR Description: CRAN Package 'latentFactoR' (Data Simulation Based on Latent Factors) Generates data based on latent factor models. 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Package: r-cran-lavaan Architecture: all Version: 0.7-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6180 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-mnormt, r-cran-pbivnorm, r-cran-numderiv, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-lavaan_0.7-3-1.ca2404.1_all.deb Size: 6120798 MD5sum: 6e90b5526ad179b21578cd18671f1c38 SHA1: bd0b1482fe52d201e7e9fa51fdc2b61da992a1c6 SHA256: 43a83a06b0b6bb8f647322d29d660353c1456f2a86302afe16a5ff5b165e714d SHA512: d21324d44506bb69970c2a94903feb2758f99c6818e44d7f7969df6948efb22bbc9fdf690b66788fd5b1e6509dda166171e7353e68a9b3f0ea768ab9c1e82166 Homepage: https://cran.r-project.org/package=lavaan Description: CRAN Package 'lavaan' (Latent Variable Analysis) Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models. 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Package: r-cran-lavaangui Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2734 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-future, r-cran-haven, r-cran-jsonlite, r-cran-lavaan, r-cran-promises, r-cran-readr, r-cran-readxl, r-cran-shiny, r-cran-colorspace, r-cran-igraph, r-cran-dt, r-cran-plyr, r-cran-digest Filename: pool/dists/noble/main/r-cran-lavaangui_0.4.0-1.ca2404.1_all.deb Size: 925286 MD5sum: 0c3e27bda399ea9d0aed004914550c35 SHA1: 8124154481a04ab3947b4044ace32a71e1d5c441 SHA256: 5fbd9936466aa37918ace14e4d2c951a957ff379278f2d71c129611135f8bc76 SHA512: 83be7c1b9ec2f7370babd015def7eb93dc5aae1eb7898b9b26fa2a6e4fff0f0ddef645ee8eb1a6c7b8da100a1bd53706788ada58322e8d3af6958fd0ce6cf47a Homepage: https://cran.r-project.org/package=lavaangui Description: CRAN Package 'lavaangui' (Graphical User Interface with Integrated 'Diagrammer' for'Lavaan') Provides a graphical user interface with an integrated diagrammer for latent variable models from the 'lavaan' package. 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'DiagrammeR' provides nice path diagrams via 'Graphviz', and these functions make it easy to generate these diagrams from a 'lavaan' path model without having to write the DOT language graph specification. Package: r-cran-lavacvxr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavash, r-cran-pracma, r-cran-cvxr Filename: pool/dists/noble/main/r-cran-lavacvxr_1.0.2-1.ca2404.1_all.deb Size: 24918 MD5sum: 99e818e2d24d84e3b4c55df6ede1c1c7 SHA1: 13b4d231b431b80fd5744949c23e54399b7bdf7e SHA256: 71359fcd9a20a5fe305e02dccc1f6bf6426656eb7a2422ce0293919e0264b377 SHA512: 18dbdb529f10cc6b923bd085115d75373512ee076f088b93f727fb877c54a9faf5dc6dc2b58df40d2d207deb5696802a0f554b539d049e84bb4dbc103f25b01e Homepage: https://cran.r-project.org/package=LavaCvxr Description: CRAN Package 'LavaCvxr' (Lava Estimation for the Sum of Sparse and Dense Signals(3Methods)) The lava estimation is used to recover signals that is the sum of a sparse signal and a dense signal. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) . Package: r-cran-lavash Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-pracma Filename: pool/dists/noble/main/r-cran-lavash_1.0-1.ca2404.1_all.deb Size: 30520 MD5sum: 1cc5d8db01ead5257c292b9b5b0ed151 SHA1: e5fc2667bd9ac1cccd7e4134df6dcbd3de809b1e SHA256: 9c2d7c3e6cac4b1244df545ed129ca85e110bf395e5c2047a056040026ba5eb4 SHA512: 82421340a2386b34ca052140f3400cf7dce52cfe31736114c943bc549ab5a1a860c05a87f6c88ace63e81c6bac695440cea2d99bdf5318c1b587f4612bdf29f0 Homepage: https://cran.r-project.org/package=Lavash Description: CRAN Package 'Lavash' (Lava Estimation for the Sum of Sparse and Dense Signals) The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. 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The package provides fast, parallel-safe factor-score prediction (lavPredict_parallel()), data augmentation with model predictions, residuals, delta-method standard errors and confidence intervals (augment()), and model-based latent grids for continuous, ordinal, or mixed indicators (prepare()). It offers item-level empirical versus model curve comparison using generalized additive models for both continuous and ordinal indicators (item_data(), item_plot()) via 'mgcv' (Wood, 2017, ISBN:9781498728331), residual diagnostics including residual correlation tables and plots (resid_cor(), resid_corrplot()) using 'corrplot' (Wei and Simko, 2021 ), and Q–Q checks of residual z-statistics (resid_qq()), optionally with non-overlapping labels from 'ggrepel' (Slowikowski, 2024 ). Heavy computations are parallelized via 'future'/'furrr' (Bengtsson, 2021 ; Vaughan and Dancho, 2018 ). Methods build on established literature and packages listed above. Package: r-cran-lavinteract Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1220 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lavinteract_0.5.3-1.ca2404.1_all.deb Size: 252506 MD5sum: 7f67db52e7db26335d2a993a6604741e SHA1: c308353bf5d9e6d41eb105ad3879fb1f624faffb SHA256: 48ed6f91b0ebb8aa54b41891d1e59635c886829238a7eeb73821a19d2efae4a1 SHA512: 91619aa9dc8110a4530c7de53dfed6db87b7a1bb47ab54ee5cb6e62064f5f25ad0c9dab0ba554411cc6304fcfd686eb8774a56e3e103ef784946fc85f71cf6b6 Homepage: https://cran.r-project.org/package=lavinteract Description: CRAN Package 'lavinteract' (Post-Estimation Utilities for 'lavaan' Fitted Models) Companion toolbox for structural equation models fitted with 'lavaan'. 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Package: r-cran-lcavarsel Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polca, r-cran-nnet, r-cran-mass, r-cran-foreach, r-cran-doparallel, r-cran-ga, r-cran-memoise Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lcavarsel_1.1-1.ca2404.1_all.deb Size: 90808 MD5sum: f368c7a5c2dc1b135e292fce635ed919 SHA1: 5a0469ef8628ddbf4776b699ee3164cc592c76f0 SHA256: accad70643e701fa71cfcb1ac3afb9277d984cefefde7636a27b876b6cd48b42 SHA512: e408ebb3025b123a13f740c7b1aad035803ac7df9df7dc51d8a973e8325b8dc9101c7dc85ffec14f2d6dddefd468556288443799638270bf5d36a3dc7725252d Homepage: https://cran.r-project.org/package=LCAvarsel Description: CRAN Package 'LCAvarsel' (Variable Selection for Latent Class Analysis) Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) and Dean and Raftery (2010) . Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines. Package: r-cran-lcc Architecture: all Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-hnp, r-cran-dosnow, r-cran-dorng, r-cran-foreach Suggests: r-cran-roxygen2, r-cran-covr, r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-lcc_3.2.2-1.ca2404.1_all.deb Size: 494278 MD5sum: 03f82278757bbcf75ee991d14c830f95 SHA1: 596a1375057124542f34d368fb842e83844c6431 SHA256: 1f04dac9fe4aba069f4496c050e4ace28e9c83d7b011b075a679546f9f0f756b SHA512: 0867d81ed538a27e25f3534efcd4bf9776dfb8fd5c9f3f9acfec73a38834790482fc580c6bd5c2f6eb4065db0114b5b4988c66bf835c6c140521b3793962a303 Homepage: https://cran.r-project.org/package=lcc Description: CRAN Package 'lcc' (Advanced Analysis of Longitudinal Data Using the ConcordanceCorrelation Coefficient) Methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via 'nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) . Package: r-cran-lccknn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-caret, r-cran-mlmetrics, r-cran-class Filename: pool/dists/noble/main/r-cran-lccknn_0.1.0-1.ca2404.1_all.deb Size: 32206 MD5sum: 9536a0e39bfd904c4750880cc1844228 SHA1: a178bc373d1da68c74ac64d49fe4bcfcf91cb9b5 SHA256: 782da4697d2bc403504ea2757ef9025d22f9e9c7c54f08f7a24f8ad73b6ba537 SHA512: 34b973a5d3332e5ead853259a071a477adabd4ae247310cf6bba22915b62e5066d50dfdb0d000724ac4d0fbe2d4386026d6785adb792389cad173ddf656cd63e Homepage: https://cran.r-project.org/package=LCCkNN Description: CRAN Package 'LCCkNN' (Adaptive k-Nearest Neighbor Classifier Based on Local CurvatureEstimation) Implements the kK-NN algorithm, an adaptive k-nearest neighbor classifier that adjusts the neighborhood size based on local data curvature. The method estimates local Gaussian curvature by approximating the shape operator of the data manifold. This approach aims to improve classification performance, particularly in datasets with limited samples. Package: r-cran-lccr Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lccr_2.0.1-1.ca2404.1_all.deb Size: 164040 MD5sum: 8a5b44149bec4123677f7f7c44cca073 SHA1: 0bd7ecb5d42f909781dab0dd65fb54b1cac3d97f SHA256: e56743e65c3c64dcecfeae374323aec882ec804c123fa9110e64126c082939b1 SHA512: 9369cfef768ce52bea51a04ccb79ea921150934daa2370dde311fbdf83aa03f2cc4bbca9206be76d270668c34684d438e9f290ccb1e54e471d8571e78a21f05b Homepage: https://cran.r-project.org/package=LCCR Description: CRAN Package 'LCCR' (Latent Class Capture-Recapture Models) Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017. Package: r-cran-lcda Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polca Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lcda_0.3.3-1.ca2404.1_all.deb Size: 85506 MD5sum: 46f823f08b3c037ac06c24855c29f33e SHA1: 622997cb586e500a92636d0b3e9613d3a9c6e601 SHA256: eb720277563f7e47b4f6b98f677f1497a579ad7959f1950ed0dab71af58ca4f6 SHA512: f10807594e9f3e1677f92d51dc27f2081c30096407766d44ed9a9cf8f9acd27a3190cb78f32ba6ed27e59fb9a96dc686e78ac8f1158f973b009969ff55c01f43 Homepage: https://cran.r-project.org/package=lcda Description: CRAN Package 'lcda' (Latent Class Discriminant Analysis) Providing a method for Local Discrimination via Latent Class Models. The approach is described in . Package: r-cran-lcf Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-lcf_1.7.0-1.ca2404.1_all.deb Size: 79750 MD5sum: 4a35df2026a52b9333b136cc2bf09f54 SHA1: 1a51d6d064dd44c440fd4f27189d20b8606a26de SHA256: 68c46f895518016b97125a03c1be4be6296e04f37838778b16ca9f6ecd740758 SHA512: 65637f2d20dc3e271c09e6b26cbec6c63867fa47c00c1dc8ae896d976dbd06960118ea2f004ccd481fcae581b97119f41ac4578e0895e88e6c87c6db4ded647c Homepage: https://cran.r-project.org/package=LCF Description: CRAN Package 'LCF' (Linear Combination Fitting) Baseline correction, normalization and linear combination fitting (LCF) of X-ray absorption near edge structure (XANES) spectra. The package includes data loading of .xmu files exported from 'ATHENA' (Ravel and Newville, 2005) . Loaded spectra can be background corrected and all standards can be fitted at once. Two linear combination fitting functions can be used: (1) fit_athena(): Simply fitting combinations of standards as in ATHENA, (2) fit_float(): Fitting all standards with changing baseline correction and edge-step normalization parameters. Package: r-cran-lcfdata Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lcfdata_2.0-1.ca2404.1_all.deb Size: 1710558 MD5sum: 120539b2b2084fe55acd739e45917b72 SHA1: 3ce791bf9e7400baef497f62ff0855168193d5a5 SHA256: 4e4082ff48e222b4d076b160f33e571072d09c8cd3b00bdabef528df9bf25bd2 SHA512: fcf7d35b6a346d4e90fa497b77a7dd4bcc17d3a2d32a94b8b5ee3892779ae19ac92616ef25c364325e1dedd6769f9cb5d886a0078df1ba0922085e0e16cf4b04 Homepage: https://cran.r-project.org/package=LCFdata Description: CRAN Package 'LCFdata' (Data sets for package ``LMERConvenienceFunctions'') This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model. Package: r-cran-lchemix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lchemix_0.1.0-1.ca2404.1_all.deb Size: 264782 MD5sum: 588e55136311dd5843b51977b7af107a SHA1: 567ad142f29feefdfb1bf446df031460a0bf2285 SHA256: 19d7be53cab63aff3c3eeeb7d9b6bfbd1aaf828f6ee56db46b26e8c32817ecff SHA512: 13c8c354d5d9d0d93b7c58a98a5ee01a43459e3d458fae63ad51a9a3706b546c8b9875914ff5184fdeb48b7f36d37b0a232345f7fe871e495c271b803f7513a7 Homepage: https://cran.r-project.org/package=lchemix Description: CRAN Package 'lchemix' (A Bayesian Multi-Dimensional Couple-Based Latent Risk Model) A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. . Package: r-cran-lcmsqa Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bsplus, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-biocmanager, r-bioc-xcms, r-bioc-msnbase, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lcmsqa_1.0.2-1.ca2404.1_all.deb Size: 564928 MD5sum: 66b55da662134a133946728c5e36e11f SHA1: 2f1f1d3d9e4bbc2a4fb7766963a856a82066272f SHA256: f3e4c96dfa809fdd2e9789e4587f5a24fd808b316b382def8f44c2eaef12b706 SHA512: 497237545d34b2f0b5ef90274989d8e68a65eacf49e3c4b1889065a727f7b18e0f27d2beee538dcbea6b28c8ef6d19f6890d5300dd2d99a82d7aedcac1fe0ebe Homepage: https://cran.r-project.org/package=LCMSQA Description: CRAN Package 'LCMSQA' (Liquid Chromatography/Mass Spectrometry (LC/MS) QualityAssessment) The goal of 'LCMSQA' is to make it easy to check the quality of liquid chromatograph/mass spectrometry (LC/MS) experiments using a 'shiny' application. This package provides interactive data visualizations for quality control (QC) samples, including total ion current chromatogram (TIC), base peak chromatogram (BPC), mass spectrum, extracted ion chromatogram (XIC), and feature detection results from internal standards or known metabolites. 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This implementation accounts for the implicit constraints on the parameter space. Other features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization. Package: r-cran-lcra Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-coda, r-cran-rjags Suggests: r-cran-r2winbugs, r-cran-gtools Filename: pool/dists/noble/main/r-cran-lcra_1.1.5-1.ca2404.1_all.deb Size: 121930 MD5sum: 5451e50906e706d1ee4471c7e69d9cda SHA1: 5404f50b9fba78d3cdf53506e17efde38f06b213 SHA256: 5a622e6d105b4a806f5bb84890eeb5bb337d758fe1ce6ea9fce05dcd78d8ac63 SHA512: c11a45ab932d7ac2eb80daf805aed601f6a022ee104e7d5fe97aa1ea3dbd6ec208d48b69463ba7164c2b071f9a312de88e95de1ae40e8833d6b95c643f94af23 Homepage: https://cran.r-project.org/package=lcra Description: CRAN Package 'lcra' (Bayesian Joint Latent Class and Regression Models) For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology . 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For details about Latent Change Score Modeling (LCSM) see McArdle (2009) and Grimm, An, McArdle, Zonderman and Resnick (2012) . The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints. The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually. Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications. Estimated model parameters and fit statistics can be extracted as data frames. Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects. This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together. Package: r-cran-lctools Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sp, r-cran-reshape, r-cran-weights, r-cran-pscl, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lctools_0.3-1.ca2404.1_all.deb Size: 1727352 MD5sum: 0f961c63e00d72d0f2c3c4cf6deed0cb SHA1: 6476a4ffc25dfc3d9dbf73037065ee2ee0c2a894 SHA256: 18c27fffef07e815261ca1cad007e0efa967a5198153dff80f687a679d2c87c0 SHA512: 037395f3a73e2051428765f9ea5610bf8a0305443c192284810da28a92f76510db683730c90cb917690ff1d4908ce529c1b762b3997c65e6f968b8a65ee5983c Homepage: https://cran.r-project.org/package=lctools Description: CRAN Package 'lctools' (Local and Geographically Weighted Spatial Statistics Tools) Provides researchers and educators with easy-to-learn, user friendly tools for calculating key spatial statistics and for applying simple as well as advanced methods of spatial analysis on real data. These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients; Spatial Inequality Measures (Gini coefficient, Spatial Gini, Location Quotient (LQ) and Focal Location Quotient); Spatial Autocorrelation indices (Global and Local Moran's I); several Geographically Weighted Regression techniques, including the Geographically Weighted Zero-Inflated Poisson Regression; tools for computing variables used in Spatial Interaction Models; and other spatial analysis tools (other geographically weighted statistics). The local correlation tools were originally developed to test for local multicollinearity among the explanatory variables of local regression models and can also be used to examine the local association between pairs of variables. The package also contains functions for measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations, and comes with two example datasets, one of which is a spatial data frame referring to the municipalities of Greece. Methods are described in Kalogirou (2012) , Kalogirou (2016) , and Rey and Smith (2013) . Package: r-cran-lcyanalysis Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantmod, r-cran-ttr, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-lcyanalysis_1.0.4-1.ca2404.1_all.deb Size: 119142 MD5sum: c8823b9fa702482730c28a061fab8c0c SHA1: 61a89f0429f95da36aa48994ba8a24eea156110d SHA256: 06f30a18e8178b961c271236467fc183b1363e65c8090ebd8d1917f6f4912734 SHA512: d46848cc27ed72708f8d64114378bf1adf39da61a86947265035308f5930fd90f626c8395aba561b741f2c5ac1e61b252ff4d034d67220d32098a987df5a0bd9 Homepage: https://cran.r-project.org/package=lcyanalysis Description: CRAN Package 'lcyanalysis' (Stock Data Analysis Functions) Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend. Package: r-cran-ldabiplots Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2986 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinyalert, r-cran-shinybusy, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-dplyr, r-cran-ggplot2, r-cran-rvest, r-cran-dt, r-cran-highcharter, r-cran-tidyr, r-cran-snowballc, r-cran-ldatuning, r-cran-topicmodels, r-cran-textminer, r-cran-chinese.misc, r-cran-stringr, r-cran-htmlwidgets, r-cran-ggrepel, r-cran-textplot, r-cran-glasso, r-cran-qgraph, r-cran-matrix, r-cran-factoextra, r-cran-quanteda Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-beepr, r-cran-readxl Filename: pool/dists/noble/main/r-cran-ldabiplots_0.1.2-1.ca2404.1_all.deb Size: 1489108 MD5sum: 0378a95eac186059b8a22613964dd4d3 SHA1: 886b0bb670391a327afb719781394fa17327c9f8 SHA256: faae709dba11db8ec816bdf627415e2d76a0c69dbcc738a574319e9bbaa0d0b6 SHA512: 5310da73d10b1f7c486303f2c4ee997dbc47959baaaab9d4283454596cfbb5ae4cb7b448f57662034d66f07bb923a2088d39dd8abba7b0249ba1a5e9f3a5298d Homepage: https://cran.r-project.org/package=LDABiplots Description: CRAN Package 'LDABiplots' (Biplot Graphical Interface for LDA Models) Contains the development of a tool that provides a web-based graphical user interface (GUI) to perform Biplots representations from a scraping of news from digital newspapers under the Bayesian approach of Latent Dirichlet Assignment (LDA) and machine learning algorithms. Contains LDA methods described by Blei , David M., Andrew Y. Ng and Michael I. Jordan (2003) , and Biplot methods described by Gabriel K.R(1971) and Galindo-Villardon P(1986) . Package: r-cran-ldacoop Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ldacoop_0.1.2-1.ca2404.1_all.deb Size: 201654 MD5sum: a65af12b05a0996e5174acdf6d58a043 SHA1: 206c9aea9714f4112bae2653d0f194ec62abcfc3 SHA256: e8798979be398e276b13e7ec12badc663497380d4028b46c8c1ae8d02df3a5be SHA512: b505121dc727ca71b3a7c54cb2a023b7b5d3bbd4b4d223a78b9dde4d9210f07da3cb72710ca0780ba498a8ad79cdbc71e32d4d773ff506d48947ee2fefbf7eed Homepage: https://cran.r-project.org/package=LDAcoop Description: CRAN Package 'LDAcoop' (Analysis of Data from Limiting Dilution Assay (LDA) with orwithout Cellular Cooperation) Cellular cooperation compromises the established method of calculating clonogenic activity from limiting dilution assay (LDA) data. This tool provides functions that enable robust analysis in presence or absence of cellular cooperation. The implemented method incorporates the same cooperativity module to model the non-linearity associated with cellular cooperation as known from the colony formation assay (Brix et al. (2021) : "Analysis of clonogenic growth in vitro." Nature protocols). Package: r-cran-ldamatch Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-runit, r-cran-data.table, r-cran-entropy, r-cran-foreach, r-cran-iterators, r-cran-iterpc, r-cran-ksamples, r-cran-car, r-cran-gmp Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-ldamatch_1.0.3-1.ca2404.1_all.deb Size: 226980 MD5sum: ef0b13dbb39bba4916d4943883206e43 SHA1: b623918effcca89e805821b85d82c33000cd5cee SHA256: 0fb5d292baaa252bd083e0d18ec345b635aeeb9c92fe129ee39d6e3043159f66 SHA512: 9d831913cb88d4c444bbf2d8b95a270c3176fb7dfa64ad41cd09496f3938f8e6935cb95a8bf87e484a9ec5b47a8b6659c4ff8bff0bf360f72b8788d971673f8e Homepage: https://cran.r-project.org/package=ldamatch Description: CRAN Package 'ldamatch' (Selection of Statistically Similar Research Groups) Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search. Package: r-cran-ldaprototype Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-checkmate, r-cran-colorspace, r-cran-data.table, r-cran-dendextend, r-cran-fs, r-cran-parallelly, r-cran-lda, r-cran-parallelmap, r-cran-progress Suggests: r-cran-covr, r-cran-rcolorbrewer, r-cran-testthat, r-cran-tosca Filename: pool/dists/noble/main/r-cran-ldaprototype_0.3.2-1.ca2404.1_all.deb Size: 261808 MD5sum: f6c81d78c14c8223fbe6c558d13051e7 SHA1: 8c9dd18a93ce75345e88a73792faa8b16edc2a73 SHA256: d5f4fbf9a8e5b1f2c2e22aa09bf8b4308f19e8684a05a70e7e92f5a800cd5c6b SHA512: 94fea5d2ff8a7a981cc5b34c22cd8c61154914e71163c39ce354392cce9dbdec58f19ae6ff091b8e96174f780871e645dc05a6187d62de2e9a952d2d008658f9 Homepage: https://cran.r-project.org/package=ldaPrototype Description: CRAN Package 'ldaPrototype' (Prototype of Multiple Latent Dirichlet Allocation Runs) Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype. 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Steps include data import and deduplication, text preprocessing (stopword removal, stemming, n-grams, sparse-term filtering), statistical inference to select the optimal number of topics via coherence, final model training, and topic trend analysis over time using linear regression. All results can be exported as Excel files, RDS objects, and publication-quality plots. Package: r-cran-ldatree Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-folda, r-cran-ggplot2, r-cran-magrittr, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ldatree_0.2.0-1.ca2404.1_all.deb Size: 1058760 MD5sum: 8c815094c4a0b3d754ba197cc6c78630 SHA1: b44ca62c8cd3d14829b397ea1599896fbf1c4308 SHA256: e1261f440a2ff4dcb7d6bb8db7cdc4b39105bc1b047e651b0bc82df68f163a28 SHA512: a4c1b3036a65c98ff1177e73195cc0ef7448f2582887a25d2902e4f3876990a9d48c10d077e7a792034b173b2e1e39116b8cee9f2eebfa62ec442421f0adef9b Homepage: https://cran.r-project.org/package=LDATree Description: CRAN Package 'LDATree' (Oblique Classification Trees with Uncorrelated LinearDiscriminant Analysis Splits) A classification tree method that uses Uncorrelated Linear Discriminant Analysis (ULDA) for variable selection, split determination, and model fitting in terminal nodes. 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Package: r-cran-ldatuning Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-topicmodels, r-cran-slam, r-cran-rmpfr, r-cran-ggplot2, r-cran-reshape2, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ldatuning_1.0.2-1.ca2404.1_all.deb Size: 516892 MD5sum: e13daba6f9dc0dcaad167f5da8ce7fe8 SHA1: 8fd6ed4fe4537bc7ea53938d17f70f3f5134ed44 SHA256: fa128778864ec974f4e3d25c6ace2ecb40947519fb5138e8fd92c31265e2ebda SHA512: 961245b8fc1d551ab76de42fb4c4c66882291452d3a763bfd51d09d7a1f782c9aac11711149a3773a51a68ad5e53d3747248463975dff1bae410b66f752a0d63 Homepage: https://cran.r-project.org/package=ldatuning Description: CRAN Package 'ldatuning' (Tuning of the Latent Dirichlet Allocation Models Parameters) For this first version only metrics to estimate the best fitting number of topics are implemented. Package: r-cran-ldavis Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2672 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proxy, r-cran-rjsonio Suggests: r-cran-mallet, r-cran-lda, r-cran-topicmodels, r-cran-gistr, r-cran-servr, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-digest, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-ldavis_0.3.2-1.ca2404.1_all.deb Size: 2209848 MD5sum: 98afbc163ad945be3fa82c3e49221bb5 SHA1: 40e3fbfddefa04d025733e4a48556855622537e9 SHA256: 1f47e58b6b745ea4b6b4855f82f1f852f4946de8bd6fcfe70205aac561dfeb57 SHA512: d4a44078b46a07939d180f538a127b9d8eefc4c004de4bef5c9073e283a690da6f3c27627b96e0d0baaedba5a81a5f5c740ec198e84a32998be129bd7ef0b449 Homepage: https://cran.r-project.org/package=LDAvis Description: CRAN Package 'LDAvis' (Interactive Visualization of Topic Models) Tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). 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Package: r-cran-ldbod Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-ldbod_0.1.2-1.ca2404.1_all.deb Size: 44500 MD5sum: 747ef1269f2565c580631c5b4e919f7c SHA1: a404df574461d8448d80e3ee0565982bf53c3a8f SHA256: 3ba190f768d3d1f37a8f96cc44eb21ac7d1731a2bf8d66cce041450659d155b1 SHA512: 14a859a134a3e1e5919f3a84fa47f24e952d5e0f13d7e88cc6cf2b1f59b7c0a8dd69b6ba258f19f458bb10cbff8113041b217542a8b55c45956ba5b29cbe8cb9 Homepage: https://cran.r-project.org/package=ldbod Description: CRAN Package 'ldbod' (Local Density-Based Outlier Detection) Flexible procedures to compute local density-based outlier scores for ranking outliers. Both exact and approximate nearest neighbor search can be implemented, while also accommodating multiple neighborhood sizes and four different local density-based methods. It allows for referencing a random subsample of the input data or a user specified reference data set to compute outlier scores against, so both unsupervised and semi-supervised outlier detection can be implemented. Package: r-cran-ldbounds Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ldbounds_2.0.2-1.ca2404.1_all.deb Size: 228764 MD5sum: 7820ca9c4859acb5decddfe61ba253b3 SHA1: f673cd87bc7ed9e5a6fc3ad7c4ebaa2fcb883b80 SHA256: 6eb55416ee6cfba240da09cadff24ee2ad10f05733060b2f5261f6db6eb9a19a SHA512: abf21c16885fc0278db5832b8639f38f89df8b40afa320541319b8443d48ad5f7d253277bc8c13b0db6d256a732d3cca6310ea3dff45bcf84d9b227d5aba44eb Homepage: https://cran.r-project.org/package=ldbounds Description: CRAN Package 'ldbounds' (Lan-DeMets Method for Group Sequential Boundaries) Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. (2000) . Package: r-cran-ldcorsv Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ldcorsv_1.3.4-1.ca2404.1_all.deb Size: 72928 MD5sum: 4fa31877a46be75725f6d74ae7359207 SHA1: 96b3da3fcf06aa265abbfa5ceffb7636efbcd140 SHA256: 69a5a3d6729f8f236f3a00d219d04cdcf7d6139d4cd147ac64f19514ccfc7229 SHA512: 117a2cca887951dce5726ddbb31e9987b1b7e9e43836861ae19aa7330d25e3d43d64a3f4a9529f978c62b06f52359af2261e15510ea79ed8d41552d68c52a26d Homepage: https://cran.r-project.org/package=LDcorSV Description: CRAN Package 'LDcorSV' (Linkage Disequilibrium Corrected by the Structure and theRelatedness) Four measures of linkage disequilibrium are provided: the usual r^2 measure, the r^2_S measure (r^2 corrected by the structure sample), the r^2_V (r^2 corrected by the relatedness of genotyped individuals), the r^2_VS measure (r^2 corrected by both the relatedness of genotyped individuals and the structure of the sample). Package: r-cran-ldhmm Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gnorm, r-cran-optimx, r-cran-xts, r-cran-zoo, r-cran-moments, r-cran-scales, r-cran-ggplot2, r-cran-yaml Suggests: r-cran-knitr, r-cran-testthat, r-cran-depmixs4, r-cran-roxygen2, r-cran-r.rsp, r-cran-shape Filename: pool/dists/noble/main/r-cran-ldhmm_0.6.1-1.ca2404.1_all.deb Size: 1467262 MD5sum: 5afe6ee863b7d438881e8a76d52671eb SHA1: 9c658f22ecd81dc9e18a10794f53062b0b4e92f1 SHA256: 857056a7d54111bfa04cbe5ba66560bfd828153ca31213e433718232bd2ff889 SHA512: 83a1a03ddc0cf6e036ae90c414e939bde28990338a3e0d4612006f72bf3b211b177333eb25241b02e8fd1c3ff7b79a27c77a71bdd70a39cd9a55ae55cfd647e9 Homepage: https://cran.r-project.org/package=ldhmm Description: CRAN Package 'ldhmm' (Hidden Markov Model for Financial Time-Series Based on LambdaDistribution) Hidden Markov Model (HMM) based on symmetric lambda distribution framework is implemented for the study of return time-series in the financial market. Major features in the S&P500 index, such as regime identification, volatility clustering, and anti-correlation between return and volatility, can be extracted from HMM cleanly. Univariate symmetric lambda distribution is essentially a location-scale family of exponential power distribution. Such distribution is suitable for describing highly leptokurtic time series obtained from the financial market. It provides a theoretically solid foundation to explore such data where the normal distribution is not adequate. The HMM implementation follows closely the book: "Hidden Markov Models for Time Series", by Zucchini, MacDonald, Langrock (2016). 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It can also calculate the variance of LDL and the Atherogenic Index of Plasma (AIP) using error propagation and bootstrapping. 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This programmatic access facilitates researchers who are interested in performing batch queries in 1000 Genomes Project (2015) data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying germline variants in human population groups of interest. For more details, please see Machiela et al. (2015) . Package: r-cran-ldm Architecture: all Version: 6.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gunifrac, r-cran-vegan, r-cran-permute, r-bioc-biocparallel, r-cran-matrixstats, r-cran-castor, r-cran-phangorn, r-cran-modeest Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-ldm_6.0.1-1.ca2404.1_all.deb Size: 786974 MD5sum: 871768ff06a4e822fb4993d4221f4e4c SHA1: 9aaad31db0424424e0ca37a9f0a9ffcf071ab5dc SHA256: 366c0af695aa727a141b4f1f19ab9cb1412d08ea4f459f5589ad73df2833aa95 SHA512: c6f295ec8923584958a79f5c55871c983652b41a532021d497dcd5238256eb1e7096a915d503e77a8591f73b979cdea15887e651756da71344745a2888c86d00 Homepage: https://cran.r-project.org/package=LDM Description: CRAN Package 'LDM' (Testing Hypotheses About the Microbiome using the LinearDecomposition Model) A single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. 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Package: r-cran-ldnn Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-devtools, r-cran-reticulate, r-cran-tensorflow Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ldnn_1.10-1.ca2404.1_all.deb Size: 31010 MD5sum: 97f92b5bbdb2d70803d2c6dd435c08cf SHA1: 9f01151bde4294ed815cdd3712eb7cb930c303e3 SHA256: f9eee996290b7119e43db0ba217e8667637e7a13708a2e0f98f974b970c3092f SHA512: 88168edd79fc8c0624aeb6fbde49681cc4c8018f57742f68bd201dfa1ff9f608afcd8e4f9292ce22b3df571da3a9aeb000eeea73783a11465472c96f1fe7ed73 Homepage: https://cran.r-project.org/package=LDNN Description: CRAN Package 'LDNN' (Longitudinal Data Neural Network) This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs. 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This package is based on the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on the description of the algorithm by Randall O'Reilly (1996) . For more general (not 'R' specific) information on the algorithm Leabra see . 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The package implements a four-stage workflow: data subset generation, functional form discovery, numerical parameter optimization, and multi-objective evaluation. It provides a high-level formula-style interface that abstracts and extends multiple discovery engines: genetic programming (via PySR), Reinforcement Learning with Monte Carlo Tree Search (via RSRM), and exhaustive generalized linear model search. 'leaf' extends these methods by enabling multi-view discovery, where functional structures are shared across groups while parameters are fitted locally, and by supporting the enforcement of domain-specific constraints, such as sign consistency across groups. The framework automatically handles data normalization, link functions, and back-transformation, ensuring that discovered symbolic equations remain interpretable and valid on the original data scale. Implements methods following ongoing work by the authors (2026, in preparation). 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Package: r-cran-leaflet.extras Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2613 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-leaflet, r-cran-htmlwidgets, r-cran-htmltools, r-cran-stringr, r-cran-magrittr Suggests: r-cran-jsonlite, r-cran-readr, r-cran-sf, r-cran-xfun, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leaflet.extras_2.1.0-1.ca2404.1_all.deb Size: 1412668 MD5sum: 8e78318836a74efe4e75bc8f286c8785 SHA1: c59f39692c2516d6c315aef0ae542c77c441f8c0 SHA256: 7f700ef31a8d82a9111ad1f01ae844fbcd017dd1e9061ad11cc625ba69362acd SHA512: 38ac0dc7cb1eaa40b4b2ba6e04fe1b9db9527061aa40a52c68813db55f185eecbae7cfe8377c4a75ed635d56a7f783aa4d3af378c0ca5d5efe0e98d42a54259f Homepage: https://cran.r-project.org/package=leaflet.extras Description: CRAN Package 'leaflet.extras' (Extra Functionality for 'leaflet' Package) The 'leaflet' JavaScript library provides many plugins some of which are available in the core 'leaflet' package, but there are many more. It is not possible to support them all in the core 'leaflet' package. This package serves as an add-on to the 'leaflet' package by providing extra functionality via 'leaflet' plugins. Package: r-cran-leaflet.minicharts Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-shiny, r-cran-manipulatewidget, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-leaflet.minicharts_0.6.3-1.ca2404.1_all.deb Size: 529778 MD5sum: cd87f0138a36acfb118b8424cebc02f7 SHA1: 0b73e31e814ead5136182e178b36ffd07daf0c2e SHA256: 1b214afa0c95c89360eee4c9a3173d5f6b2ffe92ec6024ab66d0f1c0bb2843b9 SHA512: e036d995517d0b70ce35eec1d4af6121e328346c4f4c0435f214636d6763b98e5e6cf468074cd4a86f125c5f12391a0544e26b54ee0cac33519aeab2df027583 Homepage: https://cran.r-project.org/package=leaflet.minicharts Description: CRAN Package 'leaflet.minicharts' (Mini Charts for Interactive Maps) Add and modify small charts on an interactive map created with package 'leaflet'. These charts can be used to represent at same time multiple variables on a single map. Package: r-cran-leaflet.providers Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-jsonlite, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-leaflet.providers_3.0.0-1.ca2404.1_all.deb Size: 44150 MD5sum: 9fee076603c82bc832b9377666994185 SHA1: dd0708558c6ce267ce89037b86c4d8ead733b160 SHA256: 7f31ef93a08898658c4964a8a1948199348a9aa588bb6b203e87b19041ea7cb7 SHA512: ec3e50af6aaf2dcfc3624e3a3bb879db2dd408fe3564128c74fd6751688f66ac0f2dfe31c217143c979a47d7bf5a5150e61835e4b02ea0556f21bfd63e8446fd Homepage: https://cran.r-project.org/package=leaflet.providers Description: CRAN Package 'leaflet.providers' (Leaflet Providers) Contains third-party map tile provider information from 'Leaflet.js', , to be used with the 'leaflet' R package. 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Package: r-cran-leafpm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-leaflet, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-leafpm_0.1.0-1.ca2404.1_all.deb Size: 59302 MD5sum: 078fbe8334431c26891d658a7dc1904d SHA1: b7e838f1913a634a9afeda3e69fffd7a7285640d SHA256: 5b9ef9a52beed6d9e419c74851f2afc07623cba6be8c9981bd30a6b4df8356e6 SHA512: 4aea0eb34380bbabd8db93ec55943c7a1041aa963eb81cdbcb21dfa31a1e713155c3f6214e3584d05485a44167144a2084587139b6a91a37c2f8e5b6ab18f6e4 Homepage: https://cran.r-project.org/package=leafpm Description: CRAN Package 'leafpm' (Leaflet Map Plugin for Drawing and Editing) A collection of tools for interactive manipulation of (spatial) data layers on leaflet web maps. 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Package: r-cran-leafpop Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-brew, r-cran-htmltools, r-cran-htmlwidgets, r-cran-sf, r-cran-svglite, r-cran-uuid Suggests: r-cran-lattice, r-cran-leaflet, r-cran-sp Filename: pool/dists/noble/main/r-cran-leafpop_0.1.0-1.ca2404.1_all.deb Size: 1910518 MD5sum: 72c0ec5f41b78a0471301f3351e984b2 SHA1: b0a0c7c6951e42cb8dbfceff40695011579bc937 SHA256: d5ad3b6d7ed85f90abd004ace8e2688d2bc43f6ae188572ceb2bbe950d657cfe SHA512: fbbc907fa6f286b6a435ebb13866f58fe2d93e9dbc8f236cb42d3a9c7af582959d46f75d933bc81383137cb0424c142987e19e956b56af216ea93ed1e8717584 Homepage: https://cran.r-project.org/package=leafpop Description: CRAN Package 'leafpop' (Include Tables, Images and Graphs in Leaflet Pop-Ups) Creates 'HTML' strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like 'leaflet' or 'mapview'. Handles local images located on the file system or via remote URL. Handles graphs created with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'. Package: r-cran-leafr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lidr, r-cran-sp, r-cran-data.table, r-cran-raster Filename: pool/dists/noble/main/r-cran-leafr_0.3.5-1.ca2404.1_all.deb Size: 447946 MD5sum: 88b91ac03e521ac846b346dad1aad339 SHA1: e225a93dbef1c2b2d622d9cb273f4b80d452c828 SHA256: cb8a83c43bc7ccf41bd5c22998d3974943fcde3bcc02f8fa5464c62fdfd43986 SHA512: 642006bb794c2f09bded62ce5c30bf6cddb34216e49d4867079669e9f92ed1d4911be8025b3335ada1a645da0b61f9d94415b458d93f849938a351e642dc9d73 Homepage: https://cran.r-project.org/package=leafR Description: CRAN Package 'leafR' (Calculates the Leaf Area Index (LAD) and Other Related Functions) A set of functions for analyzing the structure of forests based on the leaf area density (LAD) and leaf area index (LAI) measures calculated from Airborne Laser Scanning (ALS), i.e., scanning lidar (Light Detection and Ranging) data. The methodology is discussed and described in Almeida et al. (2019) and Stark et al. (2012) . Package: r-cran-leafstar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-leafstar_1.0-1.ca2404.1_all.deb Size: 111878 MD5sum: 51e8e55b66fdb943c646575ceda7463b SHA1: 2a183cae87ad49b58432e44868568cb0a8da0522 SHA256: 496ac537208e288f9da0372b907c6d11d15d63874c658a6ea9316931f63f15c0 SHA512: 0a0494eb641b1338ae2bb67d19c5ed2bd20674552f3d288c3ecfadbc114dc3894462da268ebde95adaf8f6a8983c03769572a79ff0e69af8c9e7e74320fc45b4 Homepage: https://cran.r-project.org/package=leafSTAR Description: CRAN Package 'leafSTAR' (Silhouette to Area Ratio of Tilted Surfaces) Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes. Package: r-cran-leafsync Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-leafsync_0.1.0-1.ca2404.1_all.deb Size: 841464 MD5sum: faf9f9182c8f0cc4be7e23fad780c0af SHA1: cfa7aa9112a77a3a34d828be88b04e1b44f1f1f5 SHA256: 078c13d0d154e7da74fb88818411200ac332a42a92c4760e5c7251834032d258 SHA512: 45961ac500e34fe6b0e79bce10914a4bec09e9ca44f575e13fbb22c75574ff0f5cf705afc2a9c74a9fa31d18302d2428d1edb037caf94374948628c29925c706 Homepage: https://cran.r-project.org/package=leafsync Description: CRAN Package 'leafsync' (Small Multiples for Leaflet Web Maps) Create small multiples of several leaflet web maps with (optional) synchronised panning and zooming control. When syncing is enabled all maps respond to mouse actions on one map. This allows side-by-side comparisons of different attributes of the same geometries. Syncing can be adjusted so that any combination of maps can be synchronised. Package: r-cran-leaftime Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-geojsonio Filename: pool/dists/noble/main/r-cran-leaftime_0.2.0-1.ca2404.1_all.deb Size: 34080 MD5sum: d1d19dfdc18f1374c61a2c100581f0ad SHA1: 1c2dfdb2a6e4de41680c6e4bf67f4e3f2c5265e6 SHA256: f6286280788dc08d366642905520a04f8de1c5adc75cf0aab89dbc43597ebe59 SHA512: bb844bcf5cdc321faf815902d359cfbf6b92833865881446da9f1a312b7e5e7ae5a4acf25273dbf4db38f0a623c1b1dde8c0e57e892685d5501fd11bdd00b4f0 Homepage: https://cran.r-project.org/package=leaftime Description: CRAN Package 'leaftime' ('Leaflet-timeline' Plugin for Leaflet) Use the 'leaflet-timeline' plugin with a leaflet widget to add an interactive slider with play, pause, and step buttons to explore temporal geographic spatial data changes. Package: r-cran-leafwax Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-rappdirs, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leafwax_0.2.7-1.ca2404.1_all.deb Size: 1858432 MD5sum: 638f2a6b1503953fc71a1dc1ae9cc737 SHA1: d800522a7a2de4342c1d66466942add17f947d9d SHA256: f16d2be943c199f54138dfda723a737944b3646e37f462e358028fa0fb941482 SHA512: e5c92256ea42c2c8d1cdb4e1ef5549bd5698727899ece13083f06a82b4ae47f3014d6654d10b91bc10716c00ff1599737791076ecc39b5f9994d38c5a6c9c82a Homepage: https://cran.r-project.org/package=leafwax Description: CRAN Package 'leafwax' (Bayesian Inversion of Leaf Wax Hydrogen Isotopes toPrecipitation) Bayesian inversion of leaf wax hydrogen isotopes to reconstruct precipitation isotopes using hierarchical spatial models. Provides fourteen Bayesian models that vary in their use of spatial Gaussian processes and ancillary covariates (precipitation amount, plant functional type, C4 fraction). Models are pre-computed using 'Stan' and stored as posterior distributions, so prediction does not require 'Stan' to be installed. A 100-draw fixture ships with the package; full 1000-draw posteriors are downloaded from a versioned 'Zenodo' deposit on first use; see Bradley (2026) . Package: r-cran-leakaudit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magick Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-leakaudit_0.1.0-1.ca2404.1_all.deb Size: 41018 MD5sum: 8dde51d91de3d98f39a0e5df4bc89ad5 SHA1: eafa7a51d8a3f066650b4512057be1f0e7b34e20 SHA256: a056b366239b69e3c9754445a26d92828f73d707c49b48f70713841ae2520703 SHA512: 4782b7a0dc8b60dc3f72d7edc627f246bfe20526a2d166c9c2d4db58201b9610a56024ed83520ea4a024f7137264f531d6716222b5d7a8a0fb75974d31c19059 Homepage: https://cran.r-project.org/package=leakaudit Description: CRAN Package 'leakaudit' (Detect and Audit Train/Test Leakage from Near-Duplicate Images) Detects near-duplicate images across dataset splits using perceptual hashing, reports the resulting train/validation/test contamination, and produces a corrected, leak-free split assignment. Intended for machine learning researchers who need to verify that image classification splits do not share near-duplicate samples across partitions before reporting model metrics. Package: r-cran-leakr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-arrow, r-cran-data.table, r-cran-digest, r-cran-htmltools, r-cran-openxlsx, r-cran-readxl, r-cran-stringr, r-cran-workflows, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-caret, r-cran-mlr3, r-cran-tidymodels, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-leakr_0.1.0-1.ca2404.1_all.deb Size: 206924 MD5sum: 76705a2207f404956d6b8f88b47a6d53 SHA1: ad4e53d8b4cda8d5cf5310fa27d85be1a27e75e0 SHA256: 1ecbf2e423177a9900b8642231589d068d755de3579c1b28efb29ae87fbbf892 SHA512: 27bceb305603f8d32e714a9f24cc98ad6cc912921d50c87515afcf30a834484ea3ce5f50108fbb242d4fc21632c0b307de3e37525b760ecb902d2543a1092c25 Homepage: https://cran.r-project.org/package=leakr Description: CRAN Package 'leakr' (Data Leakage Detection Tools for Machine Learning) Provides utilities to detect common data leakage patterns including train/test contamination, temporal leakage, and data duplication, enhancing model reliability and reproducibility in machine learning workflows. Generates diagnostic reports and visual summaries to support data validation. Methods based on best practices from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0387848570). Package: r-cran-leakyiv Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-corpcor, r-cran-glasso, r-cran-matrix, r-cran-mvnfast, r-cran-foreach Filename: pool/dists/noble/main/r-cran-leakyiv_0.0.1-1.ca2404.1_all.deb Size: 87616 MD5sum: 0637b9eb6215ad68f8d5dff8b7dfb342 SHA1: 8134ec28559fa7af702cd9ef2630a0acb1901e75 SHA256: a71fc12c4dedd99a6d4890cb69d5b3ed389834346ca260de058ed0f3a33eebae SHA512: c8572e456236b08fcd3c624be9564a3aa338bbca288765afe0363c6a3a01cd190afea60c14b331e3f6ed66b9c7c8db55385fe87b9e5a7e949bd93e101c3ede86 Homepage: https://cran.r-project.org/package=leakyIV Description: CRAN Package 'leakyIV' (Leaky Instrumental Variables) Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical methods rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. In the so-called "leaky" IV setting, candidate instruments are allowed to have some direct influence on outcomes, rendering the average treatment effect (ATE) unidentifiable. But with limits on the amount of information leakage, we may still recover sharp bounds on the ATE, providing partial identification. This package implements methods for ATE bounding in the leaky IV setting with linear structural equations. For details, see Watson et al. (2024) . 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(Also called an excursion set). Applications of level sets include confidence or credible regions for parameters of statistical models, where the function is the likelihood or posterior density; regions where classification rules assign high probability to a given class; and scientific or engineering models where one is interested in input regions for which model output is above a threshold. This package maps out the boundary of a level set by finding its intersections with collections of 1-dimensional rays, generalizing a proposal by Kim and Lindsay (Statistica Sinica 21:923-948, 2011). Tools are provided to generate rays, find intersections, and visualize results. The package makes few assumptions about the studied function: it may be discontinuous, it may have a complicated feasible region, and the target level set may be non-convex or have multiple, disconnected parts. Vignettes describe package usage and show examples with two to five input space dimensions. 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Package: r-cran-lexfindr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-future.apply, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-lexfindr_1.1.0-1.ca2404.1_all.deb Size: 282856 MD5sum: dbc6ff6066306a19cd41a12bf2fbc528 SHA1: 64af96edc25701ac47f0631bb86163695d5dd54d SHA256: 299e679b3bcb167c06d772b9d82c37f7b546f9df74be04f815349743b93e707f SHA512: 50a045cf3ad5d722177fb8c0e1daf2dcf7226c6c9c2b5f786b95d5fa0938dfe2784764bdfc4435243c13001e7dbf49408e280b0d9d442f2968f304392ec6ec33 Homepage: https://cran.r-project.org/package=LexFindR Description: CRAN Package 'LexFindR' (Find Related Items and Lexical Dimensions in a Lexicon) Implements code to identify lexical competitors in a given list of words. We include many of the standard competitor types used in spoken word recognition research, such as functions to find cohorts, neighbors, and rhymes, amongst many others. The package includes documentation for using a variety of lexicon files, including those with form codes made up of multiple letters (i.e., phoneme codes) and also basic orthographies. Importantly, the code makes use of multiple CPU cores and vectorization when possible, making it extremely fast and able to handle large lexicons. Additionally, the package contains documentation for users to easily write new functions, allowing researchers to examine other relationships within a lexicon. Preprint: . Open access: . Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). . Package: r-cran-lexicon Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3281 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-syuzhet Filename: pool/dists/noble/main/r-cran-lexicon_1.3.2-1.ca2404.1_all.deb Size: 3291958 MD5sum: 6080e7d2b3879d673ec218db12a2ee1d SHA1: e094201398d44525b6ab4688c1255a0a50c2a59c SHA256: d4aa0528cc0733b95d3ac46ce0382687a7c7955af1d2df5b04f9639247498edb SHA512: 4ef434b6ed682365c6866f8b043cdd557420cd98aea00b48d334ac2ef3e0a303a4c2745974661d188ccd82163cec4801a246cb3159dbbf56a58fc00021407865 Homepage: https://cran.r-project.org/package=lexicon Description: CRAN Package 'lexicon' (Lexicons for Text Analysis) A collection of lexical hash tables, dictionaries, and word lists. 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However, while this is a nice exercise I do recommend, not everyone has the time. This package takes files downloaded from the newspaper archive of 'LexisNexis', reads them into R and offers functions for further processing. 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The package includes utilities for recursive construction, defining-contrast identification, alias and confounding summaries, incidence matrix construction, and selected design-characteristic diagnostics. The methodological framework follows foundational work on factorial block designs, including Gupta (1983) . Package: r-cran-lfm Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2736 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-laplacesdemon, r-cran-matrixcalc, r-cran-relliptical, r-cran-elasticnet Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lfm_0.3.4-1.ca2404.1_all.deb Size: 2744808 MD5sum: 5288a398c620600e972002f9e598e8bd SHA1: 8006cec1cd35234397a6f69cefdcc00657982cca SHA256: 8a22ce6cba05037429beaee1a303f79bc5c4e0036b65371724456107a2075327 SHA512: 2af0fadf7ce65c2af2c415c28dba707f9b9a6c98274fadb6bebf219a0e0caf8abba6c09cf8b26ce4d738ae97769ebde1cdb83a70c5cc0b47777b6d23a607a81a Homepage: https://cran.r-project.org/package=LFM Description: CRAN Package 'LFM' (Laplace Factor Model Analysis and Evaluation) Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods and Farm Test methods to these models. 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Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by Välikangas et al. (2016) , Jin et al. (2021) and Srivastava et al. (2023) .This work has published by Sakthivel et al. (2025) . 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This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual. 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This package is useful for actuarial analyses and life insurance modeling, facilitating accurate financial projections. 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Package: r-cran-lifertable Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-lifertable_1.0.1-1.ca2404.1_all.deb Size: 90364 MD5sum: d3b004a2b5390b0f58b8152981a6fdf7 SHA1: 7aa06f76374bfbdf7f0dfb991509e159e17023cd SHA256: db8f6f0e93bfa7fff29d5a12f6c2415e1d49d4e5d99cf85758de6d9b2ab9afe4 SHA512: bd9487ae07888d925b60832ed9c181d23e5577f6cf9da03af55e0bdfb5a49d351b3126aeaff213e0c68180750402c76c36d511af1660839a9b79116786e15f6f Homepage: https://cran.r-project.org/package=Lifertable Description: CRAN Package 'Lifertable' (Life and Fertility Tables Specially for Insects) Life and Fertility Tables are appropriate to study the dynamics of arthropods populations. This package provides utilities for constructing Life Tables and Fertility Tables, related demographic parameters, and some simple graphs of interest. It also offers functions to transform the obtained data into a known format for better manipulation. In addition, two methods for obtaining the confidence interval are included. 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The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) and Rossini et al. (2024) . 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Each observation type (exact, left-censored, right-censored, interval-censored, or custom) contributes independently to the total log-likelihood, which is summed under an i.i.d. assumption. Provides contr_name() for standard R distributions and contr_fn() for user-defined contributions, composed via likelihood_contr() into objects compatible with the likelihood.model inference framework. Package: r-cran-likelihood.model Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-algebraic.mle, r-cran-algebraic.dist, r-cran-generics, r-cran-numderiv, r-cran-boot Suggests: r-cran-mvtnorm, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-likelihood.model_1.0.1-1.ca2404.1_all.deb Size: 206574 MD5sum: 3e0b2076e636f579a2147db4a2ac8cab SHA1: df577ac83c5a9f0974f76a3c9c0eaf0356c5c353 SHA256: fb0e1b4afff9150f8de6027350c8cd8489edd91f619494ddc8578980a3357bec SHA512: 46f9f449640409d0430a24bb8d7ebc0e401d2e1312f01348b4d94a32ee1a137ed2b1d6b6d96bf8efa33d949c071696182004b37226d16355e7834548f6f5135e Homepage: https://cran.r-project.org/package=likelihood.model Description: CRAN Package 'likelihood.model' (Likelihood-Based Statistical Inference in the FisherianTradition) Facilitates building likelihood models in the Fisherian tradition following Richard Royall (1997, ISBN:978-0412044113) "Statistical Evidence: A Likelihood Paradigm". Defines generic methods for working with likelihoods (loglik(), score(), hess_loglik(), fim()) and provides functions for pure likelihood-based inference (support(), relative_likelihood(), likelihood_interval(), profile_loglik()). 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Package: r-cran-likelihoodexplore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lazyeval, r-cran-plyr Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-likelihoodexplore_0.1.0-1.ca2404.1_all.deb Size: 93212 MD5sum: b9c398305fe032905773aa945b85649d SHA1: 2aff591f762d7577d7a3be164ffcc3b3265b9211 SHA256: d0da6088ff0c84bc6a0f4bf65b599603c2b721598e8f1210f7cc92650821e4bd SHA512: e2b8eee650ad40efdcd181f22dc3950047036675bad6cc0f4b54e0a1471a0e1a37a853dfc936c5d6adb1ec585acf0ad3802543dcf69179b7bf82b393580c1873 Homepage: https://cran.r-project.org/package=likelihoodExplore Description: CRAN Package 'likelihoodExplore' (Likelihood Exploration) Provides likelihood functions as defined by Fisher (1922) and a function that creates likelihood functions from density functions. The functions are meant to aid in education of likelihood based methods. Package: r-cran-likelihoodr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-likelihoodr_1.1.5-1.ca2404.1_all.deb Size: 148250 MD5sum: a37b27912fe4e165392bd3e27c1cc773 SHA1: ce98595cd2db9715eaf30525cd5dd5037c085693 SHA256: b06c294f4d675145e15e5572bcc191244cb6cee09fd4473c24106a497283a2d3 SHA512: f2ac33b8ef19c50e1846d39ca38ce647f8ec03c1c41bec5d024358d65b58a28484d795e45e260b0ba2d526bc9b7b193f4c615f548f940ff34b44681a384f6d16 Homepage: https://cran.r-project.org/package=likelihoodR Description: CRAN Package 'likelihoodR' (Likelihood Analyses for Common Statistical Tests) A collection of functions that calculate the log likelihood (support) for a range of statistical tests. Where possible the likelihood function and likelihood interval for the observed data are displayed. The evidential approach used here is based on the book "Likelihood" by A.W.F. Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall (2011) , "Understanding Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), S. Glover & P. Dixon and others. This package accompanies "Evidence-Based Statistics" by P. Cahusac (2020, ISBN-13 : 978-1119549802) . 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It provides functions for organizing, visualizing, and summarizing MLE outcomes, streamlining statistical analysis workflows. By improving interpretation and facilitating model evaluation, it helps users gain deeper insights into parameter estimation and model fitting, making MLE result exploration more efficient and accessible. See Goffe et al. (1994) for details on MLE, and Canham and Uriarte (2006) for application of MLE using 'likelihood'. 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It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton Jeff Xia Travers Ching, Xun Zhu, Lana X. Garmire (2018) . 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With 'liminal' you can create linked interactive graphics to diagnose the quality of a dimension reduction technique and explore the global structure of a dataset with a tour. A complete description of the method is discussed in ['Lee' & 'Laa' & 'Cook' (2020) ]. 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See Singer and Hughey (2018) . 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Although developed for data collected on river networks, it can be used with any interval or point data referenced to a 1-dimensional coordinate system. Flexible bin generation and batch processing makes it easy to compute and visualize variables at multiple scales, useful for identifying patterns within and between variables and investigating the influence of scale of observation on data interpretation. 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The reason that such a package is needed is because traditional two-dimensional nearest neighbor distance is not applicable when biodiversity data are sampled via optimal ecological survey methods, like line transects. In comparison to the entire studied region, line transect-collected local biodiversity data are spatially constrained and sampling-limited. To this end, two-dimensional nearest neighbor distance would tend to over-estimate distributional aggregation pattern of species when using this limited biodiversity information. Accordingly, one-dimensional nearest neighbor distance is needed and the associated statistical testing should be established for analyzing line transect-derived biodiversity data. 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Provides a unified interface for multinomial logistic regression, hierarchical partial-pooling models, and the Piantham approximation for relative reproduction number estimation. Features include rolling-origin backtesting, standardized forecast scoring, lineage collapsing, emergence detection, and sequencing power analysis. Designed for real-time public health surveillance of any variant-resolved pathogen. Methods described in Abousamra, Figgins, and Bedford (2024) . 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Tags datasets with unique lineage identifiers that persist through filter, join, and derive operations. Requires documented reasons for every row exclusion, capturing who was removed, why, and at which pipeline stage. Variable derivations are registered as structured specifications linking output variables back to their source. Any row in any downstream dataset can be traced back to its origin via lg_trace(). Generates structured HTML provenance reports suitable for regulatory submissions, internal audit, or analytical documentation. General-purpose: works for clinical data, machine learning pipelines, financial modelling, epidemiology, or any workflow where row-level accountability matters. Optional features support pharmaceutical users including population flag definitions, source-to-analysis variable mapping, and Reviewer's Guide-aligned report output. Complements the 'regulog' package for tamper-evident session-level audit logging. For more details see . 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Package: r-cran-linearregressionmde Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-linearregressionmde_1.0-1.ca2404.1_all.deb Size: 18676 MD5sum: f109adefd59c8f86ad47e5a679ea4218 SHA1: bb3d5635ab5736dc147e93f6478bf66fe80704d0 SHA256: ee13852337085a33107aef308947c67de46c1b26b4110adb636ca331f696dcf4 SHA512: 7ea248182c0648e2d249ade41611bafd0439fadf14c91e448eeaf81b170c1d1013cb9ec7e4116d442059237494ff92665a4adf8f1dd7f3d47ca916e2ceb83fc5 Homepage: https://cran.r-project.org/package=LinearRegressionMDE Description: CRAN Package 'LinearRegressionMDE' (Minimum Distance Estimation in Linear Regression Model) Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. 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Package: r-cran-lineartestr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-sandwich, r-cran-dplyr, r-cran-ggplot2, r-cran-viridis, r-cran-tidyr, r-cran-readr, r-cran-forecast Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lineartestr_1.0.0-1.ca2404.1_all.deb Size: 95610 MD5sum: 5d89e4b7542b16d1ad5ac83ca9a8e39c SHA1: bc97efaad4364c2039018cf9d097168d0e622f4b SHA256: 1e5fa8c496bbb807f80073cd4b38a7d8b3af5916f48b6a8192400cae4a8d46c6 SHA512: 43bd6ec715db28567dc2cc4e16f7de7b31b6c9ad77b1e6c7c93bc700bcb82e1ffaf66c66fb164d394e22e0e294c5c85e0ab79a8f10ec33c559e328ca47e2fc09 Homepage: https://cran.r-project.org/package=lineartestr Description: CRAN Package 'lineartestr' (Linear Specification Testing) Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), for ARMA (autoregressive–moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) . 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(2011) . Based on the 'Python' implementation by Ikeuchi et al. (2023) . The 'VAR-LiNGAM' residual diagnostics are inspired by the 'VARLiNGAM' R code of Moneta et al. . 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Access to Web of Science and Scopus requires personal API keys, while PubMed can be queried without one. The optional deduplication functionality requires the package 'ASySD' available from . 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Generates publication-ready bar, stacked bar, histogram, waffle, donut, treemap, alluvial, trend, co-occurrence, 'UpSet', tree, and study-by-criteria matrix figures, together with world maps and formatted summary tables. Plot functions return standard 'ggplot2' objects that can be further customized, and an interactive 'Shiny' application is included for building figures without writing code. Aims to help researchers report study characteristics consistently across many publications. Package: r-cran-litriddle Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4910 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-stylo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-litriddle_1.0.0-1.ca2404.1_all.deb Size: 4796696 MD5sum: a3e08a8cfc43472d51b4f874e5ccad94 SHA1: b6629d19f645284f416b661fba28cb17eb568921 SHA256: e12d321c8e704c7a43437186a48d8cbadd8ce9885819833dee715957901567e8 SHA512: 0d2d9e7bee437cdbe54926ed7b7ea6d5f5847da09fceeb972bef821fffc2bf811279dbbdb8f2b9ae5901d45835ddcb59e29c333e91d91987810282d1bb091b31 Homepage: https://cran.r-project.org/package=litRiddle Description: CRAN Package 'litRiddle' (Dataset and Tools to Research the Riddle of Literary Quality) Dataset and functions to explore quality of literary novels. 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Package: r-cran-litter Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-stringr, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-fs, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-yaml, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-kableextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-litter_1.0.2-1.ca2404.1_all.deb Size: 1618950 MD5sum: 1d935e5b5d0d9560e7b0c4f37805addf SHA1: dd3bc2ec0f4b6db618bca494919635519f179cb1 SHA256: 5f0e973f4c34908c7532ad302d79f4be3a5900bf8e57b8b71a5025879f8f7188 SHA512: 187f1ee075c56d97922a9ad38d5400bb658c1151a0cd7b23ed5f67e2406132de0c2571a8a30958be1ca2b4e998d65516918e7a78eb68f33120090f11d1eac55d Homepage: https://cran.r-project.org/package=litteR Description: CRAN Package 'litteR' (Litter Analysis) Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) for details. MS-Windows users are advised to run 'litteR' in 'RStudio'. See our vignette: Installation manual for 'RStudio' and 'litteR'. Package: r-cran-litterfitter Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-devtools, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-litterfitter_0.1.4-1.ca2404.1_all.deb Size: 670408 MD5sum: 1b90c102940e71a61e2fa017d4edf252 SHA1: 1c9d4470989ea2f49410dfc05e3e76f882148695 SHA256: 82795c95f77abc168defde4fcfb97b32329c4ccc703c49cdba2f47920380a513 SHA512: f1c8f2d7a4a7afe896ff77772147e9681267f08af78b7c79548b99a92b1494c86c89f9eaa45e2e10dee6f43a75abf3947b3f6646fa9ed8c23c98084bc1d04e15 Homepage: https://cran.r-project.org/package=litterfitter Description: CRAN Package 'litterfitter' (Fits a Collection of Curves to Single-Cohort Decomposition Data) Fit different model forms to single-cohort litter decomposition data (mass remaining through time) using likelihood-based estimation. 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Package: r-cran-liureg Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-liureg_1.1.3-1.ca2404.1_all.deb Size: 119686 MD5sum: 6a1f4d6a755f96585b17ce8366e2210a SHA1: 60a67cb9825ab69736d04a51426777c6371ef57b SHA256: 5343738fd8f444b5dfcce5ab5159e42bf711f8eb7c7300d3cf0ec991910643f4 SHA512: 3ab8599cbd7884ef933d751dba3ea58c6f8c005217ee7daffaa26ce10f4f169634db0230bfa9b48845bddee7557f466ad43161ae98a102f2b1674a8813dfa68d Homepage: https://cran.r-project.org/package=liureg Description: CRAN Package 'liureg' (Liu Regression with Liu Biasing Parameters and Statistics) Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc. REFERENCES i. Akdeniz and Kaciranlar (1995) \doi{10.1080/03610929508831585} ii. Druilhet and Mom (2008) \doi{10.1016/j.jmva.2006.06.011} iii. Imdadullah, Aslam, and Saima (2017) iv. Liu (1993) \doi{10.1080/03610929308831027} v. Liu (2001) \doi{10.1016/j.jspi.2010.05.030}. Package: r-cran-live Architecture: all Version: 1.5.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr, r-cran-dplyr, r-cran-breakdown, r-cran-data.table, r-cran-forestmodel, r-cran-shiny, r-cran-mass, r-cran-ggplot2, r-cran-gower, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-glmnet, r-cran-covr, r-cran-dalex, r-cran-rweka, r-cran-mda, r-cran-modeltools Filename: pool/dists/noble/main/r-cran-live_1.5.13-1.ca2404.1_all.deb Size: 200656 MD5sum: ed70b17865f6f99f61e7d895cf76e23b SHA1: 71c6341f4eec4a1a5fc765d2666b9bab0e340565 SHA256: 2c579e814288ac176a12fc8f298d2da6c09404f62746ed619e804077cc5c6bb3 SHA512: 54aba188316d00a27cd5a1b2bd65d39434e5a3e9993ecdbb1db5d0352c7a76fe25e99516a943235e3656b8d6d7812abf13ad035a676f69570ae3e82143aa4812 Homepage: https://cran.r-project.org/package=live Description: CRAN Package 'live' (Local Interpretable (Model-Agnostic) Visual Explanations) Interpretability of complex machine learning models is a growing concern. 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Package: r-cran-liver Architecture: all Version: 1.30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5898 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-class, r-cran-ggplot2 Suggests: r-cran-proc, r-cran-skimr, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-mltools, r-cran-forcats Filename: pool/dists/noble/main/r-cran-liver_1.30-1.ca2404.1_all.deb Size: 5114162 MD5sum: 5d094063e70f0d2361aec57ab8b17be3 SHA1: 4ef6ad4ec713ce980b7ed42d5f18c9176cc27ba2 SHA256: d0357beb95c1af1dceb2d8bb3a33b0b03a1921bb797a29f4a86463209cd9a61c SHA512: 746c00d6babbc23f1bb164a3d3541554298b8cf887690557a4a1e5ccaae5392f5d7a3598c64ab048b8740f85a83b6bc4b54c867b6f594627481186ea5dd71bc1 Homepage: https://cran.r-project.org/package=liver Description: CRAN Package 'liver' (Toolkit and Datasets for Data Science) Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book , making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge. Package: r-cran-ljexm Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-pdftools, r-cran-rstudioapi, r-cran-webshot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ljexm_1.0.5-1.ca2404.1_all.deb Size: 24660 MD5sum: dbf1871ffce824626b97f4029ab47072 SHA1: 885ae0a334e2d1c1059b5701207815c618e16d79 SHA256: c968f293b415cf86474215b7ed6dd5f3c502abe7dc2e55e60ce73ed742801bd3 SHA512: c187e7fa0034447d949968aa8424c1cbc5bd84d32ebb295d2f1375b3ec00b5de4be51c656b676473640155072d309cfe79b8b3486d4762f48de3b6a8515b1382 Homepage: https://cran.r-project.org/package=LJexm Description: CRAN Package 'LJexm' (Extract, Convert, and Merge 'pdf' Files from 'zip' Files) Extracts 'zip' files, converts 'Word', 'Excel', and 'html'/'htm' files to 'pdf' format. 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Package: r-cran-lkt Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparsem, r-cran-matrix, r-cran-data.table, r-cran-glmnet, r-cran-glmnetutils, r-cran-lme4, r-cran-cluster, r-cran-proc, r-cran-crayon, r-cran-hdinterval Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-caret, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lkt_1.7.0-1.ca2404.1_all.deb Size: 2658240 MD5sum: d911cccd6cfdfabf7adc9eef6314af2e SHA1: 1b4acd59153df2cdadf8b87e8991a134ee5062dc SHA256: 46a13a80e2a5bc27758f65f372fd9c36696ee5dd34f7f23880845a041e555300 SHA512: 9c7466bb6bfc2ac7ede83cb0a4f5f8bd0a909b7808626f1cbb27f98abde1168e18799abca98e8f3884e9b7b2e7119ece67cf12edd26ced3ee20e1bf52078fedc Homepage: https://cran.r-project.org/package=LKT Description: CRAN Package 'LKT' (Logistic Knowledge Tracing) Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. 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Package: r-cran-llama Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr, r-cran-rjava, r-cran-parallelmap, r-cran-ggplot2, r-cran-checkmate, r-cran-bbmisc, r-cran-plyr, r-cran-data.table Suggests: r-cran-testthat, r-cran-paramhelpers Filename: pool/dists/noble/main/r-cran-llama_0.10.1-1.ca2404.1_all.deb Size: 1951434 MD5sum: d82b9430f39fd4d7bb1187b5c4a8a7b5 SHA1: bcfe20bc76522158b9044b864e7442aeb2cd8885 SHA256: 053fee1fb059028985f1c15d51ddb96d6f83d5075571b2d5c91324d61ace59d3 SHA512: 5dbc353ef3b7605b1d3cae43e9215722567507ac8487d3701ee78e009513ceda339b9df54eb5f845d776552ec9fb20fb4689b58c63ad23a81cbc13ef468f00fb Homepage: https://cran.r-project.org/package=llama Description: CRAN Package 'llama' (Leveraging Learning to Automatically Manage Algorithms) Provides functionality to train and evaluate algorithm selection models for portfolios. Package: r-cran-llbayesireg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-stanheaders, r-cran-rcpp, r-cran-llogistic, r-cran-rstan, r-cran-mcmcpack, r-cran-mass, r-cran-coda Filename: pool/dists/noble/main/r-cran-llbayesireg_1.0.0-1.ca2404.1_all.deb Size: 49584 MD5sum: 345a7cb03a1dd3a2321ed0cd9407a432 SHA1: 7f3a0df7942d1eeb2c666fd49b7f9b66f4687542 SHA256: 42fb5acd7c2170e6b49305ace61d1ca7fd9969413a641b7518466b6fc426860f SHA512: 66a05a79a118087f9d56be9a6d97645f7120fcf12147c5981bb07a20f6226de4d4a20b56e269f610396806050d5cd0b2b517295acb1e8f9716b30c276a5df69e Homepage: https://cran.r-project.org/package=llbayesireg Description: CRAN Package 'llbayesireg' (The L-Logistic Bayesian Regression) R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, . Package: r-cran-llic Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-dplyr, r-cran-laplacesdemon, r-cran-relliptical, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-llic_3.0.0-1.ca2404.1_all.deb Size: 19790 MD5sum: 5c8a537a7da950003e51d4dd440838e2 SHA1: 5f68610f652ad204dbe5f78228056280b175ba28 SHA256: 9d69cdf77cd83a8679d6303153c642b9e6bc084a263e12236f5c9842242fb676 SHA512: 97a67b5825d845c7cbe68ea3b5bf650a72ad8b68c6a2d02037df01bb470e7f68af2dd5c2b5d0d9297bc4b7a49beb472824e8e526e5aadb9fe5474bc029d54d0f Homepage: https://cran.r-project.org/package=LLIC Description: CRAN Package 'LLIC' (Likelihood Criterion (LIC) Analysis for Laplace Regression Model) Performs likelihood criterion analysis using the Laplace regression model to determine its optimal subset of variables. The methodology is based on Guo et al. (2023), LIC criterion for optimal subset selection in distributed interval estimation . Package: r-cran-llm.api Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-tinyoauth Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-llm.api_0.1.9-1.ca2404.1_all.deb Size: 244398 MD5sum: 3c2e8902eda449f01e54d90879c33fc3 SHA1: 23be00d00470dc7d7013ec210780c4e68599f9c8 SHA256: 18ab6da418c1e70e624fc45239054ea1543bbd8d2fa317f116b05789226d1d8d SHA512: b10e32315c48933972c512929aca76b9547bcab8960ec670d3260935d200ee1e44811beedb5aaafd49f4f75ebf9234ab44df7a1385dbd118832c55a9bbd97c32 Homepage: https://cran.r-project.org/package=llm.api Description: CRAN Package 'llm.api' (Minimal LLM Chat Interface) A minimal-dependency client for Large Language Model chat APIs. Supports 'OpenAI' , 'Anthropic' 'Claude' , 'Moonshot' 'Kimi' , 'OpenAI' 'Codex' subscription endpoints, 'Ollama' , and other 'OpenAI'-compatible endpoints. Includes an agent loop with tool use and a 'Model Context Protocol' client . API design is derived from the 'ellmer' package, reimplemented with only base R, 'curl', 'jsonlite', and 'tinyoauth'. Package: r-cran-llm Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-stringr, r-cran-rweka, r-cran-survey, r-cran-reghelper, r-cran-scales Suggests: r-cran-mlbench Filename: pool/dists/noble/main/r-cran-llm_1.1.0-1.ca2404.1_all.deb Size: 74392 MD5sum: 3608867fd79b3f6e3558292ef3030758 SHA1: 2e0bea7f601ba2ac7f30c46fbcabb3c8a83b7d6b SHA256: c953b5ea384bc77dcff922fd1c7658e056777f9a7ed88c5a7273e74f6c0ee01c SHA512: af0a1536051e229ce70b2e71b9dfdc16f3a2e07095eb1aaf706ed106c4f73d6272f2c372279d7f72cebc39c64acf43bdc235507e29e17906caaf5fb7d7f3025d Homepage: https://cran.r-project.org/package=LLM Description: CRAN Package 'LLM' (Logit Leaf Model Classifier for Binary Classification) Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) ). Package: r-cran-llmagentr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-dbi, r-cran-rsqlite, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-officer, r-cran-purrr, r-cran-timetk, r-cran-pdftools, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-rsample, r-cran-modeltime.ensemble, r-cran-modeltime, r-cran-xml2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-usethis, r-cran-prophet, r-cran-forcats, r-cran-kernlab, r-cran-xgboost, r-cran-xfun, r-cran-modeltime.resample, r-cran-tidymodels, r-cran-tibble, r-cran-lubridate, r-cran-tesseract, r-cran-rvest, r-cran-fastdummies, r-cran-stringr Filename: pool/dists/noble/main/r-cran-llmagentr_0.3.2-1.ca2404.1_all.deb Size: 1696930 MD5sum: 024232317cbfa1002c70c7750ea57b0d SHA1: ab7c48c122fde44629393c29e4c61a36bc1bece8 SHA256: 66c9c63f557ff80b691e880fdf183b550b80371b318ef27336e9e5932e847128 SHA512: 0b84b3909f919a88d0dc9172542f56bd6a16b8a11695c1b77296c40fff563807fe0bd1c764edf00dc6a414a06ae56785cfcdc7ca10fa8bd2e68ad1de03a98b7e Homepage: https://cran.r-project.org/package=LLMAgentR Description: CRAN Package 'LLMAgentR' (Language Model Agents in R for AI Workflows and Research) Provides modular, graph-based agents powered by large language models (LLMs) for intelligent task execution in R. Supports structured workflows for tasks such as forecasting, data visualization, feature engineering, data wrangling, data cleaning, 'SQL', code generation, weather reporting, and research-driven question answering. Each agent performs iterative reasoning: recommending steps, generating R code, executing, debugging, and explaining results. Includes built-in support for packages such as 'tidymodels', 'modeltime', 'plotly', 'ggplot2', and 'prophet'. Designed for analysts, developers, and teams building intelligent, reproducible AI workflows in R. Compatible with LLM providers such as 'OpenAI', 'Anthropic', 'Groq', and 'Ollama'. Inspired by the Python package 'langagent'. Package: r-cran-llmclean Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httr2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-llmclean_0.1.1-1.ca2404.1_all.deb Size: 117512 MD5sum: 48c4dae6183a4e5086994e2e13a0f4b8 SHA1: 968f84bec75432a836fd85cfadfa51df5c728440 SHA256: 32f9466173584ac9a12fbc3a9a8a5a27b6cd89a55a440a924f863d68ca832d75 SHA512: 6e86519c62eedac41cdf57a4c86414838bbd1c24984bb7606d2f5aa4b5988f90b3f942998cfe6f6722ecf5ab4db54f04135118e2acbba6f7fd4ea03ee4005afe Homepage: https://cran.r-project.org/package=llmclean Description: CRAN Package 'llmclean' (LLM-Assisted Data Cleaning with Multi-Provider Support) Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include 'OpenAI' ('GPT-4o', 'GPT-4o-mini') , 'Anthropic' ('Claude') , 'Google' ('Gemini') , 'Groq' (free-tier 'LLaMA' and 'Mixtral') , and local 'Ollama' models . The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any application programming interface (API) key. Interactive fix application with human review is supported via 'apply_fixes()'. Methods follow de Jonge and van der Loo (2013) and Chaudhuri et al. (2003) . Package: r-cran-llmcoder Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstudioapi, r-cran-httr2, r-cran-miniui, r-cran-shiny, r-cran-stringi, r-cran-stringr, r-cran-rlang, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-llmcoder_1.2.0-1.ca2404.1_all.deb Size: 176340 MD5sum: 8e53185c956885e336a39baf4fb414e3 SHA1: d977f69173f3238d14c11f5cd9828db517aa379e SHA256: 62ff0dd651241c8e767e5ac6a9a0494692dc7474f94ac479cc6e6880e8cd4acc SHA512: c2577e7f978bb632adc6595d7b71dbcdadff6643e09b2642ac996fb03b277375b45b0def7f585dd187d5f1c22f48db0ff186e115129831c9ec189b0082d03e0c Homepage: https://cran.r-project.org/package=llmcoder Description: CRAN Package 'llmcoder' (LLM-Powered Code Generation, Error Fixing, and Chat for'RStudio') An 'RStudio' addin that integrates large language model (LLM) assistance directly into the code-editing workflow. Features include: (1) generate R code from inline comments; (2) obtain LLM-assisted fixes for console errors; (3) insert plain-English explanations of selected code blocks; (4) a multi-turn Chat Panel with session-context awareness (loaded packages, global objects, source editor contents, console history). Supports 'OpenAI', 'Anthropic' (Claude), 'DeepSeek', 'Groq', 'Together AI', 'OpenRouter', 'Ollama' (fully local, no API key required), and any 'OpenAI'-compatible custom endpoint (e.g. 'LM Studio', 'vLLM', 'llama.cpp'). Package: r-cran-llmflow Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-jsonvalidate Suggests: r-cran-ellmer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmflow_3.0.2-1.ca2404.1_all.deb Size: 162696 MD5sum: 833e05f953df7ed3b61964914e9d3fea SHA1: c6c27a0dddc23161f26ff2f60fbb5d9653be5ddd SHA256: e2bd02c24ef3b483f9f1e62ea13eff0364ac4c281f3b06b442cfd9622fcc2bb9 SHA512: 6dc6bc6349d334f15b00fa6cf12de1ab5061fbb5b28ff9885c0b4b462c837f43e9210eb1db486c02d939888d72c171271b903a2097687706a5fc6f0583efa675 Homepage: https://cran.r-project.org/package=llmflow Description: CRAN Package 'llmflow' (Reasoning and Acting Workflow for Automated Data Analysis) Provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management. Package: r-cran-llmhelper Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyprompt Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmhelper_1.0.0-1.ca2404.1_all.deb Size: 89940 MD5sum: 624e0a3d7e20e223d96900a5c7eed245 SHA1: e5d01004f256d676e70632fe16418c2696af36c3 SHA256: 8c75ce9bdb0aa861ee0a8a20c07de7ddfe8a9aeef0e90ee33b92a5942f27d01a SHA512: 6cd6309826b3c52c4058c74b17975b1eb1adaeff7682ae96867c59cc1e55cb5aca22f0dc6d3e65b14071c5475df284c9e0fcfd4fc1e5ec690e313f0990d71f7e Homepage: https://cran.r-project.org/package=llmhelper Description: CRAN Package 'llmhelper' (Unified Interface for Large Language Model Interactions) Provides a unified interface for interacting with Large Language Models (LLMs) through various providers including OpenAI , Ollama , and other OpenAI-compatible APIs. Features include automatic connection testing, max_tokens limit auto-adjustment, structured JSON responses with schema validation, interactive JSON schema generation, prompt templating, and comprehensive diagnostics. Package: r-cran-llmimpute Architecture: all Version: 0.1.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-llmimpute_0.1.0-1.ca2404.2_all.deb Size: 181106 MD5sum: ce850007c16ebd92796539098d9ef131 SHA1: ee2c4fccd03da61b2fc7fa12538b7b8d3688ca8f SHA256: bb9a561e6f2ec06d2aff8bbb7344d732c476f4729c1194ec51b7c35b17982eea SHA512: dadda4020b7d5786980f337e69f39036b4c5b1ecfae79d904c900f515207575a4f1aac0e1d5f8c1274fc8c01bcf4ce746c2f74580346e284f91f80f2a4fa62d1 Homepage: https://cran.r-project.org/package=llmimpute Description: CRAN Package 'llmimpute' (Missing Data Imputation via Language Models and Statistics) Provides missing data imputation through two complementary engines: a large language model engine that communicates with the 'Anthropic' 'Claude' application programming interface for context-aware semantic imputation, and a fully self-contained offline engine implementing nineteen statistical and machine learning algorithms entirely in base R with no additional package dependencies. Offline methods include mean, median, mode, last observation carried forward, next observation carried backward, hot-deck, predictive mean matching, k-nearest neighbours, ordinary least-squares regression, Lasso with coordinate descent, Ridge with closed-form solution, Bayesian Ridge regression with evidence approximation following MacKay (1992), support vector regression with a radial basis function kernel, classification and regression trees, random forests, gradient boosting, iterative random forest imputation, principal component analysis imputation via iterative singular value decomposition, and nuclear-norm minimisation via singular value thresholding. When no API key is available the package automatically falls back to the offline engine, ensuring full operation in environments without internet access. Every imputed value is accompanied by a confidence score and a plain-language reasoning string, producing reproducible audit trails. The automatic method selector chooses the best algorithm per column based on data type, skewness, missingness rate, and inter-column correlations. Package: r-cran-llming Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdpack, r-cran-quanteda, r-cran-stopwords, r-cran-stringi, r-cran-dbscan, r-cran-pracma, r-cran-caret, r-cran-word2vec, r-cran-keras3 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-llming_1.3.0-1.ca2404.1_all.deb Size: 87554 MD5sum: 4c262a5d0558e9ac1625d54493d04fa5 SHA1: a745856c9abeb0465f1042b56d7e6bbd22b602f5 SHA256: 88dad7654ea306ec4e767a9a5426ed31a51679ceb26f36fda8acd88d0b566041 SHA512: 8d7140f50a7d04f426f3d7e19701a8820c0ec89bff9a97d14c4721a6913dc77a69af6e6beab655f502f3c14a72c5c804091bb8d1447c3e640648bc352d9a7a36 Homepage: https://cran.r-project.org/package=LLMing Description: CRAN Package 'LLMing' (Large Language Model (LLM) Tools for Psychological Text Analysis) A collection of large language model (LLM) text analysis methods designed with psychological data in mind. Currently, LLMing (aka "lemming") includes a text anomaly detection method based on the angle-based subspace approach described by Zhang, Lin, and Karim (2015) and a text generation method. . Package: r-cran-llmjoin Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-config, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmjoin_0.3.0-1.ca2404.1_all.deb Size: 40196 MD5sum: 1be060f3246e97b5041fb67d7d704a99 SHA1: 3af736585de5d89eb2e94301bb58caa7dd088e68 SHA256: a673b0e03eeaa870014586bc682e7b079e11fbbbb2ca8876bc022d2ba2d82626 SHA512: a30ebca8ab7c20419fa5d28660921cfbb7b40ff045545e55483f2080ebd2e2b78d9fff1ec94bb8bfb4c562123454f87aa545c39c762eb0af3749395df34f2b46 Homepage: https://cran.r-project.org/package=llmjoin Description: CRAN Package 'llmjoin' (LLM-Powered Fuzzy Join) Resolves ambiguous links between data.frames using large language models (LLMs). Supports matching across spelling variations, translations, and differing levels of precision. Package: r-cran-llmr.shiny Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-bslib Suggests: r-cran-llmr, r-cran-dt, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-llmr.shiny_0.1.2-1.ca2404.1_all.deb Size: 290628 MD5sum: 81093a8b4a4761becb9c8db74bdb9aaa SHA1: b777a7fddc672747ef9160db83efa161106bf444 SHA256: f3815c29a3f8cd7c1c2ad22d1cc3015d73333ae22cd81a1248c0531c1c188676 SHA512: cc92289e82a7349dde53511aa64217d7d9bb28b48cb124f7ef6537a1bfc56b4bc86d5376a4fff1d9de271023815aafe13dc1d5fd3b46e3d11d11c8ea60d975c7 Homepage: https://cran.r-project.org/package=LLMR.shiny Description: CRAN Package 'LLMR.shiny' (Shared 'Shiny' Components for 'LLMR' Family Applications) Reusable 'Shiny' user interface and server components from which the graphical applications in the 'LLMR' package family are assembled. Package: r-cran-llmr Architecture: all Version: 0.8.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1385 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-curl, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-memoise, r-cran-future, r-cran-future.apply, r-cran-tibble, r-cran-base64enc, r-cran-mime, r-cran-glue, r-cran-cli, r-cran-jsonlite, r-cran-vctrs, r-cran-digest, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-progressr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-jsonvalidate, r-cran-stringi, r-cran-kableextra, r-cran-withr Filename: pool/dists/noble/main/r-cran-llmr_0.8.11-1.ca2404.1_all.deb Size: 987016 MD5sum: 3fed9399edcef83312db3c7c2331f24e SHA1: 6e49397b1aa1189f204bcf173fee34470f878679 SHA256: 3b58ab5abc261c443fb02de8891c8175ae92a4c3514f014995e79953b76956e6 SHA512: 5a86580e3f5a6d2bf3d4a30049f89e043d003c653725a82d6667e786fe2f628300f907dbd0e2cddea20d7505319d045f1e570a2d119903140c1170bd20c6f8a3 Homepage: https://cran.r-project.org/package=LLMR Description: CRAN Package 'LLMR' (Interface for Large Language Model APIs in R) One interface to many large language model providers: a single configuration object and a single calling function serve chat and embedding models alike, so research code does not change when the provider does. The same calls scale to multi-model and multi-condition studies. Package: r-cran-llmragent Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-llmr, r-cran-r6, r-cran-tibble, r-cran-rlang, r-cran-cli, r-cran-digest, r-cran-jsonlite, r-cran-httr2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-future, r-cran-future.apply, r-cran-dplyr, r-cran-withr, r-cran-callr, r-cran-jsonvalidate, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-llmragent_0.8.1-1.ca2404.1_all.deb Size: 825944 MD5sum: 4b32b2bed4777cabacbe08321a4e2e7d SHA1: 1e9e351c75fb17b2a360811f93f3c2c9fdaeca71 SHA256: c846ee33739646ba5e03740a172d1a5a82f1330500a0db5589ba795745c7c312 SHA512: 652dba082aa9b30000c6f7fc7b1d57c841db05d01c4edfca6af49ba0233dd672e1980874e4f54fdb4d16e1b9ef30c423df15684066dd7e197ad78359bafc778b Homepage: https://cran.r-project.org/package=LLMRagent Description: CRAN Package 'LLMRagent' (Reproducible Language-Model Agents for Research) Large language model agents as governed research instruments, built on 'LLMR'. The package supports designed conversations and factorial experiments with declared tools and budgets. Each run produces an inspectable record that can be archived and checked. Package: r-cran-llmrpanel Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-llmr, r-cran-tibble, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-llmr.shiny Filename: pool/dists/noble/main/r-cran-llmrpanel_0.6.1-1.ca2404.1_all.deb Size: 416982 MD5sum: b2b0f1c8b081844ea7488aa12979d5f5 SHA1: fee14c326dbedb7f9c99310b0a59134d7c2dcdee SHA256: 5f0d940add832a0147b25cf0fda33230108157c956568e3e8a41f2c26e974642 SHA512: 9fdefc6a4200004a747d5da5edf405ea91c9d16ccb34249e636c73d1220b9a36ffa1507b959c56f702c13897394f63f13da41da63d84ec5ab7d0545197e707e9 Homepage: https://cran.r-project.org/package=LLMRpanel Description: CRAN Package 'LLMRpanel' (Benchmarked Silicon Samples for Survey and Experiment Design) Administers survey and experimental instruments to panels of language-model personas, with respondent-level randomization, benchmark comparison against human data, and conjoint estimation from recorded respondent-level profile assignments. Samples of language-model personas follow Argyle et al. (2023) ; the case for benchmarking them against human data is set out in Bisbee et al. (2024) ; the conjoint estimand is the average marginal component effect of Hainmueller et al. (2014) . Package: r-cran-llmshieldr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2041 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-cli, r-cran-rlang, r-cran-digest, r-cran-stringi Suggests: r-cran-ellmer, r-cran-httr2, r-cran-processx, r-cran-filelock, r-cran-htmltools, r-cran-covr, r-cran-lintr, r-cran-plumber, r-cran-shiny, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-dplyr, r-cran-tokenizers, r-cran-snowballc Filename: pool/dists/noble/main/r-cran-llmshieldr_0.1.0-1.ca2404.1_all.deb Size: 1628062 MD5sum: 57e3f1d0f1d25b8b7eafde845e628700 SHA1: af58b09bbda8cf0f42bed97fc2dfa37e05d9ac5d SHA256: 29f7e0801f822341b92c432957bd270f5c677bb220ebb835a151025c5bd51af1 SHA512: 80d6c9806a50bcc024597158f8a96a5863e8979ec99ec1dd1580746a01386bb9c15cc959b4d95f5a12b03aaa7bee09fc029ede04ea8f4f0b25c7b76c8277b3d3 Homepage: https://cran.r-project.org/package=llmshieldr Description: CRAN Package 'llmshieldr' (Safety Guardrails for Large Language Model Workflows) A model-agnostic safety layer for developers building with large language model (LLM) applications. Maps starter controls to the Open Worldwide Application Security Project Top 10 for Large Language Model Applications 2025 risk categories via a modular rule engine. Supports regular-expression rules, lightweight natural language processing (NLP) intent checks, optional scanners, and semantic large language model reviewer checks on prompts, conversations, retrieved context, tool inputs and outputs, streaming chunks, and model outputs. Supports workflows with the 'Ollama' local web service via 'ellmer', remote reviewer endpoints, and other chat interfaces callable from 'R'. Intended as an experimental guardrail layer that teams should evaluate against their own workflows before relying on it in production. Package: r-cran-llmtranslate Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny Suggests: r-cran-dt, r-cran-readxl, r-cran-writexl, r-cran-openxlsx, r-cran-httr2, r-cran-jsonlite, r-cran-glue, r-cran-later, r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmtranslate_0.4.0-1.ca2404.1_all.deb Size: 59988 MD5sum: 1fc240c2e70fe57591c15a9df1b84ff3 SHA1: b5939b1f739d64902e022667808de1acb8ee2741 SHA256: e1e28e02dbb561d5ce64179a8fafccf2b0f28ce408bfe737debd19f13c3eee91 SHA512: fd242569589d7caa49430ce2029a81fb71784104a509f08b02d8669cccb3b4d196b7a7ec5cb366ecd5dbc316e24f691d9c47d0d604f94bf9a7ab1c63bef0894d Homepage: https://cran.r-project.org/package=LLMTranslate Description: CRAN Package 'LLMTranslate' ('shiny' App for TRAPD/ISPOR Survey Translation with LLMs) A 'shiny' application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports both item-by-item and batch translation modes for optimal performance and context-aware translations. Handles multi-sheet Excel files and supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) and Wild et al. (2005) . Package: r-cran-llogistic Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-llogistic_1.0.3-1.ca2404.1_all.deb Size: 14208 MD5sum: 2c995c18a4cb6ba4e1670a24afdc9c8d SHA1: af01140ce7d512b32601963eb8ddb6eff8dbc712 SHA256: d49a3dd296ba107be0527adbe77e0db2f9d746225183979e7a2f23ed15f20890 SHA512: 3813d77c1107b7772b17a7db10b049e375a1ea07f6626165dbec561eaa362a9a4a892511ada3325c62e6998d0deed271d8d18d9046835806e8d64370dcfbc9be Homepage: https://cran.r-project.org/package=llogistic Description: CRAN Package 'llogistic' (The L-Logistic Distribution) Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution. Package: r-cran-llsr Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-openxlsx, r-cran-digest, r-cran-svdialogs, r-cran-minpack.lm, r-cran-ggplot2, r-cran-dplyr, r-cran-nleqslv, r-cran-crayon Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-llsr_0.0.4-1.ca2404.1_all.deb Size: 453982 MD5sum: e62dcc4ef3ca28b2621af4b5b8ede2ef SHA1: 727cc9fa99f73f52d00519b61f3dbccb6298b230 SHA256: 1841d6286e554729374b8b8fb975d7518f67f35d8e9b9e27085f302835157c0f SHA512: 3698321f3601fb9901ca6df3c8a3d0dffe82c8793d39e880e75ca41ccc99e091d0deafcf9757a7a232930d4491ad766e0b88298696c236a4dac80fa03a266f1a Homepage: https://cran.r-project.org/package=LLSR Description: CRAN Package 'LLSR' (Data Analysis of Liquid-Liquid Systems using R) Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research. Package: r-cran-lm.beta Architecture: all Version: 1.7-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-lm.beta_1.7-3-1.ca2404.1_all.deb Size: 249514 MD5sum: 5bb1f3e8bafbcd15f2b8da142256bf13 SHA1: 14e5d8039d129d0be968385b0f2c2c8e08fbf6e7 SHA256: 20b0c3a3e9d4ec3fdafaa9ed52cbb8d5b2cccbe0ec2930a4457aaef71e585074 SHA512: 2abd8ad7448d21d6f2b2d8c85eb1e2b618c73af9511b3f228d2beaf82336dd3e72b2af2d03aaf4989614c49da31e6c06b5aee312954c91036e04179ec9fb00c7 Homepage: https://cran.r-project.org/package=lm.beta Description: CRAN Package 'lm.beta' (Add Standardized Regression Coefficients to Linear-Model-Objects) Adds standardized regression coefficients to objects created by 'lm'. 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Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) , the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) , and the Bayesian bootstrap by Rubin (1981) . Package: r-cran-lmd Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emd, r-cran-ggplot2, r-cran-patchwork, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggformula, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmd_1.2.1-1.ca2404.1_all.deb Size: 369418 MD5sum: 495a74195dc4a138917cf0c572f36b7e SHA1: 36aee5a9bb079773d0ee5e80c0dadc7b682fe80d SHA256: 24b429fcab8b65b6519861c0b1dd709cca718f7fc33f2eae01e7d5ff32178118 SHA512: 3fe77eb1cbaa795d260be9615cfe3bf9410fab4caea66609f02f956fd90bfeb4d83c53af691cfe8a3dd135c831944a45e602156cc687d9f187a6950db0416556 Homepage: https://cran.r-project.org/package=LMD Description: CRAN Package 'LMD' (A Self-Adaptive Approach for Demodulating Multi-Component Signal) Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) and the 'Python' library 'PyLMD'. Package: r-cran-lmdiallel Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multcomp, r-cran-plyr, r-cran-sommer, r-cran-enhancer, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-lmdiallel_1.0.2-1.ca2404.1_all.deb Size: 246496 MD5sum: 34ddf94d3b18a7b5ba0490cf57c0a85c SHA1: 05b87fdea68c409ac36d4baa58f411c24fd4efeb SHA256: f3a429162eb59cd931fcb2c77f1c56a20e6ccdebecaad5c999857c6f36eb3741 SHA512: 5c73aec75efe8dd33993a5ea04b25c45b1e30a4c69243a02cbad674ad712b97dc20840f36d34bfab5cfafad729ddbd626c5d2abdd67b46f2f75d2bb9c80f7ef1 Homepage: https://cran.r-project.org/package=lmDiallel Description: CRAN Package 'lmDiallel' (Linear Fixed/Mixed Effects Models for Diallel Crosses) Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in Onofri et al (2020) . 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Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated. Package: r-cran-lme4breeding Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-crayon, r-cran-enhancer Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-orthopolynom, r-cran-mass, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-lme4breeding_1.1.5-1.ca2404.1_all.deb Size: 350430 MD5sum: 6c33adf0ee55de25fa4b507914440a4d SHA1: 5e9951cd5d5f22e92fc837a86584cad0a41598a3 SHA256: ab6051ae98004b11a3b0dc256579d0b4cc2a972acf8066589d9bec0264bc1da6 SHA512: f774b34ba321e5c1ac68547ae8b955e05dc786c78c0d63524907987b65691ce940a53ffa54eeef339b60d0a61dcf502edf8c8c99b41f787464f3b63e787673d1 Homepage: https://cran.r-project.org/package=lme4breeding Description: CRAN Package 'lme4breeding' (Breeding-Related Mixed-Effects Models) Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'. Package: r-cran-lme4gs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrix Suggests: r-cran-bglr Filename: pool/dists/noble/main/r-cran-lme4gs_0.1-1.ca2404.1_all.deb Size: 392882 MD5sum: 07737d99bf1ec2db97b523948e356c25 SHA1: c145c91438aaa594378bc2de6e62b767a318de1f SHA256: 7178cc17c7ad78e4532936b7ea99dee28f31b80226b26147da764cd40401088e SHA512: 568df2f0f8af9230b2bfb85433da22f4cc1c18338c464ca97aaf9960b1f2068802746e4d7535172f51cad266f10b41cc0eb5eeade20bceb0626bcce8eb00a0b5 Homepage: https://cran.r-project.org/package=lme4GS Description: CRAN Package 'lme4GS' ('lme4' for Genomic Selection) Flexible functions that use 'lme4' as computational engine for fitting models used in Genomic Selection (GS). GS is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). The 'lme4' is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. The 'lme4GS' package is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data. For more details see Caamal-Pat et.al. (2021) . Package: r-cran-lmeinfo Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nlme Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-scdhlm, r-cran-mlmrev, r-cran-cardata, r-cran-lme4, r-cran-matrix, r-cran-merderiv Filename: pool/dists/noble/main/r-cran-lmeinfo_0.3.3-1.ca2404.1_all.deb Size: 137464 MD5sum: 4c9384efa2ea5378a7f4ff086d4bb410 SHA1: 08cda7b5e42f90d3fc598c406ca61402eb2c7a61 SHA256: 33386896fad4802638ff74aea25266e5ee504971710e118be7a0a906e560e94e SHA512: a05f94b746bd84974ef4682f96379fea1e5745bb4d22a9647249766431b901b37ba8c23f13fca56fd08755d7a4a365810556a985747c391beaf88fbf0e67d5b2 Homepage: https://cran.r-project.org/package=lmeInfo Description: CRAN Package 'lmeInfo' (Information Matrices for 'lmeStruct' and 'glsStruct' Objects) Provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) ) based on fitted lme or gls models. Package: r-cran-lmerconveniencefunctions Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-lcfdata, r-cran-fields, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-lmerconveniencefunctions_3.2-1.ca2404.1_all.deb Size: 279602 MD5sum: 3e5588282ef2cbda5cf3e4842d13fafd SHA1: 3de2637910df790277752acc40da0949cb4aa39e SHA256: a682d698d93802d662f88d0f001025120c86eadd7362720f22b28163b0e2e44e SHA512: 8f23e6e2d2860c71f48fc04a93c056375b446897196f453b8c49c9824ea9d546aa07ee2419634fcd0aac97cc8191d210a4af5cdddb9046ac1b18bd46f4d173ee Homepage: https://cran.r-project.org/package=LMERConvenienceFunctions Description: CRAN Package 'LMERConvenienceFunctions' (Model Selection and Post-Hoc Analysis for (G)LMER Models) The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data. 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The provided 'bootstrap()' function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) , Carpenter, Goldstein & Rasbash (2003) , and Chambers & Chandra (2013) . 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By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) ). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) ) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values. 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A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available. 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The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method. 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Several empirical datasets are also included. Package: r-cran-lmforc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 755 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmforc_1.0.0-1.ca2404.1_all.deb Size: 430586 MD5sum: 335b1f4455bfbbfcbd702f6785123d8f SHA1: a928b1c5ac827978aa6551e48a57c80ae2c3b18a SHA256: 0e30dcfa7ed984920d2ea3d4b16fda478c25204ab5fc6ab8e68048cb8613190a SHA512: e6db0153a870d1715b7f93e50f6eeb25ba09c3bbd6027ab8abc62ac501c69815648b73c32d17f221d8901b2d5676586f046d345ec1354f67dc515fe562b4cb7f Homepage: https://cran.r-project.org/package=lmForc Description: CRAN Package 'lmForc' (Linear Model Forecasting) Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast. 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It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values. Package: r-cran-lmodel2 Architecture: all Version: 1.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lmodel2_1.7-4-1.ca2404.1_all.deb Size: 343532 MD5sum: 65783a0043c3ecf16ccb0a478396c087 SHA1: 4f2d58c7244c04bb4e07b4355974f122fe70bd68 SHA256: 19ba0e85ede9e11c40f8a36e60d1fd3553b46d568a7e17cc735d63fe991697e7 SHA512: 216e623f155e609b90d82a82a4c7023e972e0fd22c30358d5276c30253f133e5f9141d0968730a44b610f7427e71163999e7f14a2270f14e04b7a0081abdd37f Homepage: https://cran.r-project.org/package=lmodel2 Description: CRAN Package 'lmodel2' (Model II Regression) Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA). Package: r-cran-lmofit Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2781 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmom, r-cran-pracma, r-cran-ggplot2, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lmofit_0.1.7-1.ca2404.1_all.deb Size: 2465702 MD5sum: 974cc5825c62d01ff68ecd08ff1fd8dc SHA1: a2514a2599ba9005d8cc1b6eb36de4b40972e659 SHA256: 2ea941c14996fee820b4c914e27357ffef31ddc0b462627e04df6362d17f17a2 SHA512: 3fa3378fe85a17be47989367fc6c653b9d784b239f4a0c185ef57e3c495d7444e26376cdf642559e0c7652a070b0e648467d8fb8eb49be438987a733916a3271 Homepage: https://cran.r-project.org/package=LMoFit Description: CRAN Package 'LMoFit' (Advanced L-Moment Fitting of Distributions) A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) , the fitting of various distributions as in Zaghloul et al. (2020) and Hosking, J.R.M. (2019) , besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner. Package: r-cran-lmomco Architecture: all Version: 2.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3783 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-goftest, r-cran-lmoments, r-cran-mass Suggests: r-cran-copbasic Filename: pool/dists/noble/main/r-cran-lmomco_2.5.7-1.ca2404.1_all.deb Size: 3365318 MD5sum: 7ee145ec60d6e3911ebb7d75862d4ef2 SHA1: 7d9791c56b1a4d0a7fb55908932dfc6156529ac4 SHA256: c007bc964d689e8c02e824bd6c718d30aea126e0b2870ac6a0408d8b5e82541d SHA512: eb4cabb181b091039ff75cd61879b6af7c84b89d6b55ec1873bb42a7fac4ed4b17f12303e8fcef299740e643647190093b674e7780f6f89bcaf8eb80b06a3f41 Homepage: https://cran.r-project.org/package=lmomco Description: CRAN Package 'lmomco' (L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments,and Many Distributions) Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. 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Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; ). Package: r-cran-lmviz Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3030 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-lmtest, r-cran-mgcv, r-cran-mass, r-cran-scatterplot3d, r-cran-rgl, r-cran-car Filename: pool/dists/noble/main/r-cran-lmviz_0.2.0-1.ca2404.1_all.deb Size: 2491330 MD5sum: b6297b25a538b386e4de60915bfbb475 SHA1: b6a97877674833421d72498212d4c3832e3a9fc2 SHA256: 1116c7ebc916c9cb32b2e6251e7efffba8206ecabb3bbe679618e7acf32f4edd SHA512: 77ba436d7a7f163a3f7d0506294aaa4d5373eafae9a09970b5b16a37702638f71872252047570e1497a8ae7d360358394f97eabccbb8d3f31a29ed262d592f64 Homepage: https://cran.r-project.org/package=lmviz Description: CRAN Package 'lmviz' (A Package to Visualize Linear Models Features and Play with Them) Contains a suite of shiny applications meant to explore linear model inference feature through simulation and games. Package: r-cran-lmw Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chk, r-cran-sandwich, r-cran-backports Suggests: r-cran-matchit, r-cran-weightit, r-cran-marginaleffects, r-cran-psweight, r-cran-estimatr, r-cran-lmtest, r-cran-ivreg, r-cran-mlogit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmw_0.0.2-1.ca2404.1_all.deb Size: 496694 MD5sum: 14e33b8d81cecb25f762d478fd382235 SHA1: e7134393f054f844ead4e25386e1023faa72bf3e SHA256: 8622db37d0f67a3f35ebdd7965ace94b8e680de5c296f6eec74371581b6c5dc1 SHA512: 5603651b1741a36629fb894f9235ba50db4ed0babf33d7b32bdff561df4fa0d84d34f7f5f47e1b850797c170eca67e899a5b57f429f1a938e60c821512d66189 Homepage: https://cran.r-project.org/package=lmw Description: CRAN Package 'lmw' (Linear Model Weights) Computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights. These diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023) where several regression estimators are represented as weighting estimators, in connection to inverse probability weighting. 'lmw' provides tools to diagnose representativeness, balance, extrapolation, and influence for these models, clarifying the target population of inference. Tools are also available to simplify estimating treatment effects for specific target populations of interest. Package: r-cran-lncdiff Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lncdiff_1.0.0-1.ca2404.1_all.deb Size: 639396 MD5sum: c03ab0addb87ae1e708279fb86ed3fa0 SHA1: 50ebface81800d6cb2a2ff173917d96af09d281d SHA256: 211da4ad93aab710b7b26bc4cf97559b15ecb4eeb9f1068e50eb0de5afbbb4a9 SHA512: 6b490f403024e72cb57050e3e151896c96fa12734e7ec16035d106497bde3d2649a3b9c74bca7b07f591bca6c30752a3f3dbd5e3ad2e998bbcf194aef7efca3f Homepage: https://cran.r-project.org/package=lncDIFF Description: CRAN Package 'lncDIFF' (Long Non-Coding RNA Differential Expression Analysis) We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) . 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It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about? 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Including automated filtering of noise parameters and determination of breakpoints. 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Streamlines the end-to-end workflow from data request to analysis-ready datasets. For advanced high-frequency econometric analysis, see the 'highfrequency' package. Package: r-cran-lobstercatch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lobstercatch_0.1.0-1.ca2404.1_all.deb Size: 64842 MD5sum: 0d69c79f5d12821b8e2e6f1bb32c01da SHA1: cd81dd1ce5f6840d8524eea717ce8d29fca7c9fa SHA256: 1261a9aeb089a82834727e6430c446ad633615d6caa4b83db5e6592ea3f1c774 SHA512: 3eea7708822814fc7fd4610dd80a46d5496086d46fc7001df49dab6f57485c7e084e60e499367e21fdbd51d98a54630125a7ab69b272ac4c3c09548ce700ce86 Homepage: https://cran.r-project.org/package=LobsterCatch Description: CRAN Package 'LobsterCatch' (Models the Capture Processes in American Lobster Trap Fishery) Simulate lobster catch process in a trap fishery. Factors such as lobster density on ocean floor, their movement, trap saturation and bait shrinkage rate can be modeled. Details of the methods for modeling those processes can be found in: Addison and Bell (1997) . Package: r-cran-localfda Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-localfda_1.0.0-1.ca2404.1_all.deb Size: 1038254 MD5sum: 056ad34391ee1b4f774be64af2020341 SHA1: acb9fe8a21813e47623276b5c7173cc77455e2cb SHA256: 0a9d8f9781236a19caf5acb195b47d02db84333bd641b9515351d702d276114d SHA512: 8041f95d9a81aa4892487bf9182837c131b8a8016bdf7faa48aab907b3ff031825ad205183c434c66806c6153094199a47c5ac089663c001af30447ada8acf9a Homepage: https://cran.r-project.org/package=localFDA Description: CRAN Package 'localFDA' (Localization Processes for Functional Data Analysis) Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) . Package: r-cran-localice Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate Suggests: r-cran-covr, r-cran-h2o, r-cran-mlbench, r-cran-randomforest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-localice_0.1.1-1.ca2404.1_all.deb Size: 30968 MD5sum: a2447ed7c68a6e3b38017982a664db02 SHA1: 9a24dbf8d9feb0af0f5c58b06af0978bef3b781b SHA256: bfa6b25b2d2ec3b0013596e2e75e42aedcb03faae04c06f7a3f91c647dfb9d2f SHA512: e071a84ecdfcc0aaf38ce2526844830f79c0fa85f32086e915c78bf3af1c2e14ef747f52104ee2e5fe31ddc29646e7f862f40efde06286f9a12964fcac63512b Homepage: https://cran.r-project.org/package=localICE Description: CRAN Package 'localICE' (Local Individual Conditional Expectation) Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are 'mlr', 'randomForest', 'caret' or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) . 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(2024) . Package: r-cran-locar Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seewave, r-cran-tuner, r-cran-matrixstats, r-cran-oce, r-cran-signal, r-cran-synchwave Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-locar_0.3.0-1.ca2404.1_all.deb Size: 2690802 MD5sum: af821358e97860af91a07c338d1818eb SHA1: 8e32827340594a04223e92b2bd8689b7fa2c5bed SHA256: 5e80e6fb7660c74155621ddbf0005b8d1531b13b117d8c0f17f4f64e2fd2edd7 SHA512: af23ec407a7c549a045a013c1d3d33daf98db0ce29043dcd582d3919d2519b840453fdf2d92b2f8719cdd2ca4c6ec7598c28725e93151e37a07382aac1c0a959 Homepage: https://cran.r-project.org/package=locaR Description: CRAN Package 'locaR' (A Set of Tools for Sound Localization) A set of functions and tools to conduct acoustic source localization, as well as organize and check localization data and results. 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It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, ) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015) Boehmke, B., & Greenwell, B. M. (2019) . 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The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available. 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Observations) Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k. 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Package: r-cran-logibin Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-doparallel, r-cran-data.table, r-cran-foreach, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-logibin_0.3-1.ca2404.1_all.deb Size: 76694 MD5sum: 481f36d767202f7f980dc05c1e919b50 SHA1: 9362c0d041ed4c6f5b2025729acef3390e1391d1 SHA256: 60890e351160c1f63f7edc4b950db4c030f17ae6421dd529d8c562b8a83428cd SHA512: 112cb8b6a7c04a660814dea5291cf3ae01f2010fc82471502939165c71a35e7155360fa291471e5e09c17bdb38fed88d474ef3c66821d1053f493bf6a53d1007 Homepage: https://cran.r-project.org/package=logiBin Description: CRAN Package 'logiBin' (Binning Variables to Use in Logistic Regression) Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) . 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Wolf, B.J., Slate, E.H., Hill, E.G. (2010) . Package: r-cran-logihist Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-popbio Filename: pool/dists/noble/main/r-cran-logihist_1.1-1.ca2404.1_all.deb Size: 53392 MD5sum: cceba80dc84992ee7e0a791ab309df5a SHA1: a50779fdbe972de59908155a960172c4f41e3240 SHA256: e05c7af727d5e1421420d7750dd7a7afdfdce9bf2eb3e969c8287c15207efff8 SHA512: 53a3abe8bd8c1f6a648e7f635fba8c8c9ede961e801d9b3c7924a84c16d267d9ff86a80e326e38733e88d7f03afedd02cca5aa5c762c39dd68648201a78d58e4 Homepage: https://cran.r-project.org/package=logihist Description: CRAN Package 'logihist' (Combined Graphs for Logistic Regression) Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions. Package: r-cran-login Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cookies, r-cran-dbi, r-cran-digest, r-cran-emayili, r-cran-htmltools, r-cran-shiny, r-cran-shinybusy, r-cran-shinyjs, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-login_0.9.3-1.ca2404.1_all.deb Size: 640770 MD5sum: c93658d87528ace9a99f229cfa031bc8 SHA1: 23357bbee8ca8fe800f2ada8bf5b14dfea006882 SHA256: 3685cc2becb3e471b3b6d2d05081924fdfa76735f3c3a6e5bf73ce7f6c4d3284 SHA512: 3e5983b98d7ad7ad4ece65626b8e5e2b43b2d864f61acf7253350060367c37e31fe94f3a8a9a338ff732489537839d528e94ce20a5da0b2701e3ed11f4a8bdb7 Homepage: https://cran.r-project.org/package=login Description: CRAN Package 'login' ('shiny' Login Module) Framework for adding authentication to 'shiny' applications. Provides flexibility as compared to other options for where user credentials are saved, allows users to create their own accounts, and password reset functionality. Bryer (2024) . Package: r-cran-logistic4p Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-logistic4p_1.6-1.ca2404.1_all.deb Size: 86104 MD5sum: 2d98881a51b562cbc129cf38b6eed256 SHA1: aa2031d95b14b664ebf6f6123c2d040069ef0725 SHA256: 451ebc67e74035edec77cde961f53764b491667d82e6f2d79b332c9d89b9c47c SHA512: c5cb9b6401d3ef19370026554545e30eccbe135e815514f5f7deb2c873cd3564fa05390bae0f9dc155550cf12a671f604e4baa4b22b6d086371e438849225131 Homepage: https://cran.r-project.org/package=logistic4p Description: CRAN Package 'logistic4p' (Logistic Regression with Misclassification in DependentVariables) Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables. Package: r-cran-logisticcopula Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brglm2, r-cran-vinecopula, r-cran-rvinecopulib, r-cran-igraph, r-cran-numderiv, r-cran-stringr Filename: pool/dists/noble/main/r-cran-logisticcopula_0.1.0-1.ca2404.1_all.deb Size: 165952 MD5sum: 897811448ea9c8ae734dd33c45609652 SHA1: 8ceaae508cf0d191ed4b2ad91058173fdedc5dec SHA256: 065619f59e88b30d0506383578c59dfb71aba4df94fb791737bc23e11b3b0dd9 SHA512: fd99ba76baf5d5edbfe41f01f3e0cdada19b3cc847b01dbca3bc17f67caa6360a09beb3f312c15cd3463452d7c12d1de6ae774a92afaed321fab3085d021a7bd Homepage: https://cran.r-project.org/package=LogisticCopula Description: CRAN Package 'LogisticCopula' (A Copula Based Extension of Logistic Regression) An implementation of a method of extending a logistic regression model beyond linear effects of the co-variates. The extension in is constructed by first equating the logistic regression model to a naive Bayes model where all the margins are specified to follow natural exponential distributions conditional on Y, that is, a model for Y given X that is specified through the distribution of X given Y, where the columns of X are assumed to be mutually independent conditional on Y. Subsequently, the model is expanded by adding vine - copulas to relax the assumption of mutual independence, where pair-copulas are added in a stage-wise, forward selection manner. Some heuristics are employed during the process of selecting edges, as well as the families of pair-copula models. After each component is added, the parameters are updated by a (smaller) number of gradient steps to maximise the likelihood. When the algorithm has stopped adding edges, based the criterion that a new edge should improve the likelihood more than k times the number new parameters, the parameters are updated with a larger number of gradient steps, or until convergence. Package: r-cran-logisticcurvefitting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-logisticcurvefitting_0.1.0-1.ca2404.1_all.deb Size: 11014 MD5sum: 45c79c592dc6568a488a16991659938c SHA1: 97f988d07cf2d2d155766d08f3f411185675cb49 SHA256: 62f68ac8803c48cacc29c0e5690f5a5fd632b712037a5f6aba1b88fba0c00ef7 SHA512: bb008127a0265ee71d9520913134d8e9742007d46506696353da18519aad0a0b9cd8326f3fb0b635ab8eba50e01770a7cd887748c29a08810bb612101479ad11 Homepage: https://cran.r-project.org/package=LogisticCurveFitting Description: CRAN Package 'LogisticCurveFitting' (Logistic Curve Fitting by Rhodes Method) A system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-logisticensembles Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adabag, r-cran-arm, r-cran-brnn, r-cran-c50, r-cran-car, r-cran-caret, r-cran-corrplot, r-cran-cubist, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-gam, r-cran-gbm, r-cran-ggplot2, r-cran-ggplotify, r-cran-glmnet, r-cran-gridextra, r-cran-gt, r-cran-htmltools, r-cran-htmlwidgets, r-cran-ipred, r-cran-klar, r-cran-machineshop, r-cran-magrittr, r-cran-mass, r-cran-mda, r-cran-pls, r-cran-proc, r-cran-purrr, r-cran-randomforest, r-cran-ranger, r-cran-reactable, r-cran-readr, r-cran-rpart, r-cran-scales, r-cran-tidyr, r-cran-tree, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-logisticensembles_1.0.2-1.ca2404.1_all.deb Size: 2297920 MD5sum: 2924d1ca7fe497500b10e762f97b88e1 SHA1: 2defeaf49a933e06c593a780da8c0e503b75ceca SHA256: d51c936385b869664fdebdb158ada2640a55564fb52371d1fada790d4124e7b3 SHA512: c6160a943fd515707af238fbf5ac6031ff986c5ae938a418180532b2a214d3651d3bdc9c223603099e6b6e5593abc8827b25d24294e5fb947963a0e0617d04d1 Homepage: https://cran.r-project.org/package=LogisticEnsembles Description: CRAN Package 'LogisticEnsembles' (Automatically Runs 18 Logistic Models-14 Individual LogisticModels and 4 Ensembles of Models) Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 18 models. Package: r-cran-logisticrci Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-logisticrci_1.1-1.ca2404.1_all.deb Size: 42040 MD5sum: 2e4132e97dec2b6fd1b64edaa32e38c9 SHA1: 83f255120a9de7fdb65d7fdc928d4a6921df4554 SHA256: 4c0ce3bcc087cae78dc8790e318cc053f4366c015cfb7ffcf8460aed4c5ed347 SHA512: 703916c0c8dd072449159087942796e7a78c3fa2887a670f27207cea7a4d0a0a2b0578902ecbde41dd1abe702bd1558631dd81cbda938cbebdf48df49f78a491 Homepage: https://cran.r-project.org/package=LogisticRCI Description: CRAN Package 'LogisticRCI' (Linear and Logistic Regression-Based Reliable Change Index) Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. . The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline. Package: r-cran-logisticrr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-logisticrr_0.3.0-1.ca2404.1_all.deb Size: 102232 MD5sum: 30c6b5eb92232859f3365109cb81db91 SHA1: 6b809457b2f676999711a8f54b9234d67d65fd77 SHA256: bc82a1b84bbd76701129187db59baf7fb91ea2161a37ee7862c2577c6a5d1a8d SHA512: 7dc9b6e7d6002c5f186302f17be287b497b9d8d544e35548b565b59ca50b5696b08c8ace91273f56ec98bd7e376b16e489d39dbdea38cf87da98e103e3e895f9 Homepage: https://cran.r-project.org/package=logisticRR Description: CRAN Package 'logisticRR' (Adjusted Relative Risk from Logistic Regression) Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders. 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The proposed methods were developed in Escabias et al (2004) and Escabias et al (2005) . Package: r-cran-logitnorm Architecture: all Version: 0.8.39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-logitnorm_0.8.39-1.ca2404.1_all.deb Size: 201718 MD5sum: 872a5fffe0f6bb4356e8265b5d2d751b SHA1: e36e17e781176c80060a9c73015d29ac22e3a8e7 SHA256: e98f112324e45e5042db035e5cbc57acb1290da2aa9b600f2c9c882121490566 SHA512: f47aeba7a3b6301b7b5c992c5ca48d29bb4a997a74e2c957ab95b6e3c6b73a041f8d03ea07570d8cdfb1c77b01e2c7da6c25226e305af5315d23a1f87dd66713 Homepage: https://cran.r-project.org/package=logitnorm Description: CRAN Package 'logitnorm' (Functions for the Logitnormal Distribution) Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters. 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Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) . More details can be found in Helveston (2023) . 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For complex survey data, models can be fitted from design objects from the 'survey' package. Currently supported models include UNIDIFF (Erikson & Goldthorpe, 1992), a.k.a. log-multiplicative layer effect model (Xie, 1992) , and several association models: Goodman (1979) row-column association models of the RC(M) and RC(M)-L families with one or several dimensions; two skew-symmetric association models proposed by Yamaguchi (1990) and by van der Heijden & Mooijaart (1995) Functions allow computing the intrinsic association coefficient (see Bouchet-Valat (2022) ) and the Altham (1970) index , including via the Bayes shrinkage estimator proposed by Zhou (2015) ; and the RAS/IPF/Deming-Stephan algorithm. 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To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) , we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications. 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Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201. 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The array output can be used by the 'keras' package. Long short-term memory neural networks are described in: Hochreiter, S., & Schmidhuber, J. (1997) . 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Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial. 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The package implements a novel longitudinal attribution method based on a semiparametric additive hazards model with time-dependent covariates, specifically designed to address interval censoring and semi-competing risks via a copula framework. The resulting age-cause-specific contributions to disability prevalence and death probability can be used to quantify and decompose differences in cohort HE between groups. The package supports stepwise replacement decomposition algorithms and is applicable to cohort-based health disparity research across diverse populations. Related methods include Sun et al. (2023) . Package: r-cran-longit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aiccmodavg, r-cran-missforest, r-cran-r2jags, r-cran-rjags Filename: pool/dists/noble/main/r-cran-longit_0.1.0-1.ca2404.1_all.deb Size: 72492 MD5sum: 295e2619be6d0cc606f7de57be4630fc SHA1: 71b85392b09533f5f9672b1617e7a509f369c4a7 SHA256: 5b35fdf4b5a9b8fcc71fbade4020a3237f01bcefc89bd4b096fceb40628fc08c SHA512: 36cc9267fd76f8f2a53f8f355c159b7428905ca94e62feceee299079c2358bfdff1a1a6932441df8e1a8ff793ddd9b123638e04ecdf7960e413f0f1f493b8c86 Homepage: https://cran.r-project.org/package=longit Description: CRAN Package 'longit' (High Dimensional Longitudinal Data Analysis Using MCMC) High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) . 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Package: r-cran-longitudinalanal Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-mass, r-cran-dlm Filename: pool/dists/noble/main/r-cran-longitudinalanal_0.2-1.ca2404.1_all.deb Size: 60702 MD5sum: 0f0a130b63f595af6953efa2f0906e67 SHA1: 3e52d8965f880341dc37bc31652994370bf72949 SHA256: 5d8fb5315b2d486b05b52518a69ebd77368dd32a7829b5e925de69e65b329015 SHA512: 3cbf6285e92736e79b951316e4d72e8e940965b800e493299bd593ba3cce362e0fedb9f7fd2f4d9afa43c0e9c8000560a944e4f9cebbfa1c7ae0551c3733f347 Homepage: https://cran.r-project.org/package=longitudinalANAL Description: CRAN Package 'longitudinalANAL' (Longitudinal Data Analysis) Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2023) and Liu, C. et al. (2023) . Package: r-cran-longitudinalcascade Architecture: all Version: 0.3.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-survival, r-cran-ggplot2, r-cran-tidyr, r-cran-zoo, r-cran-scales, r-cran-rlang, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-longitudinalcascade_0.3.2.6-1.ca2404.1_all.deb Size: 299560 MD5sum: 9f474d509bc9ea0a5e7b1b2136806d23 SHA1: e0d7180cd0eed9d62235e5a72f2054bb3267c3ed SHA256: 877218ce743ed47e66635653d9201c9faa645c4a333af912302e9aae14bac298 SHA512: 1200da875e859f4b886433bbb697db42b630081f76aec416c33d85012596775a437ee7848ce56fd0b6c4fbeec0c95b53eddbf2e7152bc9525cd3204f94327b15 Homepage: https://cran.r-project.org/package=longitudinalcascade Description: CRAN Package 'longitudinalcascade' (Longitudinal Cascade) Creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests. Package: r-cran-longitudinaldata Architecture: all Version: 2.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-class, r-cran-rgl, r-cran-misc3d Filename: pool/dists/noble/main/r-cran-longitudinaldata_2.4.7-1.ca2404.1_all.deb Size: 536228 MD5sum: b9ea5daeb6391b1c3045a3adbb2b7a2f SHA1: 5593c17bc0197a50e06954f8384911bbf56ad019 SHA256: c0b8b46a17dd6d395fc05e11e7f424fabc9c3533f379d31131f31938d4318309 SHA512: e03dbcff76d03f11028c97a7bb10669d8e6b0c17e36ffa16d64b1f2ffb0d808cdcee287bc6c22205e1d3f08b7956a8ef87452e4c6c7c1318c2f362f3173bc386 Homepage: https://cran.r-project.org/package=longitudinalData Description: CRAN Package 'longitudinalData' (Longitudinal Data) Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d). 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The package consists of three functions. The first starts a 'shiny' app which assesses how strong a time-invariant confounder needs to be associated with the exposure and outcome to explain away a proposed causal association. The second and third functions simulate a time-dependent confounder over time either using a fit from the qmle() function from the 'yuima' package or directly using the observed effect estimate. Package: r-cran-longiturf Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-rpart, r-cran-mvtnorm, r-cran-latex2exp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-longiturf_0.9-1.ca2404.1_all.deb Size: 155810 MD5sum: 3f063577c47e15b374dd0a9c3b921817 SHA1: e8d806f5844a2286c6cc1e7c5e508cef40d2e498 SHA256: 658ec626e5822db5d0f4d56f54e46cf04130b55309fa1f0312f79006c60ec52e SHA512: e60be32e564eab51f9c2e8d92e859a99ab0dd07f54f2b656fcfb896ac7e9b6102d2dac3f23a785659db634c6e93718a7a053cf2785a415918ac4292e0e62f28e Homepage: https://cran.r-project.org/package=LongituRF Description: CRAN Package 'LongituRF' (Random Forests for Longitudinal Data) Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) Random forests for high-dimensional longitudinal data. 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The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels. 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Package: r-cran-loon Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4459 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra Suggests: r-cran-maps, r-cran-sp, r-bioc-graph, r-cran-scagnostics, r-cran-pairviz, r-cran-rcolorbrewer, r-cran-loon.data, r-cran-rworldmap, r-cran-mgcv, r-cran-rgl, r-bioc-rgraphviz, r-bioc-rdrtoolbox, r-cran-kernlab, r-cran-scales, r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-png, r-cran-formatr, r-cran-covr Filename: pool/dists/noble/main/r-cran-loon_1.4.3-1.ca2404.1_all.deb Size: 2822644 MD5sum: 46b5bded4ad72e39ab3b833f189a7d2b SHA1: 81acf191ce38b1f07ee1ac705d4b8635073e758d SHA256: 18a936a213d16d4a5796a3f839d8a0e5328dc9fb783a4ee1d72d814ef8630f1b SHA512: 2b4adc93b7b7e5e594b62381c44a13417650e582700996dfe561cc7ffc1dd7dea41ec83e69a8da1510445c973f4929ed7568818c5bfe4dcc365b5110782440bc Homepage: https://cran.r-project.org/package=loon Description: CRAN Package 'loon' (Interactive Statistical Data Visualization) An extendable toolkit for interactive data visualization and exploration. Package: r-cran-loopanalyst Architecture: all Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nlme, r-cran-rlang Filename: pool/dists/noble/main/r-cran-loopanalyst_1.2-8-1.ca2404.1_all.deb Size: 230214 MD5sum: 929b2376045c959792ec2f270ccd3972 SHA1: 01a5cbe310d7298fc31eff300aeb3692baf841a2 SHA256: fdb6740f4ecd0a77bd2b182fa3ac4a12d5f82e87b665543990fd3744b4e830cc SHA512: 5315593cfc08d35a9c922be0a4657f41b04f9ea9f74b186d4dd501d7bea170c8487b4027eb62762bc16edc3a95e2c35a2553540c87c81d581b3c1fa5b1fd6951 Homepage: https://cran.r-project.org/package=LoopAnalyst Description: CRAN Package 'LoopAnalyst' (A Collection of Tools to Conduct Levins' Loop Analysis) Performs Levins' loop analysis of qualitatively-specified complex causal systems. Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means direct causal relationships and indirect causal effects are coded as sign only (i.e. increases, decreases, no change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as 'graphviz' (). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools. Package: r-cran-loopdetectr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-numderiv Suggests: r-cran-desolve, r-cran-knitr, r-cran-markdown, r-cran-remotes, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-loopdetectr_0.1.2-1.ca2404.1_all.deb Size: 262260 MD5sum: 9658e77d03f48b8277029e2be4e998da SHA1: e9db29761496744397a00fab26862208e9077945 SHA256: 5ffc7db4b093301043ecf1248454f17437114aebeec42af72e8b1c6d28d6367c SHA512: 230e39ea84d951008ac09867149b18d0b6de806246c4696ac158d7b18819743f07d72a7aa1a3f413b3f9f3c89c48ca974bce0af4ee4e8d0dafb9c2d60cab5ad4 Homepage: https://cran.r-project.org/package=LoopDetectR Description: CRAN Package 'LoopDetectR' (Comprehensive Feedback Loop Detection in ODE Models) Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. 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Package: r-cran-loopevd Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-evd, r-cran-ncdf4, r-cran-ismev Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-zoo, r-cran-ozmaps, r-cran-raster, r-cran-sp Filename: pool/dists/noble/main/r-cran-loopevd_1.0.2-1.ca2404.1_all.deb Size: 901016 MD5sum: 8e5062998a4231680afca76c5e302cc5 SHA1: 3b1446c3059b173870a001fe318e8958c1b1111e SHA256: 78e17ee8e8bb3738aba4331ff1600b5df8f4b2fb8cd5d56e0c5655e11ff7b6fb SHA512: e3e7ad888d1535eff2c4213906b504cc7fd36c7789bbff9279ef26653d0f684ab1196fce8a1376c5d1d9b2c94591c445cfb9ecfb92338e5a5095033243d6ac13 Homepage: https://cran.r-project.org/package=loopevd Description: CRAN Package 'loopevd' (Loop Functions for Extreme Value Distributions) Performs extreme value analysis at multiple locations using functions from the 'evd' package. 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Package: r-cran-looprig Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-looprig_0.1.1-1.ca2404.1_all.deb Size: 598300 MD5sum: 1181487f23108bcc40760c658933c5e9 SHA1: 97399e1a5e20851e51cfa093473e3c9a38d7040e SHA256: 6fe84bbda0db1994bfc71ff067f5c5e6d2856236d9e10827997a1fe7ab519d90 SHA512: 33f23b241387c4bf02a635d5abb3a9ee73efe77baa8323adcaed0cf31b242fc18742cbc04ec78e8e2f4c9f68a586690d54a45fc4d99a86e4b086fae84f1077ae Homepage: https://cran.r-project.org/package=LoopRig Description: CRAN Package 'LoopRig' (Integration and Analysis of Chromatin Loop Data) Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. 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Package: r-cran-lorax Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-partykit, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-aorsf, r-cran-c50, r-cran-cubist, r-cran-dbarts, r-cran-grf, r-cran-knitr, r-cran-lightgbm, r-cran-modeldata, r-cran-palmerpenguins, r-cran-randomforest, r-cran-ranger, r-cran-rpart, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-lorax_0.1.0-1.ca2404.1_all.deb Size: 443936 MD5sum: f5c04e536730504d4a2afea141e9ed09 SHA1: 4ff04397686f2e7724d69fd0fae3d6c34904fc0d SHA256: 2481003e5ff766e7e692317a4a9b2e194d8352e8f0ee9755fd7db12a3e515274 SHA512: 2de5d264c658f431814cb9225afd8adfb014b3805752001ecca64c28179264b985a125dd0139c838cb9864b794cd122a7047ebef14f1d437f21660c122ce3f8a Homepage: https://cran.r-project.org/package=lorax Description: CRAN Package 'lorax' (Speak for the Trees) Extracts decision rules from tree- and rule-based models fitted in 'R'. 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Package: r-cran-lorbridge Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cavariants, r-cran-nnet Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lorbridge_0.1.1-1.ca2404.1_all.deb Size: 89224 MD5sum: 6abada1a46e4ebb7cad72d464a04e4c0 SHA1: 48c6ed627b4ba93c17eef5cb65d83cb291f095a9 SHA256: 47cc6279767c01d6344884aaa03f5dbce86eaddf057a8bc8537049b9e85f9132 SHA512: 736c62d73722d01b357797a0d112869ad82faa871391822c3e67e4192458e85ce96ee34cc1076bf9f4c9c7424b64770540ce869a4bf926e4d8f0af34d0a579c3 Homepage: https://cran.r-project.org/package=lorbridge Description: CRAN Package 'lorbridge' (Bridging Log-Odds Ratios and Correspondence Analysis viaCloseness-of-Concordance Measures) Provides a unified analytical workflow that bridges conventional binary and multinomial logistic regression with singly-ordered (SONSCA) and doubly-ordered (DONSCA) nonsymmetric correspondence analysis. Log-odds ratios (LORs) from logistic regression are re-expressed as cosine theta estimates and closeness-of-concordance measures (CCMs) -- including Yule's Q, Yule's Y, and r_meta -- on the familiar [-1, +1] scale introduced by Kim and Grochowalski (2019) . Bootstrap confidence intervals for cosine theta are provided throughout. The package is intended to help clinical and medical researchers interpret association strength from logistic regression in an intuitive, correlation-like metric, and to connect conventional regression results with geometric correspondence analysis visualisations. Package: r-cran-lordif Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mirt, r-cran-rms, r-cran-dosnow, r-cran-foreach Filename: pool/dists/noble/main/r-cran-lordif_0.4.2-1.ca2404.1_all.deb Size: 156078 MD5sum: f0788d9ec854762b67b8c54dc92a789d SHA1: c7002b61f8a9a997ae558f01cd0f4b09d6560a43 SHA256: 73bb781f82de3d261568215b6f276aa8481a36e6ee052634228162dac797c8f6 SHA512: 5d46356657cc7cc11ad6c9b26160a06f9332477899d92d284ba62c6905e7a96e4d5e4d11b9e702a8e881922647b4acb7846b2f9afeed269af9f2a9eda2964a11 Homepage: https://cran.r-project.org/package=lordif Description: CRAN Package 'lordif' (Logistic Ordinal Regression Differential Item Functioning usingIRT) Performs analysis of Differential Item Functioning (DIF) for dichotomous and polytomous items using an iterative hybrid of ordinal logistic regression and item response theory (IRT) according to Choi, Gibbons, and Crane (2011) . 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Package: r-cran-lorenz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dineq Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lorenz_0.1.0-1.ca2404.1_all.deb Size: 41258 MD5sum: d0c706eedb53206461327652ac71b58f SHA1: a9726d8d56e9e639bb69b8d1bcfbb71f93661a9b SHA256: 44a947a2a5859cd547ce82a4dcc955719c86088d82877696177f01953e1ea669 SHA512: 4335246e4ec7cac8ada30e3a12e1939a6c6ab3c4488d1fd8b1234c717e1642cd1945fbaa8ba533583fe1779c021b382e43f7561a008e04dc959873e9763ca7e9 Homepage: https://cran.r-project.org/package=lorenz Description: CRAN Package 'lorenz' (Tools for Deriving Income Inequality Estimates from GroupedIncome Data) Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census. These methods use different interpolation techniques to infer the distribution of incomes within income bins. One method is an implementation of Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB). The other method is based on a new technique, Lorenz interpolation, which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data. These methods can be used to estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index. Jargowsky and Wheeler (2018) . 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LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) . 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It introduces an encapsulation feature that streamlines the process into a summary object. With the initial configuration of this summary object, users can execute a wide range of analyses with a single line of code, requiring only two essential parameters for setup. The package delivers comprehensive outputs including analysis objects, statistical outcomes, and visualization-ready data, enhancing the efficiency of research workflows. Designed with user-friendliness in mind, it caters to both novices and seasoned researchers, offering an intuitive interface coupled with adaptable customization options to meet diverse analytical needs. 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A subset of functions implements the design phase of the study, where the focus is on the selection of suitable subpopulations for which valid causal inference can be drawn. These functions provide summary statistics of pre- and post-treatment variables by treatment status and select suitable subpopulations around the threshold where pre-treatment variables are well balanced between treatment groups, using randomization-based tests with adjustment for multiplicities. Functions for a visual inspection of the results are also provided. Finally, the package includes a set of functions for drawing inference on causal effects for the selected subpopulations using randomization-based modes of inference. Specifically, the Fisher Exact p-value and Neyman approaches are implemented for the analysis of both sharp and fuzzy Regression Discontinuity designs. The approach is illustrated in a study concerning the effects of university grants on student dropout. 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Described further in Mantz (2016) . Package: r-cran-lrmest Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-psych Filename: pool/dists/noble/main/r-cran-lrmest_3.0-1.ca2404.1_all.deb Size: 511494 MD5sum: dc27c311dba036e07dddb0c9ab4fa366 SHA1: f3199caeb1a7f297d4f616e3d573645106ec7b90 SHA256: 2ac7ccfc67f6fdb79e94cb4b962efe2ff9a3d03b3a875a577bd11851e4385a00 SHA512: 2be671b8b1403c3cbd852ea9f8af36b0d0e10a0e07269fffb3adb7193b61a7065dac7527ebc68e0fe11130a6de4815575d4ff1ee4f673a4ebe5c1659aeb9887e Homepage: https://cran.r-project.org/package=lrmest Description: CRAN Package 'lrmest' (Different Types of Estimators to Deal with Multicollinearity) When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. 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Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically. Package: r-cran-lrmf3 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glue Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lrmf3_0.1.0-1.ca2404.1_all.deb Size: 193268 MD5sum: c9797af9cfa0de2ab12dc229165d06f5 SHA1: c9048b35b104a4691865046a7e8b035578c3b6df SHA256: 7c5ac46a9e0738040b513f8529a9424b39fd99dd7eff120fb0e73bf47517a298 SHA512: 9f2a6c84a3d575689ca2de5de23a42862b045d7429b77eb8ab134b68f345cb48fd3883380a6d8d03d2f15d135d23d3ea720715e512c1ec7dde62a294cf31d304 Homepage: https://cran.r-project.org/package=LRMF3 Description: CRAN Package 'LRMF3' (Low Rank Matrix Factorization S3 Objects) Provides S3 classes to represent low rank matrix decompositions. 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The package implements two estimators: oss_estimator(), a low-dimensional semi-supervised method, and dantzig_missing(), a high-dimensional approach. The tuning parameter can be selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026) . Optional support for the 'gurobi' optimizer via the 'gurobi' R package (available from Gurobi, see ). 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By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome. 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For actually building a vector semantic space, use the package 'lsa' or other specialized software. Downloadable semantic spaces can be found at . 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(Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung." Wien: facultas. (ISBN: 978-3-7089-1343-8, ) zur Verfügung. Package: r-cran-lsasim Architecture: all Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-cli, r-cran-polycor Suggests: r-cran-testthat, r-cran-knitr, r-cran-formatr, r-cran-rmarkdown, r-cran-naepirtparams Filename: pool/dists/noble/main/r-cran-lsasim_2.1.6-1.ca2404.1_all.deb Size: 524268 MD5sum: 66076185702f3b8151cb8cab46589fe4 SHA1: e217173e855fe07c77fbb39ca40ba96b27a6458d SHA256: 317385c8d66cc52aa0fd8a925d24117fb919411af90dc5e4282d0dd72d40c7fb SHA512: e7490aada2a5adbfebffdb51dc530796343cf24b74ab3fa4486c319508a2ccc13f733e25c4bfcab015ecd8e3bde988f6bae8789f615477974f02f0c91ce81111 Homepage: https://cran.r-project.org/package=lsasim Description: CRAN Package 'lsasim' (Functions to Facilitate the Simulation of Large Scale AssessmentData) Provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design. 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Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) ), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. 'LSD' (Laboratory for Simulation Development) is free software available at ). 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Reads object-oriented data produced by 'LSD' simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) ) is estimated using the simulation data to provide the data required by the Sobol decomposition. 'LSD' (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at ). 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It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis. Package: r-cran-lsl Architecture: all Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-lsl_0.5.6-1.ca2404.1_all.deb Size: 172066 MD5sum: 60e1130997a89cbfb1462915322b71fb SHA1: 7739b476db6128a1a7f9ec5d5558d7b1fa124966 SHA256: 7d9761fcd4efa1439fc649e41e74a083273cad157e879122cac8cf11339ec6c1 SHA512: 622c0f062d6f4b49f76c5ddaaa502ffc554e2e4999e1bff1551b92d01e5a891a947731a6496d5dfe73ecf52c001e5c5dd271c7841b48e1868aaae83ae6b77683 Homepage: https://cran.r-project.org/package=lsl Description: CRAN Package 'lsl' (Latent Structure Learning) Fits structural equation modeling via penalized likelihood. Package: r-cran-lsm Architecture: all Version: 0.2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lsm_0.2.1.5-1.ca2404.1_all.deb Size: 142624 MD5sum: 1e823b486982bf23d21bca68b7a53453 SHA1: 2dd3f5d3d71cdcc31e9dfb6029715d159a465d45 SHA256: 18494531556dc606a0ca18fb8f239c07b767b040a388b3b94e50d972d76c7109 SHA512: 91c0a0ba86e36b3eb2a22c35257226a24c18aba7e73641a6e54b92b510bf90d49c177d89aef8b0c9c778d778ed1f3bfa5de651a493ba035924e61e7bd0b35c8e Homepage: https://cran.r-project.org/package=lsm Description: CRAN Package 'lsm' (Estimation of the log Likelihood of the Saturated Model) When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K. Package: r-cran-lsmeans Architecture: all Version: 2.30-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emmeans Filename: pool/dists/noble/main/r-cran-lsmeans_2.30-2-1.ca2404.1_all.deb Size: 39222 MD5sum: d0d3d97787b567eaaa6fd65a8e1545b6 SHA1: b8751d7646bd5ce4787109b6e303a7bb7d618dbf SHA256: de516d1db01694e20e4b866e93b1f429c0a50a0d18f07e9a56975d3bb221a84d SHA512: 049e787be51de6bf07982c1137b83c977e8f1b7866ff4ec49f17b0c22737a1aa5b0826c86184ef73d1b42064329fbfc6bc12f11cce0b9bbf63f3d6093a8255b0 Homepage: https://cran.r-project.org/package=lsmeans Description: CRAN Package 'lsmeans' (Least-Squares Means) Obtain least-squares means for linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers", Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) "Population marginal means in the linear model: An alternative to least squares means", The American Statistician 34(4), 216-221 . NOTE: lsmeans now relies primarily on code in the 'emmeans' package. 'lsmeans' will be archived in the near future. Package: r-cran-lsmontecarlo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-fbasics Filename: pool/dists/noble/main/r-cran-lsmontecarlo_1.0-1.ca2404.1_all.deb Size: 105586 MD5sum: 94e0b2a32a5b68bcb374779deaed1e9d SHA1: 5da3d78972e474e9e0e9689b3211b3d83445f508 SHA256: 9dd2ddf535c0af2b6e9ea7fb44e05c0eba7f83692ea9ab908d391b4dc67cc6f3 SHA512: b292b8ed7485556787a0a9ffba1e6b7716f4fce337aa6e30f2d93ca3a2259d5661e39bfeb0ec6239aef577f69b73c1a131d486e32f7789fbf1b1ba9094bd35d0 Homepage: https://cran.r-project.org/package=LSMonteCarlo Description: CRAN Package 'LSMonteCarlo' (American options pricing with Least Squares Monte Carlo method) The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution. Package: r-cran-lsmrealoptions Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-nfcp Filename: pool/dists/noble/main/r-cran-lsmrealoptions_0.2.1-1.ca2404.1_all.deb Size: 130762 MD5sum: 0723804c38f7812b375c40847e9197e2 SHA1: ca8def9ad6e08abc1f856ba17bd99ae789e91203 SHA256: f426b5f5d3532b46a1dc39bf4069affd2db119e013de4e66bac71038f8764327 SHA512: 817ad703cab5ed7b6db3c49c794dc5b22af5fa8e87be9c2f3f4c482d81c7b171a99718297ae653fb9ecc54bff884ff3b5d01215f5a94c540af47a03954c799a3 Homepage: https://cran.r-project.org/package=LSMRealOptions Description: CRAN Package 'LSMRealOptions' (Value American and Real Options Through LSM Simulation) The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. 'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) . Package: r-cran-lsnstat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lsnstat_1.0.1-1.ca2404.1_all.deb Size: 24556 MD5sum: 33e43c0f187de9782b5bf41b30d5189e SHA1: 8859976a2bcb0e66a1df12cc4fe23581690939cb SHA256: f35d2ff093fc03d8d59c03d0642bfabcadd88e3acb2511796097f2cfa51a8a12 SHA512: 6548c00987e01a0a28f1c3b55a786a9f6d52c8deb81d812ec54012a416972b785e81a4f7ee02502d21f06713dca666af87baa7ddaa9ead6c5324e83f2f56ed41 Homepage: https://cran.r-project.org/package=lsnstat Description: CRAN Package 'lsnstat' ('La Societe Nouvelle' API Access) Tools facilitating access to the 'macro_data' service of the 'La Societe Nouvelle' API. 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H., Thyholt, K., Næs, T. (2004) "A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables" Journal of Chemometrics, 18(10), 451--464, . 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This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006) for the accelerated failure time model based on the least-squares principle. 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Every label the package adds around that content -- column headers, type names, the audit section -- is written in English, French, German, Spanish or Italian, whatever the survey languages are. A rule-based audit flags missing translations, forward filter references, duplicate codes, array-scale inconsistencies and orphan structural references. Questionnaires travel the other way too: describe one in R, or fill in a Word form, and write a '.lss' file ready to import. Meant for the people who work on questionnaires -- researchers, methodologists, ethics committees, translators and reviewers -- and fully local: the source file is the only input, and no questionnaire content is uploaded to a third-party service. 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Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). . Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). . Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). . On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). . Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). . Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014). 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This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently. Package: r-cran-lumberjack Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-lumberjack_1.3.1-1.ca2404.1_all.deb Size: 409388 MD5sum: cffa6711d4ba6c682871e85cb3dcb816 SHA1: 93a8aedc789231b551b2252fd0fa7841799e177e SHA256: b2f375e2aab102e8962e6c604b23bb54e7839d1e147907e82af85671d726a13d SHA512: c0ba305896f8560c0b83fba77f0987c0f7a720824e8b5e08a32a20dab1ce7c663960721f231ab28cdb827771a798e6b2685d82fd9ac2c5b551aedb9ac966e173 Homepage: https://cran.r-project.org/package=lumberjack Description: CRAN Package 'lumberjack' (Track Changes in Data) A framework that allows for easy logging of changes in data. 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Package: r-cran-lvnet Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx, r-cran-glasso, r-cran-qgraph, r-cran-matrix, r-cran-psych, r-cran-mvtnorm, r-cran-corpcor, r-cran-dplyr, r-cran-lavaan, r-cran-semplot Filename: pool/dists/noble/main/r-cran-lvnet_0.3.5-1.ca2404.1_all.deb Size: 151640 MD5sum: a308c9ff2b23eab0643f540b4ad2a4bc SHA1: ca9e893a0d0907973a1ed9e7216ef982f2796d9b SHA256: b4f65f36148b5f29bda1f07e66c98feabd8701eb0eba39119501ce13ccb4da60 SHA512: 31d12c6c202f0605e671f8c77940cb13c624a4212dbb094bc1bf9a24fda5e9b14cecd67da1135d6493c0ac080e5777ca573d97e2899f4717384a702bd01f5b30 Homepage: https://cran.r-project.org/package=lvnet Description: CRAN Package 'lvnet' (Latent Variable Network Modeling) Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. 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If the fitted values of a quantile regression on this transformed outcome are all greater than the negative value, then results are displayed. The resulting coefficients can be meaningfully interpreted as logarithmic intensive-margin relationships between the outcome variable and the independent variables, even with non-positive values in the outcome variable. If the condition does not hold for the specified quantile, then the command iteratively makes the value larger and checks again. After ten iterations where the condition does not hold, the functions return an error and suppress results. This is an automated adaptation of the algorithm described by Liu & Kaplan (2025) and implemented in the companion 'Stata' command 'lzqreg', described in Fitzgerald et al. (2026) . Package: r-cran-m2b Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-catools, r-cran-ggplot2, r-cran-randomforest, r-cran-caret Suggests: r-cran-adehabitatlt, r-cran-movehmm, r-cran-knitr, r-cran-diagrammer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m2b_1.1.0-1.ca2404.1_all.deb Size: 384046 MD5sum: 96a8b2fda07fa20dc83379cb5155aea8 SHA1: fa1e24e1786912feaeb72e892b9e41d894cb5f2b SHA256: ec2d34c901d32ed67e9bf38462e59b5f949b00821e4485d6ca778c0cecef1a2f SHA512: b42ee2c0fbc75211c57861a56d8c11fe86d3e78983ac67c0c61170a2fc6a236f99e2d62a4d20ff66c161bf963a1c5fbbfab1953540c2a540c68a3d8281b98ee2 Homepage: https://cran.r-project.org/package=m2b Description: CRAN Package 'm2b' (Movement to Behaviour Inference using Random Forest) Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour. Package: r-cran-m2smf Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m2smf_2.0-1.ca2404.1_all.deb Size: 62284 MD5sum: 172e70a520308904ce100ee20665f12d SHA1: a20b721816a25bdd5fdb09b726dc3b09c81adb60 SHA256: 0c6cc87fc472fe8d9121f65e834865af59effbce4e75e550c611db805178163b SHA512: e412e217345148c2e2b235684d8d280f7d7231f8cf1b9a29a7091341a843946efccc5961c492bf97055dcfa170cb8145d8e2c1a90dfa7020b343ecb4cba50150 Homepage: https://cran.r-project.org/package=M2SMF Description: CRAN Package 'M2SMF' (Multi-Modal Similarity Matrix Factorization for IntegrativeMulti-Omics Data Analysis) A new method to implement clustering from multiple modality data of certain samples, the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data. Package: r-cran-m2smjf Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m2smjf_1.0-1.ca2404.1_all.deb Size: 62546 MD5sum: e7202ff90ccae08096d70c3daeeccb73 SHA1: 26a80675fef18fbac8fdf3da68a608caed19ad89 SHA256: 9a536e4c577529d51c8597a98f0dd60edebc74fdc110e1008b7fafa4c88df12a SHA512: 5bd51f688b6db4e6e43108789d2f3175d1da7dac2f6ec68d2ee686873744b5e623f66d5b78546ce2dcf3e7586db52968e0266d4748d5fcb385c54d5e6681df04 Homepage: https://cran.r-project.org/package=M2SMJF Description: CRAN Package 'M2SMJF' (Multi-Modal Similarity Matrix Joint Factorization) A new method to implement clustering from multiple modality data of certain samples, the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data. Package: r-cran-m3 Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncdf4, r-cran-sf, r-cran-maps, r-cran-mapdata Filename: pool/dists/noble/main/r-cran-m3_0.4-1.ca2404.1_all.deb Size: 2023920 MD5sum: ed5cc7b4073703b8f3ba5d85ddfc9aa9 SHA1: 2051a42982f4a809687d11abf10de285bfdf6d95 SHA256: 20ddd2ea0426d945529a4261fa673c6bb7a68518fff2d64a23bb2bd28750a27e SHA512: 22c952e331beecb72b0f662b438fc6a0b1c91d1ff9dc180bd0cfeafe4e7961825dab2bce1bc59314b8f5d5c96734714322628387d077d1b2b261216f7dcb0952 Homepage: https://cran.r-project.org/package=M3 Description: CRAN Package 'M3' (Reading M3 Files) Provides functions to read in and manipulate air quality model output from Models3-formatted files. This format is used by the Community Multiscale Air Quality (CMAQ) model. Package: r-cran-m3jf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-snftool, r-cran-dplyr, r-cran-intersim Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m3jf_0.1.0-1.ca2404.1_all.deb Size: 85740 MD5sum: 6e9fdab103e9a0a5161e75e1b41d6576 SHA1: dfef86968784b65a5bcc25ade88b63f1d9f63be8 SHA256: 7b066e1f62494fa47107e5b62e1d18ab6d4dd86616199a3ba5957bc96d4ccccd SHA512: e2d7ebff8c05277af1370cb4e667888c3a863f8b25fde16ba1176c94af17e625d201255c6f4568ceb9c23ef02f9bb59d192f7b58d570d8b9b1f10409247e59c7 Homepage: https://cran.r-project.org/package=M3JF Description: CRAN Package 'M3JF' (Multi-Modal Matrix Joint Factorization for IntegrativeMulti-Omics Data Analysis) Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests. Package: r-cran-m5 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2794 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-lubridate Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-zeallot Filename: pool/dists/noble/main/r-cran-m5_0.1.1-1.ca2404.1_all.deb Size: 1358446 MD5sum: 458c21100d4bf15a4a0b0d1d8bebd138 SHA1: f504ff49afb6b0974a79a2bc2983abe4b17f3ba1 SHA256: 1dfdbe1c4151385acb0a1cdfcf359e3d190fc22a9d7d227cb7fe6c799f408a4a SHA512: 6ab57bf77299a4132138ac0ecfe4768c1c88373ebc7f2a16dc9164fcf5c372ba57d71b8c77ea4f88bfb0cb83e6eb200bafa6fd54daa5c8811749460cf74c306d Homepage: https://cran.r-project.org/package=m5 Description: CRAN Package 'm5' ('M5 Forecasting' Challenges Data) Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle'). The data itself is set of time series of different product sales in 'Walmart'. The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'. For detailed information about the challenges, see: Makridakis, S. & Spiliotis, E. & Assimakopoulos, V. (2020). . Package: r-cran-m61r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m61r_0.1.0-1.ca2404.1_all.deb Size: 132242 MD5sum: b111717827fbfc604a8ab2f79b943994 SHA1: ba5b8a76e4221a9c7646d25a547d00994bbaf28c SHA256: d80e5a0eda291ce435de4adf60b63c883ddb0b96e2b083209605a21e58a1a499 SHA512: 7105d4ad14345dd499fde53c6c9c17883ba289351763090bfddd845cba47cb312f418f49036c51c8c0f805587c0bc6a91dc5f9a6b6441fc923d9415fde99a84a Homepage: https://cran.r-project.org/package=m61r Description: CRAN Package 'm61r' (Package About Data Manipulation in Pure Base R) A lightweight, dependency-free data engine for R that provides a grammar for tabular and time-series manipulation. Built entirely on Base R, 'm61r' offers a fluent, chainable API inspired by modern data tools while prioritizing memory efficiency and speed. It includes optimized versions of common data verbs such as filtering, mutation, grouped aggregation, and approximate temporal joins, making it an ideal choice for environments where external dependencies are restricted or where performance in pure R is required. Package: r-cran-maaper Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-genomeinfodb, r-bioc-rsamtools, r-bioc-iranges, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maaper_1.1.1-1.ca2404.1_all.deb Size: 206812 MD5sum: 67ccafd452a01a5d4aa2798da71485e7 SHA1: 6d4311ae4e9ccec041a9bb65d947775841ad7556 SHA256: d912fdead7815006499872ec544d1d048d46996adac8953e8df86274e470a51d SHA512: a0dbe0bc8941c02d7d1e7ff340ca10efb4510a3238810edde3ea86a0ddacc6df5234a3033073aadf6f71a9ebaca3324a65435c5f3f45711c937f4a6bba7c14dd Homepage: https://cran.r-project.org/package=MAAPER Description: CRAN Package 'MAAPER' (Analysis of Alternative Polyadenylation Using 3' End-LinkedReads) A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) . Package: r-cran-maarts Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast, r-cran-tseries, r-cran-urca, r-cran-lmtest, r-cran-sandwich, r-cran-nortest, r-cran-moments, r-cran-strucchange, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maarts_1.0.0-1.ca2404.1_all.deb Size: 202308 MD5sum: 67232d145457bd0177ad55ee8e948ac7 SHA1: 13d0cde311468581eb6b1969134709b81e9b349d SHA256: 188f75307bde960e152a0a57471eaa2993b25455deb1a9c4a16602c36f95d37b SHA512: dc0b6380cdfbcc0123f2f5f2a55f26b74103279e0fad5ee4fd2e350e723778f88be99659dab8828a2398e71daa890a086dc867dbceb79aaa5de5fbface2a2e9b Homepage: https://cran.r-project.org/package=MAARTS Description: CRAN Package 'MAARTS' (Merger and Acquisition Autoregressive Time-Series Models) Implements comprehensive Merger and Acquisition ('M&A') Autoregressive ('AR') time-series models with full statistical analysis capabilities. The package provides parameter estimation, forecasting with confidence intervals (80%, 90%, 95%, 99%), descriptive statistics, stationarity tests (Augmented Dickey-Fuller ('ADF'), Phillips-Perron, Kwiatkowski-Phillips-Schmidt-Shin ('KPSS'), Dickey-Fuller Generalized Least Squares ('DF-GLS')), autocorrelation analysis (Autocorrelation Function ('ACF'), Partial Autocorrelation Function ('PACF')), model diagnostics (Ljung-Box, Box-Pierce), accuracy measures (Mean Squared Error ('MSE'), Mean Absolute Error ('MAE'), Mean Absolute Scaled Error ('MASE'), Root Mean Squared Error ('RMSE'), Symmetric Mean Absolute Percentage Error ('SMAPE'), F-statistic), residual diagnostics (normality tests, heteroscedasticity tests), model stability analysis, impulse response, information criteria (Akaike Information Criterion ('AIC'), Bayesian Information Criterion ('BIC'), Hannan-Quinn Information Criterion ('HQIC')), structural break analysis, spectral analysis, and Monte Carlo simulation. Models are based on: Kumar, Mudassir, and Agiwal (2024) , Kumar, Mudassir, and Srivastava (2025) , Kumar and Mudassir (2025) . Package: r-cran-maat Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testdesign, r-cran-readxl, r-cran-mass, r-cran-diagram Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-maat_1.1.1-1.ca2404.1_all.deb Size: 1606400 MD5sum: 3d1147eee925122e5136222adf95a1ea SHA1: 9a0708e54d1c010ea4aec958dd8d09f57db8b5f6 SHA256: b9cc8fbb315bbad6aa76e164030c956a95d0cb3e27b507e134e4101ec2fc8124 SHA512: 09de9813e50565b937332971ee982366d2d051360083d71daa3b9c960a460b810a3f3cd39a0af1f16d66d1e51bd496397ac75d3c20184bd978c5d1780edb9ca9 Homepage: https://cran.r-project.org/package=maat Description: CRAN Package 'maat' (Multiple Administrations Adaptive Testing) Provides an extension of the shadow-test approach to computerized adaptive testing (CAT) implemented in the 'TestDesign' package for the assessment framework involving multiple tests administered periodically throughout the year. This framework is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports multiple item pools vertically scaled and multiple phases (stages) of CAT within each test. Between phases and tests, transitioning from one item pool (and associated constraints) to another is allowed as deemed necessary to enhance the quality of measurement. Package: r-cran-mabacr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mabacr_0.1.0-1.ca2404.1_all.deb Size: 25438 MD5sum: 58b42d2f3ff44500624247feb31ada94 SHA1: f6a38d15b361859290bea5e3a68d64afe0f9a857 SHA256: aeb2045e896fa19115f2dacaf81b1c7122db911fe545b01ab275f1ac4e6cf633 SHA512: 4163b7c1958486155d07eb6a9b66a03c833d80fca300f6f4e84372c6ef95d72d790e41726d0110f2e54cbe6ef9b14a87b4540206b7d60b1acdb96d48976e888d Homepage: https://cran.r-project.org/package=mabacR Description: CRAN Package 'mabacR' (Assisting Decision Makers) Easy implementation of the MABAC multi-criteria decision method, that was introduced by Pamučar and Ćirović in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated. Package: r-cran-macbehaviour Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-httr, r-cran-dplyr, r-cran-rjson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-macbehaviour_1.2.8-1.ca2404.1_all.deb Size: 79972 MD5sum: 33d7fdadf2ca75240ee86c06bd58b2aa SHA1: bfe37cef01f646d9b870f2c8b6429d6a8c86065e SHA256: 2fa356b6e472e2fee9ab1b4106fde7098dce1e38ac36242c294cb81a57840dd0 SHA512: 0c85933dca748921e523f80919aaf4ae3673b262533e38ff3be0fb9a0604a3805229cf9e1a17387fd70926157d5d69355c33aa530aedcdb72a247f82e1e80716 Homepage: https://cran.r-project.org/package=MacBehaviour Description: CRAN Package 'MacBehaviour' (Behavioural Studies of Large Language Models) Efficient way to design and conduct psychological experiments for testing the performance of large language models. It simplifies the process of setting up experiments and data collection via language models’ API, facilitating a smooth workflow for researchers in the field of machine behaviour. Package: r-cran-macc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-optimx, r-cran-mass, r-cran-car Filename: pool/dists/noble/main/r-cran-macc_1.0.1-1.ca2404.1_all.deb Size: 580420 MD5sum: bdb50bc9b51bc204378d8bcf4eb64ff2 SHA1: 42cd8c0887f116a06d9b1cc2bc68f9b3cdfcd087 SHA256: 51d0c6d228cd2c678a5a6537a42dc8408e4108f464cfd69932f8003a0b98e65c SHA512: 36bd520c419e3bf3e92129015446325e504fe7d1fa75089844d2ba243aa16dbdee7a6d09ffd894cf105ae7eaca6cc0507b17761f2e9df425c3ceb6b7c2891542 Homepage: https://cran.r-project.org/package=macc Description: CRAN Package 'macc' (Mediation Analysis of Causality under Confounding) Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, for details. Package: r-cran-macer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-ape, r-cran-httr, r-cran-ggplot2, r-cran-pbapply, r-cran-png Filename: pool/dists/noble/main/r-cran-macer_0.2.1-1.ca2404.1_all.deb Size: 94776 MD5sum: ac3739f53cd37342a2763f83aefb6e3b SHA1: 9590eaabb09348b1c24ac08c3d96981dcbbdb20e SHA256: 2c906f841c552f933130f7d0dab56ca936b732402193f0ae46631a268cbc0991 SHA512: 66348ceabfca7d0da1505c92f9dde8d842583a8ef9f4b5a725cfb3ba5500785a1c542822985df323b2d6eab23fb55346ae2c9144a0f82f06dadd52931dde5c7d Homepage: https://cran.r-project.org/package=MACER Description: CRAN Package 'MACER' (Molecular Acquisition, Cleaning, and Evaluation in R 'MACER') To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. 'MACER' accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The 'MACER' package has four core functions and an example run through using all of these functions can be found in the associated repository . 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The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures. Package: r-cran-maclogp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bma, r-cran-plot.matrix, r-cran-rlist Filename: pool/dists/noble/main/r-cran-maclogp_0.1.1-1.ca2404.1_all.deb Size: 82268 MD5sum: 8a06cfa8f70aec6206fc8c002bc51518 SHA1: 346d51bf297ddadffb70dc3c45e61fdac4c11d95 SHA256: 44469712aa8cd05c267822393771f00c77a49c355c6ede80c5d25835a7334973 SHA512: 5cf28209808f270bae104aa750b7a126a987baba0e051fe1e2bea823d148419e300376c8af9379df7429346cc632014783c8dfa084f9a6fdbbbec8bf2fc665b3 Homepage: https://cran.r-project.org/package=maclogp Description: CRAN Package 'maclogp' (Measures of Uncertainty for Model Selection) Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) . 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Package: r-cran-macrosyntr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-tidyr, r-cran-reshape2, r-cran-dplyr, r-cran-stringr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-macrosyntr_0.3.3-1.ca2404.1_all.deb Size: 2351326 MD5sum: a82022bc73f681251fc9b0a0e416f3de SHA1: 15299071ab9a84f049b3a956ef32f42556c433ca SHA256: fc4a6f26436728566f4944e8b872dec4d87cb8c308e213c23e891088ed6414bf SHA512: c9d8f7abc7ee571ab0c63389f4a8e7341591cd81d63327c9c8dc95df28443302a0dae9fc9c6e5e0c6ae20874d2c9642b751200e9c87f5ab97edd916557763efb Homepage: https://cran.r-project.org/package=macrosyntR Description: CRAN Package 'macrosyntR' (Draw Ordered Oxford Grids and Chord Diagrams) Use standard genomics file format (BED) and a table of orthologs to illustrate synteny conservation at the genome-wide scale. 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F., Trigg, D. J. and Walley, W. J. (2014). Arslan, N., Salur, A., Kalyoncu, H. et al.(2016) . Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988) Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999). 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(2018) . Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) . This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health. 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This project collates all the credible data on population and GDP for 169 countries, with some dating back to the year 1 of the current era. One function makes it easy to find the leaders for each year, allowing users to delete countries like OPEC with narrow economies to focus on technology leaders. Another function makes it easy to plot data for only selected countries or years. Another function makes it relatively easy to obtain references to the original sources, which must be cited per the copyright rules of the Maddison Project for different uses of their data. 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Package: r-cran-magentabook Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx, r-cran-officer, r-cran-flextable, r-cran-pwr, r-cran-sandwich, r-cran-swcrtdesign, r-cran-bcea, r-cran-cobalt Filename: pool/dists/noble/main/r-cran-magentabook_0.1.1-1.ca2404.1_all.deb Size: 263520 MD5sum: 440bb216524634be83afbd1d4fcfe9ee SHA1: 13f58867574e4c835b6341edc03d88f0affb2ba4 SHA256: 1c1cf7730e07a5ea4ee644a17e8d98e2c6a2c85b4587b98d7bbdde72045fbc94 SHA512: 4d496fa3ee84e8418be6682423b590e78c6cf611c99427e9502de3f857d082cdb21c4114dcc3f7d9b78cdce028e9d05ded7015f745cc423883797a8baaf8341a Homepage: https://cran.r-project.org/package=magentabook Description: CRAN Package 'magentabook' (HM Treasury Magenta Book Policy Evaluation Primitives) Implements policy evaluation primitives from HM Treasury Magenta Book guidance (HM Treasury, 2026): theory of change and log-frame construction, evaluation planning and stakeholder mapping, power and minimum-detectable-effect calculations for randomised designs (including cluster and stepped-wedge designs following Hussey and Hughes (2007) and Hemming et al. (2015) ), Maryland Scientific Methods Scale ratings, structured confidence ratings, light-weight difference-in-differences and interrupted-time-series estimators (Bernal et al. (2017) ) with cluster-robust standard errors (Cameron and Miller (2015) ), pre-treatment balance checks (Stuart (2010) ), and cost-effectiveness analysis (cost per outcome, incremental cost-effectiveness ratio, acceptability curves, incremental net benefit, quality-adjusted and disability-adjusted life years). Designed as the evaluation companion to the appraisal package 'greenbook'. Bundled rubric and reference tables carry vintage metadata for reproducibility. Aligned with the May 2026 republication of the Magenta Book. Package: r-cran-magic Architecture: all Version: 1.6-1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-magic_1.6-1-1-1.ca2404.1_all.deb Size: 395978 MD5sum: afa9d5a9d96fe69a59c28d5f32378cde SHA1: 3767ee85066b987929460672730324880cbc7a9e SHA256: f6232b9e19b4c8d7d34d16f3ba5225083dc0b133e6c6a3aed1ab0ed4f98a746a SHA512: 8364271f7300f1b3839c5fed56c535340f6d0b5a563972e1f648c7ac2af0c96bcfad1a575162b16956295b1ed432dacf51d556cc24a5b049f65f1c01fc368b02 Homepage: https://cran.r-project.org/package=magic Description: CRAN Package 'magic' (Create and Investigate Magic Squares) A collection of functions for the manipulation and analysis of arbitrarily dimensioned arrays. The original motivation for the package was the development of efficient, vectorized algorithms for the creation and investigation of magic squares and high-dimensional magic hypercubes. Package: r-cran-magicaxis Architecture: all Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4050 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-celestial, r-cran-mass, r-cran-plotrix, r-cran-sm, r-cran-mapproj, r-cran-rann Suggests: r-cran-imager, r-cran-fst Filename: pool/dists/noble/main/r-cran-magicaxis_2.5.1-1.ca2404.1_all.deb Size: 3657320 MD5sum: e8dfb8d68795816b58fa27a7a20df973 SHA1: 3cb9ad5c43aa8364be0ec002c78242c1587551fb SHA256: 7c2f04aaaff9681d42e5775fa3a04099ee26636a6f861e82cf8dded218fcbdf6 SHA512: 0935ea8fcb4a676de9809c7aaa097a484c2a14fa14e54c8a35252b39a1c0e9cd5f86acb2a121782d53c1e8c925bab7b0f30d5d4a2ab8aea0e8fbe4fc4b3ec9e9 Homepage: https://cran.r-project.org/package=magicaxis Description: CRAN Package 'magicaxis' (Pretty Scientific Plotting with Minor-Tick and Log Minor-TickSupport) Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots. Package: r-cran-magicfor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-magicfor_0.1.0-1.ca2404.1_all.deb Size: 37058 MD5sum: 03e80cf00127370779fa8170bf88573a SHA1: 5f1cddba26d7e293ff8d4696c57e6a3b00034593 SHA256: dd6573921e0b61ca1430b7748d49cf85bc2354347c3425db3cc82518380e6c9f SHA512: 6ea842a323107c6bb2ba335be0194f7640f37810fed9b75883cc2ca0862ff81dace42d2faf397ff346f390a742d90d89f248687b91e2caf09182517288cd525a Homepage: https://cran.r-project.org/package=magicfor Description: CRAN Package 'magicfor' (Magic Functions to Obtain Results from for Loops) Magic functions to obtain results from for loops. Package: r-cran-magickgui Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magick Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-magickgui_1.3.1-1.ca2404.1_all.deb Size: 150366 MD5sum: 8abfe9cbca0f44b3b9747ffd7e2ae6bb SHA1: 61f7e641990488879107c0d54d7c83ad44d5462a SHA256: d6f7e3a9b82ec7a675973f40e1f04eefe575700dced91ce882d8dc7b055d3e21 SHA512: f4547ef55886e70f006463df2c15570002bd69b8eac98180381e728855f2af7f48f7691e933d490e7a5959c6448ce4868905ce23f76629d39e628265cf85e8e5 Homepage: https://cran.r-project.org/package=magickGUI Description: CRAN Package 'magickGUI' (GUI Tools for Interactive Image Processing with 'magick') Enables us to use the functions of the package 'magick' interactively. Package: r-cran-magiclamp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-magiclamp_0.1.0-1.ca2404.1_all.deb Size: 21050 MD5sum: b1100c142887db953eb1ca075cd815c3 SHA1: 2f8506743b531f37a35131b2cc387c087fb27f21 SHA256: cf34f32c0a2cc9e2df04225671ecfd87fe39078f002d0dd62edc84b374e6de00 SHA512: dac692ab186ea565bff5709ea9312204f5df5ec1e9f2f126169ae426bd7dd14249b1af002f9d23df87c372b2701a86a937d52641e340b3f12ca5977207cbb0d0 Homepage: https://cran.r-project.org/package=magicLamp Description: CRAN Package 'magicLamp' ('WeMo Switch' Smart Plug Utilities) Set of utility functions to interact with 'WeMo Switch', a smart plug that can be remotely controlled via wifi. The provided functions make it possible to turn one or more 'WeMo Switch' plugs on and off in a scriptable fashion. More information about 'WeMo Switch' can be found at . Package: r-cran-magicrect Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-magicrect_1.0.0-1.ca2404.1_all.deb Size: 43900 MD5sum: 02bfefdfdef70ae80990fcffeaffd43d SHA1: 0d1ab0900ad7efe429972ce119448f557b93e8e1 SHA256: 376162ec39c16329af242918b4caf0db3feaec72213a2defc40fa5701defbe6a SHA512: ef4ec6ef4f6acf620ce2562dea35766a79a61ef789ec04b15ba07cf27b0c727f7e62551cc22002e940db04b1e37454a69fd2ce8b30a5c8b1b2500644aabbe960 Homepage: https://cran.r-project.org/package=magicrect Description: CRAN Package 'magicrect' (Construct Magic Rectangles and Nearly Magic Rectangles) Constructs a magic rectangle or a nearly magic rectangle of order p x q for every order for which one exists, together with existence classification and verification utilities. A magic rectangle arranges the integers 1 to p*q so that all row sums are equal and all column sums are equal; it exists exactly when p and q have the same parity, excluding 2 x 2 and degenerate single-row/column cases (Hagedorn, 1999, ). When p and q have opposite parity a nearly magic rectangle exists instead, with constant sums along one direction and sums differing by at most one along the other (Chai, Singh and Stufken, 2019, Journal of Combinatorial Designs 27(6), 368-376). Implements the constructions of De Los Reyes, Das, Midha and Vellaisamy (2009) for even by even orders, Chai, Das and Midha (2013) for odd by odd orders, and Chai, Singh and Stufken (2019) for the nearly magic (even by odd) case. Package: r-cran-magma.r Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyverse, r-cran-doparallel, r-cran-foreach, r-cran-metafor, r-cran-robumeta, r-cran-psych, r-cran-ggplot2, r-cran-janitor, r-cran-flextable, r-cran-overlapping, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-stddiff Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-magma.r_1.1-1.ca2404.1_all.deb Size: 501586 MD5sum: 998bf861ef7ba2f54201a8fd01144507 SHA1: d04e5103f3009fa306b8a4c9dabf260b42454543 SHA256: 58e4f6d8eeeb3ad388c3120a76ead1d48b2fc0c249ed7f3d42895ed85e2490be SHA512: 5476abd5d3332900c0ead5b0c61144e770af8bf1dab9d4c72b9ddc646f7ab56a5ee1642f8c11aec338c9745f34b47af6e7c408730aabc151f51777c258451dd6 Homepage: https://cran.r-project.org/package=MAGMA.R Description: CRAN Package 'MAGMA.R' (MAny-Group MAtching) Balancing quasi-experimental field research for effects of covariates is fundamental for drawing causal inference. (Propensity Score) Matching deals with this issue but current techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The MAGMA R-package addresses these restrictions by offering nearest neighbor matching based on Propensity Scores or the Mahalanobis Distance for two to four groups. It also includes the option to match data of a 2x2 design. In addition, MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a General tutorial on MAGMA and a tutorial for Mahalanobis Distance Matching as vignettes. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) . Package: r-cran-magmar Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-jsonlite Suggests: r-bioc-dittoseq, r-bioc-biocstyle, r-cran-vcr, r-cran-webmockr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-magmar_1.0.4-1.ca2404.1_all.deb Size: 425980 MD5sum: 902f432c04cc50d0ca38179d5e6a6fea SHA1: cf2a2b985a5b1e96bb3e68689789b7a9aa5962b3 SHA256: 5caa2c16e579b7d49ad0a670524fa9727aaa3e8668ebe10d7d7e366c8372913a SHA512: 21f2641c76b1020f5771cbccebc3fad81bd3212e754b75fd2875766bb52b164a5140fe636fd73b22bd6237b8aa7dda6c2a6fdab791082a86a925c36b29bb25ee Homepage: https://cran.r-project.org/package=magmaR Description: CRAN Package 'magmaR' (R-Client for Interacting with the 'UCSF Data Library') A client for interacting with 'magma', the data warehouse of the 'UCSF Data Library'. 'magmaR' includes functions for querying and downloading data from 'magma', in order to enable working with such data in R, as well as for uploading local data to 'magma'. Package: r-cran-magnamwar Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-coxme, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-iterators, r-cran-lme4, r-cran-multcomp, r-cran-plyr, r-cran-qqman, r-cran-survival, r-cran-seqinr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-magnamwar_2.0.4-1.ca2404.1_all.deb Size: 1363198 MD5sum: e63be5aa14fcf607360e4bde3d0ce71d SHA1: 4dc5bcf5cd7f126522672f41234cac703312710d SHA256: 5f2f40a6800fd0c98ac2adb277c38c2a981a4a06974e4195bf40f2cd229c7bc0 SHA512: c2ea286d9bdbad7e24b5a4ac02177cbe17d4eb7ba21438c92affd5d634ef88c0e7edffbac5e797d81c45e10fc3c2dc824282d26304fbfacefd0e92e2a20f7520 Homepage: https://cran.r-project.org/package=MAGNAMWAR Description: CRAN Package 'MAGNAMWAR' (A Pipeline for Meta-Genome Wide Association) Correlates variation within the meta-genome to target species phenotype variations in meta-genome with association studies. Follows the pipeline described in Chaston, J.M. et al. (2014) . Package: r-cran-magui Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase Suggests: r-cran-tkrplot, r-cran-gwidgets2, r-bioc-rgraphviz, r-bioc-ssize, r-bioc-biostrings, r-cran-biocmanager, r-cran-rsqlite, r-bioc-geoquery, r-bioc-geometadb, r-bioc-rbgl, r-cran-wgcna, r-bioc-keggrest, r-bioc-kegggraph, r-bioc-pdinfobuilder, r-bioc-lumi, r-bioc-oligo, r-bioc-graph, r-bioc-affy, r-bioc-genefilter, r-bioc-beadarray, r-bioc-gostats, r-bioc-go.db, r-bioc-category, r-bioc-annotate, r-bioc-impute, r-bioc-limma Filename: pool/dists/noble/main/r-cran-magui_4.0-1.ca2404.1_all.deb Size: 82328 MD5sum: 31bfcaa0647d65f5e201c9318b78566e SHA1: c75ee6ad2468b0bcfc64f89522ec7a0c248b1202 SHA256: 1cb726a928224cebcf355660076be9fe4aa1b804c8cd9c8fa46922c30d09e320 SHA512: 12fe62e88cd2cffa242dd203eee45052aab74a737d8dc7c2feb43d5b6fced431df7552f008e240d81e26e67a66db0af8e3eb797e55241f7a756c3bdb1e8e5e3a Homepage: https://cran.r-project.org/package=maGUI Description: CRAN Package 'maGUI' (A Graphical User Interface for Microarray Data Analysis andAnnotation) Provides a comprehensive graphical user interface for analysis of Affymetrix, Agilent, Illumina, Nimblegen and other microarray data. It can perform miscellaneous tasks such as gene set enrichment and test analyses, identifying gene symbols and building co-expression network. It can also estimate sample size for atleast two-fold expression change. The current version is its slenderized form for compatable and flexible implementation. Package: r-cran-maic Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-matrixstats, r-cran-weights Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-maic_0.1.4-1.ca2404.1_all.deb Size: 54644 MD5sum: c22290514410d83f5efb4e0033e87e6f SHA1: bffd9c68d2c9b91648a0900e6274826969ce65bc SHA256: 74b0412ff2ea2818129f78ac17c49eb46d54d986dc8b6daeab4f2fce7205ff66 SHA512: feb57367a06d947aeb942ceb61b8482337a8d6677698f4de0fce50a4479cee6f80935518698b39b645e4692224f27d5572eb0129d1517837891d3bc146d0cd1f Homepage: https://cran.r-project.org/package=maic Description: CRAN Package 'maic' (Matching-Adjusted Indirect Comparison) A generalised workflow for generation of subject weights to be used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et al. (2012) , Signorovitch et al (2010) . In MAIC, unbiased comparison between outcomes of two trials is facilitated by weighting the subject-level outcomes of one trial with weights derived such that the weighted aggregate measures of the prognostic or effect modifying variables are equal to those of the sample in the comparator trial. The functions and classes included in this package wrap and abstract the process demonstrated in the UK National Institute for Health and Care Excellence Decision Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]), providing a repeatable and easily specifiable workflow for producing multiple comparison variable sets against a variety of target studies, with preprocessing for a number of aggregate target forms (e.g. mean, median, domain limits). Package: r-cran-maicchecks Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-ggplot2, r-cran-lpsolve, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maicchecks_0.3.0-1.ca2404.1_all.deb Size: 136608 MD5sum: 8c225303f23e456284ac99883dfde205 SHA1: 51bf86e01be4b843d6ffef46204d4e3b7479a780 SHA256: 19fe07014bf35d27206d898a6b381cd1867694d9b0cacd62b402db14454f393d SHA512: 70122ec7694edb83d6e994232f2ed211723a9bdd8394f9b9b8cd0dd60e4db5d8ceaaf0013e88c7407177a809c3266adb27d76f1953de9ef11c76611ac2d12017 Homepage: https://cran.r-project.org/package=maicChecks Description: CRAN Package 'maicChecks' (Exact Matching and Matching-Adjusted Indirect Comparison (MAIC)) The current version (0.3.0) streamlines the underlying code, adds a feasibility check to 'maicWt' and 'maxessWt', extends 'exmWt.2ipd' with options 'target.ipd' and 'method' for one-sided MAIC weighting between two IPDs, and adds 'wtTrtDiff' for the weighted treatment-effect difference with a Wald confidence interval based on Section 5 of Glimm & Yau (2026). The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2026) ). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2022) .) Package: r-cran-maicplus Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1925 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-lubridate, r-cran-matrixstats, r-cran-mass, r-cran-boot, r-cran-stringr, r-cran-lmtest, r-cran-sandwich Suggests: r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-rmarkdown, r-cran-dplyr, r-cran-survminer, r-cran-flexsurv, r-cran-tibble, r-cran-vdiffr, r-cran-checkmate Filename: pool/dists/noble/main/r-cran-maicplus_0.1.2-1.ca2404.1_all.deb Size: 1440940 MD5sum: c1722ddfa52536124b14de548d7d287d SHA1: add662cd16d551efee674a44a937c3287efaa10e SHA256: 8e571d177845c498ceaeb7e95f396d87ad3e2fc16b6d44c7ad199b1c43c9bd1c SHA512: e2fd07155e05acbfcd50c3760ddee54deab68a798a5f4d1852377f16ed8dba4435e406fbabb5f004549759cf3e33cbd2c94255c0edbd59a1342807ab29335644 Homepage: https://cran.r-project.org/package=maicplus Description: CRAN Package 'maicplus' (Matching Adjusted Indirect Comparison) Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) . Package: r-cran-maictools Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-boot, r-cran-broom, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vim Suggests: r-cran-haven, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-maictools_0.1.1-1.ca2404.1_all.deb Size: 166412 MD5sum: b2c0168779900e830cb39ad6b950e404 SHA1: bb368b7c6149de44ded441d7c2112f6526a46c11 SHA256: f7dc742b1d14384cb919f47df21b369ca289a8cfdbfdfe85a3f171d7a9903a63 SHA512: 5e3750d583061284cfdb80b34a07b457638b918757a5335c0ce9c6b24e1fe45fce4e765f3068d81652d7b3739f58e8b6e5abd31757275c9de81a3fa71274cef1 Homepage: https://cran.r-project.org/package=MAICtools Description: CRAN Package 'MAICtools' (Performing Matched-Adjusted Indirect Comparisons (MAIC)) A generalised workflow for Matching-Adjusted Indirect Comparison (MAIC) analysis, which supports both anchored and non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison is achieved by weighting the subject-level outcomes of the intervention trial so that the weighted aggregate measures of prognostic or effect-modifying variables match those of the comparator trial. Measurements supported include time-to-event (e.g., overall survival) and binary (e.g., objective tumor response). The method is described in Signorovitch et al. (2010) and Signorovitch et al. (2012) . Package: r-cran-maidr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7289 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-curl, r-cran-ggplot2, r-cran-ggplotify, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-shiny, r-cran-svglite, r-cran-xml2, r-cran-rlang, r-cran-r6 Suggests: r-cran-testthat, r-cran-lintr, r-cran-styler, r-cran-goodpractice, r-cran-cyclocomp, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-patchwork, r-cran-scales, r-cran-tibble, r-cran-tidyquant, r-cran-quantmod, r-cran-ttr, r-cran-zoo, r-cran-xts, r-cran-pkgload, r-cran-hexbin, r-cran-vioplot, r-cran-wordcloud, r-cran-sm, r-cran-gridgraphics, r-cran-proc, r-cran-yardstick, r-cran-plotly, r-cran-highcharter, r-cran-echarts4r Filename: pool/dists/noble/main/r-cran-maidr_0.5.0-1.ca2404.1_all.deb Size: 4592836 MD5sum: 970eedaa30ca55c24f85313f285ba8ab SHA1: 61a41e59fcb98c3a27e9b1d9532cb46775b4de1c SHA256: 6bbee18607c2ada9a92679aa459eb97d0ecefb6eacdeec3cfe8becf8f6731693 SHA512: 7df235f16236e5720d787ce6e8b28343209547410eff6cb07a5b7680c3e8dee8c265d4553aebda8b9901ac5e7a32cc61c0c98d1caeafd2a898983e1dfd6efbb7 Homepage: https://cran.r-project.org/package=maidr Description: CRAN Package 'maidr' (Multimodal Access and Interactive Data Representation) Provides accessible, interactive visualizations through the 'MAIDR' (Multimodal Access and Interactive Data Representation) system. Converts 'ggplot2' and Base R plots into accessible HTML/SVG formats with keyboard navigation, screen reader support, and 'sonification' capabilities. Supports bar charts (simple, grouped, stacked), pie charts, histograms, line plots, step plots, scatter plots, box plots, violin plots, candlestick (OHLC) charts, heat maps, density/smooth curves, faceted plots, multi-panel layouts (including patchwork), and multi-layered plot combinations. Also makes 'plotly', 'highcharter' and 'echarts4r' 'htmlwidgets' accessible by attaching the matching 'MAIDR' JavaScript adapter. Enables data exploration for users with visual impairments through multiple sensory modalities. For more details see the 'MAIDR' project . Package: r-cran-maihda Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2758 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-reformulas, r-cran-cli, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-generics, r-cran-rlang, r-cran-patchwork, r-cran-ggrepel, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-future, r-cran-promises, r-cran-plotly, r-cran-haven, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-brms, r-cran-wemix, r-cran-ordinal, r-cran-testthat, r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maihda_0.2.1-1.ca2404.1_all.deb Size: 1880962 MD5sum: ca45196d087193a13dc543207188ae0c SHA1: 5b521ac67d596f19e575491705936418ae881394 SHA256: f90ceb787205142265d6f1ed4480fced5244341392e16a102a21d60661359117 SHA512: fff6d3a00797a2c9fe6b4f03d7b0b818932e02115a27e42d84063e8b40f8cec9ff980bcd1999269fa117bb84b477ac75bb176e6c2b73fed5a10f5c241aa8560d Homepage: https://cran.r-project.org/package=MAIHDA Description: CRAN Package 'MAIHDA' (Multilevel Analysis of Individual Heterogeneity andDiscriminatory Accuracy) Tools for Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for intersectional inequality research. Methods are described in Merlo (2018) and Evans et al. (2018) . The package creates intersectional strata, fits multilevel MAIHDA models, estimates variance partition coefficients, proportional change in variance, stratum effects, and discriminatory-accuracy summaries, and provides diagnostic and presentation plots. Package: r-cran-mailchimpr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-mailchimpr_0.1.0-1.ca2404.1_all.deb Size: 22944 MD5sum: 68ff4d4e66d4e03e127751f140eb0c8c SHA1: 698f1e8635a1e1bb1c7e3e9f67a12bef7b74e11a SHA256: da05ff77c8e55daa42a7e38d2849ebf5808b8647093f87e96d3faa1a2a342710 SHA512: 82241b590c2040f3a6f97ddde74f3b912642559fea55c87e130d5cf33526cdf31400fedba3b95aed597ce5074b7443c01a577c61f1d319f8b3f360d7ebfbd7dc Homepage: https://cran.r-project.org/package=mailchimpR Description: CRAN Package 'mailchimpR' (Get Mailchimp Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Mailchimp using the 'Windsor.ai' API . Package: r-cran-mailmerge Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-commonmark, r-cran-gmailr, r-cran-rstudioapi, r-cran-googledrive, r-cran-rmarkdown, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-fs, r-cran-lifecycle, r-cran-shiny, r-cran-miniui Suggests: r-cran-spelling, r-cran-withr, r-cran-covr, r-cran-knitr, r-cran-testthat, r-cran-here, r-cran-mockery, r-cran-readr Filename: pool/dists/noble/main/r-cran-mailmerge_0.2.5-1.ca2404.1_all.deb Size: 91580 MD5sum: 81560f993aa80c24376f53c9d8401cef SHA1: 8f3790af27c966a055800cfe1a9a92adb5433987 SHA256: 266aa82f56067c366b3f1191bb4551200230ce1668d0c223205ec3f919910799 SHA512: a549ffc3d6a9c30de0be461dd944c03b8f07fc583aac74625362d7f73be7160a9e3084de3c044fc013212ce1b816b2bea21b46bd750d5001f8d3d3b7bff7ffd5 Homepage: https://cran.r-project.org/package=mailmerge Description: CRAN Package 'mailmerge' (Mail Merge Using R Markdown Documents and 'gmailr') Perform a mail merge (mass email) using the message defined in markdown, the recipients in a 'csv' file, and gmail as the mailing engine. With this package you can parse markdown documents as the body of email, and the 'yaml' header to specify the subject line of the email. Any '{}' braces in the email will be encoded with 'glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using 'gmailr'. Package: r-cran-mailr Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-stringr, r-cran-r.utils, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-mailr_0.8-1.ca2404.1_all.deb Size: 742104 MD5sum: a39a456c6a393e83154f928328bdac83 SHA1: 56796048972c84966fe6894b51d114283d9e2fd7 SHA256: e1611c0974312fae641db46d5eec3066e68aae17e35cedb817bdf900dd88fe8b SHA512: dadcb1cf611f85faebe0070a8aee2614615ca924c94034e91513035a4916baa239a0125db17aa7de43c831ff88fa96614d28ca47b1180da9377618fd1ce6403a Homepage: https://cran.r-project.org/package=mailR Description: CRAN Package 'mailR' (A Utility to Send Emails from R) Interface to Apache Commons Email to send emails from R. Package: r-cran-mailtor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue Filename: pool/dists/noble/main/r-cran-mailtor_0.1.0-1.ca2404.1_all.deb Size: 12964 MD5sum: d55bc61a2a423c560b408aff43bca16d SHA1: dc98ea74fe3bbe3bf0c5e60ef0c45e432f30157c SHA256: 41a236567a87bb9edcf2830e3026b4d583fab5df9bc27efa17d387de2ae7a280 SHA512: fbd9a6abc9a72a828eb4d06c59e7384fb4c0a23c5a61d9f61c55d51da8a6249e4b85431ca012361cbe64ab65984a7c7e81f00e7b9f18d32387cf6fb6e52dbe97 Homepage: https://cran.r-project.org/package=mailtoR Description: CRAN Package 'mailtoR' (Creates a Friendly User Interface for Emails Sending in 'shiny') Allows the user to generate a friendly user interface for emails sending. The user can choose from the most popular free email services ('Gmail', 'Outlook', 'Yahoo') and his default email application. The package is a wrapper for the 'Mailtoui' 'JavaScript' library. See for more information. Package: r-cran-mainexistingdatasets Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-golem, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-openxlsx, r-cran-pkgload, r-cran-processx, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-spdata, r-cran-tidyr, r-cran-tmap, r-cran-tmaptools Suggests: r-bioc-bioccheck, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mainexistingdatasets_1.0.2-1.ca2404.1_all.deb Size: 52824 MD5sum: 264fc904979ff9c28d3ee2d0008b994a SHA1: 5bf25637e3cc9e52d14c888eddfacc0c3e562254 SHA256: b1e95dd0662c3fd11336ce30bae9424af960bacb423924384c6d9c4bd446860c SHA512: 3b5590669d64688ff851a99ea1943954fbf67b190db1ba89f0797ccaff4955c7e7ff8c6f5f581b35fee75509ee47b7725e6550911f9e49785dccee96640970b5 Homepage: https://cran.r-project.org/package=MainExistingDatasets Description: CRAN Package 'MainExistingDatasets' (Main Existing Human Datasets) Shiny for Open Science to visualize, share, and inventory the main existing human datasets for researchers. Package: r-cran-maive Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-clubsandwich Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-varhandle, r-cran-metafor Filename: pool/dists/noble/main/r-cran-maive_0.3.0-1.ca2404.1_all.deb Size: 171560 MD5sum: cd4200644da7e4e87050c0bf20990ede SHA1: de1b0082d1e68cc7ad4cc293e50e6980f15a153f SHA256: 19e64abafa36c6f81803036f8e7c08fa46ffee2076c4c71660e8fb70f4d8d3a7 SHA512: d4462bbccb596ed0f8e3026ecf0e02724ec9b8eef443dd6041f94267348a16c4e017cfd690f73f36527de922388a166f04d7340fd13a8e0d2f1ba7de9b4b5d1b Homepage: https://cran.r-project.org/package=MAIVE Description: CRAN Package 'MAIVE' (Meta Analysis Instrumental Variable Estimator) Meta-analysis traditionally assigns more weight to studies with lower standard errors, assuming higher precision. However, in observational research, precision must be estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical significance. This can lead to spurious precision, invalidating inverse-variance weighting and bias-correction methods like funnel plots. Common methods for addressing publication bias, including selection models, often fail or exacerbate the problem. This package introduces an instrumental variable approach to limit bias caused by spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025) . Package: r-cran-majkmeans Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-majkmeans_0.1.0-1.ca2404.1_all.deb Size: 28804 MD5sum: 6ab2eb89aa3560514b71579bdddb7da6 SHA1: b52a3ec574debce0a5c3349c659f4031e6354e76 SHA256: 882b3bcf794866a14786cfcc390f31a946ed976230ded17875d383615a0590df SHA512: d3c6807d5d109d423b9dbfd7ed20467666c817d1f46cd29d943e9d7ce37b9ace955dbf47ed65f442dfdaf47d2c08e69d532e9e221c46f8fcd17f618c5d4da8dd Homepage: https://cran.r-project.org/package=MajKMeans Description: CRAN Package 'MajKMeans' (k-Means Algorithm with a Majorization-Minimization Method) A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) . The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. 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The two most important functions in package 'MajMinKmeans' are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator. Package: r-cran-makedummies Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-makedummies_1.2.1-1.ca2404.1_all.deb Size: 17562 MD5sum: 678f17f1912d9945c3f628626832b7d1 SHA1: e2538a0aaafadb857371fb4703dcc0955c346fd3 SHA256: 6a1c3565aa0e3b147efe23d7960eed5059653ed96917c5a007ee3b20f39913b5 SHA512: a7df23667d1c7a7b9df2e2b6335d2f7cb08efbbb3bcb9f23bb62bc5174553527f227d1b40d0f9b7837b1cddd418a6e97b089d222547c3618aef7b7f1bd98468a Homepage: https://cran.r-project.org/package=makedummies Description: CRAN Package 'makedummies' (Create Dummy Variables from Categorical Data) Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. 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In short: run an R script if underlying files have changed, otherwise do nothing. Package: r-cran-makemyprior Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3885 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-shiny, r-cran-shinyjs, r-cran-shinybs, r-cran-visnetwork, r-cran-rlang, r-cran-mass Suggests: r-cran-rstan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-makemyprior_1.2.2-1.ca2404.1_all.deb Size: 1158960 MD5sum: 3929969fb826b437117afa8580e93213 SHA1: 6ef9d4f18f4e53f67d12fb9950996ff624dde68e SHA256: b21aa45b2201387af8b76e57256f88dca7ff3a382aafd990d4af78f7d01a2b77 SHA512: efb284e859c8d4cd68a6f69c106af05a07d18fd7d97eb56f32a733d5a34b4f26fbb8a3834ccb171cd24f57c80c712526cebf96bd03f108837f41e8034b01efdd Homepage: https://cran.r-project.org/package=makemyprior Description: CRAN Package 'makemyprior' (Intuitive Construction of Joint Priors for Variance Parameters) Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, ). Package: r-cran-makepalette Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-cluster, r-cran-prismatic Filename: pool/dists/noble/main/r-cran-makepalette_0.1.2-1.ca2404.1_all.deb Size: 1101410 MD5sum: 449c798c5f495c87efbb429070406054 SHA1: 0ddc044607310f34733c8f53aa3c5a25c1eca6f8 SHA256: a4101947443e6e0850ab2639c5e790a1ef12a95f8176648026b31f1d5798ae46 SHA512: 020bae15f103ec715ada1a9cbd10657512480bd6f29d0a6641039661dcff9d7201992e0ddcf618dce0e1f9bdd617d312c565362ab319a66bfeae92329d3499ac Homepage: https://cran.r-project.org/package=makePalette Description: CRAN Package 'makePalette' (Make Palette) Functions that allow you to create your own color palette from an image, using mathematical algorithms. Package: r-cran-makepipe Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-nomnoml, r-cran-r6, r-cran-roxygen2 Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-withr, r-cran-rmarkdown, r-cran-webshot2, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-makepipe_0.2.2-1.ca2404.1_all.deb Size: 361644 MD5sum: 481043a4825f610bd8795c98c05669fc SHA1: 779bf4d8934a02dfa90b2216dce30e361ae289c3 SHA256: 7dc23041fb193dbcbdcf85cba995ba922477284ba9e3a4c4ad70653eee43003a SHA512: 7bb0625ec50b3840e25e53e4ed6f08b1ba479ecf30f548337cbb2a8fa5eca0d7ab3aa1687a540eafd014aa0205a3cf90eaa11603be0b877692a5f2fedfb56ee0 Homepage: https://cran.r-project.org/package=makepipe Description: CRAN Package 'makepipe' (Pipeline Tools Inspired by 'GNU Make') A suite of tools for transforming an existing workflow into a self-documenting pipeline with very minimal upfront costs. Segments of the pipeline are specified in much the same way a 'Make' rule is, by declaring an executable recipe (which might be an R script), along with the corresponding targets and dependencies. When the entire pipeline is run through, only those recipes that need to be executed will be. Meanwhile, execution metadata is captured behind the scenes for later inspection. Package: r-cran-makeproject Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-makeproject_1.0-1.ca2404.1_all.deb Size: 14980 MD5sum: ab4fc1aa3ac4254bd18f27bdc2c8536f SHA1: 90f0f6aa81528e22524ed313ceb948e61138c027 SHA256: 72b32bac09931402bb7e0dcbf14f7d2a2cd36e660e03dad7d12abf6995141e82 SHA512: dd37d3e2c63390fdb316d5cac917daf08dc688c19d837e046975b3f72bd29f5b174acbf8967ca3199f17e56e4e3e8aeb2d8e5490ab093493c3275a22da66edf5 Homepage: https://cran.r-project.org/package=makeProject Description: CRAN Package 'makeProject' (Creates an empty package framework for the LCFD format) This package creates an empty framework of files and directories for the "Load, Clean, Func, Do" structure described by Josh Reich. Package: r-cran-maketools Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sys Suggests: r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maketools_1.3.2-1.ca2404.1_all.deb Size: 263748 MD5sum: d26aa2a536ec1297c64f22d84f68d8c9 SHA1: 4640c2237a23def1e2f1e6535a6ed078bbd5f611 SHA256: 4672a7bf82a9503cbeb248494c1cf7a2c6b3adcd8adbea09b41ca695ae618646 SHA512: b0c92348e0165c2052db176abf2fffe047051bb66da59837b92f8f01682920ba99fbeaae5271580496d4138b174a5633980beb518243d69dacc5e445d2f60777 Homepage: https://cran.r-project.org/package=maketools Description: CRAN Package 'maketools' (Exploring and Testing the Toolchain and System Libraries) Helper functions that interface with the system utilities to learn about the local build environment. Lets you explore 'make' rules to test the local configuration, or query 'pkg-config' to find compiler flags and libs needed for building packages with external dependencies. Also contains tools to analyze which libraries that a installed R package linked to by inspecting output from 'ldd' in combination with information from your distribution package manager, e.g. 'rpm' or 'dpkg'. Package: r-cran-makeunique Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-makeunique_1.0.0-1.ca2404.1_all.deb Size: 59110 MD5sum: f4ad9531ed23e52671c75e6290ec9f94 SHA1: 4ab2896d7d3755404ff07a8579e6406c91d2f53e SHA256: cddfb9a168876b16d499cc32b60d64f60dd53a21ac9b3a2ba5f7e6c9eccf3366 SHA512: 787dc06d45ee33da254bd52e42645c77cdc6562f708b387d9be914edc58713faa786a0715a4311e08c83523aea511be466d8be424d8eefbc90d0078043af2e69 Homepage: https://cran.r-project.org/package=makeunique Description: CRAN Package 'makeunique' (Make Character Strings Unique) Make all elements of a character vector unique. Differs from 'make.unique' by starting at 1 and allowing users to customise suffix format. Package: r-cran-makicoint Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-makicoint_2.0.0-1.ca2404.1_all.deb Size: 72010 MD5sum: b6260d4334f6d6dc8e9587399aa35ab7 SHA1: c05432133de68fa3b4710ba9ee43172d96d9f8f3 SHA256: e3c55e52b16b9e944c30f06527f37e71d7422d74158524b4a2402e5f5bbc77ba SHA512: af3347f4c5c2857961181b6f0f889e7171fd92cf3bd78f6b7b467d5d464ab8b18bc037123d07845bc5c6772c1e9cd80b7b89e5ff56699ea9e9c961226c0a9c1f Homepage: https://cran.r-project.org/package=makicoint Description: CRAN Package 'makicoint' (Maki Cointegration Test with Multiple Structural Breaks) Implements the Maki (2012) residual-based test for cointegration allowing for an unknown number of structural breaks. Breaks are located by a sequential procedure and the cointegrating residual is tested for a unit root with an augmented Dickey-Fuller (ADF) regression; the test statistic is the minimum ADF t-statistic over all candidate breaks. Four model specifications are supported (level shift, level shift with trend, regime shift, and regime shift with trend) and one to four regressors. The default engine reproduces the original 'GAUSS' / 'tspdlib' implementation, with an optional break rule following Maki (2012, Steps 2 and 4). The test runs for any feasible number of breaks; beyond the five tabulated by Maki, critical values can be simulated by his Monte-Carlo design. A two-panel diagnostic plot is provided via 'ggplot2'. Package: r-cran-makl Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-auc, r-cran-grplasso Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-makl_1.0.1-1.ca2404.1_all.deb Size: 31452 MD5sum: 72754a8a69db97c5d42c5209be680e1c SHA1: e821da5a5d3264325a3b828f87c659b5b491ed64 SHA256: 2be49a6c30ac668975cca79f72e08ce5a47ede633057bbc9fd281818dae52615 SHA512: 37e09aec868c12541d6c952f4b321c571082211aff77799bddb6f87ce0bb0d3a68dc810b4b02b68f8efc5984eba60d4a5485c74502d0728eddff1edd788379fa Homepage: https://cran.r-project.org/package=MAKL Description: CRAN Package 'MAKL' (Multiple Approximate Kernel Learning (MAKL)) R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in . The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable. Package: r-cran-malani Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-malani_1.0-1.ca2404.1_all.deb Size: 39272 MD5sum: 63d79390709f615c70de37b7aeb21157 SHA1: 2b7602af3ddbe1dc2e847f28a2d992429d65a9c8 SHA256: 8f0875cb34b12e075f039c758882c083eb89a005895c394eed286ce12d74d415 SHA512: f1e1d3fcdcd8bd515cf06759669b8b6932164a30ec495472565294669faa62179fbb4dd04cc3670ca80fda4d827351ca80ea141f1b1e9a716baa79b0cab567b3 Homepage: https://cran.r-project.org/package=malani Description: CRAN Package 'malani' (Machine Learning Assisted Network Inference) Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks. Package: r-cran-malariaatlas Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xml2, r-cran-gridextra, r-cran-httr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-sf, r-cran-lifecycle, r-cran-terra, r-cran-tidyterra, r-cran-ows4r, r-cran-future.apply, r-cran-lubridate, r-cran-jsonlite, r-cran-stringr, r-cran-ggnewscale Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-palettetown, r-cran-magrittr, r-cran-tibble, r-cran-rdhs Filename: pool/dists/noble/main/r-cran-malariaatlas_1.7.0-1.ca2404.1_all.deb Size: 2111606 MD5sum: 57c0f13b3de6f9b195d145550bd41717 SHA1: bd54c0dbbbc1a40998f6970cca24f080728263e9 SHA256: 84f6508019f0cf246662974d39e5e5e92a139cad91413c3cdf498970029b914f SHA512: 70ff7e99ba49aae59ac0337e1e333a22a0829fd02ba6b7654f5eed533b383f9a48f52c89d3624d029da8f424f2ad43b35269aed33121cffb45714da9430a38c9 Homepage: https://cran.r-project.org/package=malariaAtlas Description: CRAN Package 'malariaAtlas' (An R Interface to Open-Access Malaria Data, Hosted by the'Malaria Atlas Project') A suite of tools to allow you to download all publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from the 'Malaria Atlas Project' servers as well as utility functions for plotting the downloaded data. Package: r-cran-malaytextr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-malaytextr_0.1.3-1.ca2404.1_all.deb Size: 79808 MD5sum: 93a7ff14156681e83c9df1ed448bcf00 SHA1: cc00863cf5d9c59aacc56faad8eebaac62e9d5e4 SHA256: cfb8b4f304e74d41cf6f464a1688a7593024f794e48cd4e2787cd8952f962500 SHA512: 6403050410c6ab4b3d188aef7263249d7a02c4188c0c82a71d51c0353ed756b138547f9ebfa71c1af632bc21d0cb36c0e496730aa50233cea8ad242c30275d71 Homepage: https://cran.r-project.org/package=malaytextr Description: CRAN Package 'malaytextr' (Text Mining for Bahasa Malaysia) It is designed to work with text written in Bahasa Malaysia. We provide functions and data sets that will make working with Bahasa Malaysia text much easier. For word stemming in particular, we will look up the Malay words in a dictionary and then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi (2017) . This package includes a dictionary of Malay words that may be used to perform word stemming, a dataset of Malay stop words, a dataset of sentiment words and a dataset of normalized words. Package: r-cran-maldicellassay Architecture: all Version: 0.4.47-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4939 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-nplr, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-scales, r-cran-maldiquant, r-cran-maldiquantforeign, r-cran-tibble, r-cran-svmisc, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-maldicellassay_0.4.47-1.ca2404.1_all.deb Size: 4499200 MD5sum: 27a14de7a75204164b266db98719bcd9 SHA1: a4e705e0f3e04a64495ca76da2b34b0fdfba17ed SHA256: e9c14c102c2bbf99358154f9e228eb992d74ef1323f069e74f67be8cde6eed48 SHA512: acfcbf271789ebdd4df7deb75e086c8dd7072ac9536f30ded2567b0162c46d7041018bfb97209e64e67ee4142bd46efd198520ecec3d2651aae4bed184c86517 Homepage: https://cran.r-project.org/package=MALDIcellassay Description: CRAN Package 'MALDIcellassay' (Automated MALDI Cell Assays Using Dose-Response Curve Fitting) Conduct automated cell-based assays using Matrix-Assisted Laser Desorption/Ionization (MALDI) methods for high-throughput screening of signals responsive to treatments. The package efficiently identifies high variance signals and fits dose-response curves to them. Quality metrics such as Z', V', log2FC, and CRS are provided for evaluating the potential of signals as biomarkers. The methodologies were introduced by Weigt et al. (2018) and refined by Unger et al. (2021) . Package: r-cran-maldipickr Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3362 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-maldiquant, r-cran-readbrukerflexdata, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-coop, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maldipickr_1.3.3-1.ca2404.1_all.deb Size: 2127274 MD5sum: 7354caf876d63be971461c7160ddff4d SHA1: 51f59820fe945053fbc3d35ae2e5bd7886589245 SHA256: f3d911640e82526bc2f15b1d37cd8db7b7d5b975b5948d504392973f359601e0 SHA512: c129436b9824d8c9603b34a80398df98df2fa54733787d861f8f9f2b932e0e6ed48a70da65195374d1562acc2243b9a17bc52432999d717cce6c9133dcc87ae2 Homepage: https://cran.r-project.org/package=maldipickr Description: CRAN Package 'maldipickr' (Dereplicate and Cherry-Pick Mass Spectrometry Spectra) Convenient wrapper functions for the analysis of matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra data in order to select only representative spectra (also called cherry-pick). The package covers the preprocessing and dereplication steps (based on Strejcek, Smrhova, Junkova and Uhlik (2018) ) needed to cluster MALDI-TOF spectra before the final cherry-picking step. It enables the easy exclusion of spectra and/or clusters to accommodate complex cherry-picking strategies. Alternatively, cherry-picking using taxonomic identification MALDI-TOF data is made easy with functions to import inconsistently formatted reports. Package: r-cran-maldiquantforeign Architecture: all Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maldiquant, r-cran-base64enc, r-cran-digest, r-cran-readbrukerflexdata, r-cran-readmzxmldata, r-cran-xml Suggests: r-cran-knitr, r-cran-testthat, r-cran-rnetcdf Filename: pool/dists/noble/main/r-cran-maldiquantforeign_0.14.1-1.ca2404.1_all.deb Size: 375636 MD5sum: 21026c5296fb692523d9d7d8f3225801 SHA1: cd3c3e72b6e4807e5fd228c47b039516fb65f43b SHA256: c5d4eaaeaa33917517f8cb9e1d922bba347f92b455e4c364af11215e097aa19d SHA512: 998709d00e9dc40729b4017ca88220381ba21ae606e3081487c76432ac75cf13bac16c6dc21348146a68fd0a057115d3d18c74f802dd861c7f2edaa191524dfb Homepage: https://cran.r-project.org/package=MALDIquantForeign Description: CRAN Package 'MALDIquantForeign' (Import/Export Routines for 'MALDIquant') Functions for reading (tab, csv, Bruker fid, Ciphergen XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and writing (tab, csv, mMass MSD, mzML, imzML) different file formats of mass spectrometry data into/from 'MALDIquant' objects. Package: r-cran-maldirppa Architecture: all Version: 1.1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maldiquant, r-cran-signal, r-cran-robustbase, r-cran-lattice, r-cran-waveslim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maldirppa_1.1.0-3-1.ca2404.1_all.deb Size: 1548596 MD5sum: e8875059c59171bf961072d1ea7e962f SHA1: e50c48c95f6c896adf0360462c0873a30bbf8a1d SHA256: 88c91e7050214288c54497506141866318e56e25f08aa81c61cad111d71b4cb4 SHA512: fa96cac57bdbcebc832e0c915bd5474e38e5cebb3085121b89b7dc314c23e645889920372c3c9a9771d4af2dc01470a03c215b899d34dcd7418e80f674e587e8 Homepage: https://cran.r-project.org/package=MALDIrppa Description: CRAN Package 'MALDIrppa' (MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis) Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) ). Package: r-cran-mall Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ellmer, r-cran-fs, r-cran-glue, r-cran-jsonlite, r-cran-ollamar, r-cran-rlang Suggests: r-cran-dbplyr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mall_0.2.0-1.ca2404.1_all.deb Size: 124988 MD5sum: 4e8dbb98a024a5c83350ff382e45310e SHA1: f94c58d23130cd7b3e7fa2a5c22a966af1c81a60 SHA256: 69a9eb455906a2fd68dba83a61b5d6d7fa8f8fb37d9c2004ee9b6c816f39a6df SHA512: 88925620dc68441d45243bf042c864ebbcbb69d9dcc56c4ed159244fcad9b6b2be61692d4beb43af758d539436855c2b46b1a8c0f4c523c6173fab5556ab1e16 Homepage: https://cran.r-project.org/package=mall Description: CRAN Package 'mall' (Run Multiple Large Language Model Predictions Against a Table,or Vectors) Run multiple 'Large Language Model' predictions against a table. The predictions run row-wise over a specified column. It works using a one-shot prompt, along with the current row's content. The prompt that is used will depend of the type of analysis needed. Package: r-cran-mallet Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mallet_1.3.0-1.ca2404.1_all.deb Size: 3955758 MD5sum: d689785bee1544a9600b27d441662c32 SHA1: 57c3e0ea181b0c4ae2af71b6330fbcb0be315b92 SHA256: 6263e326000f5d92c91866acdcdfd6658e3aa993ef1f992478205d51c8535d6d SHA512: 4e4cb9a81774b4438cd4ac3a9310025063041283206969afddcb748cb7333436aa9ba5cb68862f62348e5bb075066c317adc1175a96fc31b1601feda03f3ffeb Homepage: https://cran.r-project.org/package=mallet Description: CRAN Package 'mallet' (An R Wrapper for the Java Mallet Topic Modeling Toolkit) An R interface for the Java Machine Learning for Language Toolkit (mallet) to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects, run the Java implementation of mallet directly in R, and extract results as R objects. The Mallet toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the rJava package to connect to a JVM. Package: r-cran-malp Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-boot Filename: pool/dists/noble/main/r-cran-malp_1.1-0-1.ca2404.1_all.deb Size: 77046 MD5sum: 35f9136d0857531f4288f02ea1c6d613 SHA1: 75e2ad0edfca19a0e714d5c66553df68ac64dd0a SHA256: 3209d9b8ed7cd46a93f7d7399ce6d5667da4b418dde9b9c1801f557525053789 SHA512: dd5d51046ef2716ca9b554524bd7ebc30dd20e3abf7a9eb8e0c39716d95cc6663f5c45fd281abe010c741ff6875cbf4002dd7cb7207aabc19fae85eabb894838 Homepage: https://cran.r-project.org/package=malp Description: CRAN Package 'malp' (Maximum Agreement Linear Prediction) Provides tools for estimation and prediction using Maximum Agreement Linear Predictors (MALPs). MALPs provide an alternative to least squares linear predictors when agreement between predicted and observed values, as measured by Lin's Concordance Correlation Coefficient (CCC), is of primary interest. Applications include missing value imputation and calibration studies. The package includes functions for model estimation, prediction, statistical inference, cross-validation, and model diagnostics. The implemented methodology is described in Kim et al. (2026) . Package: r-cran-malvinas Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-malvinas_0.1.0-1.ca2404.1_all.deb Size: 280252 MD5sum: 84ba9c29729c7b2dd3ae586eb5e63121 SHA1: 65b29221e8a442fe5f00f25206fcc7ee693501a7 SHA256: 2486876c6de1f9e2fc245a71b62f62fdd7ee6908a2d2cc11cb7953380f27a475 SHA512: ecb1a3c6acaa82ff6c1aea4fd1bc7836aa7097883ee69657f88c283b572e1260dc02d36e4be1ef6c133be6771222f7bc7416f292f82e7e790ee620c0dd58b16e Homepage: https://cran.r-project.org/package=malvinas Description: CRAN Package 'malvinas' (Islas Malvinas, Georgias Del Sur y Sándwich Del Sur) Data sets related to the Islas Malvinas /// Sets de datos relacionados a las Islas Malvinas - La Nación Argentina ratifica su legítima e imprescriptible soberanía sobre las islas Malvinas, Georgias del Sur y Sándwich del Sur y los espacios marítimos e insulares correspondientes, por ser parte integrante del territorio nacional. La recuperación de dichos territorios y el ejercicio pleno de la soberanía, respetando el modo de vida de sus habitantes y conforme a los principios del Derecho Internacional, constituyen un objetivo permanente e irrenunciable del pueblo argentino. Package: r-cran-mammalcol Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-sf, r-cran-geodata Suggests: r-cran-dplyr, r-cran-finch, r-cran-knitr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mammalcol_0.2.9-1.ca2404.1_all.deb Size: 607142 MD5sum: 9195fe19d424579e7045407a0e8209a3 SHA1: f1a09537479f58bec951d81efa111d677200f14b SHA256: 8e97b1ff140def69fb44e8b506c7705bd2f38efb916f5cabbeae81d13562674a SHA512: 4a4cca76caccf16b40a14ba2b43b2dc4d3d50f6294ca8882672eb7273b3d0128b46bb9dc19812ceb997c5ff10cf58cd23d2b969a7d8109a0b9ae667e2fd57c4c Homepage: https://cran.r-project.org/package=mammalcol Description: CRAN Package 'mammalcol' (Access to the List of Mammal Species of Colombia) The goal of 'mammalcol' is to provide easy access to a meticulously structured dataset of Colombian mammal species in R. The 2025 update includes comprehensive, detailed species accounts, and distribution information. Package: r-cran-managedcloudprovider Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dockerparallel, r-cran-adagio, r-cran-jsonlite Suggests: r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr Filename: pool/dists/noble/main/r-cran-managedcloudprovider_1.0.0-1.ca2404.1_all.deb Size: 372838 MD5sum: de7c2481e7cd36f66d791151eafa8eba SHA1: 09f351cb4aae6e18178a6ea35e670d0da31b158d SHA256: b532bbb5a58bad92bca9f58019e503c7f25c8f3624addbadcc1126659f32501f SHA512: e7faee5d52c5bd6d842b76e6ede8874d063b78ceaa207cb9759da9504d345d47b96f890848273ab0aafcd790f38d186944e21d342851bcea4b580121df887555 Homepage: https://cran.r-project.org/package=ManagedCloudProvider Description: CRAN Package 'ManagedCloudProvider' (Providing the Kubernetes-Like Functions for the Non-KubernetesCloud Service) Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of the 'CloudProvider' class in the 'DockerParallel' package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from 'ManagedCloudProvider' and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and . Package: r-cran-managelocalrepo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-managelocalrepo_0.1.5-1.ca2404.1_all.deb Size: 24714 MD5sum: 336e84452aebedae29ce446a9b75e048 SHA1: 26136dce5560bd1e8afeee53656b0ece9d176123 SHA256: 9abb280ad10e7607ae29d83f438e433349c186f975185264bb4952ee0720e156 SHA512: 7a00e3903e8557a9f5ad787bf5f15b45aa4684c4437add1086d777fea9a65d10d4cf77a5841f4609c341ae049d84f257c756bcdab72ba4984e492086d6675704 Homepage: https://cran.r-project.org/package=managelocalrepo Description: CRAN Package 'managelocalrepo' (Manage a CRAN-Style Local Repository) This will allow easier management of a CRAN-style repository on local networks (i.e. not on CRAN). This might be necessary where hosted packages contain intellectual property owned by a corporation. Package: r-cran-mancie Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mancie_1.4-1.ca2404.1_all.deb Size: 572502 MD5sum: b2f7e7815298998f74c3c2e5e8893cb6 SHA1: b20e6b0192215728a2b15dcfa09b39a4a77f450e SHA256: 1c4453cc52bd08358fb638e76adf82d13187363e63d433f3b29b4d4645cfb210 SHA512: 2efb0b369f6bc3d23211edaffd4c11c84713aaec83d66229ad9624c0dfb9190b3cbe532ff5804d11de01e7d0aaa4dc2b7f5e0ca3cbb888e66b43534ac169f643 Homepage: https://cran.r-project.org/package=MANCIE Description: CRAN Package 'MANCIE' (Matrix Analysis and Normalization by Concordant InformationEnhancement) High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach. Package: r-cran-mand Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7678 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-oro.nifti Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-mnormt, r-cran-msma Filename: pool/dists/noble/main/r-cran-mand_3.0-1.ca2404.1_all.deb Size: 1047700 MD5sum: 399f58c32addc603f047e56f00952f87 SHA1: bad413de8ce3c6d94911594ba751e2192574f9a3 SHA256: 9eadc4bdc94b8204134d548c3f974d2b8e61efac45c1eee36bcae9874ffbf4c4 SHA512: b0c0187100f18e840cdc293d39da1a365565e73a986538d724da2c71e528883188b7db059a20ac401a245223272b0e925907220c61c6b5d212bae7774ba93643 Homepage: https://cran.r-project.org/package=mand Description: CRAN Package 'mand' (Multivariate Analysis for Neuroimaging Data) Provides functions for multivariate analysis and visualization of neuroimaging data. The package contains the functions and example data used in the book 'Multivariate Analysis for Neuroimaging Data' by Kawaguchi (2021, ISBN: 978-0367255329). It includes utilities for image visualization, image data matrix construction, basis reconstruction, multicomponent visualization, predictive modeling, simulation, and multiblock analysis. Version 3.0 preserves the public interfaces used in the accompanying package vignettes. Package: r-cran-mandalar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mandalar_0.1.0-1.ca2404.1_all.deb Size: 49648 MD5sum: fb2b300c89b108997358512cd9bdaf1b SHA1: be2739269301231557330cdbe77ddfe39ff59658 SHA256: 0a4b2a0c8ac32a188f166ea41757cc8d67f783c00089730eefa8b319c65aa817 SHA512: 7485d3e2a072bf09b7cda14ffc62dfb237d00f2d61163fc0f6ef9abceeadd506aee0c0c45b5839b7cecd6182ba78ab7a767a2c9200d104be5437dbd7c0d2fb58 Homepage: https://cran.r-project.org/package=MandalaR Description: CRAN Package 'MandalaR' (Building Mandalas from Parametric Equations of Classical Curves) Provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal. Package: r-cran-manet Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-combinat, r-cran-igraph, r-cran-mclust Filename: pool/dists/noble/main/r-cran-manet_2.0-1.ca2404.1_all.deb Size: 50968 MD5sum: a965859b1cc6544c3e27a63b0d9e7be1 SHA1: 9cc94b90dbc9730f98f0d8a10dc778db0694720b SHA256: 4a475a5fd7cb981d4e1ffe76ad5ccb0356999209321cc93e00b9c149c86e4299 SHA512: 5485b9184a30e192ab878f82a05f7b4e6d534a21f3aed9a72312e94a301bfb7fecf7d3fe0895d79ed30caf9eff0c188fce4b1a3ee3c40d40cbaaedf123232d82 Homepage: https://cran.r-project.org/package=manet Description: CRAN Package 'manet' (Multiple Allocation Model for Actor-Event Networks) Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted. Package: r-cran-mangrove Architecture: all Version: 1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-kinship2 Filename: pool/dists/noble/main/r-cran-mangrove_1.21-1.ca2404.1_all.deb Size: 344698 MD5sum: 1befe50a2010507b2589c95adc165895 SHA1: c5e9a48bcce3b13ff4f4bc4d389c19e77c407694 SHA256: 49ddac93399f9d34d064dcf53fa011021f09dcb668054dcf61d8415c1fe1af0f SHA512: ad26b502ab6116b85bcabb675f498049802c0546e54ead5540fc4218cdc117ba90e5765225cf29635c5444b9f3df8a07dee668e9c870e499746381592058da6b Homepage: https://cran.r-project.org/package=Mangrove Description: CRAN Package 'Mangrove' (Risk Prediction on Trees) Methods for performing genetic risk prediction from genotype data. You can use it to perform risk prediction for individuals, or for families with missing data. Package: r-cran-manhattanly Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-plotly, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-manhattanly_0.3.0-1.ca2404.1_all.deb Size: 431526 MD5sum: ccc958746e08d91d23fc0fd469ef50e6 SHA1: c89eabc12fcea2c5780b097b7fd132f41915f555 SHA256: 2ad8c1709c5ae351d7d78b99a2564b5b36d11e8c14e7d1808624b221cc217306 SHA512: 45d50a4c918836938de419f14a822964c35154e6aed503c93093a2a9935c4a01ec0b2afae7ac2743d8f2d8b0c54bb9d5a769ddcafac537afeecf8e1ad7896ac1 Homepage: https://cran.r-project.org/package=manhattanly Description: CRAN Package 'manhattanly' (Interactive Q-Q and Manhattan Plots Using 'plotly.js') Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a point to show details or drag a rectangle to zoom. A manhattan plot is a popular graphical method for visualizing results from high-dimensional data analysis such as a (epi)genome wide association study (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter plot against their genomic position. Manhattan plots are used for visualizing potential regions of interest in the genome that are associated with a phenotype. Interactive manhattan plots allow the inspection of specific value (e.g. rs number or gene name) by hovering the mouse over a cell, as well as zooming into a region of the genome (e.g. a chromosome) by dragging a rectangle around the relevant area. This work is based on the 'qqman' package and the 'plotly.js' engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq' functions in the 'qqman' package, with the advantage of including extra annotation information and interactive web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser. Package: r-cran-manhplot Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3516 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra Suggests: r-cran-r.utils, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manhplot_1.1-1.ca2404.1_all.deb Size: 962846 MD5sum: 9f9d3241e57b0ab2ba017bed944864c3 SHA1: d4dff2839a1c22c1e1c2cf83446abb1f9ca54959 SHA256: 724eb8206c1304c9f513aaae4e27b43d61eafeb75e9c8cbfec46f425a6e1cf99 SHA512: 43a4a54c0977a7b2b01baf1468907beef5d6811511afa74545072bf4c9771b4ace43d1bdece76a795104134828690a08ebf6264913b2aab81686b3611e4a8711 Homepage: https://cran.r-project.org/package=manhplot Description: CRAN Package 'manhplot' (The Manhattan++ Plot) This plot integrates annotation into a manhattan plot. The plot is implemented as a heatmap, which is binned using -log10(p-value) and chromosome position. Annotation currently supported is minor allele frequency and gene function high impact variants. Package: r-cran-manifesto Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-pak, r-cran-tomledit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manifesto_0.0.2-1.ca2404.1_all.deb Size: 139810 MD5sum: 142c6c933179eb56253f61f96636dbdd SHA1: eaa4557a394a74f29056254a113c65af4f68a35d SHA256: 02f022a3402d66a5787f328f5479a39074600e3a8836cc9d66deed901ed7f8e3 SHA512: 6782e2d62a8c8ffb92b2aa57f5739937d9c93915488a262a766aed79edd91411f2b8dfe5d2cf456c5d2f24a361a6876e444b7ce9d62f26a42a6a64ee5cca7d15 Homepage: https://cran.r-project.org/package=manifesto Description: CRAN Package 'manifesto' (Create Project Manifest Files) Provides 'TOML' representations of packages needed that must be installed to run a project. 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The Manifesto Project collects and analyses election programmes across time and space to measure the political preferences of parties. The Manifesto Corpus contains the collected and annotated election programmes in the Corpus format of the package 'tm' to enable easy use of text processing and text mining functionality. Specific functions for scaling of coded political texts are included. Package: r-cran-manipulate Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-manipulate_1.0.1-1.ca2404.1_all.deb Size: 45196 MD5sum: 821e7f95e2fa0036dc25228200514192 SHA1: 64077b633c2fe15c1a03c30a9545a4ed9a5ad5b4 SHA256: 926f000c67d6123e286fb3460d8f9be5bd3ad840b1faa4686c9fb19ddb90e33b SHA512: 01f39f8545ac9e3167ddad4c70bf2d6e23162db5e09f142aad064c327f2e5bdbc24232adc00d57dfcb9ea4f3b63f58f1e55fdc3c960fae7c93d003f41acbdaee Homepage: https://cran.r-project.org/package=manipulate Description: CRAN Package 'manipulate' (Interactive Plots for RStudio) Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. When a value is changed using its corresponding control the expression is automatically re-executed and the plot is redrawn. Package: r-cran-manipulatewidget Architecture: all Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-base64enc, r-cran-codetools, r-cran-webshot, r-cran-shinyjs Suggests: r-cran-dygraphs, r-cran-leaflet, r-cran-plotly, r-cran-xts, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-manipulatewidget_0.11.2-1.ca2404.1_all.deb Size: 2351204 MD5sum: 561fcaf6b4cf7d441d1d78467cc03bde SHA1: dc8cfecde9e374ddb2a94320e6be53c8bf565a24 SHA256: 875e90870804b62b56bdd430ec2f367eae4c215d14cbe7cd254a732cda3e5b0b SHA512: 209aafbe20334e429ca27951f5c408d94074e6f89de892f8c027ccacc7993fac129ccda381f5ebf01185735ea5d25f36ecdd093aa657a43ebbb240c903d82156 Homepage: https://cran.r-project.org/package=manipulateWidget Description: CRAN Package 'manipulateWidget' (Add Even More Interactivity to Interactive Charts) Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package 'htmlwidgets'. Package: r-cran-mannwhitneycopula Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-tidyverse, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-mannwhitneycopula_0.1.1-1.ca2404.1_all.deb Size: 65922 MD5sum: 4d176a9abc95b8e83c06f3517f7eefea SHA1: 0bda9b48f4d1b102dd12a05aa3d042f737a8d967 SHA256: ccdbd6cbd0b766257d27505401f57e9653141485ba6570b196d1783df85385df SHA512: c9a0ec1adfc07bd76ecc5fe159c4e2a1d3ccd29cb534f69f572e5164329bb92025fe0fe9b144dbd3ab3e1e00ce186663faee389a7289c047bb5ff95c64cd2d23 Homepage: https://cran.r-project.org/package=MannWhitneyCopula Description: CRAN Package 'MannWhitneyCopula' (Computing Mann-Whitney Effect Based on Copulas) Computing the Mann-Whitney effect based on copula models. Estimation of the association parameter in survival copula models. A description of the underlying methods is described in Nakazono et al. (2024) and Nakazono et al. (accepted for publication in Statistical Papers). Package: r-cran-manorm2 Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locfit, r-cran-scales, r-cran-statmod Suggests: r-cran-gplots, r-cran-desctools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-manorm2_1.2.2-1.ca2404.1_all.deb Size: 4001014 MD5sum: 6f01c34a955ba3fbb3ca7fe15ef0ee4a SHA1: 8ba447b5a3607847a8ca4bdc9c57c68bdc23a108 SHA256: 5fba6c33b7e467e3ee555da23c3482c8eaaee9d566311940b07939b591f5edc0 SHA512: 3c1ebf88077a6a8f2a3c449fe52bcdb8044bd3ec5074b8b95336bcfd0d7adf760aed52c2502fc34523604c86d95b655c61e2e20bfa8e052ce2fa35e5f8c03879 Homepage: https://cran.r-project.org/package=MAnorm2 Description: CRAN Package 'MAnorm2' (Tools for Normalizing and Comparing ChIP-seq Samples) Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) and Chen et al. (2022) for associated statistical details. Package: r-cran-manova.rm Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1114 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-matrix, r-cran-magic, r-cran-plotrix, r-cran-ellipse, r-cran-multcomp, r-cran-mvtnorm, r-cran-data.table Suggests: r-cran-nparld, r-cran-knitr, r-cran-rmarkdown, r-cran-hsaur3, r-cran-tidyr, r-cran-ggplot2, r-cran-gfd, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manova.rm_0.6.0-1.ca2404.1_all.deb Size: 542776 MD5sum: 2037f4447a9706d263a8a619f02e1295 SHA1: 5023a11b84d0d35c30a30e00f8600e0db1004f04 SHA256: 502fbabe4cfa9b8199bbb0aa6e502316ae7f30c88db222fc13169f565cf443cc SHA512: 5439259456415ca09b921a81c19b0ea42c2701604c78e440dfbc3f0976261b6b86c42c93d5be38668b71323c4154012d0b3683d79ec1070ea3728161ffa239ee Homepage: https://cran.r-project.org/package=MANOVA.RM Description: CRAN Package 'MANOVA.RM' (Resampling-Based Analysis of Multivariate Data and RepeatedMeasures Designs) Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) . Package: r-cran-mantaid Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biomart, r-cran-caret, r-cran-keras, r-cran-mlr3tuning, r-cran-mlr3, r-cran-ggplot2, r-cran-data.table, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-ggcorrplot, r-cran-reshape2, r-cran-scutr, r-cran-paradox, r-cran-rcolorbrewer, r-cran-purrr, r-cran-dplyr Suggests: r-cran-mlr3hyperband, r-cran-mlr3learners, r-cran-ranger, r-cran-rpart, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mantaid_1.0.4-1.ca2404.1_all.deb Size: 273424 MD5sum: 6e938ddaac60aac90ba9cb2818b26ee6 SHA1: ecd976e1153ab983f982c579e74012f49c18985c SHA256: 9017c018093e074adf52c9da6204925d0c9bc94bfb36f7d69676bd8ff15e45c3 SHA512: e104d63fc326c7c586adf02f6b6e42aa15e1c74c0a0a68a551b137f24f59743466fc671bf45c75aa0a3bc3ad8af303a714c2982c9bcd405da46108c96090ecaa Homepage: https://cran.r-project.org/package=MantaID Description: CRAN Package 'MantaID' (A Machine-Learning Based Tool to Automate the Identification ofBiological Database IDs) The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases. Package: r-cran-mantar Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdpack, r-cran-mathjaxr, r-cran-matrix, r-cran-glassofast Suggests: r-cran-numderiv, r-cran-mice, r-cran-lavaan, r-cran-qgraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mantar_0.3.1-1.ca2404.1_all.deb Size: 383542 MD5sum: 4eaa5deb93cc72ac53d3f7b9ddddb512 SHA1: 8a6aeda05bd96e38f072926f6bbfb78341f0cbe0 SHA256: 576f900067f2c472ae538846ece3c76f398d6f3bec6b1363479e96bfb0f8ce6a SHA512: df6087afe97ed2efe4433bc9330790d5219b44f4967573915402a2eefd0046a7ad8305095b0aa1fb65d688a86c1ea5c3c3304215341964fc71263b7a025f61c2 Homepage: https://cran.r-project.org/package=mantar Description: CRAN Package 'mantar' (Missingness Alleviation for Network Analysis) Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025) and Nehler and Schultze (2026) . Deletion-based approaches are also available but play a secondary role. Package: r-cran-mantis Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr, r-cran-reactable, r-cran-dplyr, r-cran-tidyr, r-cran-dygraphs, r-cran-xts, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-htmltools, r-cran-lubridate Suggests: r-cran-covr, r-cran-testthat, r-cran-codemetar, r-cran-vdiffr, r-cran-shiny, r-cran-diffviewer, r-cran-withr Filename: pool/dists/noble/main/r-cran-mantis_1.0.2-1.ca2404.1_all.deb Size: 1146182 MD5sum: c93655c71241d6773497e646d095581d SHA1: 7d06b73de9888be750b67a1a68e14d2cdde75f33 SHA256: 0d90e4880a5302cb26c8346d07a76db2d8401da45f6bd2229f4daf5c388aa8e8 SHA512: e02e3c11a6c6a7947a9837c92ee26b900852c80f5d9e2d1114d6a70c7a80cf527eb9cb89133cbeefd5f1cdf6460ba6d400064097346f9f602b7f943ff1e1564d Homepage: https://cran.r-project.org/package=mantis Description: CRAN Package 'mantis' (Multiple Time Series Scanner) Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server. Package: r-cran-manureshed Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-ggplot2, r-cran-tidyr, r-cran-jsonlite, r-cran-rlang, r-cran-magrittr, r-cran-scales, r-cran-igraph, r-cran-tigris Suggests: r-cran-nhdplustools, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-ggpubr, r-cran-viridis, r-cran-testthat, r-cran-knitr, r-cran-ragg, r-cran-progress, r-cran-rmarkdown, r-cran-shiny, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt, r-cran-exactextractr, r-cran-ggrepel, r-cran-terra Filename: pool/dists/noble/main/r-cran-manureshed_0.1.5-1.ca2404.1_all.deb Size: 615364 MD5sum: 4527a7981a5199e2364169fbcf38fe86 SHA1: 8024a98336e6d9dc6ba2046072590d776bd39928 SHA256: c480148fec78f5681be32c0363343f0f1e62588d4acd5ef13f9f2dd07ca9eec9 SHA512: 724d1951e07c8d4967910b47b6c02fb1795e2d1d5cc29518d85f8069efb1baea061140e3816797790341d74f0c512195773bce2ee43d3dd449da8d27c206957a Homepage: https://cran.r-project.org/package=manureshed Description: CRAN Package 'manureshed' (Spatiotemporal Nutrient Balance Analysis Across Agricultural andMunicipal Systems) A comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, 'HUC8', 'HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the 'ECHO' Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) . Data are downloaded on demand from the Open Science Framework ('OSF') repository to minimize package size while maintaining full functionality. The integrated 'manureshed' framework methodology is described in Akanbi et al. (2025) . Designed for nutrient management planning, environmental analysis, and circular economy research at watershed/administrative to national scales. This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. We thank Dr. Robert D. Sabo (U.S. Environmental Protection Agency) for his valuable contributions to the conceptual development and review of this work. We acknowledge Dr. Sheri Spiegal (U.S. Department of Agriculture–Agricultural Research Service) for foundational contributions to the manureshed classification framework (Spiegal et al. 2020) . Package: r-cran-manydata Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-messydates, r-cran-caret, r-cran-dtplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-remotes, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggvenndiagram, r-cran-rlang, r-cran-text2vec Filename: pool/dists/noble/main/r-cran-manydata_1.1.4-1.ca2404.1_all.deb Size: 1623486 MD5sum: 334699f3a45de91ae995a23a148e3b17 SHA1: 09da81b0b1b34ce49b769fb31d4672163dcf0f6e SHA256: 30f42671878efbc2bfc00828573cb5795019dccd6b99fa8fad549a04198d930b SHA512: 96eb2426133010ad80361fdf0d9fe2f1d6f82a0829a9f3576c36966ce30500c30b501e2820f64971a1a436ba0f73ee42c936f0e6938d943211d0147725150793 Homepage: https://cran.r-project.org/package=manydata Description: CRAN Package 'manydata' (Many Global Governance Datacubes) This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes. Package: r-cran-manydist Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2456 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aricode, r-cran-cluster, r-cran-clustergeneration, r-cran-data.table, r-cran-dials, r-cran-distances, r-cran-dplyr, r-cran-entropy, r-cran-fastdummies, r-cran-forcats, r-cran-fpc, r-cran-generics, r-cran-ggplot2, r-cran-kdml, r-cran-magrittr, r-cran-matrix, r-cran-parsnip, r-cran-philentropy, r-cran-purrr, r-cran-readr, r-cran-recipes, r-cran-rfast, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tune Suggests: r-cran-arules, r-cran-clustmixtype, r-cran-fd, r-cran-klar, r-cran-mclust, r-cran-palmerpenguins, r-cran-paralleldist, r-cran-statmatch, r-cran-testthat, r-cran-workflows, r-cran-workflowsets Filename: pool/dists/noble/main/r-cran-manydist_0.5.1-1.ca2404.1_all.deb Size: 2437254 MD5sum: 0dd5ad2d8189cdf07e8196fef4b01776 SHA1: 44ee5562e0e06825b876e2dba861d98939ca20bd SHA256: ffa6b739c92b2040217face0e0e3c72d879a339f99ec36aa81af74648f385752 SHA512: 9e7fe92d52019f32d620cd61bddb304b84b1c6a3220d386a4674d488225fe223416d4c6b899bd61581894ff8615f1f36572a12897986b9729e8f6f952196418c Homepage: https://cran.r-project.org/package=manydist Description: CRAN Package 'manydist' (Distance-Based Learning for Mixed-Type Data) Provides tools for constructing, computing, and using distance measures for numerical, categorical, and mixed-type data. The package implements a flexible framework in which continuous and categorical components can be combined under additive, commensurable, and association-aware specifications. Supported methods include classical distances such as Gower, Euclidean, Manhattan, and Mahalanobis-type distances; categorical dissimilarities such as simple matching, occurrence-frequency, and association-based measures; and mixed-type presets designed to reduce biases due to variable type, scale, distribution, redundancy, and number of categories. The package also provides scaling options, supervised and unsupervised distance constructions, leave-one-variable-out tools for distance-based variable importance, and integration with distance-based learning workflows such as nearest-neighbour prediction, partitioning around medoids, and spectral clustering. Methods are motivated by van de Velden, Iodice D'Enza, Markos, and Cavicchia (2026) and related work on categorical and mixed-type dissimilarities. Package: r-cran-manyivsnets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-aer, r-cran-lmtest, r-cran-sandwich, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-knitr, r-cran-rtransferentropy, r-cran-tidyr, r-cran-viridis, r-cran-countrycode, r-cran-spelling Filename: pool/dists/noble/main/r-cran-manyivsnets_0.1.1-1.ca2404.1_all.deb Size: 163344 MD5sum: 3d2342dfe82d367b790062b73a44e22b SHA1: aa50f9316531724718d2847cf5cb41265c8ec243 SHA256: d8c154b849cd0edd522d1c19171b20cf29f5525b993cc16bca6c2ae41b1140c6 SHA512: 1f1f322c57cbcd5aa5a26f05152e965c772d362ccafa736680ff89acae129193b6daacc8e0fe4222bc80095f10be3cc2cf5786e100489e5e90705beb5668b6cb Homepage: https://cran.r-project.org/package=ManyIVsNets Description: CRAN Package 'ManyIVsNets' (Environmental Phillips Curve Analysis with Multiple InstrumentalVariables and Networks) Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) , transfer entropy by Schreiber (2000) , and weak instrument tests by Stock and Yogo (2005) . Package: r-cran-manymodelr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3550 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-stringr, r-cran-usethis, r-cran-testthat, r-cran-caret, r-cran-metrics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-manymodelr_0.4.0-1.ca2404.1_all.deb Size: 630050 MD5sum: f870649e058e5b89f1c326673f1e902c SHA1: ba1c1fee46eb26d8555cd0788d6e3331be698388 SHA256: 884814f71a15613561da32f705c13ffce20688b7295eb1d5d278347cb25b9974 SHA512: be8089c0648d3bd9174dc8a62f605a8b68cc24769cb0f0305f242deffeba7066fb8956f91854d0358607106bbaf306169ad9fafc44ed70a1a4c0080fba043532 Homepage: https://cran.r-project.org/package=manymodelr Description: CRAN Package 'manymodelr' (Build and Tune Several Models) Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) . Package: r-cran-manymome.table Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-manymome, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-lavaan, r-cran-officer Filename: pool/dists/noble/main/r-cran-manymome.table_0.4.1-1.ca2404.1_all.deb Size: 72106 MD5sum: 441cc87b180d06ad825fe5dbf01fe97c SHA1: e6ecb3e53c93e3c5fec7e4eb6e7674d8f81b17e4 SHA256: f35af35cbf9026065485d3c4928f955fa092b5847dbaa02dc7608205ca3ebf01 SHA512: 79e1fd30e4a70e33a1e78400104f1043c685313ac54eb916890a1fe3992b68ddfdff5a1e272c913ddfe2e04fd005f0ea1144c78711c916912e0ceb18f7ba8cb5 Homepage: https://cran.r-project.org/package=manymome.table Description: CRAN Package 'manymome.table' (Publication-Ready Tables for 'manymome' Results) Converts results from the 'manymome' package, presented in Cheung and Cheung (2024) , to publication-ready tables. Package: r-cran-manymome Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3818 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-pbapply, r-cran-ggplot2, r-cran-igraph, r-cran-mass, r-cran-lmhelprs, r-cran-psych, r-cran-semtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan.mi, r-cran-amelia, r-cran-mice, r-cran-semplot, r-cran-semptools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manymome_0.3.7-1.ca2404.1_all.deb Size: 3151520 MD5sum: 3d4c6fff035f1430a861f545d17eb0f9 SHA1: 334928ea03ea3cb75d74bbae70c7f3eb82d8d586 SHA256: 54f0bd5d30e13af3426820ff6e22a65932203665769500c38bc828bd5efe2a3a SHA512: 3c15d6dbe03fe57532f91ab64172f3483be2346f35d5c3109d6df72bfddaed9fbba34e616033c9df74c97c4d9e264daab7d2e6acbb43dd452a5389f8a970bae1 Homepage: https://cran.r-project.org/package=manymome Description: CRAN Package 'manymome' (Mediation, Moderation and Moderated-Mediation After ModelFitting) Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) . Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) and Cheung, Cheung, Lau, Hui, and Vong (2022) . Model fitting can be done by structural equation modeling using lavaan() or regression using lm(). Package: r-cran-manynet Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4508 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-igraph, r-cran-network, r-cran-pillar, r-cran-tidygraph Suggests: r-cran-readxl, r-cran-rsiena, r-cran-sna, r-cran-testthat, r-cran-tibble, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-manynet_2.3.4-1.ca2404.1_all.deb Size: 3119106 MD5sum: b580a6f255ff4e70e8a5ccfbe5fc2031 SHA1: 5654e279088663456d3b12c869b2c06c5c91fa3a SHA256: a1ba6007c040b2eb9850c17bd0e2460c7d0848524e9b4b9126356f41dbbbb52d SHA512: e8e45dd3e077a9bbee8a341a14fa3817ea09b88113a52057114eb97f842cc2bee56ee79ef01778868c1e639655d1f056b0e255433d73eff4518ccedc45080497 Homepage: https://cran.r-project.org/package=manynet Description: CRAN Package 'manynet' (Many Ways to Make, Manipulate, and Modify Myriad Networks) Many tools for making, manipulating, and modifying many different types of networks. 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Package: r-cran-manystates Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manydata, r-cran-knitr, r-cran-purrr, r-cran-stringi Suggests: r-cran-pointblank, r-cran-messydates, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-manystates_1.0.3-1.ca2404.1_all.deb Size: 318640 MD5sum: 8e1e07726de1cfcb6b57cfd4a12874c7 SHA1: 0e6d3004196420a0e75a7e334a3044714934d34a SHA256: 5dd9dfd1a4c8b8129a485653287b65f82c39961b0b59525f66e9dfeaba03cdcc SHA512: 1d6c0db61da968d1b490b922a35e904164f2556b92e4b772bf92cbc040b49dc5ffedd269a2c622af19644ff4d4fa84c8c6ad870de61fdc2f02105dbb1b268f84 Homepage: https://cran.r-project.org/package=manystates Description: CRAN Package 'manystates' (Many Data on State and State-Like Actors in the InternationalSystem) Comprehensively identifying states and state-like actors is difficult. This package provides data on states and state-like entities in the international system across time. The package combines and cross-references several existing datasets consistent with the aims and functions of the manydata package. It also includes functions for identifying state references in text, and for generating fictional state names. Package: r-cran-manytests Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-manytests_1.2-1.ca2404.1_all.deb Size: 24566 MD5sum: db430aa9f44115ef087d081d2a608b27 SHA1: cdc10670f95af0b1b22eea55e04d6542771c6347 SHA256: f4d742bc77bb854bef881b7f095da35af00e30a903d32a61406c30d1a95b241a SHA512: 8134a083fd421e8498271e9a34a1ba687bf394582e2a634a49d9b25a49808d27a880ac8661210755f6eacb1259c0b4db53fce60a4d8b430e1785d64a429c91a1 Homepage: https://cran.r-project.org/package=ManyTests Description: CRAN Package 'ManyTests' (Multiple Testing Procedures of Cox (2011) and Wong and Cox(2007)) Performs the multiple testing procedures of Cox (2011) and Wong and Cox (2007) . Package: r-cran-maoea Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-nsga2r, r-cran-lhs, r-cran-nnet, r-cran-stringr, r-cran-randtoolbox, r-cran-e1071, r-cran-mass, r-cran-gtools, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-maoea_0.6.2-1.ca2404.1_all.deb Size: 192064 MD5sum: 4c6ba639fcb6ce87681da6b8b8888562 SHA1: 5c69313dfcb75e09621dada6f842cbe4d6f6489f SHA256: 6498313eb751f4cbc55572042a2cea0acd5224aa9119b27bb53c223830647706 SHA512: 9477187a58bc6f23dfc68722cf9f6324bb19cd58d4ca54cbc00864b2fcdabd52b8ceb7e99e0adba65ea9a200010aad121fb5fc0a3647409b05bbc9ea9e1fb8e6 Homepage: https://cran.r-project.org/package=MaOEA Description: CRAN Package 'MaOEA' (Many Objective Evolutionary Algorithm) A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' 'NSGA-III' 'MO-CMA-ES' The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) and 'WFG4'-'WFG9' from Huband, et al. (2005) . Package: r-cran-map2ncbi Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez Filename: pool/dists/noble/main/r-cran-map2ncbi_1.5-1.ca2404.1_all.deb Size: 83436 MD5sum: 7052795f5a25adacd0bc34aee3641e5d SHA1: 111b9b23b545c69aa9e9816744e26aa512eceec7 SHA256: d04e6b8bde60d51f14cfb8020a5ff4d3d47e344a8fca84ee5ed03415f63cf1aa SHA512: 5d2bc2d8bc70b4d7fdbb25553b83a2ee2fdcb1c26fb9a98cebd741d677908e1d57d2a8e810e85779171fad5502429f1c5e3d2b8edb99e8f81b623a9db3c30d31 Homepage: https://cran.r-project.org/package=Map2NCBI Description: CRAN Package 'Map2NCBI' (Mapping Markers to the Nearest Genomic Feature) Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or UTR) directly from NCBI database for any species with a current build available. Option to save downloaded and formatted files is available, and the user can prioritize the feature list based on type and assembly builds present in the current build used. The user can then use the list of features generated or provide a list to map a set of markers (designed for SNP markers with a single base pair position available) to the closest feature based on the map build. This function does require map positions of the markers to be provided and the positions should be based on the build being queried through NCBI. Package: r-cran-map Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexmix, r-cran-matrix, r-cran-magrittr Suggests: r-cran-knitr, r-cran-proc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-map_1.0.0-1.ca2404.1_all.deb Size: 95246 MD5sum: 122eb91a5291153b92a5f5a86a64cbc0 SHA1: d778860da9fc84c9c872089f2e277ba61b631995 SHA256: 23e3c408220ed1bb38beeb5aa5f0a1a0efcce37d7d17ac8b095493c6114eb5d9 SHA512: ffacd03998f51e78e2c467eb5d218a3e048cc336dee08ee63709aeb5f09449a94232ac33467f6401ae3810f65c1ff38a03e0f1d9c61b68978b87455abf8b0d38 Homepage: https://cran.r-project.org/package=MAP Description: CRAN Package 'MAP' (Multimodal Automated Phenotyping) Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. Towards that end, we developed an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Specifically, our proposed method, called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent mixture models on aggregated ICD and NLP counts along with healthcare utilization. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no (See Katherine P. Liao, et al. (2019) .). Package: r-cran-mapa Architecture: all Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-rcolorbrewer, r-cran-smooth Filename: pool/dists/noble/main/r-cran-mapa_2.0.7-1.ca2404.1_all.deb Size: 111288 MD5sum: cf64a0a90b959d79506d5729dfca9535 SHA1: 029467dad9c437f0d8cab9dad37008762d42ffca SHA256: 2871477f1bdaf3334af1c941dcd596be2d2f1bc9e58571b7c94215d18f67f69e SHA512: 961b73d936c43e77fbf049527d3c0118181aa7dbd1954ac4837a765f8efa218481b7d985e31b9949130a8b953ef0b763992ae4162734613b5f8e096ad43b1d08 Homepage: https://cran.r-project.org/package=MAPA Description: CRAN Package 'MAPA' (Multiple Aggregation Prediction Algorithm) Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) . 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See for more information about the 'Mapbox' APIs. 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A 'shiny' app allows users to create admixture maps interactively. Jenkins TL (2024) . Package: r-cran-mapnhanespa Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapnhanespa_0.2.0-1.ca2404.1_all.deb Size: 761618 MD5sum: 55b3d8429c61709d2d4de804aff3164e SHA1: 24a0414918020561c77a4d311e31f8dc904bf417 SHA256: b8717afebbed32917589066086ed3371c653680b2250fe1dc0f09aa6a6eaed37 SHA512: 766e0ef2aa4f0068bcb903c43c58ec6fdb5fbb1c3a725d154e0801b98d75770fa55174895b3826d35130c9c07787e61db68b4a10034ad010a17c2200cc4373ad Homepage: https://cran.r-project.org/package=mapnhanespa Description: CRAN Package 'mapnhanespa' (Map Quantiles for Physical Activity from 'NHANES') Maps physical activity from the National Health and Nutrition Examination Survey ('NHANES') study into population-based quantiles. Package: r-cran-maposm Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3629 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-lwgeom, r-cran-mapsf, r-cran-osmextract Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-maposm_0.5.1-1.ca2404.1_all.deb Size: 2452588 MD5sum: 3b02021b7f3b33e15c3b55c6dd43dd7a SHA1: 5379b7784df9d56828d3a593225f377c2a458080 SHA256: db6600cf9fa2982cd2b719cca87b61dcd7bf4593d935dd57d9a98f79cf65ca8b SHA512: d53e5aba21d3da60145a47afe1d25e7ae0a94ccb1ec4bf82249354528777568eefaf0b9313531dceebf13b615f9cfa29cf900afba938be0ee3ca956366746c24 Homepage: https://cran.r-project.org/package=maposm Description: CRAN Package 'maposm' (Get Map Layers from 'OpenStreetMap') The 'OpenStreetMap' database provides a wide range of highly detailed geographic layers on a global scale. To obtain synthetic information for cartographic purposes, layers must be selected, simplified, merged, or modified. 'maposm' downloads 'OpenStreetMap' extracts and performs these operations to create a set of composite layers of urban areas, buildings, green spaces, main roads, secondary roads, railways, and water bodies. Package: r-cran-mapper Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastcluster Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapper_2.4.0-1.ca2404.1_all.deb Size: 266738 MD5sum: 1d0c970f2078a3f94f87c7b4df29c49f SHA1: c3bc3599d48df29fbb4fb2a8b04eb06096154c04 SHA256: a3fd9bfb0dfbf14fb7394a47514fe9bc129d6fe6244cb0a037bf27e88d6d45c0 SHA512: 74e55285b521f673503969a928c0a9b61f9662c942de451276ab09fdafb637432a3e7c86833016236263ad724c0bb8918a0e54114a94718d2f737679d81c6285 Homepage: https://cran.r-project.org/package=mappeR Description: CRAN Package 'mappeR' (Construct Mapper Graphs for Topological and Exploratory DataAnalysis) Topological data analysis (TDA) is a method of data analysis that uses techniques from topology to analyze high-dimensional data. Here we implement Mapper, an algorithm from this area developed by Singh, Mémoli and Carlsson (2007) which generalizes the concept of a Reeb graph . Package: r-cran-mapperalgo Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1599 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-inaparc, r-cran-ppclust, r-cran-doparallel, r-cran-foreach, r-cran-networkd3, r-cran-plotly, r-cran-igraph, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-webshot2, r-cran-jsonlite, r-cran-rlang, r-cran-viridislite, r-cran-mclust, r-cran-nortest Suggests: r-cran-fastcluster, r-cran-cluster, r-cran-dbscan, r-cran-mlr3, r-cran-mlr3cluster, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapperalgo_1.3.0-1.ca2404.1_all.deb Size: 1455416 MD5sum: d62d9dcf2b2d976df99a081d3b9a73bb SHA1: 93f0d291870203f8dae3c15f329a638f47aa6f47 SHA256: 2acb8a3ce4976f62d67b50fcea0945e2c50156087839982799b24188f6e08f54 SHA512: 9ae0c63fcc72c71e714fa26ab417339d8dae5fd1debd71de96543489913c8289d495d4bd232129514ebb11d542d9825391171daf4f68d5ee886416cde434e2d9 Homepage: https://cran.r-project.org/package=MapperAlgo Description: CRAN Package 'MapperAlgo' (Topological Data Analysis: Mapper Algorithm) The Mapper algorithm from Topological Data Analysis, the steps are as follows 1. Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results. Package: r-cran-mappestrisk Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geodata, r-cran-ggplot2, r-cran-khroma, r-cran-nls.multstart, r-cran-progress, r-cran-purrr, r-cran-rtpc, r-cran-terra, r-cran-tidyr Suggests: r-cran-covr, r-cran-leaflet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mappestrisk_0.1.2-1.ca2404.1_all.deb Size: 358392 MD5sum: add5dd23e727514280f19cdb086d63a2 SHA1: d3a3ee968bed9c418495f9444907dfdafb654ba0 SHA256: cca91cd4272af44b96816bf0578dc5d2ff30a324c27da51aadfa60bac5b91177 SHA512: aadf92b68d04a2a57d33ba5e02dc0383ebfbc482910e727b2e68943f710ea0ae33582ff291e2d18c18b4f83662212e40b02ce080a92b1ae0dfc9c8d2121fdd87 Homepage: https://cran.r-project.org/package=mappestRisk Description: CRAN Package 'mappestRisk' (Create Maps Forecasting Risk of Pest Occurrence) There are three different modules: (1) model fitting and selection using a set of the most commonly used equations describing developmental responses to temperature helped by already existing R packages ('rTPC') and nonlinear regression model functions from 'nls.multstart' (Padfield et al. 2021, ), with visualization of model predictions to guide ecological criteria for model selection; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction and visualization inspired on previous research (Taylor et al. 2019, ), with either exportable rasters, static map images or html, interactive maps. Package: r-cran-mapping Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1296 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmap, r-cran-cartography, r-cran-ggplot2, r-cran-sf, r-cran-dplyr, r-cran-leaflet, r-cran-tmaptools, r-cran-viridislite, r-cran-httr, r-cran-curl, r-cran-htmltools, r-cran-leafpop, r-cran-leafsync, r-cran-mapview, r-cran-geojsonio, r-cran-jsonlite, r-cran-stringr, r-cran-s2, r-cran-stringi Suggests: r-cran-knitr, r-cran-diagrammer, r-cran-rmarkdown, r-cran-validate, r-cran-unrepx Filename: pool/dists/noble/main/r-cran-mapping_1.4.1-1.ca2404.1_all.deb Size: 1278444 MD5sum: 4d3674436963d7eb887fa6b8d79ca858 SHA1: f95f2913f3e092ae0a1a02672a65573c0ac48e16 SHA256: 97d660bd54a755ffa0b7439cf2e466d639e8c34aff71a70687bc102027203d9a SHA512: 26862d7a6bda812d90f45a83136284c0f47b078b0e984b60c08fb791a4fa8ddbcfa8cb90384e2a684609cf9457943a8655897e18d005e1a37a73d6a0b26d5ae2 Homepage: https://cran.r-project.org/package=mapping Description: CRAN Package 'mapping' (Automatic Download, Linking, Manipulating Coordinates for Maps) Maps are an important tool to visualise variables distribution across different spatial objects. The mapping process requires to link the data with coordinates and then generate the correspondent map. This package provide coordinates, linking and mapping functions for an automatic, flexible and easy approach of external functions. The package provides an easy, flexible and automatic unit. Geographical coordinates are provided in the package and automatically linked with the input data to generate maps with internal provided functions or external functions. Provide an easy, flexible and automatic approach to potentially download updated coordinates, to link statistical units with coordinates and to aggregate variables based on the spatial hierarchy of units. The object returned from the package can be used for thematic maps with the build-in functions provided in mapping or with other packages already available. Package: r-cran-mappingas Architecture: all Version: 1.13.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-shiny, r-cran-bslib, r-cran-leaflet, r-cran-units, r-cran-lwgeom, r-cran-readxl, r-cran-rlang, r-cran-dt, r-cran-htmltools, r-cran-htmlwidgets, r-cran-ggplot2, r-cran-plotly, r-cran-officer Suggests: r-cran-rgee, r-cran-reticulate, r-cran-writexl, r-cran-zip, r-cran-curl, r-cran-ggnewscale, r-cran-ggtrendline, r-cran-ragg, r-cran-shinywidgets, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mappingas_1.13.2-1.ca2404.1_all.deb Size: 798776 MD5sum: b1d872cfbc44f3de97f6c81a36b56951 SHA1: 878f8100c77bc85fabe38961b3973ca3d15419d0 SHA256: dc149e6c362b000bc7e28235bea32b45c1407ab6229cb244d390a8f2eaee194e SHA512: 1e15c0140ff4c9b0b4a3f611a3f05ddc2d559340e103d8f3482254de551b9b54e32c217a8f529507a0a0c2e5e3f3243979e485d0f42c364c89bdd7b4dce7540f Homepage: https://cran.r-project.org/package=mappingAS Description: CRAN Package 'mappingAS' (Spatial Metrics and Habitat Conversion for Extinction RiskAssessment) A spatial analytical framework for preliminary species extinction-risk screening following the IUCN Red List Criterion B guidelines. From occurrence points it computes the Extent of Occurrence (EOO) and Area of Occupancy (AOO) on a data-centred equal-area projection, assigns provisional Criterion B categories, and integrates 'MapBiomas' land-use/land-cover data to quantify the proportion of anthropogenic conversion versus remaining natural habitat within each range metric, with per-class breakdowns and land-cover time series. Several 'MapBiomas' initiatives are supported through one standardised legend - 'MapBiomas' Brazil, the Pan-Amazon / Amazonia collection (RAISG), Colombia, Argentina, Bolivia, Chile, Ecuador, Peru, Venezuela, Paraguay and Uruguay - so a species anywhere these products cover can be screened as readily as a Brazilian one. For ranges outside 'MapBiomas' coverage it can fall back to the global 'Esri' / 'Impact Observatory' 10 m annual land cover derived from 'Sentinel-2' (the product behind the 'ArcGIS' Living Atlas Land Cover Explorer), so a species anywhere on Earth can be screened. It also integrates 'MapBiomas' Fire to compute burned-area metrics and fire time series, and quantifies the overlap of the range with protected areas from the global World Database on Protected Areas (WDPA). 'MapBiomas' data are read either locally over the network via 'GDAL' '/vsicurl/' (no 'Google Earth Engine' account required) or server-side through 'Google Earth Engine' (GEE) for large-scale assessments. Outputs include interactive and publication ready maps and charts, spatial (shapefile/'GeoPackage') and raster exports, and a written assessment report (HTML, text or Word). An interactive 'shiny' application ties the whole workflow together for reproducible conservation planning. Package: r-cran-mappingcalc Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bslib, r-cran-ggplot2, r-cran-readxl, r-cran-haven, r-cran-writexl, r-cran-tibble Suggests: r-cran-dt, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mappingcalc_2.0.0-1.ca2404.1_all.deb Size: 244718 MD5sum: b4539a75995701fc8dfb8845c9ff94a3 SHA1: 39a18f2171de0b10ed0b153fa67f7835044fc922 SHA256: 3d49da3f67a3f63c81c1fc68becb5c3280ef24fbcdb96502d07fc6b6f5e900d7 SHA512: bb7e42bb8c5e7ccad611f05b9a8bbe13aeab5d1c78dd0d33ce83a3b8db6b35d64dffbb203762ae6e69e5b639c77d98c2345c4ed068833e4a421f2910183671e8 Homepage: https://cran.r-project.org/package=MappingCalc Description: CRAN Package 'MappingCalc' (Mapping Calculator for EQ-5D Utility Scores) Provides a 'shiny' web application to map scores from clinical instruments (PANSS, SQLS, WHODAS 2.0, PHQ-8, EQ-5D-5L) to preference-based EQ-5D-5L health utility values using validated regression-based and beta-mixture mapping algorithms developed from Singapore population studies. Intended for use in health economic evaluations and cost-utility analyses. Methods are based on: Abdin et al. (2019) , Seow et al. (2023) , Abdin et al. (2021) , Abdin et al. (2024) . Package: r-cran-mappings Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mappings_0.2.0-1.ca2404.1_all.deb Size: 41598 MD5sum: d7a6e5ddeba44f52c44f6b2f4628cb3b SHA1: 0ff9b5b6571010c9c03cecac106723d2464bd0c5 SHA256: fb4ba9113769c7939e18df18a6a9cbdb79db376ff39558c67eb83391329110fd SHA512: ccbfe6a4a837a5160f8b9b57a45af88377b75da93e83fa37b2d554f825fcb0e0a0693df87b110cdc8ce8665defc01ea1966617d377abf07e0f2055343981284d Homepage: https://cran.r-project.org/package=mappings Description: CRAN Package 'mappings' (Functions for Transforming Categorical Variables) Easily create functions to map between different sets of values, such as for re-labeling categorical variables. Package: r-cran-mapplots Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-shapefiles Filename: pool/dists/noble/main/r-cran-mapplots_1.5.3-1.ca2404.1_all.deb Size: 354750 MD5sum: 7a0c7c6146da1a479201f16aa131c86c SHA1: fc7d5e8b1eebd0fcc3e6d625127dc9427cfc1e80 SHA256: ebaa111789bf1c0326d30a3c075308801125108c613e5d482c137afc6b515898 SHA512: 5918dad6e8e96a6d87420db2fbb7fec8b4199b004e32be9a1c2ba99206e788fd8bc9e8a11a3431d4424610cb20d56ff2d0b6c9dbe3b9c2a845208fe99ad11bc6 Homepage: https://cran.r-project.org/package=mapplots Description: CRAN Package 'mapplots' (Data Visualisation on Maps) Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation. Package: r-cran-mappp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-memoise, r-cran-progress, r-cran-pbmcapply, r-cran-parallelly, r-cran-purrr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mappp_1.0.0-1.ca2404.1_all.deb Size: 24714 MD5sum: d6b2d93222d82cb2a136f0b896d04579 SHA1: 12126e3b4f951b3d09adbfb867254405e5c53c6b SHA256: 77b606edf80d1f2f1b87379df6efe4d6148229cd6d5561c89602f54d7d499102 SHA512: 73b0962c565116596cef3f220da41f514f0dc28c0fde72eeeb32d0a40b40b712b60800a941b0d436e14d400d4c650790af7751d8ca0a9f39c222df416c8d0008 Homepage: https://cran.r-project.org/package=mappp Description: CRAN Package 'mappp' (Map in Parallel with Progress) Provides one function, which is a wrapper around purrr::map() with some extras on top, including parallel computation, progress bars, error handling, and result caching. Package: r-cran-mapsapi Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-sf, r-cran-bitops, r-cran-stars, r-cran-rgooglemaps, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-mapsapi_0.5.4-1.ca2404.1_all.deb Size: 1690070 MD5sum: a160c5341028d0cd967525af0281d61d SHA1: 309c40f61781841fefce39316b583d4852b7614b SHA256: 8c682a00ec4630f781e937845fa8ecdffc48e9e5e72f779a2d527fff8991fcfd SHA512: 315fa4d6a291f681ad1017aac1650e7714943a504ebe6dd99b390e3da9e6247c83502a6093678c888352264b5d4efcc13477ade388efd2d0d404d5561d3b9254 Homepage: https://cran.r-project.org/package=mapsapi Description: CRAN Package 'mapsapi' ('sf'-Compatible Interface to 'Google Maps' APIs) Interface to the 'Google Maps' APIs: (1) routing directions based on the 'Directions' API, returned as 'sf' objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the 'Distance Matrix' API; (3) geocoded locations based on the 'Geocode' API, returned as 'sf' objects, either points or bounds; (4) map images using the 'Maps Static' API, returned as 'stars' objects. Package: r-cran-mapsenegal Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3783 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-mapsf Filename: pool/dists/noble/main/r-cran-mapsenegal_0.1.1-1.ca2404.1_all.deb Size: 3781878 MD5sum: dca379496b480ea55c630e84b29d49bf SHA1: 6d04478939b5d934e5569fa1ab58efa5c94feb84 SHA256: 0f2d15aabce55822166e05d259bbcb0d506a0b7235945103497e26ae697e229b SHA512: 304e0ae9142633f4e9bfed63ffbd807e4c88d4597bddf0dc0c01be9bd684e94ceaa1856df782eb9ffe6a14cce4d96c1c5497d04108280210d05a588135c76b49 Homepage: https://cran.r-project.org/package=mapSenegal Description: CRAN Package 'mapSenegal' (Administrative Boundaries of Senegal) The administrative boundaries of Senegal are provided at several levels, including regions, departments, arrondissements and communes. The Global Administrative Areas database, or `GADM` , is the primary source for these layers. The dataset is complemented by the incorporation of additional geographic layers, such as localities, universities, roads, or health facility locations. Package: r-cran-mapsf.gui Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2212 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-bslib, r-cran-colourpicker, r-cran-ckmeans.1d.dp, r-cran-dt, r-cran-formatr, r-cran-jpeg, r-cran-htmltools, r-cran-mapsf, r-cran-phosphoricons, r-cran-png, r-cran-sf, r-cran-shiny, r-cran-shinyjs, r-cran-shiny.i18n, r-cran-sortable Suggests: r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapsf.gui_0.1.0-1.ca2404.1_all.deb Size: 1418096 MD5sum: 7e88cc23d8e053e3a74f933ca8ba2be7 SHA1: b878878ef46bba11ad22cf7983e26a6e3a486358 SHA256: ff33772ff3cfbdfea3c3f29930f7c6be77266b0b65564de433a25958627449f2 SHA512: b349200e8e55af549462dd71306bf9e4b308781230592d2fd0f240946aae5d54d63b4741c148c964fbb40e3b2d02bc992ac52573ce15fd0b11c4a22f40d86c66 Homepage: https://cran.r-project.org/package=mapsf.gui Description: CRAN Package 'mapsf.gui' (Create Thematic Maps Interactively) A 'Shiny' application to create thematic maps interactively, based on the 'mapsf' package. Features include: a user-friendly interface to create and customize thematic maps without coding, support for various map types (choropleth, proportional symbols, etc.) and customization options (colors, legends, etc.), R code generation to ensure reproducibility and export options to save maps in different formats (PNG, SVG). Package: r-cran-mapsf Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2669 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-classint, r-cran-maplegend, r-cran-s2, r-cran-sf Suggests: r-cran-terra, r-cran-ckmeans.1d.dp, r-cran-png, r-cran-jpeg, r-cran-knitr, r-cran-rmarkdown, r-cran-svglite, r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-mapsf_1.3.0-1.ca2404.1_all.deb Size: 1900932 MD5sum: 9cba3515dfe1c7bc931306a20d82e8f2 SHA1: c3d2fe63360780e387546ab50fe0cd2af35bf268 SHA256: 8b0e8cefa67401a1d4add72f3811e287eb396aeec626478d3a3334ae6be59ee6 SHA512: b0d1bfc563f3a06cb5c970a700dccfd57212acf3fa24655a7cfbf5df07df80771ed85af7e10799d9c27f40365baa142da60dc2d936f64bd2e54da14c39d1d471 Homepage: https://cran.r-project.org/package=mapsf Description: CRAN Package 'mapsf' (Thematic Cartography) Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on 'base' graphics. 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Package: r-cran-mapycusmaximus Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf Suggests: r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-tidyr, r-cran-purrr, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-mapycusmaximus_1.0.7-1.ca2404.1_all.deb Size: 1659514 MD5sum: 4e5b99cb175de219596b6697dfd376ff SHA1: a5cf04912b6e85b1176e4d581145bdfbf0c03d0f SHA256: 1a6e025e49bc27160a674553abc4bef01ed3d7e1aa22642cc35b8da89a6ed1ce SHA512: 824259bfa20f43a665b9c15ae394b3de5430af69750a442bfe4733f30c185b1c89eb60b9f8b7fb0606d1deafd2780713b9f6947b563be51be2eaa7c4bd0bcf6c Homepage: https://cran.r-project.org/package=mapycusmaximus Description: CRAN Package 'mapycusmaximus' (Focus-Glue-Context Fisheye Transformations for SpatialVisualization) Focus-glue-context (FGC) fisheye transformations to two-dimensional coordinates and spatial vector geometries. 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Package: r-cran-mar1s Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cmrutils, r-cran-fda, r-cran-zoo Filename: pool/dists/noble/main/r-cran-mar1s_2.1.1-1.ca2404.1_all.deb Size: 227476 MD5sum: 04bf87097668ba2edf87ea84bb7c6255 SHA1: b9112db0ef8ffd47817ae34f5f18c50511b53b39 SHA256: ad492f33496efc6e0b5064bddbc56913e9d787b5a2387482c25d823bba106997 SHA512: 27b05e3bc69f76484c9f66f49f26dbd1fae7d1e687d7dbf2f0dd64035bb013b95200da23bd65ddcbdb279449199bf730a90f61aea3af91fb14366bde3592e2d5 Homepage: https://cran.r-project.org/package=mar1s Description: CRAN Package 'mar1s' (Multiplicative AR(1) with Seasonal Processes) Multiplicative AR(1) with Seasonal is a stochastic process model built on top of AR(1). The package provides the following procedures for MAR(1)S processes: fit, compose, decompose, advanced simulate and predict. 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Package: r-cran-marp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gtools, r-cran-statmod, r-cran-vgam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-marp_0.1.1-1.ca2404.1_all.deb Size: 276616 MD5sum: cfd80b2f46b7a952da9a65e252462f38 SHA1: 6810063d847b7e4bf20910711c61b5498634d604 SHA256: 7ce4a35e52e3d685f744b4a76baff346424826f26d3db6f31a7889e9123eeb1b SHA512: e430cf01ebd40d0456b3c249b12ffe86c1b4d6b663c6f433f75052ab6faf33fcc307900cb999b58769ed73731bedec56fb4d81b04299a0cb0dd847479c6a3b47 Homepage: https://cran.r-project.org/package=marp Description: CRAN Package 'marp' (Model-Averaged Renewal Process) To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. 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Package: r-cran-marradistrees Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-marradistrees_1.0-1.ca2404.1_all.deb Size: 16524 MD5sum: 382ec6ea2b891f2c58006d35ac1067a8 SHA1: b0709eb60ca0ffe47363641bda6534c65b296aa5 SHA256: 851b1b238dc7fb15ee1e149a8cc43d5ffaa9c159873e0055f7d14ad359a1890c SHA512: f38245cfd6532c0f29e40348c7c6803b26211a0590df7a83e627b1de926f873ddcde2d93f2a17e36aee53b102cbe0668e8ab0ccc5b5dc57a784e80545bcb4939 Homepage: https://cran.r-project.org/package=marradistrees Description: CRAN Package 'marradistrees' (Plots a Tree-Like Representation of a Numerical Variable(Marradi's Tree)) Provides a single function plotting Marradi's trees: a graphical representation of a numerical variable for comparing the variable mean and standard deviation across subgroups. See A. Marradi "L'analisi monovariata" (1993, ISBN: 9788820496876). Package: r-cran-mars Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-corpcor, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mars_0.2.2-1.ca2404.1_all.deb Size: 181090 MD5sum: 2465128d57c8e213976a50fd040b57c7 SHA1: 004952834cb8ac01fd2c64c5a6cb5ff45950dc91 SHA256: ddd744d67949eb2c50c70faa51ec6add436e1eafed456a9b8b16801d17fafada SHA512: 4a03e747e8776082ad89433e36531c3f6fc924cbc73d8edfd40c17aa7bac8bcf16cd1e1edd40efca3257d74dcb1d46e142a021e115739545253297785c496291 Homepage: https://cran.r-project.org/package=mars Description: CRAN Package 'mars' (Meta Analysis and Research Synthesis) Includes functions for conducting univariate and multivariate meta-analysis. This includes the estimation of the asymptotic variance-covariance matrix of effect sizes. For more details see Becker (1992) , Cooper, Hedges, and Valentine (2019) , and Schmid, Stijnen, and White (2020) . Package: r-cran-marsannhybrid Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-neuralnet, r-cran-earth Filename: pool/dists/noble/main/r-cran-marsannhybrid_0.1.0-1.ca2404.1_all.deb Size: 13302 MD5sum: 72c19132c1c93ae4a80777165a3191ce SHA1: 290aa0818f4d775d6107e621667f5373566a63bb SHA256: 461a37c32250644e0291240808cb6fc4ab28084ae3f9982d7ef63dab82f47b2e SHA512: c00f970e11400e8d3ecfdae18dcf2fcaf7ddab13e68fa0704966ee1ec73e7952ea97498be9588af2b121f050a9ffa83e2ffd09d086e68ced96089f2fd9484db9 Homepage: https://cran.r-project.org/package=MARSANNhybrid Description: CRAN Package 'MARSANNhybrid' (MARS Based ANN Hybrid Model) Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables. Package: r-cran-marsearth Architecture: all Version: 0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-marsearth_0.0.0-1.ca2404.1_all.deb Size: 41694 MD5sum: 36c788120c7049fca385c928c34c698c SHA1: 635a273be27c4e13f6f571831594ce8566ff9b07 SHA256: 58ba0b9e159e83543eb0e64b520221b5de0549e348d80ee11d9c5457bbab908c SHA512: a1c633df30d7ad85a0828b054e9fddd61c310e134999b005777ede297ece4cba11f609a9b56d061b18b30c8d1c80abc7bc1f4d80bca338be7613411277e6af5e Homepage: https://cran.r-project.org/package=marsearth Description: CRAN Package 'marsearth' (Portable Mars Runtime Replay) Loads, validates, and replays portable 'mars' ModelSpec artifacts from R. 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Package: r-cran-marsgwr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-earth Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-marsgwr_0.1.0-1.ca2404.1_all.deb Size: 28506 MD5sum: dbbb0c24f2c5d379e7fa43302a10bfc8 SHA1: 4b5e3baff03adefa54ad65160803b5e926145974 SHA256: 76edeb6ad2f87ee502a5c510906c2c98bfc7c5783634c7ab3664fff5aa5fe3c8 SHA512: 9efa19b5362d16e6a4c62362da478c8a2d3abfa68d9da0272d75a19a2a85b0fd2f38c8eb9849c80e4c7c920c9b82e0fbfd3d52d2db383e42873102f835cc3cd9 Homepage: https://cran.r-project.org/package=MARSGWR Description: CRAN Package 'MARSGWR' (A Hybrid Spatial Model for Capturing Spatially VaryingRelationships Between Variables in the Data) It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response variable. For method details see, Friedman, J.H. (1991). .The GWR model is then used to predict the response variable at testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations. Package: r-cran-marsrad Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-dt, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-marsrad_1.0.1-1.ca2404.1_all.deb Size: 152024 MD5sum: 0ff76159d4d9f89fc2abe690bbc7c0dd SHA1: a5257b9ec769abeef8b94d6eaf5d5ac6bc478c8d SHA256: d569da64939a0076a48132149da005228f64962bf3f889b4ef9d8f3fa83a2d1f SHA512: b11bb8666ea4362ddf9c610c2953938c0774c82cf62d6cf33a78dffe2c574e62e4760b48f77d15499c0f6b8f70d38fe53f2da2b1306734ad3963701586242b6f Homepage: https://cran.r-project.org/package=marsrad Description: CRAN Package 'marsrad' (Mars Solar Radiation) A set of functions to calculate solar irradiance and insolation on Mars horizontal and inclined surfaces. Based on NASA Technical Memoranda 102299, 103623, 105216, 106321, and 106700, i.e. the canonical Mars solar radiation papers. Package: r-cran-marsruntime Architecture: all Version: 0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-marsruntime_0.0.0-1.ca2404.1_all.deb Size: 41684 MD5sum: 845f2664e35adf82b1f305765f356cc8 SHA1: 32adecfe9b115c11247d3d85d7e6ddc56e47e537 SHA256: 4c205029deca570eef57046fc31bb9223db73b8e992eb37dc6ac414c3b0b4145 SHA512: 436282ab115b27297291484e35e7f23d5f52f74416161957458e1416e6a4b277b194e6d736201e99a4718c16d0fab17503c14204cd487baf2e793678ff1eada9 Homepage: https://cran.r-project.org/package=marsruntime Description: CRAN Package 'marsruntime' (Portable Mars Runtime Replay) Loads, validates, and replays portable 'mars' ModelSpec artifacts from R. The package provides helpers for constructing design matrices, generating predictions, and, when the companion runtime helper is available, fitting portable model specifications for cross-language replay. Package: r-cran-marss Architecture: all Version: 3.11.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-kfas, r-cran-mvtnorm, r-cran-nlme Suggests: r-cran-forecast, r-cran-ggplot2, r-cran-hmisc, r-cran-knitr, r-cran-lme4, r-cran-maps, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-marss_3.11.10-1.ca2404.1_all.deb Size: 3678002 MD5sum: 7e96abbadeab195e31e8f390452fa79d SHA1: dcdfd045ff08729d5bd332175da48be9985bccde SHA256: 3adff7b3927befa111776e94bd2cddd1707e3455f390c3e3d4a804305c488210 SHA512: b924c8ab4ef6b88cf8049ad94bcfae4ab262d846b50376b3d8be847e9caac4bef6235824ac5f7d85a0f7868437431a5d00d64253f00ccb7b18ad0c3e446c91a4 Homepage: https://cran.r-project.org/package=MARSS Description: CRAN Package 'MARSS' (Multivariate Autoregressive State-Space Modeling) The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at . Package: r-cran-marssvrhybrid Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-earth Filename: pool/dists/noble/main/r-cran-marssvrhybrid_0.1.0-1.ca2404.1_all.deb Size: 13414 MD5sum: 8ca5fb1ce4b4f29dd1d84f668773a3d1 SHA1: bce7da3a161a3f21dc55275d0ce940685cfde248 SHA256: 08e0e4c2b80883c83ea7d15850b2ee17f07cc9cef64f94b7fdda9ec3e5b7a2d9 SHA512: 451cfa027fa84e4588f57239c63b63b147b4c68978782b648ac2fe2267538f48648d998fa3de732c686407032de6c1762810bd65c2b657723d76c6a3c0aecfd1 Homepage: https://cran.r-project.org/package=MARSSVRhybrid Description: CRAN Package 'MARSSVRhybrid' (MARS SVR Hybrid) Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables. 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(2013) and Mandallaz (2014) . It yields smaller variances than the standard bias correction, the generalised regression estimator. 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Package: r-cran-maskedcauses Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2323 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-likelihood.model, r-cran-generics, r-cran-numderiv, r-cran-dist.structure Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-algebraic.mle Filename: pool/dists/noble/main/r-cran-maskedcauses_0.10.0-1.ca2404.1_all.deb Size: 1168322 MD5sum: 53f3af037e80df0252b0704a77d0510d SHA1: 0d3c4de4309d1b2c62cf216a8beb114dde598efc SHA256: 429c9278be11b8b6c26e6a2bd1a1db78678dc39d97094000bdbfc56b13725d8c SHA512: 0376c017a9d6c3d5fb564eefd846667457cbe01045c644cde2b4a160a8c586f532766f500d4d86a018e00b38d348a799e78327e73ee3fa67f50eefc62a3920bd Homepage: https://cran.r-project.org/package=maskedcauses Description: CRAN Package 'maskedcauses' (Likelihood Models for Systems with Masked Component Cause ofFailure) Maximum likelihood estimation for series systems where the component cause of failure is masked. Implements analytical log-likelihood, score, and Hessian functions for exponential, homogeneous Weibull, and heterogeneous Weibull component lifetimes under masked cause conditions (C1, C2, C3). Supports exact, right-censored, left-censored, and interval-censored observations via composable observation functors. Provides random data generation, model fitting, and Fisher information for asymptotic inference. See Lin, Loh, and Bai (1993) and Craiu and Reiser (2006) . 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Computes log-likelihood, score, Hessian, and maximum likelihood estimates for masked data satisfying conditions C1, C2, C3 under general component hazard functions. Implements the 'series_md' protocol defined in the 'maskedcauses' package. 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Package: r-cran-masscor Architecture: all Version: 0.0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metrology Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-masscor_0.0.7.1-1.ca2404.1_all.deb Size: 433002 MD5sum: f152b331e41661591314ab3847a1330f SHA1: 3ed5225898f5ab86e027ecc530caa78d3a81e0cd SHA256: 1a88521f4dc9dd5b8a8a137265cca5d1e329331778ae02eab96b26eed6626845 SHA512: 992f14f93df0e250e3ce85e18998dd082d7710aef6a454dcc3f2fc4bd2c45a06d3fdb43a74470819f7e0650eeb4cee83f925108dc019b7a260cd9802bebe2111 Homepage: https://cran.r-project.org/package=masscor Description: CRAN Package 'masscor' (Mass Measurement Corrections) Mass measurement corrections and uncertainties using calibration data, as recommended by EURAMET's guideline No. 18 (2015) ISBN:978-3-942992-40-4 . 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These tools are intended to simplify downstream interpretation and analysis of pangenome graphs. 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A reported number of clusters or "types" can then be tested against what the data's own margins and covariance already produce, using any clustering pipeline. A t-copula option adds tail dependence to the null, so that an apparent excess of clusters can be checked against a heavier-tailed alternative before it is read as evidence of types. Implements the matched-null procedure of Meng (2026) "Types Without Taxa" . Package: r-cran-matchfeat Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-foreach Filename: pool/dists/noble/main/r-cran-matchfeat_1.0-1.ca2404.1_all.deb Size: 188272 MD5sum: 54cd6068173760e64f7849b02a5aa261 SHA1: 317640ee5862182a218ae8d77dd6b4b28a7379ad SHA256: 43a006d3b01b397570ff756f8edf73ad5e1060abeaa788c9f98cea4c537b67c6 SHA512: 277991deefb80a404a2d2a2003280c50ad36fb225d4dd902b33f007635ad943afba81e86158be848e832ba9ce457c19701f1299a5e1ddd015448e728dc050740 Homepage: https://cran.r-project.org/package=matchFeat Description: CRAN Package 'matchFeat' (One-to-One Feature Matching) Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The 'Gurobi' software and its 'R' interface package are required for one of the package functions (match.2x()) and can be obtained at (free academic license). For more details, refer to Degras (2022) "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) "Local search heuristics for multi-index assignment problems with decomposable costs". Package: r-cran-matchgate Architecture: all Version: 0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locpol Filename: pool/dists/noble/main/r-cran-matchgate_0.0.10-1.ca2404.1_all.deb Size: 21858 MD5sum: cd4db66f17d24924b37fd450544517ab SHA1: 525f7e0002f3605cf2585b000a5897cf86d63c7c SHA256: e7b4a4d9c4e9aa8af3f3703e0a24dacbcec69af467dde2f7b55d95652c168c6e SHA512: 32da74fab0f7a15647a6fb42c614d9aae41ed5f8f993a68ea6b819f8e53204a9a0b5655536ec77f17968be46fd0637f09fe5711471758ee22e5a01580fdb3560 Homepage: https://cran.r-project.org/package=MatchGATE Description: CRAN Package 'MatchGATE' (Estimate Group Average Treatment Effects with Matching) Two novel matching-based methods for estimating group average treatment effects (GATEs). The match_y1y0() and match_y1y0_bc() functions are used for imputing the potential outcomes based on matching and bias-corrected matching techniques, respectively. The EstGATE() function is employed to estimate the GATE after imputing the potential outcomes. Package: r-cran-matchingpursuit Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6813 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-edf, r-cran-signal, r-cran-rsqlite, r-cran-imager, r-cran-raster, r-cran-digest, r-cran-egm, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-latex2exp, r-cran-remotes Filename: pool/dists/noble/main/r-cran-matchingpursuit_1.3.0-1.ca2404.1_all.deb Size: 5932148 MD5sum: 24b3ff697cee40a97015c7125478a384 SHA1: e4a04dbe8d515309223cc7d15a58139aeadf175a SHA256: f1bd09775d9c9f8aaaff86ff9d6984f3b53f318bfbabee0e890eaa7e86e18a49 SHA512: 98cb38aff56caf45fdbb39a9a56f57fc324d4668a072ff7047ec5f83be6d39da7ce2e263a62a49f6e883992573e25c88afb992cbb9602edba486677a3d5ea22e Homepage: https://cran.r-project.org/package=MatchingPursuit Description: CRAN Package 'MatchingPursuit' (Processing Time Series Data Using the Matching Pursuit Algorithm) Provides tools for analysing and decomposing time series data using the Matching Pursuit (MP) algorithm, a greedy signal decomposition technique that represents complex signals as a linear combination of simpler functions (called atoms) selected from a redundant dictionary. Support for the Orthogonal Matching Pursuit (OMP) variant of the classical MP algorithm is also provided. For more details see Mallat and Zhang (1993) , Pati et al. (1993) , Elad (2010) and Różański (2024) . Package: r-cran-matchlinreg Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-matching Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-matchlinreg_0.8.1-1.ca2404.1_all.deb Size: 473740 MD5sum: 6a8f8c1e02ef6ad7f2d496dafe6b0317 SHA1: 044db7b18327907e35a9f18c98608e14bac3484c SHA256: 13ec18d4a93d15a2a693ae66cc900b5ad4ab5469540349fe9ceec0bb600a2d7c SHA512: d66d9491d8a4fe2991b170725891697775d7c05770c871e23a45f7eeb8dcc43eab6c652121fe60a07c6e1fa7b621787784898c2089524966bc4b27d419760655 Homepage: https://cran.r-project.org/package=MatchLinReg Description: CRAN Package 'MatchLinReg' (Combining Matching and Linear Regression for Causal Inference) Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies. Package: r-cran-matchmaker Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-forcats, r-cran-cli Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-matchmaker_0.1.1-1.ca2404.1_all.deb Size: 55208 MD5sum: d454a5de1e9df38a9aa522e6a5d9781d SHA1: 605693c0217148245176ad7eac11b624067c5f1e SHA256: a11b9e8eee7ee15ce402aa37217aa81e9eb6c8f3535d6c47b40bc39454b54739 SHA512: 0612fe1f72f8c386cf17dc679a927c5bb11af6739c0c65aa152a4964f87b2eb8f5187803ace62df5661a25f71211657fa242dd431229e8d280f283dad2a2519f Homepage: https://cran.r-project.org/package=matchmaker Description: CRAN Package 'matchmaker' (Flexible Dictionary-Based Cleaning) Provides flexible dictionary-based cleaning that allows users to specify implicit and explicit missing data, regular expressions for both data and columns, and global matches, while respecting ordering of factors. This package is part of the 'RECON' () toolkit for outbreak analysis. Package: r-cran-matchmulti Architecture: all Version: 1.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-rcbsubset, r-cran-plyr, r-cran-coin, r-cran-weights, r-cran-mvtnorm, r-cran-mass, r-cran-sandwich, r-cran-magrittr Suggests: r-cran-optmatch, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-matchmulti_1.1.15-1.ca2404.1_all.deb Size: 602450 MD5sum: 7987174ec485d5a5da9d3e9e391fec61 SHA1: cb4dd03216e2b12d35baee7655198495ae9ff5e4 SHA256: 6b7c5964cb2e3780926de92fa76acee1844888557d8a9acc34261921b8f098c8 SHA512: fb1a29723ff675ecb0270a4c031b06933991e837db2c16a33d2ec4a44e41fae1743bd19317531a847e83b8f1b048e37c4d567ec678dbcbb19c958242b7b9897d Homepage: https://cran.r-project.org/package=matchMulti Description: CRAN Package 'matchMulti' (Optimal Multilevel Matching using a Network Algorithm) Performs multilevel matches for data with cluster- level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis. Details in Pimentel et al. (2018) . The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at if not available on CRAN. Package: r-cran-matchpointr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chromote, r-cran-cli, r-cran-jsonlite, r-cran-magick, r-cran-purrr, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-httr2, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-matchpointr_0.1.0-1.ca2404.1_all.deb Size: 209276 MD5sum: cd15fbd607222e17578d3d0e9e0e8010 SHA1: 7e9b842aa2f13d7767c6b7b9eb0454fda1e69155 SHA256: 2ce6f647814044f9d03600223bd2a5ae4b6d871df6ba24f341006c66c65bd81d SHA512: 988d58dc606a7c09a5f187cef25e418883cd6ea8ccefdbb94fe904ebce374c49a8db3576b8ce35be3279d0a8a47bfaabdd9d9c90631000a0b3efed3d5b7dd4af Homepage: https://cran.r-project.org/package=matchpointR Description: CRAN Package 'matchpointR' (Tidy Access to Women's Tennis Association (WTA) Data) Scrapes and tidies publicly available data from the Women's Tennis Association website (). Provides helpers to retrieve player biographies, singles and doubles career overviews, match histories, live rankings and aggregate statistics. Dynamic pages are rendered through a headless 'Chrome' session so 'JavaScript'-generated content is fully captured, and all outputs are returned as tidy data frames suitable for downstream analysis or visualisation. Package: r-cran-matchr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Filename: pool/dists/noble/main/r-cran-matchr_0.1.0-1.ca2404.1_all.deb Size: 110968 MD5sum: 9d5b87e5d2f8193f273f779444243784 SHA1: faeceaf6e9baf1e4881a21cc8d886f2cc5f75752 SHA256: 2c9c5ae275c24d60ae478fdfebce2fe6085c0f55e08d5865121ca058685d2fbf SHA512: 2ceb58f62118173f6ed46dad5716ee184661d300bce6cf50c1d8966c034e6751b4ed75d0101f203bb0295bdcf491541715d60e45ee7bc72ea8e010a04ef998dd Homepage: https://cran.r-project.org/package=matchr Description: CRAN Package 'matchr' (Pattern Matching and Enumerated Types in R) Inspired by pattern matching and enum types in Rust and many functional programming languages, this package offers an updated version of the 'switch' function called 'Match' that accepts atomic values, functions, expressions, and enum variants. Conditions and return expressions are separated by '->' and multiple conditions can be associated with the same return expression using '|'. 'Match' also includes support for 'fallthrough'. The package also replicates the Result and Option enums from Rust. Package: r-cran-matchthem Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matchit, r-cran-mice, r-cran-rlang, r-cran-survey, r-cran-weightit Suggests: r-cran-amelia, r-cran-cobalt, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-matchthem_1.2.1-1.ca2404.1_all.deb Size: 606910 MD5sum: acc5103d6a4133c05b37de81f9726d1a SHA1: 04173b94ff967d733a9e227cf96daf292ed3bc44 SHA256: 47de8c1086cf83788a5453f05bf21d9171876dd8e4da82a974d5f11b4fb620cf SHA512: f34b7e5a6156ad43f2791ebf409910cc93a9fa9b39367e756e300e2fb1b0753c30fafc4539aa87df7c875b660df4a82bd18290d906b0840615afe610d0c481d3 Homepage: https://cran.r-project.org/package=MatchThem Description: CRAN Package 'MatchThem' (Matching and Weighting Multiply Imputed Datasets) Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see for more details). Package: r-cran-matconv Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-matconv_0.4.2-1.ca2404.1_all.deb Size: 90476 MD5sum: e04c4ced252a81f34671ddbc3daad963 SHA1: cc0ef863c14708d25276bbd7c3ae3e5658262650 SHA256: 875699c0e67b8c2832707947d10a4f3d3dee81bb0e48d80d7f3ddc4a6d4ebdce SHA512: ee9141e6c07c62c9da96ec6fc4fde8c2b983cf06450b0aaa8a4f443e607f09c40e8d764cd823aa040eaf46254d4bf15deec7f26ec549531b905b78fdaaf6dbda Homepage: https://cran.r-project.org/package=matconv Description: CRAN Package 'matconv' (A Code Converter from the Matlab/Octave Language to R) Transferring over a code base from Matlab to R is often a repetitive and inefficient use of time. This package provides a translator for Matlab / Octave code into R code. 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Package: r-cran-matpow Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bigmemory Filename: pool/dists/noble/main/r-cran-matpow_0.1.2-1.ca2404.1_all.deb Size: 360742 MD5sum: 81f3dcacad24fbf65e4fddec34456734 SHA1: f92f11a63bb7351433e9a9919366c19dbff1d862 SHA256: c1beba6c1cabf9f5a3a534bfb2fd1d9f64a35f06058b4604d11dd4be44dd4e64 SHA512: 20d412084bb6b3e2da0a93a7c350e548618eea4f7bb016c5708d623f45c29774ecd5753c671b4bbe7e8f6a2f63ff9da915daea36a2c9216eaa39538fd00a2af2 Homepage: https://cran.r-project.org/package=matpow Description: CRAN Package 'matpow' (Matrix Powers) A general framework for computing powers of matrices. A key feature is the capability for users to write callback functions, called after each iteration, thus enabling customization for specific applications. Diverse types of matrix classes/matrix multiplication are accommodated. 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Package: r-cran-matrans Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-formatr, r-cran-glmnet, r-cran-mass, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-matrans_0.2.0-1.ca2404.1_all.deb Size: 155664 MD5sum: 7ff9a62c708e5987ce57f97164e57b02 SHA1: 9a6baa7449f25ee818249c75820299bb2a73b5e8 SHA256: c8bb9b5d4a43b9d537bc8d01b5b97fd3e8c55fa0b5a241ea1f8f72e74107d3ba SHA512: acf28aa811407dd3e6628937882dc5e230d5e24fb83a33c9b04f9fb0eb4ee5bbc9ddce4357f5923c6f50ad8cd6f23430cf9e15c20c138a482907881f81fd0d6a Homepage: https://cran.r-project.org/package=matrans Description: CRAN Package 'matrans' (Model Averaging-Assisted Optimal Transfer Learning) Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) . Package: r-cran-matriks Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-matriks_0.1.5-1.ca2404.1_all.deb Size: 355158 MD5sum: 5e57db38b60319be6b4810c77f4b3c4a SHA1: da78f5d2e2c75064136fb94409a00b66ffa2ab6a SHA256: 6483afae38bacf1d64e7e7ff2d8072afdffaa506365057f80b8c4a40c5bb90de SHA512: 01f3a41cfa72b03556034c9c2c486c28e8ad30e3a7c5974ac8c8037d43b8fc78cb547ca4ddc4f03f67d07adf7a66de6d58d7249c4d5fb14f5012caf972b90623 Homepage: https://cran.r-project.org/package=matRiks Description: CRAN Package 'matRiks' (Generates Raven-Like Matrices According to Rules) Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the 'DescTools' package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research. Package: r-cran-matrisk Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-sn, r-cran-dfoptim, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-matrisk_0.1.0-1.ca2404.1_all.deb Size: 37448 MD5sum: 1d393c00a02ac1b5f9b605087e3a2142 SHA1: 9698ddafbc1e27496ab9f31118df6359fb1a26a0 SHA256: 09c4eeb70851e6dab4e032ffc84d3d5a8408d9075df99a50ee6c92568d6ec639 SHA512: 63eb9aad171495a6d7a836ae7bed6a8ff60946063cf6ef4a568b6417d2944028f00f7b2f32dfc29ff672edd8cd78ce3b8dc1693e1a62f93a0c97e8cf050b0a48 Homepage: https://cran.r-project.org/package=matrisk Description: CRAN Package 'matrisk' (Macroeconomic-at-Risk) The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers. Package: r-cran-matrixcalc Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-matrixcalc_1.0-6-1.ca2404.1_all.deb Size: 202942 MD5sum: ce1df4cb0fee3008f5bb9e7e5f695674 SHA1: fe102d20f83e000cacfa5d62d698d70a14bd50fc SHA256: deb63f61dd7e47465fdfcc55876b4b69542f6399644ace19ba36a3be2a90402c SHA512: 763a4a96244487fdca8a39fcb4d3d127267da3582b4920db1a2a7533924d9ccc1727f7bbadb8e28527473d67e6bc5574e2ef00275285ad831f85f621d7c2aa8c Homepage: https://cran.r-project.org/package=matrixcalc Description: CRAN Package 'matrixcalc' (Collection of Functions for Matrix Calculations) A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563. Package: r-cran-matrixcut Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-inflection Filename: pool/dists/noble/main/r-cran-matrixcut_0.0.1-1.ca2404.1_all.deb Size: 62510 MD5sum: 6df570102980729aa26d5d807de0c8bd SHA1: b22fa85dbd0a393e398196f465e6688fef294352 SHA256: 3deead7884a2d8d79428949cc0989c4b4cd935b9042a0ff680592d68de3f44d1 SHA512: 07925e5adea7c844738dde0570d1a55d0150b13b92ac4a62243b6c4441345400efacc8fae95b5ddb8a2142aa4675048478d9f599807ea16b6aafe3f2c4d631c8 Homepage: https://cran.r-project.org/package=matrixcut Description: CRAN Package 'matrixcut' (Determines Clustering Threshold Based on Similarity Values) The user must supply a matrix filled with similarity values. The software will search for significant differences between similarity values at different hierarchical levels. The algorithm will return a Loess-smoothed plot of the similarity values along with the inflection point, if there are any. There is the option to search for an inflection point within a specified range. The package also has a function that will return the matrix components at a specified cutoff. References: Mullner. ; Cserhati, Carter. (2020, Journal of Creation 34(3):41-50), . Package: r-cran-matrixeqtl Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-matrixeqtl_2.4-1.ca2404.1_all.deb Size: 337294 MD5sum: e13a1f5c576860ba077caa4227077a7c SHA1: f9f8c901d750390492d94183718d40353e448f6a SHA256: b7bb585b870b729a2be6f99809c6149fd07020107554c916e5ecb53cb6e01115 SHA512: 3703eef9af93223e7578be472811a59d67a6469efc96d8f2f529cb1bd834d1fd1fb5bd99ef27e57b0f1123c54fc5a7761d3ae44b26e4ea5e5eee4ce758580787 Homepage: https://cran.r-project.org/package=MatrixEQTL Description: CRAN Package 'MatrixEQTL' (Matrix eQTL: Ultra Fast eQTL Analysis via Large MatrixOperations) Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for association between genotype and gene expression using linear regression with either additive or ANOVA genotype effects. 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Package: r-cran-matrixhmm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dosnow, r-cran-foreach, r-cran-laplacesdemon, r-cran-mclust, r-cran-progress, r-cran-snow, r-cran-tensor, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-matrixhmm_1.0.0-1.ca2404.1_all.deb Size: 127410 MD5sum: c35ea06f23e46ca26e43991e056e85c6 SHA1: 19b7e492ac7fb31775bf516dce71bd5e8799cc89 SHA256: a1e54ec4413d45bfc6af2f5a24f8b179282f0efc9977d7bcd74cc655b709a573 SHA512: 8485c684fa1cb3d7cfccb37e4c51d2183305639f57146cf92f5af44e1b859038a7254741879b31f5e9405a4467697afe36e9119562bd732249965ca3665dfb89 Homepage: https://cran.r-project.org/package=MatrixHMM Description: CRAN Package 'MatrixHMM' (Parsimonious Families of Hidden Markov Models for Matrix-VariateLongitudinal Data) Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. 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One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms. 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The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo. Package: r-cran-maxeff Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-groupedhyperframe, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-rpart, r-cran-spatstat.geom Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-maxeff_0.2.3-1.ca2404.1_all.deb Size: 76368 MD5sum: 1e8e71aadc113bb4c59941c418a14ef0 SHA1: 2e1e46cc17a3efa699d2a90f2bec4694229ddb5d SHA256: 5b47e7a585d8555a469e0fc2c98f81de794017383dc1d25b36532c1bbdfd62de SHA512: abb9f47caa2f712086f05b9347ad22820dff2e8864b2b82ef02197999cdbd21467f6bf4ebc41be75ca1e46ac08f83dbc93c6c9d25ee60981e6f1867c3320a59a Homepage: https://cran.r-project.org/package=maxEff Description: CRAN Package 'maxEff' (Additional Predictor with Maximum Effect Size) Methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size. Package: r-cran-maxent.ot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maxent.ot_1.0.0-1.ca2404.1_all.deb Size: 994062 MD5sum: 29c3892ccdc06c0407cb15b3059441b6 SHA1: fb3269b9fa394547694caeec5006946ee8561923 SHA256: c5c614c45cd542d30b9fcb6ba654ed7933823ec8de8cdc8ada2cc2cbabceb930 SHA512: 96451926897bd4f908fe3e8faa45ef67c23330799ee143f28f7a9d3cf00fa04c75e52bafadd9f4cb78ffd9ac589805846c0be817070ab4cd00f41f7a6110ba46 Homepage: https://cran.r-project.org/package=maxent.ot Description: CRAN Package 'maxent.ot' (Perform Phonological Analyses using Maximum Entropy OptimalityTheory) Fit Maximum Entropy Optimality Theory models to data sets, generate the predictions made by such models for novel data, and compare the fit of different models using a variety of metrics. The package is described in Mayer, C., Tan, A., Zuraw, K. (in press) . Package: r-cran-maxentvariableselection Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maxentvariableselection_1.0-3-1.ca2404.1_all.deb Size: 409330 MD5sum: 4e7504d4e91326cd3ea4977f8c958600 SHA1: 0ffb464e150beb448402250eb1cf633c888f63af SHA256: 470969cf7a396220a07b53ad496b6c4affdcfacd7872a77d30b57942318e3c12 SHA512: fa54a7da07e8df8226dc0f8cb4f54f37579add27fcbe1c35a9260d12ace12fa529b67e8719adfba693254a8decec33879b50cf3e62b1bc498d54a1dac0d1877b Homepage: https://cran.r-project.org/package=MaxentVariableSelection Description: CRAN Package 'MaxentVariableSelection' (Selecting the Best Set of Relevant Environmental Variables alongwith the Optimal Regularization Multiplier for Maxent NicheModeling) Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 ; Warren et al., 2014 ). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 ), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 ). Users of this package should be familiar with Maxent niche modelling. Package: r-cran-maximin Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plgp Suggests: r-cran-lhs Filename: pool/dists/noble/main/r-cran-maximin_1.0-6-1.ca2404.1_all.deb Size: 46084 MD5sum: 24acfaf9eaf23f9d42e3b1ef651d1446 SHA1: d05722668dd48441cdb55d05d13067dd37e5334d SHA256: 49e5c88fa8917f657343eca45c2750aa489fb1fa671ae9031155fb97a5d85a46 SHA512: 40c44a22529bf916097a7b343d5d6ec5ebc4761791e15bfbda010cd1ef2190942ce58536ceac187f7ef55cba9bd16f5d105602b86760b4b4a708092476a8bfd9 Homepage: https://cran.r-project.org/package=maximin Description: CRAN Package 'maximin' (Space-Filling Design under Maximin Distance) Constructs a space-filling design under the criterion of maximum-minimum distance. Both discrete and continuous searches are provided. Package: r-cran-maximininfer Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-cvxr, r-cran-glmnet, r-cran-intervals, r-cran-sihr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maximininfer_2.1.0-1.ca2404.1_all.deb Size: 48274 MD5sum: c05546850a4d391b925b34e09da89e11 SHA1: b6ae9166199f8658a167296c634f7c7faf01f6f1 SHA256: cc5de3d7b7e7b4d13a7b3a0628f8aa647b4edae8aefc911ed396ee24328b08ed SHA512: fea11968e9848f14099287634f4730bbd0f36d27aa706ca908bd58a93c3e747c7730508b1c5cbed213b5bfd3643ca4b43d372c2b3e4fb7fcee60e1c382d9aaf5 Homepage: https://cran.r-project.org/package=MaximinInfer Description: CRAN Package 'MaximinInfer' (Inference for Maximin Effects in High-Dimensional Settings) Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in . It constructs the confidence interval for any linear combination of the high-dimensional maximin effect. Package: r-cran-maxinttools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-reshape, r-cran-pracma Filename: pool/dists/noble/main/r-cran-maxinttools_0.1.0-1.ca2404.1_all.deb Size: 125808 MD5sum: c33cd861472ef5ef1d22544ed2210afb SHA1: d6953859a716fb8e72b06971c01d0a65719e18f8 SHA256: a989b025bdc266d7c61d8d7adb07cba3297e166e7c41a45888401e47135bd926 SHA512: 65b82499f6d323ca745bd7db0db1c1fac1e607e67c9fbaf08f9367c3567e3ccda0ed7e409214cd0df7c2ffdd1c419631c6e0ddf5e6669b55c9951ff8f2effc41 Homepage: https://cran.r-project.org/package=MaxIntTools Description: CRAN Package 'MaxIntTools' (Testing Maximal Interaction in Two-Mode Clustering via aPermutation Based Procedure) Performs maximal interaction two-mode clustering, permutation tests, scree plots, and interaction visualizations for bicluster analysis. See Ahmed et al. (2025) , Ahmed et al. (2023) , Ahmed et al. (2021) . Package: r-cran-maxlik Architecture: all Version: 1.6-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-misctools, r-cran-sandwich, r-cran-generics Suggests: r-cran-mass, r-cran-clue, r-cran-dlm, r-cran-plot3d, r-cran-tibble, r-cran-tinytest, r-cran-knitr Filename: pool/dists/noble/main/r-cran-maxlik_1.6-10-1.ca2404.1_all.deb Size: 1112194 MD5sum: 1fcb8f8482de23b6b98b0335badfe9d3 SHA1: d179164d4fff9457373fc976e98fd6dcacddb04e SHA256: dcad595f6026758b466ff9b581fc0d8e6c0ce7153392b1dd4818ffd727978cff SHA512: cba2ec67c11fedf3d172b8aa1422331f31e3f2c6d5b175d0f0e31eec789180e8bc8a5a619f6230c39ef8bb365d46548b4979be0f60e85b8e094595c33ff2146a Homepage: https://cran.r-project.org/package=maxLik Description: CRAN Package 'maxLik' (Maximum Likelihood Estimation and Related Tools) Functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models. Package: r-cran-maxlike Architecture: all Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster Suggests: r-cran-dismo Filename: pool/dists/noble/main/r-cran-maxlike_0.1-12-1.ca2404.1_all.deb Size: 518706 MD5sum: 6a385faf3ffb55c8e3379f5801c1d595 SHA1: b3df45ee7998baac05ddeaa391bffacf97059759 SHA256: 7882d8aa86b41d32b21214b99a3eb2bec315ccd0df5a59bd6286c786e8eaaeb5 SHA512: cbdb4a1be8c229da14ea21723faaeb857c693aeedee01bb8c05509298124c966f0006aa2663fe4e7dc4bbe3c3f072f7512ff622d4c9067cb56296ffe164494ad Homepage: https://cran.r-project.org/package=maxlike Description: CRAN Package 'maxlike' (Model Species Distributions by Estimating the Probability ofOccurrence Using Presence-Only Data) Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution. Package: r-cran-maxmatching Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-maxmatching_0.1.0-1.ca2404.1_all.deb Size: 60330 MD5sum: 728ffd0763df58536ffd542a67d23f33 SHA1: 5226aa2a89151d7dd8dc3568d3e65fa4d8747d78 SHA256: ea6e1b681a49e8bd3b899fda6f53e7e6720071bf774e77304df5e85383a90baf SHA512: 4226b2111260b4b6db7ca7099c18a0c02ebfd14508e1d67130816ed7924aba389ec1d44c3a4400d680006271619605a05694e8e8bf62a78fbb1600547cdc2b4a Homepage: https://cran.r-project.org/package=maxmatching Description: CRAN Package 'maxmatching' (Maximum Matching for General Weighted Graph) Computes the maximum matching for unweighted graph and maximum matching for (un)weighted bipartite graph efficiently. Package: r-cran-maxmc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gensa, r-cran-pso, r-cran-ga, r-cran-nmof, r-cran-scales Suggests: r-cran-funitroots, r-cran-microbenchmark, r-cran-boot, r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maxmc_0.1.2-1.ca2404.1_all.deb Size: 86318 MD5sum: d4feaf08f3cccc4557141567b05b57c7 SHA1: 9e48059c60bd55eeebba38f51dd863c9e9edf7c0 SHA256: 0c153863c1d10316f1077657a80b77c58be8100405ff19fdedca91f85014d9dc SHA512: 8b8bc079e4fd49490aebc2a56b1ff22c19aa22cb3db5dca94d736e29636aca61aa419ec985120096e2e439da07a2704876da8ae34892c9a51924d7384c938be7 Homepage: https://cran.r-project.org/package=MaxMC Description: CRAN Package 'MaxMC' (Maximized Monte Carlo) An implementation of the Monte Carlo techniques described in details by Dufour (2006) and Dufour and Khalaf (2007) . The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters. Package: r-cran-maxnet Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-maxnet_0.1.4-1.ca2404.1_all.deb Size: 66032 MD5sum: af1ac14290bfb2dafe51ab7cc995d4ac SHA1: f4ea6b2796bf2a256946ed8bdbb1a05ab13ae0f6 SHA256: 7ce8f32198394ad3ac2d596a4fc10867ac6ae3852539ff75879e5712c383485a SHA512: 12507d0358685a109d5317957a781dc305133a5d1e4b6e6ecf6922c3b389b78817694c39e57e2003810cf11c39595536003cad37bff04e0f4194519cc2c7b376 Homepage: https://cran.r-project.org/package=maxnet Description: CRAN Package 'maxnet' (Fitting 'Maxent' Species Distribution Models with 'glmnet') Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options. See the 'Maxent' website for more details. Package: r-cran-maxrgain Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-devtools, r-cran-usethis Filename: pool/dists/noble/main/r-cran-maxrgain_1.1.0-1.ca2404.1_all.deb Size: 75876 MD5sum: a1cd85a25865a3d5d09ec4dfd9e25799 SHA1: 475c58992b1f6aa52067978fcb14bced6db129d9 SHA256: 4d24beef09270edfb3e70444e106b139de50a5c19cb8d64fd05e66620d354e1a SHA512: 65db4a3169ac2baa0d6b1414d6f9c14768787089106394f907e719e5e0e7b263ed05a4045f2f27a5c18568411baa37301b0634c67d36b5c726532a6f535bc95e Homepage: https://cran.r-project.org/package=maxRgain Description: CRAN Package 'maxRgain' (Maximizing Polyclonal Selection Gains Using Integer Programming) Implements an Integer Programming-based method for optimising genetic gain in polyclonal selection, where the goal is to select a group of genotypes that jointly meet multi-trait selection criteria. The method uses predictors of genotypic effects obtained from the fitting of mixed models. Its application is demonstrated with grapevine data, but is applicable to other species and breeding contexts. For more details see Surgy et al. (2025) . 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Package: r-cran-maxwik Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-abc, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-maxwik_1.0.6-1.ca2404.1_all.deb Size: 1181088 MD5sum: 0d91797da0a1614d7a0e22fb47c641bb SHA1: 49cc85735e0311b08d24dd2c03359e6ce8e8555a SHA256: 622beb67ab2dae3c2b4d86f706275f37f022769bed11ce9b27fc355d819b45f9 SHA512: 0f95a9b2df6421844361a94e74cffafe646aa570a3e9ee199dd1786f0e827593723c329791c9360bb9f588b57a62d444f928ced1e132b5a8c845c71eb93fd94c Homepage: https://cran.r-project.org/package=MaxWiK Description: CRAN Package 'MaxWiK' (Machine Learning Method Based on Isolation Kernel Mean Embedding) Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) and Iurii Nagornov (2023) . 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The advantage of using a maybe type over `NULL` is that it is both composable and requires the developer to explicitly acknowledge the potential absence of a value, helping to avoid the existence of unexpected behaviour. 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Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181). Package: r-cran-mbend Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbend_1.3.1-1.ca2404.1_all.deb Size: 33950 MD5sum: 7ab283c52679b8b1d25894e727040368 SHA1: 7af107297a0ef919136551acbf1c480cd7d8399f SHA256: ec339c7c80959012fb2bdcae6ab2da52ddea227ae15ca5c59455bfec903ae658 SHA512: 1958444dfdd8c3dc49d7c2db78809b927469b107c1cad7d8f381f700282726704a919c1171a11a03126de40f75f65f533750453eda85fa12034c841691b664a4 Homepage: https://cran.r-project.org/package=mbend Description: CRAN Package 'mbend' (Matrix Bending) Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods. Jorjani, H., et al. (2003) . Schaeffer, L. R. (2014) . Package: r-cran-mbess Architecture: all Version: 5.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-lavaan, r-cran-mass, r-cran-mnormt, r-cran-nlme, r-cran-openmx, r-cran-sem, r-cran-semtools Suggests: r-cran-gsl Filename: pool/dists/noble/main/r-cran-mbess_5.0.1-1.ca2404.1_all.deb Size: 1044040 MD5sum: 212f9565e72057e3fb68f898dbf84f57 SHA1: 8163b2a3c3215365269ab123c1f4fcaa3e8335c3 SHA256: 69075c1c84ac27a05ffa7c7b790d653b00dc5d457dae6bc0e1fc0c81de5dda14 SHA512: 6a05f1da01134c2ed07828add2dac5b95f93646762909ae5c14cd953d8ddd3d39a55654c75b06b91cda05e397817d1c0a1fe2607c747a15ecc59f9d5f99959c5 Homepage: https://cran.r-project.org/package=MBESS Description: CRAN Package 'MBESS' (The MBESS R Package) Implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from the contributions of many people over the years, who are acknowledged in the package documentation. Package: r-cran-mbg Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-caret, r-cran-data.table, r-cran-glue, r-cran-matrix, r-cran-matrixstats, r-cran-purrr, r-cran-r6, r-cran-sf, r-cran-terra, r-cran-tictoc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-mbg_1.2.0-1.ca2404.1_all.deb Size: 3225138 MD5sum: 82e32420e0e5eb869e1fcec092f8a192 SHA1: 973934f52df519ce5a03b4cd6d3d26b7d89c4b1c SHA256: ed85612235b616a143405e9b3c4050aac1838d6ff96c828e52e523afb78c9e93 SHA512: d6bfd057b6cbe8bacfa9409e6eb46c8e806b7bc6c8228f7eb7974ef2bc2d3b6254dc93abaf3d50a8f15a7216af1129a4265e78a2255171867d5041534f134482 Homepage: https://cran.r-project.org/package=mbg Description: CRAN Package 'mbg' (Model-Based Geostatistics) Modern model-based geostatistics for point-referenced data. This package provides a simple interface to run spatial machine learning models and geostatistical models that estimate a continuous (raster) surface from point-referenced outcomes and, optionally, a set of raster covariates. The package also includes functions to summarize raster outcomes by (polygon) region while preserving uncertainty. Package: r-cran-mbgapp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-geor, r-cran-ggplot2, r-cran-shiny, r-cran-sf, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-magrittr, r-cran-leaflet, r-cran-leafem, r-cran-tidyterra, r-cran-stars, r-cran-riskmap, r-cran-terra, r-cran-shinyjs, r-cran-httr2, r-cran-rmarkdown Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbgapp_0.1.0-1.ca2404.1_all.deb Size: 326062 MD5sum: d584db38321c2025d552ed2dd5b2a763 SHA1: 27f2b277e03e80e3db9715473cfcea23dad9fb76 SHA256: 07f16267840efa29e0e56febb9b985fc56601272f9a32a38e53ef6b2140fc9fc SHA512: e83148de4bed5a077ebcbbd8a5b90b966217d54431dd41d84b749e2fae553e39fa184351bff5270fd2aee7f56d7f06e3298416dcb4b1c058ff010bfc3bac6743 Homepage: https://cran.r-project.org/package=MBGapp Description: CRAN Package 'MBGapp' (Interactive 'shiny' Application for Model-Based Geostatistics) Provides an interactive 'shiny' application for teaching and applied analysis of geostatistical data. Users can explore spatial data, assess spatial correlation through the empirical variogram, fit model-based geostatistical models for continuous, prevalence and count outcomes, produce spatial predictions, and download reports. The methodology follows the model-based geostatistics framework of Diggle and Giorgi (2019, ISBN:9781138732353). Package: r-cran-mbhdesign Architecture: all Version: 2.3.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-geometry, r-cran-randtoolbox, r-cran-mvtnorm, r-cran-class, r-cran-terra Suggests: r-cran-spsurvey, r-cran-mass, r-cran-fields, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mbhdesign_2.3.22-1.ca2404.1_all.deb Size: 608684 MD5sum: 43f383261199cbb391546a7339ab811c SHA1: 4346b0c75fb447d3d896eb848b0f8f198849b944 SHA256: ae06dfbdcd8e88588a901bf96aab6f62362ac2f8891950d9e54027972b590bb5 SHA512: 088e4ae40eb7163516a23d718ff0dbca96ebca122ae605e5d823e6e48956bf43e1c61a3bfb25edae8386ad919eb70b103c164b433680e2f19cd369a3ee6465c7 Homepage: https://cran.r-project.org/package=MBHdesign Description: CRAN Package 'MBHdesign' (Spatial Designs for Ecological and Environmental Surveys) Provides spatially survey balanced designs. Information about the package itself is given in Foster (2021) . Designs using MBHdesign can: 1) accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 ); 2) be based on points or transects (foster et al. 2020 and produce clustered samples (Foster et al. (in press). The base idea that these designs stem from is the quasi-random number method described Robinson et al. (2013) and adjusted in Robinson et al. (2017) . Package: r-cran-mbir Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-effsize, r-cran-psych Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbir_1.3.5-1.ca2404.1_all.deb Size: 437928 MD5sum: fa4cb50340291a282e297288d85100a5 SHA1: 5e8c4452292edad4331769b85c92d950399731c5 SHA256: f584e533ee77f1d301f93e3454a26324dd3095b06c5dfc12ff68eea4f3f4332e SHA512: 2ce6b8b7d4160b71f166d200a26c0b14f48dfa28f679bc05f6cc8d4e3885a7c9d030fc89fd2c09ed8d04ec9252af47bec1ea76a8197c01f56324b74e6cf4a3d6 Homepage: https://cran.r-project.org/package=mbir Description: CRAN Package 'mbir' (Magnitude-Based Inferences) Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently. Package: r-cran-mblm Architecture: all Version: 0.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mblm_0.12.1-1.ca2404.1_all.deb Size: 26642 MD5sum: 6cc9a8cef80eb020e491191a5e8877ce SHA1: b754c5408df813a7d144fa0d5458f67fc44ec16e SHA256: e1b86b1de521fc6dc52fc6cfb6f066a9b346d7ab09acad4dc928d8f098783677 SHA512: b6e6b22cbf000d86f5f869b17bfc47cefbd446e51f22dde05c53582c57129f169dcd017475bb79264967f67c8d959a0a986c6e6e28d3eecda676e77994df7026 Homepage: https://cran.r-project.org/package=mblm Description: CRAN Package 'mblm' (Median-Based Linear Models) Provides linear models based on Theil-Sen single median and Siegel repeated medians. They are very robust (29 or 50 percent breakdown point, respectively), and if no outliers are present, the estimators are very similar to OLS. Package: r-cran-mbmca Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-chippcr Suggests: r-cran-spelling, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mbmca_1.1-0-1.ca2404.1_all.deb Size: 308698 MD5sum: b3d19f0d15db212ec73667ee5f604fda SHA1: a47ffba93869fb04af9144ad38222a6a769a559b SHA256: 2317cc9b372ab43e28149bc0e3407ce5c678f2a93572f357ecbea3127d6dcf9c SHA512: 3670317fae8ba625de0e7ed63ccb3374f09cb8891434a7b8498da17d0633fc645cc66c8c9fccfbd90afcda2e7cb094f8c1495111384de3c5d5f1bf386d0900ff Homepage: https://cran.r-project.org/package=MBmca Description: CRAN Package 'MBmca' (Nucleic Acid Melting Curve Analysis) Lightweight utilities for nucleic acid melting curve analysis are important in life sciences and diagnostics. This software can be used for the analysis and presentation of melting curve data from microbead-based assays (surface melting curve analysis) and reactions in solution (e.g., quantitative PCR (qPCR), real-time isothermal Amplification). Further information are described in detail in two publications in The R Journal [ ; ]. Package: r-cran-mbmethpred Architecture: all Version: 0.1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-ggplot2, r-cran-catools, r-cran-caret, r-cran-keras, r-cran-mass, r-cran-rtsne, r-cran-snftool, r-cran-class, r-cran-dplyr, r-cran-e1071, r-cran-proc, r-cran-randomforest, r-cran-readr, r-cran-reshape2, r-cran-reticulate, r-cran-rgl, r-cran-tensorflow, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-scales Filename: pool/dists/noble/main/r-cran-mbmethpred_0.1.4.4-1.ca2404.1_all.deb Size: 4253314 MD5sum: 6d31bb79bb8b33b732850e00f431afcc SHA1: 200317af63f539351892fd7e1adff31557449fe9 SHA256: daf6494deb365cef22fbfbce706d5d75cc20fc1393e611e14fa3f80476b2f6db SHA512: b792a0a0f4712b8f4b73ea6c6ec640b6c15dcea6c93c102d6c374febfb5bc211daaa6f1b2e4f976949c25311eaeb466a7cac33248b62f7c73217659fedd10a66 Homepage: https://cran.r-project.org/package=MBMethPred Description: CRAN Package 'MBMethPred' (Medulloblastoma Subgroups Prediction) Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. for more details. Package: r-cran-mbmixture Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-mbmixture_0.8-1.ca2404.1_all.deb Size: 48808 MD5sum: 8ae730370cf38c7a353eba379f5c9244 SHA1: c683f6296ecd97ac598ccef523da480ace7b5056 SHA256: 375fef59d55c7d43b8aa2ed6bf0c541a2ce1b0aea16507a7c2ffccb9f934eeb2 SHA512: bd30480497a9699ec37e89b3e7e461d1d4bcc33ca0df29333cff39984fb45a659f097e8b599c30e91dd251039f1668ec120b5e33d4083486bdd4885b0956ad27 Homepage: https://cran.r-project.org/package=mbmixture Description: CRAN Package 'mbmixture' (Microbiome Mixture Analysis) Evaluate whether a microbiome sample is a mixture of two samples, by fitting a model for the number of read counts as a function of single nucleotide polymorphism (SNP) allele and the genotypes of two potential source samples. Lobo et al. (2021) . Package: r-cran-mbnmadose Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1896 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-scales, r-cran-dplyr, r-cran-r2jags, r-cran-rjags, r-cran-magrittr, r-cran-checkmate, r-cran-rdpack, r-cran-igraph, r-cran-ggplot2, r-cran-overlapping, r-cran-reshape2 Suggests: r-cran-rcolorbrewer, r-cran-coda, r-cran-testthat, r-cran-crayon, r-cran-ggdist, r-cran-zoo, r-cran-formatr, r-cran-netmeta, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbnmadose_0.5.1-1.ca2404.1_all.deb Size: 1284272 MD5sum: 8e10a7d9783ce293649cae516eb6e2bb SHA1: 4f015b3af11b59084a8055eb00bf6b7aaffaa46d SHA256: 6d9c4dec31ad150294663d9315db4fb92b0d43989962d3da28e8d2b52edb1db6 SHA512: 38364bd6f23d012538f74175af65d7a14f7d4c70e4fd3be394eeddb2f7143e32843415f56537e932b5f5b40b6e4c3579f43454b70ea373991ca2bc3d4e2add9a Homepage: https://cran.r-project.org/package=MBNMAdose Description: CRAN Package 'MBNMAdose' (Dose-Response MBNMA Models) Fits Bayesian dose-response model-based network meta-analysis (MBNMA) that incorporate multiple doses within an agent by modelling different dose-response functions, as described by Mawdsley et al. (2016) . By modelling dose-response relationships this can connect networks of evidence that might otherwise be disconnected, and can improve precision on treatment estimates. Several common dose-response functions are provided; others may be added by the user. Various characteristics and assumptions can be flexibly added to the models, such as shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting at the treatment level. Package: r-cran-mbnmatime Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3031 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-gridextra, r-cran-dplyr, r-cran-r2jags, r-cran-rjags, r-cran-reshape2, r-cran-magrittr, r-cran-checkmate, r-cran-igraph, r-cran-scales, r-cran-lspline, r-cran-crayon, r-cran-ggplot2, r-cran-ggdist, r-cran-png, r-cran-zoo, r-cran-rdpack Suggests: r-cran-overlapping, r-cran-hmisc, r-cran-rmarkdown, r-cran-testthat, r-cran-rcolorbrewer, r-cran-mcmcplots Filename: pool/dists/noble/main/r-cran-mbnmatime_0.2.6-1.ca2404.1_all.deb Size: 2543418 MD5sum: 896f821686af2a183c59891b0c880d55 SHA1: e989620fae901e0e11cf90f2c415e406ecc6848b SHA256: 2c8ce550da41d5dc21a59a6f55767ea2079756a512d56742e1b6694d37d0d05a SHA512: 44edc0d106ed4435d2a8f6145a9f43a912498411cc1fba911feebac04efb97c2e1710497ceadd3b00b5cd0908d51e837e0985fa53357bcbd05a7d5923ffe2e7d Homepage: https://cran.r-project.org/package=MBNMAtime Description: CRAN Package 'MBNMAtime' (Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models) Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) . The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting. Package: r-cran-mbr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplr, r-cran-mass, r-cran-matrix, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbr_0.0.1-1.ca2404.1_all.deb Size: 84818 MD5sum: 7701438aade7b1e1a517666b1cd61e7f SHA1: aa8240e77aea7f12600d65e0f5162e15a022ea60 SHA256: e8b15d703c7ce67175b5874a7fd535c5a76b2b6544d13881d2579daada78be37 SHA512: 1ed85e01cd0a96537d860e0e2736392490a43fd863b3373c6b7f5912087c6ae31e4f0ee5d24211e692b34eb4947fa6b49b289cb1afbc71c0a8f0665abcc5276e Homepage: https://cran.r-project.org/package=mbr Description: CRAN Package 'mbr' (Mass Balance Reconstruction) Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) . Package: r-cran-mbrdr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mbrdr_1.1.1-1.ca2404.1_all.deb Size: 62298 MD5sum: 986f692075828158570fcddbb449c47e SHA1: a27a16159961b1948ce81023269a931e03b51b0a SHA256: 0955e612c8442bc34f4721941b8c0fc4178a149b4b84ce5e4c3f32fd8283f624 SHA512: c7927bf36626cbcc80755291dfde10849b58bc7f014c6bcc121a0f581c837239b0de2900e63e99fe6d1a4a4a0f29c169a194d5c0a8b2fb1cd0b5b25ed151bb27 Homepage: https://cran.r-project.org/package=mbrdr Description: CRAN Package 'mbrdr' (Model-Based Response Dimension Reduction) Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses. The response dimension reduction is theoretically founded in Yoo and Cook (2008) . Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) and Yoo (2019) . The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace. Package: r-cran-mbreaks Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbreaks_1.0.1-1.ca2404.1_all.deb Size: 316492 MD5sum: 73da66950fe8ba1847cc82b705c01075 SHA1: acf1e92fe5ee867943fd6627eecacaedc19b789a SHA256: 68e5dbe4049599403040742658a9887d20361a8e9152154a0fcd0fded3539ed2 SHA512: d7d4a6ed2814711a095a3ce64f3dc71657d9e7520aa770c57b72e5757789256073c25fef5209594170389f9c73c235f9d310f1961f39a2e4785ed012ac913750 Homepage: https://cran.r-project.org/package=mbreaks Description: CRAN Package 'mbreaks' (Estimation and Inference for Structural Breaks in LinearRegression Models) Functions provide comprehensive treatments for estimating, inferring, testing and model selecting in linear regression models with structural breaks. The tests, estimation methods, inference and information criteria implemented are discussed in Bai and Perron (1998) "Estimating and Testing Linear Models with Multiple Structural Changes" . Package: r-cran-mbres Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-forcats, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-scales Suggests: r-cran-rprobsup Filename: pool/dists/noble/main/r-cran-mbres_0.1.7-1.ca2404.1_all.deb Size: 114340 MD5sum: 23c63d96d2c168ff4a4d96ef8baaf837 SHA1: 19350068ea80b2259819aaa820317cef596a53f2 SHA256: 1d2ca156d7d972ab1c67ff8de23246211b71c80de6ca6c2e17a6ac633b689a11 SHA512: b1772f78fff568eca767180661e549bfe0fcdd6d3c8899000af9954c00700c973259d5c3fd6f2eac0e0ddd622ef1d94f8549b4afe9092368d521abbc2c59051f Homepage: https://cran.r-project.org/package=mbRes Description: CRAN Package 'mbRes' (Exploration of Multiple Biomarker Responses using Effect Size) Summarize multiple biomarker responses of aquatic organisms to contaminants using Cliff’s delta, as described in Pham & Sokolova (2023) . Package: r-cran-mbsgs Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mass, r-cran-mgcv, r-cran-mnormt, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-mbsgs_1.2.0-1.ca2404.1_all.deb Size: 98642 MD5sum: 937d96f4737db561ed42fa5fe93deb43 SHA1: 6c434b536844e88ea024ea26ce7508a485bf8fa2 SHA256: 9a56155c6ec2b4b62fff2bdbd1536031e944fe375d4db25b2a21f5476b8853ed SHA512: 7ba379d3b4bda8f761214473627c14c0b596c874e975e4a0afe3219f8eb868e879c865f3228bbc03c801e9a98ae31fe0553c1b5a371712f3ad7b4f3e60d1e4ef Homepage: https://cran.r-project.org/package=MBSGS Description: CRAN Package 'MBSGS' (Multivariate Bayesian Sparse Group Selection with Spike and Slab) An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response. Package: r-cran-mbsp Architecture: all Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-gigrvg, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mbsp_5.0-1.ca2404.1_all.deb Size: 36938 MD5sum: 088ae2cf8507c8022929b31ed99eb636 SHA1: 5423c070d7e0458008e997070274e9fc3529bf93 SHA256: 7475570990ffc938c5881c12f531f3b7ac569cc80380a6abfad17bda253e8175 SHA512: 4f69c8cc6bc646214911e69d3237d49826c1c20014edd7e1a2b09dbd7cde064ba10ab1c911b1202866b1b9962caf11ee0aad678f28ae95d42589bd4903d18360 Homepage: https://cran.r-project.org/package=MBSP Description: CRAN Package 'MBSP' (Multivariate Bayesian Model with Shrinkage Priors) Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) . Package: r-cran-mbsts Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-matrix, r-cran-pscl, r-cran-mcmcpack, r-cran-mass, r-cran-ggplot2, r-cran-reshape2, r-cran-bbmisc, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbsts_3.0-1.ca2404.1_all.deb Size: 99918 MD5sum: 42487b1c87838b7b568c9b03665527fb SHA1: d01da4e9c6d3a7fc15f45b68f8ac16778b45f355 SHA256: c546140698551df12895b4a6ee6ff639bf6f8e0a930cfb30ea9ab5a4ec249838 SHA512: 90fa48cd6c33edd1527b4ad26ea14465b2bb37e6ee09e73fccd93f48ce810216ba0b837e414810d5d9c55314d3499109f034a70d46fbf4b2509dddff222c94bd Homepage: https://cran.r-project.org/package=mbsts Description: CRAN Package 'mbsts' (Multivariate Bayesian Structural Time Series) Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models. Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component. Package: r-cran-mbx Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rstatix, r-cran-tibble, r-cran-fsa, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbx_0.2.0-1.ca2404.1_all.deb Size: 99928 MD5sum: 7aaa495d38779c8c34751b9c314a5299 SHA1: 816ae71dd0d218105e21332140b09129462dc4d9 SHA256: 7b44d18625510a32ec1402d328e7d2fb2976e8c9a6b532846b0bb0fd6cb9582c SHA512: bf2ab4709cdfe78927d170c669041d90be87d2664f2a4c984113760d067679313381fa670c40949112fa58d2b95694165dcdcff7b932e0d68ff663404ac0573a Homepage: https://cran.r-project.org/package=mbX Description: CRAN Package 'mbX' (A Comprehensive Microbiome Data Processing Pipeline) Provides tools for cleaning, processing, and preparing microbiome sequencing data (e.g., 16S rRNA) for downstream analysis. Supports CSV, TXT, and Excel file formats. The main function, ezclean(), automates microbiome data transformation, including format validation, transposition, numeric conversion, and metadata integration. It also handles taxonomic levels efficiently, resolves duplicated taxa entries, and outputs a well-structured, analysis-ready dataset. The companion functions ezstat() run statistical tests and summarize results, while ezviz() produces publication-ready visualizations. Package: r-cran-mc.heterogeneity Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-boot.heterogeneity Suggests: r-cran-hsaur3, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mc.heterogeneity_0.1.2-1.ca2404.1_all.deb Size: 57662 MD5sum: 0becf2af8da50fb8ca6c36c44508e279 SHA1: 370315eb94fcb781063745d1b2157448c9130d4b SHA256: 960f799f6bfe5afa68bc614599b1f9f67b84c6c9fa631116133c249480ac2a09 SHA512: 3d0948fa26af7eb011d8ccf9c98d24450488f1c22b316718687dc0d51f64ef682fedc050c432dedcd0615471600dcea89bf24bda0a32deb3330641f01aac8c3e Homepage: https://cran.r-project.org/package=mc.heterogeneity Description: CRAN Package 'mc.heterogeneity' (A Monte Carlo Based Heterogeneity Test for Meta-Analysis) Implements a Monte Carlo Based Heterogeneity Test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (OR) in Meta-Analysis Studies. Depending on the presence of moderators, this Monte Carlo Based Test can be implemented in the random or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihood, so installation of R package 'metafor' is required. This approach refers to the studies of Hedges (1981) , Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) , Viechtbauer (2010) , and Zuckerman (1994, ISBN:978-0521432009). Package: r-cran-mc2d Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2990 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-ggpubr Suggests: r-cran-fitdistrplus, r-cran-survival, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mc2d_0.2.2-1.ca2404.1_all.deb Size: 2214592 MD5sum: 4888d74ab51e55e2baf3a37036761388 SHA1: 5a3719b89fc311dc70d175e7e7025da3b90f77ad SHA256: 2603763b95e3b8eff381b603d6ae2976d1144d906793071c92c4f9484ecd1afa SHA512: 20d4e8fb56021dac0e4abe190b939bd6752ff964198fcb93a8b1802296c9b701cee0b665113cb61373c5e04dc60c92e04765f3d3a5d198be77f5f3e9a44a1ecb Homepage: https://cran.r-project.org/package=mc2d Description: CRAN Package 'mc2d' (Tools for Two-Dimensional Monte-Carlo Simulations) A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous). Package: r-cran-mcanalysis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcanalysis_0.1.0-1.ca2404.1_all.deb Size: 16244 MD5sum: 9da7d461925126c63e833512a98fff2f SHA1: 88a8ea0d1229100935382986676585f5694daf6d SHA256: 04a9692be49b80568f770ada7391447a0fb9d008a35413bc42508f017aa7f1c1 SHA512: b2b5371595e4c962c135c8e3ab9dce83bd619d5d7782805b5f4262ef2540cd98f93b64214c3e70f2a57328b25ceb997ebd959ed4a9a52e12bb642b4f06865021 Homepage: https://cran.r-project.org/package=mcanalysis Description: CRAN Package 'mcanalysis' (Markov Chain Analysis for Structural Behaviour and Stability) Analyses the stability and structural behaviour of export and import patterns across multiple countries using a Markov chain modelling framework. Constructs transition probability matrices to quantify changes in trade shares between successive periods, thereby capturing persistence, structural shifts, and inter-country interdependence in trade performance. By iteratively generating expected trade distributions over time, the approach facilitates assessment of stability, long-run equilibrium tendencies, and comparative dynamics in longitudinal trade data, providing a rigorous tool for empirical analysis of export–import behaviour. Methodological foundations follow standard Markov chain theory as described in Gagniuc (2017) . Package: r-cran-mcatools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-factominer, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-mcatools_1.0.0-1.ca2404.1_all.deb Size: 53838 MD5sum: 64a0f696a895d5764eda79c7b6dc3d62 SHA1: 15aa2ee66a4d5746db44aec41def7ff2b473ca7d SHA256: ff2c86e49019a22695fbecef628266ee01f1614346de971ed0e68a2f613513f3 SHA512: a7ac4fbac9c8822466f1b4f66e140dfce482132e4de563f3d4382b0b2270e4f6924751a370e779a3487cfa46b44c1938bd50a81f8bbe4f4d1799915f128c4d39 Homepage: https://cran.r-project.org/package=MCAtools Description: CRAN Package 'MCAtools' (Multiple Correspondence Analysis Toolkit (Phi-DivergenceFramework)) A toolkit for performing Multiple Correspondence Analysis (MCA) based on the phi-divergence framework of Cressie and Read (1984) . Provides implementations for MCA computation mca_analysis(), visualization plot_mca(), and symbolic equation generation generate_mca_equation(), designed for interpretability, flexibility, and analytical consistency with generalized divergence measures. Package: r-cran-mcauchyd Architecture: all Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rgl, r-cran-mass, r-cran-lifecycle, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcauchyd_1.3.6-1.ca2404.1_all.deb Size: 60386 MD5sum: 681395d7962ad29db9e602bfe64c4224 SHA1: df243cd77f943479c065ea33b56e760c8339678a SHA256: bbbea83940502f8cb011c229718a3dc316110dda9f1158028d83652a6652f67d SHA512: ec0e132a5ccf2818fd42df7bf735efb4310ad370da59e9b17f07ae696ef87d89a7c110cdd683a2888ba4be77f48832f78c10abedb239896236ce13b1052950ef Homepage: https://cran.r-project.org/package=mcauchyd Description: CRAN Package 'mcauchyd' (Multivariate Cauchy Distribution; Kullback-Leibler Divergence) Distance between multivariate Cauchy distributions, as presented by N. Bouhlel and D. Rousseau (2022) . Manipulation of multivariate Cauchy distributions. Package: r-cran-mcavariants Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-plotly Filename: pool/dists/noble/main/r-cran-mcavariants_2.6.1-1.ca2404.1_all.deb Size: 91540 MD5sum: c716d1d412b3bbd34684a1441c9a38cc SHA1: b2091dda5b770c0d11ce0ece10c96397aa244ef2 SHA256: dada8dae5cebbc2ee2d19094cbe4025932d8c301987519be7ba6d869164851f3 SHA512: d749f1ff341c2918d38b97129387d5a420c9ef7d9f1c6f1f025c249e7704d1c4b81097de33a6a34ff04279d35c31d627ddcef816b244da88d15a41690bb6ef5d Homepage: https://cran.r-project.org/package=MCAvariants Description: CRAN Package 'MCAvariants' (Multiple Correspondence Analysis Variants) Provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson. Package: r-cran-mcb Architecture: all Version: 0.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps, r-cran-lars, r-cran-mass, r-cran-glmnet, r-cran-ncvreg, r-cran-smoothmest, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-flare, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcb_0.1.15-1.ca2404.1_all.deb Size: 62314 MD5sum: 5788f360bdcbc4e750aec38d4d5a2338 SHA1: 4a01a459ea2cfe333037b870004399c7c7e6c97e SHA256: 3d8326392fdacf55139987ae9a864f6c59bae114f5310e2941889d878105de27 SHA512: ed992d2f54ffb8ded357e94e83d166ad90d6517685d2ba1df5cda4a647dedab3cf50de996199d1ffe5cae7625b9003a173448166251b6af570b4b03eef938f0f Homepage: https://cran.r-project.org/package=mcb Description: CRAN Package 'mcb' (Model Confidence Bounds) When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. . Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in and the latest version is useful for 'mcb'. Package: r-cran-mcbackscattering Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcbackscattering_0.1.1-1.ca2404.1_all.deb Size: 89118 MD5sum: c3a0f558339d5f8694e0c1949716a7b4 SHA1: 9ee514f003f3f77dcd262dcb5f1f61b06cd17027 SHA256: 9f4b667d3b9c1900ca370db003fcc627bf77fa632af36e283b85cfb0fdb87021 SHA512: 4fea0021e12b824cc31d0efa7b02978dc9ebac1804afdaac8709e6fe736d528a31dd323d37bc4a2d2696cba7bf62b9c738837bc452c072c77bd45153295f4620 Homepage: https://cran.r-project.org/package=MCBackscattering Description: CRAN Package 'MCBackscattering' (Monte Carlo Simulation for Surface Backscattering) Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) . 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'mcbette' allows to do a Bayesian model comparison over some site and clock models, using 'babette' (). Package: r-cran-mcbiopi Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcbiopi_1.1.7-1.ca2404.1_all.deb Size: 31022 MD5sum: 17d2d7e0253854c2cc8d394c34fb8931 SHA1: e0a4d57f904933414ecbe76ab29da925e2099487 SHA256: a3ba77f4c1835953ed58f9955135204a7acc011303383fe14643205328712540 SHA512: c610783fd93c482a84d7762ee124860d9c67d81b35a84264def455dcaa05bdcae3b1a5f171a92793a82ecec283117c0e924be52bb41297f252b37df5460baf21 Homepage: https://cran.r-project.org/package=mcbiopi Description: CRAN Package 'mcbiopi' (Matrix Computation Based Identification of Prime Implicants) Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants are typically composed of a maximum of 4 or 5 literals. Package: r-cran-mcboost Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-r6, r-cran-rmarkdown, r-cran-rpart, r-cran-glmnet Suggests: r-cran-curl, r-cran-lgr, r-cran-formattable, r-cran-tidyverse, r-cran-practools, r-cran-mlr3learners, r-cran-mlr3oml, r-cran-neuralnet, r-cran-paradox, r-cran-knitr, r-cran-ranger, r-cran-xgboost, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcboost_0.4.4-1.ca2404.1_all.deb Size: 317832 MD5sum: 180c8c5e4221c504716af078dc66526b SHA1: 64591be95f31d42aef94b738df819e011f0beb8d SHA256: 685b37846cb6da85f9fc2b53957caed74fc26084555cdb19dd88f6b3d3541a85 SHA512: 870bf93a85c059c1da346a07b610631e371e4c632b16e5c7a613e6407ffc992f1a22fece1d70b76b2f8eaee3edbfd8c6882624d0224cd2c4c96470b97154ca4b Homepage: https://cran.r-project.org/package=mcboost Description: CRAN Package 'mcboost' (Multi-Calibration Boosting) Implements 'Multi-Calibration Boosting' (2018) and 'Multi-Accuracy Boosting' (2019) for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations. Package: r-cran-mcca Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-rpart, r-cran-e1071, r-cran-mass, r-cran-proc, r-cran-caret Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-mcca_0.8.2-1.ca2404.1_all.deb Size: 124406 MD5sum: 7f50b665a06f1613a2de66774fab0020 SHA1: d3d6fe3726acd8515e8e6d716ce67217a053b413 SHA256: eb2d5f1983d7faa1b3cd1ddf11a6cbdabe789758473314722aae96d5bd25d49b SHA512: b8e3f4921b3ddb6caf281690a4f3009ab6439c5e898deaee4c3e26e67a112cb00b5700f9384df69bd78472966b2c8079667fa46af932c792acdd3ab1dbe8ed1e Homepage: https://cran.r-project.org/package=mcca Description: CRAN Package 'mcca' (Multi-Category Classification Accuracy) It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) . Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) . Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) . Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) . Li et al. (2018) . PDI with variance estimation using Dover et al. (2021) . We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) . Package: r-cran-mccf1 Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rocr Filename: pool/dists/noble/main/r-cran-mccf1_1.1-1.ca2404.1_all.deb Size: 24194 MD5sum: 933e6a8bfa196090dd96148a08a69112 SHA1: fd54162fb858fc3e82035a8cbb177ac1a6125560 SHA256: 34faac3fe47621efd4779652973763388c7c94fa2df778f1ad732fa81a5be784 SHA512: e9ad5ea8a96312f1252a6f10c7c2379efe4741a0e99af1172e6d8cf81b1f2b099605ddfc4b44435c5ffd5d6a589377f374d13fefe969b8c446581b7165826884 Homepage: https://cran.r-project.org/package=mccf1 Description: CRAN Package 'mccf1' (Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric andthe Best Threshold) The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification. Package: r-cran-mccm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-polycor, r-cran-lavaan, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mccm_0.1.0-1.ca2404.1_all.deb Size: 185168 MD5sum: 54aad285240db3e4e8cb7e0180e54480 SHA1: 68f6259a5b662ace8ed7236a75f3f7c4db834853 SHA256: 4086a5b1eca8e3f07eb780442c58dd631d519116e11d14c502651718599b9e5c SHA512: 64aa354df14a7967ac7f043e69aa59644ad6766e61fabef78d76cf6c64db4841e76b179f3bfe6be7f2b34300fae4a4cb7b3334bee24f78c4212b23ad0a304a10 Homepage: https://cran.r-project.org/package=MCCM Description: CRAN Package 'MCCM' (Mixed Correlation Coefficient Matrix) The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) ; Ben Liu and Peng Zhang (2024) . Package: r-cran-mccmeiv Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-mccmeiv_2.1-1.ca2404.1_all.deb Size: 297032 MD5sum: 6e2e1266a1e6090fc93548be07c68814 SHA1: 0cbd7249f60cd9bc4ca864a2e19d25092cda40a6 SHA256: 33358f9bda3c8e11c79a95884b7191980066ebfd29c040967537a6b39d585791 SHA512: bcf11a0e8ff7f2d01383c434501741c3d6a89bc81c944a34f94209242f6e96f9b3f78f2cc08577cec4786c34d8c37562fcff3c825f699b9c795d1cb3b40629bb Homepage: https://cran.r-project.org/package=mccmeiv Description: CRAN Package 'mccmeiv' (Analysis of Matched Case Control Data with a MismeasuredExposure that is Accompanied by Instrumental Variables) Applying the methodology from Manuel et al. to estimate parameters using a matched case control data with a mismeasured exposure variable that is accompanied by instrumental variables (Submitted). Package: r-cran-mccount Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cards, r-cran-cli, r-cran-cmprsk, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-boot, r-cran-ggplot2, r-cran-knitr, r-cran-matchit, r-cran-mstate, r-cran-optmatch, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-survival, r-cran-testthat, r-cran-weightit, r-cran-withr Filename: pool/dists/noble/main/r-cran-mccount_0.1.1-1.ca2404.1_all.deb Size: 1377486 MD5sum: 311cb34c6d0b7378caaf78012e68c15d SHA1: ed13ed44792643e0abca54d989dfdeac1edf4fc1 SHA256: 1dd3b8ee2c3241137a8e7b2e9bb5721fd9f3c117e836a334a01bd56c79b13ab2 SHA512: 24cf8c1832928da4c053a7e954f24ea93d43466e07c81ad498d8e455441f7e18091bb6b28c3c5a2a23449a9822a82ef308e8f624d40af5e0c0e74450a739f17e Homepage: https://cran.r-project.org/package=mccount Description: CRAN Package 'mccount' (Estimate Recurrent Event Burden with Competing Risks) Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) . Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) . Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with 'ggplot2' for visualization. Package: r-cran-mccr Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mccr_0.4.4-1.ca2404.1_all.deb Size: 9648 MD5sum: eeeadba5ad5bfb5f820a733cf0c84055 SHA1: 0a57865e0ea8d4ac52221f7bdda0837502697b43 SHA256: 50ae754bc771015a44dffbbd2b6b60e69c5cf6bb16ac7c17a136e92dea28b584 SHA512: 7e67a73f8aac0b68d7dd2a79f9ec2b17295df8bd984fe2d729f9a94632913eb0965424563ee7e22d0f3c2d825878d9bb5422a28f15cadf9fb4d44aa60fb9eca5 Homepage: https://cran.r-project.org/package=mccr Description: CRAN Package 'mccr' (The Matthews Correlation Coefficient) The Matthews correlation coefficient (MCC) score is calculated (Matthews BW (1975) ). Package: r-cran-mcda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rglpk, r-cran-glpkapi, r-cran-rcolorbrewer, r-cran-combinat, r-cran-triangle, r-cran-plyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcda_0.1.0-1.ca2404.1_all.deb Size: 614928 MD5sum: 0df107602c1d1fa7636fde93a6caf44b SHA1: c3766858a7f3b03861974b733471ed468e2bae3d SHA256: 436608697203f61857d5f30c562e966bd842d57ad319ccee1a10c75f1db3cc72 SHA512: 40cf596316a0fcf2c6ed0c72b9adaf0689bdd46abeaf95ac2da1cc42593262c540a7e4477dedf466d1909183bf8256b17b6192a4dafe28d19e8b2db57fb1e896 Homepage: https://cran.r-project.org/package=MCDA Description: CRAN Package 'MCDA' (Support for the Multicriteria Decision Aiding Process) Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms, preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer, Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process : R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, pages 169 - 194 . Package: r-cran-mcdabench Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factoextra, r-cran-ggplot2, r-cran-gplots, r-cran-igraph, r-cran-monochromer, r-cran-networkd3 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mcdabench_1.1.2-1.ca2404.1_all.deb Size: 2147660 MD5sum: 5c06de587400ccee502bbf321ccd60fb SHA1: dbb36e1123a798ca634e8336b9f6428f775a25af SHA256: ab15b3664997a4e2b2cc5a12d87db427bfde58468b9d8fd513dfc1a90c517e4c SHA512: 2ada59f8600a7c84244596d526fade2d9b33cd7d70abcf18dc31fe4e074bb898bf85a4c239153347f52265de9c95fdec0668923c64ce1364c60bf75c3d5d031e Homepage: https://cran.r-project.org/package=mcdabench Description: CRAN Package 'mcdabench' (Benchmarking for Multi-Criteria Decision Analysis) Performs and benchmarks various Multi-Criteria Decision Analysis (MCDA) methods. MCDA is a decision-making framework used to evaluate and rank alternatives based on multiple conflicting criteria using normalization, weighting, and aggregation techniques. The package implements a wide range of MCDA methods including ARAS (Additive Ratio Assessment), AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization), COCOSO (Combined Compromise Solution), CODAS (Combinative Distance-based Assessment), COPRAS (Complex Proportional Assessment), EDAS (Evaluation based on Distance from Average Solution), ELECTRE (Elimination and Choice Expressing Reality) family (I-IV), FUCA (Faire Un Choix Adequat), GRA (Grey Relational Analysis), MABAC (Multi-Attributive Border Approximation Area Comparison), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory), MEGAN (Multi-criteria Evaluation with Gradual-weighting and Aggregation of Normalized distance matrices), MOORA (Multi-Objective Optimization on the basis of Ratio Analysis), OCRA (Operational Competitiveness Rating Analysis), ORESTE (Organisation, Rangement Et Synthese De Donnees Relationnelles), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations I-VI), RAM (Root Assessment Method), ROV (Range of Value), SMART (Simple Multi-Attribute Rating Technique), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), WASPAS (Weighted Aggregated Sum Product Assessment), WPM (Weighted Product Model), and WSM (Weighted Sum Model). The package computes comparative evaluation measures including Spearman rank correlation (Spearman, 1904) , Salabun-Urbaniak's weight similarity index (Salabun and Urbaniak, 2020), Wilcoxon signed-rank test (Wilcoxon, 1945), and permutation- and bootstrap- based entropy difference tests for pairwise method comparisons using Jensen-Shannon divergence (Lin, 1991). It also provides sensitivity and stability analysis of MCDA results. Weight sensitivity analysis is implemented through deterministic and stochastic perturbation of criterion weights, and is also integrated as a built-in step within the MEGAN method framework (Cebeci, 2026). Package: r-cran-mcgf Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1258 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sp Suggests: r-cran-testthat, r-cran-doparallel, r-cran-foreach, r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate, r-cran-dplyr, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-mcgf_1.2.0-1.ca2404.1_all.deb Size: 913692 MD5sum: 25bbb33e09f0b90b4a1f8d91e677aa95 SHA1: 8e578f3eb7828db7c297ebaa1f87384f2a08e694 SHA256: b2a76a98769bebba31fe4f0f84f0dfab6b5feb09b8a1db0e0d3e4aa50c62b3b0 SHA512: c8c41647594db760d40a0eb31e8ead579f5b11da8c337dd96609ca167766a7f04d2b39d7310ba660168a35395222447e9c713b1ca1c668bb8e56cf7c491b9a2d Homepage: https://cran.r-project.org/package=mcgf Description: CRAN Package 'mcgf' (Markov Chain Gaussian Fields Simulation and Parameter Estimation) Simulating and estimating (regime-switching) Markov chain Gaussian fields with spatio-temporal covariance functions of the Gneiting class (Gneiting 2002) , including the regime-switching framework of Jia and Sezer (2025) . It supports parameter estimation by weighted least squares and approximate conditional maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations. Package: r-cran-mcgibbsit Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mcgibbsit_1.2.2-1.ca2404.1_all.deb Size: 731680 MD5sum: 9c228dd13aedde65eafe78ea5a276b47 SHA1: 8d6a44ec03846ddeb4d23537705b3a2b8352197c SHA256: 9680ed8a2eafd40129a5d269ef3ac3e459915255a40d882d2467a53655f790cb SHA512: ab28cfa80f08f42951fe9ba6c9dff1ec2bcbb7b8f2758734931d38edf70334bb2eb892d064af495bb3e472b2bd14f7dcef1b386b390e2303f20fe18d6755ab32 Homepage: https://cran.r-project.org/package=mcgibbsit Description: CRAN Package 'mcgibbsit' (Warnes and Raftery's 'MCGibbsit' MCMC Run Length and ConvergenceDiagnostic) Implementation of Warnes & Raftery's MCGibbsit run-length and convergence diagnostic for a set of (not-necessarily independent) Markov Chain Monte Carlo (MCMC) samplers. It combines the quantile estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic. Package: r-cran-mchtest Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mchtest_1.0-4-1.ca2404.1_all.deb Size: 389416 MD5sum: dff7cb6d75b7b74f47a5281aa39e12ba SHA1: 3979e44558c5d65cc1ceb11fc710a17674108993 SHA256: 38291bef665c2133babf668f5d4f7a464b6ab0c2a8f91cfdcc6cf0eb7d141bcc SHA512: 4692a96d233944b24f6e71de479918d583c4aeb38dfb5f5087aa0c0156979a50554cf6f6fa5c948aaa0ee89b5377cf6272da3c7c0a0d21a573c75956e7b18b54 Homepage: https://cran.r-project.org/package=MChtest Description: CRAN Package 'MChtest' (Monte Carlo Hypothesis Tests with Sequential Stopping) Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 ) and a boundary proposed by Besag and Clifford, 1991 . Gives valid p-values and confidence intervals on p-values. Package: r-cran-mci Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mci_1.3.3-1.ca2404.1_all.deb Size: 297972 MD5sum: 32905f2377cf520dd1a4202994d3c5ec SHA1: 0dbb933378aa024c8331c627cf7d4e188ea4b63e SHA256: 62ac1a3db2ad7cf2d4c190256e1f0bcb76378fdbb92c83ba1989c70617e8af47 SHA512: 70d3efe18a0210c3fc0a3e68413a74b05f930de0a092583d6a936a7b0f5f32e78eb07250deb934450c623ac3de41e0dd686efc9bfc6ce5cb8e1db7a9a3031ca0 Homepage: https://cran.r-project.org/package=MCI Description: CRAN Package 'MCI' (Multiplicative Competitive Interaction (MCI) Model) Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. 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Bisson and Jiwei Zhao (2021) . Package: r-cran-mcl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm Filename: pool/dists/noble/main/r-cran-mcl_1.0-1.ca2404.1_all.deb Size: 19192 MD5sum: a317f3fb422576a78c040da2cb0873d8 SHA1: 4a956dc4bd7931e2ea113e8fc941baeeb21b8df9 SHA256: 5a565dd23182b4aebf1b5c04d43987c18d28b4f532e64102862b02605e4338fd SHA512: 817b398f2e9a302889176ed07ff78b72e93dcb727783060d0c4ef616e4d086b1ac646445ea79c3ac486630fd0ad0d3cca91c505c079999c5417c1d7811918f68 Homepage: https://cran.r-project.org/package=MCL Description: CRAN Package 'MCL' (Markov Cluster Algorithm) Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached. Package: r-cran-mclink Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-matrixstats, r-cran-stringr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mclink_1.3.1-1.ca2404.1_all.deb Size: 301570 MD5sum: 53bdd3a31ff0d86d291808f9fc3e7dff SHA1: ceecc6d3c0fa0084bb6dd41e99c6ebfe446741a0 SHA256: 34eaf97b6f5c1e23e5fa8b169ab860d19c9cf18ea2ba91508ec6f069244c52cb SHA512: 44279c6aff8139f61c3d2127ca65478a2dd2f16ae21fb701db24c30dfe4eeb6f2196a7235099ae75040f74fba5366d0d038c4bc672ace3907b863fcfb1b23d20 Homepage: https://cran.r-project.org/package=mclink Description: CRAN Package 'mclink' (Metabolic Pathway Completeness and Abundance Calculation) Provides tools for analyzing metabolic pathway completeness, abundance, and transcripts using KEGG Orthology (KO) data from (meta)genomic and (meta)transcriptomic studies. Supports both completeness (presence/absence) and abundance-weighted analyses. Includes built-in KEGG reference datasets. For more details see Li et al. (2023) . Package: r-cran-mclogit Architecture: all Version: 0.9.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-memisc, r-cran-nlme Suggests: r-cran-nnet, r-cran-ucminf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mclogit_0.9.15-1.ca2404.1_all.deb Size: 328636 MD5sum: bc3c19ef464bba8f972df7a4e85825cc SHA1: f067ad01a8c54833f20c0619a6ac0f654b0af29c SHA256: ef221a3bdbf651a6f62f3e0428e2bfab8cc1163ca56157e3ec4e6613cc82832d SHA512: ef6f43c8c477e6c747cc964b8267d2506db5de0de439485580317222c8aec9fad3689b6293205d954691a8916872e1d4b003d9a519ee2d270403aab2ed9d7468 Homepage: https://cran.r-project.org/package=mclogit Description: CRAN Package 'mclogit' (Multinomial Logit Models for Categorical Responses and DiscreteChoices) Provides estimators for multinomial logit models in their conditional logit (for discrete choices) and baseline logit variants (for categorical responses), optionally with overdispersion or random effects. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small. Package: r-cran-mcm Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey, r-cran-gee, r-cran-dplyr, r-cran-lme4, r-cran-stringr, r-cran-parameters Filename: pool/dists/noble/main/r-cran-mcm_0.1.8-1.ca2404.1_all.deb Size: 1116076 MD5sum: bdbc48441aa4564c28a79ba2f55b59ea SHA1: e54d1ee1caa1ffa6aaf5079d3db81232f8d469ad SHA256: ca63812bf8b17cdfcd1bc600734bbed064d677c90923b9049382aec74163eda4 SHA512: 5345ec982be0dbc4da8b95483c9b2daecd59bb8ede5b535bf8cc02f0ec7eccefc74f2765a748154843adaa95739267bd2c05ad4802b2c0739851a21cbc020e85 Homepage: https://cran.r-project.org/package=MCM Description: CRAN Package 'MCM' (Estimating and Testing Intergenerational Social Mobility Effect) Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data. Package: r-cran-mcmapper Architecture: all Version: 0.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcmapper_0.0.11-1.ca2404.1_all.deb Size: 62892 MD5sum: 26c251be25a4a1b1d22762aceae71618 SHA1: 163d92427aadf590da1325f933f53e74efa4db6d SHA256: 182a9beaabab6e8a71e29d04e19f876b298831e80d283979007713ae110b976a SHA512: 1482e33f40ffd6ccaad599dec9e3c9117d5014d812263a4c88d5a1cc3c300616f7ca83d7edd5c630cf7882a7f5ca4cc8e6534648a8aa0f2be78c393804af0672 Homepage: https://cran.r-project.org/package=mcmapper Description: CRAN Package 'mcmapper' (Mapping First Moment and C-Statistic to the Parameters ofDistributions for Risk) Provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) . The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions. Package: r-cran-mcmc.otu Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcglmm, r-cran-ggplot2, r-cran-coda Filename: pool/dists/noble/main/r-cran-mcmc.otu_1.0.10-1.ca2404.1_all.deb Size: 98010 MD5sum: 6ccafe7d31df10cd0a1d432d3be853b6 SHA1: b35a635c7056b04f1610424283e9057901f58de8 SHA256: 81385e03a8b0c6d13fe8a5c9622dda3cb2d04966e595bfdc52e65723eee93607 SHA512: 97fc236955540cc6ff3af1ce7b9cd08c2602dc3fb9d55f953554e5d342d89fa06a31704e0b90b924361ce71c8de868b6e8433fb365e16e960c32d76673e50eef Homepage: https://cran.r-project.org/package=MCMC.OTU Description: CRAN Package 'MCMC.OTU' (Bayesian Analysis of Multivariate Counts Data in DNAMetabarcoding and Ecology) Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt. Package: r-cran-mcmc.qpcr Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcglmm, r-cran-ggplot2, r-cran-coda Filename: pool/dists/noble/main/r-cran-mcmc.qpcr_1.2.4-1.ca2404.1_all.deb Size: 180112 MD5sum: 220e8efcae72525950bdd9e6c6de43c5 SHA1: 68da75eec1665720966f88104ec0f7edc9e10100 SHA256: 973d653d991c1803aa3db51386827a9b1a3b1ff0b35ace105b9d14e6a0482215 SHA512: a95489fa5971b57e0f010c0738be02552d9c0616b5e5dc640d39cc4663e7804b6b396215a134f01ae9b0bfe7000c3e3edebdc4f312d3f0bae16f7753989c851d Homepage: https://cran.r-project.org/package=MCMC.qpcr Description: CRAN Package 'MCMC.qpcr' (Bayesian Analysis of qRT-PCR Data) Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: . Package: r-cran-mcmc4extremes Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evir Filename: pool/dists/noble/main/r-cran-mcmc4extremes_1.1-1.ca2404.1_all.deb Size: 147444 MD5sum: 68ed938a8f658d652f7bcfd1be3c9540 SHA1: b1a5121c5442ee6eac02ff3fb487ca17b114a1fa SHA256: b81e647daf5e45564d506cf61d919362674a042e0c06217e51419ce6b80b59cc SHA512: adcc3809816be755cd208908c91d432bf17eb48f16ac2e47bb40baf8e4d9a6412334470d35287c2fe5c6171804f3752cdf0b4e7de13fdc3789858b40b18a1e66 Homepage: https://cran.r-project.org/package=MCMC4Extremes Description: CRAN Package 'MCMC4Extremes' (Posterior Distribution of Extreme Value Models in R) Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots. Package: r-cran-mcmcderive Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-chk, r-cran-extras, r-cran-mcmcr, r-cran-nlist, r-cran-purrr, r-cran-universals Suggests: r-cran-coda, r-cran-covr, r-cran-doparallel, r-cran-plyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcmcderive_0.1.2-1.ca2404.1_all.deb Size: 32530 MD5sum: f86e43bc969ccf9392ab7515f4ff3d43 SHA1: 26ac30973315d8b4ada042619ad9919ebd3bd46e SHA256: 75ab6740f28e1c7628a4f1e5723933222dd5eb8e20a3ccfb937de4bb4d06c8c4 SHA512: d2821c7fc5d78130f7e61f7d8be83b54d60cb1c132cdba69ea1dabf8e71af063117a217227a89beecbac805fcdb5394786155bb5832013715b43a79b400468d5 Homepage: https://cran.r-project.org/package=mcmcderive Description: CRAN Package 'mcmcderive' (Derive MCMC Parameters) Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code. This allows Bayesian models to be fitted without the inclusion of derived parameters which add unnecessary clutter and slow model fitting. For more information on MCMC samples see Brooks et al. (2011) . Package: r-cran-mcmcensemble Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-progressr Suggests: r-cran-bayesplot, r-cran-coda, r-cran-mockery, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mcmcensemble_3.2.0-1.ca2404.1_all.deb Size: 189970 MD5sum: 777e2e3bf4fe864dab0752bbc2b048b9 SHA1: 206637efacd7429eea6d66df47b0ba9418bb0021 SHA256: e86a9a3ecee263f20e71fd15b4a59427cf18c886a64096269504414e2c4459f9 SHA512: d79475af177b5f4d5a5ad306bdfb24c2fd53a531354e7c9433d98060bd86a9a4a30f818fa7c70cf09f4e053eb07d22ff02b7833ff19cab77b1194846d1147657 Homepage: https://cran.r-project.org/package=mcmcensemble Description: CRAN Package 'mcmcensemble' (Ensemble Sampler for Affine-Invariant MCMC) Provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the 'differential.evolution' sampler from ter Braak and Vrugt (2008) and the 'stretch' sampler from Goodman and Weare (2010) . Package: r-cran-mcmcoutput Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdinterval, r-cran-coda, r-cran-mass Filename: pool/dists/noble/main/r-cran-mcmcoutput_0.1.3-1.ca2404.1_all.deb Size: 286544 MD5sum: 0d56bf90bbac7b555cb72185e3ae8fa0 SHA1: 3e3900ad6fb61ff439da9b419168f3c62783726a SHA256: 9461d3738ee9472a412d747f839e2a81087adb98d673ada9825fceaa262da044 SHA512: c972039370d31322629fec19a0df3e148cf00434dc909164baf19aa7848227f7b3535ffe0ad898df8e7bfcf7536d9468adaae29f56117fbe77fae4fbeedd75de Homepage: https://cran.r-project.org/package=mcmcOutput Description: CRAN Package 'mcmcOutput' (Functions to Store, Manipulate and Display Markov Chain MonteCarlo (MCMC) Output) Implements a class ('mcmcOutput') for efficiently storing and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for those writing customized MCMC samplers. A range of constructor methods are provided covering common output formats. Functions are provided to generate summary and diagnostic statistics and to display histograms or density plots of posterior distributions, for the entire output, or subsets of draws, nodes, or parameters. Package: r-cran-mcmcplots Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-sfsmisc, r-cran-colorspace, r-cran-denstrip Filename: pool/dists/noble/main/r-cran-mcmcplots_0.4.3-1.ca2404.1_all.deb Size: 143712 MD5sum: a68226d8eb0f777667403f1114ada543 SHA1: d6689418e79d5e22f99574a2ce2db78921ff0a28 SHA256: ce49f270fb98c99b10f00aaa31c6b0e35b9243afa192d156bdc527a802ab2829 SHA512: b753f8df24b4ddb1f4662d92dce05ed5467c12ec260714e4db3d11276c40efb047c144b8270f4ec8a803ca9892ebc13f614a7b898685fa10f0237cfc85d57ac1 Homepage: https://cran.r-project.org/package=mcmcplots Description: CRAN Package 'mcmcplots' (Create Plots from MCMC Output) Functions for convenient plotting and viewing of MCMC output. 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For more information see Brooks et al. (2011) . Package: r-cran-mcmctreer Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7542 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-sn, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mcmctreer_1.1-1.ca2404.1_all.deb Size: 4350516 MD5sum: a8ee0129e8a54afa216b0ab30f85d381 SHA1: bfc75919c2df8fa975eaed37825b2366f9174ef4 SHA256: afa2cc3a51b1ba81379218b0f7f46cbb78230f4c5f91fe7b2dcf997f2ae43335 SHA512: a4fecd62493f5d85348aff617bb6f1bf7d9b2ea2b20efd62f0e323c5a6ad846c8f6d84738b6953c94ceb9fd319e23f7bb4ce1e287d8b501a11d45eef326a0bda Homepage: https://cran.r-project.org/package=MCMCtreeR Description: CRAN Package 'MCMCtreeR' (Prepare MCMCtree Analyses and Plot Bayesian Divergence TimeAnalyses Estimates on Trees) Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007) and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. Time-scaled phylogenetic plots can be visualised with absolute and geological timescales . All plotting functions are applicable with output from any Bayesian software, not just 'MCMCtree'. Package: r-cran-mcmcvis Architecture: all Version: 0.16.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rstan, r-cran-overlapping, r-cran-colorspace Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-posterior Filename: pool/dists/noble/main/r-cran-mcmcvis_0.16.5-1.ca2404.1_all.deb Size: 3122232 MD5sum: 1a431004f36c70f01cabd417808012d7 SHA1: 4b3ab175150fff2304f0d6b911a1d5228c8c097a SHA256: f91c9e8d5e09934afe0c3c8edbc09f0f3c0e345729da9df404bce127d99f4f66 SHA512: 4ac73348f99ebc64ff56819a4cbd95b6b0c5dd464c155b56a70572ec18fb56ba12ad9043c164fb1daedc8f359a57ec25bec1d5eeea3dfb826f597e67475ebf3a Homepage: https://cran.r-project.org/package=MCMCvis Description: CRAN Package 'MCMCvis' (Tools to Visualize, Manipulate, and Summarize MCMC Output) Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with Stan, NIMBLE, JAGS, and other software. Package: r-cran-mcmiso Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-future Filename: pool/dists/noble/main/r-cran-mcmiso_0.2.0-1.ca2404.1_all.deb Size: 119062 MD5sum: 667e758b8a294adbb9cc805bb5fdded2 SHA1: d1a44f38d07ffa978e6d6f5182d4c92c4007e136 SHA256: 4d6f0a3591e4a0e4e37e38c1922f8235687e547f07f1855a1d9bf6cc9ecdf77b SHA512: f822ec1949156b6d6c39dffe9f559063b8111d38d8489d5ceaef339dea3bc9217f28cd2a4370eb922cd7dd2b39ab723adeb258e2762a8c0ca8d8a721fb016434 Homepage: https://cran.r-project.org/package=McMiso Description: CRAN Package 'McMiso' (Multicore Multivariable Isotonic Regression) Provides functions for isotonic regression and classification when there are multiple independent variables. The functions solve the optimization problem using a projective Bayes approach with recursive sequential update algorithms, and are useful for situations with a relatively large number of covariates. Supports binary outcomes via a Beta-Binomial conjugate model ('miso', 'PBclassifier') and continuous outcomes via a Normal-Inverse-Chi-Squared conjugate model ('misoN'). Parallel computing wrappers ('mcmiso', 'mcPBclassifier', 'mcmisoN') are provided that run the down-up and up-down algorithms simultaneously and return whichever finishes first. The estimation method follows the projective Bayes solution described in Cheung and Diaz (2023) . Package: r-cran-mcmodule Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4130 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mc2d, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-visnetwork, r-cran-igraph, r-cran-ggplot2, r-cran-sensitivity, r-cran-sensobol, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-mcmodule_1.3.1-1.ca2404.1_all.deb Size: 2121984 MD5sum: 1b7151fb25da4557a259c017eb49885f SHA1: dc5b90cff12640fc6f5a40a1227eae12c070f4d9 SHA256: 8a4cfe80e308e078038298d0a86e7e8a6cc404b979615aa8cdce79b9803d6961 SHA512: 9b6661b7ff60cc8d2618786df113fa82b25c0bcae9d32e2c1ce87cd41a293feecf150f05a74bd8d4812ee1a974fb3200e847d538a5f8de0f7daf37410e6f1199 Homepage: https://cran.r-project.org/package=mcmodule Description: CRAN Package 'mcmodule' (Modular Monte Carlo Risk Analysis) Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of 'mc2d' to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, align multivariate mcnodes, automate the creation of mcnodes, visualise model structure, assess convergence, and perform sensitivity analysis. For more details see Ciria (2026) . Package: r-cran-mcmsector Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-survey, r-cran-openxlsx, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-plyr, r-cran-stringr, r-cran-labelled, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-r2jags, r-cran-rmarkdown, r-cran-testthat, r-cran-mcmsupply Filename: pool/dists/noble/main/r-cran-mcmsector_1.0.2-1.ca2404.1_all.deb Size: 235704 MD5sum: e73022944b19dd971ea6b4cd9defdeec SHA1: 03a32384ade8195f466b3ae0c76af7945c841d7d SHA256: 96bf351c2ff1eaf7caa3fd297d08ce5acf2a72d174feef57e86a4fb403e1f199 SHA512: 958a33a9e332770c03230c6c846978d4d5a4fc47556a47c82a3525214a885511586e437a8716c7f2de1b2bec30646a6fd098f4df46f60b73efa5eb80b5550e6c Homepage: https://cran.r-project.org/package=mcmsector Description: CRAN Package 'mcmsector' (Estimating Subnational Public and Private Contraceptive SupplyShares Over Time) Engaging the private sector in contraceptive method supply is critical for equitable, sustainable, and accessible healthcare systems. This package implements Bayesian hierarchical models to estimate public and private contraceptive supply shares over time at national and subnational levels, using Demographic and Health Survey (DHS) data. Penalized splines are used to track supply shares over time, and spatial correlation structures link national and subnational estimates in data- sparse settings. For more details see Comiskey (2025) . Package: r-cran-mcmst Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-ecr, r-cran-grapherator, r-cran-checkmate, r-cran-gtools, r-cran-ggplot2, r-cran-vegan, r-cran-qgraph, r-cran-viridis, r-cran-igraph Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-mcmst_1.1.1-1.ca2404.1_all.deb Size: 252514 MD5sum: 87db899c4d5b44088353dfc0645cf8db SHA1: 3d8221f53e6890df374e955d9f775e461dcfb139 SHA256: bba3a5882d77b93522050ab996b4c63955fa07c5df89d910dea1d31d81a04a0b SHA512: f65a9ea7b3d766d7d61ac6fca2ec48b571384b171612315312b6406a4067be56a69432ff10fb554f9e1a7582cc6c61404b6c393a9e7b831828693139382a446c Homepage: https://cran.r-project.org/package=mcMST Description: CRAN Package 'mcMST' (A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem) Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Package: r-cran-mcmsupply Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-magrittr, r-cran-foreach, r-cran-tidyverse, r-cran-tidybayes, r-cran-runjags, r-cran-rlang, r-cran-abind, r-cran-dplyr, r-cran-ggplot2, r-cran-plyr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcmsupply_1.1.1-1.ca2404.1_all.deb Size: 720124 MD5sum: 73b4b21479a3755a28432bce20ee0acb SHA1: b09df0fdff30be014911d6edc11ded6f2934560b SHA256: ec9a41d0183c3768560b94f21e1393c54bdb393ea9d5fb4f78453620bfb18016 SHA512: 7d9c01120fe2cd917d03de9de2b4b7d82d9491be9f89eb3b63664ab5f16419be6285bfbeef0a94e9ecc239936a10016070636ea129ba6c3f719e2f50bca544f6 Homepage: https://cran.r-project.org/package=mcmsupply Description: CRAN Package 'mcmsupply' (Estimating Public and Private Sector Contraceptive Market SupplyShares) Family Planning programs and initiatives typically use nationally representative surveys to estimate key indicators of a country’s family planning progress. However, in recent years, routinely collected family planning services data (Service Statistics) have been used as a supplementary data source to bridge gaps in the surveys. The use of service statistics comes with the caveat that adjustments need to be made for missing private sector contributions to the contraceptive method supply chain. Evaluating the supply source of modern contraceptives often relies on Demographic Health Surveys (DHS), where many countries do not have recent data beyond 2015/16. Fortunately, in the absence of recent surveys we can rely on statistical model-based estimates and projections to fill the knowledge gap. We present a Bayesian, hierarchical, penalized-spline model with multivariate-normal spline coefficients, to account for across method correlations, to produce country-specific,annual estimates for the proportion of modern contraceptive methods coming from the public and private sectors. This package provides a quick and convenient way for users to access the DHS modern contraceptive supply share data at national and subnational administration levels, estimate, evaluate and plot annual estimates with uncertainty for a sample of low- and middle-income countries. Methods for the estimation of method supply shares at the national level are described in Comiskey, Alkema, Cahill (2022) . Package: r-cran-mcode Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcode_1.1-1.ca2404.1_all.deb Size: 23046 MD5sum: 8d6f4784196fedb657608eb5812b6976 SHA1: 02b3dcdf12cf3166e8c1b50add71e4dd8727ff9f SHA256: 9855349c2c13705d65ffdd0fa89a7f94f496541ce4ad0868420455985339ea2b SHA512: 5c2e1f04703db75c6c0e4138b20897492c6e86c5ac3a112dc0b4db0d894b7212d417268d34c9a1dfc46dd3d8f8fab16bcdd81843ad330d5e2a62701ce96de249 Homepage: https://cran.r-project.org/package=mcODE Description: CRAN Package 'mcODE' (Monte Carlo Solution of First Order Differential Equations) Two functions for simulating the solution of initial value problems of the form g'(x) = G(x, g) with g(x0) = g0. One is an acceptance-rejection method. The other is a method based on the Mean Value Theorem. Package: r-cran-mcoe Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-googlesheets4, r-cran-keyring, r-cran-magick, r-cran-odbc, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcoe_0.6.0-1.ca2404.1_all.deb Size: 2458308 MD5sum: e6e3feae80f39f130ffa12fce25ac43a SHA1: 61fcafff2a1c80c4eabb1917a05755519c621702 SHA256: c438c304056f1058e008ce51b491a91ed1eabd6e60dd9330bad71977b0d541f9 SHA512: 5addd2d6f7dbe8a952eb7fcc19e9d9e81fbdc8d37b0cea74e848b589a9cd791c8b34ee8e3348b430f3196d084d85d1ae059a6140d60cea5d9f9079064fafaa35 Homepage: https://cran.r-project.org/package=MCOE Description: CRAN Package 'MCOE' (Creates New Folders and Loads Standard Practices for MontereyCounty Office of Education) Basic Setup for Projects in R for Monterey County Office of Education. It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local 'SQL' table and making thematically consistent graphs. 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A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) and Boshnakov & Iqelan (2009) . 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The package provides tools to estimate marginalized count regression models for direct inference on the effect of covariates on the marginal mean of the outcome. The methods include the marginalized zero-inflated Poisson (MZIP) model described in Long et al. (2014) and the marginalized zero- and N-inflated binomial (MZNIB) model, which extends marginalized modeling to fractional count outcomes with boundary inflation at zero and the upper limit. Package: r-cran-mcp Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-rjags, r-cran-coda, r-cran-loo, r-cran-bayesplot, r-cran-tidybayes, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble, r-cran-stringr, r-cran-ggplot2, r-cran-patchwork, r-cran-rlang Suggests: r-cran-hexbin, r-cran-testthat, r-cran-purrr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mcp_0.3.4-1.ca2404.1_all.deb Size: 753006 MD5sum: 5fe270b84b7982d58dbf05b0d2026082 SHA1: 8ee71c931fdb2f12123a35be9282e44e9fa8dc5d SHA256: 523f67f376008eb089628458bb2e1b14929fbfb7c8373444dd0e301713a14b47 SHA512: 98861db58f27c44554e0862a861fd00a87d94fd039206949faf57542ef4b4ca816781c4c179ab23ce09caabd26b63c206d6c1182779bbb2d20dcf0de7ca2388b Homepage: https://cran.r-project.org/package=mcp Description: CRAN Package 'mcp' (Regression with Multiple Change Points) Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) and generalizes the approach described in Carlin, Gelfand, & Smith (1992) and Stephens (1994) . Package: r-cran-mcpan Architecture: all Version: 1.1-22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcomp, r-cran-magic, r-cran-mcmcpack, r-cran-plyr Filename: pool/dists/noble/main/r-cran-mcpan_1.1-22-1.ca2404.1_all.deb Size: 366238 MD5sum: abe3658c37301a48516aedcabf9b349e SHA1: 7c06f6e707ac4365f434ace960f59367b075539c SHA256: 370306f55b24291ada2cde94b8955960d44cd573d26060ac35056ddf2cff91fa SHA512: df5b262b6cf01af66fc81008f02c09af8b8ec44878e02b847170b8ef3f163b2d6e7719edcd82dcd178ad393ef55c6b05cc46b714d0c64422c52aae42fa473d33 Homepage: https://cran.r-project.org/package=MCPAN Description: CRAN Package 'MCPAN' (Multiple Comparisons Using Normal Approximation) Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data) and expected values under lognormal assumption. Approximative power calculation for multiple contrast tests of binomial and Gaussian data. Package: r-cran-mcparalleldo Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-checkmate, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mcparalleldo_1.1.0-1.ca2404.1_all.deb Size: 77450 MD5sum: 79f23acda10dbc9a77177f6abfe342a6 SHA1: 5385f62deb58605a9a46ffe4cc23dd23218db349 SHA256: 072ff7b2ef761b12c08c8f3029333e0d3d636d2856cd4ffa8c763b51ee169222 SHA512: fb6f76f30f1ad0ad7a0590732654010b3e8040f15bef89124eb60becfc35cc81b3da61f51516587dfed7282cc4b6fcf9c0b40d8026c99fdf8b601a72ecc0a091 Homepage: https://cran.r-project.org/package=mcparallelDo Description: CRAN Package 'mcparallelDo' (A Simplified Interface for Running Commands on ParallelProcesses) Provides a function that wraps mcparallel() and mccollect() from 'parallel' with temporary variables and a task handler. Wrapped in this way the results of an mcparallel() call can be returned to the R session when the fork is complete without explicitly issuing a specific mccollect() to retrieve the value. Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with a single call to mcparallelDoCheck(). Package: r-cran-mcplite Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-nanonext, r-cran-otel Suggests: r-cran-ellmer, r-cran-otelsdk, r-cran-processx, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mcplite_0.1.0-1.ca2404.1_all.deb Size: 108164 MD5sum: eb305b1595e0b8970b8a7dd7a2e19280 SHA1: 106bd5c3f96151b65b28ba8e3c5dce830f9949d1 SHA256: 654ac7304911ab54f986c41684dc1747ba188d0b91d7f52561f9cb27c32a702b SHA512: 003e7f9dd7cb30989f2c7b732f6bf687276f77d4b2649cf5f337d17871eb1878388d465887488aa22185804315e8e08bb095804e6a9b2bd85ae7bedeb4e34ce5 Homepage: https://cran.r-project.org/package=mcplite Description: CRAN Package 'mcplite' (Lightweight Stdio MCP Server for R) Provides a lightweight Model Context Protocol (MCP) server for exposing 'R' functions as tools over standard input and output ('stdio'). Designed for local, client-launched integrations, with protocol-aware tool definitions and results, JSON Schema helpers, and optional interoperability with 'ellmer'. Package: r-cran-mcpmod Architecture: all Version: 1.0-10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-lattice Filename: pool/dists/noble/main/r-cran-mcpmod_1.0-10.1-1.ca2404.1_all.deb Size: 509668 MD5sum: dc793f619f9ea6f521022bdac3cc1e6c SHA1: de8ea24eb8eb6b5e4ac1bef5a80f5b4a93341cb4 SHA256: 15a2234ebd6d4a7a3e5032ee4845268a9bb954cd273d7bf5c089d1746e3361ae SHA512: f5c5534499d6b722a72539971a969d34aafb1c92938e8c5c7701352dc5b3b237c19541dff05138961c3423f811ae96fe13416e2954a77a28ad6eb4974907ccae Homepage: https://cran.r-project.org/package=MCPMod Description: CRAN Package 'MCPMod' (Design and Analysis of Dose-Finding Studies) Implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, ). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The 'MCPMod' package will not be further developed, all future development of the MCP-Mod methodology will be done in the 'DoseFinding' R-package. Package: r-cran-mcpmodbc Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dorng, r-cran-survival, r-cran-doparallel, r-cran-nleqslv, r-cran-foreach, r-cran-dosefinding, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-mcpmodbc_1.1-1.ca2404.1_all.deb Size: 76602 MD5sum: c941dc2e777e79d6b7b49207af23c532 SHA1: 5450f228897ef455cd02911afb2cdd97a54075c4 SHA256: 80dd2ebc916381e775f89105240bc49b9b096b1c03b85ec6b5776a7ee97c8233 SHA512: bb192272a4fd527f0a93780ba5f034bed99515918813cf5cbd7435a35ae4d94ed9ab50f3abd6c7abeea8aa235545b60c162e0460b1bdb633402af89da63d8ea2 Homepage: https://cran.r-project.org/package=MCPModBC Description: CRAN Package 'MCPModBC' (Improved Inference in Multiple Comparison Procedure – Modelling) Implementation of Multiple Comparison Procedures with Modeling (MCP-Mod) procedure with bias-corrected estimators and second-order covariance matrices as described in Diniz, Gallardo and Magalhaes (2023) . Package: r-cran-mcpmodgeneral Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosefinding, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/noble/main/r-cran-mcpmodgeneral_0.1-3-1.ca2404.1_all.deb Size: 237616 MD5sum: ed97c4a29101d204c77a266fcc02a56e SHA1: 141f2a1d37d628a0d3fe481c48938b351aeb1cc2 SHA256: b9138094fd1a8bb35a41863c47d7468896bfa4f914c3d5419d4656225995e91d SHA512: 08a50b5468a33f4b0989642430f80e623a7ade0aa8fff2a467520a0194728e1149582ae528f3f5feeb3e7a0bd1c19d78a266075ed75bd88960550e4463547070 Homepage: https://cran.r-project.org/package=MCPModGeneral Description: CRAN Package 'MCPModGeneral' (A Supplement to the 'DoseFinding' Package for the General Case) Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available. Bretz, F., Pinheiro, J. C., and Branson, M. (2005) . Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) . Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) . Package: r-cran-mcprofile Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-quadprog, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-markdown, r-cran-multcomp, r-cran-mass Filename: pool/dists/noble/main/r-cran-mcprofile_1.0-1-1.ca2404.1_all.deb Size: 193764 MD5sum: 4a516d4b61a0337eb728fc629f95b41d SHA1: facf11b20bcd152184db34091d008ac798aee0a3 SHA256: 5cd7ce44fd5b902038f311a2c79002927c250c3428d2f9c577a4ee5525f6a6c4 SHA512: 529b95960e08ed8178c5cb1cc1424d5c48185d4d30ce7c4ccfad80cb93f406be10b2f3c997f7a3dc3398b9bbfb19017b98652a8d88106a5b994de7ceab7eecba Homepage: https://cran.r-project.org/package=mcprofile Description: CRAN Package 'mcprofile' (Testing Generalized Linear Hypotheses for Generalized LinearModel Parameters by Profile Deviance) Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided. Package: r-cran-mcprogress Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-mcprogress_0.1.1-1.ca2404.1_all.deb Size: 377848 MD5sum: 489d9849db705a4889654de7e3e07cc8 SHA1: fecdcb084483d7165ff93b19f86f90b5923b89f4 SHA256: 9658e561afc84588c377e366c4e592e7b229099825c421c7da36a37a4b67b49f SHA512: 507bc7b6a674aea9f5b6f49680cfa948d44e54cb39efb4414353bdc6727887e6f91594390cbac284e8b591ddb1dee8a846699af5d1a31e3fc06613662fdd6165 Homepage: https://cran.r-project.org/package=mcprogress Description: CRAN Package 'mcprogress' (Progress Bars and Messages for Parallel Processes) Tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in 'RStudio' or 'Linux' environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the 'foreach' package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code. Package: r-cran-mcptests Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-smr, r-cran-writexl, r-cran-xtable, r-cran-foreach, r-cran-doparallel Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-mcptests_1.0.1-1.ca2404.1_all.deb Size: 186964 MD5sum: 56892cc0d6c1eca4908abf4074ddf00d SHA1: 7caeaebaff0ec40caa5976100affe0d95c969f95 SHA256: 083fbcc615976943c035e810daccec466de5006a531afa021abe6480981408fd SHA512: 83e6fbfff1641d6026733024d33eb6f7bcb47d08a31d72ecb1d1db2926bb45e073af21ae9db957a88981963ce7239c8e6bf39a0d393c0dbf3de80d3e3a3484a5 Homepage: https://cran.r-project.org/package=MCPtests Description: CRAN Package 'MCPtests' (Multiples Comparisons Procedures) Performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) , Batista (2016) , including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test. Package: r-cran-mcptools Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-ellmer, r-cran-httpuv, r-cran-httr2, r-cran-jsonlite, r-cran-nanonext, r-cran-openssl, r-cran-processx, r-cran-promises, r-cran-rlang, r-cran-yaml Suggests: r-cran-callr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mcptools_1.0.3-1.ca2404.1_all.deb Size: 927844 MD5sum: 93daede192a03956504849184b8e4277 SHA1: 35b762c0fbaa9842668f38b8d246e78ad251f787 SHA256: 1ad958588a82e2f107204b42400665e34b786a46d34ed33bdb8419f444893433 SHA512: 1d3ce1b521dedeb174f82396cad350e64950709ecb0f6a5fc7e62660ae166c2604786789e9c8272be85a66d5d9eaee5defed73f9a6799ce31ffaef51388b6e7d Homepage: https://cran.r-project.org/package=mcptools Description: CRAN Package 'mcptools' (Model Context Protocol Servers and Clients) Implements the Model Context Protocol (MCP). Users can start 'R'-based servers, serving functions as tools for large language models to call before responding to the user in MCP-compatible apps like 'Claude Desktop' and 'Claude Code', with options to run those tools inside of interactive 'R' sessions. On the other end, when 'R' is the client via the 'ellmer' package, users can register tools from third-party MCP servers to integrate additional context into chats. Package: r-cran-mcqanalysis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcqanalysis_0.1.0-1.ca2404.1_all.deb Size: 132616 MD5sum: dc33515492d5e2bd190bfbc40bc0e10b SHA1: c8295150ff69c1f9d41a310cd5f15bee159f5f0e SHA256: 970981f489a7ac71c0a0a358c9f8cbdc2eb2943bf792e76e829040dc16e080b7 SHA512: 8583b6c970a105231dc30b610c91bc3885173b2deb995aa63f63b176b1865b4a49d35ccd674f71b3feefb74720993a9d98883ec72b0d680fde7e4cf1136c9ca2 Homepage: https://cran.r-project.org/package=mcqAnalysis Description: CRAN Package 'mcqAnalysis' (Classical Test Theory Item Analysis for Multiple-Choice Tests) A unified toolkit for classical test theory (CTT) item analysis of multiple-choice test data, including item difficulty (p-value), item discrimination (point-biserial correlation and upper-lower 27-percent discrimination index), per-distractor analysis (frequency, proportion, and discrimination), and Haladyna's distractor efficiency. A wrapper function returns a tidy 'mcq_analysis' object with print, plot (difficulty-discrimination scatter), and APA-style table methods for direct inclusion in journal manuscripts. Implemented in pure R with no compiled code and minimal dependencies. 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Most of the methods and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) recommendations and NMPA (National Medical Products Administration) guidelines. In additional, relevant plots are constructed by 'ggplot2'. 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See Ritschard, G. and Liao, T.F. (2026): "Assessing the Impact of Timing Errors in Sequence Analysis". International Journal of Social Research Methodology . 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The number of clusters at which the index attain its maximum more frequently is a candidate for being the optimal number of clusters. Package: r-cran-mcstats Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcstats_0.1.3-1.ca2404.1_all.deb Size: 64144 MD5sum: 135e15c6f7a1fa97a2ec9d6cc1645e96 SHA1: 1aa0f23152ae3de5efd155d68cb35502422af61c SHA256: d24a88c672b81bad0efa9cabb62556582c789e25b07aa9e1b11ccb1233892e5c SHA512: 4e66acc9f77684e4e0d4659f323c4db1af8724e38fa0d7a5eeb4bbd8f08380f5424416b0ea16edf6bb57c1ab6ce9a510a88a9cb5f0cb0241fe806281df4b60d6 Homepage: https://cran.r-project.org/package=mcStats Description: CRAN Package 'mcStats' (Visualize Results of Statistical Hypothesis Tests) Provides functionality to produce graphs of sampling distributions of test statistics from a variety of common statistical tests. With only a few keystrokes, the user can conduct a hypothesis test and visualize the test statistic and corresponding p-value through the shading of its sampling distribution. Initially created for statistics at Middlebury College. 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Package: r-cran-mctest Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mctest_1.3.2-1.ca2404.1_all.deb Size: 74690 MD5sum: 189de38f617f5fdc7d9b1a06395bfcb5 SHA1: 470c21bb3c7f67968b176f55e8baecd40c4ff822 SHA256: ffb6c597a15a02a65d3a935d96e9a846a93ac54c9dc66f7264db3781b6770b00 SHA512: 481fed951b014aafceaf9d198ccc5312e2a1cdd20f025f93fb19eb60cc6f4533932a56d168ba21345ff58adaac36eb4c52e3864b7342ab746fca1dca2707ab57 Homepage: https://cran.r-project.org/package=mctest Description: CRAN Package 'mctest' (Multicollinearity Diagnostic Measures) Package computes popular and widely used multicollinearity diagnostic measures \doi{10.17576/jsm-2019-4809-26} and \doi{10.32614/RJ-2016-062}. Package also indicates which regressors may be the reason of collinearity among regressors. 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Provides data tools for panel preparation (import, outlier detection, classification harmonization), analytical methods (production function estimation, capital stock measurement, markups, intensity measures, distributions, regression, clustering), and disclosure tools for tagging outputs with dominance and observation counts before aggregation and publication. Production function estimation implements methods by Ackerberg, Caves and Frazer (2015) , Levinsohn and Petrin (2003) , Wooldridge (2009) , Petrin, Poi and Levinsohn (2004) , and Arellano and Bond (1991) with the "too many instruments" correction by Roodman (2009) . Markup estimation follows De Loecker and Warzynski (2012) . Cost-share production function estimation follows Basu and Fernald (1997) . Capital stock estimation via the Perpetual Inventory Method follows OECD (2009) . Package: r-cran-mdma Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-lme4, r-cran-mass, r-cran-performance Suggests: r-cran-clusterbootstrap, r-cran-glmmtmb, r-cran-vgam Filename: pool/dists/noble/main/r-cran-mdma_2.0.0-1.ca2404.1_all.deb Size: 166100 MD5sum: a35743ca22b39b7a019898e7e78186f7 SHA1: 909f6bff3e0141d7e3411e19d98f4e142b274fef SHA256: e5cf25b216eb344a16e7a1def4d9cf5717a2d3f154c17729010aea877f56c26d SHA512: 833bcc9be5cda831c605f6305973f8c14063ca01c94d43a37ae565b4ff9b0f84dab162f18569eafebfcd1ed664d4b47df58f07fb7b31587a322ad810bd77ca7e Homepage: https://cran.r-project.org/package=MDMA Description: CRAN Package 'MDMA' (Mathijs Deen's Miscellaneous Auxiliaries) Provides a variety of functions useful for data analysis, selection, manipulation, and graphics. 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Package: r-cran-mdsmap Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1776 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-smacof, r-cran-princurve, r-cran-rgl, r-cran-reshape Suggests: r-cran-qtl, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-mdsmap_1.3-1.ca2404.1_all.deb Size: 729574 MD5sum: 6eb18cf5b286bc88d298a0bcd851afc1 SHA1: 9d778a08d1ebce7c5c258d57e88be37bb1d86d11 SHA256: 28cb45a743adbefd8f1dd2bf3ba564dbe97ee3a9f65a9c17d473df669c18c8fb SHA512: 31ddbbbf01dcb5c7b9735c72a77906137aa755212be0365cc24b37c114f30e17f244dfcaeaca3900e5880e2d724810c10843dd1d48d2b6a2428927798560b2ce Homepage: https://cran.r-project.org/package=MDSMap Description: CRAN Package 'MDSMap' (High Density Genetic Linkage Mapping using MultidimensionalScaling) Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. 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Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties. (Borg, I., Groenen, P.J.F., Mair, P. (2013) , Walesiak, M. (2016) , Walesiak, M. (2017) ). 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This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package. 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Isobel Claire Gormley and Thomas Brendan Murphy (2010) . 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Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) . Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) and Rosner B, Spiegelman D & Willett WC (1992) , the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331). 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Package: r-cran-med Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-med_0.1.0-1.ca2404.1_all.deb Size: 44718 MD5sum: 619dd0661de5e3ed44fa0b8605e3ff27 SHA1: 50c19483efef14076be432349a9e0926d186f3bc SHA256: 2543d218a3e7c69a744af6483d8c283fcfac2842359768a3e58ab535193ab98b SHA512: 67b5456f42302fbfbb358218c4729fb76d350e4aeeee2f7c48d270c1218c7046835a107fc1f91c803134ad7f07db5dc622c7502b25ef37f00ea6ae8e46476c7a Homepage: https://cran.r-project.org/package=MED Description: CRAN Package 'MED' (Mediation by Tilted Balancing) Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) . Package: r-cran-meddatasets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3513 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-meddatasets_0.1.0-1.ca2404.1_all.deb Size: 1834200 MD5sum: 18ce9859dc9f37be015ca1dd184da3ee SHA1: 6efd944408b325c08ef921ceefabf814cc72e932 SHA256: f16e80db41e02bbc8875e4d51a6d864c4d5356532835ffe0caa337893616d443 SHA512: eade66f7b8c45dc525199d7bbb34e3076f83525bacd88076ba4d9d2ee789f5fac35669615978c876f125b3a7f0eb270befa2b490a85c4bacb246e0664b58cb40 Homepage: https://cran.r-project.org/package=MedDataSets Description: CRAN Package 'MedDataSets' (Comprehensive Medical, Disease, Treatment, and Drug Datasets) Provides an extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health. 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Package: r-cran-medesigns Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-medesigns_1.0.1-1.ca2404.1_all.deb Size: 42528 MD5sum: 69cee094c54b22bb2355707bbd1deab5 SHA1: beabbc1aeee26e8426d5974e57098a0648561ab6 SHA256: 1cb8fae11e49eec5a45ad887a2167f4bb9c6fbdbbb2335273cbc409b08826ca7 SHA512: b2a10ab09824fe3e59970ecad0d3d22da2f2d63c184a9b567ea959ca78d6cc133825526576e79be186413072dd16f8959693cdf26bb84def36c314e75ce6b11b Homepage: https://cran.r-project.org/package=MEDesigns Description: CRAN Package 'MEDesigns' (Mating Environmental Designs) In breeding experiments, mating environmental (ME) designs are very popular as mating designs are directly implemented in the field environment using block or row-column designs. 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Package: r-cran-medextractr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-medextractr_0.4.1-1.ca2404.1_all.deb Size: 1453158 MD5sum: 690abdd9c3323a4ea213d2826cd33d84 SHA1: c417a0328f81bd655592ebd84198954fe57e0db9 SHA256: 3b0b2baed2e28e4c0513c3111c9b40aad089605f17b74f39c9d175d884a22295 SHA512: 6b3f97c5472950548a99299befc5369a33dd5a6738c83877679d6f6d22de7593555e736e81740e2aa61c615f46940bd65e263a1b7dea78ef40fc0e1f6ec6e0e7 Homepage: https://cran.r-project.org/package=medExtractR Description: CRAN Package 'medExtractR' (Extraction of Medication Information from Clinical Text) Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) . The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition. Package: r-cran-medfit Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-checkmate, r-cran-generics, r-cran-mass Suggests: r-cran-lavaan, r-cran-sandwich, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-medfit_0.3.2-1.ca2404.1_all.deb Size: 1061830 MD5sum: 23b53f76f63495140e2fbe596d148c8f SHA1: be1f1d7d521aacc3cdea92949a4153ccf4b00f0f SHA256: 2e85a174ab509b4576f999a6e6db37ee05453ba24ab8a9e4ef25b267a5cebcf4 SHA512: 7240362076ea249778569660ae61350149909552cebd2c426c0a0965cfec2e80e820075136eec76e89b19114df0d1dad36a4faff7dfa514cbd54f8daac33639c Homepage: https://cran.r-project.org/package=medfit Description: CRAN Package 'medfit' (Infrastructure for Mediation Model Fitting and Extraction) Provides S7-based infrastructure for fitting mediation models, extracting path coefficients, and performing bootstrap inference. Designed as a foundation package for the mediation analysis ecosystem, supporting 'probmed', 'RMediation', and 'medrobust' packages. Implements unified interfaces for model fitting across different engines (currently generalized linear models, with future support for mixed models and Bayesian methods), standardized extraction of mediation paths from various model types, and robust bootstrap inference methods. Mediation methods are described in MacKinnon, Lockwood and Williams (2004) , Preacher and Hayes (2008) , Tofighi and MacKinnon (2011) , and VanderWeele (2014) . 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We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method. Package: r-cran-mediation Architecture: all Version: 4.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-sandwich, r-cran-lpsolve, r-cran-hmisc, r-cran-lme4, r-cran-boot Suggests: r-cran-mgcv, r-cran-quantreg, r-cran-vgam, r-cran-suppdists, r-cran-survival, r-cran-testthat, r-cran-speedglm Filename: pool/dists/noble/main/r-cran-mediation_4.5.1-1.ca2404.1_all.deb Size: 1204774 MD5sum: 167d6808e277932012652d0adb8b1f25 SHA1: 8c964e6c89bb5b062f4fd47d8bd76df52b86844f SHA256: 2ef9a290da42972c0008acf73533455eef931ad7a5e912f409bd423e7f1c8db9 SHA512: ba7c8d7e65ead2a22774ead2fd146a908f1a8a8f441bae77ebd98921d4e3efa80ce15658166fb4917ec1eaac5d2a239d6e03434a78cd1b75db5513fb33f70ada Homepage: https://cran.r-project.org/package=mediation Description: CRAN Package 'mediation' (Causal Mediation Analysis) We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) , Imai, Keele and Tingley (2010) , Imai, Tingley and Yamamoto (2013) , and Imai and Yamamoto (2013) . In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models. Package: r-cran-mediationsens Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mediation, r-cran-distr Filename: pool/dists/noble/main/r-cran-mediationsens_0.0.3-1.ca2404.1_all.deb Size: 91250 MD5sum: 10c272e6e9357c6d5cc302df33f86b24 SHA1: 085df2f02057d38d2ff714c92885363956d5c1e6 SHA256: 3c0f3ff4fa82f4b68f8f684ce433fc0f7d88c8e75997f2e4191cd0ba68ec4ab3 SHA512: 5fa822d098d28a7b8215d14120e830586c4fcdd8927dc71666a1709987cbdf8295fbe235dcb8feaa0e59c882c149b11a6bb3ae1444e630ef3c2c447c40148cb9 Homepage: https://cran.r-project.org/package=mediationsens Description: CRAN Package 'mediationsens' (Simulation-Based Sensitivity Analysis for Causal MediationStudies) Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based methods. It is applicable to both randomized experiments and observational studies. Package: r-cran-medicalcoder Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4123 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-medicalcoder_0.10.0-1.ca2404.1_all.deb Size: 3477640 MD5sum: 6c7b246e7025c658690636f56f2eb765 SHA1: eae7556a8b3b014da6dd7f68dcfa2d7bf0132fb0 SHA256: 5d8aac3d072ffab20159257ac478fbaafc156eeacf6b8577da0c3389de478948 SHA512: fd5b6c3816251af035559bc35f4c152672f7d026991c40badb1d58cb050bbc85b192aca22c62c9f747297034362056316b66302838146f7223bc28968350a4a4 Homepage: https://cran.r-project.org/package=medicalcoder Description: CRAN Package 'medicalcoder' (A Unified and Longitudinally Aware Framework for ICD-BasedComorbidity Assessment) Provides comorbidity classification algorithms such as the Pediatric Complex Chronic Conditions (PCCC), Charlson, and Elixhauser indices, supports longitudinal comorbidity flagging across encounters, and includes utilities for working with medical coding schemas such as the International Classification of Diseases (ICD). Package: r-cran-medicaldata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-learnr Filename: pool/dists/noble/main/r-cran-medicaldata_0.2.0-1.ca2404.1_all.deb Size: 662542 MD5sum: 5d802059ae5b2b587481c744c06d9232 SHA1: abe98f68673487ad1af9a69c97e87461fc107fc0 SHA256: ea564bdac040263acdd6b20a0add149554c76461c0ca5def7b6fe0356a4b2d5c SHA512: afbb2d5bbd8343dec96f46d889baff3afced3ba83594cb675654ee55f6a16d998216844a1a7394c30e7f74a6fb675a2b364faa7062ea28e52723bf80e30aeacb Homepage: https://cran.r-project.org/package=medicaldata Description: CRAN Package 'medicaldata' (Data Package for Medical Datasets) Provides access to well-documented medical datasets for teaching. Featuring several from the Teaching of Statistics in the Health Sciences website , a few reconstructed datasets of historical significance in medical research, some reformatted and extended from existing R packages, and some data donations. Package: r-cran-medicalrisk Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-reshape2, r-cran-hash Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-medicalrisk_1.3-1.ca2404.1_all.deb Size: 329984 MD5sum: e8ac8618d6a4052249134da526777a75 SHA1: d9c4c352a930f4fa031fbbb5774e3ddc7bb6df34 SHA256: a737f40fe5e61264ad7cc6481800fa6fb173510127b63a8345c77ef868c1bc0a SHA512: d9a88160520439a30ee6ff7aeab7967c4588a361298c42c55b643616d2f37da7a42d8be89c700adc190370527ed0ce05aaae139632afd8a2aba368ea035b10b8 Homepage: https://cran.r-project.org/package=medicalrisk Description: CRAN Package 'medicalrisk' (Medical Risk and Comorbidity Tools for ICD-9-CM Data) Generates risk estimates and comorbidity flags from ICD-9-CM codes available in administrative medical datasets. The package supports the Charlson Comorbidity Index, the Elixhauser Comorbidity classification, the Revised Cardiac Risk Index, and the Risk Stratification Index. Methods are table-based, fast, and use the 'plyr' package, so parallelization is possible for large jobs. Also includes a sample of real ICD-9 data for 100 patients from a publicly available dataset. Package: r-cran-medicare Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2091 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-maps, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-medicare_0.3.0-1.ca2404.1_all.deb Size: 962462 MD5sum: 70f66a7ba04887dc5fab4153292acd0b SHA1: fdcc0800be4d1cec6a3e75635cc3cd219e13dc0b SHA256: 5ec52f710de3325cf0942de1cfd6f5efa2bfcc27ab65892191c41aa7e6c6c378 SHA512: dea8340a4bd79fe5c34e04249cc10263784fa48aee343a6929bf43a154ce31533305829b1baa42aa1968bd49c60827736d2bc7e4bdd23e890fc393e86b1d92fb Homepage: https://cran.r-project.org/package=medicare Description: CRAN Package 'medicare' (Tools for Obtaining and Cleaning Medicare Public Use Files) Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See . Package: r-cran-meditations Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-meditations_1.0.1-1.ca2404.1_all.deb Size: 100204 MD5sum: 22d026d0335e72ea27f64932061ba929 SHA1: f001a1cd941a3f77c6c0c391bce93cc597fb7857 SHA256: 89f76fdb060409169fa950754d1f1f83dfd0f34f6174781c3a6fc0ce2d430649 SHA512: 3ea23120cf1e26c438e8de8bbca633e8df64751022b9b3a61941255a938b20836b9409578085c6e66c78d245c341e90de54a713a06309da2462d423d5ac2b8ec Homepage: https://cran.r-project.org/package=meditations Description: CRAN Package 'meditations' (Prints a Random Quote from Marcus Aurelius' Book Meditations) Prints a random quote from Marcus Aurelius' book Meditations. Package: r-cran-medlea Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-tm, r-cran-patchwork, r-cran-wordcloud2 Filename: pool/dists/noble/main/r-cran-medlea_1.0.2-1.ca2404.1_all.deb Size: 322616 MD5sum: a29d8af24d29d5f3a62afad584dff2d0 SHA1: e8cd88fe44a244b1862164d063e1f8dd96ce299b SHA256: 56792af61e30b761e7d133befc40ba5ac0edc5b45854d5b2679b6d928cebd35b SHA512: 479db7757c511a80a2099b8f233bbc8b9f6f627eb35593ad7d7fb84e408ebbdee186d766793c8fd31cbf3d54ded9133d62aedbe39906ee8dbf7e6f136b09aa6b Homepage: https://cran.r-project.org/package=MedLEA Description: CRAN Package 'MedLEA' (Morphological and Structural Features of Medicinal Leaves) Contains a dataset of morphological and structural features of 'Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka. Package: r-cran-medmod Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jmvcore, r-cran-r6, r-cran-lavaan, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-medmod_1.2.0-1.ca2404.1_all.deb Size: 305512 MD5sum: bfe8bdba5a64130b77adcfbc6dfcc96e SHA1: 78aaa7c8f7355c1f28efcceff8290187e6747227 SHA256: 9b8aa9f173d8a75873eab1312e3e94358a9fd49704f912fe3e2edad2d8a847f2 SHA512: f4e2918634e33d8f0bb633bc1375adbbe61a9664cc58dcbb274c66ddcb58253a82fe589bf1e78ae91028fbdadf9731051f63fb6b79157f8698eee0125b4ab36d Homepage: https://cran.r-project.org/package=medmod Description: CRAN Package 'medmod' (Simple Mediation and Moderation Analysis) This toolbox allows you to do simple mediation and moderation analysis. Models are estimated with the 'lavaan' package by Rosseel (2012) ; standard errors for the mediation estimates are computed with the delta method following Sobel (1982) or by bootstrapping. It is also available as a module for 'jamovi' (see for more information). You can find an in depth tutorial on the 'lavaan' model syntax used for this package on . Package: r-cran-medparser Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-medparser_0.2.0-1.ca2404.1_all.deb Size: 19722 MD5sum: e421ef5d5e79e118a11c3b3ac7197ab7 SHA1: df6b77af68573e421ada3e4264612622dbaafaf3 SHA256: c85ff7cf0288e791eff122ab0839709f1af06f187cc33216399f56e72faddc7a SHA512: e9b39a704db0f7e7f7edc13dc2e2eea5aecdc285874d20c425021d43b49e17a9b6d09e41bd5ed262a6d3123de2ef75f116fe83dd226d0d289c8ce03848e99b5e Homepage: https://cran.r-project.org/package=medparser Description: CRAN Package 'medparser' (MedPC Text Parser) Parses information from text files with specific utility aimed at pulling information from Med Associate's (MPC) files. These functions allow for further analysis of MPC files. Package: r-cran-medrxivr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1501 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-jsonlite, r-cran-httr, r-cran-stringr, r-cran-rlang, r-cran-bib2df, r-cran-tibble, r-cran-progress, r-cran-lubridate, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-medrxivr_0.1.4-1.ca2404.1_all.deb Size: 972654 MD5sum: 69e003b79e10ebb2cc28717096a526a3 SHA1: 2e76f9b0d2365aa39f1fda7cc1f848e7e54eb3a8 SHA256: 5652dbcab7d554fc9d84a89185e3c4488c41db5c2e2c2595cfab8e64528fda13 SHA512: 420312b7b48dd7ce7aafd9ad9be5fdeaf2fec642ae264bda482c03b23f97e841a4c26b0d3625ed6d66422e09d0af5c9a432b99de88ef85a78e685fdc1a79d857 Homepage: https://cran.r-project.org/package=medrxivr Description: CRAN Package 'medrxivr' (Access and Search MedRxiv and BioRxiv Preprint Data) An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv and bioRxiv, both of which are operated by the Cold Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 'Cold Spring Harbour Laboratory (CSHL)' API , allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. 'medrxivr' also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria. Package: r-cran-medscan Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdmt, r-cran-locfdr, r-cran-qqman, r-bioc-qvalue, r-cran-fdrtool Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-medscan_1.0.2-1.ca2404.1_all.deb Size: 33672 MD5sum: 2316ee3870dd74d4a5d9dfef875ab026 SHA1: 4da348da71ded97696ce6bb8806b0eeb7e9c9bc4 SHA256: 32b642615b9ba173c9682cd4677e48e9a62d0b9a49baf40f83ec99446688ea01 SHA512: bf04859bf65144f36678a360f59c35985c7e88d393d81aca882642e2b4919b9ae6f62364b1cd47189e6e60167c5f9faa3f0f8c1de54492c796032d7bc9fae626 Homepage: https://cran.r-project.org/package=medScan Description: CRAN Package 'medScan' (Large Scale Single Mediator Hypothesis Testing) A collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) . Package: r-cran-medseq Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-matrixstats, r-cran-nnet, r-cran-seriation, r-cran-stringdist, r-cran-traminer, r-cran-weightedcluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-medseq_1.4.2-1.ca2404.1_all.deb Size: 707270 MD5sum: a07de6eabfb1aa01749449c534fe289b SHA1: b13642456b4743c73d3ca1f46c76cbfda8cf9235 SHA256: a9f39c09d42b614e1676dcf26a898948a504a14a999e07f15cbd9eb566a416c9 SHA512: 3b23ab0fbcb054631cc9dde6fc6377e9e059661abf72ad2f26cfab8d71092115ec0a60aef1d079d3285bdb01ee7f41d9a4baeb4fb5b18d181c3505d93a6ef9a3 Homepage: https://cran.r-project.org/package=MEDseq Description: CRAN Package 'MEDseq' (Mixtures of Exponential-Distance Models with Covariates) Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) . A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided. Package: r-cran-medxr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-memoise Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-medxr_0.1.1-1.ca2404.1_all.deb Size: 313180 MD5sum: 2444c90a35af267b1ccd15890b136acb SHA1: bfeecc1ca3a134431b95f11bb9abad29fbd49d42 SHA256: 28215c8c046604b050ac095f670b66544686dfe13d4d4da96f37dfc48910c7c7 SHA512: 48a921fe70d4c645f120c113191c788a9e9fce6ec44fefd42bfa61a0d298c5efc74af438571428c0ec7b8200824390b11a9276ccfb5f96f77998c525454f80de Homepage: https://cran.r-project.org/package=MedxR Description: CRAN Package 'MedxR' (Access Drug Regulatory Data via FDA and Health Canada APIs) Provides functions to access drug regulatory data from public RESTful APIs including the 'FDA Open API' and the 'Health Canada Drug Product Database API', retrieving real-time or historical information on drug approvals, adverse events, recalls, and product details. Additionally, the package includes a curated collection of open datasets focused on drugs, pharmaceuticals, treatments, and clinical studies. These datasets cover diverse topics such as treatment dosages, pharmacological studies, placebo effects, drug reactions, misuses of pain relievers, and vaccine effectiveness. The package supports reproducible research and teaching in pharmacology, medicine, and healthcare by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'FDA API' and 'Health Canada API' . Package: r-cran-medzisc Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-betareg, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-medzisc_0.0.5-1.ca2404.1_all.deb Size: 288406 MD5sum: f8814393d9e384d410100f0a3ba713c4 SHA1: d1db0b6cf4ba2ed08ec9eeac1c8c0b78ea203f6b SHA256: 0a5140389d10ec6c21739ffc1d0acafea27437beb7b4b41a0c3c68978b23c905 SHA512: 84d986a714d69d424ffa2d0b2fbe0f9d091ef0ca876dc2d64aab24de24610c3ff154e054b8cf95e4580d923a4a0f7cbcdd1f7aedb93484ece25c26389136e4ba Homepage: https://cran.r-project.org/package=MedZIsc Description: CRAN Package 'MedZIsc' (Statistical Framework for Co-Mediators of Zero-InflatedSingle-Cell Data) A causal mediation framework for single-cell data that incorporates two key features ('MedZIsc', pronounced Magics): (1) zero-inflation using beta regression and (2) overdispersed expression counts using negative binomial regression. This approach also includes a screening step based on penalized and marginal models to handle high-dimensionality. Full methodological details are available in our recent preprint by Ahn S et al. (2025) . Package: r-cran-meerva Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-meerva_0.2-2-1.ca2404.1_all.deb Size: 329482 MD5sum: 1e0fd36f75187fb338a9ff2b401f041c SHA1: 0056e22a964ea589be28049645866cd41da73dd8 SHA256: b3a5a8009d2e11013de07c3f9137427cc9353d365ed42a8722d5e267ed3f406c SHA512: 78ff435319aa328ed01ae7aad519b7252deb1fea56f500f882c05f7beca46b84865b6c88e5db556e894f16202a42123bc3d94cbecb0572a143850eee92c328ea Homepage: https://cran.r-project.org/package=meerva Description: CRAN Package 'meerva' (Analysis of Data with Measurement Error Using a ValidationSubsample) Sometimes data for analysis are obtained using more convenient or less expensive means yielding "surrogate" variables for what could be obtained more accurately, albeit with less convenience; or less conveniently or at more expense yielding "reference" variables, thought of as being measured without error. Analysis of the surrogate variables measured with error generally yields biased estimates when the objective is to make inference about the reference variables. Often it is thought that ignoring the measurement error in surrogate variables only biases effects toward the null hypothesis, but this need not be the case. Measurement errors may bias parameter estimates either toward or away from the null hypothesis. If one has a data set with surrogate variable data from the full sample, and also reference variable data from a randomly selected subsample, then one can assess the bias introduced by measurement error in parameter estimation, and use this information to derive improved estimates based upon all available data. Formulaically these estimates based upon the reference variables from the validation subsample combined with the surrogate variables from the whole sample can be interpreted as starting with the estimate from reference variables in the validation subsample, and "augmenting" this with additional information from the surrogate variables. This suggests the term "augmented" estimate. The meerva package calculates these augmented estimates in the regression setting when there is a randomly selected subsample with both surrogate and reference variables. Measurement errors may be differential or non-differential, in any or all predictors (simultaneously) as well as outcome. The augmented estimates derive, in part, from the multivariate correlation between regression model parameter estimates from the reference variables and the surrogate variables, both from the validation subset. Because the validation subsample is chosen at random any biases imposed by measurement error, whether non-differential or differential, are reflected in this correlation and these correlations can be used to derive estimates for the reference variables using data from the whole sample. The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. The augmented estimates, as well as how measurement error may arise in practice, is described in more detail by Kremers WK (2021) and is an extension of the works by Chen Y-H, Chen H. (2000) , Chen Y-H. (2002) , Wang X, Wang Q (2015) and Tong J, Huang J, Chubak J, et al. (2020) . 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Package: r-cran-memapp Architecture: all Version: 2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinybs, r-cran-shinyjs, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-stringi, r-cran-dt, r-cran-formattable, r-cran-ggplot2, r-cran-plotly, r-cran-mem Suggests: r-cran-openxlsx, r-cran-readxl, r-cran-foreign, r-cran-haven, r-cran-readods, r-cran-rodbc, r-cran-magick Filename: pool/dists/noble/main/r-cran-memapp_2.16-1.ca2404.1_all.deb Size: 158358 MD5sum: 7a5c8f2db45206beb47b9425cd0709c3 SHA1: c8adf0b3532faa05f78fd14b6883a75ef0e1767c SHA256: e995c771ae97e9a91a7de2dde07f39ddc751da5ddef0fdf2aa434db446d71d16 SHA512: 8e83fffebb5ec19b88c373b11c84b3dc07f1f89b75d9fa3050c608b3d17be40ddb3b2e121d60dfef1614e9c91ddcaffcdde5a93f8452e267d1b602da45ce4a55 Homepage: https://cran.r-project.org/package=memapp Description: CRAN Package 'memapp' (The Moving Epidemic Method Web Application) The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) , , allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package. Package: r-cran-meme Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridgraphics, r-cran-magick, r-cran-showtext, r-cran-sysfonts Suggests: r-cran-cowplot, r-cran-ggimage, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-shadowtext Filename: pool/dists/noble/main/r-cran-meme_0.2.4-1.ca2404.1_all.deb Size: 2854668 MD5sum: 5260f9108146b2ba00d2e080af44221b SHA1: bcf2fef109185effae64d197fbfbbff79ed5021c SHA256: ce52a3b3b9e4651c4d0bb83fc9ed0dd9f35cc0e008a7697aa9efee159c8ce79f SHA512: 680ceec4d3a3f16a95c4136eb0e5e0b73a1c5852e77fc9b3fd413f9c0c98f54d2a2e1e587083724ac9c3960cdfeedc076be3bc13fa78007d41b3ff7200a0523b Homepage: https://cran.r-project.org/package=meme Description: CRAN Package 'meme' (Create Meme) The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976). It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity. The internet memes are captioned photos that are intended to be funny, ridiculous. Memes behave like infectious viruses and travel from person to person quickly through social media. The 'meme' package allows users to make custom memes. Package: r-cran-memery Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-showtext, r-cran-sysfonts, r-cran-png, r-cran-jpeg, r-cran-cairo, r-cran-ggplot2, r-cran-cowplot, r-cran-magrittr, r-cran-purrr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinybs, r-cran-colourpicker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magick, r-cran-gifski Filename: pool/dists/noble/main/r-cran-memery_0.6.0-1.ca2404.1_all.deb Size: 112340 MD5sum: 61122679ea09796d34d1990f729002de SHA1: 42c857f20674f206eb38b2e3741f33bf6d1dd8a0 SHA256: c57b6fbc6a735caf4b1f734e10a859ad6c74ae92ce49c398339b38116e33138d SHA512: 7a784e517bc03314c3e03dcf20b664d937ff247cbb335da51c01d9d168a6651bbf7657cefadd642f6f96c5694317ef30996fa953dc20bb62db574813b370dd86 Homepage: https://cran.r-project.org/package=memery Description: CRAN Package 'memery' (Internet Memes for Data Analysts) Generates internet memes that optionally include a superimposed inset plot and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. The package differs from related packages that focus on imitating and reproducing standard memes. Some packages do this by interfacing with online meme generators whereas others achieve this natively. This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites. It reads images directly from local files or via URL and meme generation is done by the package. While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on allowing for non-standard meme layouts and hybrids of memes mixed with graphs. While this package can be used to make basic memes like an online meme generator would produce, it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting foreground graphs of data analysis results. The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes rather than restricting to the standard top and/or bottom text placement. This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels. The package offers a selection of templates for graph placement and appearance with respect to the underlying meme. Graph templates also permit additional template-specific customization. Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 'magick' is not required unless gif functionality is desired. Package: r-cran-memgene Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-gdistance, r-cran-raster, r-cran-sp, r-cran-vegan Suggests: r-cran-adegenet, r-cran-geosphere, r-cran-knitr Filename: pool/dists/noble/main/r-cran-memgene_1.0.3-1.ca2404.1_all.deb Size: 1392744 MD5sum: 59fa60e102442ba4b407071b8fd3eedf SHA1: 990c6f2ef42b0b491bccee116d9fd9460d64ffe9 SHA256: c659e4e855d183282250dbf9dabffb0f4f245162b749d2c84b88e8ea443cc7bf SHA512: f705a1a0e2d89467024c7186b6a1640708be7d0cb2b31c60294c783dfa6554f2529dc8e86fff07c098fbaf84956423e142b40dee0bc350a968eea9fdde7b109e Homepage: https://cran.r-project.org/package=memgene Description: CRAN Package 'memgene' (Spatial Pattern Detection in Genetic Distance Data Using Moran'sEigenvector Maps) Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation. Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation. Package: r-cran-memify Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-memify_0.1.1-1.ca2404.1_all.deb Size: 23944 MD5sum: 36cb417369d8f327b5e63ce5b6c10a51 SHA1: 9b5e8af4707376d645d68e739a6738855c144993 SHA256: bb6e687d381cc2b135ea9fd514f3e69d9f5059c891e3ba139955149cad7b22cf SHA512: f27b85616a9bc3f0368a44938742e37d2eb226f2cbcdd04e4c6433775c90afb766604df4df81dbc55cda5e2058c3efbe6dc1923e34431ae1323343c00c223b66 Homepage: https://cran.r-project.org/package=memify Description: CRAN Package 'memify' (Constructing Functions That Keep State) A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation. Package: r-cran-memochange Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-fracdiff, r-cran-longmemoryts, r-cran-sandwich, r-cran-strucchange, r-cran-longmemo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-xts, r-cran-zoo, r-cran-data.table Filename: pool/dists/noble/main/r-cran-memochange_1.1.2-1.ca2404.1_all.deb Size: 283248 MD5sum: c94b4cb4c3308208ef241269d67a6628 SHA1: cfc5cf108939314245e23e27abc51ef803ada96e SHA256: 923835ff514d709e56478359e6fa1381237faf9c26f8b1ee505b6b3196a9cc9b SHA512: 3669780f1956ad3e481485b69d5efb652b5154f9d8da64d03919652bba4f694ed9a13b22825ccc687089faed686deb583aa2222e95aaf51dce06b88bc6feb6f2 Homepage: https://cran.r-project.org/package=memochange Description: CRAN Package 'memochange' (Testing for Structural Breaks under Long Memory and Testing forChanges in Persistence) Test procedures and break point estimators for persistent processes that exhibit structural breaks in mean or in persistence. On the one hand the package contains the most popular approaches for testing whether a time series exhibits a break in persistence from I(0) to I(1) or vice versa, such as those of Busetti and Taylor (2004) and Leybourne, Kim, and Taylor (2007). The approach by Martins and Rodrigues (2014), which allows to detect changes from I(d1) to I(d2) with d1 and d2 being non-integers, is included as well. In case the tests reject the null of constant persistence, various breakpoint estimators are available to detect the point of the break as well as the order of integration in the two regimes. On the other hand the package contains the most popular approaches to test for a change-in-mean of a long-memory time series, which were recently reviewed by Wenger, Leschinski, and Sibbertsen (2018). These include memory robust versions of the CUSUM, sup-Wald, and Wilcoxon type tests. The tests either utilize consistent estimates of the long-run variance or a self normalization approach in their test statistics. Betken (2016) Busetti and Taylor (2004) Dehling, Rooch and Taqqu (2012) Harvey, Leybourne and Taylor (2006) Horvath and Kokoszka (1997) Hualde and Iacone (2017) Iacone, Leybourne and Taylor (2014) Leybourne, Kim, Smith, and Newbold (2003) Leybourne and Taylor (2004) Leybourne, Kim, and Taylor (2007): Martins and Rodrigues (2014) Shao (2011) Sibbertsen and Kruse (2009) Wang (2008) Wenger, Leschinski and Sibbertsen (2018) . Package: r-cran-memofunc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-uuid, r-cran-magrittr Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-covr Filename: pool/dists/noble/main/r-cran-memofunc_1.0.2-1.ca2404.1_all.deb Size: 67620 MD5sum: 68d5a50c0770cd30b8d640db7d644411 SHA1: 8985fb0007370716740670ba8a1904f3c9e626c0 SHA256: 0391e64fd2fb5afd8ee55dd4210a57b9ae9ce7197acde3586ed97c3e47e465ee SHA512: 8d2bc2e6df74eac2704520dc219bbf16290425f22ccbec58636ecbe9f56b9c19b55a8850c898e2c6f0df6b87ab7828917521e1bc2e6b545c26a0512024ae7697 Homepage: https://cran.r-project.org/package=memofunc Description: CRAN Package 'memofunc' (Function Memoization) A simple way to memoize function results to improve performance by eliminating unnecessary computation or data retrieval activities. Package: r-cran-memoir Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bookdown, r-cran-distill, r-cran-rmarkdown, r-cran-rmdformats, r-cran-usethis Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-memoir_1.3-1-1.ca2404.1_all.deb Size: 156096 MD5sum: 7059af6a50afc03e337871d499a4401e SHA1: c2e92032a74ff93051cf9d275626f23b33f3435e SHA256: 166b5cc61c5a5df2ba9b5732199489854a72512ad96c6bcba9c4c5790fdb2ff9 SHA512: 28a71c4fb512cf7189e0af13da4907685d22792af45e7d96f81fd4abc2d43412ad91ef5f6f75933f6dbb0e8ac29c80dc72ed60a30bf9a0de35ce2421b74723bd Homepage: https://cran.r-project.org/package=memoiR Description: CRAN Package 'memoiR' (R Markdown and Bookdown Templates to Publish Documents) Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem. 'memoiR' makes it easy. It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration. Package: r-cran-memoise Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-cachem Suggests: r-cran-digest, r-cran-aws.s3, r-cran-covr, r-cran-googleauthr, r-cran-googlecloudstorager, r-cran-httr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-memoise_2.0.1-1.ca2404.1_all.deb Size: 49256 MD5sum: ba3abc1fe61a7e8c96aabc353f1e6f00 SHA1: 19fbbe35519dc512c15a79fe5d3079336b03a4fd SHA256: 7f4fb44e22adf52557a42b6b902b4735c58ab100510e62b872ba7c7efe64f221 SHA512: b0c67fc69ec2c0c85bd6a4fb2319cad95e7526f21b07b3cf0e176bab0906db9888dfedb1805820217fd434ed346ffd5f78436eb695fb6ac1439911452fa57497 Homepage: https://cran.r-project.org/package=memoise Description: CRAN Package 'memoise' ('Memoisation' of Functions) Cache the results of a function so that when you call it again with the same arguments it returns the previously computed value. Package: r-cran-memor Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaml, r-cran-rmarkdown, r-cran-knitr Suggests: r-cran-kableextra, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-memor_0.2.3-1.ca2404.1_all.deb Size: 191230 MD5sum: bb40e802c73557778dc08ced372a51c7 SHA1: 9d7f2e7e0868179d5029f9e206fd6107df45a563 SHA256: 6819af0b37dc3a4c0257cb39f8e0e3cc9c84d30473b882aa362832c86fc8d7e9 SHA512: 18ba0ba4860d6074f4ebfab7d5b61c7882170f1fdb9e465a09e15d4c3ed496d24af6307f1bc254b88704100953fa8125f3d03e8cdfa48a560b36a9c7e35cc720 Homepage: https://cran.r-project.org/package=memor Description: CRAN Package 'memor' (A 'rmarkdown' Template that Can be Highly Customized) A 'rmarkdown' template that supports company logo, contact info, watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 'HTML' theme will be added in the future. Package: r-cran-memoria Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ranger, r-cran-zoo, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-memoria_1.1.0-1.ca2404.1_all.deb Size: 812842 MD5sum: 27dacb888ecd6bd90182291c8457c707 SHA1: f38be4b0f724c903141dc1493ab70875173e9ac4 SHA256: 3573cdda6269e70993d4a4072345c9231208bf5ccc3908d5cbe1e9dde66ddbbf SHA512: c6207708db3ab8073ab458d61467240db0190077b08f297f402dd70d11330fc0051e6af480cd588696b5fda6ca089d93fe3366eae14b3fb755f61776a856dad2 Homepage: https://cran.r-project.org/package=memoria Description: CRAN Package 'memoria' (Quantifying Ecological Memory in Palaeoecological Datasets andOther Long Time-Series) Quantifies ecological memory in long time-series using Random Forest models ('Benito', 'Gil-Romera', and 'Birks' 2019 ) fitted with 'ranger' (Wright and Ziegler 2017 ). Ecological memory is assessed by modeling a response variable as a function of lagged predictors, distinguishing endogenous memory (lagged response) from exogenous memory (lagged environmental drivers). Designed for palaeoecological datasets and simulated pollen curves from 'virtualPollen', but applicable to any long time-series with environmental drivers and a biotic response. Package: r-cran-memss Architecture: all Version: 0.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-memss_0.9-4-1.ca2404.1_all.deb Size: 239416 MD5sum: 6a6beead55e84480e9d10353e8edecc6 SHA1: 674e632b1a3994ed1d24eb5c3d1cd27d0fa5ff35 SHA256: 6d00cf70344a7be9962f5bb0e769388f59408be0b643e5b7582e8f496cbf4ec4 SHA512: 9b5970bf3c726b69385f5c8d6e3883d23436e8847df913552f29c612e9e775f62c73b32e5c57e1e7644f9cfe65e7f8b2f940633f4bfaaf4dff9ae09211febbc5 Homepage: https://cran.r-project.org/package=MEMSS Description: CRAN Package 'MEMSS' (Data Sets from Mixed-Effects Models in S) Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000). Package: r-cran-memtoc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ps, r-cran-cli, r-cran-callr Suggests: r-cran-testthat, r-cran-withr, r-cran-future, r-cran-parallelly, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-memtoc_0.1.1-1.ca2404.1_all.deb Size: 110970 MD5sum: 97d47b982c16b06e9c4de9e1ecdc21e4 SHA1: 810b275f9df95092468e8ab18cd653fb6eaffdb5 SHA256: ddc53287983652c633e8b6fee474750618b9128199b5cfc1fb9b99b232fae5fb SHA512: 6c7d29ef3e4b4d77b003699b86fd39005a45cd465f47c3713887b6ea00bddb0dc9f8a6210ebc350afa4c7fb624c76cfca0187fdce2b7d0da157e12df90614a04 Homepage: https://cran.r-project.org/package=memtoc Description: CRAN Package 'memtoc' ('Tictoc'-Style Memory Usage Tracking) Provides simple start/stop memory tracking functions tic_mem() and toc_mem() that can be nested, inspired by the 'tictoc' package. Track RAM usage during code execution with support for logging, custom messages, nested tracking blocks, and parallel worker monitoring. Features continuous background polling to estimate peak memory usage across main process and workers. Integrates with the 'future' package ecosystem for automatic worker detection. Designed for monitoring memory consumption in parallel workflows. Package: r-cran-meow Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5523 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rfast Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-meow_1.0.0-1.ca2404.1_all.deb Size: 1663844 MD5sum: b78cae2b033ff86f1f7db3d985392003 SHA1: fd2dbe87e906d2c291f9ed211544c4926063ef72 SHA256: 056725d39d1700a80a8370d8e1def7f5d84bd9bfcdd06ab52f42a285c7d11796 SHA512: 51628e340ed341dc0d38379dee47e641c0302460ab459065d352ae171100e0bdeb3398946d59658f93dff5282f051e29a6c222771f7b09cb7270797f3e1159f2 Homepage: https://cran.r-project.org/package=meow Description: CRAN Package 'meow' (Unified Framework for Computer Adaptive Testing Simulations) Provides an extensible framework for conducting simulations to compare data generating processes, item selection algorithms, parameter update algorithms, and stopping rules in computer adaptive testing (CAT) applications. Bundled algorithms include the Elo-based update rules of Klinkenberg, Straatemeier and van der Maas (2011) and Vermeiren, Kruis, Bolsinova, van der Maas and Hofman (2025) . 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The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer". J. L. Bentley (1980) . Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys method, which offers a kernel approach for the grid method. Package: r-cran-mercator Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3474 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-thresher, r-cran-kernsmooth, r-cran-cluster, r-cran-rtsne, r-cran-classdiscovery, r-cran-polychrome, r-cran-dendextend, r-cran-igraph, r-cran-flexmix, r-cran-umap, r-cran-kohonen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mercator_1.1.8-1.ca2404.1_all.deb Size: 2482936 MD5sum: 5d48c776b5fbd12bdb578550cc90d2e5 SHA1: 71f8b14793437039ba93e58d138a76bbe673e9eb SHA256: bdc6354e78f2be82074f1022edbba99102138fd8919ef61b56a27fd9d5af9183 SHA512: f582fa37f97ecc679b3de8334a4394eea1e8d89363d152c5d245de5169d05e2087358892a1501f2967132af6d469fc187b03d889f9930c9a58e951090ce3d29c Homepage: https://cran.r-project.org/package=Mercator Description: CRAN Package 'Mercator' (Clustering and Visualizing Distance Matrices) Defines the classes used to explore, cluster and visualize distance matrices, especially those arising from binary data. See Abrams and colleagues, 2021, . 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Package: r-cran-mergekmeans Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-compquadform, r-cran-mixsim, r-cran-mclust, r-cran-broom, r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mergekmeans_0.3.0-1.ca2404.1_all.deb Size: 492818 MD5sum: 4fccfe0ed5c636baeb4d82c9a931fbe5 SHA1: 6646fd7814f7ca9bf05323aa8d3409f75ff762fd SHA256: 141bc1fa5b68a33c5b4c0b3f5235a2d3c4821ec0981d38d5cdb83ae05cca5adc SHA512: a5fcd9a772e0868df9fcf1f9dd2e5365b2720b6e35c58320942a4cf191b630e0d3ebfad63b3fab72a939b533286e9587e678e17f3bf673f0a2b884ac5a26cc7f Homepage: https://cran.r-project.org/package=MergeKmeans Description: CRAN Package 'MergeKmeans' (Clustering Large Datasets by Merging K-Means Solutions) Fast clustering of large datasets by hierarchically merging components of a K-means solution based on the pairwise overlap between the Gaussian mixture components implied by the K-means partition, as proposed by Melnykov and Michael (2020) . 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This package employs artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. This package makes it easier to use Large Language Models in your development environment by providing a chat-like interface, while also allowing you to inspect and execute the returned code. Package: r-cran-mergersim Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bb, r-cran-numderiv, r-cran-rootsolve Suggests: r-cran-antitrust, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mergersim_0.2.0-1.ca2404.1_all.deb Size: 180560 MD5sum: bd734ba6c4eff6ded68a57e2227e0e8e SHA1: 49b22a7a39f648ecc6c72dc32fa416828193e96d SHA256: aaf480e9a5c163abb6b5b317be187d958f02058e4757029ff9ba775187fa4d76 SHA512: a0b16322f306431be66e04670bbb1c6cb49179217f9fc904124604b22de0381c6a0c7f1717b40bb499e00cf1b243ea7a8fd2d43def3dc729705d01e44c54e965 Homepage: https://cran.r-project.org/package=mergersim Description: CRAN Package 'mergersim' (Merger Simulation and Calibration) Analyze mergers between firms. Models of competition include differentiated Bertrand price-setting, Nash bargaining, and second score auctions, as implemented in Panhans and Taragin (2023) . Calibrates demand systems including standard logit, nested logit, and generalized nested logit as implemented in Panhans and Wiemer (2026) . Package: r-cran-mergingtools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2044 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-rlang, r-cran-mass Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-mergingtools_1.0.1-1.ca2404.1_all.deb Size: 722192 MD5sum: 56a835e0c50d86937950d5260784581c SHA1: 636a958782b2010478bf1e1f1bb1bd6403bbb54a SHA256: add010059674a6112f7035a9f50e42d5185ea8a2ceff2c200a4c647820f4d731 SHA512: 238165ba28e15e7dc3b64c3dffb23c732bc0972ee964f76cb66777dcc7546bc33901f0fe35cc898e6151ba99444575579c3c4e6769d695ea8fba33bc7684bae8 Homepage: https://cran.r-project.org/package=mergingTools Description: CRAN Package 'mergingTools' (Tools to Merge Hardware Event Monitors (HEMs) Coming fromSeparate Subexperiments into One Single Dataframe) Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, . For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). 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This date class implements ISO 8601-2:2019(E) and allows regular dates and times to be annotated to express unspecified date or time components, approximate or uncertain components, ranges, and sets of dates. The package therefore retains, represents, and reasons about data and time imprecision, resolving to a single data/time only on demand. This is useful for describing and analysing temporal information, whether historical or recent, where date or time precision may vary. 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Package: r-cran-meta Architecture: all Version: 8.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2958 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-metabook, r-cran-metafor, r-cran-ggplot2, r-cran-lme4, r-cran-compquadform, r-cran-xml2, r-cran-readr, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-scales, r-cran-cli Suggests: r-cran-netmeta, r-cran-biasedurn, r-cran-pimeta, r-cran-estmeansd, r-cran-robvis, r-cran-brglm2, r-cran-writexl, r-cran-rmarkdown, r-cran-knitr, r-cran-ggpubr, r-cran-gridextra, r-cran-gemtc, r-cran-ragg, r-cran-metadat, r-cran-svglite Filename: pool/dists/noble/main/r-cran-meta_8.5-0-1.ca2404.1_all.deb Size: 2760014 MD5sum: ef62d70ed0a0d2ac20213984f85b9576 SHA1: 9e633aac4a4c86200ff10067d1d686dbbbe8af1f SHA256: 1d4d6e2cb05a2a0e9aa0419164e790fb80e4087957ee409f30e749690e5c5b6f SHA512: 0b7aecdd2150a7c04fa92aab09b1b11f57fbbe6d728c5325f71f195f3030ac4f3853605a6ee4403f9bab1f612f7fb2d890dc67b2353724159f069ff2fd7f55c5 Homepage: https://cran.r-project.org/package=meta Description: CRAN Package 'meta' (General Package for Meta-Analysis) User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker , "Meta-Analysis with R" (2015): - common effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - three-level meta-analysis model; - generalised linear mixed model; - logistic regression with penalised likelihood for rare events; - Hartung-Knapp method for random effects model; - Kenward-Roger method for random effects model; - prediction interval and density of the prediction distribution; - expected proportion of comparable studies with clinically important benefit or harm; - statistical tests for funnel plot asymmetry; - trim-and-fill method to evaluate bias in meta-analysis; - meta-regression; - cumulative meta-analysis and leave-one-out meta-analysis; - import data from 'RevMan 5'; - produce forest plot summarising several (subgroup) meta-analyses. Package: r-cran-metaanalyser Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-ggvis, r-cran-dt, r-cran-rstudioapi Suggests: r-cran-rmeta Filename: pool/dists/noble/main/r-cran-metaanalyser_0.2.1-1.ca2404.1_all.deb Size: 41490 MD5sum: 5f9aa719be9c7fb704b9d5ab62270f8b SHA1: eb4765845306348afa4ee8cf23c14644a8382234 SHA256: c79836d531fa5527cce1d22110e7da916e5856b141a76a152029565976638169 SHA512: c205aa798735c9a4a7de3b1d38cf83eb64a130f6192e260ecf377b8e8c12e62a070aec0dcb97b44eeec4e51f45785eccd9838baaa28f6ca58e86c96ee5ea8604 Homepage: https://cran.r-project.org/package=MetaAnalyser Description: CRAN Package 'MetaAnalyser' (An Interactive Visualisation of Meta-Analysis as a PhysicalWeighing Machine) An interactive application to visualise meta-analysis data as a physical weighing machine. 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See the packages 'PublicationBias', 'phacking', and 'multibiasmeta'. These package implement methods described in, respectively: Mathur & VanderWeele (2020) ; Mathur (2022) ; Mathur (2022) . 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Therefore, researchers need to transform those information back to the sample mean and standard deviation. This package implemented sample mean estimators by Luo et al. (2016) , sample standard deviation estimators by Wan et al. (2014) , and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis. Package: r-cran-metabodata Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-magrittr, r-cran-piggyback, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-yaml Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-metabodata_0.6.4-1.ca2404.1_all.deb Size: 1677932 MD5sum: 80b713f1b724bf689c274f666d8913e8 SHA1: e7599efd13fd5e946f39823758f04faef1c289eb SHA256: 117106a679718c0cb192e84e4dda89f2ff76e17c6248303ea29755599bbe0128 SHA512: e98cb04a8d7f93a288c0207ab1a8755e27f3fc4afe5c6a599127959ffc06ae83bd83112ab6e60cb94006cc9bb8a3c381f8ecd153a7678071f707745851645e8f Homepage: https://cran.r-project.org/package=metaboData Description: CRAN Package 'metaboData' (Example Metabolomics Data Sets) Data sets from a variety of biological sample matrices, analysed using a number of mass spectrometry based metabolomic analytical techniques. 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The deconvolution part uses the algorithm described in Koh et al. (2009) . The alignment part is based on functions from the 'speaq' package, described in Beirnaert et al. (2018) and Vu et al. (2011) . A detailed description and evaluation of an early version of the package, 'MetaboDecon1D v0.2.2', can be found in Haeckl et al. (2021) . 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Package: r-cran-metabolic Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-usethis, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfittext, r-cran-cli, r-cran-forcats, r-cran-ggimage, r-cran-patchwork, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-meta, r-cran-glue, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-here, r-cran-rd2roxygen, r-cran-kableextra, r-cran-fansi, r-cran-downloadthis, r-cran-spelling Filename: pool/dists/noble/main/r-cran-metabolic_0.1.2-1.ca2404.1_all.deb Size: 205270 MD5sum: 98bed06e61505f5cc0fa17141830567b SHA1: 968f229b9183b26958a9034263087e117126b1df SHA256: fc8cbd1c09005ca21c2d0da21bd554a62d41e51c74d17c8e20ed2d2072e5c047 SHA512: 5c17af13bd7743c43aab4f982a7a81714f8950aca62d49c18675e57c535675f0f00ab8af67557eb8a762f912d64bad271ea86759230344a70de4fc9172568148 Homepage: https://cran.r-project.org/package=metabolic Description: CRAN Package 'metabolic' (Datasets and Functions for Reproducing Meta-Analyses) Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. It contains all the data and functions to reproduce the analysis. "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis". Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) . Package: r-cran-metabolicsurv Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-superpc, r-cran-glmnet, r-cran-matrixstats, r-cran-survminer, r-cran-survival, r-cran-rms, r-cran-tidyr, r-cran-pls, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metabolicsurv_1.1.2-1.ca2404.1_all.deb Size: 523646 MD5sum: af730233ef20d7b14b00b3d3afb318d8 SHA1: 7e070fae316c13b5b0a7bb1607c258e1041e305d SHA256: 58bcd301eb073eadf3ffd25aba101588ec680cc0df52e6facf813ba4da4dd7af SHA512: cd410976c275c2c905a41e3d02c0df35abc8c3d73e544a10a1a11dfc8112dd6b9b4b4a03746f8fd6ad111c6e431892b03adbf7f671d275c2d6c457693e5d0a6f Homepage: https://cran.r-project.org/package=MetabolicSurv Description: CRAN Package 'MetabolicSurv' (A Biomarker Validation Approach for Classification andPredicting Survival Using Metabolomics Signature) An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) , the LASSO method by Robert Tibshirani (1998) , the elastic net approach by Hui Zou and Trevor Hastie (2005) . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique. Package: r-cran-metabolicsyndrome Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metabolicsyndrome_0.1.3-1.ca2404.1_all.deb Size: 15068 MD5sum: eb8b277ebe5acf6579f662cfdce1c310 SHA1: f8b9b69aa340eb5f97f0d0b3d366ed3d01b82795 SHA256: 117fafb479b876246682661d0cc8914883b73f440028d9450fd059ab45798246 SHA512: 49d3e2aa22008f65ce2c1b85910c0adf3bc56548871f85e83dfef1cdc2980bf91307ff45c185445d24e28957e785892aa01e78a808e0de4eaefbfb9946478432 Homepage: https://cran.r-project.org/package=MetabolicSyndrome Description: CRAN Package 'MetabolicSyndrome' (Diagnosis of Metabolic Syndrome) The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by American Heart Association (AHA) and National Heart, Lung and Blood Institute (NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome. The AHA-NHLBI criteria advise using parameters such as waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol (HDLC) for diagnosis of metabolic syndrome. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of metabolic syndrome in busy healthcare settings and also for research purposes. The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy et al ., (2005) . Package: r-cran-metabolomicsbasics Architecture: all Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-c50, r-cran-caret, r-cran-e1071, r-cran-fs, r-cran-hirestec, r-cran-interpretmsspectrum, r-bioc-pcamethods, r-cran-plyr, r-cran-rpart, r-cran-rlang, r-cran-webchem Suggests: r-bioc-mixomics, r-bioc-ropls, r-bioc-xcms Filename: pool/dists/noble/main/r-cran-metabolomicsbasics_1.4.7-1.ca2404.1_all.deb Size: 321986 MD5sum: 871e6eeb937cf0da4d6098c96da78323 SHA1: 7978d89076737694d0ec7ecaa663bd86713a0b5b SHA256: 3d47093a7a4442e9b72103fd0cac20ddf48b426a7a5b0a8bbb998ee0a8584b05 SHA512: ed166528e049be77569cddb88e4930862cb2cf62fd8cd358dd8de498fab759264de5d3dd5ddf282ec4575fa6ff18d5585d7129f6672848ce3f87d5cbfc1f4182 Homepage: https://cran.r-project.org/package=MetabolomicsBasics Description: CRAN Package 'MetabolomicsBasics' (Basic Functions to Investigate Metabolomics Data Matrices) A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication . Package: r-cran-metabolssmf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-laplacesdemon, r-cran-nmf, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-lsei, r-cran-mclust, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caroline, r-cran-ggsci, r-cran-biocmanager, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-metabolssmf_0.1.0-1.ca2404.1_all.deb Size: 916750 MD5sum: 4c3c0e2172f807aa663892f1e8423a1f SHA1: f308266f03cece4a9ef04c64caf64da9c80b54cb SHA256: 41bae364d506c4927d8b75f7172a48c0eda20864cd2678decdeb2064cf9c08ae SHA512: fbe754a6fe83da509feb9d1782f46d2cff5dbdba06b9e0864de4e030802fe445adc23105eb51bca171b08be84ae7a38b7e0973c1df6b707f609a0f1bf40c7afd Homepage: https://cran.r-project.org/package=MetabolSSMF Description: CRAN Package 'MetabolSSMF' (Simplex-Structured Matrix Factorisation for MetabolomicsAnalysis) Provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) . Package: r-cran-metabook Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-meta, r-cran-netmeta, r-cran-metasens, r-cran-netdose Filename: pool/dists/noble/main/r-cran-metabook_0.2-0-1.ca2404.1_all.deb Size: 177896 MD5sum: 7aa4b344572c13fb0ca40cf8b423876b SHA1: ed0cd58736ca67e91d3b6d9a6441ad275c5e0a07 SHA256: 580ecc057641d023431ddbdea5e53aa27680c285f8c32fecd4dc56e258f97828 SHA512: 39c8942278b53466831de22ef4abdd8c0ba3e16f8b608fbbee505f25178c69c506b6731e1e4f75a895b65af5415c0038f5bbb7573a8bd0030dc6caedfd807abf Homepage: https://cran.r-project.org/package=metabook Description: CRAN Package 'metabook' (Data Sets and Code for "Meta-Analysis with R") Data sets and code supporting the second edition of "Meta-Analysis with R"; first edition: Schwarzer, Carpenter, and Rücker (2015) . Package: r-cran-metaboqc Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Filename: pool/dists/noble/main/r-cran-metaboqc_1.1-1.ca2404.1_all.deb Size: 53490 MD5sum: ab49ff329f498e1e98e9fbee09073335 SHA1: f91896f282497d524ec43850a8512fda385798b0 SHA256: c8b1c9bb70975f353c2437023aea97ad43e830c2e02ba43ef378935d9db4e4b8 SHA512: 502a0d541e903d83d994d71154edb367a97e9598f6ad46c523b5592b28f50dcb9da49c6a4edc38c236dc0183f705f93f4bfbabcceabbb1a3e5bd7f726706fa7d Homepage: https://cran.r-project.org/package=MetaboQC Description: CRAN Package 'MetaboQC' (Normalize Metabolomic Data using QC Signal) Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required. Package: r-cran-metabup Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-partitions Filename: pool/dists/noble/main/r-cran-metabup_0.1.3-1.ca2404.1_all.deb Size: 22408 MD5sum: a3d8f4f9661f9318e9ce95a9ec672e3d SHA1: 04f9c1a08e3370a22baf3353a73e1d971cced751 SHA256: b46046b22ac04a5a4094567941fc790089f9a609c892fbf555af12621856bd0a SHA512: 4e6eb9bc56410b25a7c786c35c39319677bce350ca02a9781626615ca7307e7015b8f95711561327e47663d8cfe390a446dd982302cd735ada173b8c825fa209 Homepage: https://cran.r-project.org/package=metabup Description: CRAN Package 'metabup' (Bayesian Meta-Analysis Using Basic Uncertain Pooling) Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances. Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022) , Evans and Sedransk (2001) , and Malec and Sedransk (1992) . Package: r-cran-metacluster Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factoextra, r-cran-cluster, r-cran-dbscan, r-cran-dplyr, r-cran-seqinr, r-bioc-biostrings Filename: pool/dists/noble/main/r-cran-metacluster_0.1.1-1.ca2404.1_all.deb Size: 67522 MD5sum: b1bf966293e1be626c536e0e03ff60c7 SHA1: 59355ab9d968a2353c137dc0bbeed4c534a913cc SHA256: 97be8c0b313d53831a42a94cb8bb9a056436085c09deb1ce4a81993b5aee1508 SHA512: aad26dd3978f86ea866fb9d5bbf774f92ec92bfd090f495628d99fe59a9850a0c9569bcc4849392d588c206302848d0b0e08fa03da17038f5f814441c69dcb0b Homepage: https://cran.r-project.org/package=metaCluster Description: CRAN Package 'metaCluster' (Metagenomic Clustering) Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning. Package: r-cran-metacom Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacom_1.5.3-1.ca2404.1_all.deb Size: 63812 MD5sum: aa36b036d865dc92080aaba01936123a SHA1: cf1306dc90fccca9eba4fcb9e924bc733f3163fe SHA256: 4d20302503279a63a4815efb1c04761c728095ec309b6495299446afdf1ad171 SHA512: 2af8f9caf40449f2ea1053b374f8583001f75fb348fe2eb8893e42299b7b07f7eec11ae3eee8f24eed75204cca1e7de03c7b97d04b99b5e17f9eae49f8170352 Homepage: https://cran.r-project.org/package=metacom Description: CRAN Package 'metacom' (Analysis of the 'Elements of Metacommunity Structure') Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002 . The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping. Package: r-cran-metacomp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-plyr, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-cairo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacomp_1.1.2-1.ca2404.1_all.deb Size: 256100 MD5sum: ed93bdb4ee7e3ca63bbbb0386ee0780f SHA1: fda16ca566aebeba6f9bfe4cad19c2ad430a341f SHA256: b6d02b57ff6eeefec52fe0f1760cab6e652888c6d6aeac8b3700c7d92a4ff0cd SHA512: 8ff20508d8fcb6f8bf1f6c07d1e9c6e0493581e8299c0a0f2ed7ddbf24bf085ed70f28c736daa97861b58801476142ed14f9a457765f7c4505074014ec2f37c1 Homepage: https://cran.r-project.org/package=MetaComp Description: CRAN Package 'MetaComp' (EDGE Taxonomy Assignments Visualization) Implements routines for metagenome sample taxonomy assignments collection, aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2, BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots comparing taxa abundances across projects. Package: r-cran-metaconfoundr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-knitr, r-cran-metafor, r-cran-patchwork, r-cran-readr, r-cran-rio, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metaconfoundr_0.1.2-1.ca2404.1_all.deb Size: 1780642 MD5sum: 8e1caa2d55a0b662193a2878942525c3 SHA1: 13ab29c96bf50aad144a13f644023b2fb4a76aa4 SHA256: ce1a64127d3a0a208e31770b2e6d49e19c297315e096a1f24c21b30c8055a96a SHA512: 64732b9707fcca2936ed74efa7cb14046b253b5e8ebde22bae70dea9ede121943ac4dc4e8f82902262bb3de14accbaac01756934e924d0aefdea92b48adb2daf Homepage: https://cran.r-project.org/package=metaconfoundr Description: CRAN Package 'metaconfoundr' (Visualize 'Confounder' Control in Meta-Analyses) Visualize 'confounder' control in meta-analysis. 'metaconfoundr' is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important 'confounders'. Then, they evaluate whether studies controlled for these variables well. 'metaconfoundr' is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more. Package: r-cran-metaconvert Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-comparedf, r-cran-metafor, r-cran-estimraw, r-cran-rio Suggests: r-cran-testthat, r-cran-metaumbrella, r-cran-toster, r-cran-esc, r-cran-epir, r-cran-compute.es, r-cran-meta, r-cran-effectsize, r-cran-metautility, r-cran-mass, r-cran-mvtnorm, r-cran-psych, r-cran-psychmeta, r-cran-knitr, r-cran-dt, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metaconvert_2.0.0-1.ca2404.1_all.deb Size: 1322366 MD5sum: 91f2024adb71e09d41e2395ff34e8c81 SHA1: 915307b48d454ddbd9d0477c895175e7f69c8e33 SHA256: ea6d54dabad9767323979d6498bc7be2b4d77f55fd87dcd92686435f59c815af SHA512: 15dbab9cee054521ff1fd4955c56ae7cd0dc723ff478ffedc91845cf243ddab426f8cbb8fa85ceb51ed0fadeb7a6e416b959f3ebf47bbfe5bdc4b8f1c79a9a45 Homepage: https://cran.r-project.org/package=metaConvert Description: CRAN Package 'metaConvert' (An Automatic Suite for Estimation of Various Effect SizeMeasures) Automatically estimate 14 effect size measures from a well-formatted dataset, including Cohen's d, Hedges' g, mean difference, odds ratio, risk ratio, incidence rate ratio, risk difference, number needed to treat, Pearson correlation, Fisher's z, Cronbach's alpha, intraclass correlation coefficient, and single-group proportion. Provides a two-tier quality-flag diagnostic system for input validation and post-computation plausibility checks, missing-data guidance that tells users which columns would unlock additional estimators, post-hoc correction for attenuation due to measurement error, and standalone psychometric utilities (standard error of measurement, smallest detectable change, change-score reliability). Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size. Package: r-cran-metacor Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-officer, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacor_1.2.1-1.ca2404.1_all.deb Size: 76704 MD5sum: bebc53ffa332601bf433cf93e686aaad SHA1: 26f8a0d4bd842c0a30594b7d29da3a4998ec2bf7 SHA256: 567deb8ee8807f202990b7da95ac5624ee3cb6a93b6913e25c5f025f30ee2347 SHA512: ebe3a3d99e0f6f4799eb5039e9758068f23979e0a12d9ce960b17aebab2b41ebbc5a4a1c607828b2f3381854ecaea96638950d367edfdf018242531744eaf941 Homepage: https://cran.r-project.org/package=metacor Description: CRAN Package 'metacor' (Meta-Analytic Effect Size Calculation for Pre-Post Designs withCorrelation Imputation) Tools for the calculation of effect sizes (standardised mean difference) and mean difference in pre-post controlled studies, including robust imputation of missing variances (standard deviation of changes) and correlations (Pearson correlation coefficient). The main function 'metacor_dual()' implements several methods for imputing missing standard deviation of changes or Pearson correlation coefficient, and generates transparent imputation reports. Designed for meta-analyses with incomplete summary statistics. For details on the methods, see Higgins et al. (2023) and Fu et al. (2013). Package: r-cran-metacore Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacore_0.3.0-1.ca2404.1_all.deb Size: 1281890 MD5sum: bc2c4c9c1a400e997b0311715eec854a SHA1: a6092b5d26f4004c0e3fada9475c91e8bba391f7 SHA256: 6d7f3a4b3333bd29715c21dfd28e17a28b09a2d3bafcecf5e94c4eac73c5a831 SHA512: b59f6cffb6cd999360a0086a8b82418004aef873165c75e2e001004d3ff916f78b6fb6b69792d2c17eb00906e80ac0745cad97dc6c5b92ff88f2730f4df7ce46 Homepage: https://cran.r-project.org/package=metacore Description: CRAN Package 'metacore' (A Centralized Metadata Object Focus on Clinical Trial DataProgramming Workflows) Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow. Package: r-cran-metaculr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-progress, r-cran-tidyr, r-cran-verification, r-cran-clipr, r-cran-spatstat.geom, r-cran-ggrepel, r-cran-assertthat, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaculr_0.4.1-1.ca2404.1_all.deb Size: 437190 MD5sum: 01c19ea65506e368de2b2b64b79d5306 SHA1: 165266ef7f3494397c6e4f1c09e940069d188094 SHA256: 6c27c8e789b50e493ff34fad33a5a6c443b1dd0c55bbfb13911e7563ff67c5c2 SHA512: 3f4400c8b946490f2bd71e7d60ae79180e79f7551ccbaf487ba46d65cd0b6cd6db80ebb06ecde5bc6ae1a751cb8db441ca72d82c4d7460cf616f0c2810bd16c2 Homepage: https://cran.r-project.org/package=MetaculR Description: CRAN Package 'MetaculR' (Analyze Metaculus Predictions and Questions) Login, download, and analyze questions predicted by you and/or the Metaculus community by interacting with the Metaculus API, currently located at . Package: r-cran-metacycle Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gnm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metacycle_1.2.1-1.ca2404.1_all.deb Size: 1028476 MD5sum: 035dd0ddbdfbc8265f9edca208afc31d SHA1: 65e5273fd7b0391e809385d691bf1f802d21c073 SHA256: adadf7b18dd63bbed4337db8d9a7d982f7c7284c6f82b7062bad1198ab650544 SHA512: f6b430584528a5e9826718789e524e9ec2cf62f586dd7bcac89cafe5ffb27d015a5eea78ff6873ada38b22bc2595a6072a4aeed60fcb2907ba9624babad98b86 Homepage: https://cran.r-project.org/package=MetaCycle Description: CRAN Package 'MetaCycle' (Evaluate Periodicity in Large Scale Data) There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, 'meta2d' is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, 'meta3d' is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual. Package: r-cran-metadat Architecture: all Version: 1.6-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-metafor, r-cran-numderiv, r-cran-biasedurn, r-cran-dfoptim, r-cran-igraph, r-cran-ape, r-cran-testthat, r-cran-digest, r-cran-lme4, r-cran-clubsandwich, r-cran-meta, r-cran-netmeta, r-cran-mvtnorm, r-cran-gridextra, r-cran-rms, r-cran-bayesmeta, r-cran-ellipse, r-cran-metasens, r-cran-mada Filename: pool/dists/noble/main/r-cran-metadat_1.6-0-1.ca2404.1_all.deb Size: 1066034 MD5sum: ca2ae608f7d8fd210d6dd1f9e26f41eb SHA1: 9fee847ebbd06db08449aa6a70b3ecec5c416aa8 SHA256: 4725eaf0b24f2a517a368b90945935ac0869b5daa338be8805048822b98e8a51 SHA512: f17c4e4b1e7de0ab03922a813dc6a8c3f4cb5369960f36c7d69a6072cb418a63f9d33fb0ad5d650178cd4c9a5c3eb002346eef0c6f4c24e6f139d2d05b0262be Homepage: https://cran.r-project.org/package=metadat Description: CRAN Package 'metadat' (Meta-Analysis Datasets) A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses. Package: r-cran-metadeconfoundr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-detectseparation, r-cran-lmtest, r-cran-foreach, r-cran-doparallel, r-cran-logger, r-cran-lme4, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-circlize, r-cran-dplyr, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-scales, r-cran-stringr Suggests: r-cran-pander, r-cran-knitr, r-cran-gridextra, r-cran-kableextra, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-metadeconfoundr_1.0.5-1.ca2404.1_all.deb Size: 886054 MD5sum: b29f7ceaf22d71651c90681bb7a05515 SHA1: fdcfb501be54e5f14ba7927b2ea85c398bb9a2dc SHA256: 6610cba3f69502062d5bebae757d4962f923047f4ada6abaddb20d183f8cd97a SHA512: 6115219b77d1ee754340c54b8a65f534812b6405d0802c47a4ea1ca382d3ead626f65e9111044d87b974320844b58817b389733a9314f4593e7b6e55a1aa9422 Homepage: https://cran.r-project.org/package=metadeconfoundR Description: CRAN Package 'metadeconfoundR' (Covariate-Sensitive Analysis of Cross-Sectional High-DimensionalData) Using non-parametric tests, naive associations between omics features and metadata in cross-sectional data-sets are detected. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests, as first described in Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) . The generated output can be graphically summarized using the built-in plotting function. Package: r-cran-metadigitise Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-purrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metadigitise_1.0.2-1.ca2404.1_all.deb Size: 431822 MD5sum: 239bcd8d888ab1f7f6811b3976c61dab SHA1: d0c13ca84c1c969a0cbbbfe30d55541c803976d3 SHA256: d1e41618e5b271b6702390eae83a2995e0a2600d4e7528a4b19d451482f02cf6 SHA512: 84f8c49f93e06b637f5d02f6c620e1da066c5cdece80c7520b0368df977d00f383ac9e000cde65ffc99c1a4c48f91755bfcf027b1b80eef991177313a475a6cd Homepage: https://cran.r-project.org/package=metaDigitise Description: CRAN Package 'metaDigitise' (Extract and Summarise Data from Published Figures) High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms. Package: r-cran-metadose Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-metafor, r-cran-rms, r-cran-rlang Filename: pool/dists/noble/main/r-cran-metadose_1.0.1-1.ca2404.1_all.deb Size: 33464 MD5sum: cc7a16d4eaaff9f7ef5d6fffbed809b8 SHA1: 7d662ecf532088cb243f637a71e51a3ae7e72158 SHA256: 088c75dd0b83a2355e425df03ea1339aa7fa917572f228c2d524009ca614e851 SHA512: 319793ea53542bce2378cc60892aae978f95d15e38b5a9a0ce8c8bbcb012ecdfd77308ff3a379db09f163f8cf4e445bb8e815c8d233ee073e0aa6a49963164f3 Homepage: https://cran.r-project.org/package=MetaDose Description: CRAN Package 'MetaDose' (Dose-Response Meta-Regression for Meta-Analysis) Conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines. The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves. Package: r-cran-metadyn Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-openmx, r-cran-matrix, r-cran-fitvarmxid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-simstatespace, r-cran-mass, r-cran-metasem, r-cran-expm Filename: pool/dists/noble/main/r-cran-metadyn_1.0.4-1.ca2404.1_all.deb Size: 209480 MD5sum: da438a5123358f4cbcc9e0b01b5d9f4c SHA1: b6b1be9f882d76801459c0f8113f60a093742b4b SHA256: 1186b9ee19b92172b7b0c5ba8cee3bb9d9705a7fe8294edcbe0728d576dbd33b SHA512: e675e24c2cdede110644ff3f6f74146579732c6385c7847e6a100f4516c4349ae61a5df037431936daef7609b75f8312ddad628ac99b56be1d991d87fbd1b95f Homepage: https://cran.r-project.org/package=metaDyn Description: CRAN Package 'metaDyn' (Multivariate Meta-Analysis of Dynamic Model Estimates) Fits fixed-, random-, or mixed-effects multivariate meta-analysis models using dynamic model estimates from each individual building on and extending Lee and Gates (2023) . Package: r-cran-metaeeea Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-eeea Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaeeea_1.0.0-1.ca2404.1_all.deb Size: 39482 MD5sum: 011f0b468a2401fa4bd902b15a84d1df SHA1: dfdd97ee3a6f51c97e0d4b9215ae31ef8ec29fbc SHA256: 28926f1d60c2b134b919b1789b477150d461cf046908cdc409c3aa775d06f44d SHA512: de93842dfb490d1f195d44ed5f915b7f3bef0a2b4dd42470d04e0509e8f3733f9dff224193926df9154e66e28a33d06d4e92785c67afc9bf4e35cdcada76ec52 Homepage: https://cran.r-project.org/package=MetaEEEA Description: CRAN Package 'MetaEEEA' (Metaheuristic Algorithms with Explicit Exploration) Solves single-objective optimization problems by using bio-inspired metaheuristic algorithms. The implemented metaheuristics are the Butterfly Optimization Algorithm, the Ladybug Beetle Optimization Algorithm and the Prairie Dog Optimization Algorithm. For all these optimization algorithms, the search of optimal values can be reinforced with the explicit exploration strategy proposed by Salinas-Gutiérrez and Muñoz Zavala (2023) . Package: r-cran-metaensembler Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-caret, r-cran-ggplot2, r-cran-e1071, r-cran-gbm, r-cran-randomforest Suggests: r-cran-knitr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-metaensembler_0.1.0-1.ca2404.1_all.deb Size: 419086 MD5sum: 6ce5c134650859ecd7cdb2d0b5b151e0 SHA1: 4b680b567e6df9e4da5fd2c202f043a6542e3209 SHA256: 28106b926581f3f9fe13a7c891a50ef3558dedabbc4d3daadc09e36e12fde343 SHA512: 21c571386c535bdc13eecf6536e23bd55e63d4081153237b46eb5166aebf8a3d50f6bce5a8e3044a8f296ef6fea0abba3fb564f6a4a735374e3892b602eb6779 Homepage: https://cran.r-project.org/package=metaEnsembleR Description: CRAN Package 'metaEnsembleR' (Automated Intuitive Package for Meta-Ensemble Learning) Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems. Package: r-cran-metaentropy Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-beeswarm, r-cran-ggbeeswarm, r-cran-knitr, r-cran-dplyr, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-metaentropy_1.4-1.ca2404.1_all.deb Size: 234182 MD5sum: ddcd331e186474a8e308232a041f416c SHA1: 9358e5e5bf8458329863a23117eae0eaf374642e SHA256: 7a90332be20041eb942448446ec911527231f43ee77ae2aeb90e4e4d2e20d77a SHA512: 9e221b9b7113b8a3bac64b4235caa9053f77a6ad9e02f1cf423b9e7e177c0f9bcf49235426f80d1b3b3361f0e5c9813f31fab1ccc58cd59cab8da892994b97fa Homepage: https://cran.r-project.org/package=MetaEntropy Description: CRAN Package 'MetaEntropy' (Functional Shannon Entropy for Virome Mutational Analysis) Estimates Shannon entropy, per gene and per genomic position, associated with non-synonymous mutation frequencies in viral populations, such as wastewater samples. The package uses codon translations for functional insights. Each amino acid can be treated as an individual state, resulting in a 20-state entropy computation, or grouped into one of six physicochemical classes, adding further functional context. Provides normalized values (0-1 scale) to facilitate the direct comparison of different genomic positions or total functional entropy across multiple metagenomes. Designed to analyze mutational data using tabular 'Single Nucleotide Variant' (SNV) frequency tables generated by variant callers (e.g., 'iVar' or 'LoFreq'), operating independently of consensus sequence estimation and multiple sequence alignment. Package: r-cran-metafor Architecture: all Version: 5.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5776 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-metadat, r-cran-numderiv, r-cran-nlme, r-cran-mathjaxr, r-cran-pbapply, r-cran-digest Suggests: r-cran-lme4, r-cran-pracma, r-cran-minqa, r-cran-nloptr, r-cran-dfoptim, r-cran-ucminf, r-cran-lbfgsb3c, r-cran-subplex, r-cran-bb, r-cran-rsolnp, r-cran-alabama, r-cran-optimparallel, r-cran-optimx, r-cran-compquadform, r-cran-mvtnorm, r-cran-biasedurn, r-cran-epi, r-cran-survival, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-car, r-cran-multcomp, r-cran-gsl, r-cran-sp, r-cran-ape, r-cran-boot, r-cran-clubsandwich, r-cran-crayon, r-cran-r.rsp, r-cran-testthat, r-cran-rmarkdown, r-cran-wildmeta, r-cran-emmeans, r-cran-estmeansd, r-cran-metablue, r-cran-rstudioapi, r-cran-glmulti, r-cran-mumin, r-cran-mice, r-cran-amelia, r-cran-calculus Filename: pool/dists/noble/main/r-cran-metafor_5.2-1-1.ca2404.1_all.deb Size: 5348476 MD5sum: 413b4065aef5c9bb33cd7de68367f36e SHA1: 4109e1ce32d5088558bd0919b73d1d519235baf8 SHA256: 2a71c1cd25d8a650a9bd5e6263d2b4360c1133f1e214886d2212df5c4ed1f551 SHA512: bc7893704af60a2e216ee5e8e2d9a6a3d72c2f51ed8832c6697a7c1bb492e880ea6af9b748b9313e5a440c1a1b4e620ad7f3f339598d986e4b195a0794a1b8ea Homepage: https://cran.r-project.org/package=metafor Description: CRAN Package 'metafor' (Meta-Analysis Package for R) A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) . Package: r-cran-metaforest Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-data.table, r-cran-metafor, r-cran-ranger, r-cran-metadat Suggests: r-cran-testthat, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-metaforest_0.1.5-1.ca2404.1_all.deb Size: 178816 MD5sum: 3581435c0c3358c3fbfbb354ec4a8d98 SHA1: 48737282278f7002cbf6e4aa5fd1427c11a77945 SHA256: cb40472e2103f5cb8bc2761dc7d1d85d666767f8389efd4d103d453b0943df02 SHA512: 1d1a32e5b4491592d9140e96e2e70dac6c1fa8dc5502cefbf174113850449850e5d1a8cf3f62cbf023cc43c9def7cf4977ef9cf57995b0e776d814a37bf35d3b Homepage: https://cran.r-project.org/package=metaforest Description: CRAN Package 'metaforest' (Exploring Heterogeneity in Meta-Analysis using Random Forests) Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size. Package: r-cran-metafrontier Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-numderiv, r-cran-lpsolveapi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sfar, r-cran-frontier, r-cran-benchmarking, r-cran-quadprog, r-cran-plm, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-metafrontier_0.3.1-1.ca2404.1_all.deb Size: 429858 MD5sum: 7ce406f7788f20f003c2c1306fbe4bf3 SHA1: 53f71fe945df8ab8abc2c5bb87eee60293d1e1bb SHA256: 2956fcc548afc3debd155b75acd5927dbe8dd191967767e8caa7d82ad807f17d SHA512: fef1826f48a0a81839f5afc84bb538a7fc92e23ade185f83ac9474919f1cb27a38cee587208d00a06e9dfda191dfdf59334b9d3ce5bf202f85f85210b6d954ef Homepage: https://cran.r-project.org/package=metafrontier Description: CRAN Package 'metafrontier' (Analysis of Metafrontier Models for Efficiency and Productivity) Implements metafrontier production function models for estimating technical efficiencies and technology gaps for groups of firms that face different restrictions of a common underlying metatechnology (group-specific technologies in the sense of Battese, Rao, and O'Donnell, 2004). Supports both stochastic frontier analysis (SFA) and data envelopment analysis (DEA) based metafrontiers. Includes the deterministic metafrontier of Battese, Rao, and O'Donnell (2004) , the stochastic metafrontier of Huang, Huang, and Liu (2014) , and the metafrontier Malmquist productivity index of O'Donnell, Rao, and Battese (2008) . The deterministic metafrontier can be identified by either the minimum sum of absolute deviations (LP) or the minimum sum of squared deviations (QP) criterion. Additional features include panel SFA with time-varying inefficiency, bootstrap confidence intervals for technology gap ratios, a DEA poolability permutation test, latent class metafrontier estimation via the EM algorithm, Murphy-Topel corrected standard errors, convergence diagnostics, import of pre-fitted models from external estimation engines ('sfaR', 'frontier', 'Benchmarking'), and 'ggplot2' visualisation methods. Package: r-cran-metafuse Architecture: all Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-mass, r-cran-evd Filename: pool/dists/noble/main/r-cran-metafuse_2.0-1-1.ca2404.1_all.deb Size: 66414 MD5sum: 9d9911c8f93526820a17765fea855b23 SHA1: c64290bef491d1cf9a4feb2f25c8b63a52fa2913 SHA256: f87e31f80decba944c423a535ec67c99df262f752d16557ed693bd5d219079cb SHA512: 7dd2917ad38e48d55591138099df47738ca56e1acc7050a6f602593cce1f4058576c765b0b5562f04f884ca1cb00e56cbdcd47251ddeb95f08069321ede85cfa Homepage: https://cran.r-project.org/package=metafuse Description: CRAN Package 'metafuse' (Fused Lasso Approach in Regression Coefficient Clustering) Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models. Package: r-cran-metagam Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2, r-cran-metafor, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metagam_0.4.1-1.ca2404.1_all.deb Size: 594360 MD5sum: d20b535998007d82de236c6586a75a37 SHA1: a243e999c9f31a3ef0da927e459c7c74d0f8e97f SHA256: 52efb3aaaaa69cf45ba12fa7f3a422af4f8dc677974abe3faf1dbb75e8d2b827 SHA512: 21d67d421dc47002213b94b226cc7429b5fb3e2c964c931fd0a59e9633b491df9db94c509dcfe9a2fdfb8547ce49a9c63ad3148e5da763102d0908dfe3c194d4 Homepage: https://cran.r-project.org/package=metagam Description: CRAN Package 'metagam' (Meta-Analysis of Generalized Additive Models) Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. 'metagam' provides functionality for removing individual participant data from models computed using the 'mgcv' and 'gamm4' packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), , extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) . Package: r-cran-metage Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-data.table, r-cran-dplyr, r-cran-emdbook, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-gplots, r-cran-ks, r-cran-purrr, r-cran-qqman, r-cran-rfast, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-metage_1.2.2-1.ca2404.1_all.deb Size: 4701060 MD5sum: 3108ef3f0742a3366170750b8452bc2f SHA1: 5c65c6c33691557f883ae9f7af4c5ee4187b89af SHA256: 27668f78d698f464a71ad34f3cbcc07bda9621f38a7bb5b8f7ee9bd9ba26c69e SHA512: 0f445508f92f5b0e4b41e9a9060480f89b9889c3531f344bca67c7407ab64703329f0cf67ce378272b2263ea790452ab5f30061caa066e2502ae2138518ed0bb Homepage: https://cran.r-project.org/package=metaGE Description: CRAN Package 'metaGE' (Meta-Analysis for Detecting Genotype x Environment Associations) Provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi{10.1371/journal.pgen.1011553}). Package: r-cran-metagear Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-metafor, r-cran-stringr Suggests: r-bioc-ebimage, r-cran-ape, r-cran-hexview, r-cran-rcurl, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-metagear_0.7-1.ca2404.1_all.deb Size: 943154 MD5sum: 220e7dc6395a86d8bd3572cdbdb2a483 SHA1: 05e5879d0f187ecdad502802b2f3965473985ef5 SHA256: f61838cede5f2fd37115304a96d4dd90da4ac7ee6e5586a394580713a36534e3 SHA512: 52a9f99615ae46600be4ef9916ea2b4f7bf06ca2b337bd45da4971f769fe887ed993ed8a9ea76acbdb2f3f9a51f7e9ee02b195dd1fdc6eb660d51519394d1323 Homepage: https://cran.r-project.org/package=metagear Description: CRAN Package 'metagear' (Comprehensive Research Synthesis Tools for Systematic Reviewsand Meta-Analysis) Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 . Package: r-cran-metaggr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaggr_0.3.0-1.ca2404.1_all.deb Size: 331906 MD5sum: f89c916d5c8300e5189fddc061a0e218 SHA1: f93a4a654f1d206a5974c671ce099e29ddccbf27 SHA256: 4d5f08503fcdd6c6622b5189d3abce1c51c118992bfc587bb04105f59cd209a1 SHA512: 1d3da07438ff36af46f5c9e528de351463b308c8971d6b5017a6f930302fd5b63049dd956f446470fef39ad562293f6c38a28fd039477f448685e61f6198c8fb Homepage: https://cran.r-project.org/package=metaggR Description: CRAN Package 'metaggR' (Calculate the Knowledge-Weighted Estimate) According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judges’ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" proposes a procedure for calculating a weighted average of the judges’ individual estimates such that resulting aggregate estimate appropriately combines the judges' collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate -- know as "the knowledge-weighted estimate" -- inputs a) judges' estimates of a continuous outcome (E) and b) predictions of others' average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data. Package: r-cran-metagroup Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-meta, r-cran-rlang Filename: pool/dists/noble/main/r-cran-metagroup_1.0.2-1.ca2404.1_all.deb Size: 62878 MD5sum: 8b3bd2fd8f7a6a61f9d6cd7a12bf8718 SHA1: b250e49feaacf55d27f0365eb988755c46849685 SHA256: 9d5070ef1bfc64180e85d6a36eba1abf80da91a37760b26d0cbc99213e1b57e4 SHA512: d9ef0a22843bee5392f3f2140d59a243bd591c5e728288619c34eaff6fe349679a709c54078d305810d163c08f1cba2bf58ecce43a8526d0afc7433588b587bc Homepage: https://cran.r-project.org/package=metagroup Description: CRAN Package 'metagroup' (Meaningful Grouping of Studies in Meta-Analysis) Performs meaningful subgrouping in a meta-analysis. This is a two-step process; first, use the iterative grouping functions (e.g., mgbin(), mgcont() ) to partition studies into statistically homogeneous clusters based on their effect size data. Second, use the meaning() function to analyze these new subgroups and understand their composition based on study-level characteristics (e.g., country, setting). This approach helps to uncover hidden structures in meta-analytic data and provide a deeper interpretation of heterogeneity. Package: r-cran-metahelper Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-confintr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metahelper_1.0.1-1.ca2404.1_all.deb Size: 84184 MD5sum: efa2e879ec97399bc8c4b62a59c50f23 SHA1: b5b13978bafe84a3cc30febd3bddaa83af73f8b2 SHA256: 85d6f7c4bc347eeb5305b10225e893b26f74c0df08097e72fc22acbda88e1f55 SHA512: 3f53568a9c52a3225f4971e42757926f1040171b61a56d77d096f401b97b817ac94a6befc6408b3a099e3e2b880909c3883232f47fa63f5ec3d4cf4726bd2933 Homepage: https://cran.r-project.org/package=metaHelper Description: CRAN Package 'metaHelper' (Transforms Statistical Measures Commonly Used for Meta-Analysis) Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations. The methods used in this package are described in the following references: Altman D G, Bland J M. (2011) Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) Chinn S. (2000) Cochrane Handbook (2011) Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) Cohen, J. (1977) Ellis, P.D. (2009) Goulet-Pelletier, J.-C., & Cousineau, D. (2018) Hedges, L. V. (1981) Hedges L. V., Olkin I. (1985) Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) Mayer M (2023) Stackoverflow (2014) Stackoverflow (2018) . Package: r-cran-metaheuristicopt Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metaheuristicopt_2.0.0-1.ca2404.1_all.deb Size: 242718 MD5sum: 02edf66551f47553d334374d269e4d0d SHA1: a5e6ea3e7ed616a68622ff824e7ea2d37debab09 SHA256: 3f6ef4a279c173d576aaa90aafc2b9f0ae12a510651ff7854e9d652e303176d8 SHA512: 3c837ca7f1712f76e69020b00f52a3fd23295bba88e8160cce87b494e5cef9bee2003a7b5a16dc58af236e93705b3b290c12c92665fc72111f5a2058cce2a001 Homepage: https://cran.r-project.org/package=metaheuristicOpt Description: CRAN Package 'metaheuristicOpt' (Metaheuristic for Optimization) An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 ), grey wolf optimizer (Mirjalili et al., 2014 ), dragonfly algorithm (Mirjalili, 2015 ), firefly algorithm (Yang, 2009 ), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 ), harmony search algorithm (Mahdavi et al., 2007 ), moth flame optimizer (Mirjalili, 2015 , sine cosine algorithm (Mirjalili, 2016 ), whale optimization algorithm (Mirjalili and Lewis, 2016 ), clonal selection algorithm (Castro, 2002 ), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013). Package: r-cran-metahunt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog, r-cran-dirichletreg, r-cran-withr Suggests: r-cran-grf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metahunt_0.1.0-1.ca2404.1_all.deb Size: 512926 MD5sum: ddfee3b5ca6550176abd2eb67abbf935 SHA1: 519f812b41f37ce26e57d16801149e9ec8c07daa SHA256: 18032a75474d9218bb8397a5796cf94adba2abf05ff3ceb0436c5b1f682fe4b9 SHA512: 125a0c514fa963055f45ff2a18fb0d6fd756b2ad6d060e7bb3a52596ee37ee413b30ebd634d52f1c61de50d91d46a66686300d9d065b42e310e6d48d16557bb0 Homepage: https://cran.r-project.org/package=MetaHunt Description: CRAN Package 'MetaHunt' (Privacy-Preserving Meta-Analysis via Low-Rank Basis Hunting) Tools for privacy-preserving meta-analysis of function-valued quantities across heterogeneous studies. Implements the 'MetaHunt' pipeline, including the denoised functional Successive Projection Algorithm (d-fSPA) for basis hunting, constrained weight estimation, Dirichlet regression of weights on study-level covariates, target prediction, and split/cross conformal prediction intervals. Operates on aggregate-level function evaluations, so individual-level data from source studies are not required. Methodology described in Shi, Imai, and Zhang (2026) . Package: r-cran-metainc Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-meta, r-cran-ggplot2, r-cran-confintr Suggests: r-cran-metafor Filename: pool/dists/noble/main/r-cran-metainc_0.2-1-1.ca2404.1_all.deb Size: 533146 MD5sum: 12a651c3788ab8fa3ed62ba3ce07c5d4 SHA1: c701106072370343be50c376c7e0758c3e09ac55 SHA256: 0c7644ead633fc3c073b34a23e4b44051a0df1c9bffd1124c19d43f2a817c357 SHA512: e147ff5c8fed6cebed11aeb704c9cf998234c51f87291d8f7ab38e85035f73781f64a1f4862039d73bdbf9d75fdb4c47c3f75157207ac789cfd6ac35cc1e813d Homepage: https://cran.r-project.org/package=metainc Description: CRAN Package 'metainc' (Assessment of Inconsistency in Meta-Analysis using DecisionThresholds) Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds. Package: r-cran-metainsight Architecture: all Version: 7.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayesplot, r-cran-bnma, r-cran-bslib, r-cran-coda, r-cran-cookies, r-cran-dt, r-cran-dplyr, r-cran-gargoyle, r-cran-gemtc, r-cran-ggiraphextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-gt, r-cran-igraph, r-cran-jsonlite, r-cran-knitcitations, r-cran-knitr, r-cran-mcmcvis, r-cran-magick, r-cran-meta, r-cran-metafor, r-cran-mirai, r-cran-netmeta, r-cran-patchwork, r-cran-plotly, r-cran-quarto, r-cran-r6, r-cran-rio, r-cran-rintrojs, r-cran-rmarkdown, r-cran-rsvg, r-cran-shiny, r-cran-shinyalert, r-cran-shinybusy, r-cran-shinyjs, r-cran-stringr, r-cran-shinywidgets, r-cran-svglite, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-jsonvalidate, r-cran-mockery, r-cran-pdftools, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metainsight_7.1.0-1.ca2404.1_all.deb Size: 794626 MD5sum: f8435d919cea5a14ae00621898742988 SHA1: 6623a937ab0b8ed87c766d0118c3c9acf33c401d SHA256: 36a9cdb34ca5329c620caa4dc304ff14ea2c1456408bd94e1a1e9e88cab271de SHA512: f12d8e2063c64eb0aff0ce986d5df93db8d5eeb73e98cca7863b35ecdbfeb36f9c4eb17fad681f1c57496f2b6699e7613855937714caac1d267d73914a9eb6cc Homepage: https://cran.r-project.org/package=metainsight Description: CRAN Package 'metainsight' (A 'shiny' Application for Network Meta-Analysis) Conduct network meta-analyses through a graphical user interface using 'bnma', 'gemtc' and 'netmeta' with additional analysis provided by 'meta' and 'metafor'. Frequentist, Bayesian, meta-regression and baseline risk meta-regression analyses can all be conducted using a consistent data structure and terminology. Many options are provided for downloading publication-ready outputs and analyses can be reproduced outside of the application by downloading a 'quarto' file. The interface was generated using 'shinyscholar'. The initial version of the app was described by Owen et al. (2018) , Bayesian ranking visualisations were described by Nevill et al. (2023) and metaregression was described by Morris et al. (2025) . Package: r-cran-metaintegration Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-corpcor, r-cran-mass, r-cran-knitr Filename: pool/dists/noble/main/r-cran-metaintegration_0.1.2-1.ca2404.1_all.deb Size: 79146 MD5sum: 1da746b5603598073e67737baf8cbbeb SHA1: d5c09dd6bf72000f8a740bd378b5a3e40a817fe0 SHA256: 1f37cf71759792258050d5d0d72823cab705713fe5073fff1730306f509a682b SHA512: 8853f7647ac959e3346c1bdd50009c4f8802e860f41944e2a58da22a869eeb00fff2997c7d48398b591f4e7bf5983e805524164c6a82ca6c2cb8270a0aa8180a Homepage: https://cran.r-project.org/package=MetaIntegration Description: CRAN Package 'MetaIntegration' (Ensemble Meta-Prediction Framework) An ensemble meta-prediction framework to integrate multiple regression models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) . A meta-analysis framework along with two weighted estimators as the ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators. Package: r-cran-metaintegrator Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biocmanager, r-cran-rmeta, r-bioc-multtest, r-cran-ggplot2, r-cran-rmisc, r-cran-gplots, r-bioc-biobase, r-cran-rmysql, r-cran-dbi, r-cran-stringr, r-bioc-preprocesscore, r-bioc-geoquery, r-bioc-geometadb, r-cran-rsqlite, r-cran-data.table, r-cran-ggpubr, r-cran-rocr, r-cran-zoo, r-cran-pracma, r-cran-coconut, r-cran-metrics, r-cran-manhattanly, r-cran-dt, r-cran-pheatmap, r-cran-plyr, r-cran-boot, r-cran-dplyr, r-cran-reshape2, r-cran-rmarkdown, r-bioc-annotationdbi, r-cran-hgnchelper, r-cran-magrittr, r-cran-readr, r-cran-plotly, r-cran-httpuv Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-cran-snplist, r-cran-magick Filename: pool/dists/noble/main/r-cran-metaintegrator_2.1.3-1.ca2404.1_all.deb Size: 3411286 MD5sum: 3f144fef09fec0a89df9804636aa606a SHA1: 8a1a9bb203535d5b906b958fe25b9c0d6871581d SHA256: 1e28d45429e5f095e15ff9a03226d821765f8744a3f3912cd838e508bd60c4c0 SHA512: 160d4453eb9efd0fe58074b5994a1aebe7efcdee5b7b61df95eaebe0c4a0c6cb4e3bba36c67eb1edb33a62abd0ff2f517d295a2934d24d57d4c2fa36b79f7e6a Homepage: https://cran.r-project.org/package=MetaIntegrator Description: CRAN Package 'MetaIntegrator' (Meta-Analysis of Gene Expression Data) A pipeline for the meta-analysis of gene expression data. We have assembled several analysis and plot functions to perform integrated multi-cohort analysis of gene expression data (meta- analysis). Methodology described in: . 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Package: r-cran-metamedian Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-estmeansd, r-cran-hmisc, r-cran-metablue, r-cran-metafor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metamedian_1.2.2-1.ca2404.1_all.deb Size: 241416 MD5sum: 7f792afcd39efe6dfc8e486dc2783758 SHA1: 4cacf69b3f32e831c4644abefc83561846a72655 SHA256: 8801c605bd7a12da242b70fae8564fb85ebbe6d3a503abd9e8367abacf4c808d SHA512: 0e74b3b9902c04e214de0ef0323e1c791bb7212e7c4bb98122dbd9d43c350eac3dfc6dfe5b4831fd4c3ccd30b023c8eee50bb1852487c1c7529f97d7656ad6f0 Homepage: https://cran.r-project.org/package=metamedian Description: CRAN Package 'metamedian' (Meta-Analysis of Medians) Implements several methods to meta-analyze studies that report the sample median of the outcome. The methods described by McGrath et al. (2019) , Ozturk and Balakrishnan (2020) , and McGrath et al. (2020a) can be applied to directly meta-analyze the median or difference of medians between groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) , Cai et al. (2021) , and McGrath et al. (2023) ) are implemented to estimate study-specific (difference of) means and their standard errors in order to estimate the pooled (difference of) means. Methods for meta-analyzing median survival times (McGrath et al. (2026) ) are also implemented. See McGrath et al. (2024) for a detailed guide on using the package. Package: r-cran-metamer Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-progress Suggests: r-cran-shiny, r-cran-miniui, r-cran-testthat, r-cran-data.table, r-cran-covr, r-cran-sf Filename: pool/dists/noble/main/r-cran-metamer_0.3.1-1.ca2404.1_all.deb Size: 216230 MD5sum: cd30f57d1bd552c72a4d759c7dbb7829 SHA1: 254ec8d8c965ea03e4559c25be417a9fcbdf8ad7 SHA256: 99402ab13efcc97dc1454893273b5159b8c7f2b39e7cf40d3e0963a78145aedf SHA512: a4d05f23c82593c1b228cd34989a70a078623938b32bc4370991908dd86199c970bc08049b8c30c5e7cc2981826d3014588f132f8ebf8d799fc4edf9e755690f Homepage: https://cran.r-project.org/package=metamer Description: CRAN Package 'metamer' (Create Data with Identical Statistics) Creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) . Package: r-cran-metamicrobiomer Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-meta, r-cran-lme4, r-cran-gdata, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lmertest, r-cran-matrixstats, r-cran-zcompositions, r-cran-compositions Suggests: r-cran-testthat, r-cran-rcurl, r-cran-httr, r-cran-repmis, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-magrittr, r-cran-foreign, r-cran-mgcv, r-cran-reshape2, r-cran-caret, r-cran-randomforest, r-cran-tsibble, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-metamicrobiomer_1.2-1.ca2404.1_all.deb Size: 1339618 MD5sum: 3050fe13f794bac2ed5bf4d84875ee7e SHA1: 3bd4313b61857a3e7af8cd971b859d4030532695 SHA256: b23227555cba45d9b6c888a3bd34446c91ceddc8bc71750b3d3507a0c9822aee SHA512: be98bb0a5f802dc954e5395daebb8b68c0a345139aa3ee12e7aa705f71f50086d4f1634590f01579a3667e2ed7c40fde024eb34033107d418b488ed7138412cc Homepage: https://cran.r-project.org/package=metamicrobiomeR Description: CRAN Package 'metamicrobiomeR' (Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI &Random Effects) Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data (with various options for data transformation/normalization to address compositional effects) and random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies are implemented. Random Forest model to predict microbiome age based on relative abundances of shared bacterial genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple diversity indexes using linear/linear mixed effect models and some data display/visualization are also implemented. The reference paper is published by Ho NT, Li F, Wang S, Kuhn L (2019) . Package: r-cran-metamisc Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-mvtnorm, r-cran-lme4, r-cran-dplyr, r-cran-plyr, r-cran-proc, r-cran-ggplot2 Suggests: r-cran-coda, r-cran-runjags, r-cran-rjags, r-cran-ggmcmc, r-cran-logistf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metamisc_0.4.0-1.ca2404.1_all.deb Size: 561924 MD5sum: 219767d4660419d1a03f5988cb6e9bb3 SHA1: 72c2d6e971e2b73f5abd3df1d02cecef1882ec71 SHA256: 1b8b6a32ad9f7ec215571ee16e58ad9aec83a21b49a14c053cffc0f159fbcb2c SHA512: 01c41e7bdc42e0a91cba9e1234826594cb501c0f611f6678a3b2ff490e6c4b1783c24a85dbc61cb93acb38d44e60669bc1f69dc596ab44dc7b3c14199f8de6e7 Homepage: https://cran.r-project.org/package=metamisc Description: CRAN Package 'metamisc' (Meta-Analysis of Diagnosis and Prognosis Research Studies) Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) . It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) . Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) . Package: r-cran-metamorphr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-bioc-impute, r-cran-lifecycle, r-cran-magrittr, r-cran-missforest, r-bioc-pcamethods, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-tidyr, r-cran-vctrs, r-cran-withr Suggests: r-cran-knitr, r-bioc-qsmooth, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metamorphr_0.4.1-1.ca2404.1_all.deb Size: 685806 MD5sum: ed705622132413efbdc7d6b80d0927a1 SHA1: 6e51d1adfa1516e8344633d3fea852f8f2b81b06 SHA256: a50b19946e9371b0ab9cea276f7e385df9e447d6aa1248ca9a132ef79cc89b21 SHA512: 446993f92a5001999d2af887c5b3765e83ecb03a326e758c33000e7da63f15556a6b58f63bc7ccfe17dd7708c0d1c767b32a0d8e0de2c11d6932eb01c610c03d Homepage: https://cran.r-project.org/package=metamorphr Description: CRAN Package 'metamorphr' (Tidy and Streamlined Metabolomics Data Workflows) Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) , Stekhoven et al. (2012) , Tibshirani et al. (2017) , Troyanskaya et al. (2001) ), normalization (Bolstad et al. (2003) , Dieterle et al. (2006) , Zhao et al. (2020) ) transformation, centering and scaling (Van Den Berg et al. (2006) ) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) ) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'. Package: r-cran-metan Architecture: all Version: 1.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3683 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggally, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mathjaxr, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-metan_1.19.0-1.ca2404.1_all.deb Size: 3444484 MD5sum: c199104f020f4dadc40febec038f9eaf SHA1: f6ec8a7ef1646f66041a47e96eb85182897f512d SHA256: 1e8595c15ced47fb73eb0f8cda3be494ecf23b9d29103e989c2b626776042637 SHA512: b786522becfceb37254c6227943cb7b64d116322911c8c6b249fc6f65a70446620a42aaf99b62c3c8900d2f599f590e691536ac3b9488aac7cdaf598792e5214 Homepage: https://cran.r-project.org/package=metan Description: CRAN Package 'metan' (Multi Environment Trials Analysis) Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) , Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) , geometric adaptability index by Mohammadi & Amri (2008) , joint regression analysis by Eberhart & Russel (1966) , genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) , scale-adjusted coefficient of variation by Doring & Reckling (2018) , stability variance by Shukla (1972) , weighted average of absolute scores by Olivoto et al. (2019a) , and multi-trait stability index by Olivoto et al. (2019b) . Non-parametric methods includes superiority index by Lin & Binns (1988) , nonparametric measures of phenotypic stability by Huehn (1990) , TOP third statistic by Fox et al. (1990) . Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided. Package: r-cran-metanet Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4356 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-dplyr, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggrepel, r-cran-magrittr, r-cran-reshape2, r-cran-tibble, r-cran-pcutils, r-cran-rlang Suggests: r-cran-pheatmap, r-cran-vegan, r-cran-stringr, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-hmisc, r-cran-gifski, r-cran-ggraph, r-cran-networkd3, r-cran-ggpmisc, r-bioc-ggtree, r-bioc-treeio, r-cran-circlize, r-cran-jsonify, r-cran-ggpubr, r-cran-corrplot, r-cran-philentropy, r-cran-spatstat.random, r-cran-spatstat.geom, r-cran-sf Filename: pool/dists/noble/main/r-cran-metanet_0.3.2-1.ca2404.1_all.deb Size: 3997614 MD5sum: a8aca8e68408867daa2aa51d7274907b SHA1: 81793498df7ddb66d11ffe39221afdf894ce0981 SHA256: 48561a98de39834e537202a9116dcbd9abb8b010bb6fefe0eacb15b6be0f07e8 SHA512: e7dae6c7acc770d15f10d6b65c31b9e7f46a2ae6b64b01e140c4ad846788fe643c1b42373e22ca6054ac199efb33d86f89aaa3509c25e6490b4a5eac21b723f7 Homepage: https://cran.r-project.org/package=MetaNet Description: CRAN Package 'MetaNet' (Network Analysis for Omics Data) Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability. Package: r-cran-metanetwork Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggally, r-cran-network, r-cran-ggplot2, r-cran-intergraph, r-cran-dplyr, r-cran-igraph, r-cran-matrix, r-cran-visnetwork, r-cran-rcolorbrewer, r-cran-magrittr, r-cran-ggimage, r-cran-rlang, r-cran-sna Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metanetwork_0.7.0-1.ca2404.1_all.deb Size: 2766482 MD5sum: 42968a1b5ba5c89ad7e2858c07275a30 SHA1: 0d1ee59907c06a0aa721bb05855679952c91eaf4 SHA256: cfdbc20db4eb0acf4453ede22919abbfee4b8c3ed8d602c733180bff4a6be6d6 SHA512: afb186f4d67f5ca65b564592da7e76f43e910ec2ea699ebc79c95a8ce49764455cddd458d10a1e144edd4c383401f55c7d5e186c247402d54c86fb8d35a029a1 Homepage: https://cran.r-project.org/package=metanetwork Description: CRAN Package 'metanetwork' (Handling and Representing Trophic Networks in Space and Time) A toolbox to handle and represent trophic networks in space or time across aggregation levels. 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Package: r-cran-metanlp Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-tm, r-cran-textstem, r-cran-lexicon Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-wordcloud, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metanlp_0.1.4-1.ca2404.1_all.deb Size: 4710986 MD5sum: 04cd5bf50e0b1e6a072d4b7b38cfdf1e SHA1: c619a9d85dc7a1f1f2e0a006796adcb191612e12 SHA256: ed2568fb4c831bf5e046d19d10910bab9602692c6ef49a37f8dad74d02b4c2c4 SHA512: 548e2903c61d6a4d9565ab26aa8b01a25d99ddceffb72bc5887b493e425d9eebc0278211bc156961961d018208c72db4e0bed1e11498afbc6480dbd9aec87256 Homepage: https://cran.r-project.org/package=MetaNLP Description: CRAN Package 'MetaNLP' (Natural Language Processing for Meta Analysis) Given a CSV file with titles and abstracts, the package creates a document-term matrix that is lemmatized and stemmed and can directly be used to train machine learning methods for automatic title-abstract screening in the preparation of a meta analysis. Package: r-cran-metann Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metann_0.1.0-1.ca2404.1_all.deb Size: 331744 MD5sum: 69206ae78d091586d5be87c119423d97 SHA1: 6878562a0292389e309edb13a334ada4b26a3b38 SHA256: 205cfcc89ee074c367f028787589c09bdfc23c92f22fcb911d7263b3b5e01f1a SHA512: b8204f402abcf868ae53e15e981d525665043c6f8c0c624efcc1b12f05f48529403c1c1caa9f4ca6107cf6f96de7e572b63e9f7f9441c215090e1f0eefa24a11 Homepage: https://cran.r-project.org/package=metANN Description: CRAN Package 'metANN' (Metaheuristic and Gradient-Based Optimization for Neural NetworkTraining and Continuous Problems) Provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) , differential evolution Storn and Price (1997) , grey wolf optimizer Mirjalili et al. (2014) , secretary bird optimization Fu et al. (2024) , and Adam Kingma and Ba (2015) . Package: r-cran-metansue Architecture: all Version: 2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metansue_2.6-1.ca2404.1_all.deb Size: 109772 MD5sum: 2b788798271ab81cd7b65cfd4ffe7c43 SHA1: 90a785b4a312c8c752cef7864435008913a088a0 SHA256: 5a9e823a7c5e0f92b4036df2849e3bcce2cae0980b80647bdc9be7d9c9a16f2c SHA512: 7b5782edac05462861d09f4dd1cfe0fe9872a2b29cc94a926bf243ee16958b35d184b468180cf7ecc9f2cc08fe62981a71b4d1b6de0277b35370fb9593281006 Homepage: https://cran.r-project.org/package=metansue Description: CRAN Package 'metansue' (Meta-Analysis of Studies with Non-Statistically SignificantUnreported Effects) Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis. 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Package: r-cran-metap Architecture: all Version: 1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rdpack, r-cran-tfisher, r-cran-mathjaxr, r-cran-qqconf, r-cran-mutoss Filename: pool/dists/noble/main/r-cran-metap_1.14-1.ca2404.1_all.deb Size: 600614 MD5sum: 1a618c9757488567ff75975c4208485b SHA1: 5cc5bac0150c4b82a8611ed12aba7d9674cf2905 SHA256: 8be458774007dacd87814e32b9ba2d8e887ea6f9845e2d972ae0ac8cc3896cd0 SHA512: 59e185e3b87124f22ce0e4daeb014b7b5255f17f959206b4216c1ba953ebc79477fe446a5da6e579ffcf72b440913f8daa1853ce7687e7dde628f7605526aec5 Homepage: https://cran.r-project.org/package=metap Description: CRAN Package 'metap' (Meta-Analysis of Significance Values) The canonical way to perform meta-analysis involves using effect sizes. 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Package: r-cran-metaphonebr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaphonebr_0.0.5-1.ca2404.1_all.deb Size: 40370 MD5sum: 90cb0f56c03a2dfa26908e4b53b370bd SHA1: da658a7db971bf6e6eb11e43f32ae2105dda341b SHA256: 935921e0c4a8a4af360c99d56306729ef562ad45c8136b9f2d08ca706ba39f53 SHA512: 37f763347903e56c0b7958935516234fe7fd3f9c3ec0d8ca1ece41c8adc41544de10c0662236ef532f3859103908f7d298325575b0fa8da09e04de819e76e998 Homepage: https://cran.r-project.org/package=metaphonebr Description: CRAN Package 'metaphonebr' (Custom 'MetaphoneBR' Phonetic Encoding for Brazilian Names) Simplifies Brazilian names phonetically using a custom 'metaphoneBR' algorithm that preserves ending vowels. Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese. Mation (2025) . Package: r-cran-metaplot Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-encode, r-cran-lattice, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-gridextra, r-cran-gtable, r-cran-ggplot2, r-cran-scales Suggests: r-cran-csv, r-cran-nlme Filename: pool/dists/noble/main/r-cran-metaplot_0.8.4-1.ca2404.1_all.deb Size: 367628 MD5sum: 0b8d8068b6c2e5fec252fd4f2bc9d418 SHA1: 01e040f4b83227224efe06fc116ae5d6f62b0e13 SHA256: 7ac8cd853c48e03eb724a360ac7fc9a49adcf3cf5e843545e66a829b737afa80 SHA512: 0ff76a3a8039105bbc21e95acf4a569612d7a6eff402ba4a178e842e1b5eb478e5fde6a3dc6e5fe696035c164e21367c764dd456daeb484275e5ba2231f834f7 Homepage: https://cran.r-project.org/package=metaplot Description: CRAN Package 'metaplot' (Data-Driven Plot Design) Designs plots in terms of core structure. See 'example(metaplot)'. Primary arguments are (unquoted) column names; order and type (numeric or not) dictate the resulting plot. Specify any y variables, x variable, any groups variable, and any conditioning variables to metaplot() to generate density plots, boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' are honored, producing fully annotated plots with minimal effort. Attribute 'guide' is typically units, but may be encoded() to provide interpretations of categorical values (see '?encode'). Utility unpack() transforms scalar column attributes to row values and pack() does the reverse, supporting tool-neutral storage of metadata along with primary data. The package supports customizable aesthetics such as such as reference lines, unity lines, smooths, log transformation, and linear fits. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability. Package: r-cran-metaplus Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bbmle, r-cran-metafor, r-cran-boot, r-cran-numderiv, r-cran-mass, r-cran-rfast, r-cran-fastghquad, r-cran-lme4 Suggests: r-cran-r.rsp, r-cran-kableextra, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-metaplus_1.0-10-1.ca2404.1_all.deb Size: 569202 MD5sum: a700418df286c0acb91442a6d64068e4 SHA1: ecc7bf493b601f367832cefe9182f9a6a20c1f6d SHA256: c8814d61e953043bffe330f2465d7eb089b5c5a760446e8f663518bc3c7a0a7a SHA512: 6e72616e757a165349b261d105c5c07bd1552c4ea6cbd8f37d92c42f04e9864e1046d5260d60675d3aa27f10a8b1c368db35101c14f8c746c034531de5a0cb1a Homepage: https://cran.r-project.org/package=metaplus Description: CRAN Package 'metaplus' (Robust Meta-Analysis and Meta-Regression) Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 or Baker and Jackson, 2008 ) or mixtures of normals (Beath, 2014 ). Package: r-cran-metapost Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridbezier Suggests: r-cran-grimport Filename: pool/dists/noble/main/r-cran-metapost_1.0-6-1.ca2404.1_all.deb Size: 72464 MD5sum: afea7c75662b0a73e9f51d76a5f23bef SHA1: 7b7bce227cf93b9e6489221585979e66897cf032 SHA256: c05bd11c720d322d69b01cf3709a2ce637b68153d443ba83b88c3e9e8e84f135 SHA512: 1903422a556260cafb89011a4a6c582fb62f3f6127153130c44cd895a36252bf6dd1ce804252db0aae0e3cac0f02c562fec6db7746f7dbbfb12c3197d17416a4 Homepage: https://cran.r-project.org/package=metapost Description: CRAN Package 'metapost' (Interface to 'MetaPost') Provides an interface to 'MetaPost' (Hobby, 1998) . There are functions to generate an R description of a 'MetaPost' curve, functions to generate 'MetaPost' code from an R description, functions to process 'MetaPost' code, and functions to read solved 'MetaPost' paths back into R. Package: r-cran-metapower Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-tidyr, r-cran-testthat, r-cran-rlang Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metapower_0.2.2-1.ca2404.1_all.deb Size: 1448464 MD5sum: 9dbcee98dbaaced1f612ab1ca4672818 SHA1: c8cfdb7ec9cff552a62010c8494c7465bb824e80 SHA256: f6e02abfe02cdf016bb452fbf6a2baa98698651b4f3ebbe270362995d51ea789 SHA512: e4ca4b32e2c2eeb01d65ff748a7a4ec261d8f6d958b2570c11a10aa45593e7ae3173029ceb65f5d8e20c3d153a4837b3bb24d41f7b4664544d14dd47e41eaeb4 Homepage: https://cran.r-project.org/package=metapower Description: CRAN Package 'metapower' (Power Analysis for Meta-Analysis) A simple and effective tool for computing and visualizing statistical power for meta-analysis, including power analysis of main effects (Jackson & Turner, 2017), test of homogeneity (Pigott, 2012), subgroup analysis, and categorical moderator analysis (Hedges & Pigott, 2004). Package: r-cran-metapro Architecture: all Version: 1.5.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metapro_1.5.11-1.ca2404.1_all.deb Size: 24662 MD5sum: c547fbfac4bad1f35246396c2b3b7ff7 SHA1: 4d871e7db6cbef94f7bc1088bb7ce201b645fdfb SHA256: 55db49e5a6d67416a5126439ffdaff066b30aaabbf6f3e5a8f69f56e29729f38 SHA512: aa1fb85fc7ecb11481d1086995e747a4f34df2dc9fa56f61b6b478b196fe7171737e250a85079883af932b49684b64a64559c8cc6cdafeb02bef832e11de78d6 Homepage: https://cran.r-project.org/package=metapro Description: CRAN Package 'metapro' (Robust P-Value Combination Methods) The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new method named weighted Fisher’s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method is useful for detecting partially associated features. Thus, the users can choose the function based on their purpose. Yoon et al. (2021) "Powerful p-value combination methods to detect incomplete association" . Package: r-cran-metaprotr Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-dendextend, r-cran-dplyr, r-cran-ggforce, r-cran-ggrepel, r-cran-reshape2, r-cran-stringr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-metaprotr_1.2.2-1.ca2404.1_all.deb Size: 1081948 MD5sum: c00224b786fadfbe1ed7b16dc5f07e27 SHA1: 0696fb11a606febeaec57fa16a21f3308f0a07d0 SHA256: 9c3e093452e95e2764355e66f88c58998d2635df7dce3e75d5c550741fc963fd SHA512: fdcaa73b2e7c71692fc96e379fef1ef890af648a5a8efa8d51adfb7ba577912d4995d5ed2d026c0ba914a0879a1d32542ac6187402e002346a25009e59bfc1f0 Homepage: https://cran.r-project.org/package=metaprotr Description: CRAN Package 'metaprotr' (Metaproteomics Post-Processing Analysis) Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis. Package: r-cran-metaquant Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gld, r-cran-sld, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr, r-cran-dplyr, r-cran-estmeansd, r-cran-metafor Filename: pool/dists/noble/main/r-cran-metaquant_0.1.3-1.ca2404.1_all.deb Size: 126356 MD5sum: f849c96c96905ba4071ed0dd8f9dac5e SHA1: 0885d0c0dea5d7320aa8925933b3f6f54ae859c9 SHA256: 8c670412a265f0bb762b4b2919c19a653432abffecbefcb4e1a40582a03fa4b9 SHA512: 58ed9247903b10398620a7f8af9ef949ab2572fbee50e2d04879d887ffbade16abe6bbfd1916c85615151c2855c9bac15b3200b954d5f8b601a9e0a0bd7ea633 Homepage: https://cran.r-project.org/package=metaquant Description: CRAN Package 'metaquant' (Meta-Analysis of Quantiles and Functions of Quantiles) Implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. A detailed vignettes with example datasets and code to prepare data and analyses is available at . The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, ). Package: r-cran-metarnaseq Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-deseq2, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-metarnaseq_1.0.8-1.ca2404.1_all.deb Size: 1273334 MD5sum: 0022d89548edf86e963be0a180e3e064 SHA1: 65c92cfff4606cdc80c5b9bf7e8ed63ebc0b59e7 SHA256: 506eedd9a31ee40d118d1fe7d32ef8155840639f40f137e62a9c3a0615f3dcd8 SHA512: 958b048ba178e53c963e3eed887fcd25a75c713ccd2796f30eef8bc0be36b97c0160fe36285bbc0992f116529061b80add06f5c3ca5d4ecbff37cd24b742f600 Homepage: https://cran.r-project.org/package=metaRNASeq Description: CRAN Package 'metaRNASeq' (Meta-Analysis of RNA-Seq Data) Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments. Package: r-cran-metarvm Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-odin, r-cran-purrr, r-cran-r6, r-cran-tidyr, r-cran-yaml Suggests: r-cran-dde, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metarvm_2.1.0-1.ca2404.1_all.deb Size: 2567532 MD5sum: 88b2f4a98d7e88506930056818a7dab7 SHA1: 0a8781fc7e1cd96c1d3ac47b027e65a02d7ab587 SHA256: cb0eb4371de30d22fa8da53de10d85d56444f7cb1fd784a1fc8a9259f0844c04 SHA512: 7dfe6912296f41c39f98606dfa5bfac2537ab4e711f048c0bd7a876412e9ff1b46bee6f44a99f37f34d811d35b13eb2aa6ebb8fd5bd227957d0c136eef53d652 Homepage: https://cran.r-project.org/package=MetaRVM Description: CRAN Package 'MetaRVM' (Meta-Population Compartmental Model for Respiratory VirusDiseases) Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) . 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by user provided attributes. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices. Package: r-cran-metasdtreg Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ordinal, r-cran-maxlik, r-cran-truncnorm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-metasdtreg_0.2.2-1.ca2404.1_all.deb Size: 108668 MD5sum: 610e8efc551a99d0d7208444efb73bfc SHA1: 4433ce85c8535134cd879500f10559e093cdccbc SHA256: e38dbaeb2d98b2685046854dcbcb020ca00a575b989bfa9775d71c3cf853e5f4 SHA512: baa6ff47ee6e76cd6491e3108077f11df18d856adfa1b96a47fd90f1afb4c6e67ae17fe59c18750a6faab0387329ffd572117675b03bc18b8851e0b5a75003fb Homepage: https://cran.r-project.org/package=metaSDTreg Description: CRAN Package 'metaSDTreg' (Regression Models for Meta Signal Detection Theory) Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. . Package: r-cran-metaselection Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-mass, r-cran-mvtnorm, r-cran-optimx, r-cran-nleqslv, r-cran-purrr, r-cran-future.apply, r-cran-progressr, r-cran-rlang, r-cran-ggplot2, r-cran-scales, r-cran-rdpack, r-cran-simhelpers Suggests: r-cran-testthat, r-cran-future, r-cran-metafor, r-cran-metadat, r-cran-clubsandwich, r-cran-desctools, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-metaselection_0.3.0-1.ca2404.1_all.deb Size: 694964 MD5sum: c595c706219e2cf24e58e9648ff10dde SHA1: 917670ea2116eb86d405d14acdcac66d3ea55057 SHA256: 866aa549e609bf8f8cb33ad5f53818c0c87c52f2ed8f09835c16b2f7422cec49 SHA512: fa6e0e7b801a7a84bbb445cc7b1cbef206dbc1968818360811a9c647fc962c10d3090819a545bd0f7ec16d77b569f146d9afe3804f28f29a09518a69a17a93f4 Homepage: https://cran.r-project.org/package=metaselection Description: CRAN Package 'metaselection' (Meta-Analytic Selection Models for Dependent Effect Sizes) Fits a flexible class of p-value selection models for meta-analysis and meta-regression models, providing standard errors and confidence intervals based on either cluster-robust variance estimators (i.e., sandwich estimators) or cluster-level bootstrapping to handle dependent effect size estimates, as described in Pustejovsky, Citkowicz, and Joshi (2025) and Citkowicz, Pustejovsky, and Joshi (2026) . Supported models include generalizations of the step-function selection model as proposed by Vevea and Hedges (1995) and the beta-function selection model as proposed by Citkowicz and Vevea (2017) . Package: r-cran-metasem Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2941 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx, r-cran-matrix, r-cran-mass, r-cran-ellipse, r-cran-mvtnorm, r-cran-numderiv, r-cran-lavaan Suggests: r-cran-metafor, r-cran-semplot, r-cran-r.rsp, r-cran-testthat, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-metasem_1.5.0-1.ca2404.1_all.deb Size: 2100418 MD5sum: 960a99c6d7d46f762c2bb67e7d95b618 SHA1: 36a6ccd7d081d24b5c42535b33cc1b95a78a22cc SHA256: 8281e7a64df1ad065103a82df141605cb64fb85d44ea0382fb7d4ca73ac6bf6e SHA512: 7bc2116ea14ad2fd8761c763a4a77bb5b152e2e86bcd0c1c595ae14ad8e573d035c9d929105b9fb2fa7afc31a4fac6b5864ebf66e597890d397381904bf16a3c Homepage: https://cran.r-project.org/package=metaSEM Description: CRAN Package 'metaSEM' (Meta-Analysis using Structural Equation Modeling) A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices, see Cheung (2015) . Package: r-cran-metasens Architecture: all Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta Filename: pool/dists/noble/main/r-cran-metasens_1.5-3-1.ca2404.1_all.deb Size: 300558 MD5sum: 12e0156c5f42b9ce952ab81b12dcaae2 SHA1: ee80205a9e7acd726c26087cbb86d024e669bca2 SHA256: 5e595cb3077e7e59fa0acd64dc4707405e237c94f1cab17d82d706ca9a1f6b55 SHA512: 0031158603e43cb461916151862f2a9a4d1305f2edc4d7935aca9ed44c10734bb3ab44e6848049b3c35a35fcb2635010500b4614911155529395300591a9b7e2 Homepage: https://cran.r-project.org/package=metasens Description: CRAN Package 'metasens' (Statistical Methods for Sensitivity Analysis in Meta-Analysis) The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) , Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) ; - limit meta-analysis by Rücker et al. (2011) ; - upper bound for outcome reporting bias by Copas & Jackson (2004) ; - imputation methods for missing binary data by Gamble & Hollis (2005) and Higgins et al. (2008) ; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) . Package: r-cran-metasnf Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4956 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-cluster, r-cran-data.table, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-mclust, r-cran-progressr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-snftool, r-cran-tibble, r-cran-tidyr Suggests: r-cran-circlize, r-bioc-complexheatmap, r-bioc-interactivecomplexheatmap, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggalluvial, r-cran-lifecycle, r-cran-dbscan Filename: pool/dists/noble/main/r-cran-metasnf_2.3.0-1.ca2404.1_all.deb Size: 3121890 MD5sum: e01faadaa18346c5daaa70e357cdc241 SHA1: 1982b974e8880a9dcafb327eb0641f678c69296e SHA256: d79eb374ca1d7d4299514b4ea743459782ca5c6d14224379a60a2eb119b00843 SHA512: 15eca91eb8bb4b2a9498ee8489e50b27072f18e84dd8c902b545d1ff50f0f4aa2a93459c65043a37e93f8e075e5a6e6ac5004d37fbd9a6317e7ddc96b6b38f7c Homepage: https://cran.r-project.org/package=metasnf Description: CRAN Package 'metasnf' (Meta Clustering with Similarity Network Fusion) Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in . SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in . Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning. Package: r-cran-metasplines Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-meta, r-cran-optimization Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-metasplines_0.1.1-1.ca2404.1_all.deb Size: 93788 MD5sum: 3e242ca481eeda403ec4d45b3bb82636 SHA1: 5711c506f9c2216c4caea4e9add4d17267930632 SHA256: eca87de61b23827f03a514051d01779ae1319be7385f7c9e4ebec08ad0ca2355 SHA512: 3f98b4f209d157bf5de980dbdf33d048844eeef664cd182ff9da1e5c84552f71415048c4d76bb17f88ad0cadd3d4b6d1268b1ffe4fd36e2984cfa8fd5560a900 Homepage: https://cran.r-project.org/package=metasplines Description: CRAN Package 'metasplines' (Pool Literature-Based and Individual Participant Data BasedSpline Estimates) Pooling estimates reported in meta-analyses (literature-based, LB) and estimates based on individual participant data (IPD) is not straight-forward as the details of the LB nonlinear function estimate are not usually reported. This package pools the nonlinear IPD dose-response estimates based on a natural cubic spline from lm or glm with the pointwise LB estimates and their estimated variances. Details will be presented in Härkänen, Tapanainen, Sares-Jäske, Männistö, Kaartinen and Paalanen (2026) "Novel pooling method for nonlinear cohort analysis and meta-analysis estimates: Predicting health outcomes based on climate-friendly diets" Epidemiology . Package: r-cran-metasubtract Architecture: all Version: 1.60-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metasubtract_1.60-1.ca2404.1_all.deb Size: 179364 MD5sum: ca0cf604ce0cd34121dc3457416f2106 SHA1: 546bbd8c643b2b01e1f790bd99576a84faf74918 SHA256: bfc66d0a859568e32a683adf1db24b1a81d86aae87a7c7391c7b73f086bb6d34 SHA512: a37136fc43a809dc6e79c48c15da66343d8619ee5378679b52547f8acc3b7f748d1759d95f387af643ffab23497fca0d899db2617bc5f4294b2e1cce3b01532e Homepage: https://cran.r-project.org/package=MetaSubtract Description: CRAN Package 'MetaSubtract' (Subtracting Summary Statistics of One or more Cohorts fromMeta-GWAS Results) If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. See for application: Nolte I.M. et al. (2017); . Package: r-cran-metasurvey Architecture: all Version: 0.0.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3776 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-jsonlite, r-cran-r6, r-cran-survey Suggests: r-cran-archive, r-cran-convey, r-cran-digest, r-cran-httr2, r-cran-haven, r-cran-openxlsx, r-cran-visnetwork, r-cran-roxygen2, r-cran-testthat, r-cran-tibble, r-cran-dplyr, r-cran-knitr, r-cran-foreign, r-cran-rmarkdown, r-cran-rio, r-cran-here, r-cran-gt, r-cran-magrittr, r-cran-shiny, r-cran-bslib, r-cran-bsicons, r-cran-htmltools, r-cran-xml2, r-cran-eph, r-cran-pnadcibge, r-cran-ipumsr Filename: pool/dists/noble/main/r-cran-metasurvey_0.0.24-1.ca2404.1_all.deb Size: 2050868 MD5sum: ce6433ad5ddc3a8aac0f705abcf58d2f SHA1: d36596845232c9964740d39624069d905832c2e6 SHA256: 89344382b6a965b663aa515f5ea4c69eb29701a7f5e8c50b06aa70b0108ef3fb SHA512: 8e0119145d8611db6b8cb0456497a2a4e043dd719e7d99796b4586123291bf8eca9a81d442cd0a938e0c449b0ca5a31a48b7bf1873d8de796d637e9eeeb960dd Homepage: https://cran.r-project.org/package=metasurvey Description: CRAN Package 'metasurvey' (Reproducible Survey Data Processing with Step Pipelines) Provides a step-based pipeline for reproducible survey data processing, building on the 'survey' package for complex sampling designs. Supports rotating panels with bootstrap replicate weights, and provides a recipe system for sharing and reproducing data transformation workflows across survey editions. Package: r-cran-metasurvival Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metasurvival_0.1.0-1.ca2404.1_all.deb Size: 53608 MD5sum: 5a586ddb47f6a93427fabcba934a4924 SHA1: 8ab1255e0e8bcc5072f767dbfe7f0afc4e35615f SHA256: 6c3e7d89920050dddd902232a18d1227476786d9dd7d3be4bb7a6d12f63d0be6 SHA512: 2a0bffe97b9465cc7ba740da8b94c272af61d5c79774dc0441b6e3396f20dd6104e4144e5a856e46b75e660e1309cea17e46edc8f94ba1a14c54501eb0883931 Homepage: https://cran.r-project.org/package=metaSurvival Description: CRAN Package 'metaSurvival' (Meta-Analysis of a Single Survival Curve) To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity. 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(2021) ), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) ), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) ), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) ), Ant Lion Optimization (ALO) (Mirjalili (2015) ), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) ). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) and Liu et al. (2018) . Package: r-cran-metatest Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metatest_1.0-5-1.ca2404.1_all.deb Size: 40026 MD5sum: b296113c854bd9198b0bec20ea7a82ae SHA1: 381a1e032ac538cff88acc2ba237932160e46ab8 SHA256: 14da92bd9d62b854d1c18a0ffd0bd79a7efbf3bda4596255e234c60f931263bd SHA512: 6b945ac74b41851a2164f52fba64bae6a376088320ad86f833b2bfc1d7ff843028f72445edf70a89a8bf950f9e42b2868a4a1976834fa70f7c53d2b21be10005 Homepage: https://cran.r-project.org/package=metatest Description: CRAN Package 'metatest' (Fit and Test Metaregression Models) Fits and tests meta regression models and generates a number of useful test statistics: next to t- and z-tests, the likelihood ratio, bartlett corrected likelihood ratio and permutation tests are performed on the model coefficients. 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Package: r-cran-metaumbrella Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1803 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-meta, r-cran-metaconvert, r-cran-pwr, r-cran-powersurvepi, r-cran-readxl, r-cran-withr, r-cran-writexl, r-cran-xtable Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-spelling, r-cran-dt, r-cran-epir, r-cran-esc, r-cran-metafor Filename: pool/dists/noble/main/r-cran-metaumbrella_1.1.0-1.ca2404.1_all.deb Size: 758212 MD5sum: 891b7cf253d96d2a7f633d84dc56de92 SHA1: e10ea91dafcb220a87a3c6cad89114029d6a79ca SHA256: b0f301c99cfb24eb3c970eab0c6a7574caa1f1190cf1fba345860abd50c0fb3f SHA512: c8be7e2403b67a2ab04b1008831d1fd70052135ffaba6d3bdffbae2ad8622586eac18464961255eb450fb508557bc6246998701e00c298e7bde33715afe4b72e Homepage: https://cran.r-project.org/package=metaumbrella Description: CRAN Package 'metaumbrella' (Umbrella Review Package for R) A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence in R. The package accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results. Note that if you are not familiar with R, the core features of this package are available from a web browser (). Package: r-cran-metautility Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-metadat, r-cran-stringr, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-metautility_2.1.2-1.ca2404.1_all.deb Size: 258564 MD5sum: d2fd9b5c9d062c113385bc6266c71229 SHA1: 58547cf42b08f4e948681b2e6c65f6d9a04ca272 SHA256: e77b2b7bb0d4ad74c386ff1f43fc808c325d3b30a8af2b7d33161bfdefad5378 SHA512: 12688da1d197addba53183e018f7e18e8f2d1ea9ac8da1515bc7d8a5f52f4ef6dd5f8237e9139d68bab13463be66e96308d84e2ec5e1505898b1e04194f0ade7 Homepage: https://cran.r-project.org/package=MetaUtility Description: CRAN Package 'MetaUtility' (Utility Functions for Conducting and Interpreting Meta-Analyses) Contains functions to estimate the proportion of effects stronger than a threshold of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval), to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var). Package: r-cran-metavcov Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mixmeta, r-cran-metasem, r-cran-mvmeta, r-cran-mice Filename: pool/dists/noble/main/r-cran-metavcov_2.1.5-1.ca2404.1_all.deb Size: 204428 MD5sum: cc80c9bee1a80a7315a0c38e04841b72 SHA1: cf4386d0622713ae77535ef2036f86a6f9817ba9 SHA256: 69c98231ca6db1119ec197a64f259ad91b6e847a93f5e00791f0037ad7396312 SHA512: d670e2a6951e8002837a75cf290708243f4aa855db3f9a426d1bdf80d343b8d848616d9dadbe731f5b5760be028ba43f4f2fddda7945fd0c61b4610bcb83c39a Homepage: https://cran.r-project.org/package=metavcov Description: CRAN Package 'metavcov' (Computing Variances and Covariances, Visualization and MissingData Solution for Multivariate Meta-Analysis) Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD). 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Provides functions to create forest plots, funnel plots, and many of their variants, including rainforest plots, thick forest plots, additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities for visual inference with funnel plots in the context of meta-analysis are provided. Further functionalities include plots for comparing fixed-effect and random-effects models and dedicated visualizations for three-level meta-analysis. Package: r-cran-metaweave Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaweave_0.4.1-1.ca2404.1_all.deb Size: 148062 MD5sum: c6831f246613375d781e1b1c04d47f7f SHA1: 3a15d4378f2d16e8599c029f46daaed47018812e SHA256: 7d9c7790b5b64b7bc016aef983a4689d314b4b86af82bb26086e1bff5d272a56 SHA512: 5caf56eeb652077dcc4a811264a3b4ff521c13a3699fa708f0d26392e167da3ded20550f58e7a4f1dde3fc4ce41764a483bd7dae610ee017d3dc9d567af2de6b Homepage: https://cran.r-project.org/package=metaweave Description: CRAN Package 'metaweave' (Spatial Ecological Network Inference) A model-agnostic framework for reconstructing and analysing spatially explicit ecological networks from species distributions and ecological inference models. 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It allows the metabolite classification in structurally-related modules and identifies common shared functional groups. The KODAMA algorithm is used to highlight structural similarity between metabolites. See Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics , Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA , and Abdel-Shafy EA, Melak T, MacIntyre DA, Zadra G, Zerbini LF, Piazza S, Cacciatore S. (2023) Bioinformatics Advances . 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Functions for calculating: reference evapotranspiration (ETref), extraterrestrial radiation (Ra), net radiation (Rn), saturation vapor pressure (satVP), global radiation (Rs), soil heat flux (G), daylight hours, and more. [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9). Package: r-cran-metools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-lubridate, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-metools_1.0.0-1.ca2404.1_all.deb Size: 166240 MD5sum: 25a9e9ce16ab8b9c236a2409c71839d0 SHA1: e6e6cd7329c1e9fc04f105b6efb11ac9aac3f693 SHA256: 0825bcdabf8a83c68e3350f82ab5d6ea28a81acb5c33bbbc6a7ac0cc4df4ff31 SHA512: ee4e5121876bd350dec5dde68a2a01d5f0a256ad1dd1d5574577acf3bf25129b001c8fa1bf0a5e6fe9ee707618d2553ab23d7f3c8737352faa0b6f69025b35a6 Homepage: https://cran.r-project.org/package=metools Description: CRAN Package 'metools' (Macroeconomics Tools) Provides a number of functions to facilitate the handling and production of reports using time series data. The package was developed to be understandable for beginners, so some functions aim to transform processes that would be complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and working with time series using a few lines of code, so the code is clean and easy to understand/maintain. Learn more about the 'metools' at . Package: r-cran-metproc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gplots, r-cran-fastcluster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metproc_1.0.1-1.ca2404.1_all.deb Size: 315130 MD5sum: 9c5563e2e20e8ad95af55466cd3529db SHA1: 6310c767b2a03a14d70b298ff7994554095014f2 SHA256: b85714871782cda8e4a412e3f1d514049ea3e16fa42a6e0ea9939592d5565a04 SHA512: a4d340e4be2dcfd9c62b5cd6a28f1094a201036a7ba2e15bd10d8368ca9ddd9dd2391c84a780dd3716979b0cf9f07be698d750cda3a89d25c82a3563dcd65bad Homepage: https://cran.r-project.org/package=MetProc Description: CRAN Package 'MetProc' (Separate Metabolites into Likely Measurement Artifacts and TrueMetabolites) Split an untargeted metabolomics data set into a set of likely true metabolites and a set of likely measurement artifacts. This process involves comparing missing rates of pooled plasma samples and biological samples. The functions assume a fixed injection order of samples where biological samples are randomized and processed between intermittent pooled plasma samples. By comparing patterns of missing data across injection order, metabolites that appear in blocks and are likely artifacts can be separated from metabolites that seem to have random dispersion of missing data. The two main metrics used are: 1. the number of consecutive blocks of samples with present data and 2. the correlation of missing rates between biological samples and flanking pooled plasma samples. Package: r-cran-metr Architecture: all Version: 0.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4753 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-digest, r-cran-formula, r-cran-formula.tools, r-cran-ggplot2, r-cran-gtable, r-cran-isoband, r-cran-lubridate, r-cran-memoise, r-cran-plyr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-stringr Suggests: r-cran-cftime, r-cran-furrr, r-cran-gsignal, r-cran-irlba, r-cran-knitr, r-cran-kriging, r-cran-maps, r-cran-markdown, r-cran-ncdf4, r-cran-proj4, r-cran-rcdo, r-cran-reshape2, r-cran-rnaturalearth, r-cran-terra, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metr_0.19.0-1.ca2404.1_all.deb Size: 3702020 MD5sum: 13a5ae66db2f678aeaa7dbb2bb63c7f8 SHA1: ca3b2f47d3236210811c8976def1fbec0999cae8 SHA256: 574bde8de80fd21c70d9b103eefda13ab086746e56d4f6385288877b491f1985 SHA512: 317d43ccdeb2820bee71b78215451e0742a0887303becbe12a6e2f76ef5f04ab26898b3fec15d3bdbb59760ff340b8d0d8a2949ad278f1f6f3f40dff1047ec9d Homepage: https://cran.r-project.org/package=metR Description: CRAN Package 'metR' (Tools for Easier Analysis of Meteorological Fields) Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends 'ggplot2' for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences. Package: r-cran-metrica Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-dbi, r-cran-rsqlite, r-cran-ggpp, r-cran-minerva, r-cran-energy Suggests: r-cran-purrr, r-cran-knitr, r-cran-rmarkdown, r-cran-apsimx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metrica_2.1.1-1.ca2404.1_all.deb Size: 2090766 MD5sum: 8e08bdf38f91f0b5ef5e84fc5ee184eb SHA1: 8008b30e5b5e73ae0b7f10bc5b742be4787c686a SHA256: 4d527a6a8b0272fc3fb0b9bf0379c1e69e5a3578e497861c7ad709c997283dcc SHA512: 7fc1938509feb4755f89741be37018ad03f40a9507f6e1a6a2d6e7989411b4a7cc02d8251973039370bcd179f57ae0c3d33f58ca46f8d688f421d7b278933fbb Homepage: https://cran.r-project.org/package=metrica Description: CRAN Package 'metrica' (Prediction Performance Metrics) A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes . Package: r-cran-metricminer Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-openssl, r-cran-gh, r-cran-getpass, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-tidyr, r-cran-googledrive, r-cran-googlesheets4, r-cran-janitor, r-cran-stringr, r-cran-magrittr, r-cran-rvest, r-cran-rprojroot, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-metricminer_1.0.1-1.ca2404.1_all.deb Size: 218760 MD5sum: 86eb79ebc6de7d857531b965184d04b9 SHA1: 7477ef75db2f4c00585cc105ad13daf25fa5f0fc SHA256: 3a70ccac567a16802953574cff4f8eb08446b284cefddda9a5949f3bd98e1cb4 SHA512: 253e3a3a09bd2eaf29992df59b02b2e014c737e0cced96c34b3376af819d429195ee610baa65fa26774d1620ec0731eefb4a2646d21df401ba4d9b3f0a633e31 Homepage: https://cran.r-project.org/package=metricminer Description: CRAN Package 'metricminer' (Mine Metrics from Common Places on the Web) Mine metrics on common places on the web through the power of their APIs (application programming interfaces). 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Package: r-cran-metrics Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metrics_0.1.4-1.ca2404.1_all.deb Size: 83500 MD5sum: 858155f2d0ec1747c3408f435a186336 SHA1: e229dfc646c8f7d899977afbaad50e3c3adefda6 SHA256: 3af7c6b2f9b5d2407281183b8c85df221a18af2dbd6d58f2f7f9cdb963fef169 SHA512: 5a6c7aed9e6dfcd84932b7c9c7e40f90b2f7a957cb8cab8f1c134f8fe6a006f7e96e1594511a9b0df75bc26d908a00b6d4cce17ad9eda943f0a5d22b5a2e2172 Homepage: https://cran.r-project.org/package=Metrics Description: CRAN Package 'Metrics' (Evaluation Metrics for Machine Learning) An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. 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Package: r-cran-metricsgraphics Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-testthat, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-ggplot2movies, r-cran-jsonlite, r-cran-knitr, r-cran-shiny, r-cran-binom, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-metricsgraphics_0.9.0-1.ca2404.1_all.deb Size: 1084142 MD5sum: e13325bfc74b79b5c718590b5504f02a SHA1: 17894fc2670988089f3a4a6893d3a16698e0bb06 SHA256: dec27262122bd77521b75a6193d47ee7d6dcc09b46d124fb412a0d8188f71ee1 SHA512: cbbf48e3555c678eced20ea02df5377df28020d5fb056a121902b408c0e73f81c7ac82ab7efc533c830d35a332ed0934a6c7a897ec363ca4091e338d81d502a6 Homepage: https://cran.r-project.org/package=metricsgraphics Description: CRAN Package 'metricsgraphics' (Create Interactive Charts with the JavaScript 'MetricsGraphics'Library) Provides an 'htmlwidgets' interface to the 'MetricsGraphics.js' ('D3'-based) charting library which is geared towards displaying time-series data. Chart types include line charts, scatterplots, histograms and rudimentary bar charts. Support for laying out multiple charts into a grid layout is also provided. All charts are interactive and many have an option for line, label and region annotations. Package: r-cran-metricsweighted Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metricsweighted_1.0.4-1.ca2404.1_all.deb Size: 120886 MD5sum: 1202baca838dde005c0bb200f63af5bb SHA1: 3b483596c927c82f28bb45d1e6a81b611b14b710 SHA256: 0e471d900870638f8ec7310e76f26a7b79003e17ff714cec6562c17160ff6248 SHA512: 60cb87443f7ef5cad706d7c307e4167abba35f58a43704e5552118f0c1438e5307f53ab99fd7dfe2c570e26d831ea55cd184d0cf774ecf3f7dd9602a130d3fc6 Homepage: https://cran.r-project.org/package=MetricsWeighted Description: CRAN Package 'MetricsWeighted' (Weighted Metrics and Performance Measures for Machine Learning) Provides weighted versions of several metrics and performance measures used in machine learning, including average unit deviances of the Bernoulli, Tweedie, Poisson, and Gamma distributions, see Jorgensen B. (1997, ISBN: 978-0412997112). The package also contains a weighted version of generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore, 'dplyr' chains are supported. Package: r-cran-metrix Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-stringr, r-cran-vegan Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-metrix_1.1.0-1.ca2404.1_all.deb Size: 146492 MD5sum: 1541e5f4c99d9425bc919d4a5f505b49 SHA1: aa611754200ce7eaa32cd86d53581070e438d21a SHA256: 335196ef1f88fb579b1355f0d76453b7e8217ffab48833e11a46c33cfd2f0a45 SHA512: 06abb3bebf9f50df432bbac2a538d993f3fa637dbdd65f23d02742054f319e7406f50a29d51a75191346fb5427da6af951cdc15734c45259aacef1656d034f35 Homepage: https://cran.r-project.org/package=metrix Description: CRAN Package 'metrix' (Water Quality Metrics Calculator) Calculate different metrics based on aquatic macroinvertebrate density data (individuals per square meter) to assess water quality (Prat N et al. 2009). Package: r-cran-metro Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geodist, r-cran-hms, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-covr, r-cran-mockr, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metro_0.9.3-1.ca2404.1_all.deb Size: 352096 MD5sum: f1f301bcfa726cbe8102769f4e1e3e95 SHA1: 8a06726096a6c6a2a426a7fc1d93fd0e2477adfa SHA256: cf90f04fce7f0ea8865a9607a717f42e5e0c12b4fe8036e2fd858fa331d8cf8b SHA512: a0a5fc9c683c900122bc4a21c258aad2033d27c592ad90b32ffc891f68e73a13a6381a56f53b040980f7015df8a2c91ffdb301e7b659ba8074412c5a07da6d49 Homepage: https://cran.r-project.org/package=metro Description: CRAN Package 'metro' (Washington Metropolitan Area Transit Authority API) The Washington Metropolitan Area Transit Authority is a government agency operating light rail and passenger buses in the Washington D.C. area. With a free developer account, access their 'Metro Transparent Data Sets API' to return data frames of transit data for easy analysis. Package: r-cran-metrology Architecture: all Version: 0.9-29-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-numderiv, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-metrology_0.9-29-2-1.ca2404.1_all.deb Size: 830610 MD5sum: ea147f731b5a05fd55aa0400ab707494 SHA1: 61737629782f8da6c6cc76ab9541aba223ef8ccb SHA256: 0196ff52707d1ebdf50deb105241d2af1d54bed9377488936a506a63660b6cc5 SHA512: b024050e9d7b1b6fbd7fe457e50c0241be1bb0fbbea5c7f847b7b97d4d63f4588917fa690ffec08f4bc9be4ab5bfdd1b2f4c690edb36b10b00d8786a70d0ad78 Homepage: https://cran.r-project.org/package=metRology Description: CRAN Package 'metRology' (Support for Metrological Applications) Provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies. Package: r-cran-metropolis Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-metropolis_0.1.8-1.ca2404.1_all.deb Size: 634854 MD5sum: ca8afe622ece051310fc2df90dd88532 SHA1: 4e18b3ebaa67ce734986b0c61f61f5ec7803021a SHA256: 8b013204674cc4f36b0bfe5bf8cee7218d9c5ac73a593d5f8167c7e848fb2423 SHA512: 76368938560f8b04ee7eb7d1553fea0048e7e8fa86ed340db660d2025485e4e910287197c0b0311060972a7e49127d9bfef97aac5ffd99d8be5169ed913ba3c0 Homepage: https://cran.r-project.org/package=metropolis Description: CRAN Package 'metropolis' (The Metropolis Algorithm) Learning and using the Metropolis algorithm for Bayesian fitting of a generalized linear model. The package vignette includes examples of hand-coding a logistic model using several variants of the Metropolis algorithm. The package also contains R functions for simulating posterior distributions of Bayesian generalized linear model parameters using guided, adaptive, guided-adaptive and random walk Metropolis algorithms. The random walk Metropolis algorithm was originally described in Metropolis et al (1953); . Package: r-cran-metrosp Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3601 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite Suggests: r-cran-bizdays, r-cran-spelling, r-cran-dataverse, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-rmarkdown, r-cran-sf, r-cran-tarchetypes, r-cran-targets, r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/noble/main/r-cran-metrosp_2.0.0-1.ca2404.1_all.deb Size: 2788932 MD5sum: 2d564ed5cffb1e9a684c58aecd4b7b31 SHA1: a38c728586ed82d6d5843f09a3a217d0418f1662 SHA256: a7c62246af1d3838bcc3e0dca0a1fbdc61211e29e1ce08891164b88f9d5b3b84 SHA512: 799d252eb9e7433240df1eaeabab5af18b89dab668eebc58f8daaad3df2737963895a7769512464606ddcd8d899d40faf07d8f1c85d9779c1c63a1102dc6cb06 Homepage: https://cran.r-project.org/package=metrosp Description: CRAN Package 'metrosp' (São Paulo Metro Passenger Demand Data) Provides passenger demand data for the São Paulo metro system, covering 2012 to 2026. Datasets include monthly passenger entries and transported counts by line, average weekday passengers transported by station, daily station entries, and spatial geometries for metro and commuter train lines and stations. The bundled datasets are a fixed snapshot, so analyses stay reproducible and examples run offline. More recent data is published to 'GitHub' releases as the upstream sources are updated, and read_metro_demand() downloads, caches, and reads it, optionally pinned to a dated monthly batch. Package: r-cran-metsizer Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-metabolanalyze, r-cran-rfast, r-cran-shiny, r-cran-shinythemes, r-cran-vroom Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-metsizer_2.0.0-1.ca2404.1_all.deb Size: 69648 MD5sum: 3aa7e3a651fc380e061e8a97fb8d167c SHA1: 5f6ffe6cb0e6aa6d911217ac4e13a47230c76610 SHA256: e5ae3d30f94c39044ed5a3f8d4107d03df94c55cf5a782e1a789c07fa558e4e4 SHA512: 08f2b11e52c5c53a5e2525deac9d2a423d372b85562bff2505c6e8cef289a001640745b66ca75d083609c1aa912e76e2e7a2dd54368621666d524ada19d73c6e Homepage: https://cran.r-project.org/package=MetSizeR Description: CRAN Package 'MetSizeR' (A Shiny App for Sample Size Estimation in MetabolomicExperiments) Provides a Shiny application to estimate the sample size required for a metabolomic experiment to achieve a desired statistical power. Estimation is possible with or without available data from a pilot study. Package: r-cran-metsyn Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-readr, r-cran-stringr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-metsyn_0.1.2-1.ca2404.1_all.deb Size: 28424 MD5sum: c550e4542ad905d552ee1dc4c6195830 SHA1: c03e8a2c29c650b126999862f3cb237e710c739d SHA256: 613dc1fc7e998c58f4e114c26cd64685481bcd57775a488076fea954111667a2 SHA512: 88d1600a3469424bf6c621bffacefc505c6015fb720e6e0f4551585ab31547651b4130a85021e37726c6bf7692e3c9131262a162e28ea93533a0ce42b06e5aa1 Homepage: https://cran.r-project.org/package=metsyn Description: CRAN Package 'metsyn' (Interface with the Meteo France Synop Data API) Provides an interface with the Meteo France Synop data API (see for more information). The Meteo France Synop data are made of meteorological data recorded every three hours on 62 French meteorological stations. Package: r-cran-mevr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-envstats, r-cran-foreach, r-cran-doparallel, r-cran-bamlss, r-cran-mgcv Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mevr_1.1.1-1.ca2404.1_all.deb Size: 127516 MD5sum: b957de46a3a6657ab71d867e202d3098 SHA1: 6981946f31b87e05667f2e40d41246feeba8f0b3 SHA256: 80e6f066b3fc560732df088dd35377ef4017ebd926f0c4b006e0704210ea8bb3 SHA512: 78dadcd19e536bbcb8f86484cd056cf7e911a04ac6961c3fc402d347e3a1e3eb279e36c362a1a4e0bf98e9f0ccba78aaa86476c7852cc756c69d11f89f9995ce Homepage: https://cran.r-project.org/package=mevr Description: CRAN Package 'mevr' (Fitting the Metastatistical Extreme Value Distribution MEVD) Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, ) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, ) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, ). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references. Package: r-cran-mexbrewer Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4036 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mexbrewer_0.0.2-1.ca2404.1_all.deb Size: 3979218 MD5sum: b298ea51f83a78e1a03f8ad50ca69cb9 SHA1: 328f857735115456af86342463459c96648d1ff1 SHA256: 8da23e3d08544a1ce582b875c217fbb990e50b43c834e8c2bb36c0f8299bfc17 SHA512: 784511246b98edc8f054b225939c0695a7dff89af5c99d948ea11fd23ffa9e6373fd00f1ccadf1dfb929bf0905e045c284257024778746386a854877ed94d91f Homepage: https://cran.r-project.org/package=MexBrewer Description: CRAN Package 'MexBrewer' (Color Palettes Inspired by Works of Mexican Painters andMuralists) Color palettes inspired by the works of Mexican painters and muralists. The package includes functions that return vectors of colors and also functions to use color and fill scales in 'ggplot2' visualizations. Package: r-cran-mexicodataapi Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mexicodataapi_0.3.0-1.ca2404.1_all.deb Size: 509604 MD5sum: a7aaaad4567d9630eafe35cca7c90ef3 SHA1: 319c93f973dc95b8b93299b65aa04bbe3662b399 SHA256: 7f927cb8c886474f234d92205743a838b15558fa2719e7f7e683a3b75418f5f7 SHA512: f7ac8b3a8ebf450b10aa1ab0008b67ca9642cc3d33c3f11534df419dd495c277d8c951237cb33c20c75ad0a4dd49ee68aa987c990626dde14f1ef34886a7ec04 Homepage: https://cran.r-project.org/package=MexicoDataAPI Description: CRAN Package 'MexicoDataAPI' (Access Mexican Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'World Bank API', and 'Nager.Date API', covering Mexico's economic indicators, population statistics, literacy rates, and official public holidays. The package also includes curated datasets related to Mexico such as air quality monitoring stations, pollution zones, income surveys, postal abbreviations, election studies, forest productivity and demographic data by state. It supports research and analysis focused on Mexico by integrating reliable global APIs with structured national datasets drawn from open and academic sources. For more information on the APIs, see: 'World Bank API' , and 'Nager.Date API' . Package: r-cran-mexicolors Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mexicolors_0.2.0-1.ca2404.1_all.deb Size: 15268 MD5sum: 50aac6c166b562f12595138a9d40b486 SHA1: cac363f181b42f0584b5340892720c4f05d0ed8c SHA256: 3a6753563bfffe785c51a4e1bde47d4151129a7fe6d5f3a6d980a646577863e2 SHA512: 6926b764fff7b98183fbb21aef171880d0abbb28d40c92ed90944b8b6daa8f6823ab206cbaa1b408e363d54ffb346d9402fccb9778fade601ea676dd3e75b727 Homepage: https://cran.r-project.org/package=mexicolors Description: CRAN Package 'mexicolors' (Mexican Politics-Inspired Color Palette Generator) A color palette generator inspired by Mexican politics, with colors ranging from red on the left to gray in the middle and green on the right. Palette options range from only a few colors to several colors, but with discrete and continuous options to offer greatest flexibility to the user. This package allows for a range of applications, from mapping brief discrete scales (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays including dozens of shades graded from red to green. Package: r-cran-mexplorer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-bioc-qusage Filename: pool/dists/noble/main/r-cran-mexplorer_1.0.0-1.ca2404.1_all.deb Size: 59428 MD5sum: 2cddda134bd3b078d6fc5b82e81c53eb SHA1: 0a32ee91136c2a01e6758c93b41548899023e678 SHA256: c0c516f4fa3b732dba53685bbdd31cc6319e7bbf6126e335045fd0b789927c90 SHA512: af083cca3f1042bd0aa6475a894cfc9e4dffe3b0fab894200c7329a946a05524de83a97259eaac502e4eb63bc2685cda723a1e43772c1e67fda14d40b7c6d490 Homepage: https://cran.r-project.org/package=mExplorer Description: CRAN Package 'mExplorer' (Identifying Master Gene Regulators from Gene Expression andDNA-Binding Data) The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 . Package: r-cran-mf.beta4 Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-devtools, r-cran-ggplot2, r-cran-dplyr, r-cran-ggpubr, r-cran-purrr, r-cran-patchwork, r-cran-tidyr, r-cran-lme4, r-cran-lmertest, r-cran-tidyverse, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mf.beta4_1.1.2-1.ca2404.1_all.deb Size: 1182996 MD5sum: a22f5dfe3e0bcb1ac96fe5c790e65fd1 SHA1: 49853fa79b809e62c8cd31d2c20dff1b9bc6cbc5 SHA256: 360d677e3463932fd8d5c1b35cc81073f31cddcfe238a697abebf4fd316260ce SHA512: e7e5fad5b522c81316a786405a7dee052c1fe6131ed89021ce244fadc0c007a6de43fc07719bbe8ea2a43dd073ef97106f1757d5377e1792c05e9a2067db7ec2 Homepage: https://cran.r-project.org/package=MF.beta4 Description: CRAN Package 'MF.beta4' (Measuring Ecosystem Multi-Functionality and Its Decomposition) Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). 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Package: r-cran-mfag Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mfag_2.3-1.ca2404.1_all.deb Size: 81520 MD5sum: 5d8ef435e5b0654c06dcbebb0f3dbaaf SHA1: b850d99869d470eae370dd7758af948e84328403 SHA256: a34153e87e86e33c129682ec058839e9a54bd6583df72c24ec574b81ae3abc34 SHA512: 202c21d82a0f6f48954c01ae80879821b35f565914c0d227402892e6a5e9030de63880e1579f40db0f78d3c82cc5849c067b4875bb47ec44d25f8a619e1ff26e Homepage: https://cran.r-project.org/package=MFAg Description: CRAN Package 'MFAg' (Multiple Factor Analysis (MFA)) Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions. Package: r-cran-mfclim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr Suggests: r-cran-httptest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfclim_0.1.1-1.ca2404.1_all.deb Size: 42922 MD5sum: 460e197b8a54ff31b303fddc989d8437 SHA1: ef8a2abf87e7c1de51374d04b85446618ae6aab9 SHA256: ec105c032c6850d43f276d6161239ae949910e0e5b4f7fd71c451eb1063ab51d SHA512: cd1bed940623e80fb6c26895cfae5f47d908c59c8b120abb929cb9d0c11c126873836702daba93f21b22d848ecea2b22cb32da236f2a4718c567d1b5e0618b7a Homepage: https://cran.r-project.org/package=mfclim Description: CRAN Package 'mfclim' (Download Archived Meteorological Data from 'Meteo-France') Download meteorological data from the 'Meteo-France' public API 'Données climatologiques' and 'SYNOP' open data archives . 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Package: r-cran-mfcurve Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plotly, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mfcurve_1.0.2-1.ca2404.1_all.deb Size: 457468 MD5sum: f3493be0b8a9c0c73e6e7466c5f7106a SHA1: 10d269687b2a9b4cc1a468766b4e694ac86f3433 SHA256: 9226fc45a171e60edf972cb353b6b7c46c1aa3f1d2f07709029479caf728e905 SHA512: 69fd16f10dfbfe5c9551d4b89d9e1bff62d09a18044221cfe619ec8f5391494e367a2575c424b43da7ec68fd31f2751d03b5c8dda491228e75dea28eb928362a Homepage: https://cran.r-project.org/package=mfcurve Description: CRAN Package 'mfcurve' (Multi-Factor Curve Analysis for Grouped Data in 'R') Implements multi-factor curve analysis for grouped data in 'R', replicating and extending the functionality of the the 'Stata' ado 'mfcurve' (Krähmer, 2023) . Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) . Includes data preprocessing, statistical testing, and visualization of results with confidence intervals. Package: r-cran-mfd Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-betapart, r-cran-cluster, r-cran-dendextend, r-cran-factominer, r-cran-gawdis, r-cran-geometry, r-cran-ggplot2, r-cran-ggrepel, r-cran-hmisc, r-cran-patchwork, r-cran-reshape2, r-cran-rstatix, r-cran-vegan Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-mfd_1.0.7-1.ca2404.1_all.deb Size: 1887434 MD5sum: 16b7039ad7744f55e16f6c2887d59a6f SHA1: 28092923a1d679a59e3053a1eea1550dadecdd26 SHA256: d39e0f03b33bc938d8f6c18e34475e38702a38ff1f5cfaade6244cd8e46941eb SHA512: a3d57aeef558452f348e8e7268a7aadc7383d089ea423b6a1deab3688e6db5845e4a95c2bdec5bf031265d121779af9a7dab288d039faa50e69b1cc4aff4f4a2 Homepage: https://cran.r-project.org/package=mFD Description: CRAN Package 'mFD' (Compute and Illustrate the Multiple Facets of FunctionalDiversity) Computing functional traits-based distances between pairs of species for species gathered in assemblages allowing to build several functional spaces. The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) , Maire et al. (2015) , Mouillot et al. (2013) , Mouillot et al. (2014) , Ricotta and Szeidl (2009) . Graphical outputs are included. Visit the 'mFD' website for more information, documentation and examples. Package: r-cran-mfdb Architecture: all Version: 7.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-logging, r-cran-dbi, r-cran-duckdb, r-cran-getpass, r-cran-rlang Suggests: r-cran-dplyr, r-cran-dbplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-unittest Filename: pool/dists/noble/main/r-cran-mfdb_7.4-0-1.ca2404.1_all.deb Size: 616508 MD5sum: 43e8dd98b3b3b9a816169acf419f31f9 SHA1: 52f4a63e926b4409172cfb3d6b2fac955bf58994 SHA256: 0c68d5eda23a835ab4829522d3ccd7c0cbecd4ce360875f1b73c7ea7cbe313a1 SHA512: 7ea109dfd8210ab45383e55edb4bfda0faac2329276b281ac350216829c3ee37569d9ee63a3e31b668fa455089bc8df52a7b770705cab735c37a11b70a25b72d Homepage: https://cran.r-project.org/package=mfdb Description: CRAN Package 'mfdb' (MareFrame DB Querying Library) Creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget model. See for more information. Package: r-cran-mfdfa Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers Filename: pool/dists/noble/main/r-cran-mfdfa_1.1-1.ca2404.1_all.deb Size: 39804 MD5sum: cec4443c0e44df16a0165ca597f99c58 SHA1: 87abef80fb86ebd36d73cf1f8c02b99ceac6a77f SHA256: dd287866902e5d2c22c63db8b024da646ca9e932b8cd45dfc9a12ec60543f8a9 SHA512: 8d4829352061fe0b5bbb2bd925a89e10e9bdfc7daa44371fbd544f6c3add4036d98deed89e1d6cf30502871bcdbd7f2d30a50181adb54330835616411722c177 Homepage: https://cran.r-project.org/package=MFDFA Description: CRAN Package 'MFDFA' (MultiFractal Detrended Fluctuation Analysis) Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. ( and ). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub. Package: r-cran-mfdp Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mfdp_0.2.2-1.ca2404.1_all.deb Size: 29254 MD5sum: 8c72aa5756f2f2d386e8e05fd725f16f SHA1: e5b9d3bc9be72166e0f815e51c1af64ddec54c49 SHA256: 6ff5afcc6005fe6d24f47eb8daece88e0e4f0042486dd81be67ae30296a8001e SHA512: 8fccae5494a38c43e94963f573de70f188a7a2df16bed712b20615004a21465d5625adf21e208be7e5cbf89a70cd37fa7584564b989905dc352ecb29887b121a Homepage: https://cran.r-project.org/package=mFDP Description: CRAN Package 'mFDP' (Control of the Median of the FDP) Methods for controlling the median of the false discovery proportion (mFDP). Depending on the method, simultaneous or non-simultaneous inference is provided. The methods take a vector of p-values or test statistics as input. 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The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) , Possibilistic FCM (PFCM) Tak (2021) , Gustafson–Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via 'glmnet', random forests, gradient boosting with 'xgboost' and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships. Package: r-cran-mfilter Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tseries, r-cran-pastecs, r-cran-locfit, r-cran-tserieschaos, r-cran-forecast Filename: pool/dists/noble/main/r-cran-mfilter_0.1-8-1.ca2404.1_all.deb Size: 293462 MD5sum: 93a13fdf80840aeb56401995790fca57 SHA1: bab33085a6b4c5547ff876e8e04599f08a262a36 SHA256: 6132901caea71cb7128240ca0b921ac48c5915d40d75ed3bae0721075776a3ce SHA512: 0d8515500e2759117b6410ffee96935990e4320e3ac93c555e01ee804b8b90238e9da04663b532e39bb2be1988c889839320c2b5d7834534467eb3c5c27e3fdb Homepage: https://cran.r-project.org/package=mFilter Description: CRAN Package 'mFilter' (Miscellaneous Time Series Filters) The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package. Package: r-cran-mflica Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-mflica_0.1.7-1.ca2404.1_all.deb Size: 609310 MD5sum: 8b1797f15b0267ec1b26d0a647e04c92 SHA1: 354cfc59e7105eac68c060e9595577fcecda49ec SHA256: f61cf4661e5f0c7917854b0fc7161a3be7529807b10d126a8c02fe3e8b2be5e3 SHA512: e883457f887f7f92c8fe5930e4b33114e0d1243146a32f58fc290697e825dbdd29de81b9dbb593f7f45c67e35ec4816101b6ef3f357b25ad1c45601deda3c855 Homepage: https://cran.r-project.org/package=mFLICA Description: CRAN Package 'mFLICA' (Leadership-Inference Framework for Multivariate Time Series) A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) for methodology and Chainarong Amornbunchornvej (2021) for software when referring to this package in publications. Package: r-cran-mfo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-minpack.lm, r-cran-openxlsx, r-cran-readxl, r-cran-stringr, r-cran-tidyr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfo_0.1.0-1.ca2404.1_all.deb Size: 146724 MD5sum: ba36e97e1000d76ec680e67d48a93586 SHA1: e45c257274ff3adfa09563ca41dd68e4d8877c40 SHA256: 1332ae5d6727409d664cd24583f61544c8659e6e59708f95fde27dd6a92f4249 SHA512: b1f5039d893617f00e6516f74c57460e6808aa2be343a54a462c102a5f211875796e3acbb30fed883c01bbea5c3ac717201f3466eaebd1e2d240190cdf371efd Homepage: https://cran.r-project.org/package=MFO Description: CRAN Package 'MFO' (Maximal Fat Oxidation and Kinetics Calculation) Calculate the maximal fat oxidation, the exercise intensity that elicits the maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. Three variables can be obtained from the SIN model: dilatation, symmetry and translation. Examples of these methods can be found in Montes de Oca et al (2021) and Chenevière et al. (2009) . Package: r-cran-mfp2 Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival Suggests: r-cran-knitr, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown, r-cran-formatr, r-cran-patchwork, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mfp2_1.0.1-1.ca2404.1_all.deb Size: 788024 MD5sum: a4684918bb3dad07330263a1b7125a16 SHA1: b9a331331bf3d7436e09778b974c4a7def2fef6c SHA256: 4efd7e56cc566597ce4bd77646db29ab5d0bc9d2dbb1f74ae43129d488e9c20d SHA512: a5a23e0252ac900109755c3c840a0c63c99da308ee93de21687e322ce7cd62715889e08334ae1bbee6f2b446ec193f1cc54e981eb0dfee5b9a9c7e04d74dc76a Homepage: https://cran.r-project.org/package=mfp2 Description: CRAN Package 'mfp2' (Multivariable Fractional Polynomial Models with Extensions) Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references are Royston and Altman (1994) and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) . This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling. Package: r-cran-mfp Architecture: all Version: 1.5.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mfp_1.5.5.1-1.ca2404.1_all.deb Size: 143828 MD5sum: b2a8f084da6cb232d1af1a71e7537e88 SHA1: ea546f9bc7eaedac53341d121dbe5957b1971868 SHA256: 2fdad2eb9a68e7e33574f96ee5ea305aba943e3dca8ca1f03da6e894c286c975 SHA512: 1a29a9d80df16d2594aaa6d9dd9772953f552430cf4068cdc35d154735b499bcab869b61a7e1bdcfceb4435314cc692dd51e5ccd2df5b23d224e3bbc21ea0f71 Homepage: https://cran.r-project.org/package=mfp Description: CRAN Package 'mfp' (Multivariable Fractional Polynomials) Multivariable Fractional Polynomial algorithm for model-building. Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994. Package: r-cran-mfpp Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-nsga2r, r-cran-igraph, r-cran-genalg, r-cran-ggplot2, r-cran-reshape2, r-cran-rfast Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-matrix Filename: pool/dists/noble/main/r-cran-mfpp_0.0.9-1.ca2404.1_all.deb Size: 678554 MD5sum: 90042ca61f6c8b72ba84c17328c4ee93 SHA1: 34577eb783f611ec0ad131392c572ad120c642af SHA256: 4275250c83f52ad2f8151edecd146dec6037fe2ae1484b3735335cabe653593b SHA512: ce37c50b4bc685cd3ac7bc266c8afa2d07870cbf64eddd0d0b3989cc492063ae4bf526e03fb5935c47ab95d21e5da37c1a4a284fe7473f77c57b17453fab84a0 Homepage: https://cran.r-project.org/package=mfpp Description: CRAN Package 'mfpp' ('Matrix-Based Flexible Project Planning') Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) . Package: r-cran-mfrcd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mixedfact, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfrcd_0.1.1-1.ca2404.1_all.deb Size: 178178 MD5sum: 0ec75b278ac39e29f8966de4b11f2e94 SHA1: eb9c08daa34255ae017be5b2f8d82a3d17c777cf SHA256: f167502b3f75d7cf93cde2a4f911c2c4aa4a5dd9db08177419d3274785fd3aa0 SHA512: bd9250cf9aa380b1c10a4f6efb4dee4e2e805272d3d2e7accc2e6182af6eda34afaeffe963f5f82f606f774e2514afa6ef5f766d7058bf1757c62ec03cfc2e41 Homepage: https://cran.r-project.org/package=MFRCD Description: CRAN Package 'MFRCD' (Optimal Row-Column Designs for Asymmetrical FactorialExperiments) Constructs and analyzes optimal row-column designs for mixed-level factorial experiments under square and rectangular field layouts. For square field layouts, the package implements direct common-factor constructions by first forming two component treatment arrays, one for each factor or super-factor, and then combining them through a symbolic cell-wise product following Gopinath, Parsad and Mandal (2018) . For rectangular field layouts, the package constructs designs by extracting a balanced principal block from a mixed-level block design, treating it as the principal column, taking the complete treatment set as the principal row, and generating the full row-column design by cyclic modular development. The package also includes repair utilities for improving disconnected or partially connected row-column designs through bounded treatment-swap searches while preserving the row-column layout structure. The package provides diagnostic tools for connectedness, orthogonal factorial structure, balance, estimability, and selected optimality criteria for row-column designs. Package: r-cran-mfrmr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-psych, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mfrmr_0.1.4-1.ca2404.1_all.deb Size: 2344734 MD5sum: a0969a7290cf8b95bfa82d7c16e0fe12 SHA1: 3a8e00e357921b0eccd41aaf9d1488840ce51a30 SHA256: 23aa835efbee4f93dec7454a439ac1af8fe41184011c29457a59b5c63286e4d6 SHA512: a5f0455c6ed15f582cd1c8d6577325f702865f1eccf8464fde41652ab47a002eba77d25fa336a4641e6cbfeb87f663c01e9fc9da28b9e42002ac3ddee8f4583e Homepage: https://cran.r-project.org/package=mfrmr Description: CRAN Package 'mfrmr' (Estimation and Diagnostics for Many-Facet Measurement Models) Fits many-facet measurement models and returns diagnostics, reporting helpers, and reproducible analysis bundles using a native R implementation. Supports arbitrary facet counts, rating-scale and partial-credit parameterizations ('Andrich' (1978) ; 'Masters' (1982) ), marginal maximum likelihood estimation via the EM algorithm ('Bock' and 'Aitkin' (1981) ) and joint maximum likelihood estimation, plus tools for anchor review, interaction screening, linking workflows, and publication-ready summaries. Package: r-cran-mfsis Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-ball, r-cran-reticulate, r-cran-crayon, r-cran-cli, r-cran-dr, r-cran-foreach, r-cran-doparallel, r-cran-fs Suggests: r-cran-knitr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-mfsis_0.3.1-1.ca2404.1_all.deb Size: 172184 MD5sum: 4ca30c49a45beae0a47a57c8d475e784 SHA1: 6501d6962268ac0c579118799f1432d6b103c6f6 SHA256: a9fc63084e7009dc57cbb663598b07ba3d8840fcffadb1969c873adb9bb83276 SHA512: b900974e51a43dc407bb118269020ed2fadc80bea455fa4b9e4b95a96b99ca2d027eacb956a475dd9a4adbd4997bf9439e9f73a5ca1d50746712a692b78a0b20 Homepage: https://cran.r-project.org/package=MFSIS Description: CRAN Package 'MFSIS' (Model-Free Sure Independent Screening Procedures) An implementation of popular screening methods that are commonly employed in ultra-high and high dimensional data. Through this publicly available package, we provide a unified framework to carry out model-free screening procedures including SIS (Fan and Lv (2008) ), SIRS(Zhu et al. (2011)), DC-SIS (Li et al. (2012) ), MDC-SIS(Shao and Zhang (2014) ), Bcor-SIS (Pan et al. (2019) ), PC-Screen (Liu et al. (2020) ), WLS (Zhong et al.(2021) ), Kfilter (Mai and Zou (2015) ), MVSIS (Cui et al. (2015) ), PSIS (Pan et al. (2016) ), CAS (Xie et al. (2020) ), CI-SIS (Cheng and Wang. (2023) ), CSIS (Cheng et al. (2024) ) and Log-rank SIS. Package: r-cran-mft Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mft_3.1-1.ca2404.1_all.deb Size: 180076 MD5sum: b08a502296c2faef3844d35bc5a37d62 SHA1: cf6aa46f6c82584e42ecdf5fd7770c28331ae9ec SHA256: f35f55425c27971a5d33fc7c379bdf516532bfaef9a8d81cca072d8a2fe371d7 SHA512: 176b0c8dd0138673f5c39d8c5590931e2e48098eb61421c16c6bed87fdf3b652f95c58ecc350aadb4792ac21e461039618c22ca0bacabbcf5222b85e3486efd4 Homepage: https://cran.r-project.org/package=MFT Description: CRAN Package 'MFT' (The Multiple Filter Test for Change Point Detection) Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance . Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points . Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies . Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series . Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2020) A multi-scale approach for testing and detecting peaks in time series . Package: r-cran-mfusampler Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-coda, r-cran-dlm Suggests: r-cran-sns, r-cran-rcpparmadillo, r-cran-inline, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mfusampler_1.1.0-1.ca2404.1_all.deb Size: 273766 MD5sum: fac7c5d893af502a04a152ac42a51803 SHA1: 09ce134761568f20d8be75c7e2911acd9488df28 SHA256: 703e372ba1d42433f82ddbee46abf74a6efe243f2b6cdcd79bc6d15a91962d42 SHA512: 7276b0f8d3205c38186a87fea97c5416883989be08d2305ce20e8dd0cc37a5b88bd22ed373560f84099dc46bf07ab6c9542fce78fa5eb2d8de4dbc735fe16af8 Homepage: https://cran.r-project.org/package=MfUSampler Description: CRAN Package 'MfUSampler' (Multivariate-from-Univariate (MfU) MCMC Sampler) Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal (2003) ), adaptive rejection sampler (Gilks and Wild (1992) ), adaptive rejection Metropolis (Gilks et al (1995) ), and univariate Metropolis with Gaussian proposal. Package: r-cran-mfx Architecture: all Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest, r-cran-mass, r-cran-betareg Filename: pool/dists/noble/main/r-cran-mfx_1.2-4-1.ca2404.1_all.deb Size: 261038 MD5sum: 03c2e14e9214e5ea2947fcde1704a385 SHA1: fd398fa58266c9e1205a12446cf628f3c95f4251 SHA256: b3741e2b174f5e6c345937ea42a02f90728d6ed4eed1bc109ecd2272fd961f0b SHA512: fcc4f3559b76a6cdd1fa01c180b3286bf325fb1bb98ff5d9856f6959cfbb982858b9e5f65c0f5800cb40dea078aa0bc47431ae487508f9435b3a4bc74aa15c7e Homepage: https://cran.r-project.org/package=mfx Description: CRAN Package 'mfx' (Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs) Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic. Package: r-cran-mg1stationaryprobability Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-memoise Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mg1stationaryprobability_0.1.2-1.ca2404.1_all.deb Size: 117088 MD5sum: 9482da484d2a381a5625580373a21302 SHA1: 1369210c52d8bbfb4fce3cb874e852bb1b4c62a1 SHA256: 8aea5bfd6f0d6162cda9d6cbe8e7061b829d286ab500b2e3879abc8c64c54055 SHA512: 7fbba378afc4b8a631fb8bb1348334e0de2709b7e37e841b73ec47701366bf73d6e6830221507b93cba33db6f73969c65e0efec2420b123d9353373187a5eac0 Homepage: https://cran.r-project.org/package=MG1StationaryProbability Description: CRAN Package 'MG1StationaryProbability' (Computes Stationary Distribution for M/G/1 Queuing System) The idea of a computational algorithm described in the article by Andronov M. et al. (2022) . The purpose of this package is to automate computations for a Markov-Modulated M/G/1 queuing system with alternating Poisson flow of arrivals. It offers a set of functions to calculate various mean indices of the system, including mean flow intensity, mean service busy and idle times, and the system's stationary probability. Package: r-cran-mgbt Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2460 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dataretrieval, r-cran-lmomco Filename: pool/dists/noble/main/r-cran-mgbt_1.1.8-1.ca2404.1_all.deb Size: 1470770 MD5sum: 3592d1169cc5a44121baf8818aca7aef SHA1: 8f06126587e1f832fa55e99f962913d238526946 SHA256: f3ba0edfb0a046cc4ffb9180b276e00897b5dc18c4faabc329daf1ff5d89195a SHA512: 5d725b48c8fe846b37d744589e7d96a2f17d643f1276d27a87bfa9963cd063d146369627d1bdf2724230d83a23b66182a7dc6411727564df36f4db87d4347be9 Homepage: https://cran.r-project.org/package=MGBT Description: CRAN Package 'MGBT' (Multiple Grubbs-Beck Low-Outlier Test) Compute the multiple Grubbs-Beck low-outlier test on positively distributed data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow data processing discussed in Cohn et al. (2013) and England et al. (2017) . Other utilities for working with peak streamflow are provided. 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The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'. Package: r-cran-mgdrive2 Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3812 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-desolve, r-cran-statmod Suggests: r-cran-mgdrive, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mgdrive2_2.1.1-1.ca2404.1_all.deb Size: 2496048 MD5sum: 170cbbf9f16850af454c037c42d88ab4 SHA1: 1299e36d1d271563d04864f3520fb05bb777f7ff SHA256: ce0a5529212e06fec645f09970910ddd0b551518a9fd383f90dc319298e52b35 SHA512: d64635558d4dc38bb3ed7ee16b2b5b0e2a0131df5a222207a869b1f9339c696341cf5503074d61a351e2272f024a8cc9fcf54ecdfad8f4166d625f30c1773ce4 Homepage: https://cran.r-project.org/package=MGDrivE2 Description: CRAN Package 'MGDrivE2' (Mosquito Gene Drive Explorer 2) A simulation modeling framework which significantly extends capabilities from the 'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets. For more information about 'MGDrivE', see our publication: Sánchez et al. (2019) Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. 'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we suggest installing that package initially. Package: r-cran-mggd Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-rgl, r-cran-lifecycle, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mggd_1.3.7-1.ca2404.1_all.deb Size: 77518 MD5sum: efb16ebeee6fbbae96c4d506002b3fa4 SHA1: 0364eb388cb46ca5303095874a0cdcdc50c11d7e SHA256: f71953f40abc22ae3f942162782c28b96aef643f91a0bc9cf1d2d946b7bd4199 SHA512: 4c0ee04b5c6e430d174c759d509a4c428a7585c5d399fc31ae03cd68d6c835c0b461437c1397aa0f9f75f3e307df046639d300f90a11283b7a797dd64b016b4c Homepage: https://cran.r-project.org/package=mggd Description: CRAN Package 'mggd' (Multivariate Generalised Gaussian Distribution; Kullback-LeiblerDivergence) Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) . Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) and Pascal, Bombrun, Tourneret and Berthoumieu (2013) ). Package: r-cran-mgi.report.reader Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-httr2, r-cran-memoise, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-vroom Suggests: r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-mgi.report.reader_0.1.3-1.ca2404.1_all.deb Size: 155818 MD5sum: d52bdfcb1d8c895c2293040101a02db1 SHA1: 4b76b5991bb61d992ab7b1fa93c1eb45f8ae83d7 SHA256: 17a6f24dbb6f0b576ee9b4e84626bcc0974303a17b238524473211da1be7ec6d SHA512: 1521e5fd8b2a97a2fb597e4056e3ec9db95c9cc213a6e6e59280e748c63a8eb321ad50c115f31cf763d255661db14f46626201e0261b6d2e1e3685f6ec2b7b7d Homepage: https://cran.r-project.org/package=mgi.report.reader Description: CRAN Package 'mgi.report.reader' (Read Mouse Genome Informatics Reports) Provides readers for easy and consistent importing of Mouse Genome Informatics (MGI) report files: . These data are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ, Mouse Genome Informatics Group (2024) . Package: r-cran-mglasso Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-matrix, r-cran-r.utils, r-cran-reticulate, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mglasso_0.1.2-1.ca2404.1_all.deb Size: 70438 MD5sum: 826421155c17b9651bc78f47d0baa92c SHA1: 29737dbf7d83b8946db388a9a1aa1866bd23b757 SHA256: 0250e9ed44b24a881a1d974e5ec2ad3dbc2d54b16f2fa2b659a5d1a8ebbe0205 SHA512: 51be3415cb4db6d7d819d3b11476831f5078963864a153609c62e5c7af0c85c080336061f407e366e00747837717f6fbf564a493ed6c11e40a2eaf775254a2fc Homepage: https://cran.r-project.org/package=mglasso Description: CRAN Package 'mglasso' (Multiscale Graphical Lasso) Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) implemented in python. Package: r-cran-mglm Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mglm_0.2.3-1.ca2404.1_all.deb Size: 443740 MD5sum: ccd93604482eaf2682236d660085ee34 SHA1: 34a2dbd1a47c8990f88d9454f3574e551aa17891 SHA256: ce47c38436e51781af5c991681c8325924ed36015e6c1f147fe9bdd91d710e5e SHA512: 72e6c337ee46b816ac10bdabf1f1fba186fc48ba9cde6d597122d27487ec98adbb270531440ac96a44793627cbf848e2fb90bd3a81af2cc8397063424aacdea0 Homepage: https://cran.r-project.org/package=MGLM Description: CRAN Package 'MGLM' (Multivariate Response Generalized Linear Models) Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported. Package: r-cran-mglmn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvabund, r-cran-snowfall Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mglmn_0.1.0-1.ca2404.1_all.deb Size: 49374 MD5sum: 3d8d3f034be2b8ee6a23630269be27eb SHA1: 6df1dd5f94fea84fa5e42d7b23392ac8d61fbd7e SHA256: e3b0c68417a7175088394269f858fafb43ec54edcf945284ccb12163645bc105 SHA512: 8258c8f5b7fe64d21f843ad15e33083aadad226081a566dc310eb52da9778e5885902edc317f82082f13145815e7d8a22863633955b050886a82622a0d2899bc Homepage: https://cran.r-project.org/package=mglmn Description: CRAN Package 'mglmn' (Model Averaging for Multivariate GLM with Null Models) Tools for univariate and multivariate generalized linear models with model averaging and null model technique. Package: r-cran-mgm Architecture: all Version: 1.2-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-stringr, r-cran-hmisc, r-cran-qgraph, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgm_1.2-15-1.ca2404.1_all.deb Size: 923102 MD5sum: 78dd4a601a39c0117ce1b779005b0dc4 SHA1: 875434aec75352ac5fbf36e649ebc6b405acf835 SHA256: 45dbad6da32fd7bdb7096ee83c1cbe3f0d170f8462cea12bddca065f6cbea2a3 SHA512: 757c24d3fa2964f50984b229fa5b2ceb3086be94f98cc183bca2efb8df73ac605a3ccd3ad74307bb70d36a957c9a0b0ced71486fac88f990be87c91029407f54 Homepage: https://cran.r-project.org/package=mgm Description: CRAN Package 'mgm' (Estimating Time-Varying k-Order Mixed Graphical Models) Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) . Package: r-cran-mgms2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maldiquant, r-cran-maldiquantforeign Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgms2_1.0.2-1.ca2404.1_all.deb Size: 3487662 MD5sum: 3f572d05d3b6ba8abd0dad9491713e11 SHA1: dbfd0cc8300cd8fa182143e68cb69cb38b345f38 SHA256: e4f1cd3dc547ac4d5f401e6fdf033b596de0bb39d392a2089e667ba9fe4249cc SHA512: 19e1933381884661dd4e50e5cb906ce7bd41a2773558c0d06e0e14ff8fae5728e8cce476cd6d390acd8f720c482f4bbe55cc16769b6388fce9a0de74f1271717 Homepage: https://cran.r-project.org/package=MGMS2 Description: CRAN Package 'MGMS2' ('MGMS2' for Polymicrobial Samples) A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) "MALDIquant: a versatile R package for the analysis of mass spectrometry data." Package: r-cran-mgpsdk Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgpsdk_1.0.0-1.ca2404.1_all.deb Size: 78154 MD5sum: 4048d8500bbcf6680e9ce0fcdf1eeae2 SHA1: 138c048f5cf1b2a087e38074a353c519b91305fd SHA256: 8734659520f4bffe9dbaf74d13892ab91a0971fef677c84d81719fe2d023a875 SHA512: c5c0c840e16407b40240a4351d2a40a2e98f399d828ab32aa65d5450a49519565a4d07bc5c4630b4e29acf59ceb221d84c032824f7ba671aeced62283c40381d Homepage: https://cran.r-project.org/package=MGPSDK Description: CRAN Package 'MGPSDK' (Interact with the Maxar 'MGP' Application Programming Interfaces) Provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS) Open Geospatial Consortium (OGC) methods. Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON. Package: r-cran-mgpstreamingsdk Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgpstreamingsdk_0.2.0-1.ca2404.1_all.deb Size: 59490 MD5sum: 7c1c7effe9fb5145c2d1a25d1dce623e SHA1: 9c009f8fb66013b4d438874af32aa463a1f53dc4 SHA256: 5be6a27bc9c6497c7b4faa0e67125b29b1990b27ab44f9b4730e72967910fb20 SHA512: b4f91f4aa552b0b48ba63eaac21fc3da70303a61c3e4ae4fd52e6ac790d959fb95a9d0161c757cf4f2ac7b1ed93a4ac2491a66ff00ed390f5755827b699054df Homepage: https://cran.r-project.org/package=mgpStreamingSDK Description: CRAN Package 'mgpStreamingSDK' (Interact with the Maxar MGP Streaming API) This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image. Package: r-cran-mgsub Architecture: all Version: 1.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgsub_1.7.3-1.ca2404.1_all.deb Size: 38070 MD5sum: a4c6dc438f2a6733ace78251b29b5c28 SHA1: 6421f9068e90b7c499072667d034d83083cc47b4 SHA256: effe44292bd115d11cab42da4cd8c25fce41d2b13eecd36a1bb37ff4d466118b SHA512: a91463a1749fc68780b100919fd59a05e2dcfab65889c1685a7387a9f6c3bc91ca0081d5a576d1c4a7df8fd8d02301154e364fba5b9b8a4183bfd489f8881dcf Homepage: https://cran.r-project.org/package=mgsub Description: CRAN Package 'mgsub' (Safe, Multiple, Simultaneous String Substitution) Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches). Package: r-cran-mgwnbr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-mgwnbr_0.3.0-1.ca2404.1_all.deb Size: 110998 MD5sum: 3b958e9fbd64f57715784e73f601c2a1 SHA1: 3bb290ad09de9f561d5aa50297f07eba6dc3581d SHA256: 5180648661d4d61aa2148f33c38309baaaeb7a60e089a8d8b05b88ec975f7666 SHA512: 2d4117e74c18335377d3d7403030f113ba7afb55052d59f0ae142805fc7fe5010b5260a9a9fca5ba199a4b676884e36e49802e59b6409076d2b5a310e67f56db Homepage: https://cran.r-project.org/package=mgwnbr Description: CRAN Package 'mgwnbr' (Multiscale Geographically Weighted Negative Binomial Regression) Fits a geographically weighted regression model with different scales for each covariate. Uses the negative binomial distribution as default, but also accepts the normal, Poisson, or logistic distributions. Can fit the global versions of each regression and also the geographically weighted alternatives with only one scale, since they are all particular cases of the multiscale approach. Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers . Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers . Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing . Package: r-cran-mgwrhw Architecture: all Version: 1.1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spgwr, r-cran-sf, r-cran-psych, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgwrhw_1.1.1.5-1.ca2404.1_all.deb Size: 924984 MD5sum: 883afe3e377a136b5d69a9ac8f15aa26 SHA1: 9c85b54db088490da7db2c7dc2ee02a70e57ce5b SHA256: 097d544252fa9508cefb68567feb390110a827f18f8bef71dc45c3b8b2d885e6 SHA512: 59b7970e1c45e108fbaff13261149bc97b8788ca3486d343507e1a5be2d7c468af60987c03cd076d269333705ce8b92696287e901b00b6eccf22f54a54e8f134 Homepage: https://cran.r-project.org/package=mgwrhw Description: CRAN Package 'mgwrhw' (Displays GWR (Geographically Weighted Regression) and Mixed GWROutput and Map) Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196. . Package: r-cran-mhcnuggetsr Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-devtools, r-cran-dplyr, r-cran-rappdirs, r-cran-reticulate, r-cran-stringr, r-cran-tibble Suggests: r-cran-beautier, r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mhcnuggetsr_1.2.6-1.ca2404.1_all.deb Size: 146276 MD5sum: 273bbc8163323d9faece64fdb29d27f6 SHA1: 01578ab933b8bbf1ea9a3173ae8d092ffe4ee8e0 SHA256: 4f581edea7290ea5189229715ae7d2d866e24b3a5fc72137d2035fe5846ad5ba SHA512: d2cb90aff39c2dc572e0c28f72674e5167db3cbf4c9b885fa6856ae4faee0a1ee90ad1c76895984e3965a8e8cc8d9ef3bf98b2228ac44531fe94265b022427f5 Homepage: https://cran.r-project.org/package=mhcnuggetsr Description: CRAN Package 'mhcnuggetsr' (Call MHCnuggets) MHCnuggets () is a Python tool to predict MHC class I and MHC class II epitopes. This package allows one to call MHCnuggets from R. Package: r-cran-mhctools Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-mhctools_1.6.0-1.ca2404.1_all.deb Size: 256964 MD5sum: 6754a8d8b2a2604984e399f114a3c96e SHA1: 8abf4cf45d436990a628c8175f79900960a6e2ef SHA256: 852c05801e04f85eb71a003fe23ba2153162305ceec9d29286367aace48a9850 SHA512: 63896927532d27d0b4dccbf7cd45b85412f31c0b0f33f2a003d5a4244d83fa93c97c66b33ea71dc07402db627a5f86b76f76f907b985e53f8bfac3b83eee026a Homepage: https://cran.r-project.org/package=MHCtools Description: CRAN Package 'MHCtools' (Analysis of MHC Data in Non-Model Species) Sixteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the 'stats' package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analyzed if the descriptions of the required data formats are observed carefully. The SynDist() function analyses of synonymous variation among aligned protein-coding DNA sequences, that is, nucleotide substitutions that do not translate to changes in the amino acid sequences due to degeneracy of the genetic code. The SynDist() function calculates synonymous nucleotide changes per base and per codon in pairwise sequence comparisons, as well as mean synonymous variation among all pairwise comparisons of the sequences within each sample in a data set. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview of which sequences are present in which haplotypes. The HpltMatch() function compares haplotypes to help identify overlapping and potentially identical types. The NestTablesXL() function translates the output from HpltFind() to an Excel workbook, that provides a convenient overview for evaluation and curating of the inferred putative haplotypes. 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Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) . 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It is well known that the p values come from different distribution for null and alternatives, in this package we provide functions to detect that change. We provide a method for using the change in distribution of p values as a way to detect the true signals in the data. 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Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users' convenience, the functions also provide the output option for printing decision rules. 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Derives the optimal per-test significance level from the economic incentives of research production, providing a correction that lies between Bonferroni (too conservative) and unadjusted (too permissive). Supports two cost models: a Linear one calibrated to United States Food and Drug Administration (FDA) clinical-trial costs, and a Cobb-Douglas one calibrated to Abdul Latif Jameel Poverty Action Lab (J-PAL) project costs. Reports optimal, Bonferroni, Holm, Benjamini-Hochberg (BH), and unadjusted results side by side. 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We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. 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This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; ). 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Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) . 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'Mica' helps studies to provide scientifically robust data visibility and web presence without significant information technology effort. 'Mica' provides a structured description of consortia, studies, annotated and searchable data dictionaries, and data access request management. This 'Mica' client allows to perform data extraction for reporting purposes. 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Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) ; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) ; Witte J, Foraita R, Didelez V (2022) . 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A large variety of repeated statistical analysis can be performed and finally pooled. Statistical analysis that are available are, among others, Levene's test, Odds and Risk Ratios, One sample proportions, difference between proportions and linear and logistic regression models. Functions can also be used in combination with the Pipe operator. More and more statistical analyses and pooling functions will be added over time. Heymans (2007) . Eekhout (2017) . Wiel (2009) . Marshall (2009) . Sidi (2021) . Lott (2018) . Grund (2021) . 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The econometric estimation can be done by the Kmenta approximation, or non-linear least-squares using various gradient-based or global optimisation algorithms. Some of these algorithms can constrain the parameters to certain ranges, e.g. economically meaningful values. Furthermore, the non-linear least-squares estimation can be combined with a grid-search for the rho-parameter(s). The estimation methods are described in Henningsen et al. (2021) . 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Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results. 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Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel. 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His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"), but he also wrote many more works, including Rodange, Tockert (1928) ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the plain text versions of his works available on . 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Package: r-cran-microbial Architecture: all Version: 0.0.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plyr, r-cran-magrittr, r-cran-broom, r-bioc-phyloseq, r-cran-vegan, r-cran-rlang, r-cran-ggplot2, r-cran-ggpubr, r-bioc-deseq2, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-rstatix, r-cran-tidyr, r-cran-phangorn, r-cran-randomforest, r-bioc-edger Suggests: r-cran-markdown, r-bioc-dada2, r-cran-rmarkdown, r-cran-knitr, r-bioc-biostrings, r-bioc-decipher, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microbial_0.0.22-1.ca2404.1_all.deb Size: 945802 MD5sum: 54138c8a9bf9fdf0acc45f7cfe1f3709 SHA1: b89d222f55f9b31b6000f840d855d1e2457acbf9 SHA256: 16141237c1eab19103de49fefed50fcfa850aa5280898ba8aaf496c0aa6e9ef1 SHA512: e10f50dc7dd23d3d15017ff96cd4fc31b31b11d6ffe08eb82bc7c48449339dc67bf8012f252f2a3e1f114106f36078e17891d914eea8a3af1fdb42cfbdb0d053 Homepage: https://cran.r-project.org/package=microbial Description: CRAN Package 'microbial' (Do 16s Data Analysis and Generate Figures) Provides functions to enhance the available statistical analysis procedures in R by providing simple functions to analysis and visualize the 16S rRNA data.Here we present a tutorial with minimum working examples to demonstrate usage and dependencies. Package: r-cran-microbialgrowth Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlstools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microbialgrowth_1.0.0-1.ca2404.1_all.deb Size: 774540 MD5sum: 9d3e5981fe6d359418d543b8efd0044c SHA1: b68d37116d4419d2581f4994a9ace433160fb1f4 SHA256: b43a1e7f36925ae61b4a7e671b84379bdaeafcf1fda84d8cd22ea2a213fd0a22 SHA512: 50f874d67d9cc7b90c54dcda0266b0e0d94b57ddd58e02431693c68e8cf75f614c130943a29211896f52176702c6e5649c9b2492159c2b6f72b5b897219b2de1 Homepage: https://cran.r-project.org/package=MicrobialGrowth Description: CRAN Package 'MicrobialGrowth' (Estimates Growth Parameters from Models and Plots the Curve) Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) ), Baranyi model (Baranyi and Roberts (1994) ), Rosso model (Rosso et al. (1993) ) and linear model (Dantigny (2005) ). Package: r-cran-microbiomemqc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-vegan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microbiomemqc_1.0.2-1.ca2404.1_all.deb Size: 21096 MD5sum: a521708365d3076f40060884e040ba88 SHA1: bf5eb999a51cf86dff57d51de7ef082e9defcb56 SHA256: 02466c496eb6ad50e8d03a8018ba2f6092bcfb71eb1955cece05d7745bc95e28 SHA512: 725cafb1831de1c55f48ed0999968e750b7923bd451357e8f42016a30e08e7536509dedd40a2a94d4cfe6cd5f809c68089885992499493ccbb970a09f2815ea9 Homepage: https://cran.r-project.org/package=microbiomeMQC Description: CRAN Package 'microbiomeMQC' (Calculate 4 Key Reporting Measures) Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent. Package: r-cran-microbiomesurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-survminer, r-cran-glmnet, r-cran-superpc, r-cran-lmtest, r-cran-gplots, r-cran-tidyr, r-cran-dplyr, r-bioc-microbiome, r-cran-pls Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microbiomesurv_0.1.0-1.ca2404.1_all.deb Size: 754706 MD5sum: b5608939581f1d0d1a7dc88d1d74265d SHA1: e79c5774189b6f247dd0d8bd5a5f77886868f8ff SHA256: 538f5baa21c23f8f7ec030be8b85556e36fc454314d21764a5f950eb79ac97a0 SHA512: 222caabc7710a5cbce7a3931a6ee38cd82e2760baba38dc3437c272739b0e04ca2c840501670afafcb10ba1424bb0bc518e7c26f83d9c84bb40111922b51649d Homepage: https://cran.r-project.org/package=MicrobiomeSurv Description: CRAN Package 'MicrobiomeSurv' (Biomarker Validation for Microbiome-Based SurvivalClassification and Prediction) An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) , Elastic net approach by Hui Zou and Trevor Hastie (2005) , supervised principle component analysis of Wold Svante et al. (1987) , and supervised partial least squares analysis by Inge S. Helland . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique. Package: r-cran-microbtisda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5839 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cluster, r-cran-dplyr, r-cran-ggdendro, r-cran-ggplot2, r-cran-ggraph, r-cran-mass, r-cran-mgcv, r-cran-plyr, r-cran-pracma, r-cran-randomforest, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vegan, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microbtisda_0.1.0-1.ca2404.1_all.deb Size: 5365596 MD5sum: feda050e8c0888e2acfb309e2a4a3c0d SHA1: 545bee35268230aa76b5613def65a8f076aeb924 SHA256: 5f87f4e63c8276759b699240edbb74c835cd6bdf081aca6901c2a258e67a68f9 SHA512: a74080a4d486633785e36632a753002008a7fc6296ad7431c782875167a1b7e1f31041b6633ef33d5ca3598a4c0a36cdc641c9eb2b997f069afe7db8489c4582 Homepage: https://cran.r-project.org/package=MicrobTiSDA Description: CRAN Package 'MicrobTiSDA' (Microbiome Time-Series Data Analysis) Provides tools specifically designed for analyzing longitudinal microbiome data. This tool integrates seven functional modules, providing a systematic framework for microbiome time-series analysis. For more details on inferences involving interspecies interactions see Fisher (2014) . Details on this package are also described in an unpublished manuscript. Package: r-cran-microcontax Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-microseq Filename: pool/dists/noble/main/r-cran-microcontax_1.2-1.ca2404.1_all.deb Size: 2649814 MD5sum: c9d03160fcb89b0b7204efaca1a4ca03 SHA1: baf4229e1959da3c2ceede38f9568c7d4afaa232 SHA256: 552cc07ac8a333c81debf3e52ee1a072d70be97612bf5faf29a26ce75af8099c SHA512: 5406af99940fd5c0bdf80cc67ada9d7825e94b8636c19585194087d2a54822f12e5cc71ebf93c44ee8369127636ff8ce4adf86f60a41e7dabb3192e834404514 Homepage: https://cran.r-project.org/package=microcontax Description: CRAN Package 'microcontax' (The ConTax Data Package) The consensus taxonomy for prokaryotes is a set of data-sets for best possible taxonomic classification based on 16S rRNA sequence data. Package: r-cran-microcran Architecture: all Version: 0.9.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-plumber, r-cran-assertthat, r-cran-mime, r-cran-xtable Suggests: r-cran-minicran, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-microcran_0.9.0-1-1.ca2404.1_all.deb Size: 73664 MD5sum: 74f295c9b5ec5c435b2795c1d2aa08ea SHA1: 5ce8198177ba236305c887ce409f7f6df0470654 SHA256: 6d03378d54f33393a381a5c8cc56a10c6b81e699ff21ead6541901c8ba635e19 SHA512: 28b09a6a01c00cd339b9d9cfe031df8cf00780058530ded2f2d3f995ac6d01b5561349cbdad89f22fbe4a6c70978ae7a640e5cf96488288f2d6ef218bd3dc22d Homepage: https://cran.r-project.org/package=microCRAN Description: CRAN Package 'microCRAN' (Hosting an Independent CRAN Repository) Stand-alone HTTP capable R-package repository, that fully supports R's install.packages() and available.packages(). It also contains API endpoints for end-users to add/update packages. This package can supplement 'miniCRAN', which has functions for maintaining a local (partial) copy of 'CRAN'. Current version is bare-minimum without any access-control or much security. Package: r-cran-microdatasus Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1453 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-foreign, r-cran-lubridate, r-cran-magrittr, r-cran-rcurl, r-cran-read.dbc, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-zip Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-microdatasus_2.3.1-1.ca2404.1_all.deb Size: 1431932 MD5sum: f6dec10f7e4c2bd3f77335c680eab1ab SHA1: d517c91f5d8b85b61a8e9e3d74d9ab1084a6a909 SHA256: 82148cdcecbc75c54a4f44df8a45aa35e2a6f402aecc87fe4d7dc889965857d1 SHA512: 7f7501d14aa352190378b8ea3683aaf495fc1e06bde44227d23a44fb7fe035bcee5a94ad3d6c912bc8a39ce1db0ccda47930c133fd38f32a15006bc851281955 Homepage: https://cran.r-project.org/package=microdatasus Description: CRAN Package 'microdatasus' (Download and Process 'DataSUS' Files) Downloads data files from 'DataSUS' health information systems from and process the data, including labeling categorical variables. Package: r-cran-microdatoses Architecture: all Version: 0.8.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr Filename: pool/dists/noble/main/r-cran-microdatoses_0.8.15-1.ca2404.1_all.deb Size: 142742 MD5sum: fd18458b4d5da4d3be4e225e7dfba449 SHA1: 76ff5484b61941f86cfb522b5ccc9cd50856226f SHA256: 179964b355bb2651c4930bd11f367bed32f4b9fa6b15c8028dbbd7736ea1e05c SHA512: e58f6337f1adc484f39430d3c8c6aa0f2a89bd416c879dda995ccbee1eac968c8569fd95d66478db4ddab91026f188d4df34a5761b7fbde0a103abb8d5aafb5c Homepage: https://cran.r-project.org/package=MicroDatosEs Description: CRAN Package 'MicroDatosEs' (Utilities for Official Spanish Microdata) Provides utilities for reading and processing microdata from Spanish official statistics with R. Package: r-cran-microdiluter Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggthemes, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rstatix, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microdiluter_1.0.1-1.ca2404.1_all.deb Size: 194034 MD5sum: 5cc6f8e9294cedc0828159359e1bd8af SHA1: ecbc078a5d54c04e0c1f5224b9143816e1d5adb8 SHA256: 132c00e9e7c841fe347c8003e5abb1992c5d234d2dd3eec741386c54dbd7fda5 SHA512: 1eb56915585cbfe81b7fa25f1b8160cc185ec394c98f980a58fcd962147508ba4ddc267deffaf5fad64725c82176cd1226d9665d0b265ac27f766d40afcdbca4 Homepage: https://cran.r-project.org/package=microdiluteR Description: CRAN Package 'microdiluteR' (Analysis of Broth Microdilution Assays) A framework for analyzing broth microdilution assays in various 96-well plate designs, visualizing results and providing descriptive and (simple) inferential statistics (i.e. summary statistics and sign test). The functions are designed to add metadata to 8 x 12 tables of absorption values, creating a tidy data frame. Users can choose between clean-up procedures via function parameters (which covers most cases) or user prompts (in cases with complex experimental designs). Users can also choose between two validation methods, i.e. exclusion of absorbance values above a certain threshold or manual exclusion of samples. A function for visual inspection of samples with their absorption values over time for certain group combinations helps with the decision. In addition, the package includes functions to subtract the background absorption (usually at time T0) and to calculate the growth performance compared to a baseline. Samples can be visually inspected with their absorption values displayed across time points for specific group combinations. Core functions of this package (i.e. background subtraction, sample validation and statistics) were inspired by the manual calculations that were applied in Tewes and Muller (2020) . Package: r-cran-microeco Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4787 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-r6, r-cran-ape, r-cran-vegan, r-cran-rlang, r-cran-data.table, r-cran-magrittr, r-cran-dplyr, r-cran-tibble, r-cran-scales, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-igraph, r-cran-lifecycle, r-cran-withr Suggests: r-cran-gunifrac, r-cran-mass, r-cran-ggpubr, r-cran-randomforest, r-cran-ggdendro, r-cran-ggrepel, r-cran-agricolae, r-cran-gridextra, r-cran-picante, r-cran-pheatmap, r-cran-mice, r-cran-ggally Filename: pool/dists/noble/main/r-cran-microeco_2.4.0-1.ca2404.1_all.deb Size: 4609056 MD5sum: a5dafbcfb307d5ed364757426acb0fb7 SHA1: 982ce6889985177d92123137076a1bf17db9af38 SHA256: ab17a59dbcfffe968989e2ed6189b5c02eade3c7dab79f9cd5d5dfaeb6186cd9 SHA512: 22c29579fd558d3fb0b246533529fa91da2877d69e726c153d3b54a5da0f50c3f158a83af74aae9f0a34f27529ad6251fa758f0a29932d7b6cccb7f4a23a3414 Homepage: https://cran.r-project.org/package=microeco Description: CRAN Package 'microeco' (Microbial Community Ecology Data Analysis) A series of data analysis approaches for microbiome data based on the R6 class. The classes are designed for data preprocessing, niche analysis, taxonomic abundance plot, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis, functional redundancy analysis, metabolites analysis, etc. Package: r-cran-microhaplot Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-dplyr, r-cran-ggplot2, r-cran-gtools, r-cran-magrittr, r-cran-scales, r-cran-shiny, r-cran-shinybs, r-cran-tidyr, r-cran-shinywidgets, r-cran-ggiraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microhaplot_1.0.1-1.ca2404.1_all.deb Size: 1052320 MD5sum: 70cc2cdd68fa9da5b73a1e1ed724f94f SHA1: 51f9cdb668109148021423ef969467274e129dc1 SHA256: 99f04a7b76dd927a24d8aa2726eb1ea450248e24d2a7b85a534f4775d79bda95 SHA512: db95d648cb461f2daa3361a2be38604f9c8537a3fe19997d9fc0d53bc2ab3623fb7944cd095f0c0909d50d08d0aff1dfde31fda766817f09ab014fe47524ba31 Homepage: https://cran.r-project.org/package=microhaplot Description: CRAN Package 'microhaplot' (Microhaplotype Constructor and Visualizer) A downstream bioinformatics tool to construct and assist curation of microhaplotypes from short read sequences. Package: r-cran-microinverterdata Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-units Suggests: r-cran-httptest2, r-cran-knitr, r-cran-lubridate, r-cran-pins, r-cran-rmarkdown, r-cran-tibble, r-cran-tsibble, r-cran-imputets, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microinverterdata_0.4.0-1.ca2404.1_all.deb Size: 888502 MD5sum: cca8320bbcd7ed50d0dd18c80758f563 SHA1: 2ea52bdc8028d85ea7cc52e85e87781c25e57a80 SHA256: d0612f3e90d437ec9577deef6a55458cc8394fc773c8a868510bd6d8c907b7e7 SHA512: a52b1e1ec88174957892d12774011a6cc7be31d27f6696afe45d54325bea66648fd0bc24392520c689f6a7f12e934a1f6b705a1df92dc706afe334514a62bdf8 Homepage: https://cran.r-project.org/package=microinverterdata Description: CRAN Package 'microinverterdata' (Collect your Microinverter Data) Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports 'APSystems', 'Enphase', and 'Fronius' microinverters. Package: r-cran-micromacromultilevel Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-micromacromultilevel_0.4.0-1.ca2404.1_all.deb Size: 47690 MD5sum: 650fc37cc36b4969117d78455341a772 SHA1: fecb0606029c7cea47bd401655f711cbd5d7c9b9 SHA256: d53c1369213ca1d3321c7a78db74c7f803e1f18cd0dd047f27d298a1104f2356 SHA512: a9eaae87738f562e18915480cac2b1aa684d9ba508ee3fb1e0e0862139c0024b796e554d4c8110b44201cea4023792afd79b2e2f43203f31f8e917807e5aa942 Homepage: https://cran.r-project.org/package=MicroMacroMultilevel Description: CRAN Package 'MicroMacroMultilevel' (Micro-Macro Multilevel Modeling) Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way. Package: r-cran-micromap Architecture: all Version: 1.9.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-sp, r-cran-sf, r-cran-ggplot2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-micromap_1.9.12-1.ca2404.1_all.deb Size: 1358900 MD5sum: d0b6812193ae076052a58ea2992b2853 SHA1: abb2ea6b68f60501c12d1beb06d9118e6ba78beb SHA256: 67d034d8bb9556b718b7052531176ea9c1ac7242156842a7fe8c269bfab7c589 SHA512: 955a96abd0e86bb59bd4b1de976dadf2bc15617c3171838cc9cb8c5982bf964e53d45412eccf40024a75d8bf21741c51a4a1ca6eaf21da11b4547d3bb2573a64 Homepage: https://cran.r-project.org/package=micromap Description: CRAN Package 'micromap' (Linked Micromap Plots) This group of functions simplifies the creation of linked micromap plots. Please see for additional details. Package: r-cran-micromapst Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3695 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-rcolorbrewer, r-cran-labeling, r-cran-sf, r-cran-spdep, r-cran-rmapshaper, r-cran-readxl, r-cran-writexl Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-micromapst_3.1.1-1.ca2404.1_all.deb Size: 3162076 MD5sum: f1146fe572ac6225d320cc427bcfa0ba SHA1: be11707ae64ca6f0be584f306394b3b950ad1bda SHA256: a01151f368786924673ceaec7b3e7985d938c677ef54371ac92d5847ecd53de4 SHA512: acb3ea6441b2d1a7cbcb63efedf114738b3c021c366617c01f85ebfac4d7b02f22df3ef508e59a086def362091be07142bf2d246933c0d9fd9e45d23d99054db Homepage: https://cran.r-project.org/package=micromapST Description: CRAN Package 'micromapST' (Linked Micromap Plots for U. S. and Other Geographic Areas) Provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the 'micromapST' function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package with the 'BuildBorderGroup' function. In April of 2022, it was announced that 'maptools', 'rgdal', and 'rgeos' R packages would be retired in middle to end of 2023 and removed from the CRAN libraries. The 'BuildBorderGroup' function was dependent on these packages. 'micromapST' functions were not impacted by the retired R packages. Upgrading of 'BuildBorderGroup' function was completed and released with version 3.0.0 on August 10, 2023 using the 'sf' R package. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., . Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle. Package: r-cran-micromodal Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-bslib, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-micromodal_1.0.1-1.ca2404.1_all.deb Size: 29528 MD5sum: cea68519d9523126798631c9aa3512d8 SHA1: 704a577e277a8f1eb75d945d5fabf9c1b661dd4f SHA256: 8b13b15a1602c3feb022a5197100063dea4dca8c7dfe5f4bd898ac37e2902329 SHA512: 194966a1db8028f0d3e27e90693580ff3e08f85a6063eda8f603ead3a73fd08059675578f93459dd3e1a0be0950297dc58fecf454b9e9f0e767f4ce0a5f17da1 Homepage: https://cran.r-project.org/package=micromodal Description: CRAN Package 'micromodal' (Create Simple and Elegant Modal Dialogs in 'shiny') Enables you to create accessible modal dialogs, with confidence and with minimal configuration. Package: r-cran-microniche Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-microniche_1.0.0-1.ca2404.1_all.deb Size: 81664 MD5sum: aef5beea372253bae81af1132949d0b6 SHA1: 2c99c121f90a1e3571b41a4d6177ae91a5d11566 SHA256: da3921d1348aac8c45808bc47891e60f53d7e4aeaab776dae72a5a68fc354f4d SHA512: 81a6a8391a51911c6582a8f354afb6d2669ac0fc7f2846c97cfdce7e6239d2412997c0f73360b1514d6cca063d01c1963a44696ead3679e8bc70533bfb1e646e Homepage: https://cran.r-project.org/package=MicroNiche Description: CRAN Package 'MicroNiche' (Microbial Niche Measurements) Measures niche breadth and overlap of microbial taxa from large matrices. Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E., Poole, R.W. (1981) ). Niche overlap measurements include Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap. Package: r-cran-micronutr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-micronutr_0.1.1-1.ca2404.1_all.deb Size: 431464 MD5sum: afbd68121f23b97f405e28c0b86b047d SHA1: bd9f42066f0fa540fd7596f2bf7e40ca7c7c0e33 SHA256: 1e56779ba9103e36a7f048e0f1131ee9396b0d5675216139fe1ef0a9b6b5b8d8 SHA512: 05899ac94c23e690c84a6dfe69707cac44d605d2e1b29bf8ace62cf0513c266ab4a894c076299fa2ded509a8cc0decae071e1f4cee9c79a81525a1f04c81f6b9 Homepage: https://cran.r-project.org/package=micronutr Description: CRAN Package 'micronutr' (Determining Vitamin and Mineral Status of Populations) Vitamin and mineral deficiencies continue to be a significant public health problem. This is particularly critical in developing countries where deficiencies to vitamin A, iron, iodine, and other micronutrients lead to adverse health consequences. Cross-sectional surveys are helpful in answering questions related to the magnitude and distribution of deficiencies of selected vitamins and minerals. This package provides tools for calculating and determining select vitamin and mineral deficiencies based on World Health Organization (WHO) guidelines found at . Package: r-cran-micropan Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1720 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-microseq, r-cran-dplyr, r-cran-stringr, r-cran-igraph, r-cran-tibble, r-cran-rlang Filename: pool/dists/noble/main/r-cran-micropan_2.1-1.ca2404.1_all.deb Size: 1700920 MD5sum: b5277787f97f6ae6f94d2bf2057348e1 SHA1: 53aec2462ca408a8ea22c8db5fc20fe6c21dd24e SHA256: f31b4dd3a3cd8adcc3165b6da1ffea3d1714753fd7e5b02a1830085b56de39f9 SHA512: 1d4e20abff4d8e58fcf6cb1e66480285d7d94926525bfcd08eff94c41c08ed902b809a57d595cf80d0e78b5bd7e0b665337947fa48e44d603985b3a0a19813cf Homepage: https://cran.r-project.org/package=micropan Description: CRAN Package 'micropan' (Microbial Pan-Genome Analysis) A collection of functions for computations and visualizations of microbial pan-genomes. Package: r-cran-microplot Architecture: all Version: 1.0-47-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-hh, r-cran-lattice, r-cran-flextable, r-cran-officer, r-cran-ggplot2, r-cran-htmltools, r-cran-cowplot Suggests: r-cran-reshape2, r-cran-latticeextra, r-cran-xtable, r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-htmltable Filename: pool/dists/noble/main/r-cran-microplot_1.0-47-1.ca2404.1_all.deb Size: 381730 MD5sum: fa65d7a0c492004bdf1fe5ca494c416d SHA1: 7386f86d38ba6a8acf9b0f99bec217d2cf92d847 SHA256: 1dd4c9091df8b3eeb97eec6991937aef1d1b777b7ae7de8d94789e5ba6ae84a4 SHA512: 751ac586c03f7f1fa80b47120ae69682fe7aef1d617af6b36b57e7bc5a00361252aa7affefd8e85a5d543e9fbfd8e6188c5b14410eac2e362400b785f5f571ee Homepage: https://cran.r-project.org/package=microplot Description: CRAN Package 'microplot' (Microplots (Sparklines) in 'LaTeX', 'Word', 'HTML', 'Excel') The microplot function writes a set of R graphics files to be used as microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word', or 'Excel' files. For 'LaTeX', we provide methods for the Hmisc::latex() generic function to construct 'latex' tabular environments which include the graphs. These can be used directly with the operating system 'pdflatex' or 'latex' command, or by using one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an intermediary. For 'MS Word', the msWord() function uses the 'flextable' package to construct 'Word' tables which include the graphs. There are several distinct approaches for constructing HTML files. The simplest is to use the msWord() function with argument filetype="html". Alternatively, use either 'Emacs org-mode' or the htmlTable::htmlTable() function to construct an 'HTML' file containing tables which include the graphs. See the documentation for our as.htmlimg() function. For 'Excel' use on 'Windows', the file examples/irisExcel.xls includes 'VBA' code which brings the individual panels into individual cells in the spreadsheet. Examples in the examples and demo subdirectories are shown with 'lattice' graphics, 'ggplot2' graphics, and 'base' graphics. Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and 'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input files and their 'pdf' output files. Examples for 'HTML' include 'org-mode' and 'Rmd' input files and their webarchive 'HTML' output files. In addition, the as.orgtable() function can display a data.frame in an 'org-mode' document. The examples for 'MS Word' (with either filetype="docx" or filetype="html") work with all operating systems. The package does not require the installation of 'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files. Package: r-cran-micropop Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-visnetwork, r-cran-testthat Suggests: r-cran-rmarkdown, r-cran-r.rsp, r-cran-knitr, r-cran-webshot Filename: pool/dists/noble/main/r-cran-micropop_1.6-1.ca2404.1_all.deb Size: 845000 MD5sum: 937a48676de24ec5b03d7ab022526609 SHA1: 026e5edc203b5b76dfd5b04c5da99f63d581e292 SHA256: 165196ddd0cac72442bc6a6b798364c71bd9be08c59d9c03291813821105e98e SHA512: 67f8a4919febbfa92b1d971726b86b2b00803753b752ce389a44040ce8ff9f1e34f1844b3d8db6e8fa3fee46b2882a8ae7f3afbbf7add83ff482d41fca798ce0 Homepage: https://cran.r-project.org/package=microPop Description: CRAN Package 'microPop' (Process-Based Modelling of Microbial Populations) Modelling interacting microbial populations - example applications include human gut microbiota, rumen microbiota and phytoplankton. Solves a system of ordinary differential equations to simulate microbial growth and resource uptake over time. This version contains network visualisation functions. Package: r-cran-microsec Architecture: all Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2621 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-genomeinfodb, r-bioc-biocgenerics Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg38, r-cran-knitr, r-cran-rmarkdown, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.mmusculus.ucsc.mm10 Filename: pool/dists/noble/main/r-cran-microsec_2.1.6-1.ca2404.1_all.deb Size: 2566234 MD5sum: f65bdfb97768ee416ac6b641b6c030d3 SHA1: 3125eed3d508e9401f5bed6878a8f81ec5863e02 SHA256: a069754c831539e28d27c6e26437a166f0e87b36607462539d6f0b69977cb1a5 SHA512: a3f5f30956be2792d2c2bd832aac7cbda9532d41f331cdc452f6e202ee20ca72070e87612196091804ee2bf494a19730e119a93b2e5aa4ded0c73a9410c13398 Homepage: https://cran.r-project.org/package=MicroSEC Description: CRAN Package 'MicroSEC' (Sequence Error Filter for Formalin-Fixed and Paraffin-EmbeddedSamples) Clinical sequencing of tumor is usually performed on formalin-fixed and paraffin-embedded samples and have many sequencing errors. We found that the majority of these errors are detected in chimeric read caused by single-strand DNA with micro-homology. Our filtering pipeline focuses on the uneven distribution of the artifacts in each read and removes such errors in formalin-fixed and paraffin-embedded samples without over-eliminating the true mutations detected in fresh frozen samples. Package: r-cran-microsoft365r Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azureauth, r-cran-azuregraph, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-vctrs, r-cran-mime Suggests: r-cran-openssl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-blastula, r-cran-emayili, r-cran-readr, r-cran-readxl Filename: pool/dists/noble/main/r-cran-microsoft365r_2.4.1-1.ca2404.1_all.deb Size: 775308 MD5sum: 59d55b1f5bdb4b1694e56f2c3cc3c3fe SHA1: c759f3de1de990b9ffafd9d9b9bf9525ce49e7d6 SHA256: 398028c3c1bd75fd6cf482b9c9876f02ed99ade7a12f55a83b5d2fb7e51acda2 SHA512: d8f7625d1d719a901b4b5a2e7261765464cc07fee0daa6936c6c270096e28f1adf4336b5738499e3f7de7775e2f45291cffa88e816cf97a1259c23efa7098f50 Homepage: https://cran.r-project.org/package=Microsoft365R Description: CRAN Package 'Microsoft365R' (Interface to the 'Microsoft 365' Suite of Cloud Services) An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders. Package: r-cran-microsynth Architecture: all Version: 2.0.51-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-kernlab, r-cran-lowrankqp, r-cran-pracma Suggests: r-cran-mass, r-cran-xlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microsynth_2.0.51-1.ca2404.1_all.deb Size: 2133258 MD5sum: 2775516d59c9795c64a397bebcff96ac SHA1: 1cab9bbd30ddd134f0a2b2e2719c4b53f98da3d5 SHA256: f5075dbfdffb010c78f5b02f8065af29526d0871fe8e4c17b1575f4df6df91d5 SHA512: fc60f6091bce5a27a8f287913adcbaf10aabd5d2e75a679492fdbfa6f9678cda34dc65e88a000277817e9bd4f6a78448e66c2584488703bf07bf7cee1429f28c Homepage: https://cran.r-project.org/package=microsynth Description: CRAN Package 'microsynth' (Synthetic Control Methods with Micro- And Meso-Level Data) A generalization of the 'Synth' package that is designed for data at a more granular level (e.g., micro-level). Provides functions to construct weights (including propensity score-type weights) and run analyses for synthetic control methods with micro- and meso-level data; see Robbins, Saunders, and Kilmer (2017) and Robbins and Davenport (2021) . Package: r-cran-micsim Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlecuyer, r-cran-snowfall Suggests: r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-micsim_3.0.0-1.ca2404.1_all.deb Size: 517788 MD5sum: 418ce39bed96e514f0d2269a1b736b5d SHA1: dfb6a03684a8d4f1e87f039ae50df533b771fce2 SHA256: a4d0201a8fe2703aae28d0770f731d30c604676788881d8b5197aba37eba49fa SHA512: 31f3ed7b7c21d55245bedecb7f614e36488b126e8f8e94f7b5fe02c95059f444bf393dc0704d6c5eeed5adac7bccc42e6284f2830654306ae89e6d7a2f7050f0 Homepage: https://cran.r-project.org/package=MicSim Description: CRAN Package 'MicSim' (Performing Continuous-Time Microsimulation) This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) . Package: r-cran-micss Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-micss_0.3.1-1.ca2404.1_all.deb Size: 167828 MD5sum: 622ae041d629ab1b2b557ca986db6fd2 SHA1: ff25aad2fa8f58d14751fd40275a49aa40c2cf98 SHA256: cdbb014836a91c04e6a0f227cccb971169e1aeda50a41faf2bfae7c75b9a0deb SHA512: bfe026612eea5e2232b02156040a5589fe825016ed0d65e883ae6401fb0b69c9565f5f371a61ed70c28175bf614991f90ca5297b703c1d37e4a25bc2c65ca17f Homepage: https://cran.r-project.org/package=micss Description: CRAN Package 'micss' (Modified Iterative Cumulative Sum of Squares Algorithm) Companion package of Carrion-i-Silvestre & Sansó (2026): "Testing for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified Iterative Cumulative Sum of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the unconditional variance of a time series controlling for the tail index of the underlying distribution. The fourth order moment is estimated non-parametrically to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). Critical values and p-values are generated using a Generalized Extreme Value distribution approach. References Carrion-i-Silvestre J.J & Sansó A (2026) . Inclan C & Tiao G.C (1994) , Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) . Package: r-cran-mict Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-mass, r-cran-mirt, r-cran-proc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mict_2.0.0-1.ca2404.1_all.deb Size: 787108 MD5sum: 22506afe4f4e404da621be05b183b205 SHA1: 02d259b919d91aba1bce90924daf7233af2e7d3b SHA256: f8e5d18f9efde291b59f9b7ca0642d431897d20817c8136fd8bcd651ee9ce211 SHA512: 1ce62b47da9e6d2d88c5e3e7684120de6b79d62501fbafe8e30bf048d147797e11a3fa6a6dd457221b2c70cf07f51783826b6667598f568d2fb57e57dd358751 Homepage: https://cran.r-project.org/package=MiCT Description: CRAN Package 'MiCT' (Minimal Important Change and Threshold Estimation) Provides methods for estimating minimal important change (MIC) and interpretation thresholds for multi-item questionnaires and single-item continuous or ordinal measures. Methods include predictive modelling, adjusted predictive modelling, improved adjusted predictive modelling using anchor reliability, confirmatory factor analysis for anchor reliability, longitudinal confirmatory factor analysis for MIC estimation, longitudinal confirmatory factor analysis-based MIC estimation for single-item measures, and confirmatory factor analysis-based threshold estimation for single-item and multi-item measures. Implemented methods include those developed by Terluin et al. (2015) , Terluin et al. (2017) , Terluin et al. (2022) , Terluin et al. (2023) , Terluin et al. (2024) , Terluin et al. (2024) , and Terluin et al. (2026) . Package: r-cran-micvar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-micvar_0.1.0-1.ca2404.1_all.deb Size: 29590 MD5sum: afaf993230a187a25a69592172a163a2 SHA1: 5765f0bb3ca909dbafd4182b465f336d4300e72c SHA256: 22c76475920b7a1a23932a1996e72f292e04d327fa8458a7e824a8ad387ded64 SHA512: 2bcca28c3c4824f7fe186c4c8e90106c4bc6fe908ff7c494f5c8ea3b6ad962f0e705197cf18e96f69fbce4ab406930bb1a425c11d02ebaf5b0fc712a7e69e93c Homepage: https://cran.r-project.org/package=micvar Description: CRAN Package 'micvar' (Order Selection in Vector Autoregression by Mean SquareInformation Criteria) Implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) . Package: r-cran-mida Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-gbm, r-bioc-genefilter, r-bioc-limma, r-bioc-preprocesscore, r-cran-proc, r-cran-sqn Filename: pool/dists/noble/main/r-cran-mida_0.1.2-1.ca2404.1_all.deb Size: 153974 MD5sum: 3d1182d28adbf36531bb088b96242152 SHA1: 7c96a47fcf4727c2f57df0ea39ee84fb38901e28 SHA256: 63096c7198f04613661701c28f20afd2bf83b1e8d8ba97622c1763d0384eb64a SHA512: 526ab67deda34faff3e686e0612d0ca05b8f652d7240f0c03da786cf63e259393d7517cd2e06891c369bc9376064dd064aa9af96d7d6e43cb0b257e7eb3e50df Homepage: https://cran.r-project.org/package=MiDA Description: CRAN Package 'MiDA' (Microarray Data Analysis) Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers. Package: r-cran-midas2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-coda, r-cran-r2jags Filename: pool/dists/noble/main/r-cran-midas2_1.1.0-1.ca2404.1_all.deb Size: 47642 MD5sum: abc4b155b1bc2d970bbc84c8d6d7f6b3 SHA1: 09d65e78fa25ab509e844e57d5a401beea815370 SHA256: f4d9fbb4f0ae3c17c7638aa0290164a98e4b8c66fb131c2b8199b69894c3e6a5 SHA512: f6440e3498f66ce4e6ef1fae6ca7dfc2f624561db5e5602e0d06226ade6eeec0d33f82076e453faa572d7fe0779b04d5ce39ecdddf53386e093ff636d8c6927b Homepage: https://cran.r-project.org/package=midas2 Description: CRAN Package 'midas2' (Bayesian Platform Design with Subgroup EfficacyExploration(MIDAS-2)) The rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging, which necessitates innovative, integrated, and efficient trial designs(Yuan, Y., et al. (2016) ). MIDAS-2 package enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We used a regression model to characterize the efficacy pattern in subgroups. Information borrowing was applied through Bayesian hierarchical model to improve trial efficiency considering the limited sample size in subgroups(Cunanan, K. M., et al. (2019) ). MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion(Wathen, J. K., & Thall, P. F. (2017) ). Package: r-cran-midas Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-midas_1.0.1-1.ca2404.1_all.deb Size: 14498 MD5sum: fc7f676c130abced15ffec125f7c563d SHA1: a869dc829a9dd046e180828e080603a8c9d87e18 SHA256: 68f90e8f65c5e11642d5531f11beca7bc7408c218467c39dc328db3dca699fe0 SHA512: b99402b2597f10a298b0a6c3cfb1baf058ffdd94263f824e1b7a9e019c02854ac67e8c791c0ad96740657cb412d8fb77c1bb0e7d0e1e9683fee6f4fb04846520 Homepage: https://cran.r-project.org/package=midas Description: CRAN Package 'midas' (Turn HTML 'Shiny') Contains functions for converting existing HTML/JavaScript source into equivalent 'shiny' functions. Bootstraps the process of making new 'shiny' functions by allowing us to turn HTML snippets directly into R functions. Package: r-cran-midasim Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-mass, r-cran-pracma, r-cran-scam Suggests: r-cran-vegan, r-bioc-phyloseq, r-cran-rmarkdown, r-cran-knitr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-midasim_2.0-1.ca2404.1_all.deb Size: 240560 MD5sum: 5d508fee23ff83180809a5a9fc1116ba SHA1: 91e59f39502017ca7653d1d296123212b783e336 SHA256: a73bc3e095090556d5860ce5fc347b380fc8c2e0529311851c469f16dcccd449 SHA512: b4248b89a3d26c11a47ff89c079792954f7461ad3341c7d7ed1facf9ab11596a997814ef4d2fb0a76129449ca157a4afc0627b36f9dc4fb6a0576f8b7079bd8a Homepage: https://cran.r-project.org/package=MIDASim Description: CRAN Package 'MIDASim' (Simulating Realistic Microbiome Data using 'MIDASim') The 'MIDASim' package is a microbiome data simulator for generating realistic microbiome datasets by adapting a user-provided template. It supports the controlled introduction of experimental signals-such as shifts in taxon relative abundances, prevalence, and sample library sizes-to create distinct synthetic populations under diverse simulation scenarios. For more details, see He et al. (2024) . Package: r-cran-midasinla Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2879 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-midasinla_0.1.2-1.ca2404.1_all.deb Size: 2625996 MD5sum: 1219963157a2f4a0d9fd9d166da5b02e SHA1: 815ebadb78ab938ff792bd3dcd9b0d89e8dc8100 SHA256: 3dd47ac9c2c14859d1ba821763935fda597e41e0e562cf8458062ca5b30e2af0 SHA512: 2c15e3f47761442d224171e666d29991c94daa1e7420eaf29f4bc45096d65e60892fde7f41cfb0ae30726305b3dfd996540c891bba421e6253735f0c360d2d60 Homepage: https://cran.r-project.org/package=midasINLA Description: CRAN Package 'midasINLA' (Spatial MIDAS Models Using INLA) Provides tools for fitting spatial Mixed-Data Sampling (MIDAS) regression models using Integrated Nested Laplace Approximation (INLA) (Rue et al., 2009) . The package is designed for settings where responses and explanatory variables are observed at different temporal frequencies and supports both constant and spatially varying regression coefficients. Package: r-cran-midasr Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-optimx, r-cran-quantreg, r-cran-mass, r-cran-numderiv, r-cran-matrix, r-cran-forecast, r-cran-zoo, r-cran-formula, r-cran-texreg Suggests: r-cran-testthat, r-cran-lubridate, r-cran-xts Filename: pool/dists/noble/main/r-cran-midasr_0.9-1.ca2404.1_all.deb Size: 925000 MD5sum: ca892da76ab063beff53cc944705b397 SHA1: 9d6ab81e58f1332ac9e5d025152f9943c8bff487 SHA256: fc4382a6962e02819a3eb19f54ac2929e5972c4aa65f1c336dbfa43427194c48 SHA512: 95acacf75dd576c74a96c3e3cf908f48e5448a43a7f599bdad293cb9dcd8e49c77ce292f2789d9e0e9afb9ed3e93e28107b1f37139a895cf4dbb4b4f9c66c5bc Homepage: https://cran.r-project.org/package=midasr Description: CRAN Package 'midasr' (Mixed Data Sampling Regression) Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions. Package: r-cran-midastouch Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mice Filename: pool/dists/noble/main/r-cran-midastouch_1.3-1.ca2404.1_all.deb Size: 22496 MD5sum: 22330c5e61a2c551351e44f7a6fb1c45 SHA1: 04d2c7082a56881c9fc212a587a40ce974b32b6e SHA256: 3efe811d28e01256b3e42b834838d722720922fd2d2ba29f8803123b949344d6 SHA512: a9564741ef6f9b0f7811722b0d17e6cc7512ff6ab85cf628c73873f3497996cfe7538936743aa8ece111bf9a89703fddc3869f1e739d63ddb837fd8c908c8b22 Homepage: https://cran.r-project.org/package=midastouch Description: CRAN Package 'midastouch' (Multiple Imputation by Distance Aided Donor Selection) Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS. Package: r-cran-midfieldr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-wrapr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-midfieldr_1.0.3-1.ca2404.1_all.deb Size: 1152394 MD5sum: 614738fb60a3617551756f09b186892c SHA1: 7d50d90a6be3c762b02658d9d2d9670cad54a133 SHA256: f2b2c83e33e7727c4c30f8830559c48ba2a210d5898db5cc583d3c17b998833f SHA512: 2f97843219037addf4dc4794caecb822cb917b1efb83089960caa29e9018e855e5616e40a0f6bcaacead413267409786d2a6af5d76415a7718e893ce7aa2df1e Homepage: https://cran.r-project.org/package=midfieldr Description: CRAN Package 'midfieldr' (Tools and Methods for Working with MIDFIELD Data in 'R') Provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in 'R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. 'midfieldr' interacts with practice data provided in 'midfielddata', an R data package available at . 'midfieldr' also interacts with the full MIDFIELD database for users who have access. As of the transfer of MIDFIELD to the American Society for Engineering Education in 2023, the development, expansion, and study of MIDFIELD has been supported by the National Science Foundation grants 0337629, 0646441, 0729596, 0734062, 0835914, 0935157, 0935058, 0969474, 1025171, 1129383, 1232740, 1329283, 1361058, 1545667, 2142087, 2141903, and 2152441. Package: r-cran-midi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2521 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-midi_0.2.0-1.ca2404.1_all.deb Size: 2455614 MD5sum: 3199424beb56e942ed1278148961dc64 SHA1: e123886976fe8dd0c69252b4c1adbe77f114b053 SHA256: 489b863d54168fcd0c231a6208f2746b81a15bc1e75b8aea55c1d2777c995ce1 SHA512: 9e08b5fb9607cb3b2ed729d40f8d7beb4eb57fe04434b091e20c7ff0e24d4e52a4fd4b81ba301bb64513a1e5372b4f3f8d6755a88de6f642feef335bc6c09c67 Homepage: https://cran.r-project.org/package=midi Description: CRAN Package 'midi' (Microstructure Information from Diffusion Imaging) An implementation of a taxonomy of models of restricted diffusion in biological tissues parametrized by the tissue geometry (axis, diameter, density, etc.). This is primarily used in the context of diffusion magnetic resonance (MR) imaging to model the MR signal attenuation in the presence of diffusion gradients. The goal is to provide tools to simulate the MR signal attenuation predicted by these models under different experimental conditions. The package feeds a companion 'shiny' app available at that serves as a graphical interface to the models and tools provided by the package. Models currently available are the ones in Neuman (1974) , Van Gelderen et al. (1994) , Stanisz et al. (1997) , Soderman & Jonsson (1995) and Callaghan (1995) . Package: r-cran-midn Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/noble/main/r-cran-midn_1.0-1.ca2404.1_all.deb Size: 36150 MD5sum: 7ff9dc04896efe375008854f70538926 SHA1: 04e243bb048ee87dd4bc620c3939b9be0fc93ca4 SHA256: 4b098ae6bfc47b4df0b7a869ebd25fa6a197f143815ac9e0e19b980a4bc546cc SHA512: 12aaa3f0fe0264fa79a2f6a9bed284faaafcbeaef4e0f0488959b7fed6de2c91b8263ab56009c41ac1b44876ef942ea430c5c50bfaee0c63d4a790710a392547 Homepage: https://cran.r-project.org/package=MIDN Description: CRAN Package 'MIDN' (Nearly Exact Sample Size Calculation for Exact PowerfulNonrandomized Tests for Differences Between BinomialProportions) Implementation of the mid-n algorithms presented in Wellek S (2015) Statistica Neerlandica 69, 358-373 for exact sample size calculation for superiority trials with binary outcome. Package: r-cran-midoc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1081 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arm, r-cran-blorr, r-cran-dagitty, r-cran-glue, r-cran-lifecycle, r-cran-mfp2, r-cran-mice, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-midoc_1.0.0-1.ca2404.1_all.deb Size: 438634 MD5sum: 5e59dae1afee640e9b92ed12bccfd101 SHA1: 273a9facc38b21e2d2c0efc1a7e978b3df5a4e7e SHA256: b638cfeb0df9933c419e03e32ce2caa5672ed3160ed90cbce65c25447a146978 SHA512: eaeee6193d414ea4fb661a0b4e05b6654ea4b39f704ea90a92006b336abad0b531da176399d78c86325d0fbc427eacdd7685fac6220774aa39cd658f5ab52f5d Homepage: https://cran.r-project.org/package=midoc Description: CRAN Package 'midoc' (A Decision-Making System for Multiple Imputation) A guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. 'midoc' will advise which analysis approaches can be used, and how best to perform them. 'midoc' follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). . Package: r-cran-midr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppeigen, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-khroma, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scales, r-cran-shapviz, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-midr_0.5.0-1.ca2404.1_all.deb Size: 553062 MD5sum: f1f3f650f58f19918077dd26a3a62b0f SHA1: 388e6aad15aafbb21c1cf8264cea30c1678d939a SHA256: 192631792ae130144e1b3c20c7a5c7c9c660d0f33633976a06c34a7735ab8a3c SHA512: df0a8639d2753a237ff6034fd6d1547a86099cfa874d8e2036be6c090d1244a5d37646897578916b903276de1e545de3c31c31c541e2c45a518bddf4af10ba1e Homepage: https://cran.r-project.org/package=midr Description: CRAN Package 'midr' (Learning from Black-Box Models by Maximum InterpretationDecomposition) The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) . Package: r-cran-midrangemcp Architecture: all Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smr, r-cran-writexl, r-cran-xtable Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-midrangemcp_3.1.3-1.ca2404.1_all.deb Size: 160214 MD5sum: 4e8eeed113339a0d038f6eec39d3ee88 SHA1: a7199cb2a825808c9fcaf9471626c1828a43c514 SHA256: 22664eb388e9965fcf8f7c8d77bbe39be56db7858475af642514ca3a00bdbba0 SHA512: 1c2306e1869451cf9976f0108f4d30bcf56f4228783cb287fba0947f8f670fedae7146343c8fa41fb8cb9cb7ff23740d281d3413d9e6627415e642367fe4da87 Homepage: https://cran.r-project.org/package=midrangeMCP Description: CRAN Package 'midrangeMCP' (Multiple Comparisons Procedures Based on Studentized Midrangeand Range Distributions) Apply tests of multiple comparisons based on studentized 'midrange' and 'range' distributions. The tests are: Tukey Midrange ('TM' test), Student-Newman-Keuls Midrange ('SNKM' test), Means Grouping Midrange ('MGM' test) and Means Grouping Range ('MGR' test). The first two tests were published by Batista and Ferreira (2020) . The last two were published by Batista and Ferreira (2023) . Package: r-cran-miebl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-miebl_0.1.0-1.ca2404.1_all.deb Size: 21812 MD5sum: 5a92f5b052efdf4f3cfb3fa7106a6a0d SHA1: 3c9254fb44d35d5592fddaccdf310c8c552ba47a SHA256: 1923efa8a8edd96863cea98b88e1b598b94f0adb8fea5e2f0ba9cf571e03e32f SHA512: 9a545a8ba63d260ced6e7e8e29d06a3b643b0d18e106b92af6346a88f9e7f99c8ca43368a649d739db5a068c0af2b2a61e861c22308370a829a5fc42d3b4d97a Homepage: https://cran.r-project.org/package=miebl Description: CRAN Package 'miebl' (Performance Criteria Modeler for Discrete Trial Training) Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) . Package: r-cran-miesmuschel Architecture: all Version: 0.0.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2470 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paradox, r-cran-mlr3misc, r-cran-checkmate, r-cran-r6, r-cran-bbotk, r-cran-data.table, r-cran-matrixstats, r-cran-lgr Suggests: r-cran-tinytest, r-cran-mlr3tuning, r-cran-mlr3, r-cran-mlr3learners, r-cran-ranger, r-cran-xgboost, r-cran-rpart Filename: pool/dists/noble/main/r-cran-miesmuschel_0.0.4-3-1.ca2404.1_all.deb Size: 1524318 MD5sum: 5bf283d98de9914c4be66e15c946fa1e SHA1: a05e4db21b1e77ee0448d8aa5b3d29d6b20cb43e SHA256: dd63d2966183a509b982cfda734a6faeb033d7303977b924051be8e3c38e1223 SHA512: b655c3fc335f2c0f51039d1bb1f218827a222f9e98e808a0a5f9b217bbb2ed494f7d020d79e682bb0b77287fbaf1dea0b3f86bbdae6a51ccc8a78796e070baad Homepage: https://cran.r-project.org/package=miesmuschel Description: CRAN Package 'miesmuschel' (Mixed Integer Evolution Strategies) Evolutionary black box optimization algorithms building on the 'bbotk' package. 'miesmuschel' offers both ready-to-use optimization algorithms, as well as their fundamental building blocks that can be used to manually construct specialized optimization loops. The Mixed Integer Evolution Strategies as described by Li et al. (2013) can be implemented, as well as the multi-objective optimization algorithms NSGA-II by Deb, Pratap, Agarwal, and Meyarivan (2002) . Package: r-cran-mifa Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-dplyr, r-cran-checkmate Suggests: r-cran-psych, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-mifa_0.2.1-1.ca2404.1_all.deb Size: 173736 MD5sum: 929099cfde0cb7e5096a1ea0360d9906 SHA1: 2ac5fee1e5b545ba39d1a6c8aa027db7a4c42714 SHA256: 9e7e42ee0320558fdd82b22cc5cde1b06171f5a00003efac40328ab3b3242af2 SHA512: c879790a985abc42ae11b4c0337829576b3be5854e9cdb5c21cfa21da1f2d1ae8c85d101bf63c430f1f71f73d1afc176d9b536ed8023b5bd1e056191c759f72b Homepage: https://cran.r-project.org/package=mifa Description: CRAN Package 'mifa' (Multiple Imputation for Exploratory Factor Analysis) Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) . Package: r-cran-migconnectivity Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3606 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-geodist, r-cran-gplots, r-cran-mass, r-cran-ncf, r-cran-r2jags, r-cran-rmark, r-cran-sf, r-cran-shape, r-cran-terra, r-cran-vgam Suggests: r-cran-knitr, r-cran-maps, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-migconnectivity_0.5.0-1.ca2404.1_all.deb Size: 2902440 MD5sum: e1512ebcb51bbaf3a32274a6aacdbcef SHA1: 9b5cded7599e3f618d4051cad876f3e2eda1d0ef SHA256: 6c26f6ec4aff0f5b5131dce1effa5fcdb72b1f6e6f44ae2d40c30e4c22dd596e SHA512: bc330cbb7ddb28dcc85176db5852d4afa080330a7cb6f0e53666624826c7bef06a6cb586f246f1bb24f741c397ce268756b6319ba9bb6102d6fa41b2ce215bac Homepage: https://cran.r-project.org/package=MigConnectivity Description: CRAN Package 'MigConnectivity' (Estimate Migratory Connectivity for Migratory Animals) Allows the user to estimate transition probabilities for migratory animals between any two phases of the annual cycle, using a variety of different data types. Also quantifies the strength of migratory connectivity (MC), a standardized metric to quantify the extent to which populations co-occur between two phases of the annual cycle. Includes functions to estimate MC and the more traditional metric of migratory connectivity strength (Mantel correlation) incorporating uncertainty from multiple sources of sampling error. For cross-species comparisons, methods are provided to estimate differences in migratory connectivity strength, incorporating uncertainty. See Cohen et al. (2018) , Cohen et al. (2019) , Roberts et al. (2023) , and Hostetler et al. (2025) for details on some of these methods. Package: r-cran-migee Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-vim, r-cran-ggplot2, r-cran-lme4, r-cran-ggeffects, r-cran-dplyr, r-cran-readr, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-migee_0.1.0-1.ca2404.1_all.deb Size: 178546 MD5sum: b37094cc272fc29fc92412c2c9d7b65d SHA1: 0b3b39d44a0aad60f05b97f3b20c68fc77d74df6 SHA256: 5fc0f2f341f2377f27b05c470d2db9d3d38797616476b2ee90e5550452b24cd6 SHA512: 5bcf4b72f350aa0eefefdfcc7b47e455e5b58bb86c42e4db6a68220dbc67d34d12439c4c635fc57c14c86d3dd1547cb2be20f480b7e433a9a906cb6d69d77e64 Homepage: https://cran.r-project.org/package=MIGEE Description: CRAN Package 'MIGEE' (Impute Missing Values and Fitting Linear Mixed Effect Model) Implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings. Package: r-cran-migest Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-tibble, r-cran-forcats, r-cran-matrixstats, r-cran-migration.indices, r-cran-circlize, r-cran-mipfp Suggests: r-cran-spelling, r-cran-countrycode Filename: pool/dists/noble/main/r-cran-migest_2.0.6-1.ca2404.1_all.deb Size: 372130 MD5sum: 38296656ffc8b7b75811d5fd6aac3f03 SHA1: 862953c594df9e2ddf719f968beb151b2ec026b1 SHA256: 05ec8a331ee34a8793014b1d21e1d7fc3b39f6de60d812badfc9804d424bfda7 SHA512: 3df53ad40263c59892c4d9b76060ad811b1cc7232cdeb919f5eeebef75a13fbb917e35e9d75eda755859384c5ebd13004efda00b1d7749b0d538e803530b4d70 Homepage: https://cran.r-project.org/package=migest Description: CRAN Package 'migest' (Tools for Estimating, Measuring and Working with Migration Data) Provides tools for estimating, measuring, and analyzing migration data. Designed to assist researchers and analysts in working effectively with migration data. Package: r-cran-mighty.metadata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-s7schema, r-cran-tibble, r-cran-yaml, r-cran-zephyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-mighty.metadata_0.1.0-1.ca2404.1_all.deb Size: 235116 MD5sum: 9b836a8790287065495cec1790d02899 SHA1: 4a6a5ad8b0212ac50bd853f63e8cfefb11b213d6 SHA256: 22cf51aeff4ddb90054fd2ae3af1a75dca19e776085061fb02e524853189339b SHA512: 9163526de91a2e21de4b33512bec30162f1df0d59bdaa27ad1f43b1c079104e52f65f54a39b6fb650aab677ac2173d90477adf065d825739c6368658f9370afb Homepage: https://cran.r-project.org/package=mighty.metadata Description: CRAN Package 'mighty.metadata' (Manage 'CDISC' 'ADaM' Dataset Specifications in 'YAML' Format) Load, validate, and manipulate Clinical Data Interchange Standards Consortium ('CDISC') Analysis Data Model ('ADaM') dataset metadata stored as 'YAML' files. 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Package: r-cran-migraph Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4961 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-manynet, r-cran-autograph, r-cran-netrics, r-cran-dplyr, r-cran-ergm, r-cran-future, r-cran-furrr, r-cran-generics, r-cran-knitr, r-cran-learnr, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-migraph_1.7.2-1.ca2404.1_all.deb Size: 3346530 MD5sum: 34ff1869e8f21855f64d8ea87f971d6b SHA1: 76e74ce257fe1f2743698c6063e6fa58b82bf61d SHA256: 1be1d93c4f5cb65d7fdeadff75e0bf7e726f7166a06cd650025cac1e51682571 SHA512: 3be008e61d069411813c096f596bb4d1ac615be761660c6355b0d0e31e1899bb34ae6d72bf7f08e9b9d118d48e79dc77cdfb2cd68e4ff827b3c212d3b97ce95e Homepage: https://cran.r-project.org/package=migraph Description: CRAN Package 'migraph' (Inferential Methods for Multimodal and Other Networks) A set of tools for testing networks. It includes functions for univariate and multivariate conditional uniform graph and quadratic assignment procedure testing, and network regression. The package is a complement to 'Multimodal Political Networks' (2021, ISBN:9781108985000), and includes various datasets used in the book. Built on the 'manynet' package, all functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode and two-mode (bipartite) networks. Package: r-cran-migrate Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-cli, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-migrate_0.5.1-1.ca2404.1_all.deb Size: 172874 MD5sum: 112a60965885627f49d329c07aabb314 SHA1: be7e0d49f70803f622b1921c67a4b7c82e347951 SHA256: 49bb8f2f9c35bfde0fbf632e45cae0f1f00e2119cd867edea4f7ff199cd0715d SHA512: 9de47fc0ad37d7a98680b5cabf3a5ab4ee12d00f0d6d0981769a2897be363130d9ef0e03adcb2081ef2666fc184206591a061f18cddd98c13f9d8af10adbf3b0 Homepage: https://cran.r-project.org/package=migrate Description: CRAN Package 'migrate' (Create Credit State Migration (Transition) Matrices) Tools to help convert credit risk data at two timepoints into traditional credit state migration (aka, "transition") matrices. At a higher level, 'migrate' is intended to help an analyst understand how risk moved in their credit portfolio over a time interval. References to this methodology include: 1. Schuermann, T. (2008) . 2. Perederiy, V. (2017) . Package: r-cran-migration.indices Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-calibrate Filename: pool/dists/noble/main/r-cran-migration.indices_0.3.1-1.ca2404.1_all.deb Size: 158076 MD5sum: d8abc8685b22475eebf845f211708534 SHA1: 9ae743ec9e7b7a39f5ce5be767cf35f59cf5bfc5 SHA256: 6a4ea0f8d0ab8ef31ba36a7670cff4c26fa2463d1f1657874fc947c3b53e2ac6 SHA512: 9c869353b8ada3961e4074ad86d03ca41f3fd61049c006041e1ad730bc61b910576048e5bf35da6c90a0ab596ffb8b2feb5ac79cd03bb8d79a4a44425d137128 Homepage: https://cran.r-project.org/package=migration.indices Description: CRAN Package 'migration.indices' (Migration Indices) Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration. Package: r-cran-migrationdetectr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-migrationdetectr_0.1.1-1.ca2404.1_all.deb Size: 81188 MD5sum: 32eb37f99a6662a8c46a2008f4953f98 SHA1: c960d521f59e3ec335ed4c0c84d0f535eeda4f34 SHA256: 1fb88deec2a4f84bd9e20d98b868f406e9503e33cefd7ef532d6ad8934035d71 SHA512: 7b236f7fe3c1b01c13a9e14d6d1d60f779a04cd4dcba0b0ec8378f3c9200705e6fe7a1eaa61ebedcd8fd7a99c1fc367d992c3ef48b22f8b4176134a9b2762fc2 Homepage: https://cran.r-project.org/package=MigrationDetectR Description: CRAN Package 'MigrationDetectR' (Segment-Based Migration Detection Algorithm) Detection of migration events and segments of continuous residence based on irregular time series of location data as published in Chi et al. (2020) . Package: r-cran-migui Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gwidgets2, r-cran-mi, r-cran-arm Suggests: r-cran-foreign, r-cran-gwidgets2tcltk Filename: pool/dists/noble/main/r-cran-migui_1.3-1.ca2404.1_all.deb Size: 75524 MD5sum: 602f4c68469d539aa5ec85bd0bd9af73 SHA1: 88d7562d031483bd3eabb12999ef1d5deb2bae68 SHA256: bb7e34d54e8d8ada4a19209c6e2a34f24e5ba8a94c5ffe4e4dec756e7b1ae46d SHA512: 70f5583d823c2974c29eda8b072bba01149b147f4e3ca923f73f17c56d2209308946aba5f05b312c392a07271b29a8a25eaf235e90b2fda76e56bef7193e91e2 Homepage: https://cran.r-project.org/package=migui Description: CRAN Package 'migui' (Graphical User Interface to the 'mi' Package) This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data. Package: r-cran-miipw Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-spatstat, r-cran-mice, r-cran-matrix, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-miipw_0.1.2-1.ca2404.1_all.deb Size: 261226 MD5sum: 6f8a2793cb9f0f4625f3cc23d7151035 SHA1: c13f599421557c7b60739e578af384b1225cd720 SHA256: 35162350c3aece2b60e904bf5d22658ff069690ff933d77e79a6b4a9f2769a60 SHA512: d222a785c036412d4252ad6b7e6cf95489452cff193b31fe8b1d1b632ddb84e94f872441e8e7b0be7bfe50951f6c0a860be976c930cdea695291731f06d70298 Homepage: https://cran.r-project.org/package=MIIPW Description: CRAN Package 'MIIPW' (IPW and Mean Score Methods for Time-Course Missing Data) Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007). Package: r-cran-miivefa Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-miivsem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-mnormt, r-cran-lavaan, r-cran-mpsychor Filename: pool/dists/noble/main/r-cran-miivefa_0.1.2-1.ca2404.1_all.deb Size: 57476 MD5sum: 61da33e394be0d1fd7f3ff5df2f7e3ef SHA1: e3b63aaa48b3451cf9dfb229cca31f6035abc47e SHA256: 2356ac0b809b268187db632c0a3bd7c35b2a758df172d49a69f01293b336a3aa SHA512: 2fba9e77fb7c1f0f1acaf7a3c80c86290e0b0fac4f5aebd1cc713a506ef94eb38628461e9a38d5724f02481fe1bd65875054b4da86c7cc8518ac8ee26baff010 Homepage: https://cran.r-project.org/package=MIIVefa Description: CRAN Package 'MIIVefa' (Exploratory Factor Analysis Using Model Implied InstrumentalVariables) Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors. Package: r-cran-miivsem Architecture: all Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-numderiv, r-cran-matrix, r-cran-car, r-cran-boot Filename: pool/dists/noble/main/r-cran-miivsem_0.5.8-1.ca2404.1_all.deb Size: 279778 MD5sum: 13166d8afbc5eccefaa5b94efd10a2ba SHA1: 368f569fce6c8061ab3b17a15bdd47604ed350e8 SHA256: cee662f21451b3b15e03a2986c2d95221eff1a23787b3ae4c9fa785c28b5a32b SHA512: 9672a2bed8dc74f270ac50a133eef116e2c12a5c911f08789f8a41c3bde79a9223fd15e23268dd5c4138cca7b6069c6b99846feff3c5df68239dc02200894c95 Homepage: https://cran.r-project.org/package=MIIVsem Description: CRAN Package 'MIIVsem' (Model Implied Instrumental Variable (MIIV) Estimation ofStructural Equation Models) Functions for estimating structural equation models using instrumental variables. Package: r-cran-mikropml Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2520 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-e1071, r-cran-glmnet, r-cran-kernlab, r-cran-mlmetrics, r-cran-randomforest, r-cran-rlang, r-cran-rpart, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-tidyselect, r-bioc-treesummarizedexperiment, r-cran-xgboost Suggests: r-cran-assertthat, r-cran-dofuture, r-cran-forcats, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-knitr, r-cran-progress, r-cran-progressr, r-cran-purrr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rsample, r-cran-styler, r-cran-testthat, r-cran-tidyr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-mikropml_1.7.1-1.ca2404.1_all.deb Size: 2219702 MD5sum: 141d6c67614fc176c76092cb7f9cf05c SHA1: 0d508336b5652c7ab90d803d9af7b5f2306ef4ab SHA256: b50c7c258c138da662ab0e1c37fd081200a01f0d20a420ad0827f015e7f0f1ac SHA512: f5a94e6e32e8eb21c25de61e545056ef924b3b0dae64371ffcf0922271a7c4c79e5eb892fcb11cab145271501cb7cb8f2ba0afaf66c063629ef587c7bc763f21 Homepage: https://cran.r-project.org/package=mikropml Description: CRAN Package 'mikropml' (User-Friendly R Package for Supervised Machine LearningPipelines) An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Topçuoğlu et al. (2020) with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website for more information, documentation, and examples. Package: r-cran-milag Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-minpack.lm, r-cran-nlsmicrobio Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-milag_1.0.5-1.ca2404.1_all.deb Size: 445404 MD5sum: 1f68fff38ca9a3f840550f2d65ca49e2 SHA1: 830ac9ab872f3a7f2969c77e4bf5a4f197c5bc66 SHA256: 6bca9e139923098bfd16ba7fb4b90a88690ef80e588a4b2298ebc6ecd3b62e8b SHA512: b4678f724fe6eadc3578c1a7ef1fcedb2ee0c69ae7b05bb163d64178dbb1386636a816e33deeb7678e0758e918b7d0610bf8b75d870ebdd7b26463997c49d590 Homepage: https://cran.r-project.org/package=miLAG Description: CRAN Package 'miLAG' (Calculates Microbial Lag Duration (on the Population Level) fromProvided Growth Curve Data) Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 . Package: r-cran-mildsvm Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-kernlab, r-cran-magrittr, r-cran-mvtnorm, r-cran-pillar, r-cran-proc, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mildsvm_0.4.2-1.ca2404.1_all.deb Size: 455140 MD5sum: e806cb96fcb64604a14e66e89e5a0fca SHA1: 8c3662eeddac074fe42ed83f288101c2409666ae SHA256: e22d40708392bb37c1c85a17a1c61f8571b112ae63712fbfd57bed03e4c4efd6 SHA512: 05f3add6164f8c2aebeaba96093111502091daa88e1b2cb666d54617a51d92d436545ac454a2e9db823d10b53aa9891bda71b87603526b248f4dda3b22ab85e4 Homepage: https://cran.r-project.org/package=mildsvm Description: CRAN Package 'mildsvm' (Multiple-Instance Learning with Support Vector Machines) Weakly supervised (WS), multiple instance (MI) data lives in numerous interesting applications such as drug discovery, object detection, and tumor prediction on whole slide images. The 'mildsvm' package provides an easy way to learn from this data by training Support Vector Machine (SVM)-based classifiers. It also contains helpful functions for building and printing multiple instance data frames. The core methods from 'mildsvm' come from the following references: Kent and Yu (2024) ; Xiao, Liu, and Hao (2018) ; Muandet et al. (2012) ; Chu and Keerthi (2007) ; and Andrews et al. (2003) . Many functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from after obtaining a license. 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(2017, submitted). 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Package: r-cran-mimer Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4034 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amr, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-testthat, r-cran-data.table, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-mimer_1.0.6-1.ca2404.1_all.deb Size: 363838 MD5sum: 32a27394882594836184376b9be22398 SHA1: 3678eaa3aec346c51df1f77d992aab5f98f501ac SHA256: 6c77727d4078c8ac735a30f0c3c159aa9b9c42c0ff8f61a50dbea293a45734c0 SHA512: 9023d084fc3e17a2893c45c11973aeef9c6da51be985e1fd48fcb627e34d85dacf9679c4836695218c08bdffd98e55ecfc1af8f6256d4da2d3e6466b75d5ccd8 Homepage: https://cran.r-project.org/package=MIMER Description: CRAN Package 'MIMER' (Data Wrangling for Antimicrobial Resistance Studies) Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes, particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing researchers to focus on their primary inquiries without being bogged down by wrangling complexities. 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The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) . 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It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [, , , , , ]; and calibrate the metabolic surrogate values to a desired dataset. 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See De Santiago (2023, ISBN: 978-2-87587-088-9). Package: r-cran-miml Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lightgbm, r-cran-data.table, r-cran-survival Suggests: r-cran-bcclong, r-cran-testthat Filename: pool/dists/noble/main/r-cran-miml_0.1.0-1.ca2404.1_all.deb Size: 1727718 MD5sum: 90c9c3a34b75069496e7fec4efff527f SHA1: 4af8d84ea6ef87b0acd9e86a93bbd3b0912acc9d SHA256: d18ce4c770e65252032e64a5ac59ffa24b54be0af8d4cc982f524c9046f331a3 SHA512: c40a6b575cfdce1910945ab6fde06d0db53626979718b16335c805712ec72a66d5a3b2f1a9ffa5e81393ebefb10e36e9f7916ce6095a224ff08a375836bfa09d Homepage: https://cran.r-project.org/package=MIML Description: CRAN Package 'MIML' (Machine Learning Imputation, Clustering and Survival Analysisfor Longitudinal Proteomic Data) Imputes missing biomarker measurements in a wide longitudinal serum panel with gradient-boosted decision trees, groups the completed panel by Bayesian consensus clustering, and compares the resulting patient subgroups by Kaplan-Meier, log-rank and Cox analysis. The imputation learner is described in Ke et al. (2017) and the clustering method in Lock and Dunson (2013) . Imputed values are conditional-mean predictions, so the procedure is a machine-learning single imputation; the completions carry no between-imputation variance and must not be pooled by Rubin's rules. Two panels from Gene Expression Omnibus accession 'GSE65622' are included, one for each survival endpoint. 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It also includes scripts to reproduce results in the related publication (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) ). 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Use mimsy() on a formatted CSV file to return dissolved gas concentrations (mg and microMole) of N2, O2, Ar based on gas solubility at temperature, pressure, and salinity. See references Benson and Krause (1984), Garcia and Gordon (1992), Stull (1947), and Hamme and Emerson (2004) for more information. Easily save the output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). Supports dual-temperature standard calibration for dual-bath MIMS setups. Package: r-cran-min2halfffd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-hrtlfmc, r-cran-shinybusy Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-min2halfffd_0.1.0-1.ca2404.1_all.deb Size: 186456 MD5sum: 1b2f84915834949ab1c9dc22bb213a08 SHA1: abb06021ffefc0baba5b2f2c9c0b7ceea7c80989 SHA256: ce871bd33e560a07dcd64fe5501296d026ae7dd9f32575a9fbedbd4fa102882d SHA512: c1083b5f328ee0950f3cb78844ae0ec39aa4f3d7ce53122c1067b17ac34ee0300343c1bc1895d71789930c478f56d453185115d3a89a576e2a0e670174eb0640 Homepage: https://cran.r-project.org/package=min2HalfFFD Description: CRAN Package 'min2HalfFFD' (Minimally Changed Two-Level Half-Fractional Factorial Designs) In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) . This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface. 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Classical statistical methods for the analysis of count outcomes are commonly variants of the log-linear model, including Poisson regression and Negative Binomial regression. However, a typical problem with count data modeling is inflation, in the sense that the counts are evidently accumulated on some integers. Such an inflation problem could distort the distribution of the observed counts, further bias estimation and increase error, making the classic methods infeasible. Traditional inflated value selection methods based on histogram inspection are easy to neglect true points and computationally expensive in addition. Therefore, we propose a multiple-inflated negative binomial model to handle count data modeling with multiple inflated values, achieving data-driven inflated value selection. The proposed approach provides simultaneous identification of important regression predictors on the target count response as well. More details about the proposed method are described in Li, Y., Wu, M., Wu, M., & Ma, S. (2023) . Package: r-cran-mind Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-matrix, r-cran-dplyr, r-cran-jwileymisc, r-cran-tm Filename: pool/dists/noble/main/r-cran-mind_1.1.0-1.ca2404.1_all.deb Size: 755598 MD5sum: a48b9ca3f7b404e738496926f09babc6 SHA1: b21894adacce6f0dc6124f51dc86a530c31ac8b7 SHA256: 36f678a16fd81ee783dadac0e8a9b2e8ead7ca1d99626c9cb4d6ff5e526449bd SHA512: 6ad91d196452dec3a071656755b8d77c8ae3a476d1e27283b34735e04248008cfec37cf0f6fe4fd3078b6d37c5aa8b3949ad240ac906561ff212940630d2f9e1 Homepage: https://cran.r-project.org/package=mind Description: CRAN Package 'mind' (Multivariate Model Based Inference for Domains) Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. 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Package: r-cran-mindr Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3787 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-pdftools, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-mindr_1.4.1-1.ca2404.1_all.deb Size: 888456 MD5sum: 83f3ba015cadf7967fb1974e25feab31 SHA1: 86fb959d36863f869a5dee09bee328c8ccbcd79a SHA256: 577d752946aa6961fe6afc2e6e5ae74598a2938144daabb511a7462bfca81a63 SHA512: ec2df87b0ce3f62f044793555cefe015555869f4a9b6f0a552c53d51a65d0ae56f9e87ac0830cb9d7f374e2c561e92b6e0d3a9fe8b757b080ef40967dce1c7f6 Homepage: https://cran.r-project.org/package=mindr Description: CRAN Package 'mindr' (Generate Mind Maps) Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or 'Freemind' codes or 'Mermaid' mind map codes, and vice versa. 'Freemind' mind map ('.mm') files can be opened by or imported to common mind map software such as 'Freemind' (). 'Mermaid' mind map codes () can be directly embedded in documents. Package: r-cran-minecitrus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-minecitrus_1.0.0-1.ca2404.1_all.deb Size: 4587210 MD5sum: a3985ab6169716840fb112ac383f5e28 SHA1: 981285681d558f331dbeaed3e518e5a084e85286 SHA256: 7bc9be9dcd783eff534d5c73396bb7ee0d301e181132bd59be026e81bb05a795 SHA512: d82136cfc461757841a39552ca41bbc4eb2e7c2fed8021bdfa41a61d05e3eb926397a67680864e157ae1088f901c1f854f7e0845cd012f03ee4f886e540f7ce8 Homepage: https://cran.r-project.org/package=mineCitrus Description: CRAN Package 'mineCitrus' (Extract and Analyze Median Molecule Intensity from 'citrus'Output) Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from 'citrus' data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) . To download the 'citrus' package, please see . 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This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials. Package: r-cran-minesdg Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-dt, r-cran-quarto, r-cran-openxlsx2 Filename: pool/dists/noble/main/r-cran-minesdg_0.4.0-1.ca2404.1_all.deb Size: 316238 MD5sum: 21eb800d210fc1c51ceb4ce41c11d585 SHA1: e001e29071e90b758acb31e16ff0e5cc15277122 SHA256: 40126c80d5f17feff8899387348d37c234a73925172d7d13430c765d423a6f85 SHA512: be30d99b408a604d39e178f169626702f946cbecb92692e443421fbeb763ce881919c217d3dc3599d0fd825d426d0a9200873adcd822df51355e4c9549a95d5f Homepage: https://cran.r-project.org/package=MineSDG Description: CRAN Package 'MineSDG' (Mining Industry SDG Impact Calculator) Provides tools to calculate quantitative scores for the United Nations Sustainable Development Goals (SDGs) for the mining, minerals and metals sector. Retrieves official indicator data from the 'United Nations SDG API', runs trend, stability, benchmarking and convergence diagnostics, maps indicators to mining-sector materiality domains via a bundled ontology, computes site-level Key Performance Indicators (KPIs) aligned with Global Reporting Initiative (GRI) 11, International Council on Mining and Metals (ICMM) Mining Principles and Sustainability Accounting Standards Board (SASB) EM-MM conventions, scores sites on a 0-100 SDG scorecard, and ships an interactive 'shiny' dashboard with demonstration datasets. An Environmental, Social and Governance (ESG) reporting layer generates Global Reporting Initiative (GRI), International Council on Mining and Metals (ICMM) and Business Responsibility and Sustainability Reporting (BRSR) reports from a disclosure bundle interface, with framework mappings shipped as data and rendering to 'HTML', 'PDF', 'Word' and 'Excel' via 'Quarto' and 'openxlsx2'. Official Sustainable Development Goals information and indicator methodology are available from the United Nations Sustainable Development Goals website . Package: r-cran-minesweeper Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gifski Filename: pool/dists/noble/main/r-cran-minesweeper_1.0.1-1.ca2404.1_all.deb Size: 61040 MD5sum: c7ae56a82beb182008686fbdf148e521 SHA1: 46bbcb7189814929a4d555be8d333abae974244d SHA256: e6cebf3120968f4bd8a25141c52cdc4f9c6d222d738c490aca92410a554bc13b SHA512: 6b24365e04ed95d3e0cd963113bd4f5e705d3da298ae969baf3dda6b39f71f5e4f4f67c8b2ec02cc0a061f8a4dc4a3ba92d0a29b15630ec13d24179114a6ad3d Homepage: https://cran.r-project.org/package=minesweeper Description: CRAN Package 'minesweeper' (Play Minesweeper) Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) . Package: r-cran-minesweepr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-complexheatmap, r-bioc-interactivecomplexheatmap, r-cran-dplyr, r-cran-gsignal, r-cran-mgc, r-cran-mmand, r-cran-hms, r-cran-rlang, r-cran-pals Filename: pool/dists/noble/main/r-cran-minesweepr_0.1.1-1.ca2404.1_all.deb Size: 62752 MD5sum: 86dafda5b5dc8dd87071083ff806fa65 SHA1: f2f2e484dba6cc7c3c1508ce5b7e974f1daee848 SHA256: d86d7f5394a70a89b9ab6b364d35bf8ee00565dd5a9522a04e003c58dcb56319 SHA512: aadc7e225501c67d408b66a9c12240d63ffbeda9c759bb5068946e502738c329f12d92caee4557c3d2fbc33de2ee3debc9a656442a8d9ca6830ae78a80e7fdb6 Homepage: https://cran.r-project.org/package=mineSweepR Description: CRAN Package 'mineSweepR' (Mine Sweeper Game) This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`. Package: r-cran-minex Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr Suggests: r-cran-clipr, r-cran-ellmer, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-minex_0.2.0-1.ca2404.1_all.deb Size: 110334 MD5sum: ff1f45d3be62d4ff27fcce5d212a6130 SHA1: 43398dc44d796390ee32767e54b5ee581b85aed6 SHA256: d3c70f8c7974cb24d49120ec6864ac187146b9bf2317d82f56a7bf59a289a54f SHA512: 6f19a22fb78d113b9739104e4f09b71c4befe3c6463949a05b7f63d76ee112642e143f6dac7bdc99973a416219bb15a6281fdc5154f19d0fa26c9e31fdea8162 Homepage: https://cran.r-project.org/package=minex Description: CRAN Package 'minex' (Automatically Reduce Failing R Scripts to a Minimal ReproducibleExample) Shrinks a failing R script to the smallest subset of statements that still triggers the same error, using the delta debugging algorithm of Zeller and Hildebrandt (2002) . Each candidate reduction is evaluated in a separate R process, so dependencies between statements and their side effects are respected. The result is a one-minimal example, in which removing any remaining statement makes the error disappear; this is the form most useful for bug reports and for questions on community forums. When no statement can be removed, because the failure is nested inside a function body, reduction continues within the surviving statements. A general delta debugging routine and a helper for reducing data frames to the rows that reproduce a failure are also provided. Package: r-cran-minfactorial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmc Filename: pool/dists/noble/main/r-cran-minfactorial_0.1.0-1.ca2404.1_all.deb Size: 19358 MD5sum: 5aa158d265eb4c235aaa8c3c5143239b SHA1: a08b9fdd1d56e2b1cf9f8d2cd4a5a69ce4d18c94 SHA256: f66dcb043a5dc06c241e551c1a55feec0876deaf37609f0c888a1fb298d91361 SHA512: 1d7676dbd5f459f10d4f9eb213a2b73c8fd35eed949932bf7f1ddb8ec22e8cfd27e1bde34c886154c4d63b9f9efba2d54606d3d1723d0d4295d10786a276b545 Homepage: https://cran.r-project.org/package=minFactorial Description: CRAN Package 'minFactorial' (All Possible Minimally Changed Factorial Run Orders) In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017).This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign(). Package: r-cran-mini007 Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-diagrammer, r-cran-ellmer, r-cran-glue, r-cran-mirai, r-cran-r6, r-cran-rlang, r-cran-uuid Filename: pool/dists/noble/main/r-cran-mini007_0.5.0-1.ca2404.1_all.deb Size: 657228 MD5sum: 78d62a97f52768ae86c3a25cd765f6ed SHA1: ae2b34052ff8614b7d24105e16079b0b12c6031c SHA256: 1bc6dfc460ea962dd0d19b9f475fc7094463d2129bb68d877bd4ed631a45e73c SHA512: 8216a90d60f30ffa08a994e8043ce23331b2b9e06763a79a73e5f6c4e28d85d71ab65b3b8c1d3d325ae2a2991c7f06dd2bcb761679c834e41fe2fbd8393c1aad Homepage: https://cran.r-project.org/package=mini007 Description: CRAN Package 'mini007' (Lightweight Framework for Orchestrating Multi-Agent LargeLanguage Models) Provides tools for creating agents with persistent state using R6 classes and the 'ellmer' package . Tracks prompts, messages, and agent metadata for reproducible, multi-turn large language model sessions. 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The user specifies a set of desired packages, and 'miniCRAN' recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN. This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access. Package: r-cran-minidown Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-katex, r-cran-knitr, r-cran-mime, r-cran-rmarkdown, r-cran-sass, r-cran-xfun Suggests: r-cran-callr, r-cran-dplyr, r-cran-purrr, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-minidown_0.4.0-1.ca2404.1_all.deb Size: 85628 MD5sum: 5e5e14d46b1df0392bd675696241670f SHA1: 995eff77da5e9aea88d4dd64862f530773d1e8ab SHA256: 1920ada98b952b01a4c3b7d4648e823e35fe4183e0ccb737fd1cc11f9b95c990 SHA512: bf03816ec68bf8dd6408ee08dc87bf798f8e01fb935e53e581ab35ac2ba37b6334bf42d5cea0d7d74975259242a3ff344056522144958dc69a767253ab09c242 Homepage: https://cran.r-project.org/package=minidown Description: CRAN Package 'minidown' (Create Simple Yet Powerful HTML Documents with Light Weight CSSFrameworks) Create minimal, responsive, and style-agnostic HTML documents with the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'. Powerful features include table of contents floating as a sidebar, folding codes and results, and more. Package: r-cran-minigui Architecture: all Version: 0.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-minigui_0.8-1-1.ca2404.1_all.deb Size: 49812 MD5sum: e90c3cf492473d9fbd05db1126a22b9a SHA1: 34085e374e41e82b2e55f7209245c6c83197e07f SHA256: 69e1daf6510659947185760972e578296af6f9594af16fbc840599596a9b9fe2 SHA512: cef1cce8f9d1e85d2cb73dd56d23f159b6cae10270168013540cf12507b2bb9fab9d16daf15adb27acadbfcfceebf5eef31f856db6310a36469e64225ce08478 Homepage: https://cran.r-project.org/package=miniGUI Description: CRAN Package 'miniGUI' (Tcl/Tk Quick and Simple Function GUI) Quick and simple Tcl/Tk Graphical User Interface to call functions. Also comprises a very simple experimental GUI framework. Package: r-cran-minimalistgodb Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1973 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-minimalistgodb_1.1.0-1.ca2404.1_all.deb Size: 1739786 MD5sum: 8cf7cad1e4c4c9413854128d5d57542e SHA1: 418ef93bd5792a10b2bb01f7b7e2de2d844c21c2 SHA256: 23d6ed9a6a8a6c2e2b1ac7974f50ca55c02c9c4deaf616dffc6f27786d67308d SHA512: bef758b294b45cb09a55c8536ff4addd07dc6bd85431b43857866422a495add837daf72a159b79ff33587dbdd3558115bfe506db8738e68fe47d8984b8daaf5f Homepage: https://cran.r-project.org/package=minimalistGODB Description: CRAN Package 'minimalistGODB' (Build a Minimalist Gene Ontology (GO) Database (GODB)) Normally building a GODB is fairly complicated, involving downloading multiple database files and using these to build e.g. a 'mySQL' database. Accessing this database is also complicated, involving an intimate knowledge of the database in order to construct reliable queries. Here we have a more modest goal, generating GOGOA3, which is a stripped down version of the GODB that was originally restricted to human genes as designated by the HUGO Gene Nomenclature Committee (HGNC) (see ). I have now added about two dozen additional species, namely all species represented on the Gene Ontology download page . This covers most of the model organisms that are commonly used in bio-medical and basic research (assuming that anyone still has a grant to do such research). This can be built in a matter of seconds from 2 easily downloaded files (see and ), and it can be queried by e.g. w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3 is a matrix representing the minimalist GODB and hgncList is a list of gene identifiers. This database will be used in my upcoming package 'GoMiner' which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)). Relevant .RData files are available from GitHub (). Package: r-cran-minimalrsd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-minimalrsd_1.0.0-1.ca2404.1_all.deb Size: 27160 MD5sum: 8f9329ba20d4f5bd70d32d1ca59defaf SHA1: a1c863a8597e109a567c0d1aebb478b6accb55fe SHA256: 6858635abbdb4e5d9ab23e42c40e009572ac8ee02e9f0fe420db02c9012c4290 SHA512: 86328c6f3b96a3524d726923dae784ade20f3d733650a44ca2e26f36e4a457fc12f043c6155290f67e3d71d8dc39e2fa7167d013539fd3236bf6edfbf4826d21 Homepage: https://cran.r-project.org/package=minimalRSD Description: CRAN Package 'minimalRSD' (Minimally Changed CCD and BBD) Generate central composite designs (CCD)with full as well as fractional factorial points (half replicate) and Box Behnken designs (BBD) with minimally changed run sequence. Package: r-cran-minimap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-minimap_0.1.0-1.ca2404.1_all.deb Size: 49924 MD5sum: 3fa9f81904f74f472a447968003f0a06 SHA1: 2b4c43485d7aeb465143b826f8b0417e306447df SHA256: 00ac145d61bf27e061101cdcc7a1129683ddea68cac6c6240a29432b0e0ad426 SHA512: 3c3f548ca7633287bd2a1fad86030ea28f851f32ff45d30ed59ea1c520b3712b19eebb964bdcb3396549adea157721f30d311099c474f56b40985690136338a9 Homepage: https://cran.r-project.org/package=minimap Description: CRAN Package 'minimap' (Create Tile Grid Maps) Create tile grid maps, which are like choropleth maps except each region is represented with equal visual space. Package: r-cran-minimapr Architecture: all Version: 0.0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-rsamtools, r-cran-pafr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-minimapr_0.0.1.3-1.ca2404.1_all.deb Size: 36132 MD5sum: ae7e83590c00c309fde9a6a5c84a5294 SHA1: 5b18abe0798d70c0cd122be33adcd89bcfa0c3b0 SHA256: 1a1242d32e0e45bf4cb3b1b2223944acb62cf38ba19e4ce82f54af7a9931d73c SHA512: d81119797a6f862278ea9b76fe5c32105dc4fe424904e85f8fe2d744cd212b91d5649049d3197155913170ea06619d8fd3bbbd90e714609c808bd37a11238bcc Homepage: https://cran.r-project.org/package=minimapR Description: CRAN Package 'minimapR' (Wrapper for 'minimap2') Wrapper for 'Minimap2'. 'Minimap2' is a very valuable long read aligner for the Pacbio and Oxford Nanopore Technologies sequencing platforms. 'minimapR' is an R wrapper for 'minimap2' which was developed by Heng Li . *SPECIAL NOTES 1. Examples can only be run from 'GitHub' installation. 2. 'conda' or 'mamba' must be used to install 'minimapR' on your system. 3. For Windows users, 'minimap2' and 'samtools' can be installed via MSYS2, instructions are provided when 'minimap2_installation()' is run. Li, Heng (2018) "Minimap2: pairwise alignment for nucleotide sequences". Package: r-cran-minimax Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-minimax_1.1.1-1.ca2404.1_all.deb Size: 19022 MD5sum: 82694e745956738c2d2a27b6adfcfb44 SHA1: 1d43ef5c418f20c7af7d0e070dd2c58c573934f7 SHA256: 850fbd265f94a88f69e9aa089bb593887caae32f755b66b968244148dbf85c55 SHA512: a889526ad95dd3c2d68f1539f6af41a1a290e489bf6ff8cd75b8adc3c97e14cc5599cc203abf731da0ba33f6a54003399df6f4da727a0f9e585a4115a0a297d5 Homepage: https://cran.r-project.org/package=minimax Description: CRAN Package 'minimax' (The Minimax Distribution Family) The minimax family of distributions is a two-parameter family like the beta family, but computationally a lot more tractible. 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Package: r-cran-minque Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-minque_2.0.0-1.ca2404.1_all.deb Size: 226926 MD5sum: 0db1e159f96b5b37373c1d6a89d86577 SHA1: 8c80ccb7469da479dfa971d9cc5b2567600cc555 SHA256: 8ebb0224653e9b07418b51b71787ce2ee233ad50ad6122557c946c1c7eb916ae SHA512: 64a99c7f75305a90fbab79792caaf9606349b7ab9886286e0f682b3f7556f88b7a263e80f02ca0c3d65bf641d8225ff7f18b42f68055a24b2919349769610352 Homepage: https://cran.r-project.org/package=minque Description: CRAN Package 'minque' (Various Linear Mixed Model Analyses) This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures. 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Package: r-cran-minsnps Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3698 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biocparallel, r-cran-data.table Suggests: r-cran-knitr, r-cran-testthat, r-cran-pkgdown, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-minsnps_0.2.0-1.ca2404.1_all.deb Size: 355550 MD5sum: ca8d16d80e2801ccc02d1a4b3d7d5d59 SHA1: f55d26caa71b59f0800c53141ec833b4ed180dee SHA256: 5c82343d7592645825477e6eb23fda044bacfd8bf475c47842d16a1ea75e191e SHA512: 5a36c85dc8b43cd1ad10d8098b64557f8e46f5ef348b7e8df63dab5c4cb498f305bcd51f300c7b90f249f5214f0617ae75476daba75310c4dd6d6e91e608df01 Homepage: https://cran.r-project.org/package=minSNPs Description: CRAN Package 'minSNPs' (Resolution-Optimised SNPs Searcher) This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants." Package: r-cran-minter Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-minter_0.2.0-1.ca2404.1_all.deb Size: 774994 MD5sum: 198d631c81e8dfdf23b89e27cf93e2b7 SHA1: 464c03211ee47dd4787865938e958ff972a942df SHA256: 972b529fdf34f16d5b0bae562c4cca0e45111e4a24574a11f21c0ead7d0deb01 SHA512: 31873fb4c751a50b48c896106f420554d40550b685e4d3b77e877fcd0704853f4b3cd46f6a8844302a699edfa8cfb260d507b6361f9ee40038d67dd60323187c Homepage: https://cran.r-project.org/package=minter Description: CRAN Package 'minter' (Effect Sizes for Meta-Analysis of Interactions from FactorialExperiments) Compute effect sizes and their sampling variances from factorial experimental designs. The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) , Morris et al. (2007) , Lajeunesse (2011) and Macartney et al. (2022) . Package: r-cran-mintplates Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mintplates_1.0.1-1.ca2404.1_all.deb Size: 2387918 MD5sum: 736270dcdb3276396a8572c3c635b0b9 SHA1: ef21756a01dafd94f2893785260946af95522f31 SHA256: 3c29bbc8d521d3a8f5bb8947910684016c9db79f434e474fb8585dbe7940c53c SHA512: 9664e628088cae9d759ee1bad0d7dc7f8113f6df258d3823586db2d30174b8a12679662c6c3b85f9c4dd96e90569126d197af2c30a96f7e7b7ed21df5621b0d0 Homepage: https://cran.r-project.org/package=MINTplates Description: CRAN Package 'MINTplates' (Encode "License-Plates" from Sequences and Decode Them Back) It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences. While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) . It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) . Package: r-cran-mintyr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-readxl, r-cran-writexl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mintyr_0.2.0-1.ca2404.1_all.deb Size: 515298 MD5sum: c7ff2244ed661b725c3ab81579b9061e SHA1: 902fcbd047d5295c1ac4dd4dff436289ca5a05dc SHA256: 2aa81642effb3b4727b3cc5635627e89c9bbb2f198763e2cde8aacc53ca4ad33 SHA512: e8f7d4a19458287a90f57d4a3b0fcf00d83bc3a77d2ad01a3cae7997148eab9dea98cf2fec023873ffa8ead738c2ad4231db2575d447da4ab14aa1d6112e2112 Homepage: https://cran.r-project.org/package=mintyr Description: CRAN Package 'mintyr' (Grouped and Nested Data Pipelines Built on 'data.table') A toolkit for grouped and nested data pipelines built on 'data.table': import many Excel / CSV files (including multi-row headers) into one table, reshape and nest data by trait and group, run reproducible (stratified) k-fold cross-validation inside every group, summarise groups with report-ready descriptive statistics, and write each piece back to its own file or sheet. Developed for animal breeding, where it prepares phenotypic files for 'ASReml-R', 'HIBLUP' or 'DMU', but useful for any multi-group, multi-variable analysis. 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The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions. 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Package: r-cran-mirai.promises Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nanonext, r-cran-promises Suggests: r-cran-mirai Filename: pool/dists/noble/main/r-cran-mirai.promises_0.5.0-1.ca2404.1_all.deb Size: 19188 MD5sum: e64bdaaedc11eb87dbc3cc2f6173b225 SHA1: cd08baa13d07e3bbc81b77bc71577074b432cf92 SHA256: 2c1a16ad4139f4be62934ac42eb1a07ec55be41b75d7c18f86c876d8d334a196 SHA512: a8e61caa8c29c3b24179d1e2259de60468c383f091ce972701525607466d0cce13f0c92855b6c7a55497792506bbdf99eb8b72a3ba689b70b88eea6ec5bf7cf6 Homepage: https://cran.r-project.org/package=mirai.promises Description: CRAN Package 'mirai.promises' (Make 'Mirai' 'Promises') Allows 'mirai' objects encapsulating asynchronous computations, from the 'mirai' package by Gao (2023) , to be used interchangeably with 'promise' objects from the 'promises' package by Cheng (2021) . This facilitates their use with packages 'plumber' by Schloerke and Allen (2022) and 'shiny' by Cheng, Allaire, Sievert, Schloerke, Xie, Allen, McPherson, Dipert and Borges (2022) . 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Built on 'nanonext' and 'NNG', its non-polling, event-driven architecture scales from a laptop to thousands of processes across high-performance computing clusters and cloud platforms. Features FIFO scheduling with task cancellation and bounded queues, promises for reactive programming, 'OpenTelemetry' distributed tracing, and custom serialization for cross-language data types. Package: r-cran-mirdd Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-amelia, r-cran-rdrobust Filename: pool/dists/noble/main/r-cran-mirdd_0.2.4-1.ca2404.1_all.deb Size: 162046 MD5sum: d2c66ac6ef14551db816f3655a66d608 SHA1: ed4afd7dd77cda9ed668839999959dca382a71d2 SHA256: b6ec99267229fe1a11b857896693fa174aa1187d59f57e33cc016501b18e1537 SHA512: be5b94c9dadce343dcc43c6f6af550c172a3c66cba550db22072a5c5e5977838e65a335cabfb03bfd8036ca3b774d581fa71eb50341c29b1c75906b136a9aa73 Homepage: https://cran.r-project.org/package=MIRDD Description: CRAN Package 'MIRDD' (Diagnostic Tool by Multiple Imputation for RegressionDiscontinuity Designs) Estimates average treatment effects at the cutoff based on sharp regression discontinuity designs (RDD) and multiple imputation regression discontinuity designs (MIRDD). It provides diagnostic tools for RDD by comparing results with those from MIRDD, as proposed in Takahashi (2023) . The package includes datasets from Takahashi (2023) and Takahashi (2026) . Package: r-cran-mirecsurv Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-compoissonreg, r-cran-matrixstats, r-cran-stringi Filename: pool/dists/noble/main/r-cran-mirecsurv_1.0.2-1.ca2404.1_all.deb Size: 61164 MD5sum: 608e491c1ed63325f801845525585be2 SHA1: a353286a821d8ddb2d09fe2a5146d261a93cf8bc SHA256: 63e39272aa620bf55b11ce2b191254bac93d0e69b5549bddada3c9eb33cf4a2d SHA512: 34803a777739cdfac1b04027cb0350a2b512de078c486eb4a63954cb3d8a29a83a6ce2bd4d02574e0ecad79a5a4ba8976edd29091fc5e469a32ee1437383ecce Homepage: https://cran.r-project.org/package=miRecSurv Description: CRAN Package 'miRecSurv' (Left-Censored Recurrent Events Survival Models) Fitting recurrent events survival models for left-censored data with multiple imputation of the number of previous episodes. See Hernández-Herrera G, Moriña D, Navarro A. (2020) . Package: r-cran-miretrieve Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2402 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-openxlsx, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-textclean, r-cran-tidyr, r-cran-tidytext, r-cran-topicmodels, r-cran-wordcloud, r-cran-xml2, r-cran-zoo Suggests: r-cran-kableextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-miretrieve_1.3.4-1.ca2404.1_all.deb Size: 2398746 MD5sum: cf99da0cab6bdc82e195294f5bbc3cd0 SHA1: aedd7ce9a998ed508231bd585a3ed91f29a40208 SHA256: 91570d27a5673f803d27e0cf8676ec6d1931e3a73da77b89c922e949eafe0900 SHA512: b0b5926d133be66e9a201dfa35c8932d303c72fb4bf4806bfba3ec593caf118fbc3bf95ffed935c9ab3294fd6e2c7909b1f204682fa0bcea49e907788c720d8c Homepage: https://cran.r-project.org/package=miRetrieve Description: CRAN Package 'miRetrieve' (miRNA Text Mining in Abstracts) Providing tools for microRNA (miRNA) text mining. miRetrieve summarizes miRNA literature by extracting, counting, and analyzing miRNA names, thus aiming at gaining biological insights into a large amount of text within a short period of time. To do so, miRetrieve uses regular expressions to extract miRNAs and tokenization to identify meaningful miRNA associations. In addition, miRetrieve uses the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) "miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database" ) to display field-specific miRNA-mRNA interactions. The most important functions are available as a Shiny web application under . Package: r-cran-mirkat Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-compquadform, r-cran-quantreg, r-cran-gunifrac, r-cran-pearsonds, r-cran-lme4, r-cran-matrix, r-cran-permute, r-cran-mixtools, r-cran-survival Suggests: r-cran-knitr, r-cran-vegan, r-cran-rmarkdown, r-cran-magrittr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-mirkat_1.2.3-1.ca2404.1_all.deb Size: 363822 MD5sum: 60e469b68a5ed61c0de7e693f83591c0 SHA1: 3eb15ed03ff733f9022a30543a5cc2bd7fb579c3 SHA256: e5508950c690207bf89bb1b7006b700bca9da2fc40051db3d47ae6b4d469d5a3 SHA512: 356c4ee3712202813ebe25b147980ef763bdb36bc6e0fd3df3fe108c0e70053cbe1673df4b78084956f174f52a364ec6321f8986b0359bc18cc4ae8335b0ab1d Homepage: https://cran.r-project.org/package=MiRKAT Description: CRAN Package 'MiRKAT' (Microbiome Regression-Based Kernel Association Tests) Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) ), survival outcomes (Plantinga et al. (2017) ), multivariate (Zhan et al. (2017) ) and structured phenotypes (Zhan et al. (2017) ). The package can also use robust regression (unpublished work) and integrated quantile regression (Wang et al. (2021) ). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information. Package: r-cran-mirnaqcd Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-qpdf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mirnaqcd_1.1.3-1.ca2404.1_all.deb Size: 594710 MD5sum: 568e490358a256a642c073458d46a42f SHA1: 975e70a49a91f0c321dc97087112e8f00cdbcd7d SHA256: f0e21f324c0bd744cadc002668b3d368a3bf8ba4068bbb202667a781f45ff69b SHA512: 1275239c402931546a332617be3247e3272cb00cff7732940c071c7112fc7d3cae745afb985428f5fe26f63b6d52c1c5e66f420ce0bdb8b0503f4259d7200de6 Homepage: https://cran.r-project.org/package=MiRNAQCD Description: CRAN Package 'MiRNAQCD' (Micro-RNA Quality Control and Diagnosis) A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 . The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 . Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information. Package: r-cran-mirrorselect Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yulab.utils Filename: pool/dists/noble/main/r-cran-mirrorselect_0.0.3-1.ca2404.1_all.deb Size: 15394 MD5sum: 1fcc84614ef25452c0fc5c3f782aa6cb SHA1: 451a9f63fde06dd3e8ec730e67d09f98bc6b1174 SHA256: ab6bab2edfeea6fdef0d37447a62bc447a451525d79f3f53e9dc5242a07f206e SHA512: 8062936a249f07ffb801e9ee62326d284ce2352f3059a50734d1741b8fd2ea203cd01305f3afcc54ce40ebb5d1e9c892393253b73b4cc424ac222ee10501a65a Homepage: https://cran.r-project.org/package=mirrorselect Description: CRAN Package 'mirrorselect' (Test CRAN/Bioconductor Mirror Speed) Testing CRAN and Bioconductor mirror speed by recording download time of 'src/base/COPYING' (for CRAN) and 'packages/release/bioc/html/ggtree.html' (for Bioconductor). Package: r-cran-mirsea Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2028 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mirsea_1.1.1-1.ca2404.1_all.deb Size: 1351894 MD5sum: 995f05aeb605b9e88672053e97178cdc SHA1: 900f4142bac22abbc67c03bb4a3b0a8825c675d4 SHA256: 22475a80650d91098cbb42739189d3e0adea65f6238cf4c79ee7a5b28297c462 SHA512: f78965f26d184e67fbc71ce0587a9a2a44d9e3ecfeb0ca2e3a30acef654c8c90f123c713ffd27ab3bc32540f8abcc0a5bc73a383cc2f569d391ba0c5c9e4ea95 Homepage: https://cran.r-project.org/package=MiRSEA Description: CRAN Package 'MiRSEA' ('MicroRNA' Set Enrichment Analysis) The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results. 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(2020). A note on exploratory item factor analysis by singular value decomposition. Psychometrika, 1-15, . 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Package: r-cran-miscfuncs Architecture: all Version: 1.5-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roxygen2, r-cran-mvtnorm, r-cran-extradistr Suggests: r-cran-bayesgarch Filename: pool/dists/noble/main/r-cran-miscfuncs_1.5-10-1.ca2404.1_all.deb Size: 186424 MD5sum: cb9464cd29de02072ca19f3cfbe951d3 SHA1: 509c460e6c9886984108681fc2d3574f6218b851 SHA256: 1d4084fd4df887c8ce6328895e2970ea80135abf59010673c53a608782e35228 SHA512: 752786b7afb52e313fc27b529132914d0f20ca7d3a08c419ae7871428571990206a55bc51ec30c6fa46f53aabb7ba2332dea9497ff3d27fb42474dafbd584436 Homepage: https://cran.r-project.org/package=miscFuncs Description: CRAN Package 'miscFuncs' (Miscellaneous Useful Functions Including LaTeX Tables, KalmanFiltering, QQplots with Simulation-Based Confidence Intervals,Linear Regression Diagnostics and Development Tools) Implementing various things including functions for LaTeX tables, the Kalman filter, QQ-plots with simulation-based confidence intervals, linear regression diagnostics, web scraping, development tools, relative risk and odds rati, GARCH(1,1) Forecasting. Package: r-cran-miscic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnls Filename: pool/dists/noble/main/r-cran-miscic_0.1.0-1.ca2404.1_all.deb Size: 43924 MD5sum: 4813816543c0f60b9b53b8a0f7055d9a SHA1: 310bd409375388891e1a653bb10a953b6d05149b SHA256: 15481f17f2ef33d12e1559ff235ad96cedcae45093f98cd85d05b27bc5be03c6 SHA512: 00e50548f39b8abedc5175ba8f5d7cde7efd6e9f575e83d36af81b1366687b35e084963ad13c8156b0127884192c1c1d1852e6d8f31ce0ef6446f8d554840fd0 Homepage: https://cran.r-project.org/package=miscIC Description: CRAN Package 'miscIC' (Misclassified Interval Censored Time-to-Event Data) Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) . Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted. Package: r-cran-miscmetabar Architecture: all Version: 0.16.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4094 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-phyloseq, r-cran-ggplot2, r-cran-dplyr, r-cran-ape, r-bioc-biostrings, r-cran-cli, r-bioc-dada2, r-cran-divent, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-bioc-xvector Suggests: r-cran-adespatial, r-bioc-aldex2, r-bioc-ancombc, r-cran-biocmanager, r-cran-circlize, r-cran-complexupset, r-bioc-decipher, r-bioc-deseq2, r-cran-devtools, r-cran-dt, r-bioc-edger, r-cran-formattable, r-cran-ggalluvial, r-cran-ggfittext, r-cran-ggh4x, r-cran-ggnetwork, r-cran-ggstatsplot, r-cran-ggridges, r-cran-ggvenndiagram, r-cran-glmulti, r-cran-gtsummary, r-cran-gridextra, r-cran-here, r-cran-httr, r-cran-igraph, r-cran-inext, r-cran-indicspecies, r-bioc-iranges, r-cran-jsonlite, r-cran-knitr, r-bioc-lefser, r-cran-magrittr, r-bioc-mia, r-bioc-metagenomeseq, r-cran-mixtools, r-cran-multcompview, r-cran-networkd3, r-cran-pak, r-cran-patchwork, r-cran-pbapply, r-cran-permute, r-cran-phangorn, r-cran-phyloseqgraphtest, r-cran-pkgnet, r-cran-plotly, r-cran-plyr, r-cran-reshape2, r-cran-rmarkdown, r-cran-rotl, r-cran-rtsne, r-cran-scales, r-cran-seqinr, r-cran-srs, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-treemapify, r-cran-umap, r-cran-uwot, r-cran-vegan, r-cran-venneuler, r-cran-vctrs, r-cran-viridis, r-cran-withr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-miscmetabar_0.16.8-1.ca2404.1_all.deb Size: 2889574 MD5sum: ccf7c4aabfcbe65c4ea35f1f8083ab5e SHA1: 81c9b4ed3a8270b652722738995cf4e650152af7 SHA256: 7604911fa6f9f5bb1fbaea8ca6370c228d4d0878cb7b6faa00814feec14131c1 SHA512: 43253f2096f76cb4d36c7e8cd31544d1f65794d9f405c8f550eecc22dcb6e23544da9bd6d3850874fac93b6b941625cc9ae74a67ecc74a25eb03f3ffaed64b44 Homepage: https://cran.r-project.org/package=MiscMetabar Description: CRAN Package 'MiscMetabar' (Miscellaneous Functions for Metabarcoding Analysis) Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. 'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' 'R' packages. It helps to build reproducible and robust bioinformatics pipelines in 'R'. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) . Package: r-cran-misctools Architecture: all Version: 0.6-30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest Suggests: r-cran-ecdat Filename: pool/dists/noble/main/r-cran-misctools_0.6-30-1.ca2404.1_all.deb Size: 88744 MD5sum: 46af52a68c04f64b46f17deacf04e5f6 SHA1: f5a897f2909a9f3ad5942f44e7df188acfdb008d SHA256: 1f5f845e8b3b1d32cb8738987632e6d7f09a7940061866692dbbdd11118ecfbe SHA512: 77b7fc0b80a7beeb6f3fc1a33e5fc1c9387e000c3a4948cde1730d8b3cb7a360f049c78dce6d2d51584d8d86cf235eea910bf57bce2dddae92a080f078d4b792 Homepage: https://cran.r-project.org/package=miscTools Description: CRAN Package 'miscTools' (Miscellaneous Tools and Utilities) Miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. 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Package: r-cran-miselect Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mice, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-miselect_0.9.2-1.ca2404.1_all.deb Size: 145576 MD5sum: 67b42daae84d8f71a7a14242d9f8ce85 SHA1: 122f8b3dc7f40696bc1f4ff02d279c5c7f08944f SHA256: c500e396b54fb77805945f7d3ca6b7d5d8942d1a2dedc971d77dbe69085d913a SHA512: 73969d0d3f3de7aa2efcc5a2f006ce84c7c6d8c2e4d2b9730cd8a91b2ab5bfa94b6df59487ee3c9fb283b6e3d3639a81bf1ce22a4e1e50f0d782da9da6415ff8 Homepage: https://cran.r-project.org/package=miselect Description: CRAN Package 'miselect' (Variable Selection for Multiply Imputed Data) Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) . They, by construction, force selection of the same variables across multiply imputed data. 'miselect' also provides cross validated variants of these methods. Package: r-cran-misl Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-future.apply, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-stacks, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-workflows Suggests: r-cran-earth, r-cran-future, r-cran-ggforce, r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-ranger, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-misl_2.0.0-1.ca2404.1_all.deb Size: 64342 MD5sum: 68e6940be66eb339fe9a76b926b31beb SHA1: c5e6142123292b6b06487efd073be71572f804a3 SHA256: 5485d86b0d3c8e381f2460e87f308b73281a066a1fa3590415a1f252c42c4bf9 SHA512: 40123bb161f658add1e1df5968ff1a1bda8880d7c5725569a16bbbe54f6727405a26544b8df713ce6722b661709532befa20397b2546e422e1ba51ac5c29dda8 Homepage: https://cran.r-project.org/package=misl Description: CRAN Package 'misl' (Multiple Imputation by Super Learning) Performs multiple imputation of missing data using an ensemble super learner built with the tidymodels framework. For each incomplete column, a stacked ensemble of candidate learners is trained on a bootstrap sample of the observed data and used to generate imputations via predictive mean matching (continuous), probability draws (binary), or cumulative probability draws (categorical). Supports parallelism across imputed datasets via the future framework. Package: r-cran-mispitools Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forrel, r-cran-pedtools, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-patchwork, r-cran-ggplot2, r-cran-dirichletreg, r-cran-proc, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mispitools_1.4.0-1.ca2404.1_all.deb Size: 910522 MD5sum: d05c077fd3cad918e1088e0876082e8d SHA1: 027c8f53ca92b69d90aebcc1f0b8b0b650891f13 SHA256: 73d9d347de864015de034acdda679353a4011d88a24da177dc9e8334de81ce46 SHA512: 566ced2a01f733ac263b3e22fde3bd9a15801741aa849e788c6f91a43cc27172aea1c5b2f9e906dfffb53ca1882e182e71c1b91eead95759d7f8ba15c193b8bd Homepage: https://cran.r-project.org/package=mispitools Description: CRAN Package 'mispitools' (Missing Person Identification Tools) A comprehensive toolkit for missing person identification combining genetic and non-genetic evidence within a Bayesian framework. Computes likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color, and birthdate evidence. Provides decision analysis tools including optimal LR thresholds, error rate calculations, and ROC curve visualization. Includes interactive Shiny applications for exploring evidence combinations. For methodological details see Marsico et al. (2023) and Marsico, Vigeland et al. (2021) . Package: r-cran-mispr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-mass, r-cran-penalized Filename: pool/dists/noble/main/r-cran-mispr_1.0.0-1.ca2404.1_all.deb Size: 148614 MD5sum: 7bbb6852ae6d4218207f6320778f54b2 SHA1: 65701de9231fe5cd6eabd11563215ab5ecab51ca SHA256: 57ee9b56b63942e8c293d3a54e191ec69da7acef8df511498669584556dc335c SHA512: 618549ccf286e9b4c68716307b73ec836cf0415c4000b64ac9c5fd73bf53e311ce16b89a55f4a6bec49623bf83ad4e570bd15d94a77d35acb80f18ad344c33b3 Homepage: https://cran.r-project.org/package=mispr Description: CRAN Package 'mispr' (Multiple Imputation with Sequential Penalized Regression) Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) . Package: r-cran-misprime Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-mass Filename: pool/dists/noble/main/r-cran-misprime_0.1.0-1.ca2404.1_all.deb Size: 46682 MD5sum: 51333b1a5ae08a8a6d6c8d27a4912046 SHA1: 66f1d6782c31b0a371b80914f7f66bfc2c800d89 SHA256: e36250d2824b2d2c082699f6fef375a6bbadd58904a6026188664b1ce77a8658 SHA512: 71b279211114fde17c28b946cae33189eb755541990aa8c48d67953f98855ea07c00019f1df6eb312340cd6470ecb13adadb1fdd4aa63edb1522ea6c8401d543 Homepage: https://cran.r-project.org/package=misPRIME Description: CRAN Package 'misPRIME' (Partial Replacement Imputation Estimation for Missing Covariates) Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) . Package: r-cran-misreparma Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mixtools, r-cran-boot, r-cran-tseries, r-cran-r2jags Suggests: r-cran-coda Filename: pool/dists/noble/main/r-cran-misreparma_0.2.0-1.ca2404.1_all.deb Size: 56060 MD5sum: 2f77f1367d14c4d7d16ec359eb0ab080 SHA1: f4372097aa3d689c848a2df911b266882fdd684a SHA256: 88ac4f2c1ff3e20a8d620157ceb709d6d40100c55c938013e5fdcc638c904f94 SHA512: f8056c6f3610fd2a2ac4fb1574f81e98f1fcc603c74b9c8e06defb042f8537488a00279e181e182c251542e2cff558e1de09d4264338d42b9c9b0f8f8e89c892 Homepage: https://cran.r-project.org/package=MisRepARMA Description: CRAN Package 'MisRepARMA' (Misreported Time Series Analysis) Provides a simple and trustworthy methodology for the analysis of misreported continuous time series using either a frequentist (bootstrap-based EM algorithm) or a Bayesian (MCMC via JAGS) approach. The frequentist method is described in Morina et al. (2021) . The Bayesian extension fits the same ARMA model with misreporting structure using a full posterior distribution, providing credible intervals and DIC for model comparison, as described in Morina et al. (2024) . Package: r-cran-missalpha Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-deoptim, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-missalpha_0.2.0-1.ca2404.1_all.deb Size: 80422 MD5sum: e8b059b509fa7488cfab6adbac67e80d SHA1: 3824255f8bed9c477ac8edb1831a55755e670268 SHA256: e5df5d8f803d2e909c85fa1f2f9b5fdf87d08485c5ad62122118f944bfb3fbfa SHA512: 3fc34bfe3449ab1b6caf3edd7e49facc1af3de7b1f6027cae3d16b139dd242386eeb74e8bfcab0dd6ab076d573347604295d0e35313063a0c60a75ffda6a6a6b Homepage: https://cran.r-project.org/package=missalpha Description: CRAN Package 'missalpha' (Find Range of Cronbach Alpha with a Dataset Including MissingData) Provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 ) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 ; Tavakol & Dennick, 2011 ). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 ). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature. Package: r-cran-misscforest Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-misscforest_0.0.8-1.ca2404.1_all.deb Size: 22432 MD5sum: fe502a4d623f4e754397635724897001 SHA1: f20e47d188589457aef87fc6824f58a58f0fa589 SHA256: 813e20c6db8134cc7a0f8d8d9d4e0e3ae5b5f211d26f4aac345bd7cd724d1857 SHA512: 3d63e0bef289f85860690f4eba136244a672d6a8942d35c866dfc833e80033f7ed7cfc942a19e2cbad78bf66652f179a4851cf31961b0a473db759efe8c6fff3 Homepage: https://cran.r-project.org/package=missCforest Description: CRAN Package 'missCforest' (Ensemble Conditional Trees for Missing Data Imputation) Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) ). Package: r-cran-misscompare Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-amelia, r-cran-data.table, r-cran-dplyr, r-cran-ggdendro, r-cran-ggplot2, r-cran-hmisc, r-cran-ltm, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-mi, r-cran-mice, r-cran-missforest, r-cran-missmda, r-bioc-pcamethods, r-cran-plyr, r-cran-rlang, r-cran-tidyr, r-cran-vim Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-misscompare_1.0.3-1.ca2404.1_all.deb Size: 2728132 MD5sum: 811df6dc0b19c3f94fbcf52c8f088537 SHA1: 9dbc2b66cd197343b35f6b57e662e7b1552c1780 SHA256: 6299dc0c8cc178fb1765bd05e620e0cae3f3c177959a9b0a5bda60ba68012ef9 SHA512: ac30c0251266ac5b82dae65ad62e9c172e3c6796b66496becc86946319d6848af4e1e00916154c135ca0de367b4bb5cbe17594922c2b98932b04d229810b6780 Homepage: https://cran.r-project.org/package=missCompare Description: CRAN Package 'missCompare' (Intuitive Missing Data Imputation Framework) Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) ; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) ; 'missForest', described by Stekhoven and Buhlmann (2012) ; 'missMDA', described by Josse and Husson (2016) ; and 'pcaMethods', described by Stacklies et al. (2007) . The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance. Package: r-cran-missdiag Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-cobalt Suggests: r-cran-mice, r-cran-sbw, r-cran-ebal Filename: pool/dists/noble/main/r-cran-missdiag_1.0.1-1.ca2404.1_all.deb Size: 145632 MD5sum: 6b40afefeb3e2b04aa9c3d88c4a958ac SHA1: a90749c8db7033c6fe156383e1d3fda8651eaa26 SHA256: 3dd78965d3ba1cfbc4cd497d26eebda7e76d6c089eaece731d5cb69734921a03 SHA512: 2bab8a9880a0b91034eb046cec7a0868b658ce930db1544b622e858713b42d32dcebd63682043c06876ab589a6c430dcdbf592080da43056b909bd844fb48c8c Homepage: https://cran.r-project.org/package=missDiag Description: CRAN Package 'missDiag' (Comparing Observed and Imputed Values under MAR and MCAR) Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) . Package: r-cran-missforest Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-ranger, r-cran-foreach, r-cran-iterators, r-cran-itertools, r-cran-dorng, r-cran-rdpack Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-missforest_1.6.1-1.ca2404.1_all.deb Size: 274880 MD5sum: 0b471a927ca6b1907b23d7cba8e3dba6 SHA1: f408d567f94e27c917c7727f63d338ab3686f9ad SHA256: 229bc0297b7f4acf5275f232e484e9f48d1c499ebe733e2c24f0883ee13270fe SHA512: f030a3bf1df5d2195b1486d4d3cc72b632b983e9c6c788e908c41a6be16d9f4603325f7cc4d80da45ae8a32a89d7bfbaa91ed156d445f596b89360749f835388 Homepage: https://cran.r-project.org/package=missForest Description: CRAN Package 'missForest' (Nonparametric Missing Value Imputation using Random Forest) The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time. Package: r-cran-missforestpredict Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-missforestpredict_1.0.1-1.ca2404.1_all.deb Size: 218612 MD5sum: f5e425610bf0454a3863efc13e0d6769 SHA1: 54d830574f020379c12984000863c7effdf91c44 SHA256: 69f1711be5f91191e029f704961edc741e5f20de2142f8984795d713f0537738 SHA512: c9d27bc3ca3bbd51d41d4f8dfe7116dc8b42a16785bc85f09ebd1c6edcfb43e65ecc984756e5351e059ba4a45d987f76d7c33e278bd5687fe7a90cf431ff96ad Homepage: https://cran.r-project.org/package=missForestPredict Description: CRAN Package 'missForestPredict' (Missing Value Imputation using Random Forest for PredictionSettings) Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) ) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings . Package: r-cran-missinghandle Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-imputets, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-missinghandle_0.1.1-1.ca2404.1_all.deb Size: 37184 MD5sum: 548dad1c6b4d129218500dfc247aa90c SHA1: ed286f1f7746f8197c2fdeb9e68c1eaa172cd0de SHA256: 057ed766d040aa5b28c3c5ec3160548acce2506943033b456419ee15e1eab5f0 SHA512: b5990b7eb348416614cb0641c8add762b1d42714a97aafa02046cf354274b4c3255262b5fe77adefc6251e3b999a693f3ac8cbe0227f5696142527026359dcd2 Homepage: https://cran.r-project.org/package=MissingHandle Description: CRAN Package 'MissingHandle' (Handles Missing Dates and Data and Converts into Weekly andMonthly from Daily) Many times, you will not find data for all dates. After first January, 2011 you may have next data on 20th January, 2011 and so on. Also available dates may have zero values. Try to gather all such kinds of data in different excel sheets of a single excel file. Every sheet will contain two columns (1st one is dates and second one is the data). After loading all the sheets into different elements of a list, using this you can fill the gaps for all the sheets and mark all the corresponding values as zeros. Here I am talking about daily data. Finally, it will combine all the filled results into one data frame (first column is date and other columns will be corresponding values of your sheets) and give one combined data frame. Number of columns in the data frame will be number of sheets plus one. Then imputation will be done. Daily to monthly and weekly conversion is also possible. More details can be found in Garai and others (2023) . Package: r-cran-missinghe Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggpubr, r-cran-ggmcmc, r-cran-ggthemes, r-cran-bcea, r-cran-ggplot2, r-cran-bayesplot, r-cran-r2jags, r-cran-loo, r-cran-coda, r-cran-mcmcr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-missinghe_1.6.1-1.ca2404.1_all.deb Size: 1492674 MD5sum: d82441e5876a0f30629d515ebfb46e35 SHA1: 7fb32bdc5ca2eaec63a1c2872b9c857c34b99487 SHA256: a5f364a416643404e04821fa9876d4089ac944a461a3417517967cdf4684ca9d SHA512: 0d8b1f8cf5efabf28d1a88352060805588fa80c6f629f68022ca7088b4b7ab1e4d7753398a8016c7e3a41d310468686779900dd7609fa11ca6e291ac2ab98d2f Homepage: https://cran.r-project.org/package=missingHE Description: CRAN Package 'missingHE' (Missing Outcome Data in Health Economic Evaluation) Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) , Molenberghs (2000) and Gabrio (2019) . Package: r-cran-missingplotlsd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missingplotlsd_0.1.0-1.ca2404.1_all.deb Size: 13128 MD5sum: 68ba37c1ed6763fcb0547e7d01c52f22 SHA1: e7b9d27f206e746f5e38ca7db0e9b118ad2ac6d6 SHA256: b5b3b4fcc207239a49cd85e1866fae11f4c63da7e18cb3696e9eff9578a0d750 SHA512: b6c0a60217e8d46f08cb286b57509a5220534fe935450d3c299223c3b550e30fa99b6e88966b98952c09ca84ef40a23d0775821550b531712143b53b87038777 Homepage: https://cran.r-project.org/package=MissingPlotLSD Description: CRAN Package 'MissingPlotLSD' (Missing Plot in LSD) A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-missingplotrbd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missingplotrbd_1.1.0-1.ca2404.1_all.deb Size: 11804 MD5sum: 32f415669e10b3ce19bb2db0857b6ec1 SHA1: c47c38d24f96a07f98525ccfc87d421a5d4acf73 SHA256: 90085a8f611cf598573d1252f9f0e65ea7c5078d8af0ab0433c813daacf7f38f SHA512: 6cff7f2738699c23d8fa18d1b942279186411da96117e0f729d70e6f9576dd453dd7aac0d498a2826d26507be70737318ad4ec439f541ffe360e255083148fb6 Homepage: https://cran.r-project.org/package=MissingPlotRBD Description: CRAN Package 'MissingPlotRBD' (Missing Plot in RBD) A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-missmda Architecture: all Version: 1.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factominer, r-cran-foreach, r-cran-ggplot2, r-cran-mice, r-cran-mvtnorm, r-cran-doparallel Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-missmda_1.23-1.ca2404.1_all.deb Size: 440776 MD5sum: e67fc7777a72ff122fb4e3582b0048ce SHA1: 97f0d3523a1a3d63aedbce899d8b9119486f69f7 SHA256: 84fdbe1b22aa901fe585fe99f76adfad3a4cb7fc44bccadb234d71a1eaf668e7 SHA512: 6f7af32c1e0cb7c78040734118de6b99cd08d8f39c4e92552af1db12981aebd127dbc4ed78b96b91757070e5a63bedc8059b7cdc09926a7003d9c322c407eac8 Homepage: https://cran.r-project.org/package=missMDA Description: CRAN Package 'missMDA' (Handling Missing Values with Multivariate Data Analysis) Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA. Package: r-cran-missmech Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missmech_1.0.4-1.ca2404.1_all.deb Size: 147970 MD5sum: 2a9dbf72c3064b48136adc5976b56d99 SHA1: bc757815327d8734fe1ea650136b4fc1013faf34 SHA256: 3c82b9dcbee5b2075e9d08787c6c23692919150d9135110cf5ccb0d4ef1326d0 SHA512: 8c6fccf2f8bef2164dd7eccd459a6e45570ad4bae436d3901d3073ac10d5a26d24db80a32a44d7d2938a82be02a80b7eff6709528d8f7ff0bded488a4647b145 Homepage: https://cran.r-project.org/package=MissMech Description: CRAN Package 'MissMech' (Testing Homoscedasticity, Multivariate Normality, and MissingCompletely at Random) To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. . Package: r-cran-missmethods Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-lpsolve, r-cran-norm, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-missmethods_0.4.0-1.ca2404.1_all.deb Size: 265482 MD5sum: 43af2f3758f5bacf47e2db7f41e2240f SHA1: de5b4873416fbff4a916329f5129e213521514a5 SHA256: eb1038e60cdb857e3c0755ed54ecec5595e667a5ab81b91cb0dfa6a9ffcf301a SHA512: ae5a8e59242eaa437214e22904ac5050645c9982c89c58d06a78fa70dbd2a688e8904a5dfe94e75db3f30a855d7f3771492e97fc635346c2178fba7dc640dc58 Homepage: https://cran.r-project.org/package=missMethods Description: CRAN Package 'missMethods' (Methods for Missing Data) Supply functions for the creation and handling of missing data as well as tools to evaluate missing data methods. Nearly all possibilities of generating missing data discussed by Santos et al. (2019) and some additional are implemented. Functions are supplied to compare parameter estimates and imputed values to true values to evaluate missing data methods. Evaluations of these types are done, for example, by Cetin-Berber et al. (2019) and Kim et al. (2005) . Package: r-cran-misspi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lightgbm, r-cran-doparallel, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-sis, r-cran-plotly Suggests: r-cran-e1071, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-misspi_0.1.1-1.ca2404.1_all.deb Size: 648372 MD5sum: 14afd81a16ad5a88afe97346e8b5fb6e SHA1: 0cea8789ee24716972e367844ad74d10035b7ae2 SHA256: b1fd4d149547c75a3954a0082c42fff6d5c6e483b0f7d2ce71cd955c96bfa16a SHA512: bbfdccc789a3d35641251bdf388d9130c513c9823445dc8eb419783e247922e13b9b303f5a07dbf7d6e2a699bad602cdd8ffdc188b786e91b2a5b7a92972dbb3 Homepage: https://cran.r-project.org/package=misspi Description: CRAN Package 'misspi' (Missing Value Imputation in Parallel) A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017) . 'misspi' has the following main advantages: 1. Allows embrassingly parallel imputation on large scale data. 2. Accepts a variety of machine learning models as methods with friendly user portal. 3. Supports multiple initializations methods. 4. Supports early stopping that prohibits unnecessary iterations. Package: r-cran-missplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missplot_0.1.0-1.ca2404.1_all.deb Size: 17248 MD5sum: 51d95dcdf858fb05cdb83a07516b2c0a SHA1: f568ad0d7fbeb125c2946d23766b4221336fd2be SHA256: fc7c30b62ae89aec9a9b6c13ac45ae082eee405f0edae6f783c5d9e1f37dc32a SHA512: 10983cca760bdfabdadda2749fa2dd7232b14bafce33acbcba7836ef641657f0261a1964f446dcf092499b725a4bee89bdde125d1dc6c5b0eec7d381697ed6ad Homepage: https://cran.r-project.org/package=Missplot Description: CRAN Package 'Missplot' (Missing Plot Technique in Design of Experiment) A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-misspls Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-plsrglm, r-cran-vim Suggests: r-cran-bcv, r-cran-knitr, r-cran-mlbench, r-cran-plsdof, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-misspls_0.2.1-1.ca2404.1_all.deb Size: 495286 MD5sum: cc4cd4faea2b05bd23d66c285d1c9b76 SHA1: 2a42ea195a3cfb4f25f05684677c79e5aca4946e SHA256: 643474f1002d2cc0ff75cee9b3affa1a9d0701e1b927241a38ef9f3ff91d0121 SHA512: 06a159867b58e75b3a4222c7d0923c6f05d50d2fee9758bedd5096a1bebcd9015a2eaca30f599abe618c8484751d38ab8ed246b72dd51cdffe2cc19fb87b48be Homepage: https://cran.r-project.org/package=missPLS Description: CRAN Package 'missPLS' (Methods and Reproducible Workflows for Partial Least Squareswith Missing Data) Methods-first tooling for reproducing and extending the partial least squares regression studies on incomplete data described in Nengsih et al. (2019) . The package provides simulation helpers, missingness generators, imputation wrappers, component-selection utilities, real-data diagnostics, and reproducible study orchestration for Nonlinear Iterative Partial Least Squares (NIPALS)-Partial Least Squares (PLS) workflows. Package: r-cran-missr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-norm, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-missr_1.0.1-1.ca2404.1_all.deb Size: 129656 MD5sum: 356cb6370da48f43240982b8d1e920ad SHA1: bba6138312b603f5a834bea30b49d831913405da SHA256: 8f45c337ed86d71f8f7ee97db3261e9c5554b215b2a88b52fd43c511eaf9f838 SHA512: f3997cf5fce3d2adcd1202a87eccedb51b959ebbb7527d931277726a6ba77ed2b42d1da27921ce8959af57597bc0961eafc1cb398dcc23bf9102c9c684dc46aa Homepage: https://cran.r-project.org/package=missr Description: CRAN Package 'missr' (Classify Missing Data as MCAR, MAR, or MNAR) Classify missing data as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This step is required before handling missing data (e.g. mean imputation) so that bias is not introduced. See Little (1988) for the statistical rationale for the methods used. Package: r-cran-missranger Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-missranger_2.6.1-1.ca2404.1_all.deb Size: 89798 MD5sum: b705eb7e0cb71ae7e06e15f3879c8dd7 SHA1: c63dfa83230dd93976bc322ff6a7e8d7315128da SHA256: d5a2d698b903fe6dfbfd1595be81e043a5c3b25be02cc128e57388c875ed2588 SHA512: a6256709b7abeb982859cdc7327cfb1d87b64769bf35924120ff1924c2ec934986a1388951d7d0a79a4d2263ebdcf6b74972ebe58884385c844e918c43aa8c9b Homepage: https://cran.r-project.org/package=missRanger Description: CRAN Package 'missRanger' (Fast Imputation of Missing Values) Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) . Under the hood, it uses the lightning fast random forest package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). Out-of-sample application is supported as well. Package: r-cran-mistr Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1956 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-pinp Filename: pool/dists/noble/main/r-cran-mistr_0.0.6-1.ca2404.1_all.deb Size: 1655316 MD5sum: 49be1ee97e93acbd24da049a6de696d3 SHA1: fc5c838664670481c9d72f0c36a80905d536b8bc SHA256: d55e5cdfa9524d94b616235080e792ec8a63469359057f7e4e345d365d6a6cc0 SHA512: 1248962a0c78bb2e23f5221a91a39d3ca232e6e543bce2c38b32210ae930fe34eaed054af30b198ffcc59c5f8614e4f050955c3845fc984c7941a88dad3430c4 Homepage: https://cran.r-project.org/package=mistr Description: CRAN Package 'mistr' (Mixture and Composite Distributions) A flexible computational framework for mixture distributions with the focus on the composite models. 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Yanagida') Miscellaneous functions for (1) data handling (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'. 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All data sets are build on runtime downloading raw data from MITRE public services. MITRE is a government-funded research organization based in Bedford and McLean. Current version includes most used standards as data frames. It also provide a list of nodes and edges with all relationships. 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Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) ). The imputation models are: bootstrapping imputation (Lubin et.al (2004) ), univariate Bayesian imputation (Hargarten & Wheeler (2020) ), and multivariate Bayesian regression imputation. 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Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) , Boshnakov (2009) ), and the extensive references in the documentation. 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Package: r-cran-mixcure Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-flexsurv, r-cran-survey, r-cran-gam, r-cran-timereg Filename: pool/dists/noble/main/r-cran-mixcure_2.0-1.ca2404.1_all.deb Size: 82706 MD5sum: 6f870ad387d7bc2cf23ee104cfd1d1bb SHA1: 1acefce79cf16377dc831dc323731cdbf5df105b SHA256: 15583155d54c4f7b014e0a365af086840e4d489391b97e585e6f00795effc3bf SHA512: f401601ae6e5defe1becff060c80ec72b67835fc26e735611148133597ddb17652f8dc1e4cc867ab6eb955add6c2eaf3e4fafdd50ec7d96c43145673c0df549b Homepage: https://cran.r-project.org/package=mixcure Description: CRAN Package 'mixcure' (Mixture Cure Models) Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) . Package: r-cran-mixdist Architecture: all Version: 0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mixdist_0.5-5-1.ca2404.1_all.deb Size: 171222 MD5sum: f03f942c51c31fe8c4573795720afe73 SHA1: d92204d8dfd6b912cc5bbcc5bcd07fb5913a315c SHA256: aa155a4634517a15c0589ae54258d89a7cf8c603fe58fda53e3729518cc9fcd2 SHA512: 3bfde9c9e32412d9b4fe6388fb892d050e0789b63dc59e32960234fee739f878d0f3d3f986cfdb17a3eb46a8b6691aba496f2a70b48056a06d470b420a7c2505 Homepage: https://cran.r-project.org/package=mixdist Description: CRAN Package 'mixdist' (Finite Mixture Distribution Models) Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm. Package: r-cran-mixedbiastest Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-plm, r-cran-lmertest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixedbiastest_1.0.3-1.ca2404.1_all.deb Size: 51212 MD5sum: 71b4d4330ecd0106a60bdd2a5438aa38 SHA1: a1fcf51d69f389c1fff8f2e93548f033af76a6d1 SHA256: 303d65e8ff35dfe4499a5c8a655c1ab3a02699b002f91fba9292a6fdd2711255 SHA512: bb61511af2f479171dc6fd440c0d4ad58fff34ef2800d273cf1d0b9d3579a0e48774e98e4cb76ef3ae7f00d3c04099601ad8efd36379e2d8f10beed99f2d0c50 Homepage: https://cran.r-project.org/package=mixedbiastest Description: CRAN Package 'mixedbiastest' (Bias Diagnostic for Linear Mixed Models) Provides a function to perform bias diagnostics on linear mixed models fitted with lmer() from the 'lme4' package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) . Karl and Zimmerman (2020) provide R code for implementing the test using 'mvglmmRank' output. Development of this package was assisted by 'GPT o1-preview' for code structure and documentation. Package: r-cran-mixedfact Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixedfact_0.1.1-1.ca2404.1_all.deb Size: 90938 MD5sum: 086037ef1ffe3093237717aaa972e576 SHA1: 174e1144e8cd6dd68e2b9e7eff6969cdfee0267d SHA256: 674fcb568390025647dbff55aefa47861fe2983c169ed066644eb6164ffcc1b2 SHA512: 6793c8e92d6f3818b18129f9ebe9bc0e89f707c89190ad0f63c601c9cbad3778b9dd5c5344f36f88a1735c4bc6ab0290efac6affe1b2ba10fd50c591346f3658 Homepage: https://cran.r-project.org/package=mixedfact Description: CRAN Package 'mixedfact' (Generate and Analyze Mixed-Level Blocked Factorial Designs) Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) . When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results. Package: r-cran-mixediffusion Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-adaptmcmc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixediffusion_1.0.1-1.ca2404.1_all.deb Size: 230846 MD5sum: c1db063cffb3687ffc426c04805b58aa SHA1: efd74772a4e1e463ab1073fe94a00daabf26d555 SHA256: 63eafbd3130b5c379d9c45af3be582fdb9db04ffcf6d3117acc286c848edda7d SHA512: 4dd9937911adad746ec067d3b8bc6ab54c2563171830dddf82f7c20fd6a930939cb64150fe5965a5bca4c4704af78671d925d593ea469cfa785505bb1c3a9adf Homepage: https://cran.r-project.org/package=mixediffusion Description: CRAN Package 'mixediffusion' (Mixed-Effects Diffusion Models with General Drift) Provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectation–maximization (EM) algorithms. The package supports Wiener and Ornstein–Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM. Package: r-cran-mixedlevelrsds Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tords, r-cran-frf2, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixedlevelrsds_1.0.0-1.ca2404.1_all.deb Size: 183192 MD5sum: a0a76094e735046c97e2a5be733711f0 SHA1: d67caccfc1e03d71ed3a2bed561ead8a1e30665d SHA256: 98dcec71700fc8c30f252431284cede496c8cba56562a03d05dafd64baf39dce SHA512: 0c7ebf2e451d2fc84e1c9424c3a1c93c6121766353bf3991fddbfdf930fd40820d905a1bb1d5017753831b787768b9ecbb25b27e2c5ca7a3d9ca8cd36b87083a Homepage: https://cran.r-project.org/package=MixedLevelRSDs Description: CRAN Package 'MixedLevelRSDs' (Mixed Level Response Surface Designs) Response Surface Designs (RSDs) involving factors not all at same levels are called Mixed Level RSDs (or Asymmetric RSDs). In many practical situations, RSDs with asymmetric levels will be more suitable as it explores more regions in the design space. (J.S. Mehta and M.N. Das (1968) . "Asymmetric rotatable designs and orthogonal transformations").This package contains function named ATORDs_I() for generating asymmetric third order rotatable designs (ATORDs) based on third order designs given by Das and Narasimham (1962). Function ATORDs_II() generates asymmetric third order rotatable designs developed using t-design of unequal set sizes, which are smaller in size as compared to design generated by function ATORDs_I(). In general, third order rotatable designs can be classified into two classes viz., designs that are suitable for sequential experimentation and designs for non-sequential experimentation. The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go (M. N. Das and V. Narasimham (1962) . "Construction of Rotatable Designs through Balanced Incomplete Block Designs"). ATORDs_I() and ATORDs_II() functions generate non-sequential asymmetric third order designs. Function named SeqTORD() generates symmetric sequential third order design in blocks and also gives G-efficiency of the given design. Function named Asymseq() generates asymmetric sequential third order designs in blocks (M. Hemavathi, Eldho Varghese, Shashi Shekhar and Seema Jaggi (2020) . "Sequential asymmetric third order rotatable designs (SATORDs)"). In response surface design, situations may arise in which some of the factors are qualitative in nature (Jyoti Divecha and Bharat Tarapara (2017) . "Small, balanced, efficient, optimal, and near rotatable response surface designs for factorial experiments asymmetrical in some quantitative, qualitative factors"). The Function named QualRSD() generates second order design with qualitative factors along with their D-efficiency and G-efficiency. The function named RotatabilityQ() calculates a measure of rotatability (measure Q, 0 <= Q <= 1) given by Draper and Pukelshiem(1990) for given a design based on a second order model, (Norman R. Draper and Friedrich Pukelsheim(1990) . "Another look at rotatability"). Package: r-cran-mixedlsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-purrr, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mclust Filename: pool/dists/noble/main/r-cran-mixedlsr_0.1.0-1.ca2404.1_all.deb Size: 190558 MD5sum: a51cc81c29bfa1facdd42fcb413d39b1 SHA1: 057cfa546cb7d2d5d13c57f8ab087d035ac32ebd SHA256: 0a0efb3696644f22fb10c556b035e5d36dea446b7490190b8875102a3fadebfc SHA512: 8716de38f5f0f2bd7a0d844ba33c7de7bd158c9811d003532878fd694b43d6993cc9fe252a23b10d57a13fc138305362cffc932f48e243ec99d058c1bad420f0 Homepage: https://cran.r-project.org/package=mixedLSR Description: CRAN Package 'mixedLSR' (Mixed, Low-Rank, and Sparse Multivariate Regression onHigh-Dimensional Data) Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix. Package: r-cran-mixedpoisson Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gaussquad, r-cran-rmpfr, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixedpoisson_2.0-1.ca2404.1_all.deb Size: 54678 MD5sum: 92261c5ded35c911efaf8035a6b9c0f7 SHA1: e174298dc1591bbc84b114a96fd386f6e360a915 SHA256: bc033b1c7b6f174962fcfdafa424088b0e0c543b08a4ecc5e15661f3fdb0b232 SHA512: 4e83b8c55667b3d7ca916effa9158c2aa51ff223940af5f4ac297bd5143b1bad7be421009210d470463fedca0364db28e2d516c2ab9883e5dd22086118768e58 Homepage: https://cran.r-project.org/package=MixedPoisson Description: CRAN Package 'MixedPoisson' (Mixed Poisson Models) The estimation of the parameters in mixed Poisson models. Package: r-cran-mixedpsy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beepr, r-cran-boot, r-cran-brglm, r-cran-lme4, r-cran-matrix, r-cran-mnormt, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-mixedpsy_1.3.0-1.ca2404.1_all.deb Size: 111640 MD5sum: 8e4a494d04b923e51cd306b57afe2916 SHA1: ceb5ac1d3449d04becc5f9933f35b73932bcfb40 SHA256: 1d9e76974de67194b5dca869246b75500a5c821a69ae268e370f5701b111ac22 SHA512: 78c5c4728d9b9ac558db331e2652defa61b0de2c78bbbf98cc68335b91c5b632aec652fffd0fb8452114241a39b75cb0f25070cb4d01be6566fc4be94dfb69a9 Homepage: https://cran.r-project.org/package=MixedPsy Description: CRAN Package 'MixedPsy' (Statistical Tools for the Analysis of Psychophysical Data) Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012). Package: r-cran-mixedsde Architecture: all Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sde, r-cran-moments, r-cran-mass, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-mixedsde_5.0-1.ca2404.1_all.deb Size: 695774 MD5sum: 5b3ad43a7dbc37894854ef76e376ebf4 SHA1: 6e815c4815c11a1b0b4ae46c3f4462c3b8598b0f SHA256: 49d8d7515f27b4c6407332146aea9d0c710a353a88385a700b40edeccb3ff9eb SHA512: 4ec8100dbd21ac7f8a16fd42c722ff825a45f511d6c0201b2f29682d1489bbd3d5eddfe5c4a6ef68b8a9ff543362378b0a8a15227ba373b70ee26e29378e51f5 Homepage: https://cran.r-project.org/package=mixedsde Description: CRAN Package 'mixedsde' (Estimation Methods for Stochastic Differential Mixed EffectsModels) Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function. Package: r-cran-mixedsubjects Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixedsubjects_1.0.0-1.ca2404.1_all.deb Size: 188384 MD5sum: 0457c203192dfb3c7196f6aaeef017d6 SHA1: 60195d4059b0d5d1f7338977622b2019075893d0 SHA256: e51dce53356b76da528ef347b1c0ed823bec0f23340991be29fa56efdb3a59a8 SHA512: 26ca06fbbea327d87ffbf04ccba1c268525b602f7a6537b70abd9ffbffce5b02ae2124c2692f72a969ff086d8ea6fb61fa7fea8cb2a11a4f6e012eb043379473 Homepage: https://cran.r-project.org/package=mixedsubjects Description: CRAN Package 'mixedsubjects' (Causal Inference in Experiments with Mixed-Subjects Designs) Implements seven estimators for average treatment effect (ATE) estimation in mixed-subjects designs (MSDs), where human subjects data is augmented with predictions from large language models (LLMs). Includes Difference-in-Means, GREG, PPI++, Doubly-Tuned, Difference-in-Predictions (DiP), DiP++, and D-T DiP estimators. Provides point estimates, variance estimation via delta-method or bootstrap, and optimal design selection for budget allocation between human observations and LLM predictions. Package: r-cran-mixedsubjectsirt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mirt, r-cran-rmutil Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixedsubjectsirt_1.0.0-1.ca2404.1_all.deb Size: 613842 MD5sum: 10560fe33d7571dd62280d11bf213270 SHA1: 227c320d3958f0c1ac90f6cd054442eba39e54f1 SHA256: b89534ddcbd995c85d70b431f189c341db839bb9f81cd9c7ae5528eb0714638f SHA512: e9b98b1543264b9150bbcf4338c71f4e94222f5ff7e4c2dfbf7982ae5fc842f8b532937f96c4c0041608dbf7daac923180017a3d34f5e990a9b683a2bb7ce48f Homepage: https://cran.r-project.org/package=mixedsubjectsirt Description: CRAN Package 'mixedsubjectsirt' (Item Response Theory Calibration with a Mixed Subjects Design) Integrates large language model generated item responses into psychometric calibration studies through a mixed-subjects design for unidimensional two-parameter and one-parameter logistic item response theory models. Human pilot responses are augmented with model-generated responses using a prediction-powered inference estimator (Angelopoulos, Bates, Fannjiang, Jordan and Zrnic (2023) ; Angelopoulos, Duchi and Zrnic (2023) ) adapted to marginal maximum-likelihood estimation, following the mixed-subjects design of Broska, Howes and van Loon (2025) . The estimator is anchored to the human responses and is asymptotically unbiased for the human item parameters at any tuning weight; the weight on the synthetic responses is chosen to minimize propagated ability-score risk, down-weighting uninformative or biased generated responses. Louis-corrected sandwich standard errors, ability scoring, cross-fitted tuning, and scale linking are also provided. Package: r-cran-mixedts Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixedts_1.0.4-1.ca2404.1_all.deb Size: 210054 MD5sum: f11e7067e6f6e4311bd8d7097cf4ae39 SHA1: b4be9dd8ac2644c339d2af5b3c41e67ffde977e2 SHA256: 6a8d282c5ea3267b47c4b01bac1b8734581be9a26b801510708e328b49ea839a SHA512: b60aaf35286eb46e90f9f58e677960fe884fb1cf65d996636ed3b7d4a6294092d56e4c903650bb524a58acc4ec86f1878d8fca4b0c3ab91b124a94fa1e597eaf Homepage: https://cran.r-project.org/package=MixedTS Description: CRAN Package 'MixedTS' (Mixed Tempered Stable Distribution) We provide detailed functions for univariate Mixed Tempered Stable distribution. Package: r-cran-mixfim Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-mvtnorm, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mixfim_1.1-1.ca2404.1_all.deb Size: 65742 MD5sum: 2390bf7c2f354dfb9b0038607f0fea58 SHA1: 5f82e926d8de25cd963d1a8f9c2834782f49d74a SHA256: f7136899719ede25b1968ba718d013f1ec51a5a93fd75139f9eb3fde0e664f67 SHA512: 6b13a7dcbd129b7e03d1a2305a36de9b0943f6094b620a03f36d9af813a5c1a35a87892e54c816469a4d22c51788a8c1c9b7faf07a43bc425919478768020751 Homepage: https://cran.r-project.org/package=MIXFIM Description: CRAN Package 'MIXFIM' (Evaluation of the FIM in NLMEMs using MCMC) Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data. Package: r-cran-mixfrac Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixfrac_1.0-1.ca2404.1_all.deb Size: 64692 MD5sum: 5f294b3dc1ec5768725aa26ec11246b8 SHA1: 870df8fce354b5ec978cad036eee369510fd853e SHA256: 0110f8ec0ab065687c920ac874059bfc423ea7cfbaa16b7e94771f474ae07652 SHA512: 5d6d84fbf8103a2fafe67ef1cb7b3b95cca2d12d5dce7a7fe6c4375b35c145f7b6463719bea367cc5e8b341ade489ec8f1ac84ad3e9096335b59ad78e1a76c4c Homepage: https://cran.r-project.org/package=MixFrac Description: CRAN Package 'MixFrac' (Fractional Factorial Designs with Alias and Trend-Free Analysis) Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following Coster (1993) . Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) . Regular fraction identification follows Guo, Simpson and Pignatiello (2007) . Alias structure computation follows Rios-Lira et al.(2021) . Package: r-cran-mixghd Architecture: all Version: 2.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-bessel, r-cran-mvtnorm, r-cran-ghyp, r-cran-numderiv, r-cran-mixture, r-cran-e1071, r-cran-cluster Filename: pool/dists/noble/main/r-cran-mixghd_2.3.7-1.ca2404.1_all.deb Size: 311802 MD5sum: 1c81f50a00fcfb7767cdd12cab1e9a79 SHA1: d11c8d55891848c53ffaa6cba12298388920ceff SHA256: 0dd1f97646780289fc8ea809ced8c93dce2dc872f0cc87733fa7f0e2d723c4d7 SHA512: 31994c33590e3acf9d4af3f1510d28ccba28baeae32f2c9116a899fdd41017ae3f48574c96d02a46b2ca9b933e45397190455cdb2a9801f9987e71d58d8a168f Homepage: https://cran.r-project.org/package=MixGHD Description: CRAN Package 'MixGHD' (Model Based Clustering, Classification and Discriminant AnalysisUsing the Mixture of Generalized Hyperbolic Distributions) Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) ) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) ) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of multiple scaled generalized hyperbolic distributions, the 'cMSGHD' is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019). The paper related to the software can be found at . Package: r-cran-mixhvg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1446 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-scran, r-cran-seurat, r-cran-matrix, r-bioc-singlecellexperiment, r-bioc-scuttle Filename: pool/dists/noble/main/r-cran-mixhvg_1.0.1-1.ca2404.1_all.deb Size: 1343254 MD5sum: 2d4eacb72b469ee1cd8bdb25eace9562 SHA1: 82f3f24a77f9921c9cf5e09a250ea5e54c19417b SHA256: 02e027c702a1418088603930a30f11a44063f79c753e95ad6ed7271ffca2f186 SHA512: 64b360e0a26371700fd964d13db16f0463953e098a2003f998ee114b0843085a282e2d4cf39f75a33760e830f32cef6c5349076f8f9c6267e8d81d8960ff840e Homepage: https://cran.r-project.org/package=mixhvg Description: CRAN Package 'mixhvg' (Mixture of Multiple Highly Variable Feature Selection Methods) Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, . Reference: . Package: r-cran-mixindependr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mixindependr_1.0.0-1.ca2404.1_all.deb Size: 237896 MD5sum: 39d9cc89ca23c7dd48c0e017b9c024d3 SHA1: 31e88db080143e6bce467395a6875d64b89c5dc8 SHA256: 7a8cf2616c6777c2643b3a72773546807531d6f443566f84b18741dd2e3566a4 SHA512: a4c33c257920be805c10a15ac789243456dd37674c45d192d6f85d28edbfc19ae0b60df223866d7cbfb9d8621c4ff2187a6e54c7ccbdde2e9447a5d5fed0dc32 Homepage: https://cran.r-project.org/package=mixIndependR Description: CRAN Package 'mixIndependR' (Genetics and Independence Testing of Mixed Genetic Panels) Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. 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"The design of order-of-addition experiments." Journal of Quality Technology 51:3, 230-241, . Provides facility to construct component orthogonal arrays, see Jian-Feng Yang, Fasheng Sun and Hongquan Xu (2020). "A Component Position Model, Analysis and Design for Order-of-Addition Experiments." Technometrics, . Supports generation of fractional designs for order-of-addition mixture experiments. Analysis of data from order-of-addition mixture experiments is also supported. 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Package: r-cran-mixphm Architecture: all Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-lattice Filename: pool/dists/noble/main/r-cran-mixphm_0.7-2-1.ca2404.1_all.deb Size: 92898 MD5sum: 4bc4a70a52406303de9e431ff722b1ac SHA1: f209c1555b8a81a81f58d46ebc92af02f02f1133 SHA256: 45d1289514917e5748c525ffb1af32c4f1a16cfa38d3bb0b864074db9464cc75 SHA512: 06bcc30a4c367e17d7017732026f696c342f4d8abb9579e16ee1f503dd0175202964ce3b69f09ffc18faa92da2fbdaaac11950fb8f98ed2e65ff312ea37071df Homepage: https://cran.r-project.org/package=mixPHM Description: CRAN Package 'mixPHM' (Mixtures of Proportional Hazard Models) Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. 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Package: r-cran-mixpoissonreg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4828 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbapply, r-cran-formula, r-cran-rfast, r-cran-dplyr, r-cran-gamlss.dist, r-cran-generics, r-cran-ggplot2, r-cran-gridextra, r-cran-lmtest, r-cran-magrittr, r-cran-statmod, r-cran-tibble, r-cran-rlang, r-cran-ggrepel, r-cran-gamlss Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-devtools, r-cran-bbreg, r-cran-testthat, r-cran-covr, r-cran-tidyr, r-cran-ggfortify, r-cran-broom Filename: pool/dists/noble/main/r-cran-mixpoissonreg_1.0.0-1.ca2404.1_all.deb Size: 3028562 MD5sum: d65551cb58fac21542251c0ed384a63d SHA1: 420934bb2d8993230e9c1b8f19f210ade36b7bc1 SHA256: e20b6f2297e8ce81dfb91d52c106503fe52d1290fb1ffdca310ffa1c8d1930d8 SHA512: bcce598b351a3f389aacf3a2bd5e92364a4274c1da0c96300859fd36c9b424c93e669703866f7afa2a2c44aee0eb5ac07333d5f684da8d1ea768700d07fa5398 Homepage: https://cran.r-project.org/package=mixpoissonreg Description: CRAN Package 'mixpoissonreg' (Mixed Poisson Regression for Overdispersed Count Data) Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. 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Package: r-cran-mixpower Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 734 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-digest, r-cran-jsonlite, r-cran-glmmtmb, r-cran-lmertest, r-cran-pbkrtest, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mixpower_1.1.1-1.ca2404.1_all.deb Size: 432898 MD5sum: 26b867a513866fc8036676831da7e803 SHA1: cc5ed85e61eca7d9b03f90b164d65802517ef271 SHA256: 7d425fbad2a827ca63878198f5539aa1257410a67a17fbd6134430d9bbdd2b65 SHA512: 32d8372dfa79feb1fe5a0fcc10a361f249980893ce4ca016efe66141260db93b5e6497b7e98bd709b65037945eee103b4d93d5534a91faf46785cfd1313e3888 Homepage: https://cran.r-project.org/package=mixpower Description: CRAN Package 'mixpower' (Simulation-Based Power Analysis for Mixed-Effects Models) A comprehensive, simulation-based toolkit for power and sample-size analysis for linear and generalized linear mixed-effects models (LMMs and GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families via 'lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity grids; power curves and minimum sample-size solvers; parallel evaluation with deterministic seeds; and full reproducibility (manifests, result bundling, and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate, singular-fit rate, effective N) and publication-ready summary tables. References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using lme4" ; Green and MacLeod (2016) "SIMR: an R package for power analysis of generalized linear mixed models by simulation" . Package: r-cran-mixqr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-ggplot2, r-cran-rqpen, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixqr_0.2.0-1.ca2404.1_all.deb Size: 440018 MD5sum: c2562cec2ce11d2899a569c19c765b9b SHA1: 4985576f81e1216d329b4e5512e459183776a9c1 SHA256: f18bd997b8251c12f71409a20f5d820cf68ca50406fdafc783a94d17242cd6de SHA512: 5629b19c2fc691afd671f83ca3481157d7558e3b26aad1b7d919267653cecba5b0d149b9c3f33d7a436a981c191e9c8d31ff2348b9a6b19df24f39bc18682d19 Homepage: https://cran.r-project.org/package=mixqr Description: CRAN Package 'mixqr' (Extensible Finite Mixtures of Quantile and Expectile Regressions) An extensible expectation-maximization (EM) framework for finite mixtures of quantile regressions (clusterwise / mixture-of-experts quantile regression). 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Package: r-cran-mixqrgate Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mixqr Suggests: r-cran-nnet, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixqrgate_0.1.2-1.ca2404.1_all.deb Size: 225872 MD5sum: f338a5da4f2c76abeee5424d6e356ddd SHA1: 67c65a2e76365ec35056aa92f7aab1af67bdd3b1 SHA256: 9a9e8823eccb9d4ea31b64f98762ac944bf08fe45206c6f6f0f4afb40f542fcc SHA512: 93571a047a988044a7e3eb47ca164b89620628cc232270c153f1076f94765ce22f646dc4b04b77002ca2a9aa5957b90ac5bc78e50daa64771bc7e43e83833597 Homepage: https://cran.r-project.org/package=mixqrgate Description: CRAN Package 'mixqrgate' (Location-Varying Gating for Mixtures of Quantile Regressions) Extends finite mixtures of quantile regressions (the 'mixqr' package) with a concomitant-covariate, quantile-indexed mixing gate. 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The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues. Package: r-cran-mixsal Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixsal_1.0-1.ca2404.1_all.deb Size: 54564 MD5sum: 00617ced87186614ac59056461c80fb3 SHA1: db1a9ab8821d24e66a08662bd93a78554a11f9cf SHA256: 693cfd563f7098b2fc828b7ca8dfdf38484e87b824686a045229703599cef989 SHA512: c556609738a01d34fc939d15481cbc5e425f5f2558abe62a3e4c77c6933c65f7e3ca0fb38b12a520892eac3fce492cc54470694720bbc609d217158687d6a3e0 Homepage: https://cran.r-project.org/package=MixSAL Description: CRAN Package 'MixSAL' (Mixtures of Multivariate Shifted Asymmetric Laplace (SAL)Distributions) The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, , for details). Package: r-cran-mixsemirob Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gofkernel, r-cran-mass, r-cran-mixtools, r-cran-mvtnorm, r-cran-rlab, r-cran-robustbase, r-cran-ucminf, r-cran-pracma, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixsemirob_1.1.1-1.ca2404.1_all.deb Size: 493704 MD5sum: 838220e37777997fd04ca9d0389d3175 SHA1: d155fc88c0b669b3c7004f54f2d36cf8c94be015 SHA256: eca402f15a0d5101cdc676ab4eaebe7a6ae5108b61a1e31476952fd93b6bfe6f SHA512: dab3082c163e267c518f4546557bbe8a1812f6ae5818c27c53171b8cb39a51edaf27bc45481bcc1942c9e02d1806bff2ba498865c4f04d54fd37eba5e53191fa Homepage: https://cran.r-project.org/package=MixSemiRob Description: CRAN Package 'MixSemiRob' (Mixture Models: Parametric, Semiparametric, and Robust) Various functions are provided to estimate parametric mixture models (with Gaussian, t, Laplace, log-concave distributions, etc.) and non-parametric mixture models. The package performs hypothesis tests and addresses label switching issues in mixture models. The package also allows for parameter estimation in mixture of regressions, proportion-varying mixture of regressions, and robust mixture of regressions. Package: r-cran-mixsiar Architecture: all Version: 3.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-r2jags, r-cran-mass, r-cran-rcolorbrewer, r-cran-reshape, r-cran-reshape2, r-cran-lattice, r-cran-mcmcpack, r-cran-ggmcmc, r-cran-coda, r-cran-loo, r-cran-bayesplot, r-cran-splancs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixsiar_3.1.12-1.ca2404.1_all.deb Size: 1359154 MD5sum: 5572ec3936cd02bd7a18d5fa0226dc4d SHA1: dde896cb276be816fe6caefeee61803658f4f146 SHA256: fedef567e17750886558fc6107a80f19a083ba8fe22134ccab861a3e1dc3ec15 SHA512: 4a5156dab18015a68b37fddd7fd5ea65d5179583ec7b53cf12533e9f7c3f36aa5399d38d0c4d6c02bc8ace96d4c5740d7a8e6f0a28b7f2b08136dda0a00ddc15 Homepage: https://cran.r-project.org/package=MixSIAR Description: CRAN Package 'MixSIAR' (Bayesian Mixing Models in R) Creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). 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Package: r-cran-mixsmsn Architecture: all Version: 1.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mixsmsn_1.1-12-1.ca2404.1_all.deb Size: 211272 MD5sum: 15d55987ad51faf8d0022ee61d0517f6 SHA1: 4bebd9c31493e2d99b92073d8561938123261ba9 SHA256: a05c873e6c2a21ebe2311fb7e9cfab79ffc923f722c26069985f036cf91b0296 SHA512: 6e691eb1cbd616be6e81e35c90bdb4bbf4f7230e001b8a25f32e39f176969667c778812cd862487ea5c0f5b06c169d15f29dbb1c641b88f634d6566c7804d96b Homepage: https://cran.r-project.org/package=mixsmsn Description: CRAN Package 'mixsmsn' (Fitting Finite Mixture of Scale Mixture of Skew-NormalDistributions) Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) , Cabral, Lachos and Prates (2012) and Basso, Lachos, Cabral and Ghosh (2010) . Package: r-cran-mixspe Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixspe_0.9.3-1.ca2404.1_all.deb Size: 105092 MD5sum: 3bb9db4501cdedaa8579e27ebda2d29b SHA1: 546441e826657cf309a5ffa3348fcd755398332d SHA256: 5978a82b965b8a32c16b973be09a7c848b4bcdf638e38370f49c5120309517db SHA512: ec37637e92924ced6ba58a12c01098854217504d47814618e7bb50c044d7cf94321872a33ba59a35f7f5b240cca3cc24b5ae1ddea67bc449fcec22b51b6c7077 Homepage: https://cran.r-project.org/package=mixSPE Description: CRAN Package 'mixSPE' (Mixtures of Power Exponential and Skew Power ExponentialDistributions for Use in Model-Based Clustering andClassification) Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew power exponential and power exponential distributions, respectively. A generalized expectation-maximization framework is used for parameter estimation. See citation() for how to cite. Package: r-cran-mixssg Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-mass, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-mixssg_2.1.1-1.ca2404.1_all.deb Size: 86026 MD5sum: acc378abf8e4f17357c7a4be33fc31bc SHA1: 9caf6f6461fdb3c0cd8891cf20933266ae2b9e7b SHA256: 662e561d6b6734c679ba894d88a945ef2c831b06d8a05ca45745a206836ca1af SHA512: 6a49d80ae511d321c42de6f837473da6f32b640aa061df3d2b1a0dc422c1c4331f9b6929dae29701a79bbb4537bac42dd3767dea9cee45842b396649d2f0ef85 Homepage: https://cran.r-project.org/package=mixSSG Description: CRAN Package 'mixSSG' (Clustering Using Mixtures of Sub Gaussian Stable Distributions) Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian stable distributions developed by Teimouri (2022) and estimating parameters of the symmetric stable distribution within the Bayesian framework. 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It implements a diverse set of estimation methods, including quantile-based approaches, regression methods based on the empirical characteristic function (empirical, kernel, and recursive), and maximum likelihood estimation. For mixture models, it provides stochastic expectation–maximization (SEM) algorithms and Bayesian estimation methods using sampling and importance sampling to overcome the long burn-in period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes tools and statistical tests for analyzing whether a dataset follows a stable distribution. Some of the implemented methods are described in Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) . Package: r-cran-mixtox Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-mixtox_1.5.0-1.ca2404.1_all.deb Size: 599238 MD5sum: 6bd7572bad6b18fc7ed5b0b44f2632c5 SHA1: 7975a63708ab1fd3d70d5899bc0459fb9ee0a0be SHA256: 546a90e21a19c712ed6d2f527df62d3cd3e1b20dde5d46a217d7ebae1bebc35a SHA512: 37fad38dc696cd5f77381f992e8dfcc9e762eccfc2fecbc036f94e589e3191be79a60c29696c90a9466936c32b5260346128f560574a4e055dc84106d7e4c0d9 Homepage: https://cran.r-project.org/package=mixtox Description: CRAN Package 'mixtox' (Dose Response Curve Fitting and Mixture Toxicity Assessment) Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as 'concentration addition', 'independent action', and 'generalized concentration addition'. Package: r-cran-mixtree Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-treespace, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixtree_0.0.1-1.ca2404.1_all.deb Size: 128660 MD5sum: 0a6089dcbbc991ba5bb9627c814c4064 SHA1: d1b667844e3648148f93be785e3f7ab18631519a SHA256: 01ca5647948bd67fd7cc910dac08075a63bdad61c6f37be696b848ef58a16f54 SHA512: 23bdf571d7bcf90e29a7e902d0c8a765cb7e961cd12a73fe2a988b1715f19ca0178cd0bb8e5496842eb58975fdad0e496a4baaf23f82c6b637162f1f879b30ce Homepage: https://cran.r-project.org/package=mixtree Description: CRAN Package 'mixtree' (A Statistical Framework for Comparing Sets of Trees) Statistical framework for comparing sets of trees using hypothesis testing methods. Designed for transmission trees, phylogenetic trees, and directed acyclic graphs (DAGs), the package implements chi-squared tests to compare edge frequencies between sets and PERMANOVA to analyse topological dissimilarities with customisable distance metrics, following Anderson (2001) . Package: r-cran-mixtur Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixtur_1.2.3-1.ca2404.1_all.deb Size: 469996 MD5sum: 695f51bb75a7d0502d703a6fad73420d SHA1: f61fa761a95f774de4380cdde4d3e75b31af9a3e SHA256: f725c9df5f0438e7b2e02fbd96111ccaa3bc11f0c13b8cc3ba1a10ca86212fd2 SHA512: 7d8d00adb7227815c4ec4fa06f1274557a7740088e5c8bf232f43a65e95b2524e6594140219d530bc7108338350a96256b9b418a1dbd1e5d1d0be5da828f35ea Homepage: https://cran.r-project.org/package=mixtur Description: CRAN Package 'mixtur' (Modelling Continuous Report Visual Short-Term Memory Studies) A set of utility functions for analysing and modelling data from continuous report short-term memory experiments using either the 2-component mixture model of Zhang and Luck (2008) or the 3-component mixture model of Bays et al. (2009) . Users are also able to simulate from these models. Package: r-cran-mixturemissing Architecture: all Version: 3.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mnormt, r-cran-cluster, r-cran-mass, r-cran-numderiv, r-cran-bessel, r-cran-mclust, r-cran-mice Filename: pool/dists/noble/main/r-cran-mixturemissing_3.0.6-1.ca2404.1_all.deb Size: 282116 MD5sum: 71248820c89c33df704f83fa873fcc6c SHA1: f7f47a1ba0f27dfde8ea9a199500027404c7f2ed SHA256: 2bff09fb87ac3d4b2e28898b8b3ef2e9e6974ae544b3f4a06252753a75643de1 SHA512: c95b6e2d44fb41ca79e899374d73f991d452acde0061d006212c3d4bbb5365d56e689cd1c0fdcb489448be7abee1b421510c23856275dfeb43960aafa341305f Homepage: https://cran.r-project.org/package=MixtureMissing Description: CRAN Package 'MixtureMissing' (Robust and Flexible Model-Based Clustering for Data Sets withMissing Values at Random) Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, ). Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, ) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, ). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974. Package: r-cran-mixtwice Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-alabama, r-cran-ashr, r-cran-fdrtool, r-cran-iso Filename: pool/dists/noble/main/r-cran-mixtwice_2.0-1.ca2404.1_all.deb Size: 2173082 MD5sum: 1045b2f20ddd43aab552e6b011572c3f SHA1: d3733ebbfd57718e461563dfc767efd7208d1529 SHA256: c666c00030618670974629685b8dfe9e0602ab80915d05e6d7e7d4a77f589cd6 SHA512: e697d4b6fe97d8dabd27f81e1fc23e02a6ad46beff30f058e9e4acc6a4993541bb6445ac4c45ef4467cbabdf5fcff9ac1cf65010816abac489d74c9820fcb4c7 Homepage: https://cran.r-project.org/package=MixTwice Description: CRAN Package 'MixTwice' (Large-Scale Hypothesis Testing by Variance Mixing) Implements large-scale hypothesis testing by variance mixing. 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Package: r-cran-mixvir Architecture: all Version: 3.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4055 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-plotly, r-cran-readr, r-cran-shiny, r-cran-stringr, r-cran-tidyr, r-cran-vcfr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mixvir_3.5.0-1.ca2404.1_all.deb Size: 1784218 MD5sum: 932a97069f5237840447f6c0de567b2e SHA1: 6b59c554873fd042cae4ab3c44ce7a25b35139c1 SHA256: cf182bc36c8e0b0be9e693d7a6152432af38b85bfa5159852d15fde5bdad44b4 SHA512: 8f694f690007492b89ef3da310bf858fafe0b890457a212745a494f5a45ee9941e9b9935b611043a4dbb5f2dc767d0449021bf124e8d43876460f354f546a98a Homepage: https://cran.r-project.org/package=MixviR Description: CRAN Package 'MixviR' (Analysis and Exploration of Mixed Microbial Genomic Samples) Tool for exploring DNA and amino acid variation and inferring the presence of target lineages from microbial high-throughput genomic DNA samples that potentially contain mixtures of variants/lineages. 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Package: r-cran-mize Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-mize_0.2.5-1.ca2404.1_all.deb Size: 400864 MD5sum: 8ec31cbf353f9bf4ae2718fefe75b2f8 SHA1: d0ec776c533686f0fcb2d379291390ab688c4d48 SHA256: 6417951cc89abc5d93a0d2e00563616f9192e8deeb53e6adfbf613badb7df16c SHA512: dc783f9a16b8b8ab6f5f670e7eb6e6cd03ffa910726eb33c14c499c4eb5ce3fb0d5cb96a21bab6d94f1556efe38b386ce284e60b13bc840a503e024b50df343a Homepage: https://cran.r-project.org/package=mize Description: CRAN Package 'mize' (Unconstrained Numerical Optimization Algorithms) Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. 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For more details see Thaning and Nieuwenhuis (2025) . Package: r-cran-mkclass Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-mkclass_0.5-1.ca2404.1_all.deb Size: 152642 MD5sum: 1dcc9b258c34408bdd142ea07a6bb5af SHA1: 1d50c2b4d3069181dc3282a95fc58c771bec20e3 SHA256: 1ce798677ebc4786dedf4d5c2431c70a2fe7ce411dd26e2de6daa2601495e217 SHA512: 614bd6870c54f30375453967881567ed9d30cb49d32ee2b92b4bcca398106e4ee34883cba4fb0e089992e17b9ce19399feb416a9d52885834846b0309464d8fb Homepage: https://cran.r-project.org/package=MKclass Description: CRAN Package 'MKclass' (Statistical Classification) Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), ) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), ; Hosmer et al (1997), ). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), ). Package: r-cran-mkdescr Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mkdescr_0.9-1.ca2404.1_all.deb Size: 531310 MD5sum: ba5bb06a403f8957af1f36500877e12d SHA1: a6724eb52fd713157774c035c746b0e0fedc74bc SHA256: 690be8c967404d58da405249594f573c81fb3181743e9bf3dd22d059884137ad SHA512: 9335ab7c16714deb8e55a4d5e7c115aefe4c0efe40c6ccd1b8ffc2d5900d8a4fbebd164f0cfc65c2fde88e44a552d36c368893f9a544f5402c4394b2c4982552 Homepage: https://cran.r-project.org/package=MKdescr Description: CRAN Package 'MKdescr' (Descriptive Statistics) Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), ), robust coefficient of variation (CV) (Arachchige et al. (2019), ), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4). Package: r-cran-mkendall Architecture: all Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mkendall_1.5-4-1.ca2404.1_all.deb Size: 23284 MD5sum: 3c26b2ee7b328daca7bfec312344fe2b SHA1: 3630f754a0bad46a2ad015cc5febaaeb07112d7c SHA256: 8a5697c9db260b9e6277c7318c7f78919a83a20e16c9ba3dc716e46dabb9cdab SHA512: 263e86b7467d4051e370c463e6c207fbf38e21dd2377e7cf8a00691ec326daa79e09d8c29f2c12728abc86064c7ec6a46cc9f39878d61c7c8187daf9706b3aaa Homepage: https://cran.r-project.org/package=MKendall Description: CRAN Package 'MKendall' (Matrix Kendall's Tau and Matrix Elliptical Factor Model) Large-scale matrix-variate data have been widely observed nowadays in various research areas such as finance, signal processing and medical imaging. Modelling matrix-valued data by matrix-elliptical family not only provides a flexible way to handle heavy-tail property and tail dependencies, but also maintains the intrinsic row and column structure of random matrices. We proposed a new tool named matrix Kendall's tau which is efficient for analyzing random elliptical matrices. By applying this new type of Kendell’s tau to the matrix elliptical factor model, we propose a Matrix-type Robust Two-Step (MRTS) method to estimate the loading and factor spaces. See the details in He at al. (2022) . In this package, we provide the algorithms for calculating sample matrix Kendall's tau, the MRTS method and the Matrix Kendall's tau Eigenvalue-Ratio (MKER) method which is used for determining the number of factors. Package: r-cran-mkin Architecture: all Version: 1.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-r6, r-cran-inline, r-cran-numderiv, r-cran-lmtest, r-cran-pkgbuild, r-cran-nlme, r-cran-saemix, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rbenchmark, r-cran-tikzdevice, r-cran-testthat, r-cran-rmarkdown, r-cran-covr, r-cran-vdiffr, r-cran-benchmarkme, r-cran-tibble, r-cran-readxl Filename: pool/dists/noble/main/r-cran-mkin_1.2.10-1.ca2404.1_all.deb Size: 2815812 MD5sum: d5f840d8a289e3cd2b2edbf2e5cc0b0f SHA1: 6b57bfda3c64b6bff8812d8319a54ceff3fad9f9 SHA256: 7874d65574358a3e4d48c8328bac11a6cb1f45681a95de12930b0e5a982e76c8 SHA512: 1b5eee78da34059a7e80d9a2ceba404d57d1e8ece0862de6a3b90c9b69aa6896ba96752c20e78cf02bd1c3cf20a1e139853d54bbc728fcfa109554b8b7953665 Homepage: https://cran.r-project.org/package=mkin Description: CRAN Package 'mkin' (Kinetic Evaluation of Chemical Degradation Data) Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models . Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end . Please note that no warranty is implied for correctness of results or fitness for a particular purpose. Package: r-cran-mkinfer Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2083 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-mkdescr, r-cran-boot, r-cran-arrangements, r-cran-nlme, r-cran-ggplot2, r-cran-exactranktests, r-cran-miceadds, r-cran-hypergeo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-amelia, r-cran-mice, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mkinfer_1.4-1.ca2404.1_all.deb Size: 1449710 MD5sum: f6d30a3bd8ee327edaa0c2a84caa5f72 SHA1: c8f9436c1fda5bb64f5b3e589866e485ac0fb561 SHA256: a03f570b8728c617fd59e88f38fd67ed210b6755550a4bc4c8842ad0db6e6ae3 SHA512: 911d8af6de671b09ed72e34549bb2904b594447e57b9bdd03eecc8b7c5cff86ac05a562b358bf99e97c832974d0a6666bee2d927f8533fecbfa267e67a15c0f9 Homepage: https://cran.r-project.org/package=MKinfer Description: CRAN Package 'MKinfer' (Inferential Statistics) Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Xiao (Xiao (2018), ), Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), ), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), ) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), ; Shieh (2018), ), mean difference plot (Boehning et al. (2008), ), plot of test statistic for permutation and bootstrap tests as well as objects of class htest. Package: r-cran-mkle Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mkle_1.0.1-1.ca2404.1_all.deb Size: 31786 MD5sum: 26957b297d77e356c207559348b56818 SHA1: 9c04baf8bfad52125e87b8950b30e971ef31c57b SHA256: 607cbd770e669d0b821c099aac1846e338908f6f4630e1fc8a4a9cc7a2cf11ca SHA512: 132c79dc6dada5d32745e5839b926e82e27a33df9967023200f9471d81e94e5f99da81d76195d1be40aacc703fec5277788c173e44cfdc469679d79cf9d4fbd8 Homepage: https://cran.r-project.org/package=MKLE Description: CRAN Package 'MKLE' (Maximum Kernel Likelihood Estimation) Package for fast computation of the maximum kernel likelihood estimator (mkle). Package: r-cran-mkmeans Architecture: all Version: 3.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gtools Filename: pool/dists/noble/main/r-cran-mkmeans_3.4.4-1.ca2404.1_all.deb Size: 34032 MD5sum: 937a9c244d001392eadf397db6a80ecb SHA1: 3b3e11ba93f4205257de8d5dd65f765ab0de16ac SHA256: 3d0a6dac690000ff0b1dddfb68e206c8d02e306bcad6c723fbda61f90770a8ba SHA512: 406ec18dd9b8e472d05c49074ca869af156ac170a71e86c3f84c661d2f4b9e247c0b87f43adc14bfdc77f2468c12a96ef6c17f1b96bbfb4be584267524d534ba Homepage: https://cran.r-project.org/package=MKMeans Description: CRAN Package 'MKMeans' (A Modern K-Means (MKMeans) Clustering Algorithm) It's a Modern K-Means clustering algorithm which works for data of any number of dimensions, has no limit with the number of clusters expected, offers both methods with and without initial cluster centers, and can start with any initial cluster centers for the method with initial cluster centers. 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Kohl) Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices. Package: r-cran-mknapsack Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-data.table, r-cran-lpsolve Suggests: r-cran-testthat, r-cran-mockery, r-cran-rglpk, r-cran-roi, r-cran-roi.plugin.glpk Filename: pool/dists/noble/main/r-cran-mknapsack_0.1.0-1.ca2404.1_all.deb Size: 30242 MD5sum: 06e71bab2bf38e3c58b1c6c5cb3c7781 SHA1: 89bbd921b8a314dfa03cabb33501063ec429d75a SHA256: d9fc5b40f204a1df28a960f061a49929103f56fe1c302f02450bba6b014a5a28 SHA512: a60fe7fca345dfc08f103ffbacfd46db1f06fb785ac68a967220416bc1ea090a97506fd92d4a645da31ae71e2dca198ecab1ec5e6de0e0ff9f4ad781687c7500 Homepage: https://cran.r-project.org/package=mknapsack Description: CRAN Package 'mknapsack' (Multiple Knapsack Problem Solver) Package solves multiple knapsack optimisation problem. Given a set of items, each with volume and value, it will allocate them to knapsacks of a given size in a way that value of top N knapsacks is as large as possible. Package: r-cran-mkomics Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-robustbase, r-bioc-limma, r-cran-circlize, r-bioc-complexheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mkomics_0.8-1.ca2404.1_all.deb Size: 480886 MD5sum: 1fbf090a60e0954dbe5d95b6550b91db SHA1: 5c97fc7f5e6795706cc586918d17c31291ecb7b9 SHA256: e7d98c6820cbb23c3576e2b2600774149caef2b58c34831a3aca8311357d115d SHA512: 7743378bc555b6dc52d8876c5ce11d5596f77cf0c6cdd46d754b9bee35cf84a24ad9277b2e9307918981f0735007f16ed6fd8db82cfd48e2d0635438f2c89591 Homepage: https://cran.r-project.org/package=MKomics Description: CRAN Package 'MKomics' (Omics Data Analysis) Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. 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Package: r-cran-mkpower Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixtests, r-cran-ggplot2, r-cran-mkdescr, r-cran-mkinfer, r-cran-qqplotr, r-cran-coin, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-hexbin Filename: pool/dists/noble/main/r-cran-mkpower_1.1-1.ca2404.1_all.deb Size: 497088 MD5sum: 5349a52683399509acc44d180c503675 SHA1: 1891a8ef747bae51bd4e2d3542c0f7ddea48465d SHA256: fca94b3adec1aa9f6d772667051ec7f72eef7bc61980201eb94f1dce6aab24dd SHA512: 6c582cd9aea88d9b3aa0a9f98e355317b59a1f18531a95ab68078ebed5c3c904f807639b74d0edc8e2df2d490e13823d2e982e5e6ed5415fdb3bdc50fe5f62c1 Homepage: https://cran.r-project.org/package=MKpower Description: CRAN Package 'MKpower' (Power Analysis and Sample Size Calculation) Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), ; Dobbin and Simon (2007), ) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), ; Thulin (2014), ), comparing two negative binomial rates (Zhu and Lakkis (2014), ), ANCOVA (Shieh (2020), ), reference ranges (Jennen-Steinmetz and Wellek (2005), ), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), ). Package: r-cran-mkssd Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mkssd_1.2-1.ca2404.1_all.deb Size: 22280 MD5sum: 0de925c6ce68b17cc380cc4889728eea SHA1: b0d12e38a9f5a9dcb942812993c7be49131a4a22 SHA256: 058546260e9c7033648a8985e545db96c2ee7a0bc39e14bc48601d97145872ad SHA512: adf22ef539217614c63e12a5510289b6dd3e301ee933de886cbc32cd132dfbcccf7f2600b99ae687febb3fdf68c7f0428035c17a4bb60936daffa696531deaa5 Homepage: https://cran.r-project.org/package=mkssd Description: CRAN Package 'mkssd' (Efficient Multi-Level k-Circulant Supersaturated Designs) Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. Package: r-cran-ml.msbd Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-ml.msbd_1.2.1-1.ca2404.1_all.deb Size: 283204 MD5sum: e035080a7661294233f36b0d7f1c4f0a SHA1: 965c09ff1a3b14a74283c8623b34af8f01808c6d SHA256: db88506c9c52a14d4982aec1e163f1964cfa6bf2904c98ff1f2727b751f4b9df SHA512: 715a9c8fb6319b772710994472b46f552f5f6c9bcc4961be10da1fbf305164c3aefe624a4437e21e9917f18b4a0b1615ef5aa0d409ab996cb99bc8c55d8114e3 Homepage: https://cran.r-project.org/package=ML.MSBD Description: CRAN Package 'ML.MSBD' (Maximum Likelihood Inference on Multi-State Trees) Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) . Package: r-cran-ml2pvae Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-reticulate, r-cran-tensorflow, r-cran-tfprobability Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ml2pvae_1.0.0.1-1.ca2404.1_all.deb Size: 269858 MD5sum: d3d20fac732aaa4b2f8319cff300d146 SHA1: 5be6e3a2d2d333124efdca360d8eeeb24b97a27d SHA256: 6efd5e8df2a61dba9f7ace73e04ee2804c8143d471b7a18b11a79ec3b4379526 SHA512: 6b7f384d317a115b16a09bf41e2efddd49dcad4eefcfe0852fe5a74a8d578d3458f662740e033e944e3a7b274e07bc2704faf30ef106217d63fe0f36c9a09199 Homepage: https://cran.r-project.org/package=ML2Pvae Description: CRAN Package 'ML2Pvae' (Variational Autoencoder Models for IRT Parameter Estimation) Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) . This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required. Package: r-cran-mlapi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-matrix Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mlapi_0.1.1-1.ca2404.1_all.deb Size: 222120 MD5sum: e82d6dba31f4ae54bc94051069f4d23f SHA1: e780ff89ef3e7a0ed8bf86ee1b0605f169e80fbe SHA256: 06b26ca21103625c9e4707ed1a6106607de77a428285d53497b6a9bb4b4cc801 SHA512: 768b8a20e631e4a12a2938b1193b7a175ec7e14427d63b35bf3b419478ec992d965815c48b24fbc41c8fb24893c9202176cd602aebd8e24b8bb1507bc9e1a9a6 Homepage: https://cran.r-project.org/package=mlapi Description: CRAN Package 'mlapi' (Abstract Classes for Building 'scikit-learn' Like API) Provides 'R6' abstract classes for building machine learning models with 'scikit-learn' like API. is a popular module for 'Python' programming language which design became de facto a standard in industry for machine learning tasks. 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The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters. Package: r-cran-mlcm Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlcm_0.4.4-1.ca2404.1_all.deb Size: 114616 MD5sum: cce5579b5941e5796eb60b82a17ac75b SHA1: d672a93eb36e7d3b78018bfdcb47c2c7d8ddbfd0 SHA256: bb424248c66c40a0a07e3cf98954f575e20d4a48279ebc566e5dd4fe294a5ce6 SHA512: c1afcd051741e3cb65edc1d551058b318f87c3ce684ed42202329bd2395dba11d7b305e33d561534cf595398fe9e59c00194d465957f9a294f4d9da44b15e2aa Homepage: https://cran.r-project.org/package=MLCM Description: CRAN Package 'MLCM' (Maximum Likelihood Conjoint Measurement) Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. 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Package: r-cran-mle.tools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-mle.tools_1.0.0-1.ca2404.1_all.deb Size: 35600 MD5sum: 03c524d51c4848b2696fd488bb825678 SHA1: 331a6b94a08efb1ea8e9918a2709cc25a2bfb800 SHA256: 9271b386c0d14f7e194a4550eb7de976a4051a95f0721c2ff920a85b0326418a SHA512: 4ac0f1ba767361b9804ecf65f8d2ffb61667f37ba470049f73d692addbeadbedd1d872d65a5a7cdf6ee18f3dde7ee8d6e80ce246b922700af22126f4b52884fa Homepage: https://cran.r-project.org/package=mle.tools Description: CRAN Package 'mle.tools' (Expected/Observed Fisher Information and Bias-Corrected MaximumLikelihood Estimate(s)) Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology. Package: r-cran-mle Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bgfd, r-cran-bivpois, r-cran-ccd, r-cran-compoissonreg, r-cran-compositional, r-cran-directional, r-cran-foldedt, r-cran-geppe, r-cran-gp, r-cran-mn, r-cran-rfast, r-cran-rfast2, r-cran-svmf, r-cran-tpxg Suggests: r-cran-mvcauchy, r-cran-rangen Filename: pool/dists/noble/main/r-cran-mle_1.9-1.ca2404.1_all.deb Size: 188194 MD5sum: 5dfc5e1a29134f7b0cf01c00e20db84e SHA1: 6add8cd5859c9bde1c93d522c6e5de89b043fed7 SHA256: 64381d4e7f52bf2e36789cea122dd963194f1f8530aa384068097268de79879e SHA512: a20cafa2ef4431a2a08fe0c59aed83f4f6d020656592620e914eac8994c5ae953b4558a9837f4ea10d37ebc27a4feb2beaf9130a9b620a9995988a39b2ff4b59 Homepage: https://cran.r-project.org/package=MLE Description: CRAN Package 'MLE' (Maximum Likelihood Estimation of Various Univariate andMultivariate Distributions) Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" , Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". . Package: r-cran-mlearning Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-class, r-cran-nnet, r-cran-mass, r-cran-e1071, r-cran-randomforest, r-cran-ipred, r-cran-rpart Suggests: r-cran-mlbench, r-cran-rcolorbrewer, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-mlearning_1.2.1-1.ca2404.1_all.deb Size: 231286 MD5sum: 01a19a1770e7794e0fc937cc435bc586 SHA1: af3d4306a227c73afd0a0d7aa7cfa5499bfdac9b SHA256: fdc2653f26612f46a20af3479a2aa2ffa58801f75d59af37cfd1b9238698b31b SHA512: e49a4986a279dbd5c28e7f55e935d4cf93b9a214682cbe26e3ab25e755867921ef31f1ed99c9ba94badc5181280a32478cd80c9416d87d90779c3d498c29fadd Homepage: https://cran.r-project.org/package=mlearning Description: CRAN Package 'mlearning' (Machine Learning Algorithms with Unified Interface and ConfusionMatrices) A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbors, random forest, support vector machine, ... It allows to train, test, and apply cross-validation using similar functions and function arguments with a minimalist and clean, formula-based interface. Missing data are processed the same way as base and stats R functions for all algorithms, both in training and testing. Confusion matrices are also provided with a rich set of metrics calculated and a few specific plots. Package: r-cran-mlece Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-laplacesdemon, r-cran-sirt, r-cran-ggplot2, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mlece_2.1.0-1.ca2404.1_all.deb Size: 110760 MD5sum: 52ff03ca0dedd853271284a4e9b451a6 SHA1: f64bfc5d8a624361edda9483a385c4c038a08542 SHA256: 7ebea1a5a5e9c316d919a6f68741774c04dbb0f0f8028ff6944e5e37e7038fc8 SHA512: 1ef0e3d6f737cc6c606f4297f93f31a6250287850526b221f3eaea7650a9ef7e0f624c31e42e0e7e068452296610add3652e56306b961e573c8c415434694a1b Homepage: https://cran.r-project.org/package=MLEce Description: CRAN Package 'MLEce' (Asymptotic Efficient Closed-Form Estimators for MultivariateDistributions) Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in. Jang, et al. (2023) . Kim, et al. (2023) . Package: r-cran-mlecensor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlecensor_0.1.0-1.ca2404.1_all.deb Size: 162644 MD5sum: 63bd12a705f368da622fd9bb25f79aed SHA1: cf0eebee72ff3474952371cbae5dde50cbadb87d SHA256: 3355d0d81b0c6f550dfc7075af48525b4fcb31d78a1a8ec2ad4d811baca89578 SHA512: fbbcbe9cffafee3ef0e5b8f838293d46b0d5cd3584cba7290471347d9e5d26eae83113db25e65fb8392b63293dd10b2190a8ae24ef84471383611ca11d88e7c2 Homepage: https://cran.r-project.org/package=MleCensoR Description: CRAN Package 'MleCensoR' (Maximum Likelihood Estimation under Censoring Schemes) Provides generalized functions to compute Maximum Likelihood Estimation (MLE) for any univariate distribution under various censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, support bounds, and initial parameter values; the package constructs and maximizes the appropriate log-likelihood automatically. Supported schemes include right and left truncation, random, right, left, interval, and middle censoring, block random censoring, balanced joint progressive Type-II (BJPT-II), progressive first failure, joint Type-I, Type-I, Type-II, progressive Type-II, Type-II progressively hybrid, joint Type-II, hybrid, hybrid Type-I, doubly Type-II, Type-I hybrid, and hybrid Type-II censoring. Optimization methods include Newton-Raphson (NR), Broyden-Fletcher-Goldfarb-Shanno (BFGS), the BFGS algorithm implemented in R (BFGSR), Berndt-Hall-Hall-Hausman (BHHH), Simulated Annealing (SANN), Conjugate Gradients (CG), and Nelder-Mead (NM). Inference summaries provide the Akaike Information Criterion (AIC), estimated coefficients, log-likelihood, iteration count, standard errors, z-values, p-values, and the variance-covariance matrix. Methods are described in Nagar, Kumar, and Krishna (2026) , Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) , Goel and Krishna (2026) , Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) , Ding and Gui (2023) , Prajapati, Mitra, and Kundu (2019) , Yadav, Jaiswal, and Yadav (2026) , Iyer, Jammalamadaka, and Kundu (2008) , Banerjee and Kundu (2008) , Kundu and Joarder (2006) , Berndt, Hall, Hall, and Hausman (1974) "Estimation and Inference in Nonlinear Structural Models" , Fletcher (1987, "Practical Methods of Optimization", ISBN:978-0-471-91547-8), Nelder and Mead (1965) , McKinnon (1999) "Convergence of the Nelder-Mead simplex method to a non-stationary point" , Kirkpatrick, Gelatt, and Vecchi (1983) , Fletcher and Reeves (1964) , and Nocedal and Wright (2006, "Numerical Optimization", ISBN:978-0-387-30303-1). Package: r-cran-mlelod Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlelod_1.0.0.1-1.ca2404.1_all.deb Size: 15450 MD5sum: 99fed65a6d227f1b313d9ea03daefd42 SHA1: 3a4ebb7f57bb49f4848d06c8f47a03da47455d2c SHA256: 4cbbab8fa0ed682f0bb17f8c45587c184bb17a0a5d03eab0c63ba69deaecd2c6 SHA512: d86a966e85b39805bcbc0be8a402ef680791b3f69109f6726af0c45ea872abeb5875d85ad54fcec24cbec4a5be925ffd89e2dcf7e1215b04375074743da96cc0 Homepage: https://cran.r-project.org/package=mlelod Description: CRAN Package 'mlelod' (MLE for Normally Distributed Data Censored by Limit of Detection) Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on . Package: r-cran-mlergm Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ergm, r-cran-network, r-cran-matrix, r-cran-stringr, r-cran-ggally, r-cran-ggplot2, r-cran-cowplot, r-cran-reshape2, r-cran-plyr, r-cran-lpsolve, r-cran-sna, r-cran-statnet.common Suggests: r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlergm_0.8.1-1.ca2404.1_all.deb Size: 943308 MD5sum: 82cdcd565a6e7c7c9010d96169e36299 SHA1: 20ac29fefc6ba1a5431b3e1d47f6978c72d15a85 SHA256: f4f3239221c69a0027a67fe3af981867a3010f43a2ff00eb67b7b67051616903 SHA512: 4408dac6ee4c94207f90bad1b45de62aed1c7dcba82c1b115c39e35ca6c22f35f57fcde78604b554803689df949b6439d1c357b47fa3a42ef96f7c31d142126a Homepage: https://cran.r-project.org/package=mlergm Description: CRAN Package 'mlergm' (Multilevel Exponential-Family Random Graph Models) Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (JCGS, 2006). The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks. Package: r-cran-mleval Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mleval_0.3-1.ca2404.1_all.deb Size: 409454 MD5sum: 13f1a76af6bdcabc38601152064dd44f SHA1: cfeffeae1495a45bd45d8b312272e5f32fa10872 SHA256: 5d3e88354447401269b72116ec9a704b50d7f703fd09fad4439d72a293664299 SHA512: cbce25ab54cff39f73bef996220534442bec8c56d74e97b8357329b89f1294f600ec95adc792221ecd944313f402cd13da1f72c01dd744fefbb65b62e0b49ac6 Homepage: https://cran.r-project.org/package=MLeval Description: CRAN Package 'MLeval' (Machine Learning Model Evaluation) Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals. Package: r-cran-mlexperiments Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-kdry, r-cran-progress, r-cran-r6, r-cran-splittools Suggests: r-cran-class, r-cran-lintr, r-cran-measures, r-cran-mlbench, r-cran-quarto, r-cran-rbayesianoptimization, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlexperiments_1.0.1-1.ca2404.1_all.deb Size: 597116 MD5sum: 7e02f61d83a4cf76fc60687b7b8fd9a7 SHA1: 87fb2484caa2fff8cc50b12fffb16ea4509adbbd SHA256: c39786dc8dfa09efd7eea16dba57afc1aeee45fba5cff2fba323d68dd4d5577e SHA512: 0b73c156043a62682e12519585f937e9a9c307d39abb43908a5f66585b57694382e5a27c99051ff39ed388a690826799ec36ef42124758ddc79d102d9eb1b31f Homepage: https://cran.r-project.org/package=mlexperiments Description: CRAN Package 'mlexperiments' (Machine Learning Experiments) Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via 'rBayesianOptimization' ) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While 'mlexperiments' focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class. Package: r-cran-mlf Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlf_1.2.1-1.ca2404.1_all.deb Size: 47930 MD5sum: 120e17a0b28dd914ca96ae64f4e0e9d2 SHA1: b1f245f37fa6009628b3788dfa0d4e97edd2f2f7 SHA256: ea459d8e257b9af5d7ec5e4cd2bcdc497ee4c79b7f7dc9fc53e3ab93ca5f418c SHA512: 2372d913fa40916900c21e463d2ad84fa6070351666c950490e943a97c734e1db3aead685bb2e1c1d5356823adffbf898570fe751e6c8d17337f157aa4261b7c Homepage: https://cran.r-project.org/package=mlf Description: CRAN Package 'mlf' (Machine Learning Foundations) Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). . Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). . Package: r-cran-mlfdr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmof Filename: pool/dists/noble/main/r-cran-mlfdr_0.1.0-1.ca2404.1_all.deb Size: 35880 MD5sum: e5b67a5651c3ee8fbdc5cc20ee638ebe SHA1: 0ba600015ff7140ff81b30e39d2a7dcc7c57fa4c SHA256: e77df96124c077d4101862c9187bf9587a8c8e80334b4ed67fe6a015385f887d SHA512: 62adf8279a3c44999be204e36ca45af80ea28756cd524d7a0a83e2344da8f58b91b05878aa3ca5413f262cebb268d7fb9dffc1d56e452fa2027700d935c85667 Homepage: https://cran.r-project.org/package=MLFDR Description: CRAN Package 'MLFDR' (High Dimensional Mediation Analysis using Local False DiscoveryRates) Implements a high dimensional mediation analysis algorithm using Local False Discovery Rates. The methodology is described in Roy and Zhang (2024) . 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This package offers implementations for several algorithms that extend this to nested structures: 'parent' and 'child' items for both of which constraints can be provided. The fitting algorithms include Iterative Proportional Updating , Hierarchical IPF , Entropy Optimization , and Generalized Raking . Additionally, a number of replication methods is also provided such as 'Truncate, replicate, sample' . 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This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models. 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It simulates the main forest processes— radial growth, height growth, mortality, crown recession, regeneration, and harvesting—so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and Jevšenak (2022) , the basal-area increment model by Jevšenak and Skudnik (2021) , and an overview of the MLFS package, workflow, and applications is provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological Informatics . Package: r-cran-mlgdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlgdata_0.1.0-1.ca2404.1_all.deb Size: 243038 MD5sum: 493db3d414becdf41d310397b859d9b5 SHA1: f66c04fa91ab971961e3d4ba795b250f45b6a9e9 SHA256: 9db13670855cd5d1aa8ee1cc8fbf13eed83b50dd0f2ee51e507246b31a0f776e SHA512: bbe0a56bf49b675ba3c4fa1df2b936ee1a759cb46155892d8a2a9392ef3175ffce8c043807c5ddf772948adb67680e8c61eaea72e70589c8628326aa8d56fea8 Homepage: https://cran.r-project.org/package=MLGdata Description: CRAN Package 'MLGdata' (Datasets for Use with Salvan, Sartori and Pace (2020)) Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati". 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Package: r-cran-mlim Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mice, r-cran-missranger, r-cran-memuse, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-mlr3tuning, r-cran-paradox, r-cran-md.log, r-cran-readstata13 Suggests: r-cran-mlr3learners Filename: pool/dists/noble/main/r-cran-mlim_0.6.0-1.ca2404.1_all.deb Size: 942664 MD5sum: 3eeaa319e2008d257c97b8fc06d27904 SHA1: d16ea60671eb6d52fef6e5caccbf02fb980bbaa4 SHA256: 78750179c46c0574f3b2d1da4646652e68bc4479abc6d7a4b846beded4660fe0 SHA512: a6d13c6b253453e567b6e0c654de3fa1f909b437b36c6aeca4f6426613d7c7c290cb5853901a0c26003f8f347c5ff514cd44c67c6ce239fb35233fa97db32e7e Homepage: https://cran.r-project.org/package=mlim Description: CRAN Package 'mlim' (Single and Multiple Imputation with Automated Machine Learning) Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced. Package: r-cran-mllmcelltype Architecture: all Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-digest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-seurat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mllmcelltype_2.0.8-1.ca2404.1_all.deb Size: 808294 MD5sum: a822774d2b3d8417fc3d5814a6b28e08 SHA1: 269815f8231e0d9206fcd3e453759cd2a45c0ecd SHA256: 0b7145d265fae9cfa03d269afa55978f7a53f9eb915c1d649862f307d64892f4 SHA512: 664f29b88e1d6652714ad16cdaaf398fe0c6fbb7c5e98dd348399685e082384c990c27c042e9110ae33e4133d08847201229a406c249a006868ae13a27ce74af Homepage: https://cran.r-project.org/package=mLLMCelltype Description: CRAN Package 'mLLMCelltype' (Cell Type Annotation Using Large Language Models) Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models. 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Package: r-cran-mllrnrs Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1778 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-kdry, r-cran-mlexperiments, r-cran-r6 Suggests: r-cran-glmnet, r-cran-lightgbm, r-cran-lintr, r-cran-measures, r-cran-mlbench, r-cran-quarto, r-cran-ranger, r-cran-rbayesianoptimization, r-cran-splittools, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mllrnrs_0.0.9-1.ca2404.1_all.deb Size: 218168 MD5sum: e95ba933a8b5ec45baad1cd6253bc325 SHA1: 602baee8e6d7c84bf7767c1f05ee11e0db2f55c4 SHA256: 9457f5d5e81d25dbe65c97a2e30e4194232c0acc10fd7f9ba783fc0ee6b671f0 SHA512: fd7c50ac2bda99f759b8197686762ac6511aa54d5bb88205e6a47b854e4769ada5c62462c0d1e8230397aa6de05bc239ee50ab5e0ee721d908c64a561d91a633 Homepage: https://cran.r-project.org/package=mllrnrs Description: CRAN Package 'mllrnrs' (R6-Based ML Learners for 'mlexperiments') Enhances 'mlexperiments' with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: 'glmnet' , 'ranger' , 'xgboost' , and 'lightgbm' . These can be used directly with the 'mlexperiments' R package. Package: r-cran-mlma Architecture: all Version: 6.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-car, r-cran-abind, r-cran-coxme, r-cran-gplots, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlma_6.3-1-1.ca2404.1_all.deb Size: 324962 MD5sum: c5719b2c1a85a9d390746ece0f28692c SHA1: 450ad0eea264db623f367a271176b7c08f566ad5 SHA256: 092c2c265d3343a79afb127c905a1bd786f417f3178c6169418aa5543cd304d8 SHA512: 489be387c221cf899a85e819581275c448a6c87593ba1eae7b58100e9e280f5930368cd379c8913caf9699ca6d87d9c93c9b92c2ba43880a0a0e1388523bda5e Homepage: https://cran.r-project.org/package=mlma Description: CRAN Package 'mlma' (Multilevel Mediation Analysis) Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072. Package: r-cran-mlmes Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-dplyr Suggests: r-cran-brms, r-cran-bayestestr, r-cran-lmertest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmes_0.1.2-1.ca2404.1_all.deb Size: 524722 MD5sum: 832f59833b8147eb65bd38facad2152d SHA1: a79ea5f267867ba9e06a9862460ce50d92b09404 SHA256: d571472afbdfaf0cf0d7253892409666c52cb3c3c240f05e981a5d935df7107d SHA512: c62c01e5b126941964a4f377fa6def0f5d791d3455a3bcc33c714716e38390d67863e25554fc1c469ccdc8ea505b1293143582a7b78929d294e1eaec93bf5584 Homepage: https://cran.r-project.org/package=MLMES Description: CRAN Package 'MLMES' (Model-Based Effect Sizes for Multilevel Models) Computes model-based effect sizes for fixed-effect coefficients in multilevel (hierarchical) models. The coefficient effect sizes are standardized mean differences from zero (d) and unique variance-explained measures (squared semi-partial correlations, sr2). The package also reports variance components and level-specific and total R-squared values. It supports 2-level and 3-level linear and binary logistic models fitted with 'lme4' (Bates et al., 2015) , and 2-level Gaussian and Bernoulli models fitted with 'brms' (Bürkner, 2017) . Sanders, Konold, and Cheng (in press), "Model-based effect sizes for multilevel linear regression coefficients," Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, describe the 2-level linear-model methods. Package: r-cran-mlmetrics Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr Suggests: r-cran-e1071 Filename: pool/dists/noble/main/r-cran-mlmetrics_1.1.3-1.ca2404.1_all.deb Size: 74610 MD5sum: 88db24ba397dc260618822e471c311b6 SHA1: 5f3d151f4417819da1886a7522cfaca643e06d6b SHA256: e3b5969d46ed627ac809a6bf30ef6d9e92eefb1efe13d00a15d14eccac753fcf SHA512: 74c618fb23e8280e81f5e33f33f380eb8bfd8c98ec66624f49a5c9825c7c4af4083d45ac7707ccaeec34ea265f86dae400f6547ca6a5163fca17a8433623eda6 Homepage: https://cran.r-project.org/package=MLmetrics Description: CRAN Package 'MLmetrics' (Machine Learning Evaluation Metrics) A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance. Package: r-cran-mlmhelpr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-rdpack, r-cran-mathjaxr Suggests: r-cran-clubsandwich, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mlmhelpr_0.1.1-1.ca2404.1_all.deb Size: 166518 MD5sum: 280dae569b4c6f241c06b14e0e0f7a20 SHA1: 10b5848b717cec345962ee8fc37e48c0f9c3bece SHA256: 225fc7bbb80b5aa569ec102bdb6ae074274b48c0a34e73fd2c67852540a20487 SHA512: 2d291161ece15e2e2315c75db53970b4521a6d6850c642222d691412436e77661795bc2a8b6751c0e6a8290852ed86c6c1c93dd7e9b7a9a905760b9c829d3468 Homepage: https://cran.r-project.org/package=mlmhelpr Description: CRAN Package 'mlmhelpr' (Multilevel/Mixed Model Helper Functions) A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015). Package: r-cran-mlmi Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gsl, r-cran-norm, r-cran-cat, r-cran-mix, r-cran-matrix, r-cran-nlme Suggests: r-cran-bootimpute, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmi_1.1.3-1.ca2404.1_all.deb Size: 103182 MD5sum: 3db2a4025acd5d86461f3e48b6fb039d SHA1: 1513c66c2722948ddaee9053b5c662da40f9b31d SHA256: 66b1293680c693f1c7bc3d45671fefa06878f56d489127d1f5a04a1e73896072 SHA512: abc53136dbc3e080b1542a8dec85bd5890883fd79a9a32b13607370c8fdb35ee3cd233ddaf1cc163e15a6586695223662d652b3355e2ca3ee0935ef957912884 Homepage: https://cran.r-project.org/package=mlmi Description: CRAN Package 'mlmi' (Maximum Likelihood Multiple Imputation) Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) . A number of different imputation methods are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) is also available. Package: r-cran-mlml2r Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-minfi, r-cran-microbenchmark, r-bioc-geoquery, r-cran-knitr, r-cran-rmarkdown, r-bioc-illuminahumanmethylation450kmanifest Filename: pool/dists/noble/main/r-cran-mlml2r_0.3.3-1.ca2404.1_all.deb Size: 610388 MD5sum: ceaf9c12569fe710fb1a6a7e8c78c2b4 SHA1: 161042dbb49b0af06a212a8940f27632409653ad SHA256: 171014bc6dfce7e33db007f16a9c3c4af551bc9b02d77eafe13e11a783c0a328 SHA512: 9d4c2036cf4dfa279cf3a2c8a18b9e1920f15dda3913c767641d9caa047cd4eb6b1d3d7892847ef053929feb7353e612d30f226b329286f25173dc98775ffa36 Homepage: https://cran.r-project.org/package=MLML2R Description: CRAN Package 'MLML2R' (Maximum Likelihood Estimation of DNA Methylation andHydroxymethylation Proportions) Maximum likelihood estimates (MLE) of the proportions of 5-mC and 5-hmC in the DNA using information from BS-conversion, TAB-conversion, and oxBS-conversion methods. One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) and Kiihl et al. (2019) . Package: r-cran-mlmm.gwas Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcompview, r-cran-multcomp, r-cran-coxme, r-cran-sommer, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlmm.gwas_1.0.6-1.ca2404.1_all.deb Size: 732190 MD5sum: f539a5e8cdce576b4791d6eec322117c SHA1: 708bccde7d968452d8888c7f064867cf7c27985c SHA256: c9bf97ee236ab9c8c05b0a5f250134e6896909c9a6bf596ad98692262a1cef66 SHA512: c331d048cfed3c164166428044fe9e4363c5b152f589ee45139c94efb73b84bef0a8fd4274473ad202df9c145679a9967261d39197fac8a67e2f6254ba9df7dd Homepage: https://cran.r-project.org/package=mlmm.gwas Description: CRAN Package 'mlmm.gwas' (Pipeline for GWAS Using MLMM) Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) . The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions. Package: r-cran-mlmodels Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-hardhat, r-cran-insight, r-cran-marginaleffects, r-cran-mass, r-cran-matrixcalc, r-cran-maxlik, r-cran-rlang, r-cran-tibble Suggests: r-cran-boot, r-cran-dplyr, r-cran-e1071, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-wooldridge Filename: pool/dists/noble/main/r-cran-mlmodels_0.1.2-1.ca2404.1_all.deb Size: 1709898 MD5sum: 06f23ec665b379925959e7987e8533d0 SHA1: 437653c7b69fdfb1a30be9e2cb5cb667befbfaea SHA256: edd35a340a91ae03641c03a7ab8892105ef6ec780e069a2d6c7713852d75e6b0 SHA512: 4ba919dd957a80de99c7b4240484606df2dfea4f638d1f9e8540d979ce085a5d359bbb97d91ef0377af59db8bb284db69f3c20531ffbbaad258133ff40340900 Homepage: https://cran.r-project.org/package=mlmodels Description: CRAN Package 'mlmodels' (Maximum Likelihood Models and Tools for Estimation, Prediction,and Testing) Provides a collection of maximum likelihood estimators with a consistent S3 interface. Supported models include Gaussian (linear and log-normal), logit, probit, Poisson, negative binomial (NB1 and NB2), gamma, and beta regression. A distinctive feature is flexible modeling of the scale parameter (variance, dispersion, precision, or shape) alongside the location/mean parameters. The package offers unified predict() methods, multiple variance-covariance estimators (observed information, outer product of gradients, robust/Huber-White, cluster-robust, bootstrap, jackknife), and a full suite of hypothesis tests (Wald, likelihood ratio, information matrix, Vuong, overdispersion, and goodness-of-fit). It is fully compatible with 'marginaleffects' for post-estimation analysis. Methods implemented include Cameron and Trivedi (1990) , for Poisson overdispersion testing, Manjon and Martinez (2014) , for goodness-of-fit testing of count data models, Vuong (1989) , for non-nested likelihood ratio testing, and White (1982) , for information matrix tests. Package: r-cran-mlmoderator Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-lmertest, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-pbkrtest, r-cran-emmeans, r-cran-mlmrev Filename: pool/dists/noble/main/r-cran-mlmoderator_0.3.0-1.ca2404.1_all.deb Size: 230978 MD5sum: 458c892a9756c31edb1d300b8514cf85 SHA1: de2d8c4ef445431b8987faf955ab98d4d687ccd0 SHA256: d688e9bb52a8e34ec8762906023f2f042462858bde205dab80cbf0a5879b3ca4 SHA512: 4d5640a6c4ee124f269bd4bd99eeb51a2f4c3ab88a5ba8019a68bbb781b4295e55702b21fe0484caac3dfa647806415b51689c41d5d9db71912ef3c6303767c6 Homepage: https://cran.r-project.org/package=mlmoderator Description: CRAN Package 'mlmoderator' (Probing, Plotting, and Interpreting Multilevel InteractionEffects) Provides a workflow for probing, plotting, and checking cross-level interaction effects in two-level mixed-effects models fitted with 'lme4' (Bates et al., 2015) . Implements simple slopes analysis following Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals following Johnson and Fay (1950) and Bauer and Curran (2005) , and grand- or group-mean centering as described in Enders and Tofighi (2007) . Tests and intervals use Satterthwaite degrees of freedom via 'lmerTest' (Kuznetsova et al., 2017) by default, with Kenward-Roger and between-cluster alternatives. Also provides confidence and new-cluster prediction intervals for simple slopes in random-slope models, contour plots of predicted outcomes over the predictor-by-moderator space, and leave-one-cluster-out influence diagnostics for the interaction. Package: r-cran-mlmoi Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3714 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-rdpack, r-cran-rmpfr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlmoi_0.1.2-1.ca2404.1_all.deb Size: 2346262 MD5sum: 3a07b429d3bdb2cadc38b5f5877398d6 SHA1: 8836ab6b25952e155af08d613c083a945ea616d0 SHA256: aa440ba7f6e703d456ef0256538f9f97d3b542199a0333456dd348454c3e34f9 SHA512: 176c6cc6b8e5f9f68dedcc243fc0669dd2bc0b875c67b9afe62e38f3a5feec8b154a408dbfc2dbbfe409ec30c64d4ac5820d7fe519d628f4a8c6f5f8459ea43f Homepage: https://cran.r-project.org/package=MLMOI Description: CRAN Package 'MLMOI' (Estimating Frequencies, Prevalence and Multiplicity of Infection) The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) , and Schneider and Escalante (2014) . Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function. Package: r-cran-mlmorph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-caret, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-openxlsx, r-cran-plotly, r-cran-randomforest, r-cran-reactable, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmorph_0.1.1-1.ca2404.1_all.deb Size: 72130 MD5sum: a59fa3abd12d8ba81539c1e7733a3682 SHA1: 17b34d615efcff75925e132dd50f19a3f4418db5 SHA256: 2c57aedfabc98a4f9c0c992f58a91cae5149a88f29f4b8ad8cd06d129cc0e13f SHA512: 61778ba6d56a33a257883245919eca79652a86e2b36821af68392c522c8fb165744a2e8326cd53d2a81923247b0205934b0bacb6152d442a4d29405b9cea911e Homepage: https://cran.r-project.org/package=MLmorph Description: CRAN Package 'MLmorph' (Integrating Morphological Modeling and Machine Learning forDecision Support) Integrating morphological modeling with machine learning to support structured decision-making (e.g., in management and consulting). The package enumerates a morphospace of feasible configurations and uses random forests to estimate class probabilities over that space, bridging deductive model exploration with empirical validation. It includes utilities for factorizing inputs, model training, morphospace construction, and an interactive 'shiny' app for scenario exploration. Package: r-cran-mlmpower Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lme4, r-cran-lmertest, r-cran-vartestnlme Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlmpower_1.0.11-1.ca2404.1_all.deb Size: 201196 MD5sum: e3604994441c64492ee085c2669b3002 SHA1: 039275ff1fb54991c3589dad251f00357761c2cc SHA256: abe1a55964eb3c25d96091caaf2a58ab57d64d65d80e880572de111783ee16e8 SHA512: a6b4e2c62dbbb469116814a85f3100ea524877b0b0cbad8077061cf61c5106d48c008a495134fccab756fb77ab530e58d6e09aa3c13e6c391f37a6411b58cdde Homepage: https://cran.r-project.org/package=mlmpower Description: CRAN Package 'mlmpower' (Power Analysis and Data Simulation for Multilevel Models) A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects. Package: r-cran-mlmrev Architecture: all Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-mlmrev_1.0-9-1.ca2404.1_all.deb Size: 2080268 MD5sum: 2954841759eeaf35a590f1d08de9c28d SHA1: e8793cd9dd7b95d09f0478d0a9d5bc196df76635 SHA256: 09a866237b9726d46573edf8f5f7ded65939009d6781b8e80ada96787bea79cd SHA512: 01c97e0c15112460ce972d1ed2e945925af0605a64f2284761b81ce2aff4405a361b1852218a83223b46fdc73e13868a03babdb350b058b1951756dbd49b53a4 Homepage: https://cran.r-project.org/package=mlmRev Description: CRAN Package 'mlmRev' (Examples from Multilevel Modelling Software Review) Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature. Package: r-cran-mlms Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-jsonlite, r-cran-plotrix, r-cran-readxl, r-cran-sf, r-cran-stringi Suggests: r-cran-connectapi, r-cran-covr, r-cran-dm, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-fontawesome, r-cran-htmltools, r-cran-htmlwidgets, r-cran-inldata, r-cran-knitr, r-cran-pkgbuild, r-cran-pkgdown, r-cran-pkgload, r-cran-rcmdcheck, r-cran-reactable, r-cran-renv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-tinytest, r-cran-v8, r-cran-webmap Filename: pool/dists/noble/main/r-cran-mlms_1.0.2-1.ca2404.1_all.deb Size: 595098 MD5sum: f720766d9da46fb3e0927b50e865e27b SHA1: 1c3fef2b9b38e6a6de1b9f19972b62e53fa082d8 SHA256: 370cf46bbf6db64f0eb038b9f56c144400403ee4e09642826971d0da580c3b44 SHA512: eaff58ad6e1554042e234062243ef5e319bdc66efba2eea4211138c70abb44b41b88716a3108d6bbdbff575d47ac5674d1675bf11f7c21ad6273c8a7ec28de8c Homepage: https://cran.r-project.org/package=mlms Description: CRAN Package 'mlms' (Multilevel Monitoring System Data for Wells in the USGS INLAquifer Monitoring Network) Analysis-ready datasets detailing the Multilevel Monitoring System (MLMS) wells within the U.S. Geological Survey's (USGS) aquifer-monitoring network at the Idaho National Laboratory (INL) in Idaho, and the data collected within these wells. Supported by the U.S. Department of Energy (DOE), the USGS collected discrete measurements of hydraulic head at various depths from wells in the eastern Snake River Plain (ESRP) aquifer over several years. These measurements were derived from data on fluid pressure, fluid temperature, and atmospheric pressure. Each well was equipped with an MLMS, which included valved measurement ports, packer bladders, casing segments, and couplers. The MLMS facilitated monitoring at multiple hydraulically isolated depth intervals, reaching significant depths below the land surface. Additionally, groundwater samples were collected from these wells over multiple years and analyzed for various chemical and physical parameters. Package: r-cran-mlmtools Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1021 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mlmtools_1.0.2-1.ca2404.1_all.deb Size: 933120 MD5sum: e29c1664a36b4826459a5b74af4380da SHA1: 44c01aafc6c6688f31d59771ae654011d2474f5e SHA256: b4043cf5f53d04aea53d2522f95298bb3fef9496e682564ade33126cc3df1e74 SHA512: ff814b3705f05d08d397b07a3822c218459d5f230b19893fa9c897846d3b235145ea759c02410b179f9e1bc9e92f0f60c302bde7ccc63c84816c33c100241fff Homepage: https://cran.r-project.org/package=mlmtools Description: CRAN Package 'mlmtools' (Multi-Level Model Assessment Kit) Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) . Package: r-cran-mlmts Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2746 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantspec, r-cran-waveslim, r-cran-rfast, r-cran-tsclust, r-cran-forecast, r-cran-tseries, r-cran-tsa, r-cran-tsfeatures, r-cran-tserieschaos, r-cran-freqdom, r-cran-e1071, r-cran-dtw, r-cran-psych, r-cran-complexplus, r-cran-mts, r-cran-matrix, r-cran-ggplot2, r-cran-multiwave, r-cran-mass, r-cran-fda.usc, r-cran-tsdist, r-cran-geigen, r-cran-desctools, r-cran-pracma, r-cran-pspline, r-cran-rdpack, r-cran-clusterr, r-cran-aid, r-cran-caret, r-cran-ranger, r-cran-igraph, r-cran-randomforest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmts_1.1.2-1.ca2404.1_all.deb Size: 2680788 MD5sum: 402b84ece7e2010143e114ee312519ca SHA1: fd5b3190dcf5280249c2280bdf8731f357143cd7 SHA256: b6d124c842e1feb0e3cff1793c1ccc1fcc8242e3e619f65f8b2147c701d98e2e SHA512: 716094288c3a84c3506d74797eb5984c71a47400fe280799ad8783c5b0a320f28e3a3ee7cb65bb1b3c00d690ab613af3bb506c02dab28883040149c68dc189a7 Homepage: https://cran.r-project.org/package=mlmts Description: CRAN Package 'mlmts' (Machine Learning Algorithms for Multivariate Time Series) An implementation of several machine learning algorithms for multivariate time series. The package includes functions allowing the execution of clustering, classification or outlier detection methods, among others. It also incorporates a collection of multivariate time series datasets which can be used to analyse the performance of new proposed algorithms. Some of these datasets are stored in GitHub data packages 'ueadata1' to 'ueadata8'. To access these data packages, run 'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='')'. The installation takes a couple of minutes but we strongly encourage the users to do it if they want to have available all datasets of mlmts. Practitioners from a broad variety of fields could benefit from the general framework provided by 'mlmts'. Package: r-cran-mlmusingr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-matrix, r-cran-magrittr, r-cran-broom, r-cran-generics, r-cran-dplyr, r-cran-performance, r-cran-tibble, r-cran-wemix Filename: pool/dists/noble/main/r-cran-mlmusingr_0.4.0-1.ca2404.1_all.deb Size: 1063098 MD5sum: 8f9669e333c4cf1e3ddbf10e8e57712c SHA1: 8f19126476736129d7d1012a46dc4273807f491f SHA256: ba45122e560ca04882c69c61f37ef92e8d50c654c77d6d3e8c76b9cf4b3f60c6 SHA512: c8406a3a817c617bcfe388c59e5e493aa268299809f2b0a7715d3db79b603f9ab2f711fc7e53481521028fc94c242729c4e0d3f6b93be6e2877bf1431882b921 Homepage: https://cran.r-project.org/package=MLMusingR Description: CRAN Package 'MLMusingR' (Practical Multilevel Modeling) Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) and Bell and 'McCaffrey' (2002) is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X). Package: r-cran-mlogit Architecture: all Version: 2.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1662 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-lmtest, r-cran-rdpack, r-cran-dfidx, r-cran-micsr, r-cran-numderiv Suggests: r-cran-knitr, r-cran-car, r-cran-nnet, r-cran-lattice, r-cran-aer, r-cran-ggplot2, r-cran-quarto, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-mlogit_2.0-0-1.ca2404.1_all.deb Size: 1067136 MD5sum: 6632ae475ca34bdee78e4be39dbc1488 SHA1: 47a89899fc59351f1762789474be88170a8e0420 SHA256: d021832c644bc5dee3bb477528e87b62ee597ec3ed919ac45db9310e7b2cab31 SHA512: cfdba783aeb8dfcf7d119dfa43ee516b6d597d48137c032d5e32ff425e1612c4793b42d9208a1f76810cc38da6681872feea6cb71a040becd9183bd100fc398f Homepage: https://cran.r-project.org/package=mlogit Description: CRAN Package 'mlogit' (Multinomial Logit Models) Maximum likelihood estimation of random utility discrete choice models. The software is described in Croissant (2020) and the underlying methods in Train (2009) . Package: r-cran-mlpreemption Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlpreemption_1.0.1-1.ca2404.1_all.deb Size: 51100 MD5sum: 2507244c33d4196211e6fcfd4515c460 SHA1: 6f4fae0ce5344ec8cd7ce509bb0fa6581adaf316 SHA256: 9908d2f1d2edb308e1f799320c93994e2c21e043d91c4a53117d1634a297b634 SHA512: 888060eb6e0ce74b752ae87dd817d6c867c23de4ec952c171575acbb2ef488189b6cf814b3f1681331c4df73de480015f73f4b81bf45db98965d59629e3fa72b Homepage: https://cran.r-project.org/package=MLpreemption Description: CRAN Package 'MLpreemption' (Maximum Likelihood Estimation of the Niche Preemption Model) Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) . The niche preemption model is a widely used model in ecology and biodiversity studies. Package: r-cran-mlpugs Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-progress, r-cran-c50, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-mlpugs_0.2.0-1.ca2404.1_all.deb Size: 69454 MD5sum: 8e2fae15ade31803290eaea1ee184ae2 SHA1: 1de50dcd7f185246f0c2446526d5048511bedac9 SHA256: 6ec7360454c8f64d723959591be3782b82504744536db24b6794eac4a17b60e4 SHA512: 4314d2b60e567e71faf1e6a02832295bb02600592704474af384eadf34461f5102825f214458d7b72641c81a0ffafeecff57497d1fc23da14e5311a614766f85 Homepage: https://cran.r-project.org/package=MLPUGS Description: CRAN Package 'MLPUGS' (Multi-Label Prediction Using Gibbs Sampling (and ClassifierChains)) An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications. Package: r-cran-mlpwr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dicekriging, r-cran-digest, r-cran-ggplot2, r-cran-randtoolbox, r-cran-rlist, r-cran-rgenoud Suggests: r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-mirt, r-cran-pwr, r-cran-rmarkdown, r-cran-simr, r-cran-sn, r-cran-tidyr, r-cran-weightsvm Filename: pool/dists/noble/main/r-cran-mlpwr_1.1.1-1.ca2404.1_all.deb Size: 1169470 MD5sum: a95f6c5e117f6ce37aa976935d97a7e5 SHA1: b7a6ca75ded51d6d8104c26c8711dd6bbdfa04ce SHA256: 742a580551e3294b42868513cda9ba37b4bbf44cb5c0572927788c3756fde69e SHA512: 44e151e5a5102e011fc09b189a55e1dabf887da7e2c5d441fdd2c69650fee86ffad45426b1c2aead780f1fe2ef0d2f1a022592346f397157f9a9a52dbad18df6 Homepage: https://cran.r-project.org/package=mlpwr Description: CRAN Package 'mlpwr' (A Power Analysis Toolbox to Find Cost-Efficient Study Designs) We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in our paper (Zimmer & Debelak (2023) ). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) ). Package: r-cran-mlquantify Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-randomforest, r-cran-fnn Suggests: r-cran-corelearn Filename: pool/dists/noble/main/r-cran-mlquantify_0.2.0-1.ca2404.1_all.deb Size: 174296 MD5sum: d211e7761ac19178a8be12f6cfe54ec5 SHA1: 0b37caef0fac8b2f60ab0021055b9b2ee619246b SHA256: 8423be18e9115eae8461dc8c6ff5a250c5856870e52886a34508599099eabee8 SHA512: 91bbed4075b766eaf1973329e4addb4e563e40984786f213c69f98efb477a1a5bef2f1894534873967b7532767a1295107b4d6fcf0907e142554d7ecfc1aeb15 Homepage: https://cran.r-project.org/package=mlquantify Description: CRAN Package 'mlquantify' (Algorithms for Class Distribution Estimation) Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. . Package: r-cran-mlr3 Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3862 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-evaluate, r-cran-future, r-cran-future.apply, r-cran-lgr, r-cran-mirai, r-cran-mlbench, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-parallelly, r-cran-palmerpenguins, r-cran-paradox, r-cran-uuid Suggests: r-cran-callr, r-cran-codetools, r-cran-future.callr, r-cran-mlr3data, r-cran-progressr, r-cran-remotes, r-cran-rhpcblasctl, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3_1.8.0-1.ca2404.1_all.deb Size: 2833108 MD5sum: 6893cd53a8399d671bf68afa51a97023 SHA1: 49141d20350995869e9ed599733b1cd6b847b2c8 SHA256: 945b983b0b5e6be8f19bdc7aaf0e67905087b819aa4f2fbd39c704a83386ee7a SHA512: 5a98978d1852ebe47ed32ce41d66caf56368ef8d6b2a5cc1e3d908d851e51489967f9e4ecc3df2ecebd340794dd6b61da87c37e87ffa11face3dcdf72741f1ea Homepage: https://cran.r-project.org/package=mlr3 Description: CRAN Package 'mlr3' (Machine Learning in R - Next Generation) Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality. Package: r-cran-mlr3automl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1613 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3tuning, r-cran-rush, r-cran-bbotk, r-cran-checkmate, r-cran-data.table, r-cran-lhs, r-cran-mlr3learners, r-cran-mlr3mbo, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-paradox, r-cran-r6 Suggests: r-cran-callr, r-cran-e1071, r-cran-fastai, r-cran-glmnet, r-cran-kknn, r-cran-lgr, r-cran-lightgbm, r-cran-mass, r-cran-mirai, r-cran-mlr3torch, r-cran-mlr3viz, r-cran-ranger, r-cran-redux, r-cran-reticulate, r-cran-rpart, r-cran-testthat, r-cran-torch, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3automl_0.1.0-1.ca2404.1_all.deb Size: 867200 MD5sum: c4a6154ce134630744d3bec52a264451 SHA1: 79dfa899de7ec18f06b15d14e24946466dfc7629 SHA256: 1fd2a00d196e6bd4e243773729796d51ac66387dc3d3696ed5e216f962df06ed SHA512: a386ae1634c6ab14e565ecff7239aff3ebc02d3fa56591789f52b1b4d3e9f141b9437ad18172113d76070bdae4038f5140098301581971689c6e0644b1e0719d Homepage: https://cran.r-project.org/package=mlr3automl Description: CRAN Package 'mlr3automl' (Automated Machine Learning for 'mlr3') Flexible automated machine learning (AutoML) system for the 'mlr3' ecosystem. Automatically selects a suitable machine learning algorithm and tunes its hyperparameters for a given task. Constructs preprocessing pipelines with multiple parallel branches using 'mlr3pipelines' and jointly optimizes them together with the learners using 'mlr3tuning'. The optimization is driven by asynchronous decentralized Bayesian optimization by Egele et al. (2023) . Package: r-cran-mlr3batchmark Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-uuid Suggests: r-cran-renv, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3batchmark_0.2.2-1.ca2404.1_all.deb Size: 38784 MD5sum: 6078d79bb55c585940acf9c05670a694 SHA1: 9b7d8cfa8f3763d900cb455c5af63bf933144b47 SHA256: 880ab1bbce345a0824bb49ce3ec2005750b53b3da7594707e1d2cf582bcb8bb7 SHA512: 1b8213e49933ad7d0037a91b5a583f33ac1dab5c89f593952be8f2a171fb291005da3315bff4d512f7b859507282fe4a7a0c256898f57afd8536db78e3254cc3 Homepage: https://cran.r-project.org/package=mlr3batchmark Description: CRAN Package 'mlr3batchmark' (Batch Experiments for 'mlr3') Extends the 'mlr3' package with a connector to the package 'batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters. Package: r-cran-mlr3benchmark Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-mlr3, r-cran-mlr3learners, r-cran-pmcmrplus, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3benchmark_0.1.8-1.ca2404.1_all.deb Size: 161860 MD5sum: 52f2e3f48e86431e3649d1b46b473265 SHA1: 781abc94e5588a0854dc419494ac624f53a99f11 SHA256: bd8afd2e64863062949cd0fe62bd5c7578e6d7d320ef8bd868fd15f09eda1514 SHA512: fecb9c3306628d175d558d853c4a16f5685901d23f3c4ba9f17284521994f561cc46f76c4ec92e00ef5b3cd60ed5b55e561d91b5f08232b4b37d82763c31746e Homepage: https://cran.r-project.org/package=mlr3benchmark Description: CRAN Package 'mlr3benchmark' (Analysis and Visualisation of Benchmark Experiments) Implements methods for post-hoc analysis and visualisation of benchmark experiments, for 'mlr3' and beyond. Package: r-cran-mlr3cluster Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-cluster, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-apcluster, r-cran-clue, r-cran-clustercrit, r-cran-clusterr, r-cran-clustmixtype, r-cran-dbscan, r-cran-e1071, r-cran-flexclust, r-cran-flexmix, r-cran-fpc, r-cran-future, r-cran-genieclust, r-cran-gmeans, r-cran-kernlab, r-cran-klar, r-cran-kohonen, r-cran-lgr, r-cran-lpcm, r-cran-mclust, r-cran-mirai, r-cran-movmf, r-cran-mvtnorm, r-cran-protoclust, r-cran-rjava, r-cran-rweka, r-cran-skmeans, r-cran-stdbscan, r-cran-stream, r-cran-tclust, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3cluster_0.6.0-1.ca2404.1_all.deb Size: 1763582 MD5sum: dbf5831f836bab6da0e9963fef583ef7 SHA1: 126fc369adf8298f92e5188d5bbca71929465bbe SHA256: 73e1661fa97dec92229cca36d5d809e20a72c763bd6dbcc19a8f2388b05635ad SHA512: a953a5908351925df92a041228db605e48179a7ca64bb3cfa474ee492499237cb0e20523163792f33c00e4aa0083fa69af5f7450077ea5500f4da66ed4a2f7a9 Homepage: https://cran.r-project.org/package=mlr3cluster Description: CRAN Package 'mlr3cluster' (Cluster Extension for 'mlr3') Extends the 'mlr3' package with cluster analysis. Package: r-cran-mlr3data Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3244 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mlr3 Filename: pool/dists/noble/main/r-cran-mlr3data_0.9.0-1.ca2404.1_all.deb Size: 3281962 MD5sum: 7e0d45906e8bbab419d70f1d58923212 SHA1: 022d9a96c23ccda60ac21f51346e3954703aa080 SHA256: d1327a2b0f59aa58d6a7b2de0e99f49344c4c3727ed3918bfd4beac83f15f93e SHA512: 24de9971ab09f5922d31995c0761326fa2392f941ba6badcfb4997cefcc0c653a9edbfc9ffedbd75793507c0a6424a9f35adeef9e812a508c1980f7ea622b3af Homepage: https://cran.r-project.org/package=mlr3data Description: CRAN Package 'mlr3data' (Collection of Machine Learning Data Sets for 'mlr3') A small collection of interesting and educational machine learning data sets which are used as examples in the 'mlr3' book (), the use case gallery (), or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if 'mlr3' is loaded. Package: r-cran-mlr3db Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-future, r-cran-future.apply, r-cran-future.callr, r-cran-hms, r-cran-lgr, r-cran-rsqlite, r-cran-rlang, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mlr3db_0.7.2-1.ca2404.1_all.deb Size: 580978 MD5sum: 6effb85721a8f441db4d3c6dbbd20331 SHA1: f1a4b4a91a8aa613d0ed1a907623885d17725adb SHA256: ec4c588861d068c61870d480561e12af97b1bcb78a37b4091537740284f951af SHA512: 7aa8e8626d519a729490d8c1d7b2bfd06cfdbc1141a2ccdd44bcf3a23f55608f371143c3ba2eeefc8a3aad512296c712df77a9169a8155bbead106e88a834239 Homepage: https://cran.r-project.org/package=mlr3db Description: CRAN Package 'mlr3db' (Data Base Backend for 'mlr3') Extends the 'mlr3' package with a backend to transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or 'PostgreSQL'. The package provides three additional backends: 'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases and also on Apache Parquet files. 'DataBackendPolars' operates on 'Polars' data frames. Package: r-cran-mlr3fairness Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-paradox, r-cran-r6, r-cran-rlang Suggests: r-cran-cccp, r-cran-cvxr, r-cran-pagedown, r-cran-fairml, r-cran-future, r-cran-iml, r-cran-kableextra, r-cran-knitr, r-cran-linprog, r-cran-lgr, r-cran-mlr3viz, r-cran-patchwork, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3fairness_0.4.0-1.ca2404.1_all.deb Size: 919528 MD5sum: 741f34e88bf04826b3289e8230b308c0 SHA1: 9f07b548053a3fb098be4cb66d7272bd8a832faa SHA256: fca7cb9eaf78c584f7311aa24ff404f50c860d62529cbaef91922724fcc725f7 SHA512: e9d51c859524e3eac38468b4f43f7aadee66e0caa250c50e1ea8dae7156f33d8981f2340c4a0e26fb24baabfb35bf108d13e446ac2c88ed67db7e16e991a76aa Homepage: https://cran.r-project.org/package=mlr3fairness Description: CRAN Package 'mlr3fairness' (Fairness Auditing and Debiasing for 'mlr3') Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in 'Kamiran, Calders' (2012) and "Equalized Odds" described in 'Hardt et al.' (2016) . Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods. Package: r-cran-mlr3fda Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2465 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-tf Suggests: r-cran-fdboost, r-cran-lme4, r-cran-mboost, r-cran-rcatch22, r-cran-rpart, r-cran-testthat, r-cran-tsfeatures, r-cran-wavelets, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3fda_0.7.2-1.ca2404.1_all.deb Size: 2326174 MD5sum: a43517f26c89b21ed5d155d1519033db SHA1: 028163a528d3a1676ed77ab3d9a4bbfd2ce542db SHA256: a2e4b22c60fbc9bddbedc2d831f517639d99f5d62208b315e37e4b7833d6d6e7 SHA512: 29bb01ea4d94f390ad8af9841c57344c3c5305582a80180da4197483c90038727553325f7cbe2a6af180613485aeb6f2ec2e69cbfc27bc09b47752f65a2252bf Homepage: https://cran.r-project.org/package=mlr3fda Description: CRAN Package 'mlr3fda' (Extending 'mlr3' to Functional Data Analysis) Extends the 'mlr3' ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the 'tf' package. The package provides 'PipeOps' for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional 'PCA'. Package: r-cran-mlr3filters Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-boruta, r-cran-care, r-cran-caret, r-cran-carsurv, r-cran-fselectorrcpp, r-cran-knitr, r-cran-lgr, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-mlr3pipelines, r-cran-praznik, r-cran-rpart, r-cran-survival, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3filters_0.9.1-1.ca2404.1_all.deb Size: 433896 MD5sum: 787ce300392ee215729aff2b70c15a77 SHA1: f740abbc71ff453e686812349ae5b104ffff350e SHA256: b4d0ccf6293b0a9eb09f2454451e3ad3316a604c64493280d304c4187f737e75 SHA512: 14ffe6b3ffdb4dd895f1819264bbe5689cbb935eed723c874a0a16dc29e1d841b9b8d3fa543deb33ccc28b4a7559c84de4efb72882bec6d72ec9115f513ea544 Homepage: https://cran.r-project.org/package=mlr3filters Description: CRAN Package 'mlr3filters' (Filter Based Feature Selection for 'mlr3') Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported. Package: r-cran-mlr3forecast Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3929 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-generics, r-cran-lgr, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-paradox, r-cran-r6 Suggests: r-cran-distributional, r-cran-fabletools, r-cran-feasts, r-cran-forecast, r-cran-ggplot2, r-cran-greybox, r-cran-mlr3tuning, r-cran-nnet, r-cran-nnfor, r-cran-prophet, r-cran-rcatch22, r-cran-rlgt, r-cran-rpart, r-cran-smooth, r-cran-testthat, r-cran-tidyselect, r-cran-timeseries, r-cran-tsbox, r-cran-tscount, r-cran-tsfeatures, r-cran-tsibble, r-cran-tsibbledata, r-cran-vctrs, r-cran-vdiffr, r-cran-withr, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-mlr3forecast_0.2.0-1.ca2404.1_all.deb Size: 2578720 MD5sum: fef85ac9295689539899c610cda2983d SHA1: 262ab4edb3aeb8e79205c97b958b2dfc5c14f011 SHA256: 30ce912c34baaf70ecf14d701634917d5acba882934526c1ad17451760034038 SHA512: a42f01fea2c9b346e20bead7508f3e458145ccf1041b2495c4fc742a6d9f894045e8d555a7f2b17afca31d55a8a0af57228fc900ea73cdd64a509f0e74f61258 Homepage: https://cran.r-project.org/package=mlr3forecast Description: CRAN Package 'mlr3forecast' (Extending 'mlr3' to Time Series Forecasting) Extends the 'mlr3' package and ecosystem to time series forecasting. Provides forecasting tasks, learners, resampling strategies, performance measures, and 'mlr3pipelines' operators for time-series feature engineering. Machine learning regression learners can be turned into forecasters through recursive and direct multi-step strategies. Package: r-cran-mlr3fselect Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1206 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-bbotk, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stabm Suggests: r-cran-e1071, r-cran-fastvoter, r-cran-genalg, r-cran-mirai, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-processx, r-cran-redux, r-cran-rpart, r-cran-rush, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3fselect_1.7.0-1.ca2404.1_all.deb Size: 863832 MD5sum: 9168df5c7a8381c1c82bc8d3a1b4777a SHA1: 55f16935d4fb5b705babedd9d03f8044c42e692d SHA256: 3c5e9fa13e4dab1a30c2f9bd35451d0db7e4d86367ae3502fa74c0a33d96a88f SHA512: 7a3366b80325373d5782125d67d4e4f4f2d95cb5e623614517205194dca144da4fce1e09c8a7d8c2eeae40d11e01ecb3162b753db1804efb7e2e71ba240dad26 Homepage: https://cran.r-project.org/package=mlr3fselect Description: CRAN Package 'mlr3fselect' (Feature Selection for 'mlr3') Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling. Package: r-cran-mlr3hyperband Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3tuning, r-cran-bbotk, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-uuid Suggests: r-cran-emoa, r-cran-mirai, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-processx, r-cran-redux, r-cran-rpart, r-cran-rush, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3hyperband_1.1.1-1.ca2404.1_all.deb Size: 217228 MD5sum: 8352a9b15901b6b9b636fad62cee1beb SHA1: dc3f3d97925c3cdc51b8d4bc4da28c755101c259 SHA256: 28549de71179ab97574b8845d2fe375d85d21c92c88a9b8a54248d459e19b8ad SHA512: 4693904509e8f9e8438c4c686e94b6088c2af6bc406751db0b385948581e1b8ea15c807ca387340b1bb085d4f83dce674c4f1ac838bbd1860caefee66cbf39d7 Homepage: https://cran.r-project.org/package=mlr3hyperband Description: CRAN Package 'mlr3hyperband' (Hyperband for 'mlr3') Successive Halving (Jamieson and Talwalkar (2016) ) and Hyperband (Li et al. 2018 ) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk. Package: r-cran-mlr3inferr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-future, r-cran-lgr, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-withr Suggests: r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3inferr_0.2.2-1.ca2404.1_all.deb Size: 320320 MD5sum: 7ceaff58df334fb7ee90a7017336595c SHA1: 9d7e95696f0ba66eb93926a5880008a5d651a91b SHA256: f18dc843c64df9af24b93276ec9f77187d8d5983d953f7ec480ab145ba1fe187 SHA512: 3681361a3a3978a196be820210ee68a997829aa6922f4550349bdbad797bf9af5714d318cc27fafe6eecc8796da8116e3860575b2d252406c136e5bebc6f397c Homepage: https://cran.r-project.org/package=mlr3inferr Description: CRAN Package 'mlr3inferr' (Inference on the Generalization Error) Confidence interval and resampling methods for inference on the generalization error. Package: r-cran-mlr3learners Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 857 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-dicekriging, r-cran-e1071, r-cran-glmnet, r-cran-kknn, r-cran-knitr, r-cran-lgr, r-cran-mass, r-cran-nnet, r-cran-pracma, r-cran-ranger, r-cran-rgenoud, r-cran-rmarkdown, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3learners_0.12.0-1.ca2404.1_all.deb Size: 600270 MD5sum: 1bbf5a8836267a8be561832bc3bdde59 SHA1: 27313465ce875586a14b33a4a76c5c94966272dd SHA256: bd41c38732d533edf4d50e91579345f7edbd1a2b27ec783e5e36a17056e64b10 SHA512: b61622722e31d8b94bc2f100aa81a775af82362af2b501d567a15d3d6e12eaa0e4175203fc7fd187e01feafa692192e09717c89f6307b2380e8d132181520f85 Homepage: https://cran.r-project.org/package=mlr3learners Description: CRAN Package 'mlr3learners' (Recommended Learners for 'mlr3') Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting. Package: r-cran-mlr3measures Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-mlr3misc, r-cran-prroc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3measures_1.3.0-1.ca2404.1_all.deb Size: 340152 MD5sum: 8322f2b102a7c16b71b0ccae00b8d993 SHA1: df986695b8087bb4911ac68919d9a102a1b6b7bf SHA256: 540a097f41fb86431af8e085f8e89973cfaa26fb0cc26af0e58877bfb84cd001 SHA512: bda63d62326e838312e7e5ba0a7c99b13e870b88d832b734bc7e213bf0e85062bbb8baaa953083dc07b1cce746f2cc876839d3b21a508558f20fd8e44523a952 Homepage: https://cran.r-project.org/package=mlr3measures Description: CRAN Package 'mlr3measures' (Performance Measures for 'mlr3') Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are. Package: r-cran-mlr3pipelines Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3500 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-ggplot2, r-cran-glmnet, r-cran-igraph, r-cran-knitr, r-cran-lme4, r-cran-mlbench, r-cran-bbotk, r-cran-mlr3filters, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-nloptr, r-cran-quanteda, r-cran-rmarkdown, r-cran-rpart, r-cran-stopwords, r-cran-testthat, r-cran-visnetwork, r-cran-bestnormalize, r-cran-fastica, r-cran-kernlab, r-cran-smotefamily, r-cran-evaluate, r-cran-nmf, r-cran-mass, r-cran-gensa, r-cran-vtreat, r-cran-future, r-cran-htmlwidgets, r-cran-ranger, r-cran-themis, r-cran-dimred, r-cran-rspectra, r-cran-rann Filename: pool/dists/noble/main/r-cran-mlr3pipelines_0.12.0-1.ca2404.1_all.deb Size: 2270066 MD5sum: d1ee89e37a2505bfcf985cfd21f571c1 SHA1: 92e677c3b86be8360ecf2456bb840b01ba2ff1d1 SHA256: 535804841bf2222c943a6b9c0194e2a68216f680922f5c9ba9c161629eb754a8 SHA512: 42dfdbc454c7be36a187dd56fcc5566267ce3f3405cacf17d30207e850d3df82894e4f14f2245adb000f1da2162468f7cf5d0334ebc0fab707e6f2b11c94442a Homepage: https://cran.r-project.org/package=mlr3pipelines Description: CRAN Package 'mlr3pipelines' (Preprocessing Operators and Pipelines for 'mlr3') Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned. Package: r-cran-mlr3resampling Architecture: all Version: 2026.2.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-checkmate, r-cran-paradox, r-cran-mlr3, r-cran-mlr3misc, r-cran-pbdmpi Suggests: r-cran-ggplot2, r-cran-animint2, r-cran-mlr3tuning, r-cran-lgr, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-nc, r-cran-rpart, r-cran-directlabels, r-cran-mlr3pipelines, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3torch, r-cran-torch Filename: pool/dists/noble/main/r-cran-mlr3resampling_2026.2.24-1.ca2404.1_all.deb Size: 755842 MD5sum: 16f1c94376eaaf8b49bf2da64cbb20e1 SHA1: 096cc877a8d9f61d934d4dab59bdd315ff7f0a31 SHA256: 7e51a2737208cb79c65fcebadf86a163d8444e0a76f0868407289bdb819e496b SHA512: 04e0dbc3d4a6757eddf02f06382103606c387e2ef192c8ba3480c10f34c8af761c957f9d6dc87be217c7a3ac8a9f7cbe2bb1a6ebbaa279226bbeec8b87bbc638 Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr3shiny Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3measures, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlr3viz, r-cran-metrics, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinyalert, r-cran-data.table, r-cran-dt, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-purrr, r-cran-patchwork, r-cran-ggparty, r-cran-ggally Suggests: r-cran-testthat, r-cran-shinytest, r-cran-devtools, r-cran-ranger, r-cran-e1071, r-cran-xgboost, r-cran-igraph, r-cran-readxl, r-cran-dalex, r-cran-dalextra, r-cran-bslib, r-cran-haven Filename: pool/dists/noble/main/r-cran-mlr3shiny_0.5.0-1.ca2404.1_all.deb Size: 142948 MD5sum: 08531805879556f440b5f447aacf365c SHA1: dd2957aa4bc85d429a00468929c687bcb0925198 SHA256: dfd9dba5ea57191d62a2a51b24e8e87f9c9feb4fd87c56e56cda6fd9392ffe4c SHA512: 27f84e269662079b6cb9c430f66ff15ca5fa8cf6377e14257d13f7ffa9fc72da99e01c4e2db997eecac5331722c2b9957a99c3eccf5862194901d6475d97827f Homepage: https://cran.r-project.org/package=mlr3shiny Description: CRAN Package 'mlr3shiny' (Machine Learning in 'shiny' with 'mlr3') A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework. Package: r-cran-mlr3spatial Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2184 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6, r-cran-sf, r-cran-terra Suggests: r-cran-bench, r-cran-future, r-cran-future.callr, r-cran-knitr, r-cran-mlr3learners, r-cran-paradox, r-cran-ranger, r-cran-raster, r-cran-rmarkdown, r-cran-rpart, r-cran-stars, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3spatial_0.7.0-1.ca2404.1_all.deb Size: 1946518 MD5sum: 5388647ad987e8081866656ae0fc4cd0 SHA1: 6c51f32ded73c3bb8d77c10db4999e0e8d6491b1 SHA256: 15b281d086eaee029a6d3cfab4a07b7e0959e50fc0e694128d625fdfd992a298 SHA512: f7143b4402b8d198a0009f4cecf001a4e6bc9ebfde9e7e74955736144dfb6181cc780e6c0dd27956601bf8360046da8c369c85e5a1bac88546ed841353189ebc Homepage: https://cran.r-project.org/package=mlr3spatial Description: CRAN Package 'mlr3spatial' (Support for Spatial Objects Within the 'mlr3' Ecosystem) Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'. Package: r-cran-mlr3spatiotempcv Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3657 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-bbotk, r-cran-blockcv, r-cran-caret, r-cran-cast, r-cran-ggsci, r-cran-ggtext, r-cran-here, r-cran-knitr, r-cran-lgr, r-cran-mlr3filters, r-cran-mlr3pipelines, r-cran-mlr3spatial, r-cran-mlr3tuning, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-rpart, r-cran-sf, r-cran-sperrorest, r-cran-terra, r-cran-testthat, r-cran-twosamples, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3spatiotempcv_2.3.5-1.ca2404.1_all.deb Size: 2527212 MD5sum: 4a4e0cf996d080e7ae9815639502fc83 SHA1: cf570c125aa3831d14622dc542c0ea438659cd12 SHA256: 348c2a1192da6891ecb7e4ede7d85249a73053783858823fecfb3b76020eb5df SHA512: d6f745d7f491a4454384d5af3bdea4837c10ebabf95c430a290c00808bd5fe08fc2b47a0a7c0ab6e2d6dca147f1a00e06adfc7ecc2f0d39c2cc27e5921f7dcf6 Homepage: https://cran.r-project.org/package=mlr3spatiotempcv Description: CRAN Package 'mlr3spatiotempcv' (Spatiotemporal Resampling Methods for 'mlr3') Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at . Package: r-cran-mlr3summary Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-future.apply, r-cran-mlr3, r-cran-mlr3misc Suggests: r-cran-fastshap, r-cran-iml, r-cran-mlr3fairness, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlr3tuning, r-cran-future, r-cran-ranger, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3summary_0.1.2-1.ca2404.1_all.deb Size: 92788 MD5sum: 618c7696bb3a23cbd19a42819eb5fe11 SHA1: 108bee985cf3b5bfa150dd6472d637b372d2b069 SHA256: fbda8034a28372da62ae5d6f774c00038e7451638dd9fa65e80e23f74b798654 SHA512: 916b45daa7762f8d985669630400ba2a1f969d805220c94f9b8cfd51f484927128be63d5cb3877523dc371d6edf10278e3a5c1f83025d30f83600602e3450ff9 Homepage: https://cran.r-project.org/package=mlr3summary Description: CRAN Package 'mlr3summary' (Model and Learner Summaries for 'mlr3') Concise and interpretable summaries for machine learning models and learners of the 'mlr3' ecosystem. The package takes inspiration from the summary function for (generalized) linear models but extends it to non-parametric machine learning models, based on generalization performance, model complexity, feature importances and effects, and fairness metrics. Package: r-cran-mlr3superlearner Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3learners, r-cran-checkmate, r-cran-lgr, r-cran-mlr3, r-cran-data.table, r-cran-purrr, r-cran-cli, r-cran-glmnet Suggests: r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3superlearner_0.1.2-1.ca2404.1_all.deb Size: 36464 MD5sum: f2a8417ab62be03c01dd5c9c1fab4d2e SHA1: 64709ba0afc72e6bcf8256cbc15db495f099b6d3 SHA256: 2d0c6863b4be3a5c02738c26cd5bd88d3bd79c5b9b366e1d1e91910821ae3daa SHA512: b77839a10f002bfecea714bc8eb550125448a1c2636effda1940177220a82c600fd105f106c973153fa54ff649cab3599caec58fba116067354085c83dc089f8 Homepage: https://cran.r-project.org/package=mlr3superlearner Description: CRAN Package 'mlr3superlearner' (Super Learner Fitting and Prediction) An implementation of the Super Learner prediction algorithm from van der Laan, Polley, and Hubbard (2007) Installed-Size: 4666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-torch, r-cran-backports, r-cran-cli, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-withr Suggests: r-cran-callr, r-cran-curl, r-cran-future, r-cran-ggplot2, r-cran-igraph, r-cran-jsonlite, r-cran-knitr, r-cran-mlr3tuning, r-cran-progress, r-cran-rmarkdown, r-cran-rpart, r-cran-viridis, r-cran-visnetwork, r-cran-testthat, r-cran-tibble, r-cran-tfevents, r-cran-torchvision, r-cran-waldo Filename: pool/dists/noble/main/r-cran-mlr3torch_0.3.3-1.ca2404.1_all.deb Size: 2805546 MD5sum: f4749ad261187ecc53596b6863e4b0bb SHA1: 72ce963a836b176263a883568d5e6058ce8a0b9f SHA256: 23c609215a036ec2e7af5a160eb2d3902d2e21f9f49d4827ff8bfe58be019775 SHA512: 37f0d9ffc2ce70d770ae6a180fec8692467e086e8d3676c563bdf14f9835de85e42c6c79d9e5d7257ce7f9a92955f7d8df6510f2ec2df43dec13adea40bd25e4 Homepage: https://cran.r-project.org/package=mlr3torch Description: CRAN Package 'mlr3torch' (Deep Learning with 'mlr3') Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'. Package: r-cran-mlr3tuning Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1373 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-paradox, r-cran-bbotk, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-future, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-libcmaesr, r-cran-mirai, r-cran-mlflow, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-nloptr, r-cran-processx, r-cran-redux, r-cran-rush, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3tuning_1.7.0-1.ca2404.1_all.deb Size: 961072 MD5sum: a66e455fc952eb2c2ec96a91dacfa2c6 SHA1: 77f976f773e4db32b6e3dd7527e3509e04ee5ab3 SHA256: bd25598bb5c39c5bc36a279289ddf112c2273e2345a8c1219ab22e86e0b115ea SHA512: bf7e6681b6ab7dfa4848aca93e04b72cf84b84d29e5052b48ece20c6c581876438c91ab4df2f6f3bf49220e82bdd75354b116be71830afe6f0a84158bbec37c3 Homepage: https://cran.r-project.org/package=mlr3tuning Description: CRAN Package 'mlr3tuning' (Hyperparameter Optimization for 'mlr3') Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling. Package: r-cran-mlr3tuningspaces Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3tuning, r-cran-cli, r-cran-checkmate, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-e1071, r-cran-bbotk, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-ranger, r-cran-rpart, r-cran-testthat, r-cran-xgboost, r-cran-torch, r-cran-mlr3torch Filename: pool/dists/noble/main/r-cran-mlr3tuningspaces_0.7.0-1.ca2404.1_all.deb Size: 365718 MD5sum: d0e80b31a959c61d79cc66eaa7b8881d SHA1: d0c7ce7b0c9266dc6b9eee328d09a04712afab27 SHA256: aee3a1ac720555403e3d9629a593b4a392ca9c2333127c78b6feb264387cb61d SHA512: c56983ff5af394b25a19100a2be55e1007de341bca1ef9cfcaecd7861b2997fb2cfc7d984e2b90236a0b40e80b4b4700af5dc81f6c24b0c8f04be5c97a6b25bf Homepage: https://cran.r-project.org/package=mlr3tuningspaces Description: CRAN Package 'mlr3tuningspaces' (Search Spaces for 'mlr3') Collection of search spaces for hyperparameter optimization in the 'mlr3' ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets. Package: r-cran-mlr3verse Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-bbotk, r-cran-data.table, r-cran-mlr3cluster, r-cran-mlr3data, r-cran-mlr3filters, r-cran-mlr3fselect, r-cran-mlr3hyperband, r-cran-mlr3inferr, r-cran-mlr3learners, r-cran-mlr3mbo, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-mlr3torch, r-cran-mlr3tuning, r-cran-mlr3tuningspaces, r-cran-mlr3viz, r-cran-paradox Suggests: r-cran-miesmuschel, r-cran-mlr3batchmark, r-cran-mlr3benchmark, r-cran-mlr3db, r-cran-mlr3fairness, r-cran-mlr3fda, r-cran-mlr3forecast, r-cran-mlr3oml, r-cran-mlr3spatial, r-cran-mlr3spatiotempcv, r-cran-mlr3summary, r-cran-rush Filename: pool/dists/noble/main/r-cran-mlr3verse_0.4.0-1.ca2404.1_all.deb Size: 36100 MD5sum: f4201dbd30cdfb62e181aaf9e191bf0e SHA1: 0dc2a8dd097f8e4fc7f68a00578e9cef8a5aff17 SHA256: 01ff55180c33aba05c8043c92fed8749d7cb91e9df18cb722c10e2f00e473104 SHA512: 5ffe763ae18d97a166257fb4d783c6be11f7c94ecb2b6e8b69630be34342169f05bdd6d450cedfaaabbe15a5fb9535f95317aede5631e0c5c390db516fca6c80 Homepage: https://cran.r-project.org/package=mlr3verse Description: CRAN Package 'mlr3verse' (Easily Install and Load the 'mlr3' Package Family) The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at . Package: r-cran-mlr3viz Architecture: all Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-scales, r-cran-viridis Suggests: r-cran-bbotk, r-cran-clue, r-cran-cluster, r-cran-ggally, r-cran-ggdendro, r-cran-ggfortify, r-cran-ggparty, r-cran-glmnet, r-cran-knitr, r-cran-lgr, r-cran-mlr3cluster, r-cran-mlr3filters, r-cran-mlr3fselect, r-cran-mlr3inferr, r-cran-mlr3learners, r-cran-mlr3tuning, r-cran-paradox, r-cran-partykit, r-cran-patchwork, r-cran-precrec, r-cran-ranger, r-cran-rpart, r-cran-testthat, r-cran-vdiffr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3viz_0.11.2-1.ca2404.1_all.deb Size: 374206 MD5sum: 71cd7f241435e718855b8316aa01d2db SHA1: 484ef9d5dc4e1b86a9fad7fc9ee0fb74c290f27f SHA256: e84b3a21d007acdb601e31176387fa5c556cd86407d31b01413c12b502eaa072 SHA512: 5d10e3f264e1bfa67b422895c9529ec965b7d9b7addcaae440a959751ffefca0036462d2af071152cc27b0445a023d0a603aedb031374dbe957ba078d2c8beba Homepage: https://cran.r-project.org/package=mlr3viz Description: CRAN Package 'mlr3viz' (Visualizations for 'mlr3') Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves. Package: r-cran-mlrcpo Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paramhelpers, r-cran-mlr, r-cran-bbmisc, r-cran-stringi, r-cran-checkmate, r-cran-backports Suggests: r-cran-care, r-cran-party, r-cran-rpart, r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mrmre, r-cran-digest, r-cran-praznik, r-cran-randomforestsrc, r-cran-randomforest, r-cran-ranger, r-cran-rfast, r-cran-fselector, r-cran-fselectorrcpp, r-cran-e1071, r-cran-fnn, r-cran-lintr, r-cran-hmisc, r-cran-fastica, r-cran-rex Filename: pool/dists/noble/main/r-cran-mlrcpo_0.3.8-1.ca2404.1_all.deb Size: 2112376 MD5sum: b94e7e04fb9b37e97d42467690973adf SHA1: c0d62ef7114cacc28761e9e43cbffa1a11777f5e SHA256: af79b58922d92cc8fdb87099876957ba00f814905f00b63c3bedb06ca092b7b8 SHA512: 920cfcda9e6594145c45de34314d49254d6849a3dce9998e8f7ba18a59ca1daed3d91fec7c43b8af1a538d36fb3ba8e44e8ca724d2d4d4fd0185ca839ec9d9dd Homepage: https://cran.r-project.org/package=mlrCPO Description: CRAN Package 'mlrCPO' (Composable Preprocessing Operators and Pipelines for MachineLearning) Toolset that enriches 'mlr' with a diverse set of preprocessing operators. Composable Preprocessing Operators ("CPO"s) are first-class R objects that can be applied to data.frames and 'mlr' "Task"s to modify data, can be attached to 'mlr' "Learner"s to add preprocessing to machine learning algorithms, and can be composed to form preprocessing pipelines. Package: r-cran-mlrintermbo Architecture: all Version: 0.5.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-lhs, r-cran-callr, r-cran-bbotk, r-cran-mlr3tuning Suggests: r-cran-mlr, r-cran-paramhelpers, r-cran-testthat, r-cran-rgenoud, r-cran-dicekriging, r-cran-emoa, r-cran-cmaesr, r-cran-randomforest, r-cran-smoof, r-cran-lgr, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlrmbo, r-cran-ranger, r-cran-rpart, r-cran-mco Filename: pool/dists/noble/main/r-cran-mlrintermbo_0.5.1-1-1.ca2404.1_all.deb Size: 155392 MD5sum: 74558bc68c2f45b5c71c67f2c627ae67 SHA1: 3b81da042d7ad76e6f2a5d58cc3916bf7ec4406b SHA256: b8abb11323e4bd78a3696c83f698ce19bba95a1d2cd0fdab76ff6b75eb8aac5c SHA512: 078d54b9e45218ef7e62de11f7978fc866cb9d31817a58f0ad5b3534ed0a522d28736960f8cb7af2fe995246e492f634584893e65db4e26245b895d91095c2b2 Homepage: https://cran.r-project.org/package=mlrintermbo Description: CRAN Package 'mlrintermbo' (Model-Based Optimization for 'mlr3' Through 'mlrMBO') The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization of general objective functions using the 'bbotk' black box optimization framework. The control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'. Package: r-cran-mlrpro Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-dplyr, r-cran-mass, r-cran-dgof Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlrpro_0.1.3-1.ca2404.1_all.deb Size: 40394 MD5sum: 043c829d4470e5544c03419a6e6cb053 SHA1: 361211ba1df20d90ab0aa828ebc8a016cf78dbfe SHA256: 76ad254efd2d062a65e1b322941a76ecde02592240559245357aa3ed6a90c899 SHA512: cb3f2a329ec4abf470e7f63e90e5738e35c4957f2933654511f9af06981695206419ef7d7e81522e6fac6b4434d9a0c38e2126f5a0a50a02407573824a997302 Homepage: https://cran.r-project.org/package=mlrpro Description: CRAN Package 'mlrpro' (Stepwise Regression with Assumptions Checking) The stepwise regression with assumptions checking and the possible Box-Cox transformation. Package: r-cran-mls3 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-e1071, r-cran-glmnet, r-cran-lightgbm, r-cran-ranger Suggests: r-cran-caret, r-cran-knitr, r-cran-randomforest, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-mls3_0.1.1-1.ca2404.1_all.deb Size: 101198 MD5sum: 8942b5e7ef2a7e92ebb62a00089fc5da SHA1: 2cad4324b10591522af0787c1a3c2d2458e096e4 SHA256: 646afeef41fb2f04f4e14e9241dc8d0234eab5f24c960139f3f8cfe94731afc2 SHA512: b06e08ca4db18ae3e6962db521a39f2ce9fb21e165ab1d077d57ce5e8e97859c0b6151fbfe0838fc7de7b7a4f86949bf0055d38c075b235efaa7b0901cc5bf1b Homepage: https://cran.r-project.org/package=mlS3 Description: CRAN Package 'mlS3' (Unified S3 Interface to Machine Learning Models) Provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from 'glmnet', 'lightgbm', 'ranger', 'e1071', and 'caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility. Package: r-cran-mlsjunkgen Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlsjunkgen_0.1.2-1.ca2404.1_all.deb Size: 82006 MD5sum: 29e155fdf5cb355e34743620527c1fee SHA1: fbb6480787511d87e8ca576998143b247718bb07 SHA256: 1ccb231d7414142101c93d64252808eb0c3ee967cac8d482aed16b660c7e2320 SHA512: fb8f52eb7f9b318c3a68a01d447725947afa199192d22568a5a60256a9aa9a4d112d4b90265c06a333a19e8581a90e9f57c0b0208413af5eb0ee1268edd54392 Homepage: https://cran.r-project.org/package=mlsjunkgen Description: CRAN Package 'mlsjunkgen' (Use the MLS Junk Generator Algorithm to Generate a Stream ofPseudo-Random Numbers) Generate a stream of pseudo-random numbers generated using the MLS Junk Generator algorithm. Functions exist to generate single pseudo-random numbers as well as a vector, data frame, or matrix of pseudo-random numbers. Package: r-cran-mlsp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsignal, r-cran-pls, r-cran-glmnet, r-cran-cubist, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-mlsp_0.1.0-1.ca2404.1_all.deb Size: 96062 MD5sum: 80dd2e1c7360305be72b1ad44a5a4b1a SHA1: 067375b4a2ca9b8764f841bc6b4de33f701f4a40 SHA256: 11b13b19b921e5aed54faec812f5448ff59b8cf40eb13c140743e3cdbf57c3f0 SHA512: e12f81436da18340746ce614d775319209db857e0756afa1b807a27e380802fc5436e04fc173da6b3f2e7b54b01fc8ba19423036ee119d105b1930e4066e937c Homepage: https://cran.r-project.org/package=MLSP Description: CRAN Package 'MLSP' (Machine Learning Models for Soil Properties) Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types. Package: r-cran-mlspatial Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-readxl, r-cran-dplyr, r-cran-ggplot2, r-cran-randomforest, r-cran-xgboost, r-cran-e1071, r-cran-caret, r-cran-tmap, r-cran-spdep, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-kernlab, r-cran-writexl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlspatial_0.1.1-1.ca2404.1_all.deb Size: 1565784 MD5sum: de20093c97064b1a0dcaf0161ecef737 SHA1: cdbb3d77a3aae5af92a4880e55820cec7d95335e SHA256: 9c902dbdaf7eed45f02fa49f3a545468d9ea16c1a2c3e9d6c4e359a426678d59 SHA512: fe6d290acef7eb5250b102a27bdaa3380a01f751dee56f47ecb885400b61c147e61c7432b48905e93e94fcbf2167c3a72a402c2838a0c32e93f2a0ec189d061c Homepage: https://cran.r-project.org/package=mlspatial Description: CRAN Package 'mlspatial' (Machine Learning and Mapping for Spatial Epidemiology) Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' and . It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions. Package: r-cran-mlstats Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lme4, r-cran-pillar, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tinytable, r-cran-vctrs Suggests: r-cran-brms, r-cran-gt, r-cran-knitr, r-cran-lavaan, r-cran-lmertest, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlstats_0.1.2-1.ca2404.1_all.deb Size: 474202 MD5sum: 0d879000ed781ab7661b4f1e302335a0 SHA1: 8a766acdf0e6b75cab7480c04481b6aa8d3810e0 SHA256: 10163b7557381350c03d17e6ec9ec041411e6e84b25c0d59f7317aa446632364 SHA512: 1aa811fd3e7d7aea9c9563a89f7ec24789222176a09d8463b248a156d765063795091a8662c2e3489800754676545f3375d71dbfc0d652873663a068d0cf7dac Homepage: https://cran.r-project.org/package=mlstats Description: CRAN Package 'mlstats' (Multilevel Descriptive Statistics and Data Preparation) Provides tools for multilevel descriptive statistics and data preparation. Computes within-group and between-group correlations (via variance decomposition or two-level structural equation modeling), intraclass correlation coefficients (ICCs), and descriptive statistics for nested data (e.g., repeated measurements per person), supporting both frequentist (via 'lme4' or 'lavaan') and Bayesian (via 'brms') estimation. Results are formatted according to APA standards and can be exported as tables using 'gt' or 'tinytable'. Also includes functions for decomposing variables into within-group and between-group components for use in Random Effects Within-Between (REWB) models. Package: r-cran-mlstropalr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-stringr, r-cran-opalr, r-cran-fabr, r-cran-madshapr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mlstropalr_1.0.3-1.ca2404.1_all.deb Size: 71082 MD5sum: 816e308e56560906ea89f0b4579f408a SHA1: b17635eca085f19bed002c1d6660b9d1b6b64e7e SHA256: a49ca55d6c029e4e379a1c1c1c0abd346d9c668e0f34f888a83130ae4d700c64 SHA512: ad84f43f38797aa27d1d30629b989dee3dc47244d4077970644273d82ccdf678abee3acfda814d664e362ac19d0a09f56aa675897373e6d43aa5fe00c05b70b4 Homepage: https://cran.r-project.org/package=mlstrOpalr Description: CRAN Package 'mlstrOpalr' (Support Compatibility Between 'Maelstrom' R Packages and 'Opal'Environment) Functions to support compatibility between 'Maelstrom' R packages and 'Opal' environment. 'Opal' is the 'OBiBa' core database application for biobanks. It is used to build data repositories that integrates data collected from multiple sources. 'Opal Maelstrom' is a specific implementation of this software. This 'Opal' client is specifically designed to interact with 'Opal Maelstrom' distributions to perform operations on the R server side. The user must have adequate credentials. Please see for complete documentation. Package: r-cran-mlsurvlrnrs Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-kdry, r-cran-mlexperiments, r-cran-mllrnrs, r-cran-r6 Suggests: r-cran-glmnet, r-cran-lintr, r-cran-measures, r-cran-quarto, r-cran-ranger, r-cran-rbayesianoptimization, r-cran-rpart, r-cran-splittools, r-cran-survival, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlsurvlrnrs_0.0.8-1.ca2404.1_all.deb Size: 252988 MD5sum: 3ba715d46389a1349efa262c87d95ea7 SHA1: 351c9cba08e4c92bc413791fe5da7e16e368ebbb SHA256: a497bafb647fe15a047b90bf215f209e23149596b4b2a37db579924587ae63fe SHA512: d251ac12f0d5641a1663b19d2986e3f3b726316348ff26410badfd5de957ed73894dbae996c9bcdb52c2df7f1cb2c5ac0f5e01cd5d769432449d50910eb61e86 Homepage: https://cran.r-project.org/package=mlsurvlrnrs Description: CRAN Package 'mlsurvlrnrs' (R6-Based ML Survival Learners for 'mlexperiments') Enhances 'mlexperiments' with additional machine learning ('ML') learners for survival analysis. The package provides R6-based survival learners for the following algorithms: 'glmnet' , 'ranger' , 'xgboost' , and 'rpart' . These can be used directly with the 'mlexperiments' R package. Package: r-cran-mlt.docreg Architecture: all Version: 1.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlt, r-cran-numderiv, r-cran-eha, r-cran-multcomp, r-cran-lattice, r-cran-survival, r-cran-flexsurv, r-cran-truncreg Suggests: r-cran-variables, r-cran-basefun, r-cran-mass, r-cran-th.data, r-cran-knitr, r-cran-prodlim, r-cran-gridextra, r-cran-nnet, r-cran-mgcv, r-cran-hsaur3, r-cran-sandwich, r-cran-latticeextra, r-cran-colorspace, r-cran-matrix, r-cran-aer, r-cran-coin, r-cran-gamlss.data, r-cran-mlbench, r-cran-tram, r-cran-rms Filename: pool/dists/noble/main/r-cran-mlt.docreg_1.1-14-1.ca2404.1_all.deb Size: 654786 MD5sum: 80f91bfb9321bc16603a0e004856235f SHA1: 49bba8185604d6c4596cebc14f42c0cbf05adaa7 SHA256: 6ae3578d3346f31036722103584a22e1ebe03daf5f612be7b74a8543f71d1750 SHA512: f652fb91eb798d00e671a31c5ce396a6c36c4eb3a8dcec0ad117527bd76ad7d48b25ee0698e5ee2078d79c3c77b607dce1cc6ad328a003097148f3a3b63037cc Homepage: https://cran.r-project.org/package=mlt.docreg Description: CRAN Package 'mlt.docreg' (Most Likely Transformations: Documentation and Regression Tests) Additional documentation, a package vignette and regression tests for package mlt. Package: r-cran-mltest Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mltest_1.0.3-1.ca2404.1_all.deb Size: 20616 MD5sum: 59b3c4777466fa5ea4ebb345d47d77b6 SHA1: c45597a8ceca9952db2f691c7297ae5a91b54a27 SHA256: ded9ff7065e726f615712b3761a7039774ecdfbe1dc2f96a7a78e417cccc1a60 SHA512: afb6a972b475d98f356863e33a97d88c5f50520206f23be8f8f83f3a8e52f888611b814937f1df92f0bdfc167b01fa2bb007eb925f404addf00ce1e03eb91932 Homepage: https://cran.r-project.org/package=mltest Description: CRAN Package 'mltest' (Classification Evaluation Metrics) A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix. Package: r-cran-mltools Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mltools_0.3.5-1.ca2404.1_all.deb Size: 110682 MD5sum: 6218160d877dfc89b8f571346e49e56e SHA1: 7251ef0505c78fb9d918567f859d69dea80b1595 SHA256: 70f63bb475eb510d89b48b417454c22ed62c2a47549a5727fe1e619750dee4cd SHA512: e2375c87e6f479ee331a890d58b54bfc16daaba2cf92ac114b1bcba77dc3a7cb0657b0f8c5dc207e785c52a2c30209f557397aa21f13d03118920b22f9d517fe Homepage: https://cran.r-project.org/package=mltools Description: CRAN Package 'mltools' (Machine Learning Tools) A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions. Package: r-cran-mlvar Architecture: all Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-arm, r-cran-qgraph, r-cran-dplyr, r-cran-clustergeneration, r-cran-mvtnorm, r-cran-corpcor, r-cran-abind, r-cran-mplusautomation, r-cran-graphicalvar, r-cran-rlang Suggests: r-cran-bootnet Filename: pool/dists/noble/main/r-cran-mlvar_0.7.3-1.ca2404.1_all.deb Size: 318180 MD5sum: 4c539c610bb448f3feb766e1b6728182 SHA1: 5c10d0b52a4689c76ca953b158ebb63ef7aaf27e SHA256: 993fa6b1afcce3500a187635d177595f0c3b2d37861f8b0a7801c537648bda4b SHA512: d43acc7186c2b6200e77bbeca45a0d497553bc7b9df6e2ac94288dfab8b622ef3310fa414d04882449a3835da60c6630559c65b2fcb732a74779fae86053e8c8 Homepage: https://cran.r-project.org/package=mlVAR Description: CRAN Package 'mlVAR' (Multi-Level Vector Autoregression) Estimates the multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal networks, contemporaneous networks and between-subjects networks. Package: r-cran-mlvsbm Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-blockmodels, r-cran-ape, r-cran-magrittr, r-cran-cluster Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggforce, r-cran-spelling, r-cran-cowplot, r-cran-reshape2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-mlvsbm_0.2.4-1.ca2404.1_all.deb Size: 509868 MD5sum: f802a51c618f919f0e0b48655d98f269 SHA1: 87c3f28a9a53b3775f0024998eb7bf7da6c83df9 SHA256: eabff455a487f6ff5b4a6fa2abf33743c4f58a2f7c71d9e6f33fcc4907018e31 SHA512: e01966b2af9bb14bf1fe920624eda7e6ec9ead4bff0ade8328ecfc4cd8eb53cbc6b794dd75c220112b15c384647783464c7a39ea8d9a4ec44bb7319e20d26c70 Homepage: https://cran.r-project.org/package=MLVSBM Description: CRAN Package 'MLVSBM' (A Stochastic Block Model for Multilevel Networks) Simulation, inference and clustering of multilevel networks using a Stochastic Block Model framework as described in Chabert-Liddell, Barbillon, Donnet and Lazega (2021) . A multilevel network is defined as the junction of two interaction networks, the upper level or inter-organizational level and the lower level or inter-individual level. The inter-level represents an affiliation relationship. Package: r-cran-mlwrap Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 872 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-tidyr, r-cran-magrittr, r-cran-dials, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-glue, r-cran-innsight, r-cran-shapr, r-cran-diagrammer, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-sensitivity, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-patchwork, r-cran-cli, r-cran-scales Suggests: r-cran-testthat, r-cran-torch, r-cran-brulee, r-cran-ranger, r-cran-kernlab, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlwrap_0.4.0-1.ca2404.1_all.deb Size: 791004 MD5sum: afb0d7a37425568cc8ff81133be1798b SHA1: 44fe7761c43dba096f6ca5c5a475f4aa7f854238 SHA256: ed2182cdc5caed48c82f92fd6d07e50445c92546a9e4f690ba1bf525ba1180ab SHA512: dcaeadf124f8444de2e19264d3bdab06e6b26e673647df6230b3c2368f7fcaa88e9ba59f60facedb3a63b935da7a0004fa3c7d7c381b05ea6234d609e2ff0429 Homepage: https://cran.r-project.org/package=MLwrap Description: CRAN Package 'MLwrap' (Machine Learning Modelling for Everyone) A minimal library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides essential tools to structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). It also includes global and pairwise interaction analysis based on Friedman’s H-statistic to support a more detailed interpretation of complex feature relationships.These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimization, and sensitivity analysis. The user can access comprehensive model evaluation results including fit assessment metrics, plots, predictions, and performance diagnostics for ML models implemented through 'Neural Networks', 'Random Forest', 'XGBoost' (Extreme Gradient Boosting), and 'Support Vector Machines' (SVM) algorithms. By streamlining these phases, 'MLwrap' aims to simplify the implementation of ML techniques, allowing analysts and data scientists to focus on extracting actionable insights and meaningful patterns from large datasets, in line with the objectives of the KDD process. Package: r-cran-mlxr Architecture: all Version: 4.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-xml, r-cran-rcpp, r-cran-reshape2, r-cran-gridextra, r-cran-shiny Filename: pool/dists/noble/main/r-cran-mlxr_4.2.0-1.ca2404.1_all.deb Size: 614948 MD5sum: 4d34d9375b6b69673a1697c777bb8d9e SHA1: 337ff7f6357cae5bc24f9098c251951024fd77ad SHA256: 3d2d8b56f63ea511a5c0f9f139381775ef0c31023ee60cafb6bea4424d54367f SHA512: c36fb9ca5cbdba0942e0b6a3d92f5081061fe123eea1f09065872a5c99cc43c155c0adeab680281c849f11faa50da6edb2e2a17ebd6126298b419c86f194820c Homepage: https://cran.r-project.org/package=mlxR Description: CRAN Package 'mlxR' (Simulation of Longitudinal Data) Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language 'Mlxtran' and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data. Package: r-cran-mm2sdata Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4822 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mm2sdata_1.0.3-1.ca2404.1_all.deb Size: 4894808 MD5sum: 9fb40daea6c734952907d1f037ea865e SHA1: 82fab3eb0395b5bea8fa4326bb399b0f8e73048b SHA256: 0e3f1cb522e0a9a3d41aa4b492587b2b4a252f22483c079dadcb1371638dce1f SHA512: 4319e85e67df79050f26d89c513126ba878d3205ac86ad034542606ff2022d57281eb9585d08071c14f9d53516c66da922ef48f72e72b7103872a460d19cbce0 Homepage: https://cran.r-project.org/package=MM2Sdata Description: CRAN Package 'MM2Sdata' (Gene Expression Datasets for the 'MM2S' Package) Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) . Package: r-cran-mm Architecture: all Version: 1.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magic, r-cran-abind, r-cran-quadform, r-cran-partitions, r-cran-oarray Filename: pool/dists/noble/main/r-cran-mm_1.7-0-1.ca2404.1_all.deb Size: 457270 MD5sum: aa7dec77f297eacd105cc41cb78449e2 SHA1: a3222ce2b8159dc785d199afd7f3988a94ff9d3e SHA256: a8eb881d043b694a0b60dd237dd19d99eef16438ca833d7baf75cd60c3f3a3e5 SHA512: f2cf1169efdd75778e9f41c07c6e641181a2bf3a2ee9c83f0e7fc3bffe21b9799d58b9cf4b41eda586ba8a412a37a558fc66b909a73e3cb18411f62f094b082d Homepage: https://cran.r-project.org/package=MM Description: CRAN Package 'MM' (The Multiplicative Multinomial Distribution) Various utilities for the Multiplicative Multinomial distribution. Package: r-cran-mma Architecture: all Version: 10.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gbm, r-cran-survival, r-cran-car, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mma_10.8-1-1.ca2404.1_all.deb Size: 929142 MD5sum: d8965d0e21f3b707fa4b220a40050061 SHA1: 28ac441df6dea430ae7e562d1c81a68957983880 SHA256: ce06a18b523f4b5645a0112ba46e900dc990a88603e7d898d6a6bcfb93f8f6eb SHA512: 5cf45e80d445e4bfe1492b489d1353f29392a8729da00845da506b8f17e3d86bacb5fd8645f0024a3c42cab595c63e38fdf9cfde1135920011e6a5be46931c1f Homepage: https://cran.r-project.org/package=mma Description: CRAN Package 'mma' (Multiple Mediation Analysis) Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23. Package: r-cran-mmabig Architecture: all Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mma, r-cran-survival, r-cran-car, r-cran-gplots Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmabig_3.2-0-1.ca2404.1_all.deb Size: 178688 MD5sum: 31b8903770719d70a632caf037f40897 SHA1: a170a5428e00f2d8d8c76214d7acf0a8f7ddaf7d SHA256: d3b6bbf6e491775b2cb4ccf91918d98913e6d0ac311ebb8efa3c8fd7c1c97675 SHA512: 41478155403a25214d5bb3e862f87735786d8d94998d780fa97cf716309a88a46bf4d6439843941bc40d9bc7a33dc64a861f17b3295b6e2df32a884969309d9d Homepage: https://cran.r-project.org/package=mmabig Description: CRAN Package 'mmabig' (Multiple Mediation Analysis for Big Data Sets) Used for general multiple mediation analysis with big data sets. Package: r-cran-mmac Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mosaic, r-cran-mosaiccalc, r-cran-plotly Filename: pool/dists/noble/main/r-cran-mmac_1.0-1.ca2404.1_all.deb Size: 760462 MD5sum: 3a0eeadf695ef5598e8805cb9aa37073 SHA1: 7a917d0470c71b2cdb6957919ad9163f00903306 SHA256: ab06479b21d0187abf53b6269f5ea6e2c9b2402344c1fd13cf4711ffaf8caa3d SHA512: 4d6e3da0d71b7ae656aa733aa5b70f85c144c5b387b53de8d2a40c20b37aab98a4599b0eb5b87c116cec1b822aaef0e7b21ae891ea3542d856ae131add0f5b4a Homepage: https://cran.r-project.org/package=MMAC Description: CRAN Package 'MMAC' (Data for Mathematical Modeling and Applied Calculus) Contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027. Package: r-cran-mmad Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmad_3.0.1-1.ca2404.1_all.deb Size: 158860 MD5sum: 5be0ddbeda51b1f0278a2533b7c5770e SHA1: c5d8d97dd401ad7a77fd33287440ac2dc01a0e8d SHA256: b023c3be0844bf12be4d8293433ed946139d647b25b20c4f951992c5ee6e0759 SHA512: c11e9d2035d244a2f62f5f047f2a14a386a35c0ffc439c578e437f79581695707d03fc8d210da3c3f93566b5bdd94e1b6fa583b9f6ccdc079a2183ed9ea6e3fb Homepage: https://cran.r-project.org/package=MMAD Description: CRAN Package 'MMAD' (Minorization-Maximization via Assembly-Decomposition Technology) A formula-driven framework for maximizing target functions via the minorization-maximization (MM) algorithm. The package represents the target as a symbolic expression tree, infers its curvature via disciplined-convex-programming rules, and constructs a separable surrogate at each iterate using only Jensen's inequality and the supporting hyperplane. The driver maximizes the surrogate via block-coordinate Newton with line search, falling back to a multivariate step on any non-separable residue. A formula interface accepts standard R expressions (including `sum()` reductions and `X %*% theta` design-matrix products) so statistical models such as Poisson regression can be written in one line. Package: r-cran-mmaqshiny Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15863 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-cairo, r-cran-xts, r-cran-lubridate, r-cran-zoo, r-cran-catools, r-cran-ggplot2, r-cran-data.table, r-cran-dt, r-cran-dplyr, r-cran-leaflet, r-cran-stringr, r-cran-shiny, r-cran-xml, r-cran-shinyjs, r-cran-plotly Suggests: r-cran-testthat, r-cran-devtools, r-cran-usethis, r-cran-shinytest Filename: pool/dists/noble/main/r-cran-mmaqshiny_1.0.0-1.ca2404.1_all.deb Size: 3268424 MD5sum: 9a0300718156d7eb3f894907a04812c2 SHA1: dcdeddcd2fb7d2690d92ed53c62a456e5feea671 SHA256: 3474647763d044f4f0296731276174a3a9b8f748a44cf6663678fa4b0c3b0a79 SHA512: 485b6f85b8446caece4749361a7cac32811416451b39993c95452b98f471d284594c95bf94c3e5f60916996ca8305f94ee2c0a1a6c255df3095329b17a4e19db Homepage: https://cran.r-project.org/package=mmaqshiny Description: CRAN Package 'mmaqshiny' (Explore Air-Quality Mobile-Monitoring Data) Mobile-monitoring or "sensors on a mobile platform", is an increasingly popular approach to measure high-resolution pollution data at the street level. Coupled with location data, spatial visualisation of air-quality parameters helps detect localized areas of high air-pollution, also called hotspots. In this approach, portable sensors are mounted on a vehicle and driven on predetermined routes to collect high frequency data (1 Hz). 'mmaqshiny' is for analysing, visualising and spatial mapping of high-resolution air-quality data collected by specific devices installed on a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate matter with size less than 2.5 microns), Black carbon mass concentrations (BC), ultra-fine particle number concentrations, carbon dioxide along with GPS coordinates and relative humidity (RH) data collected by popular portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It incorporates device specific cleaning and correction algorithms. RH correction is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) . Provision is given to add linear regression coefficients for correcting the PM2.5 data (if required). BC data will be cleaned for the vibration generated noise, by adopting the statistical procedure as explained in Apte et al., (2011) , followed by a loading correction as suggested by Ban-Weiss et al., (2009) . For the number concentration data, provision is given for dilution correction factor (if a diluter is used with CPC3007; default value is 1). The package joins the raw, cleaned and corrected data from the above said instruments and outputs as a downloadable csv file. Package: r-cran-mmarch.ac Architecture: all Version: 3.3.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1873 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-refund, r-cran-denseflmm, r-cran-dplyr, r-cran-xlsx, r-cran-survival, r-cran-tidyr, r-cran-zoo, r-cran-ineq, r-cran-cosinor, r-cran-cosinor2, r-cran-abind, r-cran-minpack.lm, r-cran-kableextra, r-cran-ggir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmarch.ac_3.3.4.3-1.ca2404.1_all.deb Size: 1099410 MD5sum: 653ec52a57bfdcc3b8a9668dcbd1cd0c SHA1: 9db8e05344e01c8356284c43fe208ea478b1ec4b SHA256: 9699d3e91c9c3240c1cfff3a2396d5bdeabcdc482afd7d3635f2a8c6fa4182bb SHA512: f2b1fac1b7369f86fdbc2f2c06207a3c2a7e4d2ac3984f210e9c8ac1f7c4ea6d655909a9b32eb4922dcf4ae9319e4e3c05733b876a58d771a8ec125706a28997 Homepage: https://cran.r-project.org/package=mMARCH.AC Description: CRAN Package 'mMARCH.AC' (Processing of Accelerometry Data with 'GGIR' in mMARCH) Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44. Package: r-cran-mmb Architecture: all Version: 0.13.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-foreach, r-cran-doparallel Suggests: r-cran-devtools, r-cran-testthat, r-cran-covr, r-cran-e1071, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggpubr, r-cran-cowplot, r-cran-philentropy, r-cran-rtsne Filename: pool/dists/noble/main/r-cran-mmb_0.13.3-1.ca2404.1_all.deb Size: 196458 MD5sum: dea24100a4a8b807149c61e30d179558 SHA1: a12b9f9e01d0159e573a60c92ce7a471c7e6b349 SHA256: 5748aaec91c54c6e9cbc6aae0df9672b1d28f0151591a6fbf685e1705d833cf0 SHA512: e58704552182e04b62eb34b53d3e81840ac3cb61b3d3ac501b89caba39a48a620cd6cb799ec87ce621c3f5f5fbdb3114d2d7093475a32c9ffc1bcdecd94bf8e2 Homepage: https://cran.r-project.org/package=mmb Description: CRAN Package 'mmb' (Arbitrary Dependency Mixed Multivariate Bayesian Models) Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes' theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) and by Scutari (2010) . Package: r-cran-mmbcv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmbcv_1.0.0-1.ca2404.1_all.deb Size: 179712 MD5sum: b072501cf7d35545ed660bff775c079a SHA1: 282f1720dacd8dd06e40fb3f32dcb620b11103a6 SHA256: 87762571a0666a6f343e85a1e9e38d48562173065ae287302abe4510a30fe811 SHA512: 72cec5d553198b97c63248c5af29c858af95f6df373bdb81cf3724f1bfb98198688286edaf292b405d316ca4e250211e912389db2def82512cec9975a286fe8b Homepage: https://cran.r-project.org/package=mmbcv Description: CRAN Package 'mmbcv' (Multistate Model Bias-Corrected Robust Variance) Computes robust and bias-corrected sandwich variance estimators for multi-state Cox models with clustered time-to-event data. Also provides Wald tests for heterogeneity, generalized least-squares linear trends, and order-restricted trends among transition-specific coefficients. The methodology extends the marginal Cox model bias-correction framework of Wang et al. (2023) to the multi-state setting. 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Package: r-cran-mmcards Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmcards_0.1.1-1.ca2404.1_all.deb Size: 36398 MD5sum: 48d4455c81daeb22e838f23c5f442850 SHA1: 4f66b132db5fab54eb28af59d415e32d94466d3f SHA256: 7c4c209e229e2ecf6c1d287524fd4536357714b685062f97533007f6eb162201 SHA512: 5d3ee2a88dc3b47631ca896f15597b4a524909f4353de939add4235a1abd4c46060e68df68d4858b34fa1adc7705d7596d87fdda4a262ff7820568e4481b5fd9 Homepage: https://cran.r-project.org/package=mmcards Description: CRAN Package 'mmcards' (Playing Cards Utility Functions) Early insights in probability theory were largely influenced by questions about gambling and games of chance, as noted by Blitzstein and Hwang (2019, ISBN:978-1138369917). In modern times, playing cards continue to serve as an effective teaching tool for probability, statistics, and even 'R' programming, as demonstrated by Grolemund (2014, ISBN:978-1449359010). The 'mmcards' package offers a collection of utility functions designed to aid in the creation, manipulation, and utilization of playing card decks in multiple formats. These include a standard 52-card deck, as well as alternative decks such as decks defined by custom anonymous functions and custom interleaved decks. Optimized for the development of educational 'shiny' applications, the package is particularly useful for teaching statistics and probability through card-based games. Functions include shuffle_deck(), which creates either a shuffled standard deck or a shuffled custom alternative deck; deal_card(), which takes a deck and returns a list object containing both the dealt card and the updated deck; and i_deck(), which adds image paths to card objects, further enriching the package's utility in the development of interactive 'shiny' application card games. Package: r-cran-mmcmcbayes Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6042 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-ksamples, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmcmcbayes_0.2.0-1.ca2404.1_all.deb Size: 6150850 MD5sum: b3223d524ada30dd487815e3dfea268d SHA1: c7ad3dd919fced224510151ce9b085552b3940bb SHA256: 4673a6b25b593f6e59911f8fb7a13e8ddc95a3437ea2e1f8c4394cf0f1ca570e SHA512: 74888ba3c73fa66bcd94e53a3d924aedd54563e1ad7a937c604f36aa610c92088d3738f6f87ffa21e39c27a3e7c78210af8ebdc69aa5bb85c594440880e51bfb Homepage: https://cran.r-project.org/package=mmcmcBayes Description: CRAN Package 'mmcmcBayes' (Multistage MCMC Method for Detecting DMRs) Implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) . 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It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." . Package: r-cran-mmd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-plyr, r-cran-bigmemory Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmd_1.0.0-1.ca2404.1_all.deb Size: 65178 MD5sum: 17d9415b85c05731608145e663ad37da SHA1: 980c33a6b625b64c4b8cc47549a6126680f5fe00 SHA256: 57fd4ea7beaf25e403aebfb4fceec49474d623c7e4177c6f3bfddfe9902867a5 SHA512: e9307eecd45ddc89542488731dd03721839873571bcfa3ec476a7d7717cc619239efb8d64d69a4d187cedcf19691dfc7ca8e6bb2520182d7d40983e530dbfafe Homepage: https://cran.r-project.org/package=MMD Description: CRAN Package 'MMD' (Minimal Multilocus Distance (MMD) for Source Attribution andLoci Selection) The aim of the package is two-fold: (i) To implement the MMD method for attribution of individuals to sources using the Hamming distance between multilocus genotypes. (ii) To select informative genetic markers based on information theory concepts (entropy, mutual information and redundancy). The package implements the functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to efficiently attribute individuals to source populations. Scientific Reports 10, 12124 (2020) . See more details and examples in the README file. Package: r-cran-mmdai Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mmdai_2.0.0-1.ca2404.1_all.deb Size: 87856 MD5sum: 45d130cc0e4a7db4c1f1e7f7a0391c98 SHA1: 24f8660ce984c9a62b608d4e63f96ca81da750e7 SHA256: d69dd7e5fb4aea0edc4eb2c9cd91380dcba7bb9562943c2fcb8c21e056ad2af0 SHA512: ca00d7aa0a51596f734a9fc456175005ab740be33daddb331aef6c56b4e24e6e3d69c2344fb735724087e62c6e66107a7e344db4f5dcc36866fcc797756e0131 Homepage: https://cran.r-project.org/package=MMDai Description: CRAN Package 'MMDai' (Multivariate Multinomial Distribution Approximation andImputation for Incomplete Categorical Data) A method to impute the missingness in categorical data. Details see the paper . 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Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) . 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Package: r-cran-mmeln Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mmeln_1.5-1.ca2404.1_all.deb Size: 128304 MD5sum: 4abdcec6428ad65e892021e210209a74 SHA1: 8540ccaea3f5a86efb42e98980e52aaface30710 SHA256: 44c8f548f158680e559b63214458da2f40c16bbd3259ecbe323f0629f0cf1169 SHA512: 5600344b26c4556205c230ce027e91dda60a6e99ed43986876a9fa098788a77c938c33377cfe0a7f03f93e107f5f711647c0d869e3e4c9b9b3034b67374f58fa Homepage: https://cran.r-project.org/package=mmeln Description: CRAN Package 'mmeln' (Estimation of Multinormal Mixture Distribution) Fit multivariate mixture of normal distribution using covariance structure. 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Package: r-cran-mmequiv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-dplyr, r-cran-httptest2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-mmequiv_1.0.0-1.ca2404.1_all.deb Size: 164780 MD5sum: 285be41552deebcdd4f257b6ddde7ecf SHA1: 28104e7b12426c39b3d43ebdef362438342fa95e SHA256: 3dfd23b5f4fe765b7db4c2fdaf324aeed540618617fcc6b416ec020208b9ac03 SHA512: 02af711d96602ec4f1ad90e538bde988f8cd1f49b5c8140a7494184a99f25581682550772aeea9c10b570667a28c51f02dfa1e98b5e423969dd7ded2f8f0a7bd Homepage: https://cran.r-project.org/package=mmequiv Description: CRAN Package 'mmequiv' (Calculate Standardized Morphine Milligram Equivalent Doses) Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the 'NIH HEAL MME Online Calculator' API or replicate API calculations on the user's local machine from the comfort of 'R'. Creation of the 'NIH HEAL MME Online Calculator' and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) . Package: r-cran-mmibain Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2068 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bain, r-cran-broom, r-cran-car, r-cran-dt, r-cran-e1071, r-cran-ggplot2, r-cran-igraph, r-cran-lavaan, r-cran-mmcards, r-cran-psych, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmibain_0.2.0-1.ca2404.1_all.deb Size: 1837894 MD5sum: 716cfd91601a82818990209768e297e7 SHA1: 78ff0d39efd9f85d2896fe11b5eaf47ee37c5d02 SHA256: 09a988cdb898a5a43aa4062ced15467d31f3b3a0503e9bc82dd6a5f00e42e6c5 SHA512: a43b96903442d29afd0d6093356acfe4341660b3e044a3bb3d2d384fd8787f8244a13a6f81037e0905e7d61f22e30f2c92624058bcc228b7b111e08c3501a596 Homepage: https://cran.r-project.org/package=mmibain Description: CRAN Package 'mmibain' (Bayesian Informative Hypotheses Evaluation Web Applications) Researchers often have expectations about the relations between means of different groups or standardized regression coefficients; using informative hypothesis testing to incorporate these expectations into the analysis through order constraints increases statistical power Vanbrabant and Rosseel (2020) . Another valuable tool, the Bayes factor, can evaluate evidence for multiple hypotheses without concerns about multiple testing, and can be used in Bayesian updating Hoijtink, Mulder, van Lissa & Gu (2019) . The 'bain' R package enables informative hypothesis testing using the Bayes factor. The 'mmibain' package provides 'shiny' web applications based on 'bain'. The RepliCrisis() function launches a 'shiny' card game to simulate the evaluation of replication studies while the mmibain() function launches a 'shiny' application to fit Bayesian informative hypotheses evaluation models from 'bain'. 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The 'mmiCATs' package offers a suite of tools for working with CATs. The mmiCATs() function initiates a 'shiny' web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the 'clusterSEs' package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a 'shiny' application card game, aimed at enhancing users' understanding of the conditions under which CATs should be preferred over random intercept models. Package: r-cran-mminp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3666 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-omicspls Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-prettydoc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mminp_0.1.0-1.ca2404.1_all.deb Size: 3630364 MD5sum: ea6f2ad6c2be85f412233c3ba018f2a6 SHA1: 4a05ef43b200fb929d3f56c94941a2bdf0f788af SHA256: 111110cad7fac32c36d10d3d472a894a526ebce3f85ff9dcc556206bcfbc8c41 SHA512: b0bd3474f92cfa84b1578883a167f2946a1276f1877d615930efd3fdd3bce343fd215464126600e2429286cfc3cc57dbc0b16dba475750fb5a3cb5e9d22360c6 Homepage: https://cran.r-project.org/package=MMINP Description: CRAN Package 'MMINP' (Microbe-Metabolite Interactions-Based Metabolic ProfilesPredictor) Implements a computational framework to predict microbial community-based metabolic profiles with 'O2PLS' model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances. Package: r-cran-mmints Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-pool, r-cran-rpostgres, r-cran-shiny, r-cran-shinyauthr, r-cran-sodium Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmints_0.2.0-1.ca2404.1_all.deb Size: 54738 MD5sum: bef0f0ba10000f5e96ff63d0e4722134 SHA1: cee6b689bf99ffd108469f635cc2ed9c798cff35 SHA256: 2727a2c391a93825ca38636fea97213b0dcf484f04d78031d142fa9d2c1ccf47 SHA512: c2433953fa15ea2b63d94093ebd9ff13be72ada6e2b0d0481e55cec93096e17adc111a0ba24fc183c7d17d23df6699cf10e09ea0097e7477ef422c195cc6c232 Homepage: https://cran.r-project.org/package=mmints Description: CRAN Package 'mmints' (Workflows for Building Web Applications) Sharing statistical methods or simulation frameworks through 'shiny' applications often requires workflows for handling data. To help save and display simulation results, the postgresUI() and postgresServer() functions in 'mmints' help with persistent data storage using a 'PostgreSQL' database. The 'mmints' package also offers data upload functionality through the csvUploadUI() and csvUploadServer() functions which allow users to upload data, view variables and their types, and edit variable types before fitting statistical models within the 'shiny' application. These tools aim to enhance efficiency and user interaction in 'shiny' based statistical and simulation applications. Package: r-cran-mmirestriktor Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-mass, r-cran-mmcards, r-cran-pool, r-cran-restriktor, r-cran-rpostgres, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmirestriktor_0.3.1-1.ca2404.1_all.deb Size: 1859798 MD5sum: 5e881912809315cda287f45d7c0c2927 SHA1: b3507e5b8e14eb0fb9e9258e7818c7cdc9dd1996 SHA256: 41ecf47547a60a5ab0b0a9c32a3d6561201687bf8815e116cd66ceea17b47e98 SHA512: 853633c0e688b8b35f3d58052de318e306fd9a110937a155aba79ae81647945493c765484a2653fdf1b7b8a31628f98a816428546e84165962f4dcdaa7a815cc Homepage: https://cran.r-project.org/package=mmirestriktor Description: CRAN Package 'mmirestriktor' (Informative Hypothesis Testing Web Applications) Offering enhanced statistical power compared to traditional hypothesis testing methods, informative hypothesis testing allows researchers to explicitly model their expectations regarding the relationships among parameters. An important software tool for this framework is 'restriktor'. The 'mmirestriktor' package provides 'shiny' web applications to implement some of the basic functionality of 'restriktor'. The mmirestriktor() function launches a 'shiny' application for fitting and analyzing models with constraints. The FbarCards() function launches a card game application which can help build intuition about informative hypothesis testing. The iht_interpreter() helps interpret informative hypothesis testing results based on guidelines in Vanbrabant and Rosseel (2020) . Package: r-cran-mmlr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-mmlr_0.2.0-1.ca2404.1_all.deb Size: 70282 MD5sum: 3fe2b7dd2e7df1289276b3ea92eb4593 SHA1: a54df6932ddd13f38de5e5f6dfeae33ec6bf3b29 SHA256: 59c32dd8b81ce7be0de9d679caf3c56a87838059c61565c6df75c0500bf90207 SHA512: 11f885646d1f95e48e44c67b916ea23e78405e4c9e2d04b3f0afa696870609f0215277c6e07f3f1a5bccd955cd482db3f826dc478e78ab8d76d2764979fba12a Homepage: https://cran.r-project.org/package=MMLR Description: CRAN Package 'MMLR' (Fitting Markov-Modulated Linear Regression Models) A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075. Package: r-cran-mmmgee Architecture: all Version: 1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-geepack, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmmgee_1.20-1.ca2404.1_all.deb Size: 137254 MD5sum: 08bf6d86227431cc351ad75becf53fb0 SHA1: 2cb4291f96a9daf2570aadc847bdfa30051a3527 SHA256: befb9eb0936e9b68ac2c9a8b53f7a172cd599f6df03b1208a09d25c123e11dd1 SHA512: add8427045c80637dfd73b89c09103fff98a211365cb271407f27975f99a69ebbbdff12311db2925f756c672b7a5edbda94896dffe70d3196283083885dba92a Homepage: https://cran.r-project.org/package=mmmgee Description: CRAN Package 'mmmgee' (Simultaneous Inference for Multiple Linear Contrasts in GEEModels) Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package. Package: r-cran-mmoc Architecture: all Version: 0.1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4028 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spectrum, r-cran-igraph, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-snftool, r-cran-plotly Filename: pool/dists/noble/main/r-cran-mmoc_0.1.1.0-1.ca2404.1_all.deb Size: 1102396 MD5sum: d54e8714926ea8d94b05e9a210ac030f SHA1: af7355cbd698c266188d37a0fa0b6cb8d28b5ec3 SHA256: 4308970ce315eff3724b51b060b0c87363eed7839e0c8150eed889b2d6b742c3 SHA512: 53c0ce0eb4e27089ce9e23015ba3313209d269eedb4a77a3604e49aadab5c9026f256eca9d2d680e8d7af0c845a7982f612e91da04789476f9b4b1bb13bfab91 Homepage: https://cran.r-project.org/package=MMOC Description: CRAN Package 'MMOC' (Multi-Omic Spectral Clustering using the Flag Manifold) Multi-omic (or any multi-view) spectral clustering methods often assume the same number of clusters across all datasets. We supply methods for multi-omic spectral clustering when the number of distinct clusters differs among the omics profiles (views). Package: r-cran-mmod Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adegenet, r-cran-pegas Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmod_1.3.3-1.ca2404.1_all.deb Size: 290092 MD5sum: 3f5a113c0cceb28598b54d298ca644bc SHA1: 8579245bda2873b0428807f06954c473164979de SHA256: 1c55029abd6d06769735da6e5fa6d714c8890b2ad4a74182dfe2ad11b3462bcc SHA512: 237622f92535334d27e822d9b99c18310e60640bc044754b45d90cebd55ddfd85145364ab5e952f8a1586fcc8b9b8c02624ba2bbfc2260b11a80937a6733f744 Homepage: https://cran.r-project.org/package=mmod Description: CRAN Package 'mmod' (Modern Measures of Population Differentiation) Provides functions for measuring population divergence from genotypic data. Package: r-cran-mmodely Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caper, r-cran-caroline, r-cran-ape Filename: pool/dists/noble/main/r-cran-mmodely_0.2.5-1.ca2404.1_all.deb Size: 492362 MD5sum: f8cd6b269656d94dd35530dae15b3e95 SHA1: d8778269ce13d9b029b2826727e14246ea47cb3a SHA256: 3b262ce1d2a38eebe0a1e5d3f7eb7e129218de336b63d3855ac78e31cfa77b17 SHA512: 5ebe09c0677058d247d711f3690bc2ca06b6e8511e3aa02103b34a2988f5346ba202c128e375fda4d5586a53c8deac5ce5f5a5e2ee538a51dbe84f79cf132264 Homepage: https://cran.r-project.org/package=mmodely Description: CRAN Package 'mmodely' (Modeling Multivariate Origins Determinants - EvolutionaryLineages in Ecology) Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 ). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 ; Garamszegi, 2011 ) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature. Package: r-cran-mmpa Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmpa_1.2.0-1.ca2404.1_all.deb Size: 70046 MD5sum: 261fb50687a5a1de4d99be45ceb23d00 SHA1: 01fa64959047148177333bafb900aab1de5f59a1 SHA256: 4a031b1dd831dec99fe0b2c39ae3b28eaa1d8ec9a4600806eaaef8876110dbb0 SHA512: 1c27ae3dd5719a373702e9c7c8e267ed1c8590292735b37958177aecaf6da3a2dce8a02582104833c71c5f2e249cc433c596c90702339a6317e07c45ad1848d6 Homepage: https://cran.r-project.org/package=mMPA Description: CRAN Package 'mMPA' (Implementation of Marker-Assisted Mini-Pooling with Algorithm) To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). 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Package: r-cran-mmr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mmr_0.1.0-1.ca2404.1_all.deb Size: 9930 MD5sum: 15792cc7e8a176327511cf25d27c019f SHA1: 536551483603b15b98de81d4f75f7486cb9c9580 SHA256: 48cb193d63ab3bfa6ebc1f4c46c538dde6a2985940a1a9eae8d99e8172a46cf7 SHA512: e3f51363d2b880dee942325513af74db74e79dea15f96e872e02b51c74a92120f91c4c580a54fb8d8c44dc632cbd5ec8b9406aea1d5032e71f6c96556d744208 Homepage: https://cran.r-project.org/package=mmr Description: CRAN Package 'mmr' (Matrix Multiplication on Data.frames) Simple helpers for matrix multiplication on data.frames. These allow for more concise code during low level mathematical operations, and help ensure code is more easily read, understood, and serviced. 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One can designate cases as typical, deviant, extreme and pathway case and use different case selection strategies for the choice of a case belonging to one of these types. Package: r-cran-mmstat4 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1804 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-digest, r-cran-httr, r-cran-knitr, r-cran-rappdirs, r-cran-reticulate, r-cran-rio, r-cran-rstudioapi, r-cran-shiny, r-cran-stringdist Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmstat4_0.2.1-1.ca2404.1_all.deb Size: 1133878 MD5sum: d76738a0ee3547d2977d38ea329b145f SHA1: d0724edce9f38c7711bedb5028cd892c5cc96d82 SHA256: 26c529ae8786e411ea76a05f21c074750df50c5dadb06426613cb2a674c76111 SHA512: 417386c190136f1f29c43ff0bfa6637aac310dd6f6308272775b1e6e2a8fef589e730e469baebeadfc1a8240a6193fa51ad6049f957adcddf57bb01fbdc1420c Homepage: https://cran.r-project.org/package=mmstat4 Description: CRAN Package 'mmstat4' (Access to Teaching Materials from a ZIP File or GitHub) Provides access to teaching materials for various statistics courses, including R and Python programs, Shiny apps, data, and PDF/HTML documents. These materials are stored on the Internet as a ZIP file (e.g., in a GitHub repository) and can be downloaded and displayed or run locally. The content of the ZIP file is temporarily or permanently stored. By default, the package uses the GitHub repository 'sigbertklinke/mmstat4.data.' Additionally, the package includes 'association_measures.R' from the archived package 'ryouready' by Mark Heckman and some auxiliary functions. Package: r-cran-mmtdiff Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmtdiff_1.0.0-1.ca2404.1_all.deb Size: 51206 MD5sum: bd33a23204eee81aa89329dc5ba4c378 SHA1: 4c5f92cb027877dc7aef61bafb4db80aef76f781 SHA256: 235e1e732e5181226368405dc32398c0de44565a26592d3b0cb3323750c024fc SHA512: f87a739eafd922dbae8a626b49f3c51e809f0c461addd388ad2c39a6710cc2f766ec48eb55db2d811b7cdd1c6933ce80eaa113f43e9f34089ba155204ce81a7d Homepage: https://cran.r-project.org/package=mmtdiff Description: CRAN Package 'mmtdiff' (Moment-Matching Approximation for t-Distribution Differences) Implements the moment-matching approximation for differences of non-standardized t-distributed random variables in both univariate and multivariate settings. The package provides density, distribution function, quantile function, and random generation for the approximated distributions of t-differences. The methodology establishes the univariate approximated distributions through the systematic matching of the first, second, and fourth moments, and extends it to multivariate cases, considering both scenarios of independent components and the more general multivariate t-distributions with arbitrary dependence structures. Methods build on the classical moment-matching approximation method (e.g., Casella and Berger (2024) ). Package: r-cran-mmtsne Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mmtsne_0.1.0-1.ca2404.1_all.deb Size: 33784 MD5sum: 91e6a86ff99f170a41c5e00325bb46f9 SHA1: 7a58585ff77a777783a1b13560e7ee23fa86ddce SHA256: c2994f572dbd872babd62c01f4af5f26bbd4193cf15e6f796dd43f416e71085c SHA512: 2ff7d062d927d43f6abb8e685a44bc00301d26499987b9e43981eb058fa740e3d2b7056ea5d98561d807858e4ed01535850a939143277651c303e9d8189b1023 Homepage: https://cran.r-project.org/package=mmtsne Description: CRAN Package 'mmtsne' (Multiple Maps t-SNE) An implementation of multiple maps t-distributed stochastic neighbor embedding (t-SNE). Multiple maps t-SNE is a method for projecting high-dimensional data into several low-dimensional maps such that non-metric space properties are better preserved than they would be by a single map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE. When projecting onto more than one map, multiple maps t-SNE estimates a set of latent weights that allow each point to contribute to one or more maps depending on similarity relationships in the original data. This implementation is a port of the original 'Matlab' library by Laurens van der Maaten. See Van der Maaten and Hinton (2012) . This material is based upon work supported by the United States Air Force and Defense Advanced Research Project Agency (DARPA) under Contract No. FA8750-17-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and Defense Advanced Research Projects Agency. Distribution Statement A: Approved for Public Release; Distribution Unlimited. Package: r-cran-mmwrweek Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmwrweek_0.1.3-1.ca2404.1_all.deb Size: 28860 MD5sum: 6bcb645593a050ea77777276ef94a43a SHA1: a811f7a2e557b482a6bd7f808c3a88943a89ece4 SHA256: 2045c8f2560cd59e0fd480b973e6fa306210d268357c68b160ee625f8084eebd SHA512: 8516aad45e8c15382b36a6e9afb19f380cfe77ab0dfb09389ecf726504dd85d34d7202dcc3100ce7e208f6018fbce6a3ee403ff477387a167d2681b3059576c6 Homepage: https://cran.r-project.org/package=MMWRweek Description: CRAN Package 'MMWRweek' (Convert Dates to MMWR Day, Week, and Year) The first day of any MMWR week is Sunday. MMWR week numbering is sequential beginning with 1 and incrementing with each week to a maximum of 52 or 53. MMWR week #1 of an MMWR year is the first week of the year that has at least four days in the calendar year. 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(2019). Assessing and Visualizing Matrix Variate Normality. and the relevant wikipedia page. 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Including also, the estimation process by maximum likelihood method, for details see Fabio, L. C; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2023) and Fábio, L. C.; Villegas, C.; Mamun, A. S. M. A. and Carrasco, J. M. F. (2025) . 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In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) . Package: r-cran-mnet Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlvar, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mnet_0.1.4-1.ca2404.1_all.deb Size: 604054 MD5sum: a0c1923bb8828cc40d4370848a2bd6f4 SHA1: 3aea1d58bd814186b4ff91b9f330349def0241c5 SHA256: e235ed31f656d2720936b4c33fdb2d0b8ffb3e95abb509552978d92b5e812742 SHA512: be58e3bda74ebe7effe1729e73d7a8bc0b0726f0d9d6427d92728f32bda1e75a3b9b3c20a62e8872764268924e9a57b25958e057af902f576552936362d2c878 Homepage: https://cran.r-project.org/package=mnet Description: CRAN Package 'mnet' (Modeling Group Differences and Moderation Effects in StatisticalNetwork Models) A toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models. 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Import raw data from file and return time-series data and metadata. Standardised methods for cleaning, filtering, transforming, and analysing mNIRS data. Custom plot theme and colour palette. Intended for mNIRS researchers and practitioners in exercise physiology, sports science, and clinical practice. 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It takes a specified scenario and a multinomial model to predict probabilities with a set of coefficients, drawn from a simulated sampling distribution. The simulated predictions allow for meaningful plots with means and confidence intervals. The methodological approach is based on the principles laid out by King, Tomz, and Wittenberg (2000) and Hanmer and Ozan Kalkan (2016) . 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The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at . Package: r-cran-mnm Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-icsnp, r-cran-spatialnp, r-cran-ellipse, r-cran-ics Suggests: r-cran-gamlss, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mnm_1.0-4-1.ca2404.1_all.deb Size: 306968 MD5sum: 2a7ffec2f53aa7d1cbc703541c3d78e5 SHA1: 2f05c7566e200fcb569dfce139add18d3a77bfff SHA256: 6b78bd22f658536375588de518da8f3b5707c3092f8907f2c152c1586c10e8c3 SHA512: f5148f9347f3b8c34bac780147f1298dc5ad7a64cb7449ce11cfaea40fc6bc89fae4d7e70b5731fbd7441f57e06765efb6b077d53122604aec3aa3890a4e2dfa Homepage: https://cran.r-project.org/package=MNM Description: CRAN Package 'MNM' (Multivariate Nonparametric Methods. 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It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 ), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis. 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Huixuan, Gao (2005, ISBN:9787301078587), "Applied Multivariate Statistical Analysis". Package: r-cran-mnpplasmonr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mnpplasmonr_0.1.0-1.ca2404.1_all.deb Size: 14096 MD5sum: 3cc38ce96b702c2f6e8df7263458ddf5 SHA1: 623912125ce863d9f62ce6883026891937e5b79e SHA256: a5ec44bb331df57798838e2632b3920da1f8a8de7dde9a4ec758f694913d80bf SHA512: f11c4e16f0657f2e87ce2ad5f3030c0c55e37e8d0582e9b92ad58c1b6d88be9da69f3b5cc4b8af0f314fa8574ba992f4da8517cceea5bdc98a05290a9c8a1ec8 Homepage: https://cran.r-project.org/package=mnpPlasmonR Description: CRAN Package 'mnpPlasmonR' (Optical Response of Metallic Nanoparticles (Drude + Rayleigh)) Computes dielectric response and optical cross-sections of metallic nanoparticles using Drude dielectric model and Rayleigh approximation. Package: r-cran-mnreadr Architecture: all Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-nlme, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mnreadr_2.1.7-1.ca2404.1_all.deb Size: 140524 MD5sum: 12e53392b51b8a7aa76154a7126c9c7f SHA1: 7efcee7fe34fa663c730ffde66c06bf376f03251 SHA256: 5842bf993d27c19d3dce3ec84e5945655d2c5d51eb4dd9b99a9033e7bd154999 SHA512: ace77f75d15e232bf8c8710f2450ccb4ac9f3aae942121c45c69b29b6026717ad671890c56c9dc871c8d6bcf8465dc8d391cb53f6852fa4f3665e33b4b4fe844 Homepage: https://cran.r-project.org/package=mnreadR Description: CRAN Package 'mnreadR' (MNREAD Parameters Estimation and Curve Plotting) Allows to analyze the reading data obtained with the MNREAD Acuity Chart, a continuous-text reading acuity chart for normal and low vision. Provides the necessary functions to plot the MNREAD curve and estimate automatically the four MNREAD parameters: Maximum Reading Speed, Critical Print Size, Reading Acuity and Reading Accessibility Index. Parameters can be estimated either with the standard method or with a nonlinear mixed-effects (NLME) modeling. See Calabrese et al. 2018 for more details . Package: r-cran-mns Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-glmnet, r-cran-mvtnorm, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-mns_1.0-1.ca2404.1_all.deb Size: 372648 MD5sum: 08c33aa34d009ed729f1ec466a3e3eee SHA1: b647dc44208cb5ee563ea43eafef43df089957fb SHA256: 711011967e10874f82ff8c4ca695fa8f903430ab857ac9bd0262fc4a71d884f1 SHA512: aff6be65250add65310fe0977ca6360ed2a13c891f853d9648f8a67699c388c1e3287c4e04f3086744d1824a4ef852a3ee265a2769b6961c25727fd2531cf6fa Homepage: https://cran.r-project.org/package=MNS Description: CRAN Package 'MNS' (Mixed Neighbourhood Selection) An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis. Package: r-cran-mnt Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pearsonds, r-cran-rmpfr, r-cran-suppdists, r-cran-mass, r-cran-pracma Filename: pool/dists/noble/main/r-cran-mnt_1.4-1.ca2404.1_all.deb Size: 261408 MD5sum: c8529941b47a538aaa3a3b5688edf05e SHA1: c4a0e0abc8e7c20034997ad31f87642f9fd73580 SHA256: 1d52a7f363d5c5454dcf8a1709956b83e32b280989b3978d36dfb4250b88200f SHA512: 54bd481afcd6b39e00ff2018f67f8209b9a42475809c8b54d3239b5392be4b61d7479ce479583713bca0090454173b560c7ddd80638e4a460ee442d002beaedf Homepage: https://cran.r-project.org/package=mnt Description: CRAN Package 'mnt' (Affine Invariant Tests of Multivariate Normality) Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns). 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For a numeric variable, all of its monotonic functional transformations will converge to the same woe transformation. Package: r-cran-mobdb Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-cli, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-lifecycle, r-cran-digest Suggests: r-cran-testthat, r-cran-withr, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-tidytransit, r-cran-gtfsio, r-cran-keyring, r-cran-sf, r-cran-gbfs, r-cran-zip, r-cran-hms Filename: pool/dists/noble/main/r-cran-mobdb_1.0.3-1.ca2404.1_all.deb Size: 260438 MD5sum: 1dcc177e5ccb590196bfd5ca925a562c SHA1: 064e549e9cbff6ec1a72724cbb9230576bbf8c53 SHA256: f776266158385c6a21b28e38304ca2717e98f92f1250673f3e4d3ad117028c2d SHA512: 4b88d414f1a4191c117d517c3a103e5591dbbb91c1f54ed763a06dbded354c33483fe613376116d1bf371484f56f63896eab31dc534d8a78ada9a18320473099 Homepage: https://cran.r-project.org/package=mobdb Description: CRAN Package 'mobdb' (Access the 'Mobility Database' API to Discover Transit Feeds) Search and access transit feed data from the 'Mobility Database' . The package wraps the 'Mobility Database' API, allowing users to discover 'GTFS' (General Transit Feed Specification) and 'GBFS' (General Bikeshare Feed Specification) feeds from agencies worldwide. Functions are designed to integrate seamlessly with packages like 'tidytransit' and 'gtfstools' for subsequent feed analysis. Package: r-cran-mobiletrigger Architecture: all Version: 0.0.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-knitr, r-cran-kableextra, r-cran-ggplot2, r-cran-xml, r-cran-plyr, r-cran-yaml, r-cran-mailr Filename: pool/dists/noble/main/r-cran-mobiletrigger_0.0.31-1.ca2404.1_all.deb Size: 59342 MD5sum: b3b1e87df687fdce9c51d40a3bbb1763 SHA1: ac3928c1621369dfc946d70a4ce8caf0bef0e89a SHA256: 47e1d318b827b5d7ea4cd795d17cf78a6e0d098432e337cb7fd10f83b655a2b8 SHA512: e606c81cf31bffaf12c3305d035e334138ac4997c421d2793d23efc1e9e6b63a8aaffc99d191cfe3bf9f727e091aed370a7b72acbd97b687785e8234f5f04f91 Homepage: https://cran.r-project.org/package=MobileTrigger Description: CRAN Package 'MobileTrigger' (Run Reports, Models, and Scripts from a Mobile Device) A framework for interacting with R modules such as Reports, Models, and Scripts from a mobile device. The framework allows you to list available modules and select a module of interest using a basic e-mail interface. After selecting a specific module, you can either run it as is or provide input via the e-mail interface. After parsing your request, R will send the results back to your mobile device. Package: r-cran-mobilitydatapt Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-httr, r-cran-jsonlite, r-cran-sf Suggests: r-cran-curl, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mobilitydatapt_1.0-1.ca2404.1_all.deb Size: 1002374 MD5sum: c8c59ba1ab81132fcf74fd9fea2e6b9c SHA1: a59fe92a9782ff7326c2407a58f61efdf7f6b301 SHA256: 3a104b2c94eaf6f923847cf757e2edb73add0844c1143b6b084e39d3ed2f164a SHA512: d5a17ae7d444c33d252e5b6d00d25c3dee1366db9f1faf636845c6ab92395b393708cd2a8435eae16481c462df0b54188e53bc4ae88fcce0423865484c419e93 Homepage: https://cran.r-project.org/package=MobilityDataPT Description: CRAN Package 'MobilityDataPT' (Get Mobility Related Data for Portugal) A collection of methods to get mobility related data for Portugal. Package: r-cran-mobilityindexr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mobilityindexr_0.2.1-1.ca2404.1_all.deb Size: 124294 MD5sum: 27873b2a17e33c8cd1236fbd4efc28f3 SHA1: 874e77541b50fc2ebbbb0af6cba2187408118e07 SHA256: 0c8b83faf35cea64a8852ab0a423a20e9ed40954c544f3f22b85a1e28af109eb SHA512: 82bd2ec00aa11b41b850ca8c57fdd5a12c8710f26203f9677a9f93a34fae8c74bd7d5a2b87ee646caaaccf6d76d22908c27da197e7363629bc4f256c0eaeafbd Homepage: https://cran.r-project.org/package=mobilityIndexR Description: CRAN Package 'mobilityIndexR' (Calculates Transition Matrices and Mobility Indices) Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included. Package: r-cran-mobius Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-directional, r-cran-rfast, r-cran-rgl Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-mobius_1.0-1.ca2404.1_all.deb Size: 61510 MD5sum: 79620c5152a32baf863230be0b448824 SHA1: 54841b0c9ecc6d116cdc369fa48b17cee1f2cf5c SHA256: feb45278400945913865a7d7383d85ed407f3a98887fb8e3ad220b4d19dbf4f2 SHA512: 66e6a7f973626299c20d13ccef68276884008c7691dd1e65fc75cf7ee07e86a306e694d52c27aa336a258d9d783e56033399fbf225e4459c7571aba1dbbb556c Homepage: https://cran.r-project.org/package=Mobius Description: CRAN Package 'Mobius' (Mobius Transport for Directional Data) Density evaluation, random generation, and maximum likelihood estimation for the Mobius-von Mises-Fisher and isotropic scaled von Mises-Fisher distributions on the hypersphere, introduced in Garcia-Portugues and Kato (2026) . Both distributions arise from Mobius transport of a von Mises-Fisher distribution. 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Associated publication: Pook et al. (2020) . Package: r-cran-mobr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotrix, r-cran-scales, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-pbapply, r-cran-ggplot2, r-cran-egg, r-cran-tibble, r-cran-vctrs, r-cran-rlang, r-cran-geosphere, r-cran-scam, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mobr_3.0.0-1.ca2404.1_all.deb Size: 773762 MD5sum: 0c80e4eac9e429093b1cdd35aa04b52f SHA1: 80993295108e270a05411fe8190df45b0ab54e0e SHA256: d0dd61eec9e46db5bcfe6c592e157d5f19b685ac2ae9fddf04a0275943644d84 SHA512: 0519f968c717766024a0410c174eb76810f0569cb5987362e9d00126820350ad6e870f22c61f6c61d513dd35cb46f5c9b7a97fe33769e29855bd2e526dc97c18 Homepage: https://cran.r-project.org/package=mobr Description: CRAN Package 'mobr' (Measurement of Biodiversity) Functions for calculating metrics for the measurement biodiversity and its changes across scales, treatments, and gradients. The methods implemented in this package are described in: Chase, J.M., et al. (2018) , McGlinn, D.J., et al. (2019) , McGlinn, D.J., et al. (2020) , and McGlinn, D.J., et al. (2023) . Package: r-cran-moc.gapbk Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nsga2r, r-cran-foreach, r-cran-doparallel Suggests: r-cran-amap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-moc.gapbk_0.2.1-1.ca2404.1_all.deb Size: 73328 MD5sum: 4891edb3c93c903530c65914b5b1342a SHA1: d345198a2fbfeada2a1e67d326b6fe502b20d55c SHA256: cfbca2efcf6cbf80359f6aa386b32cc615384067b45e6b4b4c1f52e16c9c6e06 SHA512: 78dc2d56a7afe0e8ae4cdf4bb5737b8077faf7b9e7799bd4bfe9409ed2d7a76186d4d441cd8a3537ee9c9eddb01d2426a018d47890455c0d0386678c980588d5 Homepage: https://cran.r-project.org/package=moc.gapbk Description: CRAN Package 'moc.gapbk' (Multi-Objective Clustering Algorithm Guided by a-PrioriBiological Knowledge) Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge ('MOC-GaPBK') proposed by Parraga-Alava and others (2018) . 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Package: r-cran-mocca Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cclust, r-cran-clue, r-cran-cluster, r-cran-class Filename: pool/dists/noble/main/r-cran-mocca_1.4-1.ca2404.1_all.deb Size: 49422 MD5sum: 334aa1fb3579ad85a20ad06745a949a7 SHA1: eaee3d8d087b814e2a6b90742fbbc9c990eab354 SHA256: a3e97219ba51fa529d9d2c52cfa0d37ebd74be4fe3b2c077ccb20fd8fe92eb96 SHA512: 1a1803c8e65d45d6e61358a525e1db495768140708e2a69a118e0adbc26e96eb5d1c4f63f4f1e7df02fdab340cf01f860f0d7f85791215a60076f75c9617e85d Homepage: https://cran.r-project.org/package=MOCCA Description: CRAN Package 'MOCCA' (Multi-Objective Optimization for Collecting Cluster Alternatives) Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) . 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These novel modules remove unwanted technical variation, identify open chromatin, robustly models repeated measures in single cell data, implement advanced statistical frameworks to model zero-inflation for differential and co-accessibility analyses, and integrate with existing databases and modules for downstream analyses to reveal biological insights. MOCHA provides a statistical foundation for complex downstream analysis to help advance the potential of single cell ATAC-seq for applied studies. Methods for zero-inflated statistics are as described in: Ghazanfar, S., Lin, Y., Su, X. et al. (2020) . Pimentel, Ronald Silva, "Kendall's Tau and Spearman's Rho for Zero-Inflated Data" (2009) . Package: r-cran-mochita Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httpuv, r-cran-nanonext, r-cran-r6, r-cran-testthat Suggests: r-cran-routing, r-cran-yyjsonr Filename: pool/dists/noble/main/r-cran-mochita_1.0.0-1.ca2404.1_all.deb Size: 70484 MD5sum: da3208871e2b04c2dbe1d246f97738b8 SHA1: e5c4fba606abd1003bd404196fbaf1c7cdee27c4 SHA256: 7142488073a267f44c8e76227fa815aecd1b4d7da4fa9ec64df77c11986ba1f1 SHA512: ec4226b9cc753ddd30ce053e2b436173364044d1121dfe8e92bad69fc63a431eee697a2328805b2caa6e208a58d7c835ad1eb185c122fa593c59137813941bf9 Homepage: https://cran.r-project.org/package=mochita Description: CRAN Package 'mochita' (Test R Web Applications) Write so-called 'Integration Tests' for your R web applications by declaring an HTTP request and the expectations its response should meet. 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The package will be most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath products; , and adds the directory with the MRTSwath executable to the default R PATH by editing ~/.Rprofile. 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Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" . Package: r-cran-mod Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mod_0.1.3-1.ca2404.1_all.deb Size: 49280 MD5sum: ab125fb7b114063923cf8b85c1d47664 SHA1: d5fc5360e7e31280e7fd7daf3b2454cc8705f09a SHA256: 8020f6f187263843d94491253bc6727bc3f376a11012e42a0123280ac30db0e2 SHA512: 9520d10f4d819f035b770506528bc5e0d5e866d39f4a24474733870aa87775f7fa11269a4532a7f3e5437e653f0fd4e03a6efcfb26b817d14989c1fce64a03cb Homepage: https://cran.r-project.org/package=mod Description: CRAN Package 'mod' (Lightweight and Self-Contained Modules for Code Organization) Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment. Package: r-cran-modacdc Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ccp, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-genieclust, r-cran-genio, r-cran-ggplot2, r-cran-partition, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cca Filename: pool/dists/noble/main/r-cran-modacdc_2.0.1-1.ca2404.1_all.deb Size: 105662 MD5sum: e9e5b87f36da3e3152342593a3c651f4 SHA1: a183199012a1696415fd58d79b1af452376e3895 SHA256: 0446388123550c62cc5d02c522b6ecb4156c8503a6a4f223ac4f45c84c1738e7 SHA512: 0137b243c3e062b57cd676caaf4f510a889f1e8050d8104989a439328e567f7222674698d0f843f81581a9ea15374b23f230430397da78bb2e762cbe97eb262a Homepage: https://cran.r-project.org/package=modACDC Description: CRAN Package 'modACDC' (Association of Covariance for Detecting DifferentialCo-Expression) A series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) . Package: r-cran-modalcens Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modalcens_0.2.0-1.ca2404.1_all.deb Size: 45398 MD5sum: f4229730c8feca04a31f557e81dd193c SHA1: 53a1e2b31603e74e3a2db447af52d8947787f920 SHA256: b50b4dd0c731043e741e11fa2f9ad4e70f60273768fb48c10b3ba98123342a34 SHA512: 4b0acf27e5309f299fb5ef533d64fdf9318bff35e2e8f0eecbf9bcc1b1bf23d909a5cf3aadef8fdb47fea4f2a51d92b23dc5c9dae32490977a6287c9133f0bb4 Homepage: https://cran.r-project.org/package=ModalCens Description: CRAN Package 'ModalCens' (Parametric Modal Regression with Right Censoring) Implements parametric modal regression for continuous positive distributions of the exponential family and beyond (e.g., Log-Logistic, Birnbaum-Saunders) under right censoring. Provides functions to link the conditional mode to a linear predictor using alternative parameterizations. Includes maximum likelihood estimation via numerical optimization, asymptotic inference based on the observed Fisher information matrix, and model diagnostics using randomized quantile residuals. See Galarza and Lachos (2026) . Package: r-cran-modalclust Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-zoo, r-cran-class Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-modalclust_0.7-1.ca2404.1_all.deb Size: 412222 MD5sum: 04eabd007abb40919e103a3b76e96c8f SHA1: eae4670f485fc9fc42e6af26d160d1f3f2da1e88 SHA256: c4e25f00f579660fa73a83c3b06724ceb94befad15c4d26b5bf40ae2fa726cb2 SHA512: 6e478d0a5020dde323f194606939f53670285a953b4535ef9e76053852aa44369b6738ea44e13526d5ad48e054844bb8a39be6efb51e10a49fd250700faf9907 Homepage: https://cran.r-project.org/package=Modalclust Description: CRAN Package 'Modalclust' (Hierarchical Modal Clustering) Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default. Package: r-cran-modalforecast Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-modalforecast_0.2.0-1.ca2404.1_all.deb Size: 988352 MD5sum: 733b1b48152654f1018aa9337e7b6c03 SHA1: da3bae38608189e8d07be27de7242bb922923ff2 SHA256: 73b595de36442e2e5b47bf98994e72d64d27e70a8f61fea0493db96d228bb730 SHA512: ffbaf146fa6de03d79a4c1635c00b2179146ee2b713cdcf1c8de054b08e215dffe3d084aa36ecb1604f2d4e8ed6200c04cc705c020e92c61efc59bd25ac47c18 Homepage: https://cran.r-project.org/package=ModalForecast Description: CRAN Package 'ModalForecast' (Parametric Modal ARIMA and Seasonal ARIMA Models using the SKDFamily) Implements parametric modal Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models utilizing the Skewed Distribution (SKD) family, in which the conditional mode, rather than the conditional mean, follows the (seasonal) ARIMA recursion. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The parameters are estimated by maximum likelihood using analytical gradients. Includes residual diagnostics, simulation envelopes, automatic order selection, joint and marginal modal forecasts, exact and parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) . 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Package: r-cran-modehunt Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modehunt_1.0.8-1.ca2404.1_all.deb Size: 106924 MD5sum: f1af9fc5ef7bbba3d525bf405cf1f13a SHA1: 718bec48e5a636d890198e04927b79a163107d97 SHA256: 60fcb025ba24e2266c3fc3ae1bf7b48eb7a4b34861f097004e144c56dea32afa SHA512: 73d4b71c25feb24abd75bdbfc0e7460736b26a23ad0036bc8328c70fea8b77f1ed8ab2dff5556885c84fd14159bda45377be7fb9c4494421be34cde152e3e62f Homepage: https://cran.r-project.org/package=modehunt Description: CRAN Package 'modehunt' (Multiscale Analysis for Density Functions) Given independent and identically distributed observations X(1), ..., X(n) from a density f, provides five methods to perform a multiscale analysis about f as well as the necessary critical values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive correction term Gamma in either method offers another two approaches which are more powerful on small scales and less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically equivalent to the first and was introduced in Rufibach and Walther (2010). Package: r-cran-model4you Architecture: all Version: 0.9-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-sandwich, r-cran-ggplot2, r-cran-formula, r-cran-gridextra, r-cran-survival, r-cran-rlang Suggests: r-cran-mvtnorm, r-cran-th.data, r-cran-psychotools, r-cran-strucchange, r-cran-plyr, r-cran-knitr, r-cran-ggbeeswarm, r-cran-mass Filename: pool/dists/noble/main/r-cran-model4you_0.9-9-1.ca2404.1_all.deb Size: 152102 MD5sum: f5ef82dc868259e3dd760d837f1b6785 SHA1: 28c449d54eacfcf56879f80c92388c5087ccc7d1 SHA256: 072240da48813c50708c9bf866b5090615bfe775d7badfb7e843edc575918b3a SHA512: cc4764791ea84facdabb4b3f62895e9332ac71790f7d14ea042a11fa8bba76d4b3b147a80af7d704931af002feb1ff6d0313730898d7599b99355e8d6dfd38a1 Homepage: https://cran.r-project.org/package=model4you Description: CRAN Package 'model4you' (Stratified and Personalised Models Based on Model-Based Treesand Forests) Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) ; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) . Package: r-cran-modelbased Architecture: all Version: 0.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-insight, r-cran-parameters Suggests: r-cran-afex, r-cran-bh, r-cran-betareg, r-cran-boot, r-cran-bootes, r-cran-brglm2, r-cran-brms, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-coda, r-cran-collapse, r-cran-correlation, r-cran-coxme, r-cran-curl, r-cran-discovr, r-cran-easystats, r-cran-effectsize, r-cran-emmeans, r-cran-formula, r-cran-gamm4, r-cran-gganimate, r-cran-ggplot2, r-cran-glmmtmb, r-cran-httr2, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-mass, r-cran-matchit, r-cran-matrix, r-cran-marginaleffects, r-cran-mice, r-cran-mgcv, r-cran-mvtnorm, r-cran-nanoparquet, r-cran-nestedlogit, r-cran-nnet, r-cran-ordinal, r-cran-performance, r-cran-patchwork, r-cran-pbkrtest, r-cran-poorman, r-cran-pscl, r-cran-rcppeigen, r-cran-rdatasets, r-cran-report, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rtdists, r-cran-rwiener, r-cran-sandwich, r-cran-scales, r-cran-see, r-cran-survival, r-cran-testthat, r-cran-tinyplot, r-cran-tinytable, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-modelbased_0.17.0-1.ca2404.1_all.deb Size: 915498 MD5sum: c9c745db6c023971bde9580298eb49a2 SHA1: 5f2a8119d0972417d608b9f702f55575176955fd SHA256: 716d405132ca5a51c5e34157dd7b594f811ca530b8a6e1091d88c8e26ef596b2 SHA512: 4d159d26b20d89e6887622bf8339413daea54cc8d5a2f1068f8d435dace1b8c84f0a613d14d6ea032b76b3e1702c5db8d59e0ad77458069c818b4fda48835072 Homepage: https://cran.r-project.org/package=modelbased Description: CRAN Package 'modelbased' (Estimation of Model-Based Predictions, Contrasts and Means) Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'. Package: r-cran-modelbpp Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-pbapply, r-cran-igraph, r-cran-manymome Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-modelbpp_0.4.0-1.ca2404.1_all.deb Size: 810240 MD5sum: 32fd11e827a8c4011fd543a8d65f4feb SHA1: 9fb469157e4df14af9a70870a064fa73be637334 SHA256: d979c05282d6c184ca13db13072d71d26661f5bda858027e6d1b15cae8ddb1c3 SHA512: 2404abd92678ff083b33643885c00d5e62e518082f3d42cc0dbf69f0b486730e9f324761f68e65032ff2a1c029655ba1d0400d4f3c273eeba1cde29ee3182e3e Homepage: https://cran.r-project.org/package=modelbpp Description: CRAN Package 'modelbpp' (Model BIC Posterior Probability) Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities (BPP), using the method presented in Wu, Cheung, and Leung (2020) . See Pesigan, Cheung, Wu, Chang, and Leung (2026) for an introduction to the package. Package: r-cran-modelc Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelc_1.0.0.0-1.ca2404.1_all.deb Size: 39634 MD5sum: 3af6af0945688993b32f3a437f562427 SHA1: 6e4a79dc3d4460b0e626c395719f4658222f7052 SHA256: dc9f6552fb887a8896ee466127d3cf1837b438fb47ebbea607c98020ee8f9b2a SHA512: 86a502f8cb0f767ecaf1d9150a86e063be9e0d4a52bb1ca13ea1817f97b91fe1991821ba0805ac28093e7ecb0287a8be7e81e6f4cafa2dc23980a5810dc742d1 Homepage: https://cran.r-project.org/package=modelc Description: CRAN Package 'modelc' (A Linear Model to 'SQL' Compiler) This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) and originating in Nelder & Wedderburn (1972) . The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported. Package: r-cran-modelcardr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-withr Suggests: r-cran-testthat, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-modelcardr_0.1.0-1.ca2404.1_all.deb Size: 297474 MD5sum: 8a274cb337dc5514f8d20d1adf688a9a SHA1: ef3eb7fa2902648774591a7fb85ceac23dcf577d SHA256: a6c81e7955af21b7551091585cd61f03a2c8793bbba0a85d9db7372d5435a8e5 SHA512: aaecd0ccd5742be51f2c45d2d8d9d4980ca15c814fd59e6efac855d07b2a161f837dd69c8151a1876a7d22d0a6a85a37b0c654cf51a2d9e200d3590d0b55ef2e Homepage: https://cran.r-project.org/package=modelcardr Description: CRAN Package 'modelcardr' (Machine Learning Prediction Auditing and Model Card Reporting) Audits predictions from fitted machine learning models without requiring retraining or access to the fitted model object. It supports binary classification, multiclass classification, and regression, providing performance metrics with bootstrap confidence intervals, calibration and threshold analyses, residual diagnostics, subgroup comparisons, user-defined acceptance criteria, risk warnings, and self-contained reports. For binary-classification and regression models, the package can also assemble and render structured model cards documenting intended use, evaluation data, performance, subgroup results, assumptions, and limitations. The model-card reporting approach is described by Mitchell et al. (2019) "Model Cards for Model Reporting" . 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Package: r-cran-modeldiag Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-lmtest, r-cran-resourceselection, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeldiag_0.1.0-1.ca2404.1_all.deb Size: 77428 MD5sum: c6c1dd9ee56abfc7ed0537ed0d1c24f6 SHA1: 9f0a6f795911c1529637ab45aebe3a1a35e7b6dc SHA256: ab5976a67ffed644e88305c2466a3f01284c1c533e49fad5d2042151a5d6a80c SHA512: ae74b09ff592fd66c09c0f11184e259efbe404a44ce6d8a87d9d5a14b48231fbf8bd4af77a3f58d25bfacb6fb2059b2a40ddd0a0efea9b24b4261cfe43dd20a2 Homepage: https://cran.r-project.org/package=modeldiag Description: CRAN Package 'modeldiag' (Comprehensive Diagnostics for Statistical Models) Provides a unified framework for diagnosing common issues in statistical models including linear models, generalized linear models (logistic and Poisson regression), and survival models. Implements tests for multicollinearity, heteroscedasticity, autocorrelation, normality, influential observations, overdispersion, zero-inflation, and proportional hazards assumptions. Includes visualization methods for graphical diagnostics. Methods are based on established approaches including Fox and Monette (1992) , Breusch and Pagan (1979) , and Dean and Lawless (1989) . Package: r-cran-modeldiagramr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr, r-cran-tibble, r-cran-gtools, r-cran-forcats, r-cran-nlme, r-cran-diagrammer Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-ggthemes, r-cran-lme4, r-cran-lmertest, r-cran-knitr, r-cran-viridis, r-cran-diagrammersvg, r-cran-webshot, r-cran-fieldhub, r-cran-rsvg Filename: pool/dists/noble/main/r-cran-modeldiagramr_0.2.1-1.ca2404.1_all.deb Size: 98582 MD5sum: 4b30614a711baf91d81c349bea0cb8db SHA1: 36f4c5831da6e6de4e93afd54ee6a8cceb9c60d8 SHA256: 102176a5e9b27fafdac00004183f3a71ad653fde08510faa26e0425d04de2364 SHA512: f3a5d542824ff9f18a71581b8179dd931fb23159a90d337ae100dd357f7fdad536c6b2eb870b71d9a4db1867d3aa94d39db8017d5446b271d4e649cc1c1b72bc Homepage: https://cran.r-project.org/package=modeldiagramR Description: CRAN Package 'modeldiagramR' (Generate Model Diagrams for Linear Mixed Effect Models) Generates 'DiagrammeR' model diagrams for hierarchical linear mixed effects models. 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This package uses 'DALEX' explainers to describe global model behavior. We can see how well models behave (tabs: Model Performance, Auditor), how much each variable contributes to predictions (tabs: Variable Response) and which variables are the most important for a given model (tabs: Variable Importance). We can also compare Concept Drift for pairs of models (tabs: Drifter). Additionally, data available on the website can be easily recreated in current R session. Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 at Warsaw University of Technology, Faculty of Mathematics and Information Science. Package: r-cran-modelenv Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelenv_0.2.0-1.ca2404.1_all.deb Size: 101194 MD5sum: ca3051f33658fa9ca9387079378335ba SHA1: 37c842160db87abc48abbf28b0ebd7019a00f845 SHA256: eb263f01d1c27e10142d52b20d714422f0b59aee457a9341cf4ff3613f617423 SHA512: c92746b2fb61babbe6c9b3c3e8cd5b15b3f0782655369f559131ebdd6fcbbb4d3ba751ff1cec54b348747c53096e4b0b1f52184168fafc967b99302762479d0b Homepage: https://cran.r-project.org/package=modelenv Description: CRAN Package 'modelenv' (Provide Tools to Register Models for Use in 'tidymodels') An developer focused, low dependency package in 'tidymodels' that provides functions to register how models are to be used. 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Package: r-cran-modelimpact Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-scales, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelimpact_1.1.0-1.ca2404.1_all.deb Size: 507988 MD5sum: dd87135c0627a32c1f7590997db6d64a SHA1: cd51485cdd63d6faf8a9f2096aee32363009bad9 SHA256: 37e01edef73f053bc54457f4243eb2e30b2dba988c4e8f639d2f7a843fdf0956 SHA512: 66cbfb98594dce83c4a7a8a784f47f826d280c84c0310a0105fc97420ccdd6587f27e64b99e76c92b810c0f439ede36a7219ca8fccd206f0d7089be9bcdbca99 Homepage: https://cran.r-project.org/package=modelimpact Description: CRAN Package 'modelimpact' (Functions to Assess the Business Impact of Churn PredictionModels) Calculate and visualise the financial impact of using a classification model, such as a churn model, to target customers. 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Package: r-cran-modelimportance Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3588 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-hubutils, r-cran-dplyr, r-cran-hubevals, r-cran-hubensembles, r-cran-purrr, r-cran-furrr, r-cran-future, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-kableextra, r-cran-ggplot2, r-cran-scoringutils, r-cran-testthat, r-cran-progressr Filename: pool/dists/noble/main/r-cran-modelimportance_0.1.0-1.ca2404.1_all.deb Size: 2075518 MD5sum: 21f91b4fe3413814573bd890cc4f07e5 SHA1: 9bdc2fd6433690d7c3ebf94971270fe4ae51e12e SHA256: 1019ba95f4333d5a8ec2ec9baf55c6b27e1d36ccf01ef08db73c46bc16d5c17b SHA512: 7f2d70596598164f64d27618f30c91ab929f6f0d24a021b4819172a32e8333570b196f32e169f9e8af119ceca870544edecd0be69e79825cf664c2b969aede0e Homepage: https://cran.r-project.org/package=modelimportance Description: CRAN Package 'modelimportance' (Measuring Contributions of Component Models to Ensemble ForecastAccuracy) Provides metrics for quantifying the contribution of individual component models to the predictive accuracy of ensemble forecasts. The package implements the Leave-One-Model-Out (LOMO) and Leave-All-Subset-of-One-Model-Out (LASOMO) model importance metrics, enabling users to assess the relative importance of component models and better understand the performance of ensemble forecasting systems. Methods are described in Kim et al. (2026) . Package: r-cran-modelmap Architecture: all Version: 3.4.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-raster, r-cran-mgcv, r-cran-corrplot, r-cran-fields, r-cran-handtill2001, r-cran-presenceabsence Suggests: r-cran-party, r-cran-quantregforest, r-cran-sf Filename: pool/dists/noble/main/r-cran-modelmap_3.4.0.8-1.ca2404.1_all.deb Size: 1601468 MD5sum: f4a956487859a32239a9ea63386947f1 SHA1: 3a15aa712e09b8d9f37275cd7c720717ce0d55f5 SHA256: ba2a549a4f7d652d372a08eaf955413ec94e820569cc0cfa7da1f25e1376561a SHA512: 781f41c3fc8f19a0aae59e9bf0144ba0206ccc89a4e6abbcefc2ab02e71d14f9af922a0c6b349139f2a412b1aa7a97f316e6d4851c7bda137cb442fc328decb3 Homepage: https://cran.r-project.org/package=ModelMap Description: CRAN Package 'ModelMap' (Modeling and Map Production using Random Forest and RelatedStochastic Models) Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data. Package: r-cran-modelmatrixmodel Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-modelmatrixmodel_0.1.0-1.ca2404.1_all.deb Size: 31554 MD5sum: 5f3adc370add4fcfc81e30f693ea9176 SHA1: 72b6b6ff26b39400951b5f016bf89e536cd3b844 SHA256: 5fad74928702bea084c6d1d5a81597dcc452822b6e6386fedc4cec722d944819 SHA512: 431277896e7a802a0f83b085796e8c84781d951516a77619e73ed43622a26eb6bcadb0f175066b966680971f33a88aaf0bcc5de2bc540fea84e25e9f23855d84 Homepage: https://cran.r-project.org/package=ModelMatrixModel Description: CRAN Package 'ModelMatrixModel' (Create Model Matrix and Save the Transforming Parameters) The model.matrix() function in R is convenient for transforming training dataset for modeling. But it does not save any parameter used in transformation, so it is hard to apply the same transformation to test dataset or new dataset. This package is created to solve the problem. Package: r-cran-modelobj Architecture: all Version: 4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modelobj_4.3-1.ca2404.1_all.deb Size: 523256 MD5sum: e8524b8f5032b2b6642c33425b2e785e SHA1: c0ca59011a8a78213785ad2716f5e1fff27eba37 SHA256: 05e4726dda4d819db0334a0d0cef7ceee6c6ff02456d5446dd46cd57b5a50cb3 SHA512: c55452a07330f51e85bad7e0d8f7e211ee8b16a4235c0498fc3e46b14fd24bb90f33e14c4c37bfea423bc4f5c9b46cf360d65251ac25afbafc64d7f2a38388ba Homepage: https://cran.r-project.org/package=modelObj Description: CRAN Package 'modelObj' (A Model Object Framework for Regression Analysis) A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package. Package: r-cran-modelr Architecture: all Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-covr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelr_0.1.11-1.ca2404.1_all.deb Size: 200902 MD5sum: ecef3f297e48fe13a18f4c7d5fe81b6e SHA1: a39d6099540d59e47bdd1fed7e14de5012cce94c SHA256: 3656cf2737eba269c67f5f836a351771c8f8cc6b5c0705631940e71aca0e9d32 SHA512: 93ca202bf66e5a3748d2bfee58e8e381182b0f1de11bc0cf4418aadfc5bb26f5e3478350b55c83c2a45e952c66cddfa8680cb1f57ff7b288eb02612114ec509f Homepage: https://cran.r-project.org/package=modelr Description: CRAN Package 'modelr' (Modelling Functions that Work with the Pipe) Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation. Package: r-cran-modelscompete4 Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-ggplot2, r-cran-nonnest2, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelscompete4_0.2.6-1.ca2404.1_all.deb Size: 91464 MD5sum: 430af4f48d1122c4effa2a44b4b12d84 SHA1: 4efbb59dace42e7b5b20b71090488e23d9b5ad9f SHA256: 1ee769f92fda8e63ee99243d53b47fad09f82acf5bcf709b0353373aff63b868 SHA512: 25caced6441a251733ddc575cd4e073bb04544d6b5f150e8a1de8f246d929358e146a9b8f5d45415f6a56188b93c271aa272795dea2b3f7067b2303400db94f3 Homepage: https://cran.r-project.org/package=modelscompete4 Description: CRAN Package 'modelscompete4' (Compare Nested and Non-Nested Structural Equation Models) A comprehensive package for comparing multiple Structural Equation Models (SEM). Supports both nested and non-nested model comparisons, chi-square difference tests, and extraction of multiple fit indices including AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation), and SRMR (Standardized Root Mean Square Residual). Built on top of the 'lavaan' package for seamless SEM model comparison workflows. The Vuong test (Vuong, 1989) for non-nested models is used as the statistical test. Package: r-cran-modelskill Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2013 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-modelskill_0.1.1-1.ca2404.1_all.deb Size: 1485172 MD5sum: 82d5d9b0a2f4e9a2eafb09bc0657da15 SHA1: c853aa5c859a973b57aa61aead602342e5718ffa SHA256: a54aaf8a2f14e8adca8f45427025ce7720e2a9abde1847287d8542d43215038d SHA512: 05f0057c61c28fd1453597dcf7679264852a7dc7c8840f2f1169a88212558ad1c65bbf3fc619e159ceeb0f0050133445d9f6ef5bc8bc4bf4b1b82e5ee20ee657 Homepage: https://cran.r-project.org/package=modelskill Description: CRAN Package 'modelskill' (Assessing and Visualising the Performance of Prediction Models) Provides tools for evaluating continuous predictions and their associated predictive uncertainty from statistical, machine-learning, geostatistical, and process-based models. It implements complementary measures of prediction error, association, agreement, efficiency, uncertainty calibration, and predictive-distribution performance, together with Taylor, solar, target, coverage, probability integral transform, and quantile-coverage diagnostics. Methods include the integrated evaluation approach of Wadoux, Walvoort and Brus (2022) and the uncertainty-validation framework of Schmidinger and Heuvelink (2023) . Package: r-cran-modelsse Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-delaporte Filename: pool/dists/noble/main/r-cran-modelsse_0.1-3-1.ca2404.1_all.deb Size: 133008 MD5sum: 20f5d355df4a53947e7df3083f59068f SHA1: e8702c2b3bb5eed56df8f9893d95c1d32642b218 SHA256: 9d6a43c80674b7f74da7abf5475f4ea534388f2653c185f26fee738d263ca843 SHA512: 6773bcce254ae20e8e253fd7f77ea158e2044ecdd928138d9eac0c3e6e799f19cdf7a1c68a238263ff679ee38dc25b74ae2a107ad8fe7f2465bf2b6463eb202c Homepage: https://cran.r-project.org/package=modelSSE Description: CRAN Package 'modelSSE' (Modelling Infectious Disease Superspreading from Contact TracingData) Comprehensive analytical tools are provided to characterize infectious disease superspreading from contact tracing surveillance data. The underlying theoretical frameworks of this toolkit include branching process with transmission heterogeneity (Lloyd-Smith et al. (2005) ), case cluster size distribution (Nishiura et al. (2012) , Blumberg et al. (2014) , and Kucharski and Althaus (2015) ), and decomposition of reproduction number (Zhao et al. (2022) ). Package: r-cran-modelstudio Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dalex, r-cran-ingredients, r-cran-ibreakdown, r-cran-r2d3, r-cran-jsonlite, r-cran-progress, r-cran-digest Suggests: r-cran-parallelmap, r-cran-ranger, r-cran-xgboost, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-modelstudio_3.1.2-1.ca2404.1_all.deb Size: 333526 MD5sum: 5c24d2563191c4fc69a04ec01600fa98 SHA1: 6f4700f55efb993270901ce64f6f1e615d57deb2 SHA256: 06935a3399f59874dcae758df681cc8e450efa73097122bbafba78ed637f4410 SHA512: f16b03f404e37d83e09d4afc8bf32cd1a1c409d232aa7c0d7b747c5e12b81e6c15bd9ae8593822ed897b8a23a8cee3483d7f7d3302f7b878a7fd42c32ae91c48 Homepage: https://cran.r-project.org/package=modelStudio Description: CRAN Package 'modelStudio' (Interactive Studio for Explanatory Model Analysis) Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) . Package: r-cran-modelsummary Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-generics, r-cran-glue, r-cran-insight, r-cran-parameters, r-cran-performance, r-cran-tables, r-cran-tinytable Suggests: r-cran-aer, r-cran-altdoc, r-cran-amelia, r-cran-betareg, r-cran-bookdown, r-cran-brms, r-cran-broom, r-cran-broom.mixed, r-cran-car, r-cran-clubsandwich, r-cran-correlation, r-cran-covr, r-cran-did, r-cran-digest, r-cran-dt, r-cran-estimatr, r-cran-fixest, r-cran-flextable, r-cran-future, r-cran-future.apply, r-cran-gamlss, r-cran-ggdist, r-cran-ggplot2, r-cran-glmmtmb, r-cran-gh, r-cran-gt, r-cran-gtextras, r-cran-haven, r-cran-huxtable, r-cran-labelled, r-cran-irdisplay, r-cran-ivreg, r-cran-kableextra, r-cran-knitr, r-cran-lavaan, r-cran-lfe, r-cran-lme4, r-cran-lmtest, r-cran-magick, r-cran-magrittr, r-cran-marginaleffects, r-cran-mass, r-cran-mgcv, r-cran-mice, r-cran-nlme, r-cran-nnet, r-cran-officer, r-cran-openxlsx, r-cran-pandoc, r-cran-pscl, r-cran-psych, r-cran-randomizr, r-cran-rdatasets, r-cran-remotes, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rsvg, r-cran-sandwich, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-tibble, r-cran-tictoc, r-cran-tidyselect, r-cran-tidyverse, r-cran-tinysnapshot, r-cran-tinytest, r-cran-tinytex, r-cran-webshot2, r-cran-wesanderson Filename: pool/dists/noble/main/r-cran-modelsummary_2.6.0-1.ca2404.1_all.deb Size: 3207782 MD5sum: 70c5068be5bc5c0b6ccabd4b10948e5c SHA1: 0495412855cc23a4f0091f68a64ce43372efdf6e SHA256: b8742257bde099f26e3cf3800246574a85b560a0f64d7091b1353bed406ae655 SHA512: f58f9dc0b66e1ba58af2770b089a948c6a141209d63123593559a17bc9e1ddceab9d2dc9bf18d07c2e41ed082a0a08d361c5e9f77b03500ab07e72af88717ae0 Homepage: https://cran.r-project.org/package=modelsummary Description: CRAN Package 'modelsummary' (Summary Tables and Plots for Statistical Models and Data:Beautiful, Customizable, and Publication-Ready) Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) . Package: r-cran-modeltests Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-testthat, r-cran-tibble Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-modeltests_0.1.8-1.ca2404.1_all.deb Size: 72622 MD5sum: e1af153f33ecbd3ad8f8353e547fe77b SHA1: b533424d39689eee57b7af449484ffd86c164a3f SHA256: 73370002e3e7084cdf95f6dc5cdb914a25bb350a7dd1468fa759d5dcb2955ef8 SHA512: 29b9c70b30dd994a5135e0f7f4b8e6ddb64716420dd7e48e2468106ca08a81b8cb490282fc24d47bc3a16eef4f359fbf33d04af078ee934df27c1d5e4183fe1f Homepage: https://cran.r-project.org/package=modeltests Description: CRAN Package 'modeltests' (Testing Infrastructure for Broom Model Generics) Provides a number of testthat tests that can be used to verify that tidy(), glance() and augment() methods meet consistent specifications. This allows methods for the same generic to be spread across multiple packages, since all of those packages can make the same guarantees to users about returned objects. Package: r-cran-modeltime.ensemble Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-modeltime.resample, r-cran-tune, r-cran-rsample, r-cran-yardstick, r-cran-workflows, r-cran-recipes, r-cran-timetk, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-rlang, r-cran-cli, r-cran-generics, r-cran-magrittr, r-cran-tictoc, r-cran-doparallel, r-cran-foreach, r-cran-glmnet Suggests: r-cran-gt, r-cran-dials, r-cran-earth, r-cran-testthat, r-cran-tidymodels, r-cran-xgboost, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeltime.ensemble_1.1.0-1.ca2404.1_all.deb Size: 1458234 MD5sum: 48f9e8bef0bcfea5f8c94ad84ced4f8c SHA1: 2ad12897e0223b543710a9a02b87f86cab1ecfcf SHA256: e00889f5beeab1c789b4f53888adbf535d5e87ccdf998703e3c18f7a0a6afe1a SHA512: fa0deb4a8bccb40eb37c3c9a88f503dbea3c650965f635730601c95592afdefb1aabc6c1e8f69149ca4c19aa526b460c6ea922f5637511120f7ec64afa517e3f Homepage: https://cran.r-project.org/package=modeltime.ensemble Description: CRAN Package 'modeltime.ensemble' (Ensemble Algorithms for Time Series Forecasting with Modeltime) A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability. Package: r-cran-modeltime.resample Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-tune, r-cran-rsample, r-cran-workflows, r-cran-recipes, r-cran-yardstick, r-cran-timetk, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-ggplot2, r-cran-plotly, r-cran-cli, r-cran-magrittr, r-cran-rlang, r-cran-progressr, r-cran-tictoc, r-cran-hardhat, r-cran-withr Suggests: r-cran-testthat, r-cran-tidymodels, r-cran-tidyquant, r-cran-glmnet, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeltime.resample_0.3.0-1.ca2404.1_all.deb Size: 1802304 MD5sum: 6875d355552bec4f8fe6b4a935318ccc SHA1: 51827ff00e234aa35600b33c5b8574059aeda2c8 SHA256: 4f7f0ccad22ba9da9510a895ca901d953eab0847befdd6b6842a05236afd5ecf SHA512: 18ff1ef9121d8a73be8a04b7cdabba2434d50dc340d07b1f1ef47ae4cc55936729c2bb5802b60a92b127b122e4408f01c0373cde07b0e7811dd5bd1a41f59f7d Homepage: https://cran.r-project.org/package=modeltime.resample Description: CRAN Package 'modeltime.resample' (Resampling Tools for Time Series Forecasting) A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis. Package: r-cran-modeltime Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stanheaders, r-cran-timetk, r-cran-parsnip, r-cran-dials, r-cran-yardstick, r-cran-workflows, r-cran-hardhat, r-cran-rlang, r-cran-glue, r-cran-plotly, r-cran-reactable, r-cran-gt, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-forcats, r-cran-scales, r-cran-janitor, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-magrittr, r-cran-forecast, r-cran-xgboost, r-cran-prophet, r-cran-cli, r-cran-tidymodels Suggests: r-cran-rstan, r-cran-slider, r-cran-sparklyr, r-cran-dorng, r-cran-workflowsets, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-lubridate, r-cran-testthat, r-cran-kernlab, r-cran-glmnet, r-cran-thief, r-cran-smooth, r-cran-greybox, r-cran-earth, r-cran-randomforest, r-cran-trelliscopejs, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-qpdf, r-cran-tsrepr, r-cran-future, r-cran-dofuture Filename: pool/dists/noble/main/r-cran-modeltime_1.3.5-1.ca2404.1_all.deb Size: 3032070 MD5sum: f2d80560403c9786e8b25266ec4f4031 SHA1: 09e8cfecdb34a2c15468bad39f0c2c372c06a4e1 SHA256: e86eb59d62b328f50ac040335b22518210bf422c6253be66f4d9426dbf66d018 SHA512: feed9f3373f4fc853527699dfb7bd977e4730486b0e22afcf137952d6320b2c93427cbc60a89bc3b382cfc78450789af29714de0df8eb9d21d177a12d95d0b0c Homepage: https://cran.r-project.org/package=modeltime Description: CRAN Package 'modeltime' (The Tidymodels Extension for Time Series Modeling) The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (). Refer to "Prophet: forecasting at scale" (.). Package: r-cran-modeltools Architecture: all Version: 0.2-25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modeltools_0.2-25-1.ca2404.1_all.deb Size: 222878 MD5sum: 20c1761ee1ba84cf46712a9d13387611 SHA1: f1bd88653c8bf8ef53dcd7704ab296c78c82f204 SHA256: e708ad2a84a73b9503019ef8cd2b42c6825ab0b6c9491613637b8d498521a17c SHA512: d4f4e177edbe537d1cf7277760b0ab1e6749418ef885029eb2974b1020a945d718fd0bd78a1dc32f9b7aff51be12a8cb1b0635dbea0ee0e274ac937b9a25b57f Homepage: https://cran.r-project.org/package=modeltools Description: CRAN Package 'modeltools' (Tools and Classes for Statistical Models) A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome! Package: r-cran-modeltuning Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future.apply, r-cran-progressr, r-cran-r6, r-cran-rlang Suggests: r-cran-matrix, r-cran-e1071, r-cran-future, r-cran-parallelly, r-cran-paws.compute, r-cran-rsample, r-cran-rpart, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeltuning_0.1.4-1.ca2404.1_all.deb Size: 1635234 MD5sum: 3d9ecd585b115a6fa4545f146bd61362 SHA1: 89cd413bf2d084ed95a4c72c833e20be817a19ac SHA256: e156fbf9d9a8b0def6c374cf02f70e343233f0644a8ce2c92b677f5362769afa SHA512: 8952f23e5b5ab12ef62a7d2ede25228cf99910475c74a49a1d4c94bcbf8029f366c8fe00c10b3b79854122d6e580f071fb3a567a3eedf7924c4591fa8d179651 Homepage: https://cran.r-project.org/package=modeltuning Description: CRAN Package 'modeltuning' (Model Selection and Tuning Utilities) Provides a lightweight framework for model selection and hyperparameter tuning in R. 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Seven canonical clinical process structures are supplied with structurally valid generator matrices, so that transition matrices and starting values need not be constructed by hand. Panel data can be simulated from exact trajectories under regular or irregular observation schedules, with optional exactly observed absorption times and optional Weibull holding times for assessing the Markov assumption. A single fitting call validates the input against the assumed structure and returns the estimated generator with confidence intervals, mean sojourn times, transition probability matrices and observed transition counts, together with the optimiser's convergence code. A Monte Carlo driver reports Monte Carlo standard errors alongside bias, root mean squared error and interval coverage. Likelihood evaluation is delegated to 'msm' (Jackson, 2011, ); the panel-data likelihood is that of Kalbfleisch and Lawless (1985) . 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Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) . Package: r-cran-modopt.matlab Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roi, r-cran-roi.plugin.glpk, r-cran-roi.plugin.quadprog Filename: pool/dists/noble/main/r-cran-modopt.matlab_1.0-2-1.ca2404.1_all.deb Size: 24690 MD5sum: eb2cfc0d36b056755951ea4938a0f8e0 SHA1: 95be98b5f004e2ee75ef06fbefe19a79c49827e5 SHA256: 6a928f29c5283c3c9fbe6bf742895ca698bbeb0916fedaa7f299cffe6d61f64b SHA512: 08364626debab7994b415a7376745dc6f439745703564ef223ce4825d41d70e27b5eed802e093e96a7aa07da744beb5f72e641f4a4a2d3a40622cc7e18732fa7 Homepage: https://cran.r-project.org/package=modopt.matlab Description: CRAN Package 'modopt.matlab' ('MatLab'-Style Modeling of Optimization Problems) 'MatLab'-Style Modeling of Optimization Problems with 'R'. 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In the location case, one can thus obtain halfspace depth contours in two to six dimensions. Hallin, M., Paindaveine, D. and Šiman, M. (2010) Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669 For more references about the method, see Help pages. Package: r-cran-modstatr Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4146 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-jmuoutlier, r-cran-ellipse, r-cran-hypergeo, r-cran-gsl Suggests: r-cran-biostatr, r-cran-ggplot2, r-cran-desctools, r-cran-factominer, r-cran-ggally, r-cran-islr, r-cran-kendall, r-cran-mass, r-cran-mbess, r-cran-mvn, r-cran-pcamixdata, r-cran-rcolorbrewer, r-cran-suppdists, r-cran-ade4, r-cran-adegraphics, r-cran-broom, r-cran-car, r-cran-coin, r-cran-combinat, r-cran-corpcor, r-cran-corrplot, r-cran-devtools, r-cran-dplyr, r-cran-factoextra, r-cran-finalfit, r-cran-ggcorrplot, r-cran-ggdendro, r-cran-ggiraphextra, r-cran-ggpubr, r-cran-glmnet, r-cran-hnp, r-cran-lattice, r-cran-leaps, r-cran-mice, r-cran-mvtnorm, r-cran-naniar, r-cran-ppcor, r-cran-pspearman, r-cran-pwr, r-cran-questionr, r-cran-rgl, r-cran-rms, r-cran-vcd, r-cran-vegan, r-cran-yarrr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-modstatr_1.4.2-1.ca2404.1_all.deb Size: 3351388 MD5sum: b38c6d306dfa4f70a3732889edd4b447 SHA1: 34992239dd565e070c8a98074f8e4d6aa513e544 SHA256: 583439574896eb312d790e0f4c3f515f47d7812cfd1b3459459ea87e91eeb1ce SHA512: 380f6e48052432cfc9c6ae6414833a4754d0ca78e7fdc426a8eab77a9d9919e9b57a0234d127f31a378b59bd16f0ba9ca8a1559bbaa8b45282fe801b4d506760 Homepage: https://cran.r-project.org/package=ModStatR Description: CRAN Package 'ModStatR' (Statistical Modelling in Action with R) Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses. 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It allows to quickly fetch price candles for a particular security, obtain its profile information and so on. Package: r-cran-mofat Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-slhd Filename: pool/dists/noble/main/r-cran-mofat_1.0-1.ca2404.1_all.deb Size: 21962 MD5sum: 45b81112a8a17fd7976570c0b74fd6ab SHA1: d63ed02b407049f5c7e613989ae46db1ca478d4b SHA256: b5b3aa33dab038f940a7dd4e626d24cc1501bf1748bb8b89ccd006f90be3fab7 SHA512: 6128563a77c5d0856bef01edf3257c913c82c88bf163d26d5ae003fce9e202895b35d4f6e3dfd1d81662c96ef55f139fcd2cee92ee427a05d9969af571435207 Homepage: https://cran.r-project.org/package=MOFAT Description: CRAN Package 'MOFAT' (Maximum One-Factor-at-a-Time Designs) Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. 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The focus is on the emergence of argument-marking systems (Dowty (1991) , Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) , Lestrade (2010) ), person indexing (Ariel 1999, Dahl (2000) , Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) , Jackendoff 2002, Arbib (2015) ) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) , Givon (1995) , Croft (2000), Saffran (2001) , Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) , Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). 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The concepts are modelled directly after the Monad typeclass in Haskell, but adapted for idiomatic use in R. Package: r-cran-monan Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-monan_1.1.0-1.ca2404.1_all.deb Size: 2398796 MD5sum: 08f6958f2a6b558095b5b318205824b8 SHA1: 758fa6812f44240e133d0f55f43f7d54c5acf6ae SHA256: 63c64ea042066318027b941b23ff25d368cab1c3b7b50561256a652f924f5d23 SHA512: e44546b000885f40abe34e0d9bbac7d7a586965d027c17e983560e9179272d88273f3f9934b1e4d1cdddf1db62e38973c941e9726611d6147673872575cf8bd0 Homepage: https://cran.r-project.org/package=MoNAn Description: CRAN Package 'MoNAn' (Mobility Network Analysis) Implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) . The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks. Package: r-cran-monashtipr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvest, r-cran-httr, r-cran-magrittr, r-cran-rlang, r-cran-xml2, r-cran-glue, r-cran-lifecycle, r-cran-purrr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-monashtipr_0.1.0-1.ca2404.1_all.deb Size: 48696 MD5sum: 4a7221735a09b67b8a811ebd2ff886b9 SHA1: c4e9ff6806a1104aa77a5045c8f47504d9af9eb5 SHA256: 1febd9e4dbc3902ccbcf2a776075f9b81a18a880d49259ccfe9b4578677a5c97 SHA512: 3eae8a67095ac85ae54b46cc79ca6202a44e3d859c63267fd47275daca3886489cd984415159498f2239712ed9c20dee68f10b82470c3de014e6560d4978b04e Homepage: https://cran.r-project.org/package=monashtipr Description: CRAN Package 'monashtipr' (An R API Wrapper for the Monash University Probabilistic FootyTipping Competition) An API wrapper for the 'Monash University Probabilistic Footy Tipping Competition' . Allows users to submit tips directly to the competition from R. Package: r-cran-mondate Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-zoo Filename: pool/dists/noble/main/r-cran-mondate_1.0-1.ca2404.1_all.deb Size: 308360 MD5sum: 2542f8de42ca2f6d4f53b2fdc7011085 SHA1: 52a22fafd4452c85a0e369435a35cd1cff81baba SHA256: b182e9c1f2c7cdb9707b449447bcf2d2c57e7c7e49c4589ef69a09947eb5d4e9 SHA512: 58b73fa766674d1cc1056606345f0670748aeca061be9fc6b4fdcb81bfc8db758983ecc0f533355a13d52d33b10c7bf4211eb136771c13932f4cca372ec2fa26 Homepage: https://cran.r-project.org/package=mondate Description: CRAN Package 'mondate' (Keep Track of Dates in Terms of Months) Keep track of dates in terms of fractional calendar months per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", The Journal of Performance Measurement, 2008. Model dates as of close of business. Perform date arithmetic in units of "months" and "years". Allow "infinite" dates to model "ultimate" time. Package: r-cran-mondrian Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer Suggests: r-cran-dt, r-cran-shiny, r-cran-shinybs Filename: pool/dists/noble/main/r-cran-mondrian_1.1.2-1.ca2404.1_all.deb Size: 90388 MD5sum: 8836611560a79e2e028dbb6c0396febc SHA1: e5aeb9e9ac457c8f85a09f91b313c4169492411f SHA256: fc0c69342afe06f4f5fef2f1eb03c5d5f259edd41d24ab73a356f3fe1ea2b1a2 SHA512: e6145023d7be6b621d28e78eec22e95833e954a62fad95691cf8c652f6a6f330afbf2c522ad5def38d2439023889b623612f7273e46987df119c0699aac58552 Homepage: https://cran.r-project.org/package=Mondrian Description: CRAN Package 'Mondrian' (A Simple Graphical Representation of the Relative Occurrence andCo-Occurrence of Events) The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). We named the package after the famous painter as the graphical output recalls Mondrian’s paintings. Package: r-cran-mongolmaps Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2207 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringi, r-cran-tibble Suggests: r-cran-covr, r-cran-geofacet, r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-leaflet, r-cran-mongolstats, r-cran-rmarkdown, r-cran-s2, r-cran-scales, r-cran-terra, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-mongolmaps_0.1.0-1.ca2404.1_all.deb Size: 1946748 MD5sum: 569ad673aa9b319e949379885184f236 SHA1: 3c5db2301083706834bb192d4acfbe79db99c7e8 SHA256: 527bde8c665c5270613cd95d7c0345ec404f0467e51d8080c2451787011f8832 SHA512: b616a597e78fe2601c3961769f8c50edcd469673889d8b92ba861eedbe366f1941b74a6a4d7c9b9c7be35b5bd4077144df9df8a80a785f64d4ed78dcb68f2ca3 Homepage: https://cran.r-project.org/package=mongolmaps Description: CRAN Package 'mongolmaps' (Maps and Administrative Boundaries of Mongolia and Ulaanbaatar) Ready-to-use 'sf' maps of Mongolia at every openly available administrative level: country, economic regions, aimags (provinces), soums and Ulaanbaatar districts, and Ulaanbaatar khoroos (subdistricts), with codes and names for every bag. Provides a crosswalk between English and Cyrillic names, common spelling variants, ISO 3166-2 codes, humanitarian P-codes and National Statistics Office codes; helpers to join statistics to maps; thematic layers (settlements, rivers, lakes, roads, railways, airports, elevation, land cover, population and protected areas); and quick 'ggplot2' and 'leaflet' maps. Boundaries derive from the Common Operational Dataset published by the National Statistics Office of Mongolia and the United Nations Office for the Coordination of Humanitarian Affairs . 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Package: r-cran-mongopipe Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat, r-cran-mongolite, r-cran-nycflights13 Filename: pool/dists/noble/main/r-cran-mongopipe_0.1.2-1.ca2404.1_all.deb Size: 30854 MD5sum: c69465f4c0b6bbd4c72e1ba0a5dfd75e SHA1: 0f2ff0133e62a0c637c23577435dbdf1ebf4b4a8 SHA256: 688be5608a5623a49d369aabe69715d5cd031f660518ae92b9bfa1ffd0ea4918 SHA512: de2881a51111f7f61782a74198ecfcf2ef9b06ce51a3879b81fe76712b410a2e08cbc81b4756903d3a86bcd8706f71e89cc32c04f6fc6c6766fbd87951f632b9 Homepage: https://cran.r-project.org/package=mongopipe Description: CRAN Package 'mongopipe' (Write MongoDB Queries with R) Translate R code into MongoDB aggregation pipelines. Package: r-cran-monitor Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3717 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tuner Suggests: r-cran-fftw, r-cran-rodbc, r-cran-knitr Filename: pool/dists/noble/main/r-cran-monitor_1.2-1.ca2404.1_all.deb Size: 3242886 MD5sum: 620bdd83f7615aa694e7949e38b08325 SHA1: 648e76a8df93ed57d401af3081342a17c98f6ac3 SHA256: 41643b50667200d7adc76039a71857e23d282c3ea49c1c730972212f27201ef8 SHA512: 4b9af6e8a2f193463b743604d058cc83814fd6ce14458e1fc644d2537c5da0bb553214589b057a9aa16c559d5010b13d7021985fbfb5dbcbd6723abcd923b50d Homepage: https://cran.r-project.org/package=monitoR Description: CRAN Package 'monitoR' (Acoustic Template Detection in R) Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database. Package: r-cran-monitos Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-shiny, r-cran-shinydashboard Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-monitos_0.1.6-1.ca2404.1_all.deb Size: 166124 MD5sum: e4e9821818f8551bce7141ead3114f6b SHA1: ea6d33489b30b12a7291657522a7b7236cb7db18 SHA256: 21bb65766bd03e5f94d13d400395d12fae7e798aa4ebd3758e8db6d557473276 SHA512: 058afdd1c754fbf184b92c139b45c6ffada9bf0b9aa70ed9a44337619a4c79a15c95d0eeeef705e7ce76475d8783fd445d2865572ec1dc74f03fe0b15db51a6d Homepage: https://cran.r-project.org/package=monitOS Description: CRAN Package 'monitOS' (Monitoring Overall Survival in Pivotal Trials in IndolentCancers) These guidelines are meant to provide a pragmatic, yet rigorous, help to drug developers and decision makers, since they are shaped by three fundamental ingredients: the clinically determined margin of detriment on OS that is unacceptably high (delta null); the benefit on OS that is plausible given the mechanism of action of the novel intervention (delta alt); and the quantity of information (i.e. survival events) it is feasible to accrue given the clinical and drug development setting. The proposed guidelines facilitate transparent discussions between stakeholders focusing on the risks of erroneous decisions and what might be an acceptable trade-off between power and the false positive error rate. Package: r-cran-monmlp Architecture: all Version: 1.1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx Filename: pool/dists/noble/main/r-cran-monmlp_1.1.5-1-1.ca2404.1_all.deb Size: 58956 MD5sum: 07da4b253d713a658e4172cf966f410a SHA1: 011d0a2ff8ad3e88e35c203f099432b72fb72ba0 SHA256: 9c807bf35a9e4ff6bee118ad02b7a9e061c7db27432cab4392b6c4447f1b4782 SHA512: 40feab18c0c5c9d2d3ff97834e3f23a7d5e3cd079195b96d9565e95b086161231d0d1a6a1cbc659b8675cb562a221c93dd094f562880333ff8124b8a5ed13631 Homepage: https://cran.r-project.org/package=monmlp Description: CRAN Package 'monmlp' (Multi-Layer Perceptron Neural Network with Optional MonotonicityConstraints) Train and make predictions from a multi-layer perceptron neural network with optional partial monotonicity constraints. Package: r-cran-monobin Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-monobin_0.2.4-1.ca2404.1_all.deb Size: 122394 MD5sum: 8badf26df6dd2ecce17b3ff2909f6f57 SHA1: 5723d60dd0ec88fe418d35990395fe195da78e15 SHA256: 020b3dc98462121d1286fa666e3dbe2215c211358492507fdae61c2bf9fbfa38 SHA512: 4ada1741a688291aa1fb45093103d5ef3e885718acabaca288e67e70e68b26c6c28a45756c7fa4ab9ee51ab05a3fef8f3dc26d2aae2e3c56b060ef0c2471d963 Homepage: https://cran.r-project.org/package=monobin Description: CRAN Package 'monobin' (Monotonic Binning for Credit Rating Models) Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions' argument. Missing values and other possible special values are treated separately from so-called complete cases. 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It provides shiny-based user interface (UI) that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning of numeric risk factors when building credit rating models. The additional functions implemented in 'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, while special case and outlier imputation functions are written to be used with shiny UI. Package: r-cran-monochromer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-monochromer_0.2.0-1.ca2404.1_all.deb Size: 81516 MD5sum: dda8c29160aeae9f685b17fc4c7a6fbd SHA1: 06011236daa699210724c2be82d92904f0dc4b41 SHA256: 5254cfb1ae4b2dcd14e29bfb2d082eafaf24fab769f50cebe7885dd46357ec0b SHA512: 92cf64b6f2f72997d1347aa545c732620f8b94aa9619c353479afd4d8935694011b051327659dbde636becc2b2bbbe92a35c94310b23dcd750a399dac05757b7 Homepage: https://cran.r-project.org/package=monochromeR Description: CRAN Package 'monochromeR' (Easily Create, View and Use Monochrome Colour Palettes) Generate a monochrome palette from a starting colour for a specified number of colours. The package can also be used to display colour palettes in the plot window, with or without hex codes and colour labels. 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A lot of extensions are included in the package, including applying Monothetic clustering on data sets with circular variables, visualizations with the results, and permutation and cross-validation based tests to support the decision on the number of clusters. Package: r-cran-monographar Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-png, r-cran-raster, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rpart, r-cran-sf, r-cran-shiny, r-cran-shinydashboard, r-cran-shinythemes, r-cran-shinywidgets, r-cran-sp, r-cran-terra Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-monographar_1.3.1-1.ca2404.1_all.deb Size: 680044 MD5sum: 8a119e60525adade03d2c86051e06732 SHA1: 3c9f1ee00028653fdb710720bd57778214a45915 SHA256: 03b34151359985535132cb262a5b62d3e284a16be1092d12efa9e9289509ee7f SHA512: 735c7650f4660163673aebaf74409dff8c328e8ac959544a0b9b3c879d7719edf6eabea9365fac47ebe8d19941c170146557759e9071597762c9fda60641fd25 Homepage: https://cran.r-project.org/package=monographaR Description: CRAN Package 'monographaR' (Taxonomic Monographs Tools) Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available. Package: r-cran-monoinc Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compare, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-sitar Filename: pool/dists/noble/main/r-cran-monoinc_1.1-1.ca2404.1_all.deb Size: 171034 MD5sum: 61e998f1974596566ed5d4272d6ce6a2 SHA1: 87e6b7a6712c9c8a19f141eb6a86ee3cbba0d6b3 SHA256: 6b166a9b4813bf6f37fbe3268ec6497f9556c97b0fcfd3df61132197e4263a2f SHA512: 79b6a5919fe14de689e5fe7f093942acdb4cfeeda1b37573a33a4b609569233f65b49766f4f3d87530c43f98f2094b992a9bd6ba8d32bde34ac03e339014eb19 Homepage: https://cran.r-project.org/package=MonoInc Description: CRAN Package 'MonoInc' (Monotonic Increasing) Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range. Package: r-cran-monophy Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-phangorn, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-testthat, r-cran-paleotree, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-monophy_1.3.2-1.ca2404.1_all.deb Size: 326082 MD5sum: 9165984c02f1a2544e97d59863a1f16f SHA1: f5aecf9ccff8165c5e79d6ba84f5553e038c218a SHA256: 574b2af03032e88f365708ed1526066c0530194d06e9ecc0490d276e39d65cbc SHA512: 5b5f5b6a8cf7144255059a1763e69a6087551526869059f892c221a8e7d72cd22eadef7fadcbf40fda59bba70ce8758b6463dd82e7eb47eaa1eb1815bb10fde9 Homepage: https://cran.r-project.org/package=MonoPhy Description: CRAN Package 'MonoPhy' (Explore Monophyly of Taxonomic Groups in a Phylogeny) Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) . Package: r-cran-monotonehazardratio Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-cran-kernsmooth, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-monotonehazardratio_0.2.0-1.ca2404.1_all.deb Size: 36364 MD5sum: 8e02d17e8353df3a7d1283c19b65a4c9 SHA1: e03f242915c187b2c6255cfc293d79cfa24ba29f SHA256: dfb2686ade2503339b11b37721a54a5165506216ee2a833027d2082113942c82 SHA512: 0f7be77f0db7561661c4e735433b65d61e4f54c527876975aa56bd51bd79ab2fab1610760d85c0a082b979e8d45345980572dc27ca1965a4c90b75e6abc48305 Homepage: https://cran.r-project.org/package=MonotoneHazardRatio Description: CRAN Package 'MonotoneHazardRatio' (Nonparametric Estimation and Inference of a Monotone HazardRatio Function) Nonparametric estimation and inference of a non-decreasing monotone hazard ratio from a right censored survival dataset. The estimator is based on a generalized Grenander typed estimator, and the inference procedure relies on direct plugin estimation of a first order derivative. More details please refer to the paper "Nonparametric inference under a monotone hazard ratio order" by Y. Wu and T. Westling (2023) . Package: r-cran-monotonicity Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmtest, r-cran-mass, r-cran-sandwich Suggests: r-cran-testthat, r-cran-xts Filename: pool/dists/noble/main/r-cran-monotonicity_1.3.1-1.ca2404.1_all.deb Size: 106514 MD5sum: f4ed722e268504563700533e05390fda SHA1: 98771422f1b7c09bc6edf0b9ba04dc73a0acf50a SHA256: e0c462a24248962136645bd5ab4e7b6fa8c68573619679e4432accfe44dbe3e5 SHA512: aa9024ed9538294cb74d49690bd82f5f2ecc5a31ba205428e43d3e9c5072dc698ac43776b52821c3dd8fc67dfeac6a7f7b47a8b3b888214671b2fd207430e074 Homepage: https://cran.r-project.org/package=monotonicity Description: CRAN Package 'monotonicity' (Test for Monotonicity in Expected Asset Returns, Sorted byPortfolios) Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations. Package: r-cran-monte.carlo.se Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-monte.carlo.se_0.1.1-1.ca2404.1_all.deb Size: 173322 MD5sum: e06b6e04855e88327d4f2e88e49b11aa SHA1: e7330e0059f7bb34fc15e59edfcaf8406d2d9a84 SHA256: 70263a046e47066607eb713bb3e9a93ae3e786f773ded8007f3072b47cf6e6f2 SHA512: 099a006a20cab1211dfb0197e8c0d0d5bc71373d40c54a6d300e1a20eddb33f58a9f867d5dfa4bb3605936d9942395058c08730be74ec024b94d876be13a2c52 Homepage: https://cran.r-project.org/package=Monte.Carlo.se Description: CRAN Package 'Monte.Carlo.se' (Monte Carlo Standard Errors) Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) . Package: r-cran-montecarlo Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snow, r-cran-abind, r-cran-codetools, r-cran-rlecuyer, r-cran-snowfall, r-cran-reshape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-montecarlo_1.0.6-1.ca2404.1_all.deb Size: 98122 MD5sum: 4cd5f7dfdd0b832bdad91579235a49fb SHA1: b45e6cfc4bfecbacc63732596709473c6f6e20e5 SHA256: a26d343f776ea1d8e03e2eeedc5c9324dc0da6ac071c1e3d46cc4b9f9115da30 SHA512: 1d619d7efc580bb1cf58c8bc2545e4dcfa655c09590a4753d15e3497a3cd3514cd55891bdeacb67315d12b6ddd31f6b0c226aca7c589d7af2c6a5f219ed9b817 Homepage: https://cran.r-project.org/package=MonteCarlo Description: CRAN Package 'MonteCarlo' (Automatic Parallelized Monte Carlo Simulations) Simplifies Monte Carlo simulation studies by automatically setting up loops to run over parameter grids and parallelising the Monte Carlo repetitions. It also generates LaTeX tables. Package: r-cran-montecarlosem Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-montecarlosem_2.0.0-1.ca2404.1_all.deb Size: 72186 MD5sum: cd5050573d4eac9f35b1bdbfa54c4c13 SHA1: 815d89b1767dc2a4dbe7f51c2ca61da6c20e6d81 SHA256: 7b6d75f24d0b1d5a5320c7b69850e111417bf992b5523bb4d0d78a252bfe1178 SHA512: 6aa6a5c8a1bc370ef77cb2d53b3ca72e091315fab3a82fab948ae6d27bdeb8121a958741e3d3d0ceac53f00486c49ac7419eca02de39e7b2d4bda2cc909cfc5f Homepage: https://cran.r-project.org/package=MonteCarloSEM Description: CRAN Package 'MonteCarloSEM' (Monte Carlo Simulation for Structural Equation Modeling) Provides tools to conduct Monte Carlo simulations under different conditions (e.g., varying sample size, data normality) for structural equation models (SEMs). Data can be simulated based on user-defined factor loadings and correlations, with optional non-normality added via Fleishman's power method (1978) . Once generated, models can be estimated using 'lavaan'. This package facilitates testing model performance across multiple simulation scenarios. When data generation is completed (or when generated data sets are given) model tests can also be run. Please cite as "Orçan, F. (2021). MonteCarloSEM An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713." Package: r-cran-moode Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-far, r-cran-progressr, r-cran-rdpack, r-cran-rlang Suggests: r-cran-dofuture, r-cran-foreach Filename: pool/dists/noble/main/r-cran-moode_1.1.0-1.ca2404.1_all.deb Size: 139858 MD5sum: a48dcbacfec922eda40bb4d0bb91611f SHA1: d9415535d92b73d74e776a3425f78cf545fb1be9 SHA256: d0ce06312a3fd5c70a95a4c81a72635589b5617c6bf9fa79856d383d1dc6ff42 SHA512: 1357ebb9182342c4268caa3b508377a8ac9eec7805c85e31019aada5c2ee5914caf98613f2ccb27abb45967802a16faebd45852268ce3a2a30ad5bc473a73c58 Homepage: https://cran.r-project.org/package=MOODE Description: CRAN Package 'MOODE' (Multi-Objective Optimal Design of Experiments) Provides functionality to generate compound optimal designs for targeting the multiple experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. Designs can be found using point or coordinate exchange algorithms combining estimation, inference and lack-of-fit criteria that account for model inadequacy. Details and examples are given by Koutra et al. (2024) . Package: r-cran-moodef Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blastula, r-cran-dplyr, r-cran-glue, r-cran-magick, r-cran-readr, r-cran-readxl, r-cran-snakecase, r-cran-tibble, r-cran-tidyr, r-cran-xlsx, r-cran-xml2 Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-moodef_1.2.0-1.ca2404.1_all.deb Size: 424060 MD5sum: cd6e73ed13335358aab019c54f5e5a33 SHA1: 4478370d44c1237d592e6653d1e43f23d1d046b9 SHA256: c168a6428a01736cfbfa79a8d7845b5415f8545c61c8fdcda50f07f4a60ba853 SHA512: 552b237e56469993e9aa417d1cf7a147cc9fb9c2b6bbc08fcc70d2104670354388fb0851f602aa217949d0844fd44bfcc24eca275522f43a58001cf62d40e0fc Homepage: https://cran.r-project.org/package=moodef Description: CRAN Package 'moodef' (Defining 'Moodle' Elements from R) The main objective of this package is to support the definition of 'Moodle' elements taking advantage of the power that R offers. 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This makes it easy to quickly create a quiz that can be randomly replicated with new datasets, questions, and options for answers. 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'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit . Package: r-cran-moonbook Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nortest, r-cran-survival, r-cran-sjmisc, r-cran-stringr, r-cran-magrittr, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-sjlabelled, r-cran-ztable, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-moonbook_0.3.1-1.ca2404.1_all.deb Size: 1246618 MD5sum: 57c722313f1fec680bfa2759bc0c207a SHA1: a2965369a731a3586c8e4bc43df6133178815f56 SHA256: a22062c112bc73e0834ab0656ff0e7e99eb5877d97524ee61f2c1bcd310448a5 SHA512: 5561321983616669ab49e0e1e7a32d15ace5fffcc2a7b0c854a32fd86f35ccc14da65aef25433f0143405a83e1b1007ccd0f5735325c71bf12db9dc5c8047b47 Homepage: https://cran.r-project.org/package=moonBook Description: CRAN Package 'moonBook' (Functions and Datasets for the Book by Keon-Woong Moon) Several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions. Package: r-cran-moonboot Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-moonboot_2.0.1-1.ca2404.1_all.deb Size: 64774 MD5sum: 2f0cd147a53b7df60ec451345a95aa90 SHA1: ad71f2ca9ffdccfcb46630c2deb0ba654ffced99 SHA256: 3f13db47f56dadc205f43ea86ab658f12c1f3979a329a0773fe349eaa939ef6e SHA512: 3858e96c71469a8d114ad22c92340a68c545d9527d12019cfe282a385c3937b7cf47c5225444348b89fa1cd9ceb4dadb5f19e46c77ac017d9b0727178a2baa16 Homepage: https://cran.r-project.org/package=moonboot Description: CRAN Package 'moonboot' (m-Out-of-n Bootstrap Functions) Functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) . Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2025) . Package: r-cran-moonlit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-suncalc, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-moonlit_0.1.1-1.ca2404.1_all.deb Size: 914358 MD5sum: cfa62ad6ea75d4351f937bce074c2226 SHA1: 099399684b607916c798cf38a8ba46e4f13d86d9 SHA256: 4b0f4e9f2b11958cf2be35fbff9b97ae1093e74daae474cbf593a33f673fc6f4 SHA512: ed29afa9dc5178a13083f9c9d857a667d831d4a90a21e873875206ec3d01b090b26595afd699af855697ade9dea53f3754996089ac133fd8e46763b85c36ce88 Homepage: https://cran.r-project.org/package=moonlit Description: CRAN Package 'moonlit' (Predicting Moonlight Intensity for a Given Time and Location) Tools for predicting moonlight intensity on the ground based on the position of the moon, atmospheric conditions, and other factors. Provides functions to calculate moonlight intensity and related statistics for ecological and behavioral research, offering more accurate estimates than simple moon phase calculations. The underlying model is described in Smielak (2023) . Package: r-cran-moonshiner Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-suncalc, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-redas, r-cran-ggplot2, r-cran-progress Filename: pool/dists/noble/main/r-cran-moonshiner_1.1.0-1.ca2404.1_all.deb Size: 41932 MD5sum: 64fcfa5be27c5a9f84a980d6f408bad7 SHA1: d7941652207d2e3eed1439dd87d67444d47e1955 SHA256: c142909223b10dd1a7c9d9b3ccf52d279ddc3c041a69c6bd60a478cf6e1a91f4 SHA512: 2860e055bdb128a2ad90f0a29dd4eec8c6fba74bd208e0a4f646c51737d4e85d27b40e40a2b1e6dfcc9fc9c6e913047815d3ee34694822f6766df4b797a099d0 Homepage: https://cran.r-project.org/package=MoonShineR Description: CRAN Package 'MoonShineR' (Predict Moonlight, Sunlight, and/or Twilight Ground Illuminance) Predicts ground-level illuminance from moonlight, sunlight, and twilight for specified locations and time periods. The package is intended for field studies in ecology and behavior where natural light levels are used as predictor variables. See Poon et al. (2024) . Calculations use astronomical quantities from 'suncalc' and published illuminance models, including Austin et al. (1976) and Seidelmann (1992) . Package: r-cran-mooplot Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-collapse, r-cran-matrixstats, r-cran-moocore Suggests: r-cran-extrafont, r-cran-viridislite, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mooplot_0.1.1-1.ca2404.1_all.deb Size: 165778 MD5sum: 7ddccb1bd69d671194e498f8be5c68b6 SHA1: 55867264d9cae74935b285acfedd3d1d78018ebc SHA256: 1e20bc9c3ecc7a0977bb542b006aa89a9b82a6c89e713f5f224e4be3f2d0b325 SHA512: c2c4f78298a9da3677d081901348b88b3170554e8eecaace2626721c6239a4f5da4ca7bc22f69fd0e5b2a1d354155b0e46dff1cd3e96869d2a46648389c82670 Homepage: https://cran.r-project.org/package=mooplot Description: CRAN Package 'mooplot' (Graphical Visualizations for Multi-Objective Optimization) Visualization of multi-dimensional data arising in multi-objective optimization, including plots of the empirical attainment function (EAF), M. López-Ibáñez, L. Paquete, and T. Stützle (2010) , and symmetric Vorob'ev expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) , among others. Package: r-cran-moose Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-moose_0.0.1-1.ca2404.1_all.deb Size: 14938 MD5sum: de1e111557735f8985e9e308382db15a SHA1: b4ed145f7b66c4a373bc0a2fde72e45fd15bb52d SHA256: 4b6832a1b8f740f11e6995491f51aeb4feea16c61eefc146da4fe6fd722bf1d4 SHA512: 7e88c1dc3b41c5a4ffb6bf600e76ebdd4e64f39c6f219b04c562ffa3edca3660478c577bc3146a2bd78096b7e549f3896a96c81c04d1fa06a41564e7ce6908cf Homepage: https://cran.r-project.org/package=moose Description: CRAN Package 'moose' (Mean Squared Out-of-Sample Error Projection) Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) . It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper. Package: r-cran-mopac Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-lubridate, r-cran-tibble, r-cran-readr, r-cran-hms Filename: pool/dists/noble/main/r-cran-mopac_0.1.0-1.ca2404.1_all.deb Size: 1284376 MD5sum: 5f725a768b26b31fee0c8dbdce7b931b SHA1: c6fe69676b9683cc1d6726961e63b2bcb7ec3eac SHA256: 4f9b92acaa0cb41a0c6c658ab980685e5ca569ba2b69af88403605fea5a67967 SHA512: 78c1a7a14ba865c8b84a43faaba687506d0288bf3ce9e5a86a5c70b45b13a77168489f5c72541e306e0b9ec157f185016f08acb9e864a9d2607defcff6237e28 Homepage: https://cran.r-project.org/package=mopac Description: CRAN Package 'mopac' (Collection of Datasets Pertaining to Loop 1 "Mopac") Provides real & simulated datasets containing time-series traffic observations and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas. Package: r-cran-moqa Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-gplots, r-cran-readr Filename: pool/dists/noble/main/r-cran-moqa_2.0.0-1.ca2404.1_all.deb Size: 83774 MD5sum: 9e315a14a130e4f606bb7aa0fd1e326a SHA1: 0ff45e96dd1046367e8b9805048ac1002325a681 SHA256: d3175c17f4844e0563333ac4aa65eb31a8d8ef141e36f4347120ca5453f39d66 SHA512: c91eac911079ed90c8d001c9bb9bc56a2b8ad4759afb669b9cb859ca9e438c68a698a9a8ad770fe50d69ea01798f955f461ea36d9656947447d5186b88b735f4 Homepage: https://cran.r-project.org/package=MOQA Description: CRAN Package 'MOQA' (Basic Quality Data Assurance for Epidemiological Research) With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) . . Package: r-cran-moranajp Architecture: all Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1543 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-purrr, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-stringi, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-moranajp_0.9.8-1.ca2404.1_all.deb Size: 1482062 MD5sum: 04ef7a6283ea19284468c2ace9abcd2e SHA1: 85191585ab3d6047f8687c0963faca9dd9393aa4 SHA256: abb968a5af353fbd57cc60ace5dbea2c80e54dfb2eefe1c10c5807a459b1adfa SHA512: 422f6485b58eeef3a3e9b87bb5fad04344d3f8db654bb7369171180ffcb456e0cc4dcaf280f7160b93950b62082c696ddd20ff9fecf859812d7094b4138f7515 Homepage: https://cran.r-project.org/package=moranajp Description: CRAN Package 'moranajp' (Morphological Analysis for Japanese) Supports morphological analysis for Japanese by using 'MeCab' , 'Sudachi' , 'Chamame' , or 'Ginza' . Can input a data.frame and obtain all results of 'MeCab' and the row number of the original data.frame as a text id. Package: r-cran-moreparty Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-party, r-cran-partykit, r-cran-varimp, r-cran-plyr, r-cran-foreach, r-cran-measures, r-cran-mass, r-cran-iml, r-cran-pdp, r-cran-vip, r-cran-ggplot2, r-cran-rlang, r-cran-shiny, r-cran-shinywidgets, r-cran-rclipboard, r-cran-dt, r-cran-datamods, r-cran-phosphoricons Suggests: r-cran-doparallel, r-cran-rmdformats, r-cran-descriptio, r-cran-gdatools, r-cran-rcolorbrewer, r-cran-caret, r-cran-proc, r-cran-dplyr, r-cran-e1071, r-cran-patchwork, r-cran-scales Filename: pool/dists/noble/main/r-cran-moreparty_0.4.2-1.ca2404.1_all.deb Size: 385934 MD5sum: ec8e2e3afad0a5db9029472296a5c338 SHA1: da813966d0f2f26c5d9a457e4917d5c710491870 SHA256: a76ab160fafe9bc143c3129ab8f76a8b5e26d5f083c10512e0cec46ff71bbad2 SHA512: cc52351906a2843a3b33f62220de7701ca1c35faaed820da3057635bc25fff222983e311e1bd814bf4a2ae488f52e5a45f05117ad6bedde093da82dd1599cd4a Homepage: https://cran.r-project.org/package=moreparty Description: CRAN Package 'moreparty' (A Toolbox for Conditional Inference Trees and Random Forests) Additions to 'party' and 'partykit' packages : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017) , Hapfelmeier and Ulm (2013) , Altmann et al (2010) ) and parallelized versions of conditional forest and variable importance functions. Also modules and a shiny app for conditional inference trees. Package: r-cran-morepls Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-descriptio Suggests: r-cran-plsvarsel, r-cran-ggforce Filename: pool/dists/noble/main/r-cran-morepls_0.2.1-1.ca2404.1_all.deb Size: 179324 MD5sum: bf9092a383f94a65d003a787186168c8 SHA1: 5b27178f3b144d3229b1570d92dc63a342d50733 SHA256: 1c31c6d921398d269d3ff050dddc7c24f72669804d5fdd90727f03e0d4bd395c SHA512: 377554fdfb39bbbd9bb18a224e069d883dd1228f72084bf4d11b8753090ae5ab7538ed322df304dee1cf1ff228f01775124d34b3901ad5f8b44798bbe1fa6792 Homepage: https://cran.r-project.org/package=morepls Description: CRAN Package 'morepls' (Interpretation Tools for Partial Least Squares Regression) Various kinds of plots (observations, variables, correlations, weights, regression coefficients and Variable Importance in the Projection) and aids to interpretation (coefficients, Q2, correlations, redundancies) for partial least squares regressions computed with the 'pls' package, following Tenenhaus (1998, ISBN:2-7108-0735-1). Package: r-cran-morestopwords Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4561 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cld2 Filename: pool/dists/noble/main/r-cran-morestopwords_0.2.0-1.ca2404.1_all.deb Size: 729656 MD5sum: 451ad3a8e8e74c63fbca987a3ba492e5 SHA1: 10b50831e6c37d073110fe00903f076281c7bb76 SHA256: 411e713ed5256387895c40ca9814fef3eec4e1b1614b30bcc97ef1ca3efb63e1 SHA512: 291c664c08c06dede3f7c4e0ec2d9261ce58a35094da160cb052aa585e0d8083bd8430256ed3d3481ddae9fd08217e8e89b82f08e4085fd2d2b3edd125bb8867 Homepage: https://cran.r-project.org/package=morestopwords Description: CRAN Package 'morestopwords' (All Stop Words in One Place) A standalone package combining several stop-word lists for 65 languages with a median of 329 stop words for language and over 1,000 entries for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically gets access to all the unique stop words contained in: the 'StopwordISO' repository; the 'Natural Language Toolkit' for 'python'; the 'Snowball' stop-word list; the R package 'quanteda'; the 'marimo' repository; the 'Perseus' project; and A. Berra's list of stop words for Ancient Greek and Latin. Package: r-cran-morph Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rgl, r-cran-reshape2, r-cran-igraph, r-cran-stringr Filename: pool/dists/noble/main/r-cran-morph_2.0.0-1.ca2404.1_all.deb Size: 117258 MD5sum: 54d30bf6d2dfd8b2a90ec687da606a95 SHA1: 953e1587e550e0bc51e3284818315a745b067ddf SHA256: 4a580d0750f8e4469e7e16612d79e7f25910a94625978dafde167f138b62c4c1 SHA512: a4eb8733846f1994ce2946689b58357bb74ccfe783f863aaeba46ac541bd59232e5f93b840e3cc83c7727ae7d1d59af20fe7f0108fafe9d65be728efb6104478 Homepage: https://cran.r-project.org/package=morph Description: CRAN Package 'morph' (3D Segmentation of Voxels into Morphologic Classes) Automatically segments a 3D array that represents a volume of binary voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been published in the journal Landscape Ecology: Remmel, T.K. (2022) . The output is a cartridge (list object) that maintains the input array, the segmentation results in array format, and a summary table. Plotting functionality is provided to produce interactive visual outputs from the produced results cartridge, allowing custom plotting to be performed separately from the segmentation, which speeds-up processing. While the old functions persist, they are being phased out and will eventually be replaced with the new runmorph3d() and plotmorph3d() functions. Package: r-cran-morphemepiece.data Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3540 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-morphemepiece.data_1.2.0-1.ca2404.1_all.deb Size: 3589366 MD5sum: b4503b5aab1eb84262d206799ec876ee SHA1: f5ec7fb7f4a00d8e2f5c3c7aedb9dcc215536ca2 SHA256: b6fe801b446fa3477661133ee0c01c4aec8679afdb269bfcd2820d2e83d8e2cb SHA512: 8ad162ef4249febf1e54b6729a4b89c8fe5d684e843ff7da61943add12c6d60c872a90c28e510f8f86b1974a4d2e636caab09f2a494123250f2a39edd03f4ced Homepage: https://cran.r-project.org/package=morphemepiece.data Description: CRAN Package 'morphemepiece.data' (Data for Morpheme Tokenization) Provides data about morphemes, the smallest units of meaning in a language. Package: r-cran-morphemepiece Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dlr, r-cran-fastmatch, r-cran-magrittr, r-cran-memoise, r-cran-morphemepiece.data, r-cran-piecemaker, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr Suggests: r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-here, r-cran-knitr, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-morphemepiece_1.2.3-1.ca2404.1_all.deb Size: 68318 MD5sum: abc49bdc9b48fd4bd657ab01434a99a2 SHA1: ecf922e91d65d02ea9aff4cd1c4632b279fe597a SHA256: 6e7986b9e9d0b151682863a45408f45c0de17ae5cfc782f172fee2ec7d6a76e8 SHA512: e0be6a9bede7d50de1b8eba9e19667347bbb74602206ba6f0e1ce9a02b7b0c706eca3bb2a512b7d342ed06ebc12a7717a4cbcb83704597f1732152c00c6c59a7 Homepage: https://cran.r-project.org/package=morphemepiece Description: CRAN Package 'morphemepiece' (Morpheme Tokenization) Tokenize text into morphemes. The morphemepiece algorithm uses a lookup table to determine the morpheme breakdown of words, and falls back on a modified wordpiece tokenization algorithm for words not found in the lookup table. Package: r-cran-morpherr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lmeinfo, r-cran-matrix, r-cran-msm, r-cran-mvtnorm, r-cran-nlme, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-morpherr_1.0.0-1.ca2404.1_all.deb Size: 132610 MD5sum: 2079ea710737da5346f66bbe03739fb5 SHA1: 094f15d61dd262038897eec0452d85b771936f5d SHA256: b474a8f1654ca3f2f3e51acd197be20b098a362eebf99bc050b19ff5bbad9939 SHA512: e2e7b1e684689815bae3e72adc8428b9da4ac4ccd894aca270182a491b8aa62316a8f4d484d2b8506bdc7060acc769b30b31cdae20d86f28f923b4ba67e4bb94 Homepage: https://cran.r-project.org/package=morphErr Description: CRAN Package 'morphErr' (Measurement Error Models for Morphometric Data) Morphometric data collected on animal populations can be subject to measurement error, which leads to biased estimators using line-fitting techniques such as linear regression and reduced major axis. The models implemented in this package were described by Stevenson, Smit, and Setyawan (2026) . They explicitly accommodate measurement error, allow for multivariate data, estimate relationships between dimensions, allow missing data, and provide tests for isometric relationships between dimensions. Morphometric data of the reef manta ray, collected in Raja Ampat, Indonesia, are included. Package: r-cran-morphomap Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4967 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arothron, r-cran-lattice, r-cran-mgcv, r-cran-rvcg, r-cran-morpho, r-cran-oce, r-cran-sp, r-cran-geometry, r-cran-rgl, r-cran-colorramps, r-cran-desctools Filename: pool/dists/noble/main/r-cran-morphomap_1.5-1.ca2404.1_all.deb Size: 5047580 MD5sum: 059f940aa8ef3f4cd0ff1b8985e663e6 SHA1: 68fe7f07b4425ac5b354cb240a48f3400f8c957f SHA256: d1389a5c4a0ccaf29ddca7541629c4fab86bc097ce04d61c0a641f03632a75a6 SHA512: fb6e1b4287ee75463b6c41a740ba200a05d55e076283d923add508341a12a93a67306a9cd223b2953c68bbef51b2c1cd4f0bd3e3ad16e66eb2142348b0c16d6d Homepage: https://cran.r-project.org/package=morphomap Description: CRAN Package 'morphomap' (Morphometric Maps, Bone Landmarking and Cross Sectional Geometry) Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section. Package: r-cran-morphomenses Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dendextend, r-cran-geomorph, r-cran-cluster, r-cran-shiny Filename: pool/dists/noble/main/r-cran-morphomenses_1.0.3-1.ca2404.1_all.deb Size: 156080 MD5sum: 1dfc6d8d342c1ca85e2536ab1920a854 SHA1: fd4b2f801a98e022aff55675d0fe5e19ac0ca0fa SHA256: 07820be9f9f28119acd65451459f83b1a20dd8c38379eaf29f034876017e00eb SHA512: 33e749a4a8f48dc34a31a07a35c1b5b18dd4f46e69974bd42a24049f815e4ed2bbeaf83c8ccc8cce37d9b04be39e5e82d33d32384312891876602a0a29d56e27 Homepage: https://cran.r-project.org/package=moRphomenses Description: CRAN Package 'moRphomenses' (Geometric Morphometric Tools to Align, Scale, and Compare"Shape" of Menstrual Cycle Hormones) Mitteroecker & Gunz (2009) describe how geometric morphometric methods allow researchers to quantify the size and shape of physical biological structures. We provide tools to extend geometric morphometric principles to the study of non-physical structures, hormone profiles, as outlined in Ehrlich et al (2021) . Easily transform daily measures into multivariate landmark-based data. Includes custom functions to apply multivariate methods for data exploration as well as hypothesis testing. Also includes 'shiny' web app to streamline data exploration. Developed to study menstrual cycle hormones but functions have been generalized and should be applicable to any biomarker over any time period. Package: r-cran-morphoregions Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2776 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-cluster, r-cran-scales, r-cran-ggplot2, r-cran-arg, r-cran-pbapply Suggests: r-cran-viridislite, r-cran-patchwork, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-morphoregions_0.2.0-1.ca2404.1_all.deb Size: 1914308 MD5sum: 051620e2d24ebd8f8bf9d1a8e22e8459 SHA1: 1390c96c0dfe9625ee76acc0d32e1fb507ed18d0 SHA256: b71d12701ff508c623c6aed642f8bbfa7ea27b65165197c792bbe858db96590f SHA512: ad57454abb45acdcdbf161d3458d63263a0308da299743f45dee77f0ad6292170f17edb77b80a5e6f9b05ce44b78031559aff6200d2b34482f6558577c415dd7 Homepage: https://cran.r-project.org/package=MorphoRegions Description: CRAN Package 'MorphoRegions' (Analysis of Regionalization Patterns in Serially HomologousStructures) Computes the optimal number of regions (or subdivisions) and their position in serial structures without a priori assumptions and to visualize the results. After reducing data dimensionality with the built-in function for data ordination, regions are fitted as segmented linear regressions along the serial structure. Every region boundary position and increasing number of regions are iteratively fitted and the best model (number of regions and boundary positions) is selected with an information criterion. This package expands on the previous 'regions' package (Jones et al. (2018) ) with improved computation and more fitting and plotting options. Package: r-cran-morphoscape Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1442 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-concaveman, r-cran-ggplot2, r-cran-spatial, r-cran-sp, r-cran-automap, r-cran-scales, r-cran-viridislite, r-cran-alphahull Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-morphoscape_1.0.2-1.ca2404.1_all.deb Size: 972344 MD5sum: d6b213fc3ffffc29de771239d22fa07a SHA1: 93861fa6997b3ce5cd582aa49c2cfe5e8a3f4ca6 SHA256: 1b0ef77eeebe3ba6dc1c01c3571c73b7dd3bfb73dd5464a7de40065c9ea28d6c SHA512: 4612bdf65b76fc20396b69bb24382ec6afa2d9c0e00f74b6fb44be34523de394233183a17fee773113f2d7f8205bb1bbaad878d39110542816ba274fe12bb545 Homepage: https://cran.r-project.org/package=Morphoscape Description: CRAN Package 'Morphoscape' (Computation and Visualization of Adaptive Landscapes) Implements adaptive landscape methods first described by Polly et al. (2016) for the integration, analysis and visualization of biological trait data on a phenotypic morphospace - typically defined by shape metrics. Package: r-cran-morphotools2 Architecture: all Version: 1.0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-candisc, r-cran-car, r-cran-class, r-cran-ellipse, r-cran-heplots, r-cran-mass, r-cran-plot3d, r-cran-statmatch, r-cran-vegan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-morphotools2_1.0.2.1-1.ca2404.1_all.deb Size: 924628 MD5sum: 7f5d2b72ab435e51885d92c66ac20128 SHA1: aa5eb4d78f305e7a68a0ce449a62cdcf1f794267 SHA256: 1ba7d27d4df69fe35ff053d8c1a86d84d4d3e20835139373820e346d34edb075 SHA512: 56afd5997d587ab749c720daf0fc145ee10648eda463d11764552b6e4b04b4e14c5191fb36d41fea62302af954f6a1137dac4a28cbc43a9cf9cd5a43733d6a14 Homepage: https://cran.r-project.org/package=MorphoTools2 Description: CRAN Package 'MorphoTools2' (Multivariate Morphometric Analysis) Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022. Package: r-cran-morphsim Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-fossilsim, r-cran-phangorn Suggests: r-cran-treesim, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-morphsim_1.2.1-1.ca2404.1_all.deb Size: 281820 MD5sum: b164b196310c249f77944d7b82560418 SHA1: 4cc79983bd163f9111206184977a6464569fa1e5 SHA256: c7d72c72702f9c4151d9b43b254ca95d65883d75aaebbfe46df1a2968d93c916 SHA512: 5fa1ed78698bfd390a1d177c3b07a4e2754b2ccf1c0f20d797005d61d429d7b6f30afe1a2071f8709503f94ed7054ed4cfa10a2e9203354be7c8019dd792ad0b Homepage: https://cran.r-project.org/package=MorphSim Description: CRAN Package 'MorphSim' (Simulate Discrete Character Data along Phylogenetic Trees) Tools to simulate morphological traits along phylogenetic trees with branch lengths representing evolutionary distance or time. Includes functions for visualizing evolutionary processes along trees and within morphological character matrices. Methods are described in Mulvey et al. (2026) . Package: r-cran-morrowplots Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-morrowplots_0.2.0-1.ca2404.1_all.deb Size: 2392320 MD5sum: c418fcf9d7d6c3d1c16f45d3a1c8723f SHA1: 43c886c71211f28850ceacdebdc2477895218ee8 SHA256: 7e2e57fba50a3dad472516fc255f608946915ce9ab0b9e92f4c3317e28b5e584 SHA512: c18fdb9edba5fbc9da35d14da5ce1471202aa97fdbeaa08ccedf639e4b56a8dc16d7007293a68e3187598d51b108b612c6cc67b21aeaa97bd17b015de5d80f42 Homepage: https://cran.r-project.org/package=morrowplots Description: CRAN Package 'morrowplots' (Historical Agricultural Data from the University of Illinois) Agricultural data for 1888-2021 from the Morrow Plots at the University of Illinois. The world's second oldest ongoing agricultural experiment, the Morrow Plots measure the impact of crop rotation and fertility treatments on corn yields. The data includes planting information and annual yield measures for corn grown continuously and in rotation with other crops, in treated and untreated soil. Package: r-cran-morsedr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-rjags Suggests: r-cran-ggally, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-morsedr_0.1.3-1.ca2404.1_all.deb Size: 925834 MD5sum: a643af56168f49a23aba9147d4062d46 SHA1: c537f3ee17127052ac9b663c39d49bb1b093ad2f SHA256: ae74a6c9db1aa1146017c7b21184dcbd53d90e3f64c15545c9368d5f3a100095 SHA512: 56fbf8a1513533b46d7dd4a4744e3ca0e9ae7dee1deb00f3ff658180e37f60e8dd46308b974b9a339cfef427af4d1fdf9f31c043d3f777f2e3083ededc4f1ab7 Homepage: https://cran.r-project.org/package=morseDR Description: CRAN Package 'morseDR' (Bayesian Inference of Binary, Count and Continuous Data inToxicology) Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of several type of toxicological data. 'binary' (e.g., survival, mobility), 'count' (e.g., reproduction) and 'continuous' (e.g., growth as length, weight). Estimation procedures can be used without a deep knowledge of their underlying probabilistic model or inference methods. Rather, they were designed to behave as well as possible without requiring a user to provide values for some obscure parameters. That said, models can also be used as a first step to tailor new models for more specific situations. Package: r-cran-mort Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2058 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mort_0.0.1-1.ca2404.1_all.deb Size: 1702126 MD5sum: 8aaefac4de3828c335c11b6e12f2031a SHA1: b92f9a97774fa1183a77f090938ee35eb9845559 SHA256: 541fbb2329b4044a7399c8d781c4c6c3703d6596b6a4bb61a021743f434d0adf SHA512: eeac649481440e4b58f59076a91f8837aaf11737a01ca46086cc90a90b08a9a8744bea39158edc6d4b5d94bd888c9f055a7c5eac55700f28d6ff102355b03356 Homepage: https://cran.r-project.org/package=mort Description: CRAN Package 'mort' (Identifying Potential Mortalities and Expelled Tags in AquaticAcoustic Telemetry Arrays) A toolkit for identifying potential mortalities and expelled tags in aquatic acoustic telemetry arrays. Designed for arrays with non-overlapping receivers. Package: r-cran-mortaar Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1515 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rdpack, r-cran-reshape2, r-cran-tibble, r-cran-rlang, r-cran-flexsurv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mortaar_1.1.8-1.ca2404.1_all.deb Size: 908850 MD5sum: 9adf7dcbf401dfb0436c1f2b51417fc5 SHA1: 2fe8d206936c8b8421e231d3c2e5a34ab52cc245 SHA256: 25993898e78910fde0476824dfb92416acaefb3b89379021562d83449ec30a83 SHA512: fd83b73beb814b1e3a4a160c58ee2bb44c75bb18d29b96ab4a95397db34ffadff11ed8381d6334e0fd60b89331cf80b0db21bc2ea0ad8b47e111f2c42f994fcf Homepage: https://cran.r-project.org/package=mortAAR Description: CRAN Package 'mortAAR' (Analysis of Archaeological Mortality Data) A collection of functions for the analysis of archaeological mortality data (on the topic see e.g. Chamberlain 2006 ). It takes demographic data in different formats and displays the result in a standard life table as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population). It also checks for possible biases in the age structure and applies corrections to life tables. Package: r-cran-mortalitygaps Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-mass, r-cran-crch, r-cran-pbapply, r-cran-rdpack Suggests: r-cran-mortalitylaws, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mortalitygaps_1.0.7-1.ca2404.1_all.deb Size: 380122 MD5sum: ec51ab245bc5b4c4c1c7677ea16a80f4 SHA1: 69384fd78d80ea424ceb7c4f00f4037dc9dcc64c SHA256: 3184b275b20a9e6ae7fb53fbd54234ca97ffec2c72a750fbfd8110776216ee6f SHA512: a9da1f68418c3d06bb5ec679345192cf5dec135b6ea7d737a218a45a58a84ab15bbb3ead9bd4936146c1ec76b2a2fd88f7d9fb2f6eb2f76ecba8875737881153 Homepage: https://cran.r-project.org/package=MortalityGaps Description: CRAN Package 'MortalityGaps' (The Double-Gap Life Expectancy Forecasting Model) Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). . Package: r-cran-mortalitylaws Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5363 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pbapply, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mortalitylaws_3.0.0-1.ca2404.1_all.deb Size: 1830554 MD5sum: 344caa598dade01bf54f91d87f2fed57 SHA1: c37446071b2df56f7638ec4a7936f5ffec683bee SHA256: 0da5f28805ec0ae516c2763e242ff559cc98812624ed238c05204b553b1d7fc4 SHA512: f26912bb51a0ee691f5b7ef1998da7b0e8ce04646a6aae26d1f02ea2265bb43ea2fe96dddd212338ad9e5884e73d8b7969dec3d5e7dbb4bba352f4ba858689cd Homepage: https://cran.r-project.org/package=MortalityLaws Description: CRAN Package 'MortalityLaws' (Parametric Mortality Models, Life Tables and HMD) Fit the most popular human mortality 'laws', and construct full and abridged life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) . Practical functions for downloading data from various human mortality databases are provided as well. Package: r-cran-mortalitytables Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2725 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-pracma Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mortalitytables_2.0.5-1.ca2404.1_all.deb Size: 1256364 MD5sum: da616f05862cf31c8a17990aadf0c00a SHA1: b87311faee6bf7a9a7c246681a931fb17aeab5fa SHA256: 5aa434bd5603dd38a70f4cdacbe68794ee93b06ea42d8e771d903c37430d239d SHA512: 11333fa7e73f218c5dac17498169ff2d5b9e1344606cf78fc9b3b954d2254333082576a362e761b2b63a8f19f4f34526f547893075f4d80de14a76e087cbd2ae Homepage: https://cran.r-project.org/package=MortalityTables Description: CRAN Package 'MortalityTables' (A Framework for Various Types of Mortality / Life Tables) Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box. 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It contains helper functions to standardize (1) the organization of project repositories and (2) the creation ofpipelines from the 'targets' R Package (Landau et al. (2026) ), using the DS CoP best practices. We draw upon community developed best practices as well as certain USGS-specific requirements. See Shrycock et al. (2023) for examples of these USGS requirements. Package: r-cran-mortsoa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-mortsoa_0.1.0-1.ca2404.1_all.deb Size: 87744 MD5sum: 5a7a84ae152433ba8a7f739c2f300133 SHA1: 13f8eb497445b1344abaa54eb8cc142bd3d7e0c8 SHA256: 065032fd8be31fffb227c09a78b3e6e5288f35dedc22d7fc3967818a57b28e00 SHA512: 800ccf88f721c94a3829be4faa71aabda6def0f8a443f865323d654d050bd199fe7754de01d2f389343f5d39b22668d70d41aff68b6b1ffa635dfe11134e628d Homepage: https://cran.r-project.org/package=mortSOA Description: CRAN Package 'mortSOA' (Obtain Data from the Society of Actuaries 'Mortality and OtherRate Tables' Site) The Society of Actuaries (SOA) provides an extensive online database called 'Mortality and Other Rate Tables' ('MORT') at . This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in 'Excel', 'CSV', or 'XML' formats. This package provides convenience functions that read 'XML' formats from the database and return R objects. Package: r-cran-mos Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-hypergeo2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-moments Filename: pool/dists/noble/main/r-cran-mos_0.1.4-1.ca2404.1_all.deb Size: 123624 MD5sum: f723711c70abd7bfdb9e1d046df0eeed SHA1: 23834faf047461327b0a3ae6f89bc1487428ba61 SHA256: 8df733a7de47c9283378ee7663d4014b1b68d913e409a934a06fa2975735dfff SHA512: 71f0f7c6e63e8498dc639a84b50eb0b39a9e5e2ea90bdd3516c79f237d31d6ab9ecee4bfa8c11f7bc54213b9dbedb0e6359465aadf11b9d6d43f99eeda184f9e Homepage: https://cran.r-project.org/package=mos Description: CRAN Package 'mos' (Simulation and Moment Computation for Order Statistics) Provides a comprehensive set of tools for working with order statistics, including functions for simulating order statistics, censored samples (Type I and Type II), and record values from various continuous distributions. Additionally, it offers functions to compute moments (mean, variance, skewness, kurtosis) of order statistics for several continuous distributions. These tools assist researchers and statisticians in understanding and analyzing the properties of order statistics and related data. The methods and algorithms implemented in this package are based on several published works, including Ahsanullah et al (2013, ISBN:9789491216831), Arnold and Balakrishnan (2012, ISBN:1461236444), Harter and Balakrishnan (1996, ISBN:9780849394522), Balakrishnan and Sandhu (1995) , Genç (2012) , Makouei et al (2021) and Nagaraja (2013) . Package: r-cran-mosaic Architecture: all Version: 1.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3874 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-lattice, r-cran-ggformula, r-cran-mosaicdata, r-cran-matrix, r-cran-mosaiccore, r-cran-ggplot2, r-cran-rlang, r-cran-purrr, r-cran-mass, r-cran-tidyr Suggests: r-cran-ggstance, r-cran-ggridges, r-cran-vdiffr, r-cran-lubridate, r-cran-magrittr, r-cran-nhanes, r-cran-rcurl, r-cran-sp, r-cran-vcd, r-cran-testthat, r-cran-knitr, r-cran-mapproj, r-cran-rgl, r-cran-rmarkdown, r-cran-covr, r-cran-formatr, r-cran-palmerpenguins, r-cran-ggrepel, r-cran-readr, r-cran-ggdendro, r-cran-gridextra, r-cran-latticeextra, r-cran-glue, r-cran-broom, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-mosaic_1.10.2-1.ca2404.1_all.deb Size: 3073936 MD5sum: 7ee38d88c167ccd6790ee8173f1d841e SHA1: 50cdbc6bc61ebc9db33d78dd605bf39a9b107cd6 SHA256: 129f558ce4b67192377e412fb43c59696fa6b0fd1b1caf58a33a3a942006327b SHA512: fd2866ea3057f133d2fef31c3095795ec38ab7cb2332baea0283978d5277b5f7c150b630ca9dd25b770c7e775652887c7c138b998a48c138643d08af428f0e06 Homepage: https://cran.r-project.org/package=mosaic Description: CRAN Package 'mosaic' (Project MOSAIC Statistics and Mathematics Teaching Utilities) Data sets and utilities from Project MOSAIC () used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Package: r-cran-mosaiccalc Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3643 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-calculus, r-cran-deriv, r-cran-dplyr, r-cran-ggformula, r-cran-ggplot2, r-cran-glue, r-cran-mass, r-cran-matrix, r-cran-metr, r-cran-mosaiccore, r-cran-mosaic, r-cran-orthopolynom, r-cran-rlang, r-cran-ryacas, r-cran-sp, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-cubature, r-cran-plotly, r-cran-mosaicdata Filename: pool/dists/noble/main/r-cran-mosaiccalc_0.6.4-1.ca2404.1_all.deb Size: 2738286 MD5sum: 4385c5d772899a15f58297e3033b1d70 SHA1: 34fd79d7b9cbd013b2390af1ed5221dc7c8a9bbe SHA256: d76c13f3e1140d9a54131be5990aa0edec544fd88693112fffe351d8411cc85c SHA512: 8af53ea626293a689b36c26ab220b49fa3db81304ee59f45ccfb52d1ee81cee30675ca91329cd043a98447becdadf4224cf79f7ffbc4d0b6919e13ce60ede841 Homepage: https://cran.r-project.org/package=mosaicCalc Description: CRAN Package 'mosaicCalc' (R-Language Based Calculus Operations for Teaching) Software to support the introductory *MOSAIC Calculus* textbook ), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus. Package: r-cran-mosaiccore Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-mass Suggests: r-cran-mosaicdata, r-cran-mosaic, r-cran-ggformula, r-cran-nhanes, r-cran-testthat, r-cran-mosaiccalc Filename: pool/dists/noble/main/r-cran-mosaiccore_0.9.5-1.ca2404.1_all.deb Size: 195740 MD5sum: 94eea23a55eded7aa4ec344c7928a3cd SHA1: e873b1c77ca2023666580380f556a430afcabf36 SHA256: db0742608e3a0161661cb372d967341dc3ba41f7648faa299d49741ef085929e SHA512: c8d1cbd68303f256d7af55ced136921e6a2e005cc69809034aa203d59b7b2a5564ed6d40950837c1a485dd3e519272e90b8eb1a380b7c5e856a504748a665ce0 Homepage: https://cran.r-project.org/package=mosaicCore Description: CRAN Package 'mosaicCore' (Common Utilities for Other MOSAIC-Family Packages) Common utilities used in other MOSAIC-family packages are collected here. Package: r-cran-mosaicdata Architecture: all Version: 0.20.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-mosaic, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-ggformula Filename: pool/dists/noble/main/r-cran-mosaicdata_0.20.4-1.ca2404.1_all.deb Size: 1599156 MD5sum: da73772bc47bff14c5f6588c1c69d318 SHA1: 027e5681512778fbc812ca462f244f2baa7139ed SHA256: 83611f690cac9eebd525f2bfa69e1f2e5325682423c09e90cb3e05d942b91abf SHA512: 535fe5a2c22f2a10042fb82f93be9f00c769d3d33b6a1354d904909ed0234fc6e3375fb4855bfffc68bb998fc4d66dc62abcbc37a7bf77b1a3877eb8a6978bb5 Homepage: https://cran.r-project.org/package=mosaicData Description: CRAN Package 'mosaicData' (Project MOSAIC Data Sets) Data sets from Project MOSAIC () used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Package: r-cran-mosaicmodel Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mosaiccore, r-cran-dplyr, r-cran-caret, r-cran-ggplot2, r-cran-ggformula, r-cran-lazyeval, r-cran-knitr, r-cran-mass, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse Suggests: r-cran-mosaic, r-cran-mosaicdata, r-cran-randomforest, r-cran-rpart Filename: pool/dists/noble/main/r-cran-mosaicmodel_0.3.0-1.ca2404.1_all.deb Size: 785444 MD5sum: c969fd92d29968d0cee3d0189869b9e7 SHA1: 023dc1e879d6ab5b54643ce8607236beef2be64c SHA256: 52b7e718dadca1f3dd2e49645861051fe9fae0dc3b753c5c0e70604896bc1099 SHA512: e2039216674034374fda536f183648b37744df04b4acb05a03b3612ab6cf57646b00700834cb606b26433633a4a6c96093af9881cfb05507cb1677c380bee652 Homepage: https://cran.r-project.org/package=mosaicModel Description: CRAN Package 'mosaicModel' (An Interface to Statistical Modeling Independent of ModelArchitecture) Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures. Package: r-cran-mosalloc Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ecosolver, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mosalloc_1.2.6-1.ca2404.1_all.deb Size: 183290 MD5sum: 796730a52655a5b4b49adaf3429282f7 SHA1: 015f7f9012865405696013864f64500cd3d53110 SHA256: b7e57f714f8314b7c69ec80da5a01ee24ab3aab539b063d0be792426e448efef SHA512: a83ed2915336d037ff235f1b363caa0248ebcdff7fd045490924cfa2615b26bc2c738c48f0528b7c8e3ff60e9f8c2e70f602a2ca9b6150a038bb5e777fc3bd0f Homepage: https://cran.r-project.org/package=MOSAlloc Description: CRAN Package 'MOSAlloc' (Constraint Multiobjective Sample Allocation) Provides a framework for multipurpose optimal resource allocation in survey sampling, extending the classical optimal allocation principles introduced by Tschuprow (1923) and Neyman (1934) to multidomain and multivariate allocation problems. The primary method mosalloc() allows for the consideration of precision and cost constraints at the subpopulation level while minimizing either a vector of sampling errors or survey costs across a broad range of optimal sample allocation problems. The approach supports both single- and multistage designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user- friendly interface. Sensitivity analysis is supported through the problem's dual variables, which are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR' package. See Willems (2025, ) for a detailed description of the theory behind 'MOSAlloc'. Package: r-cran-mosclust Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-clusterv Filename: pool/dists/noble/main/r-cran-mosclust_1.0.3-1.ca2404.1_all.deb Size: 366038 MD5sum: 55c39d22d33d613a9828dafcedb2bb75 SHA1: 400bef438154e352cd32aef4d965feda97364e4a SHA256: b761f6ada298224254a337be8ba733bd557f34c4ddbccac84e3d1dad7c0ba616 SHA512: ad75f6229170e9a131330c48d6fbbbd22b8ae55e9bc685aa3367e15420d74eefdc486609173e9e8dd8ce45d5bed43fc39647bd5e706c1c2fb70f231b3f1f2502 Homepage: https://cran.r-project.org/package=mosclust Description: CRAN Package 'mosclust' (Model Order Selection for Clustering) Stability based methods for model order selection in clustering problems (Valentini, G (2007), ). Using multiple perturbations of the data the stability of clustering solutions is assessed. Different perturbations may be used: resampling techniques, random projections and noise injection. Stability measures for the estimate of clustering solutions and statistical tests to assess their significance are provided. Package: r-cran-mosemiind Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mosemiind_0.1.2-1.ca2404.1_all.deb Size: 16472 MD5sum: f9ccfb1a150ba0d83aeb966e0e23c680 SHA1: 10f6dcfc5649ead6e4b2af9036315937258bc1fa SHA256: 38f43d5e52bcb692466bdd03ca42106ad5ce5e6995c7af645ceeaf1c264a76ef SHA512: 25995c2cbccf1cd4d0bd0e3acebc38c2deeed72bd6c81f643f9f57a3c8f3203c84ba66b71ee0d02b0bc91e85c3a694b57b9d01c335f9ef7494fd5a8f7717b6b1 Homepage: https://cran.r-project.org/package=MOsemiind Description: CRAN Package 'MOsemiind' (Marshall-Olkin Shock Models with Semi-Independent Time) Provides tools for analyzing Marshall-Olkin shock models semi-independent time. It includes interactive 'shiny' applications for exploring copula-based dependence structures, along with functions for modeling and visualization. The methods are based on Mijanovic and Popovic (2024, submitted) "An R package for Marshall-Olkin shock models with semi-independent times." Package: r-cran-mosqcontrol Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-assertthat, r-cran-pracma, r-cran-nlcoptim, r-cran-nloptr, r-cran-sfsmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mosqcontrol_0.1.0-1.ca2404.1_all.deb Size: 317114 MD5sum: 47e4875ca0fe5da116176881f8abbe06 SHA1: 99b4d1e0d360b7b864b70b2d4e2447c9589abe8a SHA256: 29db892b265035ab9a76c457d1e2c6bf462b5a580f2c6db24746d6dee72f8b69 SHA512: 86d8c18a43cda92d20c71817b7ebe85ef796d2be4cc2903a41d29ef421352d1e38e955f80bbd302b3ae3f45eab8f33ae6099683e99bc089708061f337ee76ef0 Homepage: https://cran.r-project.org/package=mosqcontrol Description: CRAN Package 'mosqcontrol' (Mosquito Control Resource Optimization) This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments. Package: r-cran-moss Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-dbscan, r-cran-rtsne Suggests: r-bioc-annotate, r-cran-bigparallelr, r-cran-bigstatsr, r-cran-future.apply, r-cran-scatterpie, r-cran-clvalid, r-bioc-complexheatmap, r-cran-fpc, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggthemes, r-cran-gridextra, r-cran-irlba, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis, r-cran-spelling, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-moss_0.2.2-1.ca2404.1_all.deb Size: 3344364 MD5sum: afb4c6410f73d1842e252a20efda2958 SHA1: 091adf5a76db7b8d7bae20db7d7618d215d6327d SHA256: 77fe22b3953d80df03541a05506a052da2a7d72734f637b1ee86afb519a6854e SHA512: d86233cb050fac0e003bc7465a52e1bdaca0482e676280e347c9f5978dc39f358af3dff74a169718478464739f11ea40ba5f1440fb4e5e60b3a64a1306fadf40 Homepage: https://cran.r-project.org/package=MOSS Description: CRAN Package 'MOSS' (Multi-Omic Integration via Sparse Singular Value Decomposition) High dimensionality, noise and heterogeneity among samples and features challenge the omic integration task. Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes of variation of the combined omics, b. imposing sparsity constraints at both subjects (rows) and features (columns) levels using Elastic Net type of shrinkage, and c. allowing both linear and non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to detect clusters in very convoluted data (Gonzalez-Reymundez et. al, 2022) . Package: r-cran-most Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-most_0.1.2-1.ca2404.1_all.deb Size: 60806 MD5sum: e24ae28ae939268e90cad8a1f159e3b0 SHA1: d8fc20c697c307320e393edb2cc8c8ed35579940 SHA256: 7dc49fbbabc4b629af572d1dc9dd6059df57a5c29148d7d7a634788264c85a39 SHA512: 6cbc0b8567d8d287c7bfeb4e08ea59bb2a4bf7875a08c8e7a0539a01d4da2a6fe406c448a2ec4b44e1fbc0158d487d14d497c8c53ea15602a35bde3f00247f77 Homepage: https://cran.r-project.org/package=MOST Description: CRAN Package 'MOST' (Multiphase Optimization Strategy) Provides functions similar to the 'SAS' macros previously provided to accompany Collins, Dziak, and Li (2009) and Dziak, Nahum-Shani, and Collins (2012) , papers which outline practical benefits and challenges of factorial and fractional factorial experiments for scientists interested in developing biological and/or behavioral interventions, especially in the context of the multiphase optimization strategy (see Collins, Kugler & Gwadz 2016) . The package currently contains three functions. First, RelativeCosts1() draws a graph of the relative cost of complete and reduced factorial designs versus other alternatives. Second, RandomAssignmentGenerator() returns a dataframe which contains a list of random numbers that can be used to conveniently assign participants to conditions in an experiment with many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect size, or required sample size of a factorial or fractional factorial experiment, for main effects or interactions, given several possible choices of effect size metric, and allowing pretests and clustering. Package: r-cran-motbfs Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1499 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve, r-cran-bnlearn, r-cran-ggm, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-infotheo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-motbfs_2.0.1-1.ca2404.1_all.deb Size: 1186668 MD5sum: b63b76ed6686547ff16e6199110537e4 SHA1: d811fe62a22e2d68098ae3315f936e4aaa48d177 SHA256: cfb91cd3332554018f098f9e12df00c3d43fafa022466e8c85a5ba217e1de0fc SHA512: efa59a34da9461bfcf21ae58d40097e65e25a08409e70b1f9d3072b9270506574f02c8a125dd64f558abd890acbc8e3afa57c12dc81f2acff25a3abb8c55f51e Homepage: https://cran.r-project.org/package=MoTBFs Description: CRAN Package 'MoTBFs' (Learning Hybrid Bayesian Networks using Mixtures of TruncatedBasis Functions) Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) ; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) ; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) ). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'. Package: r-cran-mote Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mote_1.2.2-1.ca2404.1_all.deb Size: 325978 MD5sum: e5b89cefa32b97e5d09afff94eedb830 SHA1: 6c833551d4a51076d2c111efc7bec9e5f7d00d51 SHA256: 8f7536064727ee65b0037d28c9a79496c86769bf91d11ac18490f0d107a337c4 SHA512: d59074ff19d364b87a8d4819839a88f4e3f0e28dd9354d3e4ce5468a0c5678caf67fb87208b2b77d86b7e31cf60ab5c50d2d6eb9f4074b5c3a11dcc139206c3c Homepage: https://cran.r-project.org/package=MOTE Description: CRAN Package 'MOTE' (Effect Size and Confidence Interval Calculator) Measure of the Effect ('MOTE') is an effect size calculator, including a wide variety of effect sizes in the mean differences family (all versions of d) and the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central confidence intervals for each effect size, relevant test statistics, and output for reporting in APA Style (American Psychological Association, 2010, ) with 'LaTeX'. In research, an over-reliance on p-values may conceal the fact that a study is under-powered (Halsey, Curran-Everett, Vowler, & Drummond, 2015 ). A test may be statistically significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 ). Although the American Psychological Association has long advocated for the inclusion of effect sizes (Wilkinson & American Psychological Association Task Force on Statistical Inference, 1999 ), the vast majority of peer-reviewed, published academic studies stop short of reporting effect sizes and confidence intervals (Cumming, 2013, ). 'MOTE' simplifies the use and interpretation of effect sizes and confidence intervals. Package: r-cran-motherduck Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-assertthat, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-httr2, r-cran-rlang, r-cran-janitor, r-cran-tibble Suggests: r-cran-testthat, r-cran-quarto, r-cran-contoso, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-motherduck_0.2.1-1.ca2404.1_all.deb Size: 2321288 MD5sum: 8e1ae7c414a16b537264b9142776d3b1 SHA1: 6a6e741932982e6571499ecc90b33ec26fa6f7f3 SHA256: 3b8685944b8778382271f4575369c39cc9f088b50451866959aa00cc0add33a8 SHA512: 887228ea092909debeecc04b2ae38cc27c64f9e7d127d0922286c408686373f410f208b225ef146ecec1ccb3ec12c9b390e64251f93eae601edad68ec40e66fa Homepage: https://cran.r-project.org/package=motherduck Description: CRAN Package 'motherduck' (Utilities for Managing a 'Motherduck' Database) Provides helper functions, metadata utilities, and workflows for administering and managing databases on the 'Motherduck' cloud platform. Some features require a 'Motherduck' account (). Package: r-cran-motifcluster Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rspectra Suggests: r-cran-covr, r-cran-knitr, r-cran-mclust, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-motifcluster_0.2.3-1.ca2404.1_all.deb Size: 450472 MD5sum: c66deca240b2a310013539f8fed16739 SHA1: 6e1f7b20c705b08751e0f9edda08145eb82cd94b SHA256: 2f20f21eed93d6001c5b2113befd5c820f6994c41372b123e7fd58ceb1fd8d11 SHA512: 9217c6812c6769e83c167154ba2481ef1243f84dd767b4d30c1c466af66e844a6a2c951501537ce4b2b4a08338edb627bcb32985d30c1a56d10128332c1f42e4 Homepage: https://cran.r-project.org/package=motifcluster Description: CRAN Package 'motifcluster' (Motif-Based Spectral Clustering of Weighted Directed Networks) Tools for spectral clustering of weighted directed networks using motif adjacency matrices. Methods perform well on large and sparse networks, and random sampling methods for generating weighted directed networks are also provided. Based on methodology detailed in Underwood, Elliott and Cucuringu (2020) . Package: r-cran-motifr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-intergraph, r-cran-network, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-ergm, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-motifr_1.0.0-1.ca2404.1_all.deb Size: 610402 MD5sum: 02e12be390e23c9a98be73e616d0fa1e SHA1: 6259b981ba4b30de51c7f0d991ab2d08b7e11e42 SHA256: c70641ce53906407399ed942415ac7daa4067359de627ae1c8a08991ea47b8c1 SHA512: a0bc36827491d89ccbb0c301b89435b9aef8562c34c9edbe244f2b1bb02adce4d371240449bdb88c8e3dd9e80df1f4d0252031b3a0e029ea0a56aef69a22e41b Homepage: https://cran.r-project.org/package=motifr Description: CRAN Package 'motifr' (Motif Analysis in Multi-Level Networks) Tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. 'motifr' can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent 'SESMotifAnalyser' 'Python' package written by Tim Seppelt. Package: r-cran-motorneuron Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2736 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dygraphs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-motorneuron_1.0.0-1.ca2404.1_all.deb Size: 229496 MD5sum: 7085370bdf46497bcc97bba82ee4a984 SHA1: e670e169535a075729c6329085748d5202e701a8 SHA256: 60a6773068ef6c3994ea0459c8eb823a277156d18bc82718a37a811af7eea44a SHA512: cb62162a677ae2927a6786a9bae8fd4c7f34be2c99aec182000695d1b620d1222891a05ab1265ffc5755635efdaa18bd27ec76645a7f204cf09154478fd1cbf8 Homepage: https://cran.r-project.org/package=motoRneuron Description: CRAN Package 'motoRneuron' (Analyzing Paired Neuron Discharge Times for Time-DomainSynchronization) The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) for a more thorough introduction. Package: r-cran-moult Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-moult_2.3.1-1.ca2404.1_all.deb Size: 679818 MD5sum: 6dc1305fb263e1507cceca58752770c8 SHA1: 5d7559702f70ec94dce3f37e38b4c8f5b1138d2d SHA256: ae68ec56539b9a75db83c0ce150dcdd098db021c4281c32734a98e343be50a65 SHA512: c51f2e55e44a1c666df6163dfc2243a71555c0718d4b698ff8c56281d25d1270d9969e94340a2b8cc7474ea62f5bcb2bdbd840f593b26ba577acb10f7a648553 Homepage: https://cran.r-project.org/package=moult Description: CRAN Package 'moult' (Models for Analysing Moult in Birds) Functions to estimate start and duration of moult from moult data, based on models developed in Underhill and Zucchini (1988, 1990). Package: r-cran-mountainplot Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-latticeextra, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mountainplot_1.4-1.ca2404.1_all.deb Size: 110762 MD5sum: fec6d62c4c4477e15c37b42b383f22f9 SHA1: 534a277d3c3e9294f3207dbdf8df7a86dd055dcd SHA256: d7b093fe46a4296901ad8c758c4283dd029221111de46b70ebeb0f123a8c11b3 SHA512: f8c5afe108a66c8f7befd8714b433ecebbdc9869954bfa3a3668b31662213902a072c9699c0752e5be78ff55870b860114d18e8ee9367bbb15f4810e7a7b5602 Homepage: https://cran.r-project.org/package=mountainplot Description: CRAN Package 'mountainplot' (Mountain Plots, Folded Empirical Cumulative Distribution Plots) Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) . Package: r-cran-mousetrajectory Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle, r-cran-signal Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-mousetrajectory_0.2.1-1.ca2404.1_all.deb Size: 88664 MD5sum: 3d7a4b2a04f6f10e69c3d857470b5c24 SHA1: ddd3b74999a1be945d4190521bc87b6236f4afea SHA256: b4f9dc0ab7646382a567592eceab440f8b2acca428e5281f221a8fc83867d93f SHA512: 73c61d7bf16965cec04bf80de8dd73fa524d5dc3d2f61fc4a2df628fb4d21f5317c1986f3b0caddb1281f8364fd756f4af97317e42e752a12e58b2b009566bbb Homepage: https://cran.r-project.org/package=mousetRajectory Description: CRAN Package 'mousetRajectory' (Mouse Trajectory Analyses for Behavioural Scientists) Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) . Package: r-cran-move2 Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-sf, r-cran-rlang, r-cran-units, r-cran-tidyselect, r-cran-dplyr, r-cran-tibble, r-cran-vroom, r-cran-cli, r-cran-vctrs, r-cran-bit64 Suggests: r-cran-knitr, r-cran-askpass, r-cran-digest, r-cran-keyring, r-cran-xml2, r-cran-curl, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown, r-cran-lwgeom, r-cran-s2, r-cran-move, r-cran-raster, r-cran-withr, r-cran-lubridate, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-circular, r-cran-tidyr, r-cran-gganimate, r-cran-prettymapr, r-cran-gifski, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-move2_0.5.0-1.ca2404.1_all.deb Size: 3284672 MD5sum: 5731ddad2440b28061237458ade38169 SHA1: de35cef9f0104a5692af4b83408817d713e3ad0c SHA256: e1cfba0eed75888277869a0f12c3923c976c91a2aa3e3f16d74a99aef5618ab6 SHA512: 4fb7f750572091e654cce1b1f56184bdd52c46e3072c3b9cf1b76ea3205b0ef3f38cb2bf77516e96a698b42872dbe1ad855d7c76ebf7cf713600830f4d015685 Homepage: https://cran.r-project.org/package=move2 Description: CRAN Package 'move2' (Processing and Analysing Animal Trajectories) Tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from 'movebank' and files is facilitated. Package: r-cran-movecost Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-igraph, r-cran-ggplot2 Suggests: r-cran-elevatr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-movecost_3.0.0-1.ca2404.1_all.deb Size: 2854608 MD5sum: af219e3ddfeeff3626784cbf66c744c4 SHA1: 073d024fca938ef25f9fda5c1446110426ffddc1 SHA256: e32467f60c0dadf2d6a7d475afceb5e32ae9dcab77315cf5cc5365557dd7f151 SHA512: de32b70dac804706a1146121fc2c70299acfff48f70da0c9917e95b50ec293f71ec374bcea873c5ff36089f1d167598dfbbb4c0e4b4cef7d677ca2e72a7b6347 Homepage: https://cran.r-project.org/package=movecost Description: CRAN Package 'movecost' (Calculation of Slope-Dependant Accumulated Cost Surface,Least-Cost Paths, Least-Cost Corridors, Least-Cost NetworksRelated to Human Movement Across the Landscape) Provides the facility to calculate non-isotropic accumulated cost surfaces, least-cost paths, least-cost corridors, least-cost networks, ranked alternative paths, cost allocation and cost boundaries, using a number of human-movement-related cost functions that can be selected by the user. The package is built around a compute-once design: a single cost surface object is created first and then reused by every analysis function, avoiding redundant computation. Visualisation is fully decoupled from computation and is provided through 'ggplot2' methods that can be invoked, customised, and re-invoked at any time without re-running any analysis. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) . Package: r-cran-movedesign Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-bsplus, r-cran-config, r-cran-crayon, r-cran-ctmm, r-cran-data.table, r-cran-dplyr, r-cran-fontawesome, r-cran-gdtools, r-cran-gfonts, r-cran-ggiraph, r-cran-ggplot2, r-cran-ggtext, r-cran-golem, r-cran-lubridate, r-cran-parsedate, r-cran-patchwork, r-cran-quarto, r-cran-reactable, r-cran-rintrojs, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2 Filename: pool/dists/noble/main/r-cran-movedesign_0.3.3-1.ca2404.1_all.deb Size: 3844152 MD5sum: 987178b8c81911d50f57163a8b573b1b SHA1: e87bc0a73d4672423dcb2887d6425558a37087f0 SHA256: 7ffcad51ebfc86505bf5fb6e68b387a4caf4604e90bdddb1cd5953015d0d9498 SHA512: 1164846c141fa1b04081f27e5eb201be7df47b42dd719cbe5fe10a26b6be7aa1d3e64230818ae5091b633ffa5b0d0e9d3e7402f293396bc9fb5e24c2f00fb8c7 Homepage: https://cran.r-project.org/package=movedesign Description: CRAN Package 'movedesign' (Study Design Toolbox for Movement Ecology Studies) Toolbox and 'shiny' application to help researchers design movement ecology studies, focusing on two key objectives: estimating home range areas, and estimating fine-scale movement behavior, specifically speed and distance traveled. It provides interactive simulations and methodological guidance to support study planning and decision-making. The application is described in Silva et al. (2023) . Package: r-cran-moveez Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4907 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-biplotez, r-cran-gganimate, r-cran-ggplot2, r-cran-gpabin Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-tibble, r-cran-scales, r-cran-rcolorbrewer, r-cran-purrr Filename: pool/dists/noble/main/r-cran-moveez_1.3.0-1.ca2404.1_all.deb Size: 3330280 MD5sum: 2a8cabe930525c0a35d17e2754d0120b SHA1: 34d0bd884299b924df5b254286067120b5ee5b0d SHA256: b02d1542f04b08dcd7fff0181c226efe554fc31775a707303bfb61db590901e0 SHA512: 2ef51c1d37413ae5e5a1fca3cdd093e0b223393faf389c799be9dbb428dd7da9b31495c10afad90f1d550858e952ac47aded9684d8b1fe41612a99cea8161622 Homepage: https://cran.r-project.org/package=moveEZ Description: CRAN Package 'moveEZ' (Animated Biplots) Create animated biplots that enables dynamic visualisation of temporal or sequential changes in multivariate data by animating a single biplot across the levels of a time variable. It builds on objects from the 'biplotEZ' package, Lubbe S, le Roux N, Nienkemper-Swanepoel J, Ganey R, Buys R, Adams Z, Manefeldt P (2024) , allowing users to create animated biplots that reveal how both samples and variables evolve over time. Package: r-cran-movegroup Architecture: all Version: 2026.07.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2804 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-beepr, r-cran-dplyr, r-cran-ggmap, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-move, r-cran-purrr, r-cran-raster, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stars, r-cran-starsextra, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-viridis Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-movegroup_2026.07.01-1.ca2404.1_all.deb Size: 2129720 MD5sum: c52f3519d9d7b3a933022160b58361bb SHA1: a0996accfc6e60ebf5034d0f0ae697911c30d6c9 SHA256: 27a15a0255ad22f037916616e094260406648f9ea3055d7c9102c76b1dcc2ef4 SHA512: c1b299980e963b2e06610aa728c2e504c25cfab722633950b6c65fd0a7f4e466ea63c6405c8c46348a8c32fb75529581da3e06c6eea0b4e362fa42fe28b9309f Homepage: https://cran.r-project.org/package=movegroup Description: CRAN Package 'movegroup' (Visualizing and Quantifying Space Use Data for Groups of Animals) Offers an easy and automated way to scale up individual-level space use analysis to that of groups. Contains functions from the 'move' package to calculate either a dynamic Brownian bridge movement model or dynamic Bivariate Gaussian bridges from movement data for individual animals, as well as functions to visualize and quantify space use for individuals aggregated in groups. Originally written with passive acoustic telemetry in mind, this package also provides functionality to account for unbalanced acoustic receiver array designs, and satellite tag data. Package: r-cran-movementsync Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hms, r-cran-igraph, r-cran-lmtest, r-cran-osfr, r-cran-rlang, r-cran-scales, r-cran-signal, r-cran-tidyr, r-cran-waveletcomp, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-movementsync_0.1.5-1.ca2404.1_all.deb Size: 1961272 MD5sum: 62254cac70e783010fc3acf7ad6f4825 SHA1: fc57c6f5b060719c9a233c6d77d36b36c0871bfd SHA256: 39a9fd84565510a82d91035f8889cb011cd1f1caeee1f8f91eeebd753a680439 SHA512: 32beaa17d69b9434850e690dda121d859a2335a81fff182ffa1db0bb3c5971c8708ad174996dc203a6575b9e64feb22f15ae20d0c5c709e5459246caf83be172 Homepage: https://cran.r-project.org/package=movementsync Description: CRAN Package 'movementsync' (Analysis and Visualisation of Musical Audio and Video MovementSynchrony Data) Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) , while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) . Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) . Package: r-cran-movieroc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-animation, r-cran-intrval, r-cran-gtools, r-cran-e1071, r-cran-robustbase, r-cran-rsolnp, r-cran-ks, r-cran-zoo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-movieroc_0.1.2-1.ca2404.1_all.deb Size: 2068714 MD5sum: 34cae615f6e08bf81aab9eb4f087b5bf SHA1: b69c1dc866100a0613ae57b86dc4f69fc7d0cc83 SHA256: 25dc73a1cef51c5a7af739b6f1025a43b205f08f2c22dcb15df8099c7e05428b SHA512: 0d3d3a00f57afcf4974b35400e8464a5ca9aadb6fd34b5e15ff794be69555c301d4d47c1acc621d3cadb5bf6e9b306e2638124f079adf7d3de5c9c82aae835e3 Homepage: https://cran.r-project.org/package=movieROC Description: CRAN Package 'movieROC' (Visualizing the Decision Rules Underlying Binary Classification) Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function). Package: r-cran-mpactr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cli, r-cran-ggplot2, r-cran-r6, r-cran-readr, r-cran-treemapify, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpactr_0.1.0-1.ca2404.1_all.deb Size: 535764 MD5sum: 31d43e2e15bc694542cab8b6da01519a SHA1: 692acd66e5224a8556bba2690d0286ad579a5154 SHA256: 293a7b545421e380d1779608ad7b2a10c5ee67dfb6418cbd4abf5388b56737d0 SHA512: 84fffc8f9630a683555aa96d15269cefd4862bd648dc74759c4abd79220386f2b7e73590253cc27dd240fba02590c75c1c619f91e3c9f682d7acfddcac458e2a Homepage: https://cran.r-project.org/package=mpactr Description: CRAN Package 'mpactr' (Correction of Preprocessed MS Data) An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) ). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow. Package: r-cran-mpae Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-rcmdrmisc Suggests: r-cran-car, r-cran-gbm, r-cran-leaps, r-cran-lmtest, r-cran-glmnet, r-cran-mgcv, r-cran-np, r-cran-neuralnettools, r-cran-pdp, r-cran-vivid, r-cran-plot3d, r-cran-appliedpredictivemodeling, r-cran-islr Filename: pool/dists/noble/main/r-cran-mpae_0.1.2-1.ca2404.1_all.deb Size: 123154 MD5sum: c8e17acfe99283ad938a045d771c5df5 SHA1: 553c0cf6d9b8ae75eb93ec64ce0a13d16dd63956 SHA256: f7afcd1472d1d2086ea4d3a10c605fa5cb4f4e11333510ae3f028deb251006db SHA512: 9c4955f3b6e173dca887e3998f1d58fe2ac6a3c9149b224dd961ac6fe93acab594b51b8825b1a1651caae84390dfacb01d770389c588a571c24f891c5e2df8cb Homepage: https://cran.r-project.org/package=mpae Description: CRAN Package 'mpae' (Metodos Predictivos de Aprendizaje Estadistico (StatisticalLearning Predictive Methods)) Functions and datasets used in the book: Fernandez-Casal, R., Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de aprendizaje estadistico" . Package: r-cran-mpar Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-rlang, r-cran-xml2 Suggests: r-cran-openalexr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpar_0.5.0-1.ca2404.1_all.deb Size: 159134 MD5sum: 1f55be346d1b0c6d323ca07719189506 SHA1: 7f1ba858ee8996a3836730223a8b7a0e4a584f4b SHA256: dd76cb8e33957d4d76c9c7183014b58bcc41bb83eee9c59a06a5c5c41942f0a6 SHA512: 09f013b7037dd6dc2502b052e94fced2bb3bf6ff68fa4e325043244a77bd4dd69a6d5c8b8914da9b27b4d3bfd90759bfd8228dbe6f52ceb1b86e3c55016154e9 Homepage: https://cran.r-project.org/package=mpaR Description: CRAN Package 'mpaR' (Main Path Analysis for Citation and Directed Networks) Implements Main Path Analysis (MPA) as introduced by Hummon and Doreian (1989) . Given a directed acyclic graph (DAG) representing a citation or precedence network, the package computes traversal weights (SPC, SPLC, SPNP) for each edge and extracts the global, local, and key-route main paths. Also provides tools for DAG validation, node role classification (source/terminal/user), per-component path extraction for disconnected networks, and scale-free network testing. Accepts 'igraph' objects or edge-list data frames as input. Includes readers for 'Pajek' (.net) and 'Gephi' export (.gexf, .graphml) files. Package: r-cran-mpathr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-readr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpathr_1.0.4-1.ca2404.1_all.deb Size: 301192 MD5sum: 4b1f5072f9cfebad6b065e574ea8159d SHA1: d439b6d94e824d1d728ba670f672c367024e2770 SHA256: 7f007df68c06b6f9c74ffccbf941fd23d1d0d2f28b883b847f6928cb2bfcda37 SHA512: 4a062f461588aafbbc293dc11ab1fa6fc62966ca6e90753b98349b7bdb8604f476f305e6216f69902f37ba6fe6fad73b354bf73e0fe649ac881f3b29487cc50f Homepage: https://cran.r-project.org/package=mpathr Description: CRAN Package 'mpathr' (Easily Handling Data from the ‘m-Path’ Platform) Provides tools for importing and cleaning Experience Sampling Method (ESM) data collected via the 'm-Path' platform. The goal is to provide with a few utility functions to be able to read and perform some common operations in ESM data collected through the 'm-Path' platform (). Functions include raw data handling, format standardization, and basic data checks, as well as to calculate the response rate in data from ESM studies. Package: r-cran-mpathsenser Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-furrr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-rsqlite, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cli, r-cran-curl, r-cran-ggplot2, r-cran-httr, r-cran-kableextra, r-cran-knitr, r-cran-lintr, r-cran-progressr, r-cran-rmarkdown, r-cran-rvest, r-cran-sodium, r-cran-spelling, r-cran-testthat, r-cran-vroom Filename: pool/dists/noble/main/r-cran-mpathsenser_1.2.4-1.ca2404.1_all.deb Size: 3979730 MD5sum: 630c7ed77a438ebd40660f56923442e5 SHA1: fa2e3f0345d402725b3876d11c29c3f8add42b2d SHA256: 885c1c6886c0f7adda27d4d90a633eb225b83843cda6eb0815b7229238e072c2 SHA512: 2f9e3d71c70616268688c7557fed55d50f48198dde8e85ee1278aeb41b1e20c44a2da2a1cbbc93a15efb854d2fa40f3fc1cf380054f4f6bc0216d403f241fd86 Homepage: https://cran.r-project.org/package=mpathsenser Description: CRAN Package 'mpathsenser' (Process and Analyse Data from m-Path Sense) Overcomes one of the major challenges in mobile (passive) sensing, namely being able to pre-process the raw data that comes from a mobile sensing app, specifically 'm-Path Sense' . The main task of 'mpathsenser' is therefore to read 'm-Path Sense' JSON files into a database and provide several convenience functions to aid in data processing. Package: r-cran-mpci Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mpci_1.0.7-1.ca2404.1_all.deb Size: 44188 MD5sum: d2b328fc5f151e10624d4340e31d3a3e SHA1: 9790cab158d9a34093bbc56a79908847d688b2be SHA256: e742a64e3cd6b56f053dd95ae5c9ed7c017ed167ac7f26de2b1f0a811014cd9e SHA512: d9bc1ccedd893d621e483a0ba970242b1d2f306716a8b82d42eb5ff81d7e0f8509ec232189dcbfae6762f177521154a7a282aab3d803eaecc3a603322289c00c Homepage: https://cran.r-project.org/package=MPCI Description: CRAN Package 'MPCI' (Multivariate Process Capability Indices (MPCI)) It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included. Package: r-cran-mpdir Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-mpdir_0.3-1.ca2404.1_all.deb Size: 392876 MD5sum: 3c636ead53ab537764af6dc69f035208 SHA1: d116995740859d047c9253c926756b090cb02514 SHA256: cfc348f6fb02ea927883dadc3c96f620cbc6ee69d0c591d98f2a06d33f60e8b9 SHA512: f4959488bc8a318bc5dabc575b81a20b08dc4df3a1e9c27691137d9d2e246157bb458b7668452ae98d2197b1d8d7623cd705fcc2d8eec6b5c285a34117e287b8 Homepage: https://cran.r-project.org/package=MPDiR Description: CRAN Package 'MPDiR' (Data Sets and Scripts for Modeling Psychophysical Data in R) Data sets and scripts for Modeling Psychophysical Data in R (Springer). Package: r-cran-mpge Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpge_1.0.1-1.ca2404.1_all.deb Size: 78476 MD5sum: 9de90b63468e0046bc019d4669d2132c SHA1: a3d38a5a2cc371f93f86af5686d2642d3469b27c SHA256: 9f83d49f36bf6be09007fcbc06da4495f1886f4f7b5a9303fd58d55a3a2547d0 SHA512: 29783704980aeaf121d825f254742403b35bffd4b82eb07866b8e5eda68dbc6bf97cdd9726a6c93f611ad9e158ad7a2dce7c6a554b868f78ce99275b5239c09a Homepage: https://cran.r-project.org/package=MPGE Description: CRAN Package 'MPGE' (A Two-Step Approach to Testing Overall Effect ofGene-Environment Interaction for Multiple Phenotypes) Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, T Haldar, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) . Package: r-cran-mpi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-mpi_0.1.0-1.ca2404.1_all.deb Size: 37802 MD5sum: 04d3ad15f95c8f2e2924738c21acc77d SHA1: c05911422ce5f389105d95ae4690f4c03b3e6eb3 SHA256: 117caaf732c4b3f7e9ef3ca711b3f948345f7d38bcae2e249987186581246139 SHA512: c47a9bad771315e4185366db05d0cb0995da5f77df7b70d9a6310d011c069cd43667cd8482e62df0929b9bba40c21de4b89fe7c36df3a157342faef35033fc05 Homepage: https://cran.r-project.org/package=MPI Description: CRAN Package 'MPI' (Computation of Multidimensional Poverty Index (MPI)) Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) for The Alkire-Foster methodology. Package: r-cran-mpindex Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-tibble, r-cran-openxlsx, r-cran-tsg, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-tidyr, r-cran-stringr, r-cran-survey, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gt Filename: pool/dists/noble/main/r-cran-mpindex_0.3.1-1.ca2404.1_all.deb Size: 159104 MD5sum: 3fb50c78434c686e4d1b4f9191fd0e30 SHA1: 79fd9f3c5250e3bf0f8c732e61fa27dfcab3fe8c SHA256: 5b8baa7fb2cb7a69411418ac044cba7f6afffb87fceca3213f0a091cbb0c1cc2 SHA512: bce060a9f2b4753eb370ab7e330f471b3ad25767de6d8440625c4462545dd956813dd337d4636c10054e6a46e85f29de136ea16238a222b1ea7fec5abff7e07f Homepage: https://cran.r-project.org/package=mpindex Description: CRAN Package 'mpindex' (Multidimensional Poverty Index (MPI) via the Alkire-FosterMethod) Estimate Multidimensional Poverty Index (MPI) measures from household survey microdata using the Alkire-Foster dual-cutoff counting method (Alkire and Foster, 2011). Load indicator specifications from CSV, Excel, JSON, or plain-text files; compute the headcount ratio (H), intensity (A), and MPI = H x A across any subgroup; and export results to formatted Excel reports. Supports complex survey designs — stratification, clustering, and probability weights — and optionally appends design-based standard errors and confidence intervals. Package: r-cran-mpitbr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-survey Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-mpitbr_1.0.1-1.ca2404.1_all.deb Size: 1454034 MD5sum: 4ea589402d394839e0defb72f4c903dc SHA1: a21af8d1a6ac533979a9ddd40a6659a284e2f05d SHA256: 83a1201df5df0428edfa1209ac293406a5637522f1e5a607abedfa11f6f3b736 SHA512: 17bfeb0f52acb889db94c383070d38a32aa7ba7a1626985540b2a189d306dc4b54cc5a1bceb1871ad7f84bdb52e9480069f3555e9c52cf6aefc13ac9d40fefb6 Homepage: https://cran.r-project.org/package=mpitbR Description: CRAN Package 'mpitbR' (Calculate Alkire-Foster Multidimensional Poverty Measures) Estimate Multidimensional Poverty Indices disaggregated by population subgroups based on the Alkire and Foster method (2011) . This includes the calculation of standard errors and confidence intervals. Other partial indices such as incidence, intensity and indicator-specific measures as well as intertemporal changes analysis can also be estimated. The standard errors and confidence intervals are calculated considering the complex survey design. Package: r-cran-mplot Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps, r-cran-foreach, r-cran-bestglm, r-cran-doparallel, r-cran-dorng, r-cran-plyr, r-cran-shinydashboard, r-cran-shiny, r-cran-glmnet, r-cran-googlevis, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-mvoutlier, r-cran-glmulti, r-cran-rmarkdown, r-cran-dt, r-cran-mass Filename: pool/dists/noble/main/r-cran-mplot_1.0.6-1.ca2404.1_all.deb Size: 204376 MD5sum: 1c6db321c3520e5d27bbbeb26dd77d9c SHA1: 3f6b20dbefb962affc459d43576213ba24fc533c SHA256: 6c952e4771fd6170b7b1bd92b83073cde360d491a54956198b8708ce64179288 SHA512: fadcb9fbd65731d698fcbf080c09dfdddc43dc93e6cff3f013ff91e6bbc4f3d84ab63fd68b89b5a0401f5b1fed4fe19799669d368692d2a5f041d608d3affc10 Homepage: https://cran.r-project.org/package=mplot Description: CRAN Package 'mplot' (Graphical Model Stability and Variable Selection Procedures) Model stability and variable inclusion plots [Mueller and Welsh (2010, ); Murray, Heritier and Mueller (2013, )] as well as the adaptive fence [Jiang et al. (2008, ); Jiang et al. (2009, )] for linear and generalised linear models. Package: r-cran-mplusautomation Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3574 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-plyr, r-cran-gsubfn, r-cran-coda, r-cran-xtable, r-cran-lattice, r-cran-texreg, r-cran-pander, r-cran-digest, r-cran-ggplot2, r-cran-data.table, r-cran-fastdummies, r-cran-checkmate Suggests: r-bioc-rhdf5, r-cran-relimp, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mplusautomation_1.3-1.ca2404.1_all.deb Size: 2640824 MD5sum: 92b3e3e4dca18af4669933494c57eaee SHA1: eda6dfea717e074999cbe82333385fc7e41778c4 SHA256: 231228c21b9a271f62fd98559217e93ac525bea51fdc3dfe592fe84203572499 SHA512: 0de578e10c23fcc634452cb8f5a31d7fad4c9162c7ace345f5eadcb8087bc44295ff9300ca0762adb9a5e454d1ef9dc057bf67ede7e9885bb135c587ded72202 Homepage: https://cran.r-project.org/package=MplusAutomation Description: CRAN Package 'MplusAutomation' (An R Package for Facilitating Large-Scale Latent VariableAnalyses in Mplus) Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics. Package: r-cran-mpluslgm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mplusautomation, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-glue Filename: pool/dists/noble/main/r-cran-mpluslgm_1.0.0-1.ca2404.1_all.deb Size: 92090 MD5sum: 3e1558007ea884b8293d3cf81c0d52fd SHA1: 840de7b96cd7106ceb1102e30b2a2cc3e0d94f9c SHA256: ac137dd004c3dc31f43a7a9f048623f3c221651419d4a6b60efb6ece7d146471 SHA512: 550b2285beec8e4f22b92afdbf75c76451edaf01175816e20d42b7d399ee6fae0e6470a800c974f7a19b2e7fff535d0198b4149366d37ad766072037aae5474b Homepage: https://cran.r-project.org/package=MplusLGM Description: CRAN Package 'MplusLGM' (Automate Latent Growth Mixture Modelling in 'Mplus') Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. . Package: r-cran-mplusparallel.automation Architecture: all Version: 0.0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mplusautomation, r-cran-dplyr, r-cran-furrr, r-cran-future Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mplusparallel.automation_0.0.1.1-1.ca2404.1_all.deb Size: 39182 MD5sum: f4310e179e24f827fc25df2412c866ee SHA1: af1c59b051570ef5e7e18bc463a9c4f40f3f328b SHA256: ec6a175495d52b9ad5a628df5b19e4b5c9ae9559e5a7fdae7a4c2eeb30bba358 SHA512: e65024741d48033c569d3ae9a15754d308e58cedb394d2bb9925d38f94f22713b2611fc6244c1cb3c95b1fa491fc3674153f47a9ffd863c81d6317adc8714183 Homepage: https://cran.r-project.org/package=mplusParallel.automation Description: CRAN Package 'mplusParallel.automation' (Parallel Processing Automation for 'Mplus') Offers automation tools to parallelize 'Mplus' operations when using 'R' for data generation. It facilitates streamlined integration between 'Mplus' and 'R', allowing users to run and manage multiple 'Mplus' models simultaneously and efficiently in 'R'. 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Version 0.1.0 includes thirteen series covering the United States, United Kingdom, and Australia: for the US, the policy news shock of Nakamura and Steinsson (2018) , the orthogonalised surprise of Bauer and Swanson (2023) , the target and path factors of the Swanson (2021) extension of Gurkaynak, Sack, and Swanson (2005), the pure monetary policy and central bank information shocks of Jarocinski and Karadi (2020) , the informationally-robust shock of Miranda-Agrippino and Ricco (2021) , and the shadow federal funds rate of Wu and Xia (2016) ; for the UK, the UK Monetary Policy Event-Study Database of Braun, Miranda-Agrippino, and Saha (2025) , the high-frequency surprise of Cesa-Bianchi, Thwaites, and Vicondoa (2020) , and the narrative shock of Cloyne and Hurtgen (2016) ; for Australia, the three-component RBA surprise of Hambur and Haque (2023) and the credit-spread-augmented RBA narrative shock of Beckers (2020). 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The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, ) offers a principled alternative in which results for all possible combinations of reasonable modeling choices are reported. MPTmultiverse performs a multiverse analysis for multinomial processing tree (MPT, Riefer & Batchelder, 1988, ) models combining maximum-likelihood/frequentist and Bayesian estimation approaches with different levels of pooling (i.e., data aggregation) as described in Singmann et al. (2024, ). For the frequentist approaches, no pooling (with and without parametric or nonparametric bootstrap) and complete pooling are implemented using MPTinR . For the Bayesian approaches, no pooling, complete pooling, and three different variants of partial pooling are implemented using TreeBUGS . The main function is fit_mpt() which performs the multiverse analysis in one call. 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Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) and Hasler and Hothorn (2008) . Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances. 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The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models. 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Package: r-cran-mrct Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-fdapace, r-cran-ggplot2, r-cran-rdpack, r-cran-reshape2, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-mrct_0.0.1.0-1.ca2404.1_all.deb Size: 61400 MD5sum: 0748ad3115470e7c7989b2c1822bfb94 SHA1: e43a58f6a1b9c9755bcf0c3a76fbef64f3217251 SHA256: 8d8ab7973ccf1142cde054b0ef221893d13982e995577c8e52d33d2c5424ac7a SHA512: 408f198bb4615add1aa0de3f36554624b0d2e7a5a14edb7eff6a2b940d4edaae35bbe37f259a2373c8ac1ac994f6e15eab98c7b19d85f59ade0187041550155d Homepage: https://cran.r-project.org/package=mrct Description: CRAN Package 'mrct' (Outlier Detection of Functional Data Based on the MinimumRegularized Covariance Trace Estimator) Detect outlying observations in functional data sets based on the minimum regularized covariance trace (MRCT) estimator. Includes implementation of Oguamalam et al. (2023) . Package: r-cran-mrcv Architecture: all Version: 0.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tables Suggests: r-cran-geepack Filename: pool/dists/noble/main/r-cran-mrcv_0.4-0-1.ca2404.1_all.deb Size: 429870 MD5sum: 667ccc567c036c0c0a6fff5829edceff SHA1: 202fe623f63fefa00ac28eb3de80530c2041bba6 SHA256: 1dc3c2fc8485da866579387992b0f400fcc058cacdad6f69949cb6c8341ab650 SHA512: a7c2a9619b4e6fb5e2e7d3fdb8eb16d7a50b9bf0038d673d88f40246cfb198c89f4116134def91b2432b3c93f73d503d48fe0cb136732ca26a8f425f48951189 Homepage: https://cran.r-project.org/package=MRCV Description: CRAN Package 'MRCV' (Methods for Analyzing Multiple Response Categorical Variables(MRCVs)) Provides functions for analyzing the association between one single response categorical variable (SRCV) and one multiple response categorical variable (MRCV), or between two or three MRCVs. A modified Pearson chi-square statistic can be used to test for marginal independence for the one or two MRCV case, or a more general loglinear modeling approach can be used to examine various other structures of association for the two or three MRCV case. Bootstrap- and asymptotic-based standardized residuals and model-predicted odds ratios are available, in addition to other descriptive information. Statisical methods implemented are described in Bilder et al. (2000) , Bilder and Loughin (2004) , Bilder and Loughin (2007) , and Koziol and Bilder (2014) . Package: r-cran-mrds Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx, r-cran-mgcv, r-cran-numderiv, r-cran-nloptr, r-cran-rsolnp, r-cran-rdpack Suggests: r-cran-distance, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-mrds_3.0.1-1.ca2404.1_all.deb Size: 989806 MD5sum: 8b0888a41d3dbfe6197ce8ac312f8042 SHA1: 4c3e98178cf98035a1cabe2f24ef5d3fc8c7b76d SHA256: 9341fe63f00a6c0fb93b790ea87fb891fdb7fe689a269ba9c8bbf0389cd2811e SHA512: 463c0e93d0bae2d14fc4a0048f95c1438dfd27e037dbf3827ffa66a0057cdd91c28b8ec7b98ca4592b3352120d96645073de742a8fdfa14c6f808e6081a1eecf Homepage: https://cran.r-project.org/package=mrds Description: CRAN Package 'mrds' (Mark-Recapture Distance Sampling) Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. Package: r-cran-mreg Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mreg_1.2.1-1.ca2404.1_all.deb Size: 61512 MD5sum: e0ef1602516f635219e5718ca01137f2 SHA1: 65048e92c868cbe48a55035b4ab616b70e103193 SHA256: d1a399f439b04e36d2e4c6ed2f157ac435a0130d4048e12f62743cb8cc709b8b SHA512: 026609ea0f92dd88c8f568249584f95a94542a7342523618f88837b95da63c6cf9c2bc45a073637455927473e47530f05ad2893dc7c82bcfbaeb8e018db19c04 Homepage: https://cran.r-project.org/package=mreg Description: CRAN Package 'mreg' (Fits Regression Models When the Outcome is Partially Missing) Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics. 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Package: r-cran-mrf Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-forecast, r-cran-monmlp, r-cran-nnfor, r-cran-wavelets Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrf_0.1.9-1.ca2404.1_all.deb Size: 166374 MD5sum: 667c63112e3ccc1f3abf173402be792a SHA1: 338d8d57b2c4f9414dedd8aa1d66d905fc0fcce1 SHA256: 4c77f9e674d54b50a77f9051014f118191fa8283c96ca94eda077bfd03e5cb6e SHA512: 3a4dc97ff6b0d9693bbadd62e67b311c3db6f450d7ce00057c3c12587f5979a6afe239fe635b3f4e2b3dcf153c0030eaf793bb46e322d4b44a909dc6e8ee0297 Homepage: https://cran.r-project.org/package=mrf Description: CRAN Package 'mrf' (Multiresolution Forecasting) Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. . Package: r-cran-mrfa Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-glmnet, r-cran-grplasso, r-cran-plyr, r-cran-randtoolbox, r-cran-foreach Filename: pool/dists/noble/main/r-cran-mrfa_0.6-1.ca2404.1_all.deb Size: 130628 MD5sum: ee397e4831076f073462a71bc6baad76 SHA1: f33d18c1ccee08985cdc99f4b4958a3c93ec64fb SHA256: d1cb654686d540ac07986ea90779021b697b2624123911c7c9e2e86f23eb0a43 SHA512: c1074d1ebef4fd8e345850543b3ea03b8976624ea60a1e72db2378d024f1495fd27c6ddfcf1a44212280dda60896d3566d35bba72ccb7237d11de654b58af087 Homepage: https://cran.r-project.org/package=MRFA Description: CRAN Package 'MRFA' (Fitting and Predicting Large-Scale Nonlinear Regression Problemsusing Multi-Resolution Functional ANOVA (MRFA) Approach) Performs the MRFA approach proposed by Sung et al. (2020) to fit and predict nonlinear regression problems, particularly for large-scale and high-dimensional problems. The application includes deterministic or stochastic computer experiments, spatial datasets, and so on. 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These growth rates are based on mark-recapture data where an individual was captured and measured at time 1 then recaptured and measured at time 2. The sizes at each time and amount of time between captures can be used to calculate growth rates. 'MRgrowth' follows the approach in Edmonds et al. (2021) and provides functions to calculate growth using three formulas, the Faben's reformulation of the von Bertalanffy formula, the Gompertz formula, and a logistic formula. 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Functions are provided for creating inputs, simulating scenarios and plotting outputs. Package: r-cran-mrhawkes Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ihsep Filename: pool/dists/noble/main/r-cran-mrhawkes_1.0-1.ca2404.1_all.deb Size: 221588 MD5sum: 1bca7dfd8887934acdc005f367150433 SHA1: 3a55d5991afb2869b4527589629ea22d804f00b3 SHA256: 12db333ae63215ac6c206cb27ed98b6c16a5d7af345cab017127a13974f83b9b SHA512: 226f86f0c67840bd96036673aa518cbd486dbda549d9b358eaa1b2d7de038e4a2402246a3f3b55514741897af43292ccf57ea0ab9870566f74eed191f90d66a0 Homepage: https://cran.r-project.org/package=MRHawkes Description: CRAN Package 'MRHawkes' (Multivariate Renewal Hawkes Process) Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting process, with given immigrant hazard rate functions and offspring density function. Calculate the likelihood of a MRHawkes process with given hazard rate functions and offspring density function for an (increasing) sequence of event times. Calculate the Rosenblatt residuals of the event times. Predict future event times based on observed event times up to a given time. For details see Stindl and Chen (2018) . Package: r-cran-mri Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mri_1.0.1-1.ca2404.1_all.deb Size: 25260 MD5sum: c9974ee021a8ad11ef934e7ce5cfeab5 SHA1: 94bcb557a8d615cf6cc6fb8ad64880cffd7ca69a SHA256: 7a70efdc46692fa4af0e5d2f5b98c80997b6eb11fde6d171cd9ee48bfa6491cb SHA512: 4359a0682ff110ffb387acda7ca274ca5a15f33bf8f78434328d742815a6a432df4ed7a7013737f03fa9d9e01157c1ad6dafa6df1459f9d785db39d6d2695934 Homepage: https://cran.r-project.org/package=mri Description: CRAN Package 'mri' (Modified Rand and Wallace Indices) It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices. The indices are proposed in Cugmas and Ferligoj (2018) . Package: r-cran-mriml Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-purrr, r-cran-recipes, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-flashlight, r-cran-future.apply, r-cran-metricsweighted, r-cran-finetune, r-cran-hstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ape, r-cran-vegan, r-cran-hardhat, r-cran-ggrepel, r-cran-themis, r-cran-mrfcov, r-cran-lme4, r-cran-randomforest, r-cran-ggnetwork, r-cran-igraph, r-cran-tidymodels, r-cran-tidyverse, r-cran-parsnip, r-cran-gridextra, r-cran-future, r-cran-generics, r-cran-missforest, r-cran-kernelshap, r-cran-shapviz Filename: pool/dists/noble/main/r-cran-mriml_2.2.0-1.ca2404.1_all.deb Size: 1329952 MD5sum: 63a8b859289b1289e3e5766b93e20d6d SHA1: fe75c3c1c6025f94bc4919f32c4ce2e00dc71d90 SHA256: c0ef0a79384d9737605f99158e7de5ae9a2875c654ea3d7906ddb327b53b86ad SHA512: b15afc852adeffd3f6eac2394eb0b9305432db66ee5855d2891b0ef0d0700f1f91b3eddcf66a28322113608514dc80ef264487256dca66213324c60cbec32eb2 Homepage: https://cran.r-project.org/package=mrIML Description: CRAN Package 'mrIML' (Multi-Response (Multivariate) Interpretable Machine Learning) Builds and interprets multi-response machine learning models using 'tidymodels' syntax. Users can supply a tidy model, and 'mrIML' automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) and Fountain-Jones et al. (2024) . Package: r-cran-mrmcaov Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm, r-cran-progress, r-cran-tibble, r-cran-trust Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrmcaov_0.3.1-1.ca2404.1_all.deb Size: 371236 MD5sum: 78f76a7a563f67318bc40594bba7469f SHA1: 66907a96d5244ec0e8e528850178ea4fa8338fd9 SHA256: b81536a1a4c63a1185e73e4f4f86aaa471e00969c54089b2bdb7db08161bc503 SHA512: a2b83cbb590f4f7829f2400b8bbc25c9a17a42bcbd2a20084fbc65765d341ef70e78dec2f6a75d827e13bfb899cf5237e11f96c3f496a47c28e46ce4a6ecb13b Homepage: https://cran.r-project.org/package=MRMCaov Description: CRAN Package 'MRMCaov' (Multi-Reader Multi-Case Analysis of Variance) Estimation and comparison of the performances of diagnostic tests in multi-reader multi-case studies where true case statuses (or ground truths) are known and one or more readers provide test ratings for multiple cases. Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) . The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) . Package: r-cran-mrmcbinary Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-survival Filename: pool/dists/noble/main/r-cran-mrmcbinary_1.0.6-1.ca2404.1_all.deb Size: 107482 MD5sum: 2e24661fe5fff8e35bb860dd5fd3d257 SHA1: ad81446e9bd5759fe8dcdd31a8a8393bc0661b2f SHA256: 54dcd6e0bdc90face6efd1be4d984eaec16af3eab03a5aae182b92ed5911a921 SHA512: 183f3d39f092523b84bfb24215d5ec1704482fc7378efb12e1a57c146c3b281a52c462a5a22c34a4bfdc2da7200d304e395f2d01de0991c6a6cb15791da5864d Homepage: https://cran.r-project.org/package=MRMCbinary Description: CRAN Package 'MRMCbinary' (Multi-Reader Multi-Case Analysis of Binary Diagnostic Tests) Implements methods for comparing sensitivities and specificities in balanced (or fully crossed) multi-reader multi-case (MRMC) studies with binary diagnostic test results. It implements conditional logistic regression and provides score tests equivalent to Cochran's Q test (which corresponds to McNemar's test when comparing two modalities only). The methodology is based on Lee et al. (2026) . Package: r-cran-mrmcsamplesize Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fpow Filename: pool/dists/noble/main/r-cran-mrmcsamplesize_1.0.0-1.ca2404.1_all.deb Size: 32908 MD5sum: 45573a9acdba86de5e4aa3e801d22ad4 SHA1: 34dcf26876e39d099da98fd4245c1ead917bba2f SHA256: 564a8f5a841afd19d4ec6846f0ee355f1ad0569233eff442379f7423c42a5fb7 SHA512: a633122110a9f3bc0251cb25b9402d51e73357536d5c36118ff6949b7e8f2703d7f17cdaebda59d9202fa49fe417caf2f5d4c129e46f837f2c4bb3baba6d6a7b Homepage: https://cran.r-project.org/package=MRMCsamplesize Description: CRAN Package 'MRMCsamplesize' (Sample Size Estimations for Planning Multi-Reader Multi-Case(MRMC) Studies Without Pilot Data) Sample size estimations for MRMC studies based on the Obuchowski-Rockette (OR) methodology is implemented. The function can calculate sample sizes where the endpoint of interest in the study is either ROC AUC (Area-Under-the-Receiver-Operating-Characteristics-Curve) or sensitivity. The package can also return sample sizes for studies expected to have clustering effect (e.g.- multiple pulmonary nodules per patient). All calculations assume that the study design is fully crossed (paired-reader, paired-case) where each reader reads/interprets each case and that there are two interventions/imaging-modalities/techniques in the study. In addition to MRMC, it can also be used to estimate sample sizes for standalone studies where sensitivity or AUC are the primary endpoints. The methods implemented are based on the methods described in Zhou et.al. (2011) and Obuchowski (2000) . Package: r-cran-mrmediation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-mass Filename: pool/dists/noble/main/r-cran-mrmediation_1.0.1-1.ca2404.1_all.deb Size: 63446 MD5sum: 1a267be73b1d946a6d537cbcdd671b4e SHA1: ed76072d7213b9cbbf6419ea3ba7f65004e52557 SHA256: 706fbbbdfa7966070fe9262218ad20c786564c08ecba3d1e5d0a20ca2e465079 SHA512: 32f65a86c131f095d276aafa93adf40a49b79adf33f989fec8d6fe832db9b4fc19e87d538dc8923021f2c12e4c30f2de3b5289d2b0f5cbae0c7fb192bea037af Homepage: https://cran.r-project.org/package=MRmediation Description: CRAN Package 'MRmediation' (A Causal Mediation Method with Methylated Region (MR) as theMediator) A causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information. 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This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) , Badsha, Martin and Fu (2021) , Kvamme and Badsha, et al. (2025) . Package: r-cran-mrpostman Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-r6, r-cran-stringr, r-cran-stringi, r-cran-magrittr, r-cran-assertthat, r-cran-base64enc, r-cran-rvest, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-mrpostman_1.4.0-1.ca2404.1_all.deb Size: 985308 MD5sum: 93578183064a7849bc0094a5c3b65537 SHA1: 0b88b48d3bf8eccd6e36337bc3a00fee9602e62f SHA256: 6368f12bf42158471fde935995f1782ad9877a2775c48cbd2aa7561a1c77e95a SHA512: d1150c88c979a7792e8250cdeb49666df352cbd7f356924a1888b41ba65a03915024b907b949d3050003ee490d7911fe8f7531baa220b1a13ff436be1abbef95 Homepage: https://cran.r-project.org/package=mRpostman Description: CRAN Package 'mRpostman' (An IMAP Client for R) A session-based IMAP client that implements the full functionality of the IMAP4rev1 protocol (RFC 3501), allowing virtually all e-mail operations to be performed from within R, paving the way for e-mail data analysis. Package: r-cran-mrpstrata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-progress, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrpstrata_0.1.0-1.ca2404.1_all.deb Size: 755990 MD5sum: 5d45554c04f5436d3d8e650255782552 SHA1: e1c44b4080daeacbba9573c6a1fc0d0d78267762 SHA256: 230cd5bf48d2534dcdc00d7d77205da6468ceb9cfa420d5ed7fd5c2f710d45ab SHA512: 0466a798d90491329233157310051cc925b6fbf806bbb2eae8d810cc7b08280f8013fd0e1e5cb98e76b3123dd0e65aead3c6208cd1d103c6171eac781d324d27 Homepage: https://cran.r-project.org/package=mrPStrata Description: CRAN Package 'mrPStrata' (Multiply Robust Estimation in Causal Survival Analysis withTreatment Noncompliance) Provides multiply robust estimators of principal survival causal effects among always-takers, compliers, and never-takers in studies with treatment noncompliance. The methods are based on Cheng et al. (2026) . Package: r-cran-mrqol Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mrqol_1.0.1-1.ca2404.1_all.deb Size: 35488 MD5sum: 5ee08a50595dc22477f058786aacca4c SHA1: 520695cf1310591efd163c31b97ca1441a2a7e8d SHA256: 06d3cdd0ea65d911167d57ac6ddfc8ad87e28e1f01052306667140e5c41ef3a9 SHA512: 599634c73c6aa1ecd1d91e7cbc8c2ada200893ef6ac5735a556f5326e6416741be4b0b63e2d25cae21869870e9ca01effb73c4333ebda12c6faaf4cd40b17daf Homepage: https://cran.r-project.org/package=MRQoL Description: CRAN Package 'MRQoL' (Minimal Clinically Important Difference and Response ShiftEffect for Health-Related Quality of Life) To calculate the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method. Package: r-cran-mrreg Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-mrreg_0.1.6-1.ca2404.1_all.deb Size: 117194 MD5sum: d47e30ca0e0ea237110daf5de6a0d452 SHA1: d9bdfbe0afba51d5488067db6782a4ca99dd9fd6 SHA256: 5c26bd09b8b6e91d3127e56e9817db1076130804c371ce09d649ac302e74c096 SHA512: 3fcff56c89bd22525e623f8bf2449837ba827f9e28764f154474535965bb3a18abeb196470f08587fa02588774098261d89da53a413676ae1ba599ae22b9c3a5 Homepage: https://cran.r-project.org/package=MRReg Description: CRAN Package 'MRReg' (MDL Multiresolution Linear Regression Framework) We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q). We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) . Package: r-cran-mrregression Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrregression_1.0.0-1.ca2404.1_all.deb Size: 243832 MD5sum: cef7b615d812b66066311cd568367e02 SHA1: a5ab7589b29de90e6767f1c710e6b1f35cdc74fc SHA256: ef69c700439dc1e9737f029390cda822e8179e4b4c4d0191ae453f43f1a514c5 SHA512: 6629010e66c611f8493866fb6869501bfa8b5e9e6d070e9f53d63b41271109c9ddceaa2c102de9daa6f4239b25a430d2abf08acba792e6317088515f74b5a5f2 Homepage: https://cran.r-project.org/package=mrregression Description: CRAN Package 'mrregression' (Regression Analysis for Very Large Data Sets via Merge andReduce) Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). 'Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, . Package: r-cran-mrstdcrt Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-geepack, r-cran-lme4, r-cran-nlme, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrstdcrt_0.1.2-1.ca2404.1_all.deb Size: 194276 MD5sum: 0f53eac52bab28da8164adc12927dc9c SHA1: c5481b2df3d0edb18662e1acf563385af736629a SHA256: e5acfdb2af3f72ae52b36f5ecc9dd524f2a1f9ed355dd18162e887d6d412eedb SHA512: 3c4c61f2cd0474d413a781fcdbc6e00eff30cb3e88864e786186f281ce43a5a73488dd1f7c634cf9bf19a8be59ab8353a772be5d82f7b2d85c25e9ba3db0d3ee Homepage: https://cran.r-project.org/package=MRStdCRT Description: CRAN Package 'MRStdCRT' (Model-Robust Standardization in Cluster-Randomized Trials) Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) . Package: r-cran-mrstdlcrt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reformulas, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect, r-cran-gee, r-cran-lme4, r-cran-ggplot2, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrstdlcrt_0.1.1-1.ca2404.1_all.deb Size: 667374 MD5sum: 3d868159ae77da791d3f81d61d26c71e SHA1: ce475deb0c9924ee2fed9fade7b5be1205fcbd3f SHA256: e03f0341abe29febe565a69254af55f8814cdfb15dbc46b1b4019923b043fc4c SHA512: 133d8f3cd4ba24b4b6c56d6fe25adc706a3a6c8dcbc1c5674158e5791bef251c8c3ff383ffffeb51c938af3bd41394206f8cf8f3be95e338b74951a392be59d7 Homepage: https://cran.r-project.org/package=MRStdLCRT Description: CRAN Package 'MRStdLCRT' (Model-Robust Standardization for Longitudinal Cluster-RandomizedTrials) Provides estimation and leave-one-cluster-out jackknife standard errors for four longitudinal cluster-randomized trial estimands: horizontal individual average treatment effect (h-iATE), horizontal cluster average treatment effect (h-cATE), vertical individual average treatment effect (v-iATE), and vertical cluster-period average treatment effect (v-cATE), using unadjusted and augmented (model-robust standardization) estimators. The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) . 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Generates plots similar to those used previously in Alexandrov et al. (2020) and Rozen et al. (2026). Package: r-cran-msigseg Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1988 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggpubr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-msigseg_0.2.0-1.ca2404.1_all.deb Size: 1821686 MD5sum: d84f5967d54dfcb9540db1532c03ca30 SHA1: c795673dd69c6e9a0eca76a8e1df11aeef686e59 SHA256: 8d75e4fae12a8ffdcc20b11537d9c9348644d79e2d5175441aa4eeefa320846c SHA512: 85e7219feba57defd5198bbff96a88f374072a12759fa3da1de23dc455ea452498ee8820706203d6d2b1d606a55edf0a6c71a73e6e793f73f7d6bb491c97c128 Homepage: https://cran.r-project.org/package=MSigSeg Description: CRAN Package 'MSigSeg' (Multiple SIGnal SEGmentation) Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package. Package: r-cran-msigtools Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-philentropy, r-cran-quadprog, r-cran-sets Suggests: r-cran-cosmicsig, r-cran-icams, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-msigtools_1.0.7-1.ca2404.1_all.deb Size: 68204 MD5sum: ae3fa9725d2c82ca1e725ea62332025a SHA1: 8f5d6ce5fa70afe6da2d8d6a315e59efb470d504 SHA256: 6269129da6aadc59cb4d25b28cf8f9dfcc3c8a084f8d8d07e97bc11c50b2cd5a SHA512: e2eb40e4f940f5fa5e8028296bf65be7b1043b46cbfb2d3c1c1435d3d0030bc54ce8ee144d24b8aac672a2fca87721f178c8b61ea4b3eb0440157a3bfd12fb88 Homepage: https://cran.r-project.org/package=mSigTools Description: CRAN Package 'mSigTools' (Mutational Signature Analysis Tools) Utility functions for mutational signature analysis as described in Alexandrov, L. B. (2020) . This package provides two groups of functions. One is for dealing with mutational signature "exposures" (i.e. the counts of mutations in a sample that are due to each mutational signature). The other group of functions is for matching or comparing sets of mutational signatures. 'mSigTools' stands for mutational Signature analysis Tools. 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This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions. 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Get access to tumor main types, identifiers and utility routines to map across to other tumor classification systems. Package: r-cran-msltrend Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-forecast, r-cran-plyr, r-cran-rssa, r-cran-tseries, r-cran-zoo Filename: pool/dists/noble/main/r-cran-msltrend_1.0-1.ca2404.1_all.deb Size: 136538 MD5sum: 4fad216a98f977a090f11c72a3d41202 SHA1: 28200c9557fb8dc0e3418ac23ac6baf8a9f4951c SHA256: e31dc55c5861ea905ffab6dad8012aea810bf41cc60d14df1d1025886c5d2c1c SHA512: 9da110e379a4604343c5c0302221304c5eca91e21dbb28a6759c91d9b4bee5875f411569e90aed05179dc18f5797ff456e5668c4596a552ba5a77774963592b3 Homepage: https://cran.r-project.org/package=msltrend Description: CRAN Package 'msltrend' (Improved Techniques to Estimate Trend, Velocity and Accelerationfrom Sea Level Records) Analysis of annual average ocean water level time series from long (minimum length 80 years) individual records, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. 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If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided. Version 4.0 adds opt-in supervised sparse soft-structured principal component analysis, reconstruction, and repeated split reconstruction-based parameter selection while preserving the default Version 3.2 computational paths. 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Developed by the Mine Sustainability Modeling Group (MSMG) at Missouri University of Science and Technology under NSF (National Science Foundation) funding (Award No. 2219086). See Pizzol (2022) for the original 'Python' implementation that inspired this tool. 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Works with any longitudinal data where subjects accumulate repeated observations with start and end times and an optional terminal outcome. Methods are described in Grossetti, Ieva and Paganoni (2018) . 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For more details, see McCurdy (2025), "Introduction to Data Science with R" . Package: r-cran-msmwra Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-msmwra_1.5-1.ca2404.1_all.deb Size: 30086 MD5sum: 0f811673f058e86900cc8bffffd0cd11 SHA1: cdc902b004a1c5413f7d911f2c601dd13470f42d SHA256: 20b8bc5cfeea33d9e5eb73eeb8757a60b86d1dc0d43711aeaac7e5aa6e329067 SHA512: 3615ddf2292ad98faf300df06fa2891c8cb36b68abd2a769b75b263941d991279f096967117a91bc364953223f4907ed266366ff6e289f5b071d14314e5c4411 Homepage: https://cran.r-project.org/package=MSMwRA Description: CRAN Package 'MSMwRA' (Multivariate Statistical Methods with R Applications) Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler". 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It then utilizes the graphical lasso method to reconstruct network structures among multivariate time-to-event variables, accommodating both multivariate outcomes measured within a single dataset and survival times integrated from heterogeneous (multi-source) datasets.. 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Package: r-cran-msoutcomes Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-msoutcomes_0.2.1-1.ca2404.1_all.deb Size: 119988 MD5sum: f983501b8c59858c1537989c7172418d SHA1: df3a4fa19442fd6143e165ad715a1a50ec01c358 SHA256: 27aa4cc23a68157d279ce488533aae9cc69ffb63d46d53f99a43e8eed2657a34 SHA512: 47458048f4ca73fe4d12068d5b0c34da9f55ddcb366c323c525ee104f2d7feec2bf68e755ab2d6147b2bb178e46d727c8d7c0c5721aa67811da80d78cb52f456 Homepage: https://cran.r-project.org/package=MSoutcomes Description: CRAN Package 'MSoutcomes' (CORe Multiple Sclerosis Outcomes Toolkit) Enable operationalized evaluation of disease outcomes in multiple sclerosis. ‘MSoutcomes’ requires longitudinally recorded clinical data structured in long format. The package is based on the research developed at Clinical Outcomes Research unit (CORe), University of Melbourne and Neuroimmunology Centre, Royal Melbourne Hospital. Kalincik et al. (2015) . Lorscheider et al. (2016) . Sharmin et al. (2022) . Dzau et al. (2023) . Package: r-cran-msprog Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1931 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xfun, r-cran-dt Filename: pool/dists/noble/main/r-cran-msprog_1.0.1-1.ca2404.1_all.deb Size: 635642 MD5sum: a2f147a3ad48c592e410c13914401019 SHA1: 76bceba2a1f6134fb889426376e6d62d383479a4 SHA256: caab5b01ff215abaebafb74471083ff487edc53677edcecfead993f04f317d93 SHA512: 586f10853f8cd211af97efdca5d16c661a76b8eab053c01767191aa4bbee73d776b244f2af12ac80fbca133f8788077b5e08773ee92bc2afcd36c96ada92f698 Homepage: https://cran.r-project.org/package=msprog Description: CRAN Package 'msprog' (Reproducible Assessment of Disability Course in MultipleSclerosis) Analyse disability course in multiple sclerosis (MS) from longitudinal data. The package provides a flexible framework for identifying disability events under user-specified criteria, allowing adaptation to different study designs and endpoints. Tools are included to facilitate transparent and reproducible reporting of the settings used in the analysis. For consensus-based recommendations on endpoint calculation and use of the package, see Montobbio et al. (2026) . For the original description of the computational framework and illustrative applications, see Montobbio et al. (2024) . Package: r-cran-msprt Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-ggplot2, r-cran-ggpubr, r-cran-foreach, r-cran-iterators, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-msprt_3.0-1.ca2404.1_all.deb Size: 517304 MD5sum: 4196ae588dc035dd75c9ddf4b1c95dc9 SHA1: 0322925ddde240b24a2bedbb237fa59ba0aae7fd SHA256: af87c1caebf290f351961baef0b8ee8e8aff2072ced49b521c609bb11d543f8b SHA512: bb49be2d3dd14d617276c0ddcd40eee0c04d9d03ddf9f25eb2c93e90cd5f42a45b868e04a5cb8fdda1f802725ba8bedebd92e227506bcc07aaee69fc06be4b57 Homepage: https://cran.r-project.org/package=MSPRT Description: CRAN Package 'MSPRT' (A Modified Sequential Probability Ratio Test (MSPRT)) Given the maximum available sample size (N) for an experiment, and the target levels of Type I and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT the package can also obtain its operating characteristics and implement the test for a given sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. The proposed test has shown evidence of reducing the average sample size required to perform statistical hypothesis tests at specified levels of significance and power. Currently, the package implements one-sample proportion tests, one and two-sample z tests, and one and two-sample t tests. A brief user guidance for this package is provided below. One can also refer to the supplemental information for the same. Package: r-cran-msqc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-msqc_1.1.0-1.ca2404.1_all.deb Size: 132734 MD5sum: 04a793e2e411cc427dbe75d4adbcc84e SHA1: 4c51a82606cc9571b8ea8c690db9818c3fb138e3 SHA256: f6e3141e9df4fb6d901bf1003d4fcd6952c1ff84aa510ad7c3b0914783dd573e SHA512: b9341e1b4a74e44e400ae8c201b60dbcda060ca0ddfc3942cdd46bf4af55bce0d3b2b3f9253d388c58c33824161dcf108e49eb799220c1cc54d891948312d0bc Homepage: https://cran.r-project.org/package=MSQC Description: CRAN Package 'MSQC' (Multivariate Statistical Quality Control) This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. 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(2017) . It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively. It also provides the implementation of conventional design method, namely binomial tests for failure count data. 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Offers filtering of decision variables through item–module eligibility and the application of explicit bounds to simplify the MILP model and accelerate the optimization process. Supports bottom up, top down, and hybrid assembly strategies; enemy-item and enemy-stimulus exclusions; stimulus all in/all out or partial selection; anchor item/stimulus specification; and item exposure control. Accommodates both single-objective and multi-objective optimization ('weighted sum', 'maximin', 'capped maximin', 'minimax', and 'goal programming'). Enables simultaneous assembly of multiple panels with item and stimulus content balancing and exposure control. Provides analytical evaluation of assembled MST performance within seconds. Includes tools for diagnosing infeasible optimization models by systematically identifying sources of infeasibility and reformulating models with slack variables to restore feasibility.Methods implemented in this package build on established work in optimal test assembly (van der Linden, 2005 ), item-set constrained test assembly (van der Linden, 2000 ), hybrid assembly (Xiong, 2018 ), recursion-based analytic methods (Lim et al., 2021 ), and classification evaluation (Rudner, 2000 ; Rudner, 2005 ). Package: r-cran-mstats Architecture: all Version: 3.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mstats_3.4.0-1.ca2404.1_all.deb Size: 235200 MD5sum: 8f9e68fa1a0bffae02a016e59c57027a SHA1: 81dd03829adff2d3a9639a0a92416afc7d2c36f1 SHA256: 9ccd7b24b0e632e812c8cc9b3705f1ffd46042a40d2f2a82954cda12595559d3 SHA512: d71a8819b05d120bfa707c48ab4b743a6313fc5f682783ff4f955cc42cee683a52ad675066644924b78c7d9891913d46aeab4e0863302fa48d41611df3e76073 Homepage: https://cran.r-project.org/package=mStats Description: CRAN Package 'mStats' (Epidemiological Data Analysis) This is a tool for epidemiologist, medical data analyst, medical or public health professionals. It contains three domains of functions: 1) data management, 2) statistical analysis and 3) calculating epidemiological measures. 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The algorithm determines the number of clusters automatically by recursively intersecting the Minimum Spanning Tree (MST) and the k-Nearest Neighbor (kNN) proximity graphs constructed from a pairwise distance matrix. The value of k is selected via a connectivity criterion (the smallest k such that the kNN graph is connected, bounded by floor(log(n))). The package requires only a distance matrix as input and returns cluster assignments, an 'igraph' network, and partition metadata. Package: r-cran-mstr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mstr_1.2-1.ca2404.1_all.deb Size: 273302 MD5sum: fc3af613351f361853b2c02f8bc240c0 SHA1: aea4856c2804b4cc6f919a98073efb3c087926e2 SHA256: 51ac47ec9accfeca54c03bcea0e75a28ee92eef62552a889968a1c65526cf5e1 SHA512: fb752211ae7fd12d7d6623f8a87f470db4ec9e4c68554061e9472abc6b3cbcdd3e6d89297f37be65f669364870a79b30a65c5cda197c7d32042174bae91061e5 Homepage: https://cran.r-project.org/package=mstR Description: CRAN Package 'mstR' (Procedures to Generate Patterns under Multistage Testing) Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. 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Package: r-cran-mtlgmm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-mclust Filename: pool/dists/noble/main/r-cran-mtlgmm_0.1.0-1.ca2404.1_all.deb Size: 111444 MD5sum: e0a99affcb543a2ae2c2137e7c3219be SHA1: 66d1a4e7bb47294e783f591e618c4463d95af1d5 SHA256: fd60222d42977ab71c23b4c83c7135778ee25206591da10f4da0d52934e4e5ae SHA512: 4a288c9808a3fe65df34faab52dc5d4660a00bb1831f8518c9e3b58cf875f07732460ff8f90bab3bc96499df7f7792503f014bbb8f011df05d0034b7c1f302b4 Homepage: https://cran.r-project.org/package=mtlgmm Description: CRAN Package 'mtlgmm' (Unsupervised Multi-Task and Transfer Learning on GaussianMixture Models) Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) . 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(2024) . The TCC are defined using a rule based on the smallest worthwhile difference (SWD). Using the defined TCC, the NMA estimates (i.e., treatment effects and standard errors) are first transformed into treatment preferences, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). These treatment preferences are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatments ability to outperform all the other competing treatments in the network. Here the terms ability to outperform indicates the propensity of each treatment to yield clinically important and beneficial effects when compared to all the other treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list. 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Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method. 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Provides structural data cleaning and standardisation (clean_the_nest()), age categorisation against ~50 published schemes with publication-ready labelling (preening()), time-unit aggregation with zero-filling and seasonal awareness (roost()), joint aggregation of several linked event dates (e.g. onset, admission, ICU, complication, fatality) into one table of comparable rate columns (flyway()), under-ascertainment correction via a stratified, time-varying multiplier factor supplied directly, derived by the ratio (multiplier) method, or derived by inverting an externally sourced severity rate (e.g. an infection-fatality-rate anchor) against an observed severity ratio (corncrake()), comorbidity detection from ICD-10-AM clinical coding (plumage()), vaccine coverage data construction (brood()), hash-based de-identification (molting()), and relinking of previously de-identified data (homing()). brood() produces a brood_df object supporting two population models: pre-aggregated denominators (population_model = "pre_aggregated") and record-level cohort designs (population_model = "cohort"). The cohort model handles single time-point coverage snapshots, interrupted time series analysis via a built-in sweep returning monthly coverage rates (time_series = TRUE), and birth cohort designs with person-time computation. This cohort/time-series coverage model was applied in Roughan et al. (2026) to estimate infant immunisation coverage against respiratory syncytial virus over an 18-month period. Both wide format (one row per person with dose columns, from 'starling'::murmuration()) and long format (one row per dose) are accepted. corncrake() returns both a point-corrected count and uncertainty bounds wherever they can be derived, including the inverse relationship between a severity-anchored factor and the bounds of its own reference rate. Built for Australian public health surveillance practice but not specific to it -- see individual function documentation for notes on non-Australian use (e.g. Northern Hemisphere season boundaries). Package: r-cran-muerelativerisk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-muerelativerisk_0.1.1-1.ca2404.1_all.deb Size: 22532 MD5sum: bf014eefe881370d3f0793c5644544cb SHA1: c472414ac428f0aa514ab3cd902ba23d008c84b9 SHA256: 8720849521ed6709ccbe644a03cb209ffa873fe074bd5b868e90aa8bbed2f058 SHA512: f3ee55fa7690460d873f984cafb74de3b66d21e8b744f9b8aeba6363f7ffcfd703a0454cb6959aa888f26e1c016a92fca6563bd2055cce93e3bb3c9f9d5a19a2 Homepage: https://cran.r-project.org/package=mueRelativeRisk Description: CRAN Package 'mueRelativeRisk' (Relative Risk Based on the Ratio of Median Unbiased Estimates) Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) . 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See Li et al. (2024) for details. 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All inputs come with R usage examples. Package: r-cran-muir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-dplyr, r-cran-stringr Suggests: r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-muir_0.1.0-1.ca2404.1_all.deb Size: 43848 MD5sum: 3a8ccae80726d774c15b7af3b29e3e89 SHA1: 8133d5e8c3e2cbe774f9161d96b9383bde107f38 SHA256: 5e9cb18ac1e048a7f3ddbbf8092ccb48334a2789237c8115c138659b3389694a SHA512: 712591de3fbabef8bfaa0ba3189851566b790588580d78d2846763a527f0b64b16f73ebe9697ae9d6421df4d361bc35693abd51c7f2fe75c13c695095113aff6 Homepage: https://cran.r-project.org/package=muir Description: CRAN Package 'muir' (Exploring Data with Tree Data Structures) A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure. 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Package: r-cran-mulgar Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6306 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-geozoo, r-cran-tibble, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-purrr Suggests: r-cran-tourr, r-cran-ggdendro, r-cran-colorspace, r-cran-mclust, r-cran-kohonen, r-cran-ggally Filename: pool/dists/noble/main/r-cran-mulgar_1.0.8-1.ca2404.1_all.deb Size: 6381664 MD5sum: 2a6655b9abb9b3129ebee44c84792597 SHA1: ce6d1db208bd9d47375a2996df0318ef7aa5cfdb SHA256: 62b226f65473eb138d8a5c67ff8d95234ffdf2c5711a9ff83fc5380cb110493c SHA512: 6a2139773d165c65d5d7c33aa99e8160a2346a0ca71c4182e139373c9b2948e119d3680f8541a6b1f213cd0acc04bce65bd63635fa6637ef615096480c63d39d Homepage: https://cran.r-project.org/package=mulgar Description: CRAN Package 'mulgar' (Functions for Pre-Processing Data for Multivariate DataVisualisation using Tours) This is a companion to the book Cook, D. and Laa, U. 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Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist). A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process. To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time. MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs. 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The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures. 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It consists of four sections. The first section uses a dynamic scheme to indicate which possible alternatives to follow depending on the fulfillment of the assumptions of the model. It also presents an analysis on the fulfillment of the assumptions of linearity, homoscedasticity, normality, and independence in the residuals of the model, as well as dynamic statistical graphs on the residuals of the model. The second section presents an analysis with a non-parametric approach of Kruskal Wallis. After Kruskal Wallis, a Post-Hoc analysis of multiple comparisons on the medians of the treatments is carried out. The third section presents a classical parametric ANOVA. Following classical ANOVA, a post-hoc analysis of multiple comparisons on the medians of the treatments, factor levels by Dunn's test, and statistical graphs for the treatments and factor levels are shown. Additionally, a post-hoc analysis of multiple comparisons on the means of the treatments is done. 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The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the combination of inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) . 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This package conducts sensitivity analyses for the joint effects of these biases (per Mathur (2022) ). These sensitivity analyses address two questions: (1) For a given severity of internal bias across studies and of publication bias, how much could the results change?; and (2) For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount? Package: r-cran-multibiplotgui Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-tkrplot, r-cran-tcltk2, r-cran-shapes, r-cran-plotrix, r-cran-mass, r-cran-matrix, r-cran-cluster, r-cran-dendroextras Filename: pool/dists/noble/main/r-cran-multibiplotgui_1.1-1.ca2404.1_all.deb Size: 214128 MD5sum: 7658a612056c12c20730ce0f09878a3b SHA1: e19605f404a3b8b81258abc9ed037c910c34e11f SHA256: b1c5a7c1081cd3ad7fda41af72e54ea55b43199a1e66e2ffcf7472da1c859741 SHA512: 8c6adb56b8d5972ac47ae25a5407482f53bc7373decd4b90d2c6d42cff0c2417205cc643390482386f293246c9228ef231ebc9aac31d641edaa0236caaa4c77a Homepage: https://cran.r-project.org/package=multibiplotGUI Description: CRAN Package 'multibiplotGUI' (Multibiplot Analysis in R) Provides a GUI with which users can construct and interact with Multibiplot Analysis. Package: r-cran-multibreaker Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multibreaker_0.1.0-1.ca2404.1_all.deb Size: 62940 MD5sum: 8faf106da0759f0961d473214ba7fd15 SHA1: 2a2868ceefbc46bae55811bb9119cd6a066eebf6 SHA256: aa17c82e91d0a66e1841b79bb601e38755dd9c9eb1666c6d772994bd874a699b SHA512: 4d08546e5b15fbb558e6af3c72856ddc283a41f7e48e34b7a9fb4d38f01d33280bdb7cac19857bed887b2602c754835e8f50f425fcbc29f721e0e92e4aa7004d Homepage: https://cran.r-project.org/package=multibreakeR Description: CRAN Package 'multibreakeR' (Tests for a Structural Change in Multivariate Time Series) Flexible implementation of a structural change point detection algorithm for multivariate time series. It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system. Bai, Lumsdaine, and Stock (1998) . Package: r-cran-multica Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-multcomp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multica_1.2.0-1.ca2404.1_all.deb Size: 51268 MD5sum: e2041b1dd9adc13a5a83c78768224bd6 SHA1: bf05a34d73096e82e28351ceb00141bf250966a0 SHA256: a29a4a74406f40dd7bd11296da946ee15fc2e57bdb53130f79e107722dce2103 SHA512: 636c8f40d5cff6499da8185708ed4c92618f44e23ecce432d58873115f473b5c841b011d34fa192214d5ce81f0d6b1c9990b036b8af4e235a942787825171c17 Homepage: https://cran.r-project.org/package=multiCA Description: CRAN Package 'multiCA' (Multinomial Cochran-Armitage Trend Test) Implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available. Package: r-cran-multicastr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multicastr_2.0.0-1.ca2404.1_all.deb Size: 41818 MD5sum: 0d315181925a0bd9d999809eeacc55c9 SHA1: 5c4d6b9f4871371fe387ed1edd500b145aca1de7 SHA256: 58938de4e7e3f23bad166be190f91379ac80e9af6922fe6b608220768670f231 SHA512: f04a2fb6cd0ff46b7bc8e96dc4966c1aa21c1aa5aa10726ba9f116bcdcd0599780a02bca8cf0877290bf72867335f7cfd26a2a0b9f5b0bdafb23136ba8bf7a2b Homepage: https://cran.r-project.org/package=multicastR Description: CRAN Package 'multicastR' (A Companion to the Multi-CAST Collection) Provides a basic interface for accessing annotation data from the Multi-CAST collection, a database of spoken natural language texts edited by Geoffrey Haig and Stefan Schnell. The collection draws from a diverse set of languages and has been annotated across multiple levels. Annotation data is downloaded on request from the servers of the University of Bamberg. See the Multi-CAST website for more information and a list of related publications. Package: r-cran-multicca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-geigen, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multicca_0.1.0-1.ca2404.1_all.deb Size: 80326 MD5sum: 82237ce64790bd2ec1879b2400f029b3 SHA1: d3217b9567429711994a1933f9529828656677e8 SHA256: 814b8f69ac5175b9765e4a065113a46a670e76c35d054fef14bdaffa1494e870 SHA512: 5aee294db5804352bf68f9b6b71dbed8135be2bada0bfeac5ab42ecc58bc6fdd582a9e3c85d56129474f9f1063b4f9e6bb800f66e3a132757292a1a241d441d4 Homepage: https://cran.r-project.org/package=multiCCA Description: CRAN Package 'multiCCA' (Multiple Canonical Correlation Analysis (Kernel and Functional)) Implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, with a focus on multivariate repeated measures and functional data. The package provides two approaches: (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional representations for exploratory analysis and visualization of dependence structures across multiple feature sets. The implementation follows the framework developed in Górecki, Krzyśko, Gnettner and Kokoszka (2025) . Package: r-cran-multichainr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-multichainr_0.1.0-1.ca2404.1_all.deb Size: 607820 MD5sum: 2a2ff2e4c4aefcfafd5502e49bc04d28 SHA1: cfa6021776b201f2d8285402ad833c1711324c68 SHA256: e4ce4a198d4d76cb2fba7033b3586e929a729d44fbcf208bf1a3328dd6089767 SHA512: 28cb40a58b6e514f6c58926201e96d74a161ac25557f942086e15a656cc0f93f5399f9a320e4f59ecbdf8dc7cafe592416986e8287bb3c4ef3ba22e04470c570 Homepage: https://cran.r-project.org/package=multichainr Description: CRAN Package 'multichainr' (R Interface to the 'MultiChain' Blockchain RPC API) Provides a comprehensive R interface to the 'MultiChain' blockchain JSON-RPC API . Allows users to manage blockchain nodes, create and subscribe to data streams, issue assets, and manage network permissions directly from the R console. Supports both local node management and remote server interaction. Package: r-cran-multichull Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-plotly, r-cran-shiny, r-cran-shinythemes, r-cran-dt Filename: pool/dists/noble/main/r-cran-multichull_3.0.1-1.ca2404.1_all.deb Size: 76610 MD5sum: 5fff34fb79e08c57dffaa9f27385dd9f SHA1: 50f046ab8dddaeb9d124e8f9c90b628b67178fd7 SHA256: 1351e92f9a175ef78f11a9e0b6291c63b5532fdf5816074a8c88afa32a67d7d0 SHA512: 950198e02ee317a3e3046e5bed802d9bc5ee61df10cf4f953ee00698b190385b201eadc153844af034995cfcaa3bca0e216fd55b82c0cd00f6d8e934020fecec Homepage: https://cran.r-project.org/package=multichull Description: CRAN Package 'multichull' (A Generic Convex-Hull-Based Model Selection Method) Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) , determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values. Package: r-cran-multiclasspairs Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-boruta, r-cran-dunn.test, r-cran-caret, r-cran-e1071, r-cran-rdist Suggests: r-cran-biocmanager, r-bioc-biobase, r-bioc-switchbox, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-leukemiaseset, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-multiclasspairs_0.4.3-1.ca2404.1_all.deb Size: 1287986 MD5sum: 92f7392e961a2e231ebf67dd8cc4fbc9 SHA1: a88edfa19bae0f1c20d4652ee12b282269169a31 SHA256: afd89ba6ca22fc6e8cfdc28346e56e930686693ba50b3cc13831a19aeb307d24 SHA512: 06abc621ceb3da34fdafb79dd45654af6989492a8fc5688876cfe6620016d5c3bae124acf5013dec745620d8f32d9c86b729e5c839371720a8d91667d2db5c4e Homepage: https://cran.r-project.org/package=multiclassPairs Description: CRAN Package 'multiclassPairs' (Build MultiClass Pair-Based Classifiers using TSPs or RF) A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) . Package: r-cran-multiclassroc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-proc Filename: pool/dists/noble/main/r-cran-multiclassroc_0.1.0-1.ca2404.1_all.deb Size: 15686 MD5sum: dffb0c45711f46c344e30d811e6320b0 SHA1: 0f928556777acf2a1b5004c062b8f037e7e7ea82 SHA256: 7e83aa6cf84808d3c81c180aac9df7fef52151bf94286e34a565562977aacb15 SHA512: e17d5cf7b2833a5c65d87b602eb64ba9895df132e2b9135079d0963205b42bdcdb3b59f1af384545fbb226e7ca739a9ec33505b562cc798791cbeeab9992378a Homepage: https://cran.r-project.org/package=MultiClassROC Description: CRAN Package 'MultiClassROC' (ROC Curves for Multi-Class Analysis) Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029). 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Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) . Future development will extend the package to higher-dimensional data. Package: r-cran-multicoll Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multicoll_2.1-1.ca2404.1_all.deb Size: 75848 MD5sum: 576f9e8f68d0c8cc74d92a5d57498caf SHA1: 3edfe7c03db532ef19a963af6e18a41e3a7688a8 SHA256: e715b77ff70c942f034bd4a6f632d796ee79488413e9ae53738d37634030e9d5 SHA512: 77afbd45d56720c885afeb2966a1800cc7a13e40e34ebec1187abccc11935dd57a9ff77aba30d418cd8bb3bfd41d3ae326cb10500f5350de015a65324528449b Homepage: https://cran.r-project.org/package=multiColl Description: CRAN Package 'multiColl' (Collinearity Detection in a Multiple Linear Regression Model) The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed. Package: r-cran-multicorr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multicorr_0.1.0-1.ca2404.1_all.deb Size: 13444 MD5sum: 0ac14e76aff0a1c86184e78b924af6c0 SHA1: b77f2fe96ea3107e770d835ded322409d9bb5f2e SHA256: 7b9dd931235b8d44186fbd234d3be3f69af83109a80d18117bf2d846342266b4 SHA512: 180636a80dffc5e319720d2c621eba991d9a417b2d60ff28ca931b1fd7daf443e52691c4de4ade64728b74ce4ab9cc9d081fd092a15acb420d94d779aac7b8c3 Homepage: https://cran.r-project.org/package=multiCorr Description: CRAN Package 'multiCorr' (Multicovariance and Multicorrelation for p-Variables) Implements the multicorrelation coefficient for p-variables as described in Cankaya (2023). The package provides a numerically stable implementation using logarithmic transformations and a log-sum-exp approach to reduce numerical overflow and underflow when calculations involve a large number of variables. Package: r-cran-multid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glmnet, r-cran-proc, r-cran-lavaan, r-cran-emmeans, r-cran-lme4, r-cran-quantreg, r-cran-lmertest, r-cran-ggpubr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-overlapping, r-cran-rio Filename: pool/dists/noble/main/r-cran-multid_1.0.2-1.ca2404.1_all.deb Size: 227056 MD5sum: 01646396405940fde05cdb59b4afa04b SHA1: 4858b8d568c2bb5c2d2bfeee93f5e67723659e90 SHA256: 50cc98d58ed728918683030d6c632c434339e2895f11cfb263e500266b412394 SHA512: 461b73edeb7ed20d552cf31d2b23b7890be597182228689cf6c1c616de234f1331d9424fdeb20a62a90ad6f0be1e1ec822532c05a3e73d1b53ec4048c24b304f Homepage: https://cran.r-project.org/package=multid Description: CRAN Package 'multid' (Multivariate Difference Between Two Groups) Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) . Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) . Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891. Package: r-cran-multideggs Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3015 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-knitr, r-cran-mass, r-cran-magrittr, r-cran-pbapply, r-cran-pbmcapply, r-cran-rmarkdown, r-cran-sfsmisc, r-cran-shiny, r-cran-shinydashboard, r-cran-visnetwork Suggests: r-cran-kernlab, r-cran-nestedcv, r-cran-pls, r-bioc-qvalue, r-cran-randomforest, r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multideggs_1.2.1-1.ca2404.1_all.deb Size: 1183918 MD5sum: 7a5ebc77d4079ba14e24fcb3c05dee0b SHA1: e74077d76102001d4f2ca2277cb103fd098ea61c SHA256: b2009fca64db7c33633a24f4951919badd95a7a462e391f66d77683394bc04eb SHA512: c39d1c3dcee44fe18ddbe55d16f443c279ede5000277beab7f96346a5074a1741d5fbe4490d8eb57bbb134c0a9f52eb9993fac6a11dcaaada585b02f401febbd Homepage: https://cran.r-project.org/package=multiDEGGs Description: CRAN Package 'multiDEGGs' (Multi-Omic Differentially Expressed Gene-Gene Pairs) Performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) . Package: r-cran-multideploy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gh, r-cran-base64enc, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multideploy_0.1.0-1.ca2404.1_all.deb Size: 73296 MD5sum: 48a7ecab82ad4999bcf42c022569fe9b SHA1: d3f65691ae505357d5b6ffd85ac9e82a00a60d75 SHA256: e966ed9ca08b964d42f1e0ca12cc075f0503b664bccf709f6b0b569952425679 SHA512: c3f83fd13ad9e7ed62ae444682d5e3adbfc4e34649ee1796753644aef03e06c0bd8def1d253b84d0a2898bcfc877806340387fb559e63f1d441e1c2079796efb Homepage: https://cran.r-project.org/package=multideploy Description: CRAN Package 'multideploy' (Deploy File Changes Across Multiple 'GitHub' Repositories) Deploy file changes across multiple 'GitHub' repositories using the 'GitHub' 'Web API' . 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Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) . Since November 2025, this package contains code from the 'ez' R package (Copyright (c) 2016-11-01, Michael A. Lawrence ), originally distributed under the 'GPL' (equal and above 2) license. Package: r-cran-multiflexscan Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dorng, r-cran-dosnow, r-cran-foreach, r-cran-igraph, r-cran-rflexscan Suggests: r-cran-sf, r-cran-spdep, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multiflexscan_0.2.0-1.ca2404.1_all.deb Size: 78664 MD5sum: e6a316559028df30ea5082741f0ed22d SHA1: 589b4522ed90ce481c389f4e0389ad1627a07bb0 SHA256: bee5eaae160a227bda63a19b5da56e90c2aac24c8f1b02ffc82f64b6c6c22091 SHA512: 123a58afcf656cca314b8bfd3aed1d0a0a314ff4d6850b7333e7529b14e779c706c9271f709cc99fc59770acc6897c90e5d105ffbead286366c6e6ba9d0bed97 Homepage: https://cran.r-project.org/package=multiflexscan Description: CRAN Package 'multiflexscan' (Information Criterion and Scan Statistic Approach for DetectingMultiple Disease Clusters) Detecting multiple disease clusters using the information criterion and scan statistic approach developed by Takahashi and Shimadzu (2020) . 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Combines four frailty distributions -- Gamma (Clayton, 1978), Inverse Gaussian (Hougaard, 1984), and two variants of the Generalized Lindley (GL) distribution: GL Type 1, a two-component gamma mixture with distribution-specific scale/shape linkage (Pandey, Hanagal, and Tyagi, 2022), and GL Type 2, a two-component gamma mixture with a common rate parameter (Pandey and Tyagi, 2021 ) -- with two baseline hazard distributions: the two-parameter Weibull distribution (Weibull, 1951) and the three-parameter Generalized (Exponentiated) Weibull distribution (Mudholkar and Srivastava, 1993 ). A no-frailty baseline-only model is also supported for nested model comparison. Maximum likelihood estimation is conducted using Newton-Raphson and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms via the 'maxLik' package (Henningsen and Toomet, 2011 ). Provides standard errors, confidence intervals, hypothesis tests, Akaike Information Criterion (AIC, Akaike, 1974 ), Bayesian Information Criterion (BIC, Schwarz, 1978 ), corrected Akaike Information Criterion (AICc, Hurvich and Tsai, 1989), Hannan-Quinn Information Criterion (HQIC, Hannan and Quinn, 1979), a bootstrap approximation of the Widely Applicable Information Criterion (WAIC, Watanabe, 2010), k-fold cross-validation, frailty variance estimation, survival, hazard, median, risk, and marginal predictions, Cox-Snell (Cox and Snell, 1968), martingale (Barlow and Prentice, 1988), and deviance residuals with a Kolmogorov-Smirnov goodness-of-fit test, influence diagnostics (leverage, Cook's distance, difference in fits (DFFITS), difference in betas (DFBETAS); Belsley, Kuh, and Welsch, 1980), random data generation under all eight censoring mechanisms, a Monte Carlo simulation-study function, and a diagnostic and survival plotting suite. 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Package: r-cran-multigrey Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multigrey_0.1.0-1.ca2404.1_all.deb Size: 18784 MD5sum: 6ca8505aa7b3140cf09144ee640e11e2 SHA1: 19b90d49b77c0bbc4a2d6e0b1ce79f318fa17d1b SHA256: f5b503d280e518562702adb34de518bf71ef1039f5b3c499e775739e15cb8823 SHA512: 48f05cf68dec1a4d23e8e676cd47425e0867145b868c7c9b7d44b16b6b783c82d6f45dbe623b333d1bdb1f47cc75f495855a7d7dc49ef75e35f505b4b46f31fa Homepage: https://cran.r-project.org/package=MultiGrey Description: CRAN Package 'MultiGrey' (Fitting and Forecasting of Grey Model for Multivariate TimeSeries Data) Grey model is commonly used in time series forecasting when statistical assumptions are violated with a limited number of data points. The minimum number of data points required to fit a grey model is four observations. This package fits Grey model of First order and One Variable, i.e., GM (1,1) for multivariate time series data and returns the parameters of the model, model evaluation criteria and h-step ahead forecast values for each of the time series variables. For method details see, Akay, D. and Atak, M. (2007) , Hsu, L. and Wang, C. (2007).. Package: r-cran-multigroup.vaccine Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1575 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desolve, r-cran-shiny, r-cran-bslib, r-cran-htmltools, r-cran-socialmixr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multigroup.vaccine_0.1.2-1.ca2404.1_all.deb Size: 464422 MD5sum: 6cc5e8d176ce697726bfb9c8bfdfe8d6 SHA1: ea33f5247abd6892654729fa2d44f81affe5dbb6 SHA256: a4064881ec297aab6e78871d4d3d02c315c8ddca58b52fabeef402d4b5f27480 SHA512: 4c20bc3d945bc3b8bb7a4056ec344e3f19c38a42f16cf2e838f5aba0d144119edd22c1c0cb2905d6198811e5ce620491bbd4be22599a8d0e4c18d18ac3df7608 Homepage: https://cran.r-project.org/package=multigroup.vaccine Description: CRAN Package 'multigroup.vaccine' (Analyze Outbreak Models of Multi-Group Populations withVaccination) Model infectious disease dynamics in populations with multiple subgroups having different vaccination rates, transmission characteristics, and contact patterns. Calculate final and intermediate outbreak sizes, form age-structured contact models with automatic fetching of U.S. census data, and explore vaccination scenarios with an interactive 'shiny' dashboard for a model with two subgroups, as described in Nguyen et al. (2024) and Duong et al. (2026) . Package: r-cran-multigroup Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multigroup_0.4.5-1.ca2404.1_all.deb Size: 179418 MD5sum: 94326290e1b17ac63126d3e152dbcb86 SHA1: 224d8f4acaa3c83427b4281c4242fbd93ddbf923 SHA256: a1d0810090aac55cdc9265d55d3b2b6346449ca3ffdf36fe51316123a9adc1eb SHA512: 04518d4ff4ef1cab05bdfed329b07bbead94696571e3511c28a089a1cd36cf0bc253a145634f74d68b4705dcc0b125ed36a634ab728260bf51b56b7a4161e49d Homepage: https://cran.r-project.org/package=multigroup Description: CRAN Package 'multigroup' (Multigroup Data Analysis) Multivariate analysis methods including principal component analysis, partial least square regression, and multiblock analysis to describe, summarize, and visualize data with a group structure. Package: r-cran-multigroupo Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rlist, r-cran-expm, r-cran-ggplot2, r-cran-gridextra, r-cran-cowplot, r-cran-plsgenomics, r-cran-gplots, r-cran-ggrepel, r-cran-qgraph, r-cran-mgm, r-cran-lemon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multigroupo_0.4.0-1.ca2404.1_all.deb Size: 270654 MD5sum: 0b65f5a6ffafa200a0834ef183bf640d SHA1: efb36f109386897628a61d154d9fb70a3b5868ab SHA256: 55a51aea1aa671fc5739b4d21518601e986c0349a9974423ab299a0ef76fbd89 SHA512: cdb2b35e464b5fe00670c5beff4746ac2c2eaf73c9c84485edf7ae7df16b6198001d990c638bee7d5ff993968129daaa4f125989ac7850305579718261b0b4db Homepage: https://cran.r-project.org/package=MultiGroupO Description: CRAN Package 'MultiGroupO' (MultiGroup Method and Simulation Data Analysis) Two method new of multigroup and simulation of data. The first technique called multigroup PCA (mgPCA) this multivariate exploration approach that has the idea of considering the structure of groups and / or different types of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality Reduction (MDR) it is another multivariate exploration method that is based on projections. In addition, a method called Single Dimension Exploration (SDE) was incorporated for to analyze the exploration of the data. It could help us in a better way to observe the behavior of the multigroup data with certain variables of interest. Package: r-cran-multigroupsequential Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx, r-cran-hommel Filename: pool/dists/noble/main/r-cran-multigroupsequential_1.1.0-1.ca2404.1_all.deb Size: 79260 MD5sum: b0399f7c6b19a84ecd4761381d680726 SHA1: eb0ac71ed7d251c3e6a363cd79e608f55aa30c0b SHA256: fac4597f60b268cd168cd24679f010d2a8b187adb9a84173197b33a24f5741ac SHA512: 1f313fcdc7227f7d3b390f1cfded66a7653c1a947f8d3d788aaada73600cae5154aa486ef299725930f19f92154a26e6fbd1d8ccb9a793c85b959307167b437e Homepage: https://cran.r-project.org/package=MultiGroupSequential Description: CRAN Package 'MultiGroupSequential' (Group-Sequential Procedures with Multiple Hypotheses) It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) and the graphical procedure proposed by Maurer and Bretz (2013) . Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013). Package: r-cran-multijoin Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multijoin_0.1.1-1.ca2404.1_all.deb Size: 77994 MD5sum: 91221d5689c391bd77bb946feeb73cbd SHA1: 77562e3244b0f892189ea4cc75010368df4bd648 SHA256: c06f2dfcef72339cb611a920eecdc84329c955b7706f5e15638c96bd9abfad0b SHA512: 2ce7688123f323c199f797f99ba3cfeb2273f5ce9e8894c3b791c0180d5df12571305431597524625631d7cb65463f8ad7285b61b38c2ef30a1ab1994c5b818f Homepage: https://cran.r-project.org/package=MultiJoin Description: CRAN Package 'MultiJoin' (Enables Efficient Joining of Data File on Common Fields usingthe Unix Utility Join) Wrapper around the Unix join facility which is more efficient than the built-in R routine merge(). The package enables the joining of multiple files on disk at once. The files can be compressed and various filters can be deployed before joining. Compiles only under Unix. Package: r-cran-multikink Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-quantreg, r-cran-gam, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multikink_0.2.0-1.ca2404.1_all.deb Size: 83574 MD5sum: 54aad6d5d3738fc6f6db7b2d27059602 SHA1: 693b79ec373c2224b484412f051d8a6deb7cd916 SHA256: b5efadbcc40b29955745598928eefb37546ce165dc13aa0c55d5eb6761f1cd2c SHA512: fddcc593a09c05f8dc47078813f21512dd2d6b80634a4dfe9905f2d62c302ae951ff070dbbaad44ef634276df6443ac767469129cd67d38bc10d96b6930c1c6a Homepage: https://cran.r-project.org/package=MultiKink Description: CRAN Package 'MultiKink' (Estimation and Inference for Multi-Kink Quantile Regression) Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES'' and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics". Package: r-cran-multilandr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4954 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-ggally, r-cran-ggplot2, r-cran-tidyterra, r-cran-gridextra, r-cran-landscapemetrics, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multilandr_1.0.0-1.ca2404.1_all.deb Size: 4646402 MD5sum: b0835e57e4ce7fd44c43b5d12103a375 SHA1: 2ce0f2425bda66844331b5c7391658848f933b8f SHA256: d254e2a2a539e1d4858fa77795335047d0e749fc3690ef46efc13232123b32fa SHA512: 58edcaec1bc3d6461606e0093afd392a1d3f713de0148d105fd9c558ba2c585fc5bbfc9769edc27150bc9ab2d48174e4ffe1a46a7acb699fa84ef4a6e67e26e9 Homepage: https://cran.r-project.org/package=multilandr Description: CRAN Package 'multilandr' (Landscape Analysis at Multiple Spatial Scales) Provides a tidy workflow for landscape-scale analysis. 'multilandr' offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the 'landscapemetrics' package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about 'multilandr' is provided in an introductory vignette included in this package and in the paper by Huais (2024) ; see citation("multilandr") for details. Package: r-cran-multilaterals Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-igraph Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-multilaterals_2.0-1.ca2404.1_all.deb Size: 80964 MD5sum: ffeee49967c93a22075340298d2978f5 SHA1: 8397af5e9211f4b87d263976a4f8bf9e81bc48cd SHA256: 26a07a71f7044ba41fe88f9e0a91ebfd9343cc0244abec200de1e7a5484184ca SHA512: 22c082505acc89e9d047bb01d9477cb11eb9cda0e368ef4da269b0865da4f7f433acfd4793894062178a6c08297b22188c061f58db74d94bffe6c5348831dd18 Homepage: https://cran.r-project.org/package=multilaterals Description: CRAN Package 'multilaterals' (Transitive Index Numbers for Cross-Sections and Panel Data) Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 ) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 ; Rao et al., 2002 ) and Minimum spanning tree (Hill, 2004 ) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas. Package: r-cran-multilevel Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-mass Filename: pool/dists/noble/main/r-cran-multilevel_2.8-1.ca2404.1_all.deb Size: 498564 MD5sum: c0baa32e94b508f4543f29aa9f1cd9f9 SHA1: 8e510761334289deed3a70e7e82c52f6261ec36b SHA256: 1b5314bfc39dd5f9b2162669c60d65ee299f41b235a01303dab3f4f22f608fc9 SHA512: a334da5e6ad59049d8867536ee21391edda20432cbcf3e798d4bfeccc8529c068645bf9b40d777de62ab18773f3c4473c0f7459cb2616faf49017b2db6552498 Homepage: https://cran.r-project.org/package=multilevel Description: CRAN Package 'multilevel' (Multilevel Functions) Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. 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Compute multilevel compositional data and perform log-ratio transforms at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2025) , Le, Dumuid, Stanford, and Wiley (2025) . Package: r-cran-multilevelmediation Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-furrr, r-cran-future, r-cran-matrixcalc, r-cran-mcmcpack, r-cran-nlme, r-cran-parallelly, r-cran-tidyr, r-cran-brms, r-cran-posterior, r-cran-glmmtmb Suggests: r-cran-testthat, r-cran-boot, r-cran-bh Filename: pool/dists/noble/main/r-cran-multilevelmediation_0.5.0-1.ca2404.1_all.deb Size: 162508 MD5sum: 7d73875f5ab7c1902f3a99bcd6aca7da SHA1: f49f693cd0b07df4077e0c1f755cf0f9c4592013 SHA256: 2f6119b5f83b8b2550b0a67c2be51b343e4e634064e1de090ef1ce5c29e19d41 SHA512: d22457ac3a34c25be0720eebbdb5b93c42d96c2724b5fb7edb923b16e0848b1694bbeb20fe3b0cc038c6f1e52b60ee5de5d4eade5c90adb0c9c9dbcd87b06c7b Homepage: https://cran.r-project.org/package=multilevelmediation Description: CRAN Package 'multilevelmediation' (Utility Functions for Multilevel Mediation Analysis) The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) . Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented. Package: r-cran-multilevelmod Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-parsnip, r-cran-dplyr, r-cran-lme4, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-covr, r-cran-gee, r-cran-ggplot2, r-cran-knitr, r-cran-nlme, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidymodels Filename: pool/dists/noble/main/r-cran-multilevelmod_1.0.0-1.ca2404.1_all.deb Size: 198068 MD5sum: 9004961584a585a11ca89efa412b084a SHA1: 8a81ff844006656cccf25b8fed9ff349d90025fd SHA256: ad0ffd56ec39ca67e67b97635f09290adc18935a5dab58a15c55020e2a73d6fc SHA512: 757d6fd814f598b444d29ca797b39a4f5a74b3d343d57332931903c30f4a08357b9d2e00396b2cbe91338ee72de4e5ae0988becef13c63a2c71838c8432a3526 Homepage: https://cran.r-project.org/package=multilevelmod Description: CRAN Package 'multilevelmod' (Model Wrappers for Multi-Level Models) Bindings for hierarchical regression models for use with the 'parsnip' package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) , and mixed-effect models (Pinheiro and Bates) . Package: r-cran-multileveloptimalbayes Architecture: all Version: 0.0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multileveloptimalbayes_0.0.4.0-1.ca2404.1_all.deb Size: 126312 MD5sum: 289dfb9ddae4e83d9f3a8c6cd182feff SHA1: 8f70f0f56b73a2cc47ccbc1c12dbcfdfddbf0bb4 SHA256: f24e36acdad3a02f73be639c498e637fa3036f03f412ff3d7ef7f5e117249768 SHA512: 86eccae8f80f87903559881374aec9ab6c69933e9c6f353625985cef2388603cb9e43e633ee56dbcb461a3a9b8d891e75d115358f84f4f0cfcf0fd089245b3b8 Homepage: https://cran.r-project.org/package=MultiLevelOptimalBayes Description: CRAN Package 'MultiLevelOptimalBayes' (Regularized Bayesian Estimator for Two-Level Latent VariableModels) Implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. The package includes comprehensive S3 methods for result objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(), dim(), length(), names(), and update() for enhanced usability and integration with standard R workflows. Dashuk et al. (2025a) derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) extended it to the multivariate case; and Luedtke et al. (2008) formalized the two-level latent variable framework. Package: r-cran-multilevelpsa Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable, r-cran-mass, r-cran-party, r-cran-plyr, r-cran-psagraphics, r-cran-psych, r-cran-reshape Suggests: r-cran-knitr, r-cran-matchit, r-cran-mice, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multilevelpsa_1.3.1-1.ca2404.1_all.deb Size: 2948048 MD5sum: 21458c08318871c5b7525fa1f4bf3e99 SHA1: ecc1ba21e97bc73317613a649aaefad909f676b0 SHA256: b712aaf503cb7dd8c96b0f893b87c3c6c5eb764613bd5c2b766d41cc346a71cb SHA512: 3576de4cc697ef34102a3a59d05c2a4f73f7495ff39f93b20f87edb4af886a1bb9296229edaa698012b8dcb28ca4ea363657776d5601e6da17fc9be5877f6381 Homepage: https://cran.r-project.org/package=multilevelPSA Description: CRAN Package 'multilevelPSA' (Multilevel Propensity Score Analysis) Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) . Package: r-cran-multileveltools Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lmertest, r-cran-data.table, r-cran-nlme, r-cran-extraoperators, r-cran-jwileymisc, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-lavaan, r-cran-zoo, r-cran-brms, r-cran-testthat, r-cran-reformulas Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multileveltools_0.2.1-1.ca2404.1_all.deb Size: 1375104 MD5sum: f4ef48861831a59bad4a5198d6480c23 SHA1: b90ad03942bbd94e2b423649afd1b3dc6999b7ae SHA256: 2c125f3a26e2a39df98c8af4bcde56db656b3032c5b9dffade11af5bdfa4425b SHA512: ef709b93d128e6d8e72053863044a28efbee5d033719b308a3d948dbc889c70f464acc7e11cc9d4f2a47bd003bd3a54def07ecf54f324a6cbc4b5f86a9c21fc2 Homepage: https://cran.r-project.org/package=multilevelTools Description: CRAN Package 'multilevelTools' (Multilevel and Mixed Effects Model Diagnostics and Effect Sizes) Effect sizes, diagnostics and performance metrics for multilevel and mixed effects models. Includes marginal and conditional 'R2' estimates for linear mixed effects models based on Johnson (2014) . Package: r-cran-multilinguer Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sys, r-cran-rappdirs, r-cran-usethis, r-cran-askpass Filename: pool/dists/noble/main/r-cran-multilinguer_0.2.4-1.ca2404.1_all.deb Size: 618594 MD5sum: f46a5fb36e90d1b51b50c47ffc638002 SHA1: 1917fd3a769e5428bbd72a1c1ea7a092c1242ec9 SHA256: c3a98d1896bfb3e5406d3f9ea605c1c2a2d619952cccba35dabb957df2a06f2f SHA512: 3b8601b4eddde4762cbaaefda7d51d9f24c20f1112f503477d070d483d37283e704ce175fdd7403b38ccc37f6d53cec00a27d87871a107b226bb986ae6d7d2a1 Homepage: https://cran.r-project.org/package=multilinguer Description: CRAN Package 'multilinguer' (Gentle Language Installer for R User) Provides install functions of other languages such as 'java', 'python'. 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Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" . Package: r-cran-multimediate Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmutil, r-cran-mass, r-cran-mvtnorm, r-cran-timereg Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multimediate_0.1.6-1.ca2404.1_all.deb Size: 331614 MD5sum: cd5b268a7d98fcd8e5f67d27223c9792 SHA1: 4dedf6705e60d56132561457ff0210cb1d759903 SHA256: 6390650c9e58b757048fb0bd0f1fad07c02c00ce0ad092dfe59652c7bf00ed23 SHA512: a3620529b732180eaadf6c97e3af2bda56f9b07e6588c91eb16bc060e10290e1f4193616c3f24338c5113bfbf14f0086dbb6376d95c9ff42e94b2327c48b75c0 Homepage: https://cran.r-project.org/package=multimediate Description: CRAN Package 'multimediate' (Causal Mediation Analysis in Presence of Multiple MediatorsUncausally Related) Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jerolon et al., (2021) and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) . Package: r-cran-multimix Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-multimix_1.0-10-1.ca2404.1_all.deb Size: 139102 MD5sum: b95bff433dd966491d87082e301e90e6 SHA1: 0fd0bf71963645061c3d608f3d1b4768db0e215b SHA256: 4165adf02d325ed3e9cf6302b7e53a5dc82de4f2ec6289ce399f7d2ba293ed0b SHA512: 4126af006c9e5b470e9b97482e990bf95d9e14c1e7fff0add646c38c42b331b3d896d9f868dd035280542d37fa83b1e773cc5a2fc584f7498fcd3c3feb8f3e77 Homepage: https://cran.r-project.org/package=multimix Description: CRAN Package 'multimix' (Fit Mixture Models Using the Expectation Maximisation (EM)Algorithm) A set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440. Package: r-cran-multimodtest Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4472 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sis, r-cran-glmnet, r-cran-ncvreg, r-cran-mbess, r-cran-survival, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multimodtest_1.1-1.ca2404.1_all.deb Size: 4523668 MD5sum: c895d9a33556134b5df0804e93fe8559 SHA1: bf34b45db7f56074a9891abdd78652df71733f54 SHA256: 98642379fcacbf83ff5925da3682650352c37fb4c2218fce5afc18ff4e43e177 SHA512: a2286eede642637e1b952b20dab28b164e5c81249b84e39e018f852d3f87fae0a79644b0d294fdd39f71101fcc860fe1f69529b795ddc473c5585099650923dd Homepage: https://cran.r-project.org/package=multiModTest Description: CRAN Package 'multiModTest' (Information Assessment for Individual Modalities in MultimodalRegression Models) Provides methods for quantifying the information gain contributed by individual modalities in multimodal regression models. Information gain is measured using Expected Relative Entropy (ERE) or pseudo-R² metrics, with corresponding confidence intervals. Currently supports linear regression, logistic regression, and the Cox proportional hazards model. A robust Median-of-Means based estimator is also provided for heavy-tailed responses under the Gaussian and Negative-Binomial families, with basic bootstrap confidence intervals. Package: r-cran-multimolang Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-multimolang_0.1.1-1.ca2404.1_all.deb Size: 214480 MD5sum: d983fbb36775d6c96755f3e7e19c4818 SHA1: 8d3389fd4e0e023e3319e6e9f9308ce9df034e05 SHA256: 34375218a065dd0cbf09f1f591628387e8519a19e5647cd08227b854433e0021 SHA512: 33c301f1a2a921064a931e748f2ec7a3db52fd5012e2f4e029b55f70981e489056043e3364ca20dd20b1ae1959da68a6f8120e794a0955429974f1e19d1e8fbb Homepage: https://cran.r-project.org/package=multimolang Description: CRAN Package 'multimolang' ('multimolang': Multimodal Language Analysis) Process 'OpenPose' human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. 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Package: r-cran-multimorbidity Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr, r-cran-sqldf Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-multimorbidity_0.5.1-1.ca2404.1_all.deb Size: 288550 MD5sum: bd4414ccd6532f15d8cdb25f2583bded SHA1: f1015cb400df2e6d5673c8dabcd86e980b3ef7d8 SHA256: ac3e0ba44a47ed9d3b84ef0aec31020aa124c971069bba7bd6e4eb89fa9a2ba3 SHA512: e580353a7a4b57f430f40ff3393a336bb90009a5b72498f9aa5c1c3ed43822ba2f0c9197ffaf9b6ca9b39badb21a0b1d8d6f6f26d22ca38ee7254bf770c21d0d Homepage: https://cran.r-project.org/package=multimorbidity Description: CRAN Package 'multimorbidity' (Harmonizing Various Comorbidity, Multimorbidity, and FrailtyMeasures) Identifying comorbidities, frailty, and multimorbidity in claims and administrative data is often a duplicative process. The functions contained in this package are meant to first prepare the data to a format acceptable by all other packages, then provide a uniform and simple approach to generate comorbidity and multimorbidity metrics based on these claims data. The package is ever evolving to include new metrics, and is always looking for new measures to include. The citations used in this package include the following publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) , Brian J Moore, Susan White, Raynard Washington, et al. (2017) , Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) , Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) , Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) , Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) , Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) , Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) , Martin Fortin, José Almirall, and Kathryn Nicholson (2017). Package: r-cran-multiness Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-multiness_1.0.2-1.ca2404.1_all.deb Size: 373354 MD5sum: 0c0aee2b03d1d63bf13396c41700417c SHA1: f131f38850e35b756625b68d2db365be81deb521 SHA256: 0ce622e401db8cf079fecd794aa710266a367c0d693cbd4b81ea0a4412c27705 SHA512: 30adcc0d5ad8bd777079c1ecb07a33f91db51e4c2af77b6362a56a64bf184ea6f5bde2c64c14d084be9b51666a9a2faa359d369bcb17363f2ee347264f27bcdf Homepage: https://cran.r-project.org/package=multiness Description: CRAN Package 'multiness' (MULTIplex NEtworks with Shared Structure) Model fitting and simulation for Gaussian and logistic inner product MultiNeSS models for multiplex networks. The package implements a convex fitting algorithm with fully adaptive parameter tuning, including options for edge cross-validation. For more details see MacDonald et al. (2020). Package: r-cran-multinmix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-clustergeneration, r-cran-mvtnorm, r-cran-extradistr, r-cran-rstan, r-cran-abind, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multinmix_0.1.0-1.ca2404.1_all.deb Size: 177966 MD5sum: db056bd71676632dbc8cb41493f86f07 SHA1: 5b2f74fda959abb2cebe02c0417ece9ab9f29488 SHA256: 1341d1c39b5bd144c9a20fef80878619a68f51b6c280f5450c7de7b0358d8d8e SHA512: 3db151766f2cd03368b100df7de9cd37be773cc380a92174cdc6c2601715694b43b4035c1a07c45f7272ddad7149bcb10b856533b8ad251a842f1c2d4186e2b1 Homepage: https://cran.r-project.org/package=MultiNMix Description: CRAN Package 'MultiNMix' (Multi-Species N-Mixture (MNM) Models with 'nimble') Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) . Royle, J. A. (2004) . Package: r-cran-multinomialci Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multinomialci_1.2-1.ca2404.1_all.deb Size: 20336 MD5sum: 1ce411723fc8180b5640c6c46c9c4268 SHA1: 10f0b7306aff560a788e9ad978aa558ba063582e SHA256: b467f0d72a4cd6d48f896a44f50504c877cce68af74e8990866bfc09915f3263 SHA512: 77314efae3e22c89c1bfcec5a8b2af34612406523e8ef65bec3374434e9ffa4fefcb026c69ac9d5236e803dab09bca33f0c42e210af50facf25108faca3432d3 Homepage: https://cran.r-project.org/package=MultinomialCI Description: CRAN Package 'MultinomialCI' (Simultaneous Confidence Intervals for Multinomial ProportionsAccording to the Method by Sison and Glaz) An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper: Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). The method is an R translation of the SAS code implemented by May and Johnson in their paper: May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). Paper and code available at . Package: r-cran-multinttestfunc Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-statmod, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multinttestfunc_0.3.0-1.ca2404.1_all.deb Size: 144344 MD5sum: ab896ee15f5421c44a202ef49ca5899a SHA1: 291ec37673813e8da9107e16e930232e07916672 SHA256: e1e1919a4f69265b80cf603cadbc99bd60dfbe7f62b6040595df48ceb7099052 SHA512: 906294c9927f137a07eeae351f6b4a42bf6e2746da0375e78cf465b7545e8f9f5ffe0ecf884899fba593ecf6b6d940f4a604e97bb89c59cdf6879d640dfad0d9 Homepage: https://cran.r-project.org/package=multIntTestFunc Description: CRAN Package 'multIntTestFunc' (Provides Test Functions for Multivariate Integration) Provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website. Package: r-cran-multiobjectivemdp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-linprog, r-cran-lintools, r-cran-nsga2r, r-cran-pracma, r-cran-prodlim Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multiobjectivemdp_1.0.0-1.ca2404.1_all.deb Size: 222390 MD5sum: f115b83db5e91e2b92a29ce0e6ffa7d1 SHA1: a5b5711cf7038824760a26ddec923300b077c9b0 SHA256: 952547d8bd3041541e7bb39a5f0688d5a49e8fb827284780f16bea1dbcf909d7 SHA512: e3c0165b527ca48442743dfefc514d03080c19e407732688a5bed199083283acc0853efc32bdf7f895baa76a6af6a1d9b2049a29c60a3b3d84dfa90027c316a7 Homepage: https://cran.r-project.org/package=multiobjectiveMDP Description: CRAN Package 'multiobjectiveMDP' (Solution Methods for Multi-Objective Markov Decision Processes) Compendium of the most representative algorithms in print---vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach---for solving multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) ; Mifrani, A. & Noll, D. ; Wakuta, K. (1995) . 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Users are able to modify the emphasis on three different optimization goals: two different distance measures and the number of treated units left unmatched. The method is proposed by Pimentel and Kelz (2019) . The 'rrelaxiv' package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . 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Package: r-cran-multiplex Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1109 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-multigraph, r-bioc-rgraphviz, r-cran-knitr Filename: pool/dists/noble/main/r-cran-multiplex_4.0-1.ca2404.1_all.deb Size: 1030770 MD5sum: fdcde02378d9e6f2b4c02c5c643eba86 SHA1: dc18653c6cfc8ef39be8eef08963f783c64ec1cd SHA256: df90f6ce48879f8f44cd3ebc059554fa8f97fe1f467089af00926919a4858e6e SHA512: c1f0ca15ea29d7e40b773cf9803ed1653f8d7c2f203327b9e8cb357788f10129502ef9efac6541ac6fcddde4c3d5a6d87c07b575d218d88956b85eff8f54bab2 Homepage: https://cran.r-project.org/package=multiplex Description: CRAN Package 'multiplex' (Algebraic Tools for the Analysis of Multiple Social Networks) Algebraic procedures for analyses of multiple social networks are provided with this package as described in Ostoic (2020) . 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(2025) ). Supports the definition and solution of conservation problems across nested H3 resolutions with resolution-specific features, costs, and management attributes, including cross-scale connectivity penalties derived from parent-child relationships. Also includes utilities to evaluate solutions using multiscale-aware diagnostics and to post-process optimization outputs into alternative area-targeted conservation scenarios. 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Package: r-cran-multisite.accuracy Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coxme, r-cran-lme4, r-cran-lmertest, r-cran-logistf, r-cran-metafor, r-cran-proc, r-cran-survival Filename: pool/dists/noble/main/r-cran-multisite.accuracy_1.3-1.ca2404.1_all.deb Size: 38366 MD5sum: 253f0f6fa418fc0b9f8295654ed35c64 SHA1: 2b9d53239c5821e3c8ccbb121b37f45da3ee3ac0 SHA256: 903d9ce6c6e2545797ea298e3f99d3b946628a49ac200139d87e8b6e50e69ddd SHA512: 21d15696f35c703d3bbfc7570ac5282e5e76aa25c36d28605ce6482c72212fc291ab41ea7a3879cc9f150c1feba637c0209353193136c16ce47467e4b100bfeb Homepage: https://cran.r-project.org/package=multisite.accuracy Description: CRAN Package 'multisite.accuracy' (Estimation of Accuracy in Multisite Machine-Learning Models) The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set' (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes. Package: r-cran-multisitemediation Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-statmod, r-cran-psych, r-cran-mass, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-multisitemediation_0.0.4-1.ca2404.1_all.deb Size: 255452 MD5sum: 6217722f573963bfb6b5afa7e8297f9b SHA1: 8b67b1e6e4e1004f4e066e41507d4c876220061f SHA256: 437c60c8fd1a6a0d8763a6fa215b9a3c556db3240a77caa8cf2de2f4ab609b15 SHA512: 3d5e03501942421596aacf5f508269a31a283cb72f39598854d4d93b980e0f0980d9dd4038424e31f12fda1a7b8af552c7015d325e42ad7e895c07d4d299f4c9 Homepage: https://cran.r-project.org/package=MultisiteMediation Description: CRAN Package 'MultisiteMediation' (Causal Mediation Analysis in Multisite Trials) Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) , Qin, Hong, Deutsch, and Bein (2019) , and Qin, Deutsch, and Hong (2021) . It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias. 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Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods". 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O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.) Package: r-cran-multivariatetrendanalysis Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-resample, r-cran-vgam, r-cran-zoo Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-multivariatetrendanalysis_0.1.3-1.ca2404.1_all.deb Size: 61892 MD5sum: 30e45f6225ac3abd4e576a31f24da3c1 SHA1: 31d743ff3cc7ee3777f128cd1d8ddb35a71d5f01 SHA256: 9c950861ece07db40c88e83db57eb13e84d2b8dfb3594e9d7846fd82a60a40da SHA512: 86c1cd673c494466ffdb126d44e43209c962ce7cf2a8e990ffa1a1352e18c261e652b783d951edd0acfc885f63d3d960162a5538f361a2f5a74ae95ed90f8ea2 Homepage: https://cran.r-project.org/package=MultivariateTrendAnalysis Description: CRAN Package 'MultivariateTrendAnalysis' (Univariate and Multivariate Trend Testing) With foundations on the work by Goutali and Chebana (2024) , this package contains various univariate and multivariate trend tests. The main functions regard the Multivariate Dependence Trend and Multivariate Overall Trend tests as proposed by Goutali and Chebana (2024), as well as a plotting function that proves useful as a summary and complement of the tests. Although many packages and methods carry univariate tests, the Mann-Kendall and Spearman's rho test implementations are included in the package with an adapted version to hydrological formulation (e.g. as in Rao and Hamed 1998 or Chebana 2022 ). For better understanding of the example use of the functions, three datasets are included. These are synthetic data and shouldn't be used beyond that purpose. 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(2017) . 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Package: r-cran-multiverse Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-r6, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect, r-cran-formatr, r-cran-collections, r-cran-evaluate, r-cran-rstudioapi, r-cran-berryfunctions, r-cran-furrr, r-cran-styler, r-cran-distributional, r-cran-jsonlite, r-cran-readr Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-highr, r-cran-rmarkdown, r-cran-covr, r-cran-broom, r-cran-boot, r-cran-gganimate, r-cran-gifski, r-cran-forcats, r-cran-stringr, r-cran-cowplot, r-cran-tidybayes, r-cran-png, r-cran-stringi, r-cran-modelr, r-cran-future Filename: pool/dists/noble/main/r-cran-multiverse_0.6.2-1.ca2404.1_all.deb Size: 2613564 MD5sum: ff0c48b0f18b0a26c7708a230df51087 SHA1: 966acc5d51048451934af15eb6f2dc90ff5fe58b SHA256: fe4bc4b0c2a72be1bc1b4eb5276b2bbdae4b3f1ac24d29ef461b4c79163128e1 SHA512: 811f9c42b03f9716b1ad47648438b752fee895bd2c9e5ab840b8267fd537eb87b3aa549192639132724b42738dc6e0f95ae6108baf555d33da250cd5543d184b Homepage: https://cran.r-project.org/package=multiverse Description: CRAN Package 'multiverse' (Create 'multiverse analysis' in R) Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016) to show the robustness of statistical inference. 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Package: r-cran-multiwave Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Filename: pool/dists/noble/main/r-cran-multiwave_2.0-1.ca2404.1_all.deb Size: 993428 MD5sum: 4444641fc7eb0033c01baba816eb6eb4 SHA1: a828a6e2a354228fab1c26d557585cfa94e82496 SHA256: 88f3883a1bb835c8ace5993d8e53bc3fae6054bedf79d0aad123f55c7e9772b4 SHA512: 133bf6c33fb65929fdec8daaf74e32075d109fe9e4d3e1a738add852540b0d0d4aef2655efc774831115fb9ec530c06de09e4d1f4d03775a099070716de0168a Homepage: https://cran.r-project.org/package=multiwave Description: CRAN Package 'multiwave' (Estimation of Multivariate Long-Memory Models Parameters) Computation of an estimation of the long-memory parameters and the long-run covariance matrix using a multivariate model (Lobato (1999) ; Shimotsu (2007) ). 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Package: r-cran-multiwayvcov Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-boot Suggests: r-cran-lmtest Filename: pool/dists/noble/main/r-cran-multiwayvcov_1.2.3-1.ca2404.1_all.deb Size: 120322 MD5sum: 52f5babaa8503c1694c835be6fc7914c SHA1: 2019d6d7833b794ab71213273fddb6523ffd8f39 SHA256: d7bc4c639a005e04d9803434dd2fa1075d60868694b95ae77c8ab1d0a8059f50 SHA512: 836fb75744c63e2590f99874b624a8c4e12c544a624090a1561aa02038d0158f7d58484746bcbc2207e76310b53591d36b066a8be5ad3101241658f39a941413 Homepage: https://cran.r-project.org/package=multiwayvcov Description: CRAN Package 'multiwayvcov' (Multi-Way Standard Error Clustering) Exports two functions implementing multi-way clustering using the method suggested by Cameron, Gelbach, & Miller (2011) and cluster (or block) bootstrapping for estimating variance-covariance matrices. 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Package: r-cran-multregcmp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-mvnfast, r-cran-progress, r-cran-bayesplot, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-multregcmp_0.1.0-1.ca2404.1_all.deb Size: 117662 MD5sum: 6a6aebc7a4a3c7d39f6dc8ff0ba30143 SHA1: 8cee199ff561de326911187da5fd79cc0d8da5eb SHA256: a8de0c0fae5ed40f6cece49e70db10c4078acfca57f1fdd7eef423ad5f534ee5 SHA512: 6fbad89ce792d269127186ceb37e840a86ef392a396fcbb579ebc95c93cc84fc4e0b6fa82c44f892696edfd99c955bcb102bfd07643dd4001a84a701d094ee82 Homepage: https://cran.r-project.org/package=MultRegCMP Description: CRAN Package 'MultRegCMP' (Bayesian Multivariate Conway-Maxwell-Poisson Regression Modelfor Correlated Count Data) Fits a Bayesian Regression Model for multivariate count data. 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Distance between two distributions (see N. Bouhlel and A. Dziri (2019): , N. Bouhlel and D. Rousseau (2022): , N. Bouhlel and D. Rousseau (2023): ). Manipulation of these multivariate probability distributions. This package replaces 'mggd', 'mcauchyd' and 'mstudentd'. 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Package: r-cran-music Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-crayon Filename: pool/dists/noble/main/r-cran-music_0.1.2-1.ca2404.1_all.deb Size: 71172 MD5sum: 058cc68b07441a0aa612e21e5217da86 SHA1: 4197711c36780fbede2334dbf5bec54a5cd830b3 SHA256: 05b7c163e3ef530c9ad8880908e576839e271e8d5a37459741ca2cdbe51f329a SHA512: 3ece6827dadbb25cf426307f9b7c496d3ea68e96b06b8a4009c7508ce09d7d0a8856d9cdad4021756fd7afcf7e7ac928071d9ae7a10385ce38db5d0a44f407a4 Homepage: https://cran.r-project.org/package=music Description: CRAN Package 'music' (Learn and Experiment with Music Theory) An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package. Package: r-cran-musicmct Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3336 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-musicmct_0.5.0-1.ca2404.1_all.deb Size: 2336302 MD5sum: 199bee3975751880e10000b5610d5a08 SHA1: aaecf8fed401b0f91256d84ce36b970142f28292 SHA256: cb32da6fb2a193e0f5b7c9724542ea0cb91fca5d39675a914a392d72d6291c3b SHA512: 62531d5ec63671f25a9674eeabba33e74b498c9884f369118ec5748d1097f25e62c6a2c61191788166808eaaac78d0defe2001768887de507c9249247fa9eb32 Homepage: https://cran.r-project.org/package=musicMCT Description: CRAN Package 'musicMCT' (Analyze the Structure of Musical Scales) Analysis of musical scales (& modes, grooves, etc.) in the vein of Sherrill 2025 . The initials MCT in the package title refer to the article's title: "Modal Color Theory." Offers support for conventional musical pitch class set theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin (1987, ISBN: 9780300034936), as well as for the continuous geometries of Callender, Quinn, & Tymoczko (2008) . Identifies structural properties of scales and calculates derived values (sign vector, color number, brightness ratio, etc.). Creates plots such as "brightness graphs" which visualize these properties. Package: r-cran-musicnmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seewave Filename: pool/dists/noble/main/r-cran-musicnmr_1.0-1.ca2404.1_all.deb Size: 2535570 MD5sum: dbaa1b863e32903131dbd97a93ce7eb0 SHA1: 6da91b2b08f9fce2d06dbae192304a964aaafaaa SHA256: 831adfc355004b1f3d7355477df29a1cd00c5a44f1616cd3afd05952cd43f333 SHA512: d2741e37e46d9f8ddc5a83e9845aba4c4fa5d1f4a56d06c29295667878cbbbb030d4ed0227aacf415007fd239abe07783d2ba1316d30ae1bc82f95ed8e653d28 Homepage: https://cran.r-project.org/package=musicNMR Description: CRAN Package 'musicNMR' (Conversion of Nuclear Magnetic Resonance Spectra in Audio Files) A collection of functions for converting and visualization the free induction decay of mono dimensional nuclear magnetic resonance (NMR) spectra into an audio file. It facilitates the conversion of Bruker datasets in files WAV. The sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The package includes also NMR spectra of the urine samples provided by four healthy donors. Based on Cacciatore S, Saccenti E, Piccioli M. Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments. OMICS. 2015;19(3):147-56. . 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Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the 'musicXML' score, which can then be further processed using specialized music software (e.g. 'MuseScore', 'GuitarPro', etc.). Examples can be found in the blog , the presentation by Renard and Le Bescond (2022, ) or the poster by Renard et al. (2023, ). 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The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining. Package: r-cran-mvdfa Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-longmemo, r-cran-pbapply, r-cran-desolve, r-cran-robper, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvdfa_0.0.4-1.ca2404.1_all.deb Size: 170162 MD5sum: ab65bc4d913cc5190de53d1c5422a1da SHA1: dce09d60b6e9ea547725b34b974d164b013cd239 SHA256: eda91aa8d48b4a7c08407e297d434600d131fb229b3a0427390feb57198aa85d SHA512: 670a426ba3e8578101ad9cd492fb04aae28f2a61cb3d37bb8ec1e4799498353356950566c0f870bbd016f8b247e56ee12279a30a391ba8762385ec89dad79483 Homepage: https://cran.r-project.org/package=mvDFA Description: CRAN Package 'mvDFA' (Multivariate Detrended Fluctuation Analysis) This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series. Package: r-cran-mvdpd Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-robustbase, r-cran-cellwise, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-mvdpd_0.1-1-1.ca2404.1_all.deb Size: 556186 MD5sum: 4b266a73fd65b4a9d1bb42520e5baf24 SHA1: e2e69e7600fb78bbd531942cd56cdb8f627847dd SHA256: 68e674b841411fed90324e2fc178ee8600d1ef22827d566d8917c1bb5f85bee2 SHA512: a14c8228c6030525d16d536bf5cf9d0ada4e2a8c6b886b9dad4a6856954d6b5b7617db788436e49d9cecc0d4500f835bc955c8f647ef3e1170773bd37ad10493 Homepage: https://cran.r-project.org/package=mvdpd Description: CRAN Package 'mvdpd' (Robust DPD Methods for Casewise and Cellwise Contamination) Robust multivariate estimation based on multivariate, composite and componentwise Density Power Divergence (DPD) minimization in multivariate normal distribution for casewise and cellwise contamination. Robust estimation for multivariate ordered gamma model using multivariate and composite DPD minimization. See A. Ghosh, C. Agostinelli, and A. Basu (2026) A Composite Divergence Approach to Robust Multivariate Estimation under Cellwise and Casewise Contamination. for full details. Package: r-cran-mverse Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4909 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiverse, r-cran-rdpack, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-broom, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-ggupset Suggests: r-cran-tibble, r-cran-purrr, r-cran-scales, r-cran-mass, r-cran-testthat, r-cran-pkgdown, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mverse_0.2.3-1.ca2404.1_all.deb Size: 4228640 MD5sum: 1e62a0823c893f18e25f1aaf07cab38c SHA1: 90a752e38f14bcbf8ddcf22881c1b43cc04fee45 SHA256: c7355b009acfff8dc8a6cc77e031b45f3bdc50f8d4f124ed0ef07174853527b5 SHA512: b2bd58a4608782711c5ca44b6d7da6fd81d5e986f8ffadc1420daf6814bed23ce1979f25d38383c67d8b198c99079025c40344490cade1780e10621b366395db Homepage: https://cran.r-project.org/package=mverse Description: CRAN Package 'mverse' (Tidy Multiverse Analysis Made Simple) Extends 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) , which allows users perform to create explorable multiverse analysis in R. This extension provides an additional level of abstraction to the 'multiverse' package with the aim of creating user friendly syntax to researchers, educators, and students in statistics. The 'mverse' syntax is designed to allow piping and takes hints from the 'tidyverse' grammar. The package allows users to define and inspect multiverse analysis using familiar syntax in R. Package: r-cran-mvet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvet_0.1.0-1.ca2404.1_all.deb Size: 93200 MD5sum: c3302087e8be3476bac536d4790e2a4e SHA1: 2cc7b450564b797b52c614faef08c620ed68ba53 SHA256: a62b949fdb658716a8f4bd9ea01562e9d51dcfa4b3a9956ef07e995c255b6c9a SHA512: 113aef88e4f95d2513c7099f79bbbffa8223f0404a4fce7cea0b31ae646e70b5be19432a811db374473d7aac12201d3e59c8d6ee65f8e366c3fb440ea2c81d31 Homepage: https://cran.r-project.org/package=MVET Description: CRAN Package 'MVET' (Multivariate Estimates and Tests) Multivariate estimation and testing, currently a package for testing parametric data. To deal with parametric data, various multivariate normality tests and outlier detection are performed and visualized using the 'ggplot2' package. 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Package: r-cran-mvfmr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fdapace, r-cran-ggplot2, r-cran-doparallel, r-cran-foreach, r-cran-proc, r-cran-progress, r-cran-glmnet, r-cran-gridextra Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvfmr_0.2.0-1.ca2404.1_all.deb Size: 335170 MD5sum: 890cc977b857236ab67677f53ee4ce24 SHA1: b25e6bc1e12f447bbec3acb4926c1128b0b81969 SHA256: afcf89c5a897be60e7b44588431c0428a38250bcf3cf336ee77ecc669c2d1b93 SHA512: 4a0516268aee91ebb659536d3e19095bd567add1ff02c4b203bd4243606ead0359186794fb5cf5580567c79fd1478ca6d8846a4c9a8904ac7e4fca9e3e1c026a Homepage: https://cran.r-project.org/package=mvfmr Description: CRAN Package 'mvfmr' (Functional Multivariable Mendelian Randomization) Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 ) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) . Package: r-cran-mvglmmrank Architecture: all Version: 1.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvglmmrank_1.2-7-1.ca2404.1_all.deb Size: 380640 MD5sum: 9f6a1d8a8a8b13beab9aaefa6000f209 SHA1: e7fb0b9ee5b854be2174a3110170a6e1fe21c127 SHA256: 861982e55b1e51a614b4b818190c9e487ce9de68af066b001e57e59a5a752978 SHA512: 85f0b78000c51fb1bedf4055148b9ffdba4bd17096fb6a9f162a0a62832d801143ba4929ef921d8d83041916a34a5594f1b6b12917d18365220d326c7163731c Homepage: https://cran.r-project.org/package=mvglmmRank Description: CRAN Package 'mvglmmRank' (Multivariate Generalized Linear Mixed Models for Ranking SportsTeams) Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) , Karl, Yang, and Lohr (2014) , and by Karl (2012) . Karl and Zimmerman use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling. 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Package: r-cran-mvhtests Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-emplik, r-cran-foreach, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-highd2means Filename: pool/dists/noble/main/r-cran-mvhtests_1.2-1.ca2404.1_all.deb Size: 126820 MD5sum: 1014759a400fc5bd0bde86ecef12b50e SHA1: 95c783c6054efef96bdf8d08ee6064c9a58a9731 SHA256: d74e45d88c04fd8834f5b542fa042920b073ea67959de1cdb4448b8f00d53d55 SHA512: e091ee2bccd65a45f6229961f039d34481755bf1faab3b3bb84e4b924db6c2e2810009982425c95471c13be05ae59e8c1622bdaaa32c2780388e4ee67e1b4bef Homepage: https://cran.r-project.org/package=mvhtests Description: CRAN Package 'mvhtests' (Multivariate Hypothesis Tests) Hypothesis tests for multivariate data. Tests for one and two mean vectors, multivariate analysis of variance, tests for one, two or more covariance matrices. References include: Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press. 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Package: r-cran-mvmise Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvmise_1.0-1.ca2404.1_all.deb Size: 67594 MD5sum: d39d7910db61554e52e910bb1ed023be SHA1: 6f2c2abd70f23860d33a4c05d7d0ba4cb742264f SHA256: bf31678c5c22ccc5ad0887ea9001502df5c59fa63bc5bd3a0c747acb8d22dc5b SHA512: 7291ef57918b0d51d4f7cf4bcc1e1495e0c8dc327c915588471634aa490b76f12419dd274b5ec194d0c09cfddd8ae11d03434dd2bbb7e0e528009322cd5c7ee7 Homepage: https://cran.r-project.org/package=mvMISE Description: CRAN Package 'mvMISE' (A General Framework of Multivariate Mixed-Effects SelectionModels) Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) . The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness. Package: r-cran-mvmonitoring Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lazyeval, r-cran-plyr, r-cran-rlang, r-cran-xts, r-cran-zoo, r-cran-robustbase Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvmonitoring_0.2.4-1.ca2404.1_all.deb Size: 2815450 MD5sum: a5a1e4172b30b163f352a4d6e6695760 SHA1: c21eff026508c71a8c48946f4914390b143128f4 SHA256: ed08b6512ab5875f0e4f692f340ae7e9fe4a84852f48bf70bacd6d2c9e75695b SHA512: 0339758c756c8a24053d0c46c064a2c4284c3bfec1843da8e2ea6c1f01e1a55c12b814f8df7a3a7576f625efdd182350b4311bdaa2803c056f308ff2a9360926 Homepage: https://cran.r-project.org/package=mvMonitoring Description: CRAN Package 'mvMonitoring' (Multi-State Adaptive Dynamic Principal Component Analysis forMultivariate Process Monitoring) Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see , the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled "Multi-State Multivariate Statistical Process Control" by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018. Package: r-cran-mvn Architecture: all Version: 6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-moments, r-cran-mass, r-cran-boot, r-cran-car, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-ggplot2, r-cran-viridis, r-cran-cli, r-cran-energy, r-cran-plotly, r-cran-mice Suggests: r-cran-dt, r-cran-bslib, r-cran-future, r-cran-haven, r-cran-jsonlite, r-cran-readxl, r-cran-shiny, r-cran-yaml, r-cran-promises, r-cran-testthat, r-cran-zip Filename: pool/dists/noble/main/r-cran-mvn_6.3-1.ca2404.1_all.deb Size: 265954 MD5sum: 461afb2a3c924ecc76b5e6c6594895e9 SHA1: 9a2e6578ab6651a73eb3394c02bcea5e3404a7b7 SHA256: 2fafec9d89eebdf1b3905aab4356b0ca7db72b026a21d5635d854d97b73fd239 SHA512: f226c4ae342dae976797c67c6129650b3f9b3ec9f4ec12a83718a2c13e68d3eb5c6b1be70d87f906261e04ae21adf169b409eb337be34e3348236a17e025daad Homepage: https://cran.r-project.org/package=MVN Description: CRAN Package 'MVN' (Multivariate Normality Tests) A comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henze–Zirkler, Henze–Wagner, Royston, Doornik–Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Box–Cox, Yeo–Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both 'ggplot2' and interactive 'plotly' visualizations. See Korkmaz et al. (2014) . Package: r-cran-mvnbayesian Architecture: all Version: 0.0.8-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-plyr Suggests: r-cran-rgl, r-cran-rfast Filename: pool/dists/noble/main/r-cran-mvnbayesian_0.0.8-11-1.ca2404.1_all.deb Size: 68136 MD5sum: cf027f96bb6fd67663085ac732303358 SHA1: 555b118f333e2e563d33dbbf2cde3e6cdda6af2d SHA256: 4c7188537046c876b47e3d183ad941c55fc8c1b1bb80aa9ddb33f5abfe02d0cb SHA512: c959458e9fbe2fd9312ca6e1f8f51edfe0642cac90738ab8015fc5f028004fb083a8b7728aa86a2cea7cf6ab3f4c3bbfd5cf57098e589823c7bdafcc72e7078e Homepage: https://cran.r-project.org/package=MVNBayesian Description: CRAN Package 'MVNBayesian' (Bayesian Analysis Framework for MVN (Mixture) Distribution) Tools of Bayesian analysis framework using the method suggested by Berger (1985) for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution. Package: r-cran-mvnggrad Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnggrad_0.1.6-1.ca2404.1_all.deb Size: 408232 MD5sum: df9921ee3df28271e90144d4b7030729 SHA1: e81cb5242873cc3d77ac17b69df53bab36943228 SHA256: 39a8e766c19be754a1d02775445e28dc1fca41ec2db0c3a033b404c8f2bd9903 SHA512: 0df7e965fe5e30d887e1342536953836ca5d8dbfab6dcf254c675e17e6d9d0d1dd4cd5c2f792221b16c23cb7f47288e419c784d802e1e7dc6f9a58060d893e19 Homepage: https://cran.r-project.org/package=mvngGrAd Description: CRAN Package 'mvngGrAd' (Moving Grid Adjustment in Plant Breeding Field Trials) Package for moving grid adjustment in plant breeding field trials. Package: r-cran-mvngmod Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bessel, r-cran-clustergeneration, r-cran-distributionutils, r-cran-matlib, r-cran-maxlik, r-cran-truncnorm, r-cran-pracma, r-cran-matrixcalc, r-cran-purrr, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvngmod_0.1.3-1.ca2404.1_all.deb Size: 207196 MD5sum: 08efcd2376dc9db12c41031b663b1cae SHA1: d9270575e00ab41f356dc95df7d4ed72004ed1df SHA256: dd45d84b46efc860e22d6a71d8af0b505411df762a6bf79b546374cd37cc1ed1 SHA512: 5b9398e6259bcbfbad27dbf54ca808dc815602c6f2462c6761c046f2a840f5258ac29b5ab5a625dab17a1966371e16eb48edf656ec93efeba15a77501876fce1 Homepage: https://cran.r-project.org/package=MVNGmod Description: CRAN Package 'MVNGmod' (Matrix-Variate Non-Gaussian Linear Regression Models) Fits matrix-variate variance-gamma (MVVG) and matrix-variate normal-inverse-Gaussian (MVNIG) linear regression models using expectation-conditional maximization (ECM) algorithms. The models accommodate clustered matrix-valued responses, with unequal numbers of observations across subjects, correlated responses, skewness, and within-subject dependence. Functions are provided for model fitting, prediction, and subject-level influence assessment using approximate generalized Cook's distances. The package also includes motivating periodontal data from Gullah-speaking African Americans with Type-II diabetes. For details on the underlying matrix-variate distributions (MVVG and MVNIG), see Gallaugher and McNicholas (2019, ). Package: r-cran-mvnma Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1049 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-meta, r-cran-netmeta, r-cran-r2jags, r-cran-coda, r-cran-dplyr, r-cran-magrittr, r-cran-matrixstats, r-cran-rlist, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/noble/main/r-cran-mvnma_0.3-0-1.ca2404.1_all.deb Size: 1036352 MD5sum: 461c1b4d521dc06cc6516f1e247b0bb2 SHA1: 7d8b8c1849e86a4e08a0b117eabb4ad89594881d SHA256: db39e2884ceef416e21da244f754f303755dd001f14582408d9e6fa825dd641b SHA512: cba108027ba94d41f663914732d66ca5f3cf2006593ccdece2555e9853c595f2e75b0a9b0859798b75c5db1af7be49dbbb9bca6a0140198fe5c7681d553c48e8 Homepage: https://cran.r-project.org/package=mvnma Description: CRAN Package 'mvnma' (Multivariate Network Meta-Analysis using Bayesian Methods) Tools to conduct Bayesian multivariate network meta-analysis providing - the single correlation coefficient model by Efthimiou et al. (2015) ; - per-outcome consistency checks using the node-splitting method (Dias et al., 2010) ; - per-outcome treatment hierarchies using the surface under the cumulative ranking curve (SUCRA), the probability of best value, or median (or mean) ranks (Salanti et al., 2011) ; - across-outcomes benefit-risk assessment using the VišeKriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method (Opricovic & Tzeng, 2004) ; - convergence checks using trace plots, density plots, or the R-hat statistic; - forest plots of treatment estimates and consistency checks, scatter plots of per-outcome rankings, Hasse diagrams (Carlsen & Bruggemann, 2014) to visualize the partial order of the treatments across all outcomes. Package: r-cran-mvnormaltest Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-moments, r-cran-copula Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mvnormaltest_1.0.1-1.ca2404.1_all.deb Size: 124246 MD5sum: f09c11d13a31c961608c2cfe22a86a03 SHA1: 7bc076bd1995fa933ba1f4d693699b0c597b893a SHA256: 70bbf609eabaeaf790cfffbe6350b366dbb0f7a026dad1554d197a06fbe13b82 SHA512: c3d27534cfeac051bf09e40a81b4bea718a797ffc7fbe46d49954efe57003d09fd44ef3d4582b186be84281568fb644c036f7d6e772e3e9fda04559e5f18c6db Homepage: https://cran.r-project.org/package=mvnormalTest Description: CRAN Package 'mvnormalTest' (Powerful Tests for Multivariate Normality) A simple informative powerful test (mvnTest()) for multivariate normality proposed by Zhou and Shao (2014) , which combines kurtosis with Shapiro-Wilk test that is easy for biomedical researchers to understand and easy to implement in all dimensions. This package also contains some other multivariate normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical power calculation functions for these tests are also provided. In addition, this package includes some functions to generate several types of multivariate distributions mentioned in Zhou and Shao (2014). 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Package: r-cran-mvnpermute Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnpermute_1.0.1-1.ca2404.1_all.deb Size: 13128 MD5sum: 5265de005596ac0b019d55a3c9b9460d SHA1: 23d79398ce6fc14a746484302936658405758465 SHA256: e0f5de6a69d7b6cd19d6588a8d69659882645f47fb02e5353f20fff43f058b95 SHA512: e4a187d0f9d5c91cc1cede3a9acb5a78bd6a2f25521f0259405a4cca026ef7e323640213ebac449cda56baba9668ca223983c4a42332db425658a701ce4ace0a Homepage: https://cran.r-project.org/package=mvnpermute Description: CRAN Package 'mvnpermute' (Generate New Multivariate Normal Samples from Permutations) Given a vector of multivariate normal data, a matrix of covariates and the data covariance matrix, generate new multivariate normal samples that have the same covariance matrix based on permutations of the transformed data residuals. Package: r-cran-mvntest Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvntest_1.1-0-1.ca2404.1_all.deb Size: 192920 MD5sum: fb8440d14a492d2325cd75327ec6d8c7 SHA1: b7e2bcb96ac9818f6bf55f55f46b93b848e4483a SHA256: 706afaa99e807468a2c67566ec4df73d8569fb8771b932fc65bf5dc43aae3d43 SHA512: 5aad5dacb3c5d8322d88b7dc34000992855a4ef3a9f3b7b864cb93f61486a5b18e0532424f47f4f88e826a80278486d1299c6fd74ce92701240da764619bb4bc Homepage: https://cran.r-project.org/package=mvnTest Description: CRAN Package 'mvnTest' (Goodness of Fit Tests for Multivariate Normality) Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test. Package: r-cran-mvntestchar Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-knitr, r-cran-ggplot2 Suggests: r-cran-markdown Filename: pool/dists/noble/main/r-cran-mvntestchar_1.1.3-1.ca2404.1_all.deb Size: 534038 MD5sum: 41c8cd04f75b7431e934b4973480dafd SHA1: e782a5361cfec54c4504c869c814dad03ce678ab SHA256: 7821fc90ebcca0fe23fc7f251e07a1d7ebaaa3de841ff14b813c7e1a4fcb481e SHA512: d08b755f3db1d9951dd299680fa4847b500fbe5ece85c33c10d1b4fa501b85a58173f954fa2442565747004958243703fcd8e92e192544822aa029597a6ebad1 Homepage: https://cran.r-project.org/package=MVNtestchar Description: CRAN Package 'MVNtestchar' (Test for Multivariate Normal Distribution Based on aCharacterization) Provides a test of multivariate normality of an unknown sample that does not require estimation of the nuisance parameters, the mean and covariance matrix. Rather, a sequence of transformations removes these nuisance parameters and results in a set of sample matrices that are positive definite. These matrices are uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle if and only if the original data is multivariate normal (Fairweather, 1973, Doctoral dissertation, University of Washington). The package performs a goodness of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the support region of positive definite matrices for bivariate samples. Package: r-cran-mvopr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncvreg, r-cran-rrpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvopr_2.0.0-1.ca2404.1_all.deb Size: 23928 MD5sum: 219bc69a3501d416223b17de39398478 SHA1: 4b6346791869848176ce67eeb69fac8e1510e0dc SHA256: 8fe08337c43fc27bf0cfb2ba5c91dada37df5e8e399ab5c7a15e43fd04ffea67 SHA512: 7981fcd59678ad4c3678067c6678732e394898acc6cf88d65870020bac6a1f9244900765765e96029648e08e03facb3027faa6f0e160707ef0b90bc9a75b57d5 Homepage: https://cran.r-project.org/package=MVOPR Description: CRAN Package 'MVOPR' (Multi-View Orthogonal Projection Regression for Multi-ModalityIntegration) Implements the 'MVOPR' (Multi-View Orthogonal Projection Regression) method for robust variable selection and integration of multi-modality data. Package: r-cran-mvout Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-mvout_1.2-1.ca2404.1_all.deb Size: 78590 MD5sum: b351078d4540635eb7a630ae8d5ca351 SHA1: 69c51244635b4dbb75f847b3aab950a7f82a9b6f SHA256: 1c4be42062d0298c87ac60d822ea65e46b8294af72c3e119e26aa9cdd45811ac SHA512: 7dc3064038c496520e1fb06182fdb01913a2e544a1afaaca2b276d0fb4eb05287758f5fe80178a8393db4246a3a46fd275685387a2ac00b139c5570789a5ad55 Homepage: https://cran.r-project.org/package=mvout Description: CRAN Package 'mvout' (Robust Multivariate Outlier Detection) Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable. Package: r-cran-mvoutlier Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sgeostat, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-mvoutlier_2.1.4-1.ca2404.1_all.deb Size: 795476 MD5sum: a76a6a385b69fc79f1e8f894b4277864 SHA1: a39ad589d55f12f0216baa8a7a3de2bff785a3fb SHA256: eda85826ba24e203e5b79d047c540ee44666e155353bd5dfd32c4d3fdb1fab2d SHA512: b1d9ac09e3d737c4745877777406e59ceb3ebe39ac314ab6e58807697b39169f5ff0838fbf4e7779dccf7345e2bf2f692c86e8c47ff15ff3ef1afdcf83d0afc3 Homepage: https://cran.r-project.org/package=mvoutlier Description: CRAN Package 'mvoutlier' (Multivariate Outlier Detection Based on Robust Methods) Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files. Package: r-cran-mvpbt Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-metafor, r-cran-mada, r-cran-mvmeta Filename: pool/dists/noble/main/r-cran-mvpbt_1.2-1-1.ca2404.1_all.deb Size: 41610 MD5sum: 8d532a47aa7812347a6a711255eab8d9 SHA1: 65b778e4e0d953bfe88fdc2a256228784c75b138 SHA256: 2df28789f2e8edaf54b839bc416e9ce6454c2113a1769132d1b2a5ab53913519 SHA512: a1178a7a1672dc32638ecd2b75e583b963eae3b5d57d277e8b7fef2102010001c83f028a30b4386466249d416931fe80c426349749737ae7d57a164810e897e9 Homepage: https://cran.r-project.org/package=MVPBT Description: CRAN Package 'MVPBT' (Publication Bias Tests for Meta-Analysis of Diagnostic AccuracyTest) Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) , Noma (2022) ). 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Package: r-cran-mvprobit Architecture: all Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-maxlik, r-cran-abind, r-cran-bayesm, r-cran-misctools Filename: pool/dists/noble/main/r-cran-mvprobit_0.1-12-1.ca2404.1_all.deb Size: 99040 MD5sum: b379faad4081a8a945eb3732bc6afbce SHA1: be1c9e9a7ed42e1d95abbb977ea5814952975ced SHA256: c133e5319e9cef326b19daf899aca2b9b503b7089de779fd06ea88a2011506c9 SHA512: 32a9db90303ee7ed1f35c9421fdfb81ce28d5d2fca3176f11ad7870ebac654440799bd77b16d105db9e464964167ffbd1fc66e94640c3ea23b93de3a6c28b8c7 Homepage: https://cran.r-project.org/package=mvProbit Description: CRAN Package 'mvProbit' (Multivariate Probit Models) Tools for estimating multivariate probit models, calculating conditional and unconditional expectations, and calculating marginal effects on conditional and unconditional expectations. 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This grids can be based on different quadrature rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the multidimensional grid the product-rule or the combination- technique can be applied. Package: r-cran-mvquickgraphs Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-mvquickgraphs_0.1.2-1.ca2404.1_all.deb Size: 30882 MD5sum: 08cf5d28cae2b78fc54d8316dcb0fbcb SHA1: 3ff9c39cd720e0d5060b735e090d470023863f2d SHA256: 8323441dcc17aadea4bf0269d1e540075d76b232bbad9c0d901cdc94899f1913 SHA512: 3a9d90c15969551e98d8c81908138f26102936fc008db8327120c5c6328885c5942125ee2f7b469f1b623c64317ebfdafb5ecdd80e7cfd39680d5f3a252eb9b3 Homepage: https://cran.r-project.org/package=MVQuickGraphs Description: CRAN Package 'MVQuickGraphs' (Quick Multivariate Graphs) Functions used for graphing in multivariate contexts. These functions are designed to support produce reasonable graphs with minimal input of graphing parameters. The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153). Package: r-cran-mvs Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-foreach Suggests: r-cran-testthat, r-cran-mice, r-cran-missforest, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-mvs_2.1.0-1.ca2404.1_all.deb Size: 2030344 MD5sum: 18b6fc4f32e3d9c88172e4578e8d535b SHA1: 21c284d1067f541496a29aa57e01dd3976ca61f8 SHA256: be87358e2b6b3c0fc18042abddd18dd4508e0e132e88799e9e2fd4ae8ed92a18 SHA512: 3f328d4bda5157a929cb8c11b72837775b17e5a25c00eacb88902786db90ea52678d2f000732d5a7c5bcfe31a9a3d4482f39d0b4f6a8a110608a9d027dd18b06 Homepage: https://cran.r-project.org/package=mvs Description: CRAN Package 'mvs' (Methods for High-Dimensional Multi-View Learning) Methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. 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Package: r-cran-mvskmod Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-clustergeneration, r-cran-distributionutils, r-cran-matlib, r-cran-maxlik, r-cran-truncnorm, r-cran-pracma Filename: pool/dists/noble/main/r-cran-mvskmod_0.1.0-1.ca2404.1_all.deb Size: 147466 MD5sum: 4eec9aacd6c5365e0e954137510f0ecd SHA1: a60855d1242e50ca342ba3eb44844c674e88cc9b SHA256: 65f3f1c2849150f677a343f7d67ba978d983cc56df614aa434bb7eb8cd4d072a SHA512: 1931d70ced85489645a78a4b7a9e9429f4f06872ba84cf1251895317def6c0f598a6b28b3793ba6cc5993b19c07a559655b94f8e1590c0f0e44393171362b0c9 Homepage: https://cran.r-project.org/package=MVSKmod Description: CRAN Package 'MVSKmod' (Matrix-Variate Skew Linear Regression Models) An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. 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Package: r-cran-mvslouch Architecture: all Version: 2.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-ape, r-cran-mvtnorm, r-cran-matrix, r-cran-ouch, r-cran-pcmbase, r-cran-matrixcalc Suggests: r-cran-pcmbasecpp, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-mvmorph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvslouch_2.7.7-1.ca2404.1_all.deb Size: 2221908 MD5sum: fef3088de9b675c310f444c7a19047a2 SHA1: 3f1c2d22e7db0a414e07d344a6012fad608f63cf SHA256: 33d41cadb9cf593986881e6464d81c9c8c309019fa59c1296cba43d0bbc1a5d3 SHA512: ec1d91d8fc47095321ac493192bdd8ce02c461a06563f7e38143d5ab76d2833f012b511fd7fb2c2fc932979568a91cc83595d686bf33ebb24890801379124d36 Homepage: https://cran.r-project.org/package=mvSLOUCH Description: CRAN Package 'mvSLOUCH' (Multivariate Stochastic Linear Ornstein-Uhlenbeck Models forPhylogenetic Comparative Hypotheses) Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) and K. Bartoszek, and J. Tredgett Clarke, J. Fuentes-Gonzalez, V. Mitov, J. Pienaar, M. Piwczynski, R. Puchalka, K. Spalik, K. L. Voje (2024) . The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from . 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These predicted proportions can then be used for standard plotting and diagnostics. See Thorson et al. 2022 . 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Package: r-cran-mxm Architecture: all Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3762 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-ordinal, r-cran-nnet, r-cran-quantreg, r-cran-lme4, r-cran-foreach, r-cran-doparallel, r-cran-relations, r-cran-rfast, r-cran-visnetwork, r-cran-energy, r-cran-geepack, r-cran-dplyr, r-cran-bigmemory, r-cran-coxme, r-cran-rfast2, r-cran-hmisc Suggests: r-cran-markdown, r-cran-r.rsp, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mxm_1.5.8-1.ca2404.1_all.deb Size: 3423272 MD5sum: 3c774ec7188ad34c0c3637cfe0ceb932 SHA1: a2aa10e4d857dbda52e35a60a9a878edf4c37eb2 SHA256: 868e4ec1cf19df215428e483cd0a77ab6bd63bba1a37feefcd8047e971ac3e3e SHA512: ee2b4b42e147b2ed563d4f06f189481744ae51ac19a836e41ff703a51315ad5a3ddad4016d79c807fe3ddd176fd8cbc2c97f81a8f6b858e871627c55d78a4505 Homepage: https://cran.r-project.org/package=MXM Description: CRAN Package 'MXM' (Feature Selection (Including Multiple Solutions) and BayesianNetworks) Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). "Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets". Journal of Statistical Software, 80(7). . b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). "Feature selection for high-dimensional temporal data". BMC Bioinformatics, 19:17. . c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). "Constraint-based causal discovery with mixed data". International Journal of Data Science and Analytics, 6(1): 19-30. . d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. . e) Tsagris, M. (2019). "Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation". Applied Artificial Intelligence, 33(2):101-123. . f) Tsagris, M. and Tsamardinos, I. (2019). "Feature selection with the R package MXM". F1000Research 7: 1505. . g) Borboudakis, G. and Tsamardinos, I. (2019). "Forward-Backward Selection with Early Dropping". Journal of Machine Learning Research 20: 1-39. h) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. . 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Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, . 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All the relevant covariates are put on the 'variable list' to be selected. The significance levels for entry (SLE) and for stay (SLS) are usually set to 0.15 (or larger) for being conservative. Then, with the aid of substantive knowledge, the best candidate final regression model is identified manually by dropping the covariates with p value > 0.05 one at a time until all regression coefficients are significantly different from 0 at the chosen alpha level of 0.05. 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A second wave started on August 12, 2026, with this post: . A third wave started on August 17, 2026, with . A fourth wave started on August 27, 2026, with . A fifth wave ran on August 30, 2026, beginning with . A sixth wave started September 4, 2026, with . A seventh wave started September 12, 2026, with . An eighth wave started September 16, 2026, with . A ninth wave started October 1, 2026 with . A tenth wave started October 4, 2026, with . All of the over seventeen hundred posts from these series start with 'My man ...' and make for excellent input to a 'fortunes'-like package. So this small package obliges and offers a random draw each time its myman() function is called. The overall package structure follows package 'fortunes', and 'atrrr' was used to (bulk-)retrieve posts. Neither package is required to run this package to display random selections. 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MyoScore integrates results from genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) across 28 muscle-related phenotypes to quantify muscle health along five dimensions (Strength, Mass, LeanMuscle, Youth, Resilience), each scored from 0 to 100. The package provides preprocessing via counts per million (CPM) normalization, dimension-level and composite scoring, and visualization utilities including radar charts and grouped boxplots. For more information, see . 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J. and Li H. (2018). "Conditional regression based on a multivariate zero-inflated logistic-normal model for microbiome relative abundance data", . 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First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 ). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect. 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D(D(f)) gives Hessians, D(D(D(f))) gives third-order tensors for skewness of maximum likelihood estimators, and so on to any order. Works through any R code including loops, branches, and control flow. 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This package provides the function for implementing the novel community detection algorithm known as Network-Adjusted Covariates for Community Detection (NAC), which is designed to detect latent community structure in graphs with node-level information, i.e., covariates. This algorithm can handle models such as the degree-corrected stochastic block model (DCSBM) with covariates. NAC specifically addresses the discrepancy between the community structure inferred from the adjacency information and the community structure inferred from the covariates information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2023) . In addition to NAC, this package includes several other existing community detection algorithms that are compared to NAC in the reference paper. These algorithms are Spectral Clustering On Ratios-of Eigenvectors (SCORE), network-based regularized spectral clustering (Net-based), covariate-based spectral clustering (Cov-based), covariate-assisted spectral clustering (CAclustering) and semidefinite programming (SDP). 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(2018) ). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application. 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Functions allow users to discover available data collections, country codes, and access types, perform complex searches using keyword and spatial/temporal filters, and retrieve detailed study information, including file lists and variable-level data dictionaries. It simplifies access to microdata for researchers and policy analysts globally. 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This approach offers several improvements compared to past implementations including the ability to easily use random-effects specified in formulas (like y ~ (age | strata) + ...) and construction of new learners is as simple as writing and passing a new function. The super learner algorithm was originally described in van der Laan et al. (2007) . 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Package: r-cran-naprior Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-r2jags, r-cran-metafor, r-cran-purrr, r-cran-dplyr, r-cran-tibble Suggests: r-cran-rjags, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-naprior_0.2.0-1.ca2404.1_all.deb Size: 149074 MD5sum: b3cebbb624dcd534d2f4555edd322db8 SHA1: 387be0453b002f4a842f5565c8e9525031c0dfbf SHA256: 3fb8e628ec9f03a5a40c058437f3ecb2091775ff5e3788514d9e85867ba17ce4 SHA512: 5aaaeaae0f4e05ac78d70b1473de1b5cdd79e048a0207d4fcf17e4011d609b104219d4dea3ce974f51721d02e7827f05e245f6924af4828b97d02e1a91fe85ab Homepage: https://cran.r-project.org/package=NAPrior Description: CRAN Package 'NAPrior' (Network Meta-Analytic Predictive Prior for Mid-Trial SoC Changes) Implements the Network meta-Analytic Predictive (NAP) prior framework to accommodate changes in the standard of care (SoC) during ongoing randomized controlled trials (RCTs). The method synthesizes pre- and post-change in-trial data by leveraging external evidence, particularly head-to-head trials comparing the original and new standards of care, to bridge the two evidence periods and enable principled borrowing. The package provides utilities to construct NAP-based priors and perform Bayesian inference for time-to-event endpoints using summarized trial evidence. Package: r-cran-naptanr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-naptanr_1.0.1-1.ca2404.1_all.deb Size: 21882 MD5sum: e9dfd52a5f101cfc854479d1bfa1e3be SHA1: 6589d918cbcbeffe16f7b183588d1de64a9cd0eb SHA256: 66c4a61f18288fada7471d00f82718ad63661ac30726fe27fbe922fccd34fd09 SHA512: 717be7de52535c010a73c77f47dd332bd68c3c072314b339f335fde7f17abbfb4359cd7c5d11ac9e1a9f2a2c3c8729b8b581fccc7c39811bcd9590ff82d2e63b Homepage: https://cran.r-project.org/package=naptanr Description: CRAN Package 'naptanr' (Call the 'NaPTAN' API Through R) An R wrapper for pulling data from the National Public Transport Access Nodes ('NaPTAN') API (). This allows users to download 'NaPTAN' transport information, for the full dataset, by ATCO region code, or by name of region. Package: r-cran-naptime Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-naptime_1.3.1-1.ca2404.1_all.deb Size: 18830 MD5sum: f8518e4e4863d8cb41e4c2d450236231 SHA1: cfa7fefd7b0b79ca5e892cc401a8e1e40aea42b0 SHA256: ff5d57df33b35ed896004739d9a478b546096ca647551459b99c80bb729148af SHA512: 622289633966927fff1c2c62157bf620d3bd74f532e8e24327efb3e2437919b522febd3328e921757085690f97071d9cd5494748c0b11085568a4c272c0a36b3 Homepage: https://cran.r-project.org/package=naptime Description: CRAN Package 'naptime' (A Flexible and Robust Sys.sleep() Replacement) Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error. 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Package: r-cran-narfima Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-nnet, r-cran-bsts, r-cran-withr Filename: pool/dists/noble/main/r-cran-narfima_0.1.0-1.ca2404.1_all.deb Size: 88554 MD5sum: 7be18c532860102e61f7eb81a9bddae5 SHA1: 0f991ed4353a93ac6f017132f634df5d3bd754b1 SHA256: 3515e690d7eb24af9259997e8787b23591ec56301575787d6196fa879035772b SHA512: e6861db42b5b650c4a7a1621644646fdb4f7a2cc3fc28b26cd70c6b78c66fe78dcbb9adcc7e392011e963da7edf489ce4a44e1f422562973f080945dcc61fc13 Homepage: https://cran.r-project.org/package=narfima Description: CRAN Package 'narfima' (Neural AutoRegressive Fractionally Integrated Moving AverageModel) Methods and tools for forecasting univariate time series using the NARFIMA (Neural AutoRegressive Fractionally Integrated Moving Average) model. It combines neural networks with fractional differencing to capture both nonlinear patterns and long-term dependencies. The NARFIMA model supports seasonal adjustment, Box-Cox transformations, optional exogenous variables, and the computation of prediction intervals. In addition to the NARFIMA model, this package provides alternative forecasting models including NARIMA (Neural ARIMA), NBSTS (Neural Bayesian Structural Time Series), and NNaive (Neural Naive) for performance comparison across different modeling approaches. The methods are based on algorithms introduced by Chakraborty et al. (2025) . Package: r-cran-nasa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magick, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-nasa_1.0.0-1.ca2404.1_all.deb Size: 30490 MD5sum: 2c46a9c67588eb12e34940877b10bf7d SHA1: f686dcf9f5f9abdad698c309464c48fa04021834 SHA256: 58581bc1e458fea8f51f6648523b280883c8fb556b79aa01403579fac7bb5ff5 SHA512: f0571821803a98772e58040a13102c7db6cc8e6dbe762e2ad927913224f46c2b67539291d1010dce31c0c4ad8ba0c23adedd2a3dcac79f365a447391b3605f52 Homepage: https://cran.r-project.org/package=nasa Description: CRAN Package 'nasa' (Access National Aeronautics and Space Administration (NASA) APIs) Provides functions to access and download data from various NASA APIs , including: Astronomy Picture of the Day (APOD), Mars Rover Photos, Earth Polychromatic Imaging Camera (EPIC), Near Earth Object Web Service (NeoWs), Earth Observatory Natural Event Tracker (EONET), and NASA Earthdata CMR Search. 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POWER (Prediction Of Worldwide Energy Resources) data are freely available for download with varying spatial resolutions dependent on the original data and with several temporal resolutions depending on the POWER parameter and community. This work is funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, the methodologies used in creating, a web-based data viewer and web access, please see . Package: r-cran-nasaweather Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nasaweather_0.1.1-1.ca2404.1_all.deb Size: 422856 MD5sum: 1c745eeea49d17b8b5600dcb9c130e8e SHA1: 28b34c73ec58d64ec4dff302986819e0417492b0 SHA256: 392efed808ea93d3da8da51e4cea7df213ed445785e0d365991d91c264558d7e SHA512: e0fe12fa96345b44f2aebbf6ae96cd2e25ba8bdc62610366231e16d9a76f2aecc507660fb4dbc189cc226355a7849640abe371ac00c706998438005531ea9983 Homepage: https://cran.r-project.org/package=nasaweather Description: CRAN Package 'nasaweather' (Collection of Datasets from the ASA 2006 Data Expo) Tidied data from the ASA 2006 data expo, as well as a number of useful other related data sets. Package: r-cran-nascar.data Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-glue, r-cran-rlang, r-cran-stringdist, r-cran-stringr Suggests: r-cran-conflicted, r-cran-curl, r-cran-ggtext, r-cran-httr2, r-cran-jsonlite, r-cran-knitr, r-cran-paws.storage, r-cran-purrr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rvest, r-cran-scales, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-nascar.data_3.1.0-1.ca2404.1_all.deb Size: 799550 MD5sum: d1234669976ac5ff21423e62c0d9b2c4 SHA1: b6ad2c2d094bcd53c502e822523a5384961c71f3 SHA256: 30b724ba0edae8c178c7150da52773b6f44910bd0db15c51166f55a2467b38b2 SHA512: 5180ff597ce8b9a6b63f503522e36814ff9d7c09c32d36d76198b711fa8e64af0d5ca125b72cd9651bdd27b5b2c8143ed5b4445fd3804ec4ac2123a4a441d1af Homepage: https://cran.r-project.org/package=nascaR.data Description: CRAN Package 'nascaR.data' (NASCAR Race Data) A collection of NASCAR race, driver, owner and manufacturer data across the three major NASCAR divisions: NASCAR Cup Series, NXS, and NASCAR Craftsman Truck Series. The curated data begins with the 1949 season and is updated weekly during the racing season. Explore race, season, or career performance for drivers, teams, and manufacturers throughout NASCAR's history. Data was sourced with permission from DriverAverages.com. Package: r-cran-nasdaqdatalink Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-httr, r-cran-zoo, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-timeseries Filename: pool/dists/noble/main/r-cran-nasdaqdatalink_1.0.0-1.ca2404.1_all.deb Size: 72938 MD5sum: 4938c794191faca18efd07ceae3b171b SHA1: 59ae29def604fd3e033d8f1cd8083762e934196f SHA256: 2cf4c09ac37b250c7f44635744c1c0b1ab1e44e46dfc22e4001d4ded24b8ee4b SHA512: 853fbb143086ef2fbcce7fcf09d52213f2f97f187cb154ef416b2a274ec96e5debb9a08bbb9f4f5902d6b625e34c33e50b172632efafbb672252aa7fff6cb121 Homepage: https://cran.r-project.org/package=NasdaqDataLink Description: CRAN Package 'NasdaqDataLink' (API Wrapper for Nasdaq Data Link) Functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to . For more help on the package itself go to . Package: r-cran-naspaclust Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-rdist, r-cran-stabledist, r-cran-beepr Suggests: r-cran-ppclust, r-cran-cluster, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-naspaclust_0.2.2-1.ca2404.1_all.deb Size: 236054 MD5sum: 55a31c637dcb39ae2529410c9fb3d0fd SHA1: 56673d860a123ea7fe002699a37628350bc7017b SHA256: 7dec82b7b00a4e31abed9af2bbdb576e661594b294fdd71f1fc776854a8d3263 SHA512: 3d85b9006e8caae645a5b2267a075c0c4a464b14a685cd31e7dfefd79560f60ce77f9000f48707127d211d1301b26ab4c593fa351f4fa9d22938cdbda5550a42 Homepage: https://cran.r-project.org/package=naspaclust Description: CRAN Package 'naspaclust' (Nature-Inspired Spatial Clustering) Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) using Artificial Bee Colony algorithm. Package: r-cran-nat.nblast Architecture: all Version: 1.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rgl, r-cran-nat, r-cran-nabor, r-cran-dendroextras, r-cran-plyr, r-cran-spam Suggests: r-cran-spelling, r-cran-bigmemory, r-cran-ff, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nat.nblast_1.6.10-1.ca2404.1_all.deb Size: 382410 MD5sum: e5934034a516f012cf1b2877792bbba7 SHA1: 6506d21a4310b401ce6f5daf1b1e657fb3194b66 SHA256: 574a73d4afdb14a39662fb3a452456524dc1dda64926f330625dcf0890ac47dd SHA512: 27ec9ebe2f0c1b4f1bda7a88e310c96de7852ec3be6018a318ef7b8b171f3d2f415c4e1a8c46587204f1611717c07e640bb42501a4d443021cc192a536d9f6da Homepage: https://cran.r-project.org/package=nat.nblast Description: CRAN Package 'nat.nblast' (NeuroAnatomy Toolbox ('nat') Extension for Assessing NeuronSimilarity and Clustering) Extends package 'nat' (NeuroAnatomy Toolbox) by providing a collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. 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This version has a few changes compared to the previous version 1.0.0, including (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario. 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The package provides model fitting, prediction, resampling-based evaluation, cross-validation, hyper-parameter tuning, and permutation variable importance utilities for horizon-specific survival prediction. The model is the censored naive Bayes classifier of Wolfson et al. (2015) , which combines the marginal Kaplan-Meier survivor function with horizon-specific class-conditional covariate densities and inverse-probability-of-censoring weights. Resampling evaluation uses the inverse-probability-of-censoring-weighted Brier score of Gerds and Schumacher (2006) . Package: r-cran-nbtransmission Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-caret, r-cran-lubridate, r-cran-poisbinom, r-cran-tidyr, r-cran-broom Suggests: r-cran-ggplot2, r-cran-hmisc, r-cran-igraph, r-cran-knitr, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nbtransmission_1.2.0-1.ca2404.1_all.deb Size: 545660 MD5sum: 6c2ae5d4f9a778733008574b17c4d86f SHA1: ca2b7ed87c45a22c08b55469a2cd806edfac8538 SHA256: 2b07bfb59702480da9790d1adbd10e2d2ad52bc24527285b06491af63f67a162 SHA512: c83faa748d78697bd7e615462ba48ef0cb3d5ce5fda85a62e989e448a0fc6b45503831a4cf419144989305465650f7d647c31cd252818a1eb064dec5e9dab960 Homepage: https://cran.r-project.org/package=nbTransmission Description: CRAN Package 'nbTransmission' (Naive Bayes Transmission Analysis) Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) . Package: r-cran-nbtsvarsel Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-mass, r-cran-mpath, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-markdown, r-cran-formatr Filename: pool/dists/noble/main/r-cran-nbtsvarsel_1.0-1.ca2404.1_all.deb Size: 66216 MD5sum: d7f0d44aeb09fc85e1b09d34b82b965a SHA1: e6339e46911eb6d32365c9c6c2682d5bcb21666b SHA256: c6dcaf2ec7ebc1dbb8334928ed907368e1ad185e095d0168d8a5ddbdff3435b9 SHA512: 8a0c0c653fcf908184c71fa2e80a033f748b7bd41343026b26d5380202873412a06e8de9d1769784e4702ea48e726879861360562e53d864dd5082b18c2f5f63 Homepage: https://cran.r-project.org/package=NBtsVarSel Description: CRAN Package 'NBtsVarSel' (Variable Selection in a Specific Regression Time Series ofCounts) Performs variable selection in sparse negative binomial GLARMA (Generalised Linear Autoregressive Moving Average) models. For further details we refer the reader to the paper Gomtsyan (2023), . Package: r-cran-nbvarsel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2651 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-desctools, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggtext, r-cran-patchwork, r-cran-proc, r-cran-rlang, r-cran-rms, r-cran-scales, r-cran-stringr, r-cran-tidyr Suggests: r-cran-glmnet, r-cran-ggsci, r-cran-gt, r-cran-knitr, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nbvarsel_0.1.1-1.ca2404.1_all.deb Size: 1161178 MD5sum: 854445d18bb8b42701d1295341f24894 SHA1: 8284bd5f05b6dd3be662fe240011908cdd4f5f69 SHA256: d201cb20a61f1371e6e1574c97b1e1df60823bae65b5fae36455f4ec133f97de SHA512: 8b234900a4320ac43e563441a00495942600bda367a2a7559b094221b70b47aa40c94b1eb9e7e61ccc99c5e809b37ec1c83ba7394fe06cfa14a4e12dabe5e819 Homepage: https://cran.r-project.org/package=NBvarsel Description: CRAN Package 'NBvarsel' (Variable Selection via Cross-Validated Net Benefit) Performs exhaustive or groupwise (backward elimination) variable selection for binary outcome prediction models using cross-validated Net Benefit as the optimization criterion. 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Package: r-cran-nca Architecture: all Version: 5.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gplots, r-cran-quantreg, r-cran-kernsmooth, r-cran-lpsolve, r-cran-ggplot2, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-plotly, r-cran-truncnorm, r-cran-dbi, r-cran-rsqlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nca_5.0.2-1.ca2404.1_all.deb Size: 391250 MD5sum: aea3cf8020faeab03bc17ea1c2909d90 SHA1: 80c6a776c39a2c8c618fcd92634b1591ab8262a7 SHA256: 17e36c2127eaf813c97bf1a63b08395332c9509b61270ae96217f10f4a0aaa5f SHA512: 0d5c8d74afe4e32b3baf3319dbd27890e781effb00467d70085764344f89f12163b5b5e6563264d99aec603a67a03f1457d85d3c276edb0dfe12097aecd0848e Homepage: https://cran.r-project.org/package=NCA Description: CRAN Package 'NCA' (Necessary Condition Analysis) Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52) . NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations. (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g., an effort, a characteristic) is necessary but not sufficient for a (desired or undesired) level of y (e.g., good performance or disease). A quick start guide for using this package can be found here: or . 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Package: r-cran-ncappc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-gtable, r-cran-knitr, r-cran-xtable, r-cran-reshape2, r-cran-dplyr, r-cran-readr, r-cran-lazyeval, r-cran-poped, r-cran-magrittr, r-cran-rlang, r-cran-purrr, r-cran-tibble, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-bookdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ncappc_1.0.0-1.ca2404.1_all.deb Size: 1465696 MD5sum: 0ea8206408694aeb9ac37041ed7d59d4 SHA1: e9bebdf1fd9049a4376824c145db0c3beac1816c SHA256: 648c2afed9c2105acdb681308fdaf67d996f9046c7e1a68800165e59a2b81f9b SHA512: 436d3c14f11477d8484d922fce7e5b90ac976ea5031959a8770d7e909cbe64888f4564189efe8669e5f083b33fa32970d45f20480067958009afde9b28fbb363 Homepage: https://cran.r-project.org/package=ncappc Description: CRAN Package 'ncappc' (NCA Calculations and Population Model Diagnosis) A flexible tool that can perform (i) traditional non-compartmental analysis (NCA) and (ii) Simulation-based posterior predictive checks for population pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics. The methods are described in Acharya et al. (2016) . Package: r-cran-ncar Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-noncompart, r-cran-r.oo, r-cran-r.methodss3 Suggests: r-cran-openssl Filename: pool/dists/noble/main/r-cran-ncar_0.7.2-1.ca2404.1_all.deb Size: 245808 MD5sum: 9393cc75fae77802a24693966259b9f8 SHA1: 36162a005a58c9d5372f390a15fb8014132d9a30 SHA256: fa785cc4be2e706177b7e431831931e879c90f4b70f6ef40e20e3a5d7f435af9 SHA512: 16241a210f9bd264093bcd8ab0ffcff9a5b24b58152342235ee334924f2198f8bdf689b3bb1c3d80e5e50e3373554b5bd8c802665a511282f20d57ec7db533ac Homepage: https://cran.r-project.org/package=ncar Description: CRAN Package 'ncar' (Noncompartmental Analysis for Pharmacokinetic Report) Conduct a noncompartmental analysis with industrial strength. Some features are 1) CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method 5) Steady-state analysis over the dosing interval (AUCTAU, CAVG, CL and Vz from AUCTAU) 6) Produce pdf, rtf, text report files. 7) Produce Installation and Operational Qualification (IQ/OQ) reports in pdf. After installation, qualify the package in your own environment: run pdfIQ() for Installation Qualification and pdfOQ() for Operational Qualification. Run writeMD5() once after installation so the IQ file-integrity check passes. To approve a report, sign it digitally in Adobe Acrobat Reader (generate with sigField=TRUE, or run addSigField(), to add click-to-sign fields), instead of printing and scanning; or use signPDF()/verifyPDF() for a scriptable signature. * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-ncbit Architecture: all Version: 2013.03.29.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9700 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ncbit_2013.03.29.1-1.ca2404.1_all.deb Size: 9898820 MD5sum: 1cde64be7361ba7ca8c4856adfd7e8c4 SHA1: 2dad9477146259f626612c0730983f763e8515a7 SHA256: cf965d5645b8ff96ce23d1a49bb1ed75c6e3ff6760401346b3bd349202db6b71 SHA512: 75698224ab2a5fb6edf32ada327f3c624fa86fc274345137fb5a5875bc8516df51424438c30f2f7065591660489ce23ec40d73743e95aba55d47acf56c52d4cd Homepage: https://cran.r-project.org/package=ncbit Description: CRAN Package 'ncbit' (Retrieve and Build NBCI Taxonomic Data) Makes NCBI taxonomic data locally available and searchable as an R object. 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A matching R Markdown stylesheet, built from the same colors so a report and its figures share one brand palette, themes HTML and 'shiny' output. The colors approximate those described in the University's branding guidelines (). This is an independent project and is not affiliated with or endorsed by the University of Notre Dame. 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The 'negligible' package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) . Beribisky, N., Davidson, H., Cribbie, R. A. (2019) . Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) . Mara, C. & Cribbie, R. A. (2017) . Counsell, A. & Cribbie, R. A. (2015) . van Wieringen, K. & Cribbie, R. A. (2014) . Goertzen, J. R. & Cribbie, R. A. (2010) . Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) . Package: r-cran-neighboot Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdstreeboot, r-cran-igraph, r-cran-rds, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-neighboot_1.0.1-1.ca2404.1_all.deb Size: 476256 MD5sum: b7742dcd8f41adf47edca7c8fd11c74e SHA1: 0b50231c58027f28a47867cf10cde66a8d4c1dd3 SHA256: a09dbdb627abbb93c1e0f9540c6e473be47c8cb65c05446ee567183e0c104919 SHA512: dd7fd1f4f930788cb5f50a8b590f281fc1ff5cd96272772045e1e5746717b8ef70c62d9405525d035c1bfd1b734e5197e39751c5a12718f80585c49dbc8dfc2f Homepage: https://cran.r-project.org/package=Neighboot Description: CRAN Package 'Neighboot' (Neighborhood Bootstrap Method for RDS) A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty. 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These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a function neighbourfun() that constructs such neighbourhood functions, based on parameters such as admissible ranges for elements in a solution. Supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimisation applications, but can be used for other models as well. Several recipes for neighbour computations are taken from "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). Package: r-cran-neighbr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mlbench Filename: pool/dists/noble/main/r-cran-neighbr_1.0.3-1.ca2404.1_all.deb Size: 242952 MD5sum: 60cfb0281a92a00ce9861224bc1d743b SHA1: 90c06fc951aa9b269cd99db4dd15a9568c0b441e SHA256: 2f0e04db0bd4ebdd06a1583636169dd043837c6670d95273f0448094ee311e60 SHA512: a679bf47fc492603270517aa06242a8c53fb45d369e23c883ab14d463ecf1fc98fb99071fa4e4f9b18522a4d5623dde8357eb4123d0df62c545b3ab7b1a2e1cb Homepage: https://cran.r-project.org/package=neighbr Description: CRAN Package 'neighbr' (Classification, Regression, Clustering with K Nearest Neighbors) Classification, regression, and clustering with k nearest neighbors algorithm. Implements several distance and similarity measures, covering continuous and logical features. Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN. Package: r-cran-neldermead Architecture: all Version: 1.0-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimbase, r-cran-optimsimplex Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neldermead_1.0-13-1.ca2404.1_all.deb Size: 1522436 MD5sum: c2bc089439d60528fc6b3c34efb73b79 SHA1: 22071c43f624c4f8c918386042e9531fa96c5005 SHA256: a887a2e000e73d15d0735535fc6ee5394d31b1753761309fb7a88fbb419081e3 SHA512: 0bf1e444e76ad85be7e5de8acfa97669fb34b1f66d8f9e7b077a0f38a7f62bb01bb5b3f610984c0dabfcc0f8d0a45f8a391a1eb2eed0b16aca1eea9cf5adbb1b Homepage: https://cran.r-project.org/package=neldermead Description: CRAN Package 'neldermead' (R Port of the 'Scilab' Neldermead Module) Provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex, 1962) , the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made, 1965) , and Box's complex method (constrained optimization with a variable shape simplex, 1965) . 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Package: r-cran-nembm Architecture: all Version: 1.00.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ergm, r-cran-blockmodeling Filename: pool/dists/noble/main/r-cran-nembm_1.00.01-1.ca2404.1_all.deb Size: 82846 MD5sum: 0698b557fb6511b0380104ded7c502ba SHA1: f4ff6f1e673ca04a202b851a549d15fa6ae911b8 SHA256: 59a33e67f8ad2f7779db959b4a1c88b08353dd2451c76bb9cd2b26d36035c86b SHA512: f1b5e85ac54b1e1819545739d492fd943c7b24a8a3300f64e5ca1cc01f4a3ac57130c964be554d4232608ccbf511e3c7ac9ee8a45050ec424f89b69af8190f73 Homepage: https://cran.r-project.org/package=nemBM Description: CRAN Package 'nemBM' (Using Network Evolution Models to Generate Networks withSelected Blockmodel Type) To study network evolution models and different blockmodeling approaches. Various functions enable generating (temporal) networks with a selected blockmodel type, taking into account selected local network mechanisms. The development of this package is financially supported the Slovenian Research Agency (www.arrs.gov.si) within the research program P5<96>0168 and the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-nemor Architecture: all Version: 0.99.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tibble Suggests: r-bioc-biocfilecache, r-bioc-biocstyle, r-bioc-dropletutils, r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-seurat, r-bioc-singlecellexperiment, r-bioc-s4vectors, r-cran-testthat, r-bioc-zellkonverter Filename: pool/dists/noble/main/r-cran-nemor_0.99.3-1.ca2404.1_all.deb Size: 150162 MD5sum: c5c5f811cd98666bee4349fa1f026b09 SHA1: 4688b150bfdf2f2327faaf42d7e2b633f2eb76ac SHA256: f3198a53c55994a839ebacd77eb89b23b38c8336351e324e2c6cbb3422790e05 SHA512: 9187ca7d2975805f8ee3641bbed64c0c04b2c2521a9b012f2579a8ea5d2cd6cd70c3986392b7e33f2490fa6d2f790c91d63e835a22249887c643fd0f3098fae7 Homepage: https://cran.r-project.org/package=nemoR Description: CRAN Package 'nemoR' (Access Open 'NeMO Archive' Datasets) Provides helpers for discovering, planning, and downloading open-access datasets from the Neuroscience Multi-Omic Archive ('NeMO'; ). The package builds reproducible file manifests that record search parameters, file metadata, download URLs, checksums, and local file paths. It supports exploratory 'NeMO' metadata queries and provides first-pass bridges from downloaded files into 'SingleCellExperiment' and 'Seurat' workflows. 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Package: r-cran-nemtr Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nemtr_0.0.1.0-1.ca2404.1_all.deb Size: 19960 MD5sum: bfd3688b8ae2ae438ca03fd25f4310a1 SHA1: c29b1efa392ede3c4d7aac26f4f87f03b93d2411 SHA256: 16809c7bede55614e5d635d1ed90b44f62cf19da1e8c26e5b55f376b6eadf15f SHA512: 9ed2719d2808604866f54fec23ae768e27f2405eba5cd5ad0a4b295d028f9328ff51c0d49255b7032fa4a13920be57102b8f00be81c2f309db33452e390e224a Homepage: https://cran.r-project.org/package=nemtr Description: CRAN Package 'nemtr' (Nonparametric Extended Median Test - Cumulative Summation Method) Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) . It then generates a control chart to assess processes and determine if any streams are out of control. Package: r-cran-neo2r Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2 Filename: pool/dists/noble/main/r-cran-neo2r_3.1.1-1.ca2404.1_all.deb Size: 55210 MD5sum: 9d6f834a62670f790322c0b9edeecc14 SHA1: 742f6e92ef3c4f6820118f0678d0bff4fca86f28 SHA256: 3193bce8d496db69e0c2eb166e721fc79088a4040c91e13d3b37c3683569f251 SHA512: 09ce8e6a4c5bc7e1752ecf53e248eaafd38684a27e4d77fac232d143d89a920682a3d9616787e4875e40d2b93bbb79ec8c1fb82e7e8ba633a9d8284f8d6ecd55 Homepage: https://cran.r-project.org/package=neo2R Description: CRAN Package 'neo2R' (Neo4j to R) The aim of neo2R is to provide simple and low level connectors for querying neo4j graph databases (). The objects returned by the query functions are either lists or data.frames with very little post-processing. It allows fast processing of queries returning many records. And it let the users handle post-processing according to the data model and their needs. Package: r-cran-neo4jshell Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-ssh, r-cran-sys, r-cran-fs, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neo4jshell_0.1.2-1.ca2404.1_all.deb Size: 45774 MD5sum: d8094cb777a7ccf16867aa1c7ce02236 SHA1: 44dc9d38bc0c8048d034eea55b2b266d2055d5fb SHA256: 08598254fa1ffce5278bfbba447d0291c13744a6a2e865130912aaf9ce5734f1 SHA512: cd917681205e6de4ae9427bd6a60ee1b8762d46e7c19d6c35a0a1e3bea86775db0af831ccd34fe3d706a1d9c9069ded9d5243b41265192511bf5a771b5a1148d Homepage: https://cran.r-project.org/package=neo4jshell Description: CRAN Package 'neo4jshell' (Querying and Managing 'Neo4J' Databases in 'R') Sends queries to a specified 'Neo4J' graph database, capturing results in a dataframe where appropriate. Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin. Package: r-cran-neo4r Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-attempt, r-cran-data.table, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rstudioapi, r-cran-shiny, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-neo4r_0.1.4-1.ca2404.1_all.deb Size: 349920 MD5sum: daba638fa1ee118e44dae89da9a48f4f SHA1: f662b779c777955f1d9f7bcc91534a353f892f30 SHA256: 6acbea37dfbec80ed66e9b6085fabca702395ce2654124a03a946a26242a82a1 SHA512: bb714c52bf5adc927647add943a4feda59e94f22b49660aa5f33fa832cbd83bd5884cc57e3b7e6eaaadbc3fe83a4d1373ff7d49147c52fbece9c46fe2756c062 Homepage: https://cran.r-project.org/package=neo4r Description: CRAN Package 'neo4r' (A 'Neo4J' Driver) A Modern and Flexible 'Neo4J' Driver, allowing you to query data on a 'Neo4J' server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way 'Neo4J' returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user. Package: r-cran-neodistr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinythemes, r-cran-plotly, r-cran-brms, r-cran-rmpfr, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-spelling, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bayesplot, r-cran-loo Filename: pool/dists/noble/main/r-cran-neodistr_0.1.2-1.ca2404.1_all.deb Size: 4575878 MD5sum: 29bb2f0fb6ca21b866d189f8a92126ac SHA1: 140b597b46c6fdf4cc673b7741d00ca8fbfb62e0 SHA256: db17be3795b343bffa8c1b0e0d086eb3f9453a003ca92683da434e2d28bb5bbf SHA512: c1dcdcf516023e2ac02189f9ef160dd89dad92870a7e08a86194a3ce062d9dbe0e20ae34e93c64e67c0f291fb13a25fbee9e2927ec661225a3f9d1ddc226fc7c Homepage: https://cran.r-project.org/package=neodistr Description: CRAN Package 'neodistr' (Neo-Normal Distribution) Calculating the density, cumulative distribution, quantile, and random number of neo-normal distribution. It also interfaces with the 'brms' package, allowing the use of the neo-normal distribution as a custom family. This integration enables the application of various 'brms' formulas for neo-normal regression. Modified to be Stable as Normal from Burr (MSNBurr), Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power, and Jones Skew Exponential Power distributions are supported. References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation, Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) . Package: r-cran-neoniso Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-zoo, r-cran-httr, r-cran-lubridate, r-cran-neonutilities, r-cran-magrittr, r-cran-r.utils, r-cran-tidyselect, r-cran-data.table, r-cran-rlang, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-hdf5r, r-bioc-rhdf5, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lifecycle, r-cran-fasttime Filename: pool/dists/noble/main/r-cran-neoniso_0.8.0-1.ca2404.1_all.deb Size: 2662036 MD5sum: 5569d5cf5fad17a95dfbfeb36b8da449 SHA1: e473dd525de1f0d1f9e21b469ea4c62c3bc52283 SHA256: dfdd6c0a130ad6684b36613650dcb19a7737de4828ac947556a7e2bd2c5c6a77 SHA512: f98062a08bc6db51635b2122696b451b014538aacfe6a65ff60353de9e23cdb8e1150550cff303485e968e4295913a50f6c7205bf2187ebe0242565e3be019a7 Homepage: https://cran.r-project.org/package=NEONiso Description: CRAN Package 'NEONiso' (Tools to Calibrate and Work with NEON Atmospheric Isotope Data) Functions for downloading, calibrating, and analyzing atmospheric isotope data bundled into the eddy covariance data products of the National Ecological Observatory Network (NEON) . Calibration tools are provided for carbon and water isotope products. Carbon isotope calibration details are found in Fiorella et al. (2021) , and the readme file at . Tools for calibrating water isotope products have been added as of 0.6.0, but have known deficiencies and should be considered experimental and unsupported. Package: r-cran-neonos Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-curl, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-neonutilities Filename: pool/dists/noble/main/r-cran-neonos_1.1.0-1.ca2404.1_all.deb Size: 132834 MD5sum: 2824938c9190b0f5bd61e529a3979e5e SHA1: acdf5f2c8d397d5ed3d599e9b28ac84320cc3b3c SHA256: 32ed3711c6d2483f11088282e6fda848ed79b225c41745baa82407e20f9d5c49 SHA512: 094b2df7b6d61cb6977f6f901be9d46f6e8e7f30d59bea16a0d268042c0c4360b352ce730c91a08753e180d5b6bd5a6f226d7ff2cc0fbeea04f43bba02fe170e Homepage: https://cran.r-project.org/package=neonOS Description: CRAN Package 'neonOS' (Basic Data Wrangling for NEON Observational Data) NEON observational data are provided via the NEON Data Portal and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and algal chemistry) are published in a format consisting of one or more tabular data files. This package provides tools for performing common operations on NEON observational data, including checking for duplicates and joining tables. Package: r-cran-neonplantecology Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-neonutilities, r-cran-vegan, r-cran-ggplot2, r-cran-data.table, r-cran-dtplyr, r-cran-dplyr, r-cran-lubridate, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neonplantecology_1.6.1-1.ca2404.1_all.deb Size: 4331722 MD5sum: fea8ca928307f43d4fe01f5233dd958f SHA1: 56751dd47a99aef04112319fcfbe217748acd27c SHA256: 89a84b68d57a0f50d543aedb100bfdbcf43a46de6a1094f55d33264dde389ef9 SHA512: f043f3d0481a6bc36439489721f621c645900d76be9c01a4719c1c1dc263757ae469892679db1ec9e24ce647cc82f7d2e922d09eeb004cfc28bccb2c60bf886f Homepage: https://cran.r-project.org/package=neonPlantEcology Description: CRAN Package 'neonPlantEcology' (Process NEON Plant Data for Ecological Analysis) Downloading and organizing plant presence and percent cover data from the National Ecological Observatory Network . Package: r-cran-neonsoilflux Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2550 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-neonutilities, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neonsoilflux_4.0.1-1.ca2404.1_all.deb Size: 2371030 MD5sum: 818592a488ed84f95e9156df024499ab SHA1: c03ebcbdb82b29bb8fa019f8f0569f52fcd800a3 SHA256: 0618c4c96d365962180dc310d6a120a16671c1fc3e44455cef2597bd12008224 SHA512: 9a67ad08cfcc6bf79a8ac60e8adf9ae1dc7db3898a533081205f5174679a1ae2f8d916dde06dff67c9447fbf0c3a96ea471c3691e30edff58ddf75b66e933758 Homepage: https://cran.r-project.org/package=neonSoilFlux Description: CRAN Package 'neonSoilFlux' (Compute Soil Carbon Fluxes for the National EcologicalObservatory Network Sites) Acquires and synthesizes soil carbon fluxes at sites located in the National Ecological Observatory Network (NEON). 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This package provides a high-level user interface for downloading and storing NEON data products. Unlike 'neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. 'neonstore' can also construct a local 'duckdb' database of stacked tables, making it possible to work with tables that are far to big to fit into memory. 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Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at . Package: r-cran-neotoma2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-sf, r-cran-leaflet, r-cran-magrittr, r-cran-digest, r-cran-geojsonsf, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-assertthat, r-cran-stringr, r-cran-progress, r-cran-uuid, r-cran-tidyr Suggests: r-cran-bchron, r-cran-covr, r-cran-ggplot2, r-cran-httptest, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-neotoma2_1.1.0-1.ca2404.1_all.deb Size: 838172 MD5sum: e98dc5ac943e15f6450161bddc8c28ea SHA1: 40af83d7674fe6a2b1c8ce55df3565a719b26654 SHA256: 07aaf2bbd0d411a7d3f0c4114cd804fab7751e4d0e7a6e6e63f05d0f8373d1d0 SHA512: 8e60e9ea757439a8f27c159404f01ada6aa4b6ce040f300e712a6a231d579008fd96510e94fd6a7a71917b82c3e8c7124824dcde8b82978ac6b8b0822cfe125f Homepage: https://cran.r-project.org/package=neotoma2 Description: CRAN Package 'neotoma2' (Working with the Neotoma Paleoecology Database) Access and manipulation of data using the Neotoma Paleoecology Database. . Examples in functions that require API access are not executed during CRAN checks. Vignettes do not execute as to avoid API calls during CRAN checks. Package: r-cran-nephro Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nephro_1.5-1.ca2404.1_all.deb Size: 117320 MD5sum: f59960b6c9ccd9ed871cab453c9c23a4 SHA1: aedef09c1e084b99708d951dfd21a951a0816dc6 SHA256: f67a8eb2d33217356cda8e04e042f2b96130e9477a3e0315aa8afe06749019dc SHA512: 91b395d3e3c5357e2ff04eb11c57d5438c729f877e63f9c6613dd938bfdccb73983c59366a32087fd739077845be30e9fab126b15314271451690777595efea8 Homepage: https://cran.r-project.org/package=nephro Description: CRAN Package 'nephro' (Utilities for Nephrology) Set of functions to estimate kidney function and other traits of interest in nephrology. 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NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network. Package: r-cran-neptune Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-this.path, r-cran-rstudioapi, r-cran-ggplot2, r-cran-plotly, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-neptune_0.2.3-1.ca2404.1_all.deb Size: 113908 MD5sum: 9d07d72c961d745140be8efa2a9909c1 SHA1: 1eafcfbbd3aff2a13d51cab69b17911049c6b5a9 SHA256: 159efb2dc7d8453d578eddca468ede6a834a9c531b1a34be392edfb208e90ffe SHA512: 2990f5704f36b3b03c976c85cfadd096c72f963b3b20a543d02ed5f100f7929068067a785510df844274199ba021b1d1cfd081205f6a684ce9d5d3f2520bf12c Homepage: https://cran.r-project.org/package=neptune Description: CRAN Package 'neptune' (MLOps Metadata Store - Experiment Tracking and Model Registryfor Production Teams) An interface to Neptune. A metadata store for MLOps, built for teams that run a lot of experiments. 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Package: r-cran-nesrdata Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rappdirs, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-dataone Filename: pool/dists/noble/main/r-cran-nesrdata_0.3.1-1.ca2404.1_all.deb Size: 166954 MD5sum: 0d448767d6754ac9beacde5cce2966df SHA1: 6341bdc83d9bdd77a71b3fd9b5432ad14652f59f SHA256: 542373c8569c76acb88491f34f5dceb03e2f6b41e86ea7ed1a8b5b9ca1b43a70 SHA512: f405cecfa27a7cdd29b6bb94dbe8a0a7d97d4502a4af1cc5ee198fffe6a676b44657f6c24d4c6e7d28edec00bf51fd521d6e4a535bc5627307b8864a28b515f2 Homepage: https://cran.r-project.org/package=nesRdata Description: CRAN Package 'nesRdata' (National Eutrophication Survey Data) Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey . Package: r-cran-nestable Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestable_0.1.1-1.ca2404.1_all.deb Size: 58372 MD5sum: 645c611ca51142876c90425b2ec64419 SHA1: 7c5735c587c1e23483830151834a5939dcfc9dc5 SHA256: f96946fb1495ab56c54a226b80662d6a2eab3f904a2ee06ef82e6eba94f542fc SHA512: ca3474a1cba8ed0ea2389222a8eb1647f109ace1c4564da30e4a29fe3494f773e782f4b12e55e12acafcce77014795e04143435761e6404fdf056c809dbd801c Homepage: https://cran.r-project.org/package=nestable Description: CRAN Package 'nestable' (Collapsible 'HTML' Tables from Hierarchical Data) Creates collapsible, expandable 'HTML' tables from hierarchical data. Supports data frame input with multi-level grouping, custom column formatters, bottom-up rollup aggregation, and CSS-variable-based theming. 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Package: r-cran-nestcolor Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestcolor_0.1.3-1.ca2404.1_all.deb Size: 403436 MD5sum: 0531f7373b19b83c46d37b9693d2359d SHA1: 0f3ba6ef236bece15808cb1df3c813397de8996c SHA256: dad782d6e05fc9ece72c2e31b4f3d44f22c72da300cc9227c71b9251485e553c SHA512: d208f595623b5cdbbf3abef0a5dee6b87bd0223b86587daeff8010409748acd57e3df8d65f67b605861cce7848bb141656f7f4b79d81c2378c0fd5fe17413ed3 Homepage: https://cran.r-project.org/package=nestcolor Description: CRAN Package 'nestcolor' (Colors for NEST Graphs) Clinical reporting figures require to use consistent colors and configurations. 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Package: r-cran-nestedcv Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4749 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-matrixstats, r-cran-matrixtests, r-cran-proc, r-cran-rfast, r-cran-rhpcblasctl, r-cran-rlang, r-cran-rocr Suggests: r-cran-boruta, r-cran-corelearn, r-cran-gbm, r-cran-ggbeeswarm, r-cran-ggpubr, r-cran-hsstan, r-cran-mda, r-cran-mlbench, r-cran-pbapply, r-cran-pls, r-cran-randomforest, r-cran-ranger, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-knitr, r-cran-shapr, r-cran-superlearner Filename: pool/dists/noble/main/r-cran-nestedcv_0.9.0-1.ca2404.1_all.deb Size: 2088264 MD5sum: 68e77b0023badb17f95f4df73879d72c SHA1: e92393aba4a7006701d59d40db902e45919e54a7 SHA256: 0c2458a69a5d66dedf12469b144c6dba5b0a0b337824df2dcb3eb36edcffc322 SHA512: ce41d19a0ecba0d12ed2278e0befc9b2ccd3ea16c4eb4ed7ae4079f594e36eafe5c6485ded3e48adcc2c47c1d4e4914534d4b2a2677ddb13fcf22421462533fd Homepage: https://cran.r-project.org/package=nestedcv Description: CRAN Package 'nestedcv' (Nested Cross-Validation with 'glmnet' and 'caret') Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package . Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) . Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) . Package: r-cran-nestedlogit Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1445 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-dplyr, r-cran-effects, r-cran-stringr, r-cran-tibble, r-cran-scales Suggests: r-cran-aer, r-cran-cardata, r-cran-equatiomatic, r-cran-desctools, r-cran-geomtextpath, r-cran-ggplot2, r-cran-ggeffects, r-cran-here, r-cran-insight, r-cran-lobstr, r-cran-knitr, r-cran-nnet, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-see, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-mass, r-cran-vgam, r-cran-mlogit, r-cran-vcd Filename: pool/dists/noble/main/r-cran-nestedlogit_0.4.2-1.ca2404.1_all.deb Size: 758318 MD5sum: cb0019ec6181c5a31cbf136cf0822a9e SHA1: b795d9cea3fd10bc4fba210c42457a78337f9830 SHA256: 0671284782488073b67240fc301b24690941a55956c5722d15df7fee42306915 SHA512: e46e9bced54c487c37cdf1d72743a49474a411b8337b83134d97c2d3e87048c0cb5b25c040b07677ce3a1e1624548533626a76b7b722caa967270cb0e93266f3 Homepage: https://cran.r-project.org/package=nestedLogit Description: CRAN Package 'nestedLogit' (Nested Dichotomy Logistic Regression Models) Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663. 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Provides transition network analysis estimation (transition, frequency, co-occurrence, attention-weighted) Saqr et al. (2025) , psychological network methods (correlation, partial correlation, 'graphical lasso', 'Ising') Saqr, Beck, and Lopez-Pernas (2024) , and higher-order network methods including higher-order networks, higher-order network embedding, hyper-path anomaly, and multi-order generative model. Supports bootstrap inference, permutation testing, split-half reliability, centrality stability analysis, mixed Markov models, multi-cluster multi-layer networks and clustering. Package: r-cran-nestr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestr_0.1.2-1.ca2404.1_all.deb Size: 31812 MD5sum: 35e7a50e041c7a1ca1ec0073efcaafb4 SHA1: 88d778171f7c26d19291dd64640d62c2583730f1 SHA256: dc657850de7e76d4f3af4c52721a660b10682fae0075edc5e9497f3728d29a89 SHA512: a5506d3f5ee78ec4642fa488856310f5b3b16b0c03e8db5db009397bfb3f4f472a82a8bc9cb5497d0306eb2728fea018ae634b10f5f78f877c89135bcb89e341 Homepage: https://cran.r-project.org/package=nestr Description: CRAN Package 'nestr' (Build Nesting or Hierarchical Structures) Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax. Package: r-cran-net4pg Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-graph, r-cran-magrittr, r-cran-matrix Suggests: r-bioc-biocstyle, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-net4pg_0.1.2-1.ca2404.1_all.deb Size: 397384 MD5sum: bd5916226254d5739b5f9d2ee467854a SHA1: 1eebb74c791dc22c924eed75f1ffde0157abfd12 SHA256: 180eb26c350133f952e7c528e00be66811497542e1f34efa639f160b990554dd SHA512: 8d6a46a0994464b4a4832f3676f37c7a637bbb35df783efaf388f97bbf6eec0e3891555fccce49a1d4caaf4147c0c3d2393e8c52febb5264436f0e8857a800de Homepage: https://cran.r-project.org/package=net4pg Description: CRAN Package 'net4pg' (Handle Ambiguity of Protein Identifications from ShotgunProteomics) In shotgun proteomics, shared peptides (i.e., peptides that might originate from different proteins sharing homology, from different proteoforms due to alternative mRNA splicing, post-translational modifications, proteolytic cleavages, and/or allelic variants) represent a major source of ambiguity in protein identifications. The 'net4pg' package allows to assess and handle ambiguity of protein identifications. It implements methods for two main applications. First, it allows to represent and quantify ambiguity of protein identifications by means of graph connected components (CCs). In graph theory, CCs are defined as the largest subgraphs in which any two vertices are connected to each other by a path and not connected to any other of the vertices in the supergraph. Here, proteins sharing one or more peptides are thus gathered in the same CC (multi-protein CC), while unambiguous protein identifications constitute CCs with a single protein vertex (single-protein CCs). Therefore, the proportion of single-protein CCs and the size of multi-protein CCs can be used to measure the level of ambiguity of protein identifications. The package implements a strategy to efficiently calculate graph connected components on large datasets and allows to visually inspect them. Secondly, the 'net4pg' package allows to exploit the increasing availability of matched transcriptomic and proteomic datasets to reduce ambiguity of protein identifications. More precisely, it implement a transcriptome-based filtering strategy fundamentally consisting in the removal of those proteins whose corresponding transcript is not expressed in the sample-matched transcriptome. The underlying assumption is that, according to the central dogma of biology, there can be no proteins without the corresponding transcript. Most importantly, the package allows to visually inspect the effect of the filtering on protein identifications and quantify ambiguity before and after filtering by means of graph connected components. As such, it constitutes a reproducible and transparent method to exploit transcriptome information to enhance protein identifications. All methods implemented in the 'net4pg' package are fully described in Fancello and Burger (2022) . 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These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) , Langendorf (2020) , and Langendorf and Goldberg (2019) . 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The external variable may act as the dependent/response variable or as an independent/predictor variable to the network. 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For example, it may be used to detect genes that co-occur across genomes. 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The package allows users to evaluate model fit based on their own model statement, model type, and sample size. Methods are described in Du and Epskamp (2026) . 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Package: r-cran-netexplorer Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-netexplorer_0.0.2-1.ca2404.1_all.deb Size: 290652 MD5sum: 77d8c29419b47660045d42ee8b054094 SHA1: 45ee6f617d98bf1ded76126a7fd068dcedbf55bf SHA256: f7abb9d032a111b71bf943b541bda405503c8714cbda81115c318468da535e30 SHA512: 4549b9f323113c487bd3a558c2c16c1184a53144019fc2303a67e3ffd7b289972c4a87f1c3d81411ac943ef50d7e719044105fcca490f0a02bf03193ef88182f Homepage: https://cran.r-project.org/package=NetExplorer Description: CRAN Package 'NetExplorer' (Network Explorer) Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface; 2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation. Package: r-cran-netgreg Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-huge, r-cran-glmnet, r-cran-dplyr, r-cran-plsgenomics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netgreg_0.0.4-1.ca2404.1_all.deb Size: 18976 MD5sum: 573f5abae656bc984c1b1009be39d8cf SHA1: ae4eb9ecfa73773ff20dfc33f09e976d468313bb SHA256: 89c116117f841c35c18197ed3e47c59ac33ad7c6918e3039a1b6ed1b24ed6159 SHA512: 98878e38d97272bdf54090490f4677efd91fecd6ae3639bef3ea17738b92120e88e5ad8988cf1e869b87263222b28f17ea486775801055292e1ed6c6599b803c Homepage: https://cran.r-project.org/package=NetGreg Description: CRAN Package 'NetGreg' (Network-Guided Penalized Regression (NetGreg)) A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our publication by Ahn S and Oh EJ (2026) . Package: r-cran-netgwas Architecture: all Version: 1.14.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-qtl, r-cran-glasso, r-cran-mass, r-cran-huge, r-cran-tmvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-netgwas_1.14.5-1.ca2404.1_all.deb Size: 381110 MD5sum: cdafc416cdc9ec90213d2f823b73ad16 SHA1: afb13e9a4448751f226befa5fc9f48d93fc2bd34 SHA256: 084104875db36b44e5542b0283d86ee2f0d14c1452d52b3d37448dc3cb6e7b48 SHA512: 2c13ca4c51a472df7b4ea5a442e5431a969d9b9dec5ae8f21867aa076947a8354ed85e345fbdc05b444573413245eeda694195c7b40e8d16d52d537d8118c129 Homepage: https://cran.r-project.org/package=netgwas Description: CRAN Package 'netgwas' (Network-Based Genome Wide Association Studies) A multi-core R package that contains a set of tools based on copula graphical models for accomplishing the three interrelated goals in genetics and genomics in an unified way: (1) linkage map construction, (2) constructing linkage disequilibrium networks, and (3) exploring high-dimensional genotype-phenotype network and genotype- phenotype-environment interactions networks. The 'netgwas' package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is considerably larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The 'netgwas' implements the methodological developments in Behrouzi and Wit (2017) and Behrouzi and Wit (2017) . Package: r-cran-netie Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-netie_1.0-1.ca2404.1_all.deb Size: 43588 MD5sum: 6ae563720afc5fecf8fef92b10edfad5 SHA1: 3787807df8f675df4fadb669fe5dcc4a3e2824bf SHA256: a62f88ccf554fc49bf272c2ef4cf247d3669614e8e09ef824e0a4820352380df SHA512: 4a75f3e8d034955ef41f34d43543a092218ebc72c54de35dc99736a846a95cfe3ed4a11b61a3164af16634286aeacfe9e81ac8806d7f314bfdc34fd4d909c93b Homepage: https://cran.r-project.org/package=netie Description: CRAN Package 'netie' (Antigen T Cell Interaction Estimation) The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) . 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These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs. Package: r-cran-netint Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-netint_1.0.2-1.ca2404.1_all.deb Size: 200638 MD5sum: 610cbb96282060a6af8cd7b529cfcdd5 SHA1: d0e85c3599aaadb8cdacbd7c32a0bbbc93b4de03 SHA256: 7b6fc38d4f8a88695760d1ccd7451306fb984b215d6408ea651a521d03eb43ed SHA512: 6888f11e425b4ab8fb645c443a35f34fbe87cd35dac565f8ac2ee50c6e55f6579a9b771c65d0c1a6062033f3e8f1fceb2f653560a17af3bbcf377dd811d396f4 Homepage: https://cran.r-project.org/package=NetInt Description: CRAN Package 'NetInt' (Methods for Unweighted and Weighted Network Integration) Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the ''predictiveness strength'' of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction). Package: r-cran-netknitr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2724 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-shiny, r-cran-shinydashboard, r-cran-dplyr, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netknitr_0.2.1-1.ca2404.1_all.deb Size: 681862 MD5sum: 7ffb9b10aa452b24f31e360552dc490a SHA1: af3abbe2dfa71785cc2f531109ae4d1f2243c3b3 SHA256: d57d9e877d0105508d5dc251a3a68dcefda022a5a1129698ff2cf829cba2cc1d SHA512: ed0a1c358a2688093b2de49e0384ba718dca74ab36599fa86df9fec827b9188d098e5357fa182ea16cce9b61c2b1f406b22c1848fc7f1f6b6ff86c1c553ee30d Homepage: https://cran.r-project.org/package=netknitr Description: CRAN Package 'netknitr' (Knit Network Map for any Dataset) Designed to create interactive and visually compelling network maps using R Shiny. 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'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky (1999) ) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants ) and Wolf-Sheep-Predation (), and a third, Butterfly, from Railsback and Grimm (2012) , all written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide () and a dictionary of 'NetLogo' primitives () equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (). Package: r-cran-netmap Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggnetwork, r-cran-igraph, r-cran-network, r-cran-rlang, r-cran-sf, r-cran-sna Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netmap_0.1.4-1.ca2404.1_all.deb Size: 343682 MD5sum: 41a8bafdeea0b9f7370e84f464b4acfd SHA1: 1c8ed03f84c46f01e86f8df4bde6373eb9df3cda SHA256: 09cd77fd3ab5e2223dd23689b5db44fbe344141a4a9f344c2e3453022b3329cf SHA512: 30c6aef4679a730c83b3a94bc1b10b9b6ccfe693c8c8a004cb949efb10bab5758825172d4fa299fbee40e54e743dad3e31d2f1ecea3c8d0383d88e9d9cae0a21 Homepage: https://cran.r-project.org/package=netmap Description: CRAN Package 'netmap' (Represent Network Objects on a Map) Represent 'network' or 'igraph' objects whose vertices can be represented by features in an 'sf' object as a network graph surmising a 'sf' plot. Fits into 'ggplot2' grammar. Package: r-cran-netmediate Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-btergm, r-cran-ergm, r-cran-tergm, r-cran-rsiena, r-cran-sna, r-cran-network, r-cran-ergmargins, r-cran-vgam, r-cran-plyr, r-cran-lme4, r-cran-plm, r-cran-gam, r-cran-intergraph Suggests: r-cran-matrix, r-cran-igraph, r-cran-relevent, r-cran-statnet, r-cran-statnet.common Filename: pool/dists/noble/main/r-cran-netmediate_1.1.1-1.ca2404.1_all.deb Size: 304102 MD5sum: 4b641489060befcd130343522abe6997 SHA1: 52de949b4461e885fa764dc837a96077e6bb5e20 SHA256: 78217625f8958e649d63ee10c32874d31f624ba18701bfa99e160098905efbcd SHA512: c1b85dbdf4a925ca100ee8027bb519a109c81ca97763aa918ff37491791f868f9702ac80cbd38ebe2a3ff5e97c77130b6c745ca1fa6b6ee286b8089c3ecb7da4 Homepage: https://cran.r-project.org/package=netmediate Description: CRAN Package 'netmediate' (Micro-Macro Analysis for Social Networks) Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: . BugReports: . Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) . Snijders, Tom A. B., and Christian E. G. Steglich (2015) . Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) . Duxbury, Scott (2023) . Duxbury, Scott (2024) . Package: r-cran-netmem Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1479 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-usethis, r-cran-styler Filename: pool/dists/noble/main/r-cran-netmem_1.1-0-1.ca2404.1_all.deb Size: 1083196 MD5sum: 9da68d7634d203fb6b12311cd9cc3ed1 SHA1: 4873868a5d486a572526bd2ef27abbf93584c1f0 SHA256: cb90cd721e41539ffbf0476a3212ee4d80ea452684948fab70bb2145b7afea42 SHA512: 2ff60fa9e86be4d9373e760d62e89f0897fe5889b06920867500fd2a339570cc7ed1419910b3c8a1029092a1ed52fd170fbc13d521d76b2782f0e4bd843ca68f Homepage: https://cran.r-project.org/package=netmem Description: CRAN Package 'netmem' (Social Network Measures using Matrices) Provides measures to describe and manipulate one-mode, two-mode, multiplex, and multilevel networks using matrix algebra. Implements functions for network centrality, cohesive subgroups, communities, structural holes, roles and positions, similarity measures, path distances, signed networks, segregation, social influence, and random network generation. Supports ego-centric and whole-network analyses, including dyadic and triadic censuses, structural balance, bipartite projections, measures with overlapping group memberships, Q-analysis, neighbourhood-inclusion dominance, main path analysis of citation networks, and permutation tests for networks. Key references: Bonacich (1972) , Breiger (1974) , Kivela et al. (2014) , Espinosa-Rada et al. (2024) , Schoch and Brandes (2016) , Traag et al. (2019) , Everett and Borgatti (2026) . 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(2015) , Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) ; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) ; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) , or penalised logistic regression (Evrenoglou et al., 2022) ; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) ; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) ; - split direct and indirect evidence to check consistency (Dias et al., 2010) , (Efthimiou et al., 2019) ; - scatter plot to visualize local inconsistency (Wilson et al., 2026) ; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) ; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) ; - measures characterizing the flow of evidence between two treatments by König et al. (2013) ; - automated drawing of network graphs described in Rücker & Schwarzer (2016) ; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) ; (Rücker & Schwarzer, 2017) ; - contribution matrix as described in Papakonstantinou et al. (2018) and Davies et al. (2022) ; - path-based approach for detecting and assessing inconsistency; - network meta-regression with a single continuous or binary covariate (Kwarteng et al., 2026) ; - subgroup network meta-analysis. 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Measures such as the Largest Connected Component, the Relative Largest Connected Component, Proximity and Separation are calculated along with their statistical significance. Significance can be computed both using a degree-preserving randomization and non-degree preserving. 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Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) . Package: r-cran-netseg Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-scales Filename: pool/dists/noble/main/r-cran-netseg_1.0-3-1.ca2404.1_all.deb Size: 300764 MD5sum: 19378206336736409c825da783125bab SHA1: 51304373361fb11d6642d851904566c3b100bd19 SHA256: 1a58e58e13dcec6da6ddb0ff62a334c2ce66208088f79d358d5cba76c12eb867 SHA512: bc6bf27b3d463f45af8605b7e7fe87259b030e7c3f4082224b3655a9b959150166cf27289d693bb7101b048c7367f2dfb716e3422ee4fac93d1acdaee5148cd1 Homepage: https://cran.r-project.org/package=netseg Description: CRAN Package 'netseg' (Measures of Network Segregation and Homophily) Segregation is a network-level property such that edges between predefined groups of vertices are relatively less likely. Network homophily is a individual-level tendency to form relations with people who are similar on some attribute (e.g. gender, music taste, social status, etc.). In general homophily leads to segregation, but segregation might arise without homophily. This package implements descriptive indices measuring homophily/segregation. It is a computational companion to Bojanowski & Corten (2014) . Package: r-cran-netsem Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2977 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-htmlwidgets, r-cran-knitr, r-cran-magrittr, r-cran-mass, r-cran-rsvg, r-cran-svglite, r-cran-png, r-cran-segmented, r-cran-rlang, r-cran-butcher, r-cran-glue, r-cran-purrr, r-cran-tibble, r-cran-broom, r-cran-dplyr, r-cran-janitor, r-cran-rcompanion, r-cran-tidyr, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netsem_0.7.0-1.ca2404.1_all.deb Size: 1994010 MD5sum: f3a6c346c5c86aa687a931b16a27f82a SHA1: 86ecfcb6fe7ad72af6f5942b3623471ac2f11751 SHA256: 3406118044659cb2f2d388836e6db5c2dfb9121f4255c71038e848e5809b0aa7 SHA512: df284cfb4f1a374af900ffb13b11f8cebd8687920c26fe6f205b600b877eacd18826de7c505ccb3111cdcfbfab19449e0cb7c44cab28b91946ac7e1f488bfac6 Homepage: https://cran.r-project.org/package=netSEM Description: CRAN Package 'netSEM' (Network Structural Equation Modeling) The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) . Package: r-cran-netshiny Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-shinydashboard, r-cran-colourpicker, r-cran-dt, r-cran-future, r-cran-future.callr, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-igraph, r-cran-ipc, r-cran-magrittr, r-cran-matrix, r-cran-netgwas, r-cran-plotly, r-cran-promises, r-cran-readxl, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinyscreenshot, r-cran-shinywidgets, r-cran-visnetwork Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netshiny_1.0-1.ca2404.1_all.deb Size: 201554 MD5sum: 8865fbfeafa0407000b2490655ebc7af SHA1: 89e1b7c9cb44b52670c0f123b2bcd7ce2901d163 SHA256: 1c925037d6d9462c886b22c1f27abda5db9b85bd35f460d26095a60b28a33319 SHA512: f28ad7acf05f4fae68b5355e52497894047d9d831255e88d0d1e7284e71ef1b9f2c28d4b8b754ab0102f5ed55f9dc0500af4c5460f6655139777c32b8cedd575 Homepage: https://cran.r-project.org/package=netShiny Description: CRAN Package 'netShiny' (Tool for Comparison and Visualization of Multiple Networks) We developed a comprehensive tool that helps with visualization and analysis of networks with the same variables across multiple factor levels. The 'netShiny' contains most of the popular network features such as centrality measures, modularity, and other summary statistics (e.g. clustering coefficient). It also contains known tools to look at the (dis)similarities between two networks, such as pairwise distance measures between networks, set operations on the nodes of the networks, distribution of the weights of the edges and a network representing the difference between two correlation matrices. The package 'netShiny' also contains tools to perform bootstrapping and find clusters in networks. See the 'netShiny' manual for more information, documentation and examples. Package: r-cran-netsimhelpers Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-bootnet, r-cran-qgraph Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-netsimhelpers_0.1.0-1.ca2404.1_all.deb Size: 42010 MD5sum: 32ab0f05983db10ea97969ee083deec4 SHA1: 8754d660d947d29d734a6c6a7b915f9ab6fe2668 SHA256: f46af0845898b8957f200d25ce6f13574cc43ebb3ca484ecf671d447cbebbe63 SHA512: ddb99129f601e8815446cf304575f656e1c00c33c8ddf9034b635500c3e9e1c15c5a2545cf5815df4a1011ff44b6dfaaf318f5dd1a682784701615f134671c6c Homepage: https://cran.r-project.org/package=netsimhelpers Description: CRAN Package 'netsimhelpers' (Helper Functions for Simulation Studies in Network Psychometrics) Helper functions for setting up simulations in network psychometrics. Package: r-cran-netsimr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2637 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-base64enc, r-cran-bslib, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-crch, r-cran-testthat, r-cran-shinytest2, r-cran-chromote, r-cran-pkgload, r-cran-dbi, r-cran-rodbc, r-cran-rpostgresql, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-netsimr_0.3.2-1.ca2404.1_all.deb Size: 1446618 MD5sum: 0349407cf6200478c8c7b505c0a7f041 SHA1: c5682e3191b59a6e3475ce573af5dce68eae3bd2 SHA256: 1c44213548809f95a367847dccfba1296fa1d683db85e3cecc80188825755fb6 SHA512: 81dbeb4e5402988820f7802c462aa37618ca907c22b8a16145d131ae9d962e4f055ed13f9ea4b928c84dba8556abffa606a4a66c60fbdf265292db7fd9482c58 Homepage: https://cran.r-project.org/package=NetSimR Description: CRAN Package 'NetSimR' (Actuarial Functions for Non-Life Insurance Modelling) Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three 'shiny' tools: a claims simulator with reinsurance structures, a generalised linear model fitting tool, and a claims frequency and severity distribution fitting tool. Methods used in the package refer to Free for All by Yiannis Parizas (2023) ; Escaping the triangle by Yiannis Parizas (2019) ; Taken to excess by Yiannis Parizas (2019) . Package: r-cran-netstat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1664 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-netstat_0.1.2-1.ca2404.1_all.deb Size: 1651248 MD5sum: 3f69ac615b1bde8c8ce9c13c14e99c59 SHA1: a6b9c4c014dd79df4fb821b342658944c2943ad5 SHA256: 7a84034eabc7b5385262c3997ffeb3d9c6d664764d601372ec31f657a67a03e4 SHA512: 7a9363ce0ec4e883d2dd755dba2700c914634c367c3cc79da558f72aec0e2c6c4a8cb778adebe7013e7d9ad9885b0504432c41d9c2dfba6c33ee1af3c703c398 Homepage: https://cran.r-project.org/package=netstat Description: CRAN Package 'netstat' (Retrieve Network Statistics Including Available TCP Ports) R interface for the 'netstat' command line utility used to retrieve and parse commonly used network statistics, including available and in-use transmission control protocol (TCP) ports. Primers offering technical background information on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) , and on the Microsoft website (2017) . Package: r-cran-netsubsamp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-matrix, r-cran-randnet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netsubsamp_1.0.0-1.ca2404.1_all.deb Size: 67644 MD5sum: e50de28bb54a9448f15da5201380bd6b SHA1: 45498b6aa3da802116863a1c949f790964b02c52 SHA256: d4eb9fb6bde6e704391ffd4577c5d1af7ddca8376087d5681198a75648203d8c SHA512: 131a9210b77f4d84d06a03b08ce73668c59471bdb9c1a2377f5cfd0dd32e173f9e6683bee253fac501f52d7cc7132e36776804bb80ada78e679307408a1c85a4 Homepage: https://cran.r-project.org/package=netsubsamp Description: CRAN Package 'netsubsamp' (Multivariate Inference of Network Moments by Subsampling) Implements node subsampling methods for multivariate inference on network moments (rescaled motif counts), including: uniform node subsampling to approximate the joint distribution of multiple network moments (Algorithm 1); externally sparsified moments for density-matched comparisons (Algorithm 2); and a two-sample test for unmatchable networks with unequal edge densities via a split-and-sparsify subsampling procedure (Algorithm 3). Built-in support for V-shape (2-star), triangle, and 3-star motifs, with a user-extensible interface for arbitrary additional motifs. Parallel execution is supported via 'doParallel' and 'foreach'. Based on Qi, Hua, Li and Zhou (2024) . Package: r-cran-netsurvprox Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-curl, r-cran-cvtools, r-cran-dplyr, r-cran-flexsurv, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-hmisc, r-cran-httr, r-cran-igraph, r-cran-magic, r-cran-openxlsx, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-survauc, r-cran-survival, r-cran-survminer Suggests: r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-plotly, r-cran-scales, r-cran-sessioninfo, r-cran-stringr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-netsurvprox_1.0.0-1.ca2404.1_all.deb Size: 915584 MD5sum: b6f36be1ceb858097f07f9167ed9e992 SHA1: eb58743be8b1266fbb57347d3d02ba7228cf0f76 SHA256: 65be8bc7af168a80443a375b6dbf6aa76ace50488e37eefe5eb504257d02e136 SHA512: fd38ea39e788f0ed7d036e8b98a4e8fcc76ede902a093e25836f958045bc4507947e5e1091d81c964073fb033c6b3623915ea3b97e1ff8b843421a92a24575d3 Homepage: https://cran.r-project.org/package=NetSurvProx Description: CRAN Package 'NetSurvProx' ('NetSurvProx': Network-Based Survival Analysis via ProximalMethods) Introduces a novel network-constrained survival analysis framework for variable selection and parameter estimation in penalized survival models with convex penalties. The package extends two classical survival models, the Cox Proportional Hazards (PH) model and the Accelerated Failure Time (AFT) model, by incorporating prior biological knowledge from curated interaction networks (e.g., KEGG) into a double-penalty framework. The first penalty enforces variable selection through a LASSO penalty, while the second preserves gene-gene correlations by incorporating Laplacian-based constraints, ensuring that biologically relevant network structures are maintained. Using censored survival data, the method enables the identification of predictive biomarkers and pathways with potential relevance for target therapies. Model estimation is performed via proximal optimization algorithms combined with cross-validation for reliable tuning. To enhance interpretability, dedicated utility functions are implemented to consolidate results, yielding biologically coherent insights that can support personalized medicine and contribute to improved patient outcomes. Package: r-cran-netswan Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-netswan_0.1-1.ca2404.1_all.deb Size: 26934 MD5sum: f7d9e4e0da7b46c349fc03a250c65d82 SHA1: 74e1db2921c62ac3de4ae6d4faeb0f7c4fa1a366 SHA256: 49acb11374968f851d843502674c535a7444f1f852cbfcf74b6bacd6868e93d2 SHA512: e669abd582b056bbffcbb78024baa643c42173ec6e11414343ae8e08717762057b580af36dfbd7644352a3d8a06e956f3d641ebd4c7c1b45ce4c9cd87fe88526 Homepage: https://cran.r-project.org/package=NetSwan Description: CRAN Package 'NetSwan' (Network Strengths and Weaknesses Analysis) A set of functions for studying network robustness, resilience and vulnerability. 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The data from the API is richer than from the online data portal. This package is not developed by the University of Oslo IT. Mowinckel (2021) . Package: r-cran-netweaver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-netweaver_1.0.0-1.ca2404.1_all.deb Size: 1559946 MD5sum: f715c48abcfddc59e6784abffce2430e SHA1: 7da8db6286c755e13bbfddb462413bfaa3320ec0 SHA256: dca5187b124592a8b9f55d323f82e86f5a923c83e9f41e64e1851e3fce627742 SHA512: dadd630f09428061f9a5cceac9b569edde68dc06091a7d08570b8612c0055f8647f6b9624811b0fa6895465179f96fdc47603760d09c22c5d2def1b3ed3b7437 Homepage: https://cran.r-project.org/package=NetWeaver Description: CRAN Package 'NetWeaver' (Graphic Presentation of Complex Genomic and Network DataAnalysis) Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features. Package: r-cran-networkchange Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-ggplot2, r-cran-rmpfr, r-cran-abind, r-cran-mvtnorm, r-cran-tidyr, r-cran-igraph, r-cran-qgraph, r-cran-network, r-cran-mass, r-cran-rcolorbrewer, r-cran-ggrepel, r-cran-rlang, r-cran-ggally, r-cran-patchwork, r-cran-viridis Suggests: r-cran-sna, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-networkchange_1.0.0-1.ca2404.1_all.deb Size: 1139074 MD5sum: 32d2805f6059a5b69f30004a71b9bda0 SHA1: 284e02b8a39a6efe8c3ed65d228edc85d539230b SHA256: 7fc2f2df149802479f737ab04cc2abfafef62f499f09cbe15e7f3157060b0443 SHA512: 81c22dea1e41ad9c2455aaa608e99db0fbde7316efc9a7418c83e81ff6e2b02f8831663d070ecdab7dd12d2e811750657ec39c796fd14e0a69f09ff3fb7c6d51 Homepage: https://cran.r-project.org/package=NetworkChange Description: CRAN Package 'NetworkChange' (Bayesian Package for Network Changepoint Analysis) Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes. Package: r-cran-networkcomparisontest Architecture: all Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-reshape2, r-cran-qgraph, r-cran-networktools, r-cran-isingfit Suggests: r-cran-bootnet, r-cran-isingsampler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-networkcomparisontest_2.2.4-1.ca2404.1_all.deb Size: 64218 MD5sum: 2b833e6cacf3559ae97504cad7411fd6 SHA1: 85e476c8118ea6849153822b75d69a696181a5d3 SHA256: 2f307ec9d8be1b37549466a26fe3da14a5c633b90e0ea97ea6b529fd329e2a8f SHA512: e251c12817f0069fe5d3985e537c013b10059b38d86f562c2c525e4b60212ed9206a40d6c7da714be3e5f8599308564cb466fcbf14c9ca3fa0f0fac5ad1f4e16 Homepage: https://cran.r-project.org/package=NetworkComparisonTest Description: CRAN Package 'NetworkComparisonTest' (Statistical Comparison of Two Networks Based on SeveralInvariance Measures) This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021), available from . Package: r-cran-networkcomparr Architecture: all Version: 0.0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-qgraph, r-cran-igraph, r-cran-reshape2, r-cran-networktools, r-cran-gdata Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-networkcomparr_0.0.0.9-1.ca2404.1_all.deb Size: 54354 MD5sum: 4710978fdb69aa915892ce895487f7b1 SHA1: 89321cb033f34d36f89a5a0878aad3484eedca58 SHA256: b52549139fc2d5b83e98ea16cb449fbe794dc3b9965e8c9523749735f1273382 SHA512: 16558035a2df6f5b4e4df45ddba6a2411bfcb19d6ff837fcb608ea35c247f1b762e52ea7f6fd4ca04ec8a6b72d29b2b36bf2f74a1cd1ab8bb8457b4f208411d0 Homepage: https://cran.r-project.org/package=NetworkComparr Description: CRAN Package 'NetworkComparr' (Statistical Comparison of Networks) A permutation-based hypothesis test for statistical comparison of two networks based on the invariance measures of the R package 'NetworkComparisonTest' by van Borkulo et al. (2022), : network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected. Package: r-cran-networkd3 Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.tree, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-htmltools, r-cran-tibble Filename: pool/dists/noble/main/r-cran-networkd3_0.4.1-1.ca2404.1_all.deb Size: 222850 MD5sum: 649228413fb7a185c5ba179fefa4e1ae SHA1: d50fb3c4c16e730e498c83dfa3ec0ca0c2005330 SHA256: c26c448428ad8eb083cd9eb59e1b9c9c5fe47dcbfabc1010f9541973bf8f331c SHA512: f6189d8a567593aa40111126048388f810ce1296a7789c487c7e5146ea4ab07ff43dfbe2ed83e1fed73d0443560cfa6d8f6c6922e1d147357642502f16b6ee4c Homepage: https://cran.r-project.org/package=networkD3 Description: CRAN Package 'networkD3' (D3 JavaScript Network Graphs from R) Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'. 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Package: r-cran-networkextinction Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-magrittr, r-cran-network, r-cran-scales, r-cran-sna, r-cran-tidyr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-patchwork, r-cran-dosnow Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-networkextinction_1.0.3-1.ca2404.1_all.deb Size: 507774 MD5sum: 8d4c599942c23a570e9a1081698db7b1 SHA1: 9e09bb719192b802a96b192995271f9c458ef754 SHA256: 263927cc41bb4a9fbc98aeb40947de9b41adb64428081b4fae7628d47864b1e8 SHA512: 4ce935c477a8fc071c608705c018206196407f9885d8394f9c0302bacb9e91b049da68170669f96cb1c44b795ce3adaab3225ca7d326ecbe871548d0e2c5d18a Homepage: https://cran.r-project.org/package=NetworkExtinction Description: CRAN Package 'NetworkExtinction' (Extinction Simulation in Ecological Networks) Simulates the extinction of species in ecological networks and it analyzes its cascading effects, described in Dunne et al. (2002) . Package: r-cran-networkgen Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-networkgen_0.1.1-1.ca2404.1_all.deb Size: 97016 MD5sum: 1421f097ea66ed9c7418a0cd7b6e312d SHA1: fe7db519707482cb68cd10835a16e519fa4a09bb SHA256: e0e86f23681d67b12c831d522fdf4aca0d17177da2d7ad83e256bc49c7b934bf SHA512: dd518b132f900fb029086b9c46c2ac336a3444410b1ab7fae5f118fe4c9947783a8161e63175465ccacc243190819d16e58330b36628b1a0429d5fbc4871adbd Homepage: https://cran.r-project.org/package=networkGen Description: CRAN Package 'networkGen' (Network Maze Generator) A network Maze generator that creates different types of network mazes. Package: r-cran-networklite Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-tibble, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-networklite_1.1.0-1.ca2404.1_all.deb Size: 118566 MD5sum: bb3281b496bf3e0e3115fe499d899b96 SHA1: a5c82ac6c380e60badee75a1b42a2682b439ae0a SHA256: cd4a51f7384c4cee20521bc92cedaee0f6e7c3b96e73095a4de09b2a0661422c SHA512: 76421626e07f7153213178bdfd3707e0dd89056274363d914b683e7dbcc8279512b986eb6762b412020c51a0db56b825d63d0a94c7e59d24277c684a528aeb71 Homepage: https://cran.r-project.org/package=networkLite Description: CRAN Package 'networkLite' (An Simplified Implementation of the 'network' PackageFunctionality) An implementation of some of the core 'network' package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops. Package: r-cran-networkreg Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randnet, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-networkreg_2.0-1.ca2404.1_all.deb Size: 44860 MD5sum: 2048a1eb62fc7615357b542b92f944b4 SHA1: fd31b6e76531a06053ec7d2e86b2d5347722ae5e SHA256: cef1e14cf85ea1cd08d8254d8873a610ae1e3eeedc6e6591d53ec5e98489b6b8 SHA512: 1bc01f81fa22b5f64b1917d8b6e78cc178d5c28d31351df4fa17c5852a6426d23d8767073cb852f25ac33fa35195dadd7931b586e19c72762171c66441842ef7 Homepage: https://cran.r-project.org/package=NetworkReg Description: CRAN Package 'NetworkReg' (Generalized Linear Regression Models on Network-Linked Data withStatistical Inference) Linear regression model and generalized linear models with nonparametric network effects on network-linked observations. The model is originally proposed by Le and Li (2022) and is assumed on observations that are connected by a network or similar relational data structure. A more recent work by Wang, Le and Li (2024) further extends the framework to generalized linear models. All these models are implemented in the current package. The model does not assume that the relational data or network structure to be precisely observed; thus, the method is provably robust to a certain level of perturbation of the network structure. The package contains the estimation and inference function for the model. Package: r-cran-networkriskmeasures Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-igraph, r-cran-covr Filename: pool/dists/noble/main/r-cran-networkriskmeasures_0.1.7-1.ca2404.1_all.deb Size: 88432 MD5sum: 2c8b6598c5dc1a6c3f5f808f573a0d33 SHA1: dd4cccb870109acd84fc768c892103aa7ce4e228 SHA256: 6d4e8d0fb19f00d9245342309f3e5686576a982a909e9045b7bcf353fc9399c2 SHA512: 523c2f44d04d14d9287ccf8e9f9fc8d9d1f743f72fc5b307a46e5479737316c262667e1fbef7784f94234376c5ad6faa04f7c19c1512ce2eb53152dd56eb57cf Homepage: https://cran.r-project.org/package=NetworkRiskMeasures Description: CRAN Package 'NetworkRiskMeasures' (Risk Measures for (Financial) Networks) Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity. 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Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at . 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Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 ), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 ), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 ). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis. 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Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) . 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The package includes clinical, experimental, neuroimaging, behavioral, cognitive, and simulated data on conditions such as Parkinson's disease, Alzheimer's disease, dementia, epilepsy, schizophrenia, autism spectrum disorder, attention deficit, hyperactivity disorder, Tourette's syndrome, traumatic brain injury, gliomas, migraines, headaches, sleep disorders, concussions, encephalitis, subarachnoid hemorrhage, and mental health conditions. Datasets cover structural and functional brain data, cross-sectional and longitudinal MRI imaging studies, neurotransmission, gene expression, cognitive performance, intelligence metrics, sleep deprivation effects, treatment outcomes, brain-body relationships across species, neurological injury patterns, and acupuncture interventions. Data sources include peer-reviewed studies, clinical trials, military health records, sports injury databases, and international comparative studies. 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The package facilitates PET image loading, data restructuring, integration into a Functional Data Analysis framework, contour extraction, identification of significant results, and performance evaluation. It bridges established packages (e.g., 'oro.nifti') with novel statistical methodologies (e.g., 'ImageSCC') and enables reproducible analysis pipelines, including comparison with Statistical Parametric Mapping ('SPM'). 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Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) . 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Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 and Williams et al 2017 for further details of the method. 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Computes interval sums of squares, mean squares, F-statistics, significance tests, and interval-based least significant difference (LSD) comparisons. When lower and upper observations are identical (crisp data), the methods reduce to the corresponding classical ANOVA and ANCOVA. 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A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) for detailed description. 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The package takes a uniform-hazard spectrum at multiple return periods as input (any source) and produces: (1) synthetic soil profile generation and fundamental period estimation from USCS classification via Ishihara's small-strain shear-modulus model and the inhomogeneous truncated shear-beam theory of Gazetas and Dakoulas; (2) nonlinear site amplification using the NGA-East ergodic site-response models (Stewart et al. (2020) and Hashash et al. (2020) , with the 2017 PEER-report generation retained as an option), with inter-period correlation via Baker & Jayaram (2008) ; (3) Monte Carlo ensemble of six empirical Newmark sliding-block displacement models (Ambraseys & Menu (1988) , Jibson (2007) , Saygili & Rathje (2008) , Bray & Travasarou (2007) , Bray & Macedo (2017) , and the Bray and Macedo shallow-crustal update) with coherent correlated draws; (4) log-log inversion to the performance-based seismic coefficient kmax at user-specified displacement targets. 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Retrieve pre-processed datasets from reliable cloud storage with automatic type reconciliation and integrated search tools for variables and datasets. Simplifies NHANES data workflows by handling cycle management and maintaining data consistency across survey waves. Data is sourced from . Package: r-cran-nhanesdiva Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nhanesdiva_1.0.2-1.ca2404.1_all.deb Size: 52910 MD5sum: 205b5d8b74cd80e16abccfbe53367e4a SHA1: 2aea1d06af95d07a7c30e3e97a8644f8853236b1 SHA256: 872405984d28fba4347fe1747e89e54cd5a429a2017b3f1db43039faa59cfb8a SHA512: a32895813db33e02e55477ea410e2f0f2160fa22ba670d46fd23fd084bb229c62a30382c9af905c734c68a2fe07a819b542034135b8360e0c5b35fc529e77c9b Homepage: https://cran.r-project.org/package=nhanesdiva Description: CRAN Package 'nhanesdiva' (NHANES Data Search, Preview, and Download Tools) Search, preview, and download datasets from the National Health and Nutrition Examination Survey (NHANES) across survey cycles. The package provides functions to identify relevant datasets by keyword, inspect available .XPT files before downloading, and organize retrieved data locally. Data are retrieved from the NHANES web services available at . Package: r-cran-nhanesr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-haven, r-cran-readr, r-cran-rlang, r-cran-cli Suggests: r-cran-foreign, r-cran-survey, r-cran-survival, r-cran-rvest, r-cran-hmisc, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhanesr_0.1.6-1.ca2404.1_all.deb Size: 252544 MD5sum: 34292a96a9a51005d1c8cfcc1d1ed70a SHA1: 543735b589e58814612887e160a5c399da508be9 SHA256: 71aa29b187c4cfe30e6190d25336a6ba400c0679c81503aac59660421bfd40ab SHA512: 4449ad1e1522d15f6786bdba6dc7135b7cf281dd85219115210cac64fba4123e225c8cea7aca4623a8d8b87259ec2e86b72553885f870fb063d474a90a246cda Homepage: https://cran.r-project.org/package=nhanesR Description: CRAN Package 'nhanesR' (Download, Parse, and Analyze NHANES Data with Mortality Linkage) Provides tools for downloading and organizing National Health and Nutrition Examination Survey (NHANES) public-use data files and the National Center for Health Statistics (NCHS) Public-Use Linked Mortality Files (LMF). Supports structured local caching, codebook access, survey-aware merging, and preparation of survival analysis datasets using NHANES-National Death Index (NDI) linked mortality data (follow-up through December 31, 2019). NHANES methodology is described at . Package: r-cran-nhdplustools Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2246 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-hydroloom, r-cran-dataretrieval, r-cran-dplyr, r-cran-sf, r-cran-units, r-cran-magrittr, r-cran-jsonlite, r-cran-httr, r-cran-xml2, r-cran-r.utils, r-cran-tidyr, r-cran-maptiles, r-cran-mapsf, r-cran-fst, r-cran-arrow, r-cran-zip, r-cran-pbapply, r-cran-memoise, r-cran-digest Suggests: r-cran-testthat, r-cran-ggmap, r-cran-ggplot2, r-cran-lwgeom, r-cran-gifski, r-cran-leaflet, r-cran-httptest, r-cran-future, r-cran-future.apply, r-cran-streamcattools, r-cran-terra Filename: pool/dists/noble/main/r-cran-nhdplustools_1.5.2-1.ca2404.1_all.deb Size: 2190186 MD5sum: 4f92a6280a2184e6fecc3a7f3b7007fc SHA1: 33803fc32a452fcba0fcc053402c5cb43297ae9f SHA256: 4811d388ab4df3c2d0201440055c9d41a7842de5d5803e440007ed0b9e6c4340 SHA512: 90bdd7809845031712250f35c4436a17e7409bf84b9af59cd03cc9ba6ebe0b9a09b29ecd85e49b527b39199e2107d363a0c7b42f89f6bb36175dc59f7ef8a77f Homepage: https://cran.r-project.org/package=nhdplusTools Description: CRAN Package 'nhdplusTools' (NHDPlus Tools) Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency . 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Package: r-cran-nhlapi Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhlapi_0.1.4-1.ca2404.1_all.deb Size: 557880 MD5sum: 0948fc00de2c22ac8e9366085bc9c849 SHA1: 28807a62d38720e4e6a574765c1dce84c406cfa1 SHA256: aa38a2b999da6a56a4640f5b0f56f4f3b7fe25513a7174fdd49d8e6318023bdb SHA512: b199da9f7dd107c0add1844ec43ffa9286fe9f5570e38d082db9bdfa243f3b326bfea12a50742642b494f3e4fb9ab34cfb2d73ec8f08f52f32ffaa12f3139942 Homepage: https://cran.r-project.org/package=nhlapi Description: CRAN Package 'nhlapi' (A Minimum-Dependency 'R' Interface to the 'NHL' API) Retrieves and processes the data exposed by the open 'NHL' API. This includes information on players, teams, games, tournaments, drafts, standings, schedules and other endpoints. A lower-level interface to access the data via URLs directly is also provided. Package: r-cran-nhldata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nhldata_1.0.0-1.ca2404.1_all.deb Size: 542766 MD5sum: c579b55d46916e6d40de387b4f88f8dd SHA1: d07f1a3207a66abeef54ee237fc241eee15ef9c0 SHA256: cab56938b36216938ef5ae8ca7285981d653e9466b5509540b0026461e1065d0 SHA512: 2c7343f8078e848eeed45cf1ff5be820feedd33fd0bff8d8d912c2dc9ac09a3ab03b547d6f68ea7d3ec254ec64afe817c9a8ab9dc083a69e756cc6f01ae9011b Homepage: https://cran.r-project.org/package=NHLData Description: CRAN Package 'NHLData' (Scores for Every Season Since the Founding of the NHL in 1917) Each dataset contains scores for every game during a specific season of the NHL. Package: r-cran-nhlscraper Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhlscraper_0.5.0-1.ca2404.1_all.deb Size: 1237482 MD5sum: f4ee5aa3badd278f095c5416c17bf1e1 SHA1: 9d6a30e17f81a93b13fabd4f1a6ee22abc187149 SHA256: f3ae3614847425bd0243a4d7193e53193f283f3c74502944c11d1b1744a8d87d SHA512: ad39d3fdc7c98aa95c7cabb694cf4f152d6a10c5a9727e7a76ac0f7b15e03734aa869225bf644701f13d4238b4297136e22730f2438e2484dabd7eb51e836f89 Homepage: https://cran.r-project.org/package=nhlscraper Description: CRAN Package 'nhlscraper' (Scraper for National Hockey League Data) Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays. Package: r-cran-nhpoisson Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Filename: pool/dists/noble/main/r-cran-nhpoisson_3.4-1.ca2404.1_all.deb Size: 385024 MD5sum: b85cd43a624580fe0cf570a817ef37aa SHA1: 1d65b212c0be0cb1e7e1b44af4d80cca632b683a SHA256: cdd304a99909abe626149421d6884596cc30b9d4ffc16efc0b180cc28198b016 SHA512: 64a436c2a2c0dc649aa1304f442c9f54d3ff12783e48cf8a083d02b888fea281eaee01b07fcba138f7e483b0a8f90e00ccbee849ab1a1c159617ce251d3dd219 Homepage: https://cran.r-project.org/package=NHPoisson Description: CRAN Package 'NHPoisson' (Modelling and Validation of Non Homogeneous Poisson Processes) Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time. Package: r-cran-nhs.predict Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nhs.predict_1.4.0-1.ca2404.1_all.deb Size: 55426 MD5sum: 39c4d5a6bba1eccecf09afba4d2c7121 SHA1: bbe0f5adca88613b8e152f58a275d320b19b7f4d SHA256: 2bb8ec0d7b38f32791a3289aae498c1a1aa3119bbac8c3b7f90c8b08bdc1d52f SHA512: 3aca3db40eab6d892bc861a2c3d7f525c6458c938116047a6ef76808dc7be8e80369a2ecf2ac8c04cd753dcf37841078cbc643d15a9f5aba34fa9035cc6b6fcf Homepage: https://cran.r-project.org/package=nhs.predict Description: CRAN Package 'nhs.predict' (Breast Cancer Survival and Therapy Benefits) Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using 'NHS Predict'. The time interval for the estimation can be set up to 15 years, with default at 10. Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates. An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates the correct standardised inputs. This work is not affiliated with the development of 'NHS Predict' and its underlying statistical model. Details on 'NHS Predict' can be found at: . The web version of 'NHS Predict': . A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions, and an additional dataset is provided for running example with the dedicated SCAN function. Package: r-cran-nhsbsa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-curl, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nhsbsa_0.1.0-1.ca2404.1_all.deb Size: 142686 MD5sum: 173280d4e900f40ba71c3f13802d8d93 SHA1: 8d1260a4bfb81c05c17ae7b045c658ea7fdd5b46 SHA256: 1f31dff54c5a831843c5aedccf409141a4680ec2826efa5d0c98ac195ad7cb02 SHA512: 4d7d2ae4b419e0a5e1d4986c572681ce5c80910669e91e1055e27fa91f3415f01ff5471d055ddf5fbbdf7d5efef61db4ce90d012fe430f89642ac30136a0d80a Homepage: https://cran.r-project.org/package=nhsbsa Description: CRAN Package 'nhsbsa' (Client for the NHS Business Services Authority Open Data Portal) A low-level client for the National Health Service Business Services Authority (NHSBSA) Open Data Portal , a 'CKAN' data catalogue. Provides thin wrappers around the portal's API actions for listing datasets, retrieving metadata, querying the datastore and downloading resource files. Results are returned as plain data (tibbles and lists) for the caller to interpret. Package: r-cran-nhscancerwaits Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-lubridate, r-cran-rlang, r-cran-ggplot2, r-cran-scales, r-cran-lme4, r-cran-broom.mixed, r-cran-performance, r-cran-cluster, r-cran-writexl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-covr Filename: pool/dists/noble/main/r-cran-nhscancerwaits_1.0.2-1.ca2404.1_all.deb Size: 260622 MD5sum: 522089bc73f17ae3a657d6a05e873f7b SHA1: 41b8970aa356747f55ba0d93492ea632edb7d848 SHA256: d049b5527ae5574833fb70f0b51d9ba9df171b3768f3f45b3cab90b71d39ff3b SHA512: c24e4eae08edd92f66f1987ec2e2014551969fd142055de63b78fafe440fdb5d777951a3d5cbcf5f77ea3ea08c9d87fb76254819c55346b6ecdc7f7eb66b2353 Homepage: https://cran.r-project.org/package=nhscancerwaits Description: CRAN Package 'nhscancerwaits' (NHS Cancer Waiting-Time Analysis, Benchmarking and MultilevelModelling) Provides tools for importing, harmonising, cleaning, analysing, benchmarking and visualising National Health Service (NHS) England Cancer Waiting Times data. The package supports national performance monitoring, provider-level benchmarking and cancer pathway comparisons through key performance indicator summaries, provider filtering, clustering analyses, mixed-effects regression models, variance decomposition, intraclass correlation coefficient estimation, adjusted provider performance estimation and sensitivity analyses. Functions are included for exploratory analysis, publication-ready visualisations and spreadsheet exports, supporting reproducible health services research, cancer services evaluation, quality improvement and assessment of waiting-time performance across healthcare organisations. Mixed-effects modelling functionality is based on Bates et al. (2015) . Multilevel modelling concepts and variance decomposition follow Gelman and Hill (2007, ISBN:9780521686891). Cancer Waiting Times definitions and reporting standards follow NHS England . Package: r-cran-nhsdatadictionary Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nhsdatadictionary_1.2.5-1.ca2404.1_all.deb Size: 149374 MD5sum: ee705c946d2d9bfe68c5fa3db17e80e1 SHA1: b27495d4a6649b8d679966a05bea318dbfff7f22 SHA256: e659c235df68378ebabc31dbfc2cfc00f934e3f0ce35089ad8fee171bd15ad81 SHA512: 60ef06a700701cb52b8f130a5911d47b28f063823db9e242398d279f422abf3e8350ca9b677d9daea73e0fa65af71f78e18ec82dc75fdc0e40293c42a8c7aaa2 Homepage: https://cran.r-project.org/package=NHSDataDictionaRy Description: CRAN Package 'NHSDataDictionaRy' (NHS Data Dictionary Toolset for NHS Lookups) Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary . The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups. The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained. This package was commissioned by the NHS-R community to provide this consistency of lookups. The OpenSafely lookups have now been added . Package: r-cran-nhsnumber Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nhsnumber_0.1.2-1.ca2404.1_all.deb Size: 15834 MD5sum: c4fd48500d09e10f81c9c3a8466e96f8 SHA1: 391cfb94b5151837a19082dca487fa013c9ee85f SHA256: ab1bd0ae6da11407edd5cca35e2a6400f5731948399a8000083463cd5b2e8f12 SHA512: eb3682d3e8293fa23941c3530de8be6c976c833639cf929a8844e3461b4b08582d306d0cadd93dc3c4b67d0ee739c593617752a4412c2a327dbca0580e5b2760 Homepage: https://cran.r-project.org/package=nhsnumber Description: CRAN Package 'nhsnumber' (Tools for Working with NHS Number Checksums) Provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created. 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This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data. This package exists to support skills development in the NHS-R community: . 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(2022) . Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included. Package: r-cran-nhstplot Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhstplot_1.4.2-1.ca2404.1_all.deb Size: 966220 MD5sum: fcec791cc10fbff2e35aee55eb162bc8 SHA1: 392a470a61f687335991e885d0f895e542491030 SHA256: 25761714857d5ba1b0f0c955365876ca5f0a79e4413a105997eb26b7f7d60781 SHA512: 64789b63668f366e9d2d7f1ffa8a1e99465cc54fbd6f9bf53aabdc87f724ff1f8c37d362903a2c83683b81139a88324ba4d7758e5d3cd211253d1e878c908405 Homepage: https://cran.r-project.org/package=nhstplot Description: CRAN Package 'nhstplot' (Plot Null Hypothesis Significance Tests) Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings. Package: r-cran-niarules Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-niarules_0.2.0-1.ca2404.1_all.deb Size: 116400 MD5sum: e08e801b3059db995841a225d116cc25 SHA1: 4c39fcf84af125c7df30d144589a5ab3ebe36961 SHA256: 346a10c5c14175ff96e85d489e308132cedcd9469694de137daf2147821579e9 SHA512: a704bfa56950f48b4804819ba02b58fee677550e1d74bbf918784addf7773cbf4d3b16d36390d3f5e8d7a58ca91e8477b0721c9f915cab4317de58762fc9741d Homepage: https://cran.r-project.org/package=niarules Description: CRAN Package 'niarules' (Numerical Association Rule Mining using Population-BasedNature-Inspired Algorithms) Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) . Package: r-cran-nic Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-nic_0.0.2-1.ca2404.1_all.deb Size: 1602328 MD5sum: dac10adc002b9daab64268f2fab1070c SHA1: 417ffce0383ce77f9719b8098d8edcc840dfc3eb SHA256: b6057ae71216651b575688581f69dc32c4e493adee8528bc02a41e64f9056419 SHA512: 2063b35fad46f1df968c7841dd3e086803aaf5f304e6b21f24b5e4511498cdc3c5dc96154cf99509d95877ca8899f88fabfed3f83b323d1b31b5ce02328ea5d6 Homepage: https://cran.r-project.org/package=nic Description: CRAN Package 'nic' (Nature Inspired Colours) Color palettes based on nature inspired colours in "Sri Lanka". Package: r-cran-nichebarcoding Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-dismo, r-cran-e1071, r-cran-maps, r-cran-proc, r-cran-randomforest, r-cran-raster, r-cran-rjava, r-cran-spider, r-cran-vegan Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-nichebarcoding_1.0-1.ca2404.1_all.deb Size: 1062198 MD5sum: a04d3ec777457e79c6387c5eecfd80a5 SHA1: cdc5f49388f32612e47274bd3150896dbd88d7b9 SHA256: fe7135c60610be08b6bef02be70dd2f6d33f6c089a32181b5535a3b72d90d98f SHA512: 6fa45f25cddc9be859725566dbc3b9cd7d95be54be08796e96d10d240c1dbb6db79ccf12eef411bd7170907d8728131afdb7f94c757a695b946a6cd9f18ed8eb Homepage: https://cran.r-project.org/package=NicheBarcoding Description: CRAN Package 'NicheBarcoding' (Niche-model-Based Species Identification) Species Identification using DNA Barcodes Integrated with Environmental Niche Models. Package: r-cran-nicher Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4349 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra Suggests: r-cran-rgl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nicher_0.1.0-1.ca2404.1_all.deb Size: 2988918 MD5sum: 0ac73e3b0e2ac9d11d1b5139055fe491 SHA1: 0ece699302af2463bbf1980748b81f2af5f19509 SHA256: 321c306a31d54a164bf4ad83d3e7b9d401704e533d7b08bb3074555d0d39f555 SHA512: 1d059e780058fd5705de8b1453dec1fd7f8c6a93d3883503bbc1814ef4b709594f8ee25893307b56555cf1a678d6bfc830bb9e04915f3e2b9c6e5db449010e7f Homepage: https://cran.r-project.org/package=nicheR Description: CRAN Package 'nicheR' (Ellipsoid-Based Virtual Niches and Visualization) Provides a robust set of tools for researchers and modelers to construct and define virtual ecological niches using ellipsoid geometries. It enables the identification and extraction of suitable environmental areas, simulation of species occurrence points with various sampling strategies, and visualization of niche boundaries and simulated occurrences in both environmental and geographic space. Inspired by methodologies in 'NicheA' and the 'virtualspecies' R package, 'nicheR' aims to streamline the process of niche conceptualization and data generation for ecological studies. Methodological and theoretical foundations are described in Peterson et al. (2011, ISBN:9780691136882), Etherington et al. (2009) , Qiao et al. (2015) , Nunez-Penichet et al. (2021) , Cobos and Peterson (2022) , Alkishe et al. (2022) , and Leroy et al. (2015) . Package: r-cran-nicherover Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nicherover_1.1.2-1.ca2404.1_all.deb Size: 572664 MD5sum: a7e163cee6daa14556c514718e7aa3d4 SHA1: 86fdf145341c4dae71a31c226185e02d4bcf485a SHA256: 97cc757977ab908a00f708b8cdcddcca4792e3bec89b9892a39d5f7775b61445 SHA512: 34af5dc80e334d77895cfe40d3c63567c3c87eebf8bc25d3926baa980421cc61c978b67bb3156ac6c66e14941d4e8fe1c3ee6190eaa449122a3671f5acf7aea3 Homepage: https://cran.r-project.org/package=nicheROVER Description: CRAN Package 'nicheROVER' (Niche Region and Niche Overlap Metrics for MultidimensionalEcological Niches) Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. The article by Swanson et al. (2015) provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data. Package: r-cran-nichetools Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2024 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ellipse, r-cran-lifecycle, r-cran-nicherover, r-cran-purrr, r-cran-rlang, r-cran-siber, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bayestestr, r-cran-ggplot2, r-cran-ggdist, r-cran-ggtext, r-cran-ggh4x, r-cran-janitor, r-cran-knitr, r-cran-patchwork, r-cran-rjags, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-nichetools_0.3.3-1.ca2404.1_all.deb Size: 1706190 MD5sum: 6d11c9792bf855a7bee42fadbcd11906 SHA1: c2e11399ae0e6db63ae3aab17924e7f81edc721e SHA256: e44472353353ce07565a1ae55296b1c45ca5180f87a2c8b89294a4b8ef48f728 SHA512: 173714deb38bd2b35c6ba0821962cebb3e8905eb36cca6d74ed58a20def8a89c06b157df922c796e18290be900e4f2e83e6c838471c5a9adf4ba166ae505bcb7 Homepage: https://cran.r-project.org/package=nichetools Description: CRAN Package 'nichetools' (Complementary Package to 'nicheROVER' and 'SIBER') Provides functions complementary to packages 'nicheROVER' and 'SIBER' allowing the user to extract Bayesian estimates from data objects created by the packages 'nicheROVER' and 'SIBER'. Please see the following publications for detailed methods on 'nicheROVER' and 'SIBER' Hansen et al. (2015) , Jackson et al. (2011) , and Layman et al. (2007) , respectfully. Package: r-cran-nichevol Architecture: all Version: 0.1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-castor, r-cran-geiger, r-cran-stringr, r-cran-terra Suggests: r-cran-knitr, r-cran-phytools Filename: pool/dists/noble/main/r-cran-nichevol_0.1.20-1.ca2404.1_all.deb Size: 425542 MD5sum: 823d56a4dacba931aacc754f8936e6d7 SHA1: 807f4df05f93e941f769548705ba345885c787c4 SHA256: cb75b920b4d47342706d56c4fcaff5187761518d82beefb75a897e965db9fd19 SHA512: 4c7536deb094c4f4fb300eb751aa0b138e81667a208379a2244a391ce5c404cb0e46b5d6d5e0caf6a0bbb05a2f4c945a3b2a0277922bb3b23db5ee939b99fbf2 Homepage: https://cran.r-project.org/package=nichevol Description: CRAN Package 'nichevol' (Tools for Ecological Niche Evolution Assessment ConsideringUncertainty) A collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species' niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) , Peterson et al. (1999) , Soberón and Peterson (2005) , Peterson (2011) , Barve et al. (2011) , Machado-Stredel et al. (2021) , Owens et al. (2013) , Saupe et al. (2018) , and Cobos et al. (2021) . Package: r-cran-nifti.io Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nifti.io_1.0.0-1.ca2404.1_all.deb Size: 416506 MD5sum: 2b874bafccd3f01ef203151932a97576 SHA1: 2c0a0b424905f6619124673538804b9a34ecb663 SHA256: cc4b0a472b882839070b9254a3e1b95c6150d93f2773d19674972bac5ee23e3f SHA512: 3413170e9f172bfde2165c769ca281d98e0e17df44edf6c61bc7010ff47357d09582978aba1e7f719419a04c104c541c89a1acba3a3f822797baf78ecb4f7354 Homepage: https://cran.r-project.org/package=nifti.io Description: CRAN Package 'nifti.io' (Read and Write NIfTI Files) Tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII. Specification of the NIfTI-1.1 format can be found here . Scientific publication first using these tools Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764. 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Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 ). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 ; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) . Package: r-cran-nimblesmc Architecture: all Version: 0.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nimblesmc_0.11.1-1.ca2404.1_all.deb Size: 1001834 MD5sum: 099a47924611f650c8ffe90bf28d8905 SHA1: 532cde601afcab941c74c04a38a5c66b5768e095 SHA256: adc9b42e44dc49ec88ee312d93fe02650fbc5da735b1fdc92e8119d1cbdb3327 SHA512: 51e7d979e0e461e3389200c0a3e8508da88edcfc126f0dbdf2af7be4b7cce6f0abfd8dda74dd99da88afdf236c18415526da635f735c06e470ec155330d33ce8 Homepage: https://cran.r-project.org/package=nimbleSMC Description: CRAN Package 'nimbleSMC' (Sequential Monte Carlo Methods for 'nimble') Includes five particle filtering algorithms for use with state space models in the 'nimble' system: 'Auxiliary', 'Bootstrap', 'Ensemble Kalman filter', 'Iterated Filtering 2', and 'Liu-West', as described in Michaud et al. (2021), . A full User Manual is available at . 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Wombling is used widely to track regions of rapid change within the spatial reference domain. Specific functions in the package implement Gaussian process models for point-referenced spatial data followed by predictive inference on rates of change over curves using line integrals. We demonstrate model based Bayesian inference using posterior distributions featuring simple analytic forms while offering uncertainty quantification over curves. For more details on wombling please see, Banerjee and Gelfand (2006) and Halder, Banerjee and Dey (2024) . 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Optimized for speed. See Andrecut (2009) . 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Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. . 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Package: r-cran-nlmixr2rpt Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3044 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-equatags, r-cran-flextable, r-cran-ggforce, r-cran-ggpubr, r-cran-ggplot2, r-cran-knitr, r-cran-stringr, r-cran-nlmixr2est, r-cran-nlmixr2extra, r-cran-onbrand, r-cran-rxode2, r-cran-xpose, r-cran-xpose.nlmixr2, r-cran-yaml Suggests: r-cran-rmarkdown, r-cran-ggally, r-cran-ggpmx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nlmixr2rpt_0.2.3-1.ca2404.1_all.deb Size: 985080 MD5sum: d4edabdc37e021e540c990bfbab3d20d SHA1: 2517226f42b60d6e1aef64be7e449a80b29b1348 SHA256: de2a44db1224f433efd4d498d09b0acfb7a9b78b4aa8ed9bdfaad3953b909b74 SHA512: a781b93875f6666b752490460d21052899492c34614df29bb5f4fd4ff6886549306019e7fc7c922830581884d31ab88b37f70b7d77aff6fcde25146328941894 Homepage: https://cran.r-project.org/package=nlmixr2rpt Description: CRAN Package 'nlmixr2rpt' (Templated Word and PowerPoint Reporting of 'nlmixr2' FittingResults) This allows you to generate reporting workflows around 'nlmixr2' analyses with outputs in Word and PowerPoint. You can specify figures, tables and report structure in a user-definable 'YAML' file. Also you can use the internal functions to access the figures and tables to allow their including in other outputs (e.g. R Markdown). Package: r-cran-nlmixr2scm Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lotri, r-cran-nlme, r-cran-nlmixr2est, r-cran-nlmixr2utils, r-cran-rxode2 Suggests: r-cran-future, r-cran-knitr, r-cran-nlmixr2data, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nlmixr2scm_0.4.1-1.ca2404.1_all.deb Size: 200586 MD5sum: d8c7a26eeae4dc631b7ec4c8d15c4828 SHA1: afc5a864fe2d7cc0e2cc053502ed0ca3c18d8e65 SHA256: 246c9719b4fd98d07cbe8010a42f92c6d96db9644bde738a25107dda333e2337 SHA512: 8d17de46160fa74302cce90aca9878bf991e05f03da84e38874a44223954cf68cc428d13e819d6164ba1b11e6415fec7e3bec65ff13660cfb289d31784c37c05 Homepage: https://cran.r-project.org/package=nlmixr2scm Description: CRAN Package 'nlmixr2scm' (Stepwise Covariate Modeling for 'nlmixr2' Models) Stepwise covariate modeling (SCM) for nonlinear mixed-effects models fitted with 'nlmixr2'. Forward inclusion and backward elimination are driven by likelihood-ratio tests, and the covariate terms are generated inside the model body, so continuous covariates are centered and categorical covariates expanded into indicator columns without editing the model by hand. Candidate fits can be cached and resumed, fitted in parallel, and reviewed through per-step and all-candidate summary tables. The approach follows Jonsson and Karlsson (1998) , and the implementation in 'Perl-speaks-NONMEM' described by Lindbom, Ribbing and Jonsson (2004) . Package: r-cran-nlmixr2targets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-digest, r-cran-nlmixr2est, r-cran-rxode2, r-cran-targets Suggests: r-cran-covr, r-cran-knitr, r-cran-nlmixr2data, r-cran-rmarkdown, r-cran-spelling, r-cran-tarchetypes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nlmixr2targets_0.1.0-1.ca2404.1_all.deb Size: 105664 MD5sum: 8aeebac0d1d98af428e18ab3cdfb3f4a SHA1: e042d0958e677689552e33f53ecb9cc580682438 SHA256: 00abbdccb760811044c0831dd408c94fd9e60c81226cdcd99a0c32c03899f994 SHA512: c49bcf0cefb362ae4093737dce3ab6a233deb7c1539fd47975f34dd4d8318344cc8896f9c1ccea3364500d57d35355deecd90ff1c792a16a7a9e2f9d3f7932f2 Homepage: https://cran.r-project.org/package=nlmixr2targets Description: CRAN Package 'nlmixr2targets' (Targets for 'nlmixr2' Pipelines) 'nlmixr2' often has long runtimes. A pipeline toolkit tailored to 'nlmixr2' workflows leverages 'targets' and 'nlmixr2' to ease reproducible workflows. 'nlmixr2targets' ensures minimal rework in model development with 'nlmixr2' and 'targets' by simplifying and standardizing models and datasets. Package: r-cran-nlmixr2utils Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-future, r-cran-future.apply, r-cran-jsonlite, r-cran-knitr, r-cran-nlmixr2est, r-cran-rxode2 Suggests: r-cran-nlmixr2data, r-cran-progressr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nlmixr2utils_0.3.1-1.ca2404.1_all.deb Size: 224450 MD5sum: fe50b0d6bf0fe89543f913274a91ff5f SHA1: a946ebbe43d80a7fd7797a648a4acbd0d43dbbf1 SHA256: 8001fd0bd11c88f897164b71b8f44d9f4bd58e694181d4989bc86c49a60b2ce9 SHA512: 2c191a246b42ef31d0ca00687e5062db730002f2a77197d80dca7c52e2c63d1ee890394d87ed24be91ea17410334639171cb50aa0f8bc368512f14da73adf4e3 Homepage: https://cran.r-project.org/package=nlmixr2utils Description: CRAN Package 'nlmixr2utils' (Shared Infrastructure for 'nlmixr2' Extension Packages) Provides shared worker-plan helpers, covariance utilities, model reexports, equation rendering methods, and package data used by the split 'nlmixr2' extension packages, including 'nlmixr2boot', 'nlmixr2llp', 'nlmixr2scm', and 'nlmixr2sir'. These helpers give the extension packages a common canonical raw-results schema, run-cache and seeding infrastructure, and equation-printing methods so that each extension package does not need to reimplement this shared functionality separately. Package: r-cran-nlmrt Architecture: all Version: 2016.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-minpack.lm, r-cran-optimx, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-nlmrt_2016.3.2-1.ca2404.1_all.deb Size: 371472 MD5sum: 374fb7e31754f0d2649e04055cfb73d1 SHA1: 96ff39fb3fa9d4fa34580291504fd5479a9b4109 SHA256: a8b85cbafa194f0a7bd9015e9f90b1af2271253c8e94a6f68680ff696ab5608e SHA512: c1c2d45f8b4f944c062ef5755c593862d47834ccebca0ad8779e4e9e86c558fe9a1e5cfa40cdff5005d0453600b6fcb2bf6bc916c995f4b1d6204078ae99217d Homepage: https://cran.r-project.org/package=nlmrt Description: CRAN Package 'nlmrt' (Functions for Nonlinear Least Squares Solutions) Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012. Package: r-cran-nlms Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-nlms_1.1-1.ca2404.1_all.deb Size: 26112 MD5sum: f2f7926d0a54997df034aa38a01f19c3 SHA1: 3129d67d92f6232c7d326e29306a484cc985ed62 SHA256: 41a88c5b3a03f2499cbd721f03324c72f4d1d5310d0878e84651457411f7d8ee SHA512: 3ddbbff923e24eb48b60ec6dc6d504a627eaf84d90dfe888896d3502c885f9bc7df932e74be6399c7004e2e54baeaaee201ccece57408624925f8403916aa1ea Homepage: https://cran.r-project.org/package=nlMS Description: CRAN Package 'nlMS' (Non-Linear Model Selection) Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit". Package: r-cran-nlnet Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-tsp, r-cran-igraph, r-cran-fdrtool, r-cran-coin, r-cran-earth, r-cran-randomforest, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-nlnet_1.4-1.ca2404.1_all.deb Size: 83756 MD5sum: d7ca1ceb54909c819ba4c5850994e9fd SHA1: df7599c0f2d8cbe73410f5a5cd74cbaa813d2847 SHA256: 1fb83e84255bedd6f437c1911032d2bfe317d47275a595998b25c58aa91b9b42 SHA512: a794541163492bafa532503123ed47f411dd68fafa9734dd6f89290b21db456af9242772487405566583317b36af66535e51b17df11ceca8c78f61d81ca88233 Homepage: https://cran.r-project.org/package=nlnet Description: CRAN Package 'nlnet' (Nonlinear Network, Clustering, and Variable Selection Based onDCOL) It includes four methods: DCOL-based K-profiles clustering, non-linear network reconstruction, non-linear hierarchical clustering, and variable selection for generalized additive model. References: Tianwei Yu (2018); Haodong Liu and others (2016); Kai Wang and others (2015); Tianwei Yu and others (2010). Package: r-cran-nlopt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Filename: pool/dists/noble/main/r-cran-nlopt_0.1.1-1.ca2404.1_all.deb Size: 31772 MD5sum: 85d1ca220cfd60532eef2b5ec8a3223a SHA1: 97f4ade01fdf5d1c6e1631ab896b90d9ec69552e SHA256: f7fe9da9d17a08f5e220d9f93570bcc5e5b3c855ba8e070d4fba34b0eedbb93d SHA512: 43835c0396a2f3b85358c94f2087199ebe33883d70d0a0a0ce832e6872332dbb1d5bbd1b5b50507816111060e200bfaeb7f34b290d7ca49161bceb199e16a8a5 Homepage: https://cran.r-project.org/package=nlopt Description: CRAN Package 'nlopt' (Call Optimization Solvers with .nl Files) The purpose of this library is to to call different optimization solvers (such as Gonzalez Rodriguez et al. (2022) , Tawarmalani and Sahinidis (2005) , and Byrd et al. (2006) ) to solve problems given by a standard nl file. Package: r-cran-nlp Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nlp_0.3-3-1.ca2404.1_all.deb Size: 379294 MD5sum: f109b931ef189c186a41250ecf53b267 SHA1: 5fc8f0cf3fe3fd20552f28aab172b335ad9a8b8f SHA256: 4f639c596feead7a942afce979846b7f6f331a21a8784f034ae3e5089466c773 SHA512: 1ad2fd0ba8f1a647ff84ba75d00d13a727815b947e8aa4f30d073e2d642fcfe43e21681cf37f0de77d128bf1c7ab65c72a12262a1ec8580de6a56e2dbb0b1084 Homepage: https://cran.r-project.org/package=NLP Description: CRAN Package 'NLP' (Natural Language Processing Infrastructure) Basic classes and methods for Natural Language Processing. 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Stanford 'CoreNLP' integrates all NLP tools from the Stanford Natural Language Processing Group, including a part-of-speech (POS) tagger, a named entity recognizer (NER), a parser, and a coreference resolution system, and provides model files for the analysis of English. More information can be found in the README. Package: r-cran-nlpembeds Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-matrix, r-cran-rcppalgos, r-cran-reshape2, r-cran-rsqlite, r-cran-rsvd Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nlpembeds_1.0.0-1.ca2404.1_all.deb Size: 124106 MD5sum: 8bb73d9f6fe4c47abddbf82faa74faf5 SHA1: 589fd251266fe0742a8102cfabb26cc9e21fd07b SHA256: 82f8099e498dce21e6f8a3e59e88bffa0c75c3adfa6009724d8081ddaf2b7f74 SHA512: 3bdbded9074a9668ae6e681d85aeb4c3426b413dec27ce573fcde84731ae7840d22f610f1da9b84f16bddf0d7818da5cd513a5a2e14d2be42a9053f086eb5c67 Homepage: https://cran.r-project.org/package=nlpembeds Description: CRAN Package 'nlpembeds' (Natural Language Processing Embeddings) Provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the 'RcppAlgos' package and sparse matrices. Furthermore, the functions can be called on 'SQL' databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) . Package: r-cran-nlpred Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-superlearner, r-cran-cvauc, r-cran-rocr, r-cran-rdpack, r-cran-bde, r-cran-np, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-prettydoc, r-cran-randomforest, r-cran-ranger, r-cran-xgboost, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-nlpred_1.0.1-1.ca2404.1_all.deb Size: 1213428 MD5sum: 77dc289181fc6cae3ac094013a3b15de SHA1: a3e0606bddf6405b40d0e2096534b13512a256d7 SHA256: c968a27e4d61b9895b1a25ac588beebbf5ae4b58251aae2043e2a6a837a86a52 SHA512: b6e3f47b32367aafbccb29c327fa9ff475c8d7f130d687e05b0666ec98cc6e1b341950428022341f6ec60bcdb83fe2523f32c369eb964241ac8562047fd18a6c Homepage: https://cran.r-project.org/package=nlpred Description: CRAN Package 'nlpred' (Estimators of Non-Linear Cross-Validated Risks Optimized forSmall Samples) Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), ). 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Package: r-cran-nlpsem Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-matrix, r-cran-nnet, r-cran-readr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nlpsem_0.4-1.ca2404.1_all.deb Size: 5513454 MD5sum: 729c03b2846f5bef6da3a1f732813b8d SHA1: 47600758d4a6149a80f4d0f9665bc2fa716a52aa SHA256: 185f21002af292791a60a270104b1f1028a76f9b00b6edea0b1a1846a45a6805 SHA512: 334c85d1cfb9d2ac2932c66a6a163c24eb06c95a926edac386d56e4bc9361ed03c8eb24c78043193fce78db994a9525ffa45e97683d6817ccd725fc782007cec Homepage: https://cran.r-project.org/package=nlpsem Description: CRAN Package 'nlpsem' (Nonlinear Longitudinal Process in Structural Equation Modeling) Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Liu (2025) . Package: r-cran-nlputils Architecture: all Version: 0.0-5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-snowballc, r-cran-qdap Filename: pool/dists/noble/main/r-cran-nlputils_0.0-5.1-1.ca2404.1_all.deb Size: 19052 MD5sum: b568772b8038271ac3173d26dbbaf01d SHA1: 9c94c01c9ef537f48a58a3a7accb0d5b148b7f5f SHA256: 0fb36e85e220b2753ca87e252dfb01bc4164466f0fb8f4b682cbfdea86b09b26 SHA512: 09e0619c383aa9f8d43873c3d0ed273bde75debe6578b17cfcbab9b1ea7bc18a87c047b21b618920af5324aae93776818d9c327b043f56d1c99d1d30211584d5 Homepage: https://cran.r-project.org/package=NLPutils Description: CRAN Package 'NLPutils' (Natural Language Processing Utilities) Utilities for Natural Language Processing. 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One of the functions is the Beta growth function proposed by Yin et al. (2003) . There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) , a book chapter with similar material and a publication by Oddi et. al. (2019) in Ecology and Evolution . The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples. 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Linear problems are solved by dense QR decomposition from 'LAPACK' which can limit the size of treated problems. On the other side, we avoid condition number degradation which happens in classical quadratic programming approach. Inequality constraints treatment on each non linear iteration is based on 'NNLS' method (by Lawson and Hanson). We provide an original function 'lsi_ln' for solving linear least squares problem with inequality constraints in least norm sens. Thus if Jacobian of the problem is rank deficient a solution still can be provided. However, truncation errors are probable in this case. Equality constraints are treated by using a basis of Null-space. User defined function calculating residuals must return a list having residual vector (not their squared sum) and Jacobian. If Jacobian is not in the returned list, package 'numDeriv' is used to calculated finite difference version of Jacobian. The 'NLSIC' method was fist published in Sokol et al. (2012) . 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(2018) . These describe the smallest changes to the data that would result in a change of decision. 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This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns). Ludwig & Reynolds (1988, ISBN:0471832359). 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The templates for the tested packages are available in the R, R Markdown and HTML formats at and . The submitted article to the R-Journal can be read at . 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Cui, Z., Marder, E. P., Click, E. S., Hoekstra, R. M., & Bruce, B. B. (2022) . Package: r-cran-nndiagram Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2965 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nndiagram_1.0.0-1.ca2404.1_all.deb Size: 2783604 MD5sum: 1b50d49bbc8f7750a3477ea0dacfc972 SHA1: 38bf1a78e519d0946721eb669b6681e45e9b0c3c SHA256: c30f2bd57d736cbfb947b68cc3c0576738a8e20e5bf83c98f0b682a6054333a3 SHA512: 9cec4d3db227550025397194ad25cba8cab2803f96a8eeaf1d9329061643e4723afd1ff62a5993865a9405d8b85d68e5669ba883979cb45c0d1185f9613be497 Homepage: https://cran.r-project.org/package=nndiagram Description: CRAN Package 'nndiagram' (Generator of 'LaTeX' Code for Drawing Neural Network Diagramswith 'TikZ') Generates 'LaTeX' code for drawing well-formatted neural network diagrams with 'TikZ'. 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Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336-1353 and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2), 713-730 . 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(2023) , and Jentzen, Kuckuck, and von Wurstemberger (2023) . Includes neural network polynomials, transcendental-function approximations, multidimensional maximum convolution, and vectorized batch realization. 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Package: r-cran-no.ping.pong Architecture: all Version: 0.1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metafor, r-cran-mcmcglmm, r-cran-mass Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-no.ping.pong_0.1.9.1-1.ca2404.1_all.deb Size: 1095088 MD5sum: 3ebdd5ae4f73eb480385354a089596b4 SHA1: d0a39d4d4598be5965fecdc89a99584bb7bd5251 SHA256: aa831f3bd0db65b9de55680c6cab615409181157a0bad719c2eb471c113b0fad SHA512: 2707f3be340bbddaabbda84a9b317572c769e8d462b9f79da926563756e3399e9508cac1de46857ea1e8e7141d992564ba044bc0e2be1873eac3acc9bc796ca1 Homepage: https://cran.r-project.org/package=NO.PING.PONG Description: CRAN Package 'NO.PING.PONG' (Incorporating Previous Findings When Evaluating New Data) Functions for revealing what happens when effect size estimates from previous studies are taken into account when evaluating each new dataset in a study sequence. 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(2014) "Node-based analysis of species distributions." Methods in Ecology and Evolution 5(11): 1225-1235. . Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped. 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Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471. Package: r-cran-noisemodel Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-nnet, r-cran-e1071, r-cran-fnn, r-cran-classint, r-cran-ggplot2, r-cran-extdist, r-cran-lsr, r-cran-stringr, r-cran-rcolorbrewer, r-cran-rsnns, r-cran-c50 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-noisemodel_1.0.2-1.ca2404.1_all.deb Size: 710382 MD5sum: 8be81b5f7039c3275bcadd0c38bd56ab SHA1: 9ce8d4cb9e69570acf5d2f129631a1ca335c5027 SHA256: 1529f5d9b9c5d1f8551ad114e3cc84909474f2e614c0011dfdd6697069df7d65 SHA512: eb0b0122a69da569cd9295a16963705f62499425bf40d31c118cb20cb9d2d6c714a5acb0bdab4c2ad434fb2abcb2dee0692741f3d771d9b254044bd8e3518677 Homepage: https://cran.r-project.org/package=noisemodel Description: CRAN Package 'noisemodel' (Noise Models for Classification Datasets) Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) that allow corrupting class labels, attributes and both simultaneously. Package: r-cran-noisyce2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-coda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-noisyce2_1.1.0-1.ca2404.1_all.deb Size: 56824 MD5sum: 42b068da927847204d9869d13add72bd SHA1: 691f6e1038f2aa82660893106efa5d242051fcbb SHA256: da9ca9ca05fba39aaa99640d2a98f9d7189b49d16fe352be419f0f81e4a2d627 SHA512: 5b162c535dd59f3312e720b2ad25b188085cc3a9aa719d1c73a640fe0f47b06f518f04807083e6688907eecb530c0820d1cde77c1a2c6c36d942fc051d5d59e0 Homepage: https://cran.r-project.org/package=noisyCE2 Description: CRAN Package 'noisyCE2' (Cross-Entropy Optimisation of Noisy Functions) Cross-Entropy optimisation of unconstrained deterministic and noisy functions illustrated in Rubinstein and Kroese (2004, ISBN: 978-1-4419-1940-3) through a highly flexible and customisable function which allows user to define custom variable domains, sampling distributions, updating and smoothing rules, and stopping criteria. Several built-in methods and settings make the package very easy-to-use under standard optimisation problems. Package: r-cran-noisyr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-bioc-preprocesscore, r-bioc-iranges, r-bioc-genomicranges, r-bioc-rsamtools, r-cran-philentropy, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-noisyr_1.0.0-1.ca2404.1_all.deb Size: 1876428 MD5sum: e077077e56b98ad0fc073968341ca3fc SHA1: c8a406084976cc0ffa2e7d56f872b5f5de109c99 SHA256: f1f37f2e5eb4d5e4a4540e24af15d3b4461b731cd4fce21be7eb306df44faf33 SHA512: 02a5df5e54474928f782b88371ab8a40c222dc847deec4d979fe6b84c53b5078d229e3fd7f2c3e1b7a078fad7e0e255f4cc97e5be5ea8241a951157b84ec2c89 Homepage: https://cran.r-project.org/package=noisyr Description: CRAN Package 'noisyr' (Noise Quantification in High Throughput Sequencing Output) Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are used, one based on the count matrix, and one using the alignment BAM files directly. Contains several options for every step of the process, as well as tools to quality check and assess the stability of output. Package: r-cran-noisysbm Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1636 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-noisysbm_0.1.4-1.ca2404.1_all.deb Size: 1452274 MD5sum: 6c4448b0bd59cc184185d32d4caff884 SHA1: 8d0caf8e186ba96cd876bedb94efd75b638bcc83 SHA256: a0251503493f957afbf410be5a67cebc389e668309a19e483bab935d2fb4587e SHA512: 23925acaa276cad3265c10c4226a8b9b2735971f2f74e808f6f301c66ad0274da65af77bec2c19ae710abc263922de3213a0c6ac2e2590e60909babf41fe1a83 Homepage: https://cran.r-project.org/package=noisySBM Description: CRAN Package 'noisySBM' (Noisy Stochastic Block Mode: Graph Inference by Multiple Testing) Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) . Package: r-cran-nolaopendata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-nolaopendata_0.1.1-1.ca2404.1_all.deb Size: 48170 MD5sum: 1efe8dc4040e09869e648a607f790ff1 SHA1: 8d92aa692866a01dab3535f1d80135c8f3dd747d SHA256: f66eb2a74c43b2a8ebe149fc6b973ad52506d097b822c22debaba4aca08ca209 SHA512: a1b8cd33bee909e0ea5a74f753f31edb661c56c6a06b71e9d18b10d11fc43ee7d1e3a3c971e37b3725b527e56015b10d2010cffc2d312c4a0514039b8041cd59 Homepage: https://cran.r-project.org/package=nolaOpenData Description: CRAN Package 'nolaOpenData' (A Lightweight Interface to New Orleans Open Data APIs) Provides a unified set of helper functions to access datasets from the New Orleans Open Data platform . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package provides a consistent interface for discovering and downloading datasets from the San Francisco Open Data Portal using human-readable dataset keys or official Socrata dataset identifiers. Package: r-cran-nolock Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-rstudioapi, r-cran-stringr, r-cran-ncmisc Filename: pool/dists/noble/main/r-cran-nolock_1.1.0-1.ca2404.1_all.deb Size: 28530 MD5sum: fed0d105ecf610b4b35842c7ab4fb24f SHA1: df38ae269ee0427ff45fb19ab51df13577ba3b55 SHA256: 60293e3575c4327a301d0e86eadf308b6fd0b1d357efd1916169bb62ac5c7a74 SHA512: c7fbef3ed2bc9b2cf91fb287f122e31760703c7f668f06461f1f2f437a7632926cacde06051ebd3d1ccd2067e42ab602059fcd025c6a371cc992209d8bfebdbe Homepage: https://cran.r-project.org/package=nolock Description: CRAN Package 'nolock' (Append 'WITH (NOLOCK)' to 'SQL' Queries, Get Packages in ActiveScript) Provides a suite of tools that can assist in enhancing the processing efficiency of 'SQL' and 'R' scripts. - The 'libr_unused()' retrieves a vector of package names that are called within an 'R' script but are never actually used in the script. - The 'libr_used()' retrieves a vector of package names actively utilized within an 'R' script; packages loaded using 'library()' but not actually used in the script will not be included. - The 'libr_called()' retrieves a vector of all package names which are called within an 'R' script. - 'nolock()' appends 'WITH (nolock)' to all tables in 'SQL' queries. This facilitates reading from databases in scenarios where non-blocking reads are preferable, such as in high-transaction environments. Package: r-cran-nomads Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3159 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-data.table Filename: pool/dists/noble/main/r-cran-nomads_0.0.1-1.ca2404.1_all.deb Size: 3183804 MD5sum: 7d9fad2f044b2baef3f6287f6453f7e7 SHA1: 74be75a642c08277f03579753822598c9d617b40 SHA256: 5e7be7fd6e07de10ca6421884883c471a51f4e4a858e93291785cea5d3246869 SHA512: d24d428a52599330d14924e93d7957fa15ac0a32b8e092f5f9b9112fa1403bee59a0d8c7ac08eff3c3a01d1ee6df37aa2be302caaf47af6b1bd01b2c0b018c1b Homepage: https://cran.r-project.org/package=nomads Description: CRAN Package 'nomads' (Nomadic Pectoral Sandpiper Movement Data) Provides satellite tracking data from nomadic pectoral sandpipers published in Kempenaers and Valcu (2017) . The data can also serve as benchmark data for clustering movement tracks. Package: r-cran-nombre Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fracture Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nombre_0.4.1-1.ca2404.1_all.deb Size: 404454 MD5sum: 60bbb10dd95e7bc60ba8833bc657fca1 SHA1: 104d970d6884ed92c1b8a7cecd2920357ae4a222 SHA256: ae8819209e295711eadc027b4edc3423ba90d26419409b2f6810031c3809e957 SHA512: e28db000c2deaa6ff4866c167760eea0cc83a8641a74d6e848d0c4f13609c68535b1f7ff172e1feb63c34e8df34b91ff25ffca5eb1d676bca40ab5359a62b318 Homepage: https://cran.r-project.org/package=nombre Description: CRAN Package 'nombre' (Number Names) Converts numeric vectors to character vectors of English number names. Provides conversion to cardinals, ordinals, numerators, and denominators. Supports negative and non-integer numbers. Package: r-cran-nomesbr Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-httr2, r-cran-stringr, r-cran-tictoc Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nomesbr_0.0.9-1.ca2404.1_all.deb Size: 111176 MD5sum: 2bf88e2ecc80b1ec5fdbecf598eac80c SHA1: 7ab23c9c15f0c35039aac35241477d30c5e58c35 SHA256: 314181bfaabe8b889951bd76e94bd31ec796e36bb2269b49b188e37fc4465de2 SHA512: 486302a70f602f669dd6048f6ca00331d8fc09a7fa5d8b0c994d87515c93da8ee2e40e707ae8166ee8934b5396394d26a1d9e1edf15305939a15722ff35c70c8 Homepage: https://cran.r-project.org/package=nomesbr Description: CRAN Package 'nomesbr' (Limpa e Simplifica Nomes de Pessoas (Name Cleaner andSimplifier)) Limpa e simplifica nomes de pessoas para auxiliar no pareamento de banco de dados na ausência de chaves únicas não ambíguas. Detecta e corrige erros tipográficos mais comuns, simplifica opcionalmente termos sujeitos eventualmente a omissão em cadastros, e simplifica foneticamente suas palavras, aplicando variação própria do algoritmo metaphoneBR. (Cleans and simplifies person names to assist in database matching when unambiguous unique keys are unavailable. Detects and corrects common typos, optionally simplifies terms prone to omission in records, and applies phonetic simplification using a custom variation of the metaphoneBR algorithm.) Mation (2025) . Package: r-cran-nominatimlite Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-sf Suggests: r-cran-curl, r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-nominatimlite_0.7.0-1.ca2404.1_all.deb Size: 234086 MD5sum: cb7d8f2c8296cb224f68c1fb30290980 SHA1: 3a3d307edf6c7ef8705f99625e30a0f68074d580 SHA256: acc6723d6ffa70320bc73f0dd2e568d2420ba57a928bb09a248df5b8cd758406 SHA512: fd339ba524bc1ba3ae3fb89e4faf4a1db006ac91d73599855483a294cf01fcb42b78d31705464e22d2b5426cf59a460d271525e2fde92935bbb112d6aecc941a Homepage: https://cran.r-project.org/package=nominatimlite Description: CRAN Package 'nominatimlite' (Interface to the 'Nominatim' API) Provides a lightweight interface to the 'Nominatim' API . It supports free-form and structured address searches, searches for addresses from coordinates, amenity lookup and address lookup by 'OpenStreetMap' object identifier. It returns results as 'tibble' data frames or 'sf' objects. Package: r-cran-nomine Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rcurl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nomine_1.0.2-1.ca2404.1_all.deb Size: 22334 MD5sum: 37ba58f1498b434d0752214d4e57040e SHA1: c2ae67c0c014a5d11bf4f5a0e386c81a92caea87 SHA256: f1ea2a4dd80cb6bcf5ae51328e842cd647be2b40792adbaf61cb1507472d8389 SHA512: 069ba854f8a8426ea802c4b4550b5c49c459bd5b6b6246c48925ad650c1f83a005c11563cd735026a608ae9566cffe3edb71c21e3d4a7ada8815156f75a66c14 Homepage: https://cran.r-project.org/package=nomine Description: CRAN Package 'nomine' (Classify Names by Gender, U.S. Ethnicity, and Leaf Nationality) Functions to use the 'NamePrism' API or 'NamSor' API v2 for classifying names based on gender, 6 U.S. ethnicities, or 39 leaf nationalities. Updated to work with current API endpoints. Package: r-cran-nomisdata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3017 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-tibble, r-cran-digest Suggests: r-cran-cachem, r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-memoise, r-cran-rappdirs, r-cran-readr, r-cran-rmarkdown, r-cran-rsdmx, r-cran-scales, r-cran-sf, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nomisdata_0.1.2-1.ca2404.1_all.deb Size: 2460736 MD5sum: ad6b5b842e05669fb95f357fe7fc397c SHA1: cabb3a586f5acdd4ac4f7a389e7228215699a468 SHA256: 5b9c92efd49f65185d36cb1eb488e28a49c7e0a7f76885b6934295c1794996f8 SHA512: 4f5cbed54f343ddae579b7c91526930d560480f6d5e77e2a9c1f592e2244f629c529aabd7607a11b9e16d0f1c618b72ef62efbf56c55b985fc5d09d3e41280f5 Homepage: https://cran.r-project.org/package=nomisdata Description: CRAN Package 'nomisdata' (Access 'Nomis' UK Labour Market Data and Statistics) Interface to the 'Nomis' database (), maintained by Durham University on behalf of the Office for National Statistics (ONS). Provides access to UK labour market statistics including census data, benefit claimant counts, and employment surveys. Supports automatic pagination, optional disk caching, spatial data via 'sf', and tidy data output. Independent implementation unaffiliated with ONS or Durham University. Package: r-cran-nomishape Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nomishape_1.0.2-1.ca2404.1_all.deb Size: 4001938 MD5sum: f8d46c0dd7328a3cbf2ca271f88f7af4 SHA1: bc2910265bdfcdffd3034beb31fac1e3c7b994a0 SHA256: 7635c1cf75336cc0a22cce07d287ea6ead388da44dd629b9018ac3723f384469 SHA512: 4782b7b69e22ed6cec6a5be45fbea63d4a3918ea056518b8d35c472c9de6c6371d0a172a2d2bdb3e055e3619cd2d644efe44e8c14ea602fc493f29f19cc4962a Homepage: https://cran.r-project.org/package=nomiShape Description: CRAN Package 'nomiShape' (Visualization and Analysis of Nominal Variable Distributions) Provides tools for visualizing and analyzing the shape of discrete nominal frequency distributions. The package introduces centered frequency plots, in which nominal categories are ordered from the most frequent category at the center toward less frequent categories on both sides, facilitating the detection of distributional patterns such as uniformity, dominance, symmetry, skewness, and long-tail behavior. In addition, the package supports Pareto charts for the study of dominance and cumulative frequency structure in nominal data. The package is designed for exploratory data analysis and statistical teaching, offering visualizations that emphasize distributional form rather than arbitrary category ordering. Package: r-cran-nomisr Architecture: all Version: 0.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-tibble, r-cran-dplyr, r-cran-httr, r-cran-rsdmx, r-cran-rlang, r-cran-snakecase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-scales, r-cran-readr Filename: pool/dists/noble/main/r-cran-nomisr_0.4.7-1.ca2404.1_all.deb Size: 770442 MD5sum: cacadef13aecaf8a9e57146cc29dbb0a SHA1: 246ef7765d6b00ce3dfcdf8353c5f87e5826e872 SHA256: 870975c947cb46957d503254a5ccbde839ef2ea5b1698148a284b185503f1e53 SHA512: b988650f73a8daf08dcbbb9192abbf2f27a68fb296e030b51624b4bbcbb409ac2429606a2514b78a3ce25474644d63edc6aa14fda4e8bf2e23e3d7b23bcbc454 Homepage: https://cran.r-project.org/package=nomisr Description: CRAN Package 'nomisr' (Access 'Nomis' UK Labour Market Data) Access UK official statistics from the 'Nomis' database. 'Nomis' includes data from the Census, the Labour Force Survey, DWP benefit statistics and other economic and demographic data from the Office for National Statistics, based around statistical geographies. See for full API documentation. Package: r-cran-nomnoml Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-png, r-cran-webshot2, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-v8, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nomnoml_0.3.0-1.ca2404.1_all.deb Size: 241330 MD5sum: e19d6c5f51dd18ffcb09e036feb0202b SHA1: 6b8c809204893ddeec8b15104e0353562b168de3 SHA256: cb34e44973e6252bfb5e8113c454a9a136b321735186c661177eb244343e7b6c SHA512: 23270055412e2062c95a22baa91a5cbbd2277cee5c1b0f60ae1890f4179c792e89e12250ec8e84956bfbdca2527f9def5f4c83474108c942f4998d4471ff2ab6 Homepage: https://cran.r-project.org/package=nomnoml Description: CRAN Package 'nomnoml' (Sassy 'UML' Diagrams) A tool for drawing sassy 'UML' (Unified Modeling Language) diagrams based on a simple syntax, see . Supports styling, R Markdown and exporting diagrams in the PNG format. Note: you need a chromium based browser installed on your system. Package: r-cran-nomogramex Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-rms Filename: pool/dists/noble/main/r-cran-nomogramex_3.0-1.ca2404.1_all.deb Size: 14482 MD5sum: ed9473bf3f536805bc5a6db4c71696aa SHA1: b97cd76cba6bc317e1b5953f3914fea3e7c0e00e SHA256: 6c813cb1f958d195597e0435227f13348b8bc6209c96ecccdf58b25066c6b4fb SHA512: bdad834aea35e8d47930a187602ab485b090b949171799209c258f470ad60be098cf42700cb2797782dda7bfe87c2c47d05b5f477d336c88b782113d821db4cb Homepage: https://cran.r-project.org/package=nomogramEx Description: CRAN Package 'nomogramEx' (Extract Equations from a Nomogram) A nomogram can not be easily applied, because it is difficult to calculate the points or even the survival probability. The package, including a function of nomogramEx(), is to extract the polynomial equations to calculate the points of each variable, and the survival probability corresponding to the total points. Package: r-cran-nomogramformula Architecture: all Version: 1.2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-do, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-nomogramformula_1.2.0.0-1.ca2404.1_all.deb Size: 35314 MD5sum: 3a73bf64e09c58b8f129ccd8f8b49397 SHA1: 48a70fb21b5ce685a3e762de7830506c0b5a0f7c SHA256: 624d7a7e6c3e753b272134160ea5c90ba28517effc0b7841c701c3f847e8c17a SHA512: 944efa2e0154670ad204c44512a23f8986f46318f7b4c51c8eac12a4ba5053bbe96b42c525470e973494d1ce45f6b8a0c373419f4c0b9a4b5ee1c982d25e5cc4 Homepage: https://cran.r-project.org/package=nomogramFormula Description: CRAN Package 'nomogramFormula' (Calculate Total Points and Probabilities for Nomogram) A nomogram, which can be carried out in 'rms' package, provides a graphical explanation of a prediction process. However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression. For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() can be used. Package: r-cran-nonabsdid Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1384 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tibble Suggests: r-cran-didmultiplegtdyn, r-cran-panelmatch, r-cran-fect, r-cran-fixest, r-cran-haven, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-nonabsdid_0.4.1-1.ca2404.1_all.deb Size: 1077252 MD5sum: a1809dfa9a3ba1c857c8948ffce13fba SHA1: ce6461c9e9adc167d2625d7f5db5c285a64452c2 SHA256: 239871ab1a41f077025d53d96b4104defa7e443637eeb206af164eb48694baf2 SHA512: d71a378a0ebe7d6d71f28aba1a521845388b24b9691eceeb738d9315304568cf09413890b4178ec2680bbfa1b7dde673d1d2f41e0d9d8f1214a1184f93f7c7f8 Homepage: https://cran.r-project.org/package=nonabsdid Description: CRAN Package 'nonabsdid' (Visualize Heterogeneity-Robust Event Studies for Non-AbsorbingTreatments) Runs several heterogeneity-robust difference-in-differences (DID) event-study estimators for non-absorbing (i.e., treatment can switch on and off over time, allowing treatment reversal) binary treatments through their respective packages, harmonizes their output onto a common time axis and tidy data structure, and overlays them in a single 'ggplot2' panel for visual comparison. Supported estimators include those provided by 'DIDmultiplegtDYN', 'PanelMatch', and 'fect', with an optional naive two-way fixed-effects reference series via 'fixest'. The underlying methods are respectively described in Clement de Chaisemartin and Xavier D'Haultfoeuille. "Difference-in-Differences Estimators of Intertemporal Treatment Effects." The Review of Economics and Statistics (2026) , Kosuke Imai, In Song Kim, and Erik H. Wang. "Matching methods for causal inference with time‐series cross‐sectional data." American Journal of Political Science 67.3 (2023) , Licheng Liu, Ye Wang, and Yiqing Xu. "A practical guide to counterfactual estimators for causal inference with time‐series cross‐sectional data." American Journal of Political Science 68.1 (2024) , and Laurent R. Bergé, Kyle Butts, and Grant McDermott. "Fast and user-friendly econometrics estimations: The R package 'fixest'." arXiv preprint (2026) . A single nabs_event_study() wrapper runs any supported estimator with a common interface; nabs_event_study_simple() provides a one-line front door for quick exploratory runs; the S3 generic as_nabs_event_study() coerces estimator output into a tidy stable schema; and nabs_event_plot() overlays multiple methods on a single 'ggplot2' panel, with optional naive two-way fixed effects drawn in a neutral color as a reference. Package: r-cran-noncompart Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-openssl Filename: pool/dists/noble/main/r-cran-noncompart_0.8.4-1.ca2404.1_all.deb Size: 401570 MD5sum: 995f38ce92df1aea9559dbe436d2bff5 SHA1: 767b7803331352ca1fe7b92fe92c85ce499b550a SHA256: 9c24135d0d48cc1811656b7a4e8b3dd40fc72f58df915279642ec516fd63b621 SHA512: be41522daabddb43f4eabc27f198324a809f3a62e6ccee4720dc62f5921d7dd0d2c10aee36df4fb799929059c600a1d9ba754ae185e222a040871b20b0b14b00 Homepage: https://cran.r-project.org/package=NonCompart Description: CRAN Package 'NonCompart' (Noncompartmental Analysis for Pharmacokinetic Data) Conduct a noncompartmental analysis with industrial strength. Some features are 1) Use of CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method 5) Steady-state analysis over the dosing interval (AUCTAU, CAVG, CL and Vz from AUCTAU) 6) Installation/Operational Qualification (IQ/OQ) reports in pdf. After installation, qualify the package in your own environment: run IQNCA() for Installation Qualification and OQNCA() for Operational Qualification. Run writeMD5NCA() once after installation so the IQ file-integrity check passes. To approve a report, sign it digitally in Adobe Acrobat Reader (generate with sigField=TRUE, or run addSigFieldNCA(), to add click-to-sign fields), instead of printing and scanning; or use signPDFNCA()/verifyPDFNCA() for a scriptable signature. * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-noncomplyr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-noncomplyr_1.0-1.ca2404.1_all.deb Size: 59318 MD5sum: 32bd83fecbb3d716f2d8b831b24cb8d5 SHA1: 09a812d7ce4d1d85240ffa38c624545d19d902fd SHA256: fc20df1d57fd63ba5379290adaf50af734fa086587fbbfd834a12e8b3cfae523 SHA512: 32bbfde91e59eae222008a5c9c8d28c78f82af4211bebeadc054daf04d9b3cce45e80983d2d68c2a415f7ff178ee80120cf447b82171d4ce99dcae0d57ef6c17 Homepage: https://cran.r-project.org/package=noncomplyR Description: CRAN Package 'noncomplyR' (Bayesian Analysis of Randomized Experiments with Non-Compliance) Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) . Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) . Package: r-cran-nonet Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-randomforest, r-cran-ggplot2, r-cran-rlist, r-cran-glmnet, r-cran-tidyverse, r-cran-e1071, r-cran-purrr, r-cran-proc, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-clusterr Filename: pool/dists/noble/main/r-cran-nonet_0.4.0-1.ca2404.1_all.deb Size: 77600 MD5sum: b11832c4ec65fc0a2384564eea533e62 SHA1: 880408b0bc8083beb513e1577c1247fc1a1640f6 SHA256: 91900b507858b41e20330285f86eebfb0f93a3bd2eae983d9848a5e6329ce8ec SHA512: d07c2ce07c295e3104b0091a4c3e521c802fbe0f22ea71f2aa77c0738e9e9ed608a62730e01c4c974b17818bd0387c5eca4988b5e4d5643d4f7db8c043656b75 Homepage: https://cran.r-project.org/package=nonet Description: CRAN Package 'nonet' (Weighted Average Ensemble without Training Labels) It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions. Package: r-cran-nonlineardid Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2 Suggests: r-cran-did, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-nonlineardid_0.2.0-1.ca2404.1_all.deb Size: 178794 MD5sum: a9a442f2377966cf9d0b35d0d83ec624 SHA1: 098a8ce5889cf94375c45f391a612fd2d9478972 SHA256: 0a3a2a9cecd1d82ac49ad006f99df198206ef733f46e06dd71b5751bd01643e5 SHA512: 7dac66bf4ba99205814fc92afb6699eb1b92d8a0ef4ff8e21c87f23d1d5c5a9232a76478f37b072d7140dfd85320bdecbe66b3e2d70cabf9be059857bf56bcd1 Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Supports staggered difference-in-differences designs with nonlinear outcomes for both panel and repeated cross-section data. Implements estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes, extending Callaway and Sant'Anna (2021) to settings with nonlinear outcome models such as logit, probit, and Poisson. For panel data, units are followed over time and 'idname' identifies repeated observations. For repeated cross-section data, observations are independent within each time period; 'idname' is optional and may identify survey records or households, but the estimator does not require the same units to appear across periods. Repeated cross-section estimation includes pooled quasi-maximum likelihood approaches motivated by Wooldridge (2023) , with optional weighting and clustered inference. Methods also draw on Roth and Sant'Anna (2023) and Sant'Anna and Zhao (2020) . Package: r-cran-nonlineardotplot Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonlineardotplot_0.5.0-1.ca2404.1_all.deb Size: 50890 MD5sum: 95994f7f261cd42901b5d2b560e2c2e7 SHA1: 02be62149fa3adddee1543a29094b8465dbeded6 SHA256: 6ea15ca70db0e5a65242b6da13fc536ef1aed1b76ea4827867e3b7e48cc4d931 SHA512: b34d3afed37a51fe8df5931efbc23eab6ddb0904fc878533ef9a50b81f171d58dae998ec1407a9f00ed662eff746fcd5d478789b9c93322d462f4695c8dde487 Homepage: https://cran.r-project.org/package=nonLinearDotPlot Description: CRAN Package 'nonLinearDotPlot' (Non Linear Dot Plots) Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. . Package: r-cran-nonlinearicp Architecture: all Version: 0.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-condindtests, r-cran-data.tree, r-cran-catools, r-cran-randomforest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonlinearicp_0.1.2.1-1.ca2404.1_all.deb Size: 59038 MD5sum: dd5a53a97e7e4a01f5464e04b4dcb5be SHA1: ea73f8ab5bdbc90b143fa245c066abcf042a761a SHA256: dccf9c4d4fa7f644fe68cbbc39dd83fbb5d1050963362b195f587b9f2223d4ed SHA512: 5854fd7f2fde0ab2a7cf0b785e892bb9e67dcf3ce68b1cad78cd7566461f6e96477fe08b3791100a7b1d033e035a008e864f647b420154893264461ac3fb5b44 Homepage: https://cran.r-project.org/package=nonlinearICP Description: CRAN Package 'nonlinearICP' (Invariant Causal Prediction for Nonlinear Models) Performs 'nonlinear Invariant Causal Prediction' to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending 'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), , to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', . Package: r-cran-nonlineartsa Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-tsdyn, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-nonlineartsa_0.5.0-1.ca2404.1_all.deb Size: 146906 MD5sum: e4f7b137a5434077c52abf0748ec4724 SHA1: 6e204ccfd3381987b5d7cdcf924904fbd975608f SHA256: 64ad3ac8b9568d699b680b634500ebfa412325f87528f7357e9365ff273fcab9 SHA512: d62cb8ccf5f3414a463507fb63caeb245412e7bf713bdccaa14e0706bf29cb37412d4bb9198b0748f8d17e29f101664e438323611772600b320493d0067f5dba Homepage: https://cran.r-project.org/package=NonlinearTSA Description: CRAN Package 'NonlinearTSA' (Nonlinear Time Series Analysis) Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied. Package: r-cran-nonmem2r Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-mvtnorm, r-cran-lattice, r-cran-latticeextra, r-cran-mass, r-cran-splines2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nonmem2r_0.2.5-1.ca2404.1_all.deb Size: 2351890 MD5sum: 33c4603a19bcfd32266e0c87d2d02b75 SHA1: aae0ae25165b026b5a258d77d0a0f8fabd42d751 SHA256: 03c227a06a9619cb17c658139a75307b591133d5715206102636fc53eaea0728 SHA512: e1be05a2b746d9719e865743c610fb96af5a5552d3a110e8985b89a416d48b1ce36a99eeb0676822d880034e78dda009cd4867cb74c2ffa56be5cd2071c9473c Homepage: https://cran.r-project.org/package=nonmem2R Description: CRAN Package 'nonmem2R' (Loading NONMEM Output Files with Functions for Visual PredictiveChecks (VPC) and Goodness of Fit (GOF) Plots) Loading NONMEM (NONlinear Mixed-Effect Modeling, ) and PSN (Perl-speaks-NONMEM, ) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty. 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Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions ) is software for nonlinear mixed effects modeling. See 'package?nonmemica'. 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Includes tests of model distinguishability and of model fit that can be applied to both nested and non-nested models. Also includes functionality to obtain confidence intervals associated with AIC and BIC. This material is partially based on work supported by the National Science Foundation under Grant Number SES-1061334. Package: r-cran-nonnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-energy, r-cran-mgcv Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonnet_1.0.0-1.ca2404.1_all.deb Size: 23886 MD5sum: 5549edff76564d00391bf6a08945a86e SHA1: 4ec34fc078e6352a5c192c05f2c648667dbfe0f9 SHA256: 78b37778924f7f4aa92ca2dc90094c5d7b7cb66935d562cda85b3de13c1151dc SHA512: 4d33414c45fa14ef5488bab6a61080eeab6ef7891bc84a24a22b7d758df24a703b6cd92c2e90a8e824b7ff87c668b83966d3f4f0963859a773cd10fb9550882d Homepage: https://cran.r-project.org/package=nonnet Description: CRAN Package 'nonnet' (Generate and Analyze Nonlinear Networks) Creates and detects nonlinear relations using the methods described in Slipetz, Qiu, Sun, and Henry (2026) . Use the netgen() function to generate a nonlinear network and the dcor_res() function for a residualization procedure for detecting nonlinear relations. 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See Tapan Nayak (1987) . Package: r-cran-nonpar Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonpar_1.0.2-1.ca2404.1_all.deb Size: 41278 MD5sum: 9a56a1bf86d348e72edf54aa5024c6cb SHA1: 59a04d5e7da67f01daf7d1aff9033c81789f16e5 SHA256: c7258be65eaf14068a25d369b7d0b5ec849258f919166c0bb28553a36d320047 SHA512: 0130bd447335194466dd6732c2be4d2905c2d1c0b0fb0ac2149e8f0a429cb6d26e1eaf9a8520f109e2a187afc10afd0600859917359fa047d2d16d1169750fc6 Homepage: https://cran.r-project.org/package=nonpar Description: CRAN Package 'nonpar' (A Collection of Nonparametric Hypothesis Tests) Contains the following 5 nonparametric hypothesis tests: The Sign Test, The 2 Sample Median Test, Miller's Jackknife Procedure, Cochran's Q Test, & The Stuart-Maxwell Test. 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This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations. 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This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute. Package: r-cran-nonpareil Architecture: all Version: 3.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonpareil_3.5.3-1.ca2404.1_all.deb Size: 182910 MD5sum: c76cdd53532319033dea4702e7ec4248 SHA1: 348aa02b2ec70704d246ee17ceac774950c510ad SHA256: 30214e5a806802bb2b5e93e989b3c4550c46fc4a1e17d733bb83e54bccf16354 SHA512: bd2aef6c193c8ec0ca2d96a222daf18d4863990629c1c129f8035f9bad4039a9736a416472d64b46ae3783c3f67b7966c93553226a930e9c8ab158e2534c50b3 Homepage: https://cran.r-project.org/package=Nonpareil Description: CRAN Package 'Nonpareil' (Metagenome Coverage Estimation and Projections for 'Nonpareil') Plot, process, and analyze NPO files produced by 'Nonpareil' . 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The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) and Balcilar et al. (2016) . 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This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) . Package: r-cran-nonpartrendr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonpartrendr_0.1.0-1.ca2404.1_all.deb Size: 17762 MD5sum: 347e9bac1dfe565ad89eaa2b27a5146c SHA1: 313716843946f691b7dd21ec0e43f0b82aea22a4 SHA256: 8c3fbc95b92e82c40442107686c5c193018d6de398818a33353608e160be6e10 SHA512: 7d9aea564009466e2ce931de6b98614b51fce88af2cee2acc139c332daaaa85a3a3a5f629ac51207dc05e97269e6e9651d29b58e75669cf1a7445c54c60bb586 Homepage: https://cran.r-project.org/package=nonparTrendR Description: CRAN Package 'nonparTrendR' (A Nonparametric Trend Test for Independent and Dependent Samples) Implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) . The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests. Package: r-cran-nonprobest Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-sampling, r-cran-e1071, r-cran-glmnet, r-cran-matrix Filename: pool/dists/noble/main/r-cran-nonprobest_0.2.4-1.ca2404.1_all.deb Size: 195592 MD5sum: 6c80095a64a4b49b4ced4d2db80c620a SHA1: 5204014eecb46d037da34d81b72c3fda471b7f8d SHA256: 2dc302e913e4ad437291e20ce583a2b5847a7bb5492e3c409c399a3b86283882 SHA512: 9905e4d1e57ea87e5b725348079b1d81ec2720b218d7f9a450a5fab885d21861adf6c7634662c0b77e0aed91e6ff4c8fe68edab270222d6292a18442d38e42bc Homepage: https://cran.r-project.org/package=NonProbEst Description: CRAN Package 'NonProbEst' (Estimation in Nonprobability Sampling) Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample. 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The package supports estimation with multiple reference surveys, allowing auxiliary information to be combined when no single survey contains all variables relevant to participation. Optional cumulative precalibration can be applied to align weighted totals of shared variables across surveys. Methods are based on the generalized estimating equations framework of Landsman et al. (2026) for correcting participation bias. For a single reference survey, the package implements the raking ratio calibration method and includes the adjusted logistic propensity (ALP) method of Wang, Valliant, and Li (2021) , as well as the Chen-Li-Wu (CLW) method of Chen, Li, and Wu (2020) . Analytic variance estimation uses Taylor linearization and accounts for complex sampling designs in the reference surveys via integration with the 'survey' package. Package: r-cran-nonsmooth Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3099 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-nonsmooth_1.0.0-1.ca2404.1_all.deb Size: 3137148 MD5sum: ab352dc2c7bd46bd0d279f3d3e365593 SHA1: 94c41961a021e84da146e97f2d3b035effa89dd1 SHA256: f58847d41fc04217bab5aeca632f9e51db03177d9b11899b87f694d16d13671f SHA512: a766e052aa3363fe73af5ae1f789e701e80b3970a3e6015abde1b910372f9ec6f103dc8c5d4728aea9c32948ca7f7171bda648f927729f19e2c7bb8f18b495f6 Homepage: https://cran.r-project.org/package=nonsmooth Description: CRAN Package 'nonsmooth' (Nonparametric Methods for Smoothing Nonsmooth Data) Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) . Package: r-cran-nonstat Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonstat_0.0.6-1.ca2404.1_all.deb Size: 17286 MD5sum: 4f2438345e42f8e85779b1cfeb424ffd SHA1: e4e1f08e70c511e5ab8313e9042f33addfb375c6 SHA256: 02d885bffde1ad04cdcf3323ba817ae4a84c5a91fd6ccea6bf5870e86d4af895 SHA512: 10fdbde56f6051ee97f6e98fb0022a4359defaa76bf8045c631a3e6322af2153e20251faa6e8c0cdf1194f446a63d71ad2ab2b60f238286d856dac68efc9d742 Homepage: https://cran.r-project.org/package=nonstat Description: CRAN Package 'nonstat' (Detecting Nonstationarity in Time Series) Provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" . 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Fitting to data by efficient ML (Maximum Likelihood) or traditional EM estimation. Package: r-cran-nord Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nord_1.0.0-1.ca2404.1_all.deb Size: 45562 MD5sum: c81ad364d105eb9c615cb49cd134a169 SHA1: 6c1384428ebaf532a2873929addb4b58cad647d1 SHA256: 6a9ea4ab83248592ff073f67c49b698ef62ab236e8584d3f60cb5608ee018d3d SHA512: 48e4305dbaf2b602b9a203c4eae4fb63adcb0c1896b19d9a667195e24c4a207544e4b19f5dbfa8473fd417066fc7634d88b8ede3a386c55946a5993cda3f5145 Homepage: https://cran.r-project.org/package=nord Description: CRAN Package 'nord' (Arctic Ice Studio's Nord and Group of Seven Inspired ColourPalettes for 'ggplot2') Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions. 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The project contractors were NORDKLIM/NORDMET on behalf of the National meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), Norway (DNMI) and Sweden (SMHI). 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Designed for multi-user web applications where minimal fetch latency and asynchronous writes are required. Individual statistical values ("cells") are stored in a gatekeeper schema with a sidecar table for arbitrary metadata dimensions, enabling deduplication across overlapping queries. 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In Journal of Statistical Software, Vol. 12, Issue 4). 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All data structures are tailored to support downstream environmental data analysis and ecotoxicological modelling. 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It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature. 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Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) . 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Users should verify extended usage of the package on files from other assay types. 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Fitting models to data using 'MLE' (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the 'LDL' (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) . 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Norms are estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), enabling flexible modelling of the full score distribution in a normative sample. The package supports applications in psychometrics and psychological testing, and includes functions for model selection, reliability estimation, norm calculation, including confidence intervals, and sample size planning. For more details, see Timmerman et al. (2021) . 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Developed and maintained for use at the Department of Forensic Sciences, Oslo, Norway. Package: r-cran-nortest Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nortest_1.0-4-1.ca2404.1_all.deb Size: 38434 MD5sum: 28bedd6dc5b496a70038d7061d282189 SHA1: 1df0ac5f357984a756c85b2ddb02f2483d2225f8 SHA256: f99b254bdb381d0134f433afd0d7335e1e5c0b505767627eb8294ed73c66984d SHA512: 221b9f92af130619633cbfde818b651b5ccf1a9099c32db1fc9a7b664af90f8fa971dc35d7851f2cbfd0e7524a55f3a284ecc956512a6973906d8628029c3dd3 Homepage: https://cran.r-project.org/package=nortest Description: CRAN Package 'nortest' (Tests for Normality) Five omnibus tests for testing the composite hypothesis of normality. Package: r-cran-nortstest Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-nortest, r-cran-ggplot2, r-cran-gridextra, r-cran-cowplot, r-cran-tseries, r-cran-uroot, r-cran-mass, r-cran-zoo Suggests: r-cran-ggfortify, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nortstest_1.1.3-1.ca2404.1_all.deb Size: 399896 MD5sum: 2e0e8173966f821c031d59843dc1cb50 SHA1: bc2870444aebcf87512a0a4e7d05e85993cad761 SHA256: a246e07dda4a24076e25653f60c990f038b2e98e203df2bf4df5907568dd29af SHA512: c54a0bf82371cbf9d3a1d91385c33f76128f6df5c1da33de0aafa7a433a17fdb2c4a84454e01fac6ef3b7302b66b45e4717159e3f01a1f66d22d0eff6bb8a5b1 Homepage: https://cran.r-project.org/package=nortsTest Description: CRAN Package 'nortsTest' (Assessing Normality of Stationary Process) Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages. Package: r-cran-nos Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gmp, r-cran-bipartite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nos_2.0.0-1.ca2404.1_all.deb Size: 69346 MD5sum: e35b355c3df311f84db96c7f7f94bd67 SHA1: a2d12ff06612358dedb5e3947e6b302264c0ea62 SHA256: abbdaa0f04c7ae011568fda984e6f93907ce22475eac3c470e2002d19c001e43 SHA512: ef71e95189185c6e7672794a69a9740fab7d271a59150279885915f821ff58ee0a3e9c58b3357c5ee3415a7b58da39549cbfa4e13c4396e67d7e55a20aa71e95 Homepage: https://cran.r-project.org/package=nos Description: CRAN Package 'nos' (Compute Node Overlap and Segregation in Ecological Networks) Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) and Strona et al. (2018) . The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity. Package: r-cran-nose Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nose_1.0.5-1.ca2404.1_all.deb Size: 24782 MD5sum: b5255f509cf268072663bde06058f108 SHA1: 0730180fa71688e670c1657727fa1c0f06c58b3b SHA256: 7101aae4b172b01680c5c849e5b0e29ff083687258263259ef1f6846608dc8c0 SHA512: 3a658ac5485e92d7b40f5bcc00909cd006f03624b2fa902412790f45fe297e8d60a18bcdad94c8fc79123bebcd05554c50018954ea59ce067afcc6dfa083feca Homepage: https://cran.r-project.org/package=nose Description: CRAN Package 'nose' (Classification of Sparseness in 2-by-2 Categorical Data) Provides functions for classifying sparseness in 2 x 2 categorical data where one or more cells have zero counts. The classification uses three widely applied summary measures: Risk Difference (RD), Relative Risk (RR), and Odds Ratio (OR). 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This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots. 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Package: r-cran-noveldistns Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adequacymodel, r-cran-gsl, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-noveldistns_0.1.0-1.ca2404.1_all.deb Size: 95432 MD5sum: bc181bfb9783d22afca66278b7beedb5 SHA1: 6b07371a6298f789e9c37bce54d60583db3a0f61 SHA256: ca813d7e49846a1dbc387e38cd3acd17204ec129abd6cb3edc89b5c97a300159 SHA512: 919067c9bd8b8c03990653135d835e9d57f7b11e0a2a9fd885b2bdc15ef1f6ac5a386e1baa4f3c938a3d7f72946c779d674abe42410a0a2812efa471a35199a9 Homepage: https://cran.r-project.org/package=NovelDistns Description: CRAN Package 'NovelDistns' (Computes PDF, CDF, Quantile, Random Numbers and Measures ofInference for 3 General Families of Distributions) Computes the probability density function, the cumulative density function, quantile function, random numbers and measures of inference for the following families exponentiated generalized gull alpha power family, exponentiated gull alpha powerfamily, gull alpha power family. Package: r-cran-novelforestsg Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-brms, r-cran-testthat Filename: pool/dists/noble/main/r-cran-novelforestsg_2.1.0-1.ca2404.1_all.deb Size: 58836 MD5sum: 208a7e7fac4263ea09d214fa4151d03b SHA1: 3ea44e26ac0c43ab8c5d1e9a183ba0bc42c40681 SHA256: 23e8a3e6cba73de2632430e7c961eb10f131b9dd3cac5ee55934fe041be1ccbf SHA512: 1e7e960dc7638e7dc6029a6974087320139ce8c7e5ff06995ef1ca97e18afeb0e4d95ef98e5d77be6ca955505920b20f5df3d5230ab309bc79a7f78db8353628 Homepage: https://cran.r-project.org/package=novelforestSG Description: CRAN Package 'novelforestSG' (Dataset from the Novel Forests of Singapore) The raw dataset and model used in Lai et al. (2021) Decoupled responses of native and exotic tree diversities to distance from old-growth forest and soil phosphorous in novel secondary forests. Applied Vegetation Science, 24, e12548. Package: r-cran-novelqualcodes Architecture: all Version: 0.13.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-naturalsort, r-cran-ggplot2, r-cran-ggpattern Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-novelqualcodes_0.13.5-1.ca2404.1_all.deb Size: 443144 MD5sum: 84bdc7f50704227cab0fbb7925ff70d3 SHA1: 35b1fac28a2b41833c27a29f342c125720e3c78e SHA256: d2524d90410b73e63b13212d37ec9ee968d973cda6196cda0ed44fe55160116d SHA512: fb40e4ed6049b64a24ee2dcf4dcf5796aa76bbcde39363992cb6a36e4f47c6714c4f657072242984bc7a1ab861c687fe233b9418db6b4e950ca4320352dfaf7b Homepage: https://cran.r-project.org/package=novelqualcodes Description: CRAN Package 'novelqualcodes' (Visualise the Path to a Stopping Point in Qualitative InterviewsBased on Novel Codes) In semi-structured interviews that use the 'framework' method, it is not always clear how refinements to interview questions affect the decision of when to stop interviews. The trend of 'novel' and 'duplicate' interview codes (novel codes are information that other interviewees have not previously mentioned) provides insight into the richness of qualitative information. This package provides tools to visualise when refinements occur and how that affects the trends of novel and duplicate codes. These visualisations, when used progressively as new interviews are finished, can help the researcher to decide on a stopping point for their interviews. For context, see Wong et al., (2023) . 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Package: r-cran-nozzle.r1 Architecture: all Version: 1.1-1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nozzle.r1_1.1-1.1-1.ca2404.1_all.deb Size: 352182 MD5sum: 0f2e8b423784e14283ec11b8b349bd6f SHA1: 3b2c8b4c1114f8a338db4dafa646dac04c4f079d SHA256: 2681a3116158686a7301267a16eb415e7465b9ef9a6eefb1cabde5ad586a4440 SHA512: b47de1b198f814acfc4defe7b85df3d4be31dc3a3ed5febd3a666e4ba4b0e5166630917f8563862fa742717595a8029ea46227ec3aad1b5b351701ac603b412b Homepage: https://cran.r-project.org/package=Nozzle.R1 Description: CRAN Package 'Nozzle.R1' (Nozzle Reports) The Nozzle package provides an API to generate HTML reports with dynamic user interface elements based on JavaScript and CSS (Cascading Style Sheets). 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Package: r-cran-npancova Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-npancova_0.2.0-1.ca2404.1_all.deb Size: 96490 MD5sum: 2c434852cca2479abc3e0ffb8857fc80 SHA1: 0fe7d444f5277caaeb3182ea0281ba2839af914f SHA256: f308312446f08a69d7cc7181080945903f4ae6e2de5580484d95948f0c5b7215 SHA512: a7a101db5cc632ae23dc3f7bd83bbe2d3498e9ae79e4c9b1a3a5e07ede2bd7075fac8243728087623117965d2a8a08b09c07768833168eeef55b4ef7b675be25 Homepage: https://cran.r-project.org/package=npANCOVA Description: CRAN Package 'npANCOVA' (Nonparametric ANCOVA Methods) Nonparametric methods for analysis of covariance (ANCOVA) are distribution-free and provide a flexible statistical framework for situations where the assumptions of parametric ANCOVA are violated or when the response variable is ordinal. This package implements several well-known nonparametric ANCOVA procedures, including Quade, Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides user-friendly functions to apply these methods in practice. These methods are described in Olejnik et al. (1985) and Harwell et al. (1988) . Package: r-cran-nparact Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-nparact_0.9.1-1.ca2404.1_all.deb Size: 101578 MD5sum: 673be4737854dde47f3bd5c32e164ca9 SHA1: 47410dab07db896f0718d550d8c26770a3cf9884 SHA256: 9f50a9c7b6d8a8b407309343e2fb64e56a5805a3d3c1d193a1ba7491014120e0 SHA512: 8b822515f58fdba10fdb9e2c84be96beb6d4a91c2cd4e72e6cef2a0b9b4f5642475b0d63d7f27f6ee6d2afd1bbd2473916a1b1125eeab4c3d1b4cfb2cf2f6c34 Homepage: https://cran.r-project.org/package=nparACT Description: CRAN Package 'nparACT' (Non-Parametric Measures of Actigraphy Data) Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) and van Someren et al. (1999) . Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days. Package: r-cran-nparcomp Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-nparcomp_3.0-1.ca2404.1_all.deb Size: 223686 MD5sum: 92a552f22a59fc4ac46f17d206c6eab4 SHA1: c370ac75dd1baddb3e839895bb0e2ed10b24a949 SHA256: add57692871fce2102e8a60477244ac34bf8a26141c8822dfa654b20c1cc00b7 SHA512: 28418af9d5cc3ce91165a563156a03e2eac2629756355ae94587c2a67175344458df1d3501dd306c563ee0c264789576da406f82faae50235a733687052e53fe Homepage: https://cran.r-project.org/package=nparcomp Description: CRAN Package 'nparcomp' (Multiple Comparisons and Simultaneous Confidence Intervals) With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) for details. Package: r-cran-nparld Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-ggplot2, r-cran-mvtnorm, r-cran-multcomp, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nparld_2.3.1-1.ca2404.1_all.deb Size: 262020 MD5sum: dc962af6a45a90a65e0eaa904fec85b5 SHA1: 979ff982a48981ccf9127ff5d2abb290d7cc15df SHA256: 64f29c6afe651dc3f016ff05b53b87118555b4ca49e6e453efac140e14bd4320 SHA512: df8db29818b46a5890ffbd64f29d9e6efc0a8b7b649e18c97a8b020c5f949ff53df7edb47e161d9fea69d8baea879f2d84439aa35a53c755607a83587486fcbc Homepage: https://cran.r-project.org/package=nparLD Description: CRAN Package 'nparLD' (Nonparametric Analysis of Longitudinal Data in FactorialExperiments) Provides nonparametric procedures for the analysis of longitudinal data in factorial experiments. The package implements hypothesis tests on marginal distribution functions and unweighted relative marginal effects. It supports arbitrary crossed factorial designs with longitudinal or repeated-measures factors, missing observations, dependent replicates, rank- and pseudo-rank-based inference, Wald-type and ANOVA-type statistics, multiple contrast tests, and simultaneous confidence intervals. Package: r-cran-nparmd Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrixcalc, r-cran-mass, r-cran-gtools, r-cran-formula Filename: pool/dists/noble/main/r-cran-nparmd_0.2.3-1.ca2404.1_all.deb Size: 50128 MD5sum: 0a8ada95108791daa97a478fca8d18fc SHA1: e1261f5a8bb837dede75486be76ea842bdc72e63 SHA256: 3c6dd66823e07e947ae9816fdb07c1e3bc04716ca0f27b8ddd702b4ceaf8064b SHA512: 7d0bff7a5d2069b46ffe33050ad72fe2737ec34540f4424cc32a3a6a29fc520e8a584fb06a73b6f97355b65e3f4380fad56444f1350174d0bfa0e5212047fd13 Homepage: https://cran.r-project.org/package=nparMD Description: CRAN Package 'nparMD' (Nonparametric Analysis of Multivariate Data in Factorial Designs) Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) and in Munzel and Brunner (2000) and in Kiefel and Bathke (2022) . Package: r-cran-nparsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-th.data Filename: pool/dists/noble/main/r-cran-nparsurv_0.1.0-1.ca2404.1_all.deb Size: 27934 MD5sum: 23474cea83ba2f277bd2cc62de26d436 SHA1: 6036079d50d6a31394599ba17f85b15a432ec7f0 SHA256: 597ab8f071aca06f5fad4d68e35752b5131d6ce4cca0ac2f591933a9f873761f SHA512: df6ecb31bd713a927d77d1b62b3d94c44302f290e166a99c1912709f43b3d90d44163461b8613272c1325e3150e26ea1f68c4b4a1556f9fdcb12c96953671969 Homepage: https://cran.r-project.org/package=nparsurv Description: CRAN Package 'nparsurv' (Nonparametric Tests for Main Effects, Simple Effects andInteraction Effect in a Factorial Design with Censored Data) Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables. Package: r-cran-npbbbdaefficiency Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-npbbbdaefficiency_0.1.0-1.ca2404.1_all.deb Size: 16288 MD5sum: d19b79c89f385e0235932cac6eeac24f SHA1: a272cb43e364049c12a538e9c04d64aeb6dbe546 SHA256: c823bf28f92be08db2c337bae5224e3b72ae78ef1331198e553bb4a64b1b7ab0 SHA512: 8ab9a4994ce1aeb85ccb4d47208d2dab9ce153e5b4b718a3ce2c7f18c27b0f7db7ddda11422aae6b6a9d04185e66a41a7fb99881d20761fe308616d49cd14e11 Homepage: https://cran.r-project.org/package=NPBBBDAefficiency Description: CRAN Package 'NPBBBDAefficiency' (A-Efficiency for Nested Partially Balanced Bipartite Block(NPBBB) Designs) Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) . This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design. Package: r-cran-npbbbdesigns Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-npbbbdesigns_1.0.0-1.ca2404.1_all.deb Size: 73848 MD5sum: 87ad05f19d311c95fc0bc488c326ac7c SHA1: d5b1b5b514d01cc62f07a9c587cffac185e5fb3c SHA256: f7fe3760c01ae01bfd0aaeea5f17efe402a07cd5003e1cfba85d8286e06e65ad SHA512: 8b89b068e762c884fae4137c14b916a518769f1a4b0d53292bd492dd564f2d9e98cc831811a3b24f95539b67563e6f485cc62a03f2e041906022af9930efddc5 Homepage: https://cran.r-project.org/package=NPBBBdesigns Description: CRAN Package 'NPBBBdesigns' (Construction and a-Efficiency of Nested Partially BalancedBipartite Block Designs) Construction and evaluation of nested partially balanced bipartite block (NPBBB) designs for comparing a set of test treatments with a set of control treatments under a nested (blocks within blocks) structure. Six systematic construction methods are provided: composing partially balanced bipartite block designs with nested balanced incomplete block designs; augmenting nested partially balanced incomplete block designs with controls; merging rows of group-divisible nested designs; direct construction from group-divisible schemes; and expansion of partially balanced incomplete block designs (Vinayaka et al. 2026: In press). The A-efficiencies of the block and sub-block classifications are computed against the A-optimal completely symmetric reference design, following the test-versus-control optimality framework of Gupta and Parsad (1996) and Vinayaka et al. (2024) . These designs are particularly suited to agricultural, animal husbandry, industrial, and clinical trials involving multiple standard checks under nested experimental conditions, such as multi-environment trials where field heterogeneity (blocks) and within-field variation (sub-blocks) must be controlled simultaneously. Package: r-cran-npboottprm Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-fgarch, r-cran-ggplot2, r-cran-lmperm, r-cran-mass, r-cran-mkinfer, r-cran-mmints, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-npboottprm_0.3.2-1.ca2404.1_all.deb Size: 280962 MD5sum: 9145365de98fd2aa3a95a74622d59d0d SHA1: cbd946a19756c611069c152122f10def8c2e5720 SHA256: c7799d3907a555eb80b719567b6c4d8d7811cc1b1b0f08ce650e1425fe9c6ae5 SHA512: b193244c3414eef25da1c43e0add6b54fc0edc12046f3f5a04aede383efff4142eabfdcc9e134786fc107ec3be8c07c7e374550f3f1f5ac321471f579c977c07 Homepage: https://cran.r-project.org/package=npboottprm Description: CRAN Package 'npboottprm' (Nonparametric Bootstrap Test with Pooled Resampling) Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) , the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration. Package: r-cran-npboottprmfbar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-fgarch, r-cran-lmperm, r-cran-mmints, r-cran-npboottprm, r-cran-restriktor, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-npboottprmfbar_0.2.0-1.ca2404.1_all.deb Size: 49752 MD5sum: 99cecd606a7185d8510addb0d8640e95 SHA1: c21450db8c482c7a011476ca67308f0fc92fb7a5 SHA256: 7ca54f75f9ff9121abf23525afc8beed614f1b40c65db7fdc0aa840739498c5d SHA512: 6eecb26a66b3a76e9bb06182e5cf1eb891671a549ecbbfc2fff0d3a957f83bf2b235e064cfb4e348df2cc4c5804c4714d4c57e04a39a7049e40a80d6e90fc820 Homepage: https://cran.r-project.org/package=npboottprmFBar Description: CRAN Package 'npboottprmFBar' (Informative Nonparametric Bootstrap Test with Pooled Resampling) Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) , and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power. Package: r-cran-npcd Architecture: all Version: 1.0-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-r.methodss3 Filename: pool/dists/noble/main/r-cran-npcd_1.0-11-1.ca2404.1_all.deb Size: 140506 MD5sum: e14020f3f5c7fe81265336efc2943569 SHA1: b56f87ddaff330123b4c155b0b23ee9731aeb392 SHA256: ecd9cfbcee04a23a1944de0b8e7c5f94dfd91416f9b4c58f0d07b1dd90662e72 SHA512: 20efed706362182143306ece139334e2e7118d8bc20b7d7bd467c91679099edc15fc0178d419088d7e083f7510bb2261ddce0a22b64efe0fa4c373481294764a Homepage: https://cran.r-project.org/package=NPCD Description: CRAN Package 'NPCD' (Nonparametric Methods for Cognitive Diagnosis) An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model. Package: r-cran-npcdtools Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gdina, r-cran-psych, r-cran-gtools, r-cran-matrix, r-cran-shiny, r-cran-mass Filename: pool/dists/noble/main/r-cran-npcdtools_1.2.0-1.ca2404.1_all.deb Size: 218972 MD5sum: d59a1faa94c70174cc2e4e9f78e186f6 SHA1: 1883d785bb238e80e88260e0ebc10f1b87e1b375 SHA256: 514ce8b4d6ebbba343ec4ca97a5502810d4c5ac0e8568fc495261aa47397eec4 SHA512: d7cc6dcfac2befbb10bc17ec348546275f15bab155c799098d1eb1c846d56abd4b288cac8d8eed1f6833a13784ca35dfacb0e450e9b856bab95204c5b7b411a2 Homepage: https://cran.r-project.org/package=NPCDTools Description: CRAN Package 'NPCDTools' (The Nonparametric Classification Methods for Cognitive Diagnosis) Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) and Chiu and Köhn (2019) . An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) . Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation. Package: r-cran-npclust Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-npclust_0.1.1-1.ca2404.1_all.deb Size: 69618 MD5sum: 96d836e747fc1d9a3e53cd338b360ea5 SHA1: a9b85a309a16206bf9837b8b327df955e6d3d064 SHA256: acf9d1529fd9c55e77bdd017ee368653bb61d6d38056c03e0b8963d698dfba85 SHA512: 49b68a071d385021835ef9b31aac57d92e464d58ef4940b4af116ace154c0ed50630fca3f48da904afcdb8107bb4781bbcf678bef6ece762f0aa9c1d5df0e49c Homepage: https://cran.r-project.org/package=npclust Description: CRAN Package 'npclust' (Nonparametric Tests for Incomplete Clustered Data) Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) and Harrar and Cui (2022) . Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals. 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More details can be found in Lu Tian et al. (2005) . 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Package: r-cran-npexact Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-npexact_0.2-1.ca2404.1_all.deb Size: 111636 MD5sum: 321444edd91fcb454741f5da6c21c3d2 SHA1: edd135fe4117f0fbdf08e72a4ab78da3196065a9 SHA256: 6a4f135e279226fc79abb03cb750b196a1d5b94e9bced3d2cff06b42f04b5c8c SHA512: 07214f54e3d65a526951ecb946c0c38b35cc4921f66b64fa9aeb8a78218341b02abc146630f7f4c5e31faa37910ae04310c1607d865f53fe55124c652fc514c1 Homepage: https://cran.r-project.org/package=npExact Description: CRAN Package 'npExact' (Exact Nonparametric Hypothesis Tests for the Mean, Variance andStochastic Inequality) Provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input. 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This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets. Package: r-cran-npfseir Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-npfseir_0.2.1-1.ca2404.1_all.deb Size: 175316 MD5sum: 9e66c8ad87b7a0fe8a7cbd7afb2b3856 SHA1: 1215dd068a5e0cbd1ad5580be1da8e97c7afefe5 SHA256: ea5f65c0e98a18fe25f98a68d895d2acbcdd32204bc400a34c291957c82d29b5 SHA512: e35365d92b42240e3cf71b5c06518b0e3997c4e5572fcb67ccfc0301f86a64681950c22bb515ed27a11b244ee69f52e21f3aabfbc3972850442084363333810a Homepage: https://cran.r-project.org/package=npfseir Description: CRAN Package 'npfseir' (Nested Particle Filter for Stochastic SEIR Epidemic Models) Implements the online Bayesian inference framework for joint state and parameter estimation in a stochastic Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model with a time-varying transmission rate. 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A function to calculate weighted log-rank tests for the comparison of two hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) . Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate normal approximation to the set of selected parameters. 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Package: r-cran-npred Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1748 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-zoo, r-cran-spei, r-cran-wasp, r-cran-knitr, r-cran-ggplot2, r-cran-synthesis, r-cran-testthat, r-cran-bookdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-npred_1.1.0-1.ca2404.1_all.deb Size: 1594826 MD5sum: 688043ac0bf57f0e66aadf972f7e0297 SHA1: d16d1adf8953516f40ab2fcd2f0d445418d6b471 SHA256: 4a3abacfdd1d9ca739247e3d23b7fcb406d24473e617e61bd4afae3f2214705c SHA512: cf4fbac9221444c731c6f81d3714c8a5233f545f87db375bc7b4c6889e3502ef175828fe715c32edef31cd8a88ff1a83082371265f764196414151ba3fcb83e9 Homepage: https://cran.r-project.org/package=NPRED Description: CRAN Package 'NPRED' (Predictor Identifier: Nonparametric Prediction) Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). , Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). , and Mehrotra, R., & Sharma, A. (2006). . Package: r-cran-npreg Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-statmod Filename: pool/dists/noble/main/r-cran-npreg_1.1.1-1.ca2404.1_all.deb Size: 725580 MD5sum: 138823e6330875c72f7a814f0a99381d SHA1: 9d81fb7cb2d56578a0f579827a87c9a1c14f2aa1 SHA256: be41763861e27a066a7bdb060a05f0e57cf0c1807fb5f093001d3d683cacdec7 SHA512: 4d605539b68d1afcc896ee724e3de5f2d0568092c57cb7ff91cfa1a2ca68ddc4e5b8ed1da7de4702028f5fd9b57118ef7ff47ec9375fb2bbfbb68b7f2acc37fc Homepage: https://cran.r-project.org/package=npreg Description: CRAN Package 'npreg' (Nonparametric Regression via Smoothing Splines) Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) . 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Package: r-cran-nprobust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-broom, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-nprobust_1.0.0-1.ca2404.1_all.deb Size: 215736 MD5sum: 0b59e0201cc23e2a202dc8dbcc353e88 SHA1: 81d8a3c7236bcaaf92aa90533ad2cfb18b88b439 SHA256: 0c8587425afc3a8536a43fbf2674e9cb8f819f88c87deec6c7cf810e1c1e48ee SHA512: fb03a7e581702983d06760259a4ab2d1e984faa08aabf76024f9486ab809a78a4e10f91b0bb8629f7e310e3d60938a22ccfdb78abb8aa2d818855ce4cd7ac619 Homepage: https://cran.r-project.org/package=nprobust Description: CRAN Package 'nprobust' (Kernel Density and Local Polynomial Regression Methods) Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, ). The package includes 'lprobust()' for local polynomial point estimation and robust bias-corrected inference, 'lpbwselect()' for local polynomial bandwidth selection, 'kdrobust()' for kernel density point estimation and robust bias-corrected inference, 'kdbwselect()' for kernel density bandwidth selection, and 'nprobust.plot()' for plotting results. The main methodological and numerical features are described in Calonico, Cattaneo and Farrell (2019, ). Package: r-cran-nproc Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-e1071, r-cran-randomforest, r-cran-naivebayes, r-cran-mass, r-cran-ada, r-cran-rocr, r-cran-tree Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nproc_2.1.5-1.ca2404.1_all.deb Size: 402664 MD5sum: 001c1c1d9e6e8e91022c7f30c17d01b9 SHA1: 0de91fd87642ff7b4424c98971a8e754ee3f0e69 SHA256: 378e727ec3e6a02a18ecc79177b8d350889ee65d46a409eec64a72a57efb346f SHA512: 380d5d9e5ee3b047b1cfeea5a45c0d1fa9ba8344c112912b7bb1a57f9c79349fd9040268720cba96ef1176d9294d37264bf7102022f8ed4b4f3cf431a4c66ef8 Homepage: https://cran.r-project.org/package=nproc Description: CRAN Package 'nproc' (Neyman-Pearson (NP) Classification Algorithms and NP ReceiverOperating Characteristic (NP-ROC) Curves) In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers. 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Provides methods for bias reduction applying iterative procedures within a Newton-Raphson learning scheme. Cross-validation is exploited to select smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018) . 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Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics. Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems. Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method. 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Despite the multitude of options, the convention in survival studies is to assume proportional hazards and to use the unweighted log-rank test for design and analysis. This package provides sample size and power calculation for all of the above statistical tests with allowance for flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, mixture cure). It is the companion R package to the paper by Yung and Liu (2020) . Specific to the weighted log-rank test, users may specify which approximations they wish to use to estimate the large-sample mean and variance. The default option has been shown to provide substantial improvement over the conventional sample size and power equations based on Schoenfeld (1981) . 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For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package. Package: r-cran-npwbs Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-npwbs_0.3.0-1.ca2404.1_all.deb Size: 148826 MD5sum: dea2705e6c4f94abe89c8abd6811bfd0 SHA1: 491454c768426029c988d996fcb6c4c735ee5436 SHA256: 0297aea10c3879cfa667b14f5e9409aef81c18acf48690d174842b5ab9071b10 SHA512: 7767be6bb95ff98c2a005b4132432846735607fdd24536d50810f02148b305191332092ce119be596367ee04ea92f52dfc4cc7c50579203c072d2ffb94e4d836 Homepage: https://cran.r-project.org/package=npwbs Description: CRAN Package 'npwbs' (Nonparametric Multiple Change Point Detection Using Wild BinarySegmentation) Implements a procedure for detecting multiple location-scale change points in a sequence of univariate observations, as described in Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" . The method combines Wild Binary Segmentation with a rank-based statistic and provides calibrated thresholds for controlling the probability of incorrectly detecting a change point in a homogeneous sequence. Package: r-cran-nrba Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-srvyr, r-cran-survey, r-cran-svrep, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-nrba_0.3.1-1.ca2404.1_all.deb Size: 516200 MD5sum: 9311a4fdc8a1d8f3cec7bb798d22d1f6 SHA1: eb2ba2ac072cafca7bdf1e92c9e6a9e7c27ea690 SHA256: a50bdc4a9c2fc7747e0d2641b9dff435484fde0101c51d4baf4e851f62c37e47 SHA512: 1d744892237cd8cdac66b75c2add338ca3bc34e763fc40f9811d0aec11df40fc8f02eed9dc7b57694a1d5b8af9b09b838218e0bb40631169dfbf81a547862dd2 Homepage: https://cran.r-project.org/package=nrba Description: CRAN Package 'nrba' (Methods for Conducting Nonresponse Bias Analysis (NRBA)) Facilitates nonresponse bias analysis (NRBA) for survey data. Such data may arise from a complex sampling design with features such as stratification, clustering, or unequal probabilities of selection. Multiple types of analyses may be conducted: comparisons of response rates across subgroups; comparisons of estimates before and after weighting adjustments; comparisons of sample-based estimates to external population totals; tests of systematic differences in covariate means between respondents and full samples; tests of independence between response status and covariates; and modeling of outcomes and response status as a function of covariates. Extensive documentation and references are provided for each type of analysis. Krenzke, Van de Kerckhove, and Mohadjer (2005) and Lohr and Riddles (2016) provide an overview of the methods implemented in this package. Package: r-cran-nregression Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-covr, r-cran-simitation Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-nregression_0.5.1-1.ca2404.1_all.deb Size: 31674 MD5sum: 302f08934c8183c62f103043bda9ea4f SHA1: fd723570d4a6f395b713ced9da9253b9d146e3b6 SHA256: 8d6fb5c0c7bf620e3251c33dd0dc71d30d156dbdd3eb7b081b790422f0293de6 SHA512: 879928ee5c8b272823968f32251de29cacf7bad9e89ed520c12ecdec2b249ab1546cdba65691752717f06e2613a116952a5b16e6078c15e51b7d28e70edd2a26 Homepage: https://cran.r-project.org/package=nRegression Description: CRAN Package 'nRegression' (Simulation-Based Calculations of Sample Size for Linear andLogistic Regression) Provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations. Package: r-cran-nrejections Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-matrixcalc, r-cran-stepwisetest, r-cran-foreach, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nrejections_1.2.0-1.ca2404.1_all.deb Size: 57402 MD5sum: 6fbaae1a5640acf827b2070e3fd37261 SHA1: 1c9241a7c0ca316db43ca78deb1a60df81797d9b SHA256: ac896bddb75ca6e1edca40203e934a62f42c820766c6768ec27e7877112571b3 SHA512: 60a5aa507d0543bff51c15e977fe2077df28d9493705a5c557762d17e36db7f8f180ddc2c737caea21878d872ef6141fcd3d0a0ea07c8cb7c0b6aa7d5698e940 Homepage: https://cran.r-project.org/package=NRejections Description: CRAN Package 'NRejections' (Metrics for Multiple Testing with Correlated Outcomes) Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. 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Supports competitions including the National Rugby League, New South Wales Cup, Queensland Cup, Super League, and various representative and women's competitions. Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. See Woods et al. (2017) for an example of rugby league performance analysis methodology. Package: r-cran-nrmsampling Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sf, r-cran-terra, r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nrmsampling_0.2.2-1.ca2404.1_all.deb Size: 144452 MD5sum: 6f2f56ad193cdba53b67e63a0b16023f SHA1: 675de6fc5874909bc8a0af5ff0c840a1d6ccef53 SHA256: 615cd247756a7fd62c1516ad140da849fd17037c1577a096bee65e045a4f72ad SHA512: 2d75d496e43d78a61fac8cfee5f5be3512f3ab8f745c083c3aa0aec60a9b3c4e309b0f2bd5aebf52eaa6f3e9b3f9c314238bca2c77719bf2c03dc7c47e227dbd Homepage: https://cran.r-project.org/package=NRMSampling Description: CRAN Package 'NRMSampling' (Sampling Design and Estimation Methods for Natural ResourceManagement) Provides functions for probability and non-probability sampling design, sample selection, and population estimation tailored to natural resource management. Probability methods include simple random sampling, stratified sampling, systematic sampling, cluster sampling, and probability-proportional-to-size sampling. Non-probability methods include convenience, judgement-based, and quota sampling. Estimation functions cover means, totals, ratio estimators, regression estimators, and the unequal-probability estimator of Horvitz and Thompson (1952, ) for unequal-probability designs. Utilities support biomass, soil-loss, and carbon-stock estimation from field plots. Spatial extensions provide random, systematic, stratified, and raster-weighted sampling within geographic polygons using the 'sf' and 'terra' packages, with extraction of remote-sensing covariates at sample locations. Applications include forest inventory, soil erosion monitoring, watershed studies, and ecological field surveys. Package: r-cran-nrmstatsml Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kendall, r-cran-trend, r-cran-strucchange, r-cran-plm, r-cran-forecast, r-cran-lavaan, r-cran-pls, r-cran-caret, r-cran-boot, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-keras, r-cran-tensorflow, r-cran-bayesiantools, r-cran-sensitivity, r-cran-mboost, r-cran-mlr3, r-cran-covr Filename: pool/dists/noble/main/r-cran-nrmstatsml_0.1.4-1.ca2404.1_all.deb Size: 177796 MD5sum: 58731ff726a7856db68753457c25df6f SHA1: 52f6d78f15d4e9db0995b2149e6174aff21e32a2 SHA256: cd89d4d4b270c1c4bd190db1cca1e00fb26baa7bea6b18a2e98fb41081724a1a SHA512: db4b85b63f8d1864482a66545705a3c98238561d0def4302b67dc40b25c305d78778fd832e78752e169ec087cfdf26a096e74c7473248e7c7776ba773d43ea77 Homepage: https://cran.r-project.org/package=NRMstatsML Description: CRAN Package 'NRMstatsML' (Statistical and Machine Learning Engine for Long-Term NaturalResource Management Data) A comprehensive toolkit for statistical and machine learning-based analysis of long-term Natural Resource Management (NRM) datasets. Integrates formula-driven approaches, statistical inference, and machine learning (ML) models for advanced analytics. Modules cover trend and structural analysis (Mann-Kendall test, slope estimation, Chow test, structural break detection), multivariate system modelling (Partial Least Squares (PLS), Structural Equation Modelling (SEM)), response curve optimisation, time-series forecasting (Autoregressive Integrated Moving Average (ARIMA), hybrid models), panel data and treatment effects (Difference-in-Differences (DiD), causal machine learning), uncertainty and sensitivity analysis (bootstrap, Monte Carlo, Bayesian), and automated model selection and performance comparison. Designed for long-term datasets covering soil, water, crop, and climate domains. Key references: Mann and Kendall (1945) ; Sen (1968) ; Bai and Perron (2003) ; Rosseel (2012) ; Croissant and Millo (2008) . Package: r-cran-nsae Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlist, r-cran-cluster, r-cran-mass, r-cran-lattice, r-cran-matrix, r-cran-numderiv, r-cran-nlme, r-cran-spgwr, r-cran-semipar Filename: pool/dists/noble/main/r-cran-nsae_0.4.0-1.ca2404.1_all.deb Size: 200224 MD5sum: 7dd160f13a8ed4c4ee903d665baf5684 SHA1: 5263cfc3ef5a60cc206baaf29eb5c1134adef233 SHA256: 41efac346b78201887cb320100143e57b7ebafea96e8f2b20ed496f4cfe3f318 SHA512: 98f2debbadf68f9c78b987decf4e350c271ffdc7a56fe851daeffef0a7f12e1cd3b7023053434dba2f842367bf8bfedb7c81d2f9008f27c09e5aa370dfc0f6a0 Homepage: https://cran.r-project.org/package=NSAE Description: CRAN Package 'NSAE' (Nonstationary Small Area Estimation) Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) and nonstationary generalized linear mixed model was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2017) . This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress. Package: r-cran-nsarfima Architecture: all Version: 0.2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nsarfima_0.2.0.0-1.ca2404.1_all.deb Size: 58752 MD5sum: 1616f2bbbfec1bcbcac7cee24b31ea9a SHA1: 4607877edd97d6c0e4f06fe07a831a4ccba83f52 SHA256: 1ddb934df32f12439d21d7afb7bfcecd4b0b9c5b5c52ac16c2ec3f5d35d356dc SHA512: f9158e713e64040ed7ad374392d9b18e9b24df2a21e557e8af31fba225ab7a6cd3d1c168f5573ab1bcc83ba2ae87ff622d99e2b89c22f060fa861fafbe3e1f09 Homepage: https://cran.r-project.org/package=nsarfima Description: CRAN Package 'nsarfima' (Methods for Fitting and Simulating Non-Stationary ARFIMA Models) Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) . Beran, J. (1995) . Package: r-cran-nsc Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readxl, r-cran-stringi Filename: pool/dists/noble/main/r-cran-nsc_1.1.8-1.ca2404.1_all.deb Size: 22000 MD5sum: da0499d4da70ff87bdd9cd60a0bf15dc SHA1: 66abd18c2ff89adf55115edc798170725473faac SHA256: 3a7ea0d95a4244c08262403ffeb5051c72114339a2d9ebf615b1cc7be06f7ac9 SHA512: fff3d200409adc93cad6039ccbc96e6dc119498b31d0a545b73119cbacb40ef88995d202c0d0b15d4282ee2688cba8f8359fb8192e47cc5e9e512280c97a2452 Homepage: https://cran.r-project.org/package=NSC Description: CRAN Package 'NSC' (Format Student Data for the National Student Clearinghouse) Formats student records for submission to the National Student Clearinghouse. 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Necessity and sufficiency are estimated as two empty-space frontiers on diagonally opposite corners of the same scatter plot, using the 'NCA' package of Dul (2016) for the necessity side and 'SCAtools' for the sufficiency side. The two empty zones and the region between them tile the analytic scope, which yields the reported areas as one identity rather than as separate definitions: a weakest-component effect, a normalised geometric mean of the two components, the share of the scope the claims jointly rule out, and the data zone they still admit. The conjunction is decided by an intersection-union combination of the two directional permutation tests, optionally driven by a single shared permutation sequence so that a statistic of both components can also be tested. These are random-pairing screens rather than direct tests of a causal necessary-and-sufficient relation, no magnitude benchmarks are asserted for the joint indices, and a dual-threshold table separates outcome levels that are out of reach, admitted, or guaranteed. An ordinary least-squares line can be drawn alongside the two frontiers as a central-tendency reference; it is an average-effect summary and is never treated as a component of either claim. Package: r-cran-nscancor Architecture: all Version: 0.7.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cca, r-cran-glmnet, r-cran-mass, r-cran-roxygen2, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-nscancor_0.7.0-6-1.ca2404.1_all.deb Size: 60800 MD5sum: 957653285fb4f1cd6dbf94afcee031f9 SHA1: 7120fadaa575e9fa5e9cbe0d40f8423d959239b4 SHA256: 146934e245ab5bf1a64b961bca980c6eb7f36bfc1ecf2cf302170c85a42d5b1b SHA512: 3f1240c7c2df666ca941145dcc6b28669738ef419ef72d5db3b5206126f8c9e3ffcaf8e0987ba930cc0d89f007667cb984d675d91c24044e6515154465b34e8c Homepage: https://cran.r-project.org/package=nscancor Description: CRAN Package 'nscancor' (Non-Negative and Sparse CCA) Two implementations of canonical correlation analysis (CCA) that are based on iterated regression. By choosing the appropriate regression algorithm for each data domain, it is possible to enforce sparsity, non-negativity or other kinds of constraints on the projection vectors. Multiple canonical variables are computed sequentially using a generalized deflation scheme, where the additional correlation not explained by previous variables is maximized. nscancor() is used to analyze paired data from two domains, and has the same interface as cancor() from the 'stats' package (plus some extra parameters). mcancor() is appropriate for analyzing data from three or more domains. See and Sigg et al. (2007) for more details. 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Fast non-dominated sorting, crowding distance, tournament selection, simulated binary crossover, and polynomial mutation are called in the main program. The methods are described in Deb et al. (2002) . 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Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. 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These numbers are used to identify individuals (CPF), vehicles (RENAVAN), companies (CNPJ) and etc. Functions to format, validate and compare these numbers have been implemented in a vectorized way in order to speed up validations and comparisons in big datasets. 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Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions. 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The package automatically returns complete results on all 32 models, 25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data). From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20), fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration. The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set. 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Package: r-cran-oaxaca Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-oaxaca_0.1.5-1.ca2404.1_all.deb Size: 238964 MD5sum: 2c004061a478968a06195200b113916f SHA1: 8b4e6a0c10a9d2c9ba37984a561ea9904dc74397 SHA256: 3f20f2fc025f1ad995405e6d62d7d8cbbd5cb6a03877373ed0e44749f7c2ab13 SHA512: 9860f96812fdfa778a3acd9145878959b0f152eddeb45a872681213925f5b93a41dd95c025de240b6f0e57afcbe0e00a745a22f8a554cf944bda9b73ea41097c Homepage: https://cran.r-project.org/package=oaxaca Description: CRAN Package 'oaxaca' (Blinder-Oaxaca Decomposition) An implementation of the Blinder-Oaxaca decomposition for linear regression models. Package: r-cran-obanalytics Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-obanalytics_0.1.1-1.ca2404.1_all.deb Size: 3460502 MD5sum: 4cd28a7708793f080a8316b88c65f599 SHA1: b7656598e2136af4676027e8bbf037aea03838c4 SHA256: 9e018f32e5fe1ffbe11d187c83867f6f293efde8fc7a57721adc06db6669ee73 SHA512: 77f458e1ddabf1a3e31ae721f85c3a86e76552df121a602afb3ca973cbb0db00094141578e27a0df552f8048d4c1625948af1b0d8645f5fbdbed8645359b610a Homepage: https://cran.r-project.org/package=obAnalytics Description: CRAN Package 'obAnalytics' (Limit Order Book Analytics) Data processing, visualisation and analysis of Limit Order Book event data. Package: r-cran-obaspatial Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-modeest, r-cran-cubature, r-cran-truncdist, r-cran-invgamma, r-cran-laplacesdemon, r-cran-hdinterval, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-obaspatial_1.9-1.ca2404.1_all.deb Size: 222242 MD5sum: 33570d0d108cc59258523d5601d9a266 SHA1: 64fff145f87fe873709862331ca9bb014172160f SHA256: f05928c15d750ecf7afc0ba2203b3a9f3238f54707914205beec290031a8acd0 SHA512: db8cc99d24b75909814586df10d381b607b747482dcef6f4f0948e41d5dd48344bf709ecee51792a7e3a3724cae23c3d51a3c2c58df277a93915012d9b972ef5 Homepage: https://cran.r-project.org/package=OBASpatial Description: CRAN Package 'OBASpatial' (Objective Bayesian Analysis for Spatial Regression Models) It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) . Package: r-cran-obcost Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-obcost_0.1.0-1.ca2404.1_all.deb Size: 428476 MD5sum: c99794e692b2d0124c18dd0f174f1119 SHA1: 1cfe02c312e3e2209b87e01aaf538e029a27d377 SHA256: d138191a2a46ead4fa4acfc3b169df08d8b6cd99e99cd8c789911d41eca86ed5 SHA512: 2d40458cb8f89967d32bdddbcd4195e31cb818731a14e9fbef6129bd8b13d404aef9a676f93047053203ac805b5886822680918e273518e686b7650d4c259a8f Homepage: https://cran.r-project.org/package=obcost Description: CRAN Package 'obcost' (Obesity Cost Database) This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" . The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. 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Package: r-cran-obrasgovr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-obrasgovr_0.1.0-1.ca2404.1_all.deb Size: 100024 MD5sum: a7e8a7e1ec86feffa2af6abdd66dbb20 SHA1: d2fcad4b9cf2bf97d6837d7852f09e5fb61c80ef SHA256: 34497727e176911a5262b598b1f9202437955d9c7debb0ec364db11304334ff8 SHA512: 05dd582306b9d77125ed58cb67c65fa3b581180d038d8465dd0d2d26cd2e276ffa7e8c8a1c2e9420e6239e41baee986fa994fa1d91b7cac130542a1de7b7a8bd Homepage: https://cran.r-project.org/package=obrasgovr Description: CRAN Package 'obrasgovr' (Access the 'ObrasGov' Open Data API) Provides a modern interface to the Brazilian federal government's 'ObrasGov' open data application programming interface (). Retrieves data about public infrastructure projects, physical execution, contracts, commitments, geometries, feasibility studies, and project status histories. Results are returned as tidy tibbles with typed date columns, preserved nested relationships, pagination metadata, and optional multi-page collection. 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Blocks larger than pairs better distinguish effects caused by a treatment from unmeasured confounding in assignment of individuals to treatment. Somewhat counterintuitively, blocks larger than pairs can use more units while attaining better covariate balance and block homogeneity. A forthcoming manuscript by Brumberg and Rosenbaum details the design. 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Package: r-cran-oca Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mathjaxr Filename: pool/dists/noble/main/r-cran-oca_0.5-1.ca2404.1_all.deb Size: 55678 MD5sum: b5e9954839107621f089ab29dd5f4a27 SHA1: f7d4a3f2a1226c6f84f4b32528710f3ee6a23e98 SHA256: 5b33451b0f8d9f4eb7a979e1ab93caf3a78af02aae07fad5fb42c9ff30ce182a SHA512: 2bc9d74493f399befd0939d6652efe109cc775eb3f23fac5a94a539ad3cb6203916fd63c24db2fd7e77db9e4cadff8a96908a15e1210e910c048a9ac1c811134 Homepage: https://cran.r-project.org/package=OCA Description: CRAN Package 'OCA' (Optimal Capital Allocations) Computes optimal capital allocations based on some standard principles such as Haircut, Overbeck type II and the Covariance Allocation Principle. 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Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) , Fukaya and Hasebe (2025) . Package: r-cran-occupancy Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-vgam, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-occupancy_1.2-1.ca2404.1_all.deb Size: 172750 MD5sum: daf9a8c2431f5cacb24a98f6dfd5596a SHA1: 67115840371b4c658e1964d62b6a75415670b4fe SHA256: 77d5d4f0c9b3b70fd8e37da312daa91ba0eeca08ea1c0cb03df9e5dc5f9c8a84 SHA512: 50a95ad6fd4ec401657a616377cc1e3bbaeebede8b41b3932bff36a9997ac465ac2a14c8dbae50dd20d4598ce3fcae23d48c5701d797e96300ce8339ad17f6da Homepage: https://cran.r-project.org/package=occupancy Description: CRAN Package 'occupancy' (Probability Functions for Occupancy Distributions) The classical and extended occupancy distributions occur in cases where balls are randomly allocated to bins. 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(2021) and Schierholz, M., Gensicke, M., Tschersich, N., Kreuter, F. (2018) . Generate suggestions for occupational categories based on free text input, with pre-trained machine learning models in German and a ready-to-use shiny application provided for quick and easy data collection. Package: r-cran-ocd Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4449 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ocd_1.1-1.ca2404.1_all.deb Size: 4486836 MD5sum: b0e23149be7574b032d2281b607d1bc3 SHA1: 3a05f2d39b6e5e0f87d2751d55538033065d9648 SHA256: d1fd607f2cc8eeb06a9c44cfd2c835255c65008dbc8eac58a46eb9490c63f1ae SHA512: 469f2816f81f4ae17223be888ae3b2867e5ee900b4a7d40507dd8d86855be790ae3ccc68e882995addc9146f19016ab1896056f4fd21475381566c6ff9e0ab04 Homepage: https://cran.r-project.org/package=ocd Description: CRAN Package 'ocd' (High-Dimensional Multiscale Online Changepoint Detection) Implements the algorithm in Chen, Wang and Samworth (2020) for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) , Xie and Siegmund (2013) and Chan (2017) . 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It includes functions to extract NetCDF data from the repository and code to visualize several physical and chemical parameters of the ocean. A Shiny app further allows interactive exploration of the data. The methods for data collecting and quality checks are described in several papers, which can be found here: . 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Some functions use 'shiny' or 'leaflet' technologies for dynamism and interactivity. The great features are : - Create maps in a web environment where the parameters are modifiable on the fly ('shiny' and 'leaflet' technologies). - Create interactive maps through zoom and pop-up ('leaflet' technology). - Create frozen maps with the possibility to add labels. 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Package: r-cran-oceanwaves Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-bspec, r-cran-signal Suggests: r-cran-scales, r-cran-oce, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oceanwaves_0.2.0-1.ca2404.1_all.deb Size: 132176 MD5sum: 523edc0a87758ab3cfdcf833117c1540 SHA1: 139b0208b6b7754b7e5b233517b7a236dfd7c923 SHA256: cb3307f3847d75c1a87406ac5cdc861924eaa64b25737805e588ea9cf4b73494 SHA512: 3cf51aa947cc1e0e938cc2f59ae24afaa75d1402e8efe75a80058e25fa694e218f028ef537d4b13885d69d42e54cabed57fbf9e2545d618b389c6408b821282c Homepage: https://cran.r-project.org/package=oceanwaves Description: CRAN Package 'oceanwaves' (Ocean Wave Statistics) Calculate ocean wave height summary statistics and process data from bottom-mounted pressure sensor data loggers. Derived primarily from MATLAB functions provided by U. Neumeier at . Wave number calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) "Direct Solution of Wave Dispersion Equation", American Society of Civil Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, Vol 105, pp 457-459. Package: r-cran-ocecens Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ocecens_0.1.2-1.ca2404.1_all.deb Size: 89224 MD5sum: 69cf6da12f093fb4a2b4d3fc87109a0b SHA1: eae1079ca24692ad5f1380fb40871c0d3ded930e SHA256: 72da61b7a92a68f43985bc27b7121d720e312b799e4f26b0b744afc9c3165d90 SHA512: 92018a861a943bba6b70963ac6ded99b10906f8836569e9058d92007a27d43e9247be2276d81f81997759a52e12dc4520221e82ad853ebc0cfa66d3a6a9ae51c Homepage: https://cran.r-project.org/package=oceCens Description: CRAN Package 'oceCens' (Ordered Composite Endpoints with Censoring) Estimates win ratio or Mann-Whitney parameter for two group comparisons using ordered composite endpoints with right censoring as described in Follmann, Fay, Hamasaki, and Evans (2020). Package: r-cran-ocedata Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4241 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ocedata_0.2.2-1.ca2404.1_all.deb Size: 4226596 MD5sum: 4293458928e51705dbb0787e3223c2e2 SHA1: 04b051ff25776ae45ddc9bb9f47e9523c3e34217 SHA256: b6303f4d77e4791f47622ccaf82ba6f760897801c303751829ccc74e9baa1f60 SHA512: 42261128877ec0a6654e071f7f4246b8c9d13998cb9759f5ee72e33d166c9d1a8c6d3d61e9502c715a293f07262d679ff0f3f06d2704fb1d4edf54f2fe136d2f Homepage: https://cran.r-project.org/package=ocedata Description: CRAN Package 'ocedata' (Oceanographic Data Sets for 'oce' Package) Several Oceanographic data sets are provided for use by the 'oce' package and for other purposes. Package: r-cran-ocelloc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-ggplot2, r-cran-rlang, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ocelloc_1.0.0-1.ca2404.1_all.deb Size: 3585106 MD5sum: 5264fb3547108f07178f0774ebea75dc SHA1: 402aec01c9b16f022dc934acbd3d596143acba5f SHA256: 8715a0a778b1e18182a631cbbd4a07a4b925fe273c3e2d2f15270f10ae6a2ab5 SHA512: 02cf84ad6499cddd73ad40819b2543ec1671fb453acc1ee2c050499ba0cb12f37aed9a6dc8cb9ee0eb833a96017dce7d52caf9f1142e5ceaa4f9028a7d2b6dc5 Homepage: https://cran.r-project.org/package=oCELLoc Description: CRAN Package 'oCELLoc' (Predicts Suitable Cell Types in Spatial Transcriptomics andscRNA-seq Data) Picks the suitable cell types in spatial and scRNA-seq data using shrinkage methods. The package includes curated reference gene expression profiles for human and mouse cell types, facilitating immediate application to common spatial transcriptomics or scRNA datasets. Additionally, users can input custom reference data to support tissue- or experiment-specific analyses. Package: r-cran-ockc Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexclust, r-cran-seriation, r-cran-modeltools Filename: pool/dists/noble/main/r-cran-ockc_1.1.1-1.ca2404.1_all.deb Size: 144418 MD5sum: 5dbbfbd74e8bff1d8cd8f417b1b9dccf SHA1: 11e675cc4460e68098909a62a9ee6904685937ec SHA256: 02f2bb49e1cb0387e6e9a2892ad9939a7e94f27ff9a315eb84e9f30ce8ffac79 SHA512: 6fc8b8e88d883f7ee2167fe70d3b91f761b32991eed0ba846b2bd3980259cee3c08b9ec663f560227a1b9cbad40644d5ca2e1fb2f1464cae3338af60e9ace1aa Homepage: https://cran.r-project.org/package=ockc Description: CRAN Package 'ockc' (Order Constrained Solutions in k-Means Clustering) Extends 'flexclust' with an R implementation of order constrained solutions in k-means clustering (Steinley and Hubert, 2008, ). Package: r-cran-oclust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-entropy, r-cran-mclust, r-cran-mixture, r-cran-dbscan, r-cran-mass, r-cran-mvtnorm, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-oclust_1.0.0-1.ca2404.1_all.deb Size: 71282 MD5sum: da18e6fc0e450efdac8e679271165966 SHA1: 1f02d3e00450d10d7ad3f47bd521783de4fb5261 SHA256: 5afa4d743985fbb881d0a4af70c8190186918b5592d976f76b17af09baae944a SHA512: ac9fc933217ea126e85d2d53c517599701d55724507a4c1a2d56d235c590e496eae4bfb4b8e16e6ad9b466481cf43191f58c497fe33bcc8a65bcd61c246789b6 Homepage: https://cran.r-project.org/package=oclust Description: CRAN Package 'oclust' (Gaussian Model-Based Clustering with Outliers) Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2024) . Package: r-cran-ocp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2940 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ocp_0.1.1-1.ca2404.1_all.deb Size: 1585050 MD5sum: 843df29f94f7c5f8f3ddf671490af423 SHA1: 11e99087223927e3e77b942f2c731bf5ca494043 SHA256: e35954abe5f3e84b58c22330970320e36092e344400ebac3339f17bdc643f07e SHA512: 27c1ec115730f5f5f149374dade05c1408c47b5117d7d8348d26d422a20cda384199a2c44470131abdde15dc819a2f1e12e2a7e3cb18e9e484eea694ee974f51 Homepage: https://cran.r-project.org/package=ocp Description: CRAN Package 'ocp' (Bayesian Online Changepoint Detection) Implements the Bayesian online changepoint detection method by Adams and MacKay (2007) for univariate or multivariate data. Gaussian and Poisson probability models are implemented. Provides post-processing functions with alternative ways to extract changepoints. Package: r-cran-ocrrbbr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3058 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-glmnet, r-cran-matrix, r-bioc-rtracklayer, r-bioc-s4vectors Filename: pool/dists/noble/main/r-cran-ocrrbbr_0.1.0-1.ca2404.1_all.deb Size: 3046042 MD5sum: ef30fac3ac47d9ac84de1ce05667a20d SHA1: 98829ca0a87c905b9a10a1f1f7a7ad9ff066e63d SHA256: 42b3465bc3329e89f584888b7432f06cb7090c5ea33b2954f65813487984060a SHA512: a7924627578199b2b61de6c3b2f8d0b7f122d28281f04efc7290a4d5e523503d42457432c8e3478219b9c3dbf6f4cff849de344032dcade969ae204b2fecfadf Homepage: https://cran.r-project.org/package=ocrRBBR Description: CRAN Package 'ocrRBBR' (Explain Gene Expression with Boolean Rules of Chromatin States) Infers Boolean rules among cis-regulatory regions using paired chromatin accessibility and gene expression data at bulk and single-cell levels. Links regulatory regions to target genes, providing insights into gene regulation mechanisms. Package: r-cran-ocs4r Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-openssl, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-xml, r-cran-keyring Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ocs4r_0.3.1-1.ca2404.1_all.deb Size: 287536 MD5sum: 28c20bf2a4c283132c07b09bb26f8fa9 SHA1: 88331952a35f17229bc1396be17a6a555f17f891 SHA256: 163bda91c6a99558514e23cc44b00bd67059cd00cb1a4682a7a53ed504267361 SHA512: f8b66754ecdf7e87bae60423c5836b78b36c554f444b4399750de8c44887a6082e1eae2d253262505ab3a9651fd7e331245358b62c99acd1d8fa1c180f0d4cc3 Homepage: https://cran.r-project.org/package=ocs4R Description: CRAN Package 'ocs4R' (Interface to Open Collaboration Services (OCS) REST API) Provides an Interface to Open Collaboration Services 'OCS' () REST API. Package: r-cran-ocsdata Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-usethis, r-cran-purrr, r-cran-httr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ocsdata_1.0.2-1.ca2404.1_all.deb Size: 276796 MD5sum: 14355d751f1c3be49e7172fc124ebbf9 SHA1: 291a9fe6768cfafc247fbe4e7be7f0db5ec4e3de SHA256: 2d39530b3d7c1a95876f33ef150c6c1e2a5e3b7f1abcf7ed718addc797719f31 SHA512: 2cd6d983443972d3a082dd6ef1ee53982843e02746dba445b368d6d909d7105d77763059100bdf9d48c74f3bc401632011fe8b91756b38301dad5833cc7b00a6 Homepage: https://cran.r-project.org/package=OCSdata Description: CRAN Package 'OCSdata' (Download Data from the 'Open Case Studies' Repository) Provides functions to access and download data from the 'Open Case Studies' repositories on 'GitHub' . Different functions enable users to grab the data they need at different sections in the case study, as well as download the whole case study repository. All the user needs to do is input the name of the case study being worked on. The package relies on the httr::GET() function to access files through the 'GitHub' API. The functions usethis::use_zip() and usethis::create_from_github() are used to clone and/or download the case study repositories. To cite an individual case study, please see the respective 'README' file at . . Package: r-cran-octawave Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matlab, r-cran-plotly, r-cran-sync3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-octawave_0.1.1-1.ca2404.1_all.deb Size: 576146 MD5sum: fee34c5e4c3ff1e2a92312400d29deb7 SHA1: e3f7ed1582eff8533d265ad239a8fefa90ee4b56 SHA256: c01219f32c852b5f0bc0544a7fdaef3ae5dba77d66b919276353a3d6450a38fe SHA512: ee3971f69d0b67434a7055e46ba502c6cf0b8191f1e0c7f9900b4fc50276cdc6ba607c13e6cb3d8064561a457ba352540159aa8b9dc7bd51d61bb1dcbe8ad91a Homepage: https://cran.r-project.org/package=octawave Description: CRAN Package 'octawave' (Spatial Octahedral Quantum Wave Functions) Provides mathematical tools for simulating and visualizing three-dimensional octahedral quantum wave interferences and spatial resonance fields. Includes functions for MRI slice generation of fullerene structures and wave models. Computational modeling and three-dimensional visualization of fullerene and octahedral topologies are implemented within the R statistical environment, with interactive plotting powered by 'plotly'. Theoretical foundations are based on the topological frameworks of Cataldo et al. (2015) , Dresselhaus et al. (1996, ISBN:9780122218200), and Coxeter (1973, ISBN:9780486614809); the geometric principles of equations of the octahedron type are outlined in Bobenko and Suris (2012) . Additional structural and biological symmetry contexts are derived from Bragg (1914) and Caspar and Klug (1962) . Package: r-cran-octopucs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-stringr, r-cran-progress Filename: pool/dists/noble/main/r-cran-octopucs_0.1.1-1.ca2404.1_all.deb Size: 73580 MD5sum: 02661763125ab528e0fe6380a96937cb SHA1: 49c38ada2691a9f98dbb496bc137856479a2ab00 SHA256: cf1d42665eb3dc7ad830a097dd58010a98917614d6d38cc3d4c48add007aeadc SHA512: 16ad0c36a9d59a5b40045ff936e6fb18243148144e5e4e9079d478aa7a574014004b3e272f360969b62b586ed3e6c6514299741c59c9a16b60a4f920a9caaa80 Homepage: https://cran.r-project.org/package=octopucs Description: CRAN Package 'octopucs' (Statistical Support for Hierarchical Clusters) Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach. Package: r-cran-octopus Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-httr, r-cran-janitor, r-cran-rio, r-cran-shiny, r-cran-shinyace, r-cran-shinyjs Suggests: r-cran-keyring, r-cran-knitr, r-cran-odbc, r-cran-readr, r-cran-rmarkdown, r-cran-rmysql, r-cran-rpostgres, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-octopus_0.4.2-1.ca2404.1_all.deb Size: 215958 MD5sum: dd31c4d927504c8cef33c0ec5f01a6dd SHA1: 8e3f8c6b59e2f01f448665d03d7dbd29104c48bc SHA256: 919473ddd5b069c90aa603f5e423a66594c3ed38e619d531c3b6127e6775f5a0 SHA512: 836a5bb2b1a0b4c7501b4d12d81fcaf2a401b1d988d85e9db1b753e3e2b7a2fcef4114137efbec887a468c5b51d9c3eea4ab6d1d5df5fdb3b7b9560c3aafbd24 Homepage: https://cran.r-project.org/package=octopus Description: CRAN Package 'octopus' (A Database Management Tool) A database management tool built as a 'shiny' application. Connect to various databases to send queries, upload files, preview tables, and more. Package: r-cran-octopusr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-askpass, r-cran-cli, r-cran-glue, r-cran-httr2, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-lubridate, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-octopusr_1.0.1-1.ca2404.1_all.deb Size: 37918 MD5sum: c7fb0cb1c219fd95e6b2f3970ab48b23 SHA1: fce5ff55402aeabbf228c0d49d2cf3b9a27bc5bc SHA256: a73b56a0e8d1eec2c1e45d81d340e59422d8290220c3ee4b48a75249f3119e24 SHA512: aaaeb10261308c2bc178d6040e0c9fb79ba926858a4bdbcf7c59d60a1e7ef59f31cd102f89059770ec7eb4fa41ea8afb31a81c072f1c0ebc8d740917755d17a0 Homepage: https://cran.r-project.org/package=octopusR Description: CRAN Package 'octopusR' (Interact with the 'Octopus Energy' API) A simple wrapper for the 'Octopus Energy' API . It handles authentication, by storing a provided API key and meter details. Implemented endpoints include 'products' for viewing tariff details and 'consumption' for viewing meter consumption data. Package: r-cran-ocupacoesbr Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl Filename: pool/dists/noble/main/r-cran-ocupacoesbr_0.8.2-1.ca2404.1_all.deb Size: 1369252 MD5sum: 32056f487f1372230a000a93bb8ef4f7 SHA1: 68c00457d24e0ec7cbe92caff9c9266f04f4e4af SHA256: 50317aadd34773d36c2cd5366b138220ccd514d785113fb61864a082de70db6e SHA512: 0f83ba9667d19aadb11c3fcbb923b6b668fcc9cd1499288d390cbaddf44221769b290bd967c2866545a2bfb30c4605b661a1630f6bfb352c1423098ca241a7c8 Homepage: https://cran.r-project.org/package=ocupacoesBR Description: CRAN Package 'ocupacoesBR' (Traduz a Ocupação Declarada ao TSE em ClassificaçõesPadronizadas. English: Translates Occupations Declared to theTSE into Standardized Classifications) Traduz a ocupação registrada em fontes brasileiras para a Classificação Internacional Uniforme de Ocupações (ISCO-88 e ISCO-08) e, a partir dela, para medidas padronizadas de posição social: o índice socioeconômico ISEI (Ganzeboom e Treiman, 1996 ), o prestígio SIOPS (Treiman, 1977, ISBN:0-12-698750-5), o esquema de classes Erikson-Goldthorpe-Portocarero (EGP; Erikson, Goldthorpe e Portocarero, 1979 ) e um esquema de classes e estratos desenhado para o dado eleitoral. Traz ainda o ISEI-BR, uma régua estimada pelo pacote: o procedimento de Ganzeboom, De Graaf e Treiman (1992) refeito sobre a PNAD Contínua, em vez de importado de dado estrangeiro. As decisões de medida estão justificadas em Peixoto (2026) . Há duas portas de entrada: o código de ocupação declarado nas candidaturas ao Tribunal Superior Eleitoral (TSE) e a Classificação Brasileira de Ocupações (CBO-2002 e CBO-94), usada na RAIS, no CAGED e no eSocial. As tabelas de conversão derivam das sintaxes publicadas do International Stratification and Mobility File (Ganzeboom e Treiman) e da tábua oficial de conversão do Ministério do Trabalho, e são geradas por script a partir dos arquivos originais, nunca transcritas manualmente. O dicionário que leva o código do TSE à ISCO-88 é, ao lado do ISEI-BR, a outra peça autoral: foi construído pelo autor, código a código, e é validado contra patrimônio e escolaridade das próprias candidaturas. English: Translates occupation as recorded in Brazilian sources into the International Standard Classification of Occupations (ISCO-88 and ISCO-08) and, from it, into standardized measures of social position: the International Socio-Economic Index (ISEI; Ganzeboom and Treiman, 1996 ), the Standard International Occupational Prestige Scale (SIOPS; Treiman, 1977, ISBN:0-12-698750-5), the Erikson-Goldthorpe-Portocarero (EGP) class scheme (Erikson, Goldthorpe and Portocarero, 1979 ) and a class and strata scheme designed for electoral data. It also provides ISEI-BR, a scale estimated by the package itself: the procedure of Ganzeboom, De Graaf and Treiman (1992) is re-estimated on Brazil's Continuous National Household Sample Survey (PNAD Contínua) instead of importing scores estimated on foreign data. The measurement decisions are justified in Peixoto (2026) . There are two entry points: the occupation code that candidates declare when registering with Brazil's Superior Electoral Court (Tribunal Superior Eleitoral, TSE), and the Brazilian Classification of Occupations (CBO-2002 and CBO-94), used in the RAIS, CAGED and eSocial administrative records. The conversion tables derive from the published syntax files of the International Stratification and Mobility File (Ganzeboom and Treiman) and from the official conversion table of Brazil's Ministry of Labor, and are generated by script from the original files, never transcribed by hand. Alongside ISEI-BR, the dictionary that maps TSE occupation codes to ISCO-88 is the package's other original contribution: it was built by the author code by code and is validated against the declared assets and education of the candidates themselves. Package: r-cran-od Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfheaders, r-cran-vctrs Suggests: r-cran-covr, r-cran-knitr, r-cran-lwgeom, r-cran-nngeo, r-cran-rmarkdown, r-cran-sf, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-od_0.5.1-1.ca2404.1_all.deb Size: 1115974 MD5sum: d1dab94b01ebf127293bdf132ff52910 SHA1: ec7069dd7d2948ca6bb675da2fcacc0a54baae67 SHA256: 626ecbe02bbdaf5c52c34e8eaf2b4879313bd9688d4fef5d18ca24160ba076ed SHA512: 274ea1921815fe947c8075988e7a759535176107570b488f5e5f1efdd8f0f4a2f5abb6169739cce8146e127eb98d25ec801408bb2633043fd0dbab06550d8ab3 Homepage: https://cran.r-project.org/package=od Description: CRAN Package 'od' (Manipulate and Map Origin-Destination Data) The aim of 'od' is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) . The package builds on functions for working with 'OD' data in the package 'stplanr', (Lovelace and Ellison 2018) with a focus on computational efficiency and support for the 'sf' class system (Pebesma 2018) . With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of 'OD' datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries. Package: r-cran-oda Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1292 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-oda_0.1.2-1.ca2404.1_all.deb Size: 1057192 MD5sum: 079739b832573dd15cce7bcbd9d8df99 SHA1: 357b6aba68fb9fd5349af4402a56b82a9914625b SHA256: 38898eb2a178ba900638a6d88cde598c4d931da3596b3f6564a65e034ae3ba46 SHA512: 87adbb7e36fd2dd23dce3c13212c3ddc720b572e6334fd9dea0cf6b942381daf99121dced4fb2e8559d235331e97fc3d6adc87dafeb74273eb70ef0b8271be0b Homepage: https://cran.r-project.org/package=oda Description: CRAN Package 'oda' (Pure-R Core Engine for Optimal Data Analysis (ODA / MultiODA)) Pure-R implementation of univariate binary-class ODA (UniODA), univariate multiclass ODA (MultiODA), and binary Classification Tree Analysis (CTA). Supports ordered and categorical attributes, priors-on inverse-frequency weighting, MAXSENS / SAMPLEREP / first-identified tie-breaking, true leave-one-out cross-validation, and Monte Carlo Fisher-randomization p-values. Covered UniODA, MultiODA, and binary CTA fixtures are tested for parity against MegaODA.exe and CTA.exe outputs. Package: r-cran-odataquery Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-odataquery_0.5.3-1.ca2404.1_all.deb Size: 101664 MD5sum: cd16061fecd494d106294cbbaa96f193 SHA1: a581d67737fa5a737222b46f4b08da4d17385645 SHA256: e4244f99485946f446588c916414442fb084b4f73dcebe0207888a56ee646960 SHA512: 0d9d53d785a36d9bff6c6fd2f71ce2c21b72490989b2485c990ceb2904b3663720d824f17446bf57a052c6b62995945f19c6aeb5b4c19505638d59c08b5ab216 Homepage: https://cran.r-project.org/package=ODataQuery Description: CRAN Package 'ODataQuery' (Querying on 'OData') Make querying on 'OData' easier. It exposes an 'ODataQuery' object that can be manipulated and provides features such as selection, filtering and ordering. 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This package uses the Resource model, with URL "resolver" and "client", to dynamically discover and make accessible tables stored in a 'MS SQL Server' database. For more details see Marcon (2021) . Package: r-cran-odbr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-haven, r-cran-piggyback, r-cran-r.utils, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odbr_0.1.1-1.ca2404.1_all.deb Size: 140512 MD5sum: 8616be4a0163923b71d2b1cb90cf157e SHA1: 4c75cea58951157fa1d82aad599a09f9fffa3453 SHA256: e2b41d2f99988348b1e7882b30b3218aa0951946b3147cf78eae561a3aa75960 SHA512: 367d3c134e4a021d51b8454bb7c6e750e7f47d540ba21f19b7162e5238bf27d0273807d78e92e1b834a7e93191d77ce3c3791a1605ec932c6cecf03526cb3a36 Homepage: https://cran.r-project.org/package=odbr Description: CRAN Package 'odbr' (Download Data from Brazil's Origin Destination Surveys) Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years. Package: r-cran-oddnet Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-fable, r-cran-fabletools, r-cran-feasts, r-cran-igraph, r-cran-lookout, r-cran-pcapp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tsibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-urca Filename: pool/dists/noble/main/r-cran-oddnet_0.1.2-1.ca2404.1_all.deb Size: 201338 MD5sum: 9db19b07f6d181eb4428ca95bcff96bf SHA1: 7fae3ab3eefe4b0ebc9c60a3f71018fe0f787f12 SHA256: 07848f6295748e9e5e39217796addaf4a532cfda485c27e629e80b6f7e3c2c5f SHA512: 222bb04f5b4b23e4fe142157c6a312a102206783d2c9e55adad75953869a724c2f23ecbc009965dceede6db03a9e5b9bc92984c84acaae7f08f2b37292f50398 Homepage: https://cran.r-project.org/package=oddnet Description: CRAN Package 'oddnet' (Anomaly Detection in Temporal Networks) Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Sanderson, Hyndman 2024) . Package: r-cran-odds.converter Architecture: all Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-odds.converter_1.4.8-1.ca2404.1_all.deb Size: 74420 MD5sum: 6ab890f572e5fe9674958f96dafaafd5 SHA1: e9e9d40b24f2a82b84491b2d8dc68d0a9ffdd7bc SHA256: 945ceae57c3c5c71ff4beac2085de7c37d12da0389096f532e7b607b6049ff54 SHA512: 59d2d2bb1e5a49bdc6fb98199b0f98c67b4d827d3d75e28043170f77a6e12d99fcb81d6bd1a3a6bee2dcfe7cf542e8e7b7f095f11a9e6ca53dc3a2f1be5d0be8 Homepage: https://cran.r-project.org/package=odds.converter Description: CRAN Package 'odds.converter' (Betting Odds Conversion) Conversion between the most common odds types for sports betting. Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw Probability are covered in this package. Package: r-cran-odds.n.ends Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-odds.n.ends_0.1.4-1.ca2404.1_all.deb Size: 27564 MD5sum: 81d8434555339727fee23881b0448d1a SHA1: 12de45cc18729876e441b22ba1a42989802fd9e9 SHA256: 6b05b3037a31b3fa47b3908269f69b6a8e465d047f464ed417a9e9ce40ea9270 SHA512: 8a5bccf5dde21aca9999736c71ac8f323ab35677a2d72c7b5b33900a751b43fac9d5ca9b98bfde423941ab976823c1228f18110eba081efae44abb9c92bf10c3 Homepage: https://cran.r-project.org/package=odds.n.ends Description: CRAN Package 'odds.n.ends' (Odds Ratios, Contingency Table, and Model Significance from aGeneralized Linear Model Object) Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity. Package: r-cran-oddsapiio Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-oddsapiio_0.1.1-1.ca2404.1_all.deb Size: 37718 MD5sum: 77a3689e11ed4006e7dcf530fd76c9ed SHA1: dfd44fef1da18a5bc71e75b3808d63567e8bf159 SHA256: 6de297c735843e3f9138e8c2c34b378d39818b92e98515a9f839d032c4b53f17 SHA512: 96d0530a6bba176b005e172a2509d2217557f7953eda2543cc05a68606fa5e472b9318be9aedb0c599187c6db86a786a705051c9fb89fa4e043a38c05956965f Homepage: https://cran.r-project.org/package=oddsapiio Description: CRAN Package 'oddsapiio' (Client for the 'Odds-API.io' Sports Betting Odds API) Query live and upcoming sports betting odds from the 'Odds-API.io' REST API . Covers sports, bookmakers, leagues, events, per-event odds, value bets and arbitrage bets across 265+ bookmakers, returned as tidy data frames. An API key is required; a free tier is available. Package: r-cran-oddsapir Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-rvest, r-cran-tibble, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-ggplot2, r-cran-ggrepel, r-cran-gt, r-cran-knitr, r-cran-progressr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-stringr, r-cran-testthat, r-cran-tidyselect, r-cran-usethis, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-oddsapir_1.0.1-1.ca2404.1_all.deb Size: 139534 MD5sum: f7b5267dad6ae4ffeb56f8ce431c2ab7 SHA1: a851c3f571a9c8c71779a056882ce9727c3fc347 SHA256: e5b7ead84ad6ef73b58943ea5690c665bd04e473c8de4b2b9d4e0b7b59d85e64 SHA512: 3c1f1b9fc61b4d8d496213e7179ecca5a1be53e19331b76682dea6e93714e65da906a0b0ea3dba40aaa75b272f6936bb41b380e72b8cb590da0bbaa25c23f4ce Homepage: https://cran.r-project.org/package=oddsapiR Description: CRAN Package 'oddsapiR' (Access Live Sports Odds from the Odds API) A utility to quickly obtain clean and tidy sports odds from The Odds API . Provides wrappers for every version 4 endpoint -- featured-market and single-event odds (including player props and alternate lines), historical odds snapshots, scores, events, participants, and usage-quota reporting -- returning tidy tibbles ready for analysis. Package: r-cran-oddsplotty Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-mlbench, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-ggthemes, r-cran-e1071, r-cran-tidymodels, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-markdown Filename: pool/dists/noble/main/r-cran-oddsplotty_1.0.2-1.ca2404.1_all.deb Size: 99118 MD5sum: 654586e205e0704b6728227f57c0790c SHA1: e16a30f8be1b059d95f1729626f4e661242ac591 SHA256: be9378bccaf7ab6a3a79803802f84a31bdb30d1831a348922309a3652eb7ade3 SHA512: fa4ddca27f911176094a77d610cafc9db35c756c9d532c969b22cb6dd54ee1f83f6f1450224f931acfc97c07b688707772e78bf9a6d2a4af29bc2ec50e9a1eb4 Homepage: https://cran.r-project.org/package=OddsPlotty Description: CRAN Package 'OddsPlotty' (Odds Plot to Visualise a Logistic Regression Model) Uses the outputs of a logistic regression model, from caret , to build an odds plot. This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model. Package: r-cran-oddsratio Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mgcv Suggests: r-cran-gam, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oddsratio_2.0.2-1.ca2404.1_all.deb Size: 277186 MD5sum: dca2d87a0d5a8e06fded2ec03e06c02c SHA1: c7642cb86e9e2dcf493cbf59971f7e7925a53a0c SHA256: 2fa35dcfbe3c15b00d27045e088f2c63fd90e5131e59ba9c7482fd068c10ec7e SHA512: 91f2f5071f1ef714fdc66c71dd95fff87fab73f95960776dde3d1faaa16fec53c2395c65c2d034ff39074e8d53d876d1c6193111380de5ad6868a0987b05e7e2 Homepage: https://cran.r-project.org/package=oddsratio Description: CRAN Package 'oddsratio' (Odds Ratio Calculation for GAM(M)s & GLM(M)s) Simplified odds ratio calculation of GAM(M)s & GLM(M)s. Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant. Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally. Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot. Package: r-cran-oddstream Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcapp, r-cran-ggplot2, r-cran-ks, r-cran-mass, r-cran-rcpproll, r-cran-mgcv, r-cran-rcolorbrewer, r-cran-mvtsplot, r-cran-tibble, r-cran-reshape, r-cran-dplyr, r-cran-tidyr, r-cran-kernlab, r-cran-magrittr Suggests: r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-oddstream_0.5.0-1.ca2404.1_all.deb Size: 3278464 MD5sum: 477a1b973e75413ed6bc1cdc0523d1e8 SHA1: 6540fca38f8a5170b8380dadc44a67f8a2e232c2 SHA256: 4950c5d33b0bb28a815406ac0974510e885712287e59ada8c8a98a88aae1c620 SHA512: 09b370f203d0ff312a36f7a852ae94faa2b4630c8da0df10b3d3bec82eebbb175f13df97554379fe6293a96e9fdea94020aa3b6012c122db777569b48a1d0b97 Homepage: https://cran.r-project.org/package=oddstream Description: CRAN Package 'oddstream' (Outlier Detection in Data Streams) We proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) . Package: r-cran-odenetwork Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-desolve Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-odenetwork_1.3.2-1.ca2404.1_all.deb Size: 130538 MD5sum: 2b4f198f98bf21137d6115d5ebdd8ef6 SHA1: 9bd3235c335a2dbca35e163e532c8ffa152741eb SHA256: 04b9bc964d9791208f8f14a0d4df0df485eb2771c3df8bdcc79ffe5641405947 SHA512: 6e52cb2d76ed289c2b8c7a82621ab67d9fb6d366652d65c0662394583c1427f1ad3080304d1ae2d71753b18215e23504f05e12cf6e55d25f0a78aa08fdeab021 Homepage: https://cran.r-project.org/package=ODEnetwork Description: CRAN Package 'ODEnetwork' (Network of Differential Equations) Simulates a network of ordinary differential equations of order two. The package provides an easy interface to construct networks. In addition you are able to define different external triggers to manipulate the trajectory. The method is described by Surmann, Ligges, and Weihs (2014) . Package: r-cran-odesensitivity Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-desolve, r-cran-odenetwork, r-cran-sensitivity Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odesensitivity_1.1.2-1.ca2404.1_all.deb Size: 146408 MD5sum: dadcd96a5cebed31351f2c39e624fce6 SHA1: e2d8d931dd97b48ba6dc55130589fd88d137bc11 SHA256: 69145844119478232fc9e20c8a99892901d3766cce5137c1380dd8beb8033ced SHA512: 69cacb59ab79f0e1acf87bc3575a1fe2f59827e78509d0b63ab2bfce189c54e714b8480c93c81dcf3874ca1af499e442d3a5e693c43e46fbe935dd673692c201 Homepage: https://cran.r-project.org/package=ODEsensitivity Description: CRAN Package 'ODEsensitivity' (Sensitivity Analysis of Ordinary Differential Equations) Performs sensitivity analysis in ordinary differential equation (ode) models. The package utilize the ode interface from 'deSolve' and connects it with the sensitivity analysis from 'sensitivity'. Additionally we add a method to run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed plotting function provides outputs on the calculations. The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) . Package: r-cran-odetector Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ppclust Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-odetector_1.0.1-1.ca2404.1_all.deb Size: 226282 MD5sum: 837753a507c32d9d88a724e6a239fe5f SHA1: 91d8ee88e0a597b8f6c70b5b75f2958d7370203e SHA256: b4106fc6d9c4c6d7108435911b8d93ce07ad455c1134779d08794c4cf9179c7b SHA512: 9020616b0877803dcff16d59419bf66a8322fe77ad8a05d905d73bbcc004dcf6c576893179a3f56dd0bdb345acddeab6a251174e56433f0d99c2ab2bb6e47fee Homepage: https://cran.r-project.org/package=odetector Description: CRAN Package 'odetector' (Outlier Detection Using Partitioning Clustering Algorithms) An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 ). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants. Package: r-cran-odiffr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 662 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-base64enc, r-cran-digest, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-lattice, r-cran-magick, r-cran-openssl, r-cran-pdftools, r-cran-png, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-odiffr_0.6.0-1.ca2404.1_all.deb Size: 439086 MD5sum: 8eecec3eaa03e103fba81273b9bf4427 SHA1: 33d817c1fc6c122ab7b3657032281b4a1dd9e22c SHA256: 3087b9180435e271368c33c71646d7c7f5ff4be741f39fefa564f2f85d92f7de SHA512: 685402224222d60690de44c87ab8cbcefa3bf81e30adc370ace6120346624c279d047eaf7bd20af9c2c309c0505153585c1f355890610500f227557ba8586b2c Homepage: https://cran.r-project.org/package=odiffr Description: CRAN Package 'odiffr' (Fast Pixel-by-Pixel Image Comparison Using 'odiff') R bindings to 'odiff', a fast SIMD pixel-by-pixel image comparison tool . Compares PNG, JPEG, WEBP, TIFF and BMP images, plots and PDF pages with configurable thresholds, antialiasing detection and ignore regions. Provides 'testthat' expectations and snapshot testing (including for 'shinytest2' screenshots), batch and directory comparison, HTML, Markdown and JUnit reports, baseline approval and audit records. Requires the 'odiff' binary, which can be downloaded with install_odiff(). Package: r-cran-odin Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2054 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-cinterpolate, r-cran-desolve, r-cran-digest, r-cran-glue, r-cran-jsonlite, r-cran-ring, r-cran-withr Suggests: r-cran-dde, r-cran-jsonvalidate, r-cran-knitr, r-cran-mockery, r-cran-pkgbuild, r-cran-pkgload, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odin_1.2.7-1.ca2404.1_all.deb Size: 1485162 MD5sum: 8d0b1b131ef4313eec03f8c7e8dca970 SHA1: fc12e555ce286fb5cb8c6e1cadf93334ec507995 SHA256: e34e4877cbbab241f74e0803d97e82643e113d1edae9598e0a9f301102cf737e SHA512: 816bdeeb1a32069a78ea1040d3e950ecedd7bf9ffe240111fcc9099c58b4a17dab8b9f1f1e8e2c1acacbd091f784400505406f6e857f04ed43b4770e54b160e8 Homepage: https://cran.r-project.org/package=odin Description: CRAN Package 'odin' (ODE Generation and Integration) Generate systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system. A solver is not provided, but instead interfaces to the packages 'deSolve' and 'dde' are generated. With these, while solving the differential equations, no allocations are done and the calculations remain entirely in compiled code. Alternatively, a model can be transpiled to R for use in contexts where a C compiler is not present. After compilation, models can be inspected to return information about parameters and outputs, or intermediate values after calculations. 'odin' is not targeted at any particular domain and is suitable for any system that can be expressed primarily as mathematical expressions. Additional support is provided for working with delays (delay differential equations, DDE), using interpolated functions during interpolation, and for integrating quantities that represent arrays. Package: r-cran-odk Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gsheet, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-odk_1.5-1.ca2404.1_all.deb Size: 153110 MD5sum: 6504845bdd88a2f1c70cbfa6b795b976 SHA1: c842aeed42d4709428cc5ece24c6f59191fe6298 SHA256: 77793d3a5de3368d9362680347765cec37080264ee9f515179c50c17a67691b1 SHA512: dde7932b7e8031be3101fc3a08a2672fa0ce24e8bf538ff3e804a7cedc39d2d52df16446c36839a506a46495376e5414bc2a4d0bbcb8fc919482423c766b8df1 Homepage: https://cran.r-project.org/package=odk Description: CRAN Package 'odk' (Convert 'ODK' or 'XLSForm' to 'SPSS' Data Frame) After develop a 'ODK' frame, we can link the frame to 'Google Sheets' and collect data through 'Android' . This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'. Package: r-cran-odmeans Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-ggplot2, r-cran-ggmap, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-odmeans_0.2.1-1.ca2404.1_all.deb Size: 3201312 MD5sum: f81615ec2e7ed6b5ca325664bb66d85d SHA1: 57ce9e1c647544a12ef612a009f4941f7d40f0e9 SHA256: d19009bb5280765334be5b0caa751ecc7bcb7a5f749a640d518551e0529be8ab SHA512: 9797d1ee4c67200296742ec8e20c81e417d467bb6cd66a135c917b817d70e68ff4f3ace8137389d3b980446f45ce649a5f4940ea2cfb4f71d5cc30b1e82a066f Homepage: https://cran.r-project.org/package=ODMeans Description: CRAN Package 'ODMeans' (OD-Means: k-Means for Origin-Destination) OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. Package: r-cran-odr Architecture: all Version: 1.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-odr_1.8.3-1.ca2404.1_all.deb Size: 573630 MD5sum: e7a5e7b28d3f8b442806b7fa2dfb3081 SHA1: 245ac1a8a3746bd62fc512ad647c6438066259ef SHA256: 11c0c6d73607bf5b9a78330ef7fc79ffbbad2efc11a1ef1c067a0cb75355d5c5 SHA512: 9148b5c6ec01d26e028725a3c3bc637bc9e7459ba511b1bd2ae4dab2510b1087a71eefa373b1d5b23c845881e33404e54931a69bc34b46d07c1060d5456c0f7b Homepage: https://cran.r-project.org/package=odr Description: CRAN Package 'odr' (Optimal Design and Statistical Power for Experimental StudiesInvestigating Main, Mediation, and Moderation Effects) Calculate the optimal sample size allocation that uses the minimum resources to achieve targeted statistical power in experiments. Perform power analyses with and without accommodating costs and budget. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. . (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. . (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education, 90(3), 693-711. . (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). Using ant colony optimization to identify optimal sample allocations in cluster-randomized trials. The Journal of Experimental Education, 93(1), 167-185. . (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and optimal design for randomized controlled trials investigating mediation effects. Psychological Methods. . (6) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from . Package: r-cran-ods Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-survival Filename: pool/dists/noble/main/r-cran-ods_0.2.0-1.ca2404.1_all.deb Size: 248518 MD5sum: 9b7bbab4a7c69a4ad10e598e230287c1 SHA1: 03825482da64959aa6103cad07117a10be089e2e SHA256: 48113596369bc3e212ca71d88694204411698d3b9316ab284099a1f4f01f5582 SHA512: cd0080aff134d59e8fe83332bb26b3977f075fc5fb36d586eb9c2631d4fa913b71b02dfc26189b6cc65fe7958129091cbb70a0bea88628d7129c481c6ab25ac7 Homepage: https://cran.r-project.org/package=ODS Description: CRAN Package 'ODS' (Statistical Methods for Outcome-Dependent Sampling Designs) Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) . Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) . (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) . (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) . Package: r-cran-odt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-partykit, r-cran-data.tree, r-cran-magick, r-cran-diagrammersvg, r-cran-diagrammer, r-cran-rsvg Suggests: r-cran-runit, r-cran-matrix, r-cran-rmarkdown, r-cran-robustbase, r-cran-knitr Filename: pool/dists/noble/main/r-cran-odt_1.0.0-1.ca2404.1_all.deb Size: 1340754 MD5sum: ac4e5f92fc6b31f398fe6f5e56102f17 SHA1: a503d701bf4961449479ba840b1609b2afb1e091 SHA256: 6334df40ebee693e26314470fbc0708898f9fa4e7397b9968bf3735612e42205 SHA512: 01aeeb4b0bfe7260369f9c39d1406dab66cdfc7d45119fd597010ed7d575388041f999b1ae9583a084a196164729ee48128f74da1c4c28e7fb6fe52851dee342 Homepage: https://cran.r-project.org/package=ODT Description: CRAN Package 'ODT' (Optimal Decision Trees Algorithm) Implements a tree-based method specifically designed for personalized medicine applications. By using genomic and mutational data, 'ODT' efficiently identifies optimal drug recommendations tailored to individual patient profiles. The 'ODT' algorithm constructs decision trees that bifurcate at each node, selecting the most relevant markers (discrete or continuous) and corresponding treatments, thus ensuring that recommendations are both personalized and statistically robust. This iterative approach enhances therapeutic decision-making by refining treatment suggestions until a predefined group size is achieved. Moreover, the simplicity and interpretability of the resulting trees make the method accessible to healthcare professionals. Includes functions for training the decision tree, making predictions on new samples or patients, and visualizing the resulting tree. For detailed insights into the methodology, please refer to Gimeno et al. (2023) . Package: r-cran-odysseuscharacterizationmodule Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-databaseconnector, r-cran-jsonlite, r-cran-sqlrender Suggests: r-cran-andromeda, r-cran-eunomia, r-cran-featureextraction, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-odysseuscharacterizationmodule_0.0.1-1.ca2404.1_all.deb Size: 200340 MD5sum: f1529f1f570926f3346849a32504b429 SHA1: 0a02d90c8c72a2f0665450d08e9341a8135ed64f SHA256: 5081f77b255f120320bf08976fa471ba1b6819edac67305e7bfe5b7a8a2653ca SHA512: 2f8c0a05697460e05274aa0e89e0fff7c4473ec2948df3723b0c8f08cd05e2a6e0edde2f718d7ffa727d4748fccd2f6366fc99b190efee40a7fffe934ec7e271 Homepage: https://cran.r-project.org/package=OdysseusCharacterizationModule Description: CRAN Package 'OdysseusCharacterizationModule' (Handy and Minimalistic Common Data Model Characterization) Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8). Package: r-cran-odysseuspathwaymodule Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender Suggests: r-cran-eunomia, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odysseuspathwaymodule_0.0.1-1.ca2404.1_all.deb Size: 78926 MD5sum: 72d70bc171c5686abb767817830bff97 SHA1: 986472be1dac9cde0597f6539f13e7ef8765a998 SHA256: 816850df317258363b386c9ecc267a8be6273fe34316b5e83067c4cd94ac485a SHA512: 62f0a6eca537bab91a7eeb3344eb1a98cd4a0299426308e10df20ad8aa065be0442941e4c7cb32725e1966dc580b0a030ca406b39fc9c272084a4c3904c3f1e2 Homepage: https://cran.r-project.org/package=OdysseusPathwayModule Description: CRAN Package 'OdysseusPathwayModule' (Cohort Pathway Analysis with Pre-Index Event Support) Provides cohort pathway analysis for Observational Medical Outcomes Partnership (OMOP) Common Data Model databases, including both standard (post-index) and pre-index pathway analyses. The pre-index analysis identifies sequences of events occurring in a lookback window before the target cohort index date. Built on the 'CohortPathways' analysis framework originally developed by Christopher Knoll and the Observational Health Data Sciences and Informatics community through 'WebAPI'. Methodological background and the originating implementation are described in . Package: r-cran-odysseussurvivalmodule Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-ggsurvfit, r-cran-sqlrender, r-cran-survival Suggests: r-cran-eunomia, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odysseussurvivalmodule_0.1.0-1.ca2404.1_all.deb Size: 86520 MD5sum: c3217c5ee021936974141697238c39a4 SHA1: 7186d6ec5185ec53e0fa02447a92a0b07bc90357 SHA256: bf6a6cd26307bbd4165ce588591a5b2f319372fd0ab42fe0a27b023791c17ff0 SHA512: c1f96dcc802540b928953579bc0712d5a7b0ddcae99b84ccbdf01cb528402714be2854a940a7c82010715e63cea979f6bb03ecbc04570ad00376921a865e0ae6 Homepage: https://cran.r-project.org/package=OdysseusSurvivalModule Description: CRAN Package 'OdysseusSurvivalModule' (Cohort-Based Single-Event Survival Utilities) Tools to build single-event survival datasets from "OMOP CDM" cohorts and estimate survival outcomes. The package supports Kaplan-Meier, Cox proportional hazards, and parametric accelerated-failure-time models, with optional stratification by gender and age groups. Package: r-cran-odyssey Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-rlang Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-odyssey_1.0.1-1.ca2404.1_all.deb Size: 84856 MD5sum: f9c44178fad8a0d86e74c1a39cf1a3c3 SHA1: 3d75a43becc7959b21bd3a3c3428c5fc13a905b9 SHA256: c46842e4530dcdd77666a13ca70a37ce4cb774dc8512f091e0ad164785bdd666 SHA512: 446c5ea85cb268ea88b33633bc025ec95998dcd2e6cf87ccfa797ccebe91d015f62150c9d79d8b327ca8894301900cadb5d31819573e00738f10fc80b3c96df6 Homepage: https://cran.r-project.org/package=odyssey Description: CRAN Package 'odyssey' (Interface to the HAL Open Archive API) An interface to the search API of 'HAL' , the French open archive for scholarly documents from all academic fields. This package provides programmatic access to the API and allows to search for records and download documents. Package: r-cran-oecd Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-readsdmx, r-cran-xml2 Suggests: r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-oecd_0.2.5-1.ca2404.1_all.deb Size: 25828 MD5sum: 393ce33c0f4e589998f01e7a62a9ef16 SHA1: 4377fb2ee768ba84f46cf1485d76540abd2d1879 SHA256: ca4c67513de6af011f6564fce990c62f1851162222feb109ab944fe1421044f8 SHA512: 6a0bf2b96a345c9da58a44a199cad9ff4f9f1851b4c95194f6b4498d10ae46764b1b9642a1b34160988be1cdd5e9707d19255db001af5aa175f78b617e5e69a3 Homepage: https://cran.r-project.org/package=OECD Description: CRAN Package 'OECD' (Search and Extract Data from the OECD) Search and extract data from the Organization for Economic Cooperation and Development (OECD). 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ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem). 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Models with offsets are most useful when working with count data or when fitting an adjustment model on top of an existing model with a prior expectation. The former situation is common in insurance where data is often weighted by exposures. The latter is common in life insurance where industry mortality tables are often used as a starting point for setting assumptions. 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Learn more about 'OhdsiShinyAppBuilder' at . Package: r-cran-ohit Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ohit_1.0.0-1.ca2404.1_all.deb Size: 34062 MD5sum: 400f16a53912f0cededee187339db449 SHA1: 8e94c94bf7fd207e5747923988cbb0565fd73046 SHA256: acdf90402e3789f3d8954a1048be6c715265ce5bd518e325a189be9c7a0311bc SHA512: 14802b83d21c929952fc1372120c297b82611ab0e983c5c0b88362b801cabf4f82db4a6e71644be927788f99a067a24755d15a08581503e11f58410ea561a981 Homepage: https://cran.r-project.org/package=Ohit Description: CRAN Package 'Ohit' (OGA+HDIC+Trim and High-Dimensional Linear Regression Models) Ing and Lai (2011) proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. 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Package: r-cran-ohmmed Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cvms, r-cran-ggmcmc, r-cran-ggplot2, r-cran-gridextra, r-cran-mistr, r-cran-scales, r-cran-vcd Filename: pool/dists/noble/main/r-cran-ohmmed_1.0.2-1.ca2404.1_all.deb Size: 601300 MD5sum: d738911bf2342b419f68f038b9d8d5f8 SHA1: f70adf7bfffaf0192bb5308119d259388ff62483 SHA256: 717d95f774f95a86c78d4fee80195239a92fe189679e7b409ded54aef23b50fd SHA512: 3b6fe45eeed17d4c0a3fab07b9a72f5204bbf29a7c6da8886e49e0e0a66c401c388595833de86114f27520472549a435a30b699de16f29f1c4f704a68419790d Homepage: https://cran.r-project.org/package=oHMMed Description: CRAN Package 'oHMMed' (HMMs with Ordered Hidden States and Emission Densities) Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities ): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. 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Package: r-cran-oii Architecture: all Version: 1.0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rapportools, r-cran-gmodels, r-cran-deducer Filename: pool/dists/noble/main/r-cran-oii_1.0.2.1-1.ca2404.1_all.deb Size: 48344 MD5sum: 2fac486029d4ea96ecdd495995363745 SHA1: a5fabfebd36bbf1c7a5bb7a96c3c82e579ddee1f SHA256: 9010be8d6cb5b00fce9250a4ab10a58bd33f60d5fe312a4544520c97373f82a7 SHA512: 895f856d2bd1bf16d920a54a996e62cda1544c9da663a3ac585ff8d3e794966cfe21873fbe110ffbba92cdee283f9ac38bf3837e6a56965bc9a8eb25e7563b4a Homepage: https://cran.r-project.org/package=oii Description: CRAN Package 'oii' (Crosstab and Statistical Tests for OII MSc Stats Course) Provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data. Package: r-cran-ojsr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rvest, r-cran-tidyr, r-cran-urltools, r-cran-xml2, r-cran-purrr, r-cran-rlang, r-cran-rcurl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ojsr_0.1.5-1.ca2404.1_all.deb Size: 62048 MD5sum: f4068bb462e2fa546d069e6ecd72a482 SHA1: b383f0438f829a2d8996227627f9d5adaaab908e SHA256: de5dfd2a2551e74f448e059c9ffb90e865245d96d7fea4cfd93f6ff7b7a411fb SHA512: 865d9e26d16f4ba5361b7cf7e64182a45f0268cf27efdabd07ce2c7ba2308fb14ac7d33b5f8b3d536cb5051cf72229f82ece7e3b9cda0dd56894c267f78a48a1 Homepage: https://cran.r-project.org/package=ojsr Description: CRAN Package 'ojsr' (Crawler and Data Scraper for Open Journal System ('OJS')) Crawler for 'OJS' pages and scraper for meta-data from articles. You can crawl 'OJS' archives, issues, articles, galleys, and search results. You can scrape articles metadata from their head tag in html, or from Open Archives Initiative ('OAI') records. Most of these functions rely on 'OJS' routing conventions (). Package: r-cran-okbathtub Architecture: all Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-okbathtub_0.1.11-1.ca2404.1_all.deb Size: 550200 MD5sum: 524dd98cb5adb60ec33b3daaa7f856a0 SHA1: ed2a308e2954cb9e7457c1ec8a2698ce363414c9 SHA256: 1939f5ee7061ce0498df09a52134f633dd657429ffa3920200be60d96687e6cc SHA512: 66c80325613d21ce8e17a4b1b9989f28c942faf36e92feb9b899bb6815fcd53d974e100211768e6af6c4e139548434bdeebbd6b0c989d0991208073d0876a58b Homepage: https://cran.r-project.org/package=okBATHTUB Description: CRAN Package 'okBATHTUB' (Empirical Reservoir Eutrophication Modelling with OklahomaCalibration) Empirical reservoir water quality modelling using Walker's 'BATHTUB' Model 1 (second-order available-phosphorus sedimentation) from Walker (1985) and Walker (1996) as the default retention model. The Vollenweider (1976) hydraulic- residence form and the equivalent formulation of Larsen and Mercier (1976) are available as alternatives. Predicts in-lake total phosphorus, total nitrogen, chlorophyll-a, and Secchi depth from tributary nutrient and hydraulic loading inputs, and computes Carlson (1977) Trophic State Indices. Optional Oklahoma-specific chlorophyll and Secchi regression coefficients are provided, calibrated from publicly available state lake monitoring data. Supports single-segment and multi-segment reservoir configurations and load-reduction scenario analysis. Designed to complement watershed loading models such as the Soil and Water Assessment Tool ('SWAT'; ) and the U.S. EPA Hydrologic and Water Quality System ('HAWQS'; ) in a two-model nutrient management workflow. Package: r-cran-okcolors Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-okcolors_0.1.2-1.ca2404.1_all.deb Size: 25230 MD5sum: 154f7ad973547273fd0bee28749fbb35 SHA1: 7be13c01a6ea6b8c1020d76c5b62bfcb16eaea21 SHA256: 655789a51267236adac03645fd568e2157a1f3d6d98d515ae9d04bb224647447 SHA512: 36af41ef212a966eb05be9397bfcc0ebffe10522a25a987071354d21a3fd8fdc9b2d21dc839af1a0f41ba7f9bdc3deaf232e4b249bb91d4e0776c6b7e27a7c2b Homepage: https://cran.r-project.org/package=okcolors Description: CRAN Package 'okcolors' (A Set of Color Palettes Inspired by OK Go Music Videos for'ggplot2' in R) A collection of aesthetically appealing color palettes for effective data visualization with 'ggplot2'. Palettes support both discrete and continuous data. Package: r-cran-okf Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-yaml, r-cran-dbi, r-cran-duckdb, r-cran-digest, r-cran-jsonlite Suggests: r-cran-httr2, r-cran-commonmark, r-cran-testthat Filename: pool/dists/noble/main/r-cran-okf_0.7.0-1.ca2404.1_all.deb Size: 146096 MD5sum: af7e7d964c9159c11bc367203a79741c SHA1: 921990cb845bbacd000db3e258aab169ae11a619 SHA256: 6ffebe9a53b572831ad4a2f2e707d6852663ad0bd660fb253f4d65309e41dd47 SHA512: c82d4bfc1c48dcfe64de5369449bf448cf1238360174c680b4b3c4096ae033b634fb63f5adeaeb7c5b765250d115a4bf10ec230a17a3054ad7517597e95921ec Homepage: https://cran.r-project.org/package=okf Description: CRAN Package 'okf' (Open Knowledge Format (OKF) Ingestion) Read, validate, and load Open Knowledge Format (OKF) bundles (a directory of markdown files with YAML frontmatter) into a portable DuckDB catalog, build the concept graph, render to HTML, and optionally embed concept bodies for semantic search. Deterministic and agent-free: the same bundle always yields the same catalog, graph, and render, with no LLM calls in the core. Conformant and permissive per the OKF v0.1 specification. Package: r-cran-oknne Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn Filename: pool/dists/noble/main/r-cran-oknne_1.0.1-1.ca2404.1_all.deb Size: 27248 MD5sum: fbc62e17b1ea842a4dd0e6013f325c88 SHA1: 76b37e6eb2a8d65a8aaa86ab0ce1cb83194f4185 SHA256: a811162652e77174ac28e3cae682d329b2f46584e616cda734e5edb8843e649d SHA512: 022175b75c9936b49b41823babbae7fc9c99449d8f65760154b92ba87f60446c40f2fedac5db98427a9ea4a1f1fc9c9aa7ca869d03ff9b3c76df9a5857b025b6 Homepage: https://cran.r-project.org/package=OkNNE Description: CRAN Package 'OkNNE' (A k-Nearest Neighbours Ensemble via Optimal Model Selection forRegression) Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) . Package: r-cran-okxapi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-httr, r-cran-base64enc, r-cran-jsonlite, r-cran-websocket, r-cran-digest Filename: pool/dists/noble/main/r-cran-okxapi_0.1.1-1.ca2404.1_all.deb Size: 230982 MD5sum: baf9ff68f9e811c1b188371027dd65a3 SHA1: 5cc9f9c32a249dd8f23bad9db5d2e355197dd88c SHA256: d5dbd304a3f388d40c787bb78876e7fa0c516c7ef77be0dce93d959e66b8a825 SHA512: 5edf634b1f0ac1acb06670bcd46896a111453d316e39b9e5d6f6dc0227faacc92082a05ee86b8ccb799f5ee1160d8ef9224f5b840bd7c579786f1c11a80075fe Homepage: https://cran.r-project.org/package=okxAPI Description: CRAN Package 'okxAPI' (An Unofficial Wrapper for 'okx exchange v5' API) An unofficial wrapper for 'okx exchange v5' API , including 'REST' API and 'WebSocket' API. Package: r-cran-okxr Architecture: all Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-httr, r-cran-jsonlite, r-cran-digest, r-cran-base64enc, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-okxr_0.4.8-1.ca2404.1_all.deb Size: 711176 MD5sum: 2f4dbae2c4fe2dfa2a10d0f68858a99c SHA1: a4a7df95de5a833d44e5a7ed6914d1e69c5932c8 SHA256: e5a116c22efc55cc6c3bbd37afac52fe2fab9d1e92b74b737d62ac5e12b7ce1d SHA512: b7c69590a6e130e8d16965340efccfa779c17347e3a8c0c51c6d4df248823fd34c8ba205e8939b291dd6895ebc373d48231a78ba64cce4dc9e389a9dc395ffca Homepage: https://cran.r-project.org/package=okxr Description: CRAN Package 'okxr' (R Interface to the 'OKX' REST API) Provides lightweight R wrappers for the 'OKX' REST API, covering endpoints for market data, trading, account management, asset balances, and copy trading. The upstream API reference is available at . Package: r-cran-olcpm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-laplacesdemon, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-olcpm_0.1.2-1.ca2404.1_all.deb Size: 311016 MD5sum: 513bf2847a98926594c8716a889cfb5d SHA1: 39324a300c0ff49468e9792730577d8ed7a32dab SHA256: d8bb7afb008ab80f143ab69c2a2e615f72ccfe80701c9c79bc1a80c1e153fe0f SHA512: 2f5cbb072cc30fbea3a42be8549a0b936d9caacd63325a3a69fa4217c46d7d757959717a14c48d3bbfdc10221cbde5ad91082ca785577baa549ba9512d85d359 Homepage: https://cran.r-project.org/package=OLCPM Description: CRAN Package 'OLCPM' (Online Change Point Detection for Matrix-Valued Time Series) We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021). Package: r-cran-oldr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbw, r-cran-car, r-cran-withr, r-cran-tibble, r-cran-rmarkdown, r-cran-cli, r-cran-tinytex Suggests: r-cran-testthat, r-cran-covr, r-cran-diagrammer, r-cran-knitr, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-oldr_0.2.4-1.ca2404.1_all.deb Size: 1684022 MD5sum: f15cf92561c7accded3f91dd9bf574b9 SHA1: 2c722c5da3664c9799a35c76117e1cb6d0f68357 SHA256: 4037948757385ff36f6abbfb58065925d3b0150864c19089825be3825656b99a SHA512: a263ade00c88a34fbaac6d9e64cde6edda09f3fdf0d563ea57a2084ab555a001505b8b5b4129356d9b87baf73d23e64f8190b67829fe5abbc89e75ea3a54c94e Homepage: https://cran.r-project.org/package=oldr Description: CRAN Package 'oldr' (An Implementation of Rapid Assessment Method for Older People) An implementation of the Rapid Assessment Method for Older People or RAM-OP . It provides various functions that allow the user to design and plan the assessment and analyse the collected data. RAM-OP provides accurate and reliable estimates of the needs of older people. Package: r-cran-olinkanalyze Architecture: all Version: 5.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3659 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-data.table, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-forcats, r-cran-ggplot2, r-cran-pillar, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom, r-cran-car, r-bioc-clusterprofiler, r-cran-curl, r-cran-emmeans, r-cran-ggplotify, r-cran-ggpubr, r-cran-ggrepel, r-cran-lme4, r-cran-lmertest, r-cran-msigdbr, r-cran-ordinal, r-cran-readxl, r-cran-pheatmap, r-cran-scales, r-cran-showtext, r-cran-sysfonts, r-cran-systemfonts, r-cran-testthat, r-cran-vdiffr, r-cran-withr, r-cran-writexl, r-cran-zip, r-cran-umap, r-cran-rstatix, r-cran-fsa, r-cran-kableextra, r-cran-knitr Filename: pool/dists/noble/main/r-cran-olinkanalyze_5.1.0-1.ca2404.1_all.deb Size: 3111102 MD5sum: d06d1a694d876102e16a6b8913fcaed6 SHA1: 5587b836f3c24ed944b6e903ea97a4ae60c48e8d SHA256: befbc293600c3cee836abb1453487c5073ad03fc54d92d8fa07a985e950f97ab SHA512: 036569a0351d01d762edb98505415a883b62679ed520a03cde171be8cf48f270e95849c6aeeb13813adbfa1f5b88d8764576020f25ac35fd066a4cc68fe996bd Homepage: https://cran.r-project.org/package=OlinkAnalyze Description: CRAN Package 'OlinkAnalyze' (Facilitate Analysis of Proteomic Data from Olink) A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform. Package: r-cran-olinkanalyzevignettes Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-olinkanalyze, r-cran-stringr, r-cran-tidyr, r-cran-umap Filename: pool/dists/noble/main/r-cran-olinkanalyzevignettes_1.1.0-1.ca2404.1_all.deb Size: 1394542 MD5sum: 132a360a2f6391ebf4d4a6c113cf0cf6 SHA1: 6f5e80f298cbd1b52e3e26607a707a081d4fb0c9 SHA256: e7d85ad77b4543946bab1bdc13a2343109d133b3b239f1250ad9d8baa8911c16 SHA512: 21aba03aa0812bb9bbdbf27a61fb902e1d955f06dbc483b8898e74c332c2519e61bdc51961c71a848148467611b42add4a9de4d5ac0f510dcdfe3d8324c527aa Homepage: https://cran.r-project.org/package=OlinkAnalyzeVignettes Description: CRAN Package 'OlinkAnalyzeVignettes' (Vignettes for Analyzing Data using 'OlinkAnalyze') Exemplifying analysis of large-scale protein data from the 'Olink platform', primarily relative protein expression data that has been exported from 'Olink NPX Software', as well as QUANT data from 'Olink'. QUANT data is log-transformed. Materials focus on reading data, demonstrating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to guide users extract biological insights from large-scale protein data run on the 'Olink platform'. More information on 'Olink' data can be found at . Package: r-cran-ollamar Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-crayon, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ollamar_1.2.2-1.ca2404.1_all.deb Size: 615384 MD5sum: 0ccf483782ba333cb5ef6aba8f9af60e SHA1: 4d5b7c0d193e2171416f4bc6b9dcb83df1aa1ed6 SHA256: 9b01814df1f2856709ee58bcb10c0f8a11d43db8a06a5a63e094cea1280f6df5 SHA512: e634e9d1955ff4ed9420bcb19c7a5576a4832a7ccda8b050158f03ebd217ebba58c91905eee2b3b600d11a49ba789704a1f2f32b0f083fb753c1cfd260202a63 Homepage: https://cran.r-project.org/package=ollamar Description: CRAN Package 'ollamar' ('Ollama' Language Models) An interface to easily run local language models with 'Ollama' server and API endpoints (see for details). It lets you run open-source large language models locally on your machine. Package: r-cran-ollg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ollg_1.0.0-1.ca2404.1_all.deb Size: 102504 MD5sum: f951f718592cb28428097fbc1e9aea30 SHA1: cec31ecb686fe43256410fdf7dd984fffd4e765a SHA256: 1f627d299996d83d174ee5957f413ef379ad9ab5662dd9ca86b6edf3988065f3 SHA512: 3ec5e68e8d5b8acb4196bc218069ef971ca9987257cad09aa91685e2f45435d1770d1fba18c54b6266ba8cb18457844bfa8e7dbb60e3488c9b4a834af9489e51 Homepage: https://cran.r-project.org/package=ollg Description: CRAN Package 'ollg' (Computes some Measures of OLL-G Family of Distributions) Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) for example. Package: r-cran-ollggamma Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggamma Filename: pool/dists/noble/main/r-cran-ollggamma_1.0.2-1.ca2404.1_all.deb Size: 18212 MD5sum: 039e879b7c3b7abf42007654a9a714a7 SHA1: 6028b5e49515eb05996f879e3aca21787a8c3f2a SHA256: d3f61887ea9bd3190fc043336b7f0db8b93753bb84a0779e215e81dc5bd8b66b SHA512: 947e137e2b15b4b4657d88213ce7d9c817dd9c8a584ceddf3de7ec613d206f52800ed8fbc2f7efa5fcf987bc7990a69deed4296a7e913da49e2176e49bd71ff0 Homepage: https://cran.r-project.org/package=ollggamma Description: CRAN Package 'ollggamma' (Odd Log-Logistic Generalized Gamma Probability Distribution) Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) . Package: r-cran-olr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-readxl, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-olr_1.2-1.ca2404.1_all.deb Size: 66540 MD5sum: 9893bdc17a5e88e4bef94a0188101c5c SHA1: 99e76d0fc681c60612979e4872c7d56293240387 SHA256: d74dd77b831e8ef28e24c1234ffe604abcf84f7c7d64c74bdea1afde7703da03 SHA512: 477a777f66989f5c038b978f08f64c868954904b0a06f82f2e578130b8cffcea5a6235ea5bd1088ef39b7dc366164f16bab72b27cdaf28ec15bcbbbe5134632b Homepage: https://cran.r-project.org/package=olr Description: CRAN Package 'olr' (Optimal Linear Regression) The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictors—regardless of relevance—potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective model—based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at . Package: r-cran-olsengine Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-olsengine_1.2.0-1.ca2404.1_all.deb Size: 118934 MD5sum: 51553aa44219b21e8f34985dcf51dacc SHA1: 9d52467fbc70a66cca413d59e5a53f2481f05a74 SHA256: 093cf18e8f2f02103db48cfa6b667d36b4ea445261b2e27a748296129f424675 SHA512: f6e56e84282180291316b8cf2c09b90ff3ddaa38ae019da1961d0ae2fba065eaf2984b709db3505e7b6b4dc82690bf4ac946aeebb63e4f3726d31254d44f7956 Homepage: https://cran.r-project.org/package=OLSengine Description: CRAN Package 'OLSengine' (Transparent and Assisted Linear Modeling Engine) Unified estimation, diagnostics, and reporting for ordinary least squares (OLS) regression, ANOVA/t-tests, logistic regression, panel data (fixed/random effects with Hausman test), instrumental variables (2SLS with weak instrument diagnostics), and difference-in-differences. Designed for applied researchers in social sciences with integrated "Methodological Customs" that audit assumptions and provide literature references. All methods implemented in pure base R without external dependencies beyond stats and graphics packages. Package: r-cran-olsrr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra, r-cran-nortest, r-cran-xplorerr Suggests: r-cran-covr, r-cran-descriptr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-olsrr_0.7.0-1.ca2404.1_all.deb Size: 2016994 MD5sum: 4f4f63e5b75d26e37060a9611b955577 SHA1: fa403ce00a5c6853048683b9397a8c85185b6893 SHA256: 4a3a92fa3b18fb3196f7fe00238ecd186a68497ecd4970f6e572aa1bc368da5b SHA512: c9d7158f5ef2eef0cfe359c663d5b4b35c21cacd2102766c95d03e3b7d87090693701bdb2a9a9477cb82e0560b6bd0f65a5d2cfd8ca47678f4fe32114631bd26 Homepage: https://cran.r-project.org/package=olsrr Description: CRAN Package 'olsrr' (Tools for Building OLS Regression Models) Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures. Package: r-cran-olstrajr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-broom, r-cran-ggplot2, r-cran-purrr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-olstrajr_0.1.0-1.ca2404.1_all.deb Size: 209958 MD5sum: 4ff1f29d38701ff70f628f2b94c31415 SHA1: f2b15f278208205f7ed08492bbae98340a11f826 SHA256: 2b17e310602d55161247cf54524763aa7b08301c7d11996cb33f741c24a6ccbb SHA512: 42796374f294755baab2ebb30deb929ca59280247ab5b1f5136c0e900cea3f3cf25e0638e923cb2873b0db27ac6b981e2584f4a3255807f8eea940bb804a3264 Homepage: https://cran.r-project.org/package=OLStrajr Description: CRAN Package 'OLStrajr' (Ordinary Least Squares Trajectory Analysis) The 'OLStrajr' package provides comprehensive functions for ordinary least squares (OLS) trajectory analysis and case-by-case OLS regression as outlined in Carrig, Wirth, and Curran (2004) and Rogosa and Saner (1995) . It encompasses two primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies the estimation of individual growth curves over time via OLS regression, with options for visualizing both group-level and individual-level growth trajectories and support for linear and quadratic models. The cbc_lm() function facilitates case-by-case OLS estimates and provides unbiased mean population intercept and slope estimators by averaging OLS intercepts and slopes across cases. It further offers standard error calculations across bootstrap replicates and computation of 95% confidence intervals based on empirical distributions from the resampling processes. Package: r-cran-olympicathletes Architecture: all Version: 0.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-olympicathletes_0.5.10-1.ca2404.1_all.deb Size: 4829954 MD5sum: a91a33f61c9e7cb1a9b7196eec1ed6c9 SHA1: 0c6db75b87c35a4977034e910d4c68b12957061f SHA256: 8b5bdd93f93c145ef40e0697965e2f2772c3e0197bf766474c2c343d9e43d3b8 SHA512: 8740b46bbb474547a61b53bf5281e6c3252716afd4ead9cf84427a5c69f5471bb81ba356a22a6e6c81663f519222b29fcda80435120ec81dfdc3a486e5c36527 Homepage: https://cran.r-project.org/package=olympicAthletes Description: CRAN Package 'olympicAthletes' (Olympic Athlete Event Data, Athens 1896 to Milano-Cortina 2026) A tidy, long-format dataset of every athlete-event participation in the modern Olympic Games, spanning Athens 1896 through Milano-Cortina 2026 (about 315,000 rows). Extends the rgriff23 'Olympic_history' dataset (1896-2016) with five additional editions scraped from Olympedia (): PyeongChang 2018, Tokyo 2020, Beijing 2022, Paris 2024, and Milano-Cortina 2026. Companion datasets cover edition-level metadata and verified medal tables for every edition from 1896 to 2026. Package: r-cran-olympicrshiny Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3717 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-shiny, r-cran-shinybusy, r-cran-shinythemes Suggests: r-cran-olympicathletes Filename: pool/dists/noble/main/r-cran-olympicrshiny_1.1.0-1.ca2404.1_all.deb Size: 3653110 MD5sum: 15091b8ff941b7541e49f32296a45819 SHA1: 980e106bef6bef0b08716dab9f59a0551aee8b97 SHA256: 4b7d7894b8de9d24aa5aeb4e68347b84752992eab5e4a42d497af1f523235c2a SHA512: 7574a888ad2f7dec80f0602df7a81e0ff06bc3756f42d0874780f8b9142d6ceb49ccc5eda8293d95d30505db592b57e6c1f81b41302c00d6d1160fe0af875d5e Homepage: https://cran.r-project.org/package=OlympicRshiny Description: CRAN Package 'OlympicRshiny' ('Shiny' Application for Olympic Data) Provides a 'Shiny' application for exploring and visualizing Olympic Games data from 1896 onwards, including both Summer and Winter Olympic Games. The application provides interactive visualizations of athletes, countries, sports, events, and medal results. Olympic data are obtained from the 'olympicAthletes' R package. Package: r-cran-omegag Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-omegag_1.0.1-1.ca2404.1_all.deb Size: 39326 MD5sum: bc52c23ec80bbb349f666cd046c28eb9 SHA1: 4ef52cb72b9b0b5e658baf66643858695d8aad9b SHA256: 8a0745444b9aa236165581b703966efe2f67f93523ceb5a0499ab4ccc6ece58c SHA512: 07dea07ac1a795946652bbf8b7c17473496dca6ff5e158ef5957bf716ba759dc9ed8a14e4d5972c5e3082ca1029a69e6026dd8000de4dc56220189b14fb98d1c Homepage: https://cran.r-project.org/package=OmegaG Description: CRAN Package 'OmegaG' (Omega-Generic: Composite Reliability of MultidimensionalMeasures) It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) . The current version can only handle continuous data. Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC. Package: r-cran-omicflow Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-matrix, r-cran-ape, r-cran-gghalves, r-cran-ggpubr, r-cran-ggrepel, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-rbiom, r-cran-rcolorbrewer, r-bioc-rhdf5, r-cran-rstatix, r-cran-slam, r-cran-vegan, r-cran-viridis, r-cran-yyjsonr, r-cran-ggplot2 Suggests: r-cran-dt, r-cran-downloadthis, r-cran-rmarkdown, r-cran-cli, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicflow_1.3.2-1.ca2404.1_all.deb Size: 372880 MD5sum: b53621614d1ee49eae22df7f1753878e SHA1: 825a0252460ffb23a75cfa0235302c7028747882 SHA256: a9bb7fd01e933380650d816b401f08ab206bcfc9c4bc65ffff5b1ad9d9864883 SHA512: 5d7dbd25d55936cdcab7e166ea4374fdbad6fa47528b8e033bf9bbe46b0fd0b310f66ac4fb9d1964ed15388c438d6d5e8ad4fc6811e5d6305489f985bccb75d0 Homepage: https://cran.r-project.org/package=OmicFlow Description: CRAN Package 'OmicFlow' (Fast and Efficient (Automated) Analysis of Sparse Omics Data) A generalised data structure for fast and efficient loading and data munching of sparse omics data. The 'OmicFlow' requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report. Package: r-cran-omickriging Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-rocr, r-cran-irlba, r-cran-foreach Filename: pool/dists/noble/main/r-cran-omickriging_1.4.0-1.ca2404.1_all.deb Size: 1815010 MD5sum: 7185901441e079576e8feb6b0482f6e9 SHA1: 583224ff210c5748f0fdc4bd9dc8770c69a59934 SHA256: e64eafc9b133da50a2c3d5eb6824f10e8af24196f2048b8122a013bba71c3778 SHA512: 24592451fe6977d7b8ec3ebc6368a7e3a873355a7888bd08f531ff89b2a030de9b6764a19ac6bf6e467b0cdfed7c006633e42cc7a73ba7eeb1f2181f993e3adc Homepage: https://cran.r-project.org/package=OmicKriging Description: CRAN Package 'OmicKriging' (Poly-Omic Prediction of Complex TRaits) It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y. Package: r-cran-omicnavigator Architecture: all Version: 1.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-faviconplease, r-cran-ggplot2, r-cran-opencpu, r-cran-plotly, r-cran-tinytest, r-cran-ttdo, r-cran-upsetr Filename: pool/dists/noble/main/r-cran-omicnavigator_1.19.0-1.ca2404.1_all.deb Size: 866074 MD5sum: a9cafa85db587898fb739e2de610e2d1 SHA1: b6a996010f5dc6a7103182f350fbda06711b101b SHA256: 7e2dd0ac0ed16e9ef79e325721be6bd3cf20d1c99fe852b1ec529d69ea2f066e SHA512: 37871f582517fcf35ab9901a54857747b0fb66aaf9a80ab26d1380a610bbd41bde854af98bd186623b8edb7ee2153c798fd9660397ae257f61658b3bd106d6b0 Homepage: https://cran.r-project.org/package=OmicNavigator Description: CRAN Package 'OmicNavigator' (Open-Source Software for 'Omic' Data Analysis and Visualization) A tool for interactive exploration of the results from 'omics' experiments to facilitate novel discoveries from high-throughput biology. The software includes R functions for the 'bioinformatician' to deposit study metadata and the outputs from statistical analyses (e.g. differential expression, enrichment). These results are then exported to an interactive JavaScript dashboard that can be interrogated on the user's local machine or deployed online to be explored by collaborators. The dashboard includes 'sortable' tables, interactive plots including network visualization, and fine-grained filtering based on statistical significance. Package: r-cran-omicnetr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-mixomics Filename: pool/dists/noble/main/r-cran-omicnetr_0.1.1-1.ca2404.1_all.deb Size: 1096990 MD5sum: e743cf0356325e52b2187f3e3f5b19ab SHA1: 88500157eea758e9ea9220c92b28894d1bc001e1 SHA256: d9a02517f1060c443daf2742c17f27bde1cdb6acc2e972580df6a25ef1e25970 SHA512: 2e7665df8e371f490e7a453d75cc68987f13a13cbaba664218c0a682299b400ead2db98559ab166e4bdb2328f55f030e6a319155ad2bf1e8d9ef661cdc649ab8 Homepage: https://cran.r-project.org/package=OmicNetR Description: CRAN Package 'OmicNetR' (Network-Based Integration of Multi-Omics Data Using Sparse CCA) Provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) ), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) ). Package: r-cran-omics Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-pheatmap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-omics_0.1-5-1.ca2404.1_all.deb Size: 55754 MD5sum: b4aa3b9065a6becaedf8e701c1cb21af SHA1: 8025fc6bf3e0e1a1eb6a9a4bc639528739d224d8 SHA256: 6beb19b75d192d8545a514599d606ede69b8a38c1241cd57cfbcf5649c7dcc62 SHA512: b72019a62eb7dfec2f3f69044fb87c2a35eba383fab10f51775119882eeb31f732c77e867a5d240200be16ff4000563d63e755dd63f482f0c23f627ca83cf6d3 Homepage: https://cran.r-project.org/package=omics Description: CRAN Package 'omics' ('--omics' Data Analysis Toolbox) A collection of functions to analyse '--omics' datasets such as DNA methylation and gene expression profiles. Package: r-cran-omicsbraid Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-bioc-multiassayexperiment, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-omicsbraid_0.2.3-1.ca2404.1_all.deb Size: 281896 MD5sum: fa0432d44d1df3385317afbeb59eeec7 SHA1: f1474de6a086317d3e8e29fd5053ba95e9d0be66 SHA256: 6da7f819d83701e106098bd592541a721748e09fc5febdeebcbf78b5f780f133 SHA512: 931a064e66e3b70846d75cf871d29ed75f3479e42f98cba532c4da917bb08410ac91d2de8497804ad976b4ac1ca9e4d9333139a60a67054f2ce7afbe25dc4264 Homepage: https://cran.r-project.org/package=OmicsBraid Description: CRAN Package 'OmicsBraid' (Covariance-Aware Inference of Cross-Omic Effect Trajectories) A research-oriented statistical framework for comparing standardized biological effects across matched omics layers. It estimates layer-specific standardized effects, accounts for cross-omic dependence using matched-subject bootstrap correlations, tests multivariate omnibus evidence, synthesizes consensus effects with generalized least squares, quantifies cross-omic heterogeneity, performs practical-equivalence testing, fits covariance-aware ordered GLS effect trajectories, classifies hierarchical cross-layer effect patterns with separate confirmatory and suggestive states, supports analytic and subject-bootstrap confidence intervals for layer and consensus effects, supports empirical matched-subject permutation and centered-bootstrap calibration of omnibus and heterogeneity tests for non-Gaussian settings, and creates evidence-forest and effect-braid visualizations. The package is designed for analysis-ready bulk multi-omics data or externally estimated summary statistics. It does not perform raw sequencing or mass-spectrometry preprocessing. Methodological components draw on standardized mean-difference estimation described by Hedges (1981) , bootstrap resampling described by Efron (1979) , and two one-sided equivalence testing described by Schuirmann (1987) . Package: r-cran-omicsense Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicsense_0.2.0-1.ca2404.1_all.deb Size: 128772 MD5sum: 06658e2412e52dbc21ff5ee153a2c0ff SHA1: 38dbe6e3e89e50ab50df0498fb842cc26da4ff53 SHA256: b8c69196f5c6a6f867649a29d573ae8d5987267c12c5988fbbc81ac060c1e106 SHA512: b35758b6ba6bc30d95b7eb2633b60e195cd3f252760fdf3b068c963d560d37b8fedd1cf087bbf6792fdd3dab64b2d13f975bd0a748914164bb3c7db4e09c0a73 Homepage: https://cran.r-project.org/package=OmicSense Description: CRAN Package 'OmicSense' (Biosensor Development using Omics Data) A method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data. Package: r-cran-omicspls Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-softimpute, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots Filename: pool/dists/noble/main/r-cran-omicspls_2.1.0-1.ca2404.1_all.deb Size: 359340 MD5sum: 0d6c3a25b2e54b17f365c1533c95d44e SHA1: 6bbe2e66236da471272408a8c6818a834c4344e2 SHA256: a92b08647b8aea8d8ecab95a4acfafd8d8b3dda5488304846b959d47e457a182 SHA512: 5a72f6cf156035bb5e35dd967493daff0c86d046940b76c98abbe9cdc299f0e801868975a838ab168d4309131da58b3860b2a23495ce1bfcd3f7efbf0d000366 Homepage: https://cran.r-project.org/package=OmicsPLS Description: CRAN Package 'OmicsPLS' (Data Integration with Two-Way Orthogonal Partial Least Squares) Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset. The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data. It provides a rigorous and a faster alternative cross-validation method to select the number of components, as well as functions to report proportions of explained variation and to construct plots of the results. See the software article by el Bouhaddani et al (2018) , and Trygg and Wold (2003) . It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) and cross-validation for the degree of sparsity. Package: r-cran-omicsprepr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicsprepr_0.1.1-1.ca2404.1_all.deb Size: 39736 MD5sum: fedf60f7e56ccdf03f6fe6ae1353b197 SHA1: c81d5a1fc7ffc920df08bdd5bf69bed63fbe6896 SHA256: 2aff6dcce594ff4ef9707fb50446ff70e1c69026a72e787954182fb628b81596 SHA512: 7cb900777ffa6f10951bc9f80548900fda3f0a413879bbb3e68bd01b7054ca55037d1141763fee5710deba393fde3cc55a67b5f97b5a6da2494a51d39b13300b Homepage: https://cran.r-project.org/package=OmicsPrepR Description: CRAN Package 'OmicsPrepR' (Unified Preprocessing Toolkit for Proteomics and Metabolomics) Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses. Package: r-cran-omicsqc Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1917 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-lsa, r-cran-boutroslab.plotting.general Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicsqc_1.1.1-1.ca2404.1_all.deb Size: 1026708 MD5sum: 316899ff3f22f3ad1adb978671bebf6c SHA1: 973b4cb9864b2fd9be3891c70da913a81642b31a SHA256: 2e35ba7f8955f9478c9a76fbfe5f5b809677fba02d49f7d1df306eb50594d9fd SHA512: 61cfea5fb6cb955acc7c5a67270aff33e8201690fc8ed7bb9f86a4222e631d79d4e5e44b320c08b976dbbb4c3217a60d7323f8b3e4f5fc7680b07a9bdbe9a61d Homepage: https://cran.r-project.org/package=OmicsQC Description: CRAN Package 'OmicsQC' (Nominating Quality Control Outliers in Genomic Profiling Studies) A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization. Package: r-cran-omicstools Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bs4dash, r-cran-cli, r-cran-config, r-cran-dbscan, r-cran-dplyr, r-cran-dt, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggrepel, r-cran-ggsci, r-cran-ggvenn, r-cran-golem, r-cran-janitor, r-cran-magrittr, r-cran-matrixstats, r-cran-moments, r-cran-outliers, r-cran-pheatmap, r-cran-purrr, r-cran-rcolorbrewer, r-cran-readxl, r-cran-readr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-bioc-biobase, r-bioc-pvca, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-boruta, r-cran-randomforest, r-cran-caret, r-cran-recipes, r-cran-parsnip, r-cran-workflows, r-cran-rsample, r-cran-tune, r-cran-dials, r-cran-doparallel, r-cran-vip, r-cran-goplot, r-cran-openxlsx, r-cran-upsetr, r-cran-zip Filename: pool/dists/noble/main/r-cran-omicstools_1.1.7-1.ca2404.1_all.deb Size: 608440 MD5sum: 89c2164927534ee46c4b616516855109 SHA1: 25af66cfc52d70f2e231e4fdea596201073b2d2c SHA256: 110a2799ba6332c9bd258e8dc6b3134801ebae5b08977e9220fd7d56e4c53fed SHA512: f981de93131c09af1fe50eea47d75b906351fbb9eadec6cdc59f47d537c0599b8701fd04a651a201d6df554f6fb5dd37a177c97b948d8925340cee7083609e5c Homepage: https://cran.r-project.org/package=omicsTools Description: CRAN Package 'omicsTools' (Omics Data Process Toolbox) Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) . Package: r-cran-omicwas Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-data.table, r-cran-dplyr, r-cran-ff, r-cran-glmnet, r-cran-magrittr, r-cran-mass, r-cran-matrixstats, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-omicwas_0.8.0-1.ca2404.1_all.deb Size: 4148768 MD5sum: c923e8a2072276937c47118eda171eef SHA1: 1dfb4e7a536096cf94b12e7d5f997c064f34a269 SHA256: 210d6d5096ac7237c56fb6a8f9661bf56a50650b09d129c20bf94feb6537cc30 SHA512: 8ba304cdfce7c7a38baebea38118449d15eef9bf20c3f49cd72f33b164a65e0e3341af977f20cdcb37e4f7ce2e011c8c7f7c17e6abd5af1c55abd973c9e74ea7 Homepage: https://cran.r-project.org/package=omicwas Description: CRAN Package 'omicwas' (Cell-Type-Specific Association Testing in Bulk Omics Experiments) In bulk epigenome/transcriptome experiments, molecular expression is measured in a tissue, which is a mixture of multiple types of cells. This package tests association of a disease/phenotype with a molecular marker for each cell type. The proportion of cell types in each sample needs to be given as input. The package is applicable to epigenome-wide association study (EWAS) and differential gene expression analysis. Takeuchi and Kato (submitted) "omicwas: cell-type-specific epigenome-wide and transcriptome association study". Package: r-cran-omisc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-psych Suggests: r-cran-lavaan Filename: pool/dists/noble/main/r-cran-omisc_0.2.0-1.ca2404.1_all.deb Size: 237440 MD5sum: e1906a630461a193aa46b30d61287b1f SHA1: 8cec5bda3ab64e934ee65e0e73a809d3a01e7fb1 SHA256: d46fb640313723001aea61d4e87eef1f4872dd17988e7d2a2b5e06195807f58c SHA512: f442a9d3c3c84f332d11cc313ad23131eb0bd2207e0b738c04847c275d0ea754e0cdf894abc11e35f55d1d4f5b68ecbb193e3cf48ec663ae8ba8ba1784e78ee8 Homepage: https://cran.r-project.org/package=Omisc Description: CRAN Package 'Omisc' (DeFries-Fulker Analysis and Univariate Bootstrapping) Implements the Univariate Bootstrap and the Traditional (Naive) Bootstrap for resampling multivariate data while preserving covariance structure. Also provides functions for DeFries-Fulker behavioral genetics models, including the Rodgers-Kohler formulation with robust standard errors. Package: r-cran-omixvizr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-data.table, r-cran-dplyr, r-cran-genpwr, r-cran-ggbreak, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-ggtext, r-cran-gridextra, r-cran-gtable, r-cran-lulab.utils, r-cran-magrittr, r-cran-matrix, r-cran-patchwork, r-cran-pheatmap, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-showtext, r-cran-stringr, r-cran-sysfonts, r-cran-systemfonts, r-cran-tibble Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-omixvizr_1.4.0-1.ca2404.1_all.deb Size: 146398 MD5sum: 1b4d5099f3d559fb5996721d29cef820 SHA1: 9a69586d7b9e521309c976a6afbf56ce0bb08b55 SHA256: 558d53c99423dc1a99f8e58bba248b4936dae7f54d518b86d7d8827de9f5fe3c SHA512: 84f21e269b05aa8c1e84d597b2668ca3892261e60a88b32e893f892e5e5bc1cfa30919b9df940d0875a2d574b1adfebcc1f14f21ae8a5482a2d542360b7a5fda Homepage: https://cran.r-project.org/package=omixVizR Description: CRAN Package 'omixVizR' (A Toolkit for Omics Data Visualization) Provides a suite of tools for the comprehensive visualization of multi-omics data, including genomics, transcriptomics, and proteomics. Offers user-friendly functions to generate publication-quality plots, thereby facilitating the exploration and interpretation of complex biological datasets. Supports seamless integration with popular R visualization frameworks and is well-suited for both exploratory data analysis and the presentation of final results. Key formats and methods are presented in Huang, S., et al. (2024) "The Born in Guangzhou Cohort Study enables generational genetic discoveries" . Package: r-cran-omnibus Architecture: all Version: 1.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-omnibus_1.2.15-1.ca2404.1_all.deb Size: 271514 MD5sum: fc5269503c6220e742fc2d0ecbc02868 SHA1: 8833c4a480f19cbae1dd053de4e5889486fb46e1 SHA256: f1effd1ea3c8f9c6ad222b5f47c58c4c9c77c6a90052a84a1861b69461f9917f SHA512: 6bbfa649984f809d4753e1ff1ecc8359361cc60b122c6e0548dc03b4adf3808ac82dbc2d786189b5ccdc37811d0192097de07d3b35c81cd959480d7d669f5fed Homepage: https://cran.r-project.org/package=omnibus Description: CRAN Package 'omnibus' (Helper Tools for Managing Data, Dates, Missing Values, and Text) An assortment of helper functions for managing data (e.g., rotating values in matrices by a user-defined angle, switching from row- to column-indexing), dates (e.g., intuiting year from messy date strings), handling missing values (e.g., removing elements/rows across multiple vectors or matrices if any have an NA), text (e.g., flushing reports to the console in real-time); and combining data frames with different schema (copying, filling, or concatenating columns or applying functions before combining). Package: r-cran-omnibusfisher Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-stringr, r-cran-survey Filename: pool/dists/noble/main/r-cran-omnibusfisher_1.0-1.ca2404.1_all.deb Size: 93540 MD5sum: 19b2f2456ed67ac1657d18c7de207fef SHA1: 9330a30c80ffdcc53b2e2d44eac639c1952d372e SHA256: faaf1f540b859d85d996d15824e3c264270ef8f32620ba63a68c1443220a23c9 SHA512: 39506b5ac7d6bcec9dc60176f99bae442e423a5a773cb0e265dbf04080d985ab29f8d64281883b4282328313f66244c4f78874535033dcbeb0a5c3373bba8851 Homepage: https://cran.r-project.org/package=OmnibusFisher Description: CRAN Package 'OmnibusFisher' (A Modified Fisher’s Method to Test Overall Gene-Level Effect) The separate p-values of SNPs, RNA expressions and DNA methylations are calculated by KM regression. The correlation between different omics data are taken into account. This method can be applied to either samples with all three types of omics data or samples with two types. Package: r-cran-omock Architecture: all Version: 0.7.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4252 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-clock, r-cran-omopgenerics, r-cran-purrr, r-cran-readr, r-cran-rlang Suggests: r-cran-cdmconnector, r-cran-dbi, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-patientprofiles, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omock_0.7.0-1.ca2404.2_all.deb Size: 4104812 MD5sum: 606269aeaed9ae5ee602187ca8a2990e SHA1: f794cee2e5fdb765c87177d8c7236330d629d028 SHA256: 1c91bd56108166906b2d9f335008d653cf035e57c1e7bf0b94558f5dd4d30690 SHA512: 3eabb2444d62105fbc4c831277eb1299cfbf57eb27a48347c33076365d874f32f5edd52a6fb0bc8cc4f0cb0e12831c1890dbe535f366b5084e636291e10b59e8 Homepage: https://cran.r-project.org/package=omock Description: CRAN Package 'omock' (Creation of Mock Observational Medical Outcomes PartnershipCommon Data Model) Creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs. Package: r-cran-omopconstructor Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang Suggests: r-cran-bookdown, r-cran-cdmconnector, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-omopsketch, r-cran-rmarkdown, r-cran-rpostgres, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-visomopresults Filename: pool/dists/noble/main/r-cran-omopconstructor_0.3.0-1.ca2404.1_all.deb Size: 717598 MD5sum: b9386da3bd0ad5adaed8cde57a4a2bf4 SHA1: 48fc4e43b25e95f5f07c100e31a7beecc4cee229 SHA256: cff1cc4e647f4707e5a39163980999c4ceb81df7340d51a8c44df983f2d1dccb SHA512: 44bed8992d455329faee3af6798ae5a57caf6e659601c96835eac3db12c0559261716f2af672fef04e9879cdfa5e043bba46c9838a319f7b37c27e4a7c7bb404 Homepage: https://cran.r-project.org/package=OmopConstructor Description: CRAN Package 'OmopConstructor' (Build Tables in the OMOP Common Data Model) Provides functionality to construct standardised tables from health care data formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. The package includes tools to build key tables such as observation period and drug era, among others. Package: r-cran-omopgenerics Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1482 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-covr, r-cran-duckdb, r-cran-gt, r-cran-here, r-cran-jsonlite, r-cran-knitr, r-cran-omock, r-cran-openxlsx, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-omopgenerics_1.4.2-1.ca2404.1_all.deb Size: 854928 MD5sum: eb32baeed3d248bccff7e865072c036e SHA1: 7ed45e9327c43c3f905a2499966834c4c067fc60 SHA256: f088ac0c0454b2a99ac4a1b44c316cbce00cef805b00209ca851b8f3ae5cfbef SHA512: 2ae9d3b023635354a7fa2611191893e6d62dfa385f2b5ecff4e34837d7785266a758abfb8831c7a800fb084892759b8831b3fd5364bcc21e2c03005de06ddc07 Homepage: https://cran.r-project.org/package=omopgenerics Description: CRAN Package 'omopgenerics' (Methods and Classes for the OMOP Common Data Model) Provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model. Package: r-cran-omophub Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-glue, r-cran-checkmate Suggests: r-cran-testthat, r-cran-httptest2, r-cran-webmockr, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-keyring, r-cran-withr Filename: pool/dists/noble/main/r-cran-omophub_1.9.1-1.ca2404.1_all.deb Size: 476518 MD5sum: 07f2d534c83d148bfa69bb4b4cc6e91a SHA1: d10ebe8b196d98b09bca16a887b7e12f8e682dd6 SHA256: b657021e941b361ce4aa36c72d66e4789273cf2bfbaff269ff9675e7044facfd SHA512: e6154965ebd02aeb324ca198ff37afbe66f6e2905dcc00d53938c26246f7a28a3a07bb413032f988654300c772bc0c7007035125b89a704122c54c7a2457e1f2 Homepage: https://cran.r-project.org/package=omophub Description: CRAN Package 'omophub' (R Client for the 'OMOPHub' Medical Vocabulary API) Provides an R interface to the 'OMOPHub' API for accessing 'OHDSI ATHENA' standardized medical vocabularies. Supports concept search, semantic search using neural embeddings, concept similarity, vocabulary exploration, hierarchy navigation, relationship queries, concept mappings, and FHIR-to-OMOP concept resolution with automatic pagination. Package: r-cran-omopindices Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1441 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortconstructor, r-cran-dbi, r-cran-duckdb, r-cran-gt, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-phenotyper, r-cran-rmarkdown, r-cran-rpostgres, r-cran-testthat, r-cran-visomopresults Filename: pool/dists/noble/main/r-cran-omopindices_0.1.0-1.ca2404.1_all.deb Size: 1357056 MD5sum: 7fb30243a81a46e09aebda2314024648 SHA1: a94e3af51c854e6906f46a34685c2b71d137413d SHA256: 38edf4876cf31dceda73425f0a904e8f509e805c931cc7c083dda903c2e5d941 SHA512: 79b405a2dedd82cbf53f96e7734d20c3b21c3eba7f529b40c89c55c7367405a62999289947631d164832d015615e063e232315796ec2b15201bf602cdc0ca4bc Homepage: https://cran.r-project.org/package=OmopIndices Description: CRAN Package 'OmopIndices' (Patient-Level Indices from the OMOP Common Data Model) Provides tools to derive standardised, reproducible patient-level indices and covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) databases. Functions calculate comorbidity and frailty scores, including the Charlson Comorbidity Index, Electronic Frailty Index, and Hospital Frailty Risk Score, as well as body mass index, polypharmacy, ethnicity, location, and socioeconomic status measures. Package: r-cran-omoponspark Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat, r-cran-omock, r-cran-knitr, r-cran-rmarkdown, r-cran-cdmconnector, r-cran-omopsketch, r-cran-odbc, r-cran-r6, r-cran-crayon, r-cran-sparklyr, r-cran-databaseconnector Filename: pool/dists/noble/main/r-cran-omoponspark_0.1.0-1.ca2404.1_all.deb Size: 262968 MD5sum: 70ed12706707fe3b11374c7b8cc87677 SHA1: db535e8c6e2aab01749f2e9c3926c443e009394a SHA256: 2f331ab28e97ed43d747836774b130fd9599b46705d8132695bbe51b79a91021 SHA512: c6648c51045a8d98c6320824fdb320b48d42da1a8ed358721575beef67411bb99f022ebe36b4fabe1f753829f74bfc0eba4fb61014840c5eb2700773c19ec6e7 Homepage: https://cran.r-project.org/package=OmopOnSpark Description: CRAN Package 'OmopOnSpark' (Using a Common Data Model on 'Spark') Use health data in the Observational Medical Outcomes Partnership Common Data Model format in 'Spark'. Functionality includes creating all required tables and fields and creation of a single reference to the data. Native 'Spark' functionality is supported. Package: r-cran-omopsketch Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3076 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-cohortconstructor, r-cran-dplyr, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortcharacteristics, r-cran-dbi, r-cran-duckdb, r-cran-dt, r-cran-flextable, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-lubridate, r-cran-odbc, r-cran-omopviewer, r-cran-sortable, r-cran-reactable, r-cran-remotes, r-cran-rmarkdown, r-cran-rpostgres, r-cran-shinywidgets, r-cran-testthat, r-cran-withr, r-cran-omock, r-cran-covr, r-cran-ggplot2, r-cran-visomopresults, r-cran-devtools, r-cran-usethis, r-cran-plotly Filename: pool/dists/noble/main/r-cran-omopsketch_1.1.1-1.ca2404.1_all.deb Size: 2234218 MD5sum: 3db57a8802339859c1066690474a3d2e SHA1: 918d51c0d25e8fcf4c715c28ed9d5d5b2149e9bf SHA256: 621a84a3381bb06a05948526c647dac5b310a2fffd88a2cc96c9e8416ef7811c SHA512: d778800930ee1c17572e7f55045f118d081abfa4b5e431fb2b2a6f20bb72703d5975c0d900850a75ad44947dcfceee07b190092766e7da9393cfd6e619aeb547 Homepage: https://cran.r-project.org/package=OmopSketch Description: CRAN Package 'OmopSketch' (Characterise Tables of an OMOP Common Data Model Instance) Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends. Package: r-cran-omopstudybuilder Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-omopgenerics, r-cran-rlang, r-cran-whisker Suggests: r-cran-dplyr, r-cran-gert, r-cran-getpass, r-cran-gh, r-cran-here, r-cran-knitr, r-cran-processx, r-cran-purrr, r-cran-renv, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omopstudybuilder_0.1.1-1.ca2404.1_all.deb Size: 136138 MD5sum: c10f32c35d775dab50326ec79aea95ba SHA1: 99e60fc8d6b2d72d6a9962892f011a458246ab5f SHA256: 794bcd1b4a895748d831494293acc77b698ca96d55b4549b34595557459d058d SHA512: 5c92f33de9cb2dffa215b42967bd7f605dd5c683dd74458bcc8a7a868805aba9a32bffe2330d5c5d166e12b095dbc125be4282214d2aad83021f8107ddf9f524 Homepage: https://cran.r-project.org/package=OmopStudyBuilder Description: CRAN Package 'OmopStudyBuilder' (Build Reproducible Network Studies for OMOP Common Data Model) Streamlines the setup and execution of network studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). 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Package: r-cran-onc.api Architecture: all Version: 2.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-anytime, r-cran-httr, r-cran-humanize, r-cran-lubridate, r-cran-stringi, r-cran-tictoc, r-cran-crayon, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onc.api_2.0.1.0-1.ca2404.1_all.deb Size: 265382 MD5sum: 0372c0223ab745a4504693e733e22a8f SHA1: 255491e97b525fe79869f22ee2ee0f8e54be06b9 SHA256: b32af91f16af4f0dd387517e0f683fc68500404c79f0ff0fd930fbd7ce33a098 SHA512: 5b845bb778ece86894e83e73527511af6453b076f8a9f037c0f4d1bca527b46e8e38b73042aa749e065e9c4fc6822e66ea9a8f1d69855e47ceb22d39f98f84bd Homepage: https://cran.r-project.org/package=onc.api Description: CRAN Package 'onc.api' (Oceans 2.0 API Client Library) Allows users to discover and retrieve Ocean Networks Canada's oceanographic data in raw, text, image, audio, video or any other format available. Provides a class that wraps web service calls and business logic so that users can download data with a single line of code. Package: r-cran-once Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-once_0.4.1-1.ca2404.1_all.deb Size: 506874 MD5sum: f37f16c9cd569192ae61eb51394f75ac SHA1: e4d399ca91423ae2d5760b2b5b5ba3cbbeea6cd4 SHA256: 9e4653dedca0714d46f244017b61cefc5df6826c808bf2fbd49c77d1f7df9381 SHA512: 99739c4347cfa784896e0d377a5a4ebf592f0751b8e7fcc70294e211313fea8c05d7e831b1ff547468c020934a973a50d4b6da28fb30a214d56b2814ab7dc101 Homepage: https://cran.r-project.org/package=once Description: CRAN Package 'once' (Execute Expensive Operations Only Once) Allows you to easily execute expensive compute operations only once, and save the resulting object to disk. Package: r-cran-oncmap Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-readxl, r-cran-dplyr, r-cran-hms, r-cran-lubridate, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oncmap_0.1.7-1.ca2404.1_all.deb Size: 59296 MD5sum: b90f4d82937cfa111caa2a7ae18a0c43 SHA1: 7f60ee64e42876cbe933977781778ad3d1ae296d SHA256: b3c5e245c9d2628f9680655e9009f110c2f5f176019a8a607d663e6ebdb966b8 SHA512: ff1d81c26c2f4c9fcae5d889992718a03afd66a5f0a00e464ce3d3477588e85992b0de2db787f952acc22e3d41fd169eebc8c5d07d3caf1a6e2b709bca3fe0de Homepage: https://cran.r-project.org/package=oncmap Description: CRAN Package 'oncmap' (Analyze Data from Electronic Adherence Monitoring Devices) Medication adherence, defined as medication-taking behavior that aligns with the agreed-upon treatment protocol, is critical for realizing the benefits of prescription medications. Medication adherence can be assessed using electronic adherence monitoring devices (EAMDs), pill bottles or boxes that contain a computer chip that records the date and time of each opening (or “actuation”). Before researchers can use EAMD data, they must apply a series of decision rules to transform actuation data into adherence data. The purpose of this R package ('oncmap') is to transform EAMD actuations in the form of a raw .csv file, information about the patient, regimen, and non-monitored periods into two daily adherence values -- Dose Taken and Correct Dose Taken. Package: r-cran-oncodatasets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2439 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oncodatasets_0.1.0-1.ca2404.1_all.deb Size: 1312006 MD5sum: d553205799df79392eeba1d698bafd32 SHA1: c6182034d35fb417e4e9c2d9a128e9677439c4bf SHA256: 6e5f3fcbf6baf35b0fc31842691e3b033f589ef1ddf1fece563956ab7878dc86 SHA512: e8c6496023e98de1d76919b4efaacb79b2ad5e2f712a21ad653014c496fab8b4b9a59de77a7a64e578b863acd14a37fe3e3987c517a6af385cf13a1953793f24 Homepage: https://cran.r-project.org/package=OncoDataSets Description: CRAN Package 'OncoDataSets' (A Comprehensive Collection of Cancer Types and Cancer-RelatedDatasets) Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes. Package: r-cran-oncofilterfast Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survminer Filename: pool/dists/noble/main/r-cran-oncofilterfast_1.0.0-1.ca2404.1_all.deb Size: 167602 MD5sum: b836f2fdede3d36df94b93b2c673e7f4 SHA1: 37bf810afa41f1f677edc77cec95926edaa9500b SHA256: c1396089324ffa328e0e81dc0adc4123f3cc6efbdf1fb1f4653b644d57d924f8 SHA512: c8e71851429a95dec71772ce669e35d434b41e74c32ec5c887209fffe5be94aab8a266ae30599a5e5b63f225d2cfa8c6fcad611e4f92822297f7c3d5dfa59550 Homepage: https://cran.r-project.org/package=Oncofilterfast Description: CRAN Package 'Oncofilterfast' (Aids in the Analysis of Genes Influencing Cancer Survival) Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's 'survival' package (Therneau, 2024; ) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the 'survminer' package by Kassambara et al. (2021; ), enhancing survival curve visualizations with 'ggplot2'. Package: r-cran-oncopredict Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3855 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ridge, r-cran-car, r-cran-glmnet, r-cran-pls, r-bioc-sva, r-bioc-limma, r-bioc-genomicfeatures, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-tcgabiolinks, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oncopredict_1.3.1-1.ca2404.1_all.deb Size: 2442348 MD5sum: 458b6a012b395abd9c8aff924a3c0e10 SHA1: e104c494b131a132d526f4d8a09996003fa351bd SHA256: 97dca0ab0b3359f91303fe48993352bc6c77078c0cd98422d86284138d60dfae SHA512: 5d236eb1ca4d887567d18698f2670dbda9e4b6ac88be02528f840744afbc15ccfd301745d9f6ef9cc2f25618f7bdc1d9f277ee40e8d2be2931c8ef391f2c8bdd Homepage: https://cran.r-project.org/package=oncoPredict Description: CRAN Package 'oncoPredict' (Drug Response Modeling and Biomarker Discovery) Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype() is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS() is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS() can take the results from calcPhenotype() and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. The package is described in Maeser et al. (2021) "oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data" . calcPhenotype() - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS() - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS() - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies. 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Package: r-cran-oncotree Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-oncotree_0.3.5-1.ca2404.1_all.deb Size: 429358 MD5sum: b3588b98ff4c07602e62eb977b7453e3 SHA1: 9e09b4fdcc26ebff3103e0a7fd7d89b0cdc096f6 SHA256: 98e681c4e8d450dfab38022f4bdcf0262bc0fc32129ef5bd5c61711bda428a20 SHA512: 18d71d07d2b8a17470d329ea74a1b882abc3abef447d3bd06a685502f14332381a1ff814ef2fb5c925b47b2b06fc3963c1537c56b3dacf8f646afb26de7bd4f5 Homepage: https://cran.r-project.org/package=Oncotree Description: CRAN Package 'Oncotree' (Estimating Oncogenetic Trees) Construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis as described in Szabo, A. and Boucher, K (2002) . 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Package: r-cran-onearm2stage Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-flexsurv, r-cran-ipdfromkm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onearm2stage_1.2.1-1.ca2404.1_all.deb Size: 112746 MD5sum: 2c23395c584bb5af83459fbfd56f2d3f SHA1: 4eafaa8512fdfd6eef0a0b1d0badafa962d67a4f SHA256: 75bb8d0597fd0284034a7dcb61df322be638a35abb3f0d60a50b1e5d33d94c86 SHA512: 91b295e97d0b40a792a295587b72ca11c95dc86ad917d72116dd6f8d0c4336b2c7368394992b17312b40dd0a97a52541ea082757848e3591016522c1d5593a8f Homepage: https://cran.r-project.org/package=OneArm2stage Description: CRAN Package 'OneArm2stage' (Phase II Single-Arm Two-Stage Designs with Time-to-EventOutcomes) Two-stage design for single-arm phase II trials with time-to-event endpoints (e.g., clinical trials on immunotherapies among cancer patients) can be calculated using this package. Two notable advantages of the package: 1) It provides flexible choices from three design methods (optimal, minmax, and admissible), and 2) the power of the design is more accurately calculated using the exact variance in the one-sample log-rank test. The package can be used for 1) planning the sample sizes and other design parameters, and 2) conducting the interim and final analyses for the Go/No-go decisions. More details about the design method can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020). . 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Package: r-cran-onelogin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-glue, r-cran-safer, r-cran-magrittr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-onelogin_0.2.0-1.ca2404.1_all.deb Size: 90252 MD5sum: 9e480d191e606c4a4a3003fcb9123c26 SHA1: 894299bd3dc7ac908c6758b16d083ef92bb5ab84 SHA256: b0bb314271fc0f0ee2d7b2f2f8dcfd931c7b157504f4e2f17559d7a0128a10ff SHA512: 43872bf47bbe642e58ac340ec3913a3bb79827b7822782927779dc2d8fdcb8c20043969df75e4eca77e9639b3789b0bc5bab6b7feae21e6d03221aadcc2d383d Homepage: https://cran.r-project.org/package=onelogin Description: CRAN Package 'onelogin' (Interact with the 'OneLogin' API) The identity provider ['OneLogin'] is used for authentication via Single Sign On (SSO). This package provides an R interface to their API. Package: r-cran-onemapsgapi Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-httr2, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-future, r-cran-furrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-googlepolylines Filename: pool/dists/noble/main/r-cran-onemapsgapi_2.0.0-1.ca2404.1_all.deb Size: 93302 MD5sum: 6ff4a5c05d732d17d3ab125013e828f2 SHA1: d15a7f915709a401a2660154cd1a513d12df49d6 SHA256: b3161341d82c12f54498de491ef7e3701ebfd655dc1bcd624afaf4b84d4851a4 SHA512: 9a29320e3b8e00ebf704e00ab6ecee2c59a114b1417575d1ea55939e0eba7a8d6bf27392b0ea6502a131878cf8a15bc2dee0f3c7ff02e8b33d5d6ede32f922bc Homepage: https://cran.r-project.org/package=onemapsgapi Description: CRAN Package 'onemapsgapi' (R Wrapper for the 'OneMap.Sg API') An R wrapper for the 'OneMap.Sg' API . Functions help users query data from the API and return raw JSON data in "tidy" formats. Support is also available for users to retrieve data from multiple API calls and integrate results into single dataframes, without needing to clean and merge the data themselves. This package is best suited for users who would like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning. Package: r-cran-oner Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oner_2.2-1.ca2404.1_all.deb Size: 101306 MD5sum: e8e184dd7d29076df33fe8d15bdae1bf SHA1: dcd2ebc52e6403fd339a2ce8754c43ef1318189e SHA256: 5478125ae025e52ac25f466981ad0653cc4d31eb5f33d47c45af8666b369c950 SHA512: cb022199be3d5a84cf0d7c21231138e90689478951868d8541eebedb92a0f01d173594883947a24e850585cba56c89cf5d0bb511aa91a97de53bd6c6b1743f99 Homepage: https://cran.r-project.org/package=OneR Description: CRAN Package 'OneR' (One Rule Machine Learning Classification Algorithm withEnhancements) Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) ) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics. Package: r-cran-onesamplelogranktest Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-survival, r-cran-survminer, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-onesamplelogranktest_0.9.2-1.ca2404.1_all.deb Size: 192036 MD5sum: 39baa344626939de8b0fe1e2743adb36 SHA1: 8124590407981dd22846750fcb1cdb88da87647f SHA256: babfd0e60476b7ff821be404d6a610fd5f877b0201e670c35e40a9146424394f SHA512: c9af1cd5f0b6ad981e535afafd7cc2b7db239534eb2be4aae12f03442ef45679c91da17b41b0d73bb1242435086d181c13d9b45c7d8d5a86dc986b10e055edfe Homepage: https://cran.r-project.org/package=OneSampleLogRankTest Description: CRAN Package 'OneSampleLogRankTest' (One-Sample Log-Rank Test) The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003) and variation of the test for small sample sizes which is detailed in FD Liddell (1984) paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest. Package: r-cran-onesamplemr Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-gmm, r-cran-ivreg, r-cran-lmtest, r-cran-msm, r-cran-rlang Suggests: r-cran-aer, r-cran-estimatr, r-cran-fixest, r-cran-haven, r-cran-knitr, r-cran-lfe, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onesamplemr_0.1.8-1.ca2404.1_all.deb Size: 126556 MD5sum: e27201dc3b1e8301ec5b5dbba22b2fb1 SHA1: dda713d2d3dcc92714f7a13ef7813ec1d841848c SHA256: 59f6b21e7ab7dec6b4caf1723866e57dfa0b8fa96d586cea74b69a798df88cfd SHA512: a8aeea8b1367c330f74e9f97aa4a851594d3199823c0344f43dd39e1fdf0d9e6733408e8adbc4c9a64a13bfb6d64bc92b95939928984a6368f86d2c5fdbad328 Homepage: https://cran.r-project.org/package=OneSampleMR Description: CRAN Package 'OneSampleMR' (One Sample Mendelian Randomization and Instrumental VariableAnalyses) Useful functions for one-sample (individual level data) Mendelian randomization and instrumental variable analyses. The package includes implementations of; the Sanderson and Windmeijer (2016) conditional F-statistic, the multiplicative structural mean model Hernán and Robins (2006) , and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) . Package: r-cran-oneshotem Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oneshotem_0.1.0-1.ca2404.1_all.deb Size: 111098 MD5sum: 0d201ae133e60cc5d2ccbd067b9bc825 SHA1: 1ab9262f5a27207b7e99099dda8f8c93f3a89d9f SHA256: f7e9ab85bed11531a5178421f1beedb7a22b31106c52aaa7be8d13197f0ec281 SHA512: f11b34032fbd45a0515237502cfadc2f81470e17aa478446d5b017a7391aa4131b419d12368afb5d1cf1936bcee6b2c6218fb2fde0b4f4f8a015441b1008a920 Homepage: https://cran.r-project.org/package=OneShotEM Description: CRAN Package 'OneShotEM' (Efficient eM-Algorithm for One-Shot Device Data Analysis) Implements the simple and efficient Expectation-Maximization (eM) algorithm proposed by Zhu, Li, Li, and Balakrishnan (2026) for parameter estimation in one-shot device accelerated life testing (ALT) data. Unlike traditional EM algorithms that impute exact failure times, this method treats failure counts between inspection intervals as missing data, resulting in faster convergence and enhanced numerical stability. Supports Exponential, Weibull, Lognormal, Gamma, and custom user-defined lifetime distributions under log-linear stress models. Standard errors, confidence intervals, model selection statistics (AIC, BIC, AICc, HQIC), residual diagnostics, and visualization tools are provided. References: Balakrishnan and Ling (2012) , Fan, Balakrishnan, and Chang (2009) . Package: r-cran-onest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-onest_0.1.0-1.ca2404.1_all.deb Size: 121710 MD5sum: 1a6a1871a45bee07491cc49bd231b937 SHA1: 5dd6a20a38a2ce43a498396821fc8ef75d91ea3d SHA256: da06b2d72e3d925366e43c832b5b6326c4dd2814b5fe7dca9a71ff6251235192 SHA512: f5d30f336c48e37ab7ea1ed89b5158194b6fc90b650840cbb04a3c82b08e825194d952e8fd1a1bc10ce7a2d1a9476b27ce8fd7d76e24dd9f7f1b3c3aa78290db Homepage: https://cran.r-project.org/package=ONEST Description: CRAN Package 'ONEST' (Observers Needed to Evaluate Subjective Tests) This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 )), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 ). Package: r-cran-onestep Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-numderiv, r-cran-extradistr Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-onestep_0.9.4-1.ca2404.1_all.deb Size: 70882 MD5sum: 8312259105bac69fb9e4a28602b1aee5 SHA1: cca0765ef98a762af1af1eff18d2181a3e87514a SHA256: fb8126e49c2911684c5a2b25f3259f2592046d4b834a0c05deaaf6269510e549 SHA512: 6802c00c242bbbc3859989dc50e4bca1f6474756ba6e7e9d7de359e773ea8a119a969a1ad3ab3dfa363243cf59f92b7eacaf71f48daeb12d29c096fd55784a08 Homepage: https://cran.r-project.org/package=OneStep Description: CRAN Package 'OneStep' (One-Step Estimation) Provide principally an eponymic function that numerically computes the Le Cam's one-step estimator for an independent and identically distributed sample. One-step estimation is asymptotically efficient (see L. Le Cam (1956) ) and can be computed faster than the maximum likelihood estimator for large observation samples, see e.g. Brouste et al. (2021) . Package: r-cran-onetime Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rappdirs, r-cran-filelock Suggests: r-cran-callr, r-cran-covr, r-cran-devtools, r-cran-doctest, r-cran-knitr, r-cran-lifecycle, r-cran-mockr, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-onetime_0.2.0-1.ca2404.1_all.deb Size: 74500 MD5sum: fafeb36a62aca65e77ea50c8fea8cd1f SHA1: 2b4fb5d93046d49ebb9f5e80cf0a7c1a282903d8 SHA256: e0190a2712e7e62871ef0e542c03bafc0b50c9378e4a2e02c964a3b19c96df58 SHA512: c14c382b2035e2cd99757e56920eb91db12fd0aa2d413c2d79507a48be6b6de454fb0ff51c79735bac83ef0e38fd092a370992aa1f47ece6f95a450b50470508 Homepage: https://cran.r-project.org/package=onetime Description: CRAN Package 'onetime' (Run Code Only Once) Allows code to be run only once on a given computer, using lockfiles. Typical use cases include startup messages shown only when a package is loaded for the very first time. Package: r-cran-onetwosamples Architecture: all Version: 1.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-onetwosamples_1.3-0-1.ca2404.1_all.deb Size: 259400 MD5sum: b87d1883c294ac412917eaccd0725646 SHA1: 288ae2fd2d2d8c032f45481c6edc2cc1e95aa4e9 SHA256: 4712f96b42bc80f5cdd330ec2a0cbe8b880e876ce22250503d03bb9bf8ff35a1 SHA512: f6d7c12b653bc5e153ad2bea1689be5f4b7f432fc6090625ae0738780f58fe22655d7aa2a47ded171d68fbede4024f00f6f149c16fd773bc4b61a66b8ca3c9b2 Homepage: https://cran.r-project.org/package=OneTwoSamples Description: CRAN Package 'OneTwoSamples' (Deal with One and Two (Normal) Samples) We introduce an R function one_two_sample() which can deal with one and two (normal) samples, Ying-Ying Zhang, Yi Wei (2012) . For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length. Package: r-cran-oneway Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-normality, r-cran-outlying, r-cran-varequal Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oneway_0.0.2-1.ca2404.1_all.deb Size: 170624 MD5sum: d0ab9db93e3617e59de7662e9ad9ad20 SHA1: a55954fcafc9a3a600a937865058d4e063643ea3 SHA256: ac0ef9439240b857d80ea32b5e74c7b5c358332c3496643852f2f74edd2cb9a9 SHA512: 84c91f54fdb57f75c96281d05258361e0898a0d38cb7cdd988acfcf8209802305abbbf5a6b8e3ed424c01ce32cf16d9f70186c266412f8c54c7233c5f5cb0e50 Homepage: https://cran.r-project.org/package=oneway Description: CRAN Package 'oneway' (One-Way Statistical Analyses) Performs one-way tests of assumptions (normality and homoscedasticity), analysis of variance, robust and nonparametric alternatives, multiple comparison procedures, effect size estimators, confidence intervals, and descriptive summaries. Functions are designed with a consistent interface to support reproducible and user-friendly statistical workflows. For more details see Howell (2010, ISBN:978-0-495-59784-1), Zar (2014, ISBN:978-0-13-100846-5), Hollander et al. (2014, ISBN:978-0-470-38737-5), Montgomery (2017, ISBN:978-1-119-11347-8), Lakens (2013) , and Piepho (2004) . Package: r-cran-onewaytests Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-moments, r-cran-car, r-cran-ggplot2, r-cran-nortest, r-cran-wesanderson Suggests: r-cran-aid, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onewaytests_3.1-1.ca2404.1_all.deb Size: 251994 MD5sum: 119239cd870e026768f0f1ecbbbdbc93 SHA1: ed10f29c06ca015dc6bd3a9c80ba0a162cd202b6 SHA256: 0121713b3abf72495c4a73db5bf32ddc67b67e2e8a9879733d331539fb48b1dc SHA512: f39b5cc3a61edaadbd9c1bbe22983a92b1c0e7b35afd9584407d0eca53f164caa18ccfe8782d2857868c90dda3af695e9cd0ababa4e835110fcd56f419681a78 Homepage: https://cran.r-project.org/package=onewaytests Description: CRAN Package 'onewaytests' (One-Way Tests in Independent Groups Designs) Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test, Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests, Alvandi's F test, Alvandi's generalized p-value, approximate F test, B square test, Cochran test, Weerahandi's generalized F test, modified Brown-Forsythe test, adjusted Welch's heteroscedastic F test, Welch-Aspin test, Permutation F test. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, ). Package: r-cran-onlinebcp Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onlinebcp_0.1.8-1.ca2404.1_all.deb Size: 2243360 MD5sum: 728da6f1d6d5fe53685d6c7730fd9c0e SHA1: dd5164c63c8561396a06e1d85d3c76544d753875 SHA256: fcb6c90924681ad5c4649c9a0dfc2e6987d8895697d1bd8753df95927478696c SHA512: f16a69d67717c4ec94a0adee8ae83113e114086dc70c787c0ce0b2bbc344d500d525b1c197c107496343328faed6f746452f6cd78ffaadb72c12dc829f1227ad Homepage: https://cran.r-project.org/package=onlineBcp Description: CRAN Package 'onlineBcp' (Online Bayesian Methods for Change Point Analysis) It implements the online Bayesian methods for change point analysis. It can also perform missing data imputation with methods from 'VIM'. The reference is Yigiter A, Chen J, An L, Danacioglu N (2015) . The link to the package is . Package: r-cran-onlineretail Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2829 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-onlineretail_0.1.2-1.ca2404.1_all.deb Size: 2861446 MD5sum: c9fe70b052db10197796b315eba67fac SHA1: 00fa7b15dafce69485ab764b671cde758ea2aa20 SHA256: 83510784b2edd0a18c4f4cd6ce68de85ef21ff5c71e4b98e21892b9c4f481fdc SHA512: 2c1612a1f3adc0c389800333594e6b02387ce2696d21a3369aad51650011c7747ab627895051598d61f3851152a1d3e87b98bec4b0e3f284bb0527619d46b64a Homepage: https://cran.r-project.org/package=onlineretail Description: CRAN Package 'onlineretail' (Online Retail Dataset) Transactions occurring for a UK-based and registered, non-store online retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, ). This dataset is included in this package with the donor's permission, Dr. Daqing Chen. Package: r-cran-onlinesurr Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kdglm, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang, r-cran-rfast, r-cran-latex2exp, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-onlinesurr_0.0.4-1.ca2404.1_all.deb Size: 395014 MD5sum: 0327053650276af7e3f9779d435b7e57 SHA1: 5074faf393c5b36f8869735b37601a1e2261691b SHA256: cb7a83c7897a1e32c6234e2a13856f645409606187a12d6f39d7c9e163a640e0 SHA512: 32a8e7295fc51825bab68add6a8faac6e7d3f0dc06c601b2cfd2861c5a13ff9c2aebf6918023912425bdc789ee99b1ce245bc82d9b583b5dc8fa7d95440f6576 Homepage: https://cran.r-project.org/package=OnlineSurr Description: CRAN Package 'OnlineSurr' (Surrogate Evaluation for Jointly Longitudinal Outcome andSurrogate) Tools for surrogate evaluation in longitudinal studies using state-space models as proposed in Santos Jr. and Parast (2026). The package estimates treatment effects over time with and without adjustment for surrogate information, summarizes the proportion of treatment effect explained by a longitudinal surrogate, quantifies uncertainty via bootstrap resampling, and provides plotting and summary utilities for fitted models. 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Delivers the same results as the 'ODRPACK' Fortran implementation described in Boggs et al. (1989) , but is implemented in pure R. 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It is inspired by the Food and Agrilculture Organizations (FAO) caliper platform and makes use of the Simple Knowledge Organisation System (SKOS). 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It expands on the previous work of Tarasov et al. (2019) . The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest. 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Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package. 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This package provides a comprehensive toolbox for researchers and practitioners to design and analyze order-of-addition experiments. Detailed comparisons and summary of all statistical methods in this package can be found in Tsai (2026), "Order-of-addition experiments in R using OofAExp", Journal of Quality Technology (to appear). 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Package: r-cran-opencage Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-dplyr, r-cran-lifecycle, r-cran-jsonlite, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-ratelimitr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opencage_0.2.2-1.ca2404.1_all.deb Size: 148850 MD5sum: 0eae8355fd2e207b7bbbe62b2424ecc7 SHA1: a585ed25060485b21789b7dae057f29d1678b45d SHA256: 8c834d006298ff1face3537232f22787f8c918aba5f730c7d2405bea5fcc143c SHA512: ee4b9a4d5085f675d1f50a79eeeeb12413de16098bab3b674f66618b164c6ba1b48fdd0cd6310a1bf8e1dcb50ca15600db6e0b9af8a9d6f69fd3433354d04827 Homepage: https://cran.r-project.org/package=opencage Description: CRAN Package 'opencage' (Geocode with the OpenCage API) Geocode with the OpenCage API, either from place name to longitude and latitude (forward geocoding) or from longitude and latitude to the name and address of a location (reverse geocoding), see . Package: r-cran-opencameo Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-seqinr, r-cran-stringr, r-cran-tibble, r-cran-entropy, r-cran-ftrcool, r-cran-elmnnrcpp Suggests: r-cran-caret, r-cran-kernlab, r-cran-ranger, r-cran-xgboost, r-cran-gbm Filename: pool/dists/noble/main/r-cran-opencameo_0.1.1-1.ca2404.1_all.deb Size: 69574 MD5sum: 32e29982a9c5351f7a9838edb7f7811c SHA1: b718df1d5c151a4dcbff7614477e17d42faaac69 SHA256: 2c60917178d9540c149bead63992c51049e26edbf52f926649e7623ff61cb024 SHA512: b351a22457e794bb8679fa23ab225ea59dfe4f3a3d9d4effe432afb4d92f4e310ed37b4cd750a7620f14f967d7aa1b5a3db99a221f561df0939631687058c234 Homepage: https://cran.r-project.org/package=OpEnCAMeO Description: CRAN Package 'OpEnCAMeO' (Optimized Ensemble Predictor for 'C' and 'A' Methylation inOrganism) DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. 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Package: r-cran-opencast Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-seqinr, r-cran-stringr, r-cran-tibble, r-cran-entropy, r-cran-ftrcool Suggests: r-cran-caret, r-cran-kernlab, r-cran-ranger, r-cran-xgboost, r-cran-gbm Filename: pool/dists/noble/main/r-cran-opencast_0.1.1-1.ca2404.1_all.deb Size: 62696 MD5sum: db74c8992ebe28944be11ade8f4358c5 SHA1: 953b3435d0c36a038c77d9aaa57bc2b9099f3302 SHA256: 6a835e43d44780a5cb8421cdb10b1b831bb2af13e38e05e44be85456d25445e6 SHA512: 927ffdab205a34b3860e5c7ea3257af5032b6de2abb5ecf561d469832ca7c7b2370fe51f02fce4cd5f5094610c9e7578acdb17f262a87cc6d6d4911d97fe6ea0 Homepage: https://cran.r-project.org/package=OpEnCAST Description: CRAN Package 'OpEnCAST' (Optimized Ensemble Model for C and A Methylation Search in Plant) DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. 'OpEnCAST' is a plant-specific ensemble-based prediction package that identifies 4mC, 5mC and 6mA methylation sites directly from DNA sequences. It combines multiple machine learning algorithms trained on monocot (Oryza sp.) and dicot (Arabidopsis sp.) reference models to deliver accurate predictions. This methodology is being inspired by the ensemble algorithm for methylation prediction developed by Wang et al. (2022) . Package: r-cran-opencis Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-digest, r-cran-cachem, r-cran-tibble, r-cran-purrr, r-cran-haven, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-memoise Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-opencis_0.1.4-1.ca2404.1_all.deb Size: 1160322 MD5sum: c8f5c84bd7b35a6031497188219a2ddd SHA1: b2fee7c0221d62045e6e7f3b62caed123a862957 SHA256: 13131a4dc99bc28db9062514f567f63a09a8b185b02ddf6c84dd326fa38b754d SHA512: ce8d51c79b1586ff576577cb373f65c1f368ad9045a2594bfabb08e0c30f9e2390981083b79f294807d184e80d2e9335400cb224be4b3a1600ce600e211809c6 Homepage: https://cran.r-project.org/package=opencis Description: CRAN Package 'opencis' (Import Data from Spanish Sociological Research Center (CIS)) Search and import data directly to R from the Spanish Sociological Research Center (CIS) . The CIS is a public institution that conducts electoral and sociological research studies on the Spanish society. The CIS has a large database of surveys that can be accessed through its website. The package includes functions to search for surveys, survey questions and timeseries, and import the data directly to R. Package: r-cran-opencpu Architecture: all Version: 2.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-evaluate, r-cran-httpuv, r-cran-knitr, r-cran-jsonlite, r-cran-remotes, r-cran-sys, r-cran-webutils, r-cran-curl, r-cran-rappdirs, r-cran-rlang, r-cran-vctrs, r-cran-zip, r-cran-mime, r-cran-protolite, r-cran-brew, r-cran-openssl Suggests: r-cran-arrow, r-cran-unix, r-cran-haven, r-cran-pander, r-cran-svglite Filename: pool/dists/noble/main/r-cran-opencpu_2.2.15-1.ca2404.1_all.deb Size: 315512 MD5sum: 8082e99774f874b21899f5c312945b94 SHA1: 832ab9987acb3ca28f6e008ac6357539b760dcb8 SHA256: 1253704484aeec2b51a7f6a6177ac8cb37788d420823c2bf5b96b26e473925ae SHA512: 2b86bceba542d8c3f1fdf43c38cae8f41f4979f468696b8a312a6d349cd37b8fda39d4e100073c426bd25eb17d51d8574f8d1a3a77c08d318ee94097f973443e Homepage: https://cran.r-project.org/package=opencpu Description: CRAN Package 'opencpu' (Producing and Reproducing Results) A system for embedded scientific computing and reproducible research with R as described in . The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange with R. This provides a reliable and scalable foundation for statistical services or building R web applications. The OpenCPU server runs either as a single-user development server within the interactive R session, or as a multi-user Linux stack based on Apache2. The entire system is fully open source and permissively licensed. The OpenCPU website has detailed documentation and example apps. 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This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see . 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Package: r-cran-opendotar Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-opendotar_0.1.4-1.ca2404.1_all.deb Size: 21754 MD5sum: f9132e76a146a7ec7a9d174207d04778 SHA1: 8a30d181f724086ac98e4ccc8e07d2dd14379304 SHA256: 85ed7cfebbbeb02bf18f08c614045e3de6c33983cf3df858f4fa68f32ad6b804 SHA512: df77c63003f764f5f307d111c1779f51cf5fbe97f434bba318f1eba133ce8b8d483abe180333bde8b0e3724d6264b7f76c030cba217066fc26955150769602e1 Homepage: https://cran.r-project.org/package=opendotaR Description: CRAN Package 'opendotaR' (Interface for OpenDota API) Enables the usage of the OpenDota API from , get game lists, and download JSON's of parsed replays from the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file. Package: r-cran-openebgm Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-numderiv Suggests: r-cran-deoptim, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-openebgm_0.10.0-1.ca2404.1_all.deb Size: 420648 MD5sum: bf74ef185c398ca6d543fe6ecdbce330 SHA1: 22cad73599cd141b7946c8f0cf99b83d7a998cc7 SHA256: 1407d53d832658c16d7e491a5f8534fc6959d061d66346d3765beeabd957704b SHA512: f70f865c6f431659469b4f9869cd3c845303ff024834100856424e7c8dd54a6d586c2ef5dd8af4fc892cf45f394aa25f976d57aa7b048eda3dd74c80a1101b6e Homepage: https://cran.r-project.org/package=openEBGM Description: CRAN Package 'openEBGM' (EBGM Disproportionality Scores for Adverse Event Data Mining) An implementation of DuMouchel's (1999) Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package. 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This package makes the API easily accessible, returning objects which the user can convert to JSON data and parse. Kass-Hout TA, Xu Z, Mohebbi M et al. (2016) . 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It is noteworthy that histone deacetylation and histone H3 lysine 27 trimethylation (H3K27me3) play a role in repressing transcription in eukaryotes. In contrast, histone acetylation (H3K9ac) and H3K4me3 have been inevitably linked to the stimulation of gene expression, which significantly influences plant development and plays a role in plant responses to biotic and abiotic stresses. To our knowledge this the first multiclass classifier for predicting histone modification in plants. . Package: r-cran-openholidaysr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-openholidaysr_0.1.0-1.ca2404.1_all.deb Size: 29192 MD5sum: c53db0877324339514104aa65d46bd04 SHA1: dd0c4fa368351fd1d426bb6265e960e2ac01f308 SHA256: 85330c7f6f107949bd3eb57a0d6116bd4caca2504f953fc918bcf969616579bf SHA512: 6741f3c82770cd9694a6b4bdaffdbbc6268cc865565b8780c99731a0e25f64cc1d00ecc49554cc8d24cceb1e86e14810d38790739298167428c6eb45bb425db3 Homepage: https://cran.r-project.org/package=openholidaysR Description: CRAN Package 'openholidaysR' (Provides Access to the 'OpenHolidays' API) Provides dates for public and school holidays for a number of countries and their subdivisions through the 'OpenHolidays' API at . 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The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system. 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It includes support for loading spatiotemporal raster data and synthesized spatial plotting. Several LUC change (LUCC) metrics in regular or irregular time intervals can be extracted and visualized through one- and multistep sankey and chord diagrams. A complete intensity analysis according to Aldwaik and Pontius (2012) is implemented, including tools for the generation of standardized multilevel output graphics. Package: r-cran-openlineage Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-r6, r-cran-s7, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-openlineage_0.1.0-1.ca2404.1_all.deb Size: 345730 MD5sum: 0142ac4fc4333f5dca59d154105dd19b SHA1: 08121b8292a98a72a3a5da684c82a4d7e5b7fa59 SHA256: 6947159975fb089179f8219b3f19ca9fc8eeacf904d69ed73fad594501ed3db1 SHA512: 7046c1e0a28b1b265d0282c476a52aca9f79d8a1b32f331727869fc2ce177ae5484fae909367c7884c0226e2c8610e63b24e05711a376563d96b004b3fb821bb Homepage: https://cran.r-project.org/package=openlineage Description: CRAN Package 'openlineage' (Create and Emit 'OpenLineage' Events) Construct and validate run events that follow the 'OpenLineage' specification . Model run lifecycles, datasets, and extensible facets with protocol-aware 'R' objects. Serialize events into deterministic JavaScript Object Notation (JSON) while preserving wire field names. Deliver events synchronously over Hypertext Transfer Protocol (HTTP) with authentication and retries, or use offline transports for local development and testing. Package: r-cran-openmeteo Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-tibble, r-cran-tidyr, r-cran-tibblify, r-cran-dplyr, r-cran-yaml, r-cran-testthat Suggests: r-cran-httptest Filename: pool/dists/noble/main/r-cran-openmeteo_0.2.4-1.ca2404.1_all.deb Size: 62132 MD5sum: 783603fef6d1dff11769c37e538f30f2 SHA1: be1277f7bb376ee2bf4634e31039fabd305ee9a0 SHA256: a7982ce311b0e123e9b496dc063fd2c49eb09a6a20f8f492c31bd67e3a209ba5 SHA512: ccc443b22675f47c50b2ff274664823d690d03797af10d13da7b13771281efa95f6873ce1ad00c1efc3365047f9d96db9c5d38264da0a8348b098dbc5aa55b6d Homepage: https://cran.r-project.org/package=openmeteo Description: CRAN Package 'openmeteo' (Retrieve Weather Data from the Open-Meteo API) A client for the Open-Meteo API that retrieves Open-Meteo weather data in a tidy format. No API key is required. 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Package: r-cran-openml Architecture: all Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3878 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-digest, r-cran-httr, r-cran-stringi, r-cran-xml, r-cran-jsonlite, r-cran-memoise, r-cran-curl Suggests: r-cran-testthat, r-cran-mlr, r-cran-paramhelpers, r-cran-randomforest, r-cran-rpart, r-cran-rweka, r-cran-xml2, r-cran-farff, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-rex Filename: pool/dists/noble/main/r-cran-openml_1.12-1.ca2404.1_all.deb Size: 1283422 MD5sum: 396b3e728a3ac9902107f93317b18800 SHA1: eaa2677a132d3095718fc4c34b943a171f2830b3 SHA256: 9c4410508c14e5fe59693471f9c880b0642e45e9708f3da83cfac0007d41f98d SHA512: 05145b6529e283f7a7aa6754c63307aad9f6ddfe7614a74e53aa1edc1349c50436d078ecc5b35ab9c9943b9f45e28496b5d6dfa6154549e123418bdc2d4824e4 Homepage: https://cran.r-project.org/package=OpenML Description: CRAN Package 'OpenML' (Open Machine Learning and Open Data Platform) We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. 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Package: r-cran-openmse Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msetool, r-cran-dlmtool, r-cran-samtool, r-cran-crayon, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-openmse_1.0.1-1.ca2404.1_all.deb Size: 235430 MD5sum: df6b1dd77889073e1fce26bc25230844 SHA1: f06f03dff234c4807f40ac92409fb2e812950c54 SHA256: 6707d8701ef3f7e1c7d58c6ef1ce063964aa6d9bcd8ec36d9366dba9104bd0af SHA512: c17dde2275a0f483a472d0679268752e748380855789c3704aef86e25c1ddf2964625bd22b61d1d2ae29289032b8326f3c95b814deda592a1c48d66b2dadffdc Homepage: https://cran.r-project.org/package=openMSE Description: CRAN Package 'openMSE' (Easily Install and Load the 'openMSE' Packages) The 'openMSE' package is designed for building operating models, doing simulation modelling and management strategy evaluation for fisheries. 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(2019) . Processes habitat extent and condition data alongside metadata and weighting systems to produce a yearly single figure indexed relative to a base-year value of 100. Package: r-cran-opennlp Architecture: all Version: 0.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nlp, r-cran-opennlpdata, r-cran-rjava Filename: pool/dists/noble/main/r-cran-opennlp_0.2-8-1.ca2404.1_all.deb Size: 48468 MD5sum: b53db4612686ddef3f0eff45425bdbc7 SHA1: 1641d816c005d1d4873a6724cd915ff39807ce03 SHA256: 59da97edaf1c252e34ac2ea6c5c0f375d8a711dab211b106fa87da70d0104430 SHA512: 4deda8d59d72f3b48bdd1f07e09703b9d724a269d102ecf5baf4bc939eba54ed4a88554286f533af0afc2451a60cc9d78e90928be97ab916b597c295c985af79 Homepage: https://cran.r-project.org/package=openNLP Description: CRAN Package 'openNLP' (Apache OpenNLP Tools Interface) An interface to the Apache OpenNLP tools (version 1.5.3). 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Package: r-cran-openskies Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1202 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-ssh, r-cran-xml2, r-cran-ggmap, r-cran-ggplot2, r-cran-magick, r-cran-r6, r-cran-dbscan, r-cran-cluster Suggests: r-cran-knitr, r-cran-runit, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-openskies_1.3.1-1.ca2404.1_all.deb Size: 708364 MD5sum: 45c6bd77ef42167a9ccec75cd429de94 SHA1: 4725a29e25a7735c990379c9f779fcf5fd718d1f SHA256: f72b4a7cc3a28b854853c90e68db2ee3a11d78f92c5b686da010d56643ae74f3 SHA512: 7019e1428054dffd155b65e99888a6721ceaf29963fb5ad6429010c9bd11d7aa682d70cf65c95e3eefc141fc6f7e5fb504de5e394ca549da84cf3d522853543b Homepage: https://cran.r-project.org/package=openSkies Description: CRAN Package 'openSkies' (Retrieval, Analysis and Visualization of Air Traffic Data) Provides functionalities and data structures to retrieve, analyze and visualize aviation data. 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This package enables users to download predictor portfolio returns (over 200 cross-sectional predictors with multiple portfolio construction methods) and firm characteristics (over 200 characteristics replicated from the academic asset pricing literature). Center for Research in Security Prices (CRSP)-based variables such as Price, Size, and Short-term Reversal can be downloaded with a Wharton Research Data Services (WRDS, ) subscription. For a full list of what is available, see . Package: r-cran-openspecy Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4042 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-catools, r-cran-hyperspec, r-cran-mmand, r-cran-plotly, r-cran-digest, r-cran-zip, r-cran-glmnet, r-cran-cluster, r-cran-jpeg, r-cran-png, r-cran-shiny, r-cran-hdf5r, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-ranger, r-cran-sgolay, r-cran-curl, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinyfiles, r-cran-bs4dash, r-cran-dplyr, r-cran-dt, r-cran-reshape2, r-cran-ggplot2, r-cran-htmltools, r-cran-scales Filename: pool/dists/noble/main/r-cran-openspecy_2.0.3-1.ca2404.1_all.deb Size: 3115820 MD5sum: 928d85218188e2ceaffa328088413fe6 SHA1: 8fe3cba5b36e3cc7576fcdfb95a946b9d3e6d582 SHA256: 5045c510ade73c7d1811e3fbbb4264fc497bd5d1b4551583101f8f031a1915db SHA512: 6e9655875f7c243c70b4a3b5dc7b4be93e92b83906aadc10d0a86a4aef756a308c38d40b67d56360f83a51d94255b1e448833cad79f134854f17d738963aa0ce Homepage: https://cran.r-project.org/package=OpenSpecy Description: CRAN Package 'OpenSpecy' (Analyze, Process, Identify, and Share Raman and (FT)IR Spectra) Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2025, ). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library using match_spec(). A bundled Shiny app is available via run_app() or online at . Package: r-cran-openstreetmap Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rjava, r-cran-raster, r-cran-sp Filename: pool/dists/noble/main/r-cran-openstreetmap_0.4.1-1.ca2404.1_all.deb Size: 2264620 MD5sum: 8b2e2288f0069b34fe92da31018606fa SHA1: 850026e6fc8c67dc36759516e2347d3fcddcfce5 SHA256: 025b03795ea42acefe704438d536f8d08e1df135e44ef91cb26f5de9fd0abf6b SHA512: f7f097b96e3e3011581b0e5e36dbdb08590b4d4050539efb8aa91f710dea84da6b7cfb8ab173916decd7086e56f2c7ee9df9e918f9f89c10ae9d4908d094c521 Homepage: https://cran.r-project.org/package=OpenStreetMap Description: CRAN Package 'OpenStreetMap' (Access to Open Street Map Raster Images) Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. Additionally raster maps may be constructed using custom tile servers. Maps can be plotted using either base graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project. Package: r-cran-opentreechronograms Architecture: all Version: 2022.1.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-geiger, r-cran-knitcitations, r-cran-paleotree, r-cran-plyr, r-cran-rotl, r-cran-stringr, r-cran-taxize, r-cran-treebase, r-cran-usethis Filename: pool/dists/noble/main/r-cran-opentreechronograms_2022.1.28-1.ca2404.1_all.deb Size: 2702822 MD5sum: 76afb3be2ef3c42e10b7bc87b01d5edb SHA1: 38b30bf97ac09c353e3ec51c8b0687fcfbe3fdbc SHA256: fe903a96248b3262d9612ddca3332815089651b71456a9f1c16c6b9b6a07e334 SHA512: 5962605ab0992338d7c350c85c3e1c305f4dcd3a09cd0b619907af77b19f1a534e3eb885f6d7805b210e4d2825cdbe9d51a175af35e96d8388847095090cb8ca Homepage: https://cran.r-project.org/package=OpenTreeChronograms Description: CRAN Package 'OpenTreeChronograms' (Open Tree of Life Chronograms) Chronogram database constructed from Open Tree of Life's phylogenetic store. Package: r-cran-opentripplanner Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-geodist, r-cran-googlepolylines, r-cran-curl, r-cran-rjson, r-cran-purrr, r-cran-rcppsimdjson, r-cran-progressr, r-cran-sf, r-cran-sfheaders Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra, r-cran-tibble Filename: pool/dists/noble/main/r-cran-opentripplanner_0.5.2-1.ca2404.1_all.deb Size: 846406 MD5sum: 640c416f128a8c935e8888283313d3e7 SHA1: 05381d4a52f6b0cc2ce47896c4669be8ed0deb60 SHA256: 75500ea5097b3d52629b79958217c0e842399cfcfbf078454ded6efedf7a8ffd SHA512: bef0d6b70d85bb08ef3b7f9a22aa854a9d60e66fe50602d39de5f2ec4206ddee57f433910c0e2a930287dceafb5275b29327290a4577b3316a68383e2ea564b7 Homepage: https://cran.r-project.org/package=opentripplanner Description: CRAN Package 'opentripplanner' (Setup and connect to 'OpenTripPlanner') Setup and connect to 'OpenTripPlanner' (OTP) . OTP is an open source platform for multi-modal and multi-agency journey planning written in 'Java'. The package allows you to manage a local version or connect to remote OTP server to find walking, cycling, driving, or transit routes. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0). Package: r-cran-openva Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-interva5, r-cran-insilicova, r-cran-interva4, r-cran-tariff, r-cran-ggplot2, r-cran-crayon, r-cran-cli, r-cran-rlang Suggests: r-cran-covr, r-cran-eava, r-cran-knitr, r-cran-nbc4va, r-cran-r.rsp, r-cran-testthat, r-cran-vacalibration Filename: pool/dists/noble/main/r-cran-openva_1.2.0-1.ca2404.1_all.deb Size: 2289502 MD5sum: 6e612595080b1cf88c1d79ba7ffb9a21 SHA1: 1d7cb16f7c088fda712a574628686b7d9a7116ed SHA256: 6289fdb93ec0585c46d7a924d340419be9e16f83c4d65283e81b56abc0701ba8 SHA512: aab93413d4a31d9028111e96ab3d6bfec2ce0b86759615ad811e99131ea408e4e88d2574dedf5a8c727a68b8e329ff7a310d8dc09f1473b33401c566e24fe4d3 Homepage: https://cran.r-project.org/package=openVA Description: CRAN Package 'openVA' (Automated Method for Verbal Autopsy) Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) , 'InterVA5' by Byass at al (2019) , 'InSilicoVA' by McCormick et al (2016) , 'NBC' by Miasnikof et al (2015) , and a replication of 'Tariff' method by James et al (2011) and Serina, et al. (2015) . It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from . Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method. Package: r-cran-operator.tools Architecture: all Version: 1.6.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-operators, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-operator.tools_1.6.3.1-1.ca2404.1_all.deb Size: 53520 MD5sum: bf16f7703edb7ef23bd31d3b62ed361e SHA1: 3e3dfaade02667cce1b9e85174e572376a1659e9 SHA256: cd3641d4193de69e36820fe762443114d7ca9536f563f907347a62115ad3123a SHA512: c38b1b7b69ee1cbb00696d19852e907dbeb579e5b50cf1612e13aa89c5cb9ab22f4cd24363400bcf75069466518264bf8d536fcbc68d50792ab565c43ae1eb8c Homepage: https://cran.r-project.org/package=operator.tools Description: CRAN Package 'operator.tools' (Utilities for Working with R's Operators) Provides a collection of utilities that allow programming with R's operators. Routines allow classifying operators, translating to and from an operator and its underlying function, and inverting some operators (e.g. comparison operators), etc. All methods can be extended to custom infix operators. Package: r-cran-operators Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-operators_0.2.0-1.ca2404.1_all.deb Size: 61438 MD5sum: 31d203a1149eb738de0a6b5c5aa8f86a SHA1: 3bbf83ddf5fe9a9f84805b28f4d07cdcf703f787 SHA256: b012dc1d8f4022f124de8e4ee42eaa5bd876cc04a5a1169ee4da9e5b3c8ca535 SHA512: 2e2ed3aebc0ba7984a25e2022866eb3584e0a06132af53e6f1047056ee51c87db60f572f0fd5d20214c7dea06bf5e3cbd0dc1bd93d6603064ddcc56367fd8cf5 Homepage: https://cran.r-project.org/package=operators Description: CRAN Package 'operators' (Additional Binary Operators) A set of binary operators for common tasks such as regex manipulation. Package: r-cran-opgmmassessment Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adaptgauss, r-cran-datavisualizations, r-cran-distributionoptimization, r-cran-cluster, r-cran-mixtools, r-cran-foreach, r-cran-rlang, r-cran-ggplot2, r-cran-catools, r-cran-dplyr, r-cran-mclust, r-cran-mixak, r-cran-multimode, r-cran-nbclust, r-cran-clusterr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-opgmmassessment_0.4.1-1.ca2404.1_all.deb Size: 65418 MD5sum: 5840dddc44dbadcc3ba9d2c855298a1e SHA1: 6949fb47d3c786d872e9e2b87f85dface8bc0e74 SHA256: 7eec3a9795ad47014fe8a9da16cd6fef8acc0f2fd6cc187090e98b1e197de8ef SHA512: 1e9a3dc5b05dc0833ed0a31e7e7938797e48db4494b61302a139e2ec971c26eb6e3945e9041186850f107834dd45726fd3767258c919cc4f1fcb9d29ef3816c3 Homepage: https://cran.r-project.org/package=opGMMassessment Description: CRAN Package 'opGMMassessment' (Optimized Automated Gaussian Mixture Assessment) Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) . Package: r-cran-opi Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-rfast, r-cran-abind, r-cran-openssl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-opi_3.1.1-1.ca2404.1_all.deb Size: 711564 MD5sum: d1cc4d78ef0e5dc06168c9587076458e SHA1: c94e5cbb527cea6ea140267aeeca278b815e46a5 SHA256: d4cda0aa678c9ce5f79ac2ea31905edb6e506afe2e4d1294cbf04c8704d9b48f SHA512: 8f1748a5b9e5080ae76623a76b35f60db32a1a92a80c145fb31333b50d7cc4a69c896fedfee23fbb8be5bc4533bba58c369e6d64dd8ce061327cbd0fc8d647aa Homepage: https://cran.r-project.org/package=OPI Description: CRAN Package 'OPI' (Open Perimetry Interface) Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 and 'CrewT ImoVifa' ('Topcon Tempo') with partial implementations on the Centervue Compass, Kowa AP 7000 and Android phones. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data. Package: r-cran-opimputation Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfit, r-cran-caret, r-cran-abcanalysis, r-cran-ggplot2, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-missforest, r-cran-mice, r-cran-miceranger, r-cran-multius, r-cran-amelia, r-cran-mi, r-cran-reshape2, r-cran-datavisualizations, r-cran-abind, r-cran-cowplot, r-cran-twosamples, r-cran-ggh4x, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-opimputation_0.6-1.ca2404.1_all.deb Size: 143902 MD5sum: 4b0d563a2cdfd97f4f00616173a4153b SHA1: 4f97f94e1db8503fc2c18e96e39c1ee9e409358c SHA256: f58f90e3946111a99958cbeb0ea3d012fe2b98cb5ab5b1e050bc84d92fd163a5 SHA512: efb9471e5fb3e3c470a42a706d8594d426c6d1caacc0501f7d0768347668d4074ce6c4bff4d23981a0ca08f7002258b699ed3fc3f2c555773fcd30d05192e09f Homepage: https://cran.r-project.org/package=opImputation Description: CRAN Package 'opImputation' (Optimal Selection of Imputation Methods for Pain-RelatedNumerical Data) A model-agnostic framework for selecting dataset-specific imputation methods for missing values in numerical data related to pain. Lotsch J, Ultsch A (2025) "A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data" Canadian Journal of Pain (in minor revision). Package: r-cran-opinar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-janitor, r-cran-rvest, r-cran-glue, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-magrittr, r-cran-assertthat, r-cran-rlang, r-cran-sjplot, r-cran-gt, r-cran-lubridate Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-opinar_1.0.0-1.ca2404.1_all.deb Size: 72154 MD5sum: 90a28ffe7212c16d766a42ae3c897cc1 SHA1: fc05d224eb1914ec16a8d5dc5d55afcd7442addd SHA256: 9384077dd76027b6d13b5b5793acb84385955f428ce68bbb4c12cdc390abd01d SHA512: 386ff5a7e13c5d2d022180a875e9d65f3414041581b1de1513f4eb8b2d7036bc3bdcaf8d997aa97a0ebf717a5d2b937607dd5fd17d744c7d24ee36db467dd3c8 Homepage: https://cran.r-project.org/package=opinAr Description: CRAN Package 'opinAr' (Argentina's Public Opinion Toolbox) A toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo facilita el acceso a los microdatos y el cálculos de indicadores del Índice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella. Package: r-cran-opitools Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-tidytext, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-likert, r-cran-tm, r-cran-wordcloud2, r-cran-forcats, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rvest, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-opitools_1.8.0-1.ca2404.1_all.deb Size: 878616 MD5sum: e0e5de38ad671b6ec4b9656254c6772c SHA1: 084b8f20dce4a2ce3861a84f1f2ad77c4a8671e7 SHA256: 0a2a35f38375beade2dcc08a7bafaabb76ddb78d3f8175998b9cc80c6baaa8b7 SHA512: ecfd7859b1e2ae3a30d522e3988c9a3f8e0e12c6c4c5b087d9c2f6240015e9fea3687f72a6ba2b0530630993f737aec0724bffbf191607757ae31519a749ff07 Homepage: https://cran.r-project.org/package=opitools Description: CRAN Package 'opitools' (Analyzing the Opinions in a Big Text Document) Designed for performing impact analysis of opinions in a digital text document (DTD). The package allows a user to assess the extent to which a theme or subject within a document impacts the overall opinion expressed in the document. The package can be applied to a wide range of opinion-based DTD, including commentaries on social media platforms (such as 'Facebook', 'Twitter' and 'Youtube'), online products reviews, and so on. The utility of 'opitools' was originally demonstrated in Adepeju and Jimoh (2021) in the assessment of COVID-19 impacts on neighbourhood policing using Twitter data. Further examples can be found in the vignette of the package. Package: r-cran-opl Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pander, r-cran-randomforest, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-opl_1.0.2-1.ca2404.1_all.deb Size: 70338 MD5sum: 4f896c706eee4b4aab2847e582cad6ae SHA1: 5374514ed8d6df79936faeeac3b1279718051afa SHA256: 452bd310c1a10998bc15bb27fb2ba27549c0d2cf26b455ec86c1bdeef6a17bc7 SHA512: d0ac173704ebd31b2552f7d9f8b12b3bb428eca8d9ceb4b961539c3fa7abc4ef553097c58ba838afb08b977ade6301ed2b759c8eaec91a13f720a2397bfe39ae Homepage: https://cran.r-project.org/package=OPL Description: CRAN Package 'OPL' (Optimal Policy Learning) Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by several international articles via Athey et al (2021, ), Kitagawa et al (2018, ), Cerulli (2022, ), the paper by Cerulli (2021, ) and the book by Gareth et al (2013, ). Package: r-cran-opportunistic Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-hopbyhop, r-cran-endtoend Filename: pool/dists/noble/main/r-cran-opportunistic_1.2.1-1.ca2404.1_all.deb Size: 29110 MD5sum: 5a1fcd0bc73996b5ad07d68efcca3cbb SHA1: 7f750afbc983b13273f49a87f9e555c2a5735e70 SHA256: 2218110785af70a57acebef3f98794ba1accddc356381644f13c6dafd5389f76 SHA512: 734f60c94fc4a46e01867e32a83c7e1665f08487db8ffe11020219d2b470b46f157a55974f264248dd65efb70faac858517faf22eeb25b679c4fb11829254805 Homepage: https://cran.r-project.org/package=Opportunistic Description: CRAN Package 'Opportunistic' (Routing Distribution, Broadcasts, Transmissions and Receptionsin an Opportunistic Network) Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations. Package: r-cran-optband Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lambertw Suggests: r-cran-survival, r-cran-km.ci, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optband_0.2.2-1.ca2404.1_all.deb Size: 210522 MD5sum: 0b4ae8b1d77021d70f06e177061d2525 SHA1: b7b576d3eb119c877bdcc78fe9cea833caf12dcd SHA256: 3c9bc33f0b123cb485a4213784828f27fb42b3dc9cf6def3ff192c31e8d5c55a SHA512: 9aac1b72a6b10e926ad95528c3ce3e94fbd7756372b0ed59a314597ab8a39475b1b211bf028423cfe7292cbec17cb2ddcbb79b066754b8e34a80b5375a8b6251 Homepage: https://cran.r-project.org/package=optband Description: CRAN Package 'optband' ('surv' Object Confidence Bands Optimized by Area) Given a certain coverage level, obtains simultaneous confidence bands for the survival and cumulative hazard functions such that the area between is minimized. Produces an approximate solution based on local time arguments. Package: r-cran-optbdmaeat Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-optbdmaeat_1.0.2-1.ca2404.1_all.deb Size: 128348 MD5sum: 008e6c5832c9896727ef69c80aa57ebb SHA1: 3397d8a70fd9edd695278588d2b8bc26e9544ba4 SHA256: b39351b57fb75fe2d61c42944f75b9953e468b96e28b8ca37a2ebe8d9237b870 SHA512: b49c996dca2cd9ce944c4d5c6108266bd6008517ecf8ad5b045dffc3211063e97e47a3299b5dabb692ee6f5f890d0f65024504e4c2a2bbf4a60727c9d4ddf571 Homepage: https://cran.r-project.org/package=optbdmaeAT Description: CRAN Package 'optbdmaeAT' (Optimal Block Designs for Two-Colour cDNA Microarray Experiments) Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2018) . The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly. Package: r-cran-optbinningr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-lintr, r-cran-covr Filename: pool/dists/noble/main/r-cran-optbinningr_0.2.1-1.ca2404.1_all.deb Size: 805960 MD5sum: 5658a47cc83d0d5938fcd55bc1a756ef SHA1: f84747b40241b2262a5f3065311d7149ad89ac7f SHA256: 0b4753421c26dae3f1f46705c25182b2cb9929016c6dfcb529ff306c041044a0 SHA512: bc712279b591a46ed3e58389f4c5bab31a4108cda9abc2e628f44ee6a969f69b520faabea83b9abfc0f08e90426e2890fd4cdd17ded0764e64a7864d8a6b87d9 Homepage: https://cran.r-project.org/package=optbinningR Description: CRAN Package 'optbinningR' (Optimal Binning Methods for Predictive Modeling and Analytics) Native R tools for optimal binning workflows in predictive modeling. The package provides APIs for binary, multi-class and continuous targets, with multi-variable binning and scorecard workflows. Methods are informed by Navas-Palencia (2020) and Navas-Palencia (2021) . Package: r-cran-optbiomarker Architecture: all Version: 1.0-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpanel, r-cran-mass, r-cran-randomforest, r-cran-e1071, r-cran-ipred, r-cran-msm, r-cran-rgl, r-cran-matrix Filename: pool/dists/noble/main/r-cran-optbiomarker_1.0-28-1.ca2404.1_all.deb Size: 443562 MD5sum: eb74ef8b2a2be20e92842f3b577c4cc7 SHA1: 68cbee1962da622de8d0a5f0411b0e8b56b9b141 SHA256: 65a04cd021cdabbad04078c0db3ce5c9e9909921b39b7448f9bcbbfbe35f792d SHA512: 712b0b678c0211e43eba6b4f7de996d8ea26cee92f56ad3b4a93b1b3ad536b413fa1711fe60b1d2c8c367fcc90e65e94f3d142e40c8704572202cea8c98cb5e2 Homepage: https://cran.r-project.org/package=optBiomarker Description: CRAN Package 'optBiomarker' (Estimation of Optimal Number of Biomarkers for Two-GroupMicroarray Based Classifications at a Given Error ToleranceLevel for Various Classification Rules) Estimates optimal number of biomarkers for two-group classification based on microarray data. Package: r-cran-optconerrf Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optconerrf_1.0.3-1.ca2404.1_all.deb Size: 327678 MD5sum: 2e12d5b27a9dff24447a4a0f3d2cc84b SHA1: 6e263771d757f16a4275e2f109cf0247f0618e86 SHA256: 7810855579aa535bd1b658d3f7440817bc5ca5226c14ac7fd3ef8ae14d6c72dc SHA512: 08ccb1d7cf1b6dd04a648c91b6e731a619ea07268173b15dc9f39a45cbfeffb6c3575814eb7b7ec9903e1d57ea6de11073653b8eafa531b3b5dfeb75a090d3f5 Homepage: https://cran.r-project.org/package=optconerrf Description: CRAN Package 'optconerrf' (Optimal Monotone Conditional Error Functions) Design and analysis of confirmatory adaptive clinical trials using the optimal conditional error framework according to Brannath and Bauer (2004) . An extension to the optimal conditional error function using interim estimates as described in Brannath and Dreher (2024) and functions to ensure that the resulting conditional error function is non-increasing are also available. Package: r-cran-optdesignslopeint Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-optdesignslopeint_1.1.1-1.ca2404.1_all.deb Size: 50884 MD5sum: 1075d502311d00c8a3ea447fe4e7588b SHA1: ab64be606c0a1e7645d0fb551304bc66f312a114 SHA256: e8b3d6f897b693797ab23e302b4bded4d94fc55ed57fc25658e6e68dcd097ff3 SHA512: a0821704614d14e8ba4d6cfc43fdcff418a843f3cf070ef80a29b28135bf56c45de704e37c9c85a34748df296d57bd0c4254f3ae9f8453fff13dc67d730a4d08 Homepage: https://cran.r-project.org/package=optDesignSlopeInt Description: CRAN Package 'optDesignSlopeInt' (Optimal Designs for Estimating the Slope Divided by theIntercept) Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio. Package: r-cran-optecd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optecd_1.0.0-1.ca2404.1_all.deb Size: 13900 MD5sum: 8433130165755b87658cace875d5c134 SHA1: 700a4f684af147c16120896d3a6ba4faeb673f14 SHA256: d791b6294d26c057074de18c81d43bc8da44920c92b961c539a28cee9d4c3c08 SHA512: 018733cfc44034ecdc5fc1314a3434215d36c0dd73e0c307d0d42bb77722203c557b861cd5a578bcb826a9570d95048177e9b81896a2b9b458d0298929c15c30 Homepage: https://cran.r-project.org/package=OPTeCD Description: CRAN Package 'OPTeCD' (Optimal Partial Tetra-Allele Cross Designs) Tetra-allele cross often referred as four-way cross or double cross or four-line cross are those type of mating designs in which every cross is obtained by mating amongst four inbred lines. A tetra-allele cross can be obtained by crossing the resultant of two unrelated diallel crosses. A common triallel cross involving four inbred lines A, B, C and D can be symbolically represented as (A X B) X (C X D) or (A, B, C, D) or (A B C D) etc. Tetra-allele cross can be broadly categorized as Complete Tetra-allele Cross (CTaC) and Partial Tetra-allele Crosses (PTaC). Rawlings and Cockerham (1962) firstly introduced and gave the method of analysis for tetra-allele cross hybrids using the analysis method of single cross hybrids under the assumption of no linkage. The set of all possible four-way mating between several genotypes (individuals, clones, homozygous lines, etc.) leads to a CTaC. If there are N number of inbred lines involved in a CTaC, the the total number of crosses, T = N*(N-1)*(N-2)*(N-3)/8. When more number of lines are to be considered, the total number of crosses in CTaC also increases. Thus, it is almost impossible for the investigator to carry out the experimentation with limited available resource material. This situation lies in taking a fraction of CTaC with certain underlying properties, known as PTaC. Package: r-cran-optedr Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2611 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-crayon, r-cran-cli, r-cran-shiny Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-dt, r-cran-shinydashboard, r-cran-shinyalert, r-cran-plotly, r-cran-hrbrthemes, r-cran-shinyjs, r-cran-orthopolynom, r-cran-magrittr, r-cran-tidyverse, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-optedr_3.0.1-1.ca2404.1_all.deb Size: 886082 MD5sum: fdfddaaa49dbe9fd0b8f0c4d937f2046 SHA1: e76eef8f00cb9549453ad8162c4b3ca08ffdc7fa SHA256: ed3db8b8f91edd4e025da1ad021f0569c8350ab4aeacdefe56a614060381578a SHA512: a1a0b74ccbdc6dfd12fcfb03041585ebe57f33963dd7d14a56002fd7f597f5708a1891469ad7a46c19f175ef0fd46921e22b8d895766946417b3e01dc35146ae Homepage: https://cran.r-project.org/package=optedr Description: CRAN Package 'optedr' (Calculating Optimal and D-Augmented Designs for Single- andMulti-Factor Models) Calculates D-, Ds-, A-, I- and L-optimal designs, weighted combinations of these via a Compound criterion, and KL-optimal designs for model discrimination, for non-linear single- and multi-factor models, via an implementation of the cocktail algorithm (Yu, 2011, ). Multi-factor models use design variables x1, x2, … with a named-list design space; single-factor models remain backward compatible. Compares designs via their efficiency, augments any design with a controlled efficiency loss, and provides efficient rounding functions to convert approximate designs to exact ones. Package: r-cran-optextras Architecture: all Version: 2019-12.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-optextras_2019-12.4-1.ca2404.1_all.deb Size: 119916 MD5sum: c9fc5b70ecd5e3dc01f2fe1f978cef05 SHA1: b86cf0aa7362133d5e20d8174b9d11b4644126b0 SHA256: e13cb1b8b079e5398bcbc705074c791809b13da8075e06337ed69054332d0b36 SHA512: a74a8ef8448be4f266d7b7195deb98211d5b3cbf28adf2f4e9567a141cbaa71262fbf38e297c2cfe028503e7d263c4f4684d17e52151e6bb0b3ffbbe5f46bebb Homepage: https://cran.r-project.org/package=optextras Description: CRAN Package 'optextras' (Tools to Support Optimization Possibly with Bounds and Masks) Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes. Package: r-cran-optholdoutsize Architecture: all Version: 0.1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats, r-cran-mnormt, r-cran-mvtnorm, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optholdoutsize_0.1.0.3-1.ca2404.1_all.deb Size: 3330956 MD5sum: ccf196080a706fc23f6f446bec1ae73c SHA1: aa46e754ada5ce21a7192f4cec9a735730e75d85 SHA256: 84d3fe9589b3c8e1805e97ffe47c1f10edae0aed42a1421e6527ef9f9ef5abd0 SHA512: e1db5e98482224d5d4d11ed2f8d88a450f895c1c42a67909624a3d9f1e79435ac529abeef52ed932cf80611770a6eba635fbaaeacd25cfc22d0c65c3f274b178 Homepage: https://cran.r-project.org/package=OptHoldoutSize Description: CRAN Package 'OptHoldoutSize' (Estimation of Optimal Size for a Holdout Set for Updating aPredictive Score) Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) (in Annals of Applied Statistics) for details of methods. Package: r-cran-optic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-did, r-cran-dplyr, r-cran-future.apply, r-cran-lmtest, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-sandwich, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optic_1.0.1-1.ca2404.1_all.deb Size: 249316 MD5sum: 422b884c6918f717c9abe66b92566fc8 SHA1: 4d8d6be69f8c9deac4bfb2b8173746607728e3d6 SHA256: ac87a05bbd1310d41af96d3a1acbbb033178fe6aa013456a521717f04eb884aa SHA512: dbb6f9a15a7c25d34ee337cc78934417b056e4dc4925fa2590f301b4d187ca27ae6318512b64609b0fb458cb81006275aae8094994a30cfc441291f35c3ef493 Homepage: https://cran.r-project.org/package=optic Description: CRAN Package 'optic' (Simulation Tool for Causal Inference Using Longitudinal Data) Implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) and Griffin et al. (2022) for a description of our methods. Package: r-cran-optical Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optical_1.7.1-1.ca2404.1_all.deb Size: 468962 MD5sum: d4fa1f78ea0b3f9c594b42f0a118fca3 SHA1: 3b84d821bb1091b335954efc06c129335dd099fc SHA256: 97c861275b19f3ed3e07f41522761d2e1ce3b505437c83844f2d66e2cf903353 SHA512: 4434bd1778caf613a3102dae6f7395674f23aae40db3e4a8945eaaf86c79daa3630018514c21a064ed0f290b33be8fc4062bc7271a79b5670173f59e87837e2b Homepage: https://cran.r-project.org/package=optical Description: CRAN Package 'optical' (Optimal Item Calibration) The restricted optimal design method is implemented to optimally allocate a set of items that require calibration to a group of examinees. The optimization process is based on the method described in detail by Ul Hassan and Miller in their works published in (2019) and (2021) . To use the method, preliminary item characteristics must be provided as input. These characteristics can either be expert guesses or based on previous calibration with a small number of examinees. The item characteristics should be described in the form of parameters for an Item Response Theory (IRT) model. These models can include the Rasch model, the 2-parameter logistic model, the 3-parameter logistic model, or a mixture of these models. The output consists of a set of rules for each item that determine which examinees should be assigned to each item. The efficiency or gain achieved through the optimal design is quantified by comparing it to a random allocation. This comparison allows for an assessment of how much improvement or advantage is gained by using the optimal design approach. This work was supported by the Swedish Research Council (Vetenskapsrådet) Grant 2019-02706. Package: r-cran-opticskxi Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2906 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rlang Suggests: r-cran-amap, r-cran-dbscan, r-cran-cowplot, r-cran-fastica, r-cran-fpc, r-cran-ggrepel, r-cran-gtable, r-cran-knitr, r-cran-plyr, r-cran-reshape2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opticskxi_1.2.2-1.ca2404.1_all.deb Size: 2611036 MD5sum: 5de70946eec1d7e98b8d869256859e37 SHA1: 3681a8a0a1eeda20bb48a0ec9a94518a6ca183c9 SHA256: 13dcac20e0a0124ed51404119be5d5f0b5709533642bc1e6644e201a101df25f SHA512: 57a06e41864930421e63c577225b364087a7bce135824aef0b93710fbd2a6d093c0eaf8dd71b9cf4cd3d02275f9ff4a223faf175420c5759183b48a81c05a4eb Homepage: https://cran.r-project.org/package=opticskxi Description: CRAN Package 'opticskxi' (OPTICS K-Xi Density-Based Clustering) Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) . A short video tutorial can be found at . Package: r-cran-opticut Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-mass, r-cran-pscl, r-cran-betareg, r-cran-resourceselection, r-cran-mefa4 Filename: pool/dists/noble/main/r-cran-opticut_0.1-4-1.ca2404.1_all.deb Size: 403406 MD5sum: fdac4c1e6845eefc2e0b8bba10d8f17a SHA1: 27f298ee70128fe8e93b80c5da2a6b182430e870 SHA256: 9cdf8c4d3039da707a652c1e521419670e585d52028e9f16d101bbf985db220e SHA512: b12608dd2b7ac4880e3df9630119cf67da5448197f153c102940f7db786fc7e1af7ae533d0d31f8c69b6d2b9be13346961cf232f884f653273f8b91891ff9d9e Homepage: https://cran.r-project.org/package=opticut Description: CRAN Package 'opticut' (Likelihood Based Optimal Partitioning and Indicator SpeciesAnalysis) Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) . The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations. Package: r-cran-optifunset Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optifunset_1.0-1.ca2404.1_all.deb Size: 11128 MD5sum: 53976ab36fe28039243cdaa33b88af32 SHA1: 7eca2338a01729e1b9f74c4b9c73b1bcc5704e98 SHA256: 74e4aab21c770ae3b1ad7d769f98dc734fd37aedfb7bfe48e4e215696c074f87 SHA512: 8da22b0fe3d8951e5f92b800d900c4ba297ef00413678aa90ff5aa66a038d7cff08ce73db0ca59118308f2968c20b201647fe68e6dd01c068d13e321fab4fb50 Homepage: https://cran.r-project.org/package=optifunset Description: CRAN Package 'optifunset' (Set Options if Unset) A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded. Package: r-cran-optigrab Architecture: all Version: 0.9.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optigrab_0.9.2.1-1.ca2404.1_all.deb Size: 87272 MD5sum: 7e0db88fce73af72c329dcfb883aef1f SHA1: f2db3c3f4f00758413abec68d4823eb3aa027822 SHA256: e042fab75f54075e44716dccb6f29e577e077e6f0f4ab9e9850ecd903e2fa532 SHA512: a618323e93e5f783ec95bbc395338f314836ed143ab1ab7be139751f67c840dd4d8b492e126e8d34f1d0de99f65ef5548ad8de1184465eac22c91ec358b29b8a Homepage: https://cran.r-project.org/package=optigrab Description: CRAN Package 'optigrab' (Command-Line Parsing for an R World) Parse options from the command-line using a simple, clean syntax. It requires little or no specification and supports short and long options, GNU-, Java- or Microsoft- style syntaxes, verb commands and more. Package: r-cran-optim.functions Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lhs, r-cran-randtoolbox, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-optim.functions_0.1-1.ca2404.1_all.deb Size: 25010 MD5sum: 221e12935eaf854f50ff8e981e145737 SHA1: beb2ebc3ddde32f5c93ceb13ca45b76374ad8610 SHA256: 7157d3ae91e1b9169ca79bddf96b32cb8bf3f57d5d734fe5d60c53c82d24532e SHA512: abfff9a289369a97b0e63fd099b124a5bacd3ad0a87d3a9ec601dbafc6d7aaab4edb3922be9ef5d87e9029ecc38ecccb7e1f6f714dc83e1767786a5effdaf86c Homepage: https://cran.r-project.org/package=optim.functions Description: CRAN Package 'optim.functions' (Standard Benchmark Optimization Functions) A set of standard benchmark optimization functions for R and a common interface to sample them. Package: r-cran-optimalcutpoints Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optimalcutpoints_1.1-5-1.ca2404.1_all.deb Size: 178222 MD5sum: 69b3a59be0aa5302ac9b4911acfb9a45 SHA1: ae517bd6f6a4777faf1570436ccdc5f3f689d7d1 SHA256: dd1dde9659122e754addcd4a2a818cb5f09a467385eb260befccfeb99312f670 SHA512: 01969f43a6df9e66ca025b2a2c3b101c87bbc6d13e7a868ad11d088f5dd2b3493b73a069c21a19f25a519ad1c0a81b54bfad143505543340d97abc20f38c0982 Homepage: https://cran.r-project.org/package=OptimalCutpoints Description: CRAN Package 'OptimalCutpoints' (Computing Optimal Cutpoints in Diagnostic Tests) Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained. Package: r-cran-optimaldesign Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lpsolve, r-cran-matrixstats, r-cran-matrixcalc, r-cran-plyr, r-cran-quadprog, r-cran-rgl Filename: pool/dists/noble/main/r-cran-optimaldesign_1.0.3-1.ca2404.1_all.deb Size: 378912 MD5sum: c122870ea689e980cae65bca8912e329 SHA1: 365143dabdcbe5cd70ab193f03208e1e940f2fd2 SHA256: 1b8de1a5e38bde3771cac6c9d4bcfcb54772bad4c9167b125dc5d4237f62fb57 SHA512: d01dc93a364c81effa0d1d40604bcd5e511ef0df13b21311a64f17d1cff5cf3242fc7498c8d26d104dd7ca19c33e99932e9622a054cd9d29a4243a0cb17fd947 Homepage: https://cran.r-project.org/package=OptimalDesign Description: CRAN Package 'OptimalDesign' (A Toolbox for Computing Efficient Designs of Experiments) Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at and the file gurobi_inst.txt, respectively. Package: r-cran-optimalgoldstandarddesigns Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-mvtnorm, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-rdpack Suggests: r-cran-testthat, r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-fpcompare, r-cran-mnormt, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-optimalgoldstandarddesigns_1.0.1-1.ca2404.1_all.deb Size: 749878 MD5sum: 590c7607cd2e0f7535303c43cf73dfbf SHA1: dc4c233f98259c1caaaddcbd8f64cc8a4dc69213 SHA256: 1e9dc905dc405ea5d2ca63d90b5295559c8df4c4db189b4d5594f8cd22a4fad9 SHA512: 8906408162b18aa1527e50f893a4fba7111f7d09f51d95672cd924a91fb800eaaf38f2193c3b4cb6d8f595d579401fab12a1f6c4c78048351ec87d2da4b638a4 Homepage: https://cran.r-project.org/package=OptimalGoldstandardDesigns Description: CRAN Package 'OptimalGoldstandardDesigns' (Design Parameter Optimization for Gold-Standard Non-InferiorityTrials) Methods to calculate optimal design parameters for one- and two-stage three-arm group-sequential gold-standard non-inferiority trial designs with or without binding or nonbinding futility boundaries, as described in Meis et al. (2023) . Package: r-cran-optimall Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4574 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-tibble Suggests: r-cran-bslib, r-cran-diagrammer, r-cran-dt, r-cran-globals, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-shiny, r-cran-shinytest, r-cran-survey, r-cran-tidyr, r-cran-testthat, r-cran-webshot Filename: pool/dists/noble/main/r-cran-optimall_1.4.0-1.ca2404.1_all.deb Size: 3180800 MD5sum: bd47dbf446926720362f327305630e92 SHA1: a1eba5fbe54dca268f7b2b82a949a166423b2b9d SHA256: 959cec0a370ecb32c579d347169d6babcb430c1432b8568d0f3ab0e208f2e3b1 SHA512: 19b001e4a68e3c99763bacdd52f383736ef99efbd70b1c8c18ee76d575a2d39a1217d1accb9b1f7da7907ab11b07e1189a70e53e5557a9386f8856b6f995b61a Homepage: https://cran.r-project.org/package=optimall Description: CRAN Package 'optimall' (Allocate Samples Among Strata) Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) or Wright (2012) allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software . Package: r-cran-optimalrerandexpdesigns Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-momentchi2, r-cran-greedyexperimentaldesign Filename: pool/dists/noble/main/r-cran-optimalrerandexpdesigns_1.1-1.ca2404.1_all.deb Size: 75880 MD5sum: 88c26e618355c2238b3b181a2a4bbb81 SHA1: c12c70ece874f4bb635cde6c76a33289520f3d71 SHA256: df7ffeee5414533ba8f148f3a1e650d16731d68f8b281c592dce79b54d93154e SHA512: e2f76b7914dd3939667ac345f816866b2bf9692856de96fb86cc7552d664ed4e9d576736ae10e6110ac4d218bb0506ba97fb54a50ee02b1ce5ec8bde658d5b30 Homepage: https://cran.r-project.org/package=OptimalRerandExpDesigns Description: CRAN Package 'OptimalRerandExpDesigns' (Optimal Rerandomization Experimental Designs) This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) . Package: r-cran-optimalsurrogate Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-optimalsurrogate_1.0-1.ca2404.1_all.deb Size: 42836 MD5sum: d4c869aad161ed24089e09ff2774b53a SHA1: df2a96db9f9c0fff30bed743b97d729fc974bf3a SHA256: 9ae2937e3b04a18d0c243a659d54cd83bc8169af9c6038922e73aaeb2a930d17 SHA512: 75818ead2f19a57f21aea2b55314cab22ed4c6cb3656d00dde3569342401bba4806b97afc29ffb84b8191f29241f8b054dd17692895f7d9146deb0865592557c Homepage: https://cran.r-project.org/package=OptimalSurrogate Description: CRAN Package 'OptimalSurrogate' (Model Free Approach to Quantifying Surrogacy) Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) . Package: r-cran-optimalthreshold Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-rjags, r-cran-hdinterval, r-cran-mgcv, r-cran-coda Filename: pool/dists/noble/main/r-cran-optimalthreshold_1.0-1.ca2404.1_all.deb Size: 396718 MD5sum: 2c532b25bdcf989da33425ea8fd0803b SHA1: ad890c8d4e98746c7c98764262c2e6569e3ca3c8 SHA256: e605bab0027cff073fe2f3bcf6e5ba411d0d9dceecf9e50fbc2ca303483f4b8c SHA512: 4fed3b87b3db64f9ce733758445e1aa3d4db3c554b53cfea6b10b32ab9d50e4fce5fbf715a970d7c88cc767c53718c878cd982c3bfade55cf558cbd4633115e7 Homepage: https://cran.r-project.org/package=optimalThreshold Description: CRAN Package 'optimalThreshold' (Bayesian Methods for Optimal Threshold Estimation) Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) for diagnostic markers. Package: r-cran-optimaltiming Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mstate, r-cran-survival Filename: pool/dists/noble/main/r-cran-optimaltiming_0.1.0-1.ca2404.1_all.deb Size: 76446 MD5sum: 32faed1463b3d0e93427454dd5ff2689 SHA1: f54e04773ffd0a0b7b35f685b4cc11ef9829fd9f SHA256: ea298cb909bc623d1ca52dbaef07f71b300e6a16abe23e363bdfe85b491cbd82 SHA512: 21b78b10fbc91349279410dddea79fa1f6143e5393a7aa01ef8ecd6cbc507bc370d91189b13c847358b88969e0207538c65d8c060df412b2f4c5093f76a6f073 Homepage: https://cran.r-project.org/package=OptimalTiming Description: CRAN Package 'OptimalTiming' (Optimal Timing Identification) Identify the optimal timing for new treatment initiation during multiple state disease transition, including multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) . 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Response surface models can be up to cubic polynomial models in up to 5 controllable factors, or Thin Plate Spline models in 2 controllable factors. Package: r-cran-optimbase Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optimbase_1.0-10-1.ca2404.1_all.deb Size: 398200 MD5sum: 4162af40b76f80f607abc260257747c0 SHA1: e8b532a6154a746cc2664109f4576828e0e82f2e SHA256: a26338187cf2f593ac5f3a7efc03c5627836139949bfdac0ed060539b96a42b6 SHA512: b6f59c35707407adc350e16e71e84427e68ebd40b96fc830f63e1e0504522569bc8124bde90a9f2aacbbf39a759981c961950f91015c5b9109cfd6778d889b22 Homepage: https://cran.r-project.org/package=optimbase Description: CRAN Package 'optimbase' (R Port of the 'Scilab' Optimbase Module) Provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc... Package: r-cran-optimcheck Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-quantreg, r-cran-mclust, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optimcheck_1.0.1-1.ca2404.1_all.deb Size: 222612 MD5sum: 323534b273d2705ce0e3a0918298f8f1 SHA1: fb986cd4f8fa9a23ca3853b16ef48c601bfc2785 SHA256: 7e2c6fcf09c32b412f8849e7621aaa67b26bfb6b3f6455a7bc370743ee5dcff3 SHA512: 249119809c944f54a47af6fdaf4867f2a17f54806e3f32c6f246dcc2e27cff482e5f181092c6beac8c50fa281354658976251ea81b131d3d029d67ed22585ab3 Homepage: https://cran.r-project.org/package=optimCheck Description: CRAN Package 'optimCheck' (Graphical and Numerical Checks for Mode-Finding Routines) Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function. This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed. The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines. Package: r-cran-optimflex Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optimflex_0.1.7-1.ca2404.1_all.deb Size: 138946 MD5sum: d0e6b08218d0d2b629e512369a66301f SHA1: 1ca6bd2f46843263f275129246f2fe8017202e17 SHA256: 6b6dc4b5ec553a5a13d28bb9f2628e279ef6c1d0e19b948c5d60126a3622e48e SHA512: 2b6721cdc33188f39ce33c8ff151f201debce8b44f93eddf75af9ab5baaf19ec13605ecbcae0172162298d36ca7e2384ba47b0adbf3eaf04b929e592e620c634 Homepage: https://cran.r-project.org/package=optimflex Description: CRAN Package 'optimflex' (Derivative-Based Optimization with User-Defined ConvergenceCriteria) Provides a derivative-based optimization framework that allows users to combine eight convergence criteria. Unlike standard optimization functions, this package includes a built-in mechanism to verify the positive definiteness of the Hessian matrix at the point of convergence. This additional check helps prevent the solver from falsely identifying non-optimal solutions, such as saddle points, as valid minima. Package: r-cran-optimg Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-optimg_0.1.2-1.ca2404.1_all.deb Size: 32418 MD5sum: 30bd326fcecfefe17091dc6fab17c156 SHA1: 47ca84e42b1282bb924c422db36959caf0ffb211 SHA256: 34967299189f1dde3bda414544ddf859935d58eeaaa8eede2423a609c40cf60f SHA512: f0660efc10e31fce6e83a7927acc4af89d6f45fb381da9b4ffa51a8d0508eb50c44b6f6cd3cbdc46580f871961a288f6ab703eaa74f6229b2623cbd56d0e0ba9 Homepage: https://cran.r-project.org/package=optimg Description: CRAN Package 'optimg' (General-Purpose Gradient-Based Optimization) Provides general purpose tools for helping users to implement steepest gradient descent methods for function optimization; for details see Ruder (2016) . Currently, the Steepest 2-Groups Gradient Descent and the Adaptive Moment Estimation (Adam) are the methods implemented. Other methods will be implemented in the future. Package: r-cran-optimizer Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-lbfgsb3c, r-cran-numderiv, r-cran-oeli, r-cran-pracma, r-cran-r6, r-cran-testfunctions, r-cran-ucminf Suggests: r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optimizer_1.3.0-1.ca2404.1_all.deb Size: 283114 MD5sum: f252fd012b329205e99fcf3f2bde1b3a SHA1: ddab55cb99014a15228b5c8c5e9a433de541a3d5 SHA256: 609df9957c9ffba64647cafe89ac18613c637e52d34157d10b2880adbca11ae9 SHA512: ded61d0aa5aabb1a024e2fe7f273bc73e7e3363cb599eb6f14cb4a49062f1668e1edfccf5064c3b13168168c1ee8434250a30ffcec1c5a766850edd5439556d2 Homepage: https://cran.r-project.org/package=optimizeR Description: CRAN Package 'optimizeR' (Unified Framework for Numerical Optimizers) Provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks. Package: r-cran-optimizr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progressr, r-cran-future.apply Suggests: r-cran-testthat, r-cran-dofuture Filename: pool/dists/noble/main/r-cran-optimizr_1.0.1-1.ca2404.1_all.deb Size: 38088 MD5sum: 79ec836bd561fe46bc734dbce2fc6925 SHA1: 41645359f4e43f1f13dab95d951ce7237e4433df SHA256: 2b28f3cb6a3611f8da99e1fbae92cbb74a97390b96c221d454924528d6890c76 SHA512: 1f2fa0e943fd2ddc9ad913f6dc931382435174db8bcaa55677a4def69e4f72dd719d4e6d9da0a772b38b86ae3465ebe4ec931a1c9aaa46743b21ba2cbe2da512 Homepage: https://cran.r-project.org/package=optimizr Description: CRAN Package 'optimizr' (Further Numerical Optimization Algorithms) A collection of numerical optimization algorithms. One is a simple implementation of the primitive grid search algorithm, the other is an extension of the simulated annealing algorithm that can take custom boundaries into account. The methodology for this bounded simulated annealing algorithm is due to Haario and Saksman (1991), . Package: r-cran-optimlanduse Architecture: all Version: 2.0.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolveapi, r-cran-tidyr, r-cran-dplyr, r-cran-future.apply Suggests: r-cran-readxl, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-optimlanduse_2.0.0-1.ca2404.2_all.deb Size: 162138 MD5sum: 235652b6ce895150c30a2624358b8785 SHA1: 92f03455700f65030d7a1af051e59bf54047ca1d SHA256: c672ad3b6f54b952b5377883cee17f5cc71021d9a84d1b4686a869b8fcd77803 SHA512: d11bd4f368668f98dc6fe32b3d79dd4b44c63dfb3940a1912c6cde1607f7070da4be0fccdb373672b5e1792e523e3752d5f952c60375fce73d313b2dcdf30437 Homepage: https://cran.r-project.org/package=optimLanduse Description: CRAN Package 'optimLanduse' (Robust Land-Use Optimization) Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) . Package: r-cran-optimmodel Architecture: all Version: 2.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-optimmodel_2.0-3-1.ca2404.1_all.deb Size: 224120 MD5sum: 086e2005090cd239283c23cae38bd29d SHA1: 7862da05176439f05537ceef809e918f11565f1c SHA256: cb027ad6743eb961eea72bba1650d04ae2ed8471bcd5d4f395793ec1d8835094 SHA512: c835a3534f9321c1400a70f5d86196dcdc241024e3b4495420457d9da1659d961a93b0e542c0fe79acefb10d24bf8de1d19a297a0d1b6789ef8d586e97eaee1e Homepage: https://cran.r-project.org/package=OptimModel Description: CRAN Package 'OptimModel' (Perform Nonlinear Regression Using 'optim' as the OptimizationEngine) A wrapper for 'optim' for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1). Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006) . Package: r-cran-optimos.prime Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyverse, r-cran-plotly Filename: pool/dists/noble/main/r-cran-optimos.prime_0.1.2-1.ca2404.1_all.deb Size: 51824 MD5sum: a6486d108dbc6195f22a41bd00e6bd85 SHA1: 406d6d0a5b9522df723659657cccd8065d0dc205 SHA256: 10c7586ce157b104dda918edc6eb56462bbecf6ba199eec521f2586e049195d9 SHA512: 5d6e573b9ad0038b873045786eb1d122e2655e19e6b02e21cc8358ee46ae020d48e4b5da21760c0939412a80533ff5f9ae0411bc82efa49ec476599b5875cfa1 Homepage: https://cran.r-project.org/package=optimos.prime Description: CRAN Package 'optimos.prime' (Optimos Prime Helps Calculate Autoecological Data for BiologicalSpecies) Calculates autoecological data (optima and tolerance ranges) of a biological species given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa have unequal occurrences. See the detailed methodology by Birks et al. (1990) , and a case example by Potapova and Charles (2003) . Package: r-cran-optimparallel Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-r.rsp, r-cran-roxygen2, r-cran-spam, r-cran-microbenchmark, r-cran-testthat, r-cran-ggplot2, r-cran-numderiv, r-cran-lbfgsb3c Filename: pool/dists/noble/main/r-cran-optimparallel_1.0-3-1.ca2404.1_all.deb Size: 201284 MD5sum: 9911055f755f28e56a99005f3abcb17f SHA1: 4f8df97ad7ce4dc05509e6215e434d2db47797bf SHA256: 2998fed68a097d0fda43d8053cee6cbf740f7b7c00bc770e9fe59c66dab7b539 SHA512: 5c9ae7616b281ac639134f120745c01e453acc8aff8263d82c0cb3531c2377557041733a6df9a393dd7e2c00949c1dc97558c8cfd3c690e442e20614e1ae12fa Homepage: https://cran.r-project.org/package=optimParallel Description: CRAN Package 'optimParallel' (Parallel Version of the L-BFGS-B Optimization Method) Provides a parallel version of the L-BFGS-B method of optim(). 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Package: r-cran-optimsimplex Architecture: all Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimbase Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optimsimplex_1.0-8-1.ca2404.1_all.deb Size: 403704 MD5sum: 722ffdb5018dc8451becf024df82d288 SHA1: d5cea8ab24cc9a21ddbbb5daf2361fc44888ae06 SHA256: 5ce495c8197b91fb0269a7605e35d37d5fb263c0474c0a8c63ba3d8f3d329bf4 SHA512: 046d531400f0e3aae70a8251c75019067a49275e1c24267e007d12fbc9b29d148f7929567c8441002c5e29de7a347212758291e8136d82687bc627ad00cfde1d Homepage: https://cran.r-project.org/package=optimsimplex Description: CRAN Package 'optimsimplex' (R Port of the 'Scilab' Optimsimplex Module) Provides a building block for optimization algorithms based on a simplex. The 'optimsimplex' package may be used in the following optimization methods: the simplex method of Spendley et al. (1962) , the method of Nelder and Mead (1965) , Box's algorithm for constrained optimization (1965) , the multi-dimensional search by Torczon (1989) , etc... Package: r-cran-optimstrat Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-mvtnorm, r-cran-cubature Filename: pool/dists/noble/main/r-cran-optimstrat_2.4-1.ca2404.1_all.deb Size: 88332 MD5sum: 1c1305dc2a7a6bbe560ddb28ea40d370 SHA1: 94cbd497e77ee6a65bb8ffd8ec9e55c3f9bcb646 SHA256: 52062d276b64159b797baf00b32ba622be483f5238f1c380960d68d16be46af1 SHA512: afcd87aa7a12c75a9474c70371456d745f84c417fee4843a51be39cba5850b0beb7fb39d880837ea6df9886805052230afa57d731d780cf1ff1807590d813e95 Homepage: https://cran.r-project.org/package=optimStrat Description: CRAN Package 'optimStrat' (Choosing the Sample Strategy) Intended to assist in the choice of the sampling strategy to implement in a survey. Package: r-cran-optimus Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvabund, r-cran-ordinal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optimus_0.2.0-1.ca2404.1_all.deb Size: 110490 MD5sum: ecf85def979d509a7f7f6e3e89f78f5b SHA1: 87bda679e88b266ebdaee303962e685ba7d30e59 SHA256: 90ca7fa975d4d6f8af0f375a682b5983bc4d7e96d6599bbbd44b0727f83ce4aa SHA512: fc428911fffa98c92bc179ba60f11d0af74924a1f3eabeaf2070180c87ff0181f50764f11920c803b04ad708f38dd9fce98424968525848dfaad0befbe55f7cc Homepage: https://cran.r-project.org/package=optimus Description: CRAN Package 'optimus' (Model Based Diagnostics for Multivariate Cluster Analysis) Assessment and diagnostics for comparing competing clustering solutions, using predictive models. 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Package: r-cran-options Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-crayon, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rcmdcheck, r-cran-pkgload, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-options_0.3.1-1.ca2404.1_all.deb Size: 134794 MD5sum: abac6062a2b03dc8feb55e75d099f3a5 SHA1: 3b4e68c2c1350ef6858f42b57eb9146e90d07b0c SHA256: c0a3080072d8411a8aa6673d431d40589d0b6058dae1abe59ab5ac471f9c8f12 SHA512: 043c956505cd687e7c511dab52fc01428173e25efffa9e83b8cce3935d1797bfb04ea3f7a9e6efffba2221b29297c7ad9c5282237d0bcc1e9423f08d575e8a14 Homepage: https://cran.r-project.org/package=options Description: CRAN Package 'options' (Simple, Consistent Package Options) Simple mechanisms for defining and interpreting package options. 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Package: r-cran-optionstrat Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optionstrat_1.4.1-1.ca2404.1_all.deb Size: 111782 MD5sum: c5389e1aeb36ec12f2b5bae81a0c69ab SHA1: 3ab73f638dd24cf5c77d44e14482a21963dbe859 SHA256: df1ca171b04e12556abc27f877f31e536c588e7c00b23e1ea6614cc6ccbf9819 SHA512: 3247547879ed9708a59190966c24ba874c14e418c83b867bd03e64d067eb842b461ec594a8b6224dc8d66ee6aeb551a17cafb4396611a8efc8677f208d9f8474 Homepage: https://cran.r-project.org/package=optionstrat Description: CRAN Package 'optionstrat' (Utilizes the Black-Scholes Option Pricing Model to PerformStrategic Option Analysis and Plot Option Strategies) Utilizes the Black-Scholes-Merton option pricing model to calculate key option analytics and perform graphical analysis of various option strategies. Provides functions to calculate the option premium and option greeks of European-style options. Package: r-cran-optiscale Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-optiscale_1.2.3-1.ca2404.1_all.deb Size: 62008 MD5sum: c89cc67636d4c896d4a87fe70b2d0000 SHA1: 3be0b1d4adaf872e18ca3227435978fc1afafea4 SHA256: bfe6ac4f359c24e5f041a7d066be8f2674ff7ea5d03b367838dff50f06c94c41 SHA512: 99b652e192222eacf934f644fc6f1f92fe4a5ff40157a32359807485e886a210b433a22ad64a0ae29faba4c9c0ae52b0c2d40d54bcb6acfea4d21ae12e8e23a6 Homepage: https://cran.r-project.org/package=optiscale Description: CRAN Package 'optiscale' (Optimal Scaling) Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. 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Package: r-cran-optisembleforecasting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tsutils, r-cran-readxl, r-cran-tibble, r-cran-tensorflow, r-cran-metrics, r-cran-forecast, r-cran-dplyr, r-cran-neuralnet, r-cran-mcs, r-cran-caretforecast, r-cran-kknn, r-cran-metaheuristicopt, r-cran-factominer, r-cran-factoextra Filename: pool/dists/noble/main/r-cran-optisembleforecasting_0.1.0-1.ca2404.1_all.deb Size: 53314 MD5sum: 6d1859dee83afaf8507ee44b3abc2dda SHA1: c56e6edb269d3b0ee93e0129ea08ce1a4ae3a1d4 SHA256: 012b80d51387902c559a23ce518cb13cb88f032cc492c1e7449e9a43862abdf1 SHA512: 9047a39b5c08aee002bfbb20aa28a885adc7aadee128cee10f6711729c21a587eb28b3792ca09a43a7f31a5e2f9b4466754abfc70bf7531dc92237c07a9b85ec Homepage: https://cran.r-project.org/package=OptiSembleForecasting Description: CRAN Package 'OptiSembleForecasting' (Optimization Based Ensemble Forecasting Using MCS Algorithm) The real-life data is complex in nature. No single model can capture all aspect of complex time series data. In this package, 14 models, namely Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM, Deep LSTM, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbour (KNN), XGBoost (XGB), Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonality (ETS) and TBATS models, have been implemented and their accuracy have been checked. An PCA based error index has been proposed to select a group of best models using MCS algorithms. After selecting the models, the forecasts from these models have been ensembled using optimization techniques. This package allows to implement 20 optimization techniques, namely, Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Bat Algorithm (BA), Black Hole Optimization Algorithm (BHO), Clonal Selection Algorithm (CLONALG), Cuckoo Search (CS), Cat Swarm Optimization (CSO), Dragonfly Algorithm (DA), Differential Evolution (DE), Firefly Algorithm (FFA), Genetic Algorithm (GA), Gravitational Based Search Algorithm (GBS), Grasshopper Optimisation Algorithm (GOA), Grey Wolf Optimizer (GWO), Harmony Search Algorithm (HS), Krill-Herd Algorithm (KH), Moth Flame Optimizer (MFO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Shuffled Frog Leaping (SFL) and Whale Optimization Algorithm (WOA). This package has been developed using concept of Wang et al. (2022) , Qu et al. (2022) and Kriz (2019) . 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Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp'. 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Package: r-cran-optotrials Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optotrials_1.1.0-1.ca2404.1_all.deb Size: 123396 MD5sum: d84dd3c997dc767f718386c14f572900 SHA1: 8ce0e0e4e0532c8285ceb0885173ab64c9d12dc0 SHA256: 8ac13220d2bda8306e9ba811e06dbe48e253a503b36f93498ead17b42b8f221d SHA512: 309da4173b335808c416a0ca00aefb35385bf6fafa6bb5b57b505d8c3b6e10ff8cbaf4b7a28825859f86b01e83e0594af57d7c78e1c0fb4d16e51eac83aaf793 Homepage: https://cran.r-project.org/package=OptOTrials Description: CRAN Package 'OptOTrials' (Optimal Two-Stage Designs for Ordered Categorical Outcomes) Functions to design and simulate optimal two-stage randomized controlled trials (RCTs) with ordered categorical outcomes, using rank-based tests and interim decision rules for futility and superiority, following Park (2025) . The functions 'rule()', 'op()' and 'design_table()' provide a single entry point for constructing designs, evaluating their operating characteristics, and tabulating several designs at once. The separate functions provided for each combination of test statistic and stopping rule up to version 1.0.2 were deprecated in 1.0.3 and are removed here. Please see the package reference manual and the vignette for details. 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Package: r-cran-optr Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optr_1.2.5-1.ca2404.1_all.deb Size: 88822 MD5sum: 2608c84e1dc3e86ed82f434f204d6651 SHA1: 4d751b37957fed671d122d59cc03267f8a90a235 SHA256: 56e9799eb07e18ba688ba680f83107fe1a2ac3d7f04f713c3b11541e780c53a0 SHA512: 58460a90ae84ba3d949e33ab53f6de1ca4f208b3ca9f3a0ed58210b8b7064f4031b66af496a481584da28b531cbc5523d70b8a4ffcca5e820eb54e7e542f059b Homepage: https://cran.r-project.org/package=optR Description: CRAN Package 'optR' (Optimization Toolbox for Solving Linear Systems) Solves linear systems of form Ax=b via Gauss elimination, LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods. Package: r-cran-optrcdmaeat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-optrcdmaeat_1.0.1-1.ca2404.1_all.deb Size: 141646 MD5sum: b0db76181ddb495e765a37f30e6411e0 SHA1: a53594f7450e93c0e68eafeb3c80a13e06db3c57 SHA256: ea5626b074516fed5819fbc021ff32f34d76ce7c719aafe0e6952db3f5f0a111 SHA512: d4586294f7e98b7dc3c04c04c8fc992b29b1310d9bf950c0d442a6d6d90b344c3e9ba80a5e47771854c2dd034349acd409e1de06718220912e332a9868812855 Homepage: https://cran.r-project.org/package=optrcdmaeAT Description: CRAN Package 'optrcdmaeAT' (Optimal Row-Column Designs for Two-Colour cDNA MicroarrayExperiments) Computes A-, MV-, D- and E-optimal or near-optimal row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all pairwise treatment contrasts. 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To solve the linear program, the 'Gurobi' commercial optimization software is recommended, but not required. The 'gurobi' R package can be installed following the instructions at after claiming your free academic license at . 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Package: r-cran-optsig Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pwr Filename: pool/dists/noble/main/r-cran-optsig_2.2-1.ca2404.1_all.deb Size: 103262 MD5sum: 94f17475b96b65434d5291023ae37461 SHA1: ce3528d288ed40254fbfe98aaa7f6c089a4c6c32 SHA256: dcb06bdb817cc5970d077f555277341392abce87e04116b00f3a1387212364ec SHA512: 58c859460efba90514d43c38d4699a0ab0dde1257b32b3fb82800ff938633f6dfd369d3daffd2ed9f3f20d87d9c25ee11315a4705f52a8d27c9fd1d01ca4e620 Homepage: https://cran.r-project.org/package=OptSig Description: CRAN Package 'OptSig' (Optimal Level of Significance for Regression and OtherStatistical Tests) The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. 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The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure. 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Package: r-cran-oralopioids Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-purrr, r-cran-plyr, r-cran-jsonlite, r-cran-reshape2, r-cran-stringr, r-cran-openxlsx, r-cran-rvest, r-cran-xml2, r-cran-rlang, r-cran-magrittr, r-cran-httr, r-cran-writexl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oralopioids_2.0.5-1.ca2404.1_all.deb Size: 690200 MD5sum: daf1ee9177997a3f02d85529e42e504a SHA1: f5fb01cee119041edf83318f8f4f271952451a1a SHA256: 297e3ef2fee3116b88e80e90fc4d07dda709ae89c8b2ee3bf68129d80a8d8da7 SHA512: e228d06e6a3ad0f219d698a79bcd6647568bd715147bff0b7bf5016242d7cf358de6570909e44a48daf338eae134d99190dbb5faf62bdd83fe4ee748c043f523 Homepage: https://cran.r-project.org/package=OralOpioids Description: CRAN Package 'OralOpioids' (Retrieving Oral Opioid Information) Provides details such as Morphine Equivalent Dose (MED), brand name and opioid content which are calculated of all oral opioids authorized for sale by Health Canada and the FDA based on their Drug Identification Number (DIN) or National Drug Code (NDC). MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) and Von Korff et al (2008) and information obtained from Health Canada's Drug Product Database's monthly data dump or FDA Daily database for Canadian and US databases respectively. Please note in no way should output from this package be a substitute for medical advise. All medications should only be consumed on prescription from a licensed healthcare provider. Package: r-cran-orange Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-icosa, r-cran-igraph Suggests: r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-vegan Filename: pool/dists/noble/main/r-cran-orange_0.1.0-1.ca2404.1_all.deb Size: 1105728 MD5sum: 2606a59ebad793c2a1dd81cdfd99360d SHA1: 4fcd83b0a2be7ea67f75c87121832f475d754052 SHA256: 7a276d3084a007fcf0f65b4dff4f4456ab284d3b9554064c99982334bac6ead8 SHA512: b78373ece2248434ce771830ef56b9ab6a52cb0297f4534e877b27d356fbcd712836e405d7de2173bed021521674c26e2f98d481a54690ae42ad4ae79d2e019b Homepage: https://cran.r-project.org/package=orange Description: CRAN Package 'orange' (Spherical Descriptors of Geographic Distributions) Characterization of distribution data on the surface of a sphere. The primary group of these metrics describe the extent of a distribution, geographic ranges. 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Package: r-cran-orangutan Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-adegenet, r-cran-dplyr, r-cran-dunn.test, r-cran-ggplot2, r-cran-multcompview, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-vegan, r-cran-withr Suggests: r-cran-ragg Filename: pool/dists/noble/main/r-cran-orangutan_2.2.0-1.ca2404.1_all.deb Size: 161250 MD5sum: 9d0715c9b34b76d70621cb84e7cfd276 SHA1: 956ed095446f2e215317f9aa77273bfc7d2fcd0b SHA256: 6cd894bffa9178a0a53b786aa35643aee9c3672ef16a4ec83a1b01947fceb30a SHA512: 9c24e604d1a84a9f1febeddc7e47ebf0e67153501271e3908a389863a52ba35b4b68199ccfd2db651e50587a567e1cdc4fc77931c87cffbeee7ae418bf957217 Homepage: https://cran.r-project.org/package=Orangutan Description: CRAN Package 'Orangutan' (Automated Analysis of Phenotypic Data) Provides functions to analyze and visualize meristic, mensural, and categorical phenotypic data in a comparative framework. The package implements an automated pipeline that summarizes traits, identifies diagnostic variables among groups, performs multivariate and univariate statistical analyses, and produces publication-ready graphics. Earlier implementation are described in Torres (2025) (v1.0.0) and Torres (2026) (v2.0.0). Package: r-cran-orbis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3102 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-orbis_0.1.0-1.ca2404.1_all.deb Size: 994032 MD5sum: 2f664b17477c56771ebce32a618191f3 SHA1: 0ead1ee7da04daad901788d838dfd4805842a8e8 SHA256: 47ee81d0ee35003b6097ff5d4414a22f09d7b1c2007df37c6693a446e72d88ac SHA512: 42447bd9eb569ddb5766e5ed40ff336991aea123f92fc42a3565a1d0123cecbdc74959814c7aef9e247fb06f073a8156e7ef812e000701e6d373ca695cce8f7f Homepage: https://cran.r-project.org/package=orbis Description: CRAN Package 'orbis' (Interactive and High-Resolution Layered Graphics with Built-inWorld Maps) A layered grammar of graphics that compiles plots to a resolution-independent scene description and renders it through two back-ends: a self-contained SVG writer with embedded 'JavaScript' for interactive figures (tooltips, hover highlighting, zoom, pan and legend toggling) and R's own graphics devices for publication-quality output at any resolution. Geographic layers are first class: a simplified world polygon dataset ships with the package and can be drawn with several map projections, including Robinson, Equal Earth and an orthographic globe. The layered grammar follows Wickham (2010) ; projections follow Snyder (1987) and, for Equal Earth, Savric, Patterson and Jenny (2019) ; line simplification uses Douglas and Peucker (1973) ; the default colour scales follow the guidance on perceptually uniform palettes of Crameri, Shephard and Heron (2020) . Package: r-cran-orbital Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-rlang Suggests: r-cran-agua, r-cran-aorsf, r-cran-arrow, r-cran-baguette, r-cran-bonsai, r-cran-c50, r-cran-dbarts, r-cran-dbi, r-cran-discrim, r-cran-dbplyr, r-cran-dtplyr, r-cran-duckdb, r-cran-earth, r-cran-embed, r-cran-glmnet, r-cran-glue, r-cran-gt, r-cran-h2o, r-cran-hardhat, r-cran-jsonlite, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-knitr, r-cran-liblinear, r-cran-lightgbm, r-cran-mass, r-cran-mda, r-bioc-mixomics, r-cran-modeldata, r-cran-naivebayes, r-cran-nnet, r-cran-parsnip, r-cran-partykit, r-cran-plsmod, r-cran-probably, r-cran-r6, r-cran-randomforest, r-cran-ranger, r-cran-recipes, r-cran-rmarkdown, r-cran-rpart, r-cran-rsqlite, r-cran-rstanarm, r-cran-rules, r-cran-sda, r-cran-sparklyr, r-cran-sparsediscrim, r-cran-splines2, r-cran-tailor, r-cran-testthat, r-cran-themis, r-cran-tibble, r-cran-tidypredict, r-cran-withr, r-cran-workflows, r-cran-xgboost, r-cran-xrf Filename: pool/dists/noble/main/r-cran-orbital_0.7.0-1.ca2404.1_all.deb Size: 248950 MD5sum: 08c8cdb6747cde2e0d72d46bd1be2d24 SHA1: 722d51846c8b3d5aa96a66916cca82b737bf7dd0 SHA256: 8aef2dbed57cb9a301099e5579f54e4bd7aaaee06d39dacee5e61565d1ac7fc0 SHA512: 1a72d425dbf8f4905f52bf37166ab905fc8bb3cc2e1439fa211f698296eeb40c1c85d2e3fa21a52ed425490a9a456c3f122a94c64f298b8a2b136773c7d7f234 Homepage: https://cran.r-project.org/package=orbital Description: CRAN Package 'orbital' (Predict with 'tidymodels' Workflows in Databases) Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. 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Package: r-cran-orcamentobr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-orcamentobr_1.0.5-1.ca2404.1_all.deb Size: 60894 MD5sum: 45ab8e5382bb6b0cfc2f829321052389 SHA1: 5b9e6c063671945fa0f9091f602c7e4999eecd91 SHA256: e713fdac2c042967e665030a457887ccebedf4c2f1e435268b8a8e5154dc7d14 SHA512: 152b4c626bf744258450badcfcd8fa34d7b77c79b3408305ee801f6f16d7d972cb4580f9f2556d5ee63ad72339ae8df020a81da741090b258d6c920dc69ae1b7 Homepage: https://cran.r-project.org/package=orcamentoBR Description: CRAN Package 'orcamentoBR' (Download Official Data on Brazil's Federal Budget) Allows users to download and analyze official data on Brazil's federal budget through the 'SPARQL' endpoint provided by the Integrated Budget and Planning System ('SIOP'). This package enables access to detailed information on budget allocations and expenditures of the federal government, making it easier to analyze and visualize these data. Technical information on the Brazilian federal budget is available (Portuguese only) at . The 'SIOP' endpoint is available at . Package: r-cran-orchard Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-emmeans, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-latex2exp, r-cran-magrittr, r-cran-mass, r-cran-metafor, r-cran-progress, r-cran-rwishart, r-cran-tester Suggests: r-cran-broom, r-cran-glmmtmb, r-cran-gt, r-cran-knitr, r-cran-lme4, r-cran-metadat, r-cran-moments, r-cran-patchwork, r-cran-pearsonds, r-cran-purrr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-orchard_2.2.1-1.ca2404.1_all.deb Size: 580030 MD5sum: 6c029a17cff452240e3b1e9004f1a014 SHA1: 757fd7b079bf44edf907a2cc022df45921f9e643 SHA256: 6c598cf32a48f091916e7359a142a2a685d701e059c6126b79ac2c51654bac98 SHA512: 88e83302ee2bcf99cd9edd18abe5ac549449ac34d953e955679ee4b54b4ae2f0ca4878dc529ff5c29fbad9f54a7cfe5d0f0c69ba2b06413e6eb7ce535f735172 Homepage: https://cran.r-project.org/package=orchaRd Description: CRAN Package 'orchaRd' (Visualizing Meta-Analyses with Orchard Plots and PredictionIntervals) Generates prediction intervals and orchard plots for meta-analytic and meta-regression models fitted with the 'metafor' package. Orchard plots augment classic forest plots by displaying individual effect sizes together with group means and their confidence and prediction intervals, providing an enhanced visualization of meta-analytic data for ecology, evolution, and beyond. Methods are described in Nakagawa et al. (2023) . Package: r-cran-orci.welch Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orci.welch_0.1.1-1.ca2404.1_all.deb Size: 99822 MD5sum: d5432c4f07c39cf8d98d132edcf3f0e1 SHA1: 2e0d8c9951467f31da33a3a14e15390599624438 SHA256: 645886b944692f9917404b7ffbd50bb15b89ae35fb70ec17d683bd9f6c75b569 SHA512: 39f121c1fa64b591a3e70f5858bb34fb88b44da6bc6ecae8076061005a51fd0f3caa69a3eeef18e59d982a0197b827dc75783246d5668f6cd41f2cab383cdcf0 Homepage: https://cran.r-project.org/package=ORCI.Welch Description: CRAN Package 'ORCI.Welch' (Approximate Odds Ratio Confidence Intervals with Welch'sAdjustments) Calculates approximate odds ratio confidence intervals with Welch's adjustments for a given dataset and ranks different odds ratio confidence intervals in terms of multiple metrics based on the dataset given. 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Package: r-cran-orcidtr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-orcidtr_0.1.0-1.ca2404.1_all.deb Size: 181734 MD5sum: 9d7f249ebe19ea7b0ac75dc4b1273855 SHA1: 97ee2bbb8486f99b9e2e0b6340d5c7205f92ad04 SHA256: 6263e5d14a1c2827934344a9c2a352ae8b574df8e098da46d4b6c8811cc678ec SHA512: 405c03092855b11bba3f1bedb6b8461f5f106def84602effc0e7a939b7b7434f457f3a194f354112a1fa03dcea0b66ca45c44e05c8c82d37d0fcc7dce5294fad Homepage: https://cran.r-project.org/package=orcidtr Description: CRAN Package 'orcidtr' (Retrieve Data from the ORCID Public API) Provides functions to retrieve public data from ORCID (Open Researcher and Contributor ID) records via the ORCID public API. Fetches employment history, education, works (publications, datasets, preprints), funding, peer review activities, and other public information. Returns data as structured data.table objects for easy analysis and manipulation. Replaces the discontinued 'rorcid' package with a modern, CRAN-compliant implementation. Package: r-cran-orclus Architecture: all Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orclus_0.2-6-1.ca2404.1_all.deb Size: 61296 MD5sum: 19e5e438a2a55f6b31d26f91d6799431 SHA1: e8da2e5cf855aa627b4325c70beaabbeeaf9dd5c SHA256: 550dd134f952ebd43ef2960992215565801cf80d6983dc2aa89c2712585e7d11 SHA512: b27d450f91de95457034dacc334d9fe4b6860fa72a41649a3891ca1178b3e0c4ae49737589d417597155be5d5dadd64a5f86db0936fe4994f1d9999e2c96ebd7 Homepage: https://cran.r-project.org/package=orclus Description: CRAN Package 'orclus' (Subspace Clustering Based on Arbitrarily Oriented ProjectedCluster Generation) Functions to perform subspace clustering and classification. Package: r-cran-orcme Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso Filename: pool/dists/noble/main/r-cran-orcme_2.0.2-1.ca2404.1_all.deb Size: 129830 MD5sum: 6a5f73d11ab81edca6f91def93f65480 SHA1: 1a7028f820f2de9540d91ff646f2989c2524f808 SHA256: b2e9308ae803225a31381deae93c9277e68407c4997f118c0d09ffa4a4fd4dda SHA512: 2d8f2796dd6273246b2b18891491afe30ff4f0b52a20b000be11e6b91675995d4a2ec343b0e24c52902a66dd1365880a21a15246ac67fe31f0a6fd0f6c3d50ce Homepage: https://cran.r-project.org/package=ORCME Description: CRAN Package 'ORCME' (Order Restricted Clustering for Microarray Experiments) Provides clustering of genes with similar dose response (or time course) profiles. It implements the method described by Lin et al. (2012). 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For more information, see Kubinec (2023) . The package is a front-end to the R package 'brms', which facilitates a range of regression specifications, including hierarchical, dynamic and multivariate modeling. 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Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540". Package: r-cran-ordcrm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms Filename: pool/dists/noble/main/r-cran-ordcrm_1.0.0-1.ca2404.1_all.deb Size: 154684 MD5sum: 79be74082ce21bfed66cf60bf035bcb3 SHA1: 4d3ccbcc75c705ba5ad6e116e10c2662e99ee3c5 SHA256: 1c5a5d28a8436ed912477349266f8716be7aecab401ae3ae56465d9ec2030a5a SHA512: 534d515bb5b7cc1a45d44bc471ed3973a0a9d71bf49691af3ab4de279ef7c491d41ebad580643c2983d475d8de53b7ab506f41a8038970aa57d4aa9a4828a420 Homepage: https://cran.r-project.org/package=ordcrm Description: CRAN Package 'ordcrm' (Likelihood-Based Continual Reassessment Method (CRM) DoseFinding Designs) Provides the setup and calculations needed to run a likelihood-based continual reassessment method (CRM) dose finding trial and performs simulations to assess design performance under various scenarios. 3 dose finding designs are included in this package: ordinal proportional odds model (POM) CRM, ordinal continuation ratio (CR) model CRM, and the binary 2-parameter logistic model CRM. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous dose levels, combining ordinal grades 0 and 1 into one category, and incorporate safety and/or stopping rules. For POM and CR model designs, ordinal toxicity grades are specified by common terminology criteria for adverse events (CTCAE) version 4.0. Function 'pseudodata' creates the necessary starting models for these 3 designs, and function 'nextdose' estimates the next dose to test in a cohort of patients for a target DLT rate. We also provide the function 'crmsimulations' to assess the performance of these 3 dose finding designs under various scenarios. Package: r-cran-orddisp Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam Filename: pool/dists/noble/main/r-cran-orddisp_2.1.2-1.ca2404.1_all.deb Size: 82482 MD5sum: 73a18771b40407aad604e6f9558e2776 SHA1: 1946a582b5c2d102d9799281b459f75e65c90b36 SHA256: 8948c545160bfc304a16a9125e6ba174b63c23cc6d41ea03d537138f9a9bfea7 SHA512: d665f85b522d9cf038d4239c09f2e4d626482ef1df74f9c9dc5da4ee3225dc45833cdad74cd40bf1cf6c510f5f01cff71b32d9c453dbb75f81cc44edcae98f24 Homepage: https://cran.r-project.org/package=ordDisp Description: CRAN Package 'ordDisp' (Separating Location and Dispersion in Ordinal Regression Models) Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses. Package: r-cran-orderanalyzer Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyselect, r-cran-data.table, r-cran-dplyr, r-cran-matrixcalc, r-cran-quanteda, r-cran-rlist, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-digest, r-cran-lubridate Suggests: r-cran-pdftools, r-cran-tesseract, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-orderanalyzer_1.0.1-1.ca2404.1_all.deb Size: 366466 MD5sum: 7b026920f2b04b9a834f31ba35350fff SHA1: e7ce61d3cdab7f20e330723f467171d530fa88b4 SHA256: 5c7fdfb69992c00d7d4139e260376b9e9900a7b82e9639c3dd450fc03c6ed6e3 SHA512: b3c8661f634cf2432a8dac1694d53beb90a33e0c401a8ba3cad9d372d06e68677f0fdbd637eee344a95d280e2cf66da8b4bc66586c9df85fd73fcaa08a0d8cbf Homepage: https://cran.r-project.org/package=orderanalyzer Description: CRAN Package 'orderanalyzer' (Extracting Order Position Tables from PDF-Based Order Documents) Functions for extracting text and tables from PDF-based order documents. It provides an n-gram-based approach for identifying the language of an order document. It furthermore uses R-package 'pdftools' to extract the text from an order document. In the case that the PDF document is only including an image (because it is scanned document), R package 'tesseract' is used for OCR. Furthermore, the package provides functionality for identifying and extracting order position tables in order documents based on a clustering approach. Package: r-cran-ordered Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parsnip, r-cran-cli, r-cran-dials, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-mass, r-cran-ordinalnet, r-cran-vgam, r-cran-rpartscore, r-cran-ordinalforest, r-cran-qsardata, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordered_0.1.0-1.ca2404.1_all.deb Size: 97110 MD5sum: 44fd9bf0f680c037269e1e809e3d5682 SHA1: ca3454cc8ab755f707e3299cb840335a99073c2b SHA256: 9e25ee8393ae3b511f9c94d56769efc5c557cc9bd794833af78fcbc21063ae88 SHA512: 63d34f76da190d9686f865287b37463066c5d6b4b80f2e4b034fb4ecbbca26d06a12793f6d368dbbfe31408a2e2e34799c52f591584d5ac834604a4bd73110c4 Homepage: https://cran.r-project.org/package=ordered Description: CRAN Package 'ordered' ('parsnip' Engines and Wrappers for Ordinal Classification Models) Bindings, methods, and tuners for using ordinal classification models with the 'parsnip' and 'dials' packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) in 'ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) in 'rpartScore', and the latent variable ordinal forests of Hornung (2020) in 'ordinalForest'. Package: r-cran-ordering Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-na.tools Filename: pool/dists/noble/main/r-cran-ordering_0.7.0-1.ca2404.1_all.deb Size: 22178 MD5sum: 94afa4d8c7d5d9418fe9677390e1a1bf SHA1: 7d49f6768f2b32cb64c4e09fcfc9066e1cc166be SHA256: 487c5661c55e724ed1a46c498224956ff0a695f06ed836e31cd73005ec20fc6c SHA512: 3514c470df4fe42a0faf79b773c9ad09fd229ee3c63d357b83954cc668250631a8a2164e327006e324d29d260066990b43e1908e6c4744e35ecb46915e059046 Homepage: https://cran.r-project.org/package=ordering Description: CRAN Package 'ordering' (Test, Check, Verify, Investigate the Monotonic Properties ofVectors) Functions to test/check/verify/investigate the ordering of vectors. The 'is_[strictly_]*' family of functions test vectors for 'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' and 'is_incremental' test for the degree of ordering. `ordering` provides a numeric indication of ordering -2 (strictly decreasing) to 2 (strictly increasing). Package: r-cran-orderly Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1381 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-diffobj, r-cran-fs, r-cran-gert, r-cran-httr2, r-cran-jsonlite, r-cran-openssl, r-cran-pkgload, r-cran-rlang, r-cran-rstudioapi, r-cran-vctrs, r-cran-withr, r-cran-yaml Suggests: r-cran-dbi, r-cran-rsqlite, r-cran-callr, r-cran-jsonvalidate, r-cran-knitr, r-cran-mockery, r-cran-processx, r-cran-rmarkdown, r-cran-testthat, r-cran-webfakes Filename: pool/dists/noble/main/r-cran-orderly_2.0.3-1.ca2404.1_all.deb Size: 883406 MD5sum: 07a4793bf052eae003ff5547ef90f238 SHA1: ae78b88f52e6f35acd3e8dd138e71f7d3f97a7fb SHA256: 585e0b1cbcb67964e82eec53033656bfbb63fe0b4c976e9a27f74af63e1bde92 SHA512: cc2dd2c305c69cdbc18240c99b49e9a0b5430fd7451eefd7e52a3397b0fe3f69278998cc1bfd29970fbcc2ebf5036d80afbb30e18317bcdbe3e6755d925d9759 Homepage: https://cran.r-project.org/package=orderly Description: CRAN Package 'orderly' (Lightweight Reproducible Reporting) Distributed reproducible computing framework, adopting ideas from git, docker and other software. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans. Package: r-cran-orders Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-newdistns, r-cran-gamlss.dist, r-cran-actuar, r-cran-vgam Filename: pool/dists/noble/main/r-cran-orders_0.1.8-1.ca2404.1_all.deb Size: 129568 MD5sum: 98fc71b56f5b540605a8a35597930ae2 SHA1: f3f97cbbd3b5d23dda49d343195d499a5a36ab75 SHA256: 32cdcca48d48a77c6d4055983700f7e29c0a2cf7bfddf2c692e8dc4eed502341 SHA512: cef895504d3de869159f60f75c0d5bab9e9477e6d12b65d5fc579101ea40bdf6c2131f66a8cc147c69caf2fdc3ccb767e74be9ccdc2cfdcd387c85c087cac809 Homepage: https://cran.r-project.org/package=orders Description: CRAN Package 'orders' (Sampling from k-th Order Statistics of New Families ofDistributions) Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; Naradajah, S. and Rocha, R. (2016), and Stasinopoulos, M. and Rigby, R. (2015), . The families of distributions are: Benini distributions, Burr distributions, Dagum distributions, Feller-Pareto distributions, Generalized Pareto distributions, Inverse Pareto distributions, The Inverse Paralogistic distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distributions, Skew normal type 1 distributions, Skew student t distributions, Singh-Maddala distributions, Sinh-Arcsinh distributions, Sichel distributions, Zero inflated Poisson distributions. Package: r-cran-orderstats Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orderstats_0.1.0-1.ca2404.1_all.deb Size: 23824 MD5sum: 323703b1a88cc9e1bd0a38f3b83b1707 SHA1: 39319f1ba5712b123530783883648271f0252e31 SHA256: 8c5d979f2109c500824a548c036c9780fa9db0dbfbebd19fc634d731b6774d9a SHA512: 0f838924862b0a8c72e52d934b6ea7e996268cd0f0b7f9d70c64d1304329c528a9866db4ab53127837665992418a61e7306d4e318a4570a7a7f1abb95984a990 Homepage: https://cran.r-project.org/package=orderstats Description: CRAN Package 'orderstats' (Efficiently Generates Random Order Statistic Variables) All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution. Package: r-cran-ordfacreg Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-eha, r-cran-mass Filename: pool/dists/noble/main/r-cran-ordfacreg_1.0.8-1.ca2404.1_all.deb Size: 74242 MD5sum: 658d487343add1195b51dda297f0ba49 SHA1: ad67e8d857906bd04a3cc3b7ba56abbb498abf0d SHA256: b5d70cbc007f9283357e571fde3fa511d6410cc9d527f9aacc113c8b365314f6 SHA512: 9ac91ab2b8fa999e983bbe1372d3d42df2c6948a14e06177a38a20fc6b8509fe08cd8dc10df042314babd3e81719d2c378bffeffa546c3150fe9b3a89b9cffe6 Homepage: https://cran.r-project.org/package=OrdFacReg Description: CRAN Package 'OrdFacReg' (Least Squares, Logistic, and Cox-Regression with OrderedPredictors) In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators. Package: r-cran-ordgam Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubicbsplines, r-cran-matrix, r-cran-mgcv, r-cran-marqlevalg, r-cran-sn, r-cran-mass, r-cran-numderiv, r-cran-ucminf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordgam_0.9.1-1.ca2404.1_all.deb Size: 1334224 MD5sum: e5bb49b5f5d4aabbfe47ab4d99f6a71f SHA1: 71ca6a4da239c0cf669110e4703ed268f8c14f56 SHA256: a49244a4a27db69e588336e56c145eea9efcae7c53b9010017a587d48df69681 SHA512: 1a4c1af408ec09ebe687dd406c1433fc617a29c6544c0b8d7dc44ec183b40c704747084927d9284f2615026d0496c6c8c287bf38444c1a5d7373fb153fb0b922 Homepage: https://cran.r-project.org/package=ordgam Description: CRAN Package 'ordgam' (Additive Model for Ordinal Data using Laplace P-Splines) Additive proportional odds model for ordinal data using Laplace P-splines. The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) ; Preprint: ). Package: r-cran-ordibreadth Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Filename: pool/dists/noble/main/r-cran-ordibreadth_1.0-1.ca2404.1_all.deb Size: 73712 MD5sum: 36d759aff8756bcb8dce8ebf9c5ca015 SHA1: ff86f2d3aef0800ef9d846e51eacc11a9a252d4f SHA256: 2149f4cd1cefdb4a1936dc88a8b619aff43602cac018e03c78419fde98a2eec1 SHA512: d88d5c6d3cc5a68d6aa9388b2cd7a6ddc0c37968210555eb9b18d9059828a26ea26f5755cd06e5298b1479c58eac5a4d47e67815daef1410f21477d6dd9b4329 Homepage: https://cran.r-project.org/package=ordiBreadth Description: CRAN Package 'ordiBreadth' (Ordinated Diet Breadth) Calculates ordinated diet breadth with some plotting functions. Package: r-cran-ordinalbayes Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-deseq2, r-bioc-summarizedexperiment, r-cran-coda, r-cran-dclone, r-cran-runjags Suggests: r-cran-knitr, r-bioc-biobase, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ordinalbayes_0.1.2-1.ca2404.1_all.deb Size: 4323856 MD5sum: 2fe763dfaa7c235d795754666586270c SHA1: 24b8c458a3a8153fbbfe77645da535955f3c6139 SHA256: 69bb5f73fa094379e76675296da4a4ab8c6cdf472ba5593eb0a29d9a72c973a5 SHA512: 2854412e94c4165cb04dae4b297abc98fb50ff7d44eb6b954db1ceb4e2251dca5e7ba2d48964f9fc70d2916b25be2b3861e979a414ff0fbc5e8e9f6287ed952d Homepage: https://cran.r-project.org/package=ordinalbayes Description: CRAN Package 'ordinalbayes' (Bayesian Ordinal Regression for High-Dimensional Data) Provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) . Package: r-cran-ordinalcompositions Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolveapi, r-cran-extradistr Suggests: r-cran-codalm, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ordinalcompositions_0.1.0-1.ca2404.1_all.deb Size: 99210 MD5sum: e7906c2018b54bd3097e68b4bf7ef34d SHA1: ed493205ad1b4512b55a8846c365bbacace56bfb SHA256: cd459f488fb9f81014e43046d39d4d8078b9fd2c79d7ef34c5ddae3265f1080f SHA512: 19f6a5bcbfbbea190198e5c948442ea0c9e784d0e2393ffeedb4b072518f9c54f2b33e2663e09f108cb51f4779a5b3e360dd6a79e85e8dba1325a7cb395a1c30 Homepage: https://cran.r-project.org/package=OrdinalCompositions Description: CRAN Package 'OrdinalCompositions' (Wasserstein-Based Regression for Ordinal Compositional Data) Tools analyzing regression models for ordinal compositional data using Wasserstein-based distances. The package includes linear programming solvers under simplex constraints, tensor product constructions and performance metrics. Package: r-cran-ordinalcont Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-deriv Filename: pool/dists/noble/main/r-cran-ordinalcont_2.0.2-1.ca2404.1_all.deb Size: 219908 MD5sum: 69ff891d0d1c1021e92d850abb7652b6 SHA1: fd0b1d29361363219a8cebdf4840f0481de308a6 SHA256: f665979562ba76b85d3b03c1148a58e6068e700a1d1264ffd8826ac1d7d5bfc6 SHA512: 6c7e18de46cc7b6afceda2137939a584c6f4e2f17c13cb895a4e60adab294acdc030f48cb1caf725d2bcd391ed849246c6dbad1e43d284e64a45694153ef20ce Homepage: https://cran.r-project.org/package=ordinalCont Description: CRAN Package 'ordinalCont' (Ordinal Regression Analysis for Continuous Scales) A regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent non-linearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. A variety of link functions are currently implemented. Such models are described in Manuguerra et al (2020) . Package: r-cran-ordinalgof Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mass, r-cran-vgam Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-ordinalgof_0.1.0-1.ca2404.1_all.deb Size: 23642 MD5sum: 5c195aebdc9e950ac525985b18898350 SHA1: 9efd161d4dbc2862a42a73d5fd6aac59a910097b SHA256: 01c96c0de648bcd63c4f09884d0274bb3fa362663ba0f8f1185c5762c0be1005 SHA512: 4ad3757dbfd81dc36c6b3cf2a396fe4a97378c35b4eddbe399a3750d0b3d180d59accc602ffef98006905825b886eed0e416257e5ea8141ba5a33463075c4283 Homepage: https://cran.r-project.org/package=ordinalGOF Description: CRAN Package 'ordinalGOF' (Goodness-of-Fit Tests for Ordinal Regression Models) Provides goodness-of-fit tests for ordinal regression models, including the Fagerland-Hosmer ordinal test, reproducing same output as 'Stata'. Supports polr(), vglm(), and binary glm() models. See Fagerland and Hosmer (2013) and Fagerland and Hosmer (2017) for details. Package: r-cran-ordinallbm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-ordinallbm_1.0-1.ca2404.1_all.deb Size: 55492 MD5sum: f8142d9913458510db6f3d661fda0b00 SHA1: ead41a223787ab92320cb3a61e9b3a0105bdf10a SHA256: 375bafd00efaf58fad4108a90ee8870ce182af1081603394efa6dbe8950781d5 SHA512: 73728533cb2d63353285c3e5f5a8affdc99c3ebf5ae9c52176eaa2c0ed89cde81d3aa2b328325ced7d9822ac961fc878f7323bef578b5beaf3012d85d11195eb Homepage: https://cran.r-project.org/package=ordinalLBM Description: CRAN Package 'ordinalLBM' (Co-Clustering of Ordinal Data via Latent Continuous RandomVariables) It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019). Package: r-cran-ordinalnet Architecture: all Version: 2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-mass, r-cran-glmnet, r-cran-penalized, r-cran-vgam, r-cran-rms Filename: pool/dists/noble/main/r-cran-ordinalnet_2.14-1.ca2404.1_all.deb Size: 120256 MD5sum: 36d22847a7719e8ac47cc6d4bd9d28e1 SHA1: a68f60249b41d96342e4b1bd1b76764663810648 SHA256: e7d93bf81663fd2de9911d4e98e870898c9481da79b1d3b0df75e422973d9b5a SHA512: d7e6c6f37157da892a05959e2d648c118dc357073ebae3fc40f5c425c2bf8b1f8449cd1d9a7eb392cff0302d41c33c0341999ecbb94cc4317d7d0ec6fc673196 Homepage: https://cran.r-project.org/package=ordinalNet Description: CRAN Package 'ordinalNet' (Penalized Ordinal Regression) Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) . Package: r-cran-ordinalrr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags Filename: pool/dists/noble/main/r-cran-ordinalrr_1.1-1.ca2404.1_all.deb Size: 74636 MD5sum: 2244adab4cc589fc6fa35070d34fcf1e SHA1: 5658a5cec40b150ab29bb3fefa303a29bdb16a5d SHA256: eae8168d6a91261fd804524c14b95489b4e0b03fcd93e3f8ed9bfeeb3a8d6e9d SHA512: 234243998aadbd7a77daf5f54b5c71e2536c09c3500a4774f69952ff26c6ae7df3772baff82ca2a61ddcc437ad13076dffd504851a7568e2b639077cd201cded Homepage: https://cran.r-project.org/package=ordinalRR Description: CRAN Package 'ordinalRR' (Analysis of Repeatability and Reproducibility Studies withOrdinal Measurements) Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper. Package: r-cran-ordinalsimr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bslib, r-cran-callr, r-cran-coin, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-rhandsontable, r-cran-rlang, r-cran-rms, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-pkgload, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-writexl Filename: pool/dists/noble/main/r-cran-ordinalsimr_0.2.4-1.ca2404.1_all.deb Size: 1993270 MD5sum: 0dfea1e94e8fff0cbf28939aa1f06957 SHA1: 5f4cb0ebac9c40a8c546d417cd9d9f85be020026 SHA256: 659ff2cf47d3ee369d997eca54017b801eac666e225645c66231d4c7290681e2 SHA512: 53ca746561a5c7dfd6431a54ec53a153090237e9157b81eddfc8365c6ca3c8da15f96adcb3c3b5507cce4aa3d1e7949905ce3b8eda2eb55eb446a28424d9dc92 Homepage: https://cran.r-project.org/package=ordinalsimr Description: CRAN Package 'ordinalsimr' (Compare Ordinal Endpoints Using Simulations) Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included 'shiny' application allows users to simulate outcomes given different ordinal data distributions. Package: r-cran-ordinaltables Architecture: all Version: 1.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordinaltables_1.0.0.3-1.ca2404.1_all.deb Size: 992494 MD5sum: 85ff176490f63b9f689a86cd17840486 SHA1: 8563723de1377e9d955e7a217261ee0c3e59dbf1 SHA256: 54f620f04a16dfdf5efb6fff89b169b922ae4936310fadfae5b2e63e528d41fc SHA512: 63b2fca87c81dfb4fe73361c16ccad622e3678926833cc2fec933806eb18c872db04ab4b21dab117c18a89f6703b9674909a7692724d5b6214207521753578aa Homepage: https://cran.r-project.org/package=ordinalTables Description: CRAN Package 'ordinalTables' (Fit Models to Two-Way Tables with Correlated Ordered ResponseCategories) Fit a variety of models to two-way tables with ordered categories. Most of the models are appropriate to apply to tables of that have correlated ordered response categories. There is a particular interest in rater data and models for rescore tables. Some utility functions (e.g., Cohen's kappa and weighted kappa) support more general work on rater agreement. Because the names of the models are very similar, the functions that implement them are organized by last name of the primary author of the article or book that suggested the model, with the name of the function beginning with that author's name and an underscore. This may make some models more difficult to locate if one doesn't have the original sources. The vignettes and tests can help to locate models of interest. For more dertaiils see the following references: Agresti, A. (1983) "A Simple Diagonals-Parameter Symmetry And Quasi-Symmetry Model", Agrestim A. (1983) "Testing Marginal Homogeneity for Ordinal Categorical Variables", Agresti, A. (1988) "A Model For Agreement Between Ratings On An Ordinal Scale", Agresti, A. (1989) "An Agreement Model With Kappa As Parameter", Agresti, A. (2010 ISBN:978-0470082898) "Analysis Of Ordinal Categorical Data", Bhapkar, V. P. (1966) "A Note On The Equivalence Of Two Test Criteria For Hypotheses In Categorical Data", Bhapkar, V. P. (1979) "On Tests Of Marginal Symmetry And Quasi-Symmetry In Two And Three-Dimensional Contingency Tables", Bowker, A. H. (1948) "A Test For Symmetry In Contingency Tables", Clayton, D. G. (1974) "Some Odds Ratio Statistics For The Analysis Of Ordered Categorical Data", Cliff, N. (1993) "Dominance Statistics: Ordinal Analyses To Answer Ordinal Questions", Cliff, N. (1996 ISBN:978-0805813333) "Ordinal Methods For Behavioral Data Analysis", Goodman, L. A. (1979) "Simple Models For The Analysis Of Association In Cross-Classifications Having Ordered Categories", Goodman, L. A. (1979) "Multiplicative Models For Square Contingency Tables With Ordered Categories", Ireland, C. T., Ku, H. H., & Kullback, S. (1969) "Symmetry And Marginal Homogeneity Of An r × r Contingency Table", Ishi-kuntz, M. (1994 ISBN:978-0803943766) "Ordinal Log-linear Models", McCullah, P. (1977) "A Logistic Model For Paired Comparisons With Ordered Categorical Data", McCullagh, P. (1978) A Class Of Parametric Models For The Analysis Of Square Contingency Tables With Ordered Categories", McCullagh, P. (1980) "Regression Models For Ordinal Data", Penn State: Eberly College of Science (undated) "Stat 504: Analysis of Discrete Data, 11. Advanced Topics I", Schuster, C. (2001) "Kappa As A Parameter Of A Symmetry Model For Rater Agreement", Shoukri, M. M. (2004 ISBN:978-1584883210). "Measures Of Interobserver Agreement", Stuart, A. (1953) "The Estimation Of And Comparison Of Strengths Of Association In Contingency Tables", Stuart, A. (1955) "A Test For Homogeneity Of The Marginal Distributions In A Two-Way Classification", von Eye, A., & Mun, E. Y. (2005 ISBN:978-0805849677) "Analyzing Rater Agreement: Manifest Variable Methods". Package: r-cran-ordmonreg Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ordmonreg_1.0.4-1.ca2404.1_all.deb Size: 85142 MD5sum: 2b773790dd3a5f44712fd2efbe4bed71 SHA1: 0f293a6579abb44fadcbad75b1eb61dc73c11478 SHA256: fead639a22176e1a3134ac0d90abafaa0a75bce0c3e3fec03c3a7dbad5ba7fda SHA512: 910222fea66cfddaa0e83a0ea1ba38b5a19ed213bccf76f367d9a5caf3f863d9b25a190f889f6d073d43465d95425e712f195d4d2f0e5c4131d26139e5c8b8dc Homepage: https://cran.r-project.org/package=OrdMonReg Description: CRAN Package 'OrdMonReg' (Compute Least Squares Estimates of One Bounded or Two OrderedIsotonic Regression Curves) We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm. Package: r-cran-ordnor Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor, r-cran-matrix, r-cran-genord Filename: pool/dists/noble/main/r-cran-ordnor_2.2.3-1.ca2404.1_all.deb Size: 50648 MD5sum: 814faf739745feffde6fe465077dba30 SHA1: 63be282efd03f771e8466bfa00f8019a5cb833df SHA256: d9cf0f631b4b121cd6430d8d9309e30431f264d7d26b13a6285edcc88f77af4c SHA512: f11033c9d49c80e98bc0dfbe00b5a9143d4ad7230f85d2dff062794b9a1e45f672ac805c5eb815cb09f4f18d9b019f15234af0c51bbda7fd6808e983ff3a370c Homepage: https://cran.r-project.org/package=OrdNor Description: CRAN Package 'OrdNor' (Concurrent Generation of Ordinal and Normal Data with GivenCorrelation Matrix and Marginal Distributions) Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2015) . Package: r-cran-ordpanel Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-flextable, r-cran-ggplot2, r-cran-consort, r-cran-patchwork, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordpanel_0.1.2-1.ca2404.1_all.deb Size: 410882 MD5sum: bf270a9adaa607bcdc369134bff26853 SHA1: 3fbc63709dd169770943513c863c1721c79a8cf8 SHA256: 9351e84503f971b0528c541063f006b3100023d9a91a8d0f224b8b8d237fb621 SHA512: 172d448f080edda3f40b448f5ffa6ad5e8c3e73a6b2500336b40433ca626574ee0b103836a117c076c2f4fa409e68075e9be4dffdbd89c1a96652764817fe358 Homepage: https://cran.r-project.org/package=ordPanel Description: CRAN Package 'ordPanel' (Ordered Panel) The ordered panel methodology (Zezulinski et al 2025 ) provides a structured framework for identifying and organizing sets of biomarkers, such as genetic variants, that distinguish between positive and negative subjects in a study when only a training cohort is available. This approach is particularly useful in situations where an independent validation cohort does not yet exist, rendering conventional performance metrics such as the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) inappropriate or potentially misleading. The methodology emphasizes transparent construction and evaluation of ordered signatures of biomarkers, allowing investigators to examine operating characteristics without establishing predictive performance. Package: r-cran-ordpens Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grplasso, r-cran-mgcv, r-cran-rlrsim, r-cran-quadprog, r-cran-glmpath, r-cran-ordinalnet Suggests: r-cran-psy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordpens_1.1.0-1.ca2404.1_all.deb Size: 1464264 MD5sum: d114d7faec641abe7e808427d02d1647 SHA1: 8c3fb7a7f781edef2a430bfe020faa6e1683a24c SHA256: 0c54595988e51a278fc8e7967113fad0a525ee4a047f558c4dbb5985d4cf61a5 SHA512: e056c64821962f346e25d1dc8f85e87f441ccf3437e78629c5d9dcd808890ef21d17a193ce9d0101c0a66cea59d747cb3451eb4f3eaa9ae1dc3ed60b4da06f5f Homepage: https://cran.r-project.org/package=ordPens Description: CRAN Package 'ordPens' (Selection, Fusion, Smoothing and Principal Components Analysisfor Ordinal Variables) Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing penalty. Package: r-cran-ordr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-cli, r-cran-mass, r-cran-stringr, r-cran-tidyselect, r-cran-scales, r-cran-generics, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-labeling, r-cran-ggrepel, r-cran-gggda Suggests: r-cran-testthat, r-cran-sessioninfo, r-cran-gridextra, r-cran-mlpack, r-cran-ddalpha, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ordr_0.2.0-1.ca2404.1_all.deb Size: 1281320 MD5sum: eb08209bb983e57b008f16803b99b84b SHA1: 4ab57d2bbe04ad7af932486e21f0449cdde26bdb SHA256: 86758d4973c185fdbf40e4be0d04f2799433194bcacfa30ee2f1a499e4477128 SHA512: 9eb478db6590e223ab8fc3c450b4265dbf38b0bbac63a5a82d1d07a48d5ba2c69fad1a8312000bbface8350dba769191f3bcf039c4c3ba41bf9065bc2958e264 Homepage: https://cran.r-project.org/package=ordr Description: CRAN Package 'ordr' (A 'Tidyverse' Extension for Ordinations and Biplots) Ordination comprises several multivariate exploratory and explanatory techniques with theoretical foundations in geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) for foundations. Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) , is called a biplot. 'ordr' provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into 'Tidyverse' workflows provided by Wickham et al (2019) . Package: r-cran-oreo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-ggplot2, r-cran-openxlsx, r-cran-spectral, r-cran-pracma, r-cran-fftwtools, r-cran-scales Filename: pool/dists/noble/main/r-cran-oreo_1.0-1.ca2404.1_all.deb Size: 115468 MD5sum: 4a413b28d834ad4400d52d621ca025ce SHA1: b81c134e849b5f551a6deefcec3511643176b8fa SHA256: 6f8614bb0ac243f9e7974edb5dcbd54055f6df53d106dbd0f1d2948ec91f291c SHA512: 54bdc41a72f6c7d535642525564201b2f3cc25d2dd7656fe21fa5c74fbd263c54c9566a9fb8e009c533e74dc0dfff1d9e35839adc668c39f764fc32bcb3bd6ab Homepage: https://cran.r-project.org/package=oreo Description: CRAN Package 'oreo' (Large Amplitude Oscillatory Shear (LAOS)) The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) ). Package: r-cran-orfid Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-orfid_1.0.3-1.ca2404.1_all.deb Size: 167802 MD5sum: e0936be958b1f5797d891d944919363c SHA1: 1d13b45b984fcd19516a581b3cd86d6d43f52d42 SHA256: 685d43c4f013b54a5002b38d55d608c0935afad691debdbb4ee83f30970c3373 SHA512: 218adacdb5786b5656cb7d61644929bc312347e3b67e371c4f1b6256cfb80a5aba03ab916df973c2110d7dd290a8436cf192625518ce71b0065dce0842fd113b Homepage: https://cran.r-project.org/package=ORFID Description: CRAN Package 'ORFID' (Manage and Summarize Data from Oregon RFID ORMR and ORSR AntennaReaders) Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency. 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This package provides tools to set up and manage project directories, handle file paths consistently across operating systems, organize results using date-based structures, source code from specified directories, and perform file operations safely. It ensures consistency across projects while accommodating different requirements for various types of content. Package: r-cran-organik Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart, r-cran-glmnet, r-cran-matrix, r-cran-mass, r-cran-imputets Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-organik_1.0.1-1.ca2404.1_all.deb Size: 89780 MD5sum: 2516610cb1b726a6a42ac425f7eb6b54 SHA1: 85f4d5627ee83845d83f14f65fc8a2868911a9f6 SHA256: 241fe4e2b399385b68d12653f3153cdd8446bcc38d80e31da84e6d8b4f4b05b1 SHA512: b2e0b6023b41dcc5595fc6cc76ab4402e6075ee278a82178799b4b23c92d035bcee9d11210fc7e2e75016fbd79265fcf753c7a17dd0e758a5df085081ee60f9a Homepage: https://cran.r-project.org/package=organik Description: CRAN Package 'organik' (Multi-Horizon Probabilistic Ensemble with Copulas for TimeSeries Forecasting) Trains per-horizon probabilistic ensembles from a univariate time series. It supports 'rpart', 'glmnet', and 'kNN' engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons. Package: r-cran-organizr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-here, r-cran-readr, r-cran-rlang, r-cran-rstudioapi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-organizr_0.1.0-1.ca2404.1_all.deb Size: 21738 MD5sum: 2808531d9cafbd829c8cf6e15fcd8371 SHA1: c6a0a6b6a0a14f0dde91fbf59a9e04bd6ae9a297 SHA256: d02cdcc1bda1052d88b7bc9def1d9fa6dfa91faa89d7fc58916fc775f3c9c66e SHA512: 828b5e0e7861af0ac7c29f9bc1059571bd86c9161872e5b2da98cf2bf03415ac4e138dddcf6accd0a1e274728591d398af6ed7ca99a8ad68fb1dc7f61a08b41b Homepage: https://cran.r-project.org/package=organizr Description: CRAN Package 'organizr' (Shortcuts for File Creation with Informative Prefixes) Provides functions for quickly creating R and Python scripts, as well as 'Rmarkdown' or Quarto documents with automatically assigned name prefixes. 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Package: r-cran-orgheatmap Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4792 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpolypath, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-patchwork, r-cran-purrr, r-cran-stringdist, r-cran-sf, r-cran-data.table, r-cran-rcolorbrewer, r-cran-viridis Suggests: r-cran-knitr, r-cran-svglite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orgheatmap_0.3.4-1.ca2404.1_all.deb Size: 4063582 MD5sum: 172fef1b915cd843e14868cdec5fe9f8 SHA1: db77975ca2f6bef5d58f7dc727ca8719a8793864 SHA256: 2179e7347952310d5c1a9c505ddd97a6c20d857136cb97cf396a8069716b77b8 SHA512: b43e1d3b0ff17c5e187beb9d1aa66e6ea36f343db0e08c2fe741a8391208dd2963543c5b3cefdc88969ecb8ff1bed6e183a1ee6c3555defd7fa75d24734e0ac6 Homepage: https://cran.r-project.org/package=OrgHeatmap Description: CRAN Package 'OrgHeatmap' (Visualization Tool for Numerical Data on Human/Mouse Organs andOrganelles) A tool for visualizing numerical data (e.g., gene expression, protein abundance) on predefined anatomical maps of human/mouse organs and subcellular organelles. It supports customization of color schemes, filtering by organ systems (for organisms) or organelle types, and generation of optional bar charts for quantitative comparison. The package integrates coordinate data for organs and organelles to plot anatomical/subcellular contours, mapping data values to specific structures for intuitive visualization of biological data distribution.The underlying method was described in the preprint by Zhou et al. (2022) . Package: r-cran-orgmassspecr Architecture: all Version: 0.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1086 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-orgmassspecr_0.5-4-1.ca2404.1_all.deb Size: 495380 MD5sum: ddf1cff154e571515ddce0bc19617033 SHA1: 7fe202d14d9cd1604b045c88caa7a21940b35b9b SHA256: 1185a83d55c39013dbd76c08c4bd7cc113ff827de7f865e1e651fc0ea6fa9740 SHA512: 7076fc36301c7e3ce2c15fa45612a6d7e34516b576c7217da4a809c3cee72557e57319386eea76cdddfbe3206267aaef41469162a84d7338aa12beb947a13764 Homepage: https://cran.r-project.org/package=OrgMassSpecR Description: CRAN Package 'OrgMassSpecR' (Organic Mass Spectrometry) Organic/biological mass spectrometry data analysis. Package: r-cran-orgr Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggthemes, r-cran-ggplot2, r-cran-lubridate, r-cran-data.table, r-cran-stringr Filename: pool/dists/noble/main/r-cran-orgr_0.9.0-1.ca2404.1_all.deb Size: 34436 MD5sum: 775e0eb86061bf6ad044b67267748ad3 SHA1: 5fb8c105cadf0b6fc6ad0a478bcebd4307b6b34b SHA256: 7d8d772870e9c8cd714e574c1c1fef91e3f759585b9108f52b594f27c21ddf6c SHA512: 1c6db6c3ab45d16748766fd27947c0c0a381b0c0a9cfb237cd5ff13b3bc8b68e504901efac1e39222aa2df9d0c4a09fce36ed92ba60272bd45f3b1820b13ec5c Homepage: https://cran.r-project.org/package=orgR Description: CRAN Package 'orgR' (Analyse Text Files Created by Emacs' Org mode) Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc). Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects. Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours. Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files. Package: r-cran-orgutils Architecture: all Version: 0.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-orgutils_0.5-4-1.ca2404.1_all.deb Size: 168534 MD5sum: 01c1169d0ebf0270c7b7bfe854fefc8e SHA1: f69c08b7db763cbda54ab9ea8e526c6da7f9001e SHA256: 87413b4228a82e00f4da1b0c42d4cb5cb1d6880bf3d5d7220251faf8d599c909 SHA512: 3c9d494a141d4da8d327179462ce75de7f4c0a492586840343008fd488ee9a887bf124ed863494d2e782de3e663276c8a6956dd8cc2ea71c6ca9e166bd5c61bd Homepage: https://cran.r-project.org/package=orgutils Description: CRAN Package 'orgutils' (Helper Functions for Org Files) Helper functions for Org files (): a generic function 'toOrg' for transforming R objects into Org markup (most useful for data frames; there are also methods for Dates/POSIXt) and a function to read Org tables into data frames. Package: r-cran-oriclust Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oriclust_1.0-2-1.ca2404.1_all.deb Size: 263666 MD5sum: 54f786bc4c77305c8044177bd7390a57 SHA1: 8b6430ee8c1ab94b5f328ad972fc4a28519b1557 SHA256: 6fcd3428fc25bbbadd48d488a54b5a65bc8d913b8aacf807c14875fd5b3117a7 SHA512: 3af1aafa98c0405a03c0ee4fb2a41c43e02c5fde5a8059f3991013b38f4d7ef9dc1956310d10f4a931e8e6e8d4fc787fedb076660be319547b572ffe5a735ad3 Homepage: https://cran.r-project.org/package=ORIClust Description: CRAN Package 'ORIClust' (Order-Restricted Information Criterion-Based ClusteringAlgorithm) A user-friendly R-based software package for gene clustering. Clusters are given by genes matched to prespecified profiles across various ordered treatment groups. It is particularly useful for analyzing data obtained from short time-course or dose-response microarray experiments. Package: r-cran-orientlib Architecture: all Version: 0.10.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rgl, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-orientlib_0.10.5-1.ca2404.1_all.deb Size: 333322 MD5sum: 20b18d27b5d7a928acf95bf8c30708a6 SHA1: a0370010b92afa7c27bca2991a68997f60a32df0 SHA256: 4f8919557c0f0773a9a75641024adf8da83eadfcd79d252a80cd96a2d7022db3 SHA512: 3331ffc5b7eba668791e45e90bac871afa7d373fa90509b408d3124b36f4c89057a82aa3c3d2cc2cc9714c2f1a5a57912eef7d21a2b2609d0a98e11e121601eb Homepage: https://cran.r-project.org/package=orientlib Description: CRAN Package 'orientlib' (Support for Orientation Data) Representations, conversions and display of orientation SO(3) data. See the orientlib help topic for details. Package: r-cran-origami Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-data.table, r-cran-assertthat, r-cran-future, r-cran-future.apply, r-cran-listenv Suggests: r-cran-testthat, r-cran-class, r-cran-rmarkdown, r-cran-knitr, r-cran-stringr, r-cran-glmnet, r-cran-forecast, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-origami_1.0.8-1.ca2404.1_all.deb Size: 93264 MD5sum: 9586b21a677e183c954c7fef46de9ba2 SHA1: d92b5dc137aa70d942410ba007d843bcee508d21 SHA256: ad28eb42297e1e3ac43b6228e072f38fcf5610e66c426f5efcac26de96e2cb72 SHA512: deea921e11d8345e324e1adeecf234ea1e0bfcf84222156bc1676537c7a4d6976ff91451f10a7ab76a815c6cd278a10c128445ac16c5905670b9792efc6c9218 Homepage: https://cran.r-project.org/package=origami Description: CRAN Package 'origami' (Generalized Framework for Cross-Validation) A general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications. This implementation was first described by Coyle and Hejazi (2018) . Package: r-cran-origamiplot Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmsb, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-origamiplot_0.2.0-1.ca2404.1_all.deb Size: 88572 MD5sum: 8591f1e990066658ce882b4dec84e6a6 SHA1: 29c44827a77bd96e52d045555da337081a37e99a SHA256: fcaca11e5f1f2e5e1ba94e31ec9d97ce8b5e27b2cd772fa100b3c1439173efa7 SHA512: 6cd65cf4635221afe5807405f504448ca40abdc5d0fba20d81bd92046209558ebc5044c541561bfd30e96a8310b20be600efba350c92f616d39ba9de871e7024 Homepage: https://cran.r-project.org/package=OrigamiPlot Description: CRAN Package 'OrigamiPlot' (A Visualization Tool Enhancing Radar Plot Visualizations forMultivariate Data) A visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making. Package: r-cran-origin Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rstudioapi Suggests: r-cran-data.table, r-cran-dplyr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-origin_1.2.1-1.ca2404.1_all.deb Size: 457000 MD5sum: 12c4b981a7a08b21a5073f37380e83cb SHA1: 85a0e6bf1dbb753097ba0e3a575fd9100017525d SHA256: ab25f3e3d6966e84539be82ce762d30be33f8ce3cbd33969bd6d60f14204073d SHA512: 4805b7adeb4f1e31a4fcd9c6886963c8981ed47b48e1bf4f5f3faf0897fe01d244d1a3350b5cea146f3bd552b788bb68e880a16fe8886b740942ec7907525f7e Homepage: https://cran.r-project.org/package=origin Description: CRAN Package 'origin' (Explicitly Qualifying Namespaces by Automatically Adding 'pkg::'to Functions) Automatically adding 'pkg::' to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the 'RStudio' Markers pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used for checking which packages are actually used in a project. Package: r-cran-orionz.g Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orionz.g_1.0.1-1.ca2404.1_all.deb Size: 68474 MD5sum: b5b12cbe678f7f2723cd0aa061948388 SHA1: aea5f2d786fd65251360e2f4196a96735895b992 SHA256: 21ede53edf3b03fcfe92a7aad3732b2f7e975adac9d8f00398a95affbed17027 SHA512: e59d24ea1e25008cf9fdd68edd2ea1aa5f3ef85aa74c7c8689938477b2fed9a785c852d538dade3f2d14434b69de618f3a3737397a30f2981be5a192fe3d3109 Homepage: https://cran.r-project.org/package=ORIONZ.G Description: CRAN Package 'ORIONZ.G' (EAP Scoring in Exploratory FA Solutions with CorrelatedResiduals) Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure. Package: r-cran-orisma Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-jsonlite, r-cran-magrittr, r-cran-pheatmap, r-cran-readr, r-cran-stringdist, r-cran-stringr, r-cran-synthesisr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orisma_0.1.0-1.ca2404.1_all.deb Size: 390662 MD5sum: 4cab42ec7fa6e41bfd97d07a4f1e2a23 SHA1: 1bb1b305b39d5100a4de1b399880c4abbafcca6a SHA256: 72024175a894d9419d7ac3e860b91b940b218292fbef86794f0fa00c5e16b017 SHA512: 17929f080b4bbf08b9eb78a91992d9c71de49ce3f5bd6a7bbf14f3f77152ceff674ff14df83396f0a3f898cb30d51bc80f6bd70f4d6719522977053a90e212a2 Homepage: https://cran.r-project.org/package=orisma Description: CRAN Package 'orisma' (Occupational Risk Integrated Systematic Mapping and Analysis) A complete pipeline for systematic bibliometric mapping of occupational health and safety (OHS) evidence. Starting from reference files exported from major bibliographic databases such as Web of Science, Scopus, PubMed, Dimensions, EBSCO, and others, 'orisma' automates ingestion, deduplication, relevance filtering, occupational risk category extraction, bibliometric analysis, and report generation. The package is related to bibliometric science mapping and evidence synthesis workflows described by Aria and Cuccurullo (2017) , Westgate (2019) , and Lajeunesse (2016) , but adds a domain-specific occupational safety and health layer. The package implements three original bibliometric indicators: (1) the Worker-Risk Disconnection Index (WRDI), measuring the proportion of studies that characterise an occupational risk without including direct worker exposure data; (2) the Risk Category Saturation Index (RCS), measuring the relative over- or under-representation of each risk category relative to a uniform baseline; and (3) the Material-Gap Profile (MGP), measuring the ratio between a material's known hazard potential and its coverage in the occupational health literature. Two additional preventive intelligence indicators are provided: (4) the Abstract Sufficiency Score (ASS, 0-5), a cumulative hierarchical index of the preventively useful information contained in an abstract; and (5) the Bridge Article Score (0-5), identifying studies that simultaneously address technology, hazardous agent, worker population, exposure measurement, and preventive recommendations. Risk categories are extracted using a built-in occupational risk dictionary of 58 categories anchored in ISO 45001:2018, INSST, NIOSH, and EU-OSHA frameworks, organised in six blocks: Safety, Industrial Hygiene, Ergonomics, Psychosociology, Biological Hazards, and Emerging Technologies. The dictionary is user-extensible. Outputs include bilingual HTML reports, occupational risk sheets, priority reading rankings, guided extraction matrices for systematic review, and reproducibility certificates with MD5 hashes. Package: r-cran-orkm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-orkm_1.0.0-1.ca2404.1_all.deb Size: 419938 MD5sum: a7409e1543d95e16de4679270fa47aae SHA1: d6ed6a8b136590ef56411a9762901880e84f426d SHA256: 301192177a90ef4e1d757ad3c7a03f02b3a0de73ed7f306f158cf80023db9eb7 SHA512: bf1e2218b7d5b1a5d1ed18da34735f7c0270b832f343e84d4e136b9fb98f59278203dce2344edc03d278d3f3009ca02806ae078df7b046bff576bc07dcf850ee Homepage: https://cran.r-project.org/package=ORKM Description: CRAN Package 'ORKM' (The Online Regularized K-Means Clustering Algorithm) Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2024) . Package: r-cran-orloca.es Architecture: all Version: 5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orloca Filename: pool/dists/noble/main/r-cran-orloca.es_5.5-1.ca2404.1_all.deb Size: 92962 MD5sum: 2b9c24ae983be8959531265859c5b046 SHA1: 7362cd2ec57f1df80bb2d6dffbc4f415eda75d65 SHA256: f55563a89be7cf40da7a1523d9e82cd4cf52fb8848ec6c4724b4ee3cd32c3f93 SHA512: 13fa1bca9818bd5882f5ec6b6215155bc4a2e44cd336c91be545fff0efe05ce8def777bd052a981feaa2c55d58c4e573f4d9078ace535fa9a6e41f62ccea0e8e Homepage: https://cran.r-project.org/package=orloca.es Description: CRAN Package 'orloca.es' (Spanish version of orloca package. Modelos de localizacion eninvestigacion operativa) Help and demo in Spanish of the orloca package. Ayuda y demo en espanol del paquete orloca. Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. . Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo especifico Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. . Package: r-cran-orloca Architecture: all Version: 5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-png, r-cran-ucminf, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-orloca.es, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orloca_5.6-1.ca2404.1_all.deb Size: 419800 MD5sum: be90ef8f15dff8573f59537f56317a21 SHA1: 049cafd71f9729213d7533e0f7bbe4e17fc791ae SHA256: 99f9122cf403befba2f51d8e6e601e368ac1b471dd94facf11044ed5110604af SHA512: 89e7f645754c57bc1f4f99bb910061537b718e8afa18e27c456cc4ce3f59dbf1e6e7b1380476a503933e0c6c30e6756f779beca9ebaf3bca1b87b8f9a6968703 Homepage: https://cran.r-project.org/package=orloca Description: CRAN Package 'orloca' (Operations Research LOCational Analysis Models) Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. . General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. . 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Package: r-cran-ormplot Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rms, r-cran-gtable Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/noble/main/r-cran-ormplot_0.3.6-1.ca2404.1_all.deb Size: 395556 MD5sum: fc8f5756ec0bca4a1e2650a0281f98dd SHA1: 7216bd5b555f08d57594d5d925ed5536b4330ff0 SHA256: 93ecb32d92ab6ec354e9c759f124dd8e50ef724ac669156b3aa848f4fecdd1bc SHA512: f8b0fcf0aecca714c4bbb3caa0c1b6352e21cffb3a641a3e94e0f21291d97e846693ab5de97157d4df336474ad215732ef87f56b8ce700d61016e464e72929b7 Homepage: https://cran.r-project.org/package=ormPlot Description: CRAN Package 'ormPlot' (Advanced Plotting of Ordinal Regression Models) An extension to the Regression Modeling Strategies package that facilitates plotting ordinal regression model predictions together with confidence intervals for each dependent variable level. 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Package: r-cran-oro.nifti Architecture: all Version: 0.11.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6726 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-abind, r-cran-rnifti Suggests: r-cran-xml, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rticles Filename: pool/dists/noble/main/r-cran-oro.nifti_0.11.4-1.ca2404.1_all.deb Size: 6055188 MD5sum: 24cbea16a881d386e0eec7891401d164 SHA1: 67415781d952fccf371406d705fbb9177d8193da SHA256: ae13426ac2520bb1be7ca5079d39d790f629705e71acfd79a683c27dbd553c10 SHA512: f75cc06651619ab00719b006258efcfa49e435db90153e20537ab38e915d2581cd868eb9304aafd6cc9cf701909486a0485e4b23fbdc7efb9282ac050700c573 Homepage: https://cran.r-project.org/package=oro.nifti Description: CRAN Package 'oro.nifti' (Rigorous - 'NIfTI' + 'ANALYZE' + 'AFNI' : Input / Output) Functions for the input/output and visualization of medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI' formats. This package is part of the Rigorous Analytics bundle. Package: r-cran-oro.pet Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-oro.dicom, r-cran-oro.nifti, r-cran-minpack.lm, r-cran-msm Filename: pool/dists/noble/main/r-cran-oro.pet_0.2.7-1.ca2404.1_all.deb Size: 72824 MD5sum: e63823a084f6e2f2357c6203550f699d SHA1: 2618299fa73b1e2761791039ee5a3ea51941fd22 SHA256: 3d0cbb849b417835c54bfeda4f4fea8ee54776c131113b870aac023232acb9a5 SHA512: 94a9be0d8ed1d8c3d035551013e651d1285bf4aa474b9ec4cdfa217b41c85f58ad8ff31a8007bbc2a13e385406b093dcaef8b671119ca7857208e4121044ba5a Homepage: https://cran.r-project.org/package=oro.pet Description: CRAN Package 'oro.pet' (Rigorous - Positron Emission Tomography) Image analysis techniques for positron emission tomography (PET) that form part of the Rigorous Analytics bundle. 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Package: r-cran-orsifronts Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-orsifronts_0.2.0-1.ca2404.1_all.deb Size: 382168 MD5sum: 9bd3bd3e6e71f20f20a7d44b7db27e39 SHA1: c6e5f728dc8d6fd639e358fa0f9346daa3606ca0 SHA256: 372f9dd4e83270ef03dab5750de2615a2b0a83edc3e4626a622a333dfac75761 SHA512: 4cc139fabfd7f2a85d1e7452978bb89eb89fbb519203c661f6e9486fa109ec86b39af3f94cd88e63c119a227825fdeaa430564dcd22850a26a82f5b8cdadb5cc Homepage: https://cran.r-project.org/package=orsifronts Description: CRAN Package 'orsifronts' (Southern Ocean Frontal Distributions (Orsi)) A data set package with the "Orsi" and "Park/Durand" fronts as 'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details. Package: r-cran-ort Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-imager Filename: pool/dists/noble/main/r-cran-ort_0.1.0-1.ca2404.1_all.deb Size: 335590 MD5sum: f94ab4900787d839dff5b09ca2b42764 SHA1: dc1bd845202562b6f5d62c81e8075be3cfa61261 SHA256: e8d15d8b52e42374b452e447f6d9c3fe3d70b969932739243dc711c92fa399b4 SHA512: d6a66739aaee203bef901ba771ec9c0e56868cef86e4ee653f9506da7089c0e92bc2364927c0b74b316b0704f102f7b7bbbe15212b9a569e180d0fed10db8fc9 Homepage: https://cran.r-project.org/package=ort Description: CRAN Package 'ort' (Create a Data Frame Representation of an Image) Takes images, imported via 'imager', and converts them into a data frame that can be plotted to look like the imported image. This can be used for creating data that looks like a specific image. 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Package: r-cran-orus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tm, r-cran-tibble, r-cran-tidytext, r-cran-topicmodels, r-cran-rmarkdown, r-cran-xlsx, r-cran-knitr Suggests: r-cran-reshape2, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-orus_1.0.0-1.ca2404.1_all.deb Size: 474794 MD5sum: 7bfa10683cf1cd6c21a758c6d8ef4727 SHA1: 34469f8890d387d6e3653bbc719c8469d7a7cd7c SHA256: 2079f1d4a274087a546a61310dc71f0121ec3ffb14063aaa0ea8edfd37c54c63 SHA512: 6942dd5d18331c9f87258f2e3f5a27fab4e601295555963b9c9010378b883797f9f9fcf3c613614fcec8d4b50c214671d56e857fd3da00fdfc62197c69feb966 Homepage: https://cran.r-project.org/package=oRus Description: CRAN Package 'oRus' (Operational Research User Stories) A first implementation of automated parsing of user stories, when used to defined functional requirements for operational research mathematical models. 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Package: r-cran-oryzaprobe Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oryzaprobe_0.1.0-1.ca2404.1_all.deb Size: 984104 MD5sum: 091b7b5b8e2e51208bfb3c6b34cc99cd SHA1: ce5bda2f26c049582eb7875a1fa27c83e8e1fafa SHA256: c48e3c609de71424b9eda58ec27000bc1061b3b89c7c4cd3852c47b21638e06e SHA512: 9fa877ad6a54fad37006e1c37032299bcb09934a03f3f9599f13ff19c4d397d43fb7ffbd461dd2aebbb12915a045f242e278c7daf23533acae656087896b5ea9 Homepage: https://cran.r-project.org/package=OryzaProbe Description: CRAN Package 'OryzaProbe' (Rice Microarray Probe ID Conversion, from Probe ID to RAP-DB ID) Microarray probe ID is not convenient for further enrichment analysis and target gene selection. The package is created for the rice microarray probe ID conversion. This package can convert microarray probe ID from GPL6864 , GPL8852 , and GPL2025 platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID. Package: r-cran-osbng Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5011 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geos Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osbng_0.2.0-1.ca2404.1_all.deb Size: 4210130 MD5sum: c6fae17aa9326bb38516edaab1246c44 SHA1: 88a37e13f8f6c42c59f2e3839ae25e3c2e200605 SHA256: 82e0d7525add85504b1f5d2be4d725f01edc64a12099edf9741ebbc4ad6e4cb7 SHA512: 5569fe115fbd48565414aacf3fe194f2bafe80c37ccb128451a17f14122e49799857609ff5d5364747498828b3788a4cd96c446c5529f20c9ef05019c18fd77e Homepage: https://cran.r-project.org/package=osbng Description: CRAN Package 'osbng' (Geospatial Grid Indexing with the British National Grid) Offers a streamlined programmatic interface to Ordnance Survey's British National Grid (BNG) index system, enabling efficient spatial indexing and analysis based on grid references. It supports a range of geospatial applications, including statistical aggregation, data visualisation, and interoperability across datasets. Designed for developers and analysts working with geospatial data in Great Britain, 'osbng' simplifies integration with geospatial workflows and provides intuitive tools for exploring the structure and logic of the BNG system. Package: r-cran-oscars Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Filename: pool/dists/noble/main/r-cran-oscars_0.2.1-1.ca2404.1_all.deb Size: 98276 MD5sum: 5261ae48b7c6e06efc33a7df20804896 SHA1: 810a87bbbf927b984f0b7dd907b6063c529fb71f SHA256: 6c034d3e4b2ce9a5ff8b232c649a7dc9187980e7fd8ec7067349f831cbdf718d SHA512: d256287a31e4ef8afc8cac970e3dfdd5ec0fc2419fb362cecd3478cf40bf00d2e5d6fe8ca64da2c2d611247441125d5a5c4b67a2209fad9fa9666debd3990942 Homepage: https://cran.r-project.org/package=OSCARS Description: CRAN Package 'OSCARS' (Global Bounded Optimization by the OSCARS-II Algorithm) A collection of general optimization routines based on variants of the One Side Cut Accelerated Random Search (OSCARS-II) algorithm (Price et al., 2020, ). The main function , 'oscars()', performs black-box optimization of a general (including nonsmooth or discontinuous) function subject to simple bounds on the unknowns. If all bounds are finite, oscars searches globally. The main method implements a stochastic direct search method and is derivative free. Testing shows the OSCARS-II algorithm usually finds extrema with fewer function evaluations than similar global derivative-free methods. Package: r-cran-oscillatorgenerator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oscillatorgenerator_0.1.0-1.ca2404.1_all.deb Size: 73348 MD5sum: 0790ed48c64f058b60386545426c179f SHA1: f63c9a34657ba9fcba1816e7aa592d75df33e425 SHA256: e77b23090f76627725263374c6a78222f51026ea791bdeb35d189ea46b336be3 SHA512: 9a04cf493cae792c033f65b6ff342ad2c37c005b3d5251784c4ffba3adc1668443d524b15b501069b85a9c77232f655c0ee418c5df3502407f00ab94f1905c99 Homepage: https://cran.r-project.org/package=OscillatorGenerator Description: CRAN Package 'OscillatorGenerator' (Generation of Customizable, Discretized Time Series ofOscillating Species) The supplied code allows for the generation of discrete time series of oscillating species. 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Package: r-cran-oscv Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-oscv_1.0-1.ca2404.1_all.deb Size: 96516 MD5sum: 908b7623ac56efd313bc7c1c3da474ac SHA1: c3f2f47d3651443f71dde953615b81e88106067d SHA256: 0a77bb17badbc02e951b08c4c014b51a3095e92e82ee47af795ce142ff2934f9 SHA512: e512b62263f08235bce32e1473eb887d91be801a148afb8a27e63a4dc017f45e4c94fac71821e3809d3293f97df4d7111db38dee1d8f56e8bda661cb4fa3739f Homepage: https://cran.r-project.org/package=OSCV Description: CRAN Package 'OSCV' (One-Sided Cross-Validation) Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, . Package: r-cran-osd Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jade, r-cran-nnls Filename: pool/dists/noble/main/r-cran-osd_0.1-1.ca2404.1_all.deb Size: 211898 MD5sum: 1840afedad1124c5b537b84328ebc4c7 SHA1: 212b6d86fa8f640318ed9526d5af803bb95de0a0 SHA256: fe1b385cf3172bd12d74f6956644ea6f165987759310691f3f0df2d4af51f909 SHA512: 679eb29b353dd59c0b02c690106ec99b181b909525d3d4774567a89e9932dba8896f0e7be67cc38d04465056529a2be4d3744eb8db1fe64c529409755361704b Homepage: https://cran.r-project.org/package=osd Description: CRAN Package 'osd' (Orthogonal Signal Deconvolution for Spectra Deconvolution inGC-MS and GCxGC-MS Data) Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). 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Package: r-cran-osdatahub Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1890 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-geos, r-cran-httr, r-cran-jsonlite Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osdatahub_0.3.0-1.ca2404.1_all.deb Size: 1288714 MD5sum: 6efd03f3af7f804c81995931b124638e SHA1: 6c07126bafb6e93baef02290c4ecfdf26cfc99ae SHA256: fbad446265b3b05260bd661b90df4b6c40c80fcd51171615caeea0de454b7809 SHA512: a75f9736f89adf163e6eacd576ef358645d871904c8a87174f3a80cdce71482398ce48a3e1d1c9d6e3abebd6908df55adb8ec7611138ba263143bd44df6eb338 Homepage: https://cran.r-project.org/package=osdatahub Description: CRAN Package 'osdatahub' (Easier Interaction with the Ordnance Survey Data Hub) Ordnance Survey ('OS') is the national mapping agency for Great Britain and produces a large variety of mapping and geospatial products. 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Package: r-cran-osdesign Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-osdesign_1.8-1.ca2404.1_all.deb Size: 1058744 MD5sum: 86c92a88d15d7d6daec6dfe7f6626fa1 SHA1: 8f7e6146e6f97d1916ae89bf33d5962b83a91de8 SHA256: be2858ec3a25276d80f906d6769615d7296def32d8486dfeffe2241aaacaef96 SHA512: 26c1c2e4c661e647b7fd63fa2d717bdf171e3ef0b7c812102050e11c002fdba3900b42a06cf806c34a36489d88d712663735b088f2dcbf961436233c20147bcf Homepage: https://cran.r-project.org/package=osDesign Description: CRAN Package 'osDesign' (Design, Planning and Analysis of Observational Studies) A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011). Package: r-cran-osdr Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-osdr_1.1.4-1.ca2404.1_all.deb Size: 30828 MD5sum: ab1de5e3c412f77c221dfc9f75d12159 SHA1: ae3259c696b5fd7ef0cdcb95bb7170f446bb52b0 SHA256: baf980d4165018e06cc43d61130153e1ec6ae1173ac1ed98d3c66c49eb9ff608 SHA512: 9055725c524e851ade0162f74d565cea36ea5ba255bcd85fa24607c9ef207c2145019f11786c729042bc157a3d6de73c7b27c4b3fc26283ece285e25fb6d2dde Homepage: https://cran.r-project.org/package=OSDR Description: CRAN Package 'OSDR' (Finds an Optimal System of Distinct Representatives) Provides routines for finding an Optimal System of Distinct Representatives (OSDR), as defined by D.Gale (1968) . Package: r-cran-osfr Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-jsonlite, r-cran-stringi, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-fs, r-cran-memoise, r-cran-httr Suggests: r-cran-dplyr, r-cran-logger, r-cran-rprojroot, r-cran-brio, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lintr, r-cran-covr, r-cran-spelling, r-cran-vcr Filename: pool/dists/noble/main/r-cran-osfr_0.2.9-1.ca2404.1_all.deb Size: 318172 MD5sum: 158c4b0c487713b356b03d1d2cbd2d9b SHA1: 76abd96be6ee0df1f3696693ed77a1b5a2d358b5 SHA256: b0f3524aa299830884b7db5842aef97934dbe54ebd150f17e4f0212912c6413d SHA512: 53b26f4b44a25ca25afdd7e10b15176e048aa281c326fb01e2625985889cdcc56a74ff3993303fc57230c2e8b25c26d55152763d972ab410791e626c88b86afa Homepage: https://cran.r-project.org/package=osfr Description: CRAN Package 'osfr' (Interface to the 'Open Science Framework' ('OSF')) An interface for interacting with 'OSF' (). 'osfr' enables you to access open research materials and data, or create and manage your own private or public projects. Package: r-cran-oshka Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-unitizer, r-cran-covr Filename: pool/dists/noble/main/r-cran-oshka_0.1.2-1.ca2404.1_all.deb Size: 35804 MD5sum: 73c1f24898983d495426fd97e4bcc326 SHA1: 3c503bccf3dca23c1cd99dc0b1592cb9f8a78e2d SHA256: c1fbaa4577de941249a11cc34136457ecc4361538febecfe35313ef22cebc8ab SHA512: 348e2b7ec2f42401c561c680cd98168cc94dfc63c4fceff8fddbdcf6db2e389008b57ca410c43c5839e78d4102079a41c124812699c5322a8074a42a82bbb4b3 Homepage: https://cran.r-project.org/package=oshka Description: CRAN Package 'oshka' (Recursive Quoted Language Expansion) Expands quoted language by recursively replacing any symbol that points to quoted language with the language it points to. The recursive process continues until only symbols that point to non-language objects remain. The resulting quoted language can then be evaluated normally. This differs from the traditional 'quote'/'eval' pattern because it resolves intermediate language objects that would interfere with evaluation. Package: r-cran-osircr Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-osircr_0.2.9-1.ca2404.1_all.deb Size: 22468 MD5sum: bfffb7dda6e7edb729a148b53af4b05e SHA1: a4bab9d924a2e6aee6ebb924543e09ae344291b2 SHA256: 1f8f105d5f14d00f012180008c3e6c961aa5e1d9ed8b8ce9b5a9d2fab078a1cc SHA512: fd47bdb468f0dbc386b9f65cbcece9753ee146bf0ab40dfea1a2fa8a4e77eda463563f218a10b077210e671b185b59b07592b31033d8836f8fabe84095fbc5c9 Homepage: https://cran.r-project.org/package=OSIRCR Description: CRAN Package 'OSIRCR' (Cosine Regression-Based Online Sliced Inverse RegressionAlgorithm) In high-dimensional streaming data analysis, extracting core periodic features under real-time constraints remains challenging. Traditional dimension reduction methods fail to adapt to incremental data and yield low accuracy due to irrelevant variables. This package provides the Online Sliced Inverse Regression framework for cosine regression with high-dimensional irrelevant variables. It integrates subspace extraction of sliced inverse regression and incremental learning of online algorithms to efficiently handle periodic streaming data. Cai, Z., Li, R., & Zhu, L. (2020) . Package: r-cran-osldecomposition Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-luminescence, r-cran-deoptim, r-cran-minpack.lm, r-cran-gridextra, r-cran-ggplot2, r-cran-scales, r-cran-ggpubr, r-cran-rmarkdown Suggests: r-cran-kableextra, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osldecomposition_1.2-1.ca2404.1_all.deb Size: 605274 MD5sum: 5ff5e3c74deb3eff67a2f5fddb43b7f5 SHA1: 8542ba30a93e7ab4f8d09f8a898371971afe0f43 SHA256: aa8ff663b648d843e96adb6f381df427e6731bf687b117ec422eac962961917c SHA512: 3d4a1cfdb52ebf07103b32f55c76dc08c1fe9767919d86213939415bb94167163eabc0c10af950adaff58a46ea6ab547a984de8274ad8d3c85f478185e05b68c Homepage: https://cran.r-project.org/package=OSLdecomposition Description: CRAN Package 'OSLdecomposition' (Signal Component Analysis for Optically Stimulated Luminescence) Function library for the identification and separation of exponentially decaying signal components in continuous-wave optically stimulated luminescence measurements. A special emphasis is laid on luminescence dating with quartz, which is known for systematic errors due to signal components with unequal physical behaviour. Also, this package enables an easy to use signal decomposition of data sets imported and analysed with the R package 'Luminescence'. This includes the optional automatic creation of HTML reports. Further information and tutorials can be found at . Package: r-cran-osmapir Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-xml2 Suggests: r-cran-httptest2, r-cran-httpuv, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osmapir_0.2.6-1.ca2404.1_all.deb Size: 485908 MD5sum: 1ba935b6d68d12722a793963c00f1306 SHA1: 929b90272e13954d26fa6903657c624a19398a40 SHA256: 50267766d6ef27b47765353fab232c3686f8609536bc5dd8a6f2526047b6e6a4 SHA512: 2c132c6fe221ea19c13ad66b18abb77f39dbc18c134f6ec6ba203ab8c33929f8217e9eba84b1e5cb9841a63c0cabb5fed93f53c736901c89dd185694fb232ac6 Homepage: https://cran.r-project.org/package=osmapiR Description: CRAN Package 'osmapiR' ('OpenStreetMap' API) Interface to 'OpenStreetMap API' for fetching and saving data from/to the 'OpenStreetMap' database (). Package: r-cran-osmclass Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-stringi Filename: pool/dists/noble/main/r-cran-osmclass_0.1.5-1.ca2404.1_all.deb Size: 265048 MD5sum: 5b0f96e437f2b65a35f1c654a11568ae SHA1: 3b26612b78c1ccd165514aeae01d48753456258c SHA256: dd42c7fb73e22a8a7e5a3ab65c90d93f7e108dce567b039e48910a38eeccd98d SHA512: eb0daba155f4f5559e832de23267e1ac3909324e3ada2baf411e0c1aa101549495542363a3284608dfc8a22051084af30ddb0e6485a16d61cb94edb9a34e3b36 Homepage: https://cran.r-project.org/package=osmclass Description: CRAN Package 'osmclass' (Classify Open Street Map Features) Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from imported as spatial data frames. A classification consists of a list of categories that are related to certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main osm_classify() function returns a classification data table giving, for each feature, the primary and alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. The package also contains a classification of OSM features by economic function/significance, following Krantz (2023) . Package: r-cran-osmextract Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-cran-osmextract_0.6.0-1.ca2404.1_all.deb Size: 5030792 MD5sum: 4ce9616be06f4a6b8bc5d9ae496b48bd SHA1: 8f2daa97375464d47997d11967eeaa0cda8a8c2c SHA256: 224bd828beae88b05440ab8f474a1ad740a434f0259d7f39c694297c9a9418f8 SHA512: b7ff31124ce09af73681521e48acda7936255709c0c5379731cac672c62b590c2332408241b7c81c2ce7d6bc8ffb221e703435714d60e7b7be211faab86858cd Homepage: https://cran.r-project.org/package=osmextract Description: CRAN Package 'osmextract' (Download and Import Open Street Map Data Extracts) Match, download, convert and import Open Street Map data extracts obtained from several providers. Package: r-cran-osmscale Architecture: all Version: 0.5.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openstreetmap, r-cran-berryfunctions, r-cran-sf, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-osmscale_0.5.23-1.ca2404.1_all.deb Size: 96078 MD5sum: 94d44de2401964a58352000770a97efa SHA1: 60dcae16f68cbf76f3f1e7a86e35299788f81b44 SHA256: a31bf1f47d094b5c221d653867923d6f65a22d230fa77b3710b410c608a182f4 SHA512: 8bc7a900113614164ffbfbc1fc5029500799bc3077d445426b1a51a5922cc7a4ad3e6e1a2964840b6b589cc9a25fe5a78e7631c18eae7ea62bf41a1cde33eba5 Homepage: https://cran.r-project.org/package=OSMscale Description: CRAN Package 'OSMscale' (Add a Scale Bar to 'OpenStreetMap' Plots) Functionality to handle and project lat-long coordinates, easily download background maps and add a correct scale bar to 'OpenStreetMap' plots in any map projection. Package: r-cran-osnmtf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-osnmtf_0.1.0-1.ca2404.1_all.deb Size: 215444 MD5sum: c4101f0dd2eaaf458cc2d993222b51f2 SHA1: df6e772b46bde57f6b74c7e792ba255e756ba289 SHA256: 4aac178c0258c7e8df8dd24b6334eaf8912562862d8c0e96099f19b34c932ca2 SHA512: 5e8435515f8c72561a491ea70b0a66895f7ef0bee0444d36ab0001b260a5d00c3ff24f9019339859dcef6a4ac65748ae00ada0c43aedea6f9492933833674997 Homepage: https://cran.r-project.org/package=OSNMTF Description: CRAN Package 'OSNMTF' (Orthogonal Sparse Non-Negative Matrix Tri-Factorization) A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC. Package: r-cran-osrm.backend Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1391 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-processx, r-cran-ps, r-cran-viridislite Suggests: r-cran-dt, r-cran-knitr, r-cran-mapgl, r-cran-osrm, r-cran-rmarkdown, r-cran-sf, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-osrm.backend_0.4.1-1.ca2404.1_all.deb Size: 1327584 MD5sum: 96ad58ce3227b103a732983cf04eaff4 SHA1: 2b414167047a23b3cc115a0434b4699f48c61403 SHA256: 6ac3a696729b85360c254400e63926f6d907c8fe0fa07104ae2d4ac87367a272 SHA512: 55708bd35b04cb93a272a58f94c500f5071100e5484193e47d830efee560bee05bcbc1fb057e414f01df077b755fbc53bc5f51344cad7ed222bdef11815346ae Homepage: https://cran.r-project.org/package=osrm.backend Description: CRAN Package 'osrm.backend' (Bindings for 'Open Source Routing Machine') Install and control 'Open Source Routing Machine' ('OSRM') backend executables to prepare routing data and run/stop a local 'OSRM' server. For computations with the running server use the 'osrm' package for 'R' (). Package: r-cran-osrm Architecture: all Version: 6.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcppsimdjson, r-cran-curl, r-cran-mapiso, r-cran-googlepolylines, r-cran-sf Suggests: r-cran-mapsf, r-cran-tinytest, r-cran-covr, r-cran-terra Filename: pool/dists/noble/main/r-cran-osrm_6.0.0-1.ca2404.1_all.deb Size: 518114 MD5sum: bd89ba3bc9744bf60f47fd561f893e09 SHA1: 6885549f0112209d8fd8920187863a1885304cc2 SHA256: 9e147e0d9139d4688ce2fe01a2bef7a241c8d303c7a47a79f02fd0b5ee1997c3 SHA512: b1bc9f04b618089b4ecf6fce8a94b7f0b40b13486a6dbc523a043e19ba583aa87990ea490fc8f868df3014f6b3245e4180f4c6e1178f6d5fe6fd32dc7bee25b8 Homepage: https://cran.r-project.org/package=osrm Description: CRAN Package 'osrm' (Interface Between R and the OpenStreetMap-Based Routing ServiceOSRM) An interface between R and the 'OSRM' API. 'OSRM' is a routing service based on 'OpenStreetMap' data. See for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance). Package: r-cran-osrmr Architecture: all Version: 0.1.36-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-bitops, r-cran-rjson, r-cran-r.utils, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-osrmr_0.1.36-1.ca2404.1_all.deb Size: 88750 MD5sum: 15fb90db1984d54ad3aac2a64cb2e038 SHA1: 406df35cf7ddf02e6b5b40dfc08b07c89edfd836 SHA256: 6fadb14b6d4e66fe7a2252bf927c6b2c2f399afeccc9dd9de963eeb2d46bca50 SHA512: 669299d79816dc45a6e98aa1895fbb066180c5a4649d69c3bf5bb95e86d045ffe44c4f7b2778e7d0c3c739e0641d599156b1d8b073564028ba68c2fb3738822b Homepage: https://cran.r-project.org/package=osrmr Description: CRAN Package 'osrmr' (Wrapper for the 'OSRM' API) Wrapper around the 'Open Source Routing Machine (OSRM)' API . 'osrmr' works with API versions 4 and 5 and can handle servers that run locally as well as the 'OSRM' webserver. Package: r-cran-ossanma Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlcoptim, r-cran-deoptimr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-netmeta Filename: pool/dists/noble/main/r-cran-ossanma_0.1.2-1.ca2404.1_all.deb Size: 36382 MD5sum: aadb00db5d5f44e3ee253ad65d03e2bc SHA1: b9216b971a12a68e13298fa3d2357e5467682134 SHA256: 0800055fa1b926d8713a1b067c7209d2aa0933ba80f8e73a8451edb3ac87938c SHA512: a3a8e452ad50322f16bb3f510d46059c46ce7f57a35d851d38dc7216fef1f1d2534412e1b8c70867330cd66036a0e3e62cf5d07fb1bccdace9d312d333f8d490 Homepage: https://cran.r-project.org/package=OssaNMA Description: CRAN Package 'OssaNMA' (Optimal Sample Size and Allocation with a Network Meta-Analysis) A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power. Package: r-cran-osscontribs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3701 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-osscontribs_0.1.0-1.ca2404.1_all.deb Size: 3749214 MD5sum: 062be1f8c557099c7fe073a67db09e6c SHA1: 7a52e3f1d60fc929e99db6f383d3b56c1869cc11 SHA256: d541569d7beb4a4488adaeb7c3cb5be30fcd807a79f96cffc434c1565f0ecaa0 SHA512: 1cd01c2ac204a9674fe6f9e6ae4500480e3deb38a913401a571f619fa473f1c899b8ed694550297345df9cacb42ad8565ee49fc32e1aa93245a97cd806c3ac23 Homepage: https://cran.r-project.org/package=osscontribs Description: CRAN Package 'osscontribs' (Commit and Contributor Statistics for Major Open Source Projects) Over 30 years of daily commit activity and contributor growth for 'FreeBSD', 'OpenBSD', 'NetBSD', and 'PostgreSQL'. Built from cloned git repositories for complete coverage -- not limited by API quotas. Includes daily commits, daily new committers, weekly aggregates, and 'Phabricator' sign-up data. Designed for time series analysis, growth modeling, and cross-project comparison. Contains no personal data. Package: r-cran-ossurvival Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ossurvival_1.0-1.ca2404.1_all.deb Size: 85744 MD5sum: db0077ed7b45d6447a1317a5cd6d7f47 SHA1: b4d1f22b5362cb9dfaa6d9a4720d17a8a7bcfec1 SHA256: f7ea9234b90fbd589ebf126c0c486c2041c42bc4c2e98779331b1e6046c1bc29 SHA512: e7b3511676892a09d73629c4f94b94d71bc9428997531bf0bd27dbc7af779899b3d6c82f96a3442371c050caa5b1eed9ae7e63a38821ef5045439db0d0497fd8 Homepage: https://cran.r-project.org/package=OSsurvival Description: CRAN Package 'OSsurvival' (Assessing Surrogacy with a Censored Outcome) Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) . Package: r-cran-ostats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc, r-cran-matrixstats, r-cran-circular, r-cran-hypervolume, r-cran-ggplot2, r-cran-gridextra, r-cran-viridis, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ostats_0.2.0-1.ca2404.1_all.deb Size: 577174 MD5sum: b2d8c1819c73da9105205c70daae31e2 SHA1: 104f014a0e63d3ff15786e85c80281c2a21bd8f4 SHA256: 435881e820e0a640861dea6d8574a65e692aafb45d723434256c1f441408a5e9 SHA512: be9775fba2982811917506b0729db547bd9c5fdf493c7d7b5324ee3fa31d13929426ccca6a596ee9980c60e54e4a3eec8fd27d29a21a45b8cc56f896b1de30f0 Homepage: https://cran.r-project.org/package=Ostats Description: CRAN Package 'Ostats' (O-Stats, or Pairwise Community-Level Niche Overlap Statistics) O-statistics, or overlap statistics, measure the degree of community-level trait overlap. They are estimated by fitting nonparametric kernel density functions to each species’ trait distribution and calculating their areas of overlap. For instance, the median pairwise overlap for a community is calculated by first determining the overlap of each species pair in trait space, and then taking the median overlap of each species pair in a community. This median overlap value is called the O-statistic (O for overlap). The Ostats() function calculates separate univariate overlap statistics for each trait, while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. O-statistics can be evaluated against null models to obtain standardized effect sizes. 'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)." For more information on this project, see the Macrosystems Biodiversity Website (). Calculation of O-statistics is described in Read et al. (2018) , and a teaching module for introducing the underlying biological concepts at an undergraduate level is described in Grady et al. (2018) . Package: r-cran-oste Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-pec, r-cran-survival, r-cran-prodlim Filename: pool/dists/noble/main/r-cran-oste_1.0-1.ca2404.1_all.deb Size: 36010 MD5sum: a6fb3572c86ab014bb460a816ce7d0ce SHA1: 0c05ab12ff2f4cb114390f02ea5e0729866a5e84 SHA256: ce366b1afa874fcf51d751dc5628062f749b6f216f4ce1b0bd4afce28107857f SHA512: 3b7a8b836a2fffdb11d3aea9498ba35b3ad2a356add30ce391f1b282c2e909330fc987c91ec5e0c1cb81dd26845fbc8bed2c4075031893dc8131f0cf0ff420e3 Homepage: https://cran.r-project.org/package=OSTE Description: CRAN Package 'OSTE' (Optimal Survival Trees Ensemble) Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakowski', 'Zardad Khan', and 'Berthold Lausen' (2020) ) is given. The trees are grown by the method of random survival forest ('Marvin Wright', 'Andreas Ziegler' (2017) ). The survival trees grown are assessed for both individual and collective performances. The ensemble can give promising results on fewer survival trees selected in the final ensemble. Package: r-cran-osum Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-osum_0.1.0-1.ca2404.1_all.deb Size: 431676 MD5sum: 7edcb9066f87dad76f8d289d5d174cc4 SHA1: c3c92a3cacca88d9d72d6d08fc4c92d3af3d4860 SHA256: b1ee71e300d52120070ec4b6c46017aa137ba018ab284fdf1b5fd8c8136bd1e6 SHA512: ab3f40804b2a4590dab65c0539b45340e9a071b62374c7d1711e8e7bd863d90399af117241d401ae501f30868d2608c514e4fc003a431cae8bf3952d9f0b0505 Homepage: https://cran.r-project.org/package=osum Description: CRAN Package 'osum' (Provide Summary Information About R Objects) Inspired by 'S-PLUS' function objects.summary(), provides a function with the same name that returns data class, storage mode, mode, type, dimension, and size information for R objects in the specified environment. Various filtering and sorting options are also proposed. Package: r-cran-otargen Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ghql, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-httr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-otargen_2.0.1-1.ca2404.1_all.deb Size: 901380 MD5sum: 57b1d49cc65a2e3a8f16a816db111e65 SHA1: ea5fc81c0fb8ab5bafced540c12b24dfc7044155 SHA256: 0f174bca7bcad07a40c86d9b52fdffacf1cddda9c6721cdf98bb5edf29cfc792 SHA512: 167e0b689722f5bba3d2086f501f6b98571f4543e5e62a871dd6bb8ec6574917dcb7cc698fe59944b43945bafd8592401835fbc88cce45aac5057286ea8d3104 Homepage: https://cran.r-project.org/package=otargen Description: CRAN Package 'otargen' (Access Open Target) Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (). Package: r-cran-otbsegm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-terra, r-cran-link2gi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-otbsegm_0.1.2-1.ca2404.1_all.deb Size: 1577700 MD5sum: c57ccace4d95e926cfa3e7b7c7babac5 SHA1: e328cb30be777ea03bea80fe4545282f29f93c3d SHA256: ed177d85c7e2cf1991da20d9c5bee77fe2b3da37feb0eab140469f41dfd060c3 SHA512: c5dca3994b0c4f7b1cf671069cd21b29a259edc0b9523712f3aaecdd63c0f772d3f650ebc74d4860cd7d7402035314d411e10a3820c9e89c1770af6c5c00635b Homepage: https://cran.r-project.org/package=OTBsegm Description: CRAN Package 'OTBsegm' (Apply Unsupervised Segmentation Algorithms from 'OTB') Apply unsupervised segmentation algorithms included in 'Orfeo ToolBox' software (), such as mean shift or watershed segmentation. Package: r-cran-ote Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-ote_1.0.1-1.ca2404.1_all.deb Size: 86452 MD5sum: 533b77ddc7627cf01c8a32f32fb26a3c SHA1: bd617e3b5284dc110a013ea8ce11fec08bdf0113 SHA256: 971e843de97dddf23b7be0f3064c447c2922e765219e93511024d4c969dc2742 SHA512: 2be35be15e22eeb3880b9ba3cca6351354593a4c6df9a0d42de4001fb62c0099d14e867275c2549ff80d02646237aaf635f247cfdc7b068dcded80cf9a45dd75 Homepage: https://cran.r-project.org/package=OTE Description: CRAN Package 'OTE' (Optimal Trees Ensembles for Regression, Classification and ClassMembership Probability Estimation) Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) ) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) ) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data. Package: r-cran-otel Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-processx, r-cran-shiny, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-otel_0.2.0-1.ca2404.1_all.deb Size: 276782 MD5sum: 779826feec1886e3026f52a7537d15a6 SHA1: 00ebe19010f0f401efb2704799dbcc2aa38a8af0 SHA256: f781233b8826cd9a494bf6bccb409adff361bf53be1e626acdcaadcb225cbb03 SHA512: 27586e44b439f8a8520cb3cafacd74d43f5f91baa0f08d602ae84def7b78ca45c0d225671efb4b89a855ce1631c158ac1fdf5a42b96246b0a2ab569ee67db881 Homepage: https://cran.r-project.org/package=otel Description: CRAN Package 'otel' (OpenTelemetry R API) High-quality, ubiquitous, and portable telemetry to enable effective observability. 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Package: r-cran-otinference Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rglpk, r-cran-sm, r-cran-transport Suggests: r-cran-rcplex Filename: pool/dists/noble/main/r-cran-otinference_0.1.0-1.ca2404.1_all.deb Size: 25844 MD5sum: b4903f9757d7511188f86c713cacce1b SHA1: f5e0a34d861ccfd9724254bb242392c0a4df664e SHA256: 94890ba9ebe5c7832ad0dc144014a00f2257f135cd8d0ba87de59de860ac73dc SHA512: 49227f6b3ad3ffaffd44369c86873241519c5f5f16509e68bc85a29cc806b812af5fd8bf0fb0761eeaef8e43b0ee9e86d5b2b9f72794162c5efe1e48201ff617 Homepage: https://cran.r-project.org/package=otinference Description: CRAN Package 'otinference' (Inference for Optimal Transport) Sample from the limiting distributions of empirical Wasserstein distances under the null hypothesis and under the alternative. 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From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed. Package: r-cran-otrimle Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-foreach, r-cran-doparallel, r-cran-robustbase, r-cran-mclust Filename: pool/dists/noble/main/r-cran-otrimle_2.0-1.ca2404.1_all.deb Size: 189652 MD5sum: de49cdbede5145cadedc87b9d9a75a31 SHA1: 42004a0c0828a2e2c8902801bdae5a833913d715 SHA256: 9504941ee99ac4f7e665a3cfa5271e3a41bdaa384ba85945ba4bd34695d39031 SHA512: 10e9f3acc54fcbefb5c9c3e38f052709bd84bb29c8237aa71d15d297a80e6f224443d21177a22ac5c0ca1c549da8ae6ca6b8d863b475b718f1160860a261eb89 Homepage: https://cran.r-project.org/package=otrimle Description: CRAN Package 'otrimle' (Robust Model-Based Clustering) Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) , and Coretto and Hennig (2017) . Package: r-cran-otrkm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgenoud, r-cran-survival Filename: pool/dists/noble/main/r-cran-otrkm_0.2.1-1.ca2404.1_all.deb Size: 97942 MD5sum: fdaef45b5bcdb790a7fd11c7ba12e946 SHA1: f978708184b5ffedc6a5f38b1f013bf10010e641 SHA256: e0e27fddafdf9532fc040fec8a70ec04f4d949b2880e7c54c7e24eca988e3fc1 SHA512: 09c03b7989632fd16d63515193b5c7928a7fc24e911da340996a3bd6abf3e8d449cb2edd525fa57ea26d597cc28f4b0d666e8dca0da7b81301e13e46ebe24e62 Homepage: https://cran.r-project.org/package=otrKM Description: CRAN Package 'otrKM' (Optimal Treatment Regimes in Survival Contexts withKaplan-Meier-Like Estimators) Provide methods for estimating optimal treatment regimes in survival contexts with Kaplan-Meier-like estimators when no unmeasured confounding assumption is satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) ) and when no unmeasured confounding assumption fails to hold and a binary instrument is available (Xia, J., Zhan, Z., Zhang, J. (2022) ). Package: r-cran-otrselect Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lars, r-cran-survival Filename: pool/dists/noble/main/r-cran-otrselect_1.3-1.ca2404.1_all.deb Size: 43344 MD5sum: d7ceceb867430430c5c9b0c02a49edee SHA1: 684c71db039f69be3c01f21c7e85663b5494ba4a SHA256: 8fb715625171d2399674faa764f89d618b6fda3407a4aa0dca064a474e73225a SHA512: d48e2f23ecbe297f7b4d74b415b3e05a6b829fb331b26fe7cb822176acbd439b36635a4eac8d2c2e275cf2071895520a7839e7d282f5d351e92534f67de521b6 Homepage: https://cran.r-project.org/package=OTRselect Description: CRAN Package 'OTRselect' (Variable Selection for Optimal Treatment Decision) A penalized regression framework that can simultaneously estimate the optimal treatment strategy and identify important variables. Appropriate for either censored or uncensored continuous response. Package: r-cran-otsad Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2622 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-sigmoid, r-cran-reticulate Suggests: r-cran-testthat, r-cran-stream, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-otsad_0.2.0-1.ca2404.1_all.deb Size: 1651776 MD5sum: 497476578f5c8eb300627067e5a1dec9 SHA1: b176f2a8d396acdd6340b8aa8d3316c39b6a5125 SHA256: 5ec94d73d75ae471f3a8103f4f1d902751473d63b67c3e6b42d2b6992fdb72a9 SHA512: 0ef1305d17d65c8b289522c26c8dc5b17c09544fa21b72b56ed2ae4b862b2de160c5cd6fcf94299c4ab7a859e726bab14cd9bfe49e8118bca4356e9a1a9d305e Homepage: https://cran.r-project.org/package=otsad Description: CRAN Package 'otsad' (Online Time Series Anomaly Detectors) Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter et al. (2012) , SD-EWMA and TSSD-EWMA see H. Raza et al. (2015) , KNN-CAD see E. Burnaev et al. (2016) , KNN-LDCD see V. Ishimtsev et al. (2017) and CAD-OSE see M. Smirnov (2018) . The first three algorithms belong to prediction-based techniques and the last three belong to window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms designed to work in stationary environments, while the other four are algorithms designed to work in non-stationary environments. Package: r-cran-otsfeatures Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-astsa, r-cran-latex2exp, r-cran-rdpack, r-cran-bolstad2 Filename: pool/dists/noble/main/r-cran-otsfeatures_1.0.0-1.ca2404.1_all.deb Size: 313986 MD5sum: 6af50ad3447fd10a8bfc051abe194647 SHA1: 410478f943dbb66fc84c9224f1528a0b9a3ab855 SHA256: 1c5ebf4bbd0fdff18d57700ab7685461750ec9c61e3881700765a94f6b798c1a SHA512: 64f312842172f21072db238404458e7d9822fdd878680d08a06a308ce044dd17161542af4b1d7e1aa1b840862bd4eaa29a92ba506ce32a6cc3e58be849fda1f5 Homepage: https://cran.r-project.org/package=otsfeatures Description: CRAN Package 'otsfeatures' (Ordinal Time Series Analysis) An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) can be computed. 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Package: r-cran-otsufire Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-gdalutilities, r-cran-glue, r-cran-purrr, r-cran-raster, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-magrittr, r-cran-tidyr, r-cran-rlang, r-cran-otsuseg Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-otsufire_0.1.4-1.ca2404.1_all.deb Size: 204266 MD5sum: 1e3cdf7e3e34cab28258a513fc933e5d SHA1: a89108df8e77c3c0e38c1b2a4a2f647bb797b0e4 SHA256: 3b76c33969bf60c77d0af41aa7d017ece0cf77291615c07c24a35f3f8bdc856e SHA512: ac28053442ee943709f755c22be416d48efadf1e7d176e64b169e5736f312214dc773cc668272b6cd02eaea675ea7dbf7443ccc2c36efaa0dbbd145929917637 Homepage: https://cran.r-project.org/package=OtsuFire Description: CRAN Package 'OtsuFire' (Fire Scars, Severity and Regeneration Mapping Using 'Otsu'Thresholding) Tools to segment fire scars and assess severity and vegetation regeneration using 'Otsu' thresholding on Relative Burn Ratio (RBR) and differenced Normalized Burn Ratio (dNBR) image composites. Includes support for mosaic handling, polygon metrics, post-fire regeneration detection, day-of-year flagging, and validation against reference datasets. Designed for analysis of fire history in the Iberian Peninsula. Input Landsat composites follow the methodology described in Quintero et al. (2025) . Package: r-cran-otsuseg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-zoo, r-cran-sf Suggests: r-cran-testthat, r-cran-curl Filename: pool/dists/noble/main/r-cran-otsuseg_0.1.0-1.ca2404.1_all.deb Size: 40248 MD5sum: 3e1ea70b207c8f10f0454808d14a72e7 SHA1: c4c0a0a61b38b22202932393b4e12a94fae614d6 SHA256: 681946b5903a97cc33800f1077c24404c7d144a67860bab0106e865eedc7436b SHA512: 71a0bdb8bf5c3f2dba3150811a96f05c833b8820497617a5068f3392dee5e45f0d3dfa96ceae32a1edc7a7814765c31e499cf2b8d57a7c1b3a37a61d620e154f Homepage: https://cran.r-project.org/package=OtsuSeg Description: CRAN Package 'OtsuSeg' (Raster Thresholding Using Otsu´s Algorithm) Provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) , a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score. 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Package: r-cran-outlierso3 Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2180 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlist, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-robustbase, r-cran-robustx, r-cran-fastpcs, r-cran-cellwise, r-cran-ggally, r-cran-memisc Suggests: r-cran-knitr, r-cran-gridextra, r-cran-rmarkdown, r-cran-languager Filename: pool/dists/noble/main/r-cran-outlierso3_0.7-1.ca2404.1_all.deb Size: 1643238 MD5sum: 52656e0f118df9980d3d86a751901904 SHA1: 057224a1aa63150584aad9dbe66385efbdd29453 SHA256: e2037facb1f3b5adcc1cfb1ee8bf9ab91439f3a3f13d240ceb8190a73d5cccc6 SHA512: 39e1159cf94fa431fb246333096c503cb5957b24d151bf4316736ebb4f588f221c40015c849d52ec83e9f57c29bafe674f7c4423a5378aa507a1b2352bd1ba73 Homepage: https://cran.r-project.org/package=OutliersO3 Description: CRAN Package 'OutliersO3' (Draws Overview of Outliers (O3) Plots) Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) . The available methods are FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'. Package: r-cran-outlierspinner Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-outlierspinner_0.1.0-1.ca2404.1_all.deb Size: 25478 MD5sum: cca46c42de1c7eb4d99fd0e1cf0109c5 SHA1: 3cce24805651dfeadf92cf6e553518720d94ddcf SHA256: b703e50575adec651505e29fc9941ac3920d9c337e5c7b4eeb84b8a8a5866729 SHA512: dd0f5e0aa5ba95fc2d7933123a79f6e2e49c24a6c7c8734b257c3d24c47040530ef3da746578c2883eb96eeeb6fecb91c6071b886f27e97225099627a87d2459 Homepage: https://cran.r-project.org/package=outlierspinner Description: CRAN Package 'outlierspinner' (Geometric Multivariate Outlier Detection via Random DirectionalProbing) Provides tools for multivariate outlier detection based on geometric properties of multivariate data using random directional projections. Observation-level outlier scores are computed by jointly probing radial magnitude and angular alignment through repeated projections onto random directions, with optional robust centering and covariance adjustment. In addition to global outlier scoring, the method produces dimension-level contribution measures to support interpretation of detected anomalies. Visualization utilities are included to summarize directional contributions for extreme observations. Package: r-cran-outlying Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-outlying_0.0.1-1.ca2404.1_all.deb Size: 17186 MD5sum: 5e60acb78fc8d3030e065ee82bdbf591 SHA1: 36ee98f8e6fb13f23386a04e67f932ede66ae384 SHA256: 28240f90a0c1020005e0489589dd7d99834659fba206f53a88f7ac56d726df3b SHA512: d815eed15d77b34fa329be1f8dc5102e997cf9a3e249be4addce49c9d0ed9f6d7cee3b49b63ce2aab81fe5d477694a425c6ffa3a65b5a21858c3707a5984169a Homepage: https://cran.r-project.org/package=outlying Description: CRAN Package 'outlying' (Outliers Detection) Provides functions for detecting outliers in datasets using statistical methods. The package supports identification of anomalous observations in numerical data and is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows. Package: r-cran-outqrf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-dplyr, r-cran-missranger, r-cran-ggpubr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-renv, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-outqrf_1.0.0-1.ca2404.1_all.deb Size: 254380 MD5sum: 9379feda520afb8b196e4b5e2159c423 SHA1: 0990686b473d7da10c22699235c565f45b88c892 SHA256: 1df836b503ca9a272ab365d8c5c501225a2339708b2a8b93233d774dd8516a22 SHA512: 33b4fd07f044220739798afb11eeff6243121a3073dd5af58ce5e5d12d5b4bdbb84bdfce8b01c4ace0b4a38c876261313574110cb16facc895df7b983f7fe895 Homepage: https://cran.r-project.org/package=outqrf Description: CRAN Package 'outqrf' (Find the Outlier by Quantile Random Forests) Provides a method to find the outlier in custom data by quantile random forests method. Introduced by Meinshausen Nicolai (2006) . It directly calls the ranger() function of the 'ranger' package to perform data fitting and prediction. We also implement the evaluation of outlier prediction results. Compared with random forest detection of outliers, this method has higher accuracy and stability on large datasets. Package: r-cran-outreg Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-reshape2, r-cran-sandwich, r-cran-stringr, r-cran-tidyr Suggests: r-cran-aer, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-outreg_0.2.2-1.ca2404.1_all.deb Size: 48852 MD5sum: 67161a0583fcacaa5a2aee1b2756cec4 SHA1: e57b334dc75180167c2826676693c2d779ce39f7 SHA256: 2a5c98b7f5ba05d18c4be8c1492930cb8f471b925359eb262c66d1b0fb78cbce SHA512: 30cd6834867efadcd2c90d391a03edaec4fa78149288fb0cf9c78305110a95ef1a504f624a1b8ad6d9b64b57b0904360e50dd76321a227d292ce09876cb41193 Homepage: https://cran.r-project.org/package=outreg Description: CRAN Package 'outreg' (Regression Table for Publication) Create regression tables for publication. Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs. 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See Remiro‐Azócar A, Heath A, Baio G (2022) ``Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data'', Res. Synth. Methods, 1–31. ISSN 1759-2879, . 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In order to ensure the inferences from the use of count data models are appropriate, researchers may choose between the estimation of a Poisson model and a negative binomial model, and the correct decision for prediction from a count data estimation is directly linked to the existence of overdispersion of the dependent variable, conditional to the explanatory variables. Based on the studies of Cameron and Trivedi (1990) and Cameron and Trivedi (2013, ISBN:978-1107667273), the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Another advantage is that the installation of other packages is unnecessary, since the command runs in the basic R language. 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Package: r-cran-ovl.ci Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks, r-cran-matrix, r-cran-mixtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ovl.ci_0.1.1-1.ca2404.1_all.deb Size: 177610 MD5sum: 246efec0084a8533c989315ae4e72f8f SHA1: 6dd4be0436f7b1c0cccbe66f53cf490c26f56f5a SHA256: d4e8d9ba498adf8b5bb6756c125ae05c6de555a344e0bae04538e1b8f7e4c34a SHA512: 0c0bc51c475eb25e3e46ec5eea03e7239e5c44b0ab48ab3e769f8fcf52a3ca48e99ca2f6d11f1875fd22c499f81916249efff3ed8d065b19a1fe840cf22209be Homepage: https://cran.r-project.org/package=OVL.CI Description: CRAN Package 'OVL.CI' (Inference on the Overlap Coefficient) Provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) . Package: r-cran-ovtool Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2000 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-twang, r-cran-amelia, r-cran-envstats, r-cran-devtools, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-magrittr, r-cran-metr, r-cran-purrr, r-cran-progress, r-cran-rlang, r-cran-survey, r-cran-tibble, r-cran-tidyselect, r-cran-varhandle Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ovtool_1.0.3-1.ca2404.1_all.deb Size: 1597594 MD5sum: 0f07de5364b63d6e5117786f8821cfe8 SHA1: 921f9730b9b25542c0565bb75208a7bd1fe59bca SHA256: d8b70cf70fc68bde5ee9c73d44ad05f8f83c92a556b767d920e3226ea221415e SHA512: 5acc44fd6fb7cf7e19ad5e1fd3b7b56a3d2464961a28f9cceb6a226640ba07c2f1dd20b991e8b36ec69c0f778e52470cbf99b589ca9c5c7c5116e86bb00f34fd Homepage: https://cran.r-project.org/package=OVtool Description: CRAN Package 'OVtool' (Omitted Variable Tool) This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. 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Package: r-cran-owea Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-mass, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-owea_0.1.2-1.ca2404.1_all.deb Size: 301118 MD5sum: 174e9b2331eb961af1dea091f5ced81e SHA1: 1ba2d9ec3fa549d1bd3573768d9ccc0b4c5174a4 SHA256: 76710ea386eb84da75796558c3a751a2c39f77029b5e75acaa811a7c52ace4cf SHA512: 2ee4e84d23c16ec3c390086c04eaa7ac6e51adadbc9f4beef68af1d949d5fa52f1fa13ae76ab52ef3b28d5b7b895480a5eef7bf41bac373e3bfdde25af6b4240 Homepage: https://cran.r-project.org/package=OWEA Description: CRAN Package 'OWEA' (Optimal Weight Exchange Algorithm for Optimal Designs for ThreeModels) An implementation of optimal weight exchange algorithm Yang(2013) for three models. They are Crossover model with subject dropout, crossover model with proportional first order residual effects and interference model. You can use it to find either A-opt or D-opt approximate designs. Exact designs can be automatically rounded from approximate designs and relative efficiency is provided as well. Package: r-cran-owidapi Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-tibble, r-cran-rlang Suggests: r-cran-curl, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-owidapi_0.1.2-1.ca2404.1_all.deb Size: 48158 MD5sum: c09414f7451632653ca0c441a8f93543 SHA1: 6905da35d8d069520f37e267da80b7bd9d65d3f6 SHA256: e49c7a27098b84e5eab41e20b6eb05899b9c0d3f476ced0615cd23ea98929f00 SHA512: 4ae944d27129432bd2030f500d8dbab0c84ae9c2f8374f820f3e9ada49d0fbc67d10b4487fcd7c20995e9bccb02373d7ef4b8004abe6ec7c1b826195128f0e6c Homepage: https://cran.r-project.org/package=owidapi Description: CRAN Package 'owidapi' (Access the Our World in Data Chart API) Retrieve data from the Our World in Data (OWID) Chart API . 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Package: r-cran-owmr Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-jsonlite, r-cran-plyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-leaflet, r-cran-whisker, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-owmr_0.8.2-1.ca2404.1_all.deb Size: 1422580 MD5sum: c13f2bacd6fecc28fafed257f225b5f8 SHA1: 5cd5d43ffc1146da560a55e12a39fa20558cf4ee SHA256: 2a67b7372519e55fddfba14dfa33e452610062ad23ef486d31f9c3816e8b5672 SHA512: 773ccf61e32a93c92d39098a11c5f86414c432aa2ea88286e334f61d09e1620c773a24762fefc352e3119b435484894f414308e973a9af6440abcaf5d5409192 Homepage: https://cran.r-project.org/package=owmr Description: CRAN Package 'owmr' (OpenWeatherMap API Wrapper) Accesses OpenWeatherMap's (owm) API. 'owm' itself is a service providing weather data in the past, in the future and now. 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Package: r-cran-ows4r Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3861 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-openssl, r-cran-keyring, r-cran-xml, r-cran-geometa, r-cran-sf, r-cran-terra, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-ows4r_0.5-1.ca2404.1_all.deb Size: 2472882 MD5sum: 3874dd9b5cef91062098e38853683189 SHA1: a905d2b8988c60f99f09b69e7aa97ec4de7c4ef0 SHA256: 0af3bb6b9bc6b8677d0ee186f89c0369011fc83b3e4893426379ff323d33a6d3 SHA512: 1257cac3343e2e1faea2482fdbaeaddea43ced91c6228a38e5213099fa6e363111e613a9579401f9711c64ae0ed8f72272efcdb4fc499b6c912cb7cf1029fd13 Homepage: https://cran.r-project.org/package=ows4R Description: CRAN Package 'ows4R' (Interface to OGC Web-Services (OWS)) Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes, and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. 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Package: r-cran-ox Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-checkmate, r-cran-dplyr, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ox_0.1.0-1.ca2404.1_all.deb Size: 79320 MD5sum: 08c9d0220900dc4b30c6aaffe025158f SHA1: ea3c0c56566265194436ab58c88c9dd27d8f964c SHA256: 31858bde10c5149da2bd0d0e70ffb5780d6f5bc64a200a29ea50d22e0b527c94 SHA512: fff9455c267446e7b6df018e7fcaa782c6d97e6f69add8808c2d614cb768a241c62db0f70240f05faf60e588ef92b5c37ac23a1fbe8f45abd83b662fe391da5d Homepage: https://cran.r-project.org/package=ox Description: CRAN Package 'ox' (Shorthand if-Else) Short hand if-else function to easily switch the values depending on a logical condition. Package: r-cran-oxcaar Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggridges Filename: pool/dists/noble/main/r-cran-oxcaar_1.1.2-1.ca2404.1_all.deb Size: 259216 MD5sum: 1b3503e47f33bc5e70b0855760dfbcce SHA1: 188df2c31cb60f6c9b5fc226020b4b938017a949 SHA256: b1bc600f6b0d8410b9259e66369f015f6aeb1804f2bc6f81f52462a84416dd9f SHA512: e873650cdaf0eda79e9b4036135954088eae235bbc34d719d9e5ed2204274002563b5e9565937ee880cb7aa717c5490b991f121ab5bc8101cf50aef6ec433e9f Homepage: https://cran.r-project.org/package=oxcAAR Description: CRAN Package 'oxcAAR' (Interface to 'OxCal' Radiocarbon Calibration) A set of tools that enables using 'OxCal' from within R. 'OxCal' () is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration. Package: r-cran-oxsr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspec, r-cran-dplyr, r-cran-ggplot2, r-cran-janitor, r-cran-munsellinterpol, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oxsr_1.0.1-1.ca2404.1_all.deb Size: 1274956 MD5sum: a451d3ba0a1733aa5ff08fe6e5d51fe0 SHA1: 17f2284e6af2f7fc7d4647c45e9a2dc1e66bee90 SHA256: 8da611978c048432845df16dc35a7c934844121837fbd14ae05e49da13c33303 SHA512: f2e950d602feb895b975acab255aa9798460d8145f395c2f27d60ead5981a99397516acc44893375ed85364a7af5e16614c15009989d837d9947d7fcc4e1cd94 Homepage: https://cran.r-project.org/package=OxSR Description: CRAN Package 'OxSR' (Soil Iron Oxides via Diffuse Reflectance) Calculate the ratio of iron oxides, hematite and goethite, in soil using the diffuse reflectance technique. The Kubelka-Munk theory, second derivative analysis, and spectral region amplitudes related to hematite and goethite content are used for quantification (Torrent, J., & Barron, V. (2008) ). Additionally, the package calculates soil color in the visible spectrum using Munsell and RGB color spaces, based on color theory (Viscarra et al. (2006) ). Package: r-cran-oxybs Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oxybs_1.5-1.ca2404.1_all.deb Size: 52600 MD5sum: f9af3191060f385eff370f33889719d5 SHA1: acd9c743d4f2f2d4d2af3eb27ebbcbb4718ce427 SHA256: 3045b55f849ab490f09e27b7a2d5339753a0162a5ea89f4970bdc4f4e6cc59ff SHA512: 8326cec2c9406d57dc1b099658fed8618ae63b515e0b3b898d32c04d52e548f76c19b4eb79261121eecca602a9983e736e758b19f85ea0864bb35ce0ea23cce2 Homepage: https://cran.r-project.org/package=OxyBS Description: CRAN Package 'OxyBS' (Processing of Oxy-Bisulfite Microarray Data) Provides utilities for processing of Oxy-Bisulfite microarray data (e.g. via the Illumina Infinium platform, ) with tandem arrays, one using conventional bisulfite conversion, the other using oxy-bisulfite conversion. Package: r-cran-oyster Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-rjson, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-yaml Suggests: r-cran-covr, r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oyster_0.1.4-1.ca2404.1_all.deb Size: 354558 MD5sum: 18a1662ad2b44a1a85a2fbc21c311a01 SHA1: 23263dd5e0071dc2b810f592157d3446c9b43cae SHA256: 0b26349ff86383b3975344f9f5f4ba1019aeb27142cdd3ea7a8d2b443f87a837 SHA512: 08295fdb88d469757af803cb4191d90dd248c458f1332a0974df98750995692d9f985d5da695ee47acfffe603113d70f6920ad4b828dc5a7291a6aea095f0ec8 Homepage: https://cran.r-project.org/package=oysteR Description: CRAN Package 'oysteR' (Scans R Projects for Vulnerable Third Party Dependencies) Collects a list of your third party R packages, and scans them with the 'OSS' Index provided by 'Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use. Package: r-cran-oystermapr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-terra, r-cran-sf, r-cran-rlang, r-cran-cli Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytex, r-cran-httr, r-cran-jsonlite, r-cran-ncdf4, r-cran-gstat, r-cran-sp, r-cran-lubridate, r-cran-suncalc, r-cran-oce Filename: pool/dists/noble/main/r-cran-oystermapr_1.5.0-1.ca2404.1_all.deb Size: 887004 MD5sum: 3c018d198f4f51d77d6b0120f5d38cc4 SHA1: c29f6b10fc7537c0472061020c560d5e93deef19 SHA256: c677735013ae086c444af3dbf224e627ee6839fe21b92353f4aa198cb83a438f SHA512: fc94536c6cf1d846e9646ca613a93c03746e68892f034a4046a6319663be10da2ee114450edb2633c80bd58d34993b0692c1ffe746f78b5e2ba594a197ef1d40 Homepage: https://cran.r-project.org/package=oystermapR Description: CRAN Package 'oystermapR' (Predict and Map Oyster Growth Suitability from EnvironmentalData) Predicts spatial suitability for oyster growth from environmental survey data using Analytic Hierarchy Process (AHP) weighted scoring. Users supply sensor data from Acoustic Doppler Current Profilers (ADCP), Conductivity-Temperature-Depth (CTD) sensors, bathymetric sonar, and sidescan sonar, specify a target species, and receive per-location suitability scores, a five-band 'GeoTIFF' heatmap for 'QGIS', contour lines, and a formatted PDF or HTML report. Supports seventeen species across global aquaculture regions, including Ostrea edulis, Magallana gigas, Crassostrea virginica, Crassostrea hongkongensis, and thirteen further species; see list_species(). Includes ocean acidification scoring via in-house aragonite saturation state (Omega_arag) calculation using Lueker et al. (2000) and Mucci (1983) equilibrium constants (no external dependencies), variable impact diagnostics (variable_impact()), fine-scale habitat area analysis for restoration reporting in m2 with contiguous patch identification (area_summary()), tolerance curve visualisation (plot_tolerance()), season-aware scoring, tidal height correction, Bayesian tolerance parameter updating from field observations, spatial block cross-validation (Roberts et al., 2017, ), permutation variable importance, wave exposure and sediment stability modules, Harmful Algal Bloom (HAB) risk and anthropogenic disturbance scoring with optional live International Council for the Exploration of the Sea (ICES) data integration, hybrid larval dispersal connectivity scoring (union-find Gaussian kernel plus optional 'OpenDrift' or Finite Volume Community Ocean Model ('FVCOM') connectivity matrix), and batch multi-species comparison. 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Package: r-cran-packmbplsda Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-proc, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-factominer Filename: pool/dists/noble/main/r-cran-packmbplsda_0.9.0-1.ca2404.1_all.deb Size: 338394 MD5sum: dfa9f2cc8867110b77db3ff257b2ab43 SHA1: 9255f24cbb1bf8f41f7f7d77f38d60e9034769f7 SHA256: cb4c5e4186898fb9806b6a9016d1d752db0eccbc9e95e5c01defdceaf8b52ad9 SHA512: 91e0dc9b7b5afd8533cede0ca71050ae34a445d229afa00035d1b748a7d23e13b39658c342f399e9ce9bb3591968828aa5690e654f33293c89b2ee4a72c9719b Homepage: https://cran.r-project.org/package=packMBPLSDA Description: CRAN Package 'packMBPLSDA' (Multi-Block Partial Least Squares Discriminant Analysis) Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134). Package: r-cran-packrat Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1396 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-httr, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-webfakes, r-cran-withr Filename: pool/dists/noble/main/r-cran-packrat_0.9.3-1.ca2404.1_all.deb Size: 663718 MD5sum: fe4c662a12f11b2ad3c54c9423a6da4d SHA1: 9c71aa38bc370bda760867389c04b836304d5956 SHA256: 82b92b9eb9219ac85dc0697b1bd095de0b1aec4b72df16e87b103533a85172b8 SHA512: 0500f9084072a2d82e12b25ecb7020af79a388400dfcddead4ee2cd67bbd5fa4750f87db08120bc436a4ba235daf6a5c4ac7ea6e787587d020256d85b3b4905c Homepage: https://cran.r-project.org/package=packrat Description: CRAN Package 'packrat' (A Dependency Management System for Projects and their R PackageDependencies) Manage the R packages your project depends on in an isolated, portable, and reproducible way. Package: r-cran-paclasso Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-penalized, r-cran-mass, r-cran-lars, r-cran-quadprog, r-cran-limsolve Filename: pool/dists/noble/main/r-cran-paclasso_1.0.0-1.ca2404.1_all.deb Size: 77822 MD5sum: be93bd0d312809e3ac397123cd16a7ab SHA1: 516c00688c2df05c55d9ab7963bc5d08d01c3e39 SHA256: 10773560a86b9237d042ab78969ea190e7615e231571bce4188cac776b84df7c SHA512: b2094d2c6d54ab0075944bb450f3abef72035e3402cc57f8c913528ed1ba399ee910abef2614feb951c03cdb53f3a413f6e536b960bed7a08b517425e1b1aa19 Homepage: https://cran.r-project.org/package=PACLasso Description: CRAN Package 'PACLasso' (Penalized and Constrained Lasso Optimization) An implementation of both the equality and inequality constrained lasso functions for the algorithm described in "Penalized and Constrained Optimization" by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019; see for a full-text version of the paper). The algorithm here is designed to allow users to define linear constraints (either equality or inequality constraints) and use a penalized regression approach to solve the constrained problem. The functions here are used specifically for constraints with the lasso formulation, but the method described in the PaC paper can be used for a variety of scenarios. In addition to the simple examples included here with the corresponding functions, complete code to entirely reproduce the results of the paper is available online through the Journal of the American Statistical Association. Package: r-cran-pacman Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-remotes Suggests: r-cran-biocmanager, r-cran-knitr, r-cran-lattice, r-cran-testthat, r-cran-xml Filename: pool/dists/noble/main/r-cran-pacman_0.5.1-1.ca2404.1_all.deb Size: 344474 MD5sum: a100ad254453318d0963de45e96e150e SHA1: dec8584b1a643212f8e118e18be1a3bfc8619a3e SHA256: 603e981888e78647b05c74c4af2353e388e0ce3613ea8068b30e43eb3c8dbec3 SHA512: 024f494a6f5440b9436a4254f953bb5ba74eac2e03f2dac399e98453f9b5a248838b72c86eacbc9b3276a72b229f0a00a0739e1dfc16a046477ae105df87e783 Homepage: https://cran.r-project.org/package=pacman Description: CRAN Package 'pacman' (Package Management Tool) Tools to more conveniently perform tasks associated with add-on packages. pacman conveniently wraps library and package related functions and names them in an intuitive and consistent fashion. It seeks to combine functionality from lower level functions which can speed up workflow. Package: r-cran-paco Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vegan, r-cran-plyr Suggests: r-cran-ape, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paco_0.5.0-1.ca2404.1_all.deb Size: 46666 MD5sum: 35b152fed45155918e6a07cca72af33d SHA1: d78fc8fd5e61d6a214422e28640468442d4f9dbc SHA256: 9b469ba5bedec36c1175a9c62f8589b0e46b26466de3627ba634bf85993185c8 SHA512: 8fe9cf7998d725538b4bc6ff304adae102fca3594e9bc8173e6a79774ba70da1213b471d37c8775be03b479ae08b5f6ac590c976bc39db9dcfede6a7cfd1a785 Homepage: https://cran.r-project.org/package=paco Description: CRAN Package 'paco' (Procrustes Application to Cophylogenetic Analysis) Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees. Package: r-cran-pacs Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-memoise, r-cran-jsonlite, r-cran-xml2, r-cran-stringi Suggests: r-cran-remotes, r-cran-renv, r-cran-withr, r-cran-pkgsearch, r-cran-mockery, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pacs_0.6.0-1.ca2404.1_all.deb Size: 976888 MD5sum: 12ec3ff56a6ade321d29cbead7f771ab SHA1: 143ba164a896718b25387c3be668341197ba6a99 SHA256: ac57b55834c198223ad8c663b21631098ac218bcc455b9aa7b8748b555d1ffa7 SHA512: ee7185c9ed04fce8e7e65d9fdae76f681d1f46165d4a677dbf0c2d0727d5711242d6e3ed13b77f3e9abf48a2889df1688e735190b3b83db9b6d42619a0f01438 Homepage: https://cran.r-project.org/package=pacs Description: CRAN Package 'pacs' (Supplementary Tools for R Packages Developers) Supplementary utils for CRAN maintainers and R packages developers. Validating the library, packages and lock files. Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies. The shiny app complexity could be explored too. Assessing the life duration of a specific package version. Checking a CRAN package check page status for any errors and warnings. Retrieving a DESCRIPTION or NAMESPACE file for any package version. Comparing DESCRIPTION or NAMESPACE files between different package versions. Getting a list of all releases for a specific package. The Bioconductor is partly supported. Package: r-cran-pacta.loanbook Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggrepel, r-cran-magrittr, r-cran-purrr, r-cran-r2dii.analysis, r-cran-r2dii.data, r-cran-r2dii.match, r-cran-r2dii.plot, r-cran-rlang, r-cran-rstudioapi, r-cran-scales, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-covr, r-cran-diagrammer, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-writexl Filename: pool/dists/noble/main/r-cran-pacta.loanbook_0.1.1-1.ca2404.1_all.deb Size: 657566 MD5sum: 76a85a1c45ac1c89a50dd379b57c4c70 SHA1: 2e2e52f975c5712ca9a60800cc9d7fc0c75036d0 SHA256: e7faaa56fb57081ce517ce528c1cb38d7a6437ddf324de803220fc88015ba789 SHA512: 2b40d3382c1565e5465deae280ddbd2fa7971238eee4e338f6d74044e8db371665a7b6a4f9c2fdbc1ef2535623f3ae8614a7d44325ee13e58b5e9c761597c41e Homepage: https://cran.r-project.org/package=pacta.loanbook Description: CRAN Package 'pacta.loanbook' (Easily Install and Load PACTA for Banks Packages) PACTA (Paris Agreement Capital Transition Assessment) for Banks is a tool that allows banks to calculate the climate alignment of their corporate lending portfolios. This package is designed to make it easy to install and load multiple PACTA for Banks packages in a single step. It also provides thorough documentation - the PACTA for Banks cookbook at - on how to run a PACTA for Banks analysis. This covers prerequisites for the analysis, the separate steps of running the analysis, the interpretation of PACTA for Banks results, and advanced use cases. Package: r-cran-pacta.multi.loanbook Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4074 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-config, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-r2dii.analysis, r-cran-r2dii.data, r-cran-r2dii.match, r-cran-r2dii.plot, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yaml, r-cran-yesno Suggests: r-cran-diagrammer, r-cran-gt, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-usethis, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-writexl Filename: pool/dists/noble/main/r-cran-pacta.multi.loanbook_0.1.1-1.ca2404.1_all.deb Size: 1992122 MD5sum: 5fa3af89be37689c72607af182ddab34 SHA1: dfb159b461a07b0276abc245fd164cc691bf690a SHA256: b4306e8a61e6e39a53e4a4b0aaab998a56ea2e72bfdbc0efc71102f06378f82d SHA512: fd515826c00fc7db4941aad496a363fd7fabe240559a98bcf94143d12b324d1d95be5828cb3e580a4dec001fb697896e5e03c015c96bba45f2c66716ec6406d4 Homepage: https://cran.r-project.org/package=pacta.multi.loanbook Description: CRAN Package 'pacta.multi.loanbook' (Run 'PACTA' on Multiple Loan Books Easily) Run Paris Agreement Capital Transition Assessment ('PACTA') analyses on multiple loan books in a structured way. Provides access to standard 'PACTA' metrics and additional 'PACTA'-related metrics for multiple loan books. Results take the form of 'csv' files and plots and are exported to user-specified project paths. 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Includes functions to download and process raw satellite images from Sentinel-2 . Includes functions that download vegetation index statistics for a given period of time, without the need to download the raw images . There are also functions to download and visualize weather data in a historical context. Lastly, the package also contains functions to process yield monitor data. These functions can build polygons around recorded data points, evaluate the overlap between polygons, clean yield data, and smooth yield maps. 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Users can gain detailed insights into Pakistan's economic performance, visualize quarterly trends, and detect patterns and anomalies in key economic indicators. Compare sector contributions—including agriculture, industry, and services—to understand their influence on economic growth or decline. Customize analyses by filtering and manipulating data to focus on specific areas of interest. Ideal for policymakers, researchers, and analysts aiming to make informed, data-driven decisions based on timely and detailed economic insights. 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Package: r-cran-pakpmics2014hl Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pakpmics2014hl_0.1.1-1.ca2404.1_all.deb Size: 3321422 MD5sum: 32781064ffa230cdab362c394a6af886 SHA1: 457abbd8bed08629f45bfc8bd138e79c49889970 SHA256: 94e831b67f79c6a07237120b4fbb250a60fc3fcbce162f8bd084f1e373e51fae SHA512: 5aaf894c5f774f84bd377f57eb3d8dac3affd81fe13aa844af19f52af6ddba3a781a6847930ca83f95748ed470d0062d7c648ca8ca16fe64eb86952e59fc692e Homepage: https://cran.r-project.org/package=PakPMICS2014HL Description: CRAN Package 'PakPMICS2014HL' (Multiple Indicator Cluster Survey (MICS) 2014 Household ListingQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan. Package: r-cran-pakpmics2014wm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pakpmics2014wm_0.1.1-1.ca2404.1_all.deb Size: 2922446 MD5sum: 7e482be2fec6bd1c07eb61695dfdcb57 SHA1: 330728ca7019f92bfd7279a507f6286b0ca26fcf SHA256: e35eeb4a8e4f62671e11f4b86ef2193f82dc99228f480d3195d7806756dd0466 SHA512: 3038084fbe7a8704d66a883bea8c2cdf9ef1e9a1dafdda7de36ccff85f364f9ce6fa2632b38b9b04262b76e90c378f394477144737ea3451562f9ae99963e74f Homepage: https://cran.r-project.org/package=PakPMICS2014Wm Description: CRAN Package 'PakPMICS2014Wm' (Multiple Indicator Cluster Survey (MICS) 2014 WomenQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan. Package: r-cran-pakpmics2018 Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018_1.2.0-1.ca2404.1_all.deb Size: 1804450 MD5sum: 2207d657665cf7fdfd6bf2a2a9ece888 SHA1: 5823bb6b9c1badb23f0c19077dcb0bcac9f6b583 SHA256: 9c7b28ed125556e07316c634ab2698f88f17089cf5848ac041932d7b0294dcc5 SHA512: 206af14c9c53f59821a6284ff0f6e04f20331825bf018b33eb76dd675fb76506863c4e54acb16d956f9561dcb856a7603a87ec3a4b83ea8d129fbea30cff73b3 Homepage: https://cran.r-project.org/package=PakPMICS2018 Description: CRAN Package 'PakPMICS2018' (Multiple Indicator Cluster Survey (MICS) 2017-18 Data forPunjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household. Package: r-cran-pakpmics2018bh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3891 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018bh_0.1.0-1.ca2404.1_all.deb Size: 3814012 MD5sum: 92d88936b9b2782be6bcb9c2a68e0872 SHA1: bd0e92a686a9b0ac6ecc9915da5f03aca46434e4 SHA256: 239ec4cc68ed4e95a1d083cffab50435c15b16dc4f2d3212519141153cf71e2f SHA512: 14bdb046e9c58b3bd1f6eb30e6b2a1fa86ac3a6a6c94fafe3e54ba96007f6a859a479d54fb872b2ca0ce33824c5c460168c30fb6e17f964b1b5e476c8e541712 Homepage: https://cran.r-project.org/package=PakPMICS2018bh Description: CRAN Package 'PakPMICS2018bh' (Multiple Indicator Cluster Survey (MICS) 2017-18 Birth Historyof Children Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018fs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3608 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018fs_0.1.0-1.ca2404.1_all.deb Size: 3539720 MD5sum: a31fb41640d4d4451a979fb725da9f1d SHA1: fc6623ebc0f140bf0e8662738542fd621dc278d7 SHA256: ef5ead9eca3217339661791a0c306cd90ac02dc022b27067c2112033e343f1aa SHA512: 07c7b843023cfc78bf6d33d4990b5bfe442c28599224627fbc4fa9088cbdfa0b8dc9d33aab90c82295816e92140166572932b5f222fee67a42bc9ce3456d4143 Homepage: https://cran.r-project.org/package=PakPMICS2018fs Description: CRAN Package 'PakPMICS2018fs' (Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age5-17 Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018hh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4780 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018hh_0.1.0-1.ca2404.1_all.deb Size: 4637986 MD5sum: a70b6f6239d2560b0190de8c383f7dc2 SHA1: 944baaba1af9984cf86387d0e4610c23870727f7 SHA256: c89c269f3f1f8aca78ea7f11b6c9122949825348099be8ffc2a7e29d703f2693 SHA512: b0b7c1b041987788f708c5760263bc37497b6c2dca3c013b7278f6583668b1495b2a2bdbc21e2dce97db226af8f2b140e2dbbd90974b6166c02ab071b60a9e81 Homepage: https://cran.r-project.org/package=PakPMICS2018hh Description: CRAN Package 'PakPMICS2018hh' (Multiple Indicator Cluster Survey (MICS) 2017-18 HouseholdQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018mm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3553 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018mm_0.1.0-1.ca2404.1_all.deb Size: 3433750 MD5sum: 35c8870ad00da560ab0f633af1c14a18 SHA1: 3afe34a070bd497d63e18b7fad1ed2969fef3588 SHA256: 9e74af4269c6b1cbd18b70db527d358040dce175704b7e14a36f21f60c0d8d9c SHA512: 79ae9565eea497da478568fb03403a652379cfd21c79a81b99e5154b802e7e86c5c951a4f2e96c8efaf625ce7671597f964e0048d30c72bf12fa1d9436ff5419 Homepage: https://cran.r-project.org/package=PakPMICS2018mm Description: CRAN Package 'PakPMICS2018mm' (Multiple Indicator Cluster Survey (MICS) 2017-18 MaternalMortality Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018mn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018mn_0.1.0-1.ca2404.1_all.deb Size: 2919756 MD5sum: 7cd62be7e1046d63f9d169e8ff0d86b3 SHA1: e9d06cdb9ada25456513467ac61797843cf41803 SHA256: a9cea0582598fc02a47f1c4a7601452794e8a218e852018bde4863bec2fe0408 SHA512: 3f0772f6d3aa8ca1fa4ba77d9b34fc7afe2c1c8d15f3351791cfd85791f2c2e9facfcd8ebe501a268573bec0e13b68c66f6b3d391ffb43dca207cc89b8ee1933 Homepage: https://cran.r-project.org/package=PakPMICS2018mn Description: CRAN Package 'PakPMICS2018mn' (Multiple Indicator Cluster Survey (MICS) 2017-18 MenQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakret Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-readr, r-cran-rmarkdown Suggests: r-cran-callr, r-cran-pkgload, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-pakret_0.3.1-1.ca2404.1_all.deb Size: 67090 MD5sum: e682f41efa1c21de6323d57d6eef1b67 SHA1: ae5ab7947d4d0fe1f541f5a476af55dc118ae999 SHA256: 28c887960af997b3061549b32b97b90b7c332349aff28385a57deaa76f763b52 SHA512: c9f58069856e297d2c1158fe8dad50f2130cd52bec4003bcbfa37b70436839ae050891d89cb2eec5e95b7860fd08976ea2d900fe2b228b2e9106bf8b4843921f Homepage: https://cran.r-project.org/package=pakret Description: CRAN Package 'pakret' (Cite 'R' Packages on the Fly in 'R Markdown' and 'Quarto') References and cites 'R' and 'R' packages on the fly in 'R Markdown' and 'Quarto'. 'pakret' provides a minimalist API that generates preformatted citations for 'R' and 'R' packages, and adds their references to a '.bib' file directly from within your document. Package: r-cran-palaeosig Architecture: all Version: 2.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-teachingdemos, r-cran-rioja, r-cran-mgcv, r-cran-mass, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-forcats, r-cran-assertr, r-cran-vegan, r-cran-rlang, r-cran-ggrepel, r-cran-tidyr, r-cran-purrr Suggests: r-cran-units, r-cran-knitr, r-cran-rmarkdown, r-cran-gstat, r-cran-sf, r-cran-analogue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-palaeosig_2.1-5-1.ca2404.1_all.deb Size: 484048 MD5sum: 87b4229b0eb22e4aa299f5c31761c3dd SHA1: 133b66752bea2c71f4375c13a4fc18a9a9ad68ce SHA256: f19cdd02a7f555b44be5bde5be2e9b40df6a31794a981f5756b6f14c11cb9d9f SHA512: c2346911badaacf66455153a998dcc8d5410b3f6b6098325675640259e76c22cb5c9f725dd5b5e1fb6c35982fac366247e7340c004e0d21f65f3e254fc9a60ad Homepage: https://cran.r-project.org/package=palaeoSig Description: CRAN Package 'palaeoSig' (Significance Tests for Palaeoenvironmental Reconstructions) Several tests of quantitative palaeoenvironmental reconstructions from microfossil assemblages, including the null model tests of the statistically significant of reconstructions developed by Telford and Birks (2011) , and tests of the effect of spatial autocorrelation on transfer function model performance using methods from Telford and Birks (2009) and Trachsel and Telford (2016) . Age-depth models with generalized mixed-effect regression from Heegaard et al (2005) are also included. Package: r-cran-palaeoverse Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2826 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-curl, r-cran-geosphere, r-cran-h3jsr, r-cran-httr, r-cran-lifecycle, r-cran-pbapply, r-cran-sf, r-cran-stringdist Suggests: r-cran-covr, r-cran-knitr, r-cran-paleotree, r-cran-phytools, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-palaeoverse_1.5.0-1.ca2404.1_all.deb Size: 2099968 MD5sum: 4de9e27762780ca4cc0af18985d41caa SHA1: 306c7bd724171552b0577291180ec99b22ae938c SHA256: 633dbd9c0f24bb2d353c43bfa4ddb1039f82f6e2458ffcca4edbd3449471f8eb SHA512: 7852d18e8498c972cf7e19168e5362e299d61e43ddcb8c742c65dc9cd2db8e9f6d0c0caa5152168d9f0058581b15c9f75502112c9a4d942838c55e51f0b3e7d5 Homepage: https://cran.r-project.org/package=palaeoverse Description: CRAN Package 'palaeoverse' (Prepare and Explore Data for Palaeobiological Analyses) Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales ) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) . Package: r-cran-palasso Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-survival Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-remotes, r-cran-proc, r-bioc-edger, r-cran-ashr Filename: pool/dists/noble/main/r-cran-palasso_1.0.0-1.ca2404.1_all.deb Size: 176046 MD5sum: c110cda0c7a302025ec74e6624cdf3dd SHA1: da191dd4d4c86e388321e5fc163a078e43451b41 SHA256: 8043a4fa93eff7acd51740c83e50875032a39e0c73edf1e3c9d23a7cef870be5 SHA512: 6361d69982dd777c447193e2b8b2723628669ea0c5489d975bf066527d9c7029ab11d5e2112422e3663e5ae327bbdaa9052b74babc8f5f367ed185f8fbdf95b0 Homepage: https://cran.r-project.org/package=palasso Description: CRAN Package 'palasso' (Sparse Regression with Paired Covariates) Implements sparse regression with paired covariates (). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr () and CorShrink () from GitHub (see README). Package: r-cran-pald Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pald_0.0.5-1.ca2404.1_all.deb Size: 812510 MD5sum: 3d87ead1e346d68c6ab0a477540a6706 SHA1: e87b7353de5247701617bbeb99505b9934357843 SHA256: e2ee615bf458e513797dddddff948ddab52391c14ec3cf78c58de7e2d458533f SHA512: 58a3a8dd993153d5ae5942326474c80753940cf52a17e58c78457c66635efddd6871fe19a8cc88c242dbf000c0ffc6e3ccf3ea9ad91e9ba9aa027d645a4b3d1b Homepage: https://cran.r-project.org/package=pald Description: CRAN Package 'pald' (Partitioned Local Depth for Community Structure in Data) Implementation of the Partitioned Local Depth (PaLD) approach which provides a measure of local depth and the cohesion of a point to another which (together with a universal threshold for distinguishing strong and weak ties) may be used to reveal local and global structure in data, based on methods described in Berenhaut, Moore, and Melvin (2022) . No extraneous inputs, distributional assumptions, iterative procedures nor optimization criteria are employed. This package includes functions for computing local depths and cohesion as well as flexible functions for plotting community networks and displays of cohesion against distance. Package: r-cran-paleoam Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paleoam_1.0.1-1.ca2404.1_all.deb Size: 156334 MD5sum: 034ad29e6120865509e341d45b969d5a SHA1: ea944de3faf405cb668aaac90c9f096ce48d4b2e SHA256: b556e7dc1a0fe78514bee1a92544445da497e00c5eadd397f69ef428b6437483 SHA512: 37e4aee2e9898101f56b69b7e521d82abe246ae00dc1c404dfd1ffbdd66c0ff202978fd4de848c680b057922ade04da4835388fd0f791d8efd62a7b1b55e049e Homepage: https://cran.r-project.org/package=paleoAM Description: CRAN Package 'paleoAM' (Simulating Assemblage Models of Abundance for the Fossil Record) Provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages. Package: r-cran-paleobiodb Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-maps, r-cran-terra Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paleobiodb_1.0.1-1.ca2404.1_all.deb Size: 446954 MD5sum: b37e9309871ab835c91407a14497abe5 SHA1: ca7cbb915547699483a253879c957155fb4028a0 SHA256: dd6314a3f8d2d715cb071b5d318eea56607bd33a643308f51e692220816abd82 SHA512: ed6c8689409a89bd8f9ee11f83b41d7a604f79b22d562358427da6e0515ab866fc5716fc7bcd1a2fd74ec597f04788459353bea927bcaa302935119930e3449b Homepage: https://cran.r-project.org/package=paleobioDB Description: CRAN Package 'paleobioDB' (Download and Process Data from the Paleobiology Database) Includes functions to wrap most endpoints of the 'PaleobioDB' API and to visualize and process the obtained fossil data. The API documentation for the Paleobiology Database can be found at . Package: r-cran-paleobuddy Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3395 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ape, r-cran-fitdistrplus, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paleobuddy_1.1.0-1.ca2404.1_all.deb Size: 1706336 MD5sum: 8e9510edc714795453e2a5b2975e94b8 SHA1: b056c6227c10f39817118ff76037a9de35d3899c SHA256: cbc141dd79225b6230042f20d65e9132682d08ce10ff7684b6d0ee672aafb824 SHA512: 64ab027ad6e1d07e04cec4813a9138a273f82937c670821b4d63ae854e06d5dda7225da0171c7c5e6dc97bca1cbb84b8922da69ea5506e54c8bf26f002ee9974 Homepage: https://cran.r-project.org/package=paleobuddy Description: CRAN Package 'paleobuddy' (Simulating Diversification Dynamics) Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) . Package: r-cran-paleodiv Architecture: all Version: 0.4.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-stringr Suggests: r-cran-testthat, r-cran-strap, r-cran-divdyn, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paleodiv_0.4.12-1.ca2404.1_all.deb Size: 703800 MD5sum: bf01414c8b5fa1e15e08282ad83b0330 SHA1: 82e0fa04968dacd50820d7b6a43895038009f66b SHA256: 2d5d42caea8b618b199724b2f143762fece02ecd64e9d5fa38bd5e8ff95b48cb SHA512: c9120cb9ba798372299a92f26c777fbbd836b6c878b9717f10a8ab70db437f0b9fbe4baf2c0363396e446f22e3645f86df53acc5f0ab5848847592434d2c2fa5 Homepage: https://cran.r-project.org/package=paleoDiv Description: CRAN Package 'paleoDiv' (Extracting and Visualizing Paleobiodiversity) Contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database , extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy. Package: r-cran-paleomorph Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-abind, r-cran-rgl Filename: pool/dists/noble/main/r-cran-paleomorph_0.1.4-1.ca2404.1_all.deb Size: 64482 MD5sum: aa1aec88c3fecfbd0dbe2bded0d41c4b SHA1: 4c2f009c80a3fc71386c9c7cc349e1e6f8141c2c SHA256: a10d001d536b480e4eff2fc11104e2131bf2ddf9d12611afec742442de47c906 SHA512: b1d941530f32b1e04a1a7f6246790c26e44e29b202e2e959e7acf072fe3a2edd7d61a26d99b15a119a76faa7768c0da9e34687215e2cc16e87611ceb948039b4 Homepage: https://cran.r-project.org/package=paleomorph Description: CRAN Package 'paleomorph' (Geometric Morphometric Tools for Paleobiology) Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses. Package: r-cran-paleopop Architecture: all Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-poems, r-cran-r6, r-cran-sf, r-cran-trend Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-dplyr, r-cran-raster Filename: pool/dists/noble/main/r-cran-paleopop_2.1.7-1.ca2404.1_all.deb Size: 3994952 MD5sum: 88cf7f3e002aef7628bef6a638f833a2 SHA1: 1bb071d0db866808be4457803f5c787622147536 SHA256: 210e5ed3907dd7299c9b9915e41b328a1511dc9f1cb05a13ccb9563291371888 SHA512: ac448ef01b977fddc233d8894d6f3779c40119ef2460fbadd79f8a9fb8c0ee5faae998309c046fabc543ac8c4534ff7daa1979397dcf3867e14233035446d3f3 Homepage: https://cran.r-project.org/package=paleopop Description: CRAN Package 'paleopop' (Pattern-Oriented Modeling Framework for Coupled Niche-PopulationPaleo-Climatic Models) This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations. Package: r-cran-paleotree Architecture: all Version: 3.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-phytools, r-cran-jsonlite, r-cran-png, r-cran-rcurl Suggests: r-cran-spelling, r-cran-testthat, r-cran-curl Filename: pool/dists/noble/main/r-cran-paleotree_3.4.7-1.ca2404.1_all.deb Size: 1531728 MD5sum: 494db3387d4603cfd5288f6c058e5fe4 SHA1: 98463a1b26bbaf5836b9da67daaf2772f6e63683 SHA256: 777dfb228838e348e8cf68e3b01a4397af39e7c94b3ac432fba710745c90c80c SHA512: 0599d864bfc4a36a904395c48260cbe0e223e949991f1446d5300846bd9868b483740643e1ab036fb41d9e0bea14f76723025ad00e590e1a1019686432f697bf Homepage: https://cran.r-project.org/package=paleotree Description: CRAN Package 'paleotree' (Paleontological and Phylogenetic Analyses of Evolution) Provides tools for transforming, a posteriori time-scaling, and modifying phylogenies containing extinct (i.e. fossil) lineages. In particular, most users are interested in the functions timePaleoPhy, bin_timePaleoPhy, cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which date cladograms of fossil taxa using stratigraphic data. This package also contains a large number of likelihood functions for estimating sampling and diversification rates from different types of data available from the fossil record (e.g. range data, occurrence data, etc). paleotree users can also simulate diversification and sampling in the fossil record using the function simFossilRecord, which is a detailed simulator for branching birth-death-sampling processes composed of discrete taxonomic units arranged in ancestor-descendant relationships. Users can use simFossilRecord to simulate diversification in incompletely sampled fossil records, under various models of morphological differentiation (i.e. the various patterns by which morphotaxa originate from one another), and with time-dependent, longevity-dependent and/or diversity-dependent rates of diversification, extinction and sampling. Additional functions allow users to translate simulated ancestor-descendant data from simFossilRecord into standard time-scaled phylogenies or unscaled cladograms that reflect the relationships among taxon units. Package: r-cran-paleots Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paleots_0.6.2-1.ca2404.1_all.deb Size: 581372 MD5sum: a34b6f18b5bd814b619211a74d251896 SHA1: 7ced0163a2c7f4c66df0c0e44e5fd42a44c70e8e SHA256: 0b5af141c0ad004f021142086811b255fdd2ca98b72f2017d1735048d09b538f SHA512: 7204dac4f7d445944fd18801caeb23f3a6aa41f82cf3e945b173feb54f848daa2356443d8909919633f3563edd32f198500b72468234c7db6674ad47401c89b2 Homepage: https://cran.r-project.org/package=paleoTS Description: CRAN Package 'paleoTS' (Analyze Paleontological Time-Series) Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts). Package: r-cran-palette Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-pillar, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-palette_0.0.3-1.ca2404.1_all.deb Size: 121682 MD5sum: 0745d4a70be000a709cfa3b6187400be SHA1: ec1e711d8a97c03d78b306458fd75600058f3a0a SHA256: 50705f4c53f34433df71341d90f73cfeb29fc7068297721d4d40588b620a5200 SHA512: 9a4ea46bb7de102b004ccd70c0c9ebfc0325a1724efc685360cd571fae7b88aa0afabda9e2281b0a891c72fc523824248eba432f9d9f64f25a9ec2edaa617ce8 Homepage: https://cran.r-project.org/package=palette Description: CRAN Package 'palette' (Color Scheme Helpers) Hexadecimal codes are typically used to represent colors in R. Connecting these codes to their colors requires practice or memorization. 'palette' provides a 'vctrs' class for working with color palettes, including printing and plotting functions. The goal of the class is to place visual representations of color palettes directly on or, at least, next to their corresponding character representations. Palette extensions also are provided for data frames using 'pillar'. Package: r-cran-palettecore Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-palettecore_0.4.1-1.ca2404.1_all.deb Size: 71208 MD5sum: 1b34e97c0af6c3e282d3d6618be8fe1d SHA1: da21284f8e3f2ee0009804ac67f22809e9764924 SHA256: 1a21836f8708c5e8b929b5fe93ef01ed3f3a81ab721ec935dfea5a74f822978e SHA512: 21bbb297df0efedda53420293320111c319d722a13bb8e1a5c982b4b129e161e798f6ba91ca6e822a2f007db70422ffbe7a073913ec26f0f7ccbca0abc7b0985 Homepage: https://cran.r-project.org/package=palettecore Description: CRAN Package 'palettecore' (Derive, Optimise and Audit a Scientific Colour Palette from OneSeed Colour) Generates sequential, diverging and categorical colour palettes from a single seed colour in OKLCH (the cylindrical lightness-chroma-hue representation of the Oklab perceptual colour space), with spacing measured by the CIEDE2000 colour-difference formula of the International Commission on Illumination. Audits every palette under simulated colour-vision deficiency, greyscale conversion, the standard Red Green Blue (sRGB) gamut and Web Content Accessibility Guidelines (WCAG) contrast. Colour-vision deficiency is simulated at severity 1.0 with the model of Machado, Oliveira and Fernandes (2009) ; the design rationale follows Crameri, Shephard and Heron (2020) . Mirrors the 'Python' reference implementation maintained in the same repository and is validated against shared parity fixtures. Thresholds are configurable design rules, not established accessibility cut-offs. Package: r-cran-paletteer Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-prismatic, r-cran-rematch2, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggthemes, r-cran-harrypotter, r-cran-knitr, r-cran-oompabase, r-cran-palr, r-cran-pals, r-cran-rmarkdown, r-cran-scico, r-cran-testthat, r-cran-vdiffr, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-paletteer_1.7.0-1.ca2404.1_all.deb Size: 473852 MD5sum: d7edb12606823794288a3f464eae30b8 SHA1: 220645330c6e692c152e54455225f0d4a6fa101a SHA256: e31d9867fb274c8f28dc5569fec8df05db5fc65e67b9e0f982c0be856955b975 SHA512: c0aa8099abd509eb4921e5eb92dc1f2de589dd9f89d7674ab858a96af8f37f866df778c799d0ed1d7ff193f06daad4da838fc9006a65477cc07fc659c3214227 Homepage: https://cran.r-project.org/package=paletteer Description: CRAN Package 'paletteer' (Comprehensive Collection of Color Palettes) The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This packages aims to collect all color palettes across the R ecosystem under the same package with a streamlined API. Package: r-cran-paletteknife Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-paletteknife_0.4.2-1.ca2404.1_all.deb Size: 58004 MD5sum: a7fd4c1b187fa06413fde9788b415d7d SHA1: 67fd933253dd46f91f98d8a523f0a60dcd5081e1 SHA256: b91be94bfc0bb1c3ea08a5ee578e4d2b59c9107b3971393b4b3e6e4902637b52 SHA512: 62f0cfa83396549d3121cf9e4270bd82165b6e9ad996d29c46e80149dcac9bb22c9302fb4035c1cb34c444b371ce64ffe462fe4c007ed2b0472d03bba951973a Homepage: https://cran.r-project.org/package=paletteknife Description: CRAN Package 'paletteknife' (Create Colour Scales and Legend from Continuous or CategoricalVectors) Streamlines the steps for adding colour scales and associated legends when working with base R graphics, especially for interactive use. Popular palettes are included and pretty legends produced when mapping a large variety of vector classes to a colour scale. An additional helper for adding axes and grid lines complements the base::plot() work flow. Package: r-cran-palettephines Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-dplyr, r-cran-sfdep, r-cran-tidyr, r-cran-roroph, r-cran-rnaturalearth, r-cran-rnaturalearthdata Filename: pool/dists/noble/main/r-cran-palettephines_0.1.3-1.ca2404.1_all.deb Size: 54616 MD5sum: d7833d0164222a7e12c9e9deebbe2177 SHA1: c8efe58fb87b0dd7e3e8d20f8959ad1f7ad936da SHA256: ee008b82aa4ed5b8da27935268baa1906ea99d23f4ef5b99bcc212ba8ccacbfd SHA512: 0a2777ff2fa8abeb71dfa51d56e5adbb90acba14d939855e6117997e1672c116b3edd691d387a546916f7d56d7342b7c351a4ddd8b6e052307d04cc7024d2545 Homepage: https://cran.r-project.org/package=palettephines Description: CRAN Package 'palettephines' (Analytical Color Palettes for Philippine Phenology) Provides specialized color palettes representing phenological transitions and biological lifecycles within Philippine landscapes. Rather than abstract gradients, these scales are anchored to topologically grounded states such as agricultural maturity, seasonal vegetation shifts, and environmental readiness. Palettes are indexed against the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale (Meier, 2023) for terrestrial vegetation and the Reef Health Index (RHI) framework (McField and Kramer, 2007) for marine ecosystems. This ensures scientific interoperability across archipelagic spatial models, aligning with global standards for ecological state-transition modeling (Schwartz, 2013) . Package: r-cran-palettes Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-cli, r-cran-pillar, r-cran-rlang, r-cran-purrr, r-cran-prismatic, r-cran-farver, r-cran-ggplot2, r-cran-scales, r-cran-tibble Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-colorspace, r-cran-gt, r-cran-biscale, r-cran-sf, r-cran-patchwork, r-cran-metbrewer, r-cran-nord, r-cran-pnwcolors, r-cran-viridislite, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-cran-palettes_0.2.2-1.ca2404.1_all.deb Size: 613296 MD5sum: 49f79b0529baf37e85553ca1743b7450 SHA1: 6d3c0cf69c961a71141d5aae934da4377cb37f82 SHA256: 1a1b2e0dd760fa740d9f9b6149976d7db0c9062c1d80fccaf1d69fde50d26e33 SHA512: 1c29d018011b915d8acd134ffc301c5e47eb8c0ac6b74487dad2b3c4563e82a081b42b0aa7b1ed370c07d2b525a832bb3ac48e341dad90281e2fcfe33575c886 Homepage: https://cran.r-project.org/package=palettes Description: CRAN Package 'palettes' (Methods for Colour Vectors and Colour Palettes) Provides a comprehensive library for colour vectors and colour palettes using a new family of colour classes (palettes_colour and palettes_palette) that always print as hex codes with colour previews. Capabilities include: formatting, casting and coercion, extraction and updating of components, plotting, colour mixing arithmetic, and colour interpolation. 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Package: r-cran-palettetown Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-palettetown_0.1.1-1.ca2404.1_all.deb Size: 51624 MD5sum: 7c8eb923489a57807cbb082a60844222 SHA1: 60384632eedfc75a8f5e41fc61f99947a536031f SHA256: dcb0b081bcbc66c0c774e1047140b9714ded4c27ff537fe0764577e67d280e7e SHA512: a9fb7a8f2fa2af4197591cc77f9d2593c0aa9d996474853e2f4387aa9451a455b1767fe31a70a98f6e4c83d918b28705ab4788455d3f6496455c5c59ff0d7b8c Homepage: https://cran.r-project.org/package=palettetown Description: CRAN Package 'palettetown' (Use Pokemon Inspired Colour Palettes) Use Pokemon(R) inspired palettes with additional 'ggplot2' scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided. Package: r-cran-palimpsestr Architecture: all Version: 0.24.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1842 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytex, r-cran-sf, r-cran-ggplot2, r-cran-viridis, r-cran-ggrepel, r-cran-plotly, r-cran-rlang, r-cran-dbi, r-cran-rsqlite, r-cran-rpostgres, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-rcarbon, r-cran-oxcaar Filename: pool/dists/noble/main/r-cran-palimpsestr_0.24.1-1.ca2404.1_all.deb Size: 1231352 MD5sum: 64898863d4f8a3501b816345e54da62a SHA1: b908ed77597343d95fb1788481d663a8cb61fc4c SHA256: 7e83bf56033711c8fbae51355a1b96bff514dd3a27eea3ac5cce2b9ad9b5d6d0 SHA512: f76711b5358ae206fbb09e98c5303f983e8ab9d39e3825e63a4fbd797a9d1171c7935a778ef62ea9fc08fc267f27e8bbea8d0da189baa8fc5f0e352e6808e526 Homepage: https://cran.r-project.org/package=palimpsestr Description: CRAN Package 'palimpsestr' (Probabilistic Decomposition of Archaeological Palimpsests) Probabilistic framework for the analysis of archaeological palimpsests based on the Stratigraphic Entanglement Field (SEF). Integrates spatial proximity, stratigraphic depth, chronological overlap, and cultural similarity to estimate latent depositional phases via diagonal Gaussian mixture Expectation-Maximisation (EM). Provides the Stratigraphic Entanglement Index (SEI), Excavation Stratigraphic Energy (ESE), and Palimpsest Dissolution Index (PDI) for quantifying depositional coherence, detecting intrusive finds, and measuring palimpsest formation. Includes simulation, diagnostics, phase-count selection, publication-quality plots, and Geographic Information System (GIS) export via 'sf'. Methods are described in Cocca (2026) . Package: r-cran-palinsol Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4394 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/noble/main/r-cran-palinsol_1.0-1.ca2404.1_all.deb Size: 4347878 MD5sum: 2dff09a5e6dc3f323c6e6be91b671455 SHA1: e70d147001209bdf8ab362dd7070befa2299d193 SHA256: 92dc7eb7d4f64e469a32590dd488f46f380aafedf5ed70120c1698834cbc0eed SHA512: 120f540fedc031d525877880cca2bd5f9e8db3565da99723e46deef94b2f8ed86086083baf846448cc017c0f113724f3f129f42567e5dd24b5c4cbda70d0bab0 Homepage: https://cran.r-project.org/package=palinsol Description: CRAN Package 'palinsol' (Insolation for Palaeoclimate Studies) R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates. Package: r-cran-palmerpenguins Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3218 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-recipes Filename: pool/dists/noble/main/r-cran-palmerpenguins_0.1.1-1.ca2404.1_all.deb Size: 2971820 MD5sum: c338a5a9ede1d1a1d4e90e2d4afe82e9 SHA1: 69ccbdbe7c014fddd0833ffd74aa6ccccb6b9679 SHA256: 8e28cbccf253a99fc9b5e690af4166b92fe853ad4b9bfc7d1910651bd9160e9c SHA512: fee72689ebbef2d7cb2750f5c957ba8e72a9c3520a5db867730e3caed76189be6c6e80f6c2245a3fe23e74dbf6b2dcf35221825f3bbacba3b5750ff96afbee2c Homepage: https://cran.r-project.org/package=palmerpenguins Description: CRAN Package 'palmerpenguins' (Palmer Archipelago (Antarctica) Penguin Data) Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program. Package: r-cran-palmr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-palmr_0.2.0-1.ca2404.1_all.deb Size: 57062 MD5sum: bb0ab0c5a24a7ed9e044502614efdf94 SHA1: e46e0b612c99036dcb5909231738ec6797aa6ded SHA256: d62356d3f376be7932344544bd3e630884140eced14961e3357e6ea9457f7998 SHA512: dc6d48b05d034065800c4b591e3198431cbfd07296ef86eb13a531c3df1c16e986712e3968e4a22172aad244ff8903bd5e28e5a8dc03bef8396d485a12fd4759 Homepage: https://cran.r-project.org/package=PaLMr Description: CRAN Package 'PaLMr' (Interface for 'Google Pathways Language Model 2 (PaLM 2)') 'Google Pathways Language Model 2 (PaLM 2)' as a coding and writing assistant designed for 'R'. 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Package: r-cran-palmtree Architecture: all Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-formula Suggests: r-cran-mvtnorm, r-cran-psychotools Filename: pool/dists/noble/main/r-cran-palmtree_0.9-1-1.ca2404.1_all.deb Size: 33690 MD5sum: e181e9c425b765052da823aa016537c4 SHA1: 4b70e174c718e9b9ff0526b2a96d658fe5dc8592 SHA256: 334fc76f92b9d2833c032503610829b234d5aa85298255523366128202e1911e SHA512: 9c2ae97771bedd4c2172c995469677fcb39ce49459e2bf2bd6669f677ea6e6a8092d072c060521d48e3cda9ad9165a5df4c5c97b3963d56267cf2ab3b2d54b91 Homepage: https://cran.r-project.org/package=palmtree Description: CRAN Package 'palmtree' (Partially Additive (Generalized) Linear Model Trees) This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the 'partykit' package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2016) . The package offers coef(), logLik(), plot(), and predict() functions for PALM trees. Package: r-cran-palr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-raster, r-cran-testthat, r-cran-covr, r-cran-stars Filename: pool/dists/noble/main/r-cran-palr_0.5.0-1.ca2404.1_all.deb Size: 306336 MD5sum: a681b3853670afeb671445ecca585b61 SHA1: 8cc9a260ec415e9d08f19e83184512ce3530bd66 SHA256: bc67531cba545a847e0be0dd1a5b31370e6be54b5664929f44ea3c2e7df97cad SHA512: cb371b88aa6432a427326f16129b89417166540ec4adefbbab6efa47e1e15670ade1b58024e58a07d33656f75d4158378d85d90a816bb86be463dfc3e9a70d23 Homepage: https://cran.r-project.org/package=palr Description: CRAN Package 'palr' (Colour Palettes for Data) Colour palettes for data, based on some well known public data sets. Includes helper functions to map absolute values to known palettes, and capture the work of image colour mapping as raster data sets. Package: r-cran-pals Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dichromat, r-cran-mapproj, r-cran-maps Suggests: r-cran-classint, r-cran-ggplot2, r-cran-knitr, r-cran-latticeextra, r-cran-reshape2, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pals_1.10-1.ca2404.1_all.deb Size: 1845698 MD5sum: 739812c8c26b6a692de68af639e9254a SHA1: 14b141d662f0c23abcebe007e0bcab35f7f508dc SHA256: 29e0c3850af9620b112491c5f126d71bf45515904fe23d5fbb436a6d86d363bb SHA512: 0d400c9016f1d8861b48e39df2eecd158c48ff8e4a1243bca7462e5dde40e882766299a6ea96382a26d5e1a121a362311a93c9d2ff1fb6e210eb51cf44eb9cd2 Homepage: https://cran.r-project.org/package=pals Description: CRAN Package 'pals' (Color Palettes, Colormaps, and Tools to Evaluate Them) A comprehensive collection of color palettes, colormaps, and tools to evaluate them. See Kovesi (2015) . Package: r-cran-pam Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-data.table, r-cran-ggplot2, r-cran-minpack.lm, r-cran-cowplot, r-cran-gridextra, r-cran-ggthemes, r-cran-metrics Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pam_2.3.0-1.ca2404.1_all.deb Size: 190118 MD5sum: b118e27e605dc1b47a0e3611616c7e22 SHA1: 5441607761740bd4173278e0928a6406397cd2e5 SHA256: beb9c29f9e947f9abf715c95bce74b36238d56abdde72d1f38205dcadc469c7e SHA512: 043b4581b54f67867d7df391da6fd5c34cccd2a8c1d76e76ff40608b6c600fdfc69bab603ba200e48d958f9b3b1adbaf2de54532f9c5337b44ed61f0b60785d8 Homepage: https://cran.r-project.org/package=pam Description: CRAN Package 'pam' (Fast and Efficient Processing of PAM Data) Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data. Package: r-cran-pambinaries Architecture: all Version: 1.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pambinaries_1.9.3-1.ca2404.1_all.deb Size: 1489988 MD5sum: 493234880bc4602f66a6c5ca40ca0739 SHA1: 94d7af1fc2e58f9a99c460c7efe95ac8fe8ae9d4 SHA256: 3bcbcd1f15c8ecf50c0906bd75188d6fb35ac333c1ef0189e9a5b93ad067c35e SHA512: ca0cbc518dc3e877fd39ada16af86b820a8bf7f8a3fbcf6a8806649a0bf9afc3c563a47c9980af1c12bebf293b3dcf447a9326d364150efd5a359e92c85765e3 Homepage: https://cran.r-project.org/package=PamBinaries Description: CRAN Package 'PamBinaries' (Read and Process 'Pamguard' Binary Data) Functions for easily reading and processing binary data files created by 'Pamguard' (). All functions for directly reading the binary data files are based on 'MATLAB' code written by Michael Oswald. Package: r-cran-pameasures Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pameasures_0.1.0-1.ca2404.1_all.deb Size: 30416 MD5sum: bdbbc9b315e73888fe2065bf723c3837 SHA1: 2e4ca34e177ebe86c2f87be30db8b1695b03c373 SHA256: 9402d639d4ce12c04da898867dcb54bb6b9c8d4df68c77609dd0d64ecd18395c SHA512: a36dce698fecc44fb25665c1b7e0d50d8ca8db975cdfa2c6fcea7877dde8a3b8c868062e63d3e70d29615d90dd77f2ac7a6c7f05c28ec88ce959b68062566c78 Homepage: https://cran.r-project.org/package=PAmeasures Description: CRAN Package 'PAmeasures' (Prediction and Accuracy Measures for Nonlinear Models and forRight-Censored Time-to-Event Data) We propose a pair of summary measures for the predictive power of a prediction function based on a regression model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric, and correctly specified or mis-specified. The first measure, R-squared, is an extension of the classical R-squared statistic for a linear model, quantifying the prediction function's ability to capture the variability of the response. The second measure, L-squared, quantifies the prediction function's bias for predicting the mean regression function. When used together, they give a complete summary of the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) for more details. Package: r-cran-pamhm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-heatmapflex, r-cran-cluster, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-readxl, r-cran-readmore, r-cran-plyr, r-cran-robusthd Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pamhm_0.1.2-1.ca2404.1_all.deb Size: 262708 MD5sum: b3455bb155119f81f157ba024c513a3a SHA1: db564f1b4c4c982124c16688f235ca9d5fe80b99 SHA256: 56ab6a09e03a204c7741aa31fd5f9b3b6bd0e679dbbd2778669d9ce6942421be SHA512: 275a2020da34bcbd0faae3816ba5a0bb30ffc8d668017dfcbeb3980097e0ff0535ba663a1b0126dc3032790560f90bfecdcbfb6e22be6658877eed7d07a6883f Homepage: https://cran.r-project.org/package=PAMhm Description: CRAN Package 'PAMhm' (Generate Heatmaps Based on Partitioning Around Medoids (PAM)) Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the 'cluster' package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) . Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering. Package: r-cran-pamm Architecture: all Version: 1.122-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmertest, r-cran-lattice, r-cran-mvtnorm, r-cran-lme4 Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-pamm_1.122-1.ca2404.1_all.deb Size: 87228 MD5sum: 19c7b72252d9cd7e6f4748f3fd9cf7f0 SHA1: e45452a7e9b173a0aceffce4855adb7a386ece0c SHA256: d484bf78ba033886aba205a1f394e299b5c42b036783837fbf79769591b9c28d SHA512: 4dd430fd90db0eea6406274554d2204eb2518a4694ef8f5f2421e3af4e521538ad30c41fac7a7bfc3ea20becebc4102b010b147f0751f15742ea3bfb9abb7263 Homepage: https://cran.r-project.org/package=pamm Description: CRAN Package 'pamm' (Power Analysis for Random Effects in Mixed Models) Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages. Package: r-cran-pammtools Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-survival, r-cran-checkmate, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-lazyeval, r-cran-formula, r-cran-mvtnorm, r-cran-pec, r-cran-vctrs, r-cran-scam Suggests: r-cran-testthat, r-cran-mstate, r-cran-broom, r-cran-etm, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-pammtools_0.8.1-1.ca2404.1_all.deb Size: 1024028 MD5sum: efc4536a56c6a39000fc7172ff124be5 SHA1: 677eb47b9e907ecf9423d544e2d5580dae7d76af SHA256: 5dd77c18958b29f3df09d89da9dae2048e4b1f5ce108b8da5b17c6134cdafb17 SHA512: 176bff0cf1d32571053be9ca4f10a3f584b7cb5b83cf7a9469a71218608188891d9c40c7f3a48358e86bcf80a4eabec995c886e2ae2795851a274df941177b7e Homepage: https://cran.r-project.org/package=pammtools Description: CRAN Package 'pammtools' (Piece-Wise Exponential Additive Mixed Modeling Tools forSurvival Analysis) The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) ) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization. Package: r-cran-pampal Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1851 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-pambinaries, r-cran-pammisc, r-cran-tuner, r-cran-seewave, r-cran-gam, r-cran-data.table, r-cran-rsqlite, r-cran-purrr, r-cran-signal, r-cran-tidyr, r-cran-ggplot2, r-cran-knitr, r-cran-xml2, r-cran-rlang, r-cran-reticulate, r-cran-lubridate, r-cran-geosphere, r-cran-shiny, r-cran-future.apply, r-cran-audio Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pampal_1.6.1-1.ca2404.1_all.deb Size: 1191498 MD5sum: c39644ef61173561f637fd5b72228d72 SHA1: 047fd393e5ea53f2eca4f5061ef31cc313de697d SHA256: d8313e35614c3080f9d1ff4ba71aeb2a0c5615ae8607ae8fb1a6255c01f63e95 SHA512: 19f5c8020cfe3e34f0a66362ad4b269a62ae71d98109a3b0c93bb7e4020b4bf24f7d78fa880961df1d9dc41e4616a16d27ce175bd96fa8c6bffa8be451b5d590 Homepage: https://cran.r-project.org/package=PAMpal Description: CRAN Package 'PAMpal' (Load and Process Passive Acoustic Data) Tools for loading and processing passive acoustic data. Read in data that has been processed in 'Pamguard' (), apply a suite processing functions, and export data for reports or external modeling tools. Parameter calculations implement methods by Oswald et al (2007) , Griffiths et al (2020) and Baumann-Pickering et al (2010) . Package: r-cran-pampe Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps Filename: pool/dists/noble/main/r-cran-pampe_1.1.2-1.ca2404.1_all.deb Size: 60848 MD5sum: 10e9f77258027ef405da3f3f61be6f32 SHA1: 37056a7bd3ec91da80edde1921b7794adfa2d3fa SHA256: 71d5cb569320c38583dc8eaafb3188b7dcfc0068bd1cf3384b528ca1ef4ee5fd SHA512: a3e4455d8f35fe63fbc03bb82f627ee57a6fc1fdbbe04384c7731904d0f8059dcf6e6acbe6aef31cba7d0061a0287d3588f93251b44ad870c6618d638c8623c3 Homepage: https://cran.r-project.org/package=pampe Description: CRAN Package 'pampe' (Implementation of the Panel Data Approach Method for ProgramEvaluation) Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention. Package: r-cran-pamr Architecture: all Version: 1.57-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-survival Filename: pool/dists/noble/main/r-cran-pamr_1.57-1.ca2404.1_all.deb Size: 664280 MD5sum: aee0a1abf00e66113ea29b761d023359 SHA1: 62e41c60140bac4e39f475b3353fce4835b66ba0 SHA256: fff9b0eaba26b22c69fbbd247c9c1432e3e718d9f07944095bdef976632cb58d SHA512: 98b15394a5ea296d14610806f821be2f368438a3fecc64fde408595020c3305a149743dd389bd475a6e76c008a1fb98911754850baf7b6704865cd01d827cb51 Homepage: https://cran.r-project.org/package=pamr Description: CRAN Package 'pamr' (Pam: Prediction Analysis for Microarrays) Some functions for sample classification in microarrays. Package: r-cran-pams Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-smacof Suggests: r-cran-knitr, r-cran-lmtest, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-pams_0.2.0-1.ca2404.1_all.deb Size: 115060 MD5sum: 70c5e990dfaf4e14939470455a1fa74e SHA1: 91ab69bc1328aa4e9f87a03ef0442f814cc52008 SHA256: 6aeef9553b2cf4c30c524dda0d54faf6155b0248ab9623bfefe84b35c6303145 SHA512: 601ae7bf2b482bf9e78bd3e10cba939184c834a15b23c52bde9b82bb5cc5261e3c3cd7eb0dbe1955021042cabb805f6f3c1f586ebfcf0bc3a19135cc04728d73 Homepage: https://cran.r-project.org/package=pams Description: CRAN Package 'pams' (Profile Analysis via Multidimensional Scaling) Implements Profile Analysis via Multidimensional Scaling (PAMS) for the identification of population-level core response profiles from cross-sectional and longitudinal person-score data. Each person profile is decomposed into a level component (the person mean) and a pattern component (ipsatized subscores). PAMS uses nonmetric multidimensional scaling via the SMACOF algorithm to identify a small number of core profiles that represent the central response patterns in a sample of any size. Bootstrap standard errors and bias-corrected and accelerated (BCa) confidence intervals for individual core profile coordinates are estimated, enabling significance testing of coordinates that is not available in other profile analysis methods such as cluster profile analysis or latent profile analysis. Person-level weights, R-squared values, and partial correlations with core profiles are also estimated, allowing individual profiles to be interpreted in terms of the core profile structure. PAMS can be applied to both cross-sectional data and longitudinal data, where core trajectory profiles describe how response patterns change over time. Methods are described in Kim and Kim (2024) , de Leeuw and Mair (2009) , and Kruskal (1964) . 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(2019) . Package: r-cran-pandemics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pandemics_0.1.0-1.ca2404.1_all.deb Size: 82150 MD5sum: 7b3eb901dda36e9b36b69de5a96b9ff6 SHA1: a781e940eb1084c3e555c25eba0e2f5a990800a4 SHA256: 207024ea343292fa9ea5eaa5ed1ec2dcb4fb71b5b2be3a07a9f862d7f49fddc3 SHA512: b98dedea759482e0089c2010793f0969d5299ee415c635c1ed96627ae44c77db0246c944683316c1bb26d2f33f5071dbc1d994f345aa61aaa3bb2036b063712a Homepage: https://cran.r-project.org/package=pandemics Description: CRAN Package 'pandemics' (Monitoring a Developing Pandemic with Available Data) Full dynamic system to describe and forecast the spread and the severity of a developing pandemic, based on available data. These data are number of infections, hospitalizations, deaths and recoveries notified each day. 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This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis. The methods are developed by Okui and Yanagi (2019) and Okui and Yanagi (2020) . Package: r-cran-panelpomp Architecture: all Version: 1.5.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1960 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pomp, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-panelpomp_1.5.0.0-1.ca2404.1_all.deb Size: 1050166 MD5sum: 7eb33b472b6894e5be479fd44bd5fff5 SHA1: d75fa94ea7a5c02112a83304c40e9fdda1e51d96 SHA256: e97b4d85e0d3146dc04e99e53af21ca028f721754b90a83eadaa032722ae4ecd SHA512: 7c2c723f8ddcb594fb70284381ad2b3bd6dd938f637dc62d936e71d51e1bc6d7f2fd8a39e5174ccd572dfbd95e21039f39c12d68fb5242db340476c44bc157f8 Homepage: https://cran.r-project.org/package=panelPomp Description: CRAN Package 'panelPomp' (Inference for Panel Partially Observed Markov Processes) Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" . Package: r-cran-panelr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-crayon, r-cran-dplyr, r-cran-formula, r-cran-ggplot2, r-cran-jtools, r-cran-lmertest, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-reformulas, r-cran-vctrs Suggests: r-cran-aer, r-cran-brms, r-cran-broom.mixed, r-cran-car, r-cran-clubsandwich, r-cran-geepack, r-cran-generics, r-cran-nlme, r-cran-plm, r-cran-sandwich, r-cran-skimr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelr_1.0.1-1.ca2404.1_all.deb Size: 943670 MD5sum: c7fa9154ed437ccb68fb707367a59e5b SHA1: cee18e912db52456f101a9c6ba3c04b875e953ea SHA256: 867806e4455d65ead269ebb694ebf2a1ea0e40216184708db4df4cdb91307695 SHA512: 94b886a9fe10aa5cb472a115c839120fb9ca2771345fe2e11ab4d7c6f982d259ad8991f678b58cc0db3b00131f485632feaa31d3141c22bb745da74f6c8fc59f Homepage: https://cran.r-project.org/package=panelr Description: CRAN Package 'panelr' (Regression Models and Utilities for Repeated Measures and PanelData) Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 ; Bell & Jones, 2015 ). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 ) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 ) as well as a generalized linear model extension thereof using GEE. Package: r-cran-panelsummary Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fixest, r-cran-kableextra, r-cran-modelsummary, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-panelsummary_0.1.3-1.ca2404.1_all.deb Size: 680972 MD5sum: 32ee133283223b0710dd73789390aeb2 SHA1: f744a403f6931557ae3d5db2d22158f2e206f5b0 SHA256: 73535e0bd2119cab731530005e4dcefaef4db12a3c74245b2fa9bfd9a2765b64 SHA512: a4ad74f43fa9127b6a471430f27e0959d94d33072222e28d94130740776a683b6527fd9f2f51afad90dbf3a04e03bbfe1804684852734cc1420f807acd6652c6 Homepage: https://cran.r-project.org/package=panelsummary Description: CRAN Package 'panelsummary' (Create Publication-Ready Regression Tables with Panels) Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) ) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation. Package: r-cran-panelsur Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-formula.tools, r-cran-plm, r-cran-matlib, r-cran-fastmatrix Filename: pool/dists/noble/main/r-cran-panelsur_0.1.0-1.ca2404.1_all.deb Size: 73174 MD5sum: 6b230149f33dc4c18901ee488e6c613e SHA1: 6ece645b5d9f9b4e4609ece2c6ed7075e49e2cd4 SHA256: 31f2d671c196b56b8fb70bc1f34b63c1f2a77798af4e8fdd216130a3529deeb7 SHA512: 33318805e47e0171823fa96f865bbdd20512dafaa38e39c699f2def03463dae21c4ba6f11c1deaa85699b075385bb267e707928f66019f93a01e2fa9659f5d89 Homepage: https://cran.r-project.org/package=panelSUR Description: CRAN Package 'panelSUR' (Two-Way Error Component SUR Systems Estimation on UnbalancedPanel Data) Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) and Platoni, Sckokai, Moro (2012) . Package: r-cran-paneltests Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-plm, r-cran-zoo, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-paneltests_1.0.6-1.ca2404.1_all.deb Size: 172422 MD5sum: ceafef1b65d32e85bae9777bb6623bdd SHA1: da294af8ebdad79d60f478fe00e3944909789280 SHA256: 61cbe64ac757644c34c9bd341f6f734fc5dcdc33c2c2d3b3dd50d9a5c6df3a46 SHA512: 96c7feba5c9be49c9f3ffd655c37a1664392d877e5cabc1778582de0e65cfc5e507156f3371babbd5b55d0a39a778ada7439e2ad79c82c7eeef5ce02b59ea692 Homepage: https://cran.r-project.org/package=paneltests Description: CRAN Package 'paneltests' (Panel Data Pre-Testing and Diagnostic Suite) Pre-testing and diagnostic tools for panel data analysis. Researchers should run these tests before any panel regression to verify modelling assumptions. The package implements: (1) the Hsiao (2014, ) homogeneity F-tests (F1/F2/F3), Swamy (1970) parameter heterogeneity test, and Pesaran (2004) cross-sectional dependence test via xtpretest(); (2) missing-data detection, mechanism testing, and imputation for unbalanced panels via xtmispanel(); (3) quantile-regression cross-sectional dependence tests (T_tau and T-tilde_tau statistics) of Demetrescu, Hosseinkouchack and Rodrigues (2023) via xtcsdq(); and (4) the panel quantile-regression slope homogeneity S-hat and D-hat statistics of Galvao, Juhl, Montes-Rojas and Olmo (2017) via xtqsh(). Together these tests address three fundamental pre-testing questions: (i) are slopes homogeneous? (ii) is there cross-sectional dependence? and (iii) is the panel balanced and is missingness ignorable? Package: r-cran-paneltm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-mass Filename: pool/dists/noble/main/r-cran-paneltm_1.1-1.ca2404.1_all.deb Size: 142484 MD5sum: 37ad2f663d2c27d392b5c83dc5b1919d SHA1: bd5fd882ca44aaf9702d7bb6a5a23a6d0c775f5e SHA256: 77d4a13d8ec36068aeb3d632df3aa5266b33611c21a0f15da783e367cb08e221 SHA512: 9b8fcc1c6d1454313f8671b6582112c2c55d59da1c9b40acc294cb49723e5050dc6bf2fb08ff82549a8ddb12043cb1bbaddd285661ba20d4cde61db84b79dd81 Homepage: https://cran.r-project.org/package=PanelTM Description: CRAN Package 'PanelTM' (Two- And Three-Way Dynamic Panel Threshold Regression Model forChange Point Detection) Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) ; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) ) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented. Package: r-cran-paneltool Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-paneltool_0.1.0-1.ca2404.1_all.deb Size: 42802 MD5sum: 5eeab9d6cdfaedaff7d9d2b344f11be7 SHA1: 27be904179da7b29ce607ef258addc341f1d1ae9 SHA256: ed6e9874f229669aea49d52d037d8ada4817d515cf927a14901580fe2af80c76 SHA512: 620c99ae32a2f27ed34d8c6af7c9c30a146045daf40e9b69ebdd7a94176bdc37a13b2a2f31bf41931a00f8e16ba52127ffff82a4cd7c4a04a2a6a772b6afa519 Homepage: https://cran.r-project.org/package=panelTool Description: CRAN Package 'panelTool' (Build Regularly Spaced Panels from Irregularly SpacedLongitudinal Data) Aligns irregularly timed longitudinal observations to regular panel schedules. Each observation on a subject serves as a potential baseline, and all later observations are aligned to the nearest panel with minimal delay. The package provides tools to: (1) build a panel book documenting alignments and delays, (2) organize variables in longitudinal data to wide-format panel matrices, (3) summarize alignment quality and panel coverage. Ideal for cross-lagged panel analysis, clinical trials with variable visit schedules, and Electronic Health Record harmonization. Package: r-cran-panelvar Architecture: all Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2646 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-progress, r-cran-matrixcalc, r-cran-texreg, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelvar_0.5.6-1.ca2404.1_all.deb Size: 2584556 MD5sum: 9b38dbc1a61b26270e6402c18f72a55c SHA1: 92d5492fcb5ad9b8138659709c38329bdf7a3c04 SHA256: 0b80b43d896fb28fb09d3681c24ad35727cf188f3d8a84c091e570c9f706efc8 SHA512: 304b225258f83c99e4fa08b59c69682999715be78fe0e5aaeaa542d6d444ec3fef9e3998dafaa7a31e62647f5b8a738e7736a74202d603796517d47e8dcd7c6c Homepage: https://cran.r-project.org/package=panelvar Description: CRAN Package 'panelvar' (Panel Vector Autoregression) We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) , Arellano and Bond (1991) and the system GMM estimator by Blundell and Bond (1998) . We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions. Package: r-cran-panelview Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-testthat, r-cran-igraph Filename: pool/dists/noble/main/r-cran-panelview_1.3.1-1.ca2404.1_all.deb Size: 234794 MD5sum: 7fd7b81b8fa15b7219ca91319d0f3c40 SHA1: 98810cc4a6a27525767492fd7b0b8261990da2c9 SHA256: 61f1c51032bfb0b49e6edee84c069a581b8bb5fe70ad85a5079c791894020dae SHA512: 0f0b933920456d0efbff0e087dd268fc973fd934eb0f770b9fcff3cbcdf075bce9f1b5bbc85f540f5c371a89749bf23cb36ed841ebb6c20837b6b9afa3dd6907 Homepage: https://cran.r-project.org/package=panelView Description: CRAN Package 'panelView' (Visualizing Panel Data) Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see . Package: r-cran-panelwranglr Architecture: all Version: 1.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-hmisc, r-cran-caret Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-panelwranglr_1.2.13-1.ca2404.1_all.deb Size: 40210 MD5sum: 20e15a1cb12f0d9afe89f4b11ffb0a63 SHA1: 1ee4857aa9c69c43593b5fa15babc49abd8bd532 SHA256: fed9a4eb2551d263dd62d6bdbeadee02dc5e67726ac27499d51f4a24cf7456ec SHA512: f41662ed6799c9c0116c9f4e098ccd421673f08f1cc81d9aa8cd029a899458e4db69cd9c9fc740355016af5b732e419cd6740bc3bcaf34284676343ab54078b9 Homepage: https://cran.r-project.org/package=panelWranglR Description: CRAN Package 'panelWranglR' (Panel Data Wrangling Tools) Leading/lagging a panel, creating dummy variables, taking panel differences, looking for panel autocorrelations, and more. Implemented via a 'data.table' back end. Package: r-cran-pangaear Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-jsonlite, r-cran-xml2, r-cran-oai, r-cran-tibble, r-cran-hoardr, r-cran-png Suggests: r-cran-knitr, r-cran-testthat, r-cran-vcr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-pangaear_1.1.0-1.ca2404.1_all.deb Size: 89062 MD5sum: aac9c46866e9369b7b07442d9a266433 SHA1: 32abca851db78366004a273a4febec923946e435 SHA256: fc287eedcc7e9b184585f32d6cdf45a99090284e4b44029860a370b421c3421b SHA512: 3dac7f5faaf876cc6bf6eb372b70e8bfa2de9ebbd1621a56607cd6f22bc214d73049cd26357ff64ab3608df25fc799b4d7b98c0eec4e7a9c880155eb391c2ddf Homepage: https://cran.r-project.org/package=pangaear Description: CRAN Package 'pangaear' (Client for the 'Pangaea' Database) Tools to interact with the 'Pangaea' Database (), including functions for searching for data, fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea' 'OAI-PMH' service. Package: r-cran-pangoling Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cachem, r-cran-data.table, r-cran-memoise, r-cran-reticulate, r-cran-rstudioapi, r-cran-tidyselect, r-cran-tidytable Suggests: r-cran-brms, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-covr Filename: pool/dists/noble/main/r-cran-pangoling_1.0.3-1.ca2404.1_all.deb Size: 813060 MD5sum: 7b3eeec55cc0d59556fa8dfb04aebeab SHA1: 7ae9528d5570c33376580bcd40c206d9c36f6b58 SHA256: ffcab0529f9c0d2c47aae04351bb87b265056bb784d6aa1f4743597cf3fc1b2a SHA512: 9778e96b8b7e50419a55ddfba2b9572e7bbbfa9b0eccc210fedc40377d198a62c416e4ebd11b03d4813bcfd2af401c869fc2bef9f20bee6e861db002065af296 Homepage: https://cran.r-project.org/package=pangoling Description: CRAN Package 'pangoling' (Access to Large Language Model Predictions) Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem . The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2') and masked/bidirectional LLMs (e.g., 'BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) , for details on BERT and masked models, see Devlin et al. (2019) . By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP). Package: r-cran-panjen Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-panjen_1.6-1.ca2404.1_all.deb Size: 73480 MD5sum: 5a8e3823024ea2ce58f4b62b8a792db1 SHA1: b0bf26a11b7b645af434c3b3379777ce5d59a2eb SHA256: c7c4aaf5a0770197c266be19fa121542500a14fb3162a27014b4d2804fe17af3 SHA512: c20b69aa43635c36bf077990cbb04312e7a2b195eb7923a308a0bfb60da8e69543cfbbe70a9dcde9a6ea6d103dc24c05812975d2c8082b217de799e1833d7cf7 Homepage: https://cran.r-project.org/package=PanJen Description: CRAN Package 'PanJen' (A Semi-Parametric Test for Specifying Functional Form) A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations. In contrast to the classic power-transformations, the methods in this package allows for theoretical driven user input and the possibility to compare with a non-parametric transformation. Package: r-cran-pannotator Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-colourpicker, r-cran-config, r-cran-configr, r-cran-dplyr, r-cran-exiftoolr, r-cran-geojsonsf, r-cran-ggplot2, r-cran-golem, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jpeg, r-cran-jsonify, r-cran-jsonlite, r-cran-leaflet, r-cran-leafpm, r-cran-rintrojs, r-cran-rhandsontable, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-httpuv, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pannotator_1.1.0-1.ca2404.1_all.deb Size: 2257364 MD5sum: 3c5ce8df14792589c5040a762f51f631 SHA1: 943775817056fd9926f977f6568f6e452ea3a37e SHA256: e4dbd3a5a9c136409979678155035e585b3c805d984d86c116e89183b1e97425 SHA512: 914e7b56fa0119a0c3c4a95ea38e4947dfbaf2a59533deca43ab3e4a528ccbc26e31af64689c14cb368e3578def36eafb775ad73a22f00ce451667b3ac710081 Homepage: https://cran.r-project.org/package=pannotator Description: CRAN Package 'pannotator' (Visualisation and Annotation of 360 Degree Imagery) Provides a customisable R 'shiny' app for immersively visualising, mapping and annotating panospheric (360 degree) imagery. The flexible interface allows annotation of any geocoded images using up to 4 user specified drop-down menus. The app uses 'leaflet' to render maps that display the geo-locations of images and Panellum , a lightweight panorama viewer for the web, to render images in virtual 360 degree viewing mode. Key functions include the ability to draw on & export parts of 360 images for downstream applications. Users can also draw polygons and points on map imagery related to the panoramic images and export them for further analysis. Downstream applications include using annotations to train Artificial Intelligence/Machine Learning (AI/ML) models and geospatial modelling and analysis of camera based survey data. Package: r-cran-panstarrs Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-jsonlite Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-panstarrs_0.2.3-1.ca2404.1_all.deb Size: 357504 MD5sum: a390af966cb4a800c4469ed51c19a430 SHA1: 92ba6850398c5122f1709474a74a690315eaa4e9 SHA256: 3c3210a69ba77e549fd9966b8327be2b659a680bfb6dd36b099e6919101abae1 SHA512: d3df2f73dca90ab40fdc811dd3b7f36590af14a1b6ef9efd3b409111003e6da00ae3149c2e9fe76f7054ce07717d0d3ad87e393277f416a52d2aaba461656442 Homepage: https://cran.r-project.org/package=panstarrs Description: CRAN Package 'panstarrs' (Interface to the Pan-STARRS API) An interface to the API for 'Pan-STARRS1', a data archive of the PS1 wide-field astronomical survey. The package allows access to the PS1 catalog and to the PS1 images. (see for more information). You can use it to plan astronomical observations, make guidance pictures, find magnitudes in five broadband filters (g, r, i, z, y) and more. Package: r-cran-pantarhei Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-pantarhei_0.1.2-1.ca2404.1_all.deb Size: 553440 MD5sum: bff7903c3181e42b3e54346f66b59d12 SHA1: 03bef185e61daf69ba0da0aa1672e84d1469d293 SHA256: 24f289d996a5134d5bcdc1e71585e26ad44472781b6d9b4595926f2ab4cc0738 SHA512: 07e2be677a4b820d703b19e88fa5dc6a6a24fe5d718841ba9016203cfbd5bfb86def66ca7061376b4cac90ab1c2cb2c299f1961eca5f33e227b48a7ef8d1dfe7 Homepage: https://cran.r-project.org/package=PantaRhei Description: CRAN Package 'PantaRhei' (Plots Sankey Diagrams) Sankey diagrams are a powerfull and visually attractive way to visualize the flow of conservative substances through a system. They typically consists of a network of nodes, and fluxes between them, where the total balance in each internal node is 0, i.e. input equals output. Sankey diagrams are typically used to display energy systems, material flow accounts etc. Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows: flows originating from a single node can, either direct or indirect, contribute to the input of that same node. This package, named after the Greek aphorism Panta Rhei (everything flows), provides functions to create publication-quality diagrams, using data in tables (or spread sheets) and a simple syntax. Package: r-cran-papaja Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2471 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tinylabels, r-cran-bookdown, r-cran-broom, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-rmdfiltr, r-cran-yaml, r-cran-zip Suggests: r-cran-afex, r-cran-bayesfactor, r-cran-beeswarm, r-cran-boot, r-cran-car, r-cran-dplyr, r-cran-effectsize, r-cran-emmeans, r-cran-ggforce, r-cran-ggplot2, r-cran-latex2exp, r-cran-lme4, r-cran-lmertest, r-cran-mass, r-cran-multcomp, r-cran-nlme, r-cran-nnet, r-cran-r.rsp, r-cran-skimr, r-cran-spelling, r-cran-testthat, r-cran-vgam Filename: pool/dists/noble/main/r-cran-papaja_0.1.5-1.ca2404.1_all.deb Size: 1222806 MD5sum: e1ed62609c4ac34a4618216d6b65075d SHA1: 78b28ec84aa681281ebe205f327993eec441a55e SHA256: 103b413674a67871ddd6c414191a6c9fbe7f285276c85b8e2423adbf847410e8 SHA512: 19a39ff5caf4347529fd3099a896bb65e957890178618442261f1c4641cdfdf4a19b56305761c2f814f31286d27ed388abb4ffb09b4b0eeb401e7dc19a438579 Homepage: https://cran.r-project.org/package=papaja Description: CRAN Package 'papaja' (Prepare American Psychological Association Journal Articles withR Markdown) Tools to create dynamic, submission-ready manuscripts, which conform to American Psychological Association manuscript guidelines. We provide R Markdown document formats for manuscripts (PDF and Word) and revision letters (PDF). Helper functions facilitate reporting statistical analyses or create publication-ready tables and plots. Package: r-cran-papci Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyverse, r-cran-binom, r-cran-propcis, r-cran-ratesci, r-cran-hmisc, r-cran-shiny, r-cran-shinythemes, r-cran-readxl, r-cran-dt Filename: pool/dists/noble/main/r-cran-papci_0.1.0-1.ca2404.1_all.deb Size: 55022 MD5sum: c97be349a67c2c3cce64b9e2b2bedf53 SHA1: fbd8356028ccb306689d66b946d827aee4f84925 SHA256: f9eae2fed1e97515c7e3c376f9d50132457b85782368c1d42f864c696927b697 SHA512: d36127939ed517e05732550751adf5c081e8ed3882790817f6808499df661ae72b757cef8705fab36e8f3a80f4110ba90c920c9579d3cb7ad7687b781cc37e52 Homepage: https://cran.r-project.org/package=papci Description: CRAN Package 'papci' (Prevalence Adjusted PPV Confidence Interval) Positive predictive value (PPV) defined as the conditional probability of clinical trial assay (CTA) being positive given Companion diagnostic device (CDx) being positive is a key performance parameter for evaluating the clinical validity utility of a companion diagnostic test in clinical bridging studies. When bridging study patients are enrolled based on CTA assay results, Binomial-based confidence intervals (CI) may are not appropriate for PPV CI estimation. Bootstrap CIs which are not restricted by the Binomial assumption may be used for PPV CI estimation only when PPV is not 100%. Bootstrap CI is not valid when PPV is 100% and becomes a single value of [1, 1]. We proposed a risk ratio-based method for constructing CI for PPV. By simulation we illustrated that the coverage probability of the proposed CI is close to the nominal value even when PPV is high and negative percent agreement (NPA) is close to 100%. There is a lack of R package for PPV CI calculation. we developed a publicly available R package along with this shiny app to implement the proposed approach and some other existing methods. Package: r-cran-paper Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-xtable, r-cran-gmodels Suggests: r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-foreign Filename: pool/dists/noble/main/r-cran-paper_1.0-6-1.ca2404.1_all.deb Size: 587352 MD5sum: c300afab60135a8aa5c41bf3fd5ec7f9 SHA1: 779adea2ada1faab94989c92b720512a37f325fe SHA256: 3421c83943478d8d903418ee2fec121022dacc2f3aa46aa1bab21e2ff48efd80 SHA512: 215e468cd9d24028e8b6511016a60d78533b611f622f897effb4fc224d0415ede1d3cff1ea5eb7764823e5daf628c94541ab8a8c9e58a392197d15348a36aff8 Homepage: https://cran.r-project.org/package=papeR Description: CRAN Package 'papeR' (A Toolbox for Writing Pretty Papers and Reports) A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models. 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Package: r-cran-parabar Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-progress, r-cran-callr, r-cran-filelock Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-parabar_1.4.2-1.ca2404.1_all.deb Size: 1873268 MD5sum: 103f1d7c88b2e60c04ed28b26243e493 SHA1: cbac4ae18ea9c0a2cbf3a762356b2fa557ad82de SHA256: af56f916692d5c406ac4df8e504b97e58f199d30dc2d811ebf3f3e01729a1076 SHA512: dfddf4aa24e78e99733a15ae2558b7f7418336c5b9f53a3ab5204a8a946f8065f8a1bc4ced767401ed6743f4cba579f73da777eee4e45d4a1802ad832a247322 Homepage: https://cran.r-project.org/package=parabar Description: CRAN Package 'parabar' (Progress Bar for Parallel Tasks) A simple interface in the form of R6 classes for executing tasks in parallel, tracking their progress, and displaying accurate progress bars. Package: r-cran-parade Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-parade_0.1-1.ca2404.1_all.deb Size: 24394 MD5sum: 086fbef2d223faf8aef1c83308f6a1c4 SHA1: 8cbd52ac8fb5ef84e510ff6596e77de50759a411 SHA256: 5dd1bac8679b32259ea385d3e8860aabb86bdb560c18198f92c1f4eb57bef9cc SHA512: b8be7f68ea887a5defa1b8818ae35fbdbb3e81e200ec38771447570e68229037ece01300f723d66faea23dfb32390a914b8149d9a8ace297b7867ae0a001fbc0 Homepage: https://cran.r-project.org/package=parade Description: CRAN Package 'parade' (Pen's Income Parades) Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities. Package: r-cran-paradox Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-rmarkdown, r-cran-mlr3learners, r-cran-e1071, r-cran-knitr, r-cran-lhs, r-cran-spacefillr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paradox_1.0.1-1.ca2404.1_all.deb Size: 761088 MD5sum: dfc7335e64fbb779282023b433788a81 SHA1: af06aa69327f3934335aa63def655b6a55bce64b SHA256: 97db30013c9d3fbb349f5178c9c197ef128095e4d2dfbac6e418b2758c757c6c SHA512: 33f22c241c2465022d1526da876015caeebb91e28145c94a2f4f65c9c33d34f5b1f90139fd6e54aa1d350b8821461afd3bd343597beb8f94b1802fddf757d0c7 Homepage: https://cran.r-project.org/package=paradox Description: CRAN Package 'paradox' (Define and Work with Parameter Spaces for Complex Algorithms) Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. 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Package: r-cran-parafac4microbiome Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3431 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositions, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-lifecycle, r-cran-magrittr, r-cran-multiway, r-cran-pracma, r-cran-rlang, r-cran-rtensor, r-cran-tidyr Suggests: r-cran-knitr, r-bioc-phyloseq, r-cran-rmarkdown, r-bioc-treesummarizedexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-withr, r-cran-readr, r-cran-stringr, r-cran-scales, r-cran-ggpattern Filename: pool/dists/noble/main/r-cran-parafac4microbiome_1.3.3-1.ca2404.1_all.deb Size: 2891360 MD5sum: 2e0ae81261abe1b93f0100124a03b668 SHA1: d989e314a5d9bd1e85764f9a5c885476244b04a1 SHA256: 80176c0756187c7275f64a443ec77c02ee4b8327ac8b8ad1867be1592e8def75 SHA512: 9284ba4e167f1fe1e55821278aace916800572aeef4cff34b2a57580dbdc7498ef6e5e48ce65432f5fc13c1594b0e9a3f34ede59bd48a247b3e7d15fc4999d53 Homepage: https://cran.r-project.org/package=parafac4microbiome Description: CRAN Package 'parafac4microbiome' (Parallel Factor Analysis Modelling of Longitudinal MicrobiomeData) Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) and Harshman (1970) . Package: r-cran-parallellogger Architecture: all Version: 3.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snow, r-cran-xml2, r-cran-jsonlite, r-cran-rstudioapi, r-cran-memuse Suggests: r-cran-sendmailr, r-cran-testthat, r-cran-shiny, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-parallellogger_3.5.1-1.ca2404.1_all.deb Size: 588386 MD5sum: e33472ddab37647554da7b03f0434724 SHA1: 6a216a6939f8c3928639dd43070936ac2158ed42 SHA256: 8c6d2f5061fe7973c4112a5229ba1ed0f1541d0673a6c119ffca5f31382aeb5a SHA512: 85337d84fe6f057f852aaf68d623856a174c0d452cd12f693808b7b51835b1e1abf60064bcd201ecd0576c531c7cf7626436fbc167520d1707d2d743c2ec80f0 Homepage: https://cran.r-project.org/package=ParallelLogger Description: CRAN Package 'ParallelLogger' (Support for Parallel Computation, Logging, and FunctionAutomation) Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel). Package: r-cran-parallelmap Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate Suggests: r-cran-batchjobs, r-cran-batchtools, r-cran-data.table, r-cran-rmpi, r-cran-rpart, r-cran-snow, r-cran-testthat Filename: pool/dists/noble/main/r-cran-parallelmap_1.5.1-1.ca2404.1_all.deb Size: 107732 MD5sum: be726014439b7ee3c45c49e79787f989 SHA1: d8ced59bd882a31c6df751b5d7522d5ad931470a SHA256: 6a27841ae319e8e6232f6b610b286b0524f6a8559ff7a0a10bd05139fe56637a SHA512: 29f69abdb866cdf3e761506f0daeb154b92bc740dcddcd7cfc5f0d38641956b93822128d588642d1aefd65240dd489aa21eb7f08d8daea60abec108ec39cac02 Homepage: https://cran.r-project.org/package=parallelMap Description: CRAN Package 'parallelMap' (Unified Interface to Parallelization Back-Ends) Unified parallelization framework for multiple back-end, designed for internal package and interactive usage. The main operation is parallel mapping over lists. Supports 'local', 'multicore', 'mpi' and 'BatchJobs' mode. Allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation. Package: r-cran-parallelmcmccombine Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-parallelmcmccombine_2.0-1.ca2404.1_all.deb Size: 38498 MD5sum: 2120786ac645a1932080cbcb9c32b6af SHA1: 9f54f9b252964e0116c5354c57a31fcfa7698643 SHA256: 4da53d8fc43a3f6db8865b619396e099ff8f94c5b98acf801b350680237db8e5 SHA512: 6dc5fc3a728907b044a1feeff74127366f5dc814f80489aec017b54bbc30d9e2f3913185f0eb12c71830fd7e4c5c852d60faefc733681c375e982bd89c1ce759 Homepage: https://cran.r-project.org/package=parallelMCMCcombine Description: CRAN Package 'parallelMCMCcombine' (Combining Subset MCMC Samples to Estimate a Posterior Density) See Miroshnikov and Conlon (2014) . Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets. Package: r-cran-parallelpc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bnlearn, r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-parallelpc_1.2-1.ca2404.1_all.deb Size: 150578 MD5sum: 4ed3eb686174500b79f5f997330b6d07 SHA1: 52e7f1cb10db0d0b33ddf3e906ba164ecc400c84 SHA256: a16960070fc8c6099eabdc3b3ec803cdb26e932def19503d35b84f485d013bc9 SHA512: c40c6fdc3906b2d386ca95b8b113d1ea3796adf819dfcd3ffe3372a52912bddc4191b51d382566521144ee14e3598236807a03529f7d1ab590b7fdf69a97da9e Homepage: https://cran.r-project.org/package=ParallelPC Description: CRAN Package 'ParallelPC' (Paralellised Versions of Constraint Based Causal DiscoveryAlgorithms) Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient. Package: r-cran-parallelplot Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-parallelplot_0.4.0-1.ca2404.1_all.deb Size: 165934 MD5sum: 3f1c7626cf9f86f8e3096e3ba9b46cb4 SHA1: 7047c857ca1dd838690d70f4c79bf4ee82d98b7d SHA256: 8aec4e06c5682e9e8f0465e5e4080d5700e202ba48b94041f68658e99a096710 SHA512: 7c4d7397e25baa98ca661c2573882421a4bbe381463b8e822b557985775de68006ad737a57328175359b799dede87aa72290aaf9e12aaa69fbac53f7f8c8e6c5 Homepage: https://cran.r-project.org/package=parallelPlot Description: CRAN Package 'parallelPlot' (`htmlwidget` for a Parallel Coordinates Plot) Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`. Package: r-cran-paralleltree Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-paralleltree_0.2.0-1.ca2404.1_all.deb Size: 27204 MD5sum: a92b4f9db69ee373d60349fcc5de6bf0 SHA1: b75c3f3b2cfca5c96f352bb60e2d510719a2fb72 SHA256: c18ea6dde07d5935b173e8dda512f5203acf28d8a5c3f003af201ad5b078bfd6 SHA512: d95a80583fd5b9d93a612b640625ef92c9fc9c48d9aa6b65bb10281b0e59dca642f5d1f6b38b5393baa768e0d1dbde5a930f0cf7f94241e899747eb52e393f90 Homepage: https://cran.r-project.org/package=ParallelTree Description: CRAN Package 'ParallelTree' (Visualizing Multilevel Data with Parallel Tree Plots) Provides two functions: Group_function() and Parallel_Tree(). Group_function() applies a given function (e.g., mean()) to input variable(s) by group across levels of a multilevel data structure, with additional data management options. Parallel_Tree() uses 'ggplot2' to create parallel coordinate plots (technically a facsimile of parallel coordinate plots in a Cartesian coordinate system). Used in combination, these functions can create parallel tree plots, a variant of parallel coordinate plots useful for visualizing multilevel data. 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The resulting outputs are the same as in 'SAS' software. A dataset (Butterfly) to test the function is also joined. 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Package: r-cran-paramhetero Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Filename: pool/dists/noble/main/r-cran-paramhetero_1.0.0-1.ca2404.1_all.deb Size: 30708 MD5sum: 4bb43f1d66c613f51a1281308afe4910 SHA1: 38f8744423452663a10ffd205fd4a4118ca4707a SHA256: c0100c275cfb84a10f64344c5058833a4ddf615e7069083dfd0e944509db58cf SHA512: 0e354800a78295d5890ad87bb694d5ddf1a186e586fee634eff067657a2bf1c0187d140212bf85a0451cc77cf32ee36a0ee67fa55242bccf5945c1b5d7a969f8 Homepage: https://cran.r-project.org/package=paramhetero Description: CRAN Package 'paramhetero' (Numeric and Visual Comparisons of Heterogeneity in ParametricModels) Performs statistical tests to compare coefficients and residual variance across models. Also provides graphical methods for assessing heterogeneity in coefficients and residuals. Currently supports linear and generalized linear models. 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Package: r-cran-paramlink2 Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pedtools, r-cran-pedprobr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paramlink2_1.0.6-1.ca2404.1_all.deb Size: 149818 MD5sum: e0443bc5ff1b53ee9b26fc9e6969f6d4 SHA1: ca78697722d6c253b280f4912715d8996722b1dc SHA256: e921931a1f1b95909629574417684d5dc4deb9f2337dbc077243a36619938a1e SHA512: bc3401983e2b1737ee228c1167b0f611b7fcc93c7440277fcf2492fa22f2088509ae5e39164e61ad6a58c2dc4fab300b449c9a0d2c2477f96b6716b7028fdf36 Homepage: https://cran.r-project.org/package=paramlink2 Description: CRAN Package 'paramlink2' (Parametric Linkage Analysis) Parametric linkage analysis of monogenic traits in medical pedigrees. Features include singlepoint analysis, multipoint analysis via 'MERLIN' (Abecasis et al. (2002) ), visualisation of log of the odds (LOD) scores and summaries of linkage peaks. Disease models may be specified to accommodate phenocopies, reduced penetrance and liability classes. 'paramlink2' is part of the 'pedsuite' package ecosystem, presented in 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). 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A suite of tools for analysing pedigrees with marker data, including parametric linkage analysis, forensic computations, relatedness analysis and marker simulations. The core of the package is an implementation of the Elston-Stewart algorithm for pedigree likelihoods, extended to allow mutations as well as complex inbreeding. Features for linkage analysis include singlepoint LOD scores, power analysis, and multipoint analysis (the latter through a wrapper to the 'MERLIN' software). Forensic applications include exclusion probabilities, genotype distributions and conditional simulations. Data from the 'Familias' software can be imported and analysed in 'paramlink'. Finally, 'paramlink' offers many utility functions for creating, manipulating and plotting pedigrees with or without marker data (the actual plotting is done by the 'kinship2' package). 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This attempts to provide a cleaner alternative to options(). Package: r-cran-paramsim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-foreach, r-cran-doparallel, r-cran-future, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paramsim_0.1.0-1.ca2404.1_all.deb Size: 34590 MD5sum: 73c1173d16f6ed1155619d65bca406d1 SHA1: 24489df2bfea6c43494b9898bb1ba5eca60fc8d0 SHA256: 3f5dce96ae705682ca715886aa5d2c8b2e99a2e21618113985b6495b9d26ac79 SHA512: 2fe77f5821c3178f2ace4e9727faeb3f9dc932d23f8b6ffc54bac7aed5e3f84a599152d03c58ffedf75a49a639be14e6a4d6186bf330dbca894e5e464aca2ab4 Homepage: https://cran.r-project.org/package=paramsim Description: CRAN Package 'paramsim' (Parameterized Simulation) This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. 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Package: r-cran-paran Architecture: all Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-rlang Filename: pool/dists/noble/main/r-cran-paran_1.5.6-1.ca2404.1_all.deb Size: 37624 MD5sum: 9ffb507895a67e037ba7091b23f21d06 SHA1: fef7d96251559584fad3526a644b4112748aa00e SHA256: 82733ca9e364bf5d93ccc8144c0e68343b61c7bc52593c3d264365f68fcb0eba SHA512: 3a9de8141ebca0e54f96249f613ef68a6beb6e8ee5d288f464e4218275fdfa81f0b847904d0b729bb1b5fd763c25a80fa93c2a9937bd24ba5db7bb3acad9d845 Homepage: https://cran.r-project.org/package=paran Description: CRAN Package 'paran' (Horn's Test of Principal Components/Factors) An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention. 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Package: r-cran-parserpdr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-readr, r-cran-parallelly, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-progressr Suggests: r-cran-testthat, r-cran-reticulate, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-parserpdr_1.1.2-1.ca2404.1_all.deb Size: 390902 MD5sum: 879cb9a0e37c71435aa574dfbb470c81 SHA1: b21ab9ec2b07cdb9e5e45f8a72519844d3611d51 SHA256: 19ec0edf148c01763006ef833ebae4fd8bcb438407d9642f75ef0eadbb553789 SHA512: 2cc8fbf3e679eb25524f50caa952b724a8c6f9ce6b8e2c5e180010a3ce84b4f2a638657825350e7ccca5f36cce0c1fdae8ca9e1afa137a41357138f4951a931d Homepage: https://cran.r-project.org/package=parseRPDR Description: CRAN Package 'parseRPDR' (Parse and Manipulate Research Patient Data Registry ('RPDR')Text Queries) Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R. 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Package: r-cran-parsim Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-parabar Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-parsim_0.4.0-1.ca2404.1_all.deb Size: 129656 MD5sum: 49e7bb1959bdefa66ffdf8f399462b2e SHA1: 733d9b8860e00d200e73f97cce6d67cbbaf53514 SHA256: ff9e16df5e633398b5f9eb87e562b09eb89042e102d64f5fca8734bd6c5830f4 SHA512: 9bba1b52c4ebe1720354e173ad27f538e77915ae0871f1f1d49f636f361b43fba9b513899ea4caf8349661dd7d897bec8f076938ada23a5e0c7aae4de663d2ae Homepage: https://cran.r-project.org/package=parSim Description: CRAN Package 'parSim' (Parallel Simulator) Perform flexible simulation studies using one or multiple computer cores. 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Package: r-cran-parsnip Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-globals, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-magrittr, r-cran-pillar, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-sparsevctrs, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bench, r-cran-c50, r-cran-covr, r-cran-dials, r-cran-earth, r-cran-ggrepel, r-cran-keras, r-cran-keras3, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-liblinear, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-modeldata, r-cran-nlme, r-cran-prodlim, r-cran-ranger, r-cran-remotes, r-cran-rmarkdown, r-cran-rpart, r-cran-sparklyr, r-cran-survival, r-cran-tensorflow, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-parsnip_1.6.1-1.ca2404.1_all.deb Size: 1729376 MD5sum: 5362c286deb7b1938f27059457e4f07f SHA1: 2baff0d9bc30c2c23b79fd354e95c3f7627886bd SHA256: 51b18dc131f45f20904a539713084637981835d7b2b0ff3c8930453847db712d SHA512: 35ea27861a2d048434ff0f24be67575ee32f1c097e93ef9c59baec64aa2fc51f8e9f8cf40db2032ae02057414d4c7b9cc9303f7b422ce6ffe28bf4b57ed2375d Homepage: https://cran.r-project.org/package=parsnip Description: CRAN Package 'parsnip' (A Common API to Modeling and Analysis Functions) A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc). 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The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) but considering the SMN family. Package: r-cran-partdsa Architecture: all Version: 0.9.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-cran-mass, r-cran-th.data, r-cran-vgam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-partdsa_0.9.15-1.ca2404.1_all.deb Size: 569032 MD5sum: 1dd16f21f04ae5c0a49728e442f5f655 SHA1: ae5a50497b8ea0cad8c22c80e381814a642197a2 SHA256: 6589e3cc523e314eca68095b8542be3f2b22738e5e7c328c26614867b8be755a SHA512: 901b06a62fddf535ca21b3a2b39487f5e7909ceac309822f22ab4653bbab3c8fccbf1c0f07000169c122e77aac0e99a2230e610ad562515f85e833502d06e27a Homepage: https://cran.r-project.org/package=partDSA Description: CRAN Package 'partDSA' (Partitioning Using Deletion, Substitution, and Addition Moves) A novel tool for generating a piecewise constant estimation list of increasingly complex predictors based on an intensive and comprehensive search over the entire covariate space. 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A comparison of means using the partially overlapping samples t-test: See Derrick, Russ, Toher and White (2017), Test statistics for the comparison of means for two samples which include both paired observations and independent observations, Journal of Modern Applied Statistical Methods, 16(1). A comparison of proportions using the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics for comparing two proportions with partially overlapping samples. Journal of Applied Quantitative Methods, 10(3). 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Provides data generating processes and nuisance functions for simulation. 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Package: r-cran-partitionbefsp Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-partitionbefsp_1.0-1.ca2404.1_all.deb Size: 30808 MD5sum: 288d127072a360b998d394b3cde30cb9 SHA1: 36202e8823616bf264e46d9b1f951adeab0502f8 SHA256: aaaf8e4cbbd87abea16c3d2fb32aec93de5857328e8127020fe79f48b1910137 SHA512: c6243c445d0ed6970643ad319ed5478b96dfbf19ddb0589d77a560ad6dacb43419c717230efe92c74dbcb7af29bc1adce2b2148a8df2467d3a0472d66450efa5 Homepage: https://cran.r-project.org/package=partitionBEFsp Description: CRAN Package 'partitionBEFsp' (Methods for Calculating the Loreau & Hector 2001 BEF Partition) A collection of functions that can be used to estimate selection and complementarity effects, sensu Loreau & Hector (2001) , even in cases where data are only available for a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the statistical corrections used here is available in Clark et al. (2019) . 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The different measures can be assigned to three different classes: Pair comparison (containing the famous Jaccard and Rand indices), set based, and information theory based. Many of the implemented measures can be found in Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006) and Meila M (2007) . Partitions are represented by vectors of class labels which allow a straightforward integration with existing clustering algorithms (e.g. kmeans()). The package is mostly based on the S4 object system. 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When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) , 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) , and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) . 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However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. . 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Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) and demonstrated by O'Boyle and Williams (2011) and Williams, O'Boyle, & Yu (2020) . Also included are fit indices described by Hancock and Mueller (2011) . Package: r-cran-paths Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bart, r-cran-boot, r-cran-gbm, r-cran-ggplot2, r-cran-metr, r-cran-twang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paths_0.1.2-1.ca2404.1_all.deb Size: 125044 MD5sum: 9d0349321f97eb270199bb469dc42e42 SHA1: 09bb19243332ebfb2f8cd64dfa7fba33a689d456 SHA256: edf8637438b14143272ecd74376043319e6361f932ea3b312f8ce8b779c9d00e SHA512: e4cb9240e4fd75fb517e7abde2b186141a96dc3649d0d06f623aae29881e07d10eef4df6848d6858475d979cbc809b38c17cf2d94e9ccf7eee4d5543b5560756 Homepage: https://cran.r-project.org/package=paths Description: CRAN Package 'paths' (An Imputation Approach to Estimating Path-Specific CausalEffects) In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto ). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap. Package: r-cran-pathviewr Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.matlab, r-cran-data.table, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-fancova, r-cran-purrr, r-cran-ggplot2, r-cran-tidyselect, r-cran-cowplot, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-anomalize, r-cran-covr Filename: pool/dists/noble/main/r-cran-pathviewr_1.1.8-1.ca2404.1_all.deb Size: 2245336 MD5sum: fe01b1c4681d164478fa6cd1f52bbdf8 SHA1: 68132934f2caa908c75279f12d0b573b8ebd3f8e SHA256: 2d2f78b38fbcbef7cef0eac080bee39e874ba57e22ec33b20241f14e73947f92 SHA512: cbe1f102749f538ee011527d72c3aefb10c90f8df622b01c772f2690b47bb8274c4852a23c8250acf8b8ed8c42d1a97dbd5f281d2db098238afbe481e9a5d634 Homepage: https://cran.r-project.org/package=pathviewr Description: CRAN Package 'pathviewr' (Wrangle, Analyze, and Visualize Animal Movement Data) Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli. Package: r-cran-pathwayspace Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3949 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rgraphspace, r-cran-rlang, r-cran-scales, r-cran-rann, r-cran-igraph, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggrepel, r-cran-colorspace, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-reder, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-pathwayspace_1.5.2-1.ca2404.1_all.deb Size: 2818188 MD5sum: b290940bc43c7d036f1dcb712a3ed6fe SHA1: 5bde59f71f89ac23e0d02cfefcab09261b073fd8 SHA256: a3dd491344bc7530057092d75de507a545b4e9b78ccb4491e2827f25c8ae9895 SHA512: 6b2139fc3986ef6b89f07d15669ce642fc766bea439a1ec350dbb6a747834829ff444c9ae8459d6926bfea6339a9bf6be5537cf7f4e474407561f365b7ca400e Homepage: https://cran.r-project.org/package=PathwaySpace Description: CRAN Package 'PathwaySpace' (Spatial Projection of Network Signals along Geodesic Paths) For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' has various applications, such as visualizing network data in a graphical format that highlights the relationships and signal strengths between vertices. By combining graph theory, signal processing, and visualization, 'PathwaySpace' provides a way of representing graph data on a continuous projection space. Based on methods introduced in Tercan et al. (2025) and Ellrott et al. (2025) . Package: r-cran-pathwaytmb Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biocgenerics, r-cran-purrr, r-cran-glmnet, r-cran-randomforest, r-cran-survival, r-cran-survminer, r-cran-caret, r-cran-data.table, r-cran-rcolorbrewer, r-cran-proc, r-bioc-maftools, r-bioc-clusterprofiler Suggests: r-cran-stringi, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-biocmanager, r-cran-xfun, r-cran-e1071, r-cran-qpdf, r-cran-tinytex, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pathwaytmb_0.1.3-1.ca2404.1_all.deb Size: 801670 MD5sum: 0f8f4eb336c7e5a72fc49cbfbfbbcd9a SHA1: 3c05bfe76bd8f5788536d894d1cba5836a0cba8b SHA256: eab51c3e71324e28e8ebb82dce8bc0a8562d6dd9fa2183f82d5c2f3788b50f95 SHA512: deafb8a76e8f12929ba0219cbbebc1cd58b80ca14af5196d5d7be29479dceec06775d5fd9ae16959c183e34b19955d64886dc7bd67c123a4f69f633af1b65a56 Homepage: https://cran.r-project.org/package=pathwayTMB Description: CRAN Package 'pathwayTMB' (Pathway Based Tumor Mutational Burden) A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy. Package: r-cran-pathwayvote Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-harmonicmeanp, r-bioc-annotationdbi, r-bioc-clusterprofiler, r-cran-future, r-cran-furrr, r-cran-parallelly Suggests: r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-reactome.db, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-pathwayvote_0.1.3-1.ca2404.1_all.deb Size: 77594 MD5sum: 8f15dc6ae1a713508028201fe503d7ff SHA1: 1bb3ac6b75a98b3a5faa09d40d0645ebdf9fa1ea SHA256: 3fe604521bc6a5d58a0047ea2528a82f925f5e6eec6275d4d11a8c7ed52b85b1 SHA512: 6a816072270a436b01ff8dbfba79be3a58451fcf67b83aefd0b4e7612d19ea1c0610ffcfc5bc3832a2624bf252474e394568693a49f6e51cf61505e8ad291d15 Homepage: https://cran.r-project.org/package=PathwayVote Description: CRAN Package 'PathwayVote' (Robust Pathway Enrichment for DNA Methylation Studies UsingEnsemble Voting) Performs pathway enrichment analysis using a voting-based framework that integrates CpG–gene regulatory information from expression quantitative trait methylation (eQTM) data. For a grid of top-ranked CpGs and filtering thresholds, gene sets are generated and refined using an entropy-based pruning strategy that balances information richness, stability, and probe bias correction. In particular, gene lists dominated by genes with disproportionately high numbers of CpG mappings are penalized to mitigate active probe bias—a common artifact in methylation data analysis. Enrichment results across parameter combinations are then aggregated using a voting scheme, prioritizing pathways that are consistently recovered under diverse settings and robust to parameter perturbations. Package: r-cran-patientgenerator Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 991 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ellmer, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-openxlsx, r-cran-r2d3, r-cran-r6, r-cran-readxl, r-cran-shiny, r-cran-stringr, r-cran-testthat, r-cran-cli Suggests: r-cran-cdmconnector, r-cran-cohortcharacteristics, r-cran-cohortconstructor, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testgenerator, r-cran-visomopresults Filename: pool/dists/noble/main/r-cran-patientgenerator_0.2.5-1.ca2404.1_all.deb Size: 673644 MD5sum: a3424e1e95391fea113116281094a862 SHA1: f68fabda12adf2f2d6f8a0671ca2941882849b89 SHA256: c90b619bf4bacf38b4cfb250d3203aea7e8990fce8e9145734f7623d64965171 SHA512: 3c048adfa8522cba6359758dc80529a29c1906fd1bede24cf53d619fefd8bbd2b7a188a23a6601a0a634a90632ad5956ba2e427fd98db416334489df256bd422 Homepage: https://cran.r-project.org/package=PatientGenerator Description: CRAN Package 'PatientGenerator' (Generator of Synthetic Patient Data for the OMOP Common DataModel) Tools to generate synthetic patient-level test datasets in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Includes a chat-driven generator backed by large language models and an interactive 'shiny' designer for editing CDM test sets. Package: r-cran-patientlevelprediction Architecture: all Version: 6.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3392 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-andromeda, r-cran-cyclops, r-cran-databaseconnector, r-cran-digest, r-cran-dbplyr, r-cran-dplyr, r-cran-featureextraction, r-cran-matrix, r-cran-memuse, r-cran-parallellogger, r-cran-proc, r-cran-prroc, r-cran-rlang, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-brokenadaptiveridge, r-cran-curl, r-cran-eunomia, r-cran-glmnet, r-cran-ggplot2, r-cran-gridextra, r-cran-iterativehardthresholding, r-cran-knitr, r-cran-lightgbm, r-cran-metrics, r-cran-mgcv, r-cran-ohdsishinyappbuilder, r-cran-pkgload, r-cran-polspline, r-cran-readr, r-cran-resourceselection, r-cran-resultmodelmanager, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsqlite, r-cran-scoring, r-cran-survival, r-cran-survminer, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-patientlevelprediction_6.7.0-1.ca2404.1_all.deb Size: 2333430 MD5sum: 7cd59f1c9838cec24d2455522e5ec358 SHA1: 6c068a1dd9016aacce674286159fe54470b8be46 SHA256: 2a86c84f13dbcbc2f04f0cb2df0da7e0a2e49e579f74ba009109a68ea3181102 SHA512: d4569822701ec92f0777d7d6f5597e7a91ae1c6bc0ef5bf54b8f5ebf782172aaf1de13002dca7d2689a45682f1347fc92a67e25439fc0546ac84396ebab171f5 Homepage: https://cran.r-project.org/package=PatientLevelPrediction Description: CRAN Package 'PatientLevelPrediction' (Develop Clinical Prediction Models Using the Common Data Model) A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) . Package: r-cran-patientprofiles Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2727 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-lifecycle, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortconstructor, r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-dt, r-cran-duckdb, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-here, r-cran-hmisc, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-patchwork, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-withr Filename: pool/dists/noble/main/r-cran-patientprofiles_1.6.1-1.ca2404.1_all.deb Size: 879228 MD5sum: 2c2cf816a61f1d6b89aab8aac5a3e5ab SHA1: dba442da799f794bc9fc31e249bb88f778079530 SHA256: 6a3b8d25aacadf2b239befdab9f24ef387ce14ff9b21895ddbc60b1068fafe1c SHA512: ce6efd222fce1fd8e2df6f02f11e5a5d1fdf9301f9a4507bb9bdb7e29efcad08aa5e06a08f3283082ece25c62c6a0dabd8c1e5ef6b8d55056884ef228ce90fb4 Homepage: https://cran.r-project.org/package=PatientProfiles Description: CRAN Package 'PatientProfiles' (Identify Characteristics of Patients in the OMOP Common DataModel) Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Package: r-cran-patientprofilesvis Architecture: all Version: 2.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clinutils, r-cran-ggplot2, r-cran-plyr, r-cran-cowplot, r-cran-reshape2, r-cran-knitr, r-cran-stringr, r-cran-gridextra, r-cran-scales Suggests: r-cran-pander, r-cran-shiny, r-cran-testthat, r-cran-rmarkdown, r-cran-gtable, r-cran-pdftools, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-patientprofilesvis_2.0.10-1.ca2404.1_all.deb Size: 3765338 MD5sum: a6615de2b6c9d39fd31b9b2caeffc2be SHA1: 0f98ada12b9c07d584f660c3ee5673e22efe1638 SHA256: 5cb004181b35f9040a6fa1c32ceb3e72ac0ba69f5f8c26d0ba4e03c12c970f54 SHA512: 5d12204cd00259f6b2eb4792425dfc18934038e08cf9f879d8741cd88a064c17e100b9f1d73887011459a6b7636e4f2322d3ee6d4f6017f8a54df440c2117223 Homepage: https://cran.r-project.org/package=patientProfilesVis Description: CRAN Package 'patientProfilesVis' (Visualization of Patient Profiles) Creation of patient profile visualizations for exploration, diagnostic or monitoring purposes during a clinical trial. These static visualizations display a patient-specific overview of the evolution during the trial time frame of parameters of interest (as laboratory, ECG, vital signs), presence of adverse events, exposure to a treatment; associated with metadata patient information, as demography, concomitant medication. The visualizations can be tailored for specific domain(s) or endpoint(s) of interest. Visualizations are exported into patient profile report(s) or can be embedded in custom report(s). Package: r-cran-patrick Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-patrick_0.3.1-1.ca2404.1_all.deb Size: 21776 MD5sum: 888ae6160a379a007d09ab26e237cb4e SHA1: 93665d13ea6178bafc9c440c9591e92f13a622c4 SHA256: 91422a09ced8c9bf9d49e47315481c0e28da8eb01400769353d7bc3fae632339 SHA512: e36f706c2b780b4eb5deacdf566cec7e173b0a4809726191196e5125a260d4a14e3234141b2a7bf602c0d3fdd92c05fcdc7c52397e778965e6493938af139cc1 Homepage: https://cran.r-project.org/package=patrick Description: CRAN Package 'patrick' (Parameterized Unit Testing) This is an extension of the 'testthat' package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule. Package: r-cran-pattern.checks Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pattern.checks_0.1.0-1.ca2404.1_all.deb Size: 23930 MD5sum: 30eaab14c386101a2d67efbf2f918c85 SHA1: 6be34608fbdb451a6b0857128209af20622eadc0 SHA256: 34f1812aedf17e7bc379c085f91a2e811a3440a2adde341eeb64c22e39b99d9a SHA512: ade603ea0dd77bb34bb9c978c472338475e33f455841f0b68469598c7ab83255e60d58485a16103294e9bd10a96d436662f63a42c935d2c4d03c7c96fe8023dd Homepage: https://cran.r-project.org/package=pattern.checks Description: CRAN Package 'pattern.checks' (Identifies Patterned Responses in Scales) Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c). Package: r-cran-patternator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-plyr Filename: pool/dists/noble/main/r-cran-patternator_0.1.0-1.ca2404.1_all.deb Size: 29808 MD5sum: f821dc392368f79682b517868cb48e49 SHA1: fe31058afa8f154b33560feb1897379737956eff SHA256: eb4ab49e5a865c52a2b26c4c2d27a7eebecf230a0fa60ac3913d219ecee6cc6b SHA512: e72e421081e6773fdd6af57f3192038583d456b791e43601c0197b18f7b10c06709d292e4a4bdb8415d4f922de4d5a77c84a5b2c8a262992fc17819e34c56e8a Homepage: https://cran.r-project.org/package=patternator Description: CRAN Package 'patternator' (Feature Extraction from Female Brown Anole Lizard DorsalPatterns) Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width. Package: r-cran-patterncausality Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-ggplot2, r-cran-reshape2, r-cran-ggthemes, r-cran-tidyr, r-cran-statebins, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-scales, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-lintr, r-cran-zoo Filename: pool/dists/noble/main/r-cran-patterncausality_0.2.4-1.ca2404.1_all.deb Size: 2955538 MD5sum: 6ab89e7d90f6381bb52f9c292e6d7b64 SHA1: c45d3bd8ce1784037b405488fd68f99a110850c3 SHA256: 4cc4b58cbf3272a6cab74cf17b524c8c70ee6a2af6348958da071209cf78d68d SHA512: e12769c4f68522eb865551e16dd9cd2fc5386244b77f4e2b11227fe2d915dd1167affadf8eb422a77d1c467b84530ff59585018fa7a058d3d627152f7bfa0a39 Homepage: https://cran.r-project.org/package=patterncausality Description: CRAN Package 'patterncausality' (Pattern Causality Algorithm) A comprehensive package for detecting and analyzing causal relationships in complex systems using pattern-based approaches. Key features include state space reconstruction, pattern identification, and causality strength evaluation. Package: r-cran-patternize Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-abind, r-cran-morpho, r-cran-dplyr, r-cran-imager, r-cran-magrittr, r-cran-purrr, r-cran-vegan, r-cran-rniftyreg, r-cran-geomorph, r-cran-clusterr Filename: pool/dists/noble/main/r-cran-patternize_0.0.5-1.ca2404.1_all.deb Size: 834936 MD5sum: 244809e380eb9c8140fb4e90dbecda86 SHA1: 81aa8e4a12b2f7b8a30dc3ffab196af3d93cfe12 SHA256: 90b996de21c7337d3c541c04538850716de85c3b5bdac5723787df53948ea502 SHA512: 548d7cd7ddf80e6df48b19f154cc031bc3e5f10e62e74f50cf7351b455fba7e953e86e34ca0ec3bbe361c714701cd4a69cfebbc084eb3f874159b2f00aa68880 Homepage: https://cran.r-project.org/package=patternize Description: CRAN Package 'patternize' (Quantification of Color Pattern Variation) Quantification of variation in organismal color patterns as obtained from image data. Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using RGB thresholds or image segmentation. Package: r-cran-patterns Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-cluster, r-cran-e1071, r-cran-gplots, r-cran-igraph, r-cran-lars, r-cran-lattice, r-bioc-limma, r-bioc-mfuzz, r-cran-movmf, r-cran-nnls, r-cran-plotrix, r-cran-selectboost, r-cran-tnet, r-cran-vgam, r-cran-wgcna Suggests: r-cran-animation, r-bioc-biobase, r-bioc-biomart, r-cran-c060, r-cran-cascadedata, r-cran-elasticnet, r-cran-glmnet, r-cran-knitr, r-cran-pixmap, r-cran-r.rsp, r-cran-rmarkdown, r-cran-spls, r-cran-testthat Filename: pool/dists/noble/main/r-cran-patterns_1.7-1.ca2404.1_all.deb Size: 1082574 MD5sum: 73808245f528acad2578572c2b1415ef SHA1: 5538f7af11154f4cf26e08035c86499e4448a238 SHA256: c854176706ffc9ec633d4c988844af1019fa62569107cbfa7c6f1416e1dbb67c SHA512: 954668f7794af7df4e1cc4ed23f85010091d0790713a4c9ecdbaa3f57d242cecb3b783bc3541a89a2c99f974ff0596f961eb1e52f9e4af1d8d3b1db4b6e09dba Homepage: https://cran.r-project.org/package=Patterns Description: CRAN Package 'Patterns' (Deciphering Biological Networks with Patterned HeterogeneousMeasurements) A modeling tool dedicated to biological network modeling (Bertrand and others 2020, ). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, ). Example of use with microarray or RNA-Seq data are provided. 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Package: r-cran-pbd Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-ade4, r-cran-ape, r-cran-ddd, r-cran-phytools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pbd_1.4-1.ca2404.1_all.deb Size: 161970 MD5sum: 6e41bf0710328384beb0206263dfc3a4 SHA1: c73c39320cb2fd1418160d214a69367473df1c56 SHA256: c7125973d1302b943a25c17af0b2b3473915ea1a861f7e9222e38778248c33db SHA512: 44b5b8e7a0fa5ed37fbabf5efedbdc9826614b5b333a3b17c8d861367c05b9f0afe23b50b6edc7a90027b05f27f94830f86e0d2eec40b6af910745c5e1669697 Homepage: https://cran.r-project.org/package=PBD Description: CRAN Package 'PBD' (Protracted Birth-Death Model of Diversification) Conducts maximum likelihood analysis and simulation of the protracted birth-death model of diversification. See Etienne, R.S. & J. Rosindell 2012 ; Lambert, A., H. Morlon & R.S. Etienne 2014, ; Etienne, R.S., H. Morlon & A. Lambert 2014, . Package: r-cran-pbgof Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sn Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pbgof_0.1.0-1.ca2404.1_all.deb Size: 8548946 MD5sum: aa88a616ce60454ec86948d97f2663c3 SHA1: 7cc569200187458094d1f0cbbc7f5937136798a6 SHA256: 75577a335172d9e8639137c1e8f7bdc5b128f270199f372c753d7674bcb6c7f5 SHA512: 0a3e8fa9fd1a29a184842fe85187e1c019c0aeb68bfe069f22f11306a0b83de6a7bf7a2241e1889f28de08a62c32eda8bbd90507ec144eaf70918d08ede44aa5 Homepage: https://cran.r-project.org/package=PBGoF Description: CRAN Package 'PBGoF' (Parametric Bootstrap Tests for the Skew-Normal Distribution) Provides goodness-of-fit tests for the skew-normal distribution with estimated parameters. 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Package: r-cran-pbibd Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pbibd_1.4-1.ca2404.1_all.deb Size: 75648 MD5sum: b3a80bfae9e90c2e84e6e5e0d9ba5f1f SHA1: deeef14ed09eb887e4820ef3d0d4ff25f5aa9fda SHA256: 29641f49eea4e79d35df94a3d6a2e53a98f747d43c6210e77c88d62496884994 SHA512: aff07ce6bb55f5c8f027a8b98d60578f0468ce934ea4adcc7056c979a1908bc6a9dfc10ad89f8d58560cc6539f52f4d48cf008430404ff0c8d2e79be301e8379 Homepage: https://cran.r-project.org/package=PBIBD Description: CRAN Package 'PBIBD' (Partially Balanced Incomplete Block Designs) The PBIB designs are important type of incomplete block designs having wide area of their applications for example in agricultural experiments, in plant breeding, in sample surveys etc. This package constructs various series of PBIB designs and assists in checking all the necessary conditions of PBIB designs and the association scheme on which these designs are based on. It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) . Package: r-cran-pbimisc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1784 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrix Suggests: r-cran-ggplot2, r-cran-ca, r-cran-lattice Filename: pool/dists/noble/main/r-cran-pbimisc_1.0-1.ca2404.1_all.deb Size: 1777886 MD5sum: 937558d688b6475229d8df1832df5d23 SHA1: 63870c010098725b6b59f80be40493d4d4ec3585 SHA256: 0562b05f0b751616b13589422b6d917d6da1fd6e7347447302b1e1dca6a86695 SHA512: d6e0fa0649e4c823199c61462d10d83c3872db7ab2f4840153f1de56409d1a9ab07555f0fc8556777947dcb0d03bd5464e08179a8fd4c7d6d8ddaf3c264707be Homepage: https://cran.r-project.org/package=PBImisc Description: CRAN Package 'PBImisc' (A Set of Datasets Used in My Classes or in the Book 'ModeleLiniowe i Mieszane w R, Wraz z Przykladami w Analizie Danych') A set of datasets and functions used in the book 'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'. 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Implements parametric bootstrap test for generalized linear mixed models as implemented in 'lme4' and generalized linear models. The package is documented in the paper by Halekoh and Højsgaard, (2012, ). Please see 'citation("pbkrtest")' for citation details. 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Package: r-cran-pbm Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-pbm_1.2.1-1.ca2404.1_all.deb Size: 149182 MD5sum: 5f3be7dd80b7135b0c065b3273aef676 SHA1: 509a98c30ae447a2e2a6dba8f725fcebdecd2b42 SHA256: f23c40673efb018d328f852473a0226d24df9320b4bbdbfe162f8689f69303ed SHA512: b2c1d55d2c67393e7d4b53fcfc1fba74470a657e4afd9efe2029a0393578e766426973e05910a258138d43d715833b808bd25a83921e498e75e0593f7543c442 Homepage: https://cran.r-project.org/package=pbm Description: CRAN Package 'pbm' (Protein Binding Models) Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) . Hoang H. Nguyen et al. (2015) . After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression. Package: r-cran-pbnpa Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metarnaseq Filename: pool/dists/noble/main/r-cran-pbnpa_0.0.3-1.ca2404.1_all.deb Size: 63930 MD5sum: 8bf50a42d5a148f49ef943a6c2bc2e28 SHA1: 71a020c4c29424eea9e454ef58a6cc011d89c7b9 SHA256: ede1958bb57662a60b4f859217739dc7fb68f66117f36d3ca8ed0e7474847a5c SHA512: b9d6cbb1d12c828e5fda66d14a82f4264fba926572c9b1283805ef8d2f544508bead2f7deb70bebb06a9e4598bcb7cfb1012a37755ea139a6011580083307d4e Homepage: https://cran.r-project.org/package=PBNPA Description: CRAN Package 'PBNPA' (Permutation Based Non-Parametric Analysis of CRISPR Screen Data) Permutation based non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) : A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper. 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Package: r-cran-pboost Architecture: all Version: 0.4.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-betareg, r-cran-quantreg, r-cran-survival, r-cran-formula, r-cran-glmnet, r-cran-ncvreg, r-cran-rqpen, r-cran-spatialreg Filename: pool/dists/noble/main/r-cran-pboost_0.4.11-1.ca2404.1_all.deb Size: 165690 MD5sum: b59a4dedcb66f1209947854ec32b6e53 SHA1: cec6b67d6450885e3354c6bed3b7f727b1e39747 SHA256: bfcec97c4cde678886b1ca5e72cab97a4b308dfe23761a4c7bd33c78950ff1be SHA512: bc805a7b801ad18d274739784aed4672449e419bf77283758e75db257511f0c69ddb7ebcdb39be37326b9adc9e7da463398e096680c558f2c5a0db10834d7044 Homepage: https://cran.r-project.org/package=pboost Description: CRAN Package 'pboost' (Profile Boosting Framework for Parametric Models) A profile boosting framework for feature selection in parametric models. It offers a unified interface pboost() and several wrapped models, including linear model, generalized linear models, quantile regression, Cox proportional hazards model, beta regression, spatial auto-regressive models. Package: r-cran-pbox Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss.dist, r-cran-copula, r-cran-data.table, r-cran-gamlss, r-cran-purrr, r-cran-stringr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pbox_0.1.8-1.ca2404.1_all.deb Size: 404696 MD5sum: 547811d9d59d43c13bcdfefd30468f9e SHA1: c86bb187a01e0b63877ed3f6506826642190a395 SHA256: 32d6cb9546b03b323aa6437989a14da57c32755a7cbcf24962a10e02028faf91 SHA512: 3a6780065e7a39767a02f5016d70d358dedd89c152b033d948ee8159ad2bb366bc44c04567e8af0aba856e5d8aee47e3235ae8d57399309e363bab4804bfaf07 Homepage: https://cran.r-project.org/package=pbox Description: CRAN Package 'pbox' (Exploring Multivariate Spaces with Probability Boxes) Advanced statistical library offering a method to encapsulate and query the probability space of a dataset effortlessly using Probability Boxes (p-boxes). Its distinctive feature lies in the ease with which users can navigate and analyze marginal, joint, and conditional probabilities while taking into account the underlying correlation structure inherent in the data using copula theory and models. A comprehensive explanation is available in the paper "pbox: Exploring Multivariate Spaces with Probability Boxes" to be published in the Journal of Statistical Software. 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Package: r-cran-pbrackets Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pbrackets_1.0.1-1.ca2404.1_all.deb Size: 75714 MD5sum: b27c16945d6b689a38b119f4c5fd04ba SHA1: da0134295c0b25eb633e724b09a6c65c42e22809 SHA256: 25c3f5385aa4cf1ec2fbd37f754890ca208996f0c7ed8abc9c58075bf10679a2 SHA512: d99c10238ccfe3a84939bdc3a8dfc1343f9c0299d6455a3305ff2858122a5b9b6f5a757d6fe48becc3d303c8b65d45601c96186f567bc6befbdf62abb3679b71 Homepage: https://cran.r-project.org/package=pBrackets Description: CRAN Package 'pBrackets' (Plot Brackets) Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets. Package: r-cran-pbs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pbs_1.1-1.ca2404.1_all.deb Size: 19696 MD5sum: c8f1e14b95724e046c8499cc9f45ebff SHA1: c30ec0c24a489f81689561df118b832c29611f75 SHA256: 1780db0cbb5d182d87b32c386b749b5ab4172a3c5f7e01331c5cc177192ff226 SHA512: e741865c13b10619533e8c57b73d0f58ef79f6b991d0ee54fbe0dd0da66b7f3e073101dbe1ac19a5a74f904faa33db717cc43da96532e2a575b78ad7fc026067 Homepage: https://cran.r-project.org/package=pbs Description: CRAN Package 'pbs' (Periodic B Splines) Periodic B Splines Basis Package: r-cran-pbsadmb Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3091 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbsmodelling Filename: pool/dists/noble/main/r-cran-pbsadmb_1.1.6-1.ca2404.1_all.deb Size: 2836202 MD5sum: 378fc5f23052d58309a1ed86aa8baf28 SHA1: 62484f0697db4622673a5bc23bca9695195b2817 SHA256: 8a2e293dfc934ab97bc5581c64ce2b98be69c0cf2b0a3f80c164a5e058dca487 SHA512: e2a2f713d9b0c1c257b44462747d6b66117de257604166dbb2c16df58fb1f5fa57edeedf873efff6c0a38f8f7ad7ebae8df264897844173d3d1e440de0fe623f Homepage: https://cran.r-project.org/package=PBSadmb Description: CRAN Package 'PBSadmb' (ADMB for R Using Scripts or GUI) A collection of software provides R support for 'ADMB' (Automatic Differentiation Model Builder) and a 'GUI' interface facilitates the conversion of 'ADMB' template code to 'C code' followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a 'GUI'. Package: r-cran-pbtdesigns Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-pbtdesigns_1.0.0-1.ca2404.1_all.deb Size: 26478 MD5sum: 9a450dab7b586849c6de6a611354b6e1 SHA1: fdbb261591b89e873b0c726769b8ff9f3868e04b SHA256: c9c76e71704d1cbcaf4c490c15b0e3b2bc9ea21349e10a21bf385b836b648377 SHA512: d61d670c21b04a710e3bb5e78fdbb42e541e1f18e38ed8f22d9efc46a04e9ff18f4fcb6a51549bf7f9efb9b2fb2c368b01ec85823fc7ab09d06944527782f5a7 Homepage: https://cran.r-project.org/package=PBtDesigns Description: CRAN Package 'PBtDesigns' (Partially Balanced t-Designs (PBtDesigns)) The t-designs represent a generalized class of balanced incomplete block designs in which the number of blocks in which any t-tuple of treatments (t >= 2) occur together is a constant. When the focus of an experiment lies in grading and selecting treatment subgroups, t-designs would be preferred over the conventional ones, as they have the additional advantage of t-tuple balance. t-designs can be advantageously used in identifying the best crop-livestock combination for a particular location in Integrated Farming Systems that will help in generating maximum profit. But as the number of components increases, the number of possible t-component combinations will also increase. Most often, combinations derived from specific components are only practically feasible, for example, in a specific locality, farmers may not be interested in keeping a pig or goat and hence combinations involving these may not be of any use in that locality. In such situations partially balanced t-designs with few selected combinations appearing in a constant number of blocks (while others not at all appearing) may be useful (Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2021)). Further, every location may not have the resources to form equally sized homogeneous blocks. Partially balanced t-designs with unequal block sizes (Damaraju Raghavarao & Bei Zhou (1998). Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2022)." Partially Balanced t-designs with unequal block sizes") prove to be more suitable for such situations.This package generates three series of partially balanced t-designs namely Series 1, Series 2 and Series 3. Series 1 and Series 2 are designs having equal block sizes and with treatment structures 4(t + 1) and a prime number, respectively. Series 3 consists of designs with unequal block sizes and with treatment structure n(n-1)/2. This package is based on the function named PBtD() for generating partially balanced t-designs along with their parameters, information matrices, average variance factors and canonical efficiency factors. Package: r-cran-pcadsc Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-pander, r-cran-ggplot2, r-cran-matrix Filename: pool/dists/noble/main/r-cran-pcadsc_0.8.0-1.ca2404.1_all.deb Size: 59946 MD5sum: 834bb18cac6ab2ed424eb652d7d3c901 SHA1: 0b5bbd6a117a537ba0389a45496cd1030756c9f5 SHA256: a88b2d5b38c28d517d8e691532a8b0cf167390d1dbf6bf15a43d3f326f0e6682 SHA512: 623a2132fa3de74d603877d47f7720abcfcd34ff665497d8ad2fdc10fb9a2da5f450d3d2ea5697a2c331803a79eec8c65cc724d478cc28cb3d152e8b4d5bb159 Homepage: https://cran.r-project.org/package=PCADSC Description: CRAN Package 'PCADSC' (Tools for Principal Component Analysis-Based Data StructureComparisons) A suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset. Package: r-cran-pcal Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcal_1.0.0-1.ca2404.1_all.deb Size: 65118 MD5sum: 080af9d23623a50e62cc2bbba7a3e534 SHA1: 8d6995d69811b3255c2e3d05e057fe5831a5ab85 SHA256: ff03d62ae4feaa018300a456974ebe76833d2938db60dee76beacb91591dd4a5 SHA512: 1eaf6bc08181aacacb2d4bc303e6b52d331f07bce7d0e002f69465d616a1a044c963d074030f0901ba54924d3882718c33629c968d30551c6a58feeaf8605063 Homepage: https://cran.r-project.org/package=pcal Description: CRAN Package 'pcal' (Calibration of P-Values for Point Null Hypothesis Testing) Calibrate p-values under a robust perspective using the methods developed by Sellke, Bayarri, and Berger (2001) and obtain measures of the evidence provided by the data in favor of point null hypotheses which are safer and more straightforward to interpret. Package: r-cran-pcalibrate Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-exact2x2, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-pcalibrate_0.2-2-1.ca2404.1_all.deb Size: 95358 MD5sum: b1d085a686bb7c813f9b7d418c152eb5 SHA1: eedc7447a1c88314acc23e5a32ad3435bd5bc246 SHA256: c3f0075d2b83fa17c999f5410487c5a44204e068fc243ac889db8cc340d89ad9 SHA512: 09cc0267ed2009db03a772abfa95eef2011c2253d67011608740c22a5471b8fabb96b1c2f72bc9a83b92cd7c05794487e16fd78bb5412f19b35ecea111c96cc2 Homepage: https://cran.r-project.org/package=pCalibrate Description: CRAN Package 'pCalibrate' (Bayesian Calibrations of p-Values) Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, ). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test. Package: r-cran-pcalls Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcalls_1.0-1.ca2404.1_all.deb Size: 25638 MD5sum: 124ca2cb70bde48348115308528d88f8 SHA1: 18cc8290400de53d43de80afbc1b25df93de696f SHA256: 1a80055202e8086db01c5c0a927991f3eea4c64a80c77ec935cd6a9f2c414c2d SHA512: c0953129330ed8d4a32c22afa36d6b0bf02ee363dc66c330ed4c40d4b59d5ecb2d2d89734def720241185f860c7be5780fe7fe11427f607ca309db355fa1402c Homepage: https://cran.r-project.org/package=pcalls Description: CRAN Package 'pcalls' (Pricing of Different Types of Call) Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. Available methods are Montecarlo Simulation, Montecarlo Simulation with Antithetic Variates, Black-Scholes and the Binary Tree. Package: r-cran-pcamatchr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-optmatch, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pcamatchr_0.3.3-1.ca2404.1_all.deb Size: 487436 MD5sum: ebe17eb383217055dd1615a4639b2d90 SHA1: 8635d67299e67e5155f3c25fcd72eb47c49dc232 SHA256: c5dbd177dea9384ba607defc71a3f7fe91a818882335da3835bf3730e44d59bc SHA512: 2bc31d55022c43fad1d3f81deffeed143d8411b3603840d5c1971136b46c06e076a485d02e38bc53fc2a21ba6cd9d378188d3fb13ed0e9778ff58fc940f09a7b Homepage: https://cran.r-project.org/package=PCAmatchR Description: CRAN Package 'PCAmatchR' (Match Cases to Controls Based on Genotype Principal Components) Matches cases to controls based on genotype principal components (PC). In order to produce better results, matches are based on the weighted distance of PCs where the weights are equal to the % variance explained by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987) ) is used to determine matches. Package: r-cran-pcamixdata Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2585 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcamixdata_3.1-1.ca2404.1_all.deb Size: 1824902 MD5sum: d17ec8bb2cee58b7773030210ccf9a10 SHA1: 527650769c5446e6ccda2b1e69924cb89dbdee84 SHA256: 68833c7f558a88a8441a224d10662ea7b7edd74859d5cfa0be570310178b5786 SHA512: 690eaa3c07b1816d18dd6f37c6223b0ec44d61c50027afda49550813aafd0c1cc08cf3c25c34f53c800afc1b7078187bbc4c76679f1efed381cf5c12de5cffdc Homepage: https://cran.r-project.org/package=PCAmixdata Description: CRAN Package 'PCAmixdata' (Multivariate Analysis of Mixed Data) Implements principal component analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables. Package: r-cran-pcapam50 Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-lattice, r-bioc-complexheatmap, r-bioc-impute Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcapam50_1.0.4-1.ca2404.1_all.deb Size: 892690 MD5sum: 17997877174916f9c14117a8bcc8d7ca SHA1: 80174e4f21123a73724508376958bb13f93399b6 SHA256: 9735fb1ac4d0dbf97aa2d99ed3b009c4aa6e929446bfd18d52e9243a95412651 SHA512: 8b548f3a9d152adcb81f16e062ed981a111cd4ce567a5f893665555eb4edd16022f07588a5a960a471f21b211ae4600000d416fb2344635641b25b7316c5c0cf Homepage: https://cran.r-project.org/package=PCAPAM50 Description: CRAN Package 'PCAPAM50' (Enhanced 'PAM50' Subtyping of Breast Cancer) Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping. The PCA-PAM50 method is described in Raj-Kumar et al. (2019) and the package implementation is described in Raj-Kumar et al. (2026) . 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The package preserves the instrument's two-part response structure, supports original and updated Consolidated Framework for Implementation Research (CFIR) mappings, describes team agreement and disagreement, compares repeated assessments, and creates implementation action-planning outputs. It does not calculate or claim a validated total pCAT scale score. The instrument is described by Robinson and Damschroder (2023) ; updated CFIR mappings are from Domlyn et al. (2026) . 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Package: r-cran-pcbn Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-igraph, r-cran-r2r, r-cran-vinecopula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-data.tree, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-pcbn_0.1.1-1.ca2404.1_all.deb Size: 170620 MD5sum: a062b4165ab9aacd9c70a76d8b88169b SHA1: 63a0b6a3cbffcd0fee8ed5b122bc809f980220bb SHA256: 5bbe5a0c1576e4e4dbf14cffa17bdb902595cee5c5cd41ed55c010dcdd52d65a SHA512: 4706868f57b259374e34d767fe0e3c233bf0b1b95b33f2cdb724d1f0e7ff7ccdd54c9935c0d75b42b0319d0184b4f408a989ba93d4f03501f6a56b0c6ee7791d Homepage: https://cran.r-project.org/package=PCBN Description: CRAN Package 'PCBN' (Inference of Pair-Copula Bayesian Networks) Creates, fits and samples Pair-Copula Bayesian networks (PCBN) under some restrictions on the underlying Directed Acyclic Graph (DAG), that is, no active cycles nor interfering v-structures, following Derumigny, Horsman and Kurowicka (2025) . 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Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: . 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The package also has tools for generating points from these spatial patterns. The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011) ) and arc density (Ceyhan et al. (2006) ; Ceyhan et al. (2007) ). The PCD families considered are Arc-Slice PCDs, Proportional-Edge PCDs, and Central Similarity PCDs. 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This package is developed for estimating and testing partial correlation graphs with prior information incorporated. Package: r-cran-pcgse Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmtstat, r-cran-mass Filename: pool/dists/noble/main/r-cran-pcgse_0.5.0-1.ca2404.1_all.deb Size: 43236 MD5sum: 421c403e6a78ea9c3d893310d32f1930 SHA1: 942d271678b514e92f9db4ba8fe881391596f036 SHA256: a6aba9564c0c4ced9f8cc8a9c900d4bddbdee3863515e410318e1f12d9d05e1e SHA512: c5117585bedb637724d98d2beee61b8b2d3ca6b9e0fe0854e8458348268cb5faf88d0d8370bffae8f0f98fbe472fc4edfb3a648a21851da5dcee29ab0fc50e41 Homepage: https://cran.r-project.org/package=PCGSE Description: CRAN Package 'PCGSE' (Principal Component Gene Set Enrichment) Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data. 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PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. The relevant papers are: a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. . b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. . c) Sevinc V. and Tsagris M. (2024). "On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms". . 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The package is unique in providing several real-world data sets that may be used for problem assignments and student projects. The data sets include cross-sections of stock data from the Center for Research on Security Prices, LLC (CRSP), corresponding factor exposures data from S&P Global, and several SP500 data sets. Package: r-cran-pcredux Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2651 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-chippcr, r-cran-ecp, r-cran-fda.usc, r-cran-mbmca, r-cran-pbapply, r-cran-pracma, r-cran-qpcr, r-cran-robustbase, r-cran-segmented, r-cran-shiny, r-cran-zoo Suggests: r-cran-dt, r-cran-future, r-cran-knitr, r-cran-listenv, r-cran-rdml, r-cran-readxl, r-cran-rmarkdown, r-cran-shinycssloaders, r-cran-spelling, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-pcredux_1.2-1-1.ca2404.1_all.deb Size: 1829662 MD5sum: fe2013e03f6be695d54ef9227d841569 SHA1: 194d506e3034c6248e50e5a925624f54edb3ec78 SHA256: f6ee6c72a090d056f4bbc638623ab506aa082cf3cf18f016519ae02cc90b4516 SHA512: 7a0847971fc0f7c769c16bbe2b54cc182b09b177b1fb183be66bce6e140ffd330d7a6e8aca47ea910a45e50c5b1f975257c2c56f76b8380066fccf4d304dbdce Homepage: https://cran.r-project.org/package=PCRedux Description: CRAN Package 'PCRedux' (Quantitative Polymerase Chain Reaction (qPCR) Data Mining andMachine Learning Toolkit as Described in Burdukiewicz (2022)) Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve. Package: r-cran-pcreg Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-scales, r-cran-psych, r-cran-elasticnet, r-cran-robustbase Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pcreg_0.1.1-1.ca2404.1_all.deb Size: 99338 MD5sum: 9fd5b88b32e1d5bf801ffab718e36ed9 SHA1: 09fd38d310040e9672530c8fe17ac0c499d6bc05 SHA256: 073b0c3bf25f60bc046b192ea8d289ef00df0bcaaae54bc2876960a4f730f941 SHA512: 29d8f531c94db971f13110daf3b1b7c9b3a59d78f26c9a977c3d929e1279042731ab638fb8d2f39e7642760813341b1051434ef5d363eb35e0968a677c8cb9e0 Homepage: https://cran.r-project.org/package=pcreg Description: CRAN Package 'pcreg' (Advanced Methods for Principal Component Analysis and PrincipalComponent Regression) Provides a unified framework for principal component analysis (PCA) and principal component regression (PCR), including standard PCA, sparse PCA, robust PCA, and supervised PCA. The package supports automatic selection of the number of components using cumulative variance and elbow methods and integrates PCA with regression modelling through PCR models. It includes tools for PCA suitability assessment using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. Visualisation utilities such as scree plots and biplots are provided for interpretation. The methods are designed to handle multicollinearity, outliers, and high-dimensional data, making them suitable for applied statistical modelling and data analysis. The methodology is based on established approaches described in Jolliffe (2002) , Zou et al. (2006) , and Hubert et al. (2005) . Package: r-cran-pcs Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-bioc-multtest Filename: pool/dists/noble/main/r-cran-pcs_1.3-1.ca2404.1_all.deb Size: 81548 MD5sum: f7489eee6926f3cf3ccd1b6011a20bc1 SHA1: d47c6cb9dc681127bdd910da95ca541f11e2da91 SHA256: ea9afbd08854201733eb90b67dd27b99250d5fedfcb97f9a5e8a1f4b009f430b SHA512: aa5c32202b70cafc4bb0cb2119ac89cacedb6d89a1b9ac1f5e400a214a77a24be89f86d7c71203700437df932ad2664c286224ca9a4708508166acb471d577d0 Homepage: https://cran.r-project.org/package=PCS Description: CRAN Package 'PCS' (Calculate the Probability of Correct Selection (PCS)) Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations? This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition. Package: r-cran-pcse Architecture: all Version: 1.9.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcse_1.9.1.1-1.ca2404.1_all.deb Size: 388032 MD5sum: b25ece9febe003bf2298178cd31595c9 SHA1: 0ab94074b7d67e74b2d4c5ce92cf394e12f0062f SHA256: 6ed481ac711c8792eabe57f1bab405113849b6e2d7dfaccb9d118df3e431b70a SHA512: 61b1855b4b9020735e561d969ded406d5d9caf209355eaf8c33229faee86470395203800d6beba67fb556b246d3ed6c4354604fb670895ca706a5b2af88da346 Homepage: https://cran.r-project.org/package=pcse Description: CRAN Package 'pcse' (Panel-Corrected Standard Error Estimation in R) A function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels. Package: r-cran-pcsinr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pcsinr_0.2.0-1.ca2404.1_all.deb Size: 34540 MD5sum: dcebb5b62124cfc95ab6cbd12bdaab44 SHA1: 2a511631d0468c2839eb35bba2a94f54b3a4a7ce SHA256: 1aa234a265a81a1a4381be15858b3f3107b920c4aa65aadbcbfe01564dbb4288 SHA512: e90218bdac0eaf8a04f6def19b477ae395817e500c59629fa82524c28a3c681731468875631636ff539db9c172b02f36e323955e0909325ba77beb41f566a059 Homepage: https://cran.r-project.org/package=PCSinR Description: CRAN Package 'PCSinR' (Parallel Constraint Satisfaction Networks in R) Parallel Constraint Satisfaction (PCS) models are an increasingly common class of models in Psychology, with applications to reading and word recognition (McClelland & Rumelhart, 1981; \doi{10.1037/0033-295X.88.5.375}), judgment and decision making (Glöckner & Betsch, 2008 \doi{10.1017/S1930297500002424}; Glöckner, Hilbig, & Jekel, 2014 \doi{10.1016/j.cognition.2014.08.017}), and several other fields. In each of these fields, they provide a quantitative model of psychological phenomena, with precise predictions regarding choice probabilities, decision times, and often the degree of confidence. This package provides the necessary functions to create and simulate basic Parallel Constraint Satisfaction networks within R. Package: r-cran-pcsstools Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pcsstools_0.1.2-1.ca2404.1_all.deb Size: 904196 MD5sum: 2293a6c624503b2e0fb9856d2fa8949b SHA1: 9a6bce2dbcf5f1306fa3fd2f7912cadecd93fd64 SHA256: 2e741eadfd1f1103c076d23aafecb5392921778860168a302169b17777d77799 SHA512: 7076d0ddd7786f00f79aa2df237138db982d16573aef93e38e088adcb4a08ab1e50c4fb45b387c9e7b24a577d753ce1ec8444b58540fb52d223c3bf3d31446f3 Homepage: https://cran.r-project.org/package=pcsstools Description: CRAN Package 'pcsstools' (Tools for Regression Using Pre-Computed Summary Statistics) Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) Wolf et al. (2020) and Gasdaska et al. (2019) . Package: r-cran-pcsteiner Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcsteiner_1.0.0.1-1.ca2404.1_all.deb Size: 222096 MD5sum: f4440641b00ad8feceef8d79e381b60c SHA1: 980f0c2c9c7bb57ce9fb0041ac466dee652b03e7 SHA256: 998bf4e620097c566d5e94e35142c042871ceb1a1d161530faff16a9c0e275e1 SHA512: 03875b3853d6bc5d11b482208e1749017da40f68ecd7985b1854bde48d3b4c75c7b9c5bf8e9c50ddd287b015fc1153bb02b08805e2a155273040258b233d9b25 Homepage: https://cran.r-project.org/package=pcSteiner Description: CRAN Package 'pcSteiner' (Convenient Tool for Solving the Prize-Collecting Steiner TreeProblem) The Prize-Collecting Steiner Tree problem asks to find a subgraph connecting a given set of vertices with the most expensive nodes and least expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm does not exist. This package provides convenient functionality for obtaining an approximate solution to this problem using loopy belief propagation algorithm. Package: r-cran-pct Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-stplanr, r-cran-readr, r-cran-sf, r-cran-crul Suggests: r-cran-covr, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-leaflet, r-cran-pbapply, r-cran-remotes, r-cran-rmarkdown, r-cran-tmap, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-pct_0.10.0-1.ca2404.1_all.deb Size: 1083750 MD5sum: f0b737b58fb227dc631d3ac43dc64435 SHA1: f0224d86f7689f9973cf7b1802554253da9ea968 SHA256: e490964cc2ad17027aeb2a5f26b77eb1f8b22b101da7d432f8401b7ea3c19ffb SHA512: 800816827b094624075c9441e16c7f4fa84347194f214398df3444e9b0618578c71af58bbde05ab814203ceeaf51dd62cf63b18ebb03fb5e379c2b899ccbf7a6 Homepage: https://cran.r-project.org/package=pct Description: CRAN Package 'pct' (Propensity to Cycle Tool) Functions and example data to teach and increase the reproducibility of the methods and code underlying the Propensity to Cycle Tool (PCT), a research project and web application hosted at . For an academic paper on the methods, see Lovelace et al (2017) . Package: r-cran-pctax Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcutils, r-cran-dplyr, r-cran-readr, r-cran-ggplot2, r-cran-vegan, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-ggrepel, r-cran-reshape2, r-cran-tibble, r-cran-ggpubr, r-cran-patchwork, r-cran-ggnewscale, r-cran-ade4, r-cran-scales Suggests: r-cran-picante, r-cran-httr, r-cran-nst, r-cran-permute, r-cran-aplot, r-cran-ggfun, r-cran-pheatmap, r-cran-mass, r-cran-rtsne, r-bioc-mixomics, r-cran-geosphere, r-bioc-phyloseq, r-cran-phyloseqgraphtest, r-cran-plotly, r-cran-umap, r-cran-hmisc, r-cran-minpack.lm, r-cran-bbmle, r-cran-snow, r-cran-foreach, r-cran-dosnow, r-cran-tidytree, r-bioc-ggtree, r-bioc-ggtreeextra, r-cran-vctrs, r-cran-zoo, r-cran-ape, r-bioc-deseq2, r-bioc-limma, r-bioc-aldex2, r-bioc-mfuzz, r-bioc-edger, r-cran-randomforest, r-cran-knitr, r-cran-rmarkdown, r-cran-metanet, r-cran-showtext, r-cran-jsonlite, r-cran-prettydoc, r-cran-readxl, r-cran-stringr, r-cran-ggextra, r-cran-clipr, r-cran-zetadiv, r-cran-ggforce, r-cran-gggenes, r-cran-mediation Filename: pool/dists/noble/main/r-cran-pctax_0.1.7-1.ca2404.1_all.deb Size: 1614664 MD5sum: e37bff98198f4c51eec7089c308eb390 SHA1: dd4df5bbe91dbf9b2ce5e63e4dbe9dcf5ea6dd77 SHA256: 613925f5dd0d18372cd88eba9446643af07c739514f2409bad1498ad31f7c546 SHA512: df0f674f3e8a4f91f5b9912c755f830730d3d0411f0b1ec5a63c499335f1e748d243735d399dca925c162ced12820cc58b3f35b0023507c240d5000f92e7a44d Homepage: https://cran.r-project.org/package=pctax Description: CRAN Package 'pctax' (Professional Comprehensive Omics Data Analysis) Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis. Package: r-cran-pcts Architecture: all Version: 0.15.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sarima, r-cran-matrix, r-cran-bb, r-cran-polynomf, r-cran-gbutils, r-cran-zoo, r-cran-xts, r-cran-lagged, r-cran-mcompanion, r-cran-rdpack, r-cran-lubridate Suggests: r-cran-testthat, r-cran-funitroots, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcts_0.15.8-1.ca2404.1_all.deb Size: 1794334 MD5sum: 4583fce3de42c88a23d683d807377063 SHA1: efe9122c5682be8c3059fd781d4307d9a3d6f00a SHA256: a046e72e29af507a15ff17bf80fcb72582ce6ae8ae6f1aec5d95adbe9ab3a72e SHA512: 82daf0f85187ef2fcdd303e02bddb4e8fc53da851b6ec0b878cace273fb3aa7bc0ed2582a7928ab32cfa31c6912010902cdecd39ee8fa92f2fb9d3c65c0a61d0 Homepage: https://cran.r-project.org/package=pcts Description: CRAN Package 'pcts' (Periodically Correlated and Periodically Integrated Time Series) Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) , Boshnakov (1996) . Package: r-cran-pcutils Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-tidyr, r-cran-tibble, r-cran-rcolorbrewer Suggests: r-cran-agricolae, r-cran-clipr, r-cran-rlang, r-cran-biocmanager, r-cran-ggpubr, r-cran-kableextra, r-cran-htmlwidgets, r-cran-pagedown, r-cran-ggsci, r-cran-readr, r-cran-grimport2, r-cran-rsvg, r-cran-pmcmrplus, r-cran-nortest, r-cran-fitdistrplus, r-cran-ggalluvial, r-cran-gghalves, r-cran-ggspatial, r-cran-sf, r-cran-magick, r-cran-ggimage, r-cran-ggpmisc, r-cran-upsetr, r-cran-eulerr, r-cran-plotrix, r-cran-vegan, r-cran-circlize, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-htmltools, r-cran-leaflet, r-cran-relaimpo, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-stringr, r-cran-ggraph, r-cran-ggrepel, r-cran-treemap, r-cran-voronoitreemap, r-cran-devtools, r-cran-multcompview, r-cran-rio, r-cran-bookdown, r-cran-sysfonts, r-cran-showtext, r-cran-jsonlite, r-cran-httr, r-cran-r.proxy, r-cran-openssl, r-cran-styler, r-cran-lintr, r-cran-aplot, r-cran-ggbeeswarm, r-cran-ggvenndiagram, r-cran-gifski, r-cran-ggnewscale, r-cran-revtools Filename: pool/dists/noble/main/r-cran-pcutils_0.2.8-1.ca2404.1_all.deb Size: 1802640 MD5sum: 0b4f1ccb0472da767f314f9a1d4bb977 SHA1: b7a344d9d938bf3c4bf1dcb628148e4592c933b4 SHA256: d1eed2ddac58fa5ee38a8f499fcba1d0a571dea9dc13045ab7a527fb916e2da0 SHA512: 7efc0c45ddb10a849e547b883feaf77dad37c106a2a89ecc6e347dc183ec2f2e8778ee9c6033401e317ec46f3c946aa7801bcce7de5ef4b408785d272908487e Homepage: https://cran.r-project.org/package=pcutils Description: CRAN Package 'pcutils' (Some Useful Functions for Statistics and Visualization) Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group. Package: r-cran-pcv Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pcv_1.2.0-1.ca2404.1_all.deb Size: 485228 MD5sum: c795c28156d8eda2308ddba42e83e6b8 SHA1: a6f4ce0804386ce6c970ce9e87e987b3562b6d52 SHA256: 87c12c0beec24ececb80d3e03b342024489dd896dbfb6a08e3a7b23f490cab4b SHA512: 54bb9f9f44aa717bb044c3f187be076e68839f0d636ef430ca73e42676e8f2335841b227be503efdbd7e9f8fe9dfee30b7dd42950fb81ff8fe024eea0bee9f9b Homepage: https://cran.r-project.org/package=pcv Description: CRAN Package 'pcv' (Procrustes Cross-Validation) Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) . 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References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. . Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254--262. . Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. . Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. . 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Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques. 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Package: r-cran-pdrobust Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1851 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-quantreg, r-cran-rootsolve Suggests: r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pdrobust_0.3.8-1.ca2404.1_all.deb Size: 1173306 MD5sum: 5c1d46ccabb069b1873ad75ea55e40c3 SHA1: ace8fd7d978f52d1ffc4ec2e0a1965ad05edb5a8 SHA256: 6f6024f433b6eb5002256d298fcfee40c4d130f178bc6ccc022ff61521e396b0 SHA512: 883ab27611ec023042cdde7a3ae8b60d036b95cd3483b5edf440b43714af4ef0c4440cfb4f9d4a3054c3a5bd2e34fe7c0d22c41fcc589c9e45a52bdbaaf6b5e8 Homepage: https://cran.r-project.org/package=PDRobust Description: CRAN Package 'PDRobust' (Robust Longitudinal Effects Under Truncation by Death) Implements principal-stratification methods for estimating time-specific and pooled heterogeneous treatment effects in longitudinal studies where outcomes may be truncated by death. Supports continuous and binary outcomes, explicit data validation and standardization, and covariate-dependent treatment effects. Fits propensity-score, principal-score, and outcome models and provides subject-level bootstrap inference, covariate-balance diagnostics, principal-stratum summaries, treatment-group-specific survival odds ratios, and outcome-noise sensitivity analysis. Methodological background is provided in . Package: r-cran-pdshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Filename: pool/dists/noble/main/r-cran-pdshiny_0.1.0-1.ca2404.1_all.deb Size: 71356 MD5sum: d55d3e1ece1e85de249030aa7cf280f3 SHA1: fee46a7de10c4bea65eaf9323b66e3d176ce1b58 SHA256: 55c41c7caa1d853abfdba253feac8cf81afbbaebb4c61bdcc7b9a510ec6c8061 SHA512: b263dacaec7ffea969bf757abbbb3f872f535b092e9789df10c4457c816ef1f811aa86775ff3e3469960523291cd79b7f7531a0fdabde55d05de5f1b68497a74 Homepage: https://cran.r-project.org/package=PDShiny Description: CRAN Package 'PDShiny' ('Probability Distribution Shiny') Interactive shiny application for working with Probability Distributions. Calculations and Graphs are provided. 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The required distance 0,1,2,3.. is determined based on repeated dependency testing while stepwise increasing the distance. In preparation: Vroegindeweij et al. "A Permutation distancing test for single-case observational AB phase design data: A Monte Carlo simulation study". Package: r-cran-pdtoolkit Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-monobin, r-cran-dplyr, r-cran-rpart Filename: pool/dists/noble/main/r-cran-pdtoolkit_1.2.0-1.ca2404.1_all.deb Size: 528424 MD5sum: 6720323bdaeffb68dfa617f66f8349fd SHA1: e583c6e68bf9f4d636c93c993bff3a32965f6d46 SHA256: 37d3b1c8f197781b3304c2f2c9dae684f0adfca88ad2bcc7c2f24b0067ae7213 SHA512: 3c399ea50a338b81dd09c2eacdd2d325974800d17118c24d7ec5cf909e89bc9d4f33ea798241f0db6b3320f84e7cf195dee10658eb4aad662be77fd350248260 Homepage: https://cran.r-project.org/package=PDtoolkit Description: CRAN Package 'PDtoolkit' (Collection of Tools for PD Rating Model Development andValidation) The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different data transformation and modeling tasks such as: imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, heterogeneity, discriminatory and predictive power of the model. Package: r-cran-pdxpower Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-ggpubr, r-cran-frailtypack, r-cran-survival Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pdxpower_1.0.5-1.ca2404.1_all.deb Size: 192696 MD5sum: 7a84fc16b7c6374c487869d1ae6f1456 SHA1: 933e8dfebee7e3a279e62dfc64af0916c2230a59 SHA256: fe94da0dcaf77f19e5ddb7a909111236f6135878a12b8f2bae840d5b743207ac SHA512: 818314940eb75c9b313e0bacd8929320e92c70af1d0b5e785fe591bdf6f9d9a70f0229d8e235970f471540bb6ddfea0690642ea7b319916be2e58f909fa82dfd Homepage: https://cran.r-project.org/package=PDXpower Description: CRAN Package 'PDXpower' (Time to Event Outcome in Experimental Designs of Pre-ClinicalStudies) Conduct simulation-based customized power calculation for clustered time to event data in a mixed crossed/nested design, where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power, motivated by Eckel-Passow and colleagues (2021) and Li and colleagues (2025) . This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for within-subject (cell line) correlation while holding different distributional assumptions about the outcome. Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power. Package: r-cran-pdxtrees Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-infer, r-cran-moderndive, r-cran-readr, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/noble/main/r-cran-pdxtrees_0.5.1-1.ca2404.1_all.deb Size: 469024 MD5sum: 0e6303d3128df264bd4618e28a474c1d SHA1: 5dba562a14a2e32475d5b1c8163cfbc9a95e4edb SHA256: cd19fb4af043be0a4b58598c53dd83b5f4e269773994f47dfe78350e5e0d1e5c SHA512: e4e2ed346bc8b14f93cb5219110d3f03645d9805198fd7594668ff1e23244babe4b91ef15f9a7b5cec88454db52bedc0d9a92ffab3fafd3b969670a1fa2ad658 Homepage: https://cran.r-project.org/package=pdxTrees Description: CRAN Package 'pdxTrees' (Data Package of Portland, Oregon Trees) An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods. Package: r-cran-pdynmc Architecture: all Version: 0.9.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-matrix, r-cran-optimx, r-cran-rdpack Suggests: r-cran-pder, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pdynmc_0.9.13-1.ca2404.1_all.deb Size: 741996 MD5sum: 49f0e71024fb7f8e2df7a242cb55668f SHA1: 5f060726eeb135c0bccafb7a0ff91ff2c6bef8e3 SHA256: fdf6b3cb857a429440b09587d5b403f78f0061bff166f75c51588cf97bc0982b SHA512: dbb99964402d53f04af5c592d74aa8c8122f5419d90e15f5aa30d85e017f685a7896872afd14bc72e18a9b679851b458b03b3398e9b2edeb26e5602c46db2ad4 Homepage: https://cran.r-project.org/package=pdynmc Description: CRAN Package 'pdynmc' (Moment Condition Based Estimation of Linear Dynamic Panel DataModels) Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) , Ahn and Schmidt (1995) , and Arellano and Bover (1995) . Estimation of the model parameters relies on the Generalized Method of Moments (GMM) and instrumental variables (IV) estimation, numerical optimization (when nonlinear moment conditions are employed) and the computation of closed form solutions (when estimation is based on linear moment conditions). One-step, two-step and iterated estimation is available. For inference and specification testing, Windmeijer (2005) and doubly corrected standard errors (Hwang, Kang, Lee, 2021 ) are available. Additionally, serial correlation tests, tests for overidentification, and Wald tests are provided. Functions for visualizing panel data structures and modeling results obtained from GMM estimation are also available. The plot methods include functions to plot unbalanced panel structure, coefficient ranges and coefficient paths across GMM iterations (the latter is implemented according to the plot shown in Hansen and Lee, 2021 ). For a more detailed description of the GMM-based functionality, please see Fritsch, Pua, Schnurbus (2021) . For more details on the IV-based estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2026) and Han and Phillips (2010) . Package: r-cran-peacesciencer Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-geosphere, r-cran-tidyr, r-cran-stringr, r-cran-rlang, r-cran-stevemisc, r-cran-lifecycle, r-cran-isard Suggests: r-cran-countrycode, r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peacesciencer_1.2.0-1.ca2404.1_all.deb Size: 4353674 MD5sum: 7cf923c7a70c7a5b67c88991243a54fa SHA1: fc39cee7cf1053c809d69a0274bc0a322fde5361 SHA256: c36e08f69da0164928059935e3cafca801c42108bd4bb9aeb013763c45b03273 SHA512: e9bf1ca51345b654717b9f4f097d6c2b4a4efb383d22872b0d374a9aba7cf43c1d86245012b59bf678cfa5f5497e45dbab8ef9bd5ed3c81c2dc7435571b7ee92 Homepage: https://cran.r-project.org/package=peacesciencer Description: CRAN Package 'peacesciencer' (Tools and Data for Quantitative Peace Science Research) These are useful tools and data sets for the study of quantitative peace science. The goal for this package is to include tools and data sets for doing original research that mimics well what a user would have to previously get from a software package that may not be well-sourced or well-supported. Those software bundles were useful the extent to which they encourage replications of long-standing analyses by starting the data-generating process from scratch. However, a lot of the functionality can be done relatively quickly and more transparently in the R programming language. Package: r-cran-peach Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2586 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mnormt, r-cran-metap Filename: pool/dists/noble/main/r-cran-peach_0.1.1-1.ca2404.1_all.deb Size: 2611420 MD5sum: af76d75490c9d7a3e832cfad71282c1b SHA1: cae3015e075c6e500245efaebde305f074071fca SHA256: 0e154f83bff5420c7ae7101fdc0ebd2223756732f531fa4fa0cd4749d89a1ecc SHA512: cf541f83f46c9327a944d667e5d83e2f4ce0bd201a7e5ed42cd276ade7ae0b4995f3d14df90aefff412298e316bcdeb98cb969ac5d88c12489d7d51a1410bacd Homepage: https://cran.r-project.org/package=PEACH Description: CRAN Package 'PEACH' (Pareto Enrichment Analysis for Combining Heterogeneous Datasets) A meta gene set analysis tool developed based on principles of Pareto dominance (William B T Mock (2011) ). It is designed to combine gene set analysis p-values from multiple transcriptome datasets (e.g., microarray and RNA-Seq). The novel Pareto method for p-value combination allows PEACH to properly model heterogeneity and correlation in Omics datasets. Package: r-cran-peacock.test Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-peacock.test_1.0-1.ca2404.1_all.deb Size: 22574 MD5sum: fe946c7a56c778319379e0192e621437 SHA1: 9807b9e126f0a58f74afb72adcc1110a2f33d5d2 SHA256: cd5b48abe178f745b20096e574f1dbf5d9c7922fb56b888361d4865922f8518b SHA512: d2721b3ba08e7569c7fec6bf1985a3962d18754de7517762944e0c464ae68b7f1621f81f1fe8466981a3723ef5005471d590bbc703d1b87a3e3dab331d2fddd2 Homepage: https://cran.r-project.org/package=Peacock.test Description: CRAN Package 'Peacock.test' (Two and Three Dimensional Kolmogorov-Smirnov Two-Sample Tests) The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, are provided to compute the test statistics in two and three dimensional spaces, respectively. Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is a variant of the Peacock test. Package: r-cran-peacots Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-peacots_1.3.2-1.ca2404.1_all.deb Size: 152080 MD5sum: cd299132b6501f73d53073d0b2714672 SHA1: 1815141376ccc92926a11ad4b3bf8d4071be84c2 SHA256: f456a53aba4d919a8b17249bd8aed627802565b94ce9aec7c9d47481cfa22433 SHA512: 9ff04b637de89f776ab45630c6b6172a4dc48824ab8dca9c5b93bcf97c36b4c44d51ee862e2023f2f123ab88f90ab1d697be82c4227be09eed51d8ddc7ccc37d Homepage: https://cran.r-project.org/package=peacots Description: CRAN Package 'peacots' (Periodogram Peaks in Correlated Time Series) Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) . Package: r-cran-peakram Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peakram_1.0.2-1.ca2404.1_all.deb Size: 17538 MD5sum: 2a8659aaa9433261f7636283ae49f208 SHA1: d2bdafef9ee693dda07d0f11f99861b5202ffc20 SHA256: 2de131b964abadf051d48222bdfb82047c59513ce283469321a13d00e0fc60d2 SHA512: aac7e3fd3855409fcd41526bf215fe764c246ea63e91e19f88b1a01b5d9f8b011d4a75b52c16ab8ad4a065458838091da76a45035e90ecf1bfaa11ce73913971 Homepage: https://cran.r-project.org/package=peakRAM Description: CRAN Package 'peakRAM' (Monitor the Total and Peak RAM Used by an Expression or Function) When working with big data sets, RAM conservation is critically important. However, it is not always enough to just monitor the size of the objects created. So-called "copy-on-modify" behavior, characteristic of R, means that some expressions or functions may require an unexpectedly large amount of RAM overhead. For example, replacing a single value in a matrix duplicates that matrix in the back-end, making this task require twice as much RAM as that used by the matrix itself. This package makes it easy to monitor the total and peak RAM used so that developers can quickly identify and eliminate RAM hungry code. Package: r-cran-pearson7 Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pearson7_1.0-3-1.ca2404.1_all.deb Size: 35350 MD5sum: 27e04e76ab915c51fb2536bb93a080ef SHA1: 39739806fc0ea3453fb97587e7ffad7711c454b1 SHA256: 72f61d5a78d84fe570c9ab31bf215de3939f7874483fa92140cd6b3c546907f9 SHA512: c907f427911e32d2c919bfc077a6d4beb192f9b64ddea3e7a4148c21cf4733f152cc256b95deafb69f5a7399ed9584ce0e0685bef3643bc12eb7d1f596bbad5c Homepage: https://cran.r-project.org/package=pearson7 Description: CRAN Package 'pearson7' (Maximum Likelihood Inference for the Pearson VII Distributionwith Shape Parameter 3/2) Supports maximum likelihood inference for the Pearson VII distribution with shape parameter 3/2 and free location and scale parameters. This distribution is relevant when estimating the velocity of processive motor proteins with random detachment. Package: r-cran-pearsonica Architecture: all Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pearsonica_1.2-5-1.ca2404.1_all.deb Size: 36444 MD5sum: a3e6f6c9d8fa84aa05b607b9683ee836 SHA1: 7c42fdae23a4a407d7a3386c52b28e89ab1c845e SHA256: b98934f17daa21d10caafdb9ac5679c375e6decb5935c4be13c2182bb81bb45f SHA512: f412cb61b088c3d2da71f6aec12cd3315b3e0d5a1f58301063847363ed08dc706a54e7a01fbfad818b14482f4cf2497560ae65d27af379e0b34ce53ff95b72c9 Homepage: https://cran.r-project.org/package=PearsonICA Description: CRAN Package 'PearsonICA' (Independent Component Analysis using Score Functions from thePearson System) The Pearson-ICA algorithm is a mutual information-based method for blind separation of statistically independent source signals. It has been shown that the minimization of mutual information leads to iterative use of score functions, i.e. derivatives of log densities. The Pearson system allows adaptive modeling of score functions. The flexibility of the Pearson system makes it possible to model a wide range of source distributions including asymmetric distributions. The algorithm is designed especially for problems with asymmetric sources but it works for symmetric sources as well. Package: r-cran-peaxai Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-benchmarking, r-cran-caret, r-cran-dear, r-cran-dplyr, r-cran-kernelshap, r-cran-iml, r-cran-lime, r-cran-np, r-cran-prroc, r-cran-proc, r-cran-rminer, r-cran-rms, r-cran-peakram Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peaxai_1.0.3-1.ca2404.1_all.deb Size: 341110 MD5sum: 6ac20ac1b34dc982bbf41d0047597b95 SHA1: f94635512d6d397d951eed7fd804bc2f651c0eae SHA256: 335bcd64499c6b6f3264304e48d5c48ca4ce7d24438fdd8f706a66b27baedd50 SHA512: 915342dcf6b11cfdbb0e1965be8dd40077c42c3e70d29a7114a63288f25bdae76acbbf8bef18596e247f8dd58c87484e706c095833dccbf83b3a19ce77cec999 Homepage: https://cran.r-project.org/package=PEAXAI Description: CRAN Package 'PEAXAI' (Probabilistic Efficiency Analysis Using Explainable ArtificialIntelligence) Provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) with machine learning classifiers (Kuhn, 2008) to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) , enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) , Shapley value explanations (Strumbelj & Kononenko, 2014) , and sensitivity analysis (Cortez, 2011) . The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication. Package: r-cran-pecan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pecan_0.1.0-1.ca2404.1_all.deb Size: 136656 MD5sum: 99b8ff5d0cf526aecfbaca44c8bd0470 SHA1: 481b9cb42d2aa909bf71ada23589ea72ef14ba6c SHA256: 661598583117a9575aafa4c9ae661ad13a80b883dde3d75eaed1f089ce3c832f SHA512: a40982f49d6e5cd321cef1c3e2b7595500f1ae6998f6da8e75933b8cfa9e4bd7d7544810bc86c4c7faed37aea89bbd3ecf1de23076078b0f28528c7f06eeddde Homepage: https://cran.r-project.org/package=pecan Description: CRAN Package 'pecan' (Portfolio for Economic Complexity Analysis and Navigation) A portfolio of tools for economic complexity analysis and industrial upgrading navigation. The package implements essential measures in international trade and development economics, including the relative comparative advantage (RCA), economic complexity index (ECI) and product complexity index (PCI). It enables users to analyze export structures, explore product relatedness, and identify potential upgrading paths grounded in economic theory, following the framework in Hausmann et al. (2014) . Package: r-cran-pecanr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pecanr_0.3.1-1.ca2404.1_all.deb Size: 108564 MD5sum: 6622bdea7278602f5a343eee97484775 SHA1: 2ff2f36b07f2a908f9fc16ccf24c5c87605cbcab SHA256: 5108f1a74bd7906b5aa4e6ae7260d9c020d72f04c905b2d1eb2143ac38c0fefa SHA512: 9a718820909788c3b637ffbef70f31e05123ab0058e46e85ed8b1cce1b3d3883a63f067ba736315eee72b5e7df2bb0c7ac98502de8930f7ec7c9f82dd5711df0 Homepage: https://cran.r-project.org/package=pecanr Description: CRAN Package 'pecanr' (Partial Eta-Squared for Crossed, Nested, and Mixed Linear MixedModels) Computes partial eta-squared effect sizes for fixed effects in linear mixed models fitted with the 'lme4' package. Supports crossed, nested, and mixed (crossed-and-nested) random effects structures with any number of grouping factors. Mixed designs handle cases where grouping factors are simultaneously crossed with some variables and nested within others (e.g., photos nested within models, but both crossed with participants). Factor predictors are supported directly, and a single factor-level (omnibus) effect size can be obtained for a multi-level factor or multi-df interaction. Random slope variances are translated to the outcome scale using a variance decomposition approach, correctly accounting for predictor scaling and interaction terms. Both general and operative effect sizes are provided, with optional parametric bootstrap confidence intervals. For correlated predictors, per-predictor effect sizes use unique (semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd (2020) , Correll, Mellinger, and Pedersen (2022) , and Rights and Sterba (2019) . Package: r-cran-pecme Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-withr Suggests: r-cran-testthat, r-cran-waldo, r-cran-mice Filename: pool/dists/noble/main/r-cran-pecme_0.1.1-1.ca2404.1_all.deb Size: 262162 MD5sum: f4eddd19939b70859eba3fae56374638 SHA1: 202ea3a60cc4c1fc24827cad2e9da2df248d5929 SHA256: b19f336c3e3d372c8108e18eb98f484659e82ae7c084ea3a6e2395ee0b7cc07f SHA512: cfd5cd3f2a930baf153fa260ee663fad5ba220a757d58612a64c97ab963c5f1529ed30e8e8cb0e48b0c0462545bddb4608b15e62f96a1d901ade353ad4d2f488 Homepage: https://cran.r-project.org/package=pecme Description: CRAN Package 'pecme' (Penalized ECME Estimation for Censored Linear Mixed Models) Fits Gaussian linear mixed models with a random intercept when the response is subject to left, right, and/or interval censoring, using the Expectation/Conditional Maximization Either (ECME) algorithm of Liu and Rubin (1994) in the spirit of the fast censored-response mixed-model algorithm of Vaida and Liu (2009). Simultaneous estimation and variable selection is supported through coordinate-descent penalized maximization with Lasso, Adaptive Lasso, SCAD, MCP, Elastic Net, and Ridge penalties (no penalty is also supported). The random intercept is integrated out by Gauss-Hermite quadrature at every iteration, and the two ECME conditional-maximization steps respectively maximize the expected penalized complete-data objective (for the regression coefficients) and the actual observed-data marginal likelihood (for the variance components), which is the defining feature of ECME relative to plain ECM/EM. The package provides a single-fit engine, a sequential/parallel penalty-parameter grid search with information-criterion or cross-validated selection, data-dependent or user-supplied lambda grids, and an Expectation-Maximization based treatment of a completely missing (at random) response, sharing the same truncated-normal machinery used for censoring. References: Liu and Rubin (1994) "The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence" ; Vaida and Liu (2009) "Fast Implementation for Normal Mixed Effects Models With Censored Response" . Package: r-cran-pedalfast.data Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-qwraps2, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedalfast.data_1.0.2-1.ca2404.1_all.deb Size: 637902 MD5sum: ccc85699467d24188411bc7826e11c9c SHA1: 37db5513c37fc0c9de5b54b43484e7738a205576 SHA256: 191c6baaf227f8aacd2e756de553ad77b17773a1bf7c0d29fde682dda9bdb212 SHA512: e3c7ea3d92ac275383512296f5d0fec32260b7d0b10a5768e86831678319b86abd515869eeb5986c4b7fb1de10b5e26f1daf639482d37cdfd42861db45ee5ce6 Homepage: https://cran.r-project.org/package=pedalfast.data Description: CRAN Package 'pedalfast.data' (PEDALFAST Data) Data files and documentation for PEDiatric vALidation oF vAriableS in TBI (PEDALFAST). The data was used in "Functional Status Scale in Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett, Dixon, et al (2016) . Package: r-cran-pedbuildr Architecture: all Version: 0.4.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pedtools, r-cran-forrel, r-cran-glue, r-cran-mirai, r-cran-pedmut, r-cran-pedprobr, r-cran-ribd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedbuildr_0.4.0-1.ca2404.2_all.deb Size: 270502 MD5sum: d56be4fc7b56e835f83e98fa8deeb86e SHA1: 28d225c4ed5671c5c43118427c194a2f030fe9e0 SHA256: 66121fcc1d3e3706d0847e71358b685c4b9c7e4cb57189d44f30555a8caf2c5b SHA512: 1042dc7bae559f12d498d4f13c6819c6454b91f797a6cda37d638d87d5ae192fd78c1e82e655e4fa300508bddc3404766dd5136115c95c3e9ea7565dd93436c8 Homepage: https://cran.r-project.org/package=pedbuildr Description: CRAN Package 'pedbuildr' (Pedigree Reconstruction) Reconstruct pedigrees from genotype data, by optimising the likelihood over all possible pedigrees subject to given restrictions. Tailor-made plots facilitate evaluation of the output. This package is part of the 'pedsuite' ecosystem for pedigree analysis. In particular, it imports 'pedprobr' for calculating pedigree likelihoods and 'forrel' for estimating pairwise relatedness. Package: r-cran-pedfamilias Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pedtools, r-cran-pedmut Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedfamilias_0.2.6-1.ca2404.1_all.deb Size: 103186 MD5sum: fc2716c8dc57271d94fc56a017f90644 SHA1: f746aabb653cb1bd3e3de58fb234b86f01f4af99 SHA256: 27ddb5cab9b0acafb271d01293253fe96116521b0afe8d32bb00e6e24bf31ca5 SHA512: 5e411da28e0413846b42b8079ed63bd4f84d29ab1bd09ed11be2428e9e09b5829f817780e5aeb2a4a73c0a5a10d8900508802db36ba4f813af9d578e84fa3440 Homepage: https://cran.r-project.org/package=pedFamilias Description: CRAN Package 'pedFamilias' (Import and Export 'Familias' Files) Tools for exchanging pedigree data between the 'pedsuite' packages and the 'Familias' software for forensic kinship computations (Egeland et al. (2000) ). These functions were split out from the 'forrel' package to streamline maintenance and provide a lightweight alternative for packages otherwise independent of 'forrel'. Package: r-cran-pedgene Architecture: all Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-compquadform, r-cran-survey, r-bioc-pedixplorer Filename: pool/dists/noble/main/r-cran-pedgene_4.1-1.ca2404.1_all.deb Size: 281186 MD5sum: 95bc11233dc7a35829ad715b32f95ab9 SHA1: ff1dae404a6c39a335f36d19eaf1fd10feca090d SHA256: 60dc345dce2d7dc951cb6931153ac69608055287da39b0ef44ec45566bb525ba SHA512: 0aa9d56ff4f6b24192e2649687b090c53aa88dbf2c438160c3c15ca13c6fad53c36f308e4f8599ec57aaec741c88998f39d9acaaf5f15046a4194f365c30c41c Homepage: https://cran.r-project.org/package=pedgene Description: CRAN Package 'pedgene' (Gene-Level Variant Association Tests for Pedigree Data) Gene-level variant association tests with disease status for pedigree data: kernel and burden association statistics. Package: r-cran-pediatric.zcalc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gamlss.dist, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pediatric.zcalc_0.1.1-1.ca2404.1_all.deb Size: 887868 MD5sum: c48c5add844f91a7cf1960777b78fc26 SHA1: fcd61aeaa05b65551eb4e91ebf7e4754c88d404d SHA256: 1c7281b6f1456f7e14e98635673743b611a1aa3581c8d3036465f0b688909863 SHA512: b5b7e12f1c9a27bca3197771acf6cbcd9c4de1a964cedbb5650e3502d3a40d1383993a23e7ffd109e969587ad38d222bd4de8eb3c8abc93caf850074ac25afd9 Homepage: https://cran.r-project.org/package=pediatric.zcalc Description: CRAN Package 'pediatric.zcalc' (Z-Score Calculator for Biomarkers: Childhood to Young Adulthood) Provides tools to compute individual percentile ranks and z-scores for clinical biomarkers in children, adolescents and young adults, based on age-, sex-, and height-specific reference data from the IDEFICS (Identification and prevention of Dietary and lifestyle-induced health EFfects In Children and infantS) study and the Biomarkers4Pediatrics collaboration. Supports the computation of a composite Metabolic Syndrome (MetS) score and associated monitoring/action levels for health monitoring. For more details see Ahrens et al. (2014) . Package: r-cran-pedmermaid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pedmermaid_1.0.2-1.ca2404.1_all.deb Size: 263342 MD5sum: 70ac375f7a76eecb0e3316a9368953a4 SHA1: 70b1d536dfd5accfee3772708e91fe455c2d0961 SHA256: 3c564885b7fb77e465f7c7be44b00001226bb73076fb521c8923cb53e7e151df SHA512: afc1c464795c30e2da0d516f5a839a8596d9afd1230e674a577e29a9a063a73c01fea511d4f090428b6e89f99315920199b402452fa90a674843ff7c01e2eee8 Homepage: https://cran.r-project.org/package=pedMermaid Description: CRAN Package 'pedMermaid' (Pedigree Mermaid Syntax) Generate Mermaid syntax for a pedigree flowchart from a pedigree data frame. Mermaid syntax is commonly used to generate plots, charts, diagrams, and flowcharts. It is a textual syntax for creating reproducible illustrations. This package generates Mermaid syntax from a pedigree data frame to visualize a pedigree flowchart. The Mermaid syntax can be embedded in a Markdown or R Markdown file, or viewed on Mermaid editors and renderers. Links' shape, style, and orientation can be customized via function arguments, and nodes' shapes and styles can be customized via optional columns in the pedigree data frame. Package: r-cran-pedmut Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedmut_0.9.1-1.ca2404.1_all.deb Size: 147550 MD5sum: ff9da0612fa40ae202593e2279a21684 SHA1: 892d1ed01e260e5c30d653dd0c241c642c2daa97 SHA256: 5906cfb87723ee4d11c232234c98b9ebb0a74e755c3e3c290c5f638ed3d3d0e6 SHA512: 705d4f5374be7fac948b999763badaa553c0573d74e5ed4cd13165126caef0e66bc3705140c96bf1174432d7eb42aa84ae0d4c9d2acd2008a050d6a876f5da90 Homepage: https://cran.r-project.org/package=pedmut Description: CRAN Package 'pedmut' (Mutation Models for Pedigree Likelihood Computations) A collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922. Package: r-cran-pedprobr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pedtools, r-cran-pedmut Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedprobr_1.1.1-1.ca2404.1_all.deb Size: 213866 MD5sum: 28ef9340523bce6890660e081f83d748 SHA1: 77935443e842a2252dc30562e77119803b316f7d SHA256: b7431cbdb799c3c7667ab07f1eb67c4ff9343fd3ea27395f2e5015280af6ff83 SHA512: 6ad3271ed9d8ba875efaf89d6ffdbe4fddd8f645a6c6508f0d9a0ee49466c18c5fe272428b4df7a7b2803e1dfa26ae18d667f4a7bae581c2c43be2809f4c6b33 Homepage: https://cran.r-project.org/package=pedprobr Description: CRAN Package 'pedprobr' (Probability Computations on Pedigrees) An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) ). The standard algorithm is extended to allow inbred founders. 'pedprobr' is part of the 'pedsuite', a collection of packages for pedigree analysis in R. In particular, 'pedprobr' depends on 'pedtools' for pedigree manipulations and 'pedmut' for mutation modelling. For more information, see 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). 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The data sources including NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative functions for trading strategies based on the 'data.table', 'TTR', 'PerformanceAnalytics' and etc packages. 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Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) ; Nilforooshan, M.A. (2022) . 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Pedigrees can be read from text files or created on the fly with built-in functions. A range of utilities enable modifications like adding or removing individuals, breaking loops, and merging pedigrees. An online tool for creating pedigrees interactively, based on 'pedtools', is available at . 'pedtools' is the hub of the 'pedsuite', a collection of packages for pedigree analysis. A detailed presentation of the 'pedsuite' is given in the book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). Package: r-cran-pedtricks Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-kinship2, r-cran-tidyr, r-cran-matrix, r-cran-mvtnorm, r-cran-mcmcglmm, r-cran-nadiv, r-cran-genetics, r-cran-igraph Filename: pool/dists/noble/main/r-cran-pedtricks_0.5.0-1.ca2404.1_all.deb Size: 232914 MD5sum: b7d51994362d79673acf7fe61aa5ab47 SHA1: aebf37063d4d98139c72fac412fdefed59dfecff SHA256: 552fe3e679444d2087cb0ea4e9419e1a37ad1b66d1c811be5f0e5cb36797514f SHA512: 647e65757b96c06a119e987ac5061bf13e6c4850dfb87ca89850450198e591b54135e747f8165439cbc9ec93a06a02d99f8887323657ae08eecd400c747271a9 Homepage: https://cran.r-project.org/package=pedtricks Description: CRAN Package 'pedtricks' (Visualize, Summarize and Simulate Data from Pedigrees) Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees. 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For each fund, the package estimates the proportion of peers it outperforms, is equalled by, and is outperformed by, correcting for luck with the false discovery approach of Storey (2002) . Screenings can be based on factor-model alphas, Sharpe ratios, or modified Sharpe ratios, the latter using the equality test of Ardia and Boudt (2015) . Funds can be screened within a universe or against a separate peer group, over rolling windows, and results come with bootstrap confidence intervals, summary, plot, and tidy data frame methods. 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Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) . Package: r-cran-peip Architecture: all Version: 2.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bvls, r-cran-matrix, r-cran-rseis, r-cran-pracma, r-cran-geigen, r-cran-fields Filename: pool/dists/noble/main/r-cran-peip_2.2-5-1.ca2404.1_all.deb Size: 179634 MD5sum: 7e220ff65bf7aab833721235a6e539e3 SHA1: aaf433197bbbad38940255ab4a5b60552566d502 SHA256: 60a0d5a373e6ffbae2c76cf451e0932ea79057294ae2912b1b002492943ce07b SHA512: 2134289a8f27e8fd6f2aca2850637d27d756aadf1c320e0afbf38f6c701ac54d3f4f7706716944ee176e6ee3871096249005832387f0071363add763d7e3d969 Homepage: https://cran.r-project.org/package=PEIP Description: CRAN Package 'PEIP' (Geophysical Inverse Theory and Optimization) Several functions introduced in Aster et al.'s book on inverse theory. 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The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities. 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Package: r-cran-penfa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-gjrm, r-cran-trust Suggests: r-cran-cartography, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-penfa_0.1.1-1.ca2404.1_all.deb Size: 531726 MD5sum: 777624cc4a4241622d12dc7777f44060 SHA1: 8694d53b019e939e7bd40e91af6586cfe7478140 SHA256: a0d43d921e08a490d83c3e5cfbaead7e0119c4b182aaca4e03d133ab11b323a5 SHA512: 99b4bc2cc6c8d8ad05dcafa9b185f17d4aea5fe945cb837ea4bb9ea5b79b2dbe9e78b1f97e83ec04b8d333cbeff7adff37322422dfc70355cbb88d9d8674f350 Homepage: https://cran.r-project.org/package=penfa Description: CRAN Package 'penfa' (Single- And Multiple-Group Penalized Factor Analysis) Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 ). 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Derived from open ecological and biological sources such as Palmer Station studies, the package integrates datasets covering adult morphology, clutch size, blood isotope composition, and heart rate. It is designed for researchers, students, and educators to explore statistical methods including ANOVA, regression, multivariate analysis, and design of experiments in an accessible and reproducible context. 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More methods under other semiparametric models such as cure model or additive model will be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. (2019) . 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Supports guided sessions through a 'Claude Code' skill bundle and autonomous research runs from R via autoresearch(). Results land in a structured vault of markdown pages with 'YAML' frontmatter and wikilinks, ready for hand-editing in your favourite editor alongside the LLM. Vaults are seeded with 'CLAUDE.md' and 'AGENTS.md' so 'Claude Code', 'Codex' , and other agents share the same operating instructions. Can adopt an existing 'Obsidian' vault in place via init_vault(adopt = TRUE). 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Package: r-cran-peopleanalytics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-peopleanalytics_0.1.0-1.ca2404.1_all.deb Size: 124116 MD5sum: a51101db8824c90ec28a5488625eaf01 SHA1: f3003b2fb168ed6c34af3a1ef7c7c98c5b2f121d SHA256: b882d2db5978bd2c7d2590c48c4444f959fa7c035852dcce3dceb6b469eaf1ac SHA512: 8bb874913059ba060a466e11178d63fe91cfaff95b96747bb3fb07d3672246bbfa31de8820083844939521f63a47804b29b01036b620daeaa7c0d6c49c129786 Homepage: https://cran.r-project.org/package=peopleanalytics Description: CRAN Package 'peopleanalytics' (Data Sets for Craig Starbuck's Book, "The Fundamentals of PeopleAnalytics: With Applications in R") Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R". 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Package: r-cran-pep725 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-dplyr, r-cran-mgcv, r-cran-patchwork, r-cran-purrr, r-cran-robustbase, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rnaturalearth, r-cran-sf Suggests: r-cran-ggmap, r-cran-kendall, r-cran-knitr, r-cran-leaflet, r-cran-miniui, r-cran-nlme, r-cran-quantreg, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-pls, r-cran-shiny, r-cran-sp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pep725_1.1.0-1.ca2404.1_all.deb Size: 3376918 MD5sum: 51656f0a4d598126f6e6b55b59b6f078 SHA1: 9a6224bbc2747766ef6b456ec47b18bfd9875c00 SHA256: abd311e3d9341bdca1b5b22bd6f81be177fb27861d237013ae532baac7d8edcd SHA512: ddba00421e50ac04b76637bdb2c51811eebb8575890534851a4c58fe585e5746b31a95d3f6f9a2aaecd84b46e544e2b428db47d2990bf817d0a221dba74bb277 Homepage: https://cran.r-project.org/package=pep725 Description: CRAN Package 'pep725' (Pan-European Phenological Data Analysis) Provides a framework for quality-aware analysis of ground-based phenological data from the PEP725 Pan-European Phenology Database (Templ et al. (2018) ; Templ et al. (2026) ) and similar observation networks. Implements station-level data quality grading, outlier detection, phenological normals (climate baselines), anomaly detection, elevation and latitude gradient estimation with robust regression, spatial synchrony quantification, partial least squares (PLS) regression for identifying temperature-sensitive periods, and sequential Mann-Kendall trend analysis. Supports data import from PEP725 files, conversion of user-supplied data, and downloadable synthetic datasets for teaching without barriers of registration. All analysis outputs provide 'print', 'summary', and 'plot' methods. Interactive spatial visualization is available via 'leaflet'. Package: r-cran-pepa Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3195 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pepa_1.0-1.ca2404.1_all.deb Size: 3207920 MD5sum: 268d336557c46098537799e086ea1b5b SHA1: 407b5022cc730fa2daaefed6e0eebdbf7fd904c4 SHA256: 4754c31639a299ea1568b3ab90f5591b204e4d073cb6100d38bf7a8977705124 SHA512: c6241038464bbe8b9e146bc1e3f3cca2138d3dcb076f2fe20067bdc05f92ee991cd46f536c3adf704b495254666f0527f89816362d9cd44b6fc259e2f0ccdce4 Homepage: https://cran.r-project.org/package=pEPA Description: CRAN Package 'pEPA' (Tests of Equal Predictive Accuracy for Panels of Forecasts) Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) and Akgun et al. (2024) . Package: r-cran-pepdiff Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-readr, r-cran-ggplot2, r-cran-cowplot, r-cran-emmeans, r-cran-artool, r-cran-magrittr, r-cran-stringr, r-cran-forcats Suggests: r-bioc-complexheatmap, r-cran-upsetr, r-cran-rcolorbrewer, r-cran-viridis, r-cran-circlize, r-cran-factoextra, r-bioc-rankprod, r-cran-mkinfer, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pepdiff_1.0.0-1.ca2404.1_all.deb Size: 1641552 MD5sum: 5790df2f634eb37b179817835d906e34 SHA1: 8bdef7163030297d362664c95a762c335433a97c SHA256: 3351003b64aec27852e1a59d7d285a89d078b81b8dfe361dc9524ea8979b4735 SHA512: b6fcd8a3a7de5e54094432d98b1e0fa0ad2a82533631dbab9288a9fdbe1e0b44bea810ae4496f3fe53ed4040ea52857298cb5b00384b1435f0c9f11af83d3776 Homepage: https://cran.r-project.org/package=pepdiff Description: CRAN Package 'pepdiff' (Differential Abundance Analysis for Phosphoproteomics Data) Provides tools for analyzing differential abundance in proteomics experiments. Implements S3 classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) ), Aligned Rank Transform (ART; Wobbrock et al. (2011) ), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps. Package: r-cran-pepe Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-psych, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pepe_1.2.0-1.ca2404.1_all.deb Size: 877752 MD5sum: 444a9a457fd6064224b2d82da275d35c SHA1: 7503820073ec7fbebc68f5646d5a230c4541ba16 SHA256: 1d55cd6798aa57f78e4b536e7e8d1dfc40c5654e56cc02cf155b24c9e017cf06 SHA512: 5e201d151b3e0a78893b005980d32f101187ae88909e4d5357b5467adf377d60faa5b47318bb0a277b9aa487400c90b81c36ba5e2c537b3b398803aba3b713f0 Homepage: https://cran.r-project.org/package=pepe Description: CRAN Package 'pepe' (Data Manipulation) Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor. Package: r-cran-pepmapviz Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-stringr, r-cran-ggforce, r-cran-ggh4x, r-cran-ggnewscale, r-cran-data.table, r-cran-rlang, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-mzid, r-bioc-msnbase Filename: pool/dists/noble/main/r-cran-pepmapviz_1.1.0-1.ca2404.1_all.deb Size: 446384 MD5sum: 9bf73f0243630ca5874bb9b49ef11dcf SHA1: 2b704a000fa3cb693655c3289e962ebc562207a3 SHA256: d5fefae6dd34541b09708b758c73b48df60de0a31b40af27d670c608fa57ab5f SHA512: f97985490e61f6530b5226a2da9bfede8b5b28f48935d31ba87b046b228c3212508e786e95b1491876160c818eaaf16d97c8d5183900b48ae888aa4e4db40606 Homepage: https://cran.r-project.org/package=PepMapViz Description: CRAN Package 'PepMapViz' (A Versatile Toolkit for Peptide Mapping, Visualization, andComparative Exploration) A versatile R visualization package that empowers researchers with comprehensive visualization tools for seamlessly mapping peptides to protein sequences, identifying distinct domains and regions of interest, accentuating mutations, and highlighting post-translational modifications, all while enabling comparisons across diverse experimental conditions. Potential applications of 'PepMapViz' include the visualization of cross-software mass spectrometry results at the peptide level for specific protein and domain details in a linearized format and post-translational modification coverage across different experimental conditions; unraveling insights into disease mechanisms. It also enables visualization of Major histocompatibility complex-presented peptide clusters in different antibody regions predicting immunogenicity in antibody drug development. 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Package: r-cran-pepr Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-yaml, r-cran-stringr, r-cran-data.table, r-cran-rcurl, r-cran-httr2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-curl Filename: pool/dists/noble/main/r-cran-pepr_0.6.1-1.ca2404.1_all.deb Size: 285780 MD5sum: 6d80090b54166b4fb4f6d3a921d65430 SHA1: 08d8e71f8b6dfd2e7ffb00806966e5ef825bd122 SHA256: 51300f52edd810a826a12ed349ae6feee3b47cff54efdecfd5017e7af440684e SHA512: 7f3de9613ac81e75ebe7ee8ccb9fb2c4fecbe7d2bfaaee0644462ad582b61b697132730a347c4cb9f8315ca56b8aa40e67b5acdfd5fb22e38c5d2418453d9397 Homepage: https://cran.r-project.org/package=pepr Description: CRAN Package 'pepr' (Reading Portable Encapsulated Projects) A PEP, or Portable Encapsulated Project, is a dataset that subscribes to the PEP structure for organizing metadata. It is written using a simple YAML + CSV format, it is your one-stop solution to metadata management across data analysis environments. This package reads this standardized project configuration structure into R. Described in Sheffield et al. (2021) . Package: r-cran-pepsavims Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3447 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-elasticnet Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pepsavims_0.9.1-1.ca2404.1_all.deb Size: 3381028 MD5sum: 62fd57aecaed0f0afc4a992ce7bcf9a1 SHA1: 546c74a0bf6e15880c3a304d1a317f7945338c74 SHA256: 8fd971dd2e162660da0dc2f5a581502f3080be229c9029b73fcbdd29246f3f49 SHA512: ce250cf5def0a184c4b49406a59e635e1eb858faace5a505cd1912bc4ea0c63cd722380dc0e8eea6d880f852ba40792081edbba630dcf99b5e7816ef37872fe7 Homepage: https://cran.r-project.org/package=PepSAVIms Description: CRAN Package 'PepSAVIms' (PepSAVI-MS Data Analysis) An implementation of the data processing and data analysis portion of a pipeline named the PepSAVI-MS which is currently under development by the Hicks laboratory at the University of North Carolina. The statistical analysis package presented herein provides a collection of software tools used to facilitate the prioritization of putative bioactive peptides from a complex biological matrix. Tools are provided to deconvolute mass spectrometry features into a single representation for each peptide charge state, filter compounds to include only those possibly contributing to the observed bioactivity, and prioritize these remaining compounds for those most likely contributing to each bioactivity data set. Package: r-cran-peptoolkit Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-peptides, r-cran-dplyr, r-cran-stringr, r-cran-caret Filename: pool/dists/noble/main/r-cran-peptoolkit_0.0.1-1.ca2404.1_all.deb Size: 41488 MD5sum: 469c8eb6f3406cc6dca92e86fbe0f502 SHA1: 522b9f8abe8ac012bd00c9f6115c78f9f18119ce SHA256: 658fc8679b9af0143e9b81b796d2b5b512e2db7189761270eb0d6ae7092c9ef3 SHA512: 3ef6ceec79f5862b4b2f21cbb391973dba289b5631b282237fe2c6706ab56e9fe7cf99a60dc01b172e9d9368ef42d2a9bea650e98fcfcc505aade005aa424174 Homepage: https://cran.r-project.org/package=peptoolkit Description: CRAN Package 'peptoolkit' (A Toolkit for Using Peptide Sequences in Machine Learning) This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) ). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning. Package: r-cran-pequod Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-pequod_0.2.0-1.ca2404.1_all.deb Size: 32840 MD5sum: 6b7463f17839ee61fc0ecde7b9a7494c SHA1: 5e8518199ba04275c892a2b938f16ab00b10ce1c SHA256: ae13c06a418cb642bf369b5503324b5534b680126cec18bc476f2e941360f383 SHA512: 8889d64bc84aa7ea559b9426784a33ac9ea3e36beacf9f35beeefd65c53dbf9a455d5ea673494a1bafd31c6bace306c7b369fbf0ff4f060b6dda3d3c69906950 Homepage: https://cran.r-project.org/package=pequod Description: CRAN Package 'pequod' (Colour Palette for Reading and Code, Inspired by Moby-Dick) The Pequod colour palette, named after the whaler in Herman Melville's Moby-Dick. Provides the full Log base scale from warm paper (Log 50) to deep ink (Log 950), eight crew accent hues with light and dark variants, and 'ggplot2' scales for discrete and continuous mapping. Designed for long-form reading and code, with low saturation and a consistent earth- pigment register. Full design rationale and accessibility notes at . Package: r-cran-peramo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lme4, r-cran-parameters, r-cran-emmeans Suggests: r-cran-multcompview Filename: pool/dists/noble/main/r-cran-peramo_0.1.5-1.ca2404.1_all.deb Size: 150154 MD5sum: 225342398bc76a830f7f332d546e0212 SHA1: cdbebde70630a3fe5d1951939a11e53f4d42306d SHA256: 8719ef9c179d565c83ba05fff5af0e7b1fa3fe315ab2dbc5c1947b1f9bacb026 SHA512: e35467e93d797633fd0bf0fd682897954109e3939c182e816bd94601487b70b170ce29529b366554fe289bce1d2eeb1f5734f3b7155db1a11a35137d42f3d81c Homepage: https://cran.r-project.org/package=peramo Description: CRAN Package 'peramo' (Permutation Tests for Randomization Model) Perform permutation-based hypothesis testing for randomized experiments as suggested in Ludbrook & Dudley (1998) and Ernst (2004) , introduced in Pham et al. (2022) . Package: r-cran-perarma Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-gnm, r-cran-matlab, r-cran-matrix, r-cran-signal Filename: pool/dists/noble/main/r-cran-perarma_1.7-1.ca2404.1_all.deb Size: 280790 MD5sum: ec09f9e6d70c771a3292446df7e3f198 SHA1: 6d877fc9680ace93ad4028bdf5f65d57b3dfa8ad SHA256: 1d4d7fc212957aa6c06cd2548fdf69f4e5a7bcecc629b4b7e2301bd4323a932b SHA512: c58c6a6da761b107f7ec52dea4956ae6688c065bdb6ba40fe88f921bc7fd59b8ac7e36475b4a13b41a96cc53918105aa7a78040c1431ca7e9727834d3fb1d9e1 Homepage: https://cran.r-project.org/package=perARMA Description: CRAN Package 'perARMA' (Periodic Time Series Analysis) Identification, model fitting and estimation for time series with periodic structure. Additionally, procedures for simulation of periodic processes and real data sets are included. Hurd, H. L., Miamee, A. G. (2007) Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994) Brockwell, P. J., Davis, R. A. (1991, ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN: 9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6) Bloomfield, P., Hurd, H. L.,Lund, R. (1994) Dehay, D., Hurd, H. L. (1994, ISBN:0-7803-1023-3) Vecchia, A. (1985) Vecchia, A. (1985) Jones, R., Brelsford, W. (1967) Makagon, A. (1999) Sakai, H. (1989) Gladyshev, E. G. (1961) Ansley (1979) Hurd, H. L., Gerr, N. L. (1991) . Package: r-cran-perc Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-reshape2, r-cran-lattice Filename: pool/dists/noble/main/r-cran-perc_0.1.6-1.ca2404.1_all.deb Size: 137204 MD5sum: e698728ecdb69c46edeb17eacbeefca3 SHA1: b065a0691651ff808fae611c761b1a432c28046b SHA256: 46b6b49f15f8e95a00894f54992b4c7e62efbbf3fcbc2c787b1d4950a2430035 SHA512: 8a35c25dcdb05e0cd18a32d4c61cadc238ffd40d64834222dd38900a476e02e64fbcafbd332f8aa99f2ed435ddf978bb5e1d6966d4dc5634dbee805b6abd9a98 Homepage: https://cran.r-project.org/package=Perc Description: CRAN Package 'Perc' (Using Percolation and Conductance to Find Information FlowCertainty in a Direct Network) To find the certainty of dominance interactions with indirect interactions being considered. Package: r-cran-perccalc Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-multcomp Suggests: r-cran-magrittr, r-cran-spelling, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-mass, r-cran-cardata, r-cran-tidyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-perccalc_1.0.5-1.ca2404.1_all.deb Size: 572400 MD5sum: bc1c23e6fa8b187c89f2d25e5dba9a79 SHA1: 18f90c07fb8a2f908eff91c865233378d9f67060 SHA256: 41d7e0fe88e30e9ec73d1727bbf26559cad7b97290c9d97f21d0a4036dcd2bab SHA512: bb0bbc5491237fa8e28785cc18c5b4185abcd5a688ff181aaeacdac7b6ca770f03be8913ecafed15daf0fbd1ece2f16c1a6cf154c8474c59e886809764e6d029 Homepage: https://cran.r-project.org/package=perccalc Description: CRAN Package 'perccalc' (Estimate Percentiles from an Ordered Categorical Variable) An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). 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Package: r-cran-perfit Architecture: all Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ltm, r-cran-mirt, r-cran-fda, r-cran-hmisc, r-cran-irtoys, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-perfit_1.4.7-1.ca2404.1_all.deb Size: 299406 MD5sum: 2b954e418a61a48cb1c6727266ef4981 SHA1: b47c7f863766fbeff391dd66c488b0faabd04b12 SHA256: 547f88f328fe59de09661d6fd9d7d7e573e51b360d155e8786ce027e4a6b801e SHA512: 6224e6d39a45a565c09c36c2135ac2a309ca3e64d56c37e1af35d6c527c8c360512b1074e1bb95f5f69a3629cce626284d18cce0f8fb10571c0454c55f0e7587 Homepage: https://cran.r-project.org/package=PerFit Description: CRAN Package 'PerFit' (Person Fit) Several person-fit statistics (PFSs; Meijer and Sijtsma, 2001, ) are offered. These statistics allow assessing whether individual response patterns to tests or questionnaires are (im)plausible given the other respondents in the sample or given a specified item response theory model. Some PFSs apply to dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial correlation, caution index, (normed) number of Guttman errors, agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include extensions of lz, U3, and (normed) number of Guttman errors. Package: r-cran-performance Architecture: all Version: 0.18.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3444 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-insight, r-cran-datawizard Suggests: r-cran-aer, r-cran-afex, r-cran-bayesfactor, r-cran-bayesplot, r-cran-betareg, r-cran-bigutilsr, r-cran-blavaan, r-cran-boot, r-cran-brms, r-cran-car, r-cran-cardata, r-cran-compquadform, r-cran-correlation, r-cran-cplm, r-cran-curl, r-cran-dagitty, r-cran-dbscan, r-cran-dharma, r-cran-discovr, r-cran-effectsize, r-cran-estimatr, r-cran-fixest, r-cran-flextable, r-cran-forecast, r-cran-ftextra, r-cran-gamm4, r-cran-ggdag, r-cran-glmmtmb, r-cran-gparotation, r-cran-hmisc, r-cran-httr2, r-cran-ics, r-cran-icsoutlier, r-cran-islr, r-cran-ivreg, r-cran-lavaan, r-cran-lme4, r-cran-lmtest, r-cran-loo, r-cran-mass, r-cran-matrix, r-cran-mclogit, r-cran-mclust, r-cran-metadat, r-cran-metafor, r-cran-mgcv, r-cran-mlogit, r-cran-modelbased, r-cran-multimode, r-cran-nestedlogit, r-cran-nlme, r-cran-nnet, r-cran-nonnest2, r-cran-ordinal, r-cran-parameters, r-cran-patchwork, r-cran-pscl, r-cran-psych, r-cran-psychtools, r-cran-quantreg, r-cran-qqplotr, r-cran-randomforest, r-cran-rcppeigen, r-cran-reformulas, r-cran-rempsyc, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rstantools, r-cran-sandwich, r-cran-see, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tweedie, r-cran-vgam, r-cran-withr Filename: pool/dists/noble/main/r-cran-performance_0.18.2-1.ca2404.1_all.deb Size: 2905588 MD5sum: a6493042a3f658ffcd3c1f843a77aa83 SHA1: 2d19799a48289f2bd28f87db4cc33bcb77c87b5e SHA256: b1d03de0c8d11728e58e35c0c3e412e4e642756e1fb60769fd1e064b05945911 SHA512: 27097701acf202d510c32c5662b51be4ecb6941f76062e5c945ddbd628f9248203e1149d9d716ee967298fca5443a17edbb348adb6f5c6a4b26db84ef77b4e64 Homepage: https://cran.r-project.org/package=performance Description: CRAN Package 'performance' (Assessment of Regression Models Performance) Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. 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Package: r-cran-periodictable Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-periodictable_0.1.2-1.ca2404.1_all.deb Size: 45138 MD5sum: ea99f4ff68de7304e132e27afeaf295c SHA1: 3faf157067d2fa21eeace4b0e9168bf8f3564b9a SHA256: 54b2bd150d1bc81a978c87c49718fc49ebbebb2e364f69af57d71dc1bf9e2a85 SHA512: 69f91c2f2c558a43db3b6ec8d17a8477ff955d78d3f41012d5901e7bda1f4c3071514ac263fc2e4f01f4301bde88682583a60e216b2218adf6fcef964e4b5a3f Homepage: https://cran.r-project.org/package=PeriodicTable Description: CRAN Package 'PeriodicTable' (Periodic Table of the Elements) Provides a dataset containing properties for chemical elements. Helper functions are also provided to access some atomic properties. 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Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) . Package: r-cran-perm Architecture: all Version: 1.0-0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coin Filename: pool/dists/noble/main/r-cran-perm_1.0-0.4-1.ca2404.1_all.deb Size: 87458 MD5sum: 6f120c0c7044f7245c6b7e5ae745eea2 SHA1: 44abf05ac59f4235eb07e001af431deaecae615e SHA256: 8dbe058d3f1a48c2e5383e201447e1f0229c5d58a6d9d9ffb5634725b243a781 SHA512: 6b309582dc23442dc2209ef0e9c28872dda28e63bb8472addd49e308d306c9c1eeed67b17be23656c189ae1366b591441653da30a1bc8fd008cff5a2d37bcf45 Homepage: https://cran.r-project.org/package=perm Description: CRAN Package 'perm' (Exact or Asymptotic Permutation Tests) Perform Exact or Asymptotic permutation tests [see Fay and Shaw ]. 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The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon. 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Package: r-cran-permcor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-permcor_0.1.0-1.ca2404.1_all.deb Size: 43334 MD5sum: 06e3b907a5545801ace0f3c798fa6806 SHA1: f62da79912e446986892aae6acd9fa529591f724 SHA256: ea97e512b1e3e0b71209997acc512e3d8f3039af334a6637f63b4d8ff22c5e7c SHA512: 0c0a64582d1c9c857086f0a80f0e75ffbc73b488cf2ebba889595efebd390c21e6bac69059691736ed447c3952685d9597afa7b8bb2a3eea2339568fc28fd368 Homepage: https://cran.r-project.org/package=PermCor Description: CRAN Package 'PermCor' (Robust Permutation Tests of Correlation Coefficients) Provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200). Package: r-cran-permgs Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-coin Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-permgs_0.2.5-1.ca2404.1_all.deb Size: 90778 MD5sum: 55c598a8e57fcad8cd84d52a6aca7577 SHA1: a56cc7b6442a046f6812a894b0384739a7253848 SHA256: 50b4aa7729627ba36fc8ccd5e866222b971da009b56d70e7e5c6c596558d1789 SHA512: a8328ff98c1e7a8547a482c27d178fa84c628780a62aee2652184e0e4fd39dec0257ac079648e06e3446b254bcd698ccc181d73491327e97a99c6bd0b2077129 Homepage: https://cran.r-project.org/package=permGS Description: CRAN Package 'permGS' (Permutational Group Sequential Test for Time-to-Event Data) Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different. Package: r-cran-permimp Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ipred, r-cran-party, r-cran-pbapply, r-cran-randomforest, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-permimp_1.1-0-1.ca2404.1_all.deb Size: 146970 MD5sum: 75b123e2ce45f0559da1cefe84e3fd81 SHA1: 1908404eca791840ba9c38971e1d63018764308b SHA256: 3580d1933789ee8759c5709a265bc6a276dc4db8b95b3aa1a2fbba8b6871c7b8 SHA512: c0c2a66ab127473dc7e917fb023ab32800104ab5932caa9cc7eaba3ac328c55f6c1920399c8d66bee7c898b4fefe0e04ba1c891bf397c73e7f190ba2a49e841c Homepage: https://cran.r-project.org/package=permimp Description: CRAN Package 'permimp' (Conditional Permutation Importance) An add-on to the 'party' package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the 'party' package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the 'party' implementation. The package is compatible with random forests fit using the 'party' and the 'randomForest' package. The methods are described in Strobl et al. (2007) and Debeer and Strobl (2020) . Package: r-cran-permrand Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-permrand_1.0.0-1.ca2404.1_all.deb Size: 387796 MD5sum: d1ac021195b1fd05f98c89103381e3f5 SHA1: 98e6142c88e78317c97733e634ddfc7982856f75 SHA256: f5c322eb0113d6428da5877625cf394f3bb64619042f46ceafacf425a03c842c SHA512: b8e3e12c12be5385f253137b6c01e60f176b0a3c8562d90376db6bdfbf2c720d0cbc916de4fcbb121948ae0bfda1efeff9214e4d85dfcf61b73f38a843a10097 Homepage: https://cran.r-project.org/package=permRand Description: CRAN Package 'permRand' (Permutation Randomization) Provides randomization using permutation for applications. To provide a Quality Control (QC) check, QC samples can be randomized within strata. A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches. The functions are flexible for users by specifying strata size and number of QC samples per strata. The randomization meets the following requirements • QC sample requirements: QC samples not adjacent, QC samples from same mother must follow certain patterns. • Matched sample sets must be within a single strata, and next to each other. Package: r-cran-permubiome Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-dabestr, r-cran-gridextra, r-cran-matrix, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-permubiome_1.3.2-1.ca2404.1_all.deb Size: 59168 MD5sum: f3c29682989cb25d4840ee6e7267a052 SHA1: 01f6f652c70b91a291159c247e5d28e1cd6a658c SHA256: dc9b2a0ccef84f7db7a7d8f1234680305ed1e54162c725447d297e45bc4ed674 SHA512: 85686e416a87b9259c6ec5aa2480fce4be5b32154686851accfc901aa084ccaa4ace493f74d2d87a4a0534a7715baf17d0b784acf9b9bee269cffef4897eb83e Homepage: https://cran.r-project.org/package=permubiome Description: CRAN Package 'permubiome' (A Permutation Based Test for Biomarker Discovery in MicrobiomeData) The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions' or 'Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated 'dabestr' functions. Package: r-cran-permutationr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-permutationr_0.1.0-1.ca2404.1_all.deb Size: 14180 MD5sum: fd2993c16c61ff78e3a067f90f649ae1 SHA1: e8415fd5f77fda50ad15f7965b3414439186a97c SHA256: 21a6bfc9d09d490508a1d8e29462b0cb62a41d05e4f17bb6d495dc63e24bedf9 SHA512: 03442a76ed1a11d43ede7c30f3404efe1b4c5a6f1183f8b864cc248a4585b37b57c1274eaddb07df11e6416334f9f88102c98c0c08a143ccd8be86ac4ea28a0b Homepage: https://cran.r-project.org/package=PermutationR Description: CRAN Package 'PermutationR' (Conduct Permutation Analysis of Variance in R) Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs. Package: r-cran-permutations Architecture: all Version: 1.1-9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magic, r-cran-numbers, r-cran-partitions, r-cran-freealg Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-permutations_1.1-9-1-1.ca2404.1_all.deb Size: 617142 MD5sum: ce5a39c09b2ff8aa61e9d90e336bdf48 SHA1: 08a00fdae6b52fc3cfc521d44112753808e8cf2a SHA256: 19d36380d383fce3835d20c50c3b597ef51710ec9cc0adb9d87d25e4ee957b60 SHA512: 19867bda07ca154c29cbfb9d053c2fba6f0348e4e73d8774e08e511579480b2e4d8fc55fa6ee597d536fe18e387bcefe7d4ae03cf9eaa62f22c3a6c0d8fcabee Homepage: https://cran.r-project.org/package=permutations Description: CRAN Package 'permutations' (The Symmetric Group: Permutations of a Finite Set) Manipulates invertible functions from a finite set to itself. Can transform from word form to cycle form and back. To cite the package in publications please use Hankin (2020) "Introducing the permutations R package", SoftwareX, volume 11 . Package: r-cran-permute Architecture: all Version: 0.9-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vegan, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-sessioninfo Filename: pool/dists/noble/main/r-cran-permute_0.9-10-1.ca2404.1_all.deb Size: 219854 MD5sum: 0e4b377c899972887fff2c0ed3760472 SHA1: c5a43074ddc5095d5ec1aebe398c635c7f8e10a6 SHA256: bafc25c25baa0ed25c7d901d0c4a4264a3e2869dc199b8fd02d54e60fa218827 SHA512: 55ae2f9ad9d7dea048ba84fcdd80cefcb57edb9b9236dce6d5ecb3ec8612d93b20d0c5f47e4d9c32c6570c1f52bcbb66745ab2ae83992afce41d13343060d5a3 Homepage: https://cran.r-project.org/package=permute Description: CRAN Package 'permute' (Functions for Generating Restricted Permutations of Data) A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak. Package: r-cran-permutes Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Suggests: r-cran-buildmer, r-cran-car, r-cran-doparallel, r-cran-ggplot2, r-cran-glmmtmb, r-cran-knitr, r-cran-lme4, r-cran-lmperm, r-cran-permuco, r-cran-rmarkdown, r-cran-viridis Filename: pool/dists/noble/main/r-cran-permutes_2.8-1.ca2404.1_all.deb Size: 3002532 MD5sum: fcb451da910e50c649c7712ef89a4ef5 SHA1: 7511894d431350ece8c6111bee973f348ec90b94 SHA256: d14926809dcd75172adee8fa94e4af97d1e71356444073a9c46c5982922f8dea SHA512: decd4d6735201ab67a5b035bfd722b9bec71e322f972f02c164df86cfe07bf796c4e4f86b9b668802561916a560fe594be249c5c2403416477f18119e23999b9 Homepage: https://cran.r-project.org/package=permutes Description: CRAN Package 'permutes' (Permutation Tests for Time Series Data) Helps you determine the analysis window to use when analyzing densely-sampled time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007) . These permutation tests can help identify the timepoints where significance of an effect begins and ends, and the results can be plotted in various types of heatmap for reporting. Mixed-effects models are supported using an implementation of the approach by Lee & Braun (2012) . Package: r-cran-permutest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-permutest_1.0.0-1.ca2404.1_all.deb Size: 63978 MD5sum: 4c2bda4575ebb7ef212063a3cdf1ec34 SHA1: b0d1c1f9744ca01b9e946a3bb1b01f3bec88475d SHA256: 8e1585b9925f15e786ed9a458d06cd72d6afe992c0ef09f71d5e946114867694 SHA512: 77290781be6db201aa194225230c271d439ce4f2f3136d0d9e0a5cdfc5446909689ebd15185222b341e4abbe34b0b5a021f594f3da085386d3091cb3408a1486 Homepage: https://cran.r-project.org/package=permutest Description: CRAN Package 'permutest' (Run Permutation Tests and Construct Associated ConfidenceIntervals) Implements permutation tests for any test statistic and randomization scheme and constructs associated confidence intervals as described in Glazer and Stark (2024) . 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It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) . Package: r-cran-perry Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-perryexamples, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-perry_0.3.1-1.ca2404.1_all.deb Size: 239170 MD5sum: 5b1b69f3ce6ba500d105584d8bd66624 SHA1: 16d5d41f971254c183c91bc10f3713758d4618bc SHA256: 426a676993ad8137a2141adfc701cf0836c3d828e1d2ba937ac9c19557e449d9 SHA512: 7919279739d6cc163a353a8905eb7a7cafecf948e78b502c60eef06fb1810f289e88ca10feae123668dd985baa8f8f0f13c879fbf3d8dcc9792dd71e24447192 Homepage: https://cran.r-project.org/package=perry Description: CRAN Package 'perry' (Resampling-Based Prediction Error Estimation for RegressionModels) Tools that allow developers to write functions for prediction error estimation with minimal programming effort and assist users with model selection in regression problems. 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It provides the foundation for developing and running Perseus plugins implemented in R by providing all required input and output handling, including data and parameter parsing as described in Rudolph and Cox 2018 . Package: r-cran-persianstemmer Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-persianstemmer_1.0-1.ca2404.1_all.deb Size: 80106 MD5sum: ad635a7191c982aaa4cd3059dafda6b0 SHA1: 3cc0458c0b62f8943088a1c8481c9add560e464e SHA256: c5c2186bb1528f80a3e59d907ed81d9e2770a58c487429f012fe24ea9a129de3 SHA512: 2d4c834062ca950baabac8640f6656d15fe7ca252f2747a7cf1221036355ab21e8d4ad9e6718b9e511c3c83f93980d1f4a9fe7fe6df990f6832f77f2de9ba53b Homepage: https://cran.r-project.org/package=PersianStemmer Description: CRAN Package 'PersianStemmer' (Persian Stemmer for Text Analysis) Allows users to stem Persian texts for text analysis. Package: r-cran-persomicsarray Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jpeg, r-cran-tiff, r-cran-stringr, r-cran-raster Filename: pool/dists/noble/main/r-cran-persomicsarray_1.0-1.ca2404.1_all.deb Size: 724486 MD5sum: 2bfd27ad5df5456a5e3bb55ac1de61d2 SHA1: 7ae7a814558997096be6635a91f7d98f6e426c2c SHA256: 73b32fe46e245b3ea6445627e665d1b3f52bc39346119db205796882573e1777 SHA512: 7db8e89dc7b8c5fd1e102c25103025bc48828a232d64d6f0d73ef501487adcbfa63249cf41f350273b252da1f83c7546cc2498372821d0485a99eb1fbb3a22af Homepage: https://cran.r-project.org/package=PersomicsArray Description: CRAN Package 'PersomicsArray' (Automated Persomics Array Image Extraction) Automated identification of printed array positions from high content microscopy images and the export of those positions as individual images written to output as multi-layered tiff files. Package: r-cran-personalized Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mgcv, r-cran-ggplot2, r-cran-plotly, r-cran-survival, r-cran-kernlab, r-cran-foreach, r-cran-xgboost, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-nnet Filename: pool/dists/noble/main/r-cran-personalized_0.2.8-1.ca2404.1_all.deb Size: 998662 MD5sum: bee9fa3694329582f655f25e4a6cd75e SHA1: 44fafc796ae010c1a4ffc51745b6786da6fec585 SHA256: f377f354196c2906999f2ec5e0a7f8f8188f7a435b6ed17566a55b6aca3f3aa6 SHA512: 5c21aac067f97c9c7a83cc5e826f94662aeb4deeb76a52e7e5f86e8a38afb9bbef5b04e426da206664f831b6f914ee9b6234ccdc7e203a695d2536cf46838da1 Homepage: https://cran.r-project.org/package=personalized Description: CRAN Package 'personalized' (Estimation and Validation Methods for Subgroup Identificationand Personalized Medicine) Provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) . This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) . Package: r-cran-personalr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desc, r-cran-devtools, r-cran-fs, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rprojroot, r-cran-rstudioapi, r-cran-usethis, r-cran-withr, r-cran-xfun Suggests: r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-personalr_1.0.3-1.ca2404.1_all.deb Size: 61708 MD5sum: f3bf378d903d005b728c55fb9b65bdc9 SHA1: ec8b087d76901e98aef367ab8e80ea979ade4e08 SHA256: 18105e9e898d7460432128b762422ba5eecca4d4eaaf9a084230b60806092b86 SHA512: 3c0f663fca1834ed08e322566f7c07a97566cafafcab98d491e3639e31dbc06e3ec74a02b809b7fb22c0b5601919988e8c3b445f7b34d389b42a1bc80c017f6d Homepage: https://cran.r-project.org/package=personalr Description: CRAN Package 'personalr' (Automated Personal Package Setup) Functions to setup a personal R package that attaches given libraries and exports personal helper functions. Package: r-cran-personnelselectionutility Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1346 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-personnelselectionutility_1.0.2-1.ca2404.1_all.deb Size: 880384 MD5sum: b47857f71fdff95a14d8554f8db4c314 SHA1: 26294263a90446976abcc00921b7e9cff92d5bd8 SHA256: 3492b44d2aca121d89d7a6633e9ab67447eb52e040c86c680ba0f34b41b5d404 SHA512: c18e811820fd24a2c6b83012e975fa9f53e19fb2b7428081804036594d88a84b88e538e03cb85ac76864d930711d5fe3c614d8320bcc98bdb8181b58e76d0749 Homepage: https://cran.r-project.org/package=personnelSelectionUtility Description: CRAN Package 'personnelSelectionUtility' (Utility Analysis Methods for Personnel Selection) Implements classical and contemporary utility-analysis methods for personnel selection, organised by criterion scale (classification or continuous/monetary) and selection structure (compensatory or multiple-hurdle). Methods include Taylor-Russell classification (Taylor and Russell, 1939, ), Brogden-Cronbach-Gleser monetary utility (Brogden, 1949, ), Schmidt-Hunter-Pearlman intervention utility (Schmidt and others, 1979, ), Sturman comprehensive cascade (Sturman, 2001, ), Thomas-Owen-Gunst multivariate classification (Thomas and others, 1977, ), compensatory versus multiple-hurdle simulation (Ock and Oswald, 2018, ), AUC-to-effect-size conversions (Salgado, 2018, ), Pareto frontiers for validity-diversity trade-offs, and Monte Carlo uncertainty propagation. Package: r-cran-personr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-whisker Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-personr_1.0.0-1.ca2404.1_all.deb Size: 197844 MD5sum: 80698cc0f82a02b281ec0ceec9e853fa SHA1: 22ce89ce3d16720d8c7283450f2366c12ea1d997 SHA256: 38c2c7c790fd83b7013f7c6703a18df08424c106fe70d0c477b9eb35eeacfaa1 SHA512: b02ad3ff79c1c1c46033e2bb94ca1dfdb4497009f83fac32e37940844c4f012351fd7e2a3fb8f399871c469a00bbee8026bb1ceb0faaa9aea619866b7683744b Homepage: https://cran.r-project.org/package=personr Description: CRAN Package 'personr' (Test Your Personality) An R-package-version of an open online science-based personality test from , providing a better-designed interface and a more detailed report. The core command launch_test() opens a personality test in your browser, and generates a report after you click "Submit". In this report, your results are compared with other people's, to show what these results mean. Other people's data is from . Package: r-cran-perspectev Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-foreach, r-cran-ape, r-cran-doparallel, r-cran-mapproj, r-cran-boot, r-cran-sp Filename: pool/dists/noble/main/r-cran-perspectev_1.1-1.ca2404.1_all.deb Size: 98270 MD5sum: e9a0eae07c4de6bb3689f3227f25c9bd SHA1: f53767e4399b21cb50317e2089e2eddf82338eaf SHA256: 038033807f89687457b7ac0fd430f8d3a33c59c59a34e56ef1b4a77552ed1cb8 SHA512: e569275a5ad059484c5a510cb81d06e96990d39aab82392ef4c80923d00a6107d36a630cc77b810ece921df02c8a7ce50e5fe667055f3f6e944e68bd28c65b76 Homepage: https://cran.r-project.org/package=perspectev Description: CRAN Package 'perspectev' (Permutation of Species During Turnover Events) Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html. Package: r-cran-perspectiver Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-arrow, r-cran-shiny, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-perspectiver_0.3.0-1.ca2404.1_all.deb Size: 4924764 MD5sum: 8233978e57f76dc0b7d41e0c98397f61 SHA1: 8b694c94a98b5e10c32a87d0f0037aaff2788b48 SHA256: c63f6757cdee88e571a2078098c7f2e365751709dfa6d921b78119db66d156db SHA512: a2ae3e258ee01aac739a8099622205af3a0a4ee4d0aef09394180641de14101dcafd6e8a712d795b1904fc82eda3beb6e23f15fb09762f74d6ad526868b10bbb Homepage: https://cran.r-project.org/package=perspectiveR Description: CRAN Package 'perspectiveR' (Interactive Pivot Tables and Visualizations with 'Perspective') An 'htmlwidgets' binding for the 'FINOS Perspective' library, a high-performance 'WebAssembly'-powered data visualization engine. Provides interactive pivot tables, cross-tabulations, and multiple chart types (bar, line, scatter, heatmap, and more) that run entirely in the browser. Supports self-service analytics with drag-and-drop column selection, group-by/split-by pivoting, filtering, sorting, aggregation, and computed expressions. Works in 'RStudio' Viewer, 'R Markdown', 'Quarto', and 'Shiny' with streaming data updates via proxy interface. Package: r-cran-persuade Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-flexsurv, r-cran-flexsurvcure, r-cran-ggplot2, r-cran-muhaz, r-cran-rmarkdown, r-cran-rms, r-cran-sft, r-cran-survival, r-cran-survminer Suggests: r-cran-kableextra, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-persuade_0.1.2-1.ca2404.1_all.deb Size: 574116 MD5sum: 98541ea9682cffb66c0162bba79efc4a SHA1: f42b9d1340d41d66672bd4d68111bfa4f0a22597 SHA256: 6fb6192a595ec36cce8c536c95f0a782bc93130df33ff7ee469cc37cded8de54 SHA512: 9b19bd93d24052a8fdd9582733ad230d351ec6d5c086af97582c9c3f688a56f8663997adf09638f19547da9f57b3261842ff89df316a05fd5ae7996296218923 Homepage: https://cran.r-project.org/package=PERSUADE Description: CRAN Package 'PERSUADE' (Parametric Survival Model Selection for Decision-Analytic Models) Provides a standardized framework to support the selection and evaluation of parametric survival models for time-to-event data. Includes tools for visualizing survival data, checking proportional hazards assumptions (Grambsch and Therneau, 1994, ), comparing parametric (Ishak and colleagues, 2013, ), spline (Royston and Parmar, 2002, ) and cure models, examining hazard functions, and evaluating model extrapolation. Methods are consistent with recommendations in the NICE Decision Support Unit Technical Support Documents (14 and 21 ). Results are structured to facilitate integration into decision-analytic models, and reports can be generated with 'rmarkdown'. The package builds on existing tools including 'flexsurv' (Jackson, 2016, )) and 'flexsurvcure' for estimating cure models. Package: r-cran-persuasio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-persuasio_0.1.0-1.ca2404.1_all.deb Size: 120870 MD5sum: 7e7b02ac4c06bb7af7702377d1163cd4 SHA1: 194d3c23fea97253e5597aab1609f556f6b8761d SHA256: 074971396dc45c0cf163a042723f9dc99bc554758522945881a1dd540309a3b0 SHA512: f0e1c595288841ba4f7493bc19c20af95ad19d2e7378eb679eeede45adedfed12b825129bc7da62b1152b9dd3f06dfb6aa5c8ef58f3c0f6a6bef34b6f7e2f623 Homepage: https://cran.r-project.org/package=persuasio Description: CRAN Package 'persuasio' (Causal Inference on Persuasion Effects) Provides estimation and inference methods for causal persuasion rates in the potential-outcomes framework of Jun and Lee (2023, Journal of Political Economy) . The package computes bounds and confidence intervals for average and local persuasion rates under data scenarios with binary outcomes, treatments, and instruments, and also when only the outcome and instrument are observed. It also provides functions for calculating bounds from summary statistics. Package: r-cran-persval Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmsb Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-persval_1.1.2-1.ca2404.1_all.deb Size: 72184 MD5sum: 11d555fb745d79e152aa2e7a7224d8ad SHA1: d3bd0f2962f00ac3c6ac04041a222eb3948cd6a6 SHA256: 3ecde6bc88ff2edc8d7711abf33f14449bd0187ff2763585e764caa2eb8c138c SHA512: f46b45fa8ee5e2261ac9893b6201e50b5a7b83d76e36270dc9306a5206cd56bb8e4610f3dcff4d821a1079227e7166529c916fd88b320bdac9f0ab6f37e54fbc Homepage: https://cran.r-project.org/package=persval Description: CRAN Package 'persval' (Computing Personal Values Scores) Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results. Package: r-cran-perturbr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-perturbr_0.1.3-1.ca2404.1_all.deb Size: 146906 MD5sum: 4fbddf6f3cfe4487ba15b5c317772b5b SHA1: 8f1474957e24362bc1c543126a361b0e2c432a2a SHA256: 567c4ed3360bc3c61817b45ae1daa3d56fe1ba011877bd2b3ab19ac994ec9d99 SHA512: 6b43cd5aaed963a0897c53483ef543aec236323aecced1cda3014b4315a867a10014a9e494cefe92a3a13e7000c76a58d9f7982fc4f70ceaf2e18b1918467366 Homepage: https://cran.r-project.org/package=perturbR Description: CRAN Package 'perturbR' (Random Perturbation of Count Matrices) The perturbR() function incrementally perturbs network edges (using the rewireR function)and compares the resulting community detection solutions from the rewired networks with the solution found for the original network. These comparisons aid in understanding the stability of the original solution. The package requires symmetric, weighted (specifically, count) matrices/networks. Package: r-cran-peruapis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peruapis_0.1.0-1.ca2404.1_all.deb Size: 490842 MD5sum: f45a1346e444e8fb178ea03a46116f66 SHA1: 32ecaa1ce9272da20b96b409acad1d9a9e17ceb0 SHA256: 72c7575dddd3159ccc44db8ea6d3380ddfd45a698e6ca7522a05becce25af757 SHA512: 22d24747859f88c82141e4c7adcac8e8c0b4a173af4c260053eb96b3308b924523ea5faedf176f34022f778685438651dc7ad9c7b8c8a8fd2a2bedd323567b5d Homepage: https://cran.r-project.org/package=PeruAPIs Description: CRAN Package 'PeruAPIs' (Access Peruvian Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to Peru, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes curated datasets focused on Peru, covering topics such as administrative divisions, electoral data, demographics, biodiversity and educational classifications. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-peruflorads43 Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gt, r-cran-scales, r-cran-stringdist, r-cran-ggplot2, r-cran-forcats, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-peruflorads43_0.2.3-1.ca2404.1_all.deb Size: 1027166 MD5sum: bd61835982c87b817a8112c4d6247071 SHA1: e02c6c8295ed38e6ec13e144dc20517cfbdb8262 SHA256: 56f7763403a47f08f0420c911b8e3eb20dca74157efab4be5bedd054247c2185 SHA512: 4ae5a8c358cb637bff8e0c774f9bea3d8be7c5a009874b1792b2b531c091381abbf3affbc2855dd97935a98fd263fff075d28580c05751880beed35577ac1863 Homepage: https://cran.r-project.org/package=peruflorads43 Description: CRAN Package 'peruflorads43' (Check Threatened Plant Species Status Against Peru's SupremeDecree 043-2006-AG) Provides tools to match plant species names against the official threatened species list of Peru (Supreme Decree 043-2006-AG, 2006). Implements a hierarchical matching pipeline with exact, fuzzy, and suffix matching algorithms to handle naming variations and taxonomic changes. Supports both the original 2006 nomenclature and updated taxonomic names, allowing users to check protection status regardless of nomenclatural changes since the decree's publication. Threat categories follow International Union for Conservation of Nature standards (Critically Endangered, Endangered, Vulnerable, Near Threatened). Package: r-cran-perumammals Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-memoise Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-ggtext Filename: pool/dists/noble/main/r-cran-perumammals_0.0.0.2-1.ca2404.1_all.deb Size: 500764 MD5sum: 03ef339a5f7eaa568d032479568e7437 SHA1: b722209aefea2b8c70edce213ff10b65eb601aa9 SHA256: 436eb0d0b4293c37c8f2406147cc8e99d7c0a3b88c43d3319a0bdd2bf7cbc132 SHA512: 3e4630bc8e433e6fb40e413be60096464a39b3eaa703a40f51f5f8c86fc098a025a37697c648931c94786e75455389a35b3d738c7c17ded9b85588c7d01a8a9f Homepage: https://cran.r-project.org/package=perumammals Description: CRAN Package 'perumammals' (Taxonomic Backbone and Name Validation Tools for Mammals of Peru) Provides a curated taxonomic backbone of mammal species recorded in Peru, based on the checklist published by Pacheco and collaborators (2021) . The package includes standardized species data, occurrence records by ecological regions, endemic status, and tools for validating and matching scientific names through exact and approximate string procedures. It is designed as a lightweight and reliable reference for ecological, environmental, biogeographical, and conservation workflows that require verified species information for Peruvian mammals. Package: r-cran-peruocc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3044 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-geoperu, r-cran-ggplot2, r-cran-jsonlite, r-cran-readr, r-cran-rgbif, r-cran-rinat, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-peruocc_0.1.1-1.ca2404.1_all.deb Size: 2073030 MD5sum: 1ee02446818b56053bd9452255273f93 SHA1: 203add0a0544695ea1432b0812de17489a9df623 SHA256: e89873d7fc19efc3af6a45ffc0aa10b53ae1b28af720ae572d09ffb139751cc7 SHA512: c454755c71f5e4478e74e3d46a048c0d780e5a5b33f6f268bb2e2c6653d9ce8cd4a7054e65b44fe8b3aae9e143f04d6eb63e66d2efa3c74e2cc50dddd0a22732 Homepage: https://cran.r-project.org/package=peruocc Description: CRAN Package 'peruocc' (Query and Standardize Biodiversity Occurrences in Peru) Facilitates the retrieval, spatial validation, and integration of flora and fauna occurrence records across administrative units (districts and provinces) in Peru. Retrieves official boundary geometries via 'geoperu', queries and consolidates observations from the Global Biodiversity Information Facility (GBIF, ) and 'iNaturalist' (), and standardizes attributes into a unified Darwin Core aligned structure. Designed for biodiversity assessments and spatial workflows within user-defined areas of interest. Package: r-cran-peruse Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang Suggests: r-cran-testthat, r-cran-purrr Filename: pool/dists/noble/main/r-cran-peruse_0.3.1-1.ca2404.1_all.deb Size: 91914 MD5sum: 84cf4d82260976afccd6fd5fe47b82c2 SHA1: 59f31b35e3264000be44b6867ce8119e17389629 SHA256: 7857f729ddab6ae68776eccc6010286ff07fc16444ba7e111af2dd0cbf6a1c77 SHA512: d75789daeb82478f4e38cadae16c546f04209b17e1d81af10e224c8effef02dd1e07d2b565f6af5dc1c0fa935a415831ed76e61bc94c89c5aa76c5a2733a1dbe Homepage: https://cran.r-project.org/package=peruse Description: CRAN Package 'peruse' (A Tidy API for Sequence Iteration and Set Comprehension) A friendly API for sequence iteration and set comprehension. Package: r-cran-perutimber Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3946 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-perutimber_0.1.0-1.ca2404.1_all.deb Size: 3655550 MD5sum: c4181272813ad418ed221d0785196320 SHA1: 1448b3f20310e69f3228a73f185e23bd3ed9dba5 SHA256: 56e163356de7d530b06dec50f4e85ec9eb1c389ce57068efc3cbb0f981763ddf SHA512: 862f229e7f90b1fafd4815573b597e771f24b3ec7dd2d77afd77fa56cfa6477c8feb30a5f9f1318435bce275525081449f436fc95b0f1da28529ad1f2e344025 Homepage: https://cran.r-project.org/package=perutimber Description: CRAN Package 'perutimber' (Catalogue of the Timber Forest Species of the Peruvian Amazon) Access the data of the 'Catalogue of the Timber Forest Species of the Peruvian Amazon' Vásquez Martínez, R., & Rojas Gonzáles, R.D.P.(2022). Package: r-cran-pervasive Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules, r-cran-dplyr, r-cran-tibble, r-cran-psych Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-psychtools Filename: pool/dists/noble/main/r-cran-pervasive_1.0-1.ca2404.1_all.deb Size: 80198 MD5sum: 98bee344ffa9fa89c901f76fa0dd8973 SHA1: cba62947b931ae7764ee0c3e04a72cc9f77353be SHA256: ad7d66dcbbc3207f091b9673d618f6076ac3cf44ebdcaad61b3be542b6b06245 SHA512: 57074408377eecd0e542cb1a7148950c354c87c921443f0121059f5d443c796f0c77d47148ad73bc75660381d989878a38c4870fff5b8528648cce0cc0326a0c Homepage: https://cran.r-project.org/package=pervasive Description: CRAN Package 'pervasive' (Pervasiveness Functions for Correlational Data) Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the 'arules' package by Hahsler et al. (2025) and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science. Package: r-cran-pesel Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pesel_0.7.5-1.ca2404.1_all.deb Size: 27804 MD5sum: 0e69e98947ceeb2df65234357a40ab04 SHA1: efd1b39b666d78f5aff1d83b4cd448dff0144c81 SHA256: 94daf3af74e257fcf6ff85b40295371b51d99960c7e8d6443805a5ab9089a0fa SHA512: ac02ba4f51ddf376e921306d25e3e17d88f719ea27e0698764dd11d69ab11bb0fd9add00151f6a7c9979e6bc5d1754334518781f1cbf769ce491d29e44e6b07f Homepage: https://cran.r-project.org/package=pesel Description: CRAN Package 'pesel' (Automatic Estimation of Number of Principal Components in PCA) Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood" (2017) . Package: r-cran-pesticideloadindicator Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-stringr, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pesticideloadindicator_1.3.1-1.ca2404.1_all.deb Size: 91530 MD5sum: aad99efabb90446ba9632b5bcd8c7613 SHA1: 3608e04a96d9f8047857bc544c7367a196ead684 SHA256: 4fb43747c00da41dbbda9fd98247b1b1af5e4d99bdb4b24f6b5ae959b57abc1f SHA512: c4b0e679bc70c400f15ea352bdcb5107e5ef01c8987a484cd4f4437da9f5e728141fd3d219be40c39abcbb382c0339c31bcf23b1001cdda7b585b4358b293d80 Homepage: https://cran.r-project.org/package=PesticideLoadIndicator Description: CRAN Package 'PesticideLoadIndicator' (Computes Danish Pesticide Load Indicator) Computes the Danish Pesticide Load Indicator as described in Kudsk et al. 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Package: r-cran-pestr Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pestr_0.8.4-1.ca2404.1_all.deb Size: 328200 MD5sum: e3704065263f22f1daf1cfb741ac3345 SHA1: 618b48bccad5f42c89f731a0258d713359dd3110 SHA256: 291640c942831f5b93bdd990490ad5f9fa3f1fb5da92cf273e37120d205e7f3c SHA512: edd8f0c58325370d7a2de11fea6a0da273c1ec19ecabc0af4b45e22890de79789c9dada68b84c2b8f50d2a0881c1834f81496ff0aa54af7849dfb0beafc46db7 Homepage: https://cran.r-project.org/package=pestr Description: CRAN Package 'pestr' (Interface to Download Data on Pests and Hosts from 'EPPO') Set of tools to automatize extraction of data on pests from 'EPPO Data Services' and 'EPPO Global Database' and to put them into tables with human readable format. Those function use 'EPPO database API', thus you first need to register on (free of charge). Additional helpers allow to download, check and connect to 'SQLite EPPO database'. Package: r-cran-petersen Architecture: all Version: 2025.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aiccmodavg, r-cran-bbmle, r-cran-btspas, r-cran-checkmate, r-cran-formula.tools, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-numderiv, r-cran-plyr, r-cran-reshape2, r-cran-rlang, r-cran-spas, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-petersen_2025.3.1-1.ca2404.1_all.deb Size: 364020 MD5sum: f9e696a8787cfd29065556cfb6819aa5 SHA1: 9451e950c6ce47292414da0ac2ca3a6b9cde11db SHA256: 615e11e6766d6c94f85489d9ef78d4ea671e215b98c7b993f5c0e450e37efa88 SHA512: 14aa211c0f4fbd1058ba5391c941c8e2bfa95a4ea652787aff853701ea80078b773d15d3c87c6ce1fc21c4db58529ce4de1fb72accc5e86e72cdc1e1d1a18989 Homepage: https://cran.r-project.org/package=Petersen Description: CRAN Package 'Petersen' (Estimators for Two-Sample Capture-Recapture Studies) A comprehensive implementation of Petersen-type estimators and its many variants for two-sample capture-recapture studies. A conditional likelihood approach is used that allows for tag loss; non reporting of tags; reward tags; categorical, geographical and temporal stratification; partial stratification; reverse capture-recapture; and continuous variables in modeling the probability of capture. Many examples from fisheries management are presented. 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Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) and on 'CRAN': . The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) , , and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b). Package: r-cran-petests Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-petests_0.1.0-1.ca2404.1_all.deb Size: 134652 MD5sum: 70c5232c947049b004b8ba96bec2a57c SHA1: a36b07a96550d6db59fade999eceb59031ffe40d SHA256: fd681eb76f4e2aa3532fe9e47deee6734ef94cde35b2d38f0c78d4773ca84ebb SHA512: db559de3cc3b50e7372c3e83a32b30a593cf3f247162cb80cf3da83127009ffb190a30d5c058965c1104ccd887aea23420205ee2c908f139ef94f37699e64340 Homepage: https://cran.r-project.org/package=PEtests Description: CRAN Package 'PEtests' (Power-Enhanced (PE) Tests for High-Dimensional Data) Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests on mean vectors and covariance matrices for high-dimensional data. Methods of these PE tests are presented in Yu, Li, and Xue (2022) ; Yu, Li, Xue, and Li (2022) . Package: r-cran-petfinder Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-lubridate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-petfinder_2.1.0-1.ca2404.1_all.deb Size: 81128 MD5sum: 8c1a0a1063ae32ee275e3aac8cd90136 SHA1: b401a4f251b21c78095b29be75b31eee05d13fbb SHA256: c6d72727ceeaa1cbc1494f22785963a28043ff094a9aaf5f0754b94f821b2bb9 SHA512: 8b81e3bca88e34d12e46022aee96760827500ee04bc85ab5ecac75a14e4f947dcc4dbb4058ab4a4ce5597d1a7b1d21eefb5899f57663b322a2808552a51c9214 Homepage: https://cran.r-project.org/package=PetfindeR Description: CRAN Package 'PetfindeR' ('Petfinder' API Wrapper) Wrapper of the 'Petfinder API' that implements methods for interacting with and extracting data from the 'Petfinder' database. The 'Petfinder REST API' allows access to the 'Petfinder' database, one of the largest online databases of adoptable animals and animal welfare organizations across North America. Package: r-cran-peticontrast Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-peticontrast_0.1.0-1.ca2404.1_all.deb Size: 11534 MD5sum: 4b1665cb794c67a5d06cdec4948c1ae4 SHA1: 74b6525eb218d5ed2dee6aa83032d36ebbb5db99 SHA256: ac01ecd485713e7cb89bc0849239aa9745a98389e0a67d28a156f7014148b1fa SHA512: 6a505931aa53387a30212589158316234a36df3ac794ced4102a165c3307e7197b66b07ff0728e0a4bb91e7bdb33c1b1c2cd344eb51b0166a506ad63bee71779 Homepage: https://cran.r-project.org/package=peticontrast Description: CRAN Package 'peticontrast' (Professional Contrast Coding for OLS Models) Automates sum coding (also known as effect coding) for Ordinary Least Squares (OLS) regression models. 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Package: r-cran-pez Architecture: all Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-caper, r-cran-picante, r-cran-quantreg, r-cran-mvtnorm, r-cran-vegan, r-cran-ade4, r-cran-fd, r-cran-matrix, r-cran-animation, r-cran-phytools Suggests: r-cran-knitr, r-cran-lme4, r-cran-formatr Filename: pool/dists/noble/main/r-cran-pez_1.2-5-1.ca2404.1_all.deb Size: 842994 MD5sum: a48fc9cfa37934f5feda47ed92e3d928 SHA1: f63604dc37de4449b75083b3c62bdd1d04bb3fa2 SHA256: 8572394fd1bd9aef82d9c14483aefee074a1b7c6c2704a455549532f6aecdb5a SHA512: 0eedc64e78f7fd9d96265b89fe5ac8273f0f2781be7aef408c0c9c5d88ff7cda2f1a039b74d3e540d276d2531558a5637b241ef5e2d3cd4072e4adc591382d19 Homepage: https://cran.r-project.org/package=pez Description: CRAN Package 'pez' (Phylogenetics for the Environmental Sciences) Eco-phylogenetic and community phylogenetic analyses. Keeps community ecological and phylogenetic data matched up and comparable using 'comparative.comm' objects. Wrappers for common community phylogenetic indices ('pez.shape', 'pez.evenness', 'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation of Cavender-Bares (2004) correlation of phylogenetic and ecological matrices ('fingerprint.regression'). Phylogenetic Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives & Helmus (2011) and Rafferty & Ives (2013). Simulation of null assemblages, traits, and phylogenies ('scape', 'sim.meta.comm'). Package: r-cran-pfa Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars, r-cran-poet, r-cran-quantreg Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-pfa_1.1-1.ca2404.1_all.deb Size: 60378 MD5sum: b4de843980074076368f5b872cc6c565 SHA1: 9ec3a1ea4edf8c29e696ff5ca569d74eb80a6203 SHA256: d02a16dabc23f57ce5714e4380841554d7bcdc5e3745264e984d57ee70adea53 SHA512: 33fcfeafebd67c562854a4728d1f4bf51d72458109296f53542d7222c7eb60f255a7c6b714a9ffb4e571ceaa2c658ab18a7a11b2152aad2123a9ca5571a428be Homepage: https://cran.r-project.org/package=pfa Description: CRAN Package 'pfa' (Estimates False Discovery Proportion Under Arbitrary CovarianceDependence) Estimate the false discovery proportion (FDP) by Principal Factor Approximation method with general known and unknown covariance dependence. Package: r-cran-pfci Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glasso Suggests: r-cran-pcalg, r-bioc-graph, r-bioc-rbgl, r-bioc-rgraphviz, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pfci_0.1.1-1.ca2404.1_all.deb Size: 77304 MD5sum: 94708ab04f850bec32e9377eacac01c2 SHA1: f6081ee2b1af0c3931cd8846f4cd32f3b40d9f1e SHA256: d31ca27172f6f81a2df881a0f5610bd670225095998cb677c64de6c7c7a23cec SHA512: 1d983c44ba9cfa5d2655a49ea590d5034758b56f5b0adc8d4b8a2229b704b57b06b1160a4fa65e4e098d23164b2d0be000e57e2a7024e85b89676bc979e1e906 Homepage: https://cran.r-project.org/package=PFCI Description: CRAN Package 'PFCI' (Penalized Fast Causal Inference for High-Dimensional StructureLearning) Implements Penalized Fast Causal Inference (PFCI), a two-stage causal structure learning procedure for high-dimensional settings with potential latent variables and selection bias. In the first stage, neighborhood selection via the Lasso constructs a sparse undirected skeleton. In the second stage, the Fast Causal Inference (FCI) algorithm orients edges on this reduced graph, producing a Partial Ancestral Graph (PAG) that accounts for latent confounders. The method is consistent under sparsity assumptions and substantially faster than standard FCI and RFCI in high dimensions. See Pal, Ghosh, and Yang (2025) for the underlying theory. Package: r-cran-pfclust Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-flexcwm Filename: pool/dists/noble/main/r-cran-pfclust_0.1.1-1.ca2404.1_all.deb Size: 61772 MD5sum: f761e38f5bcd176998eacfc6ec0ad5fb SHA1: 5ada82942041c1ecc2aad72f5bb96b8060219091 SHA256: a8b082623d3d97c60a9db59eeb356fdc5f4f20b60da6efcf5b35110cf441ac88 SHA512: e299436d17a3b1d83daf0d81253dba97101171732e78c2450de43605b5d6df463041e9cc4786dfc8918430fc3f347e8aa44b6bf1ec99a89ffc34a4ed9147ab90 Homepage: https://cran.r-project.org/package=pfclust Description: CRAN Package 'pfclust' (Power Fuzzy Clustering and Cluster-Wise Regression) Implementations of Power Fuzzy Clustering (PFC) and Power Fuzzy Cluster-wise Regression (PFCR) for multivariate data. The package supports Minkowski distances, with the L1 case solved via iteratively re-weighted least squares and the case p > 1 solved via coordinate-wise root finding, as well as an adaptive, regularised Mahalanobis distance with per-cluster covariance matrices. Both plain fuzzy clustering and cluster-wise linear regression are provided. The corresponding paper can be found at Nguyen P.T., Tortora C., and Punzo A. (2026) . 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Various functional pre-whitening approaches are implemented as discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional settings", . Further whitening representations of functional data can be derived in terms of a few principal components, providing an avenue to explore hidden structures in low dimensional settings: see Vidal, Rosso and Aguilera (2021) “Bi-smoothed functional independent component analysis for EEG artifact removal”, . 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Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) , Retout S, Comets E, Samson A, Mentré F (2007) , Bazzoli C, Retout S, Mentré F (2009) , Le Nagard H, Chao L, Tenaillon O (2011) , Combes FP, Retout S, Frey N, Mentré F (2013) and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) . 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It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters. Package: r-cran-pglm Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-plm, r-cran-statmod, r-cran-formula Suggests: r-cran-lmtest, r-cran-car Filename: pool/dists/noble/main/r-cran-pglm_0.2-4-1.ca2404.1_all.deb Size: 329654 MD5sum: ae35eb5117e7eb050bef142b0df3d778 SHA1: bd156d7e1c5e235eca1b86119562a4cbe191c24e SHA256: 2208a3839da5458fb26d03f0c12a2126ced21418dc4e6101b20cb3c4afae2e66 SHA512: 7e0f4c701c183c5d157b6c91e89dd20d97ec090fbb33f9ff3239ae6cfac131783892a7e0ae7fe875ff6b43ad60748912540a6d1c4003320bd9825d017a6f9e37 Homepage: https://cran.r-project.org/package=pglm Description: CRAN Package 'pglm' (Panel Generalized Linear Models) Estimation of panel models for glm-like models: this includes binomial models (logit and probit), count models (poisson and negbin) and ordered models (logit and probit), as described in: Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4. Package: r-cran-pgm2 Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pgm2_2.0.1-1.ca2404.1_all.deb Size: 73284 MD5sum: 411ee4bc267d8da049601345f100baca SHA1: 5cb2b1f647ebac748332f21dffc5d4178217ecc4 SHA256: ed9eb10e0354d282e225b697f0b2e48f43d6cc9e0a5f836fcc706b38c076b0e1 SHA512: 14cc75f2b4c71a08d6cf4c9e3412bb6228a609bc07f94e086dc56e45277e95058fab73f976de6ebde73b40e3e1d74751b68094fb710a14bfd1d1d62f1565fca0 Homepage: https://cran.r-project.org/package=PGM2 Description: CRAN Package 'PGM2' (Recursive Construction of Nested Resolvable Designs andAssociated Uniform Designs over GF(p)) Recursive construction of balanced incomplete block designs (BIBDs), their successive generations, resolvable BIBDs (RBIBDs) and associated uniform designs (UDs), derived from finite projective geometries PG(m, p) over a Galois field GF(p) of any prime order p. Implements and generalises the method of Boudraa, Gheribi-Aoulmi and Laib (2013, International Journal of Research and Reviews in Applied Sciences, 17(2), 167-176), which was previously available only for p = 2, and the uniform design constructions of Fang et al. (2004) . Designs of every recursion stage can be extracted, and all constructions are validated against the parameters published in the original paper. Package: r-cran-pgnorm Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pgnorm_2.0.1-1.ca2404.1_all.deb Size: 64328 MD5sum: e33e5889a1a1c8048cfc8631991b4c3f SHA1: 5dc28df521210eba629132c2f3453d2b31917d41 SHA256: 5674fbab13dd705e7ae2059c61316eee6a7276b8953e66737a734dcbdcf80ab3 SHA512: 56a5ddf62d20e705df80906e3fbde69e1f824e25c5df69b6519df782737248c710a525977de6a52ea479c376547cc7a47069283b8292be9f9a6eb94acf0d805a Homepage: https://cran.r-project.org/package=pgnorm Description: CRAN Package 'pgnorm' (The p-Generalized Normal Distribution) Evaluation of the pdf and the cdf of the univariate, noncentral, p-generalized normal distribution. Sampling from the univariate, noncentral, p-generalized normal distribution using either the p-generalized polar method, the p-generalized rejecting polar method, the Monty Python method, the Ziggurat method or the method of Nardon and Pianca. The package also includes routines for the simulation of the bivariate, p-generalized uniform distribution and the simulation of the corresponding angular distribution. Package: r-cran-pgraph Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sam, r-cran-energy, r-cran-glasso, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-pgraph_1.6-1.ca2404.1_all.deb Size: 34108 MD5sum: b547f98762ecd29409e5bc652f7ac257 SHA1: 54ce764450a2ffe53f1ec09639e6d96529ee2986 SHA256: 2b6c65557995b6e69a53f5da9fe96df19d21c6633405cc3a37c48a4a3bf2e3a7 SHA512: 3930260e17cb6fc9b3075bbdab726df02a3464b9903b31dfae0f5ca5a59ccb6c06b56101caf1df9578be5b1a43e6f6823aec7ad4241c1bd9a877ba341a32b1a2 Homepage: https://cran.r-project.org/package=pgraph Description: CRAN Package 'pgraph' (Build Dependency Graphs using Projection) Implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019). Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph. Package: r-cran-pgrn Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-doparallel, r-cran-dtw, r-cran-foreach, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-lmtest, r-cran-proxy, r-cran-tidygraph, r-cran-visnetwork, r-cran-future Suggests: r-cran-knitr, r-cran-webshot, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pgrn_0.3.5-1.ca2404.1_all.deb Size: 211608 MD5sum: 80f7b2b8e9fcfa4f7ba36476c1f80bf8 SHA1: 7bae8fac7d690c4946b2d9933a2612c61c355308 SHA256: 31078ab5fc66069916869c1dbece48957689c92f50d7f3528a8788aad317abde SHA512: 6b7d070cf373bade14168cd42b88be05529b6a74656cf9cc110499bc0d6f9e411aa2cc540a05494ac8f4b1bba6e1337367d176f98f5152e331187780f1eb6df7 Homepage: https://cran.r-project.org/package=pGRN Description: CRAN Package 'pGRN' (Single-Cell RNA Sequencing Pseudo-Time Based Gene RegulatoryNetwork Inference) Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information. Package: r-cran-pgt Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolveapi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-frontier Filename: pool/dists/noble/main/r-cran-pgt_0.7.0-1.ca2404.1_all.deb Size: 349276 MD5sum: 4220c0d86cf94920c6fb5a3ba8e3cf97 SHA1: 5e5cdb38cdfe81b34680720d6d0a666c2eaf4e19 SHA256: b15ff9afc5fddf537f715d23dc84276a7df1ea75abd4f40eb37de23fa0023f1c SHA512: 61419d321927869c5a9186149618b08d1fdd1cf280c308039f95789351bcda589ce3238096834ea88186475ab9bfc70618505a7cb355d0a508b95c4bb6caa29f Homepage: https://cran.r-project.org/package=pgt Description: CRAN Package 'pgt' (Data Envelopment Analysis for Pollution-Generating Technologies) Nonparametric efficiency analysis for pollution-generating technologies under the materials-balance principle. Implements the weak-G-disposability model of Rodseth (2025) and its factorially determined multi-output representation, the by-production intersection technology of Murty, Russell and Levkoff (2012) , the materials-balance cost model of Coelli, Lauwers and Van Huylenbroeck (2007) and a weak-disposability reference model, with a pre-estimation audit of every materials-balance account, metafrontier decompositions, bad-output shadow prices, marginal abatement cost curves, a cross-axiom comparison harness, a global Malmquist-Luenberger productivity index and subsampling sensitivity intervals. Estimators are solved with 'lpSolveAPI'. Package: r-cran-pgtools Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-toolbox, r-cran-dbi, r-cran-odbc, r-cran-stringi Filename: pool/dists/noble/main/r-cran-pgtools_1.0.2-1.ca2404.1_all.deb Size: 135602 MD5sum: 96b417ae042ae45898a9005fae06cc3b SHA1: 5fb7797654dfccd2fd714eb1cacf5860e19feba1 SHA256: b3c751a0ea518129ad16d19c1921f5f75d6209bc5b557d853a948c34cc79793f SHA512: d676d31f0eef3113ebeaae3c1ef8f5d06f253d585ccbe97066bda86ab090baaa5e00b98da7c18e732655a2a29c89dc6d8ac140cf1ba873159427243ffc8f4a6f Homepage: https://cran.r-project.org/package=pgTools Description: CRAN Package 'pgTools' (Functions for Generating PostgreSQL Statements/Scripts) Create PostgreSQL statements/scripts from R, optionally executing the SQL statements. Common SQL operations are included, although not every configurable option is available at this time. SQL output is intended to be compliant with PostgreSQL syntax specifications. PostgreSQL documentation is available here . Package: r-cran-pguimp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-dt, r-cran-datavisualizations, r-cran-dbscan, r-cran-dplyr, r-cran-e1071, r-cran-finalfit, r-cran-ggplot2, r-cran-ggthemes, r-cran-hmisc, r-cran-magrittr, r-cran-mass, r-cran-rweka, r-cran-vim, r-cran-bbmle, r-cran-gridextra, r-cran-mice, r-cran-nortest, r-cran-outliers, r-cran-plotly, r-cran-psych, r-cran-purrr, r-cran-rcompanion, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-robust, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-writexl Suggests: r-cran-knitr, r-cran-devtools, r-cran-ellipsis, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-pguimp_0.1.1-1.ca2404.1_all.deb Size: 2265098 MD5sum: d377a81a41db9f0dd5eb97a56bb576cd SHA1: 0e4d85c56613bb64333e3d39d1fc3192e7d4b772 SHA256: 66fe9f8a98eda6f74f711c8bdc0467a8d131349947bbd03041da35a3d7d3fb06 SHA512: 14159910680ef2b6d41ed9d26d31911a53dac5e7ec2f81acc0bf442537ca2e2980d7b3ae68db1734635dbe0b1f48a7ac50d4646254996e8e6c253ba7097030a9 Homepage: https://cran.r-project.org/package=pguIMP Description: CRAN Package 'pguIMP' (Visually Guided Preprocessing of Bioanalytical Laboratory Data) Reproducible cleaning of biomedical laboratory data using visualization, error correction, and transformation methods implemented as interactive R notebooks. A detailed description of the methods ca ben found in Malkusch, S., Hahnefeld, L., Gurke, R. and J. Lotsch. (2021) . Package: r-cran-ph1xbar Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-mvtnorm, r-cran-pracma, r-cran-vgam Filename: pool/dists/noble/main/r-cran-ph1xbar_0.11.3-1.ca2404.1_all.deb Size: 82338 MD5sum: b378c317ba5a892cf252bfffe5d07bfb SHA1: 6483b8a19e6b42ad65e3b0f7fdcdf6d17923f22f SHA256: 55d52c7e58a1caff109a04307bd1c1b9104429856f95ad5e92ec10ed29c36908 SHA512: 778159292ad4571a76830c279c98f1b8603acfe9ef4f1e162f568296a10da29a5d1621d13613851b88a16394916126ddf5b77347040852061db26c5cd4583c56 Homepage: https://cran.r-project.org/package=PH1XBAR Description: CRAN Package 'PH1XBAR' (Phase I Shewhart X-Bar Chart) The purpose of 'PH1XBAR' is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) , Yao and Chakraborti (2021) , and Yao et al. (2023) . Package: r-cran-ph2mult Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clinfun Suggests: r-cran-gsdesign, r-cran-survival Filename: pool/dists/noble/main/r-cran-ph2mult_0.1.1-1.ca2404.1_all.deb Size: 58892 MD5sum: a599bba822ef640ab96b2106e42424f1 SHA1: 0d62d82fac8442a75bedbd6b08e5a5a11b6de2d3 SHA256: 31b37b09125c5e032b2e429da157e9f0f884251a6021ef24f7c2c7e56072e900 SHA512: 3b2393007c7b2a79473faa930858da20050e01f88e2042d84f62c360a52fc84d85d56f4b37e2ef75db7526918d3db4940963a0741662f34f39a93fc5844cd1c9 Homepage: https://cran.r-project.org/package=ph2mult Description: CRAN Package 'ph2mult' (Phase II Clinical Trial Design for Multinomial Endpoints) Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level). Package: r-cran-phagecocktail Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-stringr, r-cran-factoextra, r-cran-bipartite, r-cran-smerc, r-cran-rjsonio Filename: pool/dists/noble/main/r-cran-phagecocktail_1.0.3-1.ca2404.1_all.deb Size: 67978 MD5sum: 5e11add20981343f0056c4b3a05e0d5f SHA1: e6a6b00fa0310b77a02a8607b85fb9e23a2012ec SHA256: 074b4226ad46f1f958b0246080010885e71afde0b92cd47f3bd731d93f9390ef SHA512: 4905fa4138fc6895a33415ab4a78dbd47ba8f49025e22f7a8e425107bd3f10a5cf5fe288ecbc40edaceec6289615eba1fd6f695d56127073c0922b50d93fae1a Homepage: https://cran.r-project.org/package=PhageCocktail Description: CRAN Package 'PhageCocktail' (Design of the Best Phage Cocktail) There are 4 possible methods: "ExhaustiveSearch"; "ExhaustivePhi"; "ClusteringSearch"; and "ClusteringPhi". "ExhaustiveSearch"--> gives you the best phage cocktail from a phage-bacteria infection network. It checks different phage cocktail sizes from 1 to 7 and only stops before if it lyses all bacteria. Other option is when users have decided not to obtain a phage cocktail size higher than a limit value. "ExhaustivePhi"--> firstly, it finds Phi out. Phi is a formula indicating the necessary phage cocktail size. Phi needs nestedness temperature and fill, which are internally calculated. This function will only look for the best combination (phage cocktail) with a Phi size. "ClusteringSearch"--> firstly, an agglomerative hierarchical clustering using Ward's algorithm is calculated for phages. They will be clustered according to bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in order to select 1 phage per cluster. Using the phages selected during the clustering, it checks different phage cocktail sizes from 1 to 7 and only stops before if it lyses all bacteria. Other option is when users have decided not to obtain a phage cocktail size higher than a limit value. "ClusteringPhi"--> firstly, an agglomerative hierarchical clustering using Ward's algorithm is calculated for phages. They will be clustered according to bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in order to select 1 phage per cluster. Once the function has one phage per cluster, it calculates Phi. If the number of clusters is less than Phi number, it will be changed to obtain, as minimum, this quantity of candidates (phages). Then, it calculates the best combination of Phi phages using those selected during the clustering with Ward algorithm. If you use PhageCocktail, please cite it as: "PhageCocktail: An R Package to Design Phage Cocktails from Experimental Phage-Bacteria Infection Networks". María Victoria Díaz-Galián, Miguel A. Vega-Rodríguez, Felipe Molina. Computer Methods and Programs in Biomedicine, 221, 106865, Elsevier Ireland, Clare, Ireland, 2022, pp. 1-9, ISSN: 0169-2607. . Package: r-cran-phantsem Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-corpcor, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-phantsem_1.0.1-1.ca2404.1_all.deb Size: 3636470 MD5sum: eb4022e2494493913c0c11205a763045 SHA1: ad719c0cdfee53fbe34fbf3b7a0d7ae536ffd6f2 SHA256: 9d17e5baa6a496af565b0e9c77594bd4ca690b5d1082262f1bd1eb4ad80c1917 SHA512: 3fcf96527360952c42b2c278b443949250fcc2d9e363984561c0b46def2071f2e7031829b3bf3640e4d182d209130876c9be7c8e7ce514b88f8b6b3433362dde Homepage: https://cran.r-project.org/package=phantSEM Description: CRAN Package 'phantSEM' (Create Phantom Variables in Structural Equation Models forSensitivity Analyses) Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias. 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This package fills a gap where tables in certain packages can be written out to RTF, but cannot add certain metadata or features to the document that are required/expected in a report for a regulatory submission, such as multiple levels of titles and footnotes, making the document landscape, and controlling properties such as margins. Package: r-cran-pharmaverse Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pharmaverse_0.0.2-1.ca2404.1_all.deb Size: 40128 MD5sum: cebb7f1d34909d4867dc279d47e9331f SHA1: 107980b9920745564673cc3f1cde9d5644b71903 SHA256: 1f98b421a3ede84eeecffcf01d18f513077f0d726f8d5ee36456fbe024a5b81e SHA512: bf406dfde0c668fb0c5394d2b23229ec9c55a76f647073940a88efce88f3c9d799111ff6ad7a10e909b5327ccff8d415fe9e95d2659d7da719500a9047a2963e Homepage: https://cran.r-project.org/package=pharmaverse Description: CRAN Package 'pharmaverse' (Navigate 'Pharmaverse') The 'pharmaverse' is a set of packages that compose multiple pathways through clinical data generation and reporting in the pharmaceutical industry. This package is designed to guide users to our work-spaces on 'GitHub', 'Slack' and 'LinkedIn' as well as our website and examples. Learn more about the 'pharmaverse' at . 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ADaM dataset specifications are described in the CDISC ADaM implementation guide, accessible by creating a free account on . 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SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on . 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Package: r-cran-phase12designs Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trialr, r-cran-iso Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phase12designs_0.3.1-1.ca2404.1_all.deb Size: 212254 MD5sum: d81481a666976b79c8ddf88db2e311a7 SHA1: 1788c6e18196882e501231f83356ce1f7337f8d3 SHA256: 93bdba5677b81024009163325bb6592c99edd93968458ee678d9ef126ada8e2a SHA512: 67f5f507d171b12f8abe9d138d60a0714b0c631141c68bb5cfaa6018b42c9c6863f540599d2f1bb9d632c2292343e60adb85d5b67fbbd18de36067051f9f5e40 Homepage: https://cran.r-project.org/package=phase12designs Description: CRAN Package 'phase12designs' (Comprehensive Tools for Running Model-Assisted Phase I/II TrialSimulations) Provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including: - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) ), - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) ), - EffTox (Thall, P. F., & Cook, J. D. (2004) ), - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) ), - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) ), - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) ), - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) ), - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) ). Includes flexible simulation parameters that allow researchers to efficiently compute operating characteristics under various fixed and random trial scenarios and export the results. 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The package provides flags to fit a variety of model-based phase I design, including 1 stage models with or without individualized dose modification, 3-stage models with or without individualized dose modification, etc. Functions are provided to recommend dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) . Package: r-cran-phase1rmd Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-boot, r-cran-rjags, r-cran-mvtnorm, r-cran-ggplot2, r-cran-arrayhelpers Suggests: r-bioc-biocparallel Filename: pool/dists/noble/main/r-cran-phase1rmd_1.0.9-1.ca2404.1_all.deb Size: 227638 MD5sum: 8d9049e0b597fc1df7a7a216fee5a964 SHA1: f97616711663b9b4a99f6ce18a40d1dbe9e57ebf SHA256: 2f0abe8abffbc39d84969b445a1035c3df810761d9f911c7f09dcee9396da8ff SHA512: 93e478c3b4923ee2ddd252a34dda60dee3b33c3078e25a11741caec090c9fd57a45352be4f70faaacafb43d507861a4f76d7749866d129e5911a747384c752d4 Homepage: https://cran.r-project.org/package=phase1RMD Description: CRAN Package 'phase1RMD' (Repeated Measurement Design for Phase I Clinical Trial) Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) . Package: r-cran-phase Architecture: all Version: 1.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-lubridate, r-cran-plotly, r-cran-pracma, r-cran-signal, r-cran-zeitgebr, r-cran-zoo, r-cran-behavr, r-cran-wesanderson, r-cran-shiny, r-cran-shinythemes, r-cran-shinydashboard, r-cran-shinycssloaders, r-cran-shinyfiles Filename: pool/dists/noble/main/r-cran-phase_1.2.9-1.ca2404.1_all.deb Size: 395550 MD5sum: 656e27a9efac82ac404c2ff0c6328812 SHA1: ee68634e7cbd1469e145a47530ddd51f5c6a2418 SHA256: f018abd52137ad18014167b3eacec7fb53bcd75848c7a22e67efb90d65d8b436 SHA512: 3a3525dd224397b97f92655d4427a07d0411b4188dd5617b068523b476d3120b8dafe87e4fa3188aff2afbf26db6ff1b714be3b4332beda75c06424f19a9425f Homepage: https://cran.r-project.org/package=phase Description: CRAN Package 'phase' (Analyse Biological Time-Series Data) Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) , Lomb-Scargle periodogram: Lomb (1976) , Scargle (1982) and Ruf (1999) , and Autocorrelation: Eijzenbach et al. (1986) . Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) ) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) , Harrisingh method: Harrisingh et al. (2007) , and Stoleru method: Stoleru et al. (2004) . Rose plots and circular analysis are based on methods from - Batschelet (1981) and Zar (2010) . 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Package: r-cran-phdcocktail Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-here, r-cran-rcolorbrewer, r-cran-rstudioapi, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phdcocktail_0.1.0-1.ca2404.1_all.deb Size: 1243806 MD5sum: 95c8e704fa820703cdce7dcf96aaba50 SHA1: 8dd3a24c6a585d82e57f7fffd2aea763b1c5c8d8 SHA256: 93d6d6805dcfb36fe7222d72f681152e1775772864cac5f2da3b9c307203e70e SHA512: 0ef8e4049aa8df96870656e0423dbd9e84fd251e554dba414f3d5fadea67e57f0172d7497d36c2c0534df3b4f01ee62e4886bab3116bb66eb22f57c597468504 Homepage: https://cran.r-project.org/package=phdcocktail Description: CRAN Package 'phdcocktail' (Enhance the Ease of R Experience as an Emerging Researcher) A toolkit of functions to help: i) effortlessly transform collected data into a publication ready format, ii) generate insightful visualizations from clinical data, iii) report summary statistics in a publication-ready format, iv) efficiently export, save and reload R objects within the framework of R projects. Package: r-cran-phdid Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-did, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phdid_0.1.0-1.ca2404.1_all.deb Size: 491562 MD5sum: 4318b3dd49dd39919e648b37709987e3 SHA1: b63a349fcc8220f46eaad58edecd2db2b04723fb SHA256: 486ac55f3c2532df2109bf521b2a1b613281d48807465f4af3f57bfca319aec1 SHA512: 05354a3e6fbd48a70d1094fc05b91a620fc34b7f48552988beb5e49fd2197730bcfad257983e707987a350b833c6814037a9a0c87173cf21245ddd5a6a95d67f Homepage: https://cran.r-project.org/package=phdid Description: CRAN Package 'phdid' (Partial Homogeneity in Staggered Difference-in-Differences) In staggered difference-in-differences designs the treatment effect is a vector of cohort-time effects rather than a single number. Estimating each separately is unbiased but imprecise when some are equal, while pooling them all is precise but biased under genuine heterogeneity. This package treats the choice as a partition-selection problem on the cohort-time cells and provides two estimators for it: a Dirichlet process mixture fitted by a collapsed Gibbs sampler, whose posterior marginalises over the unknown partition and reports co-clustering probabilities, and an 'L0'-penalised estimator that returns a single partition and arises as the fixed-variance maximum a posteriori solution of the same model. Also provides tests for whether the cohort-time effects carry recoverable heterogeneity at all, sampler diagnostics including exact enumeration of the partition posterior for small designs, regularisation paths for both estimators, and a calibrated data-generating process. All estimators accept a vector of first-stage cohort-time effects with their joint covariance, so any heterogeneity-robust first-stage estimator may be used. Methods are described in Arora and Wagle (2026) . Package: r-cran-pheatmap Architecture: all Version: 1.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-scales, r-cran-gtable Filename: pool/dists/noble/main/r-cran-pheatmap_1.0.13-1.ca2404.1_all.deb Size: 79042 MD5sum: 8f85d097b66198cc6277793edcf02b0e SHA1: 6e3782d3d43c2bddf6c3003f80f2b5396f3df65a SHA256: a59041b99a83cc6e053798a97d3b35b21ad49dd0aaece86be034ff2f45419bc5 SHA512: 095285fd47ee77d12f840da49c00eb9ceeabbd1cd8a06fcc4f93434125530bef94b0cc5cfd69a6351f26bb0f68646b8696c3409c3b7f174aca091a82b73ec4d5 Homepage: https://cran.r-project.org/package=pheatmap Description: CRAN Package 'pheatmap' (Pretty Heatmaps) Implementation of heatmaps that offers more control over dimensions and appearance. Package: r-cran-pheble Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adabag, r-cran-c50, r-cran-caret, r-cran-catools, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-earth, r-cran-evtree, r-cran-frbs, r-cran-glmnet, r-cran-gmodels, r-cran-hda, r-cran-hdclassif, r-cran-ipred, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-mlmetrics, r-cran-nnet, r-cran-party, r-cran-pls, r-cran-randomforest, r-cran-rpartscore, r-cran-sparselda, r-cran-themis Suggests: r-cran-h2o Filename: pool/dists/noble/main/r-cran-pheble_0.1.0-1.ca2404.1_all.deb Size: 696668 MD5sum: 9305a077f441af2a673e85e95bd6a906 SHA1: 179162711b59c6e34859e67a14f3650e091dba72 SHA256: 8347488aa9638bec8d3407d077f08b9e569a71fa2b4b6adbd43844f22704298e SHA512: ab5c8970a754e74909b50fc06698a70c194f00605610ae9ff902f7a762f27bb5e4da4ba09b26667c17d5563aa2e82c3b0231c8a232fcd04cd0249a96cfcaac08 Homepage: https://cran.r-project.org/package=pheble Description: CRAN Package 'pheble' (Classifying High-Dimensional Phenotypes with Ensemble Learning) A system for binary and multi-class classification of high-dimensional phenotypic data using ensemble learning. By combining predictions from different classification models, this package attempts to improve performance over individual learners. The pre-processing, training, validation, and testing are performed end-to-end to minimize user input and simplify the process of classification. Package: r-cran-phecap Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4454 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rmysql Suggests: r-cran-ggplot2, r-cran-e1071, r-cran-randomforestsrc, r-cran-xgboost, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phecap_1.2.1-1.ca2404.1_all.deb Size: 3915746 MD5sum: d0e751ec07de76604e0f9331d708f54f SHA1: 7a43728b3a9f75a61c3ca4073ddf41c6d1a61caf SHA256: 88156bda44388e6c99d7edc5dd11cdd61a9e0733e327d552f3f9f307e1592477 SHA512: 913b5de66f30c6a2a633cf254ce39cc71ffb9c047b7afaed627253d28ab5f267c3ffa7bfd55de74a4d3879c23dcc6b0cc00737ca37c0bdb22cb5c7d3a71ff7d1 Homepage: https://cran.r-project.org/package=PheCAP Description: CRAN Package 'PheCAP' (High-Throughput Phenotyping with EHR using a Common AutomatedPipeline) Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) , Yu et al. (2017) , and Liao et al. (2015) . Package: r-cran-phecodemap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3001 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-collapsibletree, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-golem, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-rintrojs, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phecodemap_0.1.0-1.ca2404.1_all.deb Size: 2530650 MD5sum: 57c29622736e60cfef2eda283c40ab0c SHA1: e59bba0f5d96026eb66c11e4d1b52a2414615496 SHA256: d91e9c09159effcc66cd4d3caaeda3edcb22ac29262a94e0b1a3848a8bd9cf7e SHA512: 15b21336208e3a8877c39b144cdf966af89340944f0e970d68aaca6cbeec675954fbdbec4b57df163a0287d2ceef0fafe3239d5cf18f3e4a8403abedae88f5b0 Homepage: https://cran.r-project.org/package=phecodemap Description: CRAN Package 'phecodemap' (Visualization for PheCode Mapping with ICD-9 and ICD-10-CM Codes) To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree. Package: r-cran-pheindicatormethods Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pheindicatormethods_2.1.2-1.ca2404.1_all.deb Size: 494200 MD5sum: 6ede6e58da6837887e9d90b44fe33420 SHA1: a05524b63fcc148d97ed276fa26fcc480ffaab2d SHA256: bd12730ce3b1a25b90d9b8cabde116a4756825eaf66783bacc1baa8e9759afe4 SHA512: 0b532ea538670408a1eb589c8dcd005eb99f39416c2bbdc7226153b9bab4e2cb42cfffc63bff7fb4bb3d4177184315aaee389943e6354e91997c488f35f55c78 Homepage: https://cran.r-project.org/package=PHEindicatormethods Description: CRAN Package 'PHEindicatormethods' (Common Public Health Statistics and their Confidence Intervals) Functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators such as those presented via Fingertips (). It provides functions for the generation of proportions, crude rates, means, directly standardised rates, indirectly standardised rates, standardised mortality ratios, slope and relative index of inequality and life expectancy. Statistical methods are referenced in the following publications. Breslow NE, Day NE (1987) . Dobson et al (1991) . Armitage P, Berry G (2002) . Wilson EB. (1927) . Altman DG et al (2000, ISBN: 978-0-727-91375-3). Chiang CL. (1968, ISBN: 978-0-882-75200-6). Newell C. (1994, ISBN: 978-0-898-62451-9). Eayres DP, Williams ES (2004) . Silcocks PBS et al (2001) . Low and Low (2004) . Fingertips Public Health Technical Guide: . Package: r-cran-phenesse Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-fitdistrplus Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenesse_0.1.3-1.ca2404.1_all.deb Size: 47538 MD5sum: a33c638b024e96d1b77f4e8ea512fb74 SHA1: 22a6af8b87770ea1e3ae37da005bd1fdf454a1b9 SHA256: 11ad7c65178607428c6a3674b678ce3f545d1af5ea4045553c365f734c751ba1 SHA512: ff58da6723f7ab132be509fcafeac92bd5806ff3e8e7eb9a797a1fd91c139d6d40fbd4a0ef86415b83c7769c7fb79396368214b4596886df25bd1de3ff3cc002 Homepage: https://cran.r-project.org/package=phenesse Description: CRAN Package 'phenesse' (Estimate Phenological Metrics using Presence-Only Data) Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) . Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) . Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest. Package: r-cran-phenix Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ppcor, r-cran-suppdists Filename: pool/dists/noble/main/r-cran-phenix_1.3.2-1.ca2404.1_all.deb Size: 85256 MD5sum: ce32f069e6fba5456c5fbf79d9d0e7ca SHA1: 5537b3d18b653e46115d0e107911a32c7419da38 SHA256: d4aa22efe75bdef6b258a92391ad91cb8d58cd5c971d2a9c108ea3515f039933 SHA512: afc63d21cc56aaa66e00cbbb3d20db3d1a006b70424d89d885b161ad0805275910d07db59dbf3802ebfaa1690e469d01b0ce80454e1364145a6bc6494067f677 Homepage: https://cran.r-project.org/package=PHENIX Description: CRAN Package 'PHENIX' (Phenotypic Integration Index) Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) , bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares . Package: r-cran-phenmodel Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-reshape Filename: pool/dists/noble/main/r-cran-phenmodel_1.0-1.ca2404.1_all.deb Size: 192806 MD5sum: 49cada5af196aaffa31be05d8d053d68 SHA1: 8aa525d2ee7b5b49d50c3c3819319e3b11cc5b98 SHA256: f826fd6fcf2879632156187f481a48a65c2dc00691856987919e2691af94e54b SHA512: d52b676d714ce8799ae119d1b3bb1a4fc6569152265a69ff08eb293f413666cadcbb5f70ea1f1af526388086ed7d682db27d59a48e57f57a1799b45d0fc166c9 Homepage: https://cran.r-project.org/package=phenModel Description: CRAN Package 'phenModel' (Insect Phenology Model Evaluation Based on Daily Temperatures) Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) . A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour. Package: r-cran-phenocamr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-changepoint, r-cran-httr, r-cran-zoo, r-cran-memoise, r-cran-daymetr, r-cran-modistools Suggests: r-cran-shiny, r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt Filename: pool/dists/noble/main/r-cran-phenocamr_1.1.5-1.ca2404.1_all.deb Size: 249036 MD5sum: e120895fee8a52a88581c32c82276aea SHA1: 3bac18e694a3aa6583d0444bb9803211c814caca SHA256: 1b7fcca5789557186274f9592f7f35e5c0ba5f99a34ac8225b53eb95e849c8d9 SHA512: 8276bce7bd50962de806eec98dd2b2b72c000aee8207a5e9f730e23b9147177be9c41a67a4ed8d9e208c7a3a4499bab7f48813bc13e626b6dd300539bc0cbd57 Homepage: https://cran.r-project.org/package=phenocamr Description: CRAN Package 'phenocamr' (Facilitates 'PhenoCam' Data Access and Time SeriesPost-Processing) Programmatic interface to the 'PhenoCam' web services (). Allows for easy downloading of 'PhenoCam' data directly to your R workspace or your computer and provides post-processing routines for consistent and easy timeseries outlier detection, smoothing and estimation of phenological transition dates. Methods for this package are described in detail in Hufkens et. al (2018) . Package: r-cran-phenocdm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phenocdm_0.1.3-1.ca2404.1_all.deb Size: 129082 MD5sum: fdece58e58ac3c477de610aeed2a0a03 SHA1: 76193f2693a9124799bce94b27410c77970d5a8c SHA256: e589aebf9c688fbc8f7ab524aa295f41f0d71ccf61ca225e9e198c7fcba8cb7f SHA512: 43297aa4f852dc7aa5ad64c810e33234d50baed9d68d3c14610c47303928147772dcdae2c457f0f68ecec072eb33652b9c6054f6e40733fa06607e3643cdb004 Homepage: https://cran.r-project.org/package=phenoCDM Description: CRAN Package 'phenoCDM' (Continuous Development Models for Incremental Time-SeriesAnalysis) Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development. Package: r-cran-phenolocrop Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenolocrop_0.0.4-1.ca2404.1_all.deb Size: 38368 MD5sum: 6a4b15e4ac15e7d41fad26926094d1ce SHA1: cf038a624086bad3a7f2b5d0884c83e0cc1610b1 SHA256: dead453da9049e915a25a9f5fb251e58f27f2cd7c16d27c7d1305d0bd7c9606e SHA512: 6c5755b96953fbc42a22df3b31e11829ef71ccbcf8a972b8859c7831b7c4620f1eeb39b63268f7e96163b7a31b84a302fd3e98bd4d5ec52f2dae464b3e7b3839 Homepage: https://cran.r-project.org/package=phenolocrop Description: CRAN Package 'phenolocrop' (Time-Series Models to the Crop Phenology) Fit a time-series model to a crop phenology data, such as time-series rice canopy height. This package returns the model parameters as the summary statistics of crop phenology, and these parameters will be useful to characterize the growth pattern of each cultivar and predict manually-measured traits, such as days to heading and biomass. Please see Taniguchi et al. (2022) and Taniguchi et al. (2025) for detail. This package has been designed for scientific use. Use for commercial purposes shall not be allowed. Package: r-cran-phenology Architecture: all Version: 2026.8.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-optimx, r-cran-helpersmg Suggests: r-cran-shiny, r-cran-fields, r-cran-progress, r-cran-car, r-cran-multirng, r-cran-nlwaldtest, r-cran-pbapply, r-cran-cranlogs Filename: pool/dists/noble/main/r-cran-phenology_2026.8.24-1.ca2404.1_all.deb Size: 1340848 MD5sum: f41c354d4666d56884040543f9ed6912 SHA1: d7013da4479f966ef4fc0964b6d7c1a1d3c0aa85 SHA256: e2a0ac026f4f43fab52c31b0af95ecd61d31f072be55d7ba93943591cf701fd2 SHA512: cec3e5cae8746767d2a964a8b2f63ecdd953f175f454f752dfa6922e0db16bc030d79ee0ce7090825a4806d8b56a3935f8e69853c2631214eea16354a33ce9e0 Homepage: https://cran.r-project.org/package=phenology Description: CRAN Package 'phenology' (Tools to Manage a Parametric Function that Describes Phenologyand More) Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) for the phenology function, Girondot, M. (2017) for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) for tag-loss estimate, Hancock J, ..., Girondot M (2019) for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) for aggregating several seasons. Package: r-cran-phenomap Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-phenex, r-cran-plyr, r-cran-stringr, r-cran-terra, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-phenomap_2.0.1-1.ca2404.1_all.deb Size: 740584 MD5sum: 24da680fef0b1fac615f8740e856a2b3 SHA1: 16d690a6c4e96ebc5cb13de2f52edf0f8c7bc229 SHA256: d648ae9c6660c4e57b8791c35daa5653b7ed3aeb6636d60a70d6885cf4569eeb SHA512: 3d20fa372fa5d1ccdd75562df1529275af941c87cd5bfc964d07ba04d53cf0d1b2bbc88aafe96fc510d7609f49e0f351374cbeeafdb83be683cdda328991355d Homepage: https://cran.r-project.org/package=phenomap Description: CRAN Package 'phenomap' (Projecting Satellite-Derived Phenology in Space) This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) . Package: r-cran-phenopix Architecture: all Version: 2.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-plyr, r-cran-jpeg, r-cran-stringr, r-cran-strucchange, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-gtools, r-cran-raster, r-cran-sp, r-cran-terra Filename: pool/dists/noble/main/r-cran-phenopix_2.4.5-1.ca2404.1_all.deb Size: 728530 MD5sum: f47b74101151352d7db388bd4e28dc11 SHA1: eb8228df4ec983d7399d3d8b04e6fb09d31f4287 SHA256: ca98011f8e5d338a77f4def17ecbbdd3dc175d554bb44cca0e64e666d3c8a2fb SHA512: e95a716054033aba08deed74965a612ffe72038f2740cb771d10ffaea74354c74dd2fd4f009e5f316271724c5299e9a92c64aa7cf702c6bd4be5e83a78993018 Homepage: https://cran.r-project.org/package=phenopix Description: CRAN Package 'phenopix' (Process Digital Images of a Vegetation Cover) A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages. Package: r-cran-phenorm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenorm_0.1.0-1.ca2404.1_all.deb Size: 25178 MD5sum: 1bff228245ac0f1aa27b4b7a84eb0797 SHA1: afc6058a9e8a8a5cdff6f98c23c9f28c1714ab16 SHA256: 393449d8be71977a15a8c2948b9c7e76d7bf01e4f04c699ebfec97fcae8b40a0 SHA512: 18c4fae32506ff37311f0d95f874aefd9ffd3bff99c800d0faeb5fcfdaeb472fe10132caf6c7755192c7159b86df0d08f102efa0c3ae94858ec81ba21524447b Homepage: https://cran.r-project.org/package=PheNorm Description: CRAN Package 'PheNorm' (Unsupervised Gold-Standard Label Free Phenotyping Algorithm forEHR Data) The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) . 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Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) and Saric et al. (2022) for related work. Package: r-cran-phenotype Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-phenotype_0.1.0-1.ca2404.1_all.deb Size: 37632 MD5sum: 576e4a5e3d2be770d2b87446fa24ca47 SHA1: a2b1c3c6cd48c05c480f09c9304c4dfa49a3f958 SHA256: 886529440ce1f7f340459609b4e8dbcae6d4deb5c0830c14251a4faa77a42a41 SHA512: aed67a66e3a7fbadaefd59046fb44cc09d3e816a89081039c401b8227159edba4e2738630cd5bca6a14713b3b769a29873e56e3330cd62bcc5f7e0a38f439ccd Homepage: https://cran.r-project.org/package=Phenotype Description: CRAN Package 'Phenotype' (A Tool for Phenotypic Data Processing) Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) . 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Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research. Package: r-cran-phenthauproc Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3326 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-rlang, r-cran-terra Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyterra Filename: pool/dists/noble/main/r-cran-phenthauproc_1.1.2-1.ca2404.1_all.deb Size: 1216840 MD5sum: a02324a8e961e7ad49e8bfc7c0d690cb SHA1: da36eb3cb2e2538648cd5bba43d17eb82e0bde6a SHA256: 1ef461bc452cc3b3c12fe62349fbe6e1326cf67c2de53d1eb762bbd5291e8e52 SHA512: dfee28d073255ebb3917ad6954c4f06bdf4b6cc93b0cb956e0f75994047716ab10f24cd3c7d240df1cd68b5c73014113833ad2655a7725b02a7fe813d2809d17 Homepage: https://cran.r-project.org/package=PHENTHAUproc Description: CRAN Package 'PHENTHAUproc' (Phenology Modelling of Thaumetopoea Processionea) Methods to calculate and present 'PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 () at FVA () Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria. 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The method decomposes a target potential into a smooth component f(x) and a non-smooth convex component g(x), approximating only g(x) via its Moreau-Yosida envelope while retaining exact gradient information for f(x). This approach, based on the methodology described in Shukla, Vats, and Chi (2025) , yields improved Hamiltonian conservation over full-potential smoothing approaches. The package provides generalized routines accepting user-defined probability density functions, log-likelihoods, priors, and proximal operators, together with automated hyperparameter tuning for the Moreau-Yosida regularization parameter, Markov chain Monte Carlo convergence diagnostics, effective sample size computation, and model evaluation metrics including the Akaike information criterion and Bayesian information criterion. 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The method, described in Guilmineau et al (2025) is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented. Package: r-cran-phoenix Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-qwraps2, r-cran-reticulate, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phoenix_1.1.3-1.ca2404.1_all.deb Size: 266816 MD5sum: 7e05a11403e358e3175bc5171daa53d4 SHA1: 4f7bd30348e9d7fa732c237602ff4746515b76cf SHA256: 41c117f9af4ccdde7d8538ef93e1fdfddc07104f710d69d898ee41c70a729911 SHA512: 6c3a0006077cb812db551786983db32a86d86d4b6346d03b43a41389367f7483f1f8291520073e8bcb0173ccae1415f62df8eabc74f392c0b9977bf7fc2843ec Homepage: https://cran.r-project.org/package=phoenix Description: CRAN Package 'phoenix' (The Phoenix Pediatric Sepsis and Septic Shock Criteria) Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as described in "Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt, Russell et al. (2024) (Drs. Sanchez-Pinto and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr. Russell contributed equally to the manuscript), "International Consensus Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson, Sorce, Argent, et al. (2024) (Drs Schlapbach, Watson, Sorce, and Argent contributed equally) and the application note "phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria" by DeWitt, Russell, Rebull, Sanchez-Pinto, and Bennett (2024) . Package: r-cran-phonenumber Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phonenumber_0.2.3-1.ca2404.1_all.deb Size: 186422 MD5sum: 0b876640d01e74ad6fb73640766c5b29 SHA1: 15d03bdef741bdca07569cb214a05bedfb8d2525 SHA256: e0d399b3684b31c5b08847015b0d9c1f57a88431db66e0c5064d1f0a5568d012 SHA512: 90586448f0b1a25073253d7f699c3c97eaf905381c078440bcd48ce899964e6579f436878174cee85fd87cef64ededc6d37b28ec30135dc087b1e828043dc891 Homepage: https://cran.r-project.org/package=phonenumber Description: CRAN Package 'phonenumber' (Convert Letters to Numbers and Back as on a Telephone Keypad) Convert English letters to numbers or numbers to English letters as on a telephone keypad. When converting letters to numbers, a character vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F" becoming 3, etc. When converting numbers to letters, a character vector is returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and "C"). Package: r-cran-phonetisr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-unicode Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-phonetisr_0.1.0-1.ca2404.1_all.deb Size: 87486 MD5sum: 466c69c64b9981da83b0689ce8434a43 SHA1: 58e7966e62cb3fe2e284f57e4121488d6babb39b SHA256: d32db6febee839860edc38bd0b20442b086c710e549d8e25c4226728ec5945d4 SHA512: 64bac2c6cdd232afa46a87078501d62c39b07209699270a9ddf39d787f1009302dbfc3fbc6196954804e78808c48cfb8560eb54194132b957b3075e2c8466d50 Homepage: https://cran.r-project.org/package=phonetisr Description: CRAN Package 'phonetisr' (A Naive IPA Tokeniser) It provides users with functions to parse International Phonetic Alphabet (IPA) transcriptions into individual phones (tokenisation) based on default IPA symbols and optional user specified multi-character phones. 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Package: r-cran-phonevalidator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phonevalidator_1.0.1-1.ca2404.1_all.deb Size: 11354 MD5sum: 6080d6eebe4b63fce52c69a77f13f69a SHA1: 84b76d8ed6f6dab8db953d003a8936ade63a2a67 SHA256: dc38edb5e364aa981d5f0fe31cb303a35b09dd7c4ce687b0490682cff6988838 SHA512: c731bac4a44ee402004190edcebeec1fbc52ba495ace805efdee67e37af237a7a7c14fce57e6316d4b0f19c4f024658bbb9de3d4ded4250162534d4d0ddf7788 Homepage: https://cran.r-project.org/package=PhoneValidator Description: CRAN Package 'PhoneValidator' (Client for 'GenderAPI.io' Phone Number Validation and FormatterAPI) Provides an interface to the 'GenderAPI.io' Phone Number Validation & Formatter API () for validating international phone numbers, detecting number type (mobile, landline, Voice over Internet Protocol (VoIP)), retrieving region and country metadata, and formatting numbers to E.164 or national format. Designed to simplify integration into R workflows for data validation, Customer Relationship Management (CRM) data cleaning, and analytics tasks. Full documentation is available at . Package: r-cran-phonfieldwork Architecture: all Version: 0.0.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tuner, r-cran-phontools, r-cran-rmarkdown, r-cran-xml2, r-cran-readr, r-cran-mime Suggests: r-cran-knitr, r-cran-tidyr, r-cran-dplyr, r-cran-dt, r-cran-lingtypology, r-cran-testthat, r-cran-readxl Filename: pool/dists/noble/main/r-cran-phonfieldwork_0.0.17-1.ca2404.1_all.deb Size: 984310 MD5sum: b79ac191c31202fbb74079c3b4a0b814 SHA1: 7f83114528b10fec81ebaaa8924cd1837acfc22c SHA256: 64fee398f928e1d913e4cb62a290e7cb54ebf69fd23c9472f4f14bd7f26f370f SHA512: 4e065034055012a2cfa34bf22089074a64211c0b9494b2d4bac7647c5c495ce2330658f80de1b21fc29f5e4342536b14de64cf06b817ed1d52d7b400746245eb Homepage: https://cran.r-project.org/package=phonfieldwork Description: CRAN Package 'phonfieldwork' (Linguistic Phonetic Fieldwork Tools) There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Boersma & Weenink 2020 ), creating an html table with annotations and spectrograms, and converting multiple formats ('Praat' TextGrid, 'ELAN', 'EXMARaLDA', 'Audacity', subtitles '.srt', and 'FLEx' flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). 'phonfieldwork' provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: 'rPraat' , 'textgRid' . Package: r-cran-phonr Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splancs, r-cran-deldir, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-phonr_1.0-7-1.ca2404.1_all.deb Size: 130692 MD5sum: 1922e611eeecd8d5c3d869d169c9b7e2 SHA1: b925455695715368b757f2e48c5fdf2b6ba7da72 SHA256: d8cb6404d9e9269ff1ce9a622875d4db33c4dfe8f51fd3ba276e060aea159536 SHA512: 1f6858f63b20e310fdb4d1ce6b5f46354bbfa6e52d8bea7d732fb441093412cb742b726a847e403c8a8e6e494cf71ee87088c171089b63588dd17a50a0c95c72 Homepage: https://cran.r-project.org/package=phonR Description: CRAN Package 'phonR' (Tools for Phoneticians and Phonologists) Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels. Package: r-cran-phontools Architecture: all Version: 0.2-2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-phontools_0.2-2.2-1.ca2404.1_all.deb Size: 458340 MD5sum: e81dd2352dd1a279fc1223c185fc587a SHA1: a9c2a9ea30099f8f0c7ad5b7b775580fcfeaba53 SHA256: 6c23f66bc40d81f153f847da7fafb5dd37b6e5d26415ced5cacd21ddb3a09ead SHA512: 917e021fd1d1dbba772029b139fbad82e017a42d3b23457f5016fca5ae8e6fdb315554ad7a8e81703f8aabceb71178081d8e7be960005e632a6578c74a42876c Homepage: https://cran.r-project.org/package=phonTools Description: CRAN Package 'phonTools' (Tools for Phonetic and Acoustic Analyses) Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files. 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The main entry point, phontrast(), reports several contrast metrics in one call -- Jensen-Shannon divergence and distance (Lin, 1991) , the Pillai-Bartlett trace, Bhattacharyya distance and affinity, Mahalanobis distance, and proportional overlap -- globally or by group on a common separation-oriented scale, with bootstrap confidence intervals. rank_contrasts() implements a measurement protocol for ranking speakers' contrasts by Jensen-Shannon distance and checking the ranking against Pillai, with sample-size licensing and a bandwidth-sensitivity check. Also provides utilities for preparing estimates for downstream modelling such as generalized additive models and mixed-effects models. Formerly released as 'phonJSD'. 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Original spectral irradiance data for incandescent-, LED- and discharge lamps are included. They are complemented by data on the effect of temperature on the emission by fluorescent tubes. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . 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Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . 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Angular response data for broadband ultraviolet and visible radiation sensors and diffusers used as entrance optics. Data obtained from multiple sources were used: author-supplied data from scientific research papers, sensor-manufacturer supplied data, and published sensor specifications. Part of the 'r4photobiology' suite Aphalo P. J. (2015) . 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In addition data for shade light under vegetation and irradiance time series from different broadband sensors. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologywavebands Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-photobiologywavebands_0.5.4-1.ca2404.1_all.deb Size: 302868 MD5sum: 68fb4cf314dc6439fcb816f3907311c2 SHA1: a7aa208ec56487ad526fba5593c2107eb59c6817 SHA256: 6a054ae92df08138950811eab0d9381cb44f1fc1d96e7cd69f4bca2db4d8abcd SHA512: 9d37332c8728af059a7b2037b856a09e9de28a2ac3fef05aa14733eb7134893834c41ba4ef580a5c8b8596b180cb276c5ba40240eebdbd32abf3bb061e04f480 Homepage: https://cran.r-project.org/package=photobiologyWavebands Description: CRAN Package 'photobiologyWavebands' (Waveband Definitions for UV, VIS, and IR Radiation) Constructors of waveband objects for commonly used biological spectral weighting functions (BSWFs) and for different wavebands describing named ranges of wavelengths in the ultraviolet (UV), visible (VIS) and infrared (IR) regions of the electromagnetic spectrum. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photogea Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-lattice, r-cran-dfoptim, r-cran-deoptim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plantecophys, r-cran-testthat Filename: pool/dists/noble/main/r-cran-photogea_1.4.0-1.ca2404.1_all.deb Size: 2522254 MD5sum: 340979b4551fa00fca6bde64cc00447b SHA1: 6d4397ff82e6a62bab65e6f6f7a1f3d3776cb7e5 SHA256: f7c04fd9823fb9446bde7361457a347bb8fd844463bec98c9d7d37d3cbb21f63 SHA512: d6729d6bdb78cc088f67395eddda9a47ad89f39c2b23882f2f32fcd40e8c6586de276870e3a1789ce7ed899efb662ce873e13d80031025a989788c3ffd668049 Homepage: https://cran.r-project.org/package=PhotoGEA Description: CRAN Package 'PhotoGEA' (Photosynthetic Gas Exchange Analysis) Read, process, fit, and analyze photosynthetic gas exchange measurements. Documentation is provided by several vignettes; also see Lochocki, Salesse-Smith, & McGrath (2025) . Package: r-cran-photon Architecture: all Version: 1.0.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-countrycode, r-cran-httr2, r-cran-r6, r-cran-sf, r-cran-processx, r-cran-rvest Suggests: r-cran-testthat, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-webfakes, r-cran-ps Filename: pool/dists/noble/main/r-cran-photon_1.0.0-1-1.ca2404.1_all.deb Size: 280700 MD5sum: ff666bff50237489f2fbff2e8e5f6dde SHA1: 7e014afcc4f4ede5cc8ac707cf71b53bb8e5acf2 SHA256: fd11e91b23c2c3ed0e9c7e6f0ebbe436274fe9f3c3ab469cbdc9a99003bdc3c1 SHA512: 74b7c85247efb11992de99443bab0d87f21739c618c808bf71adb2d32f4d5740c60223e416886ac3063da3ff3bcf44264c80cb6993c25db8cedae8210e0d37c4 Homepage: https://cran.r-project.org/package=photon Description: CRAN Package 'photon' (High-Performance Geocoding using 'photon') Features unstructured, structured and reverse geocoding using the 'photon' geocoding API . Facilitates the setup of local 'photon' instances to enable offline geocoding. Package: r-cran-photosynq Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-getpass Filename: pool/dists/noble/main/r-cran-photosynq_0.2.3-1.ca2404.1_all.deb Size: 41942 MD5sum: db1bd3f23a91f69bdaefe88ca74090a8 SHA1: 90a9616d28c8ec71e1175597df84867fd44a7ddb SHA256: 2db623bd6ce799d057ea6b4152eda4abd584ec09287b0281e32d18951e7aaf86 SHA512: 420ab19a6066660ac1b8141697c84f1aa091e50fd9227e33e7462b3dbe8886fc4ff4a990910e615428e0b99a5d334f1390bbb67354535ce788b5d50887ca5523 Homepage: https://cran.r-project.org/package=PhotosynQ Description: CRAN Package 'PhotosynQ' (Connect to PhotosynQ) Connect R to the PhotosynQ platform (). It allows to login and logout, as well as receive project information and project data. Further it transforms the received JSON objects into a data frame, which can be used for the final data analysis. Package: r-cran-photosynthesis Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-units, r-cran-checkmate, r-cran-crayon, r-cran-dplyr, r-cran-furrr, r-cran-glue, r-cran-gunit, r-cran-lifecycle, r-cran-magrittr, r-cran-nlme, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tealeaves Suggests: r-cran-brms, r-cran-broom, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-photosynthesis_2.1.5-1.ca2404.1_all.deb Size: 1238552 MD5sum: 7f344f5f74e1c1f8a4fcf2d937e6a4cb SHA1: fa311a106d0304abf3743b408181ab4fc762f6dc SHA256: 5597f94dfa406c71e8cd5cf9910ec05836aef7afde882139c97fe4ee8307bcb8 SHA512: 877e47c7073288d0eccb6669c7b878268cf687e670de2d478cdef98c04639791c62e8ada1a338487fd38c19c2ad6b93d3895669809fb0a01afd0f85d1b817086 Homepage: https://cran.r-project.org/package=photosynthesis Description: CRAN Package 'photosynthesis' (Tools for Plant Ecophysiology & Modeling) Contains modeling and analytical tools for plant ecophysiology. MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) model as described in Buckley and Diaz-Espejo (2015) . It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) . The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry. ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) ) and AQ curves (Marshall & Biscoe (1980) ), temperature responses (Heskel et al. (2016) ; Kruse et al. (2008) , Medlyn et al. (2002) , Hobbs et al. (2013) ), respiration in the light (Kok (1956) , Walker & Ort (2015) , Yin et al. (2009) , Yin et al. (2011) ), mesophyll conductance (Harley et al. (1992) ), pressure-volume curves (Koide et al. (2000) , Sack et al. (2003) , Tyree et al. (1972) ), hydraulic vulnerability curves (Ogle et al. (2009) , Pammenter et al. (1998) ), and tools for running sensitivity analyses particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()). Package: r-cran-photosynthesislrc Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-photosynthesislrc_1.0.6-1.ca2404.1_all.deb Size: 163932 MD5sum: 4e27d7ddf4b8a2265900309357e59811 SHA1: 389df08005e28f8bfbab989bd470fc7fce6621b9 SHA256: 710c9e295d18cef3c076872ca1defac63d529d26a70fa9d0ec2c449fafb150e4 SHA512: 4a068b010ec2e337158f58c2c69c828f2d75b2a447dd534333c43b6bca1e916b89eb0be2507f6a4252e014bcedd355f2d230f9e27cb4b6754fe37c785a2c9ac6 Homepage: https://cran.r-project.org/package=photosynthesisLRC Description: CRAN Package 'photosynthesisLRC' (Nonlinear Least Squares Models for Photosynthetic Light Response) Provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) )) (eq2 (Kaipiainenn (2009) )) (eq3 (Smith (1936) ))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) )) (eq5 (Abe et al. (2009) ))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) )), exponential based models ((eq8 (Webb et al. (1974) )), (eq9 (Prado & de Moraes (1997) ))), and finally the Ye model (eq11 (Ye (2007) )). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. Gas exchange data on 28 wild sunflower species from Davis et al.are included as an example data set here. Package: r-cran-phrases Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyverse, r-cran-usethis Filename: pool/dists/noble/main/r-cran-phrases_0.1-1.ca2404.1_all.deb Size: 41930 MD5sum: 8406ff7d8ff62eaa1e7e1db26b0c8583 SHA1: 9aaea72ef44ac5ec38822f5816f0a712deade7e6 SHA256: 1d42ab2f10719ae0ade9f1d30369ed5b686410e7db488c39f237b5032d3a1381 SHA512: f220caa172d828f7820ac4b2e8574188def16c24eb159ef586a753ed173f6b368a1dc00ecb12b62263026870798a5181c2631755399d2065531279c4b34422e4 Homepage: https://cran.r-project.org/package=phrases Description: CRAN Package 'phrases' (Phrasal Verbs in English Club Website) Contains all phrasal verbs listed in as data frame. Useful for educational purpose as well as for text mining. Package: r-cran-phscs Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1230 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-psgc Suggests: r-cran-jsonlite, r-cran-testthat, r-cran-gt, r-cran-rmarkdown, r-cran-knitr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-phscs_0.2.0-1.ca2404.1_all.deb Size: 995258 MD5sum: c9489fa31afd3f14044797aadc3bf62b SHA1: 8736f25c675ae1dab95b89b98c193798e380aacd SHA256: e1715212fc81eeedafabf6c9b1fb687cd42e113e8bff9016d2560e37d85e3470 SHA512: 4bd7bf70cf96ba9507fdf88d5b8575f4478b352d4bf347c71957d6091f40165617da21c964e78114d9811fb8291af9d8e05d8c9d121ade1628bdf318ecb2c27e Homepage: https://cran.r-project.org/package=phscs Description: CRAN Package 'phscs' (Philippine Statistical Classification Systems) A unified interface to access and manipulate various Philippine statistical classifications. 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Faith, D. P. (1992) Isaac, N. J. et al. (2007) Laffan, S. W. et al. (2016) Rosauer, D. et al. (2009) . Package: r-cran-phyloregion Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-matrix, r-cran-betapart, r-cran-colorspace, r-cran-igraph, r-cran-clustmixtype, r-cran-maptpx, r-cran-terra, r-cran-vegan, r-cran-predicts, r-cran-smoothr Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-mapproj, r-cran-survival, r-cran-rjava, r-bioc-phyloseq, r-cran-v8 Filename: pool/dists/noble/main/r-cran-phyloregion_1.0.9-1.ca2404.1_all.deb Size: 1507834 MD5sum: 028eeb3dc31386455b97cf31e03fceb4 SHA1: 6bacefe9e4a979e4bd5ae7ea7b3ddc381ecb55e0 SHA256: 1f9dff515daca0befed433a63fa3729585fa4b02c7c95fbe04664aed13463883 SHA512: 4ece671286924d22000f607261cf2d2913ca2aa86930e5bb65b3fad7e36083cd354271c02139923623026eecd2516f1d09a5af1a3be37dde3175f3e62db3d883 Homepage: https://cran.r-project.org/package=phyloregion Description: CRAN Package 'phyloregion' (Biogeographic Regionalization and Macroecology) Computational infrastructure for biogeography, community ecology, and biodiversity conservation (Daru et al. 2020) . It is based on the methods described in Daru et al. (2020) . The original conceptual work is described in Daru et al. (2017) on patterns and processes of biogeographical regionalization. Additionally, the package contains fast and efficient functions to compute more standard conservation measures such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and global endangerment, as well as compositional turnover (e.g., beta diversity). Package: r-cran-phylosamp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2510 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylosamp_1.0.1-1.ca2404.1_all.deb Size: 1117226 MD5sum: 37ea2ce0c1304eb152f3965aed576b37 SHA1: e52529ab0442153120a498ac22090f047154c6fb SHA256: 4667d8396d7cf816fd7e61d1c618344b7fcf0c0e71172679028e5b51dc02bef1 SHA512: 507bf2af4441223aeb109a81d1a4a63d9f0a74f3ab81d159a69336da630383130e59453bf5bd44d7f03250b260fea8929f45105a016824e19550155726a21dc9 Homepage: https://cran.r-project.org/package=phylosamp Description: CRAN Package 'phylosamp' (Sample Size Calculations for Molecular and Phylogenetic Studies) Implements novel tools for estimating sample sizes needed for phylogenetic studies, including studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023). Package: r-cran-phyloseqgraphtest Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-bioc-phyloseq, r-cran-ggnetwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phyloseqgraphtest_0.1.1-1.ca2404.1_all.deb Size: 469816 MD5sum: 34a0ecf7789c6e3d97b8ebe7596cb7c1 SHA1: 72dcb89fe07cd92f9c64e659474625fc9474ef49 SHA256: 5a5021262b70bc9cb50321978a08b0d7cb3bffd42263e616ebbabd823f2b3813 SHA512: 66d1d733542a1524d35a207d5b727cd445cfd66703de58065455837d25b8a12ddc8e465305d75a378222eaa5c21d7192e149b3f6b0f13b89591fdc25c490358d Homepage: https://cran.r-project.org/package=phyloseqGraphTest Description: CRAN Package 'phyloseqGraphTest' (Graph-Based Permutation Tests for Microbiome Data) Provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in 'phyloseq' objects. The tests are based on those described in Friedman and Rafsky (1979) , and the tests are described in more detail in Callahan et al. (2016) . Package: r-cran-phylosignaldb Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-castor, r-cran-cluster, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylosignaldb_0.4.2-1.ca2404.1_all.deb Size: 66138 MD5sum: e3b0b28fdd8c9557878a94f43c4ab9fe SHA1: 334cecd7191f43e55a02ac6fab011cfdeebb8bc1 SHA256: 24b91a1e7f43447ad8f170ac428d859005dc3436aaff82c1351ffb97f4afe370 SHA512: 76c26befec73336edf7fabeb42f7dd7b29c32822fbf5387b59bb9c3ab3c6f93cbb9ff3979f015968e34e9ebbac0bdc8a13a76c5ff722ce5f0595eb19bdc0cef2 Homepage: https://cran.r-project.org/package=phylosignalDB Description: CRAN Package 'phylosignalDB' (Explore Phylogenetic Signals Using Distance-Based Methods) A unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. The M statistic implemented in this package is based on the methodology described in Yao and Yuan (2025) . If you use this method in your research, please cite the paper. Package: r-cran-phylospatial Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3071 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-sf, r-cran-terra, r-cran-vegan Suggests: r-cran-canaper, r-cran-furrr, r-cran-future, r-cran-testthat, r-cran-betapart, r-cran-knitr, r-cran-rmarkdown, r-cran-tmap, r-cran-magrittr, r-cran-hillr, r-cran-picante, r-cran-phytools, r-cran-nullcat, r-cran-paralleldist, r-cran-prioritizr, r-cran-highs, r-cran-matrix Filename: pool/dists/noble/main/r-cran-phylospatial_1.5.0-1.ca2404.1_all.deb Size: 2304532 MD5sum: 55dcad6f6998ef98cf69335b7315b1b6 SHA1: 271a057d09936d2bc9240dd5c85a7e80e9c6c866 SHA256: 745a24289da2a80c08ed9d0519a3863f4e83d377c8ed55f955c5bc0b1f1d3c88 SHA512: 90b25d793eac9c7a49f8081ab7f143a3764563cd38be3312de852b33a2e67764b5d3c464797d79c307eaef8f8582537397a2bf6e5b51c94d4916d7b1874f6fa1 Homepage: https://cran.r-project.org/package=phylospatial Description: CRAN Package 'phylospatial' (Spatial Phylogenetic Analysis) Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data. Package: r-cran-phylotate Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ape Filename: pool/dists/noble/main/r-cran-phylotate_1.3-1.ca2404.1_all.deb Size: 62246 MD5sum: 6949f5bd55e645d7e62c76e2c00d47fa SHA1: c5b51499c895765a655db608808cdf646ae17d50 SHA256: f3ff4988c3c8f0cd3ebd394075333220d8d28efd16239a2b671837a3117d12cb SHA512: 464a2172145f5a6b17cd2e100a3db81c732392b1e804bb794289a95b3da6e19c3ca8a187b04c025016fc989574ef250fab59e8450f8203826adecf4523cfeb18 Homepage: https://cran.r-project.org/package=phylotate Description: CRAN Package 'phylotate' (Phylogenies with Annotations) Functions to read and write APE-compatible phylogenetic trees in NEXUS and Newick formats, while preserving annotations. Package: r-cran-phylotools Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Suggests: r-cran-vegan Filename: pool/dists/noble/main/r-cran-phylotools_0.2.2-1.ca2404.1_all.deb Size: 57482 MD5sum: 8fbaead6f1eb513afe30f50c870c9d91 SHA1: ccb8459633aeb10300c7005835430bc84a51e3c2 SHA256: 29c1ccb7837a878dfcea5c4aeed478f94490a7cd18acf52353ec56104017a689 SHA512: 7e67e52a147af62cc0fcc3048b6fb3619e248196ce3ada6f6a575956c29a4aa25a3277c0bb645f31a32972ce097e6ae3572f3509cf675c7785b60f5b976a7540 Homepage: https://cran.r-project.org/package=phylotools Description: CRAN Package 'phylotools' (Phylogenetic Tools for Eco-Phylogenetics) A collection of tools for building RAxML supermatrix using PHYLIP or aligned FASTA files. These functions will be useful for building large phylogenies using multiple markers. Package: r-cran-phylotop Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-igraph, r-cran-nhpoisson, r-cran-phylobase Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylotop_2.1.3-1.ca2404.1_all.deb Size: 133116 MD5sum: 96abbea86fc75b54ecf2872db8c4aec2 SHA1: d3d777bf447b1831d34e8a29ef31087fb4a7dde3 SHA256: 9d7187e6ea6d73d8dc916e324bd4e70f79351e39f3342114f1e942795c73abac SHA512: 420c0584404b48f853a04de4d4a8ba9d48a875d12d0f30b111a98bfd4085ce9e70d55a370ffab708fcef022eb58ee0dc36e125875b2979e2add1b2ff4fecd75a Homepage: https://cran.r-project.org/package=phyloTop Description: CRAN Package 'phyloTop' (Calculating Topological Properties of Phylogenies) Tools for calculating and viewing topological properties of phylogenetic trees. Package: r-cran-phymapnet Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-compositions Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phymapnet_0.1.3-1.ca2404.1_all.deb Size: 36718 MD5sum: 0d261290a78be520bbe88bb24501b6af SHA1: b82087381202ae9eace8bf76f6f7eabc7ffb784b SHA256: 6593909a37a9113210b16d9f52cc637a6ff46bbc0ea1f99de258dd382443c797 SHA512: 70290a083eca9bfaaadee2d94333f028b751daa899c071342b3e4c60d982b6c6a4def75d751456a82e528078d0041aa5ba988524f2e6d9987bd930385b15d1f7 Homepage: https://cran.r-project.org/package=phymapnet Description: CRAN Package 'phymapnet' (Phylogeny-Guided Bayesian Microbial Network Inference) Implements a phylogeny-aware Bayesian graphical modeling framework for microbial network inference using a shrinkage precision estimator guided by a phylogenetic kernel, with optional hyperparameter-ensemble edge reliability analysis. Package: r-cran-phynotype Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-clustmixtype, r-cran-dbscan, r-cran-functionals, r-cran-ggplot2, r-cran-ggrepel, r-cran-mclust, r-cran-rlang Suggests: r-cran-factominer, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phynotype_0.6.2-1.ca2404.1_all.deb Size: 792294 MD5sum: 7788d943c6b0b81036e21e6edb78fabd SHA1: 4e2864595bf55e35b9d9e19f9ecdfaf6dd668f3a SHA256: 1121b5b3a687817979522f29cdc989f4ff3c993700c559d77b9dd92ebfd7bdf1 SHA512: 2167bc0c66574fe5068da2ea86bcc45683a91e588cc4110852346aed961cb6271bd448f0c8ba0289da794c774ce7eb209ca4178f913b6723752d803494f9d46e Homepage: https://cran.r-project.org/package=phynotype Description: CRAN Package 'phynotype' (Clustering and Consensus Meta-Clustering) Tools for clustering, consensus meta-clustering, validation, exploratory interpretation, cluster prediction, and plotting. The package provides a clustering workflow with consensus clustering following Strehl and Ghosh (2002) . Package: r-cran-phyreg Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-phyreg_1.0.2-1.ca2404.1_all.deb Size: 133812 MD5sum: f82b540007006a674f86f0874507b47b SHA1: bacd7d5dbff269ac55bd1284829875d36809b1bf SHA256: a2a56be3722c34085c6806b5e722702d8585f95669674ef26c5885b676689ece SHA512: ccaeb7da75347b5a4f838efcd7fa02e5809089709ec86fe4592434450fc9b906c6ee9eb849e134426aa01f375c552ad13b41e5a068738ab086ee8d57ddbe664a Homepage: https://cran.r-project.org/package=phyreg Description: CRAN Package 'phyreg' (The Phylogenetic Regression of Grafen (1989)) Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true sequence of binary splits). Package: r-cran-physactbedrest Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-stringr, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-physactbedrest_1.1-1.ca2404.1_all.deb Size: 42064 MD5sum: 4d9b1619ce9374ffee04f208ea9355a7 SHA1: c3928b977c77cac78c136974c4fe8c4d0b9b70ef SHA256: dadbb8e0ee7ef995b7e5ddfb025d7253074355b214d87f2210c181538ab4e51f SHA512: 2e8584d6b7c81fff419ca2057890bc63bc69770f735fe9c9602eb35df0e17ed281212c10eea7fce6b61d3e8106eabec99cf06c52145a7535c148bc49961db32f Homepage: https://cran.r-project.org/package=PhysActBedRest Description: CRAN Package 'PhysActBedRest' (Marks Periods of 'Bedrest' in Actigraph Accelerometer Data) Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active. The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively. The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package. Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation. Package: r-cran-physicalactivity Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2627 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rsqlite, r-cran-dbi, r-cran-data.table, r-cran-e1071, r-cran-keras, r-cran-randomforest, r-cran-reticulate, r-cran-rms Filename: pool/dists/noble/main/r-cran-physicalactivity_0.2-4-1.ca2404.1_all.deb Size: 2482756 MD5sum: bd01d687aa0d4d4b2c07f45d6507fbd2 SHA1: 6a89f152b2f1a7f7d49a62b38220dccf75d6af18 SHA256: 744dea9511a61a9d5a0134b5cdf6a347b63a06b68acc83dff76c532c2b1003bf SHA512: bffba8d81295f8d33cc5838b10cea49d944102ac25f2f0d7308916039da86c968795970ff538f68da91f5ce2b5f05d7ec90c5cbd6513a495b9eed92f0cf63729 Homepage: https://cran.r-project.org/package=PhysicalActivity Description: CRAN Package 'PhysicalActivity' (Process Accelerometer Data for Physical Activity Measurement) It provides a function "wearingMarking" for classification of monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity. The package also contains functions for making plot for accelerometer data and obtaining the summary of various information including daily monitor wear time and the mean monitor wear time during valid days. "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail; "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete days and removing low activity days; "markPAI" can categorize physical activity intensity level based on user-defined cut-points of accelerometer counts. It also supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions. Package: r-cran-physioindexr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-physioindexr_0.1.0-1.ca2404.1_all.deb Size: 88846 MD5sum: a42b49389bed352637a18b66e6cc8a06 SHA1: 3c6325034031f341ce317d4b5a67570eb58afb48 SHA256: 9f1a6b3e7b58c92d7321f948e5e1d0cca04ab4b7432552f4d17d1f7209e6aad4 SHA512: dfc4f915b449db4f64d6aa5208353f0ba7bb123d235271d0cfe7ce4c102816768afdbc5fa262ada53a94b873e33228002ec468e46c6865ce9f27f1ba63037bfe Homepage: https://cran.r-project.org/package=PhysioIndexR Description: CRAN Package 'PhysioIndexR' (Physiological and Stress Indices for Crop Evaluation) Crop production systems are increasingly challenged by climate variability, resource limitations, and biotic–abiotic stresses. In this context, stress tolerance indices and physiological trait estimators are essential tools to identify stable and superior genotypes, quantify yield stability under stress versus non-stress conditions, and understand plant adaptive responses. The 'PhysioIndexR' package provides a unified framework to compute commonly used stress indices, physiological traits, and derived metrics that are critical in crop improvement, crop physiology, and other agricultural sciences. The package includes functions to calculate classical stress tolerance indices (See Lamba et al., 2023; ) such as Tolerance (TOL), Stress Tolerance Index (STI), Stress Susceptibility Percentage Index (SSPI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Mean Relative Performance (MRP), and Percent Yield Reduction (PYR), along with a convenience wrapper all_indices() that returns all indices simultaneously. The function mfvst_from_indices() integrates these indices into a composite stress score using direction-aware membership values (0–1 scaling) and also averaging, facilitating genotype ranking and selection (See Vinu et al., 2025; ). The package also implements two novel composite functions: WMFVST(), which computes the Weighted Mean Membership Function Value for Stress Tolerance, and WASI(), which computes the Weighted Average Stress Index, both derived from membership function values (MFV) and raw stress index values, respectively. Beyond stress indices, the package provides functions for key physiological traits relevant to sugarcane and other crops: bmap() computes biomass accumulation and partitioning between leaf, cane/shoot, and root fractions. chl() estimates total chlorophyll content from Soil-Plant Analysis Development (SPAD) and Chlorophyll Content Index (CCI) values using validated quadratic models particularly for sugarcane (See Krishnapriya et al., 2020; ). ctd() calculates canopy temperature depression (CTD) from ambient and canopy temperatures, an important indicator of transpiration efficiency. growth() computes key growth analysis parameters, including Leaf Area Index (LAI), Net Assimilation Rate (NAR), and Crop Growth Rate (CGR) across crop growth stages (See Watson, 1958; ). ranking() provides flexible ranking utilities for genotype performance with multiple tie-handling and NA-placement options. Through these tools, the package enables researchers to: (i) quantify crop responses to stress environments, (ii) partition physiological components of yield, (iii) integrate multiple indices into composite metrics for genotype evaluation, and (iv) facilitate informed decision making in breeding pipelines, and plant physiology experiments. By combining physiology-based traits with quantitative stress indices, 'PhysioIndexR' supports comprehensive crop evaluation and helps researchers identify multi-stress-resilient superior genotypes, thereby contributing to genetic improvement and ensuring sustainable production of food, fuel, and fibre in the era of limited resources and climate change. Package: r-cran-physmove Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7717 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-ggplot2, r-cran-powerlaw, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rootsolve, r-cran-scales, r-cran-sf Suggests: r-cran-emln, r-cran-kableextra, r-cran-knitr, r-cran-maps, r-cran-officedown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-physmove_1.2.5-1.ca2404.1_all.deb Size: 4575604 MD5sum: 18c08a2e8de93e4d0465f94800be7cf7 SHA1: 0efdbec6ffe0b0738d5279484f755ece11108202 SHA256: b8663fdb88948f96e5e967ad599541b0f57f3273fab9ab0812c68c22243caf13 SHA512: effcab8a8f1ce10c9fce6823ad81196556f0aa53e70054f30692cede9ce1dae35d203f87afe894071ed65b6d4708d4cdd4fca4a97c2e30558e54890270a6e49a Homepage: https://cran.r-project.org/package=PhysMove Description: CRAN Package 'PhysMove' (Quantifying Animal Movement and Space-Use Patterns withStatistical Physics) Provides tools to analyse animal movement and space-use patterns from telemetry data using methods derived from statistical physics. Methods span displacement-based approaches, distribution fitting, space-use metrics (including the influence of correlations on space-use), network-based community detection, and measures of entropy and predictability. The package enables characterisation of these patterns across spatial and temporal scales, including variation within and among individuals (inter- and intraspecific analyses). Outputs include interpretable metrics and visualisations to support ecological analysis and the investigation of fundamental movement processes. For applications of these methods in ecological studies see Rodríguez et al. (2017) and Sequeira et al. (2018) . Package: r-cran-physortr Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-phytools, r-cran-ape Filename: pool/dists/noble/main/r-cran-physortr_1.0.9-1.ca2404.1_all.deb Size: 50524 MD5sum: 56879c889c2a17e84223ed2a83c54a89 SHA1: 33adff3ba54faa171bc5e1ca2e7d4868d1606767 SHA256: 9506fbd48e967be43ad7d7ad8c501addf085544ad055071e96801f5d458afc40 SHA512: 1028a6769326369763863dc8620ee31714970e0dda4d9e8e72761600af7649b56c9f33d9e98527c289b0a5d09ba82b81cc6cccf2114ca4a971208f7943b264f8 Homepage: https://cran.r-project.org/package=PhySortR Description: CRAN Package 'PhySortR' (A Fast, Flexible Tool for Sorting Phylogenetic Trees) Screens and sorts phylogenetic trees in both traditional and extended Newick format. Allows for the fast and flexible screening (within a tree) of Exclusive clades that comprise only the target taxa and/or Non- Exclusive clades that includes a defined portion of non-target taxa. Package: r-cran-phytoclass Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bestnormalize, r-cran-dynamictreecut, r-cran-ggplot2, r-cran-rcppml, r-cran-tidyr, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-quarto, r-cran-here Filename: pool/dists/noble/main/r-cran-phytoclass_2.3.1-1.ca2404.1_all.deb Size: 425270 MD5sum: c1674087fd75b577bf1a338f10a83804 SHA1: 34b881443af9cad4ed49b3d5e79f0d12ab4c1466 SHA256: 264198632e4004e312997f29f76becb1cd1cf5cce4ae11abb06adb82c55f8e88 SHA512: 55e2ed126cae4c7d77949bb34d4975f342f1c6d78ba4afa71ab86898f2daf6258328e5804f11bcb3e25c319d19b4cb805e071e0a3ba383bc310bed247e2eb496 Homepage: https://cran.r-project.org/package=phytoclass Description: CRAN Package 'phytoclass' (Estimate Chla Concentrations of Phytoplankton Groups) Determine the chlorophyll a (Chl a) concentrations of different phytoplankton groups based on their pigment biomarkers. The method uses non-negative matrix factorisation and simulated annealing to minimise error between the observed and estimated values of pigment concentrations (Hayward et al. (2023) ). The approach is similar to the widely used 'CHEMTAX' program (Mackey et al. 1996) , but is more straightforward, accurate, and not reliant on initial guesses for the pigment to Chl a ratios for phytoplankton groups. Package: r-cran-phytoin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-packcircles, r-cran-biomass, r-cran-scales Suggests: r-cran-httr2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-phytoin_0.2.0-1.ca2404.1_all.deb Size: 144128 MD5sum: 555ddfc7d3f85e184795bf5b7a1368b6 SHA1: 1e0b80819fd92932d4d819406ad7eb5d5c8481f3 SHA256: 0d9b1cbb120d5ff4764e8e349eaf7c9f371339b2f4acaa6bcc65d0b27d79d456 SHA512: b4a1598efe354b3d90fb49fc5d9722f3f0c8d473390691d092cad2f94d7ebda51853447949af029a71dc51938a6cc3cde12622dd3947313db2ef812c3eed0d14 Homepage: https://cran.r-project.org/package=PhytoIn Description: CRAN Package 'PhytoIn' (Vegetation Analysis and Forest Inventory) Provides functions and example datasets for phytosociological analysis, forest inventory, biomass and carbon estimation, and visualization of vegetation data. Includes functions to compute structural parameters [phytoparam(), summary.param(), stats()], estimate above-ground biomass and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) classes [stratvol()], generate collector and rarefaction curves [collector.curve(), rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including rectangular plots and individual coordinates]. Several example datasets are provided to demonstrate the functionality of these tools. For more details see FAO (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in R: A routine to estimate phytosociological parameters" . Package: r-cran-phytools Architecture: all Version: 2.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-maps, r-cran-clustergeneration, r-cran-coda, r-cran-combinat, r-cran-deoptim, r-cran-doparallel, r-cran-expm, r-cran-foreach, r-cran-mass, r-cran-mnormt, r-cran-nlme, r-cran-numderiv, r-cran-optimparallel, r-cran-phangorn, r-cran-scatterplot3d Suggests: r-cran-animation, r-cran-geiger, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-rgl Filename: pool/dists/noble/main/r-cran-phytools_2.5-2-1.ca2404.1_all.deb Size: 2848268 MD5sum: e293614985d3d2db11c206fd1674a0cc SHA1: 058bae8b3a2c56a9c4470b3412707ba05ff8fdf1 SHA256: 54746fe09a8d823a0a174d0511db3223c4ee75848a93d00d8dcf2f86f2d86bda SHA512: 0edc2971df90cf83a749f12f8047f61eafb66c521f2b2899d55130c030b7c48e03213d99a2e5a371ad641bd905b81625f9a6b64cde0a722b450750dfd98547e1 Homepage: https://cran.r-project.org/package=phytools Description: CRAN Package 'phytools' (Phylogenetic Tools for Comparative Biology (and Other Things)) A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research. Package: r-cran-phytosanitarycalculator Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-acceptancesampling, r-cran-htmltools, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-devtools, r-cran-rlang Filename: pool/dists/noble/main/r-cran-phytosanitarycalculator_1.1.3-1.ca2404.1_all.deb Size: 40068 MD5sum: 4c0630d94688b0edba5c2850a23b9aec SHA1: 2e059e16bd867946d0e5b89c01849c38e287ae72 SHA256: d365ccf81f1e16beb6400ab48401102a6e4c52f3c7dbbc2ae0bf3a21239ed998 SHA512: 4d4fc9a1678f71fd6d744f44296107402cfb1818e892bee64996779602ec301daf0694cde5df4e8393f4ecf99a756375d0d48ee196b178234f9b0639ce134c23 Homepage: https://cran.r-project.org/package=PhytosanitaryCalculator Description: CRAN Package 'PhytosanitaryCalculator' (Phytosanitary Calculator for Inspection Plans Based on Risks) A 'Shiny' application for calculating phytosanitary inspection plans based on risks. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling). Package: r-cran-piar Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-data.tree, r-cran-litedown, r-cran-rsmatrix, r-cran-spelling, r-cran-sps, r-cran-tinytest, r-cran-treemap Filename: pool/dists/noble/main/r-cran-piar_0.10.1-1.ca2404.1_all.deb Size: 419772 MD5sum: fc22dc3e48e825d520c147dd6942809e SHA1: 27bde36d28c96427b429a45e45496322b8a6f18d SHA256: cff702a3b92576831f827fee6aeca054fa51ebbdddbf9cd3962d1221f7653721 SHA512: c7e3bbe016d9b563742957a0b53623b7488c3329ca2c43199a1e1d5ad74873592af103d49f7be3e0dbc9f8bfcb32609fb451489811ab2cfb68512786724187be Homepage: https://cran.r-project.org/package=piar Description: CRAN Package 'piar' (Price Index Aggregation) Most price indexes are made with a two-step procedure, where period-over-period elementary indexes are first calculated for a collection of elementary aggregates at each point in time, and then aggregated according to a price index aggregation structure. These indexes can then be chained together to form a time series that gives the evolution of prices with respect to a fixed base period. This package contains a collection of functions that revolve around this work flow, making it easy to build standard price indexes, and implement the methods described by Balk (2008, ), von der Lippe (2007, ), and the CPI manual (2020, ) and PPI manual (2004, ) for bilateral price indexes. Package: r-cran-pic Architecture: all Version: 3.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-dbscan, r-cran-dplyr, r-cran-magrittr, r-cran-rann, r-cran-tictoc, r-cran-terra Suggests: r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-gridextra, r-cran-later, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinythemes, r-cran-scales, r-cran-shinywidgets, r-cran-testthat, r-cran-tidyr, r-cran-viridis, r-cran-withr Filename: pool/dists/noble/main/r-cran-pic_3.3.3-1.ca2404.1_all.deb Size: 756444 MD5sum: c7e2675a3e27e87bc6b5dcbbf9ae9c23 SHA1: ad7a32bd2b5d8c614d1602efad5465402777dbe6 SHA256: 6c780f240312ddaaf80b9a04b901532cb2e47a8c96deef0bc520c0678833540c SHA512: 9407e9152af8f301bd73bd7a3276620403a379472b3d268343d00b898a5e04cea41413b949a2b8c87b68991c03925c26e0b7bfb7c7aa8dbcd386ad7f0969fc6e Homepage: https://cran.r-project.org/package=PiC Description: CRAN Package 'PiC' (Interactive Processing and Segmentation of Forest TLSPoint-Cloud Data) Tools for the processing, segmentation, and analysis of terrestrial laser scanning (TLS and MLS) forest point-cloud data. The package provides fast voxel-based processing, classification of point clouds into forest floor, understory, canopy, and woody components, and algorithms for single-tree analysis and structural characterization. Methods are designed to handle large and dense point-cloud datasets efficiently, supporting applications in forest structure assessment, connectivity analysis, and fire-risk evaluation. Input data are provided as '.xyz', '.txt', '.las', or '.laz' point-cloud files. The circle-fitting routines used for diameter estimation are adapted, in base R, from the 'conicfit' package (GPL-3) by Jose Gama, based on the original algorithms and code by Nikolai Chernov. For methodological details, see Ferrara and Arrizza (2025) and Ferrara et al. (2018) . Package: r-cran-picbayes Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-mcmcpack, r-cran-survival Filename: pool/dists/noble/main/r-cran-picbayes_1.0-1.ca2404.1_all.deb Size: 647906 MD5sum: d061bc30c55219cf58a2eb5059277947 SHA1: 84a5c0a0b20ce7bd44f51cebf86cada40cce9794 SHA256: 0c8fb1da70f2c74f9a5a78563a96d7a1b2e5819528388eddc760a36cb6d75464 SHA512: dba1dab5e432621896d608d3d0b9158fd5040c2d69396e154791dda74058f50335ec6cd01e44b68d316e23dc8a519f857c865aa7c437efd9fb7fa9bd262323eb Homepage: https://cran.r-project.org/package=PICBayes Description: CRAN Package 'PICBayes' (Bayesian Models for Partly Interval-Censored Data) Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) ), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) ), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data. Package: r-cran-picclip Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-base64enc, r-cran-stringr, r-cran-htmltools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-picclip_0.1.0-1.ca2404.1_all.deb Size: 16202 MD5sum: 1145216ec1ea889bf329089a1dbc66d4 SHA1: 33ac4a0e3d6e94f031797cd84a94b146f72c45b5 SHA256: 35df84a7ec59d1c642c1555dae2c6f30722e1d3457e59ece135f083bf9323a74 SHA512: b165d4637aeac10ffdc36aebe8c43f4359b4e526880730277051a74988c346cad6eb67232ceb2f6bee38b49dfef342c44af8a86b23deb4f1496d98bf7eef28bc Homepage: https://cran.r-project.org/package=picClip Description: CRAN Package 'picClip' (Paste Box Input for 'Shiny') Provides a 'Shiny' input widget, pasteBoxInput, that allows users to paste images directly into a 'Shiny' application. 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Package: r-cran-pickmax Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-pickmax_0.1.0-1.ca2404.1_all.deb Size: 15278 MD5sum: 0f64b1886394c639a87d748b604bdc82 SHA1: 7ae637b5615e6a7aad42cb851e2857e4098bb87f SHA256: f03f077ace5f68189afe5ea3f729e819b5af892895cd7307acd27b5cb5a4a46c SHA512: 4211f4a814d9343d5682c339ab2467db59b6a8cf0b06b68ad016d2d43a0585edc2ed0d6d4d45144baf4158824a8ebce3e89be853f320df9a5c7e189b17422056 Homepage: https://cran.r-project.org/package=pickmax Description: CRAN Package 'pickmax' (Split and Coalesce Duplicated Records) Deduplicates datasets by retaining the most complete and informative records. Identifies duplicated entries based on a specified key column, calculates completeness scores for each row, and compares values within groups. When differences between duplicates exceed a user-defined threshold, records are split into unique IDs; otherwise, they are coalesced into a single, most complete entry. Returns a list containing the original duplicates, the split entries, and the final coalesced dataset. Useful for cleaning survey or administrative data where duplicated IDs may reflect minor data entry inconsistencies. Package: r-cran-picotsize Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-epir, r-cran-ggplot2, r-cran-plotly, r-cran-rlang, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-picotsize_0.1.0-1.ca2404.1_all.deb Size: 1372922 MD5sum: 447fb9552b1efa17b741016fcb406e2f SHA1: e3976bcef48d0f31deb9b0bf18623deedb4a3708 SHA256: 519f97f9252888f0f861cce4d2331fd9dfe1b75b7347ba5d64c6d6fe271cd128 SHA512: 4f9c951f36d4f8c0ea2cb7a43acb69623f363ff4409c069c1e9c245add92bf4e08529d661088dbed9f31764e975d3b7ac7f4d050cf936ae2e435b0489d8016a5 Homepage: https://cran.r-project.org/package=PICOTsize Description: CRAN Package 'PICOTsize' (Sample Size Calculation for PICOT-Based Study Designs) Provides sample size calculators for the study designs covered by the PICOT framework, including cross-sectional, case-control, cohort, superiority, non-inferiority, and equivalence clinical trials, and diagnostic test accuracy studies, following Bhardwaj et al. (2024) . Calculations are performed using the 'epiR' package as a validated computational backend. Includes a 'shiny' application with a PICOT-based design wizard, an interactive sensitivity plot, and automatically generated Methods-section text for manuscripts. Package: r-cran-picr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-picr_1.0.1-1.ca2404.1_all.deb Size: 43224 MD5sum: 939aca204ed5b6251a7ef351c2cc0303 SHA1: 0b2ebbab48b977c0b48856e5b05958b653b5d32b SHA256: a09209fc8e57e0ccaf25ac60701103fa5f5acfba1ca0c6370a9437d6cee2cee7 SHA512: 747e9a39c8585b5facf8bb6f1441bbd95534a1419b5397a61179deeba0621e0c66cf0ba99b32afb576fc8aee1dbd8866953e39f1742f9070992ee3baf4cc98d6 Homepage: https://cran.r-project.org/package=picR Description: CRAN Package 'picR' (Predictive Information Criteria for Model Selection) Computation of predictive information criteria (PIC) from select model object classes for model selection in predictive contexts. In contrast to the more widely used Akaike Information Criterion (AIC), which are derived under the assumption that target(s) of prediction (i.e. validation data) are independently and identically distributed to the fitting data, the PIC are derived under less restrictive assumptions and thus generalize AIC to the more practically relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity". Package: r-cran-pid Architecture: all Version: 0.65-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-png, r-cran-frf2, r-cran-doe.base, r-cran-frf2.catlg128 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-pid_0.65-1.ca2404.1_all.deb Size: 875094 MD5sum: 44b1329e0360bb8bb2859534d5ff50b4 SHA1: fd9a006c89c10269b19a32a8c2528a7c9fa51f2e SHA256: c86fd26f845960ff28830dcacf30fd8b068b67ac3617f8479dcab3edc5729e38 SHA512: 672aadb73f8a2facdeec4ab3ab3dd4ed88092ee51336cb8015644dfa109463fa3e0bfbccd5aff30dd6c057cdb4c5d9ca2acfcefce4f1c88af18aa64da8d21838 Homepage: https://cran.r-project.org/package=pid Description: CRAN Package 'pid' (Process Improvement using Data) A collection of scripts and data files for the statistics text: "Process Improvement using Data" and the online course "Experimentation for Improvement" found on Coursera. The package contains code for designed experiments, data sets and other convenience functions used in the book. Package: r-cran-pie Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gglasso, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pie_1.0.0-1.ca2404.1_all.deb Size: 142468 MD5sum: 2b883e3391fefdfd03973239145469c4 SHA1: b157b8165cf24581b331a0580aa897c2c2e03f42 SHA256: 843c291fb86e4a37b1d5d17c995e4b7a693a3104a83bebc19c12b58fd09687d0 SHA512: d80aa6f7323b0c1f422c46b5a833d5247ebca871f218e0a8c1bee3a55f606b76147255fa9740b62d53dbcf1b8e70b0ab3c45b9c48ba0b8053e7496088f232293 Homepage: https://cran.r-project.org/package=PIE Description: CRAN Package 'PIE' (A Partially Interpretable Model with Black-Box Refinement) Implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) . Package: r-cran-piecemaker Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-stringi, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-piecemaker_1.0.2-1.ca2404.1_all.deb Size: 41478 MD5sum: 4ec9d387f56697b58cf56112fe65e054 SHA1: d5723a76318d1034a1e08161012677bc8077460e SHA256: 1104afe442bc6094ca04563df8411beff4f21cec1f97b8cf1e8a84d34c38a092 SHA512: 012097092b924c5e634622b7ba513c21f6d557a630d6757721afef5490f1f962099b7980eab3273984e74c9d878cacfd04761bbdef2994b485cfe79380f552a9 Homepage: https://cran.r-project.org/package=piecemaker Description: CRAN Package 'piecemaker' (Tools for Preparing Text for Tokenizers) Tokenizers break text into pieces that are more usable by machine learning models. Many tokenizers share some preparation steps. This package provides those shared steps, along with a simple tokenizer. Package: r-cran-piecemeal Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-filelock, r-cran-rlang, r-cran-purrr, r-cran-rsqlite, r-cran-dbi, r-cran-cli Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-piecemeal_0.3.0-1.ca2404.1_all.deb Size: 152056 MD5sum: 89ebfe5a4ec20d7bbd60b7072de527be SHA1: 14caabf7557781e367ca55af74afdc528ec3e2e7 SHA256: b2df613d7734d014c54934b2949bd12a6c894141ab7a51de771ba8100824f55c SHA512: 5fbe01050cdb8e9bbd9863a73b6c0a27f2279a612da4768abbe6fa396ec52efa6f0726b625f932bd845f1b2660a2e3d4adece5126bc0ebd95204e5cdc23f19f8 Homepage: https://cran.r-project.org/package=piecemeal Description: CRAN Package 'piecemeal' (Wrangle Large Simulation Studies) An 'R6' class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in. Package: r-cran-piecenorms Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-scales, r-cran-r6, r-cran-classint, r-cran-univariateml, r-cran-coinr, r-cran-vdiffr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-piecenorms_1.1.0-1.ca2404.1_all.deb Size: 184076 MD5sum: 5af87b9f7bc742b8da46f6c7dcfee067 SHA1: 32774decfd49f98980b7576417082c5c3aed9bd2 SHA256: 87dfe9add67aad2034c4bba1586b00e92018e5791159d335259f8fb18efbc87c SHA512: 72e886e38be34efb65dc7d3ded015ab147a629f0e7b8678e9473698dc7650d4af801c954bdf4303b00666e92c30424af075ef21e18d65253e7bd12450eaf35f6 Homepage: https://cran.r-project.org/package=piecenorms Description: CRAN Package 'piecenorms' (Calculate a Piecewise Normalised Score Using Class Intervals) Provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. 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Package: r-cran-piecepackr Architecture: all Version: 1.16.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2414 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-affiner, r-cran-gridgeometry, r-cran-grimport2, r-cran-purrr, r-cran-jpeg, r-cran-png, r-cran-r6, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-animation, r-cran-ggplot2, r-cran-gifski, r-cran-gridpattern, r-cran-magick, r-cran-pdftools, r-cran-rayrender, r-cran-rayvertex, r-cran-readobj, r-cran-rgl, r-cran-scales, r-cran-svglite, r-cran-systemfonts, r-cran-testthat, r-cran-tweenr, r-cran-vdiffr, r-cran-xmpdf, r-cran-xml Filename: pool/dists/noble/main/r-cran-piecepackr_1.16.1-1.ca2404.1_all.deb Size: 2166356 MD5sum: 7b275a8e64ae0e7b768a15ae1557e2d1 SHA1: b87b0f20846a9ccb0c0deca662d58c5fee961247 SHA256: 0afc5c21d45cfe01da343f1ec6736700031f2e3dd6e8c57a8485ddf7e3a1f5b2 SHA512: 3ab67a13685a464f4fc55c32f4939655549b7480214de05d277edfb6cd2ffbb0ef0efb94d425b05129aeb57bc95c55445e97a7efb425abebb454712e7ff629c5 Homepage: https://cran.r-project.org/package=piecepackr Description: CRAN Package 'piecepackr' (Board Game Graphics) Functions to make board game graphics with the 'ggplot2', 'grid', 'rayrender', 'rayvertex', and 'rgl' packages. Specializes in game diagrams, animations, and "Print & Play" layouts for the 'piecepack' but can make graphics for other board game systems. Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, 'piecepack', playing cards, etc. Package: r-cran-piecewisesem Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-diagrammer, r-cran-emmeans, r-cran-igraph, r-cran-lme4, r-cran-multcomp, r-cran-mumin, r-cran-mass, r-cran-nlme, r-cran-performance Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-piecewisesem_2.3.1-1.ca2404.1_all.deb Size: 369396 MD5sum: c7a5bb13692bd0feee983eb4bf4482d0 SHA1: 71222171b19cb5dbce0e469d9c134891bea6692f SHA256: 051d1a3d845a2f58e026ab2b59f2fcb610e7dcdec9c39d60e250a34d062b405e SHA512: d5f0353949e58344ab2a8d7a542db6e79e5461d37cb354c34f7ad1813dea94704227d8f6fd6df73b95a374900ea0c138a42be1b95862d82c44dc29ed86efbc81 Homepage: https://cran.r-project.org/package=piecewiseSEM Description: CRAN Package 'piecewiseSEM' (Piecewise Structural Equation Modeling) Implements piecewise structural equation modeling from a single list of structural equations, with new methods for non-linear, latent, and composite variables, standardized coefficients, query-based prediction and indirect effects. See for more. Package: r-cran-pieglyph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-ggiraph, r-cran-ggforce, r-cran-purrr, r-cran-forcats, r-cran-plyr, r-cran-scales, r-cran-cli Suggests: r-cran-spelling, r-cran-ranger, r-cran-maps, r-cran-cowplot, r-cran-mapproj, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-pieglyph_1.1.0-1.ca2404.1_all.deb Size: 769286 MD5sum: 98d245d8603a7cc2172c2f74e801b5c7 SHA1: 5c3cfd6510a48ce74ec39a5c986de8e37303ecf8 SHA256: 14519341ea5df5f28503f61bc4546e041affd44596ecf7c1f7c0ed857ffae105 SHA512: ab07bfee8b9cd3d7558f4fe13a996a0461cc33cc371326b1bbc53e3f3d42bd426dc0dc53cae47a675d4c1785ecaa05a3268cc0df28f3c91e88771401bc4acab4 Homepage: https://cran.r-project.org/package=PieGlyph Description: CRAN Package 'PieGlyph' (Axis Invariant Scatter Pie Plots) Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. 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Package: r-cran-pigauto Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5501 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-torch, r-cran-ape, r-cran-ggplot2, r-cran-matrix, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-rspectra, r-cran-rphylopars, r-cran-jomo, r-cran-mcmcglmm, r-cran-glmmtmb, r-cran-lme4, r-cran-nlme, r-cran-phangorn, r-cran-phylolm, r-cran-rgbif, r-cran-smcfcs, r-cran-terra, r-cran-phytools Filename: pool/dists/noble/main/r-cran-pigauto_0.10.0-1.ca2404.1_all.deb Size: 5263106 MD5sum: 4ad3a2e53f31f9bbe6c89b28f7c99fc5 SHA1: 6eab66bdbc280b31813f4cb0de3f5b7369027e20 SHA256: b6c4e65ef1f7cad9749b5f085ac2e8c5353370bd159689ab7d0581a6e1ecd2d9 SHA512: a1c588810d38327291d03a26cdd891ab5352049298dac35bab0439bc11ef14db7432ea0c937ece405e71ab482798c48cca8cd3f260b6abbcb74106d7a39a2502 Homepage: https://cran.r-project.org/package=pigauto Description: CRAN Package 'pigauto' (Fill in Missing Species Traits Using a Phylogenetic Tree) Imputes missing species trait data for comparative analyses by combining three sources of information: phylogenetic similarity (closely related species share similar traits), cross-trait correlations (observed traits inform missing ones), and optional environmental covariates (climate, habitat, geography). Handles continuous measurements, counts, binary variables, ordered categories, unordered categories, bounded proportions, zero-inflated counts, and compositional multi-proportion data in a single call. The method blends a phylogenetic baseline with a graph neural network correction; a per-trait gate calibrated on held-out data ensures the network only contributes when it improves on the baseline. Provides conformal prediction intervals for continuous, count, and ordinal traits and an experimental analysis-aware multiple-imputation workflow for one missing continuous covariate in Gaussian linear, binomial-logit, and Gaussian random-intercept models, with Rubin pooling limited to fixed effects. Stochastic graph-network and posterior-tree completions are prediction diagnostics rather than validated inferential imputations. Tested up to 10,000 species. Bundled datasets include 300-species and 9,993-species bird-trait subsets with matching example phylogenetic trees. Rubin (1987, ISBN:978-0-471-08705-2); Vovk et al. (2005, ISBN:978-0-387-25061-8); Nakagawa and de Villemereuil (2019) . Package: r-cran-piggyback Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-gh, r-cran-httr, r-cran-jsonlite, r-cran-fs, r-cran-lubridate, r-cran-memoise Suggests: r-cran-spelling, r-cran-readr, r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gert, r-cran-withr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-piggyback_0.1.5-1.ca2404.1_all.deb Size: 558166 MD5sum: 097314a444d9e2b162b5049e759f031a SHA1: 5cebbb0a35b8d6486946b9803d01a43c33d79e17 SHA256: 3e74d076de887520e838739d0747a5b83a9566dbc663b5a819ab2f40c6579234 SHA512: 6ede43db71afd1b966c30b3e677eede69385e37a7bdffe581f3a86fa389c7748797c2b824eb7d22fff1a224b5cfdda059a140db264e667fa11ee1ce47c3ec3f8 Homepage: https://cran.r-project.org/package=piggyback Description: CRAN Package 'piggyback' (Managing Larger Data on a GitHub Repository) Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. This package provides a simple work-around by allowing larger (up to 2 GB) data files to piggyback on a repository as assets attached to individual GitHub releases. These files are not handled by git in any way, but instead are uploaded, downloaded, or edited directly by calls through the GitHub API. These data files can be versioned manually by creating different releases. This approach works equally well with public or private repositories. Data can be uploaded and downloaded programmatically from scripts. No authentication is required to download data from public repositories. Package: r-cran-pii Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-uuid Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pii_1.3.0-1.ca2404.1_all.deb Size: 20004 MD5sum: 512a54830ef5b58780fe3d885c59255c SHA1: 6248da0a33fe32c6541119f1886c31ee56c347b9 SHA256: d6b5d37c2214c006a80558051c37ae5832fb976b7a68fb9298bd2898bdc40328 SHA512: 747d6beb3b6b4571852c24c094c8cbc574d0552b73215f61612750df6d79456f6ba6defd442d28afd346a4808c06bd23483bc230a0303363841f8b8b6da1363a Homepage: https://cran.r-project.org/package=pii Description: CRAN Package 'pii' (Search Data Frames for Personally Identifiable Information) Check a data frame for personal information, including names, location, disability status, and geo-coordinates. Package: r-cran-piir Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-piir_0.3.0-1.ca2404.1_all.deb Size: 17256 MD5sum: 104e665c3bcc840e20b45a3fbb95649e SHA1: fb85d34090db89d7462bc6b05ca94d601a6ad58e SHA256: 8eb6f7b0523ae3ec5b7f6047d3a1cefda83c7810a57d74e38634ab8caf6db7ed SHA512: aa194fff3615ca94e4b8366ae0f91b03cf9d6f78d4e17bd09aacb13d2ce5ad3114b8d0f4e60a28bac6754a87b6728d69a43dfc0bed97046906e981e3e6aba7ee Homepage: https://cran.r-project.org/package=piiR Description: CRAN Package 'piiR' (Predictive Information Index ('PII')) A simple implementation of the Predictive Information Index ('PII'). Package: r-cran-pillar Architecture: all Version: 1.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-utf8, r-cran-vctrs Suggests: r-cran-bit64, r-cran-dbi, r-cran-debugme, r-cran-diagrammer, r-cran-dplyr, r-cran-formattable, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-nanotime, r-cran-nycflights13, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-survival, r-cran-testthat, r-cran-tibble, r-cran-units, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-pillar_1.11.1-1.ca2404.1_all.deb Size: 608500 MD5sum: 8466ef5b52027fe19f80a6ac1e16ead7 SHA1: d670adc6978e6d7eefeaed04981450e72b4edf4c SHA256: 1c55e456ee6a87ac52adc7168609f3caaf5dab04cf4394d5ef38db6ed3720d8a SHA512: f642c03370e39c8be4ac8a2d38b49c747183d5ba0212b642ec3d93c93a6f9760c535af9026a236752d2c5dcf8c3d97d9aac025c13615a8af221b30d681b7aab2 Homepage: https://cran.r-project.org/package=pillar Description: CRAN Package 'pillar' (Coloured Formatting for Columns) Provides 'pillar' and 'colonnade' generics designed for formatting columns of data using the full range of colours provided by modern terminals. Package: r-cran-pilotr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-shiny, r-cran-future, r-cran-promises, r-cran-ggplot2, r-cran-lme4, r-cran-lmertest, r-cran-callr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-pilotr_0.3.1-1.ca2404.1_all.deb Size: 451150 MD5sum: 507fd029f0e9dc1723b4542425f4e82f SHA1: 039ab3deede49784c9141e41922ddbb874c84a49 SHA256: f2117450efb92a64a2a9461e113916f12ea8fddef26b70778f03e7583e85acca SHA512: a0871c452059234b2d0b48f1fe9ed8d4f81905abb62a4dec185026b7d8222e3da8a67354a5d0643a820c52f5d38e4448f68486c77805f56216c89198becd51f1 Homepage: https://cran.r-project.org/package=pilotr Description: CRAN Package 'pilotr' (Simulate Experimental and Behavioural Data from a PortableDesign Specification) Generative simulation of experimental and behavioural data sets from a portable JavaScript Object Notation (JSON) design specification shared with the 'Python' package of the same name. Supports user-specified fixed effect sizes, crossed by-subject and by-item random intercepts and slopes, predictors measured with error, realistic response families (Gaussian, lognormal, shifted lognormal, ex-Gaussian, Bernoulli, Poisson, ordinal and Beta), and simulation-based power and precision-based design analysis, including the Type S and Type M errors of Gelman and Carlin (2014) and a region of practical equivalence. A shared cross-language random-number generator means that, given the same specification and seed, the R and 'Python' implementations produce identical data: exactly for the Gaussian family and for any family with rounding set, and to within the last unit in the last place for families applying a transcendental function to the linear predictor, whose rounding the IEEE-754 standard does not fix. Package: r-cran-pim Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-bb Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-pim_2.0.4-1.ca2404.1_all.deb Size: 647594 MD5sum: 7f46effc1cda705a5b64273a7ed3b43c SHA1: 7e6f0205ceaf2137b0b7a3b47e86c276dd941778 SHA256: aaf9c44ae9c4f7e825e5b020a732a99413cddd04304f9aea9459d1484e0bfab9 SHA512: a36ac567e45590758bd44768f993bf20c6e37e7440db36e20d096337ea1f4964a6e9cf76f3be9ca5d42afa3fd914904aaea33a53498f646b968f130323850ab9 Homepage: https://cran.r-project.org/package=pim Description: CRAN Package 'pim' (Fit Probabilistic Index Models) Fit a probabilistic index model as described in Thas et al, 2012: . The interface to the modeling function has changed in this new version. The old version is still available at R-Forge. Package: r-cran-pinference Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pinference_0.2.6-1.ca2404.1_all.deb Size: 40132 MD5sum: 4c6a9943049977f33583662e42c2c272 SHA1: 20c7fc68dad83d9473f278ce9ef24ab54da45f5d SHA256: 55dd1cd10c2ba418b6dc85760264bed74d80029f5b8b8d4ec46902054530ec28 SHA512: 1166901c36050f8f2757d50041e729475971efa52848cad3b52e82a4b170dbb56699912b996eeb52abdb84e7684633292ac4cd31cb633a331fdcf96642aa68a1 Homepage: https://cran.r-project.org/package=Pinference Description: CRAN Package 'Pinference' (Probability Inference for Propositional Logic) Implementation of T. Hailperin's procedure to calculate lower and upper bounds of the probability for a propositional-logic expression, given equality and inequality constraints on the probabilities for other expressions. Truth-valuation is included as a special case. Applications range from decision-making and probabilistic reasoning, to pedagogical for probability and logic courses. For more details see T. Hailperin (1965) , T. Hailperin (1996) "Sentential Probability Logic" ISBN:0-934223-45-9, and package documentation. Requires the 'lpSolve' package. Package: r-cran-pinfsc50 Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4234 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pinfsc50_1.3.0-1.ca2404.1_all.deb Size: 3438004 MD5sum: 63775a894bea8627cfe6d9cd7d7a7c6d SHA1: b28c26e27df5160f696623bf8ff16dab4b689191 SHA256: 0fae9df9fd2245c21f96ed23978395dd13f982bc3b70e988936b8dc8f032de90 SHA512: db6bd1484fa13c27deb1252481a83598cc7a8fecc83b942fbeafc3f455d18fd2742782263fe49403e92e14afdd412aa67762c68729002f6d6ad0ff2c8f060ad3 Homepage: https://cran.r-project.org/package=pinfsc50 Description: CRAN Package 'pinfsc50' (Sequence ('FASTA'), Annotation ('GFF') and Variants ('VCF') for17 Samples of 'P. Infestans" and 1 'P. Mirabilis') Genomic data for the plant pathogen "Phytophthora infestans." It includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file ('GFF'). 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Package: r-cran-pinnprogcens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desolve Filename: pool/dists/noble/main/r-cran-pinnprogcens_0.1.0-1.ca2404.1_all.deb Size: 27846 MD5sum: 1886b830c08c109eb916d3b54bbfa8a7 SHA1: 9ba99c501c0c16d878e4fbdf7a8a93bf236f4347 SHA256: a109f16e98a029392a0fa5a265dd8fb48887d3bc64f6e14a5e588a49525ff6ac SHA512: 87672b321fc793bebbef2d86a1dcf5c93d4a327670cc795a8f6ae7376db0c5634144af8866f9e561ced3e7c0c63e126d60fb8332e0d099cf9186457c3e1bf631 Homepage: https://cran.r-project.org/package=PINNProgCens Description: CRAN Package 'PINNProgCens' (Physics-Informed Neural Networks for Progressive Censoring) Implementation of Physics-Informed Neural Networks ('PINN') for lifetime estimation under progressive Type-II censoring schemes. Combines parametric baseline hazards with physical differential degradation models. Package: r-cran-pinochet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-sf, r-cran-tidyverse, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pinochet_0.1.0-1.ca2404.1_all.deb Size: 582678 MD5sum: 50551b819ed9384623f945aaa66e2cbf SHA1: 1b814432d925f28645a1f9a8e5b3bd75ab681e1a SHA256: 2e1b0f6bff8cbbb951440b018919597d2247af13d944ba79aa05f84afd5237cf SHA512: 9c74f27a9ee08b1aaa83aa0a0b39243baef53cc4bfa39b45db3c04700b2caae962000d9ecec06f759f74a5cad927142431af3512b56f28f3e934a44794eba357 Homepage: https://cran.r-project.org/package=pinochet Description: CRAN Package 'pinochet' (Data About the Victims of the Pinochet Regime, 1973-1990) Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463). 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Package: r-cran-pins Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rappdirs, r-cran-rlang, r-cran-tibble, r-cran-whisker, r-cran-withr, r-cran-yaml Suggests: r-cran-archive, r-cran-arrow, r-cran-azurestor, r-cran-covr, r-cran-data.table, r-cran-filelock, r-cran-gitcreds, r-cran-googlecloudstorager, r-cran-googledrive, r-cran-httr2, r-cran-ids, r-cran-knitr, r-cran-microsoft365r, r-cran-mime, r-cran-mockery, r-cran-nanoparquet, r-cran-openssl, r-cran-paws.storage, r-cran-qs2, r-cran-r.utils, r-cran-rmarkdown, r-cran-rsconnect, r-cran-shiny, r-cran-sodium, r-cran-testthat, r-cran-webfakes, r-cran-xml2, r-cran-zip Filename: pool/dists/noble/main/r-cran-pins_1.4.2-1.ca2404.1_all.deb Size: 678552 MD5sum: df5e3ed3400d7b318adddade48ab36f3 SHA1: 6bb44e0ac685603550c58af70a2d233429e4bb8a SHA256: 56bc98cee32f74c1164690ece509d527f390b420304b26be9b978f4898834460 SHA512: 7e414cca6be68a32008cc2667557c48deea687b16d15cf7517007a3c3b70d9782a0f2837e01a9992a844aaed48073e5c8efa7dda14ec613834ab3bd922776b0d Homepage: https://cran.r-project.org/package=pins Description: CRAN Package 'pins' (Pin, Discover, and Share Resources) Publish data sets, models, and other R objects, making it easy to share them across projects and with your colleagues. 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Package: r-cran-pinsearch Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-boot, r-cran-difr, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pinsearch_0.1.6-1.ca2404.1_all.deb Size: 124498 MD5sum: 79c7c340edff45ab0a7f3fcfc007fbbd SHA1: a9bd1af5767181ddab3050c447a6baf986829ca7 SHA256: e07138595f62c0b8667b6eac793be5456b2111d4eca7f02c146bca0c0604b33e SHA512: 26385706ffc8a1aabfb7bf3107a8e452ec3163263e8b0b832dfdcad75e983ce6ea2e48c029472e8960ec79cccac1f66a69c1b06c5dc635e664bbe3231f6f8ef1 Homepage: https://cran.r-project.org/package=pinsearch Description: CRAN Package 'pinsearch' (Specification Search for Partial Factorial Invariance) Automate specification search for identifying noninvariant items in factorial invariance analyses, as described in Yoon & Millsap (2007) . 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(1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data. Package: r-cran-pinterestadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-pinterestadsr_0.1.0-1.ca2404.1_all.deb Size: 22928 MD5sum: ad5f859411586c055d3e8a270d5f6046 SHA1: ba831d3811c1ea350810f8358656e2ff4900948a SHA256: c7fb97ad9363090b38fe1197bc948d15d43fb75ad5db9bd69c3d4a260c755f30 SHA512: 4bb651022d190e3542bd1351091cdd9489a48623c5bc078075da2eb152fa16c543127d7170da4a9fb06db0a22629c89d964d41b4dd00d79271c6844910ebf4b6 Homepage: https://cran.r-project.org/package=pinterestadsR Description: CRAN Package 'pinterestadsR' (Access to Pinterest Ads via the 'Windsor.ai' API) Collect marketing data from Pinterest Ads using the 'Windsor.ai' API . Use four spaces when indenting paragraphs within the Description. 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The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, 'PKbioanalysis' provides a relational infrastructure for plate management and injection sequence. 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The pharmacokinetics model explains that how the drug concentration change as the drug moves through the different compartments of the body. For pharmacokinetic modeling and analysis, it is essential to understand the basic pharmacokinetic parameters. All parameters are considered, but only some of parameters are used in the model. Therefore, we need to convert the estimated parameters to the other parameters after fitting the specific pharmacokinetic model. This package is developed to help this converting work. For more detailed explanation of pharmacokinetic parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001"; "Benet and Zia-Amirhosseini" (1995) ; "Mould and Upton" (2012) ; "Mould and Upton" (2013) . 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Create a package-verse, or meta package, by supplying a custom name for the collection of packages and the vector of desired package names to include– and optionally supply a destination directory, an indicator of whether to keep the created package directory, and/or a vector of verbs implement via the 'usethis' package. 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Spatial data at country, province, district, and tehsil levels are embedded in the package as 'sf' objects compatible with the 'tidyverse' and geospatial ecosystem. Includes utilities for geographic dictionary lookup, coordinate reference system selection, spatial measurement, and neighbour structure construction for use with 'spdep', 'ggplot2', 'leaflet', and related packages. Package: r-cran-pkmon Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pkmon_1.1-1.ca2404.1_all.deb Size: 68466 MD5sum: 9b1c8f470ef660ecb13be97bb7e8236d SHA1: 0c416f2db37897bcd70029bf28de93f16fdc9eec SHA256: dda5d1a8a223cc1b390ce78366b1320ea8c12bd679fc51e2d651f3188e76c578 SHA512: efa25f90c7de6e46e6864e7ff36f7fff47e88e82ef61180c98b4c4f97e4edde07b48a064d37ee6a35e81d88e994223362f8e15442b5fca2f108e996c0fdec940 Homepage: https://cran.r-project.org/package=pkmon Description: CRAN Package 'pkmon' (Least-Squares Estimator under k-Monotony Constraint for DiscreteFunctions) We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) . The first one is a projection estimator on the set of k-monotone discrete functions. 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Package: r-cran-pkpd.release Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-gridextra, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pkpd.release_0.1.0-1.ca2404.1_all.deb Size: 268992 MD5sum: 45928141fd6a413e063ab04bad034b20 SHA1: 3397ff83b8f5c9015cb0dc06ebc22a515b0483d2 SHA256: 82fe66e1a439dce570f58f68a5380b1ae98b317deaddb818cdca54835df1efca SHA512: df8754d83176516b81a420998f9530391e6e4977c61041a1cdb18ab08d2b112f3111174595baf36d20f2a5c012753641f61916c46ff95552aba26f99060b668b Homepage: https://cran.r-project.org/package=pkpd.Release Description: CRAN Package 'pkpd.Release' (Model Fitting and Simulation for Drug Release Kinetics and PK/PD) Provides a comprehensive framework for model fitting and simulation of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD). The package implements widely used mechanistic and empirical models for in vitro drug release, including zero-order, first-order, Higuchi, Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic functionality includes linear and nonlinear functions for one- and two-compartment models for intravenous bolus and oral administration, Michaelis-Menten kinetics, and non-compartmental analysis (NCA). Pharmacodynamic and dose-response modeling is supported through Emax-based models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax) Hill models, four- and five-parameter logistic models, as well as median toxic dose (TD50) and lethal dose (LD50) models. The package is intended to support parameter estimation, simulation, and model comparison in pharmaceutical research, drug development, and pharmacometrics education. For more details, see Gabrielsson & Weiner (2000) , Holford & Sheiner (1981) , and Manlapaz (2025) . Package: r-cran-pkpdindex Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pkpdindex_0.2.1-1.ca2404.1_all.deb Size: 31874 MD5sum: bf6cb1c9508829bcf61b2a23e20d3747 SHA1: 8ad0f8d2648492d5d4c3675fe544c03ac4026332 SHA256: 279350dcae21a16461f117f83f3f057503029c7951d9aa1266c2fad5777992cf SHA512: caeac2bfe02710e56b3d796eedf8acea66a8285e0817d2716fe8c6e8fb0d2af7c4826bd807e28cc0aa4ae1d735724313e799653e0d1e29cd0f0db2fb8d0076b8 Homepage: https://cran.r-project.org/package=PKPDindex Description: CRAN Package 'PKPDindex' (Optimal PK/PD Index Finder) Fits Emax models to pharmacokinetic/pharmacodynamic (PK/PD) data, estimate key parameters, and visualise model fits for multiple PK/PD indices. Methods are described in Macdougall J (2006) , Spiess AN, Neumeyer N (2010) , and Burnham KP, Anderson DR (2004) . Package: r-cran-pkr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-noncompart, r-cran-foreign Suggests: r-cran-binr, r-cran-forestplot, r-cran-rtf Filename: pool/dists/noble/main/r-cran-pkr_0.1.6-1.ca2404.1_all.deb Size: 382582 MD5sum: e9c37549fd66f88f2625316011589832 SHA1: cc4a8a7c4143f739a0010ff0661af3074c9ed79a SHA256: 194d9d9c4dc896a2ef1363d61f44c7e964888e9b6019eafaacbbfa89bb6d24a4 SHA512: 2b51c0622e29d4d2e1735c0a503b7aa0c7a511a48631b9e7a597f01f8cf77943f012bc8e02a840824faec5b1cc883b0e9ce8435376ae6552b19f29a5fa98fdaf Homepage: https://cran.r-project.org/package=pkr Description: CRAN Package 'pkr' (Pharmacokinetics in R) Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software. The core noncompartmental computations are delegated to the 'NonCompart' package so that the two share a single, maintained engine. Some features are 1) CDISC SDTM terms 2) Automatic slope selection with the same criterion of WinNonlin(R), restricted by default to samples after the end of an infusion 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-pks Architecture: all Version: 0.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sets Suggests: r-cran-relations, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-pks_0.8-0-1.ca2404.1_all.deb Size: 516090 MD5sum: c3dd5e39c30799a4a2dd63df7bf93b1e SHA1: 58869d4ecb18e8b8ea8623b2f8b7597b76a00cfa SHA256: 957b6702cfdb064cc42ba743a85d14efeb6c164f7bc78bffdc75bf5950760149 SHA512: 7c4f3a5bfee247f17db519d10bb1d26ee677ce4519a19a5c6a9e8ec53036eed787f6a59adbe4623855856a4621031fed12dadd5be0caf014f5a8eb8da98a17c8 Homepage: https://cran.r-project.org/package=pks Description: CRAN Package 'pks' (Probabilistic Knowledge Structures) Fitting and testing probabilistic knowledge structures, especially the basic local independence model (BLIM, Doignon & Flamagne, 1999) and the simple learning model (SLM), using the minimum discrepancy maximum likelihood (MDML) method (Heller & Wickelmaier, 2013 ). Package: r-cran-pksea Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2875 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pksea_0.0.1-1.ca2404.1_all.deb Size: 2595616 MD5sum: 557f226d0003d3c95d3baa6f5c167326 SHA1: 8e6108fb092642e973078532d865154e5f5bb095 SHA256: df481e50d3c49975f97d773030bf9ebe34a790f872698d23bd9f807bcefd04ca SHA512: 430e96eb5ab3ea2f2b64cae1c5356b915ce32ee5e89a52d7607cc8a0d29c20efb5b77a287ff97de0325df5df4408161814e66e31f73ba87ff3bab36fd821a53b Homepage: https://cran.r-project.org/package=pKSEA Description: CRAN Package 'pKSEA' (Prediction-Based Kinase-Substrate Enrichment Analysis) A tool for inferring kinase activity changes from phosphoproteomics data. 'pKSEA' uses kinase-substrate prediction scores to weight observed changes in phosphopeptide abundance to calculate a phosphopeptide-level contribution score, then sums up these contribution scores by kinase to obtain a phosphoproteome-level kinase activity change score (KAC score). 'pKSEA' then assesses the significance of changes in predicted substrate abundances for each kinase using permutation testing. This results in a permutation score (pKSEA significance score) reflecting the likelihood of a similarly high or low KAC from random chance, which can then be interpreted in an analogous manner to an empirically calculated p-value. 'pKSEA' contains default databases of kinase-substrate predictions from 'NetworKIN' (NetworKINPred_db) Horn, et. al (2014) and of known kinase-substrate links from 'PhosphoSitePlus' (KSEAdb) Hornbeck PV, et. al (2015) . Package: r-cran-pksensi Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-desolve, r-cran-foreach, r-cran-doparallel Suggests: r-cran-covr, r-cran-knitr, r-cran-httk, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pksensi_1.2.3-1.ca2404.1_all.deb Size: 236660 MD5sum: 9c479cce3ef09c5d45051033019bc305 SHA1: 631ad15a135d63e71e86dabd388b6c2f40e143c1 SHA256: 7447ba562c7c1bbfe7812244152ef12ef0b58910954f770d4723aeb8b7169b79 SHA512: 79a56e07a681741c541b7b200383e58511166e961b6f5765275cedc0c140cc15854f85d2305f9cd97662f3523afe36a2035e329100dc254ef6e21f39ef71b6c1 Homepage: https://cran.r-project.org/package=pksensi Description: CRAN Package 'pksensi' (Global Sensitivity Analysis in Physiologically Based KineticModeling) Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters. The workflow was first mentioned in Hsieh et al., (2018) , then further refined (Hsieh et al., 2020 ). Package: r-cran-pl94171 Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-stringr, r-cran-readr, r-cran-dplyr, r-cran-tinytiger, r-cran-sf, r-cran-withr, r-cran-foreign Suggests: r-cran-testthat, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-pl94171_1.2.1-1.ca2404.1_all.deb Size: 319230 MD5sum: d62b7fd21d7eda25a52abdf206b1c562 SHA1: 004a14f947a88d971ea15588f427c494c7541282 SHA256: 9c8e8f31f8411da65a994937861c1821987179f8a73fa46247c2a08e1d200f86 SHA512: 9fdd415948007726ab05e92834d3d45e0ee5d3acdef6ccc9afcd24a0dd1ff015e91cef7bfded6169f7d0116232a29e0faef0201a3107363cd9593bfe5fda5cd3 Homepage: https://cran.r-project.org/package=PL94171 Description: CRAN Package 'PL94171' (Tabulate P.L. 94-171 Redistricting Data Summary Files) Tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is. Package: r-cran-placematchr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-stringdist, r-cran-data.table, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/noble/main/r-cran-placematchr_0.2.4-1.ca2404.1_all.deb Size: 1557668 MD5sum: 5721d7a7b4802cd3ac8d668ca420ec70 SHA1: e8174a4d51cf68cc25139809299c136d8b18f65c SHA256: 5764cf7737085a0738600e88c8f4abc29f058fd13af641452c72fec82e423f35 SHA512: 627246318907ce2c2964ea8d3654ef6e0e3da94173177bec00871b64045c99b21f239357763ffc32ac2cb4da8d50526e941bd2c4d4f3b438eb0e9102d4995069 Homepage: https://cran.r-project.org/package=placematchr Description: CRAN Package 'placematchr' (Normalize and Match City Names to NUTS Regions) Normalizes city names for EEA countries and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) ). Package: r-cran-placer Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-placer_0.1.3-1.ca2404.1_all.deb Size: 49284 MD5sum: 0d06afefd334f6ca6faced9828d99c81 SHA1: 7cec3306f38859369fa066afbdccde0f314d15a4 SHA256: 532c96a37504a45069befdea7898c78721fc5b07aab56e4e522ea11fc2d690eb SHA512: 19f53e9fa75750b001d1d63475b97e0afa53eb1102ed3dd816a93f56aa5b272d5ddb0855f58a24092af899ef0464b5478015a2669c11ebf4e5a58d03860ddfa9 Homepage: https://cran.r-project.org/package=placer Description: CRAN Package 'placer' (PLastic ACcumulation Estimate using R (PLACER)) Assessment of the prevalence of plastic debris in bird nests based on bootstrap replicates. The package allows for calculating bootstrapped 95% confidence intervals for the estimated prevalence of debris. Combined with a Bayesian approach, the resampling simulations can be also used to define appropriate sample sizes to detect prevalence of plastics. The method has wide application, and can also be applied to estimate confidence intervals and define sample sizes for the prevalence of plastics ingested by any other organisms. The method is described in Tavares et al. (Submitted). Package: r-cran-plackettluce Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-igraph, r-cran-matrix, r-cran-matrixstats, r-cran-partykit, r-cran-psychotools, r-cran-psychotree, r-cran-r6, r-cran-rspectra, r-cran-qvcalc, r-cran-sandwich Suggests: r-cran-bayesmallows, r-cran-bradleyterry2, r-cran-plmix, r-cran-rologit, r-cran-statrank, r-cran-bookdown, r-cran-covr, r-cran-hyper2, r-cran-kableextra, r-cran-knitr, r-cran-lbfgs, r-cran-gnm, r-cran-pmr, r-cran-rmarkdown, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plackettluce_0.4.5-1.ca2404.1_all.deb Size: 971686 MD5sum: 24b936d1031db0a21a337dd207cc2592 SHA1: c7d95798ac5f3f6d9a3abc299882e2597e82238a SHA256: 52269d5f9db5136ad4936bd2a0298eed2b8c04fb150eb4ee5dd4a3a5780b431e SHA512: 9ef9a315380c408c9e124297025e375844c4d166c2e7393f8d123079fe8a152413911169927304e5a01f73fe830eab17946c339c8473c3ee55289b38481763ae Homepage: https://cran.r-project.org/package=PlackettLuce Description: CRAN Package 'PlackettLuce' (Plackett-Luce Models for Rankings) Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) . Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details. Package: r-cran-plainview Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-lattice, r-cran-png, r-cran-raster, r-cran-viridislite Suggests: r-cran-shiny, r-cran-sf, r-cran-sp Filename: pool/dists/noble/main/r-cran-plainview_0.2.2-1.ca2404.1_all.deb Size: 660866 MD5sum: 6382ada11fc9b9db39cd985b97d6e6ce SHA1: 1f2fee345dc6040ed22ddfc008bb8807c222f50f SHA256: 9d4952aeafda0d665b8d8a5b6e71837f1dff4b3ceed62a6812660cad6de0fb99 SHA512: aa1982d369ff888d014a2bfcb50805e713ff3ca6f6075297f9e4963b737b86a0c00956928d4e4d2ce4aea5202c69586c2116c181a743dcd3be37e1b30d352afa Homepage: https://cran.r-project.org/package=plainview Description: CRAN Package 'plainview' (Plot Raster Images Interactively on a Plain HTML Canvas) Provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas. In contrast to package 'mapview' data are plotted without background map, but data can be projected to any spatial coordinate reference system. Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick' (from package 'raster') as well as 'png' files located on disk. Interactivity includes zooming, panning, and mouse location information. In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots are created (similar to 'raster::plotRGB' - but interactive). Package: r-cran-plan Architecture: all Version: 0.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plan_0.4-5-1.ca2404.1_all.deb Size: 307466 MD5sum: 1360e6f7829b726999506021e0e66ac1 SHA1: 2ee5ed17c02b0898b16584cfc409f84e788f430d SHA256: 627cd33bb09192ee28287e102c61d58fa0ab09492d237d5fbde6cad10a509637 SHA512: 0bb0aacc687c20112e3b859a3fa4505617abf546cb404b728a25184e8b064e74c20873c5e72e1c0efb983aafce57e96b38771d24bed7af284977f0c06486da43 Homepage: https://cran.r-project.org/package=plan Description: CRAN Package 'plan' (Tools for Project Planning) Supports the creation of 'burndown' charts and 'gantt' diagrams. Package: r-cran-planegeometry Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-carlson, r-cran-cvxr, r-cran-fitconic, r-cran-r6, r-cran-rcdd, r-cran-sdpt3r, r-cran-stringr, r-cran-uniformly Suggests: r-cran-ellipse, r-cran-elliptic, r-cran-freegroup, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-sets, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-planegeometry_1.6.0-1.ca2404.1_all.deb Size: 5437152 MD5sum: 943aa0bbae72ec3bd6f873c5d9d38472 SHA1: 2a41224d358c4f900f2fbf9777e509b8347a22e0 SHA256: 19e6590eaabe3f3b16ce69b8845d0ccef6353f84b42785490f50a283a1ad132f SHA512: fdeca1652991b7cc5b5d7b36d004a1e02a707618d73c066986ca0f1aff9057c71a95a84298c5259d94dfda1ab1144cfef6824160b02092a0b9e6db62e71d65dc Homepage: https://cran.r-project.org/package=PlaneGeometry Description: CRAN Package 'PlaneGeometry' (Plane Geometry) An extensive set of plane geometry routines. Provides R6 classes representing triangles, circles, circular arcs, ellipses, elliptical arcs, lines, hyperbolae, and their plot methods. Also provides R6 classes representing transformations: rotations, reflections, homotheties, scalings, general affine transformations, inversions, Möbius transformations. Package: r-cran-planesmuestra Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-planesmuestra_0.1-1.ca2404.1_all.deb Size: 69424 MD5sum: e73d900bb69ca76c0fe657bfa5a1848f SHA1: e67541778f94cb2bdb294604fb77d11cf95b57f8 SHA256: 0f3bc0cfa3c85133ee981699efb8514884ae20f392b631535653c17394b78640 SHA512: ff7f2400db9d2862e120da69b02f6e1d2774759af50a6a1f2dc4d992e4c43950fb455ca4fbfa2eff78ba02798481ce562d2473ef866509192af477ee1dd5e42b Homepage: https://cran.r-project.org/package=Planesmuestra Description: CRAN Package 'Planesmuestra' (Functions for Calculating Dodge Romig, MIL STD 105E and MIL STD414 Acceptance Sampling Plan) Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution. Package: r-cran-planetnicfi Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-data.table, r-cran-glue, r-cran-terra Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planetnicfi_1.0.5-1.ca2404.1_all.deb Size: 2017048 MD5sum: fffa901ba439a6a5a664980e58c9d01b SHA1: d1ec626f4db4e55a5f1ea4553b0e1612b1bd56c0 SHA256: 1a949c8cd28d8998b7fdf8cd9d34a6d9500038a3398f2d063c0d9009effacbaa SHA512: f5746d15800ead7bfc0a359d6716eb1622574037ff7311b1fe689b5c18646a4fed0258166db1e7aaf1cac5196749c0ade35d0928abfee08c6e54e41f84b5ba3d Homepage: https://cran.r-project.org/package=PlanetNICFI Description: CRAN Package 'PlanetNICFI' (Processing of the 'Planet NICFI' Satellite Imagery) It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API . 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System. Package: r-cran-planets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-planets_0.1.0-1.ca2404.1_all.deb Size: 87550 MD5sum: 11d15ccd80bcf498dd391f00c652a33f SHA1: e7c60dac298244c84a617655c71913319e977ad5 SHA256: d8062f0e731ba4c514aa3653f5c56a852e54463729fd2d02006ac2d1ed03e778 SHA512: bec29ca520224deaa3501cf047704467ff6199e169a53f54d13ebcc4aaab47a29d0fe0f50f740fa04c889f1e2054b6ef8ebb0124757429d6f15296c73493489c Homepage: https://cran.r-project.org/package=planets Description: CRAN Package 'planets' (Simple and Accessible Data from all Known Planets) The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets. Package: r-cran-planisphere Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2303 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-v8, r-cran-geojsonsf Suggests: r-cran-knitr, r-cran-r2d3 Filename: pool/dists/noble/main/r-cran-planisphere_0.1.0-1.ca2404.1_all.deb Size: 1483932 MD5sum: 90b0c19e36691ae5028cf70be043c3d5 SHA1: e9fe668befb179273721f47ab13083932fe77751 SHA256: f01d08393de02a82b9118c36c4402269735e1ba4258aad5e3141bb965279ac7a SHA512: 4777b89f34c484901266a69fafba990278f9b60f5319230b01067e0567a06238070c5df462daaa6e94d95044a9eec387ebcf7c812156b9dec9f0cc4c15c4a44a Homepage: https://cran.r-project.org/package=planisphere Description: CRAN Package 'planisphere' (Map Projections) Applies cartographic projections to spatial data frames containing geographic coordinates. Projection methods are based on the 'D3.js' ecosystem and use spherical geometry rather than ellipsoidal geodesic models. Package: r-cran-planningml Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3032 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-caret, r-cran-lubridate, r-cran-matrix, r-cran-mess, r-cran-dplyr, r-cran-proc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-planningml_1.0.1-1.ca2404.1_all.deb Size: 1381694 MD5sum: 725f570515099d855d35259197408468 SHA1: d55628c319908e6e93ba9318bfb97ed5a8c9d510 SHA256: f18d01ea70f30652ac09e149d3d975d7f1076e4dc284f660b6281ac7a221ebdd SHA512: 97f0935346527aaebd166b9b0b03aefd2ee47de89f4964650e61c087ab342851997403a1366b3aa809d6b61bba5f943163377d466eefd59b0e591cdc152005a2 Homepage: https://cran.r-project.org/package=planningML Description: CRAN Package 'planningML' (A Sample Size Calculator for Machine Learning Applications inHealthcare) Advances in automated document classification has led to identifying massive numbers of clinical concepts from handwritten clinical notes. These high dimensional clinical concepts can serve as highly informative predictors in building classification algorithms for identifying patients with different clinical conditions, commonly referred to as patient phenotyping. However, from a planning perspective, it is critical to ensure that enough data is available for the phenotyping algorithm to obtain a desired classification performance. This challenge in sample size planning is further exacerbated by the high dimension of the feature space and the inherent imbalance of the response class. Currently available sample size planning methods can be categorized into: (i) model-based approaches that predict the sample size required for achieving a desired accuracy using a linear machine learning classifier and (ii) learning curve-based approaches (Figueroa et al. (2012) ) that fit an inverse power law curve to pilot data to extrapolate performance. We develop model-based approaches for imbalanced data with correlated features, deriving sample size formulas for performance metrics that are sensitive to class imbalance such as Area Under the receiver operating characteristic Curve (AUC) and Matthews Correlation Coefficient (MCC). This is done using a two-step approach where we first perform feature selection using the innovated High Criticism thresholding method (Hall and Jin (2010) ), then determine the sample size by optimizing the two performance metrics. Further, we develop software in the form of an R package named 'planningML' and an 'R' 'Shiny' app to facilitate the convenient implementation of the developed model-based approaches and learning curve approaches for imbalanced data. We apply our methods to the problem of phenotyping rare outcomes using the MIMIC-III electronic health record database. We show that our developed methods which relate training data size and performance on AUC and MCC, can predict the true or observed performance from linear ML classifiers such as LASSO and SVM at different training data sizes. Therefore, in high-dimensional classification analysis with imbalanced data and correlated features, our approach can efficiently and accurately determine the sample size needed for machine-learning based classification. Package: r-cran-planr Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-magrittr, r-cran-rcpproll Suggests: r-cran-highcharter, r-cran-knitr, r-cran-reactable, r-cran-rmarkdown, r-cran-shiny, r-cran-tidyverse, r-cran-sparkline, r-cran-dt, r-cran-networkd3, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planr_0.6.5-1.ca2404.1_all.deb Size: 158816 MD5sum: 9c601eef282952b357c2b65124f35b72 SHA1: 9e2bf9045b46ef8c9ce12cebd47bacfdfe0c1d9c SHA256: 718f9857cd06288ae9489693d7704d64926ec229ce0c573cd987c9dff9a49240 SHA512: ac95cd9812cc8e2a93d7abc6e604ed1612eec0c6d598a14ce140da1fee91a5f4b8a8b9b55a17919a3fdc496f2f4573ed2cb5a13a891db5ff3b1129ec4cdd5479 Homepage: https://cran.r-project.org/package=planr Description: CRAN Package 'planr' (Tools for Supply Chain Management, Demand and Supply Planning) Perform flexible and quick calculations for Demand and Supply Planning, such as projected inventories and coverages, as well as replenishment plan. For any time bucket, daily, weekly or monthly, and any granularity level, product or group of products. 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Allows for upload of plans from block assignment files and shape files. For shapes in memory, such as from 'sf' or 'redist', it processes them to save and upload. Includes tools for tidying responses and saving output from the website. Package: r-cran-plantecophys Architecture: all Version: 1.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-nlstools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dt Filename: pool/dists/noble/main/r-cran-plantecophys_1.4-6-1.ca2404.1_all.deb Size: 231728 MD5sum: 4a315a03345d045c8fa74c4441fc5b36 SHA1: d2412fb451cc09df9c6e95f71b20d0284c648d9f SHA256: 8a7d312be03537fb7cf9dae12375b8609112d1f8c1e20149e577647ea465d5a3 SHA512: caf6e60994727638fa34e6a092f8ea1f6c19a0f68ec4775bf129a3c7f0285710f079f631782a0c659315f7721d7e748d581b77aba13a721779f7e8656f8f9a7a Homepage: https://cran.r-project.org/package=plantecophys Description: CRAN Package 'plantecophys' (Modelling and Analysis of Leaf Gas Exchange Data) Coupled leaf gas exchange model, A-Ci curve simulation and fitting, Ball-Berry stomatal conductance models, leaf energy balance using Penman-Monteith, Cowan-Farquhar optimization, humidity unit conversions. See Duursma (2015) . Package: r-cran-plantecowrap Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-plantecophys, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plantecowrap_1.0.4-1.ca2404.1_all.deb Size: 336276 MD5sum: c966f6445b0b3290b6ec510b6b38fad9 SHA1: d0d7671314313e84c82019601ff211ab741e2474 SHA256: 6bacb1b997f5e6ab0c78787f190ea1913b4ab16438c79e75100e0b4d623790eb SHA512: f136b193f7f51f233efb6ecd622814fb57ac4482b0cb8cf8065865b8dc2cf06dc6bee363ae5a63003df9d5584eae773b4449e26b264d042d87e49f6c94f59fd6 Homepage: https://cran.r-project.org/package=plantecowrap Description: CRAN Package 'plantecowrap' (Enhancing Capabilities of 'plantecophys') Provides wrapping functions to add to capabilities to 'plantecophys' (Duursma, 2015, ). Key added capabilities include temperature responses of mesophyll conductance (gm, gmeso), apparent Michaelis-Menten constant for rubisco carboxylation in air (Km, Kcair),and photorespiratory CO2 compensation point (GammaStar) for fitting A-Ci or A-Cc curves for C3 plants (for temperature responses of gm, Km, & GammaStar, see Bernacchi et al., 2002, ; for theory on fitting A-Ci or A-Cc curves, see Farquhar et al., 1980; , von Caemmerer, 2000, ISBN:064306379X; Ethier & Livingston, 2004 ; and Gu et al., 2010, ). Includes the ability to fit the Arrhenius and modified Arrhenius temperature response functions (see Medlyn et al., 2002, ) for maximum rubisco carboxylation rates (Vcmax) and maximum electron transport rates (Jmax) (see Farquhar et al., 1980; ). Package: r-cran-plantphysior Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-plantphysior_1.0.0-1.ca2404.1_all.deb Size: 90688 MD5sum: 08ca0f78c401b001f81a622c60b55f99 SHA1: 754ec6358ba8d7a7b635a2369cb5faeba20f12fd SHA256: 48c1650627e70141617cbcb303e9c94c18956af65c8d06bfc2da7dbc0038413b SHA512: ced28c2b790dd74b2654a80e0d78d5927d6200e796e1fd82ba6f94b7170a24c37c99c46c2f8e610a2fe6e69bce4c6c301400e8fc018621e2316f112204eaa30a Homepage: https://cran.r-project.org/package=plantphysioR Description: CRAN Package 'plantphysioR' (Fundamental Formulas for Plant Physiology) Functions tailored for scientific and student communities involved in plant science research. Functionalities encompass estimation chlorophyll content according to Arnon (1949) , determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008), Geometric mean productivity (GMP) by Fernandez (1992) , Golden Mean by Moradi et al.(2012), HAM by Schneider et al.(1997),MPI and TOL by Hossain etal., (1990), RDI by Fischer et al. (1979),SSI by Fisher et al.(1978), STI by Fernandez (1993),YSI by Bouslama & Schapaugh (1984), Yield index by Gavuzzi et al.(1997). Package: r-cran-planttracker Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-sf, r-cran-units Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-minidown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planttracker_1.2.1-1.ca2404.1_all.deb Size: 2884722 MD5sum: 4a30e191e3921d452a8f0aaeb01c05c9 SHA1: d97cc902617f812b1dbb3288af8e59169899b0d8 SHA256: f1000f911d1eac3fc5666881a467465d970a033902c2f61bf6899ae2a3c929b6 SHA512: 9ce13d062ffbdfcffbdebaabee61eda61b7c2f69c296e1522dc0dce6b1d23b263800181ac4de83519471ed6037d6f680c6995bde9b981523af6dff3b6a18be5b Homepage: https://cran.r-project.org/package=plantTracker Description: CRAN Package 'plantTracker' (Extract Demographic and Competition Data from Fine-Scale Maps) Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) . 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Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples. This package has been used for Kjersti Tjensvoll, Morten Lapin, Bjørnar Gilje, Herish Garresori, Satu Oltedal, Rakel Brendsdal Forthun, Anders Molven, Yves Rozenholc and Oddmund N\o{o}rdgaard (2022) . 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Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples. Package: r-cran-plasmidplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-plasmidplot_0.1.0-1.ca2404.1_all.deb Size: 286004 MD5sum: 7d57a4e2d687f1973da5924ce72cc29a SHA1: 67201c547ed89108602fd906f535cf3c31aa91ec SHA256: e87fcafa9e0d592ccd197b618fe63b48df73796d42ca8bc8afba34b512d985b4 SHA512: 3d8a3799852796e3726e19b6e46e932d9a8e7c84a20a721ec47b8edf05dedd766b45bed7c79d71e4180a42977354b6609668632ad318693ffb92c54c49d9e13a Homepage: https://cran.r-project.org/package=plasmidplot Description: CRAN Package 'plasmidplot' (Publication-Quality Circular and Linear Plasmid Maps) Draws circular and linear plasmid maps with 'grid' graphics. Features are shown as colored arcs with optional arrowheads, callout labels that are laid out to avoid overlap, an automatic base-pair scale, and the plasmid name and size. A style is built from a handful of shape parameters, with presets as named combinations of them, and the layout follows the molecule's topology. Maps can be built up feature by feature or imported from 'GenBank', 'EMBL', 'FASTA' and 'SnapGene' files, whose format is detected from content rather than file extension. Restriction sites can be located in the sequence and labeled. Ships eight visual styles, including one inspired by the 'AngularPlasmid' JavaScript library, and seven categorical palettes checked for colorblind safety. Palettes from other packages can be used directly, as a color vector or as a palette function, and checked against the same criteria. 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Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) , the main function plasso() extends the standard 'glmnet' output with coefficient paths for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) . 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Easily read in plate-shaped data and convert it to tidy format, combine plate-shaped data with tidy data, and view tidy data in plate shape. 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Package: r-cran-platevision Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readxl, r-cran-ggplot2, r-cran-stringr, r-cran-plotly Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-platevision_0.1.0-1.ca2404.1_all.deb Size: 45624 MD5sum: bbf04ab7e3979cfbd06b3f6e7902a589 SHA1: e0659456c0553ba939e36b849bc91f6c23d0e22f SHA256: da7fc976791732cb8ca623a4ac280eeb7d726b3b845555191a9e33d2d44a02db SHA512: 592ac3cab423c04dc3f4b528d9fc46ad9387581bc56606d77e78ebdd604661b22851b0cc96204f8615403c267950fe506ec09ba4e078fd3d1527d9bcbc4c65a4 Homepage: https://cran.r-project.org/package=PlateVision Description: CRAN Package 'PlateVision' (Automated qPCR Analysis and Visual Quality Control) Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and Schmittgen (2001) . Streamlines the workflow from 'Excel' export to publication-ready plots. Integrates unique visual quality control by reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting errors, edge effects, and outliers. Key features include automated error propagation, laboratory master mix calculations, and generation of bar charts and volcano plots. 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More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) . Package: r-cran-platowork Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-platowork_0.0.1-1.ca2404.1_all.deb Size: 65126 MD5sum: 6840e1a059d0667f4af2cc437f58fe8b SHA1: 368c7933053b60ce5aa66f97171b787d65b4b9dd SHA256: 2dca450891989e03ac75f21e1f9ac43a32ba2350f55b64ad8cfdeac475abeeae SHA512: 8a5da2fdfd2e04e49b1f409b4045e29b81a3d55e75932ed13c4882bd024d845c0e67d921c9a9f08cafe5b6a59f9a4eb84f3a8f68f45f49fdb87056158c82157f Homepage: https://cran.r-project.org/package=platowork Description: CRAN Package 'platowork' (Data from a Test of the PlatoWork tDCS Headset) Data and analysis from an experiment with improving touch typing speed, using the tDCS PlatoWork headset produced by PlatoScience. Package: r-cran-plattice Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-plattice_1.1-1.ca2404.1_all.deb Size: 14424 MD5sum: e6438bca7c565bd667421bdb1b86f21a SHA1: 75dd3c7106e5a5b89da662daaca073a269f194de SHA256: f255a1d9cf700b6713c0ff65a6d2292f41a0fa4dbec1114c489e226987ed23fc SHA512: 3c278892ec701e4005fabfc7ddd184158ddec3868241e792a18afb49b0bc649a21741d567cbe8deecad799c70fb6fdd9561fc78baf17e5aae50f3a1841bf3baa Homepage: https://cran.r-project.org/package=plattice Description: CRAN Package 'plattice' (Lattice Plot for Panel Data) It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch. Package: r-cran-platypus Architecture: all Version: 3.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-cowplot, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggseqlogo, r-bioc-ggtree, r-cran-jsonlite, r-cran-knitr, r-cran-magrittr, r-cran-matrix, r-cran-plyr, r-cran-reshape2, r-cran-seqinr, r-cran-seurat, r-cran-seuratobject, r-cran-stringdist, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-useful Suggests: r-bioc-annotationdbi, r-cran-ape, r-bioc-biocgenerics, r-bioc-biomart, r-cran-circlize, r-cran-cluster, r-cran-doparallel, r-bioc-fgsea, r-cran-ggrepel, r-cran-ggridges, r-cran-gridextra, r-cran-harmony, r-cran-igraph, r-cran-inext, r-bioc-limma, r-cran-kmer, r-cran-msigdbr, r-cran-phangorn, r-cran-pheatmap, r-cran-phytools, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rstudioapi, r-cran-rtsne, r-cran-scales, r-cran-sf, r-bioc-singlecellexperiment, r-bioc-slingshot, r-cran-tidytree, r-cran-tidyselect, r-cran-tidyverse, r-cran-umap, r-cran-vegan, r-cran-viridis, r-cran-testthat Filename: pool/dists/noble/main/r-cran-platypus_3.6.0-1.ca2404.1_all.deb Size: 1499398 MD5sum: 7f3e678709f8d8edc1d1584a6f023753 SHA1: 790046b3c4ca2f027a87342ccaaecafdf5adb174 SHA256: 7958302e9eadb8b9c341bc38df076b4f8c2ee99ccee466d7c636a5e4dd43c573 SHA512: 9b1474fd36e3b2f635297c0794e55bd9e9328fe69ccd3aae90bf73801f0efb56b502c1c46e8dff63b092c1a9fd961533cb624bc61c2baf4b94b424a6684515ac Homepage: https://cran.r-project.org/package=Platypus Description: CRAN Package 'Platypus' (Single-Cell Immune Repertoire and Gene Expression Analysis) We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of 'Platypus' is part of the 'ePlatypus' ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) , Cotet et al. (2023) . Package: r-cran-plausibounds Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-mass, r-cran-magrittr, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plausibounds_1.0.1-1.ca2404.1_all.deb Size: 135212 MD5sum: 1e18c37d2a24c5634befa40e44b89265 SHA1: dc55061a5dc43f30314095a3772080dd38d91214 SHA256: 07a6ce7845abae920797cede40f71fed754608ef48ad3414a548719d5e76272f SHA512: e5fe7f9ed18429d07be1264737c3aa0dbddfb93c90ed3d102adf89a4a663431d8792bc2d6374e1c200bc6eda734f08e8025fcad82d9326393d7ae067e3eb5ae0 Homepage: https://cran.r-project.org/package=plausibounds Description: CRAN Package 'plausibounds' (Plausible Bounds for Treatment Path Estimates) Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) . Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands. Package: r-cran-plavaan Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plavaan_0.0.2-1.ca2404.1_all.deb Size: 87502 MD5sum: b34a5c694d450d26a6bb026d3be5cb45 SHA1: 166707a9b53260cc18ed9e98deee031cca23e5bb SHA256: a57d0639f850e23b0810934168d17620c2fd9020558ae4d12d91b3d868c46f9c SHA512: 620df5e99be35896ca43187f6304856e31535dabab77fa9d811c25bceaa29f37c2184e6eb43bb0d88d5b141e59bc56247b00f37d260fd4f3f5b3963f1335ddd3 Homepage: https://cran.r-project.org/package=plavaan Description: CRAN Package 'plavaan' (Penalized Estimation for Latent Variable Models with 'lavaan') Extends the popular 'lavaan' package by adding penalized estimation capabilities. It supports penalty on individual parameters as well as the difference between parameters. Package: r-cran-play Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-worldfootballr Filename: pool/dists/noble/main/r-cran-play_0.1.3-1.ca2404.1_all.deb Size: 23682 MD5sum: 64025318c366cf50ceb03ea595a5dbec SHA1: 6ffe0ca579896a7e0042cc7c9933202bf3102be7 SHA256: 1e42a83838e36b8210b4c24203be0dfa9d20e610ee9a294934485100d5b0b5f9 SHA512: d1187016b3fc5de0ef78d7050b6588cf7dae6b9e7f5d13a07b4b249cbd457896cfe3b2bc9fdf432112b9a20007fbfbf174c7b7d49c6409bfd489c2208453bed3 Homepage: https://cran.r-project.org/package=play Description: CRAN Package 'play' (Visualize Sports Data) Provides functions to visualise sports data. Converts data into a format suitable for plotting charts. Helps to ease the process of working with messy sports data to a more user friendly format. Football data is accessed through 'worldfootballR' '' which gets data from 'FBref' , 'Transfermarkt' , 'Understat' , and 'fotmob' . 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Includes coin flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut the box, spelling bee, and 2048. Package: r-cran-playerchart Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-magrittr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-playerchart_1.0.0-1.ca2404.1_all.deb Size: 18536 MD5sum: 57c170dafc0f7c12446b8cb71ccb3daa SHA1: 80a4545eb692e5ab5a700803f385f78758fdab78 SHA256: 1501b55967838dded385e43769620b7ae37fbd7b630f588633e2909998130874 SHA512: 187dd079a3f4c52586cec0185743a53c7789613fb8793cdd7723370af5d579fffb5dc501f9123e21400baa5cce4969ee9945ffa5b9db091ee26f1a2b927aaa09 Homepage: https://cran.r-project.org/package=PlayerChart Description: CRAN Package 'PlayerChart' (Generate Pizza Chart: Player Stats 0-100) Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: 'data', a dataframe containing player data for any sports; 'player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; 'name_col', specifying the name of the column in the dataframe that contains the player names; and 'player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title. Package: r-cran-pldamixture Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-pldamixture_0.1.2-1.ca2404.1_all.deb Size: 128370 MD5sum: 9791b4dc03ee132b72edacf715f71188 SHA1: 65c7bd43bdac476b8cac677a78eb9028128ad343 SHA256: bcd0faf7d074a3b8e52aa97dc4b89c7c74479398e712bcd36545ecde8d79b05a SHA512: 3c03294e4442a50ad8a2e2debfcfabe7b0b803db8319992096fc3ebeda3fdbd96b4344e682cd306f5697620aff6ff6e72175e8ee2503174c576f0fee200ff22f Homepage: https://cran.r-project.org/package=pldamixture Description: CRAN Package 'pldamixture' (Post-Linkage Data Analysis Based on Mixture Modelling) Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the 'General Framework for Regression with Mismatched Data' developed by Slawski et al. (2025) . The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches). Package: r-cran-plde Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plde_0.1.2-1.ca2404.1_all.deb Size: 44194 MD5sum: a901258b390b83662b14b3cd56816e3b SHA1: 0a0d5dd33f61dd52910c19c22ab3a43c34d3a6a4 SHA256: ef0ec94eee21e972f2bd4b0f830008e2a80181c3823a227eabd072ef9b1373af SHA512: c47917f4ee297033f390894869d6afdb90cc29bee1a7aa9804271a46852dec2f7f6568ffdc9e43e72dbd53f4309ab7591958ef60dde32ad418c5598071c2deab Homepage: https://cran.r-project.org/package=plde Description: CRAN Package 'plde' (Penalized Log-Density Estimation Using Legendre Polynomials) We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC). Package: r-cran-pleio Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-matrix Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pleio_1.9-1.ca2404.1_all.deb Size: 434288 MD5sum: 41884a8d81391cd472262114013e6272 SHA1: 6a489da03a8ba4f2f260c9c282aaf46586dd2710 SHA256: 95364768ba82ed73c27a01a959feefe495760abe8e05aa1328184cca554836be SHA512: a5c0f1bf3b811ee693914ad85caf43698126aefcc20233990bae8922cf5b0d891083f7061c90d71ff1174433a9b9a4a2b321a3cf6b8d522c1b2d78c4758ceef7 Homepage: https://cran.r-project.org/package=pleio Description: CRAN Package 'pleio' (Pleiotropy Test for Multiple Traits on a Genetic Marker) Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker. 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We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 ) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates. Package: r-cran-plelma Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlogit, r-cran-dfidx Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plelma_0.2.2-1.ca2404.1_all.deb Size: 226686 MD5sum: e6e42a2064efe04035c287b4524e3fde SHA1: 031c5c21aaafc037c9ee5dae5f43db186ae5dfb3 SHA256: 07c989dd72eefdd9c24e152365a9323f237ebcd93cadfd38dfd24c382e3122ac SHA512: 70b394dfd4326cbe67d3ce0f57143df8c8c47b6693023b0bcfb8a31808088b2724bb6b78301e989e232c1b243368245dd11eecbe43c078f0ca5037da192ccf53 Homepage: https://cran.r-project.org/package=pleLMA Description: CRAN Package 'pleLMA' (Pseudo-Likelihood Estimation of Log-Multiplicative AssociationModels) Log-multiplicative association models (LMA) are models for cross-classifications of categorical variables where interactions are represented by products of category scale values and an association parameter. Maximum likelihood estimation (MLE) fails for moderate to large numbers of categorical variables. The 'pleLMA' package overcomes this limitation of MLE by using pseudo-likelihood estimation to fit the models to small or large cross-classifications dichotomous or multi-category variables. Originally proposed by Besag (1974, ), pseudo-likelihood estimation takes large complex models and breaks it down into smaller ones. Rather than maximizing the likelihood of the joint distribution of all the variables, a pseudo-likelihood function, which is the product likelihoods from conditional distributions, is maximized. LMA models can be derived from a number of different frameworks including (but not limited to) graphical models and uni-dimensional and multi-dimensional item response theory models. More details about the models and estimation can be found in the vignette. Package: r-cran-plexi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-igraph, r-cran-keras, r-cran-ggraph, r-cran-ggplot2, r-cran-aggregation Filename: pool/dists/noble/main/r-cran-plexi_1.0.0-1.ca2404.1_all.deb Size: 90854 MD5sum: 94778a4213c986273db7fd8506634a75 SHA1: 8619e26373925da9b1040d77a2d3240cd9a68b9d SHA256: 64f303d59ac51bceb85edeec71e40d626e5de6f8bb9347d731ecd9c87e191870 SHA512: 95f84587a0eb32ff4c877f8f41f7ee8f5e443ef9da3b5a1884167916d52817d6c0a990ef5e8d57fce3e513e935da7ea55c1997a5fa08e0a678e43bf0817fe531 Homepage: https://cran.r-project.org/package=PLEXI Description: CRAN Package 'PLEXI' (Multiplex Network Analysis) Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) . Package: r-cran-plfma Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-tkrplot, r-bioc-limma, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-plfma_2.0-1.ca2404.1_all.deb Size: 133404 MD5sum: fc0e71e1047f59313c82ac750bf706f5 SHA1: d715b64ab1cd5139264cb86685699a6d8207638d SHA256: 13dbcffc91468884c6c8862b57449eb6688845939a8f08975a1ce21672ed44a2 SHA512: 57a7240923c9b3517b6ff4eec2a621c9bed27e497049847f218ec094d6c62e7ec2e3c241be1002d2a114218d0046bc3b47094e292f7b03c363eef5e839fde563 Homepage: https://cran.r-project.org/package=plfMA Description: CRAN Package 'plfMA' (A GUI to View, Design and Export Various Graphs of Data) Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image. 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Specificity, sensitivity, area under the curve and ROC curve are provided. Package: r-cran-plink Architecture: all Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-statmod Filename: pool/dists/noble/main/r-cran-plink_1.5-1-1.ca2404.1_all.deb Size: 1205134 MD5sum: f206d1835fb2792ab1739ca1a2809d26 SHA1: d33b1b2564ffb4253d6af49abdb10e22e3b93c58 SHA256: e72714df88fd7a0dbdb86594b84ade4d820d18aeb7457c2f249513a7c29dc665 SHA512: 8ffecddc4cf013147d5f16cf1715c9303d88e6f079393fe7c32b9ce31b3776dce0309e8bd52b0242178c7daf185cb3899a3154dde8430d7f0ead4fa6e0af7cb0 Homepage: https://cran.r-project.org/package=plink Description: CRAN Package 'plink' (IRT Separate Calibration Linking Methods) Item response theory based methods are used to compute linking constants and conduct chain linking of unidimensional or multidimensional tests for multiple groups under a common item design. The unidimensional methods include the Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the least squares method and extensions of the Haebara and Stocking-Lord method using single or multiple dilation parameters for multidimensional extensions of all the unidimensional dichotomous and polytomous item response models. The package also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true score and observed score equating, and plotting item response curves/surfaces, vector plots, information plots, and comparison plots for examining parameter drift. Package: r-cran-plinkfile Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plinkfile_0.2.1-1.ca2404.1_all.deb Size: 607830 MD5sum: f6b55b7c3dc7b6e9d112f537cfd07572 SHA1: 30a49d266933cc07b0c3a35f98a6b56535568bdc SHA256: f07729848bcd98c876577bbb40547dee52c95cdd06aceb54336a234ee3862528 SHA512: bcef3a9c99c111b14b84abc7d09becd2d784a6741deea7ad225bda53e8bf011e51eeed2f3eb65c858ce05d2946fbcf492339148903acf21e71cf29fab27e7a6f Homepage: https://cran.r-project.org/package=plinkFile Description: CRAN Package 'plinkFile' ('PLINK' (and 'GCTA') File Helpers) Reads/write binary genotype file compatible with 'PLINK' into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by 'PLINK' or 'GCTA' into/from a R square matrix. It is best used for bringing data produced by 'PLINK' and 'GCTA' into R workflow. Package: r-cran-plinkqc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optparse, r-cran-data.table, r-cran-r.utils, r-cran-ggplot2, r-cran-ggrepel, r-cran-cowplot, r-cran-upsetr, r-cran-dplyr, r-cran-igraph, r-cran-sys, r-cran-randomforest, r-cran-tidyr Suggests: r-cran-testthat, r-cran-mockery, r-cran-formatr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-plinkqc_1.1.0-1.ca2404.1_all.deb Size: 2321566 MD5sum: c2c2111192192e252ac007c53bfb88db SHA1: 752159a2dc9dacea35a03b5ccf440b1989d3a8c3 SHA256: 66095b61cd8a84fe61a01127bb3d9427cb8f4c2dcddfeda29bdf9910c6ba904b SHA512: ba3b5b2d85bc54feee5e36b3338b6083b252b4bcbaf01070faea12defda27382479707ee88df10a50794713fc06176e63c0e995d29b91df13d8acd98efa19504 Homepage: https://cran.r-project.org/package=plinkQC Description: CRAN Package 'plinkQC' (Genotype Quality Control with 'PLINK') Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. 'plinkQC' facilitates genotype quality control for genetic association studies as described by Anderson and colleagues (2010) . It makes 'PLINK' basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions accessible from 'R' and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed to generate a new, clean dataset. Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study. Additionally, there is a trained classifier to predict genomic ancestry of human samples. Package: r-cran-plis Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plis_1.2-1.ca2404.1_all.deb Size: 53580 MD5sum: c51d93519c8c48d2326379910ad3cfa3 SHA1: b8485229836e4e8b23dd7de1678bfaf98b876514 SHA256: 3c2b64dfd9b548ed62e1a4a1a86a3aa12ba4543206669b04c66763296d884148 SHA512: fc8ab623b70ba23618714ca0e4132151db5d817672a98f538cde8138d565ba610745d64c8c339e10bfb12049282b61376c937b410c1834b07bb81ed1e279b4d3 Homepage: https://cran.r-project.org/package=PLIS Description: CRAN Package 'PLIS' (Multiplicity Control using Pooled LIS Statistic) A multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) for more details. 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Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (), Hsiao (2014), Analysis of Panel Data (), and Croissant and Millo (2018), Panel Data Econometrics with R (). Package: r-cran-plmixed Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-numderiv, r-cran-optimx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-irtoys Filename: pool/dists/noble/main/r-cran-plmixed_0.1.8-1.ca2404.1_all.deb Size: 1181944 MD5sum: 86407a5c2b80eef4bd5ec7716447edaf SHA1: 25f838fab431e0a6714c147ce63a12edc1a2b93f SHA256: 670cc4961513928bf771d8e5acfef143048647b65211a5480082553967fd1dad SHA512: 22b141af22be1e7b5380a610d774358be4fe5b83122a74332e61de9ec6c6fc9f810f729f32aad46647f433793abdfe074837e4a53144d60db2f144ef222e979b Homepage: https://cran.r-project.org/package=PLmixed Description: CRAN Package 'PLmixed' (Estimate (Generalized) Linear Mixed Models with FactorStructures) Utilizes the 'lme4' and 'optimx' packages (previously the optim() function from 'stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) and Rockwood and Jeon (2019) . Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models. 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It provides a structured approach to running the same function multiple times with different arguments, executing multiple functions on the same datasets, and creating systematic analyses across multiple strata or variables. The framework is particularly useful for applying the same analysis across multiple strata (e.g., locations, age groups), running statistical methods on multiple variables (e.g., exposures, outcomes), generating multiple tables or graphs for reports, and creating systematic surveillance analyses. Key features include efficient data management, structured analysis planning, flexible execution options, built-in debugging tools, and hash-based caching. 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Package: r-cran-plot3logit Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggtern, r-cran-ternary, r-cran-dplyr, r-cran-ellipse, r-cran-forcats, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-mass, r-cran-mlogit, r-cran-nnet, r-cran-ordinal, r-cran-rmarkdown, r-cran-vgam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plot3logit_3.2.0-1.ca2404.1_all.deb Size: 842524 MD5sum: e3b8ec4dd9029ce29fc43534a4b710a2 SHA1: 68c8f98b4b8ef231d73f28a99a6046d1aee2d364 SHA256: c981d33f580646231550c4a4c4c8daca0e5229195a3301d172ac2884aa948b9d SHA512: 797c71b8af289b7293454220723a5b65cd36d652959eb8bb9f9d8c751ed73b6d69d9b8b36531e508db2c3cdf8a78be84f6e45e3118e7d4fcb81f66ab4fd0046b Homepage: https://cran.r-project.org/package=plot3logit Description: CRAN Package 'plot3logit' (Ternary Plots for Trinomial Regression Models) An implementation of the ternary plot for interpreting regression coefficients of trinomial regression models, as proposed in Santi, Dickson and Espa (2019) . 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Package: r-cran-plotannotate Architecture: all Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-conicfit Filename: pool/dists/noble/main/r-cran-plotannotate_1.4-0-1.ca2404.1_all.deb Size: 77260 MD5sum: c517d1a4400d277592c5e22928dca768 SHA1: c169318413ef43a4a9a569ec8942560e5f7c3a56 SHA256: 5ee83976ef0407d8a2dc43ef88aca1998e241c5b94ea362b36b816d607c16d86 SHA512: 0f7d2904fdefe5a79b6346fd28bc585fae4a4837d34babeae4571a5a4f14aee995f2b81a0e0a60a0559cb050145ba1c90d51be383caccac2750fef06f50708f1 Homepage: https://cran.r-project.org/package=plotannotate Description: CRAN Package 'plotannotate' (Annotate Plots) Interactively annotate 'base R graphics' plots with freehand drawing, symbols (points, lines, arrows, rectangles, circles, ellipses), and text. This is useful for teaching, for example to visually explain certain plot elements, and creating quick sketches. Package: r-cran-plotbart Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bartcause, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rpart, r-cran-ggdendro Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-arm, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-plotbart_0.1.7-1.ca2404.1_all.deb Size: 296938 MD5sum: f31b5c8fe8973b2797385a9d9c89b5c5 SHA1: dfbedf3b0fa42279272b6d2eaf865837a7c8d0c8 SHA256: 71517b8b246826d2db8f43464a2bd4e3f58bca38761fc92cfc703e71ef89cefd SHA512: 6500bd9dacbae69d6a451c1bd87f3b52393a4024f9c7d68c40a66949f2b91ec525b041c94eaaf7d88c8920d86b9fd5f17e4154b312b610673ec489dfc40d01b6 Homepage: https://cran.r-project.org/package=plotBart Description: CRAN Package 'plotBart' (Diagnostic and Plotting Functions to Supplement 'bartCause') Functions to assist in diagnostics and plotting during the causal inference modeling process. 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Package: r-cran-plotbivinvgaus Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3770 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-plotbivinvgaus_0.1.0-1.ca2404.1_all.deb Size: 942542 MD5sum: ee0d813895ead3aea8f1cc72997dfe9f SHA1: 5c85acbd30ab49894eba1e56c81a908449414374 SHA256: a34a3aaa3aac5545d846c09311f8800d8b52ea1e3630e64f3aad600ca70fa3d0 SHA512: 7fc5296f03286f4fe1788d9e27fe7cfd80950bb89b8e6aaf97b3eefed178cd999d3f1c232b4fbf5a1e0f2de6aeb454b3364b2c2a078a2e3ee5f51f9ae7c6cc56 Homepage: https://cran.r-project.org/package=PlotBivInvGaus Description: CRAN Package 'PlotBivInvGaus' (Density Contour Plot for Bivariate Inverse Gaussian Distribution) Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables. 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'plotdap' handles extracting and reshaping the data, map projections and continental outlines. Optionally the data can be animated through time using the 'gganmiate' package. Package: r-cran-plotdk Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-mapproj, r-cran-plotly, r-cran-purrr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-plotdk_0.1.0-1.ca2404.1_all.deb Size: 2109244 MD5sum: ae79ec36c7551991439b92741b589bc1 SHA1: aa89fb0e1437c27b909e7b541da993f4ebb37d66 SHA256: 1379ec4bdb5fd424338290ae398d4cdbf997b48a9d81e6b89d4e5bff14b3f270 SHA512: 45250d447de4983ca316c244d7e02f04fea1865f248f620da3aa3b0d70d24616126a5ef828919ecdfc7581c7ca271cfb8cc66716c8519e862bb171fe7f540832 Homepage: https://cran.r-project.org/package=plotDK Description: CRAN Package 'plotDK' (Plot Summary Statistics as Choropleth Maps of DanishAdministrative Areas) Provides a ggplot2 front end to plot summary statistics on danish provinces, regions, municipalities, and zipcodes. 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Package: r-cran-plotftir Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5398 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-scales Suggests: r-cran-testthat, r-cran-withr, r-cran-ggplot2, r-cran-gghighlight, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-ir, r-cran-chemospec, r-cran-r.utils, r-cran-readjdx Filename: pool/dists/noble/main/r-cran-plotftir_1.3.1-1.ca2404.1_all.deb Size: 4433052 MD5sum: 72d95a4924f686d943f9fc7ac5f0b0e7 SHA1: fad08dcce56410c77de32001c194fbac43d52e6d SHA256: 37ead95ebf19b765a73fc9be2d9ae5a214513276022542638849b8a04e5fbc1b SHA512: 8398585f863cef059e57c1cf736bdafd797aed7cc9e743f8ecdeb5454a286da2e530f3fa186bebb8741d58882d7ba6c20fd0a4c5a2050546e5d5e2b6ae955298 Homepage: https://cran.r-project.org/package=PlotFTIR Description: CRAN Package 'PlotFTIR' (Plot FTIR Spectra) The goal of 'PlotFTIR' is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using 'ggplot2'. The produced plots can be published directly or further modified by 'ggplot2' functions. L'objectif de 'PlotFTIR' est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de 'ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions 'ggplot2'. Package: r-cran-plotfunctions Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-sp Filename: pool/dists/noble/main/r-cran-plotfunctions_1.5-1.ca2404.1_all.deb Size: 1149490 MD5sum: 29b770f93c6553b561a31d73cc4fc8a2 SHA1: 17db45f300ece8c51ab67cdde087ad795674df88 SHA256: cec4a8ea5253ce54c54e1387764bfccb317f6bf074c3a85bff514e32181edc03 SHA512: c62e0a85f68c4caf672dfd65479ea2a6f626c8b413232d0b1610b011e06b15b1eeabb6d27766b591c2a815fabce37adc702b25aaf15896830e4c2eaed71e8be8 Homepage: https://cran.r-project.org/package=plotfunctions Description: CRAN Package 'plotfunctions' (Various Functions to Facilitate Visualization of Data andAnalysis) When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions. Package: r-cran-plotgmm Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wesanderson, r-cran-amerika, r-cran-ggplot2 Suggests: r-cran-mixtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plotgmm_0.2.2-1.ca2404.1_all.deb Size: 21548 MD5sum: d102d669d80fae4d6852b85d88c885b5 SHA1: 3acb29c373c9e699a734b707b4ea1c80f3c6a01a SHA256: e46600df6eb1688ec174869551b0794e27add1bd497424ca80f387d13ac5b0dc SHA512: d0d8dd8bde6dbdcf671ea85ffd8fbd1a107fe040b6ea08cb9f5359126426105247ef385dda95c7f2cfcc437a7bab341e0c4f6652badeaaaf83f0396ef283f051 Homepage: https://cran.r-project.org/package=plotGMM Description: CRAN Package 'plotGMM' (Tools for Visualizing Gaussian Mixture Models) The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), including both densities and overlaying mixture weight component curves from the fit GMM. 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Package: r-cran-plothelper Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-ggfittext, r-cran-magick, r-cran-gridextra, r-cran-scales, r-cran-farver Filename: pool/dists/noble/main/r-cran-plothelper_0.1.9-1.ca2404.1_all.deb Size: 304840 MD5sum: 0d44e3a4a1afe31c7635636e64d7c157 SHA1: 26e3b55f735252a88fb1fad91ef5ced83c93802d SHA256: 00c4888e080448aa16f05c7e12d31a1d6394f0e5bfc1f5b3355d652880d950a5 SHA512: ec09802f78c9e675cfe1b99e4f9c32447d573f44d23395ed63f38fda3e84c14a2c74761e2eb1000c8e423e58ff84e040aa005c9df93ace0f4102885de3134574 Homepage: https://cran.r-project.org/package=plothelper Description: CRAN Package 'plothelper' (New Plots Based on 'ggplot2' and Functions to Create RegularShapes) An extension to 'ggplot2' and 'magick'. 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Package: r-cran-plotlsirm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-scales Suggests: r-cran-plotly, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-plotlsirm_0.1.3-1.ca2404.1_all.deb Size: 164034 MD5sum: 3746bff44a8981e5b8f9c7e5c2701121 SHA1: 22acf94b28c041c7080b049ee56f61e859604a26 SHA256: 252c6c8121faed45a9432c41e380202a618c9e8cc0cdf8f611b0debd5b3b3c0e SHA512: 36e9262a6e4d87bf1568a6b2f098425573ac4c0f4935945ec8f6b9fd431c86fb8a18a7873d8868ff814fa0533cb35450ad9ef2d4120976fa6dbc708ae03d3f2a Homepage: https://cran.r-project.org/package=plotlsirm Description: CRAN Package 'plotlsirm' (Plot Toolkit for Latent Space Item Response Models) Provides publication‑quality and interactive plots for exploring the posterior output of Latent Space Item Response Models, including Posterior Interaction Profiles, radar charts, 2‑D latent maps, and item‑similarity heat maps. The methods implemented in this package are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) . Package: r-cran-plotluck Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-quantreg, r-cran-scales, r-cran-plyr, r-cran-hexbin, r-cran-rcolorbrewer, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-ggplot2movies, r-cran-mgcv, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-gapminder Filename: pool/dists/noble/main/r-cran-plotluck_1.1.1-1.ca2404.1_all.deb Size: 480754 MD5sum: 7a25f65da62125cf30fc2acc4093a88f SHA1: 12833b7e91a4d6a58d3a62eee41894a9ff0cbb75 SHA256: 8637928dd087387d3d498d78bfe698b0981dbf7c4f933ea419d191ac67d672f0 SHA512: c81d1f3faaa6d4c69012ff328c06d2976fbb43fad476db506377d2a73883b2567eeb4b537f95e517085d9e146db025b1a316a122ee8c3ac2b7d2a2071f3ce9f9 Homepage: https://cran.r-project.org/package=plotluck Description: CRAN Package 'plotluck' ('ggplot2' Version of "I'm Feeling Lucky!") Examines the characteristics of a data frame and a formula to automatically choose the most suitable type of plot out of the following supported options: scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map. The aim of the package is to let the user focus on what to plot, rather than on the "how" during exploratory data analysis. It also automates handling of observation weights, logarithmic axis scaling, reordering of factor levels, and overlaying smoothing curves and median lines. Plots are drawn using 'ggplot2'. Package: r-cran-plotly Architecture: all Version: 4.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-digest, r-cran-viridislite, r-cran-base64enc, r-cran-htmltools, r-cran-htmlwidgets, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-vctrs, r-cran-tibble, r-cran-rlang, r-cran-crosstalk, r-cran-purrr, r-cran-data.table, r-cran-promises Suggests: r-cran-mass, r-cran-maps, r-cran-hexbin, r-cran-ggthemes, r-cran-ggally, r-cran-ggalluvial, r-cran-testthat, r-cran-knitr, r-cran-shiny, r-cran-shinytest2, r-cran-curl, r-cran-rmarkdown, r-cran-cairo, r-cran-broom, r-cran-webshot, r-cran-listviewer, r-cran-dendextend, r-cran-sf, r-cran-png, r-cran-irdisplay, r-cran-processx, r-cran-plotlygeoassets, r-cran-forcats, r-cran-withr, r-cran-palmerpenguins, r-cran-rversions, r-cran-reticulate, r-cran-rsvg, r-cran-ggridges Filename: pool/dists/noble/main/r-cran-plotly_4.12.1-1.ca2404.1_all.deb Size: 3673770 MD5sum: 830c84d5534b150794cda0a1d5a34563 SHA1: 9884907483d2fb190b4bc7fd89a238d55919f15b SHA256: 9cf4e1a05a49d1bbc9b6e7c7ed750ce2ddea9d680fc9464cbe2695ad77ce924b SHA512: 0ec2d5ec5cb0c59177cca6f2595a6d0ed9ad1c46e044014af31cb06e38b168b5fc28f276059759f8cafaf85e6b2a97e465f16473dcbf7701e590aa54fb882679 Homepage: https://cran.r-project.org/package=plotly Description: CRAN Package 'plotly' (Create Interactive Web Graphics via 'plotly.js') Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics. 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Package: r-cran-plotmcmc Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1145 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-gplots, r-cran-lattice Suggests: r-cran-gdata Filename: pool/dists/noble/main/r-cran-plotmcmc_2.0.2-1.ca2404.1_all.deb Size: 1056328 MD5sum: 4845ae3069d4010f6b0a5fe0f47229c2 SHA1: b64045de5942354cd9375d066310d0651a2afdb6 SHA256: 9f0f11f796b182ea1fb51e9647b9d3275edb82723758f07a82b9df6d7dfb1cec SHA512: e0939621c637bb1dd54c89ce8319d127e333633ad81cb4a1ec59e12121e5ef8bb38edad7cc9bcfe8387145b1f581cb327e7cb613771ea1367fbc8411410f33da Homepage: https://cran.r-project.org/package=plotMCMC Description: CRAN Package 'plotMCMC' (MCMC Diagnostic Plots) Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the 'coda' and 'lattice' packages, and make it easy to adjust graphical details. 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Also plot model residuals and other information on the model. Package: r-cran-plotnormtest Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-matrix, r-cran-matrixextra, r-cran-mass, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plotnormtest_1.0.1-1.ca2404.1_all.deb Size: 833754 MD5sum: 35f10e03c3a67130857e87231e64d6c2 SHA1: 98781cec95ff58247effecb3bd6ee396f4a817c1 SHA256: b14d6a601826f9289e0ea71dadb59e9219def6e1e33dfae102ef6d13e9125372 SHA512: ca4ae62b0213e5ec224cf8e25e580ed5e5a2bd6a0c0e919f1ddcb54ea0999e93f2bb22c2f1a86f40274008466edfc990ee844d381606d0ec10eff0eb7a7769cc Homepage: https://cran.r-project.org/package=PlotNormTest Description: CRAN Package 'PlotNormTest' (Graphical Univariate/Multivariate Assessments for NormalityAssumption) Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis, . Package: r-cran-plotomics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-plotomics_0.1.0-1.ca2404.1_all.deb Size: 474938 MD5sum: b13d745bdd16f3059fd4e2fd222cf85f SHA1: bf61e5e2015e1e2ad5ae34fbed104e3098c75e09 SHA256: e22d4e5de32d0905a734822193d7a88cadd6c9d024520215c9199125024e524b SHA512: 0a4b2bbf0066309e4b201cf370adba73009fd31e7b5d93fe979e7d827b1cfd96615a91ceb446d2a7015ec295d3a136e85ccdefa07d426b6833df4a017afd26b0 Homepage: https://cran.r-project.org/package=plotomics Description: CRAN Package 'plotomics' (High-Performance Bioinformatics Visualizations) Lightweight, GPU-accelerated bioinformatics visualization widgets (volcano plots, expression and clustered heatmaps, dot plots, stacked violins, embeddings, spatial tissue maps, oncoprints, protein domain lollipops, Kaplan-Meier curves, mutational signature profiles, UpSet plots, treemaps, networks and Hi-C contact matrices) backed by a shared JavaScript core and exposed to R through 'htmlwidgets'. Designed for large datasets that render smoothly in the browser, the 'RStudio' Viewer, R Markdown, Quarto and Shiny. Package: r-cran-plotor Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-callr, r-cran-car, r-cran-cli, r-cran-detectseparation, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-gtextras, r-cran-janitor, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-here, r-cran-hmisc, r-cran-knitr, r-cran-labelled, r-cran-magrittr, r-cran-mass, r-cran-medicaldata, r-cran-nhanes, r-cran-quarto, r-cran-r4hcr, r-cran-rmarkdown, r-cran-svglite, r-cran-testthat, r-cran-vdiffr, r-cran-webshot2 Filename: pool/dists/noble/main/r-cran-plotor_1.1.0-1.ca2404.1_all.deb Size: 3772746 MD5sum: 79100bc9e21e95fb65f46a5792d91848 SHA1: a8cfa12e05afba2aaf8815e82e7fb7bd2272ffd1 SHA256: 61caa819e29e7fb5f35ca4208d0f68790d5aeed421d1edcd940d062877b57912 SHA512: 520cd62f10fa425a8d109d929ba8e8ce106512596934dec1c2da817aa4274e93dbf2a9b2ac9adbb9322403081adcb15e16117e28370c940d7d481f9072e9fb01 Homepage: https://cran.r-project.org/package=plotor Description: CRAN Package 'plotor' (Odds Ratio Tools for Logistic Regression) Produces odds ratio analyses with comprehensive reporting tools. 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Package: r-cran-plotthis Architecture: all Version: 0.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-forcats, r-cran-gtable, r-cran-reshape2, r-cran-stringr, r-cran-scales, r-cran-gridtext, r-cran-patchwork, r-cran-ggrepel, r-cran-ggnewscale, r-cran-cowplot, r-cran-zoo Suggests: r-cran-plotly, r-cran-testthat, r-cran-matrix, r-cran-alluvial, r-bioc-complexheatmap, r-cran-cluster, r-cran-clustree, r-cran-gglogger, r-cran-ggwordcloud, r-cran-ggalluvial, r-cran-ggvenndiagram, r-cran-ggupset, r-cran-ggpubr, r-cran-ggbeeswarm, r-cran-ggforce, r-cran-ggraph, r-cran-tidygraph, r-cran-ggridges, r-bioc-ggmanh, r-cran-qqplotr, r-cran-hexbin, r-cran-igraph, r-cran-inext, r-cran-scattermore, r-cran-sf, r-cran-terra, r-cran-concaveman, r-cran-plotroc, r-cran-optimalcutpoints, r-cran-proxyc, r-cran-metr Filename: pool/dists/noble/main/r-cran-plotthis_0.14.0-1.ca2404.1_all.deb Size: 4433964 MD5sum: e6439adbdc72ca10cd4967e4ab61be64 SHA1: 09b63012f884f40c6fe0ad5757ee91a52fd39d69 SHA256: 67cd77968020659c1eed9356736c164381e1af7df2064c0d01a92634cb087499 SHA512: cf8f41ffee03c18e4a7836bbcca89a0a5449417a19301f04075906ce2cd077d3c39b0d188ade0457cddeb2666123010d6535e53d3fc91cc2000deef9c04c71af Homepage: https://cran.r-project.org/package=plotthis Description: CRAN Package 'plotthis' (High-Level Plotting Built Upon 'ggplot2' and Other PlottingPackages) Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. 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Package: r-cran-plottools Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-plottools_0.4.0-1.ca2404.1_all.deb Size: 41370 MD5sum: a6fe762eb3e3f21a134ea9210cc52058 SHA1: a031a15ba21e52b418a3c8a4af2a03dcb6a09ac2 SHA256: c3ba7d45da8ac29b62e67f16b5b0721b76e9d09579ab6d4dd42e805794a2c802 SHA512: 105505ea7ee58562aee45a23f7d40c52fa770a4a61b22c6edf0d5bcb743ca2bb9005892c11dc6d9d0041ae2b6b4039e64d5a5a883553b59fad124537a69e2319 Homepage: https://cran.r-project.org/package=PlotTools Description: CRAN Package 'PlotTools' (Extended Tools for Continuous Legends, Polygon Manipulation, andVisual Display of Categorical Data) Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons. 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Package: r-cran-plrmodels Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plrmodels_1.4-1.ca2404.1_all.deb Size: 298870 MD5sum: baa1b098cc4ca31aff70f1eef2f3765a SHA1: ee428e946f71a37ea30b2bc42f111ffc7c99b803 SHA256: 1a6b12ab7d20c69d37147e38211338b157a618130cc6cc008f3c84975203fd5c SHA512: 5f4cfc524bc583528b563cb68f46efdf90e60009191c10e47cbac987f3a0fdf3b4b5a4b3457a9f299219d001d32fd51206d447243b2497f1ed4167106904e802 Homepage: https://cran.r-project.org/package=PLRModels Description: CRAN Package 'PLRModels' (Statistical Inference in Partial Linear Regression Models) Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. 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Package: r-cran-pls Architecture: all Version: 2.9-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-rmpi, r-cran-testthat, r-cran-runit, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pls_2.9-0-1.ca2404.1_all.deb Size: 1157326 MD5sum: 2e2629e4ce6697dd0acfdbe69e6ea336 SHA1: 836575e5f8a952b0e0f11d9f4e2130d60559c032 SHA256: 2aeeb1c9c3bf7cdf2771d06fb174e2452427321bc9d063954996ff42c6d6c9da SHA512: c74f4faa93dca352f2b55c82bcd6f8aa819b84352ead0048b7c1ac584c4561055ea869ffce583e06a8e9d125092bd818e1cdae6157c74a259f0ae2c6f437da48 Homepage: https://cran.r-project.org/package=pls Description: CRAN Package 'pls' (Partial Least Squares and Principal Component Regression) Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS). Package: r-cran-plsdepot Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-factominer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsdepot_0.3.1-1.ca2404.1_all.deb Size: 199942 MD5sum: 46812a55ad383bd3d009b09641bd9066 SHA1: b745db9ce07a49612b393ef77aa252d671228641 SHA256: 39b5c64f935879e889e93d55ec503403e14cdac4d111ff7f6ef2a26ef1eaffdc SHA512: a5989c52aa0ef89afcba3b1a6af40cacb28537723215e5116f0932c1f2bf93163b6394573ad95f8f59080dc03aade85894bd4fc1be7a034255cd13d0344d647d Homepage: https://cran.r-project.org/package=plsdepot Description: CRAN Package 'plsdepot' (Partial Least Squares (PLS) Data Analysis Methods) Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus. Package: r-cran-plsdof Architecture: all Version: 0.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsdof_0.5-0-1.ca2404.1_all.deb Size: 208414 MD5sum: 436b89e2a22bc00ad34f9224d4d1a3b3 SHA1: d87e09e5f72770035877df98f847b66b6ca81395 SHA256: b92a59597054c84b407bd59de51e23e576609566412408b42951fe28ced3460a SHA512: 3974193b0af141bec5f9f2f0ffae62616e5c4dcdcce56d83c02686801ae6d7d746f86bdc98f9c4de2e2a650b1fcf8c3e4cd8da8153b3dcc8dee15de47823fd42 Homepage: https://cran.r-project.org/package=plsdof Description: CRAN Package 'plsdof' (Degrees of Freedom and Statistical Inference for Partial LeastSquares Regression) The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures (Kramer and Sugiyama 2012 ). Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available. Package: r-cran-plsgenomics Architecture: all Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-boot, r-cran-reshape2, r-cran-plyr, r-cran-fields, r-cran-rhpcblasctl Filename: pool/dists/noble/main/r-cran-plsgenomics_1.5-3-1.ca2404.1_all.deb Size: 1657664 MD5sum: 59085ab76a5e86cae4d47e331eaa4bf4 SHA1: 4fdf8f9ab589076ac66d9ba07008161523fbded0 SHA256: ff7ca4c46b78153251384a95c3173ba656191dfdf88466f4e244442ed260f2dd SHA512: 3e17adaa0130dc7f70b3e80d92a9cbf075388dcc202253c1f3f01f1e2f2bd728cc815e5c8191cca457ce8e982a29376bd5d18e5289c86f9e177c57440a90ee54 Homepage: https://cran.r-project.org/package=plsgenomics Description: CRAN Package 'plsgenomics' (PLS Analyses for Genomics) Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. 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Package: r-cran-plsmmlasso Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-hdi, r-cran-mass, r-cran-mvtnorm, r-cran-rlang, r-cran-scalreg Filename: pool/dists/noble/main/r-cran-plsmmlasso_1.1.0-1.ca2404.1_all.deb Size: 695652 MD5sum: 42eda10b71dd06f244d169b0b16cebef SHA1: f04a09ff644edb826166db266324136d54c1d2e6 SHA256: ed5d2c854b2039ebe8aaaa729bef84dea5d888fcda5fb46a5b94821e8a020024 SHA512: 86f9a65486585721f7774362d22bc5b559ef5075c58af85cd7856fec01973ebb7abbf2f025b23db9c544310b477b691c7eb5e0041469134e00b1147f1c52f6c1 Homepage: https://cran.r-project.org/package=plsmmLasso Description: CRAN Package 'plsmmLasso' (Variable Selection and Inference for Partial SemiparametricLinear Mixed-Effects Model) Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. 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Package: r-cran-plsmselect Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glmnet, r-cran-mgcv, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-purrr Filename: pool/dists/noble/main/r-cran-plsmselect_0.2.0-1.ca2404.1_all.deb Size: 149676 MD5sum: 4184089cb79c0a99065b2731eaad92f0 SHA1: 287f64e1b250ff39b32d4b3ec19417a1f1af4e49 SHA256: 7f9832019808929e502537f946ba1fc7f3b231d2376a0e3b33278e52a9f77eac SHA512: 215595f2e89094a5bf9f3debb426ad46031fa0fb350985209fd1e1c17aad063442f32e544d19f25f78c902ca5cdd8b637451409dc5c0e60ab4b8b6476a2b1660 Homepage: https://cran.r-project.org/package=plsmselect Description: CRAN Package 'plsmselect' (Linear and Smooth Predictor Modelling with Penalisation andVariable Selection) Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model). 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Package: r-cran-plsrcox Architecture: all Version: 1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-plsrglm, r-cran-lars, r-cran-pls, r-cran-kernlab, r-bioc-mixomics, r-cran-risksetroc, r-bioc-survcomp, r-cran-survauc, r-cran-rms Suggests: r-cran-survivalroc, r-cran-plsdof, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsrcox_1.8.2-1.ca2404.1_all.deb Size: 2307396 MD5sum: 400d2bddbd939e1f1659807f8baa2f01 SHA1: 25be0241faf2483e555081186fc5d1b45e6ecfb7 SHA256: 98e80cb62afce09a85c9f13ebe142acc618011b5900428b5659cba672915f15d SHA512: e15c0a4c552097560bbd6b9b3433479f0cf8ce18c8bd0bb779878b59eb267f28385b4e53023b3c3354cda4a18d486aab02a3368b58497e44fa4385fa77c9cacd Homepage: https://cran.r-project.org/package=plsRcox Description: CRAN Package 'plsRcox' (Partial Least Squares Regression for Cox Models and RelatedTechniques) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings , Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in , Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data. 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Package: r-cran-plssem Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13051 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-modsem, r-cran-lme4, r-cran-lavaan, r-cran-stringr, r-cran-rfast, r-cran-collapse, r-cran-mvnfast, r-cran-reformulas, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-fnn, r-cran-mass, r-cran-pbivnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-mice, r-cran-mvtnorm, r-cran-pkgload Filename: pool/dists/noble/main/r-cran-plssem_0.1.4-1.ca2404.1_all.deb Size: 2068952 MD5sum: 5b23966cd4323b4824cbab7d1fa785a2 SHA1: 0799db3d0b289c86ccc4d9dc185382c86e495150 SHA256: a57cba3621fe5e57c7fb2e1131812fab0e23bf34d4a49302c9ee62cd33a8e1dc SHA512: d4655e6fe9e3f95bba423b2f8cadd14f2dbad344790d733028b255d1123e636c4c722926a72482593eec73cef83f49b6cc16e556c4a7fea1c48c65e7856dd143 Homepage: https://cran.r-project.org/package=plssem Description: CRAN Package 'plssem' (Complex Partial Least Squares Structural Equation Modeling) Estimate complex Structural Equation Models (SEMs) by fitting Partial Least Squares Structural Equation Modeling (PLS-SEM) and Partial Least Squares consistent Structural Equation Modeling (PLSc-SEM) specifications that handle categorical data, non-linear relations, and multilevel structures. The implementation follows Lohmöller (1989) for the classic PLS-SEM algorithm, Dijkstra and Henseler (2015) for consistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear PLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal PLS-SEM and PLSc-SEM. Additional extensions are under development. The MC-OrdPLSc algorithm, used to handle ordinal interaction models is detailed in Slupphaug et al., (2026). References: Lohmöller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., & Henseler, J. (2015). . "Consistent partial least squares path modeling." Dijkstra, T. K., & Schermelleh-Engel, K. (2014). . "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). . "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026). . "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators." 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Package: r-cran-plumberdeploy Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-analogsea, r-cran-ssh, r-cran-jsonlite, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-plumberdeploy_0.2.1-1.ca2404.1_all.deb Size: 52234 MD5sum: 5dd10ad79d62394edc858db8b1a5fd7c SHA1: 24805dd2069b61997d2091c5ca761ff9e563333a SHA256: c8d1aa67c000d48d5a96b015590ed45c0b496b7d8cafe8cba1e9a25b73c0aa77 SHA512: 0fef22ffed7bc00d2d4ecc0c72048272ea95585451507e1c2f835c34f3db4925ab804cba906a112202617d51b3436e6c704143e72983c631675b637074921b55 Homepage: https://cran.r-project.org/package=plumberDeploy Description: CRAN Package 'plumberDeploy' (Plumber Deployment) Gives the ability to automatically deploy a plumber API from R functions on 'DigitalOcean' and other cloud-based servers. Package: r-cran-plumbertableau Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2019 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plumber, r-cran-magrittr, r-cran-curl, r-cran-httpuv, r-cran-jsonlite, r-cran-later, r-cran-promises, r-cran-rlang, r-cran-htmltools, r-cran-debugme, r-cran-stringi, r-cran-markdown, r-cran-urltools, r-cran-httr, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-plumbertableau_0.1.1-1.ca2404.1_all.deb Size: 1393136 MD5sum: 1d495b51cde7b49f466cab1bb6660e70 SHA1: 43f608d9b05836569ee8e7d61c3d2a17550484ef SHA256: 63e9a5bfce83cd9821a3531ebf3fa751af5359b94ce0e9c9fecd0a3a6c94a9d0 SHA512: 943b02e184897b10ee07238ecf7a90dbab411b1453bca46cf75adf0e9df8fb8e5b54fcf66e7b7ac7858bcf07d3164bc43594503f48cc831c1da35920efa568c8 Homepage: https://cran.r-project.org/package=plumbertableau Description: CRAN Package 'plumbertableau' (Turn 'Plumber' APIs into 'Tableau' Extensions) Build 'Plumber' APIs that can be used in 'Tableau' workbooks. Annotations in R comments allow APIs to conform to the 'Tableau Analytics Extension' specification, so that R code can be used to power 'Tableau' workbooks. Package: r-cran-plumbr Architecture: all Version: 0.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-objectsignals Suggests: r-cran-plyr, r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-plumbr_0.6.10-1.ca2404.1_all.deb Size: 353278 MD5sum: a2fd5546ee1c1aacd8ee09c29273b396 SHA1: 290df165cdb065add13962ce6f215d2a761fa464 SHA256: b87d3029546e5f55db7238dcd451d0ac4b0c120a1d3dc8c5c33692ce3a9bd3b7 SHA512: 350fd8c4040cef7ac1bf9ae093bafea712d46f812b58e51a2f34b9b4530f8ec82b72e27cb5029f2f011ba308e21e8a956976e1dab8e1e834d0748fdc427c091a Homepage: https://cran.r-project.org/package=plumbr Description: CRAN Package 'plumbr' (Mutable and Dynamic Data Models) The base R data.frame, like any vector, is copied upon modification. This behavior is at odds with that of GUIs and interactive graphics. To rectify this, plumbr provides a mutable, dynamic tabular data model. Models may be chained together to form the complex plumbing necessary for sophisticated graphical interfaces. Also included is a general framework for linking datasets; an typical use case would be a linked brush. Package: r-cran-plume Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-jsonlite, r-cran-knitr, r-cran-lifecycle, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-yaml Suggests: r-cran-covr, r-cran-fontawesome, r-cran-gt, r-cran-rmarkdown, r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/noble/main/r-cran-plume_0.3.0-1.ca2404.1_all.deb Size: 447696 MD5sum: bf1b7753cb844a960f50916c99603fe0 SHA1: 2587625d3e1e4dd3ccb58f6b39af948c81c39f77 SHA256: c4734bc9808de9330d1c8fa25423c5cd4426c67dc55ad14fa7525e042f073959 SHA512: 3cc47af0cd4251e6abac70cdf4e8f544bbf538437b2b6c8c88c7b1fe97704b7cbe897f7dbf183e26cb4e5809d3cdb3a00c43b2ba3129d060747db86a3567de03 Homepage: https://cran.r-project.org/package=plume Description: CRAN Package 'plume' (A Simple Author Handler for Scientific Writing) Handles and formats author information in scientific writing in 'R Markdown' and 'Quarto'. 'plume' provides easy-to-use and flexible tools for inserting author data in 'YAML' as well as generating author and contribution lists (among others) as strings from tabular data. Package: r-cran-pluscode2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Filename: pool/dists/noble/main/r-cran-pluscode2_0.1.0-1.ca2404.1_all.deb Size: 26162 MD5sum: 5ad495d4aa6bf2f3972fe74f3825f2ea SHA1: 81f1117d6726b4284504e1b6ba1c8ca3c29344d6 SHA256: f2bbae6f67cd8ccaaa2854dd298eb877bfb92e30de08531c2d65027bb66686fc SHA512: 8861fb11e9d8349d2ddedf849e5149546a37a0ce9e9ff2e352ece274cec4b3a0b691c3a10915b964edc7afc47ce48b5b998a2fab8ab3932a6cf5214a5b379347 Homepage: https://cran.r-project.org/package=plusCode2 Description: CRAN Package 'plusCode2' (Coordinates to 'Plus Code' Conversion Tool) Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository . For details about 'Plus Code', visit or . Package: r-cran-pluscode Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-pluscode_0.1.0-1.ca2404.1_all.deb Size: 63030 MD5sum: d46a4318dc1d9d036dab2ab9cf6cc49f SHA1: 18a64b7cd303d1d3d92f4ca47ae4eecf26218b17 SHA256: 322eda76305548fde0552933b23d5f422839bffb05919eb6864a3f608c05037c SHA512: 40f287af4c7db2678f3d53ef5c9113cfd85f408b8b2871aadd4e835f4269ae1e107d4be99c338009e755f2ceecde0a90c2227a54d821e0a6d6bd2cb9a0ec3650 Homepage: https://cran.r-project.org/package=pluscode Description: CRAN Package 'pluscode' (Encoder for Google 'Pluscodes') Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'. Package: r-cran-plutor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-baizer, r-cran-cli, r-cran-dplyr, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggsci, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-repr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-svglite, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-plutor_0.1.0-1.ca2404.1_all.deb Size: 510290 MD5sum: d4c7365df4ca93307c7e9c322125a00d SHA1: 2dd2701ee21881625d5046cb27da2699cc0a7f26 SHA256: 0ec0cb2087b8f1cec697a4463335381b352c9a1acfb5bc42ee5d578766023d40 SHA512: 7cb52ac41d44f1387083040d7689de89d5c8727efe16269bb7cc168d583d7c3d0af81c2cfc3978711b796bec885551fec9a1625aa8af43814b77b14a1133f802 Homepage: https://cran.r-project.org/package=plutor Description: CRAN Package 'plutor' (Useful Functions for Visualization) In ancient Roman mythology, 'Pluto' was the ruler of the underworld and presides over the afterlife. 'Pluto' was frequently conflated with 'Plutus', the god of wealth, because mineral wealth was found underground. When plotting with R, you try once, twice, practice again and again, and finally you get a pretty figure you want. It's a 'plot tour', a tour about repetition and reward. Hope 'plutor' helps you on the tour! Package: r-cran-pm3 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tableone Filename: pool/dists/noble/main/r-cran-pm3_0.2.0-1.ca2404.1_all.deb Size: 28266 MD5sum: b557c8534ccfcb3a8add1d12fcb2c3c4 SHA1: 1273b6696a622e39d686090026722914baf16dde SHA256: 1daa36cfb9c8b1c28bd4ce5f5f435f5f13b5b22d04c282113d77485926c3a61a SHA512: 424e101113c2e38030b957a07b56b1a57da5d25cab239a19185d4c75890776aeb69912341427260828df38ffccecaed9c6913a83e5f72ed06bbd9cca98e070cd Homepage: https://cran.r-project.org/package=pm3 Description: CRAN Package 'pm3' (Propensity Score Matching for Unordered 3-Group Data) You can use this program for 3 sets of categorical data for propensity score matching. Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. The matching will make the differences in the baseline data smaller. This method was described by Alvaro Fuentes (2022) . Package: r-cran-pmapscore Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-clusterprofiler, r-cran-glmnet, r-bioc-maftools, r-bioc-org.hs.eg.db, r-cran-proc, r-cran-survival, r-cran-survminer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmapscore_0.1.1-1.ca2404.1_all.deb Size: 2612342 MD5sum: cdfec540991b82948405bd06abcb680f SHA1: 82871cec169a1a369aa6f491998ae0741dd929b9 SHA256: 582f409c80be567ca5e756cd6ed91df3c0399f41c0a2ff351790c494e2621a3e SHA512: 4c21534ec09b992ac4d73dae0023cad74311ebb06978d67c2b009733319e790f827f9b8bf6087703cc6eddac9b16dce8d2f55465befb552ddb61b1536780ef56 Homepage: https://cran.r-project.org/package=PMAPscore Description: CRAN Package 'PMAPscore' (Identify Prognosis-Related Pathways Altered by Somatic Mutation) We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) ). Package: r-cran-pmc Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geiger, r-cran-ggplot2, r-cran-ouch, r-cran-tidyr, r-cran-phytools Suggests: r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmc_1.0.6-1.ca2404.1_all.deb Size: 126064 MD5sum: 6fdb6278e642c25e1924bacf82d93f89 SHA1: 67919a28532c22b5dadcff81c89a20df01ab85d3 SHA256: ac22be73548647d172a13f856d4c5f4fadf87e4534358d470942edfb80912ce1 SHA512: d94f7d12ba3a02fd155af02e148a99fc5ab1df349482d16f2e14060db104c0590e043e391da038475a85f4634ed28f24aaca60c5119b5a48f2ff144dcf66f628 Homepage: https://cran.r-project.org/package=pmc Description: CRAN Package 'pmc' (Phylogenetic Monte Carlo) Monte Carlo based model choice for applied phylogenetics of continuous traits. Method described in Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your phylogeny informative? Measuring the power of comparative methods, Evolution 66 (7) 2240-51. . Package: r-cran-pmcalibration Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mass, r-cran-mgcv, r-cran-survival, r-cran-pbapply Suggests: r-cran-rmarkdown, r-cran-data.table, r-cran-ggplot2, r-cran-rms, r-cran-simsurv Filename: pool/dists/noble/main/r-cran-pmcalibration_0.2.0-1.ca2404.1_all.deb Size: 363578 MD5sum: 98bcef234fe4cbcc9dbc6eeaabd1088b SHA1: 083cd9fe7743ba22a59a520d1f5a65fb18c9067a SHA256: 7d284b7c285f94be87f975bea078922367e4525da37fb015e3b820d08ddd6ff4 SHA512: 5e26ffdd5a2fd9f0e61a68800a36507cbeefa36058a26f302253e464c2a7154ebc3dd9f4ce5ce0ffc6d9a431002f617d6ee4977e2d84c8410dc753964d2bea39 Homepage: https://cran.r-project.org/package=pmcalibration Description: CRAN Package 'pmcalibration' (Calibration Curves for Clinical Prediction Models) Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 'pmcalibration' allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) ; Austin and Steyerberg (2019) ; Austin et al. (2020) . Package: r-cran-pmcmr Architecture: all Version: 4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-multcompview Filename: pool/dists/noble/main/r-cran-pmcmr_4.4-1.ca2404.1_all.deb Size: 53082 MD5sum: 9bc6678dd1a0ea2b3b9b54cee097a3e8 SHA1: c40fbbe44697de9084e59f5619a9c6cc37f8866f SHA256: e9d9b0f1704a729e7883277846201304d7266b8b15d9e3c8831af10c7ed51f2d SHA512: e8b1e42eca0d6f2a165942bcfb3690b39dbac299bc1620827f858430e596bd73b09ba064ae573c761f0af46fb999b7a244327766ec32d69d14114e75f52478d5 Homepage: https://cran.r-project.org/package=PMCMR Description: CRAN Package 'PMCMR' (Calculate Pairwise Multiple Comparisons of Mean Rank Sums) Note, that the 'PMCMR' package is superset by the novel 'PMCMRplus' package. The 'PMCMRplus' package contains all functions from 'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions, such as print, summary and plot. The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time. Package: r-cran-pmd Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4603 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-igraph, r-cran-envigcms, r-cran-data.table Suggests: r-cran-knitr, r-cran-shiny, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmd_0.3.0-1.ca2404.1_all.deb Size: 3775548 MD5sum: d1902f3f1492ee6c6e57b738522cc5c6 SHA1: 6cef947ed4015923d609fbdc894e7dadfd082c61 SHA256: 75c5ef343107039ba70d9da3ee1a2744f317eadb6ef0c54185b15135a3628b99 SHA512: 65eb2999c1a228a9104a1aa7fba1ded75990e4b0802ce38c81187bd85de8b41efdb8bf570c343df75eaae5608484a082b9a032a7a13d5ecaa54d50dc6cbbb5be Homepage: https://cran.r-project.org/package=pmd Description: CRAN Package 'pmd' (Paired Mass Distance Analysis for GC/LC-MS Based Non-TargetedAnalysis and Reactomics Analysis) Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) and PMD based reactomics analysis proposed in Yu and Petrick (2020) for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications. Package: r-cran-pmetar Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1559 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-magrittr, r-cran-rcurl, r-cran-tidyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-pmetar_0.7.1-1.ca2404.1_all.deb Size: 1459190 MD5sum: a3069a7240435c6d3c3af713b67b4fa4 SHA1: 59d040654966f3e411a0d8823d1bc0e21575d50a SHA256: de9f4711adee654b767a905ac38654f354a4ae80a9000ccc17f7196157a3db61 SHA512: 0bd1272f7bcbdd776ec94c6bc3216521c5e31e70962821a4de6925cbc27cba7889f03d0b7cf0e1f536578bb8778d7cf3f2ce660ddfd63b9d8277e4dd016336f2 Homepage: https://cran.r-project.org/package=pmetar Description: CRAN Package 'pmetar' (Processing METAR Weather Reports) Allows to download current and historical METAR weather reports extract and parse basic parameters and present main weather information. Current reports are downloaded from Aviation Weather Center and historical reports from Iowa Environmental Mesonet web page of Iowa State University ASOS-AWOS-METAR . Package: r-cran-pmev Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-zoo, r-cran-rlang, r-cran-ggplot2, r-cran-scales, r-cran-vdiffr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmev_0.1.2-1.ca2404.1_all.deb Size: 112690 MD5sum: 6a1bb001f78bbfafdafb93aaa7effc0b SHA1: f744b3d671a0c0acdc8c7fc50ebbc778b9c04549 SHA256: ae453a0e07f7a20c4673dce5b1e43409af613937526f5d4edff25f588be19d2e SHA512: 10e182fcece935127b89efdcef4acd6d8b7ce7e24c527f4931aacd6c03335d5daedb703fdd13f347aabe2a580186e275716d91ffb651088c0b95713c7bd60cec Homepage: https://cran.r-project.org/package=pmev Description: CRAN Package 'pmev' (Calculates Earned Value for a Project Schedule) Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)). Package: r-cran-pmevapotranspiration Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pmevapotranspiration_0.1.0-1.ca2404.1_all.deb Size: 15842 MD5sum: 54fc25e3288358a947c2cc2b0d1a70e1 SHA1: 2d2aa5003bf0ca013a361a75c9c6c226b915e2e0 SHA256: f29d492cbc6a3b49974a449b57624d62f2ffbde941ef1985644139f9416215c4 SHA512: 934387cbafd2325e131abd1843d3a91fdcbcbff4575f78261b95eea59a61c232e7c1eab1a63ea963bf70ae99c55b3296ed945383fdab343f7aa380004f247a9a Homepage: https://cran.r-project.org/package=PMEvapotranspiration Description: CRAN Package 'PMEvapotranspiration' (Calculation of the Penman-Monteith Evapotranspiration usingWeather Variables) The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015) and Debnath et al. (2015) . Package: r-cran-pmhtutorial Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-quandl Filename: pool/dists/noble/main/r-cran-pmhtutorial_1.5-1.ca2404.1_all.deb Size: 95586 MD5sum: 09c4eb51731d097f434e2c7048d2b994 SHA1: d28827687b53ef8246f6eb1211554ef1eb9feb25 SHA256: 8056736156e93d7646bd1efd336b0d334d127b461a96ee67dcf0d3afd3cf8864 SHA512: 63def5b73a7b9871a405f65a91dafa51f61a703df025f902bad14e202991545777b6504e333b1118b998122802303f4277df5ac5ce7e30e4fe6c6b4ac9ba2435 Homepage: https://cran.r-project.org/package=pmhtutorial Description: CRAN Package 'pmhtutorial' (Minimal Working Examples for Particle Metropolis-Hastings) Routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provided an unbiased estimator of the likelihood. This package is a collection of minimal working examples of these algorithms and is only meant for educational use and as a start for learning to them on your own. Package: r-cran-pminternal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dcurves, r-cran-insight, r-cran-marginaleffects, r-cran-pmcalibration, r-cran-proc, r-cran-pbapply, r-cran-purrr Suggests: r-cran-ggplot2, r-cran-glmnet, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-ranger, r-cran-gbm, r-cran-rms, r-cran-mgcv, r-cran-mice Filename: pool/dists/noble/main/r-cran-pminternal_0.1.0-1.ca2404.1_all.deb Size: 707790 MD5sum: a06ad8d09940b6528dd19f5ccdb1bdab SHA1: 85beec61ac3c0a7c9d978c7c6759eb0c1836ae60 SHA256: f71eafa4a7e80b19be97a7fbe9142f8ca1b70aa95297e7e8364f9716682d4ac3 SHA512: 0f98ee0318ef86b24db62765f7986b9018c2a47157b767f88221008889e97ae62e425cc66b462e440ff44f0c7db4d44df7b1b36e72926c3de67465972690a723 Homepage: https://cran.r-project.org/package=pminternal Description: CRAN Package 'pminternal' (Internal Validation of Clinical Prediction Models) Conduct internal validation of a clinical prediction model for a binary outcome. Produce bias corrected performance metrics (c-statistic, Brier score, calibration intercept/slope) via bootstrap (simple bootstrap, bootstrap optimism, .632 optimism) and cross-validation (CV optimism, CV average). Also includes functions to assess model stability via bootstrap resampling. See Steyerberg et al. (2001) ; Harrell (2015) ; Riley and Collins (2023) . Package: r-cran-pmlbr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmlbr_0.3.0-1.ca2404.1_all.deb Size: 220086 MD5sum: 039083d35f21fb7f08365d7f1c3ef917 SHA1: 306d38621ac4dea7be53c9b1a8c93dcfccd6d022 SHA256: ccb9fc22768ed202d5935f4d17a67e2778c294f144259343e9d02f884f04e816 SHA512: fe46140621197ad717f4abe5c463f004945aff70adddbcf0971ba787b727d5289d800f859ef752c4390c29b391091dc6727209e496e496ca7722be941b31c1d6 Homepage: https://cran.r-project.org/package=pmlbr Description: CRAN Package 'pmlbr' (Interface to the Penn Machine Learning Benchmarks DataRepository) Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository () contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository. Package: r-cran-pmle4scr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-trust, r-cran-dplyr, r-cran-rlang, r-cran-vinecopula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-semicomprisks Filename: pool/dists/noble/main/r-cran-pmle4scr_0.1.0-1.ca2404.1_all.deb Size: 130760 MD5sum: 3288fe1fc3ab9dc08a37b23437f9d5dc SHA1: 0edc01992455792a2c7e6be3f826acc9700ed2e0 SHA256: 83067226a6bf63a0177acea7efd7ae01d124d8e64f57579a471e733bc2fc0a6a SHA512: 9af6803912d5e78afe3dfa3f393bc3fda42cd18693749fbcfc80b0c6e3e205c64c5aff01748c5a48ebba732b83935d194103467b1f366a283b03645136d6e990 Homepage: https://cran.r-project.org/package=PMLE4SCR Description: CRAN Package 'PMLE4SCR' (Pseudo Maximum Likelihood Estimation for Semi-Competing RisksData) Implements two-stage pseudo maximum likelihood estimation (PMLE) for copula-based regression models with semi-competing risks data. The marginal distributions are modeled by semiparametric transformation regression models, and the dependence between bivariate event times is specified by a parametric copula function. See Arachchige, Chen and Zhou (2025) for details. Package: r-cran-pmledecon Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splitstackshape, r-cran-rmutil Filename: pool/dists/noble/main/r-cran-pmledecon_0.2.1-1.ca2404.1_all.deb Size: 42520 MD5sum: e986cc0cc6d0a13645bf5369739c4fc5 SHA1: 4ebe250fbcf4766e4e0a80877cea5bb5b24f9645 SHA256: 6b8512be3bde2da355a1e9a81a52f3e06938b81cd2b23c531d9b7190a963ffdc SHA512: 89c118d421237474792e29fd01b9061e2e6527d8b471530c30a331d8bc6dd3036d0221e750947432d9865bbcabf47dc34e2cb28ac148f96cc168916d4fefbebc Homepage: https://cran.r-project.org/package=pmledecon Description: CRAN Package 'pmledecon' (Deconvolution Density Estimation using Penalized MLE) Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used. Package: r-cran-pmlsp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-matrix, r-cran-matrixcalc, r-cran-maxlik, r-cran-minqa, r-cran-mvtnorm, r-cran-numderiv, r-cran-qrng, r-cran-spatialreg, r-cran-spdep Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmlsp_1.0.1-1.ca2404.1_all.deb Size: 145398 MD5sum: f3867cb749c3bf9e1ef6e114427a96f8 SHA1: 6807b69bd4ce7c42e0fba9311102a3f96a6a2262 SHA256: b4599911cf0eadd23f9de75a3d08df5fad6b991703e4849079646026ce077d67 SHA512: 701e2114b48d28da4169c4326d0def63742c9efe951df47aea13f82937f413352b615e6ed46a35ab01e654f9ade4bf34588f680617722efa0c5aa44a34ac190e Homepage: https://cran.r-project.org/package=pmlsp Description: CRAN Package 'pmlsp' (Partial Maximum Likelihood Estimation of Spatial Probit Models) Estimate spatial autoregressive nonlinear probit models with and without autoregressive disturbances using partial maximum likelihood estimation. Estimation and inference regarding marginal effects is also possible. For more details see Bille and Leorato (2020) . Package: r-cran-pmml Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-stringr Suggests: r-cran-ada, r-cran-amap, r-cran-arules, r-cran-caret, r-cran-clue, r-cran-data.table, r-cran-forecast, r-cran-gbm, r-cran-glmnet, r-cran-magrittr, r-cran-matrix, r-cran-nnet, r-cran-rpart, r-cran-randomforest, r-cran-rattle, r-cran-kernlab, r-cran-e1071, r-cran-testthat, r-cran-survival, r-cran-xgboost, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-pmml_2.6.1-1.ca2404.1_all.deb Size: 625754 MD5sum: b901476c9b0e72e84428580829e807fc SHA1: 86286c858b1c6802e585c6b0ca98168202125259 SHA256: 9d6d3022bbb2454eb38061e723871a841b6f5650363eeeb77a7fd4e2661d21c7 SHA512: 5f14c534ce231c2dab99e20c3ad090a00f7d22765b7457f3fedb483a247fd46b063f16078faaf0cf1f219faea239c79e51b8696d1f69d64aeeee14625343c413 Homepage: https://cran.r-project.org/package=pmml Description: CRAN Package 'pmml' (Generate PMML for Various Models) The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at . The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products. Package: r-cran-pmmltransformations Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-pmml Filename: pool/dists/noble/main/r-cran-pmmltransformations_1.3.3-1.ca2404.1_all.deb Size: 116454 MD5sum: 3a05d3e508bb8d4254439b60122404bc SHA1: 74c11876e6e849a51433b5905df93f4ea8120ee5 SHA256: 1380d79f025853e9391e316d6367cb3cf43e01ae971c3ebf4b57bd9bad6f69b8 SHA512: 85dfbe840adcdf6f892e9d8315d248894c79bdb63efc2dd61faca05bb4834c6c806b05c4e30167cbf4f8f0fba4609da2456461fd1da55f029e3ada4dfec71429 Homepage: https://cran.r-project.org/package=pmmlTransformations Description: CRAN Package 'pmmlTransformations' (Transforms Input Data from a PMML Perspective) Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at . The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. Package: r-cran-pmparser Architecture: all Version: 1.0.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dbi, r-cran-foreach, r-cran-glue, r-cran-iterators, r-cran-jsonlite, r-cran-r.utils, r-cran-rcurl, r-cran-withr, r-cran-xml2 Suggests: r-cran-bigrquery, r-cran-doparallel, r-cran-rmariadb, r-cran-rpostgres, r-cran-rsqlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmparser_1.0.26-1.ca2404.1_all.deb Size: 182894 MD5sum: 37ea87ae32395c79fe80430ed641f565 SHA1: c592bc384f6c75fc17b95e78e745e93ecb60fc13 SHA256: d2ec60a6b363a14923943cad0cb0732993faf2597a2d65b0c0e2488e912938dc SHA512: 733bd9d2b5d167a80bf23c2856b45c7fc86dd16ffe2b942fdeed77b585199f25f899008ae73546f0cd6dabe68fb4828df342a9468132792c65260346853169aa Homepage: https://cran.r-project.org/package=pmparser Description: CRAN Package 'pmparser' (Create and Maintain a Relational Database of Data fromPubMed/MEDLINE) Provides a simple interface for extracting various elements from the publicly available PubMed XML files, incorporating PubMed's regular updates, and combining the data with the NIH Open Citation Collection. See Schoenbachler and Hughey (2021) . Package: r-cran-pmr Architecture: all Version: 1.2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pmr_1.2.5.1-1.ca2404.1_all.deb Size: 120520 MD5sum: aa6a2396d1f93563a1a0bb44c77bd934 SHA1: 9d635b3e23be272b4aa4df9a93a0d0271ede7ef4 SHA256: 2b6bc82f96d48760d854ee1b34b5088184b52b6691ff49e3a6292ae91267bd60 SHA512: 889232135716027a8193820ddae5e87083d740ae437b26589fb261f4c9f5b9e04faee88313812c75dbf1d100d6e33d8bb59775e4e545fb2505acbea87c1d9dcf Homepage: https://cran.r-project.org/package=pmr Description: CRAN Package 'pmr' (Probability Models for Ranking Data) Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package. Package: r-cran-pmrm Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-matrix, r-cran-nlme, r-cran-rtmb, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-readr, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmrm_0.0.4-1.ca2404.1_all.deb Size: 1277782 MD5sum: 5a524834429130e9d1991063e384c2f5 SHA1: b641f8a3bbdd662da41a501fc9e38974d1b4a57d SHA256: e50567ace54f1388d47060cf9804719222ed1ba7270c29359dbd0e1a5aa22eff SHA512: 95dedb67961cf16604ab62f861014d279e193ed7153649de09f61c483fb03b76afcf6eed17e37f1ccd8299ff0291720ecbd70f2d763dfc157964cd3bee432847 Homepage: https://cran.r-project.org/package=pmrm Description: CRAN Package 'pmrm' (Progression Models for Repeated Measures) A progression model for repeated measures (PMRM) is a continuous-time nonlinear mixed-effects model for longitudinal clinical trials in progressive diseases. Unlike mixed models for repeated measures (MMRMs), which estimate treatment effects as linear combinations of additive effects on the outcome scale, PMRMs characterize treatment effects in terms of the underlying disease trajectory. This framing yields clinically interpretable quantities such as average time saved and percent reduction in decline due to treatment. This package implements frequentist PMRMs by Raket (2022) using 'RTMB' by Kristensen (2016) . Package: r-cran-pmsampsize Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pmsampsize_1.1.3-1.ca2404.1_all.deb Size: 46666 MD5sum: 656b2476d630ee19df75c7de91d71651 SHA1: 77ca40f14b5527273d1c3750eb5e7f24af32c14e SHA256: 7ec7983df28346ae1717f7ce77433e598d2236f0dd12e789fe291f81a0029846 SHA512: 39347d5d5cdb108139458c6ac5330fa48c87a6528653d4960504e7f4744c30b1724568d28a826f7d7de03804b02115fe1101a52c66d73d1398866264ef0e1560 Homepage: https://cran.r-project.org/package=pmsampsize Description: CRAN Package 'pmsampsize' (Sample Size for Development of a Prediction Model) Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) . pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model. Package: r-cran-pmsesampling Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rootsolve Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmsesampling_0.1.1-1.ca2404.1_all.deb Size: 21960 MD5sum: 09f9b8ff74148e5b00ab2e64230e2b98 SHA1: 7ed6f2664aebe7d4906b27c274da9e989d963e16 SHA256: 741ff5de77ecad382ea90fd282121f28ef41e84fab9d6e13a5da27c52cbca563 SHA512: f2a072b91a88b51f639a77a9047a05097b6edb9c95e7b3885e49f4f928134582db1729351030a6738e9b5776b7403761d8965b07612708ac84b2749d75d314e7 Homepage: https://cran.r-project.org/package=pmsesampling Description: CRAN Package 'pmsesampling' (Sample Size Determination for Accurate Predictive LinearRegression) Provides analytic and simulation tools to estimate the minimum sample size required for achieving a target prediction mean-squared error (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear regression models. Functions implement the criteria of Ma (2023) , support covariance-matrix handling, and include helpers for root-finding and diagnostic plotting. Package: r-cran-pmsims Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2021 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-lifecycle, r-cran-mlpwr, r-cran-proc, r-cran-survival, r-cran-timeroc Suggests: r-cran-covr, r-cran-desctools, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-knitr, r-cran-mlbench, r-cran-mlr, r-cran-randomforestsrc, r-cran-ranger, r-cran-rmarkdown, r-cran-synthpop, r-cran-testthat, r-cran-tuneranger, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-pmsims_1.0.0-1.ca2404.1_all.deb Size: 1533970 MD5sum: e919d60291b9a21612a5b5f5d23d3603 SHA1: 2dfe256431f4c5202f9c4b5f7e8bba0f15cf9e37 SHA256: df05ebdc53dc23d4dee96be29c5c18d8eaabcc98b209ec27b3f0c1fc0d26e697 SHA512: a5e81bee93854d8b6a859b314edb52790bf7dc296d051b163a2982f7c7baeaa52e3c257bd3f87bdba1a8bb7b507dec1f091578160ecdd627c9eb19806f28a421 Homepage: https://cran.r-project.org/package=pmsims Description: CRAN Package 'pmsims' (Simulation-Based Sample Size Tools for Prediction Models) Provides a flexible, simulation-based toolkit for exploring how much data are needed to develop reliable prediction models. It works by repeatedly generating data, fitting models, and evaluating performance to show how sample size affects predictive accuracy, calibration, and overfitting. The package supports continuous, binary, and time-to-event outcomes and can be used with both regression-based modelling approaches and machine-learning methods. It is designed to help researchers plan studies, assess feasibility, and build more robust and generalisable models. The methods are described in Olaniran et al. (2026) and Shamsutdinova et al. (2026) . Package: r-cran-pmultinom Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fftw Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmultinom_1.0.0-1.ca2404.1_all.deb Size: 167684 MD5sum: 56937e403e07b513d334fc016b5e6c59 SHA1: 64e1720a94050f5c384e924edafa639c86d049c9 SHA256: 8d86c78223b7f6623851fd3e9ff61f8c1baa1f21678bdfe55ace1b2d9e051064 SHA512: 8922213ce44cc7902966952a27fa5eaa320ed519e0803181378a3b59e2956126ab766f0be793aa720717cd5b8278ab4d171e61a1000a3b2913001e8f549c5d36 Homepage: https://cran.r-project.org/package=pmultinom Description: CRAN Package 'pmultinom' (One-Sided Multinomial Probabilities) Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1. Package: r-cran-pmvalsampsize Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc Filename: pool/dists/noble/main/r-cran-pmvalsampsize_0.1.0-1.ca2404.1_all.deb Size: 36642 MD5sum: b37f3e07f3d020c344506661f4c10837 SHA1: ff2be47df1bb359e3a851383b62c14f7800836ea SHA256: c1fd4c3fac98ec5a299965cd4d6d57377e402c1afcac5f4a8c4744faf57b48a4 SHA512: 3fcb989bcfe48003f3b36e14177257af67ade9f9b6d9016d305b17c0d4a03974f40ec77913cad895d1c9f6687496b71edb17d804ede684cff8c2e9a922e6ba25 Homepage: https://cran.r-project.org/package=pmvalsampsize Description: CRAN Package 'pmvalsampsize' (Sample Size for External Validation of a Prediction Model) Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) and Riley (2021) . Package: r-cran-pmwg Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-coda, r-cran-condmvnorm, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-covr, r-cran-rtdists, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-v8 Filename: pool/dists/noble/main/r-cran-pmwg_0.2.7-1.ca2404.1_all.deb Size: 533606 MD5sum: 418ca26b95702f8f681e41bac8f1eef0 SHA1: f546ef4c8d4c5125d29211e900ba0830c697a3eb SHA256: 323a2b2224adf90a20128fcbabf5cf9f05859703fb71818746c0be335d99a48e SHA512: e799687561da43c6d5afe16e986475a200240a4d9c662783a3ec2c615cfb075a17712d5c35adad3d3ba9f7531e2842a726ffa754d30a4f816e2c81ab1c747b1b Homepage: https://cran.r-project.org/package=pmwg Description: CRAN Package 'pmwg' (Particle Metropolis Within Gibbs) Provides an R implementation of the Particle Metropolis within Gibbs sampler for model parameter, covariance matrix and random effect estimation. A more general implementation of the sampler based on the paper by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020) . An HTML tutorial document describing the package is available at and includes several detailed examples, some background and troubleshooting steps. Package: r-cran-pmwr Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmof, r-cran-datetimeutils, r-cran-fastmatch, r-cran-orgutils, r-cran-textutils, r-cran-zoo Suggests: r-cran-crayon, r-cran-rbenchmark, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-pmwr_1.2-0-1.ca2404.1_all.deb Size: 1315180 MD5sum: 006a077a956dd3c2482085ee4e9319d5 SHA1: 7b59a9831429b080213c68b8ebf5a135317f9ddd SHA256: 1a1493923241380a0f4c9725ae630fd9a9108bc3ecd297aadff9cfe65fcfba7b SHA512: 21660853be8a4e163ccec6fc4aba2eb96e7670f9a0bf7d91875a3692277c12d8a4371772c012c8be8e1312b9190eae0510c2ff80908acf909f04ece9e6347d2e Homepage: https://cran.r-project.org/package=PMwR Description: CRAN Package 'PMwR' (Portfolio Management with R) Tools for the practical management of financial portfolios: backtesting investment and trading strategies, computing profit/loss and returns, analysing trades, handling lists of transactions, reporting, and more. The package provides a small set of reliable, efficient and convenient tools for processing and analysing trade/portfolio data. The manual provides all the details; it is available from . Examples and descriptions of new features are provided at . Package: r-cran-pmxcode Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shinybs, r-cran-bsicons, r-cran-bslib, r-cran-config, r-cran-dplyr, r-cran-flextable, r-cran-glue, r-cran-golem, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-markdown, r-cran-officer, r-cran-pillar, r-cran-rclipboard, r-cran-readr, r-cran-rhandsontable, r-cran-rlang, r-cran-shiny, r-cran-shinyace, r-cran-shinyfiles, r-cran-shinyjs, r-cran-tidyr, r-cran-xfun Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmxcode_0.3.2-1.ca2404.1_all.deb Size: 871906 MD5sum: 953baadf8885d07d7a5ff88e308e296f SHA1: d4c128c3192fdaa0abe50e7e74f0a2cfc49368aa SHA256: 7aa5eca1c40d5804c288016dee90161fb8221698cb7fd94744d35bcad8de6814 SHA512: c8b7d49ba32f1e2222232185f0391ac14eef2d2fca55f4b87ab801be5fcb1f86289d1d2085c8dc79d0afe7ed28cc4fb57d8570a3b83fe70ab7ea11c0174c7e47 Homepage: https://cran.r-project.org/package=pmxcode Description: CRAN Package 'pmxcode' (Create Pharmacometric Models) Provides a user interface to create or modify pharmacometric models for various modeling and simulation software platforms. Package: r-cran-pmxcv Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmxcv_0.0.2-1.ca2404.1_all.deb Size: 28336 MD5sum: 74559f710c9e7e132f5f0a7d016e85c0 SHA1: 2753bad9ca968b18a20cb82c9727845ef2a8235c SHA256: 72e900e070ea8fdf5ea2b41ebd4441948ba1996702dbd1d12df32a39f21bf7f9 SHA512: c02a57fb18a4308cc38fa78f602c147a74a3b845e4525adad6044a2c7acdd788501b833e9ed4cd5d33a1203755202a2d9c3331aaf5c5ae4e6f177f580efa4b13 Homepage: https://cran.r-project.org/package=pmxcv Description: CRAN Package 'pmxcv' (Integration-Based Coefficients of Variation) Estimate coefficient of variation percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) . Package: r-cran-pmxnode Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyr, r-cran-ggplot2, r-cran-checkmate Suggests: r-cran-rxode2, r-cran-nlmixr2, r-cran-withr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmxnode_0.2.1-1.ca2404.1_all.deb Size: 1011180 MD5sum: 32524bd52517c4b4ac519b3c7a0732f2 SHA1: f88f8c858ef0ebb1c1c4c31917a9c664b77e2ee1 SHA256: f4f7434dc53b6ca9400a67fb29c9d6e075a749f13c277aade009c70edf006ceb SHA512: fd4d3126fd0e784d91f9ad70396a39c7b827ce915c90c84fe51cf63b8d30b9c177d0118276c59f2f77abb0ef5926706d739584ebf429f746bb53ecccff437f7e Homepage: https://cran.r-project.org/package=pmxNODE Description: CRAN Package 'pmxNODE' (Application of NODEs in 'Monolix', 'NONMEM', and 'nlmixr2') An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as 'Monolix', 'NONMEM', and 'nlmixr2', see Bräm et al. (2024) and Bräm et al. (2025) . The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with 'Monolix', 'pmxNODE' interfaces with 'MonolixSuite' via the 'lixoftConnectors' package. The 'lixoftConnectors' package is distributed with 'MonolixSuite' () and is not available from public repositories. Package: r-cran-pmxpartab Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-table1, r-cran-data.table, r-cran-htmltools, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-yaml, r-cran-linpk, r-cran-survival Filename: pool/dists/noble/main/r-cran-pmxpartab_0.5.0-1.ca2404.1_all.deb Size: 92730 MD5sum: 7f7e825a220a9552783d09a165fff777 SHA1: 8c9d5167f4787125e3f87b7d3b0f1c0ab9a0665e SHA256: e494d7e38596308f2958495db577b952c86f943bbfbcf8d058348e742a2de5fb SHA512: c7467b768be9fa681914a811fa88cc420ff347011153bffcddca21a2d87347a9430c884f21424f70583c59726f116e3cca1c5ddbd364c2c2170c35bd57db5f4b Homepage: https://cran.r-project.org/package=pmxpartab Description: CRAN Package 'pmxpartab' (Parameter Tables for PMx Analyses) Generate nicely formatted HTML tables to display estimation results for pharmacometric models. Package: r-cran-pmxtools Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1304 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-patchwork, r-cran-ggplot2, r-cran-chron, r-cran-xml2, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-ggdist, r-cran-scales, r-cran-mass, r-cran-stringr, r-cran-pknca, r-cran-magrittr, r-cran-data.tree Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmxtools_1.6-1.ca2404.1_all.deb Size: 1060750 MD5sum: ad8370f23359b35ebbbcd63d2bc28426 SHA1: 7b11af368546a31f21afe38475f22f6063b71620 SHA256: ea8da74ca6de958f7c5e62b4d7c565d413669119377806263c005ac6fc04d631 SHA512: d4fab69344bb9c09c94958c13b9665509017636597b9b463d5fbd8186009759dcd8d3f4321a0731ba2330db9935d95db9f0ae552d531557ddc649d4e443378fb Homepage: https://cran.r-project.org/package=pmxTools Description: CRAN Package 'pmxTools' (Pharmacometric and Pharmacokinetic Toolkit) Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) ); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN). Package: r-cran-pnadcibge Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-survey, r-cran-tibble, r-cran-timedate Suggests: r-cran-convey, r-cran-sipdibge, r-cran-srvyr Filename: pool/dists/noble/main/r-cran-pnadcibge_0.7.5-1.ca2404.1_all.deb Size: 216526 MD5sum: ff729fcf5a7076ad44e6087a1665b04e SHA1: 85a70f9ac8c1af40f06e7818fae3fbafb574603e SHA256: 35870d146fffe9f384e3a5cc229d546cc475ed763548df815bc806515f96811a SHA512: 3eda87c44095408d4663870c0438326519af5e3057d4c445545d557ea3515c3a947384aab30767a7a6eceb2146749ee9a120205b1982c0292d95bd4ec38d4a0c Homepage: https://cran.r-project.org/package=PNADcIBGE Description: CRAN Package 'PNADcIBGE' (Downloading, Reading and Analyzing PNADC Microdata) Provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-pnadcperiods Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-checkmate, r-cran-curl, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-dplyr, r-cran-fst, r-cran-haven, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-pnadcperiods_0.1.3-1.ca2404.1_all.deb Size: 2368578 MD5sum: f623633ef659565730bc148196121301 SHA1: de4b39a2f90ac0b4daca700ef6c1eb0ac0ec4899 SHA256: 258ca0ecc89b29c5f3dfce4144eda9e0fee25a1b18ca85830961031a1982899a SHA512: 0a33f73fb7e8e2df29c14c4331f9f63cda721f04be85905d93e274596f1fe25afd8467e56dd542aaa8dd9e4786d7e27dbf2f1b84b9edc9f3ee8b64dc3c71569d Homepage: https://cran.r-project.org/package=PNADCperiods Description: CRAN Package 'PNADCperiods' (Identify Reference Periods in Brazil's PNADC Survey Data) Identifies reference periods (months, fortnights, and weeks) in Brazil's quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios Continua) survey data and computes calibrated weights for sub-quarterly analysis. The core algorithm uses IBGE (Instituto Brasileiro de Geografia e Estatistica) 'Parada Tecnica' (technical break) rules combined with respondent birthdates to determine which temporal period each survey observation refers to. Period identification follows a nested hierarchy enforced by construction: fortnights require months, weeks require fortnights. Achieves approximately 97% monthly determination rate with the full series (2012-2025). Strict fortnight and week rates are approximately 9% and 3% respectively, as they cannot leverage cross-quarter panel aggregation. Experimental strategies (probabilistic assignment and UPA (Primary Sampling Unit) aggregation) further improve these determination rates. The package provides adaptive hierarchical weight calibration (4/2/1 cell levels for month/fortnight/week) with period-specific smoothing to produce survey weights calibrated to SIDRA (Sistema IBGE de Recuperacao Automatica) population totals. Also includes a SIDRA mensalization module that converts 86+ official rolling quarter series from the IBGE SIDRA API (Application Programming Interface) into exact monthly estimates, without requiring access to microdata. Hecksher and Barbosa (2026) . Package: r-cran-pnar Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-nloptr, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-pnar_1.8-1.ca2404.1_all.deb Size: 279332 MD5sum: 56ea7087538b8f3e0aa95062d37c8010 SHA1: a41f8a7fd07c23d0f24d8a98417a35e86ca19cff SHA256: 631ca7a40b4dd157c24969d73117f96d28da323264e932cbd2960a97e6ea0927 SHA512: d69a2a139426bb99847a4715689465865c86c11dd0c38ebe1a56eac3b07d55fa6e2686875683180bd55ad65ea4804e99be712eb3d57ecde90a3c66ac85d1d21b Homepage: https://cran.r-project.org/package=PNAR Description: CRAN Package 'PNAR' (Poisson Network Autoregressive Models) Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. . Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. . Armillotta, M., Tsagris, M. and Fokianos, K. (2023). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. . Package: r-cran-pnc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4585 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-geiger, r-cran-phytools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pnc_0.2.0-1.ca2404.1_all.deb Size: 4247274 MD5sum: 98b9c5d9385d4874d00c4c5ca2c79414 SHA1: 50c957f545ac6595ef246e7ad9fc53774a3263ef SHA256: c24b4f5921247360e6afdd7a6207b19c8fb0a792b4f90878df6024527c38bdbc SHA512: 192d1a3b1beed826b2c199e57e8badcc01f53d8c8b40c0fb029c8a24988d2bde10647880f6d647a3534e14cee64220bb7bd16f00f295d8e564d72e0788043b15 Homepage: https://cran.r-project.org/package=PNC Description: CRAN Package 'PNC' (Evaluating Phylogeny as a Proxy for Ecological Similarity) Provides a trait-based workflow for evaluating whether phylogenetic relatedness is informative about similarity in measured quantitative traits within focal species pools and across multiple communities. Functions support trait data integration, taxon-specific trait extraction, coverage assessment, optional principal component analysis, and estimation of phylogenetic signal using Pagel's lambda or Blomberg's K. Curated quantitative trait datasets are included for plants, birds, mammals, reptiles, amphibians, and fishes. Paired simulations assess how observed patterns of missing trait data affect Pagel's lambda estimates and significance classifications for individual traits. Methods for quantifying phylogenetic signal are based on Pagel (1999) , Blomberg et al. (2003) , and Münkemüller et al. (2012) . Package: r-cran-pnd.heter.cluster Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-superlearner, r-cran-ranger, r-cran-xgboost, r-cran-nnet, r-cran-origami, r-cran-boot, r-cran-tidyverse, r-cran-dplyr, r-cran-purrr, r-cran-magrittr, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pnd.heter.cluster_0.1.0-1.ca2404.1_all.deb Size: 97520 MD5sum: 980097edde1167dde1a7b11b3c289917 SHA1: e5b58e613988dfb47d9a34cad063b654693ed8d0 SHA256: 5cd0240f72a90e3b2c3793b5c87bb95e1518466a7b4e96945ed8c230017c4427 SHA512: 565bf81afef372ad237456f9bd160efde76529dbfa24e665daa0bd261721d76a1668a330bc88fc491c0390de6d9f1227e7bb5d563cd0c1999849251b35b30037 Homepage: https://cran.r-project.org/package=PND.heter.cluster Description: CRAN Package 'PND.heter.cluster' (Estimating the Cluster Specific Treatment Effects in PartiallyNested Designs) Implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) . The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering. Package: r-cran-pnd Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-numderiv, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pnd_0.1.3-1.ca2404.1_all.deb Size: 681276 MD5sum: e8cd9fc979b297267ca7ee4b9b49dace SHA1: 49fa424dc521abedb46a97ab3ec256ec95be3aeb SHA256: 4073190e4d586f118b6d5a1a57150a89edd98720be2176cd75b81d8b7831457a SHA512: 87adf5195256ef607a466a88bde63431ae1c784063b2d9e5e25468e71f6fc408e1ccd089de4128039ec0672c31f61d99d05ec82f971a4ae8e2c019a73835c9b0 Homepage: https://cran.r-project.org/package=pnd Description: CRAN Package 'pnd' (Parallel Numerical Derivatives, Gradients, Jacobians, andHessians of Arbitrary Accuracy Order) Numerical derivatives through finite-difference approximations can be calculated using the 'pnd' package with parallel capabilities and optimal step-size selection to improve accuracy. These functions facilitate efficient computation of derivatives, gradients, Jacobians, and Hessians, allowing for more evaluations to reduce the mathematical and machine errors. Designed for compatibility with the 'numDeriv' package, which has not received updates in several years, it introduces advanced features such as computing derivatives of arbitrary order, improving the accuracy of Hessian approximations by avoiding repeated differencing, and parallelising slow functions on Windows, Mac, and Linux. 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The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-pnt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3963 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pnt_0.1.0-1.ca2404.1_all.deb Size: 1000820 MD5sum: d77469ee0435700d9fbc552765d72ebf SHA1: 45184e0a162c03c5dffde1d61db5188a34b52386 SHA256: 2f8b3611af1fb060f5969c47ee4bad8ee12c026f63bffe07f452565af3f67758 SHA512: 25ddb355596cbd4ad448184284faff40c20d8ab59909a279cef0b6765b35bdedd04ca5dfa57dcb265211921a9bcf5dd3d4c91a51eb398854723e652b3482df89 Homepage: https://cran.r-project.org/package=PnT Description: CRAN Package 'PnT' (Peak Finder) This program contains a function to find the peaks and troughs of a data set. 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Then transforms this into an optimisation problem, allowing both Nash and Optimal flows to be solved by nonlinear optimisation. See and Knight and Harper (2013) for more information. 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Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user. Package: r-cran-pod Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pod_1.2.0-1.ca2404.1_all.deb Size: 172572 MD5sum: ab68b2206cd453054fd8da40fcf6cfaf SHA1: 75177f63f7feab980f23089e4dbe11ce4ba5691d SHA256: ace4a8cc2dc469a7db3de9b5c4d1ab6987bbfe9241b2e908b166d2c0eb1dc6a1 SHA512: c52f651385100d763c0d9430a8773a648e6c954279ce4847b100837614ebd1c6f6c968425e6179f36fb2fa62e8e05f69e3c00e9e1b12bea1a9fb00d7788549b6 Homepage: https://cran.r-project.org/package=POD Description: CRAN Package 'POD' (Probability of Detection for Qualitative PCR Methods) This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified. This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' . The modelling itself has been described by Uhlig et al. (2015) . 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Further attempts to match records from trades and general directories. Package: r-cran-podes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl Filename: pool/dists/noble/main/r-cran-podes_0.1.0-1.ca2404.1_all.deb Size: 212328 MD5sum: 74131ca81c397e96d5e4a9fba15b6227 SHA1: d91139b42f8e20f83b2879b2d1214f6ea51256f5 SHA256: 06c0a27ed15f191004222e1b5cd3c72f78c8a86053d41e004d649050180ba461 SHA512: 980552c27609c1682dd4adfa085544a0de6f828237448f9cf0e21e56c74c50dc7c5d33229c9891e07e0e30efeed10ba29e2268dcabb85991d8382efa261aeda2 Homepage: https://cran.r-project.org/package=PODES Description: CRAN Package 'PODES' (Village Potential Statistics of Indonesia) Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. 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Package: r-cran-poems Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abc, r-cran-doparallel, r-cran-foreach, r-cran-fossil, r-cran-lhs, r-cran-metrology, r-cran-r6, r-cran-raster, r-cran-trend, r-cran-truncnorm, r-cran-gdistance, r-cran-qs2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-scales, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-poems_1.4.0-1.ca2404.1_all.deb Size: 3737664 MD5sum: f6f9530015526e525078041d3b49c355 SHA1: fea52d888e57716767fc1741479b60f210784f01 SHA256: e093aa4c6d3f766f4bfdc401c1a0feb41e5c22a8304ccea595e58107cf814a6c SHA512: 28ac65b416aeca8ec79d066a36aa197a69998ce797b8c43f68d4b6487f93546084125c0bb841b0755edfe9a9ac3b1edbd6a7778711c3e96ffa0a2fd214a721dd Homepage: https://cran.r-project.org/package=poems Description: CRAN Package 'poems' (Pattern-Oriented Ensemble Modeling System) A framework of interoperable R6 classes (Chang, 2020, ) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, ). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, ); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, ), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes. 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Package: r-cran-pogit Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-logistf, r-cran-plyr Suggests: r-cran-count Filename: pool/dists/noble/main/r-cran-pogit_1.3.0-1.ca2404.1_all.deb Size: 338566 MD5sum: 50cc09689ab0a2662ae548b6138f081b SHA1: 91900b43b379f761ba6d4d6f7fcb860b3f7fcc62 SHA256: 02c2da751775fdb0eab3bf3faf197e1c30745b08372413feb7b992298ee7b800 SHA512: 4b4d0344a669cfec89f5123c4a3dcc554f76789425d5680cf8db22a548e3ef5c88bf661836c2e9e2a4f4ea9df69640ffc4fc50b82cbd154870bc0231119740d7 Homepage: https://cran.r-project.org/package=pogit Description: CRAN Package 'pogit' (Bayesian Variable Selection for a Poisson-Logistic Model) Bayesian variable selection for regression models of under-reported count data as well as for (overdispersed) Poisson, negative binomal and binomial logit regression models using spike and slab priors. Package: r-cran-pogromcydanych Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-smarterpoland Filename: pool/dists/noble/main/r-cran-pogromcydanych_1.7.1-1.ca2404.1_all.deb Size: 4205474 MD5sum: 33b09e165dbbb2180ac514c731c69575 SHA1: 1500be130204e09a8880c829225e167fe601109c SHA256: bfae51cb92ae5afc70e0d673a1f3511cc575661c7506c6f03310ac1583aa9156 SHA512: a7c85588065f4a8934cd426512e68fddacb45912fd3c11297fca0a90ed0af14ee457075ed24f59376af688cf1f4f240de55fc6f6f39ec5e5f68ea3c574ec0b42 Homepage: https://cran.r-project.org/package=PogromcyDanych Description: CRAN Package 'PogromcyDanych' (DataCrunchers (PogromcyDanych) is the Massive Online Open Coursethat Brings R and Statistics to the People) The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment. Package: r-cran-poiclaclu Architecture: all Version: 1.0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poiclaclu_1.0.2.1-1.ca2404.1_all.deb Size: 62312 MD5sum: 90906e7ddc64aa0828f6cf0d87663802 SHA1: 728ff76d524182a74119fb3ae2abe767d074a0e7 SHA256: 5abebbda10272a9b074c56be0ca3493daf9c2f5e6375977a83841292dc38a489 SHA512: 9e3cc88bc1debffb9db9b406cae34f8e7f4ffc9042d8f38afa4f472ce80e2f16a062da4f5ac5fc0695b95c71b6ac488a7d6362ff00a1accb5c9c7be8fa67595c Homepage: https://cran.r-project.org/package=PoiClaClu Description: CRAN Package 'PoiClaClu' (Classification and Clustering of Sequencing Data Based on aPoisson Model) Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518. Package: r-cran-point Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rarpack, r-cran-matrix, r-cran-compquadform Filename: pool/dists/noble/main/r-cran-point_1.4-1.ca2404.1_all.deb Size: 58918 MD5sum: 25284bc282d58263dd37e3074a35c26e SHA1: 67cff5b95f5cd1246a5daffede6cd30a7053e03a SHA256: 7647249ed57b91dc62248af9dc9f8048c76f513bff15a1ead696a33743af4f2e SHA512: 6c6f2fc2d7e16128d1ce2bb362763106645c6730961d64bfbe999f660e62dedb0d24c19cf593db76804ee89faa01a302c438b2d14cfceb4d82135b59a1e3355e Homepage: https://cran.r-project.org/package=POINT Description: CRAN Package 'POINT' (Protein Structure Guided Local Test) Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) . Package: r-cran-pointblank Architecture: all Version: 0.12.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3745 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-blastula, r-cran-cli, r-cran-dbi, r-cran-digest, r-cran-dplyr, r-cran-dbplyr, r-cran-fs, r-cran-glue, r-cran-gt, r-cran-htmltools, r-cran-knitr, r-cran-rlang, r-cran-magrittr, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-yaml Suggests: r-cran-arrow, r-cran-bigrquery, r-cran-data.table, r-cran-duckdb, r-cran-ggforce, r-cran-ggplot2, r-cran-jsonlite, r-cran-lubridate, r-cran-rsqlite, r-cran-rmysql, r-cran-rpostgres, r-cran-readr, r-cran-rmarkdown, r-cran-sparklyr, r-cran-dittodb, r-cran-odbc Filename: pool/dists/noble/main/r-cran-pointblank_0.12.4-1.ca2404.1_all.deb Size: 3003004 MD5sum: 0de9cf2dcb5c0eacbd34d42d837465b6 SHA1: bb683c000eaaf3a338b28ddb25064d877dda1548 SHA256: 0f8ae568ddbd31eaa9e3b976a18b11ddff4f2a331041e6f20e7eabe37564317f SHA512: 77b38c45bd2a567c29f59abeff399b28097b0b90e1b5e68b6226050e5ff47d80e4b1a6e8efb3b6f72ea648f2d5a7d17690da361e351a4a7f6cc2a8a02ad80d4b Homepage: https://cran.r-project.org/package=pointblank Description: CRAN Package 'pointblank' (Data Validation and Organization of Metadata for Local andRemote Tables) Validate data in data frames, 'tibble' objects, 'Spark' 'DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables. Package: r-cran-pointcoral Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-janitor, r-cran-magick, r-cran-png, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-devtools, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-styler, r-cran-testthat, r-cran-tidyr, r-cran-writexl Filename: pool/dists/noble/main/r-cran-pointcoral_0.1.0-1.ca2404.1_all.deb Size: 2222494 MD5sum: 9fce7fca988c5e0253dcafa09fd0bef1 SHA1: dbbd939fd9ddd7c19ff9b29d7b99d3724f1fb9b7 SHA256: 3a4c086bdd074e03e53ddf3c7c9e8a58d28a39ff47e8072566f464d3cb87f357 SHA512: d6e3701e5940b0d4bc11e0857766621d1d947994e1cbe4724fe1eed0197edfcf908825eb81a123167866a2fc7ad5c8ee145abb787bf6f9244b9d7478eeb1f44d Homepage: https://cran.r-project.org/package=pointcoral Description: CRAN Package 'pointcoral' (Local Point-Count Processing for Coral Photoquadrats) Imports Coral Point Count with Excel extensions (CPCe) point-count annotations and related exported tables, standardizes labels with a user-supplied crosswalk, creates ecological cover summaries, writes quality-control overlays, and exports machine-learning-ready point labels, image patches, sparse masks, and train/validation/test splits. The package is fully local and does not depend on third-party web platforms, user accounts, or other closed services. CPCe methods are described by Kohler and Gill (2006) "Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology" . Package: r-cran-pointdensityp Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3424 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pointdensityp_0.3.5-1.ca2404.1_all.deb Size: 3361128 MD5sum: 0d86b1c36435d81506b11bf2754d55f8 SHA1: e7e724dfa053e9b45390035345f9c928cc2f69ce SHA256: 3cfccfb1796a8def497feb8b945ceecc3f8cb408abb7429f7dd268d16ff19e80 SHA512: 3525597a10c6f4e2a524ecd042bf55de700a7a0d8b3e36838589c9d7a2fa296073c51e0aa2967bf91be2446e804f6c69740af4d56509b6dcf9e1489118fd4a61 Homepage: https://cran.r-project.org/package=pointdensityP Description: CRAN Package 'pointdensityP' (Point Density for Geospatial Data) The function pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a radius of radius*grid_size (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood. 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Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them. There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions. An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) . Package: r-cran-pointfore Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gmm, r-cran-boot, r-cran-ggplot2, r-cran-mass, r-cran-sandwich Suggests: r-cran-car, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pointfore_0.2.1-1.ca2404.1_all.deb Size: 164542 MD5sum: b99e16a20dd6dcabacb472b5842aa721 SHA1: 047b0cd9db191ce1ab6c77e1d40237884c369455 SHA256: d190e8d23e982db09cf94e5579a8e0657d5d883287c105050ba3a90932b18c7b SHA512: 496b86e8c03dd20bf51eb0b9e5c7e8d9a6fb7005ab29377318b8f0929dd5eef8b2ffb78161a40344ee359dc3ab27c6b5848bd4bd1eb7717799910d1b63be6c4f Homepage: https://cran.r-project.org/package=PointFore Description: CRAN Package 'PointFore' (Interpretation of Point Forecasts as State-Dependent Quantilesand Expectiles) Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast. 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See for additional documentation and examples. Package: r-cran-polisher Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-nailer, r-cran-shiny, r-cran-shinycssloaders, r-cran-factominer, r-cran-stringr Suggests: r-cran-sensominer Filename: pool/dists/noble/main/r-cran-polisher_1.1.1-1.ca2404.1_all.deb Size: 52380 MD5sum: 628a774f08513de29a9f10d86cd1d2e0 SHA1: 91290d9eea0f168c73b534f1a0d2580d3b6f7ef4 SHA256: 687f1604a93d322c85fdff7b84b020b8d80a1d4add5428dd0527a78fedf8bf2c SHA512: cd879431812fd04eadcb9da75bdd530134bf4a1f4b1eab0570c8211b79ca544945f054d6160599b68ac85eec1a117547ab978fe88c618c7e616132ada532d9ad Homepage: https://cran.r-project.org/package=PolisheR Description: CRAN Package 'PolisheR' (Interfacing 'NaileR' with 'Shiny') A very small package for more convenient use of 'NaileR'. You provide a data set containing a latent variable you want to understand. It generates a description and an interpretation of this latent variable using a Large Language Model. For perceptual data, it describes the stimuli used in the experiment. Package: r-cran-polite Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-magrittr, r-cran-memoise, r-cran-ratelimitr, r-cran-robotstxt, r-cran-rvest, r-cran-usethis Suggests: r-cran-dplyr, r-cran-testthat, r-cran-covr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-polite_0.1.4-1.ca2404.1_all.deb Size: 148648 MD5sum: 1dd64a2424ad2a5db190a35cae61cd51 SHA1: d926d32e52c3457052259ef7076da60f55b91376 SHA256: eb99c3c0b9abd5559f91f678f617bb5547d18af00c7b5f3afc68a0bba4f2018c SHA512: e1963d97145c65067e9d91646ced78d5d7afc48bb157e3c5165ecbdc77533e8927b67ca314ef29900c8f00dab705cb6ea98bcd2803b53158bab3e95c0fc42a5f Homepage: https://cran.r-project.org/package=polite Description: CRAN Package 'polite' (Be Nice on the Web) Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice. Package: r-cran-politeness Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tm, r-cran-quanteda, r-cran-ggplot2, r-cran-spacyr, r-cran-textir, r-cran-glmnet, r-cran-data.table, r-cran-stringr, r-cran-stringi, r-cran-magrittr, r-cran-dplyr, r-cran-ggrepel, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-politeness_0.9.4-1.ca2404.1_all.deb Size: 925054 MD5sum: c3634337136d48a807d4d69288bb6b44 SHA1: 6eb2083158028d38e551e77444a1a3f23eca94c8 SHA256: 5102ff5108529ab9391ca6f4e57262741296e52f1706f00b01a9e8ff94596942 SHA512: dcc59874b80616ce715a7d4abc8ea83f7221f696d5059b639925c42cc3dc48c73bd722814ebddca001891d8445be3c9a290584fc095bd4c7b41dd1d45ec2706e Homepage: https://cran.r-project.org/package=politeness Description: CRAN Package 'politeness' (Detecting Politeness Features in Text) Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support. Package: r-cran-politicsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq Filename: pool/dists/noble/main/r-cran-politicsr_0.1.0-1.ca2404.1_all.deb Size: 260854 MD5sum: ffed887429102ba923acb63c84af61de SHA1: dd83eb90c8d50678cd027d2048c9d448aeb45fce SHA256: e7554abb6700d64476c0a980fe5fa1d6fbc90204848cc86c3595091e188deb0e SHA512: f5a0a1bd5f855f75766d0e8b312615c7a6f4f7c8211a0f7c331fd7ede5e4f34b6eaf46ed6ca967fe0b36ca95197a52892e7a18bc4d15cb6ed5a25160aa28b36c Homepage: https://cran.r-project.org/package=politicsR Description: CRAN Package 'politicsR' (Calculating Political System Metrics) A toolbox to facilitate the calculation of political system indicators for researchers. This package offers a variety of basic indicators related to electoral systems, party systems, elections, and parliamentary studies, as well as others. Main references are: Loosemore and Hanby (1971) ; Gallagher (1991) ; Laakso and Taagepera (1979) ; Rae (1968) ; Hirschmaņ (1945) ; Kesselman (1966) ; Jones and Mainwaring (2003) ; Rice (1925) ; Pedersen (1979) ; SANTOS (2002) . Package: r-cran-polle Architecture: all Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-lava, r-cran-future.apply, r-cran-progressr, r-cran-policytree, r-cran-survival, r-cran-targeted, r-cran-dyntxregime Suggests: r-cran-dtrlearn2, r-cran-glmnet, r-cran-mets, r-cran-mgcv, r-cran-xgboost, r-cran-knitr, r-cran-ranger, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-polle_1.6.6-1.ca2404.1_all.deb Size: 774566 MD5sum: f5ddb89d301d8c1dca1d2440956cbc7e SHA1: 0bf51b52dc39bdb25356b93826947a282d9460fc SHA256: ab5b6089163cb01a1296032a9a47dd8aff965c5e36133063715431eabf08dfbd SHA512: 5e6d4d29043e3ecba381ee59b609b2d8bd7296bd85666808d98de9cd26056a64168c291b6c1cf455f509bfe647e2742fea91de828013b2fb305653df5f551c59 Homepage: https://cran.r-project.org/package=polle Description: CRAN Package 'polle' (Policy Learning) Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2026) for documentation and references. Package: r-cran-pollen Architecture: all Version: 0.83.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-purrr, r-cran-dplyr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-tidyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pollen_0.83.0-1.ca2404.1_all.deb Size: 137628 MD5sum: 38b436d529df2bc25933d27eca31c7f4 SHA1: 88a9d61ce3c5a34cef190df0e4770512628ecef0 SHA256: 1352a3c64911dec73da8fb9a5dabe85250bc584471f3e40f65e9a8fa3cf5b947 SHA512: 52f13cb035167d90df8b407936341926a82340644486f171e5f111398d9de0d17c770784e32d8432bb132a037a9fd8ab979309c29e4454406ec28d7fb1a6a7f8 Homepage: https://cran.r-project.org/package=pollen Description: CRAN Package 'pollen' (Analysis of Aerobiological Data) Supports analysis of aerobiological data. 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This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018) . Package: r-cran-pollster Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-labelled, r-cran-forcats, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pollster_0.1.7-1.ca2404.1_all.deb Size: 516346 MD5sum: a03a447ab412ea4ff1860083db00bd06 SHA1: 576c5fb86c7b6320f54e2903f0a62e9c42caf6ec SHA256: 2c2ebfd26e305b0876d9c5e0b8e0596937c0185ce9ab1032a977fea72a2cedee SHA512: e10c2ef06ed365f9908838d16e21497f1b61aca9e31b88ddcd9416758e6b295258efaf8a39f9f97c741bcdc4d2935102044edf0fc3bd1f05d74d0b596788f152 Homepage: https://cran.r-project.org/package=pollster Description: CRAN Package 'pollster' (Calculate Crosstab and Topline Tables of Weighted Survey Data) Calculate common types of tables for weighted survey data. Options include topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as well as summary tables of weighted numeric variables. Optionally, include the margin of error at selected confidence intervals including the design effect. The design effect is calculated as described by Kish (1965) beginning on page 257. Output takes the form of tibbles (simple data frames). This package conveniently handles labelled data, such as that commonly used by 'Stata' and 'SPSS.' Complex survey design is not supported at this time. 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The package offers functionality to flexibly create subcorpora and to carry out basic statistical operations (count, co-occurrences etc.). The original full text of documents can be reconstructed and inspected at any time. Beyond that, the package is intended to serve as an interface to packages implementing advanced statistical procedures. Respective data structures (document-term matrices, term-co-occurrence matrices etc.) can be created based on the indexed corpora. Package: r-cran-poly4at Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-sf, r-cran-leaflet, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-shinydashboard, r-cran-dt, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-poly4at_1.0.2-1.ca2404.1_all.deb Size: 1455944 MD5sum: ec98f89e40297a1578f6081a40e30f73 SHA1: 41c272151189f9ed684e1c5db82e34a91221b4a0 SHA256: 13911b9cc85ff9d55bc6591a83fef977af828eaa55ccd49c6f546dc4e39adee3 SHA512: 0a211933f11bccd44b3a5fe04fc1dfed1556bc28f592eb820fb2e47755b174d096482af8e261ae307df97cac4a1dfa36b29995369462c0f568b76f9b60519106 Homepage: https://cran.r-project.org/package=Poly4AT Description: CRAN Package 'Poly4AT' (Access 'INVEKOS' API for Field Polygons) A 'shiny' app that allows to access and use the 'INVEKOS' API for field polygons in Austria. 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A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) . " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations. 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Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data. Submitted to BMC Bioinformatics (2021). 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Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) . Since version 1.1.0, both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) . 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The algorithm is described in Nattino, Song and Lu (2022) . 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It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations. This package includes 3 methods of triangularization of polynomial matrices: Extended Euclidean algorithm which is most classical but numerically unstable; Sylvester algorithm based on LQ decomposition; Interpolation algorithm is based on LQ decomposition and Newton interpolation. Both methods are described in D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization, IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) and in Salah Labhalla, Henri Lombardi & Roger Marlin, Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) . Package: r-cran-polymigr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-polymigr_0.1.0-1.ca2404.1_all.deb Size: 26922 MD5sum: df82d46987aa1b7c3ab31b9652094da1 SHA1: 95a454dd49a70d570d05e757b4280b1c84f9041f SHA256: a2a59f87e06f8be133df0ddaddf3597abc964af0027e7c0427b9383cd012fdb1 SHA512: 60f63701269aeb237e9c32b93ff32ff4c71c504a01ee216fb467642a996833fecddec6a6882ef8dc56dcf634931e0bba7b773a24be1b467c0ab2a88462e9e178 Homepage: https://cran.r-project.org/package=PolyMigR Description: CRAN Package 'PolyMigR' (Analysis of Polyphenol Migration from Packaging Films) The gradual release of active substances from packaging can enhance food preservation by maintaining high concentrations of polyphenols and antioxidants for a period of 72 hrs. To assess the effectiveness of packaging materials that serve as carriers for antioxidants, it is crucial to model the diffusivity of the active agents. Understanding this diffusivity helps evaluate the packaging's capacity to prolong the shelf life of food items. The process of migration, which encompasses diffusion, dissolution, and reaching equilibrium, facilitates the transfer of low molecular weight compounds from the packaging into food simulants. The rate at which these active compounds are released from the packaging is typically analysed using food simulants under conditions outlined in European food packaging regulations (Ramos et al., 2014). 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Package: r-cran-polypatex Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-polypatex_0.9.2-1.ca2404.1_all.deb Size: 484984 MD5sum: 99ffafce049a532146fb532f8b3891e9 SHA1: 1540d1d8564eb98f40f0aa3cbab2d7cca8d87ae6 SHA256: 867fe732e682386929dbb129a7496e244bd091a71294a174f14a5a37d4a4fc1a SHA512: 81170f43750a4a2b00d1504511e72f558658ab0d310930ba91cdf1cac644edf459cb18cdc5a89850691b301456f888c13ec696a026f88c7d2e8f65ae28c3b1c7 Homepage: https://cran.r-project.org/package=PolyPatEx Description: CRAN Package 'PolyPatEx' (Paternity Exclusion in Autopolyploid Species) Functions to perform paternity exclusion via allele matching, in autopolyploid species having ploidy 4, 6, or 8. The marker data used can be genotype data (copy numbers known) or 'allelic phenotype data' (copy numbers not known). Package: r-cran-polypharmacy Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-itertools, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-polypharmacy_1.0.0-1.ca2404.1_all.deb Size: 230448 MD5sum: 5a1a44019f664506e8b4cf9d31944c05 SHA1: 53812fa8ce814459466739a9f5cfc183948640b5 SHA256: 3e325e801e327f4e749cd31bccc3414db8bbaa3ffea8bfcd206242aa63b622a6 SHA512: 027531dcb610f8d15a7c1a6ed7bee894909ed5ab29ddae107871e70b5d6a6747aa5bcfc03366b080b26c517f1588adf5ed2844f3e637db2cce77b8e2231d766b Homepage: https://cran.r-project.org/package=polypharmacy Description: CRAN Package 'polypharmacy' (Calculate Several Polypharmacy Indicators) Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature. Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) . Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) . Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) . Hovstadius, B., Astrand, B., & Petersson, G. (2009) . Hovstadius, B., Astrand, B., & Petersson, G. (2010) . Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) . Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) . Narayan, S. W., & Nishtala, P. S. (2015) . Nishtala, P. S., & Salahudeen, M. S. (2015) . Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) . Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) . 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Package: r-cran-pomade Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-future, r-cran-furrr, r-cran-stringr, r-cran-tidyr, r-cran-tibble Suggests: r-cran-covr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pomade_0.2.1-1.ca2404.1_all.deb Size: 412652 MD5sum: 18d8c3cf583fba301d88739c9ec0e019 SHA1: 77c5f66a915bcdf217066940f130405a6a241624 SHA256: 34565515ff8c670428f184b137448b6c0db302222b45613b1a71409647506b13 SHA512: b1d997d95b599c069ca1955ea24995b61dc6afc632d5b1ebdaf60da4c92f859ebd614e59f53206e1223e1e52a0ae10c24ba42f8e853819059c836289c56aae42 Homepage: https://cran.r-project.org/package=POMADE Description: CRAN Package 'POMADE' (Power for Meta-Analysis of Dependent Effects) Provides functions to compute and plot power levels, minimum detectable effect sizes, and minimum required sample sizes for the test of the overall average effect size in meta-analysis of dependent effect sizes. Package: r-cran-pomcheckr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-assertthat, r-cran-rlang, r-cran-magrittr, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pomcheckr_0.1.1-1.ca2404.1_all.deb Size: 266408 MD5sum: f5b325d0a88befa6241eb311eb04ee0a SHA1: 6538ba3db879654e0b52f2ca635f2fab56297639 SHA256: 0b9ef496e47b9d3401384a08fab3ebd6f13d95f178c96a89758f4259981c9e55 SHA512: 96132af3c1d3235af07cbe61bb51f7e5c26c7040ea669589a34496ebe7639c0ea8be91d0104a9562b6ea5ca507b2bf856f9f41423d86e8694f3611c8c0e01ad1 Homepage: https://cran.r-project.org/package=pomcheckr Description: CRAN Package 'pomcheckr' (Graphical Check for Proportional Odds Assumption) Implements the method described at the UCLA Statistical Consulting site for checking if the proportional odds assumption holds for a cumulative logit model. Package: r-cran-pomic Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pomic_1.0.4-1.ca2404.1_all.deb Size: 40260 MD5sum: ced6756e653612a0794f69fb625d2002 SHA1: 4963d09e878083a4d383f2251a76c295ccac9c5a SHA256: 60b3df7619979d193d31f0847c160fb23389510f00df816847ffe4282b34858a SHA512: 76f075a8d7763c9bb7391dfa06fd6ed113d930d642f365b1bb49e1c3db674b40c4081321df9a394192f523b6aa9d32b839ffbbfb4c323174554fdb99c99cc737 Homepage: https://cran.r-project.org/package=Pomic Description: CRAN Package 'Pomic' (Pattern Oriented Modelling Information Criterion) Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) . Package: r-cran-pomodoro Architecture: all Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-caret, r-cran-gbm, r-cran-randomforest, r-cran-proc, r-cran-ipred Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pomodoro_3.8.0-1.ca2404.1_all.deb Size: 803710 MD5sum: e89c23800883ea1061426c74295499b2 SHA1: 80fb57b89af9fc25edae6e83d92d162d41e1d860 SHA256: 92e532eb163c52181fc04a329e8e88fc9682493eead833e9a6e512aad48d1584 SHA512: 4b841c7efd4421345a413892fe2cbf47a8fe8c247516c6cb8c2718fa9614e745c3f0ca5c6a9bd657c1416d9cafa38f0a58bd831cc77d6cf5a8a611cbd46fc805 Homepage: https://cran.r-project.org/package=pomodoro Description: CRAN Package 'pomodoro' (Predictive Power of Linear and Tree Modeling) Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits. Package: r-cran-pompom Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-reshape2, r-cran-qgraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pompom_0.2.1-1.ca2404.1_all.deb Size: 1273720 MD5sum: 3efb9b9baed4aa970571d28fc90fbb82 SHA1: 61bbf599d75df9bbb86c29c387cb7d248a95d843 SHA256: 826cb53c88c78dff25ed8ae4b80662301e9cdcd3ed7ce15d37d1c40823ffa24d SHA512: 2e9fbd4c6b6e507774d06c6b60b584a01207cbd783811ec41170fba7d2011d324868736c50ca2f140f66273723f830f7383b2de6471793de44c4d0d8f6dd9c3e Homepage: https://cran.r-project.org/package=pompom Description: CRAN Package 'pompom' (Person-Oriented Method and Perturbation on the Model) An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) . Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) . Gates, K. M., & Molenaar, P. C. M. (2012) . Lutkepohl, H. (2007, ISBN:3540262393). Package: r-cran-poms Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-mass, r-cran-phangorn, r-cran-phylolm, r-cran-xnomial Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-poms_1.0.1-1.ca2404.1_all.deb Size: 109960 MD5sum: 6408cab606bb8344315df007599bc5c7 SHA1: 2b01ad97a5224feb04ed1392aaa328786be38dd8 SHA256: c3504a61308b27ceb6d6e5165bfb3db016f248ee30471cfe5f58e553b2193116 SHA512: 9244030f66438113f735e5f739091220769cf6b035c3388fa607b5613d72fd8ea83fe77e02de9a8365b66d7b8820b52327165eb738adea9484b34fe046ed5187 Homepage: https://cran.r-project.org/package=POMS Description: CRAN Package 'POMS' (Phylogenetic Organization of Metagenomic Signals) Code to identify functional enrichments across diverse taxa in phylogenetic tree, particularly where these taxa differ in abundance across samples in a non-random pattern. The motivation for this approach is to identify microbial functions encoded by diverse taxa that are at higher abundance in certain samples compared to others, which could indicate that such functions are broadly adaptive under certain conditions. See 'GitHub' repository for tutorial and examples: . Citation: Gavin M. Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022) . Package: r-cran-poobly Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plm Suggests: r-cran-rfast, r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-poobly_0.1.2-1.ca2404.1_all.deb Size: 263340 MD5sum: 92b0403e64f7f8e768f6a17e34f326e2 SHA1: 772ba31f3a0df6ae38066f486e55ce0ddad9af67 SHA256: 18f6c0cfd699c6da2d1ee1dc49855049bb4ba568a8b76d710e48b813f5aadc20 SHA512: 8113e6c324758365995f9a553e99f6761295d3e9582f4f3ccd90995a8152589f77dc8b81598f560f55fd83a0bad7bced5778e26f1ba6f71ca98fce07c68014a8 Homepage: https://cran.r-project.org/package=poobly Description: CRAN Package 'poobly' (Poolability Tests in Panel Data) Homogeneity tests of the coefficients in panel data. Currently, only the Hsiao test for determining coefficient homogeneity between the panel data individuals is implemented, as described in Hsiao (2022), "Analysis of Panel Data" (). 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Implements two ABC algorithms for performing parameter estimation and model selection using Pool-seq data. Cross-validation can also be performed to assess the accuracy of ABC estimates and model choice. Carvalho et al., (2022) . Package: r-cran-poolbal Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1170 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poolbal_0.1-0-1.ca2404.1_all.deb Size: 1135502 MD5sum: 013f00067fba75052d1217af9da737cc SHA1: 4a4660cd247e6823dfe3cf0fd79ad5aa1699dfd4 SHA256: 9ce6070c6862287b23b54486c966982804166011162baedc50d4482d0ad3a572 SHA512: 906bcb1a61b521a46e20130de827a402e80288ed324b7d53eb0f39043f59a7e21b6863dc151412969af05a616c32c4b826e12ee83b3b21811a8ca774318867df Homepage: https://cran.r-project.org/package=PoolBal Description: CRAN Package 'PoolBal' (Balancing Central and Marginal Rejection of Pooled p-Values) When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) . This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) to control the quotient's value. Package: r-cran-pooldilutionr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-pooldilutionr_1.0.0-1.ca2404.1_all.deb Size: 105792 MD5sum: f5be91b1af426e1af13fbd64e54084f6 SHA1: d61733ab86961f2e4722f0953b19f1529c15b872 SHA256: fdceddd16820eac2d82798cd9693cfdfe4b69ef4eea9eb5ef1ae5c5c65a57262 SHA512: 15e974e02ad4d7b6869bbd10047958d03e92ea9f69c7cabe8e0a7f67d2dd9fa6aac6beffac4fda2d8fe8a0bc45ba709d62d7e68076199a6bb16f264089db515c Homepage: https://cran.r-project.org/package=PoolDilutionR Description: CRAN Package 'PoolDilutionR' (Calculate Gross Biogeochemical Flux Rates from Isotope PoolDilution Data) Pool dilution is a isotope tracer technique wherein a biogeochemical pool is artifically enriched with its heavy isotopologue and the gross productive and consumptive fluxes of that pool are quantified by the change in pool size and isotopic composition over time. This package calculates gross production and consumption rates from closed-system isotopic pool dilution time series data. Pool size concentrations and heavy isotope (e.g., 15N) content are measured over time and the model optimizes production rate (P) and the first order rate constant (k) by minimizing error in the model-predicted total pool size, as well as the isotopic signature. The model optimizes rates by weighting information against the signal:noise ratio of concentration and heavy- isotope signatures using measurement precision as well as the magnitude of change over time. The calculations used here are based on von Fischer and Hedin (2002) with some modifications. Package: r-cran-pooledcohort Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-pooledcohort_0.0.2-1.ca2404.1_all.deb Size: 85214 MD5sum: caa1f0788d7efa2329cd58e2ece747e9 SHA1: 80209822dbf263138365e31400510e55bd36e0e1 SHA256: ba1315177ed92e650bc49c2a454eb9c8de3b137899532cdec654b38f0b416c1c SHA512: 8fd14c1df3a1b472969347a28600e6d92324bda0a78413d0daac864c252ff823957cea0331c528dbf75f826d012cf1f186ffabae372316d51ac45004c98529a4 Homepage: https://cran.r-project.org/package=PooledCohort Description: CRAN Package 'PooledCohort' (Predicted Risk for CVD using Pooled Cohort Equations, PREVENTEquations, and Other Contemporary CVD Risk Calculators) The 2017 American College of Cardiology and American Heart Association blood pressure guideline recommends using 10-year predicted atherosclerotic cardiovascular disease risk to guide the decision to initiate or intensify antihypertensive medication. The guideline recommends using the Pooled Cohort risk prediction equations to predict 10-year atherosclerotic cardiovascular disease risk. This package implements the original Pooled Cohort risk prediction equations and also incorporates updated versions based on more contemporary data and statistical methods. Package: r-cran-pooledmeangroup Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pooledmeangroup_1.0-1.ca2404.1_all.deb Size: 90778 MD5sum: 77fbdd0e2db45a25ceff628b31278d7b SHA1: 5a0b95e29134be07a9578b26ea8d9f651e79b0a8 SHA256: c852803f446bc1045751eb3d1517f035693403f4047e2941d34d3578977c641f SHA512: 8a626d85a9cdad11fbd4204eb2c493b047da57909085598a29cc500f7c666e2b3a895dfe7395d8050b40238f3661171d3426ddcfb0ee4facb63457efe2e28a56 Homepage: https://cran.r-project.org/package=PooledMeanGroup Description: CRAN Package 'PooledMeanGroup' (Pooled Mean Group Estimation of Dynamic Heterogenous Panels) Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) . Package: r-cran-pooledpeaks Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-fragman, r-cran-magrittr, r-cran-pdftools, r-cran-qpdf, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-plyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pooledpeaks_1.2.2-1.ca2404.1_all.deb Size: 1826432 MD5sum: d6363b2f953211a1b4698c4c0c63bb81 SHA1: f05a0bfa03a6ab7b5be648d905e78b9e70caa356 SHA256: c89fcaf5e2ca69e02e067bd30ad3c0d1e246b8336d8c6caad9c926f43690837c SHA512: f9fc0b7d3a60b010ca3eb1bf1a48520ada55cef1877f7b29aa744da4b97563dbf52bee2d4e41a76d21fb49d817eaf241be31867782f7a5be10f1ca17b5d38070 Homepage: https://cran.r-project.org/package=pooledpeaks Description: CRAN Package 'pooledpeaks' (Genetic Analysis of Pooled Samples) Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) , Jost (2008) , Nei (1973) , Foulley et al. (2006) , Chao et al. (2008) . Package: r-cran-poolhelper Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-metrics, r-cran-scrm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-poolhelper_1.1.0-1.ca2404.1_all.deb Size: 311626 MD5sum: ac4bf70a7bf9c2ab74ae16e11d2405a0 SHA1: 322891986827762b9bd47ed1e5b9239b5cf0870b SHA256: 230ed6753a6380932120ec65b347402ecfbea263dc656eb8242d2ccbbc085127 SHA512: 3301dea4349536b7a2d8bdde3885e2fabb1d482c5bc0d6ba86b17521e809bb1e89900b6509dfda5278088eaf94e04e9a6f417f305900ab5c573b17a74d1e3b54 Homepage: https://cran.r-project.org/package=poolHelper Description: CRAN Package 'poolHelper' (Simulates Pooled Sequencing Genetic Data) Simulates pooled sequencing data under a variety of conditions. Also allows for the evaluation of the average absolute difference between allele frequencies computed from genotypes and those computed from pooled data. Carvalho et al., (2022) . 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Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit . 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Method described in Ochoa and Storey (2021) . 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(2) Two models of the data: (a) a self organizing map model, (b) a centroid based clustering model. (3) A number of easily accessible quality metrics, Hamel (2016) . 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Search, download and forecast time-series from the Ministry of Economy of Argentina. Forecasts are built with the 'forecast' package, Hyndman RJ, Khandakar Y (2008) . 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While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) , Rockafellar and Uryasev (2001) and Markowitz (1952) . 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See Chapter 8 (Portfolio Backtesting) of the book: Daniel P. Palomar, "Portfolio Optimization: Theory and Application", Cambridge University Press, 2025. 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Functions map physiological and operative variables to their component scores and return the predicted probabilities of morbidity and mortality from the published logistic equations. The coefficients follow Copeland and others (1991) for POSSUM, Prytherch and others (1998) for P-POSSUM, Tekkis and others (2004) for the colorectal variant (CR-POSSUM), and Neary and others (2003) for the vascular variant (V-POSSUM). The package is intended for audit and research; it is not a validated medical device and must not be used as the sole basis for clinical decisions. 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The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" . Package: r-cran-postggir Architecture: all Version: 2.4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1691 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-refund, r-cran-denseflmm, r-cran-dplyr, r-cran-xlsx, r-cran-survival, r-cran-tidyr, r-cran-zoo, r-cran-ineq, r-cran-cosinor, r-cran-cosinor2, r-cran-abind, r-cran-accelerometry, r-cran-actcr, r-cran-actfrag, r-cran-minpack.lm, r-cran-kableextra, r-cran-ggir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-postggir_2.4.0.2-1.ca2404.1_all.deb Size: 1016252 MD5sum: e23388de405e183d9bb2267d3ec68b94 SHA1: f1349fb0f4a00ab7494567baff3ea98ab7924c6a SHA256: 3ca631a04cc3ff913420401457d56e3d66ef329bc4c21075e400f27a6c1c162d SHA512: 3e176fb79a71a012aca875836d2ad2daf6873828a6b2ce025308480e1d815f9de6c0dd2730fd7b8e7a1255949c6d00a1aa0ac99e8cace6e6176bfc84ca94313a Homepage: https://cran.r-project.org/package=postGGIR Description: CRAN Package 'postGGIR' (Data Processing after Running 'GGIR' for Accelerometer Data) Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. 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This method is built on a kernel machine regression framework and allows for flexible modeling of complex microbiome effects, adjustments for covariates, and can accommodate both continuous and binary outcomes. Package: r-cran-postpack Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-mcmcse, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-r2winbugs, r-cran-r2jags, r-cran-r2openbugs, r-cran-nimble, r-cran-rjags, r-cran-jagsui Filename: pool/dists/noble/main/r-cran-postpack_0.5.4-1.ca2404.1_all.deb Size: 516162 MD5sum: e32c727cb33d8e82eef589945b9a58b4 SHA1: 92e3783b045aa6ea1ca22b669d6a5cf8afdf5de4 SHA256: 1a453aba259f6c260ffc1c1bc0e5d1cadd21986c66cfdb6fe4f51266b006455c SHA512: dd5b4eb908a04c929bdabcf7c835655d5bc7675cc6ae55e7d446078fdc637498d1ed833abc0743ac1c13a396396b9aee0156fd0cc3840aa1a556cdf152be68f3 Homepage: https://cran.r-project.org/package=postpack Description: CRAN Package 'postpack' (Utilities for Processing Posterior Samples Stored in'mcmc.lists') The aim of 'postpack' is to provide the infrastructure for a standardized workflow for 'mcmc.list' objects. These objects can be used to store output from models fitted with Bayesian inference using 'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides some methods for these objects, it is somewhat limited in easily performing post-processing tasks for specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack' streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting. Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions. Package: r-cran-postshock Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rsolnp, r-cran-garchx, r-cran-forecast, r-cran-lmtest, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-postshock_0.2.0-1.ca2404.1_all.deb Size: 268142 MD5sum: e12a52b195b9eca2841d38aeb7192080 SHA1: 0f4857165fe178eede72d92981416bb2969510cd SHA256: e3e69a259dffd92ea6f83fd0845c669217625fad86c2f74e60c2d1ea8cf3adf7 SHA512: 19ae5be380b22d78998fb8a9a5b66f9664ac1ff0055bc659e6c27559d1f84e72b78f32a40ce9a63da5dbbb7f8616368d86d5d6a549691fa41859a9e7019c2f10 Homepage: https://cran.r-project.org/package=postshock Description: CRAN Package 'postshock' (Donor-Adjusted Post-Shock Forecasting) Implements donor-adjusted methods for forecasting conditional means and variances after structural shocks. Historical donor episodes are weighted using covariates observed before each shock, and their estimated post-shock effects are combined with forecasts from a target-series model. The methods build on Lin and Eck (2021) . The package supports donor balancing weights, structured donor pools, autoregressive integrated moving average models, and generalized autoregressive conditional heteroscedasticity models with external regressors. 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The package is designed for organizing, identifying, and screening volatile compounds in metabolomics and environmental studies. Package: r-cran-potential Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-mapiso, r-cran-doparallel, r-cran-foreach Suggests: r-cran-covr, r-cran-eurostat, r-cran-giscor, r-cran-mapsf, r-cran-knitr, r-cran-tinytest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-potential_0.3.0-1.ca2404.1_all.deb Size: 1208546 MD5sum: 3d44bb4384014c021f50763ba03f1dac SHA1: bcbe0bf5596cdf4b32463c0b9d4b724433ee4b70 SHA256: 3bf4893d65bc321d4fcc03d8565e01296f84f12a0a2853b9b9c0063ab8650dcb SHA512: e8e4178f55aa40c1f2f5c2c99dc44cfab7115ba3b8185dbbc5bde84e75f146421802b9852e3edb67fbfbbcbe55a8506d9ef9ca553cddd7b9fc083ff3fec10af0 Homepage: https://cran.r-project.org/package=potential Description: CRAN Package 'potential' (Implementation of the Potential Model) Provides functions to compute the potential model as defined by Stewart (1941) . 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Package: r-cran-potentiomap Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3752 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fields, r-cran-gstat, r-cran-sf, r-cran-terra, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-potentiomap_0.2.0-1.ca2404.1_all.deb Size: 3133166 MD5sum: b32e0931c0961bf28aae7b35798efcb2 SHA1: 996ec38c68675e9c1763b88705b0001e16547251 SHA256: 128668313068b5e89e560d0ddc503386b9c2fd6d0d6776296097085d7bcd3b7f SHA512: c5302df636546460c5080b9a5099eddc654349101a642c8fbe9a1bf3c048c02bbed12e70a347dc3fb830e0fc8ea792d26c40dbf6870f9c6b28f96376c6beea82 Homepage: https://cran.r-project.org/package=potentiomap Description: CRAN Package 'potentiomap' (Build Potentiometric Surfaces and Hydraulic-Gradient Arrows) Prepares groundwater-level observations from measured hydraulic head or from depth-to-water and land-surface elevation, interpolates potentiometric surfaces using thin-plate splines, inverse-distance weighting, ordinary Kriging, universal Kriging, or user-supplied methods, and creates raster, contour, diagnostic, support, and hydraulic-gradient products for review and export. Functions retain method conditions and fit diagnostics, validate explicit prediction tasks, inspect model-conditional uncertainty and monitoring-network sensitivity, identify limited prediction support, and check whether scaled hydraulic-gradient arrows remain within finite raster support and end at lower modeled head. Raster processing uses methods from 'terra' (Hijmans 2025) , thin-plate splines use methods from 'fields' (Nychka et al. 2021) , and geostatistical interpolation uses methods from 'gstat' (Pebesma 2004) . 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This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks. Package: r-cran-pould Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haplo.stats, r-cran-gap, r-cran-ggplot2, r-cran-reshape2, r-cran-bigdawg, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pould_1.0.2-1.ca2404.1_all.deb Size: 107474 MD5sum: c3cc5edbda49da9737222b9a577cfe2e SHA1: 77aaf3c2e1ff8e9a52427e1f2bdc9a923263ff2d SHA256: 32527d2fef0e381c0aee23f2d217a5e8844d0a7debbda8a2fc14176f4a2a8518 SHA512: e2af8da215937af1780a14b12a1d5c3a550d8a3711c4488aa3b5febcf40ae65c09a35268fce5c65230802d4881335c047496f4db064e200c719f7b30e3eea2da Homepage: https://cran.r-project.org/package=pould Description: CRAN Package 'pould' (Phased or Unphased Linkage Disequilibrium) Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. 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Derives quantitative estimates of crystalline and amorphous phase concentrations in complex mixtures. 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Package: r-cran-powerlate Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-powerlate_0.1.2-1.ca2404.1_all.deb Size: 75480 MD5sum: bf48f2137b1a9f27e921d4d4f542f0b2 SHA1: 79c4b01a291c6ee66ff77f71452608a40c2cb222 SHA256: b6b1628ed9e12406f37c85fb4f3aa85a7c450250fadcd5c2a81180c22db2b607 SHA512: 169ffd23e61e7908513a863f16065fc39608a85b8545684f9bd0ebbd2cbdf03dc15339a42a0c1a56c43dca501e9c3c10a65400a3ebe846cf2e9551d75699fbf9 Homepage: https://cran.r-project.org/package=powerLATE Description: CRAN Package 'powerLATE' (Generalized Power Analysis for LATE) An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) . Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment. Package: r-cran-powerlaw Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3736 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-powerlaw_1.0.0-1.ca2404.1_all.deb Size: 3474238 MD5sum: fc54150850d90c2361e54f44743e6396 SHA1: 737c60d0ba9e51c1737ed50ab0301ae95ddb3110 SHA256: affb55763f183341c8b26bb794a63abd032f343a890a4d6939c91d30d93a94ce SHA512: 2350b4f734332ac903d8f4fb07d20a1d7a6c9f5876d3df0c86f917d60751815fa9d464f053a24f4afb7ea2fcbe38022639adfa3ca500fdc642633d62028d714a Homepage: https://cran.r-project.org/package=poweRlaw Description: CRAN Package 'poweRlaw' (Analysis of Heavy Tailed Distributions) An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region. Package: r-cran-powerly Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2586 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-splines2, r-cran-quadprog, r-cran-bootnet, r-cran-qgraph, r-cran-parabar, r-cran-ggplot2, r-cran-rlang, r-cran-mvtnorm, r-cran-patchwork Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-powerly_1.10.0-1.ca2404.1_all.deb Size: 2143054 MD5sum: 491b85f5c820426a3b957aaedfbdb0f7 SHA1: e77bea5de90ec6b1a2f7b02f22b3d35018391b71 SHA256: ccab6385fcf8f94e2c8a39142d406ea260dc2894f02d1e989243ade65c198354 SHA512: f1a17cee0a3f1e53089bfd4bd8f8083af6b4e9a0cb27c9a68c919ab6f106ef2b53d1c4d5a6f0a980ea5590751797175929fc4f73264f9a8f0c4afc849d8feec9 Homepage: https://cran.r-project.org/package=powerly Description: CRAN Package 'powerly' (Sample Size Analysis for Psychological Networks and More) An implementation of the sample size computation method for network models proposed by Constantin et al. (2023) . The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve. Package: r-cran-powermediation Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-powermediation_0.3.4-1.ca2404.1_all.deb Size: 208818 MD5sum: ffe413a32f41481d2cf9a3e7a1ebc88c SHA1: dac413ad25f843adbc15f1a8a395bc7252a6ee04 SHA256: b4661a76da2676ee17f465cf4529f73b764b0c6f2699114149e06427ea51732c SHA512: d1be2c9e962e8d351474f95ab061e25a3c3a83f45d6e0bdd6463a70352c3ee4c2cd77a52680084f5c8fef7581056095d6bf9cf28a97f6561a84e070f0f87ec15 Homepage: https://cran.r-project.org/package=powerMediation Description: CRAN Package 'powerMediation' (Power/Sample Size Calculation for Mediation Analysis) Functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression. Package: r-cran-powernlsem Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-crayon, r-cran-lavaan, r-cran-mvtnorm, r-cran-numderiv, r-cran-pbapply, r-cran-rlang, r-cran-stringr Suggests: r-cran-knitr, r-cran-mplusautomation, r-cran-rmarkdown, r-cran-semtools, r-cran-simsem Filename: pool/dists/noble/main/r-cran-powernlsem_0.1.2-1.ca2404.1_all.deb Size: 644382 MD5sum: 61fc7e4207643444fde079257e23b503 SHA1: bf3a489947f737bff3e40390983eccc15654f73d SHA256: 51b7ba37164cb30da085a75e47ac0c7b931bcfa9c88dd8a517c7d9ebc7894657 SHA512: 47129c2257224157cda07830d343a83785b57953f2657366fdc2ff8aa8f7cb503694cf9c1200628df4fcfe8517c07853bece2a8f44e4966d87b8b08ef59253a4 Homepage: https://cran.r-project.org/package=powerNLSEM Description: CRAN Package 'powerNLSEM' (Simulation-Based Power Estimation (MSPE) for Nonlinear SEM) Model-implied simulation-based power estimation (MSPE) for nonlinear (and linear) SEM, path analysis and regression analysis. A theoretical framework is used to approximate the relation between power and sample size for given type I error rates and effect sizes. The package offers an adaptive search algorithm to find the optimal N for given effect sizes and type I error rates. Plots can be used to visualize the power relation to N for different parameters of interest (POI). Theoretical justifications are given in Irmer et al. (2024a) and detailed description are given in Irmer et al. (2024b) . Package: r-cran-powernormal Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-powernormal_1.2.0-1.ca2404.1_all.deb Size: 35260 MD5sum: 6c51ef9260e3002c489f03562de43351 SHA1: d01ced4161277ef1f0e7067770e84a1988ff08c6 SHA256: 062776b3d9f6becb325b2fa4419a1776b935b702d8797f978758d0830d9b031a SHA512: 8a832723081d5be94e912dcf98afe7ae6bb5693dcf35f7de32f0048c6f5de923b173490f35ef14ce38d9fe8c73bd86d68be52e9707813494f8af19d507f59a27 Homepage: https://cran.r-project.org/package=PowerNormal Description: CRAN Package 'PowerNormal' (Power Normal Distribution) Miscellaneous functions for a descriptive analysis and initial Bayesian and classical inference for the power parameter of the the Power Normal (PN) distribution. This miscellaneous will be extend for more distributions into the power family and the three-parameter model. Package: r-cran-powerpkg Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-powerpkg_1.6-1.ca2404.1_all.deb Size: 56186 MD5sum: 7246818d1b3c7754210ac8eaf8265554 SHA1: 387f6fe8530bdf7c379a9115c664020999a2e6d8 SHA256: b4ad2bc5a790e762c46c6a7e8909d114cdb3e9f1cbc21ab09eafea96529a01c7 SHA512: a067da67e1f3d05e76033958e3527e7e28fb09c14ec837777bf17db801e3cb4b7cfb759bca4a619b3b27f878fa1c7c24b5e278c19f6e5770f0602c4ccf11a861 Homepage: https://cran.r-project.org/package=powerpkg Description: CRAN Package 'powerpkg' (Power Analyses for the Affected Sib Pair and the TDT Design) There are two main functions: (1) To estimate the power of testing for linkage using an affected sib pair design, as a function of the recurrence risk ratios. We will use analytical power formulae as implemented in R. These are based on a Mathematica notebook created by Martin Farrall. (2) To examine how the power of the transmission disequilibrium test (TDT) depends on the disease allele frequency, the marker allele frequency, the strength of the linkage disequilibrium, and the magnitude of the genetic effect. We will use an R program that implements the power formulae of Abel and Muller-Myhsok (1998). These formulae allow one to quickly compute power of the TDT approach under a variety of different conditions. This R program was modeled on Martin Farrall's Mathematica notebook. Package: r-cran-powerpls Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-compositions, r-cran-fksum, r-cran-nipals, r-cran-mass, r-cran-foreach, r-cran-simukde, r-cran-ks, r-cran-mvtnorm, r-cran-proc, r-cran-caret Filename: pool/dists/noble/main/r-cran-powerpls_0.2.2-1.ca2404.1_all.deb Size: 204592 MD5sum: f954ad6220f0c0daa89de822b8db777d SHA1: 590b94f97d2791050aff5262043a3a0ab70d14e4 SHA256: 67de08ef4d9a449ee443dda64ff424270def3a78e6bd80976c3a686f3a901365 SHA512: 87d237bcf52b4382dcd421b90da0310fbdac66088114a7400cdbe7b474d5ac26275d08bc506432246c75d0a577c068ec3034fb99da8cf5b11144c689cc27d1e3 Homepage: https://cran.r-project.org/package=powerPLS Description: CRAN Package 'powerPLS' (Power Analysis for PLS Classification) It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), . Package: r-cran-powerprior Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-laplacesdemon, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-powerprior_1.0.0-1.ca2404.1_all.deb Size: 106148 MD5sum: a561d0abf8d3016a6fad02ff4e676a77 SHA1: 434ef0c29fcdebc4dafe16f86cb09f90801f6b42 SHA256: 38f488aa7f7a725d737fdbf211b544e4928edbe6065b0689d04075ed134e2c9c SHA512: d857a372eafcc17a619f8d3ad09109852255dd7cf0898c8cbc7531274f4be21c963a3236788bb321b31609739606f8f4e72fd62087227ca2d466f3d09e5ad904 Homepage: https://cran.r-project.org/package=powerprior Description: CRAN Package 'powerprior' (Conjugate Power Priors for Bayesian Analysis of Normal Data) Implements conjugate power priors for efficient Bayesian analysis of normal data. Power priors allow principled incorporation of historical information while controlling the degree of borrowing through a discounting parameter (Ibrahim and Chen (2000) ). This package provides closed-form conjugate representations for both univariate and multivariate normal data using Normal-Inverse-Chi-squared and Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. The conjugate framework builds upon standard Bayesian methods described in Gelman et al. (2013, ISBN:978-1439840955). Package: r-cran-powersdi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmom, r-cran-lubridate, r-cran-nasapower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-powersdi_1.0.0-1.ca2404.1_all.deb Size: 479564 MD5sum: 2c4e6e49e8625760fadcaa08b0736639 SHA1: d07e1c9a2e0d965094e0e8b6fcd67155e3d787b2 SHA256: b4c18e65decac4aed26fd845c7dd2798ee02728f736dfa2b3339f8772537a226 SHA512: 41e2c8e86fab34c4514c8d3804d3e674afd2701bd9842868cb5707ea00785cea97310a2fcaaefb9d41774b55d1f58f10fbef4f1ea2ad20b2ba02136f20376f3b Homepage: https://cran.r-project.org/package=PowerSDI Description: CRAN Package 'PowerSDI' (Calculate Standardised Drought Indices Using NASA POWER Data) A set of functions designed to calculate the standardised precipitation and standardised precipitation evapotranspiration indices using NASA POWER data as described in Blain et al. (2023) . These indices are calculated using a reference data source. The functions verify if the indices' estimates meet the assumption of normality and how well NASA POWER estimates represent real-world data. Indices are calculated in a routine mode. Potential evapotranspiration amounts and the difference between rainfall and potential evapotranspiration are also calculated. The functions adopt a basic time scale that splits each month into four periods. Days 1 to 7, days 8 to 14, days 15 to 21, and days 22 to 28, 29, 30, or 31, where 'TS=4' corresponds to a 1-month length moving window (calculated 4 times per month) and 'TS=48' corresponds to a 12-month length moving window (calculated 4 times per month). Package: r-cran-powersurvepi Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma Filename: pool/dists/noble/main/r-cran-powersurvepi_0.1.5-1.ca2404.1_all.deb Size: 207506 MD5sum: c897ec504903fd4e4679ca1cc1d161a5 SHA1: e0e76e278b0cc73d212f130d2704af52cd5d4e81 SHA256: c8de99aa00bee0c63fd0e30cbdab470183eaaa42d46b72918015a9194c9d2c33 SHA512: 90a2d7118f77a596997d8415612f9780740e5fffe05832f1bc6daea9fe90d5cb4a55edb6fe27360f28c39add5282a33b16172bbe5fa2d9839fcf8b0cd58f8e47 Homepage: https://cran.r-project.org/package=powerSurvEpi Description: CRAN Package 'powerSurvEpi' (Power and Sample Size Calculation for Survival Analysis ofEpidemiological Studies) Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study. Package: r-cran-powertools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-powertost, r-cran-hmisc, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-powertools_1.0.0-1.ca2404.1_all.deb Size: 341704 MD5sum: 3d0845d1c84c7ffa183bfcd20d7f8b07 SHA1: 6396c009d07d1ea9f11f9e1306048e890b72f381 SHA256: 07549706699f825e72ca5e768a00eeb84489fdf046ed5a9e01bfe8f5fd4f98eb SHA512: 15101b8b81cfaee948f6d329cca69f43304040ccf8cce397d3097f418d2ae84f2163918c6c9e43192c3d147d44778affb316972f6b555ce7b0baf7d25e904020 Homepage: https://cran.r-project.org/package=powertools Description: CRAN Package 'powertools' (Power and Sample Size Tools) Power and sample size calculations for a variety of study designs and outcomes. Methods include t tests, ANOVA (including tests for interactions, simple effects and contrasts), proportions, categorical data (chi-square tests and proportional odds), linear, logistic and Poisson regression, alternative and coprimary endpoints, power for confidence intervals, correlation coefficient tests, cluster randomized trials, individually randomized group treatment trials, multisite trials, treatment-by-covariate interaction effects and nonparametric tests of location. Utilities are provided for computing various effect sizes. Companion package to the book "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). Further resources available at . Package: r-cran-powertost Architecture: all Version: 1.5-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-cubature Suggests: r-cran-crossdes, r-cran-knitr, r-cran-rmarkdown, r-cran-tufte, r-cran-emmeans Filename: pool/dists/noble/main/r-cran-powertost_1.5-7-1.ca2404.1_all.deb Size: 1679376 MD5sum: 1bde85dc50cae0cae23533a0bdbab799 SHA1: 887a8cfef69473f34f7443d415253ae28fb8e174 SHA256: dbd4e69c4f67c4cc99301ade2816fdf11e857135c7e3c90f3d654c99f498d3be SHA512: 4f93b8910f175683720629ede573d6e9cb6201a91266af4210ef83006e32f8fda5e033a206743d6cad92a0e2901ab26472d5f222262b3df785426ce3a5af1f18 Homepage: https://cran.r-project.org/package=PowerTOST Description: CRAN Package 'PowerTOST' (Power and Sample Size for (Bio)Equivalence Studies) Contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information. Package: r-cran-powerupr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-powerupr_1.1.0-1.ca2404.1_all.deb Size: 416510 MD5sum: 865d234b33d67eac389ce2f853ce0a75 SHA1: cfbf737f0e096ffd4701205b91e377c1313d620e SHA256: 5062e4a8d63798c6634c5bfacc6ddd7d10c38fef7cd7acc0237bf71d1993b45e SHA512: 72cedd5e34cab0864db5d6347fdfd2f679188a26d402cdd54f9d7cc385ed1383b15ae820d0a72a221dd8de7c7747e84ca52d52874b58c7f79a945328a375b6ef Homepage: https://cran.r-project.org/package=PowerUpR Description: CRAN Package 'PowerUpR' (Power Analysis Tools for Multilevel Randomized Experiments) Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes. Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; - - ), five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; - - ), four types of partially nested (PN) design to detect main treatment effect, and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; / ). See 'PowerUp!' Excel series at . Package: r-cran-powerxgammarf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger, r-cran-coda, r-cran-goftest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-powerxgammarf_1.0.0-1.ca2404.1_all.deb Size: 90354 MD5sum: bdb1257a47884078352d61eea522a1cd SHA1: e20aa6ce67635f6fa6d60e70981a00c29f6b4b9c SHA256: cf2a8444bcd69061f76e0feaf9f7844a442f7672335c5158e1aa6d39890b98bc SHA512: a42b0059ff983b6cbadcb9fcb052c760fe58e097b2f36af3cae96002fda2db29d65693f36385ea53ad0cb9c657bee71e7ea9849c082c932e21bb09b6c93835f4 Homepage: https://cran.r-project.org/package=PowerXgammaRF Description: CRAN Package 'PowerXgammaRF' (Random Forest Regression with Power Xgamma Distribution ErrorModel) Implements Random Forest regression under the Power Xgamma distribution error model. Provides core distribution functions (density, cumulative distribution, quantile, random generation, hazard, survival), parameter estimation via Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC), non-parametric bootstrap confidence intervals (at 90%, 95%, and 99% levels), Highest Posterior Density (HPD) intervals, Heidelberger and Welch's MCMC convergence diagnostic, convergence probability, model evaluation metrics (estimated values, bias, mean squared error, risk value), homoscedastic prediction intervals, and goodness-of-fit diagnostic tests (Kolmogorov-Smirnov and Anderson-Darling tests, Akaike Information Criterion, and Bayesian Information Criterion). References: Tyagi et al. (2022, Int. J. Stat. Reliab. Eng., 9(1), 51-60); Breiman (2001) ; Wright and Ziegler (2017) ; Heidelberger and Welch (1983) ; Sen et al. (2016) . Package: r-cran-powriclpm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-future.apply, r-cran-lavaan, r-cran-lifecycle, r-cran-progressr, r-cran-rlang, r-cran-ggplot2, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-powriclpm_0.2.1-1.ca2404.1_all.deb Size: 237536 MD5sum: bee5b08d77d1d6026306442ce2731996 SHA1: 8f4dcb752eb5c99737647c95fe4287e83c64fae5 SHA256: 1d21d72ae7626bd789dd3da3d0620130807bf8e6c8ef6ddaf8037976c34b5c1e SHA512: 3df66e3e7de61ec0974e32cbd3742d6384c0efdc2f40a14993f34613631b06fcde6dcac66dbd88efa5532d57da9e06df67897e2158e347c85dfb4add5731a485 Homepage: https://cran.r-project.org/package=powRICLPM Description: CRAN Package 'powRICLPM' (Perform Power Analysis for the RI-CLPM and STARTS Model) Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus. Package: r-cran-powrpriori Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-powrpriori_0.2.0-1.ca2404.1_all.deb Size: 673446 MD5sum: 95842ad6be53b0f383919b0a34abdd1f SHA1: 58efbd2c0ff95c8cf7bd047790f15da2827e61c8 SHA256: fa7d4972eb557efcd63cc26d04cfb047f0b1a229b8fb0fce566d3928fbbcdc5a SHA512: 67bfc8f2ee2e333343c9a3f06d1ec52e2837ce14bec62839a157b9afb2788d1c75cdd799c9f389518c4d35ba8931aada7ecd8a42393829a44fe8cd4338ae37b3 Homepage: https://cran.r-project.org/package=PowRPriori Description: CRAN Package 'PowRPriori' (Power Analysis via Data Simulation for (Generalized) LinearMixed Effects Models) Conduct a priori power analyses via Monte-Carlo style data simulation for linear and generalized linear mixed-effects models (LMMs/GLMMs). Provides a user-friendly workflow with helper functions to easily define fixed and random effects as well as diagnostic functions to evaluate the adequacy of the results of the power analysis. Package: r-cran-ppbigdata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datanugget, r-cran-mass, r-cran-dplyr, r-cran-magrittr, r-cran-weights, r-cran-rstiefel, r-cran-gtools, r-cran-tourr, r-cran-mclust Filename: pool/dists/noble/main/r-cran-ppbigdata_1.0.0-1.ca2404.1_all.deb Size: 139950 MD5sum: 3940a9fa7546b75fb70e73a82bc33de7 SHA1: 625ec0159028b259d0a35ffa7d64788ce832469d SHA256: 89e18c85ee9e901713ff761da02641cdbf991e1f1cc89c68912398a0736f8f90 SHA512: 789b13ca92bd8f8feb7e8b9082632dc45bf095639699963529ab44ab35e2333b14e22511ef5e6fab130c49d157fa452644006c1b08cf4f0aab27a2d3e956f71e Homepage: https://cran.r-project.org/package=PPbigdata Description: CRAN Package 'PPbigdata' (Projection Pursuit for Big Data Based on Data Nuggets) Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. Reference papers: [1] Beavers et al., (2024) . [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." . Package: r-cran-ppcdt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ppcdt_0.2.0-1.ca2404.1_all.deb Size: 18672 MD5sum: d6f52719d2e060839cfca8ca015592d7 SHA1: e19b55ff876e6f67f6ff538ab2b740856eebf6e8 SHA256: 0508e2dada1d7f3132c99c826135b8c103f7fd5e1a501226734ed83084d762f0 SHA512: fd68b6667846ee446666bc757c9741380a8ac83063dd00fb7a5f294d76e42d53917d0be0aa0dc0976f713dc878debc4c4340bb84549d08a530946d61d09b2583 Homepage: https://cran.r-project.org/package=PPCDT Description: CRAN Package 'PPCDT' (An Optimal Subset Selection for Distributed Hypothesis Testing) In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) . Package: r-cran-ppci Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2568 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rarpack Filename: pool/dists/noble/main/r-cran-ppci_0.1.5-1.ca2404.1_all.deb Size: 2515120 MD5sum: bbbfaf190d4d753ea995c7a380d22123 SHA1: 65f04d2b663543cb870d7c7f37afa8d8a6a11d06 SHA256: a72d5340f6032d4fd946cf8a081ca9c5bfce22acd824c7eb23868befcf3fb5ea SHA512: 5d9fa3135128b82035a74fea8bc105800e79c3b433cd0ebec2976efe2f49eac48bd34091eb435239faf28d2574c50404f36ad3001a147f3b286aa5fdd0e65cf1 Homepage: https://cran.r-project.org/package=PPCI Description: CRAN Package 'PPCI' (Projection Pursuit for Cluster Identification) Implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) ; minimum normalised cut, Hofmeyr (2017) ; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) . Package: r-cran-ppclust Architecture: all Version: 1.1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-inaparc, r-cran-mass Suggests: r-cran-cluster, r-cran-factoextra, r-cran-fclust, r-cran-knitr, r-cran-rmarkdown, r-cran-vegclust Filename: pool/dists/noble/main/r-cran-ppclust_1.1.0.1-1.ca2404.1_all.deb Size: 3413048 MD5sum: beb9344bc2c4133e4f038156027687b0 SHA1: 7d44c429cc4723cdf89b8fbddb03c3326c8d1cf8 SHA256: de33ec7d0c4ec0ba7c28bc8848aaec3ec3e6d6048fa7f5e050bf7c2372a0abda SHA512: 66da05813c6d880f0792784fd4cc2ab46065edb645c9cd8c762ec467c4e25850f7867dc2ef02f4916e00a350214cc8c869cea35978b0f43fcfa88fb5c6879d6f Homepage: https://cran.r-project.org/package=ppclust Description: CRAN Package 'ppclust' (Probabilistic and Possibilistic Cluster Analysis) Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) , Possibilistic C-Means (Krishnapuram & Keller, 1993) , Possibilistic Fuzzy C-Means (Pal et al, 2005) , Possibilistic Clustering Algorithm (Yang et al, 2006) , Possibilistic C-Means with Repulsion (Wachs et al, 2006) and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package. Package: r-cran-ppcor Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ppcor_1.1-1.ca2404.1_all.deb Size: 29948 MD5sum: 11b32e5fe4f436189ef296a9d3048278 SHA1: c506910392b1ef493a1ff8f272109bc13ea251fd SHA256: 657f226ff847f8a54226c4c31128b789dd97ec5a8c1276df50758b11d474d091 SHA512: 633daed1aca507552277d796a6a28d6ee7fdd11c6bf2d4a245410555d9472153f0eb1d921000d56df2b73624f8e6410f793b1499f3db66a6c006cbeee9f284cf Homepage: https://cran.r-project.org/package=ppcor Description: CRAN Package 'ppcor' (Partial and Semi-Partial (Part) Correlation) Calculates partial and semi-partial (part) correlations along with p-value. Package: r-cran-ppcsexrx Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppcsexrx_0.1.1-1.ca2404.1_all.deb Size: 52490 MD5sum: 46949be5a2fde2ab22d66afb535556ca SHA1: cac14a0a26cff98440ad37e91840fcddb4279c61 SHA256: fefd85d9eacae64cef5d4270d2c49a763176496016e5e1f463ef0ecae82284aa SHA512: cdfc5f125f1cbde582684e58484661f7bf5a8bf4662c64d7a899843bf4249acd911cae652ad3b3fd308e34b1b5375189098e1de811f7342abaf7a90e60bbe63c Homepage: https://cran.r-project.org/package=PPCSexRx Description: CRAN Package 'PPCSexRx' (Prescribe Sub-Symptom Exercise for Adolescent Concussion) A clinical decision support system for sub-symptom threshold aerobic exercise (SSTAE) prescription in adolescents with persistent post-concussion symptoms (PPCS). 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Package: r-cran-ppcspatial Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-magrittr, r-cran-pakpc2017, r-cran-scales, r-cran-shiny, r-cran-tidyr, r-cran-tmap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppcspatial_0.3.0-1.ca2404.1_all.deb Size: 1836872 MD5sum: e77b38e6fe94a6086b502177d81ce294 SHA1: 2541c146acb2d3b841273b8a32e729452eb617b7 SHA256: 11cc04e8e6ea239ab808adf9680e0ccbb3cd283dd22ec18c53ab27f4dfaf112b SHA512: 5551d642fbda744e08dfb8a94d6d3e6e3309769e524066b5ebbc8fd435632ee874a6d544a7d268dff108c80cd55caae77f95655d818012089ea2f2703bead486 Homepage: https://cran.r-project.org/package=ppcSpatial Description: CRAN Package 'ppcSpatial' (Spatial Analysis of Pakistan Population Census) Spatial Analysis for exploration of Pakistan Population Census 2017 (). It uses data from R package 'PakPC2017'. Package: r-cran-ppdiag Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ppdiag_0.1.1-1.ca2404.1_all.deb Size: 266530 MD5sum: 838b96c67af32a545bb5536e8f57b8c0 SHA1: 6f0a67d3e79a18b8b9caff3a6ebc23daeea5b449 SHA256: e88eb6e8b00d94af250edf416a9d9fb4cea72b6c74a13209ab585dc40c6dbe05 SHA512: df2522a0068cb65d08c8bd0787ac7cf1af2a58777067d9a22747009f94eb9379fbb2386c78c3206a60e3a3e1732bb692a0ec99ae7fb0aacc32cd285b736b0fd0 Homepage: https://cran.r-project.org/package=ppdiag Description: CRAN Package 'ppdiag' (Diagnosis and Visualizations Tools for Temporal Point Processes) A suite of diagnostic tools for univariate point processes. This includes tools for simulating and fitting both common and more complex temporal point processes. We also include functions to visualise these point processes and collect existing diagnostic tools of Brown et al. (2002) and Wu et al. (2021) , which can be used to assess the fit of a chosen point process model. Package: r-cran-ppendemic Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3864 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppendemic_0.2.2-1.ca2404.1_all.deb Size: 3646204 MD5sum: 61c10b76ad5abf1fb80b4983957d3e0d SHA1: c34a7cfff9bed3d22c74d9b23103832676c1c614 SHA256: 31face003cace5829063d8ad6d893bfb164679aaffc9ce4e1a6c5bf63c9de172 SHA512: b3b2351aa1eacece594c45a100544d6fe2d1882b989a0c03b5abb1855f0e7643d676e024825f4b34dcb317ff6605889334d9ff91a202a6008760ca03c90a1a79 Homepage: https://cran.r-project.org/package=ppendemic Description: CRAN Package 'ppendemic' (A Glimpse at the Diversity of Peru's Endemic Plants) Provides an updated database of accepted endemic plant taxa from Peru. The current collection contains over 8,000 taxonomic records at species and infraspecific ranks. Data are derived from Govaerts, R., Nic Lughadha, E., Black, N. et al., 'The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) . Package: r-cran-ppgam Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-evgam Filename: pool/dists/noble/main/r-cran-ppgam_1.0.2-1.ca2404.1_all.deb Size: 130224 MD5sum: 31d7c473f83a8b4af6e669d6076d0403 SHA1: fcfd0e2c162d3c5f46ef4b706272d6c196424a50 SHA256: f00081e2cf0a831e7f59982afc619c7e13130d62a9bd349511ade80d0570bbca SHA512: 3fb601fd8830d6724678984fbd8dd1de6831645ee7efbea29fe367abbd2658606eb9a67761d50b70e41e91f6774a31fa45e60a6b862bf50ba7547b40bcf192e0 Homepage: https://cran.r-project.org/package=ppgam Description: CRAN Package 'ppgam' (Generalised Additive Point Process Models) Methods for fitting point processes with parameters of generalised additive model (GAM) form are provided. For an introduction to point processes see Cox, D.R & Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) , and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) . 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The user can specify various models of trait evolution or estimate the best fit model, include fossils, use one or multiple phylogenies for inference, and make animations of shifting suitable habitat through time. This model was first used in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and Rivera et al (2020). Lawing and Polly (2011) "Pleistocene climate, phylogeny and climate envelope models: An integrative approach to better understand species' response to climate change" Lawing et al (2016) "Including fossils in phylogenetic climate reconstructions: A deep time perspective on the climatic niche evolution and diversification of spiny lizards (Sceloporus)" Rivera et al (2020) "Reconstructing historical shifts in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.". Package: r-cran-pphotspot Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-get, r-cran-sf, r-cran-spatstat.geom, r-cran-spatstat.knet, r-cran-spatstat.linnet, r-cran-spatstat.random Suggests: r-cran-r.rsp, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pphotspot_0.1-3-1.ca2404.1_all.deb Size: 539192 MD5sum: f9cb213b6157f5288f364052a6c7accd SHA1: a5e570b8dd87ccb4e4db501aef5b3858deeab886 SHA256: 4c3ac8fd87bb6c22225517afaa3f1fa761b95b8524ae83e5fee23a047b800055 SHA512: 54f40eec4fbb41fd5bca9f7581e144e4247bdfebfbdcf9e07936324a55c26295608fd11ff0ae8e47626af3163dd5034b90c3579f00867b34afb2f5eaa13ef1a1 Homepage: https://cran.r-project.org/package=pphotspot Description: CRAN Package 'pphotspot' (Hotspot Detection of Point Events on a Linear Network) Detection of hotspots of point events on a linear network as proposed by Mrkvička et al. (2025) using the R package 'GET', see Myllymäki and Mrkvička (2024) . Package: r-cran-ppitables Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-stringr, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ppitables_0.6.1-1.ca2404.1_all.deb Size: 3137906 MD5sum: 78dcc714e15d4dd97794e173e7e62f78 SHA1: 9ec9f1a51d1f3fecb88a6bd649b501ef3112b89c SHA256: e678f3f4dfbef31f2a03fa3ab1acbbe1e1be496ad7c76dfb6993cb3848e255d6 SHA512: 421c970f37ff35a31aeded7e4d952249feedc1df0865b7424d6f2b34b05f3ac40b5fefedd9dc836673ecf72dad76e3597c96d49a660d119d8e91a2469e6bfc99 Homepage: https://cran.r-project.org/package=ppitables Description: CRAN Package 'ppitables' (Lookup Tables to Generate Poverty Likelihoods and Rates usingthe Poverty Probability Index (PPI)) The Poverty Probability Index (PPI) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household's characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line - or above by only a narrow margin. This package contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their score from the country-specific PPI questionnaire. These lookup tables have been extracted from documentation of the PPI found at and managed by Innovations for Poverty Action . Package: r-cran-ppks Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-ppks_1.0-1.ca2404.1_all.deb Size: 16974 MD5sum: 2670c4ab4a250b12baca856658136ef5 SHA1: 490eaaefee81dbc5c57dbb0e46f135364f0860d3 SHA256: 597c5fc0748239055600327d5e3da8eb8e726b69d537430ba65013c65fc8f249 SHA512: 3c4fb2c8e2167604ea7834f435d10ae8f61cbe46f4375442b0177cf93a2a8707dfc2dbd97db2e3b3c099c17a97dd92c9e8b1c04ded128657698d1b99f5a85a7e Homepage: https://cran.r-project.org/package=ppks Description: CRAN Package 'ppks' (Permutation Based Paired Kolmogorov-Smirnov Test) Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) . Package: r-cran-pplasso Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-genlasso, r-cran-ggplot2, r-cran-cvcovest, r-cran-glmnet, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pplasso_2.0-1.ca2404.1_all.deb Size: 320084 MD5sum: 249611b557c836fb9ad1de78bcc46e2f SHA1: b6b40979a56cda55939df4c0d413702eed03dcd8 SHA256: 386b9779c3b728117aa8cb67d0b6bc6d65329d4882e147ef598b1e4bd75f457d SHA512: cc8db23e2e8f92ce93fb137635dc4fe6e5acb530d7ec6fa9aa136be0260b3eee759b7a65cad64f3416fa6b202b21f059b6473d3b30db4f235c8c645ca8074a8a Homepage: https://cran.r-project.org/package=PPLasso Description: CRAN Package 'PPLasso' (Prognostic Predictive Lasso for Biomarker Selection) We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25. Package: r-cran-pplot Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mbess Filename: pool/dists/noble/main/r-cran-pplot_0.9-1.ca2404.1_all.deb Size: 27206 MD5sum: fc9640d703a3e7ec761a4643168d4fc6 SHA1: 2128e720f31add9016e35d418a0c419f4ab530e6 SHA256: e34978750370bc62c88dd5bd48aef6c13f971f6f29058c7dc04421bd856adc1d SHA512: 92680e5afc4e6cbdd4cd50efe73f61c7107a71581f3ea04df42dbbb315dca989390d3fde21069f5f6478d673b14a862aea3fbae9bc84cffca9a7504ad3350c9c Homepage: https://cran.r-project.org/package=pplot Description: CRAN Package 'pplot' (Chronological and Ordered p-Plots for Empirical Data) Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences. 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The method is described and applied to simulated and experimental data in Kraemer et al. (2008) . Package: r-cran-ppmf Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-censable, r-cran-dplyr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-zip Suggests: r-cran-roxygen2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ppmf_0.2.1-1.ca2404.1_all.deb Size: 87908 MD5sum: adc122f5b1e9e51d364f3ecede70d44a SHA1: 698f9e47587509aa46e7e84b13aff46fcf1737a7 SHA256: ce984aebbfc368f036e882c86cb02295248fdc2de1f132f1319fcfc0da064e65 SHA512: 6cc2c0f53f4cbf9e75d4c0c6ad1147024db21c04b2dc376c80191538d6d6cb96fb233f34562db49a37d450ede0092811f3dba71a2fa687d3fab3e1e12d22371a Homepage: https://cran.r-project.org/package=ppmf Description: CRAN Package 'ppmf' (Read Census Privacy Protected Microdata Files) Implements data processing described in to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata. Package: r-cran-ppmhr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-ppmhr_1.0-1.ca2404.1_all.deb Size: 147488 MD5sum: 3a5c9058915226c905fce6336d22ed2d SHA1: dbf1479899d83e101c6045ed6109fedae97643f1 SHA256: 84b4d22d1d17867eda49b857f5ba96192be17ed39157373f2957997bcfae2979 SHA512: 6e8aee05a3f2fc938ed883e8443030eaf4eef2abd0aa602e38ffd2276bc773a1c77013b0656bd42eb7251be7e3fb7c318b5de4d3ef528153a434e11c93d72cd5 Homepage: https://cran.r-project.org/package=ppmHR Description: CRAN Package 'ppmHR' (Privacy-Protecting Hazard Ratio Estimation in Distributed DataNetworks) An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) . This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented. Package: r-cran-ppmlasso Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-data.table, r-cran-lattice, r-cran-plyr, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.geom Filename: pool/dists/noble/main/r-cran-ppmlasso_1.5-1.ca2404.1_all.deb Size: 518160 MD5sum: 9b378ef9fba28e4f487cfeda1352e4cc SHA1: d32102b4082461e59e226c3e564801d7df434a6a SHA256: 8e4595dedb37eac3d817bccffd0b1944ceced89554866fa474e218b58439b610 SHA512: d3bf31ead991032ca0719179bdd9286f0094ee73d7378b372b0c51ac134f64bd3219edf27462edef65355e1d5ad91cb97598b60712034c05db5c515bffc5239f Homepage: https://cran.r-project.org/package=ppmlasso Description: CRAN Package 'ppmlasso' (Point Process Models with LASSO-Type Penalties) Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) . Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff. Package: r-cran-ppmsdr Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-grpreg, r-cran-energy Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppmsdr_3.0.1-1.ca2404.1_all.deb Size: 380060 MD5sum: c25e2e500dc4219450e8d3de16de0e29 SHA1: e38ba80c50fe700cd988e4c57fa4be666eb7a5aa SHA256: 50fe8666c1bc9fa9ce7964f69d30c69c033c0cc73a0ff93c08d473d6f39f3d9d SHA512: ea12eb8e5f9bdd2651cbe1bddb185254f4c167765eef0b9da0e10da31d637c9d8b342a6dcf17036f4d57d3036069b6b1f11fc9209884a92da66d661fdc1837a9 Homepage: https://cran.r-project.org/package=ppmSDR Description: CRAN Package 'ppmSDR' (Penalized Principal Machine for Sufficient Dimension Reduction) A unified, computation-friendly framework for penalized principal machines (P2M), a class of sparse sufficient dimension reduction (SDR) estimators for regression and binary classification. Principal machines (PM) estimate the central subspace by solving a family of convex-loss problems over several cutoffs; their penalized counterparts (P2M) add a row-group sparsity penalty so that dimension reduction and variable selection are performed simultaneously. All estimators are fitted by a single group coordinate descent (GCD) algorithm that accommodates least squares, logistic, asymmetric least squares, L2-hinge, hinge (support vector machine, SVM) and quantile losses, together with the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) penalty and the minimax concave penalty (MCP). Methods are described in Li, Artemiou and Li (2011) , Shin and Artemiou (2017) , Artemiou, Dong and Shin (2021) and Breheny and Huang (2015) . Package: r-cran-ppqplan Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-ppqplan_1.1.0-1.ca2404.1_all.deb Size: 2123376 MD5sum: 51f53e7e7b4b53d48674c8dbb8fa6663 SHA1: d5240a87f6d505d6c06369f19d5e56e3421231b0 SHA256: 4fd02dc1263fbdf7e7ddab324fc6941ab7a78b5305eb2294e84fd55b23eeb1d8 SHA512: 49b39aaba936db78e8b7bd9e0a0258c5f4f9a8cd18b4993af451717dbf9cd507a73bd9d4dcfbb80a6f30b485f284a5bb50e5d0d8d6ab6a31f187ce2150ebdd15 Homepage: https://cran.r-project.org/package=PPQplan Description: CRAN Package 'PPQplan' (Process Performance Qualification (PPQ) Plans in Chemistry,Manufacturing and Controls (CMC) Statistical Analysis) Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham. Package: r-cran-pprank Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pprank_0.1.1-1.ca2404.1_all.deb Size: 17494 MD5sum: b7b2a07a35f47884930e08b9b6a7c686 SHA1: 38c7463078754b240740c0bcbbf7c4623b0219c8 SHA256: b5a03a3e8cb86cd5a58a5626694eb87d1c8132a2070574a243e3d138cee8c2cb SHA512: 7d33a2cbf97873b0b0f9eca50ea3f3397195a4776cef7d6d629234a5a5611bb8150b8fb49a4d14239f78dfc71b9db85ccc7ea0a932e219850545ad7715a7ba5b Homepage: https://cran.r-project.org/package=ppRank Description: CRAN Package 'ppRank' (Classification of Algorithms) Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions . Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values . 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Package: r-cran-pps Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pps_1.0-1.ca2404.1_all.deb Size: 202388 MD5sum: 7b609255faceb18ccb270277155fef87 SHA1: 9af24924b18ad6171732329e1468b3d9f976b806 SHA256: 8c7ec8d675ad5ab56614dbe647efa48c78754f8d44a9a862a0fa4d2d7ad31fc7 SHA512: 26bc97a8595a31505615b094993ac0493d161365d43064014c07f74c6f858b1b50ba8a957fea5484cc84c11b502a7b5e6933cf504e12fd64fe73cbb6ba9e9c53 Homepage: https://cran.r-project.org/package=pps Description: CRAN Package 'pps' (PPS Sampling) Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references. Package: r-cran-ppsbm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast, r-cran-clue, r-cran-gtools Filename: pool/dists/noble/main/r-cran-ppsbm_1.0.0-1.ca2404.1_all.deb Size: 206144 MD5sum: 53cea5215a10f7168b770aca3b871e74 SHA1: 73619c5c52bddd5ea9771c50aa98ea13ed03aa47 SHA256: 40f18895c250af08d7047c6bb03feb302c95659f4f759ffbb1b033efda4ab908 SHA512: 4ccdfd1c7aee7e4d202519d0cd3302e1b2e156f6c8624d21f923d589a2bd194ac22207bbf4d9d5c900d08e93c76aa7c4492969abbdf4fb2995258d062aecfc1c Homepage: https://cran.r-project.org/package=ppsbm Description: CRAN Package 'ppsbm' (Clustering in Longitudinal Networks) Stochastic block model used for dynamic graphs represented by Poisson processes. To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Y. Baraud and L. Birgé (2009). . C. Biernacki, G. Celeux and G. Govaert (2000). . M. Corneli, P. Latouche and F. Rossi (2016). . J.-J. Daudin, F. Picard and S. Robin (2008). . A. P. Dempster, N. M. Laird and D. B. Rubin (1977). . G. Grégoire (1993). . L. Hubert and P. Arabie (1985). . M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). . C. Matias, T. Rebafka and F. Villers (2018). . C. Matias and S. Robin (2014). . H. Ramlau-Hansen (1983). . P. Reynaud-Bouret (2006). . 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Package: r-cran-pptcirc Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pptcirc_0.2.3-1.ca2404.1_all.deb Size: 148422 MD5sum: 859ee6bca4d1cecf401a6ee9c4effb99 SHA1: 10cd1030cd544d36becba66531ec8dcd22e037f0 SHA256: e7d58f8edd4516d492b963029e4e8467e7021fe98b0181b317f2bcdc11680f9a SHA512: 6b63435662bfa8a64354dc194d58b2b8c54070db8555b785dc6f549f45e4e839e4b6f17e374c4ec987f02540f654034da6573e5620416e3d6da36eb1b4dc59e8 Homepage: https://cran.r-project.org/package=PPTcirc Description: CRAN Package 'PPTcirc' (Projected Polya Tree for Circular Data) Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) ). Package: r-cran-ppts Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ppts_1.0-1.ca2404.1_all.deb Size: 156044 MD5sum: 7336b25eb5fd11fa77fba75b046b0e44 SHA1: 4bad9bb3f7aa01c1f4229c3382276bdcb84fd19a SHA256: f2443e2d5266f70fc8ceef6a6b5457990163f08558545e5840981df8599b6a5c SHA512: 108bc0eaa515609fedbaf2e32a358e53bd6f843474d4dbd9868226fcc46f649ede9f0de0f4ad9eff167e9bb745d1fd0f62a97e7c137d0757db07ea0ba094f624 Homepage: https://cran.r-project.org/package=PPTS Description: CRAN Package 'PPTS' (Point Process Time Series) Provides functions for point process time series. Autocorrelation functions for spatial and temporal time series, and estimation of trend-plus-seasonality models for temporal and spatial time series. See Gervini (2025) and Gervini and Kopischke (2026) . Package: r-cran-ppwdeming Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-ppwdeming_3.0.2-1.ca2404.1_all.deb Size: 163586 MD5sum: 758a97cf353c1faa7c523cd1a259b4df SHA1: fa23910f62ade2e228879d04aaa58f868e72660d SHA256: 935054c6fb245dc2f16cc2798b6b94e294ea3fd2b8b94e50b17a8a501650580d SHA512: ea1026e54738ad32f5a484e60a22b5b2a7263b7cb7b4ba7d115c2c6859368311aeb6249bd9aad1f0da7a2977d8cb1e7472dc57cae2935e38678be4634c13bfa4 Homepage: https://cran.r-project.org/package=ppwdeming Description: CRAN Package 'ppwdeming' (Precision Profile Weighted Deming Regression) Weighted Deming regression, also known as 'errors-in-variable' regression, is applied with suitable weights. Weights are modeled via a precision profile; thus the methods implemented here are referred to as precision profile weighted Deming (PWD) regression. The package covers two settings – one where the precision profiles are known either from external studies or from adequate replication of the X and Y readings, and one in which there is a plausible functional form for the precision profiles but the exact (unknown) function must be estimated from the (generally singlicate) readings. The function set includes tools for: estimated standard errors (via jackknifing); standardized-residual analysis function with regression diagnostic tools for normality, linearity and constant variance; and an outlier analysis identifying significant outliers for closer investigation. The following reference provides further information on mathematical derivations and applications. Hawkins, D.M., and J.J. Kraker (2026). 'Precision Profile Weighted Deming Regression for Methods Comparison'. The Journal of Applied Laboratory Medicine 11, 379-392 . Weighted Deming regression is also now implemented for multiple instruments , as set out in Hawkins, D.M., and J.J. Kraker (2026). 'Multiple Instrument Methods Comparison by Precision weighted Deming Regression', on Arxiv . The “multi” functions refer to the multiple instrument analysis, and the "PWD" functions refer to the two-instrument. Package: r-cran-ppweibull Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-segmented, r-cran-msm, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-ppweibull_1.0-1.ca2404.1_all.deb Size: 74998 MD5sum: c36664394bf7d50413657786e89c3e2a SHA1: 39473737268b265c7c3eb4804fe2fb27df658cc7 SHA256: f7470ffde360066bc5cb2c45fcc38c10f43898efbeb1e8d441b460aab1b37d1b SHA512: 1026dd2024bb8e1867545fcd85e3c1b18b7c47d45e57a2766b30b4955729a8f5039ccfb6bff64a408d9ac7e69f4b24949da8a7ba6d8b245de4d215d4eb07c6fb Homepage: https://cran.r-project.org/package=ppweibull Description: CRAN Package 'ppweibull' (Piecewise Lifetime Models) Provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) for methodological details. Package: r-cran-pqa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pqa_1.0.0-1.ca2404.1_all.deb Size: 69252 MD5sum: 3465fe924860c90cdc018b16e9388631 SHA1: 9a697741f4240369425404a33b44d39534ef2d23 SHA256: 92a20dd171b26877db1608f94c6244bdd6abbf3825aeb9ac051c316a2bdcb350 SHA512: f9a02ff843401b2fce6f1f36dfefe5bc67f7d0c178fb92b0fc9e6a000b9cb1ec0122547075d92e291b692788ec0b60d04a2f62434312329fe3fb2e175c89f33a Homepage: https://cran.r-project.org/package=PQA Description: CRAN Package 'PQA' (Perform the Pearson-Quetelet Analysis on Two-Way ContingencyTables) Tools to perform Pearson-Quetelet analysis on two-way contingency tables. The package computes absolute and relative frequencies, Quetelet indices, Pearson-Quetelet decomposition, apex tables, and chi-square summaries for interpreting associations between categorical variables. Package: r-cran-pqantimalarials Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-plyr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-pqantimalarials_0.2-1.ca2404.1_all.deb Size: 43666 MD5sum: 0fb95dcddf1de38506dba57f6dc64b72 SHA1: 30309ed840509d3881c2730069d9f9e3c245acb8 SHA256: 07aaf2e4513e1cb66fe679592fb002bf13d05668c45fb94d2f33428b449d826f SHA512: 99bc2d1833c8641851c51cacfb737cfa95986b35c48a4866c4a502aac8931876cc2a4c2d59ac5f080387d87ee0ec3e7654ee444ca522f3c6fd167a5afe288dec Homepage: https://cran.r-project.org/package=pqantimalarials Description: CRAN Package 'pqantimalarials' (web tool for estimating under-five deaths caused by poor-qualityantimalarials in sub-Saharan Africa) This package allows users to calculate the number of under-five child deaths caused by consumption of poor quality antimalarials across 39 sub-Saharan nations. The package supports one function, that starts an interactive web tool created using the shiny R package. The web tool runs locally on the user's machine. The web tool allows users to set input parameters (prevalence of poor quality antimalarials, case fatality rate of children who take poor quality antimalarials, and sample size) which are then used to perform an uncertainty analysis following the Latin hypercube sampling scheme. Users can download the output figures as PDFs, and the output data as CSVs. Users can also download their input parameters for reference. This package was designed to accompany the analysis presented in: J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan "Estimated under-five deaths associated with poor-quality antimalarials in sub-Saharan Africa", 2014. Paper submitted. Package: r-cran-pql Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pql_0.1.0-1.ca2404.1_all.deb Size: 31898 MD5sum: 6413ed61e5db472dfc235312ebb50e1a SHA1: 40caf6f929bb11ad9917eba5c93dd6e0f5e0336e SHA256: 160a642d3a49bbb77d58f5ae80b01dc053ffe29ef1850c0f5d574b1b2b191505 SHA512: fa14335d5ffc3e6ac9d624232cda05c22ca2fd67c94d5a3f7ce761fa8f16037c9d029f0da3ff00ec7f140cd3852ca7193253daa35ada885fb659904d0d18a81d Homepage: https://cran.r-project.org/package=pql Description: CRAN Package 'pql' (A Partitioned Quasi-Likelihood for Distributed StatisticalInference) In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) . Package: r-cran-pqtldata Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4819 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-knitr, r-cran-rdpack Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-cairo, r-bioc-ensdb.hsapiens.v75, r-bioc-ensembldb, r-bioc-iranges, r-bioc-org.hs.eg.db, r-bioc-s4vectors, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-pqtldata_0.7-1.ca2404.1_all.deb Size: 2571400 MD5sum: 370c0275896df1e58285acbdc07fb059 SHA1: 6f4ab620791263ceb54bb3ce999230b3e533ab4f SHA256: a87a8d7b1daff76c2d880194b22d104bbf4d4c1e669cfc1da5f0a11b61b39a18 SHA512: bc7da4fac5130eb316be3a653f8d473e072df078fad4c014c369851ee37ebb104db093930df7aae44bcbbbc68260be2d022be2577ce193766613d72a122359d9 Homepage: https://cran.r-project.org/package=pQTLdata Description: CRAN Package 'pQTLdata' (A Collection of Proteome Panels and Metadata) It aggregates protein panel data and metadata for protein quantitative trait locus (pQTL) analysis using 'pQTLtools' (). The package includes data from affinity-based panels such as 'Olink' () and 'SomaScan' (), as well as mass spectrometry-based panels from 'CellCarta' (), 'Seer' () and 'SWATH-MS' (). The metadata encompasses updated annotations and publication details. Package: r-cran-pra Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mc2d, r-cran-minpack.lm Suggests: r-cran-base64enc, r-cran-corrplot, r-cran-devtools, r-cran-ellmer, r-cran-ggplot2, r-cran-igraph, r-cran-jsonlite, r-cran-knitr, r-cran-networkd3, r-cran-remotes, r-cran-rmarkdown, r-cran-scales, r-cran-mcptools, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pra_0.6.0-1.ca2404.1_all.deb Size: 745372 MD5sum: bd7e56f07f2f012eead65530e0679e71 SHA1: 6d276c2f48265e1c1b0d784f89ba2ef5e36457f6 SHA256: 55a0bbf76b7cd76e32598abfe2a3b54e4bb9f0500fbb3fb45ff0ca70a5779c95 SHA512: 5949fd05218ac6f4aef1a8687ed8bbeb7ca51a9fca9aacbdae068f6356cb7d5a2294bba8e6c684fe0f3e07ac1a602db2ed38c182416146091098f3d5034fd991 Homepage: https://cran.r-project.org/package=PRA Description: CRAN Package 'PRA' (Project Risk Analysis) Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Bayesian Methods, and more. Package: r-cran-praatpicture Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1842 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-av, r-cran-bslib, r-cran-crayon, r-cran-emur, r-cran-gifski, r-cran-gsignal, r-cran-ipa, r-cran-multitaper, r-cran-phontools, r-cran-rpraat, r-cran-rstudioapi, r-cran-shiny, r-cran-shinyjs, r-cran-soundgen, r-cran-tuner, r-cran-wrassp, r-cran-zoo Filename: pool/dists/noble/main/r-cran-praatpicture_1.9.0-1.ca2404.1_all.deb Size: 1163662 MD5sum: ca59c7e42a6c56279e30fe29b4fba08d SHA1: 74fb281eb67bdd2f6b42efd891ba8162f72c597e SHA256: 5d08cb36e84c4865ddb40de8317e6214cd07c9f1eb47df0df6c7b4554f5492b1 SHA512: 7f8716bb6436b3dde605d8c7b2d07e38bd9dedb94fb15e833ae597a16835b36ecb0ce336cd91cb36b991660498bf199b51e120ced19f18df65bfbb9fd1c391d7 Homepage: https://cran.r-project.org/package=praatpicture Description: CRAN Package 'praatpicture' ('Praat Picture' Style Plots of Acoustic Data) Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures. Package: r-cran-prabclus Architecture: all Version: 2.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mclust Suggests: r-cran-spdep, r-cran-spatialreg, r-cran-bootstrap, r-cran-foreign, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-prabclus_2.3-5-1.ca2404.1_all.deb Size: 472728 MD5sum: 39859e2c69f1adeb263a6d128e3a05a8 SHA1: 53d1e01cdfe4ab287dba3b12c1e009927347ea20 SHA256: 88123e919ae24057ae4e751a3737d8bea5d99de55475a16054f71f56b457f0b5 SHA512: 01407113718ed8d784fe069d64dde8d465e59515f77894a227767466a201a98f40b3b53d7b1526bd5bb49cd8dbeeb4e3cc5f826f87dd50b546d132286c0d4d8f Homepage: https://cran.r-project.org/package=prabclus Description: CRAN Package 'prabclus' (Functions for Clustering and Testing of Presence-Absence,Abundance and Multilocus Genetic Data) Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview. Package: r-cran-pracma Architecture: all Version: 2.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nlcoptim, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-pracma_2.4.6-1.ca2404.1_all.deb Size: 1710094 MD5sum: ff43762974fafa298b22b2412cc9d426 SHA1: fd5ae13dace3cbd167739d9b4ec24ec70eabc65e SHA256: f788a4aa61f971b4196255e1e0f3ced4dcf30b621ea32a4acfae850cd2916512 SHA512: 8aad912028f4be8df73e1a6b7c7c24559ed5a5cff66eeeac214d131bc167f5eb6d152196fd0061f16405192dac7d0eed28f77a4628fa9af7ceab34d813c0d631 Homepage: https://cran.r-project.org/package=pracma Description: CRAN Package 'pracma' (Practical Numerical Math Functions) Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting. Package: r-cran-pracpac Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-glue, r-cran-fs, r-cran-rprojroot, r-cran-renv, r-cran-pkgbuild Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pracpac_0.2.0-1.ca2404.1_all.deb Size: 323394 MD5sum: 0f1d2601e51211835493b3927240e765 SHA1: 6add0967f2b7e5ac0cd37c864671fa9a2525f911 SHA256: c4aa6a72acebceb0a56aeabf428a99d0fce0423e0d5e94926f7ce202d6b3c217 SHA512: 1e004b9dc1e755b2d9be140ae7438680c2f6fd1b0a3e5f56008cef9737840eba3f6df3701bcc44823d7d781e9516187197cf584216e109cd16fccb431f7390fc Homepage: https://cran.r-project.org/package=pracpac Description: CRAN Package 'pracpac' (Practical 'R' Packaging in 'Docker') Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) . Package: r-cran-practicalequidesign Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-numderiv, r-cran-temporal, r-cran-tidyr Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-practicalequidesign_0.0.3-1.ca2404.1_all.deb Size: 60888 MD5sum: 1a9f49fea97835bad7887b47615b5ada SHA1: 3243618e40710dc1569329c996c8c2b551b1e9a9 SHA256: d4ba07f8622de2c25fd37d146911ee1076bde6d038ec13ae953f3bf75c68ed3a SHA512: d54dd621f490ee64213f964085ae28d0f8b40b7af6703c0372769ea02dd98867f431c2b770c6f780dad845f1346a57f97bb2cf701e024f20954418fae20953b3 Homepage: https://cran.r-project.org/package=PracticalEquiDesign Description: CRAN Package 'PracticalEquiDesign' (Design of Practical Equivalence Trials) Sample size calculations for practical equivalence trial design with a time to event endpoint. Package: r-cran-practicalsigni Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-generalcorr, r-cran-xtable, r-cran-shapleyvalue, r-cran-nns, r-cran-randomforest, r-cran-hypergeo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-practicalsigni_0.1.2-1.ca2404.1_all.deb Size: 497090 MD5sum: fe419f59d3ea2a9a90feec0952bb8e57 SHA1: bd451c1b4a49f4943c78d0afb7f124cda0a0e928 SHA256: e45da535d2fd68740efdd8be1575b219c8f976fbe22249a59e81e91072ec33d2 SHA512: 26b2a5c2dc48f6ea1b48fd583526b0be516b98ea6a5a1d7d448c80d2ae5d5aaeeaaac5636473bb0cfe9856b8ae1a9ba1df0e8bfba4b93137876b38baca53fc6a Homepage: https://cran.r-project.org/package=practicalSigni Description: CRAN Package 'practicalSigni' (Practical Significance Ranking of Regressors and Exact t Density) Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021) and Vinod (2022). m7= a generalization of psychologists' effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the 'np' package for kernel regressions. m9= partial (dy/dxi) using the 'NNS' package. m10= importance measure using the 'NNS' boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added. Package: r-cran-practools Architecture: all Version: 1.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5025 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-mass, r-cran-usmap Suggests: r-cran-doby, r-cran-foreign, r-cran-kableextra, r-cran-knitr, r-cran-lpsolve, r-cran-markdown, r-cran-plyr, r-cran-pps, r-cran-rcpp, r-cran-reshape, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sampling, r-cran-samplingbook, r-cran-sp, r-cran-survey Filename: pool/dists/noble/main/r-cran-practools_1.7.6-1.ca2404.1_all.deb Size: 3877792 MD5sum: 03666f1497da41189d4416765eda19ca SHA1: 8af3fa89d7f21952af47b713c76f8869feacd2a4 SHA256: 7e61223702159d8e7ba9ce166a3ebc4aa35533813f5ffe46ff5f0d02391af03f SHA512: 8916a2713ec20fbc335c6527500d17717f5d2031ea3ca31917ee2de28ad6604c04b804d3f6121eb3f78e51810480e5dbba3989dff72485c1aefd4e823c035f33 Homepage: https://cran.r-project.org/package=PracTools Description: CRAN Package 'PracTools' (Designing and Weighting Survey Samples) Functions and datasets to support Valliant, Dever, and Kreuter (2018), , "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs, sample sizes in two-phase designs, and a stopping rule for ending data collection. A number of example data sets are included. Package: r-cran-prais Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-pcse Suggests: r-cran-haven, r-cran-lmtest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prais_1.2.0-1.ca2404.1_all.deb Size: 83750 MD5sum: afdc27aa7572af26926f3c958551a8f0 SHA1: 5e6da16fd349ac93f3bac1b1713eeab7e18de5a1 SHA256: 268ba37d7d1a38406956d9631d770fff1dcbe8fcee79f33899fbbdd519ae315e SHA512: 3d4eedeb58b035df4eaa942b401663e2e0332d9c8fcbcd283d17877032613624e1948ea438832735b22d4535d5fee38b7db66b23a94c421e2263e7fa6518e506 Homepage: https://cran.r-project.org/package=prais Description: CRAN Package 'prais' (Prais-Winsten Estimator for AR(1) Serial Correlation) The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained. Package: r-cran-praise Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-praise_1.0.0-1.ca2404.1_all.deb Size: 17876 MD5sum: 3bb8f323208a8a91d95f03aaa7e262bf SHA1: 1cc51ee1af99b13d41c429787715de84f7a7deb2 SHA256: 660b10a6e8c11c22c39e7d8a6302fa9efcffee6e618142f844b33187c95bc519 SHA512: 5107900448d75a6e771740721c95208631f3ef86301f161a15dbd50de3d3bce510eb81870b75e2424af81dfb7b86bac9ff5c9be6431669173bda3a32870e6f0b Homepage: https://cran.r-project.org/package=praise Description: CRAN Package 'praise' (Praise Users) Build friendly R packages that praise their users if they have done something good, or they just need it to feel better. Package: r-cran-prakriti Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1967 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-prakriti_0.1.4-1.ca2404.1_all.deb Size: 1375376 MD5sum: 5607128810d4b8e861def552d1d86c15 SHA1: 94e8b316166bcb907efa761374886a579ba17504 SHA256: 3311ec0ea6117e95fd6e76b7747ee407bd4c517ce5c30da7123e884fe9dc846d SHA512: c2d1272c854a89c58ce76ee71f33fdd9398f16748903f06ee208455530438cb67f974984dadd6095c5e98a3fd72f4e82b05979670f3f6dee5be6da4d563b0674 Homepage: https://cran.r-project.org/package=prakriti Description: CRAN Package 'prakriti' (Color Palettes Inspired by India's Natural Landscapes) Curated color palettes drawn from India's natural beauty - Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's cold desert, Kashmir's autumn chinar, and more. Provides discrete and continuous palettes with first-class 'ggplot2' integration through scale_color_prakriti() and scale_fill_prakriti(), plus base graphics helpers for displaying palettes. Package: r-cran-prana Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-robustbase, r-bioc-minet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prana_1.0.6-1.ca2404.1_all.deb Size: 115496 MD5sum: 87a9c88df7e7571854614254155af756 SHA1: acbb8100bd080e3a2a33f08732a9675b07e5b2de SHA256: ce05c0cc9839e969510c3acd918ce6d86f7875c083fbaccbe04594a5b0f2f51c SHA512: ddfe08728ff164a8f9ab13756a1af539188a1766c5113650300bce1dabeb27bce342265af1b8e3b7162fed20f0510d847e078c2564dd9b36b9739dc9fefb5bac Homepage: https://cran.r-project.org/package=PRANA Description: CRAN Package 'PRANA' (Pseudo-Value Regression Approach for Network Analysis (PRANA)) A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) . Package: r-cran-prbmsdesigns Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-prbmsdesigns_1.0.1-1.ca2404.1_all.deb Size: 85410 MD5sum: 19e2c18e99fd047a04b7d4e34b067f23 SHA1: b788ea2cde28e72ba376f6c27077b7bfd713cc17 SHA256: 157ccef06ff970c650f6d4d1c2785663481de80c3d2485fd294a34a8d7ea097c SHA512: 390ae73d9510d0ad450b1a49ce7318d5f754bc203367d01d1c676153679688a06c1be5abe63820bcd7d13b44688de17fd2a68e0f15da03f3223e5a9efb7f1f2e Homepage: https://cran.r-project.org/package=PRBMSdesigns Description: CRAN Package 'PRBMSdesigns' (Partially Residual Balanced Multi-Session Designs) Provides functions for generating novel partially residual balanced multi-session designs. 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Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; ), with adjustments and improvements described in Fokkema (2020; ) and Fokkema & Strobl (2020; ). The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, survival and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables. Package: r-cran-precintcon Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-precintcon_2.3.0-1.ca2404.1_all.deb Size: 219394 MD5sum: 8ec4ba1be7cde6013b106a99c87ef275 SHA1: ae1d504b4a4a252941716b594c56e8586b47b11d SHA256: 268fbb2c005a8d203617da9dcaa47d4001e22e6498e9e99cf6b00b14f88615f2 SHA512: cbf9901a726d7fb1c0479fa47f2a2ea6d8c81dee1a1d6ae3ae132d12016b6a5ef6089c011454097d313d78173bed9ce842695e8bddc73a1330e35d4805c84c4d Homepage: https://cran.r-project.org/package=precintcon Description: CRAN Package 'precintcon' (Precipitation Intensity, Concentration and Anomaly Analysis) It contains functions to analyze the precipitation intensity, concentration and anomaly. 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More information and an example of implementation can be found in Vargas Godoy and Markonis (2023, ). Package: r-cran-precisely Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3075 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinythemes, r-cran-tidyr Suggests: r-cran-covr, r-cran-ggrepel, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-precisely_0.1.2-1.ca2404.1_all.deb Size: 2321880 MD5sum: 9b1bf44a7d2dd121c42f3719aaefb516 SHA1: d13407e75bc1f296a9ad5ad2d0484cb14d7dec7a SHA256: dc6425b74ab3f971a0a57cf346e9ac977c80fdbc827a5ede1751c3721fb764b8 SHA512: cdc4b6c9ad5b66f5cc5b04cab43c3f47ff6819faa3d51a0ea3bec94bcb3fa65feaaa4538315f3e45490c3dcf9fc53bdb31d2002790e3cb3ea9b3534a32db5983 Homepage: https://cran.r-project.org/package=precisely Description: CRAN Package 'precisely' (Estimate Sample Size Based on Precision Rather than Power) Estimate sample size based on precision rather than power. 'precisely' is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. 'precisely' is based on the work of 'Rothman and Greenland' (2018). Package: r-cran-preciseplacement Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-preciseplacement_0.2.0-1.ca2404.1_all.deb Size: 299778 MD5sum: 61a9b883af179d0e5b3b75c4a56cecc8 SHA1: d47817bab2be29c01e3e0146c2b0ef133d2d84d3 SHA256: 303bd1a37e57e278f11723026df17bc5e36d39f79e090703ff62918145e34274 SHA512: 177d27e46919858d54669a0594da653ab74b8016ab150b77b92b4b9e4d82d8d02545d8833daf6ad1773b24be938be347e46b3e21c9c6d7df4f90b2297572a7a2 Homepage: https://cran.r-project.org/package=precisePlacement Description: CRAN Package 'precisePlacement' (Suite of Functions to Help Get Plot Elements Exactly Where YouWant Them) Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par(). Package: r-cran-precmed Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gbm, r-cran-gam, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-mgcv, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-survival, r-cran-randomforestsrc Filename: pool/dists/noble/main/r-cran-precmed_1.1.0-1.ca2404.1_all.deb Size: 1007820 MD5sum: 78fc3b14155a79479aae248926b46611 SHA1: e42ae737dae3017dfca0a47af2bcb175153842d4 SHA256: 2876262aafdd44a35a77f83bb5669f7e43adde4ceef00e7aad835cd558e07297 SHA512: 66407d60f2f2b72986a66cdf93483c7a68710c3648df14f5e814222e998c47984c865f75ade225d709af10e0ae98ea41099c08e26b8e6fa42b838d67bca9ea3e Homepage: https://cran.r-project.org/package=precmed Description: CRAN Package 'precmed' (Precision Medicine) A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) . Package: r-cran-precommit Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-here, r-cran-magrittr, r-cran-purrr, r-cran-r.cache, r-cran-rlang, r-cran-rprojroot, r-cran-withr, r-cran-yaml Suggests: r-cran-desc, r-cran-docopt, r-cran-git2r, r-cran-glue, r-cran-knitr, r-cran-lintr, r-cran-pkgload, r-cran-pkgdown, r-cran-reticulate, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstudioapi, r-cran-spelling, r-cran-styler, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-precommit_0.4.3-1.ca2404.1_all.deb Size: 419818 MD5sum: 7786a242dedfe4c1fcbbff85bc7d9205 SHA1: 5e1247c561e95b73257684fff056b39c4314178d SHA256: df725cccaee068d2a07eb0701dcb7dede50f009bce3fb5e82f3d3760de36fe2f SHA512: 4d40f8f3017ec81d49e6ac3a0fa1cf0ce6598df5bfe64741fa37b25dfdc3320a03151b98e01bda089e75758c413fd586dcba34bef840ad11bc2bf453fa68b9f9 Homepage: https://cran.r-project.org/package=precommit Description: CRAN Package 'precommit' (Pre-Commit Hooks) Useful git hooks for R building on top of the multi-language framework 'pre-commit' for hook management. This package provides git hooks for common tasks like formatting files with 'styler' or spell checking as well as wrapper functions to access the 'pre-commit' executable. Package: r-cran-precviasbr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-rlang Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-precviasbr_0.1.1-1.ca2404.1_all.deb Size: 201794 MD5sum: d9572ed8d68e6cef0be07222e5975e06 SHA1: 6aee9e6d266c0a1bc2ae579245047a4ebf2d1a83 SHA256: e85d543066bf543a348674d12c8d3ead4e1f7736216d6af5daa81315d4d70d4d SHA512: ee8d7d40f3944ee926fe3a3913c90f795c3608ee1c163363ed617210983819e3899dac4e3c7c3abb3d1f59884b4ca0f58aee51ad17d4d706cd84cdc8c1ae6859 Homepage: https://cran.r-project.org/package=precviasBR Description: CRAN Package 'precviasBR' (Spatial Data of Road Precariousness in Brazil) Fornece acesso eficiente à malha espacial de precariedade viária brasileira. O pacote realiza o download em cache e a leitura otimizada (via Apache Arrow) de arquivos Parquet particionados, contendo o cruzamento de variáveis de infraestrutura do Entorno do Censo Demográfico 2022 (IBGE) com a malha viária aberta do Overture Maps. [English] Provides efficient access to the spatial network of road precariousness in Brazil. The package performs cached downloads and optimized reading (via Apache Arrow) of partitioned Parquet files. These files contain the intersection of infrastructure variables from the 2022 Demographic Census (IBGE) with the open street network from Overture Maps. Methodology and datasets are detailed in Passos (2026) . Package: r-cran-predcrg Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4694 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-protr, r-cran-peptides, r-cran-kernlab, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-predcrg_1.0.2-1.ca2404.1_all.deb Size: 4752156 MD5sum: 917f5ad2fc0baabe8da7dea2047e9bc1 SHA1: 887fb1d29ae275bcc52ab35dab8e21556d2f24ca SHA256: 34fc6bdb2920297f9ef57c5467b68153b77b6df037a5ce3cf5b57112b6006794 SHA512: 12541442d9be556caa50668d9f8ea3575964db7f85ce94ffa9603bcbf8dfdb2ed0cb24385ab1778c5513f2912419257f27d20f2c3b7a0fca862cc40084d015e3 Homepage: https://cran.r-project.org/package=PredCRG Description: CRAN Package 'PredCRG' (Computational Prediction of Proteins Encoded by Circadian Genes) A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set. Package: r-cran-predfairness Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-caret Filename: pool/dists/noble/main/r-cran-predfairness_0.1.0-1.ca2404.1_all.deb Size: 327484 MD5sum: 75eba62494ac5ad2ec0c4c0992a0254a SHA1: e67e6b8ef296661e8851ebdfcf6b4027ce7ef8ae SHA256: 5b73e5a30c6be2fa305c90bfce6d30fa36041ccad2dada5a3dced4d7a06b46af SHA512: 291b69647a860a869a6ad9b669cb817cd922ba32a4f1782ae79d1c9e6f639531b51b179206713e858cf9355d84067f749587e7cca5e7ff58a837fc5df49dec3b Homepage: https://cran.r-project.org/package=predfairness Description: CRAN Package 'predfairness' (Discrimination Mitigation for Machine Learning Models) Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) . Package: r-cran-predhcs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-predhcs_0.1.0-1.ca2404.1_all.deb Size: 57262 MD5sum: 1ed6e89d71e46622efe9fa59251f5143 SHA1: be9ee51607a4c9858f587af643f66006296a2074 SHA256: 476c3ba3a9cd4af30129e66af77213124a7bc183bedff8175153815e5cd27f5b SHA512: 74fc1b19ea69e8d8ccb375b1d856c12c1c72a070efd3638df2ddac79841fd35e6d6f3146ef1c167d7853c3578b116c9d18c0dc4e5efb978a5b261113c286960f Homepage: https://cran.r-project.org/package=predHCS Description: CRAN Package 'predHCS' (Point and Interval Prediction for Censored Data under VariousHybrid Censoring Schemes) Implements generalized statistical point prediction and prediction intervals for future failure times under various hybrid censoring schemes. Supported censoring schemes include Type-I, Type-II, Generalized Type-I, Generalized Type-II, Unified, Progressive Type-I, and Progressive Type-II hybrid censoring schemes. Available prediction methods include Best Unbiased Predictor (BUP), Conditional Median Predictor (CMP), Maximum Likelihood Predictor (MLP), equal-tailed classical prediction intervals, Highest Conditional Density (HCD) prediction intervals, and Bayesian prediction intervals. Algorithms accept user-defined continuous probability density functions, cumulative distribution functions, quantile functions, or survival functions along with estimated parameter values. Methodological foundations are based on Balakrishnan, Cramer, and Kundu (2023, ISBN:978-0123983879), Shafay and Balakrishnan (2012) for Type-I hybrid censoring, Balakrishnan and Shafay (2012) for Type-II hybrid censoring, Shafay (2017) for Generalized Type-I hybrid censoring, Shafay (2016) for Generalized Type-II hybrid censoring, Mohie El-Din, Nagy, and Shafay (2017) for Unified hybrid censoring, Ebrahimi (1992) , Valiollahi, Asgharzadeh, and Kundu (2017) , and Asgharzadeh, Valiollahi, and Kundu (2015) . Package: r-cran-predhy.gui Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-data.table, r-cran-dt, r-cran-predhy, r-cran-bglr, r-cran-pls, r-cran-glmnet, r-cran-xgboost, r-cran-lightgbm, r-cran-foreach, r-cran-doparallel, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-predhy.gui_2.1.1-1.ca2404.1_all.deb Size: 2307380 MD5sum: d9d56fc1c4f94bb97757058b32f05976 SHA1: c7ab579e4eadde4541098cd1896a49bdab793cd8 SHA256: 7fd85c239293895be541632fabb0360fffba66d1b195c310b172687fcb1b7a8d SHA512: 93f1dbad4c9ad98807b661bdb8e555aa3b86734f5ff77cbb1f387e66bb6485175cad82fcb5ddd7160075640d3fb3cb51f7930d3a39ed21433fe54c8a903a7ea7 Homepage: https://cran.r-project.org/package=predhy.GUI Description: CRAN Package 'predhy.GUI' (Genomic Prediction of Hybrid Performance with Graphical UserInterface) Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) ; Xu S (2017) ). A complete manual for this package is provided in the manual folder of the package installation directory. You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI"). Package: r-cran-predhy Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bglr, r-cran-pls, r-cran-glmnet, r-cran-xgboost, r-cran-lightgbm, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-predhy_2.1.3-1.ca2404.1_all.deb Size: 1263832 MD5sum: da14b995973f3ce93b09b1c42f7bc886 SHA1: e2dbee341f2d5eab5c6d3f237bc415573ce6c7d1 SHA256: 074063584e8051a998ad93303f63d76d697d44cb822836f371798faf7dc88581 SHA512: 868c3a1a8512d81aceda7e060cc4a1b32fe0805399f5853505d9b8f8c01764ad694e65ac4f7dc3fddb8c3e3302a7e72e22ded809d63395ee0f0b790dda5f146f Homepage: https://cran.r-project.org/package=predhy Description: CRAN Package 'predhy' (Genomic Prediction of Hybrid Performance) Performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) ; Xu Y et al (2021) ). Package: r-cran-predict3d Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rgl, r-cran-dplyr, r-cran-ggiraphextra, r-cran-modelr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-reshape2, r-cran-plyr, r-cran-tidyr Suggests: r-cran-moonbook, r-cran-th.data, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-predict3d_0.1.6-1.ca2404.1_all.deb Size: 549576 MD5sum: d89f8de0844b4bc821fae328a4e9cfb0 SHA1: 35daa0055d154bb1c14b2b41bf38a5ea953dd0eb SHA256: 6c6857bd3f997322543525deb241275be859651f1403effedefa14c4dd7c06e0 SHA512: 143ee5a80a320ed4eb133f8d42c22479eef8d09ac4dd1a3f2492a1279689d6069eee2f160bc7b5b721c6d604f83edf8859c4b18e05184f909021a4e386293abc Homepage: https://cran.r-project.org/package=predict3d Description: CRAN Package 'predict3d' (Draw Three Dimensional Predict Plot Using Package 'rgl') Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess). Package: r-cran-predictabel Architecture: all Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-rocr, r-cran-pbsmodelling, r-cran-lazyeval Filename: pool/dists/noble/main/r-cran-predictabel_1.2-4-1.ca2404.1_all.deb Size: 176968 MD5sum: 0b1105dbba5da80203bb8776c028710b SHA1: dc018e9ef0f4b5602a501d11b6b626e26bb93309 SHA256: 6d648a9cc1576a2a2f673414f936fa8ad1c207102839df9efae38ba379ea88cd SHA512: 08f02356696c48543428e1334531e57746c0ad82131285f24d22dc452966aa9895a7e71ef974433ba928f381d4c23ec5b251c2eb860c0b5358a0d837227f7a58 Homepage: https://cran.r-project.org/package=PredictABEL Description: CRAN Package 'PredictABEL' (Assessment of Risk Prediction Models) We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models. Package: r-cran-prediction Architecture: all Version: 0.3.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-prediction_0.3.18-1.ca2404.1_all.deb Size: 232876 MD5sum: 6e5cce65655b6ec4f1cbc273909d41d8 SHA1: 1da8fec02e23d20679f8594c8c3fc320d92d3fa4 SHA256: 78301af938c2ba05be01bfaaab235d301a4b75512f7e244251b54f227565107b SHA512: c39c3bb88c3f9e30b2d7dcc94021261f18c952fa8b2fb9318312dfea5e8403ff24cd355a92b3ed65bc2032177c51b19da322fa7eb8f19dd270890e27a9c6977d Homepage: https://cran.r-project.org/package=prediction Description: CRAN Package 'prediction' (Tidy, Type-Safe 'prediction()' Methods) A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' . The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing. Package: r-cran-predictioninterval Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mbess, r-cran-mass, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-predictioninterval_1.0.0-1.ca2404.1_all.deb Size: 60514 MD5sum: 09e6c3c2b736e705db65a2871d1e0b76 SHA1: 072be186a0c81acc2e30d1337f7a93f08954a3c5 SHA256: b1c743afa3a4e8cacf1c9cfbe86f799a6bc8eaaed4f91dbc783b7dbae2662871 SHA512: 393a0d707f523a46beeecb283cacd6cad47bd66243cb6a2164343fd55ec4239c7bcbbc45e12049ba6dc70453c977ee05b86dcb8946dcc71e6950b3f6bcc112b6 Homepage: https://cran.r-project.org/package=predictionInterval Description: CRAN Package 'predictionInterval' (Prediction Interval Functions for Assessing Replication StudyResults) A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the original study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a prediction interval). This package has functions that calculate the prediction interval for the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. Package: r-cran-predictionr Architecture: all Version: 1.0-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-renext Suggests: r-cran-actuar, r-cran-tolerance Filename: pool/dists/noble/main/r-cran-predictionr_1.0-12-1.ca2404.1_all.deb Size: 45704 MD5sum: c73c5068b8c21c1ae3fd2817dc573a30 SHA1: 58c236ee655b97c12bea6599db25ed1fdfcce0a8 SHA256: 87c676491fb7b8f8b9c05b46f9f5eede692034bd9bf474c00c3da7b472a689c4 SHA512: 3cc65da2123207ffa774db0f6a3bef9dd9448c3fff76816d18033e2dc1e998b97eac8eefe749e321938c617fa24b0a0132550feceb60300e6033bd935c56d4e6 Homepage: https://cran.r-project.org/package=PredictionR Description: CRAN Package 'PredictionR' (Prediction for Future Data from any Continuous Distribution) Functions to get prediction intervals and prediction points of future observations from any continuous distribution. Package: r-cran-predictme Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3919 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-predictme_0.1-1.ca2404.1_all.deb Size: 2348444 MD5sum: 95c7d1ade9884535a4c8567a06f7e6e8 SHA1: 91555093abb941d19e61d72faa8597fe84133f89 SHA256: d470a0036488d6db205ddeedf2544c3dda710bb82e68a9ae4e976fd7fb904157 SHA512: d4fa7247c70e35622c76b79f4af17e77b9b29423f75724d2630f12c56bcec54333f56f45f3011c1f482c34ddab03ba09f57c41ce572d565aa5849548c1b4012a Homepage: https://cran.r-project.org/package=predictMe Description: CRAN Package 'predictMe' (Visualize Individual Prediction Performance) Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. 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Details can be viewed in Parsons et al. (2023) . 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This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) . Package: r-cran-predictrace Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4935 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-predictrace_2.0.1-1.ca2404.1_all.deb Size: 4965538 MD5sum: bcfdd2ad25c5650b24554e2d4db4a4fd SHA1: dfe8d1cf80a7b6fabd43304573c548ea939406c3 SHA256: d1f2ac5c34bbf18caaf12f767c2f22f7e4134c4fc0c5a01ca8189ec4d1616b52 SHA512: 08a38c967fd7af74d4ac99d666fcc2d2965c0f0141fb461c0137bf3961ef195795a0a1b3f0b4befbee7777cc5d441d135d2c74d253b3fd0c1d32900d6457df2b Homepage: https://cran.r-project.org/package=predictrace Description: CRAN Package 'predictrace' (Predict the Race and Gender of a Given Name Using Census andSocial Security Administration Data) Predicts the most common race of a surname and based on U.S. Census data, and the most common first named based on U.S. Social Security Administration data. 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Methods include split conformal, 'CV+' and 'Jackknife+' (Barber et al. 2021) , 'Conformalized Quantile Regression' (Romano et al. 2019) , 'Adaptive Prediction Sets' (Romano, Sesia, Candes 2020) , 'Regularized Adaptive Prediction Sets' (Angelopoulos et al. 2021) , Mondrian conformal prediction for group-conditional coverage (Vovk, Gammerman, and Shafer 2005) , weighted conformal prediction for covariate shift (Tibshirani et al. 2019) , and adaptive conformal inference for sequential prediction (Gibbs and Candes 2021) . All methods are distribution-free and provide calibrated uncertainty quantification without parametric assumptions. Works with any model that can produce predictions from new data, including 'lm', 'glm', 'ranger', 'xgboost', and custom user-defined models. 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Data were collated from over 400 existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from sites around the world. These data are described in Hudson et al. (2013) . 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Includes helper functions for extracting fitted values, calculating model diagnostics, and comparing fitted models. Implemented model families include those described by: Zwietering et al. (1990) , Baranyi and Roberts (1994) , Baranyi and Roberts (1995) , Buchanan et al. (1997) , Richards (1959) , Fang et al. (2012) , Fang et al. (2013) , Huang (2008) , Huang (2009) , Huang (2013) , Geeraerd et al. (2005) , van Boekel (2002) , Peleg (1999) , Mafart et al. (2002) , Albert and Mafart (2005) , Rosso et al. (1993) , Rosso et al. (1995) , and Rosso et al. (1996) . Package: r-cran-predpsych Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-ggplot2, r-cran-caret, r-cran-rpart, r-cran-e1071, r-cran-mclust, r-cran-mass, r-cran-party, r-cran-randomforest, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-predpsych_0.5-1.ca2404.1_all.deb Size: 836320 MD5sum: 4f2faa32dcdd23bbd3cdf5307ce46304 SHA1: 567b8950b221fb036f21d1f250f3e48f4e64e856 SHA256: 30f09a92c94b0d50e154660d61647edf563d0fa7cc2e7e6a0d0194d7fa8f1dd7 SHA512: ebb834ef2e9596d9f29a198212f5e5a9fc4d10d50dfec14eb422ec156eadb328d1bad1ffa731e65494b3be7ea6b57d4e30c167388544efb8cacff88cb38c375b Homepage: https://cran.r-project.org/package=PredPsych Description: CRAN Package 'PredPsych' (Predictive Approaches in Psychology) Recent years have seen an increased interest in novel methods for analyzing quantitative data from experimental psychology. 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The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data . 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See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference Data" (2013) . 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The package provides methods to visualise the preference distribution of one contest with bar charts and pairwise comparisons of two contestants, as well as methods to visualise multiple contests through 2D and high-dimensional simplex plots both statically and interactively. HD simplex displays are implemented via projection methods using the 'tourr' and 'detourr' packages, enabling dynamic exploration of high-dimensional preference structure. For more details on HD simplex projection, see Wickham et al. (2011) . 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Calculates due dates from various starting points including last menstrual period and IVF (In Vitro Fertilisation) transfer dates, determines pregnancy progress on any given date, and identifies when specific pregnancy weeks are reached. Includes medication tracking capabilities for individuals undergoing fertility treatment or during pregnancy, allowing users to monitor remaining doses and quantities needed over specified time periods. Designed for those tracking their own pregnancies or supporting partners through the process, making use of options to personalise output messages. For details on due date calculations, see . Package: r-cran-preknitposthtmlrender Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-preknitposthtmlrender_0.1.0-1.ca2404.1_all.deb Size: 37298 MD5sum: 68cc04814e63f51cf79b23feed75c816 SHA1: 344eb195d4019b842155a8194ddf07d736c71a17 SHA256: a402d647afcc1496f180c0ac71676d94dba0db7bbf9dd6ad23c521b5cefea976 SHA512: 9ea4c46fa5fce133e31ff8afe1b7c2a71efe3b71b5f0ef16000aa5868ea77d75d07d16111135892d3c6b394a05393fb90b6bf360353e4d8b56083c917c1daa85 Homepage: https://cran.r-project.org/package=PreKnitPostHTMLRender Description: CRAN Package 'PreKnitPostHTMLRender' (Pre-Knitting Processing and Post HTML-Rendering Processing) Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc. Package: r-cran-prenoms Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4095 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-prenoms_0.0.1-1.ca2404.1_all.deb Size: 4142114 MD5sum: a12eb5a374ab4962f25460ab3784346b SHA1: a860fc0d6690c9b5ec066ada4f42582be1baae36 SHA256: 9572debdbc127402acbd67cdc3a2dd205e6ab68836edce5ea70eec8b9ba99d3b SHA512: a5041d8afad0d8d31dd2a18241aebe7085c2b31c57f2177c0e5c3220dee502db4fe15f614b3db9fadbdcdb1243051ed41b168584804f124b19f265c0281e917f Homepage: https://cran.r-project.org/package=prenoms Description: CRAN Package 'prenoms' (Names Given to Babies in Quebec Between 1980 and 2020) A database containing the names of the babies born in Quebec between 1980 and 2020. 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The finalized table contains several possibilities for dependent measures of the dependent variable. Most suitable when measuring variables in an interval or ratio scale (e.g., reaction-times) and/or discrete values such as accuracy. Main functions included are file_merge() and prep(). The file_merge() function vertically merges individual data files (in a long format) in which each line is a single observation to one single dataset. The prep() function aggregates the single dataset according to any combination of grouping variables (i.e., between-subjects and within-subjects independent variables, respectively), and returns a data frame with a number of dependent measures for further analysis for each cell according to the combination of provided grouping variables. Dependent measures for each cell include among others means before and after rejecting all values according to a flexible standard deviation criteria, number of rejected values according to the flexible standard deviation criteria, proportions of rejected values according to the flexible standard deviation criteria, number of values before rejection, means after rejecting values according to procedures described in Van Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame prep() returns can also be exported as a txt file to be used for statistical analysis in other statistical programs. 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Here, in this package, five different methods have been given to generate p-Rep designs easily. Package: r-cran-prepkit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-prepkit_0.1.1-1.ca2404.1_all.deb Size: 458710 MD5sum: 84c243bd9f2d8e5bff0e5c5d55adc0a3 SHA1: ca3d840be9438a1a4b023966658e524b963a6cd0 SHA256: 7335e30b578209fb08ba4c1c7f0c143e12382eadd415ce08e8e0eb325d93626e SHA512: 22d66c5902d9168db94aa37c2d625372ef878c9a0fe9c54389663b6e84241368a3c1150fb217a4436c66a3e598c826c679d47e912c720bc9b4aa02d097dc80e7 Homepage: https://cran.r-project.org/package=prepkit Description: CRAN Package 'prepkit' (Data Normalization and Transformation) Provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) , Yeo-Johnson transformation in Yeo and Johnson (2000) . 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In research, preregistrations are commonly used to clearly document plans and facilitate justifications of deviations from those plans, as well as decreasing the effects of publication bias by enabling identification of research that was conducted but not published. Like reporting guidelines, (pre)registration forms often have specific structures that facilitate systematic reporting of important items. The 'preregr' package facilitates specifying (pre)registrations in R and exporting them to a human-readable format (using R Markdown partials or exporting to an 'HTML' file) as well as human-readable embedded data (using 'JSON'), as well as importing such exported (pre)registration specifications from such embedded 'JSON'. 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The title, presentes, comes from present in spanish. Package: r-cran-preseqr Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom Filename: pool/dists/noble/main/r-cran-preseqr_4.0.0-1.ca2404.1_all.deb Size: 228416 MD5sum: b423b040a3fcc49b187afcc4e36a6e51 SHA1: 3375a012ab3a6a53969032b60446d8f61820d73e SHA256: 84ccca2a1d0fc4a9e34c0be3683d30eb452d0fe606af42b0db3f104e08a2e132 SHA512: cb98c2bbd730196dd7aee0b5fdaa6c058a05baa86693c32b79a47ef107a310aa811965c20c417d41b7e63e60144e1ccdb393317ebafc9f9c545fc6acd12c467f Homepage: https://cran.r-project.org/package=preseqR Description: CRAN Package 'preseqR' (Predicting Species Accumulation Curves) Originally as an R version of Preseq , the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018) . Package: r-cran-presiduals Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-formula, r-cran-rms, r-cran-sparsem Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-presiduals_1.0-2-1.ca2404.1_all.deb Size: 230468 MD5sum: 0ea468247e28c4d0ee8364ab2c434924 SHA1: 6bd124fdd5de3110c10dd84a2c6ec52692c9b0e0 SHA256: d3528ac01ea05ec552ec8dd10d3640acd71cbe15f1a04700c988722fdc37d84e SHA512: fcce2f8f298f34f87d1d320028b0b6296448a63e2bf9417fb43e80e6cb08008bd124bec7f4ee91a32fd7baf28db95a75e3bb8b821b1e9b0ffb94b556019e99c3 Homepage: https://cran.r-project.org/package=PResiduals Description: CRAN Package 'PResiduals' (Probability-Scale Residuals and Residual Correlations) Computes probability-scale residuals and residual correlations for continuous, ordinal, binary, count, and time-to-event data Qi Liu, Bryan Shepherd, Chun Li (2020) . Package: r-cran-presize Architecture: all Version: 0.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kappasize, r-cran-shiny Suggests: r-cran-binom, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-hmisc, r-cran-knitr, r-cran-magrittr, r-cran-markdown, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-shinytest, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-presize_0.3.11-1.ca2404.1_all.deb Size: 408130 MD5sum: a94a0ab0022474c103b035a020d99768 SHA1: 36c24aabf27224c7bd6930fc5789d466fcb204c6 SHA256: b0ca9874c20147f1276ab8a14b502fc8fb68ebabfa07ab48bf66007f5dae11ba SHA512: 55e43c7e291cdf04ad4137663bbf73c69e6031f1ed25399e96358fd0574546481049c4ec0b41a6e5f678561a0ee05afd44db4f04605e22ee3104dc19fbf2b80c Homepage: https://cran.r-project.org/package=presize Description: CRAN Package 'presize' (Precision Based Sample Size Calculation) Bland (2009) recommended to base study sizes on the width of the confidence interval rather the power of a statistical test. The goal of 'presize' is to provide functions for such precision based sample size calculations. For a given sample size, the functions will return the precision (width of the confidence interval), and vice versa. Package: r-cran-presmtp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survpresmooth, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-presmtp_1.1.0-1.ca2404.1_all.deb Size: 56702 MD5sum: 0732d749afc7db4c0a2eb31a72d7750b SHA1: b80721b71baa7d7cda6d57558fa42ecc5812adc0 SHA256: 66f52fdcc2a2db1827ec47c1ec62c5b30f9cf66ad3fda5007ae533af07498b96 SHA512: 74b2cb026e1897a8b8d86034c6224c350a920ee0665782202133e6db5a5f4c593dcdf51bfe28b60374cd6ac92e28e98fd2e87718e7644eb00c948746ee46337b Homepage: https://cran.r-project.org/package=presmTP Description: CRAN Package 'presmTP' (Methods for Transition Probabilities) Provides a function for estimating the transition probabilities in an illness-death model. The transition probabilities can be estimated from the unsmoothed landmark estimators developed by de Una-Alvarez and Meira-Machado (2015) . Presmoothed estimates can also be obtained through the use of a parametric family of binary regression curves, such as logit, probit or cauchit. The additive logistic regression model and nonparametric regression are also alternatives which have been implemented. The idea behind the presmoothed landmark estimators is to use the presmoothing techniques developed by Cao et al. (2005) in the landmark estimation of the transition probabilities. Package: r-cran-pressfreedom.data Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readr, r-cran-here, r-cran-rlang, r-cran-countrycode, r-cran-dplyr, r-cran-stringr, r-cran-cli, r-cran-fs, r-cran-glue, r-cran-purrr, r-cran-tibble, r-cran-stringi Suggests: r-cran-usethis, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-forcats, r-cran-patchwork, r-cran-scales, r-cran-sf, r-cran-rnaturalearth, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-pressfreedom.data_0.3.0-1.ca2404.1_all.deb Size: 800286 MD5sum: 70dc4ceab44b9e75b65efb541254858a SHA1: 7eee3aa8b349095e86d2eba9443d25375d2b6075 SHA256: 79c9811a42393bb91b5376a576848559a938d76e6206c6784cd369ac097b7e10 SHA512: 8200cc4ddcd91213afc45562d07d42f413649a8665dc95e15391d30adf3652412b6a99b81164cf08a520fd47f1e08faeabd4a84651bbc07d93d3b58746a5ea2f Homepage: https://cran.r-project.org/package=pressfreedom.data Description: CRAN Package 'pressfreedom.data' (Download and Process Reporters Without Borders Press FreedomIndex Data) Download press freedom index data from Reporters Without Borders (RSF) with period-aware encoding handling. Data are downloaded from the RSF website (). Provides infrastructure for data cleaning and ISO 3166 standardization in downstream phases. Package: r-cran-pressfreedom Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pressfreedom.data, r-cran-shiny, r-cran-bslib, r-cran-dplyr, r-cran-plotly, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-rcolorbrewer, r-cran-countrycode, r-cran-tidyr, r-cran-purrr Suggests: r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pressfreedom_0.2.0-1.ca2404.1_all.deb Size: 686144 MD5sum: 987f21baa6c64bf81e644d6739e272a5 SHA1: 0a1161c553ae5c98e97256bb764612e3b02fe250 SHA256: b00106ec1a18e2591ecf38fda932185c385978a65f5f50bdfc95d0890e1a6d93 SHA512: d938af7f0598c8d13e2f20b4698b24006a04a932ea840915f9d898e3b6e0f3ba7d1ebb47c859a41b9b98168a61a7b2d83d07eb790e8c93c971a497e20d181974 Homepage: https://cran.r-project.org/package=pressfreedom Description: CRAN Package 'pressfreedom' (Press Freedom Dashboard) A Shiny dashboard for exploring Reporters Without Borders (RWB) Press Freedom Index data from 2002 to the present. Combines RWB scores with United Nations M49 geographic classifications to enable comparisons across countries, regions, and time. Package: r-cran-presspurt Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-presspurt_1.0.2-1.ca2404.1_all.deb Size: 185456 MD5sum: 858753f8f4a1ee387b8979d60c0f79f2 SHA1: d69927315acda1aaac5ae56b49675e1716731c36 SHA256: c50ae49d3c1d0c9ee8f5a593ddf7de919708e353abe940608a423bae38154371 SHA512: ee32d06c239a43692e963e878afc7a6d36b0af7fae8406e860739be6af4248a1b893ff19817638ba4732db7fd8d04ac04035bea7b8ea7a6aebb024325006114c Homepage: https://cran.r-project.org/package=PressPurt Description: CRAN Package 'PressPurt' (Indeterminacy of Networks via Press Perturbations) This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) . Package: r-cran-pressure Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-dplyr, r-cran-gdistance, r-cran-ggmap, r-cran-ggplot2, r-cran-magick, r-cran-magrittr, r-cran-morpho, r-cran-pracma, r-cran-raster, r-cran-rdist, r-cran-readxl, r-cran-rvcg, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pressure_0.2.7-1.ca2404.1_all.deb Size: 809424 MD5sum: 13d95a960017c021aa58457034cb9947 SHA1: 4efdffbde9795467e930739e58f53ad0cb7194e3 SHA256: 71c9cf7bcb637ca4fc988b906404df355eac5712bb8aca48128a98a2c8ec5c42 SHA512: eb6dd1c0556614c397aed2a6a4066271dba2e8138b12a886dcdb3ecbd941f36b6400eab9beaff3e9c686b8f4e1e00041eae143eda9e1b34d2f2e7a24d275f20a Homepage: https://cran.r-project.org/package=pressuRe Description: CRAN Package 'pressuRe' (Imports, Processes, and Visualizes Biomechanical Pressure Data) Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) , Lee et al., (2014) , van der Zward et al., (2014) , Najafi et al., (2010) , Cavanagh and Rodgers (1987) . 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Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs. Package: r-cran-primefactr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-primefactr_0.1.1-1.ca2404.1_all.deb Size: 20390 MD5sum: e3ca7c3f431a9be53851a790d0ccbfef SHA1: 8c17010d0bd04dc310ebe043a3e7e9e49af484ee SHA256: 9d2e48f41bea8dbccd0d98fdaaee400500b8e121d4e22b9ed68190263ab660c6 SHA512: 93d159f358f61475c83b90901f2ecbaf64a859e757b86d0d0af1ba90f73b953a011432421fd174c119190be573c80252a61c570014431fa6b1be17f28ec3db70 Homepage: https://cran.r-project.org/package=primefactr Description: CRAN Package 'primefactr' (Use Prime Factorization for Computations) Use Prime Factorization for simplifying computations, for instance for ratios of large factorials. Package: r-cran-primepca Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-softimpute, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-primepca_1.2-1.ca2404.1_all.deb Size: 36004 MD5sum: 1518cb890292c5072c9810f0cf8618fb SHA1: 7606cf029685176cd15af62e0ff8408049c6e013 SHA256: 8733d6328dd28eb56f6b00969e41d2b211d777389c21559ec6ea2f5519e4ec6e SHA512: a5dc1e85ee5b3e916fdb4e3dc46cf91ceb3764254431d26cea1134f092797e0b1d6cd2eb3bb5b97f55577d4c5a338c11d51129c9992ce20fdc65fea93f613f17 Homepage: https://cran.r-project.org/package=primePCA Description: CRAN Package 'primePCA' (Projected Refinement for Imputation of Missing Entries in PCA) Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. . 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Henry H. Stevens (2009) . Package: r-cran-prindt Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-party, r-cran-splitstackshape, r-cran-stringr, r-cran-gdata Filename: pool/dists/noble/main/r-cran-prindt_2.0.3-1.ca2404.1_all.deb Size: 413836 MD5sum: b4949e5497385eb15d379821226a396b SHA1: f21e0ea4dcd2b8658550e847f298210c3729c8f5 SHA256: 18c1a187132e879aec1cf9dd9effc4b529f0f58662888f213da6c2ad172c899a SHA512: 176c31c019b92b3ccfa820c75c6a7d8b9d3c1f95f44701a2e57d3006470a6a394113ef3f46f7fcac069790398312f7047b1f3b928caacfac32d9e21a46708047 Homepage: https://cran.r-project.org/package=PrInDT Description: CRAN Package 'PrInDT' (Prediction and Interpretation in Decision Trees forClassification and Regression) Optimization of conditional inference trees from the package 'party' for classification and regression. For optimization, the model space is searched for the best tree on the full sample by means of repeated subsampling. Restrictions are allowed so that only trees are accepted which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a). The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated applications of PrInDT() for different percentages of the observations of the large and the small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel classification. In addition to these PrInDT() variants for classification, the function PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() allows for a posterior analysis of the distribution of a specified variable in the terminal nodes of a given tree. In version 2, additionally structured sampling is implemented in functions PrInDTCstruc() and PrInDTRstruc(). In these functions, repeated measurements data can be analyzed, too. Moreover, multilabel 2-stage versions of classification and regression trees are implemented in functions C2SPrInDT() and R2SPrInDT() as well as interdependent multilabel models in functions SimCPrInDT() and SimRPrInDT(). Finally, for mixtures of classification and regression models functions Mix2SPrInDT() and SimMixPrInDT() are implemented. Most of these extensions of PrInDT are described in Buschfeld & Weihs (2026). References: -- Buschfeld, S., Weihs, C. (2026) "Optimizing decision trees for the analysis of World Englishes and sociolinguistic data", Cambridge Elements. ; -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example" ; -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" ; -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles" . 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Package: r-cran-priogene Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3385 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-org.hs.eg.db Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-priogene_1.0.1-1.ca2404.1_all.deb Size: 2917682 MD5sum: 8363c44f7dd29b628bc870c46e333bdd SHA1: f848a56af3aeb16b3dbcccffdb1fcf7ab2698d20 SHA256: db9f8b6beb080e410c6ac4678e5d291e093c75137576f69f66a4b09dfa42b83a SHA512: 973f9b6998c777d50575109e1b4dbc9c385455dde0062a78179f4e6c9a8c06e68e088d48b210eb7f82ced695f16998d51900959ba9e06d54fadb0cb085e36868 Homepage: https://cran.r-project.org/package=prioGene Description: CRAN Package 'prioGene' (Candidate Gene Prioritization for Non-Communicable DiseasesBased on Functional Information) In gene sequencing methods, the topological features of protein-protein interaction (PPI) networks are often used, such as ToppNet . In this study, a candidate gene prioritization method was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease PPI networks with weights for nodes and edges based on functional information. Package: r-cran-prior3d Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4283 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-prioritizr, r-cran-terra, r-cran-maps, r-cran-highs, r-cran-viridis, r-cran-readxl, r-cran-rasterdiv, r-cran-geodiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prior3d_0.1.6-1.ca2404.1_all.deb Size: 2838412 MD5sum: 722933879b74c5437d4bcf0b0a1e1182 SHA1: 74634729170bf197751754377df1bcb60bf591d0 SHA256: f9fe3bdb3c58a6145da403f29fecbfe7e5ef1edccdf955e5f09f9e8c1973ba59 SHA512: 5450f25da41b9b6ad47536f35f7d90dd2ee6fbec079ed4ea2c5ff100815193a79afef958d60992df95508c84da012e02e2d0a544dd5f702c03863635f0d84c12 Homepage: https://cran.r-project.org/package=prior3D Description: CRAN Package 'prior3D' (3D Prioritization Algorithm) Three-dimensional systematic conservation planning, conducting nested prioritization analyses across multiple depth levels and ensuring efficient resource allocation throughout the water column. It provides a structured workflow designed to address biodiversity conservation and management challenges in the 3 dimensions, while facilitating users’ choices and parameterization (Doxa et al. 2025 ). Package: r-cran-priorcon Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1830 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-prioritizr, r-cran-terra, r-cran-highs, r-cran-tmap, r-cran-sf, r-cran-braingraph, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-priorcon_0.1.8-1.ca2404.1_all.deb Size: 960564 MD5sum: 6684609bf7fd59e87bb49ff4c3c22fce SHA1: 8869fe7dac471661171b0d53d5198f7f4f2e9410 SHA256: da9a6d0c4783d09cb4fd77a5329c4d1a44428a437b5edbc1db8a077b4e53dee6 SHA512: e502888b6d8367059731fe6c9b30f27b2afea54a19a447faca526638956bec6e439354a826aefa9653a7d7d93d923be51214b0601bd323f5797a72f17c91a32c Homepage: https://cran.r-project.org/package=priorCON Description: CRAN Package 'priorCON' (Graph Community Detection Methods into Systematic ConservationPlanning) An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, 'priorCON' focuses on clusters of features that exhibit strong ecological linkages. The 'priorCON' package is built upon the 'prioritizr' package , using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems. Package: r-cran-priorgen Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve, r-cran-nleqslv Suggests: r-cran-spelling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-priorgen_2.0-1.ca2404.1_all.deb Size: 207852 MD5sum: e5dec32a26e70f465ac412ac6700805b SHA1: 5d4fb43384da6ef6f7429307e615eea2d3e7f14e SHA256: 94cb5ab31390826ae23ef82181eb13ebfb1f38d84a5f03100c87d16a40dc40f0 SHA512: 0e6a44a749ad8109ee1ed95853c98e3c17b2392efc23ddb76bb62088ebd11093cf77a87aaf57cfdef10d373b8a7b26e9057c4eb453743ae541e5800817b4c731 Homepage: https://cran.r-project.org/package=PriorGen Description: CRAN Package 'PriorGen' (Generates Prior Distributions for Proportions) Translates beliefs into prior information in the form of Beta and Gamma distributions. It can be used for the generation of priors on the prevalence of disease and the sensitivity/specificity of diagnostic tests and any other binomial experiment. Package: r-cran-prioritizrdata Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4708 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-tibble Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr Filename: pool/dists/noble/main/r-cran-prioritizrdata_0.3.3-1.ca2404.1_all.deb Size: 4492486 MD5sum: 8dba64673bb02699d68ca7400cd47008 SHA1: 0e846099ff7aa03cf04e891bdf794d40b2fff3cd SHA256: 8e7d92a56005f664367c2378049b98ecd1c1eb6a08a486e0efe4509438e4b1e0 SHA512: bd6922b3793b8a3976e0004ce0fb41d503cb6a6543aebdd1bc4880086f3db1dea32a70c475b154d1d20465f662da716162766020ae272da370f4b496d265422c Homepage: https://cran.r-project.org/package=prioritizrdata Description: CRAN Package 'prioritizrdata' (Conservation Planning Datasets) Conservation planning datasets for learning how to use the 'prioritizr' package . 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(2018) ) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets. 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Other packages exist for formatting numbers and tests according to the APA style guidelines, such as 'papaja' () and 'apa' (), but they do not offer all convenience functionality included in 'prmisc'. The vignette has an overview of most of the functions included in the package. 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Package: r-cran-processcapabilityr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-processcapabilityr_0.1.0-1.ca2404.1_all.deb Size: 114286 MD5sum: c66a0e5f237e50a090ed6146bed06efd SHA1: e14ea7de748d141f95d40de2c8d652ee7976b8fa SHA256: b4651f8baf0596b3814086bba9b19a9fc2c39c9a41ac4276d8edd490b87e64be SHA512: 098c4d17663001285f827c0baa0322f7c0ebc20fccd41a0661a11f8b45584a651696abd93ada923f446ef9b9ce4e477e3bd4cb1c51a14eabeb4a6f40bb7450e2 Homepage: https://cran.r-project.org/package=ProcessCapabilityR Description: CRAN Package 'ProcessCapabilityR' (Classical and Generalized Process Capability Indices) Computes classical process capability indices (Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Pp, Ppk, Ppu, Ppl, Z) and the generalized process capability index Cpy (Maiti, Saha & Nanda, 2010) for any continuous or discrete quality characteristic. Users supply the probability density function (PDF) and cumulative distribution function (CDF) of the characteristic, and the package returns point estimates, bootstrap confidence intervals (percentile and BCa), and sensitivity tables/plots across ranges of short-term standard deviation (sigma), long-term standard deviation (s), desired yield (p0), and significance levels. Classical indices are recoverable as special cases under the normal distribution. The package follows the theory and notation of Kane (1986) , Chan, Cheng & Spiring (1988) , Pearn, Kotz & Johnson (1992) , Kotz & Johnson (2002) , Montgomery (2020, ISBN:978-1-119-39930-8), Juran (1974, ISBN:978-0-07-033176-1), Harry & Schroeder (2000, ISBN:978-0-385-49437-2), and the AIAG SPC Reference Manual (2005, ISBN:978-1-60534-026-3). 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Package: r-cran-prodigenr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-gert, r-cran-rlang, r-cran-rprojroot, r-cran-whisker, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prodigenr_0.7.0-1.ca2404.1_all.deb Size: 47430 MD5sum: 387876ff88415c60e6d725444998140c SHA1: d8b948016aafc5858ee6b009dee7d8190f06e7cb SHA256: 33e3ac892be0b67f0e869d27cf2b93fd22ae151ca1c4afdd878b021b5e00a1c3 SHA512: 0496796a0eeca587b9c0688e68914157e028b36a5cc8303ca74b6bf2625994ce5f36b2b28980e6733ddebf316a9b07808ca885a45b392bc6697181f11429f001 Homepage: https://cran.r-project.org/package=prodigenr Description: CRAN Package 'prodigenr' (Research Project Directory Generator) Create a project directory structure, along with typical files for that project. 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Package: r-cran-productivity Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolveapi, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-productivity_1.1.2-1.ca2404.1_all.deb Size: 418164 MD5sum: ca04afe789276d3a0cae4e66e3d5c4e3 SHA1: 7ea114a4ce4da6ac83b215a110a6d2084c3b589b SHA256: 96f6f5312a6bbc390bd93f44b8d418cb238a876f228cc3c73211619d3d8e04e3 SHA512: 9c53b544f01e3bb3c1c3c5e233e3346c91e164640c9ee3533f800f1c0aa196c11179f5867ff151264cf63e4ffb9771da7ac3dd9e6e93f07febfe096bec953a8a Homepage: https://cran.r-project.org/package=productivity Description: CRAN Package 'productivity' (Indices of Productivity Using Data Envelopment Analysis (DEA)) Levels and changes of productivity and profitability are measured with various indices. The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index. Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for multilateral or multitemporal comparison. Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are only to be used for binary comparison. All indices can also be decomposed into different components, providing insightful information on the sources of productivity and profitability changes. In the use of Malmquist productivity index, the technological change index can be further decomposed into bias technological change components. The package also allows to prohibit technological regression (negative technological change). In the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont and Lowe indices, it is furthermore possible to rule out technological change. Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as an option, depending on the user's computer configuration. All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and several assumptions regarding returns to scale are available. All DEA linear programs are implemented using 'lp_solve'. 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Package: r-cran-profilemodel Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-gnm Filename: pool/dists/noble/main/r-cran-profilemodel_0.6.2-1.ca2404.1_all.deb Size: 107404 MD5sum: a81f3feb38839a5eae4da3e20216c6ee SHA1: 452eeb079963ff561229c2f7084f3da10cb49e2b SHA256: 36d220de276929628ed51aeb5fad482a251b32760d617e042e36e177b5ae2ab0 SHA512: 729e0fdabdcec64b7893081a68ff1743844ab46f838baf7e3e7b32b8d1d84fa7a7dc9237de5dbd4e275793a90f25a3769fa779bae99e3aec9f0a73e0bc883b1d Homepage: https://cran.r-project.org/package=profileModel Description: CRAN Package 'profileModel' (Profiling Inference Functions for Various Model Classes) Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. 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Profile repeatability is an individual repeatability metric that uses the variances at each timepoint, the maximum variance, the number of crossings (lines that cross over each other), and the number of replicates to compute the repeatability score. For more information see Reed et al. (2019) . 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Package: r-cran-progvine Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-progvine_0.1.0-1.ca2404.1_all.deb Size: 20936 MD5sum: 6edb59d991fb747c1441e07f0ccb84d6 SHA1: d05146862d64293a75a7ac6bc69ed5ce8583654c SHA256: fd8e12962de105bb8ba02e102be75fceac75023f4d0d6acf62b0130fb7eee8cc SHA512: fbdbf92d1831a329713e86a56fb17eab47028ee85614643abd4b9e1027bb5319b5b63685072d12b3450575cb30c97c7c9940501aefa969df91a7de43588340dd Homepage: https://cran.r-project.org/package=ProgVine Description: CRAN Package 'ProgVine' (Progressive Regularized Vine Copula for Masked Competing Risks) Implements Progressive Regularized Vine Copula (Prog-Vine) frameworks for high-dimensional dependent competing risks with masked failure causes under Progressive Type-II Censoring. Fits Weibull marginals, estimates pair-copula trees using Expectation-Maximization (EM) algorithms, computes Louis observed information confidence intervals, and implements Data Augmentation Gibbs Samplers for Bayesian credible intervals. Package: r-cran-projections Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-incidence, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-outbreaks, r-cran-magrittr, r-cran-distcrete, r-cran-svglite, r-cran-epiestim Filename: pool/dists/noble/main/r-cran-projections_0.6.1-1.ca2404.1_all.deb Size: 781926 MD5sum: f355014d7c5cfdcd72ac34a9ca7b6f58 SHA1: 0b5720db7325a8cbed85dcc267b45a069c1401ad SHA256: 161ef1cc9e2dac15943c7cdb589196d48398f97bc8c27a7a437bc590dab21155 SHA512: 8f6fa247cea7f26f2f65a3b0e21e4c27ae928ca5684c6cdd570087f5ba8c86e425c0f22038c5bf746005a5af2101f738fe49b289bbea0f4fa03e644c66ce0297 Homepage: https://cran.r-project.org/package=projections Description: CRAN Package 'projections' (Project Future Case Incidence) Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) . The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods. Package: r-cran-projectlsa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shinybs, r-cran-colourpicker, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ggiraph, r-cran-ggplot2, r-cran-glca, r-cran-haven, r-cran-httr, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-lavaan, r-cran-magick, r-cran-mclust, r-cran-mirt, r-cran-officer, r-cran-plotly, r-cran-polca, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-semplot, r-cran-semptools, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidylpa, r-cran-viridislite, r-cran-writexl Suggests: r-cran-pdftools, r-cran-pkgdown, r-cran-semtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-projectlsa_0.1.1-1.ca2404.1_all.deb Size: 173190 MD5sum: b8a07ddcca97ecb4b15a8f9efb0f5f7c SHA1: 143b82604831d086d81b30c8f9f5b86ac17623e7 SHA256: 0423f99e4f657ddf7e02b23b17f36912d44058735fc4d1b36b86dbad9bb3197f SHA512: 4cae6961a5a925edd75425590743f8aa01e4bbe0e0db2d0afcf764b52bf45a198f17fcd5cc8b98bf72f50871f68fb95e3333d8640e07a9fb06254411a3fbe54c Homepage: https://cran.r-project.org/package=projectLSA Description: CRAN Package 'projectLSA' (Shiny Application for Latent Structure Analysis with a GraphicalUser Interface) Provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through 'tidyLPA' (Rosenberg et al., 2018) , LCA through 'poLCA' (Linzer & Lewis, 2011) & 'glca' (Kim & Kim, 2024) , LTA/IRT via 'mirt' (Chalmers, 2012) , and EFA via 'psych' (Revelle, 2025). SEM and CFA functionalities build upon the 'lavaan' framework (Rosseel, 2012) . The CFA/SEM module additionally supports multi-group invariance testing, latent growth modelling, modification indices, and path diagram visualisation. Every module can save and restore an analysis session, export an 'R Markdown' HTML report, and consult an optional AI assistant that interprets the current results through a user-supplied large language model API key. Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming. Package: r-cran-projectmanagement Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-lpsolveapi, r-cran-triangle, r-cran-tuvalues, r-cran-igraph Filename: pool/dists/noble/main/r-cran-projectmanagement_2.1.4-1.ca2404.1_all.deb Size: 163776 MD5sum: 1aad4c1dcdbc9794167125d6c9b2da83 SHA1: 3f1d650e13bde9948a4c68cd6b50fcf92ed3b967 SHA256: 604f8452d303b48c046cba7434c03ebd560732d52d81f6db7d74147219e67776 SHA512: a52149fffb73eddff5a95797f93423a9e9c2d42677084a63276dabe29e9a95178819d3657c65c2053d8cc31f17cf6c594fc51234b15bf2537ff7609fc13670ec Homepage: https://cran.r-project.org/package=ProjectManagement Description: CRAN Package 'ProjectManagement' (Management of Deterministic and Stochastic Projects) Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) ). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) ). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out. 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Package: r-cran-projmgr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gh, r-cran-magrittr Suggests: r-cran-clipr, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-reprex, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-yaml, r-cran-htmltools, r-cran-httr, r-cran-covr Filename: pool/dists/noble/main/r-cran-projmgr_0.1.2-1.ca2404.1_all.deb Size: 221356 MD5sum: 0a66a7449aa5f7b8e7558a1032b32221 SHA1: 0799ceb6661e4094adfa15cf9db15b33ba3b7803 SHA256: 01ea3c250c43a3906e59d642f587912515b8c5b0efcc689c0238afaed5782654 SHA512: 9851413d49378859dc0f06d1bba80254ff95313d767f9f52f511da9973708e7cc4a63d2844d88aab8e9806084b1838e48ac74a0137e5d06acef31f4b50854232 Homepage: https://cran.r-project.org/package=projmgr Description: CRAN Package 'projmgr' (Task Tracking and Project Management with GitHub) Provides programmatic access to 'GitHub' API with a focus on project management. Key functionality includes setting up issues and milestones from R objects or 'YAML' configurations, querying outstanding or completed tasks, and generating progress updates in tables, charts, and RMarkdown reports. Useful for those using 'GitHub' in personal, professional, or academic settings with an emphasis on streamlining the workflow of data analysis projects. 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H., Schweinberger, M., Baugh, S. (2021) , and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) . Package: r-cran-prometar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-metafor, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-prometar_1.0.0-1.ca2404.1_all.deb Size: 80136 MD5sum: 77dfcd7cee20db358b391ee9360750e5 SHA1: 12c48c95a1e86a8b45f5f549640c5bf3901916de SHA256: 77004c41899d04c9b1300d594e360c106ecfa0ffe1013e5f9245c780539e6099 SHA512: 7d74377b243c0fa0fba6f0eca670bbeb84cd8155e524e7cc113deb2884b634098b57209086814b9e8303b1c3b4bce9f73dc8c0f60c1f69fe4f0a4ff2dbb697e0 Homepage: https://cran.r-project.org/package=ProMetaR Description: CRAN Package 'ProMetaR' (Meta-Analysis of Proportions and Prevalence) Tools for meta-analysis of proportions and prevalence from studies reporting event counts and sample sizes. 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Package: r-cran-promethee123 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-promethee123_0.1.0-1.ca2404.1_all.deb Size: 27894 MD5sum: a0ed62c1c881c30e7d1e8ecd5cd43c6d SHA1: 2f8e39987ddfa2fe8e041593f083d0d5dd62144f SHA256: 052d278397b4c4a133857789cfd4a00b412340c36f8c6c9a750e01e4eff8853b SHA512: 73db7ef3914b69a0bc3d89079c78685f02c9717fcb66a59b7ac31f796e07c80fcb546cb0637ba7d8de43ea7a40d3ab2c47022f7c8d8c84e3bf3cd6434cbcc33c Homepage: https://cran.r-project.org/package=promethee123 Description: CRAN Package 'promethee123' (PROMETHEE I, II, and III Methods) The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems. The method establishes a preference structure between the alternatives, having a preference function for each criterion. 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Package: r-cran-propoverlap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4758 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-propoverlap_1.0-1.ca2404.1_all.deb Size: 4837700 MD5sum: d1e689ef71520f92ce63883238179973 SHA1: 1384522335cfd467e034fcaf563edf3ef23919ef SHA256: 0ab0ac36c00d8efc0f98bae2a479073ad786326b8f50a784aeb938988906d7b0 SHA512: f28bab3b759573a22b49540688ae666cf9b0583d67d8b6a4036d89e58447388c89ffa7a113aa5801929bf9a2d31f9e34865fdec1d7f2b2de95f61da9d2ef780d Homepage: https://cran.r-project.org/package=propOverlap Description: CRAN Package 'propOverlap' (Feature (gene) selection based on the Proportional OverlappingScores) A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced. Package: r-cran-propscrrand Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-propscrrand_1.1.2-1.ca2404.1_all.deb Size: 35658 MD5sum: 57ef3fe6a54fb6be9b3a19a8907631ee SHA1: 8a9009a1be0b22b961699f54380333b72234e2dd SHA256: aa9fe067740c63a6d5d60ce5e0a1e7e3fdc6aa3f7fe1753186b0591fe4dce025 SHA512: e69d8e9b899621a723a631c4d21299f8ad2898df861eb7bff2fb95a9439b096a5106a3833ad58f431b3f256b987b9f1b89519261d7fdffdc40c3c29450954591 Homepage: https://cran.r-project.org/package=PropScrRand Description: CRAN Package 'PropScrRand' (Propensity Score Methods for Assigning Treatment in RandomizedTrials) Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization. Currently only supports trials comparing two groups. Package: r-cran-proptestr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desctools, r-cran-ratesci Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-proptestr_1.0.0-1.ca2404.1_all.deb Size: 67474 MD5sum: f2d6e44a072f42d24739fd7220cae5d4 SHA1: 4d7ccd1bbbe2d9f806cdc85f46170d5536b452fa SHA256: aee1911e5c38c4ee68d6e03090a680f18a80669ef91f4e8a151f3fd4c8b364d9 SHA512: 4b277a26022defcfe8cc77ef49fc72ad1fa0bf08ddad760249b18389b7d5622e63b2b6feb711f26bb54e7e25fd47205c6e0d066427153a6a47c45638b91fdeb9 Homepage: https://cran.r-project.org/package=PropTestR Description: CRAN Package 'PropTestR' (Comprehensive Two-Proportion Inference) Unified methods for comparing two independent or paired proportions. Provides classical, exact, score-based, non-inferiority, equivalence, effect-size, confidence-interval, and stratified procedures with standardized publication-ready output. Farrington-Manning inference is supported through established score-based methods described by Farrington and Manning (1990) and implemented through 'ratesci', while additional established methods are provided through 'DescTools' and base R. Package: r-cran-propubbills Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-propubbills_0.1-1.ca2404.1_all.deb Size: 23224 MD5sum: 3b1aa2fa290e455017a712134d68c6a7 SHA1: db6f3acc57cb74e09725eb2bb2117ee62400d546 SHA256: 3b245144c0d77e6c8b70f13721ca8186f775e075dfa5639e83eb0522b01caeb6 SHA512: b86e7234651b739bd0f56e26d8d19541a53306ec942f8653560850481811f7397e0230f665e74636f8dd29da29e7a4cd6ed04b430907587efd594f4be4f56872 Homepage: https://cran.r-project.org/package=proPubBills Description: CRAN Package 'proPubBills' ('ProPublica' U.S. Congress Bills API Wrapper) An API wrapper around the 'ProPublica' API for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs. Package: r-cran-propublicar Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-httr, r-cran-config, r-cran-lubridate Suggests: r-cran-testthat, r-cran-httptest, r-cran-knitr Filename: pool/dists/noble/main/r-cran-propublicar_1.1.4-1.ca2404.1_all.deb Size: 292136 MD5sum: 08f395c6332a8a25a06b20e40b85b182 SHA1: 2cfd8c1831b7da90810621bd2762eb6f32b16495 SHA256: ca4d278d510395e650cd8a1c0d7d7b3521cc3bac2c03826456558bd4925f9e5b SHA512: a1f1be7a310fd5ad7bb4384f3de3865e703c9aad717ba4c894a9f05a784f40b507d884a4228ff14a8e979dbf4b038311df44146d6dca18d93f10276ce5702cc6 Homepage: https://cran.r-project.org/package=ProPublicaR Description: CRAN Package 'ProPublicaR' (Access Functions for ProPublica's APIs) Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs. The Congress API provides near real-time access to legislative data from the House of Representatives, the Senate and the Library of Congress. The Campaign Finance API provides data from United States Federal Election Commission filings and other sources. The API covers summary information for candidates and committees, as well as certain types of itemized data. For more information about these APIs go to: . Package: r-cran-proratar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-proratar_0.1.0-1.ca2404.1_all.deb Size: 17230 MD5sum: 819478fbe07baf7d595e0cb7cf001662 SHA1: 935f62f13ab928e4cf61186b7d59ab63adb9e707 SHA256: 831f8021bd03351bfede64251e3f05b933b62f60866d8aa764529f53f75d657a SHA512: f5027d02ddbddef266b524db69eada6e0e0f63e4be393138cac0c49db729062f25d4187ce2b74a1d19fe17f7a7c74f09c289eeabcbe5c2e4e70ef25a884f9757 Homepage: https://cran.r-project.org/package=proratar Description: CRAN Package 'proratar' (Proportional Allocation with Sum Consistency) Provides robust functions for proportional allocation of numeric values. It guarantees sum consistency after rounding or integer truncation using one of two adjustment methods: the largest remainder method or max-value adjustment. Handles edge cases like NA weights and vector total values seamlessly. Package: r-cran-proreg Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fmsb, r-cran-car, r-cran-rcolorbrewer, r-cran-matrixcalc, r-cran-rootsolve, r-cran-numderiv, r-cran-matrix Filename: pool/dists/noble/main/r-cran-proreg_1.3.3-1.ca2404.1_all.deb Size: 171042 MD5sum: 3145bafbbb73d7a7ac8f2aa2ae78af66 SHA1: 53b1b411fbb381e300e7deec7a050b690414e057 SHA256: 10544a77c4ece99ede5391c520240e135bf956c1ce27f60643fde46e142f51bc SHA512: 874dacdf57c420d90eb4fb2c84a3ed53edcb0f1e949fefdfb4ff18690a272a56ae90c2c124ff277c960d1e5330e2c6b1f5b0333576f67bec13fd4b81d26b48db Homepage: https://cran.r-project.org/package=PROreg Description: CRAN Package 'PROreg' (Patient Reported Outcomes Regression Analysis) It offers a wide variety of techniques, such as graphics, recoding, or regression models, for a comprehensive analysis of patient-reported outcomes (PRO). Especially novel is the broad range of regression models based on the beta-binomial distribution useful for analyzing binomial data with over-dispersion in cross-sectional, longitudinal, or multidimensional response studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019) ). Package: r-cran-proscorer Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proscorertools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-proscorer_0.0.4-1.ca2404.1_all.deb Size: 105290 MD5sum: 9e639171229783d98ee12ab3a40ddbdb SHA1: 8e0b3f4ccf230f56c4db5a296a40dc5365f89e3b SHA256: 28551ba3775e6023aab6e6953fe048d8a8cd192137fd433d9966031fc1cf132b SHA512: 9bc14a11a0c7f6a644f6afe8f16d60dffba5af01ef35fe388d804ed1340eb18ba10f7716f0bec62ff9288b2794e365f62ee8ecf1a432809bb3ebdc7d29ddbd3e Homepage: https://cran.r-project.org/package=PROscorer Description: CRAN Package 'PROscorer' (Functions to Score Commonly-Used Patient-Reported Outcome (PRO)Measures and Other Psychometric Instruments) An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. 'PROscorer', together with the 'PROscorerTools' package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The 'PROscorer' Instrument Descriptions vignette contains descriptions of each instrument scored by 'PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each 'PROscorer' function is composed of helper functions from the 'PROscorerTools' package, and users are encouraged to contribute new functions to 'PROscorer'. More scoring functions are currently in development and will be added in future updates. Package: r-cran-proscorertools Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-proscorertools_0.0.4-1.ca2404.1_all.deb Size: 78930 MD5sum: 9d97f875f831e58ac440390e8d1c598a SHA1: 7258c879724d6b4f822d2cb2aaa9108e6e06607f SHA256: a687f145d0b951c54f8354312f0368d82a235eccf9359216b848f6825ea2d71a SHA512: c5237c69b35e35120b60f9d12fed638a500edf86061902e7a3a11cecefbeb64cd37d5c80b639d77ebf92d5fa5b49beba5a9de40d4c78b98d6fb30018537d4956 Homepage: https://cran.r-project.org/package=PROscorerTools Description: CRAN Package 'PROscorerTools' (Tools to Score Patient-Reported Outcome (PRO) and OtherPsychometric Measures) Provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the 'scoreScale' function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' functions can be used to write new functions that score more complex measures. In fact, 'PROscorerTools' functions are the building blocks of the scoring functions in the 'PROscorer' package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use 'PROscorerTools' to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in 'PROscorer' (a tutorial vignette will be added soon). The long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research. Package: r-cran-prosgpv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3716 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-brglm2, r-cran-mass, r-cran-survival Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-prosgpv_1.0.0-1.ca2404.1_all.deb Size: 2341858 MD5sum: 334835fc353fb2b31ac470671c2ac323 SHA1: aff5deac5f21d1af8b5025dfb2e76578c5990024 SHA256: 3c01a244c69c085c7cad5a4aab853387ea4b7b102db76ef19d72d2fd99f683a0 SHA512: 2ffed576f7f0c9ef6d1147dfd77693ad25316a98dc0f947a623db7a91ab599b6c28a9f6864dc2a6265f8d47f8df6a3e43d752277299d5a5fb0e4000a137c3215 Homepage: https://cran.r-project.org/package=ProSGPV Description: CRAN Package 'ProSGPV' (Penalized Regression with Second-Generation P-Values) Implementation of penalized regression with second-generation p-values for variable selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details can be found at Zuo et al. (2021) . Package: r-cran-prosper Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-prosper_0.3.3-1.ca2404.1_all.deb Size: 267788 MD5sum: 3da94e9b301c75c3100b39529d36087d SHA1: 91bc501761035c9a87d3a93da03e072b4d23db49 SHA256: bbce441328762e606d4959d3eab7e4609d2b32691d1e3775a6a740f7c054d9ef SHA512: 7dd9277fa3f8f19359896040cf6930863150e3ea60b8e98c6eca910df4b870a155cb3e13d25bb893688a0dc7e6d645bdf08faa29e2acd507bc947d3e7d241efe Homepage: https://cran.r-project.org/package=PROSPER Description: CRAN Package 'PROSPER' (Simulation of Weed Population Dynamics) An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015). Renton, M, Diggle, A, Manalil, S and Powles, S (2011) Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.) Package: r-cran-prosportsdraftdata Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prosportsdraftdata_1.0.3-1.ca2404.1_all.deb Size: 2559784 MD5sum: 291e7bafef3fca341dbb47b3464e4e0b SHA1: e8130a40e8981151f02cee72213942087d71ce39 SHA256: f0d29c60476f2c6b78dd6264af8ae21585e5d7cfbf5ddedf89ec7fbf136b8d76 SHA512: 373a97fed8cac766018bbab667bf8a8d8b226047cc810d53903815a51f6fdb5a32cdd3658d6e704ce9631793f2e79168f5cfc446b4a83d3de0961718edbb7446 Homepage: https://cran.r-project.org/package=ProSportsDraftData Description: CRAN Package 'ProSportsDraftData' (Professional Sports Draft Data) We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" . Package: r-cran-protag Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-protag_1.0.0-1.ca2404.1_all.deb Size: 58834 MD5sum: a55fe96e4bb9492dc34f3c58f0caf254 SHA1: 15260de2c85bfaa747c6107332737056512ee4ea SHA256: 13800989daa5bcf256a038a1d40b409ee7f85b7c7d9be14ff37ea7377f814b50 SHA512: 09dfc7c60b370649f6bb677e6485013655e3f3fad286c764d911785cb1ac506f6df07f869c79a1fa1307ac6361c1c1fe31dd0b8df335c6197a78e287309b1ef1 Homepage: https://cran.r-project.org/package=protag Description: CRAN Package 'protag' (Search Tagged Peptides & Draw Highlighted Mass Spectra) In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) ; Elizabeth Chang (2016) . Package: r-cran-prote Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-vegan, r-cran-uniprotr, r-cran-stringr, r-cran-missranger, r-cran-car, r-cran-openxlsx, r-cran-tidyr, r-cran-broom, r-cran-reshape2, r-cran-ggpubr, r-cran-ggplot2, r-cran-vim, r-cran-forcats, r-bioc-limma, r-cran-pheatmap Suggests: r-cran-biocmanager, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-prote_1.0.3-1.ca2404.1_all.deb Size: 1917850 MD5sum: 59b347b3a575b20859c8ac692d9c5e60 SHA1: 0721e3e907d488dde0d352ca02771fb1700281d2 SHA256: 4e1131ddd0f83f4f55904344008d5e1ac94049b97c81236a81f335f8f2af5b7b SHA512: 5bba5bc44fa22cc2e5ca47b48cee3e2214f024c1adfca7e4adfe596c7f7f490a4d261567aea02b4cbc99b25596f8b45dc7f4e3f6f8431d447101859aaaebc59e Homepage: https://cran.r-project.org/package=ProtE Description: CRAN Package 'ProtE' (Processing Proteomics Data, Statistical Analysis andVisualization) The 'Proteomics Eye' ('ProtE') offers a comprehensive and intuitive framework for the univariate analysis of label-free proteomics data. By integrating essential data wrangling and processing steps into a single function, 'ProtE' streamlines pairwise statistical comparisons for categorical variables. It provides quality checks and generates publication-ready visualizations, enabling efficient and robust data analysis. 'ProtE' is compatible with proteomics data outputs from 'MaxQuant' (Cox & Mann, (2008) ), 'DIA-NN' (Demichev et al., (2020) ), and 'Proteome Discoverer' (Thermo Fisher Scientific, version 2.5). The package leverages 'ggplot2' for visualization (Wickham, (2016) ) and 'limma' for statistical analysis (Ritchie et al., (2015) ). Package: r-cran-protein8k Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-magick, r-cran-dplyr, r-cran-gridextra, r-cran-ggplot2, r-cran-rjson, r-cran-rlang, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-protein8k_0.0.2-1.ca2404.1_all.deb Size: 2214728 MD5sum: 92af5600df0da304b9b086ba3ba5d642 SHA1: 9edc287342c72f03f95d745abf425d797d1d786e SHA256: 39c162da76dffa2c9fc1a253a7e14bfbab0f504d4ae70b6a894bf2b57f38dc24 SHA512: 3dbbfbf156be919e9d487925ed2fb9502c237a4a72bfcd47d0ed5013e4f2bd171872899550b2d5a3a32c98e8b3033581d3b546abf78ae84d5070a4af898d2390 Homepage: https://cran.r-project.org/package=protein8k Description: CRAN Package 'protein8k' (Perform Analysis and Create Visualizations of Proteins) Read Protein Data Bank (PDB) files, performs its analysis, and presents the result using different visualization types including 3D. The package also has additional capability for handling Virus Report data from the National Center for Biotechnology Information (NCBI) database. Nature Structural Biology 10, 980 (2003) . US National Library of Medicine (2021) . Package: r-cran-proteinpca Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-proteinpca_0.1.1-1.ca2404.1_all.deb Size: 18360 MD5sum: 27b0789329615c70973f146e7d542cda SHA1: 6b978558ffaa8a258ad1a7138f26d16dff6aa613 SHA256: 152b83535eb13b96b50c494977f8052a9e037fcc1ca670473ba39f793fdefdb6 SHA512: 0c1eebfe2b142011708e738a22ef10dbd7346a2c890ec1a3a27c8c04c58310c60ed2e63be8144270d011660c5ddb62b83256db04b08eb8969ca2e6af72612d27 Homepage: https://cran.r-project.org/package=ProteinPCA Description: CRAN Package 'ProteinPCA' (Principal Component Analysis (PCA) Tool on Protein ExpressionData) Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) , Gabriel (1971) , Zhang et al. (2024) , and Anandan et al. (2022) . Package: r-cran-proteobayes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-extradistr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-proteobayes_1.0.0-1.ca2404.1_all.deb Size: 142640 MD5sum: be5066b80c3beb602eb147b1c687d9ab SHA1: abd6ff9b8552ff989e7574886a309ca73a6d2adc SHA256: 686619b37013e96223437fe9a5819968557b5295afb6aaa3396866804a8a2184 SHA512: 4d859e6e049d7a5d1acf3043e8f4b38aaf127447ffc4184b9fc5062244487ad1d48663729502081435f82c480dcedffb178069228d43f9ea9b32da29a860061b Homepage: https://cran.r-project.org/package=ProteoBayes Description: CRAN Package 'ProteoBayes' (Bayesian Statistical Tools for Quantitative Proteomics) Bayesian toolbox for quantitative proteomics. In particular, this package provides functions to generate synthetic datasets, execute Bayesian differential analysis methods, and display results as, described in the associated article Marie Chion and Arthur Leroy (2023) . Package: r-cran-proteomicscv Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-proteomicscv_0.4.0-1.ca2404.1_all.deb Size: 11778 MD5sum: 6e0a18bb33abfa1f0f2eba2c5391a829 SHA1: 983c356c9da82fb9dffc1343380f8af8ea357898 SHA256: 9cad6c5af943b6078be7fecff8577e59704ce488bb669dae1e97535e7e458091 SHA512: 8d2819395c845e362f8b677b6ad0482c81f16a1b8fac17131afe113ef29e5af7fe5647760a3ba09f5d3f71e4fb8467157f31fd060af50cf2ba8484ddf7cbfad1 Homepage: https://cran.r-project.org/package=proteomicsCV Description: CRAN Package 'proteomicsCV' (Calculates the Percentage CV for Mass Spectrometry-BasedProteomics Data) Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data. 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Package: r-cran-prothmm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-phontools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prothmm_0.1.1-1.ca2404.1_all.deb Size: 181198 MD5sum: 2e0ad1bd6e0dca8d8e64fb5a443ae88b SHA1: d0e9f9420e42f7a37f18882cc7902d828908348e SHA256: dd2fd928d818cff9511806e56e586d55b68961efae9d81443b8932c986019d01 SHA512: 171194048647a81856e71203f8b69c3d77ec79f0e10da759c5bdc9bfe172c3724fe905bfd1bc66d67a6efb281a050dfb2b04eec441458c359ead4f1f25dc34b1 Homepage: https://cran.r-project.org/package=protHMM Description: CRAN Package 'protHMM' (Protein Feature Extraction from Profile Hidden Markov Models) Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) and 'HMMer' Eddy (2011) . Features calculated by this package can be used for protein fold classification, protein structural class prediction, sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features extracted are found in Song et al. (2018) , Jin & Zhu (2021) , Lyons et al. (2015) and Saini et al. (2015) . Package: r-cran-proto Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-proto_1.0.0-1.ca2404.1_all.deb Size: 464840 MD5sum: fb6c95e0b2cbf9a8be0e5bb6782bc183 SHA1: a3d667584b35a7a157c8ba543ae9179878435e38 SHA256: 8095dd94eb7e983b0957f3bac2324793294472df7d379a8a1d8fba4499656278 SHA512: 082ed15be7443c6e3f48a05dc73a0619f7bc5f3870aeb8aa8351e7e5d1fdd7972cacdd1f62be7742ba22bc11d108768d040b689ef2bef296a4c922ba2b381881 Homepage: https://cran.r-project.org/package=proto Description: CRAN Package 'proto' (Prototype Object-Based Programming) An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas. 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You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit. Package: r-cran-protoshiny Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-dynamictreecut, r-cran-protoclust, r-cran-rare, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-protoshiny_0.1.1-1.ca2404.1_all.deb Size: 1431150 MD5sum: d278154f2896547016bb8a6d0327aaed SHA1: b70de28c7a363afa296335a1c60b6ab82c82fcdd SHA256: 54db928abde6b086af71ab17cd3f998d9d16967afa4349f03c1dc16151063abe SHA512: 56af961d9f9b54ea6cfcd45320f3aaf448079713e6d7a64f100e12e18ab0675a4a29e6d955025ef7f045bc209f566d49ad159fb478b98e050323f85b92925974 Homepage: https://cran.r-project.org/package=protoshiny Description: CRAN Package 'protoshiny' (Interactive Dendrograms for Visualizing Hierarchical Clusterswith Prototypes) Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) . This package currently launches the application. 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For full functionality, the software 'ncbi-blast+' is needed, see for more information. Package: r-cran-protrackr Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-lattice, r-cran-signal, r-cran-tune Suggests: r-cran-amigaffh Filename: pool/dists/noble/main/r-cran-protrackr_0.4.4-1.ca2404.1_all.deb Size: 912538 MD5sum: a3e31c9c0c7a622cc97b82f364a5e1f6 SHA1: 3902b37135a891cd17e01420d8c2ec1bae2a7e66 SHA256: 5f4ac7704951f5a0a912d6e39e0b6cba88feba8c8eb9d016ee953fddabd8aa0e SHA512: 07782d14d0fe3b7cce20986bed502e2231bae65d5f87c3855acb467b0ffee9d6a5388343783d9c2e5a15291bf83b20d35863b28ef4dcdd9a58d9bf5768cb5f2d Homepage: https://cran.r-project.org/package=ProTrackR Description: CRAN Package 'ProTrackR' (Manipulate and Play 'ProTracker' Modules) 'ProTracker' is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play 'ProTracker' module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of 'ProTracker' and the Commodore Amiga alive. Package: r-cran-protti Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-data.table, r-cran-janitor, r-cran-progress, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-forcats, r-cran-tibble, r-cran-plotly, r-cran-ggrepel, r-cran-curl, r-cran-readr, r-cran-lifecycle, r-cran-httr, r-cran-r.utils Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-r3dmol, r-bioc-proda, r-bioc-limma, r-cran-dendextend, r-cran-pheatmap, r-cran-heatmaply, r-cran-furrr, r-cran-future, r-cran-seriation, r-cran-drc, r-cran-igraph, r-cran-stringi, r-bioc-stringdb, r-cran-iq, r-cran-scales, r-cran-farver, r-cran-ggforce, r-cran-xml2, r-cran-jsonlite, r-cran-missforest Filename: pool/dists/noble/main/r-cran-protti_1.0.0-1.ca2404.1_all.deb Size: 1744390 MD5sum: 508d5f96ac79c2d23c015f50a070d7f9 SHA1: 45721966435e75aae3e6ee4e772d0bf6411badde SHA256: e14035831af642edaf75d9e4c05177b13da10f975e42674ea15fc6fb62a9bab0 SHA512: 367111ecda99a419dbb91fb84f06bf45cc25745b746bd9f6e6210412abc562579eb23f78c0ae1ecc4f2993179e88f815add3397a1f7b5d70e5e17de059f30028 Homepage: https://cran.r-project.org/package=protti Description: CRAN Package 'protti' (Bottom-Up Proteomics and LiP-MS Quality Control and DataAnalysis Tools) Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) ) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions. Package: r-cran-proustr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2823 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-tokenizers, r-cran-snowballc, r-cran-attempt Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-proustr_0.4.1-1.ca2404.1_all.deb Size: 2794110 MD5sum: fd222cb0a91dc6a2ae74187e431a4bcb SHA1: 71f3f731509cf1ab7a5533c4291b30e075500dc4 SHA256: ab1ca093142fe48ab3eb3c86bc3359c875a23cdd7bb9886eb2b4d0dfaf927f35 SHA512: 780476617621c1890db838adebd3093972565e2bb97f52e797c2f8ad8368c3a943fc336cbdbb302bf8a10a95d72daeefbc70a86f44dad1e8015808d4cc07e691 Homepage: https://cran.r-project.org/package=proustr Description: CRAN Package 'proustr' (Tools for Natural Language Processing in French) Tools for Natural Language Processing in French and texts from Marcel Proust's collection "A La Recherche Du Temps Perdu". The novels contained in this collection are "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve". Package: r-cran-prova Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18265 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-nimble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-prova_2.3.0-1.ca2404.1_all.deb Size: 2861126 MD5sum: e8a1592879b996f6db7ee6e5718b62b8 SHA1: 0eb020a46ad5f65d7a7dc1c0a64cbf1a5b1985c2 SHA256: 9a4350f1c407ef27462e59495e138d4071dad6b8d40a3976fe9f8e5b83c1669f SHA512: 664e772e980e7e285320be90952b2310b9f83a82daaa436f0908793b79c37fe1ea90ae9ac299c3f621d6e45e1c7b91b34f891b77136a054b4a4d4fba59c9beb2 Homepage: https://cran.r-project.org/package=prova Description: CRAN Package 'prova' (Nonparametric Probabilistic-Statistical Variate Analysis) Calculate posterior joint and conditional probabilities, probability distributions of population frequencies, information-theoretic measures, and expected utilities, by means of Bayesian nonparametrics. Data can be any combination of nominal, ordinal, continuous, censored, rounded types. Data imputation is automatic and done in a principled way. Markov-chain Monte Carlo calculations are automatically handled and do not require user supervision. Applications range from statistical estimation and probabilistic hypothesis testing to evidence-based inference and decision making, in a wide range of disciplines from astrophysics to medicine. For more details and examples see for instance Porta Mana & al. (2026) , Dunson & Bhattacharya (2011) , Lindley & Novick (1981) , Bernardo & Smith (2000) , Müller et al. (2015) . Data-training function requires the package 'Nimble'. Package: r-cran-provdebugr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-provgraphr, r-cran-provparser, r-cran-textutils Suggests: r-cran-knitr, r-cran-rdtlite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-provdebugr_1.0.1-1.ca2404.1_all.deb Size: 148504 MD5sum: 8d87c94d96b8a83f891b81e06e74d982 SHA1: 679238a36e923e80fd9478b97fd060c53de39407 SHA256: 01fc9e33f818e0add01c768e49000bfc4da2f16ac914864032de74a34f5d380d SHA512: cdf9076d83d720ab075f54a868b36887837cdd0720065067e344808e1d7f25dc93e560462db4003da7b87441ccc88a0d548111cce489083c629f32365bb69806 Homepage: https://cran.r-project.org/package=provDebugR Description: CRAN Package 'provDebugR' (A Time-Travelling Debugger) Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. 'provDebugR' uses provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format. Package: r-cran-provenance Architecture: all Version: 4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-transport, r-cran-t4transport Filename: pool/dists/noble/main/r-cran-provenance_4.4-1.ca2404.1_all.deb Size: 832530 MD5sum: 7505350bf29b42377744e21ce5888495 SHA1: 76a1fe2c8116819dae8d25e5e8280fb0700f7421 SHA256: 30331bf091d31f62c4d4127309359ea6c4fc0213c3233968e96e40620dfdf8b0 SHA512: 3420ab6b35f076ace9fb436096ad967cb5e1e8c2d89daa32f5bafd9a91a1b8fba18f2ad150398cc74ee81ad096cffac48a48d54b2e7a7c6ff19f18008f128605 Homepage: https://cran.r-project.org/package=provenance Description: CRAN Package 'provenance' (Statistical Toolbox for Sedimentary Provenance Analysis) Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R. Package: r-cran-proverbs Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-crayon, r-cran-httr2, r-cran-glue, r-cran-lubridate, r-cran-purrr, r-cran-rvest, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-proverbs_0.4.0-1.ca2404.1_all.deb Size: 321384 MD5sum: 536417360755f87f966c5604ae53b6fc SHA1: 9c4ea30b419fdaf1cc5a475f15f81fc9f2f3cbe5 SHA256: 8ead269e5332c84b4705718e0dc651abf814649e77a4f582317977cb8fc70807 SHA512: 153f5173b571308596ff9e7e307f9a4aab476136f603a568dacf71a067f33154c3f859397a79564ddc8abfe5abddf33fa82792e59a0aa5bac243aa50708b545c Homepage: https://cran.r-project.org/package=proverbs Description: CRAN Package 'proverbs' (Print a Daily Bible Proverb to Console) A simple package to grab a Bible proverb corresponding to the day of the month. Package: r-cran-provexplainr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-provparser, r-cran-diffobj, r-cran-digest, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-provexplainr_1.1.1-1.ca2404.1_all.deb Size: 69414 MD5sum: bd4909507918df0598ba699684f653e8 SHA1: 60c8831eac8582f931da1394a246c85c38d85542 SHA256: 54eed5e82ed53c0eb40f9736e58f121a928885362f16997b5d93559ed525f392 SHA512: f1966080e0a13b6145681109009022ea1f86e9f60ca3531217e3735416208e9f818aaef389d1c7c62530d0791ae2ff549f633e5bc4e438d92b4ad8f9efc3ab37 Homepage: https://cran.r-project.org/package=provExplainR Description: CRAN Package 'provExplainR' (Compare Provenance Collections to Explain Changed Script Outputs) Inspects provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, 'provExplainR' can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about 'rdtLite' and associated tools is available at and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-provgraphr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-provparser Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-provgraphr_1.0.1-1.ca2404.1_all.deb Size: 112126 MD5sum: 53198ca616ded8625a3b7408121f88db SHA1: 5a89de31eec750739c79b1b90fac01605f8f4919 SHA256: 33559edb77da3350ae1f9ecae7f43a2ed684ef96340e0a0bea516c450bd49911 SHA512: 508bcf151cb154a147892b511fded4ed81778b1dbcbd417cf965fab98e5ed25a3894fb3e5adca49195303e1ea494e6fdcce63da2b2d40f51bfacad80761e1b6a Homepage: https://cran.r-project.org/package=provGraphR Description: CRAN Package 'provGraphR' (Creates Adjacency Matrices for Lineage Searches) Creates and manages a provenance graph corresponding to the provenance created by the 'rdtLite' package, which collects provenance from R scripts. 'rdtLite' is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (). The extended JSON provenance format is described in . Package: r-cran-provolleyballr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-janitor, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-selenider Suggests: r-cran-chromote, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-provolleyballr_0.1.0-1.ca2404.1_all.deb Size: 431808 MD5sum: 0d7e329224493cee9efd432590c9c985 SHA1: 3c0fa112e4e5cf880683f04d14c02ac6af95a891 SHA256: 04077092adb3123ba9f9cc251c949b8efb2c3b06a904eaebf2de4b857c4e3b6d SHA512: bd94a79387184712711d66f0927752207f0617bcac64f3663b28dcef176c1dc8186276f49c1763e708e714ccbd7e535de63f471c98dbfc542403adf23619edb6 Homepage: https://cran.r-project.org/package=provolleyballr Description: CRAN Package 'provolleyballr' (Extract Data from US Women's Professional Volleyball Websites) Tools for scraping match statistics and player data from the Athletes Unlimited (UA) website , the League One Volleyball website , and the Major League (MLV) website . Package: r-cran-provparser Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-provparser_1.0-1.ca2404.1_all.deb Size: 152756 MD5sum: 46b566fbf10c7b2f34fb33c45a54145d SHA1: 51b9a2ee20e6ad77a72669b4f8b8cd355a445f1a SHA256: 2f4ce53bec1278c3610990c564d7e694d624f5b3ff38e0aca5dd6760bd236895 SHA512: 3bf12dd4c7ad8a79c5c7499b17afb4c253e7b1cce1bbacc0c67e9240faad1c3d897a9f8230fb0f71272a4a6a4e20755ad1328a76a654f45ba35db6b301eb9db6 Homepage: https://cran.r-project.org/package=provParseR Description: CRAN Package 'provParseR' (Pulls Information from Prov.Json Files) R functions to access provenance information collected by 'rdt' or 'rdtLite'. The information is stored inside a 'ProvInfo' object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in . Package: r-cran-provsummarizer Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-provparser Suggests: r-cran-digest, r-cran-knitr, r-cran-rdtlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-provsummarizer_1.5.1-1.ca2404.1_all.deb Size: 53722 MD5sum: 848afeac4a777d69c917a27b7afd47b5 SHA1: ca878907a90dac755421d9299b4f44d8045db56e SHA256: fc30fdbe18812898efb807640b32c8b5db1cefaf9b101378772df4f6a8181341 SHA512: a6a625ef19eece46d480016e03d5d9f4b9c6fe1b221771f45dbd439fefa6f855352e6ae287219e28175a3c128567a8f53bd614ae6d4b84f4d71198a3cddf06fe Homepage: https://cran.r-project.org/package=provSummarizeR Description: CRAN Package 'provSummarizeR' (Summarizes Provenance Related to Inputs and Outputs of a Scriptor Console Commands) Reads the provenance collected by the 'rdtLite' or 'rdt' packages, or other tools providing compatible PROV JSON output, created by the execution of a script or a console session, and provides a human-readable summary identifying the input and output files, the scripts used (if any), errors and warnings produced, and the environment in which it was executed. It can also optionally package all the files into a zip file. The exact format of the PROV JSON file created by 'rdtLite' and 'rdt' is described in . More information about 'rdtLite' and associated tools is available at and Lerner, Boose, and Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-provtracer Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-provparser Suggests: r-cran-digest, r-cran-knitr, r-cran-rdtlite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-provtracer_1.0-1.ca2404.1_all.deb Size: 42328 MD5sum: da99582c02084f910311cb5c2ac10a0e SHA1: 30c2df8eb16fb016ddbac097ada90930cb85e8b5 SHA256: 7c6438f99c2696a8c65207f7250c6f1084158552c787d860cac82c9c4f706c9a SHA512: b16c2d766b2b319da28ac0d673e880065b465598f5f8a37dd9c255a84b2cd7e4479a432634b1e7215d0b76cd3851ddfd008ba0b801a205131a1526613165a17e Homepage: https://cran.r-project.org/package=provTraceR Description: CRAN Package 'provTraceR' (Uses Provenance to Trace File Lineage for One or more R Scripts) Uses provenance collected by 'rdtLite' package or comparable tool to display information about input files, output files, and exchanged files for a single R script or a series of R scripts. Package: r-cran-provviz Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3481 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rdtlite Filename: pool/dists/noble/main/r-cran-provviz_1.0.9-1.ca2404.1_all.deb Size: 3127632 MD5sum: a48ab08802f9dcfb5896daf8bc2e78c5 SHA1: 7a2dca14656c2e8277f033b23452ea03e86c3ed7 SHA256: 4176ce0185c299a360ce7b3cb51d0093962b3f40cb6103b043c48fa6a4f8f62e SHA512: bc5dd7823234801ad055f87330b4cd8c05b898229229b51078ae9718b37ac217f7909711a3fa01c4346c5486d78db1f2535d7809da80e2a01c1ff0566769a5e1 Homepage: https://cran.r-project.org/package=provViz Description: CRAN Package 'provViz' (Provenance Visualizer) Displays provenance graphically for provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in . More information about rdtLite and associated tools is available at and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-proximum Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1641 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-rlang Suggests: r-cran-covr, r-cran-e2tree, r-cran-igraph, r-cran-knitr, r-cran-randomforest, r-cran-ranger, r-cran-rmarkdown, r-cran-seriation, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-proximum_1.1.0-1.ca2404.1_all.deb Size: 1282936 MD5sum: 965916385f88fc05eb66acf28bb9ad9e SHA1: 426582eceb7487d6d15df6a076560f8e1920bb67 SHA256: 83a97172c1fd80cf8b8be94df3b192b7e2b365ff2a50d86330059291f332c8cf SHA512: 2bd8e333a0f9b202116f971889e2764e878ec82e25021e175dbc56bc68def5203991c57194d8753400645a37107270b8fb49ddf086c27b8fd1b05955183d7f51 Homepage: https://cran.r-project.org/package=Proximum Description: CRAN Package 'Proximum' (Statistical Analysis of Ensemble Proximity Matrices) Treats the proximity matrices produced by tree ensembles as first-class statistical objects rather than as model by-products. Provides a unified extractor across ensemble engines, in-bag and out-of-bag definitions, transformations to dissimilarities with metric diagnostics, corrections that make an indefinite proximity usable as a kernel, and permutation inference for comparing two proximity matrices or partitioning one across the terms of a design. Also provides a Nystrom approximation, a thresholded sparse representation and a streaming form that never allocates the matrix at all, for samples too large to hold it, measures how far the proximity moves between replicates of the ensemble, and draws each object through 'ggplot2': the seriated matrix, the configuration it implies, and the thresholded graph with its communities. Package: r-cran-proxirr Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-proxirr_0.5-1.ca2404.1_all.deb Size: 55788 MD5sum: 3dfead5c9581f3b4080700c9517f24b9 SHA1: 4bf4dc81dcf45e65da55afe873ea4c2b3e3a03d3 SHA256: 56056888e48a4295734a56f6534b8098bb255589556d3c5bd137656d1c3c3204 SHA512: 5f785bbff697d92b5d91a6a7f9b8fd0e4b180ff943ac8b5e19b5b1a9e6d182a8c8e03ef53658221326a32daad1b41fff0895b76f73b089875eda39c79ccfa611 Homepage: https://cran.r-project.org/package=proxirr Description: CRAN Package 'proxirr' (Alpha, Beta and Gamma Proximity to Irreplaceability) Functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. . Package: r-cran-proxreg Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-ebimage, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-proxreg_1.1.2-1.ca2404.1_all.deb Size: 510838 MD5sum: d215b4b1723556f843471a5534ce5020 SHA1: 102a0bf55e9ad1ac4c3cac5d0c5c535523ad72ae SHA256: 4fed002b770b00b6b4204e242643129ead6f02cf100836b1da9a550e22ee229a SHA512: d7f2f8266b5be75a1c79083869cf75c88e9f331f1505a5db0aac70347041a2246d39f7146c43abe17f3ea438803be07b7b9aed095f474a53fc097aace486da06 Homepage: https://cran.r-project.org/package=ProxReg Description: CRAN Package 'ProxReg' (Linear Models for Prediction and Classification using ProximalOperators) Implements optimization techniques for Lasso regression, R.Tibshirani(1996) using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009). The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters. Package: r-cran-proxymix Architecture: all Version: 0.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5055 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-mvnfast, r-cran-cli, r-cran-rlang, r-cran-data.table, r-cran-withr Suggests: r-cran-mclust, r-cran-mice, r-cran-generics, r-cran-ggplot2, r-cran-viridis, r-cran-patchwork, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-proxymix_0.16.0-1.ca2404.1_all.deb Size: 3006168 MD5sum: a8fdf0a3925fd5e46e541ca6ddadd500 SHA1: b9eddce4b4898b04097af9323c88334176dea288 SHA256: ff14bda7423e1953d8c9c2cfac45062cc0af71f475366a7dd7c821d8c7cd00fc SHA512: a290e8bcb328b93621712abbbce28d8aa4def5ab3c384da5b008b830cd8cf6a8eebc4c4418fd95e02002bed17e29942d07dbe92eedc44cf31616d768afd86aed Homepage: https://cran.r-project.org/package=proxymix Description: CRAN Package 'proxymix' (Kullback-Leibler Optimal Gaussian Mixture Proxies for TargetDensities) Fits multivariate Gaussian-mixture proxies that are Kullback-Leibler optimal to user-supplied target densities on real Euclidean space. Three fitting regimes are unified under one verb: (i) closed-form moment matching for a single component, (ii) classical expectation-maximisation when independent samples are available, and (iii) importance-sampled expectation-maximisation that minimises the Kullback-Leibler divergence when the target can be evaluated point-wise but not (cheaply) sampled. Closed-form Gaussian-mixture operators (density, sampling, marginalisation, conditioning, divergence) round out the toolkit. The conditioning operator drives multiple imputation of data missing at random, covering the multimodal and heteroscedastic cases a single-Gaussian model cannot represent. Implements the regime hierarchy of van der Hoek and Elliott (2024) . Package: r-cran-prozor Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3805 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ahocorasicktrie, r-cran-docopt, r-cran-matrix, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-seqinr, r-cran-stringr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-prozor_0.3.1-1.ca2404.1_all.deb Size: 3020298 MD5sum: 4d4e732b9ad12de9c8452a0630fdd756 SHA1: 63daba8dd27e1d5ec82b220cfd5d807217865493 SHA256: 55c9e0e08e9c4aa351c11d3e260a8e64620438c7458c3fdf2a4c5d6cb5bfd0af SHA512: 32fb27214d85ca3944926b5f3deea3df9406d07b726f804f110b0df757d8f172024996ee35770d30556f42ba39bab4ba0607fb9bdbfb824b2bf34db00a93dc1b Homepage: https://cran.r-project.org/package=prozor Description: CRAN Package 'prozor' (Minimal Protein Set Explaining Peptide Spectrum Matches) Determine minimal protein set explaining peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants. Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions. Package: r-cran-prp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-prp_0.1.1-1.ca2404.1_all.deb Size: 36464 MD5sum: 635b68b42180b73287c4cb14181f1f61 SHA1: 4d224f115ed9f07e54d9cefcfc3fec36ebe6f652 SHA256: bd3b76153f04ba6d882dd70e7b33951d66ca330efb5a94b9afd5413b97446521 SHA512: 63a538d27343e1c1767e819e5a86643ca2ff576f291e3b9c3ee02c581f44452708695c617a1fb1855402a7fbc56c87cf6d1355f751d931fb5b97f0178744a63e Homepage: https://cran.r-project.org/package=PRP Description: CRAN Package 'PRP' (Bayesian Prior and Posterior Predictive Replication Assessment) Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) . Package: r-cran-prrd Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-config, r-cran-liteq, r-cran-data.table, r-cran-crayon, r-cran-dbi, r-cran-rsqlite Suggests: r-cran-docopt, r-cran-foghorn, r-cran-anytime Filename: pool/dists/noble/main/r-cran-prrd_0.0.7-1.ca2404.1_all.deb Size: 62368 MD5sum: 68ae4abf7689dc2e434d6de86a0f4a4e SHA1: 8cf960ecfdad30693d7319fdc6858f023676b5ef SHA256: 3e6f24b135e92fa4ddbd09ede8ed426c28041dc9ec1b8215aea6ee4870b7a5a0 SHA512: ed4d9c0745b1e56c3e1b9426f7a7d844d8e066768ec564b597b386a9b4ce9d014db3dd6c464fd758834d0825b8257c13fc11100357842191d231d76350004c84 Homepage: https://cran.r-project.org/package=prrd Description: CRAN Package 'prrd' (Parallel Runs of Reverse Depends) Reverse depends for a given package are queued such that multiple workers can run the reverse-dependency tests in parallel. Package: r-cran-prroc Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-rocr Filename: pool/dists/noble/main/r-cran-prroc_1.4-1.ca2404.1_all.deb Size: 575530 MD5sum: 4c4d0770eb03d2af097057c6bf3eb728 SHA1: 191a95d9b41a44986441c5f3d62b7e4b88f8ad77 SHA256: 82afb3e521530361b2ec9007e3a5cf15297c0f33a0921b5b44d13d6fb2308217 SHA512: aad4e0781038d7d85a2f211daccfbca3e12eda202c02409ab378fb203ebfe5046be0be6e43f195f248b0ebe9a7341809bea09baa85c5a18c2fbb74bde15313a3 Homepage: https://cran.r-project.org/package=PRROC Description: CRAN Package 'PRROC' (Precision-Recall and ROC Curves for Weighted and Unweighted Data) Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) ; Keilwagen et al. (2014) ; Grau et al. (2015) . Package: r-cran-prt Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-fst, r-cran-data.table, r-cran-vctrs, r-cran-tibble, r-cran-cli, r-cran-pillar, r-cran-crayon, r-cran-backports, r-cran-rlang Suggests: r-cran-testthat, r-cran-xml2, r-cran-covr, r-cran-withr, r-cran-nycflights13, r-cran-rmarkdown, r-cran-knitr, r-cran-bench Filename: pool/dists/noble/main/r-cran-prt_0.2.1-1.ca2404.1_all.deb Size: 108162 MD5sum: e12b0882eb17f5d92c6895deaa6fe569 SHA1: c0c1615a4842704d19d4dd7caab2d36a0d221836 SHA256: 6bf196f4d89f174f286a4434497598b5b68cab804d4df4b769ca9795b03cd08c SHA512: 513ec8ff125be1ffbd9f9ac39f321dd269447bee8f85adfaafd5cc3cfff97e8c2a811d2c02accf160a2c5bd325522bef54c055a1e7de6f6db590b36c92c8b5f3 Homepage: https://cran.r-project.org/package=prt Description: CRAN Package 'prt' (Tabular Data Backed by Partitioned 'fst' Files) Intended for larger-than-memory tabular data, 'prt' objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package. Package: r-cran-prwarp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2497 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-morpho Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-geomorph Filename: pool/dists/noble/main/r-cran-prwarp_1.0.1-1.ca2404.1_all.deb Size: 1622542 MD5sum: df56c1a46493bef8df650d94cbde3bc3 SHA1: 5968e732937d105acc747fc0ccf04ba4b2c22f6b SHA256: 8e87994c34ede0871f595b6f8d05cc83d06d244b9bf19430d1f49cf6e7fcebd0 SHA512: c2be8f7f4fcd20acae6a4b121e82aa21cf2daa107a9c020a0662cdf544be2e169e5e8aec995dc388684b442cd51cb32f4f2e4b27e51fb8ed265050a81b95f0ab Homepage: https://cran.r-project.org/package=prWarp Description: CRAN Package 'prWarp' (Warping Landmark Configurations) Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) . Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) and Grunstra et al. (2020) . Package: r-cran-przewodnik Architecture: all Version: 0.16.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pogromcydanych, r-cran-pbimisc Filename: pool/dists/noble/main/r-cran-przewodnik_0.16.12-1.ca2404.1_all.deb Size: 1583438 MD5sum: a2dab595c17f55bee902a60b01ab3eee SHA1: c7145fdc611a1b04fb16d4a56b906442f57e0e19 SHA256: e75489b91a94758781a7dbab13e660ad236adc76bc76dd6e6f51e6d41c65334f SHA512: d3d76ca0f8292ad274ad3dd054236473d74abd682bf4116d42b10b7a360e505a3858181f44a506170bc942466d4c678d2c7d112658017462a5c360ab52bb4e69 Homepage: https://cran.r-project.org/package=Przewodnik Description: CRAN Package 'Przewodnik' (Datasets and Functions Used in the Book 'Przewodnik po PakiecieR') Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at . Among others you will find here data about housing prices, cancer patients, running times and many others. Package: r-cran-psaboot Architecture: all Version: 1.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-psagraphics, r-cran-ggthemes, r-cran-matching, r-cran-matchit, r-cran-modeltools, r-cran-party, r-cran-psych, r-cran-reshape2, r-cran-rpart, r-cran-trimatch Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psaboot_1.3.9-1.ca2404.1_all.deb Size: 3544196 MD5sum: 0043c893aa5fe1d4616c4d9082617d9f SHA1: 0bec7d3745445b355ed6bc22869acfb480c124da SHA256: 4496f237268b91f6fa10dfb35dc313b061dbc9f5411e53467dba490dbca58a55 SHA512: 5265d35869063c61f56ad2f3ebf24876fabb43bec146daa7a18dbeadbe63c870273a58b894a2776d07f8fc1541a41757d97556aa6e413063e937578cb5fd2240 Homepage: https://cran.r-project.org/package=PSAboot Description: CRAN Package 'PSAboot' (Bootstrapping for Propensity Score Analysis) It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) . The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using 'rpart' and 'ctree' functions), two matching methods (using 'Matching' and 'MatchIt' packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results. Package: r-cran-psagraphics Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-psagraphics_2.1.3-1.ca2404.1_all.deb Size: 259734 MD5sum: 32c88abcde213e51dc3a57d902aabf4f SHA1: 091a611ee2f941d8e7d4d5fdc56358d9b8599efb SHA256: 9ebdb6485535553ac783e25212c07b494131b4ab94090a9429a5f0b483d69797 SHA512: 198de29e83afd7fc0aa9e77fb4d227bc7d70f6414f517262d6409ede5a12749de108879bb28efe1d9a0c7dad8687aeac8abe2cbd19fe53a81c54d7416a98c2e0 Homepage: https://cran.r-project.org/package=PSAgraphics Description: CRAN Package 'PSAgraphics' (Propensity Score Analysis Graphics) A collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate. Package: r-cran-psave Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cobalt Suggests: r-cran-matchit, r-cran-weightit, r-cran-superlearner, r-cran-rpart, r-cran-ranger, r-cran-xgboost, r-cran-survey, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psave_1.0.1-1.ca2404.1_all.deb Size: 330868 MD5sum: 918e90e68c39a4f6cedb0ce3578da5c0 SHA1: efc6fba717561ba7784add21a7566fd274ba676b SHA256: ad9ffdd03d78a809ed79c05d0c549fb30e4511aa785b1fd52e01e52eb94c1044 SHA512: deec728551ea2e5dd9d35817a5248ccae742ad1b6f426759443afc403ba95292354d07893f1800ca4ef89166d28a6ef41e70e7b261c6ace9e2bbbebde0ba0aec Homepage: https://cran.r-project.org/package=psAve Description: CRAN Package 'psAve' (Model-Averaged Propensity Scores Selected by Prognostic-ScoreBalance) Constructs a model-averaged propensity score as a convex combination of candidate propensity score models, with mixing weights selected on a simplex grid to optimize covariate or prognostic-score balance, implementing the method of Kabata, Stuart and Shintani (2024) . Prognostic scores follow Hansen (2008) : outcome models are fit on untreated units only. The resulting score is designed to be supplied directly to the matchit() function of 'MatchIt' as a distance measure or to the weightit() function of 'WeightIt' as a propensity score, with balance assessment via 'cobalt'. Package: r-cran-psawr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-psawr_0.1.0-1.ca2404.1_all.deb Size: 51838 MD5sum: 36801d1e1c713926b7430c6b9015b012 SHA1: d8c09ba2b43ac69518716d0c8a81d69ebb8fd23c SHA256: bf72132b6e394a5922ef8cee96fd4541758de6041bc22b01ec22ce38f8b2fd59 SHA512: 56cb5c130583fa27753fd71be05205cb5b5784a613e6cbcbd948437641811d595e9daa1a7321795eaefc002a18245dd2cde76225af657e18617f1a59792656de Homepage: https://cran.r-project.org/package=PSAWR Description: CRAN Package 'PSAWR' ('Pushshift' API Wrapper for 'Reddit' Submission and CommentSearch) Connects to the API of to search for 'Reddit' comments and submissions. Package: r-cran-psborrow2 Architecture: all Version: 0.0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-glue, r-cran-posterior, r-cran-generics, r-cran-matrix, r-cran-mvtnorm, r-cran-future, r-cran-simsurv Suggests: r-cran-survival, r-cran-flexsurv, r-cran-eha, r-cran-testthat, r-cran-usethis, r-cran-vdiffr, r-cran-tibble, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-bayesplot, r-cran-matrixcalc, r-cran-weightit, r-cran-matchit, r-cran-bayesppd, r-cran-ggsurvfit, r-cran-gbm, r-cran-ggplot2, r-cran-cobalt, r-cran-table1, r-cran-gt, r-cran-gtsummary Filename: pool/dists/noble/main/r-cran-psborrow2_0.0.5.1-1.ca2404.1_all.deb Size: 2689926 MD5sum: 31f268884bee340da178c3c950db3054 SHA1: df8c9d8c57a11a718ebaf394cf299b581aa9e6dc SHA256: ab6df207a6e431bef4cd5caa423e23c4075ac07cf4252b1146c9ff24f40ce346 SHA512: 71de04a9e82be60e7ce542ea51991caa84a00d54771326f18ba0cd17f349c2314b6aa147af4d95d29c0b97bc1066eef5671161c873fd182c636c90114090919b Homepage: https://cran.r-project.org/package=psborrow2 Description: CRAN Package 'psborrow2' (Bayesian Dynamic Borrowing Analysis and Simulation) Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 . There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from . Package: r-cran-psborrow Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-rjags, r-cran-mvtnorm, r-cran-ggplot2, r-cran-foreach, r-cran-doparallel, r-cran-matchit, r-cran-survival, r-cran-futile.logger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-matrix, r-cran-assertthat, r-cran-pkgload, r-cran-flexsurv, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-psborrow_0.2.4-1.ca2404.1_all.deb Size: 385210 MD5sum: 21cbac32aa1f9b1c0a3ab53e62cffe22 SHA1: 4f4481048a6dba4d00755268de0970c4b1da7a61 SHA256: 136610d97052924db820a1d4f59127a8883077a80ce659dcf4eac599b265fe48 SHA512: d0eb45302932706225572c7b2708f0e1c2b286cc2eaef27ad0fd6a3ab3215e33d1d2545c11492437af78743b3d62ca5ff32a33684f46febb712663db9cd13df0 Homepage: https://cran.r-project.org/package=psborrow Description: CRAN Package 'psborrow' (Bayesian Dynamic Borrowing with Propensity Score) A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) that combines propensity score and Bayesian dynamic borrowing methods. Package: r-cran-psc Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2644 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-mvtnorm, r-cran-enrichwith, r-cran-flexsurv, r-cran-survminer, r-cran-gtsummary, r-cran-rcolorbrewer, r-cran-ggpubr, r-cran-posterior, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psc_2.1.0-1.ca2404.1_all.deb Size: 2318042 MD5sum: 5f3381a6e52d7294c20ffea7011c54d3 SHA1: f48d3cce7fe2858dcb83887b6bb6b2bbc7f6bc86 SHA256: 0c346333990ac3a2ba1d620a1f2ba604cec8bab36f6cb090db86efaa6701548f SHA512: cb5735d4ac4e53e145223b4a51ab28b7b63ae52af2d7bb14d23a7397d2f5415a883b0c7f13ad3c06e5d2d66ccdb007e067c8dae8e2b058d074d318b087cac32d Homepage: https://cran.r-project.org/package=psc Description: CRAN Package 'psc' (Personalised Synthetic Controls) Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter. 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Package: r-cran-pscore Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-lavaan, r-cran-jwileymisc Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pscore_0.4.1-1.ca2404.1_all.deb Size: 258424 MD5sum: c4a0daf480967079bccdfddd583c37a8 SHA1: aefe166bdf72d8ba4fd9b431b9bf302441c5c0ef SHA256: 43818e3e408d65703dc3120f70a81dd3772508ec3ed0ef20dbe73507f5aa6aaa SHA512: 104677edf1f42abbc4701fc42b460d4f264164eb84d3b6a9804672c856ab8e07b3ecee11daabc612abe46cb913ce57dd4e8697183d16fb54c6809d399499ed44 Homepage: https://cran.r-project.org/package=pscore Description: CRAN Package 'pscore' (Standardizing Physiological Composite Risk Endpoints) Provides a number of functions to simplify and automate the scoring, comparison, and evaluation of different ways of creating composites of data. 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Package: r-cran-pscr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-vgam Suggests: r-cran-mstate Filename: pool/dists/noble/main/r-cran-pscr_1.2-1.ca2404.1_all.deb Size: 59800 MD5sum: 823485c91432b5945a621ca3e97c7f3e SHA1: 187007b796a43145ffdf7ceab615dc0c7935defe SHA256: 1bf4318f57c6369ec3c8690097ff307d602ce98acfd4a2a1579e694c8b840a5c SHA512: dd6202c9a54f82403786a3110844a136dee7e2aa55e07db83e86a14a6c4f5e3d70ca4fe803d2d22e497e75681ff5f1a20d40610411fef7f45a0cdfe00bdb1637 Homepage: https://cran.r-project.org/package=PScr Description: CRAN Package 'PScr' (Estimation for the Power Series Cure Rate Model) Provides estimation and simulation tools for particular cases of the power series cure rate model . For the distribution of the concurrent causes the alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which are includes in the original work), the polylogarithm model and the Flory-Schulz . The estimation procedure is based on the EM algorithm discussed in . For the distribution of the time-to-event the alternative models are slash half-normal, Weibull, gamma and Birnbaum-Saunders distributions. Package: r-cran-psdata Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-countrycode, r-cran-datacombine, r-cran-reshape2, r-cran-rio, r-cran-xlsx Filename: pool/dists/noble/main/r-cran-psdata_0.2.2-1.ca2404.1_all.deb Size: 73810 MD5sum: 2c754e8d097961e0754d628c3e33e2ea SHA1: 2f76291542c5538301c57272577a28350e6a2611 SHA256: 049aa2dd5d98c567e2d908551f2077f99bb5d9e196d1ba719000b51a28da6c72 SHA512: 6e977424c1c42218a10af7e02486b4b61885a8c4c26c9e9e05d56879fe22eea89fa56701444cd3f203294f2c829f70324a63bd3bd488aa23f9d3727cf62edfd1 Homepage: https://cran.r-project.org/package=psData Description: CRAN Package 'psData' (Download Regularly Maintained Political Science Data Sets) This R package includes functions for gathering commonly used and regularly maintained data set in political science. 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Package: r-cran-psdistr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psdistr_0.0.1-1.ca2404.1_all.deb Size: 90228 MD5sum: 82d145433e800bf7437e8c0e394f754c SHA1: c04d6966d461636730491cfff269e425b03ea156 SHA256: a951333c87be0e6451dca704226bf9daf4470921ce4078475fb541d7aea20bb8 SHA512: d76839eb29bb38a3935fc827e722022b48ace9fe3b237a431f21f42113846b8a71539c533e88b17a4db61e78fd6e70b4b130f88f9c5844248ec38f25f66f11d1 Homepage: https://cran.r-project.org/package=PSDistr Description: CRAN Package 'PSDistr' (Distributions Derived from Normal Distribution) Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) , Sulewski (2021) , Sulewski (2021) , Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) , Sulewski (2023) . Package: r-cran-psdr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-psdr_1.0.3-1.ca2404.1_all.deb Size: 224416 MD5sum: f1de0fbe414c1eada078d259c1873cc4 SHA1: 3d539a663cd6449cd0058b200736284d893c4b04 SHA256: 9f0f6267e4f02db2b657eee6fb35d463de8d10807f497cc4dd310248886724ef SHA512: a6aa0d2e2804c534c7097ed323821eb604004bff4cba42ad257393697efef9cd15ae018e0a19efcc8f51eb07cd8138504514c6b5d164d66a7359e90758ab5d8a Homepage: https://cran.r-project.org/package=psdr Description: CRAN Package 'psdr' (Use Time Series to Generate and Compare Power Spectral Density) Functions that allow you to generate and compare power spectral density (PSD) plots given time series data. Fast Fourier Transform (FFT) is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.Thus given a time series, the dominant frequencies in the time series can be identified. Additional functions in this package allow the dominant frequencies of multiple groups of time series to be compared with each other. To see example usage with the main functions of this package, please visit this site: . The mathematical operations used to generate the PSDs are described in these sites: . . Package: r-cran-pseudo Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kmsurv, r-cran-geepack Filename: pool/dists/noble/main/r-cran-pseudo_1.4.3-1.ca2404.1_all.deb Size: 43346 MD5sum: 870a0243a12629939a92a31d81464701 SHA1: f289fd1ae445077d158683cfd81247b1b4034fef SHA256: 81bbcaab8312dd85164f34fcb7de73895e4549e3c610fcafef56bf1bebc6ab01 SHA512: 517e7942748d16c6792096a64e00708ba9d7225dbad64494a7a48f0b7ccf20f8ae5b845139c79df8535e3b7f04f6867062fd3f6f052e18620e9cba4edbed8ed7 Homepage: https://cran.r-project.org/package=pseudo Description: CRAN Package 'pseudo' (Computes Pseudo-Observations for Modeling) Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean, survival function based on the Kaplan-Meier estimator see Klein et al. (2008) . 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The model combines multivariate Gaussian and Cauchy distributions within each cluster to accommodate heavy-tailed observations and decompose the data into a high-density region and a low-density remainder, with outliers more likely to arise from the latter. Package: r-cran-pseval Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-printr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pseval_1.3.3-1.ca2404.1_all.deb Size: 268680 MD5sum: 675b49043657d3e0164baf563bc4a6a5 SHA1: 61ecafe68fb2af34ca30702cf1239ae246202b1a SHA256: d1db788445333dec977a28f05b6ff17359e9cf00b7d793cd082f5ddbd5aa955c SHA512: e56d51e6451b5341d941cc1308e25273284e224d5683a0b8a0873e191614701824cdf86fac1383ec8d921e697bae8c97762879d1e61e77c005e87ffcba7fd3ad Homepage: https://cran.r-project.org/package=pseval Description: CRAN Package 'pseval' (Methods for Evaluating Principal Surrogates of TreatmentResponse) Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing. Package: r-cran-psf Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forecast Filename: pool/dists/noble/main/r-cran-psf_0.5-1.ca2404.1_all.deb Size: 110250 MD5sum: 416566fe8e3415031f90466f42aca5fb SHA1: a7d8aa84c7229998c082c4b241a10a9f2471aabd SHA256: 84e74cc3ee86a65091eb2024aa92928f541c9348bfa8e2c8f18e096c5c66f07c SHA512: b265d9b9475e907a878a3886c83575a23339137f02c3d15fe7df80ee9953f25c05b4111832206e62f524d5fde010e7b2cf7d04595f8cd5753b8d2e62dde202d4 Homepage: https://cran.r-project.org/package=PSF Description: CRAN Package 'PSF' (Forecasting of Univariate Time Series Using the PatternSequence-Based Forecasting (PSF) Algorithm) Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values. This algorithm forecasts the behavior of time series based on similarity of pattern sequences. Initially, clustering is done with the labeling of samples from database. The labels associated with samples are then used for forecasting the future behaviour of time series data. The further technical details and references regarding PSF are discussed in Vignette. Package: r-cran-psfmi Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-norm, r-cran-survival, r-cran-mitools, r-cran-proc, r-cran-rms, r-cran-magrittr, r-cran-rsample, r-cran-mice, r-cran-mitml, r-cran-cvauc, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-lme4, r-cran-car Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-readr, r-cran-gtools, r-cran-covr Filename: pool/dists/noble/main/r-cran-psfmi_1.4.0-1.ca2404.1_all.deb Size: 894016 MD5sum: 32578b5358d31bad4ffdcaad89346dcc SHA1: 9d3c3e9fe4f25366bbcfea24811a24fb1306d211 SHA256: ed765639383f645e3cc7265fa5aa042fa40b3730fa3e6c7fd6a45a1a6cd99faa SHA512: 3b31939fc274bf9df225223211423417778c8e56e5cd40635b91bbac1c50548ab5e4fad42e379ecbbd1bda878b006298631a0ab439da5fb3385f2f2aec0387ea Homepage: https://cran.r-project.org/package=psfmi Description: CRAN Package 'psfmi' (Prediction Model Pooling, Selection and Performance EvaluationAcross Multiply Imputed Datasets) Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) . Wiel (2009) . Marshall (2009) . Package: r-cran-psgc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11994 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psgc_0.1.2-1.ca2404.1_all.deb Size: 6562308 MD5sum: 6d0d75c22df57bc7e20d1a3c58d03bb1 SHA1: e46a4e6070dbac0a4439d0b684f5db377f5155ff SHA256: 9d4c0b5075c3ca48620cfafb75983f653a27f82f63cf98e8bccf3c0848706c97 SHA512: e392b356573c6eb3a647802efaace208c3c798b440c7646df92b5b61dd5721b06021ecee997351899260834983e25cb138054fdcc52a14b0c0f85a23e478015c Homepage: https://cran.r-project.org/package=psgc Description: CRAN Package 'psgc' (Philippine Standard Geographic Code) Provides access to the Philippine Standard Geographic Code (PSGC), an official classification system for geographic areas in the Philippines published by the Philippine Statistics Authority (PSA). Includes area names, geographic levels (Region, Province, City, Municipality, Sub-Municipality, and Barangay), and census population figures across multiple PSA publication releases. Offers utilities to look up individual codes, filter by geographic level, track code changes across releases via a built-in crosswalk, and retrieve population data in long or wide format. Package: r-cran-psgoft Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-moments Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psgoft_0.0.1-1.ca2404.1_all.deb Size: 27290 MD5sum: 0c11986fda2cbdab1726794b33784118 SHA1: 95628e83d3e63e5463e6e5971aa2f578071310ce SHA256: 58bdeeb0d0791867b5cdccea724e958b9dd513f24bf90b0fc5e300ca082ef536 SHA512: 4130e8511c6e631a8b22f9dce3edb892c3dd7b2604903f37cf32077d9621cc67bf55ca1e32374ac89e500fff15f45c8a2621507fa1f58a717407e32c77892fdb Homepage: https://cran.r-project.org/package=PSGoft Description: CRAN Package 'PSGoft' (Modified Lilliefors Goodness-of-Fit Normality Test) Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) . Package: r-cran-psharmonize Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-psharmonize_0.3.6-1.ca2404.1_all.deb Size: 807280 MD5sum: bd293f9b9ff52eb1a9e1ca24ed92ad5f SHA1: 4eb09c100d434a70ca4cd2e5bdff8c8ff1a000a9 SHA256: dd1d9bdb5cf2975d0070be12fc213db6986c2f37f17914c3876076acf2e64742 SHA512: 9d9024cf2249d6026405ed080ac8cddf139015f8c61f492ba07116c89a3f3c5d51f143830dccb56ac5bc21d29e2c08437ef9e864ebc235b00f050a9949adea19 Homepage: https://cran.r-project.org/package=psHarmonize Description: CRAN Package 'psHarmonize' (Creates a Harmonized Dataset Based on a Set of Instructions) Functions which facilitate harmonization of data from multiple different datasets. Data harmonization involves taking data sources with differing values, creating coding instructions to create a harmonized set of values, then making those data modifications. 'psHarmonize' will assist with data modification once the harmonization instructions are written. Coding instructions are written by the user to create a "harmonization sheet". This sheet catalogs variable names, domains (e.g. clinical, behavioral, outcomes), provides R code instructions for mapping or conversion of data, specifies the variable name in the harmonized data set, and tracks notes. The package will then harmonize the source datasets according to the harmonization sheet to create a harmonized dataset. Once harmonization is finished, the package also has functions that will create descriptive statistics using 'RMarkdown'. Data Harmonization guidelines have been described by Fortier I, Raina P, Van den Heuvel ER, et al. (2017) . Additional details of our R package have been described by Stephen JJ, Carolan P, Krefman AE, et al. (2024) . Package: r-cran-psica Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-gridbase, r-cran-randomforest, r-cran-rpart, r-cran-partykit, r-cran-party, r-cran-bayestree Filename: pool/dists/noble/main/r-cran-psica_1.0.2-1.ca2404.1_all.deb Size: 59482 MD5sum: 84e9111dd2f0c3fe6a97c12531ddec2a SHA1: e7f7d2983eeac6237b4ee28b1f7c7995d8e94210 SHA256: 981dead2ae8dc5781c3c35a47129ac87cd0321e82df524dfe5fafb9e5ce2c117 SHA512: 38b9fe3d49286323fdfac6807bf10db32c217d08c35dc538a158db2333543ff4d613f84dfdd0ed4e82bd368b63eaa04e88fe68dfca21b9f7c382a1e6b322b095 Homepage: https://cran.r-project.org/package=psica Description: CRAN Package 'psica' (Decision Tree Analysis for Probabilistic Subgroup Identificationwith Multiple Treatments) In the situation when multiple alternative treatments or interventions available, different population groups may respond differently to different treatments. This package implements a method that discovers the population subgroups in which a certain treatment has a better effect than the other alternative treatments. This is done by first estimating the treatment effect for a given treatment and its uncertainty by computing random forests, and the resulting model is summarized by a decision tree in which the probabilities that the given treatment is best for a given subgroup is shown in the corresponding terminal node of the tree. Package: r-cran-psidr Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2864 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcurl, r-cran-foreign, r-cran-sascii, r-cran-openxlsx, r-cran-futile.logger Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psidr_2.3-1.ca2404.1_all.deb Size: 1637456 MD5sum: 7c2e4c4d50980685813fc3765cbda292 SHA1: 4abb1a17b2863d30e48a7f6ddf8dd926d2681ec2 SHA256: 48417270410b074e06d104043f27b62abf39d7a74b164c14aed101d0c4760d39 SHA512: 5d5b4b52be81654de9ef766b5e698d8f340a4afc24dc29e2927c62ae8d1b240a3603f6f08c57404be19a15f9ff462866d4ae3d66ca8ed5ce2515ea866fa08113 Homepage: https://cran.r-project.org/package=psidR Description: CRAN Package 'psidR' (Build Panel Data Sets from PSID Raw Data) Makes it easy to build panel data in wide format from Panel Survey of Income Dynamics (PSID) delivered raw data. Downloads data directly from the PSID server using the 'SAScii' package. 'psidR' takes care of merging data from each wave onto a cross-period index file, so that individuals can be followed over time. The user must specify which years they are interested in, and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each year). The package offers helper functions to retrieve variable names from different waves. There are different panel data designs and sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about the PSID can be obtained at . Package: r-cran-psidread Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4469 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-asciisetupreader, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-diagrammer, r-cran-spelling Filename: pool/dists/noble/main/r-cran-psidread_1.0.7-1.ca2404.1_all.deb Size: 2416140 MD5sum: 1f5db006e1205cc4872b0ef73de9d618 SHA1: f0cc134c95962c86ca2c081442005a9c5d69c43b SHA256: 22c03a3b15c7a7da41e3caf62226069b9861c2e2d76f27d02d552be3b7b0f0f3 SHA512: 5886ca0601c91fc26c7db30c83b8357a664fa988052b513add0d8fc3a303af8d862567d15bb5013da36e24c3aec6116e2d5c5151e11826b5b0a2ebb64f03de3c Homepage: https://cran.r-project.org/package=psidread Description: CRAN Package 'psidread' (Streamline Building Panel Data from Panel Study of IncomeDynamics ('PSID') Raw Files) Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis. Package: r-cran-psim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-matrixstats, r-cran-magrittr, r-cran-tidyverse Suggests: r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psim_0.1.0-1.ca2404.1_all.deb Size: 13788 MD5sum: cec764639b1396ea5874311532df550c SHA1: ca301d4a33afaf08ad7c870b5fcd6c7ff8a5b827 SHA256: f2188841f0979965cc3b7e8b542c636ba7b8d00e5ec0d283b5d7070d343fe443 SHA512: 6f2d9a587780f634d14f9f8406c7554d00559a06a10ced058ef2bc7a49263ff826708d209658d745c71be7b4824ba21ddae4e44310ba9798ba2546e1c5e3972d Homepage: https://cran.r-project.org/package=PSIM Description: CRAN Package 'PSIM' (Preference Selection Index Method (PSIM)) The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) . Package: r-cran-psindependencetest Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psindependencetest_0.0.1-1.ca2404.1_all.deb Size: 118504 MD5sum: cef362e22bccc56ecccff578fa7fa9b2 SHA1: d555d1b75daa116d6d8e7702610d9067215805af SHA256: 609aa1dbd193da395a719c60f0d080d6cdcfe517656f5ac9a7abda2024b1160a SHA512: 829f704ee8f1505f33b9b1f3afaa026b4858e90f20be2e7f5bb09555df3e06dbfdd6e80560ad6a8dea942f0068ecd88778257d45c287ce1e2a095de54724cf06 Homepage: https://cran.r-project.org/package=PSIndependenceTest Description: CRAN Package 'PSIndependenceTest' (Independence Tests for Two-Way, Three-Way and Four-WayContingency Tables) Presentation two independence tests for two-way, three-way and four-way contingency tables. These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) , Sulewski (2018) , Sulewski (2019) , Sulewski (2021) . 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For more details see Klein et al. (2021) . Package: r-cran-psiuenginerl Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psiuenginerl_0.1.3-1.ca2404.1_all.deb Size: 16568 MD5sum: 743b864ccd944454ca5ce1eb6997240c SHA1: 56ed23d33f1fb63375b9c66bb7c00cef52238db2 SHA256: d1501e900f3d3083b88773422cceb578bdcb395bd05d18f6311771436d38f3ab SHA512: 8b2082e0f5659c9a1a7f5e69b7f5297f6b91cdc8148417730eee41d58d208627d2b0256ef52bbfb4b1fa26aae2ee4ba65bfff7dad7e8cdc6cb5e7cd16578064f Homepage: https://cran.r-project.org/package=PsiUEngineRL Description: CRAN Package 'PsiUEngineRL' (Homotopy Type Theory Engine for Reinforcement Learning) Core architecture for interpreting continuous data streams as homotopy types. It evaluates identity paths against the Gnomonic Ratio ('Lombardi', 2026) and processes them via a dynamic 'Tableau Refutation Tree'. The engine categorizes data into necessity (BOX), possibility (DIAMOND), or noise based on deviation thresholds from the invariant value. Includes adaptive auto-tuning and native high-contrast Cartesian graphics for structural entropy isolation. Package: r-cran-pslm2015 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4374 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-pslm2015_0.2.0-1.ca2404.1_all.deb Size: 4350756 MD5sum: afc3f01433deacf31a7ac5cb0dd73fb1 SHA1: a11317e80f3455426199611334c4556e8e41bc6f SHA256: 66318c89f85a3d32eff2a5a2ae6becdb41ef03ed4ad17c4bf619292ccc0ee2e4 SHA512: be19dc0f590d6b8a8d6637b84b554bc9c12bf2dd25a40214210b6562264fe221315650c27141e211f408345e322564b74a7ec4672463040ec3ca00a83174176f Homepage: https://cran.r-project.org/package=PSLM2015 Description: CRAN Package 'PSLM2015' (Pakistan Social and Living Standards Measurement Survey 2014-15) Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (). 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These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy. 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The package includes a psme() function that (1) relies on package 'mgcv' for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package 'lme4' for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) and Bates et al. (2015) for an introduction. Unlike the gamm() function in 'mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations. Package: r-cran-psminer Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bupar, r-cran-dplyr, r-cran-data.table, r-cran-forcats, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-stringi Suggests: r-cran-knitr, r-cran-eventdatar, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-edear Filename: pool/dists/noble/main/r-cran-psminer_0.1.1-1.ca2404.1_all.deb Size: 122052 MD5sum: af85688ef8b510bddfc2937b8ef612cb SHA1: 540c6dbac3eec48cb86208fb1625ab45dca59965 SHA256: 8586adc81eb2833d5cacf704e7e9aec86ec7fe661b30d1d5ef05cd73e8123dac SHA512: 0974d59ed6ccfe5113da72dd7beed10ed60dded881986cbc68826ab1fb7af7a14911fce9eb021f745b0e6bf1e0c6140ffdaeb56e4b391fdc7ee864f5dc7ccad7 Homepage: https://cran.r-project.org/package=psmineR Description: CRAN Package 'psmineR' (Performance Spectrum Miner for Event Data) Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) . 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The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), ; Rodriguez-Alvarez, M.X. et al. (2015) and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) . Package: r-cran-pspower Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pspower_2.0.0-1.ca2404.1_all.deb Size: 257382 MD5sum: e06e70b67cc0275d33a504fb22d02d1b SHA1: 24be5d0d57c8ff20cae1930c0a3f5052283dac05 SHA256: 2fa203a71c586fbb82b4b997ebca8fc1239d8eabcbc9111f1a0d18049d441300 SHA512: eaa7c435dc1124091ca0cf8aac6b6939a6ef0c73c93bf02810eaf7433ff0724017d44f52d45719a19f4799f28bfee3381b54c6294e28e7bbf4f700dfe5a00f4c Homepage: https://cran.r-project.org/package=PSpower Description: CRAN Package 'PSpower' (Sample Size and Power for Propensity Score Weighted Estimators) Computes sample size and power for causal inference studies that use propensity score (PS) weighting. Supports continuous, binary, and time-to-event (survival) outcomes under four estimands: average treatment effect (ATE), average treatment effect on the treated (ATT), average treatment effect on the controls (ATC), and average treatment effect on the overlap population (ATO). For continuous and binary outcomes, the asymptotic variance of the Hajek inverse probability weighting estimator is derived under a logit-normal propensity score model, approximated by a Beta distribution matched through the Bhattacharyya overlap coefficient. For survival outcomes, the asymptotic variance of the propensity-score- weighted partial likelihood estimator is used for randomized trials and observational studies. The Schoenfeld formula is also available for randomized trial settings. Package: r-cran-psre Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-nortest, r-cran-marginaleffects, r-cran-ggplot2, r-cran-boot, r-cran-cowplot, r-cran-fancova, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tibble, r-cran-sm, r-cran-multcomp, r-cran-rlang, r-cran-ggrepel, r-cran-mgcv, r-cran-viztest Suggests: r-cran-ggeffects, r-cran-nnet Filename: pool/dists/noble/main/r-cran-psre_0.4-1.ca2404.1_all.deb Size: 629416 MD5sum: 84d5a5d6b1d9da95a826eba9a8bc870b SHA1: c7ede5651e9c21fec18068eebf3888a0612f1e69 SHA256: cb68520a2a26320aae9b5bcf38ad85545a473fb3696e16406069875189ac6dae SHA512: e4baae759168a0980855d29280a77f519fddadccdd60cdfefa2af4df498bad8c24aa475c72f4a28173683df6b798c460fd28539f2a479dc312d8466ee275d0f7 Homepage: https://cran.r-project.org/package=psre Description: CRAN Package 'psre' (Presenting Statistical Results Effectively) Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book. Package: r-cran-psreplicate Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-dataverse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psreplicate_0.1.1-1.ca2404.1_all.deb Size: 78946 MD5sum: 875e3f413ef4cb175250c5ce30f047f9 SHA1: 1338502cdbd19ced9e30d318efb1f9ca6ce56faf SHA256: c3d31b793f2efb46c72a17ceead4bb2623bc82010586d3ead1ca1a4fcbebd222 SHA512: 6d35221cec1cf7abf9b1649e6f045276c64c8555af0e36b6aa221be8819ab1ca631dfd6370a80624c6f82f1acb1e113af1c27b95f461e06ac8ff3e05c4b7280f Homepage: https://cran.r-project.org/package=psreplicate Description: CRAN Package 'psreplicate' (Access the Political Science Replication Index from R) Search and browse the Political Science Replication Index (), a searchable, tagged index of replication packages crawled monthly from flagship political science journals' Harvard Dataverse collections, without leaving R. Provides functions to download and cache the index locally and to search and filter it by journal, method, data type, and year. Package: r-cran-psricalc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psricalc_1.0.0-1.ca2404.1_all.deb Size: 15744 MD5sum: abceab0eed451e72a260eec7e2f56780 SHA1: 804a252fe39a28f5224056524e526deb7896fe76 SHA256: fd4a50ab28e34fd1935601775f9d40ef1e3c8b15d4cb6ebeca9cf2213d642104 SHA512: cb91c36e681aaea109e258dbc08c547e53ada6ba2632c93a059cd12ec91712bd2b5214f85b9633d1538b8a4d78c4ed429b5790e308ec5e8d0d6699d292615583 Homepage: https://cran.r-project.org/package=PSRICalc Description: CRAN Package 'PSRICalc' (Plant Stress Response Index Calculator) Calculate Plant Stress Response Index (PSRI) from time-series germination data with optional radicle vigor integration. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Provides clean, direct PSRI calculations suitable for agricultural research and statistical analysis. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Package: r-cran-psricalcsm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psricalcsm_1.0.0-1.ca2404.1_all.deb Size: 46566 MD5sum: 29911ad92ce645387fca8448e5c4dab1 SHA1: 7421e93b1a9683379974d73058a51ffa595dc191 SHA256: d6016a7341a9c18459a92673d5a3f62c4be741d23037dfbf04d4021e836d2a5d SHA512: 5ef912473411b302f117527e5e1393a576aec8823222ed3b6e2eaa35122f6fef59e4fdaeb9960e165e80bf4c4979254c870d6f895d345197fb944c6b1add32f6 Homepage: https://cran.r-project.org/package=PSRICalcSM Description: CRAN Package 'PSRICalcSM' (Plant Stress Response Index Calculator - Softmax Method) Implements the softmax aggregation method for calculating Plant Stress Response Index (PSRI) from time-series germination data under environmental stressors including prions, xenobiotics, osmotic stress, heavy metals, and chemical contaminants. Provides zero-robust PSRI computation through adaptive softmax weighting of germination components (Maximum Stress-adjusted Germination, Maximum Rate of Germination, complementary Mean Time to Germination, and Radicle Vigor Score), eliminating the zero-collapse failure mode of the geometric mean approach implemented in 'PSRICalc'. Includes perplexity-based temperature parameter calibration and modular component functions for transparent germination analysis. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies. Package: r-cran-pss.health Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-easypower, r-cran-envstats, r-cran-epir, r-cran-ggplot2, r-cran-icc.sample.size, r-cran-kappasize, r-cran-longpower, r-cran-plotly, r-cran-powermediation, r-cran-powersurvepi, r-cran-presize, r-cran-proc, r-cran-pwr, r-cran-pwr2, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfeedback, r-cran-shinyhelper, r-cran-writexl Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mess, r-cran-webpower Filename: pool/dists/noble/main/r-cran-pss.health_1.1.5-1.ca2404.1_all.deb Size: 824366 MD5sum: bbb7186c789a0687d594d04bcaaf64cf SHA1: 615a8fadf7cfdf8c2e20afa7c8154192759379f8 SHA256: bd362befb93aa124f9e716490071189743610b3784c632d8b614d15192a8336a SHA512: 2d25844cf74611b01f5809e24ad28ad41eada1787c12e2d2e4fa2a1f4ab3a59fad7d14ace584b792a08fdc0ac04b30a36fdfb98e3fd3c3077247aea86a94f4ad Homepage: https://cran.r-project.org/package=PSS.Health Description: CRAN Package 'PSS.Health' (Power and Sample Size for Health Researchers via Shiny) Power and Sample Size for Health Researchers is a Shiny application that brings together a series of functions related to sample size and power calculations for common analysis in the healthcare field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, Cronbach's alpha, interobserver agreement, intraclass correlation coefficients, limit of agreement on Bland-Altman plots, area under the curve, sensitivity and specificity incorporating the prevalence of disease. You can also use the online version at . 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This package implements the key functions of two tasks: (1) computing image structural similarity measure PSSIM of Wang, Maldonado and Silwal (2011) ; and (2) test of independence between a response and a covariate in presence of heteroscedastic treatment effects proposed by Wang, Tolos, and Wang (2010) . Package: r-cran-pssmcool Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3078 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-infotheo, r-cran-phontools, r-cran-dtt Suggests: r-cran-testthat, r-cran-spelling, r-cran-waveslim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pssmcool_0.2.4-1.ca2404.1_all.deb Size: 1544642 MD5sum: ff319878a975955e97c1e5fcef92a67f SHA1: 1c6711c89c058e6eec2e9607221143eec1cb4565 SHA256: 148793ee9cb8031b0582ce706ddab31584adef532491a113a26f7bf26caa6f7f SHA512: fe9f0fc63f1aa2d54907d5ab321d858dfa9bb0879b69ed9cc9a391c649f2fdeac609d9e5c786c42871ef0d290dc045f069e65fe51fe2aab4e8fccb8cb4cdd5c5 Homepage: https://cran.r-project.org/package=PSSMCOOL Description: CRAN Package 'PSSMCOOL' (Features Extracted from Position Specific Scoring Matrix (PSSM)) Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by 'PSI-BLAST' which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see for mor details. some of these features are described in Zahiri, J., et al.(2013) , Saini, H., et al.(2016) , Ding, S., et al.(2014) , Cheng, C.W., et al.(2008) , Juan, E.Y., et al.(2009) . Package: r-cran-pssmooth Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-osdesign, r-cran-np, r-cran-chngpt, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pssmooth_1.0.3-1.ca2404.1_all.deb Size: 110032 MD5sum: b2d23ad615cfb294246d1147c8fcb3b1 SHA1: bf65473886e2db8286ebf2937f535106603600ce SHA256: 21cca37bcfce561533a3cabc490729c15d9881acdd10724ea59fb36fcbe4a464 SHA512: 0b29ada2b9d2eb7cf04e1b6b54b5403aa2e0852e9065b7487ff3bdbae8c896196b81c56c79b1a4fcc2f8c70cfacf440b5b520f2f0e1acd358916a140b8afe55a Homepage: https://cran.r-project.org/package=pssmooth Description: CRAN Package 'pssmooth' (Flexible and Efficient Evaluation of PrincipalSurrogates/Treatment Effect Modifiers) Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 . The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results. Package: r-cran-pssurvival Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-cowplot, r-cran-generics Suggests: r-cran-nnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mice Filename: pool/dists/noble/main/r-cran-pssurvival_0.2.1-1.ca2404.1_all.deb Size: 1731512 MD5sum: 227ef5a300661f65f4a916fc3b648492 SHA1: aad910f568e43a15b7427e0408ff3d002db65c1a SHA256: 7c6e85d55ddeea1a1dcfa12e5a90e51bd30b6dbbc70af8a8638607de676c692a SHA512: 64bccf670b013415b824a506eda505841f8e48a2683a41364a1660064037f5f218b9ca54dea072198f447c94098e2afca14827110f056952ddf58cb6dce127b5 Homepage: https://cran.r-project.org/package=PSsurvival Description: CRAN Package 'PSsurvival' (Propensity Score Methods for Survival Analysis) Implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) , Li & Li (2019) , and Cheng et al. (2022) . Package: r-cran-pst Architecture: all Version: 0.94.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-traminer, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-pst_0.94.1-1.ca2404.1_all.deb Size: 481968 MD5sum: 0cca5c6f78b1fde0c58d7b83df43b26b SHA1: 121d8cfa0d6a53530d9e1393c44c04bbf1810ec3 SHA256: 95010ad5d91aede99baedc6d933d820c21fc8ab101eb12f35da5914a8f7123b3 SHA512: 6c915034e114b23404fea641644920003a3344b5295a84ca55bb476641e504bcfcb05bb9d2ad0eea201f35a32db588268ac6e05a632de716d20befb6ce239b55 Homepage: https://cran.r-project.org/package=PST Description: CRAN Package 'PST' (Probabilistic Suffix Trees and Variable Length Markov Chains) Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support. Package: r-cran-pstat Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pstat_1.2-1.ca2404.1_all.deb Size: 107620 MD5sum: 4c8d4158648b65bb6f17a36454bd666d SHA1: 69bfb915afd6ff81821aaa0d431ddd82ed4c982f SHA256: d034845d613c085524557306b2813633984c76cc72a734fdfd945054458e9059 SHA512: 329ca5026bd839cd3fcd6e6bb25abd2b1c55ae504758b44c23a3a0cbb11ce06b63edfadeb4c59a853bf63a5dcbc73e5115df4ab2bbe2a01ec08e7a2eb8f2d306 Homepage: https://cran.r-project.org/package=Pstat Description: CRAN Package 'Pstat' (Assessing Pst Statistics) Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation). Package: r-cran-pstest Architecture: all Version: 0.1.3.900-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmx, r-cran-mass Filename: pool/dists/noble/main/r-cran-pstest_0.1.3.900-1.ca2404.1_all.deb Size: 30948 MD5sum: 3115c23bce37a88ec27fa2b224e889c4 SHA1: b65af0050eedcb7db27568ef37aa014f373c024b SHA256: 12cd900dc93a26d710164e696b96f3e2cc5763452dd57c33e66c2e8a943c4b82 SHA512: aab4faa162d8fd70f97bd3d38c7df37baa82914e189c54a0729ef77fbaae5ab885399fa97865bae2079ccbe76bb5e46a375906903adb69412ee3925ed76c4a43 Homepage: https://cran.r-project.org/package=pstest Description: CRAN Package 'pstest' (Specification Tests for Parametric Propensity Score Models) The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) . Package: r-cran-pstr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-r6, r-cran-knitr, r-cran-cli, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-snowfall, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pstr_2.0.0-1.ca2404.1_all.deb Size: 492586 MD5sum: 16425025ddb78c0c15449421d4e73f9e SHA1: 03690785f6a2b502ef617451c7a76c4509b543e1 SHA256: 7ed13cc776afe5b3ce533b9d1678dce469a8231a935e54af8d5e01bc9b17945b SHA512: 6a0c1ffdbc804403c757f5cb7e7c2bc2db83c6ab3b00cdea9d4c039996df5876c09e84b33c24a6895eda420813a00e71eda04b0da4e7832d5776a861bcf51ee7 Homepage: https://cran.r-project.org/package=PSTR Description: CRAN Package 'PSTR' (Panel Smooth Transition Regression Modelling) Implements the Panel Smooth Transition Regression (PSTR) framework for nonlinear panel data modelling. The modelling procedure consists of three stages: Specification, Estimation and Evaluation. The package provides tools for model specification testing, to do PSTR model estimation, and to do model evaluation. The implemented tests allow for cluster dependence and are heteroskedasticity-consistent. The wild bootstrap and wild cluster bootstrap tests are also implemented. Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests. The package supports parallel computation, which is useful for large-scale bootstrap procedures. Package: r-cran-pstrata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rstan, r-cran-lme4, r-cran-reformulas, r-cran-purrr, r-cran-stringr Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pstrata_1.0.1-1.ca2404.1_all.deb Size: 208004 MD5sum: b550f84d617120dfe31f09f9c8b8a1ac SHA1: 96adc95560aa34918b7abdd987cf3e48ca6667d1 SHA256: 17cf1d9a751c31accac5599c2227b57318ceb65e96ccd73d4be6718482d6b6b9 SHA512: 0c026226a5837136f308e04942c137b2ee69fb33a4042bba6e5875db32b09e44d6a22ca1c60534d5186030f7cdeb65413f696ce28dce8a20980bb53825a12cec Homepage: https://cran.r-project.org/package=PStrata Description: CRAN Package 'PStrata' (Principal Stratification Analysis in R) Estimating causal effects in the presence of post-treatment confounding using principal stratification. 'PStrata' allows for customized monotonicity assumptions and exclusion restriction assumptions, with automatic full Bayesian inference supported by 'Stan'. The main workflow is PStrataModel() to specify the model, fit() to run MCMC sampling, estimate() to extract potential outcomes, and contrast() to compute causal effects. Visualization tools are provided for diagnosis and interpretation. See Liu and Li (2023) for details. Package: r-cran-psvmsdr Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psvmsdr_3.0.1-1.ca2404.1_all.deb Size: 126838 MD5sum: 9e082c1637c6ea8734da35c1c9519bd7 SHA1: ef76c2ab184ba41bcd6ad7434f2e877b265044d7 SHA256: a0f8d36f1fa82aa6d8e0450ec6ffaf7e1a85d4053161c2367ca0b22c44ea5bb1 SHA512: debdfd6dbf1b7cbd4f8be132dc1bd97c88c634965c4bf2fa1a21cf893431bc6ce98369586c05ecea71961251e7cd2d5d25adb04c5b311e325e128e2dad568bd8 Homepage: https://cran.r-project.org/package=psvmSDR Description: CRAN Package 'psvmSDR' (Unified Principal Sufficient Dimension Reduction Package) A unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) ). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) ). Package: r-cran-psw Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-gtools Filename: pool/dists/noble/main/r-cran-psw_1.1-3-1.ca2404.1_all.deb Size: 129090 MD5sum: 76ad65acc5081113c41a6c75cf11f968 SHA1: f426627b9f185c60d525b5924747156427278ca7 SHA256: 0cfa4ec6bb13e0c108ff22d6d3ed11310bb09ae746ac62e999413cd6db976202 SHA512: 90ac88a050e455dfc5a943bee00dc7c51abda10b622fc54c0519357c66dec232b6b40abb8868095ea5e73fb48590f454cca5386dc2f94e9b2eaaa1090ec05035 Homepage: https://cran.r-project.org/package=PSW Description: CRAN Package 'PSW' (Propensity Score Weighting Methods for Dichotomous Treatments) Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) , Lunceford and Davidian (2004) , Li and Greene (2013) , and Li et al (2016) . Package: r-cran-psweight Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-nnet, r-cran-mass, r-cran-ggplot2, r-cran-numderiv, r-cran-gbm, r-cran-superlearner, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-psweight_2.1.2-1.ca2404.1_all.deb Size: 1090732 MD5sum: ed6bab00e90827741ff576fca83fc03e SHA1: b4df5884b84fd5321fca19799316cb6d3326d71d SHA256: 7fbb92308783803aac5f584ab321d230ff4a58711105ee9be8c9f11a11b62e17 SHA512: 2115b168304703601b91e4170b320535c63dc06f325c70bf0751d191754c130d843b3078821f2884e8919147918f4db5c3c40bec143507a120755dee808c4002 Homepage: https://cran.r-project.org/package=PSweight Description: CRAN Package 'PSweight' (Propensity Score Weighting for Causal Inference withObservational Studies and Randomized Trials) Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) and Li and Li (2019) . Package: r-cran-psy Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psy_1.2-1.ca2404.1_all.deb Size: 155714 MD5sum: df4ed7f7f6179d4d94776f5af81d001a SHA1: 528555a634a8769c0c1101a914a0bd7d9ae26587 SHA256: b153fb9c7a8efba893666a755c627d2b9d1988efe8c24ff044ae9dcb4e59debe SHA512: 9863c63382204e5ef2afa06d7170d33e638403e275155753002a97aa32b9b2add4950a1491d2bf4a746f3c9b360f2c98952bb5adda028e281cffae444dc878cb Homepage: https://cran.r-project.org/package=psy Description: CRAN Package 'psy' (Various Procedures Used in Psychometrics) Kappa, ICC, reliability coefficient, parallel analysis, multi-traits multi-methods, spherical representation of a correlation matrix. Package: r-cran-psyccleaning Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-data.table, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-covr, r-cran-misty, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psyccleaning_0.1.1-1.ca2404.1_all.deb Size: 117008 MD5sum: 962304ab76c21fca8a179bae87550b5a SHA1: 89d539861d00e57b86c8685be9bbb575d5effc87 SHA256: d870bf849ae787ef6e129475dcf2a47b55a06c2c9c0cd4f957368574ffe357ba SHA512: 1bb5cb966653204ca44757072bfdef0a577363358ec06338de7f1d067d5c676ea473e679ad0dedcdbe5ef22257731fa55fdcb57cd1e6d06b61600e36ca3f397b Homepage: https://cran.r-project.org/package=psycCleaning Description: CRAN Package 'psycCleaning' (Data Cleaning for Psychological Analyses) Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more. 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Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the web page. Package: r-cran-psychmeta Architecture: all Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3878 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-metafor, r-cran-ggplot2, r-cran-progress, r-cran-curl, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-purrr Suggests: r-cran-mass, r-cran-mvtnorm, r-cran-nor1mix, r-cran-bib2df, r-cran-rmarkdown, r-cran-knitr, r-cran-stringi, r-cran-cli, r-cran-crayon, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psychmeta_2.7.0-1.ca2404.1_all.deb Size: 2900806 MD5sum: 9cecc5c3707fe9713b3bbafcd2bbce1d SHA1: ca2fd20d5543064b894ef0cd9d24bb032eb39257 SHA256: d6c9c7cd4f09849159c4103a4f9b7ba7368427e4f29e2e749938ba8372ebd3da SHA512: 0e4b3085fb38adc14a41d7c572eca437154752056b1753b0aba1032447473b9649ddba81864e8c360454c5d95ee0cd148c1081249705197d67a072dabaf03578 Homepage: https://cran.r-project.org/package=psychmeta Description: CRAN Package 'psychmeta' (Psychometric Meta-Analysis Toolkit) Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to or . Package: r-cran-psychnets Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3009 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-cocor, r-cran-cograph, r-cran-glasso, r-cran-glmnet, r-cran-igraph, r-cran-isingfit, r-cran-knitr, r-cran-mgm, r-cran-mvtnorm, r-cran-networktools, r-cran-psych, r-cran-qgraph, r-cran-rmarkdown, r-cran-testthat, r-cran-tna Filename: pool/dists/noble/main/r-cran-psychnets_0.5.2-1.ca2404.1_all.deb Size: 2044700 MD5sum: d4a399ba426da9ef74a5c820dd784fc7 SHA1: 898c969cd250592b3ea2622d48b8408242553a34 SHA256: 002ea00b2233f5a0e24f51731c5eb831fe0d8f739682af5d4dc4c594244ffe82 SHA512: d9aa0d60c70b7c62a5fb9f13b4b4b941d6374979c99fd58dea37e596d837d1ed7af0789bca40f2ef56f7ad16bf964305771ec36e97786a8b7b84f38232d92b03 Homepage: https://cran.r-project.org/package=psychnets Description: CRAN Package 'psychnets' (Tidy Clean-Room Psychological Network Modeling) Provides clean-room implementations for estimating psychometric network models, including correlation and partial-correlation networks, Gaussian graphical models with extended Bayesian information criterion (EBIC) regularization, nonparanormal and stepwise selection variants, information-filtering networks (the triangulated maximally filtered graph and the local-global inverse covariance), relative-importance networks, and Ising and mixed graphical models . All methods are implemented from first principles in base R without compiled dependencies and return consistent, tidy outputs. Functions are designed to be transparent and report optimization diagnostics where applicable. For Gaussian graphical models, the graphical lasso stationarity (Karush-Kuhn-Tucker) residual quantifies the deviation of the estimated solution from the optimum of the corresponding convex optimization problem. Package: r-cran-psycho Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-dplyr, r-cran-stringr, r-cran-ggplot2, r-cran-insight, r-cran-bayestestr, r-cran-parameters, r-cran-effectsize Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-gparotation Filename: pool/dists/noble/main/r-cran-psycho_0.6.2-1.ca2404.1_all.deb Size: 190114 MD5sum: eb2ca87813110cb9d97eae531c143a81 SHA1: 4d03996b39a233d8ba9c17f8cc67c59c3f65b2ca SHA256: 0aeb81cffd47e32e4f097fa9b0f7c002babda6932001ed10ab706ea1ecd6d965 SHA512: 9826b63b5018db63d5dee358f7ff765e88073003c184aca867960ee8ac7e5c3dd1dcf0a5b8e52741a062cb15a9cee2acf1050ce6d1a824ce9f62ca8cabb1cb44 Homepage: https://cran.r-project.org/package=psycho Description: CRAN Package 'psycho' (Efficient and Publishing-Oriented Workflow for PsychologicalScience) The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity. Package: r-cran-psychomatic Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gparotation, r-cran-lavaan, r-cran-matrix, r-cran-psych Suggests: r-cran-flextable, r-cran-knitr, r-cran-mirt, r-cran-mvn, r-cran-officer, r-cran-openxlsx, r-cran-rmarkdown, r-cran-semtools, r-cran-sirt, r-cran-testthat, r-cran-writexl Filename: pool/dists/noble/main/r-cran-psychomatic_0.3.0-1.ca2404.1_all.deb Size: 401696 MD5sum: bb3d0768734e0c807937f46d003acfe5 SHA1: fcfc2799d3b8b9a96b1cbbd53080eeea72048e94 SHA256: 262727b7444e6bce2dbac602c561b16ca350ac8c2473f6b232ca809bd0b576c1 SHA512: 3f3d9a0a6b2d1e909a1e9997ee1935df6f94a74748a1f9eede3e116f94d861d27004feef75fcc050ed752cdfd91ff50b923b0c32a3905b96ee101bf3915dd65a Homepage: https://cran.r-project.org/package=PsychoMatic Description: CRAN Package 'PsychoMatic' (Automated Psychometric Workflows and Reporting Tools) Automates common psychometric workflows for applied researchers, including item descriptives, inter-item correlations, exploratory and confirmatory factor analysis, reliability, multi-group measurement invariance, and alignment optimization. Decision heuristics are informed by procedures such as parallel analysis (Horn, 1965, ), multivariate normality diagnostics (Mardia, 1970, ), measurement-invariance fit-change rules (Chen, 2007, ), and alignment optimization (Asparouhov and Muthen, 2014, ), among others. Results can be returned as structured R objects and exported as bilingual reports for transparent analytical documentation. Package: r-cran-psychometric Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-multilevel, r-cran-purrr, r-cran-nlme Filename: pool/dists/noble/main/r-cran-psychometric_2.4-1.ca2404.1_all.deb Size: 197938 MD5sum: c8602362a319d7633252153043f67574 SHA1: c5c1bdd42c4260cda07c592276a81d5f52e7d7d5 SHA256: eaf6a2f8df675ed799b2512da053b2695badd102407f70464cd1c164452f7038 SHA512: 7eb8158d228dddfc9d42c8f6783d75eec08e2d8593d7f26cbced2fa469043446026348075d551430b1099bd02350040fd8aae5d4589c69036b1b7e4176a22e01 Homepage: https://cran.r-project.org/package=psychometric Description: CRAN Package 'psychometric' (Applied Psychometric Theory) Contains functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility. Package: r-cran-psychomix Architecture: all Version: 1.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-flexmix, r-cran-psychotools, r-cran-lattice, r-cran-formula, r-cran-modeltools Suggests: r-cran-effects, r-cran-lmtest, r-cran-nnet, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-psychomix_1.1-10-1.ca2404.1_all.deb Size: 846138 MD5sum: 29fff5f7d9ad43a3926ab7bc461ff2ec SHA1: ed569dc80e480741c6f44b46303c77f0c7691fdb SHA256: cbf3b40a35053b58c2a7388b1fa9502acddfee0176697c27c94f0f67e3c1b1ea SHA512: d046a29b9561f16ccae3f8b0cc2bf5a0ae11a9145cd18ea1aaffaab334c041c7537ea4d32388e461d09d16cd6292869f6643b4ebf55bc8cac9ea53d8c2fdfb5f Homepage: https://cran.r-project.org/package=psychomix Description: CRAN Package 'psychomix' (Psychometric Mixture Models) Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) and Frick et al. (2015) for details on the Rasch mixture models. Package: r-cran-psychotree Architecture: all Version: 0.16-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-partykit, r-cran-psychotools, r-cran-formula Suggests: r-cran-stablelearner, r-cran-strucchange, r-cran-mirt, r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psychotree_0.16-3-1.ca2404.1_all.deb Size: 538978 MD5sum: 4a78de5482cf73ebe719c945b13c59e6 SHA1: b6b23d2f7000967bf1462da035571312b6484457 SHA256: 7902566e5d2a777adf4200b99b1794b9a199630fa26cbaa44cfea8a3a36f9582 SHA512: 1edf7ff8ba42ba4781de2b3b8ab65e57e3b1631e92755aa095098d51c8b0dc11b53026769cb39ba0ff004f05236126fcde659cd6a873b4e3f4349ed9ce6a9ced Homepage: https://cran.r-project.org/package=psychotree Description: CRAN Package 'psychotree' (Recursive Partitioning Based on Psychometric Models) Recursive partitioning based on psychometric models, employing the general MOB algorithm (from package partykit) to obtain Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and MPT trees, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial credit models. Package: r-cran-psychreport Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-xtable Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psychreport_4.0.1-1.ca2404.1_all.deb Size: 115512 MD5sum: fc6574848bed4fa3a113cd97d59e7ab9 SHA1: 6518b9987d018de5b36b7e4edac98c153ac92d52 SHA256: 9094cf8df2f98a5f909d174ef82162a5eaf35ade703bf3e888858af9832617db SHA512: 7e005e59d6f2b14d3d302b19da389e2a9292cd1c7ef1d88dc20c86f4ed84102b460415f67d00c274ebbf46ea3bd1b6968fe861cdda1357f3d257deebf6a5d23b Homepage: https://cran.r-project.org/package=psychReport Description: CRAN Package 'psychReport' (Reproducible Reports in Psychology) Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files. Package: r-cran-psychtools Architecture: all Version: 2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4940 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-psych, r-cran-foreign Suggests: r-cran-gparotation, r-cran-lavaan, r-cran-knitr Filename: pool/dists/noble/main/r-cran-psychtools_2.6.4-1.ca2404.1_all.deb Size: 4088728 MD5sum: 7acd75b37080a7fbe0e2577b43764e7e SHA1: ea6c529e3b080bfe1ca10076a6cfdd947030fbd7 SHA256: 9ec49f76f2eb8264b9c4dce1d7174708cbb473916158697ae3e06787ec6124a6 SHA512: 8b4302ba0a078b60df9235e2f0306222e78df0b914f7686ff794ac3ef9aa43b7bf40c1baa9acf2b95137008e9bdb106155af648f9b4d64b76bec8690d381b963 Homepage: https://cran.r-project.org/package=psychTools Description: CRAN Package 'psychTools' (Tools to Accompany the 'psych' Package for PsychologicalResearch) Support functions, data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as four vignettes. 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Package: r-cran-psychwordvec Architecture: all Version: 2025.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brucer, r-cran-dplyr, r-cran-stringr, r-cran-data.table, r-cran-purrr, r-cran-vroom, r-cran-cli, r-cran-ggplot2, r-cran-ggrepel, r-cran-corrplot, r-cran-psych, r-cran-rtsne, r-cran-rgl, r-cran-qgraph Suggests: r-cran-text, r-cran-text2vec, r-cran-word2vec, r-cran-rsparse, r-cran-fasttextr, r-cran-wordsalad, r-cran-sweater, r-cran-glue Filename: pool/dists/noble/main/r-cran-psychwordvec_2025.11-1.ca2404.1_all.deb Size: 3999094 MD5sum: f85fce5ff3af928ad47620bbe12720cd SHA1: 51c081b8ed6fa30b701fbac64d3343d388df7396 SHA256: 3d1ed99f32c66fcd0b07ffcd58925b7572d885d85de57f31a635189c56bbc9ac SHA512: b359cce54e97c3c74c7ae2ac2a802c8cce3b25519aa741436b511f5badfa8ff9ee07735735d14d1645b2ce9170da4ff5239d4d99dddf065320530e3371e9d870 Homepage: https://cran.r-project.org/package=PsychWordVec Description: CRAN Package 'PsychWordVec' (Word Embedding Research Framework for Psychological Science) An integrative toolbox of word embedding research that provides: (1) a collection of 'pre-trained' static word vectors in the '.RData' compressed format ; (2) a group of functions to process, analyze, and visualize word vectors; (3) a range of tests to examine conceptual associations, including the Word Embedding Association Test and the Relative Norm Distance , with permutation test of significance; and (4) a set of training methods to locally train (static) word vectors from text corpora, including 'Word2Vec' , 'GloVe' , and 'FastText' . Package: r-cran-psycmodel Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-insight, r-cran-lavaan, r-cran-lifecycle, r-cran-lme4, r-cran-lmertest, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-psych, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-correlation, r-cran-covr, r-cran-cowplot, r-cran-fansi, r-cran-ggrepel, r-cran-gparotation, r-cran-gridextra, r-cran-interactions, r-cran-knitr, r-cran-nfactors, r-cran-nlme, r-cran-pagedown, r-cran-qqplotr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sandwich, r-cran-see, r-cran-semplot, r-cran-spelling, r-cran-testthat, r-cran-lavasearch2 Filename: pool/dists/noble/main/r-cran-psycmodel_0.5.0-1.ca2404.1_all.deb Size: 869536 MD5sum: 925be4ea631dd79cc68fffea1ea8385c SHA1: 0f0cd251dd65473a188f113d99226a31c6e3169d SHA256: 0a688e983c1da508482f3ae4fb1c3ca7943997691c9fbf9222e0a9b5bd16aa7b SHA512: 24fe65a5e4656716c13a1d1159098011f5a2b22c5e1085b0822aa99e11f05f708b0ed91a2de3a97ee5b2dc397fbdfa2cad58cd59547e192b044d307782d08358 Homepage: https://cran.r-project.org/package=psycModel Description: CRAN Package 'psycModel' (Integrated Toolkit for Psychological Analysis and Modeling in R) A beginner-friendly R package for modeling in psychology or related field. 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Package: r-cran-psymetrictools Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-semtools, r-cran-psych, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-gridextra, r-cran-gtable, r-cran-pbapply, r-cran-tidyselect, r-cran-rlang, r-cran-scales Suggests: r-cran-testthat, r-cran-eganet, r-cran-bootnet, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-progress, r-cran-careless, r-cran-qgraph, r-cran-semplot, r-cran-ggridges, r-cran-ggpubr, r-cran-forcats, r-cran-rcolorbrewer, r-cran-openxlsx, r-cran-lavaan.mi, r-cran-blavaan, r-cran-hdinterval, r-cran-ggplotify, r-cran-patchwork, r-cran-wesanderson, r-cran-circlize, r-cran-shiny, r-cran-bslib, r-cran-sempower Filename: pool/dists/noble/main/r-cran-psymetrictools_1.2.4-1.ca2404.1_all.deb Size: 583344 MD5sum: f0f465c1988703d8b0f481830b9ea612 SHA1: 849cf19502fe2f69bd94779d5bcc0bc71b9fe0c6 SHA256: 6df7901eeae99828ba1284025e48df91b72c53ce62882e4cd20f86ae01b83c8f SHA512: 97225893a592028a2bb5931b058f0e48a7b335e22895a82b638357b281d6851bc1e77cf892f9e2aaa33766d21faac8a0b23cb8896a79bd3c2e99230872b73fc5 Homepage: https://cran.r-project.org/package=PsyMetricTools Description: CRAN Package 'PsyMetricTools' (Psychometric and Statistical Analysis Tools) Provides tools for psychometric and statistical analysis in social sciences, including functions for data preprocessing, factor analysis, reliability testing, descriptive statistics, visualization of Likert-scale items, measurement invariance analysis, and handling of multi-class imbalance. 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(2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185–1215. . 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Package: r-cran-publicworksfinanceit Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-leaflet, r-cran-lubridate, r-cran-rlang, r-cran-rvest, r-cran-scales, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-plotly, r-cran-spdep, r-cran-knitr Filename: pool/dists/noble/main/r-cran-publicworksfinanceit_0.3.1-1.ca2404.1_all.deb Size: 191052 MD5sum: cf3749086628dab3f717417488ecf322 SHA1: c422066c94dc7e23db112cbabc9723de7dd214b8 SHA256: f89efb02674bacc93bcf398c7838a6489a69256e64f0cfe7e2878db55dbded37 SHA512: f702ef4899c031bce438eabcc1b33f12a44b57eb48eb09ad038c912300cb2a0f75f70b1bdec8972a2a0de925149eb2df59c47c1c09f9339aa178d0bfcb5cc419 Homepage: https://cran.r-project.org/package=PublicWorksFinanceIT Description: CRAN Package 'PublicWorksFinanceIT' (Soil Defense Investments in Italy: Data Retrieval, Analysis,Visualization) Facilitates the retrieval and analysis of financial data related to public works in Italy, focusing on soil defense investments. 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Package: r-cran-publish Architecture: all Version: 2025.07.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-prodlim, r-cran-survival, r-cran-data.table, r-cran-lava, r-cran-multcomp Suggests: r-cran-riskregression, r-cran-testthat, r-cran-smcfcs, r-cran-rms, r-cran-mitools, r-cran-nlme Filename: pool/dists/noble/main/r-cran-publish_2025.07.24-1.ca2404.1_all.deb Size: 454716 MD5sum: bc985cb87d3e9a9a207948994722283e SHA1: 9d310804ba2267c0803a73655352f49cbdfaaabe SHA256: d70d4c0a101bdcc046b6b99b65d997e884b2c5f9c4d03899a8cd51dd73b67321 SHA512: 0d87da4e8b3bfd676c0d2d0037a05b3289cb27c13a1e606e32122a0fedfb718aa684745ef94fbcddef607218698bb51cc357656e81b69f256ba624660da17f26 Homepage: https://cran.r-project.org/package=Publish Description: CRAN Package 'Publish' (Format Output of Various Routines in a Suitable Way for Reportsand Publication) A bunch of convenience functions that transform the results of some basic statistical analyses into table format nearly ready for publication. 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Package: r-cran-pubmatrixr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2880 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pbapply, r-cran-pheatmap, r-cran-readods, r-cran-xml2 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pubmatrixr_1.0.1-1.ca2404.1_all.deb Size: 1800450 MD5sum: f8b1a50c7056df2870ed604aa1fa733b SHA1: 48d7b0d4312e6a5ad9505997c9d1a4d2ad364784 SHA256: fd12fb34ff5d8a60c0148d953af8a76d26e884549ad61b0becb1c17629b47df0 SHA512: 95871cd6ff4ece9f79ac83b40ba49522717f35d47850c2d43faef76acc0b039b77b66f6646b6bebb28d2f44272d3bb4b0c5a5cab1558686754541e241cb20030 Homepage: https://cran.r-project.org/package=PubMatrixR Description: CRAN Package 'PubMatrixR' (PubMed Pairwise Co-Occurrence Matrix Construction andVisualization) Queries the 'NCBI' (National Center for Biotechnology Information) Entrez 'E-utilities' API to count pairwise co-occurrences between two sets of terms in 'PubMed' or 'PubMed Central'. 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Package: r-cran-pubmed.miner Architecture: all Version: 1.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-xml, r-cran-boot, r-cran-r2html, r-cran-rjsonio Filename: pool/dists/noble/main/r-cran-pubmed.miner_1.0.21-1.ca2404.1_all.deb Size: 1255936 MD5sum: f912661610b961e018aa11ae8ed3d4d2 SHA1: eea1fe3cd2f9113284d9d749679bfe042e1144a5 SHA256: 067e14116f96716399267976672523886fd73df038e27ae224974479834d8ae3 SHA512: c7ae998e581d38ebeb9b675b43657a378512f462a322db54ca131dfdd5313d638e1a9583619d69e269e06a22899746e0e0921542da5d8befea54d1eb17b4af67 Homepage: https://cran.r-project.org/package=pubmed.mineR Description: CRAN Package 'pubmed.mineR' (Text Mining of PubMed Abstracts) Text mining of PubMed Abstracts (text and XML) from . 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The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus) is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term), a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates. 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Package: r-cran-pubmedtk Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-xml2, r-cran-jsonlite, r-cran-httr, r-cran-assertthat, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pubmedtk_1.0.4-1.ca2404.1_all.deb Size: 44778 MD5sum: c75587213cbfe9a3462237ecbf29e806 SHA1: e3d2180d024728a4b7fbb487460c115a343b176c SHA256: 057b98f8352979efe933ef33de2aebee99298461c397561dea30a6c7a1a89c95 SHA512: e8c80e4ccb0e1e1ae6903de982c94640a1b06c6648a35150a0276010b5f2902b05947eceb1fdd8b2bd4e34eaf286b61b642c80ca86e73c90a72cdd7e711f76e3 Homepage: https://cran.r-project.org/package=pubmedtk Description: CRAN Package 'pubmedtk' ('Pubmed' Toolkit) Provides various functions for retrieving and interpreting information from 'Pubmed' via the API, . 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(2024) . Numerous visualization options, including static and animated, 2D and 3D, and a site map generator based on sensor and source coordinates. 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Package: r-cran-puls Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-dplyr, r-cran-fda, r-cran-fda.usc, r-cran-ggplot2, r-cran-monoclust, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-puls_0.1.3-1.ca2404.1_all.deb Size: 293236 MD5sum: 826c4db8af6a3a48239464d7a5fef200 SHA1: adc7fe4e2f2c0e055dd3031704a3a74489056165 SHA256: 805ce79aa7400bc4b9f6572fb4dd28885842a535a8e5f8ad1859d187c84e92c8 SHA512: 5da256b791b3f59e9252bfeb8c2b3a5193d959e977512a73f76534fa76cb99c6a52ab8488eeebde6ecb715f4febd36cb65dd9d4aa83dbe18136ec6b8a7eba265 Homepage: https://cran.r-project.org/package=puls Description: CRAN Package 'puls' (Partitioning Using Local Subregions) A method of clustering functional data using subregion information of the curves. It is intended to supplement the 'fda' and 'fda.usc' packages in functional data object clustering. It also facilitates the printing and plotting of the results in a tree format and limits the partitioning candidates into a specific set of subregions. Package: r-cran-pulsar Architecture: all Version: 0.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-batchtools, r-cran-fs, r-cran-checkmate, r-cran-orca, r-cran-huge, r-cran-mass, r-cran-clime, r-cran-glmnet, r-cran-network, r-cran-cluster, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pulsar_0.3.13-1.ca2404.1_all.deb Size: 307436 MD5sum: 52ed5b1c98b338232e44506a973f2211 SHA1: bcabe98ae74a761c561ee4d970b49cfb564d7471 SHA256: 26722463f7432d8a9a7ed44895b13a35b604bc0ef6b53eaae212337b87136edd SHA512: 223b01e734cfca0843b7230b9126586ae050aec9d0d1ded43fbad8fa3a6f73e5ffcbcbbb932b2baa82493f92f98f12eaa305aed778c28e6a820310aea8a974d2 Homepage: https://cran.r-project.org/package=pulsar Description: CRAN Package 'pulsar' (Parallel Utilities for Lambda Selection along a RegularizationPath) Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) . Package: r-cran-pulso Architecture: all Version: 0.1.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tibble, r-cran-rlang, r-cran-cli, r-cran-fs, r-cran-digest, r-cran-data.table, r-cran-dplyr, r-cran-readxl, r-cran-xml2 Suggests: r-cran-haven, r-cran-vctrs, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pulso_0.1.1-1.ca2404.2_all.deb Size: 200744 MD5sum: aead3b3b038484aec6df33c323b58784 SHA1: c115e037de3912e218d497ef8ed03d172d919fff SHA256: b98b2c75a7822b08aeb0a7324426047510763867404f2c25bbbe481c719f651c SHA512: 52888c550cf80ddb74f1a5238426e04fe538b8e225bcd3af8681a9cb4ea173b8b7b29e1013cf71a574723e4f842ed9f4cbe3b4288a2ee48ca209d453fc0f4759 Homepage: https://cran.r-project.org/package=pulso Description: CRAN Package 'pulso' (Load Microdata from Colombia's 'GEIH' ('DANE')) Programmatic access to microdata from Colombia's Gran Encuesta Integrada de Hogares ('GEIH'), published by 'DANE'. Provides a tidy interface to download, parse, and harmonize labor market surveys from 2007 to present. R companion to the 'pulso-co' 'Python' package. Package: r-cran-pumilior Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-rcurl Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-pumilior_1.3.1-1.ca2404.1_all.deb Size: 36128 MD5sum: 7dc5b60222eaaea5e1c1465872181b04 SHA1: ba8a6dd7c222abf1a134fc2631fe69bb096a86a0 SHA256: 5fca8ef6d13535152f7ec014f1c92fc5a80baa1b548b5758406bb7d5a8859da6 SHA512: dc58b9b2b85f457a5887eb4036e5029cdcdeebc0d34dfe0c7647e8aee0fe7ec01943e7ff827292d958768c308490e2a7144cf880ee511822f4311c7e83f1bb74 Homepage: https://cran.r-project.org/package=pumilioR Description: CRAN Package 'pumilioR' (Pumilio in R) R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/). 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The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see . Package: r-cran-pupaim Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metrics, r-cran-ggplot2, r-cran-nls2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pupaim_0.3.1-1.ca2404.1_all.deb Size: 370664 MD5sum: f16fe57561884bbf4949141213169ef5 SHA1: c1166f47713969797d4fd4519ae0b9f6f57dc13b SHA256: 5c72657980f5d9d0e9af72f43ea47ec11344c410f8565c0ff8843eaee362e8fd SHA512: c711b02b4f54801e90281a1b90738cbee3f498a4c92b2b142f7ab7971a41c52dcb20e0eec18e951b962cc4705e8eea256eeee348fa2f4e45ad1ddaab71396221 Homepage: https://cran.r-project.org/package=PUPAIM Description: CRAN Package 'PUPAIM' (A Collection of Physical and Chemical Adsorption Isotherm Models) The PUPAIM R package can generally fit any adsorption experimental data to any of the 55 available adsorption isotherm models - 32 nonlinear models and 23 linear models. This package provides parameter estimation, model accuracy analysis, model error analysis, and adsorption plot created using the package 'ggplot2'. This package will help the users for a much easier way of adsorption model data fitting. Package: r-cran-pupak Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metrics, r-cran-ggplot2, r-cran-nls2, r-cran-segmented Filename: pool/dists/noble/main/r-cran-pupak_0.1.1-1.ca2404.1_all.deb Size: 373356 MD5sum: cc775be4619a4ba0cca551cd6184a5ae SHA1: 45b450bdff7b8aeb7773290dfc8671380dc37b47 SHA256: 46aaa7561811b0e010a512aaa8340509f0602a10a00efec44a23e5d2765d4911 SHA512: dfbd0742220ddceb65e42c0a2afc8966a04056ec7e0bc82d58ce2206fa01144d54adf4022719ea767bfcae153761b4ef0fcfc4689fa0bbca521552177e8914d1 Homepage: https://cran.r-project.org/package=PUPAK Description: CRAN Package 'PUPAK' (Parameter Estimation, and Plot Visualization of AdsorptionKinetic Models) Contains model fitting functions for linear and non-linear adsorption kinetic and diffusion models. Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) . This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model. Package: r-cran-pupillometryr Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-fda, r-cran-mgcv, r-cran-signal, r-cran-stringr, r-cran-tidyr, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pupillometryr_0.0.7-1.ca2404.1_all.deb Size: 891074 MD5sum: b2b771f510577d405971b7335661ede9 SHA1: a8aa97f5bba6459aeebf70b696ccc658feb916a4 SHA256: 65bdc5fa1cd26fdbf5caaf54575010e1bb1063cc1f88a53431d7b8dc5eb49355 SHA512: 2d2f06c831ed36ee556a60b5bf8d04acb3093a617524294f43556596434543910b79a273e32ed263318f58d4c4143cb8384118e678488185aa3f504efdbe96c2 Homepage: https://cran.r-project.org/package=PupillometryR Description: CRAN Package 'PupillometryR' (A Unified Pipeline for Pupillometry Data) Provides a unified pipeline to clean, prepare, plot, and run basic analyses on pupillometry experiments. 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This package contains functions for preparing pupil dilation data for visualization and statistical analysis. Specifically, it provides a pipeline of functions which aid in data validation, the removal of blinks/artifacts, downsampling, and baselining, among others. Additionally, plotting functions for creating grand average and conditional average plots are provided. See the vignette for samples of the functionality. The package is designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. 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Package: r-cran-pupmsi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-metrics, r-cran-minpack.lm, r-cran-nls2 Filename: pool/dists/noble/main/r-cran-pupmsi_0.1.0-1.ca2404.1_all.deb Size: 200084 MD5sum: f6a544ad612a491265f35a9047b057bb SHA1: f5b2e89f1f1606ad34fdf23db584d5368b509926 SHA256: 7b247d5cfa1a1e668fcfe93fd5f78f49c0253465a1f8fe272b43edd64226cddf SHA512: 992057ab9a4fe970740acdff607996cbdfb72f20b8687b4e0ce6095a94a409191c9d90dc615a9217ad6cf5e48f23b80b4b1d40471f1243a7b1f06a62b9bf2f6d Homepage: https://cran.r-project.org/package=PUPMSI Description: CRAN Package 'PUPMSI' (Moisture Sorption Isotherm Modeling Program) Contains sixteen moisture sorption isotherm models, which evaluate the fitness of adsorption and desorption curves for further understanding of the relationship between moisture content and water activity. Fitness evaluation is conducted through parameter estimation and error analysis. Moreover, graphical representation, hysteresis area estimation, and isotherm classification through the equation of Blahovec & Yanniotis (2009) which is based on the classification system introduced by Brunauer et. al. (1940) are also included for the visualization of models and hysteresis. 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A PubMed search resolves to a set of PMIDs, which can be used to retrieve article metadata and abstracts, author affiliations, 'iCite' citation data and links, 'PubTator3' entity and relation annotations, and open-access full text from 'PMC'. A local analysis layer operates on the retrieved tables, supporting corpus expansion through citation links, citation network construction, sentence-level entity co-occurrence, inspection of relation evidence, and 'MeSH' descriptor keyness. Package: r-cran-purge Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-survival, r-cran-lme4, r-cran-ranger, r-cran-randomforest, r-cran-rpart Filename: pool/dists/noble/main/r-cran-purge_0.2.1-1.ca2404.1_all.deb Size: 23218 MD5sum: 4db3f2252a04ac7343afe91498168ef2 SHA1: 2feb624581d929dbce896ec13be3f888597a4082 SHA256: ee4843aeb6167edc630df045191eb01c7a2b724ed64887981894ffb40c6550e3 SHA512: b9807912629764a46e422e29658d55f96d0b98f69f43b3ccbe9ba3014f58ff17fa740a7b8e3103d393c58de05a798731abfee9318e5e1b9b6b3906b3ab69182f Homepage: https://cran.r-project.org/package=purge Description: CRAN Package 'purge' (Purge Training Data from Models) Enables the removal of training data from fitted R models while retaining predict functionality. The purged models are more portable as their memory footprints do not scale with the training sample size. Package: r-cran-purging Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-purging_1.0.0-1.ca2404.1_all.deb Size: 20204 MD5sum: c938a2b6642250a87121633589e2b1d4 SHA1: e2d7b5b914932a3cdad762c7436df47a83aa937c SHA256: 203998846d47891f795068fea3169c052da0b241dbc664a2784a0e5acb3c70e3 SHA512: 122fabd1ee0f350da1240cc6c53aa52015ffb71646f95f37e06734d4ddbc2f155a73543f11110389975bad7735ee9fefb0b03449e454144fa6bb5091727e1cc3 Homepage: https://cran.r-project.org/package=purging Description: CRAN Package 'purging' (Simple Method for Purging Mediation Effects among IndependentVariables) Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) . Package: r-cran-purpleair Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-cli, r-cran-dplyr, r-cran-glue Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-purpleair_1.1.1-1.ca2404.1_all.deb Size: 90158 MD5sum: a5669dfe302a7f5cf08c138e7da96622 SHA1: 6a9c881333d08ec572431ff2c8936753c912947e SHA256: 0cbfba9cbd9aa1588ca0c357a66fd23abec2d8d7230800366e4ead398ca1a7a3 SHA512: f34e23f11321133b026b1a109d3cc07fd41f74ed7ad69b7ad978add0e113bde1a1f8c1bbce39b22985a0bc4ec81a9144ea8e41f3d284809577524d2be87f8375 Homepage: https://cran.r-project.org/package=PurpleAir Description: CRAN Package 'PurpleAir' (Query the 'PurpleAir' Application Programming Interface) Send requests to the 'PurpleAir' Application Programming Interface (API; ). Check a 'PurpleAir' API key and get information about the related organization. Download real-time data from a single 'PurpleAir' sensor or many sensors by sensor identifier, geographical bounding box, or time since modified. Download historical data from a single sensor. Stream real time data from monitors on a local area network. 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Package: r-cran-pvaclone Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcmle, r-cran-dclone, r-cran-coda Filename: pool/dists/noble/main/r-cran-pvaclone_0.1-8-1.ca2404.1_all.deb Size: 161884 MD5sum: 3800157fa53096772ef1626109224e90 SHA1: 510d6d7933150edda989859d902b23bd7ae87116 SHA256: 733f7384f310fcbfbce73e049fa18b5df373b35bf54dc0e9ac976016487aa0dd SHA512: d1998f43e4b157f236898776cb95e350749471a1d89591a6167abe36625ca8cbf80403f8da9617c857f7f4f3ee92bd1e9154504edc9d2ff290782dc38dd5e2e6 Homepage: https://cran.r-project.org/package=PVAClone Description: CRAN Package 'PVAClone' (Population Viability Analysis with Data Cloning) Likelihood based population viability analysis in the presence of observation error and missing data. The package can be used to fit, compare, predict, and forecast various growth model types using data cloning. Package: r-cran-pvaluefunctions Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-scales, r-cran-zipfr, r-cran-pracma, r-cran-gsl, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pvaluefunctions_1.7.0-1.ca2404.1_all.deb Size: 1342306 MD5sum: 99ed179c552d159960e8c3499ac70c3d SHA1: 1f34c89570e7f85bba98858d3283560fd8b38a29 SHA256: 2ab031b30773e91b44883735303d2b5619b4f091b5a811670571d3766f50377c SHA512: 1989ad1bc8e462b23e0304c5bfeaced979eb5e50ce38b615878d5c40ccd03b3637391be7d0f3a1d162d6d8492fde394e4fff2c83bf153be568f74a286f7003f5 Homepage: https://cran.r-project.org/package=pvaluefunctions Description: CRAN Package 'pvaluefunctions' (Creates and Plots P-Value Functions, S-Value Functions,Confidence Distributions and Confidence Densities) Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) ; Poole C. (1987) ; Schweder T, Hjort NL. (2002) ; Bender R, Berg G, Zeeb H. (2005) ; Singh K, Xie M, Strawderman WE. (2007) ; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) ; Greenland S. (2019) and Rafi Z, Greenland S. (2020) . Package: r-cran-pvarife Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pvarife_0.1.2-1.ca2404.1_all.deb Size: 190452 MD5sum: 9c7814d5ffe4556ae1daed1d7bb670a6 SHA1: f19c5c29d5e0d2d560353a93acc3582fd4faaf86 SHA256: 749dd3a3159b486f5b7f01518ae7bb8c645c4aee0fd4b5645420943b30a67710 SHA512: b03e8794fe385a18f74c4484449bdab788886757236fb5d8aae300b12318b453db0ed1fe62ac9ff27a65427521e87f7db2954ba6d33a739a1dd10426d17fdfde Homepage: https://cran.r-project.org/package=pvarife Description: CRAN Package 'pvarife' (Panel VAR Models with Interactive Fixed Effects) Implements the estimator of Tugan (2021) for panel vector autoregression (VAR) models with interactive fixed effects. 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From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) and Sack, Scoffoni, PrometheusWikiContributors (2011) . 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Contains various testing and post-processing functions. 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Functions included allow for data cleaning, feature correction, power predictive modeling, PLR determination, and uncertainty bootstrapping through various methods . The vignette "Pipeline Walkthrough" gives an explicit run through of typical package usage. This material is based upon work supported by the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE-0008172. This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. Package: r-cran-pvr Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splancs, r-cran-ape, r-cran-mass Filename: pool/dists/noble/main/r-cran-pvr_0.3-1.ca2404.1_all.deb Size: 190162 MD5sum: 25619e64a7a656e8d591fc9146e44f6a SHA1: 303fe09556e7f2c0872531a5d774f5fa19a0d381 SHA256: 137855a6aa03a72e5eea8d8d2cf698b4714a2ac18b386283087ddc8d610023ee SHA512: c29e6260bac96bce81fa2dada7b72b056f27eca725181635075c5b843315b9b76b028108690446647efb2ebb1666f139382bdb236a9b862f679f54c7286d0cbb Homepage: https://cran.r-project.org/package=PVR Description: CRAN Package 'PVR' (Phylogenetic Eigenvectors Regression and PhylogenticSignal-Representation Curve) Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities. Package: r-cran-pvstatem Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3724 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-nplr, r-cran-r6, r-cran-readr, r-cran-readxl, r-cran-stringi, r-cran-stringr, r-cran-png, r-cran-ggrepel, r-cran-lubridate, r-cran-r.utils, r-cran-svglite, r-cran-fs, r-cran-scales Suggests: r-cran-knitr, r-cran-qpdf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pvstatem_0.2.2-1.ca2404.1_all.deb Size: 2041240 MD5sum: 7eafe88f6f7a6e17e315bae957e6f76b SHA1: 519a0dd8a58d4a55fb8e6e6a94b38ccf2504aa2a SHA256: 0c1e9bb17ec5ea7e0d0155a793ee14cc9ebe864196d8f24ca03a842a4b8337e9 SHA512: 1936f7ecf8e40497ebb3f84740e94ad73108bb30e893aada97125bbb5a18f41a1a6a4e7c413761966d048f53843cd40cad9b21c6a3bba2cf7353e908f11eb312 Homepage: https://cran.r-project.org/package=PvSTATEM Description: CRAN Package 'PvSTATEM' (Reading, Quality Control and Preprocessing of MBA (MultiplexBead Assay) Data) Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - 'PvSTATEM', which is an international project aiming for malaria elimination. Package: r-cran-pwepred Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-segmented, r-cran-fastmatch, r-cran-foreach, r-cran-dosnow Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwepred_1.1.2-1.ca2404.1_all.deb Size: 577752 MD5sum: 1d47d64ce73c3f1b31e07012ddc3a9c3 SHA1: 3f3d9750af0476c3e19af7b4b29f7a464686f652 SHA256: 88801ebe40e308bb67ee9033e55d49cbe0d64bdeef45f97d544ed756d7974ed6 SHA512: 329624e55cc2e1d86e725d23c00232289431d047f6e691eabbfafde4ca0f09fc12a1b5ada66bdd92bec05b9338de4bacd9202528a7a1cfb07c5f633bd14e3c9f Homepage: https://cran.r-project.org/package=PwePred Description: CRAN Package 'PwePred' (Event/Timeline Prediction Model Based on Piecewise Exponential) Efficient algorithm for estimating piecewise exponential hazard models for right-censored data, and is useful for reliable power calculation, study design, and event/timeline prediction for study monitoring. Package: r-cran-pwev Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-rumidas, r-cran-rugarch, r-cran-weightedensemble, r-cran-metrics, r-cran-zoo Filename: pool/dists/noble/main/r-cran-pwev_0.1.0-1.ca2404.1_all.deb Size: 24472 MD5sum: 13d1375a1c9a7b8d2e1d5bd488dc36f3 SHA1: dfedd1a47fd9bac2291b7ca1c73150f87e6c5ba5 SHA256: 0610bba6bfab32cba879ec9d2d08ccc28468bf633364791aaae795e8eb5f65d5 SHA512: 2be5454d7baa74e7738723651e5e4d8de5c2a11c0fd476def395f474cbff1fc1ee369a8c2975318851fec789238d6f50f0477319460329a134c846569a6cfe30 Homepage: https://cran.r-project.org/package=PWEV Description: CRAN Package 'PWEV' (PSO Based Weighted Ensemble Algorithm for Volatility Modelling) Price volatility refers to the degree of variation in series over a certain period of time. This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. and Yeasin and Paul (2024) . Package: r-cran-pwexp Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-segmented, r-cran-foreach, r-cran-dosnow, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwexp_0.5.1-1.ca2404.1_all.deb Size: 485358 MD5sum: b233acbfdd2034c2a4e5895bde5ccfe8 SHA1: c7712f883f60111abfd0dd9831141cb89de9435e SHA256: 3954bb557187dae0ca0012ec679f07c0362b86d3b399e278c8aef1d8b747904d SHA512: 4e15db58d6e7c4c226d2cbd0afe83f1a704f1d1140e326a5aa386ba92763168907d5d665f18f886104acc45c307f8cc24e6b65575930637cd08fffdf6b77620c Homepage: https://cran.r-project.org/package=PWEXP Description: CRAN Package 'PWEXP' (Piecewise Exponential Distribution Prediction Model) Build piecewise exponential survival model for study design (planning) and event/timeline prediction. Package: r-cran-pwir Architecture: all Version: 0.0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bibliometrix, r-cran-igraph, r-cran-progressr Filename: pool/dists/noble/main/r-cran-pwir_0.0.3.1-1.ca2404.1_all.deb Size: 33390 MD5sum: aa75fca5f7e1ada198a7d87986e78311 SHA1: 44e3247d4b2847b51fb718b75be034fcf56bc5a1 SHA256: a668359f13fabf1f57a8d04d76a6630184f956f551b76aff85d44947961aa0e5 SHA512: 7053b0a70d9e24316ac976236528c519ff89abe80b6fdcd80569bca395f2a01d59c0e00aa5f32187dbb7fa4eae7855c923d8ceb5c07a6e7f6dbebe378f8a5a54 Homepage: https://cran.r-project.org/package=PWIR Description: CRAN Package 'PWIR' (Provides a Function to Calculate Prize Winner Indices Based onBibliometric Data) A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes. 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Methods based on Warwicker and Rebennack (2025) "Efficient continuous piecewise linear regression for linearising univariate non-linear functions" . Package: r-cran-pwr2 Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pwr2_1.0-1.ca2404.1_all.deb Size: 34752 MD5sum: 42528c7f2d7d52171126b2415e6ed41e SHA1: dd7d1c0a7e128bdf4ec0776f5c2e0c1de7a7301a SHA256: ff38c3ce4fd36dd65b4c1caab2bdf16e1512d1e43d067c65309d68a06e0c4f40 SHA512: 00529fb58057c93610fae458bb87a54c327b0be539213b793293bee63ef807569879d4b37ac2a50a1fcabd546e80a55a24773745553cd925c918fb4f67483c03 Homepage: https://cran.r-project.org/package=pwr2 Description: CRAN Package 'pwr2' (Power and Sample Size Analysis for One-way and Two-way ANOVAModels) User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers. Package: r-cran-pwr2ppl Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-phia, r-cran-afex, r-cran-mbess, r-cran-lavaan, r-cran-semtools, r-cran-quantreg, r-cran-broom, r-cran-lmtest, r-cran-lmperm, r-cran-pls Filename: pool/dists/noble/main/r-cran-pwr2ppl_0.6.0-1.ca2404.1_all.deb Size: 471458 MD5sum: dfa1a972e9f3377a17d8a130d5242b2e SHA1: 892293babd2aedc974e5eac4b01638fc93f0eda7 SHA256: 569e2119668848c83945cae8b922c91b633e32cb5abf0b5f735d787f3f2bccf7 SHA512: 79508c4090d1e928f1653d56d004a021d177b2a676a68fc67a56b379033774a0b9656523acb708254f9674e52722e4ddd6bec15d2d6297a8ff17ffeac5a318d7 Homepage: https://cran.r-project.org/package=pwr2ppl Description: CRAN Package 'pwr2ppl' (Power Analyses for Common Designs (Power to the People)) Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) . Package: r-cran-pwr4exp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emmeans, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-numderiv Suggests: r-cran-agricolae, r-cran-algdesign, r-cran-crossdes, r-cran-frf2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwr4exp_1.0.1-1.ca2404.1_all.deb Size: 252454 MD5sum: 5728ec45a5d7409303c75f5f94d0f2d8 SHA1: a5ee4c0055d8b843af069e9c3d14216f0c1d9b0b SHA256: d03edc5f4ad0842ea300036ee9dd4675a06a6434c1764287ca9979092dda5c48 SHA512: 36cf3dbbe80f3c15a21277cf884ea11ec70c2a0d5495747d26a4c154100035d9220daa87aa05d82d8005f38ffb9fde1590fba535a8e021411c62e70c0887fe0d Homepage: https://cran.r-project.org/package=pwr4exp Description: CRAN Package 'pwr4exp' (Power Analysis for Research Experiments) Provides tools for calculating statistical power for experiments analyzed using linear mixed models. It supports standard designs, including randomized block, split-plot, and Latin Square designs, while offering flexibility to accommodate a variety of other complex study designs. Package: r-cran-pwr Architecture: all Version: 1.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-scales, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwr_1.3-0-1.ca2404.1_all.deb Size: 149784 MD5sum: cc1b4cf0e0e1ace891196e119c68ba6f SHA1: ae958ffdf8624f656bd21d033729345d45befe6e SHA256: 1387a693aa097f047d29663a07d9078b6f58027ac1d38995a681e29939176e6e SHA512: 82b3aca3889c80de9b8cd056c2604ce336f433742ec61f0d745ff20d38d6f7be46d3f14a8e329d1c254ccd6cf5ea435e06004885787807e3a26abe3b32029a48 Homepage: https://cran.r-project.org/package=pwr Description: CRAN Package 'pwr' (Basic Functions for Power Analysis) Power analysis functions along the lines of Cohen (1988). Package: r-cran-pwrab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pwrab_0.1.0-1.ca2404.1_all.deb Size: 24696 MD5sum: 988ee33e2f37a3989062e19a72eab1b3 SHA1: b73a9b41282b0abfddbc4aa8b79bb01016b488f3 SHA256: 4eebc07aae9a42fb2c510a4787e1b2720881ec28d87b5ee0beb10eef772153b9 SHA512: f684bf48c62415ee5014beba6edbc64c81d78c2c80177de3e862ef5c95831aa76ca72643606a1877e3ea238bbf3038c3889f1ec91d1dab5fb86da3963fb49f8f Homepage: https://cran.r-project.org/package=pwrAB Description: CRAN Package 'pwrAB' (Power Analysis for AB Testing) Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give biased results due to using the pooled standard deviation, unlike the Welch's t-test. Package: r-cran-pwranova Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pwranova_1.1.5-1.ca2404.1_all.deb Size: 99852 MD5sum: 68dc60773f4e4f4171c9963e43104341 SHA1: b9b77f96e15c16c8b13aa4d746b68aeacd4c63c7 SHA256: 4bfbc046fb210ae6e871fb7a3c9126e64f108395ec9c63e588aa08787ef38eec SHA512: 7dc8241521be0e9edbd849e82eb409e88d3d58306f0744258e5894537f0cd28d23059215bd0b8c05aa7c038e97c127b682f80781cb1d6d9585a7791c49460153 Homepage: https://cran.r-project.org/package=pwranova Description: CRAN Package 'pwranova' (Power Analysis of Flexible ANOVA Designs and Related Tests) Provides functions for conducting power analysis in ANOVA designs, including between-, within-, and mixed-factor designs, with full support for both main effects and interactions. The package allows calculation of statistical power, required total sample size, significance level, and minimal detectable effect sizes expressed as partial eta squared or Cohen's f for ANOVA terms and planned contrasts. In addition, complementary functions are included for common related tests such as t-tests and correlation tests, making the package a convenient toolkit for power analysis in experimental psychology and related fields. Package: r-cran-pwrfdr Architecture: all Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flextable, r-cran-ggplot2, r-cran-mvtnorm, r-cran-stringr, r-cran-tablemonster Filename: pool/dists/noble/main/r-cran-pwrfdr_3.2.4-1.ca2404.1_all.deb Size: 603570 MD5sum: d692b234c682c9c4a38332a1931858ca SHA1: f7207535701e5c5d40e06cdb58f94e21800c9b9a SHA256: a7850c840c6c57dcbd12dd9b1ecce7e6d1bda5ec9636f9d30938189ef884221f SHA512: 02816be7d1302c4f475321a3336539f4c89ed9f2e39dec5fe942e6e07b09d452349bd300ad0df9eae4f1d69a32ae1424e7c2f2b4747418cfc661c8abf8df12a6 Homepage: https://cran.r-project.org/package=pwrFDR Description: CRAN Package 'pwrFDR' (FDR Power) Computing Average and TPX Power under various BHFDR type sequential procedures. All of these procedures involve control of some summary of the distribution of the FDP, e.g. the proportion of discoveries which are false in a given experiment. The most widely known of these, the BH-FDR procedure, controls the FDR which is the mean of the FDP. A lesser known procedure, due to Lehmann and Romano, controls the FDX, or probability that the FDP exceeds a user provided threshold. This is less conservative than FWE control procedures but much more conservative than the BH-FDR proceudre. This package and the references supporting it introduce a new procedure for controlling the FDX which we call the BH-FDX procedure. This procedure iteratively identifies, given alpha and lower threshold delta, an alpha* less than alpha at which BH-FDR guarantees FDX control. This uses asymptotic approximation and is only slightly more conservative than the BH-FDR procedure. Likewise, we can think of the power in multiple testing experiments in terms of a summary of the distribution of the True Positive Proportion (TPP), the portion of tests truly non-null distributed that are called significant. The package will compute power, sample size or any other missing parameter required for power defined as (i) the mean of the TPP (average power) or (ii) the probability that the TPP exceeds a given value, lambda, (TPX power) via asymptotic approximation. All supplied theoretical results are also obtainable via simulation. The suggested approach is to narrow in on a design via the theoretical approaches and then make final adjustments/verify the results by simulation. The theoretical results are described in Izmirlian, G (2020) Statistics and Probability letters, "", and an applied paper describing the methodology with a simulation study is in preparation. See citation("pwrFDR"). 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The package provides an R interface to a number of 'pysd' functions, and can read files in 'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested using 'python3'. Package: r-cran-pysparklyr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-processx, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-rstudioapi, r-cran-sparklyr, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-uuid, r-cran-withr, r-cran-connectcreds Suggests: r-cran-vcr, r-cran-crayon, r-cran-r6, r-cran-testthat, r-cran-tibble, r-cran-rsconnect, r-cran-rsample, r-cran-workflows, r-cran-tune, r-cran-parsnip, r-cran-dials, r-cran-tailor, r-cran-recipes Filename: pool/dists/noble/main/r-cran-pysparklyr_0.2.2-1.ca2404.1_all.deb Size: 321298 MD5sum: 2c8f2482d126132604ea77f5c8d96109 SHA1: cf56208ed3108f166264c6028a9779769c08f21e SHA256: a6a5ce918e6fef46ebf7531e3d3302149cc9fb20e3db5cfef722849608782889 SHA512: 14c4698dfa00f0158936e0a4dd573c3b4917cae35bd59d3bc69b61c66e139467e314dd7e36b14b654efefd040ca6b81224d24b59b307a409b97df75b484e3cdd Homepage: https://cran.r-project.org/package=pysparklyr Description: CRAN Package 'pysparklyr' (Provides a 'PySpark' Back-End for the 'sparklyr' Package) It enables 'sparklyr' to integrate with 'Spark Connect', and 'Databricks Connect' by providing a wrapper over the 'PySpark' 'python' library. Package: r-cran-pytrendslongitudinalr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-jsonlite, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pytrendslongitudinalr_0.1.4-1.ca2404.1_all.deb Size: 80502 MD5sum: ecd34fbfc83cef6743d222a9490e3f7f SHA1: 9fecce64d66bdc33c6020e0f66e3bd1da1af37d1 SHA256: 3237dfb8957b6313ca1629f06c282840cb3783529740c65b8ae091f19ab33c3e SHA512: 089280503dbee31097cfed6a18b9ef1ef7d7cfbae53c2a74d7883b56b2c2a3f3d68db3cf418a8f9e9da7087f13db788928d99f328cacb327f9b291770f268c06 Homepage: https://cran.r-project.org/package=PytrendsLongitudinalR Description: CRAN Package 'PytrendsLongitudinalR' (Create Longitudinal Google Trends Data) 'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine cross-sectional and time-series data and limitations in data formats and timelines that limit data granularity over extended time periods. This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data. This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work. During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data. In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit . Package: r-cran-pzfx Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pzfx_0.3.1-1.ca2404.1_all.deb Size: 617176 MD5sum: 682ace66601f29bc3e6d5782fe050490 SHA1: 7466b612f4e49e95e93b9223e6682a48a8233e49 SHA256: 91eb487b16bc0dfa86af8cf7674cea8528824dbf9b4a167ed95a2f1abf423eff SHA512: 3b424fa4730f3bbe7e1754bc25fef0f6c36dc3a7f1d530023bb111ca560d1ec633e7758ffc6401239f759d23b2271498738636a83d9c8f025f136f8f26b1a0ac Homepage: https://cran.r-project.org/package=pzfx Description: CRAN Package 'pzfx' (Read and Write 'GraphPad Prism' Files) Read and write 'GraphPad Prism' '.pzfx' files in R. Package: r-cran-q2q Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-q2q_0.1.2-1.ca2404.1_all.deb Size: 152152 MD5sum: 874f70bbbc9519df8e0818fc38973f21 SHA1: a27da2767cb62697cb6d71c764925f6d6a0e67e2 SHA256: eaea9637976b3bbc2c958a7ca5708f4851fb1856d1346e915d55146e646645b2 SHA512: 6141c778a761d7468553faab26088155b50d34ac42732f32d4ab00183115d276696786d5971299af0c2ab3e00a09cf5d8882a625b8a04880531a99b4981e4cf5 Homepage: https://cran.r-project.org/package=Q2q Description: CRAN Package 'Q2q' (Interpolating Age-Specific Mortality Rates at All Ages) Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', '[10, 15]', and so on. Some applications necessitate a detailed (single) age description. Despite the large number of proposed approaches in the literature, only a few methods ensure great performance at both younger and higher ages. For example, the 6-term 'Lagrange' interpolation function is well suited to mortality interpolation at younger ages (with irregular intervals), but not at older ages. The 'Karup-King' method, on the other hand, performs well at older ages but is not suitable for younger ones. Interested readers can find a full discussion of the two stated methods in the book Shryock, Siegel, and Associates (1993).The Q2q package combines the two methods to allow for the interpolation of mortality rates across all age groups. It begins by implementing each method independently, and then the resulting curves are linked using a 5-age averaged error between the two partial curves. Package: r-cran-q7 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-q7_0.1.0-1.ca2404.1_all.deb Size: 51962 MD5sum: e6c4b3548cd25072f42c84d996b34506 SHA1: 209522bd6cc3c199a3fecc7e4b026ac79a0105d4 SHA256: 5d55141f8297da4e8b4467694d25c27a81369b67e020b50191079d03957c4925 SHA512: 3281180610886537c2f1f62f35dac5607cf24b4b541477b5cf102c9c8133c6f55abacfb1756c73c02149c1900cb1ab127b56081813f94753f7ea504b14a4ea54 Homepage: https://cran.r-project.org/package=Q7 Description: CRAN Package 'Q7' (Types and Features for Object Oriented Programming) Construct message-passing style objects with types and features. 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Package: r-cran-qadf Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qadf_1.0.2-1.ca2404.1_all.deb Size: 35306 MD5sum: 32e207afaf4e2d1805f183a6cda9e040 SHA1: ff791d8891ff2d6398a004789608eec5f1ca8e21 SHA256: ef9482d5d9ff8f5c04ad6ee73be4ba71942805c7acecd041476c6757d901e62f SHA512: 4c1c190f5019effc4289d2ab28e4361279c475d1f9f68c27940bd3590b96027ca27c588c3559a8e646d54f2e002f26d5e2695b6d28c64729353629eab2782ac2 Homepage: https://cran.r-project.org/package=qadf Description: CRAN Package 'qadf' (Quantile Autoregressive Distributed Lag Unit Root Test) Implements the Quantile Autoregressive Distributed Lag (QADF) unit root test proposed by Koenker and Xiao (2004) . The test examines unit root behaviour across the conditional distribution of a time series using quantile regression, providing a richer characterisation of persistence than standard ADF tests. Critical values follow Hansen (1995) . Lag order selection is supported via AIC, BIC, or the t-statistic sequential testing approach. Package: r-cran-qaensemble Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coda, r-cran-diagram, r-cran-expm, r-cran-knitr, r-cran-rmarkdown, r-cran-svmisc Filename: pool/dists/noble/main/r-cran-qaensemble_1.0.0-1.ca2404.1_all.deb Size: 242608 MD5sum: 445e4935706dd59ef48094a52bc0d564 SHA1: 875543f1ab3aa0d54f09354e6616cdaa34f3deea SHA256: 386a7e4d4e9a5c96386f0a3efd138910c518fb52b9e1c0cefe9a7b3f3689f502 SHA512: 33db5606750160c233400033f060f4fef9b85a640da0bbd6627b0ffb4f6e15267fe4714ac4acc9b6e15f0b0be6efda3bb59c36ea2e3049c3583bfa246f19461b Homepage: https://cran.r-project.org/package=QAEnsemble Description: CRAN Package 'QAEnsemble' (Ensemble Quadratic and Affine Invariant Markov Chain Monte Carlo) The Ensemble Quadratic and Affine Invariant Markov chain Monte Carlo algorithms provide an efficient way to perform Bayesian inference in difficult parameter space geometries. 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Package: r-cran-qaig Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-formula Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qaig_0.1.7-1.ca2404.1_all.deb Size: 31294 MD5sum: dfbf2c7ce1b044cd3a6d66e81c0e5778 SHA1: b6dd621a9e9b5c055f5b52eb0cfe228967caeff9 SHA256: 31dd3716f8beb816d03c1782234d7621e67306f2e7bb5f25966bbfc746d47205 SHA512: 0d5da85a6dc0df48742b2f564cd0794a06cb39d928aa9fa7ea80d7240fa6eb5f12fd9a745ba4dee1e2f59496c26047510c773edcad4a312f92d21e807794c580 Homepage: https://cran.r-project.org/package=QAIG Description: CRAN Package 'QAIG' (Automatic Item Generator for Quantitative Multiple-Choice Items) A tool for automatic generation of sibling items from a parent item model defined by the user. 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Package: r-cran-qapproach Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1361 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fmsb, r-cran-igraph, r-cran-qmethod, r-cran-withr Suggests: r-cran-hues, r-cran-knitr, r-cran-magick, r-cran-pdftools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qapproach_0.1.2-1.ca2404.1_all.deb Size: 1204066 MD5sum: 68d57d6ea1096cee27b0cb3ae98c7e59 SHA1: e8c01f39b3b19c476ff5b544724094d0d274e529 SHA256: 234ee735d0decaa5f2411028dd5dd1c15940fe3c246f65e19f0bd1b629edc48e SHA512: 0b8414be7cb79e1fc4fad2dbe47ec0c6f6b0c2cb65a09e020040d5a55657f6ad3b6fbd4aaff29936f4a6b31b8aac5bc34661c6cd4e337741c292c7b6d9b6aa83 Homepage: https://cran.r-project.org/package=qapproach Description: CRAN Package 'qapproach' (The Q Approach to Consensus Building) Implements a workflow based on Q method to support consensus-building processes. It prepares participant rankings, selects and fits group perspectives, calculates consensus priority scores, validates results by bootstrap resampling, and produces publication-ready figures. The underlying method is described by Geschke et al. (2022) "The Q approach to consensus building: integrating diverse perspectives to guide decision-making" . Package: r-cran-qapprox Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qapprox_0.2.1-1.ca2404.1_all.deb Size: 18224 MD5sum: 6d12852fabedfa146a7e5122a9bc9948 SHA1: 5c35074893635635db8470a1b3be38be1dda1d71 SHA256: a85802e18458da75c22b9562882b2a755b43cd2bb5fac5e42631ead30c93d965 SHA512: 8041a92a812067a32c26c6e377179b29f0590527fe0c35fba3670f0f30f2216a2de98b3b814d20ad718e09009f73b9b38fd2e76d036073f2f7ea3326202f3324 Homepage: https://cran.r-project.org/package=Qapprox Description: CRAN Package 'Qapprox' (Approximation to the Survival Functions of Quadratic Forms ofGaussian Variables) Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2022) "A fast and accurate approximation to the distributions of quadratic forms of Gaussian variables" . Package: r-cran-qardlr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qardlr_1.1.1-1.ca2404.1_all.deb Size: 178816 MD5sum: 4c26427be4e6efa1f3c2ec982a1baa61 SHA1: a1250884b9c50db32e1c97ec05662ec803d8c96d SHA256: 580e0a2bd2540c42a9aa7565b80304d55feb5a9129a3d4e7afc2cd8c998f1500 SHA512: 47db256e88c09f1d2715f952afa7228ec8a444f0e6fb2a2344bb375435a999e113b3329e67d422bcad4d46bb601a69ada59dcec997742f3204c54eac2a66fec3 Homepage: https://cran.r-project.org/package=qardlr Description: CRAN Package 'qardlr' (Quantile Autoregressive Distributed Lag Model) Implements the Quantile Autoregressive Distributed Lag (QARDL) model of Cho, Kim and Shin (2015) . Estimates quantile-specific long-run (beta), short-run autoregressive (phi), and impact (gamma) parameters. 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Package: r-cran-qarpi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-qarpi_0.1.0-1.ca2404.1_all.deb Size: 109000 MD5sum: 586db0e8391aa4ba408ebd27ef2dffc5 SHA1: 056bd69faf3b3eb03869918df3e1fee2d7a4c360 SHA256: c113c26a618c36520a2019aaca89cb16c22b5b93be9e7635dfdf34c4d1e46591 SHA512: 0b42d040d4fb6a5e628b6de57eadea8aa7ffd72c904857c2375c9658d320b5a583fdfaf1ba84d40ccab852b023ba6825c9255a424e2b48d73889049f613fd5f5 Homepage: https://cran.r-project.org/package=qarPI Description: CRAN Package 'qarPI' (Prediction Intervals for Quantile Autoregression) Provides prediction intervals for classical homoscedastic autoregressive models (AR(p)) and quantile autoregressive models (QAR(p)). 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Apart from integrating different R packages devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, 'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it includes functions to perform multivariate control charts such as 'Hotelling T2', 'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric alternatives based on data depth are implemented in this package: 'r', 'Q' and 'S' control charts. In addition, Phase I and II control charts for functional data are included. This package also allows the estimation of the most complete set of capability indices from first to fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. See Flores et al. (2021) . Package: r-cran-qcrlscr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2901 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcrlscr_0.1.3-1.ca2404.1_all.deb Size: 2863266 MD5sum: 4a02561853016d6fb664033bc2fb5842 SHA1: c9fde01964ffb0f0c5e7b611a01ce32b29b49694 SHA256: d4e405cb377c1c2a0ad7a16b5e1bdbedf18d2e42b11e23886ffbc49d2d197faf SHA512: 6906d40722725d1b8dce57a543f0e84f068741ef8da19262a0733ced0b075ad357297ff85f9eaad4399ec432e75399244f87d0f5f808845f4c4d5af85597466b Homepage: https://cran.r-project.org/package=qcrlscR Description: CRAN Package 'qcrlscR' (Quality Control–based Robust LOESS Signal Correction) An R implementation of quality control–based robust LOESS(local polynomial regression fitting) signal correction for metabolomics data analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011) . The optimisation of LOESS's span parameter using generalized cross-validation (GCV) is provided as an option. In addition to signal correction, 'qcrlscR' includes some utility functions like batch shifting and data filtering. Package: r-cran-qcsimulator Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-qcsimulator_0.0.1-1.ca2404.1_all.deb Size: 137974 MD5sum: d7f6b8190403192b147052d5814657d9 SHA1: 847851d5f6647942420ebbfa388acdc01dc414e4 SHA256: 60a12112dac20c0acf7b7cae78f9d201a3ddd12b2d5251c2d705d678498cb8bd SHA512: 50d8d420087d1b92d5b45d915cbc9ae8679f9e0fc8707e537fa36367ed64b850dbe991dc573c0fe77e65d50bdd33ea9722d36b1b445373c0d333fac12a51abad Homepage: https://cran.r-project.org/package=QCSimulator Description: CRAN Package 'QCSimulator' (A 5-Qubit Quantum Computing Simulator) Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2 qubit quantum gates. Package: r-cran-qcsis Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcsis_0.1-1.ca2404.1_all.deb Size: 24768 MD5sum: 7b36fcccf4681478f5c8ba6271ecf041 SHA1: 505c0366b9443d54e8824d471f105c88a58d3e4a SHA256: b882ce5f6d5c051a57a84374a24c88b0b9d35dc68c628691386e1659f88cd66f SHA512: 3033dfa9482a576558e3a44dc3553356c92fbc5770ca7453cac5da3dd47f2fcc7e67506e73e1e9b79cb0e45aea7a866f02a7f58eefe188d1f2f23341bb38e193 Homepage: https://cran.r-project.org/package=QCSIS Description: CRAN Package 'QCSIS' (Sure Independence Screening via Quantile Correlation andComposite Quantile Correlation) Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data. Package: r-cran-qcv Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcv_1.0-1.ca2404.1_all.deb Size: 33632 MD5sum: 620aa9fddeca28ea4e2e9fee18e40334 SHA1: ac9026c8d665e2b6ed652bce572837a667599e8b SHA256: 9d4f07ad791cf19e56739d968bf1ce8e19a10d01ddf782f7975859202a084fde SHA512: f0699c2188b63fffe75065f3ebf1e877416d808fd4eb3ec2c1a6a3a7e99347c38bfdad023ade49df12a286564ed56dc5c45b4ebdc5971bcd27f85fa2f477f271 Homepage: https://cran.r-project.org/package=qcv Description: CRAN Package 'qcv' (Quantifying Construct Validity) Primarily, the 'qcv' package computes key indices related to the Quantifying Construct Validity procedure (QCV; Westen & Rosenthal, 2003 ; see also Furr & Heuckeroth, in press). 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Package: r-cran-qdap Architecture: all Version: 2.4.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qdapdictionaries, r-cran-qdapregex, r-cran-qdaptools, r-cran-rcolorbrewer, r-cran-chron, r-cran-dplyr, r-cran-gender, r-cran-ggplot2, r-cran-gridextra, r-cran-igraph, r-cran-nlp, r-cran-opennlp, r-cran-openxlsx, r-cran-plotrix, r-cran-rcurl, r-cran-reshape2, r-cran-scales, r-cran-stringdist, r-cran-tidyr, r-cran-tm, r-cran-venneuler, r-cran-wordcloud, r-cran-xml Suggests: r-cran-korpus, r-cran-knitr, r-cran-lda, r-cran-proxy, r-cran-stringi, r-cran-snowballc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qdap_2.4.6.1-1.ca2404.1_all.deb Size: 3533044 MD5sum: cb3e865f3277b10f47e148e76eed2bad SHA1: 00979422e5b4656420427db1db632092e70d5487 SHA256: 66a75f2c3bd98d74184506af654ad53b6b043f27c4c7a8c43cc26d5ecb8d0331 SHA512: 19561330c69c5a08924b51a2f846a5b6b36fe2af62c3c27caaa64b05356a3b368567500e3445c9bc5aef6311dffc4c80fc09dc0eb349044fc2c209d655023dd0 Homepage: https://cran.r-project.org/package=qdap Description: CRAN Package 'qdap' (Bridging the Gap Between Qualitative Data and QuantitativeAnalysis) Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing. Package: r-cran-qdapdictionaries Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2201 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qdapdictionaries_1.0.7-1.ca2404.1_all.deb Size: 2153384 MD5sum: 431ac5b6c6210f66373d162418288933 SHA1: 85d42ac176a203cf78a9353d5ebcbf5c0f85e8fb SHA256: bbff9b128af3d2c4a90d87beee3ec0044349034167c128107fef97916a7907bc SHA512: b2ff2f8e971542df684669b55d32f3fcf884bd65873b37e4160ba10aa9fe74a1e54aaaf788940498c1d955b86c3fff7a66b5d8c67c95fac54a29f922e8c0fe2d Homepage: https://cran.r-project.org/package=qdapDictionaries Description: CRAN Package 'qdapDictionaries' (Dictionaries and Word Lists for the 'qdap' Package) A collection of text analysis dictionaries and word lists for use with the 'qdap' package. Package: r-cran-qdapregex Architecture: all Version: 0.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qdapregex_0.7.10-1.ca2404.1_all.deb Size: 386282 MD5sum: 5d698c52133ac545471e92c7fa706ed6 SHA1: f5a5f4edc807ac31b3dfd1b784ad9a0c3e2452f1 SHA256: a1be7654c4f3ab22b9a3fb485eae0f04ecc26c1fd265032e0da5815d6aed996f SHA512: d8cf951c962278e0b948b0e7917928404f526b260b965ac3ab870d57a972a888e7d08ce0de52ae7c35d0809c1da3f7864f32aee53787d68eca0a856532e5dd82 Homepage: https://cran.r-project.org/package=qdapRegex Description: CRAN Package 'qdapRegex' (Regular Expression Removal, Extraction, and Replacement Tools) A collection of regular expression tools associated with the 'qdap' package that may be useful outside of the context of discourse analysis. Tools include removal/extraction/replacement of abbreviations, dates, dollar amounts, email addresses, hash tags, numbers, percentages, citations, person tags, phone numbers, times, and zip codes. Package: r-cran-qdaptools Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-data.table, r-cran-rcurl, r-cran-xml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qdaptools_1.3.7-1.ca2404.1_all.deb Size: 133900 MD5sum: f08e7aecbd3f8141ebfc0d17c42bb6d1 SHA1: d21d4e3e83005b2714ce79316f555abca076a97e SHA256: f36d2efeee02a04afabd6d0e74999524789d42da12c1fa12657fbecbf03b40ea SHA512: 82b825beb1229275d59c75eb46065f79e5e677804fca385fb8f33f5e541015383aae574e3751d8e5094e3a202f928ea426301fbd8385f66c78daa80560ecc527 Homepage: https://cran.r-project.org/package=qdapTools Description: CRAN Package 'qdapTools' (Tools for the 'qdap' Package) A collection of tools associated with the 'qdap' package that may be useful outside of the context of text analysis. Package: r-cran-qdar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-jsonlite, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-vegawidget, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-qdar_0.1.0-1.ca2404.1_all.deb Size: 361410 MD5sum: 346645314fc0be6d8b232c5c13c172ae SHA1: 237df4aa94ea9f298a905fba2d4a7818d1e37b44 SHA256: 8ed16e1a0f23243b1f195ac4e07764a93760821e34061add3250f80d084182aa SHA512: d45a6bc9eb7c254ad01eab56fe73e3a043eb9ef535f02dac91d29491e0a0c610ce8454ad3b37a19414956a1f454e5050aad7ce3e03e0f2ee9d776353fd43ec29 Homepage: https://cran.r-project.org/package=qdaR Description: CRAN Package 'qdaR' (Read and Analyse Qualitative Coding Exported from Zotero) Reads the versioned exchange files written by the Zotero plugins 'zotQDA' and 'qdaZ' -- coded fragments, code systems, coding histories and team-consensus results -- validates them against the shipped contract, and reproduces the plugin's graphics with 'ggplot2'. Adds what those plugins deliberately leave out: six agreement coefficients with bootstrap confidence intervals, the reliability of the segmentation itself, chi-squared tests of code by group tables with effect sizes, correspondence analysis, multidimensional scaling and hierarchical clustering of codes. Projects from other programs can be read through the 'REFI-QDA' interchange standard , which makes those analyses available to users of established software that does not offer them; the subset a '.qdpx' supports is reported on import. Reference files are included, so every function can be tried without a Zotero installation. Package: r-cran-qdcomparison Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qdcomparison_3.0-1.ca2404.1_all.deb Size: 65054 MD5sum: b972a91b7f7990724b55da157d60e087 SHA1: 236823004da12e6b40ae6ae5861a391c978dd88f SHA256: 51809daf370531013cd058deaa42760a460a318ff02fb55e52696512df32e256 SHA512: ce6f316ade36858f5f3560c0c168dbbf8a6d202c07b4655b9caeefe0a8f4fe281aaa473a9892526ed6861875783b13bd8e2fc5246204d919240097fb7fa75d07 Homepage: https://cran.r-project.org/package=QDComparison Description: CRAN Package 'QDComparison' (Modern Nonparametric Tools for Two-Sample Quantile andDistribution Comparisons) Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report). Package: r-cran-qdea Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-doby, r-cran-highs, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qdea_1.0.0-1.ca2404.1_all.deb Size: 201412 MD5sum: 37456bc67be733361ce5aa543a473547 SHA1: 40cf73f4eff5036c4fe20a421ad013b9d7e5487d SHA256: c2f025cf76c0dde4d7c9b4def98a8f5a5aac53568e6a65a6089b31656d0cbdac SHA512: 4a87483b9b33f54771f4da11aac89ffaa7a95919a7e65adcaa3031d72464d0a9091785a106ab5f1748f73318b0de380b2132e56b2d43b8d1d6752639e5f2acdb Homepage: https://cran.r-project.org/package=qDEA Description: CRAN Package 'qDEA' (Quantile Data Envelopment Analysis) R implementation of Quantile Data Envelopment Analysis. The package 'qDEA' allows a user specified proportion of observations to lie external to a given Decision Making Units's (DMU's)reference hyperplane. 'qDEA' can be used to detect and address influential outliers or to implement quantile benchmarking, as discussed in Atwood and Shaik (2020). Quantile benchmarking is accomplished by using heuristic procedures to find a DMU's closest input-output projection point in a specified direction while allowing a specified proportion of observations to lie external to the projected point's hyperplane. The 'qDEA' package accommodates standard (DEA) and quantile DEA estimation, returns to scale CRS(constant),VRS(variable),DRS(decreasing) or IRS(increasing), the use of directional vectors, bias correction through subsample bootstrapping and subsample size selection procedures. The user can also recover each DMU's reference DMUs and external DMUs if desired. The implemented procedures are based on discussions in: Atwood and Shaik (2020) Atwood and Shaik (2018) Walden and Atwood (2023) Walden and Atwood (2025) . Package: r-cran-qdiabetes Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4467 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-qdiabetes_1.0-2-1.ca2404.1_all.deb Size: 4465088 MD5sum: d73f46d31ec5bb5607157892360d3e30 SHA1: a254a3002745bef06f987d24757b00e35e126b51 SHA256: f23ba94b285735a982462ee5c2b1948df9148a11d20aa5aa624c78a5dec3193b SHA512: d25bb42f433fc052267ef312f02701fe7eab4465a8a9def44c14fa454be42aebf5d33fa1e6252afb198f0bfc41f9472a3088191a088fda7e0fca0fb00620a25f Homepage: https://cran.r-project.org/package=QDiabetes Description: CRAN Package 'QDiabetes' (Type 2 Diabetes Risk Calculator) Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'. Package: r-cran-qdm Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qdm_0.1-0-1.ca2404.1_all.deb Size: 781468 MD5sum: b73ff8d89b978e1f43d817800a4f52cf SHA1: b62d33b7dae40e236d3469273ffbc9cd19687393 SHA256: 0e55a3690c21e0fc96bb7fa38b6ee9239e22112d2322a5d68974145bef211a41 SHA512: f3f217ccabfe12bb48a55b25f2996cd71b7eb1931a1c434c2796568ee3622c8a1e9b6d4b19bfcfd2d4ec474814e46c6ac1e71282d5155d7958f872a0583bbc55 Homepage: https://cran.r-project.org/package=qdm Description: CRAN Package 'qdm' (Fitting a Quadrilateral Dissimilarity Model to Same-DifferentJudgments) This package provides different specifications of a Quadrilateral Dissimilarity Model which can be used to fit same-different judgments in order to get a predicted matrix that satisfies regular minimality [Colonius & Dzhafarov, 2006, Measurement and representations of sensations, Erlbaum]. From such a matrix, Fechnerian distances can be computed. Package: r-cran-qeml Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3783 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-regtools, r-cran-gtools, r-cran-rmarkdown, r-cran-tufte, r-cran-grf, r-cran-gbm, r-cran-toweranna, r-cran-tm, r-cran-rpart, r-cran-rpart.plot, r-cran-partools, r-cran-foci Suggests: r-cran-knitr, r-cran-partykit, r-cran-randomforest, r-cran-ranger, r-cran-e1071, r-cran-jousboost, r-cran-lightgbm, r-cran-keras, r-cran-neuralnet, r-cran-polyreg, r-cran-glmnet, r-cran-umap, r-cran-reticulate, r-cran-party, r-cran-proc, r-cran-xgboost, r-cran-rocr, r-cran-autoimage, r-cran-deepnet, r-cran-ncvreg, r-cran-uwot, r-cran-cdparcoord Filename: pool/dists/noble/main/r-cran-qeml_1.1-1.ca2404.1_all.deb Size: 1746884 MD5sum: 374d1476b35c2ba26b925635aa496219 SHA1: 2511842e55a12317ac4f53fb948705d48ec15170 SHA256: 999bb5f1f9cc45a10d86f11de6744c865b613389b230a045abe237884a1b8434 SHA512: 9db755d97b9fdacf5505b612177950e465b620067403bd871017e9ba234be94bff754afe973e1b5bbb9c2b0dd5dbb0e3912e2222292f061b79cb7337cb401116 Homepage: https://cran.r-project.org/package=qeML Description: CRAN Package 'qeML' (Quick and Easy Machine Learning Tools) The letters 'qe' in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface. Package: r-cran-qfasar Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qfasar_1.2.1-1.ca2404.1_all.deb Size: 284022 MD5sum: f7b2b6feb1280498d7b93e9629519b42 SHA1: 260208fa051bcb25a22839400df531da16df3338 SHA256: eb7cecb45bada9f819205dfb80f2acca6989b46a37ba33324446338b490624fe SHA512: ec8bd102faa99eea75c3e22b65dee29c7aac6946ea4d288e56cc0655de801a5e7c157759d4e6995fa86a57711f85dee0d1746a3a6c1331f788c34c9f47b3feda Homepage: https://cran.r-project.org/package=qfasar Description: CRAN Package 'qfasar' (Quantitative Fatty Acid Signature Analysis in R) An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the composition of fatty acids within lipids. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options and simulation capabilities are implemented. Please refer to the vignette for additional details and references. Package: r-cran-qfrm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qfrm_1.0.1-1.ca2404.1_all.deb Size: 253958 MD5sum: 8bd20b5a9669a91a8df48940ca917797 SHA1: 0962532c4365fcff017a7f7b96347b67625cf5e2 SHA256: 995d05bb4e3dfb079a5fbc815db9b7036c8b371225fade103c037c333b9c00dd SHA512: f203f48d7276290f96f35e1f41459b8519a1f07a2c413c16bebb87656245f1f71651ff402a1fe5dc78477bebe7829f0d4089ea5bf1be57d7c4648ff10494ca8d Homepage: https://cran.r-project.org/package=QFRM Description: CRAN Package 'QFRM' (Pricing of Vanilla and Exotic Option Contracts) Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015. Package: r-cran-qga Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-qga_1.0-1.ca2404.1_all.deb Size: 445868 MD5sum: 305ed09a1cef1b0aa3a35ebab7f29de6 SHA1: bdd9b536261707f19a821230b4e572727a47b78b SHA256: 699fa26babc0de797d5b0a002e4954ac9502f44e58505de9f5649d65cefaaf62 SHA512: 9947af8c57819390a6b257ca3a6a3247f0cb042cfab0411411d1ad0bd17b3c40230c050061e38365d2df6bfb01468dd65b1199ac5de63af3f538dc76171f8f2a Homepage: https://cran.r-project.org/package=QGA Description: CRAN Package 'QGA' (Quantum Genetic Algorithm) Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra (). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization. Package: r-cran-qgametheory Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-qgametheory_0.1.2-1.ca2404.1_all.deb Size: 156406 MD5sum: 8e3e80fd302a3f7c3b784dabc8804499 SHA1: a63aa05b30c30c79b824d93d292f3f3c609e2d3c SHA256: a44967c993a0b5163dd4bb9b3725c855bf09fc397608cf6a039bf03677daf2bc SHA512: 1dd0d0c3e0e51860a664dd7c9abce32c4c27389925fbee6c4a8620529b79dc9e115e5099427f310d02dfe217c309edcbb2a6ff024ea44e6fd2387fad91ed24aa Homepage: https://cran.r-project.org/package=QGameTheory Description: CRAN Package 'QGameTheory' (Quantum Game Theory Simulator) General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) . Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) , Prisoner's Dilemma (J. Orlin Grabbe 2005) , Two Person Duel (Flitney and Abbott 2004) , Battle of the Sexes (Nawaz and Toor 2004) , Hawk and Dove Game (Nawaz and Toor 2010) , Newcomb's Paradox (Piotrowski and Sladkowski 2002) and Monty Hall Problem (Flitney and Abbott 2002) . Package: r-cran-qgarch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qgarch_0.1.0-1.ca2404.1_all.deb Size: 104626 MD5sum: d3431c7b70540599c60828761dc31acc SHA1: 29068934089f013b9f035d23b135c0b519c09a09 SHA256: c852fb5ee895aa5d13c1a40e30f12a89bd0d9a44934ca2e33e0e6db49ef82f5d SHA512: a23bd3ad153c5631b08947eb134eaf52e17a9b64b7c3a014e3e828dd7449dd44b71dff07a2d1a3c312921bd2c5d68f434b18dd3572eddbf921eb8777c830019e Homepage: https://cran.r-project.org/package=qgarch Description: CRAN Package 'qgarch' (Quadratic GARCH-in-Mean Models for Volatility Feedback) Fits quadratic generalized autoregressive conditional heteroskedasticity-in-mean (QGARCH-M) models motivated by Campbell and Hentschel (1992). 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Effect measure modification in this method is a way to assess how the effect of the mixture varies by a binary, categorical or continuous variable. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; . Package: r-cran-qgglmm Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature Filename: pool/dists/noble/main/r-cran-qgglmm_0.8.0-1.ca2404.1_all.deb Size: 467152 MD5sum: 7cef125673c3466fd50ec9c9645aad74 SHA1: 0e01473d1aa0325074b4e92d2a53239d1d682b35 SHA256: a4bb11086db47bb9f2228d968a2675f2723e109dcce6ac58c8a80b2937229ee0 SHA512: cbf0ee7874866801bb729e788fc6af2b83689fc0978d12841bb1a2669b5ca8fd186d0bb8ffc6182e8cef24d9b89afc78e95d61eda94d0851da0717b2ed03c9be Homepage: https://cran.r-project.org/package=QGglmm Description: CRAN Package 'QGglmm' (Estimate Quantitative Genetics Parameters from GeneralisedLinear Mixed Models) Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance. 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The application relies on established plant breeding and quantitative genetic theory found in Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427). Package: r-cran-qhot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qhot_0.1.0-1.ca2404.1_all.deb Size: 31888 MD5sum: ae8a50a9e8575e86f98b92d3cdff20b9 SHA1: 76e1943002a527aa80b71e2784bbe4f5b8a0766d SHA256: 2c877b6629a634dbb14987a60a1a28a03c5c76a42650f34313d485dbb947033b SHA512: 226f7befb4795de40eaae2151f01ca4cfb541273e17ca51a486a8c5288d7447d9a7f8192e53e21dba9a1b8e743c77c02acbfbc1cfe3b713fd2388fcd9f442409 Homepage: https://cran.r-project.org/package=QHOT Description: CRAN Package 'QHOT' (QTL Hotspot Detection) This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs. 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Such relationships describe the changes in K+ concentration in the soil solution (or the intensity factor) in relation to the corresponding changes in K+ at exchange sites of the soil (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg is generally used as the variable denoting the intensity, whereas, change in exchangeable K is used to denote the quantity factor. Package: r-cran-qicharts2 Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-qicharts2_0.8.1-1.ca2404.1_all.deb Size: 1054856 MD5sum: 07f2466acbd015fccc874aad0bf9c8e4 SHA1: c05760ca1c3245c6217e761362950d40f50748f8 SHA256: a509d54b4237cbc52475f3797e06dad521735ecec85a0e72eabd9eb15d48a062 SHA512: 37c593c8ef0dd232327a0a33ffa0eef792a370263e0487c0fb84b22af99915906902e3a31df7f0d1e6b7e4d7a40a6b97bb9643498553cc25a384c0c767923b28 Homepage: https://cran.r-project.org/package=qicharts2 Description: CRAN Package 'qicharts2' (Quality Improvement Charts) Functions for making run charts, Shewhart control charts and Pareto charts for continuous quality improvement. 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The main function, qic(), creates run and control charts and has a simple interface with a rich set of options to control data analysis and plotting, including options for automatic data aggregation by subgroups, easy analysis of before-and-after data, exclusion of one or more data points from analysis, and splitting charts into sequential time periods. Missing values and empty subgroups are handled gracefully. 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Package: r-cran-qlearning Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qlearning_0.1.1-1.ca2404.1_all.deb Size: 23782 MD5sum: 8c7dd12b45dbcaf526474d7e1c4fec32 SHA1: 3ba9b910263f5966c2f00bf1f729d61c3ee5def4 SHA256: cb0588aa8bd1bb24d7d83855c56f5e2d14a706ddc1b5483b697f8942deee1247 SHA512: 741e4487f6434c6f617546614db4c4c17b3d46b8769b6b7eb31807f101e088ee19eedef75b5f2bc605a6fd2b6be235e8ca77d9e6634acd8cac38c7b4669f438b Homepage: https://cran.r-project.org/package=QLearning Description: CRAN Package 'QLearning' (Reinforcement Learning using the Q Learning Algorithm) Implements Q-Learning, a model-free form of reinforcement learning, described in work by Strehl, Li, Wiewiora, Langford & Littman (2006) . 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References: Pavía and Lledó (2022) . Pavía and Lledó (2023) . Pavía and Lledó (2025) . Acknowledgements: The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research. Package: r-cran-qmap Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-qmap_1.0-6-1.ca2404.1_all.deb Size: 328252 MD5sum: dc3223571a1c9a7162dbde5b10a2ae63 SHA1: f83913bba62327d4b5174d6a3f5efe8c3169b267 SHA256: c3f2d28dad0b5c810de87953e0f2fa238f819837cb97dba86efd2d19e06b274a SHA512: 29dbeafcdcf71b000ca86fb5196a7e9c485980236825cc48398d1a3c8e3e25ab9e0336807264a844aa11449c11d60d325098da2d3464be40266850110415e585 Homepage: https://cran.r-project.org/package=qmap Description: CRAN Package 'qmap' (Statistical Transformations for Post-Processing Climate ModelOutput) Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping. 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This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see ). A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors. The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options. Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards. 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Package: r-cran-qploidy Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5239 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-vroom, r-cran-ggpubr, r-bioc-multtest, r-cran-vcfr, r-cran-stringr, r-cran-magrittr Suggests: r-cran-covr, r-cran-spelling, r-cran-updog, r-cran-rmdformats, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qploidy_1.5.4-1.ca2404.1_all.deb Size: 3154388 MD5sum: 5a60919a72a13717d510c8b4fbfa89fe SHA1: b59e11403499148674511fb5b9afedb596aaa178 SHA256: 94a78fccdad598709d41ce8c53cc9ad21e6999ab56ed143dd2de8e5303f669a1 SHA512: 7d97d4ce9d17b815ce9c43df0a3559f95a8c8ab80aca4698e6e13b6f700b9d6838bd7bb89717f61bd9f1e09994a6236b0f64a16b88b4b9b83cd075a930c94420 Homepage: https://cran.r-project.org/package=Qploidy Description: CRAN Package 'Qploidy' (Estimation of Ploidy and Detection of Aneuploidy UsingGenotyping Data) Provides functions for estimating ploidy levels and detecting aneuploidy in individuals using allele intensities or allele count data from high-throughput genotyping platforms, including single nucleotide polymorphism (SNP) arrays and sequencing-based technologies. Implements method described in Taniguti et al. (2025) an extended version of the 'PennCNV' signal standardization method by Wang et al. (2007) for higher ploidy levels. Computes B-allele frequencies (BAF), z-scores, and identifies copy number variation patterns. Package: r-cran-qpnca Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qpnca_1.1.6-1.ca2404.1_all.deb Size: 885852 MD5sum: bbd6aba896f70a3f94919fcb74982182 SHA1: 29a6165977f5f6bc8307c42f5e51741fa15e486c SHA256: fe1600a5872e88387fa22adc80b640ead304dcb68c5f1e4b9663c41370b328ea SHA512: 8d7185de19fcb1a8c3bd958a34f39a8df73dcbb073a4ec4817b1f7ce4c510d1be6f3988b988f3b6475034039af670ebc0f2f64b5043928997a5840de846e4b42 Homepage: https://cran.r-project.org/package=qpNCA Description: CRAN Package 'qpNCA' (Noncompartmental Pharmacokinetic Analysis by qPharmetra) Computes noncompartmental pharmacokinetic parameters for drug concentration profiles. For each profile, data imputations and adjustments are made as necessary and basic parameters are estimated. Supports single dose, multi-dose, and multi-subject data. Supports steady-state calculations and various routes of drug administration. See ?qpNCA and vignettes. Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and Gibaldi and Perrier (1982, ISBN:978-0824710422). Package: r-cran-qpost Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fs, r-cran-glue, r-cran-here, r-cran-htmltools, r-cran-lubridate, r-cran-miniui, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-rstudioapi, r-cran-shiny, r-cran-shinyfeedback, r-cran-stringi, r-cran-stringr, r-cran-urltools, r-cran-yesno, r-cran-yaml Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-qpost_1.1.0-1.ca2404.1_all.deb Size: 239066 MD5sum: da9da79f0eedf0de6bbe001edddd7bd0 SHA1: 5470d12eefd980e19121e9c45fa9eeca4baa0bc0 SHA256: 6f8efc8b3829f3ba47db9d154e9edf004f8e5b8cd03e7ebd78bacd2fbba23e3f SHA512: 86337cde09323a60e7b6550a1b122a033d45dc89733ba3d25552e2420c558e4530aca2b81191c7e40e12fc16c88dd8399cdd746e70ec0650fc1f97d25151dbc9 Homepage: https://cran.r-project.org/package=qpost Description: CRAN Package 'qpost' (Create a 'Quarto' Blog Post) Provides an interactive 'RStudio' dialog for creating 'Quarto' blog posts with correctly structured YAML front matter. The dialog collects title, author, date, categories, and other metadata, then scaffolds the post directory, creates the 'index.qmd' file, and optionally copies an image. A companion function appends COinS (ContextObjects in Spans) metadata to posts for automatic bibliographic import into reference managers such as 'Zotero'. Package: r-cran-qpraentry Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bsplus, r-cran-dplyr, r-cran-dt, r-cran-eurostat, r-cran-ggiraph, r-cran-ggplot2, r-cran-giscor, r-cran-memoise, r-cran-purrr, r-cran-sf, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinywidgets, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qpraentry_0.1.1-1.ca2404.1_all.deb Size: 631508 MD5sum: 82ff448cfe34f92cc9cbcccccf8c32d2 SHA1: 8e4600f1ea5ee86fab75e367b0dfc87f3aeff5db SHA256: 1bdd54856335f79fcf78cbe73faa8e7915860b519a58745887652270ac7a0261 SHA512: 98c4a81200a71f4c50c6bbdd84cc69699aa32f6e1ef18f12069c7a83e747abc6bbc21d1b60db0a2d815827735d86a73f88298d36284d327cc0914ca3e78221f1 Homepage: https://cran.r-project.org/package=qPRAentry Description: CRAN Package 'qPRAentry' (Quantitative Pest Risk Assessment at the Entry Step) Supports risk assessors in performing the entry step of the quantitative Pest Risk Assessment. It allows the estimation of the amount of a plant pest entering a risk assessment area (in terms of founder populations) through the calculation of the imported commodities that could be potential pathways of pest entry, and the development of a pathway model. Two 'Shiny' apps based on the functionalities of the package are included, that simplify the process of assessing the risk of entry of plant pests. The approach is based on the work of the European Food Safety Authority (EFSA PLH Panel et al., 2018) . Package: r-cran-qqboxplot Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-gridextra, r-cran-testthat, r-cran-vdiffr, r-cran-scales Filename: pool/dists/noble/main/r-cran-qqboxplot_0.3.0-1.ca2404.1_all.deb Size: 458900 MD5sum: 2f815d53cd1f00cbc196a756ccc97542 SHA1: b2b9db8015715db4b8c77f5d2631e81baab3ba5e SHA256: 0a6e191e8b622a9f8daeb9877759d4ccdfc48dcd14892de80a6e1a64c8180259 SHA512: 2369b4834dc0939260109030acf50104810258006526d4b4acaa5b1c73e515680f256c658e98680235de78c0280c5bc6440954981c0ab7e00c7bc7e8f41c743d Homepage: https://cran.r-project.org/package=qqboxplot Description: CRAN Package 'qqboxplot' (Implementation of the Q-Q Boxplot) A system to implement the Q-Q boxplot. It is implemented as an extension to 'ggplot2'. The Q-Q boxplot is an amalgam of the boxplot and the Q-Q plot and allows the user to rapidly examine summary statistics and tail behavior for multiple distributions in the same pane. As an extension of the 'ggplot2' implementation of the boxplot, possible modifications to the boxplot extend to the Q-Q boxplot. Package: r-cran-qqkrls Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-krls, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qqkrls_1.0.0-1.ca2404.1_all.deb Size: 50714 MD5sum: 98d8ca9d77ee28ca4be8923a18a9dd27 SHA1: 1729fc18657c4527cae388a6ab6a3614392186f5 SHA256: 12b0826808591000e973483d43191b437ab725713e7182f0761021a1dbc4a458 SHA512: 5f7433d1bbb2478a2f8237e1d21701515c16bfce54e023c68e659745c81125864201c3484dffad741f1236380e8161040ceac359f1bfc071cec8140ff317cada Homepage: https://cran.r-project.org/package=qqkrls Description: CRAN Package 'qqkrls' (Quantile-on-Quantile Kernel Regularized Least Squares) Implements Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024) . Combines Kernel-Based Regularized Least Squares (KRLS) of Hainmueller and Hazlett (2014) with the Quantile-on-Quantile regression of Sim and Zhou (2015) : for each quantile theta of the independent variable the response is fit by KRLS on the corresponding sub-sample and the tau-quantile of the resulting pointwise marginal effects yields beta(theta, tau). Standard errors come from a paired bootstrap. Visualisations use the 'MATLAB' 'Parula' colour map by default. Package: r-cran-qqman Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-calibrate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qqman_0.1.9-1.ca2404.1_all.deb Size: 1106976 MD5sum: fb8e528179df8355c249c32a095eda67 SHA1: 926f0dcca41f7bd9f7ae124fcf4864ebf2c286d8 SHA256: f81cd16c7f05d65fcbc4c0718d3bb489c0322c016dc91a5fc5e4135fd4462ecc SHA512: 4f43d022237a4e27e4f2237f4a3c56400fda7211956b4b3b98c6b3ea394eb6a36aa18107163b82be28d165edf58f70c6ae5b124f8647fdd4db5abb8aaf01e4d4 Homepage: https://cran.r-project.org/package=qqman Description: CRAN Package 'qqman' (Q-Q and Manhattan Plots for GWAS Data) Create Q-Q and manhattan plots for GWAS data from PLINK results. Package: r-cran-qqplotr Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-robustbase, r-cran-mass, r-cran-opdisdownsampling, r-cran-qqconf Suggests: r-cran-shiny, r-cran-devtools, r-cran-lattice, r-cran-shinybs, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qqplotr_0.0.7-1.ca2404.1_all.deb Size: 1074404 MD5sum: e1933fe6ef998e7eb8e8d3bc2ff37d7c SHA1: 84bff29a7901f1a28533eab76e4e64ef304d39a8 SHA256: 2ef2acbf70d68e7c7101143f957b439c7b7700ec1594238bb8cc53c1c2b125a7 SHA512: 7a6e41ecb38ea74725610d558f92e42a10757d0ac775fe274b70ac387dbd8e2b0297390227eea240a64ffb5255f4ec2f25ef092595c69917dccf2a7b9ff6708c Homepage: https://cran.r-project.org/package=qqplotr Description: CRAN Package 'qqplotr' (Quantile-Quantile Plot Extensions for 'ggplot2') Extensions of 'ggplot2' Q-Q plot functionalities. Package: r-cran-qqreflimits Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qqreflimits_1.0.3-1.ca2404.1_all.deb Size: 58256 MD5sum: f0276d622d422912c629fe78ee99d32b SHA1: 735028a37b29c104b3826fac039d4d2ef069e0c2 SHA256: 36f6ded6e8d60202e7940fce6b6344ff16fe70c25388fa226aafbc37497afd5c SHA512: 83593df7ce8c21092fbaee42e0a825132a7a632b923810492e30096659ec72c0152b7d5eafd1bfe41c32967cfd347730fd878995595f03598f4fe5e70a611b4b Homepage: https://cran.r-project.org/package=QQreflimits Description: CRAN Package 'QQreflimits' (Reference Limits using QQ Methodology) A collection of routines for finding reference limits using, where appropriate, QQ methodology. All use a data vector X of cases from the reference population. The default is to get the central 95% reference range of the population, namely the 2.5 and 97.5 percentile, with optional adjustment of the range. Along with the reference limits, we want confidence intervals which, for historical reasons, are typically at 90% confidence. A full analysis provides six numbers: – the upper and the lower reference limits, and - each of their confidence intervals. For application details, see Hawkins and Esquivel (2024) . Package: r-cran-qqtest Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qqtest_1.2.1-1.ca2404.1_all.deb Size: 224410 MD5sum: 373efc0366826f1edbe5ffcae90a864a SHA1: 7e05c90dd8e237e4456c452d24e70c4513d0408f SHA256: df3e341ba8f0e860b47965f61be728f38465ac8586edbe50e6a8c96c7e1ebe15 SHA512: cb67004615ad46e313bd8cb5cdb15940e9a04b7efb9a46c23364fbc52b3a3e8678215393e4b9647aeb7ec66edce851699e85b70036b583a59ee2e5e05fd93c10 Homepage: https://cran.r-project.org/package=qqtest Description: CRAN Package 'qqtest' (Self Calibrating Quantile-Quantile Plots for Visual Testing) Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution. Package: r-cran-qqvases Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-qqvases_1.0.0-1.ca2404.1_all.deb Size: 56898 MD5sum: 12b01f83a21dfab2cd50f8c58c935211 SHA1: d17a92213558ff147b7b45d711832c3127442a2b SHA256: 18834c95d9d97e510e434bbeaef301a32b02b9c576885472a6293741fb7ae7df SHA512: cb50e673801b7a5e6e5152e0505f1b44438ab57c32338f8ce1e0212ba9a77869ac0b4a698833257363ef26958b85329fb33c4794e801d9db636b26cb76ed679a Homepage: https://cran.r-project.org/package=qqvases Description: CRAN Package 'qqvases' (Animated Normal Quantile-Quantile Plots) Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy. The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions. The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot. The package draws QQ plots for several distributions, either as samples or continuous functions. Package: r-cran-qr.break Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sparsem Filename: pool/dists/noble/main/r-cran-qr.break_1.0.3-1.ca2404.1_all.deb Size: 227934 MD5sum: 5516f27bfc4d964b67fb34ff5f0752df SHA1: 31d8dfaeb5586110f793da786584f840fd1a9cd9 SHA256: d9de939813f32dd3f6f374eb49b28a9bb1687e9999833ba54460d3370c9edbfd SHA512: bd22f03cf2011cc063bbf554549a7b655489b92f5c1480b1ebe0d2b0e27fc9c79f10cb60bbd09e911dd078b52c40572978b56422a8bfa26531c5edd99abfb443 Homepage: https://cran.r-project.org/package=QR.break Description: CRAN Package 'QR.break' (Structural Breaks in Quantile Regression) Methods for detecting structural breaks, determining the number of breaks, and estimating break locations in linear quantile regression, using one or multiple quantiles, based on Qu (2008) and Oka and Qu (2011). Applicable to both time series and repeated cross-sectional data. The main function is rq.break(). References for detailed theoretical and empirical explanations: (1) Qu, Z. (2008). "Testing for Structural Change in Regression Quantiles." Journal of Econometrics, 146(1), 170-184 (2) Oka, T., and Qu, Z. (2011). "Estimating Structural Changes in Regression Quantiles." Journal of Econometrics, 162(2), 248-267 . Package: r-cran-qra Architecture: all Version: 0.2.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-ggplot2 Suggests: r-cran-fitodbod, r-cran-vgam, r-cran-glmmtmb, r-cran-gamlss, r-cran-prettydoc, r-cran-dharma, r-cran-kableextra, r-cran-plotrix, r-cran-dfoptim, r-cran-optimx, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-qra_0.2.8.1-1.ca2404.1_all.deb Size: 1539176 MD5sum: 3c705984aa3d7c9de3b5702be44ce539 SHA1: 85a98287abee0b7b4c9a73d2297253315ccfb43e SHA256: 9f4edd20d43aeac0448718ee9cbe1bcdb6dac04221ab3f1c87cbc68015a015c2 SHA512: 74cc22633155f831c06c7cd448f95715ca6a3bba88db8e56aa2c6f2caac7437efe816effccd126969970d613b94789f1aa46cd930f86a69c1b62b2a0cad66453 Homepage: https://cran.r-project.org/package=qra Description: CRAN Package 'qra' (Quantal Response Analysis for Dose-Mortality Data) Functions are provided that implement the use of the Fieller's formula methodology, for calculating a confidence interval for a ratio of (commonly, correlated) means. See Fieller (1954) . Here, the application of primary interest is to studies of insect mortality response to increasing doses of a fumigant, or, e.g., to time in coolstorage. The formula is used to calculate a confidence interval for the dose or time required to achieve a specified mortality proportion, commonly 0.5 or 0.99. Vignettes demonstrate link functions that may be considered, checks on fitted models, and alternative choices of error family. Note in particular the betabinomial error family. See also Maindonald, Waddell, and Petry (2001) . Package: r-cran-qragadget Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1716 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-cran-leaflet, r-cran-miniui, r-cran-rhandsontable, r-cran-scales, r-cran-shiny, r-cran-shinywidgets, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qragadget_0.4-1.ca2404.1_all.deb Size: 1386540 MD5sum: 0845737d53a05273319f71ecf067855a SHA1: b4ab4e196c971fd7f5d7fc7632d72846dff11091 SHA256: 22651f895e5157704fdc5798962d797cef181d38945bd2c95f2bec076b76f04b SHA512: 30024b02834c3e831f4134266cbd5bb0df3eabd89acfb3befcc766029ea67bcc2429db0ff2b6189831d001a715e7d6f33115c6d69d2068be921e75eca7b7f60d Homepage: https://cran.r-project.org/package=QRAGadget Description: CRAN Package 'QRAGadget' (A 'Shiny' Gadget for Interactive 'QRA' Visualizations) Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. 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The 'qsort' package includes descriptions and scoring procedures for four different Q-sets commonly used in developmental psychology research: Attachment Q-set (version 3.0) (Waters, 1995, ); California Child Q-set (Block and Block, 1969, ); Maternal Behaviour Q-set (version 3.1) (Pederson et al., 1999, ); Preschool Q-set (Baumrind, 1968 revised by Wanda Bronson, ). 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These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time. Package: r-cran-qtl2pattern Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2037 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-assertthat, r-cran-qtl2, r-cran-qtl2fst, r-cran-fst, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qtl2ggplot Filename: pool/dists/noble/main/r-cran-qtl2pattern_1.2.1-1.ca2404.1_all.deb Size: 1115956 MD5sum: ce410a4b6e6b98489dc074ff156abd18 SHA1: 4b3e341e54902f4d868f4efd599c939b3131b553 SHA256: 79ab07dbe0ce170a4aa95b692b082fcf84600e27aa8a584242c90d9312251bd5 SHA512: 9df326902f761736881b2d3e84856eadb0a53b29f288770bddaed6e68e660332ecd9c9d4947d252ca60ddeeb8f0a2755c0997c4b8aaaec6f370298a55875ff80 Homepage: https://cran.r-project.org/package=qtl2pattern Description: CRAN Package 'qtl2pattern' (Pattern Support for 'qtl2' Package) Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL) mapping over a chromosome. Useful in crosses with more than two alleles to identify how sets of alleles, genetically different strands at the same locus, have different response levels. Plots show profiles over a chromosome. Can handle multiple traits together. See . 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Package: r-cran-qtlc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tiff, r-cran-rgl, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-qtlc_1.0-1.ca2404.1_all.deb Size: 158838 MD5sum: e45e0dd9c82c6efdb91548b820529de1 SHA1: b83c037205cea7918254a6d3796fc82710dd6620 SHA256: 135808f6418c6167b627c0ab565a29870a66df7f7049e1628945eac4d616acef SHA512: 8b81450058f11484015ff0c8ca2dcc68f0fd087f11e1e1829ca949997c43cd2e4f0d6e018ca8f4509c0aae85f6405b51009e34d5419b8798e75d0d92aa1b940b Homepage: https://cran.r-project.org/package=qtlc Description: CRAN Package 'qtlc' (Densitometric Analysis of Thin-Layer Chromatography Plates) Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates. Package: r-cran-qtlcharts Architecture: all Version: 0.22-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1934 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-qtl, r-cran-htmlwidgets Suggests: r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qtlcharts_0.22-1.ca2404.2_all.deb Size: 763432 MD5sum: f7154cc48cc7b88a1fa3eca53a40a9bc SHA1: ba20f6db7c15b4ad81b7e6f924b6edff213b6a7c SHA256: c052f9ca6e3bfcaf4fbd1175ba5839fb14ab805fa16dbdf6a7ba742a6d5898ae SHA512: 76ec26a051ce33c48beeb18796ae5c97fe34985809ff324235b9b78a4fc8780cb170f0855ed8b9f45ac52a0acd3e8b3e39c9227d1343127ce283cdbefd0ddddd Homepage: https://cran.r-project.org/package=qtlcharts Description: CRAN Package 'qtlcharts' (Interactive Graphics for QTL Experiments) Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) . Package: r-cran-qtldesign Architecture: all Version: 0.953-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-qtl Filename: pool/dists/noble/main/r-cran-qtldesign_0.953-1.ca2404.1_all.deb Size: 99676 MD5sum: 7853ff650569de896478c04e28f30887 SHA1: 5197842cbfa5b3406acbfcd576440b20f73c5b89 SHA256: 2316e0fec8d455d02940e646980c65d3e109e280ede6745ef555fd9df930a8f3 SHA512: e41818543b803888bdb3d3e552edcc2f43424a85a50a6c6815faf7d327b5725bb01d8ca47de4a29a6575643cc0612544f2c4ae85094ec2084a0728027ec9cb08 Homepage: https://cran.r-project.org/package=qtlDesign Description: CRAN Package 'qtlDesign' (Design of QTL (Quantitative Trait Locus) Experiments) Design of QTL (quantitative trait locus) experiments involves choosing which strains to cross, the type of cross, genotyping strategies, phenotyping strategies, and the number of progeny to raise and phenotype. This package provides tools to help make such choices. Sen and others (2007) . Package: r-cran-qtlemm Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-gtools Filename: pool/dists/noble/main/r-cran-qtlemm_3.1.0-1.ca2404.1_all.deb Size: 2423236 MD5sum: 5fd2b3c98ac84445fc45de5acf23b917 SHA1: 73c0d80bfb1080e32cf9071067edd7b940a88dd1 SHA256: 3a5288499bee3517af060c7d1f85cdf13ccaf810045581985b2d9a08019e182b SHA512: 324d2b7ea816d29bc4773c925a4d289a216a2a39d3b12fac43da95fa8ff277a9a835d79e18b25f39ce41d22f5c527194e232fb13ea985734cfcf1faf129498c6 Homepage: https://cran.r-project.org/package=QTLEMM Description: CRAN Package 'QTLEMM' (QTL EM Algorithm Mapping and Hotspots Detection) For QTL mapping, this package comprises several functions designed to execute diverse tasks, such as simulating or analyzing data, calculating significance thresholds, and visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables the fitting and comparison of various statistical models, is employed to analyze the data for estimating QTL parameters. The models encompass linear regression, permutation tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic process is utilized to compute significance thresholds for QTL detection on a genetic linkage map within experimental populations. Two types of data, complete genotyping, and selective genotyping data from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, statistical methods can be developed based on either utilizing individual-level data or summarized data. We have proposed a statistical framework capable of handling both individual-level data and summarized QTL data for QTL hotspot detection. Our statistical framework can overcome the underestimation of thresholds resulting from ignoring the correlation structure among traits. Additionally, it can identify different types of hotspots with minimal computational cost during the detection process. Here, we endeavor to furnish the R codes for our QTL mapping and hotspot detection methods, intended for general use in genes, genomics, and genetics studies. The QTL mapping methods for the complete and selective genotyping designs are based on the multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D. Teasdale (1999) and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) , respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) . Package: r-cran-qtlhot Architecture: all Version: 1.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor, r-cran-broman Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qtlhot_1.2.10-1.ca2404.1_all.deb Size: 1742952 MD5sum: d0e416b12fdfb058f9f4f6b8c539bc01 SHA1: d8cbe59216b754f88529f858b31d0ef0ec164573 SHA256: ddb21e2bc0815f54ca690c7010824acfcacfaf8a6137039a6615e23b3ff69927 SHA512: aedd597b1c4edcd88718550b534bbc99b33a5405337817472ae85cad078d78438983c8aae3bbc1e58768f7dbb4e7b94b972aa4090e59a87e5351ec4852ad1d64 Homepage: https://cran.r-project.org/package=qtlhot Description: CRAN Package 'qtlhot' (Inference for QTL Hotspots) Functions to infer co-mapping trait hotspots and causal models. Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. . Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013) Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013. . Package: r-cran-qtlnet Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4732 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qtl, r-cran-igraph, r-cran-sem, r-bioc-graph, r-cran-pcalg Filename: pool/dists/noble/main/r-cran-qtlnet_1.5.4-1.ca2404.1_all.deb Size: 3143308 MD5sum: 90d0b780f5414eae077fa0c3606c01d9 SHA1: 5cc225283d099c1f8e0dad9982e69c8bc1d75a27 SHA256: 0438b2464482629b2a6fb1f53a1edbb05e5f24d4a1f7b98348a58317a1e0de08 SHA512: 4da9e93a778a21e54700abb3e55e3c4fa94093100a8e82df5e6eb1e0153360f49658b9ef50aa528e610cd2f1fbad510e9711c0418eb7e478dd7dd3ce06d70919 Homepage: https://cran.r-project.org/package=qtlnet Description: CRAN Package 'qtlnet' (Causal Inference of QTL Networks) Functions to Simultaneously Infer Causal Graphs and Genetic Architecture. Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by a few genetic loci (QTL). Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Annals of Applied Statistics 4: 320-339. . Package: r-cran-qtocen Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rgenoud, r-cran-quantreg, r-cran-rdpack, r-cran-matrixmodels Suggests: r-cran-stringr, r-cran-testthat, r-cran-faraway, r-cran-quantoptr, r-cran-survminer Filename: pool/dists/noble/main/r-cran-qtocen_0.1.1-1.ca2404.1_all.deb Size: 132766 MD5sum: fed499c465040bd37575ed7434e20ab7 SHA1: be5ed0dd0de8e6ebd5f057b85d19b3d9aa06bbe6 SHA256: de2a5b1a2b7325d6c81255e3ab76f46e095d654cb658b8700bd18b9651a7bd31 SHA512: 23bacfcecf882d5c6290adbef0a224fb52e4bb91241a356129ff35761a0dfb35fb3acc5249e1ff9040fd840a79ae15b3c7500b5bd126ed5735d2f44c935f478e Homepage: https://cran.r-project.org/package=QTOCen Description: CRAN Package 'QTOCen' (Quantile-Optimal Treatment Regimes with Censored Data) Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature. Package: r-cran-qtsa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qtsa_0.1.1-1.ca2404.1_all.deb Size: 75600 MD5sum: c0d4325bdc7a7bcd748d148b5ffc64f4 SHA1: 7aee17c1672e25a66a81cde4b77a7a91ef11832e SHA256: 7608c913bc155910763e65b1ec4549c9afd68ed93e9c43bb783c35c88a6f9036 SHA512: 4b45095e373ab2604192ac674e3ecf1697cbf974b24eed46d2fba5f70a5c3d1fc78b07ca76f9191afe169af2e8f35c86356943b673455e5f58a61c5919211a3d Homepage: https://cran.r-project.org/package=qtsa Description: CRAN Package 'qtsa' (Quantum Time Series Analysis: Drift, Noise Spectroscopy andCalibration Forecasting) Tools for exploratory statistical analysis of quantum-hardware calibration time series. The package provides simulators for random telegraph noise (RTN), power-law noise, and Ornstein-Uhlenbeck dephasing; Welch and sine-multitaper power spectral density estimators; a lightweight two-state hidden Markov model for switching signals; cumulative sum (CUSUM) and binary-segmentation diagnostics for calibration drift; residual-quantile interval forecasts; and filter-function calculations for illustrative coherence curves. The package includes a reproducible generator of simulated superconducting-qubit calibration records; it does not retrieve authenticated live provider data. Methodological background is provided by Welch (1967) , Thomson (1982) , Rabiner (1989) , Page (1954) , Paladino et al. (2014) , and Cywinski et al. (2008) . Package: r-cran-qtwacademic Architecture: all Version: 2022.12.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2456 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qtwacademic_2022.12.13-1.ca2404.1_all.deb Size: 1698202 MD5sum: 942f0bdafc18da66e5574b09e9174855 SHA1: 8652d1dcdef1ff129aecd8659bdf077ed70c6a30 SHA256: 74d32b9de5ac1858f70590625b0f7ea8abf476650474d31b21c121c14783b4da SHA512: 6cae1ca4e05afe48cadfad04831bc7229d999e34794406b35fff1bebe376b43523142b15b07d1d3285d08092ca2e739d608cfb64aa290532043b2391eb491f64 Homepage: https://cran.r-project.org/package=qtwAcademic Description: CRAN Package 'qtwAcademic' ('Quarto' Website Templates for Academics) Provides three 'Quarto' website templates as an R project, which are commonly used by academics. 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Package: r-cran-quadform Architecture: all Version: 0.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quadform_0.0-4-1.ca2404.1_all.deb Size: 89480 MD5sum: ec37ce3e59232ceddb302b7c62e4796b SHA1: 7c78d59affd47510720544d912a8231ad3848779 SHA256: a7764a88c7677b5b0d8a6d3fee56ffd83f96b105da22ef0f8b260cbde9f05f5d SHA512: e27a78109dce79af537c78301ed785953198d3e34cdc041c3d01a98f6e4a0a7a79d389b7c9c21cec2f718ed309ab03d22226176cee58cad95349f5a4d065a9ab Homepage: https://cran.r-project.org/package=quadform Description: CRAN Package 'quadform' (Efficient Evaluation of Quadratic Forms) A range of quadratic forms are evaluated, using efficient methods. Unnecessary transposes are not performed. Complex values are handled consistently. 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Package: r-cran-quadmesh Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2501 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-raster, r-cran-gridbase, r-cran-png, r-cran-sp, r-cran-geometry, r-cran-reproj, r-cran-scales, r-cran-palr, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-quadmesh_0.6.0-1.ca2404.1_all.deb Size: 2411998 MD5sum: f91dfc7467d3715ce74473a914edc27e SHA1: fa09066deaca3e01070e1c6be69494a36ce88146 SHA256: 9e039f9eea695ec484f2a07d57ede4cfa6358e7cf033f5f3a95c370a06b52982 SHA512: 772de9dcab275b64f8caea560b2d05aef372a453e9dc330ba4c080f1db75797421da3489f954fb079c002a263598fa8860df126421305f0c58cd37832116d1c1 Homepage: https://cran.r-project.org/package=quadmesh Description: CRAN Package 'quadmesh' (Quadrangle Mesh) Create surface forms from matrix or 'raster' data for flexible plotting and conversion to other mesh types. The functions 'quadmesh' or 'triangmesh' produce a continuous surface as a 'mesh3d' object as used by the 'rgl' package. This is used for plotting raster data in 3D (optionally with texture), and allows the application of a map projection without data loss and many processing applications that are restricted by inflexible regular grid rasters. There are discrete forms of these continuous surfaces available with 'dquadmesh' and 'dtriangmesh' functions. Package: r-cran-quadprogxt Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-quadprogxt_0.0.6-1.ca2404.1_all.deb Size: 26048 MD5sum: c1ed8708143ca19ee80dacb3c63a6727 SHA1: 7ec2a4c6548aafe6afc9cd3df6fe9cfe07998e99 SHA256: 4949db571668fcd4ded913b1322beecc42f1628388aa8ba62c97d4139c4f4c75 SHA512: 9f7281b1422874860fb105181fa9b7c741d0444092fb48ccc06fc99d629e9607ed2a40d3fc87e9a39e5bd611d1fb3d4e3f8eb88ccca08c843c63a2ea12c2dd6c Homepage: https://cran.r-project.org/package=quadprogXT Description: CRAN Package 'quadprogXT' (Quadratic Programming with Absolute Value Constraints) Extends the quadprog package to solve quadratic programs with absolute value constraints and absolute values in the objective function. 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Thereafter, using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) and visualize the standard deviation (SD). For details, see Sarkar and Rashid (2019), Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10. Package: r-cran-quadriceps Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4914 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quadriceps_0.1.0-1.ca2404.1_all.deb Size: 2867198 MD5sum: b2d045042a3805344aa9fc87269dfdd1 SHA1: 7b6758534f9921daab9d27c2265faf84d872993f SHA256: 32f11c17c99568c18666e82aa6c490381a4e6dbfff5d3581878b0e991f1476db SHA512: ecc79754a5a6eda5955fdaa135987f099495e94862d91484d3dc00c8186405836887af3ea72842fbe7de0a03678789eca68e791d03ecd672deca37d61642aadf Homepage: https://cran.r-project.org/package=quadriceps Description: CRAN Package 'quadriceps' (Positive-Weight Cubature Rules for the Gaussian Weight and theCube) Positive-weight cubature rules in several dimensions for the Gaussian weight (ghpos) and the uniform weight on the cube (lepos): the smallest rules known to the author for dimensions 2 to 5, with a fallback to the cheapest tensor product of lower-dimensional rules elsewhere. The rules are described in Pinkse (2026) , . The R twin of the 'Julia' package 'Quadriceps.jl'. Package: r-cran-quadroot Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quadroot_0.2.1-1.ca2404.1_all.deb Size: 15158 MD5sum: 0a99bca7ddc713b7ef2e92d554ca7e2c SHA1: b61ae4ca50f042daac2403a665bf369e0bc84b56 SHA256: 27657dc5f876aa3733d740eed22e32de65705a364a63fe4a971101508fb3c65c SHA512: a485d8d07e3069b67c3de9f19707ff2ed62de9605d0a09d3409fc35d94b005d2a93abdee76ea1e8dd81fa8da75602a1287e18c07c42d8e4e3b446696777f7f8e Homepage: https://cran.r-project.org/package=QuadRoot Description: CRAN Package 'QuadRoot' (Quadratic Root for any Quadratic Equation) It will assist the user to find simple quadratic roots from any quadratic equation. 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(2018) , compare the performance with linear models, and construct networks with partial derivatives. Package: r-cran-quak Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dbi, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-rlang Suggests: r-cran-azr, r-cran-dbplyr, r-cran-dplyr, r-cran-nanoarrow, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-quak_0.1.1-1.ca2404.1_all.deb Size: 277936 MD5sum: 9d3700e57f521c4daba0388be4cf2dbc SHA1: e563d79ec761cc371f676d35bdf2e7cc85b47f84 SHA256: 4e7c1f6bc2e7d990bd03e9785a9b9af2a7a4a334573be5ea59fb07d5bfd4c7fb SHA512: cf6a363e51cb7aac5df6245b9e83d16c3cf8410accc214c5a3fcec7ff9ff7c822a7ea731c3fd627a9a8d156d2e2b69c9ae899a28de6eadebe02e3204fcf844f5 Homepage: https://cran.r-project.org/package=quak Description: CRAN Package 'quak' (Query 'Azure Data Lake Storage Gen2' with 'DuckDB') Provides convenience utilities for using 'DuckDB' directly over datasets stored in 'Azure Data Lake Storage Gen2' (ADLS Gen2, 'abfss://'). Opens connections configured for Azure-backed 'Delta Lake' and 'Parquet' data, registers Azure credentials as 'DuckDB' secrets, and supports optional repository mirrors for restricted networks. Integrates well with 'DBI' for SQL workflows and with 'dplyr' and 'dbplyr' for lazy table queries. Package: r-cran-qualitycontrol Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-janitor, r-cran-openxlsx, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qualitycontrol_0.1.0-1.ca2404.1_all.deb Size: 62214 MD5sum: c3c2e8afc7bb7f708cdad952cf437cc6 SHA1: ef83df30f06fb65a8b7bc1a6d11b64b9d872c549 SHA256: 497deab297d9a6b411dcf0ee0c27d03874e8f16b1b0f418ecbd142b11fd5a60e SHA512: 3d59197edbb38e3bca7ce8cf89134d9b7f7d4256ee465616e2518460e78e2f11b5c4e50a4532df01647b0c7e1561b4482cdd1a08f20ddcc722671c4a0a734289 Homepage: https://cran.r-project.org/package=qualitycontrol Description: CRAN Package 'qualitycontrol' (Unified Framework for Data Quality Control) An easy framework to set a quality control workflow on a dataset. Includes a various range of functions that allow to establish an adaptable data quality control. Package: r-cran-qualitymeasure Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-doparallel, r-cran-lme4, r-cran-foreach, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qualitymeasure_2.0.2-1.ca2404.1_all.deb Size: 2202084 MD5sum: ea722a39651d97b313ccc9b73d3325d2 SHA1: 18b32e3eab057c23066d324675203e834bf990e1 SHA256: 3f182f67b0c75a4a85e5bb8a3ca3fa19f88b6b9a66f56224bf2d89951441af69 SHA512: 0ca369028c8f8c970f879396a2e7342ffe9cda2d56ee100fd76a96e221fb65592ff68873ac1cd73394a04a26bbdd3b31754978ba23ee9f7172094ee933ac00fb Homepage: https://cran.r-project.org/package=QualityMeasure Description: CRAN Package 'QualityMeasure' (Methods for Analyzing Quality Measure Performance) Quality of care is compared across accountable entities, including hospitals, provider groups, and insurance plans, using standardized quality measures. However, observed variations in quality measure performance might be the result of chance sampling or measurement errors. Contains functions for estimating the reliability of unadjusted and risk-standardized quality measures. Package: r-cran-quallmer.app Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quallmer, r-cran-shiny, r-cran-bslib, r-cran-dplyr, r-cran-tidyr, r-cran-irr, r-cran-htmltools, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quallmer.app_0.1.0-1.ca2404.1_all.deb Size: 101936 MD5sum: 447bed5c9113686b50e5351fa028887f SHA1: 440e37f52daabc5a82b3d7780186b9ce6e932798 SHA256: 09777e3e37011262a091a99b080fe66fddb4653827c975d1316d97f505f144fa SHA512: 43221ee48bd2cd71c44021626b4081a6f1777314e0c5a806caa7d363958bc3940a15c5d67300c41eda78395ff958a8ac48ca11b706ea253c8f2e6207398448a0 Homepage: https://cran.r-project.org/package=quallmer.app Description: CRAN Package 'quallmer.app' (Interactive Validation App for 'quallmer') Companion package to 'quallmer' providing an interactive 'shiny' application for manual coding, reviewing large language model (LLM) generated annotations, and computing inter-rater reliability metrics. Supports three modes: blind manual coding, LLM output validation, and agreement calculation. Computes standard reliability metrics including Krippendorff's alpha (Krippendorff 2019 ), Cohen's kappa, Fleiss' kappa (Fleiss 1971 ), intraclass correlation coefficient (ICC), and percent agreement for nominal, ordinal, interval, and ratio data. Also computes gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009 ). Package: r-cran-quallmer Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ellmer, r-cran-cli, r-cran-curl, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-av, r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-mockery, r-cran-quanteda, r-cran-quanteda.tidy, r-cran-withr, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-quallmer_0.5.0-1.ca2404.1_all.deb Size: 2231634 MD5sum: fe1c7162019fca655e80d1be300f41ca SHA1: 98ab2b3151286d6928da7374b0bf6402f457e686 SHA256: 5426c37a90a178b84ca3b2539d0a68510fa07ba47469ee5d71d40d981d429a00 SHA512: a545fcd1a2bc99ede382f968b13b54a354dbb2be3db468bb4633a8c768d98f7830abcad283af122deb0e7436e9b3ab59c29d901f4ca4d80b8d07b043e4007b7a Homepage: https://cran.r-project.org/package=quallmer Description: CRAN Package 'quallmer' (Qualitative Analysis with Large Language Models) Tools for AI-assisted qualitative data coding using large language models ('LLMs') via the 'ellmer' package, supporting providers including 'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'. Provides a 'codebook'-based workflow for defining coding instructions and applying them to texts, images, audio recordings, and other data. Includes built-in 'codebooks' for common applications such as sentiment analysis and policy coding, and functions for creating custom 'codebooks' for specific research questions. Supports systematic replication across models and settings, computing inter-coder reliability statistics including Krippendorff's alpha (Krippendorff 2019, ) and Fleiss' kappa (Fleiss 1971, ), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, ). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research. Package: r-cran-qualmap Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-leaflet, r-cran-purrr, r-cran-rlang, r-cran-sf Suggests: r-cran-covr, r-cran-ggplot2, r-cran-testthat, r-cran-tigris, r-cran-tidycensus, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qualmap_0.2.2-1.ca2404.1_all.deb Size: 832390 MD5sum: 4998c9095bc44d683668cc39ce09a4fe SHA1: 7c1b16f630eb7ac64711d1fb183e728c6409040c SHA256: 6fc663872077a3d17dafa49de16f994ed3e600a5b6e04b189ec28e23b99f05b7 SHA512: f3d21532c7210ec7e4d8d271a0bd50d01400aba20ccd319db84fd995a26b64e1e50344f1200dfdd7ce6f8f6e6b808e4b8148e73e19117fa2100dda5b8bf3082d Homepage: https://cran.r-project.org/package=qualmap Description: CRAN Package 'qualmap' (Opinionated Approach for Digitizing Semi-Structured QualitativeGIS Data) Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and converting it to a simple features object. These tools are focused on data that are drawn on a map that contains some type of polygon features. For each area identified on the map, the id numbers of these polygons can be entered as vectors and transformed using qualmap. Package: r-cran-qualtrics Architecture: all Version: 3.3.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-archive, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sjlabelled, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-qualtrics_3.3.0-1.ca2404.2_all.deb Size: 260592 MD5sum: ef2322d28e983e1413dc780e4cdf4681 SHA1: 59add6c3b156eb250e402734869bbebc6041a55c SHA256: 13bd7ea83e31d52639222c2b0e512ba7fafe190166853e5ffeffb0c2a7368b9d SHA512: 4e05f333aa7cd96470ea0f6203bab316e581abbacf8cd3ad917c1a24963c695ff56ce79673e36ed90067ce4878487b1138283115fe33f414eb759ebd8c0e05e1 Homepage: https://cran.r-project.org/package=qualtRics Description: CRAN Package 'qualtRics' (Download 'Qualtrics' Survey Data) Provides functions to access survey results directly into R using the 'Qualtrics' API. 'Qualtrics' is an online survey and data collection software platform. See for more information about the 'Qualtrics' API. This package is community-maintained and is not officially supported by 'Qualtrics'. Package: r-cran-qualvar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggally, r-cran-dt Filename: pool/dists/noble/main/r-cran-qualvar_0.2.0-1.ca2404.1_all.deb Size: 198464 MD5sum: 5f288c54ab06377e643d13842567794d SHA1: 7ae1c85b1e38cd1766fceb1d750cb34d12a991a3 SHA256: eb2e3474751c24a34439e2a4eb8fd4f5c87f72d5eca8d8ddfafa11ec973d6bee SHA512: 09d731aa407f69e2cd89576eac747ebb266b4b00ee8c578a338e16db6af16712f3420ff04e9b6196de26cf5b0020930b442471b639b26b79b803cfa6c8c17125 Homepage: https://cran.r-project.org/package=qualvar Description: CRAN Package 'qualvar' (Implements Indices of Qualitative Variation Proposed by Wilcox(1973)) Implements indices of qualitative variation proposed by Wilcox (1973). Package: r-cran-qualypso Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-expm, r-cran-rfast, r-cran-gamlss, r-cran-gamlss.dist, r-cran-statmod Filename: pool/dists/noble/main/r-cran-qualypso_3.1-1.ca2404.1_all.deb Size: 145142 MD5sum: da743e4dc061acd144f6f21fe2cedd8a SHA1: 2dfaabdb2388c410f53d7fc9269caf7a3986e8c4 SHA256: 246ec05c816c55f29917b66a3b28c83ea8116b9336014411443cbe08199d14c0 SHA512: a053ccf5344548deb831e67d4f7d2df3b7553642283f96652c97d38e8f220012dc02237bde8c9427dab4b6bd6e6414ad93c8b8d0a0d5903b3cddbed4efaef774 Homepage: https://cran.r-project.org/package=QUALYPSO Description: CRAN Package 'QUALYPSO' (Partitioning Uncertainty Components of an Incomplete Ensemble ofClimate Projections) These functions apply an analysis of variance to incomplete ensembles of climate projections. 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Package: r-cran-quandl Architecture: all Version: 2.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-httr, r-cran-zoo, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-timeseries Filename: pool/dists/noble/main/r-cran-quandl_2.11.0-1.ca2404.1_all.deb Size: 70362 MD5sum: 84fffd19d995d7d92ce2af590d63e6d3 SHA1: 2545d2992b09dcbf5b59532b2ef99553a411c9f9 SHA256: 72f97e377805d046d71009ab0aa029c2bd61343a0e21f5fc26025985c0369822 SHA512: ec8772d260c1f08b885b542f785d4b669258d7f3b82ca999271ce4bd07a6ad4fdd23069afa2b55fa22f93f8632722608c8db644716854268c2702ff56f5dceed Homepage: https://cran.r-project.org/package=Quandl Description: CRAN Package 'Quandl' (API Wrapper for Quandl.com) Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to . For more help on the package itself go to . Package: r-cran-quantbayes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-quantbayes_0.1.0-1.ca2404.1_all.deb Size: 1313108 MD5sum: b98bb83014ff8208275df7ec9bb162f8 SHA1: 81d9b7079ae1d58a1f9a32636cb172198080e44a SHA256: 35de7a12422d0dbf93d251dd60baa52731e0bc6ab345289df7967826eada7057 SHA512: 6eadd088cbf27e25cc956ac69a198338e8189144af1aed217959a12e3c5147f5ce28d177e115b65cc07e1a23a9c1e37772f16288942617e4674d58cb5a478224 Homepage: https://cran.r-project.org/package=quantbayes Description: CRAN Package 'quantbayes' (Bayesian Quantification of Evidence Sufficiency) Implements the Quantification Evidence Standard algorithm for computing Bayesian evidence sufficiency from binary evidence matrices. It provides posterior estimates, credible intervals, percentiles, and optional visual summaries. The method is universal, reproducible, and independent of any specific clinical or rule based framework. For details see The Quantitative Omics Epidemiology Group et al. (2025) . 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It covers fixed coupon assets, floating note assets, interest and cross currency swaps with different payment frequencies. Enables the calibration of spot, instantaneous forward and basis curves, making it a powerful tool for accurate and flexible bond valuation and curve generation. The valuation and calibration techniques presented here are consistent with industry standards and incorporates author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4). Package: r-cran-quantcurves Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-locpol, r-cran-np, r-cran-quantreg, r-cran-quantreggrowth Filename: pool/dists/noble/main/r-cran-quantcurves_1.0.0-1.ca2404.1_all.deb Size: 60738 MD5sum: b0a9a83cf3aca0356347113e2bfb77b3 SHA1: 791a81715942b53e1cde93ebebd3b984f0807b53 SHA256: b0a21cc1dcdc2351439e6fd1e664ae8564338a956aab4400648611df747c97da SHA512: cb954e29364d9acfe902828c75db93f6f3a7e5b8728cd4ade40fda0a770a17fddcc7196ad8ba4be65b21215870ccfbcc6608b04111b35b6b277648ac0f4e0759 Homepage: https://cran.r-project.org/package=quantCurves Description: CRAN Package 'quantCurves' (Estimate Quantiles Curves) Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. 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Implements row operations for 'subsetting' and reordering documents; column operations for managing document variables; grouped operations; and two-table verbs for merging external data. For more on 'quanteda' see 'Benoit et al.' (2018) . For 'dplyr' see 'Wickham et al.' (2023) . 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Methods implemented in the package draw from several sources, including Alexander (1976) , Batschelet (1981, ISBN:9780120810505), Benhamou (2004) , Bovet and Benhamou (1988) , Cheung et al. (2007) , Cheung et al. (2008) , Cleasby et al. (2019) , Farlow et al. (1981) , Ostrom (1972) , Rohlf (2008) , Rohlf (2009) , Ruiz and Torices (2013) , Scrucca et al. (2016) , Thulborn and Wade (1984) . Package: r-cran-quantification Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car Filename: pool/dists/noble/main/r-cran-quantification_0.2.0-1.ca2404.1_all.deb Size: 79690 MD5sum: 336b095ac9daeb73e0f8f1a92d100dc9 SHA1: 1a4f0b4fe08c5ffe3f9613d41dc23b2117e9d201 SHA256: 0355d089c2d1fc87d86ea0b56242b389380107c14ca3b80793c143cd41f59cac SHA512: 5eb459babd14984eee84e171868d6f5fe5bf2f6857747f3a8078792ced6f1e821df032926896038f912d1b3cd0e09ce2d7d909d502ae7477f2a89f0e894ba4e7 Homepage: https://cran.r-project.org/package=quantification Description: CRAN Package 'quantification' (Quantification of Qualitative Survey Data) Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method. Package: r-cran-quantileda Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantileda_1.2-1.ca2404.1_all.deb Size: 77306 MD5sum: 47491405ef7f5136199ab640ad5725cc SHA1: 8f1ac1785d56dbcfea3528a4af6bb1f6c73f9c22 SHA256: 8d7f3b975bc85adfbc331ff033435291b4e47d8a308eddaf718fd0a410ed4a6e SHA512: 0faf09dbb0d08c225d5d9b1e236a5381df7b38a5f6fc35ec70f0931aff4a027db7532ca1fee8704db08538283f43b0c37cb0ed8a10149beba745822495ae4d99 Homepage: https://cran.r-project.org/package=quantileDA Description: CRAN Package 'quantileDA' (Quantile Classifier) Code for centroid, median and quantile classifiers. Package: r-cran-quantilegh Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Filename: pool/dists/noble/main/r-cran-quantilegh_0.3.0-1.ca2404.1_all.deb Size: 24728 MD5sum: 389a2f856e4c5be81b4c629943d72aca SHA1: 50c46a9d05ffd78051acb3e6b5fa52ba1f51cbbf SHA256: c87e5ce25e1c7b88e816796290669792e4e6aadebd10dea1c382f38a339f4a67 SHA512: bd320a4017fa228ab764081ca35581f71a71bc8b4b6f9c499791422ac5b25dd321d461c3b23dfd57b6da071269d3c75184e488e72aecf39afa06ed32b5964162 Homepage: https://cran.r-project.org/package=QuantileGH Description: CRAN Package 'QuantileGH' (Quantile Least Mahalanobis Distance Estimator for Tukey g-&-hMixture) Functions for simulation, estimation, and model selection of finite mixtures of Tukey g-and-h distributions. The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in is not maintained in versions >= 0.3.0 (July 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries. Package: r-cran-quantilegrader Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantilegrader_0.1.1-1.ca2404.1_all.deb Size: 72970 MD5sum: 0be40e50c60426570a797f643e42e041 SHA1: ea07c96895b8b89efb6629f4baaf82fcd1c0485e SHA256: 2f4b5416cd76701df89e2091e9254fbb3ebb853ef7d4b743103605d88f1f626d SHA512: 53af091b54626ea35ce2d4afbb7ae6506b93399ef635ff717c36de3e3d902e5523f5047287bbe47e541759f74ddaa17ad60724428af66853b4011d29f880797d Homepage: https://cran.r-project.org/package=QuantileGradeR Description: CRAN Package 'QuantileGradeR' (Quantile-Adjusted Restaurant Grading) Implementation of the food safety restaurant grading system adopted by Public Health - Seattle & King County (see Ashwood, Z.C., Elias, B., and Ho. D.E. "Improving the Reliability of Food Safety Disclosure: A Quantile Adjusted Restaurant Grading System for Seattle-King County" (working paper)). As reported in the accompanying paper, this package allows jurisdictions to easily implement refinements that address common challenges with unadjusted grading systems. First, in contrast to unadjusted grading, where the most recent single routine inspection is the primary determinant of a grade, grading inputs are allowed to be flexible. For instance, it is straightforward to base the grade on average inspection scores across multiple inspection cycles. Second, the package can identify quantile cutoffs by inputting substantively meaningful regulatory thresholds (e.g., the proportion of establishments receiving sufficient violation points to warrant a return visit). Third, the quantile adjustment equalizes the proportion of establishments in a flexible number of grading categories (e.g., A/B/C) across areas (e.g., ZIP codes, inspector areas) to account for inspector differences. Fourth, the package implements a refined quantile adjustment that addresses two limitations with the stats::quantile() function when applied to inspection score datasets with large numbers of score ties. The quantile adjustment algorithm iterates over quantiles until, over all restaurants in all areas, grading proportions are within a tolerance of desired global proportions. In addition the package allows a modified definition of "quantile" from "Nearest Rank". Instead of requiring that at least p[1]% of restaurants receive the top grade and at least (p[1]+p[2])% of restaurants receive the top or second best grade for quantiles p, the algorithm searches for cutoffs so that as close as possible p[1]% of restaurants receive the top grade, and as close as possible to p[2]% of restaurants receive the second top grade. Package: r-cran-quantilenpci Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quantilenpci_0.9.0-1.ca2404.1_all.deb Size: 25398 MD5sum: 58156feace7a1d8d21584c7d68aaa57b SHA1: d57056a2b4568dcec407d8189ed670f24e58d52d SHA256: 682cb58de08d84b14bccebc91d92f14c32abda865694d7b1c21d7240c042f345 SHA512: 49d5d3ac26032fe0061313dfe3bcd91f8ea70b92c456951ef40c74935023341829c22c72afd054e53fa1ce343bfa81f6d87ea2c47bbc6133b8a8dea95d6acd6c Homepage: https://cran.r-project.org/package=QuantileNPCI Description: CRAN Package 'QuantileNPCI' (Nonparametric Confidence Intervals for Quantiles) Based on Alan D. Hutson (1999) , "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353. Package: r-cran-quantileonquantile Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-quantileonquantile_1.0.3-1.ca2404.1_all.deb Size: 87024 MD5sum: 9b20aa4d088e99f01e7f4e50f3c3d267 SHA1: 6b229cf72ca3a8f0033a8c005256299bb0c18647 SHA256: bffd52d8e5297ce90329b56e038a685ddad35e7a316811ec19bfcd2cde842238 SHA512: 2620db3c0f3be9274cd2b8af24fb607c669075599fc2f1864a3980024fa05e81031f9486b0a2b47af8778ba6f8405536b6f725d4a282a5e44c74dc0a0e22d738 Homepage: https://cran.r-project.org/package=QuantileOnQuantile Description: CRAN Package 'QuantileOnQuantile' (Quantile-on-Quantile Regression Analysis) Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) . QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions. Package: r-cran-quantilogram Architecture: all Version: 3.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-np, r-cran-quantreg, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sparsem Filename: pool/dists/noble/main/r-cran-quantilogram_3.2.1-1.ca2404.1_all.deb Size: 218238 MD5sum: cd44691f26b8fbff256580e9663b67d5 SHA1: f0293e6f4da3ac1d0a6471f9160cb66c09c920a3 SHA256: 5de314dcd6a7870177f390137bb2e24d95ac26f0542105c9fa535f977ec72af6 SHA512: 746558edba30e1db3f61ab7b9be277fd3227f467e0832b5b171ecff2b6fecdbb6205bfed9758ca876c4d4788562a949bfc1a8ba06e9a69f7c04d0e9acecc1c4b Homepage: https://cran.r-project.org/package=quantilogram Description: CRAN Package 'quantilogram' (Cross-Quantilogram) Estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency. One can use the cross-quantilogram to detect the presence of directional predictability from one time series to another. This package provides a statistical inference method based on the stationary bootstrap. For detailed theoretical and empirical explanations, see Linton and Whang (2007) for univariate time series analysis and Han, Linton, Oka and Whang (2016) for multivariate time series analysis. The full references for these key publications are as follows: (1) Linton, O., and Whang, Y. J. (2007). The quantilogram: with an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282 ; (2) Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270 . Package: r-cran-quantkriging Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hetgp, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quantkriging_0.1.0-1.ca2404.1_all.deb Size: 65396 MD5sum: 23d6518a9194cc24f21ae06935f9c35e SHA1: d4af09fcb4d9d2a0b1386ea1044a68b73ee15b21 SHA256: ec91e22d5d68e1fffc46b726298640c4847d2c1d08081d25f0e169e1abd70149 SHA512: dab5a73e52aebce255b5377fa13b596dab12393307dcd58f411cb84a1957b7990e6b03fce3677bc9944eb1bf3fbc4311ceb87905f95b03d42ad4331489f2f64c Homepage: https://cran.r-project.org/package=quantkriging Description: CRAN Package 'quantkriging' (Quantile Kriging for Stochastic Simulations with Replication) A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) . With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) it is now possible to apply quantile kriging to a wider class of problems. In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation. 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The method has been published in Bioinformatics (Fei et al, 2018, ). Also available on 'GitHub' . 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For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included. 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Package: r-cran-quantreg.nonpar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-mnormt, r-cran-fda, r-cran-rearrangement Filename: pool/dists/noble/main/r-cran-quantreg.nonpar_1.0-1.ca2404.1_all.deb Size: 815492 MD5sum: f0e7b0b23112522b3cc0c3429244cefa SHA1: 270121ca57c8905465aaabfd1578727abae65596 SHA256: d1304b97f3a9b2b75dcca3a930e35ca2086312b01c7eb3bf058e6871ad41d4a3 SHA512: 1e58062ae67a963d28ddb2d67d8de5c299fb5831777806ed3cf123770bc9f6f7d8d829b542e5df88dafb266db225461b767123525a09eb1225d4f60f12e06b0d Homepage: https://cran.r-project.org/package=quantreg.nonpar Description: CRAN Package 'quantreg.nonpar' (Nonparametric Series Quantile Regression) Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods. Package: r-cran-quantregforest Architecture: all Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-randomforest Suggests: r-cran-gss, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-quantregforest_1.4-0-1.ca2404.1_all.deb Size: 34144 MD5sum: 2d446078d1ce27cc8df3927fdad7585c SHA1: 89fc5c02de2a4ca8b9576f22b30e0706e7d833fc SHA256: 62d75352f5fee1adc587161f4de872cf706b29108770ebe51cc5e6d4c1c769f3 SHA512: fd17e15cb6131be1289623dcd700ec864fe95cc0d6f25228fd5dc6cece588a6cab10a9c0fa79c238e4f00ec7776381f465b77dc1f9f96e32e4800acf5f183862 Homepage: https://cran.r-project.org/package=quantregForest Description: CRAN Package 'quantregForest' (Quantile Regression Forests) Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles. It is particularly well suited for high-dimensional data. Predictor variables of mixed classes can be handled. The package is dependent on the package 'randomForest', written by Andy Liaw. Package: r-cran-quantreggrowth Architecture: all Version: 1.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-sparsem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mgcv, r-cran-markdown Filename: pool/dists/noble/main/r-cran-quantreggrowth_1.7-2-1.ca2404.1_all.deb Size: 387078 MD5sum: 113dcde2c26c28f420a2d1cb476f6c28 SHA1: 8fae8e9e6a684e689caac14cdc86b90d8c932985 SHA256: 1e688630caab8503487172eb649051e16b779878a99052667a6c3c692dfc5821 SHA512: b5abcd6c8f990bd4a37e7b5a1474f6c479f05d0799aa46f3c86f36999f22f3b487b4f88832ad3185af26b5b3abd704852f595f98d61024c6125f3d31bd6d7633 Homepage: https://cran.r-project.org/package=quantregGrowth Description: CRAN Package 'quantregGrowth' (Non-Crossing Additive Regression Quantiles and Non-ParametricGrowth Charts) Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) . Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) , and also or some code examples. Package: r-cran-quantsig Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantsig_0.1.0-1.ca2404.1_all.deb Size: 10594 MD5sum: 74a347b02c2060e47869fbfc03ae3bc1 SHA1: cd63d71eb46761938e110b177864de9a26d8977b SHA256: 8eb1e92795af59e20cf938506b582aae17b0ed6fcaf3d1c70c75c508accdc7c7 SHA512: cf04181593f4488acec2b1ef06e1cec50e7e51635e95fff199f39c5b7bbfd9185a52998dd3f9a9343ade73b015fe1f33f6dc955ab4e6f56141e8e27c67d1e673 Homepage: https://cran.r-project.org/package=quantsig Description: CRAN Package 'quantsig' (Sigmoidal Quantile Function Estimator) A sigmoidal quantile function estimator based on a newly defined generalized expectile function. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. The package is based on the method introduced in Hutson (2024) . Package: r-cran-quantumops Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantumops_3.0.1-1.ca2404.1_all.deb Size: 268424 MD5sum: 163e4cf87733a22c2fde85482b885e0f SHA1: 2e06cf6d64ab2314a019f1360ee57f95bf249103 SHA256: c440779514328e4cce5c01ae22b80fb8759e6eabc923a6717beae4304e14e7a7 SHA512: d0fb94f89e53425cd5c2ea292c676281833b3cae86f98c220ad71f00f2025dbdb0ae7c1296c81036dac850c54a83d0361c6ad2480dbf24a7cc9a35b2888ad43a Homepage: https://cran.r-project.org/package=QuantumOps Description: CRAN Package 'QuantumOps' (Performs Common Linear Algebra Operations Used in QuantumComputing and Implements Quantum Algorithms) Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction , Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) , and a variational quantum classifier (Schuld 2018) . Can be used with the gridsynth algorithm to perform decomposition into the Clifford+T set. Package: r-cran-quaqcr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quaqcr_1.0.4-1.ca2404.1_all.deb Size: 869330 MD5sum: f5756a9b7d199b4b305c68685ad78093 SHA1: dd3fdeecb41cf28f21af45cd84c624acb5957cea SHA256: 5df7dbc0e1901b4723163a5cbd4d5fd344bf83c29c00b32f51fb72ad43a2136c SHA512: bfe0d22ee5af01e880c8cfae97d531163d9756683352bb69a1091eaa801dde217cd91ad2798962924485b7026210b0521bcf819f5e0abaf315906c235517e0c0 Homepage: https://cran.r-project.org/package=quaqcr Description: CRAN Package 'quaqcr' (Quick ATAC-Seq QC) A wrapper around the 'quaqc' program described in Tremblay and Questa (2024) . 'quaqc' allows for assay for transposase-accessible chromatin using sequencing (ATAC-seq) specific quality control and read filtering of next-generation sequencing (NGS) data with minimal processing time and extremely low memory overhead. Any number of samples can be processed, using multiple threads if desired. 'quaqc' outputs a comprehensive set of aligned read metrics, including alignment size, fragment size, percent duplicates, mapq scores, read depth, GC content, and others. Although designed for ATAC-seq data, 'quaqc' can also be used for other unspliced DNA sequencing experiments (such as chromatin immunoprecipitation sequencing, or ChIP-seq) as many of the metrics are related to general sequencing quality. This R package also provides additional utilities for custom analyses and plotting of 'quaqc' results. 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Currently plain-, age-, volatility-weighted- and filtered historical simulation are implemented in this package. Volatility weighting can be carried out via an exponentially weighted moving average model (EWMA) or other GARCH-type models. The performance can be assessed via Traffic Light Test, Coverage Tests and Loss Functions. The methods of the package are described in Gurrola-Perez, P. and Murphy, D. (2015) as well as McNeil, J., Frey, R., and Embrechts, P. (2015) . Package: r-cran-quarrint Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3927 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-quarrint_1.0.1-1.ca2404.1_all.deb Size: 3978964 MD5sum: 748ca2014f45ac6527f95741b9e3ad2d SHA1: f7b5a9ec752b01251c5d7df2cd47a5782b2f365c SHA256: f37d6b58e665bf30fadb19513aedccce87d72964e83fa618705ac4ea3b552074 SHA512: 05fa428543117436f362162e7693008387f6a55fb6efaeced541c260b326c8e8b018e89a5fce7919a90f382255b7c649994679ce0b430c689dfced928d6199e2 Homepage: https://cran.r-project.org/package=quarrint Description: CRAN Package 'quarrint' (Interaction Prediction Between Groundwater and Quarry ExtensionUsing Discrete Choice Models and Artificial Neural Networks) An implementation of two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. 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Package: r-cran-quartets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2265 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quartets_0.1.1-1.ca2404.1_all.deb Size: 2192212 MD5sum: 8c7e5bf1d438661b097ad1896cb34efb SHA1: 010c4b2a9215c0227a4bd5cc272afa33e11f1e4f SHA256: 85de67a6c1b93c34179784fc0f206f6ef0be3b0344851aad4c9f3ac11b2b048e SHA512: 4f523e9aeec2daa19582b2d59d501dcde16ac5e79242ab787c5d6c488ec5d2329275c4da896a581c17337543efa4c235272350471ae3b558edbc2909a2e18ff1 Homepage: https://cran.r-project.org/package=quartets Description: CRAN Package 'quartets' (Datasets to Help Teach Statistics) In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. 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Package: r-cran-quasar Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-matrix, r-cran-mass, r-cran-pracma, r-cran-sn Filename: pool/dists/noble/main/r-cran-quasar_0.2.1-1.ca2404.1_all.deb Size: 77598 MD5sum: a1789ee44c6173161cdcb687d42db1ed SHA1: d9072d268cd152b3e27b675fc12b7d7de20805e7 SHA256: 4923b98de499d963edca73b5ffe04cb4e1ea57aa59541fc7b4c7470e9fb22c7c SHA512: 6ae21a467ae3a36189e630482a594ed5b6f5a4b466870a86bd8435295b5900f52219294870b158cc0ee7960baee2a731333de38867f923520ef9051db4343379 Homepage: https://cran.r-project.org/package=quasar Description: CRAN Package 'quasar' (Valid Inference on Multiple Quantile Regressions) The approach is based on the closed testing procedure to control familywise error rate in a strong sense. The local tests implemented are Wald-type and rank-score. The method is described in De Santis, et al., (2026), . Package: r-cran-quaxnat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1873 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-quaxnat_1.0.1-1.ca2404.1_all.deb Size: 1847490 MD5sum: dcc4a414ddcf332d6a7a20b20452868d SHA1: 69fd1562b96d953fa9cd58abb2d245b9ec418421 SHA256: 53b5df7e8762a7b2375e16e8a813d829ca346f2e666183de28b6031e02db2382 SHA512: 21ce1d2fa30d7c7afe75774ec2bd8f7b262fd691f969ef4697945b21311b2a185ef0c48041d427ff2759f1b980f4838e80c0ab6b5b20a38486d099dff6c02222 Homepage: https://cran.r-project.org/package=quaxnat Description: CRAN Package 'quaxnat' (Estimation of Natural Regeneration Potential) Functions for estimating the potential dispersal of tree species using regeneration densities and dispersal distances to nearest seed trees. A quantile regression is implemented to determine the dispersal potential. Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) . Package: r-cran-quclu Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quclu_1.0.1-1.ca2404.1_all.deb Size: 50066 MD5sum: e93acae81b38a4af3b81456bb04354bd SHA1: 79ba761505e8b2e9eee1b1edcd2d93647d63d841 SHA256: 43d897b8325e7b50d6f0c89e1b6007ffe6a100cae0272ceed0000a9bb184dd30 SHA512: 58997726e29d244231b4e9c5d0ec9250cafbb79cca18b2e73bbcc3e0da8619a7a1d54ed110c31da2e9f7ab09b0d5551665b5ee4ca17fd21d561dd805e6bdc1cc Homepage: https://cran.r-project.org/package=QuClu Description: CRAN Package 'QuClu' (Quantile-Based Clustering Algorithms) Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883 . 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A filtering query, built as a nested list configuration, can be easily stored in other formats like 'YAML' or 'JSON'. What's more, it's possible to convert such configuration to a valid expression that can be applied to popular 'dplyr' package operations. Package: r-cran-querychat Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3141 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-cli, r-cran-coro, r-cran-dbi, r-cran-ellmer, r-cran-htmltools, r-cran-jsonlite, r-cran-lifecycle, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-s7, r-cran-shiny, r-cran-shinychat, r-cran-whisker, r-cran-yaml, r-cran-zip Suggests: r-cran-dbplyr, r-cran-dplyr, r-cran-dt, r-cran-duckdb, r-cran-ggsql, r-cran-knitr, r-cran-later, r-cran-nanoparquet, r-cran-palmerpenguins, r-cran-pins, r-cran-rmarkdown, r-cran-rsqlite, r-cran-rsvg, r-cran-testthat, r-cran-v8, r-cran-withr Filename: pool/dists/noble/main/r-cran-querychat_0.4.1-1.ca2404.1_all.deb Size: 2589906 MD5sum: b787d1373121429423649eaf8a988307 SHA1: b2bc962e34fa8c24d445ba37ba8f96cefe7ef799 SHA256: 336f25d2ff5fd9ed148a1c4bb283a55a12c2655e4d12eb27cf34fdcd7b7a7a30 SHA512: f8798ae45ccaac6bb625ef25f31f521925320604dd051a53a09693a75cbbaead585d91b1fb8db4a64b57b4b242c2673c068cb0a798d7737892ad2b2558813a87 Homepage: https://cran.r-project.org/package=querychat Description: CRAN Package 'querychat' (Filter and Query Data Frames in 'shiny' Using an LLM ChatInterface) Adds an LLM-powered chatbot to your 'shiny' app, that can turn your users' natural language questions into 'SQL' queries that run against your data, and return the result as a reactive data frame. Use it to drive reactive calculations, visualizations, downloads, and more. Package: r-cran-queryparser Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-queryparser_0.3.2-1.ca2404.1_all.deb Size: 214010 MD5sum: 2f67301c8c58be0d3e48ff4085c0361a SHA1: 4926daf55d845421b756ecf30fc1753949ac6313 SHA256: 4a52f1278b3432676ab09d9341fe16ef25ed83ce6f1f3908d0ef1b6a1c064339 SHA512: a3374f0455cc028ada926651977afe7b58691e7b7c1ed53f44c3fbf75f11214856860bca3c18394b79abba12e6ffadaf0d5ddd19de3139ce71464daf376b20ed Homepage: https://cran.r-project.org/package=queryparser Description: CRAN Package 'queryparser' (Translate 'SQL' Queries into 'R' Expressions) Translate 'SQL' 'SELECT' statements into lists of 'R' expressions. Package: r-cran-queryup Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-xml2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-queryup_1.0.5-1.ca2404.1_all.deb Size: 70186 MD5sum: 825a5169ad3d2e5dbc5f14f34ca7412f SHA1: 65d035ad42ddedb605baee0caf81a7d4e447955d SHA256: 16681f476566f8a7761be7aef321342c3518f3691ec2f7726016d94fc5e3ee1e SHA512: d67f85d3e3b294cf1c4e61a77494cf4977a9c9741f626a41f0ca30fb916aedb4c3509ade054a3889a84ebb7c9f1eb13eeb79dea817fc3f759f22aa831b593e21 Homepage: https://cran.r-project.org/package=queryup Description: CRAN Package 'queryup' (Query the 'UniProtKB' REST API) Retrieve protein information from the 'UniProtKB' REST API (see ). Package: r-cran-quest Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-str2str, r-cran-abind, r-cran-checkmate, r-cran-plyr, r-cran-car, r-cran-psych, r-cran-boot, r-cran-mbess, r-cran-nlme, r-cran-lme4, r-cran-multilevel, r-cran-lavaan Suggests: r-cran-reshape, r-cran-psychtools, r-cran-lmeinfo, r-cran-semtools Filename: pool/dists/noble/main/r-cran-quest_0.2.1-1.ca2404.1_all.deb Size: 731298 MD5sum: 1f08afca4a4c4ac0bc56707dca5c6a7d SHA1: 7f1b5f578feabb4ed3317aed9651c5e53afee7cd SHA256: 849b588459a3ee3694cb23913c5a022553ca865c66e2124f754eb2da3784f897 SHA512: d212761193e72293728cd5986dd671e016dbf2ce739a2f3b3e5d38c9a01aaa2fbe8bf988bf0bd4d9991bbcf961f9d4ae73a2a76c7f09511a5ea7fcfd065d1fca Homepage: https://cran.r-project.org/package=quest Description: CRAN Package 'quest' (Prepare Questionnaire Data for Analysis) Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame. Package: r-cran-questionr Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-highr, r-cran-styler, r-cran-classint, r-cran-htmltools, r-cran-rlang, r-cran-labelled Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-janitor, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-survey, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-questionr_0.8.2-1.ca2404.1_all.deb Size: 3869388 MD5sum: dd9381625eedb317d18139dbc11581b1 SHA1: 171312c2a3f7393ac2ec275befc3351ebb4d3f71 SHA256: e191cb170631b6d60e72bc6ac22456a8c7566c55ec13d4d9ed13f68151d66780 SHA512: a74fcb880fe8d42e7079dcde4a3dac3ae5009ceedc54f04fed8337f88c0f7272c8a36b3a1498759f29f0d533857d4f37e7c2843654481c0f242e250e41e8043b Homepage: https://cran.r-project.org/package=questionr Description: CRAN Package 'questionr' (Functions to Make Surveys Processing Easier) Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions. Package: r-cran-questr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-questr_0.1.1-1.ca2404.1_all.deb Size: 136804 MD5sum: 39952f53f2c486f4159c65580bd74d04 SHA1: c3762fad0aea29bc2c20bcb876080a4a7c36de99 SHA256: e49718d2adf91235f5c1b5742ae020834c67b444b8b1a3a7d8563cdaaa008b48 SHA512: 051efff1fdef2eaa73956c3903deb34a7efc236db473b4867128f4b1e1b84681472fa45171a6b636aeab6b7667db0efdbc6e2aeaf3466bc9c280a7edd4aac1f6 Homepage: https://cran.r-project.org/package=QuESTr Description: CRAN Package 'QuESTr' (Constructing Quantitative Environment Sensor usingTranscriptomes) A method for prediction of environmental conditions based on transcriptome data linked with the environmental gradients. This package provides functions to overview gene-environment relationships, to construct the prediction model, and to predict environmental conditions where the transcriptomes were generated. This package can quest for candidate genes for the model construction even in non-model organisms' transcriptomes without any genetic information. Package: r-cran-queueing Architecture: all Version: 0.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1217 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-queueing_0.2.12-1.ca2404.1_all.deb Size: 963162 MD5sum: 5a8c5167c267fc612e919d56713d1c15 SHA1: 98999a2a2b64f4bee6e2d986464f8c39315a2c4a SHA256: 09a3f0dcedcd64d9d417dff88e4bad5db871ff6b5f7091d7ed2dae3381d712d5 SHA512: 35a60cfba0b5b66659daba4580c558467b59994d7887f94b95117848f95e5701aa3f4d737e23b3507474283fc1463ab72bcdb9060d7b2171f09465d57189e53e Homepage: https://cran.r-project.org/package=queueing Description: CRAN Package 'queueing' (Analysis of Queueing Networks and Models) It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua. Package: r-cran-quickcheck Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat, r-cran-hedgehog, r-cran-purrr, r-cran-tibble, r-cran-data.table, r-cran-hms, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-quickcheck_0.1.3-1.ca2404.1_all.deb Size: 632054 MD5sum: 488fe08e753e575d40bd91b0eca52772 SHA1: 9bb623d46b40f9a8feaa41038b078327d4273c04 SHA256: d033567a8102310625afc5b3219c7efb5ad5bbb7a39f301c6089e1cfcb2b34d4 SHA512: faf77e3f31fb8d9de4b57ccf3516b47bf82146d6f5a6c56eedfb6ccf6326f928082a03583ea562631bbd61e520fb8b6b8aad9369f42b39f83a1d72498c3aa668 Homepage: https://cran.r-project.org/package=quickcheck Description: CRAN Package 'quickcheck' (Property Based Testing) Property based testing, inspired by the original 'QuickCheck'. This package builds on the property based testing framework provided by 'hedgehog' and is designed to seamlessly integrate with 'testthat'. Package: r-cran-quickcode Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1384 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstudioapi, r-cran-fitdistrplus Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quickcode_1.2.0-1.ca2404.1_all.deb Size: 1058770 MD5sum: e35f0ddb8b21df87640aeae0433fe880 SHA1: ba661eeb991caf79d610bcf5fb70a71c50c09933 SHA256: 604cb51f26e325886ab5d118b446463d2b024e91721b2cc9a02d0b0456ea7b6c SHA512: 7a063430a1356fb30076eb5c11a40c913a0b95a21886a43552a6f3932757cddfb108dc202b7357fb0adace1ee54424b3cc6a909983fdf1345f62f3e4f0df864b Homepage: https://cran.r-project.org/package=quickcode Description: CRAN Package 'quickcode' (Quick and Essential 'R' Tricks for Better Scripts) The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts. 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Package: r-cran-quicknmix Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-optimparallel, r-cran-doparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-quicknmix_1.1.1-1.ca2404.1_all.deb Size: 101746 MD5sum: d0b04957c64d1404b6f7a8c6d66f3eb3 SHA1: a40eaa81722986107164b33aa4b5e104d4d4d5c4 SHA256: a6c446d97327f82d49701ea323815fc2f8375db9b15881a8fbfcd73eab8a62e3 SHA512: d9c1b3f56f0a0973a47cbde95996160d457dc26e5a5cf5db52887ba730a9e384c5d765b8294b4ae069e3a717b66f8259d51afddbdb00dfc0762d76879aca975b Homepage: https://cran.r-project.org/package=quickNmix Description: CRAN Package 'quickNmix' (Asymptotic N-Mixture Model Fitting) For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) . Asymptotic N-mixture models extend the work of Dail and Madsen (2011) , to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". Package: r-cran-quickoutlier Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbscan, r-cran-ggplot2, r-cran-isotree, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quickoutlier_0.1.5-1.ca2404.1_all.deb Size: 1026402 MD5sum: fa6e9a91931e868dbfae17fa23987f73 SHA1: caff6797edc105b1bf8010b16e3bba2365afeaed SHA256: ec30d5ab8bdba7f77a2fa2c34238f8706e9c809a9e2554b24092170c7d9efd88 SHA512: 2db00caff08169fd6281cd5b82960fa07776e002a86ca69212f74ca6ed26123e39daf59e80d909d7843696d2c0fee39e83b84ea8a10a82b3f27a4d886cbe768f Homepage: https://cran.r-project.org/package=quickOutlier Description: CRAN Package 'quickOutlier' (Detect and Treat Outliers in Data Mining) Implements a suite of tools for outlier detection and treatment in data mining. 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Package: r-cran-quickreg Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-survival, r-cran-psych, r-cran-nortest, r-cran-dplyr Suggests: r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quickreg_1.5.0-1.ca2404.1_all.deb Size: 216558 MD5sum: a895c563354a361b03b312f51deb5146 SHA1: 67b16c503615b4fc141fa4e3979e3d1dbac5a3d4 SHA256: 3df8ec023d0679481ab7e0acd952f1fe76a5fec28b1edea5c4a2a33b14f4c3f2 SHA512: dd0552d0a455aafb74f4c0598b973d49f0a3138ca058dc536d4e9d964bdd691db5449f148bab34b5903305cca850ae5cab851215a591a6da8b6abbcb8f36d822 Homepage: https://cran.r-project.org/package=quickReg Description: CRAN Package 'quickReg' (Build Regression Models Quickly and Display the Results Using'ggplot2') A set of functions to extract results from regression models and plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing multivariate regression results. With specific outcome, this package could build regression models automatically, extract results into a data frame and provide a quicker way to summarize models' statistical findings using 'ggplot2'. 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Package: r-cran-quicksentiment Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-magrittr, r-cran-matrix, r-cran-naivebayes, r-cran-quanteda, r-cran-ranger, r-cran-stopwords, r-cran-stringr, r-cran-textstem, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-quicksentiment_0.3.6-1.ca2404.1_all.deb Size: 150078 MD5sum: 845260f1f3cdfeb4eaeffdcbb999b388 SHA1: 1ae0d3e4ca3c6cb8989f4f2564449b0b0c7fb872 SHA256: e3155e2733812128b94ca650f4848ea1c5569a082334995b32dbbb630220d880 SHA512: 361c89f97b774613e6da7e750ac1d0fd8e880b002c90b972efdb4d1ca49b9b5d0561c66ef95b3d23159427e832925cf0e14bb119f01a0e8a30f28a9f727df178 Homepage: https://cran.r-project.org/package=quickSentiment Description: CRAN Package 'quickSentiment' (A Fast and Flexible Pipeline for Text Classification) A high-level pipeline that simplifies text classification into three streamlined steps: preprocessing, model training, and standardized prediction. It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', 'xgboost', and 'naivebayes') and memory-efficient sparse matrix vectorization methods (Bag-of-Words, Term Frequency, TF-IDF, and Binary). Users can go from raw text to a fully evaluated sentiment model, complete with ROC-optimized thresholds, in just a few function calls. The resulting model artifact automatically aligns the vocabulary of new datasets during the prediction phase, safely appending predicted classes and probability matrices directly to the user's original dataframe to preserve metadata. Package: r-cran-quicr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 704 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-janitor, r-cran-openxlsx, r-cran-purrr, r-cran-readxl, r-cran-reshape2, r-cran-slider, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quicr_2.1.0-1.ca2404.1_all.deb Size: 506756 MD5sum: 7e284efc304990f305ac615691c67a08 SHA1: 9a80a857cb26d45a9130f08c6337ed1d209bbec9 SHA256: 4778821919e94832a44796b274556fc2bcdf43ae5afa24d4d66cfc19a547c4ec SHA512: 8dc4dc6a98645764dbd279d7733d3237b8accca0ecd68cd329256302a4f169797c7b44e94671d076776d30d6e6a2df225ddd4dd9da484c2b52fb4fe216c0ee5f Homepage: https://cran.r-project.org/package=quicR Description: CRAN Package 'quicR' (RT-QuIC Data Formatting and Analysis) Designed for the curation and analysis of data generated from real-time quaking-induced conversion (RT-QuIC) assays first described by Atarashi et al. (2011) . 'quicR' calculates useful metrics such as maxpoint ratio: Rowden et al. (2023) ; time-to-threshold: Shi et al. (2013) ; and maximum slope. Integration with the output from plate readers allows for seamless input of raw data into the R environment. 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'QuICSeedR' addresses limitations in existing software by automating data processing, supporting large-scale analysis, and enabling comparative studies of analysis methods. It incorporates methods described in Henderson et al. (2015) , Li et al. (2020) , Rowden et al. (2023) , Haley et al. (2013) , and Mair and Wilcox (2020) . Please refer to the original publications for details. 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Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable 'R' data containers. A 'shiny' application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) . 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Package: r-cran-r2dii.analysis Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-r2dii.data, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-cli, r-cran-covr, r-cran-r2dii.match, r-cran-readr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/noble/main/r-cran-r2dii.analysis_0.5.3-1.ca2404.1_all.deb Size: 149708 MD5sum: 4c40508d0eed796239ace9e940d9ddf7 SHA1: e1e7e6e19792d509f6a5a21458e05c245fe06787 SHA256: 06b46e70922486b421ac5bcedc9c35436f07caee2a374b156fd33bfaf9166a4c SHA512: 7749ab605a562a4501197da75206995b7ca0bc703f1fb87c8a42ed43856980b0ed1a4f1d45e3c2e04568f7bb8423eaa216f983bd31006b6225e9d58de1b2ac5f Homepage: https://cran.r-project.org/package=r2dii.analysis Description: CRAN Package 'r2dii.analysis' (Measure Climate Scenario Alignment of Corporate Loans) These tools help you to assess if a corporate lending portfolio aligns with climate goals. 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Package: r-cran-r2dii.data Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle Suggests: r-cran-charlatan, r-cran-covr, r-cran-readr, r-cran-rlang, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r2dii.data_0.6.1-1.ca2404.1_all.deb Size: 336390 MD5sum: 0f8c1a23d46bc095e493a5e41e960302 SHA1: e2df891a52baff0b5cf52089c7afaf845e8a0f47 SHA256: bf407a0eb7a53ed2fe2ff2ff81942f2ae5ab567fcf4e38ae90a75ec3caa59e47 SHA512: 4ec27adb4cfa77d47cfe6a28618039fe0aba1c9ea41290a4bd656457b3bed32d243cc3c4ee5d6001baac44c81e59a9fbced631e577a840ae526404e945d4f1fe Homepage: https://cran.r-project.org/package=r2dii.data Description: CRAN Package 'r2dii.data' (Datasets to Measure the Alignment of Corporate Loan Books withClimate Goals) These datasets support the implementation in R of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between corporate lending portfolios and climate scenarios (). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R. Package: r-cran-r2dii.match Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-r2dii.data, r-cran-rlang, r-cran-stringdist, r-cran-stringi, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-r2dii.match_0.4.1-1.ca2404.1_all.deb Size: 114076 MD5sum: f78e6dde7751de9b432be90b321f092c SHA1: dd3d1a36d4d9c38f1516488ba735bd8bbf59230a SHA256: 7eeab88850ec69b03a633250644a8ffed16b802d2d042ef83aff10f524d75aa5 SHA512: b9f508803f4960f75073bdd648d60e7d1ebdeb9bf84f2dbb3a5ca34092f690803553adb23968940bf28cb2b1c487084eacc506928ac709e1de21bb11b721e8e0 Homepage: https://cran.r-project.org/package=r2dii.match Description: CRAN Package 'r2dii.match' (Tools to Match Corporate Lending Portfolios with Climate Data) These tools implement in R a fundamental part of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals. Package: r-cran-r2dii.plot Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-r2dii.data, r-cran-rlang, r-cran-stringr, r-cran-scales Suggests: r-cran-cli, r-cran-covr, r-cran-r2dii.analysis, r-cran-r2dii.match, r-cran-readr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-r2dii.plot_0.5.2-1.ca2404.1_all.deb Size: 285316 MD5sum: f9f0a0cd47d271040034181aadadc366 SHA1: 7ea159f1293a731fae2c0f3f05026cd59771648f SHA256: 903de0dbe85d02cd33246187b0cc74cc1af6283ebdd426b721732484f4abf15d SHA512: 39c062c796c0ac7f2f8228c8e8fff4437f043e29da6d98c6fa433ec2ad5be85e9cb3b994c9d75fdbe9422d732e6ddff310dce221001fd5486702b4825b37ac27 Homepage: https://cran.r-project.org/package=r2dii.plot Description: CRAN Package 'r2dii.plot' (Visualize the Climate Scenario Alignment of a FinancialPortfolio) Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital Transition Assessment; ). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Package: r-cran-r2dt Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-plyr, r-cran-devfunc Filename: pool/dists/noble/main/r-cran-r2dt_0.2-1.ca2404.1_all.deb Size: 72134 MD5sum: 973a364a40f4ac8080486b2603557cd7 SHA1: 13942509c99dc47b31de6ddb9856a49b891827da SHA256: 01bc5f5fe6950c835b45811123afba74cf14ab272958d6d549ee85f96f5b3ac4 SHA512: 6f03970cbe8889cc220a7b82fc5afdce95de295653273a5fd488e5918c8b1db166ce36edf79223d2c1f9a0378a86da6307f0f1fcdeb657b3778f59f08da66240 Homepage: https://cran.r-project.org/package=R2DT Description: CRAN Package 'R2DT' (Translation of Base R-Like Functions for 'data.table' Objects) Some heavily used base R functions are reconstructed to also be compliant to data.table objects. 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Package: r-cran-r2glmm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-matrix, r-cran-pbkrtest, r-cran-ggplot2, r-cran-afex, r-cran-mass, r-cran-gridextra Suggests: r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-data.table, r-cran-dplyr, r-cran-lmertest Filename: pool/dists/noble/main/r-cran-r2glmm_0.1.3-1.ca2404.1_all.deb Size: 91838 MD5sum: ca8058c0f97f9f44f4452d5eecb55621 SHA1: cdcb1e86699db12965b2645ac86dce8cf71ab497 SHA256: e2ddc4cb454b837692561dfd053bd64c418aaa86c10ac78ace442719eb266fa9 SHA512: 332e73114d2939b30c99b777a394f19b90f05040705be9876eb3903d209903e87199e5517773e153fba035786a7178a540a61516fdcde76469f84963303add4a Homepage: https://cran.r-project.org/package=r2glmm Description: CRAN Package 'r2glmm' (Computes R Squared for Mixed (Multilevel) Models) The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 ). Three methods of computation are provided and described as follows. First, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Second, The method introduced by Nakagawa and Schielzeth (2013) and later extended by Johnson (2014) . The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file. 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Package: r-cran-r2mlm Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-rockchalk, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-r2mlm_0.3.8-1.ca2404.1_all.deb Size: 407462 MD5sum: 9b0c63fae4f812ccc75162a45de2284e SHA1: 4e060d8940a7bfe16c6e5e6f66b4bf5e90224ce2 SHA256: 767353b5979c640a949f2ac8a2c3fc7d1d2410b93c208e51fa32823b386309a0 SHA512: b935161a074ded6360b3ab50371fa94400c85637b14b95a5b8576b248d67acc4eaf18bd2b586071facf3dee800cc82c2956da586352558a354977773980bd05d Homepage: https://cran.r-project.org/package=r2mlm Description: CRAN Package 'r2mlm' (R-Squared Measures for Multilevel Models) Generates both total- and level-specific R-squared measures from Rights and Sterba’s (2019) framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. 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It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). 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The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'. The suggested package 'BRugs' (only needed for function openbugs()) is only available from the CRAN archives, see . 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Sadly, that never happens. These tutorials allow students to demonstrate (and their instructors to be sure) that all work has been completed. See Kane (2023) from the 'tutorial.helpers' package for a background discussion. 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Use tools and methods that are selected carefully to align with academic consensus, bridging the gap between theoretical knowledge and practical application. They help you find your own personalized optimal discretionary spending or optimal asset allocation, and prepare you for retirement or financial independence. The optimal solution to this problems is extremely complex, and we only have a single lifetime to get it right. Fortunately, we now have the user-friendly tools implemented, that integrate life-cycle models with single-period net-worth mean-variance optimization models. Those tools can be used by anyone who wants to see what highly-personalized optimal decisions can look like. For more details see: Idzorek T., Kaplan P. (2024, ISBN:9781952927379), Haghani V., White J. (2023, ISBN:9781119747918). Package: r-cran-r4googleads Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-r4googleads_0.1.1-1.ca2404.1_all.deb Size: 53808 MD5sum: d594a25a99ceae38178c0cde27ac1f2f SHA1: a12e8d197fa6d02f10b7c63c7faf0cd79f27676a SHA256: c45b99abfcaff1d7a3df3c810dac3ccb1ac103893e09bd293aa6a4fc13434dc0 SHA512: 0f271cf1fd8ad81efd9f5961a996105c92c41c44b6063a113c2284c696208d76785dc2e3c269020fe28c9b88d34b8469916bf6e261c0936d86e96f82909da61e Homepage: https://cran.r-project.org/package=r4googleads Description: CRAN Package 'r4googleads' ('Google Ads API' Interface) Interface for the 'Google Ads API'. 'Google Ads' is an online advertising service that enables advertisers to display advertising to web users (see for more information). Package: r-cran-r4hcr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-irr, r-cran-mada, r-cran-meta, r-cran-metafor, r-cran-survival Filename: pool/dists/noble/main/r-cran-r4hcr_0.1-1.ca2404.1_all.deb Size: 327532 MD5sum: a29fda9665ae8fe02c52d37c7b59f515 SHA1: 7c92d5597528114dea0cd98dc63dc3095417de49 SHA256: 29654535ccc02220d6fe593b413f350dfc1542f5d9c5b54d1d77e62a9d1345e5 SHA512: 95f81585f50d64acad6bbbc369b1b3abfa91cf739fb2bcc5bf97c697d8f7e9f75910b313cefd8b686f23431728e97e62fe815f4c57fa78b04138dde82de298e7 Homepage: https://cran.r-project.org/package=R4HCR Description: CRAN Package 'R4HCR' (R for Health Care Research) A collection of datasets that accompany the forthcoming book "R for Health Care Research". Package: r-cran-r4lineups Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-here, r-cran-magick, r-cran-magrittr, r-cran-proc, r-cran-psych, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/noble/main/r-cran-r4lineups_0.1.1-1.ca2404.1_all.deb Size: 531338 MD5sum: 79834f63b6695f69e2d53555cca348c8 SHA1: 60ce9edf4ed2b176d27ad889ca128fcc12c0c3a3 SHA256: 41b90db9f021ad4b5fa47078dd48ae2f7e533368fe3329a12b86dae9114c5e7c SHA512: 718a159b887512e64545b5820eec666b0e5ff36b85c82693f39842a97380b38e125dc004a01882ebba53dbdf5512359002ad6fdb8d00bf6982abc3dbf55cf867 Homepage: https://cran.r-project.org/package=r4lineups Description: CRAN Package 'r4lineups' (Statistical Inference on Lineup Fairness) Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup. Package: r-cran-r4pde Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-car, r-cran-cluster, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-lubridate, r-cran-mgcv, r-cran-magrittr, r-cran-nasapower, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-survival, r-cran-tidyr, r-cran-tibble, r-cran-httr, r-cran-jsonlite, r-cran-terra Suggests: r-cran-interval, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggdendro, r-cran-patchwork, r-cran-cowplot, r-cran-ncdf4, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-r4pde_0.2.2-1.ca2404.1_all.deb Size: 851646 MD5sum: e86c2b890e587e950953cbdd2aeda1b4 SHA1: 3d01bff3d680df98a6ec7e75706cad411b9e2bbe SHA256: c9a5243ec935a57edc7e290eaf133c5c150aa1c207a84f71b2c6304c7b7450d5 SHA512: 51cb8de1863daee0fdf15a80959c45a4ccb04963c53ee48a9fae48a6bd7f469c54e473828aab01138bd671529f3a6880550d406d59d2da21ea93ae7076442657 Homepage: https://cran.r-project.org/package=r4pde Description: CRAN Package 'r4pde' (Tools for Quantitative Plant Disease Epidemiology) Tools for quantitative plant disease epidemiology, including functional analysis of disease progress curves, spatial epidemiology, disease quantification, and weather-driven epidemic analysis. 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Package: r-cran-r4sub Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r4subcore, r-cran-r4subdata, r-cran-r4subprofile, r-cran-r4subrisk, r-cran-r4subscore, r-cran-r4subtrace, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4sub_0.1.0-1.ca2404.1_all.deb Size: 29498 MD5sum: 103926c99d4499f5f6960e686d7811ff SHA1: 352fad628303fbf1c5cf3040a9ecd6c8eb0e6998 SHA256: cc04fb0369448b276780e7de2ed1e6a7c1ea1441c00a70f50120202ee693338f SHA512: a4b88a0b913d605919959c5ba4b8060df96c9cecf510909fdd94ca04c3d1bf34a8135d24b7926819f2ef74e5b72d88f0f29fb36f7e9096c14be7a1c5a69a56a7 Homepage: https://cran.r-project.org/package=r4sub Description: CRAN Package 'r4sub' (Easily Install and Load the R4SUB Ecosystem) The 'r4sub' package is a meta-package that installs and loads core packages of the R4SUB (R for Regulatory Submission) clinical submission readiness ecosystem. 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Package: r-cran-r4subcore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subcore_0.1.0-1.ca2404.1_all.deb Size: 72016 MD5sum: 69a205fb495a4b1f9e02ebebd3344e9b SHA1: 1b1002f8c262920353d6d5a718aefe131a3b691e SHA256: aed4fac770fcf4c3e59cc2197566da61e1c02e370b2f3dd733604170c28056e6 SHA512: abb848d25eb8b61eba8720a088fb6e757896e67abc02b7536f78e10f04615f45848a2088227eb1fcb0daafcfde9f32b7c6e49400cfe11a147a67ef50f5eea0e9 Homepage: https://cran.r-project.org/package=r4subcore Description: CRAN Package 'r4subcore' (Core Data Contracts, Parsers, and Scoring Primitives forClinical Submission Readiness) Foundational package in the R4SUB (R for Regulatory Submission) ecosystem. Defines the core evidence table schema, parsers, indicator abstractions, and scoring primitives needed to quantify clinical submission readiness. Provides a standardized contract for ingesting heterogeneous sources (validation outputs, metadata, traceability) into a single evidence framework. Package: r-cran-r4subdata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subdata_0.1.1-1.ca2404.1_all.deb Size: 45446 MD5sum: 02d4d84683b2bb8ff8754b87464ad0a1 SHA1: cf616cb3e226528355b9afdcad7e8610df188c6f SHA256: 9234ba68165c7ae93bc1190af2adf02cf64c680011560d625cc0557f0ed4490c SHA512: 55f26f82fe743f63f362b2f16f18ddd34004d4b27867c14319115774491b3aa1ff6ee3514799799239dff80e3d77bc3eca8a1b30800e974cd31139ec5559dd54 Homepage: https://cran.r-project.org/package=r4subdata Description: CRAN Package 'r4subdata' (Example Datasets for Clinical Submission Readiness) Provides realistic synthetic example datasets for the R4SUB (R for Regulatory Submission) ecosystem. Includes a pharma study evidence table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model) metadata following CDISC (Clinical Data Interchange Standards Consortium) conventions (), traceability mappings, a risk register based on ICH (International Council for Harmonisation) Q9 quality risk management principles (), and regulatory indicator definitions. Designed for demos, vignettes, and package testing. Package: r-cran-r4subpharma Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-r4subcore, r-cran-tibble Suggests: r-cran-knitr, r-cran-metacore, r-cran-pharmaverseadam, r-cran-r4subdata, r-cran-r4subprofile, r-cran-r4subscore, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subpharma_0.1.0-1.ca2404.1_all.deb Size: 65944 MD5sum: 7f6a78ec5d86ad9a334073435cb2b637 SHA1: 0f92315f940bb07b1929535784f9af2628270059 SHA256: 9cdcf55b0ccd42b9815dceaa71b91446317e44b7bbd42b386f6edc17c2ca0381 SHA512: 1b47ca9b336209fbdde9b4558b85249f59aff9862c9721dd128a284f34e82a862b081458864e6170e2870b16183fd0d2038a7805e6193bc9035c917c1ba789a7 Homepage: https://cran.r-project.org/package=r4subpharma Description: CRAN Package 'r4subpharma' ('pharmaverse' Adapters for R4SUB Submission Readiness Evidence) Bridges the 'pharmaverse' clinical reporting stack and the R4SUB (Ready for Submission) ecosystem. 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Supports the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and Medical Devices Agency (PMDA), Health Canada, Therapeutic Goods Administration (TGA), and Medicines and Healthcare products Regulatory Agency (MHRA). Integrates with 'r4subscore' and 'r4subrisk' configuration systems. 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Builds risk registers from evidence, computes Risk Priority Numbers (RPN), classifies risk levels, and emits standardized R4SUB (R for Regulatory Submission) evidence table rows via 'r4subcore'. Supports risk mitigation tracking and trend analysis across submission milestones. Package: r-cran-r4subscore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subscore_0.1.0-1.ca2404.1_all.deb Size: 43938 MD5sum: b26116333dc40eebfb2175b022797591 SHA1: 61cbe056ee730cd90350da4d56d4c66a48d84669 SHA256: e6ff85cde24016842a510f9294132a9fca04e5a4dedeb3e1e9b4434e5b5ca9ac SHA512: 4aa4dee8113c7514db2bd8f874c179a53f49ed3644482d902aa27b925e09e853ea7f28dfd7e367ec41d6a87d961a12195818b0fd383afd367d70832bbbfe3fb7 Homepage: https://cran.r-project.org/package=r4subscore Description: CRAN Package 'r4subscore' (Submission Confidence Index Engine) Converts standardized R4SUB (R for Regulatory Submission) evidence into indicator scores, pillar scores, and a Submission Confidence Index (SCI). Provides sensitivity analysis, explainability tables, and decision band classification to answer the question: are we ready for regulatory submission. Package: r-cran-r4subtrace Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-igraph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subtrace_0.1.0-1.ca2404.1_all.deb Size: 51648 MD5sum: d915a4c971a3f78eb50b1c16cfce0032 SHA1: 291d080fd1d727fe70de898942045248ad583a2b SHA256: 89e4c034d49410df39e5658e14ac745e38b7ea9b5313982b11fac254d859112c SHA512: c7e0908ed0e7452c19fbf1fd473e5c3286b118afe8615c3107cc447d6bcc78849ca15bc5c9fe6045a848ce1eb4374334382fd5db4bd64d70822f19936bb25d41 Homepage: https://cran.r-project.org/package=r4subtrace Description: CRAN Package 'r4subtrace' (Traceability Engine for Clinical Submission Readiness) Quantifies and explains end-to-end traceability between clinical submission artifacts (ADaM (Analysis Data Model) outputs, derivations, SDTM (Study Data Tabulation Model) sources, specs, code). Builds trace models from metadata and mapping sheets, computes trace levels, and emits standardized R4SUB (R for Regulatory Submission) evidence table rows via 'r4subcore'. Package: r-cran-r4vn Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5347 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey Suggests: r-cran-arrow, r-cran-boruta, r-cran-bslib, r-cran-curl, r-cran-dbi, r-cran-dbscan, r-cran-dt, r-cran-e1071, r-cran-flextable, r-cran-forecast, r-cran-foreign, r-cran-geepack, r-cran-ggplot2, r-cran-glmnet, r-cran-haven, r-cran-htmltools, r-cran-jsonlite, r-cran-keyring, r-cran-knitr, r-cran-lavaan, r-cran-leaflet, r-cran-nortest, r-cran-lme4, r-cran-mass, r-cran-metafor, r-cran-mgcv, r-cran-nlme, r-cran-nnet, r-cran-officer, r-cran-openxlsx, r-cran-pagedown, r-cran-pkgdown, r-cran-pmsampsize, r-cran-proc, r-cran-presize, r-cran-psych, r-cran-quantreg, r-cran-ranger, r-cran-readstata13, r-cran-readxl, r-cran-rmarkdown, r-cran-rmariadb, r-cran-rose, r-cran-rpart, r-cran-rstudioapi, r-cran-sampling, r-cran-sandwich, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-spdep, r-cran-statpsych, r-cran-survival, r-cran-testthat, r-cran-trialsize, r-cran-tseries, r-cran-webpower, r-cran-webshot2, r-cran-writexl, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-r4vn_1.6-1.ca2404.1_all.deb Size: 4959476 MD5sum: 16b776e4b534c2fbed4a21638ca9d92b SHA1: d43365296837170ba2269d400aa7363843e42ddc SHA256: 0522c45d391f70ff4ccca70753e8d73861c1bc887f59d08cfb14e72f631dd9f3 SHA512: ddcf55455ead29b446624e1e440467c2516cfe070f7af6fa9c6a6a353349082891dc066348596bc7df8860855270c97b54fdeeeb35b2a179e849faf52b51f018 Homepage: https://cran.r-project.org/package=R4VN Description: CRAN Package 'R4VN' (Health Data Analysis and Publication-Ready Reporting) Provides short and consistent commands for data management, descriptive and inferential statistics, epidemiological analyses, regression models, survival and longitudinal analyses, diagnostic accuracy, scale assessment, meta-analysis, machine learning, study design, publication-ready tables, graphics, and reporting. Commands accept an explicit data frame or an active data frame selected with usedf(). Package: r-cran-r5r Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6155 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-isoband, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rjava, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-zip Suggests: r-cran-accessibility, r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-gtfstools, r-cran-h3jsr, r-cran-interp, r-cran-knitr, r-cran-mapview, r-cran-patchwork, r-cran-rjavaenv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r5r_2.4.0-1.ca2404.1_all.deb Size: 3282896 MD5sum: da679227a44ca81c8c44c712d3ec823e SHA1: f9f87b83adb119f7d808083775ea2e40ab3596df SHA256: 2b1caa84bd41c1e6bd1f0f0f9a285276b3cd72ded334695d84c2f405077571ca SHA512: b6a85bca0d079072a0ba43264e51f8ccae52f7f45fcfd41d558bb314d9c0f286a4f7500c13cb9f512514e03c901fea3b0edad543ff664363db860b6255977dbf Homepage: https://cran.r-project.org/package=r5r Description: CRAN Package 'r5r' (Rapid Realistic Routing with 'R5') Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine . The package allows users to generate detailed routing analysis or calculate travel time and monetary cost matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at . Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see ) corresponds with the R5 version that r5r depends on. This version of r5r depends on R5 v7.1. Package: r-cran-r5rgui Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-mapgl, r-cran-r5r, r-cran-sf, r-cran-shiny Suggests: r-cran-mockery, r-cran-quarto, r-cran-rlang, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r5rgui_0.2.0-1.ca2404.1_all.deb Size: 125788 MD5sum: eaefa1b05f1c4fd52f46559cf306717d SHA1: ab188c9614c88d23948fc7fdd62f5ac99988e9fc SHA256: 5aff0c5c912b169e2ab42481b84bf268b63b3b9614582c8d3e31c4765a527851 SHA512: 02df2388ab3796cd60c69cf081cdf3d1a2ddf2a3d28db51a51e4194253d2908e0997ba11a2c698b0a9a5a09dbacd28f86478d6dd40a5838d64f78a2635fb2daf Homepage: https://cran.r-project.org/package=r5rgui Description: CRAN Package 'r5rgui' (Graphical User Interface for 'r5r' Router) Interactively build and explore public transit routes with 'r5r' package via a graphical user interface in a 'shiny' app. The underlying routing methods are described in Pereira et al. (2021) . Package: r-cran-r62s3 Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-r6 Filename: pool/dists/noble/main/r-cran-r62s3_1.4.1-1.ca2404.1_all.deb Size: 88094 MD5sum: 4fdf33c7015c420cf954a2b89226f7de SHA1: 82b09287b7ec47f6fcbcf0365e5db12fcbe61ee4 SHA256: ed52e185c726ccd550e1839be2654f5948bb72948b6d558c18f2b65f123e5050 SHA512: ef4270eb0816c2012a6ed089b887bf49bd9d7dd3d175860a3c5ea7cbfc7687df6a7490d536fe850d705c747f8ca9440ad9f6eef8cf04beda82b2bd05b6f097f5 Homepage: https://cran.r-project.org/package=R62S3 Description: CRAN Package 'R62S3' (Automatic Method Generation from R6) After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects. Package: r-cran-r6 Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lobstr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r6_2.6.1-1.ca2404.1_all.deb Size: 86876 MD5sum: bc64defd53cc3a140be11f77067504f4 SHA1: dfa95955e572ce36ca2a13321ff53d07bb0632d3 SHA256: 1cd484e6532ee84bd84f30184996b9b55089541b41cb1c9572c10a435d2a3aa2 SHA512: 44fd217def2cb174f68c6d70e9a93a2e5375df5babe39a799b56247e18364c2006921442510fcdfd4547d0237ebe7c7ed49d5f4e4c2cac2920e1c8832d904d89 Homepage: https://cran.r-project.org/package=R6 Description: CRAN Package 'R6' (Encapsulated Classes with Reference Semantics) Creates classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. 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Package: r-cran-r6causal Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-causaleffect, r-cran-cfid, r-cran-data.table, r-cran-dosearch, r-cran-glue, r-cran-igraph, r-cran-r6, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qgraph, r-cran-sqldf Filename: pool/dists/noble/main/r-cran-r6causal_0.8.3-1.ca2404.1_all.deb Size: 735130 MD5sum: be5ce80ce989dd1c4a3bcb17a3aae4d8 SHA1: 0e36a2ece61582f02000ab2b758d47fa152f2453 SHA256: 203e8ee3ee2f57e1f0e84048717b4945035e8d4a90fb4ab4ab5cc9ce4930c318 SHA512: dd6d21553a5197832adf4b811aadaff00e8e1f68db111d11b448d4d6884ff16dbe7372bcf1997951f2d8fd55cb16b31be847e67859cd323b17f9e5b642b2424d Homepage: https://cran.r-project.org/package=R6causal Description: CRAN Package 'R6causal' (R6 Class for Structural Causal Models) The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation. Package: r-cran-r6ds Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-r6ds_1.2.0-1.ca2404.1_all.deb Size: 308668 MD5sum: 56742995203c201f5805eebd22e95a8a SHA1: a80e15c3c28cd3ee146c5bef004771450b1df7d9 SHA256: 43f38e877bf37b8721dd1a133567b73c0af24c0d08ec2cd42e1d533af9842102 SHA512: 4ad4b5ea2c4b8e4317e9949ca0946f8ce16c73d212360a03e395dd37c1211803bd370f692f18a61b15ad1b68b0ef387a420bfc825efcd6b04984f97822e7de05 Homepage: https://cran.r-project.org/package=R6DS Description: CRAN Package 'R6DS' (R6 Reference Class Based Data Structures) Provides reference classes implementing some useful data structures. The package implements these data structures by using the reference class R6. Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference. The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree. See for example for more information about the data structures. Package: r-cran-r6extended Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-magrittr, r-cran-digest, r-cran-hellno Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-r6extended_0.1.2-1.ca2404.1_all.deb Size: 48980 MD5sum: 0c503c4b38c58cc26120a0ec39d734bc SHA1: fac6cbc195263649d2428e844603b1e791e58ce0 SHA256: 0089e3f5ff6cf9bde9aae707a8edf5d413a35a851dc28e0eb825cd200684739d SHA512: debe03122b66a9b6ab4f82bf66096359864be78b20b09675f42a182547077a7faa5fb1d58142b6b84528aa9bef0a9e29e8322cc9e04ca280c7eef853954a9069 Homepage: https://cran.r-project.org/package=r6extended Description: CRAN Package 'r6extended' (Extension for 'R6' Base Class) Useful methods and data fields to extend the bare bones 'R6' class provided by the 'R6' package - ls-method, hashes, warning- and message-method, general get-method and a debug-method that assigns self and private to the global environment. Package: r-cran-r6methods Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-rstudioapi, r-cran-miniui, r-cran-shiny, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-purrr Suggests: r-cran-r6, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r6methods_0.1.0-1.ca2404.1_all.deb Size: 44428 MD5sum: 014aedb5646b5fcb683b0824b9a431f5 SHA1: 7ddfa5635c9df6419f0cad314eba88b08e3ca856 SHA256: 4121828d1d7b4c8c1df102b843b352d69bf35dc677f9df1ccd6dd9f6470195d5 SHA512: d7d1e5772ce770e759472c7b7bc11c550cca79bccf96143a8dd0a8458f21bc0747b2d5882c0715af5dfd6df31d8d5caa3bdaa46725b4acb149fe884fe2ba068a Homepage: https://cran.r-project.org/package=r6methods Description: CRAN Package 'r6methods' (Make Methods for R6 Classes) Generate boilerplate code for R6 classes. Given R6 class create getters and/or setters for selected class fields or use RStudio addins to insert methods straight into class definition. Package: r-cran-r6nomogram Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-quarto Filename: pool/dists/noble/main/r-cran-r6nomogram_1.0-1.ca2404.1_all.deb Size: 298814 MD5sum: df19382a5b91071ad9170ec4de4f821d SHA1: f1b0f42b92f3296d79a357e5214b0c719aec6cf0 SHA256: 98d120344d2e433a5a5a07772de3f2f1d1c772a4d474cb9dbde04968362bfafa SHA512: 94a6bc272293e2a3edfb08f46d42431b27e205f7c018f70af1c074199c7e58bde9117df017700c7848db2487b5b4cc3de2ad2ec4195b6f98c634a9c44622cd39 Homepage: https://cran.r-project.org/package=R6Nomogram Description: CRAN Package 'R6Nomogram' (Create, Edit, and Plot Nomograms using R6 Objects) Nomograms are a type of plot for displaying linear models. A scale is plotted for each predictor in the model that translates values of the variable into "points", the sum of the "points" is then looked up on another scale to find the final prediction from the model. This package provides an R6 object constructor that does the computations for you to create an object representing the nomogram for the model. Methods and fields in the object allow you to customize the nomogram. You can then plot the nomogram, further customize, replot, etc. These types of nomograms are described in Harrell (2015) . Package: r-cran-r6p Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-collections, r-cran-dplyr, r-cran-stringr, r-cran-r6, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-dbi, r-cran-rsqlite, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-r6p_0.4.0-1.ca2404.1_all.deb Size: 72756 MD5sum: f4139ef7d25b3f602fa4a7b754b08b98 SHA1: 5b7b3bb3277e215338816ecfd3cb205d5ff2e9f9 SHA256: 465c169b7ffc9651a0cb068c647cb0f0a079404503b4e25e03841305049c49e7 SHA512: 75a0e090c3424cd293cc5b2d76c117a74fe6fc1d1d64409b7e2ecadebab3d4db196b026b3165073b2d35029cdade62b0462b3d5836dd9262cdb30ec2fefd23de Homepage: https://cran.r-project.org/package=R6P Description: CRAN Package 'R6P' (Design Patterns in R) Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. 'R6P' provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess. Package: r-cran-r6qualitytools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1523 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-envstats, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-patchwork, r-cran-plotly, r-cran-r6, r-cran-rcolorbrewer, r-cran-rsolnp, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-r6qualitytools_1.0.1-1.ca2404.1_all.deb Size: 1378170 MD5sum: 92ae89a8dc0929c4623c58e7b37d2cfc SHA1: 368b67edd1abd9908c13e5e027e42456ad096ec9 SHA256: 1dde3d21112a38e0e92717c93f153a15877faf14936c3c07d098be892ef7e75a SHA512: 9b1f09f0645a5f7fbd3f93a786a7061f6b41b50e82a28b34b8e5d4a0f8dc48bca63903a58a6ba654f233dfd3788409aa47ae55b8d2ed610f6f7b2a2bcb93a0c2 Homepage: https://cran.r-project.org/package=r6qualitytools Description: CRAN Package 'r6qualitytools' (R6-Based Statistical Methods for Quality Science) A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science. Package: r-cran-ra4bayesmeta Architecture: all Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesmeta Filename: pool/dists/noble/main/r-cran-ra4bayesmeta_1.0-8-1.ca2404.1_all.deb Size: 215050 MD5sum: b166010b938f069a2669eec199c8888c SHA1: c0416ebdb548bf17d3876f9e52a52b9a332cf1f3 SHA256: d989f1a2bd564d3f8da390287a47de6945bb03d6f9d6ddc8275029a63ee1e4ca SHA512: 57de3726fd10468ad6d57258161a4f9c1f08243bba102c4b4260a824fbf72f33950c39e26e279e1bd420c15c5191699cd9fd8e3cf7a42b36e813d8a356e6dd77 Homepage: https://cran.r-project.org/package=ra4bayesmeta Description: CRAN Package 'ra4bayesmeta' (Reference Analysis for Bayesian Meta-Analysis) Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) . Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package. Package: r-cran-rabhit Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-cowplot, r-cran-gtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-htmlwidgets, r-cran-ggdendro, r-cran-piglet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rabhit_0.4.0-1.ca2404.1_all.deb Size: 1766924 MD5sum: 51f9cfd7e67bcc44f754052dd2dae99a SHA1: 86619ad9f71e026e0cf06029a7b98954950eaeba SHA256: 84f3378ef6407877165f8f0776f57c19b67995389785f7692b7d20846c32b503 SHA512: 0e2a9913f399ce547582e879cdcc5179eca90a0964124b76fdb9ee977dd6e757beb2d7af3bc71731a9fd8a5490812d57a9b385832bce066f06d35acc4fdd8913 Homepage: https://cran.r-project.org/package=rabhit Description: CRAN Package 'rabhit' (Inference Tool for Antibody Haplotype) Infers V-D-J (Variable-Diversity-Joining) haplotypes and gene deletions from AIRR-seq (Adaptive Immune Receptor Repertoire sequencing) data for Ig (Immunoglobulin) and TR (T cell Receptor) chains, based on J (Joining), D (Diversity), or V (Variable) genes as anchor, by adapting a Bayesian framework. It also calculates a Bayes factor, a number that indicates the certainty level of the inference, for each haplotyped gene. Citation: Gidoni, et al (2019) . Peres and Gidoni, et al (2019) . Package: r-cran-rabi Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers, r-cran-polynom, r-cran-shiny, r-cran-stringdist Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rabi_1.0.2-1.ca2404.1_all.deb Size: 68138 MD5sum: 0fe97d69a128f8e4a2fb05ea6a7bbc5e SHA1: 52ccb07e2029d7eb4566139e2d8d921efe485201 SHA256: 35964910cc1536d4e12ce2b2cb79b58b16dc73abb85b84cc782144ac111dd21a SHA512: e3102b1a3e065826982c5b0b9949d60266bbb995ffb92fbbe79434eccb43a1f19922f704569bb2f9e431ed98f261dc46b2f9d4de2d3e3e6054af47826319af74 Homepage: https://cran.r-project.org/package=rabi Description: CRAN Package 'rabi' (Generate Codes to Uniquely and Robustly Identify Individuals forAnimal Behavior Studies) Facilitates the design and generation of optimal color (or symbol) codes that can be used to mark and identify individual animals. These codes are made such that the IDs are robust to partial erasure: even if sections of the code are lost, the entire identity of the animal can be reconstructed. Thus, animal subjects are not confused and no ambiguity is introduced. Package: r-cran-rabr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-asd, r-cran-cubature, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-multcomp, r-cran-multxpert, r-cran-survival Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rabr_0.1.1-1.ca2404.1_all.deb Size: 64744 MD5sum: 3203fd075b8ce36d84734ad3a707cfb3 SHA1: 9bb349ade62979f93ac725985a730004d9b518da SHA256: 423ee062899dffb8057be7e42b5d2d977bd44836a3d4ed9bb0860e96fa5ddec4 SHA512: 11f89b241902b994962f780507a92cbdef144726fcbcad7258c06858d4ebe7ae856337175b3fa2dc1b9cb5ad131a2f3a6d294f1bf63e1a6608051684ac95f123 Homepage: https://cran.r-project.org/package=RABR Description: CRAN Package 'RABR' (Simulations for Response Adaptive Block Randomization Design) Conduct simulations of the Response Adaptive Block Randomization (RABR) design to evaluate its type I error rate, power and operating characteristics for binary and continuous endpoints. For more details of the proposed method, please refer to Zhan et al. (2021) . Package: r-cran-rac Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2789 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-rstudioapi, r-cran-plotrix, r-cran-raster, r-cran-sp Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rac_1.5.5-1.ca2404.1_all.deb Size: 1063954 MD5sum: 439653426e5eba60eec8117924bd1a8f SHA1: ea418f6d9705ee242c097e60e0d7ed642bcabf94 SHA256: cd880c8a736abbc74ba05de44532fffd34cf3a71bc6e16c16d3a0a929bbf7712 SHA512: 26dd663f75e88b49e54690d1895518fe79525d6089a70f2c947b90c9ddd5694e150ed983d70a21a15ddda11aca6bf8764e0e37a159de1b5fbdb1e8c1264df396 Homepage: https://cran.r-project.org/package=RAC Description: CRAN Package 'RAC' (R Package for Aqua Culture) Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 ) and shellfish (Mussel and Clam; Brigolin et al., 2009 ; Solidoro et al., 2000 ). Allows for spatialized model runs and population simulations. Package: r-cran-racademyocean Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-pbapply, r-cran-purrr, r-cran-rappdirs, r-cran-retry, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-racademyocean_0.3.3-1.ca2404.1_all.deb Size: 122112 MD5sum: 29546655519c86fbf6065e385258702e SHA1: e1150ab77e6e64a87a411a196d350298304ba599 SHA256: 44d0e34224bf75b18869ee86e350b62b8bd7d36803460428c614e306a4927e28 SHA512: fc4a43df0df6b79140c0d62e418a659d746f686d4661d95445592364ee9adb29fc14ae0a314215e4edbdbfbe105082da12ec7d2192b5a10e418cee666bf7b6d2 Homepage: https://cran.r-project.org/package=racademyocean Description: CRAN Package 'racademyocean' (Client for 'AcademyOcean API') Provide function for work with 'AcademyOcean API' . 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Functions for model estimation, assessment, and displaying results are provided. For more details, see Pearce and Zhou (2025) . Package: r-cran-racir Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-racir_2.0.0-1.ca2404.1_all.deb Size: 196258 MD5sum: fd2f0450c07b46c4ee928ec771bdbdae SHA1: fa87bcf86114b56aa88db6ed1deb84d0a87315ec SHA256: 3825c097ef1834963a222b8346005a9aa38d2a794907fab152ab40e23145ffa8 SHA512: 9b31ae27269c991b05fc5c0a5219d40587f5ead5f4b24eef3f4296b7b296931b406c1b8dc12a38b4635052fac562bcac037aa92fac6089b89ae3e5b82cac39d2 Homepage: https://cran.r-project.org/package=racir Description: CRAN Package 'racir' (Rapid A/Ci Response (RACiR) Data Analysis) Contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. 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Package: r-cran-ractivecampaign Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-pbapply, r-cran-stringr, r-cran-tidyr, r-cran-cli, r-cran-retry Filename: pool/dists/noble/main/r-cran-ractivecampaign_0.6.0-1.ca2404.1_all.deb Size: 150228 MD5sum: 3abd90dbda4f268795a564d98fae4d52 SHA1: dbd0e6e66e4e3f4e4231a6226721e77c9f7ffaac SHA256: 3799b316b6c178bee8bc4dc6b1e48e8766fc9862f7969ef0fe5b1a3b4a80fc2a SHA512: 921cff252f5e4022bb4d1bd9916d442cbc0b942445fc1d2fadb10a3348d8d0221226083c383487b54932f316a09da9d7f627459fbdb338b50cd76aae8d9c7332 Homepage: https://cran.r-project.org/package=ractivecampaign Description: CRAN Package 'ractivecampaign' (Loading Data from 'ActiveCampaign API v3') Interface for loading data from 'ActiveCampaign API v3' . Provide functions for getting data by deals, contacts, accounts, campaigns and messages. 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Rank abundance distributions are widely used in biology and ecology to describe species abundances, and are mathematically equivalent to complementary cumulative distribution functions (CCDFs) used in physics, linguistics, sociology, and other fields. The method is described in Saeedghalati et al. (2017) . Package: r-cran-radar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-radar_1.0.0-1.ca2404.1_all.deb Size: 101522 MD5sum: 811330f429db14c97a0875c0e61559ad SHA1: f49a768fcd3cedab928c0b92f4b91e073b617135 SHA256: 5c915af7a36b85224ab814184f9be0887983bb9d27a3085db826f11e0df608bc SHA512: 0a5335661b2117d377adde3f3ad694a955096c58862779991c91079c90dad291cad12f46b4b02175d843f8d951982f49ee4b0ee3380d857c7df803c254d73099 Homepage: https://cran.r-project.org/package=radar Description: CRAN Package 'radar' (Fundamental Formulas for Radar) Fundamental formulas for Radar, for attenuation, range, velocity, effectiveness, power, scatter, doppler, geometry, radar equations, etc. Based on Nick Guy's Python package PyRadarMet Package: r-cran-radarboxplot Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-radarboxplot_1.0.5-1.ca2404.1_all.deb Size: 196952 MD5sum: 72fb2447996ac2b1c3da198b21e976a9 SHA1: 71295d30e43d810dd11ffd432dfd1e81a90e72b3 SHA256: 548b63195fd2567f37d8688f2a1d681c3c8a622e34c231bc3e4b7ce37f90f891 SHA512: a1691a109ec11ef3f0aae86ad57f137981793cfa8a34d7a4a998b943d75cd47bd77e36c95ba6e197f8c332bbc4214a5152ca6927422f8f3e819f1f55c2c3f0e2 Homepage: https://cran.r-project.org/package=radarBoxplot Description: CRAN Package 'radarBoxplot' (Implementation of the Radar-Boxplot) Creates the radar-boxplot, a plot that was created by the author during his Ph.D. in forest resources. 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Package: r-cran-radarchart Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-radarchart_0.3.1-1.ca2404.1_all.deb Size: 324388 MD5sum: 5d08a97d8347b68657ed16c7bfd2a081 SHA1: 0042b651e27fade933ad233218477927cbf83b55 SHA256: 1ca281e9f07bb4f679a8639340cc824c48fd0dbcb485fd48ec7e3d9eeab0aebc SHA512: 91aceb9260e587533f29427e8d893ef95fed2e74996a7ca12eef872be17b7e98ba55e23bdb6f65955990b6b054d793c5a19a0659b6adcdf233f4f08c0d9e0580 Homepage: https://cran.r-project.org/package=radarchart Description: CRAN Package 'radarchart' (Radar Chart from 'Chart.js') Create interactive radar charts using the 'Chart.js' 'JavaScript' library and the 'htmlwidgets' package. 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Package: r-cran-raddata Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4451 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-radsafer Filename: pool/dists/noble/main/r-cran-raddata_1.0.2-1.ca2404.1_all.deb Size: 4517782 MD5sum: ccc75eff57491a25a88096a05bcf3ee1 SHA1: 873bfbe99e1ec6966f963304b218dd254c8532d7 SHA256: 0778abe86b3a28f493d51ba9b1159c2720083c6191e8a746b1e350f74870e53e SHA512: 9c48a853e2c5da85e3942ecea65499c3ed29462929b287b7c002e4d6a7d9fc60bf105cc3d7379108c89c10069341298803e9f68e1a426bc6b3552bf03904e7fb Homepage: https://cran.r-project.org/package=RadData Description: CRAN Package 'RadData' (Nuclear Decay Data for Dosimetric Calculations - ICRP 107) Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 . This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements. 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The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) ). Package: r-cran-radstackshelpr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vcfr, r-cran-ggplot2, r-cran-ggridges, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-radstackshelpr_0.1.0-1.ca2404.1_all.deb Size: 884920 MD5sum: 18f9d207c8b68ea356277fe8a0b549e9 SHA1: 126833c98b30bfa236f8607005012840e36a445f SHA256: 1026447e7a8c97eadfce77583b4d01290e6c9ed9215204b1184f04c04739ffd8 SHA512: 043a50260b1c4c5f2a7e0d13ba8b03e96bb2ffad349bf9f11f7ff281e319d714c2582bdd74716cffc82bec35d75ff5a017a9f5baa0aa3c52d77f2d0b191c3c98 Homepage: https://cran.r-project.org/package=RADstackshelpR Description: CRAN Package 'RADstackshelpR' (Optimize the De Novo Stacks Pipeline via R) Offers a handful of useful wrapper functions which streamline the reading, analyzing, and visualizing of variant call format (vcf) files in R. This package was designed to facilitate an explicit pipeline for optimizing Stacks (Rochette et al., 2019) () parameters during de novo (without a reference genome) assembly and variant calling of restriction-enzyme associated DNA sequence (RADseq) data. The pipeline implemented here is based on the 2017 paper "Lost in Parameter Space" (Paris et al., 2017) () which establishes clear recommendations for optimizing the parameters 'm', 'M', and 'n', during the process of assembling loci. 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Package: r-cran-raem Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Suggests: r-cran-isoband, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-raem_0.1.0-1.ca2404.1_all.deb Size: 464760 MD5sum: ac96e820b52c2aa0c1a4fdd94e32177d SHA1: ac68c85e91cd9f536a57120ea12a986c7f103569 SHA256: 75d1e04507a4483bf164d8da8551ac3bbcee786f57964b7dd79dfc2f12aac8c9 SHA512: a56ba43ca114ae6762bacfc9d525a5b4c3fe0f37e3dd11ff9ebf60898cbe8b1fdb01c6b8875fbab77fc399ded9fc2525e8e87f4dedda689dfb1185854e6f17a0 Homepage: https://cran.r-project.org/package=raem Description: CRAN Package 'raem' (Analytic Element Modeling of Steady Single-Layer GroundwaterFlow) A model of single-layer groundwater flow in steady-state under the Dupuit-Forchheimer assumption can be created by placing elements such as wells, area-sinks and line-sinks at arbitrary locations in the flow field. Output variables include hydraulic head and the discharge vector. Particle traces can be computed numerically in three dimensions. The underlying theory is described in Haitjema (1995) and references therein. 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Package: r-cran-ragflowchainr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-httr, r-cran-dplyr, r-cran-pdftools, r-cran-officer, r-cran-rvest, r-cran-xml2, r-cran-curl Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-stringi, r-cran-magrittr, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-vectrixdb Filename: pool/dists/noble/main/r-cran-ragflowchainr_0.1.7-1.ca2404.1_all.deb Size: 1571396 MD5sum: a60fc8a4903aca4ec1742ba4ace6f790 SHA1: c67d1d50f798a6518f4a7ad798f1174e57fea2a0 SHA256: eea822122aab418d802324aac4c079e7a622cbec48f529694561fdf12bac3e50 SHA512: f3ab26b0e21b61df7dcdbaa1be7f231b90a1eefc58afa5f3d451f0ed5535e9c86a4b670feb953f57b439a95bd117cfe1d83f0d86b118902b76dd48df8c10fad1 Homepage: https://cran.r-project.org/package=RAGFlowChainR Description: CRAN Package 'RAGFlowChainR' (Retrieval-Augmented Generation (RAG) Workflows in R with Localand Web Search) Enables Retrieval-Augmented Generation (RAG) workflows in R by combining local vector search using 'DuckDB' with optional web search via the 'Tavily' API. Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW' (Hierarchical Navigable Small World) indexing, and modular large language model (LLM) invocation. Designed for advanced question-answering, chat-based applications, and production-ready AI pipelines. This package is the R equivalent of the 'python' package 'RAGFlowChain' available at . Package: r-cran-raggrid Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-crosstalk, r-cran-knitr Suggests: r-cran-jsonlite, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-raggrid_0.2.0-1.ca2404.1_all.deb Size: 325630 MD5sum: 3e5d9916468de2dc6f32e341f8e2e341 SHA1: f433e274c5e1bbcc61bbbd9c53ec8ce8c70e2a68 SHA256: 6d594247e4e404957c245ce3975f4193d119c29ad3cbb05e9fab450858d7b237 SHA512: f772be3ad70cc016a0615591c91bfabdaee8e0a08ba5309617e9421205f5c84e828d73d6d1ec0c0d96e4d1c2ee10dcac290ae8bb34f675c2cc82a5a7bfa48c5a Homepage: https://cran.r-project.org/package=RagGrid Description: CRAN Package 'RagGrid' (A Wrapper of the 'JavaScript' Library 'agGrid') Data objects in 'R' can be rendered as 'HTML' tables using the 'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The 'ag-grid' library has been included in this 'R' package. The package name 'RagGrid' is an abbreviation of 'R agGrid'. Package: r-cran-ragr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-pdftools, r-cran-readtext, r-cran-tibble Suggests: r-cran-plumber, r-cran-stringr, r-cran-yaml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ragr_0.1.0-1.ca2404.1_all.deb Size: 199032 MD5sum: ff9075c95a53f97b53e8897d19fbacb5 SHA1: e60dcff4d6eda5dc246daaaceb81804f79adef77 SHA256: da494caf7a4a5684fe73617eec11e76062e6bab1df9ee8a7b295ff776157a9e7 SHA512: e8e6d41886faaec4c430f95892b97b961d5c581d722f5e92eafeb877dc22642668eebfd5f04af61778493cb82640472385b37816998f56efbe128321a2294980 Homepage: https://cran.r-project.org/package=ragR Description: CRAN Package 'ragR' (Retrieval-Augmented Generation and RAG Evaluation Tools) Provides tools for document ingestion, embedding storage, retrieval-augmented generation (RAG), and evaluation of question-answering systems. The package includes an R-native vector store, wrappers for OpenAI embedding and chat-completion application programming interfaces (APIs), question-answering logging utilities, and large language model (LLM)-based evaluation metrics for context precision, context recall, answer relevance, and faithfulness. These metrics are based on the Retrieval-Augmented Generation Assessment (RAGAS) framework. The retrieval-augmented generation methodology is described by Lewis et al. (2020) "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" . The evaluation metrics are based on Es et al. (2024) "RAGAS: Automated Evaluation of Retrieval Augmented Generation" . Package: r-cran-ragtop Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-futile.logger Suggests: r-cran-bondvaluation, r-cran-ggplot2, r-cran-knitr, r-cran-limsolve, r-cran-lubridate, r-cran-mass, r-cran-matrix, r-cran-r.cache, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-roxygen2, r-cran-stringr, r-cran-testthat, r-cran-treasury Filename: pool/dists/noble/main/r-cran-ragtop_2.0.0-1.ca2404.1_all.deb Size: 567220 MD5sum: 7ea31ef8f0bc16835d6ba0a365d9f6a6 SHA1: 6350f74ab47363c25ce13339f8b8d1444f6a89d4 SHA256: 88f61c488fb5c165c8e36dcc87e7abab723e181f82ff8500c0557e55b838eeef SHA512: 2a30c4d1b7b85c75ab7ac1ac7a964003cef6bbfa832df7681f9e6d8879918807d46acff0ed4f28ffa19940ed289eaf87f4b3d5357dea22f6e888771b4116b42a Homepage: https://cran.r-project.org/package=ragtop Description: CRAN Package 'ragtop' (Pricing Equity Derivatives with Extensions of Black-Scholes) Algorithms to price American and European equity options, convertible bonds and a variety of other financial derivatives. It uses an extension of the usual Black-Scholes model in which jump to default may occur at a probability specified by a power-law link between stock price and hazard rate as found in the paper by Takahashi, Kobayashi, and Nakagawa (2001) . We use ideas and techniques from Andersen and Buffum (2002) and Linetsky (2006) . Package: r-cran-rai Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rai_1.0.0-1.ca2404.1_all.deb Size: 83352 MD5sum: d7d45a037240ff339e2fb0e503e19e88 SHA1: 262b4b63823a240045f6431466dfcc71fe4e2fc7 SHA256: 6becf4d96b5db067aef4e40002f3c206ace06b21ce1c3f1d1fe05d6a396c35ec SHA512: 76b6232d4072f72d9d1d60b8180bf4348c153a8a5c379746b491bcc91bd9ef0df8d7326a56f02f41a31bdff5ee2c16a8f6c624b78cd53ca4dec2d096a31e6d79 Homepage: https://cran.r-project.org/package=rai Description: CRAN Package 'rai' (Revisiting-Alpha-Investing for Polynomial Regression) A modified implementation of stepwise regression that greedily searches the space of interactions among features in order to build polynomial regression models. Furthermore, the hypothesis tests conducted are valid-post model selection due to the use of a revisiting procedure that implements an alpha-investing rule. As a result, the set of rejected sequential hypotheses is proven to control the marginal false discover rate. When not searching for polynomials, the package provides a statistically valid algorithm to run and terminate stepwise regression. For more information, see Johnson, Stine, and Foster (2019) . Package: r-cran-rainbow Architecture: all Version: 3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pcapp, r-cran-hdrcde, r-cran-cluster, r-cran-colorspace, r-cran-ks Suggests: r-cran-forecast Filename: pool/dists/noble/main/r-cran-rainbow_3.8-1.ca2404.1_all.deb Size: 419766 MD5sum: 3fc42c934710bc30a730daffe41af8eb SHA1: 7230aaa21caaaf34bf589fd182a0f16d70830f55 SHA256: 674dd84b1ff12243c448d8fb58520785c4e828339f76d217355527667cfac379 SHA512: 50f205308222cf5c39c7e337c1ee7507a716f4ea0d7461160e01f072f3ae1bc40a1a7fd65887b89c06f15417b9f93ba116a4a0370a50bf662056da21e8f5b81e Homepage: https://cran.r-project.org/package=rainbow Description: CRAN Package 'rainbow' (Bagplots, Boxplots and Rainbow Plots for Functional Data) Visualizing functional data and identifying functional outliers. 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This package is initially designed for the scoring system in a high school project showcase to rank student research projects, where each judge can only evaluate a set of projects in a limited time period. See Langville, A. N. and Meyer, C. D. (2012), Who is Number 1: The Science of Rating and Ranking, Princeton University Press , and Gou, J. and Wu, S. (2020), A Judging System for Project Showcase: Rating and Ranking with Incomplete Information, Technical Report. Package: r-cran-rainerosr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rainerosr_0.1.1-1.ca2404.1_all.deb Size: 82054 MD5sum: a3725089ea4aa840a5a3c5cb9ff11002 SHA1: b950e30fe42ce30e07571ebb935a682b3591f207 SHA256: 817b93d038782348ce1129a85f401d9603144487d770412854a75a6b20eb7cc1 SHA512: ecc806eee90e5ffbed2ff9144041f8bd16af4a2b91f280df334b8d0f18c3cc89529921884bd1a58b6b97c64181c6ceb0c146a21126727ece5dfc0952b7ac79e0 Homepage: https://cran.r-project.org/package=rainerosr Description: CRAN Package 'rainerosr' (Calculate Rainfall Intensity and Erosivity Indices) Calculates I30 (maximum 30-minute rainfall intensity) and EI30 (erosivity index) from rainfall breakpoint data. Supports multiple storm events, rainfall validation, and visualization for soil erosion modeling and hydrological analysis. Methods are based on Brown and Foster (1987) , Wischmeier and Smith (1978) "Predicting Rainfall Erosion Losses: A Guide to Conservation Planning" , and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" (USDA Agriculture Handbook No. 703). 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Package: r-cran-rainfarmr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rainfarmr_0.1-1.ca2404.1_all.deb Size: 70672 MD5sum: 82630e738d0696d9c0834175417c1ec8 SHA1: a1234af0cd9788ef13ff70897949e833c60ba7ef SHA256: 32ab724990598ae66e427710962cada2fadb681f2c558fa0afdb58dac93e847e SHA512: a172c23bd0cb608ff3b5cf477e73111cab9fe496b9c1c5ed5202b22500e687c1357208ffe9a4cfdaaaca6a7723ad67259efb9f6d465af9be936a250102968949 Homepage: https://cran.r-project.org/package=rainfarmr Description: CRAN Package 'rainfarmr' (Stochastic Precipitation Downscaling with the RainFARM Method) An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) ). Adapted for climate downscaling according to D'Onofrio et al. (2018) and for complex topography as in Terzago et al. (2018) . The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field. Package: r-cran-rairtable Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble, r-cran-dplyr, r-cran-cli, r-cran-crayon, r-cran-rlang, r-cran-progress Filename: pool/dists/noble/main/r-cran-rairtable_0.1.2-1.ca2404.1_all.deb Size: 56844 MD5sum: 60688bf47b9b50f7fbed043957db622b SHA1: ed68a85f8a1b2f4680b51d74bffd214ee57acb4b SHA256: 663e58362185bffd4e8d4cdc08822d53e042506c5d8bea6ee370b6e3e2365c10 SHA512: b91a5c3113e0b9434ced6806c5268d9508d8f7560d681342cf1b3cf687e287c240a9c3aa57859ed654cb971873b8195ec38df389b3e610b46c0b18a443798374 Homepage: https://cran.r-project.org/package=rairtable Description: CRAN Package 'rairtable' (Efficient Wrapper for the 'Airtable' API) Efficient CRUD interface for the 'Airtable' API , supporting batch requests and parallel encoding of large data sets. Package: r-cran-raiser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mrfdepth, r-cran-mass, r-cran-withr Suggests: r-cran-testthat, r-cran-lmtest, r-cran-car Filename: pool/dists/noble/main/r-cran-raiser_0.1.0-1.ca2404.1_all.deb Size: 197200 MD5sum: 32299f72459be655a96fc6ee97ce8b21 SHA1: e090f5498b972f02ca3de26630b0617a29459b0d SHA256: 9057753c391c40cfe36586a29de38399051c276a3d745a49886ba68cbbe6047a SHA512: cdd4f937b773fa24dfffb0daa6d1c454df048c7aa2f531a871e5c66f7a31dd559282ae108dcc0d0713c0a1173402edad0f6bd206e49857e73ef974407b809c84 Homepage: https://cran.r-project.org/package=raiseR Description: CRAN Package 'raiseR' (Raise Regression and Robust Methods for Multicollinearity) Implements Raise Regression as an inference-preserving alternative to Ridge Regression for combating multicollinearity in linear models, including the classical single-variable Raise Regression, the Simultaneous Raise Regression (SRR) based on QR decomposition and the Sequential Variance Inflation Factor (SVIF) of Jacob and Varadharajan (2022) , and the original raise parameter selection strategy of Jacob and Varadharajan (2023) . Also implements Robust Raise Regression for data contaminated by outliers, with exact finite-sample inference (sandwich standard errors, Wald tests, Satterthwaite-corrected degrees of freedom) obtained by down-weighting observations using Stahel-Donoho projection outlyingness and Tukey's biweight function. Provides ordinary and robust Ridge Regression (Hoerl and Kennard, 1970, ), ordinary and robust Liu Regression (Liu, 1993, ), with the robust variants of both based on the MM-estimates of Yohai (1987, ) and, for Liu Regression specifically, the biasing-parameter derivation of Filzmoser and Kurnaz (2018) . Also provides the classical Variance Inflation Factor (VIF) and Condition Number (Belsley, 1991) computed from the correlation matrix of the predictors, and the Robust Variance Inflation Factor (RVIF) and robust Condition Number of Jacob and Varadharajan (2024, Sankhya B, ), which use the same projection outlyingness and biweight down-weighting scheme to obtain a weighted correlation matrix that resists the influence of outliers. A flexible scaleDat() function supports classical (mean and standard deviation), robust weighted (Stahel-Donoho and Tukey biweight), median and Median Absolute Deviation Normalized (MADN, the median absolute deviation scaled by 1.4826 to estimate the standard deviation under normality), and min-max scaling. Diagnostic and goodness-of-fit plots, and the standard influence-diagnostic suite (Cook's distance, DFBETAS and COVRATIO regression diagnostics) and heteroskedasticity tests (via the 'lmtest' and 'car' packages) analogous to those for objects of class 'lm', are provided for the exact, unbiased Raise Regression fit. Package: r-cran-raisr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-archive, r-cran-cli, r-cran-curl, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-tibble Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-raisr_0.1.0-1.ca2404.1_all.deb Size: 124672 MD5sum: 7d04e0a6910297652b0107f238ee040d SHA1: 8930b2611d1c9b386fdf911877b56d171bcd1303 SHA256: 34e24238dd6dbe77b648d4677b122d1db85ed668da343006bc90c0e1dcedadca SHA512: 263c04aed9c3eb0efc30ac3f3a927c0ad593e52ddfeb3da1534c778f36a3aea5b3c89b0ffe4cbb97d01880685672416ea56c8908d2387bc6f97cbef8abe606ea Homepage: https://cran.r-project.org/package=raisr Description: CRAN Package 'raisr' (Access 'RAIS' Microdata from the Brazilian Ministry of Labour) Download and read the public, non-identified microdata of the 'RAIS' (Relação Anual de Informações Sociais), the annual census of formal employment relationships and establishments published by the Brazilian Ministry of Labour and Employment through the 'PDET' FTP server . Lists the years and archives available on the server, resolves which regional or state archive holds a given state, downloads it with an idempotent local cache, and reads the '7z' archives as a stream, filtering by state and selecting columns before anything is kept in memory, so that a single state can be extracted from a regional file of tens of millions of records. Handles the two header generations of the files (up to the 'RAIS' 2022 and from the 'RAIS' 2023 onwards) with the same normalized column names, provides the official record layout and a helper to consolidate the employment stock, admissions, separations and December payroll. Package: r-cran-rajive Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-cowplot, r-cran-reshape2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-rajive_1.0-1.ca2404.1_all.deb Size: 220490 MD5sum: 860a992eeb568ac38e8e6b57ccb9454e SHA1: ffd4eaaef0f0b8fd44e3fe33a576f724186e93a8 SHA256: ad5337d483f303629a8c970652909d21136683998fb4ee8a7d3adc003c2ce7d5 SHA512: bcab54af34142aa2646e4e52aaed987e13d90b8c46e3c47a1f4057d6709a5ebec03c715edf25417801a9af98c02bcd80ddcba9f378bfbe879a3e7fbaf93dd79d Homepage: https://cran.r-project.org/package=RaJIVE Description: CRAN Package 'RaJIVE' (Robust Angle Based Joint and Individual Variation Explained) A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: ) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) . 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Package: r-cran-ralger Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvest, r-cran-xml2, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-robotstxt, r-cran-crayon, r-cran-curl, r-cran-stringi, r-cran-urltools, r-cran-purrr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ralger_2.3.0-1.ca2404.1_all.deb Size: 129924 MD5sum: 0f2963650a15c9994ec0388f3fda8e08 SHA1: 0fbc5b17d97c4fa94f7301c3fc9d30c15f331789 SHA256: 9e8db7d943eb2b0e107c452fe9573b97916f1aa017a0f6aaeb22062793ca01b2 SHA512: a208050f46e53f3097eabd20509526efdc1c6f679f705042597b1dd9adb4bb951078408ced0af2cf0451a7fb58f361d515bc7586edd6abd410e29b306193d5a3 Homepage: https://cran.r-project.org/package=ralger Description: CRAN Package 'ralger' (Easy Web Scraping) The goal of 'ralger' is to facilitate web scraping in R. 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Such studies are, for example, international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from 'SPSS' into 'R' data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types increases with every next release. For a general presentation of the package, see 'Mirazchiyski', 2021a (). For detailed technical aspects of the package, see 'Mirazchiyski', 2021b (). 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Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), ), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants. 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Package: r-cran-ramcharts4 Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-shiny, r-cran-jsonlite, r-cran-lubridate, r-cran-minpack.lm, r-cran-base64enc, r-cran-xml2, r-cran-stringr Suggests: r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-ramcharts4_1.6.0-1.ca2404.1_all.deb Size: 1733328 MD5sum: f319eed18e57d2e53b840dda50c64a18 SHA1: ca34a083d2db477cd2184fabcee717e5d9c10d14 SHA256: b1d53525469759ba977b7b872587ffd5fe1a4fd9432de92ceb91714721e9c34a SHA512: 93fa0d4b5636dd12d6fd9f411f0151cba51de7862912d61c7f239995c98c80d7c50463d05d30e22e07a0de887d5761ae85575904aadde274a43c4565a9ce3258 Homepage: https://cran.r-project.org/package=rAmCharts4 Description: CRAN Package 'rAmCharts4' (Interface to the JavaScript Library 'amCharts 4') Creates JavaScript charts. The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart. 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The database is named after Dr. Ransom A. Myers whose original stock-recruitment database, is no longer being updated. More information about the database can be found at . Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) . 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The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) , likelihood ratio method from Wang and Bickel (2015) , spectral methods from Le and Levina (2015) . Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 ) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 ). It also includes the consensus clustering of Gao et. al. (2014) , the method of moments estimation of nomination SBM of Li et. al. (2020) , and the network mixing method of Li and Le (2021) . It also includes the informative core-periphery data processing method of Miao and Li (2021) . The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134. 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The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA. 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Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) . Package: r-cran-randomgaussiannb Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mlbench, r-cran-testthat Filename: pool/dists/noble/main/r-cran-randomgaussiannb_0.2.4-1.ca2404.1_all.deb Size: 40512 MD5sum: 2d7fcd68455dd09f8e772f5b23bd4c63 SHA1: fc7caa3dd44514d87a5d7868b373ab1b62a4ab63 SHA256: 60dc4d980ae973e403063a767c22b411d258ba7e4f3767794c9a9252924734e5 SHA512: 12430e82bfdb172905d79e5b8d7dafdd870d8baa6ca3b2c3c30cc2758cefb8d0ba28045902688302abda0dc67620ca62f3bef6777f67c853abce504f5e79da46 Homepage: https://cran.r-project.org/package=RandomGaussianNB Description: CRAN Package 'RandomGaussianNB' (Randomized Feature and Bootstrap-Enhanced Gaussian Naive BayesClassifier) Provides an accessible and efficient implementation of a randomized feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method combines stratified bootstrap resampling with random feature subsampling and aggregates predictions via posterior averaging. Support is provided for mixed-type predictors and parallel computation. Methods are described in Srisuradetchai (2025) "Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification". Package: r-cran-randomglm Architecture: all Version: 1.10-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3556 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-foreach, r-cran-doparallel, r-cran-hmisc, r-cran-geometry, r-cran-survival, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-randomglm_1.10-1-1.ca2404.1_all.deb Size: 3610168 MD5sum: 7664c81a313c887de57e0b0ae5628249 SHA1: 8ca752a9711cedd88cbd6f9bb90e83418b63bd78 SHA256: af4f91e5efe4aaafb23103e92e770fd2457344b8b3679fdd3a69551229588255 SHA512: 8e3775f9ebfcdcac20f33a8ef4c2ba14ceca1541d246693975f23fc00c618933d38be71ad10117847c4ea9c1e643019561bcdca24c56efa868f4a39b5214013c Homepage: https://cran.r-project.org/package=randomGLM Description: CRAN Package 'randomGLM' (Random General Linear Model Prediction) A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in Song, Langfelder and Horvath (2013) . Package: r-cran-randomgodb Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-go.db Filename: pool/dists/noble/main/r-cran-randomgodb_1.1-1.ca2404.1_all.deb Size: 405676 MD5sum: f9975afaaf68016442edb6f56c3a60bb SHA1: 39c192694fefb1c17fbf0ef283bcd48f9f0efaec SHA256: 6ae3b3b274be683dca6a31fb6f0d6b0ce6f17d6f2a18e9d363ec411aebe70d3a SHA512: 48a05d2edc9691aceec5415bb59215b108843a3bc6665e6e6129802fab573bc9bb532a7276bd420e3fd5af692eaf4aada37a0b915c0c903f4f25658b75290af7 Homepage: https://cran.r-project.org/package=randomGODB Description: CRAN Package 'randomGODB' (Random GO Database) The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. The significance is traditionally determined by randomizing the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for each category. We explore here the novel alternative of randomizing the GO database rather than the gene list. 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The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously. 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This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm and a heuristic algorithm to drastically improve speed. The package also provides an option to bootstrap a consensus ranking based on resampling input rankings (with replacement). Input rankings can be either incomplete (partial) or complete. Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) . 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This method is a modified Wilcoxon signed-rank test which produces consistent and meaningful results for ordinal or monotonically-transformed data. 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Brunner, E., Bathke, A. and Konietschke, F. (2018) . Package: r-cran-rankgwask Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-qqman Filename: pool/dists/noble/main/r-cran-rankgwask_0.1.1-1.ca2404.1_all.deb Size: 57556 MD5sum: 567d43f24dc732579bc9f70723e1231f SHA1: 10f688414b4530222fa15fa20ea69685796712f7 SHA256: 4db290676a69e34a8fe49d6f34070416da7dff16d9ad19c12cfcfbdc1e5a339e SHA512: 27f9a3ceb69a33d22062ae746fbdec1c18ed61ff0ade6c057fb976a817e82f4a5ec6ca72419910d2a5ea2089e7b5b4f3c8996713f5a96674afd81ce96d5454c6 Homepage: https://cran.r-project.org/package=RankGWASK Description: CRAN Package 'RankGWASK' (Ranked Set Sampling Genome-Wide Association Studies Toolkit) Provides methods for genome-wide association studies (GWAS) using ranked set sampling (RSS) designs. 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The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate. Package: r-cran-rankicc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rankicc_1.0.2-1.ca2404.1_all.deb Size: 48724 MD5sum: 22c865e0fad635b59c022e91793df677 SHA1: 858dc2f6d0e4bee51a82e5ff49a366cb0beeff68 SHA256: 9d53922da8ee854ee73bd9c46749144c35d8d16f30ff51ff4ae0d12105b3b236 SHA512: 78ad8218ab12ea5671656abace97a852380464b30b57d3757db2e2856030c09fe36b7dd7fd4f923c8855e513f64c2bcb6039135eaabd88769af0f9137c66b872 Homepage: https://cran.r-project.org/package=rankICC Description: CRAN Package 'rankICC' (Rank Intraclass Correlation for Clustered Data) Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) for details. 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This package treats every source of importance as a judge expressing a ranking over the predictors, and synthesises those rankings into a Kemeny median ranking with ties. Uncertainty about the consensus is quantified through bootstrap rank confidence sets, top-k probabilities and clustering of disagreeing judges. 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Companion package to "A Primer on Visualizations for Comparing Populations, Including the Issue of Overlapping Confidence Intervals" by Wright, Klein, and Wieczorek (2019) and "A Joint Confidence Region for an Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020) . 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Package: r-cran-rankinplot Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-rcolorbrewer, r-cran-lifecycle Suggests: r-cran-viridis Filename: pool/dists/noble/main/r-cran-rankinplot_1.2.0-1.ca2404.1_all.deb Size: 87710 MD5sum: 274ad2eb1f872deb3534d0d562438794 SHA1: ba5a1a5a26f9a18b24f15f5377ca1622180d7d79 SHA256: e4392ce5b4eda40d622798baefceff598ca6bb22a7ec2990c1f449cc087eadd6 SHA512: e1ef2ffe50bc79087d6c289c56cd8070edf155dd50ccca89d3a512b49c54390148cdc9743bdad676c3eb16002796bd6a2cbcc74e1df4e2b55f4b8ed00642adcb Homepage: https://cran.r-project.org/package=rankinPlot Description: CRAN Package 'rankinPlot' (Convenient Plotting for the Modified Rankin Scale and OtherOrdinal Outcome Data) Provides convenient tools for visualising ordinal outcome data following conventions within stroke research literature. It currently supports the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) and Probability-Probability plots for visualising Desirability of Outcome Ranking (DOOR) scales with large numbers of categories proposed by Johns et al. (2026) . 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Statistical inference is based on a wild or a sample-specific bootstrap approach as described in 'Dobler et al. (2019) '. The unweighted treatment effects considered do not depend on sample sizes and allow for transitive ordering. The package thus provides an extension of the univariate 'rankFD' package to multivariate data. Package: r-cran-rankpca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret Filename: pool/dists/noble/main/r-cran-rankpca_0.1.0-1.ca2404.1_all.deb Size: 16924 MD5sum: 1b28ce20064d59e3fc2b0e1168656ebc SHA1: ef1a05cad42439feda8a24368734e89c086e1b77 SHA256: 1a186349e842918d437c5ba6ae304b04a044054a0531fda1080a4b940c7ffdb4 SHA512: 72151600507ff9ff8a05602d3b3ba23d5e5c390b9b9a27968ae57cdf93f941dd00cf828e5389cb5fa897fb1ac7edae0cce51a11266e96635fc09cafbfe98f15d Homepage: https://cran.r-project.org/package=RankPCA Description: CRAN Package 'RankPCA' (Rank of Variables Based on Principal Component Analysis forMixed Data Types) Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) . 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You can also determine the ("special") card of highest rank of the remaining cards for that suit. At some point, you notice that a certain opponent discards that special card. What can you infer about his holding in that suit? A series of simulation studies are reported here that allows a quantitative inference based on the conditional probability, given that the opponent has the special card. The same procedure is also used for the conditional probability, given that the opponent does not have the special card. 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This project was supported by the National Science Foundation under Grant No. 2019901. Package: r-cran-rankresponse Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rankresponse_4.0.0-1.ca2404.1_all.deb Size: 42606 MD5sum: 8aea0477a3820ac56da2bfd76276f1be SHA1: f59c18a6b016af476da4d8c24cf3db7fad0c29c2 SHA256: d9dfee8c422876fde841e4e10d9b57374e5249af1fa49cc591130852c7397001 SHA512: 7f9ab66ad3e2dd72c2d5cb2c88991538eb87b1f9e7c90eba1f46914cf8006c125074d55ccc68bf14c7f7acb8437281ff604dd33f04b048ce1a472d956d6cb305 Homepage: https://cran.r-project.org/package=RankResponse Description: CRAN Package 'RankResponse' (Ranking Responses in a Single Response Question or a MultipleResponse Question) Methods for ranking responses of a single response question or a multiple response question are described in the two papers: 1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of Applied Statistics, 35, 465-474. 2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple Response Questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 191-208. . 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This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) . 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Includes tools for crop process calculations, parameter management and customisation, enabling users to explore, modify and apply lupin model components. 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Van Everdingen, A. F. and Hurst, W. (1949) . Fetkovich, M. J. (1971) . Yildiz, T. and Khosravi, A. (2007) . 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Package: r-cran-rareflow Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-gganimate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rareflow_0.1.0-1.ca2404.1_all.deb Size: 103214 MD5sum: 1fbd9c50f5bea98132a20f4484c8dac3 SHA1: 017f2328187c27a9df9a36c45fec074cb348142f SHA256: e471c4823d3b53c62c1fa124699fb574a66b97a4a5deda80b664ecd8730a9124 SHA512: f39182388f604bcbdce84c68d78373975f913ff061aaf4f47d58bf056a5a6c8500d3e44a1a005aba4df737512ce86cd2402d4cf51554c285fdbeb66e8bafd542 Homepage: https://cran.r-project.org/package=rareflow Description: CRAN Package 'rareflow' (Variational Flow-Based Inference for Rare Events and LargeDeviations) Variational flow-based methods for modeling rare events using Kullback–Leibler (KL) divergence, normalizing flows, Girsanov change of measure, and Freidlin–Wentzell action functionals. The package provides tools for rare-event inference, minimum-action paths, and quasi-potential computation in stochastic dynamical systems. Methods are based on Rezende and Mohamed (2015) , Girsanov (1960) , and Freidlin and Wentzell (2012, ISBN:978-0387955477). Package: r-cran-rarefun Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rann, r-cran-boot, r-cran-dbscan, r-cran-geosphere, r-cran-isotree, r-cran-rdpack, r-cran-pdp Suggests: r-cran-testthat, r-cran-dplyr, r-cran-ggplot2, r-cran-heatwaver, r-cran-patchwork, r-cran-quantmod, r-cran-quantreg, r-cran-ranger, r-cran-viridis Filename: pool/dists/noble/main/r-cran-rarefun_0.1.0-1.ca2404.1_all.deb Size: 183074 MD5sum: baddd96c3feeee02ca9910d7939722e4 SHA1: 4aa964ae8d8add82decf2a8405c786b11d837d4c SHA256: 5fea9d85a24d7b43cb618badf47d4ad3c36857e7cc5aaab291ac54cf82e59632 SHA512: 3904d557982046a3e7a2347136d27bdb0c12d59d8383b12bed9965a2ca08ac9b7b3c52e9afd8e6cafc82a920e3aeec5e73f9143068b1d1986f78051e5ff0dbdd Homepage: https://cran.r-project.org/package=rarefun Description: CRAN Package 'rarefun' (Functions for Rare Events Analysis) Functions for detecting and analyzing rare events in data. Implements isolation forest (Liu et al., 2008, ) and clustering for anomaly detection in time series residuals. Decomposes time series using LOESS (Locally Estimated Scatterplot Smoothing) or STL (Seasonal-Trend decomposition using LOESS). Detects marine heatwaves and cold spells following Hobday et al. (2016) . Provides goodness-of-fit tests for quantile regression (Haupt et al., 2011, ), partial dependence with quantile random forests, MCC (Matthews Correlation Coefficient) computation and testing, knee-point detection via the Kneedle algorithm (Satopaa et al., 2011, ), and spatial point matching. Package: r-cran-rarenmtests Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Filename: pool/dists/noble/main/r-cran-rarenmtests_1.2-1.ca2404.1_all.deb Size: 116702 MD5sum: f1e080afc63eb232d34e355ccc5b8e66 SHA1: f5b63c4575d0eb9cead43a9b88a69ffc607ffe77 SHA256: 74af9e3190eac33a60cbbc30a9af69d247c0c51fd4199172448fe06097310393 SHA512: fccb3212a86c50d212750cb9e481ab9c3ef80cfabb47b83f55a274b8b92f5d49a298c83f6ec412653967eb9fff2de0bb9801faac785f5a4702967d30256cb49c Homepage: https://cran.r-project.org/package=rareNMtests Description: CRAN Package 'rareNMtests' (Ecological and Biogeographical Null Model Tests for ComparingRarefaction Curves) Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) . Package: r-cran-rarestr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rarestr_1.1.1-1.ca2404.1_all.deb Size: 65002 MD5sum: 90020e89d9d325452fd2209b3aebabe8 SHA1: 8fa6de21f508d26789b2788cb195094dd761026c SHA256: b259f4f3966eda295e02dd5545593b5854915a017bd36b598ad129f704e22720 SHA512: 8d0b1f42fc1d1965f38f14839286abcbf9cecffbfa6674c2515e44da991b002d6ed3095b5729bbb675bb14a74d6299dfa78d563319ed3bce48aa67f1af19b944 Homepage: https://cran.r-project.org/package=rarestR Description: CRAN Package 'rarestR' (Rarefaction-Based Species Richness Estimator) Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators. Package: r-cran-raretrans Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-bslib, r-cran-dplyr, r-cran-formatr, r-cran-gridextra, r-cran-huxtable, r-cran-knitr, r-cran-popdemo, r-cran-popbio, r-cran-purrr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-raretrans_1.0.5-1.ca2404.1_all.deb Size: 640608 MD5sum: f95316655eabe581489d82f5ea90b71d SHA1: 470d6ab82ddeb42d4cb092eb87f602daddf25f66 SHA256: 721aa82095ac5ecd50b0d73cc8f35083fdc00a7bad5c8b04443c81941d785cae SHA512: fff61a085c2dfe565f1b4b8ce9265d340bd683414e77f6d8efd8ff8d57a92a333a6468c53a8ebde71f882026c9c368d5c81a02be1969408655e72544337e6e24 Homepage: https://cran.r-project.org/package=raretrans Description: CRAN Package 'raretrans' (Bayesian Priors for Matrix Population Models) Provides functions to correct biased transition and fertility estimates in population projection matrices caused by small sample sizes. Small or short-term studies frequently produce structural zeros (biologically possible transitions never observed) and structural ones (transitions estimated at 100% survival, stasis, or mortality that are biologically implausible). Both distort matrix structure and bias estimates of population growth. Implements a multinomial-Dirichlet Bayesian prior for transition probabilities and a Gamma-Poisson prior for reproduction, allowing analysts to incorporate prior biological knowledge and regularise estimates from rare or unobserved events. Includes functions to compute marginal posterior credible intervals for all transition probabilities (transition_CrI()), visualise those intervals as point-range plots (plot_transition_CrI()), and display the full posterior beta density for each matrix entry (plot_transition_density()). Methods are described in Tremblay et al. (2021) . Package: r-cran-rarfreq Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-latex2exp, r-cran-magrittr, r-cran-patchwork, r-cran-tidyr, r-cran-data.table, r-cran-reshape2, r-cran-rdpack Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rarfreq_0.1.5-1.ca2404.1_all.deb Size: 201714 MD5sum: b1ce956afe0c0badd977635f9acd831c SHA1: 7e00f61b6e6291e946417002e3718d63fce9b9b8 SHA256: f6c5dbc33ff55071d19b346f707b6b49e9e20c085ba68e414a4440c1558eba78 SHA512: 5b0c650fcea7af2f0cef33b75e304a9d960ae8582de10b7c77fbc49e6158dd175b72f953d73ba80a211913012b22da03aaaf8fe45eb474051e4d86fe9235c814 Homepage: https://cran.r-project.org/package=RARfreq Description: CRAN Package 'RARfreq' (Response Adaptive Randomization with 'Frequentist' Approaches) Provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or 'ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) and 'Urn' allocations. Package: r-cran-rarity Architecture: all Version: 1.3-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rarity_1.3-8-1.ca2404.1_all.deb Size: 92438 MD5sum: 65dcf0f152b24bca8a0ae62a65d72068 SHA1: 95c97ad5bdf13dd79fd74718032faa870d9eca81 SHA256: 0a1280e6c627b98d63caae062570e6ff74d85bf253d4a7f1c6da9fcb2b846e2c SHA512: b8c423591c52222b68d0eeeb99261be0775b98291e3dcec21080cc6461ae2efd7856cad70deb0469c9a3fb20aaa87e5b7874652a6274a6ecdfd8682edc0e01a5 Homepage: https://cran.r-project.org/package=Rarity Description: CRAN Package 'Rarity' (Calculation of Rarity Indices for Species and Assemblages ofSpecies) Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 ; Leroy et al. 2013, Divers. Distrib. 19:794-803 ). Package: r-cran-rarms Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rarms_1.0.0-1.ca2404.1_all.deb Size: 14630 MD5sum: e886a0b5fc6404766c3be248cf031862 SHA1: 95f2d29b1d1478fb85c90acc9555d77af36102f3 SHA256: cde5c040f5e1850553b596fbc7149d4a4d7ab9f8035189db6507a46414c41fe5 SHA512: 983f11cbda6bd0b8e5243e154043b889e3bea916ded67dfd23fdbc967580f5b841683d6efe4ad47e74d3d191eeb434d89de533d02316f8a2aff1efad03954a7d Homepage: https://cran.r-project.org/package=rarms Description: CRAN Package 'rarms' (Access Data from the USDA ARMS Data API) Interface to easily access data via the United States Department of Agriculture (USDA)'s Agricultural Resource Management Survey (ARMS) Data API . The downloaded data can be saved for later off-line use. Also provide relevant information and metadata for each of the input variables needed for sending the data inquery. Package: r-cran-rarpack Architecture: all Version: 0.11-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra Suggests: r-cran-matrix Filename: pool/dists/noble/main/r-cran-rarpack_0.11-0-1.ca2404.1_all.deb Size: 30032 MD5sum: f86c4aeb0f3bb03e271390e8cae028e4 SHA1: 1b46335fa53386a9ddf01dafef532729a9661dda SHA256: 3a600a5f2a7f2d6fa53d810c58e430ded1522ee4a7406667f6e26d4211963527 SHA512: 9aa290abaf45cfd6228c87cdf7846a03147b0b0f3800969229a531072a29bafcc9f80473e9016d36cff749d844280b409fd4b819345960ac158eda84c2851d84 Homepage: https://cran.r-project.org/package=rARPACK Description: CRAN Package 'rARPACK' (Solvers for Large Scale Eigenvalue and SVD Problems) Previously an R wrapper of the 'ARPACK' library , and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package. Package: r-cran-rartrials Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pins, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rartrials_0.0.2-1.ca2404.1_all.deb Size: 641402 MD5sum: 9a7a8ec54bd445794fc0bcefffcaf741 SHA1: 2f65d0d40a5d59ff83154812e5bcb64984f119ec SHA256: 81fe5b8adeae7d72378fcde877678cfed6aef463040a55ebbee8c2161bc397db SHA512: 7fcbb23265806258537a3ac5de7870a3d42842aae85c53330302f52eecd064da2f68bf5fbffa28eb1ae2b85da1e688b8f8242b85939c1667fd66261fae2578fc Homepage: https://cran.r-project.org/package=RARtrials Description: CRAN Package 'RARtrials' (Response-Adaptive Randomization in Clinical Trials) Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) ), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) , Rosenberger and Lachin (2015) ) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) ) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) , Bello and Sabo (2016) ), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) ), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) ), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) ), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) ) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) , Williamson and Villar (2019) ). Package: r-cran-rasch Architecture: all Version: 1.11.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2064 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-bsicons, r-cran-knitr, r-cran-rmarkdown, r-cran-erm, r-cran-sirt, r-cran-psychotools Filename: pool/dists/noble/main/r-cran-rasch_1.11.7-1.ca2404.1_all.deb Size: 1654030 MD5sum: a252255edc27ecb56069876495d9ab00 SHA1: 2e0b593cfeb259e39942c5b5eaf9a2bfeaf7f782 SHA256: e163b02732554c168e103b75216b42d612aeba13c1ec290bf9307da150944282 SHA512: 016133d9d54cb09a44e8a04bad10020a618e620308a6c3e6ff4b06789f1d0887920c526035279f0bfd0b1edd9b03ec63cc4a6bfeb00d6e9847350c27d76d1483 Homepage: https://cran.r-project.org/package=rasch Description: CRAN Package 'rasch' (Pairwise Conditional Rasch Measurement Analysis and Diagnostics) Pairwise conditional maximum likelihood estimation of dichotomous and polytomous Rasch models (partial credit and rating scale) after Andrich and Luo (2003) and Zwinderman (1995) , with standard errors from a Godambe sandwich estimator. An optional alternative estimator reparameterises each item's thresholds as Andrich's (1978 , 1985) orthogonal-polynomial principal components (location, spread, skewness, and kurtosis; Pedler 1987), exact for items with up to 3 thresholds and a smoothed reduced-rank model for items with more, useful when some categories are sparsely populated. Person measures are Warm's (1989) weighted likelihood estimates, computed per missing-data pattern. The diagnostic suite follows the conventions set out in Andrich and Marais (2019) : the log-of-mean-square fit residual with apportioned degrees of freedom (and its natural form), infit and outfit, the item-trait interaction chi-square over automatically sized class intervals with its per-interval detail table, the class-interval ANOVA item-fit F, the person separation index with and without extremes and the item separation index, Cronbach's alpha, summary distribution statistics with skewness and kurtosis, targeting, the score-to-measure table with maximum likelihood and geometric extreme-score extrapolation options, test information, threshold and category diagnostics, residual principal-components dimensionality testing, local dependence by residual correlation, and differential item functioning by two-way residual analysis of variance over any number of person factors, factor-at-a-time (the full two-way table with partial eta-squared effect sizes) or as a full factorial with interaction precedence, Tukey HSD post-hoc comparisons on significant group terms and interaction cells, false-discovery-rate or familywise adjustment, and DIF magnitudes in logits by resolved-item locations with a practical-significance criterion. Violations of independence are quantified, not just flagged: the magnitude of response dependence between two items by the resolution method of Andrich and Kreiner (2010) (polytomous form Andrich, Humphry and Marais 2012 ), the spread-parameter least-upper-bound screen (Andrich 1985), and the magnitude of multidimensionality (latent subscale correlation and common-variance proportion) from Andrich's (2016) two-calculation reliability comparison. A likelihood-ratio test of the partial credit against the rating parameterisation is reported both raw, as conventionally displayed, and with a first-order composite-likelihood calibration (Kent 1982 ) from the Godambe matrices. Also included: anchored estimation for test equating (individual threshold and average item-location anchors), common-item equating tests and plots, item splitting to resolve invariance violations, tailored analysis for guessing with the four-step anchored comparison (Andrich, Marais and Humphry 2012 ), classical test theory companion statistics, racked and stacked reshaping for repeated measurements, model comparison by composite-likelihood information criteria whose penalty is the Godambe effective parameter count (Varin and Vidoni 2005 ; Gao and Song 2010 ), absorbing the pairwise over-counting that a nominal AIC or BIC would ignore, the many-facet Rasch model (Linacre 1989) for rated long-format data with facet severities, fit, and optional item-by-facet interactions, subtest formation for locally dependent items, multiple-choice scoring against a key with double keying and polytomous option scoring of informative distractors (Andrich and Styles 2011, with an evidence-based rescoring proposal), rest-measure distractor analysis and option curves, the Guttman scalogram with the coefficient of reproducibility, the Bradley-Terry-Luce model for paired comparisons (Bradley and Terry 1952 ; Luce 1959) as the conditional form of the dichotomous Rasch model (Andrich 1978), estimated by the same conventions with judge-clustered sandwich errors and judge fit diagnostics, and the first software implementation of the extended frame of reference model (Humphry 2005; Humphry and Andrich 2008), in which the unit of the latent scale differs across item-set by person-group frames: group units are estimated by person-free within-frame pairwise conditioning and set units by error-corrected person linking, all reported in a common arbitrary unit; its paired-comparison form estimates judge-panel and object-set units with the linking identified from cross-set comparisons alone. A modern 'shiny' interface and a one-call exporter for every table and plot are included. Implemented from published measurement theory in base R, with no dependence on other estimation engines. Package: r-cran-rasciidoc Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-fritools, r-cran-gert, r-cran-highr, r-cran-knitr, r-cran-reticulate Suggests: r-cran-devtools, r-cran-pkgload, r-cran-rmarkdown, r-cran-rprojroot, r-cran-runit, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rasciidoc_4.1.1-1.ca2404.1_all.deb Size: 103188 MD5sum: dab4ecf5acaff058d016d183bfb30173 SHA1: b2f45f7c722220a863349251cf184f2ea8ac4ee9 SHA256: bfb6f7de36afd54f2070e968964aabd003198665cc699233c5973b033996d49f SHA512: 78273ea1b8503bd9c66eb2294e5c3170d5f970e0d42b6a19ea0b5a407a3b9029df4bd887f354dfad75411c535f93f0eb796f8b8bd1f45b0105da60aaf2c825b7 Homepage: https://cran.r-project.org/package=rasciidoc Description: CRAN Package 'rasciidoc' (Create Reports Using R and 'asciidoc') Inspired by Karl Broman`s reader on using 'knitr' with 'asciidoc' (), this is merely a wrapper to 'knitr' and 'asciidoc'. Package: r-cran-rasclass Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-nnet, r-cran-rsnns, r-cran-e1071, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-rasclass_0.2.2-1.ca2404.1_all.deb Size: 113134 MD5sum: d7bfbe850027ff3d8e523ba472746814 SHA1: 5230ff738e34858b5b442125d3c43ea6f8f7ba8d SHA256: b2e7bdf6395fc75de5ecaeff407fd0b4ee648d0b782db076311b1f29a129ebc9 SHA512: 30421a62958a3cc90b68a4c94ac213c557dc7911077f014cf1204f73bfad7ca6aeb10d7f715df21c999d64a75059a50d753cdfaa16debb6fcf8aa18b67a1b8b5 Homepage: https://cran.r-project.org/package=rasclass Description: CRAN Package 'rasclass' (Supervised Raster Image Classification) Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available. Package: r-cran-rasen Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-caret, r-cran-class, r-cran-doparallel, r-cran-e1071, r-cran-foreach, r-cran-nnet, r-cran-randomforest, r-cran-rpart, r-cran-ggplot2, r-cran-gridextra, r-cran-formatr, r-cran-fnn, r-cran-ranger, r-cran-kernelknn, r-cran-modelmetrics, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rasen_3.0.0-1.ca2404.1_all.deb Size: 4184378 MD5sum: 96d2f9fa989db972fda42d4f672e7437 SHA1: 847de300cab0223aa46a19572df5880a09ff398c SHA256: a8230d0584b1b7370e2ea057f25d910d53a35ffb1e4ecb3b82ce705af8b8c349 SHA512: b8732de54c14ccb1d03bfdc83583b18158fc5dd3f4ef162688d0faffceeadddac2a2b98b5260515093f767e4c051916e21f2229664e4d96abcfa674ab023b206 Homepage: https://cran.r-project.org/package=RaSEn Description: CRAN Package 'RaSEn' (Random Subspace Ensemble Classification and Variable Screening) We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems. Package: r-cran-rashnu Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rashnu_0.1.2-1.ca2404.1_all.deb Size: 124356 MD5sum: f50d9247081af19efbd337e25c106aac SHA1: bbe0165f061e22dd857c9e360122c802005b6088 SHA256: 05f474be0b58dd5def4e9665ef350f7b87216b0fd5cfc0de239d6426126f76bf SHA512: 4b7dbc94faa81c4b3e30d16c44fc2173713889d5319b7d804c0dcda1750fd6319b51c77de46f1ebfd5ba209640f1066fba66ac07e713fc747506acf2803ef38e Homepage: https://cran.r-project.org/package=rashnu Description: CRAN Package 'rashnu' (Balanced Sample Size and Power Calculation Tools) Implements sample size and power calculation methods with a focus on balance and fairness in study design, inspired by the Zoroastrian deity Rashnu, the judge who weighs truth. Supports survival analysis and various hypothesis testing frameworks. Package: r-cran-rassta Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggally, r-cran-ggplot2, r-cran-histogram, r-cran-kernsmooth, r-cran-kohonen, r-cran-plotly, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-stringdist, r-cran-stringi, r-cran-terra Suggests: r-cran-testthat, r-cran-tinytest, r-cran-doparallel, r-cran-mgcv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rassta_1.0.6-1.ca2404.1_all.deb Size: 3575142 MD5sum: 180229d2a6ff732355c1035afe4297c3 SHA1: 197fdf0fb6fc8b17e0d120e09d54ee8932802dc3 SHA256: 161b7775b1d31ae0dc4065763d0047d939ced895a613dc1c9925015ece746580 SHA512: 191c0cf40bd3fdd33e83a0b6fc57f5dfbf540152a9dd56d2b61b1861115daa59c31c045b9e3c7a4fbbb6a68082d4fc463a21f029dd603776b38b68e679cad548 Homepage: https://cran.r-project.org/package=rassta Description: CRAN Package 'rassta' (Raster-Based Spatial Stratification Algorithms) Algorithms for the spatial stratification of landscapes, sampling and modeling of spatially-varying phenomena. These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). . 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Layers are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR) with . The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from Hansen et al. (2013) and Beaudoin et al. (2017) ; and rasterized Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018. Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these layers, and code for downloading them from their original sources can be found in the 'github' repository . 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For large rasters, the functions run from 5 to approximately 100 times faster than the 'raster' package functions they replace. The 'fasterize' package, on which one function in this package depends, includes an implementation of the scan line algorithm attributed to Wylie et al. (1967) . 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Trends in Ecology and Evolution, 37: 725-728. Package: r-cran-rata Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-glpkapi, r-cran-lpsolveapi, r-cran-rirt, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rata_0.0.2-1.ca2404.1_all.deb Size: 74946 MD5sum: b9812c8bcad90e0e090e72376f7bfe32 SHA1: bb5a8d67ddb593dbb25c5f220d4ee49bf43b6d71 SHA256: 36c5b49e6260c3b976444acc0f14d70f5c3d23ebc57a23017e8ab7daca82d519 SHA512: 3a277c2edb92e681ab6443e9d2f76bfe62787d81f7dd31c75d5d9ff59bc079fd36bed0cb4ee0580833fb31d9feb2c445b38ed8cd48dd4efe2262d945fd23bd6c Homepage: https://cran.r-project.org/package=Rata Description: CRAN Package 'Rata' (Automated Test Assembly) Automated test assembly of linear and adaptive tests using the mixed-integer programming. The full documentation and tutorials are at . 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Package: r-cran-ratecalib Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-osqp Suggests: r-cran-testthat, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-ratecalib_0.3.0-1.ca2404.1_all.deb Size: 133760 MD5sum: ecbba1b0e77c6cef3323e6fe493501a4 SHA1: f8befd502c88aacbcd06eb21c7067b9539a06455 SHA256: 238e4930a01f60951bfed3a7c172d17e221e3d30ad19344d45a6a31a31f3c345 SHA512: 897fe5331b2dff57cd2d6007025b83cf424eb0a404c6fcbc09e0778aa4ed9d9707ff861f9ad58ea156c44d384bdac8a4b4bf9ef2c1e80127914e7fa8ac9b67b3 Homepage: https://cran.r-project.org/package=ratecalib Description: CRAN Package 'ratecalib' (Calibration Weighting to Multiple Subgroup Pass-Rate Targets) Calibration weighting for binary-outcome pass rates against multiple overlapping subgroup targets. Adjusts initial positive weights so that the overall pass rate and subgroup pass rates approach (soft mode) or exactly match (exact mode) given targets, while preserving the initial weight structure and population margins. Provides a one-step interface, pre-solve data checks, target-table construction, effective sample size and design-effect diagnostics, and example data. The solver works on a bounded convex quadratic program over demographic-cell-by-outcome aggregates for efficiency on large samples. Methods follow the calibration approach of Deville and Saerndal (1992) . 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Package: r-cran-raters Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-raters_2.1.1-1.ca2404.1_all.deb Size: 51954 MD5sum: 6da0fe88c4a5cf248347acffcc1ddb9a SHA1: 52a257fa7eca9fec5152ee9c911097e9ec30f95d SHA256: 16a1fcd4c05ffa10299379493b838f6268479b1e570e6b2dae90aa0ae12b7e3f SHA512: 5130fcccc32f94f3349ab267af83fafd66e351eae06819e64c78c525b77525d2e05b28e3905c9feac5e0a1882cdd9e552095176c2a5d9929808d15b0671c26d2 Homepage: https://cran.r-project.org/package=raters Description: CRAN Package 'raters' (A Modification of Fleiss' Kappa in Case of Nominal and OrdinalVariables) The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data. 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Including the rate (or risk) difference ('RD') or rate ratio (or relative risk, 'RR') for binomial proportions or Poisson rates, and odds ratio ('OR', binomial only). Also confidence intervals for RD, RR or OR for paired binomial data, and estimation of a proportion from clustered binomial data. Includes skewness-corrected asymptotic score ('SCAS') methods, which have been developed in Laud (2017) from Miettinen and Nurminen (1985) and Gart and Nam (1988) , and in Laud (2026, under review) for paired proportions. In each case, the same score produces hypothesis tests that are improved versions of the non-inferiority test for binomial RD and RR by Farrington and Manning (1990) , or a generalisation of the McNemar test for paired data. The package also includes MOVER methods (Method Of Variance Estimates Recovery) for all contrasts, derived from the Newcombe method but with options to use equal-tailed intervals in place of the Wilson score method, and generalised for Bayesian applications incorporating prior information. So-called 'exact' methods for strictly conservative coverage are approximated using continuity adjustments, and the amount of adjustment can be selected to avoid over-conservative coverage. Also includes methods for stratified calculations (e.g. meta-analysis), either with fixed effect assumption (matching the CMH test) or incorporating stratum heterogeneity. Package: r-cran-ratest Architecture: all Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ratest_0.1.12-1.ca2404.1_all.deb Size: 415198 MD5sum: ea102516a210c667bad6f9374edfab55 SHA1: 37b3257f0946d0f595a8e0642f83630ce0d5898c SHA256: 93c242e98c7d05daa05703a2347ced734626d1c63e3c79bfe5cb0c48eaf71f4b SHA512: b92be10b1e6f57a9905d6e03110a46cb7ff4d10a977d222ec4a8fe24600dd25e3bdc37cc9c8f1fbe1e151ae08817fbba4d1161e5a694c5433498564bf59f5657 Homepage: https://cran.r-project.org/package=RATest Description: CRAN Package 'RATest' (Randomization Tests) A collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) . More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) . Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter. 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Package: r-cran-rattains Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-crul, r-cran-curl, r-cran-dplyr, r-cran-fauxpas, r-cran-fs, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-vcr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-rattains_1.2.0-1.ca2404.1_all.deb Size: 152040 MD5sum: c213592eb8509c112dd76602532730bd SHA1: 3b37e18c641bbdc65ffd99987c2e079f2d42165d SHA256: 8e40a5a5d4f27165779a00650fb7bc7b6ba8be4a7f0a9586ea5e77f4a840b185 SHA512: dac5a1eb61c84f8158fc241546d9e9153374b815f0605e9d7a6cba3ad878921bd4f09c157656149d0be0eb856c197d42d49a343828c79ef3fbb0040082f4e02e Homepage: https://cran.r-project.org/package=rATTAINS Description: CRAN Package 'rATTAINS' (Access EPA 'ATTAINS' Data) An R interface to United States Environmental Protection Agency (EPA) Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System ('ATTAINS') data. 'ATTAINS' is the EPA database used to track information provided by states about water quality assessments conducted under federal Clean Water Act requirements. ATTAINS information and API information is available at . Package: r-cran-rattle Architecture: all Version: 5.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-bitops, r-cran-ggplot2, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-xml, r-cran-rpart.plot Suggests: r-cran-ada, r-cran-amap, r-cran-arules, r-cran-arulesviz, r-cran-cba, r-cran-cluster, r-cran-colorspace, r-cran-corrplot, r-cran-descr, r-cran-doby, r-cran-e1071, r-cran-ellipse, r-cran-fbasics, r-cran-foreign, r-cran-fpc, r-cran-gdata, r-cran-ggdendro, r-cran-gplots, r-cran-gridextra, r-cran-gtools, r-cran-hmisc, r-cran-janitor, r-cran-kernlab, r-cran-matrix, r-cran-mice, r-cran-nnet, r-cran-party, r-cran-plyr, r-cran-psych, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-readxl, r-cran-reshape, r-cran-rocr, r-cran-rpart, r-cran-scales, r-cran-snowballc, r-cran-survival, r-cran-tidyselect, r-cran-timedate, r-cran-tm, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-rattle_5.6.2-1.ca2404.1_all.deb Size: 7033194 MD5sum: aca9ddff069a315d4ba480e2f42c9e41 SHA1: b95e79c911b9ca126737cbc3dbdb01ea52dca588 SHA256: 8196c15b24329d955af741f4c11b749a39a2362a76cf51ba81712490a7f175b5 SHA512: 3978fbd4dfb6de477768ddbe55002872b1a6181b7ecb82454b5e36af7ab6c9bfc2041ae2f51a15fb991d36afa9fc35f3f7177be06d36daadf87fbc79f9eaa1c3 Homepage: https://cran.r-project.org/package=rattle Description: CRAN Package 'rattle' (R Data Science Supporting Rattle) The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. This package (v5.6.0) supports the companion graphical interface with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file transform and explore the data, and to build and evaluate models. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. If you want to use the older Rattle implementing the GUI in RGtk2 (which is no longer available from CRAN) then please install the Rattle package v5.5.1. See rattle.togaware.com for instructions on installing the modern Rattle graphical user interface. Package: r-cran-ravecore Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bidsr, r-cran-data.table, r-cran-filearray, r-cran-fs, r-cran-ieegio, r-cran-jsonlite, r-cran-r6, r-cran-ravepipeline, r-cran-ravetools, r-cran-s7, r-cran-threebrain Suggests: r-cran-rpyants, r-cran-rpymat, r-cran-htmltools, r-cran-httpuv, r-cran-knitr, r-cran-plotly, r-cran-rnifti, r-cran-rniftyreg, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ravecore_0.1.1-1.ca2404.1_all.deb Size: 2126652 MD5sum: 36dda86850d2c07556d097458322d24d SHA1: dbe5e4f5b3b975144a8d765519bde466e032fa9e SHA256: 03ae7ad073921c576d1014728701ffe12d187c916296a6974eaea58c49d9b2fe SHA512: c81eccbb592c297debcdb812d0355ceae972534b436d7907b5d17337ca6bfabb2bc9051968bc180fbbaacfd9534b44b313b3033ce7bd224db87cc6228c8e0802 Homepage: https://cran.r-project.org/package=ravecore Description: CRAN Package 'ravecore' (Core File Structures and Workflows for 'RAVE') Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in 'RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, ). Supports brain imaging data structure (BIDS) and native file structure to ingest signals from 'Matlab' data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and 'FreeSurfer' formats, providing brain imaging normalization to template brain, facilitating 'threeBrain' package for comprehensive electrode localization via 'YAEL' (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, ). Package: r-cran-ravel Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny, r-cran-tibble Suggests: r-cran-keyring, r-cran-knitr, r-cran-lintr, r-cran-pkgdown, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ravel_0.1.4-1.ca2404.1_all.deb Size: 273044 MD5sum: 67c31cfe0b6904a8b957381b227967be SHA1: f842290a14a6ba2ebb0aa18b2ab3a2e9250c84ce SHA256: a9f4f66ebd513bb94ad6d2c3625f667c1af7e1ba9a1f9d3b006dd27db0232a7f SHA512: c1f4d337c7d9d48e6d998f26ad96750527fc7c89e80af9e53f980c4d879237928c2ff4d1988e794a043aa19e3abf1db9cfbffd1887dbf24f7e6955b7e320831f Homepage: https://cran.r-project.org/package=ravel Description: CRAN Package 'ravel' (AI Copilot for R Analysis Workflows in 'RStudio') An AI copilot for R users in 'RStudio' and Posit workflows with active-editor, workspace, object, console, plot, and git-aware context. Provides statistical helpers for interpreting lm() and glm() models, stages code and file actions before execution, drafts reproducible 'Quarto' content, and connects to official provider APIs or CLIs for 'OpenAI', 'GitHub Copilot', 'Gemini', and 'Anthropic'. Package: r-cran-ravelry Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ravelry_0.1.0-1.ca2404.1_all.deb Size: 102492 MD5sum: c5a64ce52afa6428e7a99be5ef7975bc SHA1: df44da1dac6fecd4d63065b2cbf5c87c06d830b4 SHA256: 96aa20e69c577230c31b85b5a9cdf18a8efdb82f68b994c30840b9e13ea0657e SHA512: 4d36c78b6c261e736dca4ffff989addb24477596b736ef71cc7c2ffe4f9542e1486523f9d7974670f8761a5b7ece976598f588de87cbaeca8af2af8125acc951 Homepage: https://cran.r-project.org/package=ravelRy Description: CRAN Package 'ravelRy' (An Interface to the 'Ravelry' API) Provides access to the 'Ravelry' API . An R wrapper for pulling data from 'Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id. Package: r-cran-raven.rdf Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-raven.rdf_0.2.0-1.ca2404.1_all.deb Size: 58634 MD5sum: a6724cd66b4c1b756079e9650cd00644 SHA1: 57c31f2ddba9d4308d0a09c4cc7e720a18616992 SHA256: df04ea240b46773ed427477583bbcedee6dea44d20282775c805dff4ac64f129 SHA512: 2e6f98f912e34c3754154dfeed14d985d915baaa23af5b236657a579d426993d49132705d8f3e71966a5996ee7a74fddcc7a2a1547d17998217de090ef5f35b1 Homepage: https://cran.r-project.org/package=raven.rdf Description: CRAN Package 'raven.rdf' (An R Interface for Raven DataFrames (Beta0)) Provides an I/O interface between R data.frames and Raven DataFrames. 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Package: r-cran-ravepipeline Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-callr, r-cran-cli, r-cran-digest, r-cran-fastmap, r-cran-future, r-cran-fst, r-cran-glue, r-cran-jsonlite, r-cran-knitr, r-cran-promises, r-cran-r6, r-cran-remotes, r-cran-rlang, r-cran-targets, r-cran-uuid, r-cran-yaml, r-cran-logger Suggests: r-cran-ellmer, r-cran-dipsaus, r-cran-filearray, r-cran-future.apply, r-cran-globals, r-cran-ieegio, r-cran-pkgsearch, r-cran-rpymat, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shidashi, r-cran-threebrain, r-cran-testthat, r-cran-visnetwork, r-cran-later, r-cran-shiny, r-cran-mirai Filename: pool/dists/noble/main/r-cran-ravepipeline_0.2.0-1.ca2404.1_all.deb Size: 1005148 MD5sum: a9c88e17915e8a3a44b4b03d0ac47d3a SHA1: f1e69eccd812df7b8828232f1e0cd90b49761f6a SHA256: fddea7e40ead87d19b7284de0d67055c116dbb394f73cb753483999081526098 SHA512: 61c01ef7ff7447ff40264836124c73379590c5e4d32ef3377e840bed6f5dd50e908709efdef8d12b04f6a29b27615a11f7d60e4d27666e179cda07046f5a02af Homepage: https://cran.r-project.org/package=ravepipeline Description: CRAN Package 'ravepipeline' (Reproducible Pipeline Infrastructure for Neuroscience) Defines the underlying pipeline structure for reproducible neuroscience, adopted by 'RAVE' (reproducible analysis and visualization of intracranial electroencephalography); provides high-level class definition to build, compile, set, execute, and share analysis pipelines. Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard templates for extending and building customized pipelines. See the full documentations at ; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, ), or run citation("ravepipeline") for details. Package: r-cran-raw Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-actuar, r-cran-chainladder, r-cran-dplyr, r-cran-devtools, r-cran-fincal, r-cran-fitdistrplus, r-cran-forcats, r-cran-ggplot2, r-cran-insurancedata, r-cran-knitr, r-cran-lahman, r-cran-lubridate, r-cran-maps, r-cran-mondate, r-cran-nlme, r-cran-nycflights13, r-cran-purrr, r-cran-randomforest, r-cran-randomnames, r-cran-readr, r-cran-readxl, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tweedie, r-cran-xml Filename: pool/dists/noble/main/r-cran-raw_0.1.8-1.ca2404.1_all.deb Size: 931504 MD5sum: cf221fc3d05af199e965a206a278a82b SHA1: 599e6f1333bbd27e1549266598f4d2f7aaf4a91c SHA256: 4e336d676d6a6171046feca02fdd20fa9cbdc9a4c3973f2b78133f3a476e985b SHA512: ee19db68b852fbc446c0aed0a9e7608ba7760d767107e393e60078e67c709322802ed49d870e04b8f68af448738ea4f2925e1375c0ea2162d297434ec510b89f Homepage: https://cran.r-project.org/package=raw Description: CRAN Package 'raw' (R Actuarial Workshops) In order to facilitate R instruction for actuaries, we have organized several sets of publicly available data of interest to non-life actuaries. 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Package: r-cran-rawks Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rocr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rawks_0.1.0-1.ca2404.1_all.deb Size: 41764 MD5sum: 7f77fc5450d533fa8ce12f98925b6ab0 SHA1: 5a1f4036a59cca0bcc2bea1764cb495d7d339f85 SHA256: ed6ee8c49f358c6706f86ffad453c95a181a405eaca289747246032295f4e86f SHA512: ca67cdd0a648e71b9df3f79149e9bad6702a53b4d9e9c2e3e48552d6ad96830d5138b39c139fff53566b2f2445059df97e6ef156740b33bd5917d4e6edc77007 Homepage: https://cran.r-project.org/package=rawKS Description: CRAN Package 'rawKS' (Easily Get True-Positive Rate and False-Positive Rate and KSStatistic) The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for credit risk scoring models. 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Package: r-cran-raws.profile Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-tibble, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-raws.profile_0.1.0-1.ca2404.1_all.deb Size: 28254 MD5sum: ccfcc300a5b13a0ed99846718c7e9ce8 SHA1: 8646d9e531dac048cecfbdc64cc4f12179f42864 SHA256: 9e635eb93d82e69b6746858ae9c252740e1fa31b3f30a9b4c8bd928c624f1b56 SHA512: 6e9cad1d5f83fc0d944d719ab2a5c073e7eee33bf4dddd6fac54e315ba142536da4dda0c497d1136c1d4e426441167eb75dfa8065014299070173d022abb5286 Homepage: https://cran.r-project.org/package=raws.profile Description: CRAN Package 'raws.profile' (Managing Profiles on Amazon Web Service) This is an R wrapper from the AWS Command Line Interface that provides methods to manage the user configuration on Amazon Web Service. 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It is possible to link behavioral labels extracted from 'BORIS' software or manually written in a csv file. Package: r-cran-rbmf Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rbmf_1.1-1.ca2404.1_all.deb Size: 79334 MD5sum: d9f4aa1a26ae941ebeb54a356578de10 SHA1: bdf1ed6248c172b1aa5a4a8d99165043166ca776 SHA256: 459ab4d858d918943336403cc67b1da9ceeda4334fb5dcefadcce783d8684940 SHA512: 6d540fa5a76caf55a384bac55d87bc569e53acb1191fcfaaaaec17e2420168d95c163638b5a64b3616fc5159f1905eac2055952230d38d445023ac956a32766b Homepage: https://cran.r-project.org/package=rBMF Description: CRAN Package 'rBMF' (Boolean Matrix Factorization) Provides four boolean matrix factorization (BMF) methods. BMF has many applications like data mining and categorical data analysis. BMF is also known as boolean matrix decomposition (BMD) and was found to be an NP-hard (non-deterministic polynomial-time) problem. Currently implemented methods are 'Asso' Miettinen, Pauli and others (2008) , 'GreConD' R. Belohlavek, V. Vychodil (2010) , 'GreConDPlus' R. Belohlavek, V. Vychodil (2010) , 'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) . Package: r-cran-rbmi Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1337 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mmrm, r-cran-pkgload, r-cran-matrix, r-cran-r6, r-cran-assertthat, r-cran-jinjar, r-cran-fs, r-cran-stringr, r-cran-lifecycle Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-testthat, r-cran-emmeans, r-cran-tibble, r-cran-mvtnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-lubridate, r-cran-purrr, r-cran-ggplot2, r-cran-rstan, r-cran-r.rsp, r-cran-withr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rbmi_1.6.1-1.ca2404.1_all.deb Size: 862918 MD5sum: dd8b3605e32c5c61af9fb9a666cfb6d1 SHA1: cb82fc546977e5d7009287bc44717acda34c3fff SHA256: 345605c307c3f3e6df3a6d13690dea133d8a1b3276c17d1c2a14640dd0c5dcdf SHA512: 4516ae92c600096e657ecde86b5185f54aed36f01dabff26bafdf0aea95380ec91a26b9504feccd043191880742adcd5c903b4a17786547d4419922b9bfe2412 Homepage: https://cran.r-project.org/package=rbmi Description: CRAN Package 'rbmi' (Reference Based Multiple Imputation) Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. 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Restrictions can be within regimes, across regimes, or both, and are supported in two forms: an affine parameterization (Form A: delta = S*theta + s) and explicit linear constraints (Form B: R*delta = r). Provides break date estimation with confidence intervals, a restricted sup-F test for the null of no structural change, simulation of critical values by Monte Carlo, and a bootstrap restart procedure to reduce the risk of convergence to spurious local optima. Also implements a generalized regression tree (linear model tree) procedure where each leaf contains a linear regression model rather than a local average. Reference: Perron, P., and Qu, Z. (2006). 'Estimating Restricted Structural Change Models.' Journal of Econometrics, 134(2), 373-399. . Package: r-cran-rbrsa Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-writexl, r-cran-rlang Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rbrsa_0.2.0-1.ca2404.1_all.deb Size: 114876 MD5sum: f2b75878e9fee10f0b98f3048c9a8470 SHA1: bf41a8b2fd469c0cd16d59edcfb72f55ccf831ea SHA256: d61750e7476bc4d8028a9fefaee44b7a529b5ffd3f076f04b1c195bf64fe3da1 SHA512: 63635ccefed5d619e1fb91288fec99757056d5a61fd08261cb9f0d89362561cb28c616f724008a2ccfc23f55d2b3264632267833fd58f3c7f5f4fa03acd822c9 Homepage: https://cran.r-project.org/package=rbrsa Description: CRAN Package 'rbrsa' (Fetch Turkish Banking Sector Data from the Turkish BankingRegulation and Supervision Agency) The goal of the 'rbrsa' package is to provide automated access to banking sector data from the Turkish Banking Regulation and Supervision Agency (BRSA, known as BDDK in Turkish). 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Package: r-cran-rbtest Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-mice, r-cran-psych Filename: pool/dists/noble/main/r-cran-rbtest_1.1-1.ca2404.1_all.deb Size: 29574 MD5sum: 185d557fe1462951dd55462c13eee6d6 SHA1: 6c74b99f6202e253fef59e13384b96b927eb3ea6 SHA256: 1178f11381e6c9ada27a6499029a784915ee8314f37f3b95dc317baf0fde378a SHA512: f60e586a218149d03e39a1d2de53591a8d9da9add87b39a5bf479e4dbd4ec7be745f304a48fd21bfccb5d2adcb53cb3522a5e7323d336316df2b0770f6048cd9 Homepage: https://cran.r-project.org/package=RBtest Description: CRAN Package 'RBtest' (Regression-Based Approach for Testing the Type of Missing Data) The regression-based (RB) approach is a method to test the missing data mechanism. 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Package: r-cran-rcbalance Architecture: all Version: 1.8.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-plyr, r-cran-rlemon Suggests: r-cran-optmatch, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcbalance_1.8.8-1.ca2404.1_all.deb Size: 75940 MD5sum: 6a6a6f8ef1fdb919f8baf0a91c7222b5 SHA1: 8df82a4e52464af44405cfb2e904c0454d8b4e94 SHA256: 263fa7c4ee31f32d3dece4bc6d56d6db4da1392765e10ce8c67693d1927561b3 SHA512: 103a2a4b84b36c6433f72b9a5ed5e988478e16bc5d619d628454a724481aa0b9f428d392a1afab5b20061f6061fc737b3ce9633eb3e5dc0a562dd6846bedf152 Homepage: https://cran.r-project.org/package=rcbalance Description: CRAN Package 'rcbalance' (Large, Sparse Optimal Matching with Refined Covariate Balance) Tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . Package: r-cran-rcbr Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rmosek, r-cran-rebayes, r-cran-orthopolynom, r-cran-formula, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-digest Filename: pool/dists/noble/main/r-cran-rcbr_0.6.2-1.ca2404.1_all.deb Size: 156430 MD5sum: 6f945c8fb3fb87d5da7c0be13397bf06 SHA1: 1e99b9f626193e818c7933d51c188c5f3e6d73ed SHA256: c9db8984f325cac489e25617693522aed5dfdeba09f8f71015741a8989caeb85 SHA512: a0d3557d57c5f31da57a0d97107c8cfd5ab94ea9b436ef5b963d511bc8d70bd4089ba9ac4ca9026af5f3fe0d3c878138f8e510326876b3d06429707afb784330 Homepage: https://cran.r-project.org/package=RCBR Description: CRAN Package 'RCBR' (Random Coefficient Binary Response Estimation) Nonparametric maximum likelihood estimation methods for random coefficient binary response models and some related functionality for sequential processing of hyperplane arrangements. See J. Gu and R. Koenker (2020) . Package: r-cran-rcbsubset Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-plyr, r-cran-rcbalance, r-cran-rlemon Suggests: r-cran-testthat, r-cran-optmatch Filename: pool/dists/noble/main/r-cran-rcbsubset_1.1.7-1.ca2404.1_all.deb Size: 56500 MD5sum: 5c9478b2944c92f324cd6265838f9734 SHA1: 0aab9e8b9fcde30ecab5545681751db82eaa89a3 SHA256: f9d48b4e3d82699efb745f61bd49e45855c1437caed4e51ccbcc1624b929f8ba SHA512: 7c4fb507a2352c6391ba3a7cf9bfabc11783b99f27658918667d7e87bff93decd3c2d52a3b663fcfed772fe2c990e249607c05e69b0867bfb98d63485fed8367 Homepage: https://cran.r-project.org/package=rcbsubset Description: CRAN Package 'rcbsubset' (Optimal Subset Matching with Refined Covariate Balance) Tools for optimal subset matching of treated units and control units in observational studies, with support for refined covariate balance constraints, (including fine and near-fine balance as special cases). A close relative is the 'rcbalance' package. See Pimentel, et al.(2015) and Pimentel and Kelz (2020) . The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . Package: r-cran-rcc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcc_1.0.0-1.ca2404.1_all.deb Size: 22580 MD5sum: 0bd36948fbae9774382f5c367d2119f2 SHA1: d0332849d7d3719cfd0557d8554782b7f0942fad SHA256: 1efb449d9c7e0eb5efd82647a263ad3c8257ba5ce8421b2a111b773ea86d0755 SHA512: e9d646ae1c2085e618aa9200383f741e2f7bbceeb2003de9f47bf16d4abe14192123f3a6c7eccb11e0edbb28e0e36607f9748fbe26cae3ea7803f95dee00ed92 Homepage: https://cran.r-project.org/package=rcc Description: CRAN Package 'rcc' (Parametric Bootstrapping to Control Rank Conditional Coverage) Functions to implement the parametric and non-parametric bootstrap confidence interval methods described in Morrison and Simon (2017) . Package: r-cran-rccola Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-redcapapi, r-cran-getpass, r-cran-yaml, r-cran-keyring Filename: pool/dists/noble/main/r-cran-rccola_1.0.2-1.ca2404.1_all.deb Size: 20512 MD5sum: 0855709b88f0af817d3b5ed4804d4113 SHA1: ceff3d4ceefde2934e7fa29749fb2bb79bfef82c SHA256: 574f1fb65c169a17c7c471b815b58a5668a1735ee2879ca73a492e9d7c0c127b SHA512: 2855e2e7698f24308027119690ab0e68de4cac64e567a2182de23da4a961ae817f661ec3c212e47212cc1786a25d5d3c02514c9fb9a07fa4fb36a0a7b657646d Homepage: https://cran.r-project.org/package=rccola Description: CRAN Package 'rccola' (Safely Manage API Keys and Load Data from a REDCap or OtherSource) The handling of an API key (misnomer for password) for protected data can be difficult. This package provides secure convenience functions for entering / handling API keys and pulling data directly into memory. By default it will load from REDCap instances, but other sources are injectable via inversion of control. Package: r-cran-rccpca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rccpca_0.1.0-1.ca2404.1_all.deb Size: 14316 MD5sum: cead4804c68d5bd6d671a7fc723822e6 SHA1: cc181a2d4eb02e05ea28fddf25f84b2b35f42441 SHA256: ccc219d8cb1994c7d76d4e0bf8c544a3801f6dafbbdc7efab374b708589b824e SHA512: 43006e13200e1f79cb7ebe84e8d5b877042424909148d1ca8f5189dc93f89d25c6712e7c8e101f559025a49b5566d7c6c9d4d837b82dd19cb450e37a6b964520 Homepage: https://cran.r-project.org/package=RCCPCA Description: CRAN Package 'RCCPCA' ("Retained Component Criterion for Principal Component Analysis") The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021). Package: r-cran-rcd3 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcd3_0.1.1-1.ca2404.1_all.deb Size: 28780 MD5sum: d1100c01e5c945084efd098f86ec7c29 SHA1: dea884ab4143d6e9c1d650946032dcea96d200f9 SHA256: 85df1ab1fccc6461372e1d63246c31e6517941c27e052798de71ab3e61d350b6 SHA512: 8611301858fa60fdfdfc5e6a00bc7fa16c662a5deb4b3ee4645cc1691d45c4b6f5cae72c01f2d0b4b8d00edad758023fcaf09c60548ec7bca3d01071d63143a4 Homepage: https://cran.r-project.org/package=rcd3 Description: CRAN Package 'rcd3' (Efficient Row-Column Designs for 3 Level Factorial Experimentsin 3 Rows) Provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) and Dash, S., Parsad, R., and Gupta, V. K. (2013) . Package: r-cran-rcdea Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-benchmarking Filename: pool/dists/noble/main/r-cran-rcdea_1.0-1.ca2404.1_all.deb Size: 57354 MD5sum: 5852e0e2785c20be4e20bf83488367f2 SHA1: 23d1fe79cf9b4a4aaf40eb7681a9be8301b3837e SHA256: 5e5bfc00840bc9eb9b411c12b17925570802ba4333a9c6d81e8c7fad42aada96 SHA512: 6930d925cbf09996ea91bdf465b112b6c8517fb36392df4f8f96120aac6167ad7057c40c9d9d8412e475e8ff9f50ef8d81992b6c60d389a70bb056bd1312d647 Homepage: https://cran.r-project.org/package=rcDEA Description: CRAN Package 'rcDEA' (Robust and Conditional Data Envelopment Analysis (DEA)) With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. The robust approach is based on the work of Cazals, Florens and Simar (2002) . The conditional approach is based on Daraio and Simar (2007) . Besides we provide graphs to help with the choice of m. We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units. Package: r-cran-rcdf Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-duckdb, r-cran-haven, r-cran-zip, r-cran-openssl, r-cran-dplyr, r-cran-jsonlite, r-cran-dbi Suggests: r-cran-arrow, r-cran-dbplyr, r-cran-openxlsx, r-cran-rsqlite, r-cran-lifecycle, r-cran-testthat, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-rlang, r-cran-tibble, r-cran-withr, r-cran-gt Filename: pool/dists/noble/main/r-cran-rcdf_0.1.6-1.ca2404.1_all.deb Size: 171668 MD5sum: 049015c7484e50903c289a046b82e9b2 SHA1: 1d60d64e2dfdce2d18c71c20cbf47f786a188569 SHA256: e66a9d250740e3663ba3b83059f4008c6797c552b40cad6daf52573143dd28a1 SHA512: 3e7d66de9ba3dae8593776e648eae3e4b5eea1aff1501db6abef56d2938706aa6aa6de6206a220604349a51d59c4f3d963fcde72cde45b53c0d4a9649b9651aa Homepage: https://cran.r-project.org/package=rcdf Description: CRAN Package 'rcdf' (A Comprehensive Toolkit for Working with Encrypted Parquet Files) Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions. Package: r-cran-rcdk Architecture: all Version: 3.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcdklibs, r-cran-fingerprint, r-cran-rjava, r-cran-png, r-cran-iterators, r-cran-itertools Suggests: r-cran-xtable, r-cran-runit, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-rcdk_3.8.2-1.ca2404.1_all.deb Size: 560360 MD5sum: 6beb94f44c7588188719be68a9ff9e42 SHA1: 759a72485d2a553ca330ba10298a09c6002c4769 SHA256: 4d505609f9dc06b22ad0838d4b41b319af8c004ba90ae0dd38427495bbe895e2 SHA512: 3a9e7cce4ad9d7418b25c0fe87ec4b8c2699e6bc2a11cad912eddd37070b5e914123e3e8f1cb79af33adf22fb38b026189e0d8c211b4d45fc03dfb8c47aa3837 Homepage: https://cran.r-project.org/package=rcdk Description: CRAN Package 'rcdk' (Interface to the 'CDK' Libraries) Allows the user to access functionality in the 'CDK', a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the 'CDK' API allows the user to view structures in 2D. Package: r-cran-rcdklibs Architecture: all Version: 2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20540 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-rcdklibs_2.9-1.ca2404.1_all.deb Size: 19255090 MD5sum: 469af198e93b0fca2656a4e01721fb3f SHA1: 650559c9ff7da0ad5b2ffc1f96ea88a48e03cf8d SHA256: be68cfe540c074c665be5d1e6262486eea017647bbeec42848f1869744de5284 SHA512: 37b9bd78a45de00d8a3278d0c108c84d316900100f86d880b3be140328a3f15aeb20947446c16d9f85c28794ccf08e7d2aaf9ad8bf50d0d502f594bc92f2b825 Homepage: https://cran.r-project.org/package=rcdklibs Description: CRAN Package 'rcdklibs' (The CDK Libraries Packaged for R) An R interface to the Chemistry Development Kit, a Java library for chemoinformatics. Given the size of the library itself, this package is not expected to change very frequently. To make use of the CDK within R, it is suggested that you use the 'rcdk' package. Note that it is possible to directly interact with the CDK using 'rJava'. However 'rcdk' exposes functionality in a more idiomatic way. The CDK library itself is released as LGPL and the sources can be obtained from . Package: r-cran-rcdo Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang Suggests: r-cran-glue, r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcdo_0.3.2-1.ca2404.1_all.deb Size: 1454100 MD5sum: 98408bbeb0741b0876b324b9e787a053 SHA1: f548355e6316056151309fb9767d8302cb51c2a0 SHA256: 07041e6ebe82e60dd4f0d07f415ca6be8a95227a719acd1512cba60bc17f2660 SHA512: 1ef21084453b937638f0c4d1c05bf6fbbddbb3436a2b9e83ce4ffc5e72b7a38b2f40d1264a9192c12e3334c6e729d53e617c1b4c923ff43e560ca6d8a3884c3d Homepage: https://cran.r-project.org/package=rcdo Description: CRAN Package 'rcdo' (Wrapper of 'CDO' Operators) Provides a translation layer between 'R' and 'CDO' operators. Each operator is it's own function with documentation. Nested or piped functions will be translated into 'CDO' chains. Package: r-cran-rceim Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1352 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rceim_0.3-1.ca2404.1_all.deb Size: 1309164 MD5sum: 6ec7c7dd3e03db8ab9aa7c3ba6ab96be SHA1: 70b0ecd75963a7ea6f3f083c8fb8180c397dbb4e SHA256: 36e11cc60be72b4b35e48ff3a61da93b6ccaebd954b40ce27882971fc9340ad2 SHA512: 13929e125aac9af92bd4e2a297058f5b3dcde78fd1f42b0ed3cf5500ae60984a488331924a25347042f533a186ef7cf5297abc720b4a354aed1c5b182e732042 Homepage: https://cran.r-project.org/package=RCEIM Description: CRAN Package 'RCEIM' (R Cross Entropy Inspired Method for Optimization) An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions. Package: r-cran-rcens Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/noble/main/r-cran-rcens_0.2.2-1.ca2404.1_all.deb Size: 195014 MD5sum: a7ec1f215463b6c038fc27e2c16e1b21 SHA1: 4c41ef05a0b56fdadd03c2765797f34bdd796302 SHA256: 7955293a9601b7940d3c625386aeccab5e6a1df4386a977930cc6836e4b88bf7 SHA512: 0f613614fcb70acac2ed5449d8a2d0312abe05354c0d8dd15097d18b4aa19e41c48b9fde19a455f9b02cf01ee9b6cb365217ea8443d91bd08a83a3d9e78e0260 Homepage: https://cran.r-project.org/package=rcens Description: CRAN Package 'rcens' (Generate Sample Censoring) Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the testing phase, with two options of fixed length intervals and random lengths. Additional functions generate complex inspection, delayed-entry, hybrid, progressive, covariate-dependent, frailty-dependent, and competing-risk observation structures while retaining a consistent and verifiable data object. 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Other functions assist in searching for available datasets, geographies, group/variable concepts of interest. Also provided are functions to access and layer (via standard piping) displayable geometries for the US, states, counties, blocks/tracts, roads, landmarks, places, and bodies of water. Joining survey data with many of the geometry functions is built-in to produce choropleth maps. Package: r-cran-rcereal Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cpp11, r-cran-rcpp, r-cran-decor, r-cran-git2r, r-cran-httr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcereal_1.3.2-1.ca2404.1_all.deb Size: 212740 MD5sum: 6f026fb23a0690e4f9cce6f845c884b7 SHA1: 63add5be5b4aed34cc89aef014051d8bf2a92a83 SHA256: 233231d70ece2c344f8cb23ec98ec8c01fd246bbf926e634540a9e58d2ad2a76 SHA512: 8228860f5e8c2033af66a2eae5d411a8fd0f3a0fdcaf658c7d4d80c6d5798fe83878b150bc653553e2d2ce732c90fd262a1e9be6f3f01327600cd6cec7de9350 Homepage: https://cran.r-project.org/package=Rcereal Description: CRAN Package 'Rcereal' ("Cereal Headers for R and C++ Serialization") To facilitate using 'cereal' with R via 'cpp11' or 'Rcpp'. 'cereal' is a header-only C++11 serialization library. 'cereal' takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, 'XML', or 'JSON'. 'cereal' was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see for more information. 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Package: r-cran-rcgls Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-ncdf4, r-cran-raster, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcgls_1.0.3-1.ca2404.1_all.deb Size: 24540 MD5sum: 7c02f9e25583a6a6af2a1e962502bb33 SHA1: 08a131535f4663f311a284143fcef2c7b517b271 SHA256: 91cb08eafcbbc5a23279c2d23513154f557698aa71671e2637107bf1ddc71d3f SHA512: bcab8f8d68fc98afe5ed4ab47484c48c5151848301a854b8acef89e765d504caa7b7a183f276c45de05cb40ad117eeb37ab62c4e6ade9660399c1e21660f80ec Homepage: https://cran.r-project.org/package=RCGLS Description: CRAN Package 'RCGLS' (Download and Open Data Provided by the Copernicus Global LandService) Download and open manifest files provided by the Copernicus Global Land Service data . The manifest files are available at: . Also see: . Before you can download the data, you will first need to register to create a username and password. Package: r-cran-rchallenge Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rchallenge_1.3.4-1.ca2404.1_all.deb Size: 112696 MD5sum: 341ba4143b69f22b76f9279ae81c1289 SHA1: a453870a7371f118abc898b1b2a56d2f8efc9df3 SHA256: 37e50d2962a6f5058df5a47a01cc78e8abaf15abbd999353eae7d1739426ee3c SHA512: 93cca39ff5457fe02b286926a07337e5614cfeb007ac49591cb4eb9f287ffe3a7c86169c2caff3a1105e0c707841753e527087798a4ae37bcd72eb47e6f5d974 Homepage: https://cran.r-project.org/package=rchallenge Description: CRAN Package 'rchallenge' (A Simple Data Science Challenge System) A simple data science challenge system using R Markdown and 'Dropbox' . It requires no network configuration, does not depend on external platforms like e.g. 'Kaggle' and can be easily installed on a personal computer. Package: r-cran-rchasm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-envstats, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-ggstar, r-cran-magrittr, r-cran-matrixstats, r-cran-mclust, r-cran-rstatix, r-cran-sirt, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-rchasm_1.0.1-1.ca2404.1_all.deb Size: 559132 MD5sum: af9f59dd35c0294b19b241f4ebd06283 SHA1: 2c223f2d2aa9558c05998a7e7c9af2682649bd14 SHA256: b2548db1ec5b3a63ccb53a5fa3c60dabfab2df29c61c701ef57d45c48a2df529 SHA512: bca0fa1f553b7e66f3f24d66ac82a856d556e1a99dcabe34048e66d03a216c0d2161d8814c714352f58ff9fe7110ff267f800435a760cee9b38f5d27626b56fa Homepage: https://cran.r-project.org/package=RChASM Description: CRAN Package 'RChASM' (Detection of Chromosomal Aneuploidies in Ancient DNA Studies) An R implementation of ChASM (Chromosomal Aneuploidy Screening Methodology): a statistically rigorous Bayesian approach for screening data sets for autosomal and sex chromosomal aneuploidies. This package takes as input the number of (deduplicated) reads mapping to chromosomes 1-22 and the X and Y chromosomes, and models these using a Dirichlet-multinomial distribution. From this, This package returns posterior probabilities of sex chromosomal karyotypes (XX, XY, XXY, XYY, XXX and X) and full autosomal aneuploidies (trisomy 13, trisomy 18 and trisomy 21). This package also returns two diagnostic statistics: (i) a posterior probability addressing whether contamination between XX and XY may explain the observed sex chromosomal aneuploidy, and (ii) a chi-squared statistic measuring whether the observed read counts are too divergent from the underlying distribution (and may represent abnormal sequencing/quality issues). 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'ChEA3' integrates evidence from ChIP-seq, co-expression, and literature resources to prioritize transcription factors regulating a given set of genes. This package provides convenient R functions to query the API, retrieve ranked results across collections (including integrated scores), and standardize output for downstream analysis in R/Bioconductor workflows. See or Keenan (2019) for further details. 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This package provides a convenient interface to interact with the REST API of 'ChromaDB' . 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ChronoModel is a friendly software to construct a chronological model in a Bayesian framework. Its output is a sampled Markov chain from the posterior distribution of dates component the chronology. The functions can also be applied to the analyse of mcmc output generated by Oxcal software. Package: r-cran-rcicr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matlab, r-cran-png, r-cran-jpeg, r-cran-dplyr, r-cran-scales, r-cran-viridis, r-cran-dosnow, r-cran-foreach, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-tibble, r-cran-yesno Suggests: r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcicr_1.5.0-1.ca2404.1_all.deb Size: 689188 MD5sum: afdd8d2b6bac91a0b5553f41f87189f5 SHA1: 7ddec6ef2d0e9570dbcbb2a24e3424f1ac19472a SHA256: a32e871c69a02b397c1537548057c10fe3b2ec7257343107191c7670a93d9fdd SHA512: c3878603f810e005611940df294042ba96f1f41b4d9339fa6cff79516e5b55c6759cfaaa495f5d8db3a726ee7865c79fe84eae75baac2224b2cf18b9aa3543af Homepage: https://cran.r-project.org/package=rcicr Description: CRAN Package 'rcicr' (Reverse-Correlation Image-Classification Toolbox) Generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces). For the method see Dotsch and Todorov (2012) ; for a practical primer see Brinkman, Todorov and Dotsch (2017) . 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Which proportion of my sample does not only change in a statistically significant way (see question one), but also in a clinically significant way (e.g. change from a test score regarded "dysfunctional" to a score regarded "functional")? This package allows you to very easily create a scatterplot of your sample in which the x-axis maps to the pre-scores, the y-axis maps to the post-scores and several graphical elements (lines, colors) allow you to gain a quick overview about reliable changes in these scores. An example of this kind of plot is Figure 2 of Jacobson & Truax (1991). Referenced article: Jacobson, N. S., & Truax, P. (1991) . Package: r-cran-rcircos Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcircos_1.2.2-1.ca2404.1_all.deb Size: 1570324 MD5sum: baf802ca25bec8560216bf79e3041164 SHA1: 94190190f7ed83a05054d6c2579c126d2d26435b SHA256: 4cbbe0e41f97dc1e7ee52e0bb256aa28290a2bc69f9643b030f8762e6d951d69 SHA512: 1daeb6b95fc846d485e8868f0ec73e5b62b8db53a69ad8c62b8a4ab344b03ca4efc977650b230d079efd96c4f8501067f064b6167f64af78d905ded8f7277a00 Homepage: https://cran.r-project.org/package=RCircos Description: CRAN Package 'RCircos' (Circos 2D Track Plot) A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation. Package: r-cran-rcissvae Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-purrr, r-cran-gtsummary, r-cran-rlang, r-bioc-complexheatmap Suggests: r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-kableextra, r-cran-mass, r-cran-fastdummies, r-cran-palmerpenguins, r-cran-glue, r-cran-withr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rcissvae_0.0.5-1.ca2404.1_all.deb Size: 3211604 MD5sum: 80a3c3aecdb12d37c294107ee8242d0d SHA1: 1e17204199d8ea6c31c28b7e49191635a99c11a2 SHA256: 2fe8d9c88fb20a61de86e7b8982a85e8f90b3911686424cd7c8dee9ffbbe5adf SHA512: fa95149313eeca93400b88bab17f7d723300f96c32497a70489a0c6be34664872abf8e51202572d2bf2efd0119893a39a3993b70377afa450c16b52aacec48f1 Homepage: https://cran.r-project.org/package=rCISSVAE Description: CRAN Package 'rCISSVAE' (Clustering-Informed Shared-Structure VAE for Imputation) Implements the Clustering-Informed Shared-Structure Variational Autoencoder ('CISS-VAE'), a deep learning framework for missing data imputation introduced in Khadem Charvadeh et al. 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Package: r-cran-rcites Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-testthat, r-cran-tibble, r-cran-rmarkdown, r-cran-rworldmap, r-cran-vcr Filename: pool/dists/noble/main/r-cran-rcites_1.3.0-1.ca2404.1_all.deb Size: 407642 MD5sum: 0f38c4f8fd311d45b54e9148fd67e103 SHA1: 1ce1c0384a92de082c75a751701db4fc1c7ac7d6 SHA256: 7828f6dd1613c776569ba9be9bad91b92618e9f8b132d08bc87fa2258637d050 SHA512: 360a42eff134ce3d699cb4de2115b2d22679c5487a8b0c7e1fa0fe7e6c0e0a56e5dce204c6a813db95250714884ee2d06d4e14e6abfa86eb1159b01f44a624d2 Homepage: https://cran.r-project.org/package=rcites Description: CRAN Package 'rcites' (R Interface to the Species+ Database) A programmatic interface to the Species+ database via the Species+/CITES Checklist API . Package: r-cran-rclabels Architecture: all Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-hmisc, r-cran-magrittr, r-cran-purrr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rclabels_0.1.11-1.ca2404.1_all.deb Size: 162942 MD5sum: e73ae1a2b7ff8a942825112fe2455fa2 SHA1: ba09a29e5582705f26507231e56644fdb3a70a25 SHA256: 3e0a0eb4bc54ffa8c5c5483b4ee2a0d867ed173d488fbc4ac50f8ed98e69ac6b SHA512: 4044f8ce13766345c5f2f05baba30e494831b334f617787bba4703fefc2bf032cca351ac3f9d1d2dcea1c0f24f6d5f4bd2f8e3abd6cf597264a79b4f08dc7119 Homepage: https://cran.r-project.org/package=RCLabels Description: CRAN Package 'RCLabels' (Manipulate Matrix Row and Column Labels with Ease) Functions to assist manipulation of matrix row and column labels for all types of matrix mathematics where row and column labels are to be respected. Package: r-cran-rclade Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-bioc-ggtree, r-cran-deeptime, r-cran-ggplot2, r-cran-rlang, r-cran-stringr, r-cran-tidytree, r-cran-viridislite Suggests: r-bioc-treeio, r-cran-phangorn, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-patchwork, r-cran-shiny, r-cran-optparse, r-cran-yaml, r-cran-vdiffr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-filelock, r-cran-withr Filename: pool/dists/noble/main/r-cran-rclade_1.1.5-1.ca2404.1_all.deb Size: 736250 MD5sum: e054d03cec73d8a495ce50e3b6d708f9 SHA1: e456a3e3ce6d0ebbefb225966ba1af6f8bccbf50 SHA256: e732e647c841a7019d88d1e7b2647b8ceda8d95447c3cb9895ac292fda8ce9a3 SHA512: 0de2dbc7651354471572d336862db045b9efea418bf29ab54399dc8295d7aa9fee96590cd22cd30fac821f0340136bb3175c25135c03a67f8e667bbdd573ed4c Homepage: https://cran.r-project.org/package=Rclade Description: CRAN Package 'Rclade' (Automated Deep-Time Phylogenetic Tree Collapsing andVisualization) Provides a single-function pipeline for automated collapsing and visualization of large phylogenetic trees with geological timescales. Automatically parses taxonomic labels from multiple formats (GTDB, Silva, NCBI, embedded, custom), identifies Most Recent Common Ancestors (MRCAs), assigns color-blind-safe palettes, executes batch collapsing with automatic nesting-aware ordering, integrates 'deeptime' geologic time scales with adaptive time breaks and unit switching, and manages smart legend layout. Supports special ancestral node identifiers (LUCA, LACA, LBCA) for highlighting key nodes in the tree of life. Provides external taxonomy file support for trees with incomplete or missing taxonomic labels. Features real-time logging with timestamps, step tracking, and multiple log levels. Includes comprehensive input validation for tree and sequence file formats. Reduces a 60-line manual workflow to a single function call while preserving full compatibility with the 'ggtree'/'deeptime' ecosystem. The visualization pipeline builds on 'ggtree' Yu et al. (2017) , 'deeptime' Gearty (2025) , and 'ape' Paradis and Schliep (2019) . Package: r-cran-rcldf Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-archive, r-cran-bib2df, r-cran-csvwr, r-cran-digest, r-cran-dplyr, r-cran-jsonlite, r-cran-leaflet, r-cran-logger, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-remotes, r-cran-rlang, r-cran-urltools, r-cran-versionsort Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-htmltools, r-cran-testthat, r-cran-mockthat, r-cran-covr, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-rcldf_1.6.1-1.ca2404.1_all.deb Size: 557598 MD5sum: d90fb99d29b7c808f512cd9d648df30f SHA1: 69d12f7371acb14252c494ae03e78641e6507a5a SHA256: 964c815a95536cd43438c71b9fb741233d588d820774313684a208540bf3c2ef SHA512: f3e05d31515e659cd1d23a95c75e9db2c46598335b75e3107268e980cc95d29d64cff1ecb3cf63f4b4e714ed7e3a51e60463e6638cda6516ab40b5960cac55d5 Homepage: https://cran.r-project.org/package=rcldf Description: CRAN Package 'rcldf' (Read Linguistic Data in the Cross Linguistic Data Format (CLDF)) Cross-Linguistic Data Format (CLDF) is a framework for storing cross-linguistic data, ensuring compatibility and ease of data exchange between different linguistic datasets see Forkel et al. (2018) . The 'rcldf' package is designed to facilitate the manipulation and analysis of these datasets by simplifying the loading, querying, and visualisation of CLDF datasets making it easier to conduct comparative linguistic analyses, manage language data, and apply statistical methods directly within R. Package: r-cran-rclimacell Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-httr, r-cran-lubridate, r-cran-rlang, r-cran-tidyr, r-cran-assertthat, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rclimacell_0.1.4-1.ca2404.1_all.deb Size: 344480 MD5sum: 81ca0a0a113776ea3a83e52975fb41b3 SHA1: 253a6067fa71bfb36239e91d02bca03c84086e15 SHA256: 38ac8237b8253a4070aa53304895b956f9f09ee7cc53765ad7dce5212f1d43b8 SHA512: 80ad382b4e3bbd36790b4cee18a5b62b42015e1b1fe801a5da711885a261c1a294b5c60decb2671b192e31e1cb9e4f2200f4ecabc12c6d1f1d403c0a87927941 Homepage: https://cran.r-project.org/package=RClimacell Description: CRAN Package 'RClimacell' (R Wrapper for the 'Climacell' API) 'Climacell' is a weather platform that provides hyper-local forecasts and weather data. This package enables the user to query the core layers of the time line interface of the 'Climacell' v4 API . This package requires a valid API key. See vignettes for instructions on use. Package: r-cran-rclipboard Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-bslib Filename: pool/dists/noble/main/r-cran-rclipboard_0.2.1-1.ca2404.1_all.deb Size: 20160 MD5sum: 978c2c1e1f67ae543586e281f4923001 SHA1: c742b1615cb993b363eccf2f047ed95af964b074 SHA256: 262b0fa1dc0690a0950393a9ab54120f82c4c4dc3dd7e596f93d99b475a682a9 SHA512: aa51b9100a30b501b69b0c4707b076e84c87446555498e826dc40b76a53cf1050f1bdd1fcc74c4029f5a5552e1bb7dcf5f6bda1ad8a7e1f795278063456be0c0 Homepage: https://cran.r-project.org/package=rclipboard Description: CRAN Package 'rclipboard' (Shiny/R Wrapper for 'clipboard.js') Leverages the functionality of 'clipboard.js', a JavaScript library for HMTL5-based copy to clipboard from web pages (see for more information), and provides a reactive copy-to-clipboard UI button component, called 'rclipButton', and a a reactive copy-to-clipboard UI link component, called 'rclipLink', for 'shiny' R applications. Package: r-cran-rcloner Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-jsonlite, r-cran-processx Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rcloner_0.0.1-1.ca2404.1_all.deb Size: 75360 MD5sum: 3268b3a67c51314d76f40e903d419744 SHA1: d80e7d5bb23e3f9489135f2dfb2b099a4e0be25b SHA256: 1498a450cb6d16d2019350de106dfdbc91703ed06ce169c37c11783e395400aa SHA512: 61687eedcd28452475b4dfca394b252bc6a2c6be8b02cbcd57b41801292e6f985a82ab582126146c7fddc6a5e6db290522613b2275143a168e99a7468879e5bb Homepage: https://cran.r-project.org/package=rcloner Description: CRAN Package 'rcloner' (Interface to 'rclone' Cloud Storage Utility) Provides an R interface to 'rclone' , a command-line program for managing files on cloud storage. 'rclone' supports over 40 cloud storage providers including 'S3'-compatible services ('Amazon S3', 'MinIO', 'Ceph'), 'Google Cloud Storage', 'Azure Blob Storage', and many others. This package downloads and manages the 'rclone' binary automatically and wraps its commands as R functions, returning results as data frames where appropriate. Package: r-cran-rclsp Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-cvxr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rclsp_2.0.1-1.ca2404.1_all.deb Size: 77352 MD5sum: d02e25781780534fd198464e266bec89 SHA1: 8b6606d6d54bdfda7c18883e3c869cfd9284ec5a SHA256: cee0d7244c9a6340953c6d012002d98876233dfb5e7616e94b5e3aa3f25da4a1 SHA512: 83cbd47c64f8ffed9adf32268b64e98272f6603ae843d4b9947b274335be10918011e20d2c4a4354af4262271d34ff316ac81aae6e9b8ed6a0af55f13c0222f4 Homepage: https://cran.r-project.org/package=rclsp Description: CRAN Package 'rclsp' (A Modular Two-Step Convex Optimization Estimator for Ill-PosedProblems) Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. 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Package: r-cran-rclustool Architecture: all Version: 0.91.61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-cran-tkrplot, r-cran-class, r-cran-cluster, r-cran-conclust, r-cran-corrplot, r-cran-e1071, r-cran-factoextra, r-cran-factominer, r-cran-ggplot2, r-cran-jpeg, r-cran-knitr, r-cran-mass, r-cran-mclust, r-cran-mda, r-cran-mmand, r-cran-nnet, r-cran-png, r-cran-randomforest, r-cran-reshape, r-cran-rlang, r-cran-searchtrees, r-cran-sp, r-cran-stringi, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rclustool_0.91.61-1.ca2404.1_all.deb Size: 1471610 MD5sum: c91908cdbe9ee51e874fda477657ecb7 SHA1: 3f30eee4682f934150f0b60cb1851f5f98568adb SHA256: f1c58da4ef13c4ae6836c4956fb6fc4586eaf19e75fb78ec73d6ebe3eaef1b86 SHA512: ee2e865bc70ad9c2a067117fe86f0870801c2faa1bb34e22bd0db1bef629c1b346fb61566e7491977642224f1ba1841a0db42d438329449877dfafea0e233084 Homepage: https://cran.r-project.org/package=RclusTool Description: CRAN Package 'RclusTool' (Graphical Toolbox for Clustering and Classification of DataFrames) Graphical toolbox for clustering and classification of data frames. It proposes a graphical interface to process clustering and classification methods on features data-frames, and to view initial data as well as resulted cluster or classes. According to the level of available labels, different approaches are proposed: unsupervised clustering, semi-supervised clustering and supervised classification. To assess the processed clusters or classes, the toolbox can import and show some supplementary data formats: either profile/time series, or images. These added information can help the expert to label clusters (clustering), or to constrain data frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by Wacquet et al. (2013) and the methodology provided by Wacquet et al. (2013) . Package: r-cran-rcma Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rjava Filename: pool/dists/noble/main/r-cran-rcma_1.1.1-1.ca2404.1_all.deb Size: 339510 MD5sum: 3b8bce84e8a2a733daeb9792b0581183 SHA1: 8c89b9276553e08a4e1441ccf3411235eb9e6882 SHA256: a6821e86da23f444b50a5575c4a53fdb745064e3138dcb858e5d101a084c659c SHA512: e8123757b6bb8740c3904b8e223042445dd4994982fddb7651bdc631458b315c5f8b870ca3d1370a8a0d18676186e75ecbb44b1e86f9690eaacedd8278699459 Homepage: https://cran.r-project.org/package=rCMA Description: CRAN Package 'rCMA' (R-to-Java Interface for 'CMA-ES') Tool for providing access to the Java version 'CMAEvolutionStrategy' of Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy, see . 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Package: r-cran-rcmdr Architecture: all Version: 2.15.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8990 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcmdrmisc, r-cran-car, r-cran-effects, r-cran-abind, r-cran-lme4, r-cran-relimp, r-cran-teachingdemos, r-cran-tcltk2 Suggests: r-cran-aplpack, r-cran-boot, r-cran-colorspace, r-cran-e1071, r-cran-foreign, r-cran-hmisc, r-cran-knitr, r-cran-lattice, r-cran-leaps, r-cran-lmtest, r-cran-markdown, r-cran-mass, r-cran-mgcv, r-cran-multcomp, r-cran-nlme, r-cran-nnet, r-cran-nortest, r-cran-readxl, r-cran-rgl, r-cran-rmarkdown, r-cran-sem Filename: pool/dists/noble/main/r-cran-rcmdr_2.15.0-1.ca2404.1_all.deb Size: 5623664 MD5sum: 065628d0cd10366113686ab67e00902d SHA1: b18a5fd813d92765af6be7d1c61c35916b88b081 SHA256: 1623208714e44d868e40bb830cb4e75311d8ba9451ab3fd2704e834a7bd12bd0 SHA512: af03eb9d5689fcda5b98c212b597f5a29e401311211a32e5e004a95faab80c5f17987560c1077e475f3a73f11f0544a619ed8f924f5a1049766396e745860ef6 Homepage: https://cran.r-project.org/package=Rcmdr Description: CRAN Package 'Rcmdr' (R Commander) A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package. Package: r-cran-rcmdrmisc Architecture: all Version: 2.10.2-1.ca2404.4 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-car, r-cran-sandwich, r-cran-abind, r-cran-colorspace, r-cran-hmisc, r-cran-mass, r-cran-e1071, r-cran-foreign, r-cran-haven, r-cran-readstata13, r-cran-readxl, r-cran-nortest, r-cran-lattice Suggests: r-cran-boot, r-cran-cardata Filename: pool/dists/noble/main/r-cran-rcmdrmisc_2.10.2-1.ca2404.4_all.deb Size: 222866 MD5sum: 6ff5d422ddcdde1263625b82b8016cb8 SHA1: e5d72edbab163a22c519b207b4fb3639f5beea22 SHA256: b452cd02a3e33580f696eacdc8435c78635d4f9077959031d8f8f932fc8dc51f SHA512: b466b5683cde5b134e1ae11504cf4a14741a9a4f9fded082dc205cb832bcf830f5a277d686df6a535e75ad1512d1c3d0fe76c9a959faaf113ef69b528dceaa6f Homepage: https://cran.r-project.org/package=RcmdrMisc Description: CRAN Package 'RcmdrMisc' (R Commander Miscellaneous Functions) Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R. Package: r-cran-rcmdrplugin.arnova Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.arnova_0.0.6-1.ca2404.1_all.deb Size: 1921052 MD5sum: 570cc89fc02e68bf28928704f7c2e4b2 SHA1: d3d62439129178346b4aebbfa748617e3a797c45 SHA256: cb7e27eab1c518c5b7d2a8a403f9e720dce7306183482253e732d48ebd503221 SHA512: 1b00d42efbd53d93e2e0da604fea8fc49ab35fa1e25705f27afc5ac30a5272befb06199dfee0ab28ce0f1575764c692bc596e72bb73741db43225916959fabf4 Homepage: https://cran.r-project.org/package=RcmdrPlugin.aRnova Description: CRAN Package 'RcmdrPlugin.aRnova' (R Commander Plug-in for Repeated-Measures ANOVA) R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. It adds a new menu entry for repeated measures that allows to deal with up to three within-subject factors and optionally with one or several between-subject factors. It also provides supplementary options to oneWayAnova() and multiWayAnova() functions, such as choice of ANOVA type, display of effect sizes and post hoc analysis for multiWayAnova(). Package: r-cran-rcmdrplugin.biclustgui Architecture: all Version: 1.1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biclust, r-bioc-fabia, r-bioc-ibbig, r-cran-superbiclust, r-cran-bcdiag, r-bioc-bicare, r-cran-s4vd, r-cran-bibitr, r-cran-rcmdr, r-cran-gplots, r-cran-viridis Suggests: r-cran-knitr, r-bioc-rqubic, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-rcmdrplugin.biclustgui_1.1.3.1-1.ca2404.1_all.deb Size: 2771874 MD5sum: 3b774b23ccb1d88491e791a8b73b43b1 SHA1: b2344624d15d2977a0d7a13191f739b41e0fb547 SHA256: 890b78b8467d891c5ec58e0f96ecefa3b8a278ef7c41180eababef986fee33e7 SHA512: f10bbba521f3063896e53ac3d8003a0e7f716ad2d8920a77ea80b2a3d7224781d9a4a2ed56446b0fa8870de349cc1024ececa147444882abcf916f5fb9741770 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BiclustGUI Description: CRAN Package 'RcmdrPlugin.BiclustGUI' ('Rcmdr' Plug-in GUI for Biclustering) A plug-in for R Commander ('Rcmdr'). The package is a Graphical User Interface (GUI) in which several biclustering methods can be executed, followed by diagnostics and plots of the results. Further, the GUI also has the possibility to connect the methods to more general diagnostic packages for biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG', 'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and 'BcDiag' are also implemented to be able to further investigate results. The GUI also provides a couple of extra utilities to export, save, search through and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific framework for biclustering in which new methods, diagnostics and plots can be added. Scripts were prepared so that R-package developers can freely design their own dialogs in the GUI which can then be added by the maintainer of 'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk' and 'Rcmdr' and are easy to fill in. (Note: rqubic currently requires manual installation through BiocManager::install('rqubic').) Package: r-cran-rcmdrplugin.bws1 Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crossdes, r-cran-support.bws, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.bws1_0.3-0-1.ca2404.1_all.deb Size: 116226 MD5sum: d02bfdda484d963bbd61caf6f3d0bd06 SHA1: 40133a1d68568498b66bf009bb4e3def8d4293fa SHA256: f30729700625f7543f582ab586172e9e7a35de39678fc513116f6ff488d5fe18 SHA512: ea75b7fbe05f5ada094730d8402088bb25508056bb919184031b808585a8961836363798e97682dc97f25fd0337ef097bafe45d4d9fa70880a182b06a34ac052 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS1 Description: CRAN Package 'RcmdrPlugin.BWS1' (R Commander Plug-in for Case 1 Best-Worst Scaling) Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) . Package: r-cran-rcmdrplugin.bws2 Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-support.bws2, r-cran-support.ces, r-cran-survival, r-cran-rcmdr, r-cran-doe.base Filename: pool/dists/noble/main/r-cran-rcmdrplugin.bws2_0.3-0-1.ca2404.1_all.deb Size: 98740 MD5sum: 1d067a93c9ad5c5ed493d46188b88187 SHA1: 80aa767a06f199c58018d858d3c343753e6e11c0 SHA256: e2eb94c214656f8563acad3404193ca08a59f767fb2d7ce429c69a91cce48457 SHA512: 7903cda202184e19caf622fd6a226a51f58e700e40ad928fbbb8416b954604ab03d7112719a73cc7572283e2690bae9df87c9ea548ac8f4184e104b10bfaf32a Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS2 Description: CRAN Package 'RcmdrPlugin.BWS2' (R Commander Plug-in for Case 2 Best-Worst Scaling) Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) . Package: r-cran-rcmdrplugin.bws3 Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.bws3, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.bws3_0.3-1-1.ca2404.1_all.deb Size: 103840 MD5sum: 16a5f51c65edbb11914bcd0e350eb57b SHA1: 37486f120c50222fc2f140c65c549899add11768 SHA256: b5feda01cf418daf31c2984185398b40022f4bc3d32812d98f09c6d1742da9b5 SHA512: f84ad291be42349141b2c778a108e4da373822b03bb0d0e687bf0b208ac61d83dba312c064b00cd064db4a62124395489b240872e49ce84c9ce46a10fe338604 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS3 Description: CRAN Package 'RcmdrPlugin.BWS3' (R Commander Plug-in for Case 3 Best-Worst Scaling) Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) . Package: r-cran-rcmdrplugin.cpd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpd, r-cran-rcmdrmisc, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.cpd_0.2.0-1.ca2404.1_all.deb Size: 90322 MD5sum: cbf600cfb4899296539a5b17e4d32042 SHA1: 7d4bd414bf314e41cb385b3c6010861b8cda8dc4 SHA256: 6c5d185ef879ce15a2a9e52303e83d20dbe39620784ac0ac1e7bc230f6c5a5f3 SHA512: 34b149c718e4d624f4a681e7272e1677dc5b3fa9263e547f770ab67e35cf413ed7d1977e40d5c1b080d538706e1d317669e92c492137aa6dbeec2240c156fea9 Homepage: https://cran.r-project.org/package=RcmdrPlugin.cpd Description: CRAN Package 'RcmdrPlugin.cpd' (R Commander Plug-in for Complex Pearson Distributions) Provides an 'Rcmdr' plug-in based on the 'cpd' package. Package: r-cran-rcmdrplugin.dccv Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcchoice, r-cran-rcmdr, r-cran-isocodes Filename: pool/dists/noble/main/r-cran-rcmdrplugin.dccv_0.2-0-1.ca2404.1_all.deb Size: 66486 MD5sum: 54d9914228fc6dda08d1983306a3b832 SHA1: 105f98fb41764ab484214b69f83b4a74cd4e00fd SHA256: 354e369ce8dd44dff82c0906e2e01175f3f74238d6093017043526165f394927 SHA512: b4d044b95a2e58cff378c9c6c4b5658209359d6b2b452c3a899ec0bbd3f13f3251f05275cc8bac002eb631910c26543bbad3a04449270483bae88817e38da39c Homepage: https://cran.r-project.org/package=RcmdrPlugin.DCCV Description: CRAN Package 'RcmdrPlugin.DCCV' (R Commander Plug-in for Dichotomous Choice Contingent Valuation) Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) for DCCV. Package: r-cran-rcmdrplugin.dce Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.dce_0.3-1-1.ca2404.1_all.deb Size: 93450 MD5sum: 9bdfc4ba07272efbbba79ba372c9c422 SHA1: 153df9e1ac6a7653d75a68ca164ae9f9102f2903 SHA256: 84cd797bcb3e9c25b219e6bc87d3774b058258e25f9fa4b71338e9f0fca9f604 SHA512: a00a333ae2eb5d204321d886279785edd8a0da5eec65a8747cbcf7cd3bec67b44578e8cc23af57355563f9a0a8208d991121f623c06d4479572cbd567bdf9a32 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DCE Description: CRAN Package 'RcmdrPlugin.DCE' (R Commander Plug-in for Discrete Choice Experiments) Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) . Package: r-cran-rcmdrplugin.depthtools Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-depthtools Filename: pool/dists/noble/main/r-cran-rcmdrplugin.depthtools_1.4-1.ca2404.1_all.deb Size: 71918 MD5sum: 4856b60108232fbe8ac166b60dd51a00 SHA1: 7b90ebfa12e25d1322fc7392d0ed7fcefb3b82b2 SHA256: b781a747b159325c8c0099a2d9f426ee23908727d63e5983d57b9188daee2a44 SHA512: f75cfd0f0a11d6168107100effe8fd2fd5b1ef48cc99a09eaf6da2ad1e687d28b8517bc89ddd331d80c1e4c6d6d3e691f4d8925674261e7145e4efd2dad96096 Homepage: https://cran.r-project.org/package=RcmdrPlugin.depthTools Description: CRAN Package 'RcmdrPlugin.depthTools' (R Commander Depth Tools Plug-in) We provide an Rcmdr plug-in based on the depthTools package, which implements different robust statistical tools for the description and analysis of gene expression data based on the Modified Band Depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. Package: r-cran-rcmdrplugin.doe Architecture: all Version: 0.12-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doe.base, r-cran-frf2, r-cran-doe.wrapper, r-cran-rcmdr, r-cran-rcmdrmisc Suggests: r-cran-frf2.catlg128 Filename: pool/dists/noble/main/r-cran-rcmdrplugin.doe_0.12-6-1.ca2404.1_all.deb Size: 2163842 MD5sum: 6524459ca3c31e80526e57476e948037 SHA1: 7a96e5645a0fc71622fdb554f57e060b8604e643 SHA256: 57ee616dd7d6d529367f5107880f7fa8bca89f954942c5c76fac26ddb3a931fd SHA512: 0ba127c57820873605c049c2f02b5f90e24e06b3285c8021412922a82b4533016fbe049eac2cfd1ea22e935113ce5a99445277469e5e727c8d439e3932832a33 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DoE Description: CRAN Package 'RcmdrPlugin.DoE' (R Commander Plugin for (Industrial) Design of Experiments) Provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus. Package: r-cran-rcmdrplugin.eacspir Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2html, r-cran-abind, r-cran-ez, r-cran-nortest, r-cran-reshape, r-cran-rcmdr, r-cran-rcmdrmisc Filename: pool/dists/noble/main/r-cran-rcmdrplugin.eacspir_0.2-3-1.ca2404.1_all.deb Size: 406328 MD5sum: 7a65e911ea18dae285a8031a28ee851f SHA1: 981e7c1438fbeaf23a782a366e8707907f6542de SHA256: f5677307540b6d7d024e1e266303fecf3ac614d4bba42b52d5dec1d0b5ede978 SHA512: 00779dc59532fae9a46d2df4598a898e46322a2ef6636650c44df7b25467577474e466d6195b7ba8267ec493ef1853d6eda0402e09db886d5255fb18351bbeeb Homepage: https://cran.r-project.org/package=RcmdrPlugin.EACSPIR Description: CRAN Package 'RcmdrPlugin.EACSPIR' (Plugin de R-Commander para el Manual 'EACSPIR') Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander. Package: r-cran-rcmdrplugin.ebm Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-epir, r-cran-abind Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ebm_1.0-10-1.ca2404.1_all.deb Size: 60594 MD5sum: d739f0ac68b4b43bdb188bb85336637b SHA1: bfdf0c42dce166c2139d9d5c2fc0611a1ee33d79 SHA256: cbd2c8bf01630c50fef23a9c9c75ac08f791c70b2c8771ca996f33fbd19a2126 SHA512: 528b94e526b63dd6cfc521c840d8d5cf43e7a12c4fba6f43a6741071b2e2fec2e11b9141973cba14d04658d6a359638078716b11dfad4286a42779fef5235653 Homepage: https://cran.r-project.org/package=RcmdrPlugin.EBM Description: CRAN Package 'RcmdrPlugin.EBM' (Rcmdr Evidence Based Medicine Plug-in Package) Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...). Package: r-cran-rcmdrplugin.ecovirtual Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-ecovirtual Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ecovirtual_1.0-1.ca2404.1_all.deb Size: 148908 MD5sum: fdd37a5d9ddb18244dacede8d471697b SHA1: d636b737982b0a68275471dc24b6e6cbba052623 SHA256: 0b4f14318f191f95b5febe5c771a8d88583b25e72caa03ecad704c445421861c SHA512: 02c547117ea6f62aff6fb65726f74357a6cb5f53877c3cd36891643b9d4d2b23b51f44a5070f3bb1b656aea7fd6e3f3d3163c5589e304c424923082b73949801 Homepage: https://cran.r-project.org/package=RcmdrPlugin.EcoVirtual Description: CRAN Package 'RcmdrPlugin.EcoVirtual' (Rcmdr EcoVirtual Plugin) A Rcmdr "plug-in" for the EcoVirtual package, designed primarily for teaching ecological models using simulations. Package: r-cran-rcmdrplugin.export Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-xtable, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-rcmdrplugin.export_0.3-1-1.ca2404.1_all.deb Size: 70706 MD5sum: 80808e6680143e943e312a2ea1f838cb SHA1: 78ff8a04a38f8710ae363822e8c56841ce84fd81 SHA256: 9227642fbb48b97bc267917e81f3d4d94101e49eaca9a0ef6f24bc6cb78f6f15 SHA512: 20b6a88f77966168dd5d196c83c8badb5d74693660c4b700af7b1cd2f6728f304d3472460d0dbfc85895660aa65a943c43ced0ab60f5f58e825fa24d17b633cb Homepage: https://cran.r-project.org/package=RcmdrPlugin.Export Description: CRAN Package 'RcmdrPlugin.Export' (Export R Output to LaTeX or HTML) Export Rcmdr output to LaTeX or HTML code. The plug-in was originally intended to facilitate exporting Rcmdr output to formats other than ASCII text and to provide R novices with an easy-to-use, easy-to-access reference on exporting R objects to formats suited for printed output. The package documentation contains several pointers on creating reports, either by using conventional word processors or LaTeX/LyX. Package: r-cran-rcmdrplugin.ezr Architecture: all Version: 1.70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-readstata13 Suggests: r-cran-abind, r-cran-aod, r-cran-aplpack, r-cran-brant, r-cran-car, r-cran-clinfun, r-cran-cmprsk, r-cran-consort, r-cran-foreign, r-cran-ggplot2, r-cran-lawstat, r-cran-meta, r-cran-metatest, r-cran-netmeta, r-cran-multcomp, r-cran-mvtnorm, r-cran-matching, r-cran-plotly, r-cran-proc, r-cran-survivalroc, r-cran-survrm2, r-cran-tableone, r-cran-readxl, r-cran-lmertest, r-cran-swimplot, r-cran-currentsurvival, r-cran-rstatix Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ezr_1.70-1.ca2404.1_all.deb Size: 1428316 MD5sum: 33dbcaf02433d4a0e3dced3bcc3125a4 SHA1: 224c2691962d450a87c4b29c9a315af634f26655 SHA256: 949326f728a6f23b279b79f0e5e4e98fd4a1de1f068f8c0a831da016480234da SHA512: a3662202165edc45d47466362914df9c5a0b0bfc97b62e20b23ea71403bc37b7a9d13011493376082b32e7ee8b1a8cf63e6d5cae3d87f349fd33802f1575993e Homepage: https://cran.r-project.org/package=RcmdrPlugin.EZR Description: CRAN Package 'RcmdrPlugin.EZR' (R Commander Plug-in for the EZR (Easy R) Package) EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles. Package: r-cran-rcmdrplugin.factominer Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factominer, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.factominer_1.8-1.ca2404.1_all.deb Size: 492216 MD5sum: 45713d6e539b2621a0a7ff104fb71c8c SHA1: e8e7f3665f873f760ce4ded07f8b58d6f66bc8ed SHA256: 04f837751c40d71c7404677fb8871a44fee146089d09d3a5362d29f3c05b69d4 SHA512: e5eddb0a7d300b20d008a50ad5b6fd31b739ff0cb4db2e1cbc62d091847655d11265fad3cb0260106b9ef150ef08cb286e76d04b3b78a049d68a935b100d03e0 Homepage: https://cran.r-project.org/package=RcmdrPlugin.FactoMineR Description: CRAN Package 'RcmdrPlugin.FactoMineR' (Graphical User Interface for FactoMineR) Rcmdr Plugin for the 'FactoMineR' package. Package: r-cran-rcmdrplugin.gwrm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gwrm, r-cran-rcmdrmisc, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.gwrm_1.0.2-1.ca2404.1_all.deb Size: 45728 MD5sum: 536cac6c787c4f7d9bd97a629cdc8c59 SHA1: b1985ec3cdd2491b337b08f2805409222a73c9dd SHA256: f74cd455abf6962b27b0e532cdd357df9973f01ab3b9d3a7beb8f459322306de SHA512: ba71b266f08a9aab663387a535dc23939ececc46d4502b0eba1abd4a53e6028bcd54cc1938d1843c0d9a8ecb3af8102d5e2f6ad975af2409e0a9c06e708a917d Homepage: https://cran.r-project.org/package=RcmdrPlugin.GWRM Description: CRAN Package 'RcmdrPlugin.GWRM' (R Commander Plug-in for Fitting Generalized Waring RegressionModels) Provides an Rcmdr "plug-in" based on the GWRM package. Package: r-cran-rcmdrplugin.hh Architecture: all Version: 1.1-51-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hh, r-cran-rcmdr, r-cran-lattice, r-cran-mgcv Suggests: r-cran-car, r-cran-leaps, r-cran-latticeextra, r-cran-rgl Filename: pool/dists/noble/main/r-cran-rcmdrplugin.hh_1.1-51-1.ca2404.1_all.deb Size: 254370 MD5sum: 6dc767232ae3bb384b2e1e853325d14e SHA1: 2abcd7cb6f0d3c661628c2a3e083f6cc713b276d SHA256: c6810499d08a503452ce676ad64820b849e78423353acc758ee0f6d7d1f237bb SHA512: 7489500a09e82913ea1979b6f4398206fff8bb0a87d5311d3376b908a069c3b4c1b64141796f96fc8b2806f6d479548fb24bd896d64d9eec1f5a6b67f5dc4adb Homepage: https://cran.r-project.org/package=RcmdrPlugin.HH Description: CRAN Package 'RcmdrPlugin.HH' (Rcmdr Support for the HH Package) Rcmdr menu support for many of the functions in the HH package. The focus is on menu items for functions we use in our introductory courses. Package: r-cran-rcmdrplugin.kmggplot2 Architecture: all Version: 0.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-ggplot2, r-cran-rlang, r-cran-ggthemes, r-cran-plyr, r-cran-rcolorbrewer, r-cran-scales, r-cran-survival, r-cran-tcltk2 Suggests: r-cran-extrafont, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcmdrplugin.kmggplot2_0.2-7-1.ca2404.1_all.deb Size: 710204 MD5sum: e91e2327e8cd80e6dcce1d93dc20aba1 SHA1: 3d757d19896556b32a6dec6143c0c1391d972b3c SHA256: 55c2c5fcfc6940af3f2d63fcf372bf95026566f02cc742c4e68f0a1c10efafe8 SHA512: 17130f02dc347ff9f36702f8825ecbfaaaef6971f8a602f24227acbe189d0071a7b6ee27c6b94695ea205bbc4dd83b477b860b6f91b94ebc77ca24c95feb97d0 Homepage: https://cran.r-project.org/package=RcmdrPlugin.KMggplot2 Description: CRAN Package 'RcmdrPlugin.KMggplot2' (R Commander Plug-in for Data Visualization with 'ggplot2') A GUI front-end for 'ggplot2' supports Kaplan-Meier plot, histogram, Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart, bar chart, contour plot, and distribution plot. Package: r-cran-rcmdrplugin.ma Architecture: all Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-mad, r-cran-metafor Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-compute.es, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ma_0.0-2-1.ca2404.1_all.deb Size: 213096 MD5sum: 41463812a14b07508191ca950d1e4084 SHA1: b147e95c08385724511789abb0592880ce1a4ea8 SHA256: c01fbebcfbdef38c6e2303a4f97bbe967054971e04bf917128462bdfa5a6f73c SHA512: 140917b8348e6cee978afe5bedcd649781f57816d60ad86aa8455db19e0248209889db0ba8fa37d6dfe3c92e4c38af7ce3300727e71d34342abd73ac35e21db5 Homepage: https://cran.r-project.org/package=RcmdrPlugin.MA Description: CRAN Package 'RcmdrPlugin.MA' (Graphical User Interface for Conducting Meta-Analyses in R) Easy to use interface for conducting meta-analysis in R. This package is an Rcmdr-plugin, which allows the user to conduct analyses in a menu-driven, graphical user interface environment (e.g., CMA, SPSS). It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). Package: r-cran-rcmdrplugin.mpastats Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-ordinal Filename: pool/dists/noble/main/r-cran-rcmdrplugin.mpastats_1.2.2-1.ca2404.1_all.deb Size: 165298 MD5sum: 665c5f1d5ff0cdd555da36e6264b50ca SHA1: e1a4dee68293fe497ad25d8f43c18041b9a73925 SHA256: ac62de8733d6c775aa8b66a1368746585e6184a61e3c4a38286ccccf78e3511c SHA512: 458d3964f056b5d8311ebf125d949e572bcfec03fb178063ffb9600b95648efc43ef204d0b5e5dcf20ac0da9f021be0728f7297b978fe39b0c33168290a8f6bd Homepage: https://cran.r-project.org/package=RcmdrPlugin.MPAStats Description: CRAN Package 'RcmdrPlugin.MPAStats' (R Commander Plug-in for MPA Statistics) Extends R Commander with a unified menu of new and pre-existing statistical functions related to public management and policy analysis statistics. Functions and menus have been renamed according to the usage in PMGT 630 in the Master of Public Administration program at Brigham Young University. Package: r-cran-rcmdrplugin.nmbu Architecture: all Version: 1.8.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixlm, r-cran-mass, r-cran-pls, r-cran-xtable, r-cran-phia, r-cran-rcmdr, r-cran-car Suggests: r-cran-lme4, r-cran-leaps, r-cran-mvtnorm, r-cran-gmodels, r-cran-abind, r-cran-lattice, r-cran-pbkrtest, r-cran-vcd, r-cran-multcomp, r-cran-e1071, r-cran-nnet Filename: pool/dists/noble/main/r-cran-rcmdrplugin.nmbu_1.8.15-1.ca2404.1_all.deb Size: 486276 MD5sum: 71a0322e8981cb7aeb97c039efa1df82 SHA1: f4466cf3ed3ebfdee4256eea49b6555be91781f7 SHA256: b0a3cbc456a551eec8a35dda42e3666701c409106da700f5adb5b0635c2f8054 SHA512: 1862ca24be519e069f8043b3af343fb19f61fe85c5526eba5fd27f09bf1cb1fcd989cac0a519935d7e9536835f01979fcbdc2b929d0d98761c9d0201c15a8b6d Homepage: https://cran.r-project.org/package=RcmdrPlugin.NMBU Description: CRAN Package 'RcmdrPlugin.NMBU' (R Commander Plug-in for University Level Applied Statistics) An R Commander "plug-in" extending functionality of linear models and providing an interface to Partial Least Squares Regression and Linear and Quadratic Discriminant analysis. Several statistical summaries are extended, predictions are offered for additional types of analyses, and extra plots, tests and mixed models are available. Package: r-cran-rcmdrplugin.orloca Architecture: all Version: 4.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orloca, r-cran-orloca.es, r-cran-rcmdr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcmdrplugin.orloca_4.8.2-1.ca2404.1_all.deb Size: 365598 MD5sum: b92c4d36ab317a0ad14cf952ac752b8b SHA1: ed11a1887f0ec79d869cb6f7b32230d364ec6933 SHA256: 191630515b391c1bb0a4b8be88c7ed8b4a96a47ad589d4cfa987e096b0122b69 SHA512: 7cde5375257372f250995491064fa0ecf4aa1c8b80f1e9b1faa35e2e4739dcd192f01b6c96b7e86cb1c72a6a09f6e339781677301f01b494decfbbb217f1c031 Homepage: https://cran.r-project.org/package=RcmdrPlugin.orloca Description: CRAN Package 'RcmdrPlugin.orloca' (A GUI for Planar Location Problems) A GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with continuos planar location problems. Package: r-cran-rcmdrplugin.pcarobust Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrcov, r-cran-tkrplot, r-cran-rcmdr, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-rcmdrplugin.pcarobust_1.1.4-1.ca2404.1_all.deb Size: 28764 MD5sum: db7a3d9d19c38fd6e9665d1be010402c SHA1: 00285ef5d2432b2b79c8406f51c511c9f7017197 SHA256: 0800e8ffa5946d2f606214c1320d1ac5c6130a76b395563f7a742def68dd73a5 SHA512: 60af0bbc238b559da9a641cdb02b546d38604a2e6bdc896ec9d50dea833936ffebee3129e902cafd6a37cf8f4050e5a9c784a7bacde2997fd20e019a35dcfbc1 Homepage: https://cran.r-project.org/package=RcmdrPlugin.PcaRobust Description: CRAN Package 'RcmdrPlugin.PcaRobust' (R Commander Plug-in for Robust Principal Component Analysis) The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method. Package: r-cran-rcmdrplugin.riskdemo Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-demography, r-cran-forecast, r-cran-ftsa, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-zoo, r-cran-data.table Suggests: r-cran-tkrplot, r-cran-rgl Filename: pool/dists/noble/main/r-cran-rcmdrplugin.riskdemo_3.3-1.ca2404.1_all.deb Size: 4421954 MD5sum: 8fdda3947359ebfeda9ee18e0c76dd7e SHA1: ee121976547d5bb0b8e5eaf8daef8e08d319baef SHA256: 524e2b34fbecceec35e80e668be3c1a4f81109bf9c3243b2719082b515f55e9a SHA512: a641f525b4c3f1f7cd56d57fc9f84b91997f1c601ca0fee53ca119297931bafeaa20e2bb73b1c1d465c9754cd9fce97f371865bb84ec07bac516ae0d9544277d Homepage: https://cran.r-project.org/package=RcmdrPlugin.RiskDemo Description: CRAN Package 'RcmdrPlugin.RiskDemo' (R Commander Plug-in for Risk Demonstration) R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic. Package: r-cran-rcmdrplugin.rmtcjags Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-runjags, r-cran-rmeta, r-cran-igraph, r-cran-coda, r-cran-rjags Filename: pool/dists/noble/main/r-cran-rcmdrplugin.rmtcjags_1.0-2-1.ca2404.1_all.deb Size: 53886 MD5sum: 0a72d94f94ad95c87e990ad6e86384b3 SHA1: 22851e7089549804abecb5d36910c21d404b59c7 SHA256: 422d79438960333e30fb86abf3ea930e7f816004702bf6f44ee8be0bd170ee73 SHA512: 62e3415ed7e6f2f609dc86e585f2b2d656bcddbd2af2931f51c41ea37667ee2c6ca9be7aa6a47d633bbaf0b42132528f0101618c6223682b78a6d90e941340ca Homepage: https://cran.r-project.org/package=RcmdrPlugin.RMTCJags Description: CRAN Package 'RcmdrPlugin.RMTCJags' (R MTC Jags 'Rcmdr' Plugin) Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). 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Package: r-cran-rcmdrplugin.roc Architecture: all Version: 1.0-19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-proc, r-cran-resourceselection Filename: pool/dists/noble/main/r-cran-rcmdrplugin.roc_1.0-19-1.ca2404.1_all.deb Size: 170982 MD5sum: e8dffc52484ce427f2e188f1b9362abd SHA1: 108e7a61d9b4ef5a4150e73adec705b179c236a0 SHA256: 0069044731142eeab184c8ced83c1876dc121a60de3b1f0dce19586a96c3a61e SHA512: daa003495318f72afea3cc8ae00ef5cff746a84090f45ea79b8ed1ad61bb7023eedaf7a821f294034833204ecfbb92b7cf0a6301ef3681769290ab71c4a27f8e Homepage: https://cran.r-project.org/package=RcmdrPlugin.ROC Description: CRAN Package 'RcmdrPlugin.ROC' (Rcmdr Receiver Operator Characteristic Plug-in Package) Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection. Package: r-cran-rcmdrplugin.sos Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sos, r-cran-rcmdr, r-cran-tcltk2 Filename: pool/dists/noble/main/r-cran-rcmdrplugin.sos_0.3-0-1.ca2404.1_all.deb Size: 30214 MD5sum: e1aac69f6df6d42bc7df479fccf843e9 SHA1: fb406ebebad4d642b758ce1e628d441abefd2c4c SHA256: 209015381391fddce02c56edaed7aa5606cdde124186d86766887ed1aa815b3f SHA512: 3966c3247678829acc9ffd2f9e08e8c6028168e0ddd7ccd74c696901993acb37c393b96c9e37593b6d03621affbd34e1847e1827799a9cd7575de2d467525a46 Homepage: https://cran.r-project.org/package=RcmdrPlugin.sos Description: CRAN Package 'RcmdrPlugin.sos' (Efficiently search the R help pages) Rcmdr interface to the 'sos' package. The plug-in renders the 'sos' searching functionality easily accessible via the Rcmdr menus. It also simplifies the task of performing multiple searches and subsequently obtaining the union or the intersection of the results. Package: r-cran-rcmdrplugin.survival Architecture: all Version: 1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-date, r-cran-rcmdr, r-cran-car Filename: pool/dists/noble/main/r-cran-rcmdrplugin.survival_1.3-2-1.ca2404.1_all.deb Size: 1108504 MD5sum: 83f19740dae0cf6faf21bb838dc6fd36 SHA1: dc23ebec7a70f4d3931cac9df6752ff9b11df6b3 SHA256: 327bcd6afffeed52b04fab029920c502b29d44882c0dbeff553db0fa231a1832 SHA512: aa6257df99e1bd7728c37de83b1c74db6ce3bbd8426981ba965f10ab23b7ef9ffd758cdfed796b17a51a9fd4aef7a09e53e8c728c5c58127c517466c9e894576 Homepage: https://cran.r-project.org/package=RcmdrPlugin.survival Description: CRAN Package 'RcmdrPlugin.survival' (R Commander Plug-in for the 'survival' Package) An R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs. Package: r-cran-rcmdrplugin.teachingdemos Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-teachingdemos, r-cran-rcmdr Suggests: r-cran-rgl, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-rcmdrplugin.teachingdemos_1.2-0-1.ca2404.1_all.deb Size: 35722 MD5sum: 11020c727df0409bb33f0e5b589c909e SHA1: 9e197d38b1ad8a2a20a70e4c3554e6f102998bef SHA256: d54bd159bba1a3b68b659f0806c410519a03296fb1914192f52952ba9ee6e10d SHA512: 410a1f4d97cd4b28558121625dcd82ed810fcb88c4901eb5e9f5c373f258d0d377d22ed7f677ab3762d2030acdf2d3820775aa59f7eba61de2f8a3bb304a79b2 Homepage: https://cran.r-project.org/package=RcmdrPlugin.TeachingDemos Description: CRAN Package 'RcmdrPlugin.TeachingDemos' (Rcmdr Teaching Demos Plug-in) Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes. 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The data sets and descriptions of the data sets may differ from what is on CRAN or other source websites. The aim of this package is to bring together data sets from a variety of ADHD research publications. This package would be useful for those interested in finding out what research has been done on the topic of ADHD, or those interested in comparing the results from different existing works. I started this project because I wanted to put together a collection of the data sets relevant to ADHD research, which I have a personal interest in. This work was conducted with the support of my mentor within the Global Talent Mentoring platform. . 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Package: r-cran-rcppad Architecture: all Version: 1.20260000.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3802 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcppad_1.20260000.0-1-1.ca2404.1_all.deb Size: 439518 MD5sum: 552d5f9a63e4b0bd22ef4f855d562cf8 SHA1: 2799b94dac0ffd0c27260fa0e0ccc4b93acc9108 SHA256: fa381622c2471f131e6131dbba9e347752b6731775249f40e9927aff159e1075 SHA512: 6b2c08faab82fc101407a265f19781ad09fb455351dab40f463c7a957d9a77f1933086b733bce1d64b3748dd5f79e5a573b093fd4d156df22c0a6e939e9b1631 Homepage: https://cran.r-project.org/package=RCppAD Description: CRAN Package 'RCppAD' ('CppAD' C++ Header Files for Automatic Differentiation) Provides the 'CppAD' C++ header library for automatic differentiation, for use by R packages via LinkingTo. Headers are vendored with CRAN-safe defaults and R-safe error handling that does not call std::cerr or std::exit. 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Package: r-cran-rcprd Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-fastmatch, r-cran-lubridate, r-cran-rsqlite, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcprd_0.0.2-1.ca2404.1_all.deb Size: 473568 MD5sum: 0c154e86e90f5a6c8b1a7396ac85acd4 SHA1: 065dc118a1b40660362c5328b79f51a7f2dce02f SHA256: 2ef0e4a76609deea9dd0d8614ea5b4adf32d6b76e905cfab9d13ffc745c69671 SHA512: cc84b13e9d398cf85274bc38a536ad6a0cca5a1332e82e1ad0088db531add0a47b935d9729bbd9e045b56852d8fa59c35dab417afa4cf56aded8b6c99849e020 Homepage: https://cran.r-project.org/package=rcprd Description: CRAN Package 'rcprd' (Extraction and Management of Clinical Practice Research DatalinkData) Simplify the process of extracting and processing Clinical Practice Research Datalink (CPRD) data in order to build datasets ready for statistical analysis. 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Package: r-cran-rcrawler Architecture: all Version: 0.1.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml2, r-cran-data.table, r-cran-foreach, r-cran-doparallel, r-cran-selectr, r-cran-webdriver, r-cran-callr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rcrawler_0.1.9-1-1.ca2404.1_all.deb Size: 149446 MD5sum: 430c23eecc553615eefb7f5534a25914 SHA1: 0c73cd820ad4e6278c434234f05b2b5d356348dd SHA256: d9cdcd6b552bbcf37d0f0676a52bd808ff89b86aa7e3c94ae44609a69ed8efa3 SHA512: 4326f6d7c7a366d32c271092d30e63d4fecddcb27aaf40333aa3a788c1529a329be8de6ea2efebbadd8cdd917c730191653dc150fc1d600c7692e917cd82e587 Homepage: https://cran.r-project.org/package=Rcrawler Description: CRAN Package 'Rcrawler' (Web Crawler and Scraper) Performs parallel web crawling and web scraping. 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Package: r-cran-rcreliability Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcreliability_0.1.0-1.ca2404.1_all.deb Size: 77946 MD5sum: 7d0a226a4dffbd80d8ee60db7cfcf5db SHA1: 22ffac6cc8bd72e7efbc03445910b7ea5151a942 SHA256: cffcda772f0829fdd20bd87b1e44a22843bc51c160967f7db55d95ba6c9fbd93 SHA512: e2fa068cb666c014285cf94878e5b2aa74e5c946d8929981fa172175a589cfe4ee58c66452c78b2f736aebcdffd21592fdc3d39bb3a728d8192348c2772f2a8d Homepage: https://cran.r-project.org/package=RCreliability Description: CRAN Package 'RCreliability' (Correct Bias in Estimated Regression Coefficients) This function corrects the bias in estimated regression coefficients due to classical additive measurement error (i.e., within-person variation) in logistic regressions under the main study/external reliability study design and the main study/internal reliability study design. The output includes the naive and corrected estimators for the regression coefficients; for the variance estimates of the corrected estimators, the extra variation due to estimating the parameters in the measurement error model is ignored or taken into account. Reference: Carroll RJ, Ruppert D, Stefanski L, Crainiceanu CM (2006) . 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Delineation entails the identification of corridor boundaries, segmentation of the corridor, and delineation of the river space using two-dimensional spatial information from street network data and digital elevation data in a projected CRS. The resulting delineation can be used to characterise spatial phenomena that can be related to the river as a central element. 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Moreover, the package features an interface to query campaign data from the Criteo API. The data can be downloaded and will be transformed into a R data frame. Package: r-cran-rcriticor Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcriticor_2.0-1.ca2404.1_all.deb Size: 104358 MD5sum: b8d01c8c81756f66e03292a49f4bacea SHA1: 79b364ecbba6ced98710df73123586eb9f6b8885 SHA256: a09a7248ae865ce069e23eff00c4f9d58c04d4fce12999798df55da3b5f4e93a SHA512: 82b00b8643dc4e44cb2ade1e185ba17b4f0180a3c6c49409002714000bcf9753a2b37c4ecb2b0cd3205cefce44210c5b031faeeac29debf96013a04d08ecfaf7 Homepage: https://cran.r-project.org/package=Rcriticor Description: CRAN Package 'Rcriticor' (Pierre-Goldwin Correlogram) Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window. Package: r-cran-rcrnorm Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-rcrnorm_0.0.2-1.ca2404.1_all.deb Size: 42040 MD5sum: d285d3eb2a52740e070a4a8e605395fd SHA1: bf7e86da19e89ca6e1269bcd14de750704ecc3ae SHA256: 5bf72251e1bfc3136fdad260720715379ec1a9ccd65415a532925cdaa2c89dc3 SHA512: 3588267c007887e76675e4dee14a47b04101be90682ba9fe3901fc1a4163362b50a45c123829febd311e47b5d9b37ad3b4d0149e937ab076e3fc4dc57541084d Homepage: https://cran.r-project.org/package=RCRnorm Description: CRAN Package 'RCRnorm' (An Integrated Regression Model for Normalizing 'NanoStringnCounter' Data) 'NanoString nCounter' is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) . Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform . It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed. 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Robust treatment assignment by strata/blocks, that handles misfits; Power calculations of the minimum detectable treatment effect or minimum populations; Balance tables of T-test of covariates; Balance Regression: (treatment ~ all x variables) with F-test of null model; Impact_evaluation: Impact evaluation regressions. This function gives you the option to include control_vars, fixed effect variables, cluster variables (for robust SE), multiple endogenous variables and multiple heterogeneous variables (to test treatment effect heterogeneity) summary_statistics: Function that creates a summary statistics table with statistics rank observations in n groups: Creates a factor variable with n groups. Each group has a min and max label attach to each category. Athey, Susan, and Guido W. Imbens (2017) . 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Package: r-cran-rctool Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4571 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rctool_3.1.0-1.ca2404.1_all.deb Size: 4145486 MD5sum: eeb501ca6a7563c64a5dfd2225490cb3 SHA1: 23d7f8052a64c556654b0e9c0f7001d9617d6e8e SHA256: fbac8ecf679fcbd479039945ab02200553e2eecce11539224ce339397a7df076 SHA512: 37878deac6e4bd60745657f1c86daa7548e4a429c294910276fdcefafd4beb0a7c7a268907935e25bbc0a8ce27a43859b9f5ff65552c16f2476598cb1542b4aa Homepage: https://cran.r-project.org/package=rCTOOL Description: CRAN Package 'rCTOOL' (Soil Organic Carbon Turnover Modelling with 'C-TOOL') Provides an 'R' interface to the 'C-TOOL' soil carbon turnover model for simulating soil organic carbon dynamics in agricultural systems. 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Richard Reeve, et al. (2016) . Package: r-cran-rdkitpyr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdkitpyr_0.2.1-1.ca2404.1_all.deb Size: 79754 MD5sum: 982391dc4c6e7723f3fe148f14a71b4b SHA1: 40685cc4cd46486053255bb9c8048f6333e111ad SHA256: 675f684717bb76f26e2a889193ed82a3ad9b7600f81a12c640bf2b122fc94bad SHA512: ff053b4b548acb4bb18bdc518a0b3d0269deade0a866777ddc78f4baae3454cf1ed8cde5c304be90f0b242fb08d587b566238cf603a2b7b5346750178054f7fa Homepage: https://cran.r-project.org/package=rdkitpyr Description: CRAN Package 'rdkitpyr' (Task-Oriented Cheminformatics in R Using 'RDKit' via 'Python') A task-oriented R interface to the 'RDKit' library through its 'Python' API via 'reticulate'. 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See Cattaneo, Titiunik and Vazquez-Bare (2020) for further methodological details. 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The German National Library is the German central archival library, collecting, archiving, bibliographically classifying all German and German-language publications, foreign publications about Germany, translations of German works, and the works of German-speaking emigrants published abroad between 1933 and 1945. Package: r-cran-rdnp Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cellwise, r-cran-mass Filename: pool/dists/noble/main/r-cran-rdnp_1.3-1.ca2404.1_all.deb Size: 15510 MD5sum: 925d05c139e53f7d9166f65f17d5136b SHA1: b453d33b3b97f3e1231491b571e6b9ea7f808762 SHA256: 77ad2582b792c1d072607f532a0025cc4088612e6f2708b6e51b6233226f91b4 SHA512: 406f8600400fdfe774b634eec6ba580c2be94c8c984a78eb84961062c276d6d25742e15b5c958001bd94c30b487ec24ee23bdad79991407329ed0e4244b4aa56 Homepage: https://cran.r-project.org/package=RDnp Description: CRAN Package 'RDnp' (Robust Test for Complete Independence in High-Dimensions) Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) . The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) . So, the Bulut's statistic can be performed robustly by using RDnp function. 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Package: r-cran-rdota2 Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rdota2_0.1.6-1.ca2404.1_all.deb Size: 104182 MD5sum: e628eeff12764126ddb808588fe8cc50 SHA1: 8ba4b82c76169fcc31f5ddecae505203cb2fc6e4 SHA256: d10e74928189211b8d05fb30759ab71e91c14dd1546ddf869c4d9853131cee40 SHA512: 612889a61417c216c28db82eae43c3c4834ddd3ec802f58bb45535793c3c68139abb61129e22901a666c5acc3eb1a9aecd79d7dddf5fd4ea1fb82100533d08a8 Homepage: https://cran.r-project.org/package=RDota2 Description: CRAN Package 'RDota2' (An R Steam API Client for Valve's Dota2) An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at . Package: r-cran-rdpack Architecture: all Version: 2.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbibutils Suggests: r-cran-testthat, r-cran-rstudioapi, r-cran-rprojroot, r-cran-gbrd Filename: pool/dists/noble/main/r-cran-rdpack_2.6.6-1.ca2404.1_all.deb Size: 621280 MD5sum: c79e8ed808817e929d4883f094226972 SHA1: 4db28aad9bd63da138037ed3e8bc51d5336149c3 SHA256: c1dc738aeb5ba85830681cda8476045ffc5215ef92368c4802c5b895b116b396 SHA512: 92699bad705d7add53c49f489cb1a2e207ed50fe6e758b22cb7f3a090789861699ca89c7cc8c4cfeeebb0e6cf6a10d589ece7fe2a5b7f775a857f29d063155f9 Homepage: https://cran.r-project.org/package=Rdpack Description: CRAN Package 'Rdpack' (Update and Manipulate Rd Documentation Objects) Functions for manipulation of R documentation objects, including functions reprompt() and ereprompt() for updating 'Rd' documentation for functions, methods and classes; 'Rd' macros for citations and import of references from 'bibtex' files for use in 'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and inserting snippets of 'R' code and the results of its evaluation or creating graphics on the fly; and many functions for manipulation of references and Rd files. Package: r-cran-rdpower Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdrobust Filename: pool/dists/noble/main/r-cran-rdpower_3.0-1.ca2404.1_all.deb Size: 98964 MD5sum: 6a48d5a133091b6b6b3c111172b54e43 SHA1: e804175671c1b938665c7c4b347d77d6f1157ea9 SHA256: 1febb4df1285be42c6f5e3fe2987f671d6783fc1c45ee1f82ce4b22f60cabfa0 SHA512: 19442996f823017fd1bbe83ec167222b9ca9f23062f27823889b2d9bc8b9f07efa147d2f2d3a358000ee35a4c43fd6e6744be84f7ef3c1db062893b7bfb1307c Homepage: https://cran.r-project.org/package=rdpower Description: CRAN Package 'rdpower' (Power, Sample Size, and Minimum Detectable Effect Calculationsfor RD Designs) The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. 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Package: r-cran-rdracor Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-data.table, r-cran-xml2, r-cran-igraph, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdracor_1.0.6-1.ca2404.1_all.deb Size: 447344 MD5sum: f56cb80546cf80a16bed07d6e9041938 SHA1: 043c988ec9531caac52ef9a91446a892213da1cd SHA256: ce5d254b7934e7dc1452ef61a5c09a70e634744be0d97620bad34a10e99c4b6e SHA512: bbefbb375a59a4596ab50cf31517932c8bb95a87cbd4780935c001c6547c72bd9a43665ac9d0ba07eecec89d5e129eb147c78448c1b903fdaedd8c9da6d0971b Homepage: https://cran.r-project.org/package=rdracor Description: CRAN Package 'rdracor' (Access to the 'DraCor' API) Provide an interface for 'Drama Corpora Project' ('DraCor') API: . Package: r-cran-rdrobust Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Suggests: r-cran-broom, r-cran-gridextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdrobust_4.1.1-1.ca2404.1_all.deb Size: 306284 MD5sum: 55d1e6d29d26c41058944d144852ae73 SHA1: f2808eca7ba6bc0a56dc773f17bfbd1b4d4a2c4f SHA256: 6e436b8d53008d170cac62e56738ac7a54a73d5a46485ce00f695a33c33482b6 SHA512: 2a60e14d68308b439b9a8224bb0e9d8fbb49ad459b72a5fd0ea7b6710b667c90718c77d3a8d866f2b861161814dfe6942833137cfc324b0458e73ebf890d9908 Homepage: https://cran.r-project.org/package=rdrobust Description: CRAN Package 'rdrobust' (Robust Data-Driven Statistical Inference inRegression-Discontinuity Designs) Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots). Package: r-cran-rdrw Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-rdrw_1.0.4-1.ca2404.1_all.deb Size: 58212 MD5sum: ade1222c1b15728fdd59f30e75f12029 SHA1: ca51be9c3f102d71a2ebaa736c7170bba48711c9 SHA256: 489c770ddf28295324579adc24452c7f5784f896ca6b61f0c91c9885e4479935 SHA512: 0842ed16b3f4e01f7448a676ba8df638f599ed6cf9769e1f6710f7180cbd53b90d53966c840a0a2d9d9e7d2c98408142092c5f4032b9e4d7f149c558d160bab2 Homepage: https://cran.r-project.org/package=Rdrw Description: CRAN Package 'Rdrw' (Univariate and Multivariate Damped Random Walk Processes) Provides tools for fitting and simulating univariate and multivariate damped random walk processes, also known as Ornstein-Uhlenbeck processes or first-order continuous-time autoregressive models, CAR(1) or CARMA(1, 0). 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Package: r-cran-rdryad Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-curl, r-cran-tibble, r-cran-hoardr, r-cran-zip, r-cran-jsonlite, r-cran-mime Suggests: r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-rdryad_1.0.0-1.ca2404.1_all.deb Size: 83490 MD5sum: 301bc043f2fca0f1fdfaa8f3563eec97 SHA1: 59829f7ad7bee2f7d8846c7ed93b01f7105e53b2 SHA256: a393c27e0c024c45aad3cc169c5063154f4a772685e44dfaadb201d36e9fc1d9 SHA512: 19893a267f038aab0bb43972a1a41569e969b96fe04d84c52d20fcf3daf7b87de5c9f94493f9c20c25464811453dae07e6b03b458e23a7e97241a30b403cacdd Homepage: https://cran.r-project.org/package=rdryad Description: CRAN Package 'rdryad' (Access for Dryad Web Services) Interface to the Dryad "Solr" API, their "OAI-PMH" service, and fetch datasets. Dryad () is a curated host of data underlying scientific publications. 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Package: r-cran-rdstagger Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-ggplot2, r-cran-rdrobust Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-rdstagger_0.1.0-1.ca2404.1_all.deb Size: 101220 MD5sum: c770db820fa5a939a7e2a34e11375ed0 SHA1: 56c27b461df60d5726f712ba9d0ef025ae588a5d SHA256: 75e3ea4182618bed866b18002e12c398b4a4328d322d3317358e8588585dc829 SHA512: 2894977b38d42761464ab728782c5e7643c112f9fc9c66691def2d3e8a98fa3fc29b941034f93f7e814260ae09d59531eedbffa85978a52c2717e0a1dbcea8a6 Homepage: https://cran.r-project.org/package=rdstagger Description: CRAN Package 'rdstagger' (Staggered Regression Discontinuity with Network Interference) Implements a unified framework combining staggered difference-in-differences with regression discontinuity designs and network interference. Extends Callaway and Sant'Anna (2021) to settings where treatment assignment is determined by a running variable crossing a cutoff, adoption timing is heterogeneous across units, and spillover effects operate through a known network structure. Provides group-time average treatment effects (direct and spillover), aggregation schemes, bandwidth selection, and pre-treatment falsification tests. Package: r-cran-rdstreeboot Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rdstreeboot_1.0-1.ca2404.1_all.deb Size: 60876 MD5sum: 1b4139ca671603b29cb203794f67d317 SHA1: 0f223b8d95c970356077b1c8f864565d280ae3c3 SHA256: e06f2acd3df8cadfc0d4cc9e6a2a2ca257c5e99fb9400e6678895196b4d015f4 SHA512: ec5797272fb3802c0aca2592597485badc91b476c624671e551f4de47583a22c63a4241ce5512ba11003b5a2e05385c4635a1c38f5806133457800db097cffdd Homepage: https://cran.r-project.org/package=RDStreeboot Description: CRAN Package 'RDStreeboot' (RDS Tree Bootstrap Method) A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process. Package: r-cran-rdta Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-rdta_1.0.1-1.ca2404.1_all.deb Size: 38322 MD5sum: 5f926ab4e32891db24be7c19605a1973 SHA1: 82a31b556a209c704eb4f52545221a2c559af105 SHA256: 5008214ba947bdeb596d10a9a8ff1b650eb1405317dfd5d8381edbaac6e0f090 SHA512: f55da151595e323b6ba7de39ac1d71feb94e352bc3c468538647e2c5fce091611021958494ad7a7ecda3229e76ee49f2e8b6152b751c79d35a2a601a06e208b4 Homepage: https://cran.r-project.org/package=Rdta Description: CRAN Package 'Rdta' (Data Transforming Augmentation for Linear Mixed Models) We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) . 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The output is a text file in 'PROV-JSON' format. Package: r-cran-rduckhts Architecture: all Version: 1.5.1-0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 30915 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-duckdb Suggests: r-cran-rtinycc, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rduckhts_1.5.1-0.1.3-1.ca2404.1_all.deb Size: 7393086 MD5sum: 4826a1cb5406b531638ea8f2405be964 SHA1: a655c9df1d8f3a5c1179dd30fbee28c4f2dd7d40 SHA256: 4a1dcd82781f9a9c16206ea7d4e53151466c4b1fca59a6eea8dd4bd040213e91 SHA512: 63feddcf86943b8ccb72a801d3f1dd909447cdf345a51f5869829d20517a1c232ccb0a788aceeecdf8241d3214d0a2fafdfe684f822ae75b371f33608af20b93 Homepage: https://cran.r-project.org/package=Rduckhts Description: CRAN Package 'Rduckhts' ('DuckDB' High Throughput Sequencing File Formats ReaderExtension) Bundles the 'duckhts' 'DuckDB' extension for reading High Throughput Sequencing file formats with 'DuckDB'. 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The package includes functions for preprocessing digital elevation and streams data, and one function to compute all the spatially explicit land use metrics described in Peterson et al. (2011) and previously implemented by Peterson and Pearse (2017) in ArcGIS-Python as IDW-PLUS. 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Package: r-cran-reacnorm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-stringi, r-cran-matrixstats Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-reacnorm_0.2.1-1.ca2404.1_all.deb Size: 2126586 MD5sum: ac5e3f8d3c66efd8db9301e09200ad25 SHA1: 7bafab1c0d8cecbed69850ffae12e8406f5620d9 SHA256: 15a3aa63c8b89c16d8192d2aec4e84d45cd946c436024b682dc864eb4c8d0722 SHA512: 5f91a4c6ec77129af21ca012703c466e82929f1cfb23c4b0e93fddb6da46dd03da71bfef1b80f2e8601e2f42159fe9390523dd930dcba369cd3e2564dcb1f1a2 Homepage: https://cran.r-project.org/package=Reacnorm Description: CRAN Package 'Reacnorm' (Perform a Partition of Variance of Reaction Norms) Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. 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Package: r-cran-react Architecture: all Version: 2024.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang Filename: pool/dists/noble/main/r-cran-react_2024.1.0-1.ca2404.1_all.deb Size: 12040 MD5sum: affc73ce11d2a992915a59db5b6c7fbf SHA1: b846f612c2bbc83e4700738bc45dedbe8d967624 SHA256: e7c4c5e71bc0b4788ce6ee91b5c4206d10f8d001ef2ba7f20db0b4e11d51ce5d SHA512: dc52f383bbec67ed18ac2d3c036a30f6f794f62d4f02d9db55cf3a00bd567e345698e34f0b40f6341f4c7f393e6262ad473d8f00ea68f59f90d38354ac8ef926 Homepage: https://cran.r-project.org/package=react Description: CRAN Package 'react' (Reactivity Helper for 'shiny') Tools to help with 'shiny' reactivity. The 'react' object offers an alternative way to call reactive expressions to better identify them in the server code. 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Apply conditional formatting to cells with data bars, color scales, color tiles, and icon sets. Utilize custom table themes inspired by popular websites such and bootstrap themes. Apply sparkline line & bar charts (note this feature requires the 'dataui' package which can be downloaded from ). Increase the portability and reproducibility of reactable tables by embedding images from the web directly into cells. Save the final table output as a static image or interactive file. 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Package: r-cran-reactlog Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3502 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-shiny, r-cran-fontawesome, r-cran-knitr, r-cran-rmarkdown, r-cran-htmltools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reactlog_1.1.1-1.ca2404.1_all.deb Size: 1688680 MD5sum: 84029e562ab362da7d35a2659f640ea5 SHA1: cbb787187e98196ea96587f7374997b71a5278fd SHA256: 866c0b8ca3a5ef5156b984aa56d25831cc4dfb7e2bab88df059554a784f8b923 SHA512: 15795efad33ae46a4cdc04e0e3db741908fcfdc2216c7b6c99f868a94c0e51d887bdc278d7b66996d909d2022c253fbb3a1bcccc1aa9d1226be4cda2afb58979 Homepage: https://cran.r-project.org/package=reactlog Description: CRAN Package 'reactlog' (Reactivity Visualizer for 'shiny') Building interactive web applications with R is incredibly easy with 'shiny'. 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Package: r-cran-readoecd Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-readoecd_0.3.4-1.ca2404.1_all.deb Size: 88910 MD5sum: 1c842a435e9d40e4239d902307b14297 SHA1: d1d210435149324a2e213507dbad7614830b763c SHA256: aad33bb18376cf9657ebe421011ea84e6f7787e5ca57054e14e2f6583f5f9b9e SHA512: 58dfc229745c411d79ac67650aafddfac511802e37d4ad795588ed6e1483a5686add0352a4d983de82d5b4d8ad6e3e389447c161279a96b154fd8adf81c1ab48 Homepage: https://cran.r-project.org/package=readoecd Description: CRAN Package 'readoecd' (Download and Tidy Data from the 'OECD') Provides clean, tidy access to key economic indicators published by the 'Organisation for Economic Co-operation and Development' ('OECD'), covering GDP, CPI inflation, unemployment, tax revenue, government deficit, health expenditure, education expenditure, income inequality, labour productivity, and current account balance across all 38 'OECD' member countries. 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References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) . Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) . Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) . Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) . Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) . Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) . Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) . Carlisle JB, Loadsman JA. (2017) . Carlisle JB. (2017) . 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Package: r-cran-reconstructkm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-survival, r-cran-rlang, r-cran-survminer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reconstructkm_0.4.0-1.ca2404.1_all.deb Size: 375374 MD5sum: 3400a9ef8bca9c163e688c48cb389f2c SHA1: 8e29181caef7a9e3919992063d33bb2e8255766e SHA256: 1b87fd697fa449fc38805aa453ff19369d0b3cc30ed7ee5a73c708bcd5ff8d61 SHA512: c37a5039613426180f9069ba2e04d002873fd2aaeda2e16592641e8cb618dd8cf557a138c416613a73df5d51f3af36698366f2a1df7aca7e6009113d6b9a4f3b Homepage: https://cran.r-project.org/package=reconstructKM Description: CRAN Package 'reconstructKM' (Reconstruct Individual-Level Data from Published KM Plots) Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) . Please see the vignette for a quickstart guide. 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The validation process consists of two steps: (1) record relevant statistics and meta data of the variables in the original training data for the predictive model and (2) use these data to run a set of basic validation tests on the new set of observations. 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Package: r-cran-recurrentpseudo Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-survival, r-cran-geepack, r-cran-stringr, r-cran-prodlim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-recurrentpseudo_1.0.0-1.ca2404.1_all.deb Size: 65010 MD5sum: 34ec9b8f3f0dc0b76afb085d33d04ad5 SHA1: b7696fd16af8e6dbdc568775dfab58113beed65c SHA256: 48ebecb1f9112b30e4048c9d8ec8b421dbd2b50e1b9c5bb9462c97c8f39e5332 SHA512: d53269c0534a7c8cb61b475904dc6402210a19d30bc2d871b4d0d6a9a55ee31084e163121056a547b26e68492afa070c29af3c2f16e490c5ca6f57ca1f672808 Homepage: https://cran.r-project.org/package=recurrentpseudo Description: CRAN Package 'recurrentpseudo' (Creates Pseudo-Observations and Analysis for Recurrent EventData) Computation of one-, two- and three-dimensional pseudo-observations based on recurrent events and terminal events. 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It provides functions to create, modify and export data models in json format. It also allows importing models created with 'MySQL Workbench' (). These functions are accessible through a graphical user interface made with 'shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility. Package: r-cran-redas Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-car, r-cran-cluster, r-cran-foreign, r-cran-gparotation, r-cran-mass, r-cran-mclust, r-cran-psych, r-cran-usingr, r-cran-vcd Filename: pool/dists/noble/main/r-cran-redas_0.9.4-1.ca2404.1_all.deb Size: 85876 MD5sum: 473e13c7027c36291e3dce3e324bf581 SHA1: e9921747747992c7e09f93ed25999ceecdc739e2 SHA256: efb9fb605ec52bbe26e72bee136c00f9b227565e44f5287598ff667f3ab35fd2 SHA512: 074c7baea54f8714b887ca3744884ba8dd8a304bae9577848ed3a19059d3a8a2367c1050ff4e9c11836fc7bbaafba468cfc994683705df15366748100935e4af Homepage: https://cran.r-project.org/package=REdaS Description: CRAN Package 'REdaS' (Companion Package to the Book 'R: Einführung durch angewandteStatistik') Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition). Package: r-cran-redbookperu Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redbookperu_0.0.3-1.ca2404.1_all.deb Size: 874910 MD5sum: e0284e6f0bba1dcc71a9bf6333d29074 SHA1: e8e16524c14f1f2af0546399f0a70fac2eb1f352 SHA256: 16fe9aebe8a205497edb0039d5c5c8e013a118374c9809d366f41ac123d57083 SHA512: aae82c280a154b5eb077a4417a0649cc4ab4e1452ad0c3c46c031aae08248938486292649e0ebbe999b61cdf187ebaf6673e2f18ebf8cb95267fd6a6327e3178 Homepage: https://cran.r-project.org/package=redbookperu Description: CRAN Package 'redbookperu' (Access and Analyze Data from the Red Book of Endemic Plants ofPeru) Provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) . This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset. Package: r-cran-redcapapi Architecture: all Version: 2.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3715 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-chron, r-cran-curl, r-cran-jsonlite, r-cran-labelvector, r-cran-lubridate, r-cran-mime, r-cran-shelter Suggests: r-cran-testthat, r-cran-hmisc, r-cran-mockery, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-redcapapi_2.12.0-1.ca2404.1_all.deb Size: 3266922 MD5sum: 78e6560d1b7af91f195f9ffa51bbbddc SHA1: 03db1b61a17071e851a7073495e5dc0c5c266bed SHA256: f8dba4fb8d8d537a188f10626d5123b505143bb5f527813bf24a05104688a623 SHA512: b483bb0021e5f95be2fdf89b4f6756dc9a3aa16bb35669ddc8c200308f25955563690ea605f0486c50c7e6de4341bd6aea8f35880431c2c791b5f9bdc4a14f4d Homepage: https://cran.r-project.org/package=redcapAPI Description: CRAN Package 'redcapAPI' (Interface to 'REDCap') Access data stored in 'REDCap' databases using the Application Programming Interface (API). 'REDCap' (Research Electronic Data CAPture; , Harris, et al. (2009) , Harris, et al. (2019) ) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The API allows users to access data and project meta data (such as the data dictionary) from the web programmatically. The 'redcapAPI' package facilitates the process of accessing data with options to prepare an analysis-ready data set consistent with the definitions in a database's data dictionary. Package: r-cran-redcapcast Architecture: all Version: 26.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-redcapr, r-cran-tidyr, r-cran-tidyselect, r-cran-keyring, r-cran-purrr, r-cran-readr, r-cran-zip, r-cran-assertthat, r-cran-forcats, r-cran-vctrs, r-cran-gt, r-cran-bslib, r-cran-here, r-cran-glue, r-cran-gtsummary, r-cran-shiny, r-cran-haven, r-cran-openxlsx2, r-cran-readods Suggests: r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-spelling, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redcapcast_26.1.1-1.ca2404.1_all.deb Size: 265086 MD5sum: 51fc89b480cb1873126f162d18b46aac SHA1: 18874150e3113a9bb1582a87a328b49338bf4362 SHA256: 6c10dd55c8cdd240cb76ad6c40366ca34dbcf58661a2fcffd716576b312a13ba SHA512: 01e1cee55e4f1c5c7e340d85a28c1bd69cae1ee27d1d2fb6ebc4fd9baf843c6527fa16e04765812762d85bf0ff333caa6b68c9f98afc1b2565c007ced0d87536 Homepage: https://cran.r-project.org/package=REDCapCAST Description: CRAN Package 'REDCapCAST' (REDCap Metadata Casting and Castellated Data Handling) Casting metadata for REDCap database creation and handling of castellated data using repeated instruments and longitudinal projects in 'REDCap'. Keeps a focused data export approach, by allowing to only export required data from the database. Also for casting new REDCap databases based on datasets from other sources. Originally forked from the R part of 'REDCapRITS' by Paul Egeler. See . 'REDCap' (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (Harris et al (2009) ; Harris et al (2019) ). Package: r-cran-redcapdm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-openxlsx, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-labelled, r-cran-stringi, r-cran-cli, r-cran-forcats, r-cran-lifecycle, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-mockery Filename: pool/dists/noble/main/r-cran-redcapdm_1.0.1-1.ca2404.1_all.deb Size: 414656 MD5sum: a840154f7f8d36c9b7780a258ef78595 SHA1: 2210da79096104879b1e960741cf6fc6c595f63f SHA256: 2cb8d425bc224a44d0cff77c5e6732d9f945be57c8c6f7c9af4ba6cc6dcc2676 SHA512: e784cd38423bcdf3c50573823e6eba03364d77bb06852cf722ce23f0c4b423dbd50c7a1a906917d1357579a32bbabcd0c68f119e130a512fcc5feb2cb92c4236 Homepage: https://cran.r-project.org/package=REDCapDM Description: CRAN Package 'REDCapDM' ('REDCap' Data Management) REDCap Data Management - 'REDCap' (Research Electronic Data CAPture; ) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries. Package: r-cran-redcapexporter Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-keyring, r-cran-lubridate, r-cran-rjson Suggests: r-cran-getpass, r-cran-kableextra, r-cran-knitr, r-cran-qwraps2, r-cran-rmarkdown, r-cran-roxygen2, r-cran-secret Filename: pool/dists/noble/main/r-cran-redcapexporter_0.3.6-1.ca2404.1_all.deb Size: 123950 MD5sum: 292586600ea16224d504b07f686627c4 SHA1: d49631f992a7447827634d566e602f8e37c3dcce SHA256: fdfb7633f4d6d9558a4664cf54261683104fe0f1796e14ba56f9f2ee254250d2 SHA512: f7faa2a967a44d84276f4a64f2b5c347cb5b4711417757a0bc28f206f50206a6603c5bc1ca5c15549866c11fb84391b599da642bf4c5cf345cb5a01b89014fef Homepage: https://cran.r-project.org/package=REDCapExporter Description: CRAN Package 'REDCapExporter' (Automated Construction of R Data Packages from REDCap Projects) Export all data, including metadata, from a REDCap (Research Electronic Data Capture) Project via the REDCap API . The exported (meta)data will be processed and formatted into a stand-alone R data package which can be installed and shared between researchers. Several default reports are generated as vignettes in the resulting package. Package: r-cran-redcapr Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2577 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-spelling, r-cran-covr, r-cran-dbi, r-cran-kableextra, r-cran-knitr, r-cran-odbc, r-cran-purrr, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-testthat, r-cran-tidyselect, r-cran-yaml Filename: pool/dists/noble/main/r-cran-redcapr_1.7.0-1.ca2404.1_all.deb Size: 1261088 MD5sum: f3c785846c09f749ac5199297a488f31 SHA1: f29221114d0aea7a2b12e453e681addedcb938a0 SHA256: 360eff04e2105fc7b2da44c962cf2530cad7ba5361e582ab59e98ac3b77da412 SHA512: 3625e9f7a7692acd7413a52de56ae9ef4e0ecb9ea25d9a92a1789c4d5468174b476a6b72ba45306c9fc6abddb1a1c35a9cc7f595ac7d0665ff8af2c5ba9f84bd Homepage: https://cran.r-project.org/package=REDCapR Description: CRAN Package 'REDCapR' (Interaction Between R and REDCap) Encapsulates functions to streamline calls from R to the REDCap API. REDCap (Research Electronic Data CAPture) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The Application Programming Interface (API) offers an avenue to access and modify data programmatically, improving the capacity for literate and reproducible programming. Package: r-cran-redcapsync Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-hoardr, r-cran-lubridate, r-cran-openxlsx2, r-cran-r6, r-cran-readxl, r-cran-redcapapi, r-cran-redcapr, r-cran-skimr, r-cran-stringr Suggests: r-cran-keyring, r-cran-knitr, r-cran-listviewer, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-redcapsync_0.2.1-1.ca2404.1_all.deb Size: 2226914 MD5sum: c89089fe955f62ff7888bbcc1a4877a4 SHA1: 58631ded5d1f3dde1d668683c540f8e7317a0287 SHA256: f3b8178916ba049124a999e179ef009845fb90bb4d1363d5f06da2d93352bf11 SHA512: 0b8a17f1b0b93d15eaf75630c080307b90345a064f574948dfbc9cc5b09629e7c96b8e2b70bde32f74e9058bd4f4cff8627c765d0719e64fe0338c782b3b1c7f Homepage: https://cran.r-project.org/package=REDCapSync Description: CRAN Package 'REDCapSync' (Encapsulated 'REDCap' Projects for Synchronized Data Pipelines) Wraps dozens of 'REDCap' API endpoints into a standardized R6 object. Research Electronic Data Capture ('REDCap') is a survey and database web application software maintained by Vanderbilt University. It has a robust application programming interface (API) utilized by several R packages. 'REDCapSync' uses 'redcapAPI' and 'REDCapR' behind-the-scenes to retrieve all metadata, data, and log details for a project. To minimize unnecessary server calls, the interim 'REDCap' log is analyzed and used to only update necessary records. Furthermore, the user can define custom datasets that save to a directory. Those datasets continue to refresh when projects are synced. Having a secure, standardized, API-efficient, project-agnostic R object for 'REDCap' projects, streamlines downstream use in scripts, functions, and shiny applications. Package: r-cran-redcaptidier Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2996 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lobstr, r-cran-lubridate, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-formattable, r-cran-pillar, r-cran-vctrs, r-cran-readr, r-cran-forcats Suggests: r-cran-covr, r-cran-knitr, r-cran-labelled, r-cran-lintr, r-cran-openxlsx2, r-cran-prettyunits, r-cran-rmarkdown, r-cran-skimr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-redcaptidier_1.2.5-1.ca2404.1_all.deb Size: 2008630 MD5sum: e907b08545c44be9d56ce09a7e7aaa60 SHA1: a8724042f755ad34c61c89c36eee813a35bee0ea SHA256: c0ec4399aed3c7539430070d0966c156bd0568d9dbcd6491649386306678037a SHA512: b3c95e7ac50a9c189bd49ccb77328858e00e841450f544d90eb79c6b0608a96d8c9580d584df6d314e019ac4b2b45c7fb41fa3a6129450ec66ed9416da3bb25e Homepage: https://cran.r-project.org/package=REDCapTidieR Description: CRAN Package 'REDCapTidieR' (Extract 'REDCap' Databases into Tidy 'Tibble's) Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms. Package: r-cran-redcas Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-redcas_0.1.1-1.ca2404.1_all.deb Size: 368622 MD5sum: 698458d3ced645c953fb3079da8f7398 SHA1: f6b65d94dcd2845338b410ed9c71ac4aa8af0ee9 SHA256: 800148bf6c7d6ec6f45577f2aff1a7627e1d6deb72f6868f47b8fe51a698e6a2 SHA512: 6679cbe1bf36c03126ccc6bcf6ea169ce28a48e9b41cf8e8e5e64479de4d1ed78a9cfbe399ab71c4eac3cfd8aa4ae4668ef435046e4355244082cd3109546218 Homepage: https://cran.r-project.org/package=redcas Description: CRAN Package 'redcas' (An Interface to the Computer Algebra System 'REDUCE') 'REDUCE' is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in 'LaTeX' format. 'REDUCE' is based on 'Lisp' and is available on the two dialects 'Portable Standard Lisp' ('PSL') and 'Codemist Standard Lisp' ('CSL'). The 'redcas' package provides an interface for executing arbitrary 'REDUCE' code interactively from 'R', returning output as character vectors. 'R' code and 'REDUCE' code can be interspersed. It also provides a specialized function for calling the 'REDUCE' feature for solving systems of equations, returning the output as an 'R' object designed for the purpose. A further specialized function uses 'REDUCE' features to generate 'LaTeX' output and post-processes this for direct use in 'LaTeX' documents, e.g. using 'Sweave'. Package: r-cran-redditadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-redditadsr_0.1.0-1.ca2404.1_all.deb Size: 22982 MD5sum: 986327192457f107a0c76b95d26085fd SHA1: 7cb0bc6deb298afa41fb0f7700603cd2d71839f3 SHA256: 0561033fb3709eb9e7ce7e7868ca10a0dadd65b6f77397e15bd0447b525594ef SHA512: 8fadd5db35f0f2e3b88f23f24bcd9d18bb87c5ebe72ed5aaee3aa2b196e01b7a2603f2d6b2c0d3b262474a121118d09334d001795a1935ed6288587958e87133 Homepage: https://cran.r-project.org/package=redditadsR Description: CRAN Package 'redditadsR' (Get Reddit Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from reddit Ads using the 'Windsor.ai' API . Package: r-cran-redditextractor Architecture: all Version: 3.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjsonio Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redditextractor_3.0.9-1.ca2404.1_all.deb Size: 442616 MD5sum: 3ff13bb16ccb27ff2270fa209d9d1432 SHA1: 065786ea4137c7b2800211eb960a41fa153223ef SHA256: ceed5841ca8822b1008d0d5a54af4e5ed5fab839c1912f04f3604b246099357d SHA512: 413f46173fa35fa631039f12ac33a0b9718633224d1af66c87d07fbd520435febe20fcf4d1d3251311e76ba0a09e064a65f145d5d3c471c32b43862c65871a02 Homepage: https://cran.r-project.org/package=RedditExtractoR Description: CRAN Package 'RedditExtractoR' (Reddit Data Extraction Toolkit) A collection of tools for extracting structured data from . 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Package: r-cran-reddyprocncdf Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reddyproc Suggests: r-cran-ncdf4, r-cran-rnetcdf, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-reddyprocncdf_1.1.4-1.ca2404.1_all.deb Size: 479118 MD5sum: 6ae25f71014e9c7da5d74a79f072f3d7 SHA1: 2035edb832d20b250aeb05c85366a16e309e3193 SHA256: 5e5caf8d57dfc92d6b207fc293541ab4704832c4ae9a885c91ec975250e7073d SHA512: 05b378cc3b27fd7b47c31d2c1298c0a92b03cfc2b93f8174727954ed9917835ed6e70712289ac5fc7405f6e19253b052a7b94bd13aedde535e4aaf19e052f476 Homepage: https://cran.r-project.org/package=REddyProcNCDF Description: CRAN Package 'REddyProcNCDF' (Reading Data from NetCDF Files for 'REddyProc') Extension to 'REddyProc' that allows reading data from netCDF files. Package: r-cran-redeagroradar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-magick, r-cran-shiny, r-cran-bslib Filename: pool/dists/noble/main/r-cran-redeagroradar_0.1.1-1.ca2404.1_all.deb Size: 59692 MD5sum: 9665d906662c3431e7a3e6edc8869a4a SHA1: b374df4e3f8e2aca0b305d1ff0b223ee47914457 SHA256: 616b3d9391b80495fa8b058d6cf57a61de6621ed2825290578bd77f993ce66d2 SHA512: 75852fcdcb934743496a57a2e88334e1a772446df87cb8231de63c082cc2b489d7dade73a607ad85004fb2f6f4a12cbe08b29997868d261905e3d13222cf7c77 Homepage: https://cran.r-project.org/package=RedeAgroRadar Description: CRAN Package 'RedeAgroRadar' (Weather Radar Monitoring and 'Telegram' Alerts for AgricultureResearch) Provides tools to download, process, and analyze real-time meteorological radar images from Simepar (Paraná, Brazil) . Designed to support the 'Rede Agropesquisa' hydrological monitoring, it includes functions to detect rainfall intensity based on Red, Green, and Blue (RGB) color values within predefined circular study areas. Features automated integration with the 'Telegram Bot API' to send spatialized image alerts and an interactive 'shiny' dashboard for easy configuration and continuous weather tracking. Package: r-cran-redi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-redi_1.0.0-1.ca2404.1_all.deb Size: 140062 MD5sum: 2fcd95ae9c35545eb194e0aed2c1d33a SHA1: 3ab7d137b5a1444cd0c3f628040c150d0a527d5c SHA256: 93f2ec36d4ccca9269ef5be8a8b9fc1738ad1e27376392b847036daeba2ce874 SHA512: 39185dcbc7baf61cb1b86fe49e851b6faea55e380e12ef174b58f29d7e5ba9a7d9661caa5255a9847aa074917db59ac63feb76daa56c8673056279ff383fe8d1 Homepage: https://cran.r-project.org/package=REDI Description: CRAN Package 'REDI' (Robust Exponential Decreasing Index) Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) . The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way. Package: r-cran-redirection Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-redirection_1.0.1-1.ca2404.1_all.deb Size: 34068 MD5sum: af844bc023cd3da5e9db97b1b7ff153e SHA1: 12af0b0410d40792645faf34032a613466aa55c1 SHA256: 036b94b4eb440444143ff9bc1a57aa7f67aa6ea89e5743caeb43558269c76af4 SHA512: 8abbcc9727aa3bf3e78d1bebbef105cd1ace060c6503c0426c9b83e03c7d10f2aab02fb1c6ea53be0feefe5dd74e4e7be7ca6064b319539fbb7b247f1b376ae2 Homepage: https://cran.r-project.org/package=ReDirection Description: CRAN Package 'ReDirection' (Predict Dominant Direction of Reactions of a Biochemical Network) Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. 'ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external. Package: r-cran-redisbasecontainer Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dockerparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-redisbasecontainer_1.0.1-1.ca2404.1_all.deb Size: 265986 MD5sum: 75536bb99c22a42c86a81b2ebb159400 SHA1: 6187b7de160062a2f7fc255079ab40895f0d845f SHA256: a18c98852677aeebe04d9aa9b2a2efb7ac8017eb125c4ecf814b603125dc4a9a SHA512: 33a8a20f02b8b45281fa258cc6c818f9913d4d8f4a93388f7ffcbf08184e9c74138b19c9f85d464f49a1b7335888e7f829a133058b45649d5aaeb74da1b0e2a2 Homepage: https://cran.r-project.org/package=RedisBaseContainer Description: CRAN Package 'RedisBaseContainer' (The Container for the DockerParallel Package) Providing the container for the DockerParallel package. Package: r-cran-rediscover Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-poissonbinomial, r-cran-shiftconvolvepoibin, r-cran-matrixstats, r-bioc-maftools, r-cran-data.table, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-bioc-biocstyle, r-cran-dplyr, r-cran-kableextra, r-cran-magick, r-bioc-qvalue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rediscover_0.3.3-1.ca2404.1_all.deb Size: 2759164 MD5sum: 2828f124ba8a98bcf2b8b8c2b3e5ec89 SHA1: de69b5221870891bfab20b01298d91eb97abacd8 SHA256: b95b95e270ff30f29a4a366d8da192015051a47787f673f138e357383613f735 SHA512: a1029213d5a67c21ea686720f49be6421d5fc9e3a3cb4866b1603565b1cdfba0ab60187c8f65942f2aba9c1e874392be46f9de966017c664e3d6b15ef5b80ec0 Homepage: https://cran.r-project.org/package=Rediscover Description: CRAN Package 'Rediscover' (Identify Mutually Exclusive Mutations) An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. ]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources. Package: r-cran-redistverse Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-redist, r-cran-redistmetrics, r-cran-geomander, r-cran-ggredist, r-cran-sf, r-cran-censable, r-cran-tinytiger, r-cran-easycensus, r-cran-pl94171, r-cran-alarmdata, r-cran-cli, r-cran-birdie, r-cran-baf Suggests: r-cran-wacolors, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-redistverse_0.1.2-1.ca2404.1_all.deb Size: 101308 MD5sum: 909f95c540ebcec35ca8991d49d49477 SHA1: 9d23149bff781415444d25f598d2c70b3469a3c2 SHA256: 27e453ba9acb4618a25993eb7fd999f414a1b353dacc8f52b0cf9f83e8c7c949 SHA512: 272cb3291f315b4b08d679928f0d9c45a31b9cd44903ed36de9f7897fb30304b5862e5da7deb2723a5d091158af501642bce0e4cc7377ac7876dcb659b5ba8ab Homepage: https://cran.r-project.org/package=redistverse Description: CRAN Package 'redistverse' (Easily Install and Load Redistricting Software) Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple 'redistverse' packages at once. The 'redistverse' is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see . Package: r-cran-redlist Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3586 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-magrittr, r-cran-rlang, r-cran-rvest Suggests: r-cran-coordinatecleaner, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-rgbif, r-cran-rmarkdown, r-cran-scales, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redlist_0.3.0-1.ca2404.1_all.deb Size: 1701216 MD5sum: 93063fed5799abee20bdeb913911d2d7 SHA1: d49061aa49d60303c391d58fa6ff85c5a6a0f957 SHA256: 7d4d313d264df238b05dd8745ca66adea4eb4aac9dfa97b37d9d1f8a4e6a3b56 SHA512: cb153ef130a62e810418fc1a6051c2440ca6759be429589787792ff7fc61a65d552b05966c02d9bd406e476da35c65905f2823a3e2b453d4aa46158df78b55c6 Homepage: https://cran.r-project.org/package=redlist Description: CRAN Package 'redlist' (Interface to the IUCN Red List Data with Risk Metrics) Access species conservation data from the International Union for Conservation of Nature (IUCN) Red List API , including assessments, taxonomy, threats, habitats and historical status. The package also reconciles taxonomic names between the IUCN Red List and the Global Biodiversity Information Facility (GBIF), retrieves and checks GBIF occurrence records, and computes the range and population metrics of the IUCN Red List Categories and Criteria (IUCN Standards and Petitions Committee, 2024, ): extent of occurrence and area of occupancy for criterion B, and population reduction for criterion A. Package: r-cran-redlistr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5252 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-units, r-cran-stringr, r-cran-mgcv Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-redlistr_2.1.0-1.ca2404.1_all.deb Size: 1974154 MD5sum: 2b8396f6d3ab75e8bebfeb4358c4462d SHA1: 60e46b030ef563636bde6b13fe23283df2065505 SHA256: 457d0ad357a6fa4c8bfaf40810debb108d6c0cac08df50a71e13116d88bce024 SHA512: d89ab9f1106ebacec2fdeba901270267c2d97633bdc7ff195e9f92a54aad96d7ccc34e06447577323488b5966e5539f32f1ca18a059327ee1a66ca482feb4a0e Homepage: https://cran.r-project.org/package=redlistr Description: CRAN Package 'redlistr' (Tools for the IUCN Red List of Ecosystems and Species) A toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information. Package: r-cran-redm Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rann Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/noble/main/r-cran-redm_2.0.2-1.ca2404.1_all.deb Size: 865004 MD5sum: fec0562fd2d0ea8177ecaefcb88c7985 SHA1: 79310dcdb185d06b0eead84a45380ace7779f912 SHA256: 48b6d521acaecdb9220f213f3d2761f2b1397a24b4ec604dc720268fa3492871 SHA512: 5bea9ed57143b52fabfd8dee87c500cace40526648a9590d90192706953a4c42519a87c6ce32738648c781cb4fef91f90389fc0d9e2dc09b333744f0f5ce9c67 Homepage: https://cran.r-project.org/package=rEDM Description: CRAN Package 'rEDM' (Empirical Dynamic Modeling ('EDM')) An implementation of 'EDM' algorithms based on research software developed at the Sugihara Lab ('UCSD/SIO'). Primary methods include 'Simplex' projection from Sugihara & May (1990) , Sequential locally-weighted global linear maps 'S-map': Sugihara (1994) , Convergent cross mapping described in Sugihara et al. (2012) , and, 'Multiview embedding' from Ye & Sugihara (2016) . Package: r-cran-redmonder Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-redmonder_0.2.0-1.ca2404.1_all.deb Size: 49158 MD5sum: 2b6720957c4146b09899723e4be0ba1b SHA1: 37c3351768977076f4028320635e74b58710164f SHA256: 7e9c3b49a63a98b0cb6b31afebe11c5d1527159c0877ea6583dd60455e54f135 SHA512: 319135ef9615db3520e8c7bdb73f0a9881e3ec80bfb3b038f80b7499218e78d6cf6619eff93ac33b02bdc89a823478f1b15aea6344ab4c3049b768469ab00edf Homepage: https://cran.r-project.org/package=Redmonder Description: CRAN Package 'Redmonder' (Microsoft(r)-Inspired Color Palettes) Provide color schemes for maps (and other graphics) based on the color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2. 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(2012) RE-EM trees: a data mining approach for longitudinal and clustered data . 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This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects. 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Provide functions to download sequence data from NCBI GenBank . Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies. Package: r-cran-refer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-elist, r-cran-matchr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-refer_0.1.0-1.ca2404.1_all.deb Size: 132352 MD5sum: 79d378c8a1d1e62aba1ac24eecebf966 SHA1: 73af567f17d1e1d5e81973d3ac9a26739556ba1f SHA256: 4d69bbb41bacf54aa7af744ec4e46eac88f8736a8eb61f4feaa3d791b8f2b0d6 SHA512: 451f0cf74257aeb42bdacb0f8a07c29937fa08577fd4168e136a69653fb4304ab4088964da74a26d1f95d5c7084a94dd97b70900ddb05fc920d79a9927b77a86 Homepage: https://cran.r-project.org/package=refer Description: CRAN Package 'refer' (Create Object References) Allows users to easily create references to R objects then 'dereference' when needed or modify in place without using reference classes, environments, or active bindings as workarounds. 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Package: r-cran-referenceintervals Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-extremevalues, r-cran-mass, r-cran-outliers Filename: pool/dists/noble/main/r-cran-referenceintervals_1.3.1-1.ca2404.1_all.deb Size: 77454 MD5sum: 07ea38528425281950b4729b82276b64 SHA1: a037c1ec9f71b092ab60e2043de420c582d7b237 SHA256: 28efcf4334e1ed8be7f99560e2e4cf6234364e97f9fdd838ff012316ae318f9e SHA512: 628833293ba5594738a4710e0c66708aac1744a8b711d5015d419c19b057becc96ca6540da7ecdf453303f9072ad09632829d5b5d5534246aaf02aeb644833aa Homepage: https://cran.r-project.org/package=referenceIntervals Description: CRAN Package 'referenceIntervals' (Reference Intervals) This is a collection of tools to allow the medical professional to calculate appropriate reference ranges (intervals) with confidence intervals around the limits for diagnostic purposes. Package: r-cran-reffectivepred Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaml, r-cran-config, r-cran-zoo Filename: pool/dists/noble/main/r-cran-reffectivepred_1.0.1-1.ca2404.1_all.deb Size: 110198 MD5sum: 37d7752976bb9116ab4561de7357392e SHA1: 1dd493a990e221a8d8a3ef47b1b9f26519591adc SHA256: 322cacb433cdaa7231fe318932203dd315a8ba39cc6bf2b99f5dd6ede98b6665 SHA512: 53bf1734f06a699339602d677a8dd964935ea17eadd38f3bb22abae463b2d39cab23d335967dd2a5c3335c4811ea373fc243d7aded723c2df456374abd5a4b6a Homepage: https://cran.r-project.org/package=REffectivePred Description: CRAN Package 'REffectivePred' (Pandemic Prediction Model in an SIRS Framework) A suite of methods to fit and predict case count data using a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) model, based on an assumed specification of the effective reproduction number. The significance of this approach is that it relates epidemic progression to the average number of contacts of infected individuals, which decays as a function of the total susceptible fraction remaining in the population. The main functions are pred.curve(), which computes the epidemic curve for a set of parameters, and estimate.mle(), which finds the best fitting curve to observed data. The easiest way to pass arguments to the functions is via a config file, which contains input settings required for prediction, and the package offers two methods, navigate_to_config() which points the user to the configuration file, and re_predict() for starting the fit-predict process. The main model was published in Razvan G. Romanescu et al. . Package: r-cran-refiner Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ash, r-cran-future, r-cran-future.apply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-refiner_2.0.0-1.ca2404.1_all.deb Size: 4475352 MD5sum: 8388cd593adbe9dbf15c9fe9dc4dcb40 SHA1: 9563c065d570d00d632f1840f399eb789491bc21 SHA256: e4f3345c035a6847e80881e141a6d7ac68992982b17a1258d45dae7bc712fead SHA512: c5e5d706fdb8b3fd3eef4a23589fdbdfb2b0d56f2a5a40c6cae624dc7837cc0e4698427a634d0b137feaa4b480cccbdc206a4a4934b5779f7c989df072137c0f Homepage: https://cran.r-project.org/package=refineR Description: CRAN Package 'refineR' (Reference Interval Estimation using Real-World Data) Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in 'Ammer et al. (2021)' . Additional guidance is in 'Ammer et al. (2023)' . The verification method is described in 'Beck et al. (2025)' . Package: r-cran-refitgaps Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-refitgaps_0.1.2-1.ca2404.1_all.deb Size: 108244 MD5sum: 145b83cfed234616a827f6e498d95324 SHA1: c0166f2807565a770727841e67bb7218663ab49e SHA256: f214a50535775fdd83c2588a60f2db666f2c00bede529b6ec084dbf781ffd433 SHA512: 765ded90c85b1dab460ec691fbe4d968b2b6a46e18c729cef07b3f1acb8319cbd33b541c9a19d563573d823102830150766e7ae1b6e5642b9eb09a8ff0d64e49 Homepage: https://cran.r-project.org/package=refitgaps Description: CRAN Package 'refitgaps' (Reduce the Number of Holes in the School Timetable) Reallocating the respective lessons by hours (respecting the constraints induced by the existence of coupled lessons) so that the total number of gaps is as small as possible. Package: r-cran-refitme Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-vgam, r-cran-vgamdata, r-cran-caret, r-cran-expm, r-cran-mvtnorm, r-cran-sandwich, r-cran-dplyr, r-cran-scales Filename: pool/dists/noble/main/r-cran-refitme_1.3.1-1.ca2404.1_all.deb Size: 2022490 MD5sum: e1bd82e29f42eb4fd15440bdd69a5c54 SHA1: f477120956d872c888e5fe6be041e98304b92fc6 SHA256: d30ed3ee4d48a432482f344f9467c712c3b09b257655f4df518b9f6251a7d437 SHA512: a7f835b9fa732f5b3dff2b3dcfca69fca6edfb9b6f544fce265f47dc99324dccc77abb23aa976dd7b4491571ee09230be3392718a3869203df91ae06a4bbab2f Homepage: https://cran.r-project.org/package=refitME Description: CRAN Package 'refitME' (Measurement Error Modelling using MCEM) Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial". 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It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) . It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model. 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The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included. 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(doi below) for a full description of and motivation for the methodology. Package: r-cran-regsubseq Architecture: all Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-regsubseq_0.12-1.ca2404.1_all.deb Size: 1736352 MD5sum: 102c71ccbed7f1f738a5e59d35435ed7 SHA1: 5ac515aa57c14c1a1e0c3be99bad668ef25f2ac0 SHA256: 0f6e994d1240907e9b65228ddad449d47e0a30d08acff88be28fc3cf129e195c SHA512: 9ff1f9c3f5d95c1f1c4d09c203cdb5a7662c0eff1efd7275d4410f4a41f1fafc15aa9e3f4c7b16f247b29ab02047f36b124f0900c8b6726f16b2a725e607ba18 Homepage: https://cran.r-project.org/package=regsubseq Description: CRAN Package 'regsubseq' (Detect and Test Regular Sequences and Subsequences) For a sequence of event occurence times, we are interested in finding subsequences in it that are too "regular". 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See Dimitriadis, Gneiting, Jordan (2021) . 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Package: r-cran-reliar Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-reliar_0.2-1.ca2404.1_all.deb Size: 585754 MD5sum: 69cfb0f33988fd85abf73225a5d85bd8 SHA1: fdd6ce8182e13a00a2090c715a21292e3e885dc8 SHA256: 58f1e31ad5fc8e28227421219921f1b56a9bbcc8a1ed94b403e8d7335d2f26eb SHA512: 1a6972c72809362a2fa2c09bce2e81c9443aaea332fa2a6d07aa2a42856b158c804764c9536664c52a23dd189c9127ddf591f0c60cd505fff8c6a5c880d529cc Homepage: https://cran.r-project.org/package=reliaR Description: CRAN Package 'reliaR' (Comprehensive Tools for some Probability Distributions) Provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling. Package: r-cran-reliashiny Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-reliagrowr, r-cran-reliaplotr, r-cran-shinycssloaders, r-cran-weibullr, r-cran-weibullr.alt, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reliashiny_0.5-1.ca2404.1_all.deb Size: 106466 MD5sum: 09f407289f83155718dd35a15e459e2d SHA1: 18430a24f0f01bec7d2a9a537d335fe64ecaa30c SHA256: af5b27c5d6a31b2af38b5435792a7c9e6eb2504b2257e250c0e3c66b0cd33e35 SHA512: 1de0f3a1f09a0cfca35fc9f170976744b958c7d92c56ea5d2c26d03743e75e1ba639b6f1a434e95a44340e952fa133b29b09d9d10a2a4c6e0a7d01d87cec3d97 Homepage: https://cran.r-project.org/package=ReliaShiny Description: CRAN Package 'ReliaShiny' (A 'Shiny' App for Reliability Analysis) An interactive web application for reliability analysis using the 'shiny' framework. The app provides an easy-to-use interface for performing reliability analysis using 'WeibullR' and 'ReliaGrowR' . Package: r-cran-relimp Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-nnet, r-cran-mass, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-relimp_1.0-5-1.ca2404.1_all.deb Size: 77546 MD5sum: 08375299a37fc8c571d9653533b833b2 SHA1: 088452b50cead12ea9685b0faab919cb2ba2c70a SHA256: e91205cf092e5b57a161d0f9a3fd1ddbc8f7180cab9b8fe182e2d15170d22765 SHA512: 1fc775ce1609f709f1858806874af72c28e6db68da3d7a97a64070caaf00ab7a82e00950998c9b7f2177cee38be676771169ea52c23d868ef6a39654171f8529 Homepage: https://cran.r-project.org/package=relimp Description: CRAN Package 'relimp' (Relative Contribution of Effects in a Regression Model) Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets. Package: r-cran-relimppcr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-relaimpo, r-cran-rmisc, r-cran-caret, r-cran-ggplot2, r-cran-reshape2, r-cran-logger Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-relimppcr_0.3.0-1.ca2404.1_all.deb Size: 34936 MD5sum: 9e4b6a38fba23b81153fdfd63fd24cc2 SHA1: 1a09eef9458e3f58a3c8836da24e1f6024e640c1 SHA256: 659482cf7bf743c4a378f6dc3bc08df86a0df8e83c5d8e28fa1ed74a436c5bf0 SHA512: 905b3e29d22de54f78a69e569f45d9e7a0c4dfdc752959311464261e6c12f38655b4481a40660af8320a08c5b8937a36d69dc99cd88cb66b5b3e1d5d72fa156b Homepage: https://cran.r-project.org/package=RelimpPCR Description: CRAN Package 'RelimpPCR' (Relative Importance PCA Regression) Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) . Package: r-cran-relmix Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-familias, r-cran-flextable, r-cran-officer, r-cran-pedfamilias, r-cran-pedtools Suggests: r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-relmix_1.4.1-1.ca2404.1_all.deb Size: 260454 MD5sum: 16ab8cb8571e1a72b8208c3f67c06dd9 SHA1: 60009cb34b3095d4fc5c6a8bc62f68ade209310b SHA256: 908ac8cdd85e7e984f6c1b244d735275891411e2cd4b1c587de163d2f5f21d92 SHA512: 0d82043e3ff5273d90229f03c0d28edc3127c02fd521344b29658ff739a5a31c13e6f267a7b098f2fa0ab7fa350cdc1ece6277b757ef6872693bf7d0628e48e0 Homepage: https://cran.r-project.org/package=relMix Description: CRAN Package 'relMix' (Relationship Inference for DNA Mixtures) Analysis of DNA mixtures involving relatives by computation of likelihood ratios that account for dropout and drop-in, mutations, silent alleles and population substructure. This is useful in kinship cases, like non-invasive prenatal paternity testing, where deductions about individuals' relationships rely on DNA mixtures, and in criminal cases where the contributors to a mixed DNA stain may be related. Relationships are represented by pedigrees and can include kinship between more than two individuals. The main function is relMix() and its graphical user interface relMixGUI(). The implementation and method is described in Dorum et al. (2017) , Hernandis et al. (2019) and Kaur et al. (2016) . Package: r-cran-remap Architecture: all Version: 0.3.3-1.ca2404.3 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-units Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-mgcv, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-remap_0.3.3-1.ca2404.3_all.deb Size: 573298 MD5sum: 97256aefebe8fa8db007c6fe85fdef10 SHA1: 9c91e6e9f3fa512f9a343b580a2bbb3e45bbdcc7 SHA256: ac289149726ae3104d2965acb8b71c1f64e9b48208786acd965643f3d3239110 SHA512: 78cf8bcbe14db74d54f01ea99bfab24617a73dfb34b341e78cd64ec10c01dcc4e5e58aadc98b63bea3033112a0547c5b12e47b16df71c441bd900c8075aa082c Homepage: https://cran.r-project.org/package=remap Description: CRAN Package 'remap' (Regional Spatial Modeling with Continuous Borders) Automatically creates separate regression models for different spatial regions. The prediction surface is smoothed using a regional border smoothing method. 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Package: r-cran-rematch2 Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rematch2_2.1.2-1.ca2404.1_all.deb Size: 47134 MD5sum: a58c3be12c7241979dc8a1c05dc2c932 SHA1: ce6fa09ca8b2b49a695f40527b8eac2129b2b335 SHA256: d650f0b35f18605c265a2680d2b19e7f46adf1a29c1f122d4a7fc57fc02ea0a0 SHA512: 5a6f5b34e0572b93079b568f3bccae80e39778f345164c82ff626f3a07d7491d1ffefed7a07f34c4b32990a0778165a6c9fe58c721e140d86050e3992155631b Homepage: https://cran.r-project.org/package=rematch2 Description: CRAN Package 'rematch2' (Tidy Output from Regular Expression Matching) Wrappers on 'regexpr' and 'gregexpr' to return the match results in tidy data frames. Package: r-cran-rematch Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rematch_2.0.0-1.ca2404.1_all.deb Size: 17694 MD5sum: c1376b635862c2262a331d986512f596 SHA1: d846cadd977856aa56802b4bd15912c48288e979 SHA256: 231fa71a35c8ab093239843ddf7e8cbe862ac63b87f1d364e57ec827b0e033d0 SHA512: b3cf559bbaf03cd035d2f1cd7c25189b4e8ec5b60ef52535bb5a0c191ff42fd781700794668b2604a1316d0c6532485bded8c82baa9e647dc38f1edc2f3f4dd3 Homepage: https://cran.r-project.org/package=rematch Description: CRAN Package 'rematch' (Match Regular Expressions with a Nicer 'API') A small wrapper on 'regexpr' to extract the matches and captured groups from the match of a regular expression to a character vector. Package: r-cran-rembg Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-onnxr, r-cran-jpeg, r-cran-png, r-cran-matrix Suggests: r-cran-tinytest, r-cran-openssl Filename: pool/dists/noble/main/r-cran-rembg_0.1.1-1.ca2404.1_all.deb Size: 110550 MD5sum: 692f8d7b356cf3dd2e01bc7b4a2e54e3 SHA1: 73b2e43c77b3043760a4adf2d6e26955ef16ba16 SHA256: 1a4e21e47c5b12bb712fffa20167a61f4d4c4572b9c98acfebb273fde7797548 SHA512: 335c602b336d21ab27ad6fc308fc15537a10aca6cb4b241a5f803cc2db6877610703ddb52e47427d755cba2e9b7bbfbc4d38bfb03affc689a5c6dabe48d23e5b Homepage: https://cran.r-project.org/package=rembg Description: CRAN Package 'rembg' (Remove Image Backgrounds with Pre-Trained Segmentation Models) Remove the background from an image using pre-trained deep learning segmentation models ('U-2-Net', 'ISNet', 'BiRefNet' and others) run through the 'ONNX' Runtime via the 'onnxr' package. Given an image, a model predicts a foreground alpha matte which is composited into a cutout with a transparent (or solid-colour) background; optional closed-form alpha matting (ported from 'pymatting') refines soft edges. An R port of the Python 'rembg' package (). Models are downloaded on first use and cached in a per-user cache directory. Package: r-cran-remdata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-remdata_0.2.1-1.ca2404.1_all.deb Size: 1926728 MD5sum: b8f1f0622a86dc90306f613e0e99e80d SHA1: a7b8534fc13250d5cc00c197fcfaaf4e52b873cf SHA256: c60494ee4262990827b7544a9f85b171d34826d0b3ec675a9bf091ef301a6e0e SHA512: da3479674391cd6a3e41dc22f9e3fd2753db125934f815908f90397d2b20c8592d219081b5493c184d0002fe3063af30b5f581f2260a5e2b93e118adbea8beff Homepage: https://cran.r-project.org/package=remdata Description: CRAN Package 'remdata' (A Collection of Empirical and Simulated Relational Event DataSequences) Empirical and simulated data for relational event analyses. Each dataset consists of a relational event sequence and optional actor attributes. Individual datasets are redistributed under their original licenses as documented in inst/DATA_LICENSES. Package: r-cran-remedy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-rstudioapi, r-cran-rematch2 Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-remedy_0.1.0-1.ca2404.1_all.deb Size: 99200 MD5sum: 7efb684d1b4a56073b8a66134c3ba07a SHA1: f8ab1c7b0adb9ac1457279a567a57dfcc6cac80a SHA256: 3b91004d739ab9d2565e768387a20b48035255cf43665118954ea3e0902cecb6 SHA512: e318ac41b9148e9f1e6c77a71c4ab967010b1017875737b1e4ae061acb5bac5af4000a2a4079f31886e94498b77ab2e8e1c3da072f267c5ad19700941a40087c Homepage: https://cran.r-project.org/package=remedy Description: CRAN Package 'remedy' ('RStudio' Addins to Simplify 'Markdown' Writing) An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of functions that allow the user to be more efficient when using 'Markdown'. 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Package: r-cran-remfpca Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2770 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-fda, r-cran-expm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-remfpca_2.0.0-1.ca2404.1_all.deb Size: 2752130 MD5sum: 56a042eb9474ffeff9ad34f08b9b96de SHA1: d9ce4993721c1677ca38509b271bbb99bb154864 SHA256: 9d71fa6b9e9f6ce66563158dc3770c074aeec30b007aabac106b74817eeaf2c0 SHA512: 738c4e8bda7f35730ad9f42bf36685710f61e9d008a01b2f6ead78c2338a12b5da45ae6f4d0a91710eba159e36d6c4349efd83171df36b6a5faf8e2bbfaeb164 Homepage: https://cran.r-project.org/package=ReMFPCA Description: CRAN Package 'ReMFPCA' (Regularized Multivariate Functional Principal Component Analysis) Methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. 'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) . Package: r-cran-remindr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-remindr_0.0.1-1.ca2404.1_all.deb Size: 27894 MD5sum: 39968890795add6173bbe1e633a340f4 SHA1: af7ef5823a5dceb48cc12350431c7444205f732d SHA256: 4be6b5355bb147bbc2c0b9b4bc0ee0bdbfc0cc73f81cbcee97c71e27a1fbf22b SHA512: 9c8385c0f8826303c4501781ff4324091839b783fadc188da6510fd3c1fed920811efefb319bfaa6d713146009e0b88439c4fe2ad60868bb20e1eeffe8208bfc Homepage: https://cran.r-project.org/package=remindR Description: CRAN Package 'remindR' (Insert and Extract "Reminders" from Function Comments) Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. 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Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis. Package: r-cran-remixed Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-desolve, r-cran-rsmlx, r-cran-dosnow, r-cran-dplyr, r-cran-fastghquad, r-cran-ggplot2, r-cran-snow, r-cran-stringr, r-cran-rmpfr Filename: pool/dists/noble/main/r-cran-remixed_1.1.2-1.ca2404.1_all.deb Size: 1039950 MD5sum: 164e422b4f89540ec8607ada9c7a12b3 SHA1: ee1cc7d50e45f6f5fadc4e81d7e8dc7d339f441e SHA256: 29715edf8475488d2a96161c0f41dd91f7b8516a36efa8c34a595dd24b66c9ac SHA512: 09df7c6574fd2d18d275c8d40492819ad3585bab76d590b08359ae9433b948c1c8852f6ffe226d036f603bf6a8126fd17c0c93cffef12142309852e444c1f50d Homepage: https://cran.r-project.org/package=REMixed Description: CRAN Package 'REMixed' (Regularized Estimation in Mixed Effects Model) Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses 'Monolix' software (), which provide robust statistical method for non-linear mixed effects modeling. 'Monolix' must have been installed prior to use. Package: r-cran-remla Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gparotation, r-cran-geex Suggests: r-cran-knitr, r-cran-lavaan, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-remla_1.2.0-1.ca2404.1_all.deb Size: 95760 MD5sum: 77119cd45423290815c025f9659c951f SHA1: 1f8456c748b716cf6e26c5f4010128d666dc786f SHA256: fec29733c3cca61d268877b5e0022af9d68d23c9892e3ebd1e6828d3ede3d539 SHA512: 3e4f0218cfa204154802520d594d620a58b34959473d314380c8f8e81ddd8ce4219175a9ca3d6cf7d124c1c3a6c6e1439fc23d0623d9b9b0e437798ce9dfd2b2 Homepage: https://cran.r-project.org/package=REMLA Description: CRAN Package 'REMLA' (Robust Expectation-Maximization Estimation for Latent VariableModels) Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) . The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model. Package: r-cran-remm Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stream, r-cran-cluster, r-cran-clustergeneration, r-cran-mass, r-cran-proxy, r-cran-igraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-remm_1.2.1-1.ca2404.1_all.deb Size: 911804 MD5sum: 157000d84cddb50bd0db41e2989a9fd3 SHA1: c4cd1b1bc7d82e7ccb56fa207273fbb39a05b028 SHA256: 604c111c4792e4beee14c6e5ccf99eb3723a8b1e0cd4a88a233d88fa5da537c7 SHA512: 4dd3db25b25f01c2dd5d78301bbc8d5bf6770ff896e5e4d9e2cc1ad942707f51d58e753baeb45c8b2dc4ae0a785ec19a984b5f4e047bcd00e2d503987ff0cf07 Homepage: https://cran.r-project.org/package=rEMM Description: CRAN Package 'rEMM' (Extensible Markov Model for Modelling Temporal RelationshipsBetween Clusters) Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute. Hahsler and Dunham (2010) . Package: r-cran-remmy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-httr2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-remmy_0.1.0-1.ca2404.1_all.deb Size: 362240 MD5sum: b9ff7f5e67738f9b99917789e21a196b SHA1: 0261915142157fd45ec5140217f8a79dde36233a SHA256: a913f386266f58d071893a9190e396b6d543cd6e6b8a2f2509050bcd9f597b84 SHA512: 7139785a213409eafa865aa6edc30ea4dea4fbedc6850cdeac81f36060cf708af65c06877828a08527317de02c96b0bcccd4c0bfd6e7b3e1058e53ecf010de0b Homepage: https://cran.r-project.org/package=remmy Description: CRAN Package 'remmy' (API Client for 'Lemmy') An HTTP API client for 'Lemmy' () in R. Code and documentation are generated from the official 'JavaScript' client source (). Package: r-cran-remode Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-remode_0.1.0-1.ca2404.1_all.deb Size: 35834 MD5sum: e996536f7354e92037ec5db890373fc5 SHA1: 9d4c151335a2413e9e84e87b935a76563f406d21 SHA256: 2512c913f40294da2a35b49351f2172652a2b6a1fb765521c10da7d9f5222dcb SHA512: 5847068cf44d5eeeb71440acee13addd3f6ce54dae2dafdfb0248f65273c8afd74d12fea453776a13027b121a7772abf4deaaa53c4c12e81d1c49d7e6d668dde Homepage: https://cran.r-project.org/package=remode Description: CRAN Package 'remode' (Recursive Mode Detection for Distributions of Ordinal Data) Provides the function remode() for recursive modality detection in ordinal data. 'remode' is an algorithm specifically designed to estimate the number and location of modes in ordinal data while being robust to large sample sizes. 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Package: r-cran-rempsyc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3704 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr Suggests: r-cran-flextable, r-cran-ggplot2, r-cran-effectsize, r-cran-performance, r-cran-insight, r-cran-correlation, r-cran-datawizard, r-cran-report, r-cran-modelbased, r-cran-see, r-cran-lmtest, r-cran-ggrepel, r-cran-boot, r-cran-bootes, r-cran-ggsignif, r-cran-qqplotr, r-cran-broom, r-cran-emmeans, r-cran-ggpubr, r-cran-interactions, r-cran-openxlsx2, r-cran-patchwork, r-cran-psych, r-cran-venndiagram, r-cran-rmisc, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rempsyc_0.2.0-1.ca2404.1_all.deb Size: 2178314 MD5sum: 810e11ce81f000bc499f588a161d0943 SHA1: 2cff6d61068bf02388ba9392c0dde24bbab0717b SHA256: d194bfd05311ce4366ef63e6e51ff405e545a58f803c358d42c79954611cfa56 SHA512: 348205cfb1e34bd15ec63c4b1e97d275c8edb20004992634b11658849f2f91998e264af20328ca9725e26d475686e4c0bdb74621576d0a79cf6ff2384be9928b Homepage: https://cran.r-project.org/package=rempsyc Description: CRAN Package 'rempsyc' (Convenience Functions for Psychology) Make your workflow faster and easier. Easily customizable plots (via 'ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via 'flextable'), easily run statistical tests or check assumptions, and automatize various other tasks. 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TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) . 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Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power. Package: r-cran-rentrez Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rentrez_1.2.4-1.ca2404.1_all.deb Size: 121898 MD5sum: 3d39a0f72208c123d5b388b5a1697011 SHA1: 91c3e24c04c2028fd5fbdbaae8764dbe4ab6e5a3 SHA256: 9d61f2efa966e6b6c3b0a5311ce8cac896c04377f71f0b49aa1834fecf433a40 SHA512: 4ce4b9058cc7699095ba287509f608bba78df8aef3b709ccd7c61f6603d00448b4c1cbfa9d5e4863b814b3b9da66e3969e1f7ef64791c9bd49e06e3c489be196 Homepage: https://cran.r-project.org/package=rentrez Description: CRAN Package 'rentrez' ('Entrez' in R) Provides an R interface to the NCBI's 'EUtils' API, allowing users to search databases like 'GenBank' and 'PubMed' , process the results of those searches and pull data into their R sessions. 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This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) . 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"bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session. Package: r-cran-renyiextropy Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-renyiextropy_0.4.0-1.ca2404.1_all.deb Size: 176612 MD5sum: 842729809cf6860c3a6680b852f308fb SHA1: a50673697ff50a430796ee072589224649beb622 SHA256: c26e8996151ee57eab5fc5226f83bc1006e6d0175495c9fb83b3f4996a3e390e SHA512: 63068ebee07fdf9fba7464ce689236877ea5195406765cd56e07b40c5edabbb5e12be8264a23261acf2bcec9cad21b35a9a3ffa072a777845b396a97501c992b Homepage: https://cran.r-project.org/package=RenyiExtropy Description: CRAN Package 'RenyiExtropy' (Entropy and Extropy Measures for Probability Distributions) Provides functions to compute Shannon entropy, Renyi entropy, Tsallis entropy, and related extropy measures for discrete probability distributions. Includes joint and conditional entropy, KL divergence, Jensen-Shannon divergence, cross-entropy, normalized entropy, and Renyi extropy (including the conditional and maximum forms). All measures use the natural logarithm (nats). Useful for information theory, statistics, and machine learning applications. Package: r-cran-renz Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-renz_0.2.1-1.ca2404.1_all.deb Size: 544282 MD5sum: bef3a908ebbc2a6423a4c4e70d535fc8 SHA1: 1f13193cabf970ea1ca6a59dc1c8047501198d6d SHA256: c2852ad8a63b007979156640773a2348610f33bbcf22bb82c41cbe9d18f82635 SHA512: c9c71550bcda87731d0039a874d1e187e697430a9e3454ba152d8ed4bd06a094a026fa8f78607f267324869abe1910b75b7c29ab14a3b7f73305773c7a839eab Homepage: https://cran.r-project.org/package=renz Description: CRAN Package 'renz' (R-Enzymology) Contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation of kinetic parameters (Aledo (2021) ). To avoid data transformation and the potential biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme. Package: r-cran-repairdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-repairdata_0.1.0-1.ca2404.1_all.deb Size: 1207834 MD5sum: f469eba87f62b16ce4e41a40b7a78235 SHA1: 5e2d63d5236194688260e20581cc9552238740df SHA256: 00210ede8b07e99c86fd7ede69c0454ebad46e1cce6e9e6ae9a0ec5850035d8c SHA512: 439b835d0a556f88c63c732611ebd79f645f6be27ffe44ba34fd1b1524e81ebe1c15e33b8484a81665e0243f03ef44eb3931e77b803ed67d872bfc00f10a2eea Homepage: https://cran.r-project.org/package=repairData Description: CRAN Package 'repairData' (Open Repair Alliance Datasets 2021) The complete data set of open repair data, full compliant with the Open Repair Data Standards (ORDS). It combines the datasets contributed by partner organizations of the Open Repair Alliance (ORA). Last updated: 2021-02-22. The package also contains via quests enriched datasets on batteries, printer, mobiles, and tablets. Package: r-cran-repana Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-config, r-cran-dplyr, r-cran-magrittr, r-cran-lubridate, r-cran-rmarkdown, r-cran-processx, r-cran-readr, r-cran-rstudioapi, r-cran-pool, r-cran-openxlsx, r-cran-yaml, r-cran-digest Suggests: r-cran-spelling, r-cran-testthat, r-cran-knitr, r-cran-rsqlite, r-cran-rpostgres, r-cran-duckdb, r-cran-pacman, r-cran-targets Filename: pool/dists/noble/main/r-cran-repana_2.2.1-1.ca2404.1_all.deb Size: 150876 MD5sum: 99f589910b361bdaf14277876b534e09 SHA1: 199ee54f2fe3cf1df4e4daeb0bd0c3d21dd89d70 SHA256: c169478973e96918925d0159eb43ab69df966855cf9a902d741882ac2718789d SHA512: 5d0f24e4773c58201d0766bc1361db391ab7cdbed4cf7ffb9f31844c3a53b91ed656172412746aacf67a12b6f9236f508f7a364b9511b5d884654faa08efc2ae Homepage: https://cran.r-project.org/package=repana Description: CRAN Package 'repana' (Repeatable Analysis in R) Set of utilities to facilitate the reproduction of analysis in R. It allow to make_structure(), clean_structure(), and run and log programs in a predefined order to allow secondary files, analysis and reports be constructed in an ordered and reproducible form. Package: r-cran-repaymentplan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-repaymentplan_0.1.0-1.ca2404.1_all.deb Size: 18866 MD5sum: b1584d268aee85eda36e6b2851333495 SHA1: adeb38dfde2d3727db4eeb8afef7e6229759ca7e SHA256: 89082ee2f1d00e0bb675a257f9248a8973805a4811f3697159b591a360cb1386 SHA512: 895bbe7ea6027687720ebc639233b7ee9193062b7da51f5997b46d07940a27afccd914cd9259bca2260e6a96a6ccdda5937be3e33f4a526b3b15a95e33e081de Homepage: https://cran.r-project.org/package=RepaymentPlan Description: CRAN Package 'RepaymentPlan' (Calculation of Mortgage Plan or Repayment Plan) The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans. Package: r-cran-repeatabel Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hglm Filename: pool/dists/noble/main/r-cran-repeatabel_2.0-1.ca2404.1_all.deb Size: 85816 MD5sum: 0a376b3606d932ca20e0c3552b3de560 SHA1: d3faddc428a2fd974809bc1eb4854f396542214c SHA256: 86fda51e26f40cc393b86c58d2557410ff4f9a2b48b5833a451060d01738836f SHA512: 317d4f93a552f3ce91c4b5dba52eba6de245773b238ebb78ec93e51f3af526df5e6bc4f0a625a95e037983d5367613cc7d7f681d382edc4f5e25b558a4e2d25f Homepage: https://cran.r-project.org/package=RepeatABEL Description: CRAN Package 'RepeatABEL' (GWAS for Multiple Observations on Related Individuals) Performs genome-wide association studies (GWAS) on individuals that are both related and have repeated measurements. For each Single Nucleotide Polymorphism (SNP), it computes score statistic based p-values for a linear mixed model including random polygenic effects and a random effect for repeated measurements. The computed p-values can be visualized in a Manhattan plot. For more details see Ronnegard et al. (2016) and for more examples see . 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Package: r-cran-repec Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-repec_0.1.0-1.ca2404.1_all.deb Size: 42254 MD5sum: 85936934c2e463408687f7b0269ba241 SHA1: c8acc7cef3892f2a42c1849b2936af6c30d64d4f SHA256: 9f5b2b04a6621d58a2934da8f23cfbbca991c844b9d7f0f8707f4572dd5e25a7 SHA512: 80de08d3efea4cc80a2ae51d7dc8b8c535af0cee6aa1b095801622cb7954544987138c5768ea838ba84df6385d7dba8d8d2fd43078694bdcbd3b24be5b1f46ec Homepage: https://cran.r-project.org/package=repec Description: CRAN Package 'repec' (Access RePEc Data Through API) Utilities for accessing RePEc (Research Papers in Economics) through a RESTful API. You can request a code and get detailed information at the following page: . Package: r-cran-repello Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-repello_1.0.1-1.ca2404.1_all.deb Size: 47070 MD5sum: 0537ca592ba2a0e0edb561c88c0fb156 SHA1: cee239fb85bf93dd8b18c4dbf1fb695091ca7194 SHA256: db495a50b110b9cc195d8b1a2490a07d05ab34774b06d5358790cddae35fadd5 SHA512: c91fd5d00571e94b4525720728419d5664cdce1ca15b646d1b820e294ce8fa5428c152a43c91ee99feeb79237c73dfd224abdd6b23b2e7fceffb6218d46104a6 Homepage: https://cran.r-project.org/package=repello Description: CRAN Package 'repello' (Reports from Trello in R) Creates reports from Trello, a collaborative, project organization and list-making application. Reports are created by comparing individual Trello board cards from two different points in time and documenting any changes made to the cards. Package: r-cran-repertoir Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circlize, r-cran-igraph, r-cran-reshape2, r-cran-stringdist, r-cran-stringi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-repertoir_0.0.1-1.ca2404.1_all.deb Size: 51356 MD5sum: b9d51c84bcd4383b2a99f27a64cd84a5 SHA1: 075e5f9dbc4b38d4b86265df5f97b9f1dfa3347a SHA256: 40fd2d0a3519b5e6e9c9175a1a6a58c269301aaf25fc0b050139513de0f01ef6 SHA512: ab0770efcae797335cb1c4b2fbc63c3e1ad78c11f454cae14c80cfe9f79aaf69f0e30278dcb013a03216bd828d0ef8da781584e464c0eb08e69166b6cd0b8555 Homepage: https://cran.r-project.org/package=RepertoiR Description: CRAN Package 'RepertoiR' (Repertoire Graphical Visualization) Visualization platform for T cell receptor repertoire analysis output results. It includes comparison of sequence frequency among samples, network of similar sequences and convergent recombination source between species. Currently repertoire analysis is in early stage of development and requires new approaches for repertoire data examination and assessment as we intend to develop. No publication is available yet (will be available in the near future), Efroni (2021) . Package: r-cran-repfun Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-glue, r-cran-jsonlite, r-cran-rlang, r-cran-xportr, r-cran-hmisc, r-cran-r2rtf, r-cran-haven, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rprojroot, r-cran-dt, r-cran-kableextra, r-cran-admiral, r-cran-tibble, r-cran-lubridate, r-cran-ggplot2, r-cran-devtools, r-cran-spelling Filename: pool/dists/noble/main/r-cran-repfun_0.1.2-1.ca2404.1_all.deb Size: 2053960 MD5sum: 0a0ae8ad729d39f540b1eea609ae686b SHA1: 1d27177ff7761521cdc9a19ae92118a24536998d SHA256: 55bad564fd488f389450bae11aed8378ec831391bd695d2855b130f6be2937c0 SHA512: 72729761a8b5498e5ee7dc5fbb5bb0cfc2da2f910a0211ef1ff3b580b3545d0a5132c811565a6e1d77a770c2c3a597bfcb864185794b60fce51ccd9ad708ce04 Homepage: https://cran.r-project.org/package=repfun Description: CRAN Package 'repfun' (Create Tables, Listings and Figures using Functions Styled afterSAS™ Macros) Mimic the style of traditional reporting macros for clinical trials. The purpose is to generate tables, listings and figures that support clinical research. This package is well suited for firms or individuals who wish to incorporate R without changing their ways of working as it follows a traditional clinical research workflow. Invoke functions (instead of macros) to summarize data and produce formatted reports. This package differs from others in that it includes tools (wrappers) for both analyzing and reporting data. Package: r-cran-replacer Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-cardata, r-cran-testthat, r-cran-checkmate, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-replacer_1.0.2-1.ca2404.1_all.deb Size: 78830 MD5sum: a7184a6e93ff074ad55cf13cf95b0e0c SHA1: 9e02c913eedad1105656d5f36c2228b6c9f0ddc5 SHA256: 311acbf847e8bb36488d60ba6c578f7cbfe85072d22bb56a6eedbc74a5621a98 SHA512: f184e6f8c53faedc9f94d7852a75876287fe27ce261fd084f7779af786ef9c3df6f84dc6b9445e84c9da8dc10742fec19dabfe1415317172466d27badae54b46 Homepage: https://cran.r-project.org/package=replacer Description: CRAN Package 'replacer' (A Value Replacement Utility) Updates values within csv format data files using a custom, User-built csv format lookup file. Based on 'data.table' package. Package: r-cran-replesentr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-modules, r-cran-dat, r-cran-knitr Suggests: r-cran-testthat, r-cran-covr, r-cran-txtplot Filename: pool/dists/noble/main/r-cran-replesentr_0.4.1-1.ca2404.1_all.deb Size: 34732 MD5sum: 857eadd7e5abb81b37ee00ddb3164e3d SHA1: 8ee903fd56dc1c8973ec62657d28c92b34aac161 SHA256: 19cc68a56841763d1ee0c678a72bc5929a2e151db5c121331ae92d0b7cd60454 SHA512: c068a04168ff27605aec8ec3969eba938f26a59a2372966e6707fd09510ca399e7924e3194c935656267b9cdfa5d72cda14754dee5dfe0aa239e0bdf4b5e1c02 Homepage: https://cran.r-project.org/package=REPLesentR Description: CRAN Package 'REPLesentR' (Presentations in the REPL) Create presentations and display them inside the R 'REPL' (Read-Eval-Print loop), aka the R console. Presentations can be written in 'RMarkdown' or any other text format. A set of convenient navigation options as well as code evaluation during a presentation is provided. It is great for tech talks with live coding examples and tutorials. While this is not a replacement for standard presentation formats, it's old-school looks might just be what sets it apart. This project has been inspired by the 'REPLesent' project for presentations in the 'Scala' 'REPL'. Package: r-cran-replicate Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-replicate_1.2.0-1.ca2404.1_all.deb Size: 37188 MD5sum: 9596b56865d3e80a066e2043085c8d33 SHA1: 477156998169611166068c2e47f39ff8909532b2 SHA256: 53b5b0cdd6efc826865ff3ce8eb8cd157cf016e67a6eea6c895341767ccc39a4 SHA512: 673e47b0bf968019c9db41445267ac94fb0173f355f930e360bc5d69a6c4385a102d5acfc042545bb5f7467a8baaa423f66b187dc656f2c012330abbf7f80a16 Homepage: https://cran.r-project.org/package=Replicate Description: CRAN Package 'Replicate' (Statistical Metrics for Multisite Replication Studies) For a multisite replication project, computes the consistency metric P_orig, which is the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study were statistically consistent with the replications. Other recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (2) the probability of a true effect of meaningful size in the direction opposite the original study's estimate. These two can be computed using the package \code{MetaUtility::prop_stronger}. Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; ) for details. Package: r-cran-replicatebe Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 827 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-powertost, r-cran-lmertest, r-cran-nlme, r-cran-pbkrtest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-replicatebe_1.1.3-1.ca2404.1_all.deb Size: 466576 MD5sum: 5e62ac87924236ca6f8c4beeae05bed0 SHA1: adc526c9b8ec6c0955a96206214e6d7ec7234247 SHA256: 03afd2d41770dc8f40333530456c0256493947c4dbf112255ea2fa88f214e401 SHA512: 759ca007888f1a0bbc1b7b6bd11be42edc9acb2efdf64633e3f3f3a6177bf35af57f1dca6e83b159953fdac6354a83f92f362c18ef262cd471f824ec3f8bd84b Homepage: https://cran.r-project.org/package=replicateBE Description: CRAN Package 'replicateBE' (Average Bioequivalence with Expanding Limits (ABEL)) Performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' / 'Method B' and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well. 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Package: r-cran-represent Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-represent_1.0.1-1.ca2404.1_all.deb Size: 48048 MD5sum: 7e0592892e9b4d0a99e7c15970dde771 SHA1: 0f4806a4f4b0514d617a6cd57ae2b42f4cf20255 SHA256: bf408beeca6c2f798e5991facf76d6b839e8066e83d49fed296671cb4deb4f82 SHA512: d4ac5c36de3f94dbd91ee27b6e6c9738012764b57641b3a86374d89dd7e97fcbb6c56e4e742c1b1a165cc79f0b9da0f76552a4b40ec9cbad4badb0c2b84fde93 Homepage: https://cran.r-project.org/package=represent Description: CRAN Package 'represent' (Determine How Representative Two Multidimensional Data Sets are) Compute the values of various parameters evaluating how similar two multidimensional datasets' structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), . 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Functions will create appropriate modules which may pass data from one step to another. Package: r-cran-reprex Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-clipr, r-cran-fs, r-cran-glue, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-withr Suggests: r-cran-covr, r-cran-fortunes, r-cran-miniui, r-cran-rprojroot, r-cran-sessioninfo, r-cran-shiny, r-cran-spelling, r-cran-styler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reprex_2.1.1-1.ca2404.1_all.deb Size: 491482 MD5sum: e51619dc70fe64068cb8194e088ff94d SHA1: 104d2d9c00f3c7e2bd43f9112b9a05bf32abb89e SHA256: 86c830754914f7820694e47b261a0c407d499de0593bc4fbe0d1d0e94aea4474 SHA512: e68f21a6ca0cd7c6e5c8276f8cd732dc00133e760d98add76c98063bb7b959f69e6cfdd63a17663e55ee57c1c894a3aac6ecd6367c34100340eff7f12921e09d Homepage: https://cran.r-project.org/package=reprex Description: CRAN Package 'reprex' (Prepare Reproducible Example Code via the Clipboard) Convenience wrapper that uses the 'rmarkdown' package to render small snippets of code to target formats that include both code and output. The goal is to encourage the sharing of small, reproducible, and runnable examples on code-oriented websites, such as and , or in email. The user's clipboard is the default source of input code and the default target for rendered output. 'reprex' also extracts clean, runnable R code from various common formats, such as copy/paste from an R session. Package: r-cran-reproducer Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-getoptlong, r-cran-ggplot2, r-cran-gridextra, r-cran-httr, r-cran-jsonlite, r-cran-lme4, r-cran-mass, r-cran-metafor, r-cran-nortest, r-cran-openxlsx, r-cran-readr, r-cran-reshape, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xtable Suggests: r-cran-assertthat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reproducer_0.7.0-1.ca2404.1_all.deb Size: 1556078 MD5sum: b206873b233a2183b9a490bc48d842f8 SHA1: b54795762bcd49a220dd5886fe897b69093aee30 SHA256: 0a328703fd78dfffadc662ac20e0b0e47332fd2b740d4687505009c724439ae3 SHA512: 2a764e1f05b36f95e8d2250be940ad6595610a3274d9b66c58693a5631d8ad7ba8f49fcd90664b2ae2ac2ae039ed9d37c5d6d6bf38bbfdbc61b954903dfeeb6f Homepage: https://cran.r-project.org/package=reproducer Description: CRAN Package 'reproducer' (Reproduce Statistical Analyses and Meta-Analyses) Includes data analysis and meta-analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for AB/BA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram),and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi- site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering. Package: r-cran-reproducible Architecture: all Version: 3.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-filelock, r-cran-fpcompare, r-cran-fs, r-cran-lifecycle, r-cran-lobstr Suggests: r-cran-archive, r-cran-covr, r-cran-curl, r-cran-dbi, r-cran-future, r-cran-geodata, r-cran-glue, r-cran-googledrive, r-cran-httr, r-cran-httr2, r-cran-knitr, r-cran-parallelly, r-cran-qs2, r-cran-raster, r-cran-rcurl, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-r.utils, r-cran-rvest, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-reproducible_3.2.1-1.ca2404.1_all.deb Size: 1530802 MD5sum: 21f62ef304d351f59d6eaaf3a94cb993 SHA1: 2719c07598821aa1d730810e4d601538596950d6 SHA256: df4cbd302d6c58bc74816059ed664f1ffd5cefdc79ca216c9e7abb980cea43eb SHA512: 5dffd7cc6775525f6a0309089e6f0833a9ffd0b8a03bd722e06b55c7c6de2e9bd5427642a0d0b822e12ce4af5fc9d9676803e9531d2ac19cac1de1664d23049e Homepage: https://cran.r-project.org/package=reproducible Description: CRAN Package 'reproducible' (Enhance Reproducibility of R Code) A collection of high-level, machine- and OS-independent tools for making reproducible and reusable content in R. The two workhorse functions are 'Cache()' and 'prepInputs()'. 'Cache()' allows for nested caching, is robust to environments and objects with environments (like functions), and deals with some classes of file-backed R objects e.g., from 'terra' and 'raster' packages. Both functions have been developed to be foundational components of data retrieval and processing in continuous workflow situations. In both functions, efforts are made to make the first and subsequent calls of functions have the same result, but faster at subsequent times by way of checksums and digesting. Several features are still under development, including cloud storage of cached objects allowing for sharing between users. Several advanced options are available, see '?reproducibleOptions()'. Package: r-cran-reproduciblerchunks Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-knitr, r-cran-digest, r-cran-rstudioapi, r-cran-rmarkdown Suggests: r-cran-spelling, r-cran-mass, r-cran-psych, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reproduciblerchunks_1.2.0-1.ca2404.1_all.deb Size: 792480 MD5sum: f7f30b3cccccd34fa90ff06911327a06 SHA1: 2916ff2df6e4a52fdba9bf73e1dc73943c5268d0 SHA256: c1f1309c9c78dec61a61f06cf2a8bc7479933ef4c962382540657100d602dfd5 SHA512: 5936085b15a4c7790da327fdfe153a0a249d00e4e7d20de450063dcc4afd4f37d4219191b87a1cc0f20d91b404112260531d4cd076ccfb3107aa57fad1caaaff Homepage: https://cran.r-project.org/package=reproducibleRchunks Description: CRAN Package 'reproducibleRchunks' (Automated Reproducibility Checks for R Markdown Documents) Provide reproducible R chunks in R Markdown document that automatically check computational results for reproducibility. This is achieved by creating json files storing metadata about computational results. A comprehensive tutorial to the package is available as preprint by Brandmaier & Peikert (2024, ). Package: r-cran-reproducr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-digest, r-cran-jsonlite, r-cran-commonmark, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-reproducr_0.2.0-1.ca2404.1_all.deb Size: 188686 MD5sum: e8ea8903cf4bc84c5831ed7e53707625 SHA1: c17c9e44921d59fc3e5f1a11e857ab6e4c8cac25 SHA256: 400205604e06123c73b1d732ebbc252276e856bee095eb868fa9c6241de1b36b SHA512: 48643ad86a28490f04acbe29253f64a5042725fd5cacc97fcc49c89f75887b99e8e6731052ec0557ffb126b61bb04459635097b3c71266d80f04950fa7ae6684 Homepage: https://cran.r-project.org/package=reproducr Description: CRAN Package 'reproducr' (Behavioural Reproducibility Auditing for R Projects) Audits R scripts for behavioural reproducibility risk. Scans scripts for qualified package::function calls and checks them against a curated database of known silent breaking changes across popular CRAN packages. Flags stochastic calls lacking set.seed() and detects locale-sensitive operations that may produce different results across systems. Supports baseline certification of analytical outputs so that silent numerical drift can be detected across package upgrades or platform changes. Generates human-readable audit reports suitable for academic submission or pharmaceutical QC workflows. For more details see . Package: r-cran-reproj Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crsmeta, r-cran-proj, r-cran-wk Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-reproj_0.8.0-1.ca2404.1_all.deb Size: 274804 MD5sum: 890d377245e8c047686a3dc8706477bc SHA1: 7f8befdd195a367742fb1ddca76d57837fd8d1f4 SHA256: 4b064f7fce458c7ab527f5e67886ad6cc902000a6084fdfdb03d7441f2407e60 SHA512: 70f8325f1fa3dd0a65630e1b52ec721b03d1b10f36179876423fdf9c61fb91d7292edaf85614215eecd1c277aca08c5833d578cb33dcf833868d8719a0c3060d Homepage: https://cran.r-project.org/package=reproj Description: CRAN Package 'reproj' (Coordinate System Transformations for Generic Map Data) Transform coordinates from a specified source to a specified target map projection. This uses the 'PROJ' library directly, via the 'PROJ' package. 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Package: r-cran-reproresearchr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-reproresearchr_0.1.2-1.ca2404.1_all.deb Size: 146738 MD5sum: 37c7280673aee691c2905f19ede38000 SHA1: 92de8f25daa97f007f80326ca8f5428f08270614 SHA256: 0278bfeeea01ae78df5e178ecb6a87b8ea6bd9d79efaadd3359cef62985a6615 SHA512: 48f123c2d0b1a5796ac1a672c8a802200de04507ce0f2e8251c997cc94b05d7c7f2316e4a633369c75629513ebd92e3294e9c6904216e3d97348a3c66c857077 Homepage: https://cran.r-project.org/package=reproresearchR Description: CRAN Package 'reproresearchR' (Companion Package for 'Reproducible Research Using R') Provides teaching datasets and helper functions to support the open educational resource Martinez (2026) "Reproducible Research Using R" . The package includes datasets used throughout the book and utilities to list and copy chapter scripts shipped with the package. 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It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about 'reservoirpy', please see Trouvain et al. (2020) . This package was developed in the framework of the University of Bordeaux’s IdEx "Investments for the Future" program / RRI PHDS. Package: r-cran-reset Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-seurat, r-cran-seuratobject, r-cran-sctransform Filename: pool/dists/noble/main/r-cran-reset_1.0.0-1.ca2404.1_all.deb Size: 357090 MD5sum: aa53354c71aa22c3fe81a07032dcf05c SHA1: 31020a4a9303cb269da322bbc5c2d7da4e0b02eb SHA256: 1fb2876d534f459989f6bfd2fcd210b2fca0ec5285e9835189872d59834a2680 SHA512: 612d3c7f541430b826545eb35b22a7e07b91841c45034c407a9168e8d7ccfa61905075a57fa16a01799f6b015e33df50dbe219ac2a470c2a446e3f6155c9ead1 Homepage: https://cran.r-project.org/package=RESET Description: CRAN Package 'RESET' (Reconstruction Set Test) Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. 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Package: r-cran-reslik Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-reslik_0.1.2-1.ca2404.1_all.deb Size: 53922 MD5sum: 806304e0b9b139ba14e8eb67cd196409 SHA1: 75a4084786afc5720cf3701d67e28f52f56d193c SHA256: 0851ce107df106229bebb8b92c6dde965d8c80e5f6402c4daac2288e69416055 SHA512: 148513f226abb576318dbc119b55067a1ccbb914031b277ec9c9aab0770cc8caf457bf74baf18bcb06613ee6e2580fb94fccd48216ee1b4c90652fe0d494485e Homepage: https://cran.r-project.org/package=resLIK Description: CRAN Package 'resLIK' (Representation-Level Control Surfaces for Reliability Sensing) Implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. 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Package: r-cran-reslr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4334 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-fastdummies, r-cran-fields, r-cran-geosphere, r-cran-ggplot2, r-cran-magrittr, r-cran-ncdf4, r-cran-plyr, r-cran-posterior, r-cran-purrr, r-cran-r2jags, r-cran-stringr, r-cran-tidybayes, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-reslr_0.2.0-1.ca2404.1_all.deb Size: 2809896 MD5sum: 51557e271fbcd934abbb65cb5fe68f45 SHA1: c7a465bad4f52781d9bd6f61e9c235c1071fe8cc SHA256: 6187c436b1dff1aa52445092eae05a10c132dd7ba58a33c67f72ad30c2d16f8b SHA512: 5be464aa69b450a71613b21fbd60eb75c01ba485fb48e8304d4c953ddb12e29ab581ffc34327263b21182687b90695d61322a9c51d697109e830da9657f6ecda Homepage: https://cran.r-project.org/package=reslr Description: CRAN Package 'reslr' (Modelling Relative Sea Level Data) The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. 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Package: r-cran-resmush Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-httr2 Suggests: r-cran-knitr, r-cran-png, r-cran-quarto, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-resmush_1.0.3-1.ca2404.1_all.deb Size: 800398 MD5sum: 2afdbb75b736db5800b2de9d5a829e74 SHA1: e31e064eccfef7d28d4ef873d960d4dc2bcf23bd SHA256: ecd6a0ae3068382129d34fbdc9abf44b445baa0ee8fc76b955d82c8bfb4ede7c SHA512: 7781c2263b07530e2970ba2aa2f0de372223498143d2bbb07589458adb84bbd8419dab1baca9fc84b61348a56e244ba79d77b58885e71d53c7fcef70e6522bba Homepage: https://cran.r-project.org/package=resmush Description: CRAN Package 'resmush' (Optimize and Compress Image Files with 'reSmush.it') Optimize and compress local and online image files with the 'reSmush.it' 'API' . Process individual files or entire directories. 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Package: r-cran-resourcecodedata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7523 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-resourcecodedata_1.0.0-1.ca2404.1_all.deb Size: 7664680 MD5sum: b95b1ea0328bc60b8254acea42373144 SHA1: 969bf506b75430a2fc0760a95cc0cef05028be9d SHA256: 18b005e2860c9578fe7c51caf153f40c7f2e314a584e4743a21fe1971d38a626 SHA512: 47622cadc67649b79dfa7e5391a0fc7e4b8fdd105053b82122271fa7cd357321127521721884d064817b859911fd8db4cfc498b8599ce7ebb7642b2e44298db2 Homepage: https://cran.r-project.org/package=resourcecodedata Description: CRAN Package 'resourcecodedata' (Resourcecode Database Configuration Data) Includes Resourcecode hindcast database (see ) configuration data: nodes locations for both the sea-state parameters and the spectra data; examples of time series of 1D and 2D surface elevation variance spectral density. 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In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) . Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively. In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size. Package: r-cran-respirometry Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-birk, r-cran-dplyr, r-cran-lubridate, r-cran-marelac, r-cran-measurements, r-cran-minpack.lm, r-cran-plyr, r-cran-rlang, r-cran-seacarb, r-cran-segmented Filename: pool/dists/noble/main/r-cran-respirometry_2.0.2-1.ca2404.1_all.deb Size: 534138 MD5sum: bdcd656e0313f3b550711c06fe615e1a SHA1: 6506e9b1d24540005eba3652b2ccaf320e225c6b SHA256: 476242c50aa09da987e506fa24954b28b5326c53a4749cbe5596ba2721f6adf7 SHA512: 5a005788781527426dc7345588ca24c7ef3f7786fad0d27343a8bfcd00edd9f7ad17b7bb228ac21f096531c82039214b2ce9df75eb91e6b80e3638397d63f8b8 Homepage: https://cran.r-project.org/package=respirometry Description: CRAN Package 'respirometry' (Tools for Conducting and Analyzing Respirometry Experiments) Provides tools to enable the researcher to more precisely conduct respirometry experiments. Strong emphasis is on aquatic respirometry. Tools focus on helping the researcher setup and conduct experiments. Functions for analysis of resulting respirometry data are also provided. This package provides tools for intermittent, flow-through, and closed respirometry techniques. 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Given the mean change, standard deviation and sample size per arm across studies, respondeR estimates the proportion of patients who cross a minimal important difference (MID) threshold under a parametric model for the change scores, and contrasts the arms as a risk difference, risk ratio, odds ratio or number needed to treat. It provides median, unweighted-mean, weighted-mean and per-study (fixed- or random-effects) pooling, the standardized-mean-difference to odds-ratio bridge of Anzures-Cabrera, Sarpatwari and Higgins (2011) , a threshold-free common-language effect size, and a point-and-click 'Shiny' application. The estimation methods were evaluated in a simulation study by Sofi-Mahmudi (2024) . Package: r-cran-responsepatterns Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-responsepatterns_0.1.1-1.ca2404.1_all.deb Size: 111602 MD5sum: ebaa350fb12b64acc4f94957ad35d1b5 SHA1: 27e733833a9738c5d9648720720f5f6dee487f0a SHA256: 70b258162fff83780114ca1deb59e590386779e4520d32530708ee217328a26a SHA512: 6d5ed47b51d9863ab8a1a4943949140140fb97e64dc991ac1620e6c7db9865572d1e74b424f41e584f3c3f293b80592d41d367832c76542bc9cd3bd6768492af Homepage: https://cran.r-project.org/package=responsePatterns Description: CRAN Package 'responsePatterns' (Screening for Careless Responding Patterns) Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic. See Gottfried et al. (2022) for more information. 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"Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). 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Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. Although this assumption makes inference easier, in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. Therefore we extended the base model by introducing random effects for the clones. In this extended formulation the dynamic parameters are estimated using a tailor-made expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) . 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'ResultModelManager' provides utility functions to allow package maintainers to migrate existing SQL database models, export and import results in consistent patterns. Package: r-cran-resumer Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-useful, r-cran-dplyr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-resumer_0.0.5-1.ca2404.1_all.deb Size: 38980 MD5sum: eb21b7897bf1260d14067f49cb656d64 SHA1: 05641e3efe89c6679c79bcda638488da41a426cb SHA256: 24609edc6b01d6c77df0ef4b01b509b8d40a1cf4c13364baadced355b565286b SHA512: 9607350469fe0a02dc07454c1b8f1efb83a43b49419cb922e35dc83a4c58bc33a84ac393e655fad412b19991c83f3f5f37616b2b6110c8a0bd2dbac9b2760ca2 Homepage: https://cran.r-project.org/package=resumer Description: CRAN Package 'resumer' (Build Resumes with R) Using a CSV, LaTeX and R to easily build attractive resumes. 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También permite obtener tablas de frecuencia clásicas y gráficos cuando se desea realizar un análisis de series agrupadas. Su objetivo es de aplicación didáctica para un curso introductorio de Bioestadística utilizando el software R, para las carreras de grado las carreras de grado y otras ofertas educativas de la Facultad de Ciencias Agrarias de la UNJu / It generates summary measures and graphs for discrete or continuous numerical data in simple series. It also enables the creation of classic frequency tables and graphs when analyzing grouped series. Its purpose is for educational application in an introductory Biostatistics course using the R software, aimed at undergraduate programs and other educational offerings of the Faculty of Agricultural Sciences at the National University of Jujuy (UNJu). 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Sankey diagrams visualize flows between states with link widths proportional to flow counts; see Kennedy and Sankey (1898) "The Thermal Efficiency of Steam Engines" and Schmidt (2008) "The Sankey Diagram in Energy and Material Flow Management: Part I: History" . The minimum input schema is one row per person per term with an identifier, term, and categorical state. 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Design retimings and pitch (f0) transformations with tidy data and apply them via 'Praat' interface. Produce spectrograms, spectra, and amplitude envelopes. Includes implementation of vocalic speech envelope analysis (fft_spectrum) technique and example data (mm1) from Tilsen, S., & Johnson, K. (2008) . Package: r-cran-retmort Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-retmort_1.0.0-1.ca2404.1_all.deb Size: 71530 MD5sum: e3bd0dae55beed8e15c9c61e569e2581 SHA1: 36ca5284d7f7142ae81f2f6d60e286e1fabd5738 SHA256: 42c4a56bb3a506303da1e5e8f2e82f94b9b6f4fa48be4c4e8559285072b887c1 SHA512: 78bae626130a837e1289efaa700cb87f9148c87a6cc7c0faf2a9daad263f7062e77afa419c5dd3ebe13510d981103cbeefc7b21ef69495f1868407c557550580 Homepage: https://cran.r-project.org/package=retmort Description: CRAN Package 'retmort' (Estimate User-Based Tagging Mortality and Tag Loss inMark-Recapture Studies) We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an 'rmarkdown' printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies"). 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'retraction' reads bibliographic formats (BibTeX, BibLaTeX, CSL-JSON, RIS, EndNote XML) and document formats (R Markdown, Quarto, 'LaTeX', Markdown, HTML, JATS XML, Word, and PDF), extracts and normalizes identifiers, and checks them against retraction data. The default data source is the Retraction Watch database served through the 'XeraRetractionTracker' API; 'Crossref', 'OpenAlex', 'Europe PMC', 'PubMed', 'DataCite', and a preprint source ('arXiv' and 'bioRxiv' withdrawals) are available as additional sources ('OpenAlex' retraction data is itself derived from Retraction Watch). Within each source, matching proceeds from exact Digital Object Identifier (DOI) and 'PubMed' identifier lookups to fuzzy title matching, and results are returned as a tidy table with a match-quality score and an optional self-contained HTML report. 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Package: r-cran-revecor Architecture: all Version: 0.99.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-xml, r-cran-stringr, r-cran-magrittr, r-cran-gtools, r-cran-plyr, r-cran-purrr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-revecor_0.99.3-1.ca2404.1_all.deb Size: 1153966 MD5sum: 6fe7a30cf56027148f52e08a9c6933f7 SHA1: 13201df824a7b8b703b3cd18c2d25f08595076ea SHA256: c1294b77a36f21ed291e4bf6def1393d68917055b9f8207833bbf656a2590be1 SHA512: d870c77f8463618374db4784e7c4a650faec2af46a3c228c2749eefb5dfa417d827375edebcc121836af6332880d63ca9023636ea505b48101994c8c9a6e2941 Homepage: https://cran.r-project.org/package=RevEcoR Description: CRAN Package 'RevEcoR' (Reverse Ecology Analysis on Microbiome) An implementation of the reverse ecology framework. 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Package: r-cran-revenerar Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-jsonlite, r-cran-purrr, r-cran-httr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-revenerar_1.0.1-1.ca2404.1_all.deb Size: 62648 MD5sum: 4dbba772c607043bf7bc8ea91d5e40b2 SHA1: 980221c7ecbfda2d7035fd7f5049233852822f74 SHA256: 29fe8d631c592dbb25e054d40d4bfd332ad5deb855aa73464baa4ea887f81747 SHA512: 2892a801ddda4ee6d2fac12e4476e999bca382da4b3968b5333fd88d63989ecc4c1dc827723f45896d4f8dfe96dd1cd037960f85da2ffa28345dab4cb0321190 Homepage: https://cran.r-project.org/package=reveneraR Description: CRAN Package 'reveneraR' (Connect to Your 'Revenera' (Formerly 'Revulytics') Data) Facilitates making a connection to the 'Revenera' API and executing various queries. 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The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table. The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities. Package: r-cran-reverser Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-boot.pval, r-cran-l1pack, r-cran-quantreg, r-cran-isotree, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-reverser_0.2-1.ca2404.1_all.deb Size: 91166 MD5sum: 745e24d6dacd57a4517e69a3eac233c4 SHA1: 29f1dcb3969cb6d3bf930c112c8ac56fbf6986ae SHA256: a7273c6c3b3bab5423e8a08361f8516c9dfdb0bbae02316972079ba86850620f SHA512: d9167c1578c34e9927f0d7332f9157e40e19ec63e1dccbc73518c0cacd1b8b67c7f8fae9925fbe57da94de0fae9e77483ef26b9a98b066d7aefca1c9007126a3 Homepage: https://cran.r-project.org/package=reverseR Description: CRAN Package 'reverseR' (Linear Regression Stability to Significance Reversal) Tests linear regressions for significance reversal through leave-one(multiple)-out. 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This tool supports browsing clinical data in many different formats including multiple versions of the 'OMOP' common data model as well as the 'MIMIC-III' data model. In addition, chart review information is captured and stored securely via the Shiny interface in a 'REDCap' (Research Electronic Data Capture) project using the 'REDCap' API. See the 'ReviewR' website for additional information, documentation, and examples. 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The package converts between wide revision triangles and tidy long vintages, extracts selected releases, computes revision series, visualizes vintage paths, and summarizes revision properties such as bias, dispersion, autocorrelation, and news-noise diagnostics. It also identifies efficient releases and estimates state-space models for revision nowcasting. Methods are based on Howrey (1978) , Jacobs and Van Norden (2011) , and Kishor and Koenig (2012) . 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Package: r-cran-revsd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-revsd_0.1.1-1.ca2404.1_all.deb Size: 25322 MD5sum: 0b94fd4a8a25a54bb27eb8f4b0d27d0d SHA1: cc0357cdd8a2fc9db20f1fac97ea7ff724f22855 SHA256: 56e7b58fc9d1392ea972e266471ebcfee54b19bc9a58d030ffb8a50a07bd27c3 SHA512: bdf2f577daa83eeee0b68f10346fc440a83669d592748dcfa7bf9281515df4084a6c1ed1ba81ed0b1e9802dd0714e100153db817056efe29d3bb137659a78ec5 Homepage: https://cran.r-project.org/package=RevSD Description: CRAN Package 'RevSD' (Visualizing the Standard Deviation via Revolution) We visualize the standard deviation of a data set as the radius of a cylinder whose volume equals the total volume of several cylinders made by revolving the empirical cumulative distribution function about the vertical line through the mean. For more details see Sarkar and Rashid (2016) . 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Package: r-cran-revulyticsr Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-jsonlite, r-cran-purrr, r-cran-httr, r-cran-tidyselect, r-cran-tidyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-revulyticsr_0.0.3-1.ca2404.1_all.deb Size: 62530 MD5sum: b6c76b1b1e9655884fe084d00c915ec4 SHA1: 42579b194549b6a6267c8449e4270ce015747575 SHA256: c640a0f11c16f50edba7ba4d47a1054848b08526298d6a23e90922d3db131baf SHA512: 0201cba5a75cc58db438e4dbf9d9585655e567ffa092fd7c26bd753d6fde2da66b4d90d8fcf7630ef71b8123f5a1d383824195d838a481c8f8317fe5bcd67705 Homepage: https://cran.r-project.org/package=revulyticsR Description: CRAN Package 'revulyticsR' (Connect to Your 'Revulytics' Data) Facilitates making a connection to the 'Revulytics' API and executing various queries. 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Package: r-cran-rfacebookstat Architecture: all Version: 2.16.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-tidyselect, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rfacebookstat_2.16.1-1.ca2404.1_all.deb Size: 473222 MD5sum: a9d5f7ebfeeac514410b50fbd7a91a95 SHA1: 28218b89e7d21a0816e166ae9745e726f6e4001d SHA256: ac928a4fb4a3cb1f1de2860811bdf1203aa5315d0e32416bf243ef2aa8575041 SHA512: fff972b6dacac1fe9347730feab9d435a35b7f52a6759f27c82ff927d2c68d9680ae8d558d97bc2c79bb599edf19f16f3f775bc66fde0fbce1377cd85a52404c Homepage: https://cran.r-project.org/package=rfacebookstat Description: CRAN Package 'rfacebookstat' (Load Data from Facebook API Marketing) Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. 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The package identifies rainfall events, calculates continuous rolling rainfall intensities, rainfall kinetic energy, and event EI30 erosivity, aggregates contributing event erosivity to monthly and yearly totals, and calculates multi-year mean monthly and annual rainfall erosivity. Storm separation, event omission criteria, intensity durations, and rainfall kinetic-energy equations are configurable. Rainfall kinetic energy can be calculated using the formulations of Brown and Foster (1987) , McGregor et al. (1995) , and Laws and Parsons (1943) . Package: r-cran-rfacts Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-fs, r-cran-tibble, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rfacts_0.2.1-1.ca2404.1_all.deb Size: 174438 MD5sum: 1217750992edf458a39c9f48ade07ba9 SHA1: 54a70dc2c0b32b8562401ac894ec4d6aa7283b5f SHA256: 4931f41fbbc5fdd10639581c40fd353169cf091b26f44764671cf0cfffe3fa5d SHA512: df2ae529265c6f6ca13813f20c7107d5d90b60cc28c9da658dc1377ee2177a2144c455a94576d75f84f6064408fe7c8bd0aaba1b9632b24577f7be0fc2a375d0 Homepage: https://cran.r-project.org/package=rfacts Description: CRAN Package 'rfacts' (R Interface to 'FACTS' on Unix-Like Systems) The 'rfacts' package is an R interface to the Fixed and Adaptive Clinical Trial Simulator ('FACTS') on Unix-like systems. It programmatically invokes 'FACTS' to run clinical trial simulations, and it aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility. For more information on 'FACTS' itself, please visit . Package: r-cran-rfae Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-foreach, r-cran-matrix, r-cran-mgcv, r-cran-ranger, r-cran-rann, r-cran-rspectra, r-cran-tibble Suggests: r-cran-arf, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfae_0.1.0-1.ca2404.1_all.deb Size: 98364 MD5sum: 6189b40cef9d94015bc453440918baff SHA1: 58f5515ea02899fc036ee5881dd16442875e6602 SHA256: 806292ce5bbdc5c9f8a19616cca3a522ab0f8d29903e542486472566e5871149 SHA512: 18c6dab82ee39b93de3531f141b3e226f8f9627192dc9434d38c7ff124159f2c75b5c83c6bea08b9b4e3681c52798d2545856afa7dc2577364e4f2f665e609b5 Homepage: https://cran.r-project.org/package=RFAE Description: CRAN Package 'RFAE' (Autoencoding Random Forests) Autoencoding Random Forests ('RFAE') provide a method to autoencode mixed-type tabular data using Random Forests ('RF'), which involves projecting the data to a latent feature space of user-chosen dimensionality (usually a lower dimension), and then decoding the latent representations back into the input space. The encoding stage is useful for feature engineering and data visualisation tasks, akin to how principal component analysis ('PCA') is used, and the decoding stage is useful for compression and denoising tasks. At its core, 'RFAE' is a post-processing pipeline on a trained random forest model. This means that it can accept any trained RF of 'ranger' object type: 'RF', 'URF' or 'ARF'. Because of this, it inherits Random Forests' robust performance and capacity to seamlessly handle mixed-type tabular data. For more details, see Vu et al. (2025) . Package: r-cran-rfair Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-mime, r-cran-rvest, r-cran-stringdist, r-cran-xml2, r-cran-yaml Suggests: r-cran-bslib, r-cran-chromote, r-cran-dt, r-cran-jsonld, r-cran-knitr, r-cran-plumber, r-cran-rdflib, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rfair_0.2.0-1.ca2404.1_all.deb Size: 787322 MD5sum: 8280e24905de6b3227be7b8499f38b3b SHA1: 5ba6805b6835f256910e15a9751a558e7529fe66 SHA256: 0372793d95e21cbbfba6b4d2a46d9ed0420d24df96f8526520ef1ed0d5cd0ca7 SHA512: 343c2307ef5d13c807968ffcef07e624024c07d71d695d4d36b63657947284fb8d33f8a1624e61de9f96a435d184c13038c582e7ef58594fd2af577c8307edd5 Homepage: https://cran.r-project.org/package=rfair Description: CRAN Package 'rfair' (Assess the FAIRness of Research Data Objects and Software) A native R implementation of the F-UJI (FAIRsFAIR Research Data Object Assessment) and FRSM (FAIR for Research Software) metrics for evaluating how well a research data object or piece of research software satisfies the FAIR principles (Findable, Accessible, Interoperable, Reusable). The software metrics operationalize the FAIR Principles for Research Software (FAIR4RS) of Chue Hong et al. (2022) . Given a persistent identifier, URL, or code repository, 'rfair' resolves it, harvests metadata from landing pages and registries, and scores it against the FAIRsFAIR metrics of Devaraju and Huber (2020) entirely in R, without requiring an external assessment server. 'rfair' began as a fork of the 'rfuji' F-UJI API client and reimplements the assessment engine natively. Package: r-cran-rfams Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-fsa Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-metr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-zipfr, r-cran-quarto, r-cran-fsadata, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-rfams_0.0.3-1.ca2404.1_all.deb Size: 225434 MD5sum: 2f9c426c3b5077c130b48ab371035d1f SHA1: bde9c95717d0cb70426f35a1d5c330452fb1741b SHA256: 6972fe7a43e049ce0a51b49c76654a5fbfaefaf8a64ded25caf1589b8234d5fa SHA512: 96015d28adc0a1f4cee018bcca92bf20885d23a8773877c94ebae28adb072bfcae697c86aef3b00128c3f57c7ea9268e3352a951f7bc22f9198676c6e517570b Homepage: https://cran.r-project.org/package=rFAMS Description: CRAN Package 'rFAMS' (Fisheries Analysis and Modeling Simulator) Simulates the dynamics of exploited fish populations using the Jones modification of the Beverton-Holt equilibrium yield equation to compute yield-per-recruit and dynamic pool models (Ricker 1975) . Allows users to evaluate minimum, slot, and inverted length limits on exploited fisheries using specified life history parameters. Users can simulate population under a variety of conditional fishing mortality and conditional natural mortality. Calculated quantities include number of fish harvested and dying naturally, mean weight and length of fish harvested, number of fish that reach specified lengths of interest, total number of fish and biomass in the population, and stock density indices. Package: r-cran-rfars Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-downloader, r-cran-dplyr, r-cran-haven, r-cran-janitor, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-leaflet.extras, r-cran-ggplot2, r-cran-scales, r-cran-stargazer, r-cran-viridis, r-cran-lme4, r-cran-tidyverse, r-cran-tidytext, r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfars_2.0.4-1.ca2404.1_all.deb Size: 2080824 MD5sum: e614d632192171b37f4a1d3e0b74901b SHA1: 5f5e63d5ed252f33e7bfdaac49070bcb283e843f SHA256: 14f96fb50a60d13b098a7d68233e5af47cad97530d16855fda05bad7c545a94b SHA512: df8de252297ab55fefbb2a20f0152b88bd2e675dcfa6b64081075e59e755e72f85153156ed5449b1ce52a482e2ba357ad321d8cf1483baf7b8d66493445faa73 Homepage: https://cran.r-project.org/package=rfars Description: CRAN Package 'rfars' (Download and Analyze Crash Data) Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration. Package: r-cran-rfclust Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1763 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-consensusclusterplus, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-rfclust_0.1.2-1.ca2404.1_all.deb Size: 1771124 MD5sum: 8b3d5b6237fda9deeca15a1e41741180 SHA1: ac7439ec7e65b53009617061430ec033d44ebcfd SHA256: a533d4d1c6be5a5bdf960ea664d53ec8a2c2e22ec61ea8a48528bf4105741d28 SHA512: d50165c3786d16ba57b2f6dee86a1549a1c9c90fcc5bbb190647ef5d47743e556ffe4a3c342e22353bb4eb90ff5938c27c2a74732f41fd65c8e54122d4992f26 Homepage: https://cran.r-project.org/package=RFclust Description: CRAN Package 'RFclust' (Random Forest Cluster Analysis) Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) & Monti et al (2003) . Package: r-cran-rfdp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rfdp_0.1.1-1.ca2404.1_all.deb Size: 14444 MD5sum: c35129e710a93ce9a2a13e7b65774236 SHA1: ee61eb8c7ac6e1cb3f0d6fb0981230d6807f74d5 SHA256: 7000496bfa54e4ac27fae88e0589578f0d72b89e6e3adc3450774b0bcc684154 SHA512: 36f7bd16fa57b1f1f0844d2aa9eeb48c5f418559caa7e1b99b2acc13e0667fcd3d4e12db19b2a5b8c245b56b7088717cf8c926fb10510c357926ad8343d745d0 Homepage: https://cran.r-project.org/package=rFDP Description: CRAN Package 'rFDP' (Resampling-Based False Discovery Proportion Control) Methods for Resampling-based False Discovery Proportion control. A function is provided that provides simultaneous, multi-resolution False Discovery Exceedance (FDX) control as described in Hemerik (2025) . Package: r-cran-rfempimp Architecture: all Version: 2.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-ranger Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rfempimp_2.1.8-1.ca2404.1_all.deb Size: 208992 MD5sum: aa65a23f9489b22bef242c558073834d SHA1: c4f7568fb399c64ceae2e5100aa3c4f48f86b341 SHA256: 874f2c98a786f021b2e779e3cb936d8ce86ea44ace0f104103e44b02aa04868c SHA512: 592190729ab146d32fe6edba69bc5a42785739b3d258dcc936b00dcc3fb688eeda562107277a1493c17f9220119371e47ad6992a2fa44afbeed75485981528d2 Homepage: https://cran.r-project.org/package=RfEmpImp Description: CRAN Package 'RfEmpImp' (Multiple Imputation using Chained Random Forests) An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) . Package: r-cran-rfia Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7744 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dtplyr, r-cran-tidyr, r-cran-stringr, r-cran-sf, r-cran-data.table, r-cran-tidyselect, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gganimate, r-cran-r2jags, r-cran-coda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfia_1.2.0-1.ca2404.1_all.deb Size: 7614952 MD5sum: a8e73ec44913af2b39f6e05af813c629 SHA1: f763548b705340994ec4c89e831aa7dfae5ebe1a SHA256: 642cbdb44ab5846bdea832c9dc9ede9a99bf391e4a77ffc443136687b6e2ee2e SHA512: 274b52288589707af9a7e27d208e09fac41b74d4b04e4fae3f69a6819b3f471b18390bf70ade37885666eb637577708ef9f201538ceea4717cc26dd79c6da179 Homepage: https://cran.r-project.org/package=rFIA Description: CRAN Package 'rFIA' (Estimation of Forest Variables using the FIA Database) The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) , and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Package: r-cran-rfieldclimate Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfieldclimate_0.1.1-1.ca2404.1_all.deb Size: 31536 MD5sum: ccf8dc10b3bd4d06abb8b93cbe781d12 SHA1: bbb3f2fade3abd242d9f90cdd5cc6bdbb7ab2e4f SHA256: 5c93fedc1d47bda3fbc2fcf4c9d45a5c348e29761208c7d56ffd02821c05f031 SHA512: 531fb044ae0aad089e3b489aafc9af46d819cc1fd468f7670524cf490f87dfa42e8bbc2370d2b036676b62ef9956b07ff0adca72c3c6c6cef979070d44d49bfc Homepage: https://cran.r-project.org/package=rfieldclimate Description: CRAN Package 'rfieldclimate' (Client for the 'FieldClimate' API) Provides functionality to interact with the 'FieldClimate' API . 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Package: r-cran-rfinterval Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfinterval_1.0.0-1.ca2404.1_all.deb Size: 132494 MD5sum: 2c8a6dd85d523b6d67cf3a88857c9eb2 SHA1: c77648f73503ce4128fd1be2a5329a3a2bc2dc25 SHA256: a9e56cd48ee217c3d86d06b5561863c6cf32bbfb2327d077721627ec1d1fb3f8 SHA512: 8bab559f24343ee20a894713ba262ee5a5df9ed698917322d73bdc6a33a58ba6dca995721a97d07bf0129b391dd6c0477958ab0fa26907177365904af1ceb11d Homepage: https://cran.r-project.org/package=rfinterval Description: CRAN Package 'rfinterval' (Predictive Inference for Random Forests) An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) : the out-of-bag prediction interval, the split conformal method, and the quantile regression forest. Package: r-cran-rfishbase Architecture: all Version: 5.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2030 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-stringr, r-cran-purrr, r-cran-dplyr, r-cran-duckdbfs, r-cran-rlang, r-cran-magrittr, r-cran-memoise, r-cran-xml2 Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-spelling, r-cran-curl Filename: pool/dists/noble/main/r-cran-rfishbase_5.0.3-1.ca2404.1_all.deb Size: 563042 MD5sum: 3c1675ad8545b5f20a595d703074c2db SHA1: a129e6be1f016a723a16cf76a37a4bb6cb046549 SHA256: ac44fc1833839cc1e25037c5a05b4ac6c37b2890f4aad499e0070fb1ef1721c1 SHA512: 89dde2efd7e2331a804e51bdf15c1bca1496b32a9d3b87a5a39a7aa2463187b5476780bd750eccbed23c2c4a977c41f4be1e1c6c3e6abe0b22ef5fe1a236428c Homepage: https://cran.r-project.org/package=rfishbase Description: CRAN Package 'rfishbase' (R Interface to 'FishBase') A programmatic interface to 'FishBase', re-written based on an accompanying 'RESTful' API. Access tables describing over 30,000 species of fish, their biology, ecology, morphology, and more. This package also supports experimental access to 'SeaLifeBase' data, which contains nearly 200,000 species records for all types of aquatic life not covered by 'FishBase.' Package: r-cran-rfishbc Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-clisymbols, r-cran-crayon, r-cran-readbitmap, r-cran-rlang, r-cran-settings, r-cran-stringr, r-cran-tidyr, r-cran-withr Suggests: r-cran-fsa, r-cran-covr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfishbc_0.2.7-1.ca2404.1_all.deb Size: 194392 MD5sum: 7431c8af7d13dd9b0003c78aabcff1a3 SHA1: 495fe5528c8f696d3db9628a872eb1fe861d781c SHA256: a78fc04ee75e3ff98c234cd28623b023ec7c8c2e691f6ec14014d6181a157431 SHA512: 541daea6b1ac0eb5dfb00b0f1599472da8036c6ece3f5f55e9347d90b303ec491205204bbe2fb755c19d9d9335bbac746514242d3a4eaaee30caea7b69bc404d Homepage: https://cran.r-project.org/package=RFishBC Description: CRAN Package 'RFishBC' (Back-Calculation of Fish Length) Helps fisheries scientists collect measurements from calcified structures and back-calculate estimated lengths at previous ages using standard procedures and models. 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Package: r-cran-rfishdraw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-prettydoc, r-cran-patchwork, r-cran-devtools Filename: pool/dists/noble/main/r-cran-rfishdraw_0.1.0-1.ca2404.1_all.deb Size: 681676 MD5sum: 848a40295971d647d7c059342b6bb43e SHA1: 927a6d4dca5ebc3edb14b31160754bfb7167a0d4 SHA256: a09c70a063b32535860b2c18afd31fe0d86c01eb600abcd64602ae6093682cd5 SHA512: 8dda072b56a0c084e8292527afaf3af2c7b38de8803760df71af9ecf1f58307a167eb8b516d76035c3a41df9c2a04ca41c7700b434654dff3b4469249c4660b9 Homepage: https://cran.r-project.org/package=rfishdraw Description: CRAN Package 'rfishdraw' (Automatically Generated Fish Drawings via JavaScript) Automatic generation of fish drawings based on JavaScript library , including JavaScript code for dynamic generation of fish drawings. Package: r-cran-rfisheries Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-data.table, r-cran-assertthat, r-cran-ggplot2, r-cran-rjson Filename: pool/dists/noble/main/r-cran-rfisheries_0.2-1.ca2404.1_all.deb Size: 242330 MD5sum: 3cbbd6ce173e8a055b7e569cb75d9ac7 SHA1: 7a3e98f4283ef6eec4460337153311bec943449f SHA256: 078c066f1d3667905759c680d122c3106a73560619bda1467d8b92efaeaa4f6c SHA512: 0e867be832b19b7c644b6c8105684c2eadb60c6cab20a8b0bc2f8a187552314b82cfc6f71a4e6f69c50a0e18339ae70dc9abe7fabb3d5056fa6c482d4e79b228 Homepage: https://cran.r-project.org/package=rfisheries Description: CRAN Package 'rfisheries' ('Programmatic Interface to the 'openfisheries.org' API') A programmatic interface to 'openfisheries.org'. This package is part of the 'rOpenSci' suite (http://ropensci.org). Package: r-cran-rfishnet2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-pracma, r-cran-ggplot2, r-cran-sf, r-cran-rworldmap Filename: pool/dists/noble/main/r-cran-rfishnet2_0.2.0-1.ca2404.1_all.deb Size: 833896 MD5sum: c4fd4e5190edb1aa4cb14020eefd8114 SHA1: f2700786ab9a7d378842240fa87c936f52e4bffb SHA256: 939be748d236d14b5f19da19c3e577fefa38b032e44784af205622511e79ac2b SHA512: ae29b8885dc10635217b3643b93e68f0dd706d721980b819d440546d24256f098ce62a258b4b7580f33060b33b4c9c79e7370d919c508f2e5e2101634cf0c9c6 Homepage: https://cran.r-project.org/package=rfishnet2 Description: CRAN Package 'rfishnet2' (Exploratory Data Analysis for FishNet2 Data) Provides data processing and summarization of data from FishNet2.net in text and graphical outputs. Allows efficient filtering of information and data cleaning. 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Package: r-cran-rflsgen Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4748 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-terra, r-cran-jsonlite, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-landscapemetrics Filename: pool/dists/noble/main/r-cran-rflsgen_1.2.2-1.ca2404.1_all.deb Size: 4334822 MD5sum: a05943fbe5d440dfb07b974abebc4cfb SHA1: 93cc36b245cbb84c42ba066547c73b86dcd97f72 SHA256: 4c75408cf99865a08d1e91493176d917d26345c0d53e3c2b60c1a48a0a6cbc80 SHA512: 3b4cf0c0cd2e6e84d395418401a2905eede6cfe31efa2134bae8f175dd803ec8e5bb8ac2417aab54b99e673b9b5e4a6b13163a64a4c47b1d17e140ccc19b3a39 Homepage: https://cran.r-project.org/package=rflsgen Description: CRAN Package 'rflsgen' (Neutral Landscape Generator with Targets on Landscape Indices) Interface to the 'flsgen' neutral landscape generator . 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Package: r-cran-rfm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gganimate, r-cran-ggplot2, r-cran-magrittr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-treemapify, r-cran-xplorerr Suggests: r-cran-cli, r-cran-covr, r-cran-dt, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-rmdformats, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-rfm_0.4.0-1.ca2404.1_all.deb Size: 1426420 MD5sum: abf9fc2225db9326d942792bcc0a2bd6 SHA1: 96072cb1d57ab8f9063f3657aeaeaeceef9f1463 SHA256: 85575ebd84dc546471601ad172f413bb04c10f619689d8f742a2de7561556c11 SHA512: 0d564a1527ddc2af41b7b3ce91d48b2e40ed3e50198c798adf0f307a03ba01c8849be2f1fef4fe80c49adba612c55723726963ca8c2923edd8f685c31247f870 Homepage: https://cran.r-project.org/package=rfm Description: CRAN Package 'rfm' (Recency, Frequency and Monetary Value Analysis) Tools for RFM (recency, frequency and monetary value) analysis. Generate RFM score from both transaction and customer level data. Visualize the relationship between recency, frequency and monetary value using heatmap, histograms, bar charts and scatter plots. Includes a 'shiny' app for interactive segmentation. References: i. Blattberg R.C., Kim BD., Neslin S.A (2008) . Package: r-cran-rfmerge Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-randomforest, r-cran-zoo, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rfmerge_0.3-3-1.ca2404.1_all.deb Size: 2119960 MD5sum: f263132c0f97d1dce7ba49b8627f082d SHA1: 47c0262613aa36578af4784350e611609841315a SHA256: 08f31f1520355c34ca32aa07b91fc7236e1aaccc85a74ad8c6776ad4f30d6090 SHA512: a2acc134c97e8d3a68fa2679a06a1f34706e434fd69b2b87469c10e8ce95383fa2f3a2a77cfeeba3cbf6004088351e45745d8a27fb300c07b8d165de7fdf8126 Homepage: https://cran.r-project.org/package=RFmerge Description: CRAN Package 'RFmerge' (Merging of Satellite Datasets with Ground Observations usingRandom Forests) S3 implementation of the Random Forest MErging Procedure (RF-MEP), which combines two or more satellite-based datasets (e.g., precipitation products, topography) with ground observations to produce a new dataset with improved spatio-temporal distribution of the target field. In particular, this package was developed to merge different Satellite-based Rainfall Estimates (SREs) with measurements from rain gauges, in order to obtain a new precipitation dataset where the time series in the rain gauges are used to correct different types of errors present in the SREs. However, this package might be used to merge other hydrological/environmental gridded datasets with point observations. For details, see Baez-Villanueva et al. (2020) . Bugs / comments / questions / collaboration of any kind are very welcomed. Package: r-cran-rfmstate Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3551 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfmstate_0.1.9-1.ca2404.1_all.deb Size: 2164172 MD5sum: 29309e6404ec4e7cef3ebf159a0122ea SHA1: 197bf836ae118de2b7558a773795084a8b670bb2 SHA256: d59780bc8f20dafb143bfd8b7a6b8d11d0e19e5ca002b41d9a3abbc05548ee4e SHA512: 7d6ef2735b7b040c0c1770a3cd1b4aad1c7c10f22d86654822939e888bef4410d0eaba61b8943ccd7700c87f88552bddabef048ad56852e455deca06962caa96 Homepage: https://cran.r-project.org/package=RFmstate Description: CRAN Package 'RFmstate' (Random Forest-Based Multistate Survival Analysis) Fits transition-specific cause-specific random survival forests on a clock-reset duration scale for acyclic, non-recurrent multistate processes. Entry-conditioned state-occupation probabilities are assembled from predicted cumulative hazards by semi-Markov entry-mass and sojourn convolution on a validated regular grid. The one-row-per-subject interface supports one common initial state, one recorded entry per state, baseline time-fixed covariates, competing exits, and independent right censoring. Left truncation, recurrent visits, directed cycles, time-dependent covariates, and ongoing-sojourn dynamic prediction are not supported. The package also provides calendar-time Aalen-Johansen point estimates as a covariate-free descriptive baseline, transition-specific permutation importance, genuine ranger edge OOB concordance, and patient-level cross-validated IPCW state-probability scoring. Methods are described in Ishwaran et al. (2008) for random survival forests, Putter et al. (2007) for multistate competing risks decomposition, and Aalen and Johansen (1978) for the nonparametric estimator. Package: r-cran-rfoaas Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-rfoaas_2.4.0-1.ca2404.1_all.deb Size: 52932 MD5sum: a52463f858192a3176414481a20148b6 SHA1: cf6d3eac6391b77deae618dc221ab88723afefc5 SHA256: 0fd711d1b856dcf00749726226be72b67b5f9c6e6d79fa845bce2bfccdeb5ffe SHA512: 4e42459d86c04bfe6a20392bbe97e3e8669afe531b69421bf03083e56eeb5ffe754fff263363c430dd236b47f64e3ea959c1d091845a522f672564cd5a0e8f01 Homepage: https://cran.r-project.org/package=rfoaas Description: CRAN Package 'rfoaas' (R Interface to 'FOAAS') R access to the 'FOAAS' (F... Off As A Service) web service is provided. 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Package: r-cran-rfold Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-glue, r-cran-here, r-cran-usethis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfold_0.2.0-1.ca2404.1_all.deb Size: 59868 MD5sum: 09150949efa342bccc3f1d389e351491 SHA1: 80f2ff69d879a3eda99fbe2945f2103e3fbb6cb4 SHA256: 0945bd7875f7d8ca4b3c8b8e04fb1145b0730ff15bac56bc4fde7300dc0ed5a7 SHA512: 37c84831ed3513c232b1af759ed6d46a776ebef0c131594ccf3c34857ed774dd9c30381cc61412c263186374950989a806fe6afd0f3211058bb184064f5b04f7 Homepage: https://cran.r-project.org/package=rfold Description: CRAN Package 'rfold' (Working with many R Folders Within an R Package) Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into the R folder while making additional checks. 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This package stems from the following research publication: Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018. . 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In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using 'f_' (user f_riendly) prefix followed by the normal function name. This third version of the 'rfriend' package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), summary figures, f_scan(), outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations, f_boxcox() in part based on 'MASS/boxcox' and 'rcompanion', and f_bestNormalize() which wraps and extends functionality from the 'bestNormalize' package. Furthermore, 'rfriend' can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), and histograms f_hist(). Additionally, the package includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), f_lmer(), f_glm(), f_t_test(), f_wilcox_test(), for sequential testing and visualisation of the similar named 'stats' functions. These functions, except for f_chisq_test(), support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes 'RStudio' themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email. 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Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained for least median of squares in Hawkins (1993) . The feasible solution algorithm comes up with model forms of a specific type that can have fixed variables, higher order interactions and their lower order terms. 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The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split. This gain is aggregated for each split variable in a tree and averaged across trees. 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The main function of the package is rfvimptest(), which allows to test for the statistical significance of predictors in random forests using different (sequential) permutation test strategies [1]. The advantage of sequential over conventional permutation tests is that they are computationally considerably less intensive, as the sequential procedure is stopped as soon as there is sufficient evidence for either the null or the alternative hypothesis. Reference: [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation testing of variable importance measures by the example of random forests. Computational Statistics & Data Analysis 181:107689, . 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Package: r-cran-rgabriel Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rgabriel_0.9-1.ca2404.1_all.deb Size: 20808 MD5sum: 11cc74acba5232471b6f4532326b25cb SHA1: 8990ca0bb8295941080c937b9bb55a079381834d SHA256: fc0a1a988a990a8c26ec48ccf51ecab992a179951e3a46e6769d6ee0e9b7ffdf SHA512: 6ce67d0ecfa6d0299a189edb6c61640c8b412dcfdc4a6d3b3444a15036f9238dbf7f56df45c298b492bf2dbff039df13f056890a9c15c92bcc3a337c6a8b4be3 Homepage: https://cran.r-project.org/package=rgabriel Description: CRAN Package 'rgabriel' (Gabriel Multiple Comparison Test and Plot the ConfidenceInterval on Barplot) Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. 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A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available. Package: r-cran-rgap Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1817 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-zoo, r-cran-dlm, r-cran-openxlsx, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rgap_0.1.1-1.ca2404.1_all.deb Size: 1690704 MD5sum: 5d39abc69c6e5a65fcd97747d2b5bc8c SHA1: d97627a1e0cbaf5a6172218c6f4d3a95ff057f41 SHA256: 6f2401bb01a21e55b88db5761a28a5d560e352898acd70b6c63becbcd8fc7a99 SHA512: 8b220285a2cd52e6cddb236238dfec47ef63445b48130e43741818fef2888a3f171bc0c843131b8730c6a6842af8ef6d597c512f512c7f5d5216081fb8060354 Homepage: https://cran.r-project.org/package=RGAP Description: CRAN Package 'RGAP' (Production Function Output Gap Estimation) The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) . To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' database and automated model selection procedures. See the paper by Streicher (2022) for details. Package: r-cran-rgbif Architecture: all Version: 3.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml2, r-cran-ggplot2, r-cran-crul, r-cran-data.table, r-cran-whisker, r-cran-magrittr, r-cran-jsonlite, r-cran-oai, r-cran-tibble, r-cran-lazyeval, r-cran-r6 Suggests: r-cran-testthat, r-cran-png, r-cran-terra, r-cran-magick, r-cran-protolite, r-cran-sf, r-cran-vcr, r-cran-knitr, r-cran-rmarkdown, r-cran-bit64 Filename: pool/dists/noble/main/r-cran-rgbif_3.9.0-1.ca2404.1_all.deb Size: 1579464 MD5sum: 279cb6696fbe7c85a231fad657f08ef3 SHA1: 9041222d8a6510406d9ebdff4499ac12f1192a79 SHA256: 40032e08bd2f91bc3490752677b354a134e91bcc586482529d3ccd318525240b SHA512: 3c8597e58b78af394b672eb3ab77bfffed87f09b9c15fc91a31f873eee78b04aa0fa0ef9d7545958e8f70739d62bb07459af78cae766b15def30ec7b6649ed2e Homepage: https://cran.r-project.org/package=rgbif Description: CRAN Package 'rgbif' (Interface to the Global Biodiversity Information Facility API) A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; ). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data. Package: r-cran-rgbindices Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rgbindices_0.1.1-1.ca2404.1_all.deb Size: 1102390 MD5sum: 9d9e07c09a0ceb5a563ff2694ac436e7 SHA1: e1054b705b36427575088d4963fef4a108bedafd SHA256: f1ef7cf7655a71466d864a7987c4e000ac13c64e3e31f066d449deb63eae0ca8 SHA512: 3911212e633206d9b213751b18f02cf21f2a065b932fb26bfd9ec75ada0e405b4d53ebc7a09ca3c4f87d989c3716035fb629073a7a0fd956c93312ee27e30590 Homepage: https://cran.r-project.org/package=rgbIndices Description: CRAN Package 'rgbIndices' (RGB Visible Indices for Image Analysis) Computes RGB-based vegetation, color, and spectral indices from digital images for applications in agriculture, crop phenotyping, and remote sensing. The methods are based on digital image processing and plant phenotyping approaches (Singh et al. (2023) ). Package: r-cran-rgbp Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sn, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-rgbp_1.1.4-1.ca2404.1_all.deb Size: 198230 MD5sum: b9e06f6d450bcff4ea529d5a1988066b SHA1: bf28c46818124052a7c512e89be6a231a4f6b040 SHA256: 20c9d267dc307b29169d4f5659d7f937d75280d5ceb11a0ceb1d5c244c91cf2f SHA512: ac076bb30c6b73e6537ba1b3106817948b6ed1be4d0f1169844e5c2071c45f9f59de9d1041fa12fffb11396f441c1867fe729d238ba4d42e547c676ca753e3ca Homepage: https://cran.r-project.org/package=Rgbp Description: CRAN Package 'Rgbp' (Hierarchical Modeling and Frequency Method Checking onOverdispersed Gaussian, Poisson, and Binomial Data) We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking. Package: r-cran-rgcca Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-deriv, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-matrixstats, r-cran-pbapply, r-cran-rlang Suggests: r-cran-devtools, r-cran-factominer, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-rticles, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-rgcca_3.0.3-1.ca2404.1_all.deb Size: 617630 MD5sum: a719573f8617633ff95bc150fc5532b2 SHA1: d7485a6799d68091443c6f01ea1318e63732c26f SHA256: 80826d12cd71f6393a7e06155df018244c922d1627cf25d52f521787b3400ac0 SHA512: 33c43878f4164573e0465869e21696b17bdfa00fb6780a5b58cf0795ee9d0efc35c6e7d3e9cbfa59282b4c7b2835bfdb71f8b37c141f8551981f7f5b61a614ff Homepage: https://cran.r-project.org/package=RGCCA Description: CRAN Package 'RGCCA' (Regularized and Sparse Generalized Canonical CorrelationAnalysis for Multiblock Data) Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: to study the relationships between blocks and to identify subsets of variables of each block which are active in their relationships with the other blocks. This package allows to (i) run R/SGCCA and related methods, (ii) help the user to find out the optimal parameters for R/SGCCA such as regularization parameters (tau or sparsity), (iii) evaluate the stability of the RGCCA results and their significance, (iv) build predictive models from the R/SGCCA. (v) Generic print() and plot() functions apply to all these functionalities. Package: r-cran-rgcxgc Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rnetcdf, r-cran-ptw, r-cran-colorramps, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-rgcxgc_1.2.0-1.ca2404.1_all.deb Size: 3189782 MD5sum: 5a57a677077e7d64b70397d168a26161 SHA1: f01a3ca8a8958ddd77fa4f9fef03d01c76b2802d SHA256: 18d4627aa298971340c721c0a4d542bb052690eda4cc570edfbeba9e2c068d1f SHA512: f04149e4cf3a7963635fd10041548745d20aff70e16e74e10464ccc971690a7759a5232f793ed42604a8507fa8fb8d16301c1a9650181250eb1075f6a4eda922 Homepage: https://cran.r-project.org/package=RGCxGC Description: CRAN Package 'RGCxGC' (Preprocessing and Multivariate Analysis of Bidimensional GasChromatography Data) Toolbox for chemometrics analysis of bidimensional gas chromatography data. This package import data for common scientific data format (NetCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated. Package: r-cran-rgdax Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-jsonlite, r-cran-rcurl, r-cran-httr, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rgdax_1.2.1-1.ca2404.1_all.deb Size: 66266 MD5sum: 16cc838d49e00b8fbe0cf60e8af02595 SHA1: 728930c7ec77f7f6b3ed656c4870f8030a711320 SHA256: 06ab27bb3066888f4d566f6a72a6dcd97e500b9a90d6e74ebe1a0d3b6d8792ab SHA512: a8a50f076901a80f5c7c484a49e3884cc7c5a96ffd3ca43041515995f6c3025bf007619a42dd51fbbc785de0e3f5f4bb0fac1318563aad9b8696457bb46e3d1c Homepage: https://cran.r-project.org/package=rgdax Description: CRAN Package 'rgdax' (Wrapper for 'Coinbase Pro (erstwhile GDAX)' CryptocurrencyExchange) Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect. Package: r-cran-rgdrivers Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-igraph, r-cran-dplyr, r-cran-sf, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rgdrivers_0.1.0-1.ca2404.1_all.deb Size: 43516 MD5sum: 0d0635564450b0d66fe7426ec9b0672b SHA1: 3be88a384b6bbcc3b3c1aab53f58feb37c6123d9 SHA256: 0f6a2353d416eefa8636725e1846dba87c7d20b570bcb7d9ea114b425ab188d8 SHA512: b2e4acdc48dc4da1f57d0ce259a974222b0b696c689fe7e627ada79d1c0f248d47bb7febe6343841c22a1673d1d489cfeb836d0bfafd9eaed8213faf8e57dee6 Homepage: https://cran.r-project.org/package=RGDrivers Description: CRAN Package 'RGDrivers' (Analysis of Stream Network Topology and Order) Provides tools for analyzing stream networks, including graph construction, calculation of Link Magnitude, D-LINK, and export of spatial data. The Link Magnitude (Shreve stream order) method follows Shreve (1966) . The D-LINK metric follows Osborne & Wiley (1992) . Package: r-cran-rge Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixmodels, r-cran-coda Filename: pool/dists/noble/main/r-cran-rge_1.0-1.ca2404.1_all.deb Size: 1585884 MD5sum: a0b87ceaeb77ed3c3383c9cee15734d9 SHA1: 2d5125b4f412b40925fea092d43c80f12f2d15e0 SHA256: be7f11ff293c67d3745e5e7506d6d709a377c92ced6f99bc776e049ba6ba6526 SHA512: c186e065c1fc5d6b84a8d596ba4ac2753e4109dc098bd7a570039d9c4fb13e3e181f32dde06da990b8ba95e14448515343bcb28ad2152eb000de48c3be47ff94 Homepage: https://cran.r-project.org/package=RGE Description: CRAN Package 'RGE' (Response from Genotype to Environment) Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico. Package: r-cran-rgee Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-rstudioapi, r-cran-leaflet, r-cran-magrittr, r-cran-jsonlite, r-cran-processx, r-cran-leafem, r-cran-crayon, r-cran-r6, r-cran-cli Suggests: r-cran-magick, r-cran-geojsonio, r-cran-sf, r-cran-stars, r-cran-googledrive, r-cran-gargle, r-cran-httr, r-cran-digest, r-cran-testthat, r-cran-future, r-cran-terra, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-png, r-cran-googlecloudstorager, r-cran-leaflet.extras2, r-cran-spelling, r-cran-raster Filename: pool/dists/noble/main/r-cran-rgee_1.1.8-1.ca2404.1_all.deb Size: 2699660 MD5sum: cc91eb69988895cbcbd99aaaff6b7c3e SHA1: 58793fef84756d5abb44a66cf82a1ba7ddec5db0 SHA256: c9701fc8a47c1660894ed6c88028da15d8344579897a0747c48cc93f4cd48c7e SHA512: 313e452f9a913d14b0a8182b9083ce17a0a44f344349d028f70e26ccc348e666fc89fe89d82af83e5f3d0b51ca2b1e512ab1614d2f12d2d795518bff690e1b2a Homepage: https://cran.r-project.org/package=rgee Description: CRAN Package 'rgee' (R Bindings for Calling the 'Earth Engine' API) Earth Engine client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See for further details. Package: r-cran-rgeedim Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-terra, r-cran-raster, r-cran-tinytest, r-cran-litedown Filename: pool/dists/noble/main/r-cran-rgeedim_0.4.0-1.ca2404.1_all.deb Size: 362660 MD5sum: 78e6f05de576053423d6ee0c9fe2a0bf SHA1: 7e4a53d3a90037b1a9836e5b372130f72b3ec0bc SHA256: 54321f4b3f0ddec31cc75d85de22528fad08612edf66d572bdea18b39b26c870 SHA512: 559e2a008983897a9448e50188cacb701afba7f0ca61f61e7dfe4d01ad8bffe38c40bba218913c192b2fc2b762c07b5bdc9624dfc641ea79458a5cd0f9d7637f Homepage: https://cran.r-project.org/package=rgeedim Description: CRAN Package 'rgeedim' (Search, Composite, and Download 'Google Earth Engine' Imagerywith the 'Python' Module 'geedim') Search, composite, and download 'Google Earth Engine' imagery with 'reticulate' bindings for the 'Python' module 'geedim' by Dugal Harris. Read the 'geedim' documentation here: . Wrapper functions are provided to make it more convenient to use 'geedim' to download images larger than the 'Google Earth Engine' size limit . By default the "High Volume" API endpoint is used to download data and this URL can be customized during initialization of the package. Package: r-cran-rgen Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rgen_0.0.1-1.ca2404.1_all.deb Size: 16412 MD5sum: 4bf92a5ff2a1178e5ae42af6196b3753 SHA1: 3fe138788be21bef226974e8b3e3eb65367d36a6 SHA256: b5bfdd8181d56eaceb9eb3544ee89b174223c1f2186313ebcb9052dab62cf718 SHA512: 6e321d3d4f893c8a843a6abe78c36199b7e6cb4ed8c69d15cd140f8e1eb5a14aaf1ab826dc4faa92f8018d789f75a923678236f3ee6eedccc0bf667b283fce60 Homepage: https://cran.r-project.org/package=rgen Description: CRAN Package 'rgen' (Random Sampling Distribution C++ Routines for Armadillo) Provides popular sampling distributions C++ routines based in armadillo through a header file approach. Package: r-cran-rgendata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rgendata_1.0.1-1.ca2404.1_all.deb Size: 33228 MD5sum: 3aabca6374050dea99c99260af47c3ad SHA1: 1696bb8415b39e49b506abb38b11590593284c9d SHA256: c66265c732a080494c42fa026701e85f2eed68d773bb53fc756b84a8a74f6430 SHA512: d38c4fa8ad1ae9c8a8468aff709a156c5f8e5031d234a3f0c526165187888f589d4c7b7fdb44a556e0c174bb68f1f169a87dd8f83bf2c3e55dbcc375d6b2eff2 Homepage: https://cran.r-project.org/package=RGenData Description: CRAN Package 'RGenData' (Generates Multivariate Nonnormal Data and Determines How ManyFactors to Retain) The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. . . Package: r-cran-rgenerate Architecture: all Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmawgen, r-cran-magrittr, r-cran-vars Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rgenerate_1.3.8-1.ca2404.1_all.deb Size: 3144324 MD5sum: 467165c96ee3becb4e253a78292e0f6c SHA1: 920ea17b1511b87bd9236b6df20776e8f3cf092b SHA256: 6478670d9a7d027b54984351e5a90177600f4cb78d19c4091b1e5472bd141c5c SHA512: 47d0f1124d23f1c8d10a0796c58f91a20e774c35cdafccdb8ba43f9afe947b7d480c7236c14ac1f77df1789f9d8c0f5191752a8603b4471b38d7c879cc253aa4 Homepage: https://cran.r-project.org/package=RGENERATE Description: CRAN Package 'RGENERATE' (Tools to Generate Vector Time Series) A method 'generate()' is implemented in this package for the random generation of vector time series according to models obtained by 'RMAWGEN', 'vars' or other packages. This package was created to generalize the algorithms of the 'RMAWGEN' package for the analysis and generation of any environmental vector time series. 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The online method (GPlates Web Service) makes the rotation of static plates, coastlines, and a low number of geographic coordinates available using nothing but an internet connection. The offline method requires an external installation of the GPlates Desktop Application, but allows the efficient batch rotation of thousands of coordinates, Simple Features (sf) and Spatial (sp) objects with custom reconstruction trees and partitioning polygons. Examples of such plate tectonic models are accessible via the chronosphere . This R extension is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions). 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The original interface package for 'GRASS 5' (2000-2010) is described in Bivand (2000) and Bivand (2001) . This was succeeded by 'spgrass6' for 'GRASS 6' (2006-2016) and 'rgrass7' for 'GRASS 7' (2015-present). The 'rgrass' package modernizes the interface for 'GRASS 8' while still permitting the use of 'GRASS 7'. 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The method is provided in Yichuan Bai and Lynna Chu (2023) . Package: r-cran-rgtmx Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rgtmx_0.1.4-1.ca2404.1_all.deb Size: 52398 MD5sum: f0aa0bae8367774a96b351690a72bafc SHA1: 7d8cae3925082d7a554747b91eddd766c129e0d4 SHA256: 2f5ecf2e6ff859f132283ba62f874ac603123e58807aff6df98c6fbb1fbce344 SHA512: cc9e5805f812ae248c250b3f6b84a4d70727bbc4aaac22a78fe37174a9019a372f020c83dabfc64b632f64a8cb7038f2e26a22e6842b5660c56392101b0dab8e Homepage: https://cran.r-project.org/package=rgtmx Description: CRAN Package 'rgtmx' (Manage GTmetrix Tests in R) This is a library to access the current API of the web speed test service 'GTmetrix'. 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See for details. Package: r-cran-rhawkes Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ihsep Filename: pool/dists/noble/main/r-cran-rhawkes_1.0-1.ca2404.1_all.deb Size: 1006450 MD5sum: 3d38623c7d82ad7228c407968e6ab309 SHA1: f1f204c1820c93b087bf248568e4fe67930b4c62 SHA256: 6746bf36ac983c6571a87aabe746fcc62a31c75edf8316059390104540b63a8f SHA512: 42d1abfdff6999181751e0992b50cb4868c652e67ae2be1d196096d6229b00deb4b9c0d2814b0e556bb36678360126bc8f6354b2988a5f4a26696fbf45563084 Homepage: https://cran.r-project.org/package=RHawkes Description: CRAN Package 'RHawkes' (Renewal Hawkes Process) The renewal Hawkes (RHawkes) process (Wheatley, Filimonov, and Sornette, 2016 ) is an extension to the classical Hawkes self-exciting point process widely used in the modelling of clustered event sequence data. 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This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions. 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It can be used to explore important co-clusters consisting of important samples and their regulatory significant features. Please see Hasan, Badsha and Mollah (2020) . 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Package: r-cran-rhnerm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rhnerm_1.1-1.ca2404.1_all.deb Size: 31936 MD5sum: 880f41b78652e28ed31480f59ca64c25 SHA1: 2a7d90c79efb9c6951414f92c84c5f127ded170a SHA256: a7c0c17b9f89210843332e3f3b9336c5f6b5e661d277821a738059e2c6383cf5 SHA512: da492523e5e52f2e435eb9cb4a37ceb2f1419cd49ee671a1b0a6d188552a4eaabebf1c4e88c275cfea5905167a30c7bb668e322b3ce41e0a26715ba06eb4efae Homepage: https://cran.r-project.org/package=rhnerm Description: CRAN Package 'rhnerm' (Random Heteroscedastic Nested Error Regression) Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) . Package: r-cran-rhoneycomb Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rhoneycomb_2.3.4-1.ca2404.1_all.deb Size: 156894 MD5sum: b1c7f7eaaca490a727a941956c9c0e8d SHA1: 99a76187d727bd8628b88b1349f2d55615e51162 SHA256: 21144a018ffdfccb7b89c73f80ec269cf454a04687bdd4afbbba98049c6ab5ae SHA512: b947e0782dce488ccd72e50f08cdec5cd0108149be5592c7e0bf9e3d235f06cf75155894c4c59d1274bf79adf3bb241fd8cffbc0ef60412fbb6bad908618b5a5 Homepage: https://cran.r-project.org/package=rhoneycomb Description: CRAN Package 'rhoneycomb' (Analysis of Honeycomb Selection Designs) A useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) Fasoula V.A., and Tokatlidis I.S. (2012) Fasoulas A.C., and Fasoula V.A. 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There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic. 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This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) . 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Package: r-cran-rhybridfinder Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-seqinr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rhybridfinder_0.2.0-1.ca2404.1_all.deb Size: 1655782 MD5sum: 2e3326adc3cf8bfb1e030b2e2f97f9d8 SHA1: d72eb19542dc6c34c506543433fe2b43d799e614 SHA256: ecef2abc24262a45a3e3746e049d4937436459d176946284183ae3ff00a9b90b SHA512: dd02df6a0e435046d8f43d080f7b63296f1890731b33e6779cd4c9dfb90f0cc59f63c32f252374064d364dd7777a66a78d5d7d91846aac374df3b44ed2bba846 Homepage: https://cran.r-project.org/package=RHybridFinder Description: CRAN Package 'RHybridFinder' (Identification of Hybrid Peptides in Immunopeptidomic Analyses) Tool for the analysis Mass Spectrometry (MS) data in the context of immunopeptidomic analysis for the identification of hybrid peptides and the predictions of binding affinity of all peptides using 'netMHCpan' while providing a summary of the netMHCpan output. 'RHybridFinder' (RHF) is destined for researchers who are looking to analyze their MS data for the purpose of identification of potential spliced peptides. This package, developed mainly in base R, is based on the workflow published by Faridi et al. in 2018 . 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Learn more about the 'Datamuse' API here . Package: r-cran-rhype Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-r6, r-cran-rspectra Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rhype_0.3.0-1.ca2404.1_all.deb Size: 121072 MD5sum: 0e73d5956e08a59dbefe13ca8309ef8b SHA1: ffdb1c58f42b74232a09c16ae2067fd252cc8746 SHA256: 7a82bc7584ef2a12eaa851ebc9d740cea640de3f88d5629aac88733c41c979fd SHA512: 7d959e63f022f331aeb99c063a4997200ba031f89d22181194167ffbc5272fbaab02efe2ba0a4a8dc3816508e570205db2330e8ff82504d5ce42afabe05d6992 Homepage: https://cran.r-project.org/package=rhype Description: CRAN Package 'rhype' (Work with Hypergraphs in R) Create and manipulate hypergraph objects. This early version of rhype allows for the output of matrices associated with the hypergraphs themselves. 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The 'rhythm.metrics' package provides a standardised workflow to compute common metrics including Delta C, Delta V, VarcoC, VarcoV, the percentage of vocalic intervals (%V), and both raw and normalised Pairwise Variability Indices (rPVI, nPVI). It includes functions for calculating and visualising these measures to facilitate cross-linguistic and developmental rhythm research. Delta C, Delta V, and %V measures are based on Ramus et al. (1999) ; VarcoC and VarcoV measures are based on Dellwo (2006, ISBN: 9783631554777); and rPVI-C and nPVI-V are based on Grabe & Low (2002) . 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Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated. 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Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty. To include any indirect method, a custom wrapper function needs to be provided. The package offers functions for generating the test sets, executing the indirect method and evaluating the results. See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) . Package: r-cran-ribiosgraph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-magrittr, r-cran-plotly, r-cran-ribiosutils Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ribiosgraph_1.1.0-1.ca2404.1_all.deb Size: 133702 MD5sum: 5477d8823631f9d18c1a451508323a57 SHA1: 3416e6cf5e61ebb2fac80fba1b93bc53728ea867 SHA256: fb7e8ca4ed9c94c53fcf3cdeccb944f6e1affa9461ba09fffbe0cfca0b11e60c SHA512: 40c73dbb357e7b5de7a6a669a344cd6f7ae244c4a42327fc3f29c1920c776558864e254dc80ab819f84126cd1fbef5b29366fd0471423700deda93a9de25bb7f Homepage: https://cran.r-project.org/package=ribiosGraph Description: CRAN Package 'ribiosGraph' (Manipulate and Visualize Graphs in the 'ribios' Software Suite) Tools to manipulate and visualize graphs (networks) for computational biology in drug discovery, for instance functions for creating bipartite graphs and for interactive visualizations. Zhang (2025) . 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This is achieved by allowing (upper and lower) index labeling of arrays and making use of Ricci calculus conventions to implicitly trigger contractions and diagonal subsetting. Explicit tensor operations, such as addition, subtraction and multiplication of tensors via the standard operators, raising and lowering indices, taking symmetric or antisymmetric tensor parts, as well as the Kronecker product are available. Common tensors like the Kronecker delta, Levi Civita epsilon, certain metric tensors, the Christoffel symbols, the Riemann as well as Ricci tensors are provided. The covariant derivative of tensor fields with respect to any metric tensor can be evaluated. An effort was made to provide the user with useful error messages. 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Supporting publication: Blaauw, M., Reimer, P.J., 2026. An open-source toolkit for radiocarbon dating and calibration. Radiocarbon . The methods follow long-established recommendations such as Stuiver and Polach (1977) and Reimer et al. (2004) . This package uses the calibration curves from the data package 'rintcal'. Package: r-cran-ricegeneann Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 903 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-riceidconverter Filename: pool/dists/noble/main/r-cran-ricegeneann_1.0.2-1.ca2404.1_all.deb Size: 893090 MD5sum: 90b25155600bda5685279bf468662aa9 SHA1: 56745276a353cbc61cddc5ff032cda9bea8261a3 SHA256: ab6d0622320d8fa07279d0a25f05f2b98ddd2784189fd0eb742d13a823009fdd SHA512: e1c71901112b9b4bcf315b262f9a69990f023b116c7c09223ccec4f79df8818a3efe5f117dd0f558464168bd223fd064e4d283ecf3dfa3eef668121f2a49dc8a Homepage: https://cran.r-project.org/package=ricegeneann Description: CRAN Package 'ricegeneann' (Gene Annotation of Rice (Oryza Sativa L.spp.japonica)) Gene annotation of rice (Oryza Sativa L.spp.japonica). The package is based on the annotation file from the website . 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Rice(Oryza sativa) has more than one form gene ID for the genome. The two main gene ID for rice genome are the RAP (The Rice Annotation Project, , and the MSU(The Rice Genome Annotation Project, . All RAP rice gene IDs are of the form Os##g####### as explained on the website . All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website . All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, . The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######. The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI. There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as 'ID Converter' . But it is difficult to convert super multiple IDs on these websites. The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number. 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Package: r-cran-ricrt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-randomforest, r-cran-tidyverse, r-cran-superlearner, r-cran-glmnet, r-cran-rlang, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-ricrt_0.1.0-1.ca2404.1_all.deb Size: 56728 MD5sum: 11a7492d3206671003cde1c459c23a49 SHA1: 395f3042e08df86c92b408e8e09fa662df21398e SHA256: 8f687c4ddb84fda1aaba8674a4a77bd8fc2117b502bc9aa1c0f5e71332cec72a SHA512: 9cbad07c71a829150ecc1b8914e41e8f19d00262adc393041e39602181fecc7f48b60499e33e54437f24778a63d24ddc2c4c5f9219595915b9861073c8a6c6d2 Homepage: https://cran.r-project.org/package=Ricrt Description: CRAN Package 'Ricrt' (Randomization Inference of Clustered Randomized Trials) Methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) . Package: r-cran-rict Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-geomander, r-cran-geos, r-cran-ggplot2, r-cran-gt, r-cran-purrr, r-cran-redist, r-cran-redistmetrics, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rict_0.0.1-1.ca2404.1_all.deb Size: 366144 MD5sum: 19547727fc323aeac981f3915d26bb5c SHA1: 728f395d82d9e8b6ecc7d9c5065352d0950442c8 SHA256: 74bdf88a454d843d3a72f19d9f8a8e348b0664a533c2bccaae3078465a1c67fa SHA512: 6ea60da97d0764a0323bb755dd549c5d961c33bd909f47b7f51d7fa889b7d4f7433764e2def8d8b3bc9e5af2dfb4d815aa3e32132e760fce05bee78d0e953ddd Homepage: https://cran.r-project.org/package=rict Description: CRAN Package 'rict' (Redistricting in Clean Tables) Provides a suite of tools to create tables that accompany maps. The tools create clean, informative tables for electoral outcomes, compactness, and other district-level quantities. Most tools are aimed at the redistricting context, but are broadly applicable to other electoral data. Package: r-cran-ricu Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-curl, r-cran-assertthat, r-cran-fst, r-cran-readr, r-cran-jsonlite, r-cran-prt, r-cran-tibble, r-cran-backports, r-cran-rlang, r-cran-vctrs, r-cran-cli, r-cran-fansi, r-cran-openssl Suggests: r-cran-xml2, r-cran-covr, r-cran-testthat, r-cran-withr, r-cran-mockthat, r-cran-pkgload, r-cran-progress, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-cowplot, r-cran-survival, r-cran-forestmodel, r-cran-rticles, r-cran-kableextra, r-cran-units, r-cran-pdftools, r-cran-magick, r-cran-pillar Filename: pool/dists/noble/main/r-cran-ricu_0.6.3-1.ca2404.1_all.deb Size: 1373318 MD5sum: 526c5ea2640acbea5b3d510f738d9607 SHA1: c5aa7dfef634a936550840e25e480f3ff313710a SHA256: 9f464c4d57bb8d06382b3953e20cc6a3a65d697a74e4d7024c757d97d4a956dd SHA512: 7638fe2d70951865df7c60f7f18d43f9e1f42aae889c8e71a7fcccf5a46d63aaae49f37b88e8bdb5258934c043779bb7f980d683f9daeeb94d44491b73b2dd50 Homepage: https://cran.r-project.org/package=ricu Description: CRAN Package 'ricu' (Intensive Care Unit Data with R) Focused on (but not exclusive to) data sets hosted on PhysioNet (), 'ricu' provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented. Package: r-cran-rideogram Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-grimport2, r-cran-rsvg, r-cran-scales, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rideogram_0.2.2-1.ca2404.1_all.deb Size: 2632842 MD5sum: 78a11d63a3aa62ea1dbf151730c045ba SHA1: 24a601a728b353afd5eff675dd4c60070db45400 SHA256: 693dde4b289d3427afddcfdd894ec82d841fd294380dea6976946a4b8ec60772 SHA512: fce9f82f6e9d5f5edfc16c4bb184c278db4929cc955b5e6289e05a7c217ba948d01b0d003a175748a6e1644a99575d27cb41a933af469542ace9ed88bee4ab31 Homepage: https://cran.r-project.org/package=RIdeogram Description: CRAN Package 'RIdeogram' (Drawing SVG Graphics to Visualize and Map Genome-Wide Data onIdiograms) For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species. Package: r-cran-ridgregextra Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-isdals, r-cran-mctest Filename: pool/dists/noble/main/r-cran-ridgregextra_0.1.1-1.ca2404.1_all.deb Size: 27636 MD5sum: ed4798e1a2b44bcab42e5a8874d68662 SHA1: aae89bac0413d03933bfb2585adc2ae363a29f3e SHA256: a617a84a18c5acfbf0542a1249491b64afeb55ad22221ef32251e79653c8040b SHA512: bf12b53a09bb7fc08966ee6e3f95b70941ddef646ea46c810c63082bdc761d44bf935039f7b8a6789b6384fc7f4e48fa3dc0fa11db6df1180cd5258d535948ae Homepage: https://cran.r-project.org/package=ridgregextra Description: CRAN Package 'ridgregextra' (Ridge Regression Parameter Estimation) It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them. Package: r-cran-ridigbio Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4096 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-httr, r-cran-jsonlite, r-cran-leaflet, r-cran-kableextra, r-cran-tidyverse, r-cran-cowplot Suggests: r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ridigbio_0.4.1-1.ca2404.1_all.deb Size: 1509674 MD5sum: 88b44c2b73214e01977e85bb88bce34e SHA1: 87fe64e263b8de6cd27cc21f29291bb667e061d2 SHA256: 0430682ff4bcdc755eb9ec4174f2b29a1a465b233cb0e31b4f730563ca2cab79 SHA512: 03a6e9bb237d95acb155e03b96cdb2b952dc05c5c883595c54eddf54889a86a84605e67dd3dad1228241d3ae721e7e7ad88b8c46beb5cd0074ee90a2a0755b20 Homepage: https://cran.r-project.org/package=ridigbio Description: CRAN Package 'ridigbio' (Interface to the iDigBio Data API) An interface to iDigBio's search API that allows downloading specimen records. 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Package: r-cran-ridittools Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ridittools_0.1-1.ca2404.1_all.deb Size: 39254 MD5sum: 86631dae5be2bed4aed15455b139c499 SHA1: 58a4a868e90453db27750d102800265d4202ed70 SHA256: 2c61abd6b564c8e3bf026d4b8e562d37a4ec4ee71a520d19aee0f78fa6e2fbed SHA512: c3e0b458d9eddcdd254575f7a0e9db7030dbe728b62fe8f265643ff2fc17718569cffd8054b03428bba40d2950c8f1d376110ae2e5573356566401bd034e68ae Homepage: https://cran.r-project.org/package=ridittools Description: CRAN Package 'ridittools' (Useful Functions for Ridit Analysis) Functions to compute ridit scores of vectors, compute mean ridits and their standard errors for vectors compared to a reference vector, as described in Fleiss (1981, ISBN:0-471-06428-9), and compute means/SEs for multiple groups in matrices. Data can be either counts or proportions. 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Package: r-cran-riem Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-weathermetrics, r-cran-xts Filename: pool/dists/noble/main/r-cran-riem_1.0.0-1.ca2404.1_all.deb Size: 25430 MD5sum: 8f935ba84b0330204520000668dd10d8 SHA1: 341293a2659291eea889deebbe6e7d0d09bbdba9 SHA256: 87ab4bc218bb0a5987f870cfe2397f4db93894bde2c742e331c21d6a061f6f9f SHA512: 4f292741a620738ea3d303290224ba38b981251cac3ca329a6e4a8b0a56fa020f4c553de95f68b66e1845bdafc4a7bf618ef3a508c1338c718cc5e00aeb00a8f Homepage: https://cran.r-project.org/package=riem Description: CRAN Package 'riem' (Accesses Weather Data from the Iowa Environment Mesonet) Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website. Package: r-cran-riemannianstats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5135 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-ggrepel, r-cran-uwot, r-cran-vegan, r-cran-dbscan Suggests: r-cran-scatterplot3d, r-cran-plotly, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-riemannianstats_0.2.0-1.ca2404.1_all.deb Size: 1823342 MD5sum: 9743e9d13d40feab7450e48a96b3774d SHA1: cb2ef11dbe24e173536ad6f5776d49cdd73cf2a9 SHA256: f4d7c41b18df56907619eee6a2ccde7da18a333f7ad16811b275987185332d1a SHA512: 1a754c47d12e5c63ebbcc882116fbadc2f72648ad469581fe99513635285410e4893c1365ac90d97a9f034fe386d94d8bcdf5eb49ccb29efa799d7507debbc4f Homepage: https://cran.r-project.org/package=riemannianStats Description: CRAN Package 'riemannianStats' (Riemannian Methods for Principal Component Analysis, Regressionand Visualization) Provides tools for statistical analysis on Riemannian manifolds using local geometry derived from Uniform Manifold Approximation and Projection (UMAP), Isometric Mapping (Isomap), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The package supports dimensionality reduction, visualization, Riemannian principal component analysis, and Riemannian linear regression for multivariate data analysis. Methods based on Uniform Manifold Approximation and Projection follow McInnes et al. (2018) . Package: r-cran-riemstats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-covtools, r-bioc-sva, r-cran-purrr, r-cran-riemtan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-riemstats_0.2.0-1.ca2404.1_all.deb Size: 52440 MD5sum: 8c61c15fd50ed2760936ac72959a56f4 SHA1: 36409884e714f98edb993eb7435932375ee87b25 SHA256: 75f70092af8ff3d7311f8db42af11a8d7b302b8deb992798da9e780ef271a6f3 SHA512: 78a72261b1e5fbb35e622155f72f3694e829f80c663d56f98733b40474f1b2e6657c73e9cff68df31f63dadf27121b0089d6b6171e088b9ccdec0ad3ca42e6e2 Homepage: https://cran.r-project.org/package=riemstats Description: CRAN Package 'riemstats' (Riemannian ANOVA Statistics) Provides statistical methods for analyzing samples of symmetric positive definite (SPD) matrices, particularly functional connectivity matrices from neuroimaging data. Implements Fréchet ANOVA (Dubey and Müller (2019) ) for testing differences between groups in metric spaces, and Riemannian ANOVA methods that leverage tangent space geometry with classic multivariate test statistics including Wilks' Lambda and Pillai's trace. Also includes harmonization techniques for removing batch effects in multi-site studies: ComBat-based harmonization (Honnorat et al. (2024) ) and rigid harmonization (Simeon et al. (2022) ). Builds on 'riemtan' package infrastructure for efficient computation with multiple Riemannian metrics. Package: r-cran-riemtan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-r6, r-cran-purrr, r-cran-mass, r-cran-furrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-riemtan_0.1.0-1.ca2404.1_all.deb Size: 210224 MD5sum: 90952c078d8562ca99fadd8a656f3bd6 SHA1: 0d3d3e45bf6cf3c2b75995ae967e6a16fc43da67 SHA256: 1e71340bbde661bfecb3f49447639cf4dd7d8fadcdfec5837b8fd3746d667f09 SHA512: b2dae67e1d0d212e15f8a6c3659627987c09092f51d530408a3c685252ad923a58f2d3998feea0489fc248283807efdc88b0a9a4be3d519ad7a2f0a3f5f50a27 Homepage: https://cran.r-project.org/package=riemtan Description: CRAN Package 'riemtan' (Riemannian Metrics for Symmetric Positive Definite Matrices) Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, see Pennec, Fillard, and Ayache (2006) ), Log-Euclidean (see Arsigny, Fillard, Pennec, and Ayache (2006) ), Euclidean, Log-Cholesky (see Lin (2019) ), and Bures-Wasserstein metrics (see Bhatia, Jain, and Lim (2019) ). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices. Package: r-cran-riex Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-ttr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-purrr, r-cran-rjson, r-cran-stringr, r-cran-urltools, r-cran-quantmod Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-riex_1.0.2-1.ca2404.1_all.deb Size: 127822 MD5sum: 5110d82278603d0de96d1861b06f0711 SHA1: d32c0bde6d906c190ba655f569fc4abc4dd9c3f6 SHA256: e1d7457876ad9d67860f966c161141733cca488bdea55399258b2cf4700a2123 SHA512: b5ec465ac8e13e237f4f0009cafa08997da3820fb5e86990a3ad5cce695e0e047749105571fb61da6dd1b127652c5b24eb470bc8de16db1b3fa0e69bba0106be Homepage: https://cran.r-project.org/package=Riex Description: CRAN Package 'Riex' (IEX Stocks and Market Data) Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group). Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions. In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements. Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (). Package: r-cran-rifanalysis Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-cowplot, r-cran-igraph, r-cran-openxlsx, r-cran-powerlaw, r-cran-rlang, r-cran-ggrepel, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rifanalysis_0.9.3-1.ca2404.1_all.deb Size: 314646 MD5sum: 6de8dcad46bb9cb88fb03c162b0284ab SHA1: 8b9b027900027689a66708679f6b805143444510 SHA256: b0de61fe36e1d335bf71a97fba6b48ceb8bca6252fada972e9edaa295c13faed SHA512: 337dcce2f7b80f0f793a283667afbcbc6f859569d09ad1ec59a9b73cbf7d394bc6177552bb903c3d543f80facbd037ab27d8d183506bb5c6ff0d9bc5ddcbba01 Homepage: https://cran.r-project.org/package=RIFanalysis Description: CRAN Package 'RIFanalysis' (Relative Importance Factor Analysis) Tools for estimating, comparing, and visualizing Relative Importance Factor (RIF) indices based on rank-frequency distributions and discrete power-law models. The package provides reproducible workflows for data preparation, model fitting, goodness-of-fit assessment, bootstrap inference, and publication-ready outputs. The implemented methodology is described in Llinas et al. (2026) . Package: r-cran-rifexpectile Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-wooldridge Filename: pool/dists/noble/main/r-cran-rifexpectile_0.1.1-1.ca2404.1_all.deb Size: 235246 MD5sum: 8f1ecddf56dac50a5c5f9cccd43880fd SHA1: ab5b20fc21a369cc14e2b474435eb09c0da343f3 SHA256: 91f7ee2a604d09d0e28ad72cca5da0809feb99c8dbdb3e6c5aeb5f73c514f95c SHA512: 76e3f589404533c58f02d7bf396da84d7d6edf8b3cde739ea8154ed6d776ea468cf604fa6d327a605de0f7e2cdab3d801df896a4cf7a3c1ab8b2be066c8bb6cb Homepage: https://cran.r-project.org/package=rifexpectile Description: CRAN Package 'rifexpectile' (Density-Free RIF Decompositions for Unconditional Expectiles) Implements a density-free recentered influence function (RIF) regression framework for unconditional expectiles, and embeds it in a two-sample Oaxaca-Blinder decomposition indexed continuously by the expectile level. Unlike quantile-based RIF decompositions, which require estimating an inverse density term at each quantile, the expectile RIF depends only on primitive moments of the outcome distribution and requires no density estimation, no bandwidth selection, and no kernel smoothing. The package provides expectile estimation by iteratively reweighted least squares, closed-form RIF construction, two-sample composition/structure decomposition across a grid of expectile levels, bootstrap-based inference, and plotting methods. The underlying methodology is described in Ndoye (2025), "Semi-Nonparametric Expectile RIF Regression for Distributional Decomposition," presented at the 2025 World Congress of the Econometric Society, Seoul, Korea, . Package: r-cran-rifle Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-rifle_1.0-1.ca2404.1_all.deb Size: 23562 MD5sum: d694c56ea6c3fb52b282877200209251 SHA1: bbc7c44361afb8a3a147ab58cec2cfe4e7a226c1 SHA256: ef2d505f9334305fc726b8858938297fd86f68480448676bb6134c9d481a94fa SHA512: 67e95e1961a4557f7e45b51b2656953fda3c5837aa5124a3a2898d09b5f6c59da9ffa0b1037f77b396f4fde0f4348973866baa1e1288c00d41f73023468dfc6b Homepage: https://cran.r-project.org/package=rifle Description: CRAN Package 'rifle' (Sparse Generalized Eigenvalue Problem) Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow. To appear in Journal of the Royal Statistical Society: Series B. . 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The package allows to regress the RIF on any number of covariates. Generic print, plot and summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) . "Unconditional Quantile Regressions.". 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The user provides the final analytical dataset and specifies the design of the table, with rows and/or columns defined by exposure(s), effect modifier(s), and estimands as desired, allowing to show descriptors and inferential estimates in one table -- bridging the rift between epidemiologists and their data, one table at a time. See Rothman (2017) . 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The package currently works with (generalized) linear models, survival models, complex survey models, and marginal effects probit models. originally forked by Brecht Devleesschauwer from the 'decomp' package (no longer on CRAN), 'rineq' is now maintained by Kaspar Walter Meili. Compared to the earlier 'rineq' version on 'github' by Brecht Devleesschauwer (), the regression tree functionality has been removed. Improvements compared to earlier versions include improved plotting of decomposition and concentration, added functionality to calculate the concentration index with different methods, calculation of robust standard errors, and support for the decomposition analysis using marginal effects probit regression models. The development version is available at . 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Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" . Package: r-cran-ringbp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-sn Suggests: r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-tinyplot Filename: pool/dists/noble/main/r-cran-ringbp_1.0.0-1.ca2404.1_all.deb Size: 341402 MD5sum: 41d3add6937078c35e3ee966d4349646 SHA1: 44814728eef299863d461b2d32181df2c9494763 SHA256: 7395209ffb34dd1bc48817399e2165b7f0eadeaccc787995570aeecccfa49ca7 SHA512: 5c288b530950b493b88262fa6db8267e17d4df7d0f2ee25f59dd8079a8121e4df5bfc978999b59678a8fbad76b5ec5dc4c627c2abc3c4c0a7b9a5e514347aa0b Homepage: https://cran.r-project.org/package=ringbp Description: CRAN Package 'ringbp' (Simulate and Evaluate Targeted Interventions in InfectiousDisease Outbreaks) Branching process simulation model of infectious disease transmission with flexible parameterisation of epidemiology and targeted interventions, including isolation, contact tracing and quarantine, to reduce transmission, together with functions to evaluate outbreak control. Introduced in Hellewell et al. (2020) . Package: r-cran-ringostat Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-stringr, r-cran-cli, r-cran-readr Filename: pool/dists/noble/main/r-cran-ringostat_0.1.5-1.ca2404.1_all.deb Size: 89206 MD5sum: d5b1e6c67156af17dab63b790fd6780b SHA1: 9a02a5f203059ca8e09a7fc45135b7cf391352a5 SHA256: 5dcb8f73e5253d10691e6eb6b7263aebe3413ce2f4ec91d45af7ad4ccf0ee29f SHA512: 69eca5337a359714c5c2da506bdcf894db9b0a59dc51adf6fc3d7c47883013945191e957acc3c8cb9f6e71c6192c031383c2f11c57e0a36a5f4df44f1d7ff460 Homepage: https://cran.r-project.org/package=ringostat Description: CRAN Package 'ringostat' (Load Data from 'Ringostat API') Loading calls data from 'Ringostat API'. See . Package: r-cran-ringseg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ringseg_0.1.0-1.ca2404.1_all.deb Size: 93532 MD5sum: 0bcf040ca672d9a47aa5f3d9e2093adf SHA1: 1cfb1026171b99de2feb412fbeb16bc77efb8ca7 SHA256: 4673934adf690a15789be19719127ec5d0d7dbfef176bdd77d94e5f83797b4f2 SHA512: 89ac24300146663cce5b0e1ba08be0f8de97560c0106e2fad3eb8f7474fc1660b4f5853ceeb2e2ac48cda2c8a71cc8f2817f7f1bca633d135343612acc7e4278 Homepage: https://cran.r-project.org/package=ringSeg Description: CRAN Package 'ringSeg' (Asymptotic Distribution-Free Change-Point Detection via a NewRanking Scheme (RING)) Rank-based, asymptotic distribution-free change-point detection for modern (high-dimensional, non-Euclidean) data, based on the graph-induced ranking scheme of Zhou and Chen (2025) . Given a rank matrix built from a pairwise similarity, the method scans for a single change-point or a changed interval using three statistics (weighted 'WR', max-type 'MR', and generalized 'TR') and returns analytic distribution-free p-value approximations (with an optional skewness correction) as well as optional permutation p-values. Package: r-cran-rintcal Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4267 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-rintcal_1.4.2-1.ca2404.1_all.deb Size: 2706566 MD5sum: 2505b5653f75e7fc689d8653b7ebabd6 SHA1: 06a4cda1b13d7da06082c90aa5868447c8f7db99 SHA256: 0ecb098adf3bedf0bdcda54a4df4d0925b104bace42dad03b853a5d6fb289632 SHA512: 98dff42a9ea60463e87cdda865cbb45178b339b373d7b048a6d63a39d332ff9fef0f75e32102f23bcfc62e4c67c5b9f37a201b634ceed02b67e7ff11f1fdb979 Homepage: https://cran.r-project.org/package=rintcal Description: CRAN Package 'rintcal' (Radiocarbon Calibration Curves) The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 ) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951, Stuiver & Suess 1966, Pearson & Stuiver 1986) and postbomb curves. 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An additional convenience function, 'convert()', provides a simple method for converting between file types. Package: r-cran-rioplot Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-mass Filename: pool/dists/noble/main/r-cran-rioplot_1.1.2-1.ca2404.1_all.deb Size: 689768 MD5sum: c5252be12f2ba435e6966c84d32c5238 SHA1: f27f3c5b18300727d748b6a64a8da3e1b98da11c SHA256: 36cef1268d84567fc762f416b59cdc291728bbb8fa14c5a30eff0e1485cab7f9 SHA512: 895a6d3f0ef5248b582475875a6ebe9ac851f599f7298d65a7ee651b04285933dc29455275999f633d812d2d77e421239d98b0af1e91c94099477f125fb4f8ef Homepage: https://cran.r-project.org/package=rioplot Description: CRAN Package 'rioplot' (Turn a Regression Model Inside Out) Turns regression models inside out. Functions decompose variances and coefficients for various regression model types. Functions also visualize regression model objects using techniques developed in Schoon, Melamed, and Breiger (2024) . Package: r-cran-ripc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-countrycode, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ripc_1.0.0-1.ca2404.1_all.deb Size: 156936 MD5sum: 811989d27edd16721b731a7c09179623 SHA1: f5d7f945a017e2bc6b7a75ddf0199bff073ca464 SHA256: e1a3fea5f383a2e40c75b3fcbc4e17eb437e51cd57af836e95ea74e8e05577e4 SHA512: 5aa59166780cc6b6fa248b2c88c3bcf5718069e950a2e1a6a0155d2e854c559bfe4fec382926662a45a70654a9925301769771236204637757bcc0949f2f179e Homepage: https://cran.r-project.org/package=ripc Description: CRAN Package 'ripc' (Download and Tidy IPC and CH Data) Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the 'IPC-CH' Public API () simplified and advanced endpoints to easily download the data in a clean and tidy format. Package: r-cran-rirods Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rappdirs, r-cran-testthat, r-cran-withr Suggests: r-cran-httptest2, r-cran-kableextra, r-cran-knitr, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rirods_0.3.0-1.ca2404.1_all.deb Size: 332334 MD5sum: 31d7fa0a4789bbe116e1604d28680f88 SHA1: 4d9dd8339e6aa02fb608afb771c990f4b1637040 SHA256: 636e67fb2b304a21993bd89367a3582eea3fa3469edb5dc3153fcc307717f49a SHA512: 9c90807222d8961c04c9d8fd89903d0dc472be3aac682ad5569f4782dd98caef2404fe4624138d7850e2e4e3d8f616c0febae723874d5f5e01a880ab19587631 Homepage: https://cran.r-project.org/package=rirods Description: CRAN Package 'rirods' (R Client for 'iRODS') The open sourced data management software 'Integrated Rule-Oriented Data System' ('iRODS') offers solutions for the whole data life cycle (). The loosely constructed and highly configurable architecture of 'iRODS' frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the 'iRODS' HTTP API, allowing you to manage your data and metadata in 'iRODS' with R. Storage of annotated files and R objects in 'iRODS' ensures findability, accessibility, interoperability, and reusability of data. Package: r-cran-risca Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-relsurv, r-cran-reticulate, r-cran-tune, r-cran-nlme, r-cran-mass, r-cran-mvtnorm, r-cran-statmod, r-cran-doparallel, r-cran-foreach, r-cran-nnet, r-cran-kernlab, r-cran-glmnet, r-cran-caret, r-cran-superlearner, r-cran-rpart, r-cran-mosaic, r-cran-cubature Filename: pool/dists/noble/main/r-cran-risca_1.0.7-1.ca2404.1_all.deb Size: 1365174 MD5sum: daebd9b43564a23e1ffc498150e21b7b SHA1: 68a20482c76b70e97c5e21696a8a7e1fd25cb98e SHA256: e5d2cd8b58437620dbc5823e7c432c9a6b445b8b290c4039f6ebd072e0b6015a SHA512: 036eaadedbda227f06f5444e771838e054e7818e449ac56f4e02217c6e4927085145b8a8aaaab269ce8e7410f77fffc2c945a9407d86c38acdca5d264b644861 Homepage: https://cran.r-project.org/package=RISCA Description: CRAN Package 'RISCA' (Causal Inference and Prediction in Cohort-Based Analyses) Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data. Package: r-cran-risdr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-dplyr, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-vim Filename: pool/dists/noble/main/r-cran-risdr_0.3.1-1.ca2404.1_all.deb Size: 392736 MD5sum: d88b1304eea532e0166cb55a69a96e44 SHA1: bfcf7a75cae7f9245e5cd361688802d0dbe2c7df SHA256: 098003bf7a71089440d6f7a964a50a20423741ef3874fe55f0def86e7ad2b35a SHA512: 2f8e5a287336946f3419e84dccbd3815e2f7a8f11807910f8e42608e2ec9856e713ee4d6468ae034ed7627956f1a1f924dad50454e8e4a5715a32593f8b539f9 Homepage: https://cran.r-project.org/package=risdr Description: CRAN Package 'risdr' (Regularised and Information-Theoretic Sufficient DimensionReduction) Implements covariance-stabilised sufficient dimension reduction for continuous responses with information-theoretic structural dimension selection. Supported methods include sliced inverse regression, sliced average variance estimation, directional regression, and principal Hessian directions. Sample, ridge, Oracle Approximating Shrinkage, Ledoit-Wolf, and Maximum Entropy Covariance (MEC) estimators are provided alongside prediction, resampling, simulation, and diagnostic utilities. The sufficient dimension reduction methods build on Li (1991) , Li (1992) , and Li and Wang (2007) . Covariance shrinkage follows Olorede and Yahya (2019) , Ledoit and Wolf (2004) and Chen et al. (2010) . Package: r-cran-rise Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rise_1.0.4-1.ca2404.1_all.deb Size: 18648 MD5sum: 826382a6ff1e9bc046a4e9599735f1f9 SHA1: 77fe5302b54d0c9efcfa1c5f8edcc063a0bd9cf3 SHA256: 8bc3e4a0f5cbb23f710cfb3a4f0ed4177dcce28bedba47c1b090a796278d82a1 SHA512: 48655bfc6aad5538140d868ec9cff31d176a592edfd1885683ff8866a2a380499f70f75bc3736b7e571a22fe7da88b7bacb96c875781e1c1d12ae467e9af07a5 Homepage: https://cran.r-project.org/package=rise Description: CRAN Package 'rise' (Conduct RISE Analysis) Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) . Automates the process of identifying learning materials that are not effectively supporting student learning in technology-mediated courses by synthesizing information about access to course content and performance on assessments. Package: r-cran-risk.assessr Architecture: all Version: 4.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3253 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-remotes, r-cran-test.assessr, r-cran-checkmate, r-cran-desc, r-cran-dplyr, r-cran-fs, r-cran-purrr, r-cran-rmarkdown, r-cran-rcmdcheck, r-cran-rlang, r-cran-xml2, r-cran-stringr, r-cran-tidyr, r-cran-curl, r-cran-jsonlite, r-cran-memoise, r-cran-biocmanager, r-cran-glue Suggests: r-cran-devtools, r-cran-forcats, r-cran-here, r-cran-htmltools, r-cran-htmlwidgets, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-openxlsx, r-cran-pkgload, r-cran-r6, r-cran-s7, r-cran-readr, r-cran-roxygen2, r-cran-testthat, r-cran-tibble, r-cran-tidyselect, r-cran-withr, r-cran-mockery, r-cran-ggplot2, r-cran-dt Filename: pool/dists/noble/main/r-cran-risk.assessr_4.1.3-1.ca2404.1_all.deb Size: 1944558 MD5sum: b0110b97a39bd4a937ad6278649019bb SHA1: 60381d861f4c9493327acb2d3d185f77f0798995 SHA256: f83acff43bf3fc03b67942498633427fd2fd63d061a68ec876ce2a122ea20ef2 SHA512: a2c9d4584a03482c4e247311cb8ee53fb07fb0506d2c0a3800a4b0e33f383f52c891a74b8769172ccdd2186b24a1674060aaff49c404bd26cdf0c31f1900a980 Homepage: https://cran.r-project.org/package=risk.assessr Description: CRAN Package 'risk.assessr' (Assessing Package Risk Metrics) A reliable and validated tool that captures detailed risk metrics such as R 'CMD' check, test coverage, traceability matrix, documentation, dependencies, reverse dependencies, suggested dependency analysis, repository data, and enhanced reporting for R packages that are local or stored on remote repositories such as GitHub, CRAN, and Bioconductor. Package: r-cran-risk Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-risk_1.0-1.ca2404.1_all.deb Size: 86658 MD5sum: b49d9dadcc41d9ef367b6eeb44b276fd SHA1: e240f804d9f771938cfb73c170358895f54c6040 SHA256: d9c294cafc765ad0ec46a8956229c117ac22ef4574d48255aeeb47aa9a7cfef0 SHA512: de2e8dc20ba3349bef28714f8f671b2b0d92a057708b6e1c485d73f7f10437acf3229bf95d074f3cb4a4a6114e0e30e15416366cf1c83019307fbf93fdd22d1c Homepage: https://cran.r-project.org/package=Risk Description: CRAN Package 'Risk' (Computes 26 Financial Risk Measures for Any ContinuousDistribution) Computes 26 financial risk measures for any continuous distribution. The 26 financial risk measures include value at risk, expected shortfall due to Artzner et al. (1999) , tail conditional median due to Kou et al. (2013) , expectiles due to Newey and Powell (1987) , beyond value at risk due to Longin (2001) , expected proportional shortfall due to Belzunce et al. (2012) , elementary risk measure due to Ahmadi-Javid (2012) , omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa due to Kaplan and Knowles (2004), Wang (1998)'s risk measures, Stone (1973)'s risk measures, Luce (1980)'s risk measures, Sarin (1987)'s risk measures, Bronshtein and Kurelenkova (2009)'s risk measures. Package: r-cran-riskclustr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlogit, r-cran-stringr, r-cran-matrix Suggests: r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-dplyr, r-cran-knitr, r-cran-usethis, r-cran-spelling Filename: pool/dists/noble/main/r-cran-riskclustr_0.4.1-1.ca2404.1_all.deb Size: 386072 MD5sum: 86b186b8c5e29dec65b240f0287efa78 SHA1: b5d07ce33ca4362bf9fed46ced0bb46e882b95a4 SHA256: bde645f08c09176aaed2771905ccc40150bf2520d3232df77a4b96d7ec66abbf SHA512: b5969bb35ae313747a5e2f7113968b281c3b3cf1b3d1beb5b2dddfb8d1f270affd5a510d3b0d2003dac1b2b5de4ec6fd0e0a3bab9b192bfed2a0ec4d88b01d58 Homepage: https://cran.r-project.org/package=riskclustr Description: CRAN Package 'riskclustr' (Functions to Study Etiologic Heterogeneity) A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) . Package: r-cran-riskcommunicator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-printr, r-cran-stringr, r-cran-formatr, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-riskcommunicator_1.0.1-1.ca2404.1_all.deb Size: 1093346 MD5sum: a3834f1d8d8cee00fb4e353f11e3fde3 SHA1: c7d149059d65342ba4c9bfb72b92801acc442562 SHA256: 9a5c94641758eb6c71b9c287d14aa1321af13ee7d22566efbcb624e9081a1528 SHA512: 3a16dc6bf8738a0aad844258eb98763261aa9c8fb6b4276c0c44336d68bf1cb0d2b182dc2d9d03a57d04158a7458b03f8e851d7b7a9da99427044a012c147cf9 Homepage: https://cran.r-project.org/package=riskCommunicator Description: CRAN Package 'riskCommunicator' (G-Computation to Estimate Interpretable Epidemiological Effects) Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) and has been described in detail by Ahern, Hubbard, and Galea (2009) ; Snowden, Rose, and Mortimer (2011) ; and Westreich et al. (2012) . Package: r-cran-riskdiff Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-ggplot2 Suggests: r-cran-kableextra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-mockery Filename: pool/dists/noble/main/r-cran-riskdiff_0.3.0-1.ca2404.1_all.deb Size: 412010 MD5sum: c1a94cb26472dbbab741b23259c1167d SHA1: 1893aa6e666790bd3db14484dfa0da5d0d480d6b SHA256: bcbabcae4388d5a54b91fec6bd443d9fe28925aec24069669510458629a60dc3 SHA512: 175fdb4e9d703bfa621e8bfa60a645f1645c4ba9f4af430dd9498b54f72b5b98e5c2378bfc60990f5dae33d5c6c52e5e4c8d5c460e93138bbdfefaeb87d6804e Homepage: https://cran.r-project.org/package=riskdiff Description: CRAN Package 'riskdiff' (Risk Difference Estimation with Multiple Link Functions andInverse Probability of Treatment Weighting) Calculates risk differences (or prevalence differences for cross-sectional data) and Number Needed to Treat (NNT) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference with NNT calculations, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology". Package: r-cran-riskmap Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-terra, r-cran-xtable, r-cran-spatialeco, r-cran-spatialsample, r-cran-deriv, r-cran-numderiv, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-sf, r-cran-sns, r-cran-stars Filename: pool/dists/noble/main/r-cran-riskmap_1.0.0-1.ca2404.1_all.deb Size: 1068004 MD5sum: 899e154ea24fa0ba058f2ae3d4f600b3 SHA1: b001db6f9b4d3128d83aead698308be966765aa6 SHA256: c272b86993c609d32331acb3717cf0e5d0d2592109a25a4b1e394ee130743086 SHA512: 161531a1844ca12ebe7e2c786ea57696688cb6d98da471f5f0b87866b09389d0f8b0187ea9ca74871bb3d2094ce55d0fa3140b72da63bcf95f63d6a34dbf2640 Homepage: https://cran.r-project.org/package=RiskMap Description: CRAN Package 'RiskMap' (Geostatistical Modeling of Spatially Referenced Data) Geostatistical analysis of continuous and count data. Implements stationary Gaussian processes with Matérn correlation for spatial prediction, as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7). Package: r-cran-riskmetric Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-xml2, r-cran-httr, r-cran-curl, r-cran-urltools, r-cran-memoise, r-cran-biocmanager, r-cran-cranlogs, r-cran-covr, r-cran-vctrs, r-cran-pillar, r-cran-tibble, r-cran-pkgload, r-cran-devtools Suggests: r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-magrittr, r-cran-pkgbuild, r-cran-rmarkdown, r-cran-testthat, r-cran-webmockr, r-cran-withr Filename: pool/dists/noble/main/r-cran-riskmetric_0.2.7-1.ca2404.1_all.deb Size: 760938 MD5sum: 9a681abf2412a84ae95a2565bef12c10 SHA1: 8fbcb33ce489f316b4f5f79a6522ca95874a3855 SHA256: e706e363f180634ddc8f4bf0d3a1451be78031ce803b420a301da413d4d49fc4 SHA512: 9927c317dc20fa5c12de64f21e304584a4f0170010637cde307af6745ef917246a5855aa81151a1d9d265d51f7b1ee1c484ec6244df19aa6ffc02a78c27b47d1 Homepage: https://cran.r-project.org/package=riskmetric Description: CRAN Package 'riskmetric' (Risk Metrics to Evaluating R Packages) Facilities for assessing R packages against a number of metrics to help quantify their robustness. Package: r-cran-riskportfolios Architecture: all Version: 2.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-riskportfolios_2.1.8-1.ca2404.1_all.deb Size: 124746 MD5sum: f75225f93aadbf57a0d4a7d81e8662df SHA1: f4a4542c5bd3b93d0a387134fbd874c14c03a8a8 SHA256: b37a904f3258174cabfdee03ad802b120b521b69be83a8fa78ff53689a05b76c SHA512: 2f10fc83f2f86b52d5c39f5d074f8039528cca315149258e705b79a8c12e7d3d410ee184b119cb68ddde516491774bd8e6d93717d1c95576002850abbf1228be Homepage: https://cran.r-project.org/package=RiskPortfolios Description: CRAN Package 'RiskPortfolios' (Computation of Risk-Based Portfolios) Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) and Ardia et al. (2017) . Package: r-cran-riskpredictclustdata Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-gee, r-cran-hmisc, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-riskpredictclustdata_0.2.7-1.ca2404.1_all.deb Size: 92246 MD5sum: bc5c518b92c7166e370121dc18ff4c8b SHA1: aebad47e38e9ecc7fa9fe02f315d70fc0e3dfbfe SHA256: 0c86f96fbe8f6aedd88d805c4bcfc6993eed11703d54e5f7841d39ac51673643 SHA512: bd51b5512840ac7b24b0b619112e90142fa00f66393700d999b40c55037e6c2906134cbcc70217d1a85ec18d5f84f7fcf258c308a9892430ba49425738ae7ac2 Homepage: https://cran.r-project.org/package=riskPredictClustData Description: CRAN Package 'riskPredictClustData' (Assessing Risk Predictions for Clustered Data) Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) . Package: r-cran-risks Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-bcaboot, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-sandwich, r-cran-tibble, r-cran-tidyr Suggests: r-cran-addreg, r-cran-covr, r-cran-knitr, r-cran-logbin, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-risks_0.4.3-1.ca2404.1_all.deb Size: 1387748 MD5sum: f7efcb0e176dd92e93fef8079d9ebfdd SHA1: dbccc466a6b274c5ee204b5caafc41c95d966c21 SHA256: 0704f4db0eb1014748e6068c42965f712c1d6c0508703b901431af6afcf13bf4 SHA512: 3127c8c9448fe7f33de538fbecb97e6dc64d24eacce992fcf3658afe57c9fd84043f9bd5b7bd9afc8d52c437e608f5a2504df07df4b88d266b6177acff161f4c Homepage: https://cran.r-project.org/package=risks Description: CRAN Package 'risks' (Estimate Risk Ratios and Risk Differences using Regression) Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, ), log-binomial (Poisson) models with empirical variance (Zou 2004, ), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, ), and others. Package: r-cran-riskscores Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-proc, r-cran-prroc Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-riskscores_1.3.0-1.ca2404.1_all.deb Size: 164882 MD5sum: 5d2c2c992e6244cf79c789f96f118d49 SHA1: 5a9abaa84d083d058ee638f5033188420f68e8df SHA256: 90d3bbc9bbff98bbc372fdd250d3dfa56feed1444e306243263d6e33f29434c3 SHA512: 95230e530fec6f439d54619faedc93dc48dcb20ff400bb7aa9d6bb4f7a4e0236086528485454aaf527cfd3e7f111c9132d4eff59c2256cbc890b6088655c874b Homepage: https://cran.r-project.org/package=riskscores Description: CRAN Package 'riskscores' (Optimized Integer Risk Score Models) Implements an optimized approach to learning risk score models, where sparsity and integer constraints are integrated into the model-fitting process. Package: r-cran-riskscorescvd Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pooledcohort Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-purrr, r-cran-testthat, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-riskscorescvd_0.3.1-1.ca2404.1_all.deb Size: 545384 MD5sum: f9f739ac2ee09cdc48a52f5d85adbaf7 SHA1: daca85ea24fe6e666f55c230b60944257113ee94 SHA256: cb67c237e1609b04bff014b6b299183ad8b9f65f26645bd72751386f532af850 SHA512: f3103d9984c82e0ee178981ddeedcb0fb883aae784c07dd5b438d251bd2f5f1ea9c995fa0ff186e09f8c4a882b3b2be8617ae71ea1d7c7723dd256e761be5e51 Homepage: https://cran.r-project.org/package=RiskScorescvd Description: CRAN Package 'RiskScorescvd' (Cardiovascular Risk Scores Calculator) A tool to calculate Cardiovascular Risk Scores in large data frames as published in Perez-Vicencio, et al (2024) . Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using 'RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) . EDACS described in Mark DG, et al (2016) . GRACE described in Fox KA, et al (2006) . HEART is described in Mahler SA, et al (2017) . SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) . TIMI described in Antman EM, et al (2000) . SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) . SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) . Package: r-cran-risksetroc Architecture: all Version: 1.0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-risksetroc_1.0.4.1-1.ca2404.1_all.deb Size: 87034 MD5sum: 2cddb82d4ec5de6e770177cc3bb34c23 SHA1: cf034703b9b51567741ffc7c1a211c0b8704edf1 SHA256: d09c4d2635d9bf0accccfdf40cc38daeea920ab398ffec98ce1700d11465dff6 SHA512: 51befcfe3f9b4320ae12ab1e8e55c4145628774dc93cd1f8d122f8c4920242f52b16145f92c2b5cc0d83a63366b20f8f0c329cb2fe7887a6a17f097a3d309fea Homepage: https://cran.r-project.org/package=risksetROC Description: CRAN Package 'risksetROC' (Riskset ROC Curve Estimation from Censored Survival Data) Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105). Package: r-cran-risksimul Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-runuran Filename: pool/dists/noble/main/r-cran-risksimul_0.1.2-1.ca2404.1_all.deb Size: 64366 MD5sum: 9ded81b62302430426259c481e9c2374 SHA1: d499f924565245681dcf574050708100043b69db SHA256: 07c6b480e87d297d7f1b85256167d95e38f7b1f18ec59855518d9c3dcf2b6d30 SHA512: a3779768a419beb9ed6b2451864d062fe0572eed6607b39d9b24286c9a65650aafd0142f6cca14e54be9c4e9689786783b03977db7e78b3215b1f012ca7e429b Homepage: https://cran.r-project.org/package=riskSimul Description: CRAN Package 'riskSimul' (Risk Quantification for Stock Portfolios under the T-CopulaModel) Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals. Package: r-cran-riskutility Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-mass, r-cran-vim, r-cran-randomforest, r-cran-ranger, r-cran-reshape2 Suggests: r-cran-simpop, r-cran-synthpop, r-cran-caretensemble, r-cran-kernelshap, r-cran-uwot, r-cran-rtsne, r-cran-dbscan, r-cran-rpart, r-cran-xgboost, r-cran-partykit, r-cran-testthat, r-cran-torch, r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-misc3d, r-cran-sdcmicro, r-cran-caret, r-cran-robustbase, r-cran-gridextra, r-cran-hmisc, r-cran-robcompositions, r-cran-clue Filename: pool/dists/noble/main/r-cran-riskutility_0.2.0-1.ca2404.1_all.deb Size: 2233332 MD5sum: 7f266752025f957f0169684b3943f50e SHA1: cf054e30a27074871e7bcdbfb38ba0e31bf27619 SHA256: 64c15fbe2383468de9f380fafc8def694681b501cdb945f828282141becfed3c SHA512: f3bca408c74ed023e9fb23fa58ca9a0a1d44baa703b41aa4b4af0f906b626fa7a587d884b332412c43dde25b3fe7656cdc80127df7c08b1cd2064a996101e4ae Homepage: https://cran.r-project.org/package=riskutility Description: CRAN Package 'riskutility' (Disclosure Risk and Data Utility Metrics for Synthetic andAnonymized Data) Provides comprehensive methods to measure disclosure risk and data utility for anonymized and synthetic data. Implements attribution-based risk metrics including Correct Attribution Probability (CAP), Targeted CAP (TCAP), Within Equivalence Class Attribution Probability (WEAP), and RAPID (Risk of Attribute Prediction-Induced Disclosure). Also provides distance-based privacy metrics such as Distance to Closest Record (DCR), Nearest Neighbor Distance Ratio (NNDR), and Identical Match Share (IMS). Utility assessment includes propensity score analysis, distribution comparisons, and various statistical tests. Methods are based on Taub et al. (2018) and related literature. Designed for integration with 'simPop' S4 classes. Package: r-cran-riskweightedassets Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-jsonlite, r-cran-openxlsx, r-cran-readxl, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-riskweightedassets_1.2.4-1.ca2404.1_all.deb Size: 646706 MD5sum: ce0405e2bbb40b342db2055476eeb229 SHA1: b36ef4f2dbe4a7a902835eef24eced07aa50edd9 SHA256: 58499192622aa4bcdf22253730d3ec7b6a476b2e9a14b7dbbb8a378cb3ba0eb2 SHA512: a5d3dd023d43e3119d1455667c4be530b30287e7133bca23067c18e83334815fbbc35fa98c714c307fa44e054894cd2bbd0996dc9c8e72d5734f829431d90dac Homepage: https://cran.r-project.org/package=riskweightedassets Description: CRAN Package 'riskweightedassets' (Reproducible Risk-Weighted Asset Calculations) Provides transparent, deterministic and auditable calculations of risk-weighted assets, own-funds requirements, interest-rate risk in the banking book and related capital metrics. It supports canonical in-memory tables and versioned spreadsheet datasets, strict validation, synthetic reference profiles, bitemporal snapshots, calculation controls and traceable regulatory source metadata. Methods are parameterised against the European Parliament and Council (2013) Capital Requirements Regulation and its amending Regulation (EU) 2024/1623 . A granular analyst API exposes individual formulae, domain views, controls, schemas and auditable parameter overrides. The implementation is intended for analytical, educational and model-validation use and does not constitute legal or supervisory advice. Package: r-cran-riskycnv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-genomicranges, r-cran-rlang, r-bioc-s4vectors, r-cran-tidyr Suggests: r-cran-biocmanager, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-riskycnv_0.1.0-1.ca2404.1_all.deb Size: 91370 MD5sum: e98abb4af0e218be2b16ded8a8665672 SHA1: f6286ec880947d0b4a85682ade14d320230f57e2 SHA256: c276d7fbf2ecac499ee46f022b6f0aef9da40df6d92d1428fdc268d7c95e540f SHA512: 3288aedcbddd91df597505a01574aad401c83845af775172797a7b13f4ea49f81d1963d1fb8f5cfad9d6915e05333c9af894cef6a30e0c1ceca670be0cb62129 Homepage: https://cran.r-project.org/package=RiskyCNV Description: CRAN Package 'RiskyCNV' (Risk Analysis of Genomic Copy Number Variation) Provides a complete seven-step workflow for copy number variation (CNV) analysis applicable to any disease or condition where samples with genomic copy number data is available. Supports built-in grading and risk stratification presets for seven major cancers (viz. prostate, breast, colorectal, lung, cervical, lymphoma, melanoma) based on clinically validated systems including ISUP Grade Groups, Nottingham Grading System, Dukes staging, IASLC TNM, FIGO, Ann Arbor/Lugano classification, and Breslow depth. Generalizable to other disease types. An automatic mode derives a normalised Risk Score from the data using min-max normalisation and adaptive binning. Custom user-defined thresholds are supported for any other disease type. Downstream functions for CNV aberration detection, recurrence analysis, gene annotation, CNV matrix generation, and CNV-RNA expression correlation are disease-type agnostic. Package: r-cran-riskyr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4331 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-riskyr_0.5.0-1.ca2404.1_all.deb Size: 2737360 MD5sum: 7447d37a088522af33d056c371a2afb9 SHA1: f58f5afc1274ad00a0e7d7b79b7cdc4becdb6d27 SHA256: e7434ae2871a9a79d0718fff707c806e9e057773aa1709fbefbd58b149e230b4 SHA512: 2befdb466400c4e297f95c9a55197a4602fc45482904c6830dc435438bce89be9cc35b80c7a568a658d831c057d8f500e08865371c03afd8c9efdeb6c270bfcc Homepage: https://cran.r-project.org/package=riskyr Description: CRAN Package 'riskyr' (Rendering Risk Literacy more Transparent) Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see , for details). 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Monitor changes in representativity during data collection. Improve representativity through adaptive survey design. Supports both R-indicators and coefficients of variation. See also Schouten, Cobben, Bethlehem (2009) , Shlomo, Skinner, Schouten (2012) and Schouten, Shlomo (2017) . 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Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. . . 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Package: r-cran-riverbuilder Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1170 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-riverbuilder_0.1.1-1.ca2404.1_all.deb Size: 1111278 MD5sum: 089d4b5b8bf1d177ff1cad600af59112 SHA1: 950b41631f95ed2c42933f2e1ed3c12c7fe7f7e5 SHA256: 69def22186ca708d5f5820beb88deb61ad1414c8b3d90d6f4ab3e086ff1da0f3 SHA512: 7647cd0a61cb4f68df2c06217f6cb6b2b8ef7763512f91affc106fb0632ba5a6bded5824fbd7b1608993c2cfe2a5fc6cc86a950312c5d59d830089d60da3d8ad Homepage: https://cran.r-project.org/package=RiverBuilder Description: CRAN Package 'RiverBuilder' (River Generation for Given Data Sets) Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function. 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For a review, see Jumani et al. (2020) and Baldan et al. (2022) Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included. 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Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software. 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Define derivations in 'R', 'Python' or 'Julia', chain them into a composition of pure functions and build the resulting pipeline using 'Nix' as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the 'targets' package. 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Package: r-cran-rjd3bench Architecture: all Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rjava, r-cran-rjd3jars, r-cran-rjd3toolkit, r-cran-rprotobuf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rjd3bench_3.1.3-1.ca2404.1_all.deb Size: 879704 MD5sum: 081762ca867d2c76d15fd89c1dce7894 SHA1: 268952185a0d1e44c78794219cb58de4c39ced6c SHA256: ecd5286a1ed0d7f7769d702cc2351f4469ce76b37283c17d98debd783e875475 SHA512: 3ddab0e4d0201779e04280ef650251c7223284aef6dde1a4eec931fe7e3181bb5b93ff1f0bf24c8a2d485be7e3f8ad7edb4d85b22ee31e2a49ac54c7522bddbb Homepage: https://cran.r-project.org/package=rjd3bench Description: CRAN Package 'rjd3bench' (Temporal Disaggregation and Benchmarking in 'JDemetra+' 3.x) Interface to 'JDemetra+' 3.x () time series analysis software. 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Streamline your data analysis workflows by importing datasets effortlessly and focusing on insights rather than manual data handling. Perfect for data enthusiasts and professionals looking to integrate Kaggle datasets into their R projects with minimal hassle. 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Package: r-cran-rkin Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ks, r-cran-sf, r-cran-ggplot2, r-cran-mass, r-cran-rcolorbrewer, r-cran-randomcolor, r-cran-shades, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rkin_1.0.4-1.ca2404.1_all.deb Size: 100948 MD5sum: 1af2306f7aa76aae79d76377ae3239fd SHA1: 49ed0cc0e0f7f36048b9ee4b27a5e4c48c584c1f SHA256: ceefefea3753e914893d5aeba40a60ef11bb3f089e091feaef74843e5620c5c6 SHA512: 8709cacb3ffd7a56ca9a1a012942c9263d437206a4982b0b18ae83ba2366b1706f1609872962c5393ab3480238945085f3f5810afa264b41edcc31309b44ac46 Homepage: https://cran.r-project.org/package=rKIN Description: CRAN Package 'rKIN' ((Kernel) Isotope Niche Estimation) Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities. Package: r-cran-rkmetrics Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rkmetrics_1.3-1.ca2404.1_all.deb Size: 97118 MD5sum: f52d449cfc67a1716b5dec6a2246ce4e SHA1: 595cf68b32b445c4fb4e1633dc190a6d22f9ad70 SHA256: 8ba4ef5b752aaf852399af5873cdbabc8f3bcbd041922d5514817d57ec152135 SHA512: 2ca4ff2241d058520845f86b3ee4456aa177ead532886a28700c1b62ca5dcfd4237e92be39a3ea595aaad4903b10b185d4b45ad64c0d281bdc61a3866685cdbc Homepage: https://cran.r-project.org/package=RkMetrics Description: CRAN Package 'RkMetrics' (Hybrid Mortality Estimation) Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality. Package: r-cran-rkolada Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-ggplot2, r-cran-scales, r-cran-progress Filename: pool/dists/noble/main/r-cran-rkolada_0.3.1-1.ca2404.1_all.deb Size: 689670 MD5sum: 9e6c623ffa14baadf425c4b87f3e8c1c SHA1: 3b872a42e243b73e14bc98c4fccd0a64c4e025fe SHA256: 814681df0c63675b86a252778ccdee47402d7e924eb31c0296ef49a13d59f2d4 SHA512: 7a9d032cfa8ab0e39d582df5ed801d89a3995432a8ae22038fd7fd21bd59c36adcf51ab956c528424576ea05d7bb4616e97a4b3cbd794b27842f49bf92310160 Homepage: https://cran.r-project.org/package=rKolada Description: CRAN Package 'rKolada' (Access Data from the 'Kolada' Database) Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database . Package: r-cran-rkomics Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24870 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ape, r-cran-circlize, r-bioc-complexheatmap, r-cran-reshape2, r-cran-dplyr, r-cran-factoextra, r-cran-factominer, r-cran-ggpubr, r-cran-magrittr, r-cran-stringr Suggests: r-cran-viridis Filename: pool/dists/noble/main/r-cran-rkomics_1.3-1.ca2404.1_all.deb Size: 1770044 MD5sum: fb53cd803e4daaabe38fa4d5e8f216e4 SHA1: 83da1d4744b9426491f87aae01c5b8d6f7b9fc78 SHA256: 29e9bde29d07ef9562077b66c9b38696a507ee7891131da5d91a9d63e9968bee SHA512: 9368e82bebaa41163087a5bd0d5a709afbd6be46dab9c8d3943ab90d8bd7a9c32d4d5d678deaf84d02a3a78837a011518895dd0801b7d6b671bf05c2e1382ffe Homepage: https://cran.r-project.org/package=rKOMICS Description: CRAN Package 'rKOMICS' (Minicircle Sequence Classes (MSC) Analyses) This is a analysis toolkit to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies. The rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history. ***** !! WARNING: this package relies on dependencies from Bioconductor. For Mac users, this can generate errors when installing rKOMICS. Install Bioconductor and ComplexHeatmap at advance: install.packages("BiocManager"); BiocManager::install("ComplexHeatmap") *****. Package: r-cran-rkorapclient Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 934 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r.cache, r-cran-broom, r-cran-ggplot2, r-cran-tibble, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-lubridate, r-cran-highcharter, r-cran-jsonlite, r-cran-keyring, r-cran-httr2, r-cran-curl, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-urltools, r-cran-xml2 Suggests: r-cran-lifecycle, r-cran-testthat, r-cran-tidyllm, r-cran-htmlwidgets, r-cran-rmarkdown, r-cran-shiny, r-cran-vcd, r-cran-kableextra, r-cran-knitr, r-cran-purrrlyr, r-cran-raster, r-cran-tidyverse, r-cran-sf, r-cran-geojsonsf, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-rkorapclient_1.4.1-1.ca2404.1_all.deb Size: 730878 MD5sum: 67c8753b9b7c8930fd8aeeb079649ced SHA1: 8d0587350bf97b5b643efd7261f6dd6ab3261658 SHA256: 0ebd03e0f91ba40700378f0ecc3fdbc459ffd657f349b30b10302843ea188972 SHA512: a8235f1145e56bdfc98340425be4a453247cc212e40d9d4f475877038eda1b8d703efcce750166e0d424735327053eb1e9c933849e4e08128f7cb071962aed40 Homepage: https://cran.r-project.org/package=RKorAPClient Description: CRAN Package 'RKorAPClient' ('KorAP' Web Service Client Package) A client package that makes the 'KorAP' web service API accessible from R. The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at . 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See for more information. The original Mapzen has gone out of business, but 'rmapzen' can be set up to work with any provider who implements the Mapzen API. 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It outputs the properties of all detected events and exceedances. Package: r-cran-rmark Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2659 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixcalc, r-cran-msm, r-cran-coda Suggests: r-cran-lattice, r-cran-nlme, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-rmark_3.1.0-1.ca2404.1_all.deb Size: 1436250 MD5sum: 5158011948f23e7654b64cd3322b6aa2 SHA1: 98855d9424e9a8611de87e7fb272b656a7dd18b1 SHA256: fff9f8daa205d6cd1b3b463a3592e6ac358dba616650a56c4527e9d9e14cfc4e SHA512: 623efdecd2defbb11653e118937baf4ff92030a1a1357d4145e3be8999e76ff0c81c137dc2ec2f700542fda06d60048a4524e7d6c57dfeeae70b8f8b44ff42c5 Homepage: https://cran.r-project.org/package=RMark Description: CRAN Package 'RMark' (R Code for Mark Analysis) An interface to the software package MARK that constructs input files for MARK and extracts the output. 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Package: r-cran-rmass2 Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmass2_0.0.0.2-1.ca2404.1_all.deb Size: 44030 MD5sum: fb17a7441a3313d0f55bdef23b1c65db SHA1: fd96544f75bcef30b575fb29eb8097094c6063e3 SHA256: 26e67b07974bcd551c43bb570b1ed9606c845a48301a30b2af30d867f11458e9 SHA512: c8421c7d7741f9f870e37e5ad5bcf51ed3a8ff9cdc1a784221506dee870f93bcdfa4a6ab774383fe19528877fd11bd6690475b823366ff0c1f8e1a3b48580a08 Homepage: https://cran.r-project.org/package=rmass2 Description: CRAN Package 'rmass2' (Repeated Measures with Attrition: Sample Sizes and Power Levelsfor 2 Groups) For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) . Package: r-cran-rmat Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-rmat_0.2.0-1.ca2404.1_all.deb Size: 54276 MD5sum: b783869e9e624baadeadb11893d7a840 SHA1: 91c60c73aae37fd484f4b52f12303a4e583ac0b9 SHA256: c5b3ba0b3151827361a11fd5c5a9da57cc46581da989bde6f8f5cf5b8c2a150f SHA512: 67b66f073b9fe4d3e7ac9c415423c13f660de8131664c30c5d7a14e128a240b724598ba7ad65a1f3828a6832aabd4d7655b3a78a304f07b53e379116dc39cb8c Homepage: https://cran.r-project.org/package=RMAT Description: CRAN Package 'RMAT' (Random Matrix Analysis Toolkit) Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions. Package: r-cran-rmawgen Architecture: all Version: 1.3.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2007 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-date, r-cran-vars, r-cran-matrix Suggests: r-cran-lubridate Filename: pool/dists/noble/main/r-cran-rmawgen_1.3.9.3-1.ca2404.1_all.deb Size: 1986580 MD5sum: 4847aa4555ac695140e95c773385155a SHA1: 4fdcb15e554b998098551977f7687f0c6ba49ca5 SHA256: b029c3cc5afd910e0010907986753d7d2f9858f2f7a0b2f46b044f83fdcd4f19 SHA512: d3c978f3ef1163ff0feec3d5968a38eda5028e7c12a8a6ea56519e16cf091425086cc065119c993c6ed5b3ca7a422c6c63bd349237775ede9bd3866b346468fc Homepage: https://cran.r-project.org/package=RMAWGEN Description: CRAN Package 'RMAWGEN' (Multi-Site Auto-Regressive Weather GENerator) S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the 'vars' package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series. Package: r-cran-rmbc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ktaucenters, r-cran-mvtnorm, r-cran-mass Suggests: r-cran-tclust, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmbc_0.1.0-1.ca2404.1_all.deb Size: 56462 MD5sum: 461cf48742bb318e1cfe258f5da86243 SHA1: bd64b55fc55bbaa5dc4076ea70fbc2e827594162 SHA256: fed00dd26af83c31692a214e156708dc6893e8a8bdd5f4c1b740aa58705bd8c8 SHA512: 5fb2b713f75ef44dd45a53589e41a27aee41815c34f64d545bd30fce3f52df7f6bc22f9e0c75c34f07fdbc6e921f3302e6eedbafab967c6114991822d3f60cff Homepage: https://cran.r-project.org/package=RMBC Description: CRAN Package 'RMBC' (Robust Model Based Clustering) A robust clustering algorithm (Model-Based) similar to Expectation Maximization for finite mixture normal distributions is implemented, its main advantage is that the estimator is resistant to outliers, that means that results of parameter estimation are still correct when there are atypical values in the sample (see Gonzalez, Maronna, Yohai and Zamar (2021) ). Package: r-cran-rmcda Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-fmsb, r-cran-lpsolve, r-cran-matlib, r-cran-nloptr, r-cran-matrixstats, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmcda_0.3.1-1.ca2404.1_all.deb Size: 270422 MD5sum: bd72431ad8243402657547fee9fc6c3a SHA1: 9c5bba6fd6c092100da291324236af80011967bd SHA256: 53ee02a78082223b6598e970e325161a6b8d41321faf9d89b0182cb1c5dbc0b3 SHA512: 3bb63df1233708a1283441e8ad7f199c1ef73ad994f3b912e4d961a676569249a8d44f9abbdb92770dccb3bc8fd2ee9f45f0df15412418d54cafeda72f42b5df Homepage: https://cran.r-project.org/package=RMCDA Description: CRAN Package 'RMCDA' (Multi-Criteria Decision Analysis in R) Provides different methods of multi-criteria decision analysis. Package: r-cran-rmcfs Architecture: all Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4940 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-yaml, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-dplyr, r-cran-stringi, r-cran-igraph, r-cran-data.table Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rmcfs_1.3.6-1.ca2404.1_all.deb Size: 4683468 MD5sum: 501b1fb7a7d726c60772136bd349531f SHA1: 9a1596370c0f6675fd238c2f24d7b3ac38764b82 SHA256: 02dbb168953b31e3655d100e612be1d53e4a0f6e2ef6d18e376ed748a046b009 SHA512: 2738fe184c42fe1e07938359abc5aa626edfdcf4a62b72ec4372bac071e9e9d516c4835232cc7a4e9b04efbd82b5d02e1b1e08b33201e7aeaac419cf367bc907 Homepage: https://cran.r-project.org/package=rmcfs Description: CRAN Package 'rmcfs' (The MCFS-ID Algorithm for Feature Selection and InterdependencyDiscovery) MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) . Package: r-cran-rmchsptt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmchsptt_0.1.0-1.ca2404.1_all.deb Size: 71226 MD5sum: 2727b0454d118264db221c2a2c632b99 SHA1: c5449c165fde560c76137b90cd3651f121063e72 SHA256: 60fe5e4849554d1f0b2663dc641234945177f942f73815bdb6bc7229a2311bea SHA512: 0daae654c60b3ddfc03110569380c4d111e0c614d49abdc5b084183b0752e97ef1b27768134e55d363cfad46a7b94a225d957d453f2d4f35a41fd74f7d568821 Homepage: https://cran.r-project.org/package=rMCHSPTT Description: CRAN Package 'rMCHSPTT' (Modified Chain Sampling Inspection Plan for Time-Truncated LifeTests) Designing and comparing modified chain sampling inspection plan (MChSP). This package implements ChSP-1, MChSP-1, Multiple Dependent State Sampling (MDS), and Modified Chain Sampling plans (MChSP). The plans use user-supplied failure probabilities and determine the minimum sample size subject to consumer's risk constraints. Functions are provided to compare the required sample sizes of the four plans and visualize their performance. The distribution-free formulation allows the methods to be used with different lifetime distributions. Luca (2018) ; Tripathi et al. (2021) ; Tripathi et al. (2023) ; Rao et al. (2025) ; Tripathi and Saha (2023) . Package: r-cran-rmcmc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-withr Suggests: r-cran-knitr, r-cran-posterior, r-cran-progress, r-cran-ramcmc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmcmc_0.1.2-1.ca2404.1_all.deb Size: 288416 MD5sum: b0ab88e2ecc16c533e9aca38ea2b66fa SHA1: 96a5bc03d93a54de72edbdd9ba346af977285eb0 SHA256: 6841913003d6978dd84a9ca8f3310c8044241adff0fc34e4279bf277db97b30a SHA512: 3a749671b49a24431ead839957801df1af3688a9c11e0d3d24012bafca1f05d65c2dff312e371b9a2431f49c1314419c82d5ae74c64975cf372e4f3b18252004 Homepage: https://cran.r-project.org/package=rmcmc Description: CRAN Package 'rmcmc' (Robust Markov Chain Monte Carlo Methods) Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) , is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface , available on GitHub , is installed, then 'BridgeStan' can be used to specify the target distribution to sample from. Package: r-cran-rmcorr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plotrix, r-cran-lme4, r-cran-mertools, r-cran-pwr, r-cran-aiccmodavg, r-cran-pals, r-cran-testthat, r-cran-vdiffr, r-cran-corrplot, r-cran-cocor, r-cran-covr, r-cran-ggextra, r-cran-gglm, r-cran-dplyr, r-cran-esc, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-rmcorr_0.7.0-1.ca2404.1_all.deb Size: 1470460 MD5sum: 8e6dea3c364eaa51d89ab1436718bec6 SHA1: 50e75c8c55159a418c2273d765b14c34ec1fc0fe SHA256: 911631135dc0d2187f0755df8fb0278ef9dbaea8e5b3b5b501ec3d91f03e4957 SHA512: 0d4f96c49041aadef1e150cdb24ddf8dafeca07fa902e697fbb55bc1f6ac5f426135b852fa7663a2ddedb0acae583fd45194fc0c14c28051efe6d34240878b12 Homepage: https://cran.r-project.org/package=rmcorr Description: CRAN Package 'rmcorr' (Repeated Measures Correlation) Compute the repeated measures correlation, a statistical technique for determining the overall within-individual relationship among paired measures assessed on two or more occasions, first introduced by Bland and Altman (1995). Includes functions for diagnostics, p-value, effect size with confidence interval including optional bootstrapping, as well as graphing. Also includes several example datasets. For more details, see the web documentation and the original paper: Bakdash and Marusich (2017) . Package: r-cran-rmda Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape, r-cran-pander, r-cran-mass, r-cran-caret Filename: pool/dists/noble/main/r-cran-rmda_1.6-1.ca2404.1_all.deb Size: 110436 MD5sum: 824dbc973f79e8133dad683e2a5d1317 SHA1: c3cdd7e0462f27d59c4f3ae4ed781edf4451adac SHA256: 10bb552e48222d6d50669927990ad0fceabbf26ba9087db6ebbdb2bc7668ac79 SHA512: e10e15c6cb590245ad9d489e8cd5bcc97cd8fbb007c816ee4668ff72d8b3f43a2c011b5589d6d93b4e5b4bd717dc7cceb2770932c795c41526b97d06b2fb4f06 Homepage: https://cran.r-project.org/package=rmda Description: CRAN Package 'rmda' (Risk Model Decision Analysis) Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention). Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making. Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies. Curves can be estimated using data from a prospective cohort. In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available. Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided. 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This currently allows the original source location in the '.Rmd' file of errors detected by 'HTML tidy' to be found more easily, and potentially allows forward and reverse search in 'HTML' and 'LaTeX' documents produced from 'R Markdown'. The 'LaTeX' support has been included in the most recent development version of the 'patchDVI' package. 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Package: r-cran-rmdhelpers Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-rmdhelpers_1.3.1-1.ca2404.1_all.deb Size: 38050 MD5sum: 56a6d1d838e20437210b9e873ea801f7 SHA1: 14dd97949eeeed940e3c80504de0b34c5bfdea2c SHA256: 7d1c7a6c49729f40c13b84f65ac8d0fef21db6f772ab3d99337d950753c06aef SHA512: ef5f2deff70665c35f2082b0647596bcc186e2362cb01bc3f3d21c6f251807ff9c2022512056fbed5e2800c001022382cdb52f6e843ad64cae0db8ddc661f832 Homepage: https://cran.r-project.org/package=rmdHelpers Description: CRAN Package 'rmdHelpers' (Helper Functions for Rmd Documents) A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. 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This system includes the addition of formal attributes that modify base `R` objects, including terms and formulas, with a focus on variable roles in the "do-calculus" of modeling, as described in Pearl (2010) . For example, the definition of exposure, outcome, and interaction are implicit in the roles variables take in a formula. These premises allow for a more fluent modeling approach focusing on variable relationships, and assessing effect modification, as described by VanderWeele and Robins (2007) . The essential goal is to help contextualize formulas and models in causality-oriented workflows. Package: r-cran-rmdpartials Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-knitr, r-cran-rlang Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rmdpartials_0.6.5-1.ca2404.1_all.deb Size: 48728 MD5sum: 06814ea3446e1fa928f06dcf79da1bc3 SHA1: a6ed54fefbc582e5814850a9d57c344878a07f02 SHA256: 699342a4b238c68575439b28057273b5051692f395cf49827e1fe4ac2b67d5e7 SHA512: d37711d906fae4caad27df2f924ed10e86b9dec00be446537a7e28b208ff9de97f95be722a9630237b6c3c3d0086b7f98e4c60a1237c091c2212ab54386b6840 Homepage: https://cran.r-project.org/package=rmdpartials Description: CRAN Package 'rmdpartials' (Partial 'rmarkdown' Documents to Prettify your Reports) Use 'rmarkdown' partials, also know as child documents in 'knitr', so you can make components for HTML, PDF, and Word documents. 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Package: r-cran-rmdplugr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown, r-cran-bookdown Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-knitr Filename: pool/dists/noble/main/r-cran-rmdplugr_0.4.1-1.ca2404.1_all.deb Size: 295390 MD5sum: 5a2b9fb693184284c0e5e30252b04983 SHA1: 3647abe5022f5dfbe9ced8adcacf0855ecd7dac3 SHA256: a88834ffdc5d097b03b22f311fba20dedb9cd18eec96aca4922f06588de39870 SHA512: 821ebdf046f1943ea1ef56e66728158f95a2582e6cfbaece176fd4479c99ad3c10bea1f1b09db9cf66c358849e7ab896863ad130908d62afc1c3e6a24e583528 Homepage: https://cran.r-project.org/package=rmdplugr Description: CRAN Package 'rmdplugr' (Plugins for R Markdown Formats) Formats for R Markdown that undo modifications by 'pandoc' and 'rmarkdown' to original 'latex' templates, such as smaller margins, paragraph spacing, and compact titles. 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The package determines the minimum sample size required to satisfy a specified consumer's risk constraint and evaluates the probability of acceptance under different quality and termination ratios. Users can directly provide failure probabilities, allowing the sampling plan to be applied to different lifetime distributions without requiring distribution-specific functions. Provide operating characteristic analysis, sample size analysis, and graphical comparison with single sampling inspection plans. Aslam et al. (2016) ; Rao et al. (2020) ; Balamurali et al. (2017) ; Saha et al. (2021) ; Tripathi et al. (2020) ; Tripathi et al. (2023) . Package: r-cran-rmdwc Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-knitr, r-cran-rstudioapi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmdwc_0.3.1-1.ca2404.1_all.deb Size: 38996 MD5sum: e2d3a3899410b043ca499e1e490f7346 SHA1: f2e98a011209b0330230915e2b75c01085591504 SHA256: 06e81c32c49bbd0b0db0cf4f345c7ebdd47fd9e42158a6f83ad09cdfb31aea17 SHA512: 086787a44f28772e12d285fec1ef629af05604b5b721553e2c9ed727ba80c6eb4e69bf0075e9da5e272d091d1cb7bc7944bdcc674e5adb3259d5abc3368710e9 Homepage: https://cran.r-project.org/package=rmdwc Description: CRAN Package 'rmdwc' (Count Words and Characters in R Markdown and Jupyter Notebooks) Computes word, character, and non-whitespace character counts in R Markdown documents and Jupyter notebooks, with or without code chunks. Returns results as a data frame. Package: r-cran-rmea Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2410 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmea_1.2.2-1.ca2404.1_all.deb Size: 938434 MD5sum: 6d04d2cdfa8f5833ceff06be64db0c5f SHA1: 9c1481b4a41144c85cbbc68e33afcef46f5ee4e3 SHA256: b51f15821539171b21e9d3f9e9ca5d47ea1a66f8cd9bf07a98ed72315bc8b39f SHA512: 422c1d98d4b8e1dc17cd3e7328df5e4138ca873c51d477896276b13376b536f2a38e6d162563c93aeffa0b4503197639a83e1a82d3b4d89cb502b310ae23dfe5 Homepage: https://cran.r-project.org/package=rMEA Description: CRAN Package 'rMEA' (Synchrony in Motion Energy Analysis (MEA) Time-Series) A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. . Package: r-cran-rmediation Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cubature, r-cran-lavaan, r-cran-mass, r-cran-s7 Suggests: r-cran-knitr, r-cran-medfit, r-cran-openmx, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmediation_1.6.1-1.ca2404.1_all.deb Size: 996544 MD5sum: a7895f86eb92a96d5ed22b5944f1512b SHA1: 86897eb3d019498eaede1184f99b27e8edb6baca SHA256: 837d1d56485da687a125be85fcebd17d3dee14985442101c246a7f4b9a1fa8f5 SHA512: 6efaab4177b984defb21e1ccdaf088847330a9eb007565a86d0c79da2bc58d8e2b25d9bee796a6014f3c0c33c949107725f90d38ad301765a7fd364e2d6b19c3 Homepage: https://cran.r-project.org/package=RMediation Description: CRAN Package 'RMediation' (Mediation Analysis Confidence Intervals) Computes confidence intervals for nonlinear functions of model parameters (e.g., product of k coefficients) in single-level and multilevel structural equation models. Methods include the distribution of the product, Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based Constrained Optimization (MBCO) procedure for hypothesis testing of indirect effects. References: Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692-700. ; Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods, 25(4), 496-515. ; Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. . Package: r-cran-rmedpower2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8607 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-dplyr, r-cran-simr, r-cran-magrittr, r-cran-ggplot2, r-cran-ggtext, r-cran-quantreg, r-cran-tibble, r-cran-lmertest, r-cran-dharma, r-cran-influence.me, r-cran-envstats, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmedpower2_1.0.2-1.ca2404.1_all.deb Size: 5398182 MD5sum: ca228b77b4eb60f1042a2948f97c404d SHA1: ed56e88e39c927848f2643646a0e437219227f0d SHA256: 95858bec170ab0838d360285b849ace3512b47e2688b2af96b4fc9eccb372918 SHA512: 51301eafd1401968b29a0a7ef7ece61560f6c66714d2b9965683fca1f04930ab3da075f2770a850e71407c6e002195cce1c9e8d20f097907bcc50bfafd50fb3c Homepage: https://cran.r-project.org/package=RMeDPower2 Description: CRAN Package 'RMeDPower2' (Design and Modeling for Repeated Measures Studies) Provides complete functionality to analyse data from repeated measures experiments with hierarchical or crossed experimental designs. Supports testing modeling assumptions, identifying outlier observations and experimental units, estimating statistical power, and performing sample size calculations. Uses linear mixed effects models via 'lme4' and simulation-based power analysis via 'simr'. Handles both normal and non-normal error distributions including binomial and Poisson families. For more details see Shin et al. (2022) , Bates et al. (2015) , Green and MacLeod (2016) , Hartig (2024) , Nieuwenhuis et al. (2012) , Millard (2013) and Kuznetsova et al. (2017) . Package: r-cran-rmedsem Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-mvtnorm, r-cran-ggplot2 Suggests: r-cran-blavaan, r-cran-csem, r-cran-hdinterval, r-cran-modsem, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmedsem_1.1.0-1.ca2404.1_all.deb Size: 228760 MD5sum: 7cb12a928dff51a98327687b4a93ee37 SHA1: bcf6b41f7f657e9a800c0e65ef4eaee3ddaafaea SHA256: 9aa4b9df094720579a0743d2912a68464bc6e0a0419573d421347254e72bc38d SHA512: b02d0f16f66140a9848327d8743e210d11549ac7b7ed9c68c935ea2d23a3f762ed50c565b9ec32e3df2a244fa4f8589decf8bc8e0c2e3fdd4fce3af03840b819 Homepage: https://cran.r-project.org/package=rmedsem Description: CRAN Package 'rmedsem' (Statistical Mediation Analysis for SEMs) Conducts mediation analysis for structural equation models (SEM) estimated with 'lavaan', 'blavaan', 'cSEM', or 'modsem'. Implements the Baron and Kenny (1986) and Zhao, Lynch & Chen (2010) approaches to determine the presence and type of mediation. Supports covariance-based SEM, partial least squares SEM, Bayesian SEM, and moderated mediation and mediated moderation models. Tests indirect effects with the Sobel, Delta, Monte-Carlo, and bootstrap methods or, for Bayesian models, with posterior summaries and equal-tailed or highest density credible intervals. Reports the effect size measures RIT, RID, and Upsilon of Lachowicz, Preacher and Kelley (2018) . Results can be summarized, extracted with standard methods such as summary(), coef() and confint(), and plotted. Package: r-cran-rmerec Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmerec_0.1.1-1.ca2404.1_all.deb Size: 14010 MD5sum: e754ef7d4c3ee61a58eec80cae92955f SHA1: 3b0a61867ceb483cb08aaf15ff34e9721c3e8c33 SHA256: 22938d74336fe03af7b6ec9807057b9b28e3119ef84144ab1359fd006c4cc0e4 SHA512: efd4ce03f73ba566943a0bc501c3b602326ebc756f97c819ae7e1583a3888a83ae45c2c6cd21c975f103fa6ae61ceabf72cbcaec32bf728ed7944744332a5c36 Homepage: https://cran.r-project.org/package=rmerec Description: CRAN Package 'rmerec' (MEREC - Method Based on the Removal Effects of Criteria) Implementation of the MEthod based on the Removal Effects of Criteria - MEREC- a new objective weighting method for determining criteria weights for Multiple Criteria Decision Making problems, created by Mehdi Keshavarz-Ghorabaee (2021) . Given a decision matrix, the function return the Merec´s weight vector and all intermediate matrix/vectors used to calculate it. Package: r-cran-rmet Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmet_0.2.0-1.ca2404.1_all.deb Size: 358648 MD5sum: 4667698f4a140b53be14870fcc05dea4 SHA1: a1fcc78e2af61285472a3066888ab7276dacabd2 SHA256: b2de403608e19b62b304c62092c312ec850565ba8533c9340693dfd1814a20a3 SHA512: 6a0a6acbefe2e531d529cbe6e08774e54fd10618b96ee24b6fefd1442951cdfec945c16fee06eb89a4678e93fcdaeb65176038e345753492a1b3ca9a06e982ad Homepage: https://cran.r-project.org/package=rmet Description: CRAN Package 'rmet' (Download and Read Brazilian Meteorological Data from INMET) Automates the download and processing of historical weather data from the Brazilian National Institute of Meteorology (INMET). It provides a cached catalogue of automatic stations, resumable and validated downloads, and parsers for formatting inconsistencies in raw CSV files across different years. It removes structural artifacts, standardizes column names, parses timestamps, and returns data frames ready for analysis. Data are retrieved from and . 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This package provides functions to build these distributions from raw data. Resulting metalog objects are then useful for exploratory and probabilistic analysis. Package: r-cran-rmfanova Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-mass, r-cran-refund Filename: pool/dists/noble/main/r-cran-rmfanova_0.1.0-1.ca2404.1_all.deb Size: 61246 MD5sum: 7ed811d299d2a54e14206f5127c6bdca SHA1: e44b1ec22fadc2fe66d576445f5014bafd83221a SHA256: 7643ab81d8465449b250ab9d327070ea9695e3032a5a235c9233d640aee44d4b SHA512: 21c3a6a7bc207eec91089b197d496b6394df108c288831dae0a0afefc1bb18e5b4d081cc46f7b26bbbc09bcfba4176c48eb768fd2e09c53127b5f62f1d1eec45 Homepage: https://cran.r-project.org/package=rmfanova Description: CRAN Package 'rmfanova' (Repeated Measures Functional Analysis of Variance) The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, ). 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Package: r-cran-rmfrac Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parallelly, r-cran-ggplot2, r-cran-zoo, r-cran-matrixstats, r-cran-proxy, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-fields Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmfrac_1.0.0-1.ca2404.1_all.deb Size: 316062 MD5sum: 49d1fdff75ffb135cedfd7fb90fe0486 SHA1: 5b74b4c611d0f52b07fc0dcf1dec9061bafbd940 SHA256: 398951b1ce3a3cdf0696880b15f78f2cd51b69bbf49877a2886978a518c0f4ff SHA512: 7a980fc24b84b07e8f70a08995926d8e4875f805993599f2ee0d154f4135f6ebfa41b52942e0d0a5164e24fc035e9d489d0797e6c044bbaad30e711a3448cf55 Homepage: https://cran.r-project.org/package=Rmfrac Description: CRAN Package 'Rmfrac' (Simulation and Statistical Analysis of Multifractional Processes) Simulation of several fractional and multifractional processes. Includes Brownian and fractional Brownian motions, bridges and Gaussian Haar-based multifractional processes (GHBMP). Implements the methods from Ayache, Olenko and Samarakoon (2026) for simulation of GHBMP. Estimation of Hurst functions and local fractal dimension. Clustering realisations based on the Hurst functions. Several functions to estimate and plot geometric statistics of the processes and time series. Provides a 'shiny' application for interactive use of the functions from the package. Package: r-cran-rmidas2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-processx, r-cran-rlang Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-reticulate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmidas2_0.2.0-1.ca2404.1_all.deb Size: 124746 MD5sum: 41a567cad24541eccabbe68dd8ed74fc SHA1: 1e2724c1b938373dd6e79afea15473931f84ffed SHA256: bbe15bd7db834ed5be216fc774b06c2af84b490ea86feb759692e006bd681a2d SHA512: 030ff18cc2c614205f0d345e995ded5f6b3f2faef4ff469c632d593bed7c883cf35dd8d1c997515291c0526b2d57f58e2c0734af0acde7026600a157cd902de2 Homepage: https://cran.r-project.org/package=rMIDAS2 Description: CRAN Package 'rMIDAS2' (Multiple Imputation with 'MIDAS2' Denoising Autoencoders) Fits 'MIDAS' denoising autoencoder models for multiple imputation of missing data, generates multiply-imputed datasets, computes imputation means, and runs Rubin's rules regression analysis. Wraps the 'MIDAS2' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. Methods are described in Lall and Robinson (2022) and Lall and Robinson (2023) . Package: r-cran-rmidas Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mltools, r-cran-reticulate, r-cran-rappdirs, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmidas_1.0.1-1.ca2404.1_all.deb Size: 160040 MD5sum: d3921f873c2a0cb46e7958358c357593 SHA1: d1f86ccd0996f49140fe6c74d8ce7f5a6fa970e4 SHA256: 1c670a2a78af0b03150c44755ec32e212c996da394a6a1778a10c750d5d98450 SHA512: 794c86270d30e04c55edd476a4ed4867c5b3a5b05670b6f3341f1969e4511f1a5828dfac2ec9e0e9d4ddd7d9a9a436cb8de1fb39d43b212050945145b28beaf8 Homepage: https://cran.r-project.org/package=rMIDAS Description: CRAN Package 'rMIDAS' (Multiple Imputation with Denoising Autoencoders) A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; ). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) . This package is deprecated in favor of 'rMIDAS2'; it remains available for existing workflows but will receive only compatibility and documentation updates. Package: r-cran-rminer Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1052 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-lattice, r-cran-nnet, r-cran-kknn, r-cran-pls, r-cran-mass, r-cran-mda, r-cran-rpart, r-cran-randomforest, r-cran-adabag, r-cran-party, r-cran-cubist, r-cran-kernlab, r-cran-e1071, r-cran-glmnet, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-rminer_1.5.0-1.ca2404.1_all.deb Size: 928524 MD5sum: 3b9883e0b296d829eced1500b824fe5e SHA1: 2937a514409633c3e87d6ba82a66d57b6486d351 SHA256: ea283abcc46c9b79b6220a044787fc4532e1b13b76d4dacf80cd2eb423be58c8 SHA512: 9d45384301be24bb5664b554f44ff15848548195ee446ad41e636bba7169f35181f36e6a934e1293562056db584097cb2505eb0d12369a9ac097632a79eab651 Homepage: https://cran.r-project.org/package=rminer Description: CRAN Package 'rminer' (Machine Learning Classification and Regression Methods) Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version. 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Package: r-cran-rmlnomogram Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2645 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-broom, r-cran-ggplot2, r-cran-ggpubr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-caret, r-cran-randomforest, r-cran-iml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmlnomogram_0.1.2-1.ca2404.1_all.deb Size: 1472176 MD5sum: 55f0b9b638831f90e0a95f122288e1c7 SHA1: 1fc697ae6fe77b8d6d6606d0bf27dff34043897f SHA256: 299451481d511524dafd53dcc0d3f104d61a84dd466bd54e1238d03f63f9e7d8 SHA512: 189ccdeb2b70057a4edb50671a3495decab232b3ec8cbe9793a9af24c64c0d2d55368cea581633292dab64efd9716697ff7562736565943cf0c9eb79928b653f Homepage: https://cran.r-project.org/package=rmlnomogram Description: CRAN Package 'rmlnomogram' (Construct Explainable Nomogram for a Machine Learning Model) Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at . 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Package: r-cran-rmost Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmost_1.0.1-1.ca2404.1_all.deb Size: 190092 MD5sum: 6ca0bd1bef2ba81623fc6deaa77817ee SHA1: f78e8fbd6af6ec4d07f827cfd8cff1d1f5dd153e SHA256: c5c6c753927ad618ee2817bfe988ab12b4b43c130599cb1b35849ba75fed6e92 SHA512: 0a577dbea8529ea61332ad1d96d49c30ab0561d61bf2c7ec7925518e3abaf301fd2d67c0b11a6a62431e6a8669f1bce46c7a2c93381f3b08a0d64022f012a04b Homepage: https://cran.r-project.org/package=rMOST Description: CRAN Package 'rMOST' (Estimates Pareto-Optimal Solution for Hiring with 3 Objectives) Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) . 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This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) . 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By restricting attention to models with at most M regressors, the package supports reduced model space inference, thereby preserving degrees of freedom for estimation. It provides posterior summaries, Extreme Bounds Analysis, model selection procedures, joint inclusion measures, and graphical tools for exploring model probabilities, model size distributions, and coefficient distributions. When the model space is too large to enumerate, it can be explored by Markov chain Monte Carlo model composition instead. The methodological approach follows Doppelhofer and Weeks (2009) and Madigan and York (1995) . 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The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in 'RMSD' package. See Wada and Tsubaki (2013) for the detail of the algorithm. Package: r-cran-rmsfact Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmsfact_0.0.4-1.ca2404.1_all.deb Size: 21750 MD5sum: f0e7e6518b45f342e1a7240ce428fdf8 SHA1: 292e283eb35627b107cb2f6b26d7768a89f0da81 SHA256: e228085ce9104c31e9aa8a881cae586a78db794154e2bad49959d7427b0551be SHA512: da9c9530ea6d86d51b8679be36a980f3710094ce15ec021c2c3f8d5370fc2c7917460b7fd879d39196231f30002c0ecc46020abec6c8366ad17e554949c708a3 Homepage: https://cran.r-project.org/package=rmsfact Description: CRAN Package 'rmsfact' (Amazing Random Facts About the World's Greatest Hacker) Display a randomly selected quote about Richard M. Stallman based on the collection in the 'GNU Octave' function 'fact()' which was aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site stallmanfacts.com (which is accessible only via ). 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The 'modelsummary_rms()' function produces concise summaries for linear, logistic, and Cox regression models, including automatic handling of models containing restricted cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into publication-ready documents using the 'flextable' and 'officer' packages. The 'ggrmsMD()' function creates clear and customizable plots ('ggplot2' objects) to visualise RCS terms. Package: r-cran-rmst Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rata, r-cran-reshape2, r-cran-rirt Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmst_0.0.3-1.ca2404.1_all.deb Size: 61822 MD5sum: 51b13e94b010bc5d96383c91427fd5fb SHA1: cb9725af55faf0cc3e356e124fd5d5962abce351 SHA256: f00f737cee91be33a1bb85f1bfaacea2b18dc286b1c6173a073bc9a40f3057a9 SHA512: 99797a0e060b29eb1d11e728b93d56cc0046abe3cf9e81b994a19d936facf9abe66f6cc1359c6eeb5190bba6c320450ff365c11ca81a17e243b52a99364261ef Homepage: https://cran.r-project.org/package=Rmst Description: CRAN Package 'Rmst' (Computerized Adaptive Multistage Testing) Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at . Reference: Luo and Kim (2018) . 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Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples. 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Package: r-cran-rmtl Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-psych, r-cran-corpcor, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmtl_1.0.0-1.ca2404.1_all.deb Size: 380682 MD5sum: fc66f0c324b98874ca5da6ee211c36cd SHA1: fb7e22d63eedc9d950b34e06493ab664acf6b01f SHA256: c274b97bd31938e9a41ce7fbc51591c8f6e39cede9bc5b27d5307b20f7d937c2 SHA512: e72b3af8cc02fc8522ef90d805b24bf5fcffdda73054c0764d6934aa4367fb48113eb3258640448363d1efb13ab497758332401a54a0965e8fbb804ee98d8de8 Homepage: https://cran.r-project.org/package=RMTL Description: CRAN Package 'RMTL' (Regularized Multi-Task Learning) Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. 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The tables for computing the Tracy-Widom densities and distribution functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram. 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The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) . 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The meteorological normalisation technique uses predictive random forest models to remove variation of pollutant concentrations so trends and interventions can be explored in a robust way. For examples, see Grange et al. (2018) and Grange and Carslaw (2019) . The random forest models can also be used for counterfactual or business as usual (BAU) modelling by using the models to predict, from the model's perspective, the future. For an example, see Grange et al. (2021) . 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The current version provides the plotPIccc() function, which extracts from the return object of the originating package all information required to draw an extended Person-Item-Map (PIccc), showing any combination of * category characteristic curves (CCCs), * threshold characteristic curves (TCCs), * item characteristic curves (ICCs), * category information functions (CIFs), * item information functions (IIFs), * test information function (TIF), and the * standard error curve (S.E.). for uni- and multidimensional models (as far as supported by each package). It allows for selecting dimensions, items, and categories to plot and offers numerous options to adapt the output. The return object contains all calculated values for further processing. Package: r-cran-rmytarget Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-lubridate, r-cran-stringr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmytarget_2.4.0-1.ca2404.1_all.deb Size: 201148 MD5sum: e0e7424198367d208d5a1bea4466c255 SHA1: 2a47a5221148117f96b19eb9e79f6cd676748421 SHA256: 2d2893d7b79f85cad0630a4f914218f05321c12cf94fc0eaebb43f0245af5e2e SHA512: 2d694a3f3999a9cdc25cca66bbdb2de0d925b5085fc537ef9e332c96e6663162a36a71772adfd4906d04a67e4d853f7b883d8103a58199febb47928634b7d25d Homepage: https://cran.r-project.org/package=rmytarget Description: CRAN Package 'rmytarget' (Load Data from 'MyTarget API v2 and v3') Allows work with 'MyTarget Statistics API v2' and 'MyTarget Statistics API v3' load data by ads, campaigns, agency clients and statistic from your ads account. 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The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information. The package is fully described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive" . Package: r-cran-rnumerai Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-lubridate, r-cran-jsonlite, r-cran-ghql Filename: pool/dists/noble/main/r-cran-rnumerai_3.0.1-1.ca2404.1_all.deb Size: 89592 MD5sum: e75f8b3238f6cbc3e0e21a0299444a49 SHA1: b378ba97081fe47e2e132d77751f395704380cdb SHA256: 54308b564b65e5c84545d4d10fe9dbc08e3e9018ad06bb74c35b46c49cac00f6 SHA512: 84235fc84d9bc457abc693766013992bb236bc2ed6206b91cebf5517d49fe9d0e81ebae65b7e07676e7f14fe686308e0ea434e597632dc48994a8d2010dc72a6 Homepage: https://cran.r-project.org/package=Rnumerai Description: CRAN Package 'Rnumerai' (Interface to the Numerai Machine Learning Tournament API) Routines to interact with the Numerai Machine Learning Tournament API . 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Package: r-cran-roaddb Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rpostgres, r-cran-rsqlite, r-cran-assertthat, r-cran-dplyr, r-cran-stringr, r-cran-glue Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roaddb_1.0.0-1.ca2404.1_all.deb Size: 172198 MD5sum: 15c0a17deef9dcde6c52ab2678d99f17 SHA1: 6211227aa3c63897289ad69b22f6010b9ba3270e SHA256: 27986895f576b0b1cbf0bbb90ec86c6702421af07065fcd0f992e87bf17e6ffa SHA512: 5abce0fb5f2755e75284d2030fb01e341b945fb734c74c67fa4a4a99c056802c38746f8e650a6190c438191209b1df4ef4d5597b4c40146f1a4b057507fc0d28 Homepage: https://cran.r-project.org/package=roadDB Description: CRAN Package 'roadDB' (Access Data from the ROCEEH Out of Africa Database (ROAD)) Provides an R interface to the ROCEEH Out of Africa Database (ROAD) (), a comprehensive resource for archaeological, anthropological, paleoenvironmental and geographic data from Africa and Eurasia dating from 3,000,000 to 20,000 years BP. 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Package: r-cran-roadoi Architecture: all Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-plyr, r-cran-purrr, r-cran-tibble, r-cran-miniui, r-cran-shiny, r-cran-tidyr, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-lintr Filename: pool/dists/noble/main/r-cran-roadoi_0.7.3-1.ca2404.1_all.deb Size: 261908 MD5sum: 12c0540df045930e57a2c28b75110ad0 SHA1: 750e54a3293c84fedacdc9ad02b3995251427e1e SHA256: dd5a0ad90d5677f91c0222655762d3c3cc66b2190b078d9b2a6aec1ca2745515 SHA512: 706d1c85ff48a5280a2e5588ebf433b159834cd910f005c6e4dc5a832a0d407780868cb6e5885ec23bb680a5b297938aa6bdd2a57af55e0e1841dec4948b8efe Homepage: https://cran.r-project.org/package=roadoi Description: CRAN Package 'roadoi' (Find Free Versions of Scholarly Publications via Unpaywall) This web client interfaces Unpaywall , formerly oaDOI, a service finding free full-texts of academic papers by linking DOIs with open access journals and repositories. 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Package: r-cran-robincar2 Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-numderiv, r-cran-mass, r-cran-sandwich, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-speff2trial, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robincar2_0.2.4-1.ca2404.1_all.deb Size: 230292 MD5sum: 1125322eca64a735b4c540e7acca60be SHA1: 1f2e398382420cfecda5fc2ae1c1f77146705670 SHA256: b109d1ed303a16509e71a3f89cb08d8f18065ab3b445fdc9e1f8395cc5371b7a SHA512: 5367fb9c324d774bfc1c87f4c4941c9042a8511ea3c7a875cf3d66fbb1d98e3f1a103aef27d7afce6d5021b35f1abec704b3a35a1b76b47e9919d231269faf50 Homepage: https://cran.r-project.org/package=RobinCar2 Description: CRAN Package 'RobinCar2' (ROBust INference for Covariate Adjustment in Randomized ClinicalTrials) Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials. This package is trimmed to reduce the dependencies and validated to be used across industry. See "FDA's final guidance on covariate adjustment", Tsiatis (2008) , Bugni et al. (2018) , Ye, Shao, Yi, and Zhao (2023), Ye, Shao, and Yi (2022), Rosenblum and van der Laan (2010), Wang et al. (2021), Ye, Bannick, Yi, and Shao (2023), and Bannick, Shao, Liu, Du, Yi, and Ye (2024). 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Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) . Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) . Ting Ye, Jun Shao, Yanyao Yi (2023) . Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) . Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) . 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Heagerty, Ting Ye (2025) . Package: r-cran-robinhood Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-lubridate, r-cran-profvis, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-uuid Filename: pool/dists/noble/main/r-cran-robinhood_1.7.0-1.ca2404.1_all.deb Size: 219348 MD5sum: 829346ab744419271b6b8e1824800eba SHA1: 46e812713525ac025995502e14930034a348ea72 SHA256: 8481c3608e48aeda6d8c1561c072056df40144475760f8daeb46330d1b3b7507 SHA512: abe3b2edbfed939ab7582c5e5627eed7b87e47a1fae6a0c9bf0f65349ee05208c686d9f7956ef44cafa8b08afbbebfadf4ae6c62d0e5c0bb42eadc1b56de8b61 Homepage: https://cran.r-project.org/package=RobinHood Description: CRAN Package 'RobinHood' (Interface for the RobinHood.com No Commission Investing Platform) Execute API calls to the RobinHood investing platform. 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Package: r-cran-roblox Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distrmod, r-cran-robastbase, r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biobase, r-cran-randvar, r-cran-distr Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-roblox_1.2.3-1.ca2404.1_all.deb Size: 425884 MD5sum: 82538a33f75324606ad5865e48c13875 SHA1: f4704d944e3385fb48e4d9c0406af15fe476b362 SHA256: 65d7f7b563e4dfef170a531d4823e2d97bdb7044087a9d0061d07838bf08980c SHA512: 74d3a5602b5f68266c25cc5dd7bc5a89fa328981fe42569335b4078a8d069b53feef8608e9b605c126fd5a40fc3f34fa806d5d430a00302672bbc52e757c8d76 Homepage: https://cran.r-project.org/package=RobLox Description: CRAN Package 'RobLox' (Optimally Robust Influence Curves and Estimators for Locationand Scale) Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) ). 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Package: r-cran-robmed Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-robustbase, r-cran-boot, r-cran-quantreg, r-cran-sn Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robmed_1.3.0-1.ca2404.1_all.deb Size: 969868 MD5sum: e39d3d90d25381f3163808a5ac0f12b4 SHA1: b22108c626bf1fdccff94d12a4cc6db613d12e60 SHA256: 2c12cf000a32779e1071cdaeb00666005f74d05d60d4953fb714064f7b4840f4 SHA512: 71f03af3ba6a01414381e133e89b378561d3c12d56352a445d2b4837ac8e97b3b1f0d4f4cffc7371216c702d4838baa50c3fc5c702297c8b67e894d19f7b049d Homepage: https://cran.r-project.org/package=robmed Description: CRAN Package 'robmed' ((Robust) Mediation Analysis) Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; ), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) . Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) . 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They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) . NeyKov N, Filzmoser P, Dimova R et al. (2007) . Bai X, Yao W. Boyer JE (2012) . Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) . Package: r-cran-robnptests Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1676 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-gtools, r-cran-robustbase, r-cran-statmod, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-covr Filename: pool/dists/noble/main/r-cran-robnptests_1.1.0-1.ca2404.1_all.deb Size: 489088 MD5sum: f9f8e3a2af996c60d8a3871d36a1edc1 SHA1: 54bafa40a43f4ebef01078fe97ee226b95f6a332 SHA256: d0baac31e994f1c65766ac8e513b37d69300a544d5cb4f03bcc182ca7cbee607 SHA512: d8366ef15995e8cf7ab27d21f89f3e313c91fe238ee2e2fdffee1505df1ed05b5dfdbc7cf3073f80c89d810a239f61020480edc120b9f5f9a0b78e43ec5c332f Homepage: https://cran.r-project.org/package=robnptests Description: CRAN Package 'robnptests' (Robust Nonparametric Two-Sample Tests for Location/Scale) Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) . The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) . Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero. Package: r-cran-robomit Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plm, r-cran-dplyr, r-cran-ggplot2, r-cran-broom, r-cran-tidyr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robomit_1.0.7-1.ca2404.1_all.deb Size: 102530 MD5sum: 54f74942455180ccf6a1ee76dc1a7d61 SHA1: 9a640f6e0e25303fa3ffd9cd29cd90bb33246c45 SHA256: 6a81b092d8819088faf44e75cc33b9174ef75eb6e2dac498c3522652b75c357e SHA512: abbcac6ab44b53e9414b24af96fe10b1fe8e4d2518190250820e14f10d88e2067ad68743841743c2c47d97aab4aa41c91bed7efe139a0ce468d1210e026f4133 Homepage: https://cran.r-project.org/package=robomit Description: CRAN Package 'robomit' (Robustness Checks for Omitted Variable Bias) Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The 'robomit' package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the 'psacalc' command in 'Stata'. Additionally, 'robomit' offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. . Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: . Package: r-cran-robosrmsmote Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov, r-cran-meanshiftr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-robosrmsmote_1.0.0-1.ca2404.1_all.deb Size: 39758 MD5sum: 4657a11751c10150b7d2b0d2a9c38049 SHA1: e185ce08ccb6a54b8a64e0ba0aff965f7154a4a2 SHA256: aa924b6ef71b45c7611bb5431c3454d9a4fa6bda5bc3d9dd8769752ef7f15707 SHA512: e3abbea11818baaf0bfa3a76d552779724d838b898230858894a3f3c14799e31987b0645d10e498c76dcebfd611d4e822276caef7881a2cfaf04140a7172ed08 Homepage: https://cran.r-project.org/package=ROBOSRMSMOTE Description: CRAN Package 'ROBOSRMSMOTE' (Robust Oversampling with RM-SMOTE for Imbalanced Classification) Provides the ROBOSRMSMOTE (Robust Oversampling with RM-SMOTE) framework for imbalanced classification tasks. This package extends Mahalanobis distance-based oversampling techniques by integrating robust covariance estimators to better handle outliers and complex data distributions. The implemented methodology builds upon and significantly expands the RM-SMOTE algorithm originally proposed by Taban et al. (2025) . Package: r-cran-robotoolbox Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-rcppsimdjson, r-cran-data.table, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect, r-cran-tibble, r-cran-stringi, r-cran-glue, r-cran-dm, r-cran-labelled, r-cran-readr, r-cran-cli Suggests: r-cran-roxygen2, r-cran-devtools, r-cran-vcr, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-sf, r-cran-mapview Filename: pool/dists/noble/main/r-cran-robotoolbox_1.6.2-1.ca2404.1_all.deb Size: 1067912 MD5sum: eb1204efc2631c11b50ad21bb21ca2d7 SHA1: 27a74d0bc81764e1b9979e200e21ae4d53e6ac83 SHA256: 2ac32b4e529a442ea98d71a6848821fcfcb0e5a421f57c062e8824c7d6dcfc55 SHA512: 7855e99ef974a8a7e6dcd10005f30d472e9870062d64fbe98b70952896f322920292e3369a3cadf864eb48134c0dc89e9c12926fcbb98f0b0df9f74fa462bf98 Homepage: https://cran.r-project.org/package=robotoolbox Description: CRAN Package 'robotoolbox' (Client for the 'KoboToolbox' API) Suite of utilities for accessing and manipulating data from the 'KoboToolbox' API. 'KoboToolbox' is a robust platform designed for field data collection in various disciplines. This package aims to simplify the process of fetching and handling data from the API. Detailed documentation for the 'KoboToolbox' API can be found at . 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Package: r-cran-robpc Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-trimcluster Filename: pool/dists/noble/main/r-cran-robpc_1.4-1.ca2404.1_all.deb Size: 13200 MD5sum: cf33aa4b9e6ea9fc8b8b6e511e55ce1f SHA1: 589c91c5a0347effc456fb47c1a85c3042cac967 SHA256: fcedd385701eaee2de1c4a78c3f38235403fd8f59704351f496e29b9044a91f4 SHA512: 8d35782784dfc8a747c69604d53a13e591d98c69f0ce159c4514688aa2e5ef19e71e5dc9f059f1f58b45ba860b655dfe33029561a3065a717af024863bd9d8be Homepage: https://cran.r-project.org/package=RobPC Description: CRAN Package 'RobPC' (Robust Panel Clustering Algorithm) Performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) , Cuesta-Albertos et al. (1997) . 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Package: r-cran-robreg3s Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gse, r-cran-mass, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-robreg3s_0.3-1-1.ca2404.1_all.deb Size: 47630 MD5sum: b7ba9c4b4ffedeed93e1dbf12a9e713a SHA1: 98ad91a18480070aa3f43f5666f568868a2709db SHA256: 1981ef731b887c35eb6974a8cab1778684c613b91150e803b9df5e70c556aaff SHA512: a021481f1d1d113d6e37ece4f9b7264ebf1f8d22f32484421413bdd3b88ccab3b43de31b28b46be04152d739dbf6edbb791dc343b6cfadea6cf163f4ced04df6 Homepage: https://cran.r-project.org/package=robreg3S Description: CRAN Package 'robreg3S' (Three-Step Regression and Inference for Cellwise and CasewiseContamination) Three-step regression and inference for cellwise and casewise contamination. 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Package: r-cran-robustadaptivedecomposition Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustadaptivedecomposition_0.1.0-1.ca2404.1_all.deb Size: 12678 MD5sum: 5052bf76f396702ca9116dac72268d71 SHA1: be225d65d310356d40bf7b6049633e09845865d6 SHA256: a52d5f94d3a0f53d6f48171e5616c59faebbcd0c1cfb0187efe6288d88a498d8 SHA512: df063d8b6fd58754bc89beeaab14ea90210a98bf1057a3b163c33f36271686a3b47d86a9e90e32b38a4af9ea3aeab362762e1c50e39d3993d0c5cef77a3f12b2 Homepage: https://cran.r-project.org/package=RobustAdaptiveDecomposition Description: CRAN Package 'RobustAdaptiveDecomposition' (Decomposes a Univariate Time Series into Subcomponents) Provides a method to decompose a univariate time series into meaningful subcomponents for analysis and denoising. Package: r-cran-robustanova Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-peip, r-cran-optimbase Filename: pool/dists/noble/main/r-cran-robustanova_0.3.0-1.ca2404.1_all.deb Size: 37344 MD5sum: cd65d05e07aa103392595d7ea8e4bbed SHA1: f2449dc079f72f61829df540ff7ce7d8e25b33ab SHA256: e06fb5c987fdf7dd1eaa7d345aaf677248809d5a0e1a3743bdb55826e311f54a SHA512: 52b49d72df2e80e21678cb0f525cac6d5c574019def7efbf87628127d07ffd70a8a6dd5cef172eb4f3dc6cc11244adc0b4da56a1b09697a0ad3d302eb759414a Homepage: https://cran.r-project.org/package=RobustANOVA Description: CRAN Package 'RobustANOVA' (Robust One-Way ANOVA Tests under Heteroscedasticity andNonnormality) Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) , parametric bootstrap test proposed by Krishnamoorthy et. al (2007) ; and generalized F test proposed by Weerahandi (1995) ;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) , . Package: r-cran-robustarithmetic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-rmpfr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-robustarithmetic_0.1.0-1.ca2404.1_all.deb Size: 369096 MD5sum: 9d3cde2c82247524fd7d19cd0e1a0280 SHA1: 108db4a54e3f71a3430b9ce26e08bdec5b9bbccd SHA256: d86b3a3469bdfc5ed91d61d68450b3e14fa1ec8c71621d0fdf108f192b2712c2 SHA512: 7b2ef39d86ecf5a4ee59c47e482fdda0b2c5a29ad97a62da26260e08fa9f646894cecd4c2b2d47a881d10033559b10df4fa209eea484a6d050148767e8e81c35 Homepage: https://cran.r-project.org/package=RobustArithmetic Description: CRAN Package 'RobustArithmetic' (Verified Interval Arithmetic and Rigorous Enclosures in Pure R) Verified interval arithmetic for R, in the inf-sup (endpoint) representation of the set-based flavor of the interval standard. Every operation returns an enclosure that provably contains the exact result: outward rounding is obtained from the predecessor and successor formulas of Rump, Zimmermann, Boldo and Melquiond (2009) , which are valid under round-to-nearest and therefore need no change to the floating-point rounding mode. That mode is not reachable from R, and changing it would not be a local act: it is per-thread state of the processor, so it would govern every floating-point operation executed afterwards on that thread, in this package or anywhere else. Elementary functions are provided at two levels: a fast level over the system math library, widened by a declared slack derived from published accuracy measurements, and a rigorous level over 'Rmpfr' with a directed-rounding bridge, reached by an escalation ladder of precisions when a verdict would otherwise fall inside the slack. On top of the kernel the package builds natural and centered interval extensions of expressions, a monotonicity test, the Hansen-Sengupta interval Newton operator with extended division and epsilon-inflated candidate verification, and a subdivision (paving) engine whose only failure mode is a named abstention with its budget printed. Conformance with IEEE Std 1788.1-2017 is not claimed, and the reason is the standard's own: its subclause 1.5 makes conformance a list of requirements that an implementation shall satisfy, with no partial grade to claim. What this package follows, measured one requirement at a time and stated in the package documentation, is the interval type and the decoration system of clause 5, 22 of the 39 arithmetic operations of Table 4.1, and the seven numeric functions of Table 4.3. What it does not provide is the cancellative operations, the interval comparison relations, the text input and output of subclause 6.8, the interchange representation of subclause 7.3, and the tightest accuracy that subclause 6.5.2 requires of the basic operations, which here are one unit in the last place wider at each end. Package: r-cran-robustbetareg Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-rmpfr, r-cran-rstudioapi, r-cran-crayon, r-cran-pracma, r-cran-numderiv, r-cran-formula, r-cran-robustbase, r-cran-zoo, r-cran-bbmisc, r-cran-mass, r-cran-misctools, r-cran-matrix Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustbetareg_0.3.1-1.ca2404.1_all.deb Size: 229392 MD5sum: 69845e9e3d40c311fa04637972188f11 SHA1: 15e50848a9d790408c0329805de6b181c75a6574 SHA256: da662299e2b0995ad4f4f35b564dee3d82627fc579bdb1ff22d3eed96fb7a4b7 SHA512: df0760cfa6bca7cdc1f0c79aa72ba2cbee64ca9c9412f30a9a15d54fb58c663a1a22a751a0461d2154f6814ab3a13dd9de867a46784c759fd0749b8d5201777a Homepage: https://cran.r-project.org/package=robustbetareg Description: CRAN Package 'robustbetareg' (Robust Beta Regression) Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2025) . Package: r-cran-robustbf Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustbf_0.2.0-1.ca2404.1_all.deb Size: 24992 MD5sum: 3d81efd7a2e30b25c2740a560b427d50 SHA1: b997d4d111a72c759860265836115bf588029c35 SHA256: 681502ec19cb2ef81424c6cb81b1f27d6919d32cb627e7b9ba3a21a726241124 SHA512: 7c1c06b84196bcad4bc8e03f6412dead5f3dbecef51cdc9b245c06e5a43ac976dc4e667b90b9bd744fb96acde9090ce98da9623cbb0eb603bc1b9cc20cd8cb48 Homepage: https://cran.r-project.org/package=RobustBF Description: CRAN Package 'RobustBF' (Robust Solution to the Behrens-Fisher Problem) Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) and Donmez (2010) . Package: r-cran-robustda Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mclust, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-robustda_1.2-1.ca2404.1_all.deb Size: 24032 MD5sum: 13b33a1610ab8c19dd14f6a727bef587 SHA1: 6bb7af7d95be8ce36cf2b8feb350ea3d9a03cdea SHA256: a60994b55351376700dac1789b1cac89bea60040dc4851c169524697ac1399f3 SHA512: f50d66bb6a84b314fd5cbf14c4bcb72f8732d2b234a4363ae7c500a6b3e30f7f5100893fe96d37e72eab0edcb66f712e1871440d2efdfa0a8ee697d58ea6ef54 Homepage: https://cran.r-project.org/package=robustDA Description: CRAN Package 'robustDA' (Robust Mixture Discriminant Analysis) Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 , allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels. Package: r-cran-robustdif Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mirt, r-cran-lavaan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustdif_0.2.0-1.ca2404.1_all.deb Size: 108874 MD5sum: 4828298068d64de394b57e089e6b7244 SHA1: 36fe95a8e1127c424fbacfbf65c615dce7349588 SHA256: 14b4d3c5370c4aaacfaa93915446933477845fe54cf0720c0dc9cb221ee182fc SHA512: 991fd497684e9da9ee9dd502d0f628f3df2b9a95d991713065aab82529dfdb86f09a1836e8a26a5992f43734c0782b4a67ab8fd140265f0526e4e6164261276d Homepage: https://cran.r-project.org/package=robustDIF Description: CRAN Package 'robustDIF' (Differential Item Functioning Using Robust Scaling) Provides tools for testing differential item functioning (DIF) and differential test functioning (DTF) in two-group item response theory models. The package estimates robust scaling parameters via iteratively reweighted least squares with Tukey's bisquare loss, and supports Wald-type tests of item-level and test-level differences from robust scaling parameters. Inputs can be supplied directly from model parameter/covariance objects or extracted from fitted 'mirt' and 'lavaan' models. Methods are described in Halpin (2022) . Package: r-cran-robustfa Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov Suggests: r-cran-lattice, r-cran-cluster, r-cran-mclust, r-cran-mass, r-cran-ellipse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustfa_1.2-0-1.ca2404.1_all.deb Size: 1379662 MD5sum: f2cb321aaee791f7c09419889b33e23e SHA1: ea54047c3213b6023bfab8cc2c1e1986c9e01c65 SHA256: f9bf84b44a04c72d169b7dd0633203598bda97a52b06ab3ab2f07adb939268cf SHA512: bcdba74d90052ea58ecf5694c64f7192d7b4e6a6231aaec9e54068d9b791084efe2392d0ab215446ed36791f5749ae5be30961150e3b8728f1fc97ea4cffe5aa Homepage: https://cran.r-project.org/package=robustfa Description: CRAN Package 'robustfa' (Object Oriented Solution for Robust Factor Analysis) Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) ). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) ) (Todorov, V. and Filzmoser, P. (2009) ), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes. Package: r-cran-robustflow Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-golem, r-cran-bslib, r-cran-ggplot2, r-cran-dt, r-cran-plotly, r-cran-scales, r-cran-htmltools, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-knitr, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-cran-robustflow_0.1.1-1.ca2404.1_all.deb Size: 129672 MD5sum: b53efa40341b5364082c6d7568ee90a5 SHA1: 0818cdc700adeee5248c4caa744dffa381dcb9da SHA256: 5665858fcd130c0815825c769e1206417d4270ba63354bf8da693a8185de3516 SHA512: ec319e03fd5f02c39a39b3960132e9a7aa2d980af58dbee0642fb5d82e7e7a5e4b8be921be3717729d182c72938285399503db2f9e8fcff142b97a05d2d7ea80 Homepage: https://cran.r-project.org/package=RobustFlow Description: CRAN Package 'RobustFlow' (Robustness and Drift Auditing for Longitudinal Decision Systems) Provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive 'shiny' application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731). Package: r-cran-robustgarch Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-nloptr, r-cran-rugarch, r-cran-zoo, r-cran-xts Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-pcra Filename: pool/dists/noble/main/r-cran-robustgarch_0.4.2-1.ca2404.1_all.deb Size: 78416 MD5sum: a46384414e6f1173f76f5425b9c7cbe7 SHA1: de01bd126cae989c7768ff828d27fcd83051957b SHA256: 5e281af51cd83d26adbe1b94af09470495bd3a54c38bae19562b486718ac99aa SHA512: cdc0fa6d79f35575ef674c82b0fc71e5d1de66fc315b81b3bc2ca07fc214bcf33bb7a711ca81d82cef083b1cc97f639952dad797ced8295ac9bf904c23fdfdd2 Homepage: https://cran.r-project.org/package=robustGarch Description: CRAN Package 'robustGarch' (Robust Garch(1,1) Model) A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) . Package: r-cran-robustiv Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-igraph, r-cran-intervals, r-cran-cvxr Filename: pool/dists/noble/main/r-cran-robustiv_0.3.1-1.ca2404.1_all.deb Size: 130378 MD5sum: b9c3103c828b89cf23ed380252a8241a SHA1: 0e4ea03fec9a42d3d5ea43b5a5da75b943d326e6 SHA256: f9f55c1eeecfb1e24df51290a952a4b1d4fe4b2a3b34b5d7f0bb224dffed73b0 SHA512: 67f913f37a2c1c07a0ee4a065422b446f00ed50ea9dc903483bd35a18097ff983e7e5465bffb98700d3b565a09d375d458eda8c9e1b9a1f6cc4e29ca6cee0083 Homepage: https://cran.r-project.org/package=RobustIV Description: CRAN Package 'RobustIV' (Robust Instrumental Variable Methods in Linear Models) Inference for the treatment effect with possibly invalid instrumental variables via TSHT ('Guo et al.' (2018) ) and SearchingSampling ('Guo' (2023) ), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2018) ). Package: r-cran-robustlinearreg Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustlinearreg_1.2.0-1.ca2404.1_all.deb Size: 18172 MD5sum: c7f6330f7c6d53fe299f29fa1d9a37b7 SHA1: 23c5fc361c1bcb1b83e266782bd3799da6e886e4 SHA256: 9f06eeff2378eb4d71b3cee3d4c4b6677bd1b3dd9cb5300baf4d5af92ac32b6f SHA512: de3e124855f6fc2031673fdbdb89deb2191ebfaf8d073e357f94f76081ba0c5ee7da56346d0892109004bd7420e3bda2cac44b8e96d632e2ec57a7316b6bc14f Homepage: https://cran.r-project.org/package=RobustLinearReg Description: CRAN Package 'RobustLinearReg' (Robust Linear Regressions) Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) , Sen, P. K. (1968) . Package: r-cran-robustlm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-robustlm_0.1.0-1.ca2404.1_all.deb Size: 159848 MD5sum: c385cf2f72b76823dfe8cd886dbd111e SHA1: fce8e446022065596decb83a646ffe28b85520f8 SHA256: 984ec6429e13e9ea498a25ed1610c6c83c6df8a071be8f21d840ebff5e576a61 SHA512: aae45c32bf3d7dd87e13c07c35b5a0c94f34c2b879387ba3fdf292a0fead45be4f9fc840e040a91920dbe78a0c43383099dabd865350f2294883950d6320464d Homepage: https://cran.r-project.org/package=robustlm Description: CRAN Package 'robustlm' (Robust Variable Selection with Exponential Squared Loss) Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) . Users can enjoy the near optimal, consistent, and oracle properties of the procedures. Package: r-cran-robustmediate Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang, r-cran-scales, r-cran-broom Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-lme4, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-robustmediate_0.1.1-1.ca2404.1_all.deb Size: 227270 MD5sum: e3a6c8bb251790a03b951e1d75c01aeb SHA1: c2869085fb997495fd66c887e67f21e97c82727e SHA256: 50c321e7088a0f6d6743c6f90ab8367bfeb77a31e4b229f560461f221d3591a3 SHA512: bb09373d37ef9ffa2ed5b84ee082eadba5d8e17a06b0ce001c3a6076773a54609dbb79175c0bf6b7ec6a204da6f68af802f2e541fa23fa285cb594273b0b827f Homepage: https://cran.r-project.org/package=RobustMediate Description: CRAN Package 'RobustMediate' (Causal Mediation Analysis with Diagnostics and SensitivityAnalysis) Provides tools for causal mediation analysis with continuous treatments using inverse probability weighting (IPW). Estimates natural direct and indirect effects over a user-defined treatment grid and supports flexible dose-response mediation analysis. Includes diagnostic procedures for assessing covariate balance in both treatment and mediator models using standardized mean differences. Implements pathway-specific extensions of the impact threshold for a confounding variable (ITCV; Frank, 2000 ) adapted to mediation settings. Provides joint sensitivity analysis combining E-values (VanderWeele and Ding, 2017 ) and violations of sequential ignorability (Imai, Keele, and Yamamoto, 2010 ). Additional utilities include visualization of dose-response mediation functions, robustness profiles, fragility summaries, and formatted outputs for applied research. Supports clustered data structures and multiple outcome families. Package: r-cran-robustmeta Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor Filename: pool/dists/noble/main/r-cran-robustmeta_1.2-1-1.ca2404.1_all.deb Size: 34412 MD5sum: 37cb97a5b0868b9eef9f13578054676d SHA1: 5111f5594dc202761b6bd55432522ced5f30df6f SHA256: eb4528996620a4da348e354be0116d9701302d8db279d88f0bc3254c97894980 SHA512: 91c4ff364949ad47bfcdc2de62870b2681c1d67ad7aaa02e30a279a619552a293a17040411f0c86c305cc075fece59be55ca3c639a7b1bcea2dea19f78c7bfbe Homepage: https://cran.r-project.org/package=robustmeta Description: CRAN Package 'robustmeta' (Robust Inference for Meta-Analysis with Influential OutlyingStudies) Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) , and the meta-analysis methods are developed by Noma et al. (2022) . Package: r-cran-robustmetrics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustmetrics_1.0.0-1.ca2404.1_all.deb Size: 324194 MD5sum: 472a9a9cfa2a6835a9b19442a2ca1b30 SHA1: 9c3bb804d4113ba938759ae26cb7a15317305f0f SHA256: 41744278f86fad0c3fb401cc04d3ce7ff64fd8fd01d62c3f5f829c4728595088 SHA512: 4a1165b3ee8feb7e8e5046aebebb1aa742c333fe9e8741abc4ac7ae0f58df8a38b55ddf60354c6f8bbb16630a0e2082bd810c123bbda153543c8f7a195b2e2fc Homepage: https://cran.r-project.org/package=RobustMetrics Description: CRAN Package 'RobustMetrics' (Calculates Robust Performance Metrics for ImbalancedClassification Problems) Calculates robust Matthews Correlation Coefficient (MCC), Cohen's Kappa, and robust F-Beta Scores, as introduced by Holzmann and Klar (2026) . These performance metrics are designed for imbalanced classification problems. Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve. Package: r-cran-robustprediction Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mboost, r-cran-mlr, r-cran-ranger, r-cran-e1071, r-cran-proc Filename: pool/dists/noble/main/r-cran-robustprediction_0.1.7-1.ca2404.1_all.deb Size: 301496 MD5sum: 5fd53355b05472e94ff3c11bdfca3550 SHA1: c00df8ce90f3e67b1fc9b96353ed9c649d55c84c SHA256: b7784603bfc013a2ca2b4e41954d5d06b4dc72412108152ad9ac77b1271ef9d9 SHA512: ea4dbaf078042fbc34f9675b2d4d33f949c2f71c3b0327dbfa70eba279300f12fec0ba3d64a0ebba87f3c3a25e82825e11a1ea878e18f7d89f18b7dc4cc1fe3c Homepage: https://cran.r-project.org/package=RobustPrediction Description: CRAN Package 'RobustPrediction' (Robust Tuning and Training for Cross-Source Prediction) Provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" . Package: r-cran-robustrankaggreg Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustrankaggreg_1.2.1-1.ca2404.1_all.deb Size: 50028 MD5sum: fc69bcd42ad95f6ef545fc7f728d2bf8 SHA1: a93e5f6e75f41de015cbb1fab5b04bde81105af7 SHA256: 50cbf38fc0f34bbd5240804b4c23b2776d71ea351396a0afa334afdc65d20d50 SHA512: 52efa6a4695a458c2435d94012f42cd6b6df8871d9df0e962b8a908e5cb3503e2d61f9810ea023b5ea1bf5ca51bf3175991fa1d1183026c6cb6ca002e4259dd2 Homepage: https://cran.r-project.org/package=RobustRankAggreg Description: CRAN Package 'RobustRankAggreg' (Methods for Robust Rank Aggregation) Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation Kolde et al (2012) and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking. Package: r-cran-robustrao Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-gmp, r-cran-iterpc, r-cran-quadprog, r-cran-igraph, r-cran-foreach Filename: pool/dists/noble/main/r-cran-robustrao_1.0-5-1.ca2404.1_all.deb Size: 194790 MD5sum: 2e6bb94df393a8ce3d31f03cfb92ffeb SHA1: 54d25ff55240c08563f32992076428eaab13817f SHA256: 02276ecaa34ba24f5cbbb2427b6a9753c6038a59827043079f47f9a183f464ab SHA512: ff9dfe13a58ff6710fe48bccc424836e6043480521072082d1dc674e74dcaee4c7889f4ca95791b0138d18ac69019924cc812bb78c2de523103115b477e755ff Homepage: https://cran.r-project.org/package=robustrao Description: CRAN Package 'robustrao' (An Extended Rao-Stirling Diversity Index to Handle Missing Data) A collection of functions to compute the Rao-Stirling diversity index (Porter and Rafols, 2009) and its extension to acknowledge missing data (i.e., uncategorized references) by calculating its interval of uncertainty using mathematical optimization as proposed in Calatrava et al. (2016) . The Rao-Stirling diversity index is a well-established bibliometric indicator to measure the interdisciplinarity of scientific publications. Apart from the obligatory dataset of publications with their respective references and a taxonomy of disciplines that categorizes references as well as a measure of similarity between the disciplines, the Rao-Stirling diversity index requires a complete categorization of all references of a publication into disciplines. Thus, it fails for a incomplete categorization; in this case, the robust extension has to be used, which encodes the uncertainty caused by missing bibliographic data as an uncertainty interval. Classification / ACM - 2012: Information systems ~ Similarity measures, Theory of computation ~ Quadratic programming, Applied computing ~ Digital libraries and archives. Package: r-cran-robustrcp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustrcp_0.1.0-1.ca2404.1_all.deb Size: 31612 MD5sum: d9412f8b6c666f6bafdb20a50e475702 SHA1: b4394c4b4d224fcd66b0bc813a9c6142d8440dca SHA256: 4f99148333ad581ae294f98000163b242353937d2e7033583ce2c89df7125707 SHA512: d4d952a03b3ca708044bd2a9b7b6ff28b840924ced18f6b74a71e780b0338697cbd35f6f2dc193e5221d36f3e31fd67da7d0a5ed10bca3dab864d18a774d30d5 Homepage: https://cran.r-project.org/package=robustrcp Description: CRAN Package 'robustrcp' (Outlier-Robust Ratio-cum-Product Estimators of Finite PopulationMean) Implements robust ratio-cum-product estimators using auxiliary medians for estimating the population mean under simple random sampling without replacement (SRSWOR). Provides analytical optimal tuning parameters, bias, Mean Squared Error (MSE), and Percent Relative Efficiency (PRE) evaluations. Package: r-cran-robustsur Architecture: all Version: 0.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-robreg3s, r-cran-matrix, r-cran-gse Suggests: r-cran-systemfit Filename: pool/dists/noble/main/r-cran-robustsur_0.0-8-1.ca2404.1_all.deb Size: 46736 MD5sum: d288fe401a7a1f441a797c8c7686b806 SHA1: 9947625da70125cdfebe155e5e0fcf88190ba50d SHA256: 642b165f7e978be0763f3f95c31efcf7a855a8decaeae4d2c18fcd2975a64738 SHA512: 188d85bf6c1beb5b5f361f903352495b1f22cfc4875e1c5cab1f4a9e467af8af27b837796aadefd3b898ff4dec632285171ebbb8d9abb836d5486ce386923f17 Homepage: https://cran.r-project.org/package=robustsur Description: CRAN Package 'robustsur' (Robust Estimation for Seemingly Unrelated Regression Models) Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) . Package: r-cran-robustt2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-ggplot2, r-cran-rrcov, r-cran-shiny, r-cran-ggrepel, r-cran-forcats, r-cran-matrix Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-robustt2_0.1.0-1.ca2404.1_all.deb Size: 94448 MD5sum: c9186a1b89badf793811c0a90cb57979 SHA1: f3760185aff6f7ce9fddcb9e3c0e6cfb7bb8b53c SHA256: 22337be4cdbc28da0dd6089abf83bdf3971191be4dd3d0a2c15fc5799ddd6b1d SHA512: 1b0c0fb15f5403ef26ce13e1bf76025079fcc295c75dc04e5d274bcd3e654161ed15bfc9b256f1cfd258fc6c1d90cdffffbbdd6df8224327c5e21115d58831c8 Homepage: https://cran.r-project.org/package=robustT2 Description: CRAN Package 'robustT2' (Robust Hotelling-Type T² Control Chart Based on the Dual STATISApproach) Implements a robust multivariate control-chart methodology for batch-based industrial processes with multiple correlated variables using the Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework. A robust compromise covariance matrix is constructed from Phase I batches with the Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is applied for anomaly detection in Phase II. The package includes functions to simulate clean and contaminated batches, to compute both robust and classical Hotelling T² control charts, to visualize results via robust biplots, and to launch an interactive 'shiny' dashboard. An internal dataset (pharma_data) is provided for reproducibility. See Lavit, Escoufier, Sabatier and Traissac (1994) for the original STATIS methodology, and Rousseeuw and Van Driessen (1999) for the MCD estimator. Package: r-cran-robusttseq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma Filename: pool/dists/noble/main/r-cran-robusttseq_0.1.0-1.ca2404.1_all.deb Size: 20408 MD5sum: 01444a752d3b415f3210b743c90b6135 SHA1: 08934013b365bd0316e636de99f6d2f95447b0f1 SHA256: 13d3678a6b24e132e3184a96b6683a853d5666a6d02e116505073e3eaad354f0 SHA512: 89301218ff8b2c3c8254dd4b02cb920a6d9172dfa18bc8bceafb0cfcb87da5cf3f0f23ad7dabc991af29d86e6e7519115208add81b822062c18c6bcb2cf4c945 Homepage: https://cran.r-project.org/package=robusttseq Description: CRAN Package 'robusttseq' (Robust Statistical Methods with Huber Estimators) Provides robust statistical methods for analyzing numeric data, including robust estimation of location and scale using Huber M-estimators and a robust two-sample t-test. Methods are based on Huber (1981, ISBN:0471418056) "Robust Statistics" and Smyth (2004) . Package: r-cran-robustvis Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-robustvis_0.1.2-1.ca2404.1_all.deb Size: 33614 MD5sum: 4a32b3e5ad2f23427ea0572f23cd8fbb SHA1: 1edc10070dfa6a42c01e7c741fd45f5278248d6e SHA256: 3cf076fda65d9741bc411ca371b1bc1e70b074ac5de35f252f110dbca58d469d SHA512: 3ef521272edc3c679b1e499b7f6bf85fb8f5ad8c60194732bab61466e950477df930fd97b06709dbca920c1997f8bbfdc8e6f7f81657c1f8f8fdc501a13a903d Homepage: https://cran.r-project.org/package=RobustVis Description: CRAN Package 'RobustVis' (Visualize ROBUST-RCT Risk of Bias Assessments) Provides functions to visualize ROBUST-RCT assessments, as introduced by Wang et al. (2025) . Through a two-step workflow (step 1 and step 2), the package generates bar plots and traffic-light plots that match standard Cochrane styles. Package: r-cran-robustx Architecture: all Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase Suggests: r-cran-mass, r-cran-lattice, r-cran-pcapp Filename: pool/dists/noble/main/r-cran-robustx_1.2-8-1.ca2404.1_all.deb Size: 128878 MD5sum: 77c065f67cc64399143f1b6c79c742ae SHA1: 349862c45c012070cde7cf9d33404943dbf741e5 SHA256: 5543f8c3347c97a98c5e1668d6476e852e418fc17ffee0a122cc54a200a6edf3 SHA512: 2670a09992ba5073ac3a0c02a6b889df6684f15aae1975a14e9f8c0d6e0cb1a0bea40ec87bf4eefddbd4e3c334c342e066b1c31b97a6b51a706116b74983da6c Homepage: https://cran.r-project.org/package=robustX Description: CRAN Package 'robustX' ('eXtra' / 'eXperimental' Functionality for Robust Statistics) Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary' Functionality for Robust Statistics. Hence methods which are not well established, often related to methods in package 'robustbase'. Amazingly, 'BACON()', originally by Billor, Hadi, and Velleman (2000) has become established in places. The "barrow wheel" `rbwheel()` is from Stahel and Mächler (2009) . Package: r-cran-robvis Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2203 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robvis_0.3.1-1.ca2404.1_all.deb Size: 1270566 MD5sum: 1ab97bf0fcf0e6b9c4d238b1e28b4a0b SHA1: a910e988f2459a820fb4bd750315397eff4ac68c SHA256: 27ffca06dc24e0c4d26a9914eb2e8c39e067bb9b314660f80942cda358364ac4 SHA512: bc96e5e857c64b3896617179cd910924214ccc2b3f6c7fc7ab32403c4ef544e062b6ce9bd6e4f61785ec50ce5a8f7181f9dab4710c8d2e28eea8ade66fffa5e7 Homepage: https://cran.r-project.org/package=robvis Description: CRAN Package 'robvis' (Visualize the Results of Risk-of-Bias (ROB) Assessments) Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. It allows users to create weighted bar-plots of the distribution of risk-of-bias judgments within each bias domain, in addition to traffic-light plots of the specific domain-level judgments for each study. The resulting figures are of publication quality and are formatted according the risk-of-bias assessment tool use to perform the assessments. Currently, the supported tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019) ), ROBINS-I (for non-randomised studies of interventions; Sterne et al (2016) ), and QUADAS-2 (for diagnostic accuracy studies; Whiting et al (2011) ). Package: r-cran-robyn Architecture: all Version: 3.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggridges, r-cran-glmnet, r-cran-jsonlite, r-cran-lares, r-cran-lubridate, r-cran-nloptr, r-cran-patchwork, r-cran-prophet, r-cran-reticulate, r-cran-stringr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-robyn_3.12.1-1.ca2404.1_all.deb Size: 1573388 MD5sum: a74b48f1fdd2e7e6cd623c7e85c28195 SHA1: 02ef28d7fc12ca63962a5ef8daae1761149ab75c SHA256: 1dfd4abe89b91500c54b8345fee3444632c0b109c9a79f898fc9033b05860d42 SHA512: 3b767bab8a2c663a7365d59fc189cf30fbf23ff0f58e7862a9513ad2ea0baa67bbb0da10a5da06266e4151e65a92c3501d7e540e9046fbecf9c33bf501dda69f Homepage: https://cran.r-project.org/package=Robyn Description: CRAN Package 'Robyn' (Semi-Automated Marketing Mix Modeling (MMM) from Meta MarketingScience) Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation. Package: r-cran-rocaggregator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat, r-cran-mockery, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown, r-cran-rocr, r-cran-proc, r-cran-pracma Filename: pool/dists/noble/main/r-cran-rocaggregator_1.0.1-1.ca2404.1_all.deb Size: 44132 MD5sum: 921c6f28b2c181943915d0d65d6e8a7e SHA1: cac3b6f7f9621d38eb273bb2473412937a477a41 SHA256: 037b823b93e177826d13c5a64281aba41ba595f567a19af89a86c6a164b69f96 SHA512: 3f30c1c4bec396f61c81c4772196dc03bb8a4f8b5ab13c0cf6367ce5c7da9f524eafeba285af2475f270821645d898f55725f32d24432dfa88b0a04255353289 Homepage: https://cran.r-project.org/package=ROCaggregator Description: CRAN Package 'ROCaggregator' (Aggregate Multiple ROC Curves into One Global ROC) Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, it’s also possible to calculate the aggregated precision-recall (PR) curve. Package: r-cran-rocbc Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-clinfun, r-cran-splancs, r-cran-mvtnorm, r-cran-formattable, r-cran-mrmcaov, r-cran-proc Suggests: r-cran-knitr, r-cran-markdown, r-cran-matlab Filename: pool/dists/noble/main/r-cran-rocbc_3.2.0-1.ca2404.1_all.deb Size: 994802 MD5sum: c58b732af0c982dcb4abe705ecff8f9c SHA1: fb2de72b041def01f0e743274bb8b25cab8260bc SHA256: 8497fc04bfc28456d61d89be25e76c8311c379ec411b890579a21c1204a645a2 SHA512: 22f92f8d6e56ddb0fba3e85c661e22d1fd66fff25d28bcab536b198cb92688e17ab04d230dd6ef2287c5612dd51782c4bf1b357eb5d50a873eb07b24532fb7e6 Homepage: https://cran.r-project.org/package=rocbc Description: CRAN Package 'rocbc' (Statistical Inference for Box-Cox Based Receiver OperatingCharacteristic Curves) Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff. Package: r-cran-rocc Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr Filename: pool/dists/noble/main/r-cran-rocc_1.3-1.ca2404.1_all.deb Size: 37950 MD5sum: 6a38ddff58b9ee5390b7222137e8af4e SHA1: 83596575b6362854981e13ef158151cb0fccd27b SHA256: 9335edcb05cb07b325d81f98b0ad2127a5f763088151cc773ad0717b0845f473 SHA512: e71f59af85010427ff4ef23594e2c8053a081196c31904cbde92a41ceff4f0798f9212e29a0ad6801cf1f13284bf5f81cc94e78c2b59aafd318f2a2c37bcfb01 Homepage: https://cran.r-project.org/package=rocc Description: CRAN Package 'rocc' (ROC Based Classification) Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems. Package: r-cran-roccv Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-proc Filename: pool/dists/noble/main/r-cran-roccv_1.2-1.ca2404.1_all.deb Size: 24858 MD5sum: 336dbed899f152478a7834ea55c070ee SHA1: 7c0f292582cb094838969b59ed26eecad86b2494 SHA256: c7bb41e2a8278f8ce48c2bf997897bb5b55b9c5726c268d853ec0f0d8c14799c SHA512: d19d60b2b145342d5b828d528291b8d97b08c3ba1e709161f44abc1f1e8f72900208423eaf1a3a19a169e93ec25bac8d17ead4d2d195e75436410493e3b08aa8 Homepage: https://cran.r-project.org/package=roccv Description: CRAN Package 'roccv' (ROC for Cross Validation Results) Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data. Package: r-cran-rocean Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ff Filename: pool/dists/noble/main/r-cran-rocean_1.0-1.ca2404.1_all.deb Size: 45402 MD5sum: 7ea66be44bbc4522898f24ec12a93ca5 SHA1: 6d9232b341ef4af4d7f1842d6bf78fdea637ffe9 SHA256: 2e6f5bc684c64512ffa4bc47b8cb107ee822aa746605987d52298d23f557c45d SHA512: c80515fd555672d1fe0c189fa9e627b932437e634505343481327f2c70fd72b900a7c132978d7864ea2e1a408a832be072bef4f9379d5d0880cf89688a680336 Homepage: https://cran.r-project.org/package=rOCEAN Description: CRAN Package 'rOCEAN' (Two-Way Feature Set Testing for Multi-Omics) For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) . Package: r-cran-rocftp.mms Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocftp.mms_1.0.1-1.ca2404.1_all.deb Size: 20974 MD5sum: ddaaca87f6ede383b624493c333052e6 SHA1: cae07159d6422a02e8a5208355b9fe0b3246cc2b SHA256: 1ed0ff5889a1a99a534c251b0900585b5cb3f6c12bb883b83833f0cc4ab009ac SHA512: 9d5136956ddeac8cc57d3458b07abd1d64bb97e9ba7b513bca06930e8e13977c4dc5a70461f57fbc9241be1d83014a8152b2b85c237577225833ada7d35d3d41 Homepage: https://cran.r-project.org/package=ROCFTP.MMS Description: CRAN Package 'ROCFTP.MMS' (Perfect Sampling) The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution. Package: r-cran-rocit Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rocit_2.1.2-1.ca2404.1_all.deb Size: 280634 MD5sum: 8fdb77a2a578a60cfea4f9bc9fdbe8f8 SHA1: 97e8dac7c333478aecadf56956b66e029d52eb27 SHA256: 6ab254daa16b12f85e54ad2c621a262672b114eadb0d290fde215dff32cdcce5 SHA512: f3df650bb8e9cae80de3c557f95d22ea82ee3f4c13bffac4c62fb96ffe97bc0fe2322e93c68a6b9b49f15fe92d4ba2500499f9caad98f30dc58fb5ebe449dce9 Homepage: https://cran.r-project.org/package=ROCit Description: CRAN Package 'ROCit' (Performance Assessment of Binary Classifier with Visualization) Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use. Package: r-cran-rock Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-ggplot2, r-cran-glue, r-cran-htmltools, r-cran-markdown, r-cran-purrr, r-cran-squids, r-cran-yaml, r-cran-yum Suggests: r-cran-covr, r-cran-googlesheets4, r-cran-haven, r-cran-justifier, r-cran-knitr, r-cran-limonaid, r-cran-openxlsx, r-cran-pdftools, r-cran-pkgdown, r-cran-preregr, r-cran-readxl, r-cran-rmarkdown, r-cran-rvest, r-cran-rsvg, r-cran-rstudioapi, r-cran-striprtf, r-cran-testthat, r-cran-writexl, r-cran-xlconnect, r-cran-xml2, r-cran-zip Filename: pool/dists/noble/main/r-cran-rock_0.9.6-1.ca2404.1_all.deb Size: 1214310 MD5sum: 829ba88f42e22734e4c10e00eb42dd38 SHA1: 4b3b5e8ecaa00fd1b723870a1542ccaba395919d SHA256: de71fa91ec325713ecd954735b5539afa2305c2ff211847e9db87d6744de16be SHA512: f5b099ef34742015cca4d23d2cb3f48df69b6be7411c0c36e0acb3cae99218250ba27e328fff47f8fb46d1dd94b2b10cd30d03de857d7d9f0ec5a1031e037ab6 Homepage: https://cran.r-project.org/package=rock Description: CRAN Package 'rock' (Reproducible Open Coding Kit) The Reproducible Open Coding Kit ('ROCK', and this package, 'rock') was developed to facilitate reproducible and open coding, specifically geared towards qualitative research methods. It was developed to be both human- and machine-readable, in the spirit of MarkDown and 'YAML'. The idea is that this makes it relatively easy to write other functions and packages to process 'ROCK' files. The 'rock' package contains functions for basic coding and analysis, such as collecting and showing coded fragments and prettifying sources, as well as a number of advanced analyses such as the Qualitative Network Approach and Qualitative/Unified Exploration of State Transitions. The 'ROCK' and this 'rock' package are described in the ROCK book (Zörgő & Peters, 2022; ), in Zörgő & Peters (2024) and Peters, Zörgő and van der Maas (2022) , and more information and tutorials are available at . Package: r-cran-rockchalk Architecture: all Version: 1.8.164-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2827 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-cardata, r-cran-mass, r-cran-kutils, r-cran-reformulas Suggests: r-cran-tables, r-cran-hmisc, r-cran-car, r-cran-mvtnorm, r-cran-scatterplot3d, r-cran-hh Filename: pool/dists/noble/main/r-cran-rockchalk_1.8.164-1.ca2404.1_all.deb Size: 2379608 MD5sum: d1b6d22e0d25ef1df67dae116d25dc4f SHA1: 602d1f6305c43eb1696b7c0a8882417509e76bb7 SHA256: 68772fa610a538b1b762365e66654755f4083c222b5feff0ff4bc79ec5c78670 SHA512: cdcb5cd7be252d4b5b86e47398bff175e5555bb1a769a6ed3dc482e63cb864dbe5d9d1ceea8dc1309da287923f6198f5f4203316065857c11fea78457c94f396 Homepage: https://cran.r-project.org/package=rockchalk Description: CRAN Package 'rockchalk' (Regression Estimation and Presentation) A collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in . Includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview. The vignette 'Rstyle' has advice about coding in R. The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'. Package: r-cran-rocker Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-r6, r-cran-sodium Suggests: r-cran-covr, r-cran-crayon, r-cran-knitr, r-cran-rmariadb, r-cran-testthat, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-rocker_0.3.2-1.ca2404.1_all.deb Size: 350420 MD5sum: 95f77aa1312698280232c88689670ebd SHA1: 5944413dba7be921110815977f14fdf4311cf13c SHA256: 53d054a9206ab9980b455ec92e19ad3320ed72a12afc6f77a205de45cdae185e SHA512: 7aac69dee34fdda5b3eb17af14423c19e37f03b4e996ebbb79492dd7a7e25b5fb5a1a17b2949259f93f700bfdb0119b40439d86c72c9f0496952bbb6f4955151 Homepage: https://cran.r-project.org/package=rocker Description: CRAN Package 'rocker' (Database Interface Class) 'R6' class interface for handling relational database connections using 'DBI' package as backend. 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Package: r-cran-rocket Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocket_1.0.3-1.ca2404.1_all.deb Size: 128596 MD5sum: 0a6a19373251a5ac4916b65a4d38cb62 SHA1: e51ba334a4621e82995df184b05b0bc3fbbcde53 SHA256: d1a2660e4d31abd4bea326c6c89007538716da0e75d6892d86723a842919aec6 SHA512: e5551af45a64f3b7c012390cca9bcc580d14a5d2ebcdaefaca58483111d00e9d42f405e810628da19c18e689855b6284a6aded049c7e64d1d166d3dcdef69fd8 Homepage: https://cran.r-project.org/package=ROCket Description: CRAN Package 'ROCket' (Simple and Fast ROC Curves) A set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In 'ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004). Package: r-cran-rockfab Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-bioc-ebimage Filename: pool/dists/noble/main/r-cran-rockfab_1.2.1-1.ca2404.1_all.deb Size: 119082 MD5sum: 1cd2b99d6c581ca6f793110545e70e5b SHA1: f680b74e033f5a02609d1eeb659d47b658e542b4 SHA256: 2606eee8c2777759a9b1989b48a38b523b402e3f5e18071e9c725b1f68b3c0e6 SHA512: f0d7a690563942421484fb3a403bf6b18280db3b91c09a3ced793daa1b5e8a48fddf40adf849d1fdd6455f037fefb3ca0f7d4d398571f6f4c247c96837b957b0 Homepage: https://cran.r-project.org/package=RockFab Description: CRAN Package 'RockFab' (Rock Fabric and Strain Analysis Tools) Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality. Package: r-cran-rockr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-mime, r-cran-progress Suggests: r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rockr_1.0.0-1.ca2404.1_all.deb Size: 129006 MD5sum: 44a98e626fe30aceb0e5992404c53efb SHA1: 8d22d38ed50da5cb7e5cd31a7d8b95e2119731a9 SHA256: 9189ae48f55ff97b33135e35f7daf01e081a13f16ec08d4728d848f947713aa2 SHA512: 9cbdd551545e2aeaa02a0d4ff3c2717e7d6022fe560eb725e5aabb3df923ccb303444af2693d27680edd75a77a84b93756824f17a8ff21835f07f3b9c255f238 Homepage: https://cran.r-project.org/package=rockr Description: CRAN Package 'rockr' ('Rock' R Server Client) Connector to the REST API of a 'Rock' R server, to perform operations on a remote R server session, or administration tasks. See 'Rock' documentation at . 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This is an early release intended for testing and feedback. Package: r-cran-roclab Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-fastdummies, r-cran-kernlab, r-cran-pracma, r-cran-rsample, r-cran-dplyr, r-cran-caret, r-cran-proc Suggests: r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roclab_0.1.4-1.ca2404.1_all.deb Size: 244158 MD5sum: c550062ecb3776da6f5182af149c321f SHA1: 219cd974f34433467d0783e80d2c9e0b49510e4c SHA256: 3a1c6481d9f4c3a2bf5cbc2c3d1f04384a86336e350ad3276cc313e46a639517 SHA512: bf0d621ffb5262d125b31237b5a1980dc2456240ee4fb7e3fa8b8179377624e6474a5fdeea51dd6bebb77406e2ed1a364824a295fac2380ede1873a65397f8d2 Homepage: https://cran.r-project.org/package=roclab Description: CRAN Package 'roclab' (ROC-Optimizing Binary Classifiers) Implements ROC (Receiver Operating Characteristic)–Optimizing Binary Classifiers, supporting both linear and kernel models. Both model types provide a variety of surrogate loss functions. In addition, linear models offer multiple regularization penalties, whereas kernel models support a range of kernel functions. Scalability for large datasets is achieved through approximation-based options, which accelerate training and make fitting feasible on large data. Utilities are provided for model training, prediction, and cross-validation. The implementation builds on the ROC-Optimizing Support Vector Machines. For more information, see Hernàndez-Orallo, José, et al. (2004) , presented in the ROC Analysis in AI Workshop (ROCAI-2004). Package: r-cran-roclang Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-magrittr, r-cran-rlang, r-cran-roxygen2, r-cran-rex Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roclang_0.2.3-1.ca2404.1_all.deb Size: 36684 MD5sum: ea9acc8c3cabdfbd83384a4506154f49 SHA1: bcf4bb1424b3c9c1c2021c1a9f563b01266f7171 SHA256: ba37e33486aa337061f4730743c57c21ff3a31b8f900d2c7f0f64394a3bb3cf7 SHA512: e02961257068700d6c4d7988eae0a882360049e6030bb3c8c2720dddc798a6f7ed863b43427251891d51c0f48d56d8e13013bc5df053347dca8d41fd69a4f0ab Homepage: https://cran.r-project.org/package=roclang Description: CRAN Package 'roclang' (Functions for Diffusing Function Documentations into 'Roxygen'Comments) Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code. Package: r-cran-rocmodels Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-kedd, r-cran-dplyr, r-cran-survival, r-cran-nleqslv, r-cran-hdinterval, r-cran-rocit, r-cran-doparallel, r-cran-foreach, r-cran-pbivnorm, r-cran-nor1mix, r-cran-readr, r-cran-mass, r-cran-dorng Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rocmodels_1.0.0-1.ca2404.1_all.deb Size: 174378 MD5sum: fe48b5ef0724eb33642fabaada50ea6d SHA1: 513e02ed5f32e077ec694d6e82ae7f29b0bda95e SHA256: 22561f9dfef2563f7cf784d571399181d1b36fab2067f933da1c55f5686e2604 SHA512: b038e47e17ed605ec6b8adc1c10897cefbdea951dd7fabb3bcbc09bb23c515959b2bfbb162bf4b7c36c8c07698cef210acb0534cbedcf945ba858851e28642ba Homepage: https://cran.r-project.org/package=ROCModels Description: CRAN Package 'ROCModels' (ROC Models and AUC Estimation) The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. 'ROCModels' helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette 'ROCModels_Package_Doc' for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) , Andrews, D. F., and Herzberg, A. M. (1985) , Bamber, D. (1975) , Cox, D. R. (1972) , Cox, D. R. (1975) , DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) , Dorfman, D. D., and Alf, E. (1969) , Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) , Erkanli, A., Sung, L., and Stamey, J. D. (2006) , Faraggi, D., and Reiser, B. (2002) , Ghebremichael, M., and Habtemicael, S. (2018) , Ghebremichael, M., and Michael, H. (2024) , Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) , Gönen, M., and Heller, G. (2010) , Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) , Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) , Gu, Y., Ghosal, S., and Roy, A. (2008) , Guidoum, A. C. (2020) , , Guo, B. (2015) , Hanley, J. A., and McNeil, B. J. (1982) , Hsieh, F., and Turnbull, B. W. (1996) , Hussain, E. (2012) , Ishwaran, H., and James, L. F. (2002) , Jokiel-Rokita, A., and Topolnicki, R. (2020) , Krzanowski, W. J., and Hand, D. J. (2009) , Kundu, D., and Gupta, R. D. (2006) , Lloyd, C. J. (1998) , Lehmann, E. L. (1953) , Metz, C. E., Herman, B. A., and Shen, J. H. (1998) , Pepe, M. S. (2003) , Pundir, S., and Amala, R. (2014) , Silverman, B. W. (2018) , Yeo, I. K., and Johnson, R. A. (2000) , Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) , Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) . Package: r-cran-rocngo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocngo_0.1.0-1.ca2404.1_all.deb Size: 236520 MD5sum: d33e89a49a1e5209d5136cdaa13fb6c5 SHA1: f3591085857c8a62c27b034156b19947c0e50a6d SHA256: 6296ffebb57898a6b3ac91d4ab6960837ece58288e9ede6dbc2fd418760a4066 SHA512: 73b358b2e1496295c4c626ed876aa8cff52c97213d42f2984f6dbf9338f9717a8f5869caea214698cf7e30c5f9e94e402cfc013394df2aa6e9c165e52368547a Homepage: https://cran.r-project.org/package=ROCnGO Description: CRAN Package 'ROCnGO' (Fast Analysis of ROC Curves) A toolkit for analyzing classifier performance by using receiver operating characteristic (ROC) curves. Performance may be assessed on a single classifier or multiple ones simultaneously, making it suitable for comparisons. In addition, different metrics allow the evaluation of local performance when working within restricted ranges of sensitivity and specificity. For details on the different implementations, see McClish D. K. (1989) , Vivo J.-M., Franco M. and Vicari D. (2018) , Jiang Y., et al (1996) , Franco M. and Vivo J.-M. (2021) and Carrington, André M., et al (2020) . Package: r-cran-rocnit Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rocnit_1.0-1.ca2404.1_all.deb Size: 14312 MD5sum: b9f3a5720a7aeddcbb7e4538da544003 SHA1: f53c6ea8521fbdd218397d74a64fa536f36c5ea3 SHA256: 7967782919b8d3dcdc563aa7839ed7b8a92f1ac9ebf0c2b99d5b2b23224af3e0 SHA512: 1300c61e0806e47fcdccaac13ffc6789bbaccfb4d8c11edeb247de365da050aeda602b3d3a667e2e58b915a4c7ab65ce2331f2d757b460649227801e257d6f07 Homepage: https://cran.r-project.org/package=rocNIT Description: CRAN Package 'rocNIT' (Non-Inferiority Test for Paired ROC Curves) Non-inferiority test and diagnostic test are very important in clinical trails. This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test. Package: r-cran-rocnp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocnp_0.1.0-1.ca2404.1_all.deb Size: 38210 MD5sum: 054b9881b79eaff10c81732043371de5 SHA1: 3b463cf45b08c8258d5844e8b3eb0fce0b9bd689 SHA256: ba5cd14d33e6153dee0ead749f1ce18053619fe3b228b19e4175b940e8b9d499 SHA512: a9eea58d5a5d3c70aff6cc120c404c1780d060bc727962f50dd9376ce32acb1cbacfe6a4f952cfe106a54a59a04d4f97b6ad8fea03cda7fced1ef2f8b6fae0bd Homepage: https://cran.r-project.org/package=rocnp Description: CRAN Package 'rocnp' (Work with Romanian Personal Numeric Codes PNC / CNP) A set of tools for working with Romanian personal numeric codes. The core is a validation function which applies several verification criteria to assess the validity of numeric codes. This is accompanied by functionality for extracting the different components of a personal numeric code. A personal numeric code is issued to all Romanian residents either at birth or when they obtain a residence permit. 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Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) ; Faraggi, D. (2003) ; Gu, J. et al. (2008) ; Inacio de Carvalho, V. et al. (2013) ; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) ; Janes, H., and Pepe, M.S. (2009) ; Pepe, M.S. (1998) ; Rodriguez-Alvarez, M.X. et al. (2011a) ; Rodriguez-Alvarez, M.X. et al. (2011a) . Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) for more details. 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Enables automated selection of the distribution families to be fit when smoothing ROC curves via the population probability density function estimation strategy described by Leeflang et al. (2008) , as well as generation of diagnostic performance and cutoff estimates from the resultant smoothed curves. 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Inspired by the lintr package. 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Supported cone types include the positive orthant, second-order (SOC), rotated second-order (bridged automatically to standard SOC), exponential (primal and dual), power (primal and dual), and semidefinite (symmetric-vectorised) cones, as well as their mixed-integer variants. The reader translates a .cbf file into an ROI 'OP' object, handling coordinate-convention differences between CBF and ROI transparently; the writer serialises an ROI 'OP' object back to CBF plain-text. 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Package: r-cran-roi.plugin.quadprog Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-roi, r-cran-slam Filename: pool/dists/noble/main/r-cran-roi.plugin.quadprog_1.0-1-1.ca2404.1_all.deb Size: 17096 MD5sum: ef07b2b922faec201b5aeabc58c5a74b SHA1: cfa33cbdbb5c91e02795b1ab78fd753617d29c98 SHA256: 78011fbf81f7fa3c50ec06ed3d73f21f0be6db1d9d06bfa7c21b3733ca6d93e3 SHA512: 9fbbf82a93cce0142d715628615f1ca432dc097d5587205b1d98c8974d5fc6563c271dc3cf2d0076e1e2afea8d5c332d84f9c83b3069f43f4b748fd3ac17b2b1 Homepage: https://cran.r-project.org/package=ROI.plugin.quadprog Description: CRAN Package 'ROI.plugin.quadprog' ('quadprog' Plug-in for the 'R' Optimization Infrastructure) Enhances the R Optimization Infrastructure ('ROI') package by registering the 'quadprog' solver. It allows for solving quadratic programming (QP) problems. Package: r-cran-roi.plugin.scs Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam, r-cran-roi, r-cran-scs Filename: pool/dists/noble/main/r-cran-roi.plugin.scs_1.1-2-1.ca2404.1_all.deb Size: 36890 MD5sum: 2910ec5c85ca7a8c8edaef72c1f54b12 SHA1: bbeb5bdbb6e4e7ed43f6e8389f351cacb6fbfdae SHA256: 02e27d3f8ea61da8656b19825b901c9eabe1ac57476084b97cdf4d911ea4ae40 SHA512: 4a312a4abc06710a51f93f91196d0b697048a32db2334a388cccdf739249c91dafe6ffeac17775fad4d94a3bcb9e40a0a5638317f32c3f9db829ac7a35609d2e Homepage: https://cran.r-project.org/package=ROI.plugin.scs Description: CRAN Package 'ROI.plugin.scs' ('SCS' Plug-in for the 'R' Optimization Infrastructure) Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'SCS' solver for solving convex cone problems. Package: r-cran-roi.plugin.symphony Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roi, r-cran-rsymphony, r-cran-slam Filename: pool/dists/noble/main/r-cran-roi.plugin.symphony_1.0-0-1.ca2404.1_all.deb Size: 16304 MD5sum: 5250cad3aedf39a1077380651c63f5b4 SHA1: 359e4dd598b0e6eb4be15f8cb392fe0e1d94896d SHA256: 8bf2ad147a8760320ae30fd4ae5bd41a22f3af5b253539145bb3c0752c719b47 SHA512: f8b7e83da17ffb072b2f1094b0f9f51f00ae13fc4cc01c5efeef3badd3c78114bf41cdf4419c7c20ee4294f4bfe0f8191a7ffc6f2deb2aecac74650141d6ebe4 Homepage: https://cran.r-project.org/package=ROI.plugin.symphony Description: CRAN Package 'ROI.plugin.symphony' ('SYMPHONY' Plug-in for the 'R' Optimization Interface) Enhances the R Optimization Infrastructure ('ROI') package by registering the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for solving mixed integer linear programming (MILP) problems as well as all variants/combinations of LP, IP. Package: r-cran-roi Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-registry, r-cran-slam, r-cran-checkmate Suggests: r-cran-numderiv Filename: pool/dists/noble/main/r-cran-roi_1.0-2-1.ca2404.1_all.deb Size: 471946 MD5sum: e84b552ed62763ac157c28d0a819f7a1 SHA1: 6ee5c67dd530d57bdfaabaa472936b694e87fb88 SHA256: 9a014bb488d29d3a1f3c6fc00d9ed7188412a40818eea2007659505ed3459c52 SHA512: 427cf16bd1eb6c86120af25822ff0f4e918f39f0d252ddb320b2467e3a787b1208d06ab408c9aec31d54b508eef3354453aabb4f62fae3c5b77a4cab49fbb657 Homepage: https://cran.r-project.org/package=ROI Description: CRAN Package 'ROI' (R Optimization Infrastructure) The R Optimization Infrastructure ('ROI') is a sophisticated framework for handling optimization problems in R. 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Package: r-cran-rolap Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17646 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dm, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-snakecase, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-when, r-cran-xlsx Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-maps, r-cran-pander, r-cran-pivottabler, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rolap_2.5.2-1.ca2404.1_all.deb Size: 2343890 MD5sum: 5065622f89bd6a0880d00e78c66251cc SHA1: 98675169a07bf8ce6e6123477255617a26d1fb2d SHA256: 2091ec3ed39cd34c54cad6104cd363739a9bff8d5a2ed20a5e371d22ae124f75 SHA512: f4aff96f5e8f23a861a25a28afb308430c662daab8f5e96445c040ea632ffec24d3578d58cfaf7408e96d408afc332e924ced78df03f0e04a6809a1cf68b7b6b Homepage: https://cran.r-project.org/package=rolap Description: CRAN Package 'rolap' (Obtaining Star Databases from Flat Tables) Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. 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Package: r-cran-rolescry Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-moments, r-cran-diptest, r-cran-stringdist, r-cran-readxl, r-cran-openxlsx, r-cran-haven, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rolescry_0.2.0-1.ca2404.1_all.deb Size: 170882 MD5sum: 2582ff1b3e676a29aeadcbb8c9162e86 SHA1: 014c00a5e26bb4372423d025b2ecc58a5533f55c SHA256: 77faff80dd0c33b6598f6f0b8d8a58d6b4f835567d456b942272c3083c178e87 SHA512: 23925769bde8a41950dbbc6e47184a38e021a27c2302565c663c863407024279f846258c6ae416c4900ce1e912666c0e959b877dfa90b47c7a84ec5b21246725 Homepage: https://cran.r-project.org/package=rolescry Description: CRAN Package 'rolescry' (Name-Blind Variable-Role Detection by Data Signature) Deterministic, name-blind detection of variable roles (group, outcome, survival time and event, paired and agreement measurements, repeated measures, scale items, subject identifier, covariate) in tabular data. Roles are assigned from each column's information-theoretic signature -- Shannon entropy, normalized mutual information, and distributional shape -- rather than from column names, so renaming columns to 'col_1', 'col_2', ... does not change the result ("Data inspice, non nomen"). An optional, capped name-based hint and automatic header-row detection are also provided. No large language models and no external data transmission. Extracted from the 'MDStatR' biostatistics engine; see Boynukara (2026) . Package: r-cran-rollama Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4838 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-base64enc, r-cran-covr, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-s7, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rollama_0.3.1-1.ca2404.1_all.deb Size: 3350456 MD5sum: 5785bfd042455c995d19404aed32aeb9 SHA1: a75e1d056fff08042006533acc830421da90f8c6 SHA256: 267711a6e148fc6ebaf2a20e7b0743dadb9e5dabf90a99b259fcdaaa8d581279 SHA512: 7a6be0a01fc5a3a2df946fc7646e3bef3e59284040bb882e8504f5133d9bc6abe1b146a4a4d673998510bfb15896f9427ee7952e674249a2382060a556e4222e Homepage: https://cran.r-project.org/package=rollama Description: CRAN Package 'rollama' (Communicate with 'Ollama' to Run Large Language Models Locally) Wraps the 'Ollama' API, which can be used to communicate with generative large language models locally. Package: r-cran-rollbar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-rollbar_0.1.0-1.ca2404.1_all.deb Size: 22806 MD5sum: b2da09dd24559ba915589b7c3b54da52 SHA1: 3dbd89721bff1135a71de7d666b951f501da9975 SHA256: 441f9d9b464e20f63117411044e92b7fe56449a49b844126eed9e08ec95ad325 SHA512: 18c8e2387c00e1dda1b854cdf89e4ac2406a5094f569d419abaeba872574bc77356504fd70ca5a7bf66f7e6c639e5fe703f7f69752a239b32465ee86f10b1c00 Homepage: https://cran.r-project.org/package=rollbar Description: CRAN Package 'rollbar' (Error Tracking and Logging) Reports errors and messages to Rollbar, the error tracking platform . Package: r-cran-rolldown Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-bookdown, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rolldown_0.2-1.ca2404.1_all.deb Size: 86258 MD5sum: 93b8ed7b79558e2e9d78acf707fefaac SHA1: 016fc5b77ed8f758684cf306c1ab190e0613330f SHA256: 57847d7e5fba0016af7120980bedc68b234f558559189fd8fbc30d7f6cc85f88 SHA512: c130803abc87b71dadc1d5f71dc567215aebf85d86b7e32a4fc46beb159ca2916eddf070b26eda8c51fff2d57ab46412e875a5a62d4ac790580080bfde4b7cd0 Homepage: https://cran.r-project.org/package=rolldown Description: CRAN Package 'rolldown' (R Markdown Output Formats for Storytelling) R Markdown output formats based on JavaScript libraries such as 'Scrollama' () for storytelling. Package: r-cran-rollmatch Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2413 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rollmatch_2.0.4-1.ca2404.1_all.deb Size: 2372046 MD5sum: e242e1bad1f077f62bc4f4c79c231fcb SHA1: 2e708cbb09b1a26d6d8abafb68f91da78f8aeb6a SHA256: 3d6c1eb1c9e8e9cf258793811b2af7f71049aaa9fa4dccc225a9a717f79b94fe SHA512: ba5fc14f82bc4edf197dcf5ff7d87da78a66bb119c030ee2dde0dfbb7a0b8b888d25cc7e8b6c9aa27e49194bf75ec5fc332d32e5f95b4f0368b5646a8e29c2f2 Homepage: https://cran.r-project.org/package=rollmatch Description: CRAN Package 'rollmatch' (Rolling Entry Matching) Functions to perform propensity score matching on rolling entry interventions for which a suitable "entry" date is not observed for nonparticipants. For more details, please reference Witman et al. (2018) . Package: r-cran-rollout Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom.mixed, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-pbapply, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-broom, r-cran-lme4, r-cran-lmertest, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rollout_0.2.0-1.ca2404.1_all.deb Size: 113698 MD5sum: ffeb43f51c2875aab4dd53705cd2704b SHA1: 0228e7156ea2eaaa8aa0f481dc4a3cf3cc028de4 SHA256: 7cc3856c06b581b87afc9ce5ab31ca33dbdce63632655ba6f3dba7908fc7900d SHA512: f91c747fc3e1b5882d5adb30c96b6e9ca11cdd9294cf864c9e81eecb838bdfe61e9763ecbf772d5d7f6ff0ae3a45e8ba6e46b579cb3c51b7881d8572a85f2dc4 Homepage: https://cran.r-project.org/package=rollout Description: CRAN Package 'rollout' (Tools for Designing, Simulating, and Analyzing ImplementationRollout Trials) Provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians. Package: r-cran-rollup Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-sparklyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rollup_0.1.0-1.ca2404.1_all.deb Size: 323420 MD5sum: cb590a97d95aae773411c1b3a248bfab SHA1: 6de38ae324e390ad23b1814b0fd4f1694aee9a22 SHA256: 13765366b31effab120625d84be58a9eb7138a839dda7361e690084ad3118ea9 SHA512: c86f9ceecb0e2a190de837f1b09c86ec6985ec02e67dea2fa4bdc4717f06a67f488fff2d1cb3e726474ce30baabf29b89c3a4b69fbc013bc6cf51d3bac568a9a Homepage: https://cran.r-project.org/package=rollup Description: CRAN Package 'rollup' (A Tidy Grouping Set Aggregation) A Tidy implementation of 'grouping sets', 'rollup' and 'cube' - extensions of the 'group_by' clause that allow for computing multiple 'group_by' clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" . Package: r-cran-rolluptree Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rolluptree_0.4.1-1.ca2404.1_all.deb Size: 267328 MD5sum: 87a52ace0818c1f5468fabab2f032a29 SHA1: ae0ec4ff044ba257d5373abc29b0a2c72f8c2d12 SHA256: affbf733901c78f1c80f25d612d57d320f1add0faefa46cc3c991b71a4a860c2 SHA512: 9b7dd4bce3528e7bfdd95b9f08a5c1893fa53c186ba104e19740bbf78364fe30302a237c9442deead17b8c7dd94c60da07c0edb73e5a5a42c5104e7a0e2fda97 Homepage: https://cran.r-project.org/package=rollupTree Description: CRAN Package 'rollupTree' (Perform Recursive Computations) Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the 'igraph' package of Csárdi, et al. (2006 ). Package: r-cran-roloc Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roloc_0.1-2-1.ca2404.1_all.deb Size: 115580 MD5sum: a347df80aa50c0e41ea3d324e01f9f63 SHA1: b8e8b988baf350d18ef8c6ba0d764257b77fe65d SHA256: adaf5a2e8c7302c69ace2bed1643a133f69688982040cd175ed41912b9b8ee34 SHA512: c2ac62edf12f57fb2c75ea440a9e4a040c8df53694924b1e0b4cf6c483c8d5e2b4ac2b4429e1a13f279eb48c2026f8330043306f4750659ae6ac3350b0361801 Homepage: https://cran.r-project.org/package=roloc Description: CRAN Package 'roloc' (Convert Colour Specification to Colour Name) Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion. Package: r-cran-rolocisccnbs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6090 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roloc, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-rolocisccnbs_0.1-1.ca2404.1_all.deb Size: 5300736 MD5sum: 3454636121c75bd809253ba5fb310382 SHA1: 3e6152ef03919e5f245f4f18d9f6654daa8d2431 SHA256: c261bea5eebffc8636a0ce9fd9e36a778ac22b1af4e76ec6b41bf4db6b03697a SHA512: 9379d1770243e60698d5df919d6a0a054d0cd2f8f684cc51cd79b23ce4818cf14d5601d9fb01e6d8213c8e76462dd3dca6cda10063f06c10a9d7d870ec294d75 Homepage: https://cran.r-project.org/package=rolocISCCNBS Description: CRAN Package 'rolocISCCNBS' (A Colour List and Colour Metric Based on the ISCC-NBS System ofColor Designation) A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names. Package: r-cran-rologit Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-evd Filename: pool/dists/noble/main/r-cran-rologit_0.1.3-1.ca2404.1_all.deb Size: 125690 MD5sum: 4962668099d574744f41bee1fa1979f8 SHA1: 4ebd34546f9c0a859d8321ab7dc5f592220be37f SHA256: 170e980b3684c01e494585f8496d0fded4fd7bd3256cb6db8a7665ab61c6a5f7 SHA512: 9153315f8d18193f5ad0dfea2fc6c8811b33a2c96a98b8c2ec0d32eb91cf2166a6579767882bc1ceead6bcc1dcf9a1d4adb92e3ea302d35692568c63e8657bf7 Homepage: https://cran.r-project.org/package=ROlogit Description: CRAN Package 'ROlogit' (Fit Rank-Ordered Logit (RO-Logit) Model) Implements the rank-ordered logit (RO-logit) model for stratified analysis of continuous outcomes introduced by Tan et al. (2017) . Model diagnostics based on the heuristic residuals and estimates in linear scales are available from the package, and outcomes with ties are supported. Package: r-cran-rolr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rolr_1.0.0-1.ca2404.1_all.deb Size: 47558 MD5sum: de93b33ab53a659293fde451ba700ce8 SHA1: 893fdf081f09864468ea0ac5aab3d9915c05e768 SHA256: dc37670c6efcbffd3f9b247f83f9dbe16f40fdced50d5c45927fedfb361cdc05 SHA512: 032aa7bba0611fa6b2b088652eacdc0c7d15d5728058f8e5a911130541f4c8ab7e2be3d47092ccc159311d82928502722c37460081294b8ef805c682207706d4 Homepage: https://cran.r-project.org/package=rolr Description: CRAN Package 'rolr' (Finding Optimal Three-Group Splits Based on a Survival Outcome) Provides fast procedures for exploring all pairs of cutpoints of a single covariate with respect to survival and determining optimal cutpoints using a hierarchical method and various ordered logrank tests. Package: r-cran-rolwinmulcor Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-zoo, r-cran-pracma, r-cran-colorspace, r-cran-scales Filename: pool/dists/noble/main/r-cran-rolwinmulcor_1.2.0-1.ca2404.1_all.deb Size: 127394 MD5sum: 58c04840a3aa3882b7ea046863d6f7af SHA1: 0f28d3a3242fa39927eb8d6c9d24a80e45ff4602 SHA256: a3d7de4708957b187ad89d4cac85238fb559c49ccfb8ddd671d7907cd7ebac18 SHA512: 7a2c3975859643cd5f7a909a337b2383f1e22ef71d258372b3d834f9b6b5c3cc85525c523a8a739ba3bd031ff0932f41c73764deb69fcf627e48be42dc7311f0 Homepage: https://cran.r-project.org/package=RolWinMulCor Description: CRAN Package 'RolWinMulCor' (Subroutines to Estimate Rolling Window Multiple Correlation) Rolling Window Multiple Correlation ('RolWinMulCor') estimates the rolling (running) window correlation for the bi- and multi-variate cases between regular (sampled on identical time points) time series, with especial emphasis to ecological data although this can be applied to other kinds of data sets. 'RolWinMulCor' is based on the concept of rolling, running or sliding window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains six functions. The first two focus on the bi-variate case: (1) rolwincor_1win() and (2) rolwincor_heatmap(), which estimate the correlation coefficients and the their respective p-values for only one window-length (time-scale) and considering all possible window-lengths or a band of window-lengths, respectively. The second two functions: (3) rolwinmulcor_1win() and (4) rolwinmulcor_heatmap() are designed to analyze the multi-variate case, following the bi-variate case to visually display the results, but these two approaches are methodologically different. That is, the multi-variate case estimates the adjusted coefficients of determination instead of the correlation coefficients. The last two functions: (5) plot_1win() and (6) plot_heatmap() are used to represent graphically the outputs of the four aforementioned functions as simple plots or as heat maps. The functions contained in 'RolWinMulCor' are highly flexible since these contains several parameters to control the estimation of correlation and the features of the plot output, e.g. to remove the (linear) trend contained in the time series under analysis, to choose different p-value correction methods (which are used to address the multiple comparison problem) or to personalise the plot outputs. The 'RolWinMulCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from H. Abdi. (2007) , R. Telford (2013) , and J. M. Polanco-Martinez (2020) . Package: r-cran-rolwinwavcor Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveslim Filename: pool/dists/noble/main/r-cran-rolwinwavcor_0.4.0-1.ca2404.1_all.deb Size: 95418 MD5sum: ef3e3e76accdc42279bebe1872b9f0a0 SHA1: d37e3bcfb6ef1f89d5e53892fc3e6fba1614d9e2 SHA256: 9110b6915e4d1f6eb87a03b462ed7f4cf6b0daca2bd4083df31de1b123b5b37b SHA512: d7ca65da54412ee56118ab4d5cedd06d0f2bd81a4b778446728d06b5a8f3913783d64cb21fc35a6bcc617b3495b2cbd698988deacf9fa380ecf00860b8662e65 Homepage: https://cran.r-project.org/package=RolWinWavCor Description: CRAN Package 'RolWinWavCor' (Estimate Rolling Window Wavelet Correlation Between Two TimeSeries) Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. 'RolWinWavCor' also plots at the same graphic the time series under study. The 'RolWinWavCor' was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in 'RolWinWavCor' are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in 'RolWinWavCor'. Methods derived from Polanco-Martínez et al (2018) ). Package: r-cran-romdb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-httr, r-cran-stringr, r-cran-rodbc, r-cran-magick Filename: pool/dists/noble/main/r-cran-romdb_0.1.0-1.ca2404.1_all.deb Size: 130740 MD5sum: 7087e036e05cd112e7d3bce10b7a816d SHA1: 7f041e749dd98e7ae862c74c388b4fea9da54a4b SHA256: b708827d1bafc2597ca9d3f547e071a5668ec57496a14743cccec1b341dd5d6a SHA512: 4161ab3d5bf88355d10dc32aaa851fa0681e664606188060c6c895b7dfd5b107409d97bd84f710c060a48be5b7a4a60a65b65045e6f1664a649b53103721e645 Homepage: https://cran.r-project.org/package=ROMDB Description: CRAN Package 'ROMDB' (Get 'OMDB' API Multiple Information) Load multiple movies, series, actors, directors etc from 'OMDB' API. More information in: . Package: r-cran-rome Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4025 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-timedate, r-cran-stringr, r-cran-ggplot2, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-zip, r-cran-maps, r-cran-sp, r-cran-dplyr, r-cran-ggrepel, r-cran-magrittr, r-cran-geosphere, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-svdialogs, r-cran-shinyjs Filename: pool/dists/noble/main/r-cran-rome_0.2.4-1.ca2404.1_all.deb Size: 3975998 MD5sum: 5c388f258e391aa6994cdc5ae7d2650d SHA1: 0766e6991c351bc4f7b21331eb0a54d8740b80af SHA256: f5b326da36aec5289effe80c044633f78e272405214f72a0312ee97b1f6a147e SHA512: c254d4e7571a6261e5bda1bb923c47109c4573d37c5831e4c636ca263e948829b3fa6be8b55656d293cb33883ebc27803f5a8dd761bba8d3c3941a6f3ec49157 Homepage: https://cran.r-project.org/package=RoME Description: CRAN Package 'RoME' (Multiple Checks on MEDITS Trawl Survey Data) Provides quality checks for MEDITS (International Bottom Trawl Survey in the Mediterranean) trawl survey exchange data tables (TA (Haul data), TB (Catch data), TC (Biological data), TE (Biological individual data), TL (Litter data)). The main function RoME() calls all check functions in a defined sequence to perform a complete quality control of TX (Generic exchange data) data, including header validation, controlled-vocabulary checks, cross-table consistency tests, and biological plausibility checks. No automatic correction is applied: the package detects errors, warns the user, and specifies the type of error to ease data correction. Checks can be run simultaneously on multi-year datasets. An embedded 'shiny' application is also provided via run_RoME_app(). References describing the methods: MEDITS Working Group (2017). Package: r-cran-romeb Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-romeb_0.2.0-1.ca2404.1_all.deb Size: 64490 MD5sum: 7e1b40c9a8230e55fc4cafb1e0812e2b SHA1: 0c93e2ca87a041ce8f53100f67f15a1a165d5363 SHA256: 7c4aa99ad3f107bc8f79bbeb5ac7188e553f9c558067ecea4d97fcf58e24b528 SHA512: 656ed8a6f047db234fc934b2c73a126e7611c1d9060398f1e6bc672f67a3e158339cedaf25da517dd65e0595e47c08cd018e4013863897e425a5b9727a8e078d Homepage: https://cran.r-project.org/package=Romeb Description: CRAN Package 'Romeb' (Robust Median-Based Bayesian Growth Curve Modeling) Implements robust median-based Bayesian linear growth curve models for complete data and for data with Missing Completely at Random (MCAR), Missing At Random (MAR), or Missing Not At Random (MNAR) mechanisms. Models are fitted using 'rjags' through 'JAGS' and posterior summaries are computed with 'coda'. The main function allows users to specify outcome variables, auxiliary variables for MNAR missingness models, prior hyperparameters, and initial values directly through function arguments. 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Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap. Package: r-cran-romney Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-psych Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-romney_0.1.1-1.ca2404.1_all.deb Size: 159386 MD5sum: 133747f2dcbce5ca19b6a569609470b5 SHA1: 494ebc491231cf66b36f71a24bac56bcd9669c2f SHA256: a7bcab96d9c0828dd2840d52c8c221d8f76890704e6d1c03bdecb3cea9aa84d9 SHA512: 66103571c45d4d6a2401fbb043553f8a0c9649b62e57eb65e9607d16b47d605b7f052ffea92b4a83cf8986e0c948bec3efaba93ddd211566fd803fc2791129fc Homepage: https://cran.r-project.org/package=Romney Description: CRAN Package 'Romney' (Classical Cultural Consensus Analysis) Implements classical cultural consensus analysis with formal, informal, and covariance agreement models, 'UCINET'-aligned minimum-residual factor extraction, competence estimation, and answer-key estimation. Based on the classical framework of Romney, Weller, and Batchelder (1986) , Romney, Batchelder, and Weller (1987) , and Weller (2007) . Package: r-cran-ronfhir Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ronfhir_0.4.0-1.ca2404.1_all.deb Size: 147412 MD5sum: bc9690128603a13527da1c7de30cd15a SHA1: 7c2cece4d73bf6d3b77533e7045c2e555c088165 SHA256: cf0c0ef649b04e4259504c33a3901cdb6234d63a949cd9ac1c0c0a782836bc25 SHA512: 51a291de151bb7afba7356894930a7e338a70b6030ec1e996e7b23eeaffe1dae91332cd23432a2ca5accc683eaacdc3735aa093ea213090a1ca9a5ca1e062e0a Homepage: https://cran.r-project.org/package=RonFHIR Description: CRAN Package 'RonFHIR' (Read and Search Interface to the 'HL7 FHIR' REST API) R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely. Package: r-cran-ronfig Architecture: all Version: 0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carrier, r-cran-cli Suggests: r-cran-litedown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ronfig_0.0.10-1.ca2404.1_all.deb Size: 29246 MD5sum: 20b7a073312f6909c6c369e6f130e6da SHA1: e104bd82ece474178c58587d60305779a9971b44 SHA256: c265962e1463a3e2b08ce032c67a43b406b496bd911e3b114335fb40002d7028 SHA512: 5fc3a89e7a60edd66e205c2aa165ee44042d236c060aebf0e8c88f2f2e8af3f4e7fefc7f34533dfb98976595de139b3e2a03820a2d78539db1695349a50c50a5 Homepage: https://cran.r-project.org/package=ronfig Description: CRAN Package 'ronfig' (Load Configuration Values) A simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly. Package: r-cran-roopsd Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-lmoments, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-roopsd_0.3.9-1.ca2404.1_all.deb Size: 459568 MD5sum: 0983d59d0305fcf4ccf44ce2f9609d19 SHA1: aacd3c59d1deb456fc4fdb54a020e314c98aeb76 SHA256: 3af2ff16e0755499dd21cff9609f21562b914db2f29689e07b8f4f6c413e9443 SHA512: 07b0163703dc275fb891cf537d62284340f1529e9cc2960b012d65b274064008e61beffe9093a2a8f24b0d3e6b100c550885223b7223f08f3e8db0c4290c3e03 Homepage: https://cran.r-project.org/package=ROOPSD Description: CRAN Package 'ROOPSD' (R Object Oriented Programming for Statistical Distribution) Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) . Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) . Package: r-cran-root Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-rpart, r-cran-gbm, r-cran-withr, r-cran-rpart.plot Suggests: r-cran-mlbench, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg Filename: pool/dists/noble/main/r-cran-root_0.2.0-1.ca2404.1_all.deb Size: 600884 MD5sum: 61cb1a9b19fe61eb1934af6d93f795c2 SHA1: 4d8964f630cf691cccb95c6cee754912585a6c45 SHA256: 4340fa6054fc70dfc468d024b48084a0d383c6005e3e739733cd54e31b561e8f SHA512: 1ba9a145f52ebc94a7caac08e6e38fca5ed66e8c22be0fe573c2abb96b84f0dfb1723c862095a129d0b23b48e4f40a08c5d7ce807c97452702dbe764f389cc8a Homepage: https://cran.r-project.org/package=ROOT Description: CRAN Package 'ROOT' (Rashomon Set of Optimal Trees) Implements a general framework for globally optimizing user-specified objective functionals over interpretable binary weight functions represented as sparse decision trees, called ROOT (Rashomon Set of Optimal Trees). It searches over candidate trees to construct a Rashomon set of near-optimal solutions and derives a summary tree highlighting stable patterns in the optimized weights. ROOT includes a built-in generalizability mode for identifying subgroups in trial settings for transportability analyses (Parikh et al. (2025) ). Package: r-cran-roots Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-animation, r-cran-rarpack, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-roots_1.0-1.ca2404.1_all.deb Size: 123154 MD5sum: deb36b64e0f314c7df54cd6dcde8a4e3 SHA1: f74bec022c930b5542dd54af71a82d4e13497e0f SHA256: 2ce19ce0b5401df2c1ceffe5fa749adefb64a54cdb7e2598adf25aba1b9a2767 SHA512: b9f5ed4106809d9f15cbf274231a9030a79ad4b6066871eb8909c68025e8ef8c3722687e9ada8f4a27f9c08f1476d1f2bc9705d5909ff9f568b6af7e7a8e18ad Homepage: https://cran.r-project.org/package=roots Description: CRAN Package 'roots' (Reconstructing Ordered Ontogenic Trajectories) A set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data. Package: r-cran-rootscanr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-png, r-cran-abind Filename: pool/dists/noble/main/r-cran-rootscanr_0.0.1-1.ca2404.1_all.deb Size: 182700 MD5sum: 5390aabc70a8673c921dd34b2785973b SHA1: 3be28a934b1460e71dbfe7c6ab1622693efc4637 SHA256: 54131681480a09b79f552b1051f1eb23869c75053319f10cd3891e8ec6efb17f SHA512: a9c63cad14ea2e917e8d3896f45d092d0f82fe5c053459c0aeacd5d4ee5e9c36d9d89fa6a6536fbf0d220010b8e8b7f572fe00278ac71bae373bbb928bf1146b Homepage: https://cran.r-project.org/package=RootscanR Description: CRAN Package 'RootscanR' (Stitching and Analyzing Root Scans) Minirhizotrons are widely used to observe and explore roots and their growth. This package provides the means to stitch images and divide them into depth layers. Please note that this R package was developed alongside the following manuscript: Stitching root scans and extracting depth layer information -- a workflow and practical examples, S. Kersting, L. Knüver, and M. Fischer. The manuscript is currently in preparation and should be citet as soon as it is available. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM – Artificial Intelligence in Farming funded by the German Federal Ministry of Research, Technology and Space (No. 03WIR4805). Package: r-cran-rootsextremainflections Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iterators, r-cran-foreach, r-cran-doparallel, r-cran-inflection Filename: pool/dists/noble/main/r-cran-rootsextremainflections_1.2.5-1.ca2404.1_all.deb Size: 176224 MD5sum: 88a26b4f20b4248571a5892e83d071bb SHA1: 501193761bbbf4ad1abe71042445ec34b5ba8b18 SHA256: 2b445872314d7c5ed033179aadcfd803b00e09ab4ed8b526aec54294b5df0415 SHA512: 4dcaf9d1d396a9637a6839e117eb0076a8dce1f8b1badc4019161937956fbfb4ff427328f7be42160f8ce36f707865f2f5106720d5f5f9a882fd520621b6c7ff Homepage: https://cran.r-project.org/package=RootsExtremaInflections Description: CRAN Package 'RootsExtremaInflections' (Finds Roots, Extrema and Inflection Points of a Curve) Implementation of Taylor Regression Estimator (TRE), Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE), Integration Extreme Finding Estimator (IEFE) and Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve . Christopoulos, DT (2019) . Christopoulos, DT (2016) . Christopoulos, DT (2016) . Christopoulos, DT (2014) . Package: r-cran-rope Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rope_1.0-1.ca2404.1_all.deb Size: 963602 MD5sum: 629a026f3ca8803a4268751c3b765c11 SHA1: bcb7b354d555c3f9c3c2c6f7f41a288bb39901cd SHA256: 6ea4fd91e54869f37d43c7e528812828facbc452209c2a02513265f5a508d9f0 SHA512: e618b3b1f8595c8e09f4109f2f48e57643d687d239462465fe59b84712cc757d32ef9aceb73fe664917126a1be67930d54d4a606cad2cc44c1034e9ff17e61ce Homepage: https://cran.r-project.org/package=rope Description: CRAN Package 'rope' (Model Selection with FDR Control of Selected Variables) Selects one model with variable selection FDR controlled at a specified level. 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The Roper Center for Public Opinion Research maintains the largest archive of public opinion data in existence, but researchers using these datasets are caught in a bind. The Center's terms and conditions bar redistribution of downloaded datasets, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one employed. The `ropercenter` package cuts this knot by providing registered users with programmatic, reproducible access to Roper Center datasets from within R. 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(2010), , and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), . Package: r-cran-roptimus Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3635 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-roptimus_3.0.0-1.ca2404.1_all.deb Size: 1113002 MD5sum: 62fa8ad888b9bbca10c414ec40b335d2 SHA1: 42c6b1bbc3a56da4eb13d6301d35c29a2b79bdeb SHA256: 92137c921c9921e500549b2ec8a4b06dd18944c75876e90928e1d24d737b10c9 SHA512: cad67d79fc1fb8ea08f965681538b26d0ab103c193f51b4cbb6f1625da66a966c524bb2abd276ac85a224a33aaf4a6ac7a6651cab0b0b862471a44b6985ef926 Homepage: https://cran.r-project.org/package=ROptimus Description: CRAN Package 'ROptimus' (A Parallel General-Purpose Adaptive Optimisation Engine) A general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. 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'routr' is a package implementing a simple but powerful routing functionality for R based servers. It is a fully functional 'fiery' plugin, but can also be used with other 'httpuv' based servers. Package: r-cran-roxut Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-tinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roxut_0.4.0-1.ca2404.1_all.deb Size: 30364 MD5sum: 2ac74b58d3afe69b23807e19e8c9db08 SHA1: e197fe061297c03d849258ea9d959a5895d426ad SHA256: 6d8836e49945bc1d370f716a3be7d387606df554333902da39cdb61bd98607f1 SHA512: 49eaf1041e1bacbfedb21982a5bb6256b674fa859af19d05f8ab10efbbcf36654dc58114c49d9997a9ace93df4aac68f5d0080edd81ab213c90a2ed40f5adf5a Homepage: https://cran.r-project.org/package=roxut Description: CRAN Package 'roxut' (Document Unit Tests Roxygen-Style) Much as 'roxygen2' allows one to document functions in the same file as the function itself, 'roxut' allows one to write the unit tests in the same file as the function. Once processed, the unit tests are moved to the appropriate directory. Currently supports 'testthat' and 'tinytest' frameworks. The 'roxygen2' package provides much of the infrastructure. Package: r-cran-roxy.shinylive Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-jsonlite, r-cran-lzstring, r-cran-roxygen2, r-cran-stringr Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-roxy.shinylive_1.0.0-1.ca2404.1_all.deb Size: 20290 MD5sum: 3a2a1969caaea5a85c02f8b78a669186 SHA1: 9080765e18573e65a5b7049ec4853b8d8c6fa313 SHA256: b7ff3a86e6072323864fe3f29040da1026700512e0defdec98d28ae6556f92f0 SHA512: f049a1fcd38df63abaf58433b77fd45edfc8fde293e8ecae3325dab8843fa4c09bf13a8a4c31c1f6e6295ebf94c2f08bcf536f43b25437ed14ce730c9fe64f1e Homepage: https://cran.r-project.org/package=roxy.shinylive Description: CRAN Package 'roxy.shinylive' (A 'roxygen2' Extension for 'Shinylive') An extension for 'roxygen2' to embed 'Shinylive' applications in the package documentation. Package: r-cran-roxygen2md Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-desc, r-cran-devtools, r-cran-rex, r-cran-rlang, r-cran-tibble, r-cran-usethis Suggests: r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roxygen2md_1.0.1-1.ca2404.1_all.deb Size: 61318 MD5sum: 8d2ed1120d2858feff44dc5a99b88398 SHA1: 4f23de0fd6970c787323111fee0c84ac077a6471 SHA256: fec8851b958097c33eb947566a225e14f0398f6eb435d8f282078162a4daa3aa SHA512: 23ccb637dee639f59002d9e6081f4ab0f08225b7898e5ffa0146e8eae3c370a8d49c8badff3c3277b2608eff02d7cb4cbb9e5f6ba9212b5195eeb056507b9d4e Homepage: https://cran.r-project.org/package=roxygen2md Description: CRAN Package 'roxygen2md' ('Roxygen' to 'Markdown') Converts elements of 'roxygen' documentation to 'markdown'. Package: r-cran-roxyglobals Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brio, r-cran-codetools, r-cran-desc, r-cran-roxygen2 Suggests: r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-roxyglobals_1.0.0-1.ca2404.1_all.deb Size: 32526 MD5sum: d97e4a5a8b734ebb9e892479914a0d25 SHA1: f1becaa74780282b3e55e0533783759317d1b17d SHA256: 825d91e67b648d3ccd23f0a89be2e8ba6ffdcc36b58e38f5da8425b8301b44cc SHA512: bc67dfa325740ad24598ea10278cbae6850232916c5b921eccb272625dd596beb4597b134eb1b2e4de598b27b53379858e7d3ec4a6785d0574912c0b72ce0a0e Homepage: https://cran.r-project.org/package=roxyglobals Description: CRAN Package 'roxyglobals' ('Roxygen2' Global Variable Declarations) Generate utils::globalVariables() from 'roxygen2' @global and @autoglobal tags. Package: r-cran-roxylint Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-roxylint_0.1.0-1.ca2404.1_all.deb Size: 51784 MD5sum: 86948873f823e6378458b6c00dcd3938 SHA1: 731519597609d8b661e27e4c40183f7ef99cd37d SHA256: 59aff51839722c819166ad6b254752cfd0ddf3ab0dcc54813355054c1c2602be SHA512: 6a42d20413752c307aa4334deea385b36681be24cfd68947d4ca3747ea3a324bcdc165bf143d2393a34b2647e684497b852ea6534a6821b04e5945259545c3bb Homepage: https://cran.r-project.org/package=roxylint Description: CRAN Package 'roxylint' (Lint 'roxygen2'-Generated Documentation) Provides formatting linting to 'roxygen2' tags. Linters report 'roxygen2' tags that do not conform to a standard style. These linters can be a helpful check for building more consistent documentation and to provide reminders about best practices or checks for typos. Default linting suites are provided for common style guides such as the one followed by the 'tidyverse', though custom linters can be registered by other packages or be custom-tailored to a specific package. Package: r-cran-roxyreqs Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-r6, r-cran-rlang, r-cran-roxygen2, r-cran-stringr, r-cran-testthat, r-cran-withr Suggests: r-cran-spelling, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-roxyreqs_1.3.0-1.ca2404.1_all.deb Size: 99310 MD5sum: 1176ee43fce5ede3c62269763a394c7b SHA1: b0d46a3d7b67625ab0e15a5f180caf3921f185d3 SHA256: 6ab589f950a7afb5de199fabdd9516976bffac38bb2a33eba1addd8db62d455e SHA512: 2eeefa09dae2020078bef34916fb8bdc661523dfc020f600f6656c9f1f20343a4d3e619772e8941bac724cd87b40a03bfc00f2d91d0b4b9104783366e8df36ce Homepage: https://cran.r-project.org/package=roxyreqs Description: CRAN Package 'roxyreqs' ('roxygen2'-Style Metadata for Test Cases and FunctionDocumentation) Extends 'roxygen2' to support '@meta' tags for documenting 'testthat' test cases and function specifications. Includes a custom 'JUnit' reporter that exports test metadata as XML properties and validation functions to ensure all exported functions and tests contain required tags. Designed for traceability between requirements and tests in regulated industries such as pharma and finance. Package: r-cran-roxytest Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-testthat, r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-roxytest_0.0.2-1.ca2404.1_all.deb Size: 59568 MD5sum: 8f3e64c15de24e37fa62223b50c8092d SHA1: d316dc7b32c563799f0151aa987da36c15d3ab2f SHA256: 14d1b29111a2ff65e2f6f46a837e5390d8d769e9ad168ab78a0a43ca1625fe92 SHA512: 84935be4c8436b464073faf7368310fc9a2e5378c0f1e6943225926589d9dc4dfbef605e28c608706f40567966a1312f16ef662c063e02a3696ac0e66bfe6a61 Homepage: https://cran.r-project.org/package=roxytest Description: CRAN Package 'roxytest' (Various Tests with 'roxygen2') Various tests as 'roxygen2' roclets: e.g. 'testthat' and 'tinytest' tests. Also other static analysis tools as checking parameter documentation consistency and others. Package: r-cran-roxytypes Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-roxygen2 Suggests: r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roxytypes_0.1.2-1.ca2404.1_all.deb Size: 116472 MD5sum: d9a284454d0040e0c27e17d7805add76 SHA1: 500680c82dfbe1fa42300b43157cf87cc7739fed SHA256: 3758baf0e43cf1670e46906479ec5ad7042314de028746b8c55c3f3ee53459d0 SHA512: 42e9054a54d08c19f207cfb598fa2d1573fcc47b7124cb21da51e94884597482af8ead2647ea6a3514356952e084c014ac6152a054103841932dcc147dc480df Homepage: https://cran.r-project.org/package=roxytypes Description: CRAN Package 'roxytypes' (Typed Parameter Tags for Integration with 'roxygen2') Provides typed parameter documentation tags for integration with 'roxygen2'. Typed parameter tags provide a consistent interface for annotating expected types for parameters and returned values. Tools for converting from existing styles are also provided to easily adapt projects which implement typed documentation by convention rather than tag. Use the default format or provide your own. Package: r-cran-rpaci Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bnlearn, r-cran-ggplot2, r-cran-tidyr, r-cran-ggpubr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpaci_0.2.2-1.ca2404.1_all.deb Size: 1024666 MD5sum: cfb94ff024c42875a95c4e2ac148d4bc SHA1: 5b820afee4ffc24b7f9f60c05d749e4855552ac2 SHA256: 696e0133443f53d908f739203e3584fa949e82ec41eb4440a516b54ae68db560 SHA512: 1a7458332f7f54b201074173e05a64946162b209b9bb0c1173e8bec1d993b4426a52797dab29d3c95567a4fc3312930e3cd2de94f699d1af928a1456e6787cd3 Homepage: https://cran.r-project.org/package=rPACI Description: CRAN Package 'rPACI' (Placido Analysis of Corneal Irregularity) Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)], which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)], [Ramos-Lopez et al. (2013)], and [Ramos-Lopez et al. (2011)]. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician. Package: r-cran-rpackedbar Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-data.table, r-cran-scales, r-cran-shiny Suggests: r-cran-covr, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rpackedbar_0.2.2-1.ca2404.1_all.deb Size: 1105650 MD5sum: b48368481ca462f032e980f3ff734eb3 SHA1: 8d162010e96bb4f871c84f251b4e71d95bb02e66 SHA256: f64f26a857e75dddc78d0bf3b12718dfe0f732be8b6d6efcc2eb5d3f7dde552e SHA512: 52574a8e22a1e75bdc5db650f5780d582d83c207e2694995a6002fb54b416d1737dfd9b91b2f038c57155613ecfacefa3d3744620966cbfbbcaade48756507b6 Homepage: https://cran.r-project.org/package=rPackedBar Description: CRAN Package 'rPackedBar' (Packed Bar Charts with 'plotly') Packed bar charts are a variation of treemaps for visualizing skewed data. The concept was introduced by Xan Gregg at 'JMP'. Package: r-cran-rpadrino Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ipmr, r-cran-curl, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-truncdist Suggests: r-cran-covr, r-cran-knitr, r-cran-maps, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpadrino_0.0.5-1.ca2404.1_all.deb Size: 323582 MD5sum: 2523ebe8ac990da3921c9cbff1425799 SHA1: 3dc4f2a095185c3c19c4c696398d74a7ffad5dd0 SHA256: fdff016bea2313386e3d07297c3bf7ecd67aa0f7985e478118aee52596c7644e SHA512: 9b9be5b7d095e152e51124365e1dd60dd78589d1a34c39431a726b005f50543495062d24d928daa33caac6118c314b1ccc1d0a94e1971d8a486113f5597f07cf Homepage: https://cran.r-project.org/package=Rpadrino Description: CRAN Package 'Rpadrino' (Interact with the 'PADRINO' IPM Database) 'PADRINO' houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. 'Rpadrino' is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) , Merow et al. (2013) , Rees et al. (2014) , and Metcalf et al. (2015) . See Levin et al. (2021) for more information on 'ipmr', the engine that powers model reconstruction . Package: r-cran-rpaex Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-agricolae Filename: pool/dists/noble/main/r-cran-rpaex_1.0.5-1.ca2404.1_all.deb Size: 946240 MD5sum: 092a0d9bbfef9ec869ba6a79218cb2ae SHA1: fd78045a1cfc5b9025919b08afacf69998e134b0 SHA256: db004e06f45742d175c638ca8fb232406b27891a81c02a07304288423b538729 SHA512: 134c492babef65b1347cc4100c1a43542ee24924173dc10deb7ec6e563bbb2ed9c8866f9bb5f61ee73bcd9c815c4c993b4cf7f08ff756f7e53a8a525a1562025 Homepage: https://cran.r-project.org/package=rPAex Description: CRAN Package 'rPAex' (Automatic Detection of Experimental Unit in PrecisionAgriculture) A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop. Package: r-cran-rpaleoclim Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-fs, r-cran-httr, r-cran-rlang, r-cran-terra Suggests: r-cran-knitr, r-cran-covr, r-cran-raster, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpaleoclim_1.1.0-1.ca2404.1_all.deb Size: 491920 MD5sum: 71947cbb7afe0cdb4edc0725456938f2 SHA1: e666a9c4d45e697cf4247284790495a03d2e1e5c SHA256: a7e2d29c16c38e28d686f67d767da9c193f66b571cb0a97fa366fad6e53b3c9d SHA512: 40b9821301be984b528fa3ecfe88673579555a1dc571a1fc02d3e75566be61ccb43cc4ee88a8e68578452916228076d6c39fdc69059eae326ece728e10de6a25 Homepage: https://cran.r-project.org/package=rpaleoclim Description: CRAN Package 'rpaleoclim' (Download Paleoclimate Data from 'PaleoClim') 'PaleoClim' (Brown et al. 2019, ) is a set of free, high resolution paleoclimate surfaces covering the whole globe. It includes data on surface temperature, precipitation and the standard bioclimatic variables commonly used in ecological modelling, derived from the 'HadCM3' general circulation model and downscaled to a spatial resolution of up to 2.5 minutes. Simulations are available for key time periods from the Late Holocene to mid-Pliocene. Data on current and Last Glacial Maximum climate is derived from 'CHELSA' (Karger et al. 2017, ) and reprocessed by 'PaleoClim' to match their format; it is available at up to 30 seconds resolution. This package provides a simple interface for downloading 'PaleoClim' data in R, with support for caching and filtering retrieved data by period, resolution, and geographic extent. Package: r-cran-rpandas Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpandas_0.1.4-1.ca2404.1_all.deb Size: 97816 MD5sum: ad0592b08b55be041972950bf168b9d1 SHA1: b91b3d3576a551c346951e4746f0c6fd9b134101 SHA256: 2994a6138713d97297a91ace53d9e29f302d7be837b4586626a5827dda90ef79 SHA512: c91d87e9a27502943771420bf1b6730e0db93d66a1fbb0509ec45d19959fb738ca94a95c1f87b48f896345881a003b7ae5c6c2e159df1bde28799a64732ef15c Homepage: https://cran.r-project.org/package=rPandas Description: CRAN Package 'rPandas' (Translating from R to Python's Pandas Package) Provides an R interface to Python's 'pandas' library using non-standard evaluation. Users can write R code (e.g., rp_filter(), rp_select(), rp_mutate()) that is translated into pandas commands and executed via 'reticulate'. Supports chaining, grouping, and 'summarisation', and includes a 'table_name' parameter to generate 'copy-pasteable' Python code. Ideal for leveraging pandas' speed and flexibility within the R ecosystem. 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Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above. 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Package: r-cran-rphylo Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-expm Filename: pool/dists/noble/main/r-cran-rphylo_0.1.1-1.ca2404.1_all.deb Size: 647318 MD5sum: 3d06e1e63b7bd10f20c2a72f200d07a2 SHA1: 8e99bd9912964ab3090d78a63b44f49fb871c5b7 SHA256: f00ddb91a7006b9adf906a426450cad0b29454ab2d5dd5f9df03610716d9ae9b SHA512: 27e4b81f53a4651f28442b0931a67f02a87d26f70dea7e5336bf047cedb84dad1dc7bbd47c66ae338d8995dde250c7333fa9aaab2e74fe2b8a7ffea6b4102f95 Homepage: https://cran.r-project.org/package=rphylo Description: CRAN Package 'rphylo' (Phylogenetic Analysis with Dependent Discrete Models) Implementation of dependent discrete models (with reversible jump MCMC) derived from 'BayesTraits' V5.0.3 . Original software copyright Andrew Meade and contributors, distributed under GPL-3. Modifications for this package by Vivian G. Li . The following articles should be referenced when using this package: Pagel, M., A. Meade and D. Barker (2004) "Bayesian estimation of ancestral character states on phylogenies" ; Pagel, M. (1994) "Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters" ; Pagel, M. and A. Meade (2006) "Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo" . Package: r-cran-rphylopic Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2773 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-jsonlite, r-cran-png, r-cran-grimport2, r-cran-rsvg, r-cran-httr, r-cran-httpcache, r-cran-curl, r-cran-lifecycle, r-cran-pbapply, r-cran-knitr, r-cran-scales Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-rmarkdown, r-cran-covr, r-cran-phytools, r-cran-palaeoverse, r-bioc-ggtree, r-cran-deeptime, r-cran-palmerpenguins, r-cran-maps, r-cran-sf, r-cran-igraph, r-cran-ggraph, r-cran-devtools Filename: pool/dists/noble/main/r-cran-rphylopic_1.7.0-1.ca2404.1_all.deb Size: 2109726 MD5sum: e6b206725c9b62aab7d36d692a6af440 SHA1: 828046791dbdb8ebc60446e201ee5a7631ddef9b SHA256: 11edea1ee95f2f389f66373d2e0d1b85831af8e879b49da6ed4310575137f72c SHA512: b47eb7d7b37143f45ec16ce5c5eae224fa8fee1b1987c52e58a43de9f4a4531ab9aab29e9a3ff9e799d8d114660a864eb14cde8a15c4329b1a8e99eecf353156 Homepage: https://cran.r-project.org/package=rphylopic Description: CRAN Package 'rphylopic' (Get Silhouettes of Organisms from PhyloPic) Work with the PhyloPic Web Service () to fetch silhouette images of organisms. Includes functions for adding silhouettes to both base R plots and ggplot2 plots. Package: r-cran-rpinterest Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-rjson, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-rpinterest_0.3.1-1.ca2404.1_all.deb Size: 36696 MD5sum: 5a03c86123a32a00e96582a541f62b2b SHA1: 4967a0a8cf71040f5bd78c5a34f7cea36f3f141a SHA256: a9432262dfda79d952156dcbc6ae601b5b23506a43e549a1f66f38441a6e1f73 SHA512: 7c9b246551f7fe6d190747df53a2a7d1e0d9be94c0dd439ce338c3c21ef2875cb3568bcbec560e03463c0f3eae22937c2a116c611f21a194728df4076c03c440 Homepage: https://cran.r-project.org/package=rpinterest Description: CRAN Package 'rpinterest' (Access Pinterest API) Get information (boards, pins and users) from the Pinterest API. Package: r-cran-rpiv Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpiv_1.1.1-1.ca2404.1_all.deb Size: 75264 MD5sum: a2b03a606cd1c340e71faabb95e3fc45 SHA1: 5f658b58c6def3ca582e47594798d5ed31a59ed2 SHA256: c37fec64fffdf09b32a3eec44571d08a80c48021023b819b1c40750b17673024 SHA512: 378f19c3ee9fbff130b4518c1788242c3a8e6cb052f23f66b5ba0be65b49831c6c289dc66f38ce1ffb3d5275f59d383378624030771b897ec8a855d6a466e496 Homepage: https://cran.r-project.org/package=RPIV Description: CRAN Package 'RPIV' (Residual Prediction Tests for Well-Specification of InstrumentalVariable Models) Two tests for the well-specification of the linear instrumental variable model. The first test is based on trying to predict the residuals of a two-stage least-squares regression using a random forest. The second test is robust to weak-identification and based on trying to predict the residuals for a particular candidate parameter and can also be used to construct confidence sets with an Anderson-Rubin-type inversion. Details can be found in Scheidegger, Londschien and Bühlmann (2025) "Machine-learning-powered specification testing in linear instrumental variable models" . Package: r-cran-rpivottable Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-data.table, r-cran-devtools, r-cran-dplyr, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rpivottable_0.4.0-1.ca2404.1_all.deb Size: 354730 MD5sum: b35441aa07062cfabaaf265972b047ab SHA1: ae475bf606e75dcc014e8883a4640030a92f50fa SHA256: b6fa96075b3d3bf47955cbd90345b1bea5b4ae1030770f358100c18aff4cef79 SHA512: ae70418a18084fd9cfe697041a0f0e0cb5ceaf02b8be53280f93075c787c2ea4a448e794b780d9bfaac47f95a7b208cf835da0d3de75c98621343b9d1eaec99c Homepage: https://cran.r-project.org/package=rpivotTable Description: CRAN Package 'rpivotTable' (Build Powerful Pivot Tables and Dynamically Slice & Dice yourData) Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab) and dynamically slice & dice / drag 'n' drop your data. 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Package: r-cran-rplec Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3966 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-pbapply, r-cran-purrr, r-cran-rpmm, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rplec_0.1.3-1.ca2404.1_all.deb Size: 3248464 MD5sum: 678b424b7b497ecba8147e42e35c2aa5 SHA1: 4bbe2eb2856da20a58c314d8895bca4578594ca6 SHA256: a97d97ce39c232b9f22c6443ff2f89dc3303b6085fdd7a32965fb433c0892d28 SHA512: a6d9694b7955f68e505c8768772361f495c923d37da60fd2ee2fe627f8aec066ee725f9b4b6b4ef1bec36959bf9b40e71a61408290ee669694e9fe1c01e90a41 Homepage: https://cran.r-project.org/package=rplec Description: CRAN Package 'rplec' (Placental Epigenetic Clock to Estimate Aging by DNA Methylation) Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at . Package: r-cran-rplotengine Architecture: all Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xtable Filename: pool/dists/noble/main/r-cran-rplotengine_1.0-9-1.ca2404.1_all.deb Size: 114038 MD5sum: 8dc2c63192fa31f5df44065601459cc7 SHA1: b2ffc0300d08d761929acd07af909a9b66eb8804 SHA256: bc7c71714dd3d73922947cf4259c2d7a8debfe4175a6a520479f417dca4786bc SHA512: f6ae00eb10e4b7cf51e47b975db218141ea0e1fe9d3977e05d782d199bcf09e8b4c9de2f74a5c55a10f66108f94da58186952b941d66cca605c6e500a149324f Homepage: https://cran.r-project.org/package=rplotengine Description: CRAN Package 'rplotengine' (R as a Plotting Engine) Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function 'rplotengine()'. 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Package: r-cran-rpmodel Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rpmodel_1.2.3-1.ca2404.1_all.deb Size: 556822 MD5sum: 48f6a96ac305c7f6b2cf66ad65a253dd SHA1: 0b1df167e8331fd77b97a16e511d403e71381b56 SHA256: 33a5d461580b4d1401b1f415ce5116c7429ac732ab6392ccd7c91a5e22f3863f SHA512: 9e22cb6d5b859fdc0fb0781ee8a7de4d0496ad86aa0890413bbce586afc530f2a777b9a9045e06c71ea5ede77cabb08ad9d3aed738797575860e4a8712946a77 Homepage: https://cran.r-project.org/package=rpmodel Description: CRAN Package 'rpmodel' (P-Model) Implements the P-model (Stocker et al., 2020 ), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure). Package: r-cran-rpnf Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpnf_1.0.5-1.ca2404.1_all.deb Size: 108276 MD5sum: 8afd291903dc72f739dee3a5341a32d4 SHA1: 23f3c90ee9fbbfbc95d59146157e249be8ddd107 SHA256: 2e88916dc006a0b15f2a2a7c0bc82f1386df8fb66bb14c4f33709f631fc8e862 SHA512: 404cdba25cbef99ba881224bbdd424fed8897945d3d1aadac232e383b924ec0581cfe2ecc8baf1909195a99474c0783e63bf7a1bd00cd880a3ace2f98f5739f6 Homepage: https://cran.r-project.org/package=rpnf Description: CRAN Package 'rpnf' (Point and Figure Package) A set of functions to analyze and print the development of a commodity using the Point and Figure (P&F) approach. A P&F processor can be used to calculate daily statistics for the time series. These statistics can be used for deeper investigations as well as to create plots. Plots can be generated as well known X/O Plots in plain text format, and additionally in a more graphical format. Package: r-cran-rpoet Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-httr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rpoet_1.1.0-1.ca2404.1_all.deb Size: 18524 MD5sum: 85c8f8a7653eab53413693e117d01580 SHA1: 0d23c7d01e17fd905790fbaef599d0cd3683eb8d SHA256: 6e2ece01bab8ce50e55257bc2c6c35d755c4432aac6b9cd197828fe0ca91e363 SHA512: 3c4d16473aa9510c24cb29315df55c6225dba78c910e8616bcc9d76da0d4a6506ae3a29d1f8704bf590a8de9ed9bfe557be8cf2fa235a727db3b6f9385ee0675 Homepage: https://cran.r-project.org/package=Rpoet Description: CRAN Package 'Rpoet' ('PoetryDB' API Wrapper) Wrapper for the 'PoetryDB' API that allows for interaction and data extraction from the database in an R interface. The 'PoetryDB' API is a database of poetry and poets implemented with 'MongoDB' to enable developers and poets to easily access one of the most comprehensive poetry databases currently available. 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The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the 'TDA' package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of 'RPointCloud' is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) and (2026) . Package: r-cran-rpolyhedra Architecture: all Version: 0.6.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3047 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-geometry, r-cran-rgl, r-cran-stringr, r-cran-xml, r-cran-digest, r-cran-lgr, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-covr, r-cran-codemetar Filename: pool/dists/noble/main/r-cran-rpolyhedra_0.6.0-1.ca2404.2_all.deb Size: 1546582 MD5sum: 1dc9e62f85d5a0533d46cd49620a1dca SHA1: 6503619cd67d76c5f53edcdf25d9d90c5a95189c SHA256: 0beb68937eb45b537d5c174f583eea646fb3c52abfdab0f23f4444585fa86265 SHA512: ffaccb123bb2743ac1fd80e2f24cd2082c8618ea27ef842a9ccedd802555faec617e3ac1014f2cf4d6ef9c4b688ca89d4e4c0d21ff8de0c5c1799cd132963fb2 Homepage: https://cran.r-project.org/package=Rpolyhedra Description: CRAN Package 'Rpolyhedra' (Polyhedra Database) A polyhedra database scraped from various sources as R6 objects and 'rgl' visualizing capabilities. Package: r-cran-rportfolio Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-rportfolio_0.0.3-1.ca2404.1_all.deb Size: 56178 MD5sum: 9bedb8b47c14c0bf47492e3fd8b61139 SHA1: 4469d6873c8226b710f4344fec16001a1eb478ac SHA256: 68d92f6a2de6652fb3c016eacee9c79a572db1ab3864160ef5854742e15eb7d0 SHA512: e169abc900d85344adeebab2b23e18b2304182e13852c88d73839bcd499244be57ac47db4a511f827d8e09ef0e214dc4afea9871dd64f6637f900c848c6ca04d Homepage: https://cran.r-project.org/package=rportfolio Description: CRAN Package 'rportfolio' (Portfolio Theory) Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. 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This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) . Package: r-cran-rppairwisedesign Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rppairwisedesign_1.0-1.ca2404.1_all.deb Size: 65322 MD5sum: cdaaa2a2ef1fc3ae8bd7544b58121bcd SHA1: 0df3c5e880d2eb19e1b7b3ff00f7f57fadf3ad57 SHA256: 689432a8d89ac16fcf5dfe77edacc224c61bd34977783ae68d62972aec6755b6 SHA512: 3a8014c54aa1a293f2f04c1f0d0d0e181812d7ff7824d89c5ed1977a459443005caa5432ce04fe6b0aed55d1f6e764c5732f70f3c501b32edcfd9720d1689142 Homepage: https://cran.r-project.org/package=RPPairwiseDesign Description: CRAN Package 'RPPairwiseDesign' (Resolvable partially pairwise balanced design and Space-fillingdesign via association scheme) Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs. Package: r-cran-rppanalyzer Architecture: all Version: 1.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5431 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-bioc-limma, r-cran-lattice, r-cran-gam, r-cran-gplots, r-cran-ggplot2, r-cran-hmisc, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-rppanalyzer_1.4.9-1.ca2404.1_all.deb Size: 1523370 MD5sum: 31bb596796fc3c82c70b0ec87d3f5d78 SHA1: 8e7478287f63b514b77e09c0a396de39e94ae66d SHA256: 7feaec33e68a9ec725359c9c7b233379388ddb9694449c4c3394901cddbb4711 SHA512: b5ab2b324ea6d5284398c86ebaa3439baa0c6c9338fb2a32939ea45a95004d29c7eee58cfd7c0f4a6ab8495eeb22772e730572369d4b2bf638275d27dfad4aee Homepage: https://cran.r-project.org/package=RPPanalyzer Description: CRAN Package 'RPPanalyzer' (Reads, Annotates, and Normalizes Reverse Phase Protein ArrayData) Reads in sample description and slide description files and annotates the expression values taken from GenePix results files (text file format used by many microarray scanner and software providers). 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The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage information, and a list of related papers describing methods used in the package please review the vignette 'Guide_to_RPPASPACE'. 'RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data'. Bioinformatics Nov 15;38(22):5131-5133. . Package: r-cran-rpraat Architecture: all Version: 1.3.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-readr, r-cran-dygraphs, r-cran-tuner Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpraat_1.3.2-1-1.ca2404.1_all.deb Size: 747442 MD5sum: 1743c38fe3f9408293caaa08261b9257 SHA1: 2eb8387f58c4f5495f04f29e66daff3d7efe7bb9 SHA256: 0c4539929c997347a4cb9c8e72d87c158af4617784c099f333179927f8cddf07 SHA512: ea53f2220f2dcb317938222fbe62dff04294f6941cec17370e8d5f03c36e7da3bc1918def82d36e203c6386177c24a15caea207f3149af242fd03716bbe8b0f4 Homepage: https://cran.r-project.org/package=rPraat Description: CRAN Package 'rPraat' (Interface to Praat) Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files . Package: r-cran-rpredictit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-dt, r-cran-dygraphs, r-cran-magrittr, r-cran-quantmod, r-cran-xts, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpredictit_0.1.0-1.ca2404.1_all.deb Size: 244924 MD5sum: 514523908cd7d8823fe4b31b26701155 SHA1: 03a2b1f5b3e52765d65b49e05de39d500ff9e2c0 SHA256: 339dc9fef04405976edbc657c52b81192655bed1529a559ae6d18dc63dbbef8b SHA512: 003c35dc565103b89ae2e779054e8e827c28e480b431a747d3d8d5461ca0ea464799877bd8d6cd80db04adaedab317d0e2271509f7726080b49c3e3c8afa42c2 Homepage: https://cran.r-project.org/package=rpredictit Description: CRAN Package 'rpredictit' (Interface to the 'PredictIt' API) Wrapper to retrieve market data, explore available markets, and plot historical price data from the 'PredictIt' public API (). The package comes with a demo 'shiny' application for illustrating example use cases. License to use data made available via the API is for non-commercial use and 'PredictIt' is the sole source of such data. Package: r-cran-rpregression Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stargazer, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rpregression_0.1.0-1.ca2404.1_all.deb Size: 14426 MD5sum: e6081daa04486709deb55458a814c814 SHA1: 529b3f09482e62f60bf302bbe244927cd6476f7c SHA256: fc9a5e6ffe4767fa1122181006f85e5730fd22f04b01847559b54117a70f22e6 SHA512: 6b14cabf43359c103e4088007be1bbb22e0af4e58f4119e0a505b1a3a28848521cdb3b00061d93770b3cfc036da363f6b4ea270be0fe596ebf739db7594a8482 Homepage: https://cran.r-project.org/package=RPregression Description: CRAN Package 'RPregression' (A Simple Regression and Plotting Tool) Perform a regression analysis, generate a regression table, create a scatter plot, and download the results. It uses 'stargazer' for generating regression tables and 'ggplot2' for creating plots. With just two lines of code, you can perform a regression analysis, visualize the results, and save the output. It is part of my make R easy project where one doesn't need to know how to use various packages in order to get results and makes it easily accessible to beginners. This is a part of my make R easy project. Help from 'ChatGPT' was taken. References were Wickham (2016) . 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Package: r-cran-rrepast Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lhs, r-cran-sensitivity, r-cran-ggplot2, r-cran-digest, r-cran-xlsx, r-cran-dosnow, r-cran-rjava, r-cran-gridextra, r-cran-foreach Filename: pool/dists/noble/main/r-cran-rrepast_0.8.0-1.ca2404.1_all.deb Size: 295652 MD5sum: 25a9d57cafab191cad29e14087c2fdaf SHA1: cf60beb81e51473a44aeab241652b482781c480e SHA256: 9d4e74a318f638834f09c1b682ba1ac27573eb1214ab95cb9fad570b011cd86a SHA512: b947515742db16b9c68370970d352153731805fee3ff2ec8923f9ec29bd7cf5b83f084c2a330a015b8b8a09131d0b578abf344b9bea8a836c2375dbd4a0e4726 Homepage: https://cran.r-project.org/package=rrepast Description: CRAN Package 'rrepast' (Invoke 'Repast Simphony' Simulation Models) An R and Repast integration tool for running individual-based (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework directly from R code supporting multicore execution. 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Package: r-cran-rrepest Architecture: all Version: 1.6.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-flextable, r-cran-foreach, r-cran-labelled, r-cran-magrittr, r-cran-officer, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-rrepest_1.6.14-1.ca2404.1_all.deb Size: 957564 MD5sum: e537f18234c02dbae9a81179b102351a SHA1: f4d7c296fb1a872ba6d75ef1c412f91eb86c6dbf SHA256: a45d8c00002c66591ece9e6db7a743a52ce254ba15d09bdb4a93fe4b1e116321 SHA512: d36f115aa75eb795ffd5c29b60b90ec53a8f047f970f9bb509a635e7c850594fac80db14dc34c28d2933d624f1d821af09041d1d8d00ee3f836df5e7d36e9324 Homepage: https://cran.r-project.org/package=Rrepest Description: CRAN Package 'Rrepest' (An Analyzer of International Large Scale Assessments inEducation) An easy way to analyze international large-scale assessments and surveys in education or any other dataset that includes replicated weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...) while also allowing for analysis with multiply imputed variables (plausible values). It supports the estimation of univariate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies, correlation, linear regression and any other model already implemented in R that takes a data frame and weights as parameters. It also includes options to prepare the results for publication, following the table formatting standards of the Organization for Economic Cooperation and Development (OECD). Package: r-cran-rrgeo Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-pbapply, r-cran-rphylopars, r-cran-rrphylo, r-cran-dismo, r-cran-gtools, r-cran-terra, r-cran-adehabitatma, r-cran-ecospat, r-cran-foreach, r-cran-doparallel, r-cran-presenceabsence, r-cran-ade4, r-cran-sp, r-cran-sf, r-cran-scales, r-cran-ks, r-cran-leastcostpath, r-cran-dosnow, r-cran-biomod2 Suggests: r-cran-ggplot2, r-cran-cowplot, r-cran-openxlsx, r-cran-bchron, r-cran-curl, r-cran-rnaturalearth, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-knitr, r-cran-httr, r-cran-jsonlite, r-cran-adehabitaths Filename: pool/dists/noble/main/r-cran-rrgeo_0.0.6-1.ca2404.1_all.deb Size: 2377116 MD5sum: a84c79b7e54e9a8c8de2f95212c23e91 SHA1: a811b315db0e05f1b453a594cfe4a07017d689e0 SHA256: f10b9c57258203e2f9e44b04ff00c30fbd01746b14a297c19f2e92dea737fed7 SHA512: 91f016348558b194a3ae2979c78e119b13b05419ef1f359854e1932d23b561ce7bcd26308b1f1f963df3b41e5f3ab646f75f3a4b9a601d768ca3ca502e97fc84 Homepage: https://cran.r-project.org/package=RRgeo Description: CRAN Package 'RRgeo' (Species Distribution Modelling for Rare Species) Performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 ) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 ). 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Package: r-cran-rrmlrfmc Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-rrmlrfmc_0.4.0-1.ca2404.1_all.deb Size: 70658 MD5sum: 3cdb849f3e654726c0d468e5453fae32 SHA1: fb40f2be6d619c728283208b395574cff01320e3 SHA256: a92274e77d8beb9ee7ad857133d2fce702d81379f17db697db2b34b0fd9d7104 SHA512: 107e49b39a34cfea654faa4b78ed57ac0dd6ba0871da50d758b6ba3d380725826ad2ba8c3ea5945f789ef513541d7faaf10102116af44453c1b8e7d35917f9a8 Homepage: https://cran.r-project.org/package=RRMLRfMC Description: CRAN Package 'RRMLRfMC' (Reduced-Rank Multinomial Logistic Regression for Markov Chains) Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021) in R. 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Package: r-cran-rrmorph Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-morpho, r-cran-rgl, r-cran-rvcg, r-cran-rrphylo Suggests: r-cran-inflection, r-cran-ddpcr, r-cran-ape, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rrmorph_0.0.2-1.ca2404.1_all.deb Size: 1757412 MD5sum: 5fcbfcf02c887024c6a5c6b96f481c98 SHA1: 497272168f9993d448c562fbac42d307831cbbcb SHA256: 592211e871d84bd585868ae4bcf7f09e19b5665c61f583667e6bd629d0b89581 SHA512: 3a0aad89c4d13c907d9abc9734cfcdc0eabd4409cae649a8c6288e3f21bd9e2ab4c5018eb59a272d018978f4216f418005a340dffd5d170ea716c8e2c745eb81 Homepage: https://cran.r-project.org/package=RRmorph Description: CRAN Package 'RRmorph' (3D Morphological Analyses with 'RRphylo') Combined with 'RRphylo', this package provides a powerful tool to analyse and visualise 3d models (surfaces and meshes) in a phylogenetically explicit context (Melchionna et al., 2024 ). Package: r-cran-rrna Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rrna_1.2-1.ca2404.1_all.deb Size: 120454 MD5sum: deb111532f82bd6700d77ed0bd4d3ed7 SHA1: 856c9b0189a28ff3d6cce748447f7c64c3266370 SHA256: 8863a7ef89e36835741f7535bd297b9e76f8ce0ad88dbd4b13469a66cea4128d SHA512: b7ac4aaffc4ca5e9410ea674b5ca9b63a66d22c9c2673005ecf3a8334670d3292c490c5c0efdfec9fcf010d92e4a89c8af928d0bb245cc9f0a78ca23785e20c0 Homepage: https://cran.r-project.org/package=RRNA Description: CRAN Package 'RRNA' (Secondary Structure Plotting for RNA) Functions for creating and manipulating RNA secondary structure plots. Package: r-cran-rroad Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-zoo, r-cran-biwavelet Filename: pool/dists/noble/main/r-cran-rroad_0.0.5-1.ca2404.1_all.deb Size: 379730 MD5sum: 134d952c4e27f099618b1b82dc7b08bc SHA1: 9e2bfae6ebce85aa36eb200e6eeedbe5512b1bdb SHA256: 314f2b7c3f51b04eb51ad5917b7372062e5c50dbfea5f26b6226f596a7cfe4b3 SHA512: 2294e550b54f33368af92e6bc1809757831d42ab077713cf32b8f57e6656c50f62fe0272d5dc422f9b35a56d596edc80345ed97fcbec121295460c719fd039b3 Homepage: https://cran.r-project.org/package=rroad Description: CRAN Package 'rroad' (Road Condition Analysis) Computation of the International Roughness Index (IRI) given a longitudinal road profile. The IRI can be calculated for a single road segment or for a sequence of segments with a fixed length (e. g. 100m). For the latter, an overlap of the segments can be selected. The IRI and likewise the algorithms for its determination are defined in Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements. World Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available from . 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For more details see Saluja, Parlak, and Mejia (2026+) . 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The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence. Package: r-cran-rrpp Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1958 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-matrix, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rrpp_2.2.0-1.ca2404.1_all.deb Size: 1661686 MD5sum: 0ee0b405119735b4d52b085cf0eb57df SHA1: fa1dae68b2ab5ac69b116f74722fa9658a3d6dc5 SHA256: 952ef7957f272c982d4d51ae2f8c527b953fb8aee204e2737873ecb5d6a40742 SHA512: e1de9a0a42237ed933ecfcac9e24141e3681f0fdb7b69816370424506f31b57b3a0654e084b2c74038c258019aaedb3aabccc0ff215b236172f6c1015010ab40 Homepage: https://cran.r-project.org/package=RRPP Description: CRAN Package 'RRPP' (Linear Model Evaluation with Randomized Residuals in aPermutation Procedure) Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well. Package: r-cran-rrr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-ggally Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/noble/main/r-cran-rrr_1.0.0-1.ca2404.1_all.deb Size: 1936258 MD5sum: 37b02df7d2386ed18cfaed535f8064d0 SHA1: 95db558c8eb701b711d586092a2483d8b536b3be SHA256: b47e5ea2aeebf8ee883628477e84f9eeba01e6911ca3f9413e3c5991813d59a3 SHA512: a266be81dca060151e8586f0a370af6da27555fe5c2441a7b9446f73de2964131af69a31d0af608a60bfa56946aeb817118fab3311951f384c6ccab4d07ebf97 Homepage: https://cran.r-project.org/package=rrr Description: CRAN Package 'rrr' (Reduced-Rank Regression) Reduced-rank regression, diagnostics and graphics. Package: r-cran-rrreg Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-lme4 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rrreg_0.7.6-1.ca2404.1_all.deb Size: 880420 MD5sum: 31f2c88561a9ee3bf4a73f2da99340d8 SHA1: bd708d442e7716a39717441912c758c9457da48c SHA256: 4cccc72811ead1f4906e5e712d656048052655359b233c91a1706589834777b2 SHA512: 851e0d19e3725dd85715e6cbc0c6e1ff77e46669fa608046a368d65616c2cee2714b79774e7ba674c6bb62ee24e6da3559cc3d9578f2516b164a26f6adc4ff2e Homepage: https://cran.r-project.org/package=RRreg Description: CRAN Package 'RRreg' (Correlation and Regression Analyses for Randomized Response Data) Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69, ). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 1–29, ). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724–732, ). Package: r-cran-rrrr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc, r-cran-expm, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm Suggests: r-cran-lazybar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rrrr_1.1.1-1.ca2404.1_all.deb Size: 180258 MD5sum: 17141d36f4959fa317c189592ce7635d SHA1: 4cdf35307d61b54277e354fe797f1aee9e5a8f91 SHA256: 594f745dbeeca220768634163eb67ce519540ee2050602951846e8f1e1fad58e SHA512: bac7fbb36085ecb9bbb61ca4863952cd46f48dbe5ea341783fb4fdc8eac7b84aca68c74f6105f2c97a9033aaa10f94484cd02f0d02ae72053c08fa250a599a5e Homepage: https://cran.r-project.org/package=RRRR Description: CRAN Package 'RRRR' (Online Robust Reduced-Rank Regression Estimation) Methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) . The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) . The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) . Package: r-cran-rrscale Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim, r-cran-nloptr, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-rrscale_1.0-1.ca2404.1_all.deb Size: 209238 MD5sum: c2c41e9cb10cd6005c4acc4ed3ef8e32 SHA1: cb7fc9f82e1dccb03ff9bd0e917cbed29462eec1 SHA256: 9fde4cee61b4fa410716a9de1669d32570d35849a42e412379232db5482d9841 SHA512: 1a452b14aaeee07b594b28faff3cc2fc573aec70f4dcee3239dda272c7490c76550c98233194453d702fbd1bc4e977cfd79f58d333bbf8eb400e29c5a50f6cd6 Homepage: https://cran.r-project.org/package=rrscale Description: CRAN Package 'rrscale' (Robust Re-Scaling to Better Recover Latent Effects in Data) Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, . Package: r-cran-rrtable Architecture: all Version: 0.3.4-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-ggplot2, r-cran-officer, r-cran-purrr, r-cran-flextable, r-cran-rvg, r-cran-magrittr, r-cran-devemf, r-cran-moonbook, r-cran-rmarkdown, r-cran-shiny, r-cran-editdata, r-cran-shinywidgets, r-cran-ggpubr, r-cran-rlang, r-cran-readr, r-cran-ztable Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rrtable_0.3.4-1.ca2404.2_all.deb Size: 2208670 MD5sum: 9d78b3c1c467e8170c32c9ecfc790d8a SHA1: 65294fe3ed1a9302226c811c8283a14587647393 SHA256: 16e28e99e96c12901f93648a5f7c78945c90eaeded18ce002c3ccb160a9460bb SHA512: a4e608fa412bb61072174d4b09bad1427c0417e66a90643e20065ee827951801c7010c6136e110246cef3df2f46cfa8474229d12e9abe443e59be0ce81761783 Homepage: https://cran.r-project.org/package=rrtable Description: CRAN Package 'rrtable' (Reproducible Research with a Table of R Codes) Makes documents containing plots and tables from a table of R codes. Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications. Package: r-cran-rrtcs Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampling, r-cran-samplingvarest Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-rrtcs_0.0.4-1.ca2404.1_all.deb Size: 479620 MD5sum: 71444c76e7d5688227d8bcbdba361268 SHA1: 112499314a240683bfc2aff526950d0f909db157 SHA256: 1becdc9ca1b9f686cd97f7f4262cc5d85c9ef1b3ad1ff6aa827bda959875bbab SHA512: 15249dbf845f3a7c465344f89f3a4f822207b6b846d0e53f8dda19f901b36be9dd6966655623ca7b76d8af185f71ef7fcfbcba5af7afd088a75a13f24e39d2ec Homepage: https://cran.r-project.org/package=RRTCS Description: CRAN Package 'RRTCS' (Randomized Response Techniques for Complex Surveys) Point and interval estimation of linear parameters with data obtained from complex surveys (including stratified and clustered samples) when randomization techniques are used. The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. Estimators and variances for 14 randomized response methods for qualitative variables and 7 randomized response methods for quantitative variables are also implemented. In addition, some data sets from surveys with these randomization methods are included in the package. Package: r-cran-rsa Architecture: all Version: 0.10.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-lattice, r-cran-plyr, r-cran-rcolorbrewer, r-cran-aplpack Suggests: r-cran-fields, r-cran-rgl, r-cran-qgraph, r-cran-tkrplot, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rsa_0.10.8-1.ca2404.1_all.deb Size: 410952 MD5sum: b90ec8d13a5404ce9f875636cd3d2ccb SHA1: 421bbc244766d9ebf4d7c3e78e27767fca5a98a5 SHA256: 4e4d8057d7c88dd86ca74dfb64c92fa8a3f4414823a766fd208fba02ccf79c54 SHA512: c47859b85e2890bd2890ac6e24a5312d8335c7aae59e10bbe9d258d38a2768b966af6bb8e745ba689173c212f50fc0ca13875289045da3ab9062659a6ceb31a3 Homepage: https://cran.r-project.org/package=RSA Description: CRAN Package 'RSA' (Response Surface Analysis) Advanced response surface analysis. The main function RSA computes and compares several nested polynomial regression models (full second- or third-order polynomial, shifted and rotated squared difference model, rising ridge surfaces, basic squared difference model, asymmetric or level-dependent congruence effect models). The package provides plotting functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. Calculates many surface parameters (a1 to a5, principal axes, stationary point, eigenvalues) and provides standard, robust, or bootstrapped standard errors and confidence intervals for them. Package: r-cran-rsadbe Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsadbe_1.0-1.ca2404.1_all.deb Size: 114290 MD5sum: 4b6ab73bf028d1fbdbeef956b71603c9 SHA1: 37663878cf5217ce2713d0f51379247bfbd95ba0 SHA256: 0d8416fd6ea296832941eaef5e3a8130b2b659df3e4efe96e782d01eff3808af SHA512: 3e2fb200cfd3340444acecd3dc8f5769d286039031b5235704d9602d98ccb40a0bed085cd5cd8c1e66faeee2fba9eb316b32a1518b8af9a43417463dc19756ee Homepage: https://cran.r-project.org/package=RSADBE Description: CRAN Package 'RSADBE' (Data related to the book "R Statistical Application Developmentby Example") The package contains all the data sets related to the book written by the maintainer of the package. 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For more information see: Gosiewska et al. (2020) . Package: r-cran-rsaga Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2502 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gstat, r-cran-plyr, r-cran-shapefiles, r-cran-magrittr, r-cran-stringr Suggests: r-cran-gam, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rsaga_1.4.2-1.ca2404.1_all.deb Size: 2072482 MD5sum: bb84a13b919da525471e892e7dd758e3 SHA1: a3bd27deba332d925121207a01b2069e27d4bc81 SHA256: 777f7b59bb4847c70aaf22a4b55917370ae5ae768f7c9b392c52d778be563362 SHA512: b62afc2f4c920d0c7d8d28ccb23a63daf3830ca0f5905ef4f792848be1416e107c6b10d5ebe152062d5096a7bfd71886bf823c639c39024898c6c01c9993ff32 Homepage: https://cran.r-project.org/package=RSAGA Description: CRAN Package 'RSAGA' (SAGA Geoprocessing and Terrain Analysis) Provides access to geocomputing and terrain analysis functions of the geographical information system (GIS) 'SAGA' (System for Automated Geoscientific Analyses) from within R by running the command line version of SAGA. This package furthermore provides several R functions for handling ASCII grids, including a flexible framework for applying local functions (including predict methods of fitted models) and focal functions to multiple grids. SAGA GIS is available under GPL-2 / LGPL-2 licences from . Package: r-cran-rsagacmd Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics, r-cran-sf, r-cran-terra, r-cran-stars, r-cran-foreign, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-processx, r-cran-rvest Suggests: r-cran-dplyr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rsagacmd_0.4.3-1.ca2404.1_all.deb Size: 824922 MD5sum: 833266aed995706ab7c733e3d4963699 SHA1: db0f950c2da072ea0980a5284301a6af26149c2f SHA256: 62da8f7408d56ab2ac844a986c50b5442d46b15de5a6e903ef251bf7c7a71ccc SHA512: 5e23a38a5966e6e17214ae707988889666f7d8a2334392d208af15e63397b50f40077e4d5499352f7e969cb0ea7b05dd90644550d296dbc07bacea326f7a97e1 Homepage: https://cran.r-project.org/package=Rsagacmd Description: CRAN Package 'Rsagacmd' (Linking R with the Open-Source 'SAGA-GIS' Software) Provides an R scripting interface to the open-source 'SAGA-GIS' (System for Automated Geoscientific Analyses Geographical Information System) software. 'Rsagacmd' dynamically generates R functions for every 'SAGA-GIS' geoprocessing tool based on the user's currently installed 'SAGA-GIS' version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of 'SAGA-GIS' geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in 'RStudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the 'terra', 'sp', and 'sf' packages) along with non-spatial data are automatically passed from R to the 'SAGA-GIS' command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual 'SAGA-GIS' tools can also be chained using pipes from the 'magrittr' and 'dplyr' packages to combine complex geoprocessing operations together in a single statement. 'SAGA-GIS' is available under a GPLv2 / LGPLv2 licence from including Windows x86/x64 and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories. Rsagacmd has currently been tested on 'SAGA-GIS' versions from 2.3.1 to 9.5.1 on Windows, Linux and macOS. Package: r-cran-rsahmi Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blit, r-cran-cli, r-cran-rlang, r-bioc-shortread Filename: pool/dists/noble/main/r-cran-rsahmi_0.0.2-1.ca2404.1_all.deb Size: 4716940 MD5sum: f1b1703ec032303fc3b0a7f0d647aecd SHA1: 705cc5f7b2e4e4ae4fb8873f273eeed9372f4767 SHA256: ee71f9d1acdf6c81a606d164b1422e7d9dac1d86bb5e7665be4a895c5bb275a3 SHA512: 5a00e5f1ff56f0bda669c444910116a6d8b1f675c98359be6928ae037fedd21b803cb7c5373da5a94e45242f255274c889cc6a72c72d81ad372020e6209db7e8 Homepage: https://cran.r-project.org/package=rsahmi Description: CRAN Package 'rsahmi' (Single-Cell Analysis of Host-Microbiome Interactions) A computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) . This implementation leverages the 'polars' package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing. 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The workflow is (1) to define a statistic of interest that can be calculated from a data table, (2) to randomize rows ad/or columns of a data table to simulate a null hypothesis and (3) and to score the value of the statistic from many randomizations. The relative frequency distribution of the statistic in the simulations is then used to infer the probability of the observed value be generated by the null process (probability of Type I error). This package intends to translate this logic for R for teaching purposes. Keeping the original workflow is favored over performance. Package: r-cran-rsat Architecture: all Version: 0.1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-curl, r-cran-httr, r-cran-leafem, r-cran-leaflet, r-cran-rjson, r-cran-rvest, r-cran-tmap, r-cran-xml2, r-cran-zip, r-cran-rdpack, r-cran-fields, r-cran-calendr, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-sp, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rsat_0.1.21-1.ca2404.1_all.deb Size: 2912346 MD5sum: 434cb6d38f178ec9df1938fe1c3d463a SHA1: a797e0bbe22386b9803d6b1294d500b352105de4 SHA256: 5cc45f7365a3fef07c67a4dc5bf0ae22005892f96019eb4ad8207d80e3e1aca9 SHA512: b4bfe08d7ab6f0dd4f058cc4bef0acc8a7f7a32112ddfdb6fbc8fc683a404f0a1b86aab244f9d6a8826d6479d2f30b559c018f343ea18f31a2e6dd80f3962a57 Homepage: https://cran.r-project.org/package=rsat Description: CRAN Package 'rsat' (Dealing with Multiplatform Satellite Images) Downloading, customizing, and processing time series of satellite images for a region of interest. 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Many of these tools are based upon and extend the 'RSA' package, by testing a larger scope of polynomials (+27 families), more diverse response surface probing techniques (+acceleration points), more plots (+line of congruence, +line of incongruence, both with extrema), and other useful functions for exporting results. Package: r-cran-rsatscan Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-rsatscan_1.0.10-1.ca2404.1_all.deb Size: 135752 MD5sum: f5e54fc4a7a484546595c13d77112b21 SHA1: b3c4ceb844ad8601d14c30bceb1364494397654d SHA256: 4b0b80bbd0019202d25700ce60b2de2506c8c57931cfb1e1a01cafec43d8c366 SHA512: 3526d77176039572a6bc38ea636f9fd784d080b75bcedb07152627e1536bd13722ca9bf7a54e61cddcc1269d6e743c0b9f35b59160536cbd1ea8a92b07912486 Homepage: https://cran.r-project.org/package=rsatscan Description: CRAN Package 'rsatscan' (Tools for Running the External 'SaTScan' Software using RClasses and Methods) The 'SaTScan'(TM) software uses spatial and space-time scan statistics to detect and evaluate spatial and space-time clusters. With the 'rsatscan' package, you can run the external 'SaTScan' software from within R using R data formats. To successfully select appropriate parameter settings within 'rsatscan', you must first learn 'SaTScan'. 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Package: r-cran-rsdne Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsdne_1.3.0-1.ca2404.1_all.deb Size: 63876 MD5sum: cc3add5b14df01aeb01b335bb37acbb9 SHA1: bbe8274b49f293b703c2805bec8a50ba19ead33b SHA256: 91250f18c8bb8b1f172b7be72c4ef7e61d008d3ddc4f50194ff45b1f3dd2f371 SHA512: 6a0391623d0f1ed9b8160b9f20adfef3e6a6d183e5956de51b8dbfe98790fa22930e10f6cb1fa4117256e964af48820acc5e7e1c6af05389f3899c86e2cb8a49 Homepage: https://cran.r-project.org/package=rsdNE Description: CRAN Package 'rsdNE' (Response Surface Designs with Neighbour Effects (rsdNE)) Response surface designs with neighbour effects are suitable for experimental situations where it is expected that the treatment combination administered to one experimental unit may affect the response on neighboring units as well as the response on the unit to which it is applied (Dalal et al.,2025 ). Integrating these effects in the response surface model improves the experiment's precision Verma A., Jaggi S., Varghese, E.,Varghese, C.,Bhowmik, A., Datta, A. and Hemavathi M. (2021)). This package includes sym(), asym1(), asym2(), asym3() and asym4() functions that generates response surface designs which are rotatable under a polynomial model of a given order without interaction term incorporating neighbour effects. Package: r-cran-rsdr Architecture: all Version: 1.0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manifoldoptim, r-cran-rcpp, r-cran-rstiefel, r-cran-scatterplot3d, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggsci Suggests: r-cran-expm, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-cran-rcppnumerical, r-cran-fdm2id Filename: pool/dists/noble/main/r-cran-rsdr_1.0.3.0-1.ca2404.1_all.deb Size: 134232 MD5sum: c7814e25b5f477543764c7061716c451 SHA1: 56ea4bcbc373486de4b8046f0104e3164841867d SHA256: 72c05dbaa207deaab23ec9c3cba8f561b73a97279da1a3f150d0789a3f4c9f8e SHA512: 5e399aa59104148cbb3b2a9823adb44175c6faca862f8da939ca1042079fe1c8f2e112bc1439f37421c63cdf5dc8b0f4aba3f416c20921fdf2a3479f6de3dbe7 Homepage: https://cran.r-project.org/package=rSDR Description: CRAN Package 'rSDR' (Robust Sufficient Dimension Reduction) A novel sufficient-dimension reduction method is robust against outliers using alpha-distance covariance and manifold-learning in dimensionality reduction problems. Please refer Hsin-Hsiung Huang, Feng Yu & Teng Zhang (2024) for the details. 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Includes a metadata system with column types and primary keys, declarative constraints enforced via rejection sampling, conditional sampling, and quality, validity and privacy reports modeled on those of the 'SDMetrics' library. Inspired by the Python 'SDV' (Synthetic Data Vault) library by 'DataCebo'; see Patki, Wedge and Veeramachaneni (2016) "The Synthetic Data Vault" . Package: r-cran-rse Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rse_1.3-1.ca2404.1_all.deb Size: 79514 MD5sum: a55b91b31005b2c59c074b76f6c5f7d0 SHA1: 4b9504efe26ca62763f2ecb12179640c7ce6fc62 SHA256: d3293d357797e827d9933c1cfc7ab5d76ddf8b113f7903a05bc0d051de273a7a SHA512: 0bccafe8937475456576667501fc494b1110bbfc84790ceca16ad09f38620132c588436fc4c84bdfb07c9e333576a8a9f0480da2590a0478b30d5df614b82ea0 Homepage: https://cran.r-project.org/package=RSE Description: CRAN Package 'RSE' (Number of Newly Discovered Rare Species Estimation) A Bayesian-weighted estimator and two unweighted estimators are developed to estimate the number of newly found rare species in additional ecological samples. Among these methods, the Bayesian-weighted estimator and an unweighted (Chao-derived) estimator are of high accuracy and recommended for practical applications. Technical details of the proposed estimators have been well described in the following paper: Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press. Package: r-cran-rsea Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hommel, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rsea_2.1.2-1.ca2404.1_all.deb Size: 57140 MD5sum: 9a3c390d6ace3a3876b7bc46490feaea SHA1: c2324098a22f3fa942e27943d6a94740c3a889e9 SHA256: 1e2f8cd61520c9a5233277365b2006595422ad331a121fed49c064d4fcd890a4 SHA512: 17f8026ebcb245aa6edb4aef834066dde2eb9acdb40fd6f8c4b2339638e52e07eb3448c88ca32dabe6beeeccc428a02635e2ef68e35634fb64cf2bd7d9fc9c66 Homepage: https://cran.r-project.org/package=rSEA Description: CRAN Package 'rSEA' (Simultaneous Enrichment Analysis) SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. 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Package: r-cran-rsearch Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-cowplot, r-cran-dplyr, r-cran-ggextra, r-cran-ggplot2, r-cran-microseq, r-bioc-phyloseq, r-cran-rcolorbrewer, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rsearch_1.2.0-1.ca2404.1_all.deb Size: 421992 MD5sum: c64676774c61c8f711133214061469cf SHA1: 6f12f58481c4adf4a14ada15c1200a2c3ea6e1c9 SHA256: 260f1b6da636815cbefe99cba883d3b07ebd8311a98eb42d7f9c2db6825059cc SHA512: 60cd04a6f00adbfc2640a9101da3e16a5609fc7042a010d6e62d408192f6d3f8871aa3e479b82e7844a11025c64319a7317b0878ea8153b675a8088110f1fcee Homepage: https://cran.r-project.org/package=Rsearch Description: CRAN Package 'Rsearch' (Processing and Analyzing Amplicon Sequence Data) Processing and analysis of targeted sequencing data. 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Package: r-cran-rseedcalc Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rseedcalc_1.3-1.ca2404.1_all.deb Size: 32082 MD5sum: 5e0c2a4ecd3512eee0af579898294e8d SHA1: 5c311cc757049a735ec8b8a9fb821e544589cd28 SHA256: 3314abecf5974e3ead68adad7e2b178b9d2e353481c56d718368fae7d3ebe604 SHA512: b05527394b7893f4b6b6ceca5818f49a065abe9698fce26cd1dbac7915ee7e6a1eba2562815ca0d8921b65a4b7be58777c8a5e46487fd1dc4aac750659a84676 Homepage: https://cran.r-project.org/package=rseedcalc Description: CRAN Package 'rseedcalc' (Estimating the Proportion of Genetically Modified Seeds inSeedlots via Multinomial Group Testing) Estimate the percentage of seeds in a seedlot that contain stacks of genetically modified traits. Estimates are calculated using a multinomial group testing model with maximum likelihood estimation of the parameters. 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Package: r-cran-rselenium Architecture: all Version: 1.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools, r-cran-httr, r-cran-wdman Suggests: r-cran-binman, r-cran-xml, r-cran-testthat, r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-selectr Filename: pool/dists/noble/main/r-cran-rselenium_1.7.10-1.ca2404.1_all.deb Size: 279332 MD5sum: c3d0d1278d525cdab958aeb91618a55d SHA1: f3ba6d880aba876d41a7b5cd2b43ee6cc23ef59f SHA256: e1a05a71eeb3c6a0b8b7091849d4c315adf9122da5268cba83897f5cff754616 SHA512: 7f32dd7d4ac74bf3c74661d4ebfa7986e3105b001f5e053d3119655e9291e7994dead1538fff4b30d694ad924bce0f3f26be1417c6ceac20b9705280a3fa20f5 Homepage: https://cran.r-project.org/package=RSelenium Description: CRAN Package 'RSelenium' (R Bindings for 'Selenium WebDriver') Provides a set of R bindings for the 'Selenium 2.0 WebDriver' (see for more information) using the 'JsonWireProtocol' (see for more information). 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Model equations are specified as plain text (e.g. "y == a*exp(b*eta)"). Gradients are obtained by automatic differentiation via 'RTMB', and the constrained problem is solved with 'nloptr' (SLSQP or augmented Lagrangian). Missing data are handled case-wise. The methodology is described in Oldenburg (2024) and Oldenburg (2025) . Package: r-cran-rsentiment Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-stringr, r-cran-opennlp, r-cran-nlp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rsentiment_2.2.2-1.ca2404.1_all.deb Size: 114548 MD5sum: 3a68f4979adc6bbf1e39d7f649dee3d9 SHA1: 8bb6829ec7cba6d6c662dc2936eeeb0fd8510a3b SHA256: f83ebee0fad46d1a00eddbce13dc006b10391797a1a86855e4e9321736ca8376 SHA512: a2b6e995c37787dd02199ee2fd5059c87be3916e6ca8e444c02d2fb948ede374fba49104b9cd1cdf71ad2ed4825a6b384c9f5cf6eb7a6acbaba5e9074537df60 Homepage: https://cran.r-project.org/package=RSentiment Description: CRAN Package 'RSentiment' (Analyse Sentiment of English Sentences) Analyses sentiment of a sentence in English and assigns score to it. 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Package: r-cran-rservets Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-objectproperties, r-cran-objectsignals, r-cran-rlang, r-cran-rserve Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rservets_0.8.3-1.ca2404.1_all.deb Size: 366528 MD5sum: 8ca050c4f424eea6e5945036d3c19807 SHA1: ea6e3601b6e15bbbb25ba8222176cda0af646299 SHA256: 77c813e0007989d6a313b41e02e5bf27d0a0eb7daa0898ba54851471e87a382f SHA512: 3b5604a31ab7750a8107177d53f0e783082b18d1297fe859bab22c7c8510f98bc52f0f2fd44580aff59a68588d8aeaa254223f86491e2797c68152ea778e64a3 Homepage: https://cran.r-project.org/package=RserveTS Description: CRAN Package 'RserveTS' (Typed Application Contracts for 'Rserve') Defines a typed application contract between R backends and 'TypeScript' clients over 'Rserve'. 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Package: r-cran-rsetse Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-rlang, r-cran-igraph, r-cran-purrr, r-cran-tibble, r-cran-minpack.lm, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-ggraph, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-rsetse_0.5.0-1.ca2404.1_all.deb Size: 429110 MD5sum: 62339895937f38090cdf6a3d968d68cc SHA1: ae8253a394e89aa6ed6378416f0ca026e63de4bb SHA256: 22e5229bbbcf289a36da33887c3d5b37c851e9222912f368bdf58b036cd9d1ee SHA512: 45d9326e1079544686845251a9d0469ecb0b9f09ca16727fb33c3f549c752182d8baa43308d0467519506d10c7b8569492ce86d3c61b9b9ea46e0b54627a6a6d Homepage: https://cran.r-project.org/package=rsetse Description: CRAN Package 'rsetse' (Strain Elevation Tension Spring Embedding) An R implementation for the Strain Elevation and Tension embedding algorithm from Bourne (2020) . The package embeds graphs and networks using the Strain Elevation and Tension embedding (SETSe) algorithm. SETSe represents the network as a physical system, where edges are elastic, and nodes exert a force either up or down based on node features. SETSe positions the nodes vertically such that the tension in the edges of a node is equal and opposite to the force it exerts for all nodes in the network. The resultant structure can then be analysed by looking at the node elevation and the edge strain and tension. This algorithm works on weighted and unweighted networks as well as networks with or without explicit node features. Edge elasticity can be created from existing edge weights or kept as a constant. Package: r-cran-rsf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gert, r-cran-renv Filename: pool/dists/noble/main/r-cran-rsf_1.0.0-1.ca2404.1_all.deb Size: 25380 MD5sum: 434d169051ae0ba99b144d33e2e4b7ae SHA1: 098988e7f3983eabe2fa98dbc4e66a22b0f20642 SHA256: be46f8950a2333deb641869ad4c8cabc090ab132b3329d45e4e43be791ff17ea SHA512: 1929bfcfcc8b669ce8a7208a94be0d9e0b481df179f605d67743a14a93211a94bb5c9d59758185fe7e84c20938291a4b8b441c04a2083e1166ef88e30a6b7377 Homepage: https://cran.r-project.org/package=rsf Description: CRAN Package 'rsf' (Report of Statistical Findings in Quarto) A report of statistical findings (RSF) project template is generated using a Quarto book. Package: r-cran-rsfa Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-rsfa_1.5-1.ca2404.1_all.deb Size: 150800 MD5sum: dfb5aad9a05efbd0f90e385a4f17083d SHA1: cb4b6e2d3b14b5b3468363c63fd0956673fd7f0d SHA256: 443d674de03cf222b97791fd4f497f2b86d3acd145e9f06a97523437c11e7dc1 SHA512: 9a34915f96f5986f04aa58e5be03c6c52325928d3f803a523392297ba13b2eb370d15ac03b805dc2a29aca307f0e660379b6b18c4cc5b205d6dd2418ac42748f Homepage: https://cran.r-project.org/package=rSFA Description: CRAN Package 'rSFA' (Slow Feature Analysis) Slow Feature Analysis (SFA), ported to R based on 'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit' 2.8 by Wolfgang Konen. Package: r-cran-rsfar Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda Filename: pool/dists/noble/main/r-cran-rsfar_0.0.1-1.ca2404.1_all.deb Size: 30052 MD5sum: 9c469ba4f0af022b990dda2353aa4627 SHA1: 8e74b1da031e17e002992bcbe82685a338fb1e94 SHA256: 4a9fee49c383d729fea525627053302dcbd1a5186bc836f49eebb8feb0665d59 SHA512: 6c971330f383b032b9c704778a7bdc096a6aeb1c9cda6dcacbde7152d5f9173459a30fae5449990562b6e95817051b7c2f2b33ef0757c37cf4ff3b387549f0aa Homepage: https://cran.r-project.org/package=Rsfar Description: CRAN Package 'Rsfar' (Seasonal Functional Autoregressive Models) This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: . Package: r-cran-rsgf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-png, r-cran-stringi, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rsgf_1.0.0-1.ca2404.1_all.deb Size: 143298 MD5sum: ee8cd7f977c8ade1e5203907fd052da3 SHA1: 30dd6531fba007620b60073c4edfeb224b5c01dc SHA256: 26a586123164804589efef57151ff383b887dc076a4a4cd8f529fadd13fb75f7 SHA512: fde91bd6f816cd30be6ed1f6694c1a0f9ad88952afc0a62ce4208b6254b30bbf071832840cd35714ef4686462a04ddf11a830f044906f4dd71da7b760c62ee2f Homepage: https://cran.r-project.org/package=Rsgf Description: CRAN Package 'Rsgf' (SGF (Smart Game File) File Format Import) Import SGF (Smart Game File) into R. Package: r-cran-rshape Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-sn, r-cran-vgam, r-cran-evd, r-cran-rsqlite, r-cran-dbi, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-rshape_0.3.2-1.ca2404.1_all.deb Size: 335054 MD5sum: 44bb4b04baa1b07a475c3debbfe43359 SHA1: a233cf2add50f3902ea0cffc25e7c66749365ae5 SHA256: 1435f78f6addb5cb76123c4e53b67a8699da3c4a61f5a8f11ca1b4360b9377cf SHA512: cf8ed910fe156b853ad2323c3a23d1600ae49536ecb3e44227e879b91c59a53891405e765033c69567140758aa3c2421c979146a84d17bd9e5a5a9c7bdd91b51 Homepage: https://cran.r-project.org/package=rSHAPE Description: CRAN Package 'rSHAPE' (Simulated Haploid Asexual Population Evolution) In silico experimental evolution offers a cost-and-time effective means to test evolutionary hypotheses. Existing evolutionary simulation tools focus on simulations in a limited experimental framework, and tend to report on only the results presumed of interest by the tools designer. The R-package for Simulated Haploid Asexual Population Evolution ('rSHAPE') addresses these concerns by implementing a robust simulation framework that outputs complete population demographic and genomic information for in silico evolving communities. Allowing more than 60 parameters to be specified, 'rSHAPE' simulates evolution across discrete time-steps for an evolving community of haploid asexual populations with binary state genomes. These settings are for the current state of 'rSHAPE' and future steps will be to increase the breadth of evolutionary conditions permitted. At present, most effort was placed into permitting varied growth models to be simulated (such as constant size, exponential growth, and logistic growth) as well as various fitness landscape models to reflect the evolutionary landscape (e.g.: Additive, House of Cards - Stuart Kauffman and Simon Levin (1987) , NK - Stuart A. Kauffman and Edward D. Weinberger (1989) , Rough Mount Fuji - Neidhart, Johannes and Szendro, Ivan G and Krug, Joachim (2014) ). This package includes numerous functions though users will only need defineSHAPE(), runSHAPE(), shapeExperiment() and summariseExperiment(). All other functions are called by these main functions and are likely only to be on interest for someone wishing to develop 'rSHAPE'. Simulation results will be stored in files which are exported to the directory referenced by the shape_workDir option (defaults to tempdir() but do change this by passing a folderpath argument for workDir when calling defineSHAPE() if you plan to make use of your results beyond your current session). 'rSHAPE' will generate numerous replicate simulations for your defined range of experimental parameters. The experiment will be built under the experimental working directory (i.e.: referenced by the option shape_workDir set using defineSHAPE() ) where individual replicate simulation results will be stored as well as processed results which I have made in an effort to facilitate analyses by automating collection and processing of the potentially thousands of files which will be created. On that note, 'rSHAPE' implements a robust and flexible framework with highly detailed output at the cost of computational efficiency and potentially requiring significant disk space (generally gigabytes but up to tera-bytes for very large simulation efforts). So, while 'rSHAPE' offers a single framework in which we can simulate evolution and directly compare the impacts of a wide range of parameters, it is not as quick to run as other in silico simulation tools which focus on a single scenario with limited output. There you have it, 'rSHAPE' offers you a less restrictive in silico evolutionary playground than other tools and I hope you enjoy testing your hypotheses. Package: r-cran-rsi Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-proceduralnames, r-cran-rlang, r-cran-rstac, r-cran-sf, r-cran-terra, r-cran-tibble Suggests: r-cran-curl, r-cran-knitr, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rsi_0.3.3-1.ca2404.1_all.deb Size: 1200690 MD5sum: 001fb253eab467d61602800cb82fd395 SHA1: fecfd4e5f6094dbafcdfb1e88742d62da170e661 SHA256: e9cc4dc7d1c2dd5ea3d93e2a4669be2d3edaa04bcc0e6db3524943665eb6ae77 SHA512: 7f6b73b80d142d00c77648fd1d6328fbe9b1db7d670dd8729c4af384cce13e58dfdcf0b4fdf382b2e865f93f7a1c74a1d057c33642206302c2eb48043f3da30d Homepage: https://cran.r-project.org/package=rsi Description: CRAN Package 'rsi' (Efficiently Retrieve and Process Satellite Imagery) Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) ) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'. 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This package is modelled on the 'simsum' user-written command in 'Stata' (White I.R., 2010 ), further extending it with additional performance measures and functionality. Package: r-cran-rsinaica Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rsinaica_1.2.0-1.ca2404.1_all.deb Size: 220906 MD5sum: 94321c87d08824332c33e131c0366d51 SHA1: cde4e56a1d1fa248820e061c5cf5a8fa73101009 SHA256: fedb1077b75553bdf54549a798ba8a0525da9477d1c03523be6d5763165b659c SHA512: 15d0c751032bcbf4c2d66cc26f1040547a7cb1e7b8e273b52de0bec3b78ba5aabacb7c48a71d6a8f703ec0f11fa248c62587bd7d7612fdc7a34074e898bbebfd Homepage: https://cran.r-project.org/package=rsinaica Description: CRAN Package 'rsinaica' (Download Data from Mexico's Air Quality Information System) Easy-to-use functions for downloading air quality data from the Mexican National Air Quality Information System (SINAICA). Allows you to query pollution and meteorological parameters from more than a hundred monitoring stations located throughout Mexico. See for more information. Package: r-cran-rsizebiased Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-rsizebiased_0.1.0-1.ca2404.1_all.deb Size: 61230 MD5sum: 57010ada485f91314af747817bdbbeb5 SHA1: 2d26185a5591fc4a510506ed71010362b898fd31 SHA256: eb08332945d73934b1c9b98ef8d40f7d82f46c81a2d4d160c5fa8a6f3556e246 SHA512: 3fd9c8c6d3810e627f0b30a3b415065da1bb68cad3d83e43ddbe69ff6085a03b4e32cbcb1fe2bad80f8aaad68d74249ed73617d31022c32f79e430ab58a0d053 Homepage: https://cran.r-project.org/package=RSizeBiased Description: CRAN Package 'RSizeBiased' (Hypothesis Testing Based on R-Size Biased Samples) Provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes. Package: r-cran-rskey Architecture: all Version: 0.4.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstudioapi, r-cran-berryfunctions Suggests: r-cran-knitr, r-cran-pillar Filename: pool/dists/noble/main/r-cran-rskey_0.4.19-1.ca2404.1_all.deb Size: 51790 MD5sum: 215e63eb20909194fc3de99993e3491f SHA1: 2900d1d743e26c84d18b283e50649e2fd5298166 SHA256: 54344918ed1843da8b45db6b50df649e63ca405ecce3c56145fcf8dc11406bfb SHA512: 11763fd13efdd55683088d7607d9c49a97296e544a3bcf5fe167e95ec45bb2f80ce604747f5c0cca2df252a1f27fc53c58d068a648cb5d4087ff4309d9e3373f Homepage: https://cran.r-project.org/package=rskey Description: CRAN Package 'rskey' (Create Custom 'Rstudio' Keyboard Shortcuts) Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor. F3 can for example yield 'str(selection)' and F7 open the source code of CRAN and base package functions on 'github'. 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Package: r-cran-rslurm Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-whisker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-rslurm_0.6.2-1.ca2404.1_all.deb Size: 155736 MD5sum: d8096e5146a4aabcaee520f9ee5cb19a SHA1: 65908a565aa8ecbaa0e55304b39eaa5d6a18965c SHA256: 6f7dbc5833f136db944d0083adc1622a83572ff168050804e344a962e63d3b3b SHA512: e881f094beb1a31c6f0caa11293835f3b483df6ef17f75ef1802926f025f3cbaa0a522dbd2c309c086715e1b4893fbb0a514dedd132a51a286984cca69006cd0 Homepage: https://cran.r-project.org/package=rslurm Description: CRAN Package 'rslurm' (Submit R Calculations to a 'Slurm' Cluster) Functions that simplify submitting R scripts to a 'Slurm' workload manager, in part by automating the division of embarrassingly parallel calculations across cluster nodes. Package: r-cran-rsm Architecture: all Version: 2.10.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-estimability Suggests: r-cran-emmeans, r-cran-vdg, r-cran-conf.design, r-cran-doe.base, r-cran-frf2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rsm_2.10.6-1.ca2404.1_all.deb Size: 860532 MD5sum: 9a7359917bb986b4818520d9b1383e7c SHA1: 1acd1d8b680234ea248b7594ea02df700ef22021 SHA256: 92cd8fa4489d4b80804471faf293dfd415c7b4dca881d9ec36fd9863f512ab21 SHA512: 331becaa0c59cc457d597e977f805fd3d9845e122eb61d10dc3afd1f8dd63fa2c5cc785aa9a81eac21a65c7b04ecfa551ffc5bf6cb0281465d178fa3f92f2a01 Homepage: https://cran.r-project.org/package=rsm Description: CRAN Package 'rsm' (Response-Surface Analysis) Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software . Package: r-cran-rsmalltelescopes Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsmalltelescopes_1.0.4-1.ca2404.1_all.deb Size: 20584 MD5sum: e0e2b108d6665ac6320506f288d5e3e6 SHA1: c68f7404b1b80252d796e512e460a68f5a060088 SHA256: 6e38347ad08c7773391ddb2b040f80add5e87dc3d8128b8a2883afaa0eb33e42 SHA512: bdbeccfb68c8b9e597ddc5d44f39db7a7e193ff8c362b90eb0a9fe1399018225758998fee292e87cde84b92133f8d2efa4e6088808bba71f6c5a0e528344ec95 Homepage: https://cran.r-project.org/package=RSmallTelescopes Description: CRAN Package 'RSmallTelescopes' (Empirical Small Telescopes Analysis) We provide functions to perform an empirical small telescopes analysis. This package contains 2 functions, SmallTelescopes() and EstimatePower(). Users only need to call SmallTelescopes() to conduct the analysis. For more information on small telescopes analysis see Uri Simonsohn (2015) . 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Package: r-cran-rspde Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-fmesher, r-cran-lifecycle, r-cran-broom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-lattice, r-cran-splancs, r-cran-optimparallel, r-cran-rspectra, r-cran-numderiv, r-cran-inlabru, r-cran-sn, r-cran-viridis, r-cran-doparallel, r-cran-foreach, r-cran-tidyr, r-cran-dplyr, r-cran-generalizedhyperbolic, r-cran-gridextra, r-cran-metricgraph, r-cran-sf, r-cran-flexiblas, r-cran-withr Filename: pool/dists/noble/main/r-cran-rspde_2.6.0-1.ca2404.1_all.deb Size: 4579448 MD5sum: d02338c5b9c8089bfda7ca43895f651d SHA1: 9b0e60cdaaa63bec4dfa00da0df5c2dee1d00e93 SHA256: 90db796728733695bef5780764e01bcfc49f1128d78b50e5aa918478895224d7 SHA512: e4bfac252beb12a16c779f1f338f57b73509ef052669878ad415fb27d2b25f72434a576b0cc4042ca03daf11f0006b006ca74631ecf44cf2ae93e535886355a3 Homepage: https://cran.r-project.org/package=rSPDE Description: CRAN Package 'rSPDE' (Rational Approximations of Fractional Stochastic PartialDifferential Equations) Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) for the covariance-based method and Bolin and Kirchner (2020) for the operator-based rational approximation. These can be generated by the citation function in R. Package: r-cran-rspincalc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rspincalc_1.0.2-1.ca2404.1_all.deb Size: 148642 MD5sum: 01b69f6867faa1b1e548019bf43f24d6 SHA1: 1a4f1817f261e33d3c794cfd1f45a1e987a32142 SHA256: ffc505f139adf5cf87d7166d018847c546305a43cad96800aa610c0c711dc5db SHA512: 22523465a8edac046087150c5c446b3092bb9aed0169b8bc18c2a1f7d61d73308081e7c6c038745ed5dfdc83c5b0400f824b581c431697f89520f8b5c8b94902 Homepage: https://cran.r-project.org/package=RSpincalc Description: CRAN Package 'RSpincalc' (Conversion Between Attitude Representations of DCM, EulerAngles, Quaternions, and Euler Vectors) Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors. Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx). 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Package: r-cran-rspiro Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rspiro_0.5-1.ca2404.1_all.deb Size: 314086 MD5sum: 05b5f5384e5c0904651a727fb5425b45 SHA1: 825b5f772c64f259618fd2e60b0a0c3478fc8000 SHA256: f60e6e5d78b1be0fa544755dc3acbc593cbf523b083b0a5b95fa8d816e6a4956 SHA512: c44e5288e7d3088793e679194c2dca38ac69b25150b3ce0f136630014f14b160a69a14b8a8cd158bc2faa2b3cb9776595dad0f0d9d951bedd36f865b2e512eb1 Homepage: https://cran.r-project.org/package=rspiro Description: CRAN Package 'rspiro' (Implementation of Spirometry Equations) Implementation of various spirometry equations in R, currently the GLI-2012 (Global Lung Initiative; Quanjer et al. 2012 ), the race-neutral GLI global 2022 (Global Lung Initiative; Bowerman et al. 2023 ), the NHANES3 (National Health and Nutrition Examination Survey; Hankinson et al. 1999 ) and the JRS 2014 (Japanese Respiratory Society; Kubota et al. 2014 ) equations. Also the GLI-2017 diffusing capacity equations are implemented. Contains user-friendly functions to calculate predicted and LLN (Lower Limit of Normal) values for different spirometric parameters such as FEV1 (Forced Expiratory Volume in 1 second), FVC (Forced Vital Capacity), etc, and to convert absolute spirometry measurements to percent (%) predicted and z-scores. 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Package: r-cran-rsubgroup Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2982 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-foreign Filename: pool/dists/noble/main/r-cran-rsubgroup_1.1-1.ca2404.1_all.deb Size: 2657702 MD5sum: ce2c3e86f597690713f54db379d8f528 SHA1: b83753214561873f2b26f0eaa526a883daadd94a SHA256: 17b19e6a7eb54f7b1e9229c37c5b333433c7452cd63f3ad901d12e26b5383648 SHA512: c587a368845fb7ebb384eb16003f37adca1fdd7131ad429f527f7388fee62abfe7c3da312d4ac68fd31723f7e48b20a17f08e3dab3c562516fc85ede3ad1f8da Homepage: https://cran.r-project.org/package=rsubgroup Description: CRAN Package 'rsubgroup' (Subgroup Discovery and Analytics) A collection of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system implementing subgroup discovery, pattern mining and analytics in Java. 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(2012) design_ccd(), and analyzes CCD data with response surface methodology ccd_analysis(). A rotatable CCD provides values of the variance of the predicted response that are concentrically distributed around the average treatment combination used in the experimentation, which with uniform precision (implied by the use of several replicates at the average treatment combination) improves greatly the search and finding of an optimum response. These properties of a rotatable CCD represent undeniable advantages over the classical factorial design, as discussed by Panneton et al. (1999) and Mead et al. (2012) among others. 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The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) . 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These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (). Many of the 'RSurveillance' functions are incorporated into the 'epitools' website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: . 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Features reasoning, structured output, memory management, and tool use. Supports 'Ollama' , 'OpenAI'-compatible , and 'Anthropic'-compatible endpoints. Runs Apple's on-device 'Foundation Models' through the 'rtemis-afm' bridge . Package: r-cran-rtemis Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3504 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-future, r-cran-htmltools, r-cran-rtemis.core, r-cran-s7 Suggests: r-cran-arrow, r-cran-bit64, r-cran-car, r-cran-colorspace, r-cran-dbi, r-cran-dbscan, r-cran-dendextend, r-cran-duckdb, r-cran-e1071, r-cran-farff, r-cran-fastica, r-cran-flexclust, r-cran-future.apply, r-cran-future.mirai, r-cran-futurize, r-cran-geosphere, r-cran-ggplot2, r-cran-glmnet, r-cran-geojsonio, r-cran-glue, r-cran-gsubfn, r-cran-haven, r-cran-heatmaply, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-later, r-cran-leaflet, r-cran-leaps, r-cran-lightauc, r-cran-lightgbm, r-cran-matrixstats, r-cran-mgcv, r-cran-mice, r-cran-mirai, r-cran-missranger, r-cran-nanonext, r-cran-nanoparquet, r-cran-networkd3, r-cran-nmf, r-cran-openxlsx, r-cran-parallelly, r-cran-partykit, r-cran-plotly, r-cran-proc, r-cran-progressr, r-cran-psych, r-cran-pvclust, r-cran-ranger, r-cran-reactable, r-cran-readxl, r-cran-reticulate, r-cran-rocr, r-cran-rpart, r-cran-rtsne, r-cran-seqinr, r-cran-sf, r-cran-shapr, r-cran-survival, r-cran-tabnet, r-cran-threejs, r-cran-testthat, r-cran-tibble, r-cran-timedate, r-cran-torch, r-cran-uwot, r-cran-vegan, r-cran-vroom, r-cran-withr Filename: pool/dists/noble/main/r-cran-rtemis_1.2.7-1.ca2404.1_all.deb Size: 3321828 MD5sum: eb632b760e42e18a3f25409a539dd2de SHA1: 4987a356c04d1228eedc29d1c1a84302922b009b SHA256: 68ce333c98e062a295fc48b4d91fae0877cb3bc7ec196778f09ff9925e18d6da SHA512: 607f03e73bf8a2d49fe96969cd2edac138c078c3950d19ed167eaa288c898105db51e6f2da8a4adea675ce362e98ada20120c8a96404224ce0d56474e591ae15 Homepage: https://cran.r-project.org/package=rtemis Description: CRAN Package 'rtemis' (Machine Learning and Visualization) Machine learning and visualization package with an 'S7' backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. All configuration steps are performed using setup functions and validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017) . 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(2021) ). Disease-agnostic: works for any pathogen given a known generation interval. 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Package: r-cran-rtmpinv Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rclsp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtmpinv_2.0.0-1.ca2404.1_all.deb Size: 40582 MD5sum: 73bf456655cdd09bbd282aa6f9e39494 SHA1: ffe76cd9faa1242b2b16d1b8cee4b076b6fde942 SHA256: c8ab438536aab1577022c34f90c524781232c156d2beaa7bc1cddbd4b0495275 SHA512: c0591c02defa86031d50348daf7e122fbd9eb9fca7490e2999fafbc0ae6a894f3bdcaad279cb226ea5876a89ccf65c35f459ed02225a02cc9f9a22f87f788e86 Homepage: https://cran.r-project.org/package=rtmpinv Description: CRAN Package 'rtmpinv' (Tabular Matrix Problems via Pseudoinverse Estimation) The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net). 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The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net). 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Scores are based on player-specific input probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether a player is "fast" (e.g. the player could stretch home). These probabilities may be calculated from common player statistics that are publicly available on team's webpages. Scores are evaluated based on a nine-player lineup and may be used to compare lineups, evaluate base scenarios, and compare the offensive potential of individual players. Manuscript forthcoming. See Bukiet & Harold (1997) for implementation of discrete Markov chains. 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Package: r-cran-rusda Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-httr, r-cran-plyr, r-cran-foreach, r-cran-stringr, r-cran-testthat, r-cran-taxize, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-rusda_1.0.8-1.ca2404.1_all.deb Size: 63128 MD5sum: cc669a58a7479d05e026588c3cce9cc2 SHA1: e2d07a67fc9c939346348f53e4f09ce45e0b2d1e SHA256: a8621d13df8566eeda5aa39fe5c95174b99e583e3856e66635e5833d894535a8 SHA512: 0ce6a50091881f05b6baef93cb878901137a52caa66e9deb3093232ecf6b45675ed2185d829b44e620dcf5f53cfd96da12273b3ac155c8c3ac59dab50833806b Homepage: https://cran.r-project.org/package=rusda Description: CRAN Package 'rusda' (Interface to USDA Databases) An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. 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Package: r-cran-rusk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-reshape2, r-cran-shiny, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-rusk_0.1.1-1.ca2404.1_all.deb Size: 23880 MD5sum: a3aa9a99ea73c174112e951166f4dc8e SHA1: 45d4461663fe124f59de746ea4439107c116cc2f SHA256: 2b033513e92614f9dca808bbd5915679579babc7b401dfda91a0eac66730d162 SHA512: 3fbfea4a7de2d064c1f7cb2cfc189d0f12b09e5ec06b3f18343da13fb9cdbdc58fbfe00e5411ee9ef61ed86103aa32a286649d052f3b51911cc4cb12fe5937fe Homepage: https://cran.r-project.org/package=rusk Description: CRAN Package 'rusk' (Beautiful Graphical Representation of Multiplication Tables on aModular Circle) By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. 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Package: r-cran-rusquant Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantmod, r-cran-data.table, r-cran-jsonlite, r-cran-httr, r-cran-xts, r-cran-xml, r-cran-stringr, r-cran-jose, r-cran-rvest, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-rusquant_1.1.4-1.ca2404.1_all.deb Size: 201194 MD5sum: f9bf4396bb593bbe5d7933561b59629f SHA1: 669933db3b5aaa481918bbc791110c61eaf30743 SHA256: 50d8432df1aab93d6b173a3c57c77bc6215f3a7785a138bf6c4d56c37bbd3921 SHA512: c94d531b90555362b0e70dfb233a56bfaee4ca54367cce4bc337497c686d45d9e20af6225da11288923d61ed689df4d87cd08de4f009c6fb8493c1f56044f380 Homepage: https://cran.r-project.org/package=rusquant Description: CRAN Package 'rusquant' (Quantitative Trading Framework) Collection of functions to retrieve financial data from various sources, including brokerage and exchange platforms, financial websites, and data providers. 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This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) , Salmerón, R., Rodríguez, A. and García C. (2020) , Salmerón, R., García, C.B, Rodríguez, A. and García, C. (2022) , Salmerón, R., García, C.B. and García, J. (2025) and Salmerón, R., García, C.B, García J. (2023, working paper) . You can also view the package vignette using 'browseVignettes("rvif")', the package website () using 'browseURL(system.file("docs/index.html", package = "rvif"))' or version control on GitHub (). 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The overall approach consists of four sequential steps, each of which has a function in this package: (1) estimate the template spectrum with the function estimate_template(), (2) find absorption features in the estimated template with the function findabsorptionfeatures(), (3) fit Gaussians to the absorption features with the function Gaussfit(), (4) apply the HGRV with simple linear regression by calling the function hgrv(). This package is meant to be open source. But please cite the paper Holzer et al. (2020) when publishing results that use this package. 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Package: r-cran-rweaveextra Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rweaveextra_1.3-1-1.ca2404.1_all.deb Size: 42504 MD5sum: 86e955f19bfe009120f13873155b3d2f SHA1: 2287f221e38dc21665cb5002fc3451bbe1d29f06 SHA256: ff48a89eb434deb5de7b39d0988aaa7e930f734b5a187bad1d88e2f9e140308a SHA512: 1a0e9a04e899d9a822dee12cfa8c66c3929278b6d97fe72ef686bd30d99c436c733f2740304913c926ef5bb04cc70d3fc24157949ba4537574dd5ca95a4f305a Homepage: https://cran.r-project.org/package=RweaveExtra Description: CRAN Package 'RweaveExtra' (Sweave Drivers with Extra Tricks Up their Sleeves) *The package is deprecated. It uses the standard drivers on R >= 4.6.0 since they incorporate all the functionalities below.* Weave and tangle drivers for Sweave extending the standard drivers. 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Package: r-cran-rwetools Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-survey Suggests: r-cran-openxlsx, r-cran-ggplot2, r-cran-matchit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rwetools_0.5.0-1.ca2404.1_all.deb Size: 418824 MD5sum: 52904981c71be67b232b92845eb2c022 SHA1: 0f5116a0129c49f7a37d1f3a4eb7e9d50bafd632 SHA256: 85598c274954b9f7dc3aad12775353839d022f7a1565e0e37ea6341d8f02cd54 SHA512: efc9ac7ce13873811b1c270055473b2e082d9dd6daf92891db42a2a545e9712295af11ce490a19b37303bf53d9ca3d64ad1d52c03549da10df924bdf12100ecc Homepage: https://cran.r-project.org/package=rwetools Description: CRAN Package 'rwetools' (Estimating Propensity Scores (PS), PS-Based Weights, and Effects) Toolbox that provides a streamlined, end-to-end workflow for propensity score analysis in generating real-world evidence from real-world data. 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From a single function call it produces a structured validation report in Hypertext Markup Language (HTML) and JavaScript Object Notation (JSON) covering concept coverage, cohort attrition, temporal data density, and covariate feasibility against a comparator. The checks are aligned with the United States Food and Drug Administration (FDA) guidance on real-world data and evidence, FDA (2023) , the Harmonized Protocol Template to Enhance Reproducibility (HARPER), Wang and others (2022) , and the Reporting of Studies Conducted Using Observational Routinely-Collected Data for Pharmacoepidemiology (RECORD-PE) statement, Langan and others (2018) . A self-contained example database is bundled so the checks can be run without a live database connection. 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Visualizing MCMC Convergence in Phylogenetics) Implements various tests, visualizations, and metrics for diagnosing convergence of MCMC chains in phylogenetics. It implements and automates many of the functions of the AWTY package in the R environment, as well as a host of other functions. Warren, Geneva, and Lanfear (2017), . Package: r-cran-rwunderground Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-countrycode, r-cran-lubridate, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rwunderground_0.1.8-1.ca2404.1_all.deb Size: 269126 MD5sum: c55197c2b93d814fd5b98e3712cff413 SHA1: 9ef8c25f7f2fc93cceedde0782d3cc74e523e8da SHA256: 03659ff3899c6b88530dfa61bdb3f8f56eca81ceead50e96d7f256abb81b0a6d SHA512: f2ccb093a076c8d5a90ddc5a98a48a43ead2ffdfb417c742a9b3e78e4b7462f3f03de393c1d8c0649debe221424a9270276159adf834c54b3113c84589a4ddb4 Homepage: https://cran.r-project.org/package=rwunderground Description: CRAN Package 'rwunderground' (R Interface to Weather Underground API) Tools for getting historical weather information and forecasts from wunderground.com. 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Package: r-cran-rxkcd Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-jpeg, r-cran-png, r-cran-dbi, r-cran-duckdb, r-cran-matrix Filename: pool/dists/noble/main/r-cran-rxkcd_2.0.2-1.ca2404.1_all.deb Size: 36542 MD5sum: 4012d294c11db0aa1742fb1174f2dd80 SHA1: 316d850617079d64a38ce0ea46671582dbf8f7c3 SHA256: ef481c8e49701ff509e216bd4512b1f5ee6ddb32e255d949573f86cf3c48be00 SHA512: 5a1f0330552e6d7c3df31cd02b5cb261f3f7c4b1ad3931404ab80d56bffc39b0fa4d278b33d134b3c78b472f87a082c1d6bb4a554198fba8493859b6f117dc15 Homepage: https://cran.r-project.org/package=RXKCD Description: CRAN Package 'RXKCD' (Get XKCD Comic from R) Visualize your favorite XKCD comic strip directly from R. Includes full-text search with BM25 ranking and semantic similarity search via latent semantic analysis, powered by a local 'DuckDB' cache. 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Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality. Methods for reaction similarity and sub-structure masking are as described in: Giri et al. (2015) . 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Package: r-cran-rxref Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cachem, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-memoise, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rxref_0.4.0-1.ca2404.1_all.deb Size: 479270 MD5sum: 32e02297a548e0edbe2824faee7305df SHA1: b7c40ce4b8fb3e27801f1f55be8a4c54473b356d SHA256: c5f764f27fcf7f10f0f4df1e9db062b1c65247977eae27a9b1d3cf05de6b1f8e SHA512: 53459dda02e0b6bb668718158e5418055d67086d5a6de1d8f54e7d34c24fb6372486295744e77a62cf0af32c01e73927d80e8af5389cc491e4041e7d4e82eff2 Homepage: https://cran.r-project.org/package=rxref Description: CRAN Package 'rxref' (Tidy Utilities for RxNorm and NDC Resolution) Provides a tidy, vectorized interface to the 'RxNorm' / 'RxNav' API for resolving drug names, RxCUIs, National Drug Codes (NDCs), and related drug concept metadata. 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Package: r-cran-rxseq Architecture: all Version: 0.99.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-vgam, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-rxseq_0.99.3-1.ca2404.1_all.deb Size: 473616 MD5sum: 07d3aac6f34c20371dc884239957aae2 SHA1: 8d8eaf1c36fb0935526427be5688a99d0d039ccc SHA256: 3cc9fa11b5f636692d157ea29ec42cb8d182343d27a7cc8c18d9ff63e845caa2 SHA512: b5c38f9891745bca31fc84978b9cbf5beaeb554110d1840a00465d79f0b3e0926c7d053ef5997fe5a4aacae43e523bbcdf0f973c626c9e33057df96436e2982d Homepage: https://cran.r-project.org/package=rxSeq Description: CRAN Package 'rxSeq' (Combined Total and Allele Specific Reads Sequencing Study) Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data. Package: r-cran-rxshrink Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars, r-cran-ellipse Suggests: r-cran-mgcv Filename: pool/dists/noble/main/r-cran-rxshrink_2.3-1.ca2404.1_all.deb Size: 292134 MD5sum: 445cf7070206dd55dc8e2cbb05803a60 SHA1: cb29e288ffd866578953b1979fd27b58027ae9fe SHA256: 7d61a363e4605235e4321c0978e71f7337d3413900979c1a8efbb4f61b1d0dde SHA512: bc15dcb0368459e84b5e329bd6723f6898b5c2ac99bcc9b18edcec9240ae856d148799fd7d5056b306f565bda09effe37d2c265dcfd68e404b973c4662a44851 Homepage: https://cran.r-project.org/package=RXshrink Description: CRAN Package 'RXshrink' (Maximum Likelihood Shrinkage using Generalized Ridge or LeastAngle Regression) Functions are provided to calculate and display ridge TRACE Diagnostics for a variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood estimation of unknown true effects under normal distribution-theory, some estimates are modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain (2022) . This "p-Parameter" Shrinkage-Path always passes through the vector of regression coefficient estimates Most-Likely to achieve the overall Optimal Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken regression coefficients. Functions for plotting TRACE Diagnostics now have more options. Package: r-cran-ryandexdirect Architecture: all Version: 3.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4869 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-bitops, r-cran-jsonlite, r-cran-xml2, r-cran-data.table, r-cran-readr, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-httr2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-googleanalyticsr, r-cran-rym Filename: pool/dists/noble/main/r-cran-ryandexdirect_3.6.6-1.ca2404.1_all.deb Size: 3288468 MD5sum: 4d66507230f3a2de80788b10bf488e72 SHA1: 1aaa82172159cfe80bd8b126ce678f609e1b8bdf SHA256: 4409c68ef283a55c61ad97ab2e575f328319c54be8b8da9c0aa723efeb3bcfd5 SHA512: 20dc3899c240fcc4a130cb4f9727beb406618147a12e21321241e25d0d035c37a6d17821ab7b9394a1e70c66dfe42ac496768be513597f6cb9b1fb78781b0efd Homepage: https://cran.r-project.org/package=ryandexdirect Description: CRAN Package 'ryandexdirect' (Load Data From 'Yandex Direct') Load data from 'Yandex Direct' API V5 into R. Provide function for load lists of campaings, ads, keywords and other objects from 'Yandex Direct' account. Also you can load statistic from API 'Reports Service' . And allows keyword bids management. 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Package: r-cran-rym Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-httr2, r-cran-stringr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rym_1.1.2-1.ca2404.1_all.deb Size: 267596 MD5sum: 81bae32022e26121d60ec9aeb4502b6f SHA1: 63e97d1dd5c49785d464d6b82a425aec5ba18a6a SHA256: ca8638d7ad7cad6d1d62d0ca7bce643cdefab38537f06f68c88de5b890c5d85f SHA512: 5624410b7680100a27675ef227f31f9aa484492e4abef94622cff42254967ebcf976b5ead934d5aa80f4619c08a73e72dc9166b8b2a32782fe5c8827c9561745 Homepage: https://cran.r-project.org/package=rym Description: CRAN Package 'rym' (R Interface to Yandex Metrica API) Allows work with 'Management API' for load counters, segments, filters, user permissions and goals list from Yandex Metrica, 'Reporting API' allows you to get information about the statistics of site visits and other data without using the web interface, 'Logs API' allows to receive non-aggregated data and 'Compatible with Google Analytics Core Reporting API v3' allows receive information about site traffic and other data using field names from Google Analytics Core API. For more information see official documents . Package: r-cran-ryoutheria Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-rjsonio, r-cran-reshape2, r-cran-rcurl Suggests: r-cran-knitr, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ryoutheria_1.0.3-1.ca2404.1_all.deb Size: 41192 MD5sum: ba9659b9197aa587baa7a8d8acc2f042 SHA1: a5eff3b1e848ec103bde309b34bd225f0c312c86 SHA256: eeadcc8261a27d38da538d1a72f684669436e1851a4362592e6718b55b4064d0 SHA512: b6396b7594bb5ad027ca50935ead11d719b1d19ce030e5fb0f12e895eb4723189da52c2f0915f5275f0b9ef8d1c1ebf86b1cd4fa1a56580d73c90b8548cd674e Homepage: https://cran.r-project.org/package=rYoutheria Description: CRAN Package 'rYoutheria' (Access to the YouTheria Mammal Trait Database) A programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data . 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Package: r-cran-rywaasb Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-factoextra, r-cran-factominer, r-cran-mathjaxr Suggests: r-cran-car, r-cran-metan, r-cran-devtools, r-cran-usethis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rywaasb_0.4.1-1.ca2404.1_all.deb Size: 387158 MD5sum: 404ad304344c2828a152fe1c8c15c3bf SHA1: 76203f83a1b8fbc52d148255c86f9c16b111721c SHA256: 26f99b18a7725ab43793bf387ef762c03b5156f899ee2090093f5c125aaf71b0 SHA512: 50ffe66b3a047930f535a73c2efcd9d37b4eeeca476cd96adbd0b72df306d561d9a3ce15283016643c0bbe66fddc0043510792821f4df5a410654fe2c1dadf2b Homepage: https://cran.r-project.org/package=rYWAASB Description: CRAN Package 'rYWAASB' (Simultaneous Selection by Trait and WAASB Index) This tool proposes a new ranking algorithm that utilizes a "Y*WAASB" biplot generated by the 'metan'. The aim of the current package is to effectively distinguish the top-ranked genotypes in MET (Multi-Environmental Trials). For a detailed explanation of the process of obtaining "WAASB", "WAASBY" indices, and a "Y*WAASB" biplot, refer to the manual included in this package as well as the study by Olivoto & Lúcio (2020) . In this context, "WAASB" refers to the "Weighted Average of Absolute Scores" provided by Olivoto et al. (2019) , which quantifies the stability of genotypes across different environments using linear mixed-effect models. To run the package, you need to extract the "WAASB" and "WAASBY" coefficients using the 'metan' and apply them. This tool utilizes PCA (Principal Component Analysis) and differentiates the entries which may be genotypes, hybrids, varieties, etc using "WAASB", "WAASBY", and a combination of the specified trait and WAASB index. 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Package: r-cran-rzentra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rzentra_0.1.0-1.ca2404.1_all.deb Size: 28096 MD5sum: 39d14011738a953c59974ff8d1a25e39 SHA1: a5da2ec841a39e6f53bbdda4488b81bdd9587265 SHA256: 3b7bbf7400862df629b6acc5d9751ff850e10d4a044e8ba06aa87c4aa0f841ed SHA512: 6a75dce0f5fb327b3e3f24eadd110875a4db0c96301b1e051dc3c94a575441960b644b25d3bd323509214c9720caae605b38f67d7d5316771139af0449e7444c Homepage: https://cran.r-project.org/package=rzentra Description: CRAN Package 'rzentra' (Client for the 'ZENTRA Cloud' API) Provides functionality to read settings, statuses and readings of weather stations from the 'ZENTRA Cloud' API . 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Package: r-cran-s20x Architecture: all Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggally, r-cran-ggplot2, r-cran-nlme, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi Suggests: r-cran-bootstrap, r-cran-dafs, r-cran-emmeans, r-cran-formatr, r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-s20x_3.3.0-1.ca2404.1_all.deb Size: 653678 MD5sum: 4f3427de6a23214e44d3de1b6cb5189b SHA1: 12c64ae0b38b59136be37b95ed3953d59fac8402 SHA256: 3c6d4daa88090129d7f59b947b926dac192ba6d5f2f686683acbf14a2bcafe1a SHA512: a7488c222e1907758a188c52a7147d568e531c24401d2d6f1e77872259d8a2b643b74517d2b220dfe336bc6e3d12d21a2fab3cca8306929bdb9963e0ebce5912 Homepage: https://cran.r-project.org/package=s20x Description: CRAN Package 's20x' (Functions for University of Auckland Course STATS 201/208 DataAnalysis) A set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. 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Package: r-cran-s2dv Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-bigmemory, r-cran-maps, r-cran-mapproj, r-cran-climprojdiags, r-cran-plyr, r-cran-ncdf4, r-cran-nbclust, r-cran-multiapply, r-cran-specsverification, r-cran-easyncdf, r-cran-easyverification, r-cran-signal, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-s2dv_2.3.0-1.ca2404.1_all.deb Size: 2306366 MD5sum: d7b384203937120ff021e5a0df028e76 SHA1: 8318802c0bb5e6a4e21e507568967de4d964c20f SHA256: 85955d1701eb5c144f0d7b77bd2a0e8d84323055b4513b6cf127d97f8e5999d5 SHA512: 7b8397bc86de2c661713e88054e023f1a7de9a67b9e3ace0e8ad0489ffb83f3b9e239b6a7f3590172f34775422860f79436ea3822b7bc2daa7243423cbf70668 Homepage: https://cran.r-project.org/package=s2dv Description: CRAN Package 's2dv' (Seasonal to Decadal Verification) An advanced version of package 's2dverification'. Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) , DelSole and Tippett (2016) , Kharin et al. (2012) , Doblas-Reyes et al. (2003) . Package: r-cran-s3.resourcer Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-resourcer, r-cran-aws.s3, r-cran-sparklyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-s3.resourcer_1.1.2-1.ca2404.1_all.deb Size: 96406 MD5sum: f3e00ab937e0d49644dfad3479a24fac SHA1: 24bb57c37e7b72d7315417c7aab3b26805373e3a SHA256: 5025dd5009fcd58546a978ddf09cfe6a2fa8a2e727be150d07d8cfbccfa16356 SHA512: 7ba45489759c9acc1cf3b0f39e4ce2ffaacf030b359750172dce9372d6f22e8d20b4f46feaed4801138670de366b88d0d989a5996063e81216270651c558ba56 Homepage: https://cran.r-project.org/package=s3.resourcer Description: CRAN Package 's3.resourcer' (S3 Resource Resolver) A S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). 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Package: r-cran-s3 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-prettyunits, r-cran-fs, r-cran-rlang, r-cran-httr, r-cran-glue, r-cran-purrr, r-cran-dplyr Suggests: r-cran-aws.signature, r-cran-testthat, r-cran-digest, r-cran-withr Filename: pool/dists/noble/main/r-cran-s3_1.1.0-1.ca2404.1_all.deb Size: 29492 MD5sum: 7ae93bc85537b59cf83da9e146b8977f SHA1: ba0ee56c9101d7f690077b4354e2fa24996c9e34 SHA256: a695f77334e51f207a8bb6c5f0599f25451da550bd03a77262bbeaa3521937d1 SHA512: 3ee0cde34229d275c87b558e7a5ab12961b6be9e4412cd62528d41643a8c3fa9fc2d7c8a6211f7747533e67c553e05cd9d95134a3ba4570b1ca8c1d4af3dca13 Homepage: https://cran.r-project.org/package=s3 Description: CRAN Package 's3' (Download Files from 'AWS S3') Download files hosted on 'AWS S3' (Amazon Web Services Simple Storage Service; ) to a local directory based on their URI. 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Package: r-cran-s3vs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-survival, r-cran-modelselection, r-cran-eha, r-cran-pec, r-cran-aftgee, r-cran-afthd Suggests: r-cran-rjags, r-cran-knitr, r-cran-doparallel, r-cran-future, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-s3vs_1.1-1.ca2404.1_all.deb Size: 778502 MD5sum: ca7d0629715cc8713fde1169b57c12f1 SHA1: c852375e99c12d560fc8079619f48f73ffedd8de SHA256: cc013894553ffc1f8a3c7064456970f04e3c89a9e1a23941bd8d5f35a7db42e5 SHA512: d5da4aa44cf09ae3a45a561afc0e35a734698d7092ce639b8a694d40dfb4dbf501a6410868b64caf51c410641ee689afa917ee4621d7d0b8cfb24d4c598179a9 Homepage: https://cran.r-project.org/package=S3VS Description: CRAN Package 'S3VS' (Structured Screen-and-Select Variable Selection in Linear,Generalized Linear, and Survival Models) Performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model. Package: r-cran-s4dm Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-densratio, r-cran-flexclust, r-cran-geometry, r-cran-kernlab, r-cran-maxnet, r-cran-mvtnorm, r-cran-np, r-cran-proc, r-cran-robust, r-cran-rvinecopulib, r-cran-sf, r-cran-terra, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-geodata, r-cran-bien, r-cran-ggplot2, r-cran-tidyterra, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-s4dm_0.0.2-1.ca2404.1_all.deb Size: 241076 MD5sum: 0a80fa078fcf25835b2c00c6401bc730 SHA1: 3ea0f04216025b2c14f8eb37d405d0532c5839dc SHA256: 953c708acdae9a130cddd164070c84db588cbcace06851d49e7cca19860fa746 SHA512: 9fdf12f9345d6ba9d3a0a4feea4912dd6a38bc506f246a81069476bf1e54facd6be5ece3f91b7747e405d74b5eaadf0d1c071814b696f819a83094e1b39a8202 Homepage: https://cran.r-project.org/package=S4DM Description: CRAN Package 'S4DM' (Small Sample Size Species Distribution Modeling) Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). 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Package: r-cran-s4vd Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biclust, r-cran-irlba, r-cran-foreach Filename: pool/dists/noble/main/r-cran-s4vd_1.1-1-1.ca2404.1_all.deb Size: 173124 MD5sum: 553502eafcfa3bb5a2fcfb689fd79eda SHA1: dae834ed14eb518f62fb46bab59f49b4c11cfa3e SHA256: 89c08d47bc0cf8f1da620dc4b3c80ac2f035a2c88e3f3c0506bd43e2635c1497 SHA512: 2e53c964bf8e86acd2e4485d150261fc6105de8140971647745daa5b97458d7d5b7c08c92481a6f4abfb99d36e7b5af228709404ac41154973d6049f4720479a Homepage: https://cran.r-project.org/package=s4vd Description: CRAN Package 's4vd' (Biclustering via Sparse Singular Value DecompositionIncorporating Stability Selection) The main function s4vd() performs a biclustering via sparse singular value decomposition with a nested stability selection. The results is an biclust object and thus all methods of the biclust package can be applied. Package: r-cran-s7contract Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-s7contract_0.2.3-1.ca2404.1_all.deb Size: 279066 MD5sum: ad8baddf3e1f2747dd85948a321b89ac SHA1: 7b00e25ddff211d9312648269c9c8cca0ccbb919 SHA256: 0004a5c93cb0611204aff705ef58de3f47ebc80ddb301cc251ebbb7559e0d2f4 SHA512: 6e0aea290392893c2ed683be86f33063cdf309f5bc9f5d4ff58cb9f8351804f9647f27cf5f7fd2d52522ee53f51a2acfdc619b4007ca672631001439f15db414 Homepage: https://cran.r-project.org/package=s7contract Description: CRAN Package 's7contract' (Behavioral Contracts and Generative Laws for 'S7') Contract helpers built with 'S7' for expressing runtime protocols around ordinary 'S7' dispatch. Structural interfaces describe small sets of required 'S7' generics, while explicit traits record registered implementations with optional default methods and associated metadata. Optional runtime checks can validate argument and return specifications in contract-scoped evaluation. Generative laws combine generators, deterministic shrinking, and one-result 'tinytest' expectations. Package: r-cran-s7schema Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-s7, r-cran-v8, r-cran-yaml Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-s7schema_0.1.2-1.ca2404.1_all.deb Size: 207804 MD5sum: b314f15edc5c69ee7a41816f1b5d7d4a SHA1: a8861f700170656408f67c04017c52f3589ba064 SHA256: ab8b2cae150eeabf96721c382343df5657e0994c949096cef1ab61d1e7fb69e8 SHA512: 0fc39c98607c5c442f597e926380aeff742c79feeb34759822715701ea2b44d9e668d47c74212b4fe89bb08052bf94ebb7b81ec3eb2cf10fbd7d44a0b03f5b3c Homepage: https://cran.r-project.org/package=S7schema Description: CRAN Package 'S7schema' ('S7' Framework for Schema-Validated YAML Configuration) Provides a generic framework for working with YAML (YAML Ain't Markup Language) configuration files. 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Package: r-cran-saascnv Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1063 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-saascnv_0.3.4-1.ca2404.1_all.deb Size: 992670 MD5sum: 2964caa22f7fde2df064227dfbb1dd0f SHA1: 21c1a56450a3a786d9d6f1abfe1218ba7d40022c SHA256: 64baa01d294595a7e418e9cb71a5afd46bf4e1eb13a376013c486dd156cac122 SHA512: d27316833468e38a8bcbc1b39caf5e11543c503ce50cb66ba86a3347f0d5d3b4db370a6f74f8e31f15c2b114d89aa5b81754f30625c70150cafd751cd538a74a Homepage: https://cran.r-project.org/package=saasCNV Description: CRAN Package 'saasCNV' (Somatic Copy Number Alteration Analysis Using Sequencing and SNPArray Data) Perform joint segmentation on two signal dimensions derived from total read depth (intensity) and allele specific read depth (intensity) for whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data. Package: r-cran-saber Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-saber_0.7.2-1.ca2404.1_all.deb Size: 131836 MD5sum: 735c21d6905c820172469b727a52dd31 SHA1: 8ad7fac0329f780295056dcd3f37168126fe7029 SHA256: 266ce7acf94ff8ea9e79acbad33726c1728cc5541d38db7abc6540669814c6d3 SHA512: ae46e2ea70b337354a96d38479b014ff650eca39c33bdcc5e53dc9dd8d5bf49a797fa5ce63176ae988f21a267b06622a6866d8f9ae48c2921643bf27a53282b9 Homepage: https://cran.r-project.org/package=saber Description: CRAN Package 'saber' (Context Engineering for Large Language Model Agents) Context-engineering primitives for Artificial Intelligence (AI) coding agents working in R. Assembles agent context from memory and instruction files ('AGENTS.md', 'CLAUDE.md'), traces function call blast radius across projects, generates project briefings, parses source into Abstract Syntax Tree (AST) symbol indices, discovers dependency graphs, and introspects installed packages. Zero dependencies. Package: r-cran-sabre Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-entropy, r-cran-raster, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sabre_0.4.3-1.ca2404.1_all.deb Size: 631936 MD5sum: da6ad983e65bee47f503dd203333af6d SHA1: 670d378237425468e33cf3b7da8027a3351ae472 SHA256: bbfdaf90f537d67127fd98b197a59af472a9c07258f919e64e5d1dd87f1765e8 SHA512: df05772fd425523eed5d7b8ef9b1682fe3fb0d5fd7f669400a241616fcdd7bf891b63837673c96b35e52bfee5532b29313cb327a7548a71f90f9015eb549965b Homepage: https://cran.r-project.org/package=sabre Description: CRAN Package 'sabre' (Spatial Association Between Regionalizations) Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) ). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) ). Package: r-cran-sac Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sac_1.0.2-1.ca2404.1_all.deb Size: 92218 MD5sum: 80422e65d6f4a73fa1c1323615b003a0 SHA1: ddb45fc5145ee3fa50bceec226dced62ca4fcd31 SHA256: ec9f916665ea5a616be83de6aead5d8c41b8d31020b7e95e1e3ae399ef9b289a SHA512: 92558d709b79ab76828b4b0715973b85d4fd1624578085118f42a3957b06075a7c537d1faed7739be4e8aab376728a252d92a8fa773b61b9e6c75ffa76d00d9d Homepage: https://cran.r-project.org/package=sac Description: CRAN Package 'sac' (Semiparametric Analysis of Change-Point) Semiparametric empirical likelihood ratio based tests of change-point with one-change or epidemic alternatives with data-based model diagnostic are contained. Package: r-cran-saccr Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-jsonlite, r-cran-trading Filename: pool/dists/noble/main/r-cran-saccr_3.4-1.ca2404.1_all.deb Size: 112012 MD5sum: 5948c0ece6a860acdd8024e00f819d28 SHA1: 556b20b2cefae308c1a425827acce649cfc91ab7 SHA256: 1acfe2f70424f5f2a4483c612de9347d08a75c4b4199c7e2e7982cffd1001abb SHA512: 0fa08c72466d9a73c86937bbdcd5d2ca7bd9df5ffc93c9ace36f40c78abc229dcdaffbc4bc7f80d4e89e92c13ffb913a73832788629e98b2b0616bb6d0f1d65d Homepage: https://cran.r-project.org/package=SACCR Description: CRAN Package 'SACCR' (SA Counterparty Credit Risk under CRR2) Computes the Exposure-At-Default based on the standardized approach of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology. Multiple trade types of all the five major asset classes are being supported including the Other Exposure and, given the inheritance- based structure of the application, the addition of further trade types is straightforward. The application returns a list of trees per Counterparty and CSA after automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the netting sets and the risk factors. The basis and volatility transactions are also identified and treated in specific hedging sets whereby the corresponding penalty factors are applied. All the examples appearing on the regulatory papers (both for the margined and the unmargined workflow) have been implemented including the latest CRR2 developments. Package: r-cran-saci Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-shiny Filename: pool/dists/noble/main/r-cran-saci_0.1.0-1.ca2404.1_all.deb Size: 30228 MD5sum: f303dea015d5db53cc7d8ee6b0e63635 SHA1: 5ffb7dfdbf001eb925e9bcf41791e364236743c4 SHA256: a106d591c16c64671f98270087282167d3d7df5e30cb16d0c8e4bbc32a54fce2 SHA512: e32c1ddb00c540e35e1a70e0c2d881d27c318ef36f3d1af7ffb0eeb644d11b7e588a7a74fb5f0aa18aa35feebac98a793ac490611ab07a61d611ae25d7387ce6 Homepage: https://cran.r-project.org/package=saCI Description: CRAN Package 'saCI' (Stochastic Approximation Confidence Interval for Correlation) Implements stochastic approximation method for constructing nonparametric confidence intervals for correlation coefficient, based on Xiong & Xu (2016). Package: r-cran-sad Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 841 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dualtrees, r-cran-emdist Filename: pool/dists/noble/main/r-cran-sad_0.1.3-1.ca2404.1_all.deb Size: 825868 MD5sum: 50014ea92338f9986c61bd45bb9f10ca SHA1: 2aeb0bdd25ec3c9f53adccca5ab1d11c96471c11 SHA256: 9f18f5b8071308ed939fa705ce2cc4bb606b08eb8ef045268389aeb9d0008c33 SHA512: 314eb188d904aafc3e8f39b3ddc810378490f448c679d61343b26d3b67a78a042aa5ff121f11e70199d5b671f128d0bf2523eaaaca458fe6bada08d35f5c9f8b Homepage: https://cran.r-project.org/package=sad Description: CRAN Package 'sad' (Verify the Scale, Anisotropy and Direction of Weather Forecasts) Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well. Package: r-cran-sadeg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sadeg_1.0.0-1.ca2404.1_all.deb Size: 50382 MD5sum: 69430742120e5ae9a31a595269721d0a SHA1: 757b344a204ecb4a56f135095cf68c5f93c61389 SHA256: 62c8e07d894c7a5d8ea989b4ff5af1972dde030c48f842f4b61ec611cfe81227 SHA512: 3f8903f40cec8b6729c162da0ad99211ed21b5ad4eb494554883a9d6c7eecfac5a4b154275506cf84d67d4c07fc0059c0c7bff2ceec80a386e796fe66f215b19 Homepage: https://cran.r-project.org/package=SADEG Description: CRAN Package 'SADEG' (Stability Analysis in Differentially Expressed Genes) We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level. Package: r-cran-sadisa Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-ddd Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sadisa_1.2-1.ca2404.1_all.deb Size: 129898 MD5sum: 1c98725427cb43602cc14ef0af4c3141 SHA1: 8ca1a419e6dc6f7875971a87fbea6b203653081e SHA256: 9513f3b11de993f95447dce2db8efe50f83180c38d56f27d9ca3220ecd538ae3 SHA512: e770da40b0ce87cdccb1f2a0cfbdbf230bb4f7ad636601c0e349b539c3f07401df5666af24ca51804dd9d1aa23e50919a0f7930ea85f39ad3e46de04abe9aa90 Homepage: https://cran.r-project.org/package=SADISA Description: CRAN Package 'SADISA' (Species Abundance Distributions with Independent-SpeciesAssumption) Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 . Package: r-cran-sadists Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8202 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pdqutils, r-cran-hypergeo, r-cran-orthopolynom Suggests: r-cran-sharper, r-cran-shiny, r-cran-testthat, r-cran-ggplot2, r-cran-xtable, r-cran-formatr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sadists_0.2.6-1.ca2404.1_all.deb Size: 4980486 MD5sum: 15f0346b7eba5b3984ff00373113f49e SHA1: f18249b6c307db7f9aa5e2c8bd441f45ed33c1b4 SHA256: fe1c9e47ed8b0ef27210c5cd62cb22647779bfcafcb23eb82ca69820105e0373 SHA512: 33731b7d68effc92bb94f9b9ba8818b15142726e7b13ccb33c69c350011326f721499022acfbde71db6bb0ed651a68197876f1d0b6c589aaa50868ef5a91b88b Homepage: https://cran.r-project.org/package=sadists Description: CRAN Package 'sadists' (Some Additional Distributions) Provides the density, distribution, quantile and generation functions of some obscure probability distributions, including the doubly non-central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares; the product of non-central chi-squares to powers; the product of doubly non-central F variables; the product of independent normals. Package: r-cran-sae.projection Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidymodels, r-cran-survey, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-parsnip, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-bonsai, r-cran-ranger, r-cran-randomforest, r-cran-themis, r-cran-lightgbm, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sae.projection_0.1.5-1.ca2404.1_all.deb Size: 1450742 MD5sum: ebbd120c7ec8f6d37fc00167e18f6f1b SHA1: fa4341357eb6cae7809444e24b599d3d5320c180 SHA256: 6bcfb86c1dcad9492fbc192e3add6da10c38b7414341a619c8bbdf2dfdd35252 SHA512: 37063c16b167cf4c9643ac6a25beac7db17932589d4f42f3ec74e1897824103a470916c144ccbfd774186d2b2bd394a9c27e88ba6cd9083f1f04b0ca9bd2bd0f Homepage: https://cran.r-project.org/package=sae.projection Description: CRAN Package 'sae.projection' (Small Area Estimation Using Model-Assisted Projection Method) Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) . Package: r-cran-sae.prop Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-mass, r-cran-corpcor, r-cran-progress, r-cran-fpc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sae.prop_0.1.2-1.ca2404.1_all.deb Size: 350224 MD5sum: 43f789d78838debb42b0885443de460a SHA1: 7a68f2e4754be1c4b77489a2f32ad2bcbd85b0e4 SHA256: 14ec9523651dc4a93c91e4438f8683f35c35dfabb45eb44c69734d3d9a53ef17 SHA512: 9f440e5a607857eae302d660092ba83c83cc6c3bcae1dec2855f16524a7faca0cf8814d7c06be4a7262d0f556e7382070dfb6d80493aff71a18f66aac964919d Homepage: https://cran.r-project.org/package=sae.prop Description: CRAN Package 'sae.prop' (Small Area Estimation using Fay-Herriot Models with AdditiveLogistic Transformation) Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A . The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I , with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A . Package: r-cran-sae2 Architecture: all Version: 1.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survey Suggests: r-cran-sae Filename: pool/dists/noble/main/r-cran-sae2_1.2-2-1.ca2404.1_all.deb Size: 146732 MD5sum: 875866b61e71b591553a9d3a102a091a SHA1: 68f8b56f547d1837178757c77ef28589098fbcc8 SHA256: 55feb4021d0a0666f46ce7e0163062392b05aa10a24eba8f69c9f0c318290416 SHA512: 60231642ae442176e44c1cc25ec229ff09af3dbfc07776e76b45076cad69ce739758317d20c1bf53c0afe6d9d2d88f666baebb7b75958c6e052935204b15a753 Homepage: https://cran.r-project.org/package=sae2 Description: CRAN Package 'sae2' (Small Area Estimation: Time-Series Models) Time series area-level models for small area estimation. The package supplements the functionality of the sae package. Specifically, it includes EBLUP fitting of the Rao-Yu model in the original form without a spatial component. The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey. Key references to the methods include J.N.K. Rao and I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994) , and R.E. Fay and R.A. Herriot (1979) . Package: r-cran-sae4health Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-gridextra, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-r6, r-cran-sf, r-cran-sp, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-summer, r-cran-surveyprev, r-cran-survey, r-cran-geodata, r-cran-bookdown, r-cran-markdown, r-cran-haven, r-cran-ggridges, r-cran-ggthemes, r-cran-rcolorbrewer, r-cran-viridislite, r-cran-scales, r-cran-patchwork, r-cran-leaflegend, r-cran-leafsync, r-cran-plotly, r-cran-readr, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sae4health_1.2.3-1.ca2404.1_all.deb Size: 1074646 MD5sum: 560fe61e6315379124bc410ada45777a SHA1: e002ebd8c07cc4a9d1fad7090c6f0163f6117083 SHA256: 9b042aa3cd11584778c576f90c93dc03c0389633675deb72e70912270ce7463b SHA512: 2bf8a0ff566527d5a0972b5d4fa99ca23fa9b730e79161bb49bb46d57fafb5c15188cd1d85ba6f7b34f018eaae0bdc1032d7f9aa78071142a186642df1ffc3f5 Homepage: https://cran.r-project.org/package=sae4health Description: CRAN Package 'sae4health' (Small Area Estimation for Key Health and Demographic Indicatorsfrom Household Surveys) Enables small area estimation (SAE) of health and demographic indicators in low- and middle-income countries (LMICs). It powers an R 'shiny' application for generating subnational estimates and prevalence maps of 150+ binary indicators from Demographic and Health Surveys (DHS). It builds on the SAE analysis workflow from the 'surveyPrev' package. For documentation, visit . Methodological details can be found at Wu et al. (2025) . Package: r-cran-sae Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-sae_1.3-1.ca2404.1_all.deb Size: 1178688 MD5sum: 13777c15d42b6ebcad07db535a0053c6 SHA1: 9fad5e9280ced0f0f0f53ac6222f209c9820f5ec SHA256: 1621a1ac9ead904a18f9ba678d0d6c51cd2eaab7be00c9b30e291be90f0fc016 SHA512: 63bf874a1d412cc29a2bc2fd36660682975a548608d4cb8f7907428962737e2f1a0ffdd0d1e868ebb3da4ff2b1d7f9cedda8503c81ddc9bc1362e1cb4807ae75 Homepage: https://cran.r-project.org/package=sae Description: CRAN Package 'sae' (Small Area Estimation) Functions for small area estimation. Package: r-cran-saebenchmarking Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sae, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saebenchmarking_0.1.0-1.ca2404.1_all.deb Size: 72918 MD5sum: f71d9ae630c0aa380f6519f1c791d9ac SHA1: 09c42e92554006142608977fefe0c51a888f548e SHA256: 0e321020e0bc2cd08ac68f048f36eb6397aa26101f989bd6b173bf6b476a12c7 SHA512: ebf4b2c65358de45156a609a32f62980d683b8a075f7e970960f105f0d4b85d3140860fb01cabbfde9c68bc4aa613d509d77650d6ab2768eb4641e4d40ce715d Homepage: https://cran.r-project.org/package=saebenchmarking Description: CRAN Package 'saebenchmarking' (Benchmarking Small Area Estimates and Their Mean Squared Errors) Adjusts model-based small area estimates so that their weighted aggregate agrees with the weighted aggregate of the direct estimates, using the difference, ratio, and optimum benchmarking methods described in Rao and Molina (2015, ISBN:978-1-118-73578-7) and Wang, Fuller and Qu (2008). The mean squared error (MSE) of the benchmarked empirical best linear unbiased predictor (EBLUP) under the Fay-Herriot model is estimated with the second-order approximation or the parametric bootstrap of Steorts and Ghosh (2013) . The posterior MSE of the benchmarked hierarchical Bayes (HB) estimator follows Datta, Ghosh, Steorts and Maples (2011) . Package: r-cran-saebest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sae Filename: pool/dists/noble/main/r-cran-saebest_0.1.0-1.ca2404.1_all.deb Size: 19040 MD5sum: b4102a1be16367d097230684db430b3e SHA1: 5134bf70a1b045dda53b2a2d9a60833b37640d4e SHA256: a67934b51f7a7b73d22c567a6d48de8eb857333fd58a4a181618af88f25e863e SHA512: 4b6be2d9860b8d38779f2c30f20e506b616b61e5ae4474f0d56fba799eb4bd3bb57098a07180ce6c0a3bdbe65f6cffa3c756095e3719f5a27ad169efbde4e215 Homepage: https://cran.r-project.org/package=saeBest Description: CRAN Package 'saeBest' (Selecting Auxiliary Variables in Small Area Estimation (SAE)Model) Select best combination of auxiliary variables with certain criterion. Package: r-cran-saebnocov Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-descr, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-saebnocov_0.1.0-1.ca2404.1_all.deb Size: 81060 MD5sum: ccd13066cab8779f0633472db69768c0 SHA1: da3d8c1f5435bed91400850bf96a5c64226f2f98 SHA256: 5214362069359c4f0467969f909feb2f6172a0d185cc350a8f6ae3d9d142748a SHA512: f65bb9413a5ac6111d2e6556d4f1d964af3bb417cde26072e4bb10b46d14557528e61c9af2cc2fbfc630c7e3256768cacab7d12952f2326c79e871448e929f41 Homepage: https://cran.r-project.org/package=saebnocov Description: CRAN Package 'saebnocov' (Small Area Estimation using Empirical Bayes without AuxiliaryVariable) Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) and Kleinman (1973) . Package: r-cran-saeeb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-count, r-cran-mass Filename: pool/dists/noble/main/r-cran-saeeb_0.1.0-1.ca2404.1_all.deb Size: 33078 MD5sum: 8d3d6be3980a5f8c15ac780cab3ad890 SHA1: 358f88174abad17a87379700e932cf59e328f1f1 SHA256: a60023af767ed8e017bda77dffec74349c644ea4d8058302e5bcb2c49349a0ce SHA512: a49f774b5b8ab50cd41edbf73f0fb0f620f58992700ae3e1f325856906f536f50c30e4b8df0ea9f2afe93cbe628e5d44d99ad332e1d35298d0ae356db92234a2 Homepage: https://cran.r-project.org/package=saeeb Description: CRAN Package 'saeeb' (Small Area Estimation for Count Data) Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) , the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) . Package: r-cran-saehb.me.beta Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saehb.me.beta_1.1.0-1.ca2404.1_all.deb Size: 49008 MD5sum: 25280adcc2392a9c446b0cf932b89686 SHA1: e61376a814b4e4b4937644e7c3bb36f81c745a94 SHA256: 92dc7e812bbc93ebcd8204ac3afb237329bf64a26ed3b6949211732c649141b1 SHA512: f268faec8713c321e10abf9557a8a01188c258440889e9029f09c1830d2f6b29dd866541c9835dc20f22b135eed988559d0309fef0a5a269a7eba8d86dceeaf1 Homepage: https://cran.r-project.org/package=saeHB.ME.beta Description: CRAN Package 'saeHB.ME.beta' (SAE with Measurement Error using HB under Beta Distribution) Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) , Ybarra and Sharon (2008) , and Ntzoufras (2009, ISBN-10: 1118210352). Package: r-cran-saehb.me Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr Filename: pool/dists/noble/main/r-cran-saehb.me_1.0.1-1.ca2404.1_all.deb Size: 58608 MD5sum: 0a43cef969ab6ed26f911e5e23b9a11f SHA1: 2130e5359169a4ed55a446c2ede21342006b740a SHA256: c0899181d0f5de7aad16cd7ba021b8c56aa3109089e956f8dd72b9c377f5710c SHA512: 9eb1932f25101a9c243dc13ff6652a05498c142957a4f3b2da3c4c38c279e9b34653a0ac63d93ac0499fa5a7f32208d8999857ddd044de071a193bb10060c1bc Homepage: https://cran.r-project.org/package=saeHB.ME Description: CRAN Package 'saeHB.ME' (Small Area Estimation with Measurement Error using HierarchicalBayesian Method) Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) , Ybarra and Lohr (2008) , and Ntzoufras (2009, ISBN-10: 1118210352). Package: r-cran-saehb.panel.beta Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rjags, r-cran-stringr Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saehb.panel.beta_0.1.5-1.ca2404.1_all.deb Size: 80768 MD5sum: aa627015ef590796454aae0f70ce151e SHA1: f46fd2ded7649a2d3ca7e7bbe962b79c1151c9b5 SHA256: 1473d5662fed1aacbbd532d0f042b68345c3b2454ab962d69570f276c299ca34 SHA512: 1c0d93f091c8a503c951f8ce735baf8fd1ef33a61eedbd58dcda86c321a9defc31206cbf0446162807f16024a89e4c338cb5cac6c6a252d635886d91a41190d7 Homepage: https://cran.r-project.org/package=saeHB.panel.beta Description: CRAN Package 'saeHB.panel.beta' (Small Area Estimation using HB for Rao Yu Model under BetaDistribution) Several functions are provided for small area estimation at the area level using the hierarchical bayesian (HB) method with panel data under beta distribution for variable interest. This package also provides a dataset produced by data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators, which include the mean and the variation of the mean. For the reference, see Rao and Molina (2015, ISBN: 978-1-118-73578-7). Package: r-cran-saehb.panel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-saehb.panel_0.1.1-1.ca2404.1_all.deb Size: 90130 MD5sum: 7b3e25788612d9d50cb67ecfbceee1ff SHA1: 1c57987117c6fbf1ea41eeaf720d55da16b22b9f SHA256: b40cd4732b3d0a40e332e2a7af43637fcc40d407ee8bbc332fc4afb1bfc9aa57 SHA512: f8eb975c8737edc9bc80e74c9e7a60b4ceff76f7c5d965dec1cfc359cdf1569b10e2b6c524a5844f27e6d99b217cd25085620014dd9e28aa67047ee6438e0a64 Homepage: https://cran.r-project.org/package=saeHB.panel Description: CRAN Package 'saeHB.panel' (Small Area Estimation using Hierarchical Bayesian Method for RaoYu Model) We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015). Package: r-cran-saehb.spatial.beta Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-runjags, r-cran-coda, r-cran-sf, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-saehb.spatial.beta_0.2.0-1.ca2404.1_all.deb Size: 266528 MD5sum: df83349d343108a42e514b7fa13c699e SHA1: 36550c3462355feac4e22a686b8e55fedf43b9cd SHA256: d15370ee66d3b41c86ff255005b6b787c5ff1bea1ff6125e833fe9cdeb41f2af SHA512: c6ed602c40b4cbcd2936a1d8eea17a078b0d892da00ab9e4581e14d5d2cb3aaf65c2e79c657f0ed449065b9bebe8f15b4dd4daa805427866fd05ad060bb32a45 Homepage: https://cran.r-project.org/package=saeHB.Spatial.Beta Description: CRAN Package 'saeHB.Spatial.Beta' (Small Area Estimation Hierarchical Bayes for Spatial Beta Model) Provides several functions and datasets for area-level Small Area Estimation using the Hierarchical Bayesian (HB) method. Model-based estimators are designed for variables of interest that follow a Beta distribution (proportions bounded between 0 and 1). The package supports both non-spatial and spatial models based on Simultaneous Autoregressive (SAR) and Leroux Conditional Autoregressive (CAR) structures for area-level random effects, with optional survey design effect (DEFF) adjustments for sampling variances. In addition, it provides utility functions for constructing spatial weights matrices and performing spatial autocorrelation diagnostics. The 'runjags' package is used to obtain posterior estimates via Markov Chain Monte Carlo (MCMC) with parallel computing capabilities. For references, see Rao and Molina (2015) , Liu et al. (2014) , Kubacki and Jedrzejczak (2016) , Leroux et al. (2000) , Chung and Datta (2020) , Figueroa-Zúñiga et al. (2013) , Denwood (2016) , Anselin (1988) , and Anselin and Morrison (2019) . Package: r-cran-saehb.spatial Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-saehb.spatial_0.1.1-1.ca2404.1_all.deb Size: 44190 MD5sum: f59720c42de8e5cbbb6b3e6a06d892d4 SHA1: 1089a21e51d6481da234501378dce78a1f6cd796 SHA256: 6d5cbdb3566a96c347c3a3d800cf62f2de1195d3081f10f6310c47df7055b357 SHA512: 6fda5e7164553304f923cde80543dc28acd558692411f251e7bb82ca1c2d75e9900fead35c87841f356fc522d838ddc6d0234d4ef966a2f5d4ce7c895ddc1cff Homepage: https://cran.r-project.org/package=saeHB.spatial Description: CRAN Package 'saeHB.spatial' (Small Area Estimation Hierarchical Bayes For Spatial Model) Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) . Package: r-cran-saehb.twofold Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-stringr, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saehb.twofold_0.1.2-1.ca2404.1_all.deb Size: 108204 MD5sum: 51eaf4cfbc45d330951b4deee842665c SHA1: 93ca9c6b2d5d59f15d32999924669d465deb5f52 SHA256: 3a2653c5e299903b8c275d9b22e395553e0d62934c3ca667d527383d9528f9ed SHA512: 9ab1479bf11988891157781a1dcbba3cb836562c0424d90e8a2adde56c903ab544a5a98f207483efd52553601154be8590c1a82e78d3ede02bb4895a4acc50fd Homepage: https://cran.r-project.org/package=saeHB.twofold Description: CRAN Package 'saeHB.twofold' (Hierarchical Bayes Twofold Subarea Level Model SAE) We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , and Erciulescu et al.(2019) . Package: r-cran-saehb.unit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-coda, r-cran-dplyr, r-cran-rjags Filename: pool/dists/noble/main/r-cran-saehb.unit_0.1.0-1.ca2404.1_all.deb Size: 352974 MD5sum: f1ae816f0a4478d18b5cde51fcb6cf93 SHA1: bb8db0ee7045b91114f3a2aff44451b551dd2f3f SHA256: 6c8c33239c6e116ef19a97f7473823aad8019ac0260d63801835bc914fef1295 SHA512: dcdf434c40a32b47cfbe79eae9060a8dcc5b14aa3192f273e4bd7079c111c6e6ef9373453498bea5213588bbe3bed5833f5e977bc69872494be3eb82cbe2984e Homepage: https://cran.r-project.org/package=saeHB.unit Description: CRAN Package 'saeHB.unit' (Basic Unit Level Model using Hierarchical Bayesian Approach) Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) . Package: r-cran-saehb.zib Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-saehb.zib_0.1.1-1.ca2404.1_all.deb Size: 37372 MD5sum: 0991f2c4026f030180096c6cef315bd3 SHA1: b5389246acf82eb66af644c8ef30e5ef14c0c7bb SHA256: f46ec9ac67e1b7f5d8c50d9cf0b8e59aef162958b3e20e604dea53763bde9e99 SHA512: 114bec1c1141123a5221e883e9d924820b26e5578b619a240b0a7b78432b4cac6b4181ec08a64d8349174d5f7a8b46e0e9cadd9dab789fdfa1398bb12e3d44d8 Homepage: https://cran.r-project.org/package=saeHB.ZIB Description: CRAN Package 'saeHB.ZIB' (Small Area Estimation using Hierarchical Bayesian under ZeroInflated Binomial Distribution) Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. Package: r-cran-saehb Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nimble, r-cran-mass, r-cran-carbayesdata Filename: pool/dists/noble/main/r-cran-saehb_0.2.3-1.ca2404.1_all.deb Size: 237920 MD5sum: 3ba27266f6d6c517d7f53623dfd49031 SHA1: 661c259d70b0bb37789d8c0ebe508ce267a1b8c1 SHA256: 846f9cddde59617511194876bc25488222b86e9c2975e9237bb2f2aa8e3e2414 SHA512: 25b18324d719daf0063b5e4f710d8a66d74d6f35fd570550f72ffea3e21897edc92e47faafa4ce85b7760db8e0df563c883c5ee27605a31054db4ea224b3a94b Homepage: https://cran.r-project.org/package=saeHB Description: CRAN Package 'saeHB' (Small Area Estimation using Hierarchical Bayesian Method) Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) . Package: r-cran-saekernel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-saekernel_0.1.1-1.ca2404.1_all.deb Size: 25566 MD5sum: 96bde9754222001c46ce3530b10573da SHA1: 0b133e2e55b277f7d6dc10848f491cdac78ff8d7 SHA256: d58d0e95fa0740f9d58e32dcd91c11b4072c706337846844d7d20631844dfb77 SHA512: 07463af2d750b3da117f9bf79cd8caad1f2f644af5badf0546870d46e7b2990547298aed4036df45d77419d392b45eb83d29dd39c2c1ef27e1b99b7eba305d94 Homepage: https://cran.r-project.org/package=saekernel Description: CRAN Package 'saekernel' (Small Area Estimation Non-Parametric Based Nadaraya-WatsonKernel) Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) . Package: r-cran-saeme Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-saeme_1.3.1-1.ca2404.1_all.deb Size: 56208 MD5sum: 0d26facf8d9617f3ea2ab8e32ce6b9f9 SHA1: 201777a23ba9f58e22391c95bb2a7e9bd7027a95 SHA256: d14bce3b246590faf2fda7dd98b0ae0fce1b49f4b3e552eb7dab9798d9c2e5a9 SHA512: 2390391c986adab9ce0748a22931921358669816d0aeafd6feeeb5bfe77eb898b00a9bccb0eff4bbed334b17b918ddf0c598d173364364253bc7731ba7496ecb Homepage: https://cran.r-project.org/package=saeME Description: CRAN Package 'saeME' (Small Area Estimation with Measurement Error) A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) . Package: r-cran-saemix Architecture: all Version: 3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-npde, r-cran-gridextra, r-cran-ggplot2, r-cran-rlang, r-cran-mclust, r-cran-scales, r-cran-mass Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-saemix_3.5-1.ca2404.1_all.deb Size: 3279194 MD5sum: 8a8a27d46ad6437b06a67ade7f77d6bb SHA1: 2b3f05c8360cb90d9a5a59dc3b83ed24f6597504 SHA256: 20f08c1dcf681510ed6e7298024605b0af8db7b259d35a4b387db32c851d4102 SHA512: 2020870f6a786e8c6684f75f4b348f6b3281356888342c62e3ac62b2d3518877c57e3e67c2e5c7a50facd80f1bcb305c8f600fa9896046ede692175e654f15e5 Homepage: https://cran.r-project.org/package=saemix Description: CRAN Package 'saemix' (Stochastic Approximation Expectation Maximization (SAEM)Algorithm) The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) ). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': . Package: r-cran-saens Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-saens_0.1.2-1.ca2404.1_all.deb Size: 285096 MD5sum: 094cada7bf6ecd4b4d1e947b3bc7dd0d SHA1: 0db9b889fa0fff9aa747e67c7bdf10072fc827b7 SHA256: 6c095e8d6d418aff728cf05323bf7c2abb8d82790bfdb9b2f90a3c79c67a5488 SHA512: 532ee48e3c4c6df519f7c29db3339b1f952f6de8b17db867da69cd222c97a904f762b383ce015c564dc1799ab1da947c5f3dc482c4c872e3b52fd4eb1d37a314 Homepage: https://cran.r-project.org/package=saens Description: CRAN Package 'saens' (Small Area Estimation with Cluster Information for Estimation ofNon-Sampled Areas) Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) . Package: r-cran-saeproj.multilevel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lme4, r-cran-reformulas, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saeproj.multilevel_0.1.1-1.ca2404.1_all.deb Size: 418548 MD5sum: b4607ae1c86f5357d703299598431d71 SHA1: e5c6c51bd02e9c8084d270541c6740e02c8db4d0 SHA256: c6ecd6d9c2e7b7ea1e611d1fce1c1d3eac796557c1eba39faeff6d515ae6045e SHA512: 1a7f2cc5319f323e192f511171febd6fa6a036abb7e592b7e64146f54b7347a8581c39acdc8d68cf578e5df74eea9c400eb014cf20cafdd5018cabdf0e91e945 Homepage: https://cran.r-project.org/package=saeproj.multilevel Description: CRAN Package 'saeproj.multilevel' (Small Area Estimation Using a Projection Estimator with aMultilevel Regression Model) Provides tools for small area estimation using a projection estimator with a linear multilevel working model. The main function fits a multilevel model to a smaller survey containing the response variable and auxiliary predictors. The fitted model is used to predict outcomes in a larger projection survey, and domain-level estimates are computed by combining synthetic predictions with a design-based residual correction. For methodological references, see Kim and Rao (2012) , Food and Agriculture Organization of the United Nations (2021) , and Moura and Holt (1999) . Package: r-cran-saepseudo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sae Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saepseudo_0.1.0-1.ca2404.1_all.deb Size: 28438 MD5sum: 430a9b3f667f9188786939adcd50a356 SHA1: f4e7c2e75782c4f2e9bf3fc03e3e6a1bb38f172a SHA256: 8795bfd0a0844d01c52a8f98d66543e351baca98a01775602fb48af484dc173a SHA512: 535a7b9af9134d9a510c7da1dbc7b0566b284a7107890b3907f53d065af528537d0b2a5a731406732b1da1155603f9c02260ce6e1c81d77a7a9f946bc11c70e7 Homepage: https://cran.r-project.org/package=saePseudo Description: CRAN Package 'saePseudo' (Small Area Estimation using Averaging Pseudo Area Level Model) Provides function for small area estimation at area level using averaging pseudo area level model for variables of interest. A dataset produced by data generation is also provided. This package estimates small areas at the village level and then aggregates them to the sub-district, region, and provincial levels. Package: r-cran-saery Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-saery_2.0-1.ca2404.1_all.deb Size: 163886 MD5sum: 713edf5a51fceebf8b9d20c69b0a8b20 SHA1: f6b3e46f32c263caab86f0eab77178487ea8f278 SHA256: 7213496bb10a3e001d409fa3f59ef4151de9d2c15028ab3cab6aeb9082564449 SHA512: bf7fd357e3ccae2f5ddc2625f237527dbaa33382d2ebb9e70f2d43ba6302e07849da858e3262293d53caa2b2e41785df081167168d26e3e516b745bd523ca263 Homepage: https://cran.r-project.org/package=saery Description: CRAN Package 'saery' (Small Area Estimation for Rao and Yu Model) Functions to calculate EBLUPs (Empirical Best Linear Unbiased Predictor) estimators and their MSEs (Mean Squared Errors). Estimators are based on an area-level linear mixed model introduced by Rao and Yu (1994) . The REML (Residual Maximum Likelihood) method is used for fitting the model. Package: r-cran-saesim Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-functional, r-cran-ggplot2, r-cran-mass, r-cran-spdep, r-cran-parallelmap Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-saesim_0.13.0-1.ca2404.1_all.deb Size: 265200 MD5sum: cad7caf2dbe3cbd5f09e285ed63144c2 SHA1: bdf7483ba44849d8c6fcf8328a8c5ba63c9f3b8e SHA256: 11c9e5544f3a59e34b669cb2f9955b6cf0ad6f65fc175643d5bc669e46b9f1fd SHA512: f2764225984c53e60206bdc23cc8d0023eeb905e6fb61075af3273d325056154b5203bfbf3a64adfe7f25bb13307de936ebb72ee0291174a5802ff212191b027 Homepage: https://cran.r-project.org/package=saeSim Description: CRAN Package 'saeSim' (Simulation Tools for Small Area Estimation) Tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel. Package: r-cran-saetrafo Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2002 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-emdi, r-cran-sfsmisc, r-cran-parallelmap, r-cran-ggplot2, r-cran-moments, r-cran-openxlsx, r-cran-reshape2, r-cran-hlmdiag, r-cran-gridextra, r-cran-stringr, r-cran-readods, r-cran-rlang Suggests: r-cran-simframe, r-cran-sf, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-saetrafo_1.0.6-1.ca2404.1_all.deb Size: 1962884 MD5sum: 9f4e64a29a8e361bea3130196d6ddbeb SHA1: f66193c7a00ed7e8fea37de04852f2dd6e91855a SHA256: 3b0511222a9d334dbc55a4c6547df5ef1acb01acdd31efdae144597da3664fdd SHA512: 8511245f86d87f47c0039896c5a01eca512481dbcc8e138a016b4f035855a7fa9aa75be6a2f20df4d872979af3fcc9b17544bf2700d089f45c708a7b65a02219 Homepage: https://cran.r-project.org/package=saeTrafo Description: CRAN Package 'saeTrafo' (Transformations for Unit-Level Small Area Models) The aim of this package is to offer new methodology for unit-level small area models under transformations and limited population auxiliary information. In addition to this new methodology, the widely used nested error regression model without transformations (see "An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data" by Battese, Harter and Fuller (1988) ) and its well-known uncertainty estimate (see "The estimation of the mean squared error of small-area estimators" by Prasad and Rao (1990) ) are provided. In this package, the log transformation and the data-driven log-shift transformation are provided. If a transformation is selected, an appropriate method is chosen depending on the respective input of the population data: Individual population data (see "Empirical best prediction under a nested error model with log transformation" by Molina and Martín (2018) ) but also aggregated population data (see "Estimating regional income indicators under transformations and access to limited population auxiliary information" by Würz, Schmid and Tzavidis ) can be entered. Especially under limited data access, new methodologies are provided in saeTrafo. Several options are available to assess the used model and to judge, present and export its results. For a detailed description of the package and the methods used see the corresponding vignette. 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Package: r-cran-safetygraphics Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4889 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-datamods, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-rclipboard, r-cran-rlang, r-cran-safetydata, r-cran-safetycharts, r-cran-shiny, r-cran-shinyjs, r-cran-sortable, r-cran-stringr, r-cran-tidyr, r-cran-yaml Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-shinytest, r-cran-testthat, r-cran-usethis, r-cran-listviewer, r-cran-shinybusy, r-cran-shinywidgets Filename: pool/dists/noble/main/r-cran-safetygraphics_2.1.1-1.ca2404.1_all.deb Size: 3108734 MD5sum: 00c2feba2301ea2280165c3fc119b8db SHA1: 759cec043f096878ad14ab6d609ed0e0c2bb4f36 SHA256: b54c50b95f251c37f5de682ebdfa6697a26408734bcc4021e6b4b2bd480303fe SHA512: 59e0c79eeeba0e00d32bb7861e280c5270349c07fa726aaf403d89e4a2c4505d3f4233aab6cc0324efced1f5388cfc1fcb864e7fa8f92d8a2ecc4f0d215ea464 Homepage: https://cran.r-project.org/package=safetyGraphics Description: CRAN Package 'safetyGraphics' (Interactive Graphics for Monitoring Clinical Trial Safety) A framework for evaluation of clinical trial safety. Users can interactively explore their data using the included 'Shiny' application. Package: r-cran-safevote Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formattable, r-cran-knitr, r-cran-fields, r-cran-ggplot2, r-cran-data.table, r-cran-stringr, r-cran-forcats, r-cran-dplyr Suggests: r-cran-testthat, r-cran-vote, r-cran-stv Filename: pool/dists/noble/main/r-cran-safevote_1.0.2-1.ca2404.1_all.deb Size: 738960 MD5sum: 124d0125dffc2c1279619516c4e0e5bd SHA1: 820b1edb19537cc4e5926f3b879047620f587c04 SHA256: 324caa4c471e71576f9471726939a6a1d8c3dab5a9e8608fcbd14fa99eb6a003 SHA512: 404e89a5d0e43ace50eb59b6f058270d5cadf1ca9f2c9d51630c4681b7fed59bb124b32fb735a2834356710bc54464c20316bd25ec4be9ad56690f0ef014f688 Homepage: https://cran.r-project.org/package=SafeVote Description: CRAN Package 'SafeVote' (Election Vote Counting with Safety Features) Fork of 'vote_2.3-2', Raftery et al. (2021) , with additional support for stochastic experimentation. Package: r-cran-safuzzy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-safuzzy_0.1.1-1.ca2404.1_all.deb Size: 95622 MD5sum: e03ee74c881d546209d8dfe9a987c70c SHA1: 48a30ef00836828d3dc9d857e8819887a3da2f6d SHA256: 0e7ef60bb640a8eaa609fcf64e5384e4986348d6cc15870f974878d66c502868 SHA512: a3e80f5ea8149bce986b0fe92d9db33c6b51f5f08b4b2130a95d9a6f9004c6ddd9f6f10f6b16704b7383d2b8ae7bbeb8e30ef723df27073b27bd21e97d1d5804 Homepage: https://cran.r-project.org/package=safuzzy Description: CRAN Package 'safuzzy' (Stability Analysis with Fuzzy Logic) It integrates 'fuzzy logic' into the analysis of genotype adaptability and stability. 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Package: r-cran-sager Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-markdown, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-learnr, r-cran-spelling, r-cran-lattice, r-cran-spatstat.data, r-cran-matrix, r-cran-mgcv, r-cran-pmcmrplus, r-cran-devtools, r-cran-desctools, r-cran-ggally, r-cran-vgam, r-cran-car, r-cran-dplyr, r-cran-tibble, r-cran-ggiraphextra, r-cran-scales, r-cran-spatstat, r-cran-vcd, r-cran-factominer, r-cran-cluster, r-cran-caret, r-cran-corrplot, r-cran-actuar Filename: pool/dists/noble/main/r-cran-sager_0.7.0-1.ca2404.1_all.deb Size: 501678 MD5sum: b7669cede4c5885ae9b30406e6fee9a7 SHA1: 5c8f178246b60714dc14091c5f4a7ef94173874d SHA256: 9a389036aac462a930375ffd335cc07f99224a7c788f8a57a096f47c20eefb96 SHA512: 746816adca2e52e61ec706980c2d99c6501acc693ec9e3ffae15a8572835245c95941d83c9c72ee3b70ddbfe2f720d1740a9920b0bd3860322da769ec6215354 Homepage: https://cran.r-project.org/package=sageR Description: CRAN Package 'sageR' (Applied Statistics for Economics and Management with R) Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). The first chapter of the book is dedicated to an introduction to statistics and their world. The second chapter deals with univariate exploratory statistics and graphics. The third chapter deals with bivariate and multivariate exploratory statistics and graphics. The fourth chapter is dedicated to data exploration with Principal Component Analysis. The fifth chapter is dedicated to data exploration with Correspondance Analysis. The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. The seventh chapter is dedicated to data exploration with automatic clustering. The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions. The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests. The tenth chapter is dedicated to an Gaussian linear model. The eleventh chapter is dedicated to an introduction to time series. The twelfth chapter is dedicated to an introduction to probit and logit models. Various example datasets are shipped with the package as well as some new functions. Package: r-cran-sagm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix, r-cran-gigrvg, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sagm_1.0.0-1.ca2404.1_all.deb Size: 36214 MD5sum: ecb7d51931d17b7d80ee28dd1ea9ab05 SHA1: c373c9c2b1677f9f4a890527a96171be55620b41 SHA256: 69e43e0b93a2f14fd1e0b942e93e493431b0f71b2207f8eac0aaf6f4820bb36f SHA512: 9c9faed165896983ac31f0267df387fc0443af68e77ce58647a5dae0e77d6ab7f2c3237b43213c2a5b6debbe97025879935d35663159bfebda8b15c6bc4bbd6d Homepage: https://cran.r-project.org/package=SAGM Description: CRAN Package 'SAGM' (Spatial Autoregressive Graphical Model) Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) , and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. The package allows for the generation of spatial weight matrices to capture asymmetric effects for strip-type intercropping designs, although it can handle any type of spatial data commonly found in other sciences. Package: r-cran-sahpm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sahpm_1.0.1-1.ca2404.1_all.deb Size: 23920 MD5sum: 23995ebd8ff25bb4a354849ee259a164 SHA1: fb12e7474e998d65149cf3328423585b0ec133ed SHA256: 87ef0d90289ff86d410eeeb9fbf361d1b8545c18410e0c081cb216a157091f4c SHA512: cab53179876dc5de6d557c9c568c764649d8c73ca9c0b239425bda3ad5cf08f9019c1735dbeb49f6a9d51c413e6010e1630c0c9cdc715052e2ac7c9ec91d1ff7 Homepage: https://cran.r-project.org/package=sahpm Description: CRAN Package 'sahpm' (Variable Selection using Simulated Annealing) Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. 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Package: r-cran-saic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-glmnet, r-cran-glasso Filename: pool/dists/noble/main/r-cran-saic_1.0.1-1.ca2404.1_all.deb Size: 19496 MD5sum: 72f39694567ee4ace666381b58f9fde2 SHA1: 3879b38bf2095486c5dccfd6fd1624eabe6c5ee3 SHA256: f823c5446f19bf737f638607fb9bd6b9bdc1e6ba497dcd24c2baa51cffe353e7 SHA512: 352d811f3f0f77d3ec01a2e0acb952b479289c7084fbc05dc0532e242b2b37804f493971ee641a4e23a7282c3e6c85576787958df5353e19e449c780d786ef7f Homepage: https://cran.r-project.org/package=sAIC Description: CRAN Package 'sAIC' (Akaike Information Criterion for Sparse Estimation) Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso. Package: r-cran-sailor Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3623 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sailor_1.2-1.ca2404.1_all.deb Size: 3669462 MD5sum: ae6e1b5fb44f896553b51ddc2b176061 SHA1: 977a4fe92425c0c2bdb24afc4962cb09df1b13ea SHA256: 9478b76f2d23b2685987962be0d94cf17f4d0c78c153f8b27b621514fe62d09c SHA512: dc8033be26d90f1223ce3259eaec4a4951fb64bbe58d86f7c2f69a70c056a43e1c4b6b2596ad4181f3c0356668aa672f759aca9935a3db78bba4193dfc03761a Homepage: https://cran.r-project.org/package=SailoR Description: CRAN Package 'SailoR' (An Extension of the Taylor Diagram to Two-Dimensional VectorData) A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. 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Package: r-cran-saive Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1871 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-crayon, r-cran-doparallel, r-cran-proxy, r-cran-rlang, r-cran-terra, r-cran-vsurf Suggests: r-cran-ranger, r-cran-testthat, r-cran-vdiffr, r-cran-whitebox Filename: pool/dists/noble/main/r-cran-saive_1.0.6-1.ca2404.1_all.deb Size: 1337996 MD5sum: 737170c7fe323188cc4178e38006f145 SHA1: 8f147fb49c2f048c79f98d7095cee3fca18029d5 SHA256: 143e585cd2d6e788f65a4b9aa0126d65a3ce5d85958cebebcf479e0821086292 SHA512: ce556228dbd2420bbbcbc548fa39c01c28d0c3b7ecc6ff83957ee2c6c9150ec63bf61b1dbaf3e069104d6bcc0c4aab035ed53a4bb2e6cc82462b7aa2d9d1e9d9 Homepage: https://cran.r-project.org/package=SAiVE Description: CRAN Package 'SAiVE' (Functions Used for SAiVE Group Research, Collaborations, andPublications) Holds functions developed by the University of Ottawa's SAiVE (Spatio-temporal Analysis of isotope Variations in the Environment) research group with the intention of facilitating the re-use of code, foster good code writing practices, and to allow others to benefit from the work done by the SAiVE group. Contributions are welcome via the 'GitHub' repository by group members as well as non-members. Package: r-cran-sakernas Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl Filename: pool/dists/noble/main/r-cran-sakernas_0.1.0-1.ca2404.1_all.deb Size: 714724 MD5sum: c6acfb5cab4c90514c48aec3376def1e SHA1: 935944c573cbd986bd1ff025278794201be5353b SHA256: 12f29f18c31eb7a18b76db5c42b7db19d1dafe3f7cb5aabc36ef70223f9c5ec1 SHA512: 35c60bab1efe1e471c519589e45538c696e917d658e0cb34d98012a65703b56d629f82f70d3df6edc39ef2fcef0a2ba97a5039cdad37658b3e84a86ada3b7250 Homepage: https://cran.r-project.org/package=SAKERNAS Description: CRAN Package 'SAKERNAS' (A National Labor Force Survey of Indonesia) Surveys to collect employment data so as to obtain data estimates on the number of employed people, the number of unemployed, and other employment indicators. Package: r-cran-salad Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-salad_1.2-1.ca2404.1_all.deb Size: 444814 MD5sum: dca13fa82bb50b8df312a17faa5b9ca0 SHA1: 946faa3574307bf14a4764eb2217cf6136cff1e7 SHA256: 749b8edc073103ea907310ccede9f6c26a2f0578293394ccb5d87e67cd9b3a71 SHA512: 468eae9ba80f82ef2c4945745667a95826c8d129c83d32c6483b73598ef2c31e1ed9fa0d66631740f0a9293ce440645befbee7d7ffcc5bd3cf5240f3684ff352 Homepage: https://cran.r-project.org/package=salad Description: CRAN Package 'salad' (Simple Automatic Differentiation) Handles both vector and matrices, using a flexible S4 class for automatic differentiation. The method used is forward automatic differentiation. 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Package: r-cran-salem Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4026 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-salem_1.0.1-1.ca2404.1_all.deb Size: 2798554 MD5sum: 4b3f8119aedceb5d2457e5491e85417a SHA1: 5b0bb978a838e3f9d36b04346b020abfb2f78467 SHA256: b1fe1b4cb93b4e10d58289036684c1c041a9c83d6cad68465c2f3ea13581e339 SHA512: 734dd302e4f398af9c948aad12c1fcc95ed6dbe05362d89d3b7a34bbe9d2e6c0f0f16a3886ea37b1193eb4a64ffcc166125f402e86f70d7fb38e8f260b00c849 Homepage: https://cran.r-project.org/package=salem Description: CRAN Package 'salem' (Provides Access to Salem Witchcraft Data) Data related to the Salem Witch Trials Datasets and tutorials documenting the witch accusations and trials centered around Salem, Massachusetts in 1692. Originally assembled by Richard B. 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Package: r-cran-salmonmse Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-rtmb, r-cran-dplyr, r-cran-ggplot2, r-cran-gsl, r-cran-reshape2, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-bookdown, r-cran-envstats, r-cran-ggrepel, r-cran-knitr, r-cran-rstan, r-cran-scales, r-cran-testthat, r-cran-tmbstan Filename: pool/dists/noble/main/r-cran-salmonmse_3.0.0-1.ca2404.1_all.deb Size: 741236 MD5sum: 59e27678b94367532535ef9145a4e71c SHA1: 4ea7fb00554940dfebf83dc3f4c5a72959fa460e SHA256: 7f8453a8c1c6ec7395346b849fca54cd29ddefdd5b9d60c1e6df62d1d99ba178 SHA512: f4a5f155e8ebb0daaf397c86bdcfa92b40f70eff2189f1d2e2d776cd5291f042a37d874b937ce79f28618138ad331fa65e861f2c817c9a080c75b3f74b286a8b Homepage: https://cran.r-project.org/package=salmonMSE Description: CRAN Package 'salmonMSE' (Management Strategy Evaluation for Salmon Species) Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and habitat actions. The stochastic age-structured operating model accommodates complex life histories, including freshwater survival across early life stages, juvenile survival and fishery exploitation in the marine life stage, partial maturity by age class, and fitness impacts of hatchery programs on natural spawning populations. 'salmonMSE' also provides an age-structured conditioning model to develop operating models fitted to data. Package: r-cran-saltsampler Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-coda Filename: pool/dists/noble/main/r-cran-saltsampler_1.1.0-1.ca2404.1_all.deb Size: 68424 MD5sum: 4cff1b27fc0566362c6b7214d1178829 SHA1: cc754864d2ebb27217f2d8f0d97fdc01f83bc32d SHA256: e50d7173f5e6ab066080e9e9dbe60c44ca3603b8f187fd85911776b2753ccda0 SHA512: 134e51a53db59a992a553883d5727f92f440edf77e42f3202e65bab99ce52ec15e413d3ec049668707e10f4b5f9c0ceb28b4873a561474df6b3dba931eb22cc8 Homepage: https://cran.r-project.org/package=SALTSampler Description: CRAN Package 'SALTSampler' (Efficient Sampling on the Simplex) The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of random variables on a simplex. A Self-Adjusting Logit Transform (SALT) proposal is used so that sampling is still efficient even in difficult cases, such as those in high dimensions or with parameters that differ by orders of magnitude. Special care is also taken to maintain accuracy even when some coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are included in the package, enabling easy assessment of the convergence and mixing of the chain within the constrained space. Package: r-cran-salty Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-purrr, r-cran-stringr Suggests: r-cran-charlatan, r-cran-testthat, r-cran-tibble, r-cran-covr Filename: pool/dists/noble/main/r-cran-salty_0.1.2-1.ca2404.1_all.deb Size: 91150 MD5sum: d89ec29805b4232a441fa5b143627c88 SHA1: f42be3ba21a8eac5894c51c9b15919efb7d01920 SHA256: 022f54fff43369fc19871e63eef30ddbef2d236b42cf1b86317bdf086db19b0b SHA512: fc235eb2a620e8ca3f77b0aa27aa10cb1587e78ed46c40c9f1f2a39dc613ee918268f8ff67df26c10178c4271767d09bd21bf97cd2f9ca71342eb4c8e8e1d2a3 Homepage: https://cran.r-project.org/package=salty Description: CRAN Package 'salty' (Turn Clean Data into Messy Data) Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc. Package: r-cran-samadb Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-writexl, r-cran-data.table, r-cran-collapse Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-samadb_0.3.1-1.ca2404.1_all.deb Size: 69264 MD5sum: e894c2be21801a3932344d1dd74e8343 SHA1: 177f8db03d902526f79b6f4e796c6b98b68f2a62 SHA256: 32f8ec12e5e17582bf445f747f74324b52503a810f9cb213ce25c3e053412bcf SHA512: 2b85ab2044ca9609c2cc60827ffcdbf5c0ceace0fcc05f29340eac42db950dcd044337d7184f41446d6ca27cc238d656e6958738bd3d598c8a5dcbe0abcf6be3 Homepage: https://cran.r-project.org/package=samadb Description: CRAN Package 'samadb' (South Africa Macroeconomic Database API) An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University. Package: r-cran-samba Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-optimx, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-mass Filename: pool/dists/noble/main/r-cran-samba_1.0.0-1.ca2404.1_all.deb Size: 405234 MD5sum: 9db7fcd20b83e70dc6d96a98cc33a9d7 SHA1: 1bc1d42828629bd462b28426f6c857073f0d4315 SHA256: be1a48a1bb2b4065bf708af5e02d4e498aa559eab03b8478ece38d16ba691cd7 SHA512: 55f947e72997a6a72edc7bcf3a1ccde8aa83ecaf169493f6aa261426c1eb9431c5395a338f5f38ae8da858af40bf0645e2a44c9b959dacc44bc927f01cfbde7c Homepage: https://cran.r-project.org/package=SAMBA Description: CRAN Package 'SAMBA' (Selection and Misclassification Bias Adjustment for LogisticRegression Models) Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. 'SAMBA' implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) , Biometrics. Package: r-cran-sambia Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-dplyr, r-cran-smotefamily, r-cran-e1071, r-cran-ranger, r-cran-proc, r-cran-fnn Filename: pool/dists/noble/main/r-cran-sambia_0.1.0-1.ca2404.1_all.deb Size: 60108 MD5sum: ac57779bf669b4c8b9195c033b55d238 SHA1: 3d3fe11eda9b6b1f7dd33f1a586aec810fd237d2 SHA256: 5c54d8553dad5df5dbb4a9282333a2ed1a5ecef91d782a473fd4838c81907e0c SHA512: c2ec1cb59ac5d5c1c443095c0d547e588823597435999fecb1f5f1c24b8381b1827c438ef44452d238c2cc59345ba0742a6114fd45b466cde3715fe1a643e0d2 Homepage: https://cran.r-project.org/package=sambia Description: CRAN Package 'sambia' (A Collection of Techniques Correcting for Sample Selection Bias) A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) . The methods may be used for further purposes where weighting and generation of new observations is needed. Package: r-cran-same Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-rjags, r-cran-coda, r-cran-extradistr, r-cran-survival, r-cran-ggplot2, r-cran-expint Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-same_0.1.0-1.ca2404.1_all.deb Size: 87464 MD5sum: fe167dfa9df9870c4638e747e9337152 SHA1: 974efecca0951e21f7e0dd1ee37528010388dfa9 SHA256: bfdd404111693968db5f39f49f3a0ea32fb854940593c69f7f9fe8c7d9aacf75 SHA512: 6ddf0868388436d0298608bd928eb3668fa1519237e75b5d9861b3e5b1714bee711c0704151246cf323a4c6d3a8847ad75fbb3f2f5c3e249881d6591dea2ca5d Homepage: https://cran.r-project.org/package=SAME Description: CRAN Package 'SAME' (Seamless Adaptive Multi-Arm Multi-Stage Enrichment) Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) . Guosheng Yin, Nan Chen, J. Jack Lee (2018) . Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) . Package: r-cran-sameplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-ragg Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sameplot_0.1.0-1.ca2404.1_all.deb Size: 51822 MD5sum: 6ebb7c341794255810e05bdc18a341e6 SHA1: 6495dd282c5a5d36bdbbd1d08022cfcbae19e4de SHA256: f9daa222db61319c37a84dced58fc57b841d81fccf46b9f94bcbee6cea834734 SHA512: cdf75c39d065a179f1c1840bdb06755d965860526c324e83d9fb2dddb469addf0b48f843f382c5d78817437c76a279d543849a662964acf59d54ceebd52cff2d Homepage: https://cran.r-project.org/package=sameplot Description: CRAN Package 'sameplot' (Consistent Plot Rendering and Saving Across Interactive Sessionsand Reports) Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. This helps ensure consistent appearance across interactive sessions, saved files, and knitted documents. For more details see Pedersen and Shemanarev (2025) . Package: r-cran-samesies Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-purrr, r-cran-scales, r-cran-stringdist Suggests: r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-samesies_0.1.0-1.ca2404.1_all.deb Size: 416454 MD5sum: 651f1cf078e17d0abb3a0e438d369266 SHA1: 686a780e7ee39ced68d1c28d004eb6f503e92390 SHA256: 05d0ebd179cc4d2ce325e3c7db6bd6b3142fee199ad952f0d807518d83e3e6a6 SHA512: f43387fa8984c532e355579987f83480724797bdde1fe6072c4b7442831f8ee7892e4e2603bca0a0e118900aa033fcbdbd8603fdc3b731e2ee105ba81852dfe4 Homepage: https://cran.r-project.org/package=samesies Description: CRAN Package 'samesies' (Compare Similarity Across Text, Factors, or Numbers) Compare lists of texts, factors, or numerical values to measure their similarity. The motivating use case is evaluating the similarity of large language model responses across models, providers, or prompts. Approximate string matching is implemented using 'stringdist'. Package: r-cran-sampbias Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-viridis, r-cran-terra, r-cran-sf, r-cran-rnaturalearth Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sampbias_2.0.0-1.ca2404.1_all.deb Size: 694252 MD5sum: 5811b157c7c3d5181626782fd19e3815 SHA1: f52e5fdee3c36d0ae03d9227237d68142bbc68bc SHA256: 4252014b5a3622f0f4002ef55e0d01ea44dde72d116421327cac236bd08b868b SHA512: defd3ab0169ea2df33eeeffa585f26d5d7f3a664b68dac6fd1524b8b1f4276787248f7f0a3c0f747982e5e88197c252175d2fbdd1ad44f383ff2f90003a7f908 Homepage: https://cran.r-project.org/package=sampbias Description: CRAN Package 'sampbias' (Evaluating Geographic Sampling Bias in Biological Collections) Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as 'shiny' app. The reference for the methodology is: Zizka et al. (2020) . Package: r-cran-sampcompr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-hmisc, r-cran-lmtest, r-cran-magrittr, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-reshape2, r-cran-sandwich, r-cran-survey, r-cran-svrep, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-jtools, r-cran-stargazer Filename: pool/dists/noble/main/r-cran-sampcompr_0.3.3-1.ca2404.1_all.deb Size: 614352 MD5sum: b057ca68567d5102113d42bcd849da9d SHA1: 773b794d31fd44fb2fed13133ebb077a9f2012da SHA256: 64cc06ac3c4ea011361ced03bca1835e0beb8bee78dd709963f8a5c6074c5528 SHA512: e3b00836286f6c88c5ceac5d43307b062501ac390288768d278b5908bd6516814cf3365f3ab52febb6af72868e9ae20ed60aec7dd54cbf28c174cf511e580c73 Homepage: https://cran.r-project.org/package=sampcompR Description: CRAN Package 'sampcompR' (Comparing and Visualizing Differences Between Surveys) Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology . Package: r-cran-sample Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rmisc, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sample_1.0.1-1.ca2404.1_all.deb Size: 61114 MD5sum: c033fdeb33e60f79faea56755bb7188f SHA1: bd1870751f850411c8a54559cecdc387846549c2 SHA256: c0669dc9e0e0c6980a962f2eff6ae496b8d09894b057fab837f9ec4e316a86ad SHA512: b7821bfec2fe688cc126d20fc16bc451e3440985fd89082166ef265f0d8f6c3836ca4974c756b247d557bca3b6e884c0bbaf65ee2119a000045cb0f575cc9614 Homepage: https://cran.r-project.org/package=SAMPLE Description: CRAN Package 'SAMPLE' (Estimate Sampling Effort for Species Occurrence Rates) Estimates the sampling effort needed to obtain stable species occurrence and prevalence rates from presence-absence data. The method repeatedly subsamples observations, evaluates changes in estimated rates, and reports the minimum stable sample size. For methodological details, see Bravo et al. (2025) . Package: r-cran-samplecore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-diversitystats, r-cran-ggplot2, r-cran-igraph, r-cran-mass, r-cran-mathjaxr, r-cran-prospectr, r-cran-rdpack, r-cran-rtsne, r-cran-vegan Suggests: r-cran-biotools, r-cran-dbscan, r-cran-evaluatecore, r-cran-fastcluster, r-cran-knitr, r-cran-pander, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samplecore_0.1.0-1.ca2404.1_all.deb Size: 284094 MD5sum: 35e5f103ddade4efd02c460718bd4f56 SHA1: 9a9f6989024d16ff0451a0a51638633570959232 SHA256: 5911af744b260c64c7b2afd7733d510993f65a937c133d8101e474d043318bd2 SHA512: 29922afe1314bd8efc5b8a7a580eb117a9445184c3bfbda86322f761b9555960ddcc6553005b043a7130b81f564fb94c8b025451f0ef14c3e28a0c3909bcbe2f Homepage: https://cran.r-project.org/package=SampleCore Description: CRAN Package 'SampleCore' (Sampling Strategies for Constructing Core Collections) Implements multiple allocation and selection strategies of sampling to construct core collections primarily from clustered or grouped germplasm collection data. Provides methods for allocating entries to clusters/groups based on group sizes, group-wise distance-based diversity metrics, and group-wise diversity index estimates. Includes procedures for selecting entries within clusters/groups through random sampling, genetic distance-based approaches, and optimized diversity metric–based selection methods. See the package documentation for more, including full list of references for the methods implemented. Package: r-cran-sampledatasets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sampledatasets_0.1.0-1.ca2404.1_all.deb Size: 89402 MD5sum: 809a769d12a6e91c1c1706bab89be572 SHA1: 077cdf9670cadb100de8acb6374f359896386c07 SHA256: 5c6af87b63328811263a806a5d1c821cee8f8b683083b6f08f11da8233f4cf4e SHA512: 681027147ff0e15a9289d1f2533448db1e4cca33145a403be9729f6c15e898ae2eae5246964ba4d58a460d0ec5168b0bbb738fbec506307eaad78977ede9329d Homepage: https://cran.r-project.org/package=sampledatasets Description: CRAN Package 'sampledatasets' (A Collection of Sample Datasets) Provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R. Package: r-cran-sampler Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-reshape, r-cran-purrr Filename: pool/dists/noble/main/r-cran-sampler_0.2.4-1.ca2404.1_all.deb Size: 260918 MD5sum: 58fe56e7517f995a48b31fcde672fa58 SHA1: 645826ea01313dc0fcc135a50d66e3ba17352bc0 SHA256: cb5b52cd4cf87cde4c355a1a3b31ff23fa37528e5078ff93f1b969ef2099cc72 SHA512: dbc67429b26b449c86cf35e927ebb00bcb1dcb8bbca3110266e0f1144ab6c6022edc7c96987595de13f15eaf36f610c3021890f931d63cd9566db5afb2e5a005 Homepage: https://cran.r-project.org/package=sampler Description: CRAN Package 'sampler' (Sample Design, Drawing & Data Analysis Using Data Frames) Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs. Package: r-cran-sampleselection Architecture: all Version: 1.2-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1744 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-misctools, r-cran-systemfit, r-cran-formula, r-cran-vgam, r-cran-mvtnorm, r-cran-maxlik Suggests: r-cran-lmtest, r-cran-ecdat Filename: pool/dists/noble/main/r-cran-sampleselection_1.2-16-1.ca2404.1_all.deb Size: 1594332 MD5sum: d234fed4e1eebfd7c318d762b8e4872e SHA1: 84bfa748981827234fc523af3a91eab476086c88 SHA256: 79cd9208da70bf2a46d82f0ef785996c11ff1db26c99b3d8b47c32a016d8f22f SHA512: e695a62e182e871933cff76963cdb9d989aa42e68f08d16705e95a88b4c0df2af32b790433f7c5ac2f97bb60c2ead1203a4f262fa050dc567f277cdc4fa786f7 Homepage: https://cran.r-project.org/package=sampleSelection Description: CRAN Package 'sampleSelection' (Sample Selection Models) Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson). Package: r-cran-sampleselectr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rlang, r-cran-tidytable Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sampleselectr_1.0.0-1.ca2404.1_all.deb Size: 823412 MD5sum: ce8828c5362c154ae5bdd408e23e4c65 SHA1: 8db7502eeed12d7f69c239d1e99e6a097bfebed2 SHA256: 14247cedcf2b3cf59a3e10579832bb55dcb6a78a794dd3f14541b32384c8ad80 SHA512: a840675dee78f888bc407ab1ced500c1fecff9ecdae80eab98e2ce7bed1ba019ff8416cd23e9804484192e523d8ebddb5614a4a72d6d071847252dfa45876901 Homepage: https://cran.r-project.org/package=SampleSelectR Description: CRAN Package 'SampleSelectR' (Randomly Select Samples for Various Probability-Based Methods) Randomly select samples using simple random sampling (SRS), systematic sampling, and various probability proportional to size (PPS) methods, including systematic PPS and sequential PPS (i.e., Chromy's method). Also includes functionality to allocate sample sizes across strata using proportional, power, Neyman, and optimal allocation methods, and to select samples within strata. Designed to make survey sample design and selection reproducible, efficient, and transparent for survey statisticians and researchers. Sampling methods follow Kalton (1983) and Chromy (1979) . Package: r-cran-samplesize4clinicaltrials Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesize4clinicaltrials_0.2.3-1.ca2404.1_all.deb Size: 18030 MD5sum: aad7039e68c2acde27c27cc757e20240 SHA1: ad7df033f073c3a83618b391ce1c1b43259240d7 SHA256: 9ad1cd712b38c822c74628900f5a8d40f1db250ee85a31db3348490f8b4f6cab SHA512: 2a4995049f65bec18d960341e5a57c3c762bf594e50553fec2c64c5f7c8eb505b70b0be67727f4fa9b9366e43d1ba90d06b6c31a19e473b10a7e154312887b16 Homepage: https://cran.r-project.org/package=SampleSize4ClinicalTrials Description: CRAN Package 'SampleSize4ClinicalTrials' (Sample Size Calculation for the Comparison of Means orProportions in Phase III Clinical Trials) There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals. Package: r-cran-samplesize4surveys Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-teachingsampling, r-cran-timedate, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-samplesize4surveys_4.1.1-1.ca2404.1_all.deb Size: 1741608 MD5sum: b4d3e383451776fe42972163fc7cd56a SHA1: b6717f64d01c4d4f116dcfc5bc6efab8e52156c5 SHA256: 3747808ea93298641278b97344198b3cc8e1e3f5dbe711094b28acc508bf245f SHA512: 7f2f8b1070866b90ea4197577839ad5b9c47856111480d7d86386117f127a5f5c542c5754fc5e834ddce0b1c4a47fb2ebb5ca02d083e0069674213031fbd9761 Homepage: https://cran.r-project.org/package=samplesize4surveys Description: CRAN Package 'samplesize4surveys' (Sample Size Calculations for Complex Surveys) Computes the required sample size for estimation of totals, means and proportions under complex sampling designs. Package: r-cran-samplesize Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesize_0.2-4-1.ca2404.1_all.deb Size: 23566 MD5sum: 89f6558f1544e6f5adaaf8bd3ef4dc74 SHA1: b0300143be1b8a32abf0192841378bbeeb518e4a SHA256: f394b35cf14ece553cdf9715aee3b6d2f2cc8375164fbc86762f96f8b2e99dd9 SHA512: cf3f89c8029dad4e7d2810b28c91b529d1a0414ead27ddf233fd6897c8f751a5001cf0fc98ec307f2e4120cffe9244dd4fdb27419b34640e608888b0c7ee0e0f Homepage: https://cran.r-project.org/package=samplesize Description: CRAN Package 'samplesize' (Sample Size Calculation for Various t-Tests and Wilcoxon-Test) Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties. Package: r-cran-samplesizecalculator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-dt, r-cran-bslib Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-samplesizecalculator_0.1.0-1.ca2404.1_all.deb Size: 197006 MD5sum: a742c7fa1455ae0fbc192971f397c09b SHA1: 77593e86c95de4377419d760944c95454d51a35b SHA256: 45e31473ea3649c7a1f683545867e148d889e6d52c13f5645fecad27eb342548 SHA512: 11f0920410499c7f28be1b060d5a71cd95e1dd363f77fb98836a143c1ea4bba2a0d305fbd60efdabdd1e3fc296f2b8ef40c1aed7899506e38182ed0072f8aa5a Homepage: https://cran.r-project.org/package=SampleSizeCalculator Description: CRAN Package 'SampleSizeCalculator' (Sample Size Calculator under Complex Survey Design) It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement. When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design, it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata, standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" . Package: r-cran-samplesizecmh Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-desctools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-samplesizecmh_0.0.3-1.ca2404.1_all.deb Size: 74258 MD5sum: e65810b8aa57998e57c540d60108eabe SHA1: 5061c1feae8584c7b29651c6753fae8ff1d29e15 SHA256: aba301e64e8143b6d8bf776216807770a78fa63a0e9947a1e00ebf16f4a7933a SHA512: 3aba9a7f64d71c59a80d499ed88db4a2f8962de0d94e17f31fcf47d426edc12aacc5cca968e24e78746d723421d9274c3b1bb680b5c65213892838a6d0d266f8 Homepage: https://cran.r-project.org/package=samplesizeCMH Description: CRAN Package 'samplesizeCMH' (Power and Sample Size Calculation for theCochran-Mantel-Haenszel Test) Calculates the power and sample size for Cochran-Mantel-Haenszel tests. There are also several helper functions for working with probability, odds, relative risk, and odds ratio values. Package: r-cran-samplesizediagnostics Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samplesizediagnostics_0.1.1-1.ca2404.1_all.deb Size: 17598 MD5sum: 24ee8ff6672e0b7efb4446efc561c63e SHA1: 227f445f68c30b01326d3e13591c40703b4b4e04 SHA256: edf4b7bb745144613a12da93139a7d575dc9dad38f7f20b713d354de146faaa5 SHA512: a3a42a719212672d275d1e1bfeea6faac424ebe935057224f7fe2d4f16d5d3371c0417587595c16ab6ed73efb2f12dce79ce60bedbc9207e157c0cf678ba7cf7 Homepage: https://cran.r-project.org/package=SampleSizeDiagnostics Description: CRAN Package 'SampleSizeDiagnostics' (Choosing Sample Size for Evaluating a Diagnostic Test) Calculates the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision. Based on Buderer (1996) . Package: r-cran-samplesizeestimator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Filename: pool/dists/noble/main/r-cran-samplesizeestimator_1.0.0-1.ca2404.1_all.deb Size: 67348 MD5sum: d7a582ddc01926893809cd76c7f6d9a2 SHA1: c804eab121827754bdcfd4f45ba80e869bbbb10d SHA256: 6587d9d1b98ebfd375690a948bf63d21b2da49e3d550ab2662bc38fb9d0bfa02 SHA512: 11b4e7b52460b2985ba245190875f662f931be213ff3b41369ea05bd649a8be39b4e5feb3570645363461d23d4b5b762ab2e863d7de6a06ddc437b9b86c865f4 Homepage: https://cran.r-project.org/package=samplesizeestimator Description: CRAN Package 'samplesizeestimator' (Calculate Sample Size for Various Scenarios) Calculates sample size for various scenarios, such as sample size to estimate population proportion with stated absolute or relative precision, testing a single proportion with a reference value, to estimate the population mean with stated absolute or relative precision, testing single mean with a reference value and sample size for comparing two unpaired or independent means, comparing two paired means, the sample size For case control studies, estimating the odds ratio with stated precision, testing the odds ratio with a reference value, estimating relative risk with stated precision, testing relative risk with a reference value, testing a correlation coefficient with a specified value, etc. . Package: r-cran-samplesizelogisticcasecontrol Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-samplesizelogisticcasecontrol_2.0.2-1.ca2404.1_all.deb Size: 511466 MD5sum: b7c3bb06a9f1657de7d888509415178a SHA1: 5e8dc3830cad130fd57dbfb4f277c1b6c7ad25ac SHA256: c6d54a802da947515c955c2bac4e07907269adf47340a3758b9d76f377e977cb SHA512: 4bc093d92615526976c21bbe0cba295162edc89f2ee2cded003541aecf135f57237311e95be0ae7b58fc208f4d6502fdf0bd734b2c345c3078f2b9e6805b2925 Homepage: https://cran.r-project.org/package=samplesizelogisticcasecontrol Description: CRAN Package 'samplesizelogisticcasecontrol' (Sample Size and Power Calculations for Case-Control Studies) To determine sample size or power for case-control studies to be analyzed using logistic regression. Package: r-cran-samplesizemeans Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesizemeans_1.2.3-1.ca2404.1_all.deb Size: 194740 MD5sum: ac91160b80bad62b1138c9baf6cdec86 SHA1: 476902667d69ae0af93d953fae27ed8f9a7f86c8 SHA256: 0bd8a63ee260ad7f5cb338eb248c2cd55af9beb97bba6ac5707c40a656f30d91 SHA512: 04358f3a7f4b93b6b31e045e9de2b160fafcbcd2279c49aceb261b4d0efb65dca747a23f5011af650d84b40395116ea4647515193e660a4702be7dddef8a52d4 Homepage: https://cran.r-project.org/package=SampleSizeMeans Description: CRAN Package 'SampleSizeMeans' (Sample Size Calculations for Normal Means) Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate a normal mean or the difference between two normal means. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of normal means are provided. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L. and Bélisle P. (1997) . Package: r-cran-samplesizeproportions Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesizeproportions_1.1.3-1.ca2404.1_all.deb Size: 91028 MD5sum: 422660c8b8b0a1ea5ac8f44c0e7971dc SHA1: bafbf1fe703b77acd7b0b74b73524ab9d5482d20 SHA256: 21c79349016ddd0c145831ddd8f1a0fe8fba0789c9ea4d86375036f1e450a70e SHA512: 09dcaa35f0e5f4ebfff07b95cc60fb6526146a456418da657d5850515c958eda1117a00202f032c07f2ff8783e1f1c745adb0336b6f3067cc8cb20a8325b2b5f Homepage: https://cran.r-project.org/package=SampleSizeProportions Description: CRAN Package 'SampleSizeProportions' (Calculating Sample Size Requirements when Estimating theDifference Between Two Binomial Proportions) Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of binomial observations are provided. In all cases, estimation of the difference between two binomial proportions is considered. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L., du Berger R. and Bélisle P. (1997) . Package: r-cran-samplesizer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-samplesizer_0.1.0-1.ca2404.1_all.deb Size: 121742 MD5sum: 14b3b263a93540a6556fd799464370c8 SHA1: 1f886a8ddce446eaaea7b2f3c3f075a79c9fe234 SHA256: 08fceaf6ace76d246733f5b044a0560c6152d4b1c5034139b16a26ff7f2b9b4c SHA512: dbefa604bf1f91d84afc865f8b7c6ac0e3dc6d46f2baefda695a59deed17034b5ca3cabf7d67e7a1a8a97057667c88b565f47c631eddd5723af5b882160e9c68 Homepage: https://cran.r-project.org/package=SampleSizeR Description: CRAN Package 'SampleSizeR' (Sample Size Calculations for Epidemiological, Clinical, andDiagnostic Studies) Provides comprehensive methods for sample size determination for epidemiological studies, clinical trials, diagnostic accuracy studies, and diagnostic agreement studies. The package supports prevalence surveys, cluster prevalence studies, unmatched case-control studies, cohort studies, superiority, non-inferiority, and equivalence clinical trials, diagnostic sensitivity, diagnostic specificity, receiver operating characteristic (ROC) area under the curve (AUC), and diagnostic agreement studies. Functions include optional adjustments for finite population correction, design effect, unequal allocation, anticipated response rate, and dropout. Results are returned as standardized 'SampleSizeR' objects with print, summary, plot, and data frame methods. Package: r-cran-samplesizesinglearmsurvival Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-samplesizesinglearmsurvival_0.1.0-1.ca2404.1_all.deb Size: 21164 MD5sum: 798adc5c612e68fcfab773b8557c20ef SHA1: cced8ecc874c0e704165beef75fe87536df51048 SHA256: 6a71fef4b281e544bbe5db169535e29b21a2ce28dc0825be88e4281d89a9fedd SHA512: db5f98bad3b72493aeeca69fe367a697849dfc8a56e1319be1d1ebf2f41585888419e54dc08a09727fa56d5d5f805a175cf58f1bbccd57376ff89a34c9d6b363 Homepage: https://cran.r-project.org/package=SampleSizeSingleArmSurvival Description: CRAN Package 'SampleSizeSingleArmSurvival' (Calculate Sample Size for Single-Arm Survival Studies) Provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) . Package: r-cran-samplevadir Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-splitstackshape Suggests: r-cran-haven, r-cran-rio Filename: pool/dists/noble/main/r-cran-samplevadir_1.0.0-1.ca2404.1_all.deb Size: 2614850 MD5sum: 879d294b9cb4a951547599241775152c SHA1: cc95ab421620048b7a5c9f0ae7a5bcbacc154e86 SHA256: ae2a6cc6d522353ca6870e4d7779eaeb1d697feac2a4e794b147e0033869ec24 SHA512: d1887fd1b17437fdb871899a0f78079f2b752589ad1926b5a1f425310bdb8aad768907d660a41281f8563d4bd31ea38fe83d78e0f2dd5e807847f95549b145d7 Homepage: https://cran.r-project.org/package=sampleVADIR Description: CRAN Package 'sampleVADIR' (Draw Stratified Samples from the VADIR Database) Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) . Data from VADIR are not provided as part of this package. Package: r-cran-samplex Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-samplex_0.3.0-1.ca2404.1_all.deb Size: 31800 MD5sum: 6189193850cd31725cd387e188574a27 SHA1: b4eed40308e2a03cc3e3db77c091b3d66c7a4874 SHA256: 45d1ab40290c50e1956b84541f2ec1ff047624a7567eac964415c1f34dafe06b SHA512: a96dd8524ec1b429bbafe38a7afde41246b5f74a98b157bfdc2d6dd9a88881757735cff7c3f66a88f228e953f46e76bf3305c1d668db65d32613f3e488a39088 Homepage: https://cran.r-project.org/package=samplex Description: CRAN Package 'samplex' (Shiny Tool for Sample Size Calculation) An interactive 'shiny' application to assist in determining sample sizes for common survey designs such as 'simple random sampling', 'stratified sampling', and 'cluster sampling'. It includes formulas, helper calculators, and illustrative examples. 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Data is available in multiple sizes—small, medium, and large. For more information, refer to the package documentation. 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Package: r-cran-sanketphonetictranslator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-readr, r-cran-stringi, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sanketphonetictranslator_0.1.0-1.ca2404.1_all.deb Size: 19786 MD5sum: 908e959f10e49fafe111e440362f9bdd SHA1: b6497d1306e540d190214896d55363f1dd2e3248 SHA256: b0e2b2030ff9ba2702faaeff90530a4cbdc2c5df0fff154f5d6bccdcf39ed520 SHA512: 3f08548e07a5efc7c40cced5bec1f667f3bf5892215c628e061815f98b7f6bdb67b809e27451e6431936fceff48d7d048705f01fc6eb1e2ccef8b0166eb2ffa3 Homepage: https://cran.r-project.org/package=sanketphonetictranslator Description: CRAN Package 'sanketphonetictranslator' (Phonetic Transliteration Between Hindi and English) Facilitate phonetic transliteration between different languages. With support for both Hindi and English, this package provides a way to convert text between Hindi and English dataset. Whether you're working with multilingual data or need to convert dataset for analysis or presentation purposes, it offers a simple and efficient solution and harness the power of phonetic transliteration in your projects with this versatile package. Package: r-cran-sankey Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-simplegraph Suggests: r-cran-covr, r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sankey_1.0.2-1.ca2404.1_all.deb Size: 221270 MD5sum: da03a132fc95485e7c54f0b2f1305a5a SHA1: 15d7521cbb7bdd015c19aacc86cc67a9c9a62607 SHA256: 81358692b1bc6c17e81f8356372f6ae1d897a9afbb7ba5cdd1ba908f4bd09796 SHA512: 26a6d498333af16840491e6bd3b64493a84e86225e3c9fb79f4fd6f5c22bcbb0be47219ca0d64eea2db01a2f74a45334246d252043ca80602fbefc0790480dd7 Homepage: https://cran.r-project.org/package=sankey Description: CRAN Package 'sankey' (Illustrate the Flow of Information or Material) Plots that illustrate the flow of information or material. Package: r-cran-sankeywheel Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-colourpicker, r-cran-manipulatewidget, r-cran-dt Filename: pool/dists/noble/main/r-cran-sankeywheel_0.1.0-1.ca2404.1_all.deb Size: 2475786 MD5sum: 3a79b123d36b690c92d85e953121d031 SHA1: 0d0549ce74f94e027799127e5bfa7910c94043a4 SHA256: 4d7c709541ba5c96d2b5526fb1e015cc40797ee1f96d7b000f9086483092ec7f SHA512: 43f3a5777e32e0221ac44632a8f81751b10286a126f36e92d3e66e83715a9e7ec41af44bf42fe69a5f007a8c17a546c1d6a8d5f887037d9a2c76c498b88069ae Homepage: https://cran.r-project.org/package=sankeywheel Description: CRAN Package 'sankeywheel' (Create Dependency Wheels and Sankey Diagrams) By binding R functions and the 'Highcharts' charting library, 'sankeywheel' package provides a simple way to draw dependency wheels and sankey diagrams. Package: r-cran-sanon Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sanon_1.6-1.ca2404.1_all.deb Size: 93500 MD5sum: b3f6504c99457dab2be15eb1f399127c SHA1: 1653776708337e61e3b8959e2ece23deae470e89 SHA256: 555c48181853ba6953c18ed6c83cb19235d585dcf3d48ed8f36de0d476a41700 SHA512: 1359cd57566c1acbcd4aa735549a08479b2b617830d0117419e68a5dbf04fe9c3ac304b42d699ae70e1b8b621c1587a22ddde470878a98e2181fb7b50288ff72 Homepage: https://cran.r-project.org/package=sanon Description: CRAN Package 'sanon' (Stratified Analysis with Nonparametric Covariable Adjustment) There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided. Package: r-cran-sansa Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-fnn, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sansa_0.0.1-1.ca2404.1_all.deb Size: 37694 MD5sum: 601aec9b5db578e3674ea4de5d1eea5c SHA1: fbfa6445ce0df4a139e0e5c98fc5effd00370f8b SHA256: d3e56920dcbc96d970d286f0d982f65e5b9d2cd686f0527e1e3ab82daf1baf7f SHA512: 60e0ebab1435350a719770838d51f37b6f2006bca9f923375607a4b1cee513c567b209425926d47e858556348a69b7dcc78c836c92be142a1b97fa58748f6d5b Homepage: https://cran.r-project.org/package=sansa Description: CRAN Package 'sansa' (Synthetic Data Generation for Imbalanced Learning in 'R') Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This 'R' package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022) . 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This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, 'santaR' is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users. Package: r-cran-sanzo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sanzo_0.1.0-1.ca2404.1_all.deb Size: 118318 MD5sum: 2a3665cd25c142f97807b8268ee1cc89 SHA1: 01519f4cc1781eef3bc17e0cf2b8371f16c525a7 SHA256: 4f053cfa3b37ccdf4dbd1f72c0e49a240178c0369fd22b790bfd655dbc776b9f SHA512: 8b10f21cf73be0836ddce318a70b4fc3e91dd9951b47dfb0a02eeb7e906b04845b757fb61b2c5acf092c9930872878693efa7e254317a77c33a0290870b84d03 Homepage: https://cran.r-project.org/package=sanzo Description: CRAN Package 'sanzo' (Color Palettes Based on the Works of Sanzo Wada) Inspired by the art and color research of Sanzo Wada (1883-1967), his "Dictionary Of Color Combinations" (2011, ISBN:978-4861522475), and the interactive site by Dain M. 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Package: r-cran-sap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bsda Filename: pool/dists/noble/main/r-cran-sap_1.0-1.ca2404.1_all.deb Size: 14052 MD5sum: b0e285ddcf7eb697e81870a57336cbba SHA1: 338d175c402ee07efe649852c9907a69edb297a3 SHA256: 2833434da7670584540ff2eb1ed12e4d2fde56621e790ebaea91e171621b2782 SHA512: 66a1d25ef37b901a0a24e6bf763c8ed9dc1efd76d3dfb945b5841fb59d1e56740783cea25d3184fc920fec12078734dc064f1e7783f801679b70a51347bd61cc Homepage: https://cran.r-project.org/package=SAP Description: CRAN Package 'SAP' (Statistical Analysis and Programming) The Hypothesis tests for the means of independent or paired groups. This package investigates the normality assumption automatically. Then, it tests the hypothesis tests for two independent or paired group means by using parametric or non-parametric tests. It uses the Shapiro-Wilk test to test the normality assumption. For independent two groups, If data comes from the normal distribution, the package uses the Z or t-test according to whether variances are known. For paired groups, it uses paired t-test under normal data sets. If data does not come from the normal distribution, the package uses the Wilcoxon test for independent and paired cases. 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This method provides alternatives ranking given decision makers' preferences: criteria preferences and alternatives preferences for each criterion.This method is described in Gomes et al. (2020) . 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Package: r-cran-sappviz Architecture: all Version: 1.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-mass Filename: pool/dists/noble/main/r-cran-sappviz_1.0.16-1.ca2404.1_all.deb Size: 47450 MD5sum: d7d1ac3357814ee341aa02614add512c SHA1: bcb386094fbc76f7eda51069cc37f669d29d2c44 SHA256: ff9e08487ec6a9e8f55ab55137eb2c697ddb254a25a1019986d5c40b354233ff SHA512: fa23fac1109cae145b4b173d2385765dfdbfe290769c0fc8256140ffb96e857b496812579c3b823871a4581f143ac7c76e73aa756a2904dad8b9e41d9a1d8039 Homepage: https://cran.r-project.org/package=sappviz Description: CRAN Package 'sappviz' (Sector-Adjusted Points Plot for Feature Dominance) Visualizes feature influence by projecting data into 2D via PCA and adjusting points toward sector centers weighted by importances. Supports linear models and optionally tree-based models via SHAP values from the 'fastshap' package. For tree-based models, please install 'fastshap' manually from the CRAN archive: . Package: r-cran-saqgetr Architecture: all Version: 0.2.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-lubridate, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-magrittr, r-cran-httr Suggests: r-cran-openair Filename: pool/dists/noble/main/r-cran-saqgetr_0.2.24-1.ca2404.1_all.deb Size: 78932 MD5sum: 9e231c6bb8ed16d082df29b08161e35b SHA1: 4c8afde909fcd6b686ef87bbaf9fbbdb6ed6f9df SHA256: 04d0f3c3b728c4d6f17bac51c22a15def8329f25cf5d2b49aed32c0dd2ed4fa6 SHA512: 67a0944f4ce7548c93f3f54d3b0e92bbe32b6419876611a739bdb1ac63d47e2acf56a31efc7bce007f5aee3b96670fc24d1b2934b949276898c9131871288da3 Homepage: https://cran.r-project.org/package=saqgetr Description: CRAN Package 'saqgetr' (Import Air Quality Monitoring Data in a Fast and Easy Way) A collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. 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Package: r-cran-sara4r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2555 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tcltk2, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sara4r_0.1.0-1.ca2404.1_all.deb Size: 1986246 MD5sum: bb4e33a01d5759db2b88304edf14099f SHA1: 0ba27b423d9ff5d12023e1140cfecd431489f9f9 SHA256: 4d1c9e57f0d9dc543dfe01a73d36fde0f18ab37e9efd7f56dff00bce0a145e30 SHA512: 15d49659ff401df3b87cbdbb7c140c6134afc0a5f0b43227fcb2da6866bf43509508d4296146c9668ad7a9eb1002cc80f4a8a223bf5a28721d11115bb9fa7897 Homepage: https://cran.r-project.org/package=sara4r Description: CRAN Package 'sara4r' (An R-GUI for Spatial Analysis of Surface Runoff using theNRCS-CN Method) A Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011). It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps. This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021) but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.). Package: r-cran-saros.base Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-tidyselect, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-stringi, r-cran-forcats, r-cran-fs, r-cran-yaml, r-cran-zip, r-cran-rstudioapi, r-cran-bcrypt Suggests: r-cran-covr, r-cran-haven, r-cran-srvyr, r-cran-readr, r-cran-qs, r-cran-purrr, r-cran-writexl, r-cran-webshot, r-cran-usethis, r-cran-quarto, r-cran-labelled, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-saros.base_1.2.0-1.ca2404.1_all.deb Size: 516212 MD5sum: 89302ff3a1f6df648f7f8e81f0265df0 SHA1: 5e6af95182a451d874a8a1acb317bf1677366ec2 SHA256: 6602a3d63ba27dfc6cf61e1ed79682cf358a1ee3ba1c39baeb64d594a19e9388 SHA512: fa28f91c7dd9e183b765eb1e479ab785aa0105d1728ad2dd165995d825dbc38137513292e0df1d80642d379fe59865011e4d7bb7fd1151f46496e80759ff1cbb Homepage: https://cran.r-project.org/package=saros.base Description: CRAN Package 'saros.base' (Base Tools for Semi-Automatic Reporting of Ordinary Surveys) Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting, e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites where each organization wants to obtain results for their organization compared with everyone else. See 'saros' () for tools used for authors in the drafted reports. Package: r-cran-saros Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-ggiraph, r-cran-ggplot2, r-cran-glue, r-cran-mschart, r-cran-officer, r-cran-rlang, r-cran-stringi, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-covr, r-cran-haven, r-cran-labelled, r-cran-pdftools, r-cran-quarto, r-cran-knitr, r-cran-readr, r-cran-scales, r-cran-spelling, r-cran-srvyr, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-withr, r-cran-writexl, r-cran-readxl Filename: pool/dists/noble/main/r-cran-saros_1.6.2-1.ca2404.1_all.deb Size: 676500 MD5sum: 8a8cea29d96e5b001e38104d13e3ac6e SHA1: 50af15394993dae9eda5ae73fd3e8e149c9debf9 SHA256: f7226d2e98fbd24241025b69c2fe03a947a222d1383cf0a36fa99d82be7aa841 SHA512: 9a552e91e542a7587587cca76fc554abea455aff69c39d261046b0043dd89d6ad2bfabba6660f5238d313e753b8ebbaed7af10591783d534cc2b53b66f73ee22 Homepage: https://cran.r-project.org/package=saros Description: CRAN Package 'saros' (Semi-Automatic Reporting of Ordinary Surveys) Offers a systematic way for conditional reporting of figures and tables for many (and bivariate combinations of) variables, typically from survey data. Contains interactive 'ggiraph'-based () plotting functions and data frame-based summary tables (bivariate significance tests, frequencies/proportions, unique open ended responses, etc) with many arguments for customization, and extensions possible. Uses a global options() system for neatly reducing redundant code. Also contains tools for immediate saving of objects and returning a hashed link to the object, useful for creating download links to high resolution images upon rendering in 'Quarto'. Suitable for highly customized reports, primarily intended for survey research. Package: r-cran-sarp.compo Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-car Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-sarp.compo_0.1.8-1.ca2404.1_all.deb Size: 247070 MD5sum: 1e43d3072c20a129232e447b7ea96c7b SHA1: 56408c10effa0ac9ff4ee8dfa0dc0af905c1acab SHA256: d189b07cb83a901571a09d94bf5a1c926cb7dce5ffe1f59d28435e371bb54fb2 SHA512: e0923ca0b92611376d297b7cee80a114f4cd284ef38a164953dfd4819bcac7c646a678bb08b645230fbff6d6bc117c2297bc27efc5c6369bfc662d0f24f15a51 Homepage: https://cran.r-project.org/package=SARP.compo Description: CRAN Package 'SARP.compo' (Network-Based Interpretation of Changes in Compositional Data) Provides a set of functions to interpret changes in compositional data based on a network representation of all pairwise ratio comparisons: computation of all pairwise ratio, construction of a p-value matrix of all pairwise tests of these ratios between conditions, conversion of this matrix to a network. Package: r-cran-sarp.moodle Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-magick Suggests: r-cran-readods, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-sarp.moodle_1.2.3-1.ca2404.1_all.deb Size: 391808 MD5sum: 7ab1b76c6ba9c295568276bca573d0c4 SHA1: 4c4c2ea40a21f2b3f7973b24d8e17cb30b4cd5a4 SHA256: 36afc9e90fbf1540fcbed768916bfc07cb36f7aa151d149187c789f1786b1030 SHA512: 3e27db0c9079a1ed43281e9637f2114831346f6854afab4299f50d1b8d7a5771af40907422e42efb6183c0842e424b3665a713e600c442276ae309919460a6f0 Homepage: https://cran.r-project.org/package=SARP.moodle Description: CRAN Package 'SARP.moodle' (XML Output Functions for Easy Creation of Moodle Questions) Provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see for more information). Package: r-cran-sarp.snowprofile.alignment Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sarp.snowprofile, r-cran-cluster, r-cran-dtw, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-dendextend, r-cran-smacof, r-cran-testthat, r-cran-progress Filename: pool/dists/noble/main/r-cran-sarp.snowprofile.alignment_2.0.2-1.ca2404.1_all.deb Size: 2824942 MD5sum: 6c57643055fa2c9c7017f1b4cee63723 SHA1: 166f54e5124fb5e770f93ae940eb1622fb8c53f0 SHA256: 4f44e0734acf3dbc4591168d96dd7b3303fcbcdb661e2cf306774c4aa9f82219 SHA512: f95218414d09a7e7debb394d641577d9b97129c9fe7491cd309ea145bb3dbc5c5648ae76cba960d1b6c6939a6419a05a663e36b78ecb99ce5809686a5875be92 Homepage: https://cran.r-project.org/package=sarp.snowprofile.alignment Description: CRAN Package 'sarp.snowprofile.alignment' (Snow Profile Alignment, Aggregation, and Clustering) Snow profiles describe the vertical (1D) stratigraphy of layered snow with different layer characteristics, such as grain type, hardness, deposition date, and many more. Hence, they represent a data format similar to multivariate time series containing categorical, ordinal, and numerical data types. Use this package to align snow profiles by matching their individual layers based on Dynamic Time Warping (DTW). The aligned profiles can then be assessed with an independent, global similarity measure that is geared towards avalanche hazard assessment. Finally, through exploiting data aggregation and clustering methods, the similarity measure provides the foundation for grouping and summarizing snow profiles according to similar hazard conditions. In particular, this package allows for averaging large numbers of snow profiles with DTW Barycenter Averaging and thereby facilitates the computation of individual layer distributions and summary statistics that are relevant for avalanche forecasting purposes. For more background information refer to Herla, Horton, Mair, and Haegeli (2021) , Herla, Mair, and Haegeli (2022) , and Horton, Herla, and Haegeli (2024) . Package: r-cran-sarp.snowprofile.pyface Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sarp.snowprofile, r-cran-reticulate, r-cran-data.table Filename: pool/dists/noble/main/r-cran-sarp.snowprofile.pyface_0.4.3-1.ca2404.1_all.deb Size: 459368 MD5sum: e76d48a3e6a427a8aadf71d65b3281b3 SHA1: dad44e4ae252c996529d9201fcd4525c8a24e4a4 SHA256: c6df8033be21fac46a69014fca3a103444136dc3dfda978e9b8fdb052e76428a SHA512: 88359ccd4e7b457d0c4471cab6bc4d469a95d99af4309063c8b48c22f1d67d28fcc1ca5964b9c6c63a4929abd2a550c200a96fc67e77361422bcca6200e6f264 Homepage: https://cran.r-project.org/package=sarp.snowprofile.pyface Description: CRAN Package 'sarp.snowprofile.pyface' ('python' Modules from Snowpack and Avalanche Research) The development of post-processing functionality for simulated snow profiles by the snow and avalanche community is often done in 'python'. This package aims to make some of these tools accessible to 'R' users. Currently integrated modules contain functions to calculate dry snow layer instabilities in support of avalache hazard assessments following the publications of Richter, Schweizer, Rotach, and Van Herwijnen (2019) , and Mayer, Van Herwijnen, Techel, and Schweizer (2022) . Package: r-cran-sarp.snowprofile Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-sarp.snowprofile.alignment Filename: pool/dists/noble/main/r-cran-sarp.snowprofile_1.4.1-1.ca2404.1_all.deb Size: 559332 MD5sum: 81831ecff74cfbd3dc060e95b22951db SHA1: c12377e5c360d05d310b5fbf1b5a650c4baf91cb SHA256: 9bf964b9804f2408c91d589adc903872a4fb0fbe8d53fb629ce5a86c3d72097e SHA512: 452324b8e6acfb532a9ff9a3d4f28ae0643fd7ec5d80f4a442f60a06a516d4897ece06face9da654c47f697eb6c02d58c9b4d523636f690f043268b76409c3bf Homepage: https://cran.r-project.org/package=sarp.snowprofile Description: CRAN Package 'sarp.snowprofile' (Snow Profile Analysis for Snowpack and Avalanche Research) Analysis and plotting tools for snow profile data produced from manual snowpack observations and physical snowpack models. The functions in this package support snowpack and avalanche research by reading various formats of data (including CAAML, SMET, generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and produce graphics such as stratigraphy and time series profiles. Package developed by the Simon Fraser University Avalanche Research Program . Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, ). Package: r-cran-sars Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-crayon, r-cran-cli, r-cran-numderiv, r-cran-doparallel, r-cran-foreach, r-cran-aiccmodavg, r-cran-minpack.lm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sars_2.1.1-1.ca2404.1_all.deb Size: 757918 MD5sum: 171fcbb47c8685b6b1c90c465e354ca8 SHA1: 57c778b44273d357359e5de2049e86510b3c09e1 SHA256: 0853051aedddb077ddb67318d7006d115f90e745b991a02bccbf404f6a6df56a SHA512: d8b9561937e84c99a4f5fe521a0fb8092fcab73ec9ef3a7ee90c3f4f42e035e84b33d73bc09650c48b0427e376b452b62a5334ce3355dd48f502814bbc4bfdff Homepage: https://cran.r-project.org/package=sars Description: CRAN Package 'sars' (Fit and Compare Species-Area Relationship Models UsingMultimodel Inference) Implements the basic elements of the multi-model inference paradigm for up to twenty species-area relationship models (SAR), using simple R list-objects and functions, as in Triantis et al. 2012 . The package is scalable and users can easily create their own model and data objects. Additional SAR related functions are provided. Package: r-cran-sas7bdat Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sas7bdat_0.8-1.ca2404.1_all.deb Size: 202784 MD5sum: f37c88ce60d86edbafb8e6c7fcbfba28 SHA1: b328f24e149ff2e121b62966e520caf724e6f5ff SHA256: ac168bb2893c066575ad723d8885772ea887c3344dee1c35b5d1fec842487589 SHA512: d089cfdbdd635966b22674a36187f14181c7c4c1dd231a0fd1f923dda175151ae1b356c3e1f16875aa6f27632a4357dd4aaa4ec0b318ca4ea99e657aae502419 Homepage: https://cran.r-project.org/package=sas7bdat Description: CRAN Package 'sas7bdat' (sas7bdat Reverse Engineering Documentation) Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages may contain more robust readers for sas7bdat files. Package: r-cran-sascii Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sascii_1.0.2-1.ca2404.1_all.deb Size: 43318 MD5sum: 45fb367284d6e1e8f3a7b50e1fca10ba SHA1: 0fecd174bbceb6c71c4d75677804dc59d6bc13ac SHA256: 087775603aa59636d24aa54f0f19d44d21fa7ba22b77c7f204fb1442f3251a8f SHA512: f75dfa6c0397d70d3a6db2e9768cadf35a1485250b616d6932f280bf5f1408498c32f4f3f37627037465953d6896b0341771f3f4b275ff9d721bc1d1d83e6d2c Homepage: https://cran.r-project.org/package=SAScii Description: CRAN Package 'SAScii' (Import ASCII Files Directly into R using Only a 'SAS' InputScript) Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call. This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step. Package: r-cran-sasctl Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-uuid, r-cran-rocr, r-cran-reshape2, r-cran-base64enc, r-cran-glue Suggests: r-cran-future, r-cran-furrr, r-cran-testthat, r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-tidymodels, r-cran-xgboost, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-sasctl_0.9.0-1.ca2404.1_all.deb Size: 320326 MD5sum: 0e2f04a8674a849b703ca7a974d9d1a5 SHA1: 5ed8ba1c463d40776d03bfa0736bb881f761b5f2 SHA256: 6281d5b6f6c05aee5b87ffb78e366a9fc08bea8329a39b4f63310eb84f7b4ae3 SHA512: 83774ef830a3d50ccf1234e653d2c076fdcd72bcc9c6ab37d4cf9b9e09b9ade8f04a86942526b1cce00f8e876bd7398c646602ad7c66029b40ef9a7dae9d4669 Homepage: https://cran.r-project.org/package=sasctl Description: CRAN Package 'sasctl' (Easily Communicate Between the "SAS Viya" Platform and R) The 'sasctl' (sas control) package enables easy communication between the "SAS Viya" platform APIs and the R runtime. It offers convenient wrappers to some most used endpoints. Package: r-cran-sasdates Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sasdates_0.1.0-1.ca2404.1_all.deb Size: 19518 MD5sum: b3225a3493115861173f250301a0619c SHA1: e7564241e25ceef3ea785df19ab98da57ba14673 SHA256: b41f21e550e50c83ca7247eed276a66d04bccc827c7bf9521485ac5a09f1b769 SHA512: c21661e473211ca8af7a07be6b1ae8540e055f5e37fbbefb54db41ee6081a9642f169b030ad1e34f9960bca42e1016fd86dc02167ab84362c4525ca4c0da343a Homepage: https://cran.r-project.org/package=SASdates Description: CRAN Package 'SASdates' (Convert the Dates to 'SAS' Formats) Converts the dates to different 'SAS' date formats. In 'SAS' dates are a special case of numeric values. Each day is assigned a specific numeric value, starting from January 1, 1960. This date is assigned the date value 0, and the next date has a date value of 1 and so on. The previous days to this date are represented by -1 , -2 and so on. With this approach, 'SAS' can represent any date in the future or any date in the past. There are many date formats used in 'SAS' to represent date-time. Here, we try to develop functions which will convert the date to different 'SAS' date formats. Package: r-cran-sasif Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sasif_0.1.3-1.ca2404.1_all.deb Size: 33958 MD5sum: a5676a295bd6d1ff6fbd160e34539716 SHA1: 1e6f53a05b7d30dd9f962b6ccf996b5d58cd0bd1 SHA256: 8f51fc86fd3c9943dbf702b247afa4270e7d236c5dd688b8e5655548393c5fc4 SHA512: 054bcea9448d918cd8ae05ab3456bd8a7b14ac01f16e0890a0e49549010bd9e91461b17432bb788360ea3930155b5bf0b7cb5361283d60380c63b37dccbd5221 Homepage: https://cran.r-project.org/package=sasif Description: CRAN Package 'sasif' ('SAS' IF Style Data Step Logic for Data Tables) Provides 'SAS'-style IF/ELSE chains, independent IF rules, and DELETE logic for 'data.table', enabling clinical programmers to express Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM)-style derivations in familiar SAS-like syntax. Methods are informed by clinical data standards described in CDISC SDTM and ADaM implementation guides. See and . Package: r-cran-saslm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-openssl Filename: pool/dists/noble/main/r-cran-saslm_1.0.1-1.ca2404.1_all.deb Size: 1294994 MD5sum: 778b4e278978d47bd2ebf594d66b35a2 SHA1: eeccbf07bd13db65501b68a7d6f66bbe4f12e5b0 SHA256: f10d9df37fa92490e6f120a733b7f1ab0d31ae396c600fbad6b29be41eabb437 SHA512: d9d412fc0987f894963e94dd3bd4a51736996c0129fa1a6fa20ca3f5df3183702368f7c5ee944f19a6f053a73826ec5b541f917feb915b6055d563a6bcb521f2 Homepage: https://cran.r-project.org/package=sasLM Description: CRAN Package 'sasLM' ('SAS' Linear Model) This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide Type II and Type III SS. However, the results of nested and complex designs are often different from those of 'SAS'. Different results do not necessarily mean incorrectness. However, many want the same results as 'SAS'. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0). 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Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the probability that a jury will find a defendant guilty given jurors' preferences for a guilty verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical trial conditions to conduct harmful error analysis. The conceptual framework is discussed by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors, UCLA Criminal Justice Law Review (2024) and Barry Edwards, If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence, Virginia Journal of Social Policy & the Law (2025) . The relationship between individual jurors' verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) ; MacCoun & Kerr (1988) , and Devine et el. (2001) , among others. 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Reading functions can import a user defined geographical extent of data stored in netCDF files. Currently supported ocean data sources include NASA's Oceancolor web page , sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS. Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others. Data sources specific for SST that can be imported too includes Pathfinder AVHRR and GHRSST . In addition, ocean productivity data produced by Oregon State University can also be handled previous conversion from HDF4 to HDF5 format. Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases , namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast. 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This is a package that contains the Saturn_coefficient() function that reads an input matrix, its dimensionality reduction produced by UMAP, and evaluates the quality of this dimensionality reduction by producing a real value in the [0; 1] interval. We call this real value Saturn coefficient. A higher value means better dimensionality reduction; a lower value means worse dimensionality reduction. Reference: Davide Chicco et al. (February 2026), "The advantages of our proposed Saturn coefficient over continuity and trustworthiness for UMAP dimensionality reduction evaluation", PeerJ Computer Science 12:e3424 (pp. 1-30), . 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This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) . 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The package solves an optimization problem that balances the contribution of each predictor variable to ensure estimation stability in the presence of multicollinearity. It supports two distinct parameterization methods, a Budget-based approach that allocates a fixed loss contribution to each predictor, and a Target-based approach (t-tuning) that utilizes a relative elasticity weight for the response variable. The package provides comprehensive tools for model estimation, risk distribution analysis, and parameter tuning via cross-validation (PR1, PR2, and PR3 model types) to optimize predictive accuracy. Methods are based on Asimit, Chen, Ichim and Millossovich (2026) . 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(2021) . Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density or a non-parametric Kaplan-Meier estimator are used, which either ignore censoring or competing events. This package contains functions to easily conduct the proposed improved AE analyses. 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Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. . Package: r-cran-saws Architecture: all Version: 0.9-7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-saws_0.9-7.0-1.ca2404.1_all.deb Size: 340994 MD5sum: 0195b41e84a19cb3dd8cb23317617d3a SHA1: bb8f184af9027805603ad784e4c1e8f6e933e56d SHA256: 79d851dbec68bc53c09bcf165704f60ad565f885b2ed79a1349b3bfcc641d2aa SHA512: 98d831934f07edb813d2b86e137a2a5fc5f28839f4ec10cb5261bcd95fe435ff26bed08c95893b03351a7e68724da85dda847fce987dc19ad9fd087e62282269 Homepage: https://cran.r-project.org/package=saws Description: CRAN Package 'saws' (Small-Sample Adjustments for Wald Tests Using SandwichEstimators) Tests coefficients with sandwich estimator of variance and with small samples. 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"Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 . 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'CytoSig' database is described in: Jiang at al., (2021) . 'Reactome' database is described in: Gillespie et al., (2021) . The 'VAM' method is outlined in: Frost (2020) . 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This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), ). Package: r-cran-scbursts Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytex Filename: pool/dists/noble/main/r-cran-scbursts_1.6-1.ca2404.1_all.deb Size: 657950 MD5sum: 87867004534b184f13a0eef5fa7aa8d1 SHA1: 78c29e0ddc5b461700b7bfe1c40031c8ff1eb994 SHA256: b5bc4bca7ccd69acdacb4d6bd783b1f160ddcb3c5251d2708cb3f1c32ec977ed SHA512: 20d2d048b1d591ec8a885e8db9db8baf475757a7bd7f73b93acfd63f01a630c27e1948cb606f2de264239cf8c1e232219dd984671189d14a53c4ebd0ab84c3cb Homepage: https://cran.r-project.org/package=scbursts Description: CRAN Package 'scbursts' (Single Channel Bursts Analysis) Provides tools to import and export from several existing pieces of ion-channel analysis software such as 'TAC', 'QUB', 'SCAN', and 'Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting. Package: r-cran-sccan Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scdha, r-cran-fnn, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sccan_1.0.5-1.ca2404.1_all.deb Size: 3359654 MD5sum: 7d6f957199aba6461263591180ddd008 SHA1: 2f1f2cc3f3c16951a80f07af1b7eef375a082154 SHA256: e37f158516655a9fdb65ab7d7f6ce7d57515459082820afde445773cd897aadb SHA512: 22982bb9a614fb6e1c82ca1c282a8dd474a4959c39f76d5178e616ed6d1e8cd3b0ff9fd6b8a55f5eefca1a48a16049616379c68efa68ff0e0c2837669862897e Homepage: https://cran.r-project.org/package=scCAN Description: CRAN Package 'scCAN' (Single-Cell Clustering using Autoencoder and Network Fusion) A single-cell Clustering method using 'Autoencoder' and Network fusion ('scCAN') Bang Tran (2022) for segregating the cells from the high-dimensional 'scRNA-Seq' data. The software automatically determines the optimal number of clusters and then partitions the cells in a way such that the results are robust to noise and dropouts. 'scCAN' is fast and it supports Windows, Linux, and Mac OS. Package: r-cran-sccatch Architecture: all Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2484 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-progress, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-sccatch_3.2.2-1.ca2404.1_all.deb Size: 2329690 MD5sum: 933463aa84c2eeac4e35506868097ae4 SHA1: 300dc4524a306e6b55e96391bce250bbd4f351e3 SHA256: fbae1bb196dec0bb11324ed6e48fd9670db45e212f7426ce95666c2819f599bd SHA512: 2622a7ccbd29e5c952eb1c393d3e8cedc3329647f2516ecb88eb700c8ca605d5bbb1fe6a66f9b569e0d89ac151723514db34008bba160081794e7bbb2d0be615 Homepage: https://cran.r-project.org/package=scCATCH Description: CRAN Package 'scCATCH' (Single Cell Cluster-Based Annotation Toolkit for CellularHeterogeneity) An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) for more details. Package: r-cran-sccca Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seurat, r-cran-dplyr, r-cran-plyr, r-cran-scales, r-cran-hgnchelper, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-sccca_0.1.1-1.ca2404.1_all.deb Size: 60580 MD5sum: a0bb0376d113047af8552b4355ed61ad SHA1: ad49cc2e986df0afd2508bf845a2e91b2a2bb1ac SHA256: 5562100d79545f0b7cb2e503efc092dddcf2c4ce7f65b190512987f6f0f4a198 SHA512: 5e734b86d122a8622ddb95054733da02f87ba7ce9274faa1695f95c0b48203bee6f74ae1bbfe34b74f81e6e9a2f780e7f2020c19ed555e8a52479205cad2ca72 Homepage: https://cran.r-project.org/package=sccca Description: CRAN Package 'sccca' (Single-Cell Correlation Based Cell Type Annotation) Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) ). Package: r-cran-sccddesign Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pracma, r-cran-algdesign, r-cran-leaps Filename: pool/dists/noble/main/r-cran-sccddesign_0.2.0-1.ca2404.1_all.deb Size: 107006 MD5sum: f72fb47df506c0c00fdc42934b5ca96d SHA1: 68a60328f3778f99f927080c4536499dfc5fbb65 SHA256: 76c7d2962975a571131d9b96111b1f5145087c549e97762bf6c5149c4dd365e6 SHA512: 0e348afae673e0d0f084dde16c97627e167443f61064545acba2acf68e8078f2aa19fcc5a2ad2ebd5262083079a7574eeb2b2866655441572d33d48d49201f0c Homepage: https://cran.r-project.org/package=SCCDdesign Description: CRAN Package 'SCCDdesign' (Construction of Screening Designs for Mixed Level Continuous andCategorical Factors) Constructs screening designs for experiments involving continuous and categorical factors with multiple levels. The package implements methods for constructing mixed-level screening designs, involving factors with more than two levels. It also evaluates the statistical performance of screening designs throughdev power to identify active effects and Type I error rates. The package implements three methods proposed by Jones, B., Lekivetz, R., Majumdar, D. and Nachtsheim, C. (2025) for generating efficient screening designs involving three-level continuous and two-level categorical factors for even run sizes. It also includes Paley Type I and Type II constructions for conference matrices and pseudo conference matrices obtained using the coordinate exchange algorithm by Jones, B. and Nachtsheim, C. J. (2011) which are used in the development of these screening designs. Package: r-cran-sccic Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-wooldridge, r-cran-qte, r-cran-synth Filename: pool/dists/noble/main/r-cran-sccic_0.1.1-1.ca2404.1_all.deb Size: 115624 MD5sum: f1ba9df42b80d431b11aa3ce1304012e SHA1: a95d8fe8207bf5b0d28988849914751ac0458f91 SHA256: 001274eb7b85e4f74b2379c5e329e280437760bacf2567931b49529296e14cb8 SHA512: 81a260e33a72a16e5e8c32899b0e9cdc74f7a6cd66d95999faf729f86e30f3536c74521997d50763fe9d815f54b601c82ad1a0ceb5f15d704fe7a48044af9fbb Homepage: https://cran.r-project.org/package=sccic Description: CRAN Package 'sccic' (Synthetic Control Changes-in-Changes Estimator) Implements the Changes-in-Changes (CIC) estimator of Athey and Imbens (2006) combined with synthetic control methods. Provides both the continuous CIC estimator (Theorem 3.1) and the discrete CIC estimator (Theorem 4.1) for integer-valued outcomes, with analytic and bootstrap inference. Also provides nonparametric estimation of the entire counterfactual distribution of outcomes for a treated group, allowing evaluation of average, quantile, and distributional treatment effects. Synthetic control weights are constructed via elastic net regularization to handle settings with many potential control units. Package: r-cran-sccr Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-sccr_2.1-1.ca2404.1_all.deb Size: 25930 MD5sum: c9da3a893aa0564eec4af14f8e40c429 SHA1: 6262c092cac025796ca6d11563714a3baa067d30 SHA256: 60f6fc4d9c52aab32a092449b6b9cd7ee20481a86410aacf3c9bee18d2da560d SHA512: 71f3ad14db121c8f7c1cc6ec0c02e371f6ebb5abbc88d68b76ddc6788044124ac208745be7cd5d31dae72db41dcac8077e6fa8e64cc35d4ab05c93c33c5cae53 Homepage: https://cran.r-project.org/package=sccr Description: CRAN Package 'sccr' (The Self-Consistent, Competing Risks (SC-CR) Algorithms) The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added. Package: r-cran-sccs Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-corpcor, r-cran-fda, r-cran-r.methodss3, r-cran-gnm Filename: pool/dists/noble/main/r-cran-sccs_1.7-1.ca2404.1_all.deb Size: 604450 MD5sum: 6ffc209d0e021b571f8fc515e49fda11 SHA1: 97db9d8f2e02bd128fbbfb7292f4cc7c621154f5 SHA256: 2b8d8869d9b9c55d3cde486af3facb4ab130a1adad9f002b815c4938fe3fa205 SHA512: eddfe971ae2af853f67cc2257e0bee7122d25ce42167ee99731058f6cc86aa5b37d204930e75ee67b889858a86a9b39817876980e2fe3539557fbf6a18ae0ef5 Homepage: https://cran.r-project.org/package=SCCS Description: CRAN Package 'SCCS' (The Self-Controlled Case Series Method) Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and . Package: r-cran-sccustomize Architecture: all Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2375 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seurat, r-cran-circlize, r-cran-cli, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggprism, r-cran-ggrastr, r-cran-ggrepel, r-cran-glue, r-cran-janitor, r-cran-lifecycle, r-cran-magrittr, r-cran-matrix, r-cran-mcprogress, r-cran-paletteer, r-cran-patchwork, r-cran-pbapply, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-scattermore, r-cran-seuratobject, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-bioc-biocfilecache, r-bioc-complexheatmap, r-bioc-dittoseq, r-bioc-dropletutils, r-cran-ggpubr, r-cran-hdf5r, r-cran-knitr, r-bioc-nebulosa, r-cran-remotes, r-cran-reticulate, r-cran-rliger, r-cran-rmarkdown, r-bioc-scuttle, r-cran-tidyselect, r-cran-qs2, r-cran-viridis Filename: pool/dists/noble/main/r-cran-sccustomize_3.3.0-1.ca2404.1_all.deb Size: 1772534 MD5sum: 94ca1c507423db8f7ff6e58d3d46f691 SHA1: 248bf3ea512d72d9eda6a5ce5babbbd98c8dd903 SHA256: 1a344b7dad7d396d0fad8240a6e8e98024a31d6cb1a9b578153ffed752f4ab67 SHA512: e49725ee42fdd3dfcd81164d0ee0134aaf19e508d35c161aed47d719d56a41671dabe9ea1f7c27c7804bfdc75fc514d09b5ab5495cd4e7a03bf6d0db42f0e124 Homepage: https://cran.r-project.org/package=scCustomize Description: CRAN Package 'scCustomize' (Custom Visualizations & Functions for Streamlined Analyses ofSingle Cell Sequencing) Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" RRID:SCR_024675. Package: r-cran-scda Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatialreg, r-cran-sp, r-cran-spdep, r-cran-rlang, r-cran-performance, r-cran-dplyr, r-cran-sf, r-cran-nbclust, r-cran-ggplot2, r-cran-ggspatial Suggests: r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-scda_0.0.2-1.ca2404.1_all.deb Size: 952652 MD5sum: afd3c879efc453324293fbd56206e448 SHA1: f5e2a2e33c2683c9b9d44a6c85a47e9c33a218b0 SHA256: 300f2a72723891ee6aa777ecbe5bdc1cdab31b4dfec4c7fb405828ea85fcd50f SHA512: 60f4a6888049a0fdf5d76eec8c66aa4b82b519535c5094ef30db3f9a34c51a8528f111b1f6b459062579e69bdce7b224e5b15700144785a6bd77c94bab2498c5 Homepage: https://cran.r-project.org/package=SCDA Description: CRAN Package 'SCDA' (Spatially-Clustered Data Analysis) Contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 . Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) . Package: r-cran-scdb Architecture: all Version: 0.6.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-openssl, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-r6, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr Suggests: r-cran-callr, r-cran-conflicted, r-cran-devtools, r-cran-duckdb, r-cran-ggplot2, r-cran-here, r-cran-jsonlite, r-cran-knitr, r-cran-lintr, r-cran-microbenchmark, r-cran-odbc, r-cran-pak, r-cran-rmarkdown, r-cran-roxygen2, r-cran-pkgdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyverse, r-cran-withr Filename: pool/dists/noble/main/r-cran-scdb_0.6.2-1.ca2404.2_all.deb Size: 372366 MD5sum: 276992f04065393538cefe31c918376b SHA1: 1914b938e83a80e14a1a4d6f8514bde631a06bd0 SHA256: 5b3707c28e155fd6b283836c4421c874ffd047d279d938a30ace52f042098e8e SHA512: 5ff3f86f48fcaca8dd3e0e2a60cca0e2f5671f64a028b12bbd539c0a770cf81ec2a79e60898157d18e0d619ff20b7a3c10916462f62f46c36604d1c0315dd05c Homepage: https://cran.r-project.org/package=SCDB Description: CRAN Package 'SCDB' (Easily Access and Maintain Time-Based Versioned Data(Slowly-Changing-Dimension)) A collection of functions that enable easy access and updating of a database of data over time. More specifically, the package facilitates type-2 history for data-warehouses and provides a number of Quality of life improvements for working on SQL databases with R. For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801). Package: r-cran-scdeco Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rjags, r-cran-msm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scdeco_0.1.1-1.ca2404.1_all.deb Size: 83336 MD5sum: f7cfaaf4001b5cd891b29a02ed3710d4 SHA1: b2b94e987e1a2c303df5b2a66db67073f8c9ffc4 SHA256: 810583e6673350ce43fb3565a987730bf0dd210d45654155474074411ebe359a SHA512: d2c31770c3bed7367d47914170e615d7874ffbe0817be9041690d78e430e212870410fdc3722e75c8461452f3c66c1e54786bd27d135facb31ce34ee81bbec58 Homepage: https://cran.r-project.org/package=scDECO Description: CRAN Package 'scDECO' (Estimating Dynamic Correlation) Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) , while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) . 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Provide various visualization functions. Package: r-cran-scdensity Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scdensity_1.0.3-1.ca2404.1_all.deb Size: 1062960 MD5sum: 11ccedb25bfb55897f1c8a03d7675553 SHA1: d526ffc48e9f61d57a7a5cd8c62b53c73054c235 SHA256: f2a95aed0231481816eb3930bba9fd43e5135979c684b686b403b084b55947ab SHA512: 58a655c75a2202289b5b9df571fc7328544c7a2ccde362232f05604c59d1cf1a2048f32760f4c418c860246bbcf7cabe07fa01ff0f030d2a2c03416f3ff60ff7 Homepage: https://cran.r-project.org/package=scdensity Description: CRAN Package 'scdensity' (Shape-Constrained Kernel Density Estimation) Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in 'stats', allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) , Wolters (2012) , and Hall and Huang (2002) . See the scdensity() help for for full citations. Package: r-cran-scdhlm Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-readxl, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-magrittr, r-cran-lmeinfo Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plyr, r-cran-boot, r-cran-shiny, r-cran-shinytest, r-cran-glue, r-cran-janitor, r-cran-rclipboard, r-cran-rvest, r-cran-testthat, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-iterators, r-cran-reshape, r-cran-rlecuyer Filename: pool/dists/noble/main/r-cran-scdhlm_0.7.4-1.ca2404.1_all.deb Size: 844792 MD5sum: 61e019bf111b922e7ddb26ea0ba88d95 SHA1: fbc5758ffd678928c69572059741ad37d274692b SHA256: d04495be4d93a69527490bf462b51f47a55c7c3e994fde2fe57304d12fa64abf SHA512: ec7557db7574667c23fd2fab8d44ea5ee93b798c8935924aeed86c3ad8d99fe5c0de90100339c45e17f5e8528f6b3cb1edf4d0ba387f15fbf1225073a310feb0 Homepage: https://cran.r-project.org/package=scdhlm Description: CRAN Package 'scdhlm' (Estimating Hierarchical Linear Models for Single-Case Designs) Provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) ; Hedges, Pustejovsky, and Shadish (2013) ; Pustejovsky, Hedges, and Shadish (2014) ; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) . Includes an interactive web interface. Package: r-cran-scdiffcom Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-delayedarray, r-cran-future, r-cran-future.apply, r-cran-lifecycle, r-cran-magrittr, r-cran-seurat Suggests: r-bioc-biomart, r-cran-covr, r-cran-dt, r-cran-ggplot2, r-bioc-gosemsim, r-cran-igraph, r-cran-kableextra, r-bioc-keggrest, r-cran-knitr, r-cran-ontologyindex, r-bioc-ontoproc, r-cran-pkgdown, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rmarkdown, r-bioc-rrvgo, r-cran-spelling, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-scdiffcom_1.2.0-1.ca2404.1_all.deb Size: 3291246 MD5sum: e649cc802a7b2dcbb983fefc9cc8402b SHA1: 97179e4d4875c7cb85f4211133f07bd960e485b8 SHA256: e3b17ad7f284b3c76544147420b8fb3bde539a71488a0442a8b61974fecec344 SHA512: b8d68e6307a8dcdde5bd4a2a8c44ae0bd52a7e014843d172367464ca9ce5707e43e6509d0249a55056b97a11f94cf648378c1f1b25161b6323d37a74e8475c54 Homepage: https://cran.r-project.org/package=scDiffCom Description: CRAN Package 'scDiffCom' (Differential Analysis of Intercellular Communication fromscRNA-Seq Data) Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) for a full description of the methodology. Package: r-cran-scdiftest Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-strucchange, r-cran-mirt, r-cran-zoo Suggests: r-cran-mvtnorm, r-cran-psychotree, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scdiftest_0.1.1-1.ca2404.1_all.deb Size: 43430 MD5sum: 589496e71cf636b3506f454b5590c8ab SHA1: 08bbe17da644d84e09fb9a17c058e8463164c238 SHA256: 196f6497975805cc759dc75d0d68f5f3e905ab4a1950b6f3e1641ad6d2ec95f6 SHA512: 8198be553ece50a0eecd98a55df12ec63c8197e37a01398715aa0c552cf08887af25c8018b84233ed831b27ae8386d2bfa4c258e2691296e8f4cad61630e6484 Homepage: https://cran.r-project.org/package=scDIFtest Description: CRAN Package 'scDIFtest' (Item-Wise Score-Based DIF Detection) Detection of item-wise Differential Item Functioning (DIF) in fitted 'mirt', 'multipleGroup' or 'bfactor' models using score-based structural change tests. Under the hood the sctest() function from the 'strucchange' package is used. Package: r-cran-scdtb Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom.mixed, r-cran-dt, r-cran-ggplot2, r-cran-mass, r-cran-mmcards, r-cran-mmints, r-cran-nlme, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scdtb_0.2.0-1.ca2404.1_all.deb Size: 1883952 MD5sum: 4523b073ebdfb6649f9dbfec62bb2b66 SHA1: 18cfa49fcb73ab7f1894a2c6accae3b68b477975 SHA256: de355270eafea50fb5918922596b37b5d8a13afe76369e557d3a81dab4952d7f SHA512: e7013c30087d1e930c4bf8244744e83e8e44dd7c253abfc512483a576ac4d5dc917e21b68828159665e93bafbf683393bb4ebaa51b462e6dc9bdc379bf685286 Homepage: https://cran.r-project.org/package=scdtb Description: CRAN Package 'scdtb' (Single Case Design Tools) In some situations where researchers would like to demonstrate causal effects, it is hard to obtain a sample size that would allow for a well-powered randomized controlled trial. Single case designs are experimental designs that can be used to demonstrate causal effects with only one participant or with only a few participants. The 'scdtb' package provides a suite of tools for analyzing data from studies that use single case designs. The nap() function can be used to compute the nonoverlap of all pairs as outlined by the What Works Clearinghouse (2022) . The package also offers the mixed_model_analysis() and cross_lagged() functions which implement mixed effects models and cross lagged analyses as described in Maric & van der Werff (2020) . The randomization_test() function implements randomization tests based on methods presented in Onghena (2020) . The scdtb() 'shiny' application can be used to upload single case design data and access various 'scdtb' tools for plotting and analysis. Package: r-cran-sce Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sce_1.1.4-1.ca2404.1_all.deb Size: 219474 MD5sum: 01c031e14bfd875b588317a656e7e8d5 SHA1: c7bab4d2c25b30567da02914d48bb0e76515b280 SHA256: dbf9e089c8f615e1826c26bbe2280e4527aa073c50e077ab67081b8c84376491 SHA512: cf4338954526b4dc52164203d050aff3defd39db862ec4125e5059576122788ef16b6cc345c11ae1ffd4711c9da9d06934bf3a89a384bf91e314616068b3da5c Homepage: https://cran.r-project.org/package=SCE Description: CRAN Package 'SCE' (Stepwise Clustered Ensemble) Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) . Package: r-cran-scem Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-scem_1.2.0-1.ca2404.1_all.deb Size: 120210 MD5sum: ea485b936af64cc3270600b93cc572ee SHA1: 34490081bf2ef94074a82a86bc1b72d5a94a141f SHA256: e37bdf311f34ceaced89e837a2d5aa60553cb5b8b7bc93b0a0aff0afb18f2d06 SHA512: eb081584f7ca77b58f7f32de2c99346852238169820a5bc65a35277959071ecc8708acfadbad399d9f54789aa9b80256bd139d3673662864549c6c6039ae907c Homepage: https://cran.r-project.org/package=SCEM Description: CRAN Package 'SCEM' (Splitting-Coalescence-Estimation Method) We introduce improved methods for statistically assessing birth seasonality and intra-annual variation. The first method we propose is a new idea that uses a nonparametric clustering procedure to group individuals with similar time series data and estimate birth seasonality based on the clusters. One can use the function SCEM() to implement this method. The second method estimates input parameters for use with a previously-developed parametric approach (Tornero et al., 2013). The relevant code for this approach is makeFits_OLS(), while makeFits_initial() is the code to implement the same method but with given initial conditions for two parameters. The latter can be used to show the disadvantage of the existing approach. One can use the function makeFits() to generate parametric birth seasonality estimates using either initialization. Detailed description can be found here: Chazin Hannah, Soudeep Deb, Joshua Falk, and Arun Srinivasan (2019) "New Statistical Approaches to Intra-Individual Isotopic Analysis and Modeling Birth Seasonality in Studies of Herd Animals". Package: r-cran-scenes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cookies, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-shiny Suggests: r-cran-covr, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scenes_0.1.0-1.ca2404.1_all.deb Size: 252666 MD5sum: 57599ae389c4c3a1d6e90e0f5f3602d9 SHA1: 5f9a7f95c5a7a82bc7908295b3e1d62f9a6b8589 SHA256: 40d5439495dbeebc8786a61569d1fb59f3467105a9d30842f04eb7eb0e2b6c84 SHA512: 3852d8128776c9f376acb381d0b053ceb77830cadc6b30fe73c9329cd62ec73177355136ebd6145811931b653c9e042aa789f10ca93937237563ff96902bd9df Homepage: https://cran.r-project.org/package=scenes Description: CRAN Package 'scenes' (Switch Between Alternative 'shiny' UIs) Sometimes it is useful to serve up alternative 'shiny' UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches. Package: r-cran-scenfire Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-readr, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-rlang, r-cran-stringr, r-cran-doparallel, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-scenfire_0.1.0-1.ca2404.1_all.deb Size: 68310 MD5sum: 9984cdbc52cd8f76b1ec24f0922ed58c SHA1: 05773e53af4334c05259ebf77193a40bfc060a22 SHA256: 9300859065b5c2620357da593ac13cd14caeb73f57f5d874f80d4f479ec47471 SHA512: 452cd0b2f6f228b4c9aa8558cc9738a104c49e2d729cd9acbd9d1513ebdaa63c70e6b5b397b7de35ed76b5def358f6716986d42b92850f7fd123547f9e4c88cb Homepage: https://cran.r-project.org/package=scenfire Description: CRAN Package 'scenfire' (Post-Processing Algorithm for Integrating Wildfire Simulations) A specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) . Demo data and code examples can be found in Rodrigues (2025b) . Package: r-cran-scent Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-entropy, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scent_0.0.1-1.ca2404.1_all.deb Size: 31726 MD5sum: b1883a06fd0588765cc96375c7488976 SHA1: 4a20f810ae38298a5df5fb78c5b14416be2ac866 SHA256: 24142ec6e8c4377077eb9bf137f818d310ce4acfd840dd60bdead00d44ccbc95 SHA512: 9012e4a61c8b7ff7dfbeada7069bd514e8dcecc071986c314da80a723cec6366a728b97c3fbb079baf8e227a3d17ddb5ac31020086f807c2554f57a963528fb7 Homepage: https://cran.r-project.org/package=SCEnt Description: CRAN Package 'SCEnt' (Single Cell Entropy Analysis of Gene Heterogeneity in CellPopulations) Analyse single cell RNA sequencing data using entropy to calculate heterogeneity and homogeneity of genes amongst the cell population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia and Ben D. MacArthur. Package: r-cran-scf Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-haven, r-cran-httr, r-cran-rlang, r-cran-survey, r-cran-quantreg Suggests: r-cran-dplyr, r-cran-hexbin, r-cran-kableextra, r-cran-knitr, r-cran-mitools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scf_1.0.10-1.ca2404.1_all.deb Size: 1517772 MD5sum: d6c7f5c7b550008292e13002e2e98b7c SHA1: 3b25f3768c73c668a1db0976ec5073cd5925601d SHA256: cab584f88125bba2718ffc1f2cda8ced5e2042dc0b4f50c262dbd7f31b00de89 SHA512: 751eec5be8e550d963b787b2539b15e83a66c8aead4d85761fe9206acc78e5b57e05cd64642641c2e3906e7cb963933098cb396189cf57aaa5ad496303a8ce04 Homepage: https://cran.r-project.org/package=scf Description: CRAN Package 'scf' (Analyzing the Survey of Consumer Finances) Analyze public-use micro data from the Survey of Consumer Finances. Provides tools to download prepared data files, construct replicate-weighted multiply imputed survey designs, compute descriptive statistics and model estimates, and produce plots and tables. Methods follow design-based inference for complex surveys and pooling across multiple imputations. See the package website and the code book for background. Package: r-cran-scflex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-reticulate, r-cran-seuratobject Suggests: r-cran-hdf5r, r-cran-seurat, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scflex_0.1.0-1.ca2404.1_all.deb Size: 1019768 MD5sum: 38a2b2ce1c7c9208a5db211b3ea3ef7b SHA1: 0c0855efdf74a66f2bc10bff6440842e2697920f SHA256: 15b148cddf64a42245af927dcc0856c4faba64a1238ac58bed3df14b46ef8564 SHA512: 06b95615d5f72ad93e8c278ca01822fb104981cd90ffa6be21b68395747a08f3ed925b87cafb355df3cca8890b99b77ff83cded06ced0b7d16280ec8a5c60c27 Homepage: https://cran.r-project.org/package=scFlex Description: CRAN Package 'scFlex' (Flexible Conversion Between Single-Cell Data Objects) Provides conversion among 'Seurat', 'SingleCellExperiment', 'AnnData', and 'Loom' single-cell data representations while preserving expression matrices, cell and feature metadata, and dimensionality reductions when supported by the target format. The package performs alignment and validity checks during conversion and reports unsupported or unavailable components rather than silently reconstructing them. Package: r-cran-scfmonitor Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-stringr, r-cran-tidyselect, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-tidyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-scfmonitor_0.3.5-1.ca2404.1_all.deb Size: 397302 MD5sum: 18aa326337a4777f3b99ca9216280202 SHA1: 1f486e02f362d5d7ebd3959329b00e186637118e SHA256: 35fbbb092b2ebb551ff881555ad791eb830a5d802d81448d3bff1b8adcb44248 SHA512: 87f8d1e80c7cc26398be94f40dc1fadb95b20db9eb03e0ea38ef9ed57c6875b1866f5130c6ade3231ff34c979a3e306dc50b9dbbf0fc15e7bf86dfe0d9096087 Homepage: https://cran.r-project.org/package=SCFMonitor Description: CRAN Package 'SCFMonitor' (Clear Monitor and Graphing Software Processing Gaussian .logFile) Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). However, the most prevailing software in this area, 'Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). 'SCFMonitor' enables 'Gaussian' quantum chemistry calculation software users to easily read the 'Gaussian' .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using 'tidyverse' to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of 'Gaussian'. Package: r-cran-scgate Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-bioc-ucell, r-cran-dplyr, r-cran-patchwork, r-cran-ggridges, r-cran-colorspace, r-cran-reshape2, r-cran-ggplot2, r-bioc-biocparallel Suggests: r-cran-ggparty, r-cran-partykit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scgate_1.7.2-1.ca2404.1_all.deb Size: 1940832 MD5sum: 627947a280b3194db49c0b4b66612472 SHA1: 780ab44af3629afc884ba3f4012fa3b59c4022d0 SHA256: 60185b73a911246ecf1ee2a3ac68bb502faab7a7547ce6707d34c1295eab0a83 SHA512: ad811105b779ba627d94c1ba5dd1af7a0e82a21015112f882347ac52287c1fdbb9638b03207c4fa6628fc8504c10c1e643c444ba93aea86966451cfcdea38d98 Homepage: https://cran.r-project.org/package=scGate Description: CRAN Package 'scGate' (Marker-Based Cell Type Purification for Single-Cell SequencingData) A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) . 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Use this package to analyze and have fun with text from the best series of all time. 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The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. . 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Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package. Package: r-cran-scopr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-behavr, r-cran-data.table, r-cran-readr, r-cran-stringr, r-cran-rsqlite, r-cran-memoise Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-ggetho, r-cran-zeitgebr Filename: pool/dists/noble/main/r-cran-scopr_0.3.5-1.ca2404.1_all.deb Size: 235780 MD5sum: 0a8f2cda1eaca9ad863a6d225c8cc9bb SHA1: dd4a9d3faf6eae6c26f6ec990ba4605c2e7714ed SHA256: aa1229db06986d4bdc8dd2c1051ce717f419dd183316fbd2b3c38f22a9f884af SHA512: 30929be72f6fbddaffd718d3dcb1295dddd15f1c5b0f2a21d8ccc27f4af4c5509b2ee8d3458dac050ec5331f65afed2e01b6a9da77aeaec832ea2387cb464d87 Homepage: https://cran.r-project.org/package=scopr Description: CRAN Package 'scopr' (Read Ethoscope Data) Handling of behavioural data from the Ethoscope platform (Geissmann, Garcia Rodriguez, Beckwith, French, Jamasb and Gilestro (2017) ). Ethoscopes () are an open source/open hardware framework made of interconnected raspberry pis () designed to quantify the behaviour of multiple small animals in a distributed and real-time fashion. The default tracking algorithm records primary variables such as xy coordinates, dimensions and speed. This package is part of the rethomics framework . Package: r-cran-scopro Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-ciara, r-cran-rmarkdown, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-scopro_0.1.0-1.ca2404.1_all.deb Size: 332386 MD5sum: 5e42ff7f6817f484786ba6f63141cf0e SHA1: 5fb73b137f850f75b86327cfcabfc1d4ea6b2196 SHA256: d6f42b53782281ad1147e79cdb9207443df08bf1740d5b05c9b693f85c6680a7 SHA512: 59ab63f9d0ce0c0c5a5895922264bd8672136d2660c952cb7e21ccc1430e2c2a3c38e02ff92cc87f0385845d8d1e3e769fedf3f941d16549c8058520b5c2e3af Homepage: https://cran.r-project.org/package=SCOPRO Description: CRAN Package 'SCOPRO' (Score Projection Between in 'Vivo' and in 'Vitro' Datasets) Assigns a score projection from 0 to 1 between a given in 'vivo' stage and each single cluster from an in 'vitro' dataset. 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Package: r-cran-scopusflow Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-bslib, r-cran-callr, r-cran-fansi, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-scopusflow_0.4.0-1.ca2404.1_all.deb Size: 711906 MD5sum: f3e520ac3a0ed1f527901b01ad3a0852 SHA1: 5cd5f198b73c1941b3d945c3ef660edaf9aec2a7 SHA256: 4376664cae08b026e09e7f8a0628f183fb6b70561d9dc0bd18aed74688e11750 SHA512: 42eace75119a9c47ae88944c39ef440293b20cdea7ee4992d07ac6cf81c3466abe899f9a524640901c458eaafe734b4fba9d129d61d220e0993f5969b9ab062e Homepage: https://cran.r-project.org/package=scopusflow Description: CRAN Package 'scopusflow' (A Reproducible Workflow Layer for 'Scopus' BibliographicSearches) A coherent, quota-aware workflow layer over the Elsevier 'Scopus' Search 'API' . It builds reproducible search plans, retrieves records with rate-limit handling, retry with back-off and optional resumable caching, normalises results to a stable tidy schema, extracts and tracks changes in Digital Object Identifiers (DOIs), sizes sets of concepts and their intersections, compares publication trends across topics, writes the search up as a reproducible record for a methods section following the 'PRISMA-S' reporting standard (Rethlefsen and others, 2021) and exports to formats compatible with downstream bibliometric tools. Network and 'API' errors are surfaced as typed conditions so that callers can respond to them programmatically. 'Scopus' is a trademark of Elsevier. This package is an independent client and is not affiliated with or endorsed by Elsevier. Package: r-cran-scor Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-scor_1.1.2-1.ca2404.1_all.deb Size: 62918 MD5sum: e657ebb91c03369b0eba297b8523e03b SHA1: f957f2295d75cdbeb16049bba5ed7be962a482b0 SHA256: 38450684d7f9bbd3faf8380d9217141c2301232a2925eb3b1a5ee98ff1f49e76 SHA512: f6594f3194d2c0464093aa0fbe8954ea008032491846f844f17e16b0914a8fe855c0aa26f8b78da9cdb69d194cd78cd790b460085351d51ffaafd0edb06edd6c Homepage: https://cran.r-project.org/package=SCOR Description: CRAN Package 'SCOR' (Spherically Constrained Optimization Routine) A non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) . Package: r-cran-score Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msm Filename: pool/dists/noble/main/r-cran-score_1.0.2-1.ca2404.1_all.deb Size: 37792 MD5sum: 07c963900ea24f6d406182c4ca5b0782 SHA1: 473a5bc0b65146ef682b23cc53d1e348b5d37429 SHA256: 1dba47d40df7bfc60b1a6a2ee33d28f82e2a5adf266ed388a7c23abdc13c3623 SHA512: 54271172519676848c230a6cae7b7fd9d6cd68ff804fb4f3424fea85861191b544611aff28d08eebd1f75045f615d81e9d56aa69781b38e2c9b3ba2c3fb9a9ef Homepage: https://cran.r-project.org/package=score Description: CRAN Package 'score' (A Package to Score Behavioral Questionnaires) Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' . Compares physical functional performance to the age- and gender-specific normal ranges. Package: r-cran-scorecard Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-foreach, r-cran-doparallel, r-cran-openxlsx, r-cran-stringi, r-cran-cli, r-cran-xml2, r-cran-xefun Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scorecard_0.4.6-1.ca2404.1_all.deb Size: 367744 MD5sum: fab3522600abce86a8d00acc0f6341ec SHA1: 371049b714389333542bce4a2036952213afd018 SHA256: d57127aae0ef9b87ed14a2a78ae797dd135d3cb96e83b32fb39ea2a41e0ae428 SHA512: 3a535e39b2c8574b6f71e0f8993486687ed021282ec6d8eff6152e25b06b13c25e708a412b1bedd04222f251554e4c325d176f82715f387924f7e38e83ff40c0 Homepage: https://cran.r-project.org/package=scorecard Description: CRAN Package 'scorecard' (Credit Risk Scorecard) The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring. 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Package: r-cran-scoreplus Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-limsolve, r-cran-rspectra, r-cran-igraph, r-cran-igraphdata Filename: pool/dists/noble/main/r-cran-scoreplus_0.1-1.ca2404.1_all.deb Size: 38898 MD5sum: 03b2decdff54c46e178f5d57b8bbf96f SHA1: 0c36bc166acb687ac932032d3134bda85f189e54 SHA256: 1c2bf3741ccbfabd69751649e6c93ef2834029f4db24636037ae58024cab991d SHA512: 56cb97289fdfbec13fff3c96cec2faa976c9d37d6601cc436bfd5662cb79f8528dd855aa32c327a7d6d9e4f87272649dafcf238189a7df9a30a2c79ecb4aed31 Homepage: https://cran.r-project.org/package=ScorePlus Description: CRAN Package 'ScorePlus' (Implementation of SCORE, SCORE+ and Mixed-SCORE) Implementation of community detection algorithm SCORE in the paper J. Jin (2015) , and SCORE+ in J. Jin, Z. Ke and S. Luo (2018) . Membership estimation algorithm called Mixed-SCORE in J. Jin, Z. 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Acceptable input includes both one-dimensional and two-dimensional data for linear, logistic, functional, and spatial generalized least squares regression models. Functions are also available for constructing simultaneous confidence bands (SCBs) for these models. The definition of simultaneous confidence regions (SCRs) follows Sommerfeld et al. (2018) . Methods for estimating inverse regions, SCRs, and the nonparametric bootstrap are based on Ren et al. (2024) . Methods for constructing SCBs are described in Crainiceanu et al. (2024) and Telschow et al. (2022) . 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Pointwise losses and realised average scores are both available. Detailed documentation of the functions' properties is included for facilitating the interpretation of results. Package: r-cran-scoringutils Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3974 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-ggplot2, r-cran-lifecycle, r-cran-purrr, r-cran-scoringrules Suggests: r-cran-ggdist, r-cran-kableextra, r-cran-knitr, r-cran-metrics, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-scoringutils_2.3.0-1.ca2404.1_all.deb Size: 3163962 MD5sum: 70aa0ed520e688d7c7d9715a7d30fc01 SHA1: bd94c189576c7a5ea27cc7fe71b687f0e8b84ab6 SHA256: 219c881ed8d7652d803710c4e7e740c0215fe99a07e7fd11640e60d334edc06f SHA512: 0c072ec5d0daff1c8195a69997a940a8641359f76c97a0c27ed5c882f034a88433e0c999249f0ae96a2882573aaa73ec990712bac67b004d22c2a1727a952f3b Homepage: https://cran.r-project.org/package=scoringutils Description: CRAN Package 'scoringutils' (Utilities for Scoring and Assessing Predictions) Facilitate the evaluation of forecasts in a convenient framework based on data.table. 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Package: r-cran-scottknott Architecture: all Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-emmeans, r-cran-xtable Suggests: r-cran-lme4, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scottknott_1.4-0-1.ca2404.1_all.deb Size: 398932 MD5sum: b81a0bb6fe8c6762a8bac3ff8cf6aeb7 SHA1: 7f1907906201425b157fee3d1f82e8a187de4c52 SHA256: c880f4e3eb8be98cf4ed37f5e977fa6b5ef542dd4689ad71206d8ade546a6f19 SHA512: 40c0107ec136eafdaf43f80689b919a5d90d03b663cedc6a28923ebf6e435937f6cf500a4a4869da6398bf7147f362990e946525f606609a40beb22da105f45c Homepage: https://cran.r-project.org/package=ScottKnott Description: CRAN Package 'ScottKnott' (The ScottKnott Clustering Algorithm) Performs the Scott & Knott (1974) clustering algorithm as a multiple comparison method in the Analysis of Variance context, for both balanced and unbalanced designs. 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Package: r-cran-scpoisson Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-glmpca, r-cran-seurat, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-matrix, r-cran-rdpack, r-cran-seuratobject, r-cran-wgcna, r-cran-broom, r-cran-matrixstats Suggests: r-cran-renv, r-cran-testthat, r-cran-vdiffr, r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-scpoisson_0.0.2-1.ca2404.1_all.deb Size: 1373082 MD5sum: 1d0e587354c2357df68eaca4e16880c3 SHA1: 0ac36797d3ded7dcf572cc6b5dc1cd59e0a8a4c8 SHA256: a5bf87cf98a5f6498a9b7005041977f7be50712d902dd1726de91a5b482c7143 SHA512: c87aeb27eb1ac2999da3e560f7363f1cc76e24acc0fa4b19466347b2232e93b152b365ed34633f6d11e8460836badc4e32eea65c6b012fe415ac65a99e40f783 Homepage: https://cran.r-project.org/package=scpoisson Description: CRAN Package 'scpoisson' (Single Cell Poisson Probability Paradigm) Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation. 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(2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. . 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The threshold chosen by 'ScreeNOT' is optimal (asymptotically, in the sense of minimum Frobenius error) under the the so-called "Spiked model" of a low-rank matrix observed in additive noise. In contrast to previous works, the noise is not assumed to be i.i.d. or white; it can have an essentially arbitrary and unknown correlation structure, across either rows, columns or both. 'ScreeNOT' is proposed to practitioners as a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966. If you use this package, please cite our paper: David L. Donoho, Matan Gavish and Elad Romanov (2023). "ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise." Annals of Statistics, 2023 (To appear). . 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Package: r-cran-scspatialsim Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-spatstat.geom, r-cran-crayon, r-cran-ggpubr, r-cran-pbmcapply, r-cran-spatstat.random, r-cran-tidyr, r-cran-proxy Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spatialtime, r-cran-spdep Filename: pool/dists/noble/main/r-cran-scspatialsim_0.1.4-1.ca2404.1_all.deb Size: 1515918 MD5sum: 8b625a481e8654f670494d1aca0a17de SHA1: cf62e66aac3b22155d609af9eebc33f53f744fb2 SHA256: f92fdc261a61748fed3a0d3e58e28ae116d65b2685308e5baa64953d6249de5c SHA512: 0e427d11e2b845bc370db6662fb676c952566e742ccd621639e8e6128aff969c23773bd1d2dd4ba69b14a047887c2af3e9d685f76369385e452903b23a0e878f Homepage: https://cran.r-project.org/package=scSpatialSIM Description: CRAN Package 'scSpatialSIM' (A Point Pattern Simulator for Spatial Cellular Data) Single cell resolution data has been valuable in learning about tissue microenvironments and interactions between cells or spots. This package allows for the simulation of this level of data, be it single cell or ‘spots’, in both a univariate (single metric or cell type) and bivariate (2 or more metrics or cell types) ways. As more technologies come to marker, more methods will be developed to derive spatial metrics from the data which will require a way to benchmark methods against each other. Additionally, as the field currently stands, there is not a gold standard method to be compared against. We set out to develop an R package that will allow users to simulate point patterns that can be biologically informed from different tissue domains, holes, and varying degrees of clustering/colocalization. The data can be exported as spatial files and a summary file (like 'HALO'). . Package: r-cran-scstability Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aricode, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-magrittr, r-cran-pcapp, r-cran-rlang, r-cran-rtsne, r-cran-seurat, r-cran-uwot, r-cran-vegan Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-bioc-scrnaseq, r-bioc-summarizedexperiment, r-cran-matrix, r-cran-biocmanager, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scstability_1.0.4-1.ca2404.1_all.deb Size: 117774 MD5sum: 4a9b71ff840045535546d6399e221b27 SHA1: 56a26d48459bb4ec950b3daa8919f429011b6489 SHA256: 096e98515db7db2b23857142bf501a9603eb686d5c902b253cf9d1c720998261 SHA512: 16f9971f6ccbc6ddf9c16f36378c80506d62a751782d83c45a6d7101028c57f8953366dab33e6a70574cd0b1ccc81d1067d8162f4e6227aba3ccb33a843427a4 Homepage: https://cran.r-project.org/package=scStability Description: CRAN Package 'scStability' (Measuring the Stability of Dimension Reduction and ClusterAssignment in scRNA-Seq Experiments) Provides functions for evaluating the stability of low-dimensional embeddings and cluster assignments in single‑cell RNA sequencing (scRNA‑seq) datasets. Starting from a principal component analysis (PCA) object, users can generate multiple replicates of t‑Distributed Stochastic Neighbor Embedding (t‑SNE) or Uniform Manifold Approximation and Projection (UMAP) embeddings. Embedding stability is quantified by computing pairwise Kendall’s Tau correlations across replicates and summarizing the distribution of correlation coefficients. In addition to dimensionality reduction, 'scStability' assesses clustering consistency using either Louvain or Leiden algorithms and calculating the Normalized Mutual Information (NMI) between all pairs of cluster assignments. For background on UMAP and t-SNE algorithms, see McInnes et al. (2020, ) and van der Maaten & Hinton (2008, ), respectively. Package: r-cran-sctenifoldknk Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-sctenifoldnet, r-cran-cli, r-cran-enrichr, r-cran-igraph, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-locfdr Filename: pool/dists/noble/main/r-cran-sctenifoldknk_2.0.0-1.ca2404.1_all.deb Size: 172506 MD5sum: deeb7a0cb86f65b5da3ed4bca47846fb SHA1: 25b179765ea671dbaf729a7f48a84ae9f943ed45 SHA256: b49357c8c255d8e93ff1ba75bf83d64c6f8083f2a51f28fe3267aaa72775b214 SHA512: 318eb12cd20d1e09e6299960ec92ba7dfdca3897a442f7c6560e2b117662f483d6cdbdf34f116db0e9c5add0aa75f10eed2eaae8dd6177b33af3445eda846737 Homepage: https://cran.r-project.org/package=scTenifoldKnk Description: CRAN Package 'scTenifoldKnk' (In-Silico Knockout Experiments from Single-Cell Gene RegulatoryNetworks) A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells. It also predicts the direction (up or down) of the response of each gene from the WT expression, and reads all knockouts of a network from a single heat kernel, which makes transcriptome-wide knockout screens practical. Package: r-cran-sctenifoldnet Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rspectra, r-cran-matrix, r-cran-mass, r-cran-cli, r-cran-ps, r-cran-rhpcblasctl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sctenifoldnet_1.4.3-1.ca2404.1_all.deb Size: 113826 MD5sum: cc5fe9444dc81fba69d6cdb41c6c94cd SHA1: f1c16a8f5bd4f823b299916a55dc88f0af3c1076 SHA256: 829beb1e991efd94badebc8378f6f024ad13e5e6ec73a93e7c5a3a94d9b7cb6c SHA512: d7f566f12961167712d2a55a35a523e5a2753cdb85dad69531ff83e7d1711f616ef42b111bd80e607aa724427cab8528ffa39b1b14971162e8eba6d671336802 Homepage: https://cran.r-project.org/package=scTenifoldNet Description: CRAN Package 'scTenifoldNet' (Construct and Compare scGRN from Single-Cell Transcriptomic Data) A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See for more details. Package: r-cran-sctools Architecture: all Version: 0.3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-ggplot2, r-cran-synth, r-cran-stringr, r-cran-cvtools, r-cran-furrr, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sctools_0.3.3.1-1.ca2404.1_all.deb Size: 1362558 MD5sum: c5e4bced48aac1d28cd402cad9dedda6 SHA1: 1be91945698dbec40b3fa21dba2072989ce6b7a8 SHA256: aadfdafec44b947a5c531a514094063c6f1b3245cb509a7634b4a2d2ac8b4dc0 SHA512: 31c67a47240db3f04d7abc49abf00c39bd68090dc2644f60f39b7921241ac19f5a0e038c682dbab5d2ff43681f518ef195d8a036c569ebe58398a6f08dcec65f Homepage: https://cran.r-project.org/package=SCtools Description: CRAN Package 'SCtools' (Extensions for Synthetic Controls Analysis) Extends the functionality of the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) . Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units. Package: r-cran-scutils Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-scales, r-cran-assertthat, r-cran-dplyr, r-cran-viridis, r-cran-viridislite Suggests: r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-scutils_0.1.0-1.ca2404.1_all.deb Size: 89128 MD5sum: c86814f16b11344e942534a231ccfa53 SHA1: 0077a8972bd8680c75e2f5defde20b80d58cf317 SHA256: 48bba93ab34732b52d0a1a5fbb388b711396f8d563189aa060b400fe839e1015 SHA512: 2cc9f5fb4d7f9236d57418f2b093cf9a382cea2fa2f45ed8b73cf9aeef2d8ed3c9a3f6defdd466e956fb821349fffbfe4aa5750539570497f43074625ac4221a Homepage: https://cran.r-project.org/package=scUtils Description: CRAN Package 'scUtils' (Utility Functions for Single-Cell RNA Sequencing Data) Analysis of single-cell RNA sequencing data can be simple and clear with the right utility functions. This package collects such functions, aiming to fulfill the following criteria: code clarity over performance (i.e. plain R code instead of C code), most important analysis steps over completeness (analysis 'by hand', not automated integration etc.), emphasis on quantitative visualization (intensity-coded color scale, etc.). Package: r-cran-scutr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-smotefamily, r-cran-mclust Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scutr_0.2.0-1.ca2404.1_all.deb Size: 244082 MD5sum: 294deae890c3790dac927452b2eb45d5 SHA1: 74f8faa4accd93c343704820e63aa87348dce97e SHA256: 1ea5e9c55334e03c43ff999e9b2f679d22e6dda65ce2868eefcf0657924ff19a SHA512: c23c9fec7cb95bb12676abcfdbc233849614b7a677e2dddfbf664751c037610dca64e416bfe6d4fe9733b9c51db69018fedda148eda589bade3020b58a767d6a Homepage: https://cran.r-project.org/package=scutr Description: CRAN Package 'scutr' (Balancing Multiclass Datasets for Classification Tasks) Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) . Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package. Package: r-cran-scva Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggextra, r-cran-ggplot2, r-cran-plotly, r-cran-scales Filename: pool/dists/noble/main/r-cran-scva_1.3.1-1.ca2404.1_all.deb Size: 229892 MD5sum: b427ff98fc476be38ecbafcb1f8a0eb5 SHA1: f81fcbbfc3c577d62e7c25fe2bdff12a332dcaee SHA256: 6c4744abdec8760ce32fe259214ec99fde5083bb8b7dfee5519f814c5697e845 SHA512: 0c366c5aa3a8825b176258a4f41c197c0c6405fc1c53170a2b19212fdbe73387c225cc7e324e895f081cda603496b8228930c129759cce66721d08905e1edfcc Homepage: https://cran.r-project.org/package=SCVA Description: CRAN Package 'SCVA' (Single-Case Visual Analysis) Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) . The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend. Package: r-cran-sda Architecture: all Version: 1.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4003 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-entropy, r-cran-corpcor, r-cran-fdrtool Suggests: r-cran-crossval Filename: pool/dists/noble/main/r-cran-sda_1.3.9-1.ca2404.1_all.deb Size: 4048426 MD5sum: 444eaab8cba4fae1f31440635333f0e8 SHA1: dea1281d995ec0d3fd3becf9c97688f593054b48 SHA256: aef0595e1b828bcce5fbef9a67191fed9958cef49d687437f69ea782d5a05894 SHA512: a9b6e6cbb62cc132a867adf846b3108cd69070c0f624353d52308025b0a6e3edf85ff4b23003ca29c945f5a436d6b841c00fee5ff8b8add3caf56434b3f79635 Homepage: https://cran.r-project.org/package=sda Description: CRAN Package 'sda' (Shrinkage Discriminant Analysis and CAT Score Variable Selection) Provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism. Package: r-cran-sdaa Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-survey, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sdaa_0.1-5-1.ca2404.1_all.deb Size: 567122 MD5sum: a074e9712d4ec21c86e5577d2880b89f SHA1: 0e5a052328defa0906a925b27f0897d9d29257c0 SHA256: 2be137401637c0c38e6580c5cdf5dada372695aaa333a6de31c461f9d1d0dd9a SHA512: b2ac06de832524fa7ce6ddec8c2b997fb1ae3725d573695d943a014dfba30baae9cd317dcb63e9ec367d8aeecdeb1a1b157f68b52bc4ac1fada3c56fd3657490 Homepage: https://cran.r-project.org/package=SDaA Description: CRAN Package 'SDaA' (Sampling: Design and Analysis) Functions and Datasets from Lohr, S. (1999), Sampling: Design and Analysis, Duxbury. Package: r-cran-sdafilter Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-glasso, r-cran-poet, r-cran-selectiveinference Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-sdafilter_1.0.1-1.ca2404.1_all.deb Size: 43310 MD5sum: fde19b030613ac29bf6d1d586730e90e SHA1: d99a9c1171228b6758698c33cfaf23fa2ab05c80 SHA256: def949ef2ef98402526394c8005cce4a203c66a7a4b67c3db76ebcf751b99cad SHA512: cb4334d50ff3ee5fa4cf1e40817d95b2c52c6e5358a95bfea5bceadd965ba6f805ec69c656c8eeaa47c4c18bbee331b2a97b4e2bc231c4d9562a4df3cb5bafd2 Homepage: https://cran.r-project.org/package=sdafilter Description: CRAN Package 'sdafilter' (Symmetrized Data Aggregation) We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2023), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", . Some optional functionality uses the archived R packages ‘huge’ and ‘pfa’, which are not available from CRAN’s main repositories. Users who need this optional functionality can obtain them from the CRAN Archive as follows: ‘huge’ at ; ‘pfa’ at . Package: r-cran-sdam Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grimport2, r-cran-multiplex Suggests: r-cran-httr, r-cran-rjson, r-cran-knitr, r-cran-rmarkdown, r-cran-multigraph Filename: pool/dists/noble/main/r-cran-sdam_1.1.4-1.ca2404.1_all.deb Size: 2557614 MD5sum: 185c9e4728505262e4b7a5dbcf08617a SHA1: 56295e4797c1e7d9865807c5467b0b7d65f5f304 SHA256: 30b63555a1fbdb281acc0a257c9f0b42efa61c97fb26405fb9f0a6998600fc88 SHA512: 227829728d2f0f6d82e7cfcd85da86881d1dbeadd7cd8b9cf8493af76ea7f70df9506b8d8531513f462056f22ea499b8ce462627278fb5f5b818da35abf4ddfd Homepage: https://cran.r-project.org/package=sdam Description: CRAN Package 'sdam' (Social Dynamics and Complexity in the Ancient Mediterranean) Provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University. Package: r-cran-sdamr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-car Filename: pool/dists/noble/main/r-cran-sdamr_0.2.0-1.ca2404.1_all.deb Size: 226470 MD5sum: 9c717184e0210c1bb88aa3845ff3cac5 SHA1: 61f6ed4d657488e0e55ba6fb6739b1452c2cb8f0 SHA256: 7f37d01bdb6f91240286a22a7e6724431888923dfc144da1977e8c5e0f5deb73 SHA512: c51abe00233c853f976513ea5e595e0d618ef4323866042908e40ca221752e69e3be403c5920df4d4d2a481a919e0d0c551a8145684e9f204af2b5080646bcf6 Homepage: https://cran.r-project.org/package=sdamr Description: CRAN Package 'sdamr' (Statistics: Data Analysis and Modelling) Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) . All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts. Package: r-cran-sdar Architecture: all Version: 0.9-55-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-linbin, r-cran-grimport2, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sdar_0.9-55-1.ca2404.1_all.deb Size: 1480786 MD5sum: 1678ade2f654f1008db73e52378c437c SHA1: d7fa049b2b1204fa3a497c7f6f772fcd1a7b9ece SHA256: a3401a0a373b531071e3dc92e32820ab5cd6f4a1955e89c95ef5f29c40e4431e SHA512: 46e9a67d05171502f7589bdda48ab1a3815094b7cddbd7f8de740d20a030ee1eade7dbd6dc2baf85b637b3c0caf3e0ace199802bd32531dc4753b4ec8192c3bb Homepage: https://cran.r-project.org/package=SDAR Description: CRAN Package 'SDAR' (Stratigraphic Data Analysis) A fast, consistent tool for plotting and facilitating the analysis of stratigraphic and sedimentological data. Taking advantage of the flexible plotting tools available in R, 'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness, lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, gamma ray logs) (Johnson, 1992, ). Package: r-cran-sdaresources Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3166 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sdaresources_0.1.1-1.ca2404.1_all.deb Size: 3110816 MD5sum: ad77816194c1c9de50baad3c7c6c0d34 SHA1: f9ca4276e714125ae57dcbe465fdfe1dbcc64559 SHA256: 7921f59564beb87d0e16003a3b5b7a5f79a581351041ca488684fdbf4d622939 SHA512: fcee9b8a05e42f18b3d1bd40f4b0bb69e403b3488f4d5e6ee93d84c09a0c51afd4d99d28d09fdae9630295cc2b5726ce572bdbe6f035864d3b0eee2221668184 Homepage: https://cran.r-project.org/package=SDAResources Description: CRAN Package 'SDAResources' (Datasets and Functions for 'Sampling: Design and Analysis, 3rdEdition') Includes all the datasets of 'Sampling: Design and Analysis' (3rd edition by Sharon Lohr) in R format and additional functions for analyzing and graphing probability samples. Package: r-cran-sdbuildr Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1478 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-desolve, r-cran-diagrammer, r-cran-future, r-cran-future.apply, r-cran-htmltools, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-juliaconnector, r-cran-plotly, r-cran-progressr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-textutils, r-cran-withr, r-cran-xml2 Suggests: r-cran-callr, r-cran-gt, r-cran-diagrammersvg, r-cran-kableextra, r-cran-knitr, r-cran-qgraph, r-cran-rsvg, r-cran-testthat, r-cran-this.path, r-cran-webshot2, r-cran-bslib Filename: pool/dists/noble/main/r-cran-sdbuildr_2.2.3-1.ca2404.1_all.deb Size: 1400122 MD5sum: 68af43533fc175050926b3bf369f66ef SHA1: 39888b60226c20a1b028dd733d1ebd0d4d0d72c7 SHA256: 38ae5e0af0b3689ccbbf4ea95fe92a7f6d29a518efee2d076ec6417c341ea343 SHA512: cfbe64023949134a9c0a3bf6358ba78d36ec8d6fed02ff04583bf53af46c6748804e2e05df890ee886d873949381ac8b3b5a6276537a6d04be293ee30c068fc5 Homepage: https://cran.r-project.org/package=sdbuildR Description: CRAN Package 'sdbuildR' (An Accessible Interface for Stock-and-Flow Modelling) Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. 'sdbuildR' (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. 'sdbuildR' simulates models in 'R' and 'Julia', and supports computationally intensive ensemble simulations. Additionally, 'sdbuildR' can import models created in 'Insight Maker' (). Package: r-cran-sdc.redistribute Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-areal, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-sdc.redistribute_0.1.0-1.ca2404.1_all.deb Size: 37736 MD5sum: d275a1932fc3bb61ec4bf60185de67d2 SHA1: b822b76b443d875f56eaf8026a9db6758666002d SHA256: 59bd1fc52bf175db1784a02390d53906886beab3ce308088b3c7c91010ffb482 SHA512: 84fb56d50e30b5565b3f011feaf70a9e4c9e4378e6f71ae977c82fc72d86b1c8a4d9f2361657db6abf5fca2a2f30fd9818763a3ad41ecfadc91525b2157cea15 Homepage: https://cran.r-project.org/package=sdc.redistribute Description: CRAN Package 'sdc.redistribute' (Redistribute Values Between Geographic Areas) Estimate attribute values for one set of polygons from values measured on a different, misaligned set. Provides area-weighted areal interpolation and a dasymetric method that distributes values across a point layer (such as parcel centroids). Count (extensive) measures are total-preserving; rate (intensive) measures use area-weighted means. Package: r-cran-sdclog Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-mathjaxr Suggests: r-cran-cffr, r-cran-knitr, r-cran-lfe, r-cran-rmarkdown, r-cran-skimr, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sdclog_0.5.1-1.ca2404.1_all.deb Size: 99258 MD5sum: 538572a218604b6288bf4546b2efeda6 SHA1: 8865faff91910f8495ebaa0b41c8b35a88d9a0d8 SHA256: c235d2bb2aff9978059cbf044f7861b5f7399477d2cfdb477364dd3ce8c428ea SHA512: f6e4f9aa2c1530061578f76e841a2cffbf4b25ba8b95f44aeddbb51cc5637496491ba0eb11f1ab2da8cd7b8fe6d34a1d7dcbc8413331ff8f83f35b84f206552e Homepage: https://cran.r-project.org/package=sdcLog Description: CRAN Package 'sdcLog' (Tools for Statistical Disclosure Control in Research DataCenters) Tools for researchers to explicitly show that their results comply to rules for statistical disclosure control imposed by research data centers. These tools help in checking descriptive statistics and models and in calculating extreme values that are not individual data. Also included is a simple function to create log files. The methods used here are described in the "Guidelines for the checking of output based on microdata research" by Bond, Brandt, and de Wolf (2015) . Package: r-cran-sdcnway Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 967 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-plyr, r-cran-dplyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-sdcnway_1.0.1-1.ca2404.1_all.deb Size: 805382 MD5sum: 34e74c343ab0dd345c3d7851c3977dc3 SHA1: c8cf9744459c64435fe53b1f21de88f0427c02dd SHA256: 0bd02aeb71388c6e734f3c2035f2a4069cd7b44780644d3effbdd6dd0ad45f37 SHA512: 5ace2716f702e20d8bcaef9e25097befd9e48c613685b343458a97f4a1131e9f54efb50f463b2768eaa14466feef0582c86ed163eb0353206cd7ee0b61eb05ff Homepage: https://cran.r-project.org/package=SDCNway Description: CRAN Package 'SDCNway' (Tools to Evaluate Disclosure Risk) Tools for calculating disclosure risk measures for microdata, including record-level and file-level measures. The record-level disclosure risk is estimated primarily using exhaustive tabulation. The file-level disclosure risk is estimated by fitting loglinear models on the observed sample counts in cells formed by key variables and their interactions. Funded by the National Center for Education Statistics. See Skinner and Shlomo (2008) for a description of the file-level risk measures and the loglinear model approach. Package: r-cran-sdcspatial Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-sf, r-cran-fnn Filename: pool/dists/noble/main/r-cran-sdcspatial_0.5.2-1.ca2404.1_all.deb Size: 1404228 MD5sum: da94ff01c7f27c90070b2709108c730b SHA1: 8a77ce82df920d9800100e3940875a634391070c SHA256: 3530af059e738bd7273a7d8438fe2f4edf5fc3fed91261cc22c9dee265d8c25e SHA512: c0f289b0d776ab13cad275c5db2292c93666e20954ed98f9584e3b5b3ff98d1af601201e48324b637d3bf9bfa99c78900703ae3c26ba20e2780381c5eba77198 Homepage: https://cran.r-project.org/package=sdcSpatial Description: CRAN Package 'sdcSpatial' (Statistical Disclosure Control for Spatial Data) Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) , continuous variables by de Wolf and de Jonge (2018) , suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) . Package: r-cran-sddr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-reticulate, r-cran-spdep, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sddr_0.1.1-1.ca2404.1_all.deb Size: 94560 MD5sum: 4ebd86014dcb11d3b08d6975d721754f SHA1: 3f522fdb9b4a4f91c80344232b0c24118607904d SHA256: 03781d40ca509de2a70d6f872ae3391c6621359a41a1b1196b7c556f58bafd93 SHA512: 8317e15517626695eae5b1f501002858655543d56aa3e3bdf63c710b65eea27e2947a116d9b6dd3f35bbed12ca5ac3b23d2136c505446083b1d984fda10b0e95 Homepage: https://cran.r-project.org/package=sddr Description: CRAN Package 'sddr' (Spatial Distribution Dynamics) A tidy toolkit for distribution dynamics: analysing how a cross-sectional distribution of values evolves over time and where it settles in the long run. Provides discrete-time, spatial, rank and local indicator of spatial association ('LISA') Markov transition estimation, ergodic analysis (steady-state, mean first passage and sojourn times), rank-mobility measures (Kendall's tau and the Theta statistic) and Markov mobility indices. Methods use long-format 'id'/'time'/'value' data rather than transition matrices and build on the distribution-dynamics literature (Quah (1993); Rey (2001) ). Results are validated for numerical parity against the reference 'giddy' library. Package: r-cran-sdear Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-optisolve, r-cran-dear, r-cran-foreach Suggests: r-cran-doparallel Filename: pool/dists/noble/main/r-cran-sdear_1.1.1-1.ca2404.1_all.deb Size: 301860 MD5sum: 3fbf4bb61a4b45dca37a441e49a11b3c SHA1: bd7d8223b0a346e057f0bdc23931abc41b5b7348 SHA256: f18e3da674f7b84e85a43bcb9f957c7a798e252525af281835a3e65c43d37409 SHA512: 560dfe4db86899e0dae2281b7d2f13f42aee4076fe9ff7d9f266f4ae01e2eb70647a75636d4931e3809c7655522cf0315357856f69c5d9dab4565b123126b344 Homepage: https://cran.r-project.org/package=SdeaR Description: CRAN Package 'SdeaR' (Stochastic Data Envelopment Analysis) Set of functions for Stochastic Data Envelopment Analysis. Chance constrained versions of radial, directional and additive DEA models are implemented, as long as super-efficiency models. See: Cooper, W.W.; Deng, H.; Huang, Z.; Li, S.X. (2002). , Bolós, V.J.; Benítez, R.; Coll-Serrano, V. (2024) . Package: r-cran-sdefsr Architecture: all Version: 0.7.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sdefsr_0.7.22-1.ca2404.1_all.deb Size: 773244 MD5sum: 02e3d104670a9ba8c986464816787a6f SHA1: 0fab4ac3988ea29caa92e3acbe051f5ff556f202 SHA256: 0d746e9fe6579e3ecb4c90ff4b99a24a6b7bc29da3f5aa8f2f63ff7daf385e85 SHA512: 3a6d4f97d870d856d4ac8c794d6f1e6ba55d5fcb367c050021554ee32df1aef8a59761a256e933acfacb4b4742dc1a1181e08d26a1ab328786f0c18e7000954d Homepage: https://cran.r-project.org/package=SDEFSR Description: CRAN Package 'SDEFSR' (Subgroup Discovery with Evolutionary Fuzzy Systems) Implementation of evolutionary fuzzy systems for the data mining task called "subgroup discovery". In particular, the algorithms presented in this package are: M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) M. J. del Jesus, P. Gonzalez, F. Herrera (2007) C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in KEEL, ARFF and CSV format and also with data.frame objects. Package: r-cran-sdf.test Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dtt Suggests: r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sdf.test_0.0.1.0-1.ca2404.1_all.deb Size: 320896 MD5sum: 7a659ba71170f5aad766c727f891a843 SHA1: fc227126b722d6b40af99cd0d7e26e15f79bfad0 SHA256: dd42a3ef5c180a5254b90e9b5d79354cb161f28700e549eb31e8173234852b23 SHA512: a50aa81cc12dd43eb4a8c77061a2343d75efa957cf2276041f2b2efa6060fb06ad1cf6e02c793ac6a9d17355d509230def1e1d9ffc07979c3795c89066d15d6f Homepage: https://cran.r-project.org/package=sdf.test Description: CRAN Package 'sdf.test' (Nonparametric Two Sample Test for Equality of Spectral Densities) Nonparametric method for testing the equality of the spectral densities of two time series of possibly different lengths. The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), . Package: r-cran-sdgdetector Architecture: all Version: 2.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-ggplot2, r-cran-tidyr, r-cran-rnaturalearth, r-cran-scales, r-cran-magick Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sdgdetector_2.7.3-1.ca2404.1_all.deb Size: 1776724 MD5sum: 15ddb294f3ee047cf322de758d8f74a4 SHA1: 4e69174213c33c81a0299225a55f30d069b06130 SHA256: 850952b8947edd74a44ff010ac823cc72a36396dd17f8548fba3a74f35df8882 SHA512: c7ceb0f57dc4202415713c5f51840fecda3fb265afd44bef8d410762f883716ca8c3fc85a61b4b8d6cef5a555284add77c6036aa5edfa39c17c5431064ce2e31 Homepage: https://cran.r-project.org/package=SDGdetector Description: CRAN Package 'SDGdetector' (Detect SDGs and Targets in Text) Identify 17 Sustainable Development Goals and associated 169 targets in text. Package: r-cran-sdglm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sdglm_0.4.0-1.ca2404.1_all.deb Size: 67890 MD5sum: faa4a9d1cb959faf584fe6de138bd46f SHA1: d537bef1d53877986a7d17275bc2ac9a5f8c3685 SHA256: 30983f3df99db9a25bb5b36d8d4d94f3e47bf337f1a246b002c2553c52428435 SHA512: 1cae1a4133cdc2728699d4d540508f4cfce9eaaadea825e80ae9bfe674076da16d22ca9ebd0f267947a4051880c5516a9bbbbd7334fa9e83a6bdafceff6bbc55 Homepage: https://cran.r-project.org/package=SDGLM Description: CRAN Package 'SDGLM' (Scalable Bayesian Inference for Dynamic Generalized LinearModels) Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025). Package: r-cran-sdi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-topsis, r-cran-liver Filename: pool/dists/noble/main/r-cran-sdi_0.1.0-1.ca2404.1_all.deb Size: 39254 MD5sum: 8c442146fcacf9ad118b2fd6ab28d22c SHA1: 027f8347341f3893c0dd46fa552257def750a3ee SHA256: a1a6edeee612cddf8daa333eec3bb72573102b80caa540c75285084299ceb8dc SHA512: ac77dfd6fbce85308b838d354ca6110bcd4388d853f6a40e66175e38039e1325f6fd11faa08578fe86794db22fb68868ddeb4b397f6e8353a993c4e2443e11a8 Homepage: https://cran.r-project.org/package=SDI Description: CRAN Package 'SDI' (Slow Digestibility Index) The Slow Digestibility Index (SDI) is a tool that helps users evaluate the slow-digestion properties of crops or food matrices by combining multiple factors into a single score. It considers parameters related to starch composition [total starch (TS), amylose/amylopectin ratio (Aratio), total amylose content (TAC), and total amylopectin content (TAPC)], starch digestibility [rapidly digestible starch (RDS), slowly digestible starch (SDS) and resistant starch (RS)], structural properties [relative crystallinity (RC)], non-starch components [total protein, total oil content (TOC), and total phenolic content (TPC)], and pasting behaviour [peak viscosity (PV), pasting temperature (PT), holding strength (HS), and final viscosity (FV)].The SDI is flexible and allows users to calculate the index using all parameters or only selected ones, depending on the data available. Users can also compute a starch-based SDI (using only starch-related parameters) or a principal component analysis (PCA)-based SDI, where weights are determined automatically from the data. Thus, the SDI provides a simple way to compare and rank crops or food samples based on their slow digestion potential. The package implements SDI(),starchSDI(), genSDI(), scoreSDI(), and pcaSDI() for estimating slow digestibility index using predefined weighted TOPSIS, starch-specific TOPSIS, user-defined weighted TOPSIS, score-based normalization, and PCA based approaches, respectively. The package has been developed using the algorithm of Pandey et al. (2026) . Package: r-cran-sdlfilter Architecture: all Version: 2.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-geosphere, r-cran-data.table, r-cran-gridextra, r-cran-ggmap, r-cran-maps, r-cran-pracma, r-cran-lubridate, r-cran-dplyr, r-cran-emmeans, r-cran-sf, r-cran-stars, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-sdlfilter_2.3.3-1.ca2404.1_all.deb Size: 1111350 MD5sum: d213f6bda9ef6aae6f1e2b54c3fc4ae7 SHA1: 142f3a411389eecd600d630bdd89e429a51712b5 SHA256: 3a066836c63eb10d1e35350a57e5646a1bec7f082f19e2f57b5a8c1e1d24e23a SHA512: 4315ba7d6375a15a6455bbb7f3d61ebc5ee046fba1a8720965e3f8a17f4152fb5035695a6de2cedaf46b74c1f2c5be8b77ba7437392173fabef484b12c178b28 Homepage: https://cran.r-project.org/package=SDLfilter Description: CRAN Package 'SDLfilter' (Filtering and Assessing the Sample Size of Tracking Data) Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) and Shimada et al. (2016) . Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (2021) . 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The package provides a set of functions for a complete analysis of integer-valued data, where the dependent variable is assumed to follow a modified SDL distribution. This regression model is useful for the analysis of integer-valued data and experimental studies in which paired discrete observations are collected. 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These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) ) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) ). 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Package: r-cran-sea Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8586 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-kscorrect, r-cran-data.table Filename: pool/dists/noble/main/r-cran-sea_2.0.1-1.ca2404.1_all.deb Size: 7493754 MD5sum: 07a5a576d1747ac1d60be4520395f130 SHA1: eaeb493b9237bf3f2b5eca5576db2e6b813eecdc SHA256: 444b83d7f758dda13c4a674f53d873637174087e32252052055c5ee5a00eb517 SHA512: 4da49e32906a199a1cdcc54bdd491b9492d1e815df930b20c51ee19c17620cf34db0d83233f571563438699121329af84faf8d03174ab2bf8bc3d64d37a229c4 Homepage: https://cran.r-project.org/package=SEA Description: CRAN Package 'SEA' (Segregation Analysis) A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, ). Package: r-cran-seacarb Architecture: all Version: 3.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-oce, r-cran-gsw, r-cran-solvesaphe Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seacarb_3.4.1-1.ca2404.1_all.deb Size: 826322 MD5sum: 4cf36cffdb7b76f5e0b05db5094e7ac5 SHA1: 05987b0b7d30e2bc9f8b9eae4152e7171350087e SHA256: 677886922266c71ac787472258218b0d28230eae0e2ac9abc0b37661915a4304 SHA512: 8a99d6abbbf7d174914fff622e1397b3a5e391d4d8db355505a9d33b73b49db67e6ce69ddd5a2c904a88d5dda325176555571de95972aa15d4ecbf80caeec5ae Homepage: https://cran.r-project.org/package=seacarb Description: CRAN Package 'seacarb' (Seawater Carbonate Chemistry) Calculates parameters of the seawater carbonate system and assists in design of ocean acidification perturbation experiments. Package: r-cran-seagle Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3012 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-compquadform Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-seagle_1.0.1-1.ca2404.1_all.deb Size: 401550 MD5sum: b96ef7796038ad014c912bbbfbb7da93 SHA1: 1a206ae19ceb948386d2e1bb25ce5bc3060a044e SHA256: e02d51aa4e53afa6d8af83afe42947db006563b6d45ad481968eaa6d7f7c8862 SHA512: 300bafb4472e40b6e06b402826ea9e164edec29a26d10c46b4a64d5d56a806a82a2a82bba11016e5f446764d47c95378079be3c24b0449b8b26ba28afe9dd05c Homepage: https://cran.r-project.org/package=SEAGLE Description: CRAN Package 'SEAGLE' (Scalable Exact Algorithm for Large-Scale Set-BasedGene-Environment Interaction Tests) The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in GxE assessment, especially for set-based GxE variance component (VC) tests, a widely used strategy to boost overall GxE signals and to evaluate the joint GxE effect of multiple variants from a biologically meaningful unit (e.g., gene). We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern matrix computations to achieve the same “exact” results as the original GxE VC tests, and does not impose additional assumptions nor relies on approximations. 'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable on standard laptops, and does not require specialized equipment. The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+) . Package: r-cran-seagraphs Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfnetworks, r-cran-sf, r-cran-terra, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-leaflet.extras2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seagraphs_0.1.3-1.ca2404.1_all.deb Size: 1507722 MD5sum: 52a9c63c9e214f1f8e960a56169b2ddb SHA1: d2a3b7a022449efa22f7b631743c5ac1e131356d SHA256: d7a2cbace50ced5e9f13698cc6f9ed1503abc43e91df898785a8352d03e0cc43 SHA512: 656afdd4023576688005c885bdd683701c04b6bf0de65b6273103ba7495f530c46fe13bf75e5bd7fbd61bbe374c967530ba96d1c7827b48d86631910fb8cd4ed Homepage: https://cran.r-project.org/package=SeaGraphs Description: CRAN Package 'SeaGraphs' (Sea Currents to Connectivity Transformation) Transformation of sea currents to connectivity data. Two files of horizontal and vertical currents flows are transformed into connectivity data in the form of 'sfnetwork', shapefile, edge list and adjacency matrix. An application example is shown at Nagkoulis et al. (2025) . Package: r-cran-seahors Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-shinythemes, r-cran-shinyjs, r-cran-shinywidgets, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-plotly, r-cran-dt, r-cran-mass, r-cran-readxl, r-cran-raster, r-cran-stringr, r-cran-viridis, r-cran-rmarkdown, r-cran-htmlwidgets Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-seahors_1.9.0-1.ca2404.1_all.deb Size: 418394 MD5sum: f21e7c412cf62116e371d9f15d78a09f SHA1: e0c790e5469d7133e123cca0b28a1f6710acc425 SHA256: 66617d83027c02cc7c9f859a12680f516b2c34ef428fd28c5944ce95f9651d89 SHA512: e9806d4baa94e86d9ed22eb48f76ebab3bdf152bef1cd85c4cc5b915c1d9a8360c7f64c45ceb4b6ed77d52d159e54a2c085fb8051dfc6ff5a1a291f048e8f78a Homepage: https://cran.r-project.org/package=SEAHORS Description: CRAN Package 'SEAHORS' (Spatial Exploration of ArcHaeological Objects in R Shiny) An R 'Shiny' application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible. Documentation about 'SEAHORS' is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, ). Package: r-cran-seairmobility Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seairmobility_0.1.0-1.ca2404.1_all.deb Size: 70734 MD5sum: 409ebcc4fb2b03b177b519dc6afe9f0e SHA1: 442c6347e2d678a60a6f442c50e9f5fd5998049a SHA256: 46da8894404f0ad948e67d53b3e14161bcea05589cb58fded770542808e11733 SHA512: f12a7e016e6e50828432a0d98bf40ebeaf0cdd7a9fa8612d2675eceb9f2274801c8e6cb883e37e60fe0b7afc8a1762db6eb36d3a23618eb3a12d57aca6b04411 Homepage: https://cran.r-project.org/package=seairmobility Description: CRAN Package 'seairmobility' (Mobility-Based SEAIR Epidemic Models) Tools for simulating, analysing, and fitting mobility-based SEAIR (Susceptible-Exposed-Asymptomatic-Infectious-Recovered) compartmental epidemic models with heterogeneous individual mobility. Each individual carries a fixed mobility trait that scales susceptibility and infectiousness through a rank-one kernel, extending the mobility-based compartmental framework of Jiang et al. (2025) by adding a latent stage and a behavioural split between asymptomatic and symptomatic infectiousness. Provides a numerical solver for the underlying partial differential equation system, closed-form computation of the basic reproduction number R0 and the final epidemic size, and a parametric least-squares routine for recovering the mobility distribution from an observed aggregate symptomatic time series. Package: r-cran-sealasso Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars Filename: pool/dists/noble/main/r-cran-sealasso_0.1-3-1.ca2404.1_all.deb Size: 158294 MD5sum: 1b5319d30a20f790863e48df0f9ece27 SHA1: 2fea6c7ff2d6bf4c6d09620bd3636a9615fc9187 SHA256: 3450a66e37695b8e8a8ae6e85a0b4707649673ae352fa8eb7875af67d1eb7843 SHA512: 87624369bc27683d8af7676e0ff0cd7fc04df9e223a7cf33ac7fa13e5b09f95689833e3a9b75cb60b4c62d23b84fb927d21311c8c8d331bf14f94a94be1fdb2d Homepage: https://cran.r-project.org/package=sealasso Description: CRAN Package 'sealasso' (Standard Error Adjusted Adaptive Lasso) Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern. Package: r-cran-sealeveltools Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-sealeveltools_0.3.0-1.ca2404.1_all.deb Size: 17934 MD5sum: 0f9089edccf0ecfafa1991bba3acf7cd SHA1: 50fb1ca273cf34660110d69fe3bd786fd971d052 SHA256: 0a8cae645e5380ba6434e4973e577c699ae8a5b8974e8a75f33b935513c500c0 SHA512: e42006927f7c740491131923b600276e179f973089e7135dfb3393ca2d0723897c5c327da711bb1d9a9110a9ee73cef465c4f34b7ec8e34753bacbd3d31757a4 Homepage: https://cran.r-project.org/package=sealeveltools Description: CRAN Package 'sealeveltools' (Sea Level Adjustment and Coastal Terrain Modeling) Simulates sea-level rise and fall from raster-based elevation models through functions for relative vertical datum transformation of Digital Terrain Models (DTMs) and integration with bathymetric data to produce continuous terrestrial–marine Digital Elevation Models (DEMs). Supports coastal exposure modeling, paleogeographic reconstruction, submerged landscape analysis, and climate change impact assessment. Optional gap-filling and threshold-based terrain filtering facilitate reconstruction of incomplete elevation surfaces and scenario-based landscape simulation. Applications span coastal engineering, geomorphology, archaeology, environmental modeling, and geospatial analysis. Package: r-cran-seamless Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-ggplot2, r-cran-optparse, r-cran-data.table Suggests: r-cran-ggtern, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-seamless_0.1.1-1.ca2404.1_all.deb Size: 3022362 MD5sum: 59917d2f13c438c27e6859b1bae3783d SHA1: a44db784ce94d2d463c0b2ea56d27ae6836fe269 SHA256: f2d66450ca8b6e4e4af2bdf247a6ae335450b26c55f48a8b07d3250a066f730a SHA512: 0ba0c8b25dd4801e0d596bb1a7547eec02bc75d023a513ec0d257e6901ea556829d2ee7e5b4e44a39e3eae9eff84da623664545fb3e0e9a865763665cc358578 Homepage: https://cran.r-project.org/package=seAMLess Description: CRAN Package 'seAMLess' (A Single Cell Transcriptomics Based Deconvolution Pipeline forLeukemia) Given a bulk transcriptomic (RNA-seq) sample of an Myeloid Leukemia patient calculates immune composition and drug resistance for different small-molecule inhibitors. Published in . Package: r-cran-searchanalyzer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-ggplot2, r-cran-openxlsx, r-cran-stringdist, r-cran-lubridate, r-cran-digest Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr, r-cran-shiny, r-cran-shinydashboard, r-cran-flexdashboard, r-cran-plotly, r-cran-dt, r-cran-boot, r-cran-pwr, r-cran-progress, r-cran-rmarkdown, r-cran-knitr, r-cran-glue, r-cran-rentrez, r-cran-xml2, r-cran-readr, r-cran-jsonlite, r-cran-revtools, r-cran-tidyr, r-cran-patchwork, r-cran-metagear Filename: pool/dists/noble/main/r-cran-searchanalyzer_0.1.0-1.ca2404.1_all.deb Size: 844204 MD5sum: d25f265e8ef1ab7cd2b4305713ed7e46 SHA1: 1d00efb1fc8417cdcae961e426bac3a261bfe0ce SHA256: 3a988a5000d16098a88367484a4fb142e5f3affe4e005e030d5c7848d62ea4fc SHA512: 4a6ee104347a63f5a38010ca31bd32974a1edfa972ee32fb66f0bc7d54c005b3b37184308ff54879a9b1ece38dbec5a30ddd930bb7ab74810f4e4aad42c948a7 Homepage: https://cran.r-project.org/package=searchAnalyzeR Description: CRAN Package 'searchAnalyzeR' (Advanced Analytics and Testing Framework for Systematic ReviewSearch Strategies) Provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized 'PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including 'CSV', 'Excel', 'RIS', 'BibTeX', and 'EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to 'PubMed' for direct database searching and real-time strategy comparison using the 'E-utilities' 'API'. Enhanced with bootstrap comparison methods, 'McNemar' test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for 'PRISMA' guidelines, and Sampson et al. (2006) for systematic review search methodology. Package: r-cran-searcher Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-searcher_0.0.7-1.ca2404.1_all.deb Size: 52340 MD5sum: eba070eee96433c92bf5c54c2b92b7c0 SHA1: 44a12a91222d2add931e258d11d883e639106326 SHA256: e8904a344fbb911858a1ea71819bf7d192d2a5b493fec82f01a7ce66b8cbd407 SHA512: bf3466c58aa1dbc7be5f35ee5f2c47b9d71ae52b816da920f1c5e7d2023957282730df91d35797ee85d9306c6868aa4c7c2387dba5b5c75ea30f2a4d3c62cea7 Homepage: https://cran.r-project.org/package=searcher Description: CRAN Package 'searcher' (Query Search Interfaces) Provides a search interface to look up terms on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek', 'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'. Upon searching, a browser window will open with the aforementioned search results. Package: r-cran-searchlight Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3249 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carbayes, r-cran-cli, r-cran-commonmark, r-cran-digest, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-mass, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-spdep, r-cran-suncalc, r-cran-posterior, r-cran-ggplot2, r-cran-tidyr, r-cran-tibble, r-cran-withr, r-cran-xml2 Suggests: r-cran-testthat, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-lintr, r-cran-spelling, r-cran-vdiffr, r-cran-zip Filename: pool/dists/noble/main/r-cran-searchlight_0.1.0-1.ca2404.1_all.deb Size: 1734442 MD5sum: e965da1746af994cfa7448d28078d1d4 SHA1: aa871cc1ab2ec2472376f5abff87200a96763f2d SHA256: 3a7210ee2ba00be48e0c990a93dc969ac4fca9e6a651e814c2bf3dca5ca2485c SHA512: ec6aeabcb1bbe510271f9e665887d8f50750792e4a61bf50fe8b430800044e357c12ddf4ae2c05a7c028c8ff756a21c5a660c59c9a7f68b9df081e8716d5b5e4 Homepage: https://cran.r-project.org/package=searchlight Description: CRAN Package 'searchlight' (Audited Analysis of Police Stop and Search Records) Acquire and audit public police stop and search archives for England and Wales, retaining provenance and explicit coverage information. Designed for exposure-based ethnic disparity analysis with separately reported sampling and assumption uncertainty. Contains public sector information licensed under the Open Government Licence v3.0. Package: r-cran-sears Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boin Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sears_0.1.0-1.ca2404.1_all.deb Size: 43460 MD5sum: 3e75303dfdc754fc934e4b13d4cfb9be SHA1: 41ebb62908735880412e3d2b95799eb9c4de07ad SHA256: fca95a4709f0f70ac1a5e6bb1ef47acfcc8a4d4e7326ec483146684b041e20a4 SHA512: 50f6f4fe044c5a20b5ea6528da3fe3237892bc3c312a432160fd17106705772a9bcddb5f14f864486007ccf4a5f13c818e1677c4ecb00e41438b49ee4861e833 Homepage: https://cran.r-project.org/package=SEARS Description: CRAN Package 'SEARS' (Seamless Dose Escalation/Expansion with Adaptive RandomizationScheme) A seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy. Package: r-cran-seasepi Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-ngspatial Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seasepi_0.0.3-1.ca2404.1_all.deb Size: 132564 MD5sum: bb88c5e8ae4534479fbb9343330f0990 SHA1: d6615424d39fea200e33e47522ad81edabd84d3a SHA256: 3a566cb8046d851f49bf944a50211680b77c6856988fcd4a5d9f5945975be405 SHA512: b923a293993a7347fa33e2a9be31349d4c6a96147518509adcf7f0b652798011412f8b1fc32f60d33422a03e5155f20e0e0f973958b5045da0c102d3d48b5585 Homepage: https://cran.r-project.org/package=SeasEpi Description: CRAN Package 'SeasEpi' (Spatiotemporal Modeling of Seasonal Infectious Disease) Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations. Package: r-cran-season Architecture: all Version: 0.3.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-survival, r-cran-coda, r-cran-stringr Suggests: r-cran-dlnm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-season_0.3.16-1.ca2404.1_all.deb Size: 513214 MD5sum: f25b3d3af1318f0d073f54360faf360a SHA1: ea91d3c4bb5f01b6a8a3e3f95978c67d94a75e15 SHA256: f7912484dbcff7b54a9acf69ca1c1b2dc05cbb0e4f2a8adab88d03e996630e57 SHA512: 25b225fa3bb5dc44ea17872a3f42d3617613c871fbc4067c245b344454fa90765524025bfc98fd79493b556c0aae45fb89ef89074838a1f481134db490fe0807 Homepage: https://cran.r-project.org/package=season Description: CRAN Package 'season' (Seasonal Analysis of Health Data) Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code. Package: r-cran-seasonal Architecture: all Version: 1.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-x13binary Suggests: r-cran-seasonalview, r-cran-generics, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-seasonal_1.11.0-1.ca2404.1_all.deb Size: 567142 MD5sum: 2759285990c042ee9785ce5bd9e73d7b SHA1: 550515c4f2386f24b18a5223836fdb3dc9210810 SHA256: 753982319b6f36a630a250d24986f33b0ef89345b6f7aa06b1a939a4c244bc50 SHA512: 9885e4d6c6bd7545dc77756ff7ecd545727f1c3e9a756ec501d776354a0be2f78fd44fe536abff30b1304315b4fc3d2753476cedae15d5f891d35584edd61ce1 Homepage: https://cran.r-project.org/package=seasonal Description: CRAN Package 'seasonal' (R Interface to X-13-ARIMA-SEATS) Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package. Package: r-cran-seasonalityplot Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-quantmod, r-cran-dygraphs, r-cran-plotrix, r-cran-htmltools, r-cran-zoo, r-cran-lubridate, r-cran-crypto2, r-cran-ttr, r-cran-assertthat Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seasonalityplot_1.3.1-1.ca2404.1_all.deb Size: 939724 MD5sum: fe90be128c3ec60c0347a3af7b998330 SHA1: be2e83e939f25b8e087aa1110d770213aa638d25 SHA256: fc65858b4090d3f4e5f18ba1bdaf8878a0486e82313060105b89245b278cfa89 SHA512: ceb827a4d9026c8f3ab5e5c50ad4c5d9c5f2f8eb2c7e90c7112d75389f6580dc87682f0b38be9dcaa314d0de08adf908bd25e0e7216a03f5f3980fe4112c89d8 Homepage: https://cran.r-project.org/package=seasonalityPlot Description: CRAN Package 'seasonalityPlot' (Seasonality Variation Plots of Stock Prices and Cryptocurrencies) The price action at any given time is determined by investor sentiment and market conditions. Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the 'quantmod' package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc). Package: r-cran-seasonalview Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seasonal, r-cran-shinydashboard, r-cran-shiny, r-cran-dygraphs, r-cran-htmlwidgets, r-cran-openxlsx, r-cran-xtable, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-seasonalview_1.0.0-1.ca2404.1_all.deb Size: 88278 MD5sum: aa99bd4b2abf1d6a2b86271bbde1eb2b SHA1: d3fc8a44e1dcebd60ad2f728d082532f0d229db2 SHA256: 379ea569c2eadefc5e375202ea9a852a2920f9f42fdf20014a28bfaa6d9a9295 SHA512: 2d7a14f3ef1e972d5f810919419645dcec4687bd797d50daa552e222198926cdd0579ada5be97f3f3f8fce897d4d08f2a0a9e92d983e7c6092418b2ee039aeb5 Homepage: https://cran.r-project.org/package=seasonalview Description: CRAN Package 'seasonalview' (Graphical User Interface for Seasonal Adjustment) A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. Package: r-cran-seasonalytics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seastests Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seasonalytics_0.1.0-1.ca2404.1_all.deb Size: 16022 MD5sum: 56e41e99c6cac4b7bd241045cd4235bd SHA1: 454276139b182f48ddf856a359063b83d86bcb11 SHA256: ade88daedeb49ff82893a2b4b4c75b575cf50b1666c4c3701e1a49eb3c214529 SHA512: 8d6761d4840e2f9a88a9e992d43c416c8159c12b460a7599360a05a52cedaac4b00a1a892738b62c91f0cae75e243b93171f6848eabd26c2b52206be2296cc95 Homepage: https://cran.r-project.org/package=seasonalytics Description: CRAN Package 'seasonalytics' (Compute Seasonality Index, Seasonalized and Deseaonalised theTime Series Data) The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies — for each season (e.g. month, quarter, week) — the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data. Package: r-cran-seasonder Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bit64, r-cran-bitops, r-cran-constants, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-pracma, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uuid, r-cran-whisker, r-cran-yaml, r-cran-zoo Suggests: r-cran-here, r-cran-mockthat, r-cran-testthat, r-cran-openssl, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seasonder_0.2.8-1.ca2404.1_all.deb Size: 4811640 MD5sum: dac10ca47be5b59180493c9b29944ece SHA1: 0db54958342886f2ec41148da6f3cbdda1d2110a SHA256: 823f79256732d0547b63fcd1cc0369a3952d446a17f4ce307777179625607755 SHA512: f6cff0e5c64b8992a54fbe8391738d63908ee3b6a79be24e1dfe602dbab6eebacc746cc764bb49f787c4746f74afbb1cfc45486ed08c4c029096e4c20df99000 Homepage: https://cran.r-project.org/package=SeaSondeR Description: CRAN Package 'SeaSondeR' (Radial Metrics from SeaSonde HF-Radar Data) Read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. Implementation is based in technical manuals, publications and patents, please refer to the following documents for more information: Barrick and Lipa (1999) ; CODAR Ocean Sensors (2002) ; Lipa et al. (2006) ; Paolo et al. (2007) ; CODAR Ocean Sensors (2009a) ; CODAR Ocean Sensors (2009b) ; CODAR Ocean Sensors (2016a) ; CODAR Ocean Sensors (2016b) ; CODAR Ocean Sensors (2016c) ; Bushnell and Worthington (2022) . Package: r-cran-seastests Architecture: all Version: 0.15.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-forecast Filename: pool/dists/noble/main/r-cran-seastests_0.15.4-1.ca2404.1_all.deb Size: 74252 MD5sum: ce1ac1bc647b43a4699ea3880a8748f8 SHA1: 79520b5e5fca8914186ead728615c1b5472066b6 SHA256: 3cbd77f88816eb47d5274a54c7ec6ded89b6c0cae25d8a6c56ee7c109090e65f SHA512: e6114b9195cdde4f89b34d0bf4a4b54e62387f70ffb2ddd1ba44cb809cf029575fe0ced094f88c319e3621c3af24382f0fef46d5d09f4553014a4de9ec415a88 Homepage: https://cran.r-project.org/package=seastests Description: CRAN Package 'seastests' (Seasonality Tests) An overall test for seasonality of a given time series in addition to a set of individual seasonality tests as described by Ollech and Webel (forthcoming): An overall seasonality test. Bundesbank Discussion Paper. Package: r-cran-seattleopendata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-seattleopendata_0.1.0-1.ca2404.1_all.deb Size: 50314 MD5sum: fc97c026a73e72f26c0cfb3aa073d44e SHA1: f33fcd6d08d344a8abb25b79676c3b2fbfa3b904 SHA256: 10db1625eff374268831716143fb8beb548cf1810cd121d479dcc192fab1b3db SHA512: 924dfb320130ea172f2d521deb4a31b4a2dd37a093e863124556232db69b5fd72118adfb38a9025a3c87956f610bf8c1f99b39e1e8776d0126b0fe30ba07799a Homepage: https://cran.r-project.org/package=SeattleOpenData Description: CRAN Package 'SeattleOpenData' (A Lightweight Interface to Seattle Open Data APIs) Provides a unified set of helper functions to access datasets from the Seattle Open Data platform . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package provides a consistent interface for discovering and downloading datasets from the Seattle Open Data Portal using human-readable dataset keys or official Socrata dataset identifiers. Package: r-cran-seaval Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19674 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggplotify, r-cran-lifecycle, r-cran-maps, r-cran-ncdf4, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-scales, r-cran-stringr Filename: pool/dists/noble/main/r-cran-seaval_1.2.0-1.ca2404.1_all.deb Size: 3260004 MD5sum: 5e3e939d089b29ce3c02aaf3a4fb8777 SHA1: 69d40ea6fc7bd5f7e9aacb592f13058f676c022e SHA256: ba2385616a3474edd747ca547df452d52e652c09995717aab0af4259aae8c64b SHA512: 613f4dd99a1009a1f40a7c037ae3aabe32dff9423f722d6fa3a7b4cdb747933d1eaac29104ec868cd76b87241c586016438c0055e1f2cf157d803441b0f09b52 Homepage: https://cran.r-project.org/package=SeaVal Description: CRAN Package 'SeaVal' (Validation of Seasonal Weather Forecasts) Provides tools for processing and evaluating seasonal weather forecasts, with an emphasis on tercile forecasts. We follow the World Meteorological Organization's "Guidance on Verification of Operational Seasonal Climate Forecasts", S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: ). The development was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869730 (CONFER). A comprehensive online tutorial is available at . Package: r-cran-seawaveq Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rms, r-cran-lubridate, r-cran-reshape2, r-cran-plyr Suggests: r-cran-waterdata Filename: pool/dists/noble/main/r-cran-seawaveq_2.0.2-1.ca2404.1_all.deb Size: 1003502 MD5sum: a49c7530ed5808859b9514c8701e56e9 SHA1: 5453ab1341f936b8ab001bb8107306ff8167809a SHA256: 992836a9c1e00d1118712aafd40e71a6b85d8df3da2fae23a3a6e12cfd486a64 SHA512: d089b53d1f97a41a58b038fe3d6d39dbffc9dfc26f417f4b7e6ada71c93d7c0cc418d3a170cde7afd85a43b77d813b7cc15b245ae3fb95595bd652e0d96f9ce0 Homepage: https://cran.r-project.org/package=seawaveQ Description: CRAN Package 'seawaveQ' (SEAWAVE-Q Model) A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables. See Ryberg and York, 2020, . Package: r-cran-sebkc Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5411 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-gstat Filename: pool/dists/noble/main/r-cran-sebkc_1.0-6-1.ca2404.1_all.deb Size: 3514456 MD5sum: f5ed42291cf595156c37ed881644972d SHA1: 864c8e6faee2f17434a578bc10f09865cfed020c SHA256: ddbbca00d67d659b7d8cbe9f56e00c8e7df127de2034d829e1e85d4ff44d15b4 SHA512: 86fa848b9bbc3b5854f8c030099fba6ff7aaa965d5bc90bae51d968f78bfbf82884b7e4aeb6f5177be2ce9c171cf7110b2f9597585a0e16955080097a4e71d7d Homepage: https://cran.r-project.org/package=sebkc Description: CRAN Package 'sebkc' (Surface Energy Balance and Crop Coefficient EvapotranspirationEstimation) Computes and integrates surface energy balance components of evapotranspiration (ET), sensible heat (H), soil heat flux (G) and net radiation (Rn) into the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 (FAO-56) water balance model. The package can perform single crop coefficient (Kc), dual Kc and the integration of thermal-based evaporative fractions in a water balance model. The surface energy balance models include Two-Source Surface Energy Balance (TSEB) models, namely the Priestley-Taylor TSEB (TSEB-PT), the Penman-Monteith TSEB (TSEB-PM) and TSEB-Parallel, as well as One-Source Surface Energy Balance (OSEB) models, namely the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), Surface Energy Balance Index (SEBI), Simplified Surface Energy Balance (SSEB) and Surface Energy Balance System (SEBS). Methods are described in Allen et al. (2007) and Bastiaanssen et al. (1998) . Package: r-cran-sebr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fields, r-cran-gpgp, r-cran-knitr, r-cran-mass, r-cran-plyr, r-cran-quantreg, r-cran-rmarkdown, r-cran-spikeslabgam, r-cran-statmod Filename: pool/dists/noble/main/r-cran-sebr_1.1.0-1.ca2404.1_all.deb Size: 626390 MD5sum: e0a31cddcfebb66a3e42e14fd1890363 SHA1: ceef3b83f1d77d2ddeea384b4a624f18e589e59b SHA256: f89d73d10e51f6e8a9c72583ccfda545bc398529725c902c928b044ea3793c32 SHA512: 25e6bd85c569529cdf4d3293175d945128bfd51bc89327d405eec9c58733eb3f760d67ad98357877b3987947d594fbd7e2144f59200ca0b464e10c1a976ae72e Homepage: https://cran.r-project.org/package=SeBR Description: CRAN Package 'SeBR' (Semiparametric Bayesian Regression Analysis) Monte Carlo sampling algorithms for semiparametric Bayesian regression analysis. These models feature a nonparametric (unknown) transformation of the data paired with widely-used regression models including linear regression, spline regression, quantile regression, and Gaussian processes. The transformation enables broader applicability of these key models, including for real-valued, positive, and compactly-supported data with challenging distributional features. The samplers prioritize computational scalability and, for most cases, Monte Carlo (not MCMC) sampling for greater efficiency. Details of the methods and algorithms are provided in Kowal and Wu (2024) . Package: r-cran-secdim Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2474 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-geosphere Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-secdim_3.2-1.ca2404.1_all.deb Size: 2394410 MD5sum: d68ed166f876b148aad0dc2d97a4ed07 SHA1: 53cf25bbbc48ad5e377e20373d8d8cfa4a062e13 SHA256: 7583f6c76634103d991696bd0c05accd653c2178b536d6355ff6e22c5829b306 SHA512: 75caa2f872e67cf830827ec191e7b632e1c04eb316cea706f8af97ebe8557095de3342065c24b53a0b76631f484bcb86b68b955e1c6026e29ccfae6752ea289c Homepage: https://cran.r-project.org/package=SecDim Description: CRAN Package 'SecDim' (The Second Dimension of Spatial Association) Most of the current methods explore spatial association using observations at sample locations, which are defined as the first dimension of spatial association (FDA). 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Package: r-cran-secp Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spsl Filename: pool/dists/noble/main/r-cran-secp_0.1.5-1.ca2404.1_all.deb Size: 47140 MD5sum: b842f1769194bd22ca3bb263141f3450 SHA1: dd1a7af8827b1a151bcf4dc164c8591d4fef5592 SHA256: 7a8d9d9a4ebed2b301446f9c3ba696b76c9a6c7563258d00910a6040109d6e7e SHA512: ac823b2a2cded90b3284bcf61792b956ec8b26ccc7fd5c24cc795ea8d1603faff01d8d376a9e4a2191648c63f806af9f7ba802b0e387123449b29cd4910b6391 Homepage: https://cran.r-project.org/package=SECP Description: CRAN Package 'SECP' (Statistical Estimation of Cluster Parameters) Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) . 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The resulting text can be decoded using decode() function and the two numeric keys specified during encryption. Package: r-cran-secrlinear Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-secr, r-cran-mass, r-cran-sp, r-cran-igraph, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-secrdesign, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-secrlinear_1.2.5-1.ca2404.1_all.deb Size: 736476 MD5sum: 592ec1836c77af1c5e42bfd4b98579f9 SHA1: 6f2baababdc5a6d347c360d4368a1c3e96658236 SHA256: be4778da2df6d7965a5017c3b5b1fa3c16b7238af39b26b3489ac256976c3972 SHA512: 7f7fad3a6bb852b61650acdb7be77d460ffc790caa74b8da1f17996de1b9f64fa3c1f8555bfc0897be34a089d4629ded5849299485325b94d03264df257935cc Homepage: https://cran.r-project.org/package=secrlinear Description: CRAN Package 'secrlinear' (Spatially Explicit Capture-Recapture for Linear Habitats) Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'. Package: r-cran-sectorgap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-zoo, r-cran-ggplot2, r-cran-mcmcpack, r-cran-dplyr, r-cran-tidyr, r-cran-tempdisagg Filename: pool/dists/noble/main/r-cran-sectorgap_0.1.0-1.ca2404.1_all.deb Size: 317332 MD5sum: 5436036b6973ba3f8145072180f8457d SHA1: 2d792d098cd9698175c316f9209e693ed16d2a59 SHA256: 39a1f63bdf344dd09bb94749514679f5f9d9bbb751deed57166ba36b1ac54f43 SHA512: 732c1130d391366adefc4819e161812a926d87ddd6288add6761b29175d048954f7d0712a1bb2b845c299acf1a77cca6db5e8eaad3d14327bc4d65f93fb138f5 Homepage: https://cran.r-project.org/package=sectorgap Description: CRAN Package 'sectorgap' (Consistent Economic Trend Cycle Decomposition) Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. 'sectorgap' features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) . Package: r-cran-secutrialr Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-magrittr, r-cran-purrr, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-lubridate Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat, r-cran-tufte, r-cran-igraph Filename: pool/dists/noble/main/r-cran-secutrialr_1.3.3-1.ca2404.1_all.deb Size: 1329250 MD5sum: beb506b71e94fc03102931df9893a5da SHA1: 8dc654f33c30f5fbe171ebad5ac6b835de75f947 SHA256: 6c02a41b53a9ae0da2bb28c2865a7c483cabcaccfc498e539c6151eaf24b28f7 SHA512: d9726a450701df70ab5cd77b648c3a5920f6ca328f1da3de42f543e7ad1390307bd4db30c7e1be6235afa35976b153620c053c906799721313262e158da3e439 Homepage: https://cran.r-project.org/package=secuTrialR Description: CRAN Package 'secuTrialR' (Handling of Data from the Clinical Data Management System'secuTrial') Seamless and standardized interaction with data exported from the clinical data management system (CDMS) 'secuTrial'. 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Package: r-cran-sedproxy Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2968 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sedproxy_0.7.6-1.ca2404.1_all.deb Size: 2392072 MD5sum: dc8ece874faf1d035cd3c8b0700e25bc SHA1: aa8e491061296ca4ea7c373ffa973a4d971a823e SHA256: bc16081add1a9618c317f35134d95e714965c74d8cdd2c8d0ae6c173431bee44 SHA512: a879d1bda82e4d2ad1dc4df9af4587efeb90fd859ca47b5658478fdf12577455c549fff55418b495eef71c4bb01d045c76e7632ccd3d3ac66ae71b53655b1c2f Homepage: https://cran.r-project.org/package=sedproxy Description: CRAN Package 'sedproxy' (Simulation of Sediment Archived Climate Proxy Records) Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) . Package: r-cran-see Architecture: all Version: 0.14.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-correlation, r-cran-datawizard, r-cran-effectsize, r-cran-ggplot2, r-cran-insight, r-cran-modelbased, r-cran-patchwork, r-cran-parameters, r-cran-performance Suggests: r-cran-bh, r-cran-brms, r-cran-collapse, r-cran-curl, r-cran-dharma, r-cran-discovr, r-cran-emmeans, r-cran-factoextra, r-cran-formula, r-cran-ggdag, r-cran-ggdist, r-cran-ggraph, r-cran-ggrepel, r-cran-ggridges, r-cran-ggside, r-cran-glmmtmb, r-cran-httr2, r-cran-lavaan, r-cran-lme4, r-cran-logspline, r-cran-marginaleffects, r-cran-mass, r-cran-mclogit, r-cran-mclust, r-cran-merderiv, r-cran-mgcv, r-cran-metafor, r-cran-nbclust, r-cran-nfactors, r-cran-psych, r-cran-qqplotr, r-cran-randomforest, r-cran-rcppeigen, r-cran-rlang, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-testthat, r-cran-tidygraph, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-see_0.14.2-1.ca2404.1_all.deb Size: 716490 MD5sum: 38a069608a7a9b31047a33a0cdb51375 SHA1: 3192fccc222b3eaa8a02a039d80fa9af1b5692d5 SHA256: 25f4c825fe3c7708c8fa2f4e6892228d59497c48a77a17bcfcf9e97910b499b7 SHA512: 6b82ad6c3a4da19d8d90948d5c1281fba8f4d1b0d0200725b48eb8ab3360383b8678a4c76ba38dc41933b2ab56477a843048ce8788d6a5b71e5c9f6fe84caf03 Homepage: https://cran.r-project.org/package=see Description: CRAN Package 'see' (Model Visualisation Toolbox for 'easystats' and 'ggplot2') Provides plotting utilities supporting packages in the 'easystats' ecosystem () and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on . References: Lüdecke et al. (2021) . Package: r-cran-seeclickfixr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-seeclickfixr_1.1.0-1.ca2404.1_all.deb Size: 51178 MD5sum: 39ca24d72f09c56b955211362f282942 SHA1: a2e53e1e5eddbaf4f1093c50c195dbf27e996a32 SHA256: e6673e6860b26f73118238cf9d4a4c4718d14ae365f566d80af21537185a6d2c SHA512: eeeed35b25e2cfc7e1b4a2e0aa70adb635df83616a75c8edd881297b74647ceba3131be0ce51ca8be75812caa8fbe876699cd2dca738410b99ba6e1ca02e6660 Homepage: https://cran.r-project.org/package=seeclickfixr Description: CRAN Package 'seeclickfixr' (Access Data from the SeeClickFix Web API) Provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions. Package: r-cran-seecolor Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-crayon, r-cran-purrr, r-cran-stringr, r-cran-rstudioapi, r-cran-magrittr, r-cran-ggplot2, r-cran-fansi Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seecolor_0.2.0-1.ca2404.1_all.deb Size: 1143632 MD5sum: 39d8d7bcde78dc5cad1997ff61c3cd18 SHA1: 10ffebd0c6f5d02fc7a008996581753426edf6b4 SHA256: fb1c471dc127c21d8cd9f738b0dd9b94273a4313bc2f5487a923e569f8fa8f5f SHA512: 3dc2ff5f66718b8c14511ab23adb0497828451fad6428da58b7178499b0b598b11331e2ccc09fb5097bcef40ab5f0a406a1148b421cff0c163c926e874a5c1eb Homepage: https://cran.r-project.org/package=seecolor Description: CRAN Package 'seecolor' (View Colors Used in R Objects in the Console) Output colors used in literal vectors, palettes and plot objects (ggplot). Package: r-cran-seedcalc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seedcalc_1.0.0-1.ca2404.1_all.deb Size: 73644 MD5sum: abe31ea15b20e4033823723d2a4bf943 SHA1: 0618a7183c7ff2a12a8d4c6b1c07ccc2c4e2bb3e SHA256: 435f326830ca2f05cb73c7183f6c9686e261808baf0c25c0b476d77dc0edca77 SHA512: 4f205cbbdee9605c2cdde1727659e8996922e7e813f8da86fdcab12e088a2e352c2eeba5d863fe1ca424d97ed2da48aa7f9bb12bcd77d9943f75cc374105d826 Homepage: https://cran.r-project.org/package=SeedCalc Description: CRAN Package 'SeedCalc' (Seed Germination and Seedling Growth Indexes) Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) ; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) ; and Vigour, in Medeiros and Pereira (2018) . Package: r-cran-seedcca Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cca, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-seedcca_3.1-1.ca2404.1_all.deb Size: 358100 MD5sum: ec1e7a51e01bf36aad31b587d49a9552 SHA1: b2ae7b2b79b2fa9df8c78735a505c9e545b0bc6c SHA256: f25a83f925a635b679bd79d93c0deb41e545b557693898c45ef7d2b32087c70e SHA512: 2e1ff787c362933f02baf607b79dd6ec1e3fd57556041682e4307d80caf3d731b4022ed29d984b6acd1fe703aee19c5a53cfc14d478a24e0fec6705675abed33 Homepage: https://cran.r-project.org/package=seedCCA Description: CRAN Package 'seedCCA' (Seeded Canonical Correlation Analysis) Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \doi{10.1111/anzs.12057}. Then, the canonical correlation is finalized with the initially-reduced two sets of variables. Package: r-cran-seedimbibition Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seedimbibition_0.1.0-1.ca2404.1_all.deb Size: 9914 MD5sum: 9179b7ab5842e3b8492b73295458bb67 SHA1: 97f5db181271e21e6e61c80de73f796a18854e37 SHA256: 4ca8c48a5f3a985b672b8fd699f89f23795de215ed572b8d29df036f3dc10705 SHA512: faf72ab287ae9d2b3d755eab625b0de0c0fab1603873ce8bd7b6bce3c431496ebc98b673aaa9116f4448ae00ce3aac39efa88e2a79117315fafcb6fb6df31ec9 Homepage: https://cran.r-project.org/package=SeedImbibition Description: CRAN Package 'SeedImbibition' (Seed Imbibition Percentage) Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption. Package: r-cran-seedmaker Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-seedmaker_1.0.0-1.ca2404.1_all.deb Size: 45062 MD5sum: 12534746334c0311c5e3899125684208 SHA1: bbec6880d08563da12fd964a43514e257aedd8ec SHA256: 6d233d095c5ec6ebf8b0be84589e2b1e59244026a27e7c457e7278677c9f0cb6 SHA512: 6a54380b6dff79e408dbef7a923c3a74ca692e42c2ed05f905d7ed03c7b519bf72e2b269709d5bde017f58e5b39e9f85643d529265021852502ee5cc7471aed4 Homepage: https://cran.r-project.org/package=SeedMaker Description: CRAN Package 'SeedMaker' (Generate a Collection of Seeds from a Single Seed) A mechanism for easily generating and organizing a collection of seeds from a single seed, which may be subsequently used to ensure reproducibility in processes/pipelines that utilize multiple random components (e.g., trial simulation). Package: r-cran-seedmatchr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-biostrings, r-bioc-ggmsa, r-bioc-msa, r-cran-ggplot2, r-bioc-annotationhub, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-cran-cowplot, r-cran-testit, r-cran-lifecycle, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-org.rn.eg.db Filename: pool/dists/noble/main/r-cran-seedmatchr_1.1.1-1.ca2404.1_all.deb Size: 590712 MD5sum: dd553a922d63a76aee5d65e5b0224e24 SHA1: e9a2ed5052222fb00b6729544db20477fee8755c SHA256: fa653c2e806c94395380bf458bc2d70640ef5609427ed086fad96907248a06ea SHA512: 99eb590f412f28df70fa2d7c5609f7a49d67f05bbb89fa69d9ac855296bcdd74ebcf36c2d99ce9017f5b38992bc09d60430a517bf2acede4c244ec0db5eb80df Homepage: https://cran.r-project.org/package=SeedMatchR Description: CRAN Package 'SeedMatchR' (Find Matches to Canonical SiRNA Seeds in Genomic Features) On-target gene knockdown using siRNA ideally results from binding fully complementary regions in mRNA transcripts to induce cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ~8 nucleotides at the 5’ end of the guide strand, called a seed region, bind the 3’ untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with potential off-target effects driven by the seed region. 'SeedMatchR' provides tools for exploring and detecting potential seed-mediated off-target effects of siRNA in RNA-seq experiments. 'SeedMatchR' is designed to extend current differential expression analysis tools, such as 'DESeq2', by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of 'SeedMatchR' to detect cumulative changes in differential gene expression attributed to siRNA seed regions. Package: r-cran-seedr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-binom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seedr_0.3.0-1.ca2404.1_all.deb Size: 146058 MD5sum: 6839a8282019fc958f3f9a61ddee4164 SHA1: 3934c68eeebf918819f5586cf7159b7c343ebe4e SHA256: 47f9aba376288e0bf9d25939ef58fac93d6d27bfebccf10a3e0ed41ae5b06e8c SHA512: 2cf43ff27a20b6852386fc9b3f99d3e389d7719d42986acf30f03d5043cfc1ae09f7709d5064a796702d4200a4b3a461417e965ae041f7b6f122918265b3a59b Homepage: https://cran.r-project.org/package=seedr Description: CRAN Package 'seedr' (Hydro and Thermal Time Seed Germination Models in R) Analysis of seed germination data using the physiological time modelling approach. Includes functions to fit hydrotime and thermal-time models with the traditional approaches of Bradford (1990) and Garcia-Huidobro (1982) . Allows to fit models to grouped datasets, i.e. datasets containing multiple species, seedlots or experiments. Package: r-cran-seedreg Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-drc, r-cran-ggplot2, r-cran-car, r-cran-crayon, r-cran-emmeans, r-cran-multcomp, r-cran-hnp, r-cran-boot, r-cran-multcompview, r-cran-stringr, r-cran-sf, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-seedreg_1.0.3-1.ca2404.1_all.deb Size: 292058 MD5sum: 3ad4ac948b5054c899c09909b71ef7ec SHA1: 8416eb692632b3752d6c6da69cff0b28edafcde7 SHA256: 7d5f8e6b943b472b88febad33ed0632b7e654294f488622efff13dc234eae6e3 SHA512: 2d5636d09c737dda8daa3a548947a42e384a0ca6919a556e30349771a87bdb6e415c138bbdb2d2fbacf3ec2ff8e25778f57308fc0f86323759ffd737b22d1fa0 Homepage: https://cran.r-project.org/package=seedreg Description: CRAN Package 'seedreg' (Regression Analysis for Seed Germination as a Function ofTemperature) Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) ), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) ) and others with potential for use, such as those implemented in the 'drc' package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). ), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically. Package: r-cran-seeds Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-pracma, r-cran-deriv, r-cran-ryacas, r-cran-mvtnorm, r-cran-matrixstats, r-cran-statmod, r-cran-coda, r-cran-mass, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-hmisc, r-cran-r.utils, r-cran-callr Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-rsbml Filename: pool/dists/noble/main/r-cran-seeds_0.9.1-1.ca2404.1_all.deb Size: 440578 MD5sum: 322b3a80a8a69b93ad0c5a8a87433c4b SHA1: ca29503c8a36af65b426ee8ed8a7ae4fd5be59a6 SHA256: 90b6b22cf76e46387fb4e0f8d8055fb46005512397bca99ed29ce7644e9d4cf4 SHA512: e937fab02bec197dc71bba60c1d7822f82e10279eb52b548fdb3b54ca018552fd8797ae51e025ea565925e5e5189e2d94e5377a9cb112975b82a64d2bdbf50c4 Homepage: https://cran.r-project.org/package=seeds Description: CRAN Package 'seeds' (Estimate Hidden Inputs using the Dynamic Elastic Net) Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" (Engelhardt, Froelich, Kschischo 2016) . To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: . Package: r-cran-seedvigorindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seedvigorindex_0.1.0-1.ca2404.1_all.deb Size: 10116 MD5sum: 44b7cd9f1c8e8a45dbf337be8daaa43b SHA1: 5bcf5206691115b44a1d7ed636e06b74463ceb66 SHA256: 269baa8acf27cfc7c88e9ab3248b800ed219f31606acf2f096af36009b9f3442 SHA512: 32634fcee6bdf4997c164ad4ad371e863816aa4c40482a120a46f24c6c4df8f976ef8ae85c3bd3d7f2e8125bd286c1eeb9a331ad0306ba34efa9e0564dd8cffd Homepage: https://cran.r-project.org/package=SeedVigorIndex Description: CRAN Package 'SeedVigorIndex' (Seed Vigor Index) Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence. Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) . Package: r-cran-seeker Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-affy, r-bioc-annotationdbi, r-cran-biocmanager, r-bioc-biomart, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-foreach, r-bioc-geoquery, r-cran-glue, r-cran-jsonlite, r-cran-qs, r-cran-r.utils, r-cran-rcurl, r-cran-readr, r-cran-sessioninfo, r-bioc-tximport, r-cran-withr, r-cran-yaml Suggests: r-bioc-arrayexpress, r-bioc-biobase, r-cran-doparallel, r-cran-knitr, r-bioc-org.mm.eg.db, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seeker_1.1.6-1.ca2404.1_all.deb Size: 162710 MD5sum: 152a2eb3c7f934b3b6c2acb029f51fa3 SHA1: 9ff14daa8c35a1602aa9a31ca3c5721ffd9ab954 SHA256: fe60e29f59f779351fd38ae479b98c8ff339ecb1fe298fdb3bf31a9a83cdbfdd SHA512: 23616cb07f2b85a0a3152f2d6b12eab590c43ecba924d95e4f20233405b669f63ef2f68972cfe03a3bc3a2ee29bddb1302bcfdf2ec935c3b81e855dce0b661e8 Homepage: https://cran.r-project.org/package=seeker Description: CRAN Package 'seeker' (Simplified Fetching and Processing of Microarray and RNA-SeqData) Wrapper around various existing tools and command-line interfaces, providing a standard interface, simple parallelization, and detailed logging. For microarray data, maps probe sets to standard gene IDs, building on 'GEOquery' Davis and Meltzer (2007) , 'ArrayExpress' Kauffmann et al. (2009) , Robust multi-array average 'RMA' Irizarry et al. (2003) , and 'BrainArray' Dai et al. (2005) . For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and quality trimming using 'TrimGalore' Krueger , performs quality control checks using 'FastQC' Andrews , quantifies transcript abundances using 'salmon' Patro et al. (2017) and potentially 'refgenie' Stolarczyk et al. (2020) , aggregates the results using 'MultiQC' Ewels et al. (2016) , maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) , and summarizes transcript-level quantifications for gene-level analyses using 'tximport' Soneson et al. (2015) . Package: r-cran-seekr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-fs, r-cran-glue, r-cran-mime, r-cran-pillar, r-cran-processx, r-cran-purrr, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-testthat, r-cran-withr, r-cran-diffobj, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-seekr_0.2.0-1.ca2404.1_all.deb Size: 391526 MD5sum: 0d1739eb40d6b0ebdbd4c7419b560a07 SHA1: 01b1b179ea0006147ac8afbb9a5da66095b5c065 SHA256: 62bdd30526e49ace0e89f27875f5bed673d1faeb9b155be7c12d0391a238225e SHA512: b4a5bafc30d4fe3990a3cab71e4ebd5ccfe0c8c1004446084289601f80c5186d249b503d2ca59c3d7e9ad0ed5f9ae7a15693df876a180fb7d57a757ef89fdc27 Homepage: https://cran.r-project.org/package=seekr Description: CRAN Package 'seekr' (Search, Inspect, and Replace Text Across Files) An inspectable and composable workflow for search-and-replace in text files. Files can be listed, filtered, and searched separately, with inspectable exclusions showing what was excluded and why. Matches are represented as structured vectors that can be printed with context, summarized, and filtered. Replacements can be defined at search time or set and updated after the search. Only matches present in the vector are modified when files are written. Backup and restore helpers are provided for file workflows. Text that has already been read can also be searched and updated directly, giving users control over input, output, and encoding when needed. Package: r-cran-seer Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-urca, r-cran-forecast, r-cran-dplyr, r-cran-magrittr, r-cran-randomforest, r-cran-forectheta, r-cran-stringr, r-cran-tibble, r-cran-purrr, r-cran-future, r-cran-furrr, r-cran-tsfeatures Suggests: r-cran-testthat, r-cran-covr, r-cran-repmis, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-mcomp, r-cran-ggally Filename: pool/dists/noble/main/r-cran-seer_1.1.8-1.ca2404.1_all.deb Size: 134406 MD5sum: 4afd2188d3b3935cb9b41af4cc613a50 SHA1: 3fbd24c54f445df6b29a3402787185778a4e765c SHA256: 73b0a98c15f133d497cea43d31acdab1456383b7c48be2377df31a322541b002 SHA512: b518734dd1c451b4d1a3cf038e2ea56ab35dc22333ca542f6c2a047ead30d9281063570e237a9a286aa176ad273a7dffde31ee7bed773f73f55286154e545fa0 Homepage: https://cran.r-project.org/package=seer Description: CRAN Package 'seer' (Feature-Based Forecast Model Selection) A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at . Package: r-cran-seewave Architecture: all Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3808 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner Suggests: r-cran-audio, r-cran-circlize, r-cran-factominer, r-cran-fftw, r-cran-ggplot2, r-cran-rgl, r-cran-rpanel, r-cran-phontools, r-cran-signal Filename: pool/dists/noble/main/r-cran-seewave_2.2.4-1.ca2404.1_all.deb Size: 3549332 MD5sum: ec420e13f624ee276c491a8424e299b4 SHA1: a5396813cfe1b10f1a02f277afa6c3cf23ec4e6a SHA256: 590e20fc7dbc6027fcc03e378675e4658719c35a05a9e71d4fe406ec89cd4027 SHA512: 839bf4b7034239205770339ad9662ea1875599a140da69ef1a8cd6bc7ab1143a948b7ea04d58fd37c19c524a15fd053e267d68013ad918d75e3e8e9c42544c6c Homepage: https://cran.r-project.org/package=seewave Description: CRAN Package 'seewave' (Sound Analysis and Synthesis) Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) and Sueur (2018) . Package: r-cran-segcorr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jointseg Filename: pool/dists/noble/main/r-cran-segcorr_1.2-1.ca2404.1_all.deb Size: 308278 MD5sum: ffc2bb512de6e6ceda3594c2a4724086 SHA1: b77481912af5cdc73c2662c3384a22c76bf9cf5e SHA256: 208ee6f6d542633252cd5ee93613748a3627fe39083c4277abe395346b96bd78 SHA512: 0e3d0b263ac50f5e50111eb809cfc92eecb1ebcb2a80b9105d81f22db723e95891582131b00d8a1cb58c8311fffd4878d91604340f57e25e202f30f9ee96d024 Homepage: https://cran.r-project.org/package=SegCorr Description: CRAN Package 'SegCorr' (Detecting Correlated Genomic Regions) Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression. Package: r-cran-segen Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-segen_2.0.1-1.ca2404.1_all.deb Size: 95252 MD5sum: 636032624ebbfb7052a3db1b81dcb93a SHA1: 82145d5aed5f6cf102b38f8ad6a2c3721532344f SHA256: 4dac5b74f9f5e164e5f5eea76f34ee04cfd05682110fb0ddac1a64bc0411c544 SHA512: daf0af7c5125f1919d68f483f3ea1470f93a11b822ebc9d53ef6e52d72cd32db6f73b2326d3e64bb7561ef99427868de155c25bb6afdca49f92a443b6736b17d Homepage: https://cran.r-project.org/package=segen Description: CRAN Package 'segen' (Sequence Generalization Through Similarity Network) Proposes an application for sequence prediction generalizing the similarity within the network of previous sequences. Package: r-cran-segenvineq Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-oasisr, r-cran-outliers Filename: pool/dists/noble/main/r-cran-segenvineq_1.2-1.ca2404.1_all.deb Size: 52854 MD5sum: 51d20486f4496fe7abc3e8824c6bf29e SHA1: aa2e4b3faa4c032ea20e9c76eee0a9743d269088 SHA256: c5d00a406526ee57e0e49e42bcf94ff72156ce45a12bb4a09a3234e83e53db3a SHA512: e3f2d19e9f5b4476b795492018992c7b8fae6f5deb2f462129d4e0a23262363d431ecee6b4b383ad38dfaad4fd64e722fd4f370f4b33686f4159d98dcdaa0a46 Homepage: https://cran.r-project.org/package=SegEnvIneq Description: CRAN Package 'SegEnvIneq' (Environmental Inequality Indices Based on Segregation Measures) A set of segregation-based indices and randomization methods to make robust environmental inequality assessments, as described in Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities: Insights from the Residential Segregation Literature" . Package: r-cran-segmented Architecture: all Version: 2.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-nlme Filename: pool/dists/noble/main/r-cran-segmented_2.2-2-1.ca2404.1_all.deb Size: 1427318 MD5sum: aebf05d3a53466bede4bded0c4451e16 SHA1: f110e7e2c9bd89af9968b02524e996f78c54eb2b SHA256: 78d19edf38a41ac30e607805cfb28869ee4a0fec29de0ebd6c898dc1f89dcd78 SHA512: 2438d27ddcce3e7bb3d11e14d67c1e439a729284ec699d0ccd7ae9e98acc7c70756dfd120aa30ba5991d4a70b1f706d91d1ea6b317b292d99b5ed8f1b6266c5d Homepage: https://cran.r-project.org/package=segmented Description: CRAN Package 'segmented' (Regression Models with Break-Points / Change-Points Estimation(with Possibly Random Effects)) Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, ) and illustrated in Muggeo (2008, ). An approach for hypothesis testing is presented in Muggeo (2016, ), and interval estimation for the breakpoint is discussed in Muggeo (2017, ). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, ). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, ). Package: r-cran-segmetric Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-magrittr, r-cran-units Suggests: r-cran-classint, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-segmetric_0.3.0-1.ca2404.1_all.deb Size: 1370786 MD5sum: 85be14dbdcb93dbfe52dc86f6e5aecd6 SHA1: 6215383789d4fde4dfedb4e8d5f10ddc05015ab8 SHA256: f6e2d611c79fa91b37d368e45524146c74a27bb750ab4d11636f7999ff985fa6 SHA512: 07103d9398abedb336e315f7b296bbd50383fcd625719e3f52728fc2b2228ac65dccdc9761c08b68a76fec772f380db1660edc3b4b7ea40ec3daaf9263bd81da Homepage: https://cran.r-project.org/package=segmetric Description: CRAN Package 'segmetric' (Metrics for Assessing Segmentation Accuracy for Geospatial Data) A system that computes metrics to assess the segmentation accuracy of geospatial data. These metrics calculate the discrepancy between segmented and reference objects, and indicate the segmentation accuracy. For more details on choosing evaluation metrics, we suggest seeing Costa et al. (2018) and Jozdani et al. (2020) . Package: r-cran-segrda Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-segrda_1.0.2-1.ca2404.1_all.deb Size: 168114 MD5sum: e356e44578657388b7d46d4f6a6b6614 SHA1: a4d74b7143bc0110b9b82d125ae77ae14722e755 SHA256: dccbef26c93bcdb58444f3de71e8342328801c1d12f76f9f4d32b4de5837e2ff SHA512: 345316dbf7c97b6b3c4a5c7344ec1d7b62067214408ff1dd945300fa23d1d677c1860049b871b301da6ae59b1ea7da2e9561a40915acea458e717b8a9520e265 Homepage: https://cran.r-project.org/package=segRDA Description: CRAN Package 'segRDA' (Modeling Non-Continuous Linear Responses of Ecological Data) Tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) and Legendre and Legendre (2012, ISBN: 9780444538697). 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The package was developed as a result of an internship in MI2 Group - , Faculty of Mathematics and Information Science, Warsaw University of Technology. Package: r-cran-sejong Architecture: all Version: 0.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sejong_0.01-1.ca2404.1_all.deb Size: 1617082 MD5sum: 53cd64787ed2972b6eb0148de0a3d4f9 SHA1: 414b87b867ebb16e944e40f25dd5e08a2adf6598 SHA256: cb7727c28838a70db0513e6f05c787a4117ca7585665a28f69a16eec12604b1c SHA512: c3dd3b1a99f0a06dc403c41d6b53ea38361653846e5731ac2f7473f6e58d1478cdcf4e3efcecd1cbbe2133d7963b137022b6ca9fe979cdfadce34e6f081a2d9f Homepage: https://cran.r-project.org/package=Sejong Description: CRAN Package 'Sejong' (KoNLP static dictionaries and Sejong project resources) Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP Package: r-cran-selcorr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-selcorr_1.0-1.ca2404.1_all.deb Size: 20616 MD5sum: 2141e4822c0b9f6617e4de3b70944b49 SHA1: 00cf4c384b323a935484586b2d2e2a4ffbe125a2 SHA256: 30bb7d5ac7d599adb566325e28d2387bae5a2768087cf7d36a7bc9a2d4b242a8 SHA512: 7fd4fa7bab3106216ba8ba40fc0652c861497139b6b9ddb6490660dba960652d084ba2434e223f99d61b0dd144a55a2338546ad98dde0fce790d099e19342533 Homepage: https://cran.r-project.org/package=selcorr Description: CRAN Package 'selcorr' (Post-Selection Inference for Generalized Linear Models) Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models. Package: r-cran-select Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-latticeextra, r-cran-lattice, r-cran-ade4 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-fd Filename: pool/dists/noble/main/r-cran-select_1.6-1.ca2404.1_all.deb Size: 166376 MD5sum: 3c6b922fdd7409af2ec150b5bd770d2c SHA1: d4253aeac58dc7679b8f7b7331b416480977e9db SHA256: f54e704e130e93c2b78c4d62346ee05ef6199d78c69be720fae7b9f68223bc26 SHA512: 023f08029c25f90b2731f48350b84935cb94cf4256abf4e9a8d33eca57ae089f4eab204008ab7ed3d11e5d2ea02d1a1f2769c297383652c6dfa3ef117b67fd14 Homepage: https://cran.r-project.org/package=Select Description: CRAN Package 'Select' (Determines Species Probabilities Based on Functional Traits) The objective of these functions is to derive a species assemblage that satisfies a functional trait profile. Restoring resilient ecosystems requires a flexible framework for selecting assemblages that are based on the functional traits of species. However, current trait-based models have been limited to algorithms that can only select species by optimising specific trait values, and could not elegantly accommodate the common desire among restoration ecologists to produce functionally diverse assemblages. We have solved this problem by applying a non-linear optimisation algorithm that optimises Rao Q, a closed-form functional trait diversity index that incorporates species abundances, subject to other linear constraints. This framework generalises previous models that only optimised the entropy of the community, and can optimise both functional diversity and entropy simultaneously. This package can also be used to generate experimental assemblages to test the effects of community-level traits on community dynamics and ecosystem function. The method is based on theory discussed in Laughlin (2014, Ecology Letters) and Laughlin et al. (2018, Methods in Ecology and Evolution). Package: r-cran-selecta Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selecta_0.6.1-1.ca2404.1_all.deb Size: 1298468 MD5sum: 4b11023de50e04b452af5e008f39232d SHA1: 0399e5bf7d9b3db94d02c4a2619feb718441411b SHA256: 2166e197acdf8d3433a556b9fc8e6b60f4a1f7beef81df6a2ae37678a5e398a0 SHA512: 71b56dcde3a3e14413fbe92eefa09f7aaf4afaa6e50069df08b4b625c284efd209b265ee410b47c2ce66d7f06865a9741bbb4b848a5f0517251bde2001885218 Homepage: https://cran.r-project.org/package=selecta Description: CRAN Package 'selecta' (Declarative EQUATOR-Style Flow Diagrams for Clinical Studies) Build EQUATOR-style flowcharts for clinical studies by sequentially defining inclusion and exclusion criteria, study arms, and endpoints. The pipe-friendly API supports CONSORT (randomized trials), STROBE (observational cohorts), STARD (diagnostic accuracy), PRISMA (systematic reviews), and MOOSE (observational meta-analysis) diagram layouts, as well as multi-source convergence, split-and-recombine, factorial, and hybrid topologies. Diagrams are rendered via 'grid' graphics in both data-driven (automatic counting) and manual-count modes, with optional 'DiagrammeR'/'Graphviz' output. Package: r-cran-selectapref Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-selectapref_0.1.2-1.ca2404.1_all.deb Size: 15440 MD5sum: 1092034437102499f9e5575dc3502968 SHA1: 2132887340d0086386de633bd4e9cacea4aaac40 SHA256: e106a57435f2f32138c8922e11098e5049e75ba51c242da1669b7a6c068dc377 SHA512: dd120676db1a3c91794a44e889eaf59928db180fc8c0975fb710454e7b948d688fa446734a8650bd5b886d8cada4bf638f3f6ea0a6aebc1bf46ee5b5a8a1ef85 Homepage: https://cran.r-project.org/package=selectapref Description: CRAN Package 'selectapref' (Analysis of Field and Laboratory Foraging) Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies. Package: r-cran-selectboost.fda Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-selectboost Suggests: r-cran-fdboost, r-cran-glmnet, r-cran-grpreg, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-sgl, r-cran-stabs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selectboost.fda_0.5.0-1.ca2404.1_all.deb Size: 673712 MD5sum: 0ad7643f25c3e9992c1ea4069e69c5a2 SHA1: 226dc9091ee7a0186353756626874aabb7aa0022 SHA256: 07977d3ba9c40e7202bc0bd3263bb8b668ca9291d4c5a96712cc713fe0f48914 SHA512: b5d14d9f196964b6262bc34dce10d245a78f4382d64930b7786fcb29d652cfb97d0ecdc3441c35800c11b7dbb97caa8a77e12dc4bf793d9af2f0c699a61e85be Homepage: https://cran.r-project.org/package=SelectBoost.FDA Description: CRAN Package 'SelectBoost.FDA' (SelectBoost-Style Variable Selection for Functional DataAnalysis) Implements 'SelectBoost'-style variable selection workflows for functional data analysis. The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for 'SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities and shipped benchmark summaries for mean 'F1' comparisons between FDA-aware and plain 'SelectBoost' workflows; small example datasets; and an optional adapter to the native stability-selection interface from the 'FDboost' package. Package: r-cran-selectboost.quantile Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 717 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-movmf, r-cran-quantreg, r-cran-withr Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selectboost.quantile_0.3.1-1.ca2404.1_all.deb Size: 465376 MD5sum: 5774b9238d3024d5823872795255ca10 SHA1: 1b23caff1358084f6d50c0498ed26db615d1d3cd SHA256: 2495a0ab9cd4a4673119d7726ba2861a94af37801a97b15115ff24db8d4aad6e SHA512: 5a36da46e616e5145cb0b8e0f3daec2fbce5952488184971eb76cf038cf1d2e0d129f7ebd4231ed68fa3392383d5bbf7d3edae75dc871fbaec197cad914ccc74 Homepage: https://cran.r-project.org/package=SelectBoost.quantile Description: CRAN Package 'SelectBoost.quantile' ('SelectBoost'-Style Variable Selection for Quantile Regression) A 'SelectBoost'-inspired workflow for sparse quantile regression. The package builds correlation neighborhoods, perturbs correlated predictors with a directional sampler inspired by the original 'SelectBoost' internals, refits penalized quantile regression models on the perturbed designs, and aggregates variable-selection frequencies across a path of correlation thresholds. Package: r-cran-selectboost Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2618 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lars, r-cran-glmnet, r-cran-igraph, r-cran-msgps, r-cran-rfast, r-cran-cascade, r-cran-varbvs, r-cran-spls, r-cran-abind Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-mixomics, r-cran-cascadedata, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selectboost_2.3.0-1.ca2404.1_all.deb Size: 1878968 MD5sum: 2a0935910c36569cc0b6d2153048ee1e SHA1: 47e1a88faea1a4ae3394cd45e8abb689cedc4db7 SHA256: 4fad4259858f6f18ffc64d03c9b66ba87e48b1037b4fb75ef0d71cd3680fc4c0 SHA512: f54b868f8ee9490a8f0fc3718a37d28386281d48d264cb2dc53c0e676145def75c347fcf2ed9f7db7d4c79f7f7e8bdee2297e1b986b08d235a71f0912388de84 Homepage: https://cran.r-project.org/package=SelectBoost Description: CRAN Package 'SelectBoost' (A General Algorithm to Enhance the Performance of VariableSelection Methods in Correlated Datasets) An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', ), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective. Package: r-cran-selection.index Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selection.index_1.2.1-1.ca2404.1_all.deb Size: 51234 MD5sum: d97720ac3112e3666123022fbeba6aa3 SHA1: ed15ae909c93341a09d65f0b46089679a897d6a0 SHA256: 62d4031b216716b004f4ef7168b8e2ed44c9000b17e74112d03297712d681b6a SHA512: 67571f78ef01defe94d2020277892137955219157323a180f9803290f2f957c84537649e37bd2bc2f4523b630b6fea44128b275269080eab749c467a9ebdb2a7 Homepage: https://cran.r-project.org/package=selection.index Description: CRAN Package 'selection.index' (Analysis of Selection Index in Plant Breeding) The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 ) based on the Fisher's (1936 ) "discriminant function" Dabholkar (1999 ). In this package selection index is calculated based on the Smith (1937) selection index method. Package: r-cran-selectionbias Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-table1, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selectionbias_2.1.0-1.ca2404.1_all.deb Size: 234998 MD5sum: e26af6f2c69eca69d00513b9063561e3 SHA1: 2082ba6a9bc2b16ae26fdf6069e73a1d6aab3903 SHA256: 7f6a14ada96de1e1a52fd8c96986aa15ca7999291f90a851a7624ed33e4d9982 SHA512: ddd23e11f512208d27192c781a9bdbc8079cde5f9dc47e282a1fdb88d0bdf0e72014a548894e62e5500622c0729ba8b9cc7f999815bb55e7d7f06b6c5eab3a68 Homepage: https://cran.r-project.org/package=SelectionBias Description: CRAN Package 'SelectionBias' (Calculates Bounds for the Selection Bias for Binary Treatmentand Outcome Variables) Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), sharp bounds, AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV, sharp, and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) , Zetterstrom, Sjölander, and Waernabum (2025) , Zetterstrom and Waernbaum (2022) , and Zetterstrom (2024) . Package: r-cran-selectiongain Architecture: all Version: 2.0.710-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-selectiongain_2.0.710-1.ca2404.1_all.deb Size: 229928 MD5sum: 0f47bc8ca93aa6a03f28fca4587d7680 SHA1: 1752fbc53675565731ead064b26a6a9a5605e5b5 SHA256: 2de27d92f39dfa96c3eaa84d42f22f5648faf0afd9327ed45ddfec1b808ef4c4 SHA512: 3dc61d326d1a30cf5c986f0d0893b06ee0641ce2bdd9e983a9c0c8a3c45b079d3b0a07a36266e3894bdbcfc82a104955ece58c969ddd15be42b1c24754d8aa76 Homepage: https://cran.r-project.org/package=selectiongain Description: CRAN Package 'selectiongain' (A Tool for Calculation and Optimization of the Expected Gainfrom Multi-Stage Selection) Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage. Package: r-cran-selectmeta Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim Filename: pool/dists/noble/main/r-cran-selectmeta_1.0.9-1.ca2404.1_all.deb Size: 89872 MD5sum: 9a3ff0cf1fab452855b61324f7cf6653 SHA1: e5bc6f99ba36e7ac48860a86ff5878e07bb1c13c SHA256: d454d02a95660446ef50a7d6b162b1daad27c25d481c5ee03dece2b0478b677c SHA512: 7c680ff555276af38eb29eace51e1cec84b02764690e163af221fd6a3828920432c8c3e95ec9affafcf361bd1e35232eeb996fca51a7d4fb4480d8f2434bc2fd Homepage: https://cran.r-project.org/package=selectMeta Description: CRAN Package 'selectMeta' (Estimation of Weight Functions in Meta Analysis) Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6). Package: r-cran-selectr Architecture: all Version: 0.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-xml, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-selectr_0.8-0-1.ca2404.1_all.deb Size: 254142 MD5sum: 8811bd4cc2a89e1be62c6260429c7adc SHA1: eaf60f4251d1635000578c3326fc524fd2976525 SHA256: c77f2fb87dcd4b33d52b389797de4cc4021dac5313bb0f56931bb623c55e8753 SHA512: a59a8fa6414117b030fb4d0e3bb0a633ce7862f1ac42e7cd5f358e91d24a03db8ccda16c43e67010e63fc88df4a60b2ca9f52dd6019d943a2a1685f2f15b71a2 Homepage: https://cran.r-project.org/package=selectr Description: CRAN Package 'selectr' (Translate CSS Selectors to XPath Expressions) Translates a CSS selector into an equivalent XPath expression. This allows us to use CSS selectors when working with the 'XML' and 'xml2' packages, which can only evaluate XPath expressions. 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Package: r-cran-selfcontrolledcohort Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender, r-cran-parallellogger, r-cran-rateratio.test, r-cran-checkmate, r-cran-dplyr, r-cran-empiricalcalibration, r-cran-resultmodelmanager, r-cran-andromeda, r-cran-readr, r-cran-rlang, r-cran-cli, r-cran-cohortgenerator Suggests: r-cran-withr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-eunomia, r-cran-duckdb, r-cran-diagrammer, r-cran-tidyr, r-cran-r.utils, r-cran-jsonlite, r-cran-dt, r-cran-ggplot2, r-cran-shiny, r-cran-shinycssloaders, r-cran-plotly, r-cran-reactable, r-cran-shinywidgets Filename: pool/dists/noble/main/r-cran-selfcontrolledcohort_2.1.0-1.ca2404.1_all.deb Size: 198774 MD5sum: d6e62d600a487dbda82e56e85b0c25f8 SHA1: 97e4658d8919a1b24f5eba6b19312df579392967 SHA256: bbc8a9d5bca719740a76093e1679b2703aa651b89304bd4c9a50dfe673676258 SHA512: 9d44134c72c85080d46e17373acb35c6212cdb08aebe1aaba09d489f0c2f5162ca794fa1e1d91b178cdc8c3950ce7ffa80f9713fb82c966f3e5acf20e7f15698 Homepage: https://cran.r-project.org/package=SelfControlledCohort Description: CRAN Package 'SelfControlledCohort' (Self-Controlled Cohort Population-Level Estimation) Estimates incidence rate ratios by comparing time exposed with time unexposed among an exposed cohort using self-controlled cohort methodology as described in Ryan et al. (2013) . Functions used for empirical calibration of effect estimates, confidence intervals, and p-values are included to control for residual bias. 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This package implements functionality to compute probability trees for two- and three-marker genotypes in the F2 to F7 selfing generations. The conditional probabilities are derived automatically and in symbolic form. The package also provides functionality to extract and evaluate the relevant probabilities. 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It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) . 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Package: r-cran-semantic.dashboard Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shiny.semantic, r-cran-htmltools, r-cran-glue, r-cran-checkmate Suggests: r-cran-testthat, r-cran-lintr, r-cran-shinydashboard, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semantic.dashboard_0.2.1-1.ca2404.1_all.deb Size: 229864 MD5sum: d7bfb9e7d054b8a9617b720e41b3342d SHA1: 7528a3c35f26ba92bd30499bc609d13d9a8ab0d2 SHA256: 560b9f44880ea9c1a758645f05ce8463abba0683745fecacc4be0e7e4e610b48 SHA512: 10d97a5a977629a8b20fe61c3245b28cdd09f5f5f401ca967566d94ae9343b253e7cd0084f4cbdaac75d3305928dcb73a6f30ed0e325324c63f44b5c460fed64 Homepage: https://cran.r-project.org/package=semantic.dashboard Description: CRAN Package 'semantic.dashboard' (Dashboard with Fomantic UI Support for Shiny) It offers functions for creating dashboard with Fomantic UI. Package: r-cran-semanticdistance Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-cluster, r-cran-dendextend, r-cran-dplyr, r-cran-httr, r-cran-igraph, r-cran-lsa, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-textstem, r-cran-tidyselect, r-cran-tm, r-cran-tidyr, r-cran-textclean, r-cran-wesanderson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semanticdistance_0.1.1-1.ca2404.1_all.deb Size: 1222746 MD5sum: 67585b73c745db46981cfac99cdfd662 SHA1: 3b20ace4febd54850aef292fd4cbc865c84964b4 SHA256: ca8d976adfa3ef88e6c21744a01e1576c9523900682c62e9cee46c5b9fc95034 SHA512: 404793e3302a6985b90c838134ca1af493c62b82665219c40b8d2055adc363d67080062cc837c4f1a19a57443eada0625e1249789ead9249e1e0b8a22a4db47c Homepage: https://cran.r-project.org/package=SemanticDistance Description: CRAN Package 'SemanticDistance' (Compute Semantic Distance Between Text Constituents) Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). 'SemanticDistance' computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. 'SemanticDistance' can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). 'SemanticDistance' includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network). Package: r-cran-semanticfa Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-gparotation, r-cran-psych, r-cran-reticulate, r-cran-rtsne, r-cran-uwot, r-cran-withr Suggests: r-cran-efatools, r-cran-jsonlite, r-cran-eganet, r-cran-httr2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semanticfa_0.5.1-1.ca2404.1_all.deb Size: 1006788 MD5sum: 4b5fd4c53ba12f8cdc7e8a36b73e1ccd SHA1: cc1c543dac68301850eb2202e708755043c13a75 SHA256: 5bea42b1d6fcce83f619cc3303afa26b047bbea475dfcd8187706a46ac8c46d6 SHA512: f5af532c40335ca4dcc97896a8bd34881bee95f41cd09747bbc3caedafd9855eda4bbd791714a9a491207668690fe9a3bba6a7711058e3401f3c0bf3845bd64a Homepage: https://cran.r-project.org/package=semanticfa Description: CRAN Package 'semanticfa' (Semantic Factor Analysis of Language Model Embeddings) Performs exploratory factor analysis on language model embeddings of psychological scale items. Embeds item text with sentence transformers or other language models, transforms the embeddings into item-by-item similarity matrices, and extracts latent factor structure via standard exploratory factor analysis, using several similarity transforms (atomic reversed, SQuID centering, mean-centered Pearson) and fit diagnostics tailored to embedding matrices (TEFI, RMSR, CAF, McDonald's omega). Factor retention spans embedding-adapted parallel analysis, the empirical Kaiser criterion, Velicer's minimum average partial, a comparison-data misfit profile, and a calibrated learned rule that reports conformal intervals. Further tools orient factor axes toward retrieved construct terms by lexical target rotation, and audit whether a scale's items cover their construct's semantic region without collecting responses. The underlying methods are documented with full citations in the corresponding function help pages. Returns objects compatible with 'psych' and 'EFAtools' workflows. Package: r-cran-semblance Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-performanceanalytics, r-cran-desctools, r-cran-msos Suggests: r-cran-kernlab Filename: pool/dists/noble/main/r-cran-semblance_1.1.0-1.ca2404.1_all.deb Size: 33914 MD5sum: 8aad9c9dc98b4c3a49ef3ca7a478d498 SHA1: b60c1dcf918401784cb5e2af7a9fc625e6cf139f SHA256: b7493baa399641086b2ef1363ad8f72363399c74b7bdd3cba2cdf67da9a6e0e3 SHA512: bd956d2d4895169eca8da045224e6be370433e218340385385856e3edc03aa16046127aa2d6ebfd72fb0930ad367b3a5561f1fdbc97a63cd0623c35db85d227b Homepage: https://cran.r-project.org/package=Semblance Description: CRAN Package 'Semblance' (A Data-Driven Similarity Kernel on Probability Spaces) We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations. Package: r-cran-semboottools Architecture: all Version: 0.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-lavaan, r-cran-psych, r-cran-lavaan.printer, r-cran-ggplot2, r-cran-ggally, r-cran-rlang, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semboottools_0.1.2.1-1.ca2404.1_all.deb Size: 320248 MD5sum: 87ac749be3c790ead8a898cee97360fe SHA1: f9a90dcf31a5aafd5cc961563b9eb726e860ed65 SHA256: 3b626719c654f6669b6a6d64ab88cc409924c4eea73071da0e73ef758f806eda SHA512: 2ca588ea900c60a8a53843c7eb99a0081cdd719a9895777ba29f5edef29c5db664b1d71ad09fb66e15e39bc727615b3dd16b3d25d4e7c154fb7cee2572891806 Homepage: https://cran.r-project.org/package=semboottools Description: CRAN Package 'semboottools' (Bootstrapping Helpers for Structural Equation Modelling) A collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling, introduced in Yang and Cheung (2026) The function currently support models fitted by the 'lavaan' package by Rosseel (2012) . Package: r-cran-semdeep Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-semgraph, r-cran-igraph, r-cran-coro, r-cran-corpcor, r-cran-kernelshap, r-cran-lavaan, r-cran-neuralnettools, r-cran-parabar, r-cran-progress, r-cran-ranger, r-cran-rpart, r-cran-torch, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-semdeep_1.1.1-1.ca2404.1_all.deb Size: 297608 MD5sum: 36b2c4b3e1abd9a3b5ca750fb1c79299 SHA1: d697839cce4ab631ac6976a74676b7ffa081763f SHA256: e910a419fe759113cc10ad914af9cd18c611a29dc32642c7470512a34a196a07 SHA512: c4e244823738ec54bfd52a8d2616e7690c4a02fb4c0a07f112da7c436d32acdf81a3fc4752195db7a3acbde42a9675ecd1b0df7c86eb89191da46ec2af5b9c8c Homepage: https://cran.r-project.org/package=SEMdeep Description: CRAN Package 'SEMdeep' (Structural Equation Modeling with Deep Neural Network andMachine Learning Algorithms) Training and validation of a custom (or data-driven) Structural Equation Models using Deep Neural Networks or Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package . Package: r-cran-semdrw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyace, r-cran-lavaan, r-cran-semplot, r-cran-dplyr, r-cran-semtools, r-cran-psych Filename: pool/dists/noble/main/r-cran-semdrw_0.1.0-1.ca2404.1_all.deb Size: 73912 MD5sum: 0bc04d672525ba3469ae994b6642058f SHA1: 4a34d3f6ec1062f4a709f5d46de33d10c6b46402 SHA256: 427926f0d1bc538b2836ec66915d70fd9c22a3901a11f1d73ee4df8f5aabae88 SHA512: 9226b86928f1e05b69169282131200a9a90ef6ab53dc0c5e4e99b1b1257225d4125447f5b1e104cdf0de0d551538d6dafe8d7fa03088baedd7b7f517291537e2 Homepage: https://cran.r-project.org/package=semdrw Description: CRAN Package 'semdrw' ('SEM Shiny') Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at . Package: r-cran-semds Architecture: all Version: 0.9-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-minpack.lm Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-semds_0.9-7-1.ca2404.1_all.deb Size: 93960 MD5sum: 762ffb39c9b2f27cc147f6eef62f0b19 SHA1: d01d3bfd04cb68cc45a3595826d54bf38daa1bc1 SHA256: 98b60ef93f8406121a6859bc8b3950a4e0089603c024ffd65e606c9a60ba7bde SHA512: c24057143ccc4e680be78808b6e617c978a6cc1b02e455ea30e0b2a83bcfacfb7ccd35a1a7017329f9ad9613d8ea49050e189b6f1796dd9408a7621f76282ad2 Homepage: https://cran.r-project.org/package=semds Description: CRAN Package 'semds' (Structural Equation Multidimensional Scaling) Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. 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Package: r-cran-semeff Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-gsl, r-cran-lme4 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-piecewisesem, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semeff_0.7.2-1.ca2404.1_all.deb Size: 2242986 MD5sum: d889ed42fa1f46acb928ab91ab89c2df SHA1: df090689ec2211a2d525fcb00c37c3806a7dba1c SHA256: b10a323c9e162c2f777cb04247257439756d89725e9069ef6fec4ef157cf6471 SHA512: 1b0777f90e3638e2b26901ecc86965b41ae7f699cf789c1a3a8e30d9f03fa634970d274095faa5ca084c7565baa6645252547c2141d5058b7ba6c854712c3b0f Homepage: https://cran.r-project.org/package=semEff Description: CRAN Package 'semEff' (Automatic Calculation of Effects for Piecewise StructuralEquation Models) Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 ). 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Package: r-cran-semeffect Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-piecewisesem, r-cran-plspm, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-checkmate, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semeffect_1.2.3-1.ca2404.1_all.deb Size: 26992 MD5sum: 5f7b6701d35bf1c6ecb3dc88676497d5 SHA1: 41def64201f779ec756c833c160ebc4e3e245339 SHA256: 6a57121b267fa71bacd02914cbb225c703ae230ced45a91a7ae1881af87ae79b SHA512: e445cb5d67575d4d8e92368b77f7f3531f0bf2eacbe70f3c12160a404b91d6e2f8ab6f1ba4b48b8af46c41549bac52d0fa5e9d63e4d66845070a856b7b99e477 Homepage: https://cran.r-project.org/package=semEffect Description: CRAN Package 'semEffect' (Structural Equation Model Effect Analysis and Visualization) Provides standardized effect decomposition (direct, indirect, and total effects) for three major structural equation modeling frameworks: 'lavaan', 'piecewiseSEM', and 'plspm'. Automatically handles zero-effect variables, generates publication-ready 'ggplot2' visualizations, and returns both wide-format and long-format effect tables. Supports effect filtering, multi-model object inputs, and customizable visualization parameters. For a general overview of the methods used in this package, see Rosseel (2012) and Lefcheck (2016) . Package: r-cran-semeqmodels Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-modelbpp, r-cran-digest, r-cran-manymome, r-cran-igraph, r-cran-semplot, r-cran-semptools, r-cran-rcolorbrewer, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dagitty Filename: pool/dists/noble/main/r-cran-semeqmodels_0.1.0-1.ca2404.1_all.deb Size: 876220 MD5sum: 50bf1b29d5009a2c12c73f831699cc8f SHA1: 36709ad12735b14c16afb6757e1cd29b1b8476a1 SHA256: 18c12e270c1669959c83a255a598eb4554236324303b089b0e0b864356bb2b10 SHA512: 02cd67ee89d8a7879db0e4d07c16b5b7fb7fdf7ecd2d9ee6221255cb20eab7a81bedb64c2c7d2daf704d49b37d8d80f270fdb0328a24672488c80ca2ec659abe Homepage: https://cran.r-project.org/package=semeqmodels Description: CRAN Package 'semeqmodels' (Equivalent Models in Structural Equation Models) For identifying the sets of empirically equivalent models for structural equation models fitted by the 'lavaan' package developed by Rosseel (2012) . Package: r-cran-semfindr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-matrix, r-cran-pbapply Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-modi, r-cran-mass Filename: pool/dists/noble/main/r-cran-semfindr_0.2.1-1.ca2404.1_all.deb Size: 979742 MD5sum: 312c8b63aad66a6a7b9102d98acaec79 SHA1: 1670d5fbf5599b9e5927da36e2ae4d0237fcf463 SHA256: 96fb262b09413a89eb88af4a2392aaa4e06b29c8a857b0fd0b4c7f6756341790 SHA512: 862e4f86ea73433a5c460f847d755dc4d2dd49193b484337d1a908f58e6ed48ed9c9e0b6920154b4ae9a79f617043fdb583a42f439d914c94c85b8f3500e382e Homepage: https://cran.r-project.org/package=semfindr Description: CRAN Package 'semfindr' (Influential Cases in Structural Equation Modeling) Sensitivity analysis in structural equation modeling using influence measures and diagnostic plots. Support leave-one-out casewise sensitivity analysis presented by Pek and MacCallum (2011) and approximate casewise influence using scores and casewise likelihood. An introduction to the package can be found in Cheung and Lai (2026) . Package: r-cran-semfromkeys Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-lavaan, r-cran-openssl, r-cran-withr, r-cran-matrixcalc, r-cran-matrix Suggests: r-cran-testthat, r-cran-here Filename: pool/dists/noble/main/r-cran-semfromkeys_0.5.5-1.ca2404.1_all.deb Size: 217374 MD5sum: b0696ad89e5156d71203da63b4b4679f SHA1: a1c64fbeb48431e315db0ed4dd41e48f87fb2065 SHA256: c2995a03af491b0965c3c792786eea7e017ea0110e93948a713688cfd2be8dec SHA512: 91c44ed7fcb42832e190e3234a01baa217accc57af07fd2dc3821d09c352c6eb1b1c28787a32900994965bd2573912196260822441dbf7faa71bb2e7b8e012b6 Homepage: https://cran.r-project.org/package=semFromKeys Description: CRAN Package 'semFromKeys' (Run 'lavaan' Models from Keys Lists) Specifying 'lavaan' models manually can be time consuming when multiple similar models are required. The 'semFromKeys' package streamlines the process of running 'lavaan' models by generating model code from simple keys lists and running entire collections of models at once. The package was inspired by the process used in the code for Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022) . The package also optionally checks that identical models have not been run on the same data, which saves time when code needs to be run again. Package: r-cran-semgram Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rsyntax, r-cran-stringr Filename: pool/dists/noble/main/r-cran-semgram_0.1.0-1.ca2404.1_all.deb Size: 116372 MD5sum: 337cf5b29fefb6739e23a84fdacf3529 SHA1: f79f071425a19d7d2ddc339d9d489160839e3b3f SHA256: a4ecb290f4e39288b71f862fc0bbae71315074b77f796451872e1f782d088365 SHA512: b3694bf69410ee49b63f46b1c8a7b6fd962b88b864f30df4b53de26cc771fbc9cf65d229454e6e4d1cd8c97588a82d3b85599ec0b8a32b9cac9c3cc938b0830f Homepage: https://cran.r-project.org/package=semgram Description: CRAN Package 'semgram' (Extracting Semantic Motifs from Textual Data) A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) . Package: r-cran-semgraph Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-lavaan, r-cran-aspect, r-cran-boot, r-cran-corpcor, r-cran-dagitty, r-cran-flip, r-cran-gdata, r-cran-ggm, r-cran-glasso, r-cran-glmnet, r-bioc-graph, r-cran-mgcv, r-cran-mvtnorm, r-cran-pbapply, r-cran-protoclust, r-bioc-rbgl, r-bioc-rgraphviz, r-bioc-graphite, r-bioc-annotationdbi Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-semgraph_1.2.4-1.ca2404.1_all.deb Size: 3922094 MD5sum: 3d3024709ca6cc42ef6271c72b53d2ce SHA1: c9b912124bebfbacc75aa754861ea6d3fac6b3f7 SHA256: 6d1df0f390f511995212b54cb526e466476e35599445de3b54fad482a5a3e462 SHA512: 2411ad7731c94e8f4b480a3567ad8ce2731e8e714d8a6d22e97cad9e1a8ec285a8fc0c8efbe6afae0feccfc9c7d1834e0e7e3cf2db800e0234150dc79f746a12 Homepage: https://cran.r-project.org/package=SEMgraph Description: CRAN Package 'SEMgraph' (Network Analysis and Causal Inference Through StructuralEquation Modeling) Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) . Package: r-cran-semhelpinghands Architecture: all Version: 0.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1345 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-rlang, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-semtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semhelpinghands_0.1.15-1.ca2404.1_all.deb Size: 1177688 MD5sum: 9ec1bba6a8b9e503d4dbb36de84e774b SHA1: d2758204951b7688c622837589df118d48c7c999 SHA256: 82979bf0057397d2531e1fcc27ea9f90a52a9d1a7cb296bc85a2e07238b867c1 SHA512: bf1875464cce56f3b7139cdfe43184e2609adb4f1ceaa8a09f519710e232392508f9cae86c68975c1e80d55f2628d670bb81b87086288eb864772fd399ad14fa Homepage: https://cran.r-project.org/package=semhelpinghands Description: CRAN Package 'semhelpinghands' (Helper Functions for Structural Equation Modeling) An assortment of helper functions for doing structural equation modeling, mainly by 'lavaan' for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible. Package: r-cran-semiartificial Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corelearn, r-cran-rsnns, r-cran-mass, r-cran-nnet, r-cran-cluster, r-cran-fpc, r-cran-timedate, r-cran-robustbase, r-cran-ks, r-cran-logspline, r-cran-mcclust, r-cran-flexclust, r-cran-statmatch Filename: pool/dists/noble/main/r-cran-semiartificial_2.4.1-1.ca2404.1_all.deb Size: 206944 MD5sum: 9477ae66e6d0af1ec985665f33ffb1a3 SHA1: 3dbf21e95ea1fee665ccff2e9598c22906cdc3a1 SHA256: 3aa08ae97f17e9da18b989e0ef330b74b97939269d255507bfc942f49dd54da1 SHA512: a1f2869044f951d99f9313f6bdd27c147a3b0a1769de97cddcbf7e62aab10045ad465bb745456e637cbd5029160e41050a86b83bf44e5ff97e3df3f7f74dd2ef Homepage: https://cran.r-project.org/package=semiArtificial Description: CRAN Package 'semiArtificial' (Generator of Semi-Artificial Data) Contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package 'RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests. Package: r-cran-semicmprskcoxmsm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-twang, r-cran-fastghquad, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semicmprskcoxmsm_0.2.0-1.ca2404.1_all.deb Size: 116704 MD5sum: cb973c1f5f2bbd01829c6593daeda79c SHA1: 3546fc2737630b2d6627c7d7f342c20c2be70e6a SHA256: cb6787ddb9f8e929f890be54f289593126aa0b04c26656565f9dcb9e0b596489 SHA512: f92b01e0ba171a4947ddde4bce2b508f2805f5e5b750b1b568fc09d4124c507b97b980ee1015e370a44686a71a075b6e9b9e8e4349056ca7f0d6268a60887860 Homepage: https://cran.r-project.org/package=semicmprskcoxmsm Description: CRAN Package 'semicmprskcoxmsm' (Use Inverse Probability Weighting to Estimate Treatment Effectfor Semi Competing Risks Data) Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) . Package: r-cran-semicontmanova Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-semicontmanova_0.2-1-1.ca2404.1_all.deb Size: 81942 MD5sum: 915dace2a75cb2ab9d899433b445c569 SHA1: d33927ad1044bfb73af8b3135269f761caa0b10a SHA256: d9162d6c0677fdd9f51f787bdffab02f399289aa23ccbc68b6b147256aa88b47 SHA512: 47419fcd4d5f208517da71b3a51a027be64f45495b604715256d0b38195982e98cd24ec47956c489dd74500f02809ec61918be265e709d513fa25e49fe5b564f Homepage: https://cran.r-project.org/package=semicontMANOVA Description: CRAN Package 'semicontMANOVA' (Multivariate ANalysis of VAriance with Ridge Regularization forSemicontinuous High-Dimensional Data) Implements Multivariate ANalysis Of VAriance (MANOVA) parameters' inference and test with regularization for semicontinuous high-dimensional data. The method can be applied also in presence of low-dimensional data. The p-value can be obtained through asymptotic distribution or using a permutation procedure. The package gives also the possibility to simulate this type of data. Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054. DOI , arXiv DOI . Package: r-cran-semid Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.oo, r-cran-r.methodss3, r-cran-igraph, r-cran-r.utils, r-cran-rje Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semid_0.5.1-1.ca2404.1_all.deb Size: 380078 MD5sum: dcf7e4a0470ead96f128a1d33dbd8f3c SHA1: f934ff230a36a04929548e7b7ef26b9953fac50f SHA256: 16c19464721214a9e88680d84b1a2a6472cd6ca36c01521f52f389814570c924 SHA512: 73c109c6218ed17ac29428dfa1c463eebd9287f51ce4215f00c9446e58d8fc2b6f838d8f5236c01b299464b9cf6a831226e302228304b300a120d21695a6be83 Homepage: https://cran.r-project.org/package=SEMID Description: CRAN Package 'SEMID' (Identifiability of Linear Structural Equation Models) Provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models. Package: r-cran-semiestimate Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-numderiv, r-cran-purrr, r-cran-rlang, r-cran-testthat, r-cran-bb, r-cran-nleqslv, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-semiestimate_1.1.3-1.ca2404.1_all.deb Size: 60462 MD5sum: 53455c27baa454245ee2399777510b02 SHA1: fb8c4565f29ba4af9a1c6af596c888b7f068181e SHA256: 2a379463fcc0745f66325083eb3a219d8ecaba2fc0bfe2284e3ab0a25d49fe5f SHA512: 8c1e818ce2c56446f9e7efaf497b1698ab934a3ed9f14abf4b34330bea2914a9e64d94a8a8f9c1d10812970a707df2c43b2561d91fd300399dd2cbbbf8950726 Homepage: https://cran.r-project.org/package=SemiEstimate Description: CRAN Package 'SemiEstimate' (Solve Semi-Parametric Estimation by Implicit Profiling) Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix. Package: r-cran-semimarkov Architecture: all Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-mass, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-semimarkov_1.4.6-1.ca2404.1_all.deb Size: 238402 MD5sum: cffc927f096025747b0ea4aa7b39d07c SHA1: 30b4849c8d4d074ab42ee1016871b2e685709b10 SHA256: 9807b9d7dfa47b8f0e9c778371ebf96abc9fc17f4e99ff61124b32774dac55b2 SHA512: ce24611c11cfaf48522c9fc16983ae74c76988fbd2b4f9f1dc38bb0ed3c155bdc98f3abd2be1d5feccf8a8554d11fd7a233428dc6391853064052370dbd82075 Homepage: https://cran.r-project.org/package=SemiMarkov Description: CRAN Package 'SemiMarkov' (Multi-States Semi-Markov Models) Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) . Package: r-cran-seminr Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2345 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-glue, r-cran-diagrammer, r-cran-diagrammersvg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-webp Filename: pool/dists/noble/main/r-cran-seminr_2.5.0-1.ca2404.1_all.deb Size: 1574276 MD5sum: 575d71612b9ac792d3722a75137ee2da SHA1: 3187a31cbddd6acd1f425dabe52c5c82539d0a14 SHA256: 2f640c4d8d305d22419aac594da6271bc55d529aff93a0a8b6a36457e7ddb3f8 SHA512: f0b0185fdc454ce2127627bc2f0bdc55dd5cf2abcc6b5763016952d9879647634d182aae186224c232b298989d1210ec720853a2fe8900a1192d29e0b52b6f1a Homepage: https://cran.r-project.org/package=seminr Description: CRAN Package 'seminr' (Building and Estimating Structural Equation Models) A powerful, easy to use syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 ). Package: r-cran-seminrextras Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seminr, r-cran-rpart Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-paran, r-cran-psych, r-cran-learnr Filename: pool/dists/noble/main/r-cran-seminrextras_1.0.3-1.ca2404.1_all.deb Size: 844988 MD5sum: 74c53d1b71df8b1b2e2df1c069cd62cd SHA1: 5112ff3632375cc9d090d586ddd3970bb5c95e09 SHA256: 9cea7597fb57d506235e3a89b8dedd48d31c9725273e813773ecafa3bacdb535 SHA512: b77d3869fb1122348ddfb85e717833b9beef57531d4a82b167c9a0a857cfce07f117d030ff58d3646cd470f9e7b19eb002eaa4d64810a3cd7c1af48ba7061f64 Homepage: https://cran.r-project.org/package=seminrExtras Description: CRAN Package 'seminrExtras' (Conduct Additional Modeling and Analysis for 'seminr') Supplementary tools for evaluating and validating partial least squares structural equation models estimated with 'seminr'. Provides methods for predictive model assessment, importance-performance analysis with necessary condition testing, overfitting diagnostics, measurement model verification, mediator contribution analysis, unobserved heterogeneity detection via latent class and prediction-oriented segmentation, and congruence coefficient testing. All functions accept estimated 'seminr' model objects and return results with print, summary, and plot methods. Package: r-cran-semipar.depcens Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-copula, r-cran-foreach, r-cran-doparallel, r-cran-pbivnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semipar.depcens_0.1.3-1.ca2404.1_all.deb Size: 105682 MD5sum: 8e33b713201b18cc53bdb7adc9d2dbae SHA1: cf9dd277382ee689576d7ffa09340502cf9b90a4 SHA256: cf0cc030a1267f26abb2a2ff214d142ea9acc23d57ed7edc78f7a11f444593ed SHA512: 1023d7b76fe27e053bd88d5f2186bbf508acb190d633b62e6f70f758ecff3105af50fb5d53122395f0220b57b4a805b395c0d781cc141be9cfb77a6e6b418c36 Homepage: https://cran.r-project.org/package=SemiPar.depCens Description: CRAN Package 'SemiPar.depCens' (Copula Based Cox Proportional Hazards Models for DependentCensoring) Copula based Cox proportional hazards models for survival data subject to dependent censoring. This approach does not assume that the parameter defining the copula is known. The dependency parameter is estimated with other finite model parameters by maximizing a Pseudo likelihood function. The cumulative hazard function is estimated via estimating equations derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any parametric model that satisfies certain identifiability conditions could be used. Implemented methods are described in the article "Copula based Cox proportional hazards models for dependent censoring" by Deresa and Van Keilegom (2024) . Package: r-cran-semipar Architecture: all Version: 1.0-4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-cluster, r-cran-nlme Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-semipar_1.0-4.2-1.ca2404.1_all.deb Size: 316222 MD5sum: 30efc171e7873db8e26657d32816b334 SHA1: 02a1ef7bb17dac2152268d844ad2163d3bc57e63 SHA256: 91154fa2cee8a223d2da10be5b63887a577693ff77121c6fe5ed79b493305e00 SHA512: 2532cd96e8af16bff768ac06a59d3a8984a8bc8f3d12a6a1663c555a005ce0966f7139cf05996725f7eefd010f21ef4702b93cc48bace6d8499db955b2dbfca7 Homepage: https://cran.r-project.org/package=SemiPar Description: CRAN Package 'SemiPar' (Semiparametic Regression) Functions for semiparametric regression analysis, to complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J. (2003). Semiparametric Regression. Cambridge University Press. Package: r-cran-semiparambernsteindepcs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semiparambernsteindepcs_0.1.0-1.ca2404.1_all.deb Size: 112464 MD5sum: 718888441fa8c7a801ea5f7069a7d514 SHA1: 72adbbcc33d7940639be4a576ce0070b46405274 SHA256: dc31cec23bf7a828c956b5f89c478bd4721c128c10ed77b38a2f4d8cd0a538bd SHA512: 78ac7099148234347a261d474e346ef37659a99c45f7b507399ffdf08cceb7b6dbab1f73a932ee509498a3a9dc37c682cef39a766941fb884ac62ded96b1b5d1 Homepage: https://cran.r-project.org/package=SemiParamBernsteinDepCS Description: CRAN Package 'SemiParamBernsteinDepCS' (Semiparametric Bayesian Regression for Dependent Current StatusData) Implements a semiparametric Bayesian regression framework using Bernstein polynomial baseline models for analyzing dependent current status data. The package accommodates proportional hazards (PH) and proportional odds (PO) regression models with Archimedean copulas ('Gumbel', 'Frank', and 'Clayton') to model the joint dependence structure between event and observation or censoring times. Estimation is performed using a Robust Adaptive Metropolis (RAM) Markov Chain Monte Carlo ('MCMC') algorithm. Model comparison metrics including Deviance Information Criterion ('DIC') and posterior summaries with Highest Posterior Density ('HPD') intervals and Kendall's tau are provided. Methodological details are described in Sharma and Balakrishnan (2026) . Package: r-cran-semiparmf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semiparmf_1.0.0-1.ca2404.1_all.deb Size: 64702 MD5sum: be1200922ddd94f5e4923a21aa576cd4 SHA1: efa667f510713b730515f70d61cf8d5e003b7301 SHA256: cc41363a627a2bae5f3782e16610e7755dca10f94a814a6873e978756f55896d SHA512: 6c3d1df5b71bcae3b7596c37bff1af4dac29d87c090375ce1941e2562b62cbd7ff0664434a50bbb20d05f6936a90f5c4451f05d98e6bff8c653850c6381e11d9 Homepage: https://cran.r-project.org/package=SemiparMF Description: CRAN Package 'SemiparMF' (Semiparametric Spatiotemporal Model with Mixed Frequencies) Fits a semiparametric spatiotemporal model for data with mixed frequencies, specifically where the response variable is observed at a lower frequency than some covariates. The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects, and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022) . Package: r-cran-semlbci Architecture: all Version: 0.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-nloptr, r-cran-mass, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-pbapply, r-cran-callr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semlbci_0.12.1-1.ca2404.1_all.deb Size: 815626 MD5sum: 1b9fe7cb7dd43e6120ac75457fd2b242 SHA1: 6295f784e6ac1e016125a63417d3e252a01c62a5 SHA256: 95551eb02f8ddf10423e2e84879a14cbc687057378feb00c1ba1f22cff72b80e SHA512: 8f26cf24c0ad4d4c83b7396e0b28cc476de6fa94fb365f88a6787b14c536e5ced054595d74c7eceda09e9994a59bbfbbf6b174d2635b601859956758ddc13cfa Homepage: https://cran.r-project.org/package=semlbci Description: CRAN Package 'semlbci' (Likelihood-Based Confidence Interval in Structural EquationModels) Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) . Currently implements the algorithm illustrated by Pek and Wu (2018) , and supports the robust LBCI proposed by Falk (2018) . Package: r-cran-semlrtp Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semlrtp_0.1.2-1.ca2404.1_all.deb Size: 542042 MD5sum: 1d7f2636ab4e9b2f40772eee1f44a0d6 SHA1: 753749b353c96f34bcf031e170ace93280c37a39 SHA256: 7f183eb0446b60c918cdc6f731c86594ccc43c4b542a5eb34f6f1737ab682950 SHA512: 1b1d5269e2f854c6a3c4059c008bbabef7a88a95a21ca8caa499d4b7fd9e1874ff0f54caa320a61ea3be1fc4dd56c5f037ef38729c5cfa48470c9791e4babfac Homepage: https://cran.r-project.org/package=semlrtp Description: CRAN Package 'semlrtp' (Likelihood Ratio Test P-Values for Structural Equation Models) Computes likelihood ratio test (LRT) p-values for free parameters in a structural equation model. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) . Package: r-cran-semmcci Architecture: all Version: 1.1.6-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-psych, r-cran-amelia, r-cran-bmemlavaan Filename: pool/dists/noble/main/r-cran-semmcci_1.1.6-1.ca2404.2_all.deb Size: 105644 MD5sum: 7a68597ad25b0431d86e70001518bde2 SHA1: dba18b540b6c6a33cf912cf17a29400f5556d73e SHA256: b360994d336a4f441cd8bd2cb186a4936170823fa0383a9c29e1ac67cae45515 SHA512: 2078e374f69c18ca8b47b1d2e039345dc73a55b77f76e1489061dbe7284d6bf9651b4fc88abdc233a90ed78a68eb8c92139ad7505d16066d1ba98314ee766b28 Homepage: https://cran.r-project.org/package=semmcci Description: CRAN Package 'semmcci' (Monte Carlo Confidence Intervals in Structural Equation Modeling) Monte Carlo confidence intervals for free and defined parameters in models fitted in the structural equation modeling package 'lavaan' can be generated using the 'semmcci' package. 'semmcci' has three main functions, namely, MC(), MCMI(), and MCStd(). The output of 'lavaan' is passed as the first argument to the MC() function or the MCMI() function to generate Monte Carlo confidence intervals. Monte Carlo confidence intervals for the standardized estimates can also be generated by passing the output of the MC() function or the MCMI() function to the MCStd() function. A description of the package and code examples are presented in Pesigan and Cheung (2024) . Package: r-cran-semmcmc Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-msm Filename: pool/dists/noble/main/r-cran-semmcmc_0.0.6-1.ca2404.1_all.deb Size: 27006 MD5sum: fa848d1735627a7bc83ea995e64800fa SHA1: 31e3fd43751b4ce8a39ab308099fde3cd7339194 SHA256: 429bc77c0e3dd995bf7f4ad906a507124df4ccd8efa81257f197abc89150186c SHA512: 58e6f66e78e53a8b83950c67f7ed2e9b7356e966dabdf64d506b883b3c02efd5fece8b118a9cea38c5239c020dab027746d87eb311efa5fdff8d1ced9e6b5495 Homepage: https://cran.r-project.org/package=semmcmc Description: CRAN Package 'semmcmc' (Bayesian Structural Equation Modeling in Multiple Omics DataIntegration) Provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) . This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms. Package: r-cran-semnar Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-lubridate, r-cran-parsedate, r-cran-leaflet, r-cran-urlshortener Suggests: r-cran-curl, r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-semnar_0.8.2-1.ca2404.1_all.deb Size: 81614 MD5sum: b49cc9af1b1847b7b147c406258ef490 SHA1: d3bbac2fc01bdca58bdead4159ca685ff005ef3f SHA256: c0da130dd4f09cebd6efa95fc8f458a3d8343b64fb0af8d74d0fca0a533233d7 SHA512: ff7476815714684bcf00a07e046a5440ddcd24c009d83c9d39d5e2b0e5c5ceba18e0eeef62e6596d92a447e558f8ad691a47f59fe52c4b8d7f4fcd0f3cb2cecf Homepage: https://cran.r-project.org/package=semnar Description: CRAN Package 'semnar' (Constructing and Interacting with Databases of Presentations) Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on). Package: r-cran-semnet Architecture: all Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2911 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-dplyr, r-cran-plyr, r-cran-magrittr, r-cran-ggplot2, r-cran-igraph, r-cran-qgraph, r-cran-scales, r-cran-car, r-cran-broom, r-cran-effects, r-cran-philentropy Suggests: r-cran-shiny, r-cran-shinyjs, r-cran-shinyalert, r-cran-shinymatrix, r-cran-shinybs, r-cran-animation, r-cran-r.matlab, r-cran-foreign, r-cran-readxl, r-cran-data.table, r-cran-networktoolbox, r-cran-semnetcleaner, r-cran-semnetdictionaries Filename: pool/dists/noble/main/r-cran-semnet_1.4.5-1.ca2404.1_all.deb Size: 2620198 MD5sum: 50af3cb226e0031aa61bdb7a004cc116 SHA1: cbbfb4b0c6bc32b97fd85c76aec4b0044c3ddecb SHA256: eed5e7724d1197a750cdf572334a9f8cf51b13fd102db3d6b48e24bdad45250b SHA512: e5f4c9eea88efe2d8d9db1969996d673ede781d58bd63b37e7dbe114a806294ad99a0dcff1c919e644930fe32631a229a68d25f2394e2e8558435891dfa6d8dc Homepage: https://cran.r-project.org/package=SemNeT Description: CRAN Package 'SemNeT' (Methods and Measures for Semantic Network Analysis) Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 ), random walk simulation (Kenett & Austerweil, 2016 ), and a function to compute global network measures. Significance tests and plotting features are also implemented. Package: r-cran-semnetcleaner Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-semnetdictionaries, r-cran-foreign, r-cran-pbapply, r-cran-r.matlab, r-cran-readxl, r-cran-rstudioapi, r-cran-searcher, r-cran-shiny, r-cran-stringi, r-cran-stringdist Suggests: r-cran-dt, r-cran-hunspell, r-cran-easycsv, r-cran-htmltable, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semnetcleaner_1.3.7-1.ca2404.1_all.deb Size: 597044 MD5sum: 93044ce7f2432ebcdcf039350e1f6787 SHA1: 8487670a65f3179d470db159e57845760cecf3a1 SHA256: 1df9a91ef120f73da8f191c8df007fc42f66c4e3c4f5ea577f005c1aec0b8487 SHA512: 5d5790851d72792bdd1c67db756d0a5ed3a76dad80280ab3fabe6c8d0e7b3e71aa19e0137e99c6d719f38ee4b323773a3c47c9fc9d619aba14d0d8266c63ecb6 Homepage: https://cran.r-project.org/package=SemNetCleaner Description: CRAN Package 'SemNetCleaner' (An Automated Cleaning Tool for Semantic and Linguistic Data) Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible. 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Package: r-cran-semnova Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-matrix, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semnova_0.1-6-1.ca2404.1_all.deb Size: 113880 MD5sum: b49bcab0538a0c97a15d1a2691d1e718 SHA1: 74343aa295d4e8c771ba75dddb08adc9c019c379 SHA256: 791129d9d25c35454b1b1f58f760776d338018a1bbed155d3ab8338353430788 SHA512: 6ed4531922d0fde96c3e19b8d0183bdaf43077e00cede81d61196891cb0985bdf74ca6ca4bab6161f68d300d95abf5ff54f05af62f24df2191e47d4a5316d4d2 Homepage: https://cran.r-project.org/package=semnova Description: CRAN Package 'semnova' (Latent Repeated Measures ANOVA) Latent repeated measures ANOVA (L-RM-ANOVA) is a structural equation modeling based alternative to traditional repeated measures ANOVA. 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Package: r-cran-semptools Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1731 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-rlang, r-cran-semplot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-semptools_0.4.0-1.ca2404.1_all.deb Size: 1219618 MD5sum: 18b1603e29a5024dcfc9566882c44039 SHA1: 911ba6f164ecda4c2319e2982f292b7ca8a02f43 SHA256: 4ee9a8a4d981dae01e72f0ee214c68f60606d5808cd279b521c232c96e79bbf5 SHA512: d12b115cbe787c89e84aa0c6b9c715e4fc65d8bf0d5e46137f758a808ee98a2b53b64ace47a7db121137a8da6745ec568e011fc44bdcf61577b083fd4a24f02d Homepage: https://cran.r-project.org/package=semptools Description: CRAN Package 'semptools' (Customizing Structural Equation Modelling Plots) Most function focus on specific ways to customize a graph. They use a 'qgraph' output as the first argument, and return a modified 'qgraph' object. This allows the functions to be chained by a pipe operator. Package: r-cran-semrulesid Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semrulesid_0.4.2-1.ca2404.1_all.deb Size: 133084 MD5sum: fd603ba834f682fdbe22e6e901f9e64b SHA1: 2dc8b4e9b0e47e25741d317c42b210727043812e SHA256: 484ccedba7af62aa3fedbb034e92d5eebfe7da5d291fcced178fcd5aa29df94a SHA512: c0896bb2cfd22968432dc58875e9c1a81d153b96361f6b23b91a98a33fe3b636dbc7a3b5cfa7ebb477cf19c41f9a54b81d8a67bedbd8118c8fbd94662462c996 Homepage: https://cran.r-project.org/package=semrulesid Description: CRAN Package 'semrulesid' (Evaluate Structural Equation Model Identification Rules) Evaluates selected necessary and sufficient identification conditions in structural equation models (SEMs), including latent-variable scaling constraints. Output reports rule status and applicability and provides diagnostic messages to support model specification and respecification. The package is intended as a diagnostic aid and does not implement a universal identification algorithm. For more details, see Bollen (2026, ISBN:978-1009312820). Package: r-cran-semsens Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semsens_1.5.5-1.ca2404.1_all.deb Size: 192382 MD5sum: 8e87be571f012edd5c231720c345ebe6 SHA1: 9dd8054f13e181c10cee6e75f80dd536f813dda1 SHA256: 56113e65e30072118573b11243deec9ce1fd589eb707011545a32f99fb348571 SHA512: 3241caf0a5e29ba06dbaabf53a75d6038e53279fc86b07a1c14dc43f79c1ed51f0cfc14aac9adb38f8e5904b0c9ef01b6c3e6656beac56bce5fff68e02fbe812 Homepage: https://cran.r-project.org/package=SEMsens Description: CRAN Package 'SEMsens' (A Tool for Sensitivity Analysis in Structural Equation Modeling) Perform sensitivity analysis in structural equation modeling using meta-heuristic optimization methods (e.g., ant colony optimization and others). The references for the proposed methods are: (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017) ; (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. (2022). ; (4) Socha, K., & Dorigo, M. (2008) . We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization algorithm with continuous domains and associated R code, which provided the base for the development of this package. Package: r-cran-semsensitivity Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-dplyr, r-cran-r.utils, r-cran-semfindr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semsensitivity_0.1.0-1.ca2404.1_all.deb Size: 167894 MD5sum: 964d1844b2d116aae3dc66f988180bb7 SHA1: 4764e08672c244cf352e6e34f583505389ae8e60 SHA256: a2f1684c74449fed561f8fe12a7edd6e8a55352237e5cfb68ac8ba91af42b334 SHA512: cd0b65bcc99b51e85fab75f0dd09b5134c03ab182cad8df72b62592142d48fccbaaafe344c4766771e42e871f5986e5c6c82f57df22f4c2854bda4cc2e4608fb Homepage: https://cran.r-project.org/package=SEMsensitivity Description: CRAN Package 'SEMsensitivity' (SEM Sensitivity Analysis) Performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation. 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Package: r-cran-sensiphy Architecture: all Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phylolm, r-cran-ggplot2, r-cran-caper, r-cran-phytools, r-cran-geiger Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sensiphy_0.8.5-1.ca2404.1_all.deb Size: 1125036 MD5sum: 2cc1135afe36d6143517d57f67012ecd SHA1: 75772d0b59da92afe78cbb982bb07f7969e2c6db SHA256: 2bd78e8e24bb3a53d321c7dfbdbe6555b0339245ceb5744c3298b37555294012 SHA512: a0d68297f60b25f3090a8be9f114ab05cea171c618806f6fa284f80f8277d00bdb3c20e0eb53e698bb0d550b49cdc171962b825055da4776af2ef9bc3418ef49 Homepage: https://cran.r-project.org/package=sensiPhy Description: CRAN Package 'sensiPhy' (Sensitivity Analysis for Comparative Methods) An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error). Package: r-cran-sensitivity2x2xk Architecture: all Version: 1.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sensitivity2x2xk_1.01-1.ca2404.1_all.deb Size: 66626 MD5sum: e28bd307d72325533aa5b99cb7f42e51 SHA1: 177c496bbee4a4ee6b63297137b81f6cf092d866 SHA256: 67274a87a62b381a841bef3a7896434de07b9f0369aacaecbf2b16b6b856e035 SHA512: 958fb2b225452fbc8796b226f15a28666bf62ae9ae98db14d87d7a384ff2f73386ebc4389bc601aac3c472185c204d9c2c92cb6f866703dd7d33bef7c0549f3e Homepage: https://cran.r-project.org/package=sensitivity2x2xk Description: CRAN Package 'sensitivity2x2xk' (Sensitivity Analysis for 2x2xk Tables in Observational Studies) Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies. Package: r-cran-sensitivitycalibration Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-relaimpo, r-cran-splitstackshape, r-cran-ggrepel, r-cran-stringi, r-cran-plotly Filename: pool/dists/noble/main/r-cran-sensitivitycalibration_0.0.1-1.ca2404.1_all.deb Size: 115892 MD5sum: f9274226c8a2810643fd65925f233422 SHA1: f5dda41d12216adae105edc337d4320f89fa33be SHA256: 8b944c68cebc64ce7db4d833c891f9d970413cdad15e812b440e98d2ac7ff0ac SHA512: a44d5891cccc92b4bb6b986600c74ad2f283651f21f8f8a3fab25b2b46c1002fba1b28565121eca2330a138c0541a53d2f85047a306dc4dd22393fd8b2927038 Homepage: https://cran.r-project.org/package=sensitivityCalibration Description: CRAN Package 'sensitivityCalibration' (A Calibrated Sensitivity Analysis for Matched ObservationalStudies) Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). . Package: r-cran-sensitivitycasecontrol Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitycasecontrol_2.2-1.ca2404.1_all.deb Size: 51248 MD5sum: 9ff56c811ae8f3dfc8aec28b308a331f SHA1: a0b8935aa6cad81e03059318e33c0f997ad21f88 SHA256: 3661f013a61088389f1360e6d76936adbf43298a6eaa32f7eb00169bd8e100d2 SHA512: bd87f7da64cd1e53204f15cfbe2d7b264bca4c60964fc0340c6ecdde9453de9028ce13deeed80d4d5bcc1e719f766314e545436c45e9b2735caab67efd7c8e48 Homepage: https://cran.r-project.org/package=SensitivityCaseControl Description: CRAN Package 'SensitivityCaseControl' (Sensitivity Analysis for Case-Control Studies) Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions. Package: r-cran-sensitivityfull Architecture: all Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivityfull_1.5.6-1.ca2404.1_all.deb Size: 46380 MD5sum: fd8bb3009312110bb0a35614b1c68df7 SHA1: 4b1b7d20532a2a18196fa3b682329cf534887ad8 SHA256: 6849ce85dc2962b05850c1c143e608d3b79f883b1b25083a3ac50e6762c50365 SHA512: 524a0b05bd6d845b2665837319e986c8118c292dc091ad0884b43f38fa055d6c0a959824ac00eaec2223ebcbbd2c5f34f03f5bb23ebb8212c000dc60324fe31a Homepage: https://cran.r-project.org/package=sensitivityfull Description: CRAN Package 'sensitivityfull' (Sensitivity Analysis for Full Matching in Observational Studies) Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) . Package: r-cran-sensitivitymult Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitymult_1.0.2-1.ca2404.1_all.deb Size: 125596 MD5sum: f82ebba0d587535d9593bceede7c02b6 SHA1: 51ec21df2fc5a30faea32181933a6b1edf163ff2 SHA256: 2c37e612ec6a64f8e028dc46200b2fadb4ff44ac1689036122808348ce104b98 SHA512: 9c90c8847b3c4a52812fdfa43caab856a62c173f553dab6a944cd3a90a6492c712e986399f228286f5bacb48c8e8e5413eb9891ece6ff7f471975006000dc79b Homepage: https://cran.r-project.org/package=sensitivitymult Description: CRAN Package 'sensitivitymult' (Sensitivity Analysis for Observational Studies with MultipleOutcomes) Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) . Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function. Package: r-cran-sensitivitymv Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitymv_1.4.4-1.ca2404.1_all.deb Size: 76536 MD5sum: 5916c3eee23b117b510455a2cc57210d SHA1: 385c32addfe82ea208a092a27ef504636fb8b99c SHA256: 3f761db3b2c0b008d7e2894dfd1bc103da6859d953ca7a34a38c594dcc78d4e3 SHA512: f20f864fd5c2bd63edc6ce868c57d0312d6760133cc5de41ca2a2421de0005f9c04365aaf053fc1738e2814595bfd8d79571f1df175386ef16aaa3deea68d39a Homepage: https://cran.r-project.org/package=sensitivitymv Description: CRAN Package 'sensitivitymv' (Sensitivity Analysis in Observational Studies) The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, . Package: r-cran-sensitivitymw Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitymw_2.1-1.ca2404.1_all.deb Size: 60126 MD5sum: 9f213e8ad9dd6d1dc693a53f60c6679d SHA1: 24538339ce0231048618a3e5951520bf5ce59c88 SHA256: cbe48af6db9773bdcbb17577346c0d74fe15ed1bd9bb3985ad8955d726429f12 SHA512: 17706e505c2a71f3499d75f064adfe07ac633fbe816209181a0a1f9fe3379409482cca30e208a5a06d9daa6faf5c9f77ba0390f9dd18314996e1d99c29fc7fd7 Homepage: https://cran.r-project.org/package=sensitivitymw Description: CRAN Package 'sensitivitymw' (Sensitivity Analysis for Observational Studies Using WeightedM-Statistics) Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 . Package: r-cran-sensmap Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doby, r-cran-lattice, r-cran-factominer, r-cran-mcmcpack, r-cran-factoextra, r-cran-fields, r-cran-ggdendro, r-cran-glmulti, r-cran-mgcv, r-cran-plotly, r-cran-shiny, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensmap_0.7-1.ca2404.1_all.deb Size: 120468 MD5sum: 911205b5b67a3911ed3dd3f5dfa9fe73 SHA1: e1dba6831ba31c81683fd3fcda366dde8e485944 SHA256: 646e49ba99073a52de639f7c250181e787695082e06f9211ba039f099a943418 SHA512: d5ef25e316c63af74e982a424af178b5c11d97b83a3a1bdd41acd923cc92dc512ac181e627d3997c643a016205eb39b1e46ea03976fece43f25d2916f341cb36 Homepage: https://cran.r-project.org/package=SensMap Description: CRAN Package 'SensMap' (Sensory and Consumer Data Mapping) Provides Sensory and Consumer Data mapping and analysis . The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A 'shiny' application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available. Package: r-cran-sensmediation Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensmediation_0.3.1-1.ca2404.1_all.deb Size: 233830 MD5sum: 466242e1f84eec2191af0ad0a4307518 SHA1: 573aa9e5018a3508d43560cadd2b2aebaccaa9bc SHA256: ad6aef5b1c8dd02b484c208d185020650ca4f32c978fe1822d4937bd56302423 SHA512: 72a2e83b5c6e25cf18de46bf27b68a3dd0099f3c9fce25326ec6f91ae20b64ba33b667e2ecfbdb556e8e5a0d59b738da29238c4ef267cc17a105eada96c709c0 Homepage: https://cran.r-project.org/package=sensmediation Description: CRAN Package 'sensmediation' (Parametric Estimation and Sensitivity Analysis of Direct andIndirect Effects) We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) and Lindmark (2022) . The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships. Package: r-cran-sensominer Architecture: all Version: 1.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-kernsmooth, r-cran-ggplot2, r-cran-reshape2, r-cran-algdesign, r-cran-gtools, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-sensominer_1.28-1.ca2404.1_all.deb Size: 999178 MD5sum: a8fa5b744210dc06d88a45845df78ff8 SHA1: db15d2c772a82e40396fe3d8073c42fcb3d87c0a SHA256: 423cc18f1d39136aa9c8b7ee5f4a70e970b92c2623674c6ff8e45be32f5f72cc SHA512: cef2093cb97181f74b7fe7897189f52d4f3b6d36a3e3788fc14e8413c8f98fe3802839609be9e672fa37af33ab412ce9c512dcdefd7500217594c8b9abf78cda Homepage: https://cran.r-project.org/package=SensoMineR Description: CRAN Package 'SensoMineR' (Sensory Data Analysis) Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008). Package: r-cran-sensorleak Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensorleak_0.1.0-1.ca2404.1_all.deb Size: 51934 MD5sum: 9c9cc1aa6b1a87702dcf16171db3211a SHA1: 88ef83e23a5d3cca46178df5edc86d5c25c6f7cd SHA256: ccb1962b7b4a105571203707dba8e724c2f59ad8fb76383dd48e490d23ce2e61 SHA512: d58967a1df62ea4de3419f2199711ce9be89ade10629f87f905e77fc8570cae42340abeb6dcbb92f328cae7e7335ae0f8dd14449421ee8de214ced2e8984cb65 Homepage: https://cran.r-project.org/package=sensorLeak Description: CRAN Package 'sensorLeak' (Leakage Detection for Environmental Sensor Data) Provides tools for identifying potential information leakage and validation risks in machine learning workflows using environmental sensor data. The package includes checks for temporal ordering, shared sensors, spatial proximity, and overlapping temporal windows. The diagnostics are motivated by considerations of spatial and temporal structure in model validation (Roberts et al., 2017) . Package: r-cran-sensortowerr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sensortowerr_2.0.0-1.ca2404.1_all.deb Size: 173978 MD5sum: 5fee0da17e976f92907a03dff4333baf SHA1: dbd248f10673990bf9ce25a3612afadfc2ce8707 SHA256: 1f5fb998236e425996e538f34c2d88814c1775ac8770704a87c94a9c810c4048 SHA512: 2f683ea0bfcff0b4419c75712a3ac7c88804df226410c18cf75d860de00cbee387d21aa4fa43bef40407ed9c6288f09b6fe5b785ae91eef79da155441de6cc5d Homepage: https://cran.r-project.org/package=sensortowerR Description: CRAN Package 'sensortowerR' (Tidy Pipelines for the 'Sensor Tower' API) Retrieves mobile app intelligence from the 'Sensor Tower' API . Composes discovery, metadata, rankings, sales, audience and specialist estimates through ordinary data frames with explicit identifiers, units and error handling. Package: r-cran-sensory Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-gtools, r-cran-mass Filename: pool/dists/noble/main/r-cran-sensory_1.1-1.ca2404.1_all.deb Size: 77876 MD5sum: f12f61fbd7ddb961650e2748b562d50b SHA1: 3ca64696bdb23f487a0918bbc19a25612b5e4d92 SHA256: 80ae113fb28cd475427682e0bd9c93f11ba4aa371a6f72ec2db64c15bc5676ae SHA512: ea4d5e6017d41230c44e51cf0f8400aa0607001e6c3272ca77cec08014030f190d64e438ff8ebcd23aa100b2955b99c0641e2ecd568bdb0ac54ecf01ae3235cb Homepage: https://cran.r-project.org/package=sensory Description: CRAN Package 'sensory' (Simultaneous Model-Based Clustering and Imputation via aProgressive Expectation-Maximization Algorithm) Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously. Package: r-cran-sensorydatasets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensorydatasets_0.1.0-1.ca2404.1_all.deb Size: 2280904 MD5sum: 5aee598b8502b49bc526652f1127346a SHA1: ed8fc5db03f45bc8eecaf1588fe5d04dd969ff87 SHA256: 7be9a54c19a82fc04a42a9eda92d9b09f608a7ce07bb58f6e80f15f1bbad5514 SHA512: 88ec732fc89bbeb2066eac6d7995d6af2e89e8252c319b8b3727e8e7fa6b052adada51b008cc56585a86f3891517c231ba0af6fa467f26cf3cd2c968921519e7 Homepage: https://cran.r-project.org/package=SensoryDataSets Description: CRAN Package 'SensoryDataSets' (A Collection of Sensory Evaluation and Consumer Science Datasets) Provides a curated collection of datasets for sensory evaluation, consumer research, and related statistical applications. The collection includes consumer acceptance and liking scores, sensory profiles, descriptive evaluations, physical and chemical measurements, wine quality and bitterness assessments, and data from products such as bread, olive oil, orange juice, grape blends, wine, cocktails, and perfume. The package is intended for teaching, exploratory data analysis, statistical modeling, multivariate analysis, consumer studies, and methodological research in sensory and consumer science. The original sources and applicable licensing terms are documented in the 'LICENSES_DETAILS.md' file. Package: r-cran-sensr Architecture: all Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1299 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp, r-cran-mass, r-cran-numderiv Suggests: r-cran-ordinal, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensr_1.5-3-1.ca2404.1_all.deb Size: 996164 MD5sum: 3b37d8f2957f4c191db58517d6c6809d SHA1: 5ff3fb8cd243a9ae7198d723543494bc34d0d020 SHA256: 0f78bb1d87f74c636d31969307b0654cdf94a0ac7cea92236722547eb8fff4fe SHA512: 59b1a96992bfbd5ce61dfaf71d800b01f00b293b2c3aaadf5e6ce410749ffc6223796966e3c4f484328ddc1974bfb09afaa705da56f04487edcf8127a4bb8206 Homepage: https://cran.r-project.org/package=sensR Description: CRAN Package 'sensR' (Thurstonian Models for Sensory Discrimination) Provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included. Most methods are described in Brockhoff, P.B. and Christensen, R.H.B. (2010) . Package: r-cran-sensrivastava Architecture: all Version: 2015.6.25.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-leaps, r-cran-car Filename: pool/dists/noble/main/r-cran-sensrivastava_2015.6.25.1-1.ca2404.1_all.deb Size: 151762 MD5sum: dc5bb2539e6e2ba99bc32813b0606e66 SHA1: 8cfa4f15d8e2c3073043d70b24138bd96759a960 SHA256: b7a0b1723121eebdd5ea929fde07d74b138993ed5782e87a8f86268a457aa8a5 SHA512: 2782df9e3e520a90ee3a0d250987452a6360da38f99a58d440274190401a22d0891e6d25a7c274323e42ede57445192951274a87cdfab38a1b677dfbecfa198f Homepage: https://cran.r-project.org/package=SenSrivastava Description: CRAN Package 'SenSrivastava' (Datasets from Sen & Srivastava) Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book. 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Package: r-cran-sentiment.ai Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-reticulate, r-cran-roperators, r-cran-tensorflow, r-cran-tfhub, r-cran-xgboost Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-magrittr, r-cran-microbenchmark, r-cran-prettydoc, r-cran-rappdirs, r-cran-rstudioapi, r-cran-text2vec Filename: pool/dists/noble/main/r-cran-sentiment.ai_0.1.1-1.ca2404.1_all.deb Size: 874168 MD5sum: e4d760ef65a693b311e0282705b488fc SHA1: 12ef90d4e0156c40bf6fa7b0ac64a2f86f011827 SHA256: f5195584fd8cd8a71492b7c603f95e047895d599dbaadb2acc34587a3db00add SHA512: 7c7e820411ead8025140d0d2946c2701177378723e71bce15fbbb601005d28e8c98970d29d929909acd2fd13be721862ff3d53bb77fe748a1980dbb2a12c629e Homepage: https://cran.r-project.org/package=sentiment.ai Description: CRAN Package 'sentiment.ai' (Simple Sentiment Analysis Using Deep Learning) Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. In addition to out-performing traditional, lexicon-based sentiment analysis (see ), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux. 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Package: r-cran-sentinmixt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-snow, r-cran-tsdist, r-cran-tidyr, r-cran-data.table, r-cran-expint, r-cran-zipfr, r-cran-mclust, r-cran-rlist, r-cran-withr Filename: pool/dists/noble/main/r-cran-sentinmixt_1.0.0-1.ca2404.1_all.deb Size: 126648 MD5sum: ada52b57e98ba919656756e3c7b235bd SHA1: 9012e649dbfe5972c7fedca56e5d1817907fe596 SHA256: a12a86b88d223a5a7c5470b15a0851b39808076cd9d0395b45867823ce5af9d8 SHA512: f680fec5b9ef344cdc7f54e9b7c0c8350b8afbc9300080e4abd4c6cb1f0ea6761ad70c137bdaa2f96d645f6877af3ce190acb3a181380b85b716befdebbe6e6d Homepage: https://cran.r-project.org/package=SenTinMixt Description: CRAN Package 'SenTinMixt' (Parsimonious Mixtures of MSEN and MTIN Distributions) Implements parsimonious mixtures of MSEN and MTIN distributions via expectation- maximization based algorithms for model-based clustering. 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Lexicons included: Sentix 3.0, MAL, ElIta VAD and basic emotions (Plutchik's wheel of emotions). For more details about the lexicons, see Basile & Nissim (2013), "Sentiment Analysis on Italian Tweets", ; Vassallo et al. (2019), "The Tenuousness of Lemmatization in Lexicon-based Sentiment Analysis", ; Di Palma (2024), "ELIta: A New Italian Language Resource for Emotion Analysis", . Package: r-cran-sentryr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr, r-cran-tibble, r-cran-uuid Suggests: r-cran-httptest, r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sentryr_1.1.2-1.ca2404.1_all.deb Size: 317074 MD5sum: 8177d588e48c455c2cd57a4e8acbed19 SHA1: bd3135cf6169c4cd635afd121681f8fd6c8efc78 SHA256: 7b7fd8e535178c5753ec7d89428c32b81494beb94c38bafb465205670ed6fc5f SHA512: 4bc6ca807b33f3eb008043ea4ceaca79f5c6b9d17a30f3da9a752dcaaf62f42e227105dd008c25f6d430e7b7d26a3f21e19460ee5c9dd6ac0a2c8f87beda91b8 Homepage: https://cran.r-project.org/package=sentryR Description: CRAN Package 'sentryR' (Send Errors and Messages to Sentry) Unofficial client for 'Sentry' , a self-hosted or cloud-based error-monitoring service. 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Package: r-cran-sepa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-writexl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sepa_0.1.0-1.ca2404.1_all.deb Size: 1757126 MD5sum: 20c7e473243547a69a72458b8a7dbf49 SHA1: 33e3a3aa4c7d03cffa1b538ee24eae1806eb4876 SHA256: 2f350e11bfe4b82b4a7eb35708b47c0f57dd8aaac829794207ef4b17c3443e00 SHA512: f45dde6dd292d8b14ede350bf120ee2692e2b57f74f5ebc4a88e5240c965c105e48220f87b628f89ddc32508e23aa7f979fc8b07d0b6e5a68393fc3d86628eeb Homepage: https://cran.r-project.org/package=SEPA Description: CRAN Package 'SEPA' (Segment Profile Extraction via Pattern Analysis) Implements the Segment Profile Extraction via Pattern Analysis method for row-mean-centered multivariate data. Core capabilities include SVD-based row-isometric biplot construction, bias-corrected and accelerated, and percentile bootstrap confidence intervals for domain coordinates and per-person direction cosines, Procrustes alignment of bootstrap replicates across planes, parallel analysis for dimensionality selection, and segment profile reconstruction in planes defined by pairs of singular dimensions. A synthetic Woodcock-Johnson IV look-alike dataset is provided for examples and testing. The method is described in Kim and Grochowalski (2019) . Package: r-cran-sepals Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sepals_0.1.0-1.ca2404.1_all.deb Size: 302556 MD5sum: be6b437feaeb29bb16abdc70bf28cbac SHA1: 51d3af51937eeb052d2e87decbe0cb2e1936a487 SHA256: eaff9e9aed8971c7341d2c457e2369d9b1997a467ce8a359d7f831c83173857d SHA512: f44b8edd7220dfcfcae78894dc8a8fa45c3ed89500b6bc0181bddc28d8242413d4a652b5ebaee2d75748957e9c208306a0b91f84583e017588f0c8fe1d0f25ed Homepage: https://cran.r-project.org/package=SEPaLS Description: CRAN Package 'SEPaLS' (Shrinkage for Extreme Partial Least-Squares (SEPaLS)) Regression context for the Partial Least Squares framework for Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares (SEPaLS) estimators, an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework. The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and Julyan Arbel. R code to replicate the results of the paper is available at . Extremes within PLS was already studied by one of the authors, see M Bousebeta, G Enjolras, S Girard (2023) . Package: r-cran-separate Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-separate_0.3.2-1.ca2404.1_all.deb Size: 68874 MD5sum: 0e129f0bcd06ce11abff28db7f3a01a7 SHA1: a72d911dc233ab06f92c329d02a58c4c86e845e0 SHA256: 262130b59c9dce2b6a1e019780f7d585b65546a1fc389ebea4c1e6003b28d3d1 SHA512: 32a30d91ff33ef763b6799b26ae6c24a1e7b9429b2abf95580a3193ef4c84ec796a2260177b51d0aac423f4c589a2adfcbb6098405cc0b0e5af2bc1238cd4c30 Homepage: https://cran.r-project.org/package=sEparaTe Description: CRAN Package 'sEparaTe' (Maximum Likelihood Estimation and Likelihood Ratio TestFunctions for Separable Variance-Covariance Structures) Maximum likelihood estimation of the parameters of matrix and 3rd-order tensor normal distributions with unstructured factor variance covariance matrices, two procedures, and for unbiased modified likelihood ratio testing of simple and double separability for variance-covariance structures, two procedures. References: Dutilleul P. (1999) , Manceur AM, Dutilleul P. (2013) , and Manceur AM, Dutilleul P. (2013) . Package: r-cran-separationplot Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-hmisc, r-cran-mass, r-cran-foreign Filename: pool/dists/noble/main/r-cran-separationplot_1.4-1.ca2404.1_all.deb Size: 35104 MD5sum: cd99daeed7de4e74a7dc7ece51099b7b SHA1: cf300e14116784fff6c956f42f52d66ca90bfe5d SHA256: c555fa7ebb8a9409a201255b781d218d02fa373e22d63e2ab0d0c1888d7a2feb SHA512: f8bb5387e66a761fd4ba25f9497dd95c7b472993b2d5d1b11d2fd36f601d3496a83394d77fd3757ed4b4e25008accfc39072d71e0133bc034377c2df56171bb6 Homepage: https://cran.r-project.org/package=separationplot Description: CRAN Package 'separationplot' (Separation Plots) Visual representations of model fit or predictive success in the form of "separation plots." See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002. 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Package: r-cran-sepkoski Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-sepkoski_0.0.1-1.ca2404.1_all.deb Size: 1420862 MD5sum: 978f86c3c2d4230478404738cee9cde4 SHA1: 51927094623316b65af773d1d24fb8ed1e37611c SHA256: 9f91ea2cc34e2f21852259a9a12ed9dc28b8ec8a8ceb6060d204c8488c06e291 SHA512: 3a3b638e374e819d1a7e9968f541e018d827e9f178efe4785724789b7abe0265762dff61aaa0e285727619dba208fd2c1cd4b54ca7750a8f3b2c6d6aafa5974e Homepage: https://cran.r-project.org/package=sepkoski Description: CRAN Package 'sepkoski' (Sepkoski's Fossil Marine Animal Genera Compendium) Stratigraphic ranges of fossil marine animal genera from Sepkoski's (2002) published compendium. No changes have been made to any taxonomic names. However, first and last appearance intervals have been updated to be consistent with stages of the International Geological Timescale. Functionality for generating a plot of Sepkoski's evolutionary fauna is also included. For specific details on the compendium see: Sepkoski, J. J. (2002). A compendium of fossil marine animal genera. Bulletins of American Paleontology, 363, pp. 1–560 (ISBN 0-87710-450-6). Access: . Package: r-cran-septest Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-dhsic, r-cran-fields, r-cran-get, r-cran-ggplot2, r-cran-mass, r-cran-patchwork, r-cran-reshape2, r-cran-scatterplot3d, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-splancs Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-knitr, r-cran-spatstat, r-cran-stpp, r-cran-rcolorbrewer, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-septest_0.0.1-1.ca2404.1_all.deb Size: 224522 MD5sum: d6cb4b385276f985ecddb87cfaaa0bab SHA1: b987b4f5ff5026d502ea56ef2f748b83843f0474 SHA256: 424ae820a3b9dac9ee285672233be8f6bd98b7774e14bbf7605b7562971c2a17 SHA512: 104800e2082c4a783cca5080ce041609f59a3aca7eb7e73ff2dd6b6670b69d897ace4368e5d6a0681ded54b1e499fd6f902e14ec9b3ec3c56787f911d7f2df91 Homepage: https://cran.r-project.org/package=SepTest Description: CRAN Package 'SepTest' (Tests for First-Order Separability in Spatio-Temporal PointProcesses) Provides statistical tools for testing first-order separability in spatio-temporal point processes, that is, assessing whether the spatio-temporal intensity function can be expressed as the product of spatial and temporal components. The package implements several hypothesis tests, including exact and asymptotic methods for Poisson and non-Poisson processes. Methods include global envelope tests, chi-squared type statistics, and a novel Hilbert-Schmidt independence criterion (HSIC) test, all with both block and pure permutation procedures. Simulation studies and real world examples, including the 2001 UK foot and mouth disease outbreak data, illustrate the utility of the proposed methods. The package contains all simulation studies and applications presented in Ghorbani et al. (2021) and Ghorbani et al. (2025) . Package: r-cran-seqalignr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plot.matrix Filename: pool/dists/noble/main/r-cran-seqalignr_0.1.1-1.ca2404.1_all.deb Size: 36210 MD5sum: c984d73f048be066e1818ebc6707c5ac SHA1: 800389c413eb615bd73dd2e64e440afbe92091a3 SHA256: 8d1d0f664df91e224bbe0eef27625301249427001165e65b6b59f5522390211b SHA512: 76fa5e10f45b9da761adf8c91d1d4271d73723322102ef440190576b0bd6977f1855a956b894587fdd6eb49fbdca1eb015b8576750c119597d54f9871303a8c3 Homepage: https://cran.r-project.org/package=SeqAlignR Description: CRAN Package 'SeqAlignR' (Sequence Alignment and Visualization Tool) Computes the optimal alignment of two character sequences. Visualizes the result of the alignment in a matrix plot. Needleman, Saul B.; Wunsch, Christian D. (1970) "A general method applicable to the search for similarities in the amino acid sequence of two proteins" . Package: r-cran-seqalloc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seqalloc_1.0-1.ca2404.1_all.deb Size: 58132 MD5sum: 26252179aa1129a9f45b8067a3fe6ba7 SHA1: 0ec35ececd1ac57faa60e3f2a66e37cbb7dbf606 SHA256: d4407107448c4e2a5842ec1baeabf9cde7c8e1d4c074a170e7ccf1b7b57156ed SHA512: 0e54aecfd68e5e28064130554b64e01c10122bfbe50c919164bac4debc7693d6cbf54e5b72a791a1bbdbcedb9b7f9726ceefad58c9080e7683cff163f7e6af9a Homepage: https://cran.r-project.org/package=SeqAlloc Description: CRAN Package 'SeqAlloc' (Sequential Allocation for Prospective Experiments) Potential randomization schemes are prospectively evaluated when units are assigned to treatment arms upon entry into the experiment. The schemes are evaluated for balance on covariates and on predictability (i.e., how well could a site worker guess the treatment of the next unit enrolled). Package: r-cran-seqbench Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-generics, r-cran-ggplot2, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seqbench_0.1.0-1.ca2404.1_all.deb Size: 185682 MD5sum: c4852f07d227de95efeba6572a5de383 SHA1: 58ec25d836e804e125fd55c1315b7fbb9178acae SHA256: 4a6e07dc9bd33f6e40ecd0f17414caa04273603480e5161dc3e5fd7a75718264 SHA512: 20beab3c27e6b71a65e10890b220bfc075e581f1b1c6b9a165c8be8b541209301d5bfd0c997f2c3180924e9b84ed4e08b3d61757cc076c80199ad13d35598c53 Homepage: https://cran.r-project.org/package=seqbench Description: CRAN Package 'seqbench' (Anytime-Valid Sequential Benchmarking of Algorithms) Sequential, anytime-valid comparison of algorithms on paired losses with a practical-equivalence margin. Confidence sequences for the mean paired difference, superiority/equivalence/continue decisions, cost accounting and auditable trajectories. Package: r-cran-seqcomp Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lamw Suggests: r-cran-scoringrules, r-cran-vgam, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seqcomp_0.3.0-1.ca2404.1_all.deb Size: 294666 MD5sum: 90514fe199b5ce34e7a413d30dfc2b10 SHA1: 968e1874fadbe7f5276d9e3b77b3cee97a79e608 SHA256: 359de032307d191f889c6f3f112fff2d84aaee5f6f14b0ceda1734a2ce0cf5ed SHA512: 53db5d1ba0d72581ad35d02fa4c57bdfa1c39cd892862794a00b7ba3a620adb87c3806d2c2019c7e81e8048999effd40cb14d08899b0c947b2e8103596ee5f89 Homepage: https://cran.r-project.org/package=seqcomp Description: CRAN Package 'seqcomp' (Sequential Comparison of Probabilistic Forecasts) Implements tools for the anytime-valid sequential comparison of two or more probabilistic forecasters. Provides binary, categorical, and quantile scoring rules, together with finite-sample confidence sequences and e-processes following Choe and Ramdas (2024) . Extends to multi-model evaluation via Sequential Model Confidence Sets, following Arnold, Gavrilopoulos, Schulz, and Ziegel (2026) , using closure principles, joint confidence sequences, and accelerated closed-testing. Adaptive betting fractions for the strong null (aGRAPA and ONS-m) are adapted from Waudby-Smith and Ramdas (2024) . Also includes Winkler-score comparisons, lag handling, and predictable-bound betting e-processes. Package: r-cran-seqdesign Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-seqdesign_1.2-1.ca2404.1_all.deb Size: 424436 MD5sum: 4313072dee70dca9f9d55ef0f3744ce1 SHA1: 6a360bfbbd01cfc0b56374f02852bb1ef0f8b874 SHA256: d9312332244a21aed4fe26663d46b64036bd2f4bc4d24039646886954904aaa9 SHA512: a2e371b6b9698269556c850171d20dbb88e398491db32ab49e1017a24086637c8a080052944bf1002660981377a263580f75db53681acb253f58d4bed2444064 Homepage: https://cran.r-project.org/package=seqDesign Description: CRAN Package 'seqDesign' (Simulation and Group Sequential Monitoring of RandomizedTwo-Stage Treatment Efficacy Trials with Time-to-EventEndpoints) A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design. Package: r-cran-seqexpmatch Architecture: all Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-checkmate, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-seqexpmatch_0.1.1.1-1.ca2404.1_all.deb Size: 145602 MD5sum: c50711b50e6c56a8c85b0dd2bd832975 SHA1: c3a50c0d9183728711d837158042a3f719765c3b SHA256: c093dd48efe00ebfd920cf2db24ca02a8504521a9a8aa21c4f6b2bd374934664 SHA512: c5eafcf88226cbb521fe4edb8278772d5d31b692b784d9e2bb100d3eb1e053954e518f6eacbc984b2ec4fe2b4f6b70e671087d9d8130c9178f0713a08b3ab208 Homepage: https://cran.r-project.org/package=SeqExpMatch Description: CRAN Package 'SeqExpMatch' (Sequential Experimental Design via Matching on-the-Fly withEstimation and Testing) DEPRECATED. This package is deprecated and no longer maintained; all of its functionality has been superseded by the 'EDI' package, which provides faster, more general, and actively maintained implementations of the same sequential designs and inference procedures. Please migrate to 'EDI'. This package previously generated the following sequential two-arm experimental designs: (1) completely randomized (Bernoulli) (2) balanced completely randomized (3) Efron's (1971) Biased Coin (4) Atkinson's (1982) Covariate-Adjusted Biased Coin (5) Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly (6) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Stepwise) and also provides the following types of inference: (1) estimation (with both Z-style estimators and OLS estimators), (2) frequentist testing (via asymptotic distribution results and via employing the nonparameteric randomization test) and (3) frequentist confidence intervals (only under the superpopulation sampling assumption currently). Details can be found in Kapelner and Krieger (2021) . The 'EDI' package is on CRAN at and on GitHub at . Package: r-cran-seqfeatr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-bioc-biostrings, r-cran-plotrix, r-cran-plyr, r-cran-phangorn, r-bioc-qvalue, r-bioc-widgettools, r-cran-calibrate, r-cran-ggplot2, r-cran-r2jags, r-cran-coda, r-cran-scales, r-cran-ape Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-seqfeatr_0.3.3-1.ca2404.1_all.deb Size: 910152 MD5sum: d0c4eb27dc87b769b8a1c97df4d47bd3 SHA1: ace3cb477999ce18b8cdf617d533cb5b0069d4fb SHA256: cb81f6ec3c871bc5e23e531e2d1e1d1cdee161bf03942d5d6cca53c4ff9c5886 SHA512: c4405fe7113fc7f91ff8d07ae1f6dea98139129f86e8c1929ee21ad11aca3dd13c93e181af5bba2dafb3a30b68c66e93c60bb6afa2ee7913c685ae288846e4c5 Homepage: https://cran.r-project.org/package=SeqFeatR Description: CRAN Package 'SeqFeatR' (A Tool to Associate FASTA Sequences and Features) Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) . Package: r-cran-seqgendiff Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-irlba, r-bioc-sva, r-cran-pdist, r-cran-matchingr, r-cran-clue Suggests: r-cran-covr, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-deseq2, r-cran-knitr, r-cran-rmarkdown, r-bioc-airway, r-bioc-limma, r-bioc-qvalue, r-bioc-edger Filename: pool/dists/noble/main/r-cran-seqgendiff_1.2.4-1.ca2404.1_all.deb Size: 397122 MD5sum: a44b95a951f4a0d913f5f87bccf609d8 SHA1: 5b7573bdc4821a235f4ea88b5bd295ce04ba2f46 SHA256: 7301714c3d562f638766d4c33edbf8cfc63bfa781e59f1d2618d7c8634cd629b SHA512: 02d458ca7a7342b81607b933046abf3a37fb4ca5a416356814fc429e82693bb174dd2f0131ae482f8858602b50ee1522b164a812c8883bf13f00aca2a99e3e56 Homepage: https://cran.r-project.org/package=seqgendiff Description: CRAN Package 'seqgendiff' (RNA-Seq Generation/Modification for Simulation) Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) for details on the implemented methods. Package: r-cran-seqhandbook Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-traminer Suggests: r-cran-factominer, r-cran-descriptio, r-cran-rcolorbrewer, r-cran-traminerextras, r-cran-weightedcluster, r-cran-ade4, r-cran-cluster, r-cran-questionr, r-cran-rmdformats, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-seqhandbook_0.1.2-1.ca2404.1_all.deb Size: 78834 MD5sum: 6a140fb89b938d353e001445b2e23fbb SHA1: c78691a21caa529913edeb6b542eba4d41288fd6 SHA256: 495fe2b222a26320a9fde3d9a90cfb62c7f739aee834cf22aa184df5d9b087f5 SHA512: e88912405bfae4e3a78d16ad47d34a710ccc35c6efff20653578ec718f9ae1cd9d6f2880b9aaa7ac1f24f03517d55e3626ee4b68dbbf0f91723f4c989a1284bc Homepage: https://cran.r-project.org/package=seqhandbook Description: CRAN Package 'seqhandbook' (Miscellaneous Tools for Sequence Analysis) It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) ), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) ), smoothing sequences for index plots (see Piccarreta (2012) ), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) ), symmetrical (or canonical) Partial Least Squares (see Bry (1996)). Package: r-cran-seqicp Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dhsic, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-seqicp_1.1-1.ca2404.1_all.deb Size: 98838 MD5sum: f20411df10e04743f320aaff3488b978 SHA1: 9870a3621f72e096125312b0c3693c880c112215 SHA256: 32934a16c93431b8d2aea95ca419389dc6f960d082ae231bc0e109522734b874 SHA512: cb00bc86214b7744963fa868dfb002a8a84d2aa56c9daa1bfdb8f7c71da9f78b423c985b353e404974b008e4e1779e29a0f2a59d6f06b49995b850751ff97d73 Homepage: https://cran.r-project.org/package=seqICP Description: CRAN Package 'seqICP' (Sequential Invariant Causal Prediction) Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods. Package: r-cran-seqimpute Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-cluster, r-cran-dfidx, r-cran-dorng, r-cran-dosnow, r-cran-dplyr, r-cran-foreach, r-cran-mlr, r-cran-nnet, r-cran-plyr, r-cran-ranger, r-cran-rms, r-cran-stringr, r-cran-traminer, r-cran-traminerextras, r-cran-mice, r-cran-parallelly Suggests: r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seqimpute_2.2.1-1.ca2404.1_all.deb Size: 1024836 MD5sum: a3840fac080005cc602b845256609ede SHA1: d21251e754dbb5050980632d00bab10732c31b2e SHA256: 1c92217b2faed5be39ef8ebaead9b1d636332502d991613c866e07b8ca7e54a6 SHA512: 52e15d11c89cc17e2bb1458f50cee82092bdf571cacbf41a68352d1d6013f2a09fb74d335da321557323dfd447e76ab2bfd36fe4895c9292bcf91d505359c464 Homepage: https://cran.r-project.org/package=seqimpute Description: CRAN Package 'seqimpute' (Imputation of Missing Data in Sequence Analysis) Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model. Package: r-cran-seqmade Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-seqmade_1.0-1.ca2404.1_all.deb Size: 41584 MD5sum: 659a0446e076ba1547a79c374473d0b5 SHA1: 0cf598942dc4ef94032f54ba49d1a00c1cd50f91 SHA256: 1d7c91fdc6b9b0e1c1180e5d45a3cea1fa831feb703c8948bfe47525cd8f4c9b SHA512: 0d2e881b32e0ac15a9237678641ac03e5fd1b366e5cb2f1453202f1b8c56df9d14f6c61fdf6e9ceed3936503dfb7f17c5fed2392bf705d02888c9dbebb1c8af4 Homepage: https://cran.r-project.org/package=SeqMADE Description: CRAN Package 'SeqMADE' (Network Module-Based Model in the Differential ExpressionAnalysis for RNA-Seq) A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq. Package: r-cran-seqmagick Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-yulab.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-genomicalignments, r-bioc-genomicranges, r-bioc-iranges, r-bioc-muscle, r-bioc-rsamtools, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-seqmagick_0.1.9-1.ca2404.1_all.deb Size: 214290 MD5sum: fd86e1c881138ae99d84aa4ada09f338 SHA1: fed33eeb94bd13c48181c117f0401c3dc845a05d SHA256: 09399c275c46380d1c105d3821dde3c64139ffddf5ea3ba649ea46c3abb1976a SHA512: d36aa9aab95e1a64fa5c8a5e8d99c450bbb353b5e782e7a9a2bc7ead99dbc8797af779b88c1fc77b2c958de587f7a41b08a654735fe977018056cf8a47b4535f Homepage: https://cran.r-project.org/package=seqmagick Description: CRAN Package 'seqmagick' (Sequence Manipulation Utilities) Tools for reading and writing biological sequences in multiple formats, including 'FASTA', 'PHYLIP', 'CLUSTAL', 'STOCKHOLM', 'MEGA' and 'GenBank'. Supports interleaved and sequential layouts where applicable, and reads or writes gzip, bzip2 and xz compressed files transparently. Converts between formats, renames sequences via a two-column mapping, summarizes sequence counts, lengths, GC content and ambiguous characters, and manipulates sequence sets (e.g., filtering by patterns and computing consensus sequences from alignments). Also includes functions to download nucleotide records from NCBI by accession. 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The package is primarily designed for French-language statistical and economic publications. It includes functions to describe changes in levels, percentages, trends, accelerations and short-term economic developments using consistent linguistic rules. The package supports automated reporting workflows and reproducible economic writing. Fournit des outils permettant de générer des textes économiques dynamiques et standardisés dans des documents R Markdown. Le package est principalement conçu pour les publications statistiques et économiques en français. Il propose des fonctions permettant de décrire les évolutions de niveaux, de pourcentages, de tendances, d'accélérations et les évolutions conjoncturelles à l'aide de règles linguistiques homogènes. Le package facilite l'automatisation de la rédaction et la reproductibilité des publications économiques. 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Fits two-phase within-host models capturing antibody rise, peak, and decay following pathogen infection, using 'JAGS' for posterior inference. Designed as the upstream companion to the 'serocalculator' package for end-to-end seroepidemiological analysis. Methods are described in Teunis and colleagues (2016) and Teunis and van Eijkeren (2020) . Package: r-cran-serolyzer Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3873 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-nplr, r-cran-r6, r-cran-readxl, r-cran-stringi, r-cran-stringr, r-cran-png, r-cran-ggrepel, r-cran-lubridate, r-cran-r.utils, r-cran-svglite, r-cran-fs, r-cran-scales, r-cran-rlang, r-cran-patchwork, r-cran-cellranger Suggests: r-cran-knitr, r-cran-qpdf, r-cran-furrr, r-cran-future, r-cran-shiny, r-cran-shinyfiles, r-cran-dt, r-cran-qs2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-serolyzer_1.5.0-1.ca2404.1_all.deb Size: 2077854 MD5sum: 41782b3d92564ea24ab32e4041d5d3a2 SHA1: caa45125d9194c6ca508fe338a3aaf111255c672 SHA256: cc0efb61f4617f04be5853216553d978501fa098c2f9422c11e47194f8c487c8 SHA512: e9eb35d9319650a77132cdfa814519a829aed27163de7d2908ac808842dec8d1280db94a5e609b0708dd4fbf66c2ef30a57bae1c0aa706f56c8eab5bb4ce99fe Homepage: https://cran.r-project.org/package=SerolyzeR Description: CRAN Package 'SerolyzeR' (Reading, Quality Control and Preprocessing of MBA (MultiplexBead Assay) Data) Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project 'PvSTATEM', which is an international project aiming for malaria elimination. Package: r-cran-serotrackr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3170 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-drc, r-cran-forcats, r-cran-ggplot2, r-cran-here, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-openxlsx, r-cran-parsnip, r-cran-purrr, r-cran-ranger, r-cran-readxl, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-workflows Suggests: r-cran-glue, r-cran-htmltools, r-cran-httr, r-cran-jsonlite, r-cran-readr, r-cran-rsconnect, r-cran-shiny.fluent, r-cran-tidyverse, r-cran-zoo Filename: pool/dists/noble/main/r-cran-serotrackr_1.1.1-1.ca2404.1_all.deb Size: 1207796 MD5sum: eae4f07bd220140951b6f143573ddfdb SHA1: 696fa19fb7bfd92da8be4b62c1540296152002bc SHA256: e0f51bbe099548341762fc7bb094eb1c146d5477481960d765a1de89a67b8621 SHA512: 58eab54a4df5c465b81952dc9044965388f5089a464d15383b3fd094d7fb6fffcf7aa61266704cd4c28c28ec2eecf7f35bf83f493686fc4efe9c7f0702201842 Homepage: https://cran.r-project.org/package=SeroTrackR Description: CRAN Package 'SeroTrackR' (Serology-Based Data Analysis and Visualization) Data wrangling and cleaning, quality control checks and implementation of machine learning classification algorithm. Package: r-cran-serp Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ordinal, r-cran-crayon Suggests: r-cran-covr, r-cran-testthat, r-cran-tibble, r-cran-vctrs, r-cran-pkgdown, r-cran-vgam Filename: pool/dists/noble/main/r-cran-serp_0.2.5-1.ca2404.1_all.deb Size: 178960 MD5sum: cbf4cf95eecc9ba9e3be35f5c8a86e3c SHA1: a778105d2ea3f74420376be0f55dfa73cc84371f SHA256: 843319d2a091b558970b617a6aeaeb18967698385ab8d1f9e04427f2b2ca5701 SHA512: 47e1b1e7d10ebdfe69dc958fc641ba7376e12c1c38f55a74635a9dd6b241219b59d0c9da1e3601956b4835cb61e9bcd443014dadbbe2189b0458bfab1d2fe9ab Homepage: https://cran.r-project.org/package=serp Description: CRAN Package 'serp' (Smooth Effects on Response Penalty for CLM) Implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). 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Package: r-cran-servospherer Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-data.table, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-servospherer_0.1.1-1.ca2404.1_all.deb Size: 135470 MD5sum: acfac8c3dd3676918d583ebb039e627f SHA1: 6afce514b9233d64493076c9048f30d0caf60f50 SHA256: 25a8121791c279053c41d0f60eb3e1ec68a73559e096b6fc83b777dfc5311c3a SHA512: 75370c62a3a53ac50c3ad4fcc3e705f40d60c6ef4a4d2d48782d69170a42bfff566ed5a94d96ad622e0d93dbf7920b77d50837ca5dd1c12d35ac21d3df1e982a Homepage: https://cran.r-project.org/package=servosphereR Description: CRAN Package 'servosphereR' (Analyze Data Generated from Syntech Servosphere Trials) Functions that facilitate and speed up the analysis of data produced by a Syntech servosphere , which is equipment for studying the movement behavior of arthropods. 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Package: r-cran-setmethods Architecture: all Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qca, r-cran-admisc, r-cran-ggplot2, r-cran-ggrepel, r-cran-stargazer, r-cran-scatterplot3d, r-cran-fmsb, r-cran-betareg Filename: pool/dists/noble/main/r-cran-setmethods_4.1-1.ca2404.1_all.deb Size: 537028 MD5sum: 2d0898d966f4b24ffc79b511522fb635 SHA1: 1b3fde81e37dc2020c6718ffbc59c3d1d6a76cbb SHA256: 09dc36088e0150736b4c226ac66c2d3e1c1c5ac6db7e82603cf7bfaeef67ad70 SHA512: 9116615dc2744552762a2e8a7586f30d8f3025fc2832a799e70c1e2a6dff164e921d5f7abe9e01b051502a78d32b3d878dbdb5ea8b567abfa321dd5b61ac67ed Homepage: https://cran.r-project.org/package=SetMethods Description: CRAN Package 'SetMethods' (Functions for Set-Theoretic Multi-Method Research and AdvancedQCA) Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press. 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Package: r-cran-sfclust Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-sf, r-cran-sparsem, r-cran-stars, r-cran-dplyr, r-cran-matrix, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-ggraph, r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfclust_1.1.1-1.ca2404.1_all.deb Size: 3397756 MD5sum: 594e1b4bb6e3e9ae1817677873d202f0 SHA1: 245858bc3bbc0d03d21d4c293660f51b3f0d723c SHA256: 2ba4cdba0ac6947e6ff65d66bcf3eca0e65b77fa672c4c273aeecb25fa4c06a0 SHA512: 92262bca3846f89badd2bbabae4b098740cab18818d0f8fd40e14d2aedde5978fde458d35c295306095535adb710bdb96ca96ea512d35f68f7bf12ee18b83212 Homepage: https://cran.r-project.org/package=sfclust Description: CRAN Package 'sfclust' (Bayesian Spatial Functional Clustering) Bayesian clustering of spatial regions with similar functional shapes using spanning trees and latent Gaussian models. The method enforces spatial contiguity within clusters and supports a wide range of latent Gaussian models, including non-Gaussian likelihoods, via the R-INLA framework. The algorithm is based on Zhong, R., Chacón-Montalván, E. A., and Moraga, P. (2026) , extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023) . The package includes tools for model fitting, convergence diagnostics, visualization, and summarization of clustering results. Package: r-cran-sfd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2569 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-cli, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfd_0.1.0-1.ca2404.1_all.deb Size: 2450210 MD5sum: 55744fea5c7106e2f93c4e1c6d479c38 SHA1: 876e3610b7d05789697b76c9a51f28f9ee798615 SHA256: 3e308cb9cb7acf9a1db56ab059632301f4131a72dfb6bf265f336de1c5c5e36f SHA512: 8ce4060304dd97c05baaecc183b0033a4dda947fb59622b37edfb028b9d6e0ea29baba83544a3f8ddb6184ebc6913a8a3583999652cc05a0515858f81221b6aa Homepage: https://cran.r-project.org/package=sfd Description: CRAN Package 'sfd' (Space-Filling Design Library) A collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) and Wang and Fang (2005) . 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Package: r-cran-sfdct Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3895 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rtriangle, r-cran-sf, r-cran-sp, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-maps, r-cran-rmarkdown, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-sfdct_0.3.0-1.ca2404.1_all.deb Size: 2844310 MD5sum: a288a48ff6d0d46acddb52bc33458e4f SHA1: 18d83da5d4d00de77e9e3f005755219f16b0e9a6 SHA256: 0aaa6ab6a7dc314c2bc31cc1dd09554e8b4691a3f2220bc3775761acad8907dc SHA512: ddabeea4382681bc11591428d54b5d3a08fc433aa16d6b2a0d1164bb7d856fbc60b6512c1968e5aef79027d8ce979ba31edc697f904efcbb7ea49e995a111f9f Homepage: https://cran.r-project.org/package=sfdct Description: CRAN Package 'sfdct' (Constrained Triangulation for Simple Features) Build a constrained high quality Delaunay triangulation from simple features objects, applying constraints based on input line segments, and triangle properties including maximum area, minimum internal angle. The triangulation code in 'RTriangle' uses the method of Cheng, Dey and Shewchuk (2012, ISBN:9781584887300). For a low-dependency alternative with low-quality path-based constrained triangulation see and for high-quality configurable triangulation see . Also consider comparison with the 'GEOS' lib which since version 3.10.0 includes a low quality polygon triangulation method that starts with ear clipping and refines to Delaunay. Package: r-cran-sfdep Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-cli, r-cran-spdep, r-cran-rlang Suggests: r-cran-broom, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-pracma, r-cran-rmarkdown, r-cran-sfnetworks, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-vctrs, r-cran-yaml, r-cran-zoo, r-cran-kendall, r-cran-igraph, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-sfdep_0.2.5-1.ca2404.1_all.deb Size: 1063706 MD5sum: 0c4fbb8d16f67cb8c62e78eccb83607e SHA1: a4fd2fdd865839d945ced68b19f9bd3b63694df4 SHA256: 55dfa68cadc96f6f088f7821d138548dda91af5047bf8db406ea6a9fe9e27073 SHA512: cf1590ffef76ee5b4bf435387c389f8b89f76f4ca46105d8560a74dfe606b74b16ab65ee2c71aa7f1a0cb38edf565dc11b2c9764d8bc57e9ccff652a22652563 Homepage: https://cran.r-project.org/package=sfdep Description: CRAN Package 'sfdep' (Spatial Dependence for Simple Features) An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'. Package: r-cran-sffdr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-locfit, r-cran-ggplot2, r-cran-patchwork, r-bioc-qvalue, r-cran-fastglm, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sffdr_1.1.0-1.ca2404.1_all.deb Size: 421888 MD5sum: aebcf967d36ec72b452244247eaea9c9 SHA1: 7bd3e14a51f8566c6ee52a99768bab4837731c64 SHA256: 7877100ca68f3b964febae5613c93b85b9651b1bae4928b80e653488098441f9 SHA512: a0186e47f2a3d560e2804fdceda1ac288b9bb89cb862176c1aee46914442423daa19fa68fcff097214a593e7eb5c472c58b409120fc3f5a38606c04c4fbd7d5d Homepage: https://cran.r-project.org/package=sffdr Description: CRAN Package 'sffdr' (Surrogate Functional False Discovery Rates for Genome-WideAssociation Studies) Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). Package: r-cran-sfflhd Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doe.base, r-cran-conf.design, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfflhd_0.1.2-1.ca2404.1_all.deb Size: 194660 MD5sum: 5ff1664ad48dc8d694c48e4fd46ccdf2 SHA1: 0010e9e1e75a903933a0a54562ea413102521c86 SHA256: 4604313ed2f4887b6fce43e9c58a8af4a258181d8a8735ae6a0283f3326e5769 SHA512: 5b61c79f3d781bc26b2bececd6c5d5691daaaf8d9ec3d4adc463ad868fc12a7123b3a886ea816ea80fed89f465c112f6d2bab21ac7581775425da7c83f3c01fb Homepage: https://cran.r-project.org/package=sFFLHD Description: CRAN Package 'sFFLHD' (Sequential Full Factorial-Based Latin Hypercube Design) Gives design points from a sequential full factorial-based Latin hypercube design, as described in Duan, Ankenman, Sanchez, and Sanchez (2015, Technometrics, ). Package: r-cran-sfhelper Architecture: all Version: 0.2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-rjson, r-cran-mapview, r-cran-sf, r-cran-stringr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfhelper_0.2.2.0-1.ca2404.1_all.deb Size: 35180 MD5sum: 7a0b11e46b502548dcf4e00c1b4673cc SHA1: 28d59cf31b9385ef63909c4b7dcc1d7fbf77297c SHA256: d7edfbad930cba0fbd6b4be2116edd8896e13b6091521d383d52d3d84ac4bb57 SHA512: 611685358915f0a8871beab6a6bbb8f7305989cadfe9de0e088e36ab54003448cd01942f3a43a3fdfdb623b592cc963a2b16d70ab1de6cad2d85e629dd9b5525 Homepage: https://cran.r-project.org/package=sfhelper Description: CRAN Package 'sfhelper' (Repair Functions for 'sf' Package Objects) A group of functions that support the 'sf' package, focused primarily on repairing polygons that break when re-projected. Package: r-cran-sfhnv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-future.apply, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfhnv_0.1.0-1.ca2404.1_all.deb Size: 54120 MD5sum: cf98dbd738175d7f02b89bc0e6a826bd SHA1: d208f117972c750a62169156fbcbd0674922eece SHA256: c88a17107e9eb56e124cedbe975abc3fe0e10eed82eccbeb69c4688d0fa2b23e SHA512: 017ce1cad31f5dce15f946588de719dec30888d52788aa6ee6f0021b913c1dfa2bc4dda7c8aae87fb02f8d620f2440f9525a2fcae88bb11216a49c8ea1ab32e8 Homepage: https://cran.r-project.org/package=SFHNV Description: CRAN Package 'SFHNV' (Structural Forest for the Heterogeneous Newsvendor Model) Implements the structural forest methodology for the heterogeneous newsvendor model. The package provides tools to prepare data, fit honest newsvendor trees and forests, and obtain point and distributional predictions for demand decisions under uncertainty. Package: r-cran-sfhotspot Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-classint, r-cran-cli, r-cran-dbscan, r-cran-ggplot2, r-cran-ggspatial, r-cran-isoband, r-cran-rlang, r-cran-sf, r-cran-spatialkde, r-cran-spdep, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-quarto Filename: pool/dists/noble/main/r-cran-sfhotspot_1.1.1-1.ca2404.1_all.deb Size: 1196836 MD5sum: cafeb3dc52d4aa3b16fd4dc1b6ab8957 SHA1: 8971aa05310ad9b7a2ea13745269774fc7ff5633 SHA256: a9e6fa7a7882565ffc3d4d1e514a9f3a45b1fd9e49472feb7ceb31a72f66674c SHA512: 2cb508afd4b7464d612f570c45f2790307d82310662b33e89d5a3ac822d48ea4fb2bea6f2d286e5054495bf0cdd6edf13f11edf2ebefc1115c1a4101c2dd25ab Homepage: https://cran.r-project.org/package=sfhotspot Description: CRAN Package 'sfhotspot' (Hot-Spot Analysis with Simple Features) Identify and understand clusters of points (typically representing the locations of places or events) stored in simple-features (SF) objects. This is useful for analysing, for example, hot-spots of crime events. The package emphasises producing results from point SF data in a single step using reasonable default values for all other arguments, to aid rapid data analysis by users who are starting out. Functions available include kernel density estimation (for details, see Yip (2020) ), analysis of spatial association (Getis and Ord (1992) ) and hot-spot classification (Chainey (2020) ISBN:158948584X). Package: r-cran-sfinx Architecture: all Version: 1.7.99-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfinx_1.7.99-1.ca2404.1_all.deb Size: 70802 MD5sum: 7cedb741bd0e530d6131087e70a0eec0 SHA1: f6ca8b866296674e8638fc8ad5e23236d791f5dd SHA256: e777c7f718e4d68b9e97657e4df0f796bb932782d4130829455abbbcd20909b4 SHA512: 8e2cdb543c14b486fbb925a68772d57ce4f4a50e8429a721c03b454554414035201f5a9e78e1e823a63f29ff4081ac6f208956b3bf9baf5a60068fab28836b5a Homepage: https://cran.r-project.org/package=sfinx Description: CRAN Package 'sfinx' (Straightforward Filtering Index for AP-MS Data Analysis (SFINX)) The straightforward filtering index (SFINX) identifies true positive protein interactions in a fast, user-friendly, and highly accurate way. It is not only useful for the filtering of affinity purification - mass spectrometry (AP-MS) data, but also for similar types of data resulting from other co-complex interactomics technologies, such as TAP-MS, Virotrap and BioID. SFINX can also be used via the website interface at . Package: r-cran-sfislands Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4714 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-tidyr, r-cran-broom.mixed, r-cran-lifecycle Suggests: r-cran-mgcv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfislands_1.1.2-1.ca2404.1_all.deb Size: 2835844 MD5sum: 07c736cddce35b7a579c21bed845bfc6 SHA1: 1612eb6bc2fbf7e6e3c893c665acc0c3301b40ff SHA256: 1af989fb3f0a3105bbe4e461a06d2f4416b553cf899975f6a7428ee99999b648 SHA512: 435c7a7cef0492051845b9cb73ab962a2c8ad75acce18e3d40ed15b8bce06f450f2546e7a88def6be1513ed44a79a11e37fafe1f0dbd98c6a5a6e4f7e53fd16b Homepage: https://cran.r-project.org/package=sfislands Description: CRAN Package 'sfislands' (Streamlines the Process of Fitting Areal Spatial Models) Helpers for addressing the issue of disconnected spatial units. It allows for convenient adding and removal of neighbourhood connectivity between areal units prior to modelling, with the visual aid of maps. Post-modelling, it reduces the human workload for extracting, tidying and mapping predictions from areal models. Package: r-cran-sfm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-sopc, r-cran-matrixcalc, r-cran-sn, r-cran-psych Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sfm_0.2.1-1.ca2404.1_all.deb Size: 1485132 MD5sum: 2946c7c867cf6b6107205c41b9199e88 SHA1: af3b45648ef1abb32cccf6e70697ae0ddb534126 SHA256: 2fba7ae2926fc0cacab5773740ef15baef4cb2e82bb1a94c0fb7c4dce31b3c7c SHA512: c8fa0d595579a8e868e30202feb5841cba44ae598ef4ed4c76ec5b879e2f49373aea3ce1f5d87cdef223feddd4e34c22b0bdd16e9e69c44273fffe376d3ff30d Homepage: https://cran.r-project.org/package=SFM Description: CRAN Package 'SFM' (A Package for Analyzing Skew Factor Models) Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projected Principal Component (PPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix.The philosophy of the package is described in Guo G. (2023) . Package: r-cran-sfnetworks Architecture: all Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-igraph, r-cran-lwgeom, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-tibble, r-cran-tidygraph, r-cran-units Suggests: r-cran-dbscan, r-cran-fansi, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-s2, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-testthat, r-cran-tsp Filename: pool/dists/noble/main/r-cran-sfnetworks_0.6.6-1.ca2404.1_all.deb Size: 2797802 MD5sum: 90d68e267c29f6eb9bb541457469ffdb SHA1: ffedfc20a2bae1a6522b44dd4ee73fcd289e8928 SHA256: 98364f2d0843de1cdeec168606ededd544a9dc47bcd0c4f95b9154cc3a29650d SHA512: 7b0ec6c06a16304224a186bde444f2d914e61da5687e91334c0913cf1fcfb815904f1daecb5dcc96ea00ece6b3aa8014f965cf08434fe0270f2d4ff2747388aa Homepage: https://cran.r-project.org/package=sfnetworks Description: CRAN Package 'sfnetworks' (Tidy Geospatial Networks) Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'. Package: r-cran-sfo Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5908 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-magrittr, r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-sfo_0.1.2-1.ca2404.1_all.deb Size: 1990572 MD5sum: bda9e29875f74ee0d95794cae2c0b159 SHA1: 619f4f1332a696f1e332101efe04cb4b84bd0f81 SHA256: 660e73d52ff44bdfc9d6d2615aa8b633c0264af04fa8ac6dfc8d06253b479003 SHA512: 3c40cbe9051e11b9d9da665576feda575592423f3232019769e7adf1277a5cd316d1bf8db58cec3fe01196d3d4d4ccf8869d32ba7bfc0bf9556ca2a69c25b503 Homepage: https://cran.r-project.org/package=sfo Description: CRAN Package 'sfo' (San Francisco International Airport Monthly Air Passengers) Provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc. Data source: San Francisco data portal (DataSF) . Package: r-cran-sfocds Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gtools Filename: pool/dists/noble/main/r-cran-sfocds_1.2.0-1.ca2404.1_all.deb Size: 53098 MD5sum: c30a1cba8e5a8e271fbd4559c28ccfe2 SHA1: db59d066a0dd157770e150e78317e75bfc3ce069 SHA256: a4a48304df395cc7b54cced779b9a69a61db5dffad251aa9cdbb4c27623506a0 SHA512: 753a82e1fc893bbe947338776d2e0aac9e4974bb1e96026613ab32e6b2f92576c44c17c0968423c92b84fb633559f042623478a02d6990751f09577bf0b7f31c Homepage: https://cran.r-project.org/package=SFOCDs Description: CRAN Package 'SFOCDs' (Space Filling Optimal Covariate Designs) We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points. Package: r-cran-sfopendata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-sfopendata_0.1.2-1.ca2404.1_all.deb Size: 49980 MD5sum: bbe5f39f5a6bd18e235296324d063377 SHA1: ed244f68e75bc3c0f8d8bac1dcb5c12f62c4b4ed SHA256: 3a58cfe71f3909836f1aea7b497a62cdb3baed1cad8020840ec7f80198de9ad4 SHA512: e9208315c1cac1b7e8e83c40ed20914cd8613b310a9d76bdf67f7c74ce7b220087a97fb80aa61835a5aecdfacedc3e32291a40fc34e6f91c5849a218bdbbf358 Homepage: https://cran.r-project.org/package=sfOpenData Description: CRAN Package 'sfOpenData' (A Lightweight Interface to San Francisco Open Data APIs) Provides a unified set of helper functions to access datasets from the San Francisco Open Data platform . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package provides a consistent interface for discovering and downloading datasets from the San Francisco Open Data Portal using human-readable dataset keys or official Socrata dataset identifiers. 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For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole(). 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Package: r-cran-sftrack Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1666 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-adehabitatlt, r-cran-knitr, r-cran-geosphere, r-cran-scales, r-cran-covr, r-cran-rmarkdown, r-cran-lwgeom Filename: pool/dists/noble/main/r-cran-sftrack_0.5.5-1.ca2404.1_all.deb Size: 1019912 MD5sum: 7c37bc7ae2efa1fbf459a90bbb616761 SHA1: 3068c063022dcda944803bb52b83cccfa3cd7dce SHA256: 6519e0b9fef6aaf8dee854fc395c24516530577766ef9cc44970fd6b311a989b SHA512: 1e5f58a2493d4e46be050587dac0865fed99f88e32f611bded1ddecd9ed3c6bbcf80de0c3d61894ca3929d927dfb59946af1d72d21b3ac4e85bc843642f9fe0f Homepage: https://cran.r-project.org/package=sftrack Description: CRAN Package 'sftrack' (Modern Classes for Tracking and Movement Data) Modern classes for tracking and movement data, building on 'sf' spatial infrastructure, and early theoretical work from Turchin (1998, ISBN: 9780878938476), and Calenge et al. (2009) . Tracking data are series of locations with at least 2-dimensional spatial coordinates (x,y), a time index (t), and individual identification (id) of the object being monitored; movement data are made of trajectories, i.e. the line representation of the path, composed by steps (the straight-line segments connecting successive locations). 'sftrack' is designed to handle movement of both living organisms and inanimate objects. Package: r-cran-sg Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-httr2, r-cran-mime, r-cran-rlang Filename: pool/dists/noble/main/r-cran-sg_0.2.0-1.ca2404.1_all.deb Size: 20730 MD5sum: b177999a056124561ebf1c31089bdcfa SHA1: b779c180552f3530b893fcb91f1327a37f4376ae SHA256: e6f402794e4b5bfee28a771ecc07ccf8c1f7d97de845dc262dd0d99ec348a10d SHA512: 20d61ca551cdf9ef037f0dd93086a51995fa9ef795db089fd5b0c7705821bd1fc9aa81f74d8573ee6a692adc749a4fd4ac391f43a7ac0fa26922673507769a9c Homepage: https://cran.r-project.org/package=sg Description: CRAN Package 'sg' ('SendGrid' Email API Client) Simple 'SendGrid' Email API client for creating and sending emails. For more information, visit the official 'SendGrid' Email API documentation: . 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Simplifies process of querying 'nomis' datasets and extracting desired datasets in dataframe format. Extracts area shapefiles at chosen resolution from 'Office for National Statistics Open Geography' . 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It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package. Package: r-cran-sgbj Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gbj, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sgbj_0.1.1-1.ca2404.1_all.deb Size: 29474 MD5sum: 17c777ff8de483ac8d0f4c25eb1ea0ad SHA1: 16945f34da7f89a70bde1bea019009c7a40d2d7f SHA256: 5f7df9cb507db5fd7fe8ea65132fa2afca97d19ac801212878896e15f51678d9 SHA512: 0cfa7aa4bc4b93b2841bd53604911fb5c560314893d7cde5f7f04f0baa24e80a511f81083108d43a270d942200dbcfbffe935a1a9910653c4d5f38dc0dfb5e55 Homepage: https://cran.r-project.org/package=sGBJ Description: CRAN Package 'sGBJ' (Survival Extension of the Generalized Berk-Jones Test) Implements an extension of the Generalized Berk-Jones (GBJ) statistic for survival data, sGBJ. 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Package: r-cran-sglg Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-survival, r-cran-adequacymodel, r-cran-ggplot2, r-cran-plotly, r-cran-moments, r-cran-gridextra, r-cran-pracma, r-cran-progress, r-cran-rcpp, r-cran-plot3d, r-cran-magrittr, r-cran-teachingsampling Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sglg_0.2.7-1.ca2404.1_all.deb Size: 1496132 MD5sum: 136880dae8dfc9297f219bae789c39d2 SHA1: 21efb37daa8f794f22ba5b780ebb1ca8cb713820 SHA256: 3ec44a7ad745f25e1a79552ebddbe4b38aa91fde755c7bd911f854b1674b020c SHA512: 43f1f720d65a36a08088537f025ad9cb7326935f71bcd663842afc98aeff2bbc2cee3f4af6559803f4b3552b9ba605a7e1af8b052520e49c39b25768cabe42ae Homepage: https://cran.r-project.org/package=sglg Description: CRAN Package 'sglg' (Fitting Semi-Parametric Generalized log-Gamma Regression Models) Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right or left censoring. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression or semi-parametric functions, whose non-parametric components may be approximated by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods. Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered. Package: r-cran-sglr Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-shiny Filename: pool/dists/noble/main/r-cran-sglr_0.8-1.ca2404.1_all.deb Size: 52628 MD5sum: 0b659df22b882e54f4e2d5c783501f11 SHA1: f2e20d1882e192a10febe22375196f7da5ad32f2 SHA256: 5d78d0249dc28ec4997ee4f95e427fb29d11a7d0ce1ffe2deeb303707246160f SHA512: 8a46a0c16c4dca9b9f8c691c4e5466a418d5a2fc795ab53cccfbc3016861a6d75dcc8fb7cc698ddd61bd98c2412345e5ed7454abd487cb798d42ea27138dfa0a Homepage: https://cran.r-project.org/package=sglr Description: CRAN Package 'sglr' (Sequential Generalized Likelihood Ratio Decision BoundariesProposed by Shih, Lai, Heyse and Chen (2010,)) We provide functions for computing the decision boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen (2010, ). Package: r-cran-sgmean Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sgmean_0.1.1-1.ca2404.1_all.deb Size: 20212 MD5sum: 7a2fc4a6dc2b813aa2d87d3ac5f27bc4 SHA1: 3777eaf2bfe4805a3cb96c4de06bf27e627286f0 SHA256: d5ea56484694c72258cef6d1d83926c70f02693fe8e68373e1276f9e5f62e68c SHA512: fb3b112f29c92971fe695b3926669b1d506429c7d70219d8575eb242aa9f133de718f28b763d7284b79294e49ab5798c46c5c203b894555355abdbe81cf9ed0b Homepage: https://cran.r-project.org/package=sgmean Description: CRAN Package 'sgmean' (Proportional Trimmed Mean) Computes a proportional trimmed mean that resolves the integer truncation problem of base R's mean(..., trim). When k = trim * n is non-integer, a fractional discount (1 - delta) is applied to boundary observations, where delta = k - floor(k). The resulting estimator is continuous in alpha for any fixed n, syntactically identical to mean(..., trim), and compatible with the 'Statgraphics' implementation. See Gaviria Chaverra (2026) . Package: r-cran-sgmodel Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ramify, r-cran-rtauchen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sgmodel_0.1.2-1.ca2404.1_all.deb Size: 50344 MD5sum: d5a910a425a76bd6b33d6392ba5b6457 SHA1: 1a39ef859f4608844a0795c9114f583c02290724 SHA256: ced3b278abbb45d9e5b2502bc8b4b9a2d8877848ccc074ba6a6fe98e126c15f3 SHA512: 85694dda62ac3254f68991c1b03b0a8d81f593383952bb56b7ce167f12b04ae4c846387e50358a3111ae88fa422a81227d6c37451e290acfced95efb474a0378 Homepage: https://cran.r-project.org/package=sgmodel Description: CRAN Package 'sgmodel' (Solves a Generic Stochastic Growth Model with a RepresentativeAgent) It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes the solutions to the model, plots of the solution, a summary of the features of the model, a function that covers different types of consumption preferences, and a function that computes the moments of a Markov process. Merton, Robert C (1971) , Tauchen, George (1986) , Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ). Package: r-cran-sgmrfmix Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-glasso, r-cran-mvtnorm, r-cran-tidyr, r-cran-zoo Suggests: r-cran-dplyr, r-cran-modelmetrics, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sgmrfmix_0.3.0-1.ca2404.1_all.deb Size: 1270558 MD5sum: fa21136d04893e48f57a82c561f305c1 SHA1: a8e52be7a0fad493c8c768c201008655ddabeacd SHA256: 838550728c8296899bca62a3cb099ee0f810b53051832a0f3e0fae889c76798e SHA512: d941b779e04caa9fa7fd8ebae0d9655658bcaf323f8825480302c11ce557b7b4654846724c7b2cbd7fac167952612b6149f008160f9a426f3423b4665a20fac4 Homepage: https://cran.r-project.org/package=sGMRFmix Description: CRAN Package 'sGMRFmix' (Sparse Gaussian Markov Random Field Mixtures for AnomalyDetection) An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. 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There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference. Package: r-cran-sgr Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-polycor Filename: pool/dists/noble/main/r-cran-sgr_1.3.1-1.ca2404.1_all.deb Size: 83718 MD5sum: 10e057328ed0f323104471f991b9161b SHA1: e7b7bad67c1a00b30dde64cd24afe0bffb63162f SHA256: d20aad0a71383c1d865358d7dba7c7b9f8f2194f1b2dd664f5ac83393fc81826 SHA512: ea03caa78dd04201bb6267d486c542963580786f37999f1082bffd83f11c2405b72ca51aaadda0d09b83d35d480b73bbd07fce0c9a44b5ff3d811688b483b4b2 Homepage: https://cran.r-project.org/package=sgr Description: CRAN Package 'sgr' (Sample Generation by Replacement) Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results. Package: r-cran-sgraph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-stringi Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sgraph_1.1.0-1.ca2404.1_all.deb Size: 220392 MD5sum: 2fd2ee6add824509196e0cecd9a9f8d9 SHA1: 129e300fb57b4d61d61573e85fd819b703489f5d SHA256: f078b50cbdc6e1405bed690d2ea689795e004a5ffd0a58ec209fb6c89333ecd3 SHA512: 42cba5627024e4d66050284f1cc5f98fdf8cfc0ff8f107e5db1582b3f5a417c40ea76ea2249dafe3d9bf048a36c02d7def11ce1d152ca4bee9db069dff23c0f8 Homepage: https://cran.r-project.org/package=sgraph Description: CRAN Package 'sgraph' (Network Visualization Using 'sigma.js') Interactive visualizations of graphs created with the 'igraph' package using a 'htmlwidgets' wrapper for the 'sigma.js' network visualization v2.4.0 , enabling to display several thousands of nodes. While several 'R' packages have been developed to interface 'sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the 'sigmaNet' package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic 'sigma.js' visualizations by overriding the underlying 'JavaScript' code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding 'sigma.js' code include toggling node visibility when clicked using a node attribute and highlighting specific edges. 'sigma.js' is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available. Package: r-cran-sgsr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-clhs, r-cran-samplingbigdata, r-cran-balancedsampling, r-cran-spatstat.geom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rfast, r-cran-testthat, r-cran-doparallel, r-cran-dosnow, r-cran-snow, r-cran-foreach, r-cran-entropy, r-cran-roxygen2, r-cran-covr, r-cran-rann, r-cran-spelling Filename: pool/dists/noble/main/r-cran-sgsr_1.5.0-1.ca2404.1_all.deb Size: 2437368 MD5sum: d21594bb76fbc14078129ed22d5fc254 SHA1: d2a94f2bd7d633c128de34387c239def48c0e77c SHA256: a4dc923e1d6bb537ec278093fbeccd4d9d58177a1eeb45ba1862095d484cccb0 SHA512: d868baa9cc68ccb5147a529cd816fb811836a648cc55f864028137e0418618044de5806e36c9b3adf86bbad3cd83e0b11981f471e441bbcf2a76283ec4e97ae3 Homepage: https://cran.r-project.org/package=sgsR Description: CRAN Package 'sgsR' (Structurally Guided Sampling) Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches. Package: r-cran-sgstar Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sgstar_0.1.2-1.ca2404.1_all.deb Size: 55768 MD5sum: 960b58d5069cbfff537ea799284dfe64 SHA1: 095dec7f2dccba5b08ceb981f5388715f7f45c04 SHA256: 5690baceaf1c9b3b73221e0e55fdcc93626f13c31d9065a2a028b5b125ff28be SHA512: 3688f6ca19aa7db4937fce44dc300854be2fe616aff4fceb215770390a1ef05b9786439b1f385949b1207c9f93c4161f50a5efb31d3099ced845bf296cd4444b Homepage: https://cran.r-project.org/package=sgstar Description: CRAN Package 'sgstar' (Seasonal Generalized Space Time Autoregressive (S-GSTAR) Model) A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016). Package: r-cran-sgt Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-sgt_2.0-1.ca2404.1_all.deb Size: 265348 MD5sum: cd8caef468a9207b5ac1e36eb6f75c3f SHA1: f4d17db66c7763d421dd275258425dcdf023af41 SHA256: 23672bc0088c8525d1f69c3bf8f467487715a28525a8ef67daad928dd611dda1 SHA512: 11abdb0d6f0231bc4e290f6beda1141c824072c890549d1500e81ae5b4519f2cec814b69290a5273fff8f2e3f2051fbeb80eeee8969a5fb9efb3b226ccb851d5 Homepage: https://cran.r-project.org/package=sgt Description: CRAN Package 'sgt' (Skewed Generalized T Distribution Tree) Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation. Package: r-cran-shades Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-shades_1.5.0-1.ca2404.1_all.deb Size: 110580 MD5sum: 9fbeee392454cccc6522ec0a656397e0 SHA1: f5075e2c8e4f12ec0acd07faa294f6fdd86ebca0 SHA256: 3d99c9b1c54a5099119a6162e721658d1c33f3c7748bc594f99ae2b58921cb5f SHA512: e6752e986a7c01a08edcb500ab1b6c46836acfa04a17f37d21c272e7f7740abf89a2f40c5d81099b2d72733fb65bb91d38881b48994aca650851ec149af339e8 Homepage: https://cran.r-project.org/package=shades Description: CRAN Package 'shades' (Simple Colour Manipulation) Functions for easily manipulating colours, creating colour scales and calculating colour distances. Package: r-cran-shadowr Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rselenium Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shadowr_0.0.2-1.ca2404.1_all.deb Size: 359566 MD5sum: 74eb0c5b80654810ebfb86d301cef8e3 SHA1: caf629a62655d3677699aa0dfc4d4c775873b499 SHA256: 35a624820a9e0e1333b7fbd736ebac8acbd45959dadb7b35a88724bef0c95a39 SHA512: 54afb360c792a76e1881d0d268454980ee795c83c44bdbde63781cfdd19a37b3ca96a647c08512bf7713c3c3ab3be62e7837518fa68b0c10eca1fc5c83e948a7 Homepage: https://cran.r-project.org/package=shadowr Description: CRAN Package 'shadowr' (Selenium Plugin to Manage Multi Level Shadow Elements on WebPage) Shadow Document Object Model is a web standard that offers component style and markup encapsulation. 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Package: r-cran-shakti Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-shakti_0.1.0-1.ca2404.1_all.deb Size: 24926 MD5sum: d7d573eba254efaa6f7b94a7c1231680 SHA1: b60835f3b8b4680ac993673a1d75d88ec11cb8a9 SHA256: be66cdb6526d018ae416b9d0634e2c8be3c50b4f975861b502ae3e8b3ca19730 SHA512: 70c9b167caa80ced2cc285abb545bdbaae2aabe6d9fe0ff3f2dd6f45c5d6b4730d77e2bfad924b91f0cef1c4d99010eb4d37d09411ac1ffe723984cfebada7a0 Homepage: https://cran.r-project.org/package=SHAKTI Description: CRAN Package 'SHAKTI' (Suite for Heat-Related Adsorption Knowledge and ThermodynamicInference) A comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactions—changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)—is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surface–solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as “AdIsMF” , and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) . Package: r-cran-shannon Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vares, r-cran-extradistr Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shannon_0.2.0-1.ca2404.1_all.deb Size: 372312 MD5sum: da4f357ac36832f3da1ea3ddad505998 SHA1: 921fec04560f8de64d0f2fbb8a0a15f380248453 SHA256: a137ee50080c0601eb2a08c48d0016a1b1a946464c833ada9b792c39737b46c9 SHA512: 929e33c9c9fd99528e6678ff1a8a4731b58efed990542b1fffe494adf30088d96ecb64ff8050d000b7908e4444fe0f276eecd09418c6fb7924c71e5026118ee0 Homepage: https://cran.r-project.org/package=shannon Description: CRAN Package 'shannon' (Computation of Entropy Measures and Relative Loss) The functions allow for the numerical evaluation of some commonly used entropy measures, such as Shannon entropy, Rényi entropy, Havrda and Charvat entropy, and Arimoto entropy, at selected parametric values from several well-known and widely used probability distributions. Moreover, the functions also compute the relative loss of these entropies using the truncated distributions. Related works include: Awad, A. M., & Alawneh, A. J. (1987). Application of entropy to a life-time model. IMA Journal of Mathematical Control and Information, 4(2), 143-148. . Package: r-cran-shapboost Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-shapforxgboost, r-cran-caret, r-cran-matrix Suggests: r-cran-flare, r-cran-survival Filename: pool/dists/noble/main/r-cran-shapboost_1.0.3-1.ca2404.1_all.deb Size: 178700 MD5sum: 998e789ceff0655aa59e0e22ac7e51e0 SHA1: 5df1c2b3185ef366246210e0142f455297e41656 SHA256: 547601c33bf3b94a0af4e1152a2d86b30fc11a5f129fcd913f78baffeb314178 SHA512: eb8f9af7f00417cc4bbc9c62dc6dd05646ee8ba131f8711b81f13bf1788455c71ce8db3f85cacf6863e4e0ab0c15cecb07bb114700c12695ee126fd8d42f7d12 Homepage: https://cran.r-project.org/package=SHAPBoost Description: CRAN Package 'SHAPBoost' (The SHAPBoost Feature Selection Algorithm) The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values. Package: r-cran-shapdoe Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-shapdoe_1.0.0-1.ca2404.1_all.deb Size: 55714 MD5sum: 97c6939a51d9617cadc5cc1db613bd4c SHA1: 3ff1326ba6effdf391110f7bc0dff6453a9fc75e SHA256: b854a2868db19f89bc498b185b62b347646d6f41de883732a138432af85fcda8 SHA512: 7b4422a023f7a01f41eaa3c64393b86f9165766bf0c715f67373c2a4a9c1426fb6985ae58fcc36d238976e07215870fc0e8a351fca105c3663ee2a1c39662775 Homepage: https://cran.r-project.org/package=ShapDoE Description: CRAN Package 'ShapDoE' (Approximation of the Shapley Values Based on ExperimentalDesigns) Estimating the Shapley values using the algorithm in the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and Wei Zheng (2024) "Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs". You provide the data and define the value function, it retures the estimated Shapley values based on sampling methods or experimental designs. Package: r-cran-shape Architecture: all Version: 1.4.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-shape_1.4.6.1-1.ca2404.1_all.deb Size: 747218 MD5sum: a5bec55064e249abbbdeac8f21e4e64a SHA1: 76c297f95c8ebac66eee80e230d9b6c03dc0a948 SHA256: ca2c5d4815525f3972a41d64fd7c2d9cae2b6deaaa61d22af3b876ca2edb231e SHA512: 9615d301508297fa6c51726c014f7f098d7465e22160a58e47146a1ae1215725160ec81acc3fc5ee563a5150a38682c3387309360bef279ca0556fddcbccc8a0 Homepage: https://cran.r-project.org/package=shape Description: CRAN Package 'shape' (Functions for Plotting Graphical Shapes, Colors) Functions for plotting graphical shapes such as ellipses, circles, cylinders, arrows, ... Package: r-cran-shapechange Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coneproj, r-cran-quadprog Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-shapechange_1.5-1.ca2404.1_all.deb Size: 203496 MD5sum: 7d794f74dbb58482ce87616814b2db5c SHA1: 1bb1d9a3f72d4e5cc5bfefa0b5927ea19b41c99a SHA256: 1701a04a9515df602705d59a7d0974c8c750df8b61cb9b1e1e32a44d18d30a12 SHA512: 0553305c7d4d728efc9478ea02c9819a222946e618af6471dd1ab0f38518ea19b9d61d563a4b38349e5bab1f4010285f0f1dfe8b07d5105ef2081dd94f1fba62 Homepage: https://cran.r-project.org/package=ShapeChange Description: CRAN Package 'ShapeChange' (Change-Point Estimation using Shape-Restricted Splines) In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine. Package: r-cran-shapefiles Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign Filename: pool/dists/noble/main/r-cran-shapefiles_0.7.2-1.ca2404.1_all.deb Size: 52030 MD5sum: 8b25ed57eb171ef67255e3803ff3691a SHA1: ab8ff214b0edd2f3feadaa153da85b65f9cc2b24 SHA256: c69cd09c143b5c87e2cf4d3bc143f40bc95fdc9d43bbf302a39942e4ca2899d9 SHA512: 80581d9eebf876528dfb17c67c3a75a332ec733358da0f589571663df09569632cce3840a6719a9d5c53b86dd485c8a00283acbaff87cf6545360c8f5d5d163d Homepage: https://cran.r-project.org/package=shapefiles Description: CRAN Package 'shapefiles' (Read and Write ESRI Shapefiles) Functions to read and write ESRI shapefiles. Package: r-cran-shapena Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-mice Filename: pool/dists/noble/main/r-cran-shapena_0.0.2-1.ca2404.1_all.deb Size: 84072 MD5sum: afb1a23a90026761706222845a79cdfd SHA1: 7ea822848ad7d8cab42f823733252e922ba0e2f4 SHA256: 5264a12255ead14f1234efd7b518f97a95da906c57c1cf1ebd0cb08f1c1c314b SHA512: 44574cee65becac5e851f12861ac31a1c0a2e1ca40a6e173f7f8da9cc58d0da78a6ab058b6082f76bb32cc32ca1f6544fd4285b929b0b3a280f92f46ab690d9a Homepage: https://cran.r-project.org/package=shapeNA Description: CRAN Package 'shapeNA' (M-Estimation of Shape for Data with Missing Values) M-estimators of location and shape following the power family (Frahm, Nordhausen, Oja (2020) ) are provided in the case of complete data and also when observations have missing values together with functions aiding their visualization. Package: r-cran-shapepattern Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-igraph, r-cran-terra, r-cran-landscapemetrics, r-cran-raster Filename: pool/dists/noble/main/r-cran-shapepattern_3.1.0-1.ca2404.1_all.deb Size: 196426 MD5sum: 9576ee2ea847cc37f74858b3dd4201f2 SHA1: 93341b4ec9d17db2d53bf9f486e49f5c75ca1729 SHA256: 3831c7651512fbfc53d86f2c11caa9c51683c8e65d5e0542c159e0bfb3bd868f SHA512: 25d7a3acc852bc30d18f8f1f6adad79747e3427251d505655b8b8c0215bd577c461ca2f6ac7fcc71d409005a3e85f5bc8e3ac4126fb503ab79a2658f6582b4bf Homepage: https://cran.r-project.org/package=ShapePattern Description: CRAN Package 'ShapePattern' (Tools for Analyzing Shapes and Patterns) This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) , (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) , (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) , and (5) computing the porosity of raster patches . NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived. Package: r-cran-shaper Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-jpeg, r-cran-pixmap, r-cran-wavethresh, r-cran-vegan, r-cran-mass Filename: pool/dists/noble/main/r-cran-shaper_1.0-2-1.ca2404.1_all.deb Size: 3623264 MD5sum: 8a0b4b1bd20f04900e1a7ff30da88edb SHA1: 39d9cfc8b938f5a7e1f996fa9b3f42d29cc891d0 SHA256: 70c8fc46236c5f8a42ffec9f638951e922a5e1dc00ca31c3cad6a811b4f5af3b SHA512: d2d209018ecf0ba10266d50776b59bc9c6fc9703f87afa967640200062c7ff9708cacfd9c137ee167f9690ac6f3d71b4d30f37821212fa1b70c3cc241dd0441f Homepage: https://cran.r-project.org/package=shapeR Description: CRAN Package 'shapeR' (Collection and Analysis of Otolith Shape Data) Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) , choose from conducting either a Fourier or wavelet see Gençay et al (2001) transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects. Package: r-cran-shaperotator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plot3d Suggests: r-cran-geomorph Filename: pool/dists/noble/main/r-cran-shaperotator_0.1.0-1.ca2404.1_all.deb Size: 91006 MD5sum: f5de2c14e4bfef50a6628bd774ea3f18 SHA1: 2a05aa799ea3829b095172e031f9e7f08cc10964 SHA256: bde2511cb66d794282c637d80cf36f1af1ef8980809cf7e24de238e7603ab09c SHA512: 891ed6dca5ea59d83e29633ac7d2106b98dd2b425eeab45e42797dfa10d243a27d57ab9ebd81821dbfd05c6d6a10b05142532f638bcdc49b69c173e310412d42 Homepage: https://cran.r-project.org/package=ShapeRotator Description: CRAN Package 'ShapeRotator' (Standardised Rigid Rotations of Articulated Three-DimensionalStructures) Here we describe a simple geometric rigid rotation approach that removes the effect of random translation and rotation, enabling the morphological analysis of 3D articulated structures. Our method is based on Cartesian coordinates in 3D space so it can be applied to any morphometric problem that also uses 3D coordinates. See Vidal-García, M., Bandara, L., Keogh, J.S. (2018) . Package: r-cran-shapes Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-scatterplot3d, r-cran-rgl, r-cran-mass, r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-shapes_1.2.8-1.ca2404.1_all.deb Size: 899970 MD5sum: 5a2f4b92da1f8cd8d8a2026b5809dfe1 SHA1: 376b414234f0debfc2748b96ca4c5dd1c6e00556 SHA256: 5f5f4c8b2cbada54c90708bd66c97c06ce688db1386a7dda832318ada62b6ff4 SHA512: eb27b3f2052ba4506276fd795b9b48d9aedff9de49306433549371c0b7c4c0bcef0d4de614a652244fdd0c242151dfb98bf02d7f7cd3c0f2d2e58ce8287debc1 Homepage: https://cran.r-project.org/package=shapes Description: CRAN Package 'shapes' (Statistical Shape Analysis) Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons. Package: r-cran-shapeselectforest Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coneproj, r-cran-raster Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-shapeselectforest_1.7-1.ca2404.1_all.deb Size: 179500 MD5sum: 1f3679e89c73d05fb1117afe10d3602b SHA1: ac4132a216a9c8d121c9ccd64eeac9a3c99ec357 SHA256: e3f8f8f59d077c56ea69839c371074a1440adf364205b274605c7879a63f1b10 SHA512: 5422524e91320a2c20316221bb775e23f19e532485f2d5d812a8665de37c846f2799933337d57ec113ab774f376b27595330f43ae50325283a840925016ecb66 Homepage: https://cran.r-project.org/package=ShapeSelectForest Description: CRAN Package 'ShapeSelectForest' (Shape Selection for Landsat Time Series of Forest Dynamics) Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site . By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery. Package: r-cran-shapforxgboost Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xgboost, r-cran-data.table, r-cran-ggforce, r-cran-ggextra, r-cran-rcolorbrewer, r-cran-ggpubr, r-cran-bbmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-here, r-cran-lightgbm Filename: pool/dists/noble/main/r-cran-shapforxgboost_0.2.0-1.ca2404.1_all.deb Size: 804640 MD5sum: 3494a5b2e7d808a85a73d0b5a2b75929 SHA1: dbcb35480adbc2ed9f516abe263ca4253f8d7e93 SHA256: c730a6d90dc8b5dec559e1c690f8276c13540347062d739c07b00a4d7bf58cc6 SHA512: f0a49f78e5166e73b868520f44238537cb8ab5858970b7240dc5f47b5ca80ee3d2a531692612bd3ab910f1017d3aa2dbac1541cdeff0a30c820f8d7f8dd4a318 Homepage: https://cran.r-project.org/package=SHAPforxgboost Description: CRAN Package 'SHAPforxgboost' (SHAP Plots for 'XGBoost') Aid in visual data investigations using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. It provides summary plot, dependence plot, interaction plot, and force plot and relies on the SHAP implementation provided by 'XGBoost' and 'LightGBM'. Package: r-cran-shapley Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-h2o, r-cran-curl, r-cran-pander Filename: pool/dists/noble/main/r-cran-shapley_0.7.0-1.ca2404.1_all.deb Size: 805864 MD5sum: 07f9571dd440f466e9672b416ff95f83 SHA1: 95699aea61fd95532adc3bbf1502c00e80fdbac6 SHA256: d6ff06cc0fb82feb950a0d60e76ee88f80469ab7e6c89e2ad41c4c1474042320 SHA512: 17f76991fe7db935171ee28f7285ddff76aaa4ad44cd180d81b01e1bb021f225962cf986204b34c2b8eef5fc20cbc5684e3faa78086c6601814ed6fa1015684e Homepage: https://cran.r-project.org/package=shapley Description: CRAN Package 'shapley' (Weighted Mean SHAP and CI for Robust Feature Assessment in MLGrid) This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios. Package: r-cran-shapleyhale Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-doparallel Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shapleyhale_0.1.0-1.ca2404.1_all.deb Size: 69458 MD5sum: d60c0a3cd59f89327f6c8d98316020e6 SHA1: 238f8790e3f294a50a6b5f67ec67ebc66d5b5177 SHA256: 3013ad204355979c358c9ad4014cdfedf797a94ef0ca76f8c7f08702db2e95d3 SHA512: a7a5474e9b41a17b21e81506ce5c04d367f35974aacaebd6dad589c7ff5559cf539685e14d4e749f372e653b09e9566d5ccb0de5db51768f1100eaf4207d5dfd Homepage: https://cran.r-project.org/package=shapleyHALE Description: CRAN Package 'shapleyHALE' (Shapley Value Decomposition of Health-Adjusted Life Expectancy) Implements a Shapley-value framework to decompose changes in health-adjusted life expectancy (HALE) into additive contributions of individual diseases or causes, fully accounting for higher-order interactions. Separates mortality and disability effects and provides second-order Shapley interaction indices. Uncertainty is propagated via parametric bootstrap. Package: r-cran-shapleyoutlier Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rdpack, r-cran-tibble, r-cran-tidyr, r-cran-robustbase, r-cran-forcats, r-cran-egg, r-cran-ggplot2, r-cran-gridextra, r-cran-rcolorbrewer, r-cran-magrittr Suggests: r-cran-cellwise, r-cran-robusthd, r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shapleyoutlier_0.1.2-1.ca2404.1_all.deb Size: 790656 MD5sum: 903dc7e32172b1f2e28f76aa8ebe8a8e SHA1: 493b7a1a1c0f0880d639e8ac93580565a629559e SHA256: 79c8d070b5748d33a689bd4f727adace1cc0db64f8411b462c6d8e1afbd84dc2 SHA512: f5d2fcbf233def431b59946f3dc5722ad0d8937a6e404689915443508ceb5853731f29e580d50fc5b5c08405f6de02ae739e6d4e549394e174e28ef6d6347727 Homepage: https://cran.r-project.org/package=ShapleyOutlier Description: CRAN Package 'ShapleyOutlier' (Multivariate Outlier Explanations using Shapley Values andMahalanobis Distances) Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2023) . Package: r-cran-shapleyvalue Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyverse, r-cran-kableextra, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shapleyvalue_0.2.0-1.ca2404.1_all.deb Size: 21154 MD5sum: d8f5b4eb39398ea7ba22aa43d1b60f4a SHA1: 261b649cda30680e8d1858f665e5af3267b917a3 SHA256: 50383c237029fe26f3c3c3607a0b75ed0dc5d047a176bb2844ba765b15d5052e SHA512: 94b09e8b6fa13d7388c78ff64ee9562512e2e93b2b12e5f488b7586baa572309083b84a30abb682e17b0a857d29c542aafa10fbb563c9534371ae8d7cabd3cbf Homepage: https://cran.r-project.org/package=ShapleyValue Description: CRAN Package 'ShapleyValue' (Shapley Value Regression for Relative Importance of Attributes) Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity. Package: r-cran-shapper Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-dalex, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-knitr, r-cran-randomforest, r-cran-rpart, r-cran-testthat, r-cran-markdown, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-shapper_0.1.3-1.ca2404.1_all.deb Size: 67540 MD5sum: b614716d377bc593a992b80c3554f34b SHA1: 4257f72b1ecc43ccc5049d924ed486e01b2e670a SHA256: 805adb17b67ae25a648cf2976bd45f7a71ecaf6c7b3f9379d8fcf6e249cc2747 SHA512: 539df21643c1a1c6791d4c36c9f5a2e631c62767c618afba0ec562f66de6edb227c6fefc8ebbf208abf5d43b3f0bcfefceb89e85ce71a69ed651a7f9d5111ff4 Homepage: https://cran.r-project.org/package=shapper Description: CRAN Package 'shapper' (Wrapper of Python Library 'shap') Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'. Package: r-cran-shapviz Architecture: all Version: 0.10.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2752 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggfittext, r-cran-gggenes, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-rlang, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shapviz_0.10.4-1.ca2404.1_all.deb Size: 2025482 MD5sum: c1287aa5b3ca0ddd5c2bcfb92cd9e54f SHA1: 51b7a9202d1bf4fc18212d49c641ac15f49e4ec3 SHA256: ea72547480bfb5726890cc178ebd6f92904494b75126992a65c6035988388752 SHA512: e2d53c62b153b3d4fd091bdc2354341d573d933b565b255d2c8f43be4b63b3ed4b48ca43e1fb49aa000ba27519e1d9d87e8ce982e6c35d04b1d8b154778ff43c Homepage: https://cran.r-project.org/package=shapviz Description: CRAN Package 'shapviz' (SHAP Visualizations) Visualizations for SHAP (SHapley Additive exPlanations), such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a 'shapviz' object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages 'xgboost', 'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and 'kernelshap' are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the 'shap' package in Python, but there is no dependency on it. Package: r-cran-shar Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-classint, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-spatstat.random, r-cran-terra Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spatstat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shar_2.3.1-1.ca2404.1_all.deb Size: 1236908 MD5sum: 04e22d92b014f0ff4ae81bfd0ff274b4 SHA1: a9ecd3287c6b480faa7ea79f913ea59fcc8fc091 SHA256: 36a5d97377b6990048a983f9f2f57e740c235a8e2c491c3bab8de80331ca25a4 SHA512: 7a4b398c8f08692a18eacd07bacee38f9d0a79ee471d977b70d75c676cf4f3caa7ff4a287176e89093f0cde4311f7d722531c4880bc447992556a101f754fd28 Homepage: https://cran.r-project.org/package=shar Description: CRAN Package 'shar' (Species-Habitat Associations) Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) and Harms et al. (2001) . Package: r-cran-shark4r Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1708 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-leaflet, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-sf, r-cran-stringi, r-cran-terra, r-cran-tidyr, r-cran-vroom, r-cran-worrms Suggests: r-cran-ggtext, r-cran-htmltools, r-cran-irfcb, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-skimr, r-cran-spelling, r-cran-shiny, r-cran-bslib, r-cran-bsicons, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shark4r_1.2.1-1.ca2404.1_all.deb Size: 1334890 MD5sum: 04d29e2998a24602f8c446e4043459b9 SHA1: d13f548a0c8eee8af942fa7df52cb95b8eff7703 SHA256: b71245e8b3130df6e663672c5417644abf6a647482e0c478d907ee67d998700f SHA512: 87f4ae918341eed8692f454cfbf82b1b637acba09bd2f84db04ad56ef52e7280f3dd05da1d4bb6e802193e4146380d677acea68a3ce6792438e81ec7c1cd150b Homepage: https://cran.r-project.org/package=SHARK4R Description: CRAN Package 'SHARK4R' (Accessing and Validating Marine Environmental Data from 'SHARK'and Related Databases) Provides functions to retrieve, process, analyze, and quality-control marine physical, chemical, and biological data. The main focus is on Swedish monitoring data available through the 'SHARK' database , with additional API support for 'Nordic Microalgae' , 'Dyntaxa' , World Register of Marine Species ('WoRMS') , 'AlgaeBase' , OBIS 'xylookup' web service and Intergovernmental Oceanographic Commission (IOC) - UNESCO databases on harmful algae and toxins . Package: r-cran-sharkdemography Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-interp, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-popbio, r-cran-iterators, r-cran-dofuture, r-cran-doparallel, r-cran-foreach, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sharkdemography_1.1.0-1.ca2404.1_all.deb Size: 115968 MD5sum: b78a1ca12faa16cbe33c050ae4be5c05 SHA1: 426ff8bd9ab93b782970cd17d989267c3cb6cb1a SHA256: 7f3d5737d34214c746be13eadd6cdf98925a8fdef8f3320b157bd0d2a32d8fb9 SHA512: 529b2916a21aa0541fc4acd49c10d3a77694f88789cc1c3cd099e73220aa5f6d9bcc85c693931a20056d750cb4f2966b7539bc66dd53d01822a4f1bc197db4e8 Homepage: https://cran.r-project.org/package=SharkDemography Description: CRAN Package 'SharkDemography' (Shark Demographic Analyses Using Leslie Matrix Models) Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020) "Can multi-species shark longline fisheries be managed sustainably using size limits? Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847–1860. . Package: r-cran-sharp Architecture: all Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fake, r-cran-abind, r-cran-beepr, r-cran-future, r-cran-future.apply, r-cran-glassofast, r-cran-glmnet, r-cran-igraph, r-cran-mclust, r-cran-nloptr, r-cran-plotrix, r-cran-rdpack, r-cran-withr Suggests: r-cran-cluster, r-cran-corpcor, r-cran-dbscan, r-cran-elasticnet, r-cran-gglasso, r-bioc-mixomics, r-cran-nnet, r-cran-openmx, r-bioc-rcy3, r-cran-randomcolor, r-cran-rmarkdown, r-cran-rpart, r-cran-sgpls, r-cran-sparcl, r-cran-survival, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-sharp_1.4.8-1.ca2404.1_all.deb Size: 1461288 MD5sum: 6d121f2a9ec0ef8656d3d7779ff42980 SHA1: cb86f02921873c98e5d9cc51ab8bd409d06d7a16 SHA256: 6f88b6dbabe48570c6ff9ca2d1c523596f693afc2f1a56f774ce11a56320801f SHA512: 397f93a75d5345a4abb82b8e660ce616c9e37bb25f5ec49bb7caf45356472abd0e31dba4eda643744688279a55d5df9260d5e7d7124a56bed10271a8c99c6be9 Homepage: https://cran.r-project.org/package=sharp Description: CRAN Package 'sharp' (Stability-enHanced Approaches using Resampling Procedures) In stability selection (N Meinshausen, P Bühlmann (2010) ) and consensus clustering (S Monti et al (2003) ), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) ), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) and B Bodinier et al (2023b) ). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering. Package: r-cran-sharper Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3094 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc, r-cran-zoo, r-cran-epsiwal Suggests: r-cran-xtable, r-cran-xts, r-cran-timeseries, r-cran-quantmod, r-cran-mass, r-cran-ttr, r-cran-testthat, r-cran-sandwich, r-cran-txtplot, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sharper_1.4.0-1.ca2404.1_all.deb Size: 2647784 MD5sum: 00716c6bf545f5ee032b36464e77e064 SHA1: 31e983fc40bae40297860661e10b38d126582faf SHA256: 6689e259c1c490af5b6bc512e3244ef5f308c23245bf30c0940d3bc95f1509af SHA512: f1ffe5d7ec800f626f64a52a9c8922db5c288065c39b5fd799089adbd9d7be9903ae80a1d92b565ccccd7054e5b04e84b23903d1806c49645774491165948c2f Homepage: https://cran.r-project.org/package=SharpeR Description: CRAN Package 'SharpeR' (Statistical Significance of the Sharpe Ratio) A collection of tools for analyzing significance of assets, funds, and trading strategies, based on the Sharpe ratio and overfit of the same. Provides density, distribution, quantile and random generation of the Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe ratio over multiple assets. Computes confidence intervals on the Sharpe and provides a test of equality of Sharpe ratios based on the Delta method. The statistical foundations of the Sharpe can be found in the author's Short Sharpe Course . Package: r-cran-sharpr2 Architecture: all Version: 1.1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sharpr2_1.1.1.0-1.ca2404.1_all.deb Size: 184016 MD5sum: 9faef414a8108ed1ef93bcf65e248e31 SHA1: 7b519b0c50f6e5f31af8949a46794b00cb6c4068 SHA256: b758faddeb968e57b502b1086e4a9176116342de0120852569875544878d193e SHA512: 630aff21b68afe4fafc7cb4f4dc7464a940a61c996499f2ffe4d12fec82a92e3ea73776eddbe34d58244d0ede7059d082222f47452b9bb40e78afbdd2d21143e Homepage: https://cran.r-project.org/package=sharpr2 Description: CRAN Package 'sharpr2' (Estimating Regulatory Scores and Identifying ATAC-STARR Data) An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) . Package: r-cran-sharpshootr Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aqp, r-cran-ape, r-cran-soildb, r-cran-cluster, r-cran-lattice, r-cran-reshape2, r-cran-scales, r-cran-circular, r-cran-rcolorbrewer, r-cran-plyr, r-cran-digest, r-cran-e1071, r-cran-stringi, r-cran-curl Suggests: r-cran-mass, r-cran-spdep, r-cran-circlize, r-cran-rvest, r-cran-xml2, r-cran-terra, r-cran-raster, r-cran-exactextractr, r-cran-httr, r-cran-jsonlite, r-cran-igraph, r-cran-dendextend, r-cran-testthat, r-cran-latticeextra, r-cran-farver, r-cran-venn, r-cran-gower, r-cran-daymetr, r-cran-elevatr, r-cran-evapotranspiration, r-cran-zoo, r-cran-soiltaxonomy, r-cran-sf, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-vegan Filename: pool/dists/noble/main/r-cran-sharpshootr_2.5-1.ca2404.1_all.deb Size: 532502 MD5sum: 5497047b1d186f26c428175c1fccb1e2 SHA1: 3d83a426afa78af38dc617d618ee2cc066a6b8d4 SHA256: d6604dba7b7c999c64f85c22d393c5206fd444df85ff8ad64172950a579cfe45 SHA512: 17462b72084cd61f6027fbf5f7877c2cebd966704dcc9db2678a81071e25c72d217d8b73cf74b5cc196975b6853926bd8ea422540ff1966aa5cec51ed799a1c8 Homepage: https://cran.r-project.org/package=sharpshootR Description: CRAN Package 'sharpshootR' (A Soil Survey Toolkit) A collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes. Package: r-cran-sharx Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-dcmle, r-cran-dclone Filename: pool/dists/noble/main/r-cran-sharx_1.0-7-1.ca2404.1_all.deb Size: 201222 MD5sum: 4820a0446545ce06c35a03966aebf880 SHA1: a1ae79863db4055da9a27e377d18a8c05db906e8 SHA256: af7312da8e5867df4fa1954a79256aa608676665216c51f505167bf6b17eb193 SHA512: eacc315a866f5afcdab4c51923f1b0cb3151b8dffb2f920144577671efede04e17b50fe76268106d806e33bbba1c2e7510741a9f11928caef515a7ce4859402c Homepage: https://cran.r-project.org/package=sharx Description: CRAN Package 'sharx' (Models and Data Sets for the Study of Species-Area Relationships) Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) . Package: r-cran-shattering Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-pdist, r-cran-slam, r-cran-ryacas, r-cran-rmarkdown, r-cran-pracma, r-cran-e1071, r-cran-nmf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shattering_1.0.7-1.ca2404.1_all.deb Size: 71692 MD5sum: 80de2c5263f87c6bd6cfac27e64c2914 SHA1: 15832b5a072fe977dfc30dfd5512b93d31065de7 SHA256: 4bbdae64f24f2fa462a54a1619ac925e7e3bb11e663b706ae68fa79d5cb16930 SHA512: 2ae36880a569afefc743947ea870c225dd32497de920e11e773f0be6d9fc21c91a68e6c3c6fa7e1a91ff7595045a9f80cba33b25114ca93951c3edbd476f76f6 Homepage: https://cran.r-project.org/package=shattering Description: CRAN Package 'shattering' (Estimate the Shattering Coefficient for a Particular Dataset) The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" ; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory". Package: r-cran-shazam Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2553 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-alakazam, r-cran-ape, r-cran-diptest, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-igraph, r-cran-iterators, r-cran-kernsmooth, r-cran-lazyeval, r-cran-mass, r-cran-progress, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringi, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-shazam_1.4.0-1.ca2404.1_all.deb Size: 2178356 MD5sum: e9581c7d07cab1d192fde78f06f675da SHA1: fefee9827a79e72229019b551295b9a918d5e6c2 SHA256: a60e0d56a7a3ce785d11f545c6df3f1087e004d6da95566e7f5cc429a93b5ba1 SHA512: 657d37675b2c68129b1552caa2e72768587b4479ac9ba7c2a36a4a9de0787133b5bb7aacc5e602289327a621dc85e8ab498e921b8f9d4cf2df831ed08c38049c Homepage: https://cran.r-project.org/package=shazam Description: CRAN Package 'shazam' (Immunoglobulin Somatic Hypermutation Analysis) Provides a computational framework for analyzing mutations in immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of antigen-driven selection pressure, mutational load quantification, building of somatic hypermutation (SHM) models, and model-dependent distance calculations. Also includes empirically derived models of SHM for both mice and humans. Citations: Gupta and Vander Heiden, et al (2015) , Yaari, et al (2012) , Yaari, et al (2013) , Cui, et al (2016) . Package: r-cran-shelf Architecture: all Version: 1.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-ggextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-hmisc, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinymatrix, r-cran-sn, r-cran-survival, r-cran-survminer, r-cran-tidyr Suggests: r-cran-ggally, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-shelf_1.13.0-1.ca2404.1_all.deb Size: 703844 MD5sum: 6445ee678ec8cb65300e6bc5ce9fb9bb SHA1: ca38fd2f8491eaed9fdf91715483a4e0fb57c3be SHA256: b72dae0de8ee7e6afcc1d3a67757c49cc34aed296b7f06be4066f1a5e7106c46 SHA512: 5a45137db93f7a40a6a1c4cd9c1b48517f0605e2ae1f7b2982217fd61a2c55385012b710177246e81c085ea14efa3638565230d347b98f47f1c6ef7c649ecf54 Homepage: https://cran.r-project.org/package=SHELF Description: CRAN Package 'SHELF' (Tools to Support the Sheffield Elicitation Framework) Implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included. Package: r-cran-shellchron Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtop, r-cran-zoo, r-cran-ggplot2, r-cran-ggpubr, r-cran-tidyr, r-cran-scales, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shellchron_0.4.0-1.ca2404.1_all.deb Size: 522716 MD5sum: c0899b34dff846178b17002dc37266c1 SHA1: cc18ed4aa6d754f42ae72c62e778b700f39dca03 SHA256: e3011081a19673e00d851771eec1c3aaf54c829919f093d2dad1975caaec03f3 SHA512: 91520901ec840bd8c2e639c0816ea815255712ff6f45d4cf33b1d34199c26081a6ee84cde3a9445fff6fb4737a5a4318a43177d7f44340d1c854a2184c36b7a7 Homepage: https://cran.r-project.org/package=ShellChron Description: CRAN Package 'ShellChron' (Builds Chronologies from Oxygen Isotope Profiles in Shells) Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) ). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters. Package: r-cran-shellgame Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8600 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tidycensus Suggests: r-cran-geodeltaaudit, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shellgame_0.1.1-1.ca2404.1_all.deb Size: 1709272 MD5sum: 1631158e7e2ec34dede0785965c3a229 SHA1: a7b0889509ba2d587c50d5e12427c37ee851f962 SHA256: 4b3a4c96ee928554953e4b05fbd3388225a751d25876170708b948626162ba7b SHA512: 323958aaa154e8afea93e3c640d8b31ff0ce978a1a8e17d7559fda28757a3622785e5e87c0124051192e0b71f89ee68a2c121fbde13fbbca4fa6c415dcb358c5 Homepage: https://cran.r-project.org/package=shellgame Description: CRAN Package 'shellgame' (The Shell Game - Audit Geographic Data Transformations) Reveals how data quality silently degrades during geographic transformations while variable labels remain unchanged. Demonstrates that transformation error is agnostic to both the variable (population, income, etc.) and the tool ('R', 'Python', etc.). Provides a reproducible audit framework for quantifying the shift from observed to imputed data at each transformation hop. Package: r-cran-shelltrace Architecture: all Version: 3.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xlsx, r-cran-bmp, r-cran-tiff Filename: pool/dists/noble/main/r-cran-shelltrace_3.5.1-1.ca2404.1_all.deb Size: 2536652 MD5sum: c50573a2719ed5ec6769e44a429838d5 SHA1: 1cc0004f052e0e0238795d731031f115f36f1dde SHA256: 872c9d77ed8ec25df2e715e0246531b11ff3037866da24dba89966cd3141008f SHA512: 47742ccfcbc984722ae31321431a6eba727cc2ffe829193f48f90f26ade2e0e80374edc03d5f4f66582aa229c52d9ac9c40c078dc8d52e169801037b4870fcd3 Homepage: https://cran.r-project.org/package=shelltrace Description: CRAN Package 'shelltrace' (Bivalve Growth and Trace Element Accumulation Model) Contains all the formulae of the growth and trace element uptake model described in the equally-named Geoscientific Model Development paper (de Winter, 2017, ). The model takes as input a file with X- and Y-coordinates of digitized growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in the form of XLSX files. Package: r-cran-shelter Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-getpass, r-cran-yaml, r-cran-filelock, r-cran-rappdirs, r-cran-sodium Suggests: r-cran-testthat, r-cran-rstudioapi, r-cran-mockery, r-cran-keyring Filename: pool/dists/noble/main/r-cran-shelter_0.2.1-1.ca2404.1_all.deb Size: 75348 MD5sum: 640e1823644055ef7fa51e68a5487d1c SHA1: b8c585b286d8a9a064d824481480bc5054c1ce88 SHA256: 7972172050f6cfbc8d7a7aa48ed7c4f24ff5c2196239179520231e90c4b4e143 SHA512: a3bb3785b9f12e0ea50b067d73d0853835dc844d92b570e6ab33fe20da1cba7dc332ec4076665a8efa4c0c8b512284d337e2a35d027b43d790a3724d802a05c2 Homepage: https://cran.r-project.org/package=shelter Description: CRAN Package 'shelter' (Support for Secure API Key Management) Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments. Package: r-cran-sherlock Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-forcats, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-cowplot, r-cran-scales, r-cran-ggh4x, r-cran-stringr, r-cran-plotly, r-cran-readr, r-cran-openxlsx, r-cran-purrr, r-cran-fs, r-cran-rstudioapi, r-cran-tidytext Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-sherlock_0.7.0-1.ca2404.1_all.deb Size: 494990 MD5sum: d1c447d0fc9d5dfd3efb511aeb78085d SHA1: a7462192feb3b8ceee9a3fa9288130fb2f8a2594 SHA256: 9a8a1f51fb3046d39314ea9ac454830c595a11a28359fbd352d9ed836ef94294 SHA512: b63ca466e9480fc00112c9578b32c5ae9d4e44ba0f05eb5fa2ddebbcce3b93a11c5fce9b7e979d1bc477dcd3ed4bbb8ed13830f16989e5fa4942aacc3c3cb06e Homepage: https://cran.r-project.org/package=sherlock Description: CRAN Package 'sherlock' (Graphical Displays for Structured Problem Solving and Diagnosis) Powerful graphical displays and statistical tools for structured problem solving and diagnosis. The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique. The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file. References: David Hartshorne (2019, ISBN: 978-1-5272-5139-7). Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467). Package: r-cran-sherlockholmes Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf, r-cran-stringr, r-cran-dpseg, r-cran-plotrix, r-cran-zoo, r-cran-stargazer, r-cran-textboxplacement, r-cran-plot.matrix, r-cran-devtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sherlockholmes_1.0.2-1.ca2404.1_all.deb Size: 2320882 MD5sum: e1197c069b9cc2e2d0f10968dc7253c0 SHA1: 3cdecf7946870cfdc18853e654f16b5095a79849 SHA256: 431799266ac88db011319a9de6793eb271e9bad0fb2c35d6b3962b184fb1e9a9 SHA512: 0f68a91c54e4f385ffbb51884f7a3e6168729c98f050b9523287ba6752b2daa92655d33ab918513b346f189587a4b77d2cc5d308a3a7973fcf909644092172a8 Homepage: https://cran.r-project.org/package=SherlockHolmes Description: CRAN Package 'SherlockHolmes' (Building a Concordance of Terms in a Series of Texts) Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes' popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at . Package: r-cran-shewhartr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3703 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-ggplot2, r-cran-broom, r-cran-slider Suggests: r-cran-patchwork, r-cran-plotly, r-cran-qcc, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-shewhartr_1.4.0-1.ca2404.1_all.deb Size: 2398616 MD5sum: 4fbb82dad598156bf0a4b99ded1c9068 SHA1: a386fa994730fec12ec9aaeb3129f5d18c77f3e6 SHA256: af1fe913d8e14425c7d409b575a1425c32bee58df7481f3fe63e9a39fc6e702d SHA512: efcb82f6c9c4c165108a827c26139225574c6f17eb42024a96b1a616a955edbe6aaa106b88811ec990ffa249776a6f9b004706d461fb7d0681f97f8aca9c3712 Homepage: https://cran.r-project.org/package=shewhartr Description: CRAN Package 'shewhartr' (Statistical Process Control with Tidyverse-Native Workflows) A comprehensive toolkit for Statistical Process Control (SPC) that combines the rigor of classical Shewhart methodology with modern tidyverse-native interfaces. Provides classical control charts for variables (I-MR, Xbar-R, Xbar-S) and attributes (p, np, c, u), as well as regression-based control charts for processes with trend. Includes Nelson runs tests, Average Run Length (ARL) simulation, process capability indices with bootstrap confidence intervals, Box-Cox transformation guidance, and a clean Phase I / Phase II workflow. All chart objects integrate with broom via 'tidy', 'glance' and 'augment' methods. References: Shewhart (1931, ISBN:0-87389-076-0); Montgomery (2019, ISBN:978-1-119-39930-8); Nelson (1984) ; Woodall (2000) ; Box & Cox (1964) . Package: r-cran-shidashi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-digest, r-cran-ellmer, r-cran-fastmap, r-cran-formatr, r-cran-httr2, r-cran-s7, r-cran-shiny, r-cran-shinychat, r-cran-yaml, r-cran-jsonlite, r-cran-htmlwidgets Suggests: r-cran-cli, r-cran-coro, r-cran-logger, r-cran-processx, r-cran-promises, r-cran-later, r-cran-rstudioapi, r-cran-htmltools, r-cran-httpuv, r-cran-ggplot2, r-cran-ggextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shidashi_0.2.0-1.ca2404.1_all.deb Size: 1293104 MD5sum: 47d110afb5f13d891ff61cc2f39f32e2 SHA1: 6390d15a1a1c088a5764ed5eb59a69179c8c143d SHA256: 2c201521d66a7d0e2d30146037034253811199e4522d80fb65f69c7cbc536862 SHA512: b44043b801b4fa768dd5c4ff9fd296d5832073b3be5e80abcba243d0bf3f7ddefcbd8676cf1c052d648a45a57f5d9fa9331ac95c1327562269395ae212fc8fd8 Homepage: https://cran.r-project.org/package=shidashi Description: CRAN Package 'shidashi' (A Shiny Dashboard Template Modular System with Chat Bot Support) A template dashboard system with AI agent integrated. 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Package: r-cran-shifthappens Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-rbam, r-cran-ggplot2, r-cran-scales, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shifthappens_1.0.1-1.ca2404.1_all.deb Size: 379736 MD5sum: 7b2635f8482b6e831a19604480e93500 SHA1: 8db5408bf11efa53d5e83d5d008fdded56baa31f SHA256: 8226756bfc462c21bc9bebe96b2264f80545e1c324d1cfdd45715a3de0bb5a31 SHA512: decc28732ec4bb143361786416c1320478d98546e1c7e7ba658426fe52af7aed6c802e6a01b977c8e7cd61a8772c74525698022b4d5d958837f9f5e6f67e82dc Homepage: https://cran.r-project.org/package=ShiftHappens Description: CRAN Package 'ShiftHappens' (Detecting, Visualizing and Estimating Shifts) Detecting, visualizing and estimating shifts, with a specific focus on stage-discharge rating shifts. 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Package: r-cran-shinipsum Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-attempt, r-cran-dt, r-cran-dygraphs, r-cran-ggplot2, r-cran-magrittr, r-cran-plotly Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinipsum_0.1.1-1.ca2404.1_all.deb Size: 355646 MD5sum: 951cb136b1f2c77eb0caeb2c9b087d6c SHA1: c86978ce34880a7ac706cb18407fad69a2f8ba60 SHA256: 83eda302ed12f25604f030903af914ba0a1a5d8200c81f9941e58ac6c1626a91 SHA512: 19dc5bfd4fe38a962c024c50393df8b3956a58ffbc156ec698455a284258b392731745406ea6e3e74ecc04924930807ea5bb20994cb4dccec16e7953afcbf2e2 Homepage: https://cran.r-project.org/package=shinipsum Description: CRAN Package 'shinipsum' (Lorem-Ipsum-Like Helpers for Fast Shiny Prototyping) Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers. 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Package: r-cran-shiny.blueprint Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-htmltools, r-cran-shiny, r-cran-shiny.react Suggests: r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-purrr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-shiny.router Filename: pool/dists/noble/main/r-cran-shiny.blueprint_0.3.0-1.ca2404.1_all.deb Size: 658206 MD5sum: 6fb6229bfa7bbca3c330c903f0713fae SHA1: e616571878f38c305a56d60791f95eebb0daa910 SHA256: 0b0ca4e81ebddd6b739161bd957c8729acfae97472dbaa08b46d0206fdb2ed16 SHA512: 17e0c70f5d17a011a085380472d8be57c4f9aead8108754e5badeb3d716b3d1460a4aa3d4ec59d51bdaeb60a7cc1ef28072f2b5f3afcb0f8e08b7dc4d2f52260 Homepage: https://cran.r-project.org/package=shiny.blueprint Description: CRAN Package 'shiny.blueprint' (Palantir's 'Blueprint' for 'Shiny' Apps) Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app. 'Blueprint' provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in 'R' without writing 'JavaScript' code. Package: r-cran-shiny.destroy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-rlang, r-cran-shiny Suggests: r-cran-bslib, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shiny.destroy_0.1.0-1.ca2404.1_all.deb Size: 309352 MD5sum: 38ee803f97bcab8c903281e45b234bad SHA1: 29171ab093c7df09aa16c96ca2a3b02ce1590a34 SHA256: cc7d43e6d98f91cf7a3aec0791d3f8d64ddf59c874a6c115ed9629ab383edc67 SHA512: bb8f66462060095a2f9cf46195199543de9399c2b5bf1203cbf85413ac44014ac41f108ede8361d01acba46937ce870cdaaf9edc58923196370524135cc7700b Homepage: https://cran.r-project.org/package=shiny.destroy Description: CRAN Package 'shiny.destroy' (Create Destroyable Modules in 'Shiny') Enables the complete removal of various 'Shiny' components, such as inputs, outputs and modules. It also aids in the removal of observers that have been created in dynamically created modules. Package: r-cran-shiny.emptystate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-r6, r-cran-shiny Suggests: r-cran-bsicons, r-cran-chromote, r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.emptystate_0.1.0-1.ca2404.1_all.deb Size: 126238 MD5sum: 1a119bb31886d17e1ee12f85561eaf51 SHA1: e195aa9fcc0753b500c6b8664c6c063537a5f2b4 SHA256: 11b46738a512f60a2620b563fffe6d654eb89cb6600d98d6489a60fd7f54f7c6 SHA512: d9fe98b02f2ef8640e1a8f98680433713f49bc51fd5e02462a0340642432956baadac7859c26054ca93e77ac9dcbf9897b59e99349054aa49ad9cea8db5fe44c Homepage: https://cran.r-project.org/package=shiny.emptystate Description: CRAN Package 'shiny.emptystate' (Empty State Components for 'Shiny') Offers a comprehensive solution for managing 'empty states' in 'Shiny' applications. 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It generates a script in the open shiny project then create a shortcut in the same folder that allows you to launch the app by clicking.If you set `host = 'public'`, the application will be launched on the public server to which you are connected. Thus, all other devices connected to the same server will be able to access the application through the link of your `IPv4` extended by the port. You can stop the application by leaving the terminal opened by the shortcut. Package: r-cran-shiny.fluent Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3422 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-purrr, r-cran-shiny, r-cran-shiny.react Suggests: r-cran-chromote, r-cran-covr, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-glue, r-cran-knitr, r-cran-leaflet, r-cran-mockery, r-cran-plotly, r-cran-rcmdcheck, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-sass, r-cran-shiny.i18n, r-cran-shiny.router, r-cran-shinyjs, r-cran-shinytest2, r-cran-sortable, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.fluent_0.4.1-1.ca2404.1_all.deb Size: 1253636 MD5sum: ff6934e0fbff76f26bc63d12985b830e SHA1: 7d048fcb1758ebe3f4fb61ea449612f4a88d77bd SHA256: 27a257e895365ed20c2c6c483e3f58af9d98a49dfb603a1a56d3425f81de798a SHA512: 44b3d98cd1fed55b9e31a3d7a92bc73432a715c5a14df044265b2c1171251c04a9e1c9857bc69a86de9cb70534bc85ecfc48361857d076d50c77674b5cd2a8ac Homepage: https://cran.r-project.org/package=shiny.fluent Description: CRAN Package 'shiny.fluent' (Microsoft Fluent UI for Shiny Apps) A rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. 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Package: r-cran-shiny.router Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue, r-cran-rlang, r-cran-shiny Suggests: r-cran-covr, r-cran-lintr, r-cran-rcmdcheck, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shiny.router_0.3.1-1.ca2404.1_all.deb Size: 84580 MD5sum: ebfb1d9fec7592aca45c2299fba2a8e7 SHA1: 3d6c8235e608d7ba349a993c1bea4533b733c924 SHA256: c42b015354f6889ff4930401925690ca8c8cf61acda67817288d37daf33bc78c SHA512: 6fb576b816b99689aa4080065c676db08195fe8708ddd818694af3da170d7516b1410754b0d9d4fc7e584afb082eae71a679a1b5cc1d10c0bc4c6ae9d8801a62 Homepage: https://cran.r-project.org/package=shiny.router Description: CRAN Package 'shiny.router' (Basic Routing for Shiny Web Applications) It is a simple router for your Shiny apps. 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Package: r-cran-shiny.tailwind Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-fontawesome, r-cran-palmerpenguins, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shiny.tailwind_0.2.2-1.ca2404.1_all.deb Size: 176788 MD5sum: 31ba1c57e6d3a7df48d64781242051ca SHA1: 18af59e503d625ce9f38dea3c1364fcaa39a5532 SHA256: 9b438eb0f15198f91c84803f9bd4b852f78cae4c4f38319af0dfa3201723c24f SHA512: 72489a600ed6759d5c626ceb936b4788ab556abb26d1eeee7c10256ada6c4d4f0862df10fa55add5915df3fd5923472dd07b5850ad31812afb36d0bba8d35d6c Homepage: https://cran.r-project.org/package=shiny.tailwind Description: CRAN Package 'shiny.tailwind' ('TailwindCSS' for Shiny Apps) Allows 'TailwindCSS' to be used in Shiny apps with just-in-time compiling, custom css with '@apply' directive, and custom tailwind configurations. Package: r-cran-shiny.telemetry Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-htmltools, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-logger, r-cran-lubridate, r-cran-odbc, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-rsqlite, r-cran-shiny, r-cran-stringr, r-cran-tidyr Suggests: r-cran-box, r-cran-config, r-cran-dt, r-cran-knitr, r-cran-mongolite, r-cran-plotly, r-cran-plumber, r-cran-rcolorbrewer, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpostgresql, r-cran-scales, r-cran-semantic.dashboard, r-cran-shiny.semantic, r-cran-shinyjs, r-cran-testthat, r-cran-timevis, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.telemetry_0.3.2-1.ca2404.1_all.deb Size: 1565532 MD5sum: d6fe845cc39f5d1b9e53c2bb53ffb9aa SHA1: 19758b5dcc8a91948e3a8ced1b2f0905a758404a SHA256: 9ea2712df4581d5be5dbd711b34eef3b43bf0e8d6cf891ca558be36f88990a2e SHA512: 7e58da492ef37e4b99e2e1da996c3b33cccee1ffc74d281ba6eeb524cff6b38758d6ac0b4a64b48eefab80fccdde3d3f6dabb26158772a3337add9adee14f49c Homepage: https://cran.r-project.org/package=shiny.telemetry Description: CRAN Package 'shiny.telemetry' ('Shiny' App Usage Telemetry) Enables instrumentation of 'Shiny' apps for tracking user session events such as input changes, browser type, and session duration. 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Package: r-cran-shiny.worker Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-shiny, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-shiny.worker_0.0.1-1.ca2404.1_all.deb Size: 74734 MD5sum: b128dbcf893810e4615b3f5e042da84d SHA1: 8394c80f9ffc8ded5e4ceaf004fec333910193f0 SHA256: 03b3453f7f5ee8e0ef291dd66e9fd1f275db5671cff7eed6f1d48d9d64e8eff4 SHA512: 20f0b0a5b6a6c7ae7ca0ad20735da88f267e99a52d60d879c9e921d0ab909f12ad16cf1279a0e1ebabba50633d0bb13fd3112b74f0ba2638c69ffd04f3b88933 Homepage: https://cran.r-project.org/package=shiny.worker Description: CRAN Package 'shiny.worker' (Delegate Jobs for Shiny Web Applications) It allows you to delegate heavy computation tasks to a separate process, such that it does not freeze your Shiny app. Package: r-cran-shiny2docker Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-attachment, r-cran-cli, r-cran-dockerfiler, r-cran-here, r-cran-yesno Suggests: r-cran-knitr, r-cran-mockery, r-cran-renv, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shiny2docker_0.0.4-1.ca2404.1_all.deb Size: 35516 MD5sum: e4d5b875cdda20a4efec16c45e686dff SHA1: 74ac012ff222e6799d6efdac72feb27b10d3ea12 SHA256: 25fa7bc494bfc9969ce88401e1d9dbc79715d5748c337334fcca49022fbde875 SHA512: 9d524241ac4bb6e12fa1dcbb440ff10475fd408f75b4a6a266a517ececc493d7aad8d841be648842721be79ba14fbea8055eafed205d221eb772cc253c7e926d Homepage: https://cran.r-project.org/package=shiny2docker Description: CRAN Package 'shiny2docker' (Generate Dockerfiles for 'Shiny' Applications) Automates the creation of Dockerfiles for deploying 'Shiny' applications. 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Package: r-cran-shinyalert Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-uuid Suggests: r-cran-colourpicker, r-cran-shinydisconnect Filename: pool/dists/noble/main/r-cran-shinyalert_3.1.0-1.ca2404.1_all.deb Size: 442382 MD5sum: e5f6fad429d101477ee79f5138646126 SHA1: fc269d1fc042afc72c04d03be49ebb488caeaa8f SHA256: 0b018d35b77bd2545fef090498b31c056493eccdc6256c85e81e4f6de3dc688d SHA512: bdf8320d117030b7fb428e51855d28d6a454bc49c86c9c52e780fc621c8e51ee85a73c78d88bfe5b50dec7b72caadc8f5e7b0c81f66e4e30c411c8020a121d95 Homepage: https://cran.r-project.org/package=shinyalert Description: CRAN Package 'shinyalert' (Easily Create Pretty Popup Messages (Modals) in 'Shiny') Easily create pretty popup messages (modals) in 'Shiny'. 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Package: r-cran-shinyblock Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-reticulate, r-cran-reactable, r-cran-networkd3, r-cran-bslib, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shinyblock_0.1.3-1.ca2404.1_all.deb Size: 26656 MD5sum: d3884e70fe7401b9e078afbb2bbda720 SHA1: 8e1594b084d1960979f8e8826e259b3ea5684c4f SHA256: 7a3450cc1d4ec00e3c7d37a9c33c36aecfff9b1f72dbac0e79685e7a63e7e1e5 SHA512: f5cabb51bdac7187de62aaf5fc5822c4fd4459cd8f3c4259121d1b595899ae3de901b3db692723ef404a20473bffaee9f9841281907d167610c68daba96d191b Homepage: https://cran.r-project.org/package=ShinyBlock Description: CRAN Package 'ShinyBlock' (Multi-Protocol Blockchain Simulator and Enterprise LedgerFramework) An interactive framework for simulating blockchain protocols using a hybrid 'R-Shiny' and 'Python' architecture. 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Package: r-cran-shinybody Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1521 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-crosstalk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-shinybody_0.1.3-1.ca2404.1_all.deb Size: 503986 MD5sum: 781062c0f3aec6df254fbd05dc673150 SHA1: 625d28f14859eecf7e6b45c6768012bfd13f9cbf SHA256: 77446ed878d6b1e02fd0d449faa87a7ace90c9d6ace28c7253208a72bc81f2fe SHA512: a75c34eedf89d60966bc0a5adfd2e4ff7453cbf54308b626dc0a9009a5d10d1da51d4a18f3546c9e2e5e1ffc7f2aa404176fc85eae281a430fe3dcb61332c2b9 Homepage: https://cran.r-project.org/package=shinybody Description: CRAN Package 'shinybody' (An Interactive Anatomography Widget for 'shiny') An 'htmlwidget' of the human body that allows you to hide/show and assign colors to 79 different body parts. 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Package: r-cran-shinychakraui Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20896 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-reactr, r-cran-shiny, r-cran-jsonlite, r-cran-rlang, r-cran-stringr, r-cran-formatr, r-cran-fontawesome Suggests: r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-shinychakraui_1.1.1-1.ca2404.1_all.deb Size: 3784300 MD5sum: b4ce652dd82839ebe7316c3747918bc6 SHA1: 79c0d34a9ff8be33a0a28a9eee8b0820adbd3a05 SHA256: 6c8b06960a7395196f1220b809ecc78bdb274e416790f551fedc3328365db9c5 SHA512: 8e141e295b4e8c4da3158d5245d813ebf06596280b773fb8a1229937d1e6a9f9f50fafd61d9d54cc957bd6102e3931c0af0287bd9b2818ccf31fd4357e7d61f2 Homepage: https://cran.r-project.org/package=shinyChakraUI Description: CRAN Package 'shinyChakraUI' (A Wrapper of the 'React' Library 'Chakra UI' for 'Shiny') Makes the 'React' library 'Chakra UI' usable in 'Shiny' apps. 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Package: r-cran-shinychat Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-bslib, r-cran-cli, r-cran-coro, r-cran-ellmer, r-cran-fastmap, r-cran-htmltools, r-cran-jsonlite, r-cran-lifecycle, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-s7, r-cran-shiny Suggests: r-cran-chromote, r-cran-covr, r-cran-knitr, r-cran-later, r-cran-rmarkdown, r-cran-shinytest2, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-shinychat_0.5.0-1.ca2404.1_all.deb Size: 1866536 MD5sum: fc4d14b740148192f5fd378bac9a2a61 SHA1: 8d9e61335c6e8eed3dee11ff8d6f9ccb7e9555b5 SHA256: 98a9f69b16bb532092bdf0b087a5c82974649757d1d71f1cd4197d8bb36649f3 SHA512: 97fe7d5f237ec38868f75ab95534ed6ad8dca4482e28e3b6bbe511dadeca27b46aea85253fca1277461aadda5d4cd8e76ed5f09c48ab11578f52c2dd2e2d7582 Homepage: https://cran.r-project.org/package=shinychat Description: CRAN Package 'shinychat' (Chat UI Component for 'shiny') Provides a scrolling chat interface with multiline input, suitable for creating chatbot apps based on Large Language Models (LLMs). Designed to work particularly well with the 'ellmer' R package for calling LLMs. Package: r-cran-shinychatr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-purrr, r-cran-r6, r-cran-shiny Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinychatr_1.2.0-1.ca2404.1_all.deb Size: 126436 MD5sum: 83405a519fab03acc624b07b7ba2fea2 SHA1: fc29fff2efde083130396dde38adcf6477541a4a SHA256: eee371f3b804ccfd1a528305751885f7f3c1b3f39043292bdda6eb5a4a75c540 SHA512: 5ac32b98c3d37b6c30f71b6dfd0b2a121eb14077767f28fcb5883f638cc7d6055b4ea882e31520406f6e74797cadc62189c2e87b7b1e01a19998321f05b710d2 Homepage: https://cran.r-project.org/package=shinyChatR Description: CRAN Package 'shinyChatR' (R Shiny Chat Module) Provides an easy-to-use module for adding a chat to a Shiny app. Allows users to send messages and view messages from other users. 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Package: r-cran-shinyclt Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat, r-cran-purrr, r-cran-shiny, r-cran-gamlss, r-cran-dplyr, r-cran-plotly, r-cran-future, r-cran-shinycssloaders, r-cran-waiter, r-cran-shinythemes, r-cran-shinywidgets, r-cran-cachem, r-cran-knitr Filename: pool/dists/noble/main/r-cran-shinyclt_0.9.4-1.ca2404.1_all.deb Size: 41350 MD5sum: 2c38c71e2a97fcd8acb0f573a72596d0 SHA1: 17aedad6ff90557709ef90ed6f08f390e369d447 SHA256: 91bffb9dca46305b379bbdfbcc98ea9de1661ef7304e7edf1f140e7a54da3c20 SHA512: 3b38189421e923511c8f81fba6f578cc9952eb899128d0eab145a14e8400d65b20cd60d9005e0af9b1024c72cb5d60a59556e86b459e1f758bfd977833b5c532 Homepage: https://cran.r-project.org/package=shinyCLT Description: CRAN Package 'shinyCLT' (Central Limit Theorem 'shiny' Application) A 'shiny' application estimating the operating characteristics of the Student's t-test by Student (1908) , Welch's t-test by Welch (1947) , and Wilcoxon test by Wilcoxon (1945) in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) . Package: r-cran-shinycohortbuilder Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2993 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glue, r-cran-bslib, r-cran-jsonlite, r-cran-purrr, r-cran-htmltools, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinywidgets, r-cran-dplyr, r-cran-cohortbuilder, r-cran-s7, r-cran-trycatchlog, r-cran-highr, r-cran-shinygizmo, r-cran-rlang, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-querybuilder, r-cran-shinyquerybuilder, r-cran-shinychat, r-cran-magrittr, r-cran-pkgload, r-cran-sass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest2, r-cran-withr Filename: pool/dists/noble/main/r-cran-shinycohortbuilder_1.0.0-1.ca2404.1_all.deb Size: 1902412 MD5sum: a3619e0a59a48f03da43ae58c5899145 SHA1: eaa6652ae5107b3d6ecc49509cbdc0999320dac6 SHA256: 2c26278d60f1ab74b02829c7144c90e14ab98e3f3f0babc2fefc067c7c0cb633 SHA512: 07ce847971d2b9d04d1e4dcc5cf21e01d67c485479773742e950bbab9ddb6c5a45d51d5321832dc64b4a8e9222f2e02e6cea1045641471cca5223fe216f3e860 Homepage: https://cran.r-project.org/package=shinyCohortBuilder Description: CRAN Package 'shinyCohortBuilder' (Modular Cohort-Building Framework for Analytical Dashboards) You can easily add advanced cohort-building component to your analytical dashboard or simple 'Shiny' app. Then you can instantly start building cohorts using multiple filters of different types, filtering datasets, and filtering steps. Filters can be complex and data-specific, and together with multiple filtering steps you can use complex filtering rules. The cohort-building sidebar panel allows you to easily work with filters, add and remove filtering steps. It helps you with handling missing values during filtering, and provides instant filtering feedback with filter feedback plots. The GUI panel is not only compatible with native shiny bookmarking, but also provides reproducible R code. Package: r-cran-shinycox Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-survival Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-shinycox_1.1.3-1.ca2404.1_all.deb Size: 111530 MD5sum: 3ec5143b4c08f26c91e9d010308a6051 SHA1: 801bf7c7669500acf0cac8c5bd8b9175e224af46 SHA256: 6cb060fb24028ee49ad642b279c5d0fd58beeb71d7fe6a9a70164f0a978ee2ae SHA512: ca4af697923ff5fdff532be12e631fe152c88c800212b5964359c00eb58ca4e1c6ed9fda9c2ce3226ccad31305dcc0be74a5cecc6499e5175e478c822cb496c9 Homepage: https://cran.r-project.org/package=shinyCox Description: CRAN Package 'shinyCox' (Create 'shiny' Applications for Cox Proportional Hazards Models) Takes one or more fitted Cox proportional hazards models and writes a 'shiny' application to a directory specified by the user. The 'shiny' application displays predicted survival curves based on user input, and contains none of the original data used to create the Cox model or models. The goal is towards visualization and presentation of predicted survival curves. Package: r-cran-shinycroneditor Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-shinycroneditor_1.0.0-1.ca2404.1_all.deb Size: 34872 MD5sum: c2c90c96e4de2fb6908a4a720a7bbb54 SHA1: 99d2d36fcec2d741be03ee77e0d918b47364d3ba SHA256: 9706f3ccdc1eb65ec4acca1c7395224ded6baa623e74c1f9be9783048148d7b3 SHA512: 3c46cb86243cd2c0ec39d70e7db428dab89cd7086ba107e693c7b0c8174c5879a371b4d9ab29cdb59fe580684f2bc53a23baece9b995f7c7fa39311c4ae3df65 Homepage: https://cran.r-project.org/package=shinycroneditor Description: CRAN Package 'shinycroneditor' ('shiny' Cron Expression Input Widget) A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. 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Package: r-cran-shinydlplot Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-shinyjs, r-cran-plotly, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shinydlplot_0.1.4-1.ca2404.1_all.deb Size: 32386 MD5sum: 10ba65ef5795fae9880923a86fd3c65c SHA1: 82135af3dfbda1b0a26d19707208911e31eab120 SHA256: fc71e9b9496235044338ae328da3ed4e6f1dc0f4eb9c6d96dbc5bf8866cd2785 SHA512: 389b9459886ca791c720d24f28cd189e988cfe855ea474cc60293ef32eeed26b56506f223c8401925a9fe9b3af83851eb100a1c8751745565f3ac124169dadd5 Homepage: https://cran.r-project.org/package=shinydlplot Description: CRAN Package 'shinydlplot' (Add a Download Button to a 'shiny' Plot or 'plotly') Add a download button to a 'shiny' plot or 'plotly' that appears when the plot is hovered. 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Package: r-cran-shinydrive Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-yaml, r-cran-dt, r-cran-r.utils, r-cran-knitr, r-cran-zip Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinydrive_0.1.5-1.ca2404.1_all.deb Size: 1625430 MD5sum: b64250fec51aa24b335a604a7eb5591f SHA1: c666d6e4185416cdfc482151268ddfb2c6680258 SHA256: 5cafc8d9be70b825bd3439d5c90ceb7888bb6cfd617bd9145566861a29d3452f SHA512: 101c18016cd1a76f98f14864611e8ce35cbcca35ae15a6c83e782effef4fe919165c61fd7ece9ad8a4259489e8a268f962954d0c438f2235bc8f4e858eb7f42a Homepage: https://cran.r-project.org/package=shinydrive Description: CRAN Package 'shinydrive' (File Sharing Shiny Module) Shiny module for easily sharing files between users. Admin can add, remove, edit and download file. User can only download file. 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Package: r-cran-shinyfiles Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-shiny, r-cran-fs, r-cran-tibble Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-shinyfiles_0.9.3-1.ca2404.1_all.deb Size: 403512 MD5sum: 5433e7f1fd2d544502e363f6901303af SHA1: c607f0d12e98b202bce3c37648cbe3a6947f78a5 SHA256: ca5e5500a4dbbc502409b6bdfe805176e4b78bbe9e894b48d5fc9b71c785d6b8 SHA512: e52ec5943e709dfa06ab01037b23e23e813878bd969f93d382b38a6000b13bdca76bf308282a2065b64a1cf60f301f69d78c948513dab92f173dc18dbe99db3e Homepage: https://cran.r-project.org/package=shinyFiles Description: CRAN Package 'shinyFiles' (A Server-Side File System Viewer for Shiny) Provides functionality for client-side navigation of the server side file system in shiny apps. 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Package: r-cran-shinyfilter Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-reactable, r-cran-shinybs, r-cran-shinyjs, r-cran-stringr Filename: pool/dists/noble/main/r-cran-shinyfilter_0.1.1-1.ca2404.1_all.deb Size: 302208 MD5sum: 2d88781b31485feae5bb50fb3c1ec9d5 SHA1: ef83c23033b1f89fb125946dd98dc6964e265d36 SHA256: 0890350503202d4356004185acb55d34497aa440783694b4ed29de10cf2f12ce SHA512: 871759a322d44d4fe335a9a6544656cd23c2c62dfaf80ef23fd93bea03ade6197c4f03bdd234f5b93014040d170f187d0c2ed4c7ddec09cdae6b7e4292629bb8 Homepage: https://cran.r-project.org/package=shinyfilter Description: CRAN Package 'shinyfilter' (Use Interdependent Filters on Table Columns in Shiny Apps) Allows to connect 'selectizeInputs' widgets as filters to a 'reactable' table. 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Package: r-cran-shinygizmo Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-rlang, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-purrr, r-cran-shiny, r-cran-shinywidgets Filename: pool/dists/noble/main/r-cran-shinygizmo_0.5.0-1.ca2404.1_all.deb Size: 129562 MD5sum: e07779ac5f855b6ad85ac58e155e74f0 SHA1: e6a4d826834f22dbea9ece59fc14056087bfd34a SHA256: de850245cb6db43ac6068c882c5bdd662d5a78a0b03e7dbf4c57c41bf6668ee5 SHA512: 77bf64546e04c8a574a66d282680be65041735a489a9b6d457e133bf9678cb8d0dcd3def9f1e8e1ac43b3b59b40cd9e22a687b5887258d32d0ccba2152237913 Homepage: https://cran.r-project.org/package=shinyGizmo Description: CRAN Package 'shinyGizmo' (Custom Components for Shiny Applications) Provides useful UI components and input widgets for 'Shiny' applications. 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Package: r-cran-shinylive Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-archive, r-cran-brio, r-cran-cli, r-cran-fs, r-cran-gh, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-pkgdepends, r-cran-rappdirs, r-cran-renv, r-cran-rlang, r-cran-whisker, r-cran-withr Suggests: r-cran-httpuv, r-cran-pkgcache, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinylive_0.5.0-1.ca2404.1_all.deb Size: 106978 MD5sum: a82225515240f229f8e2a39b8ae50486 SHA1: ba5a4366b27b06966d4eb5f2b5488621b20b8bbe SHA256: d3ec095094e1e3eaf9465d498d22607658525bb9cb83a2fb56bf65a4b9cec23d SHA512: 1eab1a8e6085ae1514ddcc3304a9d92fd9630fa8ba9ae5ab63ffc47ca1313cc92d9517e37b91b4c282f71fb7a18ada59df44bb485f47ac46be6a73a713cec2e9 Homepage: https://cran.r-project.org/package=shinylive Description: CRAN Package 'shinylive' (Run 'shiny' Applications in the Browser) Exporting 'shiny' applications with 'shinylive' allows you to run them entirely in a web browser, without the need for a separate R server. 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Package: r-cran-shinyloadtest Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-httpuv, r-cran-jsonlite, r-cran-magrittr, r-cran-r6, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-svglite, r-cran-vroom, r-cran-websocket, r-cran-xml2 Suggests: r-cran-getpass, r-cran-glue, r-cran-gtable, r-cran-htmltools, r-cran-lubridate, r-cran-progress, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinyloadtest_1.2.1-1.ca2404.1_all.deb Size: 573922 MD5sum: 0c4d7b1292cf7c631de5df7a8cd59039 SHA1: 81a2f61d6c59c568120100d1ba01fdab7879f0c2 SHA256: 2d8d4702d5ae276ef442f8dd7309dba82a17fe61e0e73bfde2caf48ce8a9ee1a SHA512: 7d4264d061f442723c589dcbd28fe0174d54b1f9f9254bf5d8cedc2a7851543d3060aa07988f07b71b213202b3f6b4b53892da5e218826413abefed4446e9fc6 Homepage: https://cran.r-project.org/package=shinyloadtest Description: CRAN Package 'shinyloadtest' (Load Test Shiny Applications) Assesses the number of concurrent users 'shiny' applications are capable of supporting, and for directing application changes in order to support a higher number of users. 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Package: r-cran-shinymergely Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-shinyjqui, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-shinymergely_0.2.0-1.ca2404.1_all.deb Size: 590844 MD5sum: e087e56087373bbc16ac43641ad68669 SHA1: 7217e6014e2669e3a8f0502a36fe021094ef7ed8 SHA256: ced6aa620c2c8f72ebba8fe8b9af35d613de0631463be30c20d928d5a3d96d14 SHA512: a4f68e98055762704ada691375d02553affc492015460dbc49252fc12cd7773fd38d430098c2385107ab58e083ad95d17fab9c505a39a7d76aa6259225b60834 Homepage: https://cran.r-project.org/package=shinyMergely Description: CRAN Package 'shinyMergely' (Compare and Merge Two Files with a 'Shiny' App) A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages. Package: r-cran-shinymeta Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-fastmap, r-cran-fs, r-cran-htmltools, r-cran-rlang, r-cran-shiny, r-cran-sourcetools, r-cran-styler Suggests: r-cran-clipr, r-cran-cranlogs, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-shinyace, r-cran-stringr, r-cran-testthat, r-cran-xfun, r-cran-zoo Filename: pool/dists/noble/main/r-cran-shinymeta_0.2.2-1.ca2404.1_all.deb Size: 133740 MD5sum: bd0ea03d0363db2d02bae5d4ef64e01a SHA1: 7a60251a76569ca55d65904188efe286436c3466 SHA256: a8a0486ba5b251e7d4fafff0dd8f64deed722f32461a0937aea0a80d3a6e9c72 SHA512: 3b2246dfa2b935b7356bba977e00ba5fed4ec9176c67dde8d73a4cc0792ab3b526edde9babcca47d0723c049657fd931c055550bf9cd082aa1f9b5f1e456404c Homepage: https://cran.r-project.org/package=shinymeta Description: CRAN Package 'shinymeta' (Export Domain Logic from Shiny using Meta-Programming) Provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). 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Package: r-cran-shinymgr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5383 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-dbi, r-cran-reactable, r-cran-renv, r-cran-rsqlite, r-cran-shinyjs, r-cran-shinydashboard Suggests: r-cran-fs, r-cran-learnr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinymgr_1.1.0-1.ca2404.1_all.deb Size: 2435376 MD5sum: 50502d82038a5c1e054f6e856ea6a24f SHA1: 3f9fd4adb1c25de8572273df35e6f2ca0ef9bd43 SHA256: 2fb6590594eaf3b3c4a988a7fd71c74de0e70cf713e0f82ddbd0e0d424e77fe5 SHA512: 134d0701361c62e431b87fb416944cda981ee035ed2a96e641e0a3912f4cb85433137f3c5e2312c6fb5b9dd821ce9995c3c588d81480202556c2e6f00ebbf7bb Homepage: https://cran.r-project.org/package=shinymgr Description: CRAN Package 'shinymgr' (A Framework for Building, Managing, and Stitching 'shiny'Modules into Reproducible Workflows) A unifying framework for managing and deploying 'shiny' applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The 'shinymgr' app builder "stitches" 'shiny' modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of 'shinymgr' apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the 'shinymgr' framework to write modules and seamlessly combine them into 'shiny' apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. Package: r-cran-shinymixr Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-gridextra, r-cran-collapsibletree, r-cran-shinyace, r-cran-dt, r-cran-bs4dash, r-cran-shinywidgets, r-cran-stringi, r-cran-r3port, r-cran-whisker, r-cran-plotly, r-cran-patchwork, r-cran-shinyjs, r-cran-ps, r-cran-xfun, r-cran-fresh, r-cran-nlmixr2est, r-cran-magrittr, r-cran-rxode2, r-cran-cli Suggests: r-cran-xpose, r-cran-nlmixr2, r-cran-nlmixr2plot, r-cran-xpose.nlmixr2, r-cran-nlme, r-cran-testthat, r-cran-shinytest2, r-cran-knitr, r-cran-rmarkdown, r-cran-rlang, r-cran-miniui, r-cran-shinyfiles, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-shinymixr_0.5.3-1.ca2404.1_all.deb Size: 954978 MD5sum: 6c7a7227a8d4f7d47c8e8cec01382df7 SHA1: e24e862499e6568bb09e79437269f735dd69ccfb SHA256: 150de0f9a43a84445ac454250e287da8f4cc795e554334deaaefcac3151a03d7 SHA512: e41bc3548677cf3edc185c1eb872895740a205ce20f961fc280b903b0e677b2cdf903e9599c73ddc357d72bd128e2d0e43657e308716ba1ffdfd361ab2b3d6f8 Homepage: https://cran.r-project.org/package=shinyMixR Description: CRAN Package 'shinyMixR' (Interactive 'shiny' Dashboard for 'nlmixr2') An R shiny user interface for the 'nlmixr2' (Fidler et al (2019) ) package, designed to simplify the modeling process for users. 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Package: r-cran-shinyml Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-shiny, r-cran-argondash, r-cran-argonr, r-cran-shinyjs, r-cran-h2o, r-cran-shinywidgets, r-cran-dygraphs, r-cran-plotly, r-cran-sparklyr, r-cran-tidyr, r-cran-dt, r-cran-ggplot2, r-cran-shinycssloaders, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinyml_1.0.1-1.ca2404.1_all.deb Size: 158488 MD5sum: 3fb98cad71803b352aef07b75f7ab5e6 SHA1: f5c8e2c1307a2819f6ef60ec737b53c077611870 SHA256: 27577567d204f3ca6978c83e22e0a02bcffe536452f8de766daf6b85dddbe87b SHA512: 1d6340a9fdba8a868717cb704a2e49353484fa29a1ef91f5574659d451d7596f9affdb9b6e552cc7ee25159786c2c4e3e9627144e32179e95a3d569debc77c9e Homepage: https://cran.r-project.org/package=shinyML Description: CRAN Package 'shinyML' (Compare Supervised Machine Learning Models Using Shiny App) Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148). Package: r-cran-shinymobile Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4511 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-gplots, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-testthat, r-cran-rstudioapi, r-cran-shinywidgets, r-cran-apexcharter, r-cran-ggplot2, r-cran-dplyr, r-cran-bslib, r-cran-shinytest2, r-cran-thematic Filename: pool/dists/noble/main/r-cran-shinymobile_2.0.1-1.ca2404.1_all.deb Size: 1634218 MD5sum: 47ea46785d11352f8298b32ebaef6a58 SHA1: fa58888d0e10c26712d43ff4683a43fd3d0eab2a SHA256: 3d5cd26791f8defe6129bba5f04c01bf04657360c57c6d161a1f384a52657a78 SHA512: 30bd6f617de19ffaa785f481b97ab3f979c59ca7f298d5c7e7be8cbcb44ef6940a27e4c7d7c71df55183763aa4d0a29cd0b1e550b7e4ab0b64ea5a446d73299f Homepage: https://cran.r-project.org/package=shinyMobile Description: CRAN Package 'shinyMobile' (Mobile Ready 'shiny' Apps with Standalone Capabilities) Develop outstanding 'shiny' apps for 'iOS' and 'Android' as well as beautiful 'shiny' gadgets. 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It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck'). Package: r-cran-shinymrp Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-checkmate, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-highcharter, r-cran-htmlwidgets, r-cran-httr2, r-cran-loo, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-patchwork, r-cran-posterior, r-cran-purrr, r-cran-qs2, r-cran-r6, r-cran-rcolorbrewer, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-tidyr, r-cran-waiter Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinymrp_0.10.0-1.ca2404.1_all.deb Size: 2145540 MD5sum: b199e3a8fda459855c732cdae53c8dba SHA1: 45a5558877c6216314e20dffc7a7f4b007807869 SHA256: 193156fa2b3ff23ce78e054efcb38feab60891b7e1eebd7790a646a5f62df684 SHA512: fcfb2a6fe5cc7692616d72c9ee4b6773ad772901e619224aa25264d0a779103f5b3ad71fae1cf70eed65b2315390735d49d08db421050b50c165c78fbb56f95f Homepage: https://cran.r-project.org/package=shinymrp Description: CRAN Package 'shinymrp' (Interface for Multilevel Regression and Poststratification) Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). 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Package: r-cran-shinynextui Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4692 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-shiny.react, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-shinytest2, r-cran-purrr, r-cran-thematic, r-cran-shiny.router, r-cran-roxy.shinylive Filename: pool/dists/noble/main/r-cran-shinynextui_0.1.0-1.ca2404.1_all.deb Size: 1565244 MD5sum: 35099b96cf99efdc4ac6c895f03f2683 SHA1: bdab7988d3c70543aea593fa7db8d1c4f6f3b8a0 SHA256: aa304715eadb296451f49b4e716cf65e61289d38e4920518bd558e745ad9f0f8 SHA512: bc788ad88dc5892a54e41aaf86622e5988a56c72ee90ade8b629ebb51e91e724b06164ac5f1c82bb9b092014b21a2c6082e49c0bdc4b80e8c2d6783b0238c9eb Homepage: https://cran.r-project.org/package=shinyNextUI Description: CRAN Package 'shinyNextUI' ('HeroUI' 'React' Template for 'shiny' Apps) A set of user interface components to create outstanding 'shiny' apps , with the power of 'React' 'JavaScript' . 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The package includes a 'shiny' module that can be included in any 'shiny' application to create a panel containing searchable, editable text broken down by section headers. Can be used with a local 'SQLite' database, or a compatible remote database of choice. Package: r-cran-shinyoauth Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2996 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-r6, r-cran-rlang, r-cran-shiny, r-cran-jsonlite, r-cran-openssl, r-cran-httr2, r-cran-curl, r-cran-urltools, r-cran-cachem, r-cran-jose, r-cran-lifecycle, r-cran-cli, r-cran-htmltools, r-cran-otel Suggests: r-cran-bslib, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-testthat, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-webfakes, r-cran-promises, r-cran-mirai, r-cran-future, r-cran-withr, r-cran-later, r-cran-callr, r-cran-processx, r-cran-pkgload, r-cran-chromote, r-cran-sodium, r-cran-shinytest2, r-cran-xml2, r-cran-otelsdk Filename: pool/dists/noble/main/r-cran-shinyoauth_0.6.1-1.ca2404.1_all.deb Size: 2294616 MD5sum: b400ec5caa407b5d608dd47ba0804899 SHA1: 8e8660a51b626f053bcc06de3b0673c32d8ff1e9 SHA256: 785052cf2c808ab9725992208056abd14d861389bc95a4551e6a229aeddf1335 SHA512: e87292bc90cf91106ed7e58bc30b522abd1d720aa9b5a14372ab01264bdcb6430342f2e66f46d455a792ae8a107e2a957a086c77f07f8b7f4085342fb13fa0fd Homepage: https://cran.r-project.org/package=shinyOAuth Description: CRAN Package 'shinyOAuth' (OIDC Authentication and OAuth Authorization for 'shiny'Applications) Provides a simple, configurable framework for 'OpenID Connect' (OIDC) authentication and 'OAuth 2.0' authorization in 'shiny' applications using 'S7' classes. Defines providers, clients, and tokens, as well as various supporting functions and a 'shiny' module. Features include cross-site request forgery (CSRF) protection, state encryption, 'Proof Key for Code Exchange' (PKCE) handling, validation of OIDC identity tokens (nonces, signatures, claims), automatic user info retrieval for OIDC and supported 'OAuth' providers, asynchronous flows, and hooks for audit logging. 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Package: r-cran-shinypanel Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinyjs, r-cran-shiny, r-cran-shinybs, r-cran-htmltools, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-shinypanel_0.1.5-1.ca2404.1_all.deb Size: 42574 MD5sum: 9aac8f1d1f6f6471484dfa67ee434121 SHA1: 96f31b8a110824f7e0e09107d70221148fa2f197 SHA256: 3affe37844edd9fb60529290dfc413a16ac39097469e3c3735ff4900d0088f1c SHA512: 0604ccac68f3d7fcce55c98f71ab2e46e425f8038ac8104a251d5ad6c6857fb1a8274530d7d107beafa4a06727273f36022245228abed211208f1bb51707b6aa Homepage: https://cran.r-project.org/package=shinypanel Description: CRAN Package 'shinypanel' (Shiny Control Panel) Add shiny inputs with one or more inline buttons that grow and shrink with inputs. Also add tool tips to input buttons and styling and messages for input validation. 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Package: r-cran-shinyreact Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-htmltools, r-cran-jsonlite, r-cran-rlang, r-cran-shiny Suggests: r-cran-knitr, r-cran-later, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-shinyreact_0.1.0-1.ca2404.1_all.deb Size: 170882 MD5sum: 2f5d27f3cf02ff9e8e01bd98e2d67cc5 SHA1: 5dc52da3a3c03145b0613d92e2e27b2f63c9934d SHA256: 229b10fe348453c17fdebf4f017a812a9c38d7c41a78d91c849fced11b27efec SHA512: 3ce8404ed80c9cd49bce077c5e4c6e25bc83673713990dfea6676378bc8dd3e79d715966385aba4549e67c507c6293fa0efccabe1fcb4e9a5c85ad2a34ddbcd7 Homepage: https://cran.r-project.org/package=shinyreact Description: CRAN Package 'shinyreact' (Client-Side 'React' Interface for 'Shiny') Server-side plumbing for the 'ui.tsx' pattern in 'Shiny': the user interface is defined in a client 'React' () bundle, and the 'Shiny' server contains only reactive computation. Provides page builders that discover and serve the client bundle, a render function that publishes any JSON-serializable value to the client, and custom messages to 'React' components. Ships no user interface components, so the app author owns the whole front end. The 'React' runtime and the client hooks are bundled, so no JavaScript build step is required to get started. 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Build your 'recipe' by dragging the variables, visually analyze your data to decide which steps to use, add those steps and preprocess your data. 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This enables users to reproduce tables and visualisations outside the interactive UI, facilitating integration into static reports or automated workflows without requiring access to the original application source code. 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Forked from 'wallace'. Code is split into modules that are loaded and linked together automatically and each call one function. Guidance pages explain modules to users and flexible logging informs them of any errors. Options enable asynchronous operations, viewing of source code, interactive maps and data tables. Use to create complex analytical applications, following best practices in open science and software development. Includes functions for automating repetitive development tasks and an example application at run_shinyscholar() that requires install.packages("shinyscholar", dependencies = TRUE). A guide to developing applications can be found on the package website. 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Host apps can expose these modules as extension points where user-supplied code augments or replaces built-in logic, without requiring users to modify the app's source. Each module embeds an 'Ace' editor with a structured argument table, an in-frame R console rooted in the paused function's local environment, and a step debugger that handles for, while, repeat, and if/else blocks at any nesting depth. Two module flavours are provided: solo editors for testing a function in isolation with literal argument values, and embedded editors for pausing a function mid-execution inside a larger host program. Package: r-cran-shinystoreplus Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-htmltools, r-cran-shinywidgets Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-shinystoreplus_1.6-1.ca2404.1_all.deb Size: 111982 MD5sum: 4e2074817080205b81113350e7f5e7d8 SHA1: 16b19335b6f4bf639b2924e5eb4bc8b67a37f343 SHA256: 364205b3846ccd85e07a007b7c5e4c5e0033354239d16fbba6c973a172384549 SHA512: caa9fe7105c2bb55547e56cce62ecbb59a628c306e7a9078e3546a9674be70acadd833bb60d3d7e4e3dc270f7cb044f8942fe5aa3c081cc19815ed7ccf48591f Homepage: https://cran.r-project.org/package=shinyStorePlus Description: CRAN Package 'shinyStorePlus' (Secure in-Browser and Database Storage for 'shiny' Inputs,Outputs, Views and User Likes) Store persistent and synchronized data from 'shiny' inputs within the browser. Refresh 'shiny' applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using 'Dexie.js' , a minimal wrapper for 'IndexedDB'. Transfer browser link parameters to 'shiny' input or output values. Store app visitor views, likes and followers. Package: r-cran-shinysurveys Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-sass, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-shinysurveys_0.2.0-1.ca2404.1_all.deb Size: 539280 MD5sum: dd0a73908905687ee3ffc3aa7fe51cfa SHA1: 6c1b4cf83733b2420553dc96b7593c14c27d806d SHA256: 568ebbf0d4fa6a9076c9f4848ffbaeca0a476da3ba8704633830409f61857a89 SHA512: 1a36d6abc9d6416da7053776c58a88c2e98272e3cf6081dbf0498e5a9a34dd332b71bce544e172f2612b6c06e7697d9cf686547a22d8739b6158eeaea45e4c5f Homepage: https://cran.r-project.org/package=shinysurveys Description: CRAN Package 'shinysurveys' (Create and Deploy Surveys in 'Shiny') Easily create and deploy surveys in 'Shiny'. This package includes a minimalistic framework similar to 'Google Forms' that allows for url-based user tracking, customizable submit actions, easy survey-theming, and more. Package: r-cran-shinytempsignal Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-forecast, r-cran-ggplot2, r-cran-ggprism, r-cran-ggpmisc, r-bioc-ggtree, r-cran-golem, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-bioc-treeio, r-cran-yulab.utils, r-cran-nlme Suggests: r-cran-attempt, r-cran-conflicted, r-cran-config, r-cran-glue, r-cran-htmltools, r-cran-knitr, r-cran-prettydoc, r-cran-processx, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinytempsignal_0.0.8-1.ca2404.1_all.deb Size: 804024 MD5sum: 73e037159bd37d2bb026f8ebc178bea9 SHA1: 8adc00ed877ec82727beebb1cf0111825ba78579 SHA256: 8ad84935711be80d31104a6125cd86a1eda685ee8b7dad2431936dff436881ea SHA512: 57d17a717d35d88992069b74f46b360e1173cde0183d155cd1f4ad31f04dba6e56087ec44dd886fad905f2cce6fe647e7992830d4d25517877beef9709ea9682 Homepage: https://cran.r-project.org/package=shinyTempSignal Description: CRAN Package 'shinyTempSignal' (Explore Temporal and Other Phylogenetic Signals) Sequences sampled at different time points can be used to infer molecular phylogenies on natural time scales, but if the sequences records inaccurate sampling times, that are not the actual sampling times, then it will affect the molecular phylogenetic analysis. This shiny application helps exploring temporal characteristics of the evolutionary trees through linear regression analysis and with the ability to identify and remove incorrect labels. The method was extended to support exploring other phylogenetic signals under strict and relaxed models. Package: r-cran-shinytest Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-callr, r-cran-crayon, r-cran-debugme, r-cran-digest, r-cran-htmlwidgets, r-cran-httpuv, r-cran-httr, r-cran-jsonlite, r-cran-parsedate, r-cran-pingr, r-cran-r6, r-cran-rematch, r-cran-rlang, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat, r-cran-webdriver, r-cran-withr Suggests: r-cran-flexdashboard, r-cran-globals, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shinytest_1.6.1-1.ca2404.1_all.deb Size: 399472 MD5sum: a524749eafdd0b098f041cd899a653c9 SHA1: 06614801b26c6fc9b9044b6218f2bcbe925b8b78 SHA256: 697db4331f646d915b27c2fd3fec5f8826505e5bfaf682497146547d887544f7 SHA512: 61c06abc2fe8535179b2ea14137bf7d78c306bdb7791827909e33a0a8c7e2447560fe2f912ea4e76c02585cda80f171915d5163537dc2ab425bdfea18ffa1f36 Homepage: https://cran.r-project.org/package=shinytest Description: CRAN Package 'shinytest' (Test Shiny Apps) Please see the 'shinytest' to 'shinytest2' migration guide at . Package: r-cran-shinytester Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-shinytester_0.1.0-1.ca2404.1_all.deb Size: 24418 MD5sum: 2370cc64d83d21faba8e98b811afc9a7 SHA1: 06a105b5391d63619ba9d7adfe8e4b9126f25782 SHA256: 063ea31c514814e83319b775683b1dc1add9c7d7ee9883c1efbdad35dc21eb40 SHA512: 21c13608c121e188b3808c72c2ad103ca9b16a35d5ce08bf182ad6aa643e60ba98d9c9ff34d854c1b2c09c33c2a69c6c00ef55e00b77e42591dc4ba77b787fa3 Homepage: https://cran.r-project.org/package=ShinyTester Description: CRAN Package 'ShinyTester' (Functions to Minimize Bonehead Moves While Working with 'shiny') It's my experience that working with 'shiny' is intuitive once you're into it, but can be quite daunting at first. Several common mistakes are fairly predictable, and therefore we can control for these. The functions in this package help match up the assets listed in the UI and the SERVER files, and Visualize the ad hoc structure of the 'shiny' App. Package: r-cran-shinytesters Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinytesters_0.1.0-1.ca2404.1_all.deb Size: 23912 MD5sum: 3b4051fde33b0d40112a43c04a1d3826 SHA1: e3d82b9c1ba2acef8fec3e0967666d605f09727d SHA256: 985d578d647a2f7033c5bb5df97ecf8842319785856377668cac5b1f466f219a SHA512: 87528ed9b4da557ea331b041fafb9aef2baca20cbb8e1c1d838b7a163fad89bfd822cd9ff46aed7c849f8296771d65c75a7d7c651513e60357dfc969b526b746 Homepage: https://cran.r-project.org/package=shinytesters Description: CRAN Package 'shinytesters' (Update 'Shiny' Inputs when using testServer()) Create mocked bindings to 'Shiny' update functions within test function calls to automatically update input values. The mocked bindings simulate the communication between the server and UI components of a 'Shiny' module in testServer(). Package: r-cran-shinythemes Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Filename: pool/dists/noble/main/r-cran-shinythemes_1.2.0-1.ca2404.1_all.deb Size: 572656 MD5sum: 36449953c631595e1da7e4411492f767 SHA1: c0be11a67dcd0029345b6a11f971061f28ef2cd9 SHA256: 6d7c569ef31927611fba9c743a7097438c16e68061d8844126bac08ae0987b09 SHA512: f4e783a523c8f54db7ff858ce46d6cbe24f0a6c1e1dd20cecc915e1ba01292948c97ad85ee3e8201dffb8f75a069a193c5b3a1e5045a0b0ecbbb8e98f7068397 Homepage: https://cran.r-project.org/package=shinythemes Description: CRAN Package 'shinythemes' (Themes for Shiny) Themes for use with Shiny. Includes several Bootstrap themes from , which are packaged for use with Shiny applications. Package: r-cran-shinytime Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling, r-cran-hms Filename: pool/dists/noble/main/r-cran-shinytime_1.0.3-1.ca2404.1_all.deb Size: 42344 MD5sum: 2ae8c9f6f364299b9d8b30eb147a5fb4 SHA1: 6a80758cb34bcec4c2ff54d6c534b7049fce08bc SHA256: 8cca891fbc970f620fa6825818df07ed10530bd1c503de1a5bc9506fe3f10f00 SHA512: ef84f5c4094800292160c535aa97b5cf534cebaf35bcd6b1bcfc3b913b8ae3993cd0f81c26084e05f6ee58346a8138674434968aed93706f9af1329971d2b21f Homepage: https://cran.r-project.org/package=shinyTime Description: CRAN Package 'shinyTime' (A Time Input Widget for Shiny) Provides a time input widget for Shiny. This widget allows intuitive time input in the '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time component. 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Key features include countdown and count-up mode, multiple display formats (including simple seconds, minutes-seconds, hours-minutes-seconds, and minutes-seconds-centiseconds), ability to pause, resume, and reset the timer. 'shinytimer' widget can be particularly useful for creating interactive and time-sensitive applications, tracking session times, setting time limits for tasks or quizzes, and more. Package: r-cran-shinytitle Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinytitle_0.1.0-1.ca2404.1_all.deb Size: 284716 MD5sum: b595f50b6031f1378094ff6e9e416f3c SHA1: e67322875836c84fdda587fbd42d3465047f209c SHA256: a2d3808c677bc37414382767ac3fe42363b1f16efb1383db3155f8249fa1c7d1 SHA512: 414d32d6810419bfac228b7d0a41584d9f925d4c055edc5a61619e6426ae51685082d05d343abbe52923f0fd40c08108dec2acd679aab02968c76df52bffe103 Homepage: https://cran.r-project.org/package=shinytitle Description: CRAN Package 'shinytitle' (Update Browser Window Title in 'shiny' Session) Enables the ability to change or flash the title of the browser window during a 'shiny' session. Package: r-cran-shinytoastify Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-reactr, r-cran-shiny, r-cran-fontawesome Filename: pool/dists/noble/main/r-cran-shinytoastify_2.0.0-1.ca2404.1_all.deb Size: 205630 MD5sum: e6c04ac0839be332e651fdfccec6e2e8 SHA1: acbde51d452ecd4bfdd109bd9a40d65013802964 SHA256: ff6e51aa793da86b00f0f80bbc9130d430db95c5acc99c260b28afd80d3b39b4 SHA512: 29b7b08d96cee9380945d29b05111b524f721423eca32c4b06e1ed1afca61218d6f0038d3628e3616fa93aac18fe9000e34a89211c53589bde7fe80677bc4525 Homepage: https://cran.r-project.org/package=shinyToastify Description: CRAN Package 'shinyToastify' (Pretty Notifications for 'Shiny') This is a wrapper of the 'React' library 'React-Toastify'. It allows to show some notifications (toasts) in 'Shiny' applications. There are options for the style, the position, the transition effect, and more. Package: r-cran-shinytoastr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Filename: pool/dists/noble/main/r-cran-shinytoastr_2.2.0-1.ca2404.1_all.deb Size: 82698 MD5sum: 998cbf7e08862a8ebe44e7b21d3b23a0 SHA1: 5827d75e1e2ccca5c5b2a6bb4a8613960310b35b SHA256: 388df99d7ac067dda471c16adaa6e1ca2cc65b77f5536e998f1979f4299d44e3 SHA512: 33fbaf0096b1fbb083e313bcd4703ff3f9d039f35fd64396fbad3397eb2dbf651c8e5d05e964142f8d7af19bc4c06c7ea72c29c2affa8166bf05620539601d82 Homepage: https://cran.r-project.org/package=shinytoastr Description: CRAN Package 'shinytoastr' (Notifications from 'Shiny') Browser notifications in 'Shiny' apps, using 'toastr': . Package: r-cran-shinytree Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-stringr, r-cran-promises Suggests: r-cran-testthat, r-cran-shinytest, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-shinytree_0.3.1-1.ca2404.1_all.deb Size: 520210 MD5sum: ee99a9c8d02bdca9d5d2422ba8a39802 SHA1: c7f0cf8a2578fed7be34372496e9353efa194ff5 SHA256: ec3264507da39dcb87455c17df480443353ec5129a491f4d1fbf4792db066730 SHA512: b05753789012d1c6e64cab4c6f9656b6c09d1d2ce929d9fe10e615ab66d0aa11011169610f74bdd10373b22b1aebb61871ff4f0d7f5c900f30e750af752f1400 Homepage: https://cran.r-project.org/package=shinyTree Description: CRAN Package 'shinyTree' (jsTree Bindings for Shiny) Exposes bindings to jsTree -- a JavaScript library that supports interactive trees -- to enable a rich, editable trees in Shiny. Package: r-cran-shinyvalidate Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-rlang, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-shinyvalidate_0.1.3-1.ca2404.1_all.deb Size: 180928 MD5sum: 6ec67d0bdc7eacb5ef5059ef41bee008 SHA1: 39a87ca9b3466c6aa7a11a7a4c07fe39b9a27362 SHA256: a9956a442925389678c2174f2384e53fd151eb071a94474e59d7443f19388805 SHA512: 17b7cd793a301cacf9d6dad3bdb9395a775c560dc20da20401e21caa0f578ebe3842e7f4574da251c7864030e3b8a58aa061082bf7ebe13d093b07310b407c6b Homepage: https://cran.r-project.org/package=shinyvalidate Description: CRAN Package 'shinyvalidate' (Input Validation for Shiny Apps) Improves the user experience of Shiny apps by helping to provide feedback when required inputs are missing, or input values are not valid. Package: r-cran-shinywgd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyalert, r-cran-stringr, r-cran-vroom, r-cran-fs, r-cran-tidyr, r-cran-data.table, r-cran-dplyr, r-cran-ape, r-cran-ks, r-cran-mclust, r-cran-htmltools, r-cran-seqinr, r-cran-httr, r-cran-jsonlite Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-argparse, r-cran-bslib, r-cran-bsplus, r-cran-english, r-cran-fontawesome, r-cran-igraph, r-cran-shinybs, r-cran-shinyfiles, r-cran-shinywidgets, r-cran-shinyjs, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinywgd_1.0.0-1.ca2404.1_all.deb Size: 893570 MD5sum: 2f30b0a43fbf4c82c1c6e1ed777a773e SHA1: 51c490dbd8d89dad8ee7b192bbf45e75b5f3c76a SHA256: ea55de09740be6a6ab7088ddbc67c2ab8d703f4b65064918200004c652893337 SHA512: 56278b0709fd4b03d87cda95a6ca4c85394cdede2f61e099e65d3420a3568498c0ec2983c9121923f8fd362937f3a454b8ee49763254d40405e9315f6e575286 Homepage: https://cran.r-project.org/package=shinyWGD Description: CRAN Package 'shinyWGD' ('Shiny' Application for Whole Genome Duplication Analysis) Provides a comprehensive 'Shiny' application for analyzing Whole Genome Duplication ('WGD') events. This package provides a user-friendly 'Shiny' web application for non-experienced researchers to prepare input data and execute command lines for several well-known 'WGD' analysis tools, including 'wgd', 'ksrates', 'i-ADHoRe', 'OrthoFinder', and 'Whale'. This package also provides the source code for experienced researchers to adjust and install the package to their own server. Key Features 1) Input Data Preparation This package allows users to conveniently upload and format their data, making it compatible with various 'WGD' analysis tools. 2) Command Line Generation This package automatically generates the necessary command lines for selected 'WGD' analysis tools, reducing manual errors and saving time. 3) Visualization This package offers interactive visualizations to explore and interpret 'WGD' results, facilitating in-depth 'WGD' analysis. 4) Comparative Genomics Users can study and compare 'WGD' events across different species, aiding in evolutionary and comparative genomics studies. 5) User-Friendly Interface This 'Shiny' web application provides an intuitive and accessible interface, making 'WGD' analysis accessible to researchers and 'bioinformaticians' of all levels. Package: r-cran-shinywidgets Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3640 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-sass, r-cran-shiny, r-cran-htmltools, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-dt, r-cran-scales, r-cran-shinydashboard, r-cran-shinydashboardplus Filename: pool/dists/noble/main/r-cran-shinywidgets_0.9.1-1.ca2404.1_all.deb Size: 1276970 MD5sum: f04905e76dee92d7980d2caea98c53df SHA1: 7ad4ec9ac7471fe5917bd4d1d4fc0ba068f48bc4 SHA256: 18164971616cd578e59faf3a07bfc559eee2c9c83e39089a3f1f4893fea995c0 SHA512: d1ea8071d94f456d74e8e223fbb9e6168e5d7730d49425ee3a80e33f9d79f78a20a3a2189370cf3500c16562e2244264b9a81257ce6c1447bc2d907ed0d1df2a Homepage: https://cran.r-project.org/package=shinyWidgets Description: CRAN Package 'shinyWidgets' (Custom Inputs Widgets for Shiny) Collection of custom input controls and user interface components for 'Shiny' applications. Give your applications a unique and colorful style ! 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Package: r-cran-shorm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-srmers, r-cran-ggplot2, r-cran-scales, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-shorm_0.2.1-1.ca2404.1_all.deb Size: 24186 MD5sum: 7fed877dab7d8cf43bf8ad0808273436 SHA1: 83a8aa35d7978ecfdc056ac4f9c8a5bf6f1f5402 SHA256: a294f5e78eed0a6bc0b9ca708b2caaf1a5d7f2702cbdef06c271feb66d819597 SHA512: 19f945558b8b991a8a509c5502009da3a2b8d6c4dce4fb5a568fa7a4ba9e55bbcae583850d1a23f94db017cba9ecd9c71f66afac52cc903040c91b3c5991a1de Homepage: https://cran.r-project.org/package=shorm Description: CRAN Package 'shorm' (Detect the Shape of Dose-Response Curves) Provides functions for hormesis screening by classifying the shapes of dose-response curves based on semiparametric tests. The shapes are indications of different potential toxicology effect. 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The procedures are flexible enough to be adopted for the development of short forms of full-length tests. Different procedures are considered (Epifania, Anselmi & Robusto, 2022 and Epifania & Finos, 2025 ). The main difference between the presented procedures refers to the degree of control that they allow for targeting specific latent trait levels. The simplest procedure, denoted as benchmark procedure, does not allow for any control on the latent trait levels of interest, while the other procedures allow for specifying either discrete latent trait levels for which the information needs to be maximized (theta-target procedure, ) or a target information function that needs to be recreated with the selected items (item selection algorithm -ISA- denoted as Frank in ). Another difference concerns the definition of the number of items to be selected. 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The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) , Greene PR. (1986) , Chelly SM, Denis C. (2001) , Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) , Samozino P. (2018) , Samozino P. and Peyrot N., et al (2022) , Clavel, P., et al (2023) , Jovanovic M. (2023) , and Jovanovic M., et al (2024) . 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Package: r-cran-signaturesurvival Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forestplot, r-cran-gplots, r-cran-gtools, r-cran-survival, r-cran-survminer, r-cran-ggplot2 Suggests: r-cran-rmisc Filename: pool/dists/noble/main/r-cran-signaturesurvival_1.0.0-1.ca2404.1_all.deb Size: 4632416 MD5sum: a2e8b6919acb34ee829606bc72e62a10 SHA1: eb753c70b0c42234b5764c130cba926acb9a73c2 SHA256: 03df08e2b892c96e90dd1d41729329c4d98697578db962a0e6adfcd5cec263a3 SHA512: b90449cfe104991adf020e372ba4e4ac0cc31ec92d282e9326afc1c262d0f75bf740fad62cb477f6c3be0a576ec557a22a0723e69328f201a06a2adfe8891721 Homepage: https://cran.r-project.org/package=signatureSurvival Description: CRAN Package 'signatureSurvival' (Signature Survival Analysis) When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. 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Package: r-cran-signed.backbones Architecture: all Version: 0.91.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-signed.backbones_0.91.5-1.ca2404.1_all.deb Size: 25328 MD5sum: 5c90a7e0979403dbc45de9262b6e5d2b SHA1: 3d433ea0d5a1f2669222026ea82c5f01184baeee SHA256: ab718449cde15c70f659cfd99922b53b2378f8707aa7b13631c2e0be0091f209 SHA512: 740149bd9fafb20d064b451d512d3ac793452b93aa4efd6492fe33c9a1da143b477627434a5cc572cc402f17140d6923894113e783f5336c1dbbfac06f7230c7 Homepage: https://cran.r-project.org/package=signed.backbones Description: CRAN Package 'signed.backbones' (Extract the Signed Backbones of Weighted Networks) Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work. Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. . Package: r-cran-signibox Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-signibox_1.0-1.ca2404.1_all.deb Size: 10140 MD5sum: 50252ac7776ff901e6508b4865d3559b SHA1: 16282fed626d86e6fae752cf53b17413ab75ffe1 SHA256: 4135e8ee98cd2895cb0ab5fee321fa5357d01ff829b9ec237cafef06e3caf9e9 SHA512: 10b6e0d0b4c1f1b90b4742f2490e7cde5586047c727f819f9024d9015dfae79eed6530144cc4b52865fa07785075641ebd3e2b78d74c14970de196f0dd4d8e6b Homepage: https://cran.r-project.org/package=signibox Description: CRAN Package 'signibox' (Statistical Significance Marks on Boxplots) Add significance marks to any R Boxplot, including a given significance niveau. 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Package: r-cran-siie Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyfst Filename: pool/dists/noble/main/r-cran-siie_0.4.0-1.ca2404.1_all.deb Size: 35010 MD5sum: 2ac66b629de8ecc22eaa29229b65e803 SHA1: 63db26260c6da6e33ecda6234cc81370d228e384 SHA256: 90689d1365e7abb6e6680e36ec1fd6e552455250a6325217e05201b8a4d5f207 SHA512: e2bf4d02b6ff30c206967b4fbf9898ed06662d19a2034b4fe5d91694213527f69d97095b53122c3a6947aae726e93545b32a00a04e323688374853d5dea93853 Homepage: https://cran.r-project.org/package=siie Description: CRAN Package 'siie' (Superior Identification Index and Its Extensions) Calculate superior identification index and its extensions. 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These methods could be extended to evaluate other entities such as institutes, countries, etc. Package: r-cran-siland Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1827 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-lme4, r-cran-sp, r-cran-raster, r-cran-ggplot2, r-cran-ggforce, r-cran-fasterize, r-cran-reshape2, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-siland_3.0.2-1.ca2404.1_all.deb Size: 1716108 MD5sum: 81899cca1f6ed83181692ab216f8a724 SHA1: 66b97a928ab01c76615525fcdc714bc63c166dd4 SHA256: fc349e6af8fadd7d3a1f9ad428c2643174e6467c123ab2320b161b308383860c SHA512: fe40fd0089df9870877a167165fb941959f75f1c4512b416004ccb955f79ad175572ced9bd30d49fcaafb2475489ae230a65a0f05b8a5b5ac443a4d09a977999 Homepage: https://cran.r-project.org/package=siland Description: CRAN Package 'siland' (Spatial Influence of Landscape) Method to estimate the spatial influence scales of landscape variables on a response variable. 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This package provides functions for two subgroup identification methods based on penalized functions, both of which utilize factor model structures to adapt to data with cross-sectional dependency. The first method is the Subgroup Identification with Latent Factor Structure Method (SILFSM) we proposed. By employing Center-Augmented Regularization and factor structures, the SILFSM effectively eliminates data dependencies while identifying subgroups within datasets. For this model, we offer optimization functions based on two different methods: Coordinate Descent and our newly developed Difference of Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the latter can be applied to cases where the distance function in Center-Augmented Regularization takes L1 and L2 forms. The other method is the Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017) . Additionally, we provide a function for the Standard CAR (S-CAR) method, which does not consider the dependency and is for comparative analysis with other approaches. Furthermore, functions based on the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are also included in 'SILFS' for selecting tuning parameters. For more details of Subgroup Identification with Latent Factor Structure Method, please refer to He et al. (2024) . 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Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette (Van der Laan et al., 2003) , Probability of Alternative Cluster normalization methods (Raymaekers & Rousseeuw, 2022) , fuzzy clustering and silhouette diagnostics using membership probabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat & Kiruthika, 2024) , , , and multi-way clustering extensions such as block and tensor clustering (Schepers et al., 2008; Bhat & Kiruthika, 2025) , . Provides tools for computation and visualization (Rousseeuw, 1987) to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings. 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Our simulation studies reveal that while 'silp' may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, 'omega' = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, 'silp' achieves estimations akin in quality to those derived from LMS. In conclusion, the 'silp' package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Cheung et al.(2021) Hsiao et al.(2018). 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Package: r-cran-sim.diffproc Architecture: all Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deriv, r-cran-mass Suggests: r-cran-desolve, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-sm Filename: pool/dists/noble/main/r-cran-sim.diffproc_5.0-1.ca2404.1_all.deb Size: 1468832 MD5sum: d29a50757341e11c95e29165d06df1c7 SHA1: 1ee824da429ee2fbd46683411583b3a07a8ccd97 SHA256: f09b5f8624eca79711c9b11ea289ebbaeafe90faca76e996392f3efea8062d58 SHA512: 34fb1d2c3bf9d14cd71fbca1155c0a21d8e62d817f73d312b97b0631a2e6aca74c46c27859b850680f4d55343a767d9d60bda3e2e4e3327584941fff6ac5722b Homepage: https://cran.r-project.org/package=Sim.DiffProc Description: CRAN Package 'Sim.DiffProc' (Simulation of Diffusion Processes) It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs . Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2. Package: r-cran-sim.plfn Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-distrib Filename: pool/dists/noble/main/r-cran-sim.plfn_1.0-1.ca2404.1_all.deb Size: 50592 MD5sum: 1622edd22bafc84f8c23103a61500ce8 SHA1: 228f2b124301d28cd9ba57f410bdefc2767f523e SHA256: 04d0380fd1ee0df19936e01288c67b9ef5f69907777e6dd40dfb4dd99c36046d SHA512: 20940d4550ad9dfd96b80781e0da553409f591fc8c185c87db4769d35aa59a1b19c5e56522375661ea66ea1d60b47b5271f8af37d59b5bc1cac4282b18fd632f Homepage: https://cran.r-project.org/package=Sim.PLFN Description: CRAN Package 'Sim.PLFN' (Simulation of Piecewise Linear Fuzzy Numbers) The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are: (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package. Package: r-cran-sim1000g Architecture: all Version: 1.40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hapsim, r-cran-mass, r-cran-stringr, r-cran-readr Suggests: r-cran-knitr, r-cran-prettydoc, r-cran-testthat, r-cran-gplots, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sim1000g_1.40-1.ca2404.1_all.deb Size: 1150272 MD5sum: 5398d77a4097ef03380de4c4353c7e91 SHA1: 3f3ee0359328e2dbf1c8a4ec7f72e6b2462ebe92 SHA256: 30f8428dacdaf4c74932fb742196cbd9d96302d7d67d4d3700c108ca2fd28959 SHA512: 2c9a5539f3bf258f58c00f3ba4fb96c4f0de39bccec91e7a3d5cfaf0c0f1517792b3a5d12420c604e1662df162436232b8dee1919c2380f71ec901bbf382be04 Homepage: https://cran.r-project.org/package=sim1000G Description: CRAN Package 'sim1000G' (Genotype Simulations for Rare or Common Variants UsingHaplotypes from 1000 Genomes) Generates realistic simulated genetic data in families or unrelated individuals. Package: r-cran-sim2dpredictr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rdpack, r-cran-spam, r-cran-tibble, r-cran-dplyr, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-sim2dpredictr_0.1.1-1.ca2404.1_all.deb Size: 198922 MD5sum: bc0ef7961f2bcef04bd99e781db081bc SHA1: 076fd5a43977f390e03ff0706b38a8dc71768363 SHA256: 32be35f14ad9368ecbd5071f3b827c279e87c7e524cf1288d78b924b4d9f2d7d SHA512: e33384e1c8cc5b72f91c5e0705411c69c0f0d2d844d001064f1183d2a4a99ed8cc8350eac0e9eca7a8d4ac613d51c1ccc2fc7e046df8f6d37aea3bc22c218d2b Homepage: https://cran.r-project.org/package=sim2Dpredictr Description: CRAN Package 'sim2Dpredictr' (Simulate Outcomes Using Spatially Dependent Design Matrices) Provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) and Furrer and Sain (2010) ), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user. Package: r-cran-simaerep Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-forcats, r-cran-cowplot, r-cran-rcolorbrewer, r-cran-furrr, r-cran-progressr, r-cran-knitr, r-cran-tibble, r-cran-dbplyr, r-cran-glue Suggests: r-cran-testthat, r-cran-devtools, r-cran-pkgdown, r-cran-spelling, r-cran-haven, r-cran-vdiffr, r-cran-lintr, r-cran-dbi, r-cran-duckdb, r-cran-ggextra Filename: pool/dists/noble/main/r-cran-simaerep_1.0.0-1.ca2404.1_all.deb Size: 619250 MD5sum: b42bfa4f10c744d544dc8a73b9d19019 SHA1: 746fec7e2b7042be50b712f523aab8a63184047b SHA256: 21b0451385f3eb4ef890bcf26cee5e025b4e3b74d4800caa0e315468afab88a3 SHA512: dccfa380e98ea956a5c3270ed0c20f086a44f3e446887388961f25a773ef677cc77785079dc070dc8e9f2a7efab8f5710aa7f0ce45be4d20320a0f5416b73585 Homepage: https://cran.r-project.org/package=simaerep Description: CRAN Package 'simaerep' (Detect Clinical Trial Sites Over- or Under-Reporting ClinicalEvents) Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 ). Package: r-cran-simbarepro Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ddalpha, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simbarepro_0.1.0-1.ca2404.1_all.deb Size: 233470 MD5sum: 6ca1a3b4f71a21fd1a061d71c9a89e14 SHA1: 5be9a4fdace379b161971605b4b2e616d05cbb34 SHA256: edec1ef393035479c9151a8a987b410aa250eaac71134ed8e0f543a23edaa3ea SHA512: 4785534c414b6813508b5fd6d19ca2980b6eaf330f32fed63f8fca70e50a95c1834e4b43e5f1151dc06fe22e29825238a7cf707d5b612039d20bca15a565018f Homepage: https://cran.r-project.org/package=SimBaRepro Description: CRAN Package 'SimBaRepro' (Simulation-Based, Finite-Sample Inference via Repro Samples) Functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", . Package: r-cran-simbkmrdata Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-bkmr, r-cran-fields, r-cran-gt, r-cran-quarto, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-simbkmrdata_0.2.2-1.ca2404.1_all.deb Size: 584788 MD5sum: 831e2dd3d3335f6bced442cbb5567b2f SHA1: e5a106c0844cf90da090a3dbcbfef522690f8a6a SHA256: d9e2bc933f801cf5c74566eb968cf3bddc25c14283cd69ade861ad06771315d3 SHA512: a3996fc3cc0d740c99e409b6068bfcc279b5ec96cb55d4eaf45db127a0145320ea85b24e3becbf79983ab828b40a047eacf06cbc8489ab97d8eea98ec63a5934 Homepage: https://cran.r-project.org/package=simBKMRdata Description: CRAN Package 'simBKMRdata' (Helper Functions for Bayesian Kernel Machine Regression) Provides a suite of helper functions to support Bayesian Kernel Machine Regression (BKMR) analyses in environmental health research. It enables the simulation of realistic multivariate exposure data using Multivariate Skewed Gamma distributions, estimation of distributional parameters by subgroup, and application of adaptive, data-driven thresholds for feature selection via Posterior Inclusion Probabilities (PIPs). It is especially suited for handling skewed exposure data and enhancing the interpretability of BKMR results through principled variable selection. The methodology is described in Hasan et al. (2025) and . Package: r-cran-simboot Architecture: all Version: 0.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-simboot_0.2-8-1.ca2404.1_all.deb Size: 120640 MD5sum: 66639ebb075043e5e3dfa732d611cd47 SHA1: 8f1cf732dcf968c15b08bd1bcb42f1009dbc99b9 SHA256: 8693188a8a661f79e9d8a20f935f161dcd1b23c06524f25a0b57fce66b62b338 SHA512: c71d71b52a601c72861e939551e7414884c9c0707fa5cbf93cf62aa73dc549e12ef5bc5c878df23a90ff203f2d4e41b18dc3f0df8df9de22d8963e10ec883d6a Homepage: https://cran.r-project.org/package=simboot Description: CRAN Package 'simboot' (Simultaneous Inference for Diversity Indices) Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity. Package: r-cran-simcat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mirt, r-cran-mirtcat, r-cran-shiny, r-cran-shinycssloaders Filename: pool/dists/noble/main/r-cran-simcat_1.0.1-1.ca2404.1_all.deb Size: 80826 MD5sum: 4cd88e5c25636179d469a9d2c647d96b SHA1: 1ca3a869f23a3131af58ea84dc1dcc7933d3843f SHA256: 28058af6eb7de896ef86acc88f2f4ad63291e5f0a9b81d95e83bd25bd2d85087 SHA512: 6ae8410d4b1b63b10e6f9726770f02901931776030bb27d82b136d999da0a3bb0b52103e40bc5cf23eac27e70dafcc5a92b1f9a25708b32e30784e27b2a832b8 Homepage: https://cran.r-project.org/package=simCAT Description: CRAN Package 'simCAT' (Implements Computerized Adaptive Testing Simulations) Computerized Adaptive Testing simulations with dichotomous and polytomous items. Selects items with Maximum Fisher Information method or randomly, with or without constraints (content balancing and item exposure control). Evaluates the simulation results in terms of precision, item exposure, and test length. Inspired on Magis & Barrada (2017) . Package: r-cran-simcausal Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-stringr, r-cran-r6, r-cran-assertthat, r-cran-matrix Suggests: r-cran-copula, r-cran-runit, r-cran-ltmle, r-cran-knitr, r-cran-ggplot2, r-cran-hmisc, r-cran-mvtnorm, r-cran-bindata Filename: pool/dists/noble/main/r-cran-simcausal_0.5.7-1.ca2404.1_all.deb Size: 957322 MD5sum: d96ee569ba4dc39fa0e89a1a0c0b6d64 SHA1: f5842780aecdc73ef231e2fd9e3d59b7a41fc3d0 SHA256: 0bacc643104b8595ec03c3431e7a162eaa38baea5d25e70695cb1bb331d5350d SHA512: e92e30b990ce887f39d4fbf658627130f20629f81dbc2de113915dc590202b36592825532608d9c654186cff241b4004c6a6e4bcaddf0b342101912b2713c3db Homepage: https://cran.r-project.org/package=simcausal Description: CRAN Package 'simcausal' (Simulating Longitudinal Data with Causal Inference Applications) A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples. Package: r-cran-simcomp Architecture: all Version: 3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcomp, r-cran-mratios Filename: pool/dists/noble/main/r-cran-simcomp_3.6-1.ca2404.1_all.deb Size: 186078 MD5sum: d6cda1290141d7e5440e54a33b05910c SHA1: 937a711dcc587f9e6a871e63f5800a7d1fc2d290 SHA256: 8aafa36f19f417af48237225779a7257f003dabc99fef82cb5ef0cc2c44d7d3c SHA512: e0407dc5f5a9b40c155225d9ccdd2076ea3575078ce5ef2fa2b63db05275d53978b82f131ff2d6250719bb7e998bf6a4795e5023e6bf9deadbe8a400fc2e3198 Homepage: https://cran.r-project.org/package=SimComp Description: CRAN Package 'SimComp' (Simultaneous Comparisons for Multiple Endpoints) Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 ) or ratios (Hasler and Hothorn, 2012 ) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 ) or covariance matrices (Hasler, 2014 ). Package: r-cran-simcop Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-rgl Filename: pool/dists/noble/main/r-cran-simcop_0.7.4-1.ca2404.1_all.deb Size: 374606 MD5sum: 4119ad6fc55b2d0ec83df647231e1424 SHA1: ae8d83e292aa80d609fff0235a1cbc04ad88c15d SHA256: efe4780b0c3b9f08cb76bee37ccce72eef6885355eb147030f74529f1db9b687 SHA512: 03ae326869a5021ef9c0f3f123800f40d4fe5bf66cc0dc2d634c09c27ede571c752adffd5a1aae8844d0a8a8de154013d8c9b777eeab491d5b78831baf20e693 Homepage: https://cran.r-project.org/package=SimCop Description: CRAN Package 'SimCop' (Simulate from Arbitrary Copulae) Provides a framework to generating random variates from arbitrary multivariate copulae, while concentrating on (bivariate) extreme value copulae. Particularly useful if the multivariate copulae are not available in closed form. Detailed discussion of the methodologies used can be found in Tajvidi and Turlach (2018) . Package: r-cran-simcormultres Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd Suggests: r-cran-bookdown, r-cran-covr, r-cran-gee, r-cran-knitr, r-cran-multgee, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simcormultres_1.9.0-1.ca2404.1_all.deb Size: 579208 MD5sum: 58442386d959b3d9113185bcf9c1db16 SHA1: 833248fd8a0580c17dc548681c64938645b824c3 SHA256: bc607997a2bc301dfe1f761b54c2bebc5ec45804e1d55cd03c5011564bb71210 SHA512: 51464752ef594d45aafea724162e29d0ef110ac1dfe8cf08f419318aa7345b99f5c8a41136cccbef36e74a7f7197d64ce3872cb72f539bf1105b296552d3c91a Homepage: https://cran.r-project.org/package=SimCorMultRes Description: CRAN Package 'SimCorMultRes' (Simulates Correlated Multinomial Responses) Simulates correlated multinomial responses conditional on a marginal model specification. Package: r-cran-simcorrmix Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4620 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-simmulticorrdata, r-cran-bb, r-cran-nleqslv, r-cran-mass, r-cran-mvtnorm, r-cran-matrix, r-cran-vgam, r-cran-triangle, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-printr, r-cran-bookdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simcorrmix_0.1.1-1.ca2404.1_all.deb Size: 853142 MD5sum: d56fa0a7492f399af21b64dcbbb84500 SHA1: b8aab8dace2f9c3f3bcb22919de252b9d078812f SHA256: 34b002c9e54b294487f9b023f6b3840ee0e71faf4867e1a11c0ad47eb47349f2 SHA512: 32007233be2ae5baa77adfced361c25a3a800653f69bb4bc957e9b21afffa8701ebfa9025b8fae822f7952cf1c977b5f47bc09d1fe0291db5a0990e168a5b3d5 Homepage: https://cran.r-project.org/package=SimCorrMix Description: CRAN Package 'SimCorrMix' (Simulation of Correlated Data with Multiple Variable TypesIncluding Continuous and Count Mixture Distributions) Generate continuous (normal, non-normal, or mixture distributions), binary, ordinal, and count (regular or zero-inflated, Poisson or Negative Binomial) variables with a specified correlation matrix, or one continuous variable with a mixture distribution. This package can be used to simulate data sets that mimic real-world clinical or genetic data sets (i.e., plasmodes, as in Vaughan et al., 2009 ). The methods extend those found in the 'SimMultiCorrData' R package. Standard normal variables with an imposed intermediate correlation matrix are transformed to generate the desired distributions. Continuous variables are simulated using either Fleishman (1978)'s third order or Headrick (2002)'s fifth order polynomial transformation method (the power method transformation, PMT). Non-mixture distributions require the user to specify mean, variance, skewness, standardized kurtosis, and standardized fifth and sixth cumulants. Mixture distributions require these inputs for the component distributions plus the mixing probabilities. Simulation occurs at the component level for continuous mixture distributions. The target correlation matrix is specified in terms of correlations with components of continuous mixture variables. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. However, the package provides functions to approximate expected correlations with continuous mixture variables given target correlations with the components. Binary and ordinal variables are simulated using a modification of ordsample() in package 'GenOrd'. Count variables are simulated using the inverse CDF method. There are two simulation pathways which calculate intermediate correlations involving count variables differently. Correlation Method 1 adapts Yahav and Shmueli's 2012 method and performs best with large count variable means and positive correlations or small means and negative correlations. Correlation Method 2 adapts Barbiero and Ferrari's 2015 modification of the 'GenOrd' package and performs best under the opposite scenarios. The optional error loop may be used to improve the accuracy of the final correlation matrix. The package also contains functions to calculate the standardized cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables. Package: r-cran-simdag Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2637 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rfast, r-cran-rlang, r-cran-igraph, r-cran-dagitty, r-cran-ggdag Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-ggplot2, r-cran-ggforce, r-cran-mass, r-cran-covr, r-cran-foreach, r-cran-dosnow, r-cran-dorng, r-cran-simr, r-cran-rsurv, r-cran-survival Filename: pool/dists/noble/main/r-cran-simdag_1.0.1-1.ca2404.1_all.deb Size: 1475416 MD5sum: dc38b1031cfc855844cbb3232f259389 SHA1: 4b6fa3dca3c3d98e6f5280cc6754bf9b7886b112 SHA256: fa9b80bb32d88068d148831be7bbeaca8cc4efa64e5bcd71b62b24749a82e24d SHA512: 0aae7b631b46996eff816874d98fb233b485a6232c4e7019c1e555ebaa6f0408ec98da9330739cb2b7273c4f3961de8c01404558935a387c473a44dc6aeba454 Homepage: https://cran.r-project.org/package=simDAG Description: CRAN Package 'simDAG' (Simulate Data from a (Time-Dependent) Causal DAG) Simulate complex data from a given directed acyclic graph and information about each individual node. Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to one of many implemented regressions, such as logistic regression, linear regression, poisson regression or any other function. Also includes a comprehensive framework for discrete-time simulation, discrete-event simulation, and networks-based simulation which can generate even more complex longitudinal and dependent data. For more details, see Robin Denz, Nina Timmesfeld (2026) . Package: r-cran-simdata Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-igraph Suggests: r-cran-doparallel, r-cran-dorng, r-cran-dplyr, r-cran-fitdistrplus, r-cran-forcats, r-cran-ggplot2, r-cran-ggally, r-cran-ggcorrplot, r-cran-knitr, r-cran-patchwork, r-cran-purrr, r-cran-reshape2, r-cran-rmarkdown, r-cran-nhanes, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simdata_0.4.1-1.ca2404.1_all.deb Size: 746174 MD5sum: b0fa013fc9bec3468fdc99ca892c7bf7 SHA1: e4a26f87c44c46df107313ce51d3d16b5c50d1ee SHA256: 65e5882a8f07de7f27a4f0e93dfd78a655568d57a98e3cc92526c2a5d9e16e5f SHA512: 7d7f4492d23bed97e20975fd82720dafdad7ab1cbbc71d96f537324f9ebedcbdea5e5e2a4eb13852d49b7144b10a6093c0538f0f9473da3d175d8914d5bc290c Homepage: https://cran.r-project.org/package=simdata Description: CRAN Package 'simdata' (Generate Simulated Datasets) Generate simulated datasets from an initial underlying distribution and apply transformations to obtain realistic data. Implements the 'NORTA' (Normal-to-anything) approach from Cario and Nelson (1997) and other data generating mechanisms. Simple network visualization tools are provided to facilitate communicating the simulation setup. Package: r-cran-simdd Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-circstats, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simdd_1.1-2-1.ca2404.1_all.deb Size: 55084 MD5sum: 4b1914f27b4067db48608738db091af5 SHA1: 444c480e0e7715e105ee8d140696e9e0abaa7257 SHA256: fbdf585ec53e21ced7d0d6071d2e8ea017266224041f0dd4f39ebb7ffc90082b SHA512: ee867c9f78f0cea6801789dc74b3c774c1bb76554df19f71e7e0882835e897728d82354dee92490225b6c3d5a4981f83e249a20dc0dee35bbf08be434400d620 Homepage: https://cran.r-project.org/package=simdd Description: CRAN Package 'simdd' (Simulation of Fisher Bingham and Related DirectionalDistributions) Simulation methods for the Fisher Bingham distribution on the unit sphere, the matrix Bingham distribution on a Grassmann manifold, the matrix Fisher distribution on SO(3), and the bivariate von Mises sine model on the torus. The methods use an acceptance/rejection simulation algorithm for the Bingham distribution and are described fully by Kent, Ganeiber and Mardia (2018) . These methods supersede earlier MCMC simulation methods and are more general than earlier simulation methods. The methods can be slower in specific situations where there are existing non-MCMC simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018) for further details). Package: r-cran-simdesign Architecture: all Version: 2.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7529 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-testthat, r-cran-parallelly, r-cran-dplyr, r-cran-sessioninfo, r-cran-beepr, r-cran-pbapply, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-r.utils, r-cran-codetools, r-cran-clipr, r-cran-e1071, r-cran-qs2 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-shiny, r-cran-copula, r-cran-extradistr, r-cran-renv, r-cran-cli, r-cran-job, r-cran-future.batchtools, r-cran-frf2, r-cran-rmarkdown, r-cran-rpushbullet, r-cran-mirai, r-cran-httr Filename: pool/dists/noble/main/r-cran-simdesign_2.28-1.ca2404.1_all.deb Size: 1180376 MD5sum: 436827ef730c0cdc380331114c85467c SHA1: ef5f08698888d2f36c1b4785ed1e61e9ad1d205a SHA256: df88c4eed3210ad3db7650609b7c5119cade451d60dad06946a1df5e7460a1b5 SHA512: d96c1d4ced06a56ea1f647b47cbed528e7bf1f66cb4fcbaa0c0a888e168a22a77cc81a5bce9c575558b2c0f66c35a34eb75eb280c0a2defb7295df9ac336bbe5 Homepage: https://cran.r-project.org/package=SimDesign Description: CRAN Package 'SimDesign' (Structure for Organizing Monte Carlo Simulation Designs) Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) . For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) . Package: r-cran-simdissolution Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-alabama, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-simdissolution_0.1.0-1.ca2404.1_all.deb Size: 40530 MD5sum: 7ad2defb24d7d89562ce6b2b9cd2355d SHA1: 24ac382a24eba9cdbbadc9f16937dc69276f7dc5 SHA256: 68622528973cdc76bf2bfe86a67a717f8ac95cca150c1fcb0fcda2fffc372d5d SHA512: 7a849f0916183596df59b6dca8d89e82aaff4421636973ce29403fc14893ff3e72cd4b04637b2a31286540c9069ba8a2eb6a1420db02bf57d89eb9962be4bdff Homepage: https://cran.r-project.org/package=SimDissolution Description: CRAN Package 'SimDissolution' (Modeling and Assessing Similarity of Drug Dissolutions Profiles) Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) for details. Package: r-cran-simdistr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-simdistr_1.0.1-1.ca2404.1_all.deb Size: 23770 MD5sum: 7b3cc1603e17b00bba0d4ea49e65ee93 SHA1: 5b3d8269672ad620e3ce85cab0e0a06c206f24ce SHA256: 8c25c6d2bc33201fcf0017dd41ceafdc3f5b4241732cbc984cd24c04f4ba7078 SHA512: 4bb2e6833e69c21528ab79a8bece642f2345ed6d0422e89710057b43dda3eda51f8b86f5a23d21b5268fcd2d5cf3a7ce6444493ca853d93c617170cac7f4d761 Homepage: https://cran.r-project.org/package=simdistr Description: CRAN Package 'simdistr' (Assessment of Data Trial Distributions According to theCarlisle-Stouffer Method) Assessment of the distributions of baseline continuous and categorical variables in randomised trials. This method is based on the Carlisle-Stouffer method with Monte Carlo simulations. It calculates p-values for each trial baseline variable, as well as combined p-values for each trial - these p-values measure how compatible are distributions of trials baseline variables with random sampling. This package also allows for graphically plotting the cumulative frequencies of computed p-values. Please note that code was partly adapted from Carlisle JB, Loadsman JA. (2017) . 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Package: r-cran-simed Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstream, r-cran-shape Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-simed_2.0.2-1.ca2404.1_all.deb Size: 807558 MD5sum: cc7833acfc96b9eed07ead0790dab36c SHA1: 42afb4d9beb65a741d33905998e6acb49ac893e4 SHA256: fed159136b4ff8d87513086461e2a73b7e2c81697f9eb76b8ee4d7a0e7d9253b SHA512: 5cc03f44246bdaa27b88a8cef7b5be65a928bbdf817250e412101438e0220742110cee55ef7836b2b329fc66b3a4cd3f33fcf965f148e3dd5a0c2524de0d77b3 Homepage: https://cran.r-project.org/package=simEd Description: CRAN Package 'simEd' (Simulation Education) Contains various functions to be used for simulation education, including simple Monte Carlo simulation functions, queueing simulation functions, variate generation functions capable of producing independent streams and antithetic variates, functions for illustrating random variate generation for various discrete and continuous distributions, and functions to compute time-persistent statistics. Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) , in Kudlay, Lawson, and Leemis (2020) , and in Lawson and Leemis (2021) . 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These calculations main refer to Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006) . 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To obtain an 'SimFin' API key (and thus to use this package), you need to register at . 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Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package. Package: r-cran-simgof Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ddst Filename: pool/dists/noble/main/r-cran-simgof_1.0.2-1.ca2404.1_all.deb Size: 31168 MD5sum: bf21eb57d88f5bad6ce3e79a781afa5a SHA1: 44b00a53653110a15d1038f3d737e7f442ae0507 SHA256: b92228fbd0495a206068f3d50d302994eb104dc0c53bac474164dc3aa8cf1403 SHA512: 7e6942d4739725b2ed4e0a50e936cdfd3a28eca8c68f51ce022e6ee5b8b45436e56a44cff5a0d7d9f7138d66f256df030ed24650964c35976ab88788669eef12 Homepage: https://cran.r-project.org/package=simgof Description: CRAN Package 'simgof' (Simultaneous Goodness-of-Fits Tests) Routine that allows the user to run several goodness-of-fit tests. It also combines the tests and returns a properly adjusted family-wise p value. Details can be found in . Package: r-cran-simhaz Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-simhaz_0.1-1.ca2404.1_all.deb Size: 86416 MD5sum: 3b5dca8b1dfe1b369a01f4bbd81fc57e SHA1: 6d7203b0184dd9325227aecff2de81bca82c3583 SHA256: 0f7d5bc71edca0754de9357e166ecb81af293b11f9e7c64b0a4fe91d01afd324 SHA512: b28fe6698f629ec98a1dd54d5fd3e265a75f1b967a26254e3c157fd46c60a4b979941465d8acfffd9e82b9ba5a26792f36a92883267a5cd4ae8cc781511f0787 Homepage: https://cran.r-project.org/package=SimHaz Description: CRAN Package 'SimHaz' (Simulated Survival and Hazard Analysis for Time-DependentExposure) Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. 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Package: r-cran-similaritymeasures Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-similaritymeasures_1.4-1.ca2404.1_all.deb Size: 89456 MD5sum: dcf1212fb5b1204294176ab8cad16a55 SHA1: 6d487e56433e31c4f8cf898f6be70b26f0f3182a SHA256: 71784c231bdced8488d9a41389897f046bbd2890e2c39fcd76e80b974451fd6d SHA512: 36e1ecffc126a86f89e62701884ced6a5de59d0f8ee2dc5bb5c44d4f9cf96339af6e5fc82b09ad233ba08bebbcd6d5b01edc85598a0437019d27f10697b38f17 Homepage: https://cran.r-project.org/package=SimilarityMeasures Description: CRAN Package 'SimilarityMeasures' (Trajectory Similarity Measures) Functions to run and assist four different similarity measures. The similarity measures included are: longest common subsequence (LCSS), Frechet distance, edit distance and dynamic time warping (DTW). Each of these similarity measures can be calculated from two n-dimensional trajectories, both in matrix form. Package: r-cran-simile Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-simile_1.3.4-1.ca2404.1_all.deb Size: 61854 MD5sum: bfcb497b62918ffa52a4b6d83aaee37c SHA1: 3981864582ec31adb808bea69cfa68e96ee7c662 SHA256: b282c1e487b494da954afc5b6425730a7e4124a0399210c5f699ce8ab98a5b45 SHA512: d70a72af11e75cde067fcc92a84d528df1e1fe4675d5b21f5b703817b16e12de6f0125631997cb1dc3e9e49703c1780aff19962e6051ae55f9964e706e70b383 Homepage: https://cran.r-project.org/package=Simile Description: CRAN Package 'Simile' (Interact with Simile Models) Allows a Simile model saved as a compiled binary to be loaded, parameterized, executed and interrogated. This version works with Simile v6 on. Package: r-cran-simindep Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-simindep_0.1.2-1.ca2404.1_all.deb Size: 19554 MD5sum: 2548c75bf2e55e4b29448c160671423e SHA1: c959f8a7a2d7581985e0f9d731cc60aeb8903644 SHA256: d8b4dbd667cefa5e9e7d920673cabc5f3826494e9462a8ff860e6d672f6aaf48 SHA512: 49aa6162197457cbeab692a6a81e61d3336df583df99a5c9abaa8c492932eeefa07f1fa0dc822ea941428937c005f232c3f32b0ba9fc511c91f8b1f010e705ca Homepage: https://cran.r-project.org/package=SimIndep Description: CRAN Package 'SimIndep' (WISE: a Weighted Similarity Aggregation Test for SerialIndependence) A fast implementation of the weighted information similarity aggregation (WISE) test for detecting serial dependence, particularly suited for high-dimensional and non-Euclidean time series. Includes functions for constructing similarity matrices and conducting hypothesis testing. Users can use different similarity measures and define their own weighting schemes. For more details see Q Zhu, M Liu, Y Han, D Zhou (2025) . 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Package: r-cran-simitation Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-devtools, r-cran-markdown, r-cran-formatr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simitation_0.0.7-1.ca2404.1_all.deb Size: 234628 MD5sum: 4f13f04dd8825e908d6ed5ee97def188 SHA1: bde040973eb20dadb0ba17753a2780425d5d95ae SHA256: 0ed9f4f00ae3161aa7ae0d61e0f10fd12ded6551074adfde1ae931f7667ed4f9 SHA512: 2ff1c0f96d45466b06e73d87f736c1375a52b2e843f99fec1ad59eec468eecf871644804f9b84c6911dd5a4533368a40751e86fbe62f8d57ac96201e4beb9e6c Homepage: https://cran.r-project.org/package=simitation Description: CRAN Package 'simitation' (Simplified Simulations) Provides tools for generating and analyzing simulation studies. Users may easily specify all terms of a simulation study, often in a single line of code. 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Package: r-cran-simits Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-arm, r-cran-ggplot2, r-cran-knitr, r-cran-plyr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-simits_0.1.1-1.ca2404.1_all.deb Size: 329608 MD5sum: 744b980d5ca44838f1af4189f9238e21 SHA1: 7c3164d76fce6d0ff1471c07c41dd0a79bd534c1 SHA256: 05c747b676d4f6f46e0c9216e4c5f7e41955f37b27f1092204ab3128f82da313 SHA512: 6b5eced9ea9239cf712847749839a6e1dacff9d551bd7fa9bf928ff57f5caac90dcfcc3d131cc7b4f7c33d4864fc2516afeed967ae97e3f10dc8ffb5326ec238 Homepage: https://cran.r-project.org/package=simITS Description: CRAN Package 'simITS' (Analysis via Simulation of Interrupted Time Series (ITS) Data) Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) . 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The method is based on the potential landscape definition by Wang et al. (2008) (also see Zhou & Li, 2016 for further mathematical discussions) and can be used for a large variety of models. 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Package: r-cran-simmetric Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simmetric_0.1.1-1.ca2404.1_all.deb Size: 381988 MD5sum: 4adfcee7b88736da27b91720c0b8eefa SHA1: 8e1b56814f619345a4a4719c3802ca5fe498f2a0 SHA256: b4d77e422364886f4db50de6f7e04e3fb8e08d7ef2a1b573009b3b5a073a6c47 SHA512: ed77a45e91c596be3ce0d02f60ad521b83489608bf3b466940540f6fe9c2ce9af59360bb75b98cafbd781daf95624c625c7c8fa0d53ccbfd534558077a3013fb Homepage: https://cran.r-project.org/package=simMetric Description: CRAN Package 'simMetric' (Metrics (with Uncertainty) for Simulation Studies that EvaluateStatistical Methods) Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies. 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The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) and Park, Petkova, Tarpey, and Ogden (2020) (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml(). 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It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order () or Headrick's fifth-order () polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from 'GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, ). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, ). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds. 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'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) . 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Dattner & Yaari (2018) . Dattner et al. (2017) . Dattner & Klaassen (2015) . 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To better understand the usage of the package and the algorithm used, please refer to Perera, A., and Ramanayake, A. (2019) . Package: r-cran-simrec Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt Filename: pool/dists/noble/main/r-cran-simrec_1.0.1-1.ca2404.1_all.deb Size: 912652 MD5sum: ee96b40b33848717ebfe6e42e21c4905 SHA1: 08dfcbf322ece1c224b2a8a6481f1ce8f6412b83 SHA256: 5e72da3e39f780fa5a4fc04aea473177eea9b6e807fa0cf1e09fc9eebd36fbcb SHA512: 7d1f89f4c9090567dc5f8e2307639dc9004995d9ceb47d7583692f8c2c3b198e87924ee422e99428c7edea6cdd7c0a85c5e7bd51752140723fa8ce0f8d8ffe97 Homepage: https://cran.r-project.org/package=simrec Description: CRAN Package 'simrec' (Simulation of Recurrent Event Data for Non-Constant BaselineHazard) Simulation of recurrent event data for non-constant baseline hazard in the total time model with risk-free intervals and possibly a competing event. Possibility to cut the data to an interim data set. Data can be plotted. Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) . Package: r-cran-simrel Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1935 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-frf2, r-cran-ggplot2, r-cran-gridextra, r-cran-jsonlite, r-cran-magrittr, r-cran-miniui, r-cran-purrr, r-cran-reshape2, r-cran-rstudioapi, r-cran-scales, r-cran-sfsmisc, r-cran-shiny, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-testthat Suggests: r-cran-covr, r-cran-knitr, r-cran-pls, r-cran-markdown, r-cran-doe.base Filename: pool/dists/noble/main/r-cran-simrel_2.1.0-1.ca2404.1_all.deb Size: 355158 MD5sum: 8244d630a0237aefbf965dbfce473137 SHA1: 099dfdc4b30fdfcde22d79d18fa88e00d2003e76 SHA256: 5001f60654405cc804daafcb6be7133baa89ed798a5a07ed14eed52377b76493 SHA512: 00cb804ee0683b5a147a681cd2c3865422668f194db876843e4b012004710b93f93522616066cea3b5e0b06a85fa314a89892fdd7ff1b7ed558178d8333c01f6 Homepage: https://cran.r-project.org/package=simrel Description: CRAN Package 'simrel' (Simulation of Multivariate Linear Model Data) Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) and Rimal, R., Almøy, T., Sæbø, S. (2018) . Package: r-cran-sims Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-chk, r-cran-future.apply, r-cran-nlist, r-cran-yesno Suggests: r-cran-covr, r-cran-future, r-cran-knitr, r-cran-progressr, r-cran-rjags, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sims_0.0.5-1.ca2404.1_all.deb Size: 64014 MD5sum: d96ac0295f1dc23d39e945cd3a849453 SHA1: d7920f5e94a9763a81359870e5ac0a453ebc742d SHA256: 7167825a8f05590d8fa6292e6d20af1cd1ddba34daa86ba2ea7ee51f6d377897 SHA512: a896d4371eb2b3c1ed8cffacb14b191840bf1052aa6ca7d44152562b8994e8aeafdefe4996b806327620450ee5dae01f42cf07bcc52557fb365083ba8a349b84 Homepage: https://cran.r-project.org/package=sims Description: CRAN Package 'sims' (Simulate Data from R or 'JAGS' Code) Generates data from R or 'JAGS' code for use in simulation studies. The data are returned as an 'nlist::nlists' object and/or saved to file as individual '.rds' files. Parallelization is implemented using the 'future' package. Progress is reported using the 'progressr' package. Package: r-cran-simsalapar Architecture: all Version: 1.0-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-gridbase, r-cran-colorspace Suggests: r-cran-lattice, r-cran-rmpi, r-cran-hmisc, r-cran-copula, r-cran-foreach, r-cran-doparallel, r-cran-fgarch, r-cran-robustbase, r-cran-codetools Filename: pool/dists/noble/main/r-cran-simsalapar_1.0-13-1.ca2404.1_all.deb Size: 464474 MD5sum: 6a99b87121dfdffd2cff78181201261d SHA1: a5d61be562cafd8e72d0f6ec39eab2bb1cdf30e9 SHA256: ecefd97df0715dfb9fffc308ef220ca1502145942e9e42028f06319a0282613b SHA512: df8bfc8395f3a9a55d85fed2421c356b92220af3bded75350b303d3fccd2ac5afa9e6e148cfb5af1d0e322f984d10959a97b41ce928e196ceffb057d032b6254 Homepage: https://cran.r-project.org/package=simsalapar Description: CRAN Package 'simsalapar' (Tools for Simulation Studies in Parallel) Tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations. Package: r-cran-simsem Architecture: all Version: 0.5-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-quantreg, r-cran-kernsmooth, r-cran-semtools, r-cran-lavaan.mi, r-cran-openmx, r-cran-copula Filename: pool/dists/noble/main/r-cran-simsem_0.5-17-1.ca2404.1_all.deb Size: 1147278 MD5sum: 5ab69adf6738fdb711088cbcea2a07b3 SHA1: 90f4e3ab33dad3040213fdfd216e9f7c8aa2e804 SHA256: 67530b0e5b5b1cc8e805722786816cb83ef5478d41a82ff5501d18792ef077bd SHA512: 3f6f62f361e8c418dfbbebc2f7fa8eec17479c42d029791c95ffa60cb1f4b7551edac2a50640215c2c9adda0a1ce9dbd3f418757f44c06fc141190a00f7371fd Homepage: https://cran.r-project.org/package=simsem Description: CRAN Package 'simsem' (SIMulated Structural Equation Modeling) Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning. Package: r-cran-simseq Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4383 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool Filename: pool/dists/noble/main/r-cran-simseq_1.4.0-1.ca2404.1_all.deb Size: 4443118 MD5sum: 54be1180c5bed287aaa13403e9caaca4 SHA1: 06cf6b45e7d394725f0d1122ba6720f2613deb6f SHA256: 63cc0b2db9970ac2ea56ba0ad5bb9077242bebcf024418c8469c62bb7f0b01fe SHA512: a6a4af08e1852b89934879aaed64638336a51b4358a2c8643826b675751f5c6a24a900976603c75e7c60da85c2f51b0c0182aa9e6c0351a7558917f31c6ebfd6 Homepage: https://cran.r-project.org/package=SimSeq Description: CRAN Package 'SimSeq' (Nonparametric Simulation of RNA-Seq Data) RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard. Package: r-cran-simsl Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-simsl_0.2.1-1.ca2404.1_all.deb Size: 216676 MD5sum: 34904b6685e646bf8b1a918cfd6c64cd SHA1: e6f01f089532bfa08ac32d42f81e1453567c3794 SHA256: 88ad89a335bd7434c4da38b78e6cd4b02e183d9376dc2f36a28b277bcdc305b8 SHA512: ec1ad95d3c2b827fd0651ab5163caa2e6453b5a17e900bb020dddeba1d0eb090b69594acde5a39a9291ae25836637de1cf738265792f3b8ca2998825f9cd141a Homepage: https://cran.r-project.org/package=simsl Description: CRAN Package 'simsl' (Single-Index Models with a Surface-Link) An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl(). Package: r-cran-simsst Architecture: all Version: 0.0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gamlss.dist, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simsst_0.0.5.2-1.ca2404.1_all.deb Size: 56160 MD5sum: 9af6f1928b97bf3b5e8f3571ba1d375f SHA1: 033691fcf2a75d7b37992677b154a4c3a974dc7d SHA256: a7462303bc86765a376513f303b9245b4e85a5dfa024b5daf3221ff9447bafff SHA512: f355c3bfe60dde27593b12cbdc3eac146aaf6f33cb02ffede3148a71fd44c1db662af0a06d0660906a86507d1924329c79ad88b286c13d5a3f2209427d24c768 Homepage: https://cran.r-project.org/package=SimSST Description: CRAN Package 'SimSST' (Simulated Stop Signal Task Data) Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) , Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) , Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) . 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Data generation methods are described in Schneider (2013) . Package: r-cran-simsurv Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bb, r-cran-eha, r-cran-flexsurv, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-rstpm2, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simsurv_1.0.1-1.ca2404.1_all.deb Size: 148974 MD5sum: c6df0fc883267b72bf0017f4cc7109ca SHA1: 81ffe762ac9b7a70af3cf275ffe31e43fc649712 SHA256: cfa3bc1c400ec1e87c56744562e3066291d5f08b2cacdb72b2f03f12fd30f376 SHA512: 91a244b859613fc7d7a6bd9e726793bdcd8fb584d117f6eadadce1a55a9ba6fc7fa475cd319ff80882d6da013e54790246fce25744eb2e1291c6a01f65ad502f Homepage: https://cran.r-project.org/package=simsurv Description: CRAN Package 'simsurv' (Simulate Survival Data) Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper . Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) or Crowther and Lambert (2013) ). Package: r-cran-simsurvey Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-data.table, r-cran-progress, r-cran-doparallel, r-cran-foreach, r-cran-plotly, r-cran-rlang, r-cran-lifecycle, r-cran-matrix Suggests: r-cran-fields, r-cran-rmarkdown, r-cran-flexdashboard, r-cran-shiny, r-cran-crosstalk, r-cran-htmltools, r-cran-viridis, r-cran-lme4, r-cran-ggplot2, r-cran-inlaspacetime, r-cran-knitr, r-cran-bezier Filename: pool/dists/noble/main/r-cran-simsurvey_0.1.8-1.ca2404.1_all.deb Size: 3627692 MD5sum: 740fd68f04bf50557e1077b52c1eb2ef SHA1: 5875eab900f5d0addf73b23512d040cf592a0627 SHA256: cfd24316e56af0f9193b7799b97568c70f41168c0db1242e8f0f4612e6c3e3a1 SHA512: 646586ba8b6d65ff7eb8cebd6c6a733c823cc23afd9c9dd848fa626e79cbc77d88a159e49885b6e01ce467687630dbd965b8611c534ef482c9fc52dc80b77afd Homepage: https://cran.r-project.org/package=SimSurvey Description: CRAN Package 'SimSurvey' (Test Surveys by Simulating Spatially-Correlated Populations) Simulate age-structured populations that vary in space and time and explore the efficacy of a range of built-in or user-defined sampling protocols to reproduce the population parameters of the known population. (See Regular et al. (2020) for more details). 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Features a grammar for publication-ready epidemiological tables built on design-aware effect measures, prevalence and odds ratio calculations, and seamless integration with 'flextable' for exporting results to 'Microsoft Word' and 'PowerPoint'. Package: r-cran-simtargetcov Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simtargetcov_1.0.1-1.ca2404.1_all.deb Size: 15510 MD5sum: 43749a3cbb14378a4c9d0849ad76d8e3 SHA1: 6a632360455145b0a775f8247eb1a4e3a3269e96 SHA256: 72047549077bfa4364debcc88c80f76318a40a25912d6d23bb077dc99e5fb755 SHA512: 84701393c4aee32d5bfb36fe409a4df5da02dcd65581acf11cbeb5977a452ed3968df10d7bbc1fd1ed61daf743b4b4044bf0d34cce50c28bd587e24c940dcc9c Homepage: https://cran.r-project.org/package=simTargetCov Description: CRAN Package 'simTargetCov' (Data Transformation or Simulation with Empirical CovarianceMatrix) Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) . Package: r-cran-simtimer Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-simtimer_4.0.0-1.ca2404.1_all.deb Size: 207710 MD5sum: ece5dd99e181eab337cd94d29fd06411 SHA1: 518567f6643905fffcee4862efa6b51573011bca SHA256: 72bb482103272295ba2f259dc64eae5767f794922c298df4ae5d6c9efa331c53 SHA512: a3fb46b000421a1210de0a610b05fd15cdcb9468277e4b2cf0a2067bdfb1de99b84df209960c85e4f8ab02498b8e865d9b4d0a53fcafe8011133cc91b9d23366 Homepage: https://cran.r-project.org/package=simtimer Description: CRAN Package 'simtimer' (Datetimes as Integers for Discrete-Event Simulations) Handles datetimes as integers for the usage inside Discrete-Event Simulations (DES). The conversion is made using the internally generic function as.numeric() of the base package. DES is described in Simulation Modeling and Analysis by Averill Law and David Kelton (1999) . Package: r-cran-simtimevar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-mvtnorm, r-cran-icc, r-cran-misctools, r-cran-car, r-cran-plyr, r-cran-corpcor, r-cran-psych Filename: pool/dists/noble/main/r-cran-simtimevar_1.0.0-1.ca2404.1_all.deb Size: 77942 MD5sum: 70cb113e820708230c443441ecadbb33 SHA1: d5b8045b6146184957df5eb2643722cf3f820fe1 SHA256: db25e0a6ac6a15053347c8f9a8afda0cdb429bfdd2c51daf00d05b3fb957cf8a SHA512: 3af86f5132d732560391ef0a4d8e1165320a4d65ca90623ee708c6046cbfd6a3e16732f237b851c639d46a22fec23d625eb2d20beb895921e7b041c818f1d13e Homepage: https://cran.r-project.org/package=SimTimeVar Description: CRAN Package 'SimTimeVar' (Simulate Longitudinal Dataset with Time-Varying CorrelatedCovariates) Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. for methodological details. Package: r-cran-simtool Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-boot, r-cran-broom, r-cran-rmarkdown, r-cran-tinytest, r-cran-lintr, r-cran-roxygen2, r-cran-covr Filename: pool/dists/noble/main/r-cran-simtool_1.1.9-1.ca2404.1_all.deb Size: 322124 MD5sum: 64e535da19d0a8cae58dede76db9dc98 SHA1: fc5429985da92d3d62ebfde3df76bfe9e80d87d0 SHA256: 45c5a9ca61e58b087977c2117076bd4be20063811634dde1542dae5df8de7a25 SHA512: cd101cb49b6177309cee5ef3cd9c2808f4e20bd220e5ba7a1a031455880774b4dbd8ca6b114b7ad5de2fd374980594b137cc1e2ac6f33a7b3ca70cd43b640675 Homepage: https://cran.r-project.org/package=simTool Description: CRAN Package 'simTool' (Conduct Simulation Studies with a Minimal Amount of Source Code) Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results. Package: r-cran-simtrait Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prroc Suggests: r-cran-popkin, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bnpsd, r-cran-bedmatrix Filename: pool/dists/noble/main/r-cran-simtrait_1.1.3-1.ca2404.1_all.deb Size: 346660 MD5sum: 9f002be58bac16eec9bc78173b48baa1 SHA1: 8f867a14d80e424428bcb15a6c19c2fb8d35f3ea SHA256: 8c98e92faf37f7131be0fd724042b13cec5325125e6955979aee665e505b87dd SHA512: 403fe45430c6f019dc29d8ded834e2bb53901753ec4632bb964cbd26130bd8d6b4b82b494e5dd4672d516c6f9133561a2fcda0a19e02039c3542be46d8c92d42 Homepage: https://cran.r-project.org/package=simtrait Description: CRAN Package 'simtrait' (Simulate Complex Traits from Genotypes) Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset). Emphasis on avoiding common biases due to the use of estimated allele frequencies. The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects. Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal). GWAS method benchmarking functions are also provided. Described in Yao and Ochoa (2022) . Package: r-cran-simtte Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mrgsolve Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simtte_1.0.2-1.ca2404.1_all.deb Size: 59648 MD5sum: 2a6fbca0302e2f0d27e73763e4438777 SHA1: 94844a1c640e0eff47c82ecf83fe092718a2d007 SHA256: d53b029ef85b5732bb5ff1095eef4b74b37d94d009e3c7dca98f94e4d8b718c7 SHA512: a80259ea525acef97d6705b63b40916fa0634f94c9704591390d1dda06edf278da38a4c1777efc9293ec042e15bc7f6d58a7108d4c2e1f0ebbe54a99ef16c720 Homepage: https://cran.r-project.org/package=simtte Description: CRAN Package 'simtte' (Simulate Bespoke Time-to-Event Models Using ODEs) Simulates time-to-event (survival) datasets for clinical trial design and analysis using ordinary differential equation (ODE) models solved via the 'mrgsolve' backend. Built-in Weibull and flexible M-spline baseline hazard models are provided out of the box, and fully bespoke hazard models can be implemented as custom 'mrgsolve' ODE systems. Event times are generated by inverse transform sampling from the resulting cumulative hazard functions. See Bender et al. (2005) for the inverse transform sampling methodology and Royston and Parmar (2002) for flexible parametric survival models. Package: r-cran-simuclustfactor Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simuclustfactor_0.0.3-1.ca2404.1_all.deb Size: 150138 MD5sum: a2e79278568e2479c6472df3897f2bd6 SHA1: de1ba556f66be702706b9f39d49219dfd8f0577e SHA256: f94efc98737ab76212eed14b6935423e47d07f90d82551ea6cb06d3d8ec24fdc SHA512: 2bee3d2c44f71bc677531144010e2849296de01c5c235f078066a8f93f7126812e65c40d704e5dad5955347c64a64a9d7988e5c8bc1c956365d0231cd055537c Homepage: https://cran.r-project.org/package=simuclustfactor Description: CRAN Package 'simuclustfactor' (Simultaneous Clustering and Factorial Decomposition of Three-WayDatasets) Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) . Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) . Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) . Package: r-cran-simukde Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ks, r-cran-mvtnorm, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simukde_1.3.0-1.ca2404.1_all.deb Size: 47042 MD5sum: bd918f0dbade6425554786aabb4732c9 SHA1: 4e74b180fb5af6c52f33cf559ea41be104f44e9e SHA256: cd341b074a2cd38f48fa84547a6139e47fb681d61f627bb8162ef72d923fa03e SHA512: faf6887a762ef14a367ace00e157a2fb8d5599fe830f869a4be964e4324003dd9b3775159eeb799a5e0c8ba6d57f1d7caac111c5cf5f49138b7970e7b3272c3c Homepage: https://cran.r-project.org/package=simukde Description: CRAN Package 'simukde' (Simulation with Kernel Density Estimation) Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) , Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) . Package: r-cran-simulariatools Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-reticulate, r-cran-scales, r-cran-terra Suggests: r-cran-magick, r-cran-testthat, r-cran-openair, r-cran-sf Filename: pool/dists/noble/main/r-cran-simulariatools_3.1.0-1.ca2404.1_all.deb Size: 968492 MD5sum: ce3a4e8412db79902ad84eae4a76722f SHA1: 21a8810fbb1f85e2eb38813dea237e40c0f95f62 SHA256: ca9e4d7d8597210d20da1ef736e24b854ce9d247c154359df8fc38025225a953 SHA512: 87f48fc685daa7b12ba763518c9677a2dc46bfd31a9b325cc229ec067dc671849796e77d269b7ffd32a6265359cc38a7a76219a83503419db1325ed48408f185 Homepage: https://cran.r-project.org/package=simulariatools Description: CRAN Package 'simulariatools' (Simularia Tools for the Analysis of Air Pollution Data) A set of tools developed at Simularia for Simularia, to help preprocessing and post-processing of meteorological and air quality data. 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It helps to quickly test different designs against each other and compare the performance of new models. The goal of 'simulateDCE' is to make it easy to simulate choice experiment datasets using designs from 'NGENE', 'idefix' or 'spdesign'. You have to store the design file(s) in a sub-directory and need to specify certain parameters and the utility functions for the data generating process. For more details on choice experiments see Mariel et al. (2021) . Package: r-cran-simulator Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2026 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-digest, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-glmnet, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-simulator_0.2.5-1.ca2404.1_all.deb Size: 1372594 MD5sum: bab27f51e8744648a788c9e0fd27a9e8 SHA1: 0f5c2d276671bed4c9192856cd0bc3161da33eb1 SHA256: 3e1ee936022f3f55459c2401727fbe32fd202757a23f1d4d9fffabc71f2a0782 SHA512: a127ad4bbdd676529ec4203006dee9e367ae5e85a2e07eb9f05262e9f6a9dd6f4069d7f87788644c6d9f2d7390774f2bde7f01fd813a7cd274cbfa37eb865e79 Homepage: https://cran.r-project.org/package=simulator Description: CRAN Package 'simulator' (An Engine for Running Simulations) A framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at . Package: r-cran-simule Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-pcapp, r-cran-igraph Filename: pool/dists/noble/main/r-cran-simule_1.3.0-1.ca2404.1_all.deb Size: 376800 MD5sum: c490e2dacc13cff5914c459c639b37b2 SHA1: fe74b36b15c93287b3dfa2130eec3d841ee0c89d SHA256: 655054c9039ab7e4a8b4f0071b1c8f9aabb32b6fcd6f52cb64ea5fb93b33f2e7 SHA512: 4dabc4ce09435dcc108f32740abcd4444684cfc39be24214d04819421e28e673c7287afe7f9f1fb917e24313a2cd9ea725f4db5d710e4ee64b24da18e8a51bf3 Homepage: https://cran.r-project.org/package=simule Description: CRAN Package 'simule' (A Constrained L1 Minimization Approach for Estimating MultipleSparse Gaussian or Nonparanormal Graphical Models) This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) . Package: r-cran-simulist Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-english, r-cran-epiparameter, r-cran-grates, r-cran-randomnames, r-cran-rlang Suggests: r-cran-dplyr, r-cran-epicontacts, r-cran-ggplot2, r-cran-incidence2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-simulist_0.7.0-1.ca2404.1_all.deb Size: 1768254 MD5sum: 827922a764dffcba67141c4eacebb773 SHA1: 3b9322f57dd1cb1176772905b38e8fabc8423a31 SHA256: d9436881d4910dab0b746a076dd935d9025a145aa3112d7dc4cbe0d745f968d6 SHA512: 65b1b104772aa2c5e190277d3d744f0ea302e8ba585ffd2dce799080056aef22518ccfad05b62c12591b435badfd8f81a3edc64b63218ab8dd697db1598ae72a Homepage: https://cran.r-project.org/package=simulist Description: CRAN Package 'simulist' (Simulate Disease Outbreak Line List and Contacts Data) Tools to simulate realistic raw case data for an epidemic in the form of line lists and contacts using a branching process. Simulated outbreaks are parameterised with epidemiological parameters and can have age-structured populations, age-stratified hospitalisation and death risk and time-varying case fatality risk. Package: r-cran-simulmgf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-simulmgf_0.1.1-1.ca2404.1_all.deb Size: 31976 MD5sum: 30665ca2f22e52f1ad81666336998192 SHA1: c1838a4b93ee992bc1fc61075d23c4a62b2231dd SHA256: 8474394023fd708f8652ef2003f576ea9158fd0ae586b829c1d29bb7c024964f SHA512: be3ffef28eddb46d26de489a28b41970d2504eea65aeda91adf988a8121708453a3c0ea910064a2900339852d11ecc536267bc3a1cd72ba6838f7424e9374996 Homepage: https://cran.r-project.org/package=simulMGF Description: CRAN Package 'simulMGF' (Simulate SNP Matrix, Phenotype and Genotypic Effects) Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) , or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes. 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Supports generation of estimation scenarios and control files for external engines (e.g., 'Monolix'), simulation of models using 'rxode2', and creation of goodness-of-fit diagnostics. Includes tools for covariate modeling, virtual population design, and local and global sensitivity analyses. Package: r-cran-simvitd Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-simpleboot Filename: pool/dists/noble/main/r-cran-simvitd_1.0.5-1.ca2404.1_all.deb Size: 559590 MD5sum: 08776709f951b62c72965a2be74cbd34 SHA1: ce1eab3aba5b7b976622d1be07959045721b5727 SHA256: dbd27aae84cd139003ab0c84b4f6a224faa9a83fe6ad23a37485aaf4e79776ef SHA512: 7d486287320954d49b184e876ca00c20f67ebf565cfd7e3b90cd1e47c1568036bf9d246ba8fe2e2d73889233e32205e93e6c8390398597b2dd4f07670a3f95f1 Homepage: https://cran.r-project.org/package=SimVitD Description: CRAN Package 'SimVitD' (Simulation Tools for Planning Vitamin D Studies) Simulation tools for planning Vitamin D studies. Individual vitamin D status profiles are simulated, modelling population heterogeneity in trial arms. Exposures to infectious agents are generated, with infection depending on vitamin D status. Package: r-cran-sinaplot Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-sinaplot_1.1.0-1.ca2404.1_all.deb Size: 1156888 MD5sum: 7c491cdb263968de52b3c5b316dd2944 SHA1: 1ece9b2561d63e5dae849ac7266450bbc40f18fc SHA256: 6d2c997797fae50db7e72459e3f34b36def3172d16b6c5950ac18df2dfe14aac SHA512: 7ae5fcdfcc69aecf403a27b3e0be5df41a495082ef7cc39597d1fba939035ffda28b7484654ea4aa7e4a387d4fe33609d5c8ff4e30d0403f5f4326b708782025 Homepage: https://cran.r-project.org/package=sinaplot Description: CRAN Package 'sinaplot' (An Enhanced Chart for Simple and Truthful Representation ofSingle Observations over Multiple Classes) The sinaplot is a data visualization chart suitable for plotting any single variable in a multiclass data set. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class. Package: r-cran-sinar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-sinar_0.1.0-1.ca2404.1_all.deb Size: 38932 MD5sum: 50e81e2eadfb7cb2adb4ab245ebc9d6a SHA1: 364eefbebc24aa7cdd0aeecfecc1bdc5385df32d SHA256: 46131d54b0b0849c38661373937ad649a00787756d0cf6769b7e7d65037f2c58 SHA512: 53887fcfdf00511a1ad295bed472a8be796d5b7a38a8557eb7b2e7b18df3262ced14ab0e2597d655c74f26224d9f8c8b2a90bab122461ee34b8b1d28044fc963 Homepage: https://cran.r-project.org/package=sinar Description: CRAN Package 'sinar' (Conditional Least Squared (CLS) Method for the Model SINAR(1,1)) Implementation of the Conditional Least Square (CLS) estimates and its covariance matrix for the first-order spatial integer-valued autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012) . Package: r-cran-sindyr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrangements, r-cran-matrixstats, r-cran-igraph, r-cran-pracma Filename: pool/dists/noble/main/r-cran-sindyr_0.2.4-1.ca2404.1_all.deb Size: 43730 MD5sum: 920bf1b54c220fa9ed4253cfcd43210a SHA1: ca47c262af7f8f0888b7da3df026fd90ca3dbaf4 SHA256: ad9f2404ce9f91eb326056b95103dee4aa8bee0892747a5e736a6906c0ce5628 SHA512: 261e263e2fbf128052eb60008ae204dc9045b94bb196eafbda61723bfef2ad39d843bcecd608c0cb3599530fa22cb97a8cca9f4e7f0065532a73db765bd2a941 Homepage: https://cran.r-project.org/package=sindyr Description: CRAN Package 'sindyr' (Sparse Identification of Nonlinear Dynamics) This implements the Brunton et al (2016; PNAS ) sparse identification algorithm for finding ordinary differential equations for a measured system from raw data (SINDy). The package includes a set of additional tools for working with raw data, with an emphasis on cognitive science applications (Dale and Bhat, 2018 ). See for examples and updates. Package: r-cran-sinew Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstudioapi, r-cran-sos, r-cran-stringi, r-cran-yaml, r-cran-crayon, r-cran-cli, r-cran-rematch2 Suggests: r-cran-rcmdcheck, r-cran-git2r, r-cran-shiny, r-cran-miniui, r-cran-withr, r-cran-usethis, r-cran-fs, r-cran-details, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sinew_0.4.0-1.ca2404.1_all.deb Size: 246198 MD5sum: 06457a48a9bcd94d6bd2de136e19a2b5 SHA1: 9a8a3fca510b3acf86171eaef0033fbd925a764e SHA256: a1b6e491febe9544ac41fd7e64e078341d3ee9ef01a7cf5c487dbcedc744540a SHA512: ebe6a937a69f278e2561040ec38935bc36e156a4f0d30061af757758f2e03b237c81468ff0c85511c9c918193610015c822853be8dc0a8aced7552902d02a38a Homepage: https://cran.r-project.org/package=sinew Description: CRAN Package 'sinew' (Package Development Documentation and Namespace Management) Manage package documentation and namespaces from the command line. Programmatically attach namespaces in R and Rmd script, populates 'Roxygen2' skeletons with information scraped from within functions and populate the Imports field of the DESCRIPTION file. Package: r-cran-singcar Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cholwishart, r-cran-mass, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-lme4, r-cran-lmertest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-singcar_0.1.5-1.ca2404.1_all.deb Size: 231840 MD5sum: 00f202b8e7b7c74828344ff3f189f16d SHA1: 0986b9a31730c8c0a3e5c2338200c2207bf36b51 SHA256: ed65e59774195668d4a3bb60dba559b3d32916138ec244008737bb22165d9367 SHA512: 2d31884b68afc4c633bdd2c7fd16286c1c3306e8ecb77ae0007a722d2efbd4807ff8ca3cf3fce68ac67476e669c19fe7687f07408f18a117cec3f671eb170fbe Homepage: https://cran.r-project.org/package=singcar Description: CRAN Package 'singcar' (Comparing Single Cases to Small Samples) When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) , Crawford and Garthwaite (2005) , Crawford and Garthwaite (2007) and Crawford, Garthwaite and Ryan (2011) . The package is also equipped with power calculators for each method. Package: r-cran-singlearmmrct Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-singlearmmrct_0.1.1-1.ca2404.1_all.deb Size: 270380 MD5sum: e88ab82383e904b84029584847ce0f43 SHA1: 345c6dc983200330c351756981454daf4ece7519 SHA256: 8021d2d022b649f633bf440c730e61493f1616ef4340473ff3dd0bbe2bf9ce58 SHA512: 239521f4ed39d581e8237dece329d711344d356ea60dbc3fbc22fac416b8a42309c3aaf4a85e813d483f8cdb9f23e29bc98b3291890f2cf2c5db72916902f106 Homepage: https://cran.r-project.org/package=SingleArmMRCT Description: CRAN Package 'SingleArmMRCT' (Regional Consistency Probability for Single-Arm Multi-RegionalClinical Trials) Provides functions to calculate and visualise the Regional Consistency Probability (RCP) for single-arm multi-regional clinical trials (MRCTs) using the Effect Retention Approach (ERA). Six endpoint types are supported: continuous, binary, count (negative binomial), time-to-event via hazard ratio, milestone survival, and restricted mean survival time (RMST). For each endpoint, both a closed-form (or semi-analytical) solution and a Monte Carlo simulation approach are implemented. Two consistency evaluation methods are available: Method 1 (effect retention in Region 1 relative to the overall population) and Method 2 (simultaneous positive effect across all regions). Plotting functions generate faceted visualisations of RCP as a function of the regional allocation proportion, overlaying formula and simulation results for direct comparison. The methodology follows the Japanese MHLW guidelines for MRCTs. Abbreviations used: RCP (Regional Consistency Probability), MRCT (Multi-Regional Clinical Trial), RMST (Restricted Mean Survival Time), MHLW (Ministry of Health, Labour and Welfare). Package: r-cran-singlecasees Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-spelling, r-cran-shiny, r-cran-shinytest2, r-cran-stringr, r-cran-rvest, r-cran-ggplot2, r-cran-purrrlyr, r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-cleanrmd, r-cran-kendall, r-cran-kableextra, r-cran-covr, r-cran-readxl, r-cran-glue, r-cran-janitor, r-cran-rclipboard Filename: pool/dists/noble/main/r-cran-singlecasees_0.7.4-1.ca2404.1_all.deb Size: 295542 MD5sum: f89f7bc369f1ce915f3920335b123431 SHA1: 39b8cca8833f0f52b63097d83992f7b0d3a40b96 SHA256: aa0510c43ab428682c5b8b7e56b87e1286b2c89f34f9934af9d435e3baac83d6 SHA512: 0fc9987cc409f31c1bce3babdf8418ce9a8508e413372edc0f71529849d8f099cc0a2dd7fc9ec048151e8013a97860b9a3d4d2c4550634357e191dbb79f075b3 Homepage: https://cran.r-project.org/package=SingleCaseES Description: CRAN Package 'SingleCaseES' (A Calculator for Single-Case Effect Sizes) Provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) . Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions. Package: r-cran-singlecellcomplexheatmap Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-complexheatmap, r-cran-seurat, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-circlize, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-viridis, r-cran-devtools, r-cran-biocmanager, r-cran-ggsci, r-cran-seuratobject Filename: pool/dists/noble/main/r-cran-singlecellcomplexheatmap_0.1.2-1.ca2404.1_all.deb Size: 656684 MD5sum: d14be2dbd8f8ac0a949cf6cc5b604413 SHA1: 01d695f933efc05dfbbbc7e3a9fc6e6f37bbc59f SHA256: 25c82a4da95e02d86bff1c51856c39873b5ba6d865473970a7eb5f01454cd7e0 SHA512: b408eb8d64da9667bb99ce2d2db4118eb14ff7cf99d26064d33c281305d8d8cf9f89aaee8170a5e1b6cc24e8624a9f674ae5d1260226c2cb185f41be42085338 Homepage: https://cran.r-project.org/package=SingleCellComplexHeatMap Description: CRAN Package 'SingleCellComplexHeatMap' (Complex Heatmaps for Single Cell Expression Data with DualInformation Display) Creates complex heatmaps for single cell RNA-seq data that simultaneously display gene expression levels (as color intensity) and expression percentages (as circle sizes). Supports gene grouping, cell type annotations, and time point comparisons. Built on top of 'ComplexHeatmap' and integrates with 'Seurat' objects. For more details see Gu (2022) and Hao (2024) . Package: r-cran-singlecellhaystack Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-seuratobject, r-cran-cowplot, r-cran-wrswor, r-bioc-sparsematrixstats, r-bioc-complexheatmap, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-singlecellhaystack_1.0.3-1.ca2404.1_all.deb Size: 732896 MD5sum: e80298f525f39fe986a7440ff5bc72ed SHA1: 6d91fb3bc494c001eabc949edddf4a6bfaf7348e SHA256: 0f9ad74444b034b8443a74f213cbb24014c9d227e0f3ec4c7f8c66994f7c7214 SHA512: b5302c15ffd7397e4ff59870d811de21917fdc91e80a460a75c3d8a4a434a2092e2b6612794b969ea7b4a827e32f96c2f51d4fbc523941e277cec1b5dc2317ca Homepage: https://cran.r-project.org/package=singleCellHaystack Description: CRAN Package 'singleCellHaystack' (A Universal Differential Expression Prediction Tool forSingle-Cell and Spatial Genomics Data) One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) and our update Vandenbon and Diez (Scientific Reports, 2023) . Package: r-cran-singlecellstat Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-singlecellstat_0.3.1-1.ca2404.1_all.deb Size: 2595048 MD5sum: 3703b10e31ea3215c0903541290cce83 SHA1: ca67eba489d70d841c5e455a6dea201ef90947e2 SHA256: 0c25d8529e1ed1b35279a85d190dc523a996b673e393afd1d7569f5300dbcc2f SHA512: 9f7c83ed1eac502a9125bbe7188fe2117e5e5512a5c45f4f4f787013a9344d5eb7cc381f7a359f910be243a6583ba361cf5a9893877ca1dc3f1cabc0f851dda1 Homepage: https://cran.r-project.org/package=SingleCellStat Description: CRAN Package 'SingleCellStat' (A Toolkit for Statistical Analysis of Single-Cell Omics Data) A suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics). Package: r-cran-singlercapture Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2633 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lamw, r-cran-mathjaxr, r-cran-sandwich, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest, r-cran-covr, r-cran-vgam, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-singlercapture_1.1.0-1.ca2404.1_all.deb Size: 1909820 MD5sum: 0f7dc84a36a3482a77960f79432685a9 SHA1: c8a8951c1fe17aaf486855c9268cf56421c6fd14 SHA256: 90b155388e0dc8cb8582123b93845ff4e62b445c9142a928b95eab2cdd3f386c SHA512: b58f07e83ed14f583d50684a2eea53ee3b4845f774cefb49271148591ff673b2a33aff9fa509582734c4491fc3306c617ba599bbe122f3ae8942a70c53d4267f Homepage: https://cran.r-project.org/package=singleRcapture Description: CRAN Package 'singleRcapture' (Single-Source Capture-Recapture Models) Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's, Chao's and ratio-regression estimators. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) , Böhning and van der Heijden (2019) , Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) . Package: r-cran-singregkrig Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-randomforest, r-cran-gstat, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-singregkrig_0.1.0-1.ca2404.1_all.deb Size: 102600 MD5sum: b6cbea9f29c84700c6a43cff46f7776a SHA1: 5d867d9955275002023b6badfc1b25faf068537e SHA256: 3ab77c72193b62e13198e439425a717c48104fcff5743589d6069b8d6e44bbd0 SHA512: 4e379c7fbcfd8175f7a5b35017e1ee40bc31dba71af6ee22a9a91f7913b450606bb2f9029fb4552c6895e7888fcfb6eb06b929faa39c04f6b6f96d0b58db013d Homepage: https://cran.r-project.org/package=SingRegKrig Description: CRAN Package 'SingRegKrig' (Singularity Regression Kriging for Spatial Prediction) Implements the Singularity Regression Kriging ('SRK') model for spatial prediction by integrating covariate singularity feature construction, nonlinear trend estimation via random forest, and geostatistical interpolation of residuals using ordinary kriging. Singularity-based anomaly indices are computed from environmental covariates at multiple spatial scales to capture local multiscale heterogeneity and augment the random forest feature set for trend estimation. The resulting residuals are interpolated using ordinary kriging to generate final spatial predictions with uncertainty quantification. Tools for spatial block cross-validation, parameter sensitivity analysis, and diagnostic visualization are also provided. Methods are based on Ren, Song, Chen, and Yu (2026) , with singularity theory from Cheng (2012) and Cheng (2017) , random forest methodology from Breiman (2001) , and regression kriging framework from Hengl, Heuvelink, and Rossiter (2007) . Package: r-cran-siniar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-dt, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-siniar_0.1.0-1.ca2404.1_all.deb Size: 206888 MD5sum: 071bb44f3283bb60e3485d6966ba0e76 SHA1: bf8fb898397bd7d0ebdea2673b74dbd3dcea3dcc SHA256: 90a273bf41e94ed63dd92af87776756ccf6e9cf5378adb1d9f7bcfe1b8e3bf29 SHA512: 40f7815d066cb664a40d57b5fc72f9dcd6a595e57f3abb598ce48d8c7a520b010f1924bbd3a7734870be1e8854a323060233eeeb800d110c3f0ee9745ad36f59 Homepage: https://cran.r-project.org/package=siniaR Description: CRAN Package 'siniaR' (Access to Peru's Environmental Statistics ('SINIA' / 'MINAM')) Provides programmatic access to official environmental statistics and data of Peru from the National Environmental Information System ('SINIA', ), Ministry of the Environment ('MINAM'). Includes indicator catalogs, technical metadata sheets ('fichas técnicas'), and structured historical series for 'R'. Package: r-cran-sinib Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sinib_1.0.0-1.ca2404.1_all.deb Size: 25212 MD5sum: 8c95f1a1fbc9446b1e50a81bf4fce619 SHA1: fbae88df7442baf88a763535514d9ccc1f9360a9 SHA256: 8f1090cc95e6ea915ad3a0fb1592ca7a56d558678b0ab8c13e6e67e166e66719 SHA512: 2a97b9c5102b3f53dd1ce788768e3af881f4e09b689b541b9326d152f97ba5dfb0d890a6cb344577716dfed68d8c24eb01b71dabfb8fbf8de56b645932a07c2f Homepage: https://cran.r-project.org/package=sinib Description: CRAN Package 'sinib' (Sum of Independent Non-Identical Binomial Random Variables) Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \code{size} and \code{prob}. Package: r-cran-sinrelef.ld Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs Filename: pool/dists/noble/main/r-cran-sinrelef.ld_1.1.0-1.ca2404.1_all.deb Size: 86562 MD5sum: 8cabb2cf1ccf5a8b5e8922bf0844200d SHA1: 17c82f5e16ace8102c40aada30a44d036ac03a65 SHA256: 8ac642ef95495270902d4ca699dcacf806ac0717b66beac0d73ab2ed518a77c6 SHA512: b05fb1ff13eb57685cf9646ab228db2ecf54e5fd3cd53eac5a7d7cc476b3e7808b109f9dde5103b4c81239657c333af0451c4d5358e8670b372a343e9702b7c8 Homepage: https://cran.r-project.org/package=SINRELEF.LD Description: CRAN Package 'SINRELEF.LD' (Reliability and Relative Efficiency in Locally-DependentMeasures) Implements an approach aimed at assessing the accuracy and effectiveness of raw scores obtained in scales that contain locally dependent items. The program uses as input the calibration (structural) item estimates obtained from fitting extended unidimensional factor-analytic solutions in which the existing local dependencies are included. Measures of reliability (Omega) and information are proposed at three levels: (a) total score, (b) bivariate-doublet, and (c) item-by-item deletion, and are compared to those that would be obtained if all the items had been locally independent. All the implemented procedures can be obtained from: (a) linear factor-analytic solutions in which the item scores are treated as approximately continuous, and (b) non-linear solutions in which the item scores are treated as ordered-categorical. A detailed guide can be obtained at the following url. Package: r-cran-sint Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-knitr, r-cran-network, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sint_0.1.0-1.ca2404.1_all.deb Size: 92818 MD5sum: 298db05818728df8925734d90438a34e SHA1: 6bbb3505c22bccd1d1a10f955321ee38f6398be6 SHA256: 2e13f59f1a4677cd3598cf18f119f323088d7e90269663b19ef23fb7c9118c71 SHA512: 554607260609cc16efe436bbb0f606320950af87dafbde0e27aea1a09bb850ab936d41d7d246dfd21ed001dcf5c945b6d72ea62ac123f69289316ef5d909a8e2 Homepage: https://cran.r-project.org/package=SINT Description: CRAN Package 'SINT' (Simulation and Analysis of Social Influence Network Models) Tools for specifying, analyzing and simulating models of social influence network theory based on the Friedkin-Johnsen model, Friedkin and Johnsen (1990) , which includes the consensus model of DeGroot (1974) as a special case. Equilibrium opinions, total influence matrices and convergence diagnostics are computed in closed form, also for signed networks with antagonistic ties, Altafini (2013) . Simulations allow influence weights and susceptibilities to depend on time and on the state of the system, and can couple latent opinions with manifest responses through logistic or threshold response functions, whose results are aggregated into collective outcomes by quota rules. Package: r-cran-sip Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sip_0.1.0-1.ca2404.1_all.deb Size: 2444768 MD5sum: f27b0e5c6f0689be9071cf465af386aa SHA1: d8cdaf2df7de0341cd32c296810ab40047d6b3d8 SHA256: 7b9dd207fc270441f007fb4ebb5582bd474106b4f08cfd41d4fe04a99ba43035 SHA512: acbd80443d4b97eb4d2ac84bf7269c131639885fc7ae556fb2f9c1d8e02c1e50b5f169eef313a443d463bc39685deafb18d0503715e32b26fbc8c97aed9945f7 Homepage: https://cran.r-project.org/package=SIP Description: CRAN Package 'SIP' (Single-Iteration Permutation for Large-Scale Biobank Data) A single, phenome-wide permutation of large-scale biobank data. When a large number of phenotypes are analyzed in parallel, a single permutation across all phenotypes followed by genetic association analyses of the permuted data enables estimation of false discovery rates (FDRs) across the phenome. These FDR estimates provide a significance criterion for interpreting genetic associations in a biobank context. For the basic permutation of unrelated samples, this package takes a sample-by-variable file with ID, genotypic covariates, phenotypic covariates, and phenotypes as input. For data with related samples, it also takes a file with sample pair-wise identity-by-descent information. The function outputs a permuted sample-by-variable file ready for genome-wide association analysis. See Annis et al. (2021) for details. 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More information can be obtained from the official website . Package: r-cran-sipetool Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-convolutioner Filename: pool/dists/noble/main/r-cran-sipetool_0.1.0-1.ca2404.1_all.deb Size: 236502 MD5sum: a4ff1d0ea4ea981ba940193304002ec7 SHA1: 92b2365060d0c338f940f80556da27414d03d515 SHA256: 28f0a2cea066b78817cc6827ebe8b6bf06c0903fb589d37263efbbed397c5c15 SHA512: f6c57c41da8013daece22846bc42bde37c2b92dabd2ae07b9dfbb09b48fc5dc22339ed9dfdf44acae451feadf4ef722a644065a5d22c65bd2a15fa8f3bd2e5d6 Homepage: https://cran.r-project.org/package=SIPETool Description: CRAN Package 'SIPETool' (SIFT-MS and CPET Data Processor) Processor for selected ion flow tube mass spectrometer (SIFT-MS) output file from breath analysis. 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The FAO Penmann-Monteith equation to calculate evapotranspiration is also included. 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For theoretical reference see Faliva (1992) and Faliva and Zoia (1994) . 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Package: r-cran-sirthresholded Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-strucchange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sirthresholded_1.0.2-1.ca2404.1_all.deb Size: 666126 MD5sum: ebc2867c4581545a7e607f0edb63de37 SHA1: dc93073475524fcc58cb3d4dce5e83593687784f SHA256: fa014a05fdc70b71d9e1e388029a1e626dc9e8b1e690309b527c169c8c215f0f SHA512: d545d761972967da636ba5f627eaabf541421d1b65f490841049873c77bbb67ca6117d195414b7a985a26df40f9354afd8a0d96207854b2b1a719cab3f16ae21 Homepage: https://cran.r-project.org/package=SIRthresholded Description: CRAN Package 'SIRthresholded' (Sliced Inverse Regression with Thresholding) Implements a thresholded version of the Sliced Inverse Regression method (Li, K. C. (1991) ), which allows to do variable selection. Package: r-cran-sis Architecture: all Version: 0.8-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2580 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-survival Filename: pool/dists/noble/main/r-cran-sis_0.8-8-1.ca2404.1_all.deb Size: 2606564 MD5sum: f82fe5a135561edd9301e4c30b75a574 SHA1: fe15ecb56a329a910ba019b66718abb881916c9e SHA256: 5f0bde6a8a8b9521072860653c9cc6058c66b6ec6662e558dadd406383b6c89a SHA512: 57ca7aca649dd3525283387bb07e100a9f80a27d02e435ca161fe95525501af5cb31eeaf9cec8e7dccf2c2705f211a50bcf49e448e65138d80912c48ff6af621 Homepage: https://cran.r-project.org/package=SIS Description: CRAN Package 'SIS' (Sure Independence Screening) Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)). Package: r-cran-sisal Architecture: all Version: 0.49-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-lattice, r-cran-mgcv, r-cran-digest, r-cran-r.matlab, r-cran-r.methodss3 Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sisal_0.49-1.ca2404.1_all.deb Size: 639368 MD5sum: bb0696335b3732895451e5006230b49c SHA1: 1c7e6fc7923723fd07705b96296fcfb80174f6d6 SHA256: 2f450acf0a449e348704c5900b2d4de879b08e68495f6e066e896a2c7436b3db SHA512: cdd0826e70fe119748818966d16ee6c55520cfa16f7877c8cdc26a05436926df9f9b83040ef4132b50c3c3a97049bdafbc91cff02a9bf0a3153f75372a5ba6b0 Homepage: https://cran.r-project.org/package=sisal Description: CRAN Package 'sisal' (Sequential Input Selection Algorithm) Implements the SISAL algorithm by Tikka and Hollmén. 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Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in . 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(2010)) This data package contains four datasets of quantitative PCR (qPCR) amplification curves that were used as supplementary data in the research article by Sisti et al. (2010), . The primary dataset comprises a ten-fold dilution series spanning copy numbers from 3.14 × 10^7 to 3.14 × 10^2, with twelve replicates per concentration. These samples are based on a pGEM-T Promega plasmid containing a 104 bp fragment of the mitochondrial gene NADH dehydrogenase 1 (MT-ND1), amplified using the ND1/ND2 primer pair. The remaining three datasets contain qPCR results in the presence of specific PCR inhibitors: tannic acid, immunoglobulin G (IgG), and quercetin, respectively, to assess their effects on the amplification process. These datasets are useful for researchers interested in PCR kinetics. The original raw data file is available as Additional File 1: . 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Package: r-cran-sitar Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-splines2, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sitar_1.5.0-1.ca2404.1_all.deb Size: 792598 MD5sum: e3c4c5b68f85fbf40ef6c6c1c84fddb4 SHA1: 278053315ed1ca4e4449b611a2f7d73ae6725d5d SHA256: 3803fa9f2eb7f20640eac2f46102f2a848f607b9d29dec6924acc29c089906d7 SHA512: bba5845e8731971c839954404cdb672fcd42996534f0504e537184e8a008dbf21bab582e45a6e14691aae58db0132490b5aed5a32c00d6beb67b9c74bc701be7 Homepage: https://cran.r-project.org/package=sitar Description: CRAN Package 'sitar' (Super Imposition by Translation and Rotation Growth CurveAnalysis) Functions for fitting and plotting SITAR (Super Imposition by Translation And Rotation) growth curve models. 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The procedure aims to reduce bias (and/or loss of external validity) with respect to the target population. In selecting units and sub-units, 'sitepickR' uses the cube method developed by 'Deville & Tillé', (2004) and described in Tillé (2011) . The cube method is a probability sampling method that is designed to satisfy criteria for balance between the sample and the population. Recent research has shown that this method performs well in simulations for studies of educational programs (see Fay & Olsen (2021, under review). To implement the cube method, 'sitepickR' uses the sampling R package . To implement statistical matching, 'sitepickR' uses the 'MatchIt' R package . 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Package: r-cran-sitools Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sitools_1.4-1.ca2404.1_all.deb Size: 16552 MD5sum: 7bf032db96a10b0a614d6780ec926940 SHA1: 531b7010c106477c6d0f9a6072f0c277cc932803 SHA256: a03a4ea3649108397ca30ef86ff6e4d3eb701a5bd2b3c8cce5aea1c441c6005d SHA512: f4ebf43f575cc5280d1fdfc1c3ef1d93cbf74ebe8c12035d2e9961765ef05ebc5601724bd7df0fb4ac8d63b615a8097e2666e27240c9cd7be7cd1b38b2a669f8 Homepage: https://cran.r-project.org/package=sitools Description: CRAN Package 'sitools' (Format a number to a string with SI prefix) Format a number (or a list of numbers) to a string (or a list of strings) with SI prefix. Use SI prefixes as constants like (4 * milli)^2 Package: r-cran-sitree Architecture: all Version: 0.1-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2409 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sitree_0.1-15-1.ca2404.1_all.deb Size: 1088526 MD5sum: 777a9dd6c0ff5fde681a66b2c6004fc9 SHA1: aafafb10dbd2b5a834d9fab1f721a14fe33c64ac SHA256: 0386ba6cb5a5ff951fee3502547ea3b318348e745f4204944fd53ed6c77e1702 SHA512: 7904c0016cfcaff0d9a0a37cfc5b233424f647e94dfaac3b0ba80696354f775794384938b66cb0dd4b51a2158ba3aa836670e9eea297b6d041f49913de2b2a9d Homepage: https://cran.r-project.org/package=sitree Description: CRAN Package 'sitree' (Single Tree Simulator) Framework to build an individual tree simulator. Package: r-cran-sitreee Architecture: all Version: 0.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-sitree Filename: pool/dists/noble/main/r-cran-sitreee_0.0-10-1.ca2404.1_all.deb Size: 70674 MD5sum: 061b051f2bbc1c875ac50e12cbfc9fe2 SHA1: e62603256d66d1ed5d2ba90f99c566ef00bad7cb SHA256: 1d00776dce335b8096b5951d0e5b568975d8b856a9027d7165d7f1b0de2048d7 SHA512: 0c461740f82bd6e3acb8306775a41f4ccc3707ac60da8f17eb334d408474cf8aa8bf23ca2a277c3bcdbd4790fb767a139d69a002b2d04b65c2f8d5a072ac0ffa Homepage: https://cran.r-project.org/package=sitreeE Description: CRAN Package 'sitreeE' (Sitree Extensions) Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions. Package: r-cran-sitrep Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-apyramid, r-cran-epidict, r-cran-epikit, r-cran-epitabulate Suggests: r-cran-anthro, r-cran-binom, r-cran-broom, r-cran-clipr, r-cran-covr, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggspatial, r-cran-glue, r-cran-gtsummary, r-cran-here, r-cran-janitor, r-cran-knitr, r-cran-labelled, r-cran-lubridate, r-cran-matchmaker, r-cran-pacman, r-cran-parsedate, r-cran-patchwork, r-cran-purrr, r-cran-rio, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-sessioninfo, r-cran-sf, r-cran-slider, r-cran-srvyr, r-cran-stringr, r-cran-summarytools, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tsibble, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-sitrep_0.4.1-1.ca2404.1_all.deb Size: 951990 MD5sum: f07433898a75163ed027781f45a59536 SHA1: fdcfebc878605e580fa3d3f7844753733e18c6a1 SHA256: 0c0582bcf3caeb8518e27857fe789375e65396b4e5a3ac3fefbb5d68b7aac282 SHA512: 35215c4a8d1923d948bc24e77dd0c4b2db65e13b7f89256d6129782a1a9271b0b81035120aa092ea52188d6e90b8d7690bb2f4bc7337372e6bc27be60dcaad4d Homepage: https://cran.r-project.org/package=sitrep Description: CRAN Package 'sitrep' (Report Templates and Helper Functions for Applied Epidemiology) A meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. This package provides report templates and automatically loads companion packages, including 'epitabulate' (for epidemiological tables), 'epidict' (for data dictionaries), 'epikit' (for epidemiological utilities), and 'apyramid' (for age-sex pyramids). Simply load 'sitrep' to access all functions from the ecosystem. Package: r-cran-sivirep Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-epitrix, r-cran-ggplot2, r-cran-httr2, r-cran-kableextra, r-cran-readxl, r-cran-rlang, r-cran-sf, r-cran-showtext, r-cran-stringr, r-cran-sysfonts, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sivirep_1.0.1-1.ca2404.1_all.deb Size: 1558132 MD5sum: 1715e2225d3a620c483d12821b3f8aba SHA1: 3459840b2776bc7a6d3841bbc1431fbf316020c5 SHA256: 66985ddfcc69f0c5dd9293510191420a9d13e783a1bbc642626988fadcf0668b SHA512: a90eee8bafb4ec33f3aeae2a1a47526e1eb00fc70fe98372c2ac6bcce1897d1de21fbaca538d22d4ea119bb318f341831b07ebfdec413e44af25beabd0ddabc5 Homepage: https://cran.r-project.org/package=sivirep Description: CRAN Package 'sivirep' (Data Wrangling and Automated Reports from 'SIVIGILA' Source) Data wrangling, pre-processing, and generating automated reports from Colombia's epidemiological surveillance system, 'SIVIGILA' . It provides a customizable R Markdown template for analysis and automatic generation of epidemiological reports that can be adapted to local, regional, and national contexts. This tool offers a standardized and reproducible workflow that helps to reduce manual labor and potential errors in report generation, improving their efficiency and consistency. Package: r-cran-sivmethod Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sivmethod_0.1.0-1.ca2404.1_all.deb Size: 46630 MD5sum: e75626e0eb0758efcd2e3dadda27f6a4 SHA1: e7b2bd1f11c4e01e787016ac1b97ef6779289686 SHA256: f3eeccfd81b0ca62a1b3ac4bbaac9e81bae7291bd14fb70589d0cf0a298cbfe6 SHA512: 66d2317369df904ceb57398cf52cd91f29d37c08fd886918e8c0bee33668bc77f01d31362f94a01fcc87bd167e1aa9c569b0a8718218c5208b07050681200466 Homepage: https://cran.r-project.org/package=SIVMethod Description: CRAN Package 'SIVMethod' (Identification, Estimation and Inference Based on StructuralError Projection) Estimation and inference for regression models with endogenous regressors using a semiparametric projection approach. Instrumental variables are constructed internally from observed regressors by projecting out a space of basis functions used to represent the conditional mean of the structural error. A least absolute shrinkage and selection operator (LASSO) procedure selects basis functions for the projection. Tools are provided for simulation studies and empirical applications. The methods are based on Dong, Gao, Linton and Peng (2026) . Package: r-cran-sivs Architecture: all Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-proc, r-cran-varhandle Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sivs_0.2.11-1.ca2404.1_all.deb Size: 204146 MD5sum: 78305d48c9b2dad60ad6997280600456 SHA1: b3a3c55d9cb7473c59928f35845e3aa5ab7bb0f0 SHA256: 11d8df5f1c6e19cbf9ba516b71350a8af3098f9837ae02560dfcc4e5b0862781 SHA512: bae8e6693c3ba6ff421fdd1df54d616bfe3408040767bd274aefd23d78ee704ccd5ce191427addd099eef08eba539c4dad7882bdfa7273cf54e56aec8a67779a Homepage: https://cran.r-project.org/package=sivs Description: CRAN Package 'sivs' (Stable Iterative Variable Selection) An iterative feature selection method that internally utilizes various Machine Learning methods that have embedded feature reduction in order to shrink down the feature space into a small and yet robust set. Package: r-cran-sixsigma Architecture: all Version: 0.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-ggplot2, r-cran-reshape2, r-cran-nortest, r-cran-e1071, r-cran-scales, r-cran-testthat, r-cran-xtable Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-sixsigma_0.11.1-1.ca2404.1_all.deb Size: 605936 MD5sum: be6f9794501a1b84dbe5c6c7bb3ce141 SHA1: a7872b4c9262edfe16edcd78d5eba3625406ddb1 SHA256: d9a5d1be2ab7d2f515ee2d994b03e44439b684e7277af7b6a98cdb9788b6baa0 SHA512: 7d9979297a06b06f231a4a6202ebb6d6fc230d03cc01af4de4a5d7f3506c5b27fccb4cbe7e9fbb177a87d3e068a8e7ba99d7dbce379df3565a9639cb8831f7a8 Homepage: https://cran.r-project.org/package=SixSigma Description: CRAN Package 'SixSigma' (Six Sigma Tools for Quality Control and Improvement) Functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package. Package: r-cran-sixtyfour Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-paws.common, r-cran-paws.security.identity, r-cran-paws.storage, r-cran-paws.compute, r-cran-paws.database, r-cran-paws.cost.management, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-fs, r-cran-s3fs, r-cran-cli, r-cran-glue, r-cran-memoise, r-cran-uuid, r-cran-jsonlite, r-cran-curl, r-cran-tidyr Suggests: r-cran-knitr, r-cran-withr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-dbi, r-cran-rpostgres, r-cran-rmariadb, r-cran-ggplot2, r-cran-lubridate, r-cran-testthat, r-cran-vcr, r-cran-webmockr, r-cran-clipr, r-cran-ipaddress Filename: pool/dists/noble/main/r-cran-sixtyfour_0.2.4-1.ca2404.1_all.deb Size: 681760 MD5sum: 1b616ee9cd6557bafccb57b42eee0c4f SHA1: 98f4a247a48c0d3d1ac65d3719024d518ef53c96 SHA256: c95dd502c848c89b05c2b60b554248b8f067c08ae692339a23caf7e8298ff011 SHA512: d2569c17f3ab69a03ca5183ddc08a5eaa7e06a29c66b7b70ffa944ab79c37e7877486831ebd1900639a65094f73d2f234ca6b1bb1b604b2d7ee3f487de7262c2 Homepage: https://cran.r-project.org/package=sixtyfour Description: CRAN Package 'sixtyfour' (Humane Interface to Amazon Web Services) An opinionated interface to Amazon Web Services , with functions for interacting with 'IAM' (Identity and Access Management), 'S3' (Simple Storage Service), 'RDS' (Relational Data Service), Redshift, and Billing. Lower level functions ('aws_' prefix) are for do it yourself workflows, while higher level functions ('six_' prefix) automate common tasks. Package: r-cran-sizeestimation Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-msm Filename: pool/dists/noble/main/r-cran-sizeestimation_1.1.1-1.ca2404.1_all.deb Size: 37532 MD5sum: 5091f88810308d676f653fddc367f5fa SHA1: 84edfb19ea7e4407e8215c9c5a2104375a0835fa SHA256: bac193a83c8bad03f448437c66f3f0ef79f1cc618650ec717c544480c2ae3ca5 SHA512: 1bc507c63c7480e13d0b038d5f4d0f83d6ba985c0349c5f06d6476db8b9b31b45b2e750b46e6ad6319b91b2eabab37951cf42fcb3b5458cb8dd2dcb5a5044bf6 Homepage: https://cran.r-project.org/package=SizeEstimation Description: CRAN Package 'SizeEstimation' (Estimating the Sizes of Populations at Risk of HIV Infectionfrom Multiple Data Sources Using a Bayesian Hierarchical Model) This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion. Package: r-cran-sizemat Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mcmcpack, r-cran-matrixstats, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sizemat_1.2.0-1.ca2404.1_all.deb Size: 409358 MD5sum: 797279bd4dc3467f2f6c0c2ac3f669b8 SHA1: 84e94d867997c17b28cd74414f2bad3f0977a4f6 SHA256: 72c3825646f566a7eb20bcc4283e4e2687929ae3d99196daad795e4069e9a5c8 SHA512: da1ddc4525ea9ad34786bb3e056676fb4bf70ed8444cbdef18ed98402ae8caf92c11b80e34aa178f5a8c094d41388e97acf37f69b1d7929df52b881a9aa557a9 Homepage: https://cran.r-project.org/package=sizeMat Description: CRAN Package 'sizeMat' (Estimate Size at Sexual Maturity) Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots. Optional ggplot-style graphics are available for selected plot methods. Package: r-cran-sizer Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-sizer_0.1-8-1.ca2404.1_all.deb Size: 81824 MD5sum: 04b290729c94620418dd718f15b45abe SHA1: 5998b9bfb1c423997bf501411fce1fcfc6c696ce SHA256: deb3d44c6729e9c2618bc4bae2907ba6d3d2498cca2fea0b948a91343a374dfd SHA512: e818192e450a32bae318a45a699ef42cc3a2bf0fe9474fba79d7f901d9e268dbc8cebc7e2ad78558fde2c7bd8203ef2cafcdce1b3dcd8099963717537e0ef334 Homepage: https://cran.r-project.org/package=SiZer Description: CRAN Package 'SiZer' (Significant Zero Crossings) Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths. Package: r-cran-sjdbc Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-sjdbc_1.6.1-1.ca2404.1_all.deb Size: 65962 MD5sum: 762c44ac1285929c3531ed10556f9252 SHA1: 6315f9990fb17a76215d99423b16f9d76a2edcf1 SHA256: 99f715d21d948e38778964c93e722b1fef359222d3d22442286daeadbd1e7470 SHA512: 77a40de7b08b7f876378216fdf93d1f1abdb5f06e0374e1ce5220ee647d67b378e1aa8b1bce2b8f8c46628f5a832e9f8f4f0ef817a101b26511d13001bb7e249 Homepage: https://cran.r-project.org/package=sjdbc Description: CRAN Package 'sjdbc' (JDBC Driver Interface) Provides a database-independent JDBC interface. Package: r-cran-sjlabelled Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-insight, r-cran-datawizard Suggests: r-cran-dplyr, r-cran-haven, r-cran-magrittr, r-cran-sjmisc, r-cran-sjplot, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-snakecase, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjlabelled_1.2.0-1.ca2404.1_all.deb Size: 293536 MD5sum: 5bc6907328226f896fc4c4b0898bcd7e SHA1: df2fa6e20901e91230e07a699a5762af6d98245b SHA256: 7428c67ba3c0f55a699f8caf9244ec22e7c56d8f4305bd8b01b352afb0633219 SHA512: a2d60e88abd13851e33ac8d0f54714d32e9007f258713d04699f6a2a5be93daff65f6b5dc66e6237e2b69f42bb7990d043d00d367e4b15cbc09fcdc671c29bd2 Homepage: https://cran.r-project.org/package=sjlabelled Description: CRAN Package 'sjlabelled' (Labelled Data Utility Functions) Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values. Package: r-cran-sjmisc Architecture: all Version: 2.8.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-insight, r-cran-datawizard, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-sjlabelled, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-haven, r-cran-mice, r-cran-nnet, r-cran-sjplot, r-cran-sjstats, r-cran-knitr, r-cran-rmarkdown, r-cran-stringdist, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-sjmisc_2.8.11-1.ca2404.1_all.deb Size: 516036 MD5sum: 0a9484be0841c299ed4c25d7582f2367 SHA1: 19fd97f820d02cc9b59bf7b0bae00b81dea28807 SHA256: f0ee33befe3784dc115d0f19270872aeefe321850f855c8b442d0d05e28c3131 SHA512: 84e9bfbeedd2be2c91ff8c01b2e6741c548e051dc2f68745e711ca08335fb967d867c24e258656058a3de1639fc5866b3ff6aae3c66a8e501ff5c9d647c98158 Homepage: https://cran.r-project.org/package=sjmisc Description: CRAN Package 'sjmisc' (Data and Variable Transformation Functions) Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow. Package: r-cran-sjplot Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-dplyr, r-cran-ggeffects, r-cran-ggplot2, r-cran-knitr, r-cran-insight, r-cran-parameters, r-cran-performance, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-sjlabelled, r-cran-sjmisc, r-cran-sjstats, r-cran-tidyr Suggests: r-cran-brms, r-cran-car, r-cran-clubsandwich, r-cran-cluster, r-cran-cowplot, r-cran-effects, r-cran-haven, r-cran-gparotation, r-cran-ggrepel, r-cran-glmmtmb, r-cran-gridextra, r-cran-ggridges, r-cran-httr, r-cran-lme4, r-cran-mass, r-cran-nfactors, r-cran-pscl, r-cran-psych, r-cran-rmarkdown, r-cran-rstanarm, r-cran-sandwich, r-cran-survey, r-cran-tmb, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjplot_2.9.0-1.ca2404.1_all.deb Size: 1397940 MD5sum: 2f2957e77498958245881e991ada7165 SHA1: 527d5d1d6fdd443933aac53d757a317daebb914f SHA256: 07ed58bec316f8f4ba2ebd9440c3a0a07ac0c68d64beb2ee8ec0bf895a804ef3 SHA512: 154e7f9e220e1633cf496e6df099c724f2b0b48e4c9554e5300809c5d79b51cf4d2b5311f1666f9b3022fad1f0cfbdfb06e3971643053fa04459940c999ee5e8 Homepage: https://cran.r-project.org/package=sjPlot Description: CRAN Package 'sjPlot' (Data Visualization for Statistics in Social Science) Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data. Package: r-cran-sjsdm Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-mvtnorm, r-cran-rstudioapi, r-cran-abind, r-cran-metrics, r-cran-mgcv, r-cran-cli, r-cran-crayon, r-cran-ggplot2, r-cran-checkmate, r-cran-mathjaxr, r-cran-beeswarm, r-cran-qgam, r-cran-scales, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-iml, r-cran-fields Filename: pool/dists/noble/main/r-cran-sjsdm_1.0.7-1.ca2404.1_all.deb Size: 1084536 MD5sum: e8c4d06725291bb275acbac4140fe77a SHA1: a0bcc581c7d3ac57ddd554a26c9d0d162311b557 SHA256: cd7e729ec89cfa8d708087a7d003648c6034abd0494d4076b84b1c2b861179d4 SHA512: 4df082fdd264bf4e6db5070dfc59e6c12b2919c5e85f785e99ea279990141aa857edee23e9ad988592859eae8c6f1c718a1c7b88e7141fba9d614ff50e109b34 Homepage: https://cran.r-project.org/package=sjSDM Description: CRAN Package 'sjSDM' (Scalable Joint Species Distribution Modeling) A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., . The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) , scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs. Package: r-cran-sjstats Architecture: all Version: 0.19.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-datawizard, r-cran-effectsize, r-cran-insight, r-cran-parameters, r-cran-performance Suggests: r-cran-brms, r-cran-car, r-cran-coin, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-pscl, r-cran-pwr, r-cran-survey, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjstats_0.19.1-1.ca2404.1_all.deb Size: 376686 MD5sum: c600c3d40cb54c9349d05f6d4b01141a SHA1: 55935093883e77a6de35ef5cabd58bb316499043 SHA256: 1fb4e998771d334fa8fdc9acc1f24f265a6fab1e9646ec8cd1406afe0fd9f1a7 SHA512: 942f254eec02a2d02a55a5b5fb2cccd13c303e60713a11c355ab7f03cce2d37cdda1b9664eee941c0f458bd8f05e4d698bcc07e6055d3f1c117ff45175d583da Homepage: https://cran.r-project.org/package=sjstats Description: CRAN Package 'sjstats' (Collection of Convenient Functions for Common StatisticalComputations) Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more. Package: r-cran-sjtable2df Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-kableextra, r-cran-rlang, r-cran-rvest, r-cran-xml2 Suggests: r-cran-lintr, r-cran-lme4, r-cran-mlbench, r-cran-quarto, r-cran-sjplot, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjtable2df_0.0.6-1.ca2404.1_all.deb Size: 37786 MD5sum: 7dfed265d0422af51d0e9763055d8e0e SHA1: a7fcbf392cff2c9fe2e1e4f527a071ccc57ac2a6 SHA256: 843c81639a81a56cec918bb8e18027001cdda3dc3bfc5ee89759c88fa2cef6d9 SHA512: c782f810b74de8e2c244bf832726f9c0797c18c9fd2c07a155d4f51176265c721e250c8e099bd9bb3b52671effee13ef5e6f0aeed231a16a0f942bd6920c7ccd Homepage: https://cran.r-project.org/package=sjtable2df Description: CRAN Package 'sjtable2df' (Convert 'sjPlot' HTML-Tables to R 'data.frame') A small set of helper functions to convert 'sjPlot' HTML-tables to R data.frame objects / knitr::kable-tables. Package: r-cran-skater Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-purrr, r-cran-kinship2, r-cran-corrr, r-cran-tibble, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-skater_0.2.0-1.ca2404.1_all.deb Size: 455364 MD5sum: 485bee43041ed1c38a28e137dedfdc82 SHA1: bdc39e4d19dc71beeeec8f6e5be48a4cd9de36da SHA256: 27f3fe1be7da931dcb9dff1803ee01448f598425519551d9a5ac5b941b279efe SHA512: 1360b4aa2236b88bf707e5b45e16ed0243836419349a48bd227f219d8b0d720e90b62a47fafea061c3ba7f3a1bf4381ff2612261082e1016bc1bdda986a6d76c Homepage: https://cran.r-project.org/package=skater Description: CRAN Package 'skater' (Utilities for SNP-Based Kinship Analysis) Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The 'skater' package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" . Package: r-cran-skbd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny Suggests: r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-skbd_0.1.1-1.ca2404.1_all.deb Size: 140216 MD5sum: 0726fbb4d66c76960b40b7f60d3ba4d9 SHA1: beb3216af81912d268f13a3b69e61c75ad067260 SHA256: a28f83032446346acab607c14bf0d35a0c5642603691c2ddf66d0b6219993ef2 SHA512: 738c23f1a6d92a03a6d9ac33d41ff38f1a4561288f875d1d62e520f5c8ceeed0444a217086a1b1bbe7bf4e04682b82672833c116bd17df6833b5eb2ced494f7e Homepage: https://cran.r-project.org/package=SKBD Description: CRAN Package 'SKBD' (Shared Keyboard Designs for Phase I Dose-Finding Trials) Implements the shared keyboard design (SKBD) for model-assisted phase I dose-finding, including decision-boundary construction, operating-characteristic simulation, and extensions for dose insertion and time-to-event settings. The package also provides an interactive Shiny interface for trial-planning workflows. For more details, see Zhao, Shi, and Xu (2026) . Package: r-cran-skedastic Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-broom, r-cran-pracma, r-cran-compquadform, r-cran-mass, r-cran-quadprog, r-cran-inflection, r-cran-rfast, r-cran-caret, r-cran-matrix, r-cran-quadprogxt, r-cran-slam, r-cran-roi, r-cran-osqp, r-cran-mgcv, r-cran-roi.plugin.qpoases Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-lmtest, r-cran-car, r-cran-tseries, r-cran-tibble, r-cran-testthat, r-cran-mlbench, r-cran-expm, r-cran-arrangements, r-cran-quantreg, r-cran-gmp, r-cran-rmpfr, r-cran-cubature, r-cran-mvtnorm, r-cran-lmboot, r-cran-sandwich, r-cran-cmna Filename: pool/dists/noble/main/r-cran-skedastic_2.0.3-1.ca2404.1_all.deb Size: 4184452 MD5sum: 964b653e06f80cb9ab2ca9802a3a194a SHA1: 8a7e6601200061f64a5082fbcbbd24eca5731229 SHA256: 751f596f8fee53bbcfc785a84899f750b91c71310e4b17512366e66096baf02c SHA512: 69872e6d7b427d061b974bc016f1faa472706abc76550bf5a65d3477aaf929f63892f6e8b7c515253234b99828d2527626f41d632ef606bea7c114dc7a37d193 Homepage: https://cran.r-project.org/package=skedastic Description: CRAN Package 'skedastic' (Handling Heteroskedasticity in the Linear Regression Model) Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) . The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) . The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) . Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) . A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) , Ramsey's (1969) BAMSET Test , the tests of Bickel (1978) , Breusch and Pagan (1979) with and without the modification proposed by Koenker (1981) , Carapeto and Holt (2003) , Cook and Weisberg (1983) (including their graphical methods), Diblasi and Bowman (1997) , Dufour, Khalaf, Bernard, and Genest (2004) , Evans and King (1985) and Evans and King (1988) , Glejser (1969) as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) , Goldfeld and Quandt (1965) , Harrison and McCabe (1979) , Harvey (1976) , Honda (1989) , Horn (1981) , Li and Yao (2019) with and without the modification of Bai, Pan, and Yin (2016) , Rackauskas and Zuokas (2007) , Simonoff and Tsai (1994) with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) , Szroeter (1978) , Verbyla (1993) , White (1980) , Wilcox and Keselman (2006) , Yuce (2008) , and Zhou, Song, and Thompson (2015) . Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) , the conditional two-sided p-values of Kulinskaya (2008) , and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) , MacKinnon and White (1985) , Cribari-Neto (2004) , Cribari-Neto et al. (2007) , Cribari-Neto and da Silva (2011) , Aftab and Chang (2016) , and Li et al. (2017) . Package: r-cran-skeletalvis Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr, r-cran-igraph, r-cran-miniui, r-cran-pbapply, r-cran-plotly, r-cran-rlang, r-cran-shiny, r-cran-tidyr, r-cran-visnetwork Suggests: r-cran-mockery, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-skeletalvis_0.1.2-1.ca2404.1_all.deb Size: 1296330 MD5sum: 6b88c1c34790d93f12d9b11f8f30fc8d SHA1: ba0563e424ce05f257951658816c2e198f203c54 SHA256: ead37954ba63ef92638cea1078457d2b7861c56315c17ded133858a6109725a6 SHA512: 1a28a827ed4600f3055ed79c48dc984c766a8e13ce7709f13e827850dc6cc4f3a7f65ad29923d6776ff2800ac8c5ffc4040a907e493a781e2a7805f7cc0512cd Homepage: https://cran.r-project.org/package=SkeletalVis Description: CRAN Package 'SkeletalVis' (Exploration and Visualisation of Skeletal Transcriptomics Data) Allows search and visualisation of a collection of uniformly processed skeletal transcriptomic datasets. Includes methods to identify datasets where genes of interest are differentially expressed and find datasets with a similar gene expression pattern to a query dataset Soul J, Hardingham TE, Boot-Handford RP, Schwartz JM (2019) . 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Package: r-cran-skewhyperbolic Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distributionutils, r-cran-generalizedhyperbolic Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-skewhyperbolic_0.4-2-1.ca2404.1_all.deb Size: 172652 MD5sum: 9054bd1ab6ad22d36c487983ce74a53f SHA1: 707340865cfbf64bac35c60fbb48326dd4f6dacd SHA256: 97519e97a65abaf4005974202da587f47b775cff540c1c69fd5329ff22f30439 SHA512: cb61ac5ce4272b59d35c071d0f058f7db5e3fe2ba6bceeab4cd53e1d5aca419e22bff2f220e3712c009a6f2bcaa5a1b7c4c8e29bca5ddec596daaacfefcc81ea Homepage: https://cran.r-project.org/package=SkewHyperbolic Description: CRAN Package 'SkewHyperbolic' (The Skew Hyperbolic Student t-Distribution) Functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. 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This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) . This software is also implemented as a web application through the Shiny R package . Package: r-cran-sleacr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-parallelly Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sleacr_0.1.3-1.ca2404.1_all.deb Size: 2474516 MD5sum: 58b2a29d3767953fae7384792b490037 SHA1: 7b419cadceeca420e330cbe2b9ea52699b15adee SHA256: 913e70d4f0f09b4dd47213d962fb7d4b49979c55b014ddf0736ae6bf74e5a511 SHA512: a42c31d31c44be37face3d9ad939633383892acd3e8aca21d0f6d6e101587071770dc8140d89b76a9c29c20b0a89c2d0c26d6aa5ccb1cf7948bc7179f84690e9 Homepage: https://cran.r-project.org/package=sleacr Description: CRAN Package 'sleacr' (Simplified Lot Quality Assurance Sampling Evaluation of Accessand Coverage (SLEAC) Tools) In the recent past, measurement of coverage has been mainly through two-stage cluster sampled surveys either as part of a nutrition assessment or through a specific coverage survey known as Centric Systematic Area Sampling (CSAS). However, such methods are resource intensive and often only used for final programme evaluation meaning results arrive too late for programme adaptation. SLEAC, which stands for Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage, is a low resource method designed specifically to address this limitation and is used regularly for monitoring, planning and importantly, timely improvement to programme quality, both for agency and Ministry of Health (MoH) led programmes. SLEAC is designed to complement the Semi-quantitative Evaluation of Access and Coverage (SQUEAC) method. This package provides functions for use in conducting a SLEAC assessment. Package: r-cran-sleekts Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sleekts_1.0.2-1.ca2404.1_all.deb Size: 19946 MD5sum: 8da6fa0db8b9ea72d0cc5d15d1e7f4e1 SHA1: 406ab4db5df7d8d4ebb2a325de3591335a2d0b18 SHA256: 7a410bba25ef8a6e011b23bfcbfee8e53c067f1fde48374ed956eaeec761bbc4 SHA512: 9911ca4cf9183943aec13ce500298a58ba9b6442a766c40ca961c0655a130c3c46234959e12d26f3936768af419b6dd33430c24192ab9d8bb805f34b21eadd72 Homepage: https://cran.r-project.org/package=sleekts Description: CRAN Package 'sleekts' (4253H, Twice Smoothing) Compute Time series Resistant Smooth 4253H, twice smoothing method. Package: r-cran-sleepcycles Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr, r-cran-stringr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sleepcycles_1.1.4-1.ca2404.1_all.deb Size: 99142 MD5sum: d0c7e9712c68008632c835c0b1f2f9f8 SHA1: 3eba2636cfc7ef468b9620f3de96d7a23153f008 SHA256: e4651acbc2a786e3fc2a120c6b8d54f2d42b7f1e35a0435d482520bc1a0bc736 SHA512: 8e42f7250502ff58c342571d3b9324acc98b054ea42f2505203376147f2a49e947d2048ed57a0b9a40b40c42af4648e9d1de96026d35a69142eafcbcca8d70a2 Homepage: https://cran.r-project.org/package=SleepCycles Description: CRAN Package 'SleepCycles' (Sleep Cycle Detection) Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) as described in Blume & Cajochen (2021) . Package: r-cran-sleeper Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4895 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-dplyr, r-cran-reticulate, r-cran-rlang Suggests: r-cran-callr, r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sleeper_0.5.0-1.ca2404.1_all.deb Size: 4858884 MD5sum: 4ed7ceffcbdcb43ca73f8ed4bf1d793c SHA1: d72205b3038d3364962e07ece49d3932b04015a5 SHA256: aa90cf740628e8d30c96adf7255f3f6b53a000e08b892b5f448c808b8a0c479b SHA512: 74a2f16e4906151601a9f3c0fe7c60e2f072c4ec15fc9ea3dac8ea3d68569ada179fa72f47c376b812207c9afc4ec5e5160fa2426899126331910a83f0e4f4f1 Homepage: https://cran.r-project.org/package=sleeper Description: CRAN Package 'sleeper' (Estimate Sleep Status from Accelerometry Data) Wraps the classifier from the Sundararajan (2021) to estimate sleep using a random forest. Users must download the model files from Sundararajan (2020) in order to use this method. Package: r-cran-sleeperapi Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-plotly, r-cran-dplyr, r-cran-purrr, r-cran-scales, r-cran-dt, r-cran-shiny, r-cran-htmlwidgets, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-rcolorbrewer, r-cran-htmltools, r-cran-tidyr, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-sleeperapi_1.1.2-1.ca2404.1_all.deb Size: 155470 MD5sum: a4778fdb679eada3b941e5193d6f81e3 SHA1: f2229f0753f4121372386e0d96988e4800bb3149 SHA256: 31195d2c09720a6ce22c2cafe1081d456c882bdadbc7fe54b749e3a27e9cb4d8 SHA512: 945381c68b7c4554eb2f39bd37ed91d161b606580ba4ef68214d812b386f5cabfbf2c86828dba3cc1f4a2b891e692726568e631069b13d66e552286cfa1f95e8 Homepage: https://cran.r-project.org/package=sleeperapi Description: CRAN Package 'sleeperapi' (Wrapper Functions Around 'Sleeper' (Fantasy Sports) API) For those wishing to interact with the 'Sleeper' (Fantasy Sports) API () without looking too much into its documentation (found at ), this package offers wrapper functions around the available API calls to make it easier. Package: r-cran-sleepr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-behavr, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sleepr_0.3.1-1.ca2404.1_all.deb Size: 43000 MD5sum: 4530424dad9d9688ed88c4c6afc00a90 SHA1: 8c4ee7fd2b5018cb9dd706f40f2b467a96804ef2 SHA256: c762bf2b615adaba9499c3d2600f35bfea4908390e83c3536a7640d1d27200ef SHA512: 23132541f3c02cfd29b81b1e7c2755566facd1aa0ec5ecdf193dd111d4af16de226b99b9c30b938ad031a048d2346ff3ea4ca78cb92b36324cb7ae8c26e6d7d2 Homepage: https://cran.r-project.org/package=sleepr Description: CRAN Package 'sleepr' (Analyse Activity and Sleep Behaviour) Use behavioural variables to score activity and infer sleep from bouts of immobility. It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data. It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) ), activity classification for Ethoscopes (Geissmann et al. (2017) ) and a new algorithm to detect when animals are dead. Package: r-cran-sleepwalk Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jrc, r-cran-cowplot, r-cran-httpuv, r-cran-jsonlite, r-cran-scales, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sleepwalk_0.3.2-1.ca2404.1_all.deb Size: 121726 MD5sum: c22102030ba4cf91c42d50868e4d3842 SHA1: e05800fa09b43f341fb584a0ad7af6bb41a1bbb9 SHA256: 52ff300ba17fefdf818eddd4e579c4f5067bb928b07ada6e9822522518f57e32 SHA512: 69ae0c9167510b72a2bc62d3326a7faec079f7dc6f242fc4b9e0efd5b9c9cf396cb79968ff07588861f838bce53ec13ecf94453601b10edadcb2ec7641f66570 Homepage: https://cran.r-project.org/package=sleepwalk Description: CRAN Package 'sleepwalk' (Interactively Explore Dimension-Reduced Embeddings) A tool to interactively explore the embeddings created by dimension reduction methods such as Principal Components Analysis (PCA), Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other. Package: r-cran-slemi Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-ggplot2, r-cran-gridextra, r-cran-nnet, r-cran-hmisc, r-cran-reshape2, r-cran-stringr, r-cran-doparallel, r-cran-caret, r-cran-corrplot, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-data.table, r-cran-covr Filename: pool/dists/noble/main/r-cran-slemi_1.0.2-1.ca2404.1_all.deb Size: 1465914 MD5sum: 2cd223d45d74999ff1053d96920edb31 SHA1: 4b48e7dfabb555dbc3a4cfa717c13c0f48ab1946 SHA256: 88c39d1313abc2eab7e4c1b7e6ae2c04a2d8a0db6d5fcea97374d060c7941291 SHA512: 021dc21804b92f057b427c2753145ee132451f8be2d583e4e24f7738976a08e93449c99e9ed793ea8e6e1aad12f5746ac8d970fabbabed1e6358f1989939f939 Homepage: https://cran.r-project.org/package=SLEMI Description: CRAN Package 'SLEMI' (Statistical Learning Based Estimation of Mutual Information) The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) . Package: r-cran-slendr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4600 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-magrittr, r-cran-reticulate, r-cran-tidyr, r-cran-png, r-cran-ijtiff, r-cran-ape, r-cran-scales, r-cran-digest, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-sf, r-cran-stars, r-cran-lwgeom, r-cran-rnaturalearth, r-cran-gganimate, r-cran-knitr, r-cran-rmarkdown, r-cran-admixr, r-cran-units, r-cran-magick, r-cran-cowplot, r-cran-forcats, r-cran-shinywidgets, r-cran-shiny, r-cran-rsvg Filename: pool/dists/noble/main/r-cran-slendr_1.5.0-1.ca2404.1_all.deb Size: 2463084 MD5sum: 9d64362fff82a4008349a58d3d1c66f6 SHA1: d6b84228605d9787f66a12a9567996ee84cd17ee SHA256: b17a740f5787941ceb5c50fa41d2de136d686088e84e7223d31576323769b24d SHA512: 00c63a3876194fa49313d6ddc0d11494886c924deaa4106a6c9c386af60b1f413e87450d4aeda91cfbf7964db58cd1463227b63a16204c196c9f619e578fe66c Homepage: https://cran.r-project.org/package=slendr Description: CRAN Package 'slendr' (A Simulation Framework for Spatiotemporal Population Genetics) A framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the 'SLiM' software by Haller et al. (2019) behind the scenes, using a custom built-in simulation 'SLiM' script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the 'SLiM' built-in back-end script, or using an efficient coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022) with a custom-built 'Python' script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the 'Python' module 'tskit' by Kelleher et al. (2019) . Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis. Package: r-cran-sleuth2 Architecture: all Version: 2.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5072 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-knitr, r-cran-mass, r-cran-agricolae, r-cran-car, r-cran-gmodels, r-cran-leaps, r-cran-mosaic Filename: pool/dists/noble/main/r-cran-sleuth2_2.0-7-1.ca2404.1_all.deb Size: 3899320 MD5sum: bdbdeca932d472ea12a4317b545969a4 SHA1: 0f181d90e43b26db83071439efcd40ab29311257 SHA256: 579b0f60726ef39c497671af9896ba194e028217accd462b2003143999c8c5d3 SHA512: 445c3fd3a242e7f40ba66631b57479744b1571db286e21c64c4b7351f380ccf78ae79f75aaab251cf69ec45c8bb47a7a876972a97052615bbf3e30b1e47199f7 Homepage: https://cran.r-project.org/package=Sleuth2 Description: CRAN Package 'Sleuth2' (Data Sets from Ramsey and Schafer's "Statistical Sleuth (2ndEd)") Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury. Package: r-cran-sleuth3 Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5859 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cca, r-cran-hmisc, r-cran-mass, r-cran-agricolae, r-cran-car, r-cran-gmodels, r-cran-knitr, r-cran-lattice, r-cran-leaps, r-cran-mosaic, r-cran-multcomp Filename: pool/dists/noble/main/r-cran-sleuth3_1.0-6-1.ca2404.1_all.deb Size: 4675498 MD5sum: 060db7e373e03c5500e7501beb56d6f5 SHA1: a25737f691d6cd7e410cda60104d68f68e5d59f6 SHA256: d65a3a1b0b454e4b3276a82548a814c5ae37f9f25a78c9b90b3d7db5398a075a SHA512: ba7a68e8cd28309185572f8501fceddce35f34a1739eba7495d11cac8e32b466849f54363c4937fd154a188efada74f1e0f1ac45f3d3db53bcbabb60aade5d51 Homepage: https://cran.r-project.org/package=Sleuth3 Description: CRAN Package 'Sleuth3' (Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rdEd)") Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning. Package: r-cran-slfpca Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-fdapace, r-cran-psych Filename: pool/dists/noble/main/r-cran-slfpca_3.0-1.ca2404.1_all.deb Size: 44040 MD5sum: 644d91cad5aae2b3fcd959cc6bd66436 SHA1: 28f50bbcbbf8405604845300a730232c0394cf72 SHA256: 1e10e3d12724649e20820a6f735410d2488fc37fe0cd4dc0fbe8b5eb31e35696 SHA512: bd09676ebe489ab1b128380ece508fea7e6fe9e3bc3b2910a9866b4e842e13217036fcee51b0eec971126d4e2f0b3b68525c947eb03640874efbd3c371e36879 Homepage: https://cran.r-project.org/package=SLFPCA Description: CRAN Package 'SLFPCA' (Sparse Logistic Functional Principal Component Analysis) Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA(). Package: r-cran-slgf Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-numderiv Suggests: r-cran-knitr, r-cran-formatr, r-cran-rcrossref, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-slgf_2.0.0-1.ca2404.1_all.deb Size: 80864 MD5sum: be7e5d14b8e98b8a3d6aacc3673e48e1 SHA1: f617169c9bae29f2c363e061c156b9a1ae15a35d SHA256: f89878403a451a4c55c5f8a058bea6e6394be13b805e45d3cd7561a37e881197 SHA512: 7a062a97769ee0159cea96d461b1596b25eaba3fd2dc5d4dae3b3bbe225c91878556940ca3e27e6de3960937c20452f367de37876694eb494c82b2045e9f6f39 Homepage: https://cran.r-project.org/package=slgf Description: CRAN Package 'slgf' (Bayesian Model Selection with Suspected Latent Grouping Factors) Implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), . SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor. Package: r-cran-slic Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-laplacesdemon, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-slic_0.3-1.ca2404.1_all.deb Size: 32458 MD5sum: e56ad910751c7b4bf98e72a131d51761 SHA1: 1471c98df467076b110f0cf3d9aefff669228e23 SHA256: 53c12a1a71c462ebee575d39ff47857a89f76d75d270bad15c9b8ce3a34baa67 SHA512: cf1933a822cd99c43fcadb4c81342089acb4da0f9e2c879bf0541aee0aa56bbdacd5ec2e766394c569e79516c2f9f6f20a6b7f485da63a39a30675795eead437 Homepage: https://cran.r-project.org/package=SLIC Description: CRAN Package 'SLIC' (LIC for Distributed Skewed Regression) This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) . Package: r-cran-slicedlhd Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-slicedlhd_1.0-1.ca2404.1_all.deb Size: 35238 MD5sum: d55ea7ecf2d9f31b6f4433529553248b SHA1: 823c82e58cf5658641453898ccdb29884cec56af SHA256: ba486b847cce1a44ba78614d910ec1602b0294d02007e843c8bb21b4f88d00cf SHA512: a4a2727146ad7d79400c065a6ad2fe2a779baf5a2a56239bb1b8a86fa76a398b394eb496ca7bf8429108a29f2d50ae56107d31beb31500d689ec0a3e0bdb093e Homepage: https://cran.r-project.org/package=SlicedLHD Description: CRAN Package 'SlicedLHD' (Sliced Latin Hypercube Designs) A facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, . Package: r-cran-slick Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggrepel, r-cran-golem, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-bookdown, r-cran-colourpicker, r-cran-colorspace, r-cran-cowplot, r-cran-esquisse, r-cran-flextable, r-cran-fresh, r-cran-httr, r-cran-kableextra, r-cran-knitr, r-cran-msetool, r-cran-openmse, r-cran-rcolorbrewer, r-cran-shiny.i18n, r-cran-shinyalert, r-cran-shinybs, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinywidgets, r-cran-rmarkdown, r-cran-testthat, r-cran-waiter Filename: pool/dists/noble/main/r-cran-slick_1.0.2-1.ca2404.1_all.deb Size: 1295878 MD5sum: 9ee7fa5c7b85f770c828998fa3be6cf2 SHA1: 27663edefb10afd2af98def9fd7fd7e59bae7ffc SHA256: c0bc10e7d33ab939226d29489ef1f47ac59b43051c8d5899862f34b14e3969a7 SHA512: 22629ce742828b7f9021ac1ff99543c6a726ba8d4b862cf1511eb6516a2f3087911db55d1e86795324df147639d7496f12cf54a5b49d6d93feb32fd2c2cba000 Homepage: https://cran.r-project.org/package=Slick Description: CRAN Package 'Slick' (Interactive Visualization of MSE Results) A framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). The publication quality figures and tables can be developed directly from the R console, or interactively explored with the 'Slick' App. For more details, see the 'Slick' website . 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Package: r-cran-slide Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-slide_1.0.0-1.ca2404.1_all.deb Size: 59610 MD5sum: b9e25f9668eefd3d4d289417a4c01816 SHA1: 07d11f413625024b86229ecf541733d3bf6d4cdf SHA256: 5c4302e5625542fc3c5c30990ad36207ef849d18724c64e6afae66a7633bcc03 SHA512: 4fc95d3c05907443d244a6ff32f7f2ce5741414fdd5aefde5fbb225eeba1a46da3fbd8da3ed5319083cf70c7bca5dda52c52f7348c2da51dcd3812a65f5f1a3a Homepage: https://cran.r-project.org/package=SLIDE Description: CRAN Package 'SLIDE' (Single Cell Linkage by Distance Estimation is SLIDE) This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the two cells being compared. 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Package: r-cran-slimrec Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-matrix, r-cran-glmnet, r-cran-bigmemory, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-slimrec_0.1.0-1.ca2404.1_all.deb Size: 123528 MD5sum: 4f5161781f756ec742e073420d9ed765 SHA1: 1a530e3a2cb1c1c5692a5b99e182d9bb2792c329 SHA256: cbc5340e5b82025f0df42d67abececd5e200ca6971c2adc11f266b51e0e51966 SHA512: 4682ee50e8a1422b761a3719d0b0fc1e412a8660bf4bf95d813e9f7c345494956739e7ed5d2b94e8e842384322f47b088dc3cf52021fd5b2b543fd7e6451df45 Homepage: https://cran.r-project.org/package=slimrec Description: CRAN Package 'slimrec' (Sparse Linear Method to Predict Ratings and Top-NRecommendations) Sparse Linear Method(SLIM) predicts ratings and top-n recommendations suited for sparse implicit positive feedback systems. 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The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples. See Warton et al. 2012 for methods description. 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Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. . 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Package: r-cran-smidm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-extradistr Suggests: r-cran-testthat, r-cran-covr, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-hdrcde, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-smidm_1.0-1.ca2404.1_all.deb Size: 155008 MD5sum: 8e0329879e2d51689056e7b8ed2f67e1 SHA1: 93be4b22151325dbf9c90afb9b8a07e34971d76f SHA256: 17d2e28a6ebd381e0097bb10b9479d606be68eaf5515252d83bdecfe348a1c28 SHA512: 9fef76beaef08b523ba29fd42019455c304c119f16ac8869e836a14723169f39b65e8767520be877f90aefbd1057edfe4c4b52fd4542957b8ede02da1cf395c7 Homepage: https://cran.r-project.org/package=smidm Description: CRAN Package 'smidm' (Statistical Modelling for Infectious Disease Management) Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) . Package: r-cran-smiles Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-meta Suggests: r-cran-bookdown, r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smiles_0.1-0-1.ca2404.1_all.deb Size: 155786 MD5sum: 57534c89b9efd6cc7a39e45abe17040d SHA1: 628b885ed775ad8c1e02c09cf4067e5a190ad9e6 SHA256: 224c246f5e09d1395410471748eb2a746778bb48102e3e7251a902a8fd5573f4 SHA512: 46fd77fb0da127c932e09bfbe0e8eab1fbc57688cc6f419067e1d0b2e9234b7064e4fb22035c32b25abe0d25abb26ee8d9540bd18c2b0539f0357932fde7feeb Homepage: https://cran.r-project.org/package=smiles Description: CRAN Package 'smiles' (Sequential Method in Leading Evidence Synthesis) Trial sequential analysis emerges as an important method in data synthesis realm. It is necessary to integrate pooling methods and sequential analysis coherently, as discussed in the Chapter by Thomas, J., Askie, L.M., Berlin, J.A., Elliott, J.H., Ghersi, D., Simmonds, M., Takwoingi, Y., Tierney, J.F. and Higgins, J.P. (2019). "Prospective approaches to accumulating evidence". In Cochrane Handbook for Systematic Reviews of Interventions (eds J.P.T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M.J. Page and V.A. Welch). . Package: r-cran-smimodel Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cgaim, r-cran-conformalforecast, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-gratia, r-cran-gtools, r-cran-matrix, r-cran-mgcv, r-cran-purrr, r-cran-roi, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-tsibble Suggests: r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smimodel_0.1.3-1.ca2404.1_all.deb Size: 572024 MD5sum: 8cf43a8537c5641de654f8b06d13770b SHA1: 08d1ed40ba8fd0c65685abcf0e9ad4559c9b1f5b SHA256: 6b43517bc1a5ac69232e61084562ad137547e63ed863a526bb7a2ca88c618e7f SHA512: 43cfc2daad939b7649ca78f9fb9e1e09289aee497d07b178d1914e7cf1523ed1fe78c53c61d57bf38e2380a2b5412a46c87fc1a52abbff12f26dbb9881b25672 Homepage: https://cran.r-project.org/package=smimodel Description: CRAN Package 'smimodel' (Sparse Multiple Index Models for Nonparametric Forecasting) Implements a general algorithm for estimating Sparse Multiple Index (SMI) models for nonparametric forecasting and prediction. Estimation of SMI models requires the Gurobi mixed integer programming (MIP) solver via the gurobi R package. To use this functionality, the Gurobi Optimizer must be installed, and a valid license obtained and activated from . The gurobi R package must then be installed and configured following the instructions at . The package also includes functions for fitting nonparametric additive models with backward elimination, group-wise additive index models, and projection pursuit regression models as benchmark comparison methods. In addition, it provides tools for generating prediction intervals to quantify uncertainty in point forecasts produced by the SMI model and benchmark models, using the classical block bootstrap and a new method called conformal bootstrap, which integrates block bootstrap with split conformal prediction. Package: r-cran-smirnov Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-smirnov_1.0-1-1.ca2404.1_all.deb Size: 11546 MD5sum: 19cb311987e3f29a27e9d6ab3d5a1ffd SHA1: 7319ef5d8d391394ebb3a648c50e0c0c2cf8f396 SHA256: 7aefe9aade3825db5bf0a2bb191d5f52b2c0b94d76b8f13d962aab15b5b673ec SHA512: 068db08e5c7d5f107437a851435f5740c10f48240c53687d65122d1f0e14b332688819554f562ef1f2dbaef62430da45f84feb73ed2bfa73edc93688548c682d Homepage: https://cran.r-project.org/package=smirnov Description: CRAN Package 'smirnov' (Provides two taxonomic coefficients from E. S. Smirnov"Taxonomic analysis" (1969) book) This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). These two characteristics may be used for the analysis of similarities between any number of taxonomic groups, and also for assessing uniqueness of giving taxon. It is possible to use smirnov() output as a distance measure: convert it to distance by "as.dist(1 - smirnov(x))". Package: r-cran-smithwilsonyieldcurve Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-smithwilsonyieldcurve_1.1.1-1.ca2404.1_all.deb Size: 189474 MD5sum: 70439e8bf3a6875fb5dc7d863a8e6982 SHA1: 9d3f1127cdb19555248a2b3182b80ee7d3603908 SHA256: ad8549f3c055d649e39840d2e0295f01f81d5d02e988b3929ef6b331e55b8777 SHA512: 6ed7de3cf487633d6e6ad93687f48444198b553a6cb8b48e22f8ca6860c907da2587caa9993c9d9120d65629f052f2b8326ec81aa445d5b0559fda3d9bb5c2c2 Homepage: https://cran.r-project.org/package=SmithWilsonYieldCurve Description: CRAN Package 'SmithWilsonYieldCurve' (Smith-Wilson Yield Curve Construction) Constructs a yield curve by the Smith-Wilson method from a table of libor and swap rates. Now updated to take bond coupons and prices in the same table. Package: r-cran-smitidstruct Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-bioc-biostrings Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-smitidstruct_0.0.5-1.ca2404.1_all.deb Size: 216292 MD5sum: f624bb846c519324ffe0d42d274b12c6 SHA1: 7d1338f6ad9b51bba6d95264f0652aa77e2810fb SHA256: 77e30eb54712bff983a77f37c97691a2d528c82cb39613165c3b4288054a0831 SHA512: 28349e09cb572f98d565b85bb93a64e2e76e135b8f48bcd350bcb2748a8e0b47c69d93ec28d374f50504205b0e8fff384b2496dc922ebb29920c43f00a4e1083 Homepage: https://cran.r-project.org/package=SMITIDstruct Description: CRAN Package 'SMITIDstruct' (Data Structure and Manipulations Tool for Host and ViralPopulation) Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID). It allow sequence-space-time host and viral population data storage, indexation and querying. Package: r-cran-smle Architecture: all Version: 2.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrixcalc, r-cran-mvnfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smle_2.2-3-1.ca2404.1_all.deb Size: 3215416 MD5sum: 6d92b5cda572561c4aa01f2e5f52fcec SHA1: d2d17420cc46865598554721319333e2b9fefe6f SHA256: c6af27d8bcf79f80144586720a326f813ca0760cfb8908513a201c731c1cc997 SHA512: 7e19b34c8de8b8b00d537ab59a2a8dcb38f7ab99e7027129080a17609cbfec6e46c8affe9032ef60410bb792b234f0d93afcb0af7c725c5f6b1b88c34479ac46 Homepage: https://cran.r-project.org/package=SMLE Description: CRAN Package 'SMLE' (Joint Feature Screening via Sparse MLE) Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014) proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025). Package: r-cran-smleph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-splines2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-smleph_0.1.1-1.ca2404.1_all.deb Size: 26982 MD5sum: 4c3624bc6eeef24a6fb9e1f81ebbdbb3 SHA1: 61e897f3ed816a9029360cf0748dc6fa223035ee SHA256: 1cbaf0d04c25b8e12231251ccb60ba52ef2e89687b9ad7a3dfc958cf17509a87 SHA512: 36cdee61342858c2040c084c09b73c6ce58f94cc8b5399558b2eda5889a7c2e85f473672e7e9b3dab88b03317038ea0520f89a70ba99550f8cac4457b95fe405 Homepage: https://cran.r-project.org/package=smlePH Description: CRAN Package 'smlePH' (Sieve Maximum Full Likelihood Estimation for the Right-CensoredProportional Hazards Model) Fitting the full likelihood proportional hazards model and extracting the residuals. Package: r-cran-smlmkalman Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3013 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdep, r-cran-pracma, r-cran-scales, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-smlmkalman_0.1.1-1.ca2404.1_all.deb Size: 3017314 MD5sum: 2333e1201d9ee079a6b33e9dcb9b9ce5 SHA1: 661e0d7d052c086d961bfbc35361f7ddead7a130 SHA256: 643d91078eed54d4ef265c1c9237386b4f4ad560c5eace29f7f297cb495258d7 SHA512: af2c2eafdb6e2898fd9604d9b54f3104f87350651d45d21d207068e01c623b9e85390d310a71b694f5ff456d1bfd2d283f1441d39b29267e7e59b9e15c313fc8 Homepage: https://cran.r-project.org/package=smlmkalman Description: CRAN Package 'smlmkalman' (Generation and Tracking of Super-Resolution Filamentous Datasets) A pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension, primarily for use in super-resolution plant micro-tubule image segmentation. Package: r-cran-smloutliers Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-smloutliers_0.1-1.ca2404.1_all.deb Size: 17512 MD5sum: 7e6cc2e342a346771d94b4bccfe7250b SHA1: 454683a06d96ebaecf1298691a72ea59674a1f0f SHA256: 7b779f640b9595d9be7bb8365feb1717e0544609713528f1aa940a0a91fb2144 SHA512: 8d492a7873792aac8fdd0c1988011507f6cf36197c65dbf274161e63dce3ea2b8697d632cf1e07562a964ad906415fd006abc66a474fb9f7db91a134618feb0a Homepage: https://cran.r-project.org/package=SMLoutliers Description: CRAN Package 'SMLoutliers' (Outlier Detection Using Statistical and Machine Learning Methods) Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see . Package: r-cran-smm Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seqinr, r-cran-discreteweibull Filename: pool/dists/noble/main/r-cran-smm_1.0.3-1.ca2404.1_all.deb Size: 553698 MD5sum: 77a8e4d776f15397f97005b8da68c131 SHA1: d877f5e02c60c46f173d8624c4a29b72d4a6d4c7 SHA256: e46a4dc1eb0c04de2868e7a23daff2362d771c385a44d4405653986ef828629e SHA512: 9ca58aeb2973ed203f908e1c3f82255e309d63f925d5e3f0cfd91cd36b7bb2738c3905c815797871184aacc8695cbbbccf4d3603ccfc6e87269845cdbbd08b38 Homepage: https://cran.r-project.org/package=SMM Description: CRAN Package 'SMM' (Simulation and Estimation of Multi-State Discrete-TimeSemi-Markov and Markov Models) Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) , Barbu, V.S., Limnios, N. (2008) and Trevezas, S., Limnios, N. (2011) . Estimation and simulation of discrete-time k-th order Markov chains are also considered. Package: r-cran-smmal Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-splines2, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smmal_0.0.5-1.ca2404.1_all.deb Size: 1520416 MD5sum: 6de645dcba9015d38606e77539a59791 SHA1: 9434da8ed645cefd85fa221017b69f160319a5c4 SHA256: 531d7f70a6a87d47210fe286724ae7b3d6a454d3e10203f81415b82539b82ae3 SHA512: e996685fb5004695ac4d66874a5c17eda04839040a632541450fc0b1b654de9a2bea0ad54f630e412f6cdf9441c9b4c95a448759d40689e14ac6009736af22e2 Homepage: https://cran.r-project.org/package=SMMAL Description: CRAN Package 'SMMAL' (Semi-Supervised Estimation of Average Treatment Effects) Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) . A detailed vignette is included. Package: r-cran-smmt Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-xml, r-cran-tibble, r-cran-curl, r-cran-rvest, r-cran-xml2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-smmt_1.2.0-1.ca2404.1_all.deb Size: 206682 MD5sum: 94add33196af7e49b4365f751f20d8c0 SHA1: 9cee04dbaef95cbc3f2845fd995d2584362f670b SHA256: 7aac8f10ca1fe6bb36adfec850b636c9232424219bf327d8e638ddb4c2ca81e7 SHA512: 4e1b85557f8bf458ccf6aeeb97d104bf6fc328e378782e567c1be764ef9c56282e17954f4fb546555c86bbc2875ab069b5a737689351eed4f9e4baa4985aeac2 Homepage: https://cran.r-project.org/package=SMMT Description: CRAN Package 'SMMT' (The Swiss Municipal Data Merger Tool Maps Municipalities OverTime) In Switzerland, the landscape of municipalities is changing rapidly mainly due to mergers. 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Package: r-cran-smncensreg Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-performanceanalytics Filename: pool/dists/noble/main/r-cran-smncensreg_3.1-1.ca2404.1_all.deb Size: 88404 MD5sum: 527dcef331d7708c7ecc1eb8437697bf SHA1: 8a0a6a37e3ef7bacd276023849113c27b5daa617 SHA256: 643507c5ae29cb0a1cda797fca75e5ddbdbc98040d19d25949cc2a8556a8438b SHA512: 33346b7d39f8330833eca1f8a736e732312a9931d13beeee0df8da0b08ccc5859d43f0df7f66232de8785c2e6d99dadd61f3f2ae1825ca9651a463702ed1eb95 Homepage: https://cran.r-project.org/package=SMNCensReg Description: CRAN Package 'SMNCensReg' (Fitting Univariate Censored Regression Model Under the Family ofScale Mixture of Normal Distributions) Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions. 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For more details, see Kelin Zhong, Fernanda L. Schumacher, Luis M. Castro, Victor H. Lachos (2025) . Package: r-cran-smoarima Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast Filename: pool/dists/noble/main/r-cran-smoarima_0.1.1-1.ca2404.1_all.deb Size: 32022 MD5sum: 3744a24cb7359c8024a2983eff579e2e SHA1: 881654dd5b150d0742db11d720b118b676cba43c SHA256: 69260cc0306cf302be479be136352ea4428cf7c96b38d442d0a8fd78ee75fb08 SHA512: 393f82ce4b4fcf41d463aebb5a53ff875358daf213ead8e1fc27fef860274e7ea23610b7679070ca815f285bb3a728b0fd2aba7ec516e4763e212cf6d99e92bc Homepage: https://cran.r-project.org/package=SMOARIMA Description: CRAN Package 'SMOARIMA' (Automatic ARIMA Order Selection Using Spider Monkey Optimization) Implements automatic autoregressive integrated moving average order selection using the Spider Monkey Optimization algorithm. Candidate orders are evaluated using a weighted objective function that combines the Akaike information criterion and root mean square error. Functions support model fitting, forecasting, and forecast accuracy evaluation. 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Two variants of the present approach have been developed, one in each of the next references: Azzalini (2023) , (2024) . Package: r-cran-smoke Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-smoke_2.0.1-1.ca2404.1_all.deb Size: 1604646 MD5sum: e8ee02fa54b234d859914a89cd697dc9 SHA1: b08690a83591152f0d43eb180c781297913e1c31 SHA256: 676542e76d62b2576ebc6ae37ba53415307048451db4966ec153a8fb83b04d5c SHA512: a52ae1bca8732b3d8d18ce1aebdf03f26b40b09e75856c56f456ba364d9abade776a9e62253a887328e27ff59ed6c06c2c3db8ad49757997582b1aee6ba51636 Homepage: https://cran.r-project.org/package=smoke Description: CRAN Package 'smoke' (Small Molecule Octet/BLI Kinetics Experiment) Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) . In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure. Package: r-cran-smoothapc Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-sparsem, r-cran-lmtest, r-cran-rgl, r-cran-colorspace Suggests: r-cran-testthat, r-cran-demography Filename: pool/dists/noble/main/r-cran-smoothapc_0.3-1.ca2404.1_all.deb Size: 84726 MD5sum: 45e7a3ebed17e257885ca83caf0bf69f SHA1: 8603d2f813f5d921f066211d934719de964f1fe7 SHA256: 1017adb61778a097955a501c83c6c45ee3c14337c28a1f1ea4e7b55920339d5f SHA512: 58aeaad000b2215682fbff103bcd1880f0bc4ccb5165db86ea45513e2de7ccb3d786d785fff5df9fbc66edc758e44b2466a60df3701b181d681d87c65c737af9 Homepage: https://cran.r-project.org/package=smoothAPC Description: CRAN Package 'smoothAPC' (Smoothing of Two-Dimensional Demographic Data, Optionally Takinginto Account Period and Cohort Effects) The implemented method uses for smoothing bivariate thin plate splines, bivariate lasso-type regularization, and allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors. Package: r-cran-smoothedipw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-smoothedipw_0.1.0-1.ca2404.1_all.deb Size: 285460 MD5sum: 9970b2431cec014a2809dad14983c326 SHA1: a6837d4dec61b202f59367d053c13df3c37ef1f6 SHA256: 96b3a29386df405be2e716f8a28b6c551b5f003cdc1ea59d11d703887a017210 SHA512: 585442d6e98aba024a7c3b36f33c7c8118debf41941700aa745db90e0ef95271ab9061e6eabdfc308ca7dc92543b91a1489f22e805ee065df887764a332b818b Homepage: https://cran.r-project.org/package=smoothedIPW Description: CRAN Package 'smoothedIPW' (Time-Smoothed Inverse Probability Weighting for RepeatedlyMeasured Outcomes) Implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) . Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest. Package: r-cran-smoothedlasso Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-matrix Filename: pool/dists/noble/main/r-cran-smoothedlasso_1.6-1.ca2404.1_all.deb Size: 56542 MD5sum: cf96a4c9eac38c4e1a058a15c3c12e05 SHA1: 919936d6fd810516df6e912cf821475e581c5827 SHA256: 7807f03fd32a95cf8ea09b75670c8675351d56f766d8f757467a76b55b0f6df1 SHA512: cb1ce022166c89242d71612f41445be04561d3e7f98670570e0f24a35c29af154cd1539a1031ec8560dc67fc4ece2fa08b3d465444b4bfdefb2bf88cf930ba81 Homepage: https://cran.r-project.org/package=smoothedLasso Description: CRAN Package 'smoothedLasso' (A Framework to Smooth L1 Penalized Regression Operators usingNesterov Smoothing) We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) ), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) ). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided. Package: r-cran-smoother Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ttr Filename: pool/dists/noble/main/r-cran-smoother_1.3-1.ca2404.1_all.deb Size: 24080 MD5sum: 5c2162e91b72ae069a5be5c101071ae3 SHA1: 532f7720b3eb03c1c72fa8d2e73f73611e8b0a86 SHA256: 2ea97e70211dde95a01df0f2ae04be3c0d994e2160cabfda984b37389484b3ec SHA512: 093568ba7486a62ab2795eff96d866a8980ba240b8f936fc19a5c11ede2b6031a389d2d9e5f06793bacf9788d9f979811e961f92d399f61a69758926598e99db Homepage: https://cran.r-project.org/package=smoother Description: CRAN Package 'smoother' (Functions Relating to the Smoothing of Numerical Data) A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included. Package: r-cran-smoothhr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-smoothhr_1.0.5-1.ca2404.1_all.deb Size: 118826 MD5sum: 0accdf4230ab30703f9b28157a077185 SHA1: ec796c0e2d0fde5ad3960ef7b2b20ffe357c6475 SHA256: b69d19915a20c116fe0018dbc73314b30bece5f5b26faa8ca8c2caf0a5f74cea SHA512: 61fc465e014a359369a7733e0ec0288419c61cc0969d4cc3e367e8e8ae32ae12ef133be67eb72ec7c26b92262950eb67e805da3712b3aeb96090dd90ed2d3e52 Homepage: https://cran.r-project.org/package=smoothHR Description: CRAN Package 'smoothHR' (Smooth Hazard Ratio Curves Taking a Reference Value) Provides flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived. Package: r-cran-smoothic Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-numderiv, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-toordinal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-smoothic_1.2.1-1.ca2404.1_all.deb Size: 346416 MD5sum: e98eb6e0815dd9db411d8cbdb69e297b SHA1: adcd11f4252bc7d1c57ff04131f0f8c39d874bde SHA256: 1eeeade4b7b97c19b34d4a9fbb46584b5444fa4dfb6b3c7e0f4e45891ae26710 SHA512: a7662f0d951ad59a6a68c8a4e0cbbd26aa786c5cdd34ae5dc9c77799c679d1e4af9f9e2736091d20b176ae415ed6fb2b460d66881ed8315def006660b0d77802 Homepage: https://cran.r-project.org/package=smoothic Description: CRAN Package 'smoothic' (Variable Selection Using a Smooth Information Criterion) Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. . This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". . Package: r-cran-smoothie Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spatialvx, r-cran-fields Filename: pool/dists/noble/main/r-cran-smoothie_1.0-4-1.ca2404.1_all.deb Size: 59958 MD5sum: 9d34184a44d51367ac7ec4a0a132ef66 SHA1: f4db92fbde9d46d53eb33af49ad8a0189ecbf423 SHA256: 34fd2cd4e2c7a5657d43c880c9bb1d66b1b3540df76005ba4218f88a0c2206b8 SHA512: 743e849bc5014370858885c054aeeb4a85ff883623034c238348cd9ce9a1534d462491e853787405e5ae0f2853cd579f3d13b1d8e53ace572754fb925c90ace3 Homepage: https://cran.r-project.org/package=smoothie Description: CRAN Package 'smoothie' (Two-Dimensional Field Smoothing) Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, ). Package: r-cran-smoothmest Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-smoothmest_0.1-3-1.ca2404.1_all.deb Size: 59320 MD5sum: 76c1e1e26de9ed782a0ee2bd0a2134d6 SHA1: a89ea56378d7b8fb5b6d6b0c26f8b7ab954059a4 SHA256: 2a1c4b2ba918daee5e99e3ed937b720c16071451e86899d2db69c6898d68c055 SHA512: e7b96de17651e4276067bcce6e5a782382edb7dd819cc9f30ebc4fb31d7af7dd1b6400b684f6366dfbb0b3e514a99ca3cc677323dedf5b3c961985866f0b1fa3 Homepage: https://cran.r-project.org/package=smoothmest Description: CRAN Package 'smoothmest' (Smoothed M-Estimators for 1-Dimensional Location) Some M-estimators for 1-dimensional location (Bisquare, ML for the Cauchy distribution, and the estimators from application of the smoothing principle introduced in Hampel, Hennig and Ronchetti (2011) to the above, the Huber M-estimator, and the median, main function is smoothm), and Pitman estimator. Package: r-cran-smoothpls Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3099 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cfda, r-cran-dplyr, r-cran-fda, r-cran-ggplot2, r-cran-mass, r-cran-mgcv, r-cran-pls, r-cran-pracma, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-future.apply, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smoothpls_0.1.5-1.ca2404.1_all.deb Size: 1157330 MD5sum: 51a904561a362dcd0d408e023c17cda5 SHA1: a54d253a00b840200854370ae9f6aa8e3c0ea838 SHA256: bb3d0813d9cf9867802c058327bff1a38769ab0265bd2980c3610820c8d68df1 SHA512: caf85616c743692761f9a23e029da2622d98a2e15992184f9a2101a00ef7d02f93eb92bec6265c9cd3fd451eea2023564a3c5faff4d3936670167214f9e23f17 Homepage: https://cran.r-project.org/package=SmoothPLS Description: CRAN Package 'SmoothPLS' (Partial Least-Squares Algorithm for Categorical and ScalarFunctional Data) Performs the Partial Least-Squares ('PLS') algorithm for functional data through the concept of active area integration. This approach builds upon the basis expansion methods for functional 'PLS' regression described in Aguilera et al. (2010) . The package seamlessly handles both Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), providing interpretable regression curves even for discrete state changes. It was developed during a PhD thesis between 'DECATHLON' and French research institute 'INRIA' 2022-2026. The 'SmoothPLS' method does not directly decompose the data into a basis; rather, it assumes the data is known as precisely as desired, and for every 'PLS' component, the weight functions are decomposed into the basis. For both single-state and multi-state 'CFD' as well as 'SFD', the algorithm is implemented for a scalar response. To provide a baseline, a naive 'PLS' method on time-value functions and standard Functional 'PLS' are also implemented. Package: r-cran-smoothr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-units Suggests: r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-terra Filename: pool/dists/noble/main/r-cran-smoothr_1.3.0-1.ca2404.1_all.deb Size: 899016 MD5sum: cfcb6f9aab8ff9d532871560027c687c SHA1: c6626d7fab26f2baee60080885fcf77d6bcac90f SHA256: e2a7572c98d596cf23edc53483da7a69d77f9be44bfcffc0a9e41efc66ad4fff SHA512: de79418266525c8fb07a9b4032f1aababc831a766d0604d51331b17aa694817cef92a4fbcf36afadd22cbfb644cd8e3818f38f9c6efcd223b374cb58cbd589bc Homepage: https://cran.r-project.org/package=smoothr Description: CRAN Package 'smoothr' (Smooth and Tidy Spatial Features) Tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons. 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The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0]. 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For a detailed introduction for the model and estimation techniques, see the paper by Chanwoo Lee and Miaoyan Wang (2021) "Smooth tensor estimation with unknown permutations" . 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Package: r-cran-snowfall Architecture: all Version: 1.84-6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snow Suggests: r-cran-rmpi Filename: pool/dists/noble/main/r-cran-snowfall_1.84-6.3-1.ca2404.1_all.deb Size: 248898 MD5sum: 488982a5d653694a71c8318be22fe5a2 SHA1: 4aead979c627faadadd8f441986d6958e23ee6ae SHA256: f1596efdcabe42dd75f6dc661b0b893dd577700d04f92759cd8fe269a49ae94a SHA512: f5d3eef18a54bbfac74bd649f592bfbfb4424b2608bc66cb23fc0c6d2a902c71edab45a6e2d4ec007e44c5497637894274933a840100cf48f9303e233de57603 Homepage: https://cran.r-project.org/package=snowfall Description: CRAN Package 'snowfall' (Easier Cluster Computing (Based on 'snow')) Usability wrapper around snow for easier development of parallel R programs. This package offers e.g. extended error checks, and additional functions. All functions work in sequential mode, too, if no cluster is present or wished. 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Package: r-cran-snowquery Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-reticulate, r-cran-rpostgres, r-cran-rsqlite, r-cran-dbi, r-cran-duckdb, r-cran-dplyr, r-cran-dbplyr Filename: pool/dists/noble/main/r-cran-snowquery_1.3.0-1.ca2404.1_all.deb Size: 31694 MD5sum: a65fee5a57f49431d79dc6c5af863efe SHA1: 716089ac9b5042460d99eb8acfdb38c3ca8d29d8 SHA256: a3f9f502a5205152417c9fa3e7b11a3c4453ed931882dd9e11ddb2899ded6a78 SHA512: dd70c3568d9ccb54cc75733274cd8f21bc909d393c8ad507d5cdb850cd3bf9d525b0daf3971fcaa35a9038a3b0e1b89ed8fc513dfc26027afcc9be33447676df Homepage: https://cran.r-project.org/package=snowquery Description: CRAN Package 'snowquery' (SQL Interface to 'Snowflake', 'Redshift', 'Postgres', 'SQLite',and 'DuckDB') Run 'SQL' queries across 'Snowflake', 'Amazon Redshift', 'PostgreSQL', 'SQLite', and 'DuckDB' from R with a single function. 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Package: r-cran-snpannotator Architecture: all Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-xml2, r-cran-openxlsx, r-cran-progress, r-cran-ggplot2, r-cran-kableextra, r-cran-rmarkdown, r-cran-ini, r-cran-igraph, r-cran-png, r-cran-ggraph, r-cran-logger, r-cran-readr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-snpannotator_1.4.8-1.ca2404.1_all.deb Size: 1767694 MD5sum: e434fb844ee632b5180078020b745dac SHA1: a3277fa48d72305e1fd69c3a394eecf5886ebf7b SHA256: 0fe1be034ef1ede8c96f8ea43eb594842a977b0a492bb550ef155b425af64da7 SHA512: caf70b9decc76739bfb8739a299dfd72ede729fc77871d167d0c95b8687f9958d95495c40c91b34624b500fa195ecc8023b63d0803e1b88a8196bdc8c3a2047f Homepage: https://cran.r-project.org/package=SNPannotator Description: CRAN Package 'SNPannotator' (Automated Functional Annotation of Genetic Variants and LinkedProxies) To automated functional annotation of genetic variants and linked proxies. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis. Package: r-cran-snpassoc Architecture: all Version: 2.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haplo.stats, r-cran-mvtnorm, r-cran-survival, r-cran-tidyr, r-cran-plyr, r-cran-ggplot2, r-cran-poisbinom Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biomart, r-bioc-variantannotation, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/noble/main/r-cran-snpassoc_2.1-2-1.ca2404.1_all.deb Size: 1189176 MD5sum: cc97751e88ed96c72e6c241f795ce614 SHA1: 736537bd4f4ed6b59207f49649f2b5b6369a9646 SHA256: 3aff44d8c7dd83d6f64c0351554728570f524affc73c797be8a2b114b13e7fba SHA512: b1497b808c4145eb3bfbf54b32be14614691d9d09e00a4539a81a35f84a6ed5c89f203f045e0b5cdbb38abbf3844844193bc36f9a99bbbfcc9302ffdbd7a3e3d Homepage: https://cran.r-project.org/package=SNPassoc Description: CRAN Package 'SNPassoc' (SNPs-Based Whole Genome Association Studies) Functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 . Package: r-cran-snpfiltr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcfr, r-cran-ggplot2, r-cran-rtsne, r-cran-cluster, r-cran-adegenet, r-cran-gridextra, r-cran-ggridges Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-snpfiltr_1.0.7-1.ca2404.1_all.deb Size: 661470 MD5sum: 477eda7911185ab8e9cbcf4624e79566 SHA1: 0a4d3a33494f14a91ec3b0da8b839c95e02b4e73 SHA256: f992e9a52c254767cddebf20032821e7f3ebf2ee2d51cc59ae1d45ac60b6a18f SHA512: 77ec2baa9a782c6126f0982a43a00306faa9e86e1989bc1be97727d8f091ebf1588126775aa10ec5ec5cff09556c0a14deb06d4a05eecd152fd4f5fe01733d68 Homepage: https://cran.r-project.org/package=SNPfiltR Description: CRAN Package 'SNPfiltR' (Interactively Filter SNP Datasets) Is designed to interactively and reproducibly visualize and filter SNP (single-nucleotide polymorphism) datasets. This R-based implementation of SNP and genotype filters facilitates an interactive and iterative SNP filtering pipeline, which can be documented reproducibly via 'rmarkdown'. 'SNPfiltR' contains functions for visualizing various quality and missing data metrics for a SNP dataset, and then filtering the dataset based on user specified cutoffs. All functions take 'vcfR' objects as input, which can easily be generated by reading standard vcf (variant call format) files into R using the R package 'vcfR' authored by Knaus and Grünwald (2017) . Each 'SNPfiltR' function can return a newly filtered 'vcfR' object, which can then be written to a local directory in standard vcf format using the 'vcfR' package, for downstream population genetic and phylogenetic analyses. Package: r-cran-snplinkage Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gwastools, r-bioc-biomart, r-cran-cowplot, r-cran-data.table, r-bioc-gdsfmt, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtable, r-cran-knitr, r-cran-magrittr, r-cran-reshape2, r-bioc-snprelate Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-snplinkage_1.2.0-1.ca2404.1_all.deb Size: 4720654 MD5sum: a2e96c13456740be81d031a76808b797 SHA1: 22b995831b17c531b7043127430d939729af7508 SHA256: 22860b0ec37e23cf0f59d5680da8912e206e4dcd2f0153fb6bfb6cfc0f420391 SHA512: 00da913fedcf05ffd12b6a9b2c638ae03742f030e4b56ded1e2ca0a928bbaaa8ea6659c26fcc07ddd0093ff3d76fe585582cc3f0f4c16076026a246bcd2302c3 Homepage: https://cran.r-project.org/package=snplinkage Description: CRAN Package 'snplinkage' (Single Nucleotide Polymorphisms Linkage DisequilibriumVisualizations) Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) . Package: r-cran-snpls Architecture: all Version: 1.0.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clickr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-ks, r-cran-mass, r-cran-matrix, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-snpls_1.0.27-1.ca2404.1_all.deb Size: 92364 MD5sum: 3ca07e90b3b0a2ae764a17be4f6e67ad SHA1: 60ece0ac4747741bbe6e8a04e802270319ba8f33 SHA256: fbffc49ce82ae7cfae244ca19c114829125b28d456fa9baa70e783b9ba0c3d02 SHA512: 53c66f2be0806a663544a0639adc5392cdc044d7b47a6adb3bfbaf52b84ba059c007e64067a55fb594e0c51419722e837c6e027d638a9d6b82bb8d7c75f6f6d3 Homepage: https://cran.r-project.org/package=sNPLS Description: CRAN Package 'sNPLS' (NPLS Regression with L1 Penalization) Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 ) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores. Package: r-cran-snpready Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-stringr, r-cran-rgl, r-bioc-impute Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-snpready_0.9.6-1.ca2404.1_all.deb Size: 361372 MD5sum: d6b0a4d46e6b454a74446d7803c83f0f SHA1: 59c7a1bce67b07a7cd9bf297ba27eb508a6c0de6 SHA256: a27e3db9e7b46e3953ae3423480e967a03aa91d0a4fd87ba718d3f4604593196 SHA512: a5f2dde1805cd55be1aca3d0cceebc2174b7810682aec482ace25d5a8a9a8bb83533c86d8f8e9bc9aff0426770ea677f716a41c52930d3202297cf0fcc713772 Homepage: https://cran.r-project.org/package=snpReady Description: CRAN Package 'snpReady' (Preparing Genotypic Datasets in Order to Run Genomic Analysis) Three functions to clean, summarize and prepare genomic datasets to Genome Selection and Genome Association analysis and to estimate population genetic parameters. Package: r-cran-snqtl Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rarpack, r-cran-mass Filename: pool/dists/noble/main/r-cran-snqtl_0.2-1.ca2404.1_all.deb Size: 200868 MD5sum: c3243f9fa048fd0f8965885928c058f5 SHA1: 9b9c3305a70413cf4ef2edaf7d598988cf106aac SHA256: b96b68838a78d8af57826a3bc874816deca8c3585da3d2c5e148fd7f369c0d07 SHA512: 88f90cbe98de3be75a8ebb7b063367749f66be40430aa99ef59cc518bd4ea9b03bd8a2c8072f050fca651830af6b55abd404932fb6c8437d605b0be5388aacce Homepage: https://cran.r-project.org/package=snQTL Description: CRAN Package 'snQTL' (Spectral Network Quantitative Trait Loci (snQTL) Analysis) A spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) . Package: r-cran-sns Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda, r-cran-numderiv Suggests: r-cran-regressionfactory, r-cran-mfusampler Filename: pool/dists/noble/main/r-cran-sns_1.2.2-1.ca2404.1_all.deb Size: 542706 MD5sum: db5ca1f40fca500b26ff7e11a2c51a51 SHA1: 75b35ff7cbcdfa2d8ba3dbe21f8534de3a16b074 SHA256: 4f99adb1f7fb5c8ab878a44720c4b7eba73d9bd7db14a214a68bfb342b9d3d28 SHA512: 3ee2e1fe6042570b28946741799681562a581332f2b4a421a0c7973d46a60b0312550b516f62ce5dfe9d18bdf090eb224ca5bbd73d84de5a7f0bd2826b0a8c9d Homepage: https://cran.r-project.org/package=sns Description: CRAN Package 'sns' (Stochastic Newton Sampler (SNS)) Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive . Package: r-cran-snschart Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-snschart_1.4.0-1.ca2404.1_all.deb Size: 236944 MD5sum: 608ed38f928065743372b7e128dec4a2 SHA1: 6af7ce42ea744adff5f7031098d73ecd83d98be9 SHA256: 453f053228afd7443e48e0beacb55250241187579b036fbf1f401d2d2fd40c69 SHA512: 31f13489a7401e49f97df1656f7bd69289c60b0c8f136ff63e048c888902c0d577291f93351deb123b12c946e6dd98fc51e64bc6d3951171bed2542b4ebc8b74 Homepage: https://cran.r-project.org/package=SNSchart Description: CRAN Package 'SNSchart' (Sequential Normal Scores in Statistical Process Management) The methods discussed in this package are new non-parametric methods based on sequential normal scores 'SNS' (Conover et al (2017) ), designed for sequences of observations, usually time series data, which may occur singly or in batches, and may be univariate or multivariate. These methods are designed to detect changes in the process, which may occur as changes in location (mean or median), changes in scale (standard deviation, or variance), or other changes of interest in the distribution of the observations, over the time observed. They usually apply to large data sets, so computations need to be simple enough to be done in a reasonable time on a computer, and easily updated as each new observation (or batch of observations) becomes available. Some examples and more detail in 'SNS' is presented in the work by Conover et al (2019) . Package: r-cran-snsequate Architecture: all Version: 1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-ake, r-cran-equate, r-cran-moments, r-cran-emdbook, r-cran-plyr, r-cran-statmod, r-cran-knitr, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-snsequate_1.3-5-1.ca2404.1_all.deb Size: 320616 MD5sum: 787a14e38160726942c0f968f30bfb95 SHA1: 885055fde9344893907fb7fcfa0e32274d543d3b SHA256: 3c0e7c6a0138a40ed106189f16cedc37340c7f0e2cc9b8aaa5258bac2ab93a3f SHA512: 615189cb9b2b93657e578a6a9637ec043c58ba7276ad4fedd76f723d8d435816c9eabc519cf03348425efd126d35f6161a550f7ad7620b33082bc26d0274aca2 Homepage: https://cran.r-project.org/package=SNSequate Description: CRAN Package 'SNSequate' (Standard and Nonstandard Statistical Models and Methods for TestEquating) Contains functions to perform various models and methods for test equating (Kolen and Brennan, 2014 ; Gonzalez and Wiberg, 2017 ; von Davier et. al, 2004 ). It currently implements the traditional mean, linear and equipercentile equating methods. Both IRT observed-score and true-score equating are also supported, as well as the mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. It also supports newest methods such that local equating, kernel equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT parameter linking methods based on asymmetric item characteristic functions. Functions to obtain both standard error of equating (SEE) and standard error of equating differences between two equating functions (SEED) are also implemented for the kernel method of equating. Package: r-cran-snsfdatasets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-snsfdatasets_0.1.1-1.ca2404.1_all.deb Size: 22430 MD5sum: 4d043ec1bbd50bc73571a2e0256a0ad1 SHA1: d6fd761fedb54c85d71a675bbc1e00aa79e7db01 SHA256: 281f2bd971e445cd99fea9dd5fda49b5ff127ad2e6d34f707300411f6307896c SHA512: 1b961e783dee0a120025ee0b9431923484d4c1daab6a5685c567b926f35880802fdce8f000e931575281be67fa4a1d85ded5c9332df0adfb2eb69fd7d6b9e852 Homepage: https://cran.r-project.org/package=SNSFdatasets Description: CRAN Package 'SNSFdatasets' (Download Datasets from the Swiss National Science Foundation(SNF, FNS, SNSF)) Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; ). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries. Package: r-cran-snsmart Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-envstats, r-cran-bayestestr, r-cran-condmvnorm, r-cran-cubature, r-cran-geepack, r-cran-hdinterval, r-cran-pracma, r-cran-rjags, r-cran-tidyr, r-cran-truncdist Suggests: r-cran-coda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-snsmart_0.2.4-1.ca2404.1_all.deb Size: 155902 MD5sum: a8f87d025cec31884cb37b875b88dbdf SHA1: 8d6507f55182fff22cc07f07aa37f0125407b052 SHA256: fbe259edf6864d97f764833b9a9dd9a477efe871bbc81c35b2f88c608d50a927 SHA512: ea5d1a4f458a7c885668ec970a64531fb99ddf9de4780ef23e8528d2855b8645ecc3db442c91883b22190cf25b390e8765b3e170832eeeeeb91b3559a6c02790 Homepage: https://cran.r-project.org/package=snSMART Description: CRAN Package 'snSMART' (Small N Sequential Multiple Assignment Randomized Trial Methods) Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 . Package: r-cran-snvecr Architecture: all Version: 3.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-readr, r-cran-tidyselect, r-cran-rlang, r-cran-glue, r-cran-backports, r-cran-stringr Suggests: r-cran-astrochron, r-cran-ggplot2, r-cran-tidyr, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-withr, r-cran-curl, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-snvecr_3.10.1-1.ca2404.1_all.deb Size: 1330442 MD5sum: 6e95cea908fc4d5e01ab96f0b8433011 SHA1: a4ac23b12f3fb71718be41e035941833369e9c9f SHA256: 4e64e8080bb2d35c77ae056ee86920aa74590121e8e6ecac395d0d2f87daf74b SHA512: 09d731df9a42b1b8e5768c6d7554d43e686e0fe2d9eff3a41171cf4023fe54db7a215b846957f0cfe2d1a01b167e809c488027c9e32a029e4659121e19cd12b3 Homepage: https://cran.r-project.org/package=snvecR Description: CRAN Package 'snvecR' (Calculate Earth’s Obliquity and Precession in the Past) Easily calculate precession and obliquity from an orbital solution (defaults to ZB18a from Zeebe and Lourens (2019) ) and assumed or reconstructed values for tidal dissipation (Td) and dynamical ellipticity (Ed). This is a translation and adaptation of the 'C'-code in the supplementary material to Zeebe and Lourens (2022) , with further details on the methodology described in Zeebe (2022) . The name of the 'C'-routine is 'snvec', which refers to the key units of computation: spin vector s and orbit normal vector n. 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Package: r-cran-socialrisk Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-socialrisk_0.5.1-1.ca2404.1_all.deb Size: 36398 MD5sum: 8319d6817a69870c9b8b371360e17e9d SHA1: 4bab1feed8ba3fb921dc60e62892d095798bfbca SHA256: 7ed00f5fc39532a62b835cc87382cee6d3e954d4615e61a943995150731e3589 SHA512: 5573278386341f1f2eae425b0f96d4c95ae689763019db7ff8b82c4caea249453be42d58e9727ad81f4d916fa20068066174eddcfb2c4e29882832b5d8fc4039 Homepage: https://cran.r-project.org/package=socialrisk Description: CRAN Package 'socialrisk' (Identifying Patient Social Risk from Administrative Health CareData) Social risks are increasingly becoming a critical component of health care research. One of the most common ways to identify social needs is by using ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies of ICD-10-CM Z-codes from administrative health care data. The conceptual taxonomies come from: Centers for Medicare and Medicaid Services (2021) , Reidhead (2018) , A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) . Package: r-cran-socialsim Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-future, r-cran-future.apply Suggests: r-cran-rstan, r-cran-devtools, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-socialsim_0.1.9-1.ca2404.1_all.deb Size: 40454 MD5sum: f80b65e56fc3daf681704960030e7c58 SHA1: eaf14930d1875e8b2678296b0589c3cbe884f983 SHA256: b6f680c6d296414e3ad7dce79132e58cb929fa0df6fac4870ac04c3ca2ba8571 SHA512: bb309c82db147ab3344feadaf26f193a3c2fc845131c40364fd4e8a1f2de70856a251599103d19f8468ed333125f4bef945e274da414513b532fc682d69b66c2 Homepage: https://cran.r-project.org/package=socialSim Description: CRAN Package 'socialSim' (Simulate and Analyse Social Interaction Data) Provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan. Model fitting is performed via the 'rstan' package. Users can generate realistic interaction data where individual phenotypes influence and respond to those of their partners, with control over sampling design parameters such as the number of individuals, partners, and repeated dyads. The simulation framework allows flexible control over variation and correlation in mean trait values, social responsiveness, and social impact, making it suitable for research on interacting phenotypes and on direct and indirect genetic effects ('DGEs' and 'IGEs'). The package also includes functions to fit and compare alternative models of social effects, including impact–responsiveness, variance–partitioning, and trait-based models, and to summarise model performance in terms of bias and dispersion. For a more detailed description of the available models and impact–responsiveness, see the accompanying article Wijnhorst et al. (2026) . 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Package: r-cran-sodavis Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-mass, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sodavis_1.2-1.ca2404.1_all.deb Size: 2702200 MD5sum: db2bea5448fd0ca2d866de6780030180 SHA1: 0a30bbe09f8061a4edf7a162ce4fba36ce1248e0 SHA256: 840b606f7fec6f5638f799a736d5f1cb48c159475a943a7013f7d57d799754c2 SHA512: b5d2c612f23eb36c5cb78cc755b7d6a8775d19c8b448d539838db1a200199e3566e8f2f47b234b1bc54fd303649d314c1812b533508b9aed27e9b83ca0bc5580 Homepage: https://cran.r-project.org/package=sodavis Description: CRAN Package 'sodavis' (SODA: Main and Interaction Effects Selection for LogisticRegression, Quadratic Discriminant and General Index Models) Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses. Package: r-cran-sofa Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2552 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crul, r-cran-jsonlite, r-cran-r6, r-cran-mime Suggests: r-cran-testthat, r-cran-cli, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sofa_0.4.2-1.ca2404.1_all.deb Size: 621342 MD5sum: be42bc2f1d458bfe5955b7bd2eaf96ce SHA1: cd6a5a12b0429b58ed61eadf1261c97cfbbb0f74 SHA256: 310491e4ab7a3e2f71b18a16499b0c5dbc74c925386ec5735efc4e2e18eb81d8 SHA512: 34e8931964e5b2fa0dad524eeb6dff9c414a915403fb7b5f0af386059ba85207961017eca7cfd9f5acb2e8e52483ae7f0acb2c6105917444dacf75bd43b7b0eb Homepage: https://cran.r-project.org/package=sofa Description: CRAN Package 'sofa' (Connector to 'CouchDB') Provides an interface to the 'NoSQL' database 'CouchDB' (). 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Package: r-cran-sofi Architecture: all Version: 0.16.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-sampling, r-cran-foreign Suggests: r-cran-ggplot2, r-cran-vgam, r-cran-shinyjs, r-cran-ggextra, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-sofi_0.16.4.8-1.ca2404.1_all.deb Size: 1374538 MD5sum: 1514a6c2f58668980ad4e4ddfbeb47d0 SHA1: b7d96b15133dd105d5b766cbe15be68b46440ec3 SHA256: 5c03945f96cc673a29424528d3d0147f85aad3c3aa33d6c443cd8d01c2e14531 SHA512: ed4b10eb2d316c6d39c5d2d107082237231a2a7cb3cc7a269f881ba9bc704db9e7f9c62d804b338e0a8cd8a4ebb2b960c707fb139dc2261d6dd5edd2b24ff20a Homepage: https://cran.r-project.org/package=Sofi Description: CRAN Package 'Sofi' (Interfaz interactiva con fines didacticos) Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos. Package: r-cran-sofia Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-png Filename: pool/dists/noble/main/r-cran-sofia_1.0-1.ca2404.1_all.deb Size: 186078 MD5sum: 0182c637ba0e1b8209cea482cbe69968 SHA1: ab185ff35045c2b0dcd1e9ed6284125347b11c53 SHA256: 5e0e9f3ddcfa35291679c4b7f3536e4bd70135a73a709395c04b6d16d0c22750 SHA512: af88eb7e25d046935568227d200ef700833d14d875747dba72edd7680f1b39a546b1a8f03813debc85f563cd966fbe3cd824096e342314c88d897607ca246a79 Homepage: https://cran.r-project.org/package=SOFIA Description: CRAN Package 'SOFIA' (Making Sophisticated and Aesthetical Figures in R) Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see . Package: r-cran-softbib Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-renv, r-cran-rmarkdown, r-cran-bibtex Filename: pool/dists/noble/main/r-cran-softbib_0.0.2-1.ca2404.1_all.deb Size: 19428 MD5sum: 8d1904edd521f11b60a0e209e5f608f9 SHA1: 804488c201849fc95a945472439315c79a2aa551 SHA256: 1a5f538e839d3932dd20c4ea768ac47ffbaa1d2be71c8da53b2ca4d9d3f047b1 SHA512: 6be249d29a90e4faad6b820d09a62a6713a25d80c49258a7394ad7a6b6123c0fa6a7744affda9f91005ae2aaf1e430ab6397360f57d59abbd45b8186c6ec0a30 Homepage: https://cran.r-project.org/package=softbib Description: CRAN Package 'softbib' (Software Bibliographies for R Projects) Detect libraries used in a project and automatically create software bibliographies in 'PDF', 'Word', 'Rmarkdown', and 'BibTeX' formats. Package: r-cran-softclassval Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrayhelpers, r-cran-svunit Filename: pool/dists/noble/main/r-cran-softclassval_1.1.0-1.ca2404.1_all.deb Size: 69876 MD5sum: a6aba03eefdee5bd0486c1da7c8e39b3 SHA1: 6fcd2a0de6766835278cf279d8c99214921ca69e SHA256: b8370e0a6355234716a2524c1ad4b4d050bce5f242af01a1aa8b07b3ff24c60b SHA512: 025db7b26f732ea6b4659944d1255e62213091bd90b6b25cc26f91fe4bd909e3274499ef6ba1e21f59470bbc65c5df5de68a652eb3b4c414912fb0a8a9230b8a Homepage: https://cran.r-project.org/package=softclassval Description: CRAN Package 'softclassval' (Soft Classification Performance Measures) An extension of sensitivity, specificity, positive and negative predictive value to continuous predicted and reference memberships in [0, 1]. Package: r-cran-softclustering Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-softclustering_2.1.3-1.ca2404.1_all.deb Size: 79608 MD5sum: d628deb5667459a7c4708594e9c36f7f SHA1: 28432c087bcecbadb9af47dea05e550935d21a9c SHA256: f5c5831a37b38dea7b47cdbe06e3dbe5a46d17577009bfae01385d0c5ab8d358 SHA512: 18fbcd766a7e85cf6d6b7bd8c434b96c111c172238d5d9dbacd0f36417d90ba71193af99102d44773a8053d463e32bb12dbf91bc3b62b65c0174ae9394fbad06 Homepage: https://cran.r-project.org/package=SoftClustering Description: CRAN Package 'SoftClustering' (Soft Clustering Algorithms) It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo. Package: r-cran-softwarerisk Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-codetools, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-ineq, r-cran-purrr, r-cran-scales, r-cran-tibble, r-cran-tidygraph, r-cran-rlang, r-cran-sensobol Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-softwarerisk_0.3.0-1.ca2404.1_all.deb Size: 1467352 MD5sum: 01d6de65656e27ddcadb0703166c4c05 SHA1: b8808ed517a11c68f368efc8a153b1272382aace SHA256: de486b398d3857ce4cd72364b2911bf4d56fb11e5f0391159fd024fe65e86e01 SHA512: aaacc22d49ee9d233180892a1c4fa084dcf271ee60d3840e18437c2ef60b19a191f4c35ef04a64df8617fbcf86c8c521b35dfc22f7fffb66ccffc7ee5c5832ca Homepage: https://cran.r-project.org/package=softwareRisk Description: CRAN Package 'softwareRisk' (Computation of Node and Path-Level Risk Scores in ScientificModels) It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) to assess how risk scores change under alternative risk definitions. Package: r-cran-sohpie Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-dplyr, r-cran-fdrtool, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sohpie_1.0.6-1.ca2404.1_all.deb Size: 153008 MD5sum: 56b5e9d33084b190870fbdad957b481c SHA1: e5e3c9bc181de8854a3954bfd4348604188a23df SHA256: 82469ec1462469c800189256ce6012d24dea6329546c6e8927688055ab3c64b3 SHA512: 8bc90ac96c88eef5a4312f90d0d4c8a27eeb35025c96cdc530850e5f966d7afa36cfb4416ad412cf0299d413c86f73795bba6e5ebaaf5cdba68b40e90a22821b Homepage: https://cran.r-project.org/package=SOHPIE Description: CRAN Package 'SOHPIE' (Statistical Approach via Pseudo-Value Information and Estimation) 'SOHPIE' (pronounced as SOFIE) is a novel pseudo-value regression approach for differential co-abundance network analysis of microbiome data, which can include additional clinical covariate in the model. The full methodological details can be found in Ahn S and Datta S (2023) . Package: r-cran-soil Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-mass, r-cran-brglm2 Filename: pool/dists/noble/main/r-cran-soil_1.1-1.ca2404.1_all.deb Size: 41146 MD5sum: f6406e2dc46142b97fbcfe23b392e0ae SHA1: 807a7fa5940be42bc34bbc73673bb0a2a1dd6173 SHA256: 64e43355557f195fc922002bb6c114a57dac9357daa3aaee1b51f6023ae3ad4e SHA512: c3d8fd9521b2bf17a06e1066109671132eaf391ba9eba0c6ceac1a20c7e0080231843776b7de4d10dcf318ec0a5fa5a46562544012ad6fbd0c53eacf0c5f80f7 Homepage: https://cran.r-project.org/package=SOIL Description: CRAN Package 'SOIL' (Sparsity Oriented Importance Learning) Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+). Package: r-cran-soilassessment Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-desolve, r-cran-e1071, r-cran-fuzzyahp, r-cran-googledrive, r-cran-hmisc, r-cran-nnet, r-cran-png, r-cran-randomforest, r-cran-raster, r-cran-soiltexture, r-cran-sp, r-cran-sf, r-cran-terra, r-cran-curl, r-cran-httr, r-cran-withr Suggests: r-cran-arm, r-cran-cubist, r-cran-aqp, r-cran-kernlab, r-cran-nlme, r-cran-ranger, r-cran-rpart, r-cran-plyr, r-cran-qrnn, r-cran-quantregforest Filename: pool/dists/noble/main/r-cran-soilassessment_1.3.1-1.ca2404.1_all.deb Size: 2262806 MD5sum: 90a2698fab394ad0ead13c9aab4810c7 SHA1: db8a62c1f315ee042aad51251909b721d07a05f3 SHA256: b46b5461cc81be1661a84313239fb806069ac87974478793693e0fa9c2f720be SHA512: f7e374e3437d8106a4b0f3520332b89a5c59bf943043b9a88ac10c94e31a1a7caf69e965be0bca9f49b9e63043cd5ea43bd0076f6c48acebe3034a989b7dad9a Homepage: https://cran.r-project.org/package=soilassessment Description: CRAN Package 'soilassessment' (Soil Health Assessment Models for Assessing Soil Conditions andSuitability) Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO ], groundwater recharge, fertility, erosion, salinization [], carbon sequestration, irrigation potential, and status of soil resources. Package: r-cran-soilchemistry Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilchemistry_0.1.0-1.ca2404.1_all.deb Size: 29864 MD5sum: 3f1a268e5c01e2d020cbc13b455b2eed SHA1: 624d0dfcd252c3522c278e294c162968e330ab5b SHA256: 26f2f7379df9b8c8181d61f3266cdd375c3a2eea0a0cc18e92d25ed08381559d SHA512: a868f0072c4a03643fa66690458511a8665f80b4550a76b653247f518d0c51454567ac6764f34a3718566e48695882c0d895e859648d672c9fc7e864bad89527 Homepage: https://cran.r-project.org/package=soilchemistry Description: CRAN Package 'soilchemistry' (Computation of Properties Related to Soil Chemical Environmentand Nutrient Availability) Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). . Datta SP, Bhadoria PBS (1999). ."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). "Solution ion activity and plant growth". Package: r-cran-soilconservation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilconservation_1.0.1-1.ca2404.1_all.deb Size: 53022 MD5sum: 02b77877e1ef956f932cfd46af8eb949 SHA1: 438131d2b47b7a5e6dd3d933be38c50fa28480d0 SHA256: da4deb25c492d01904e4705257599ce2ea284af866b2bf7df90e1d03db1c6f8b SHA512: e0608f4e628868490773b5c31aa1531fb063575c470a2147b5d72f5ef35c2ab1abbdc006892ed62d001e2aa96e71985f2f94bbe54f207334f94ad4c9cac82267 Homepage: https://cran.r-project.org/package=SoilConservation Description: CRAN Package 'SoilConservation' (Soil and Water Conservation) Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results. Package: r-cran-soildb Architecture: all Version: 2.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-curl, r-cran-jsonlite Suggests: r-cran-aqp, r-cran-xml2, r-cran-httr, r-cran-rvest, r-cran-odbc, r-cran-rsqlite, r-cran-sf, r-cran-wk, r-cran-terra, r-cran-raster, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soildb_2.9.2-1.ca2404.1_all.deb Size: 1572690 MD5sum: ab22980677b6e50f3f567f6df3728ce5 SHA1: 534ddff01d595d9c977ea23dcbc3f7d16b03213a SHA256: c78497c1e04d4e2c89e2afc4a02f035e0bcdeb26a55264123eb880efcc112dc6 SHA512: d098e2ed25db5353d727fa203490163a0369bf9f1e88a085aecf8faf04bc41b2dad08838c4d823698ebcc454bc3a3bbc1dc5467c058841c4442064b81117b8b7 Homepage: https://cran.r-project.org/package=soilDB Description: CRAN Package 'soilDB' (Soil Database Interface) A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases. Package: r-cran-soilfda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilfda_0.1.0-1.ca2404.1_all.deb Size: 17790 MD5sum: 39e62fddff84da2b24cbd6f5ab1e79e5 SHA1: 2980f23759f2e150b6e4bb942098089bf70cdf41 SHA256: 86b768acffd0898eda5cf6243fafad71315ff3891aaded733d4c7da9b1efa0f1 SHA512: 18d3a3d09d0b4f93946280d1886b0ecc15065001bf1e823ce1bf230e953b07dd85387a5ad1f5e4ab64a8eb29b15b32022a6630df9be67488c6c0524ffe150dc9 Homepage: https://cran.r-project.org/package=SoilFDA Description: CRAN Package 'SoilFDA' (Fractal Dimension Analysis of Soil Particle Size Distribution) Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) . It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) . Package: r-cran-soilflux Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-reticulate, r-cran-tensorflow, r-cran-rlang Suggests: r-cran-keras3, r-cran-ggtern, r-cran-readxl, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-soilflux_0.1.5-1.ca2404.1_all.deb Size: 3668218 MD5sum: 62b5b460ab8f4fcf56f41324f259c70b SHA1: f14e9b24033daa9dc67ffc3dd34e6c01b27bf1d2 SHA256: 117500d757951ce83c6f9edb728207177b15cca03c8daad3950303de5ae077d0 SHA512: f70c876fee020a1be71daf654b8082a74ea1fdebe68bb95e2bd03274b1796e793c1df69c5cb082af9836e51950e41e17cdf9dcec39503bfd005d7fe70bc26213 Homepage: https://cran.r-project.org/package=soilFlux Description: CRAN Package 'soilFlux' (Physics-Informed Neural Networks for Soil Water Retention Curves) Implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation. Package: r-cran-soilfoodwebs Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-diagram, r-cran-quadprog, r-cran-lpsolve, r-cran-rootsolve, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soilfoodwebs_1.0.2-1.ca2404.1_all.deb Size: 262090 MD5sum: 5e92361765269be380c42b223561f934 SHA1: d34971670a8e0b02c2320e074df202a7e6ae39d5 SHA256: c60317fbcc77a18131b6bee8ea46959275753e2f172e8acc8b8aac0393e1809f SHA512: a4e04cfb0f9b005e72ef8ca9c83940be8da1787b3688668fce4d67b8e4a2c71d084563cdf9cae53272552e9ad21dd98771ef10ca73eca6dfba6c22e15d639d3e Homepage: https://cran.r-project.org/package=soilfoodwebs Description: CRAN Package 'soilfoodwebs' (Soil Food Web Analysis) Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) , de Ruiter et al. (1995) , Holtkamp et al. (2011) , and Buchkowski and Lindo (2021) . The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments. Package: r-cran-soilfunctionality Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilfunctionality_0.1.0-1.ca2404.1_all.deb Size: 15270 MD5sum: ff029eb970a86a5b50d2f656c19a3bed SHA1: f45c7a4e91f6a6efbb56581325f2045c9f476049 SHA256: 472973b9a69c36f578feea452f9a5e0bfd86456dfa6bf58e7c508d20a8b02d09 SHA512: f163602e16c4e79926e6b23485832ad3a3f0118fd348917485204dd163896039dfd07855d9dcaff6fe1920edeac1013f09037dee7685e89f7457a8d828822c2d Homepage: https://cran.r-project.org/package=SoilFunctionality Description: CRAN Package 'SoilFunctionality' (Soil Functionality Measurement) Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems' relative operational supremacy. Package: r-cran-soilhyp Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-soilhyp_0.1.7-1.ca2404.1_all.deb Size: 230112 MD5sum: cb4ba109a505856ec7aaea3197428480 SHA1: 859250a8bc36f7478c07012e1766bfdbf32806d3 SHA256: b99410f3566b2af8b9ee1c2e446b7817c30e9885299cbd5440943bcc57e902d7 SHA512: de7510bf6cebf542946b3b7f99fcb4e168362e835e5591c5470fea70b08ab9717ad9a07c52ecb6548b02bbff1a3c1e5a40cfa8805cfdeea05501864bd5ca782c Homepage: https://cran.r-project.org/package=SoilHyP Description: CRAN Package 'SoilHyP' (Soil Hydraulic Properties) Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7]. Main references: [1] van Genuchten (1980) , [2] Mualem (1976) , [3] Peters (2013) , [4] Iden and Durner (2013) , [5] Peters (2014) , [6] Wind G. P. (1966), [7] Peters and Durner (2008) and [8] Brooks and Corey (1964). Package: r-cran-soilhypfit Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-snowfall, r-cran-mgcv, r-cran-quadprog, r-cran-rmpfr, r-cran-soilhyp Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-soilhypfit_0.1-8-1.ca2404.1_all.deb Size: 370756 MD5sum: 17248ece36809b13656e2e89ded8e298 SHA1: 5dd2ccd1694f8334186e9aaf6c0a7d3ee8145b02 SHA256: 2e0043c3938324d846980e3ccfef695bcb8011cc17fe2d2d9b33abc43481f466 SHA512: e8699877f4c2eb848d36cc004fe4944c081828bc87e73aea96ff70fefb5dc5c2ec7db0a4f6cd1a1f445690f54251d041f2b4b6d48d6395f30bd70cff00c1fbb1 Homepage: https://cran.r-project.org/package=soilhypfit Description: CRAN Package 'soilhypfit' (Modelling of Soil Water Retention and Hydraulic ConductivityData) Provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" ). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M. and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" ). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" ) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results. Package: r-cran-soilkey Architecture: all Version: 0.9.184-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14303 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-yaml, r-cran-cli, r-cran-rlang Suggests: r-cran-aqp, r-cran-soiltaxonomy, r-cran-mpspline2, r-cran-terra, r-cran-foreign, r-cran-sf, r-cran-chromote, r-cran-munsellinterpol, r-cran-pls, r-cran-prospectr, r-cran-resemble, r-cran-ellmer, r-cran-httr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-pdftools, r-cran-magick, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-shinywidgets, r-cran-plotly, r-cran-leaflet, r-cran-htmltools, r-cran-withr, r-cran-dbi, r-cran-rsqlite, r-cran-base64enc, r-cran-maps, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-soilkey_0.9.184-1.ca2404.1_all.deb Size: 7755226 MD5sum: efc9f1991a7773dba6bdb8ff2fe656e4 SHA1: c79b0c5c6725a77c58ecc255ccc5c950fc941d93 SHA256: 0868aac391e20fc04eb46145a3d2291a56f1e420acaecf05ab8dee20c5f6a2f1 SHA512: 90a0dc10122c69a95ba6ac6b566648ac54e435330ee462ea5442717497d1a9e2fc65de4e533daddd2f1a98ebf9e137bb8fb4dc579fd280b4105b1b0a3b1195db Homepage: https://cran.r-project.org/package=soilKey Description: CRAN Package 'soilKey' (Automated Soil Profile Classification per WRB 2022, 'SiBCS' 5and USDA Soil Taxonomy 13) Implements deterministic classification keys for the World Reference Base for Soil Resources 2022 (4th edition) and the Brazilian System of Soil Classification ('SiBCS', 5th edition). Provides a unified profile representation with explicit per-attribute provenance, multimodal extraction from field reports and photos via vision-language models, spatial priors from 'SoilGrids' and national soil maps, and gap-filling of soil attributes from Vis-NIR or MIR spectra via the Open Soil Spectral Library ('OSSL'). The taxonomic key itself is never delegated to a language model; LLMs are restricted to schema-validated extraction. Each classification result reports a key trace, a provenance-aware evidence grade, and ambiguities that further measurement would resolve. Package: r-cran-soilmanager Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2, r-cran-ggthemes, r-cran-tidyr, r-cran-tibble, r-cran-readxl, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soilmanager_1.1.0-1.ca2404.1_all.deb Size: 861000 MD5sum: 9d4efb8584dc9fc22a292a3f1fc5c9b7 SHA1: 923e27c2f261b72fae196c303d45afb094ea2c2a SHA256: c7cabcc8e086c96ab2a748e3102ac3e205532f0868c53f9f0e0c6ab52ee79bd8 SHA512: b3a78a09a636ff64fad54c34d967fec5e7c8461fdbaac1d4da2b8b007bf2c1a2d0759241813f3032f438f001f7df891421fd5b5d04381b6724af884936d15b87 Homepage: https://cran.r-project.org/package=SoilManageR Description: CRAN Package 'SoilManageR' (Calculate Soil Management Indicators for Agricultural PracticeAssessment) Calculate numerical agricultural soil management indicators from on a management timeline of an arable field. Currently, indicators for carbon (C) input into the soil system, soil tillage intensity rating (STIR), number of soil cover and living plant cover days, N fertilization and livestock intensity, and plant diversity are implemented. The functions can also be used independently of the management timeline to calculate some indicators. The package contains tables with reference information for the functions, as well as a '*.xlsx' template to collect the management data. Package: r-cran-soilphysics Architecture: all Version: 5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mass, r-cran-shiny, r-cran-rhandsontable, r-cran-shinydashboard, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rpanel Filename: pool/dists/noble/main/r-cran-soilphysics_5.1-1.ca2404.1_all.deb Size: 1767650 MD5sum: 8390b36f8d338eace48d092dd4f96f42 SHA1: 69763f4ce1764e5fd1caf1dc64ea526be1d9a5d1 SHA256: fbe20b8d2d3d46b3eac7f0001b95d0de571ebaf904ac56f6e98059ca4b3e1c9d SHA512: 36c870aa07652e6041cf5a26006c894fc4d718d5ad29a893ea47e3e5a729ea2c4a26f9340371b661fda91240b33e22321785033c5c94c5c35a76ed81709967d9 Homepage: https://cran.r-project.org/package=soilphysics Description: CRAN Package 'soilphysics' (Soil Physical Analysis) Basic and model-based soil physical analyses. Package: r-cran-soilr Architecture: all Version: 1.2.107-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3705 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-igraph, r-cran-assertthat, r-cran-expm, r-cran-sets, r-cran-purrr Suggests: r-cran-fme, r-cran-lattice, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-getopt, r-cran-tinytex Filename: pool/dists/noble/main/r-cran-soilr_1.2.107-1.ca2404.1_all.deb Size: 2000576 MD5sum: e24d353b3f5267de098ac4d616a84de4 SHA1: 3bc136890e414b5d6811b631bc88090337b6ead0 SHA256: ab886278ce8a0297c5feb700fec10dc7af7ad115761a1efae7aa2d6d306d237d SHA512: c55fb57c947b8b0ea199417c0dd187ce619e3a82650a824157c7c1bbb8d73e95fd586485bd1517bc2be576fb2938c091c1a62d4ebeb6e044c1170c89fa8a923b Homepage: https://cran.r-project.org/package=SoilR Description: CRAN Package 'SoilR' (Models of Soil Organic Matter Decomposition) Functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) and Sierra and others (2014) . Package: r-cran-soilsaltindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4777 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soilsaltindex_0.1.0-1.ca2404.1_all.deb Size: 4400100 MD5sum: f7b72ed8a85e1545b406f2cd4ff232b7 SHA1: 31c495a180dc5dab4b876b5348d4243af77ed0b0 SHA256: df37207e5cf24d677085b6aa31b652dd45fc0e5f11ac5a3abc3630d119fa3a15 SHA512: 55e2fa39d3a8ffc1a8b196cdd75ed5d3b288bc11d2c4f5926fd0addf361d4d1d0ff727383422f7b6d66564861feb0798c342e11c567055ac85ad0782724a2f55 Homepage: https://cran.r-project.org/package=SoilSaltIndex Description: CRAN Package 'SoilSaltIndex' (Soil Salinity Indices Generation using Satellite Data) The developed function generates soil salinity indices using satellite data, utilizing multiple spectral bands such as Blue, Green, Red, Near-Infrared (NIR), and Shortwave Infrared (SWIR1, SWIR2). It computes 24 different salinity indices crucial for monitoring and analyzing salt-affected soils efficiently. For more details see, Rani, et al. (2022). . One of the key features of the developed function is its flexibility. Users can provide any combination of the required spectral bands, and the function will automatically calculate only the relevant indices based on the available data. This dynamic capability ensures that users can maximize the utility of their data without the need for all spectral bands, making the package versatile and user-friendly. Outputs are provided as GeoTIFF file format, facilitating easy integration with GIS workflows. Package: r-cran-soiltaxonomy Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-soildb, r-cran-ape, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-soiltaxonomy_0.2.8-1.ca2404.1_all.deb Size: 410554 MD5sum: 2b157f69c4afb57670b87de3ed8f9982 SHA1: e96db760403bcddc86c73a0769c1eaa052db8adf SHA256: f785d573ba280a0d544c336ac85801a388129c42d63f00c655b20a01b37566bd SHA512: 78f483d7b8ffdc41f851dfe606d4cfdfd7e589b7e537dbc8b98933a995e749c2b37282b3526721c8e786b26a5cd7ad5ab196c58320fa2ed7a28c4d08d5c4216e Homepage: https://cran.r-project.org/package=SoilTaxonomy Description: CRAN Package 'SoilTaxonomy' (A System of Soil Classification for Making and Interpreting SoilSurveys) Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup. Package: r-cran-soiltestcorr Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-ggplot2, r-cran-ggpp, r-cran-nlstools, r-cran-minpack.lm, r-cran-modelr, r-cran-nlraa, r-cran-aiccmodavg, r-cran-smatr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soiltestcorr_2.2.1-1.ca2404.1_all.deb Size: 1508630 MD5sum: c0abb0f63c60732d38074687ca9e9f84 SHA1: 05e726b5c4e612e1dd43045f818ef6ed75dc35f9 SHA256: dcc9d8132ed9fa30544d450a609243c984c9b6cfb1871ca9abcae5aa21274999 SHA512: e90b193cb43b773ef291b0072ef89f6e6a57baf02970e3834e4fc357ebde3cce294b149e28bd149c2a6d87f935e7a074b76b7a2101766e6b06c6d650f416a65d Homepage: https://cran.r-project.org/package=soiltestcorr Description: CRAN Package 'soiltestcorr' (Soil Test Correlation and Calibration) A compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) ); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) ), 4) the linear-plateau regression (Anderson & Nelson (1975) ), 5) the quadratic-plateau regression (Bullock & Bullock (1994) ), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) ). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects. Package: r-cran-soiltesting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soiltesting_0.1.0-1.ca2404.1_all.deb Size: 48880 MD5sum: bfcbc4304c1de27d24e85ef05f58b684 SHA1: cfdbbe187db58baf67fffcf726f28804ab93764b SHA256: 45d9621d7373dc749553a2a5b17f512c0cd9a593b58949653d4316b61546379a SHA512: 44ef46033694ea40c1a101a218b3e9b1c99b8288cc54a45087c414f2e48f5f244f01b356e8476cdf50858a728a944953b2f4421b6af84707fb9421e37ffa8c07 Homepage: https://cran.r-project.org/package=SoilTesting Description: CRAN Package 'SoilTesting' (Organic Carbon and Plant Available Nutrient Contents in Soil) Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings. Package: r-cran-soiltexture Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-mass Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-soiltexture_1.5.3-1.ca2404.1_all.deb Size: 764168 MD5sum: 2282b810d3ee811bb304ecfb6a5725a4 SHA1: 3eb8b553543107962d0ca7e46633fb6192f04c09 SHA256: 28212256c6da4b418911a7f18fdcb53bc9312ff8dbbaeab4d6fc2a4a845fc274 SHA512: 85672de31c1172c2fedfa996fc271518a3c5544073cc07e5b6f39652de20b13ff4df63402d24dac2517488d451ef06685f222f0e1e224fb19baf8dea6fa87d6a Homepage: https://cran.r-project.org/package=soiltexture Description: CRAN Package 'soiltexture' (Functions for Soil Texture Plot, Classification andTransformation) "The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui(). Package: r-cran-soiltillr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-soiltillr_0.1.0-1.ca2404.1_all.deb Size: 112790 MD5sum: 338b01c7f475ed23c54b058b0b033e17 SHA1: c00a9dc1cd9c21f5e342644bd721ad631ee00726 SHA256: ce8d85639b1047eaae714d1957e9cf1f06662def1c64fea095f5609efa6197bd SHA512: 9c78d93c23dfee08677de220350e7681deb13d90f20f4e318b88cb54c4106b599d274877dd83c3538eca8fb6f277418d30f7226c0d3a485cf90f53526081fd09 Homepage: https://cran.r-project.org/package=soiltillr Description: CRAN Package 'soiltillr' (Analyse Soil Tillage Depth and Erosion Over Time) Provides tools to record, validate, and analyse soil tillage depth and erosion across years and field treatments. Includes functions for year-wise tillage operation summaries, erosion depth tracking, compaction detection, soil loss estimation, and visualisation of temporal changes in tillage and erosion profiles. Methods follow Lal (2001) and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" . 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Package: r-cran-sokoban Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sokoban_0.1.0-1.ca2404.1_all.deb Size: 31582 MD5sum: 92c596a33633b2ee6667cfadc8b330de SHA1: d3110e48792586e58541200a7779b60f928ac688 SHA256: 4a6c95cd04c0ed1ee84c8153737da8e7aa445fecf56083e0e044efb6bde4192f SHA512: 8dd2eaefd2a60346ff19f8c0a68576da35899b46a40a3b3016bbdefe1e513c3357954b186a2fdcf89e705aee72813e8008312a05847436410fe7d5cfdafb0782 Homepage: https://cran.r-project.org/package=sokoban Description: CRAN Package 'sokoban' (Sokoban Game) Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations. 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The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. Details about the methods and equations are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link. 'On the role of vegetation density on net snow cover radiation at the forest floor.' Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374, . Package: r-cran-solrium Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 653 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-plyr, r-cran-crul, r-cran-xml2, r-cran-jsonlite, r-cran-tibble, r-cran-r6 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-solrium_1.2.0-1.ca2404.1_all.deb Size: 404728 MD5sum: ad90d45f2210e278caf0d5b106112242 SHA1: 8c66f2aa9bedf63c9d1f620bd04be1a582e8b89f SHA256: c04a9f1d90262454149df2a5cbc8d6611bdcdfba6c86ce1c8a046f4e50b96451 SHA512: 9327e91a9788481563b5913869cc19c36621a3fc3d71ef140376dfeec8097b41f1602c073004dae214f848a9571b9606a70999a8c2a32386769789ef8e11615e Homepage: https://cran.r-project.org/package=solrium Description: CRAN Package 'solrium' (General Purpose R Interface to 'Solr') Provides a set of functions for querying and parsing data from 'Solr' () 'endpoints' (local and remote), including search, 'faceting', 'highlighting', 'stats', and 'more like this'. In addition, some functionality is included for creating, deleting, and updating documents in a 'Solr' 'database'. Package: r-cran-solvebio Architecture: all Version: 2.15.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-mime Suggests: r-cran-testthat, r-cran-shiny, r-cran-shinyjs, r-cran-openssl Filename: pool/dists/noble/main/r-cran-solvebio_2.15.1-1.ca2404.1_all.deb Size: 260764 MD5sum: 00ed5ae43201637c4014c28e4096e435 SHA1: e82ea746cd4dca3f1968f66ebba227d1c725404f SHA256: 2d59ef5a7fb02922d135985d2c1e57c5e9cf5cdc8f212764569e2eff62b4065d SHA512: deb00ff007f7ea4e3e58d1eccc987672c7ec3b7f5bb5d42c37b36bec0cfc5a512da4fc09173b221f22ee8f583d506582fd9211ca5c391d3c645f66a5f0b2b308 Homepage: https://cran.r-project.org/package=solvebio Description: CRAN Package 'solvebio' (The Official SolveBio API Client) R language bindings for SolveBio's API. 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Package: r-cran-solvency2rfr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-readxl, r-cran-tibble, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-solvency2rfr_0.1.0-1.ca2404.1_all.deb Size: 31768 MD5sum: bb62e4bb8fae896ea5170ae9d53aadfa SHA1: be63f23db1694f63bf82163b999c4ae411e0eb45 SHA256: fd60bd0b76aa7424192f05f99c08dac2be8ef2619c84b9afeec4d3650b5304c2 SHA512: 5ee19dedb9e4a8529b63c36c5c4815d0b37e6d7fe2bf9e4a972c4d2c3825978ac19b9d29fc85c2dd9f64324c53cecc5b4a09d00b565d5b9657684d3c43f07753 Homepage: https://cran.r-project.org/package=solvency2rfr Description: CRAN Package 'solvency2rfr' ('EIOPA' Risk-Free Interest Rate Term Structures for Solvency II) Downloads and parses the risk-free interest rate ('RFR') term structures published monthly by the European Insurance and Occupational Pensions Authority ('EIOPA') for Solvency II calculations. 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Samples of unknown class are predicted by mapping them on the SOM and analysing class membership of neurons in the neighbourhood. Package: r-cran-soma Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reportr Suggests: r-cran-tinytest, r-cran-covr, r-cran-shades Filename: pool/dists/noble/main/r-cran-soma_1.2.1-1.ca2404.1_all.deb Size: 35840 MD5sum: 3e76dcda144b5911ec164aff7f0cc7fe SHA1: 741c1af139f89b36eeed5992369b2e971d376ba1 SHA256: 0e601068b3024fb3d2d6b480938ee36ff1d8f4c3d7de3f2598d4627630f686fb SHA512: 55530d3c661a924e91b61a1ca86650f0f08128354c552c0ee27b0bdd4c9d36f600552a7d76d5eac944d215cdcc04959a0e079671da11443527841fe9df0a8e79 Homepage: https://cran.r-project.org/package=soma Description: CRAN Package 'soma' (General-Purpose Optimisation with the Self-Organising MigratingAlgorithm) An R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of "migrations" by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima. Package: r-cran-somadataio Architecture: all Version: 6.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5124 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-readxl, r-cran-tibble, r-cran-tidyr Suggests: r-bioc-biobase, r-cran-knitr, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-somadataio_6.6.1-1.ca2404.1_all.deb Size: 4158648 MD5sum: 6110a5ec7899837c6d36e6f2398cbd9e SHA1: 0ef73f7a36d2ffc6994b292bcee3a8b27a8c3b52 SHA256: 5edd30e937899f5ebf38407ce254abd14cd2af689d8a7d364dbd84f8772b0f7d SHA512: 3470236b96dcf0d3c565a28420a40507fad548f116fe2baf107b5f6b85e5d110ca436372b1ee07208976d8e8b4a8a224b833cd66a5aaa51ab2d0e07c5db74e1c Homepage: https://cran.r-project.org/package=SomaDataIO Description: CRAN Package 'SomaDataIO' (Input/Output 'SomaScan' Data) Load and export 'SomaScan' data via the 'SomaLogic Operating Co., Inc.' structured text file called an ADAT ('*.adat'). For file format see . The package also exports auxiliary functions for manipulating, wrangling, and extracting relevant information from an ADAT object once in memory. Package: r-cran-somaticflags Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-usethis, r-cran-devtools Filename: pool/dists/noble/main/r-cran-somaticflags_0.1.0-1.ca2404.1_all.deb Size: 15478 MD5sum: 4a142efb03ed8ac4afa2e248961e344b SHA1: b166dc9c780938cae1cc6aad31e64625063f8a7d SHA256: 982ecce9377e2668391502fdf041a50325672026e48efc8e725e632124670a04 SHA512: bd333d01597285f13427aef5b0c3843ba1a109884756efc34bb36aea4a0d52e3b8bcdccbe5a05a8ac07e948334e7bc65d268ad914dde33e6917b448a26724801 Homepage: https://cran.r-project.org/package=somaticflags Description: CRAN Package 'somaticflags' (Database of Somatic Flags) Database of genes which frequently sustain somatic mutations, but are unlikely to drive cancer. Package: r-cran-sombrero Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1704 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-markdown, r-cran-scatterplot3d, r-cran-shiny, r-cran-ggplot2, r-cran-ggwordcloud, r-cran-metr, r-cran-interp, r-cran-rlang Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-hexbin, r-cran-shinycssloaders, r-cran-shinybs, r-cran-shinyjs, r-cran-shinyjqui, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-sombrero_1.5.0-1.ca2404.1_all.deb Size: 872998 MD5sum: 46a48c6268fb012e4df371214802b4c6 SHA1: 99f8f0599dbfff5dea326d1887010cfe687cb7f5 SHA256: b1b7538024b175bfaee4b622c2901b01824c106eec3b129ec36e8389556d2ddf SHA512: 2bbf09cbe84586d32d453a632c95b3979a943a9b0d971a5e92e4f16e52d6b9d39f6dcec14be1e6c1a4d2d87a226e06478a640a037fff022d5db2b181c9d1c65b Homepage: https://cran.r-project.org/package=SOMbrero Description: CRAN Package 'SOMbrero' (SOM Bound to Realize Euclidean and Relational Outputs) The stochastic (also called on-line) version of the Self-Organising Map (SOM) algorithm is provided. Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) , Olteanu & Villa-Vialaneix (2005) and Cottrell et al (2004) . The package also contains many plotting features (to help the user interpret the results), can handle (and impute) missing values and is delivered with a graphical user interface based on 'shiny'. Package: r-cran-somemtp Architecture: all Version: 1.4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-somemtp_1.4.1.1-1.ca2404.1_all.deb Size: 200650 MD5sum: cde817975618ac31cdd2c1a4f3d27a1d SHA1: 4f894f177d91a7640a6b93141d7358fe67481e5d SHA256: e0862252c6be0ccdde1a792d8a81954e8ff01839fd28c2b32a7e851e9494c2c9 SHA512: 047ef5e2e252233d9807fa2563a994a19b84db8405bee2ecc99afa6d1f6dc9f480ea0ce7ddb24f6af7258d746aa307bcb90b11b375591d2c51fd206ff16bfb54 Homepage: https://cran.r-project.org/package=someMTP Description: CRAN Package 'someMTP' (Some Multiple Testing Procedures) It's a collection of functions for Multiplicity Correction and Multiple Testing. Package: r-cran-somenv Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlist, r-cran-kohonen, r-cran-shiny, r-cran-dplyr, r-cran-plyr, r-cran-openair, r-cran-colourpicker, r-cran-shinycssloaders, r-cran-shinycustomloader Filename: pool/dists/noble/main/r-cran-somenv_1.1.2-1.ca2404.1_all.deb Size: 151816 MD5sum: 402f2958d2c2d60201669357576b68e4 SHA1: 75b6ea4eab21d0d6d27633dbb8f32eb8c168633d SHA256: 867f375bd74751030d0b137ad29935627d09c9d4ab31a354cf2ca9439c1488be SHA512: cd333890ea77a5bc463d2363f83baade14bae83d9cefba5280ed633476911739b94f4046da1cab23cfcc65e949493f4566960641a4236954bf06dc1a9d551226 Homepage: https://cran.r-project.org/package=SOMEnv Description: CRAN Package 'SOMEnv' (SOM Algorithm for the Analysis of Multivariate EnvironmentalData) Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) are provided. Package: r-cran-somhca Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kohonen, r-cran-awesom, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-maptree, r-cran-fpc Filename: pool/dists/noble/main/r-cran-somhca_0.4.0-1.ca2404.1_all.deb Size: 60648 MD5sum: e96e7b7eeec57d65089b410fa1fdc79a SHA1: 8658c7ceb58896b04ffc1adbd0f084121a849f8d SHA256: a1cb060c6955e66d2a95452ebdac31aa70e06a349871dc9e76cfb56013c9a135 SHA512: 80d5e1579b54a1b26d73d0a0b8126895653fc1d83fc1a7f66847f7e3f4708307e1087b23bc09824222595ec51c0b22989e70b9997e1a8919e5403fab6734370c Homepage: https://cran.r-project.org/package=somhca Description: CRAN Package 'somhca' (Self-Organising Maps Coupled with Hierarchical Cluster Analysis) Implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data. The package includes functions to estimate the optimal map size based on various quality measures and to generate a model using the selected dimensions. It also performs hierarchical clustering on the map nodes or other data to group similar units. Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024) . Package: r-cran-somnmr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3614 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-pracma, r-cran-minpack.lm, r-cran-quadprog, r-cran-intervalsurgeon, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-somnmr_0.3.0-1.ca2404.1_all.deb Size: 3634884 MD5sum: a3a0bed189cb5552d2b58041f7c6d872 SHA1: 1f2a982df6139cdfa972cb9b2848d66517894ef9 SHA256: 70b1aecac100fdc471341190b772635b96a583031cd1b09e4b440b412604bd66 SHA512: 7c2a9e36b4433d82964ff898911dbf7db3fe8cab14dc89d85c8be38d93c43bbd7152b4556587dabf5e63216a6f3eba629609871b66eb1770079f390b1dc9c19b Homepage: https://cran.r-project.org/package=SOMnmR Description: CRAN Package 'SOMnmR' (Analysis of Soil Organic Matter using Nuclear Magnetic Resonance) Integrates the 13C nuclear magnetic resonance spectra using different integration ranges. Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: . Package: r-cran-somspace Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3994 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-kohonen, r-cran-maps, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-somspace_1.2.4-1.ca2404.1_all.deb Size: 3568690 MD5sum: a6be6b5263bba0319fb0cdf9087e3ef8 SHA1: 7f17dbc0ade0840e6768de92018532b888d01c91 SHA256: 427e0aafed408bb3876c43e631faab581c042a995a17a9593d7bd09cb74ce0cc SHA512: 9d86dc7b253de2b23107903fe1d32ad9fc221abe321f10544ace4437d9f7f50de294afbef359ea6ddf434c20e04ce51c552536d656b44fa9467deb2d08afaedc Homepage: https://cran.r-project.org/package=somspace Description: CRAN Package 'somspace' (Spatial Analysis with Self-Organizing Maps) Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2020, ). Package: r-cran-sonar Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sonar_1.0.2-1.ca2404.1_all.deb Size: 204918 MD5sum: 7d518dcfcf26c5d1ad4127a3328f6bb8 SHA1: c05a18507bb51f3328354c26f902d311013bb31f SHA256: e0d2e14af68abbf5c694bd8bab38748e0ff5aff3c7edfbdd2b8ae1e18a0204b8 SHA512: 5ce1e88755af2dde704e5c08a9ba07b2fd012d86e5bc4864997549eb27a3e55032fd1589df198e19bf1bfd519e547319e85e5a2168b68b5400ac29338db81871 Homepage: https://cran.r-project.org/package=sonar Description: CRAN Package 'sonar' (Fundamental Formulas for Sonar) Formulas for calculating sound velocity, water pressure, depth, density, absorption and sonar equations. Package: r-cran-songevo Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-geosphere, r-cran-lattice, r-cran-sp Suggests: r-cran-reshape2, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-songevo_1.0.0-1.ca2404.1_all.deb Size: 619844 MD5sum: 20880f460f19b44231e6e9a50f83cccc SHA1: 480cd5cff7b280b370d344d67670477c4fd78564 SHA256: 8ffe74c71bed6857002bd40c3676dd5e287ba519b23b3c913b2db1034bbb520c SHA512: d6dba48f075765a4c7dd066b9ef26eeecc38208c4137e0cb091620f4ce2dc7c0b54c9e746f8b3f28b5acdc284cc87725cad3174fec34e732423d6bb2f90d9831 Homepage: https://cran.r-project.org/package=SongEvo Description: CRAN Package 'SongEvo' (An Individual-Based Model of Bird Song Evolution) Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing. Package: r-cran-soniclength Architecture: all Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soniclength_1.4.7-1.ca2404.1_all.deb Size: 387272 MD5sum: 6aea6b9ae930f3e9687e43fde736cf3c SHA1: 55679e1e412e311f2a01598e8198bf1c7eb54bc3 SHA256: 692061f9a4b6d4932b4a03fb98915f2129070162b926cad0530f7248a9b80fb1 SHA512: 2d2916b4a5871b4164693c806e88665a246645b06c1701be39d4598cfb0f7c89df800bc97bd4fceed884852597307b809e82e26ec079a7349cdc3a63a13d58a5 Homepage: https://cran.r-project.org/package=sonicLength Description: CRAN Package 'sonicLength' (Estimating Abundance of Clones from DNA Fragmentation Data) Estimate the abundance of cell clones from the distribution of lengths of DNA fragments (as created by sonication, whence `sonicLength'). The algorithm in "Estimating abundances of retroviral insertion sites from DNA fragment length data" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR, Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented. The experimental setting and estimation details are described in detail there. Briefly, integration of new DNA in a host genome (due to retroviral infection or gene therapy) can be tracked using DNA sequencing, potentially allowing characterization of the abundance of individual cell clones bearing distinct integration sites. The locations of integration sites can be determined by fragmenting the host DNA (via sonication or fragmentase), breaking the newly integrated DNA at a known sequence, amplifying the fragments containing both host and integrated DNA, sequencing those amplicons, then mapping the host sequences to positions on the reference genome. The relative number of fragments containing a given position in the host genome estimates the relative abundance of cells hosting the corresponding integration site, but that number is not available and the count of amplicons per fragment varies widely. However, the expected number of distinct fragment lengths is a function of the abundance of cells hosting an integration site at a given position and a certain nuisance parameter. The algorithm implicitly estimates that function to estimate the relative abundance. 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The package validates that values are physically possible wherever feasible. Package: r-cran-sonify Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tuner Filename: pool/dists/noble/main/r-cran-sonify_0.1-0-1.ca2404.1_all.deb Size: 23606 MD5sum: fe4fc39412f1662c9d2efee01a5b4429 SHA1: d73b99acf0f51cb64eb2f0657c32660041f3c8ef SHA256: f417517b26bc20f35599b2ba1f0555eec1113c34fec0c8bbac7689a8b1bc398c SHA512: 2907de573a3abf6af986adc8b4d4b73d764309ff26c899156608acd83c6aa5e8cfa3f633a7d6c618ab37c9c60a633f344d6103c261e2bfa92cdc77c6d6c02e9e Homepage: https://cran.r-project.org/package=sonify Description: CRAN Package 'sonify' (Data Sonification - Turning Data into Sound) Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired. Package: r-cran-sono Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-desctools, r-cran-ggplot2, r-cran-rdpack, r-cran-rje Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sono_1.2-1.ca2404.1_all.deb Size: 170842 MD5sum: 7fd8f8a06fd497311fe974115a32b4d1 SHA1: 3eab439e2a32681b72449d097e8d9159797e0434 SHA256: e5726351d8fce6458fdbe8edad193490ecdd5b0b846e31e78c47cb05e47d18ca SHA512: a7c58178432fe028812ae086573567e325f83bef991f82018c451ed2dcad96f1f3245c8b9341b0c300d4307688d5424a39decbcde4cebfcee20594d3c9c7198a Homepage: https://cran.r-project.org/package=SONO Description: CRAN Package 'SONO' (Scores of Nominal Outlyingness (SONO)) Computes scores of outlyingness for data sets consisting of nominal variables and includes various evaluation metrics for assessing performance of outlier identification algorithms producing scores of outlyingness. The scores of nominal outlyingness are computed based on the framework of Costa and Papatsouma (2025) . Package: r-cran-sooty Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2914 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-curl, r-cran-s7, r-cran-tibble Suggests: r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sooty_0.6.1-1.ca2404.1_all.deb Size: 1450284 MD5sum: 9ae0d018f83f3a7d60b394ee228bd411 SHA1: d3478ca03a4cc046a1187f7a1db4e85a6c916398 SHA256: 59a4e59495a4b10087af7b3e7fc200a8e980bd5d1d0a4f2358dbc06ca15e009b SHA512: d136aa9d18d2cb7ff4fc50f01e5f1dfa39b893bd41bf35e60788a06984448455dfd300eaa3141f402d47856bfdbbbd6f5e0a045d56a8bd0e0ae4d5ce6f8454c9 Homepage: https://cran.r-project.org/package=sooty Description: CRAN Package 'sooty' (Data Source Catalogues Online for Southern Ocean EcosystemResearch) Obtains lists of files of remote sensing collections for Southern Ocean surface properties. Commonly used data sources of sea surface temperature, sea ice concentration, and altimetry products such as sea surface height and sea surface currents are cached in object storage on the Pawsey Supercomputing Research Centre facility. Patterns of working to retrieve data from these object storage catalogues are described. The catalogues include complete collections of datasets Reynolds et al. (2008) "NOAA Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1" , Spreen et al. (2008) "Artist Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice concentration" . In future releases helpers will be added to identify particular data collections and target specific dates for earth observation data for reading, as well as helpers to retrieve data set citation and provenance details. This work was supported by resources provided by the Pawsey Supercomputing Research Centre with funding from the Australian Government and the Government of Western Australia. This software was developed by the Integrated Digital East Antarctica program of the Australian Antarctic Division. 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While historically the State of the Union was often a written document, in recent decades it has always taken the form of an oral address to a joint session of the United States Congress. This package provides the raw text from every such address with the intention of being used for meaningful examples of text analysis in R. The corpus is well suited to the task as it is historically important, includes material intended to be read and material intended to be spoken, and it falls in the public domain. As the corpus spans over two centuries it is also a good test of how well various methods hold up to the idiosyncrasies of historical texts. Associated data about each address, such as the year, president, party, and format, are also included. 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Package: r-cran-soundclass Architecture: all Version: 0.0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-htmltools, r-cran-seewave, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-rsqlite, r-cran-signal, r-cran-tuner, r-cran-zoo, r-cran-magrittr, r-cran-shinyfiles, r-cran-shiny, r-cran-generics, r-cran-keras, r-cran-shinyjs Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-soundclass_0.0.9.2-1.ca2404.1_all.deb Size: 133600 MD5sum: b5abc0defc6bfb124274696f0481cb33 SHA1: 7d2ad2ac1528fd6239efcc25fbeaa43c34c19352 SHA256: 615c85118ec551ed02a00c6a5b45988e391a1307e4ab2f19852ce5c2e4256100 SHA512: 7001f38ba7dd0e54e5d2b7b8b5a932e167ba9ced05850906637727ae363185d30d14c899c23743abba0141f86550821a87ad0d81ee63b3da9c52152c6c483bd6 Homepage: https://cran.r-project.org/package=soundClass Description: CRAN Package 'soundClass' (Sound Classification Using Convolutional Neural Networks) Provides an all-in-one solution for automatic classification of sound events using convolutional neural networks (CNN). The main purpose is to provide a sound classification workflow, from annotating sound events in recordings to training and automating model usage in real-life situations. Using the package requires a pre-compiled collection of recordings with sound events of interest and it can be employed for: 1) Annotation: create a database of annotated recordings, 2) Training: prepare train data from annotated recordings and fit CNN models, 3) Classification: automate the use of the fitted model for classifying new recordings. By using automatic feature selection and a user-friendly GUI for managing data and training/deploying models, this package is intended to be used by a broad audience as it does not require specific expertise in statistics, programming or sound analysis. Please refer to the vignette for further information. Gibb, R., et al. (2019) Mac Aodha, O., et al. (2018) Stowell, D., et al. (2019) LeCun, Y., et al. (2012) . Package: r-cran-soundecology Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-oce, r-cran-ineq, r-cran-vegan, r-cran-tuner, r-cran-seewave Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-soundecology_1.3.3-1.ca2404.1_all.deb Size: 824350 MD5sum: 1b646d37b3b1332fc282ab127648fa17 SHA1: 7f14e113f97595d40d213d083c4a3fcf0d27fd5d SHA256: 54ea82cb885d65da40a91b8635246fcc3cb34fb8111bce3df5ae4668d6a8d229 SHA512: 9a85fde3876f9d86790bacae9811ba8dc37bef81257072af6e0235760f4682bcadcd24fad433d379c20c97509aa9c661f435cf48145803afd220983cd0f44b52 Homepage: https://cran.r-project.org/package=soundecology Description: CRAN Package 'soundecology' (Soundscape Ecology) Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings. Package: r-cran-soundgen Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2733 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tuner, r-cran-signal, r-cran-phontools Suggests: r-cran-base64enc, r-cran-dtw, r-cran-nonlineartseries, r-cran-shiny, r-cran-shinyjs, r-cran-bslib Filename: pool/dists/noble/main/r-cran-soundgen_3.0.0-1.ca2404.1_all.deb Size: 2115824 MD5sum: f94e92c655674ec693427f5f203ac888 SHA1: 699c5f7c1358bc2f204330e6c3c5aa4e08c1aac0 SHA256: 97c11c67086f9369ed7c169bd86cb48b8500d80c80f7122c511e6c1f6d7fd03b SHA512: 478c673100bf5c185245117272f9b3430b8d0591dc95819dcd9564f33922ae60d5054b58e8ed6117e88405b46f1c2a390cfcd866454e5d28c1e9bef3871824cf Homepage: https://cran.r-project.org/package=soundgen Description: CRAN Package 'soundgen' (Sound Synthesis and Acoustic Analysis) Parametric source-filter synthesis of harmonic-noise signals, such as animal vocalizations and human voice, with control over pitch, formants, noise, amplitude modulation, nonlinear phenomena, and morphing. General signal processing tools for audio analysis and manipulation: pitch tracking, formant and vocal tract length estimation, reassigned and auditory spectrograms, modulation spectra and psychoacoustic roughness, self-similarity and surprisal, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for audio synthesis, annotation, formant analysis, and manually correcting pitch contours. Reference: Anikin (2019) . 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Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates . 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Evaluates voter influence through an a posteriori analysis of relative preferences. Supports weighted voting and various voting thresholds. Compatible with ideal point estimates from NOMINATE, Optimal Classification, and 'MCMCpack'. The method builds on Bibina and Dougherty (2025) . 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Package: r-cran-soyurt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3985 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-soyurt_1.0.0-1.ca2404.1_all.deb Size: 4043540 MD5sum: d4a07d15886afc8b601620a338c73f84 SHA1: 2a9eb1d4967a7ab28af8b8413f01c8daf61422b8 SHA256: 952c80c3aedee239ca0cb088b9a1274cc91939c0b7ba703636f1155c6812abf8 SHA512: b9f91d60d6c40551ea0ffc2c845eb3df3284fca64b10a0a201a1ff0909975def17bf4fd050bd413767eb1b17088210df214b70cfe1f031a5648dd1481264f427 Homepage: https://cran.r-project.org/package=SoyURT Description: CRAN Package 'SoyURT' (USDA Northern Region Uniform Soybean Tests Dataset) Data sets used by 'Krause et al. (2022)' . It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. 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Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global , China animal scientific database and catalogue of life Taiwan . The development of 'SP2000' package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project and Yunnan University's Research Innovation Fund for Graduate Students <2019227>. 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SPA uses linear interpolation via the base approx() function with constrained extrapolation (rule = 1) to preserve stratigraphic order and avoid estimation beyond observed depths. The method aligns all datasets to a common depth grid, enabling high-resolution multivariate analysis and stratigraphic interpretation of core-based datasets such as those from the Utica and Point Pleasant formations. See R Core Team (2025) and Omodolor (2025) for methodological background and geological context. 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Package: r-cran-spaero Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-earlywarnings, r-cran-knitr, r-cran-moments, r-cran-np, r-cran-pomp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spaero_0.6.0-1.ca2404.1_all.deb Size: 487246 MD5sum: 413f08007accd32e415762852ca08af0 SHA1: ec1a2a1ab4a455e90e28c07e634403571acd6d80 SHA256: 8ba0d2b27540ef2bebd2ba2389d7530e1b8a8239456ee8bde894b2876220e49b SHA512: 5390730a040fbd1807fa7816168e6dec1fae343275461d641c913bc63fa438edf6b47335ba2e8068cf308bb714eba4ce87153c1c51cf3360a16a3a053a93d592 Homepage: https://cran.r-project.org/package=spaero Description: CRAN Package 'spaero' (Software for Project AERO) Implements methods for anticipating the emergence and eradication of infectious diseases from surveillance time series. 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See Davies & Lawson (2019) for example. Package: r-cran-spanish Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-xml2 Suggests: r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-spanish_0.4.2-1.ca2404.1_all.deb Size: 41462 MD5sum: c03aebba30417fe8bebb69307dbd36d6 SHA1: 8833ebbdca60f3c0d7ae72f0212de53a04f205e4 SHA256: a7c675cbbf26dea7ee41d4a58da53f9839816eae5deeba996918c75a34e99b84 SHA512: 0540d9f7c051133b0e708c6127a3cd6d5ada489aa62f669287e4f7f0b52fdba903fa4d22038e9b9d62ee789a7c22235f8f0146944613f21951051c999305938d Homepage: https://cran.r-project.org/package=spanish Description: CRAN Package 'spanish' (Translate Quantities from Strings to Integer and Back. MiscFunctions on Spanish Data) Character vector to numerical translation in Euros from Spanish spelled monetary quantities. Reverse translation from integer to Spanish. 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Package: r-cran-spanishoddata Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-digest, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-openssl, r-cran-parallelly, r-cran-paws.storage, r-cran-purrr, r-cran-rdpack, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-cli, r-cran-flowmapblue, r-cran-flowmapper, r-cran-furrr, r-cran-future, r-cran-future.mirai, r-cran-ggiraph, r-cran-hexsticker, r-cran-htmlwidgets, r-cran-mapspain, r-cran-quarto, r-cran-remotes, r-cran-scales, r-cran-testthat, r-cran-tidyverse, r-cran-withr Filename: pool/dists/noble/main/r-cran-spanishoddata_0.2.6-1.ca2404.1_all.deb Size: 2751214 MD5sum: 0d0cf7ddce63b5e5ffcb3594f8a23795 SHA1: f6c9b44a87df90905ebe1faa58c851de59c9aa7e SHA256: 54f72d0a019e7a7cc45f5f9d61abeb7f57e17a2caa15a47f15c4989a7c62c370 SHA512: 5e0bd324b1453827d64ef166b37e12eb0073a9e29ee4f955bfb3e050ddd73953f2434ebce4dbc0b288b239c535d784740e223e35eb4aa9d65f889fc2f83228f6 Homepage: https://cran.r-project.org/package=spanishoddata Description: CRAN Package 'spanishoddata' (Get Spanish Origin-Destination Data) Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at . 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Package: r-cran-spark.sas7bdat Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spark.sas7bdat_1.4-1.ca2404.1_all.deb Size: 21022 MD5sum: 13397b67d0be546f1a615e4f747a3dda SHA1: 1657e2aae7c121265a8e1ca74f86940072caa4f5 SHA256: 43400fedbdd9b976f74c9b4c00037d72047a9c2e41f9eb7b3b0af838540a59aa SHA512: a8b8e13e9ca7bd89f0130455735092235f9aa5009802ed7d57362e49c30291a5bbee5da60509b093e97569a04fd4e272a816acf66fc82646a67d5a86180077eb Homepage: https://cran.r-project.org/package=spark.sas7bdat Description: CRAN Package 'spark.sas7bdat' (Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark') Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at . 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Package: r-cran-sparklyr.nested Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr, r-cran-jsonlite, r-cran-listviewer, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-reactr Filename: pool/dists/noble/main/r-cran-sparklyr.nested_0.0.4-1.ca2404.1_all.deb Size: 49502 MD5sum: 0a682a493fa0c4fa8a000b94e1cd0605 SHA1: f365c23527b08393a916a2b21b33e1ddce5ea98c SHA256: 40103d2db266247d89910b22b44e88d9a544a3495bfd0bebbc75fdb4de09ade7 SHA512: 72dac4fe17733aee0774d7898664a107bbcbe6eb7191def3c28acdb61989469ec5d6c2eb40faf583cf2f7c7989a7809bfc27c2ab1a5bc44314b2f836cde02d39 Homepage: https://cran.r-project.org/package=sparklyr.nested Description: CRAN Package 'sparklyr.nested' (A 'sparklyr' Extension for Nested Data) A 'sparklyr' extension adding the capability to work easily with nested data. Package: r-cran-sparklyr Architecture: all Version: 1.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4779 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-config, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-generics, r-cran-globals, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-rstudioapi, r-cran-tidyr, r-cran-tidyselect, r-cran-uuid, r-cran-vctrs, r-cran-withr, r-cran-xml2 Suggests: r-cran-arrow, r-cran-broom, r-cran-diffobj, r-cran-foreach, r-cran-ggplot2, r-cran-iterators, r-cran-janeaustenr, r-cran-lahman, r-cran-mlbench, r-cran-nnet, r-cran-nycflights13, r-cran-r6, r-cran-r2d3, r-cran-rcurl, r-cran-reshape2, r-cran-shiny, r-cran-parsnip, r-cran-testthat, r-cran-rprojroot Filename: pool/dists/noble/main/r-cran-sparklyr_1.9.6-1.ca2404.1_all.deb Size: 3806038 MD5sum: 8a5ee5697aeadcdfb7f4285250b1dcde SHA1: 61ff461504f7734e59684ad52f44deda653a4038 SHA256: bd7d9cd7f4b027a289344fbb3e9615cb674db639d0e8127175be7c1211a272e8 SHA512: a8ed5f9566c67cb88f55053216732e8db9d071985c45b9ea985d917313c82053c8725eec978bb9321f70e3b7dd10ea959efba2ffadc0d30252bfe2eed1d8fb1c Homepage: https://cran.r-project.org/package=sparklyr Description: CRAN Package 'sparklyr' (R Interface to Apache Spark) R interface to Apache Spark, a fast and general engine for big data processing, see . 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Package: r-cran-sparktf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-sparktf_0.1.0-1.ca2404.1_all.deb Size: 19404 MD5sum: 95f17aa7c6b64ea98222333c8a785a0f SHA1: e54f85ebeeaa7619abf5eace8c218490a2627273 SHA256: 48dc114e78e7cead0165f3355c1ac6d92ed386f08258fc9fd6194bd92cac05f1 SHA512: bbcef499b23ed47277f8214565a613af534c62513d73be09edcda0700348542edbfc296202f5a3cae188e595420a7b606b2d426fed3091a36222ced43c383d93 Homepage: https://cran.r-project.org/package=sparktf Description: CRAN Package 'sparktf' (Interface for 'TensorFlow' 'TFRecord' Files with 'Apache Spark') A 'sparklyr' extension that enables reading and writing 'TensorFlow' TFRecord files via 'Apache Spark'. Package: r-cran-sparkxgb Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparklyr, r-cran-rlang, r-cran-magrittr, r-cran-vctrs, r-cran-fs Suggests: r-cran-dplyr, r-cran-purrr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sparkxgb_0.2.1-1.ca2404.1_all.deb Size: 70572 MD5sum: 3a99c7d7f4b0d362f119149de83227e0 SHA1: 04f1830841fea61ff69aeba955bf132413e3467f SHA256: 454cd90c65eb40d4e333c1e6f0556ef274eefa960df78309308bb07acb2155d7 SHA512: c3c5f1f95467c9b18076cad691e2f57699950451c3005f92a29271ec0bf9ecdaa78c3f8f6114f84bdd767eadd4b26fc75cb1d21c2e2dee956dd4e8e7d528122a Homepage: https://cran.r-project.org/package=sparkxgb Description: CRAN Package 'sparkxgb' (Interface for 'XGBoost' on 'Apache Spark') A 'sparklyr' extension that provides an R interface for 'XGBoost' on 'Apache Spark'. 'XGBoost' is an optimized distributed gradient boosting library. Package: r-cran-sparqlr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-styler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparqlr_0.1.0-1.ca2404.1_all.deb Size: 64024 MD5sum: ad35ad20782a38bcd2893e336dcfef61 SHA1: 280035e422936357bf1659834bc5e4cfbb769d45 SHA256: a963ad265add785e74629e36e5bef62a031b5dab24066290cbe32bf9adf020d6 SHA512: 299c1cccaff6500a8b07323aaeb1535eba1b613e9988801f75ddf30b3f11a951c302491aad36739b81fc6878fe7cbd810b2483e111cd8f3f4a857c1dcad5e119 Homepage: https://cran.r-project.org/package=sparqlr Description: CRAN Package 'sparqlr' (A SPARQL Client for R) Provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format. Package: r-cran-sparr Architecture: all Version: 2.3-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.utils, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random, r-cran-spatstat.univar, r-cran-doparallel, r-cran-foreach, r-cran-misc3d Suggests: r-cran-fftwtools Filename: pool/dists/noble/main/r-cran-sparr_2.3-16-1.ca2404.1_all.deb Size: 623084 MD5sum: e155d42912a7d492b22b205ef46bdbbf SHA1: d5d8e702d369215a29d8f99083ebeffe89015d21 SHA256: 08838e7b83074897715508e2aa1ea854f4a22ad1a55ab40923fa1b574ebafc11 SHA512: a210f36926a55edcbc222439bb7825409fa711ce6bea6bb9ba8ee0d285a2e8c74ccd61775d9aec06de02c26203f7c37f38d9653df321f0e2e25597445993025e Homepage: https://cran.r-project.org/package=sparr Description: CRAN Package 'sparr' (Spatial and Spatiotemporal Relative Risk) Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) . Package: r-cran-sparrafairness Architecture: all Version: 0.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1551 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-ranger, r-cran-mvtnorm, r-cran-cvauc, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparrafairness_0.1.0.0-1.ca2404.1_all.deb Size: 1479624 MD5sum: 21c7c03f33a062e5c7c435c48516a7a2 SHA1: 3a186e466e366077442798350b3ee8a35fcf9f09 SHA256: ebf7994ccb613215048756be3989c3ea862dc69a0ad73c1b0432002d1fbacb7c SHA512: 413a482c4ad503c37a4bad9b515819530fb607b2c01913cfd5f7b2ab03b8311a04b72dfad17d7d6a2d7e64b08e309da126f4fe6abdd7d791298b82591bddd9d8 Homepage: https://cran.r-project.org/package=SPARRAfairness Description: CRAN Package 'SPARRAfairness' (Analysis of Differential Behaviour of SPARRA Score AcrossDemographic Groups) The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset. Our manuscript can be found at: . Package: r-cran-sparrpowr Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-dorng, r-cran-fields, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-lifecycle, r-cran-sparr, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-terra Suggests: r-cran-geojsonsf, r-cran-ggmap, r-cran-ggplot2, r-cran-sf, r-cran-spatstat.data, r-cran-spelling, r-cran-testthat, r-cran-tidyterra, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-sparrpowr_0.2.9-1.ca2404.1_all.deb Size: 918844 MD5sum: bbc98ee94c3a9769c175bd37f6461e96 SHA1: f89d628cb1c743e624568cccb3cd2412bcd7b926 SHA256: 5000ac7530d5e9e7c9d439bb9740ad869178ea5a24a4feef128578fc3ee19ab6 SHA512: a50661e1a43c292e31e21ffdd740d9a1d1a4ac678e925594923f38fcbfbd5ca32e9790d535bc85394d69bf55020b189659bf9771ffbe1178dfa5040d3c547ec7 Homepage: https://cran.r-project.org/package=sparrpowR Description: CRAN Package 'sparrpowR' (Power Analysis to Detect Spatial Relative Risk Clusters) Calculate the statistical power to detect clusters using kernel-based spatial relative risk functions that are estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) . Details about kernel density estimation can be found in J. F. Bithell (1990) . More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) . Package: r-cran-sparsebc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso, r-cran-fields Filename: pool/dists/noble/main/r-cran-sparsebc_1.2-1.ca2404.1_all.deb Size: 2246428 MD5sum: fc0ea119837b0890f7f8c1d1cea37095 SHA1: aa585161844fe14e0983069d60fad87ea621ebe9 SHA256: e0fbb79d5fb6f8fdcd3ffa21372fe8622bf12177ea30899067bfcbb70759d55b SHA512: a405ba5550ff8825cfa147c41e12b988abd1b1754a94695948e11a896e22534f740d46803580cd82a76924898c9eb9436b6968ff998fc5f3949caac9b68f3613 Homepage: https://cran.r-project.org/package=sparseBC Description: CRAN Package 'sparseBC' (Sparse Biclustering of Transposable Data) Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics 23(4):985-1008. Package: r-cran-sparsebiplots Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-sparsepca Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsebiplots_4.1.1-1.ca2404.1_all.deb Size: 50116 MD5sum: 0ff2e1ebec9454cbf0e4340808d9dbd4 SHA1: 7d068592314c2481309f83ca54dcd3cef607c4f0 SHA256: 04fd10204f7c78dedbe5ab99d9871f0c2760f6e5ce11758e075f0036a874e85a SHA512: 09f288d6a0618c35a25d83bedb07467f87262e69359497fdabbbc941f338b78dba0d9f77aa8899d679457feca30499661cd475343684a0262e0172447a390dbd Homepage: https://cran.r-project.org/package=SparseBiplots Description: CRAN Package 'SparseBiplots' ('HJ-Biplot' using Different Ways of Penalization Plotting with'ggplot2') The 'HJ-Biplot' is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the information’s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using 'ggplot2'. Package: r-cran-sparsecommunity Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra Suggests: r-cran-irlba, r-cran-clue, r-cran-igraph, r-cran-igraphdata, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsecommunity_0.1.1-1.ca2404.1_all.deb Size: 112292 MD5sum: 265e6793788f94911d88a5bfb583f897 SHA1: 6fbe0acad0b50aafdf7adae69f795b71efbbf341 SHA256: 2c3da27494f0e707b74234de31460228b8ddaf72335c3c411817e880ce1a1a7d SHA512: d9ac3426946f54fc029f571d4b2c83ee84d43bb707a4aa06317909f68cad301e15dba7d95f77af824dec4628571812bcfa1d1ee386d3096c62398d9af3f08f52 Homepage: https://cran.r-project.org/package=sparsecommunity Description: CRAN Package 'sparsecommunity' (Spectral Community Detection for Sparse Networks) Implements spectral clustering algorithms for community detection in sparse networks under the stochastic block model ('SBM') and degree-corrected stochastic block model ('DCSBM'), following the methods of Lei and Rinaldo (2015) . Provides a regularized normalized Laplacian embedding, spherical k-median clustering for 'DCSBM', standard k-means for 'SBM', simulation utilities for both models, and a misclustering rate evaluation metric. Also includes the 'NCAA' college football network of Girvan and Newman (2002) as a benchmark dataset, and the Bethe-Hessian community number estimator of Hwang (2023) . Package: r-cran-sparsecov Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvnfast, r-cran-rfast, r-cran-sparsemvn Filename: pool/dists/noble/main/r-cran-sparsecov_0.0.1-1.ca2404.1_all.deb Size: 39002 MD5sum: fb64dcb9c4be87e09648278098e521a4 SHA1: 84b4d6b9c8f0051e6bf8ebbce4ffad7eb3ba131a SHA256: 1ffa995fd54db7ccd0a12a8cecc23abf6bf0f715622150a568cc41d80bbae961 SHA512: 9dfd49f25bb4b66ae0392383933ef8d0702ce2816cefce30b2b9b09919eba2f987f05529c5344e09ffc6e729138cd5c6fef805fb7b8f1dcb353cf26cbf9dbb7e Homepage: https://cran.r-project.org/package=sparseCov Description: CRAN Package 'sparseCov' (Sparse Covariance Estimation Based on Thresholding) A sparse covariance estimator based on different thresholding operators. Package: r-cran-sparsedc Architecture: all Version: 0.1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsedc_0.1.17-1.ca2404.1_all.deb Size: 4818408 MD5sum: bb5102c9df55b87902bd55d3c4dc8914 SHA1: 6fbdfbc417eafdcc0fcaf47d7a45359dca8d1309 SHA256: 1099ece67e35950f04259ce966b1d36df3528290e0efbbf754ca6a971dd362a9 SHA512: 92c5511c15a0f136cb28e43769aebcff8eef33b87cfd727c03a2e71eeb9404ee17c5f6c3baf65e396fb3594355f83cb8f1f474702ace80f09a1a5eeca364c07b Homepage: https://cran.r-project.org/package=SparseDC Description: CRAN Package 'SparseDC' (Implementation of SparseDC Algorithm) Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, . This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. Package: r-cran-sparsediscrim Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bdsmatrix, r-cran-corpcor, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-testthat, r-cran-mass, r-cran-covr, r-cran-modeldata, r-cran-spelling Filename: pool/dists/noble/main/r-cran-sparsediscrim_0.3.0-1.ca2404.1_all.deb Size: 340212 MD5sum: bb02a678803be4bb50968871ca33a2bd SHA1: bb456a5df3e83799205c15218628ca7558bc3dd1 SHA256: f657141ccf7d5401c7abe9ec65627d5b3a06ac78a8e7ee59b1c0299020e8e925 SHA512: 89f1f95dd5f1b5314a7598256348bd0c52a857a373538719222c28d595356147b4110738aa9a0b7b630c530be0599f272757021a480c58a403878e1fdf9d8c93 Homepage: https://cran.r-project.org/package=sparsediscrim Description: CRAN Package 'sparsediscrim' (Sparse and Regularized Discriminant Analysis) A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) . Other classifiers include those from Dudoit et al. (2002) , Pang et al. (2009) , and Tong et al. (2012) . Package: r-cran-sparseeigen Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparseeigen_0.1.0-1.ca2404.1_all.deb Size: 326586 MD5sum: 6c5f7a05466833ac9259e31a0e80759e SHA1: d64401ce5bc292e3242e3c0d68db1b951b76f4fe SHA256: 0d3caa638493ea3c1fe3fb6569d919b86284d7396769c7f6e98c452034b66d72 SHA512: 0ae813b89eeb26e5df3f60901676f7c8c530cc07bc7ba743f962ded53d43b012b45cf00887c578397cfe907cd3bf8fd8c4e0c68dc74abe6a3a5a3aeaef5a01b4 Homepage: https://cran.r-project.org/package=sparseEigen Description: CRAN Package 'sparseEigen' (Computation of Sparse Eigenvectors of a Matrix) Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing . Package: r-cran-sparseflmm Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-refund, r-cran-mass, r-cran-matrix, r-cran-data.table Filename: pool/dists/noble/main/r-cran-sparseflmm_0.4.2-1.ca2404.1_all.deb Size: 385016 MD5sum: b6cd1d62458c4fea21a180636f59c35c SHA1: 3ccd8d5827207a9fdd96b927969120590653cd0d SHA256: aeb8fc39cd8c97974ecc74a294aaef6af0f1e57d46908cef29d1ae10e4b9f01d SHA512: 20e092268867e84bc12b2a313696323f938380388a6dd11eebac1e9ef04d864fbf099c51ab5168b02be2ad2638072796a224bb716e96d07b236db780a030462a Homepage: https://cran.r-project.org/package=sparseFLMM Description: CRAN Package 'sparseFLMM' (Functional Linear Mixed Models for Irregularly or SparselySampled Data) Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis. Package: r-cran-sparsefunclust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsefunclust_1.0.0-1.ca2404.1_all.deb Size: 129832 MD5sum: 67de88999a3db5d550ae7c2cd98480f5 SHA1: a742a4e4b02f70cbd002e285dd557b40c0b96e12 SHA256: a758c46755776b401c9136c1c1ce7ea1b1c9896df0b407dc38869c76430d7924 SHA512: 295fd6744ee9c3e4df92b28e31d2cb7d33b9c4864db20b3c41b673344cd4b85f3565a3635d5282a509a7d434fa4e35f1a48ddb82991a04e30f972ce98000ccb4 Homepage: https://cran.r-project.org/package=SparseFunClust Description: CRAN Package 'SparseFunClust' (Sparse Functional Clustering) Provides a general framework for performing sparse functional clustering as originally described in Floriello and Vitelli (2017) , with the possibility of jointly handling data misalignment (see Vitelli, 2019, ). Package: r-cran-sparsegfm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gfm, r-cran-mass, r-cran-irlba Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsegfm_0.1.0-1.ca2404.1_all.deb Size: 61646 MD5sum: 3c2121e9b64c1588bcf3ef0e75691a89 SHA1: 0080421aa54f07b8028d8aaf4886a876e6909b87 SHA256: c482d7bfb2d3fb80e4ea5eeb5ab4ef3cd76c265bbc3028265f476f67a6176b58 SHA512: 146e9f622b36bc6a79693e9db0f75e3c4319d9f88f44543f11d83e6dd8b728ae5f6bf96654a31d78938051e961fd74126167e76e2c71a290bca6925c0fd0922f Homepage: https://cran.r-project.org/package=sparseGFM Description: CRAN Package 'sparseGFM' (Sparse Generalized Factor Models with Multiple Penalty Functions) Implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) , Fan and Li (2001) , and Zhang (2010) . Package: r-cran-sparsegrid Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-statmod, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sparsegrid_0.8.2-1.ca2404.1_all.deb Size: 174080 MD5sum: 28b6fac7263632c9eff7829c16dc7841 SHA1: 352705dd835ae425f86f309c65e412054e3f8956 SHA256: 6a1501504997a3022c9e42cdadaa33329b10d515ae9ec9a49bfb816e59be7794 SHA512: a664fbc6b53b01e000af1ccb1f5de90d5a8412177a218c766d45abda1b879dcaa349d7dd764b926b9a2130710897a8db1c916908c3360d1cf49368f1ada83fd3 Homepage: https://cran.r-project.org/package=SparseGrid Description: CRAN Package 'SparseGrid' (Sparse grid integration in R) SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de Package: r-cran-sparseindextracking Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2962 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bookdown, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-xts Filename: pool/dists/noble/main/r-cran-sparseindextracking_0.1.1-1.ca2404.1_all.deb Size: 2383010 MD5sum: a429f984de804a9b8378d06ea5f78ab7 SHA1: 8256d4dd955bb234a9227bf4c89ada8529f82bc6 SHA256: 34429394b8ba051050de933cbb3f95de91dc5ab466b03a824daa85a6fb3a471f SHA512: 5a571b5972d0cc30d1cb6b2683c05dbfb8ab2b450e37d0e7e00f719e360d0345612db57a122e1af5e1612502fdb61b9bd7d33d7cb041e458b717de069e5fe358 Homepage: https://cran.r-project.org/package=sparseIndexTracking Description: CRAN Package 'sparseIndexTracking' (Design of Portfolio of Stocks to Track an Index) Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. . Package: r-cran-sparselda Architecture: all Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-elasticnet, r-cran-mass, r-cran-mda Filename: pool/dists/noble/main/r-cran-sparselda_0.1-9-1.ca2404.1_all.deb Size: 380286 MD5sum: 2e3c513c848e30487f20f0d80c4cb361 SHA1: a27b1e3e490be6fddfdc214c177c2195407f0b9e SHA256: 7f04dff69e0d40e18b667dde1bba17a0e3c6295e7d0a4ae771b8f8ab385ee3fc SHA512: 53eff85e4347a1a54d3c3fd4b125c4e915899a3849701fc5c3a5f2e517ab3abc6afdd29784e36fd4c3f0c61d267b036bfc12188747657a2185674416ad78c57d Homepage: https://cran.r-project.org/package=sparseLDA Description: CRAN Package 'sparseLDA' (Sparse Discriminant Analysis) Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models. Package: r-cran-sparselink Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-proc, r-cran-mvtnorm, r-cran-spls, r-cran-xrnet Suggests: r-cran-knitr, r-cran-testthat, r-cran-remotes, r-cran-glmtrans, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparselink_1.0.0-1.ca2404.1_all.deb Size: 171886 MD5sum: afcb5b439bd3dc76c2b571be58214ee5 SHA1: da2a0565cb4d6717d49bbcf86a791021d697507f SHA256: 21f2004d17bf09779fd059776093eda438a1cfb45bd361a645551846b570b857 SHA512: 406b0223d5443cf0c423ca2cb0b56e5c02d3ba13bbab8a491496c2a59042031119544c9332b2227684f9c0ee920d40f1a8fd80edb3aa859fa0fb08c7fd96ae55 Homepage: https://cran.r-project.org/package=sparselink Description: CRAN Package 'sparselink' (Sparse Regression for Related Problems) Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) . Package: r-cran-sparselrmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rspectra Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparselrmatrix_0.1.0-1.ca2404.1_all.deb Size: 27404 MD5sum: 92d2c80095f5a2731778e58ce0e29b7b SHA1: 7ae0aee31ec8bf4360393cd26ebde1688111bc3a SHA256: a16f25e72a75bf2fab65e09baf79a9e3e64ec54431e3e2332fed1d4a655aa190 SHA512: 90d0a8ec5a7ebfe4e3d336da0d6ca581e68b517ea930fd8593555dafc9346b783e3cde5baf05d61cbbc6a3c7a9cdd33704a3b0ba75f348a79d92c42a1414ced4 Homepage: https://cran.r-project.org/package=sparseLRMatrix Description: CRAN Package 'sparseLRMatrix' (Represent and Use Sparse + Low Rank Matrices) Provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects. Package: r-cran-sparsematest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Filename: pool/dists/noble/main/r-cran-sparsematest_1.0.0-1.ca2404.1_all.deb Size: 41376 MD5sum: 0d3753198c70e443bb4bebed958b007b SHA1: d2b2730820d32478571f9eb2336d78053449b991 SHA256: 2c6f9eac8c6db43bb5af23673d4851dff835414d82d8b72f6abf25470e8f5d2f SHA512: 674e27de3ba8a1e19036af1712038a2d861edd6ea803c734aa22ff9e443ad990afc1460ed9fadb84c612fac5822f86bb67d3b2f0d5f1088b5e354d52e02e88ae Homepage: https://cran.r-project.org/package=sparseMatEst Description: CRAN Package 'sparseMatEst' (Sparse Matrix Estimation and Inference) The 'sparseMatEst' package provides functions for estimating sparse covariance and precision matrices with error control. A false positive rate is fixed corresponding to the probability of falsely including a matrix entry in the support of the estimator. It uses the binary search method outlined in Kashlak and Kong (2019) and in Kashlak (2019) . Package: r-cran-sparsemdc Architecture: all Version: 0.99.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4800 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dorng, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsemdc_0.99.5-1.ca2404.1_all.deb Size: 4829744 MD5sum: e1de8e9be36b16abdc0b149a0bdf8170 SHA1: 4f89f79748b2f1a2a12aaf6da0d635570bd69d1c SHA256: 92279ca5d426cf010caea62a8f8311f75cbd6615fc6ce39e9b412f80fef4acde SHA512: 878fa897d8fe2a7b7bc2d1799717fd5100e605cb5f52fe32584e6634ad458b0bf83ef5057807e8b2fd5befecc19ce4590df61747897be22118074d205ba4eed8 Homepage: https://cran.r-project.org/package=SparseMDC Description: CRAN Package 'SparseMDC' (Implementation of SparseMDC Algorithm) Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. Package: r-cran-sparsemse Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rcapture Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsemse_2.0.1-1.ca2404.1_all.deb Size: 116136 MD5sum: 625e476eafd5d1a69883ee54bb1982a2 SHA1: fc8ca79f39a66823d214992f7d0802c73b4b0f01 SHA256: 50a9b2c68145bd8182c1a01e3333ef00beea2e52917e5e796377d86e5881603b SHA512: 274acbf74d6d7944071a3395b5d7789c19e4443c444bae318bf0e56c47eaa5b354116614afe5e244d91db2e031ecf793b8c27b88a592a75ba6d380c197171911 Homepage: https://cran.r-project.org/package=SparseMSE Description: CRAN Package 'SparseMSE' ('Multiple Systems Estimation for Sparse Capture Data') Implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) . This package explicitly handles situations where there are pairs of lists which have no observed individuals in common. It deals correctly with parameters whose estimated values can be considered as being negative infinity. It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates. Package: r-cran-sparsemvn Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-forcats, r-cran-knitr, r-cran-bookdown, r-cran-kableextra, r-cran-testthat, r-cran-scales, r-cran-trustoptim Filename: pool/dists/noble/main/r-cran-sparsemvn_0.2.2-1.ca2404.1_all.deb Size: 275256 MD5sum: 39d27c0ab1f00f4e1c6fc842bdb71b42 SHA1: 4ce75acadee2fe78a8ae23bee404a3129362316e SHA256: 3b71c4e8d64ef829da45f10781917c3e8fd530c3cd9d5765565c40d9fa0d0a62 SHA512: 787d5d72c918d6e7b147c83adb93d2b9a7e1fe37180335cef94568be38d3d6b29e84cc2a5cecb06727f605710ac00ddec5612a2187af9aa9b5262dd5b1b444ae Homepage: https://cran.r-project.org/package=sparseMVN Description: CRAN Package 'sparseMVN' (Multivariate Normal Functions for Sparse Covariance andPrecision Matrices) Computes multivariate normal (MVN) densities, and samples from MVN distributions, when the covariance or precision matrix is sparse. Package: r-cran-sparsepca Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsvd Filename: pool/dists/noble/main/r-cran-sparsepca_0.1.2-1.ca2404.1_all.deb Size: 49750 MD5sum: d51d102de703a0d5d784a0527b16cae3 SHA1: e9924f82327070d559fedcbd68cf07f532fda9d0 SHA256: da2dfdae9626b5af308cac0d5b4190e9668e6ed83d5dd9a38c13b881e5eec7b9 SHA512: 588548d632585c12bd4bac61d5b2f628c98623c3fc355cf5deb2a41ea1cfbc371825d41e91dbce6d32d5c5889b9ba47b14b3a8f56b619e985e6f7ce5ecd213ea Homepage: https://cran.r-project.org/package=sparsepca Description: CRAN Package 'sparsepca' (Sparse Principal Component Analysis (SPCA)) Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) . Package: r-cran-sparsepp Architecture: all Version: 1.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sparsepp_1.22-1.ca2404.1_all.deb Size: 111924 MD5sum: 66a220891d0526d45ce5212c660a741f SHA1: 10b8a786b25cb1f4cb5b158e8d80d8d7916aae0f SHA256: 4774cb8cd241136886423e867f1bd67123ccf9273be8c72f2550ba3f24bf028b SHA512: 2082a4b9a06d58b8403e55e6953646737c29dfa87eabc1aae0f554fd484003fbda16b9ad3c7deaa8732f22f26858730583a741af43eece275ea65d18d89ea1bb Homepage: https://cran.r-project.org/package=sparsepp Description: CRAN Package 'sparsepp' ('Rcpp' Interface to 'sparsepp') Provides interface to 'sparsepp' - fast, memory efficient hash map. It is derived from Google's excellent 'sparsehash' implementation. We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance). Package: r-cran-sparser Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncvreg, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-recipes Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-covr, r-cran-modeldata, r-cran-mass Filename: pool/dists/noble/main/r-cran-sparser_0.3.2-1.ca2404.1_all.deb Size: 1666232 MD5sum: 5fdfb43b067ba5b5e5ce9c2f1489bcd8 SHA1: 52617405a31463da0cd6915d02c18533f7cdab6d SHA256: d1864eec14a1ef3b9c3bf0cd469e046d18b67af56b13cd8db7937cbc03544bc2 SHA512: 94fac4e74676d264f4c21ef07c9d33083e94d3e72c4c618ef7c09245a13d5418d0f118f38b7d1ad33a600abb995a9a0fc8cd0a3366878182644255c95bd53416 Homepage: https://cran.r-project.org/package=sparseR Description: CRAN Package 'sparseR' (Variable Selection under Ranked Sparsity Principles forInteractions and Polynomials) An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) , ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods. Package: r-cran-sparsestep Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sparsestep_1.0.1-1.ca2404.1_all.deb Size: 49830 MD5sum: cdb4aed50f6ffdeaafecd3def14c41af SHA1: 5e6ae31d65aa20f5b74062136158e33bd74312fc SHA256: 094eac900457d2a3dd7ecff650b064c71483482bf1ba156450539cd7ddf4f701 SHA512: 25d2ab0ea1a482f6409dfb85fc3b2f3e6f84ccfb109cc64afe4037e3045903dd84ceb67a5ae1538c924a1f0f22ba49e0b3a563f2a67fb66c511115def35952c7 Homepage: https://cran.r-project.org/package=sparsestep Description: CRAN Package 'sparsestep' (SparseStep Regression) Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) . In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter. Package: r-cran-sparsesurv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rjags, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-sparsesurv_0.1.1-1.ca2404.1_all.deb Size: 130536 MD5sum: c06b2f30743ff853aa9291683adea2ac SHA1: b225808d9e13e9f2d20c7ed424a6b2a72d83d9d9 SHA256: 626c713a7975a38c4b0b34ce6608dd9c7de2663d6397ac036c3946fc8640c710 SHA512: 6ba138aee16cd2ace974051641aee8b95b7e26471f50755b30941894b3cb640c04d9194fdce647627a31d02434b6b35ad349ec0c797ee0d421d905d0096d1175 Homepage: https://cran.r-project.org/package=sparsesurv Description: CRAN Package 'sparsesurv' (Forecasting and Early Outbreak Detection for Sparse Count Data) Functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics. Package: r-cran-sparsevar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-ncvreg, r-cran-doparallel, r-cran-glmnet, r-cran-ggplot2, r-cran-reshape2, r-cran-mvtnorm, r-cran-corpcor, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsevar_1.0.0-1.ca2404.1_all.deb Size: 230348 MD5sum: a3948610d5d828b0aadf6ad636fc8c4c SHA1: b876e879a9d9ce88adb0d4092a1078f6db0287cc SHA256: f11661311f7040cda9bac3623cddf95d8eba15904cd17210360bf99e3b0b3293 SHA512: 051d6c7fd1785d3d0b02433b8f37ed9154f8aae9c3de7c6e83ebcb7dea01314057b0fb19aa646449bdd55a68f707848fd32b885d1499090910dbcff1e816fc05 Homepage: https://cran.r-project.org/package=sparsevar Description: CRAN Package 'sparsevar' (Sparse VAR (Vector Autoregression) / VECM (Vector ErrorCorrection Model) Estimation) A wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) . Package: r-cran-sparsevfc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2878 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdist, r-cran-purrr Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sparsevfc_0.1.2-1.ca2404.1_all.deb Size: 2782434 MD5sum: 6cf92b711063925c3c58e7f64337e3dd SHA1: 8646ce3239f4cd8c09b0731c19ab356c93e96d8f SHA256: c4be31945e34c6ebc9c1ea3ad5304e0440b32fb4197ec400b627d9ae574feea8 SHA512: 0b73e2291163aadd9d57ca364b32a91fa60d3939602cb73ad6a43151230d0c6156e7c556abb5899cfccba01803da2c0d39b5eb4b651db883d6e15719875b1f66 Homepage: https://cran.r-project.org/package=SparseVFC Description: CRAN Package 'SparseVFC' (Sparse Vector Field Consensus for Vector Field Learning) The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 ) for robust vector field learning. Largely translated from the Matlab functions in . Package: r-cran-spartaas Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3173 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factominer, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-plotly, r-cran-stringr, r-cran-colorspace, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinyjqui, r-cran-fpc, r-cran-ggdendro, r-cran-htmltools, r-cran-htmlwidgets, r-cran-shinythemes, r-cran-explor, r-cran-shinywidgets, r-cran-scatterd3, r-cran-ks, r-cran-cluster, r-cran-leaflet, r-cran-ape, r-cran-caret, r-cran-mass, r-cran-ade4, r-cran-lmtest, r-cran-nor1mix, r-cran-shinycssloaders, r-cran-scales, r-cran-fastcluster, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-spartaas_1.2.7-1.ca2404.1_all.deb Size: 2519194 MD5sum: af14ad34c22c982f968dc816d3fc7e7d SHA1: bf876ea2d86847b1a38a1e06736da5748d8309df SHA256: ba6f13feb4030aa9168761b222c565daf84e3c2e93d938c7e7ee2f2617cf05f7 SHA512: 0f6c6c0c8b7ebdea8fc5db5405ad55094ec21f60238aa066389502b41a46782f7a08238709612b68e55e5e2ebcaa0b5aae03ae385850e490f02518c6e0c4912f Homepage: https://cran.r-project.org/package=SPARTAAS Description: CRAN Package 'SPARTAAS' (Statistical Pattern Recognition and daTing using ArchaeologicalArtefacts assemblageS) Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4). mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) . seriograph(): Desachy Bruno (2004) . cerardat(): Bellanger Lise, Husi Philippe (2012) . Package: r-cran-spatcovar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-exactextractr, r-cran-sf, r-cran-terra, r-cran-units Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatcovar_0.1.0-1.ca2404.1_all.deb Size: 104622 MD5sum: 108f2143d572ed8ef99bffa5398a7eb3 SHA1: a5ff760473af5d878c96928d2a75541d889d947b SHA256: 23e6979af97c4271961d0df5b63da3575dfd6ea1ec5504e47e24b4331ad0cc82 SHA512: 3bcfa7d620f750eeaaa44038a00a148c0a64e3b55e4abd19df09f99f8d400464455ccdced2113780097ff800b1f9154d31f243461825e4e23f54e8695643f786 Homepage: https://cran.r-project.org/package=spatcovar Description: CRAN Package 'spatcovar' (Construct Spatial Covariates from Polygon Data) Provides a consistent interface for constructing commonly used spatial covariates from polygon data. Computes polygon areas, distances to reference features, point and line intersection counts, line lengths within polygons, polygon overlap areas and shares, and raster zonal summaries. Handles coordinate reference system validation, geometry repair, unit conversion, row preservation, and standardised missing value semantics while relying on established spatial libraries for the underlying geometry operations. Package: r-cran-spatemr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-sphet, r-cran-matrix Suggests: r-cran-testthat, r-cran-sp, r-cran-spdep Filename: pool/dists/noble/main/r-cran-spatemr_1.4.0-1.ca2404.1_all.deb Size: 69522 MD5sum: 87619b7d1195055540eecdd7ef11da4c SHA1: eb077c39ad115a9e7892ac7439fe8b9ff86058f1 SHA256: fb4b6accfcb203b1be9956aa9af4022eee1330904a0d0a912676dd98f4393171 SHA512: 481304124a3c8e7866bfb5561692ff581c9a4461f6158f647a157a823827fb372f6dba1eb511138f90eedc7b99fce3d140643c198ae794b8a2066cffcaea01ef Homepage: https://cran.r-project.org/package=spatemR Description: CRAN Package 'spatemR' (Generalized Spatial Autoregresive Models for Mean and Variance) Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". . b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". . Package: r-cran-spatentropy Architecture: all Version: 2.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random Filename: pool/dists/noble/main/r-cran-spatentropy_2.2-4-1.ca2404.1_all.deb Size: 453024 MD5sum: acd0807315f74426d0ecb2a12f5f27c3 SHA1: 42afa9a0a3ea2ba658f457b1b36a13a82335d631 SHA256: 3621482db8c575c2d7c20fd9c802f0de0979760b8bd439460ed4b8956ee39124 SHA512: 4e13358f19cc51beff0ff943967ee45382463ff50c39092b8c9483767a3208783b61fb8cd74cb6128601c60df69e10dfdf8adbd176f036ac29360efb4816708f Homepage: https://cran.r-project.org/package=SpatEntropy Description: CRAN Package 'SpatEntropy' (Spatial Entropy Measures) The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 ), Batty's spatial entropy (Batty, 1974 ), O'Neill's entropy (O'Neill et al., 1998 ), Li and Reynolds' contagion index (Li and Reynolds, 1993 ), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 ), Leibovici's entropy (Leibovici, 2009 ), Parresol and Edwards' entropy (Parresol and Edwards, 2014 ) and Altieri's entropy (Altieri et al., 2018, ). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 3.0-2). Package: r-cran-spatest Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spatest_3.1.2-1.ca2404.1_all.deb Size: 120430 MD5sum: 79003167263154e63694eec72cdd1f65 SHA1: 3a7aba3f259175e59ee329b8dbab4b61d09c1b7b SHA256: f52be1983e223d8aa635cec05765ab536299eb53b2601078239fa8144c497b71 SHA512: 6f8137ac6ec7812a1b14a64d1e44de1a36c43ecaf9341a523e8fa8dac4a4929aed6125d6528907cb30208223a3aa6d52aa9494339501763ea7fe2e8708fb59d8 Homepage: https://cran.r-project.org/package=SPAtest Description: CRAN Package 'SPAtest' (Score Test and Meta-Analysis Based on Saddlepoint Approximation) Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check . Package: r-cran-spatfd Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3720 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-dplyr, r-cran-proxy, r-cran-reshape, r-cran-gstat, r-cran-sp, r-cran-fda, r-cran-sf, r-cran-mass, r-cran-geor, r-cran-tidyr, r-cran-fda.usc Filename: pool/dists/noble/main/r-cran-spatfd_0.0.1-1.ca2404.1_all.deb Size: 3741456 MD5sum: 18241d94973a8427972db506f3881744 SHA1: d05812121765701c3bd6f87d178a7a91f3498ca6 SHA256: 82d2c5f4422006a5f09efca3629dd05fe55f16efbfa32436f7559f68f636fbe4 SHA512: de42fa4db1bcd1a591c7b805e25dd96d285f7b62e09320cc33e403f59e321180e8e391d4c8cafb0440bec5dfc41ac32352a03c04ce775ddad7fb99d7fa2d8a14 Homepage: https://cran.r-project.org/package=SpatFD Description: CRAN Package 'SpatFD' (Functional Geostatistics: Univariate and Multivariate FunctionalSpatial Prediction) Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. 'SpatFD' is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) , Bohorquez, M., Giraldo, R., and Mateu, J. (2016) , Bohorquez M., Giraldo R. and Mateu J. (2021) . Package: r-cran-spatgc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-spdep, r-cran-sf Filename: pool/dists/noble/main/r-cran-spatgc_0.1.0-1.ca2404.1_all.deb Size: 56634 MD5sum: ff09d6e9e98cf36047f28b26823cea9d SHA1: 646201aa8598185dc641c3be054976f47df92b18 SHA256: c8ac312436988376e0416bdccd25eb6d4a212fc58179c6a0e9c9bf297f53d22b SHA512: eef2a9e6ec75c0a8b16864a8ada1b51d8473b36bd3585a18dce5a5c536f665e4c994401aa6a644a3a738edad17f1672846db632cd6671c3318c94e005a0e63ac Homepage: https://cran.r-project.org/package=SpatGC Description: CRAN Package 'SpatGC' (Bayesian Modeling of Spatial Count Data) Provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) . Package: r-cran-spatgeom Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-sf, r-cran-dplyr, r-cran-lwgeom, r-cran-cowplot, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatgeom_0.3.0-1.ca2404.1_all.deb Size: 46618 MD5sum: e0f4d0441141fd6b513036c937e5dea1 SHA1: bad962eb3ec9cd2c3b3322f459036b14338c2d29 SHA256: 3ee03deb7069649e36a64c0c66821e6515c66ad0589e2c132bcf34629f765bf6 SHA512: f167115561e5da755675cbfb65c6af396ec2caa22cb37a022a0fa88f424e4f6ca7ec23e436f33fffce9d7ad13ac11e3594f4fdf80530fde5ecbcfcc545184df7 Homepage: https://cran.r-project.org/package=spatgeom Description: CRAN Package 'spatgeom' (Geometric Spatial Point Analysis) The implementation to perform the geometric spatial point analysis developed in Hernández & Solís (2022) . It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the 'sf' package. The package has methods to print and plot the results. Package: r-cran-spatgrid Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatgrid_0.1.0-1.ca2404.1_all.deb Size: 25204 MD5sum: 736dcb6e2f9162422116bc11b2886578 SHA1: 3bd38783f33321e92f0a34c32b4f1aabf0dbec5a SHA256: cc3b41adec188dd2486b371c45e872f7b00aa930a6159372ad9fafce10ee8b70 SHA512: dd3bec0bfd909d2e1e1aa426bafb3af6c8e723ef7c7b611d3bd1ef5ff5a88763921c1bb1ae809d8a59dc285c03163630707fdab304a1c75ade041a923ba02f10 Homepage: https://cran.r-project.org/package=SpatGRID Description: CRAN Package 'SpatGRID' (Spatial Grid Generation from Longitude and Latitude List) The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values, ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. For method details see, Brus,D.J.(2022).. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path. The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization. Package: r-cran-spathial Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-igraph, r-cran-matrixstats, r-cran-mass, r-cran-rtsne, r-cran-class, r-cran-knitr, r-cran-rmarkdown, r-cran-irlba Filename: pool/dists/noble/main/r-cran-spathial_0.1.2-1.ca2404.1_all.deb Size: 1891776 MD5sum: dcfc59db46553256159482e4f26f1c24 SHA1: 72304d3f4b682eb4645d5c4a96f81edf94835af3 SHA256: ec470252d52853bdacd185d2981af62f53b6941dc2fb166148898643808b1ede SHA512: d0a918b94fadec867c1c491a36e2ac3e7be57d7400bb4eeaaa980c7d853a87742ed4e69a289f39a6bf2ed4b2cbb9304221719850b7a37819aa028275976a3cc0 Homepage: https://cran.r-project.org/package=spathial Description: CRAN Package 'spathial' (Evolutionary Analysis) A generic tool for manifold analysis. It allows to infer a relevant transition or evolutionary path which can highlights the features involved in a specific process. 'spathial' can be useful in all the scenarios where the temporal (or pseudo-temporal) evolution is the main problem (e.g. tumor progression). The algorithm for finding the principal path is described in: Ferrarotti et al., (2019) ." Package: r-cran-spatialacc Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-spatialacc_0.1-6-1.ca2404.1_all.deb Size: 51132 MD5sum: 1c3593dbca2c3deb02f2808de853a755 SHA1: 82f6fdf2ba0901887f48698e588cced07bf1512a SHA256: 7e6d075a5b86e6a0aa754950ad37258794dda646240138193377d32acdc6a110 SHA512: 672a8df5a599234f53f75db2dde7ee1c593960808f15dfb4e8ceb3de1ae007dd5effaf0be16adb5c31dbebce5552cdac78996a6271d9ed6802660069e87e6be9 Homepage: https://cran.r-project.org/package=SpatialAcc Description: CRAN Package 'SpatialAcc' (Spatial Accessibility Measures) Provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece. Package: r-cran-spatialatomizer Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-sp, r-cran-sf, r-cran-spdep, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-reshape2, r-cran-coda, r-cran-biasedurn, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tigris Filename: pool/dists/noble/main/r-cran-spatialatomizer_0.2.8-1.ca2404.1_all.deb Size: 4712414 MD5sum: b9ce3fa8520f4ba8e39fcbc3999f7f67 SHA1: ff7d29ae8aca157f765768b6b70c7e7e5c4e2348 SHA256: d1ff5b586a7f72a1fb06e269e3353edf86db50753d4283233522f7012a87450c SHA512: 54f2019fdc743d2edd576197c9316143531b173a46d52993f96eceac62efc29440277f1f981845af2783d1fe3bf5d773d4657c92a1df42a16dd9ff439bef6d09 Homepage: https://cran.r-project.org/package=spatialAtomizeR Description: CRAN Package 'spatialAtomizeR' (Spatial Analysis with Misaligned Data Using Atom-BasedRegression Models) Implements atom-based regression models (ABRM) for analyzing spatially misaligned data. Provides functions for simulating misaligned spatial data, preparing NIMBLE model inputs, running MCMC diagnostics, and providing results. All main functions return S3 objects with print(), summary(), and plot() methods for intuitive result exploration. Methods originally described in Mugglin et al. (2000) , further investigated in Trevisani & Gelfand (2013), and applied in Nethery et al. (2023) . Package: r-cran-spatialcatalogueviewer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-leaflet.extras, r-cran-shiny, r-cran-shinythemes, r-cran-dt Filename: pool/dists/noble/main/r-cran-spatialcatalogueviewer_0.2.1-1.ca2404.1_all.deb Size: 38296 MD5sum: 7880ba47148062cb6802b3e2c3e61592 SHA1: e01fe772331bd273d50a006c5f1f9fa817664c0c SHA256: 9160ce32c24518962c2149395c0aaf980ad524ae6ae65b6310e28531f39b290b SHA512: bcc5c2381b1cbd0393f2de2d5595dcac3142d0ea14cfe8e0bfe79baf4f5686e492fb8cb51e48c575a730c7d9f1c3e25d09aa7cd5be3b66d7a031e274f09a506f Homepage: https://cran.r-project.org/package=spatialCatalogueViewer Description: CRAN Package 'spatialCatalogueViewer' (A 'Shiny' Tool to Create Interactive Catalogues for GeospatialData) Seamlessly create interactive online catalogues for geospatial data. Items can be mapped as points or areas and retrieved using either a map or a dynamic table with search form and optional column filters. Package: r-cran-spatialcovariance Architecture: all Version: 0.6-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spatialcovariance_0.6-9-1.ca2404.1_all.deb Size: 95260 MD5sum: 84f6663245bc94778db13e50937baae2 SHA1: 1d143dba433a31986821d23974ed90f9e16c6e4a SHA256: 4aab47a57d35ce803061f9335ecdb056b49fb24bc6dc4ff7241fefe33124d68c SHA512: 8d90edb280be4500da506fcece55b6d2dd64e537458b225a194fdb5b3fb0315e59004f38988ddd742056ecc7848cc3d57e01c3e8e770a75daf89b5c706e75be2 Homepage: https://cran.r-project.org/package=spatialCovariance Description: CRAN Package 'spatialCovariance' (Computation of Spatial Covariance Matrices for Data onRectangles) Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration. Package: r-cran-spatialcvr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-tidymodels, r-cran-rsample, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-terra Filename: pool/dists/noble/main/r-cran-spatialcvr_0.1.0-1.ca2404.1_all.deb Size: 152030 MD5sum: 340966e04c91aed8e14585576f2a789e SHA1: 43d4c37a86e4a4e3cc1179d287f81f772a77c479 SHA256: b9f982af5d5f18580aeb4b4315f068a3279a532761531a231f4bd333c962e168 SHA512: 9b4d7a301184cebc5c817126dd7c6235cc0c73bd561711851a9b80fb1ee1aef7676b26aeb2641654e9ce7f6a647ad7a46ccf274e048e7a0705ae60c20989b915 Homepage: https://cran.r-project.org/package=spatialcvR Description: CRAN Package 'spatialcvR' (Spatial Cross-Validation for Machine Learning) Spatial cross-validation and model evaluation for geospatial machine learning applications. Addresses spatial dependence in observations by implementing spatial block, buffered, and clustering cross-validation methods. Includes spatial leakage detection, model performance metrics, and spatial residual diagnostics for assessing model generalization across geographic space. Methods based on Brenning (2012) , Pohjankukka et al. (2017) , and Roberts et al. (2017) . Package: r-cran-spatialdata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4366 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialdata_1.0.1-1.ca2404.1_all.deb Size: 4419880 MD5sum: 40d3c456ac51027a6500dfdb71eab5a5 SHA1: 5f7eb8a400e657afbb5f2c38117b5bf5806ae1c6 SHA256: fcd975e0a61cf846282a5077f78f0eef417b7fda461f47b03d45eb4ef5e2c832 SHA512: 07d17a0099f93bd8e888bedd211874ea52d03f78baaeeb2d839ab5bc3886ad638a53e2487eba6ce9cb8b053a014d670e5f693ca972dddff554b07e886ffea2ac Homepage: https://cran.r-project.org/package=spatialData Description: CRAN Package 'spatialData' (Spatial Datasets for Ecological Modeling) Provides spatial datasets ready to use for ecological modelling and raster companion data for prediction: Neanderthal presence during the Last Interglacial (Benito et al. 2017 ); Plant diversity metrics for the World's Ecoregions (Maestre et al. 2021 ); tree richness across the Americas (Benito et al. 2013 ); plant communities from the Sierra Nevada (Spain) with future climate scenarios (Benito et al. 2013 ); butterfly-plant interaction data from Sierra Nevada (Spain) (Benito et al. 2011 ); plant species occurrences in Andalusia (Spain) (Benito et al. 2014 ); presence of the plant Linaria nigricans and greenhouses (Benito et al. 2009 ); global NDVI and environmental predictors, and European oak species occurrences. All datasets include pre-processed environmental predictors ready for statistical modelling. Package: r-cran-spatialddls Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-grr, r-cran-matrix, r-bioc-spatialexperiment, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-zinbwave, r-cran-pbapply, r-bioc-s4vectors, r-cran-dplyr, r-cran-reshape2, r-cran-gtools, r-cran-reticulate, r-cran-keras, r-cran-tensorflow, r-cran-fnn, r-cran-ggplot2, r-cran-ggpubr, r-bioc-scran, r-bioc-scuttle Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocparallel, r-bioc-rhdf5, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-cran-testthat, r-bioc-complexheatmap, r-bioc-bluster, r-cran-lsa, r-cran-irlba Filename: pool/dists/noble/main/r-cran-spatialddls_1.0.3-1.ca2404.1_all.deb Size: 3264814 MD5sum: 844aababa46ed2cfd06e46c43c777f35 SHA1: 066d3597c53773bd1e0ea96dd5f2c49f6852cc10 SHA256: 6a08585d0541903ec8f2aedce95a3bf81daf3baf90bc725754b73924b816b01d SHA512: d2338cd2e3b835d75eb4ded5a9b968af64e211f0943d69d49bdff8150a93ef5ef518705632c53bbeef3c2d64c1cd2bc39f679fc5dfb2cfa2f9f9b40ea9143c3d Homepage: https://cran.r-project.org/package=SpatialDDLS Description: CRAN Package 'SpatialDDLS' (Deconvolution of Spatial Transcriptomics Data Based on NeuralNetworks) Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) and Mañanes et al. (2024) to get an overview of the method and see some examples of its performance. Package: r-cran-spatialdownscaling Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras3, r-cran-magrittr, r-cran-rdpack, r-cran-raster, r-cran-abind Filename: pool/dists/noble/main/r-cran-spatialdownscaling_0.1.2-1.ca2404.1_all.deb Size: 1781060 MD5sum: 1965a040afa0ea81b45dc069d8d50047 SHA1: 8e4f843fe12aeb5adbf86461bfa062319140302c SHA256: 3ea7a53abe7a3a3749d4b6b8786e7c8575bded171169cd2cdd6322e8677255ee SHA512: ae556e3659e0f1c6c22ceb96233224a94e792ba7c811922dec09c1c1faba4b8046dd965c6465496795a169b6dc7b55f3ca6eed166f2e183c30359f78b7f03488 Homepage: https://cran.r-project.org/package=SpatialDownscaling Description: CRAN Package 'SpatialDownscaling' (Methods for Spatial Downscaling Using Deep Learning) The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 ) and UNet (Ronneberger et al., 2015 ), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 ). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) . Package: r-cran-spatialeco Architecture: all Version: 2.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spdep, r-cran-ks, r-cran-cluster, r-cran-readr, r-cran-rcurl, r-cran-rann, r-cran-rms, r-cran-yaimpute, r-cran-mgcv, r-cran-zyp, r-cran-spatialpack, r-cran-mass, r-cran-caret, r-cran-dplyr, r-cran-earth, r-cran-matrix, r-cran-gstat, r-cran-spatstat.data, r-cran-units, r-cran-sp, r-cran-stringr, r-cran-lwgeom, r-cran-geodata Filename: pool/dists/noble/main/r-cran-spatialeco_2.0-5-1.ca2404.1_all.deb Size: 2158002 MD5sum: 9a99457ece0d8d4c027fe54885882b76 SHA1: 2547332178c01b40a498612ed59b1f652d945a27 SHA256: 925270c8e171a448d1fc8b88cfbd6eb527a6ebdfba63af84f2b568676a4356b0 SHA512: 19e8c4855ce07180e1338fe1be2e814f67519a39b78447d8f66ed0f387360d9da3daf4906fcb3c6e6cd5dc6581fa8d75db6f0b8e71cc65b144b344b6876228f3 Homepage: https://cran.r-project.org/package=spatialEco Description: CRAN Package 'spatialEco' (Spatial Analysis and Modelling Utilities) Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics. Package: r-cran-spatialfdar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-rgl, r-cran-geometry, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-spatialfdar_1.0.0-1.ca2404.1_all.deb Size: 201158 MD5sum: 85a33ba850dfe76233b1b4bf3f11adf3 SHA1: 2bc6cf9b1b4b93426b8a9f7dd78ef7e679c43f90 SHA256: d9cfcb8ea3500a14e79add8661aa4c0799a98149caf88618fad4c9de57a867af SHA512: c93e941f40b6e136ee2561675e767e94e30980a9f883e516307eb2a1d17b5a8b4b582259fa3b0e568085ea22e0d1e999406313394a0e5412d3b52913eba87ec7 Homepage: https://cran.r-project.org/package=SpatialfdaR Description: CRAN Package 'SpatialfdaR' (Spatial Functional Data Analysis) Finite element modeling (FEM) uses meshes of triangles to define surfaces. A surface within a triangle may be either linear or quadratic. In the order one case each node in the mesh is associated with a basis function and the basis is called the order one finite element basis. In the order two case each edge mid-point is also associated with a basis function. Functions are provided for smoothing, density function estimation point evaluation and plotting results. Two papers illustrating the finite element data analysis are Sangalli, L.M., Ramsay, J.O., Ramsay, T.O. (2013) and Bernardi, M.S, Carey, M., Ramsay, J. O., Sangalli, L. (2018). Modelling spatial anisotropy via regression with partial differential regularization Journal of Multivariate Analysis, 167, 15-30. Package: r-cran-spatialfusion Architecture: all Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-sp, r-cran-sf, r-cran-fields, r-cran-spam, r-cran-deldir Suggests: r-cran-testthat, r-cran-tmap, r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spatialfusion_0.7-2-1.ca2404.1_all.deb Size: 423590 MD5sum: da2e147e3e5446cb82006dc5ef51e00b SHA1: 01a17a1c69f0b97bea73bba43c1bd19247c0ec34 SHA256: 805372d9199976486cce171d4120b9d0b5c1c2933083a875664fde622fc6108a SHA512: f764fc06e184c7cfac6dc98c4a86f597fa21b991ac824d274069df3fa9eed097a0a61efc09840f34eab5088c47da28ac2914a9d1999c38b21bb7b0ad1ea3d87a Homepage: https://cran.r-project.org/package=spatialfusion Description: CRAN Package 'spatialfusion' (Multivariate Analysis of Spatial Data Using a Unifying SpatialFusion Framework) Multivariate modelling of geostatistical (point), lattice (areal) and point pattern data in a unifying spatial fusion framework. Details are given in Wang and Furrer (2021) . Model inference is done using either 'Stan' or 'INLA' . Package: r-cran-spatialgraph Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-pracma, r-cran-sf, r-cran-shape, r-cran-sp, r-cran-splancs Filename: pool/dists/noble/main/r-cran-spatialgraph_1.0-4-1.ca2404.1_all.deb Size: 124950 MD5sum: 35d6d64674aaf4703fe885a344ca4188 SHA1: 54f58c8924cacfaee33331ca5485843a609a1e44 SHA256: 6eb0ba58b2493df20ae24b94a5d8634b3d8ff073295a7948e6797d5c9b468b6b SHA512: 3fdbbccd3b11f63f3873f34f34fad06faf1817dc2cbac90a05aaf03387d2d7ebe60df1084e1064aec6ef1282ccf1783011cd5d9e89e4c62d609bf2ba4e49370a Homepage: https://cran.r-project.org/package=SpatialGraph Description: CRAN Package 'SpatialGraph' (The SpatialGraph Class and Utilities) Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg(). Package: r-cran-spatialkit Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-logger, r-cran-digest Suggests: r-cran-sp, r-cran-gwmodel, r-cran-ranger, r-cran-brms, r-cran-loo, r-cran-tibble, r-cran-geometry, r-cran-gstat, r-cran-ggplot2, r-cran-patchwork, r-cran-fnn, r-cran-matrix, r-cran-spdep, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spatialkit_2.0.0-1.ca2404.1_all.deb Size: 1607434 MD5sum: 0a05f882bd60149e3c4010fc744cc431 SHA1: 1529a5d90a3a4341ef44dd7d903b6e082b23ee52 SHA256: d88d9a15ecfac962e162739e2d6f54c7cb8d255ee943a52916620eb662d99974 SHA512: a755ca4c6d9dad91266e26535c50b8117ed0a5509772bbb6eb2d9b60af6abac951ed2225eaa8d2acbeed8d314a73ab73cbec80b73d942670392ade0151a11c28 Homepage: https://cran.r-project.org/package=spatialkit Description: CRAN Package 'spatialkit' (Spatial Tessellation, Modeling, and Cross-Validation Toolkit) Constructs analysis regions from the distribution of the data itself, as an alternative to aggregating onto administrative boundaries that were drawn for unrelated purposes. Seeds and builds Voronoi, Delaunay, hexagonal and square tessellations with reproducible identifiers, selects a cell count from the spatial structure of the observations, assigns features to cells, and aggregates to cell level with optional design-effect corrections so that standard errors account for within-cell autocorrelation. Also manages coordinate reference systems. Fits geographically weighted regression (via 'GWmodel'; Lu et al. (2014) ), Bayesian spatial Gaussian process regression (via 'brms', using the Hilbert space approximation of Riutort-Mayol et al. (2023) ) and random forests (via 'ranger', with the permutation importance of Strobl et al. (2007) ), each behind one S3 class with consistent predict, fitted, residuals and plot methods. Provides spatial cross-validation with random, block, buffered, leave-location-out and nearest-neighbour distance-matched folds (Mila et al. (2022) ), forward variable selection, model comparison, prediction onto a regular surface, and the area of applicability of Meyer and Pebesma (2021) to flag where a fitted model extrapolates beyond its training data. Package: r-cran-spatialml Architecture: all Version: 1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ranger Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialml_1.8.2-1.ca2404.1_all.deb Size: 115776 MD5sum: 0b1aa38339604cdc27adbc5df9b83d33 SHA1: ce637fdfc5a03d11bb4090b82345c58ccedca6fe SHA256: 943b146e15dcf492735690f6370c6bc854f0185425c56556cc5c40c82adda9d4 SHA512: 1a3b5db10b8eec5a232f0f69492e343ba6be2a7398886f97712339d2d9ae9bda0f0603c873e8d53aae8a89f9902f3a43415032588291d299dabe1c85bd636ef3 Homepage: https://cran.r-project.org/package=SpatialML Description: CRAN Package 'SpatialML' (Spatial Machine Learning) Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) ). Provides a Geographically Weighted Random Forest regression and a routine to find the optimal bandwidth (Georganos and Kalogirou (2022) ). A lightweight cross-validation helper for tuning the 'mtry' parameter of a random forest and a generator of synthetic spatial test data are also included. The package depends on 'ranger' as its single random-forest back-end. Package: r-cran-spatialpop Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-qpdf, r-cran-numbers Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialpop_0.1.0-1.ca2404.1_all.deb Size: 22508 MD5sum: 6042638675bf1157ca72474910c7cde5 SHA1: 08b70312a637636f9155468a2a742c3d4c9faff9 SHA256: a99fc468d5616f556767536f8469bf4459c21247a2d61fcaec9ca49775486dc0 SHA512: a7f593c285f453c1e9f8f9e2aad51a92239376312359976bfc7f6523a9a688bf9698febe69943d18ed4684f6eef66ecb59f6c600eb6b0288ac76bda0d8989f5d Homepage: https://cran.r-project.org/package=SpatialPOP Description: CRAN Package 'SpatialPOP' (Generation of Spatial Data with Spatially Varying ModelParameter) A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).. This spatially generated data can be used to test different issues related to the statistical analysis of spatial data. This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables. Package: r-cran-spatialposition Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-isoband, r-cran-raster Suggests: r-cran-lwgeom, r-cran-doparallel, r-cran-foreach, r-cran-cartography, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spatialposition_2.1.3-1.ca2404.1_all.deb Size: 478804 MD5sum: 6a9d4e537a34051df337abfd7c4f3d51 SHA1: 2e71a8bc2697d313f162f20a9ea783dc08c10776 SHA256: dd1090683fec001fd783521e66a50332c2f0b04559a0a7ba3f950b2261c613ef SHA512: f6dee7c8b80e71a1ce01bd6c001a6b5d20cfed647371a30c24915c89ffc31ea39573856bdb73403037b4150118a84ab969298bab58750337c82fc3668d743717 Homepage: https://cran.r-project.org/package=SpatialPosition Description: CRAN Package 'SpatialPosition' (Spatial Position Models) Computes spatial position models: the potential model as defined by Stewart (1941) and catchment areas as defined by Reilly (1931) or Huff (1964) . Package: r-cran-spatialprobit Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-spdep, r-cran-spatialreg, r-cran-mvtnorm, r-cran-tmvtnorm Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialprobit_1.0.4-1.ca2404.1_all.deb Size: 271992 MD5sum: bf8a2c19c2db8302961677c9c457fe63 SHA1: 9e872c20df6a945941c9e7afdcb86e27dde034ea SHA256: fbbafbf9c3e6eb1e4f50dd43abc30a58b2fc6c6e240b6eed535efee0b77355ee SHA512: 17bff52d1d856b2fdfcb70a2998c71e5396acb349474cbcf9e15bce0d4c8645096684d982a2dba904fd124a12f8b1884837444f0ddbba469348cca0dfaaa1dab Homepage: https://cran.r-project.org/package=spatialprobit Description: CRAN Package 'spatialprobit' (Spatial Probit Models) A collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of 'LeSage and Pace' (2009, ISBN: 1420064258) were ported and adjusted for R, as described in 'Wilhelm and de Matos' (2013) . Package: r-cran-spatialrdd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-ggplot2, r-cran-rdrobust, r-cran-lmtest, r-cran-sandwich, r-cran-cowplot, r-cran-magrittr, r-cran-rlang, r-cran-broom Suggests: r-cran-knitr, r-cran-tmap, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-lfe, r-cran-stargazer Filename: pool/dists/noble/main/r-cran-spatialrdd_0.1.0-1.ca2404.1_all.deb Size: 1678414 MD5sum: 24c9d108c4113abf663705d4c9e786f6 SHA1: 94d411c470f7949aabe6f2659cd8198387457e47 SHA256: 0765f284812479a3f01eb176f210f7a6419f8116227e4607aa210c2088e326ce SHA512: 3942b23522b5a71abc070f4299796079568a7677c9c3237154364bc200fedff17294a3c21ef68e20a222330cde08038b2f0ee508ba1f65c49c8099cc16a1a611 Homepage: https://cran.r-project.org/package=SpatialRDD Description: CRAN Package 'SpatialRDD' (Conduct Multiple Types of Geographic Regression DiscontinuityDesigns) Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on 'APIs' of external 'GIS' software ensures replicability and raises the standard for spatial RDDs. Package: r-cran-spatialreg.hp Architecture: all Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatialreg, r-cran-ggplot2, r-cran-vegan, r-cran-spdep Filename: pool/dists/noble/main/r-cran-spatialreg.hp_0.0-2-1.ca2404.1_all.deb Size: 45384 MD5sum: 82c485cfd1976dec57ecdbc0861b63ff SHA1: 59b98ab72d87be69019292f331f6fefc8c9a34f7 SHA256: 992a0de5622b8db860af7ab321bb17f05f65489b41567a1574fabbe5656fd1eb SHA512: c586bf058625d076e3b4153925483f9683d021566e678c7441d7efc047479474ebbde314e39e5f0ac94380b86b4a13a073f62fd827b3fc8a747e01a0a70010eb Homepage: https://cran.r-project.org/package=spatialreg.hp Description: CRAN Package 'spatialreg.hp' (Hierarchical Partitioning of R2 for Spatial SimultaneousAutoregressive Model) Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model. Package: r-cran-spatialregimes Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-quantreg, r-cran-gwmodel, r-cran-plm, r-cran-spatialreg Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-spatialregimes_1.2-1.ca2404.1_all.deb Size: 454198 MD5sum: 4f4edc0374e7c177e073540d2e7fe963 SHA1: aabe221b9338b40cdcb7b5e6561a43bd5c9b77e7 SHA256: 987a4d79347f800069404191d5db19f39ce1db3ebe344e66d0377f06c27c83c1 SHA512: a413477b43a406eba69859f7d357d6e171b3bcb1d3a6298580275b84f415ea5967100fa81fe30e79b5a1b74dbaf13b9d8c2ba23cfa88eb3a9e56050b8bc6d757 Homepage: https://cran.r-project.org/package=SpatialRegimes Description: CRAN Package 'SpatialRegimes' (Spatial Constrained Clusterwise Regression) A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process. Package: r-cran-spatialregroup Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-spdep, r-cran-igraph, r-cran-rlang Filename: pool/dists/noble/main/r-cran-spatialregroup_0.1.0-1.ca2404.1_all.deb Size: 54812 MD5sum: 423d8a9fec319e0f987ef6372e2d15d6 SHA1: 9ff38835ac5a5417b78a1798ebe6a81b053a851e SHA256: 39122a3b55d008296f92598387668402ce6c0137476a82dcc16ce13bbc3f1f3c SHA512: 5d5b2f51db6881dfe0dfdedb8303ec540459d6e86305123e8eabefc81c2bf4d5044a144a7bda90701389416dce57cae3e0e78dd02cf780a748920e0ae885f531 Homepage: https://cran.r-project.org/package=spatialRegroup Description: CRAN Package 'spatialRegroup' (Iterative Spatial Regrouping of Administrative Units byAttributive Affinity) Evaluates the statistical coherence of existing administrative partitions (e.g. inter-municipal groupings, districts) by identifying spatial units whose attributive profile is more similar to a neighbouring group than to their own. Border units are iteratively reassigned to the group they are most affine with, based on Euclidean or Mahalanobis distance computed on user-supplied numeric variables, with optional per-variable weighting and standardisation. Spatial contiguity is enforced throughout: isolated candidates are reintegrated into their original group, disconnected fragments are resolved, and empty groups are restored. Convergence is monitored via an eta-squared cohesion criterion. The resulting partition can be compared to the original administrative delineation using multilevel models, providing a quantitative measure of boundary inefficiency. Package: r-cran-spatialrf Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-foreach, r-cran-doparallel, r-cran-ranger, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-huxtable, r-cran-patchwork, r-cran-viridis Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-spatialrf_1.1.5-1.ca2404.1_all.deb Size: 6976754 MD5sum: 09e0cf3c340d40527aca9069f35da32a SHA1: 0493772ace91a1fe5400f53a062ab543f8c785dc SHA256: 99571e3a7f97400e1a84c7c4c516207931f2aeb67693d6151acf4e1ec2d6bc79 SHA512: 909e2fc52f53633b37ecbe2591c4e69eed18a6e45e91e25125c7544925297dadd83ba5fd4d16f99cfaa0cfce8f07b6813a90191fd2bb6c88b295fa515ea8778b Homepage: https://cran.r-project.org/package=spatialRF Description: CRAN Package 'spatialRF' (Easy Spatial Modeling with Random Forest) Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 ) or the RFsp approach (Hengl et al. ). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 ). Package: r-cran-spatialromle Architecture: all Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spatialromle_0.1.1.1-1.ca2404.1_all.deb Size: 42318 MD5sum: 60aadc6a548e8f2d83df39781c67b26c SHA1: 47e679963bbfd9d563a10536a148f026a2a5816d SHA256: f7aa31c64964c83ba05b988c82ec611604247a40febe8b738e7257fb2fbf1f88 SHA512: 5039223e198db8f48ac7893062a8bf5532e88bb6c02868019aea5876a920a9138c5f19f58bb5f3207441cc2ff2e28df14eecfc0bff3099e129009d4a41bad0c7 Homepage: https://cran.r-project.org/package=SpatialRoMLE Description: CRAN Package 'SpatialRoMLE' (Robust Maximum Likelihood Estimation for Spatial Error Model) Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, ). Package: r-cran-spatialtime Architecture: all Version: 1.3.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-rcolorbrewer, r-cran-furrr, r-cran-future, r-cran-tidyselect, r-cran-crayon, r-cran-pbmcapply, r-cran-dixon, r-cran-tibble, r-cran-stringr Suggests: r-cran-knitr, r-cran-devtools, r-cran-rmarkdown, r-cran-testthat, r-cran-gridextra, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-spatialtime_1.3.4-5-1.ca2404.1_all.deb Size: 3400578 MD5sum: bab67bdf8bbfaca0c4e78e4787b12416 SHA1: d6547063f8e90940c1837c7c93d6ad205a9ec42a SHA256: 09847dcc613d255ff9f3a38a146d74843346df8a8287562b2e1979b427cc05a8 SHA512: 47b34187c436e36005cb332d0278cf644f836915210795278cc16319899ead75f6c6e010281ac66a35d68ec33cce47919c5b158fd3a10251195c64e4c0bc1cdd Homepage: https://cran.r-project.org/package=spatialTIME Description: CRAN Package 'spatialTIME' (Spatial Analysis of Vectra Immunoflourescent Data) Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. . Package: r-cran-spatialvs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-fields Filename: pool/dists/noble/main/r-cran-spatialvs_1.1-1.ca2404.1_all.deb Size: 343172 MD5sum: 8ac35bf656805a90eeff1c5ea25a6796 SHA1: e8b0f28a6a4da27245079ca81efc4573e59568f3 SHA256: 2f26eab7d98ce28511da98ff0fb5cc505b923e294eda227cf96b1efe7e7d3518 SHA512: ccc4f0d614073648c532bbeb98f6dfb83a091a024d6ba12c105a45fdc3fb459b68891dcd209c03f0e7f5c2b762a0249892cd34ad1a027d802d321a97ab02886d Homepage: https://cran.r-project.org/package=SpatialVS Description: CRAN Package 'SpatialVS' (Spatial Variable Selection) Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, ). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006. Package: r-cran-spatialvx Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-fields, r-cran-smoothie, r-cran-smatr, r-cran-turboem, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-distillery, r-cran-maps, r-cran-boot, r-cran-circstats, r-cran-fastcluster, r-cran-waveslim Filename: pool/dists/noble/main/r-cran-spatialvx_1.0-3-1.ca2404.1_all.deb Size: 3888912 MD5sum: 0fcb91660c2ded21742756dc0565f78e SHA1: 93dfb9a38c06db8a787250a7b6b33a7f8f8c6f7b SHA256: ebaf8fb6f3e7229487dd5602a40f9fbefeb3de03cb23fde0d0130c68446e54c2 SHA512: e49b9d9c29d214807e8354e8ea2c1081a2b82a8ecb203bd45f1ba496b956cbdf71d69f074364e020114861d33c7726c8b1d390f3b78d3bfd9b4c6f367f316c3c Homepage: https://cran.r-project.org/package=SpatialVx Description: CRAN Package 'SpatialVx' (Spatial Forecast Verification) Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) and for a tutorial on some of the main functions available here, see Gilleland (2022) . Package: r-cran-spatpersist Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatpersist_0.1.0-1.ca2404.1_all.deb Size: 121476 MD5sum: f077dd55b941c2e2002ea203ff1fb7d2 SHA1: 4a51c44d2da84b438b41fb08569170fa6b56fff4 SHA256: bf3667421ff7ef147decf999a41b73015351b3a720927f71f26c0bd4edbeeae3 SHA512: 53eda54fb9ef8518f7638316caa352bb731a374243cc70915a17af54dda1772e7f8667b07083c91e806e9ed7d848a003dd6230954b99b223551b77930ec5d7ad Homepage: https://cran.r-project.org/package=spatpersist Description: CRAN Package 'spatpersist' (Create Persistent Identifiers for Longitudinal Spatial Data) Creates dataset-local persistent identifiers for polygon units observed across time. Configurable overlap metrics, thresholds, and one-to-one matching rules identify continuity while separate identifiers track boundary versions and broader lineages through splits, mergers, and replacements. Candidate-link diagnostics, validation checks, registry reconciliation, and lineage summaries keep the resulting decisions inspectable and reproducible. Package: r-cran-spats Architecture: all Version: 1.0-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fields, r-cran-spam, r-cran-data.table Filename: pool/dists/noble/main/r-cran-spats_1.0-20-1.ca2404.1_all.deb Size: 184390 MD5sum: e8aa112b09dd37a3ee71b99fbe695a85 SHA1: 9106d429303c69fe4a7afd3690a6850a0211ff9f SHA256: 189c93302a7cdcfd5897aa959180cabf8bf57edb46fd5061858dd610d950f489 SHA512: 4ac0e29fdf118a2a58bc9f3a47e4055f4d6e692d88ded246f36f03a9127084288a53afcec937c5abc582cb109f4391a2b863e2b18a356b9a16482d5588959e97 Homepage: https://cran.r-project.org/package=SpATS Description: CRAN Package 'SpATS' (Spatial Analysis of Field Trials with Splines) Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models. Package: r-cran-spatsoc Architecture: all Version: 0.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adehabitathr, r-cran-data.table, r-cran-igraph, r-cran-sf, r-cran-lwgeom, r-cran-circstats, r-cran-units, r-cran-rlang Suggests: r-cran-asnipe, r-cran-knitr, r-cran-markdown, r-cran-move2, r-cran-rmarkdown, r-cran-s2, r-cran-sftrack, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatsoc_0.2.13-1.ca2404.1_all.deb Size: 1022678 MD5sum: ddfdccdf1458b08d58bc8c98450dd991 SHA1: 5960df1e8d164feb4d105b18e23494465595a3c8 SHA256: dd2b5da692b1b6521f185d4e0626a422e07921c3515a4d95d3a17c3ffa94edec SHA512: 83fb3836b95ebd548163bf919096840bc09540d5cf5169dd01a4d2a9c781febe380b6fb87e45a494d6243cdd4f141d4f0a66a554643eda5046b54853ecb0c9d9 Homepage: https://cran.r-project.org/package=spatsoc Description: CRAN Package 'spatsoc' (Group Animal Relocation Data by Spatial and TemporalRelationship and Measure Intragroup Social Dynamics) Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) ). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks, and perform data-stream randomization methods suitable for GPS data. Also provides measures of intragroup social dynamics including distance and direction to leaders, centroids and nearest neighbours. Package: r-cran-spatstat.convert Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-sp, r-cran-spatstat.utils, r-cran-spatstat.univar Suggests: r-cran-terra, r-cran-sf Filename: pool/dists/noble/main/r-cran-spatstat.convert_1.0-1-1.ca2404.1_all.deb Size: 197178 MD5sum: 48aa610165090f1e4a11fa667431832d SHA1: fa3bdb271c954f8d5d29cda69def3a7f2299dae1 SHA256: c7bde988421c4bd2d6958ccb09f38eb9bda165fc40f13fd3c4cf360f96c968a3 SHA512: 829bdc183d55818b41ebb07032d66e44d6073f08b3ce19850745090a47f6d472e9dd91088c5488f8b3d4ac25aae7d15de5b95dc4cf738d2644161dd97f84ba86 Homepage: https://cran.r-project.org/package=spatstat.convert Description: CRAN Package 'spatstat.convert' (Extension to 'spatstat' for Converting Data Formats) Extension to the 'spatstat' package, enabling the user to convert spatial data between formats defined in `spatstat' and formats defined in 'sp', 'sf' and 'terra'. Package: r-cran-spatstat.data Architecture: all Version: 3.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.utils, r-cran-matrix Suggests: r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet Filename: pool/dists/noble/main/r-cran-spatstat.data_3.1-9-1.ca2404.1_all.deb Size: 4154170 MD5sum: e855e9f78c76da030fba655ba2d410fb SHA1: 85283b45c19c68e9be6ee9ed61315af55d84ef8e SHA256: db3ff88e125d9cf64c3fcf9fda8bf10fe70dcbbb7e4e07a419e252940def202a SHA512: 446421069c4fa956f4bcf22b14f62de6106ab56dee5b2d6d478119026cb687532e092bbc9540f0f3b264a51a6aec6c536e840b6cd9f05b6e9f63671fd814f116 Homepage: https://cran.r-project.org/package=spatstat.data Description: CRAN Package 'spatstat.data' (Datasets for 'spatstat' Family) Contains all the datasets for the 'spatstat' family of packages. 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Package: r-cran-spatstat.local Architecture: all Version: 5.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.sparse, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat.local_5.1-0-1.ca2404.1_all.deb Size: 349182 MD5sum: d36ff32d3099b5f242fd6269a501ffff SHA1: b2c7ac9162e7727a80bfc98584accaacdb7d9dd6 SHA256: db72389765b1abe43fc6207673ffcee1a94fc14223f6a758455d77d5f70fbdba SHA512: 9bac8f3b6578a448af12746a4d9fe4de23493ac78a12999992cdc32c7b457cae76f83ad376142cbfb1af19a1f6063d62fa5f370768466a46802936e3ff5a2edc Homepage: https://cran.r-project.org/package=spatstat.local Description: CRAN Package 'spatstat.local' (Extension to 'spatstat' for Local Composite Likelihood) Extension to the 'spatstat' package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression'). Package: r-cran-spatstat Architecture: all Version: 3.6-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5510 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat_3.6-3-1.ca2404.1_all.deb Size: 4351260 MD5sum: 9f55ee859c4e02e7977c6945bf6f8e3e SHA1: 4fc891d30809b463764914845724dcaf3c8e5324 SHA256: 9ef4d97bff48079f5de80e4d546f03375a0fcc1e7e25f00c9dd6c2b99b1f8251 SHA512: 11cf90f92279bfc5d5b59421f20161e93cd9eddbd26754781f2a159985390f0b97af6e9c29dea93654696ed4db35d7182f11e1be2e17e8a94bc7a1dc393c2026 Homepage: https://cran.r-project.org/package=spatstat Description: CRAN Package 'spatstat' (Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests) Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. 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This is a clinical trial design where patients initially on placebo who did not respond are re-randomized between placebo and active drug in a second phase and the results of the two phases are pooled. The method of analyzing binary data with this design is described in Fava,Evins, Dorer and Schoenfeld(2003) , and the method of analyzing continuous data is described in Chen, Yang, Hung and Wang (2011) . 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This is motivated by the application of spatial splines for adjusting for unmeasured spatial confounding in regression models, but the calculation of effective range can be applied to smoothing matrices in other contexts. For algorithmic details, see Rainey and Keller (2024) "spconfShiny: an R Shiny application..." and Keller and Szpiro (2020) "Selecting a Scale for Spatial Confounding Adjustment" . Package: r-cran-spconform Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1478 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sp Filename: pool/dists/noble/main/r-cran-spconform_0.1.1-1.ca2404.1_all.deb Size: 959842 MD5sum: c608bac815b175a99a97959d8cd16224 SHA1: d01539e1b393eb9f9c628b2ec1e19f026daa4b77 SHA256: 4a6349ec1c61ab069a5ef92531192f5b8653b5d48b2c67ab23addddbb17a09d3 SHA512: 20f2f0cec461837821211b8b5c01b7fadef7ae3692f4bacca1dd2e19aa7e752c3a374758b966fb4be5a4135a0c2f88677c30778ae54876d999f200b0e7b6d37c Homepage: https://cran.r-project.org/package=spconform Description: CRAN Package 'spconform' (Conformal Prediction for Spatially and Spatio-TemporallyDependent Data) Provides distribution-free, model-agnostic prediction intervals for spatially and spatio-temporally dependent data using localized conformal calibration. Implements locally weighted split conformal prediction for geostatistical (point-referenced) data based on spatial-distance kernels, and a neighbourhood-weighted conformal procedure for areal (lattice) data based on graph adjacency structures. Relaxes the standard exchangeability assumption using spatial proximity, following the localized conformal framework of Mao, Martin and Reich (2024) . Includes comprehensive spatial diagnostic tools to audit empirical coverage, conditional spatial strata, and boundary proximity effects. 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Package: r-cran-spd Architecture: all Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-spd_2.0-1-1.ca2404.1_all.deb Size: 220212 MD5sum: ed7f54f6687cb7036e594c74882ae58e SHA1: 1fa34578fc8eb822a628bf16aa4f5563200168ff SHA256: 6e8d7532ee879142470ad98603c26c9d8045e70248f9145a66234bd35e8cfdcd SHA512: 53530ceebf9e094737e9ad4e3fd0a52e19ea6836955e2bcf166f698ac6273378bbd8bc09b14f2d9ff122e15374b97c1bc913e4fce3f57db117b38e5e09d6bf19 Homepage: https://cran.r-project.org/package=spd Description: CRAN Package 'spd' (Semi Parametric Distribution) The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior. Package: r-cran-spdata Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6532 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sp Suggests: r-cran-foreign, r-cran-sf, r-cran-spdep, r-cran-spatialreg Filename: pool/dists/noble/main/r-cran-spdata_2.3.5-1.ca2404.1_all.deb Size: 3683410 MD5sum: ee17802725fb433b7f06f8ec4e82376a SHA1: 30c27bf9e5d97554404845cf52dafe76527b8f49 SHA256: 7bb02f4a6579caa8bda28d86e448e5441955d4e3d8bd90c1dbb077c782233893 SHA512: ce1dfd3f8e24fbac90f005c7886af054bce60a07c65439b6a431405fe8c169076d00f608466dae3eb3ba42cb1670ffa13d738bd5a138a2e3dd72421b5670f28e Homepage: https://cran.r-project.org/package=spData Description: CRAN Package 'spData' (Datasets for Spatial Analysis) Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). 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Package: r-cran-spdesign Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-cli, r-cran-future, r-cran-randtoolbox, r-cran-matrixstats, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spdesign_0.0.7-1.ca2404.1_all.deb Size: 331622 MD5sum: ffebc5d30884c0a5d8c540ec40fab1c8 SHA1: a7b042e8bb345336b681801c26279466ab6ade48 SHA256: a0479f4dedc14d44e92051613d5aa30dac5c694d61cc05d30628de8aa8c369e7 SHA512: b2e3025f737c35fcca7e0253dcbb0a4a73f5b695449ae1837abde17807ddf16d92e039f0287f087d58ebc35c48ea815e07540e26c4a47791eb5b477cc6697d08 Homepage: https://cran.r-project.org/package=spdesign Description: CRAN Package 'spdesign' (Designing Stated Preference Experiments) Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. 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Package: r-cran-spdgp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-mass, r-cran-matrix, r-cran-rlang, r-cran-sf, r-cran-smoothmest, r-cran-spatialreg, r-cran-spdep, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-spdgp_0.1.0-1.ca2404.1_all.deb Size: 86722 MD5sum: 841af479dbff10f46257af22d1796636 SHA1: 4b1899e27f8dc468204b7892f8c720c963ba75cc SHA256: aafbc1a3fb178fa1f7d3707945228fee27c17d6ea9043c725ad10ada85ced94e SHA512: 543a119634ab99913b5987e63ce12289500adc1ef5600f44bdde46b69052cc84fe61c8c20bf2f98b2a3ed2922856c022c0546a53f906ebb370993ae4553d3404 Homepage: https://cran.r-project.org/package=spdgp Description: CRAN Package 'spdgp' (Simulate Spatial Data Generation Processes) Provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the 'dgp' module of the 'Python' library 'spreg' . 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Package: r-cran-speccurvier Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1562 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-combinat, r-cran-fixest, r-cran-pbapply, r-cran-lmtest, r-cran-sandwich, r-cran-generics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-kableextra, r-cran-flextable, r-cran-modelsummary Filename: pool/dists/noble/main/r-cran-speccurvier_1.0.0-1.ca2404.1_all.deb Size: 1220088 MD5sum: 1326294692d993a6d01d7292cd6082b1 SHA1: 3b37b27aa216214762a4cd4935e0934e7bf5355c SHA256: da2e1043fdb3d6f08ea0ef83f9bcec78798f89bf816027cf89234fb4dd042615 SHA512: 4163f0318e42b8ab8a4d3febe0f440bce5f34ab6e412f307c16a462a9d78839b506c8d0289fd271b8276253d030d1dad3c4b7cac9f10739536eaabc49406af11 Homepage: https://cran.r-project.org/package=speccurvieR Description: CRAN Package 'speccurvieR' (Easy, Fast, and Pretty Specification Curve Analysis) Making specification curve analysis easy, fast, and pretty. It improves upon existing offerings with additional features and 'tidyverse' integration. Users can easily visualize and evaluate how their models behave under different specifications with a high degree of customization. For a description and applications of specification curve analysis see Simonsohn, Simmons, and Nelson (2020) . Package: r-cran-specdetec Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-specdetec_1.0.0-1.ca2404.1_all.deb Size: 66892 MD5sum: 0f1d5ea748f86d7e14cacd5c409c85d7 SHA1: 2e785e7934e3bd8183454986a37f5cc048c0f386 SHA256: 66f5137479d99b21fa839d76bd8bec8eae23273423aea62243a5e50db245d9ef SHA512: 19aff97a6e510b8f9a0f431fce3c080b0de7bc400ce1bb9ca9017f032566dae1d1fbadc90801f3ab0df72347d1056e5e17959ce934f9ede2565258406e1c4868 Homepage: https://cran.r-project.org/package=SpecDetec Description: CRAN Package 'SpecDetec' (Change Points Detection with Spectral Clustering) Calculate change point based on spectral clustering with the option to automatically calculate the number of clusters if this information is not available. Package: r-cran-spechelpers Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsubfn, r-cran-splancs Filename: pool/dists/noble/main/r-cran-spechelpers_0.3.2-1.ca2404.1_all.deb Size: 118328 MD5sum: e36dba17b555ba8e2fb0b18e20abf544 SHA1: 8300e288cedfc277ba9314c3e9ced00c93c333f9 SHA256: 446b804f1e8788e72e3dacef9cb6cf08679eefab3d415a1e6c599b2d7b96d360 SHA512: 7e1994de49f13ac6daa9c82bcdd7db7e441896d755f31897084ee5775b5e01c797a2845617630bb615f940bfb31c8ee1a2c8ddcce36652d2a5c950e7520b8975 Homepage: https://cran.r-project.org/package=SpecHelpers Description: CRAN Package 'SpecHelpers' (Spectroscopy Related Utilities) Utility functions for spectroscopy. 1. Functions to simulate spectra for use in teaching or testing. 2. Functions to process files created by 'LoggerPro' and 'SpectraSuite' software. Package: r-cran-specieschrom Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorramps, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-abind Filename: pool/dists/noble/main/r-cran-specieschrom_1.0.0-1.ca2404.1_all.deb Size: 102080 MD5sum: 90e0dad017ac014ad17bd5ad18cc5ee8 SHA1: f22b87bd27620ec2d58dd1ff51cc4205c4dd04ab SHA256: e0b51debc3a72ed2bdc756b9e5eeabf5dbc3c2b31122fc54a6f9757824e9f4c4 SHA512: f3127a5297d0d5a494b4e995d72ae819b93d376997e4da5fd33edf4f17e7526aa0c654ff836e5e2a68340bdaa388b270cd841c9dd46dd955f68796ecd20238e8 Homepage: https://cran.r-project.org/package=specieschrom Description: CRAN Package 'specieschrom' (The Species Chromatogram) A simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) for further details. Package: r-cran-speck Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-magrittr, r-cran-matrix, r-cran-rsvd, r-cran-seurat Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-seuratobject, r-cran-usethis Filename: pool/dists/noble/main/r-cran-speck_1.0.1-1.ca2404.1_all.deb Size: 4086420 MD5sum: fd91d378f7ca790a1f8df47511520971 SHA1: 8f2da9374870ae619bfe8996e71986e1d868c54c SHA256: ff3fed0dde8d84202fe606117c967c91e8ec80758aa09fc872c5a15db230b030 SHA512: 72f6663e6c91d55f682177b38df9e965cd67a6a5c68c5894b81b244172ed05fc12f6ff4cd96221a0f41e0dcf8b4dc69108f0d363181f8c291ecb3347a1df2dbb Homepage: https://cran.r-project.org/package=SPECK Description: CRAN Package 'SPECK' (Receptor Abundance Estimation using Reduced Rank Reconstructionand Clustered Thresholding) Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for the RRR is further detailed in: Erichson et al., (2019) and Halko et al., (2009) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) . Package: r-cran-specleanr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dbscan, r-cran-e1071, r-cran-isotree, r-cran-robust, r-cran-robustbase, r-cran-usdm, r-cran-mgcv Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-ggpmisc, r-cran-tibble, r-cran-rinat, r-cran-rvertnet, r-cran-rgbif, r-cran-curl, r-cran-rfishbase, r-cran-sf, r-cran-terra, r-cran-tidytext, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-specleanr_1.0.0-1.ca2404.1_all.deb Size: 2137332 MD5sum: 7d58e5d028d1976b996142af735440ac SHA1: b91edd3ebe0445f49c4b558bf965f01f98910d25 SHA256: 86508dba976ed7bb9c5ebc119569d74d66a467bfabf0bbb47ebb559f308415c8 SHA512: 9ab2cc6ea84c3d1cb9335c9743ae8971a85721b3d7d2be085906b76766e2cbb7f24e6d283971fe8cc204d4ae04e0c08adc0c19f63d3a8bea438c5dae7f58d419 Homepage: https://cran.r-project.org/package=specleanr Description: CRAN Package 'specleanr' (Detecting Environmental Outliers in Data Analysis Pipelines) A framework used to detect and handle outliers during data analysis workflows. Outlier detection is a statistical concept with applications in data analysis workflows, highlighting records that are suspiciously high or low. Outlier detection in distribution models was initiated by Chapman (1991) (available at ), who developed the reverse jackknifing method. The concept was further developed and incorporated into different R packages, including 'flexsdm' (Velazco et al., 2022, ) and 'biogeo' (Robertson et al., 2016 ). We compiled various outlier detection methods obtained from the literature, including those elaborated in Dastjerdy et al. (2023) and Liu et al. (2008) . In this package, we introduced the ensembling aspect, where multiple outlier detection methods are used to flag the record as either an absolute outlier. The concept can also be applied in general data analysis, as well as during the development of species distribution models. Package: r-cran-specmine.datasets Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1315 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-specmine.datasets_0.0.3-1.ca2404.1_all.deb Size: 1302002 MD5sum: 8792acb3fe6039721e4ace64b92c9a4b SHA1: 6069d0bc6c803a4dd22a6495182cd2574ce3bf18 SHA256: 38a5543bb7db3535d8f87fd68db8080025db81910f1421e8e9ea1c900eb853df SHA512: 632506893906b0f831fc7e0faf3e463c03f1d7996f85d940e85b645d7f74b18f3c4cd7a695dce6ae28dead1d17c9f65d791521dbd458f885428b17e4032650d6 Homepage: https://cran.r-project.org/package=specmine.datasets Description: CRAN Package 'specmine.datasets' (Data Sets for 'specmine') Provides the data sets used to exemplify 'specmine'. These data sets were formerly distributed with 'specmine', but they exceed current CRAN policy for package size. Package: r-cran-specmine Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1563 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-baseline, r-cran-caret, r-cran-compare, r-cran-ellipse, r-bioc-genefilter, r-cran-ggally, r-cran-ggplot2, r-bioc-impute, r-cran-imputets, r-cran-mass, r-cran-matrix, r-cran-metrics, r-cran-narray, r-cran-pcapp, r-cran-plotly, r-cran-pls, r-cran-rcolorbrewer, r-cran-readjdx Suggests: r-cran-clustercrit, r-cran-curl, r-cran-dbscan, r-cran-fastica, r-cran-ggdendro, r-bioc-kegggraph, r-bioc-keggrest, r-cran-knitr, r-bioc-mait, r-cran-mclust, r-cran-pins, r-cran-qdap, r-cran-qpdf, r-cran-rcurl, r-cran-cyjshiny, r-cran-reticulate, r-cran-rgl, r-cran-rtsne, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-specmine.datasets, r-cran-uwot, r-cran-randomforest, r-bioc-xcms Filename: pool/dists/noble/main/r-cran-specmine_4.0.1-1.ca2404.1_all.deb Size: 1482618 MD5sum: ed3b79a12eca40a2fa546f7fb749d92b SHA1: 2b3cfa8af6112235745aedfe1cf8c1be7d9242a1 SHA256: a2950030fe11f06ba9622f7bbdeb8a97c78ce339a2a87f0cc91828f704d8ee3f SHA512: ba077bbe8cccf34ba0fb5d3d16f1c2f0c53e7dd336768678b3ac957afd0d73523bf6c5023fe7dcff762d13efbe93195c5fcea94476b921dd74fe4c489dd0b80d Homepage: https://cran.r-project.org/package=specmine Description: CRAN Package 'specmine' (Metabolomics and Spectral Data Analysis and Mining) Provides methods for metabolomics and spectral data analysis, including data import, preprocessing, visualization, univariate and multivariate analysis, machine learning, feature selection, and pathway analysis. The package supports analytical workflows for different data types used in metabolomics and spectroscopy. Some optional functionality uses the suggested packages 'cyjShiny' and 'specmine.datasets'. The package 'specmine.datasets' is maintained separately at . Package: r-cran-specr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-cowplot, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-glue, r-cran-igraph, r-cran-lifecycle, r-cran-lme4, r-cran-magrittr, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom.mixed, r-cran-gapminder, r-cran-ggridges, r-cran-knitr, r-cran-lavaan, r-cran-testthat, r-cran-tidyverse, r-cran-performance, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-specr_1.0.0-1.ca2404.1_all.deb Size: 2345790 MD5sum: 7c83f8e7c3936d6dbd238fe84920caaa SHA1: e92bf71e30152378e4ec3477d2e8d1a46f5f4776 SHA256: 7553722bc75c0da75051cce7cf273a6954fdb2b94119604a8ff4565fe66143b7 SHA512: 12d663f7c263bc9be7c0f480d77e05a57ccdc2fde10683ef18108593f874ae332ca3cf0b75d162cb8bf9c2d0de7c8d38881754ff50512f740d62697f44417c2b Homepage: https://cran.r-project.org/package=specr Description: CRAN Package 'specr' (Conducting and Visualizing Specification Curve Analyses) Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, ) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, ) including functions to setup, run, evaluate, and plot all specifications. Package: r-cran-spect Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-futile.logger, r-cran-dplyr, r-cran-doparallel, r-cran-ggplot2, r-cran-survminer, r-cran-riskregression, r-cran-caret, r-cran-caretensemble, r-cran-survival, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-kernlab, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spect_1.0-1.ca2404.1_all.deb Size: 148372 MD5sum: d28306cb03806960e26f64f492bd6bda SHA1: 85abe5f268866560f5c3af38deaeac28882785a6 SHA256: aa89f3122db0de18efd3a5c540bb8ba4e9f47a1d11716fbb9380a0922befbe67 SHA512: 5e8520b7a69343e7ab57ce89b092c8ee1ebf175094b5dc1b3da336c7851d8fa249abf1bf7405545d0274bf09fb47b3bda590c3e281f4e38c97236b218ada37e4 Homepage: https://cran.r-project.org/package=spect Description: CRAN Package 'spect' (Survival Prediction Ensemble Classification Tool) A tool for survival analysis using a discrete time approach with ensemble binary classification. 'spect' provides a simple interface consistent with commonly used R data analysis packages, such as 'caret', a variety of parameter options to help facilitate search automation, a high degree of transparency to the end-user - all intermediate data sets and parameters are made available for further analysis and useful, out-of-the-box visualizations of model performance. Methods for transforming survival data into discrete-time are adapted from the 'autosurv' package by Suresh et al., (2022) . Package: r-cran-spectacles Architecture: all Version: 0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2819 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-stringr, r-cran-baseline, r-cran-signal, r-cran-epir Suggests: r-cran-caret, r-cran-pls, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-spectacles_0.5-5-1.ca2404.1_all.deb Size: 2465024 MD5sum: 44187c3c59c9efe715ecd34b2be5031f SHA1: fe1cd7667309714434cba6ed77a9281fdea6c87b SHA256: 736f7a45859bf17a0ea5438931f0548d0f9bbea5b7bd064904746948a85b8596 SHA512: ed352b5e5e7278b2f58c3095aed120c29812164d817bfc0e30fd5f085e480eb4c954d44024a26bc2fe0017565f6c40cc5304162cf868ef5d8594bc9c68d29977 Homepage: https://cran.r-project.org/package=spectacles Description: CRAN Package 'spectacles' (Storing, Manipulating and Analysis Spectroscopy and AssociatedData) Stores and eases the manipulation of spectra and associated data, with dedicated classes for spatial and soil-related data. Package: r-cran-spectator Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr, r-cran-sf Suggests: r-cran-calendar, r-cran-calendr, r-cran-httptest, r-cran-knitr, r-cran-lubridate, r-cran-lutz, r-cran-maps, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spectator_0.2.0-1.ca2404.1_all.deb Size: 115302 MD5sum: 4ad29061d852986a564b14b2766fc0cf SHA1: dae358124da8f39217446819624ab69ed55c923a SHA256: e03ac5c1a1cf84982d39581e151d78c1aa3d3fb5375aeae7db27e375bfbff7b2 SHA512: 86cae8fa27d2d4fa1857a93cbb90b979819fc34f283880e2006eb124a20dbab90af95fa3ac821979a4b6d3ec330903403eb326d42548af71fd055b5aba15b7b4 Homepage: https://cran.r-project.org/package=spectator Description: CRAN Package 'spectator' (Interface to the 'Spectator Earth' API) Provides interface to the 'Spectator Earth' API , mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can also be obtained for a much larger set of satellites. It is also possible to search the archive for available images over the area of interest for a given (past) period, get the URL links to download the whole image tiles, or alternatively to download the image for just the area of interest based on selected spectral bands. Package: r-cran-spectr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-lomb Suggests: r-cran-doparallel, r-cran-knitr, r-cran-qs, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spectr_1.0.1-1.ca2404.1_all.deb Size: 22722 MD5sum: 23f8f2680a5afd21a70e024fc37db0b6 SHA1: 37cda96c291df8f86fe6d9cb549d77495a9def0e SHA256: e18ad1121c7bb92bb2cf4faf79ac49cf756a84711c417dbc33f5eef05f59e281 SHA512: 3da5877be13d7c6d0206a6dea3d7ac5f381f4f095afbb9ee93337f214aca1e034cdc926bca4f3f0b131b7672817b92a5576c1cc4e9c6325fd7609e5347348c69 Homepage: https://cran.r-project.org/package=spectr Description: CRAN Package 'spectr' (Calculate the Periodogram of a Time-Course) Provides a consistent interface to use various methods to calculate the periodogram and estimate the period of a rhythmic time-course. Methods include Lomb-Scargle, fast Fourier transform, and three versions of the chi-square periodogram. See Tackenberg and Hughey (2021) . Package: r-cran-spectrakit Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-ggplot2, r-cran-ggrepel, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-magick, r-cran-glue, r-cran-data.table, r-cran-scales Suggests: r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-spectrakit_0.2.0-1.ca2404.1_all.deb Size: 65286 MD5sum: 51ba70e217d1569c8791cd79b8bda80c SHA1: bc3216d58099588a77e9a715abb05d2e1892cb90 SHA256: 494cce2b527e402eb31662e51763793664ea787cac768ed04f23e33bb94a2cdc SHA512: dde2b5776cae6dae062b3f4a62ad80cfbeabb877654a0fa4a99c1a209b20cab67effc4bbdf7d4202fdad495506216a3c93a68f8e20b35d185875111134028211 Homepage: https://cran.r-project.org/package=spectrakit Description: CRAN Package 'spectrakit' (Spectral Data Handling and Visualization) Provides functions to combine, normalize and visualize spectral data, perform principal component analysis (PCA), and assemble customizable image grids suitable for publication-quality scientific figures. Package: r-cran-spectral Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rasterimage, r-cran-lattice, r-cran-rhpcblasctl, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-spectral_2.0-1.ca2404.1_all.deb Size: 159082 MD5sum: e693f3d461e1ceb6c943a4400825a8a3 SHA1: 76bfb560b80d5a6b6f68b41ae2ac32a7a1df0acc SHA256: 13499a99caf011e3b4be3593ce5ed556a7dff37fef063be201dad9c4c325eaac SHA512: 96a3934fac052a2612424df09c91effb89cc5424fcf48688d1b957768a4c42da2383921ade10cbd8083cf1b8ef6c8fb769bcdfa4e44f25ba600859a6aaca3707 Homepage: https://cran.r-project.org/package=spectral Description: CRAN Package 'spectral' (Common Methods of Spectral Data Analysis) On discrete data spectral analysis is performed by Fourier and Hilbert transforms as well as with model based analysis called Lomb-Scargle method. Fragmented and irregularly spaced data can be processed in almost all methods. Both, FFT as well as LOMB methods take multivariate data and return standardized PSD. For didactic reasons an analytical approach for deconvolution of noise spectra and sampling function is provided. A user friendly interface helps to interpret the results. Package: r-cran-spectralanalysis Architecture: all Version: 4.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-baseline, r-bioc-biocgenerics, r-cran-data.table, r-cran-ggplot2, r-cran-jsonlite, r-cran-magrittr, r-cran-nnls, r-cran-nmf, r-cran-plotly, r-cran-plyr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-signal, r-cran-viridis, r-cran-hnmf, r-cran-zoo, r-cran-pls Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-spectralanalysis_4.3.3-1.ca2404.1_all.deb Size: 2146062 MD5sum: 3695351167be738c58689089800991a3 SHA1: 80b6ab9be24f0c7b307b47fb515c377e69709ef0 SHA256: b15d1077ea3eed359b35550bb7d473e87fa72dce84193c5d82d25ebaf696b175 SHA512: b1787d740ea175938ccc57b1665a6bb904154279f09fc8354134d9e33ab8fe8c782424fdb5d0cb87e1b20db02a0d34093b327896632d53fd5ca92365524ee42d Homepage: https://cran.r-project.org/package=spectralAnalysis Description: CRAN Package 'spectralAnalysis' (Pre-Process, Visualize and Analyse Spectral Data) Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc. Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions. See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package. Package: r-cran-spectralanomaly Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spectralanomaly_0.1.1-1.ca2404.1_all.deb Size: 85436 MD5sum: 52a1ca2a29c3b803a18b797307b662df SHA1: 80d201e19aa20306a054804f78614035db3239c2 SHA256: 0f0bad1fc916388227b29cc804412c67950d51be7bc6adc0672fa54c93d5b25c SHA512: e6e07a0915ed8056e81117c1ae85d40024dba160b21b85934b54a7abe0d7aaf98282d8b7d9c902a4958056de3adfef586aad057f8be0ca5c663ad38476a516f4 Homepage: https://cran.r-project.org/package=spectralAnomaly Description: CRAN Package 'spectralAnomaly' (Detect Anomalies Using the Spectral Residual Algorithm) Apply the spectral residual algorithm to data, such as a time series, to detect anomalies. Anomaly scores can be used to determine outliers based upon a threshold or fed into more sophisticated prediction models. Methods are based upon "Time-Series Anomaly Detection Service at Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019) . Package: r-cran-spectralclmixed Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-cluster, r-cran-ggplot2, r-cran-ggally Filename: pool/dists/noble/main/r-cran-spectralclmixed_1.0.2-1.ca2404.1_all.deb Size: 25338 MD5sum: f7e0b5ae7f12461f232c1b66d5eb6a33 SHA1: 9b31ccedbc81b8ca47160f059a38557002561e52 SHA256: bcbcf28377af5b8eb13e7e183565227e3bb5cf91877aa924fab607663510edb0 SHA512: 969aead12349e51533ff55eead00fad1d5525ff620f52ab3435b0e674cb645edbe4d98677cdab5206ad17e2e589a27368ad26e7167b5076d1aedc10ce8529311 Homepage: https://cran.r-project.org/package=SpectralClMixed Description: CRAN Package 'SpectralClMixed' (Spectral Clustering for Mixed Type Data) Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) . Package: r-cran-spectralgp Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spectralgp_1.3.4-1.ca2404.1_all.deb Size: 138522 MD5sum: b114e5934e7ab02cb1e15501c0321dc9 SHA1: b70de69214ad42194602c526af46fee8ef3be357 SHA256: 62b5a171f0d1d39e07837a9256e5ceee5ebe464fd7362bbba638e1d480602ec9 SHA512: ca8528a22cdb812c61958cf9397aed53d885a8aa84a32e5068ce1b4ec491b1c73554ce699368610488209bf0fdc448a8381fd414aaa153f13a0890cf43723db9 Homepage: https://cran.r-project.org/package=spectralGP Description: CRAN Package 'spectralGP' (Approximate Gaussian Processes Using the Fourier Basis) Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers. Package: r-cran-spectralmap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scatterplot3d, r-cran-fields Filename: pool/dists/noble/main/r-cran-spectralmap_1.0-1.ca2404.1_all.deb Size: 14628 MD5sum: 29fd4187e0bf6c3609525c1380d39a4b SHA1: 33f5a301070a47fb6a049b0a6b545542f21a061e SHA256: 7f01a523063123442bbf816a3f2dc0d3c27460130effce393f7ad221fe1657cf SHA512: 2819a6547840ae972baa4e99a96d15c3b1d133303df3524bd738841537d95457bc32e1f515e3df7ef889749245bf99cf8062c732ce89285fd12831e7c9d567b1 Homepage: https://cran.r-project.org/package=SpectralMap Description: CRAN Package 'SpectralMap' (Diffusion Map and Spectral Map) Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps. Package: r-cran-spectralr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgee, r-cran-geojsonio, r-cran-sf, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-spectralr_0.1.4-1.ca2404.1_all.deb Size: 955130 MD5sum: 3547111d08fe059dd9f5b3941868b92a SHA1: 88a6ac8a63cf5b2318ec0410e5f70378cddbf21e SHA256: f6fda336db51640f7f59bf524beef0204f863ccfdb7d487aee1f617a8970d2fc SHA512: b6adc2cad159e097817873dc3eaab03c23365f5b2095293440972fcd6b6628022ec62ce9a699260e845bebbcf2968d83f50135f1db26a1a812aea2b960f1756b Homepage: https://cran.r-project.org/package=spectralR Description: CRAN Package 'spectralR' (Obtain and Visualize Spectral Reflectance Data for Earth SurfacePolygons) Tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service () and used as a source of spectral data. Package: r-cran-spectran Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chromote, r-cran-colorspec, r-cran-cowplot, r-cran-dplyr, r-cran-gghighlight, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-ggtext, r-cran-gt, r-cran-htmltools, r-cran-magrittr, r-cran-openxlsx, r-cran-pagedown, r-cran-patchwork, r-cran-png, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinydashboard, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spacesxyz, r-cran-spscomps, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-waiter, r-cran-webshot2, r-cran-withr Suggests: r-cran-config, r-cran-here, r-cran-magick, r-cran-pkgload, r-cran-readxl, r-cran-rhub, r-cran-rmarkdown, r-cran-rsconnect Filename: pool/dists/noble/main/r-cran-spectran_1.0.6-1.ca2404.1_all.deb Size: 2725456 MD5sum: 37ba7efe1f699bb733cbe3fcff0b04ae SHA1: 2f466a221c5cbece083de1756e186bf6974ea68c SHA256: 9e610902f6e94849346cac7b35064760c00bf16fb8199f5c836e91ed663790ae SHA512: 6b824ef7ffcdceb384024f3354405cd3578c2029366a9a5c29ffc3d9ac5ad4cf54c65d1143547aabc6cd876f17f3ca4efda41d5ea670758ada2df2954a1037f3 Homepage: https://cran.r-project.org/package=Spectran Description: CRAN Package 'Spectran' (Visual and Non-Visual Spectral Analysis of Light) Analyse light spectra for visual and non-visual (often called melanopic) needs, wrapped up in a Shiny App. 'Spectran' allows for the import of spectra in various CSV forms but also provides a wide range of example spectra and even the creation of own spectral power distributions. The goal of the app is to provide easy access and a visual overview of the spectral calculations underlying common parameters used in the field. It is thus ideal for educational purposes or the creation of presentation ready graphs in lighting research and application. 'Spectran' uses equations and action spectra described in CIE S026 (2018) , DIN/TS 5031-100 (2021) , and ISO/CIE 23539 (2023) . Package: r-cran-spectrolab Architecture: all Version: 0.0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3438 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-covr, r-cran-tinytex, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spectrolab_0.0.19-1.ca2404.1_all.deb Size: 1727278 MD5sum: b348e5d593ea937fd566054e17227f3b SHA1: affd733424e83cafc63e773b7a02ed36d56a23c5 SHA256: 77703312dacb39571fd2776e2f2b39eabe2f2e9850060914d358935d0d47bc1c SHA512: 984ea0ebec84df73dd8fdbf9f267291b070755866722f9b7bb732aa6a756fc9eb235ee34cb938def887803e027b6181800b3e351d4dcd8872e974939ec3d6c1d Homepage: https://cran.r-project.org/package=spectrolab Description: CRAN Package 'spectrolab' (Class and Methods for Spectral Data) Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra. Package: r-cran-spectrum Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3913 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-clusterr, r-cran-rfast, r-cran-diptest Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-spectrum_1.1-1.ca2404.1_all.deb Size: 3535992 MD5sum: a1e91b3a1793a6144422690f14d11e50 SHA1: fed64eb7aad9b02798cba685b6466e5b9a84f06f SHA256: 29b04eb6d4689662c6d503b83044e89540a0ead7e7153387c0e11e8a8c64ad2f SHA512: 8b89e98f46eb0a5a8fc8f603c611eb58740d3ab5aa0399ab0286a013f51afac4ffceea45c7d45e59d2a6f3eae846c14efc469dea132efd21cb88638963cf2c10 Homepage: https://cran.r-project.org/package=Spectrum Description: CRAN Package 'Spectrum' (Fast Adaptive Spectral Clustering for Single and Multi-View Data) A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K. Package: r-cran-spedecon Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-spedecon_0.1-1.ca2404.1_all.deb Size: 49656 MD5sum: 6fee69291fd107c7faace08234aceadc SHA1: 74268b0f54f2bd3246a49c8b99e9ed09dcdf1b13 SHA256: cb514466ab6b6f37ad21c6a106d8b7c9590dccbba2842093d0922350619a6bbf SHA512: b94f79190e86cfeb957a266f232653d2b56093b12b9efdcfbdc4ac0fe00f6364caeb5fd8e4f8a36d4062693297f8786a79feda88090899444c505f096bdb9c58 Homepage: https://cran.r-project.org/package=spedecon Description: CRAN Package 'spedecon' (Smoothness-Penalized Deconvolution for Density Estimation UnderMeasurement Error) Implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) . The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function. Package: r-cran-spedinstabr Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2939 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-beanplot, r-cran-raster, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-spedinstabr_2.2-1.ca2404.1_all.deb Size: 2520456 MD5sum: 3a1a7b438398ed442cc585137003394c SHA1: 4c9f915d7f3fac12f6b909b3f95d08b07cc1db3f SHA256: a68b4ec226b4e98ddd45468b4488c3e3934ddc7bd0f08e6a5520f97eca792fb3 SHA512: 966903cf4c7035eebac7380c34ab3ac2bd7b5c9ba8d42432ed41c34ec2b9480a3aeecd1a47bbb5c2eda5503d95295ee41f8ec41073bf058d601b35b44c3c96e3 Homepage: https://cran.r-project.org/package=SPEDInstabR Description: CRAN Package 'SPEDInstabR' (Estimation of the Relative Importance of Factors AffectingSpecies Distribution Based on Stability Concept) From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent. Package: r-cran-speech Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tm, r-cran-tidyr, r-cran-pdftools, r-cran-rvest Filename: pool/dists/noble/main/r-cran-speech_0.1.5-1.ca2404.1_all.deb Size: 3632006 MD5sum: 453b0e318a04c6914a05b51822f4dc2a SHA1: 7b54fbc164ae4bcc7f2d5b0abf94adafef7d1d8e SHA256: d038f4d22c60b9f995f1fed5e1bf03141d1f4cfd8640a6790d4a63795b212493 SHA512: 95340d4822ee5e4c98257e87c8e681ee60b40aec946403790dd3ac8a9ced55a7bd1d842ce9c1b95748e931df850742062363344d2f319619ce4d0896e2d150b6 Homepage: https://cran.r-project.org/package=speech Description: CRAN Package 'speech' (Legislative Speeches) Converts the floor speeches of Uruguayan legislators, extracted from the parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator. Package: r-cran-speechbr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abjutils, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-xml2, r-cran-rvest, r-cran-rlang, r-cran-tibble, r-cran-glue, r-cran-httptest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-speechbr_2.0.0-1.ca2404.1_all.deb Size: 539894 MD5sum: 5eb8eb6930373c306bb3c5d80515972e SHA1: a787fcddb48fec1f750df6cd2f4a73520809d7c8 SHA256: 38f147bfdd17648a14ab767decbee4296119f58737ba96257cc4d0ff2c420f3a SHA512: 6aa683303131ec182152786f8ffb48e370bb70912fcf540ed38f56769a13f0d2905abb36a205b439e29c028ffb1135c95c02211f2d97875f9e443a42c4842d37 Homepage: https://cran.r-project.org/package=speechbr Description: CRAN Package 'speechbr' (Access the Speechs and Speaker's Informations of House ofRepresentatives of Brazil) Scrap speech text and speaker informations of speeches of House of Representatives of Brazil, and transform in a cleaned tibble. Package: r-cran-speechmatics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-speechmatics_0.1.0-1.ca2404.1_all.deb Size: 99762 MD5sum: 79c9672f2b82d74bbda0d6085611b4e4 SHA1: 45d39f02cda13af3c75ce7295a29a9a1c994649d SHA256: a6ffe0542f81e28185a290d4e26acdc0b9f56dbc31829672d57e968ec3d03522 SHA512: d4c8883a16a9ec8af815c62d9de0b7d630926ead692e79e2aa1558f515776cad7f2f77467699f637f8781b4ae1846d0693d6ffa1cc88f51bbadb55634f34376e Homepage: https://cran.r-project.org/package=speechmatics Description: CRAN Package 'speechmatics' (Client for the 'Speechmatics' Speech-to-Text API) Transcribe audio files using the 'Speechmatics' speech-to-text API . Supports custom vocabulary, speaker diarization, punctuation control, and audio filtering. 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Package: r-cran-spex Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadmesh, r-cran-raster, r-cran-reproj, r-cran-sp, r-cran-crsmeta Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spex_0.7.1-1.ca2404.1_all.deb Size: 126972 MD5sum: d25b5c56154ee465e0d6e097c3b5bf18 SHA1: 1efb1de5255c45303c6668f29eb8eff1b03c0696 SHA256: 28d6ae06d8f2e16180ff42918d6592a7b9ac7a3d642e8cfd76d0aca861172313 SHA512: b89f38c618ec0ebf25923800be83c15bf593c7d02182b38f9b5fab6938253cca7efe17fa9e8e8da84f6a5b9406851cef6357ecdb4a1682546cb34afd1f4f8bb7 Homepage: https://cran.r-project.org/package=spex Description: CRAN Package 'spex' (Spatial Extent Tools) Functions to produce a fully fledged 'geo-spatial' object extent as a 'SpatialPolygonsDataFrame'. 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Package: r-cran-spfsr Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3, r-cran-future, r-cran-tictoc, r-cran-mlr3pipelines, r-cran-mlr3learners, r-cran-ranger, r-cran-ggplot2, r-cran-lgr Suggests: r-cran-caret, r-cran-mass Filename: pool/dists/noble/main/r-cran-spfsr_2.0.4-1.ca2404.1_all.deb Size: 74922 MD5sum: 2f04f2544f4d4b1377d4e89604a15324 SHA1: cbd4f7b1f7c7fc1663afaab501054a24c8eb7539 SHA256: f977f9b438cf8cc577386e530e5514b7430a49a99f8315c45631f761e16b307e SHA512: 6bca15e3d2719ee2da44063ed4ea972c22bd6b7b8f6406f6eb58777b3b0af48f80ed31ebeed35d1cca9e6c4d4e7066d146fddb49c1baf6147b79842cd6229b63 Homepage: https://cran.r-project.org/package=spFSR Description: CRAN Package 'spFSR' (Feature Selection and Ranking via Simultaneous PerturbationStochastic Approximation) An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems). Package: r-cran-sphereclust Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-directional, r-cran-mixture, r-cran-rangen, r-cran-rgl, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-sphereclust_1.2-1.ca2404.1_all.deb Size: 106118 MD5sum: b4b6dff5997265824dab2aa00db204a5 SHA1: b845252893d2815852493304f961a68ade8697de SHA256: d688dc191ea8c154419e2d4d6fd7d8161e0e3621a4ce6b37423e184c68ea9080 SHA512: d3b71f600835cf9dfa89788b8297bbc0d950660104056d0b350d6c838692babb435e9ee67044afa156c748ad20959dc4ff0615bb5ad29326a1aa4930099f62e8 Homepage: https://cran.r-project.org/package=sphereclust Description: CRAN Package 'sphereclust' (Model Based Clustering for Spherical Data Using EllipticallySymmetric Distributions) Model based clustering with spherical data using mixtures of elliptically symmetric distributions, namely mixtures of spherical elliptically symmetric projected Cauchy (SESPC) or mixtures of elliptically symmetric angular Gaussian (ESAG) distributions. The relevant paper is: Perdikis T., Alharbi N. and Tsagris M. (2026). . Package: r-cran-spheredata Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spheredata_0.1.3-1.ca2404.1_all.deb Size: 184816 MD5sum: c79d3ed64575ab2c469f26ecb8ab3623 SHA1: 4b9682342cd6c95b9202be5357ddd58553e1a68d SHA256: 915a2d27cdc98a1435f7caad2b9f49e3214c8d090fe8b02e3686ac8540483f22 SHA512: e8780707300b7ec2a722e63ffc1f0cb53057e01d01777f6a03f6fa0885357a4bf6e06ed9a9df91caf8726449bc64974f0dd656170c60fef867ee7e3aae876e4d Homepage: https://cran.r-project.org/package=spheredata Description: CRAN Package 'spheredata' (Students' Performance Dataset in Physics Education Research(SPHERE)) A multidimensional dataset of students' performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) ]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students' attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results administered using teachers' developed items are also reported in this dataset. Package: r-cran-sphereml Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-spheredata, r-cran-lavaan, r-cran-semplot, r-cran-ctt, r-cran-mirt, r-cran-shinycssloaders, r-cran-fselectorrcpp, r-cran-randomforest, r-cran-caret, r-cran-catools, r-cran-proc, r-cran-ga, r-cran-readxl Filename: pool/dists/noble/main/r-cran-sphereml_0.1.1-1.ca2404.1_all.deb Size: 194950 MD5sum: 95322c0528d74b24ba0ba230338d8f48 SHA1: 6b891d95d835004ebdf1b7389da7efcf79b69d50 SHA256: c445ed1b43fab4dd420370239797d1816462430a734317aadc905afe1150614d SHA512: f564b2a8063da6d7ee59160643c0d56b881fc8b25e3ad900fb528ca1ccd5f7baeda34b0b591d2e4ef9c97a23195fe6d65e339d486be34c1117fecee6db50adf0 Homepage: https://cran.r-project.org/package=sphereML Description: CRAN Package 'sphereML' (Analyzing Students' Performance Dataset in Physics EducationResearch (SPHERE) using Machine Learning (ML)) A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated. Package: r-cran-sphereoptimize Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sphereoptimize_0.1.1-1.ca2404.1_all.deb Size: 17296 MD5sum: 5c90e8c537447f21f54fe4c4266c8036 SHA1: efe3580d5e04494fe1bb5fdbe37918d70b3b7ca7 SHA256: d259f4dcaf2b7cc8c0288bbf16535d23f96acb0512ee9c92defda2205f23f2a2 SHA512: c44ef018481aa0ae257a7a226e622d1589c3791fb449d881daae97723ecc25657c1e845040b84ccf9ad5189459d214bd6503aafe3755d8944533fb31226b5d9d Homepage: https://cran.r-project.org/package=SphereOptimize Description: CRAN Package 'SphereOptimize' (Optimization on a Unit Sphere) A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) . Package: r-cran-spherepc Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-rgl, r-cran-sphereplot Filename: pool/dists/noble/main/r-cran-spherepc_0.1.7-1.ca2404.1_all.deb Size: 114444 MD5sum: 32e2173f6baaa2a24c5a3c14704c9626 SHA1: 8b33553606a0f706466860334ecc44a3717e0c1e SHA256: 2fe958d7ddbc80ac52aa27813e99d72efbd9b452d173815930027fcd9c00407e SHA512: 3dda0c88defff193a3be23182698912614fbe1b35a70128ff240913337a697a4540e5acba63aa2bcf5d2ac8a4d1c18cdc685e57474e89a4a3702951bbf8d4441 Homepage: https://cran.r-project.org/package=spherepc Description: CRAN Package 'spherepc' (Spherical Principal Curves) Fitting dimension reduction methods to data lying on two-dimensional sphere. This package provides principal geodesic analysis, principal circle, principal curves proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally defined principal geodesics which is underway. The detailed procedures are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) . Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) . Package: r-cran-sphereplot Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-sphereplot_1.5.1-1.ca2404.1_all.deb Size: 42200 MD5sum: 7a10f4010090c58bca69f14805d7d85e SHA1: 47cbdb53749b55fd2b819000f00b08f54e1c2ec3 SHA256: b7326fc708473cdb79fbf0f5608e27354545ef14d68c652cf325f110a1ffc44d SHA512: aec3de417684296caef7d5758a1036869210816d11f712623d3f7a5117be1b25288efa46c2d5dcb0bdede82c16e55accde919e9c90de2aa8cbd4f99f652522fb Homepage: https://cran.r-project.org/package=sphereplot Description: CRAN Package 'sphereplot' (Spherical Plotting) Various functions for creating spherical coordinate system plots via extensions to rgl. Package: r-cran-spheresmooth Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sphereplot, r-cran-rgl, r-cran-ggplot2, r-cran-rworldmap, r-cran-sf Filename: pool/dists/noble/main/r-cran-spheresmooth_0.1.3-1.ca2404.1_all.deb Size: 218872 MD5sum: 991451b412404275277dc6c64d755d51 SHA1: 39a2052e3d736cc47f4143a92451d2938af6bf06 SHA256: 4cd179b0c2c9d6879823ceec57b7246871dd8b8e5b148e29aed940d13fb275b3 SHA512: e1b206f3ac38161e54a8f09ef649ce0d760c04a52fd357def9da1043b0124183bb49efeafb7033d760fdadcd4e4d197231935ff55cd1499cfacd89a54633c9b9 Homepage: https://cran.r-project.org/package=spheresmooth Description: CRAN Package 'spheresmooth' (Piecewise Geodesic Smoothing for Spherical Data) Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data. Package: r-cran-sphericalcubature Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-simplicialcubature, r-cran-mvmesh, r-cran-abind Filename: pool/dists/noble/main/r-cran-sphericalcubature_1.5-1.ca2404.1_all.deb Size: 102014 MD5sum: 6403512883632f9034e2215205324db9 SHA1: 4c0e30c0f632393f2168d067079c9f28257a6c81 SHA256: 4ce43777374de913cdfaeaa11327f873f53e1fcd3158744a3ce9cd81ca6c9d16 SHA512: 8bf81c62d21f897f31cf298b0bfd31ba4c7a559a2115ecdcdb85e41087fabd2130150c2a17d561eb5e2d0edaaf5fa925c058d8e9e2523606d72bb1a1d7afe487 Homepage: https://cran.r-project.org/package=SphericalCubature Description: CRAN Package 'SphericalCubature' (Numerical Integration over Spheres and Balls in n-Dimensions;Multivariate Polar Coordinates) Provides several methods to integrate functions over the unit sphere and ball in n-dimensional Euclidean space. Routines for converting to/from multivariate polar/spherical coordinates are also provided. Package: r-cran-sphet Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-spatialreg, r-cran-spdep, r-cran-matrix, r-cran-sp, r-cran-mvtnorm, r-cran-stringr, r-cran-coda, r-cran-spdata, r-cran-sf Filename: pool/dists/noble/main/r-cran-sphet_2.1-1-1.ca2404.1_all.deb Size: 543588 MD5sum: 41517fa1224f227cb6002f50410bb1a0 SHA1: 79eb0d0b1010703d7ae9b6789732aba1a77f30a1 SHA256: 1da55a7f6300de54158d91cbdd57e655a8a9b8b7a8123b6060fa2c5778492045 SHA512: 20fb97f4dbf75cae0f3cfba2171cc0ff4f71e248cf30cd33990c3b5a5c34a0e6c16005bdfdb322fb135caeabb0daf106a1dc774ca442af2cb88fc6a9e3a8fdbe Homepage: https://cran.r-project.org/package=sphet Description: CRAN Package 'sphet' (Estimation of Spatial Autoregressive Models with and withoutHeteroskedastic Innovations) Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables. Package: r-cran-spicefp Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-stringr, r-cran-tidyr, r-cran-matrix, r-cran-genlasso, r-cran-purrr Suggests: r-cran-fields Filename: pool/dists/noble/main/r-cran-spicefp_0.1.2-1.ca2404.1_all.deb Size: 800632 MD5sum: 3eb6d98856484979c1c53485b9860cc1 SHA1: 1bf3064218605446ca52dad2e9d3bc0212976c35 SHA256: c305c5c57d1fadcf67815d34e435ed55df5b16302a776a3967d3973a1035715c SHA512: c579250c225a752133eecf6b76b989ad2a14eee4587f7c062296da8d8508f571b4d0ce6f1cbc90fd1fb141981bb2a355ac214d7937d83b36af35e0028c6b45fe Homepage: https://cran.r-project.org/package=SpiceFP Description: CRAN Package 'SpiceFP' (Sparse Method to Identify Joint Effects of Functional Predictors) A set of functions allowing to implement the 'SpiceFP' approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" . Package: r-cran-spichanges Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dglm, r-cran-lubridate, r-cran-rlang, r-cran-brglm2, r-cran-zoo Suggests: r-cran-archive, r-cran-curl, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-knitr, r-cran-ncdf4, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-sf, r-cran-purrr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-spichanges_0.3.0-1.ca2404.1_all.deb Size: 723142 MD5sum: f322c65a8a1e6726d4de975a54bcdec3 SHA1: c66b19f92610049e1d378f1cb783d79985368930 SHA256: 093c38d3d692b078c641537adfbcf804c55c16f944af66519e5bb7fa79c3a9b9 SHA512: 5960b4eb10c857faf6c52162c334b517b0eba524ff6d056ae481ba86a79c0be6330e4e4e2675c371b30e09901040f17b53eb5980ce50366f653c6e397eff8581 Homepage: https://cran.r-project.org/package=SPIChanges Description: CRAN Package 'SPIChanges' (Improves the Interpretation of the Standardized PrecipitationIndex Under Changing Climate Conditions) Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring. Package: r-cran-spicy Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3553 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-labelled, r-cran-rlang, r-cran-sandwich, r-cran-stringr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-bit64, r-cran-boot, r-cran-broom, r-cran-car, r-cran-clipr, r-cran-clubsandwich, r-cran-dt, r-cran-effectsize, r-cran-emmeans, r-cran-estimatr, r-cran-fixest, r-cran-flexsurv, r-cran-flextable, r-cran-geepack, r-cran-gt, r-cran-haven, r-cran-htmltools, r-cran-insight, r-cran-glmmtmb, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-lmtest, r-cran-merderiv, r-cran-marginaleffects, r-cran-modelsummary, r-cran-collapse, r-cran-mass, r-cran-mgcv, r-cran-nlme, r-cran-nnet, r-cran-numderiv, r-cran-ordinal, r-cran-aer, r-cran-betareg, r-cran-mlogit, r-cran-pscl, r-cran-quantreg, r-cran-rlrsim, r-cran-reformulas, r-cran-rms, r-cran-sampleselection, r-cran-posterior, r-cran-rstanarm, r-cran-brms, r-cran-loo, r-cran-rstan, r-cran-survey, r-cran-survival, r-cran-officer, r-cran-openxlsx2, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tinytable, r-cran-withr Filename: pool/dists/noble/main/r-cran-spicy_0.13.0-1.ca2404.1_all.deb Size: 3386222 MD5sum: 0ded42d60c9b745a25576fb769ae582f SHA1: 13d8f7cc5a6a54e4801267bb28e02dd8f25966a9 SHA256: 6d096e78a41a6ccdf0917aaca182fa220ee3f80b8f1717742c473323cf1417e6 SHA512: 2ef6fbc8cb0d49fa7a36d28a1e30b343c00469603641160036f986de4313645f243c407403102134693ad6f54ed753f14f1051254e9602ad3dc913559a011fc5 Homepage: https://cran.r-project.org/package=spicy Description: CRAN Package 'spicy' (Publication-Ready Tables for Descriptive Statistics andRegression Models) Provides publication-ready tables for descriptive statistics and regression models: frequency tables and cross-tabulations with association measures (Cramer's V, Kendall's Tau-b, and others), categorical and continuous summary tables, by group or from a complex survey design, and regression tables for one or more models side by side, across more than thirty model classes from mixed-effects to survival and Bayesian, with robust standard errors, average marginal effects, and univariable screening. Tables follow APA conventions by default, can switch to named journal styles such as JAMA, NEJM, or The Lancet, and render identically in the console and in 'gt', 'tinytable', 'flextable', 'Word', 'Excel', or the clipboard. Declared missing values in labelled data are honored and disclosed throughout the descriptive tables. Helpers cover codebooks, variable inspection, and row-wise summaries. Package: r-cran-spider Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-pegas Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spider_1.5.3-1.ca2404.1_all.deb Size: 259254 MD5sum: f9034f536896da4103bdb1a6b7d7ee0c SHA1: 3da22eeadf51bfb8faca3754dbaee4b16e359ac7 SHA256: 129b8a69ef5afd5122305b43d74079c90f051fc5ac4fc8fd459bf607a2172a3c SHA512: 00afc4bdacf12830b11658fe5c1432ef04ea59df0f23e103cb4900c4dd0520f0083c0d3b69e73b36c7de1b7fe1ce7ce69c78185dc21a06cb2894baecef026acd Homepage: https://cran.r-project.org/package=spider Description: CRAN Package 'spider' (Species Identity and Evolution in R) Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) . Package: r-cran-spidr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rgbif, r-cran-rworldmap, r-cran-rworldxtra Filename: pool/dists/noble/main/r-cran-spidr_1.0.2-1.ca2404.1_all.deb Size: 72810 MD5sum: f743d12929bf44ac97c4481c8a5decf9 SHA1: 947611fd128a813e84b86f19f094ff1d19f6be2a SHA256: 14083292afbb27cf05425882828fbd003eec9b1ef7209b4294193b15f2a17b0b SHA512: 7c1241c82cf7dad9b35c3bfac20348c4b8e9d86c462db4d5c8c633a327f6664548d80abdc13395054784a69392d10f8669c29a1f61afd19440e4e42b4d6aa887 Homepage: https://cran.r-project.org/package=spidR Description: CRAN Package 'spidR' (Spider Knowledge Online) Allows the user to connect with the World Spider Catalogue (WSC; ) and the World Spider Trait (WST; ) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; ), and mapping. Package: r-cran-spiga Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ga Filename: pool/dists/noble/main/r-cran-spiga_1.0.0-1.ca2404.1_all.deb Size: 68396 MD5sum: 47d30aa7be6b70f36b0f461b7d404353 SHA1: 84b45afabb5244a6a4dbaa7b6ee296473cbec8f3 SHA256: a0b7de7ff9c0cc21bdf26db7342c93656b70a3944a6305836a7fac16ad11236a SHA512: 870e1cf3e5be706077fae4a302276c868030643e3cf66e9a4c42cdd9b80d607cf19e405d4ba0625e624ccf84ee94af2905eef751094653a0a5fa24b458708bc4 Homepage: https://cran.r-project.org/package=SPIGA Description: CRAN Package 'SPIGA' (Compute SPI Index using the Methods Genetic Algorithm andMaximum Likelihood) Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx. Package: r-cran-spikes Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emdbook Filename: pool/dists/noble/main/r-cran-spikes_1.1-1.ca2404.1_all.deb Size: 687360 MD5sum: 48dc89d2fcc66ce6309a970d4b29cb32 SHA1: da4d87ffdaaa2ff30a0df9c21f8238eeb7de30cd SHA256: b7db9621f7d358359f49c8819ec3d7ecc27fe2f8ed1d2e08d7facd54d6fad8d4 SHA512: d1b83d6de6ea9b3b607f2cd72f64f968fde1807f7582693b1c257a5d2f10f4b0fa61014eb6146e6b1627e2b7353b10dddc88d12226763049586c0500fdceb0fa Homepage: https://cran.r-project.org/package=spikes Description: CRAN Package 'spikes' (Detecting Election Fraud from Irregularities in Vote-ShareDistributions) Applies re-sampled kernel density method to detect vote fraud. 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Package: r-cran-spillover Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vars, r-cran-dplyr, r-cran-ggplot2, r-cran-fastsom, r-cran-tidyr, r-cran-zoo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spillover_0.1.1-1.ca2404.1_all.deb Size: 587122 MD5sum: 7233f15383c0ea1e87b3e4fc92acd572 SHA1: b7aba118be33a090ff02445051e7758546ac3767 SHA256: d6026b93150e5e4210eeb5469eac6185b6b9f088431445c7e74797e66202126e SHA512: bcb405a39d76c6a026dde8729c28302fb78aa172178e07bfca1a5e410eccd59512a98215edf6f74d571a57af2c6ee5bfa449c6296edc339665eed26cbb91825e Homepage: https://cran.r-project.org/package=Spillover Description: CRAN Package 'Spillover' (Spillover/Connectedness Index Based on VAR Modelling) A user-friendly tool for estimating both total and directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at and . Package: r-cran-spina Architecture: all Version: 5.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spina_5.1.0-1.ca2404.1_all.deb Size: 116218 MD5sum: ceac75c4733cfb1bd0941de959143939 SHA1: 1808ebe48c1d11ff6b7fe609b894bf6981af7804 SHA256: 01ad0f134c01fa6d87f1c46be59134c33b0a94f63e3f0985ae118b73637e0224 SHA512: eadee73e9ca64d55b23031e7294574a3db187e684e84c892316958fb4a42e820a0630c65619b47673422622b97cbeac8047019d3ffe35363675a6b19871b0fb1 Homepage: https://cran.r-project.org/package=SPINA Description: CRAN Package 'SPINA' (Structure Parameter Inference Approach) SPINA (Structure Parameter Inference Approach) is a methodology to calculate constant structure parameters of endocrine homeostatic systems from steady-state hormone and metabolite concentrations. 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Package: r-cran-spinar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spinar_0.2.0-1.ca2404.1_all.deb Size: 130630 MD5sum: dfa4c3771d134b46dda4b5fccff9cd2e SHA1: 18e279607594be8c115201b2b9c4e32bbcb6b15d SHA256: d84789a0e61d88724238065fb0624711390edf3893755544b6d72a896d217384 SHA512: 3605ecbfdf98ef527d89698fd9c236fe3cafb56411a79198257f614dfac21f2c4e7f6cce39dc031d13f65c3c9bda479fa8a24d07c88574f6d0d695189f8f5aae Homepage: https://cran.r-project.org/package=spINAR Description: CRAN Package 'spINAR' ((Semi)Parametric Estimation and Bootstrapping of INAR Models) Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) ) for the semiparametric setting introduced in Drost et al. 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Package: r-cran-spinebil Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tourr, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tictoc, r-cran-cassowaryr, r-cran-rlang Suggests: r-cran-minerva, r-cran-testthat, r-cran-purrr, r-cran-furrr, r-cran-future, r-cran-quarto, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spinebil_1.0.5-1.ca2404.1_all.deb Size: 2240194 MD5sum: a9b280648c1370bdd071f9d83205d6f8 SHA1: ef96b92bc4ba7ee596db3e97670a7b654681e631 SHA256: 583187c8c59d1ba3396ac543eecb8bf9304db2a7fe615f043d25e7c7c12ab548 SHA512: d0adfa46aabd5ac905b6e22310919cf4fe98f11e85e549148981bb4198259e7429f9aa302fceb893548a2d614019f6645143871361f0be1b06cf3a534f6d45eb Homepage: https://cran.r-project.org/package=spinebil Description: CRAN Package 'spinebil' (Investigating New Projection Pursuit Index Functions) Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. 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The 'spinifex' packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) . Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the 'tourr' package Wickham et al. . 'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 'plotly' graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) . 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Package: r-cran-spinyreg Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spinyreg_0.1-0-1.ca2404.1_all.deb Size: 146024 MD5sum: b4826cd41c73e26d9bb27efbb2bbb5fd SHA1: fdb5a0dfa9647ab147030e59e169c3dc7da81bba SHA256: 803e6e6fee590ee4a7be53b92fb1560a7b9f2d70a80c30ea64cc94d59db4175a SHA512: e6e5ad7e9edc00d6340bf7213d767798320e21cac126142c1847af7131f95950b53f64c62bce3d2175dce94591a0c3153136414d1b58fd387cce61dc70ea8b56 Homepage: https://cran.r-project.org/package=spinyReg Description: CRAN Package 'spinyReg' (Sparse Generative Model and Its EM Algorithm) Implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood. 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It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns. Package: r-cran-spiritr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-spiritr_0.1.1-1.ca2404.1_all.deb Size: 61642 MD5sum: da8cf81fcc7af4e93a2726888c6a3eba SHA1: 24abd56eba9aaf2ca61a97edc3fbf1cdc9e1097e SHA256: 57ae487cd485b61995b8f58ac8391ffc63bf1162fad847b770a1e4179571da69 SHA512: 838f8bee3979a2ff4597eb81a27cbe912eb4d254760d5b33f2f4948b59cb32a613ab983f61078a0a51c0cfa6887a45db1aa311e5d26533733692522174f4b070 Homepage: https://cran.r-project.org/package=spiritR Description: CRAN Package 'spiritR' (Template for Clinical Trial Protocol) Contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. 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Package: r-cran-spldv Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-matrix, r-cran-maxlik, r-cran-sphet, r-cran-memisc, r-cran-car, r-cran-numderiv, r-cran-mass, r-cran-spatialreg Suggests: r-cran-spdep Filename: pool/dists/noble/main/r-cran-spldv_0.1.3-1.ca2404.1_all.deb Size: 112166 MD5sum: 7dbd2bc264c35e482ca4f19df1b0eaed SHA1: 2c94e90cbbc6e0d3b76938def065730c181a2c96 SHA256: 32496f8bd90c7277311f7b32ffafd78a1152328f993f7bb3c5c5c39008244805 SHA512: 69b8be166d3cbc684121c80551e334d1aaf3703de2043ae459e186253ebcfe76b57a2bb87e3f8b9124e6b71ce7d54e219b18b207e45ba29b7b045c8e1308486e Homepage: https://cran.r-project.org/package=spldv Description: CRAN Package 'spldv' (Spatial Models for Limited Dependent Variables) The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators . It supports one-step (Pinkse and Slade, 1998) and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) . It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) . Package: r-cran-splice Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2711 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-synthetic, r-cran-zoo, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-actuar Filename: pool/dists/noble/main/r-cran-splice_1.1.2-1.ca2404.1_all.deb Size: 2059012 MD5sum: fce01b3ef634f5b7ae7c5e344bb7eb8c SHA1: 707ec15eda5fce6e0de2ee9098af771c056a7388 SHA256: ec22c459425a1f164a37a0623d48f33877121097fe4d390a965be1197f753758 SHA512: d4b5b7537a660c9f7fcb4600f761bfc95defafa44ef91bf3686a687261520d0f3c2ae77b54e59e6d052add1bd65aafefb97f6bc384029f1e59b80a28e6e8ae79 Homepage: https://cran.r-project.org/package=SPLICE Description: CRAN Package 'SPLICE' (Synthetic Paid Loss and Incurred Cost Experience (SPLICE)Simulator) An extension to the individual claim simulator called 'SynthETIC' (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" . Package: r-cran-splinecox Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-joint.cox Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-splinecox_0.0.8-1.ca2404.1_all.deb Size: 83616 MD5sum: 8ed725ed5cab6d38898a4fcecb483455 SHA1: 764822b5d4242b168b961b12ccf220bb8f6e1c54 SHA256: baa27e4ae1e75e07c7da87497a00cc378e77f1137d20465fec874b4c34dd2b4c SHA512: 98edb7a49547fa375a40284d2153401b963ba133a978ff8f04f1e2f5c987f04de052d91e031412a288691eecfe7245f307bb373c489f29f948aa64d20b3466e8 Homepage: https://cran.r-project.org/package=splineCox Description: CRAN Package 'splineCox' (A Two-Stage Estimation Approach to Cox Regression Using M-SplineFunction) Implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters. Package: r-cran-splinemixmeta Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mgcv, r-cran-mixmeta Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-splinemixmeta_1.0.1-1.ca2404.1_all.deb Size: 92560 MD5sum: ce97fbb2895d6bd248a3b41777710aa1 SHA1: 9dc485d6b16a9538d7625290858f4a5f01d0fe18 SHA256: 0afa4d22bff75aa9389cf31b4b175c036515330ec279bdeb8a0ba9ca4484ee9e SHA512: 9a1eb303a86adcd18bfe864a71422d32482b2526cbe643673e8c8a9c8797aa89cc9f9e5b6eeb770549f684de0e4d3e2f0599f6b54e0fe36939c919695f4248c8 Homepage: https://cran.r-project.org/package=splinemixmeta Description: CRAN Package 'splinemixmeta' (Additive Mixed Meta-Analysis with Spline Meta-Regression) Fit additive mixed meta-analysis (AMMA) models, extending the 'mixmeta' package to allow for spline-based meta-regression. Functions combine features of 'mgcv' for building spline components and 'mixmeta' for estimating general mixed-effects meta-analysis models. Package: r-cran-splineplot Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mgcv, r-cran-survival, r-cran-survey, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-splineplot_0.3.0-1.ca2404.1_all.deb Size: 231512 MD5sum: 0515cbc98ff99457ad442bdca0e3b362 SHA1: d4cb108fd11232e7546a8cf1c325f4564010f2ff SHA256: 769b162209ac4e3201aba5beac7f9349e693700227b07c1eb20261d2239d6713 SHA512: f1722b01ed7eed0f9806c8c5df9b7e1b7150a1d3096e18c836ae7c264973ce536fbe3b4b26b71edeadabd8ffe8d05ba3a7e948b2e919b71f5ad809254a4fe039 Homepage: https://cran.r-project.org/package=splineplot Description: CRAN Package 'splineplot' (Visualization of Spline Effects in GAM and GLM Models) Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports survey-weighted models ('svyglm', 'svycoxph') from the 'survey' package, interaction terms, and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models. 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Package: r-cran-splinets Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-splinets_1.5.1-1.ca2404.1_all.deb Size: 4097690 MD5sum: 295f748ebc83263bf3c1abfd4cecf2ba SHA1: e5c64041241837951e5e16db1ce13e2b894fdf20 SHA256: b93b9ed777c88fcf052863a2408108ac2c28e7ad46968b21b1d45640e3358f97 SHA512: 3c8046ba55fd7e12fd43277a51475428f02eec63ce33c2addd54a41205dff1d3eb7a2758aa03972a1c5b342f4a1ae9b242f050f382498a80e0261872805df626 Homepage: https://cran.r-project.org/package=Splinets Description: CRAN Package 'Splinets' (Functional Data Analysis using Splines and Orthogonal SplineBases) Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. 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Package: r-cran-splitfngr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lbfgs Filename: pool/dists/noble/main/r-cran-splitfngr_0.1.2-1.ca2404.1_all.deb Size: 24916 MD5sum: 1b3ed6b25528315f371f5954dbe5a67c SHA1: 00b47691aed58c36239a7528b3389f6160fba2cc SHA256: 1d7805b907648c9c721572d556c35fe30135f8802a0f8ff3c30a0ab9dc600602 SHA512: d557b6754e7a2c6489aec7118270370b5e395e230e1c7b9be94ca7cc502e5e1994e23bff4a23c2e497746a9b2db2fac6edebaff2c3a25b10ec409f30b1d73248 Homepage: https://cran.r-project.org/package=splitfngr Description: CRAN Package 'splitfngr' (Combined Evaluation and Split Access of Functions) Some R functions, such as optim(), require a function its gradient passed as separate arguments. 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The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals. Package: r-cran-splitknockoff Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-latex2exp, r-cran-rspectra, r-cran-ggplot2, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-splitknockoff_2.1-1.ca2404.1_all.deb Size: 79856 MD5sum: a90def7fd0d6cd5844d5dd2f9a14a90b SHA1: d1731926691968d20714212debb0f2b130e43873 SHA256: 73598e6cd854094d3efe490c7efae40152d2771fbbcbdee7bf9bb1dbcd9ee02f SHA512: a90277eee8077da715bed151a7c5cd766750016b8e571a5a6bbd4c4d331023e59189425ee072d4a2451fd74189e404a624405d1551bb08859cbccad98162e7c5 Homepage: https://cran.r-project.org/package=SplitKnockoff Description: CRAN Package 'SplitKnockoff' (Split Knockoffs for Structural Sparsity) Split Knockoff is a data adaptive variable selection framework for controlling the (directional) false discovery rate (FDR) in structural sparsity, where variable selection on linear transformation of parameters is of concern. This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. 'Split Knockoffs' is first defined in Cao et al. (2021) . Package: r-cran-splitpopsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maxlik Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-splitpopsurv_0.1.0-1.ca2404.1_all.deb Size: 53592 MD5sum: 72209ef8ed30abc364b96c4d5c874884 SHA1: f03a7b2964345d05b7e5ab6397d44cb1b256f329 SHA256: 2af9d66ef0b59a100ebc36e1a61c13ac7036e9c2fbe8c15e41c043e5121c6abb SHA512: 95693b6845b5602eebb1aedc31069eb3a424f605327bd1eae440b76ced24c02e1893e5b0736fd502a9cb5a5d029ef13d84aadbdfbab10fc4e853a2600e80dfc0 Homepage: https://cran.r-project.org/package=splitpopsurv Description: CRAN Package 'splitpopsurv' (Split-Population (Cure / Mover-Stayer) Survival Models) Maximum-likelihood estimation of split-population (cure / mover-stayer) survival models: an accelerated failure-time regression for event timing among "movers", combined with a logistic regression on the probability of belonging to the immune "stayer" population. Five baseline timing distributions are provided -- log-logistic, Weibull, log-normal, gamma, and the generalized gamma that nests the other four -- following Schmidt & Witte (1989, Journal of Econometrics) and Yamaguchi (1992, 1998, Sociological Methodology). This is an R translation of a set of 'Stata' ml programs, with the log-likelihood corrected to match the published model and verified by simulation against known parameters. Package: r-cran-splitr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-splitr_0.1.0-1.ca2404.1_all.deb Size: 14482 MD5sum: 58d9e399ca8063836423b30691dc15dc SHA1: c19ba96e9cef4372f12a5e3cc7954c79c5b7ed3e SHA256: 8c79556ce3491655bebdd3e9ec82b68e818398c7ac0f4365053ef1ee67a8fdb2 SHA512: dc5844709f9c3df66b8bbcc5c09f1c60dabcfc88a3732f5a0ef8c178cf0ca3f81fca4a80943d9423cdbe1ad2711219364292806def618fe52401b6594eed0edf Homepage: https://cran.r-project.org/package=splitr Description: CRAN Package 'splitr' (Fast Utilities for Splitting Excel Sheets) Provides tools for splitting large Excel worksheets into multiple smaller sheets based on a specified number of rows per chunk. 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Supports convex combinations of multiple spatial weight matrices and Bayesian Model Averaging (BMA) over subsets of weight matrices. Implements the convex combination spatial weight matrix methodology of Debarsy and LeSage (2021) and the Bayesian spatial panel data models of LeSage and Pace (2009, ISBN:9781420064247). 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Additional datasets include historical competitions, referee decisions, fan engagement, economic indicators, and sports management data obtained from public repositories, official organizations, research publications, and educational resources. Designed for sports scientists, coaches, analysts, researchers, educators, students, and data scientists, this package facilitates exploratory data analysis, statistical modeling, machine learning, visualization, and sports analytics research. 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Package: r-cran-spotoroo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geodist, r-cran-progress, r-cran-dplyr, r-cran-cli, r-cran-patchwork, r-cran-ggrepel, r-cran-ggextra, r-cran-ggbeeswarm, r-cran-ggplot2 Suggests: r-cran-sf, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-spotoroo_0.1.5-1.ca2404.1_all.deb Size: 584446 MD5sum: 65f2d1cd09386001f0d206d305000b33 SHA1: aedb62976085a9ddb04eee10c6b6dcb76ba7cbf8 SHA256: 8af1600516cbf995bc49c92b241d22bbbbe9de40839649b187fc194200157d24 SHA512: 77c616ba472767bfe5e629aec9b88007c2ce52f2b82f12ea72557e642a253cc5cb1180c57a30f32328d5cca15e24f2232d996d7a546f59c0ad4f39ca60865bad Homepage: https://cran.r-project.org/package=spotoroo Description: CRAN Package 'spotoroo' (Spatiotemporal Clustering of Satellite Hot Spot Data) An algorithm to cluster satellite hot spot data spatially and temporally. Package: r-cran-spouse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spouse_0.1.0-1.ca2404.1_all.deb Size: 17092 MD5sum: a6a6009a39536fd7ce0f1ec9802a5bf7 SHA1: 84324fe367087e53986abb3fe8ad552b98e0a81d SHA256: bffad1d3909ceb744d09181acb79281b5d3e772e47bcd0a19308fcb737cf7f57 SHA512: 4b522c1064cf96eb8df1e181554bcfec3640d60c32eb4e79bb2ed622df861ecd1c3677b1ed9ec7f24696a35f088803bae7e5360ef535bd61880efab0d57b7ef0 Homepage: https://cran.r-project.org/package=SPOUSE Description: CRAN Package 'SPOUSE' (Scatter Plots Over-Viewed Using Summary Ellipses) Summary ellipses superimposed on a scatter plot contain all bi-variate summary statistics for regression analysis. Furthermore, the outer ellipse flags potential outliers. Multiple groups can be compared in terms of centers and spreads as illustrated in the examples. 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The package focuses exclusively on Monte Carlo simulation experiment variants of (expected) prospective power analyses, criterion analyses, compromise analyses, sensitivity analyses, and a priori/post-hoc analyses. The default simulation experiment functions defined within the package provide stochastic variants of the power analysis subroutines in G*Power 3.1 (Faul, Erdfelder, Buchner, and Lang, 2009) , along with various other parametric and non-parametric power analysis applications (e.g., mediation analyses) and support for Bayesian power analysis by way of Bayes factors or posterior probability evaluations. Additional functions for building empirical power curves, reanalyzing simulation information, and for increasing the precision of the resulting power estimates are also included, each of which utilize similar API structures. For further details see the associated publication in Chalmers (2025) . Package: r-cran-sppcomb Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-sppcomb_0.1-1.ca2404.1_all.deb Size: 257688 MD5sum: 207596e39fd3adbc9cf7701c600091ed SHA1: 648a152752c2c0943e3fb02b1ed26976652b8d5a SHA256: 5d0481a4ddfc442756c59a707574a2e0e2ae0d74a3da3bd642da664705cf9c92 SHA512: 6eb44d65cc0639c1a5a702b415b48c3ea7a7bdb8a9932759eea490dc003b7b2174d9066de20d01bf6e00e691659d761999bb2c7454600615fb020940c3c9702a Homepage: https://cran.r-project.org/package=SPPcomb Description: CRAN Package 'SPPcomb' (Combining Different Spatial Datasets in Cancer Risk Estimation) We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, . Package: r-cran-spphpr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spphpr_1.1.5-1.ca2404.1_all.deb Size: 342720 MD5sum: f72e34e87efa238b847490a66c47b203 SHA1: 05fbb7d3eafbfc4dee6b9c9b8a6b2e5f995d714d SHA256: f750c679dd645b4b720206a702ea21313e317585c48624a37256c545b26e51cb SHA512: cd2dcbf2e3fc246b0fcc3e5d35b2254bf2a0583a2a16094fec387e70917eb5607a09fca1c59a50ae0d6da92527507d3307762bbfb3578802b259eda62f551449 Homepage: https://cran.r-project.org/package=spphpr Description: CRAN Package 'spphpr' (Spring Phenological Prediction) Predicts the occurrence times (in day-of-year) of spring phenological events. Three methods, including the accumulated degree days (ADD) method, the accumulated days transferred to a standardized temperature (ADTS) method, and the accumulated developmental progress (ADP) method, were used. See Shi et al. (2017a) and Shi et al. (2017b) for details. Package: r-cran-sppop Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sppop_0.1.0-1.ca2404.1_all.deb Size: 59762 MD5sum: 09d931efe80aed22d7f1b01c5f8808ab SHA1: e7d8fb971462618a74b0486ec764b308ef04a843 SHA256: 6fe398ad8806b0f1d63985f9f50a63c96174087c6e343e5603866dfccb6ef8a0 SHA512: 010451b968d09ea72440977ae00c076bc1f4b0b708e018656cf02f7f251afdbe101b280c11c9d777ca8e61fc5a2b0401bd7ee79007847b33ac52cbd1b3cefc0d Homepage: https://cran.r-project.org/package=SpPOP Description: CRAN Package 'SpPOP' (Generation of Spatial Population under Different Levels ofRelationships among Variables) The developed package can be used to generate a spatial population for different levels of relationships among the dependent and auxiliary variables along with spatially varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).. The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially generated data. Package: r-cran-spptrend Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3779 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork, r-cran-sf, r-cran-stringr, r-cran-terra Suggests: r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-spptrend_0.5-1.ca2404.1_all.deb Size: 2743414 MD5sum: 7e726dfb9a30c41543a2f30f1cbb46e3 SHA1: 23a109f610cc9cae548a92f10335ad58a505cdaf SHA256: cf28a8d1e490d6208e738cf0ec0581d75bc99539af180f7aad9564fc9b8efac6 SHA512: e3d66d2be0a3e8a82f0816c7980d334784818a072ccadb4bc3bc06963be31dec61dd91f129627cb5b25ae3d04bac30b9f18d7260684d79a6a768d62960b69cb0 Homepage: https://cran.r-project.org/package=SppTrend Description: CRAN Package 'SppTrend' (Analyzing Linear Trends in Species Occurrence Data) Provides a comparative framework to detect species-specific spatial and thermal responses to climate change using opportunistic occurrence data. Species temporal trends in geographic position (via Earth-Centred Earth-Fixed vector analysis) and environmental variables (temperature and elevation) are contrasted against the overall trend of the complete dataset, allowing classification of species into ecologically interpretable response categories. Approach described in Lobo et al. (2023) . Package: r-cran-spqdep Architecture: all Version: 0.1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1601 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-gtools, r-cran-igraph, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-rsample, r-cran-sf, r-cran-sp, r-cran-spatialreg, r-cran-spdep, r-cran-tidyr, r-cran-units Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spqdep_0.1.3.6-1.ca2404.1_all.deb Size: 1318258 MD5sum: 4960022b1eed4cb3e8a2c3c7a97a9738 SHA1: dfeca9b0469bbaeda95c50e960b14ed0267b8c60 SHA256: e4b5d24480b44e6d876f02aa21bcb955898b5287fc2c8f8a8794e792c66dc3d1 SHA512: a75b23d4ab840ab5b906e7d436d343ced6a669a6449d96f15dbadb893a0b9bf446101f13ae1a403a14317375f9822b3f4c20ecb97904ce1f55b7b7df9f60a468 Homepage: https://cran.r-project.org/package=spqdep Description: CRAN Package 'spqdep' (Testing for Spatial Independence of Cross-Sectional QualitativeData) Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in and . 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Provides a clustering-based approach (build a nearest-neighbour graph in a dimensionality-reduced space and iteratively split large components by edge weight), a threshold-based approach (classify sample pairs as belonging or not-belonging from a pairwise distance cutoff), parameter optimization over distance metrics and cutoffs, and a pairwise random-forest classifier for protein importance ranking. This is a native R port of the author's Python package 'spqrp' (), implementing methods from an associated manuscript currently in preparation. Package: r-cran-spreda Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-nlme Filename: pool/dists/noble/main/r-cran-spreda_1.2-1.ca2404.1_all.deb Size: 858618 MD5sum: e2cf9cf0b8c7b1dff0bbfb2eb873b38a SHA1: 207998734505413729df451dcb2a2a83981f0463 SHA256: a03ead52b128e8286e66f553c56fa9906aeecdd0e2400265a51c28bd3bcc4181 SHA512: 1e636c57343984cc375a3a47a05ff56e58cca98116cc2416b08eb8d353f1666bfdc118e5405e52be5cb6882235791bde877e01d92e6ad8ef761d08e13c13d423 Homepage: https://cran.r-project.org/package=SPREDA Description: CRAN Package 'SPREDA' (Statistical Package for Reliability Data Analysis) The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged. Package: r-cran-spreg Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sn, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-spreg_1.0-1.ca2404.1_all.deb Size: 31190 MD5sum: 10d8c165c2a3bd4ad0665c4db8bad009 SHA1: 954b7ab34b0e814b7ae398d10ed3732b45da4e65 SHA256: ce2dbdf3ecb7884093e9541343621eb2e676b128692d3a54858da3d9fac9044a SHA512: 135716a1a79110fc6389112370b3292d877432ff3cf3b84eb5303c5617c6a993a6b953475fb812b23bc4b8c772b75ef52932e9fb978b869411454201c6d68882 Homepage: https://cran.r-project.org/package=SPreg Description: CRAN Package 'SPreg' (Bias Reduction in the Skew-Probit Model for a Binary Response) Provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) . Package: r-cran-spreval Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-timedate, r-cran-interp Suggests: r-cran-fields, r-cran-knitr, r-cran-rmarkdown, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-spreval_1.1.0-1.ca2404.1_all.deb Size: 687742 MD5sum: 069008bb48cb07b09b6d6b941f83922e SHA1: 4169f2d19858b60e7f8398269d3809238e449cf5 SHA256: 997a7573700d7ab4f43864572c0da2571daf17a50fdb9d9c1c640ec69c5c285e SHA512: 3c43c92e55f287aac895ecd48344b9ce32eed697a17a493f2928e2c63fde46c6e52c29861a4614e3ce269c2bd4de65638602408b47761bfad2cfae77ad5e2855 Homepage: https://cran.r-project.org/package=spreval Description: CRAN Package 'spreval' (Evaluation of Sprinkler Irrigation Uniformity and Efficiency) Processing and analysis of field collected or simulated sprinkler system catch data (depths) to characterize irrigation uniformity and efficiency using standard and other measures. Standard measures include the Christiansen coefficient of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295, "Irrigation by Sprinkling"); and distribution uniformity (DU), potential efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ) that are implementations of measures of the same notation in Keller, J. and Merriam, J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management" . spreval::DU.lh is similar to spreval::DU but is the distribution uniformity of the low half instead of low quarter as in DU. spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead of lateral move or solid-set sprinkler systems. The function spreval::eff is analogous to the method used to compute application efficiency for furrow irrigation presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated depth compared against soil-moisture deficit (SMD), when the argument "target" is set equal to SMD. The other functions contained in the package provide graphical representation of sprinkler system uniformity, and other standard univariate parametric and non-parametric statistical measures as applied to sprinkler system catch depths. A sample data set of field test data spreval::catchcan (catch depths) is provided and is used in examples and vignettes. Agricultural systems emphasized, but this package can be used for landscape irrigation evaluation, and a landscape (turf) vignette is included as an example application. Package: r-cran-sprex Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-swfscmisc Filename: pool/dists/noble/main/r-cran-sprex_1.4.3-1.ca2404.1_all.deb Size: 73138 MD5sum: 2f02dcc8caca2a846dce0f86f1118d26 SHA1: 706c8a2ce938ee999a59adfc35eb307c6b5f6bb0 SHA256: c9fbb8c5f6d0c0dfb5b7d534584dfa5a77b6d2e395394a0d78d20bfd7ea649e3 SHA512: f714e1b99618f67a01eb5057e303a723e873aa6d4cb7a8408c35bc3e2ff838e93c4062dd4478c4857db3ec0590c48975df4d30b7ce758f2e9161423980104e2c Homepage: https://cran.r-project.org/package=sprex Description: CRAN Package 'sprex' (Species Richness and Extrapolation) Functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices. Package: r-cran-springpheno Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-springpheno_0.5.0-1.ca2404.1_all.deb Size: 149806 MD5sum: 25c629c68d97566e06d1e10f8ff7a342 SHA1: da7fd6daf8426bcc97d6d9f135e93ca06216fe7f SHA256: b853879fa9faf113470bd7ec35b386ea24856534c1117b785d0be04d84e483bf SHA512: c49effbdeb210eee0186e9b0ac1e2992cf684d2522c05b394f7f128da5393ccd31033b30053fb66c9776ca2881bfb25c5ced0bf96d150e07ef6e5db1268e4b71 Homepage: https://cran.r-project.org/package=springpheno Description: CRAN Package 'springpheno' (Spring Phenological Indices) Computes the extended spring indices (SI-x) and false spring exposure indices (FSEI). The SI-x indices are standard indices used for analysis in spring phenology studies. In addition, the FSEI is also from research on the climatology of false springs and adjusted to include an early and late false spring exposure index. The indices include the first leaf index, first bloom index, and false spring exposure indices, along with all calculations for all functions needed to calculate each index. The main function returns all indices, but each function can also be run separately. Allstadt et al. (2015) Ault et al. (2015) Peterson and Abatzoglou (2014) Schwarz et al. (2006) Schwarz et al. (2013) . Package: r-cran-springsteen Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-rlang Suggests: r-cran-tidytext, r-cran-dplyr, r-cran-lubridate, r-cran-readr, r-cran-stringr, r-cran-magrittr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-springsteen_0.1.0-1.ca2404.1_all.deb Size: 728000 MD5sum: 5ecd31d472e395cb8457c432342a62cc SHA1: 48fcea4f034f62da86e855d7cdd3cca382e13215 SHA256: 56d7cfd900366070a3c370a593e49851aedb757eacaf11ba56121e40ac51ed74 SHA512: 07a73d122e6f4d00aee07c9c13c03ed909ad96404d8f3329390806700b12573105aae5b02e9ac3dbd3b91a9aa8faa65aac1747466e70e0ea9da2ce2c5bb522d6 Homepage: https://cran.r-project.org/package=spRingsteen Description: CRAN Package 'spRingsteen' (All Things Data and Springsteen) An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. Also includes all his song details such as lyrics and albums. Data extracted from: . Package: r-cran-sprt Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sprt_1.1.0-1.ca2404.1_all.deb Size: 34014 MD5sum: e0f436b10013c0976d754cd6b882f15b SHA1: c6cfc118b6c285438b45b60487ce8936251631dc SHA256: af23b34caaefe060682b636aed9f21158f62a4bd29d5b753660616686d8690a2 SHA512: f079f77faa3b79fa241297a4317838a1de0ae04eeb1a61c2c7489f938c2130f56b5133b01d398b9d6b027cc71a9d9438a28b8231bfa3d1cb387b067cf0aab19d Homepage: https://cran.r-project.org/package=SPRT Description: CRAN Package 'SPRT' (Sequential Probability Ratio Test (SPRT) Method) Provides functions to perform the Sequential Probability Ratio Test (SPRT) for hypothesis testing in Binomial, Poisson and Normal distributions. The package allows users to specify Type I and Type II error probabilities, decision thresholds, and compare null and alternative hypotheses sequentially as data accumulate. It includes visualization tools for plotting the likelihood ratio path and decision boundaries, making it easier to interpret results. The methods are based on Wald (1945) , who introduced the SPRT as one of the earliest and most powerful sequential analysis techniques. This package is useful in quality control, clinical trials, and other applications requiring early decision-making.The term 'SPRT' is an abbreviation and used intentionally. Package: r-cran-sprtt Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rappdirs, r-cran-dplyr, r-cran-mbess, r-cran-purrr, r-cran-glue, r-cran-ggplot2, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-ggtext, r-cran-scales, r-cran-piggyback Suggests: r-cran-testthat, r-cran-testthis, r-cran-withr, r-cran-xml2, r-cran-effsize, r-cran-effectsize, r-cran-vdiffr, r-cran-diffviewer Filename: pool/dists/noble/main/r-cran-sprtt_0.3.1-1.ca2404.1_all.deb Size: 1648986 MD5sum: 9614bd5018e6f80c33501bc5950ecebe SHA1: a3540c3e6a66a98fa994a2a00365dda22eb7812f SHA256: 51f3c10d77fd5cfd491420f84ebafdcb32a01340fa0d1723d76da587c9b3599a SHA512: b1026d630be87c49f12d66efd8f7ce98895a03932d4483fcb141f0d4b4e55d999bd495902c461933904a161d63c08dbd1cb7bf9ae7b8f3bedb6f0a1e226126b8 Homepage: https://cran.r-project.org/package=sprtt Description: CRAN Package 'sprtt' (Sequential Probability Ratio Tests Toolbox) A toolbox for Sequential Probability Ratio Tests (SPRT) based on Wald (1945) . SPRTs are applied during the sampling process, ideally after each observation, and at every stage return a decision to either continue sampling or terminate and accept one of the specified hypotheses. The `seq_ttest()` function performs one-sample, two-sample, and paired t-tests for one- and two-sided hypotheses (Schnuerch & Erdfelder (2019) ). The `seq_anova()` function performs a sequential one-way fixed effects ANOVA (Steinhilber et al. (2024) ). The `plan_sample_size()` function helps plan sequential studies by simulating required sample sizes across a range of effect sizes. For more information, see the vignettes browseVignettes(package = "sprtt") or the package website . Package: r-cran-sps Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-kit, r-cran-litedown, r-cran-spelling, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-sps_0.7.0-1.ca2404.1_all.deb Size: 167980 MD5sum: 3655a11558ccd9f61bda892916a6d6f6 SHA1: f6452bd3be48ddd719894bda991454c0c37fd735 SHA256: 5aab1db7123074dbd29beaed68791c9171833785d9ccc468dd8460d380a33f0e SHA512: f2ce7e2506e025ffff1b3988ab5d109dcf1eaf88d2ddae8a72ae4a5b194533e98a9e8923d2ef047e25d3acaab66c176be58b0c4ab39bbd71a1fee05aa40ce9f6 Homepage: https://cran.r-project.org/package=sps Description: CRAN Package 'sps' (Sequential Poisson Sampling) Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, ), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, ). Package: r-cran-spscomps Architecture: all Version: 0.3.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-assertthat, r-cran-stringr, r-cran-glue, r-cran-magrittr, r-cran-shinytoastr, r-cran-shinyace, r-cran-htmltools, r-cran-r6, r-cran-crayon Suggests: r-cran-testthat, r-cran-shinyjqui, r-cran-spsutil Filename: pool/dists/noble/main/r-cran-spscomps_0.3.4.0-1.ca2404.1_all.deb Size: 286238 MD5sum: 16962e4b14cad85cd5614ce2d43ffdc5 SHA1: 480d1f408fb6e35e349a37cd2194eb8899bd0585 SHA256: 599d4f086302c6629287e331c3e14c85386f5569a2137443f7f0adeb31a59a7f SHA512: 205efc5a7d4c83002a050af469698b6a7bd2fa29ab93f5e0c858d9503de6acacc8b74511007503940f0f13a3699431d3ef921aec596496861c8c97435bc35cbd Homepage: https://cran.r-project.org/package=spsComps Description: CRAN Package 'spsComps' ('systemPipeShiny' UI and Server Components) The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package. Package: r-cran-spscsfa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-np Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spscsfa_0.1.0-1.ca2404.1_all.deb Size: 83742 MD5sum: 9f4161bc9b28a1cb5cef96e17fe0fac5 SHA1: 8adc1716b40c6fa37467d88cf5bed84ab2fd9b70 SHA256: 0e2323585f7a08f0d269a6459f2c2222a0d254994b6b0698f48ca26a1daaf1a3 SHA512: 6bd7bbf3dfce95e347bc5af4850f08d9cefbfe40ebc34a2cb8b3fcdb04135c1c53db75f4fa14a716f68dce822b2fdff6e278f44e34a20a4e4735e29efb921c0a Homepage: https://cran.r-project.org/package=spscsfa Description: CRAN Package 'spscsfa' (Semiparametric Smooth-Coefficient Stochastic Frontier Analysis) Provides semiparametric smooth-coefficient stochastic frontier analysis following Sun and Kumbhakar (2013) where the coefficients of the parametric part vary smoothly with a set of nonparametric variables. Inefficiency term is allowed to depend on a set of determinants through heteroskedasticity. Smooth coefficients are estimated using nonparametric regression and the remaining frontier parameters are estimated by maximum likelihood. Technical efficiency and inefficiency are computed using the Battese and Coelli (1988) and Jondrow et al. (1982) methods, respectively. Confidence intervals for technical efficiency are computed using the approach of Horrace and Schmidt (1996) . Package: r-cran-spselect Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tester, r-cran-magic, r-cran-pracma Filename: pool/dists/noble/main/r-cran-spselect_0.0.1-1.ca2404.1_all.deb Size: 92756 MD5sum: 255649a2bc83dc1567c3ff13f7c36ff4 SHA1: 88919a0554edf1ec4a5f53b4d942bce3859a20a7 SHA256: cfbae475c99c85e17e309d499384e762f0600ea38efa0dc31e3fd8476f42382f SHA512: 5e02c3be78f106f9f7efa94c80e103162919f361c6e7289d131fe6ef623ec76b92322b16cb2a1d44da9f0c9f4fb4e97e390a603ba8ec828b14347ce13d995f52 Homepage: https://cran.r-project.org/package=spselect Description: CRAN Package 'spselect' (Selecting Spatial Scale of Covariates in Regression Models) Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models. All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale. Package: r-cran-spsh Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim, r-cran-lhs, r-cran-pracma, r-cran-fme, r-cran-hypergeo, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-spsh_1.1.0-1.ca2404.1_all.deb Size: 189918 MD5sum: f86c0ca733f719c08a7eb6ead80fcb0d SHA1: d0d3fbb2bc709b842764c2e6720650ccc444a28d SHA256: a09aa40c3a86f700b6754bb0a79762ae6b0f0edd6f6abd98b267d01d32193be1 SHA512: 2c9b4d5260a04f3c13992d052bcae45ab8ce2cef7910c90a55384a872be274497dc34c8374279e8fc6f9f725c2f5947f6efdf75588ac2b54ec5731e6d4cad57c Homepage: https://cran.r-project.org/package=spsh Description: CRAN Package 'spsh' (Estimation and Prediction of Parameters of Various SoilHydraulic Property Models) Estimates model parameters of soil hydraulic property functions by inverting measured data. A wide range of hydraulic models, weighting schemes, global optimization algorithms, Markov chain Monte Carlo samplers, and extended statistical analyses of results are provided. Prediction of soil hydraulic property model parameters and common soil properties using pedotransfer functions is facilitated. Parameter estimation is based on identically and independentally distributed (weighted) model residuals, and simple model selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018) ) can be calculated. The included models are the van Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2 parametric-Mualem model, and the Fredlund-Xing model. All models can be extended to account for non-capillary water storage and conductivity (Weber, T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019) . The isothermal vapour conductivity (Saito, H., Simunek, J. and Mohanty, B.P. (2006) ) is calculated based on volumetric air space and a selection of different tortuosity models: (Grable, A.R., Siemer, E.G. (1968) , Lai, S.H., Tiedje J.M., Erickson, E. (1976) , Moldrup, P., Olesen, T., Rolston, D.E., and Yamaguchi, T. (1997) , Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2004) , Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005) , Millington, R.J., Quirk, J.P. (1961) , Penman, H.L. (1940) , and Xu, X, Nieber, J.L. Gupta, S.C. (1992) ). Package: r-cran-spsl Architecture: all Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-spsl_0.1-9-1.ca2404.1_all.deb Size: 115376 MD5sum: e57b1554adc96bbd4ea0a7e0d6b2018f SHA1: d8ae3631362dd740d7144e194518830534661f71 SHA256: c6d91674e81bf027e186c4e7ac6f4cd1194ec131dcbc26c17feb8180e378196c SHA512: 05d884f057775abf16289ccc6b4d3508281b7d66978abd337311e5c8fa00d7e6135ef56372d8a5d7990c82c4bef7d33b237a968685cd525262a27cf33dffe36b Homepage: https://cran.r-project.org/package=SPSL Description: CRAN Package 'SPSL' (Site Percolation on Square Lattices (SPSL)) Provides basic functionality for labeling iso- & anisotropic percolation clusters on 2D & 3D square lattices with various lattice sizes, occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random variables weighting the percolation lattice sites. Package: r-cran-spsur Architecture: all Version: 1.0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-ggplot2, r-cran-gmodels, r-cran-gridextra, r-cran-mass, r-cran-matrix, r-cran-minqa, r-cran-numderiv, r-cran-rdpack, r-cran-rlang, r-cran-sparsemvn, r-cran-spatialreg, r-cran-spdep, r-cran-sphet Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-spsur_1.0.2.6-1.ca2404.1_all.deb Size: 1893442 MD5sum: a928721cc38bee5fa4a66f4b6c75b369 SHA1: 4694a264843d11e86cf2ee6da51ac43218aa34ab SHA256: 40e15d900b453cf759b6a9a093236cdafc72d1aa2bcffa5bbbebb5a8f246b9e2 SHA512: cea044b9f101b67061a392fe927336839e1bdd082ff3d7c768d228751f592ab83374ed9500d5eb509234fd46b784d450c37a4b8af6545af62194827fcd478ed7 Homepage: https://cran.r-project.org/package=spsur Description: CRAN Package 'spsur' (Spatial Seemingly Unrelated Regression Models) A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references Minguez, R., Lopez, F.A., and Mur, J. (2022) Mur, J., Lopez, F.A., and Herrera, M. (2010) Lopez, F.A., Mur, J., and Angulo, A. (2014) . Package: r-cran-spsurvey Architecture: all Version: 5.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2882 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-survey, r-cran-boot, r-cran-crossdes, r-cran-deldir, r-cran-lme4, r-cran-mass, r-cran-sampling, r-cran-units Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spsurvey_5.7.0-1.ca2404.1_all.deb Size: 1923216 MD5sum: 0547f82664ab043f439bcb3e4173e5aa SHA1: 922ee526419a8e0968624828a99498bee7ee8a86 SHA256: 3e84895991116fac32b09b9b0d58bb673170bc5234e475fe5789d8cd18e0ae1c SHA512: 5b8731d6a3540e9b26997e16ee56382635a532c274c9e74a77178b0e5ba1142d1074d9cf391aa9a2fd0dddf3ad3e647f75b7aadd784ac6561fb0fb9cc9a32574 Homepage: https://cran.r-project.org/package=spsurvey Description: CRAN Package 'spsurvey' (Spatial Sampling Design and Analysis) A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) . Package: r-cran-spsutil Architecture: all Version: 0.2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-assertthat, r-cran-stringr, r-cran-glue, r-cran-magrittr, r-cran-crayon, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spsutil_0.2.2.1-1.ca2404.1_all.deb Size: 126952 MD5sum: 9e593d28cac2f5768c4945ec987438fd SHA1: f712bdaf1fd270d60d64d33b3d14e6abd1d061dd SHA256: c8d301b0aff3b0426de10b79392e773020ab579f198d24dbaff587337c1128f8 SHA512: 04902e9016e9092dab8875c99d20347b97ac7af5d30713e8b8f93c9ce6228b8111fe090c9bb734f759e898681fac1909a8af85e8dadaa001d5e00d4c3d0c78a5 Homepage: https://cran.r-project.org/package=spsUtil Description: CRAN Package 'spsUtil' ('systemPipeShiny' Utility Functions) The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough. Package: r-cran-spthin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-fields, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spthin_0.2.0-1.ca2404.1_all.deb Size: 94326 MD5sum: be115abc8d2e53a0b2d98de6bfea3b10 SHA1: 4ad5026673899827ad7f7b20c1f629d69c017867 SHA256: c66a92fde2a94c95a1c59d5875077f6fc492d9abb8b79417cde975345b38173d SHA512: 42bd1f897df8c4ca8d5c5a2300716c753de5afd3cf6586d618aea97a110d42e2c66598be5432f40c62b3de194c046b16410547d9e5054129c34e1b91701a1751 Homepage: https://cran.r-project.org/package=spThin Description: CRAN Package 'spThin' (Functions for Spatial Thinning of Species Occurrence Records forUse in Ecological Models) A set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling. Package: r-cran-sptotal Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-prettydoc, r-cran-tibble, r-cran-gstat Filename: pool/dists/noble/main/r-cran-sptotal_1.0.1-1.ca2404.1_all.deb Size: 1157848 MD5sum: 993b98c7bd857d75cc99033dac399bb3 SHA1: df54d7f2c4056ea5ec3b4b65add1f20e0719beae SHA256: c047f3b142b8ef214a77ee2d134e9d828a31316b39aa6412a57ccf56e95cb7be SHA512: 9f6ef74dfae4a29e86aedcef27d9d4b1ba39657a7553d43cde960907922152c4d44f734e33e0e330674569e47fff91a4285ff64bcf88260ccc2fbdabae70284d Homepage: https://cran.r-project.org/package=sptotal Description: CRAN Package 'sptotal' (Predicting Totals and Weighted Sums from Spatial Data) Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) . The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package. Package: r-cran-sptrends Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-matrix, r-cran-withr Suggests: r-cran-fields, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ncdf4, r-cran-kendall, r-cran-modifiedmk, r-cran-rkt, r-cran-robslopes, r-cran-trend, r-cran-zyp Filename: pool/dists/noble/main/r-cran-sptrends_1.6.3-1.ca2404.1_all.deb Size: 6553094 MD5sum: c4b2e2010f092a5b6232668680e6e247 SHA1: d1e1f8f99b808c0bad2e24d3192a1abe7284aa89 SHA256: 3ace4d114fce208934c6e75c889552faf555a2131aa7959c16143d36891cbbff SHA512: 86f0aece109ebf3ecd08fa3b8539b5005399ec1eb2965e9cd88c3c9af501dff3123437ec5759a848d36a045c0f497822bbcd10c31e59a18df1daefc9db3d3e3d Homepage: https://cran.r-project.org/package=sptrends Description: CRAN Package 'sptrends' (Statistical Inference for Spatiotemporal Trends in Gridded Data) Provides a unified and reproducible framework for statistical inference of spatiotemporal trends in gridded environmental data. The framework addresses the interconnected challenges of serial correlation, spatial dependence and multiple testing that commonly arise when analysing gridded environmental time series. Its core methods support serial-correlation treatment through trend-preserving prewhitening, pixel-wise and spatially explicit trend inference, slope estimation and multiple-testing correction. These methods may be applied independently or integrated within configurable analytical workflows. Dedicated workflows are also provided to reproduce methodologies published in the scientific literature: Gutiérrez-Hernández and García (2025) for the True Significant Trends workflow, Gutiérrez-Hernández and García (2024) for the Robust Trend Analysis workflow, and Gutiérrez-Hernández and García (2025) for the adaptive false discovery rate procedure. Supporting utilities facilitate raster data import and inspection, anomaly calculation, spatial autocorrelation diagnostics, simulation studies, benchmarking, visualisation, mapping, and reporting. Package: r-cran-spup Architecture: all Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2364 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gstat, r-cran-magrittr, r-cran-mvtnorm, r-cran-purrr, r-cran-raster, r-cran-whisker Suggests: r-cran-dplyr, r-cran-ggally, r-cran-gridextra, r-cran-knitr, r-cran-png, r-cran-readr, r-cran-sp, r-cran-testthat, r-cran-sf Filename: pool/dists/noble/main/r-cran-spup_1.4-0-1.ca2404.1_all.deb Size: 1712490 MD5sum: 4a3e3fc4005ac373bc621a016595524b SHA1: d29aa0a1a8bce891fef556c937666dd5b1bdd47c SHA256: 1e92f26e374124649d9f80499fee8ddd4cd879ff737b6daa55a5bd7c0255c65a SHA512: 9ee0117ec68ecb3a3f09b86164b1a940b6b139e9ce78bd29593f08960dd5b2ac222901b7fcb1c4eda6e1e539079b7c42eaff378bc235f1faebe57f068915f7b1 Homepage: https://cran.r-project.org/package=spup Description: CRAN Package 'spup' (Spatial Uncertainty Propagation Analysis) Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) and Brown and Heuvelink (2007) . The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally. Package: r-cran-spuriouscorrelations Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spuriouscorrelations_0.2-1.ca2404.1_all.deb Size: 99440 MD5sum: bba8d7fcb1131fb36b4b6842f098bc44 SHA1: 56ea4ff52853f2bb3243d189f31e229b4289cb55 SHA256: 6f6578913bd6a4f1a52fe77629b2d35a047ec4d46173a3daacad98df94447003 SHA512: a9c744b09df4fca4c9aef9b1c749c02e433dd4a3c2918589ed70e7d651f31bf6e262b56ad929ebfdb49b089a1c0d5ed0c823ca81ecfee575d6977c780510f87e Homepage: https://cran.r-project.org/package=spuriouscorrelations Description: CRAN Package 'spuriouscorrelations' (Datasets with Strong and Spurious Correlations) Provides datasets from Vigen (2015) rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation. Package: r-cran-spuriousmemory Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spuriousmemory_1.0.0-1.ca2404.1_all.deb Size: 74828 MD5sum: f10ebd447c57fab3aa805a20ae9b978c SHA1: 0482b29d746c9ade13b4bdc8c591d92f880b7333 SHA256: 759884b4b37d7fed7336940e1365b225599e31d80c620dd59363cd6df4217422 SHA512: 94d352758b3be1ae3d0b612af22b96e156c3fe55196bddeaa5aa6cc6cf827203f110cc6e16da80595fa6479d0108340a02a1f6222a96947c7bf39e1066bcce01 Homepage: https://cran.r-project.org/package=SpuriousMemory Description: CRAN Package 'SpuriousMemory' (Testing True Long Memory Against Spurious Long Memory) Implements a test for distinguishing between true long memory and spurious long memory. Reference: Qu, Z. (2011). "A Test Against Spurious Long Memory." Journal of Business & Economic Statistics, 29(3), 423–438. . Package: r-cran-spurs Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-spurs_2.0.3-1.ca2404.1_all.deb Size: 198424 MD5sum: 7ab76682ade3a7eacccfdddeb5c541eb SHA1: 1504564d97d38c159b1e5fd42bbfd21de8e696ed SHA256: b23544cd23d71f4f730917eb6c3d4b2ad8364e929acec759c3a4949800dd686d SHA512: 137f651964432823bb42c2490a73d6f361063b38832ad787f926c0b9221254d8797aa13e149a4f1f1e670f81e587dfbd89bd0768ab32408d3cb4043d465c0f6c Homepage: https://cran.r-project.org/package=spuRs Description: CRAN Package 'spuRs' (Functions and Datasets for "Introduction to ScientificProgramming and Simulation Using R") Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC. Package: r-cran-sputnik Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-bioc-edger, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-viridis, r-cran-ggplot2, r-cran-reshape, r-cran-imager, r-cran-infotheo, r-cran-irlba, r-cran-dosnow, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sputnik_1.4.3-1.ca2404.1_all.deb Size: 222284 MD5sum: 0dc578cba825bae2c1329bc0d527243c SHA1: 8a1dbc605144612a3e3c0649719eceea0724b76e SHA256: 1c159d19802d08bcbb6be324f2c0f4fafe52c8eb6d90544166cc39139a7ef262 SHA512: 5ce46fae409a6d8ccc12fd3535d0a33674e4c43b4b86cd476862e8eef8ea3b1b3bd3ccd18612d3c4838503260a35683002be4eeca0aa8e08c5ce502b98b43c7d Homepage: https://cran.r-project.org/package=SPUTNIK Description: CRAN Package 'SPUTNIK' (Spatially Automatic Denoising for Imaging Mass SpectrometryToolkit) Set of tools for peak filtering of mass spectrometry imaging data based on spatial distribution of signal. Given a region-of-interest, representing the spatial region where the informative signal is expected to be localized, a series of filters determine which peak signals are characterized by an implausible spatial distribution. The filters reduce the dataset dimension and increase its information vs noise ratio, improving the quality of the unsupervised analysis results, reducing data dimension and simplifying the chemical interpretation. The methods are described in Inglese P. et al (2019) . Package: r-cran-spyvsspy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spyvsspy_0.1.1-1.ca2404.1_all.deb Size: 13562 MD5sum: a80f30e06ac7a89d12ab509611fde54c SHA1: 79892d6e5fc18b52a463df71918f21a8c5260b31 SHA256: c2614e89362b446974d8bd70d829ac581c877f9a66c8eadf4a9366a092d04163 SHA512: 0924c984cc082a44cd5a9bc7c2fef299f9fed5a256be5c0ff7a0fc35b49888e63d8974a89b64410b12ec4b4aac80633dc15e6cc35497717e7568bf9706d70bb6 Homepage: https://cran.r-project.org/package=SPYvsSPY Description: CRAN Package 'SPYvsSPY' (Spy vs. Spy Data) Data on the Spy vs. Spy comic strip of Mad magazine, created and written by Antonio Prohias. Package: r-cran-sqi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-matrixstats, r-cran-olsrr, r-cran-factominer Filename: pool/dists/noble/main/r-cran-sqi_0.1.0-1.ca2404.1_all.deb Size: 86638 MD5sum: 4cca5bbd57771c678ae107ece34b9bb7 SHA1: cf68f4720e3a2d505aefca8988e42d71d9d8e54f SHA256: a55a44b61b084ae642f8e458e960068d216ecb9a7242a93e001603e274f253ea SHA512: 171ca95996ec01399115716e0a58c7511ba74bd3395073cc0875d0f156e4193b6e2d11e5d21a5ac56797a22dc204b1f3450420f9fc1029b3fad3481f2ed755a6 Homepage: https://cran.r-project.org/package=SQI Description: CRAN Package 'SQI' (Soil Quality Index) The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) . Package: r-cran-sqipro Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-factominer, r-cran-factoextra, r-cran-rlang, r-cran-matrixstats, r-cran-glmnet, r-cran-car Suggests: r-cran-openxlsx, r-cran-readxl, r-cran-ggpubr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-sqipro_0.1.0-1.ca2404.1_all.deb Size: 251704 MD5sum: b2d1fba464a547fb188eb8bde5de8b23 SHA1: 94540aa04f146d75b507f70a975764639591ff54 SHA256: 66edc580a930197dbc114f035754f40c505b2d553d1ca11e2c9ad829cbc7a1c0 SHA512: 619747ed9a8a9ca1751501fc22eeea0e0496de557313be11d13fd566c22d9ecddae17c8414008cb311ceca2a0cb451d4f745385e0218206ceca416e3e595f5bf Homepage: https://cran.r-project.org/package=SQIpro Description: CRAN Package 'SQIpro' (Comprehensive Soil Quality Index Computation and Visualization) Provides a comprehensive, modular framework for computing the Soil Quality Index (SQI) using six established methods: Linear Scoring (Doran and Parkin, 1994, ), Regression-based (Masto et al., 2008, ), Principal Component Analysis-based (Andrews et al., 2004, ), Fuzzy Logic, Entropy Weighting (Shannon, 1948, ), and TOPSIS (Hwang and Yoon, 1981, ). Implements four variable scoring functions: more-is-better, less-is-better, optimum-value, and trapezoidal, following Karlen and Stott (1994, ). Includes automated Minimum Data Set selection via Principal Component Analysis with Variance Inflation Factor filtering (Kaiser, 1960, ), one-way ANOVA with Tukey HSD post-hoc tests, leave-one-out sensitivity analysis, and publication-quality visualization using 'ggplot2'. Package: r-cran-sql Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-arrow, r-cran-stringr Filename: pool/dists/noble/main/r-cran-sql_0.1.1-1.ca2404.1_all.deb Size: 16756 MD5sum: 1fafdf5a63b6c8f8c4d2934558c8c1bd SHA1: 0e47a4099653c8b854c22023055aaa037e526e04 SHA256: 7412079168e9347de177bb08f4a0f737b1bc8de85912512b95cd56868f3a2e71 SHA512: b5a3c3c9ce114c6861e2a914d1a5175783c134f2f450d0646f82c2811f5787c1abbded9f1cea91f8df21a6eeb3bd675103f8071ff51ef457587553bb6e67fea2 Homepage: https://cran.r-project.org/package=SQL Description: CRAN Package 'SQL' (Executes 'SQL' Statements) Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base. Package: r-cran-sqlcaser Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sqlcaser_0.2.1-1.ca2404.1_all.deb Size: 41266 MD5sum: 67b05745f164278de91b66f35ff7d956 SHA1: ba335d693508127e47349d49b04b469c3cfb0eff SHA256: 9ade53193224adf3fd33c72c57f4a68a3bcda91d6991feead42a353ee9c68539 SHA512: 24f38cdbd9c773a09cf6e6476764e62da969706401ae03419ea38bcfe3133f0a781bc9a5524cb6a6d52f26f165b7b72986a8f7a76eda5f9a9271eb15ec007b16 Homepage: https://cran.r-project.org/package=sqlcaser Description: CRAN Package 'sqlcaser' ('SQL' Case Statement Generator) Includes built-in methods for generating long 'SQL' CASE statements, and other 'SQL' statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to 'SQL'-like databases, such as when using the 'RODBC' package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements. Package: r-cran-sqldf Architecture: all Version: 0.4-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsubfn, r-cran-proto, r-cran-rsqlite, r-cran-dbi, r-cran-chron Suggests: r-cran-rh2, r-cran-rmysql, r-cran-rpostgresql, r-cran-svunit, r-cran-mass Filename: pool/dists/noble/main/r-cran-sqldf_0.4-12-1.ca2404.1_all.deb Size: 76944 MD5sum: 8a82e9ca57d5431857c2a0b6b8511c6e SHA1: 12438cbee9d65af3e21291b0e9a6f0953fe0966c SHA256: 55efa3d8658a4721903147f4ec39a3b45b5e34c33448d0925223aad52b3dfe18 SHA512: 3689d4e82032a2cb81dd8c8134f499691a8b9866a32dc512e43c996e2dfc9512efc446827440a0d994b9291b8749ecd4305585fb5000600d44e98c6714fadb8b Homepage: https://cran.r-project.org/package=sqldf Description: CRAN Package 'sqldf' (Manipulate R Data Frames Using SQL) The sqldf() function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names. sqldf() transparently sets up a database, imports the data frames into that database, performs the SQL select or other statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame. The sqldf() or read.csv.sql() functions can also be used to read filtered files into R even if the original files are larger than R itself can handle. 'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported. Package: r-cran-sqlfluffr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat, r-cran-glue, r-cran-rstudioapi, r-cran-withr Filename: pool/dists/noble/main/r-cran-sqlfluffr_0.1.0-1.ca2404.1_all.deb Size: 55042 MD5sum: 090997b5640b7d5dc4181c6510998029 SHA1: 64656f50f98cc56fc60390fa34200e2546f27ab9 SHA256: 33df6bef6049dee03e2a753ff82752bab8d87321414afa9863c49390d2ec64b4 SHA512: 00f1e0a9ce3b20d13090e0fef566abf634c5113cab98ff9b1faa3efc876e254527733cd6f49120fa9915789a237aae502ca0187eae14c6407679f5b5d91bb979 Homepage: https://cran.r-project.org/package=sqlfluffr Description: CRAN Package 'sqlfluffr' (Wrapper to the 'SQL' Linter and Formatter 'sqlfluff') An R interface to the 'Python' 'sqlfluff' 'SQL' linter and formatter via the 'reticulate' package. Enables linting, fixing, and parsing of 'SQL' queries with support for multiple dialects. Includes special handling for 'glue' 'SQL' syntax with curly-brace placeholders. Package: r-cran-sqlhelper Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-yaml, r-cran-rappdirs, r-cran-stringr, r-cran-glue, r-cran-pool, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-sf, r-cran-rlang Suggests: r-cran-dplyr, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-odbc, r-cran-rsqlite, r-cran-rpostgres, r-cran-rmariadb, r-cran-bigrquery, r-cran-spdata Filename: pool/dists/noble/main/r-cran-sqlhelper_0.2.2-1.ca2404.1_all.deb Size: 103686 MD5sum: 1dc79a67a220bf049797a7433aeb1d76 SHA1: adeb7f1e8fc20d6e99827123ddb01475ac142cce SHA256: 6f839f53912f942b7610253dccf6a52f2f409342dfa5a5d85e9246cc86f35e6a SHA512: 996ef6304c6cb51e4acbae1159f1753b27b30d3777713a846dca16e0767473dae10988db33ad8e68306962a39c987a6db4042828380b6c1a5e6f57ce59fa0291 Homepage: https://cran.r-project.org/package=sqlhelper Description: CRAN Package 'sqlhelper' (Easier 'SQL' Integration) Execute files of 'SQL' and manage database connections. 'SQL' statements and queries may be interpolated with string literals. Execution of individual statements and queries may be controlled with keywords. Multiple connections may be defined with 'YAML' and accessed by name. Package: r-cran-sqlhelpers Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-toolbox, r-cran-dbi, r-cran-odbc, r-cran-stringi Filename: pool/dists/noble/main/r-cran-sqlhelpers_0.1.2-1.ca2404.1_all.deb Size: 66048 MD5sum: c3db64c5effab0ca5beb4cce75cc6934 SHA1: 20ddf6d2b99ecc2fe3dd80c36635a93698985184 SHA256: cc05cc5b60f3e16c3f9377d96bf6983596cc0f76dc5656753b5af0f46da1b308 SHA512: dba6075ebdf1f004f33a8b2d38aa40dd470efd328340051cf005282c9ab62719c049d9aa617fdef5aa2caf20fe379e9b1f550df033b7d64c908e1a44dce4cd5b Homepage: https://cran.r-project.org/package=sqlHelpers Description: CRAN Package 'sqlHelpers' (Collection of 'SQL' Utilities for 'T-SQL' and 'Postgresql') Includes functions for interacting with common meta data fields, writing insert statements, calling functions, and more for 'T-SQL' and 'Postgresql'. Package: r-cran-sqliter Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-functional, r-cran-dbi, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-sqliter_0.1.0-1.ca2404.1_all.deb Size: 22070 MD5sum: 31e0749d37a9bbcb12045ea79f80aa65 SHA1: 02dc9641af29e37e81c2fd7224812dbaa3719ff2 SHA256: 4b869321524ae46c9332bde4c9c0f3dea98ef3150841c90cf315f35b0859ca39 SHA512: 622d2605659aad314c2a65bb7d3d7b1e7b484821fdc8b3f18effd59de4034c49c03dbeeaad7c584a6a22d4f4b44ec51df7a8c7b41620078b6750f7c84428adc9 Homepage: https://cran.r-project.org/package=sqliter Description: CRAN Package 'sqliter' (Connection wrapper to SQLite databases) sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats. Package: r-cran-sqliteutils Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsqlite, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sqliteutils_0.1.0-1.ca2404.1_all.deb Size: 16470 MD5sum: 2db4e6ed6cb8ed6c49be03345f920f59 SHA1: 9bdc9ee3207343098878cdc769b9fccdad98e17e SHA256: 23b013e21ccc9f28222033796d70b3342d5287827de704be6df875ec86280ea9 SHA512: 2172e73f404c9d6c3c501dacb0142be29555f31726a8f05b85e909c19575a28c3257ada40da1380f74e343f264d88b6defa569a5aebaf01f2f6a5edd2d3c55f2 Homepage: https://cran.r-project.org/package=sqliteutils Description: CRAN Package 'sqliteutils' (Utility Functions for 'SQLite') A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. 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Package: r-cran-sqlm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dbplyr, r-cran-dbi, r-cran-glue, r-cran-purrr, r-cran-s7, r-cran-mass, r-cran-broom, r-cran-tibble Suggests: r-cran-testthat, r-cran-duckdb, r-cran-orbital, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto Filename: pool/dists/noble/main/r-cran-sqlm_0.1.0-1.ca2404.1_all.deb Size: 49484 MD5sum: c23225b69628678c1b559315ad7f9ab9 SHA1: 7a8c5d9046b86739964af327520db67469f8d9f0 SHA256: 31f736918d4778d6fec66340df541a58c36435a923a0acf4c67b441d05a59d2b SHA512: 83bc6f651bfc8bb717e2e9cfba9a9bf4c01017b1e09ce56523ce3265f46defe9731e24d6bda568aad28fbfef51df6393d72c4312accca3f8ce750ca63ea9b066 Homepage: https://cran.r-project.org/package=sqlm Description: CRAN Package 'sqlm' (SQL-Backed Linear Regression) Fits linear regression models on datasets residing in SQL databases without pulling data into R memory. Computes sufficient statistics inside the database engine via a single aggregation query and solves the normal equations in R. Package: r-cran-sqlove Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-readr, r-cran-rjdbc, r-cran-odbc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sqlove_1.0.2-1.ca2404.1_all.deb Size: 24292 MD5sum: 1a2badfeb02a17639ccb047c46f2b0e4 SHA1: 9ff4c1eddf32b33b0b353ab94f9103b91dec44c7 SHA256: 67d548fbb5a71a2ed8f6c6854bdc3da153f8fb2f6b8dbcfd6ee58fe45bfb0d20 SHA512: e45c99e6d6ea41679980aab4d2afc8ecefb9825f30cda798edddb9f1eff4e1be6e63447b8d0ca864fd90700c55f378cc7a2899e8deb012b59e62f7921e6c0032 Homepage: https://cran.r-project.org/package=SQLove Description: CRAN Package 'SQLove' (Execute 'SQL' Scripts in 'R' Containing Multiple Queries) The nature of working with structured query language ('SQL') scripts efficiently often requires the creation of temporary tables and there are few clean and simple 'R' 'SQL' execution approaches that allow you to complete this kind of work with the 'R' environment. This package seeks to give 'SQL' implementations in 'R' a little love by deploying functions that allow you to deploy complex 'SQL' scripts within a typical 'R' workflow. Package: r-cran-sqlparser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-sqlparser_0.1.0-1.ca2404.1_all.deb Size: 24468 MD5sum: 0d58f58ad39a0f3f725ad63fe8fb6955 SHA1: cbf0574f74449cfa5f4132775e6dc5a822003505 SHA256: 10580aae0067bc7117b2356adf0144d388f0ca4dd1b8b684e02ada3a64808cbf SHA512: b69d536781b295744aa34575c538008cacf91a60a323e5329074aa29e25a21627f5567ceeee695eb79e17c8dfaaded1ce9b6bc8c8346c6d3df719b9ce18a7e3a Homepage: https://cran.r-project.org/package=sqlparseR Description: CRAN Package 'sqlparseR' (Wrapper for 'Python' Module 'sqlparse': Parse, Split, and Format'SQL') Wrapper for the non-validating 'SQL' parser 'Python' module 'sqlparse' . It allows parsing, splitting, and formatting 'SQL' statements. Package: r-cran-sqlq Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-chk, r-cran-dbi Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-cyclocomp, r-cran-lintr, r-cran-lgr, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-sqlq_1.0.1-1.ca2404.1_all.deb Size: 942738 MD5sum: 196ef1f54a7fa91130689ed7808dad71 SHA1: f26dc17202e60556df8c7e281699f04fe4089e80 SHA256: eded04b5bb2d339a1b743a0d6de31c78fe7131a03943da9fc4388d40e8f6ceee SHA512: c063aafe511112e8344367489b59a5060fd7e7539fcf9a0f7df3a482d2afb0546a4d2812aeab8a3b74716abf3f5459e42748fe09662df45b8dfe62d412677034 Homepage: https://cran.r-project.org/package=sqlq Description: CRAN Package 'sqlq' ('SQL' Query Builder) Allows to build complex 'SQL' (Structured Query Language) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either ANSI (American National Standards Institute) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords. Package: r-cran-sqlrender Architecture: all Version: 1.19.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rjava, r-cran-rlang, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-sqlrender_1.19.7-1.ca2404.1_all.deb Size: 460506 MD5sum: 41da3a9c2fc62ee1d409ece7d5355a1b SHA1: a9719f9d5a692e3647965bdd18c494c5db103029 SHA256: 76e440bbc5f502c768cff9a4ab19c2d99af40ce6ff27e2ac16a3f7847ee03bb7 SHA512: 81010f3990bb8523518623fb7c211b12985b69df6f2591272fe6aea5a727aae92ca573901d8ddc95ba4cf93c4da732d623ddcffc3919edc0d18cfd14a5828b27 Homepage: https://cran.r-project.org/package=SqlRender Description: CRAN Package 'SqlRender' (Rendering Parameterized SQL and Translation to Dialects) A rendering tool for parameterized SQL that also translates into different SQL dialects. 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Package: r-cran-sqlscore Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbplyr Suggests: r-cran-testthat, r-cran-arm, r-cran-glmnet, r-cran-mboost, r-cran-covr Filename: pool/dists/noble/main/r-cran-sqlscore_0.1.4-1.ca2404.1_all.deb Size: 48086 MD5sum: 05ce9390dec0f2ce82dfa6773b7abef9 SHA1: 3871d87d7611862163304f41f5489981d0f1ba0c SHA256: 45bc17847af4a5381608709fe0d3152a8011a7aa5228a7ee7b80756d24a1650c SHA512: 2d4930624a281c683f352b65371bcf7133571c6d426cd85125fc792783fd502a0b98ef099ea9e24a8f764fa8a0c9a8447c8b9677c515860ce69b908f81c21e53 Homepage: https://cran.r-project.org/package=sqlscore Description: CRAN Package 'sqlscore' (Utilities for Generating SQL Queries from Model Objects) Provides utilities for generating SQL queries (particularly CREATE TABLE statements) from R model objects. 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Package: r-cran-sqlserverconnect Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-odbc, r-cran-pool, r-cran-cli Filename: pool/dists/noble/main/r-cran-sqlserverconnect_0.1.0-1.ca2404.1_all.deb Size: 17588 MD5sum: d4203b04edaaf6ec03217586139eed97 SHA1: d804295dd7646b13dd4035c589871150a163b9c0 SHA256: ad045a9186529a9272a1f940f4e2b1ac48d6c48a68f0c1f5fa6c4703adc34d1f SHA512: 6094b1cc486b25613c0a8b9c38d54b50fd84b5d276d87ab774a9262b99422b07d3ae6acf1965ff58dba135a38e29945a55c6f7c4090c6cb7180423c2f7ab541f Homepage: https://cran.r-project.org/package=sqlserverconnect Description: CRAN Package 'sqlserverconnect' (Simple Helpers for Connecting to 'SQL Server') Lightweight helpers for connecting to Microsoft 'SQL Server' using 'DBI', 'odbc', and 'pool'. Provides simple wrappers for building connection arguments, establishing connections, and safely disconnecting. Package: r-cran-sqlstrings Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-readr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sqlstrings_1.0.0-1.ca2404.1_all.deb Size: 12620 MD5sum: ea5dc07b720f025bbcf5d2a45b0e211b SHA1: c4aba5b6e0978a06795e170ee958095351331e27 SHA256: 343d7e9596274a408bedb8f6a33c1278cbe88bc2523b75dd118d1e71ec7cbea8 SHA512: 4d79b8e78f5dee8e79ccaada775cef5e4f805ca0be33b1a45b5029e036e1700c57f9264b048db7b7bc0d4cf99cb58ab35a0f2e13329555b78ee23c06451ba599 Homepage: https://cran.r-project.org/package=sqlstrings Description: CRAN Package 'sqlstrings' (Map 'SQL' Code to R Lists) Provides a helper function, to bulk read 'SQL' code from separate files and load it into an 'R' list, where the list elements contain the individual statements and queries as strings. 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The shorthand creates two targets, the query file and the query result. Package: r-cran-sqmtools Architecture: all Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3469 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-zip, r-cran-reshape2, r-cran-ggplot2, r-bioc-pathview, r-cran-data.table, r-bioc-biostrings Suggests: r-cran-vegan, r-cran-microeco, r-bioc-phyloseq Filename: pool/dists/noble/main/r-cran-sqmtools_1.8.1-1.ca2404.1_all.deb Size: 3502340 MD5sum: edfc352f3bbc250354353d0c3c37eaef SHA1: b70ed756e4e07893b43eee92a20cad76b59938e1 SHA256: 79253c3844ddad1387eae9a853ae776b87051d429a3fa5dc9db4df1f97fec68a SHA512: 828b4f3f407f345774b05c6c25a44789b267ed808b16ff55c41e09cbed517068c5977ed0ee384e6ca6aa2ae6858685ff02f98250f906b56e162ee05878a0c1d2 Homepage: https://cran.r-project.org/package=SQMtools Description: CRAN Package 'SQMtools' (Analyze Results Generated by the 'SqueezeMeta' Pipeline) 'SqueezeMeta' is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (). This package provides functions loading 'SqueezeMeta' results into R, filtering them based on different criteria, and visualizing the results using basic plots. The 'SqueezeMeta' project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as 'vegan' or 'DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) . Package: r-cran-sqn Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-nor1mix Filename: pool/dists/noble/main/r-cran-sqn_1.0.6-1.ca2404.1_all.deb Size: 106866 MD5sum: 7b39df364bd04218bd2e1eb516744656 SHA1: 8728668e564dfc5041bb70aa2c81d649ae6d9ec2 SHA256: 76d0f839cfaf9e3f1cf980f6a6cad6e8c3675be1594c36cd2f71874e6ab3ce20 SHA512: 9047c91df2606258d840624a8df14353b885b7f1c5fc4bd3d9a22551db08b206fcca25a441ee99a579937941f438d3033e7e6655e92ddbf11906ba963a48975b Homepage: https://cran.r-project.org/package=SQN Description: CRAN Package 'SQN' (Subset Quantile Normalization) Normalization based a subset of negative control probes as described in 'Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876]. Package: r-cran-sqrl Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-sqrl_1.0.3-1.ca2404.1_all.deb Size: 396268 MD5sum: 5645faf35b64ad72a9b17cf9a7c8ec3a SHA1: 9cf24a9014b7bd07ec7c68d922a035796bcbc2e6 SHA256: e00da87b7880c3e5e702276a8865702883948620d32a6ec60bd3457559082d66 SHA512: 7b487506c75de3712acdd1449e9850d680f0eadbf00bd2493aea5e4f1489dac56f3311c532df7bdbb3f46d6868db5fb2152b883dc7d13b08efda192411566495 Homepage: https://cran.r-project.org/package=SQRL Description: CRAN Package 'SQRL' (Enhances Interaction with 'ODBC' Databases) Provides simple and powerful interfaces that facilitate interaction with 'ODBC' data sources. Each data source gets its own unique and dedicated interface, wrapped around 'RODBC'. Communication settings are remembered between queries, and are managed silently in the background. The interfaces support multi-statement 'SQL' scripts, which can be parameterised via metaprogramming structures and embedded 'R' expressions. Package: r-cran-squant Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-squant_1.1.7-1.ca2404.1_all.deb Size: 127978 MD5sum: 1edf1bb5710e69cbb81360971b02e1a7 SHA1: c85c3965d234c893c5c7c0ef7beeb63374b85aba SHA256: 4504e065e971381c229ea9cabbdea78d1116d67ea14f052b466194c7b4f7bb04 SHA512: a9dbbebb742e4a73b6e2f02f3c3da4ff49fa74f4eb28591384fa0e98bff3b21e7d7866e2d7da6f19331f22de1b00e72e7da6ebb00b6b072f961c09b981624353 Homepage: https://cran.r-project.org/package=squant Description: CRAN Package 'squant' (Subgroup Identification Based on Quantitative Objectives) A subgroup identification method for precision medicine based on quantitative objectives. This method can handle continuous, binary and survival endpoint for both prognostic and predictive case. For the predictive case, the method aims at identifying a subgroup for which treatment is better than control by at least a pre-specified or auto-selected constant. For the prognostic case, the method aims at identifying a subgroup that is at least better than a pre-specified/auto-selected constant. The derived signature is a linear combination of predictors, and the selected subgroup are subjects with the signature > 0. The false discover rate when no true subgroup exists is controlled at a user-specified level. Package: r-cran-squarem Architecture: all Version: 2026.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-setrng, r-cran-interval Filename: pool/dists/noble/main/r-cran-squarem_2026.1-1.ca2404.1_all.deb Size: 176960 MD5sum: 585b502d13c9f43343db39a0f4ded847 SHA1: f46eafb4e8b262dbd41751db6aff9cb8b90a04db SHA256: 0291c0ee35678780323b48fae48451cfc372e0b4e4e188429896592ae9e0657a SHA512: 50e51ab76c5ed98a2ce1ead49b53e4faa8f069954a9c7d7f863959cc97ad49374ca4fa0fe92b36e45efb08339b421602f8889940d018884f48de9e36faaa4ea3 Homepage: https://cran.r-project.org/package=SQUAREM Description: CRAN Package 'SQUAREM' (Squared Extrapolation Methods for Accelerating EM-Like MonotoneAlgorithms) Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mappings such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent scheme. 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Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more. 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The group penalties correspond to groups of covariates defined by a co-data group set. The method accommodates inclusion of unpenalised covariates and overlapping groups. See Van Nee et al. (2021) . Package: r-cran-squid Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinymatrix, r-cran-ggplot2, r-cran-plotly, r-cran-mass, r-cran-lme4, r-cran-arm, r-cran-data.table, r-cran-brms Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-squid_0.2.1-1.ca2404.1_all.deb Size: 3850522 MD5sum: 0b5d09c6435b53fdf5d886e8806e9f7a SHA1: c7a39b91db19be314f6e0a8873347da0922df764 SHA256: d5fd8dd191949eac8b2306af24b3e1410fc6cfc39f84ead6908da989b518cc97 SHA512: 469c56d183ed3bf3aa77a7c6c6b8df921cb08a8d8b7d244861066d04de60436b4a9c8ed82c38d496f972ed757386832b78c743b958ff0f1655068243b38c806a Homepage: https://cran.r-project.org/package=squid Description: CRAN Package 'squid' (Statistical Quantification of Individual Differences) A simulation-based tool made to help researchers to become familiar with multilevel variations, and to build up sampling designs for their study. This tool has two main objectives: First, it provides an educational tool useful for students, teachers and researchers who want to learn to use mixed-effects models. Users can experience how the mixed-effects model framework can be used to understand distinct biological phenomena by interactively exploring simulated multilevel data. Second, it offers research opportunities to those who are already familiar with mixed-effects models, as it enables the generation of data sets that users may download and use for a range of simulation-based statistical analyses such as power and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) ]. Package: r-cran-squids Architecture: all Version: 25.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-squids_25.6.1-1.ca2404.1_all.deb Size: 228642 MD5sum: 95900f0fb996df6516ce5ab8ce8e7fc6 SHA1: 968def3520a6781b3f0d7048744795ff9cce1ff6 SHA256: afb577a986409388102a5d10a505a7bcb744c4bb6664088058dc40cbb3be56fc SHA512: bdc6bbd951d79b267e07066de618b1bab3ad89b06228ee8b86f13752fb2b4cfa298762aeb27c13deb611042b01a66166660e1674494b37148a1bc3406ca464cb Homepage: https://cran.r-project.org/package=squids Description: CRAN Package 'squids' (Short Quasi-Unique Identifiers (SQUIDs)) It is often useful to produce short, quasi-unique identifiers (SQUIDs) without the benefit of a central authority to prevent duplication. Although Universally Unique Identifiers (UUIDs) provide for this, these are also unwieldy; for example, the most used UUID, version 4, is 36 characters long. SQUIDs are short (8 characters) at the expense of having more collisions, which can be mitigated by combining them with human-produced suffixes, yielding relatively brief, half human-readable, almost-unique identifiers (see for example the identifiers used for Decentralized Construct Taxonomies; Peters & Crutzen, 2024 ). SQUIDs are the number of centiseconds elapsed since the beginning of 1970 converted to a base 30 system. This package contains functions to produce SQUIDs as well as convert them back into dates and times. Package: r-cran-squire Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-galahad, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-squire_1.0.1-1.ca2404.1_all.deb Size: 44184 MD5sum: 644165221f27ccf32d40d96231cb2ffe SHA1: dfa6fc5ab3b6b991ce541446a17cf5d556f4ac6c SHA256: 53479d7800f6c586e483be37d6da8b88b7964af3ad1731d9c20e612307e57959 SHA512: 80b3aa2cfc4a493f77145fe27f8aac42147eebc50b82a90259670ef4e2163adf515508a3d627382f5e54e187efab9251847de60ff79bcfb26bb8a0809e293101 Homepage: https://cran.r-project.org/package=SQUIRE Description: CRAN Package 'SQUIRE' (Statistical Quality-Assured Integrated Response Estimation) Provides systematic geometry-adaptive parameter optimization with statistical validation for experimental biological data. Combines ANOVA-based validation with systematic constraint configuration testing (log-scale, positive domain, Euclidean) through T,P,E testing. Only proceeds with parameter optimization when statistically significant biological effects are detected, preventing over-fitting to noise. Uses 'GALAHAD' trust region methods with constraint projection from Conn et al. (2000) , ANOVA-based validation following Fisher (1925) , and effect size calculations per Cohen (1988, ISBN:0805802835). Designed for structured experimental data including kinetic curves, dose-response studies, and treatment comparisons where appropriate parameter constraints and statistical justification are important for meaningful biological interpretation. Developed at the Minnesota Center for Prion Research and Outreach at the University of Minnesota. Package: r-cran-sr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-progress, r-cran-rann, r-cran-vdiffr Suggests: r-cran-knitr, r-cran-magrittr, r-cran-nnet, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sr_0.1.0-1.ca2404.1_all.deb Size: 231440 MD5sum: 261824dfe57fe8995436f5d65ed2e9ce SHA1: 79ebc8f81e610f4c0b213aa047b808bd7f8133e2 SHA256: 3faf35985ab79352796d7db77b212d68727898e09c3f5ca88de10d523df56ac0 SHA512: ec2e7651fe27eb14bc0359936e4b6e021b593b36d6bcbb283aa6ff89e126ab01cb824db2e4e593055f48e3eda809affc770d5f3d3809a35a952f4fa458255faa Homepage: https://cran.r-project.org/package=sr Description: CRAN Package 'sr' (Smooth Regression - The Gamma Test and Tools) Finds causal connections in precision data, finds lags and embeddings in time series, guides training of neural networks and other smooth models, evaluates their performance, gives a mathematically grounded answer to the over-training problem. Smooth regression is based on the Gamma test, which measures smoothness in a multivariate relationship. Causal relations are smooth, noise is not. 'sr' includes the Gamma test and search techniques that use it. References: Evans & Jones (2002) , AJ Jones (2004) . Package: r-cran-sra Architecture: all Version: 0.1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sra_0.1.4.1-1.ca2404.1_all.deb Size: 142602 MD5sum: 722ebdcb9d183e5095c6ce0be88c4809 SHA1: 53e5166bbb96a6924aba29f2ecf569a2b728c82b SHA256: 247f37c87290725d42773503ba7cffe6a8601c7a276526ea8eded1b207cc2771 SHA512: 2316fab48a6d8844406ad6b43f7664053052ac44c74dd93e92f659416fcad81ef54f8698d4860430e8e6726a10f0124b10bb2afcdc15e8711eee748eee934027 Homepage: https://cran.r-project.org/package=sra Description: CRAN Package 'sra' (Selection Response Analysis) Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. 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SRCS: a technique for comparing multiple algorithms under several factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012. 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Package: r-cran-sreg Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-extradistr, r-cran-rlang, r-cran-cli, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-haven, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sreg_2.1.0-1.ca2404.1_all.deb Size: 280830 MD5sum: 5a6776319b2120659d0e175cbc54ffe7 SHA1: 3581306a96a7827dafe598b9f3bf421b42dfaea4 SHA256: dd6175f503fe8d6e9331098ec9be7f95c064b0cd00c0f7bb1dbe28e6caf6dd7a SHA512: ff741a154ccca9c5915122507afb5393c10ba552eb035bf85a34dd641ad839bbcb56a3fb0e304c0a81ce9ce8b9c6f6044268bb2c4490779911b6afed8926c765 Homepage: https://cran.r-project.org/package=sreg Description: CRAN Package 'sreg' (Stratified Randomized Experiments) Estimate average treatment effects (ATEs) in stratified randomized experiments. 'sreg' supports a wide range of stratification designs, including matched pairs, n-tuple designs, and larger strata with many units — possibly of unequal size across strata. 'sreg' is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. 'sreg' computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) ; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) ; Jiang, Linton, Tang, Zhang (2023+) ; Bai, Jiang, Romano, Shaikh, and Zhang (2024) ; Bai (2022) ; Bai, Romano, and Shaikh (2022) ; Liu (2024+) ; and Cytrynbaum (2024) . Package: r-cran-sregsurvey Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-teachingsampling, r-cran-dplyr, r-cran-caret, r-cran-magrittr Suggests: r-cran-survey Filename: pool/dists/noble/main/r-cran-sregsurvey_0.1.3-1.ca2404.1_all.deb Size: 44944 MD5sum: ad36ac5b5ff06a313a534410d338900e SHA1: 6bc0e8fec6d6528edf395993fdc5929100e48d39 SHA256: 750c493d3d5d559df00034f3f4c8bed4a39166f5436cc7c8ff509a5c39535646 SHA512: 72324d2e7facc00487187dfccc12b11655906b2aca7c0a85a29d6e908347fb2065076e864083615b229c10a1ba13f6fd01aed6cc3fbaa9c79af26487de02a360 Homepage: https://cran.r-project.org/package=sregsurvey Description: CRAN Package 'sregsurvey' (Semiparametric Model-Assisted Estimation in Finite Populations) It is a framework to fit semiparametric regression estimators for the total parameter of a finite population when the interest variable is asymmetric distributed. The main references for this package are Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) Cardozo C.A, Paula G.A. and Vanegas L.H. (2022) "Generalized log-gamma additive partial linear mdoels with P-spline smoothing", Statistical Papers. Cardozo C.A and Alonso-Malaver C.E. (2022). "Semi-parametric model assisted estimation in finite populations." In preparation. Package: r-cran-srlars Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cellwise, r-cran-robustbase, r-cran-mvnfast Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-srlars_3.1.0-1.ca2404.1_all.deb Size: 102880 MD5sum: e0913b3b7d760da05be19009ff01163e SHA1: 308ec561c4f74f5216da9bae6c78f71a71e57537 SHA256: 4ef10b6bea6164bd474d16fd8797ce91ff4a7be0051f49a6f7cf4c659227458b SHA512: 533a1c2310ec92b1ac706414e2a58352883697e45ef742835174c2311486554da9bdc0a5878e5f671274fa8b90b66c3092293ea101cc3270f17ebd22e529aba0 Homepage: https://cran.r-project.org/package=srlars Description: CRAN Package 'srlars' (Fast and Scalable Cellwise-Robust Ensemble) Functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) engine proposes candidate variables and cross-validation arbitrates their assignment. A final robust MM-estimator is applied to the selected predictors. Package: r-cran-srlts Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ncvreg, r-cran-rcpproll, r-cran-rlang, r-cran-yardstick Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-srlts_0.1.1-1.ca2404.1_all.deb Size: 377652 MD5sum: 7b766c39afc9343c8287e2e67e160973 SHA1: 27ff3d82509a8d6661c8bed32bfe57e833bcbf2d SHA256: 8c9eb06853066fbd6f2a330eef07f78e5c557174ca0c6b3f1783b6d2916f5b03 SHA512: 22bae63778fc1e5ae78210b9abf8e94dee03f8eb08328b806da2b9f2fbdf4ff7baa4bbb5298e74c12f464a5bc4e7b7263f3de408c7d13991db07d014f9b19d6a Homepage: https://cran.r-project.org/package=srlTS Description: CRAN Package 'srlTS' (Sparsity-Ranked Lasso for Time Series) An implementation of sparsity-ranked lasso for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2023+) , which also describes this package in greater detail. The Sparsity-Ranked Lasso (SRL) for Time Series implemented in 'srlTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The SRL is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature. Package: r-cran-srmdata Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-srmdata_1.0.2-1.ca2404.1_all.deb Size: 392946 MD5sum: e10f84109eb553e40b8c8e7507f4b358 SHA1: bad6e1325d99fc7d9267a7cf5297fa78f2e965b8 SHA256: 8c8206c8835f9966791c06ca2eb7b7a1912c69f817f4bd8d41c5f30acca93c6f SHA512: efee03601a7283aa44f72249468ac8390b7d4e767451a14cdf9d5c46c96e1bdd37c2cf84c1232400c8fc590fd878cfb57cdd6306bd6987fd079c6e41755a0087 Homepage: https://cran.r-project.org/package=SRMData Description: CRAN Package 'SRMData' (Data Files Supporting "Scientific Research and Methodology" byPeter K. Dunn (2025)) Provides most of the data files used in the textbook "Scientific Research and Methodology" by Dunn (2025, ISBN: 9781032496726). Package: r-cran-srmers Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splines2, r-cran-lme4, r-cran-nloptr, r-cran-dplyr, r-cran-mass, r-cran-coneproj, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-srmers_0.1.1-1.ca2404.1_all.deb Size: 234036 MD5sum: 4ac4b6ec777df66a22107bce9d9205f9 SHA1: 82607aeec46293ba7f981548f6bf4b8c459ee67d SHA256: 4c654132f3a1f89179f0cd178ed474d73436c3874047950956ad3c1b01712035 SHA512: 874c6a28904cf8c55566e1f4b5effaa5c6abad6ac0e16cfcd85ab04664f776ec68f163e3490ab002e0f962fd11184c973abf47c8aa90ec8b0ff0c5987558985c Homepage: https://cran.r-project.org/package=SRMERS Description: CRAN Package 'SRMERS' (Semi-Parametric Shape-Restricted Fixed/Mixed Effect(s)Regression Spline) Select the most suitable shape to describe the relationship between the exposure and the outcome among increasing, decreasing, convex, and concave shapes (Yin et al. (2021) ); estimate the direct and indirect effects with prior knowledge on the relationship between the mediator and the outcome with binary exposure (Yin et al. (2024) ); estimate the direct and indirect effects using linear regression-based approach (VanderWeele (2015, ISBN:9780199325870)). Package: r-cran-srnagenetic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-deseq2, r-cran-futile.logger, r-cran-ggplot2, r-cran-ggsci, r-cran-plyr, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-srnagenetic_0.1.0-1.ca2404.1_all.deb Size: 104732 MD5sum: cc2d23e8fd3de065363878f6bb628339 SHA1: c4ee4244833b3882aedc91b02635624017052aa8 SHA256: 1cd05f1fa1c77d02e10ac61909249e68a7cd2e9a0b99f9149b8d2047430f899e SHA512: 6ff551e7438dcc252d6693cfa6e271a7573cc94332de4aad50261cf1c4b7dfab4412eb7b8dc3d27ee8b7dc89f887932ca025c113dd42a875ad1098d218fbccf5 Homepage: https://cran.r-project.org/package=sRNAGenetic Description: CRAN Package 'sRNAGenetic' (Analysis of Small RNA Expression Changes in Hybrid Plants) The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species. Package: r-cran-srp Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-srp_1.2.0-1.ca2404.1_all.deb Size: 64266 MD5sum: fce111a0e068aa152945fe194e458b30 SHA1: c2f000d13f7b210b55fce4e26f37fcffddbcc3f9 SHA256: 21de99e3bd51ff8e97fe60423149b0268ab3da6b35539e77a4914fc39f66b621 SHA512: 23f40dd523816719e0d61638709b168a6fcfdf0e734543f91295d5a8b52665459852a0ab7cf1fe0705443ca77ed03aec29313f76c5747466e4fca80dcc70dbfe Homepage: https://cran.r-project.org/package=srp Description: CRAN Package 'srp' (Smooth-Rough Partitioning of the Regression Coefficients) Performs the change-point detection in regression coefficients of linear model by partitioning the regression coefficients into two classes of smoothness. The change-point and the regression coefficients are jointly estimated. Package: r-cran-srpi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-srpi_0.1.0-1.ca2404.1_all.deb Size: 21014 MD5sum: 2503e408f678a33129c323f0efc8e038 SHA1: 16412b2f6e5c6fe60883732162aef3e67d097b94 SHA256: 1c428f56b592ecf17258acb16592671dd32300ce9b4d6f868fefa7afb120303c SHA512: f8925c8c327a6e711ac8d71d5dd9134e7c8c8af3cb2acfc278de1bbcad5b5a0e5deb3128859e65e5d8d27b1ee53f7da09a9147a4d0421ee85df88f178fae1199 Homepage: https://cran.r-project.org/package=srpi Description: CRAN Package 'srpi' (Standardized Ranking Performance Index for Model Selection) Flexible implementation of the Standardized Ranking Performance Index (sRPI) for model selection based on multiple evaluation criteria. The package combines multiple statistical measures into a single index to provide an objective and robust ranking of models across calibration, validation, and combined scenarios. It supports evaluation of statistical, machine learning, and other predictive models using user-defined performance criteria. For more details see Aschonitis et al. (2019) and Singh et al. (2023) . Package: r-cran-srppp Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dm, r-cran-xml2, r-cran-tibble, r-cran-stringr, r-cran-dplyr, r-cran-cli, r-cran-data.tree, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-srppp_2.0.3-1.ca2404.1_all.deb Size: 2555276 MD5sum: b1075fb63d2138a04f9b4309e49685b8 SHA1: 18f558dbea3771d1126c1b5746143f425170b767 SHA256: 65846b19e014ff9c025fd06303f0027df34bf87ff223c8f671f2a48f96d519e1 SHA512: 48c9f87c0d472f0474db62c7159bd84b452fda4849d10fb606604370e6d1d74ed92e21a37c1a6db5f8223c1ef30df77815a79b568f940296870f4b08579f328d Homepage: https://cran.r-project.org/package=srppp Description: CRAN Package 'srppp' (Read the Swiss Register of Plant Protection Products) Generate data objects from XML versions of the Swiss Register of Plant Protection Products. An online version of the register can be accessed at . There is no guarantee of correspondence of the data read in using this package with that online version, or with the original registration documents. Also, the Federal Food Safety and Veterinary Office, coordinating the authorisation of plant protection products in Switzerland, does not answer requests regarding this package. Package: r-cran-srs Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-shiny, r-cran-dt, r-cran-shinycssloaders, r-cran-shinybusy Filename: pool/dists/noble/main/r-cran-srs_0.2.3-1.ca2404.1_all.deb Size: 60576 MD5sum: b5652993f4e27bc21becf6a5b9f0a249 SHA1: fbe3b04bd2265ef58f0be0f4e3792e8709303319 SHA256: 6a78a63c0adfee7dfba3d89cfc2c72265fae81a5fc038d22c3a31fbbfe8c9a61 SHA512: cb5082091c9ca4cfeef4aa2b35251d001e3e25387c6d81cb53b5b585acefd0fa71add47886274be642b4e9f51ba474e085fcfe80ab4dbe550151e4c7b1fe9708 Homepage: https://cran.r-project.org/package=SRS Description: CRAN Package 'SRS' (Scaling with Ranked Subsampling) Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) for details. Package: r-cran-srsbench Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-srsbench_0.1-1.ca2404.1_all.deb Size: 25432 MD5sum: 0c41471b45281effbc796800bc41dd2a SHA1: 3420fb3fc11dc363534943a68ab68ee460f0df7c SHA256: 76b93bb0fea6df08c8d7e50126187d167bd58a80a7a2af5d01ecf1b1d66db76f SHA512: dac35335caf966a67a8dc2486ff91ac12b36d57fccb717d312513ce053f7bea70b51a40a7f4258e004ef4b752f5c886298bbc065fe5becf56928a54e2ac82034 Homepage: https://cran.r-project.org/package=srsbench Description: CRAN Package 'srsbench' (Evaluation Metrics for Spaced Repetition Schedulers) Calibration and discrimination metrics for spaced-repetition memory models. Provides the sample-weighted binned root mean squared error (RMSE(bins)) used to rank schedulers in the open spaced repetition benchmark, together with log loss, the area under the ROC curve, and calibration curves. 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It consists of curated data sets for individual chemicals from Australian, New Zealand and Canadian organizations, several data sets from anonymous sources, and larger uncurated compilations drawn from the ANZTOX, WQBench and EnviroTox databases. It also includes a data set of the results of fitting various distributions using different software. 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Package: r-cran-ssdgsa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gsva, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-vctrs, r-cran-tidyselect, r-bioc-clusterprofiler, r-bioc-org.hs.eg.db Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssdgsa_0.1.1-1.ca2404.1_all.deb Size: 193552 MD5sum: 6dbd88fac5ae0ab90e7ec6b3add5d084 SHA1: 32f3b9cae7369447b34f4173ec8066f0ad7c19e8 SHA256: f630a9eb55c2b4b2abc296c39b699c60474db9d285a07660fcd2666ea9cf9274 SHA512: d1176f0fb3c799a1dc4bdfe2e830a9676a7c9bbe309f66a9088ef6258c6ab7fd8a4f76970c388ee9da5de97ec5f45568052c71522dddbe659d3f2b12c073ab71 Homepage: https://cran.r-project.org/package=ssdGSA Description: CRAN Package 'ssdGSA' (Single Sample Directional Gene Set Analysis) A method that inherits the standard gene set variation analysis (GSVA) method and also provides the option to use summary statistics from any analysis (disease vs healthy, lesional side vs nonlesional side, etc..) input to define the direction of gene sets used for directional gene set score calculation for a given disease. Note to use this package, GSVA(>= 1.52.1) is needed to pre-installed. Hanzelmann, S., Castelo, R., and Guinney, J. (2013) . 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Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface. 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Multi-response problems use iterative input-space expansion related to Spyromitros-Xioufis et al. (2016) , with Jacobi or 'Gauss-Seidel' sweeps, package-defined companion gates and per-response iteration stitching. A package-defined pseudo-label stage promotes prediction rows by a cross-model and cross-dataset range ratio and accepts rounds with an out-of-fold squared-correlation gauge. Package: r-cran-sseparser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-stringr Suggests: r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sseparser_0.1.0-1.ca2404.1_all.deb Size: 54848 MD5sum: 7531b8aec747ec62523ede0a926f4dff SHA1: 64e9f64a717dfa0249a38364fa5dce31928b0d39 SHA256: d56c6eba9db63fc9f5d2289e552b2992b62f355ca0b46fef983c61347cce8020 SHA512: 353db9655c2cdfc3a79e49733275c6c2188bd90cb0f3da4d97b5147caccf296394ba8354b7c9ec317d459ca9803c046701bd251ba29ee3edabc38eff23a9f9fb Homepage: https://cran.r-project.org/package=SSEparser Description: CRAN Package 'SSEparser' (Parse Server-Sent Events) Functionality to parse server-sent events with a high-level interface that can be extended for custom applications. Package: r-cran-ssev Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pwr, r-cran-mess Filename: pool/dists/noble/main/r-cran-ssev_0.1.0-1.ca2404.1_all.deb Size: 28844 MD5sum: 38eb5fb852864cdda866f6ed02a4c182 SHA1: 52e05479393e69dce6813d634f510f9863057d5f SHA256: add2b4aabf64aa3dcd7a63b2a3876808a29a4079809fb8c6b9a5dcecfc2706d4 SHA512: 5a8e42446457d728161d3dc94fe73afc794ace47263d7415e19289a533f67bbbc8211a82a2e6d3e57009d7898a1b877b15902b90941bebbd717c1db88d4f9708 Homepage: https://cran.r-project.org/package=ssev Description: CRAN Package 'ssev' (Sample Size Computation for Fixed N with Optimal Reward) Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. 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Package: r-cran-ssfa Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-maxlik, r-cran-spdep, r-cran-sp, r-cran-spatialreg Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ssfa_1.2.3-1.ca2404.1_all.deb Size: 1461816 MD5sum: f19b730ba6c2d6bf9f55cf69562147de SHA1: 7790952eb55254a7ad8ae75f9ad066d7e1b638f2 SHA256: a832d350cc079ba2e476b92d29a73d5f0338ea553e86d43735af20c5c6367efe SHA512: 8f88d7120717d3addd3e1fdc012908c018ac1b49e860264ea35e5a59a80aa5204daf543e03e8e4ddf3b0d0ac200480ac750870c4c4b489272912475371132c2e Homepage: https://cran.r-project.org/package=ssfa Description: CRAN Package 'ssfa' (Spatial Stochastic Frontier Analysis) Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term. Package: r-cran-ssfit Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey Filename: pool/dists/noble/main/r-cran-ssfit_1.2-1.ca2404.1_all.deb Size: 20206 MD5sum: 1bc100d5474aaa831e03b3e04032863b SHA1: 5df96e83a00b33b12c78aef87233c359fe9469e5 SHA256: fae634bde835cf88fa5524064a3d7c80343454db1cf5fc5611e0e6ecf1476881 SHA512: f585b104a0641450d1a65e3354a34634797793aae1698f34ed2bcdad59b6c4f31009fb90b891cbb2fd96add69adfd128217db68966c01cd42c1a84fde3f26cf8 Homepage: https://cran.r-project.org/package=ssfit Description: CRAN Package 'ssfit' (Fitting of Parametric Models using Summary Statistics) Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods. 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The geographical SSIM method incorporates well-developed 'geographically weighted summary statistics'('Brunsdon', 'Fotheringham' and 'Charlton' 2002) with an adaptive bandwidth kernel function for irregular lattice-based maps. Package: r-cran-ssimparser Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-airportr Filename: pool/dists/noble/main/r-cran-ssimparser_0.1.2-1.ca2404.1_all.deb Size: 42652 MD5sum: ee39cd59a5ff3b86fbad4d5f8efeaf0b SHA1: 16c8797a264f6d25910bcc0b89c9841e23609c3a SHA256: dec6c3d6efbac15188d91cc1ab27ebd39677b755ed513babec23343f0ea8a94d SHA512: c0f4839bfc426f1138683c27e99410bd65db3ef127821c5c9421c803c4c701a227dad71000bc1b4aaa0aeb62d422f0adbf25681fb7dead53591f15cf6bb310ec Homepage: https://cran.r-project.org/package=ssimparser Description: CRAN Package 'ssimparser' (Standard Schedules Information Parser) Parse Standard Schedules Information file (types 2 and 3) into a Data Frame. Can also expand schedules into flights. Package: r-cran-ssize.fdr Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ssize.fdr_1.3-1.ca2404.1_all.deb Size: 83434 MD5sum: b2f70d1ca866f210be79c357f7987e4a SHA1: 65ae8aa15aad18ea12232aa836db9da453be1a87 SHA256: 333c0d91cd7093dcf1a99939b0bd102ac9dbd4a512fd5480ab2458a981d541ee SHA512: a913fa09e022e0d2e8660301093612e2b6473c79bf5f4deba431bf5a826cbc1e47efcdd1cc6529c7130bde40e974eafd8c7c8b6762475daa9b4aef83dc01a708 Homepage: https://cran.r-project.org/package=ssize.fdr Description: CRAN Package 'ssize.fdr' (Sample Size Calculations for Microarray Experiments) Functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. Package: r-cran-ssizerna Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-bioc-biobase, r-bioc-edger, r-bioc-limma, r-bioc-qvalue, r-cran-ssize.fdr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-ssizerna_1.3.3-1.ca2404.1_all.deb Size: 454886 MD5sum: db93deda9f2de4390d3307fb8c0a1bec SHA1: 470a744dcc7f453fc6d502c7a28da12e43c11d2f SHA256: 57493bfa2309200e0384468f891778ccf8d40007f289e22ad453916776a4b3c4 SHA512: 122157150f2705d84e3f258ec97df29d406ed0ae4abb0869c1c85758cbea293f0adbcd983ccbd283a54dcccca988eff325e151dff29fb81124ef0a8b44b47910 Homepage: https://cran.r-project.org/package=ssizeRNA Description: CRAN Package 'ssizeRNA' (Sample Size Calculation for RNA-Seq Experimental Design) We propose a procedure for sample size calculation while controlling false discovery rate for RNA-seq experimental design. Our procedure depends on the Voom method proposed for RNA-seq data analysis by Law et al. (2014) and the sample size calculation method proposed for microarray experiments by Liu and Hwang (2007) . We develop a set of functions that calculates appropriate sample sizes for two-sample t-test for RNA-seq experiments with fixed or varied set of parameters. The outputs also contain a plot of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. To install this package, please use 'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. For R version 3.5 or greater, please use 'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'. Package: r-cran-sslfmm Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sslfmm_0.2.2-1.ca2404.1_all.deb Size: 158940 MD5sum: f0d908636fd31a37724b2eb96b45bcde SHA1: 9ab2c9f7abccf2eff68b3cc4d2cb8f38ca48f1be SHA256: 39722b945ca971f6175fc120fbcad8e55548585522cbcd63388335724c0d5100 SHA512: bbab22905f3bb10756867f176a305a4d5ca7e4f8713acb368feaaeeb9db9bd3b10cd0adce6e2edcae9bd3e32f8adaa0215982318ae6251bbf8c4f291ace370eb Homepage: https://cran.r-project.org/package=SSLfmm Description: CRAN Package 'SSLfmm' (Semi-Supervised Learning with Mixed Missingness in FiniteMixture Models) Semi-supervised Gaussian finite mixture models for partially labelled data under complete-case, missing completely at random (MCAR), entropy-dependent missing at random (MAR), and mixed MCAR/MAR label-missingness formulations. For the mixed formulation, the source of a missing label may be observed or latent. The package supports equal and component-specific covariance matrices, model fitting, simulation, initialization, prediction, classification performance assessment, and entropy-based diagnostics. A semi-synthetic Blood Transfusion data set is included to illustrate the applied workflow. Package: r-cran-ssm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssm_1.0.1-1.ca2404.1_all.deb Size: 275442 MD5sum: a3ccf3a626e1671d3cfff64d82196c39 SHA1: 1bc48a6a74426fd22a139cc7c8e954b5796f9331 SHA256: 317579584d931a15fff444d4a8b9dca5893e3161bcd989b18893461f7b1f9b2a SHA512: 23593ae43942af10b275287c75fe1add0676848ce8d31ea4f052d716b8d3244e27a441e5e143d9581cccc7d4a8474dbc59ab2a6c861ed93e9b5cfb74324d5850 Homepage: https://cran.r-project.org/package=SSM Description: CRAN Package 'SSM' (Fit and Analyze Smooth Supersaturated Models) Creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions. Package: r-cran-ssmn Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-moments, r-cran-truncdist, r-cran-sn Filename: pool/dists/noble/main/r-cran-ssmn_1.1-1.ca2404.1_all.deb Size: 125326 MD5sum: 03364d1904c16b9f68e4bc36fbde5897 SHA1: 14e6a02d8a8118077d79c4ddc7cc3ce9d015623f SHA256: 8d676b7737fda614cbd0e7705ce808c46391e44070350a827ed60c9fbab770ce SHA512: e8fa00b31c7bee2b398d56e4021d88e4f955988edfb104d403ceeeb196a4e6f7ee3009a4adc6e5174870157fb66ba44ccd79ea1929a474a0d873e072f72d8c30 Homepage: https://cran.r-project.org/package=ssmn Description: CRAN Package 'ssmn' (Skew Scale Mixtures of Normal Distributions) Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions. Package: r-cran-ssmodels Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sn, r-cran-misctools, r-cran-rdpack Suggests: r-cran-knitr, r-cran-testthat, r-cran-numderiv, r-cran-maxlik, r-cran-mvtnorm, r-cran-sampleselection, r-cran-kableextra, r-cran-kfigr, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-ssmodels_2.0.2-1.ca2404.1_all.deb Size: 1099502 MD5sum: 59c0abe85bae7bf4a6c581202535bc93 SHA1: fe79904cb2cfa95965abdba5b4d1d4278969eb31 SHA256: 1fa58fe39f5af34f6fc3a2d0f72b3a536db620224410629883bf390a5a91119a SHA512: 63362b990bc5c6d5d504931e75432ceecbcb32adbd690feeef8074713fbcde69eafab7d0808a5457e437c20761c571c5231688e8cbc1c4d8c11ee0df9373782a Homepage: https://cran.r-project.org/package=ssmodels Description: CRAN Package 'ssmodels' (Sample Selection Models) In order to facilitate the adjustment of the sample selection models existing in the literature, we created the 'ssmodels' package. Our package allows the adjustment of the classic Heckman model (Heckman (1976), Heckman (1979) ), and the estimation of the parameters of this model via the maximum likelihood method and two-step method, in addition to the adjustment of the Heckman-t models introduced in the literature by Marchenko and Genton (2012) and the Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016) . We also implemented functions to adjust the generalized version of the Heckman model, introduced by Bastos, Barreto-Souza, and Genton (2021) , that allows the inclusion of covariables to the dispersion and correlation parameters, and a function to adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020) that uses the Birnbaum-Saunders distribution as a joint distribution of the selection and primary regression variables. This package extends and complements existing R packages such as 'sampleSelection' (Toomet and Henningsen, 2008) and 'ssmrob' (Zhelonkin et al., 2016), providing additional robust and flexible sample selection models. Package: r-cran-ssmrcd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1099 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-scales, r-cran-car, r-cran-dbscan, r-cran-plot3d, r-cran-dplyr, r-cran-ggplot2, r-cran-expm, r-cran-foreach, r-cran-doparallel, r-cran-rrcov, r-cran-desctools, r-cran-rootsolve, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssmrcd_1.1.0-1.ca2404.1_all.deb Size: 951618 MD5sum: 115d281bf54a075811e216e6e4ff65eb SHA1: 4e89efc53f0025a0fc3971cee2dbb82eb4bc65ad SHA256: b4f914b9de1ac2829597646179bdc5566a56685304a39b35ad8ac5d02436d6af SHA512: 5990a082767b325bb798fe53033aa5c68f7ed989735c8f3b99599b95fd575e90be064fccc8709ffaa6771320d268e47c1b41407a3edf69408f6ff43d914a30bb Homepage: https://cran.r-project.org/package=ssMRCD Description: CRAN Package 'ssMRCD' (Spatially Smoothed MRCD Estimator) Estimation of the Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage in an ssMRCD-based outlier detection method as described in Puchhammer and Filzmoser (2023) and for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) . Included are also complementary visualization and parameter tuning tools. Package: r-cran-ssmrob Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampleselection, r-cran-robustbase, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ssmrob_1.0-1.ca2404.1_all.deb Size: 253466 MD5sum: ce69aa51ec673d4558536efff77ebed8 SHA1: 4a233830fcb51cf3301dc5e8a11c228bbd99b169 SHA256: ef1a7a3ad5d834a957a7634ff337ceaec044c605ef35caca9696c89bf0323c90 SHA512: 6da4c3adaead536ebf11e3e9a9c89aeab550ed3e53970bf1a251d4b1c84a042e1cc686678e3faef3d37edf537ce00b00f5e278af80a17d7bf3e2434f63ca1a4c Homepage: https://cran.r-project.org/package=ssmrob Description: CRAN Package 'ssmrob' (Robust Estimation and Inference in Sample Selection Models) Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) . Package: r-cran-ssmsn Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-ssmsn_0.2.0-1.ca2404.1_all.deb Size: 16482 MD5sum: dee94f11e2dc0e915b7022af9bb5e629 SHA1: 8b9e5cf74dccc1425c14f685551e84e6c77cec6d SHA256: f06f9d08d9ad8ae31168dd0b31c6e6db7173935c9a4d338f6cf2bb9806d39dac SHA512: 047a5279dd5b63f561fda131eb66c48b128c93901ada2b565a0389377d3a174cfaa7684faed359babe795b743665067d6d1361b943f7be27d65d5e117a1234ee Homepage: https://cran.r-project.org/package=ssmsn Description: CRAN Package 'ssmsn' (Scale-Shape Mixtures of Skew-Normal Distributions) It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) . Package: r-cran-ssmutpa Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggridges, r-cran-igraph, r-cran-kernlab, r-bioc-maftools, r-cran-matrix, r-cran-nbclust, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-survival Suggests: r-cran-knitr, r-cran-qpdf, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssmutpa_0.1.2-1.ca2404.1_all.deb Size: 1974780 MD5sum: c522ba85a7db6f9e15e5a5f06d8deb49 SHA1: 4389e00b02af17c0a1fb8758d769439144014cf1 SHA256: 70de09375d1780077bfe2fc76981f82287420ebcb055972b2e4c64bb38a2ebe3 SHA512: e6c8d9929fa8b288b6be1bb51fc0481fb906d4b3c198dd106753a646735ff50d5d0d571a41b3cbd1160e388c202c56acd27e375899e5c48c5ae7bf64fdcc7294 Homepage: https://cran.r-project.org/package=ssMutPA Description: CRAN Package 'ssMutPA' (Single-Sample Mutation-Based Pathway Analysis) A systematic bioinformatics tool to perform single-sample mutation-based pathway analysis by integrating somatic mutation data with the Protein-Protein Interaction (PPI) network. In this method, we use local and global weighted strategies to evaluate the effects of network genes from mutations according to the network topology and then calculate the mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. Subsequently, the ssMutPES profiles are used for unsupervised spectral clustering to identify cancer subtypes. Package: r-cran-ssnbayes Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-dplyr, r-cran-rstan, r-cran-sf, r-cran-ssn2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssnbayes_0.0.3-1.ca2404.1_all.deb Size: 816030 MD5sum: 806b6ae648331daaa6c4ce9bef8251bc SHA1: 857b545da9179388547151bc277344a834d98bc7 SHA256: ed5c391a85a4d35af2ce94a9d1cc3165a83934767665234fec44a53f2cd81319 SHA512: 78fb365614185c20313f227f07e19590d7956ce0e0aa5fa5fe240636b39ac36db844fc6f17874e19769c10e0db08c6c651379e5fe036d17a30bc6aac38443667 Homepage: https://cran.r-project.org/package=SSNbayes Description: CRAN Package 'SSNbayes' (Bayesian Spatio-Temporal Analysis in Stream Networks) Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". . In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods. Package: r-cran-ssnbler Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2582 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-pdist, r-cran-igraph, r-cran-rsqlite, r-cran-withr, r-cran-ssn2, r-cran-doparallel, r-cran-foreach Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssnbler_1.1.2-1.ca2404.1_all.deb Size: 879036 MD5sum: 5af9bdc97d4c446a7cdfb44e334e8f5a SHA1: ed1403dd96e255b98fffd782937552151cad100e SHA256: f2e0a48398b986ddde4cb775bcb7e280d9a044a06853c25d8b33d7b252fb42a5 SHA512: c4e1d0d5ffb9509b05d269aedc57e79a4331b490ab736728e8447eb99104228b06d96831a97015f899f344aa45bb6f2ffd9c1b89b3d4c2cae69b92fe68782a28 Homepage: https://cran.r-project.org/package=SSNbler Description: CRAN Package 'SSNbler' (Assemble 'SSN' Objects) Import, create and assemble data needed to fit spatial-statistical stream-network models using the 'SSN2' package for 'R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in 'R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) . Package: r-cran-ssp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-sampling, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ssp_1.1.0-1.ca2404.1_all.deb Size: 216116 MD5sum: c7721be9197252362483713c6dc92a19 SHA1: afd98de95b06f8016e4680590eda817ff2ceac36 SHA256: f42f332dfe133647bb3c89aa1ed1c8e5a4bc2159bc450a99c9e45d1d6501c5f1 SHA512: 0115344e5caa70a646ed710fb6b77b5c085a0b42685c61187362175a8d4ba86b8afa745047d6754e1da78491a84e0c279167ca9274b8c5a3ad16cf35d4052e3b Homepage: https://cran.r-project.org/package=SSP Description: CRAN Package 'SSP' (Simulated Sampling Procedure for Community Ecology) The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015, ) and the simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020, ). Package: r-cran-sspilot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sspilot_1.0.0-1.ca2404.1_all.deb Size: 53348 MD5sum: b79f160150bcf598633e7c3d362f15ec SHA1: 0d65dd9e620a68c55e083374db2479c938596c53 SHA256: e0cc83ce9d25b4e6f33fa17b666ce1bd1cc6fc8226f821a148afd3fd38943b2d SHA512: 0e41708f06297d6b66df2184c76bc67b573bcbbc5055ef61aa53aee3371d39fa592cd36d92a096989815e167a3aa9d64081d8e375a351423bc26b93c2b0fa2a2 Homepage: https://cran.r-project.org/package=ssPilot Description: CRAN Package 'ssPilot' (Sample Size Calculation for External Pilot Studies for aContinuous Endpoint) An implementation of sample size calculations for external pilot studies that minimize the overall trial sample size for the external pilot and main trial for a continuous endpoint as described in Whitehead et al. (2016) . Package: r-cran-ssplots Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo Filename: pool/dists/noble/main/r-cran-ssplots_0.1.2-1.ca2404.1_all.deb Size: 52880 MD5sum: 72898e0480661e190c5b7f5e5a1857a5 SHA1: ca9b24f6a5915a9025b4005d19434dc332d6ef21 SHA256: 1fe135074633d6787ad4f927b2f00bc064190632157a2136858108987577e21c SHA512: f0502139dd34c5fb3bc277a964f511c43070a58af720601ec8b45f33b53eddd0f2af8bb2e2babfd6e8aef9b36302df2bbda08f46e7f7c9784157b2ba06f79941 Homepage: https://cran.r-project.org/package=SSplots Description: CRAN Package 'SSplots' (Stock Status Plots (SSPs)) Pauly et al. (2008) created (and coined the name) 'Stock Status Plots' for a UNEP compendium on Large Marine Ecosystems(LMEs, Sherman and Hempel (2009)). Stock status plots are bivariate graphs summarizing the status (e.g., developing, fully exploited, overexploited, etc.), through time, of the multispecies fisheries of a fished area or ecosystem. This package contains three functions to generate stock status plots viz., SSplots_pauly() (as per the criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria proposed by Kleisner and Pauly (2011) and Kleisner et al. (2013) )and SSplots_EPI() (as per the criteria proposed by Jayasankar et al.,2021 ). Package: r-cran-sspm Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-mgcv, r-cran-units, r-cran-checkmate, r-cran-cli, r-cran-tibble, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-ggforce, r-cran-lwgeom, r-cran-tweedie, r-cran-sfdct, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sspm_1.1.0-1.ca2404.1_all.deb Size: 2803538 MD5sum: 77b4c92e9b133a54e2693619dc2577fa SHA1: a00d0d32b1cc5290d69095355680eb091be11274 SHA256: c11d7f83e2086577de91073b8d58c7ae5ab770bdc45531b8035a5938130977fe SHA512: 633715ea2a8e8a3691f5d8e437f95cb620b205cc0420ad50820381055dac0b2774afd2e5fd0754a656a573a3dfa9b8c34e5a6a2404dd1fc391e3ace52c2c5cd0 Homepage: https://cran.r-project.org/package=sspm Description: CRAN Package 'sspm' (Spatial Surplus Production Model Framework for Northern ShrimpPopulations) Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, ). Package: r-cran-ssr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tgp Filename: pool/dists/noble/main/r-cran-ssr_0.1.1-1.ca2404.1_all.deb Size: 192706 MD5sum: ec5da00eb58d3a418cb7912e2b80a76a SHA1: a7f9909071baf153b6f5a39ba9cb1a24aa054203 SHA256: 0d5d9f1534abe6ee4f4200a4c75d8ad84a935c4005dca9308df41edb491bd016 SHA512: c01e3818a1aad4ec8da9c724c28fb7357deeaa821b5d0181cfd5c9368bc25a1e06896b3b5ce126102afc68638bb314cd85716b6d22ab6a3f988b17bdd085e03a Homepage: https://cran.r-project.org/package=ssr Description: CRAN Package 'ssr' (Semi-Supervised Regression Methods) An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) . Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions. Package: r-cran-ssra Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shape, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ssra_0.1-1-1.ca2404.1_all.deb Size: 103526 MD5sum: e48093252ef1b52e1b08d03a3ed9f7a7 SHA1: 3955672a583107c6a7fedc92a11c68862116490e SHA256: bc3a2c51bd2360342a040df9fc3b12c0cf6da1f04fee501965d7f754a2c7498d SHA512: f401961ed3cf4d66af7cceade972aa3577f35c61eba4549eec36b40f63a459947fb57f5d9260e9c7588627c32267601a5d79b66f5bf52ff064b122f5b19523cf Homepage: https://cran.r-project.org/package=SSRA Description: CRAN Package 'SSRA' (Sakai Sequential Relation Analysis) 'Takea Semantic Structure Analysis' (TSSA) and 'Sakai Sequential Relation Analysis' (SSRA) for polytomous items. Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items. Package: r-cran-ssrat Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-sna Filename: pool/dists/noble/main/r-cran-ssrat_1.1-1.ca2404.1_all.deb Size: 128124 MD5sum: 8c6433485803fb6613d64a0a59a9a87d SHA1: 266a86ea07d26a1680df1a48fab78020340a7586 SHA256: cd5383881afe223450ccddbbd51e672a252fffa893b93375a6bdb82abd059309 SHA512: a9f78885778e46efe9ec47b45711f786cb6e078e166a6f4a807163be29c28df4f65100ce9816441c799c65eddd0567117c9adac92abc0e4c968cdb81f2a3aab4 Homepage: https://cran.r-project.org/package=SSrat Description: CRAN Package 'SSrat' (Two-Dimensional Sociometric Status Determination with RatingScales) A set of functions for two-dimensional sociometric status determination with rating scales. For each person assessed, SSrat computes probability distributions of the total scores for `Sympathy' (S), `Antipathy' (A), social `Preference' (P) and social `Impact' (I), and applies a set of criteria for sociometric status categorization. Package: r-cran-ssreliabilityclaytonmwd Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dorng, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-numderiv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssreliabilityclaytonmwd_1.0.2-1.ca2404.1_all.deb Size: 500736 MD5sum: 518ff0e3e658c4e376da225c0f395b21 SHA1: ae27913a06300d0bb717cb224231939322bd3372 SHA256: f01278c522b9bdee702dc466893e313ead510711a926df6a2b76093528cc4a72 SHA512: 3182873a32546c209394c7f60a17be9350032e7ea99f427e014e4fabd00291df4985bb5eaf2ee2a0286a46697ab9bebc9c0335c43fc1a0ca9489228d27f35010 Homepage: https://cran.r-project.org/package=SSReliabilityClaytonMWD Description: CRAN Package 'SSReliabilityClaytonMWD' (Stress-Strength Reliability Model with MWD Marginals via ClaytonCopula) Implements stress-strength reliability models under a dependent framework, where both stress and strength variables follow modified Weibull distributions and their dependence is modeled using a Clayton copula (Kizilaslan (2026) ). The package provides several estimation procedures for model parameters and the stress-strength reliability R, including two-step maximum likelihood estimation (MLE), least squares estimation (LSE), weighted least squares estimation (WLSE), and maximum product of spacings (MPS). It also provides interval estimation using asymptotic confidence intervals based on MLE and bootstrap confidence intervals for all methods. In addition, functions are included for parameter estimation of the modified Weibull distribution (Lai et al. (2003) ) and the two-parameter Weibull distribution, along with utilities to compute their probability density function, cumulative distribution function, quantile function, and to generate random samples. Package: r-cran-ssrm.logmer Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-sfsmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssrm.logmer_0.1-1.ca2404.1_all.deb Size: 26036 MD5sum: a7713b6f953d041b8bb8c30e29f053e6 SHA1: ef8b09c1b593edb8b83bac6cbe77ef938911bc59 SHA256: 6c92322867036668beaab467df789eda447f9885b3086da808cea13fd7365ce4 SHA512: 5ecce961434ee37b27b895699030fcf12117855b9af3b96171efb90cde54a247e947cadeb8e8c1e3fb5976dfe69dc03a07a326963c74ee7809825a3047713293 Homepage: https://cran.r-project.org/package=ssrm.logmer Description: CRAN Package 'ssrm.logmer' (Sample Size Determination for Longitudinal Designs with BinaryOutcome) Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) . Package: r-cran-ssrmst Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survrm2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssrmst_0.1.1-1.ca2404.1_all.deb Size: 36016 MD5sum: f9b62d038b3dd9935ce44e9de88b7ef8 SHA1: 3bf326ea3d38e0ec95b44a00c27260efedf151df SHA256: e4b84aef02791513f07f9527983ea73a67a674e1ab2876870cdeb4a049c41e57 SHA512: 0c0e47e871f9d23503acf25a8f60e8e3995f62dd520e69837155303771c6f72ae3731cf873adeb6d55be5d3f34298b453fd9cd8d21e3abf41b12e25fe9ee58e6 Homepage: https://cran.r-project.org/package=SSRMST Description: CRAN Package 'SSRMST' (Sample Size Calculation using Restricted Mean Survival Time) Calculates the power and sample size based on the difference in Restricted Mean Survival Time. Package: r-cran-ssrn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-scanstatistics Filename: pool/dists/noble/main/r-cran-ssrn_0.1.0-1.ca2404.1_all.deb Size: 618332 MD5sum: cd5b9c471f9aa2ba7b385f6a4d18ffab SHA1: fdbca5394d5ac5acef3795b1a8cd7b499079766e SHA256: bfe4d6800cb17d85030eaa4f9bd44ac8c08546f72d0876efa1e1c3c98be799dc SHA512: 3bf3387ad2a741c0a2c527ee70c5af134c267511c1d206fb23833051e9f7a71266db49c0568213429d9b8d20eceb592cd6d6d1fff078b3d49d99206c2a2d3c08 Homepage: https://cran.r-project.org/package=ssrn Description: CRAN Package 'ssrn' (Scan Statistics for Railway Network) Implement the algorithm provided in scan for estimating the transmission route on railway network using passenger volume. 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Package: r-cran-ssrtcalc Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mass, r-cran-rstan, r-cran-posterior, r-cran-bayesplot, r-cran-loo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssrtcalc_2.1.1-1.ca2404.1_all.deb Size: 207478 MD5sum: 95ac0266a01e2ab23828b0b82664f1ed SHA1: b23e903bf0affc6b1ffc07cd2be5edd2985103bc SHA256: 300237bec5876e0b571419371468b5b00497d5d172151824a8b569bc61f7a4b9 SHA512: e89e295ed244d07d710ee3d5462ba665636f01c0e526548592cd63448ed4dd4aa96b6fa4ab06ecab4eb301d21c88ee461841d678f7e0aa4064c7043fb7ca1f2a Homepage: https://cran.r-project.org/package=SSRTcalc Description: CRAN Package 'SSRTcalc' (Monte Carlo and Bayesian Stop-Signal Reaction Time Estimation) Estimates stop-signal reaction time (SSRT) in the stop-signal task using the integration and mean methods described by Verbruggen and colleagues (2019) . In addition to point estimates, the package provides Monte Carlo tools (nonparametric bootstrap confidence intervals, parametric ex-Gaussian simulation, minimum-trial-count and power analysis, and sensitivity analysis under violations of the horse-race assumptions) and Bayesian estimation via 'Stan', including single-subject and hierarchical ex-Gaussian horse-race models with an optional trigger-failure parameter following Matzke and colleagues (2013) , posterior inhibition functions, and posterior predictive checks. The Bayesian layer works with either the 'cmdstanr' or 'rstan' backend. Package: r-cran-sss Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-assertthat Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-spelling, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-sss_0.2.2-1.ca2404.1_all.deb Size: 95068 MD5sum: ecc2b4b43536557af7c8bdda3f5ab871 SHA1: 2eee287f567c056974bd1d0484124e98d0f16c51 SHA256: 55f9e6dbf08caad0abe06953a642a3a6e68f1666f3b1cb4fe5910c4b3a029790 SHA512: 45512d98e1ba43e8f4ed0f6f309579142cb7a51ba84c3839a8e60336533018c0a9cd9665d809fba83a70571437398289cdd7eeaf100d29694e70186f03c01247 Homepage: https://cran.r-project.org/package=sss Description: CRAN Package 'sss' (Import Files in the Triple-s (Standard Survey Structure) Format) Tools to import survey files in the '.sss' (triple-s) format. The package provides the function 'read.sss()' that reads the '.asc' (or '.csv') and '.sss' files of a triple-s survey data file. See also . Package: r-cran-sssc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-e1071, r-cran-ggplot2, r-cran-vgam Filename: pool/dists/noble/main/r-cran-sssc_1.0.0-1.ca2404.1_all.deb Size: 232556 MD5sum: dbc7d4e1ce5a77cfa4e0bbefdf0058c1 SHA1: 47d6d8e9879a045c46c7d6e661a5d8929f385550 SHA256: 9fa1ac635cd1b5da60a736ce3eb0b74a61d15794505ee5a0374744be2b8b6d2b SHA512: b002b5cde9a6abc85cda3470152b58dffc51438030440c58be14ee9332c487d2b0c99ea063415d0ced801cf7acede980e3f53f39d75db2846f817d5553329219 Homepage: https://cran.r-project.org/package=sssc Description: CRAN Package 'sssc' (Same Species Sample Contamination Detection) Imports Variant Calling Format file into R. It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected. Package: r-cran-sssimple Architecture: all Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-maps Filename: pool/dists/noble/main/r-cran-sssimple_0.6.6-1.ca2404.1_all.deb Size: 357164 MD5sum: 78135fd53a93ef825165fb17a4716257 SHA1: 5d7558f5064027da8ec5a896f828d85b4d19420d SHA256: b7370a2121daf49d488901092684974f6964d973b1427dc6e21a5ff5e2899a64 SHA512: 68fd8875018849caedcd3225cdf839751a4355baa161c718548cf017f7e36f1b49b952139a6189b6eb50673c81ce5cdbed99b188e5f14be97c27f71b076941e2 Homepage: https://cran.r-project.org/package=SSsimple Description: CRAN Package 'SSsimple' (State Space Models) Simulate, solve state space models. Package: r-cran-sssvcqr Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-igraph, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sssvcqr_0.0.4-1.ca2404.1_all.deb Size: 1434742 MD5sum: 18517e458088f36a1191bfd892dd3ee2 SHA1: 8390b3b43f81773ad4fe3625b8b96295c358d2cb SHA256: 0c1adb486dd7926ce41244f0a29495ca56e6a9c344afe628193b6f142890bd78 SHA512: 47463120a6e588dfee81e626d7e38cac5dfeb0c7b2386c9e2b44403fb0ae4ac654847cb801159f3a8584b89f786bc55877f248d86c165f31f51573e5e63120fa Homepage: https://cran.r-project.org/package=sssvcqr Description: CRAN Package 'sssvcqr' (Sparse-Smooth Spatially Varying Coefficient Quantile Regression) Implements sparse-smooth spatially varying coefficient quantile regression (SS-SVCQR), combining quantile regression of Koenker and Bassett (1978) , grouped variable selection of Yuan and Lin (2006) , graph regularization, and the alternating direction method of multipliers of Boyd et al. (2011) . The package provides graph-regularized estimation, spatially blocked cross-validation, prediction, diagnostics, and simulation helpers for global-local spatial quantile regression. Package: r-cran-sstack Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-dplyr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-sstack_1.0.1-1.ca2404.1_all.deb Size: 659674 MD5sum: 5e77264cf0af6594c24bea0f82539d6d SHA1: 85fe9911c88baf846d99bf9fbcbce37aba60f850 SHA256: 5b89e8f3138997e0561fa1fa7e847135a5c1de34e846fa45193c04ff3639ecb3 SHA512: 5e1de5f7db386299f54551b3efa374fe405d8a7cd0004509c374d404053a5feccd8feb67f7495e66d530268071bbc6cb47983072822eb79b6dba6f618d84881f Homepage: https://cran.r-project.org/package=Sstack Description: CRAN Package 'Sstack' (Bootstrap Stacking of Random Forest Models for HeterogeneousData) Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). 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Package: r-cran-sstn Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7062 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sstn_1.0.2-1.ca2404.1_all.deb Size: 7179158 MD5sum: c77e8baad1a091d7cb68dddf6193c14e SHA1: aee22e09778f4a07f0bb2ebf6b73dd583cd06ae2 SHA256: b0478cf07f2d3a5f0075647570ed518078733feb5e9bb14cf4ae9c5c51a96146 SHA512: e28fcd15da0542dfa89498856e0a1fcedfff6ffe028da882101379f640d19b2dd4d54ba7ccc93e844fcfddaaad898c9ef899bc2df924724940010b2c7a3d53fe Homepage: https://cran.r-project.org/package=sstn Description: CRAN Package 'sstn' (Self-Similarity Test for Normality) Implements the Self-Similarity Test for Normality (SSTN), a new statistical test designed to assess whether a given sample originates from a normal distribution. The method exploits the self-similarity property of the normal characteristic function by iteratively transforming and comparing standardized empirical characteristic functions. The null distribution of the test statistic is obtained via Monte Carlo simulation. Details of the methodology are described in Anarat and Schwender (2026), "A test for normality based on self-similarity", . Package: r-cran-ssutil Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-broom, r-cran-mass, r-cran-mvtnorm, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssutil_1.2.0-1.ca2404.1_all.deb Size: 412852 MD5sum: 5bc3cedd2fa3387d0ca3004a6a81b289 SHA1: 2222e031e36573429c0912773e038507f859c364 SHA256: 12237643efcbca048507036834a133934675802dd43ecab3b3bdd279fff887df SHA512: 3420af2f67dd4b83442ad8de483b80ecb79422be0ff8114c0bc927fb6c6db1c2ff612d4ecd246e0935718f3658cce6e92d03fb1d86b3dbc5c6463bf049ea3a80 Homepage: https://cran.r-project.org/package=ssutil Description: CRAN Package 'ssutil' (Sample Size Calculation Tools) Functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) . The Indifference-zone approach is based on Sobel and Huyett (1957) and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9). Package: r-cran-ssvs Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-boomspikeslab, r-cran-checkmate, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-magrittr, r-cran-gridextra Suggests: r-cran-aer, r-cran-bslib, r-cran-foreign, r-cran-glue, r-cran-knitr, r-cran-mice, r-cran-psych, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssvs_2.2.0-1.ca2404.1_all.deb Size: 167894 MD5sum: a30daa287be2670df78ce0e5d2ecf284 SHA1: 2261b41034a9072aaf790023096dba3de47a199c SHA256: 7d06f9630c4317e5cdc837efa9e8031e026f6d7c53d9f7d160338edcaae816ae SHA512: 9adcf208c19a83651e250b6eccdec3467853586903a6ab82cc87bd39d662dd700f72f743dd33d7a89c99bb71f07c0a0c13e39edf15e926b4713d3712961c30bb Homepage: https://cran.r-project.org/package=SSVS Description: CRAN Package 'SSVS' (Functions for Stochastic Search Variable Selection (SSVS)) Functions for performing stochastic search variable selection (SSVS) for binary and continuous outcomes and visualizing the results. SSVS is a Bayesian variable selection method used to estimate the probability that individual predictors should be included in a regression model. Using MCMC estimation, the method samples thousands of regression models in order to characterize the model uncertainty regarding both the predictor set and the regression parameters. For details see Bainter, McCauley, Wager, and Losin (2020) Improving practices for selecting a subset of important predictors in psychology: An application to predicting pain, Advances in Methods and Practices in Psychological Science 3(1), 66-80 . Package: r-cran-ssw Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssw_0.2.1-1.ca2404.1_all.deb Size: 265574 MD5sum: ff854a46017d30a811f1a911c591e1aa SHA1: 03ea0fc4e2ecb107a2652da95b82e103561d4edf SHA256: a96494f5a631e381e1da5b3c8ca6c5bdaa4555a5d5b0ca60c0128306df9fa732 SHA512: 111d916bf76f15a86dd0a559d9c28a71765b0bfb63e4cd2f1378ec970087fc9464f622d1f5d9b9194cc7e1b2333eece9ea17c221c755e649b15f9730d5a14e14 Homepage: https://cran.r-project.org/package=ssw Description: CRAN Package 'ssw' (Striped Smith-Waterman Algorithm for Sequence Alignment usingSIMD) Provides an R interface for 'SSW' (Striped Smith-Waterman) via its 'Python' binding 'ssw-py'. 'SSW' is a fast 'C' and 'C++' implementation of the Smith-Waterman algorithm for pairwise sequence alignment using Single-Instruction-Multiple-Data (SIMD) instructions. 'SSW' enhances the standard algorithm by efficiently returning alignment information and suboptimal alignment scores. The core 'SSW' library offers performance improvements for various bioinformatics tasks, including protein database searches, short-read alignments, primary and split-read mapping, structural variant detection, and read-overlap graph generation. These features make 'SSW' particularly useful for genomic applications. Zhao et al. (2013) developed the original 'C' and 'C++' implementation. Package: r-cran-ssym Architecture: all Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gigrvg, r-cran-numderiv, r-cran-normalp, r-cran-formula, r-cran-survival, r-cran-sandwich Suggests: r-cran-nistnls, r-cran-gam, r-cran-sn, r-cran-mass Filename: pool/dists/noble/main/r-cran-ssym_1.5.8-1.ca2404.1_all.deb Size: 345242 MD5sum: 900947ca2f69ae0c41448fbb5e4fe729 SHA1: 953dc71642e0bf2b93da1b63da89b6b6ffcbf116 SHA256: ef5b720d33988c13f70fa971845fb62140e672cc568ecfbf13264c6839a45930 SHA512: c16e7029ce8294a08a006abc247e46756889c8c15d58ea840854d2028d464a5694336b5f16dab61769850520848bfeecafed6d184d62afe427c5fb41cfe48c7b Homepage: https://cran.r-project.org/package=ssym Description: CRAN Package 'ssym' (Fitting Semi-Parametric log-Symmetric Regression Models) Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions. Package: r-cran-st Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sda, r-cran-fdrtool, r-cran-corpcor Suggests: r-bioc-limma, r-cran-samr Filename: pool/dists/noble/main/r-cran-st_1.2.7-1.ca2404.1_all.deb Size: 586686 MD5sum: 9805213b169101050f5c768573284281 SHA1: 0ca4e7f474fc00ff6773df3e138b25e6dd0c15f8 SHA256: 81a6bf770fbe9d06b04d53e36358977298d494fe97c67d0559002d288a2a9cf7 SHA512: dd923cea8fa5394d9c01505052e82c380e1e2960adaa74ed3762a3e7d217ca613ed4574dd5e0a4a22c867cf1f03e9e8962453acdccf0edb7d11561661dd0babc Homepage: https://cran.r-project.org/package=st Description: CRAN Package 'st' (Shrinkage t Statistic and Correlation-Adjusted t-Score) Implements the "shrinkage t" statistic introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described in Zuber and Strimmer (2009) . 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Package: r-cran-sta Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-geots, r-cran-foreach, r-cran-trend, r-cran-doparallel, r-cran-mapview, r-cran-rcolorbrewer Suggests: r-cran-sp Filename: pool/dists/noble/main/r-cran-sta_0.1.7-1.ca2404.1_all.deb Size: 2045964 MD5sum: 998c7f7793cbc21d23e6259918c60901 SHA1: 2a3b987eb5de9281f24cc9cfdd73614a8bb4a70e SHA256: 6d3ecd116410522c69fb460ea3640b8a328ede605fede52c1abab29675cd0355 SHA512: 50f38e57e12836c8530d0307ee2c87d00ede7ece3256c4f1c80430599e7fec499b5ad0b2e0ce2b1fd218d46f99cbc2a79279d41f69e6e7d282a4c239ad19e839 Homepage: https://cran.r-project.org/package=sta Description: CRAN Package 'sta' (Seasonal Trend Analysis for Time Series Imagery in R) Efficiently estimate shape parameters of periodic time series imagery with which a statistical seasonal trend analysis (STA) is subsequently performed. 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Package: r-cran-staat1cho Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-readr, r-cran-rlang, r-cran-shiny Suggests: r-cran-bslib, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-readxl, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-writexl Filename: pool/dists/noble/main/r-cran-staat1cho_0.2.0-1.ca2404.1_all.deb Size: 171888 MD5sum: 7071dfc91a5827ebb62cbb781a57ba50 SHA1: e3ea42dffc049744b5612b9fb541eb86e3669b4a SHA256: 8b90ef6967a41d5f835a61c0684a1a75c4b7f1e61457ee25a86660d0c0f9b34b SHA512: c5455908facd3f5864c74537be33d856c23f249cb2e7487b9f8aa935110bf86c2b5c85432234edb1c4c1e2d68bbf92fee6cb5231f7dde349e613881e6a77c697 Homepage: https://cran.r-project.org/package=staat1cho Description: CRAN Package 'staat1cho' (Study Indicators Based on Dutch Higher Education Data (1CHO)) Calculates enrolment, graduation, dropout, and programme-switch indicators from the Dutch higher education registration data (1CHO) supplied by DUO. 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Lima et al (2021) , Meinshausen and Buhlmann (2010) . Package: r-cran-stability Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfortify, r-cran-lme4, r-cran-magrittr, r-cran-matrixstats, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-stability_0.6.0-1.ca2404.1_all.deb Size: 125336 MD5sum: a612bbd9aaa3913eed8ff191b35e6b50 SHA1: ca38dc9079983548c526f4d4b59eb6b8b90e7f3e SHA256: b1eb9fd95c609c21b4b0b6ebbdb4469d2750aa2cd739b779980c6a06228f9a5f SHA512: 5cd60fe80aca3ca2b1c53c27b96405ede7a91ee72a9ee5b2c1e12b04d76f22afa9639a981100b9e6914ef310dca7deb45e09caa7f30675a466be7815b3a0bff8 Homepage: https://cran.r-project.org/package=stability Description: CRAN Package 'stability' (Stability Analysis of Genotype by Environment Interaction (GEI)) Provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. 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Package: r-cran-stabilityapp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-gridextra, r-cran-patchwork, r-cran-stability, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboardplus Suggests: r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stabilityapp_0.1.0-1.ca2404.1_all.deb Size: 984248 MD5sum: ab9283d3cc93c9fb7e9c5d75b42b1d94 SHA1: d6ccac8b30c7f0cfa533143ba34df22e0edb7ffe SHA256: d9751294732b5a069c480a42ad9a49d1e82d2ed3f08e0812fa7bce982d81b4a3 SHA512: ba689df17d11d93f1e8c1171d7be3c36ad2b000a2c3b50b80a0069c3d28ec3ac61646b535256c81f5bc2d512a9bb63f47cfd506ba9d81008a05a9c6239b8a54b Homepage: https://cran.r-project.org/package=StabilityApp Description: CRAN Package 'StabilityApp' (Stability Analysis App for GEI in Multi-Environment Trials) Provides tools for Genotype by Environment Interaction (GEI) analysis, using statistical models and visualizations to assess genotype performance across environments. It helps researchers explore interaction effects, stability, and adaptability in multi-environment trials, identifying the best-performing genotypes in different conditions. Which Win Where! Package: r-cran-stabilizedregression Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-r6, r-cran-glmnet, r-cran-corpcor, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-stabilizedregression_1.1-1.ca2404.1_all.deb Size: 133434 MD5sum: bda341096d4e2341ff170c58f0d30809 SHA1: 62b3d19777b229917487993458776cd5abe3146f SHA256: 5c8086234b79557f2eaa3d18a212a2abba9a60e0b5cd25df0371aee059e177e1 SHA512: 6e60565deb00d8b4dc96055a0ddbd1c4c5a79e2ccbfb806b7d0ddc1adf2868cd83daa833984f40b4a122855c86fa5e535c3b12cddea3587f88b844cdb3bb37ba Homepage: https://cran.r-project.org/package=StabilizedRegression Description: CRAN Package 'StabilizedRegression' (Stabilizing Regression and Variable Selection) Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) . 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Package: r-cran-stabilo Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-pracma Filename: pool/dists/noble/main/r-cran-stabilo_0.1.1-1.ca2404.1_all.deb Size: 45402 MD5sum: f116334ff5d449acc562fa66afc55121 SHA1: 8fe3092bd3158c89a98d243d5324b9146a1d0460 SHA256: 4db0c726c022c71244b63929428f6c62fdb97237927a4563153c0d103cd30c9d SHA512: 53187e4c4db32af0c278c93291e3e200d77c837bbbd8f9cd457ee9ab07128850b3d53e67a1e5cdf7446df114de87a60479cbe67f490cda22e18e06679c9da251 Homepage: https://cran.r-project.org/package=stabilo Description: CRAN Package 'stabilo' (Stabilometric Signal Quantification) Functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) "Calculation of area of stabilometric signals using principal component analisys". 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This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) . Package: r-cran-stablelearner Architecture: all Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-partykit, r-cran-party, r-cran-randomforest, r-cran-ranger Suggests: r-cran-formula, r-cran-nnet, r-cran-rpart, r-cran-evtree, r-cran-rchallenge, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stablelearner_0.1-9-1.ca2404.1_all.deb Size: 393574 MD5sum: 4ed21b528a4f224613921a7827b83c32 SHA1: b16621e5fbfdd4ea91175a5c912820ac1adacaf4 SHA256: 8d64c232835d03c608962f566afea22fdc2e5ffb3db8fadd4934adb275d9695f SHA512: 0e7f761df703e67d7f160e0b8263e4ece6a892ee5690dad803eaed4e77619b1737ff7d70d5f1a5fc7d94257660401a3dd90295389f76f0ad2d8221ea1a31be2b Homepage: https://cran.r-project.org/package=stablelearner Description: CRAN Package 'stablelearner' (Stability Assessment of Statistical Learning Methods) Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning. Package: r-cran-stablepopulation Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-stablepopulation_1.0.3-1.ca2404.1_all.deb Size: 27940 MD5sum: d249437177af8725c363342ba1070f8f SHA1: b6912a5cfecd964e48b93d77cebb528a177aa17b SHA256: 7bb8b362bcbd364b86abd6179b85f9750afed55b5f228576bb33a0a0adf492be SHA512: 7b7a73490b59a7642e05efd9419e7845f44778ea921f4d735b3cd4f0886b59b12a665d8b2b72077bb0495aef516f6263dad07577b8b1931293db6897f8d2bdae Homepage: https://cran.r-project.org/package=StablePopulation Description: CRAN Package 'StablePopulation' (Calculates Alpha for a Stable Population) Provides tools to calculate the alpha parameter of the Weibull distribution, given beta and the age-specific fertility of a species, so that the population remains stable and stationary. Methods are inspired by "Survival profiles from linear models versus Weibull models: Estimating stable and stationary population structures for Pleistocene large mammals" (Martín-González et al. 2019) . Package: r-cran-stablespec Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggm, r-cran-matrixcalc, r-cran-sem, r-cran-nsga2r, r-bioc-graph, r-bioc-rgraphviz, r-cran-polycor, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stablespec_0.3.0-1.ca2404.1_all.deb Size: 147338 MD5sum: d57fa9a92a90e6ec6d2335f3d5924589 SHA1: 182833bf226eee85b29f936737583831c1ce0848 SHA256: 22c87d73d089c858c5730a0e78dc4f70461e3793187fcc523817cacca83aa2e0 SHA512: d6b3fb1e73e19afb3cf84b74882b470ef86127648aae90bfeac36fd5cfb0d1f814f2640798aeaf2a79872398eee69fd52abb5e5e3d48812d0e28e84852bb705b Homepage: https://cran.r-project.org/package=stablespec Description: CRAN Package 'stablespec' (Stable Specification Search in Structural Equation Models) An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model. Package: r-cran-stabm Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-matrix Suggests: r-cran-cowplot, r-cran-ggdendro, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-mlbench, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stabm_1.2.2-1.ca2404.1_all.deb Size: 478180 MD5sum: 2d35c65961b4ad74ac3b00b09e53b910 SHA1: f175aa6dab6d20b26a55699d30b8e735eccdb1e1 SHA256: 0f3df40cba502a51f9cb9451c9defad07f19c061bd7f686b49f030b70e646eee SHA512: e42817ca6c95154ecfeaac518a0194e893f52c6b0df3d8aabc824a8d436b99bc8e9bb133845ed6b9502eb38d01151e23f1008fa5442b2b49c199d92dc7bc9f02 Homepage: https://cran.r-project.org/package=stabm Description: CRAN Package 'stabm' (Stability Measures for Feature Selection) An implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available, see Bommert (2020) . Package: r-cran-stabplot Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-latex2exp, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-stabplot_0.0.1-1.ca2404.1_all.deb Size: 30150 MD5sum: de93817976eae5921dd2f99751316253 SHA1: 4098606d61c488b637601c09740ec3565976ea1c SHA256: 5555774cc06295147fe38d0757ca5c8f1fd2c845e12526e8b312b011e6a6aee8 SHA512: 2db342e4f91991ffeb9182f906680daf1ee8f70625993be1350cf46bd2db2596335fbeaacf9b7a8d1f7ff298cbf6780d46c5ff327244f30b0f29fdbfc2e124eb Homepage: https://cran.r-project.org/package=stabplot Description: CRAN Package 'stabplot' (Stability Plots for Lasso Stability Selection) Provides stability selection with Lasso and two diagnostic plots for assessing selection stability. The Regustab plot shows stability across the regularisation parameter grid, while the Convstab plot shows stability as a function of the number of subsamples. Methods are described in Nouraie and Muller (2026) . Package: r-cran-stabs Architecture: all Version: 0.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-glmnet, r-cran-lars, r-cran-mboost, r-cran-gamboostlss, r-cran-th.data, r-cran-hdi, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stabs_0.7-1-1.ca2404.1_all.deb Size: 144898 MD5sum: 95b97c80265c9b0aa3b810e898104b82 SHA1: 4bd4e656c338161109974bb5817ea6ce7192bd42 SHA256: 6def80979d408d13335710e4969e44625d5debb711ebc0b0db4740788b9eb6fd SHA512: 487bda631dd632d7cabdfa863f66787de12636d3d185d5bc0b3abc1a792e0519354738810a24ef08b06a8986933b31c1601aa76f0ce89b247806e9b4de8c0574 Homepage: https://cran.r-project.org/package=stabs Description: CRAN Package 'stabs' (Stability Selection with Error Control) Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, ) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013, ) are implemented. The package can be combined with arbitrary user specified variable selection approaches. Package: r-cran-staccuracy Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-autogam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-staccuracy_0.2.2-1.ca2404.1_all.deb Size: 67640 MD5sum: ac625d38f646060f76e90728890e673d SHA1: 8a9a2a575c2b1bfd9c9f91945889f99730f05098 SHA256: c5199d4b1db6cf2fff0d481108f7e4009e6bb2c8e514c17e2458a0652fa759a9 SHA512: cdf3cf9cd1f8cb366a9326ea0ce44750daaea05cdc379671661fe4c9b9f1d43371dc70d1abb0205a57aa7f124c162240c7654404f8db3219de4dde1e36478d5e Homepage: https://cran.r-project.org/package=staccuracy Description: CRAN Package 'staccuracy' (Standardized Accuracy and Other Model Performance Metrics) Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The 'staccuracy' package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable. Package: r-cran-stackgbm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc, r-cran-progress, r-cran-rlang Suggests: r-cran-knitr, r-cran-lightgbm, r-cran-msaenet, r-cran-rmarkdown, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-stackgbm_0.1.0-1.ca2404.1_all.deb Size: 378436 MD5sum: 1af9cf4d05b4cf665629bfa64107d1b5 SHA1: 4cedaf3c89a73ef162888fc95b2565222e4bce2e SHA256: 5856b4dc92132db328449ae2dad8065a3167c04a7fe497ee8bc93914ea0bf652 SHA512: 5ca9606a1d733b254cbe2da05d67edcec8e0a8ce54a88ac3498be9ad590ace9a10117c051aa645300ebc76ba759a7c7ed3ac173213bc29ad95fd634729d7714d Homepage: https://cran.r-project.org/package=stackgbm Description: CRAN Package 'stackgbm' (Stacked Gradient Boosting Machines) A minimalist implementation of model stacking by Wolpert (1992) for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness. Package: r-cran-stackimpute Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-zoo, r-cran-mice, r-cran-dplyr, r-cran-mass, r-cran-magrittr, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stackimpute_0.1.0-1.ca2404.1_all.deb Size: 2056832 MD5sum: c27c54c52dad7bb74834dad6d025ab12 SHA1: a6a23be04befd0ead146c20bff9cb4f122593ec9 SHA256: 8d7c462686ed555a7566f70776e7bfdea1712b777badfb294513c5afa3579d37 SHA512: 14ae03b602ab29e34483fb5ecd37fdc90901d5d347d145fe450cacf43ef6a9c816e8b77205a02a64731d8cf576000b19fa56eb512740b6c80f2c15de3a5102e6 Homepage: https://cran.r-project.org/package=StackImpute Description: CRAN Package 'StackImpute' (Tools for Analysis of Stacked Multiple Imputations) Provides methods for inference using stacked multiple imputations augmented with weights. The vignette provides example R code for implementation in general multiple imputation settings. For additional details about the estimation algorithm, we refer the reader to Beesley, Lauren J and Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple imputation incorporating the substantive model” , and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random missingness through imputation stacking” . Package: r-cran-stacking Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret Filename: pool/dists/noble/main/r-cran-stacking_0.2.1-1.ca2404.1_all.deb Size: 56518 MD5sum: cbf782772eb64e987548c2a8c1c6c78d SHA1: 2479ef0d711d14e4bc73d02693d0175d470128cd SHA256: 52d5513ce0d70dfc2c46ca4119e998684facd04bda33b72f7369c789975cbbcc SHA512: 5ea80c95508765f18b2c5f490f8c1cf1f6625dc25b5cc51065d470425e12e0790eb21b54f465ae1ebeda21287e209ab2b73542b790962205aec2337ce6b5f375 Homepage: https://cran.r-project.org/package=stacking Description: CRAN Package 'stacking' (Building Predictive Models with Stacking) Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) . Package: r-cran-stacks Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-butcher, r-cran-cli, r-cran-dplyr, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-glmnet, r-cran-glue, r-cran-parsnip, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs, r-cran-workflows Suggests: r-cran-covr, r-cran-h2o, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-modeldata, r-cran-nnet, r-cran-ranger, r-cran-rmarkdown, r-cran-testthat, r-cran-workflowsets, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-stacks_1.1.1-1.ca2404.1_all.deb Size: 1694748 MD5sum: a36019e5e96c29689180b159180051f0 SHA1: 17fc5feaeb9553235becc03851e2f74590b40b73 SHA256: a9d2f8cd60492b1fff43bc335c3de5f09966e148c9f1d07e1c2c3eddf0d66e69 SHA512: e8f225aaa64dae1cfcb9e9d794e7009c4d37c9fe8bc333356595131e980829f6321ad21013ee1082720b31f135420143faf7905169950d336afc52e78c6a7144 Homepage: https://cran.r-project.org/package=stacks Description: CRAN Package 'stacks' (Tidy Model Stacking) Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking. Package: r-cran-stacomir Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2360 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stacomirtools, r-cran-magrittr, r-cran-intervals, r-cran-rcolorbrewer, r-cran-stringr, r-cran-rpostgres, r-cran-ggplot2, r-cran-reshape2, r-cran-lattice, r-cran-hmisc, r-cran-lubridate, r-cran-dplyr, r-cran-xtable, r-cran-mgcv, r-cran-rlang, r-cran-pool, r-cran-dbi, r-cran-withr, r-cran-scales Suggests: r-cran-testthat, r-cran-viridis, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stacomir_0.6.1-1.ca2404.1_all.deb Size: 1703384 MD5sum: 1c4ed5de8e3f2d1132cb032a9810db98 SHA1: b36eaea6e6301751fc4ff1f2f93e13073e740c1f SHA256: 49678ea3b545f2910c0af72dbd0dba4b5fa5e8b6e8b85bec16d348d595619831 SHA512: 9cea8af5bd8dca34f64121d7833d84ce198256d69ebb4a0963bb4e6f3b36d495f7a9334d3d8597a7e83de2f3d804b695f9467f6702c7c3d62a67d4dae0b4e2d5 Homepage: https://cran.r-project.org/package=stacomiR Description: CRAN Package 'stacomiR' (Fish Migration Monitoring) Graphical outputs and treatment for a database of fish pass monitoring. It is a part of the 'STACOMI' open source project developed in France by the French Office for Biodiversity institute to centralize data obtained by fish pass monitoring. This version is available in French and English. See for more information on 'STACOMI'. 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It is used by the package 'stacomiR' for connections to the database. Development versions of 'stacomiR' are available in R-forge. Package: r-cran-stacr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstac, r-cran-tibble, r-cran-cli Suggests: r-cran-gdalcubes, r-cran-jsonlite, r-cran-leaflet, r-cran-sf, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stacr_0.1.0-1.ca2404.1_all.deb Size: 59198 MD5sum: 1d64d7fb7fd6120187207e3cb8ddc268 SHA1: d1901d64b26aec89bf6b5a92514fd9b79c9a2233 SHA256: 54d71499bd81cbea0bb015b1d7244e0f4814c19f276526daeed44dc4c0fd16fa SHA512: a7ee258106b97efabccb0554ac5420af21cb9abf8869d4d351199fb783eba6f530eca82d468aaf4e3dc00e5463bc20f667a1136d8b825fda935b007a7f1e38a8 Homepage: https://cran.r-project.org/package=stacr Description: CRAN Package 'stacr' (Tidy 'STAC' Workflows for R) Wraps the 'rstac' package with a pipe-friendly, tidy API. All results return 'tibbles' instead of nested lists. Ships with a catalog registry of known 'STAC' endpoints including Planetary Computer, Earth Search, and 'USGS', while supporting any 'STAC' API URL. Package: r-cran-stagedtrees Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-bnlearn, r-cran-covr, r-cran-clue, r-cran-igraph Filename: pool/dists/noble/main/r-cran-stagedtrees_2.3.0-1.ca2404.1_all.deb Size: 773156 MD5sum: b5e000449bdd964a71e6361eff90594d SHA1: 69fbe42d3d195689805cd2b6c7d227585c2fd97d SHA256: adcaf9a6c48fa6ae29e45d8f6ecea6031d20c67b94b7937d9da0ad73be52e9ad SHA512: d3f81ca471efb211e64493d512c5a2504af80831b9700c3105016912574fb43e25aa555e3dcaab2c5d3168ffaac6752654d1ec21fa25163309c391b6ad61e14f Homepage: https://cran.r-project.org/package=stagedtrees Description: CRAN Package 'stagedtrees' (Staged Event Trees) Creates and fits staged event tree probability models, which are probabilistic graphical models capable of representing asymmetric conditional independence statements for categorical variables. Includes functions to create, plot and fit staged event trees from data, as well as many efficient structure learning algorithms. References: Carli F, Leonelli M, Riccomagno E, Varando G (2022). . Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604). Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) . Thwaites P. A., Smith, J. Q. (2017) . Barclay L. M., Hutton J. L. and Smith J. Q. (2013) . Smith J. Q. and Anderson P. E. (2008) . 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Mitchell R., Agle B.R., & Wood D.J. Hester, P.T., & Adams, K.M. (2013) . 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This method is used when there are multiple raters for an object, typically an image, and this method fuses these ratings into one rating. It uses an expectation-maximization method to estimate this rating and the individual specificity/sensitivity for each rater. Package: r-cran-staplr Architecture: all Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-assertthat, r-cran-glue, r-cran-xml, r-cran-rjava, r-cran-fs, r-cran-purrr, r-cran-pdftools Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-staplr_3.2.2-1.ca2404.1_all.deb Size: 3717194 MD5sum: 7d152806932af9c6fdaa8a6d6cfafa26 SHA1: 6e2818046993441d271d076d0cdeb44a52a27cff SHA256: 8a51e4eb929df2434ff9eba05af7a06054fd72721754de536537b2db0eeb63ac SHA512: 51cf3450fe35a1992e5859e07a3d1d853ab31d20d2341f0891e12ae535f851b685d771d94d3dc06dff80f01eae8e734f29e97d7bf7097fde68266270ba4a9778 Homepage: https://cran.r-project.org/package=staplr Description: CRAN Package 'staplr' (A Toolkit for PDF Files) Provides functions to manipulate PDF files: fill out PDF forms; merge multiple PDF files into one; remove selected pages from a file; rename multiple files in a directory; rotate entire pdf document; rotate selected pages of a pdf file; Select pages from a file; splits single input PDF document into individual pages; splits single input PDF document into parts from given points. Package: r-cran-starburst Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3021 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-globals, r-cran-paws.compute, r-cran-paws.storage, r-cran-paws.management, r-cran-paws.cost.management, r-cran-paws.security.identity, r-cran-qs2, r-cran-uuid, r-cran-renv, r-cran-jsonlite, r-cran-crayon, r-cran-digest, r-cran-base64enc, r-cran-processx Suggests: r-cran-future.apply, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-mockery Filename: pool/dists/noble/main/r-cran-starburst_0.3.9-1.ca2404.1_all.deb Size: 1718638 MD5sum: 558d153a903ee6f0f9cf9cfde6eba9e8 SHA1: 094c633a21b9612160b790a7a232003487a89eab SHA256: 95522dc7724aedf1e2eb45370f5b6ce5be4bed15020bbad908c2969db8207f7e SHA512: df7d71ad39dfa3c1ba01bce0d76a0e524f50a6d24f7bfb8f56d5da8568915d786af4ae741228f3785139e57133a157e2680a3149359dddb5017c0b3cdd9b9481 Homepage: https://cran.r-project.org/package=starburst Description: CRAN Package 'starburst' (Seamless AWS Cloud Bursting for Parallel R Workloads) A 'future' backend that enables seamless execution of parallel R workloads on 'Amazon Web Services' ('AWS', ), including 'EC2' and 'Fargate'. 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Package: r-cran-stardom Architecture: all Version: 1.1.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-pracma, r-cran-zoo, r-cran-tibble, r-cran-multiway, r-cran-ggally, r-cran-doparallel, r-cran-drc, r-cran-foreach, r-cran-data.table, r-cran-matrixstats, r-cran-mba, r-cran-cdom, r-cran-r.matlab, r-cran-readr, r-cran-gtools, r-cran-viridislite, r-cran-purrr, r-cran-rlang Suggests: r-cran-plotly, r-cran-xlsx, r-cran-knitr, r-cran-kableextra, r-cran-askpass, r-cran-httr, r-cran-rmarkdown, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-stardom_1.1.32-1.ca2404.1_all.deb Size: 3240722 MD5sum: 1e016fa8f39fd0682a3de4043af8e88d SHA1: f0ce6885d75a9e504184866182968477d054a2b6 SHA256: db1f2fb26001f02b67f4e4ec90be08561e248a7da3348d7bce15d70dcd2e268b SHA512: f4e56036bc45a9d32a1d43373c48fabea3205089dd82b0f0266af2d75e9ebcf355a94e3ca997b3bcab2aaa6ee9cd7b74b0ded5a0b8930e542adb50a0eb35026e Homepage: https://cran.r-project.org/package=staRdom Description: CRAN Package 'staRdom' (PARAFAC Analysis of EEMs from DOM) 'This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) . The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.' Package: r-cran-stargazer2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-fixest, r-cran-alpaca, r-cran-plm, r-cran-sandwich, r-cran-wooldridge, r-cran-stargazer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stargazer2_0.1.0-1.ca2404.1_all.deb Size: 174484 MD5sum: 55888e821d1097d0eb6f4b2a8e4b142c SHA1: 2b98ae1893e065f4e406542e5d2667d9d473c7c6 SHA256: e3358ec7c7020fbed42551ffab83f2d965fefd2ca6b2ed88cce58d1a81ad90aa SHA512: cd9abff4d721f9134810c862ac05323d454571f354772b186f744c1e7ed083806c8bda1d48c15da75bf1e09f37c62c01bb10ac568d0e7e09ee455003517aa86b Homepage: https://cran.r-project.org/package=stargazer2 Description: CRAN Package 'stargazer2' (Beautiful Regression Tables for Modern Econometrics Packages) Produces publication-quality LaTeX, ASCII, and HTML regression tables. Designed as a drop-in replacement for the 'stargazer' package for 'lm' and 'glm' models, with added native support for 'fixest', 'plm', and 'alpaca' objects including automatic fixed-effect and random-effect indicator rows and SE-type detection. Standard errors can be supplied as variance-covariance matrices, numeric vectors, or auto-extracted from supported model objects. Follows the interface of Hlavac (2022) . Package: r-cran-stargazer Architecture: all Version: 5.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stargazer_5.2.3-1.ca2404.1_all.deb Size: 612354 MD5sum: 9b9665bfc319e6ea326b83aa9cb6881d SHA1: 5e5f3cec168aaec4bd22dddcd2f5d777ad962039 SHA256: 7fd672f790bb7b4a00501f50f49a31aa4d311cf64458e7b4d42c6c2bb2e7f87a SHA512: 39b5b4b9107249e7193f2fef5c4447848b54216ec841152b01703c4b4ef94ba1598ee3e1b2432b88d72e634cfcdab9a67cb5774cf5c8c50ec80cb1bedca8f09a Homepage: https://cran.r-project.org/package=stargazer Description: CRAN Package 'stargazer' (Well-Formatted Regression and Summary Statistics Tables) Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold regression analysis results from several models side-by-side, as well as summary statistics. Package: r-cran-starling Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-reclin2, r-cran-dplyr, r-cran-stringr, r-cran-lubridate, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-patchwork, r-cran-plotly, r-cran-phonics, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-starling_1.1.0-1.ca2404.1_all.deb Size: 632656 MD5sum: 7bcd6c300556b2c119542b9813b6329f SHA1: 66a47b15482989aac58dd58f9b7ee73f4ee34adc SHA256: 71606f14fe9ce7edf1a56dde44774478b34e4ec8d6986977a373bdf4b905d73b SHA512: 0bc466c40c8bade6f495617545dc086f38cabfe7b89aacf066bac148b20d9d25ee5c9269cee40809fb6fc01b7574769f47d1009d92a1720a43486d0caf5a50c8 Homepage: https://cran.r-project.org/package=starling Description: CRAN Package 'starling' (Record Linkage for Public Health Surveillance) Record linkage for public health surveillance datasets using either the Fellegi-Sunter probabilistic framework (via reclin2) or deterministic exact-key matching. Provides pre-linkage data quality auditing (preflight()), Medicare number checksum validation (check_medicare()), blocking variable construction (flock()), and the murmuration() linkage engine. murmuration() expresses linkage as a single cohort-to-event concept (linking a linelist of people to a dated stream of vaccination, hospitalisation, or case events within a before/during/after time window), with the historical four-code taxonomy (case-to-hospitalisation, vaccination-to-case, vaccination-to-hospitalisation, vaccination-to-event) retained as deprecated aliases. Also provides linkage weight distribution visualisation (murmuration_plot()) and threshold sensitivity analysis (perch()) with annotated AIHW, WA Data Linkage Unit, and PHRN reference benchmarks. perch() can be called standalone or triggered automatically mid-linkage via murmuration(perch_before_linking = TRUE). Includes synthetic datasets (cases_notifiable, vax_air) and three vignettes demonstrating the complete linkage workflow. Part of the aviary public health surveillance ecosystem. Package: r-cran-starm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-starm_0.1.0-1.ca2404.1_all.deb Size: 209510 MD5sum: 1e6b9ef331ab0a30d3ea5ae0740e5b6c SHA1: 9598985ed57a0dcd4d0367813c46cf07066e44cc SHA256: 035f9edca8d489dfc32bcb74d03ffd7c823dfe8b2081770b5a05d57a62b2992e SHA512: 35480d38b21318b795bf1ca465d702bd39c3bdd45832c99b1fced4e794f49af7ccd2bce7a56606e181fe5537ef9fa61c07c2a30f582790dca23b37f86179ba44 Homepage: https://cran.r-project.org/package=starm Description: CRAN Package 'starm' (Spatio-Temporal Autologistic Regression Model) Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. , using a grid of binary variables to estimate the spread of a disease on the grid over the years. Package: r-cran-starnet Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-cornet, r-cran-matrix Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-cvxr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-starnet_1.0.2-1.ca2404.1_all.deb Size: 315328 MD5sum: d02ec761a1d2b1139b594b357898217c SHA1: 805e815766c7700f43cdbf32fa243da7b44ab5fd SHA256: a56e8bb8d9cfe8bbcada93d58a031816bd9a27b6dcadc821a774a15110524ae9 SHA512: 1659c08b2832fc3617d1d61d7e4827f345b4d55400232665973c3c3ea7e2814395de7b11caa34c61f63426c8cc14156f2718e0b575712c22719ad5cbe1878940 Homepage: https://cran.r-project.org/package=starnet Description: CRAN Package 'starnet' (Stacked Elastic Net) Implements stacked elastic net regression (Rauschenberger 2021 ). The elastic net generalises ridge and lasso regularisation (Zou 2005 ). 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Package: r-cran-starry Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-car, r-cran-corrr, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-stringr, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-starry_0.1.2-1.ca2404.1_all.deb Size: 178276 MD5sum: 24a8a653e6c4309dc81bcfb924ef8282 SHA1: f147fae51eefc7a55748166fe7520c79f1026545 SHA256: ec412a75ce6fc4b75b547171f943af9da225947d23c6cc10ee83bf9b2604f6b8 SHA512: 50a1827adb9f6862be0acc26b25b1b7d24d04c6b29450c5e49ecfcd9912b3945a25a2d9bdf768c4de71fee20d2823aa66ddc208d65f2c9c7a6d1dc3d5ae4231a Homepage: https://cran.r-project.org/package=starry Description: CRAN Package 'starry' (Explore Data with Plots and Tables) Provides modular functions and applications for quickly generating plots and tables. 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Package: r-cran-stars Architecture: all Version: 0.7-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6853 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-sf, r-cran-rlang, r-cran-units Suggests: r-cran-cairo, r-cran-cftime, r-cran-classint, r-cran-clue, r-cran-covr, r-cran-cubble, r-cran-cubelyr, r-cran-digest, r-cran-dplyr, r-cran-exactextractr, r-cran-fnn, r-cran-future.apply, r-cran-ggforce, r-cran-ggplot2, r-cran-ggthemes, r-cran-gstat, r-cran-httr, r-cran-jsonlite, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mapdata, r-cran-ncdfcf, r-cran-ncdfgeom, r-cran-ncmeta, r-cran-openstreetmap, r-cran-pbapply, r-cran-plm, r-cran-randomforest, r-cran-raster, r-cran-rmarkdown, r-cran-rnetcdf, r-cran-sp, r-cran-spacetime, r-cran-spatstat, r-cran-spatstat.geom, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-viridis, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-stars_0.7-3-1.ca2404.1_all.deb Size: 4393438 MD5sum: 649b13b2f400115820f7853c87dc69d2 SHA1: 50b38d83e802ecd7c68084c5118782db84c0c76b SHA256: a6b9980552991d23b416c84385b51ae383d37d36e9347faa082ffdb2ec56411f SHA512: 0968871a7452760a97458c185c7b83fd93c612db76c55e263818f2ce2e05214330f37201b0fd1c7b52baa843987d364f1f3a929e0bd0e7b06f59de0fcf846f4a Homepage: https://cran.r-project.org/package=stars Description: CRAN Package 'stars' (Spatiotemporal Arrays, Raster and Vector Data Cubes) Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'. Package: r-cran-starschemar Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-starschemar_1.2.5-1.ca2404.1_all.deb Size: 865348 MD5sum: 5d6ee405a28b12bc4c11fbc36dc0054b SHA1: c23874c690c87ea5617aad7836031e7ac263b171 SHA256: 4b0988b452cd8a452835fb341bc90500e54eed8d4bb907f7762b7ab5fd2df1b4 SHA512: f0a9dd1c81490a1f3cf5bcfb3ca72e62a569e9ef5b7293913dd830b89228ae00aead2926425984411edf8b6b5f685780573ba84cd1e209ced68bfbd257399c2f Homepage: https://cran.r-project.org/package=starschemar Description: CRAN Package 'starschemar' (Obtaining Stars from Flat Tables) Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a star schema. Transformations can be carried out using professional extract, transform and load tools or tools intended for data transformation for end users. With the tools mentioned, this transformation can be carried out, but it requires a lot of work. The main objective of this package is to define transformations that allow obtaining stars from flat tables easily. In addition, it includes basic data cleaning, dimension enrichment, incremental data refresh and query operations, adapted to this context. Package: r-cran-starstileserver Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-leaflet, r-cran-plumber, r-cran-png, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-units, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet.extras, r-cran-webshot, r-cran-mapview, r-cran-callr, r-cran-magick, r-cran-shiny, r-cran-dplyr, r-cran-magrittr, r-cran-abind, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-starstileserver_0.1.1-1.ca2404.1_all.deb Size: 1003484 MD5sum: 33fa90b2c462fa85913ed85cf65d2f04 SHA1: 9004c60975821ea151f6d2a009e1b74a3ae55533 SHA256: a1c473448ea529944c88572d83fc5ceef927c23434d37a77889c7872e6d39e1e SHA512: 9bd052c1ecf04b74cbd992dad46fd52d58a7848ee3880ba6b346f2eca50102bb46f40d24436724c901d91343866cc836096f2d75a919564e52c786495d5b3d82 Homepage: https://cran.r-project.org/package=starsTileServer Description: CRAN Package 'starsTileServer' (A Dynamic Tile Server for R) Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. 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Package: r-cran-statnet Architecture: all Version: 2019.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tergm, r-cran-ergm.count, r-cran-sna, r-cran-tsna, r-cran-ergm, r-cran-network, r-cran-networkdynamic, r-cran-statnet.common Suggests: r-cran-ergm.rank, r-cran-ergm.ego, r-cran-epimodel, r-cran-degreenet, r-cran-latentnet, r-cran-relevent, r-cran-ndtv Filename: pool/dists/noble/main/r-cran-statnet_2019.6-1.ca2404.1_all.deb Size: 27206 MD5sum: 6ee723ac1424023f4a682a74eb99bc2f SHA1: 823f76863a17208ff63248561b7d27456d406f15 SHA256: a5f26b0fe65657772eed74e2ad70e8910f2ff34c0ef36bd6f672dff46efc05cb SHA512: 09a2635e838e74229006975fddfa9b05f62e05aca0c035b628d35dc8f2ab3721ee6f8af61d8fdc0447f2e3f766d7b5f927bbcac5a94f39532d745b054d9a8293 Homepage: https://cran.r-project.org/package=statnet Description: CRAN Package 'statnet' (Software Tools for the Statistical Analysis of Network Data) Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and 'API' design. 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(4.6) of Engle et al. (2016) ). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) , Amendola et al. (2015) and Becker et al. (2015) . Package: r-cran-statprograms Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-statprograms_0.3.0-1.ca2404.1_all.deb Size: 78216 MD5sum: c5ef1b7e5ef0cb5dee8237cb46e5785c SHA1: f1c524769177c088f68e34ad89fead60a2eba49e SHA256: 3660a15cf300b0ee91036d87e0b1019a5ad44cc49442b45c0660a24bed620310 SHA512: 9a664d87db3b775fd44c5ef1d85879b80dd16f61b641cc09e92ec8a074fed5ea402af0b02daf7282cd3bd4beb888901666eed8cf0d86190ec10654e8ef5f9794 Homepage: https://cran.r-project.org/package=statprograms Description: CRAN Package 'statprograms' (Graduate Statistics Program Datasets) A small collection of data on graduate statistics programs from the United States. 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Package: r-cran-statrank Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncdist, r-cran-plyr, r-cran-ggplot2 Suggests: r-cran-gridextra, r-cran-testthat, r-cran-lattice Filename: pool/dists/noble/main/r-cran-statrank_0.0.6-1.ca2404.1_all.deb Size: 225770 MD5sum: 500065827c02107ce31ce4c43eaa0c57 SHA1: 43d6dc6c03a299c80e5b294fa579dba65d29cc3a SHA256: 5baf2b953580e270d43597c73c0b3cc5f63d818f808be0471f4d470aa6164e67 SHA512: 7744473fdcef0164a7331544097338149617601c118d9c2572f36ee2db17c7374ebaecc046b1a4b66dd7e8eca9a3ae211437f5ce41af8badec98b7449e409b0e Homepage: https://cran.r-project.org/package=StatRank Description: CRAN Package 'StatRank' (Statistical Rank Aggregation: Inference, Evaluation, andVisualization) A set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. 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Package: r-cran-stats19 Architecture: all Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5943 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-curl, r-cran-readr, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-jsonlite, r-cran-glue Suggests: r-cran-duckdb, r-cran-dbi, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-tidyselect, r-cran-pkgdown, r-cran-kableextra, r-cran-leaflet, r-cran-geojsonsf, r-cran-htmltools, r-cran-tmap, r-cran-spatstat.geom, r-cran-osmdata, r-cran-covr, r-cran-readods, r-cran-gt, r-cran-clock, r-cran-waldo Filename: pool/dists/noble/main/r-cran-stats19_4.1.0-1.ca2404.1_all.deb Size: 2772134 MD5sum: ebf58eb8ee24a895b12c53639409924a SHA1: 2ef194c65a97480cf5356434f639264b01edd247 SHA256: decb40bdc7435186ea634ab48f56e82f434367de27f1d1c0089c4f2e982f276d SHA512: 167c6b6d747f4dcdf74b61a28040ca5e27d830a6473a208ef21f145fb01dc5a49eb4dfacb928d06749fcef30e66e5533b11f479ed25df41da2778b92afe7db1f Homepage: https://cran.r-project.org/package=stats19 Description: CRAN Package 'stats19' (Work with Open Road Traffic Casualty Data from Great Britain) Work with and download road traffic casualty data from Great Britain. 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STEPCAM is a STEPwise Community Assembly Model that infers the relative contribution of Dispersal Assembly, Habitat Filtering and Limiting Similarity from a dataset consisting of the combination of trait and abundance data. See also for more information. Package: r-cran-stepcount Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-reticulate, r-cran-rlang Suggests: r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-spelling, r-cran-callr Filename: pool/dists/noble/main/r-cran-stepcount_0.6.0-1.ca2404.1_all.deb Size: 2655744 MD5sum: 4ba8ed457091c02d2df24a342578c8a5 SHA1: 543166b0843833d4b890203706ab3cf69e579f54 SHA256: f5b69d2635d454f58e82ca9e1e3cb6d94f4f156a3012ea96042f8ac1f099bb95 SHA512: e952887c0908cd2480ec883b881f08095f3bb1cf1d53d44b9b858b4c59b830588108ef8bae9e1f1737c1dca3096cc1c3bb1b46aee35687989abd09dd5b8c286f Homepage: https://cran.r-project.org/package=stepcount Description: CRAN Package 'stepcount' (Estimate Step Counts from 'Accelerometry' Data) Interfaces the 'stepcount' Python module to estimate step counts and other activities from 'accelerometry' data. Package: r-cran-stepdownfdp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-stepdownfdp_1.0.0-1.ca2404.1_all.deb Size: 25022 MD5sum: 4dc16134cb4137c2dcc00b4d081a1866 SHA1: a00b2e2a0b8dd55936beed077b6372eb3719928c SHA256: ee643590f1035c3e5544d322bfcff854535b92ca4100e0b9cfd0756e72a9375c SHA512: 1a627640574afed260dfc8de4b464d2fe2a8b08c276e608709726dc9580a9c2d6fe58325d21936c137f7f1f6542c1d22cd79092ef27f89b5d0c4712f2e7019fb Homepage: https://cran.r-project.org/package=stepdownfdp Description: CRAN Package 'stepdownfdp' (A Step-Down Procedure to Control the False Discovery Proportion) Provides a step-down procedure for controlling the False Discovery Proportion (FDP) in a competition-based setup, implementing Dong et al. (2020) . Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression. Package: r-cran-stepgbm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spm, r-cran-steprf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-lattice Filename: pool/dists/noble/main/r-cran-stepgbm_1.0.1-1.ca2404.1_all.deb Size: 30292 MD5sum: d3a29897cb5adeeb3f55f08ad57ce357 SHA1: db0794f62aef0a77c1680498dca894446e30d0ef SHA256: 313c33d80b0174c0b34365188f15603ebce2c56c59d53be9f46588233b2b13e7 SHA512: 81c8f6e194c218bdf35af93e3ebbaf00cb20a5c4874f21a0b13d6a83edb01747b4ed3f6e1c673bfb75959c3fecd4a7918d41145419a385dfb30ab70c80ceceb9 Homepage: https://cran.r-project.org/package=stepgbm Description: CRAN Package 'stepgbm' (Stepwise Variable Selection for Generalized Boosted RegressionModeling) An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). . Package: r-cran-stepgwr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stepgwr_0.1.0-1.ca2404.1_all.deb Size: 27478 MD5sum: 2cdc5bc92dc3a400352f1e225a3cc9eb SHA1: b7d90612e244ef95e371a72606df713c89b815e6 SHA256: 8c424556fe8fa4e37bc54b139ba8c01aa3620b81ceb98fc090aeee4578f3cd9b SHA512: c3825261f2ff6650462c71f122837f1add32effe3c6f0ee7c47faf368feb8fdd046025ecb5750a1a2d803d8df32de5dc027410e219f4908fd035ed2021b8dafb Homepage: https://cran.r-project.org/package=StepGWR Description: CRAN Package 'StepGWR' (A Hybrid Spatial Model for Prediction and Capturing SpatialVariation in the Data) It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000)..This hybrid spatial model aims to improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly useful for modeling spatially varying relationships and improving the accuracy of spatial predictions. Package: r-cran-stepjglm Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsq Filename: pool/dists/noble/main/r-cran-stepjglm_0.0.1-1.ca2404.1_all.deb Size: 65078 MD5sum: 17d657c02878d71206f38d009dc797df SHA1: 8d07c4527bdefabf9bb2f625b5fcdd51850392ab SHA256: fc22c4f92b50befcf999c206911165d234ed4f235b8ca6779a0700a1ca3db56b SHA512: c38240b3cd90726cc2ab5ebbdbf99cb359778494bbbe46e63b157cdab00e3e6bf013ca5e3ee94e8ff6c94ebc8b1b94b43532cf587a9d14d69e62525b9dc07f99 Homepage: https://cran.r-project.org/package=stepjglm Description: CRAN Package 'stepjglm' (Variable Selection for Joint Modeling of Mean and Dispersion) A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) . Package: r-cran-stepmetrics Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-physicalactivity Suggests: r-cran-testthat, r-cran-rsqlite, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-stepmetrics_1.0.3-1.ca2404.1_all.deb Size: 1715884 MD5sum: e5f57a756d2e42c2c03b136d2d17620c SHA1: 4c2106c136388390a46e0ddef7930569059f9796 SHA256: 7db0bfb64aaaa5e64724f8b7a94b6530191e11431f48d06d88debed595a29879 SHA512: 4eadec0706aa9de5d47799bf8e2d7d342749e4ec3c70495d9372b474c82e4a52441287b1ac95dfc3984ebbc8fc88f5c982415895f8df98865cf068f4899b8128 Homepage: https://cran.r-project.org/package=stepmetrics Description: CRAN Package 'stepmetrics' (Calculate Step and Cadence Metrics from Wearable Data) Provides functions to calculate step- and cadence-based metrics from timestamped accelerometer and wearable device data. Supports CSV and AGD files from 'ActiGraph' devices, CSV files from 'Fitbit' devices, and step counts derived with R package 'GGIR' , with automatic handling of epoch lengths from 1 to 60 seconds. Metrics include total steps, cadence peaks, minutes and steps in predefined cadence bands, and time and steps in moderate-to-vigorous physical activity (MVPA). Methods and thresholds are informed by the literature, e.g., Tudor-Locke and Rowe (2012) , Barreira et al. (2012) , and Tudor-Locke et al. (2018) . The package record is also available on Zenodo (2023) . Package: r-cran-stepmixr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-stepmixr_0.1.3-1.ca2404.1_all.deb Size: 65816 MD5sum: 7721da73d666b6b6d375fb81af48ae93 SHA1: 074b32713e937a35c3e50db690f2417aa7d92dc9 SHA256: bb5bd3ac685261bdc9507b29b035b96876532876de7d40eb71c80437a50e42f0 SHA512: 6befbbfd5aa34452fec6182c2011ca7ab121a5c880c5e08926bd24c84f39dd5b4efd753ceec53f33e2195ecfab72e9478b47f35d84a9599d58cba6a9180c9b92 Homepage: https://cran.r-project.org/package=stepmixr Description: CRAN Package 'stepmixr' (Interface to 'Python' Package 'StepMix') This is an interface for the 'Python' package 'StepMix'. It is a 'Python' package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. 'StepMix' handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings. Software paper available at . Package: r-cran-steppedpower Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6322 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-plotly, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pwr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-steppedpower_0.4.0-1.ca2404.1_all.deb Size: 771066 MD5sum: 2e2bb63fb56fdffc4828d2db411f3ce9 SHA1: 8ef88d33db4084895fa6ee6af84097018740f9eb SHA256: 76f6b08c4ce4e3c63469b6b9136a6147b91732c5a64fe6332efbd69a61d61e35 SHA512: de4e2086f750bbb845b058b55ef1b46a6500bbd12eeaac023de8471e795d27059cd2363843a95772421828a149d10ab7f2a74c53dfc4725c71e415904d468918 Homepage: https://cran.r-project.org/package=SteppedPower Description: CRAN Package 'SteppedPower' (Power Calculation for Stepped Wedge Designs) Tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) , extensions are discussed in Li et al. (2020) . Package: r-cran-steppedwedge Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-stringr, r-cran-magrittr, r-cran-ggplot2, r-cran-lme4, r-cran-geepack, r-cran-performance Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-steppedwedge_1.0.0-1.ca2404.1_all.deb Size: 515750 MD5sum: 13dcbf930ae4b63366e3ce8669cc462b SHA1: f5a1ca8c732f02cf2e71a9a9f83fb3eb1e621a64 SHA256: ddd3f1f0b39bd560c7ede91a726c4353ea3c04ae7c2f71807b57f32dd340cdf7 SHA512: 34720e961c8edda397eba9da1dfca3a660a9639e38d4ddcffe292f322b6e48a4a278484c8b5c2cb68862bdcb714cd44dbeee589baf694280292bb5b9b99b8d3b Homepage: https://cran.r-project.org/package=steppedwedge Description: CRAN Package 'steppedwedge' (Analyze Data from Stepped Wedge Cluster Randomized Trials) Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", . Package: r-cran-steppenal Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm, r-cran-proc, r-cran-dfoptim, r-cran-caret Filename: pool/dists/noble/main/r-cran-steppenal_0.2-1.ca2404.1_all.deb Size: 80202 MD5sum: 1813704bdd73b6a22573d30e4ab2d80e SHA1: a612897d635120e1dbe9393381b7b8e5499087d4 SHA256: 012bcace8d5d54dd59eafb3e7d4372a85a4c59de426b21ade20cac6dffeaa037 SHA512: fe03a742a759730ae1560857acb3b2cb4beb43162c3357608ba5f4b69f42ef099365a63acb8a1d3692de343fa6ed119e1de8cb27dd46251c179aaf68d5e2ee91 Homepage: https://cran.r-project.org/package=stepPenal Description: CRAN Package 'stepPenal' (Stepwise Forward Variable Selection in Penalized Regression) Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms. Package: r-cran-stepreg Architecture: all Version: 1.6.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4840 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-mass, r-cran-survival, r-cran-survauc Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-kableextra, r-cran-cowplot, r-cran-rmarkdown, r-cran-flextable Filename: pool/dists/noble/main/r-cran-stepreg_1.6.9-1.ca2404.1_all.deb Size: 3056344 MD5sum: ef8324d925dbbcae4a7019a35d686bd7 SHA1: 2a4a027d16e2b692bee4e5981f368f308aa95005 SHA256: a59804f2c99c83620fdb210811dc8ee5cee667a591d3a1c67057c5b471869054 SHA512: 7ee4bdc3f8f598971b9dd3866acdc13e97c4aa678e6b6b064c6d6a65f223e76cfa56a022a939fcc0e4f2c9973bbcd7ee7673dd3b3a34d30eb73ba445ef591391 Homepage: https://cran.r-project.org/package=StepReg Description: CRAN Package 'StepReg' (A Comprehensive and Intuitive R Package for Stepwise RegressionAnalysis) Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. For an interactive web interface, users can install the companion 'StepRegShiny' package. The methodology is described in Li et al. (2026) . Package: r-cran-stepregshiny Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stepreg, r-cran-shiny, r-cran-dt, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-dplyr, r-cran-ggplot2, r-cran-stringr, r-cran-flextable, r-cran-cowplot, r-cran-ggcorrplot, r-cran-tidyr, r-cran-summarytools, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-survival Filename: pool/dists/noble/main/r-cran-stepregshiny_1.6.1-1.ca2404.1_all.deb Size: 800696 MD5sum: 2bbe731a34364c92022c4cfe4406c716 SHA1: 2fdad0b56acd331adae39f1ec8ae79e8fa765946 SHA256: 195429c3227a52433619a5b434539fb0c260bda620a2999e57d216a4e2435de5 SHA512: bb495217366521fdb715e0b19654a99b02825a9a7c43defba28303d6ceabdc75b826402ef031ff4d2e450bb717d97e8509f7a5a39b801171412d4f3064582ab1 Homepage: https://cran.r-project.org/package=StepRegShiny Description: CRAN Package 'StepRegShiny' (Graphical User Interface for 'StepReg') A web-based 'shiny' interface for the 'StepReg' package enables stepwise regression analysis across linear, generalized linear (including logistic, Poisson, Gamma, and negative binomial), and Cox models. It supports forward, backward, bidirectional, and best-subset selection under a range of criteria. The package also supports stepwise regression to multivariate settings, allowing multiple dependent variables to be modeled simultaneously. Users can explore and combine multiple selection strategies and criteria to optimize model selection. For enhanced robustness, the package offers optional randomized forward selection to reduce overfitting, and a data-splitting workflow for more reliable post-selection inference. Additional features include logging and visualization of the selection process, as well as the ability to export results in common formats. Package: r-cran-steprf Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spm, r-cran-randomforest, r-cran-spm2, r-cran-psy Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-steprf_1.0.2-1.ca2404.1_all.deb Size: 51332 MD5sum: e8603838a249903fa3d0509c6eb62b4b SHA1: 897ed0e7c38f4772212405365b8147fde2eb66b9 SHA256: e4dade43c921dacbce575528a4f0f096bd98854c5eb4a68c4a069b25b4741b05 SHA512: 4cdb45b00aae9e10be22bd45d9d1ca00175f2db94030895d64968f6abc05367064c0e3ce8c5145043213d96bb2f7fefd502a2aed628f8741f8a01d4e5f6934cd Homepage: https://cran.r-project.org/package=steprf Description: CRAN Package 'steprf' (Stepwise Predictive Variable Selection for Random Forest) An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). . Package: r-cran-stepssurvey Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ggplot2, r-cran-glue, r-cran-haven, r-cran-janitor, r-cran-patchwork, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-survey Suggests: r-cran-knitr, r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-stepssurvey_0.1.0-1.ca2404.1_all.deb Size: 679604 MD5sum: 0e6e20f8342c08d0ff39f12889d3c67c SHA1: 722d74297e9a5431732778479883a0cb4ba84ede SHA256: 5d1ed57bfed9a88402c4a41a4a0c5fb4a65b155145c93b6f4112548f8204d937 SHA512: 3149e36cf0370610627f1f435e5ebd2542571ccc3d2724cf9b21cbbdf60a3b039a711ccc8aef7b81f02f56f11915ba310c683668c966699ea954c12d05c4abb0 Homepage: https://cran.r-project.org/package=stepssurvey Description: CRAN Package 'stepssurvey' (Analyse WHO STEPS Survey Data) Provides a complete analysis pipeline for the WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) as described in Riley et al. (2016) . Imports raw survey data ('CSV', 'Excel', 'Stata', 'SPSS'), applies WHO-standard cleaning and recoding, sets up complex survey designs, computes all standard NCD indicators (tobacco, alcohol, diet, physical activity, anthropometry, blood pressure, biochemical), and generates publication-ready tables, visualisations, and 'Word'/'HTML' reports (fact sheet, data book, country report). Package: r-cran-stepwedgepower Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stepwedgepower_0.1.3-1.ca2404.1_all.deb Size: 77734 MD5sum: aa9703b0d78e47413459aada1bcd0953 SHA1: 55e3116a32a4505f502c8ac8a2ce7007a10e1dda SHA256: 6435d8cb5d80f57fd13c495828a3838925cda868acc89c915bd581e52f175786 SHA512: 5bcf9265a822b76861a1340aab9a2bc90f29398ccd75377ab6e89ff41f33ba850491e18de89da63afda8375280c176e39a2fdbbaca227a9e6e982b068a59a0fd Homepage: https://cran.r-project.org/package=stepwedgepower Description: CRAN Package 'stepwedgepower' (Stepped-Wedge Clinical Trial Analysis and Power Simulation) Provides reusable functions for aggregated cluster-period data, mixed-effects analysis, and simulation-based power and type I error evaluation in stepped-wedge cluster randomized trials. The design and mixed-effects analysis follow Hussey and Hughes (2007) . Intraclass correlations for binary outcomes are converted to logistic-normal random-intercept standard deviations following Eldridge, Ukoumunne and Carlin (2009) . Monte Carlo uncertainty in estimated power is summarized using the exact binomial interval of Clopper and Pearson (1934) . The simulation engine supports sequence-specific baseline risks, cluster random effects, direct intraclass-correlation specification, Monte Carlo uncertainty intervals, and model-fitting diagnostics. Applied physician and specialty helpers are retained for backward compatibility and for an example health-services workflow. Package: r-cran-stevedata Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4501 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stevedata_1.9.0-1.ca2404.1_all.deb Size: 4530218 MD5sum: 6a81cf41e27740f50b0b9d178ee68be1 SHA1: c238d27f1f9b9e54e31358fd63bc11ca52a72d89 SHA256: 210a12fb2ae438f92d99c0e96a5de158606a6c1cbcf746f72ce91287fee4045c SHA512: 0f746cdcb2f697547e6dedbd771f6fe36ce5e514e176671fe176b9a88b122a0d3d5d231b8d32b11fa5157018ff759c00e9d833508c0343b1d0eb2af8e7d83b84 Homepage: https://cran.r-project.org/package=stevedata Description: CRAN Package 'stevedata' (Steve's Toy Data for Teaching About a Variety of Methodological,Social, and Political Topics) This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy. Package: r-cran-stevedore Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2026 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-curl, r-cran-jsonlite, r-cran-yaml Suggests: r-cran-knitr, r-cran-openssl, r-cran-redux, r-cran-reticulate, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-stevedore_0.9.6-1.ca2404.1_all.deb Size: 1032560 MD5sum: e8e03279539338f58d3fb669dc5ce9bf SHA1: eee93d23fbe85e76542331445b571dbbc556b20c SHA256: 5feb7f4ea1148bd1679293fce1ef57198815674bda3cf53ade168d642378dbd1 SHA512: 54060b262ff436bfc382e2d48bcadb9d254f5240dabe5c100caddbbc8ab435befa53f05b5bfd612f4782118e7f857461d8b8532443cf184ecd2b48be1407fc8d Homepage: https://cran.r-project.org/package=stevedore Description: CRAN Package 'stevedore' (Docker Client) Work with containers over the Docker API. Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the 'OpenAPI' (a.k.a., 'swagger') specification, and all return values are checked in order to make them type stable. Package: r-cran-stevemisc Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1083 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-labelled, r-cran-arm, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-lme4, r-cran-rlang, r-cran-forcats, r-cran-stringr, r-cran-httr, r-cran-rmarkdown, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dbi, r-cran-rsqlite, r-cran-dbplyr Filename: pool/dists/noble/main/r-cran-stevemisc_1.9.0-1.ca2404.1_all.deb Size: 946208 MD5sum: dcfd3a304bfd9c256e18487f8988752c SHA1: 99a5afebd8c27703bbca5a1aab36b6be41d2bce4 SHA256: 813334a5b98831ae4bb8c09face0e501e115bc3da7cdb206faa089a327b95447 SHA512: 8b6e3fdad3320befe945834a1b1e7993825abf1cfc3910c686790ba1841de609b328d084d219206009a8492a5bdc3f6fea06a07e0225f62e644a240a473c3c47 Homepage: https://cran.r-project.org/package=stevemisc Description: CRAN Package 'stevemisc' (Steve's Miscellaneous Functions) These are miscellaneous functions that I find useful for my research and teaching. The contents include themes for plots, functions for simulating quantities of interest from regression models, functions for simulating various forms of fake data for instructional/research purposes, and many more. All told, the functions provided here are broadly useful for data organization, data presentation, data recoding, and data simulation. Package: r-cran-steves Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1436 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-knitr, r-cran-leaflet, r-cran-maps, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-steves_0.1.1-1.ca2404.1_all.deb Size: 907138 MD5sum: e4062db11cdbc9037c23a0ec74e3a7cd SHA1: 45fa80f0cd3ee8e1a9d12e8618bf9f0828b525a0 SHA256: 9f6fcd58eff9d8e19d9815b7147c609099079f9c492fae924ce76a9b9d84cc75 SHA512: 5a53b986b3af72cb1c4417a7fe685458827fb404d37a40fd47bb99a8fb17b1b66bbec3b0c3c16778647fb744935690ef5b9f321b8c61a4225136b65043f3c4ee Homepage: https://cran.r-project.org/package=steves Description: CRAN Package 'steves' (Rick Steves' Europe Episodes for Teaching Data Analysis) Tidy snapshot of every episode of the public-television travel series 'Rick Steves' Europe' (2000-2025), enriched with IMDB ratings, geocoded destinations, ISO country codes, episode thumbnails, and descriptive summaries. Designed as a companion dataset for introductory data analysis and visualization in the spirit of the 'moderndive' textbook: every row is an episode, every column is a candidate for a plot or a join. Compiled from public sources for teaching purposes; not an official or verified 'Rick Steves' Europe' dataset, and shared with the permission of the Rick Steves' Europe team. Package: r-cran-stevetemplates Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stevetemplates_1.4.0-1.ca2404.1_all.deb Size: 199164 MD5sum: e22cdbcbbd095c747d96e6c6f0c97f2d SHA1: 64a3e73092345cbe5a3a284152e145a5bdcc237f SHA256: 76df112f5067d32bf84453662fc020263f6ed2f6d8d36ef00b7003fed6086082 SHA512: c8ecd6030259c6e5d5bdafa50aad69a84d2a47916ae73e872bbc344173f09cad18f95e8d1f9d5556009881421f207b6dea0df39343612f102d12c8c8047538ef Homepage: https://cran.r-project.org/package=stevetemplates Description: CRAN Package 'stevetemplates' (Steve's R Markdown Templates) These are my collection of 'R Markdown' templates, mostly for compilation to PDF. These are useful for all things academic and professional, if you are using 'R Markdown' for things like your CV or your articles and manuscripts. Package: r-cran-stevethemes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-systemfonts Filename: pool/dists/noble/main/r-cran-stevethemes_0.1.0-1.ca2404.1_all.deb Size: 934616 MD5sum: 6e0ef8bea40740d18e4332781b0d2dd3 SHA1: da25bc38ec29a24c86b94184309eb6ad61a8ebe2 SHA256: f57603e38283325711cff97c1216da20625f7d07ac4c346754f1a092c0cf0f60 SHA512: a7f65bb2b370e0648c69665c0a771ba1180cc44737d5b013abb9f0cd13dd462828f578dabbe52d5f4b0ab8de668ab8829349b801cb1f6c80385a74a868bb939c Homepage: https://cran.r-project.org/package=stevethemes Description: CRAN Package 'stevethemes' (Steve's 'ggplot2' Themes and Related Theme Elements) This is a compilation of my preferred themes and related theme elements for 'ggplot2'. I believe these themes and theme elements are aesthetically pleasing, both for pedagogical instruction and for the presentation of applied statistical research to a wide audience. These themes imply routine use of easily obtained/free fonts, simple forms of which are included in this package. 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While it will include more tests when the related literature are enriched, this package contains the following key tests: functional stationarity test, functional trend stationarity test, functional unit root test, to name a few. Package: r-cran-stgam Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3243 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-cran-glue, r-cran-foreach, r-cran-doparallel, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-stringr Suggests: r-cran-cols4all, r-cran-knitr, r-cran-ggplot2, r-cran-cowplot, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-lubridate, r-cran-sf, r-cran-gratia, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-stgam_1.2.1-1.ca2404.1_all.deb Size: 2246980 MD5sum: 37b4911e36589ab26f6a096ff073e9c4 SHA1: 4e43321369a07118e4a3753dc49eb20b648224f3 SHA256: d0b7f668bf8f788888eea9cdb28a805a96f7a1d1fec24262a425e292d12aa0bc SHA512: 843f2c2e55200ee3ac0c9028fb0f2ff451d54f9a0db4f91873fb3cbcf4266de2686cbfc721c2737f151a2c23c353c0584276b10b8c98e2da59455407a099b404 Homepage: https://cran.r-project.org/package=stgam Description: CRAN Package 'stgam' (Spatially and Temporally Varying Coefficient Models UsingGeneralized Additive Models) A framework for undertaking space and time varying coefficient models (varying parameter models) using a Generalized Additive Model (GAM) with smooths approach. The framework suggests the need to investigate for the presence and nature of any space-time dependencies in the data. It proposes a workflow that creates and refines an initial space-time GAM and includes tools to create and evaluate multiple model forms. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Create all possible space and/or time models in which each predictor is specified in different ways in smooths. iii) Evaluate each model via their AIC value and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. The number of knots used in each smooth can be specified directly or iteratively increased. This is illustrated with a climate point dataset of the dry rain forest in South America. This builds on work in Comber et al (2024) and Comber et al (2026) . 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GS is one of the promising tool for improving genetic gain in animal and plants in today’s scenario. This package is basically developed for genomic predictions by estimating marker effects. These marker effects further used for calculation of genotypic merit of individual i.e. genome estimated breeding values (GEBVs). Genomic selection may be based on single trait or multi traits information. This package performs genomic selection only for single traits hence named as STGS i.e. single trait genomic selection. STGS is a comprehensive package which gives single step solution for genomic selection based on most commonly used statistical methods. Package: r-cran-sticky Architecture: all Version: 0.5.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-magrittr, r-cran-testthat, r-cran-data.table, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sticky_0.5.6.1-1.ca2404.1_all.deb Size: 31770 MD5sum: 70c2c01a127add96b0417091fd2b81a2 SHA1: b5b2bb1837c267c53eee09e8dc07a695c3aa0e32 SHA256: e4216ac6c44175d81725efe089c22e87730c6c1b7dc3a958aa51be8d548a6c99 SHA512: ccea93050836ce51874c476b282dd8e3325ad627df20f63d048f776f63066617c99d5401dbceeaf3fe1f3b7686c172ee8d94b1cf15c198384ba42673f7bee6b1 Homepage: https://cran.r-project.org/package=sticky Description: CRAN Package 'sticky' (Persist Attributes Across Data Operations) In base R, object attributes are lost when objects are modified by common data operations such as subset, filter, slice, append, extract etc. This packages allows objects to be marked as 'sticky' and have attributes persisted during these operations or when inserted into or extracted from list-like or table-like objects. Package: r-cran-stickyr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stickyr_0.1.3-1.ca2404.1_all.deb Size: 38968 MD5sum: 624cf7373ae70eb9a0f295c7d3eb343a SHA1: c3a65badb267dd80ca319cacefb3722b07dffb26 SHA256: f7e108dfca3bb20004947f0ba5e422ccdc738da28c9e0a8420ff83b4f2ebdc36 SHA512: 7520e2c3116a9f82dd90e9a6d906aa47b3393533282863cf36e2eb5ab001dd510dea0da601f1397dac0ee2ab4de9f0596ee6342ff9936a2701c92821fe5c6839 Homepage: https://cran.r-project.org/package=stickyr Description: CRAN Package 'stickyr' (Data Frames with Persistent Columns and Attributes) Provides data frames that hold certain columns and attributes persistently for data processing in 'dplyr'. Package: r-cran-sticr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sticr_1.1.2-1.ca2404.1_all.deb Size: 124684 MD5sum: bc23d8ad5a79296949b1b20d1cc9875e SHA1: e0a5ecbcb05779f0f5867385d689e9e50b14e117 SHA256: 2b81bbbf56e24c83233ad0b594a6a33f51ff70e2fb7e3bc32246325e37ed28f2 SHA512: 4e0e6cb4d084f4952ef56ab17a73376656da6fd12a0199da0f22d7380cac367e2bd4aa891b708ea7858257dde32e09dfcdc1d1b9d6097f3b072699011c30ca45 Homepage: https://cran.r-project.org/package=STICr Description: CRAN Package 'STICr' (Process Stream Temperature, Intermittency, and Conductivity(STIC) Sensor Data) A collection of functions for processing raw data from Stream Temperature, Intermittency, and Conductivity (STIC) loggers. 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(2025) ), the Generalized Iterated Poisson Process (Soni & Pathak (2024) ), the Generalized Fractional Risk Process (Soni & Pathak (2024) ), and the Tempered Space-Time Fractional Negative Binomial Process (Garg et al. (2025) ). Package: r-cran-stochlab Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6855 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-logr, r-cran-magrittr, r-cran-msm, r-cran-pracma, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-stochlab_1.1.2-1.ca2404.1_all.deb Size: 6878094 MD5sum: e4667efb7f129c0b308e6213e074306b SHA1: 966eb6e0469fc4c07c16da0dbcf0827a7334c5c7 SHA256: 508ec5edba752f65b878f89d17605b8bf78bc0882f1965031a51d3c759240e43 SHA512: 480bea17850289eec84220b7805de4c86fe3acec1f254631e4796df61a217decb36ba314b56beb66afcfe6be545740ca399e7088f32169345b2eac57510f7112 Homepage: https://cran.r-project.org/package=stochLAB Description: CRAN Package 'stochLAB' (Stochastic Collision Risk Model) Collision Risk Models for avian fauna (seabird and migratory birds) at offshore wind farms. The base deterministic model is derived from Band (2012) . This was further expanded on by Masden (2015) and code used here is heavily derived from this work with input from Dr A. Cook at the British Trust for Ornithology. These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, many of the species included in the stochastic collision risk models can also be found in the North Atlantic in the United States and Canada, and could be applied there. Package: r-cran-stochsimr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1975 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-future, r-cran-future.apply Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stochsimr_1.1.0-1.ca2404.1_all.deb Size: 1478954 MD5sum: f427a1c76f887df5cba3c8cdad690bed SHA1: 0214a55ea915f1f364fa7dda1eeab55e632920fc SHA256: e075d23a353da9d61eab4db9c277c434c288a0d63890c188fdf1fd3cf1e29980 SHA512: 43d6b5d18b7657dbbd82d74ca6b0002b6e799a83572e165cafc7b67a29a6399bcdfc883ae3553d8bcca58082abab169b6b672ee3b7cc221f6f3984e4f55104ec Homepage: https://cran.r-project.org/package=StochSimR Description: CRAN Package 'StochSimR' (Stochastic Process Simulation Engine) A modular simulation engine for a wide range of stochastic processes. Provides exact and approximate simulation methods for Poisson processes (homogeneous and inhomogeneous), Brownian motion (standard, drifted, and bridge), discrete- and continuous-time Markov chains, birth-death processes, the Yule pure-birth process, infinitesimal generator matrix utilities, Markovian queuing systems (M/M/1, M/M/c, M/M/c/K) with exact steady-state statistics, Levy processes (gamma, normal inverse Gaussian, variance-gamma, alpha-stable), Merton jump-diffusion models, Hawkes self-exciting processes, geometric Brownian motion, and Ornstein-Uhlenbeck mean-reverting diffusions. Includes variance reduction techniques (antithetic variates, control variates, importance sampling, stratified sampling), parallel simulation via the 'future' framework, rare-event simulation (cross-entropy and multilevel splitting), path visualisation, and summary statistics. Methods are based on Glasserman (2003) , Asmussen & Glynn (2007) , Norris (1997) , and Kleinrock (1975, ISBN:0471491101). Package: r-cran-stockanalyst Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stockanalyst_1.0.1-1.ca2404.1_all.deb Size: 185626 MD5sum: 819d80775937980e2afe49e62e3bd443 SHA1: a95a410d4b0230b1262cfd3485e8f108d6ca2311 SHA256: 7867607d2527bdb708d839bbf022b7227bbf93e7a79972b40472ae27e7778a16 SHA512: a9fc2efefc8827f28eb2630fca6e541d3a294092a2f69a33a0e5f4de84248c33f6778ed5ac8444a3cf0e989f9c987653eb6184f677ea0f132cb2dd40631adc59 Homepage: https://cran.r-project.org/package=stockAnalyst Description: CRAN Package 'stockAnalyst' (Equity Valuation using Methods of Fundamental Analysis) Methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (“Equity Asset Valuation (4th Edition)”, 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614). Package: r-cran-stockdistfit Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1523 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fgarch, r-cran-fbasics, r-cran-fitdistrplus, r-cran-xts, r-cran-magrittr, r-cran-zoo, r-cran-quantmod, r-cran-ghyp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stockdistfit_1.0.0-1.ca2404.1_all.deb Size: 845668 MD5sum: 67c8364c9e300f6528a4f282f0668516 SHA1: 6a1a9f370fbcdaaf6555a259deeb16e7c78d5369 SHA256: d697f32a96b667f2f9fc7237836afae06a838dfe19d26bf608e34f353596431b SHA512: 8ffdd9aad2791cee687401d06d740376637b40a2a33cd3284c60124f937b1190e2257bcbb97c46f3b68fd75f61eb586799b54b5a19fb4e84a0567f217a8614e5 Homepage: https://cran.r-project.org/package=StockDistFit Description: CRAN Package 'StockDistFit' (Fit Stock Price Distributions) The 'StockDistFit' package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) and Benth et al. (2008) . Package: r-cran-stodom Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-pracma, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stodom_0.0.1-1.ca2404.1_all.deb Size: 49378 MD5sum: 61b65a853adb1e1292b47fd3f89170fe SHA1: 1cf0b04a8c41445a87f6d24097ac16508256fdca SHA256: 84f19fa50bdf2b6452e02d406eb15f8fc954af08bf446d12b7c1b995efeae255 SHA512: 9f5ad1274ee592e9389cd6126cf2f400654cf69393544dd5180594034be2974776341c1748a2a2be87ee95ddfa9a38515bd4fa08e38c18aebf610088b15d2041 Homepage: https://cran.r-project.org/package=stodom Description: CRAN Package 'stodom' (Estimating Consistent Tests for Stochastic Dominance) Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) . Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1. Package: r-cran-stoichcalc Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stoichcalc_1.1-5-1.ca2404.1_all.deb Size: 38158 MD5sum: bda34cd5ab463bb0970ab727fbbb2d24 SHA1: 045bcc35506d55bb4e82109c3c1c5b40229bbffa SHA256: 48b0aced943072e9069a4482b5ccf7493805e366ad6bc5f0cf6e9167d52a905e SHA512: 168c9419fd13c8b58510a36e36b31f5dda0212223a9469856fe9eea3636d81f630af5c6f7c1e825301c4c9748a602c1ce5f82ef105e3c81fb5c8bb049e83e179 Homepage: https://cran.r-project.org/package=stoichcalc Description: CRAN Package 'stoichcalc' (R Functions for Solving Stoichiometric Equations) Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.) Package: r-cran-stoichutilities Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-purrr, r-cran-magrittr, r-cran-lubridate, r-cran-sf, r-cran-rlang, r-cran-units, r-cran-jsonlite Suggests: r-cran-tidyverse, r-cran-maps, r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-glue, r-cran-gluedown, r-cran-kableextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stoichutilities_1.0.2-1.ca2404.1_all.deb Size: 285868 MD5sum: fccfb8d414c8c5ac3ac5d9feba505241 SHA1: 63ba62ea94177b1e800288a74e291f055369ea3d SHA256: a73218252559a65c217641efa8073340626fa63eab8f2ad2995fb550761790d1 SHA512: 7be05dc32ad58db27593b0440ea50dd147fdfa4c2b17b877412ac780fe708b42c58eef1fb7631d83521480e5db66802f8e8bcba58555adc75ce5bc44b6e9f39a Homepage: https://cran.r-project.org/package=stoichUtilities Description: CRAN Package 'stoichUtilities' (User Tools for Accessing the STOICH Project Database) User tools for working with The STOICH (Stoichiometric Traits of Organisms in their Chemical Habitats) Project database . This package is designed to aid in data discovery, filtering, pairing water samples with organism samples, and merging data tables to assist users in preparing data for analyses. For additional examples see "Additional Examples" and the readme file at . Package: r-cran-stokes Architecture: all Version: 1.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-permutations, r-cran-partitions, r-cran-disordr, r-cran-spray Suggests: r-cran-knitr, r-cran-deriv, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown, r-cran-quadform, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-stokes_1.2-3-1.ca2404.1_all.deb Size: 331416 MD5sum: 0f5d190ea17c8a63534c3c4a51eb8fff SHA1: 499d9f6dce52c858a84021000dd95b9beacebd35 SHA256: 9b2e58d3ef020e5fb5da4e677312a075dc012c8b4511e395c827729bab17b4d4 SHA512: 0a6cee83be327a0c2e868335f8db3b8945ec8b6f100eb88918c95135fd212b2231a3934b4681f954f1cbbdb1a4a3ab79d9d0ebcb2fbcc9a5ff40f77dc9639fc4 Homepage: https://cran.r-project.org/package=stokes Description: CRAN Package 'stokes' (The Exterior Calculus) Provides functionality for working with tensors, alternating forms, wedge products, Stokes's theorem, and related concepts from the exterior calculus. Uses 'disordR' discipline (Hankin, 2022, ). The canonical reference would be M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds". To cite the package in publications please use Hankin (2022) . Package: r-cran-stopdetection Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-geodist, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stopdetection_0.1.2-1.ca2404.1_all.deb Size: 285398 MD5sum: e161898bee40f9395246b29061ba4ba5 SHA1: 0d6c97a2fe4e79b01363849537ebc762da599df6 SHA256: d1a6cc9e40605cc317634ddc0307bc945625de0554d050902facdf345067d877 SHA512: 052f6d7d4252daf4b87fdc45c92044b0415cfb91aa0c19dc99a388774c71e5d588842d6151b95c99bf9081b90a804303170319b1aeb6e2748990c7d3a8edad35 Homepage: https://cran.r-project.org/package=stopdetection Description: CRAN Package 'stopdetection' (Stop Detection in Timestamped Trajectory Data usingSpatiotemporal Clustering) Trajectory data formed by human or animal movement is often marked by periods of movement interspersed with periods of standing still. It is often of interest to researchers to separate geolocation trajectories of latitude/longitude points by clustering consecutive locations to produce a model of this behavior. This package implements the Stay Point detection algorithm originally described in Ye (2009) that uses time and distance thresholds to characterize spatial regions as 'stops'. This package also implements the concept of merging described in Montoliu (2013) as stay point region estimation, which allows for clustering of temporally adjacent stops for which distance between the midpoints is less than the provided threshold. GPS-like data from various sources can be used, but the temporal thresholds must be considered with respect to the sampling interval, and the spatial thresholds must be considered with respect to the measurement error. Package: r-cran-stopes Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-cvtools, r-cran-glmnet, r-cran-changepoint Filename: pool/dists/noble/main/r-cran-stopes_0.2-1.ca2404.1_all.deb Size: 44484 MD5sum: dfa747b7483bcdfa5235548ab956698c SHA1: 690f7cc9270eae04f1c214d5b1382eb2607f3fbd SHA256: dfeea628879fe8b8539565951d5d5d7bcfbc034cf02a2baf9fe2ede02291ecf8 SHA512: e804f3a2626aafb77edcbe2deb7ed528a5a62d8fbe586bb1a25ca4bdc2356039ebf67b39b3efee6e0a47e0c6054c023b51b2f39a983c8345df43876fb5c1b332 Homepage: https://cran.r-project.org/package=STOPES Description: CRAN Package 'STOPES' (Selection Threshold Optimized Empirically via Splitting) Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models). Package: r-cran-stopmotion Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-checkmate Suggests: r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-stopmotion_0.1.0-1.ca2404.1_all.deb Size: 573012 MD5sum: ee97fefa64e25310bec6ee4cc498c9ed SHA1: d33d081c9c4d1ba5627974c5607f3cdebe303139 SHA256: fe917213a4b0973d23dc55637f333492aa63e6707510e0a05397b5a934d0fc5c SHA512: 655446aa5cfe8b4d8601abb11205c19c038380114ce18c85dba14ad07389b46c54fb0138f3e4b62b62eeafb4624368b717cfa2cff3a426009a0322ac56936ff8 Homepage: https://cran.r-project.org/package=stopmotion Description: CRAN Package 'stopmotion' (Build Stop Motion Animations from Image Sequences) A pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a 'frames' argument to target any subset of frames, bridging the gap between 'magick' functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via 'ImageMagick Studio LLC' (2024) . Package: r-cran-stopp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-mass, r-cran-fields, r-cran-optimx, r-cran-plot3d, r-cran-sparr, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat.random, r-cran-splancs, r-cran-spatstat.model, r-cran-spatstat.utils, r-cran-stlnpp, r-cran-stpp, r-cran-mgcv, r-cran-spatstat.univar Filename: pool/dists/noble/main/r-cran-stopp_1.0.0-1.ca2404.1_all.deb Size: 1781408 MD5sum: 5f657689433f4b79c5b0481a29e5c8d8 SHA1: 53eb0140411efc82ffd1be0bf1f6193e7ee88039 SHA256: 127d32349d8607afdb336aa5420ee51329d9bb5190f90a01fc841413fb362fc3 SHA512: 9e834b8293deff44278d325208d59be5ddf57c1bcdbcafeaf5d605848f341da5b16dc2707734af81835dd8be423a70a6212f711113df61a3d00488721bbaba04 Homepage: https://cran.r-project.org/package=stopp Description: CRAN Package 'stopp' (Spatio-Temporal Point Pattern Methods, Model Fitting,Diagnostics, Simulation, Local Tests) Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018; Siino et al. 2018; Adelfio et al. 2020; D’Angelo, Adelfio, and Mateu 2021; D’Angelo, Adelfio, and Mateu 2022; D’Angelo, Adelfio, and Mateu 2023). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks. Version 1.0.0 has been updated for accompanying the journal publication D Angelo and Adelfio 2025 . Package: r-cran-stoppingrule Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-stoppingrule_0.6-1.ca2404.1_all.deb Size: 167412 MD5sum: 184c545835638a1da787399e1e5d879b SHA1: c5623fe9e4b1e10daf735323eb30535a1e8faf28 SHA256: b257f17ca30a275c3530bacc34a0b986b46a6dadf866ed82c48ef99af2d956ea SHA512: 23f62009591306ed252bc25498876f06e5fe068a2650a82f11a4722531e019acd7a5fcb3109a3d5dcf28e6d05d304b8a24f7f3344ca3573d196cdf07902e7e98 Homepage: https://cran.r-project.org/package=stoppingrule Description: CRAN Package 'stoppingrule' (Create and Evaluate Stopping Rules for Safety Monitoring) Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies. Package: r-cran-stops Architecture: all Version: 1.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-smacofx, r-cran-acepack, r-cran-clue, r-cran-cmaes, r-cran-cordillera, r-cran-dfoptim, r-cran-energy, r-cran-minerva, r-cran-nloptr, r-cran-pomp, r-cran-pso, r-cran-registry, r-cran-scagnostics, r-cran-smacof, r-cran-tgp, r-cran-vegan Suggests: r-cran-r.rsp, r-cran-diceoptim, r-cran-dicekriging Filename: pool/dists/noble/main/r-cran-stops_1.9-1-1.ca2404.1_all.deb Size: 704232 MD5sum: b0dda5b3b0812a9198ad0d3c9777df2f SHA1: 83970ff15ad34d08b727cfee0d592be424ea8f7a SHA256: 4a42a8ec2e3bbe9dd41cc78011961f57d61571e217db6f4317a295425d18813a SHA512: a6197c9772439d7815e9f31aac69d4264ea80ba5b9a7a115dc097c52d1806e52379728a7b6553005da5911b98d48c00a9e93f54a399c8e78937f3a061cca485d Homepage: https://cran.r-project.org/package=stops Description: CRAN Package 'stops' (Structure Optimized Proximity Scaling) Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices. Hyperparameter optimization can be done with a number of techniques but we recommend either Bayesian optimization or particle swarm. For using "Kriging", users need to install a version of the archived 'DiceOptim' R package. Package: r-cran-stopwords Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-isocodes Suggests: r-cran-covr, r-cran-quanteda, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stopwords_2.3-1.ca2404.1_all.deb Size: 226068 MD5sum: 471f2cba64a6f5429ecb2079ba6e0e1d SHA1: 62d4eabe53986989148a9092c87be75e495196b9 SHA256: 25e7e73d724c8f06688c849696436148635caa2d52d7a83cf0dcceb08d94c3c1 SHA512: cb871afe48833649073e3a0ec040a4f171005b28175b9ee505528d719964789f12148beda6a07c94050b473f1b987180bd7963f68a47f975acee2ddb3751e8d9 Homepage: https://cran.r-project.org/package=stopwords Description: CRAN Package 'stopwords' (Multilingual Stopword Lists) Provides multiple sources of stopwords, for use in text analysis and natural language processing. Package: r-cran-storm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-permute, r-cran-rjson Filename: pool/dists/noble/main/r-cran-storm_1.2-1.ca2404.1_all.deb Size: 138538 MD5sum: 95331cba4a47e3488382705fe4f0436c SHA1: aa1b5b72cc8fd350793fb5d40b41f63d83313eac SHA256: a251af19d0c94317143243ed5c820bd6fee8d0f3cda21904a149008e24e4686e SHA512: d6d194c0d0ded83eb234f198704c94ccc4b5cc7c2157b6504e9751cfaa32b3c1afb657c7b2452d13582b18347a3532200ffeb7980a6e9ffcb92aa73de6a6831a Homepage: https://cran.r-project.org/package=Storm Description: CRAN Package 'Storm' (Write Storm Bolts in R using the Storm Multi-Language Protocol) Storm is a distributed real-time computation system. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing real-time computation. Storm includes a "Multi-Language" (or "Multilang") Protocol to allow implementation of Bolts and Spouts in languages other than Java. This R extension provides implementations of utility functions to allow an application developer to focus on application-specific functionality rather than Storm/R communications plumbing. 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To learn more about the project visit and . Package: r-cran-storywranglr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble, r-cran-urltools Filename: pool/dists/noble/main/r-cran-storywranglr_0.2.0-1.ca2404.1_all.deb Size: 54116 MD5sum: dc9e05ece5eb130505d9d25989717ed8 SHA1: b597cccbb16165100d0781c105ea6eeca122cb85 SHA256: 3c809f6affac685043023074ffb01f9719ddedae514dc433f7a395a5f4c1bce1 SHA512: 21f3ef7adacfcfd7a98fa6f076b81f878086e68d14361e61021b0a5196e810585e5ec2e449e87c286791c1c34bba7dcfaf897d9b9c1ba4ff0a56ca36ce7e4951 Homepage: https://cran.r-project.org/package=storywranglr Description: CRAN Package 'storywranglr' (Explore Twitter Trends with the 'Storywrangler' API) An interface to explore trends in Twitter data using the 'Storywrangler' Application Programming Interface (API), which can be found here: . Package: r-cran-stow Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-digest Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-stow_0.3.0-1.ca2404.1_all.deb Size: 80858 MD5sum: 1a613540d3f669e05ca8ccae5bacb7d6 SHA1: f1e0c50883aaacc956962771a77e4b533fdab242 SHA256: 93b9695f5bec46b7f684e13aa987ea39a5ae1da654b89727d49737a99ed6b88d SHA512: 5a21f35e73eaf537bd7787eb301153b16e9fe14c4baa75b3122225b41a550ae571336a30d1a2d0e82cde84a24476828bf44d81f661a9561cb03032f9ea30f52b Homepage: https://cran.r-project.org/package=stow Description: CRAN Package 'stow' (Durable Managed Local Copies of Remote Files) Turns remote file URLs into paths to durable managed local copies stored in a fixed subdirectory of platform-appropriate, package-specific user data directories. 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Package: r-cran-stpga Architecture: all Version: 5.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-algdesign, r-cran-scales, r-cran-scatterplot3d, r-cran-emoa Suggests: r-cran-r.rsp, r-cran-emmreml, r-cran-quadprog, r-cran-usingr, r-cran-glmnet, r-cran-leaps, r-cran-matrix Filename: pool/dists/noble/main/r-cran-stpga_5.2.1-1.ca2404.1_all.deb Size: 536116 MD5sum: c3401627af8db108fead0475b70b4edb SHA1: 08d8d1b732d81247c7d1a69bc4284ed96027a026 SHA256: f586ab43d8b271f59dad317ba0034a93297d0d8744bfdc4ce6ea1129bef51291 SHA512: 74fe4f442b794a316e4c1308832c82622014d4ce72946d2a36ce44487337814152d6d2ed4f6066eeb125e35ac186727ddef37c845ee00dc07f30239467f83c05 Homepage: https://cran.r-project.org/package=STPGA Description: CRAN Package 'STPGA' (Selection of Training Populations by Genetic Algorithm) Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems. Package: r-cran-stplanr Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2964 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-httr, r-cran-jsonlite, r-cran-lwgeom, r-cran-magrittr, r-cran-nabor, r-cran-od, r-cran-pbapply, r-cran-rcpp, r-cran-rlang, r-cran-sf, r-cran-sfheaders Suggests: r-cran-cyclestreets, r-cran-dodgr, r-cran-geodist, r-cran-igraph, r-cran-knitr, r-cran-leaflet, r-cran-mapsapi, r-cran-opentripplanner, r-cran-osrm, r-cran-pct, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-stplanr_1.2.3-1.ca2404.1_all.deb Size: 2129972 MD5sum: 01432b99e3875e91fdf99216a15bec33 SHA1: f006b911e910321251198728ee53e47a25f2e80b SHA256: c118aed71a6792957eacadc7d3c73239251dedfca3fa3bcbf4adf60999500e1d SHA512: ab6268f65d3ed27c06abfa74982fef96d66478c306304f4e014ef1215f0cb96b81e03eb99d007a382982a48b589c226e3447858bf5a4b67efff1617347052bbe Homepage: https://cran.r-project.org/package=stplanr Description: CRAN Package 'stplanr' (Sustainable Transport Planning) Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) , but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) . The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as (Desjardins et al. 2021) ; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) and the 'OD jittering' method described in Lovelace et al. (2022) . Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) , and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) . Package: r-cran-stppsim Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splancs, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-sf, r-cran-sp, r-cran-ks, r-cran-terra, r-cran-raster, r-cran-simriv, r-cran-data.table, r-cran-tibble, r-cran-stringr, r-cran-lubridate, r-cran-spatstat.geom, r-cran-sparr, r-cran-chron, r-cran-ggplot2, r-cran-geosphere, r-cran-leaflet, r-cran-cowplot, r-cran-gstat, r-cran-otusummary, r-cran-progressr, r-cran-future.apply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stppsim_1.3.4-1.ca2404.1_all.deb Size: 1443226 MD5sum: 6132f341e1a94f7b1f56b3422e2435c2 SHA1: 699970e42f162c8f1baf3597429c74b0317a8e61 SHA256: 58234d65f699fabebf31d4c4274cf8716f6323fb54cec517611d01c08cb87973 SHA512: 91dd7090075b51f99d133ca3bcaf892d900f02ee602ba509eede56f7e602dcab1ef9f83bd57d210c18d06c17d04eb49d3bd4b20ce458eeb3d2ac0b4777b02264 Homepage: https://cran.r-project.org/package=stppSim Description: CRAN Package 'stppSim' (Spatiotemporal Point Patterns Simulation) Generates artificial point patterns marked by their spatial and temporal signatures. The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)) and agent-based models (Bonabeau, E., (2002)), beginning with the configuration of movement characteristics for the specified agents (referred to as 'walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences. Package: r-cran-str2str Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-checkmate, r-cran-plyr, r-cran-reshape Filename: pool/dists/noble/main/r-cran-str2str_1.0.0-1.ca2404.1_all.deb Size: 344986 MD5sum: 54f1010f9b96bccbb5af62351367225d SHA1: 036839adee219d01a6766fc632b902543a04d86a SHA256: 2b0b81f3f25d7116fd031ab05d1cb034e4a9bd2478527f74b505a47ddeaaf862 SHA512: 601b1262e793fc682bedf9b70fb4e172039709b2f15df25fd0de41f21ba00a2c1369acea8c0b7a4e86edd691125388bd6051cc60975099f14833c61c1622f39b Homepage: https://cran.r-project.org/package=str2str Description: CRAN Package 'str2str' (Convert R Objects from One Structure to Another) Offers a suite of functions for converting to and from (atomic) vectors, matrices, data.frames, and (3D+) arrays as well as lists of these objects. It is an alternative to the base R as..() functions (e.g., as.data.frame.array()) that provides more useful and/or flexible restructuring of R objects. To do so, it only works with common structuring of R objects (e.g., data.frames with only atomic vector columns). 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In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting. Package: r-cran-strand Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1977 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-matrix, r-cran-rglpk, r-cran-dplyr, r-cran-tidyr, r-cran-arrow, r-cran-lubridate, r-cran-rlang, r-cran-yaml, r-cran-ggplot2, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-dt, r-cran-rsymphony, r-cran-officer, r-cran-flextable, r-cran-plotly Filename: pool/dists/noble/main/r-cran-strand_0.2.3-1.ca2404.1_all.deb Size: 1762546 MD5sum: 110b3e23cf01c26b887d95488a7dbf17 SHA1: d66013e9ac7226838218e8ab1ebadaca656ddcc0 SHA256: bc9f2cfe997341a5d55ce499421fdc7abd691d679f7974e42f9b0ea472e76d46 SHA512: 209b4683b29315c6746cf7fc23580c1586a5bbe115968bd36269bc1d6713d687e328e206251f677d50ed14b94749009d4a3df62f8eeaf6d9339ab37c8f28b262 Homepage: https://cran.r-project.org/package=strand Description: CRAN Package 'strand' (A Framework for Investment Strategy Simulation) Provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure. For background see L. Chincarini and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management". 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It also provides cost-constrained allocation with unequal per-stratum costs, a design-efficiency comparison (compare_designs), two- and three-dimensional and interactive visualisations, solution-quality diagnostics (a Cauchy-Schwarz optimality gap and KKT first-order residuals for the derivative-free solvers) and a self-contained 'shiny' application, while remaining backward compatible with the strata.data() and strata.distr() interface of version 1.x. The methodology follows Khan et al. (2008) , Reddy and Khan (2018) and Reddy and Khan (2020) . 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Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178. The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. ; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, . Package: r-cran-stratigraphr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rdpack, r-cran-checkmate, r-cran-glue, r-cran-igraph, r-cran-pillar, r-cran-purrr, r-cran-relations, r-cran-rlang, r-cran-sets, r-cran-stringr, r-cran-tidygraph, r-cran-vctrs, r-cran-vroom Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggraph, r-cran-knitr, r-cran-oxcaar, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-stratigraphr_0.5.0-1.ca2404.1_all.deb Size: 186534 MD5sum: f1007c3a8d4c757ae62ec48a7c4ad6a5 SHA1: 4ece27a4ef0da2b37a0c8f515057356acc320382 SHA256: 4c5fd0d1911659ce9ee6b4590e47b2fb3a6ffb96d514e4f9f94a60c0275d2205 SHA512: f9c8df210f8c27f91d8c90cb2c9dda71c6405baa797ce021606ca96c00533ce37735c4c5766bb36c72058a407e813975457e46afdaccae698325f821e5e33178 Homepage: https://cran.r-project.org/package=stratigraphr Description: CRAN Package 'stratigraphr' (Archaeological Stratigraphy and Chronological Sequences) A tidy framework for working with archaeological stratigraphy and chronology. Includes tools for reading, analysing, and visualising stratigraphic sequences (Harris matrices) as directed graphs following the definition of Dye and Buck (2015) and an R interface to the Chronological Query Language (CQL) used in 'OxCal' by Bronk Ramsey (2009) . Package: r-cran-stratpal Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admtools, r-cran-paleots Suggests: r-cran-ape, r-cran-fossilsim, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stratpal_0.7.1-1.ca2404.1_all.deb Size: 724808 MD5sum: b388e295cbbd2c792637405dfe798862 SHA1: ddc1c3ed366d386405f56d7f91429a0096446a6f SHA256: c241a93eefc3489b1635bb6bda322157e4cdc36ef15bb0f563af18a06a6f9412 SHA512: 9074e0f2b39b091ed6a6b6ab051dc15c47c89161d373f29c3892874620b94dfc41a7925b2a3fb09c4bf133e70d5b9a0c058579132fa2c7f6fd646b09a526c360 Homepage: https://cran.r-project.org/package=StratPal Description: CRAN Package 'StratPal' (Stratigraphic Paleobiology Modeling Pipelines) The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation. Package: r-cran-stratsel Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-memisc, r-cran-formula, r-cran-mnormt, r-cran-pbivnorm Filename: pool/dists/noble/main/r-cran-stratsel_1.4-1.ca2404.1_all.deb Size: 148156 MD5sum: 4727d4c2dd689963baeb6996f32e424f SHA1: 62afa54f12e0e1b5255f91edf905ccdb41779350 SHA256: dc72d4a081f0a3ec11270ac5f5114fc6424cb70e0db7f4b85918f74a35b56454 SHA512: 05d77b082fcdb9197df0ec077a614b0bd57dba1f8209f8fd657f20e774144d5f8e34445f1448dff677f3f0db64b56d476d738b7db0f4b677df75899248bd5ccb Homepage: https://cran.r-project.org/package=StratSel Description: CRAN Package 'StratSel' (Strategic Selection Estimator) Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets. Package: r-cran-stratus Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-igraph, r-cran-gmp, r-cran-ggplot2, r-bioc-ggtree, r-cran-rcppalgos Filename: pool/dists/noble/main/r-cran-stratus_1.1.2-1.ca2404.1_all.deb Size: 81750 MD5sum: a85a9e082bd839a0ca994413eea59742 SHA1: 8f9523a128aa4709ec7f911e3721aaec16dfa0ca SHA256: 23f604cf0ac6c2c7a49a816d4f606dc00ca973168eef270a616a857769151056 SHA512: ff39c34161e701ebc3e46af051f801bbbe96fcd60996b34e37a5a98595da4147d6111d6b25e7b5e53176650b9c09db6299d00542a396f2d7baabd137f7ac4e29 Homepage: https://cran.r-project.org/package=STraTUS Description: CRAN Package 'STraTUS' (Enumeration and Uniform Sampling of Transmission Trees for aKnown Phylogeny) For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) for methodology. Package: r-cran-stratvns Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-partitions, r-cran-multalloc Filename: pool/dists/noble/main/r-cran-stratvns_1.1-1.ca2404.1_all.deb Size: 52742 MD5sum: 08671f8da04d97bcf6e0fe475dec84f7 SHA1: 7627e38e6a85d5ef513802d71c7f351e5bec8fce SHA256: 9275afc6b43a73147f0fd59f55ccec3d7560fa28cf662bd2a271bee3fb1667bb SHA512: fdc0ef1dd76e041adae69a7438769657634900c5aa1462e8267bc00bdd305798affb03ce970d8e90b127e5d60983d643f7d754efe65656e5f3795e16e8f5268a Homepage: https://cran.r-project.org/package=stratvns Description: CRAN Package 'stratvns' (Optimal Stratification in Stratified Sampling) An Optimization Algorithm Applied to Stratification Problem.This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called vns. Package: r-cran-straweib Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-straweib_1.1-1.ca2404.1_all.deb Size: 77282 MD5sum: 2e1d23250dd1ccfac88a1e0e9e6a6300 SHA1: d55893f047b893e1cb6c5c57216276aa3c0c21c1 SHA256: adb1d3e5f23b4f710fb860af27b82ead353fad6bdcac41c8a28f6cef60309847 SHA512: d6d9075e485d4016ba7801e07660e2894d9f100795905d5b4a93145b75140e32c60a997e436d922680093564c2d37742861fb5b2b7e34ca6b180f26ebfd14efe Homepage: https://cran.r-project.org/package=straweib Description: CRAN Package 'straweib' (Stratified Weibull Regression Model) The main function is icweib(), which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata. Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and Raji Balasubramanian (2014) for more details. 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However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) for detecting anomalies in high-dimensional data that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. Package: r-cran-streak Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-matrix, r-cran-seurat, r-cran-speck, r-cran-vam Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-streak_1.0.0-1.ca2404.1_all.deb Size: 3148820 MD5sum: ed8b625f04593e1f9aa7bbf107372b05 SHA1: 20dc63473d47cab89a7bdfc7070c6c7b708bfa16 SHA256: e2b17aa516ef4e03a379c2a40e7b46b872d13b8a29d340626f22fe04fb34db90 SHA512: 78a1e3ff178623d0685ae115486a1221ca3088c9534e6f0312c98258eb72508865005b9acd4c17230512725f42422fe008867a8ecfd0a57daffdb882e6d25cd9 Homepage: https://cran.r-project.org/package=STREAK Description: CRAN Package 'STREAK' (Receptor Abundance Estimation using Feature Selection and GeneSet Scoring) Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) . Gene set scoring procedure is described in: Frost et al., (2020) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) . 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Convenience functions in the package wrap the API for 'StreamCat' on . 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Functions are broadly split into two groups: (1) analytical streamflow depletion models, which estimate streamflow depletion for a single stream reach resulting from groundwater pumping; and (2) depletion apportionment equations, which distribute estimated streamflow depletion among multiple stream reaches within a stream network. See Zipper et al. (2018) for more information on depletion apportionment equations and Zipper et al. (2019) for more information on analytical depletion functions, which combine analytical models and depletion apportionment equations. 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MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604). Package: r-cran-streamr Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-rjson, r-cran-ndjson Suggests: r-cran-roauth Filename: pool/dists/noble/main/r-cran-streamr_0.4.5-1.ca2404.1_all.deb Size: 68834 MD5sum: 123a3df9bc8b9b44c180e225d0c45e3a SHA1: a73b2eaf95d0f35dc01831184440d96c462b2213 SHA256: f2ca2739d955598446e804ffea4e8ae4d8f981aaddda8f70ac9fb9d2922daf1c SHA512: 12005074c5a0da5cfafdd426a79d4dfb11abcc75eeb49294b9ac0dee1fcc84ac222560929a3df744e30e7ad030816ccdd896459bc81252f5be8ce1ec5df1cded Homepage: https://cran.r-project.org/package=streamR Description: CRAN Package 'streamR' (Access to Twitter Streaming API via R) Functions to access Twitter's filter, sample, and user streams, and to parse the output into data frames. 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Package: r-cran-streg Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adgoftest, r-cran-numderiv, r-cran-mcmcpack, r-cran-matlab, r-cran-tseries Filename: pool/dists/noble/main/r-cran-streg_1.1-1.ca2404.1_all.deb Size: 115294 MD5sum: 4f67ea8f138ba2e2cba8ff3cc767277b SHA1: f60567cb08f2f65fee1607237aaad3c65d18b5d3 SHA256: ee8d43b566e18402c0285f2e2ec55e4330b27451776de08fd57e3f7c3e226639 SHA512: 73272c1733cd549d4c97fcd6cc7b0e981ac266bfffe6f5381dd7d23d8f31a14bed865016282d57a42df92c2011844f2b1796263398a97950332f0c94aaee6edd Homepage: https://cran.r-project.org/package=StReg Description: CRAN Package 'StReg' (Student's t Regression Models) It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form. Poudyal, N., and Spanos, A. (2022) . Spanos, A. (1994) . Package: r-cran-strength Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-strength_0.1.0-1.ca2404.1_all.deb Size: 54840 MD5sum: 0f3e6100c958d9b57927d4f9c7215acf SHA1: 5aacdadec869f80a11df6c040cabc48c86b4783a SHA256: e5eb1f927b4db6b4865a817557360096e4ee5a536821ee3d2fe88ce9e9872e4d SHA512: 3e9b61508ce1ff93f6a757bb8579d51686b33a66aeebf0370fc765d8b121cd282578fedac04dc475719684337e4d7f23be04c9d3d6c005fbdfd70f27a52a7505 Homepage: https://cran.r-project.org/package=strength Description: CRAN Package 'strength' (Operations Designed for Tidy Strength Data) Mappings for estimated one rep max from commonly used formulas. Convenience functions for turning mass/rep/set data into useful derived quantities. Package: r-cran-stressaddition Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-drc, r-cran-plotrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stressaddition_3.1.0-1.ca2404.1_all.deb Size: 79518 MD5sum: 36b59ea95c905544a979278158ef770f SHA1: b2940bd3ad280960a54fd4db3c77b34f14ced404 SHA256: 323000cf2a3c09d30cd03766a96f76b31c40e4c2a64750ceedbd288628b7d8cd SHA512: 59e2d5c762b8e8fd348ba408617e70bf9c4c22314e7fa61737d804ca29f30ad2b8b5ebd2e1c925bc52b5516fba4e89f7b242a326c5e9754a5122655ece61af13 Homepage: https://cran.r-project.org/package=stressaddition Description: CRAN Package 'stressaddition' (Modelling Tri-Phasic Concentration-Response Relationships) The stress addition approach is an alternative to the traditional concentration addition or effect addition models. It allows the modelling of tri-phasic concentration-response relationships either as single toxicant experiments, in combination with an environmental stressor or as mixtures of two toxicants. See Liess et al. (2019) and Liess et al. (2020) . Package: r-cran-stresscensor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stresscensor_0.1.0-1.ca2404.1_all.deb Size: 128892 MD5sum: b5d405db59bf5b412ea11e0687bd0ef7 SHA1: 924053805e0c5b3bd769ca311046b2cb3fa6078f SHA256: 289958f8235d069fd26dca8ce98f138d6d55d76eff922439dbea7c5849ecb067 SHA512: c60c172d990554b0237f0c550ba53e8412e50d8d978888f86406626c48e2793720267140d66f5c9f6b96dafda743970d7b5eba5e26b5cec55e530596947c34a7 Homepage: https://cran.r-project.org/package=StressCensoR Description: CRAN Package 'StressCensoR' (Generalized Stress-Strength Reliability Estimation UnderCensoring Schemes) Generalized framework for data generation, Maximum Likelihood Estimation, and Bayesian estimation of stress-strength reliability R = P(Y < X) for arbitrary continuous distributions under censoring schemes based on Chapter 9 of 'Balakrishnan', 'Cramer', and 'Kundu' (2023) . Users provide probability density functions, cumulative distribution functions, survival functions, support bounds, parameter ranges, and sample sizes. Implements data generation under Type-I, Type-II, progressive Type-II, Type-I hybrid, Type-II hybrid, generalized hybrid, progressive hybrid, joint, block random, middle, and truncation censoring schemes, accompanied by diagnostic histograms, dot plots, and autocorrelation plots. Maximum Likelihood Estimation supports optimization routines including 'Newton-Raphson', 'Broyden'-'Fletcher'-'Goldfarb'-'Shanno' ('BFGS'), 'BFGS' in R ('BFGSR'), 'Berndt'-'Hall'-'Hall'-'Hausman' ('BHHH'), Simulated Annealing ('SANN'), Conjugate Gradients ('CG'), and 'Nelder'-'Mead' ('NM'), returning summaries ('AIC', 'coef', 'logLik', 'nIter', 'stdEr', summary, 'vcov'). Bayesian estimation of stress-strength reliability R = P(Y < X) is performed via Gibbs sampling, Metropolis-Hastings algorithm, Importance Sampling, and 'Lindley' approximation (1980). Methods and censoring schemes are described in 'Balakrishnan', 'Cramer', and 'Kundu' (2023, ISBN:978-0-12-398387-9), 'Lindley' (1980) , 'Geweke' (1989) , 'Metropolis' (1953) , 'Hastings' (1970) , 'Geman' and 'Geman' (1984) , 'Kundu' and 'Gupta' (2005) , 'Kundu' and 'Gupta' (2006) , 'Berndt', 'Hall', 'Hall', and 'Hausman' (1974) , 'Fletcher' (1987, ISBN:978-0-471-91547-8), and 'Nelder' and 'Mead' (1965) . Package: r-cran-stressor Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-mlbench, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stressor_0.2.0-1.ca2404.1_all.deb Size: 291214 MD5sum: 2ec1308ca2e4f4e7fca97e051f6c5154 SHA1: 1cb59031485e23989901c245840d8ccca1ce4f43 SHA256: 377216220d5f16bc70935557bf83408561bf91f57ee15b6f982c9f23168ac8ff SHA512: 1321272cb7e664e9326150cd23a178c13cc3769c641fc1c43ab41a912164a07dac83ad8b96cb2ee3b0334562338d88554c4b7657c278e8baf91c74b18c918a72 Homepage: https://cran.r-project.org/package=stressor Description: CRAN Package 'stressor' (Algorithms for Testing Models under Stress) Traditional model evaluation metrics fail to capture model performance under less than ideal conditions. This package employs techniques to evaluate models "under-stress". This includes testing models' extrapolation ability, or testing accuracy on specific sub-samples of the overall model space. Details describing stress-testing methods in this package are provided in Haycock (2023) . The other primary contribution of this package is provided to R users access to the 'Python' library 'PyCaret' for quick and easy access to auto-tuned machine learning models. Package: r-cran-stressr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2896 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-xml, r-cran-lattice, r-cran-latticeextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stressr_1.0.0-1.ca2404.1_all.deb Size: 2414834 MD5sum: 72229a465e853cd41aad7a4a66a1ab3c SHA1: dd89349d84cc010fafb4238844a645bf71af8325 SHA256: 26973da3195629a4123d7b6c937072a944474a8eba52abcdacdb7b4083aeee61 SHA512: caea7cc319fc5f62fb717dfb93d9605a7d0f5fa056343379fb4f6b9fdd2e91cd5c3cffa098bb1bc9ef5adf06c56f9c96c4c7679a541931dc755ee22832ade739 Homepage: https://cran.r-project.org/package=stressr Description: CRAN Package 'stressr' (Fetch and plot financial stress index and component data) Forms queries to submit to the Cleveland Federal Reserve Bank web site's financial stress index data site. Provides query functions for both the composite stress index and the components data. By default the download includes daily time series data starting September 25, 1991. The functions return a class of either type easing or cfsi which contain a list of items related to the query and its graphical presentation. The list includes the time series data as an xts object. The package provides four lattice time series plots to render the time series data in a manner similar to the bank's own presentation. 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Package: r-cran-string2adjmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Filename: pool/dists/noble/main/r-cran-string2adjmatrix_0.1.0-1.ca2404.1_all.deb Size: 15296 MD5sum: 6ef51b7b6e8f968cfbff7ad53c751457 SHA1: 80639f5c955e29c3c8184e84d5133976b55a1811 SHA256: 560f14dc49eba659e4c659c33bc705c31a5d84c1eaff7dd87c45ca8167dae666 SHA512: 3d0477efd470dddce2288fd0d85f3e54cc5747ab4e70538905083f66b0bdaed7e9aa9e545aaecb6aa135e15bc2cda49f123b76d1259ff5f25295aeb2d2ad3d98 Homepage: https://cran.r-project.org/package=String2AdjMatrix Description: CRAN Package 'String2AdjMatrix' (Creates an Adjacency Matrix from a List of Strings) Takes a list of character strings and forms an adjacency matrix for the times the specified characters appear together in the strings provided. For use in social network analysis and data wrangling. 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The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to 'ICU' (International Components for Unicode) and 'stringi', they are fast, reliable, and portable across different platforms. Package: r-cran-strip Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlist Suggests: r-cran-caret, r-cran-e1071, r-cran-knitr, r-cran-randomforest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strip_1.0.0-1.ca2404.1_all.deb Size: 30276 MD5sum: eb2a1c5aaa0aae6635b88409d6dc0a07 SHA1: 89a3dbb5fef33fe8ec27b8db5d6c09294ac50795 SHA256: b7bd5daf02b0e008f8e45310171b4f03a5c806c9a1509d38c9b262806e0de0ef SHA512: 1e5302c8e72ff6ec86e7637a3dfb4d763d63f7f7016471c8ebf209ed8e5013889456fd7fd187da614fe5b020b0d5fa988be73535a36f5eef1b41221655dae1e1 Homepage: https://cran.r-project.org/package=strip Description: CRAN Package 'strip' (Lighten your R Model Outputs) The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc. Package: r-cran-stripless Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-faraway, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stripless_1.0-3-1.ca2404.1_all.deb Size: 633360 MD5sum: 4c65ef245b9aa7e904351f0bc2e0ec57 SHA1: a8f009753ebef004f606f2ce992b34f67af87532 SHA256: e9a2ced132138022ce8034620f3e38daa2ac991ea28bccdc9a62ef0f3c1d0087 SHA512: ba68a2c559b597b1a2384e602d2fdf5288fd09d71c6fe713921efc8d5318db4ba0c9833b3fa8d0aa888747ef2b03056bfd99587aa1142b76e2693eebc79dd3df Homepage: https://cran.r-project.org/package=stripless Description: CRAN Package 'stripless' (Structured Trellis Displays Without Strips for Lattice Graphics) For making Trellis-type conditioning plots without strip labels. 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Package: r-cran-stroupglmm Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aod, r-cran-broom.mixed, r-cran-car, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-lmertest, r-cran-magrittr, r-cran-mass, r-cran-mutoss, r-cran-nlme, r-cran-parameters, r-cran-phia, r-cran-scatterplot3d, r-cran-survey Filename: pool/dists/noble/main/r-cran-stroupglmm_0.3.0-1.ca2404.1_all.deb Size: 140360 MD5sum: 68e2ddd7b19fde20e6bc58085c8111ca SHA1: 754c48df871b2fccef5ff78e63eb49f0f92092c6 SHA256: bcdb8a687500bb8f9df5e4cc7647302f73a22cedb023d5e94392cc5e9fac88cd SHA512: 71b4e280260a0b7d6ce0c96479f75f3d2464d7ebb8cda1d5d7fba9e39af806729fd792794922cbf0567f8fcfa2ef65af6b59202f0a694cf2261d5608584f6abf Homepage: https://cran.r-project.org/package=StroupGLMM Description: CRAN Package 'StroupGLMM' (R Codes and Datasets for Generalized Linear Mixed Models: ModernConcepts, Methods and Applications by Walter W. Stroup) R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press. Package: r-cran-strs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-strs_0.1.0-1.ca2404.1_all.deb Size: 85060 MD5sum: 816031aabdd159e26d60f1c0341b3807 SHA1: 0569786364705044a0259f3795a26ae29738466e SHA256: 2fde3a30b98c9a8b14cce8f242bb42b07a55497b994dc2a63ddc4c500122300d SHA512: 80807b02170a9ab8f0457396f205ca9ed52be926be04dc69cf54333d1aaabd4b9ed7a5e5ee9f34321755d2815cc7a4659e030f5c9234ca01953119dd1cd4f83b Homepage: https://cran.r-project.org/package=strs Description: CRAN Package 'strs' ('Python' Style String Functions) A comprehensive set of string manipulation functions based on those found in 'Python' without relying on 'reticulate'. It provides functions that intend to (1) make it easier for users familiar with 'Python' to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings. Package: r-cran-structfdr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-ape, r-cran-cluster, r-cran-dirmult, r-cran-matrixstats Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape Filename: pool/dists/noble/main/r-cran-structfdr_1.4-1.ca2404.1_all.deb Size: 476152 MD5sum: 555e2f03cd27c4db27357d3fa290a378 SHA1: d69a0628a1c65c1eee5a77edfda46a82aa027f9d SHA256: 97578cee7cd4ba02d7dd68aa7f43b8473814d6cabdf7c504250c3bbee797cbd0 SHA512: 036e0248459cd5291c5afe58564cc4308ff52c09664fdddf2f1e3e2de2530bfe0b798a001676afd30546b15c7d29f7e927fe0a444e6975a3cd7d944a5e4c563c Homepage: https://cran.r-project.org/package=StructFDR Description: CRAN Package 'StructFDR' (False Discovery Control Procedure Integrating the PriorStructure Information) Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species. 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Package: r-cran-structree Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-lme4, r-cran-penalized Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-structree_1.1.7-1.ca2404.1_all.deb Size: 328730 MD5sum: 7b81baaf7f46c611cd2b030a7b92707d SHA1: f4455a10898c4982880d34fc7f80d6c3e9d07c36 SHA256: 122251659d1a40e724720805e3204598ca27b4695cc1881d59eeaad5f4f8f1e9 SHA512: a5bd61d408ae99abf9d087cb99eefb183eee3a4e0c884f54a8f2d3d528aabbb31483048da2687b0434d93321d91cc7ca94f7de1fe867c36453995bcae2ed5aaf Homepage: https://cran.r-project.org/package=structree Description: CRAN Package 'structree' (Tree-Structured Clustering) Tree-structured modelling of categorical predictors (Tutz and Berger (2018), ) or measurement units (Berger and Tutz (2018), ). Package: r-cran-structssi Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-multtest, r-cran-igraph, r-cran-jsonlite, r-cran-ggplot2, r-bioc-phyloseq, r-cran-reshape2 Suggests: r-cran-ape, r-cran-testthat Filename: pool/dists/noble/main/r-cran-structssi_1.2.1-1.ca2404.1_all.deb Size: 293806 MD5sum: 5e55f096b14f53ec40e3a5704544ebac SHA1: 0874f687932130852325b4b04e0200819b4910b9 SHA256: 67a5496979d8faf6d8506faa5f5a61666d47298da76ba1760bc2a08a7f5d9ea0 SHA512: 4e6c14b803cb782978b520a945ed29a191da1657e23225b08ad6c676a7639bbb062e411689f1f1600e4e5a8de67c8a454c96603ec04fe9ed83163f5a65f14df9 Homepage: https://cran.r-project.org/package=structSSI Description: CRAN Package 'structSSI' (Multiple Testing for Hypotheses with Hierarchical or GroupStructure) Performs multiple testing corrections that take specific structure of hypotheses into account, as described in Sankaran & Holmes (2014) . Package: r-cran-structuraldecompose Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-segmented, r-cran-strucchange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-structuraldecompose_0.1.1-1.ca2404.1_all.deb Size: 111708 MD5sum: 13bc624fe9d8030e5fbf6ac1d256a133 SHA1: 18cd9f9ef184d040eaac36d8ab8cce11c3d53a6c SHA256: b2983954ca39c6af5687035fa2beb771f2092ea886dd9411f36f6a2d42b7dc23 SHA512: 3f9445745c1b400a701b65fb917aa1fd58cf36ef8416b291c11ff1f2d3dbfb7204e8654276deffc5b042043235f7d5421ae1ecadbb5dfd6f4694733111157618 Homepage: https://cran.r-project.org/package=StructuralDecompose Description: CRAN Package 'StructuralDecompose' (Decomposes a Level Shifted Time Series) Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. 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Package: r-cran-structuremc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-structuremc_1.0-1.ca2404.1_all.deb Size: 17188 MD5sum: 45ac13cead6733ca6d6e855dda332a4c SHA1: 7055d357dca7484347301c5d90689e3761579f8c SHA256: b3525ba2761fcc5eab8fa1343484690ca4f836ac12c82c99518ddf6edadaa46e SHA512: 28e8fd41757f8e75d284cebcdcf7ff9ea7a68fe16c4fc5c44a15d21ea5825e067dac8bacc725bad74f898d4c0deb28968182956ba7e2fe51b5aacfa8e6e41dc6 Homepage: https://cran.r-project.org/package=StructureMC Description: CRAN Package 'StructureMC' (Structured Matrix Completion) Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) ]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21. Package: r-cran-strvalidator Architecture: all Version: 2.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3738 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-gridextra, r-cran-gtable, r-cran-plyr, r-cran-scales, r-cran-data.table, r-cran-dt, r-cran-dplyr, r-cran-plotly, r-cran-mass Suggests: r-cran-resourceselection, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strvalidator_2.4.2-1.ca2404.1_all.deb Size: 2231950 MD5sum: 9da318ea59cf4af4cce4aa857ed1aa3a SHA1: 5b6cfd55013cae303b18069db98bd8cc28042868 SHA256: 0454922df5e3efc4dea78a4da3218df9949d551a70745e496926f33bb2c9a5f2 SHA512: e609736169c6db7e7aec50d09f52742907ec63bed018c707c3e27bae4a6c0859a4a62b3434e33e9aacd1c58c7f6c6882d317eed96bebdb79f0fc6a809e27e3a7 Homepage: https://cran.r-project.org/package=strvalidator Description: CRAN Package 'strvalidator' (Process Control and Validation of Forensic STR Kits) An open source platform for validation and process control. Tools to analyze data from internal validation of forensic short tandem repeat (STR) kits are provided. The tools are developed to provide the necessary data to conform with guidelines for internal validation issued by the European Network of Forensic Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group on DNA Analysis Methods (SWGDAM). A front-end graphical user interface is provided. More information about each function can be found in the respective help documentation. Package: r-cran-stsd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stsd_0.2.0-1.ca2404.1_all.deb Size: 215248 MD5sum: a5da64f4b1f46a44f3ceaa9eb2146201 SHA1: 33b287504eaafce191e4cbeceeb8005900df5707 SHA256: 197f9b5a645001c647e1b2600eeea4ae6ead060595fdeb1486b73d6cdb13aaaf SHA512: ca7949bcfd897edf4a87044e11c20f78e6026b41181ccf7f9fabcd44dbde8767165e554e72f143210cd3eebe74fd8eada76ba3c5472fd52457674f3329f5507e Homepage: https://cran.r-project.org/package=sTSD Description: CRAN Package 'sTSD' (Simulate Time Series Diagnostics) These are tools that allow users to do time series diagnostics, primarily tests of unit root, by way of simulation. While there is nothing necessarily wrong with the received wisdom of critical values generated decades ago, simulation provides its own perks. Not only is simulation broadly informative as to what these various test statistics do and what are their plausible values, simulation provides more flexibility for assessing unit root by way of different thresholds or different hypothesized distributions. 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Package: r-cran-subincomer Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-countrycode, r-cran-curl, r-cran-dplyr, r-cran-rlang, r-cran-sf, r-cran-tidygeocoder, r-cran-zip Suggests: r-cran-fixest, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-subincomer_0.6.0-1.ca2404.1_all.deb Size: 649456 MD5sum: 3435fc7204646869e52ffaaaceffe6ce SHA1: d43292e5412cb17be5185ef55d3b92a67f869a66 SHA256: d859be4e3279d352a5d6a504e96d5cacd77d43eccc6a3e41c81e86c2b0bc17b0 SHA512: 8ca5a00895dc6f502f065fa19a34420bbe3c2cabdb82862303f0e4d9be3f64580ee08b197b3f73850ca5ce039b453afd67c527f9e5f4c4d96a96818bf45b0d95 Homepage: https://cran.r-project.org/package=subincomeR Description: CRAN Package 'subincomeR' (Access to Global Sub-National Income Data) Provides access to granular sub-national income data from the MCC-PIK Database Of Sub-national Economic Output (DOSE). 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Package: r-cran-submax Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-sensitivityfull Filename: pool/dists/noble/main/r-cran-submax_1.1.5-1.ca2404.1_all.deb Size: 84500 MD5sum: ade378681470e7eb536ec9a94ca6603f SHA1: f589c7dadd105f3ebf8a28778368ca6325afbd54 SHA256: ec99610461961f159a94d05caa1d4a8cb1d2e9b2b7285689a28c56f01bec8325 SHA512: 3ded61244bf5282e67bc220d21dc522ab2543f97e1a5114e418fd3b01eb082a0cc7f164e563a02796fff390ef5def0147ecd3d4a55a135dad4910b53ac748782 Homepage: https://cran.r-project.org/package=submax Description: CRAN Package 'submax' (Effect Modification in Observational Studies Using the SubmaxMethod) Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2018) does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution. Package: r-cran-submitr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2334 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-readr, r-cran-yaml Suggests: r-cran-containr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-toolero, r-cran-withr Filename: pool/dists/noble/main/r-cran-submitr_0.1.0-1.ca2404.1_all.deb Size: 1915658 MD5sum: 4bc10fa87955b9bc469dafbd0badfdff SHA1: fe004da33330580870a2194d95932168be62ee78 SHA256: 0509e5a45deb882fae2c26832a692522db5269be964cdf5a4ecbc74bc076616c SHA512: 56d70265747f33b75c1a28b763e2e892bd68bb3712c02c12b6bad3fb379fd5e06b3e18c0faf2c92ee313cc51ae88329d40eaf5409a921f6526a3304b07560c04 Homepage: https://cran.r-project.org/package=submitr Description: CRAN Package 'submitr' (Scaffold and Submit Computational Jobs to HTC Schedulers) Provides scaffolding tools to help researchers prepare and submit computational jobs to high-throughput computing (HTC) schedulers. Generates the files required to run containerized R analyses on 'HTCondor', including submit files and executable scripts, and wraps the system commands needed to stage files, submit jobs, monitor status, and retrieve results from a CHTC submit node. Provides 'htc_config()' for managing connection details and SSH connection reuse guidance. Works naturally alongside 'containr' for container image management and 'toolero' for dataset splitting and project scaffolding. Package: r-cran-subniche Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-polyclip, r-cran-wordcloud Suggests: r-cran-adegraphics, r-cran-ape, r-cran-circstats, r-cran-deldir, r-cran-lattice, r-cran-mass, r-cran-pixmap, r-cran-spdep, r-cran-splancs, r-cran-waveslim Filename: pool/dists/noble/main/r-cran-subniche_1.6-1.ca2404.1_all.deb Size: 187032 MD5sum: b9b189820486978456938fce42926936 SHA1: a84f3248239dcc3584575e46394c4e71452e2323 SHA256: fde235b5851d1a9dda1b47b75d8b6048c94b2505de1512a02a98b90f60d4fda0 SHA512: d5e067db020107fcf06e78bf40f5bda9069cea23f9680285790ce536c5880d723908317731d2af6a970ba553aeba2a16eb1fdb7b3da3df668824332f365a116f Homepage: https://cran.r-project.org/package=subniche Description: CRAN Package 'subniche' (Within Outlying Mean Indexes: Refining the 'OMI' Analysis) Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz 'et al.' (2017) . Package: r-cran-subpathwaylnce Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-rbgl, r-cran-biasedurn, r-bioc-graph Suggests: r-cran-xml Filename: pool/dists/noble/main/r-cran-subpathwaylnce_1.0-1.ca2404.1_all.deb Size: 3111326 MD5sum: f18b557a9b76741614ed45cd29ad4b4f SHA1: 0b2f0a7d67c3d6d77d1d94f42e8a3714c7176a3e SHA256: 885ead4b0a9cbe1025c08c870385036a75644d29242efd5764af91f2a7b88d15 SHA512: fde376093dad2b74e5cc4eaaa8d81d6457d413eba2e4a5eec842b67d78de5b618b13d785ebfa49b90beeb0000c872d8a2c1f825f8ff80ba681f37564ff1263bc Homepage: https://cran.r-project.org/package=SubpathwayLNCE Description: CRAN Package 'SubpathwayLNCE' (Identify Signal Subpathways Competitively Regulated by LncRNAsBased on ceRNA Theory) Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies. Package: r-cran-subscore Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ctt, r-cran-irtoys, r-cran-sirt, r-cran-ltm, r-cran-cocor, r-cran-boot Filename: pool/dists/noble/main/r-cran-subscore_3.3-1.ca2404.1_all.deb Size: 110552 MD5sum: 33120f0517eff6fb4b62cbb0f3c6c0a9 SHA1: 46877f2424982af7bfc88e61c34f0f906f311b84 SHA256: 1d6e0fc23766e55ae4caf4fcd7127563a5b2f4d90dca7926d8f5cab3b6167d28 SHA512: f870028b3b5e47199ecdc8c0282c107f19a69168cfe19b79ff349cd6994be2c518c49aeba7a35f70cb16a4296eb897fb59d1943ea30772ae84caef6eca61d19c Homepage: https://cran.r-project.org/package=subscore Description: CRAN Package 'subscore' (Computing Subscores in Classical Test Theory and Item ResponseTheory) Functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) , (2) Haberman's subscoring methods (Haberman, 2008) , and (3) Yen's objective performance index (OPI; Yen, 1987) . It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 ; Yen, 1993 ). Package: r-cran-subscreen Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-stringr, r-cran-shiny, r-cran-dt, r-cran-shinyjs, r-cran-bsplus, r-cran-colourpicker, r-cran-dplyr, r-cran-ranger, r-cran-shinywidgets Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-subscreen_4.0.1-1.ca2404.1_all.deb Size: 1933602 MD5sum: b233bd335008451702deb33bac7aad9f SHA1: 7129719013be61bc5d37ce0aa7f4903f5946da51 SHA256: b7f885af139cb1962a65c53a6a49a0de22684f6a019af6bdfe894df1ed4692f6 SHA512: 22775169d2e2178d6c27dfa42e37130a09b24ba2c733076a4faad6e1acaf8f3184cdaf9dc0f82e8591318bcf5271dd0ca14d71012391e9880aa77060cd548034 Homepage: https://cran.r-project.org/package=subscreen Description: CRAN Package 'subscreen' (Systematic Screening of Study Data for Subgroup Effects) Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation. The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup. The diagram may show thousands of them. The position of the dot in the diagram is determined by the sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore, the diagram shows the line of no effect and the overall study results. For small subgroups, which are found on the left side of the plot, larger random deviations from the mean study effect are expected, while for larger subgroups only small deviations from the study mean can be expected to be chance findings. So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups. 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Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) . 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The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) ; Han et al (2019) ; Lamb and Justin (2006) ). Please cite using . Package: r-cran-subvis Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-bioc-biostrings Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-subvis_2.0.2-1.ca2404.1_all.deb Size: 50090 MD5sum: 9d021529e780f56591d8319e2ecaaf15 SHA1: 73558dcfa3e7e287ca1149cef876e51dce5878fd SHA256: 48b2ddce1f53f9db8bd89c7e0d19c8fc23e81c7a2522ae1d47ace9f7d6856dbd SHA512: 7841f7f07c45987909e1ed7a5a0fea5f135e878d24943e214a11caaebb1daeac5ace0d6c80bc0cfd20451b2135f4765fa5a7d37286105f9c80bcc694a3cbc7eb Homepage: https://cran.r-project.org/package=SubVis Description: CRAN Package 'SubVis' (Visual Exploration of Protein Alignments Resulting from MultipleSubstitution Matrices) Substitution matrices are important parameters in protein alignment algorithms. 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Allows users to construct the Continuous Time Generalized Rapid Response CUSUM (CGR-CUSUM) , the Biswas & Kalbfleisch (2008) CUSUM, the Bernoulli CUSUM and the risk-adjusted funnel plot for survival data . These procedures can be used to monitor survival processes for a change in the failure rate. Package: r-cran-sudachir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-reticulate, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sudachir_0.1.0-1.ca2404.1_all.deb Size: 79262 MD5sum: 5ba1668bd705854bf37286f85dd68245 SHA1: 8ee68f84f3991fd5a58c9a893503040f7ff87a10 SHA256: 3b41485c73575c7df96bb4ab2a5a7914353ee5573e7e883782e8aad6e5906a34 SHA512: 52bc5cdecf8096358a150ea0352c0d2ff20867b5ca5212bc57ee8f48c89e4f63ec9508f6aa40f9e97a2d010be2d491b530b56a811d11c5aaee55381cfffcbd76 Homepage: https://cran.r-project.org/package=sudachir Description: CRAN Package 'sudachir' (R Interface to 'Sudachi') Interface to 'Sudachi' , a Japanese morphological analyzer. This is a port of what is available in Python. Package: r-cran-suddengains Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-ggplot2, r-cran-psych, r-cran-readr, r-cran-tidyr, r-cran-ggrepel, r-cran-patchwork, r-cran-forcats, r-cran-naniar, r-cran-scales, r-cran-cli Suggests: r-cran-haven, r-cran-writexl, r-cran-knitr, r-cran-dt, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-suddengains_0.7.2-1.ca2404.1_all.deb Size: 669134 MD5sum: f444789c43afe67935b143febc56f4b1 SHA1: 65aa0f49fad7c0efd1ce33004d1ae0dcd89c84c3 SHA256: 7d983c6590da8de6f94d72fced108f5840d180580305bbc4d0e3ac05d94eeced SHA512: dd00df971764f7c1a17fd11a2f18472a7e2efeb9e8592234edb12c0cf7c1aeb5b0ce1dbeff0798e158da4d17b5be5265435571aab0ff2f8c2fd54046e7f16da7 Homepage: https://cran.r-project.org/package=suddengains Description: CRAN Package 'suddengains' (Identify Sudden Gains in Longitudinal Data) Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases. Package: r-cran-sudoku Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-sudoku_2.8-1.ca2404.1_all.deb Size: 51170 MD5sum: e20801ed2226aeb91954d23ee71a1d86 SHA1: 4857784876b57ac475bf63faf2e7b83966325bf0 SHA256: ca27c5c9d5d53505cb21647996e8995cf9d69cbe2cdec4de20805503568d4be1 SHA512: 65cbf6b09cbb3a6a1be5409166cc88fc2f84617df662c91c940bf3804cdcec46b2f8a42583923c6f176ae30db5866524d7e6209ba71b8ee7808d506434452304 Homepage: https://cran.r-project.org/package=sudoku Description: CRAN Package 'sudoku' (Sudoku Puzzle Generator and Solver) Generates, plays, and solves Sudoku puzzles. The GUI playSudoku() needs package "tkrplot" if you are not on Windows. 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The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints. Package: r-cran-sudokudesigns Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sudokudesigns_1.2.0-1.ca2404.1_all.deb Size: 55366 MD5sum: b0a0bdacf1910735e9dc627800018dc4 SHA1: 5777a7ff93d51bd2bfd7f651888b7d0ece1c98a3 SHA256: b265ac52bbcb0b3a6adc932dc68aa076f928c52b35384c06570e4634ca8c91fd SHA512: 5e89eab3821d4f3e459b2c19e2cbf11fbe7bd96bf26472ca965fe92d742f387109bf541725b94f06ee3b5844f209fc8666a6b677981ad9756958074d1788b234 Homepage: https://cran.r-project.org/package=SudokuDesigns Description: CRAN Package 'SudokuDesigns' (Sudoku as an Experimental Design) Sudoku designs (Bailey et al., 2008) can be used as experimental designs which tackle one extra source of variation than conventional Latin square designs. Although Sudoku designs are similar to Latin square designs, only addition is the region concept. Some very important functions related to row-column designs as well as block designs along with basic functions are included in this package. Package: r-cran-suessr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-suessr_0.1.7-1.ca2404.1_all.deb Size: 721214 MD5sum: 0804eff10aa13217976a5554cb83c664 SHA1: 7fd1223d5dfef7b707c623a3768367b869573a1c SHA256: 81edfb6044bb490419670ebfcbd430f572f59ba62fb5f1be60df96ad7b95eba0 SHA512: 990eb494f6027790a5f186b96c2528f0056634d42e03811334aafda89753c6696b640e618c956c5b8a668bc67871e3b1759073c5846be858f2fcf4bf91cf43b7 Homepage: https://cran.r-project.org/package=SuessR Description: CRAN Package 'SuessR' (Suess and Laws Corrections for Marine Stable Carbon Isotope Data) Generates region-specific Suess and Laws corrections for stable carbon isotope data from marine organisms collected between 1850 and 2025. 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See the Journal of Statistical Software reference: Zhang, H. S., Cook, D., Laa, U., Langrené, N., & Menéndez, P. (2024) . The manuscript for this package is currently under preparation and can be found on GitHub at . 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Compute day and/or night length using different twilight definitions or arbitrary sun elevation angles. This package is part of the 'r4photobiology' suite, Aphalo, P. J. (2015) . Algorithms from Meeus (1998, ISBN:0943396611). Package: r-cran-sunclarco Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-sunclarco_1.0.0-1.ca2404.1_all.deb Size: 155418 MD5sum: 53b9216a631420826724e7a5a9f8438b SHA1: 4419255dd242e4a079e20794f26006f2c1f80671 SHA256: b649d3ede95da7510bb315ddf30e5067bf6b8b864ff514a74720671e1e58541f SHA512: 73342bf4b7974fac2a7f724d73d195e7e0680b56328136d83ae4dfb906d5d29b52d47822eabb01dcd53443dee081bee4e152de4e67d72b5ff1401ee9cc3b0b07 Homepage: https://cran.r-project.org/package=Sunclarco Description: CRAN Package 'Sunclarco' (Survival Analysis using Copulas) Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) ). 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Part of the Sub-National Geospatial Data Archive System. For the underlying methods, see Zhukov, Byers, Davidson, and Kollman (2024) "Integrating Data Across Misaligned Spatial Units," Political Analysis, Volume 32, Number 1, pp. 17-33 . Package: r-cran-sunsvoc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3975 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ddiv, r-cran-magrittr, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-data.table, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sunsvoc_0.1.2-1.ca2404.1_all.deb Size: 3981556 MD5sum: 51bb34f8c5a4269cd9e15995d3eea74f SHA1: 7e441e73270dfdfee271c53f1ef809da154f1149 SHA256: 526dbf15408497ffafe7f53bda1c51edee5dfcfa35c7e085f01428d211f946cf SHA512: 65314572b5298b6e880717d5cb1629f9f05644f97d4c73f79d5f4fbce154f274adbfaf18f336656af0df852556eb054675ff4693d9999a29cde86abd43b1babd Homepage: https://cran.r-project.org/package=SunsVoc Description: CRAN Package 'SunsVoc' (Constructing Suns-Voc from Outdoor Time-Series I-V Curves) Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the diode of photovoltaic cells without the effect of series resistance. Here, Suns-Voc curves can be constructed with outdoor time-series I-V curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab. Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. [1] Wang, M. et al, 2018. . [2] Walters et al, 2018 . [3] Guo, S. et al, 2016. . Package: r-cran-suntools Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-suntools_1.1.0-1.ca2404.1_all.deb Size: 78730 MD5sum: e79ad74b60068ca797101e0d30315192 SHA1: fa0ee9786b1858c1a10e0a76efb4c752595fea4e SHA256: 4c049b2f29a0866fa08a3e848c07be903714a62e1bd48b4567079730a8197488 SHA512: f1cc42dee633d37aecc9046f247de62c2e57ed19aa184d4480132d2697169536988c5f65f3dc62ba64470bc170545c9ab2b37e20cee9ffdc32854a953f2d0ba4 Homepage: https://cran.r-project.org/package=suntools Description: CRAN Package 'suntools' (Calculate Sun Position, Sunrise, Sunset, Solar Noon and Twilight) Provides a set of convenient functions for calculating sun-related information, including the sun's position (elevation and azimuth), and the times of sunrise, sunset, solar noon, and twilight for any given geographical location on Earth. These calculations are based on equations provided by the National Oceanic & Atmospheric Administration (NOAA) as described in "Astronomical Algorithms" by Jean Meeus (1991, ISBN: 978-0-943396-35-4). 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The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) or Cousineau (2017) for a review as well as Cousineau (2005) , Morey (2008) , Baguley (2012) , Cousineau & Laurencelle (2016) , Cousineau & O'Brien (2014) , Calderini & Harding for specific references. The documentation is available at . 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Package contains functions for generating robust biclusters with respect to the initialization parameters for a given bicluster solution contained in a bicluster set in data, the procedure is also known as ensemble biclustering. The set of biclusters is evaluated based on the similarity of its elements (the overlap), and afterwards the hierarchical tree is constructed to obtain cut-off points for the classes of robust biclusters. The result is a number of robust (or super) biclusters with none or low overlap. 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'SuperCell' uses 'velocyto.R' for RNA velocity and 'WeightedCluster' for weighted clustering on metacells. We also recommend installing 'scater' Bioconductor package . 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Laqueur, H. S., Shev, A. B., Kagawa, R. M. C. (2021) . 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This includes proportional reduction in error and formatting to improve ease the transition between the book and R. Package: r-cran-superpc Architecture: all Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-superpc_1.12-1.ca2404.1_all.deb Size: 290418 MD5sum: b72c2f30ce4adc3b9157fa3d917e17a6 SHA1: 82a2a902f78a1ab716a5178e809f11bccb2e1e99 SHA256: d2bce7a9592b3283e311b82e6ef7f736d442616d38a727f42a3fc90e49cdd16b SHA512: 69916a4652c9567dc91a4c9678355a6c41f1b83972e18e600c36ef8c1eaa3df30da81580e266d88e37b59813160e9a5680bf33ff99f7449e461610b3cab67517 Homepage: https://cran.r-project.org/package=superpc Description: CRAN Package 'superpc' (Supervised Principal Components) Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 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The package fits convex combinations of parametric, semiparametric, and machine learning survival learners by minimizing cross-validated risk using inverse probability of censoring weighting (IPCW). It provides tools for automated hyperparameter grid search, high-dimensional variable screening, and evaluation of prediction performance using metrics such as the Brier score, Uno's C-index, and time-dependent area under the curve (AUC). Additional utilities support model interpretation for survival ensembles, including Shapley additive explanations (SHAP), and estimation of covariate-adjusted restricted mean survival time (RMST) contrasts. The methodology is related to treatment-specific survival curve estimation using machine learning described by Westling et al. (2024) , and the unified ensemble framework described in Lyu et al. (2026) . 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See Louviere et al. (2015) for details on best-worst scaling, and Aizaki and Fogarty (2023) for the package. 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These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'. The package also contains a tutorial on basic data frame management, including how to handle missing data. Package: r-cran-sure Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra Suggests: r-cran-mass, r-cran-ordinal, r-cran-rms, r-cran-testthat, r-cran-vgam Filename: pool/dists/noble/main/r-cran-sure_0.2.0-1.ca2404.1_all.deb Size: 141346 MD5sum: e9eae1e34e3c7e03b82e2906f5b7f88e SHA1: b624a2109ecd674f3e4ac317731e0e51c9894ced SHA256: 0e9e195f2b0a9e248879f78830f8bb0c1c568309c0cf3b8393aa443d031c3f13 SHA512: 7fe323ca0802ed8fa7715b22bc1abe6494eb2f4a512c9d05fd7b701bea7b767605d08bdc0f97fe8c2feb27214c6790913486ef11aa2b94d961862f717d9d61b3 Homepage: https://cran.r-project.org/package=sure Description: CRAN Package 'sure' (Surrogate Residuals for Ordinal and General Regression Models) An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017) . These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available. Package: r-cran-surf.vs Architecture: all Version: 1.1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-dplyr Suggests: r-cran-foreach, r-cran-doparallel, r-cran-knitr Filename: pool/dists/noble/main/r-cran-surf.vs_1.1.0.1-1.ca2404.1_all.deb Size: 96290 MD5sum: c87d84fc4dfc8c42f22a1aaece6b9981 SHA1: 0ae648aaabb53d453f55721a04b3034c59b00f3e SHA256: 068ba67b8c7cf66f178bb2aea73518c932e80f8e69a69744e75c6017b0359739 SHA512: 53793cb1eea88e33fd28c550d539fc2f255028e46bbfd04bf7cedb520f7dc2121b35b30a106a6faa42d2484d0253dc0315306464353ae447715e04e8c9226240 Homepage: https://cran.r-project.org/package=SuRF.vs Description: CRAN Package 'SuRF.vs' (Subsampling Ranking Forward Selection (SuRF)) Performs variable selection based on subsampling, ranking forward selection. Details of the method are published in Lihui Liu, Hong Gu, Johan Van Limbergen, Toby Kenney (2020) SuRF: A new method for sparse variable selection, with application in microbiome data analysis Statistics in Medicine 40 897-919 . Xo is the matrix of predictor variables. y is the response variable. Currently only binary responses using logistic regression are supported. X is a matrix of additional predictors which should be scaled to have sum 1 prior to analysis. fold is the number of folds for cross-validation. Alpha is the parameter for the elastic net method used in the subsampling procedure: the default value of 1 corresponds to LASSO. prop is the proportion of variables to remove in the each subsample. weights indicates whether observations should be weighted by class size. When the class sizes are unbalanced, weighting observations can improve results. B is the number of subsamples to use for ranking the variables. C is the number of permutations to use for estimating the critical value of the null distribution. If the 'doParallel' package is installed, the function can be run in parallel by setting ncores to the number of threads to use. If the default value of 1 is used, or if the 'doParallel' package is not installed, the function does not run in parallel. display.progress indicates whether the function should display messages indicating its progress. family is a family variable for the glm() fitting. Note that the 'glmnet' package does not permit the use of nonstandard link functions, so will always use the default link function. However, the glm() fitting will use the specified link. The default is binomial with logistic regression, because this is a common use case. pval is the p-value for inclusion of a variable in the model. Under the null case, the number of false positives will be geometrically distributed with this as probability of success, so if this parameter is set to p, the expected number of false positives should be p/(1-p). Package: r-cran-surface.analytics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-effectsize, r-cran-factominer, r-cran-factoextra, r-cran-randomforest, r-cran-multcompview Suggests: r-cran-testthat, r-cran-scales Filename: pool/dists/noble/main/r-cran-surface.analytics_0.1.0-1.ca2404.1_all.deb Size: 41870 MD5sum: 4b10efa15d9a324c65c01daa77bd659a SHA1: 836a33bafa462dec0ad05f84c0209f11af45baf0 SHA256: 3162843b0e65ae3c54f9dbb1add7bcb7129c0c562cbe69dbf617ed16ec06ffe3 SHA512: 10a885c682f865d56e94c92d86ff0da4156bd120c2400f584cca3d5ff8de904755ab4d6d09baf4551fd8b18f885ee316f5a88ac7c7db15bb2140f29f8f0be9ec Homepage: https://cran.r-project.org/package=suRface.analytics Description: CRAN Package 'suRface.analytics' (Statistical Analysis and Visualization of Surface-EngineeredMaterial Properties) A collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structure–function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) , and Kwon et al. (2020) . Package: r-cran-surface Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ouch, r-cran-mass, r-cran-geiger, r-cran-phytools Suggests: r-cran-igraph Filename: pool/dists/noble/main/r-cran-surface_0.6-1.ca2404.1_all.deb Size: 344282 MD5sum: 9d099e21d8cb61f70d6e1d98d52cf3e2 SHA1: 4efa6f77597e062a69b849f57e80410d4283116d SHA256: ee99100d3389f446bc595d70d8c855550f53b95e927ce2158c14129939cfce54 SHA512: 105063ca9dedd91e86a6855c8d001043cdcaedd06beb1cd63c0ad9769e3a7366af914a1f4726639b2e316cfa9461563853cc6e1369da695d83b7aa9b84ae51df Homepage: https://cran.r-project.org/package=surface Description: CRAN Package 'surface' (Fitting Hansen Models to Investigate Convergent Evolution) This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the 'ouch' methodology of Butler and King (2004) . The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) . Package: r-cran-surfacetortoise Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-gstat, r-cran-sf Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surfacetortoise_2.0.1-1.ca2404.1_all.deb Size: 40860 MD5sum: edb25ea25ee7efbe231026d563d8ba48 SHA1: 6cd06827e96ff858e9f794a981fac0238cbedaf8 SHA256: ab11ac15d2b1ae52afd09ee08330c4910656cbd7d4e8898210badeb1c0978a26 SHA512: 06cf86dfb7928f5717d9b085162ba3e40daba865fe6ee28f7780028d21590684670d26a278249c273162d7788b1e4ddab7912fd36f22bc624c3b6c191485ca5b Homepage: https://cran.r-project.org/package=SurfaceTortoise Description: CRAN Package 'SurfaceTortoise' (Find Optimal Sampling Locations Based on Spatial Covariate(s)) Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden. Package: r-cran-surfrough Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-surfrough_0.0.1.0-1.ca2404.1_all.deb Size: 4072642 MD5sum: 80b45309616b0119146ac665291f32ec SHA1: 8939356224043051ca1743238eaf1eaabebe6251 SHA256: 72e812a0eb9bc2de1cb5bc99a3a3027174da262ad6326fc2bf0b5c3b08307d41 SHA512: e64da4bf5ee4aacccf374367ff4dac4728e820d0644754ed08809053baf29d886329051f417b0f04c509237b1b29e50950adb82ba5f936f5da3b8efdbe3fbffa Homepage: https://cran.r-project.org/package=SurfRough Description: CRAN Package 'SurfRough' (Calculate Surface/Image Texture Indexes) Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) ). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. Package: r-cran-surprisalanalysis Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2561 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlib, r-cran-shiny, r-cran-ggplot2, r-cran-shinythemes, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-patchwork, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pheatmap, r-cran-peakram, r-cran-data.table, r-cran-biocmanager, r-bioc-clusterprofiler, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-httpuv Filename: pool/dists/noble/main/r-cran-surprisalanalysis_3.0.1-1.ca2404.1_all.deb Size: 2343830 MD5sum: b779a629f293b5a2a92fe3dfb39d4468 SHA1: 0f43eab743ff26b54126a0833df22568cdd592ff SHA256: 8f64f5173a5416b828150d13a36aab85dfed3c4225d1c3f4bc182b9906c3547b SHA512: a78b8df94ebbd8cfdf40771c43ea9a5f48dcbb33f4bb3a0cb772d3eef1ddba1ab39105b8fec152eded1cb3f163d508ec7efad58b12970dddaed270586f971fab Homepage: https://cran.r-project.org/package=SurprisalAnalysis Description: CRAN Package 'SurprisalAnalysis' (Information Theoretic Analysis of Gene Expression Data) Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) . Zadran S. et al. (2014) . Su Y. et al. (2019) . Bogaert K. A. et al. (2018) . Package: r-cran-surreal Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1303 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-png Suggests: r-cran-bmp, r-cran-bslib, r-cran-jpeg, r-cran-rsvg, r-cran-shiny, r-cran-testthat, r-cran-tiff, r-cran-withr Filename: pool/dists/noble/main/r-cran-surreal_0.0.3-1.ca2404.1_all.deb Size: 1216382 MD5sum: 7f002f14434053202db3f01b75228194 SHA1: 1bbcdf33030bf26e178758f4cf1e1d7d60711d24 SHA256: a956cf9c02b86f6ca335c3147a9fc3745569f215d6292cf903c340174d3f6234 SHA512: 9a1d3fbe19b35efaf00d733d2187c0df7d199dbd3a870f2d059ec7a0bd50ac9296b62e316698324abf5d0a3de83f394bd5ddf4c1febd9726f7155b8b5beacdc1 Homepage: https://cran.r-project.org/package=surreal Description: CRAN Package 'surreal' (Create Datasets with Hidden Images in Residual Plots) Implements the "Residual (Sur)Realism" algorithm described by Stefanski (2007) to generate datasets that reveal hidden images or messages in their residual plots. It offers both predefined datasets and tools to embed custom text or images into residual structures. Allowing users to create intriguing visual demonstrations for teaching model diagnostics. Package: r-cran-surrogate Architecture: all Version: 3.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-lattice, r-cran-latticeextra, r-cran-survival, r-cran-nlme, r-cran-lme4, r-cran-logistf, r-cran-rms, r-cran-ks, r-cran-extradistr, r-cran-pbapply, r-cran-flexsurv, r-cran-rvinecopulib, r-cran-maxlik, r-cran-purrr, r-cran-mbess, r-cran-tidyr, r-cran-dplyr, r-cran-tibble, r-cran-lifecycle, r-cran-fastmatrix, r-cran-mratios, r-cran-matrix Suggests: r-cran-copula, r-cran-fnn, r-cran-mgcv, r-cran-testthat, r-cran-vdiffr, r-cran-fitdistrplus, r-cran-kdecopula, r-cran-cubature, r-cran-mvtnorm, r-cran-withr, r-cran-stringr, r-cran-ggplot2, r-cran-sn, r-cran-pracma Filename: pool/dists/noble/main/r-cran-surrogate_3.4.2-1.ca2404.1_all.deb Size: 2317304 MD5sum: 8c57dfcb7c9a605f8d64d0901074e867 SHA1: 8d7481cc5651baf3f5ed316ab1d9e23c0e87863d SHA256: fc1dfc98faebdccee3223fcd658ff55f1ece82c263c04312dec0ba352f18db32 SHA512: ba2e38eac271568839229d940bb9d80c5bcdf784f83205e09008b2ba156c5415d701c869ceade1a22208943c705bc1a28cf5a347d9d28044778ccd61d124d22b Homepage: https://cran.r-project.org/package=Surrogate Description: CRAN Package 'Surrogate' (Evaluation of Surrogate Endpoints in Clinical Trials) In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine. Package: r-cran-surrogateoutcome Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-surrogateoutcome_1.2-1.ca2404.1_all.deb Size: 174646 MD5sum: 0838376e54b3ffc68597ef525bd8c5de SHA1: 88d8c04b009a60fce931eda43251501dde2d5225 SHA256: 2c48064a59d862c1c200e9e184c6536b752500333e0d8277f0814af97897c635 SHA512: f16a3e4ac7ed2d91c89fab798bf4abe008f6a04ff363cc80481fddb6286525b3ac732a790e5256cd1696067a58de4882314a82fe2d0858dc40f5e05d4d630018 Homepage: https://cran.r-project.org/package=SurrogateOutcome Description: CRAN Package 'SurrogateOutcome' (Estimation of the Proportion of Treatment Effect Explained bySurrogate Outcome Information) Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" . A tutorial for this package can be found at . Package: r-cran-surrogateparadoxtest Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-monotonicitytest Filename: pool/dists/noble/main/r-cran-surrogateparadoxtest_2.0-1.ca2404.1_all.deb Size: 50808 MD5sum: ccc5d18935d3faeafdc65f85c885ce73 SHA1: 800985fb21f28b6b99bc356b66949dd257067aa1 SHA256: be44aa83674c817043cedb1d6d764f9a9136814327d8f9e94cd953c1c0f800fa SHA512: 907be0fd9af2a3a9e4ff71fa5fbe9312427dd1630f77cd86f7260f6e193bf13adfb9354076e562ee9e48234fb7c13a48e85ce97f23ec50b8bd4963bf23727548 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at . Package: r-cran-surrogaterank Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pbmcapply, r-cran-cowplot, r-cran-tidyr, r-cran-complexupset, r-cran-ggvenndiagram, r-cran-tibble, r-cran-glue, r-cran-scales, r-cran-mass Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-surrogaterank_3.0-1.ca2404.1_all.deb Size: 836556 MD5sum: bfe3160dba01b2d3a554abd48f5b3a58 SHA1: bef4da293cedbf6882a9a397de0521d66ec853be SHA256: 654e5154e16911392cc8ec1cac44661baf6eb3bea849d1ef29cdbd2694a0bbce SHA512: 3b144325c39958011985592130789bb3e21efc6d13bd0b42477f82fbb97daed4886cb45a86219919600cc1bf2a4e194236b7ffcd93afc28a4fa79b560c852d3f Homepage: https://cran.r-project.org/package=SurrogateRank Description: CRAN Package 'SurrogateRank' (Rank-Based Test to Evaluate a Surrogate Marker) Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. Details are described in Parast et al (2024) , in Hughes A et al (2025) , and in Hughes A et al (2026) . A tutorial for this package can be found at and a Shiny App implementing the package can be found at . Package: r-cran-surrogatersq Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-passo, r-cran-progress, r-cran-scales Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-surrogatersq_0.2.1-1.ca2404.1_all.deb Size: 1220444 MD5sum: 57c9af8bb07265b24a0aeccb82bee473 SHA1: fc1dc36e932fda9a4a08b9d412f7c281fa85fd73 SHA256: 0272f0327e29625cead9269a4f8eba0992a8cb415d9ce1032ec313157967ca78 SHA512: 1ffb8f1e73de9dca2f296f3d4889723d0e3f9081be483b0ddb92869bff69d838100e7d2b0271fa971ed235c1d7537b49e7fd72d1ad8aa4d1f61ed2da69e86e35 Homepage: https://cran.r-project.org/package=SurrogateRsq Description: CRAN Package 'SurrogateRsq' (Goodness-of-Fit Analysis for Categorical Data using theSurrogate R-Squared) To assess and compare the models' goodness of fit, R-squared is one of the most popular measures. For categorical data analysis, however, no universally adopted R-squared measure can resemble the ordinary least square (OLS) R-squared for linear models with continuous data. This package implement the surrogate R-squared measure for categorical data analysis, which is proposed in the study of Dungang Liu, Xiaorui Zhu, Brandon Greenwell, and Zewei Lin (2022) . It can generate a point or interval measure of the surrogate R-squared. It can also provide a ranking measure of the percentage contribution of each variable to the overall surrogate R-squared. This ranking assessment allows one to check the importance of each variable in terms of their explained variance. This package can be jointly used with other existing R packages for variable selection and model diagnostics in the model-building process. Package: r-cran-surrogateseq Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-surrogateseq_1.1-1.ca2404.1_all.deb Size: 130358 MD5sum: 39785f6c6fd617e812b2dabafef21ec3 SHA1: 0f241533b6b70c92119f3eb5337caf9c9851efe7 SHA256: 430e5235f91d2a766aa1092ce5d29aeb325127798353be0ba399aa69cacf7f65 SHA512: 0075bb15636109d20a1c8fdf29db6db7053f8e8a21e3cc2edfc0b21db5077c26f85df9fcb10e485d742ddb09a359c23307308c5e1103a88a9cf25b8fca163d4c Homepage: https://cran.r-project.org/package=SurrogateSeq Description: CRAN Package 'SurrogateSeq' (Group Sequential Testing of a Treatment Effect Using a SurrogateMarker) Provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping. More details are available in: Parast, L. and Bartroff, J (2024) . A tutorial for this package can be found at . A Shiny App implementing the methods can be found at . Package: r-cran-surrogatetest Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-surrogatetest_1.3-1.ca2404.1_all.deb Size: 159470 MD5sum: 807a9db43bc3309d06af8bfbc82d13f5 SHA1: c7b157a78e78897881c1f8a46bf7b04b5fc856f3 SHA256: d0c9b0037700ced99c0b8b98278ffc10eb661fff051db4d7f6dec67e020cc127 SHA512: 788d946f6066b55bbde95b19d33d8fbc6b602c50140668943881816d4892db0626a8a5820c7eee316735ff15acd8e2c776707afab1334c1d375b75f7446bfa03 Homepage: https://cran.r-project.org/package=SurrogateTest Description: CRAN Package 'SurrogateTest' (Early Testing for a Treatment Effect using Surrogate MarkerInformation) Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. . 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It includes semiparametric, parametric, tree-based, ensemble, boosting, kernel, and deep-learning survival learners, together with benchmarking, scoring, calibration, and model-agnostic interpretation utilities. Representative methodological anchors include Cox (1972) , Royston and Parmar (2002) , Ishwaran et al. (2008) , Jaeger et al. (2019) , Harrell et al. (1982) , Graf et al. (1999) , Friedman (2001) , Apley and Zhu (2020) , and Lundberg and Lee (2017) , and other related methods for survival modeling, prediction, and interpretation. 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(2007) , Blanche, Dartigues, and Jacqmin-Gadda (2013) , Blanche, Latouche, and Viallon (2013) , Harrell et al. (1982) , Peto and Peto (1972) , Schemper (1992) , and Uno et al. (2011) . 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Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" , where the method is described as Ensemble 1. 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Package: r-cran-survdt Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survdt_0.9.0-1.ca2404.1_all.deb Size: 409224 MD5sum: ee129c9d6cbc52032920afb6de0337e0 SHA1: b5a6241c659b5a57bd845af3325b86f87e65ff84 SHA256: 73e1db361d8c94c9c76a6730c89a41744eea75b0488ed07d66a1523a37154fba SHA512: 2bf727d811feff0403e5959599d03eb9ee46e95543de2f15d60a963cbe935b363d58fe8a9c39afe606583917c058bd1ff7ecceb90a37da69fafc01465ec93633 Homepage: https://cran.r-project.org/package=survdt Description: CRAN Package 'survdt' (Improved Methods for Survival Analysis under Double Truncation) Contains existing and novel methods for nonparametric analysis and Cox regression analysis with doubly truncated survival data, as described in Vazquez and Xie (2025) . Includes survival curves and hazard estimates through nonparametric maximum likelihood estimation, various tests for detecting group differences or non-ignorable sampling bias, and inverse probability weighted Cox regression with several options of nonparametric weights. Also implements diagnostics for key modeling assumptions such as quasi-independent truncation and the positivity assumption. Closed-form standard errors are available for all estimates, i.e. bootstrapping is not required. Package: r-cran-surveltest Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-nloptr, r-cran-plyr, r-cran-survival Filename: pool/dists/noble/main/r-cran-surveltest_2.0.1-1.ca2404.1_all.deb Size: 178392 MD5sum: fa2dff99f93340256cce2d1858242311 SHA1: 089b4c036d0f23339be65717aaed5249595e19ac SHA256: b9a513d71934126ed5cbae405b9c9f0b33cce9f6f63e004fe2292f745841c06d SHA512: 934d0c636f3eb95aeda6837344c55043582273335a301aa3971625caf4764b6d350dedf70a48b31377b389b95ee083cf893f7983559bb95a64e972515eba0e26 Homepage: https://cran.r-project.org/package=survELtest Description: CRAN Package 'survELtest' (Comparing Multiple Survival Functions with Crossing Hazards) Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions. Package: r-cran-survex Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dalex, r-cran-ggplot2, r-cran-kernelshap, r-cran-pec, r-cran-survival, r-cran-patchwork Suggests: r-cran-censored, r-cran-covr, r-cran-flexsurv, r-cran-gbm, r-cran-generics, r-cran-glmnet, r-cran-ingredients, r-cran-knitr, r-cran-mboost, r-cran-parsnip, r-cran-progressr, r-cran-randomforestsrc, r-cran-ranger, r-cran-reticulate, r-cran-rmarkdown, r-cran-rms, r-cran-testthat, r-cran-treeshap, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-survex_1.2.0-1.ca2404.1_all.deb Size: 1190290 MD5sum: cd5b9e2d6b51177e80114ea90e71d447 SHA1: 8d7aa65b10c02ec8598d05bf0c8d61f24ee62c01 SHA256: eabc14074c68c8a4211fdd17eca89957b1ccb932dcbcfb786dc3131381cec5d2 SHA512: e2ef34528dbea17c39360b0d4ca8545ec8acdcadd1f2cda7c7da76faf433beac76fec5c67a6d75a9d8b31dd28b904a4920f316cb43930b9822c555c8cd59e620 Homepage: https://cran.r-project.org/package=survex Description: CRAN Package 'survex' (Explainable Machine Learning in Survival Analysis) Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) , SurvLIME described in Kovalev et al., (2020) as well as extensions of existing ones described in Biecek et al., (2021) . Package: r-cran-survexp.fr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-writexls Filename: pool/dists/noble/main/r-cran-survexp.fr_1.2-1.ca2404.1_all.deb Size: 83082 MD5sum: 92c8d11819853cfa06988ca6405d8eca SHA1: 4fd1566b7395ddc5b9ae69e9649adba4057111b7 SHA256: 7adcb7e29938b9ef6c512a6ad49a597c6ba37ea2ae16e57fe6870c36e9ef0294 SHA512: e79dd16978579ea573a0c1a82b9a7a34b8f80d4af264ab55e376a9ef9bfa82f9a624a0485695bac8855fab0fd5fa1a91073b1d16727a844bfaeb4406c28a9bb0 Homepage: https://cran.r-project.org/package=survexp.fr Description: CRAN Package 'survexp.fr' (Relative Survival, AER and SMR Based on French Death Rates) It computes Relative survival, AER and SMR based on French death rates. Package: r-cran-surveycc Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-candisc, r-cran-survey Filename: pool/dists/noble/main/r-cran-surveycc_0.2.1-1.ca2404.1_all.deb Size: 154668 MD5sum: c29d4001f09d45a876f3188709457c4c SHA1: be7a89e776b004ace36403854ee15bb2f7535e3f SHA256: 9b48776125f174a31952f2f66c0d003217814024623580656ae3d9e630c821ab SHA512: 12c11542729575e9eb3e666404c35d9f25ccf346784c004f91803daeb4bd1e72d7fefef5dd454b48657d86efa7ff707ddcbbcc3ae24b69ea99ad6e3bc6d99ebd Homepage: https://cran.r-project.org/package=SurveyCC Description: CRAN Package 'SurveyCC' (Canonical Correlation for Survey Data) Performs canonical correlation for survey data, including multiple tests of significance for secondary canonical correlations. A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review. Package: r-cran-surveycore Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-rlang, r-cran-tidyselect, r-cran-cli, r-cran-tibble, r-cran-dplyr, r-cran-marginaleffects, r-cran-pbivnorm Suggests: r-cran-testthat, r-cran-withr, r-cran-surveytidy, r-cran-survey, r-cran-survival, r-cran-srvyr, r-cran-haven, r-cran-lifecycle, r-cran-broom, r-cran-polycor, r-cran-jtools, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surveycore_1.0.0-1.ca2404.1_all.deb Size: 5875000 MD5sum: d85914914f6e7808b54e34d19b713e6c SHA1: 2dfc10f415b49f74a96d852ab33a2491a6885a9e SHA256: ba4cd16b03e36a6a4c6223cc0b2408a5dd29ee084251cb79441605dfe1281f72 SHA512: 5c27f5f496a0cb3248816cef8c68a0f16abceafa9454bd84852cb2e71af249a23cb9b889cc7c91d9be48c82bf80e4f154c10bdce43bd39d68400f960ca3dc12d Homepage: https://cran.r-project.org/package=surveycore Description: CRAN Package 'surveycore' (Core Survey Analysis Infrastructure) A modern, 'S7'-based foundation for survey analysis spanning both probability and non-probability samples. Probability sample designs include Taylor series linearization, replicate weights (BRR, Fay, jackknife, bootstrap), and two-phase estimation, following 'Lumley' (2004) . Non-probability sample designs support bootstrap and jackknife variance estimation for opt-in panels and convenience samples. Provides a unified estimator interface for means, frequencies, totals, quantiles, ratios, correlations, regression, and t-tests, with weighted 'polychoric' and 'polyserial' correlation following 'Mannan' (2025) . A metadata system preserves 'haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a 'tidyselect' interface throughout. Package: r-cran-surveycv Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-magrittr Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-islr, r-cran-knitr, r-cran-rmarkdown, r-cran-rpms, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveycv_0.2.0-1.ca2404.1_all.deb Size: 740684 MD5sum: fe94bfa63976b47f40ca238bcb619b03 SHA1: 3ce0f442fd1063e46ad83267472f40902f5b44e5 SHA256: 29208932620425d7507e7886e0485d19f1a377beab4388d3f1e1bc14cf9f1162 SHA512: a0ce99c09faf70b3e588c7da99c3bb4e4a2182331befe08cd2e1cd9d989f6030901f8961b3f9da523b5c483427e498e3c545fcedf5a720237f4513cb10e1094a Homepage: https://cran.r-project.org/package=surveyCV Description: CRAN Package 'surveyCV' (Cross Validation Based on Survey Design) Functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) explains why differing how we take folds based on survey design is useful. Package: r-cran-surveydata Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-scales, r-cran-tidyr, r-cran-dt, r-cran-assertthat Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-covr, r-cran-rprojroot, r-cran-spelling Filename: pool/dists/noble/main/r-cran-surveydata_0.2.8-1.ca2404.1_all.deb Size: 372142 MD5sum: 2d2ff1d64a6b78c6fd4d51cd8c6b0fdf SHA1: 4a6bcf7ba77347f635b1dbbaea8bb6242b3b93da SHA256: 3f7a4eb33ac10acdea8f8b5af9f69b203ddc10cb7e7438249ec27bd1fcba19a0 SHA512: 4628ad376cc4b09dd342f36af92490b5fbd15fe6bcb3d6e966502a90a30f19419c5c766b4d2be86a151eba26e2b610561ab0be117f67bdaa951f7e959bc66593 Homepage: https://cran.r-project.org/package=surveydata Description: CRAN Package 'surveydata' (Tools to Work with Survey Data) Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The 'surveydata' package makes it easy to keep track of this metadata, and to easily extract columns with specific questions. Package: r-cran-surveydefense Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-flextable Suggests: r-cran-officer Filename: pool/dists/noble/main/r-cran-surveydefense_0.2.0-1.ca2404.1_all.deb Size: 42468 MD5sum: 10869f90b6db9795a5178935f74fc417 SHA1: 7946c810afecd495b7f9e33325d42c8348d03973 SHA256: 9074b0eecd6d83d04dab26e3aed698d5c5a375b9c5e05970a38f66c808bce5e8 SHA512: b885e3f16d475b95d8fa30fb99dbf051269af472fd238658987c8c1d6d32a31d156ba6c396d5e69e073417dd29ffaa60fbcef964234fa57fe8eb09de9a8d40e4 Homepage: https://cran.r-project.org/package=SurveyDefense Description: CRAN Package 'SurveyDefense' (Survey Defense Tool) This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at . Package: r-cran-surveydown Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dotenv, r-cran-fs, r-cran-htmltools, r-cran-jsonlite, r-cran-markdown, r-cran-miniui, r-cran-pool, r-cran-quarto, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rstudioapi, r-cran-rvest, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-xml2, r-cran-yaml Suggests: r-cran-glue, r-cran-knitr, r-cran-leaflet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveydown_1.3.0-1.ca2404.1_all.deb Size: 975272 MD5sum: 052d25f396c5861252501cb9312111e6 SHA1: 521ab55a9f2cafcf26ed95d564d220d97e4180a9 SHA256: bd87b89d5f4b30c4e7b86f031b1ce00f01db21b38d805fde5362926fe42df3af SHA512: a8ba75456a18d01918ddc4e6e344ba1254a3f10a927f25c8d4ef11fd54fdda027440f69822c5c07e488248bac89ebb5602ca81625a75f9e5d3486f23c90dc892 Homepage: https://cran.r-project.org/package=surveydown Description: CRAN Package 'surveydown' (Markdown-Based Programmable Surveys Using 'Quarto' and 'shiny') Generate programmable surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd 'Quarto' file defining the survey content (pages, questions, etc), and an app.R file defining a 'shiny' app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a 'PostgreSQL' database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates. Because the surveys render into a 'shiny' app, designers can also leverage the reactive capabilities of 'shiny' to create dynamic and interactive surveys. Package: r-cran-surveyexplorer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggupset, r-cran-gt, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveyexplorer_0.2.0-1.ca2404.1_all.deb Size: 572908 MD5sum: 98ca0b07a50b29b80b0356bbd0665325 SHA1: 33884b98513ec4ba739531d18dc0deeac1ba190a SHA256: 52964f19b018beb1fe6f2fa9737957175f1456123cd7dc35826391136c87bc17 SHA512: 227ce793210a8bf0efd8a4730e2a935e4a060dad9455e866b6e1766c0f6e85a1508b04638fc109dec31970e31b2229ff00a78dfb6ad360b09a0be143f93745cc Homepage: https://cran.r-project.org/package=surveyexplorer Description: CRAN Package 'surveyexplorer' (Quickly Explore Complex Survey Data) Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data. Package: r-cran-surveyframe Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4802 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-rlang, r-cran-openssl Suggests: r-cran-ggplot2, r-cran-googlesheets4, r-cran-shiny, r-cran-psych, r-cran-mass, r-cran-nnet, r-cran-logistf, r-cran-digest, r-cran-lavaan, r-cran-seminr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-naniar, r-cran-pagedown, r-cran-rmcda, r-cran-rstudioapi, r-cran-jmv, r-cran-tidytext, r-cran-quanteda, r-cran-stm, r-cran-topicmodels, r-cran-igraph, r-cran-v8, r-cran-haven, r-cran-callr, r-cran-chromote, r-cran-httpuv, r-cran-pkgload Filename: pool/dists/noble/main/r-cran-surveyframe_0.4.2-1.ca2404.1_all.deb Size: 2715368 MD5sum: 8882dd431d0348ee91e0802a12bc699e SHA1: 0741d1fbec06708e92133ec3faaab52209a7921d SHA256: 8748044e90779e081027826ed103d7dce14a36b8ec82c86270fa97d54a5ec90d SHA512: 6655db07b3822d74a5b08e8138624ea028865bb990474f5a38916f490320913a0e66acf1e917d4b7e612988b864e4bd5035dc475b4e852ff1be2836907b13abd Homepage: https://cran.r-project.org/package=surveyframe Description: CRAN Package 'surveyframe' (Survey Instrument Workflows) Provides a design-first survey research workflow. An instrument, an analysis plan declared before data collection, and a measurement or structural model are held together in one typed, integrity-checked object (the 'sframe'), so a study's confirmatory tests are fixed before responses arrive rather than chosen afterward. Includes visual instrument design via a browser-based builder or 'Shiny' studio, export to a self-contained static HTML survey, an embeddable 'Shiny' module, SHA-256 integrity-checked serialisation to the '.sframe' format, multi-page survey rendering with branching logic, response quality checking, scale scoring, psychometric diagnostics, analysis-plan execution, model syntax generation for EFA, CFA, CB-SEM, and PLS-SEM, an interactive response dashboard, codebook generation, and reproducible HTML reporting. Also supports multi-criteria decision analysis (AHP, ANP, DEMATEL, TOPSIS, VIKOR, MOORA, SMART, WASPAS, PROMETHEE II, ELECTRE I), small-sample survey helpers, and text and open-ended response analysis (term and n-gram frequency, keyword in context, co-occurrence and co-occurrence networks, sentiment, document-feature matrices, and topic modelling via LDA or structural topic models). Package: r-cran-surveyncd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-survey, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-srvyr, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-surveyncd_0.1.0-1.ca2404.1_all.deb Size: 97252 MD5sum: 5d86ad7b8bbb51f7a028effa732c0e60 SHA1: 5c6f97e6d6b957c08bd2fcdb52efd51504f3cde7 SHA256: 6d6ef232c836d2deb143fd24c769172f709e4836cecc646d9e54ec00f9694450 SHA512: 96fa7ed69bd4c7714ed63e140ee6d3e8f179a0165d01a1c00579459a86abf8702a819ab11d5b12d31ff8b1b5cca034aa2af0e58cee465ce4f6fc0ba9310685d2 Homepage: https://cran.r-project.org/package=SurveyNCD Description: CRAN Package 'SurveyNCD' (Survey-Weighted Analysis of Self-Reported Health Indicators) Analyses population health survey data from the World Health Organization (WHO) Stepwise Approach to Non-Communicable Disease (NCD) Risk Factor Surveillance (STEPS), Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and similar complex sample surveys, where chronic conditions are self-reported rather than coded using the International Classification of Diseases (ICD) and estimates must account for stratification, clustering, and sampling weights. Includes a self-reported multimorbidity index based on the Functional Comorbidity Index (FCI) described by Groll et al. (2005) , design-weighted population prevalence estimation via the 'survey' package, a survey-weighted concentration index for health inequality analysis, a DHS anthropometric z-score categoriser, a choropleth mapping helper, and exploratory survey-weighted gradient boosting (via 'xgboost') with SHapley Additive exPlanations (SHAP) based explainability. The gradient boosting component applies case weights but does not yet propagate cluster and strata design effects into variance estimates; it should be treated as exploratory rather than as design-based inference. Package: r-cran-surveynnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-nnet, r-cran-practools, r-cran-survey, r-cran-survival Filename: pool/dists/noble/main/r-cran-surveynnet_1.0.0-1.ca2404.1_all.deb Size: 134012 MD5sum: 94dacc8076166a5704066324a102b8d3 SHA1: c0f9393061bb25a4055b9efc5fac84e946da8e2e SHA256: 8c7e00b7c743b2c0730542688bc2a488c99382ffb23b387da65fa94d7144bb40 SHA512: ed535e65be424021bb4374234078bd8967413ae7e669cdf5600ca267e370a211af08054894fa0bb9a96641797f0c866c627547e7d1ebc3d4da66831a57cecb6a Homepage: https://cran.r-project.org/package=surveynnet Description: CRAN Package 'surveynnet' (Neural Network for Complex Survey Data) The goal of 'surveynnet' is to extend the functionality of 'nnet', which already supports survey weights, by enabling it to handle clustered and stratified data. It achieves this by incorporating design effects through the use of effective sample sizes as outlined by Chen and Rust (2017), , and performed by 'deffCR' in the package 'PracTools' (Valliant, Dever, and Kreuter (2018), ). Package: r-cran-surveyprev Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4068 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey, r-cran-rlang, r-cran-rdhs, r-cran-summer, r-cran-ggplot2, r-cran-ggridges, r-cran-raster, r-cran-terra, r-cran-sp, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-dplyr, r-cran-data.table, r-cran-matrixstats, r-cran-scales, r-cran-expss, r-cran-labelled, r-cran-sjlabelled, r-cran-naniar, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-kableextra, r-cran-geodata, r-cran-patchwork, r-cran-htmltools, r-cran-leaflegend, r-cran-leaflet, r-cran-plotly, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-surveyprev_2.0.0-1.ca2404.1_all.deb Size: 2986572 MD5sum: c9d205d5e86f9321c2b3ff871718b5ab SHA1: 03878351d43febc7534087be5a7ab2c311e467c1 SHA256: d17babbd2c7cd906048035e7639755d829d4a1a848d759ef588658e6602f2c17 SHA512: 40774745e0e88ba93787d947537a4bc3946e54643d0a01d04006fbe268b2c55ac0c1734bbfe2db630486b521d08618d53a8caa216a5d5396b9d3a36358162ecd Homepage: https://cran.r-project.org/package=surveyPrev Description: CRAN Package 'surveyPrev' (Mapping the Prevalence of Binary Indicators using Survey Data inSmall Areas) Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Dong et al. (2026) , Wakefield et al. (2025) and Wakefield et al. (2020) . Package: r-cran-surveysearch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveysearch_0.1.0-1.ca2404.1_all.deb Size: 23776 MD5sum: f533f62303d5113eb0adcb3480245da6 SHA1: 86b0fc05f05a0c0b5a34c4470993468528504e8e SHA256: d0d14f60c689dfe3a672db492a741a8e56f3da3b6bfa60ab3926d580d7b3a197 SHA512: d1125eb6e2d7a11fad3d5dc38fe6a36e89361e9a01f537fe0888cd12df83a72b298f5d220d1f2724fe3fbce5e7e2af05412f5cafd60cbbd877e6915e01ca20ac Homepage: https://cran.r-project.org/package=surveysearch Description: CRAN Package 'surveysearch' (Search and Examine Variables Across Survey Datasets) Search for variables across multiple survey datasets, examine variable properties (labels, values, missingness), and explore variable context within datasets. Useful for navigating complex survey data with many variables and understanding variable relationships and metadata. Package: r-cran-surveysimr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-moments Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveysimr_0.1.0-1.ca2404.1_all.deb Size: 38862 MD5sum: 908c23c17c9548eb233279a92990343e SHA1: f26d09c07328c1241f656d29ed0b5f6bc0816dd0 SHA256: 4f33a08952a2d11b0ae36cda5d2dc464f060b3f5ed5d762cf7e24eba559fdaf9 SHA512: 99f9227e097c1fcf07dca1d1097aa97e61677009f2b4fee02e457f712f93f9d8669a028f9913ddc9cec0e78d9000837cab7eec249c6ade7d302850537a05e6fa Homepage: https://cran.r-project.org/package=surveySimR Description: CRAN Package 'surveySimR' (Estimation of Population Total under Complex Sampling Design) Sample surveys use scientific methods to draw inferences about population parameters by observing a representative part of the population, called sample. The SRSWOR (Simple Random Sampling Without Replacement) is one of the most widely used probability sampling designs, wherein every unit has an equal chance of being selected and units are not repeated.This function draws multiple SRSWOR samples from a finite population and estimates the population parameter i.e. total of HT, Ratio, and Regression estimators. Repeated simulations (e.g., 500 times) are used to assess and compare estimators using metrics such as percent relative bias (%RB), percent relative root means square error (%RRMSE).For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" . Package: r-cran-surveystat Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveystat_1.0.3-1.ca2404.1_all.deb Size: 70104 MD5sum: 40c3eebf245470c40fa7795a07af1a17 SHA1: 9fac67daf41b779b582fa4794898633126adcfc5 SHA256: b257766ad5cc60917634d36f9e964ad7cc4bdeae3897614628be72f129370e73 SHA512: 6627e526c1d2a9b80e93a3f43b78872f508c30d637fef373356e4543240f86db766eca1f21378aeee9ea8f78641dd0b9a328b8f20a206f211477aa374b9a833d Homepage: https://cran.r-project.org/package=SurveyStat Description: CRAN Package 'SurveyStat' (Survey Data Cleaning, Weighting and Analysis) Provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) . Package: r-cran-surveytable Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6233 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-magrittr, r-cran-glue, r-cran-survey, r-cran-huxtable Suggests: r-cran-gt, r-cran-kableextra, r-cran-openxlsx2, r-cran-mschart, r-cran-flextable, r-cran-officer, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surveytable_0.10.0-1.ca2404.1_all.deb Size: 1292444 MD5sum: f0c30decf4f839a673fa450c2f3d6769 SHA1: 434bd03be5937a12ec622f98613b8d94f79344ca SHA256: a1256b2e18a9e86e9bba4317ec32ba332d02a0a39aece4bf0f3966d4c28c9691 SHA512: f43301fd2ffe941ead786b84ae144e009b07bcf52181723b88c0760ac4cf2ed0e958f40f75605e4074cea5329e855145e9b7743d07dfc4c1015b313d79465547 Homepage: https://cran.r-project.org/package=surveytable Description: CRAN Package 'surveytable' (Streamlining Complex Survey Estimation and ReliabilityAssessment in R) Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) ) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) , Parker et al. (2023) ). Package: r-cran-surveytidy Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-haven, r-cran-rlang, r-cran-s7, r-cran-surveycore, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-mockery, r-cran-pkgdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-surveytidy_0.6.0-1.ca2404.1_all.deb Size: 468588 MD5sum: 6c6851091bafd0a576e83ea9a2da2fb6 SHA1: e368973c9ba78f932c7f2b48e892637868346bec SHA256: e5b61487126af45edc9a33afd5f102fbc5eb05e06d8bc93517eb93c89839c777 SHA512: 75154d3fc80d6ceb239c4c9bbfbe029e49fc479952c5645953284769a81f8714e1da460758cdca9f1d1a3165097e10f4b0fb35f03af79f6c842c4983994d987a Homepage: https://cran.r-project.org/package=surveytidy Description: CRAN Package 'surveytidy' (Tidy 'dplyr'/'tidyr' Verbs for Survey Design Objects) Provides 'dplyr' and 'tidyr' verbs, survey-aware recoding helpers, and row-wise statistics for survey design objects created with the 'surveycore' package. filter() uses domain estimation to preserve variance estimation validity; other verbs preserve design variables and metadata automatically. Also supports survey_collection objects for applying the same operation across a list of surveys. Package: r-cran-surveyverse Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-survey, r-cran-srvyr, r-cran-svrep, r-cran-svyvgam, r-cran-svylme Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-surveyverse_0.1.1-1.ca2404.1_all.deb Size: 29796 MD5sum: cfc0edab5c264122a0904e617999da72 SHA1: bc7d5026ccb4c5ea02ac1ed0da5d759dc139b00d SHA256: a6043da526e5e03edd594308bf0873ebbb36f5066ca676c92733e2ed7a2b5cdc SHA512: 2fa48321520df23bb499bddc41224665c9a67212625b37c926928694f09f3fdc923fa5d1573d41670e532b49db1b77c5e2fb975333aed6d9318f113636664331 Homepage: https://cran.r-project.org/package=surveyverse Description: CRAN Package 'surveyverse' (Easily Install and Load Survey Analysis Packages) Makes it easy to install and load a collection of packages for survey analysis that build upon the foundational 'survey' package of Lumley (2004) . Schneider (2025) describes the three core packages in this collection. Package: r-cran-survgme Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ncvreg, r-cran-glmnet, r-cran-survival, r-cran-ahaz, r-cran-ggally, r-cran-network, r-cran-sna, r-cran-scales Filename: pool/dists/noble/main/r-cran-survgme_0.1.0-1.ca2404.1_all.deb Size: 71938 MD5sum: 109fbda3ca500b79cda955e8afd5e8c0 SHA1: 034fab1f4d3b5735e170339172c661de5dcb9c11 SHA256: 20a206242267a142538c770159484a1d22ea7eb86ae708963fdba524a573a434 SHA512: 56ac95b45ef8d978ac43e1fa3652055a4e5f89cefd6404e7d6188dc943f587ff350d9ba2ae73e272dfa94011b38ddc276c0624abf4709d11c99bf155f97e820e Homepage: https://cran.r-project.org/package=SurvGME Description: CRAN Package 'SurvGME' (Analysis of Survival Data under Graphical and Measurement ErrorModels) The estimation method proposed by Chen and Yi (2021) is extended to the analysis of survival data, accommodating commonly used survival models while accounting for measurement error and network structures among covariates. Package: r-cran-survhe Architecture: all Version: 2.0.51-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-dplyr, r-cran-ggplot2, r-cran-rms, r-cran-xlsx, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rstan, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survhe_2.0.51-1.ca2404.1_all.deb Size: 299524 MD5sum: 31b4c707f89a1a0b06b4445102dfef51 SHA1: 7d923e167a9d8bc1f03a46f9dc5f0d5b32de5024 SHA256: 5a76f58a789f58101a715268001e3a9d1fb0b54776b1b6280d9ae443e81525d1 SHA512: fd015e7706824d6e621b06eedb05b660bda7b352eae31dbc192067e2d0e1568b4f3db749d586111855f059836494cdd5eb325a6befa69b521dd97c69a832a194 Homepage: https://cran.r-project.org/package=survHE Description: CRAN Package 'survHE' (Survival Analysis in Health Economic Evaluation) Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed from using 'install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' and 'install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' respectively. 'survHEinla' is based on the package INLA, which is available for download at . The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). . Package: r-cran-survhidim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rdpack, r-cran-readr, r-cran-tidyverse, r-cran-glmnet, r-cran-factoextra, r-cran-igraph, r-cran-useful Filename: pool/dists/noble/main/r-cran-survhidim_0.1.1-1.ca2404.1_all.deb Size: 445040 MD5sum: 9b9e9fc7e5a2dc49785c86d6127dfacb SHA1: ef17927ae0cea022be2300fe7d43c1ed669ee454 SHA256: f1baa7b6d2cdcddc5ac0f15993ac6475539346d2c74d14032e4aa10f1db185e0 SHA512: 01facc08fc2336781c351fdb80d4f515f1344bed60fd2f58fe0cae8105c3e60066f70720820221c2e656cf03b8917991041994ca15ab6676cd4d987909f29c45 Homepage: https://cran.r-project.org/package=SurvHiDim Description: CRAN Package 'SurvHiDim' (High Dimensional Survival Data Analysis) High dimensional time to events data analysis with variable selection technique. Currently support LASSO, clustering and Bonferroni's correction. Package: r-cran-survimchd Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-survimchd_0.1.2-1.ca2404.1_all.deb Size: 406114 MD5sum: a7c321b768b4fc89d20981b098947941 SHA1: d5f1a80b624c64921f2dfc39caebb0407603e900 SHA256: d9f7450cd7f406c96c0efa2de59a821d3fc992da0ca597a023aabfee3c103525 SHA512: 0baf44611a861c6ba86ebd9f41eeaaf4489f4fe79fac00e032d8d7edc615a2deff2305778f6a18492374f9609e5ef3732167ef7bb92b181970d91bbef78d61ff Homepage: https://cran.r-project.org/package=SurviMChd Description: CRAN Package 'SurviMChd' (High Dimensional Survival Data Analysis with Markov Chain MonteCarlo) High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data. Package: r-cran-survimpute Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-vgam, r-cran-mass Suggests: r-cran-mitools Filename: pool/dists/noble/main/r-cran-survimpute_0.1.0-1.ca2404.1_all.deb Size: 65810 MD5sum: 783bacf78a9e66c9978e296c44b4ced0 SHA1: 6db0dd0f7774569f1c98c6b6a56b41bf10a4d385 SHA256: b72e186134aa4efaea9d277e44c8ef16533b79991df4518f986c7ebc7da418d9 SHA512: 2a42ddea4e343528853b8a9e22e8771e7e160898a5afb87cc52008df6507f73ab02c36bf737f8c574fa9d9e73c0510c0c7c665ae2dbcb0450a5642d25d8d918a Homepage: https://cran.r-project.org/package=SurvImpute Description: CRAN Package 'SurvImpute' (Multiple Imputation for Missing Covariates in Time-to-Event Data) Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication. Package: r-cran-survinger Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-mass, r-cran-matrix, r-cran-nloptr, r-cran-survey, r-cran-tidyr, r-cran-vctrs, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-survinger_0.1.1-1.ca2404.1_all.deb Size: 590966 MD5sum: ef40c1f008ca654c343f9c308a38b0f2 SHA1: 218a42559aa2d31a092e5cad9c1c89b3d8e16556 SHA256: 058994d13e40ef0a2d239e8e00436b7f14eab8afa5a9f0b384069d694356f240 SHA512: cc5ff6f6c11e8925d4b67cd5cdc244006832423034ef262728f3f14e9db2699ef1ead3175b0f6b5c9051e8400adf0a6d8681bbecf04981610152ed7466e9d2e3 Homepage: https://cran.r-project.org/package=survinger Description: CRAN Package 'survinger' (Design-Adjusted Inference for Pathogen Lineage Surveillance) Provides tools for optimizing sequencing resource allocation and estimating pathogen lineage prevalence under real-world genomic surveillance conditions. Implements constrained allocation optimization for limited sequencing capacity across multiple regions and sample sources. Includes Horvitz-Thompson and post-stratified estimators that account for unequal sequencing rates, delay-adjusted nowcasting for right-censored reporting data, and combined design-weighted delay-corrected inference with uncertainty propagation. Package: r-cran-surviv Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-surviv_0.1.3-1.ca2404.1_all.deb Size: 234078 MD5sum: 5c6192dc106db1a7ddb56c0033642542 SHA1: 7b393a83acfac656e220e474266350a0193c9866 SHA256: 8009754d7c5413c479a39b0cb12ceb4f51790f2458c8ef86b67ce35a4519f5d9 SHA512: fc5a2bf8989ed2c869c18b371ff112a595cb5f52a09ebf0920e88e450539d72aa6b16a0dc247efa4dc67d3a343e38faec710f4ad466daf4da8c3a17620d5ab98 Homepage: https://cran.r-project.org/package=surviv Description: CRAN Package 'surviv' (Flexible Instrumental Variable Methods for Survival Analysis) Instrumental variable analysis methods for causal inference on survival data based on the Cox model allowing for various treatment and effect types, including orthogonality method-of-moments instrumental variable estimation for the Cox model, two-stage residual inclusion Cox estimation with frailty, sequential trial emulation, sequential Cox analyses, and sequential two-stage residual inclusion Cox analyses. Methodological background includes MacKenzie et al. (2014) , Martinez-Camblor et al. (2019) , Martinez-Camblor et al. (2019) , Gran et al. (2010) , and Keogh et al. (2023) . Package: r-cran-survival666 Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survminer, r-cran-ggplot2 Suggests: r-cran-tidyverse, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survival666_0.5-1.ca2404.1_all.deb Size: 2045392 MD5sum: ced36b329d3883ae324e219e98b1096d SHA1: fbb37970ada3a10dfa74e827da3c8cd3cd9db0a5 SHA256: 86c32972aba176313699f355fe7762528fe5a1f8e6390e1231fb6adbf0bc467f SHA512: 345048efbe11d9c43df3ac6ad7ffa6d2ddfa58be67efaeccd173505622d088ede4f6f276babb4d11185650b65cd50916152b292a277e5ca3f28755aa843a83c9 Homepage: https://cran.r-project.org/package=survival666 Description: CRAN Package 'survival666' (Eliminate the Influence of Co-Expression Genes on Target Genes) Functions can be used for batch survival analysis, but not only for it. Most importantly, it can verify any P-value calculated according to the gene expression level and eliminate the influence of co-expression genes. Package: r-cran-survivalanalysis Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rlang, r-cran-dplyr, r-cran-forcats, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-gridextra, r-cran-ggplot2, r-cran-scales, r-cran-survminer, r-cran-cowplot, r-cran-tidytidbits Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-survivalanalysis_0.4.0-1.ca2404.1_all.deb Size: 392794 MD5sum: 8133908e818182fe306342765a00cab6 SHA1: 286e79c68f783c50e1d0adfb85af806b3b3eca6b SHA256: ac1e113d26710189d9ef9aa44ec6cebf9d93ef667f98249b8509d1b986def01d SHA512: b8b137cdadac1cf95e6fd53f6c0bb54e50e9eaba670d7680e108f76e4be683f359501dff4734d1f1b0a35be93b8fe7a8ad5188f102a50acb07500e130d9a84bc Homepage: https://cran.r-project.org/package=survivalAnalysis Description: CRAN Package 'survivalAnalysis' (High-Level Interface for Survival Analysis and Associated Plots) A high-level interface to perform survival analysis, including Kaplan-Meier analysis and log-rank tests and Cox regression. Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting. Builds upon the 'survminer' package for Kaplan-Meier plots and provides a customizable implementation for forest plots. Kaplan & Meier (1958) Cox (1972) Journal of the Royal Statistical Society. Series B (Methodological), Vol. 34, No. 2 (1972), pp. 187-220 (34 pages) Peto & Peto (1972) . Package: r-cran-survivalmpl Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-survivalmpl_0.2-4-1.ca2404.1_all.deb Size: 143890 MD5sum: 78c4531fb4dca5b0207bbcdc2730cc8a SHA1: a803b0e76cd8dbc59934808c6819234c17b4c000 SHA256: b73c5582bde0460f9313358e517afe26340f0e963b369ae2785a8bf616a7ac0e SHA512: 62709feaf952f8beacfab9903e518797ae9222a9d4eefc1860691efdd1b0833ea160ae495dcc1f3a8b3db1718cd469a8df92cf076370d34f1703dae86249dfa8 Homepage: https://cran.r-project.org/package=survivalMPL Description: CRAN Package 'survivalMPL' (Penalised Maximum Likelihood for Survival Analysis Models) Estimate the regression coefficients and the baseline hazard of proportional hazard Cox models with left, right or interval censored survival data using maximum penalised likelihood. 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Package: r-cran-survivalmpldc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-splines2, r-cran-survival, r-cran-matrixcalc Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survivalmpldc_0.1.1-1.ca2404.1_all.deb Size: 429016 MD5sum: fefb8d0332435bed61ba2c67d23225c0 SHA1: e67e50f9785d6b77a9a761a7160b5c9593c3f27b SHA256: 27a2a150fed60da23a0729d8d36ab3400ee6cd1cb1236a1a572a277acee94cf0 SHA512: a96f17a71beab45590c7088c1b6de4dc021baa23d7751dd248d03a589de42d2bee5a5781a1e7c22e1ce5dd21fa25ad5744b6e33a1228615ec2130ab6ff5e0281 Homepage: https://cran.r-project.org/package=survivalMPLdc Description: CRAN Package 'survivalMPLdc' (Penalised Likelihood for Survival Analysis with DependentCensoring) Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods. Package: r-cran-survivalplann Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nnet, r-cran-risca Filename: pool/dists/noble/main/r-cran-survivalplann_0.4-1.ca2404.1_all.deb Size: 267630 MD5sum: 107988a52571e9f4c52f75165f6c8374 SHA1: 95bf8f4541be1072e1644bc968eadf13edaaa2fc SHA256: 6924cb70a6565ca829c0929ce689d5b3ae5ed10fcdc061be0e94180642d06743 SHA512: 9e572d3da9595e621c2b44254dd3286b8ee4efc4797a024acd99fba0be980cad8e38cc1eb436fcd14cf63ed2765bd3a4eeb725e3e36238c93ce2ba4e5f82db06 Homepage: https://cran.r-project.org/package=survivalPLANN Description: CRAN Package 'survivalPLANN' (Neural Networks to Predict Survival) Several functions and S3 methods to predict survival by using neural networks. We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed by Biganzoli et al. (1998) . Package: r-cran-survivalsl Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-date, r-cran-mass, r-cran-glmnet, r-cran-caret, r-cran-flexsurv, r-cran-randomforestsrc, r-cran-hdnom, r-cran-survivalplann, r-cran-dplyr, r-cran-rpart Filename: pool/dists/noble/main/r-cran-survivalsl_1.1-1.ca2404.1_all.deb Size: 409946 MD5sum: dd7779a5859354bead5e4363e68dd0ad SHA1: e53042a56937ff4e7a2eb01f4cd14769d1198c0f SHA256: f978b89e5b36f17f3140161e690a1e345d591c9ecf12cec0d8cc9cf8c90d188b SHA512: 7c8f73876015f46abd9a0fcf08a50240aa8bbc3d906eafe019560417e0673f62f184ced38a7555f5cf83766ca6f01f6fe01c6d2d7884df3c38b2d20c0699b823 Homepage: https://cran.r-project.org/package=survivalSL Description: CRAN Package 'survivalSL' (Super Learner for Survival Prediction from Censored Data) Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities. Package: r-cran-survivalsurrogate Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-glue, r-cran-mlr3, r-cran-purrr, r-cran-sparsem, r-cran-rbeta2009, r-cran-data.table, r-cran-rpart Suggests: r-cran-mlr3learners Filename: pool/dists/noble/main/r-cran-survivalsurrogate_1.1-1.ca2404.1_all.deb Size: 173492 MD5sum: c68f14894c3fea475444695998ad7c01 SHA1: 4302f9647246c970b604c6d71b6ac6ab15329da0 SHA256: a0158a7ac5d67ba6e89a962b4aaa78c1a8722e340488f9bfe86015854b7fac68 SHA512: e883883c27f6392ab90b6ff07f2fd5870434e34ea1b4e7626173bba8a3f873dc7e0ce128ec0cbef82487116ad6eac618f403c7acf86c7b39f6ff16290ff08755 Homepage: https://cran.r-project.org/package=survivalsurrogate Description: CRAN Package 'survivalsurrogate' (Evaluate a Longitudinal Surrogate with a Censored Outcome) Provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B . A tutorial for this package can be found at and a Shiny App implementing the package can be found at . Package: r-cran-survivalsvm Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-kernlab, r-cran-matrix, r-cran-hmisc Suggests: r-cran-testthat, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-survivalsvm_0.0.6-1.ca2404.1_all.deb Size: 295430 MD5sum: 1ff2af79247345dea3335520db71e699 SHA1: 3108ee8d46736e2c6e2ae3c1e4acc17303c72596 SHA256: 888ea6e37283aa3dbcfa64f366d7942cd9970f0cdd8c412ea01c7865184f801b SHA512: 2c686ebe3286523502de5fb758c72a384cf580a9a3be9159b2d0355ebce6ad56c00f10378c00ee0f1a5f218f2753b15880da59ec047661214654dd3d4987420a Homepage: https://cran.r-project.org/package=survivalsvm Description: CRAN Package 'survivalsvm' (Survival Support Vector Analysis) Performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model. Package: r-cran-survivaltests Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-weibullness, r-cran-ggplot2 Suggests: r-cran-survival, r-cran-arules Filename: pool/dists/noble/main/r-cran-survivaltests_1.0-1.ca2404.1_all.deb Size: 51948 MD5sum: 9d0e16fd09e5a28bd665aef2d9aec276 SHA1: ccdd613184553c58873707cd39a8b41d1e44778c SHA256: 4c8ac0311b7e4f35ceab875d1c3707ec38ee92efa0a01b2015448654eeb31a38 SHA512: f400263f44d6e255db835be9875442300cc76eed6a16e4bd0e36962986bee72008f7413431514149480abe2c7c8bd2000ce4d6f2e3c2cab7a074e9ca08f75b59 Homepage: https://cran.r-project.org/package=SurvivalTests Description: CRAN Package 'SurvivalTests' (Survival Tests for One-Way Layout) Performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) and (Weerahandi, 2004; ISBN:978-0471470175)). It also performs pairwise comparisons and graphical approaches. Moreover, it assesses the weibullness of data in each group via test. The package computes mean and confidence interval under Weibull distribution. Package: r-cran-survivalvignettes Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-survey, r-cran-geepack, r-cran-mstate, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-survivalvignettes_0.1.6-1.ca2404.1_all.deb Size: 552686 MD5sum: eb3e175cb0371742481417eb88a8aeba SHA1: af1999f15c57928ac35db11fb20b6f1950ea9c71 SHA256: 60afc9209b4cd71bb479ef0a171939c0374445ad208e0426e60c68c001b581d8 SHA512: 65741520c2b74af7fa79bb7e7d6f44c6623e1a54c34976bb2f174d2b25992fa3721ec26459b10f5825771cbf73b918b6e9a3e30b4ea18ae67c8af5941d6f95b4 Homepage: https://cran.r-project.org/package=survivalVignettes Description: CRAN Package 'survivalVignettes' (Survival Analysis Vignettes and Optional Datasets) Vignettes for the 'survival' package. Split from the 'survival' package since the vignettes were getting large. Also, since 'survival' is a recommended package it cannot make use of other packages outside of base+recommended (e.g. 'rmarkdown'). Package: r-cran-survivor Architecture: all Version: 2.3.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyr, r-cran-ggplot2, r-cran-stringr, r-cran-magrittr, r-cran-glue, r-cran-shiny, r-cran-purrr, r-cran-dplyr, r-cran-crayon, r-cran-readr, r-cran-shinycssloaders, r-cran-lubridate, r-cran-dt, r-cran-shinyjs Suggests: r-cran-forcats, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survivor_2.3.12-1.ca2404.1_all.deb Size: 3606820 MD5sum: e187ed557150756022e60ecbe02ee5be SHA1: 732e9ebf39b293d43fca04fb86c29cd4115e3113 SHA256: 66cb42d5a10ab078bdb8986b9247b838722c9c59a12613bc8a2151ec1abb1d44 SHA512: 579f5f402f9a4814e6a04fa5a60be25b9f1fa37f85a19b6d3ec879a77691df9a5c6af9df5de1b92c67246ea0c70ad776699aea0d430557225eea4f8775995a4d Homepage: https://cran.r-project.org/package=survivoR Description: CRAN Package 'survivoR' (Data from all Seasons of Survivor (US) TV Series in Tidy Format) Datasets detailing the results, castaways, and events of each season of Survivor for the US, Australia, South Africa, New Zealand, and the UK. This includes details on the cast, voting history, immunity and reward challenges, jury votes, boot order, advantage details, and episode ratings. Use this for analysis of trends and statistics of the game. Package: r-cran-survlab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-survival, r-cran-truncnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survlab_0.1.0-1.ca2404.1_all.deb Size: 1092478 MD5sum: 91a7b3ace02056fefa693009529370ea SHA1: 19ca5aadfa9d45e373200ddfc4ec61c8929d14da SHA256: 52549d2317ff10ad5fc730e89193701377b094af96fd2aeb999575faf95500ab SHA512: e432d0da783d3dcd001d57cc868e672ab2891ac715c4130d8861bb5fd9110fbca66d3c68dee0b8b088b2c607c608c1759638dedf664bd3a24ec2d90dad7341a1 Homepage: https://cran.r-project.org/package=survlab Description: CRAN Package 'survlab' (Survival Model-Based Imputation for Laboratory Non-Detect Data) Implements survival-model-based imputation for censored laboratory measurements, including Tobit-type models with several distribution options. Suitable for data with values below detection or quantification limits, the package identifies the best-fitting distribution and produces realistic imputations that respect the censoring thresholds. Package: r-cran-survlong Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survlong_1.5-1.ca2404.1_all.deb Size: 109252 MD5sum: 605c49172470d73f16460dccc24341fa SHA1: 272ad06a23ffeadae85fa2cb94cdd1019a46368b SHA256: 9682889e5c8de19f4d86fb6b26b6aabcf81a185cf986ccdb781a62f4214d8f6f SHA512: 97a0e10f1b3bbc2e5ade254405c19caf762f75c3051282a1bb640527a09bba43fcc244b4297bf7077844085c3dd83970f781203f0f424d935c41b0695b3b6ab0 Homepage: https://cran.r-project.org/package=SurvLong Description: CRAN Package 'SurvLong' (Analysis of Proportional Hazards Model with Sparse LongitudinalCovariates) Provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) . Package: r-cran-survma Architecture: all Version: 1.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-maxlik, r-cran-pec, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-survma_1.6.8-1.ca2404.1_all.deb Size: 80756 MD5sum: ad03f59c8d9e462790974dedc246d0f1 SHA1: 645474867b1aa53a0c9e07e1c0f71a4451288b5c SHA256: f8f9e126efb555663aa2e88531b3596a4353dc3fd44d2ede5502bef1d087533f SHA512: 8f00f28e52d0a813547d4aca2921a06dac3bfb95bdd664cf7cd51a6369aeba5ca2b09b7af57378161e48ce8af772e039ae8fba89e714083a206504cd22fbb559 Homepage: https://cran.r-project.org/package=SurvMA Description: CRAN Package 'SurvMA' (Model Averaging Prediction of Personalized SurvivalProbabilities) Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) . Package: r-cran-survmetrics Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survminer, r-cran-survival, r-cran-mass, r-cran-pec Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-ggplot2, r-cran-ggpubr, r-cran-randomforestsrc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survmetrics_0.5.1-1.ca2404.1_all.deb Size: 99064 MD5sum: 7f389c7e0aaa4c08f86950cb91937d46 SHA1: 84c2fb60bb00774e13bf952e90b3bd1a66bdabba SHA256: 13d1d4a707b819ea277095216f98711181a70d1d800a0a5b3df65ef423a74ac7 SHA512: 2cbeeb1a6f2459f8bef71104ffbdf9b4bcffcd31ea892fc1a4561b0986be4b3c8df009799383e43473d786adbab9a0514e057abbc4c4c3202a91aa29b6178001 Homepage: https://cran.r-project.org/package=SurvMetrics Description: CRAN Package 'SurvMetrics' (Predictive Evaluation Metrics in Survival Analysis) An implementation of popular evaluation metrics that are commonly used in survival prediction including Concordance Index, Brier Score, Integrated Brier Score, Integrated Square Error, Integrated Absolute Error and Mean Absolute Error. For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) ) , (Moradian H, Larocque D and Bellavance F (2017) ), (Hanpu Zhou, Hong Wang, Sizheng Wang and Yi Zou (2023) ) for different evaluation metrics. Package: r-cran-survmi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-zoo Filename: pool/dists/noble/main/r-cran-survmi_0.1.0-1.ca2404.1_all.deb Size: 97262 MD5sum: ccf576ca7d33b0511ff4c7100039b7b9 SHA1: 2c2aa0f2dc89c3fa792ff7c063d7a755891ce231 SHA256: 69d8db53fb163d833f57c12849af192c72e5171aac039ccfe05e3c412d091530 SHA512: f0fc70f0909d0ed78dc24acf411cba931652358e17bf404ce1c60b237ecfca1ac7852a62866bf9727e54ec604afdb854d55b81fd15822e236fa70d74001ccfc9 Homepage: https://cran.r-project.org/package=SurvMI Description: CRAN Package 'SurvMI' (Multiple Imputation Method in Survival Analysis) In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods. 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Other functions are also available to plot adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions. 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Huang, R., Xu, R. and Dulai, P.S.(2020) . Package: r-cran-survsim Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-eha, r-cran-statmod Filename: pool/dists/noble/main/r-cran-survsim_1.1.9-1.ca2404.1_all.deb Size: 126082 MD5sum: 934fc8df594ccd31d7992ba87a8c9b26 SHA1: f58cc5b830b12f90e9df081db041bba4a6d6db98 SHA256: bb041cfd79d5caa04ddfe6182a74d740069e4db3461ea64edf8beb8ce8f35a55 SHA512: ec74fdc615875907b73975ce873a0e3711b5fcf9d162967814ad6b75f1af5446e7436890e6c645ae5255894ed178576b72194fd9fc33391df6b0abd80230e4b1 Homepage: https://cran.r-project.org/package=survsim Description: CRAN Package 'survsim' (Simulation of Simple and Complex Survival Data) Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) and Moriña D, Navarro A. (2017) . Package: r-cran-survsparse Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-nloptr, r-cran-nleqslv, r-cran-tibble, r-cran-foreach, r-cran-gaussquad, r-cran-purrr Filename: pool/dists/noble/main/r-cran-survsparse_0.1-1.ca2404.1_all.deb Size: 64214 MD5sum: 887ddac905244514271e170c3fe0afde SHA1: 4f3fbbc939eaf1fead9092ab8c8fe9fd2c214b8f SHA256: edb24be8b7d536d857c6e004a1cb8be738bd4cfc2f8db0d3eec2be60c9de56ce SHA512: 1e96cdb76f67ca068bd0a898f93af1867b3a3e97db2f38f906ed3d404fc18f7db98947c7b5c3007d8a36994cefd48f12356bd71c93e1138161d15a3fcb14553b Homepage: https://cran.r-project.org/package=SurvSparse Description: CRAN Package 'SurvSparse' (Survival Analysis with Sparse Longitudinal Covariates) Survival analysis with sparse longitudinal covariates under right censoring scheme. Different hazards models are involved. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2022) , Sun, Z. and Cao, H. (2023) and Sun, D. et al. (2023) . Package: r-cran-survspearman Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survspearman_1.0.1-1.ca2404.1_all.deb Size: 315476 MD5sum: a28535a50e6759c4b5037aed95665fd0 SHA1: ba258e2e9e288b08453ffce61a6feb79945f20f8 SHA256: 89786d4f4a790fa81e60c7b30014c7f33ceddd389d3ce112b2495a73b9f0d5a4 SHA512: e03b8caeeff92edc000f3a94b41b2f00a6fcd2e70d7820d9589e83feef7bb65cb8d7c44b6fd366856f997357029d3884ce8ee27d31f4e8f05967df73b23ad36f Homepage: https://cran.r-project.org/package=survSpearman Description: CRAN Package 'survSpearman' (Nonparametric Spearman's Correlation for Survival Data) Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function. Package: r-cran-survspro Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-mgcv, r-cran-survival, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-survspro_0.1.0-1.ca2404.1_all.deb Size: 584036 MD5sum: 0cf5b188bec05f48d76e247c8a9859fc SHA1: a6e0d39dd05695b6d119d5f9c504dd208b8634e8 SHA256: 45fe917e0fd42a0201632f9241de836fb85d0655b808b39779d529bb6ece818e SHA512: 074b252d132256cd051de78edf93d52c41437c2467dba9b298a6cce0b7c5a35f2294d8520a2985defd3ae3fbd27dc1bf2517e3aa537c068bde8d8761b5a7f386 Homepage: https://cran.r-project.org/package=SurvSPro Description: CRAN Package 'SurvSPro' (Survival Prediction with Spatially Adjusted Protein Summaries) A survival prediction framework using spatially adjusted protein summaries from spatial proteomics data, including imaging mass cytometry data. Cell-level protein intensities are modeled with spatial spline regression to estimate spatially adjusted mean expression and residual variance. Methodological details are described in Ahn et al. (2026) . Package: r-cran-survtrunc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-survtrunc_0.2.0-1.ca2404.1_all.deb Size: 76782 MD5sum: 58c665814ca947c7c2160d403c291c45 SHA1: 288a1a0e411b162bdad34df41f9660dafa63eeea SHA256: 1ba5b137a86a07a0b889520a728a10d738611d489829cd6d36167302dc9eaad4 SHA512: 4469bf2d3d8f553728c6160653bd0204853fc21e15a3bcccd58955466553dc07771952b074bf5e6d7051aae799dac169a12789ab52772df28a433bdc690ba334 Homepage: https://cran.r-project.org/package=SurvTrunc Description: CRAN Package 'SurvTrunc' (Analysis of Doubly Truncated Data) Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) , Shen (2010) , Martin and Betensky (2005) . When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) . Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert for questions regarding function coxDT and Yidan Shi for questions regarding function coxDTdep. Package: r-cran-susenas Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl Filename: pool/dists/noble/main/r-cran-susenas_0.1.0-1.ca2404.1_all.deb Size: 1245902 MD5sum: e8dda19fc87d75abfb38d98ee4188ab0 SHA1: 93c0e74c0384042044c3ea8d05e4257571de6c36 SHA256: 95f6a64f68e74335880fd5657bff53b704947919e025557b7fbdd216778c9288 SHA512: 8bbf737cd0a81bc071aedc163e606b386c1027e06e2ab2acfd935849beab0acfb276b0f43be76139064fa8a451153a3b37b760c13f34c6ba6b4379d052bf880a Homepage: https://cran.r-project.org/package=SUSENAS Description: CRAN Package 'SUSENAS' (National Socio-Economic Survey Data Collection Indonesia) Survey to collect data about the social and economic conditions of Indonesian society. 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Package: r-cran-susier Architecture: all Version: 0.14.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-mixsqp, r-cran-reshape, r-cran-crayon, r-cran-ggplot2 Suggests: r-cran-curl, r-cran-testthat, r-cran-microbenchmark, r-cran-knitr, r-cran-rmarkdown, r-cran-rfast, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-susier_0.14.2-1.ca2404.1_all.deb Size: 2025790 MD5sum: 05fb58ce272f06f05732d46f9b585b47 SHA1: 2779703e84e6bc7653b20116326e6bc492045c20 SHA256: 586303b533357ca3d36ec2e3598e1860c828fc2e99deb7d4eee276f7ea599887 SHA512: dd20dc77b08d5bc69a0bcde87f66f9ab8904d4915035bdc4b98bae2e243d89f14ece5122424228d9253e0cbcecfc96df323bb6b584d75549e0811b243821498a Homepage: https://cran.r-project.org/package=susieR Description: CRAN Package 'susieR' (Sum of Single Effects Linear Regression) Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) and Zou et al (2021) . 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Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) and Clark (2019) . 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Package: r-cran-svemnet Architecture: all Version: 3.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-cluster, r-cran-ggplot2, r-cran-lhs Suggests: r-cran-foreach, r-cran-testthat, r-cran-rhpcblasctl Filename: pool/dists/noble/main/r-cran-svemnet_3.6.2-1.ca2404.1_all.deb Size: 717684 MD5sum: 25b7cf0a86c997b78d28e0093adaf74a SHA1: 9e28b08a159f98b497d9ebe6e0402d9ba227432b SHA256: a630db8535eae7a2454b912f973d54adb563d568d9d65ce97db5aa997c1dae67 SHA512: 9a3fae4b5f9a4a4d27fbb3d009ffd47150cf7b4e31e2d3dc5371b22dce0d32b0ac9c9e4d46fe06de9b9e720dccf44e09abc43e94465927542925eed09ed050d8 Homepage: https://cran.r-project.org/package=SVEMnet Description: CRAN Package 'SVEMnet' (Self-Validated Ensemble Models with Lasso and Relaxed ElasticNet Regression) Implements the self-validated elastic-net and relaxed elastic-net ensemble modeling and multi-response optimization workflow described in Karl (2026) . Self-validated ensemble models (SVEM; Lemkus et al. (2021) ) are fitted for small-sample design-of-experiments and related workflows using 'glmnet' (Friedman et al. (2010) ). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, and a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions. Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl (2024) ). Package code was drafted with assistance from generative AI tools. Package: r-cran-svenssonm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-svenssonm_0.1.0-1.ca2404.1_all.deb Size: 34012 MD5sum: 9e3e25692cc6bd01e76ae7776e898635 SHA1: eeba23585dbd13536ee1300de71acabcd163186e SHA256: 14c90691f64dfb1f73538d9ae814ef5ed19ec65e86c0e86fa06db7893dd4954b SHA512: fd6f868ee57d35089c265d8bf8893163ac0244668eafdcd23dbc789ac600433cf578355c15b8a91086b7b873f64546529549285e3ceab3bd4b41b43207579dcf Homepage: https://cran.r-project.org/package=svenssonm Description: CRAN Package 'svenssonm' (Svensson's Method) Obtain parameters of Svensson's Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. 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To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021). 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Recursively builds classification trees using linear Support Vector Machines (SVM) hyperplanes at each node instead of axis-parallel splits, creating oblique decision boundaries. Features include multiple feature selection methods, dynamic feature subset strategies, class weight support for imbalanced datasets, pruning and feature penalization. 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As described in Capanu (2015) , the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots. Package: r-cran-svyroc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-svyvarsel Filename: pool/dists/noble/main/r-cran-svyroc_1.1.0-1.ca2404.1_all.deb Size: 425776 MD5sum: 545b2cad51e26a3a8f92a33ae7788fdc SHA1: ffbc1669d7e50bcb30ef5d78eef9caf9b82ba7d5 SHA256: 617b7c75fdf8adbb7d5b0bc596fa206560467b20288b0d9f0366904b50c29aad SHA512: 431b0b01972cfba2b26f4eececd2be69f95b144a86a940a6d141a206323f141d918c909520c48e2e363b64debfaacb9eb4ade719604744dd420aeb52152c5500 Homepage: https://cran.r-project.org/package=svyROC Description: CRAN Package 'svyROC' (Estimation of the ROC Curve and the AUC for Complex Survey Data) Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) ; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) ; A. Iparragirre, I. Barrio (2024) . Package: r-cran-svyse Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 837 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey, r-cran-openxlsx Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-svyse_0.3.0-1.ca2404.1_all.deb Size: 659008 MD5sum: 93e9d81c96e8a97c81e531372d2b6b8b SHA1: 15b4866b1e88718fc7b92036771c9626ef6871b1 SHA256: 9dddde4001ebc73eed6b335a913929d4bb679fde31acdef4cbd37e47330e5e0c SHA512: 07076c1f8beb54d6581afba9f70fd06f8bb3eacb1556a670b7e65ba89f286565cd7429950bdf7547b9987e638ddfba5b90201438a958c44e34eb970b1f1ff6df Homepage: https://cran.r-project.org/package=svySE Description: CRAN Package 'svySE' (Sampling Error Estimation for Complex Surveys) Estimates sampling errors and produces indicator tables for complex survey data. Supports weighted totals, proportions, standard errors, confidence intervals (Wald or logit-transformed for proportions), coefficients of variation, design effects, unweighted frequencies, grouped estimates, domain estimates, optional stratification and clustering variables, and customizable exports to '.xlsx' files. Survey estimation is based on design-based inference using Taylor series linearization implemented in the 'survey' package (Lumley, 2004, ; Lumley, 2010, ISBN:9780470284308). The package provides a reproducible workflow for official statistics, household surveys, and applied survey research. Package: r-cran-svyvarsel Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 741 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-svyvarsel_1.0.1-1.ca2404.1_all.deb Size: 716094 MD5sum: 820a6b9924d88a1248e3a371120a4029 SHA1: b1abdf7bb9931ca1838a1c69b251728d17df787a SHA256: b56db17e3388718002d89964d385ccacb7ba3c0c31800d922b360fcf4c5b84e5 SHA512: 64c33de48482e479670aea2c08fd6be7f6b2966fbde675e0ecc0770009489e23bb16540667bb7a82bf43beaec5d43745ef792a97fe098dce4ef71a64025a46cc Homepage: https://cran.r-project.org/package=svyVarSel Description: CRAN Package 'svyVarSel' (Variable Selection for Complex Survey Data) Fit design-based linear and logistic elastic nets with complex survey data considering the sampling design when defining training and test sets using replicate weights. Methods implemented in this package are described in: A. Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) . Package: r-cran-svyvgam Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-survey Suggests: r-cran-pscl, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-svyvgam_1.3-1.ca2404.1_all.deb Size: 76030 MD5sum: 623721629aef6c5be0836b9d2a884d89 SHA1: 34ae7a52ac00627e2bec91cab36260e3dae57025 SHA256: 551e5974a968f436e0f19add315f8e80b24eb52b1ba54308962a8a8530ce8d0f SHA512: 3f30af8653877c36302e326e861b4f0cf8c8fb33e33196407b0040305f5134d1ce4a0d58bfc9b0c8c15c24e0e05590f28d59177a438e8dff97038f3eb6efc9d6 Homepage: https://cran.r-project.org/package=svyVGAM Description: CRAN Package 'svyVGAM' (Design-Based Inference in Vector Generalised Linear Models) Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package. Package: r-cran-svyweight Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-gdata Suggests: r-cran-dplyr, r-cran-srvyr, r-cran-testthat, r-cran-mice Filename: pool/dists/noble/main/r-cran-svyweight_0.1.1-1.ca2404.1_all.deb Size: 99902 MD5sum: a13a7fb5f26eaf841509e827ea8a773d SHA1: d08c12aea2cfce2bee58b6aea4154778b0dc57ee SHA256: 3012313cb0a088dd4835af074af42cdd0c1bcd944fd3416d74427cadabec4030 SHA512: db423d50a1e3a9e79f08b189f0cd98114d452ac106fb8de014f932f2829bbb094fcf6d4220d396761ae14785be8a0d114a833a1f1d9f7db132c6e66ddf18a6bb Homepage: https://cran.r-project.org/package=svyweight Description: CRAN Package 'svyweight' (Quick and Flexible Survey Weighting) Quickly and flexibly calculates weights for survey data, in order to correct for survey non-response or other sampling issues. Uses rake weighting, a common technique also know as rim weighting or iterative proportional fitting. This technique allows for weighting on multiple variables, even when the interlocked distribution of the two variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking to the base weighting functionality in 'survey.' 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Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) . 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This package works on top of the 'caret' package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners. Package: r-cran-swagger Architecture: all Version: 5.32.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6379 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-jsonlite, r-cran-plumber, r-cran-testthat Filename: pool/dists/noble/main/r-cran-swagger_5.32.1-1.ca2404.1_all.deb Size: 1138064 MD5sum: b61a32cc5c7598ca26b2bf9380127bd4 SHA1: ee747695978f424741a55d429f81500899030bf6 SHA256: 1f1258306266556c5a2433993c374246e8881910d56af68ccbddac2ae755c041 SHA512: a119903a56667a8c8ce9c5a659fc3aceaa173c5374377305c29e19be3bd567dd2625d8ff6edcd543b844b73e985ac97ca79f55b7708e7d6be8cd45188d252336 Homepage: https://cran.r-project.org/package=swagger Description: CRAN Package 'swagger' (Dynamically Generates Documentation from a 'Swagger' CompliantAPI) A collection of 'HTML', 'JavaScript', and 'CSS' assets that dynamically generate beautiful documentation from a 'Swagger' compliant API: . 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Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components. Package: r-cran-swaprinc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-dplyr, r-cran-gifi, r-cran-lme4, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-swaprinc_1.0.1-1.ca2404.1_all.deb Size: 43434 MD5sum: df4422107f08851e4b05acb8f70311cf SHA1: 14fd94828112e3cdc6db621d7317f7dd939d0f9d SHA256: 799cd774286b817c1448de6763c422fe27a74c60e68e3706445094910d89e568 SHA512: d02527856d8d4eb015b9e4bb88cf51ff68fa642165bd5c43e6cee1de8ad71fa5e6029abbf2f0c6a80a0b04c6033447c4affb1fdfac2911fdd3b32f5bd9e520da Homepage: https://cran.r-project.org/package=swaprinc Description: CRAN Package 'swaprinc' (Swap Principal Components into Regression Models) Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) . The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) . Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) . 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Package: r-cran-swarmverse Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbapply, r-cran-rtsne, r-cran-trackdf, r-cran-swarm, r-cran-geosphere Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-swarmverse_0.1.1-1.ca2404.1_all.deb Size: 330602 MD5sum: 69da60003b1f4c82e52baa2bf5bf2c7e SHA1: 0d891f26db07c4b904d7ffe3e8197030f80a63c2 SHA256: 1e378ae1ae29f7970b8ccdfc5309ebdde69d9db0b86f72fb371b917fa975d76f SHA512: ebb97fade219165f07654849ffa6f16ad7f5127396911ff470e92290b42db09605332d2d13edef156300b2e1ee6a217f4c9126314e1a3e775a1381b8216cd119 Homepage: https://cran.r-project.org/package=swaRmverse Description: CRAN Package 'swaRmverse' (Swarm Space Creation) Provides a pipeline for the comparative analysis of collective movement data (e.g. fish schools, bird flocks, baboon troops) by processing 2-dimensional positional data (x,y,t) from GPS trackers or computer vision tracking systems, discretizing events of collective motion, calculating a set of established metrics that characterize each event, and placing the events in a multi-dimensional swarm space constructed from these metrics. The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) . Package: r-cran-swash Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5903 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-sf, r-cran-spdep, r-cran-zoo, r-cran-strucchange, r-cran-sfdep Filename: pool/dists/noble/main/r-cran-swash_3.0.0-1.ca2404.1_all.deb Size: 5792636 MD5sum: d5695915d4ed10c0909b7ffc3866ddb2 SHA1: 5cdc7c62c64b6b6722b91f6e83865c627a21d949 SHA256: 59d8e92594f9323ca2149dec5f4f78896d6fadd4987f24e68f6cd7f3d1d767cc SHA512: 1b8afbcb88342a1e1f2df02db9e4d5f7800380b7f05e24040cfd59f7066f1c90ed15f89e794ff815db7d9b8805b117ab3592355970015e803fc6353fb904f674 Homepage: https://cran.r-project.org/package=swash Description: CRAN Package 'swash' (Health Geography Toolbox for Model-Based Analysis of InfectionsPanel Data) Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. 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Package: r-cran-swcecon Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-swcecon_0.1.0-1.ca2404.1_all.deb Size: 146450 MD5sum: 9a7a658d5477a22e93883cfeaa515198 SHA1: 3d891867fc8fdc51d4b8cb8e78a81a5ae1add93e SHA256: 05acd14bbf8dc6d9e00e778ab9f5d3da13589e8036d6de0f9ccfe3f2a85530f4 SHA512: 73a9fbbcaf97ffa36c7192262c8c354b0431efeb7ab82442e1b1c4c23bdaec534e0927091e1c360de679513b7c22e501f6f943dbc30c50c5728b6418fcf1ae09 Homepage: https://cran.r-project.org/package=swcEcon Description: CRAN Package 'swcEcon' (Economic Analysis of Soil and Water Conservation Measures inWatersheds) Provides functions and benchmark datasets for the economic appraisal of soil and water conservation (SWC) measures in watershed development projects. Implements benefit-cost ratio (BCR), net present value (NPV), internal rate of return (IRR) via the bisection method of Brent (1973, ISBN:9780130223715), modified BCR, marginal rate of return using the CIMMYT (1988, ISBN:9686127127) method, payback period, soil loss economic valuation via the Universal Soil Loss Equation of Wischmeier and Smith (1978, ISBN:0160016258), groundwater recharge valuation, employment generation ratio, sensitivity analysis, switching value analysis, and Monte Carlo simulation. Six datasets are included: state-wise BCR benchmarks from NABARD (2019) watershed evaluations, USLE erodibility parameters for Indian soil orders from NBSS and LUP, rainfall erosivity for twenty Indian districts from IMD data, SWC unit cost norms from PMKSY-WDC (GoI 2015), and two hypothetical datasets for illustration. Methods follow Gittinger (1982, ISBN:9780801825439) and Squire and van der Tak (1975, ISBN:9780801816697). Package: r-cran-swcrtdesign Architecture: all Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lmertest, r-cran-glmmtmb Filename: pool/dists/noble/main/r-cran-swcrtdesign_4.1-1.ca2404.1_all.deb Size: 227154 MD5sum: 4bdf8d9b0975a630f24afda90a0b3ecb SHA1: eaf30e50ab4abd42e6f108a380ed0e4cdb22fba1 SHA256: 4921848efcf2b0323c621334471012a511f85ccd28f5d6311abab85e0673ced5 SHA512: c47b4a607567f1437006ec9f0ac3470e6ca60b62decead30eb683c6dda6175112ba38cf850fdb562a31fd5580daac21d810829004ebb3c2a0f4f8391ff1980a4 Homepage: https://cran.r-project.org/package=swCRTdesign Description: CRAN Package 'swCRTdesign' (Stepped Wedge Cluster Randomized Trial (SW CRT) Design) A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional or cohort sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191). Package: r-cran-swdft Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1433 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-fftwtools, r-cran-fields, r-cran-signal, r-cran-nloptr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-swdft_1.0.0-1.ca2404.1_all.deb Size: 996150 MD5sum: b7325f618eb3cae63b2d2985fa4c1cc4 SHA1: d03090875dc53341cf7cb1f380422110cad8d8be SHA256: be38c7db6c2a43fafe23ea218b369b2b64b9be824dd8c08f84b6f0dc6033c506 SHA512: d3844a0a3b82b5c15762339970c3c3ea2ca0478f985f61d2134b33edf2e972cfaf6b25818fd5ee2954f1e8986efda8c821ca195de5c3d7a1774ceac59934aea1 Homepage: https://cran.r-project.org/package=swdft Description: CRAN Package 'swdft' (Sliding Window Discrete Fourier Transform (SWDFT)) Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides statistical methods based on the SWDFT, and graphical tools to display the outputs. 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Package: r-cran-sweepdiscovery Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1705 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-sweepdiscovery_0.1.1-1.ca2404.1_all.deb Size: 1683686 MD5sum: 0c0839ed5fbb0811b11d9830981b4a45 SHA1: 2391d58fa7a67d15160d86db6e952da3c2f6afb3 SHA256: 2d69201c6123b2229785d5ee92190a1db26c638ee32ac30bd3a096cc7647adb7 SHA512: c07b372a98032bfbcbc01353c46ec975ed43c6b739db70e407d96c8be7f85e579529cfe1a7d18b3253058eadf320d56b8d32cb7bc9524e98e5e3493c7f0cd8ac Homepage: https://cran.r-project.org/package=SweepDiscovery Description: CRAN Package 'SweepDiscovery' (Selective Sweep Discovery Tool) Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. Detection of selective sweep is not well acquainted as well as it is a laborious job. This package is a user friendly approach for detecting selective sweep in genomic regions. It uses a Random Forest based machine learning approach to predict selective sweep from VCF files as an input. Input of this function, train data and new data, can be computed using the project in 'GitHub'. This package has been developed by using the concept of Pavlidis and Alachiotis (2017) . Package: r-cran-sweidnumbr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-stringr, r-cran-checkmate Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sweidnumbr_1.5.0-1.ca2404.1_all.deb Size: 130306 MD5sum: eb546374d2f6bb1746521c9042395d71 SHA1: 4db903dc6ceaf4ecc4b0c727f9566a3802fe40f2 SHA256: ac573bc97c2fa7972c25695325d419747168f003c68fdfad2f913275be8e74e5 SHA512: 8835b99698eb55159150af451dda8e9e1ebcbc03c175d7a70e676762d9444005e890f3da9f840f0e159a382b31c911badb9f3f5997d5c6c8e55d1a74be0e1b68 Homepage: https://cran.r-project.org/package=sweidnumbr Description: CRAN Package 'sweidnumbr' (Handling of Swedish Identity Numbers) Structural handling of identity numbers used in the Swedish administration such as personal identity numbers ('personnummer') and organizational identity numbers ('organisationsnummer'). Package: r-cran-swelex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-urlchecker Filename: pool/dists/noble/main/r-cran-swelex_0.1.0-1.ca2404.1_all.deb Size: 32524 MD5sum: 9d30835e45be632786b2fe4c31e93ab6 SHA1: 030777caad7988204a9184152dc112dd2442f591 SHA256: 63769fcf80828fa130ba3fde04d9dbab93b21bea593a47f1dc111aeec3741a6a SHA512: b9dc56d9e8c73931923954722cc06371227a21cc14875362b2bc3ed1bf8ac6c558be9f460a451c91f68a7fbdbce5bce14f9b49a9c87b4397a26284df7a3700ee Homepage: https://cran.r-project.org/package=swelex Description: CRAN Package 'swelex' (Access the Swedish Code of Statutes via Riksdagen's Open Data) Provides functions to search and retrieve the consolidated text of Swedish statutes (Svensk författningssamling, SFS) via the Riksdag's open data API. Part of the lexverse ecosystem of packages for accessing legal and regulatory data. Package: r-cran-swfscairdas Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-swfscdas, r-cran-swfscmisc, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-swfscairdas_0.3.1-1.ca2404.1_all.deb Size: 1064102 MD5sum: d0b2c0d2ed3afa5af1ec341ab9c28b5d SHA1: 9b6ad7c9915d51a6f69f5324d60f9323afcf9e91 SHA256: c427de9f23fffcb3245c134bd0462c867513c19c9436ceb9917496d18061b7d0 SHA512: 5853293155bd7e11436b593b5abd5d8ab646509a123955ee17d54c1d79a645160ac79345b6819c4923304b5b1e10b7bf00c3c29741c72028021f2a5d9c8e883e Homepage: https://cran.r-project.org/package=swfscAirDAS Description: CRAN Package 'swfscAirDAS' (Southwest Fisheries Science Center Aerial DAS Data Processing) Process and summarize aerial survey 'DAS' data (AirDAS) collected using an aerial survey program from the Southwest Fisheries Science Center (SWFSC) . PDF files detailing the relevant AirDAS data formats are included in this package. 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These files are typically, but do not have to be DAS data produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'. This package standardizes and streamlines basic DAS data processing, and includes a PDF with the DAS data format requirements expected by the package. Package: r-cran-swfscmisc Architecture: all Version: 1.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-hdinterval, r-cran-kknn, r-cran-tibble, r-cran-rlang, r-cran-sf, r-cran-spatstat.geom, r-cran-tidyr Suggests: r-cran-coda, r-cran-runjags, r-cran-rstan Filename: pool/dists/noble/main/r-cran-swfscmisc_1.7.6-1.ca2404.1_all.deb Size: 207652 MD5sum: 1fd6f8d920c06acd24ac04442c231127 SHA1: c24c7dc6a2f4e2d5924a05ec82e6010a10ebf720 SHA256: b82c0359935c2cd5fe971e1009506e9085d5e999ef73ef77fc75a4479606c4ea SHA512: 32de1c7310bb45036b863021fe864ec4a946e0818c5eff0a794a3da63f59c9dd0523dcedd49d61bed40b42cb68ade7a38bdfc310c2eea36fc760167126e9acd6 Homepage: https://cran.r-project.org/package=swfscMisc Description: CRAN Package 'swfscMisc' (Miscellaneous Functions for Southwest Fisheries Science Center) Collection of conversion, analytical, geodesic, mapping, and plotting functions. Used to support packages and code written by researchers at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration. Package: r-cran-swgee Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-geepack, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-swgee_1.4-1.ca2404.1_all.deb Size: 58844 MD5sum: 4c0a91cf3941488818a17378059e5620 SHA1: 3490dd47482c3cf999ad35571c853446bc7a166d SHA256: 72df68bfaa286bddcc66a4ecbb860798343969364534aefd1ed97936cd5198cd SHA512: 402e812a0b168fd604048424f6bed9f85af00ad3ee3d570ed6327f28e158fe5091873deff4ff75dd9beafe29e3f8c29cbdfb101ec08e33cba5c5ebca1e89e960 Homepage: https://cran.r-project.org/package=swgee Description: CRAN Package 'swgee' (Simulation Extrapolation Inverse Probability WeightedGeneralized Estimating Equations) Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) ; Cook, J. R. and Stefanski, L. A. (1994) ; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0). Package: r-cran-swim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4521 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-hmisc, r-cran-nleqslv, r-cran-reshape2, r-cran-plyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-mvtnorm, r-cran-spelling, r-cran-weighted.desc.stat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-swim_1.0.0-1.ca2404.1_all.deb Size: 3712760 MD5sum: 83774a2161885410f78d102f0237cdff SHA1: 0091766ae70566a89ca3a3b18b356e2b3a913124 SHA256: 991090d9338370f65b1ba2751da122e5049a886348f9f838b45bd63e7ca9aa1f SHA512: bc1909f552f33d06ddcabb597e0430044043bfa14eda4518560ae9350cd336001a5c7d03d840deda04a8621a46a2be4db1a04185a73b89c8a93db9e1d7aa8da9 Homepage: https://cran.r-project.org/package=SWIM Description: CRAN Package 'SWIM' (Scenario Weights for Importance Measurement) An efficient sensitivity analysis for stochastic models based on Monte Carlo samples. Provides weights on simulated scenarios from a stochastic model, such that stressed random variables fulfil given probabilistic constraints (e.g. specified values for risk measures), under the new scenario weights. Scenario weights are selected by constrained minimisation of the relative entropy to the baseline model. The 'SWIM' package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019) "Reverse Sensitivity Testing: What does it take to break the model" and Pesenti S.M. (2021) "Reverse Sensitivity Analysis for Risk Modelling" . 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To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, 'Omega' results, and (on a development basis) '.hy3' files. Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. 'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature. Package: r-cran-swimplot Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2033 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-swimplot_1.2.0-1.ca2404.1_all.deb Size: 667312 MD5sum: e5b09d79514d48a473410a5f93c15c60 SHA1: 881eadefb96948d7a94662a6145fc90ad1a4a6c1 SHA256: ec0caaf8659ebfaec39761564082ac00c0ab133da5188221da0602d13a7a0baf SHA512: 27f7edc8aaef24b8cf7b72538129807261bb62e3cf1c64cf8018f03529eb526d0d2b292489c73c95f2ae79dfa7d840192d316f0afd12a674af423010afe77f66 Homepage: https://cran.r-project.org/package=swimplot Description: CRAN Package 'swimplot' (Tools for Creating Swimmers Plots using 'ggplot2') Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows. 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These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices. For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) , Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) . 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To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (). 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It was originally developed for and part of the 'koRpus' package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (). 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(Milligan, G.W., Cooper, M.C. (1985) , Breiman, L. (1996), , Hubert, L., Arabie, P. (1985), , Ichino, M., & Yaguchi, H. (1994), , Rand, W.M. (1971) , Breckenridge, J.N. (2000) , Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) , Dudek, A. (2007), ). 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Package: r-cran-syncerdata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14730 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-syncerdata_1.0.0-1.ca2404.1_all.deb Size: 15038230 MD5sum: 75979f7e8d12cdddc493c56feddcf679 SHA1: 32f6d10e6679734a951552ee0edc8bbdcb876748 SHA256: 6ed8b93e9813d270f1feedcb53a4618f2c7241729e15991954d820607898dca4 SHA512: a912c7eec738a1369acde9e6394301ba5908a0f8d084a62ad2e6603850d6790ab23b03b8d63249675cb7fe9f47908c3fa9c214708f601c9431ef14b97a655143 Homepage: https://cran.r-project.org/package=SyncERdata Description: CRAN Package 'SyncERdata' (Example Datasets for 'SyncER') Bundled example datasets used by the 'SyncER' package vignette and test suite: synthetic radiocarbon-dated event records for five cores and the corresponding completed 'rbacon'/'rplum' age-depth model output (raw, synchronized, and synchronized-without-14C variants). These data let 'SyncER' demonstrate and test its full workflow reproducibly, without requiring users to install 'rbacon'/'rplum' or re-run Bayesian age-depth modelling. This package contains the synthetic data from Wils & Ramisch (2026) . Package: r-cran-synchrony Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-synchrony_0.3.9-1.ca2404.1_all.deb Size: 166736 MD5sum: 73ab21768a95e226b0eb643f83a349a6 SHA1: 000b4ca02e00ac28a6189a772b8021833183af3f SHA256: 6cbf8453db871350baf82177643e164d951464e05447340dbb88e96084f53cd7 SHA512: c3a301ffbc87f0684c616b8521ca65af737c4f09c8e387c82501b04e27bdaa77204aaafea4345ddf57d7ca08d5f3c73083944f56c46907476104fd2348af36e5 Homepage: https://cran.r-project.org/package=synchrony Description: CRAN Package 'synchrony' (Methods for Computing Spatial, Temporal, and SpatiotemporalStatistics) Methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) . These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices. Package: r-cran-syncmove Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-syncmove_0.1-0-1.ca2404.1_all.deb Size: 63840 MD5sum: 314328776553c2b1344495dee0b47922 SHA1: f288f164984f5d1d93bb757a6bf348845d2e7847 SHA256: 52ee00b35d38ddfd6e492d7eed26e4bfe002f89d203ad7a3605c8b39a28beede SHA512: 9d582d82cc38876a4874856e55d50d71b3eef7b9a4df1b9c4538fb4149085f8a41d8e88d6cddd2d165fd7d3458a737eb8a182a504d48659e1ff3c567652d026b Homepage: https://cran.r-project.org/package=SyncMove Description: CRAN Package 'SyncMove' (Subsample Temporal Data to Synchronal Events and Compute the MCI) The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'. Package: r-cran-syncons Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-purrr, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-syncons_0.1.1-1.ca2404.1_all.deb Size: 1396296 MD5sum: 323b24e3b20087a06b09c8edaa849204 SHA1: fdfb8a5b0cc3e97485716499eeca85f25d850d7d SHA256: 924f0ad0ff8cdff5ac00c8acc4a63576115ff99e6abfd0973f4bce2230f09360 SHA512: a8ecd63721f00480ee4c2f2223e105afa162ed03879c695eb6474de01bb2e424547cf6892ebbc29f94a22abaf6f2e450bf0b9c7a0a04b6b6f5c806269d99ba53 Homepage: https://cran.r-project.org/package=syncons Description: CRAN Package 'syncons' (Efficient and Low-Cost Construction of Synthetic Communities) Provides functions and a Shiny application to design plate layouts for constructing synthetic communities (SynComs) from a pool of strains using 24-, 96- and 384-well microplates. The method generates all possible strain combinations in a scalable, low-cost workflow suitable for high-throughput experiments. Reference: Tian et al. (2024) . Package: r-cran-syncsa Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-fd, r-cran-permute, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-syncsa_1.3.5-1.ca2404.1_all.deb Size: 234930 MD5sum: 0a42bc3cd1c761f999aef9b8d08b7193 SHA1: a8b73d311392f56a5791e273f0c127125ae07b31 SHA256: 0feb5d952f7c341c9f461e62eec2d198b0955acade2b0a6a73c614cfdd39852d SHA512: e2a49fdfa7dbfc2a06058fb40fb47aaaf0dfa8d7b2e8b2f3558dfb275fb8af2f132828f6390dab4a9ddf59c61bc5a26e1baf5b882448665811abff56d9d7a5be Homepage: https://cran.r-project.org/package=SYNCSA Description: CRAN Package 'SYNCSA' (Analysis of Functional and Phylogenetic Patterns inMetacommunities) Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the 'SYNCSA' application written in C++ (by Valerio Pillar, available at ). Package: r-cran-syndi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-magrittr, r-cran-dplyr, r-cran-stackimpute, r-cran-arm, r-cran-boot, r-cran-broom, r-cran-mvtnorm, r-cran-randomforest, r-cran-mass, r-cran-knitr Suggests: r-cran-markdown Filename: pool/dists/noble/main/r-cran-syndi_0.1.0-1.ca2404.1_all.deb Size: 133082 MD5sum: f2107e4fe33d186f664958ddb39500dc SHA1: 82d31437420a9fcaa6c5852a29ed9ed201ad8719 SHA256: b11140f38990cb298c2ef0b65fd7d81e6c98421de01f8f69495ee96f4fe3448b SHA512: d787e6a7ccadfd5be37bad5cf1f5207a6b7876dffc965afae21e9bd4fb252db9d0fad3d24c7a8604dc8fb821f712dfefb7578763ec04856bae86150d91a3e13a Homepage: https://cran.r-project.org/package=SynDI Description: CRAN Package 'SynDI' (Synthetic Data Integration) Regression inference for multiple populations by integrating summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2021) A synthetic data integration framework to leverage external summary-level information from heterogeneous populations . 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'SynergyLMM' provides functions to analyze longitudinal tumor growth experiments using mixed-effects models, perform time-resolved analyses of synergy and antagonism, evaluate model diagnostics and performance, and assess both post-hoc and a priori statistical power. The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, ). The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, ), and Gałecki and Burzykowski (2013, ). Package: r-cran-synopr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-synopr_1.0.0-1.ca2404.1_all.deb Size: 156714 MD5sum: 0a626531e54f387bb7e322ae028f5470 SHA1: a02b5b04a5da4f6e5a2010222a82d6ccaa5704cf SHA256: f5259cd81dbe3972ef4fc52086525105b242fcff2fbd4fd1c01d00b6881aea63 SHA512: b35bd2e83c04879842eb761d2840006cdda2d1fb4c90dff5c057428dedcb85e20608dd4a7817adcdb6e2bb308b6beb839962802a2c651ceb3576f9b712853aaf Homepage: https://cran.r-project.org/package=synopR Description: CRAN Package 'synopR' (Fast Decoding of SYNOP (Surface Synoptic Observations)Meteorological Messages) Decode raw SYNOP (surface synoptic observations) messages into data frames, extracting data from Sections 0, 1, and 3, including temperature, dew point, pressure, wind, clouds, and precipitation. Available functions to download SYNOP messages from Ogimet if needed. The decoding logic follows the specifications defined in the World Meteorological Organization (2019) "Manual on Codes, Volume I.1 (WMO-No. 306)". 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Package: r-cran-syntaxr Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-covr, r-cran-haven, r-cran-testthat Filename: pool/dists/noble/main/r-cran-syntaxr_0.8.0-1.ca2404.1_all.deb Size: 24280 MD5sum: a9fe018cb136734ffcffa3695f96cd8b SHA1: 6329a814cc0a9e55a023c17f63b09073fc9b5ea0 SHA256: fc43e898c108474a3d0591e8e7b0064d87d6278fdaa637d07cf636972d4e99b1 SHA512: f808540176ed4fb10616cd1c4b018c23af4b51504f528b78b788fb7e4552563a25558b07714d4ae3d33ed94ccca2c917a82c714eb6e51fd883f6637cb9ea31cb Homepage: https://cran.r-project.org/package=syntaxr Description: CRAN Package 'syntaxr' (An 'SPSS' Syntax Generator for Multi-Variable Manipulation) A set of functions for generating 'SPSS' syntax files from the R environment. Package: r-cran-syntenyplotter Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2862 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-syntenyplotter_1.0.0-1.ca2404.1_all.deb Size: 2336804 MD5sum: 29477993db9cbba8d05c29a583dcb4b1 SHA1: c8f4566ec575ff7acab04eef7a4665e2db9a1f22 SHA256: 3e9ac2050f2528a778f12c8b35d744e24c45bcc282b50d25604db55d1262590f SHA512: c346c0323020f9e40b820dcba18a96e0296472074b958fb7167efb5152b60c30c9e7a09942dd70e9671870dbacc9a1d7c92b7de2131b16edc378972760e933d5 Homepage: https://cran.r-project.org/package=syntenyPlotteR Description: CRAN Package 'syntenyPlotteR' (Genome Synteny Visualization) Draw syntenic relationships between genome assemblies. There are 3 functions which take a tab delimited file containing alignment data for syntenic blocks between genomes to produce either a linear alignment plot, an evolution highway style plot, or a painted ideogram representing syntenic relationships. There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format to the required data structure. Package: r-cran-synth Architecture: all Version: 1.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernlab, r-cran-optimx, r-cran-rgenoud Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-synth_1.1-10-1.ca2404.1_all.deb Size: 130474 MD5sum: c83bfef47941f028ec7ee59c3bbb1828 SHA1: 0eab2194749612f48b8a921710d1ee63f1cf65e7 SHA256: c878edd92bc6759b512071886228d5f28613dd331e55039a98d8b735176cb35a SHA512: 77631ffb069a1737a14e1047a28b0b60b684f343921cf7b2291cf8fd1e243090b3358855011a4052bc389b6a21641f3890cddcfc7b42cfbd9d7092fd74dea30e Homepage: https://cran.r-project.org/package=Synth Description: CRAN Package 'Synth' (Synthetic Control Group Method for Comparative Case Studies) Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone. Package: r-cran-synthesis Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-zoo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-synthesis_1.2.5-1.ca2404.1_all.deb Size: 1742062 MD5sum: 6ecb96efc719090c01d1fd3b526ede82 SHA1: a0d348a3583d21591b4acd2afcbde5fb47fbc1cb SHA256: d6e4c6dbd48579a2e94d206ecea54761aeb6fea14c6402d3efb91760c9fe03b0 SHA512: efb5a1bfd2c48923325561ccd171a4dd2927ce6f75ee9acd6dd507bd88d23944d8dd14be3909d8eaa4aa5130e5844079419cc4372d7a4edce4b6c6631578ac7c Homepage: https://cran.r-project.org/package=synthesis Description: CRAN Package 'synthesis' (Generate Synthetic Data from Statistical Models) Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) and Jiang, Z., Sharma, A., & Johnson, F. (2020) associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) . Package: r-cran-synthesisr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-stringdist, r-cran-stringr, r-cran-tibble, r-cran-unglue, r-cran-vroom Suggests: r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-synthesisr_0.4.1-1.ca2404.1_all.deb Size: 353198 MD5sum: 90327c0549d19e9770b31c48adfc1fe9 SHA1: c14e5e368f445106bfdbc2d3e118555e32154363 SHA256: 6a15b14931dd6bdc996e1281556da87641d38cff4227bf46985e700d74612843 SHA512: d45644a755ed0c4573b57e154d55c5c099612a8d5701de676755b25c113a12b3e36484622a5211757c7012655142a0eea02c5e769791633ed069f84f01352c83 Homepage: https://cran.r-project.org/package=synthesisr Description: CRAN Package 'synthesisr' (Import, Assemble, and Deduplicate Bibliographic Datasets) A critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as 'Web of Science' or 'Scopus'. These databases often export in different formats or with different metadata tags. 'synthesisr' expands on the tools outlined by Westgate (2019) to import bibliographic data from a range of formats (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging and deduplication of the resulting dataset. Package: r-cran-synthesizer Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-synthesizer_0.6.0-1.ca2404.1_all.deb Size: 124560 MD5sum: 6f005953bfbdb5522a0c7bed2dc6d1bb SHA1: 81c7d6367431514bb5cf1415ea9b5b49204163ee SHA256: 69a23a6c51daeef59f6e6c33105ecf4a2fe0c15e45d16674ba596e8d2173f9be SHA512: 1c5e1792e16f2466fbd3cff7444113b36cc9b74167be40494a6304b5408b837ae6d83ebe5033312c960e49f615cdc0324febb5b5d193799ac1606d65898ac462 Homepage: https://cran.r-project.org/package=synthesizer Description: CRAN Package 'synthesizer' (Fast, Robust, and High-Quality Synthetic Data Generation with aTuneable Privacy-Utility Trade-Off) Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset. Package: r-cran-synthetic Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4980 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-actuar, r-cran-testthat Filename: pool/dists/noble/main/r-cran-synthetic_1.1.2-1.ca2404.1_all.deb Size: 4279006 MD5sum: 7d2263059eb1d9b424db192c55fafa34 SHA1: eba08c8e55656caa30d0b0f99fd5c8d577167397 SHA256: af9bbdb27bc37fa973d2def65b8c17e83088ac00f9d35be51949355c1819c795 SHA512: 769b9e34b104cf71ffe33ea13fc15e24be06384b4249d9fc2ecf98d2bd1e38c0fa3a1689f1ce83372ef86e77c5bdaf04c141d53ca737839d4152bf1acdb47637 Homepage: https://cran.r-project.org/package=SynthETIC Description: CRAN Package 'SynthETIC' (Synthetic Experience Tracking Insurance Claims) Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" . Package: r-cran-syntheticdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-syntheticdata_0.1.0-1.ca2404.1_all.deb Size: 186532 MD5sum: dde4c511bd2d93ad34c86491725820d2 SHA1: 7e6284b5577f153ba876c26292433c6309349ded SHA256: 89123f5a59785715e9c303a4bcc4b44f4675aa2e26f30a52b069aa60c793ae4c SHA512: f86bd8176d8b3bb18c51585f1f1148ec3a3164d3436776c385aa4506f72906d465826f0ef2a98d08bf3f3531bb0a73e8b3b903e1cd1c1d572614ac398f231099 Homepage: https://cran.r-project.org/package=syntheticdata Description: CRAN Package 'syntheticdata' (Synthetic Clinical Data Generation and Privacy-PreservingValidation) Generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Implements Gaussian copula simulation, bootstrap with noise injection, and Laplace noise perturbation, with built-in utility and privacy validation metrics. Useful for privacy-aware data sharing in multi-site clinical research. Validates synthetic data quality via distributional similarity (Kolmogorov-Smirnov), discriminative accuracy (real-vs-synthetic classifier), and nearest-neighbor privacy ratio. Methods described in Jordon et al. (2022) and Snoke et al. (2018) . Package: r-cran-synthpop Architecture: all Version: 1.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-nnet, r-cran-ggplot2, r-cran-rpart, r-cran-party, r-cran-foreign, r-cran-plyr, r-cran-proto, r-cran-polspline, r-cran-randomforest, r-cran-ranger, r-cran-classint, r-cran-mipfp, r-cran-survival, r-cran-stringr, r-cran-rmutil, r-cran-broman, r-cran-forcats Filename: pool/dists/noble/main/r-cran-synthpop_1.9-3-1.ca2404.1_all.deb Size: 1756714 MD5sum: 104e8915e1f80dbfe988015cfc104059 SHA1: 48e19c84ce6b0d276dbf4f51e5e2d37189ad34fa SHA256: c851eb5fab7585376f0d0693e8d797e1c29c1920fb53aa61c87b2fad8fe2ed59 SHA512: 5d7b1957bfbb450c8903482b0076feedf45eb0be7f08e362f0d55dbfe476c5fd8bc2ba99e7b256c1ca1ed331454802565c9c1403b800c93e6221eaa7de418365 Homepage: https://cran.r-project.org/package=synthpop Description: CRAN Package 'synthpop' (Generating Synthetic Versions of Sensitive Microdata forStatistical Disclosure Control) A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Most synthesising methods available in the package synthesise from conditional distributions where variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Methods that are not sequential, but synthesise all variables at once, are 'sample', 'ipf', and 'catall'. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. The package also includes functions to assess the utility and disclosure risk of the synthetic data compared to the original. These are described in vignettes (Utility - Assessing, Visualizing and Improving the Utility of Synthetic Data) and (Disclosure - Practical Privacy Metrics for Synthetic Data). Package: r-cran-synthreturn Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-data.table, r-cran-quadprog, r-cran-mirai Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-synthreturn_1.0.0-1.ca2404.1_all.deb Size: 306502 MD5sum: 704f04d74ec8281243757650bb97caa1 SHA1: 6dd1d10c949b1f93f75426e5d9f233ca5db7eda9 SHA256: a6dc62fbe7ecd380e68ede14af8596a03c65fa5f6acfd594f623929680bf1254 SHA512: 3f180c62dada698609ce0558475e8041ab3d8187dbea825422ff8462a8241d7103fd3ab5c2b54588f2e9d744afa41f7fde703fd22b45f1079882f8d8c430ae37 Homepage: https://cran.r-project.org/package=synthReturn Description: CRAN Package 'synthReturn' (Synthetic Matching Method for Returns) Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) , building on the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) , to estimate the cumulative treatment effect of an event on treated firms’ stock returns. Package: r-cran-synthtools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rdpack Suggests: r-cran-synthpop, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-synthtools_1.0.1-1.ca2404.1_all.deb Size: 101406 MD5sum: efc52e451d32c9b036889a5f84f4f1ee SHA1: f3085f62184e5358788bdd2a2f191585380ebcdc SHA256: 19eb7a9e11eeb77946a36c42439e8c1eb4af756c2852c6993db29eba76051dff SHA512: 79702a60377537fbe881e130b651a50f423fd1f9da4ac2c0008e9fd7089688c313fb930fa20889649198bedb6fd641dc7952c19b3956c8560c4851930c551148 Homepage: https://cran.r-project.org/package=SynthTools Description: CRAN Package 'SynthTools' (Tools and Tests for Experiments with Partially Synthetic DataSets) A set of functions to support experimentation in the utility of partially synthetic data sets. All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) . Package: r-cran-syrona Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dbplyr, r-cran-dbi, r-cran-cdmconnector, r-cran-omopgenerics, r-cran-tibble, r-cran-tidyr, r-cran-cli, r-cran-rlang, r-cran-shiny, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggtext, r-cran-scales, r-cran-readr, r-cran-meta Suggests: r-cran-testthat, r-cran-duckdb, r-cran-rpostgres, r-cran-cohortconstructor, r-cran-dt, r-cran-shinycssloaders, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-syrona_0.2.1-1.ca2404.1_all.deb Size: 533312 MD5sum: 2e25497f09484719521a954641be8acc SHA1: f5b0e61932029838d5f82f21baa1b2742880b14d SHA256: 44d60ccf181570e00aed40928dacb21fa37e65d3cf2bce6bfe47fc67e1b89fd4 SHA512: c10ae55e98a54650c6a12b5bb5c9ac1c49881bc17b88e593af26b44a67e1e5d771b5998b0add4888eb0a1256d738d03a93591f29ddf273092cc280a16aaba6e8 Homepage: https://cran.r-project.org/package=syrona Description: CRAN Package 'syrona' (Stratified Prevalence Comparison Across OMOP CDM Datasets) Derives stratified prevalence tables from the condition, procedure, and drug records in OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) databases, computes log2 prevalence ratios between paired datasets, and synthesizes them via random-effects meta-analysis at multiple aggregation levels (year, age group, and sex). Between-study variance is estimated with the Paule-Mandel method, as described in Paule and Mandel (1982) . Package: r-cran-syrup Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bench, r-cran-callr, r-cran-dplyr, r-cran-ps, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-syrup_0.1.4-1.ca2404.1_all.deb Size: 110348 MD5sum: 7b706f5d4d9db17516cf23f72d22b5b9 SHA1: 18328ecc07119b119d09611e97dbcc9d6421b59e SHA256: a7d9c8e219bfebdd6bef8448f701e0f5aa952f270a543bbea17ef936c11809e0 SHA512: 681251f59d59feb1e31dcec98d35c5c05e717f07e74b63ce3c0ade415cedad4a4c799e0d5f8e860354583787cb9cad2ef301b6b52a5c6c800e620fe110bca907 Homepage: https://cran.r-project.org/package=syrup Description: CRAN Package 'syrup' (Measure Memory and CPU Usage for Parallel R Code) Measures memory and CPU usage of R code by regularly taking snapshots of calls to the system command 'ps'. The package provides an entry point (albeit coarse) to profile usage of system resources by R code run in parallel. 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To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs. Package: r-cran-syscselection Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-syscselection_1.0.2-1.ca2404.1_all.deb Size: 81018 MD5sum: 70a1ff5f4df56ee436b60f807b98aa3b SHA1: cd6613cbbcd8eeac98a54b43afd4cb71a90c2e5a SHA256: 0337167f32d0e66f9137280fad261c2e9b08737fe2ce4c6260d31a3217a21fc3 SHA512: 4d47024a4b64d7d55ff15fadab5b64268f0bf8ed97880ea8f20865768bb9fc08e5792d236834f4ca001873a3218bf942fcacc068b687d889fca8f6b57f24344a Homepage: https://cran.r-project.org/package=SyScSelection Description: CRAN Package 'SyScSelection' (Systematic Scenario Selection for Stress Testing) Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) ). This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper. Package: r-cran-sysid Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal, r-cran-tframe, r-cran-ggplot2, r-cran-reshape2, r-cran-polynom, r-cran-bitops, r-cran-zoo Filename: pool/dists/noble/main/r-cran-sysid_1.0.5-1.ca2404.1_all.deb Size: 636130 MD5sum: f35f3585b52f7f1b160e3d9fcf8051d0 SHA1: a53531c24ecbca91b3ad2940a175c74547d743f5 SHA256: 35f19f9d72df48ef54ff1956dfe405d3b8c15ea202c425dadfaacca60b264e5b SHA512: ce68ef7dbb5014e17e3fbeb22dd09352be714ba36e8e009ecb4c7be32a0f521fe7699138b0b09a5ce594c89ce386eb537df12d51699d6554dc9e202e2db34308 Homepage: https://cran.r-project.org/package=sysid Description: CRAN Package 'sysid' (System Identification in R) Provides functions for constructing mathematical models of dynamical systems from measured input-output data. Package: r-cran-syslognet Architecture: all Version: 0.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-syslognet_0.1.2.1-1.ca2404.1_all.deb Size: 11420 MD5sum: 9c03de56aef5fb34fd7f96c804964faf SHA1: b9167bd9283ac967fa632c77c628525866e87bde SHA256: 7fb8e218c4959bc8b7b294af576dd144b4702004d801fe9dbdbc4c4b8491ed98 SHA512: 68a8d00f1cbf8615fc1e195afca1bbcf9cb503825a7b8d50cf3044f02b38b22557c667bec0224eaac290545fe3dc374967dd23760a490bdc78953efff74d0fbb Homepage: https://cran.r-project.org/package=syslognet Description: CRAN Package 'syslognet' (Send Log Messages to Remote 'syslog' Server) Send 'syslog' protocol messages to a remote 'syslog' server specified by host name and TCP network port. Package: r-cran-sysrecon Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-ggplot2, r-bioc-ggtree, r-cran-magrittr, r-cran-patchwork, r-cran-plyr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-snowballc, r-cran-stringr, r-cran-tm Filename: pool/dists/noble/main/r-cran-sysrecon_0.1.3-1.ca2404.1_all.deb Size: 196784 MD5sum: cca747641e45c3cac8a858e14599d38b SHA1: 8b3354ba7ca68800def1a1edebfe805916220adc SHA256: 8424eca8293ec022c1a4b12220d20951b945aa7fb504edfc13a26e0c65944da7 SHA512: d878a61808f32a4ac6ccedda3f77509c0311c8cf2cce35bc69e7f4697febc57a1a6a41bed5e641dd6baf5ac3a4ef93a60ee98725180da85b23165d540078db57 Homepage: https://cran.r-project.org/package=Sysrecon Description: CRAN Package 'Sysrecon' (Systematical Metabolic Reconstruction) In the past decade, genome-scale metabolic reconstructions have widely been used to comprehend the systems biology of metabolic pathways within an organism. Different GSMs are constructed using various techniques that require distinct steps, but the input data, information conversion and software tools are neither concisely defined nor mathematically or programmatically formulated in a context-specific manner.The tool that quantitatively and qualitatively specifies each reconstruction steps and can generate a template list of reconstruction steps dynamically selected from a reconstruction step reservoir, constructed based on all available published papers. Package: r-cran-sysreqr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sysreqr_0.1.0-1.ca2404.1_all.deb Size: 348668 MD5sum: 77c8befd3b9c28c5d8566ccedf39266f SHA1: 8148bcab939667a30f1457de7900c5d72a038413 SHA256: f9b8b093e070d9ff885385bd981c72ebaf768d4209b29a704fd0252e81b31a25 SHA512: 96ae5d21c8c13fe8b53f292e58ceba04667e8c6f016a57929f8470c12e83135a934681762751c43509e5150b033ef9ceb9b91f8866e01a47647e66f5f6c09edd Homepage: https://cran.r-project.org/package=sysreqr Description: CRAN Package 'sysreqr' (Preflight Checks for 'R' Package System Requirements) Helps users on 'Linux' (and, where applicable, 'macOS') find the system packages they need before installing 'R' packages from source. Queries maintained system requirement sources, reports missing system packages, and generates installation commands, 'Dockerfile' snippets, 'GitHub Actions' steps, administrator request templates, and diagnostic reports from failed installation logs. Package: r-cran-systemfit Architecture: all Version: 1.1-30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-car, r-cran-lmtest, r-cran-sandwich, r-cran-mass Suggests: r-cran-knitr, r-cran-plm, r-cran-sem Filename: pool/dists/noble/main/r-cran-systemfit_1.1-30-1.ca2404.1_all.deb Size: 599594 MD5sum: 76606dcc75dc81a04966628ffb2232d3 SHA1: ec2aa5b781d868f14f01d29293be5edc34871006 SHA256: b301b1e29b5ffeafaa6666075a3e5d1f6bf9ee74edb17fc44ed3afdcf81cc199 SHA512: 84e44b0fd864966cd67c37938f0b4b2bdce20422acf4ffd64652f27ed19c5a63a87ea6dcce8e93e5fef85eac40b6e45ab4c5dc3865b7758fe054879bc3207236 Homepage: https://cran.r-project.org/package=systemfit Description: CRAN Package 'systemfit' (Estimating Systems of Simultaneous Equations) Econometric estimation of simultaneous systems of linear and nonlinear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g., by Zellner (1962) , Zellner and Theil (1962) , and Schmidt (1990) . Package: r-cran-systemicr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-quantreg, r-cran-xts Filename: pool/dists/noble/main/r-cran-systemicr_0.1.0-1.ca2404.1_all.deb Size: 2598476 MD5sum: dd6a185f8b8f27774d00bf178018a0b5 SHA1: 8201d7b500675b3c8397f28d22c5a2cb535621e3 SHA256: 63be96a5a50a3b0d6720ff3436d2db91fdd95b68b29cbd9186c9db7ced23348e SHA512: f603df74aedc8ce2fd77caff6b539a828c23080193df6aace27c734389172d98ef5bf86f0d798127e3f0517c2b8e66300487995baf1fd6d87360a08bab64a350 Homepage: https://cran.r-project.org/package=SystemicR Description: CRAN Package 'SystemicR' (Monitoring Systemic Risk) The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) and Billio, Getmansky, Lo and Pelizzon (2012) . Package: r-cran-syt Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-partitions Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-syt_0.5.0-1.ca2404.1_all.deb Size: 165504 MD5sum: 61896bc564c347444b36e93efe9823ac SHA1: 92b935f1e892dd5b57a8b24e7c296f3bf256072b SHA256: 4894ac0a439e638ddcf5218153c690be4c2ce16e61d16a316be5463f21de7367 SHA512: 90974df7a4981d6bcd2265299b6816c3037c45d97bac16e2840e49ab48eb5ef06c7135b64a2b06ff28c876b573d9e47f62812cadc9056bb5fd40d4f7564604c2 Homepage: https://cran.r-project.org/package=syt Description: CRAN Package 'syt' (Young Tableaux) Deals with Young tableaux (field of combinatorics). For standard Young tabeaux, performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice. Also performs enumeration and counting of semistandard Young tableaux, enumeration of skew semistandard Young tableaux, enumeration of Gelfand-Tsetlin patterns, and computation of Kostka numbers. Package: r-cran-syuzhet Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-textshape, r-cran-nlp, r-cran-zoo, r-cran-dtt, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-devtools, r-cran-knitr, r-cran-pander, r-cran-readxl, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-syuzhet_1.0.7-1.ca2404.1_all.deb Size: 2839780 MD5sum: b0e28484cde542f0146f32aa0575112d SHA1: ee428820f546d25e134ec49f9634af6fa5ec7cb4 SHA256: 0ba1539b0e91ee29e94524ae852b08a078ae80167f58cc11803bcf7776d3538a SHA512: 5750812efa7e7918c86281df7ac262e400fb19d8154be374df5f33a69c51c981e9cce2af022cd8a88f73f082cab31181f920ddd5bb3216e10495de6a4a91dcf1 Homepage: https://cran.r-project.org/package=syuzhet Description: CRAN Package 'syuzhet' (Extracts Sentiment and Sentiment-Derived Plot Arcs from Text) Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include "syuzhet" (default) developed in the Nebraska Literary Lab "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the "get_sentiment" function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization. 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Package: r-cran-table1heatmap Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorramps Filename: pool/dists/noble/main/r-cran-table1heatmap_1.2-1.ca2404.1_all.deb Size: 20640 MD5sum: c599bdcec5955f79de6085b78e548647 SHA1: 8e5bc3e475005a074906ceda5a3142907499c263 SHA256: 7b3ccf1173980a4a548b9ab90d2b05ed00f04046dab4046e0b8037a11431e591 SHA512: 53df2ac04a47e987a60f15f75811cb10121f07ce92b4dd2d30e47c0bf3076b6964a2ebe5521b76c34954a138f3d296adca2b6540f8a9e305df353ab0029a6615 Homepage: https://cran.r-project.org/package=Table1Heatmap Description: CRAN Package 'Table1Heatmap' (Table 1 Heatmap) Table 1 is the classical way to describe the patients in a clinical study. The amount of splits in the data in such a table is limited. Table1Heatmap draws a heatmap of all crosstables that can be generated with the data. Users can choose between showing the actual crosstables or direction of effect of associations, and highlight associations by number of patients or p-values. v1.2 - fixed "missing "no visible global function definition for .." Package: r-cran-tablecompare Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tablecompare_0.1.1-1.ca2404.1_all.deb Size: 79038 MD5sum: e2966218897a15d800561608382302c5 SHA1: caa95dd8975695a0ea7a75adc9b2538e98e48198 SHA256: 6817888dfadf9139b8c1ef60117f3d4424cc6dc629c6240f2e29a049b3139db7 SHA512: cef2be76fd35952fa7e0eac9a7b9a84a3bb8b46ca06baf5199f80b813d23678dc3b23c71efc1fd32a777345860976ea0dac930c57d59790abbce5746c3c1333e Homepage: https://cran.r-project.org/package=tablecompare Description: CRAN Package 'tablecompare' (Compare Data Frames) A toolbox for comparing two data frames. This package is defunct. I recommend you use the "versus" package instead. Package: r-cran-tablecontainer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tablecontainer_1.0.0-1.ca2404.1_all.deb Size: 195494 MD5sum: 398feaa05215cf36fa8c98aa030b5abb SHA1: d8062d6251c840843557f01d2c2b59b8addedd6d SHA256: 758c6c25628917ae1fa3857071c8cb1f7689a75133b3d48fde04bfb25dfce7b5 SHA512: 9e73363851ec151ffa93fb1d06ae819e24fbd94875ecf66a3b50d98f79c28f3821a460a97f19892ce700971bc9378960bc9dae64e4e9b6956f415c47c3f715cc Homepage: https://cran.r-project.org/package=TableContainer Description: CRAN Package 'TableContainer' (Create a Table with Row, Column, and Table Annotations) Offers a TableContainer() function to create tables enriched with row, column, and table annotations. This package is similar to 'SummarizedExperiment' in Bioconductor , but designed to work independently of Bioconductor, it ensures annotations are automatically updated when the table is subset. Additionally, it includes format_tbl() methods for enhanced table formatting and display. 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Package: r-cran-tableeasy Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-lmtest, r-cran-mgcv, r-cran-nortest, r-cran-tableone Filename: pool/dists/noble/main/r-cran-tableeasy_1.1.2-1.ca2404.1_all.deb Size: 105652 MD5sum: 6bc0c8ba9f403e468f58e75825f40a40 SHA1: ce5a87bc526d735094421282a6a8d265ab803952 SHA256: 914d5d62c13579505aec5dfc77aa34b7d6970b1f1cacfb4ebcfd8bf7ff795139 SHA512: 2e1ecbcf4015130757ad3690395dc7a2761e01c5d5529f684192cc0bd298469082fe79eaa686d862062acf448cd5f3848a11af0b83b1db6d5c9257d494e0aac5 Homepage: https://cran.r-project.org/package=tableeasy Description: CRAN Package 'tableeasy' (Tables of Clinical Study) Creates some tables of clinical study. 'Table 1' is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of 'Table 2' is to explore influence factors. And 'Table 3' created by table3() is able to make stratified analysis. Package: r-cran-tablehc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tablehc_0.1.2-1.ca2404.1_all.deb Size: 25486 MD5sum: 51c0dd2c9c16ae7c39fca48fb237c3fa SHA1: db0641234f3d73727807ad71ed7361d7c0ff54e2 SHA256: c48101260758f7575d2ad838963d548aa85f501df6755936d0fe60cb2f1cb2d0 SHA512: 76185c607f89dd8cf61908c06d5b55bc1a042aac7f3b7fc70e39ba3cfd06ac556432c6da5f6802319248fd4aa1ebc1d2a1cb4f4309d89951a0105f24178745c8 Homepage: https://cran.r-project.org/package=TableHC Description: CRAN Package 'TableHC' (Higher Criticism Test of Two Frequency Counts Tables) Higher Criticism (HC) test between two frequency tables. 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See for more information and examples. Package: r-cran-tablemonster Architecture: all Version: 1.7.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable Filename: pool/dists/noble/main/r-cran-tablemonster_1.7.8-1.ca2404.1_all.deb Size: 42040 MD5sum: 99e3c73538ae6a5ec0677b7845a607ae SHA1: 7b6398919e17c597099f5fc4f0cfe4b095aaad2b SHA256: 692d17b691391938263ebddbb9405fd7e3c6a7359f709f70d8f7ca881ed20dfc SHA512: 286b736d5ec67946f33005fcb4d575a69846bfc6f1a02cdc8d76aed3c90a0ef8acdbe92c922201bf4fd24bf71dc6aa94fd307e83e35dc1bbe64bb29f117a4513 Homepage: https://cran.r-project.org/package=TableMonster Description: CRAN Package 'TableMonster' (Table Monster) Provides a user friendly interface to generation of booktab style tables using 'xtable'. Package: r-cran-tableone Architecture: all Version: 0.13.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-mass, r-cran-e1071, r-cran-zoo, r-cran-gmodels, r-cran-nlme, r-cran-labelled Suggests: r-cran-survival, r-cran-testthat, r-cran-matrix, r-cran-matching, r-cran-reshape2, r-cran-ggplot2, r-cran-knitr, r-cran-geepack, r-cran-lme4, r-cran-lmertest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tableone_0.13.2-1.ca2404.1_all.deb Size: 371796 MD5sum: 32c08f2650f6c354bac894ad32c9cf6c SHA1: 6259e752aaa78b80b13b99bd52148691d9d3897e SHA256: b663a98acee9d941c8ee64530ff2d3c712076c61a0943a8fd24e019f9489bc2c SHA512: 2db511bceec3877ae975a7e2f61188bbe85e1c0fe66c87d03ff70e86ba6d77dda86ae2d5d355f92926e81f9785560bad1c56b50674a19f0aa1e2c3efc2e698e6 Homepage: https://cran.r-project.org/package=tableone Description: CRAN Package 'tableone' (Create 'Table 1' to Describe Baseline Characteristics with orwithout Propensity Score Weights) Creates 'Table 1', i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the 'survey' package. Package: r-cran-tableparser Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jatsdecoder, r-cran-tabulapdf Filename: pool/dists/noble/main/r-cran-tableparser_1.0.5-1.ca2404.1_all.deb Size: 388322 MD5sum: c0ee1bb4c6dfa65f4666f7367675a960 SHA1: 9ebc632869caa5b4f234740b4f1a3ee671bd46c1 SHA256: 43217740dcb8b21c4dd25a8b81943f0979437b334ff96653122a8aed95a10dcd SHA512: 139343f04b357e63c56485719caafd31bf239319e26cb22e0eea957f6690997ac53b970caf061c42089de3d551e44be176ac821b6de2e48be16f35faceef753d Homepage: https://cran.r-project.org/package=tableParser Description: CRAN Package 'tableParser' (Parse Tabled Content to Text Vector and Extract StatisticalStandard Results) Features include the ability to extract tabled content from NISO-JATS-coded XML, any native HTML or HML file, DOCX, and PDF documents, and then collapse it into a text format that is readable by humans by mimicking the actions of a screen reader. As tables within PDF documents are extracted with the 'tabulapdf' package, and the table captions and footnotes cannot be extracted, the results on tables within PDF documents have to be considered less precise. The function table2matrix() returns a list of the tables within a document as character matrices. table2text() collapses the matrix content into a list of character strings by imitating the behavior of a screen reader. The textual representation of characters and numbers can be unified with unifyMatrix() before parsing. The function table2stats() extracts the tabled statistical test results from the collapsed text with the function standardStats() from the 'JATSdecoder' package and, if activated, checks the reported and coded p-values for consistency. Due to the great variability and potential complexity of table structures, parsing accuracy may vary. A detailed description of how 'tableParser' works is provided here: . Package: r-cran-tabler Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tabler_0.1.0-1.ca2404.1_all.deb Size: 3489750 MD5sum: caaef7a448f09ab8c971e4e01916dcd7 SHA1: d23eda52f44d6a6d08e431ae9ce77fa6817520e2 SHA256: 0c701a809ad132c8bb4986513a1fe34e87701b74c8ee272b85c147229dad1c61 SHA512: 5959e29fa60233cd0b8181b4cfc8379a63e7c03b10266b08dcf3c57848013b7033a24895d8a7e1bcd79df8a51f68a7a3275b200e257243132e64c33d5a2f65d5 Homepage: https://cran.r-project.org/package=tabler Description: CRAN Package 'tabler' (Create Dashboards with 'Tabler' and 'Shiny') Provides functions to build interactive dashboards combining the 'Tabler UI Kit' with 'Shiny', making it easy to create professional-looking web applications. 'Tabler' is fully responsive and compatible with all modern browsers. Offers customizable layouts and components built with 'HTML5' and 'CSS3'. The underlying 'Tabler' () and 'Tabler Icons' () were pre-built from source to eliminate the need for 'Node.js' and 'NPM' on package installation. Package: r-cran-tablerdash Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools Suggests: r-cran-shinywidgets, r-cran-shinyeffects, r-cran-echarts4r, r-cran-knitr Filename: pool/dists/noble/main/r-cran-tablerdash_0.1.5-1.ca2404.1_all.deb Size: 1047726 MD5sum: 30de6e16fe8409f4ee40d6196c8a6657 SHA1: c75791d880943c43c100d6ed1336b94aba8ee0b4 SHA256: ec92cf5c610368b61c332d735e324cef9865f06524806a8d21a9e2e7dfa857f2 SHA512: dc770abf1942cfcbb3faf80f612998590589d76009a5fa0a2ae27894c4eac72f59f9bbdde3015c81c8155612a31869a3c448227131494d4757b23aeaa8b6b905 Homepage: https://cran.r-project.org/package=tablerDash Description: CRAN Package 'tablerDash' ('Tabler' API for 'Shiny') 'R' interface to the 'Tabler' HTML template. See more here . 'tablerDash' is a light 'Bootstrap 4' dashboard template. There are different layouts available such as a one page dashboard or a multi page template, where the navigation menu is contained in the navigation bar. 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Although there are other packages that format tables for display, this package is unique in combining two features: (a) It is aware of the logical structure of the table being presented, and makes use of that for automatic layout and styling of the table. This avoids the need for most manual adjustments to achieve an attractive result. (b) It displays tables using 'ggplot2' graphics. Therefore a table can be presented anywhere a graph could be, with no more effort. External software such as LaTeX or HTML or their viewers is not required. The package provides a full set of tools to control the style and appearance of tables, including titles, footnotes and reference marks, horizontal and vertical rules, and spacing of rows and columns. Methods are included to display matrices; data frames; tables created by R's ftable(), table(), and xtabs() functions; and tables created by the 'tables' and 'xtable' packages. Methods can be added to display other table-like objects. A vignette is included that illustrates usage and options available in the package. Package: r-cran-tablespan Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gt, r-cran-openxlsx, r-cran-rlang, r-cran-scales, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tablespan_0.3.2-1.ca2404.1_all.deb Size: 1938516 MD5sum: dd16591bb75f89dd514b4fb32a1292c1 SHA1: ef8af3aa5bd73588a235713d64fb3db5dd528f95 SHA256: 3ee84dc83ea66556c18b6ccc63e5b5bc1f9e05e993036fdc092f7b8537adfd97 SHA512: 1ea8798344f3c7d82c1ff713d76993de4f6ca3621f63abfa6099003c20e4bab0a1cdf19f35665c50c867ab8131a895547403aba40b4be0174159e67db1ae13da Homepage: https://cran.r-project.org/package=tablespan Description: CRAN Package 'tablespan' (Create Satisficing 'Excel', 'HTML', 'LaTeX', and 'RTF' Tablesusing a Simple Formula) Create "good enough" tables with a single formula. 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Displays these by groups, if any. Highly customizable, with support for 'html' and 'pdf' provided by 'kableExtra'. Respects original column order, column labels, and factor level order. See ?tablet.data.frame and vignettes. Package: r-cran-tabletolongform Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tabletolongform_1.3.2-1.ca2404.1_all.deb Size: 86222 MD5sum: cb12d6e52b2e3c9d1a962d20a8dc91f4 SHA1: 0e04c8727382b1cf33eca4b30edcd7beb5435250 SHA256: 0a2a78fd64a76d11455b09b5a83333e3b85f5bd5a5cbf7e68f4d4d95bf749725 SHA512: 9eab8c1fd953a0cbbe4fc2ceccfc12e06e6f7a2933b552e9794ad0fa64089dd6d46419faef8cdaca05ee3cd6b49270bf79e110a2cf2f720dace553d942a3c6e7 Homepage: https://cran.r-project.org/package=TableToLongForm Description: CRAN Package 'TableToLongForm' (Automatically Convert Hierarchical for-Human Tables toMachine-Readable LongForm Dataframes) A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes. Package: r-cran-tablexlsx Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-openxlsx, r-cran-cli Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tablexlsx_1.1.0-1.ca2404.1_all.deb Size: 668046 MD5sum: f2608531d412fd3ecd05bf88c30f6def SHA1: 2d2834d089ed4fdeb191c300d97a216a487678e5 SHA256: eb07f92508c53ae2217914be41fcc37800c01ae15248eed97692bce212bbe37c SHA512: 9700f83b9a894c0e2e71cec1c00b303ccfcd3c7156bd69443c14ae44e4dfbe0bbb1c797dfa80a90466537e085bd1539146cc33df240c8e6adc98cb24e13909dc Homepage: https://cran.r-project.org/package=tablexlsx Description: CRAN Package 'tablexlsx' (Export Data Frames to Excel Workbook) Collection of functions that allow to export data frames to excel workbook. Package: r-cran-tabnet Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-coro, r-cran-data.tree, r-cran-dials, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-hardhat, r-cran-magrittr, r-cran-parsnip, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-torch, r-cran-tune, r-cran-vctrs, r-cran-withr, r-cran-zeallot Suggests: r-cran-cli, r-cran-knitr, r-cran-modeldata, r-cran-patchwork, r-cran-quarto, r-cran-recipes, r-cran-rmarkdown, r-cran-rsample, r-cran-spelling, r-cran-testthat, r-cran-tidymodels, r-cran-tidyverse, r-cran-visdat, r-cran-workflows, r-cran-xgboost, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-tabnet_0.9.1-1.ca2404.1_all.deb Size: 4051726 MD5sum: 563e73bdfcf8d40b439b93d1b3efcf63 SHA1: b36de568267705f13534a6f2e7c1f1a6f0d4d1b9 SHA256: f1492be45e37300184b759ab6548ab5f83c9ee2198e9af82d3b770e21a181daf SHA512: c5b715da62d3fd39945bd9da6260235d8e4cbed54d8e062c3b46561a36d43154c20fc9bb9fe55df11c53f737af2a0bd515a2b47df06db5bf2e95889df41d8dfd Homepage: https://cran.r-project.org/package=tabnet Description: CRAN Package 'tabnet' (Fit 'TabNet' Models for Classification and Regression) Implements the 'TabNet' model by Sercan O. Arik et al. (2019) with 'Coherent Hierarchical Multi-label Classification Networks' by Giunchiglia et al. and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the 'tidymodels' ecosystem. Package: r-cran-taboolar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-taboolar_0.1.0-1.ca2404.1_all.deb Size: 22942 MD5sum: 349bb78fa45e5c3e72d3ab0648b9e9f8 SHA1: 775527fcf2fe5e9d8e0bf4e49515ac60fd5cffec SHA256: 6d711cb76c42e50df951be705e40e3dc1e6a4137f10072c3c330bc52de3a0c92 SHA512: a057508e3a585eab0879b5b8d5e334838149b254ef83ce74bfa8becb3e24e04ffed8b4c16751c2f769e75664858d7101f0edfc6484393f6c5e9d998c5c5c089f Homepage: https://cran.r-project.org/package=taboolaR Description: CRAN Package 'taboolaR' (Get Data from 'Taboola' via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from 'Taboola' using the 'Windsor.ai' API . Package: r-cran-tabpfn Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-hardhat, r-cran-jsonlite, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-ggplot2, r-cran-mass, r-cran-modeldata, r-cran-recipes, r-cran-rstudioapi, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tabpfn_0.4.0-1.ca2404.1_all.deb Size: 307824 MD5sum: 913935dee1b0004bc0bb1e1ebc86dab3 SHA1: 34235f6aa3622ad1dfe57bd8b86bb48d9ca245f2 SHA256: 17c785651d13db538badf84725a8d1738899d8010103b4c34b021b4c6e9a6504 SHA512: 6acda4065a09bf8e75112fd2d0b5978e2044ce4665115d6666d710895643cd917c60971e7c6fc3d8565d278ab7d126e1e82a5c0f2a7c9970725c6662b67702d9 Homepage: https://cran.r-project.org/package=tabpfn Description: CRAN Package 'tabpfn' (Prior-Data Fitted Network Foundational Model for Tabular Data) Provides a consistent API for classification and regression models based on the 'TabPFN' model of Hollmann et al. (2025), "Accurate predictions on small data with a tabular foundation model," Nature, 637(8045) . The calculations are served via 'Python' to train and predict the model. Package: r-cran-tabr Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-fansi, r-cran-gridextra, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-tuner Filename: pool/dists/noble/main/r-cran-tabr_0.5.5-1.ca2404.1_all.deb Size: 859894 MD5sum: 992dc1daea9103812a2926b5fa422771 SHA1: 19da4a640df318e6d5c99a59f5d770138137bb94 SHA256: 3d92682cf13a912e8ce1f0419754b8fdf8b61c80433eb4aef0ee64d3bc9dc755 SHA512: 138d40ef21dcc7d18cca57070d0cbd8cca39614a466cffa76639822d2870e6ecfbcd9ab7c8dc97ff5c62ea0279bb2c21c76b074a461a2a0f4044587931c1280e Homepage: https://cran.r-project.org/package=tabr Description: CRAN Package 'tabr' (Music Notation Syntax, Manipulation, Analysis and Transcriptionin R) Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing, and analyzing musical information in R. Music syntax can be entered directly in character strings, for example to quickly transcribe short pieces of music. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can be organized in tidy data frames for a familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these formats and their representations of musical information. The package also provides an API to 'LilyPond' () for transcribing musical representations in R into tablature ("tabs") and sheet music. 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to the 'LilyPond' command line interface to be rendered into sheet music PDF files or inserted into R markdown documents. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package. 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Package: r-cran-tabshiftr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1521 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tidyselect, r-cran-testthat, r-cran-crayon, r-cran-purrr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-readr, r-cran-covr Filename: pool/dists/noble/main/r-cran-tabshiftr_0.4.1-1.ca2404.1_all.deb Size: 621516 MD5sum: e79ac4a1d017e21639c108875d22549b SHA1: c2ac26ecb86f484e4d52714a2312064f34e210a0 SHA256: fa251e5f2592c0a93c41f6d40d2514ba226ff5e2987b501480d6d4c39c29973e SHA512: fef346b9890904710cf3741a01c83f375e79680fad65f69bf0732dce013518e944995534888b423db1b5665f6882cbc28c6b2aef7edbc69037e243dc2446424c Homepage: https://cran.r-project.org/package=tabshiftr Description: CRAN Package 'tabshiftr' (Reshape Disorganised Messy Data) Helps the user to build and register schema descriptions of disorganised (messy) tables. Disorganised tables are tables that are not in a topologically coherent form, where packages such as 'tidyr' could be used for reshaping. The schema description documents the arrangement of input tables and is used to reshape them into a standardised (tidy) output format. Package: r-cran-tabstats Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-tibble, r-cran-vctrs Suggests: r-cran-broom, r-cran-rstatix, r-cran-glue, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-crayon, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-tabstats_0.2.1-1.ca2404.1_all.deb Size: 358798 MD5sum: 6eda40a7ff78478e4c55631765363cb7 SHA1: 890a42d7416f3206f9189b0014d14cb2e4ced19c SHA256: bc4a2be3f7d474dd9ee970475a1dc773bac54eb482bb1ff077d04fd042928cb0 SHA512: 8d45a7d53e375c3145e0b980628d9bfdc1550afdcfe46259133a2e9566063cf0f3e1cfd9753eb45d9723ed67ddf73c49c4a693ab5c01ae3ff345a6ee9f071385 Homepage: https://cran.r-project.org/package=tabstats Description: CRAN Package 'tabstats' (A Lightweight Toolkit for Displaying Customizable Tables) A lightweight toolkit that provides functions for printing tables from input data in the R console or terminal with customizable formatting. Supported outputs include American Psychological Association (APA)-style tables (American Psychological Association, 2020, ISBN:9781433832178), correlation matrices, contingency tables, and two-column summary tables. 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It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc. Package: r-cran-tabulapdf Architecture: all Version: 1.0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13936 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-png, r-cran-readr, r-cran-rjava Suggests: r-cran-knitr, r-cran-miniui, r-cran-shiny, r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-tabulapdf_1.0.5-5-1.ca2404.1_all.deb Size: 12491332 MD5sum: 02b37db7b45a0caa03f806fbf765556e SHA1: 486ea7871c0acdf55990eafcf6a91c9534678721 SHA256: 0379abf6bb4d0ab5a36e6d8aba326f3c9b71cf184fc34da7c615fdcb03867b2b SHA512: 698f15956a0e6c7d506c86cc7dc1b71ce78bdf129355659d8886abe4ac70281d7019af604a2a362b5d4198cff22ea8a10ec9b64907388cd3a0de0329cc78d32b Homepage: https://cran.r-project.org/package=tabulapdf Description: CRAN Package 'tabulapdf' (Extract Tables from PDF Documents) Bindings for the 'Tabula' 'Java' library, which can extract tables from PDF files. This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a 'Shiny' interface, enabling manual areas selection\ with a computer mouse for data retrieval. 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Features include decimal alignment via font metrics, multi-level column headers with passthrough leaves, predicate-targeted cell styling, footnotes, group-aware pagination, and figures that wrap a plot or image in the same page chrome as a table. Built for Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) workflows and regulatory submissions to agencies such as the Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceuticals and Medical Devices Agency (PMDA). 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When dealing with geospatial, it corrects for differences in visibility between areas. Package: r-cran-tabulator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-data.table, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tabulator_1.0.0-1.ca2404.1_all.deb Size: 24136 MD5sum: e3acab6b183836f5976e0c04f753cfaa SHA1: 03cf1739ab350530ad7aa1448d7f21d0f6b0c17c SHA256: d3f7d9a95fc67270298751c034493563149c84285a314eb5180bbe1466942d59 SHA512: 9eee76923be7194ff9639aaa8c0b99f64e18ec709bab61fa7a3a1ac4de45e897d4da515941a45f20e8285031934432e800a14e8cbe22d59b9891259e161c7d10 Homepage: https://cran.r-project.org/package=tabulator Description: CRAN Package 'tabulator' (Efficient Tabulation with Stata-Like Output) Efficient tabulation with Stata-like output. 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Create the same kind of tables for regression models, with a framework to compare model effects with their crude/observed counterpart systematically. All functions render data frames which can be easily manipulated. All tables can be exported with formats and colors to 'Excel', html and markdown. 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This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve). 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The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis). Package: r-cran-tai Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tai_0.2.2-1.ca2404.1_all.deb Size: 54532 MD5sum: 58835c8c93a6f696000b3d939c284346 SHA1: 357f8bfbccae8c2b9e5b069dc89d80e0443b002b SHA256: 6813d7cf190a255f1502d1b1ef209ec16e7eabb16a6514ead692128deb29957f SHA512: 04bccd98ce7c344259a7185ed7d44881ad36345c415f48754008e38b51ac3bbee1f5eed9d8dbf8420a6f25589311698d83c28f3a0bdd373ff4c1719fa8c7742f Homepage: https://cran.r-project.org/package=tAI Description: CRAN Package 'tAI' (The tRNA Adaptation Index) Functions and example files to calculate the tRNA adaptation index, a measure of the level of co-adaptation between the set of tRNA genes and the codon usage bias of protein-coding genes in a given genome. The methodology is described in dos Reis, Wernisch and Savva (2003) , and dos Reis, Savva and Wernisch (2004) . Package: r-cran-tailclassifier Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-scales Filename: pool/dists/noble/main/r-cran-tailclassifier_0.1.2-1.ca2404.1_all.deb Size: 39156 MD5sum: 9203359d6932de75522327ec2d4adfe8 SHA1: cd74727475322a6eb91444839ac9f84e825a3553 SHA256: a6a95d12eb1e5934119953344d1c7470c6c33b780f301493bdbd64404f33af5d SHA512: bc5e84be7eeb65f58531caf5c0935e9d19015cb52758a05e8ce804ac5ffd432a2ac413220bff423f0cf1e6ae67b28ff3a0e72a6c491909ac20bb3b6e6180e91a Homepage: https://cran.r-project.org/package=TailClassifier Description: CRAN Package 'TailClassifier' (Tail Classifier) The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference. Package: r-cran-tailid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ismev, r-cran-scales Filename: pool/dists/noble/main/r-cran-tailid_1.0.0-1.ca2404.1_all.deb Size: 32694 MD5sum: 368756c2abb659d7505e247d29c2eb6f SHA1: 37652d4684dffc8c83603800162d44a2bcafd2c0 SHA256: 57ee98d47270d7ffd87160087ede13090cffaafc4311c7c00bdfe5164ac205fd SHA512: 4e27860a3e125bebbebba4beda11c0e050ce2e961634574d1bd31afa378c81f50d4a60bf7d295ffd181917e4eabcc21b5b2c9252faccdb002e4ad0c78716fc25 Homepage: https://cran.r-project.org/package=TailID Description: CRAN Package 'TailID' (Detect Sensitive Points in the Tail) The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025). Package: r-cran-tailloss Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-tailloss_1.0-1.ca2404.1_all.deb Size: 212658 MD5sum: ef81975754050e77b4f82dd8cce8c2a4 SHA1: 382969f201b2621f124fe7b01ae732f59e83574d SHA256: 9b6b954763684358cad703df6312a5858f68eda14eabfb1e8b6fed330fc4fb47 SHA512: 843a2ec76dac56948e0a0bba1ffd0c11092124a7cd790aba2e4ac45cff14860a8aa75446322be8d53efd6448dbf4da59be9b4728bf3828fb36ed496b57f9f189 Homepage: https://cran.r-project.org/package=tailloss Description: CRAN Package 'tailloss' (Estimate the Probability in the Upper Tail of the Aggregate LossDistribution) Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound. Package: r-cran-tailor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-hardhat, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-betacal, r-cran-dials, r-cran-mgcv, r-cran-modeldata, r-cran-probably, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tailor_0.1.0-1.ca2404.1_all.deb Size: 483940 MD5sum: 4b26577144179fe8871b086157ade29c SHA1: 67c2210c89225fd04e5b0fcfc77dc237ea9b2e71 SHA256: b1a8b349d2ebc587703a275d9632711d8ef1ecc61455e449374a9fbb988f5972 SHA512: dfe1e4a3c991c2c54226a08e7216b0b679a637b6c88b21513191b72fc26d7dcbc5f58078d00f4d7c8e9089706a5bff4f6e8882137d87cc305a48f956b2beb664 Homepage: https://cran.r-project.org/package=tailor Description: CRAN Package 'tailor' (Iterative Steps for Postprocessing Model Predictions) Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces 'tailor' objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) . Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the 'tidymodels' framework. Package: r-cran-tailplots Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-resample Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-tailplots_0.1.0-1.ca2404.1_all.deb Size: 60910 MD5sum: f2ec2c5b585e4502c861c816b0c750c3 SHA1: d184609e0ba421c56bede9d60857f5c669b78b5b SHA256: ab3de8e48a65b178be0b0a37c50eeb902cd96a255bc2acb591216b8d3a832e06 SHA512: 97a8c3e18dc997e23d8d8e56805ac57a8e055073e0172e1e11489a8277baa8875a98b9e72a0c7915abef7f0b813b2c257043e177982d8704d0eccf293484b127 Homepage: https://cran.r-project.org/package=tailplots Description: CRAN Package 'tailplots' (Estimators and Plots for Gamma and Pareto Tail Detection) Estimators for two functionals used to detect Gamma or Pareto distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) and Klar (2024) . One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) , distinguishes between log-convex and log-concave tail behavior. The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values. Package: r-cran-tailrank Architecture: all Version: 3.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-oompabase, r-bioc-biobase, r-cran-oompadata Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-tailrank_3.2.6-1.ca2404.1_all.deb Size: 211168 MD5sum: c8063b4cd81a5972df4e90b5e2349f81 SHA1: cb9b009d7ecc4cebc4b4e02eb1ae4ae8c4323ecc SHA256: 9ccc422b1f921d0462053328ac790e871bb916beb75f7b4f3a4c953fb895cea2 SHA512: 7d84e9aa9c6b3e8001db9077d2b369a298799786643a1b372f2c449ce4e517185ebb22cc13bf81176b21e1c094da95408a8d7c41413948d1b13286d21ec8b244 Homepage: https://cran.r-project.org/package=TailRank Description: CRAN Package 'TailRank' (The Tail-Rank Statistic) Implements the tail-rank statistic for selecting biomarkers from a microarray data set, an efficient nonparametric test focused on the distributional tails. See . Package: r-cran-tailtransform Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sensitivitymw, r-cran-sensitivitymult Filename: pool/dists/noble/main/r-cran-tailtransform_2.0.0-1.ca2404.1_all.deb Size: 60094 MD5sum: 87b9f9ecfc95635f887d8bfc50a7f099 SHA1: ce17de08697a8362293ccefc8a7a86a21f5333b7 SHA256: ba748bde781e00e9daeb8942adc7385c17217cf73e0b08ddc7aaacf86b23fb8f SHA512: 7f547286b9fbee93c0da7da60ca1b6db1799fb883c0142b9d2c35471b1d6782b8739a926acea5d4632748868f769675658bd3f5f31ed5a034d9d13b9c7b85271 Homepage: https://cran.r-project.org/package=tailTransform Description: CRAN Package 'tailTransform' (Symmetric Transformation of Tails for Plotting Differences) When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting. The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) . Package: r-cran-taipan Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-ggplot2, r-cran-purrr, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-taipan_0.1.2-1.ca2404.1_all.deb Size: 1234874 MD5sum: 7770d680b6bc4e24d1ee429a9058e187 SHA1: 6c01f54df98937d000fb242775afbba90c37ab69 SHA256: 6237db48cc08fd790dcd17860b334f534e2d30d1e09f5637ba7f4b19bcb3ae0a SHA512: bafb0a1cf74f4da3523bf2d9ab84d17d78b00ac871a5aab4e393f7cac013d85411724c9f9457f89effe89124bd499f11c0a0e7972cff0edd8f261adf10238565 Homepage: https://cran.r-project.org/package=taipan Description: CRAN Package 'taipan' (Tool for Annotating Images in Preparation for Analysis) A tool to help create shiny apps for selecting and annotating elements of images. 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Package: r-cran-takos Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-devemf, r-cran-segmented, r-cran-sfsmisc, r-cran-smoother, r-cran-desolve, r-cran-pracma, r-cran-data.table, r-cran-broom, r-cran-colorramps, r-cran-minpack.lm, r-cran-baseline Filename: pool/dists/noble/main/r-cran-takos_0.2.0-1.ca2404.1_all.deb Size: 140122 MD5sum: fc55fa1811ca2a3837bb544a96fffce1 SHA1: ea218747f06acf3bf74209ad037458fd70b47bf9 SHA256: f0e2a288ba7aecc7adae7021fcbba0c25bde3223a9a7ef85f1ec24a754c2c1a6 SHA512: 65c87d0b8adf5c746581a59c4cb5802184451cb240bde39be498f203c4ca03ac4d1339111ec3b39836a21480290a1ba3971345958efc3969e097f2297eca9b1d Homepage: https://cran.r-project.org/package=takos Description: CRAN Package 'takos' (Analysis of Differential Calorimetry Scans) It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. . Package: r-cran-talkr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-pkgdown, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-talkr_0.1.3-1.ca2404.1_all.deb Size: 251742 MD5sum: 5331a458279b00c3c0e9217d1bc0ac02 SHA1: 5e1af4bf2af668f1530e3be79c8a188939417f68 SHA256: 0e04a1fcbcb39f63cce2b9124b24f87369a564589e64dc9cf07f8503cf434279 SHA512: 7bfadf78cf47ede0d7826f7a0448ca6b5f85a0de03e36c422f1b1f2a91e0db0ddd3f87d54ae1364b141a7f627fcf46f7de15d51783165fc58a359cbe273eb726 Homepage: https://cran.r-project.org/package=talkr Description: CRAN Package 'talkr' (Plotting Conversation Data) Visualisation, analysis and quality control of conversational data. Rapid and visual insights into the nature, timing and quality of time-aligned annotations in conversational corpora. For more details, see Dingemanse et al., (2022) . 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TAMD augments the Expectation-Maximization (EM) algorithm with analytic barrier terms built from the Hellinger affinity that diverge on the singular locus, actively preventing component coalescence and weight degeneracy. Provides the core TAMD fitting function, closed-form Hellinger affinity and gradient computations, the Transcendental Affinity Criterion (TAC) for geometry-aware model selection, the regularity index rho (a scalar diagnostic for mixture fit quality), and reproduction scripts for all simulation studies. Methods are described in Fokoue (2024) . See also Titterington, Smith and Makov (1985, ISBN:0-471-90510-4) and Watanabe (2009, ISBN:978-0-521-86408-7). 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The extracted features are meant to be fed into simple explainable models such as linear or logistic regressions. The package is useful in the field of explainable modelling as a way to understand variable behavior. 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Original coordinates or raster geometries are transformed using randomized or predefined vertical shifts, horizontal shifts, and rotations. Compatible with point-based data in 'matrix', 'data.frame', or 'sf' formats, as well as 'terra' raster objects. Supports reversible anonymisation workflows, hash-based validation, shapefile export, and consistent tangling across related datasets using stored transformation sequences. Approach informed by the De-Identification Decision Making Framework (CM O’Keefe, S Otorepec, M Elliot, E Mackey, and K O’Hara 2017) . Package: r-cran-tangpoemr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jiebar Filename: pool/dists/noble/main/r-cran-tangpoemr_0.1.0-1.ca2404.1_all.deb Size: 20754 MD5sum: 6238ed409d662c7a60a43df0cfedb714 SHA1: d85da994d972aaa555a2228ad8adbf2a171a439a SHA256: 822bfdec6fb06ac0e329d2e720253db8c05a95733dcdba0f66bed6c0f5103b06 SHA512: d7abe8d061328ecf77f7dc1e75c22ce065ed66d8492f08f6f2eccba350fdcb142723d70a84e364df7c61eb893fb159b6fa78ad555a8399e1388f03a2418791c0 Homepage: https://cran.r-project.org/package=TangPoemR Description: CRAN Package 'TangPoemR' (Write Chinese Tang Poems) Write Chinese Tang Poems automatically. Package: r-cran-tangram.pipe Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tangram.pipe_1.1.2-1.ca2404.1_all.deb Size: 186822 MD5sum: 129dd0283c83ef8d29d851a46f0cc54f SHA1: 2d9eab508c17e5ddb8384219e6a5bff9c601fc3c SHA256: 3b1b524f2825529acb04ef54b69ac1daaea149371c8cfa1b05408992f2b53a23 SHA512: cdd3b89030fb7ff1ea326ff76fda5bbd9b0b216d40a65362bf55760d37b4b52455ef88bbd74a73a6fd48987bd2e67673a026eb7c8014320ba467f8eb2c615092 Homepage: https://cran.r-project.org/package=tangram.pipe Description: CRAN Package 'tangram.pipe' (Row-by-Row Table Building) Builds tables with customizable rows. Users can specify the type of data to use for each row, as well as how to handle missing data and the types of comparison tests to run on the table columns. Package: r-cran-tangram Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-magrittr, r-cran-knitr, r-cran-stringi, r-cran-stringr, r-cran-base64enc, r-cran-digest, r-cran-htmltools Suggests: r-cran-testthat, r-cran-rms, r-cran-rmarkdown, r-cran-hmisc, r-cran-sandwich, r-cran-dplyr, r-cran-matching, r-cran-epitools Filename: pool/dists/noble/main/r-cran-tangram_0.8.3-1.ca2404.1_all.deb Size: 801440 MD5sum: 1110dd101f9fa77e4e7134e9d299dcb7 SHA1: 8d5b82ce761c4b09ddf7ac1b29b9edbf5c4ab56e SHA256: d6b596648dbb227769cdad7532487dbacd0961a828dda476eb8b6cd9304530ea SHA512: a99aa1330b3c4f7b596a7d437bd973f2083db576cefff05d74dd840b0146d7b2e8bac67ad2c82fb78623554915b55dddbb54b7f6c255041ac1fbf64c5c3aa4dc Homepage: https://cran.r-project.org/package=tangram Description: CRAN Package 'tangram' (The Grammar of Tables) Provides an extensible formula system to quickly and easily create production quality tables. 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Package: r-cran-tanner Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-haven Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tanner_1.8.0-1.ca2404.1_all.deb Size: 746170 MD5sum: 83d9cb16979ae21079db35e051830548 SHA1: 34766551ff9c7d11d21cdb6cbf16910cab6abd15 SHA256: 20ffbaceaadb7ab1a410515715d3f9eb20ad40d083aa8752997b680f1576d55b SHA512: 9c988db129398e0855dcc43a7346e78ab159b40a30c6672d9ba672eee6f9e3562828537b3099ff914f4c5b8b1ca97ce2840131f6273c2c496a34f06b1ddcc51b Homepage: https://cran.r-project.org/package=tanner Description: CRAN Package 'tanner' (Puberty Stage Line Diagrams and SDS for Tanner Pubertal Stages) Plots Tanner pubertal stage measurements (genital, pubic hair, testicular volume, breast, menarche) against Dutch 1997 growth-study references as stage line diagrams, and converts observed stages to age-conditional standard deviation scores (SDS). Testicular volume can be entered as a raw orchidometer reading in ml. Implements the method of van Buuren and Ooms (2009) . Package: r-cran-taper Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-taper_0.5.3-1.ca2404.1_all.deb Size: 341946 MD5sum: b6ab7fb695e6f329d8e1be5bb1ef0fd6 SHA1: 609d87cd48234ff377219e2f98af007bac03cd6e SHA256: 88acbf5dfa1dd1f84d960e85ecd1d996d78794960739a294892976d369f6b3d2 SHA512: e0382679b026be816b5deb8e86dd8e754b7033b6b4d813e4e65b9af6a69b1817ef58e3cd8e5611d4464fcd276c1b6c360e0c900575515d26f517f71e7021a3d6 Homepage: https://cran.r-project.org/package=TapeR Description: CRAN Package 'TapeR' (Flexible Tree Taper Curves Based on Semiparametric Mixed Models) Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree heights are not measured, methods for estimating additional variance in volume predictions resulting from uncertainties in tree height models (tariffs) are provided. The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013) . Package: r-cran-tapernor Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-taper Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tapernor_0.2.1-1.ca2404.1_all.deb Size: 50410 MD5sum: f26f56a3d5afdd03279b280c48f44ac9 SHA1: 68be68f6031970f44739800d1e2aacb7d3651085 SHA256: 39d3cba61250fe4fc64dfda4fed748a48dedeb6ebc6d5b30de7fb3a7400655f3 SHA512: 10bc345538ee8d264a92d7935fc8ac813e4533d0881abfa387cb031db595cb5f744be759a4653860e39637cb7c1dc1f2a4ef83ee07904cf41b1ee5f64a163412 Homepage: https://cran.r-project.org/package=taperNOR Description: CRAN Package 'taperNOR' (Taper, Volume and Bark Thickness Models for Spruce, Pine andBirch in Norway) Stem taper, bark thickness, and volume models for the main Norwegian tree species (Norway spruce, Scots pine, and birch), based on Hansen et al. (2023) and the correction . Provides functions to predict stem diameter along the bole, bark thickness, and stem volume by numerical integration of the taper curve, and to invert these relationships (e.g. estimating height and diameter at breast height from measured diameters). Package: r-cran-tapkee Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-rgl, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tapkee_1.2-1.ca2404.1_all.deb Size: 224582 MD5sum: 89e2d4866606462a8b441bbc3cd31fe5 SHA1: 4ed99fdd72414a4040a914898bc87009de6a6de1 SHA256: 015db124e2beec41c51d9e0656a0bf1497932e787a1b100529864a2a8f2f17f4 SHA512: dddf0ce22bfae38809e9fdbd837b5d219b86f6c855530a20b27d68b280f9427c9c7ee6cda0ec49cc96fafe894cc501086eb60349e9921e8b32d4bf514e3034c9 Homepage: https://cran.r-project.org/package=tapkee Description: CRAN Package 'tapkee' (Wrapper for 'tapkee' Dimension Reduction Library) Wrapper for using 'tapkee' command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. 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Package: r-cran-taplock Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-glue, r-cran-httr2, r-cran-jose, r-cran-lubridate, r-cran-promises, r-cran-purrr, r-cran-stringr, r-cran-shiny, r-cran-tower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taplock_0.2.0-1.ca2404.1_all.deb Size: 487852 MD5sum: 25c6b136bff83ee228599b5257a6ab06 SHA1: 7355897681a4ba05d012fa0783a06016915a9cd9 SHA256: 9a37d71e13998d4800b37c493f27bea78fe6fdb7ef49763a6093824249f068f5 SHA512: 513d570ff301559721c8eb1aa5647c0685e3708798e16011fee148aa1107d6e8fe78400935ee917033ccb85f41f5b35a19ca6cf471cb3446f1e0be24a70abca8 Homepage: https://cran.r-project.org/package=tapLock Description: CRAN Package 'tapLock' (Seamless Single Sign-on for 'shiny') Swift and seamless Single Sign-On (SSO) integration. Designed for effortless compatibility with popular Single Sign-On providers like Google and Microsoft, it streamlines authentication, enhancing both user experience and application security. Elevate your 'shiny' applications for a simplified, unified, and secure authentication process. Package: r-cran-tapnet Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-bipartite, r-cran-mpsem, r-cran-phytools, r-cran-vegan Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-tapnet_0.6-1.ca2404.1_all.deb Size: 281726 MD5sum: 4037fd7e07cdfa4874f75312324933d8 SHA1: e4a3982f0b026657d2c5c391910b485691081aed SHA256: f9425c857c65f5a1496a2aaae03d9cbd23248cf610278955ec8d9694a858c86a SHA512: 6fbc1e827a7867b9f67e8d10bedf32a4bbba42c1144aeb449d2ed16e01b6dead65e75df5a9cc89cf469935628df758db002867159ddbfec058164b1737a6fa73 Homepage: https://cran.r-project.org/package=tapnet Description: CRAN Package 'tapnet' (Trait Matching and Abundance for Predicting Bipartite Networks) Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press. Package: r-cran-tar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-tar_1.0-1.ca2404.1_all.deb Size: 157366 MD5sum: 45d9cb2db9dbf649e92c9275ee83bc77 SHA1: 2167ce62d8d5465e4c68086505b350ade00fff27 SHA256: 9edeca361bb5aab584b56964467da116d0588811e37086aa9f258bd44051c2b4 SHA512: 454a05b07cfc4c89d316b9957c27acfd0381768db006788bb8815724dcfc52500ce77490b38bae4c1dee446559b608f78b038fa14c70945b5e186759e0c848f1 Homepage: https://cran.r-project.org/package=TAR Description: CRAN Package 'TAR' (Bayesian Modeling of Autoregressive Threshold Time Series Models) Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data . 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As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) . Package: r-cran-tarchives Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-fs, r-cran-rlang, r-cran-targets, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tarchives_0.2.0-1.ca2404.1_all.deb Size: 127104 MD5sum: 2e0bcc00a88e28ea0ecf4283128205c8 SHA1: 742798efbf64d8ea2d4d400f98c210bc0f94956b SHA256: 7fc4bece7a988b4a59f65ba429ba5ae3f3685b51f0e71d32a11c83f14b0fd4c9 SHA512: d3ae0ea19a390e5d9a0b728a232d6ef0739572a3e8b71b9094b30314dcc9c858a4c4da1cfcb2c2d163a8585feac1ed1f51aa6cfd06148658a4090bf1663802e0 Homepage: https://cran.r-project.org/package=tarchives Description: CRAN Package 'tarchives' (Make Your 'targets' Pipelines into a Package) Runs 'targets' pipelines bundled inside a package and caches the results in the R user cache directory, so that users of the package do not need to rerun the pipeline themselves. 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Package: r-cran-targets Architecture: all Version: 1.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2922 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64url, r-cran-callr, r-cran-cli, r-cran-codetools, r-cran-data.table, r-cran-igraph, r-cran-knitr, r-cran-prettyunits, r-cran-ps, r-cran-r6, r-cran-rlang, r-cran-secretbase, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-yaml Suggests: r-cran-autometric, r-cran-bslib, r-cran-clustermq, r-cran-crew, r-cran-curl, r-cran-dt, r-cran-dplyr, r-cran-fst, r-cran-future, r-cran-future.batchtools, r-cran-future.callr, r-cran-gargle, r-cran-googlecloudstorager, r-cran-gt, r-cran-keras, r-cran-markdown, r-cran-nanonext, r-cran-rmarkdown, r-cran-parallelly, r-cran-paws.common, r-cran-paws.storage, r-cran-pkgload, r-cran-processx, r-cran-qs2, r-cran-reprex, r-cran-rstudioapi, r-cran-r.utils, r-cran-shiny, r-cran-shinybusy, r-cran-shinywidgets, r-cran-tarchetypes, r-cran-testthat, r-cran-torch, r-cran-usethis, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-targets_1.12.0-1.ca2404.1_all.deb Size: 2362068 MD5sum: a4b8a32148974604dce3b8948d58d8d3 SHA1: f52c102f647504f6d19df6bf6ec93aaa7f16bf57 SHA256: a27ecb96b09f03c1f651fd7f4a57579d4be3d29fe525417fdd0b37f8bc0b9e26 SHA512: be94563173ab18b32f60a53808b4f222cd7a6cbda520699d03f9b841054a1328b6f13cf5c783b0557423c6736a9bc6343a085103a9d2466dfcdb7db30732d49f Homepage: https://cran.r-project.org/package=targets Description: CRAN Package 'targets' (Dynamic Function-Oriented 'Make'-Like Declarative Pipelines) Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ). Package: r-cran-tariff Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tariff_1.0.5-1.ca2404.1_all.deb Size: 55648 MD5sum: 3bfd1c2aadfcf85d1866476eeba4f31d SHA1: 47bad47194b6fa62877102e1910281b533f83ac6 SHA256: c08aae679671ae866786cb0e9bb6742b78b49b00be2001042a1a0e28807e0b2e SHA512: 08ea254b3e1715de0e9fb2ee237fbe6e239d3c54dac94b60168612bbdb9ffcfd20adfff4c2ff9440d16e6e5b530736b036bfcc7d96650dbaae4967c3af7b3d63 Homepage: https://cran.r-project.org/package=Tariff Description: CRAN Package 'Tariff' (Replicate Tariff Method for Verbal Autopsy) Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) and later refined as Tariff 2.0 in Serina, et al. (2015) . Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME. Package: r-cran-tarpolyglot Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-targets, r-cran-reticulate, r-cran-juliacall, r-cran-crew, r-cran-rextendr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tarchetypes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tarpolyglot_0.2.1-1.ca2404.1_all.deb Size: 238804 MD5sum: fcca6d21dbf9ea5439e64049ef470de9 SHA1: c7b764c7511359b1617cf6c0d8bfa4e35aa6092e SHA256: 8e5120e04719affbfeb19e838ec6ded376f473c70ab4dcb6126d5b115bbfab73 SHA512: d8cdbbbdd37dfbc66c4cec3d3cc36ad0142259f0e571ffb5ea1e6b3aff167070dd166867636421419aa8f22e6bdee0cde26708e63327a966f42031e8cbf6124d Homepage: https://cran.r-project.org/package=tarpolyglot Description: CRAN Package 'tarpolyglot' (Run Python, Julia, and Rust Inside 'targets' Pipeline Steps) Adds target constructors that make it easy to use Python, Julia, and Rust inside a 'targets' pipeline using 'reticulate', 'JuliaCall', and 'rextendr'. Provides tar_target_py(), tar_target_jl(), and tar_target_rs() (with matching _raw() variants), each mirroring 'targets::tar_target()' and 'targets::tar_target_raw()'. Python and Julia steps run a script via a live interpreter with optional R pre- and post-scripts; Rust steps compile '#[extendr]' functions and call them from an R post-script. Results are returned either as converted R objects or as files written to disk (format = "file"). Dynamic branching, environment/version selection, a 'crew' controller for isolation, and the full set of tar_target_raw() arguments are supported. Package: r-cran-tashiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-igraph, r-cran-tm, r-cran-snowballc, r-cran-dplyr, r-cran-wordcloud2 Filename: pool/dists/noble/main/r-cran-tashiny_0.1.0-1.ca2404.1_all.deb Size: 71202 MD5sum: 1ebf24c282377490c79a6dd4960a93ab SHA1: 43e5b0af4d01d86b9fae14f119df0c23a13d097f SHA256: f0c855db93d6ae4598d55b765e7b6315a41b97557aadd14ea0b406a42bf6b499 SHA512: 37a1b4c17f768a4fe32c66f13ed8fe2b9f7a38b75645301ce40fbed789d0aa8b1e03dc7d2c99418e524a3bfbacb9b2a3f983733e9a56974002072875cb446ffd Homepage: https://cran.r-project.org/package=TAShiny Description: CRAN Package 'TAShiny' ('Text Analyzer Shiny') Interactive shiny application for working with textmining and text analytics. Various visualizations are provided. Package: r-cran-tashu Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-dplyr, r-cran-randomforest, r-cran-plyr, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-drat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tashu_0.1.1-1.ca2404.1_all.deb Size: 83068 MD5sum: b565da59d668b63562e64e796344f868 SHA1: 56ab9778d5c6b1466d65b1d4250460642250782d SHA256: 5ef3bb43c85ee7e19fa68f23b6f4035c42c83ed343e477b668674b4b38af0e54 SHA512: 4d96684e13e72d46ba3665efe48eeb2b58f24438cc78ba4da5f93c46bd7b017d3687ac7eea50ec1f3fd1aca6a9127645347d18c9b2b394b0b662d79bfed76838 Homepage: https://cran.r-project.org/package=tashu Description: CRAN Package 'tashu' (Analysis and Prediction of Bicycle Rental Amount) Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions. Functions on this package interacts with data on 'tashudata' package, a 'drat' repository. 'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. To install this data package, see the instructions at . top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths. daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental. create_train_dataset(), create_test_dataset() is data processing function for prediction. Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset. Users can make random-forest prediction model by using create_train_model() and predict amount of bicycle rental in 2015 by using predict_bike_rental(). 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Package: r-cran-tastyr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tastyr_0.1.0-1.ca2404.1_all.deb Size: 1968670 MD5sum: ad5f0e139298da40450ac7aa7d1580fb SHA1: 33a958d1bda38ef92f3b5ba7aa3e702cb6d93588 SHA256: d69a9b2e2025b66df3f1331d67cb8f6b55720756edeb23ef8f5f454b30c6395c SHA512: 0be8921b211ccabcd2e29bcebf84bab3e558d780ca379b4d52e53222516d485bd4f7f47fc932a5829617cdfdd4ee5a1a7584cfc78d932813d0060d7095cd58fd Homepage: https://cran.r-project.org/package=tastyR Description: CRAN Package 'tastyR' (Recipe Data from 'Allrecipes.com') A collection of recipe datasets scraped from , containing two complementary datasets: 'allrecipes' with 14,426 general recipes, and 'cuisines' with 2,218 recipes categorized by country of origin. 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Package: r-cran-tauturri Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-magrittr, r-cran-plyr, r-cran-purrr, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tauturri_0.3.0-1.ca2404.1_all.deb Size: 317910 MD5sum: cafeb4cdf1d4f6b91f13a90e801bdd71 SHA1: fcb0878c686ef8fa115c6cfd434f64ca585dbb88 SHA256: 00503c411ebfd4126be2efe52ba01aa49ceca495c5b4c25cc6d7d2703978da35 SHA512: 3b416584d193ee16f64726140c2cd8faec7d2d0383e9651ea4ffc7c75de24a6b80239fa5ae997d6c56716cef78088bfc15da25642d4e6d2297f485c134a4df37 Homepage: https://cran.r-project.org/package=tauturri Description: CRAN Package 'tauturri' (Get Data Out of 'Tautulli' (Formerly 'PlexPy')) 'Tautulli' () is a monitoring application for 'Plex' Media Servers () which collects a lot of data about media items and server usage such as play counts. This package interacts with the 'Tautulli' API of any specified server to get said data into R. The 'Tautulli' API documentation is available at . Package: r-cran-taxa Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-rlang, r-cran-stringr, r-cran-crayon, r-cran-pillar, r-cran-viridislite, r-cran-cli Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-taxa_0.4.4-1.ca2404.1_all.deb Size: 373582 MD5sum: 77359294bb40105efe5a33b0d73b890c SHA1: fee6d0b7e47062060a0fd10b50cd2901b499129b SHA256: 6124e6a7b10826b906ae0f0b90fcf255f8db8742ccb2b42681da95e0fe04324c SHA512: 367a9badf8e01ee3b385be0dab1c6c9c703e742cbd4d524a9b5fe5734600d8b89a1000d3cb85e617c8300c5150cbf68ec03cbd10cc329b3ceb04e4afd790370b Homepage: https://cran.r-project.org/package=taxa Description: CRAN Package 'taxa' (Classes for Storing and Manipulating Taxonomic Data) Provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them. Package: r-cran-taxadb Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-tibble, r-cran-dplyr, r-cran-dbplyr, r-cran-rlang, r-cran-magrittr, r-cran-stringi Suggests: r-cran-spelling, r-cran-testthat, r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-crayon, r-cran-withr Filename: pool/dists/noble/main/r-cran-taxadb_0.3.0-1.ca2404.1_all.deb Size: 394932 MD5sum: 3fff2fbfa75c9bc7f8dc53f4f1639e62 SHA1: b735b14730f33aa58a97785c25269aa06f80b42c SHA256: b9afc5d073466dc542d5ceae75d3d6839701955c12095e6c89e1f40236a0e195 SHA512: 244eadc3b38177a489db8aece7476bfa1480bf1270dd009a4017666af22f3bf3ad6b01978be06458acaae6f7f0865c5dd53e4f7c48a860d80843660abbb0d282 Homepage: https://cran.r-project.org/package=taxadb Description: CRAN Package 'taxadb' (A High-Performance Taxonomic Database Interface) Provides fast access to many commonly used taxonomic authorities in a uniform Darwin Core format. 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Package: r-cran-taxalight Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-thor, r-cran-contentid Suggests: r-cran-jsonlite, r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-progress, r-cran-utf8, r-cran-crayon Filename: pool/dists/noble/main/r-cran-taxalight_0.1.5-1.ca2404.1_all.deb Size: 53444 MD5sum: 0978f13c8ef12e997219e5c36fd5d5bd SHA1: 73a7d96ccdb0a2692cd9d4b82f8d288c10834817 SHA256: 53a41caf5e59141b10846b4cdbf6e33c5f1e5dfb4b549a00566149cda89e75a4 SHA512: 64c124fa5d19f0347c0d88a9b2d3c01c78b4fcd9de46580f889b75c20e26bdb52641db9216b70d512d1c487c081412e55b6bef0950c6462a8df72f7e6017fcac Homepage: https://cran.r-project.org/package=taxalight Description: CRAN Package 'taxalight' (A Lightweight and Lightning-Fast Taxonomic Naming Interface) Creates a local Lightning Memory-Mapped Database ('LMDB') of many commonly used taxonomic authorities and provides functions that can quickly query this data. Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using 'LMDB' can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included. Package: r-cran-taxanorm Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-microbiome, r-bioc-phyloseq, r-bioc-s4vectors, r-bioc-biocgenerics, r-cran-vegan, r-cran-mass, r-cran-future, r-cran-future.apply, r-cran-matrixstats, r-cran-pscl, r-cran-parallelly, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-taxanorm_2.4-1.ca2404.1_all.deb Size: 717170 MD5sum: e01bb07ee8e6932884cd61e2eb9fcf09 SHA1: ba26683baefcdce3bf65b3bd518e42bb0baacf7b SHA256: b0e369f53d397839af0c334efb222440e1db80122cb063b77c7eaec59bf109eb SHA512: 6c43f77c0301299a316496fbdbdfaf3461ec5b1653b568665b769ee346ae96a8a835fea027436b2808b6cda0bd3b19c9a7931f87914d5bdb5ba0f4c0ae77166d Homepage: https://cran.r-project.org/package=TaxaNorm Description: CRAN Package 'TaxaNorm' (Feature-Wise Normalization for Microbiome Sequencing Data) A novel feature-wise normalization method based on a zero-inflated negative binomial model. This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) . Package: r-cran-taxdiv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4094 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-taxdiv_0.1.0-1.ca2404.1_all.deb Size: 2524110 MD5sum: f703efb3e956e4a430196472460d20c4 SHA1: e48ad1414abff16c332d38987b658a2934048139 SHA256: 64c219c6dc93f11e8a50814f9b27eab10811ec68d50a8e83ec26dd20eda020dd SHA512: fe68216a06a7ff14b3c3f025eff1ac760b3863c5e927596f204294c3bbaa138ff7af706dbf977a3453f0fd257edae5870bbdc7a8c897387b28d85fcaae67611d Homepage: https://cran.r-project.org/package=taxdiv Description: CRAN Package 'taxdiv' (Taxonomic Diversity Indices Using Deng Entropy) Calculates taxonomic diversity indices for ecological community data using Deng entropy framework and classical approaches (Shannon, Simpson, Clarke & Warwick). Provides functions for computing taxonomic distinctness, average taxonomic distinctness (AvTD/Delta+), variation in taxonomic distinctness (VarTD/Lambda+), and Deng entropy-based measures that incorporate taxonomic hierarchy information. Includes tools for constructing taxonomic trees and computing pairwise taxonomic distances. Package: r-cran-taxicabca Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ga, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxicabca_0.1.2-1.ca2404.1_all.deb Size: 83432 MD5sum: f5eb1b9190e841768b4a338c65078dc2 SHA1: bfcbd74c4def8d043eb2a7b2ecdeb936aa0f6b21 SHA256: 8c030fd51b22e022d8cdd2027b79b948604a373aa051c59b78a2abc13b2cdd2b SHA512: 89aff15a338a84adc6734ec74f74fef8b1a9671935feff187a789959ff563afff0924dd04a64f9aa904d9eb47c3c8a4272b82cccdec1f619f1cd588fcb14beed Homepage: https://cran.r-project.org/package=TaxicabCA Description: CRAN Package 'TaxicabCA' (Taxicab Correspondence Analysis) Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) . Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod). Classical CA is based on the Euclidean distance. Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance. For some tables, Taxicab CA gives more informative results than classical CA. Package: r-cran-taxify Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4201 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-rlang, r-cran-vectra Suggests: r-cran-ape, r-cran-dbi, r-cran-knitr, r-cran-openxlsx2, r-cran-rgbif, r-cran-rmarkdown, r-cran-rsqlite, r-cran-sf, r-cran-terra, r-cran-testthat, r-cran-tr8, r-cran-withr Filename: pool/dists/noble/main/r-cran-taxify_0.6.0-1.ca2404.1_all.deb Size: 2034042 MD5sum: 60b1b0fd22eb0abeb47bda979a20387f SHA1: 728796062c716639ef8d82f7c26633f79330c46f SHA256: b2c1eb232254e2fb1c857eb7ed0e400a0d82e662102f624570b0dc0b91938129 SHA512: df7882bb45f433942b658d666323e620f8fa5a02e28fc771eb2c995cabf3d970e9e7e0acf016aa1a32447ebeef0d5501a6a172f1b822ce594c2aefc908abbdaf Homepage: https://cran.r-project.org/package=taxify Description: CRAN Package 'taxify' (Offline Taxonomic Name Matching Against Darwin Core Backbones) Match taxonomic names against locally stored Darwin Core backbone databases ('WFO', 'COL', 'GBIF', 'ITIS', 'NCBI Taxonomy', 'Open Tree of Life', 'WoRMS', 'Euro+Med', 'Species Fungorum', 'AlgaeBase', 'FishBase', 'SeaLifeBase', 'Reptile Database', 'LCVP', 'WCVP', 'Mammal Diversity Database', 'AviList', 'LPSN'). Provides offline fuzzy and exact matching with synonym resolution, hybrid name detection, and a unified output schema across all sources. All heavy computation runs in the 'vectra' C11 columnar engine. Package: r-cran-taxize Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-xml2, r-cran-jsonlite, r-cran-ape, r-cran-zoo, r-cran-data.table, r-cran-tibble, r-cran-rredlist, r-cran-rotl, r-cran-ritis, r-cran-worrms, r-cran-natserv, r-cran-wikitaxa, r-cran-r6, r-cran-crayon, r-cran-cli, r-cran-phangorn, r-cran-lifecycle, r-cran-curl, r-cran-stringi Suggests: r-cran-testthat, r-cran-vegan, r-cran-vcr Filename: pool/dists/noble/main/r-cran-taxize_0.10.1-1.ca2404.1_all.deb Size: 1548732 MD5sum: 130515e21a9975293c7aa959163f94c3 SHA1: d170989555d8a6ffee2f45194e3c6498151b9596 SHA256: 36796fb03f5944c8d52b59b42b0e0341d7db4aa16e91adc8861d98d6f428524f SHA512: 10abbe3f40d3f17bba48cca3823fde867eb0b464c28e201b674bcf12a707140d20aa8d50bb37bb46d64bf66e4a763e93eb117b84e8adb9977ed2defe59c442b3 Homepage: https://cran.r-project.org/package=taxize Description: CRAN Package 'taxize' (Taxonomic Information from Around the Web) Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include 'NCBI E-utilities' (), 'Encyclopedia of Life' (), 'Global Biodiversity Information Facility' (), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package. Package: r-cran-taxizedb Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-rsqlite, r-cran-dplyr, r-cran-tibble, r-cran-rlang, r-cran-readr, r-cran-dbplyr, r-cran-magrittr, r-cran-hoardr, r-cran-vroom Suggests: r-cran-taxize, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxizedb_0.3.2-1.ca2404.1_all.deb Size: 142964 MD5sum: 26a239c55d4f8c5178a8c51334122ea7 SHA1: 3207ab961d2dbeff7312ecbe51ffde274488059f SHA256: ac341660812b548dd5348a1e20e68ba7c01e8342c334cf8f08342d686fe29d49 SHA512: d1088bf6db98319bc934e9cf337f2fd071cf5c6a31e5fc9dfe9b0b36068b8aa96bb8f0fcce27d08d436faa73d3cf555d7c59702eefb5e64ec4c9280ab24c8d3d Homepage: https://cran.r-project.org/package=taxizedb Description: CRAN Package 'taxizedb' (Offline Access to Taxonomic Databases) Download taxonomic databases, convert them into 'SQLite' format, and query them locally for fast, reliable, and reproducible access to taxonomic data. Package: r-cran-taxlist Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biblio, r-cran-foreign, r-cran-stringdist, r-cran-stringi, r-cran-stringr Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-taxa, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxlist_0.3.5-1.ca2404.1_all.deb Size: 1722032 MD5sum: cbaf637ddb7a7870ce59a5f258030b4d SHA1: 452239136988e97523f369b4ce5b8ab2bc153089 SHA256: ba2a3244781fa94061f180ec1ee0e9a8e84e3f717f70e3cbf4b601d8e373ab4b SHA512: a8ac0170977049f84fe722c5701516579bfd07daee24bacb930f4410c7cadbcab2f59e8e45a5d8825f06d81534f6901626763d4ea100e903ab9b6a6dddeffa2e Homepage: https://cran.r-project.org/package=taxlist Description: CRAN Package 'taxlist' (Handling Taxonomic Lists) Handling taxonomic lists through objects of class 'taxlist'. This package provides functions to import species lists from 'Turboveg' () and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods. Package: r-cran-taxnames Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-taxlist Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxnames_0.1.0-1.ca2404.1_all.deb Size: 27714 MD5sum: ee1578edcaf0bd4a09ca7bdf505e36e0 SHA1: e84a88bbb6be32aa089a492cd7c3e744a7bdd152 SHA256: f006dd58980be5ea6c14e0afb8488142f0db2216a84bae7cb1bc737796e23f9c SHA512: 4c38ec2780315f6e368c9a7283189d5f2cbfc1244a9f357efd97f1ad1d8bf59eef15eccc0c417a4b97b62559108112a1fcbdccbdf370d2d1a2836afe440e8b4e Homepage: https://cran.r-project.org/package=taxnames Description: CRAN Package 'taxnames' (Formatting Taxonomic Names in Markdown) A collection of functions used to format taxonomic names in Markdown documents. Those functions work with data structured according to Alvarez and Luebert (2018) . Package: r-cran-taxodist Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rvest, r-cran-stringr, r-cran-purrr, r-cran-cli, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown, r-cran-xml2, r-cran-ape, r-cran-vegan, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-taxodist_0.8.0-1.ca2404.1_all.deb Size: 809346 MD5sum: 764d6649e08a658678fe5dcace93b492 SHA1: 3ced796e77e114834f35acd172e0279d1556a435 SHA256: 33503cc01d1968e902e000227a03c6afcb238d9bd2cb0cc65c703fd458ad9077 SHA512: 7ad6ddfbd00bd9dca945dc60a8bb279993c56ad4a2c1b878d7515ff74c57931ce07d65963842aa34bce7ee17f9a2146a344b87481d0f55428f5bc9b5fdabc32e Homepage: https://cran.r-project.org/package=taxodist Description: CRAN Package 'taxodist' (Taxonomic Hierarchy Distances and Lineage Analysis) Computes distances between taxonomic hierarchy nodes using lineage data retrieved from The Taxonomicon . For distinct nodes, distance is defined as the reciprocal of the depth of their most recent common ancestor; identical nodes have distance zero. This definition yields an ultrametric within each connected hierarchy. Functions are provided for auditable name resolution, online or user-supplied lineage analysis, clade membership, pairwise and matrix distance calculation, hierarchical clustering, principal coordinates analysis, portable JSON analysis bundles, and cache management. Distance matrices are returned as base R 'dist' objects. The distances represent classification depth rather than evolutionary time or phylogenetic branch length. Package: r-cran-taxonbridge Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-vroom, r-cran-ggplot2, r-cran-rje, r-cran-withr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxonbridge_1.2.2-1.ca2404.1_all.deb Size: 479868 MD5sum: b11bc123d1dff747d41c0ef51abeec82 SHA1: 37ee092054c1ea6b791901107f704f85f7ceb274 SHA256: dfc51cc548cae4878b6d36da0e49d9171f0c53a5d51f0cd446b6d92e02af2a80 SHA512: 62bcca61606387bf80acb19cabe095efc51b5589a20cf56617f6035a7e96e2047d3d99a09524bb1558491e5c3a0ceaf82be481802e3bfaf7a5c17ba3ed49ed41 Homepage: https://cran.r-project.org/package=taxonbridge Description: CRAN Package 'taxonbridge' (Create Custom Taxonomies Based on the NCBI Taxonomy and GBIFBackbone Taxonomy) The NCBI taxonomy is a popular resource for taxonomic studies but it only contains data on species with sequence data whereas the GBIF has a more extensive coverage of extinct species. Taxonbridge is useful for the creation and analysis of custom taxonomies based on the NCBI taxonomy and GBIF backbone taxonomy. Package: r-cran-taxotools Architecture: all Version: 0.0.148-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-taxize, r-cran-wikitaxa, r-cran-plyr, r-cran-sqldf, r-cran-stringr, r-cran-stringdist, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/noble/main/r-cran-taxotools_0.0.148-1.ca2404.1_all.deb Size: 202288 MD5sum: 4e0f6553f301e451f3183a0fbd5d45bf SHA1: 4002038a53a87c016e5c3be4343852cd1278c9ea SHA256: c72b301b1bc5ca7719b4f2f4e63607cac34d08bdfda7bbcb5b62c1e40bc7667b SHA512: a59eca55aa3b74a348edef8bfce1403fd90eb14ce86710d6d8ce4a05fa3dbccfd45567f81e9eefc85ad0c12b53d93decf407ceddbf721568b3b0ca382fe3bb6d Homepage: https://cran.r-project.org/package=taxotools Description: CRAN Package 'taxotools' (Taxonomic List Processing) Taxonomic lists matching and merging, casting and melting scientific names, managing taxonomic lists from Global Biodiversity Information Facility 'GBIF' or Integrated Taxonomic Information System 'ITIS', harvesting names from Wikipedia and fuzzy matching. Package: r-cran-taxresolver Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxresolver_0.1.0-1.ca2404.1_all.deb Size: 98186 MD5sum: 06d5c5c5f4f811dd0314af68bbdd1840 SHA1: c5f7848291967341e09a84b0ef40e388fad4d5ef SHA256: f2f8e465727480af7ec2ef30c04c1d1bd09e991372f689172c2d85bd384b10e6 SHA512: 9a42680446a5b7592dcad1b4a06c22e64b52ed9414b03f2e08279eeec8ab01adac9fe3e047acbf892080ead193138370b9e0b5ad5fc9e016a3a3b13947cd0e5b Homepage: https://cran.r-project.org/package=TaxResolveR Description: CRAN Package 'TaxResolveR' (Taxonomic Name Resolution and Validation Tools) Provides reproducible tools for cleaning, parsing, classifying, standardising, validating and resolving scientific names in ecological and biodiversity datasets. Taxonomic matches can be assessed for match quality and taxonomic status, records requiring manual review can be identified, and resolution results can be summarised, reported and exported. Taxonomic name resolution can use the 'GBIF' species matching service and the GBIF Backbone Taxonomy described by GBIF Secretariat (2023) . Package: r-cran-taylor Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-spotifyr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bookdown, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-taylor_4.0.0-1.ca2404.1_all.deb Size: 2054804 MD5sum: b33dda504655215223568c5a07bc25b0 SHA1: 2e93a7ad1147ee9164a8e8471f3d524c7b4fe573 SHA256: 8342a57f804b9c59f29a8c3c9d0e3192f7eada7e9e9bc9cf08065bb978110bbc SHA512: 120fb5349de7be96b636b1f90c3d98553b524ffdce0953b114e4eb95b63007fcbd97f8c057fc31978fa86897e62439009719ee2a101de074f8b7957c027afa02 Homepage: https://cran.r-project.org/package=taylor Description: CRAN Package 'taylor' (Lyrics and Song Data for Taylor Swift's Discography) A comprehensive resource for data on Taylor Swift songs. Data is included for all officially released studio albums, extended plays (EPs), and individual singles are included. Data comes from 'Genius' (lyrics) and 'SoundStat' (song characteristics). Additional functions are included for easily creating data visualizations with color palettes inspired by Taylor Swift's album covers. Package: r-cran-taylorrussell Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-shiny, r-cran-shinywidgets Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-taylorrussell_1.2.1-1.ca2404.1_all.deb Size: 38622 MD5sum: 33a8048ef6a0c6a331b90c6acf21b331 SHA1: 8fc3e14b6e1d19dc28a9663f1fb479451e221dbb SHA256: 7490afed1834e36d6334d129863549179143d675738e6520baed99d36597ae5e SHA512: f4059c19d681ea5580f8ea0f9e1d0ad27e60dad34e069fbf8519b1ee747af3944aa9b380214cd4376a3fb9348e407c210f332639e2efaab0e42115dae5ee5c77 Homepage: https://cran.r-project.org/package=TaylorRussell Description: CRAN Package 'TaylorRussell' (A Taylor-Russell Function for Multiple Predictors) The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples). The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors). To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a 'shiny' program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" . Package: r-cran-taylorswift Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-taylorswift_0.1.0-1.ca2404.1_all.deb Size: 301456 MD5sum: 6120b7fbf875a071be00a0043186fd77 SHA1: 21202d811a746a07f1984672740e76376ed4478b SHA256: 008449f31f233dd2be23cc599637e610da8704a356f603078cf724f4a697bdd2 SHA512: d95b23ebacce732ec746883a5d4258165e03dc37161fc66ce275086961f7bc2fc47c8dc59e74f884cf350caaa8cc80af80696dc06d938aa86b5b76e58f9d43bb Homepage: https://cran.r-project.org/package=tayloRswift Description: CRAN Package 'tayloRswift' (Color Palettes Generated by Taylor Swift Albums) For when your colors absolutely should not be excluded from the narrative. Package: r-cran-tba Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-readxl, r-cran-reshape2, r-cran-shinybusy Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tba_0.1.0-1.ca2404.1_all.deb Size: 113610 MD5sum: 63d58e259196087e5dda1834799b18e2 SHA1: 1b940ad56c646a830f1267be298e1a2f310f16a7 SHA256: 7e82a3724ee5e4a3e321f9c28992af4628e6b65548be5deddc18935b08883fa5 SHA512: 5b8b6f135df1124c469c2913a7d02bc0009969c0e379f93b695a9011f28c86e551158c30e5b7e480b13751843f7dc1e90251896834cb093acfbd07adbe8c5c42 Homepage: https://cran.r-project.org/package=TBA Description: CRAN Package 'TBA' (Collection of 'shiny' Apps for Tree Breeding Analysis) A collection of interactive 'shiny' applications for performing comprehensive analyses in the field of tree breeding and genetics. The package is designed to assist users in visualizing and interpreting experimental data through a user-friendly interface. Each application is launched via a simple function, and users can upload data in 'Excel' format for analysis. For more information, refer to Singh, R.K. and Chaudhary, B.D. (1977, ISBN:9788176633079). Package: r-cran-tbd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-tbd_0.1.0-1.ca2404.1_all.deb Size: 164526 MD5sum: f46a9b88ee1c9e7cad36c45a5353303b SHA1: 3e160734a8040a6ff60e416576e6dc377041d102 SHA256: 666079db54aa66658f08ec1a3f4129e83e53499c2515114587a70c639356afb1 SHA512: 39bfec62588a5f578ab75931805bbf87c9ab25afd77fc8bcec670f14399da1ddf63a22b34c0a722571d27e1915905a2415bb5789791420291727b54619258968 Homepage: https://cran.r-project.org/package=tbd Description: CRAN Package 'tbd' (Estimation of Causal Effects with Outcomes Truncated by Death) Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data. Package: r-cran-tbea Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-rfit, r-cran-boot, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-kendall Filename: pool/dists/noble/main/r-cran-tbea_1.8.0-1.ca2404.1_all.deb Size: 518726 MD5sum: 540ac1bf3ce90b81e0b2587ed5dd8c4f SHA1: 0c4c35a1018a9ff15fc23123cfcb44cb7eb61384 SHA256: 787da741f7538367a8aad766c0d1ca56c1e55af3bf46e0102e2db227acc07c6e SHA512: ec828117db564927d06ab51fd8190395db57a497b939f5e5832c79b75fcc611ea1990a23e867b1a10fe9b27bc242bcb6e3b9247441ce286900b831d7afed63b6 Homepage: https://cran.r-project.org/package=tbea Description: CRAN Package 'tbea' (Pre- And Post-Processing in Bayesian Evolutionary Analyses) Functions are provided for prior specification in divergence time estimation using fossils as well as other kinds of data. It provides tools for interacting with the input and output of Bayesian platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes', or 'MCMCTree'. It Implements a simple measure similarity between probability density functions for comparing prior and posterior Bayesian densities, as well as code for calculating the combination of distributions using conflation of Hill (2008). Functions for estimating the origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and stratigraphic intervals (Marshall 2010) are also available. Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. , Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. , Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. . 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Package: r-cran-tbl.now Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-generics, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-pillar, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-baselinenowcast, r-cran-data.table, r-cran-epinow2, r-cran-knitr, r-cran-modifiedmk, r-cran-nobbs, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-scoringutils, r-cran-surveillance, r-cran-testthat, r-cran-tsibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-tbl.now_1.0.0-1.ca2404.1_all.deb Size: 2911990 MD5sum: 384d4c5e2a0332b599e3f38037d64c24 SHA1: f49a666ca95a3ca921c0d46b59d64e418a79b591 SHA256: 9fe8ac2aface4c0f867b28fe836f9001a7b2d4d4bc8bd25a89b11583b348f209 SHA512: aad9c77443c089c867d87707c984de5d9bd9e67d535fba0e46b107e4c86b13aa635d64720eccb3ac718172560b6dc95ed01893a02faede83f3acd541e996574c Homepage: https://cran.r-project.org/package=tbl.now Description: CRAN Package 'tbl.now' (Tidy Data and Workflow Layer for Epidemic Nowcasting) Defines tidy data structures and package-agnostic workflows for epidemiological nowcasting. 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The classifier computes a continuous log-likelihood ratio score per document and uses a data-driven decision threshold estimated via K-fold cross-validation on a user-selected criterion (accuracy, F1 score, Matthews correlation coefficient, balanced error, etc.). An optional iterative refinement procedure locally re-estimates the threshold in regions of class overlap using either Gaussian kernel density estimation or a Central Limit Theorem bootstrap approximation. The package exposes an idiomatic R formula + data.frame interface together with a 'quanteda'-based text preprocessing pipeline, supports user-supplied document-feature matrices, and includes an optional word-embedding extension that augments the Bag-of-Words with K nearest semantic neighbours of each token. The package additionally implements the p-value extension proposed by Romano (2025) for both document- and feature-level interpretability via tbnb_pvalues(). Methods are described in Romano, Contu, Mola, Conversano (2024) , Romano, Zammarchi, Conversano (2024) , and Romano (2025) . 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Package: r-cran-tca Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-data.table, r-cran-futile.logger, r-cran-gmodels, r-cran-matrixcalc, r-cran-matrixstats, r-cran-nloptr, r-cran-pbapply, r-cran-pracma, r-cran-rsvd, r-cran-quadprog, r-cran-matrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tca_1.2.1-1.ca2404.1_all.deb Size: 657120 MD5sum: b44f5c60bb858e4449a5cf2bc3e92629 SHA1: 38710c0b2348d526394eee0583e7d522cf2c92dd SHA256: 6e642b0e4d4824909ef532cb62cdf0f50571712d0e58a062ceacc43fe9a6bed1 SHA512: fe83e01aa5a4c2dd9d682f6fa1f34da85e4042c41688641092e7882e2ca2bec612be6750c950a8ce8b5939e258a998d9ef675c8f5008f959974516fb5f3581f7 Homepage: https://cran.r-project.org/package=TCA Description: CRAN Package 'TCA' (Tensor Composition Analysis) Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) . Package: r-cran-tceper Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-tibble, r-cran-purrr, r-cran-janitor Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-stringr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-tceper_0.1.4-1.ca2404.1_all.deb Size: 324910 MD5sum: a5f599462bcd8df5ac84a594d9d6048f SHA1: 0118bc95637b18a8ff040135c41961604f4c5ad4 SHA256: f0d3179275ee740ceb4ad19dbb2919f6b3224113e6008ebbe261d497f442dc7c SHA512: 062442dc1ed30ac3a3980ab7d6296c5daf8c41fc39140f28bb50edcc826fae6c616cc2b5b863571442594c9323bf76063c828f449f3609e470ce6de8af89de0d Homepage: https://cran.r-project.org/package=tceper Description: CRAN Package 'tceper' (Access the 'Open Data API' of Pernambuco Court of Accounts) An R interface to the 'Open Data API' of the Tribunal de Contas do Estado de Pernambuco (TCE-PE), the Court of Accounts of the State of Pernambuco, Brazil. Provides tidy, ready-to-use functions to query public data on revenues, expenditures, commitments, procurement, contracts, agreements, public works, legal processes, personnel and reference tables for all state and municipal government entities in Pernambuco. All results are returned as tibbles with column names converted to 'snake_case' by default. Uses 'httr2' for HTTP requests and 'cli' for user-friendly messages. See for the API documentation. 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Package: r-cran-tcgaretriever Architecture: all Version: 1.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-reshape2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-tcgaretriever_1.10.3-1.ca2404.1_all.deb Size: 596912 MD5sum: 588af1df8da22c84bfce2530344c6aa0 SHA1: 6efc6420218563ffd935ba265e1c432706ac2423 SHA256: b1e62f0ce445ea45b82c5792730ad3eab01dc7701e0786f30592b4035606a3ba SHA512: f6076f6235f3a5492e86d8d1e7f015ee3f60463d692dcfa25374b2b6f64330646729928f8f38eba8815fe69903223912ae3712011f5cc0f08f6d3e6ce1cefdd2 Homepage: https://cran.r-project.org/package=TCGAretriever Description: CRAN Package 'TCGAretriever' (Retrieve Genomic and Clinical Data from CBioPortal IncludingTCGA Data) The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see for more information). 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Package: r-cran-tcgsa Architecture: all Version: 0.12.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lme4, r-cran-reshape2, r-cran-gsa, r-bioc-multtest, r-cran-cluster, r-cran-cowplot, r-cran-gtools, r-cran-stringr Suggests: r-cran-biocmanager, r-cran-foreach, r-cran-doparallel, r-bioc-dearseq, r-cran-knitr, r-bioc-geoquery, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tcgsa_0.12.13-1.ca2404.1_all.deb Size: 602524 MD5sum: 9a689c1a2999dc7f64aad62c345e70a7 SHA1: c8f2f888c85a951b12c2d993b4db6b97801b0e2a SHA256: 9bf0537cf32b1f518ce05d5f72658370947a3d5d757e55710bc0b8c964d00810 SHA512: 61c871c8827694c1f1edb39cbc91f86e75ce2ce191d03e7f97b618924a14b0ff91865a6ba4bf0104d2c1818565fe3f5b6e5dce2a8ce2d594f689f237607ebf42 Homepage: https://cran.r-project.org/package=TcGSA Description: CRAN Package 'TcGSA' (Time-Course Gene Set Analysis) Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level. Method is detailed in: Hejblum, Skinner & Thiebaut (2015) . Package: r-cran-tciapathfinder Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-oro.dicom, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tciapathfinder_1.0.6-1.ca2404.1_all.deb Size: 86474 MD5sum: 724e76b962e805d24118e8cfb3309277 SHA1: b3f4e03cc59fdf743675889cbb58efba53b117fe SHA256: ea03f7d713d0601ed49cbb4f4877ef0b2fdd7f9db1ab2378d45c02d1051843e9 SHA512: 80a97313a10390fc368d995ab34823945778126e8c21cc22b8e8e30b4e874ee0d3cd64f7855566a0d5a6c38d91974e65afaec6da2278bd44c7131e49a6dc029e Homepage: https://cran.r-project.org/package=TCIApathfinder Description: CRAN Package 'TCIApathfinder' (Client for the Cancer Imaging Archive REST API) A wrapper for The Cancer Imaging Archive's REST API. The Cancer Imaging Archive (TCIA) hosts de-identified medical images of cancer available for public download, as well as rich metadata for each image series. TCIA provides a REST API for programmatic access to the data. This package provides simple functions to access each API endpoint. For more information, see and TCIA's website. Package: r-cran-tcl Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-erm, r-cran-psychotools, r-cran-ltm, r-cran-numderiv, r-cran-mass, r-cran-matrix, r-cran-lattice, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tcl_1.0.1-1.ca2404.1_all.deb Size: 279434 MD5sum: 7df6b79de21f2e79572b5b3d51788e1b SHA1: 2cbc45631be112b711441ab59c462fac056bd38e SHA256: 2216d2ee3b4978708048e0941dde139f3a1085d7d9337b2b10ca9752cdb28a9a SHA512: a5a64a5aa1bc35e969ecbc694d34c01a32d0a81e7877aa29e230355c12b5988f42078eee0ccecb8f20924357241b8b916e674ab4a516d57f3cd54a30460327e0 Homepage: https://cran.r-project.org/package=tcl Description: CRAN Package 'tcl' (Testing in Conditional Likelihood Context) An implementation of hypothesis testing in an extended Rasch modeling framework, including sample size planning procedures and power computations. Provides 4 statistical tests, i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three types of functions for power and sample size computations are provided. Firstly, functions to compute the sample size given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the power of the tests given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the so-called post hoc power of the tests. This is the power of the tests given the observed deviation of the data from the hypothesis to be tested and a user-specified level alpha of the test. Power and sample size computations are based on a Monte Carlo simulation approach. It is computationally very efficient. The variance of the random error in computing power and sample size arising from the simulation approach is analytically derived by using the delta method. Additionally, functions to compute the power of the tests as a function of an effect measure interpreted as explained variance are provided. Draxler, C., & Alexandrowicz, R. W. (2015), . Package: r-cran-tcltk2 Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tcltk2_1.6.1-1.ca2404.1_all.deb Size: 1070342 MD5sum: 41a952c952dc6ab6fa9dad2fe088f69b SHA1: c231246a739eb6c33865988606148e00e540beb6 SHA256: 7b1f9083b61fb06e6fd02f2b6babfe6e7fccadae781d6edf7d231fa57eade30b SHA512: 44ef7e0b94cb4019601a0ef7671a93946fa0bbaa3b761411084fa61041decd32b960735ade2b9665e009af8cc4866c2e3c721ecf582830887a8fc0d157eea9fd Homepage: https://cran.r-project.org/package=tcltk2 Description: CRAN Package 'tcltk2' (Tcl/Tk Additions) A series of additional Tcl commands and Tk widgets to supplement the tcltk package. Package: r-cran-tcomp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcomp, r-cran-forecast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-xtable Filename: pool/dists/noble/main/r-cran-tcomp_1.0.1-1.ca2404.1_all.deb Size: 483064 MD5sum: 6d65600f8f84ca1dc810c309db386421 SHA1: a542aa21b3030684fc33e4e8af9157dbcfc996db SHA256: 2f86b54bdb4385a363b41d4a6f3656f690d1b5beb6dfc1c080294fb093e442a3 SHA512: 3593272695bf97002a1e848726cc031cc70fc06719591aef6f77cf57a1cc2315eb34ffa085c126eb5bee13c9ae45bd8f63e8823c5371e69426fe12c4127e6143 Homepage: https://cran.r-project.org/package=Tcomp Description: CRAN Package 'Tcomp' (Data from the 2010 Tourism Forecasting Competition) The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) . Package: r-cran-tcpl Architecture: all Version: 3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-rmariadb, r-cran-numderiv, r-cran-rcolorbrewer, r-cran-sqldf, r-cran-dplyr, r-cran-tidyr, r-cran-plotly, r-cran-tcplfit2, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-rlang, r-cran-ctxr, r-cran-viridis, r-cran-gt Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-htmltable, r-cran-testthat, r-cran-reshape2, r-cran-kableextra, r-cran-colorspace, r-cran-magrittr, r-cran-vdiffr, r-cran-httptest, r-cran-rmdformats, r-cran-gtable Filename: pool/dists/noble/main/r-cran-tcpl_3.3.1-1.ca2404.1_all.deb Size: 2194204 MD5sum: 531b61ed4005f2816fe34666368127fc SHA1: b9f6a6d97fa2073a9dc398863a67a5af7fdad9ae SHA256: 582817ec13e5d77b2ea12a9f605c678f8beca3d458a35f32bd37a844c92d774d SHA512: c1d47f2ba8583ba0f54dfe754fc42d240ab8e7cd20b7b23d7fa274d1df27b2c928f922a84286a07b4a9af60a889625edb07bfb4a29d76e3038391a14bc0f4852 Homepage: https://cran.r-project.org/package=tcpl Description: CRAN Package 'tcpl' (ToxCast Data Analysis Pipeline) The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, 'invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but 'tcpl' can be used to support diverse chemical screening efforts. Package: r-cran-tcplfit2 Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-rcolorbrewer, r-cran-stringr, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-dt, r-cran-data.table, r-cran-tcpl, r-cran-prettydoc, r-cran-testthat, r-cran-here, r-cran-htmltable, r-cran-tidyr, r-cran-dplyr, r-cran-gridextra, r-cran-rmdformats Filename: pool/dists/noble/main/r-cran-tcplfit2_0.1.9-1.ca2404.1_all.deb Size: 2332810 MD5sum: c495475b65ff3b601a466e4108ded0f0 SHA1: 38568e6d4753fdc40f4aaa03b7ed64de945f7bca SHA256: 9a8bec35765ebf2ec7e4d43f7097929090b14cab4a234a56773b45cb9b3a5186 SHA512: 3cf5a60b89b0b9213c6bbbe32c3d50df349755dc11c16c0e82b2de4d2e8384c46ceabba88f9761e0c9cdd460bb93360f1ccd370adeba4cc5389329dd9604f418 Homepage: https://cran.r-project.org/package=tcplfit2 Description: CRAN Package 'tcplfit2' (A Concentration-Response Modeling Utility) The tcplfit2 R package performs basic concentration-response curve fitting. The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts. Package: r-cran-tcpmor Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-semipar Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tcpmor_1.0-1.ca2404.1_all.deb Size: 25132 MD5sum: 792995e9555ad5484fe15e298259cdc6 SHA1: 2e5b9b8c01bf8ad269edbbf93b08d43230a853eb SHA256: 7cc36bea4f9c73a939cd29e8e4822c92739b1a9fb16d591e496d7adb4b3bbbee SHA512: 4d1f51d00e4401532da0e509daf3e3f2f6b229ca654899dd73f5d811b292415407cc4415e37d5aa00780c3706ecae1a8a6b0633c97976a2fd1ef95e9ca0997cf Homepage: https://cran.r-project.org/package=TCPMOR Description: CRAN Package 'TCPMOR' (Two Cut-Points with Maximum Odds Ratio) Enables the computation of the 'two cut-points with maximum odds ratio (OR) value method' for data analysis, particularly suited for binary classification tasks. Users can identify optimal cut-points in a continuous variable by maximizing the odds ratio while maintaining an equal risk level, useful for tasks such as medical diagnostics, risk assessment, or predictive modeling. Package: r-cran-tcprepdesigns Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-tcprepdesigns_0.0.2-1.ca2404.1_all.deb Size: 42966 MD5sum: b2ba6d8169772d4152c37bdda0184fb5 SHA1: eee8e86c3b62ce62d66fab7b16ed7e81ee0aa11d SHA256: 466202c4fb3c8932209108601ff4212c2b04f6a81897c00da266fab308f7a8bf SHA512: f524085dc25e0887704f32e60a353f82154ec5703904349e2965f5bf074bd1ecfe3babd3fe06353e9420f6c4fb69f1071cd8d01d2b135662025d72924e6d905a Homepage: https://cran.r-project.org/package=TCpRepDesigns Description: CRAN Package 'TCpRepDesigns' (Partially Replicated Test-Control Designs for Early GenerationVarietal Trials) Provides functions for generating partially replicated (p-rep) test-control designs for early generation varietal trials conducted across multiple environments. The package implements three construction methods for obtaining p-rep test-control designs with one or more control treatments. The package extends the partially replicated design framework of Vinaykumar et al. (2026) to accommodate test-control comparisons in breeding trials. Functions are provided for generating randomized and non-randomized layouts and for displaying the design parameters and treatment allocations for each environment. The proposed designs are useful for large-scale varietal evaluation trials where a large number of test lines are assessed under limited experimental resources. Package: r-cran-tcrconvertr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-mockery Filename: pool/dists/noble/main/r-cran-tcrconvertr_1.0-1.ca2404.1_all.deb Size: 78530 MD5sum: 806b6b5f0a639c8e130e55bc1ab23caf SHA1: fb61b3ff7599b80bc14eb88e5df2cf0dbac062da SHA256: c3900b6fa8f1ede49c6a9b0fd34a8b7576a21cf783d8c2662abd92c5ad721000 SHA512: c76d03ddd3f0145bbd724a99820d5bab718076259f986364802585517a5f06bfc23fabb4065896c2f381be7f272739208834b9ef02544f0a5c1dde385ed59089 Homepage: https://cran.r-project.org/package=TCRconvertR Description: CRAN Package 'TCRconvertR' (Convert TCR Gene Names) Convert T Cell Receptor (TCR) gene names between the 10X Genomics, Adaptive Biotechnologies, and ImMunoGeneTics (IMGT) nomenclatures. Package: r-cran-tcxr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tcxr_0.1.0-1.ca2404.1_all.deb Size: 19754 MD5sum: 38fba7edb899c66792ee260c66a80cb0 SHA1: 74e3d552768239f16f2be287d3c2f3069ca8c4a4 SHA256: 7ad92165b496b60c141c685114e8af1467d09cdf4f5dc80ef7bff3ddc9f78168 SHA512: 1b367bc508b2c4564aa697e072a887890d8f7f806fe8bcf807fe29d718f0839e377a2ed0453ba7b3f6bd202f4ffa28faa0f4e4e06d72bc00b189d14055c0a0bd Homepage: https://cran.r-project.org/package=tcxr Description: CRAN Package 'tcxr' (Parse and Analyze TCX Files) Framework provides functions to parse 'Training Center XML (TCX)' files and extract key activity metrics such as total distance, total time, calories burned, maximum altitude, and power values (watts). This package is useful for analyzing workout and training data from devices that export 'TCX' format. Package: r-cran-td Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcppsimdjson Suggests: r-cran-tinytest, r-cran-xts Filename: pool/dists/noble/main/r-cran-td_0.0.7-1.ca2404.1_all.deb Size: 95528 MD5sum: 939a6c10b48ba51262ec0ea4c30e751b SHA1: 36a98d48a15ccd9daefa6778ade5951db3e64616 SHA256: 54823d8cf7ba71fec1b85c8d572bacea7938be6e53d86d0a1bccd3d4208eee75 SHA512: 3977380011d01cd2c4301927a52d08496f5f63ac0a7c7ef311cc6f74860136038a5b252ad3b8775b8b5ee49f98f96f0740a4fb2c9c8ef7f339abd6b61dbcef30 Homepage: https://cran.r-project.org/package=td Description: CRAN Package 'td' (Access to the 'twelvedata' Financial Data API) The 'twelvedata' REST service offers access to current and historical data on stocks, standard as well as digital 'crypto' currencies, and other financial assets covering a wide variety of course and time spans. See for details, to create an account, and to request an API key for free-but-capped access to the data. Package: r-cran-tdarec Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-recipes, r-cran-dials, r-cran-rlang, r-cran-vctrs, r-cran-scales, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-cli, r-cran-ripserr, r-cran-tda, r-cran-tdavec, r-cran-testthat, r-cran-modeldata, r-cran-tdaunif, r-cran-knitr, r-cran-rmarkdown, r-cran-tidymodels, r-cran-ranger Filename: pool/dists/noble/main/r-cran-tdarec_0.2.1-1.ca2404.1_all.deb Size: 887398 MD5sum: d6f8df5cd615e0f5147d6923f278053b SHA1: c090ed8b061f9de6adde3a97124aac0b5fe84c2f SHA256: 538d59c8c9e6a01626906f307d582ef732264be0612ba705432628c70c6e71b6 SHA512: d5912bdab900313757793dd44db54da6e11cf13850821c4c001f354ebfb9bfc8a5c6d973a7e606e97425ed4c0ff9eaecd6da6751eb842f1989321a1c26743c3d Homepage: https://cran.r-project.org/package=tdarec Description: CRAN Package 'tdarec' (A 'recipes' Extension for Persistent Homology and ItsVectorizations) Topological data analytic methods in machine learning rely on vectorizations of the persistence diagrams that encode persistent homology, as surveyed by Ali &al (2000) . Persistent homology can be computed using 'TDA' and 'ripserr' and vectorized using 'TDAvec'. The Tidymodels package collection modularizes machine learning in R for straightforward extensibility; see Kuhn & Silge (2022, ISBN:978-1-4920-9644-3). These 'recipe' steps and 'dials' tuners make efficient algorithms for computing and vectorizing persistence diagrams available for Tidymodels workflows. Package: r-cran-tdaunif Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-tdaunif_0.2.0-1.ca2404.1_all.deb Size: 346598 MD5sum: 8e64733644d55e7550b47784805cd597 SHA1: 017ff1c370fd1e1ccb36d97f1df5acd6169841b8 SHA256: 62f4c4cb7e2cdb42dd16a9a3d488ac641f7429532addadae14dcfd0e512c81a5 SHA512: 3e2d4add33c6bf0fb2157aad8118822e12b2174c6c87983c838e6dbdc6ff80d63931e12a84fbd2a9af2b0bbab3d30d8c83f4beaffd802a9276ff7248e70dbc74 Homepage: https://cran.r-project.org/package=tdaunif Description: CRAN Package 'tdaunif' (Uniform Manifold Samplers for Topological Data Analysis) Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) , and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) . 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Package: r-cran-teal.modules.clinical Architecture: all Version: 0.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5099 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-teal, r-cran-teal.picks, r-cran-teal.transform, r-cran-tern, r-cran-broom, r-cran-bslib, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-formatters, r-cran-ggplot2, r-cran-ggrepel, r-cran-lifecycle, r-cran-rlistings, r-cran-rmarkdown, r-cran-rtables, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinyvalidate, r-cran-shinywidgets, r-cran-teal.code, r-cran-teal.data, r-cran-teal.logger, r-cran-teal.reporter, r-cran-teal.widgets, r-cran-tern.gee, r-cran-tern.mmrm, r-cran-tidyselect, r-cran-vistime Suggests: r-cran-forcats, r-cran-knitr, r-cran-logger, r-cran-lubridate, r-cran-nestcolor, r-cran-pkgload, r-cran-roxy.shinylive, r-cran-rvest, r-cran-shinytest2, r-cran-styler, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-teal.modules.clinical_0.14.0-1.ca2404.1_all.deb Size: 2654134 MD5sum: d1db3759cfd5a591b34d95631037adf0 SHA1: 0aa8cc3bad7a7d237a09402644db46d1750092b2 SHA256: f674e487eb9d06f66d5fe3b1bae09eb4b5993969464059ce0b8fd40e40910d5a SHA512: 1cadbb7a22433f01c2f89383bb472b3da33c5681e539b4e4b7f10378dbbc1ec74201749a3d90800ac42e9590d71b843b6a7e2419be25cfd0b6877e4cfdfbc5ee Homepage: https://cran.r-project.org/package=teal.modules.clinical Description: CRAN Package 'teal.modules.clinical' ('teal' Modules for Standard Clinical Outputs) Provides user-friendly tools for creating and customizing clinical trial reports. 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Package: r-cran-teal.picks Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bsicons, r-cran-checkmate, r-cran-dplyr, r-cran-htmltools, r-cran-logger, r-cran-rlang, r-cran-shiny, r-cran-shinywidgets, r-cran-teal, r-cran-teal.code, r-cran-teal.data, r-cran-teal.logger, r-cran-tidyselect, r-cran-yaml Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rvest, r-cran-shinytest2, r-cran-teal.transform, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-teal.picks_0.3.1-1.ca2404.1_all.deb Size: 269986 MD5sum: d9200c0dc0caa423387355e6fea3c292 SHA1: 057c28a7bfb150e3e8f60cd858f1808d3a3e6dd5 SHA256: ba5d97afb40ac93440b2230b04eea34a3d32d8aed5d1d1e3518f839eaa9ee382 SHA512: 01c1ad031e755b28d23536181af9be774e00c48a09e7337087e19e22607cd265926d8cb4e4abd8558c6e40e2276bc24bbf3e1200d05c9c76ccc5abfd2a7490ee Homepage: https://cran.r-project.org/package=teal.picks Description: CRAN Package 'teal.picks' (Dataset and Variable Picker and Merge Module for 'teal'Applications) Allows users to interactively select datasets, variables, and values within 'teal' applications using a 'tidyselect'-style interface. 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Enables the manipulation of application layout and plot or table settings. Package: r-cran-teal Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2500 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny, r-cran-teal.data, r-cran-teal.slice, r-cran-bsicons, r-cran-bslib, r-cran-checkmate, r-cran-cli, r-cran-htmltools, r-cran-jsonlite, r-cran-lifecycle, r-cran-logger, r-cran-rlang, r-cran-shinyjs, r-cran-teal.code, r-cran-teal.logger, r-cran-teal.reporter, r-cran-teal.widgets Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mirai, r-bioc-multiassayexperiment, r-cran-r6, r-cran-renv, r-cran-rmarkdown, r-cran-roxy.shinylive, r-cran-rvest, r-cran-shinytest2, r-cran-shinyvalidate, r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-teal_1.2.1-1.ca2404.1_all.deb Size: 1454200 MD5sum: c9ed7616a7dd04c10d31ef6022468e9c SHA1: 182bdb2fb5957f6f4153b5c5e83fbbf978758d27 SHA256: 640590fd26e4ed6058674a700b589909b4f3fec4e979c49877b5ac097728bc37 SHA512: 7998e78d74dcb69e6c679101b07892e6e5c1d5d76fea9f0bedf26ad2539627d9f6f14988e473d45a345d8fa4f41512ee49c0513fc0d61581b10bddd8b397221b Homepage: https://cran.r-project.org/package=teal Description: CRAN Package 'teal' (Exploratory Web Apps for Analyzing Clinical Trials Data) A 'shiny' based interactive exploration framework for analyzing clinical trials data. 'teal' currently provides a dynamic filtering facility and different data viewers. 'teal' 'shiny' applications are built using standard 'shiny' modules. Package: r-cran-tealeaves Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-units, r-cran-checkmate, r-cran-crayon, r-cran-dplyr, r-cran-furrr, r-cran-glue, r-cran-magrittr, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-stringr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tealeaves_1.0.7-1.ca2404.1_all.deb Size: 431490 MD5sum: 591cf038a5fe317262b8e770e7f58afd SHA1: 178c356a34632e59aac8eebcb937053c0e570c3d SHA256: affe512c4aeedce29d22ce96d6805eb1d9a7d19fba750334bb97650d26d4e3bd SHA512: cc51ba303ab8ad0759fda72ccc428df67cbcaed41978b23c42e3d3e443afb33e213307ba42de6c8b8287d1ab31ce078e2a4e2851bc15208e31ab15a446310e69 Homepage: https://cran.r-project.org/package=tealeaves Description: CRAN Package 'tealeaves' (Solve for Leaf Temperature Using Energy Balance) Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately as in Foster and Smith (1986) . It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) . Package: r-cran-team Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-ggplot2, r-cran-ks Filename: pool/dists/noble/main/r-cran-team_0.1.0-1.ca2404.1_all.deb Size: 37436 MD5sum: 2f1ab84baf5398fca3529e9fbb73ba0e SHA1: a6393b5068984072ae205272fa2e74dff7b35251 SHA256: 26352d2a4936fe29cbab8e3648179fdf053c39da19e5c6d577c9327ad80f1591 SHA512: 6fee3fb99bd2fb4e8c091cb7428d12ecaec3136f50dcf6ed8477024057fc1f9e8df5ef2312c2fab36e930a871eaaa3ba1d879d48e5b9e87ac06f54cc8aa1dd90 Homepage: https://cran.r-project.org/package=TEAM Description: CRAN Package 'TEAM' (Multiple Hypothesis Testing on an Aggregation Tree Method) An implementation of the TEAM algorithm to identify local differences between two (e.g. case and control) independent, univariate distributions, as described in J Pura, C Chan, and J Xie (2019) . 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Statistical algorithms to evaluate the modeling results compared with the observed data. Provides plots to visualize the results. Methods described in Stephan et al. (2023) and Wdowinski (1998) . Package: r-cran-teda Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-teda_0.1.1-1.ca2404.1_all.deb Size: 33426 MD5sum: 977cd6e7783868f6386d73dfcbda4eeb SHA1: 6524ad13a94d1ff3086c97018ef79430a539b789 SHA256: 8ac906e087849b62cae3ca25d5e51c89a064b9c0f8ad0334ff26413d1c9f2014 SHA512: f56950e5518aa3e9523aeb543253cc33a2105c49b6f4a4030b394a66ef5f787cd2685ece1695929042730d3d32925aa975d512f1c6c3e1639d1c761a99abb57e Homepage: https://cran.r-project.org/package=teda Description: CRAN Package 'teda' (An Implementation of the Typicality and Eccentricity DataAnalysis Framework) The typicality and eccentricity data analysis (TEDA) framework was put forward by Angelov (2013) . It has been further developed into multiple different techniques since, and provides a non-parametric way of determining how similar an observation, from a process that is not purely random, is to other observations generated by the process. This package provides code to use the batch and recursive TEDA methods that have been published. Package: r-cran-tehtuner Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-party, r-cran-glmnet, r-cran-rdpack, r-cran-rpart, r-cran-stringr, r-cran-superlearner, r-cran-randomforestsrc, r-cran-earth, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tehtuner_0.3.0-1.ca2404.1_all.deb Size: 189216 MD5sum: 2ace728b1f15275a5ede27f396b5fa17 SHA1: 372d858c7c15bbb39e7a4b9c1175b23e7d61e40a SHA256: ba1e4c3b1fb01f425cdb586db779f7c675083949934405bf5421c5d907a31448 SHA512: 6682e5a72121b5c354c84a5d28bd62a7d7b0ebcf10819dd2e0762e38a3e880231bcdbfdd9712ef7d4d7ec375dee51f209ab9cdcee37e88205f42e3e65a46f1c1 Homepage: https://cran.r-project.org/package=tehtuner Description: CRAN Package 'tehtuner' (Fit and Tune Models to Detect Treatment Effect Heterogeneity) Implements methods to fit Virtual Twins models (Foster et al. (2011) ) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) . Package: r-cran-tejapi Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-tejapi_1.0.1-1.ca2404.1_all.deb Size: 23624 MD5sum: 599c9997f1dc897f1ba096b52ed741a8 SHA1: 6236aded190532db39ff8604188a49622cea4dfd SHA256: 2168c2f044938e88111ede63bab99b1317748a706947f78a0238298be1da7952 SHA512: 5c65b3c595d7d26940d6ce2f1e079a5a07281fef325b5742c9fc58f143b5828dd7444815d43b6d60980ce5b99cbc231422761711520e6077fb81ba48788f724a Homepage: https://cran.r-project.org/package=Tejapi Description: CRAN Package 'Tejapi' (API Wrapper for Taiwan Economic Journal Data Service) Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to . Package: r-cran-tejor Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-jsonlite, r-cran-digest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tejor_0.2.2-1.ca2404.1_all.deb Size: 47150 MD5sum: e826375dba8641b1574b55d4d72c5184 SHA1: 1af9f1f05915222060884709d7bd782174e746a0 SHA256: 5a80c637572fec5f805177278b8291164c7bda8b52169afd65496bda96ab69c6 SHA512: f9a5d43f42517cf130aab7549040528e6a11d23eebd06d71483987fca60377643af13c9fee54c5e4e9b70073d2ecfa86011ce2c753a99a7cb4b660d89ccf831a Homepage: https://cran.r-project.org/package=tejoR Description: CRAN Package 'tejoR' (Statistical Harmonization of Territorial Series Across ChangingGeographies) Builds, validates, seals and applies weighting matrices ('crosswalks') to carry statistical series across changing zoning systems, such as the transition from the 112 Unidades de Planeamiento Zonal (UPZ) to the 33 Unidades de Planeamiento Local (UPL) in Bogota (Decree 555 of 2021). Implements the 'tejo-crosswalk/0.2' specification shared with the 'Python' package 'tejo': 'sha256'-sealed artifacts, non-negative weights that sum to one for each source unit, dasymetric weighting with vector ancillary data via 'sf', rates that are never interpolated directly, and missing values that propagate instead of being silently imputed. Methods: Tobler (1979) ; Mennis (2003) . Descripcion en espanol: construye, valida, sella y aplica matrices de ponderadores ('crosswalks') para trasladar series estadisticas entre mallas geograficas que cambian, como la transicion de UPZ a UPL en Bogota (Decreto 555 de 2021): integridad por 'sha256', pesos no negativos que suman uno por unidad fuente, metodo dasimetrico con ancilar vectorial via 'sf', tasas que nunca se interpolan directamente y valores faltantes que se propagan en lugar de imputarse. Package: r-cran-telegram.bot Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httpuv, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-r6 Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-promises, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-telegram.bot_3.0.2-1.ca2404.1_all.deb Size: 542296 MD5sum: cace525b775ce04e0550699d2a37bcb2 SHA1: f25d86f5e0a945b1c6d44cf0e8ab2dfd3bdd7002 SHA256: 7d120e27513dfa0f98436245a74256accda000aadcad5f4074ac1e96328868cc SHA512: 8c15a6ee0430c51011320be6415671c8c7a93a786c0545df549e674513ab87895d15d4b2fae171fa09f029fae2a328bbb80d1ce587b65b1b116cf48fb28ca982 Homepage: https://cran.r-project.org/package=telegram.bot Description: CRAN Package 'telegram.bot' (Develop a 'Telegram Bot' with R) Provides a pure interface for the 'Telegram Bot API' . 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Package: r-cran-telegram Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-curl Filename: pool/dists/noble/main/r-cran-telegram_0.7.1-1.ca2404.1_all.deb Size: 182002 MD5sum: 1b46c5634cf6f21febfbc61040a21f49 SHA1: 0c7dee5b0a231b5b777dd9e98c83a86b6fbade9f SHA256: de6545e48763f69a3af8c7c9b2150f862f8623b95635e4637fc78d9467f92be5 SHA512: 9fff5be95d0b5d36be06e74458a8823afe8190d84a17eb01046d10146bd9d8ab83216598aff0cfa6a7dcdea142478c50650b86780802e86d6c1268361d8e2c9d Homepage: https://cran.r-project.org/package=telegram Description: CRAN Package 'telegram' (R Wrapper Around the Telegram Bot API) A simple wrapper around the Telegram Bot API () to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone). Package: r-cran-telemetr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1673 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-zoo, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rerddap, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-telemetr_1.0-1.ca2404.1_all.deb Size: 1493522 MD5sum: 2d908a650bdfdd6d60ba6eb467ff75ee SHA1: 03439bff55aff78a02a7745719c84371df515bd2 SHA256: 5ca919760be7ba0b9a49516277cc034b411b0cadab04db6e6d7e4b9b05c537b9 SHA512: c884b1a4e17ca9490cc8afedefed41bab9563bfbf7e7dab4ffe4311b0eb12494aa4758c93370406f90c6121ecdd2b317497e1d09038345f72a3e80cc3e15c7be Homepage: https://cran.r-project.org/package=telemetR Description: CRAN Package 'telemetR' (Filter and Analyze Generalised Telemetry Data from Organisms) Analyze telemetry datasets generalized to allow any technology. 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Package: r-cran-telescope Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-bayesm, r-cran-dirichletreg, r-cran-extradistr, r-cran-mcmcpack, r-cran-mvtnorm Suggests: r-cran-invgamma, r-cran-klar, r-cran-knitr, r-cran-mclust, r-cran-polca, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-telescope_0.2-2-1.ca2404.1_all.deb Size: 1246910 MD5sum: 79e8940185f11f456e510f667c6899b1 SHA1: c6d52c4c5faf0a31c00d394aff77174a16b5f695 SHA256: bf1ec8bb7d94b9a4a1b81426f5895aa671641a071b9026f31ea81b3d7d5397c0 SHA512: f3240087bca9c5d4462876e0a9bd08b4e6b666a321c6f4fb4d2471e522d48b620092d73a752923fee8685aa243cca298e1fd8792ad2537992fbbeac2fd43a806 Homepage: https://cran.r-project.org/package=telescope Description: CRAN Package 'telescope' (Bayesian Mixtures with an Unknown Number of Components) Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) , Malsiner-Walli et al. (in press) and Malsiner-Walli et al. (2026) . 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Package: r-cran-telraamstats Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-paletteer, r-cran-purrr, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-telraamstats_1.1.2-1.ca2404.1_all.deb Size: 2332022 MD5sum: 52b42775c9c0bba1cdf2ee69aefbbc03 SHA1: be31a5b4299f99576f7e31649b0f4b2547d3f08a SHA256: a5a9f731d7bfc0f4f296093def6c8299bc828a839235ad05ea19b26748276683 SHA512: ef715d1fec43e7dfa7d2e3713482d56e24d9656c7f331cb6d09926450fada7be2682d653261b2d6c2321c5d3ad1f279f5841779cf0716039e8e21ba728c9d7e0 Homepage: https://cran.r-project.org/package=telraamStats Description: CRAN Package 'telraamStats' (Retrieval and Visualization of Mobility Data from 'Telraam'Sensors) Streamline the processing of 'Telraam' data, sourced from open data mobility sensors. These tools range from data retrieval (without the need for API knowledge) to data visualization, including data preprocessing. Package: r-cran-tempcont Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-tempcont_0.1.0-1.ca2404.1_all.deb Size: 49034 MD5sum: 2cb3976f2759129548da594f5e9f24fb SHA1: 6d90831bb03e93cd59f882ecfa268c97c2d90b72 SHA256: d21bd349c32a40c155623694234d43ec8a1aafb80fd05d51530eae375a0deb64 SHA512: fe978db7fca83dc555e4c4ca5bf18e02f3d4047a6296be20b509ff98d3f84e108332c86197766127e2dfbcfc2fe10e8d064bbb9c45c885245a11cb67e535a300 Homepage: https://cran.r-project.org/package=TempCont Description: CRAN Package 'TempCont' (Temporal Contributions on Trends using Mixed Models) Method to estimate the effect of the trend in predictor variables on the observed trend of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et al. (2017 and 2019) . Package: r-cran-tempdisagg Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tsbox, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-xts Filename: pool/dists/noble/main/r-cran-tempdisagg_1.2.0-1.ca2404.1_all.deb Size: 389032 MD5sum: 58fa02d3454429f799d00c40f8de0bf5 SHA1: 1da9ae64e1fe57f02578a92565229277e97fffd0 SHA256: a492c375e864c5e17fc91e5af6e392d3c80694223752ebe7bbc0ea9de8520258 SHA512: 4509a7c32566570fe18fb6d32f366e41e461f3eb849253a4803d2ac083c3b5e7cb67e33f465cff7270fbf19fd5dbff13e62f80a7feab6a0b336162e47fbccca8 Homepage: https://cran.r-project.org/package=tempdisagg Description: CRAN Package 'tempdisagg' (Methods for Temporal Disaggregation and Interpolation of TimeSeries) Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series, where either the sum, the mean, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and Steiner (2013) . Supports most R time series classes. Package: r-cran-temper Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-torch, r-cran-purrr, r-cran-imputets, r-cran-lubridate, r-cran-ggplot2, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-temper_1.1.1-1.ca2404.1_all.deb Size: 117540 MD5sum: da6e6db57539f7d9e97b9c02e3984d21 SHA1: e46489e30f55ec46f72466517d32086bd066b74f SHA256: 2017817a37d75aee41d576c41be3ec544f7939da67a8daf32af307dfca3a5c56 SHA512: e238d3f1609cd941d3851e4d87a757d9ef09f4912f2d7288428e15fa8ba7aa0dd036b1d3642f830abbf9726185f4ba07c768d2492d78f62b25ea2f6668422898 Homepage: https://cran.r-project.org/package=temper Description: CRAN Package 'temper' (Temporal Encoder-Masked Probabilistic Ensemble Regressor) Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing. Package: r-cran-temperatureresponse Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-rootsolve, r-cran-minpack.lm, r-cran-aiccmodavg, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-temperatureresponse_0.2-1.ca2404.1_all.deb Size: 70372 MD5sum: 1cac1723d3e2e674ff1c8b99dbde6a0e SHA1: ea24d32e24e3d2e24ad74b150dcede02106f9bfa SHA256: 6f32346ee9cd560091cd6f2ed694c0d35df1ae684e9d80472da9cdd546ad38b2 SHA512: 494dfb42cbf32fca1a48316e4000e93b20b88892ddb37a350bebc86c45e463fb3a7f67f6d9d48324905260aa6ee782968836954b8353df0e9297e84afe838e93 Homepage: https://cran.r-project.org/package=temperatureresponse Description: CRAN Package 'temperatureresponse' (Temperature Response) Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) . Package: r-cran-templateicar Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-fmritools, r-cran-fmriscrub, r-cran-foreach, r-cran-ica, r-cran-matrix, r-cran-matrixstats, r-cran-pesel, r-cran-squarem Suggests: r-cran-ciftitools, r-cran-excursions, r-cran-rnifti, r-cran-oro.nifti, r-cran-gifti, r-cran-covr, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-templateicar_0.11.3-1.ca2404.1_all.deb Size: 509370 MD5sum: 4e4c3dc63ec260060225047c94871c7f SHA1: 1d311283f2576d4ea53026be2cb448305436ad0f SHA256: 1b80b7a8973d3ac326e3ae1dde1f8d8832651d4b1c99874e91ed0d9f9f7fc876 SHA512: a699f213a4d448a0d7fdf62829b135650b66e42e989f337657faeb9b19b8e9ce0627070cd5a1d5d6c6d3e11b003d6d96b402fe92fda9195921e91d35ff05ac75 Homepage: https://cran.r-project.org/package=templateICAr Description: CRAN Package 'templateICAr' (Estimate Brain Networks and Connectivity with ICA and EmpiricalPriors) Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) and the spatial template ICA model proposed in Mejia et al. (2022) . Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Note, this package has been deprecated and superseded by 'BayesBrainMap', which includes model improvements and new names for the core functions. Package: r-cran-templates Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-templates_0.4.0-1.ca2404.1_all.deb Size: 29482 MD5sum: 69001c8139bc26a6f722c20774867b1a SHA1: 7d7fd7fe301d69f44b722fe8114e85f2a5f845af SHA256: 693ec2b6e56327d85ae2cb721e6d0565ebabc8d853be37d843af9ca1e6915abc SHA512: ec94952e1276211ac94cdc5f8e4cc0832f389d8ca8aaa1d1681620c44960f4a26826b0f2049c29a4609b11f3fa1859cacea8578282616e089735dfbfa8c715e8 Homepage: https://cran.r-project.org/package=templates Description: CRAN Package 'templates' (A System for Working with Templates) Provides tools to work with template code and text in R. It aims to provide a simple substitution mechanism for R-expressions inside these templates. 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Package: r-cran-templr Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-remotes, r-cran-xml2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-future, r-cran-mco, r-cran-praise Filename: pool/dists/noble/main/r-cran-templr_0.2-4-1.ca2404.1_all.deb Size: 88592 MD5sum: 701988fa1567d68f80007296ba0e3442 SHA1: 2d18fc3b832833d295d3e45ad29778f2cbf8cfa0 SHA256: 51b39266688a7cfe49fde2123711ae6f299b16169ecfb9f145e5bf8fe06bec0c SHA512: d311033532a7e47473ec2691f226b20b4d89a88bb7f3bef4769edc7bf599e44a5211095625973282fb855910e37a8631535d27b6435a56396e911a8dceb56e2b Homepage: https://cran.r-project.org/package=templr Description: CRAN Package 'templr' (MASCOTNUM / RT-UQ Algorithms Template Tools) Helper functions for MASCOTNUM / RT-UQ algorithm template, for design of numerical experiments practice: algorithm template parser to support MASCOTNUM specification , 'ask & tell' decoupling injection (inspired by ) to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R, basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim(). Package: r-cran-tempo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tempo_0.1.0-1.ca2404.1_all.deb Size: 44258 MD5sum: 5c3936cf718954ce270e5bff3aac95c6 SHA1: 23f97d4e6bd510dc34963d4d2b0941b6ff8506f6 SHA256: dd7dc5eb96f0ba11557a0d8fa51e23c291926fb6af29b0dda57603d0092f5ce7 SHA512: 2bb67e6bb469a0510393b30ccb123447e16bbf1884af2736caec4b4781063def9293d2b810ec053d020a3e8063265e6ad32fa82781d767114a0e131e905c5bee Homepage: https://cran.r-project.org/package=TEMPO Description: CRAN Package 'TEMPO' (Tools for Romania's National Institute of Statistics Tempo Data) Download table metadata and data from the Romanian National Institute of Statistics' TEMPO Online database. Tables can be listed in Romanian or English and downloaded as CSV files. Package: r-cran-tempodisco Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rwiener Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tempodisco_2.1.0-1.ca2404.1_all.deb Size: 516934 MD5sum: 4c98f34eb50bfcf3eca171eb37829b0e SHA1: bb36abbb155610056c3b4920bca3479fb145247e SHA256: 6a73af0c4f7a0decba44ea87195dddec3d6371aca01e9dcf58972ff4a4c2a75e SHA512: 7097b9e3fe49b23d9a553be7be7e862650eaf8a388bc7845667957b6971d40b7262b151025a0af15f11005c0664e0e53f3bd614ef8d9cdb9628e7999f2c2882d Homepage: https://cran.r-project.org/package=tempodisco Description: CRAN Package 'tempodisco' (Temporal Discounting Models) Tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, ), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, ), scoring questionnaires according to the methods of Kirby et al. (1999, ) and Wileyto et al (2004, ), Bayesian model selection using a range of discount functions (Franck et al., 2015, ), drift diffusion models of discounting (Peters & D'Esposito, 2020, ), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, ) and ED50 (Yoon & Higgins, 2008, ). Package: r-cran-temporal Architecture: all Version: 0.3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-expint, r-cran-numderiv, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-temporal_0.3.0.2-1.ca2404.1_all.deb Size: 593504 MD5sum: f7921f9d0d6225a5da1e997a02a562eb SHA1: 777a892512698195d16a1059ee83192aedb0b3f8 SHA256: 449d5b81bc687f3f3238b637ed5d26c29fa3ff94efcc9d035330a8813f0ac796 SHA512: 465f78a54ee5dbcb7d8216dd82e57fd7b59a899b30dfba9634413b7df600f8d52b282e33841f1cd56ac5c33b83bc14697318874c3a43a3b108e57a32a9abd979 Homepage: https://cran.r-project.org/package=Temporal Description: CRAN Package 'Temporal' (Parametric Time to Event Analysis) Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull. Package: r-cran-temporalforest Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wgcna, r-cran-dynamictreecut, r-cran-flashclust, r-cran-glmertree, r-cran-partykit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-temporalforest_0.1.4-1.ca2404.1_all.deb Size: 69182 MD5sum: 8e12792ec56e6b4344070f3f38e27a81 SHA1: 5fdcf86d3a1fbc2e98e52e4edf664dc2d7d89231 SHA256: 7ec12ae5b05b406fef42b8213de8ffeffbcac9a161cc20ffced45a6c2288f810 SHA512: efbe06ce3675ce40378d13fb82c2e266130e5d11cd028086bdb42829d587dde3dae3f4759895d091bb440ed2e66c379c3dba421115fbb56c9b4fc8b5fafa76b3 Homepage: https://cran.r-project.org/package=TemporalForest Description: CRAN Package 'TemporalForest' (Network-Guided Temporal Forests for Feature Selection inHigh-Dimensional Longitudinal Data) Implements the Temporal Forest algorithm for feature selection in high-dimensional longitudinal data. The method combines time-aware network construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability selection using tree-based models. This package provides tools for reproducible longitudinal analysis, closely following the methodology described in Shao, Moore, and Ramirez (2025) . Package: r-cran-temporalgssa Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-temporalgssa_1.0.1-1.ca2404.1_all.deb Size: 25406 MD5sum: 78397a5e696fefb8a00b407955f0cd40 SHA1: e93825dcffa2d56ae3f0781779469f89dfda4fa7 SHA256: 53b77b6d8502d46dfe32f227816ad36953544fd7dedc23e58f60aa7e310ba6a8 SHA512: 24f4b1d31868fd9ab522dcae0a9e88a3a6f3c30590afe6c2d91020286295c4196c4c4c86d0c4e1b8bc7413aacb4a23d6a8c206e8673eb816731214b0d702d395 Homepage: https://cran.r-project.org/package=TemporalGSSA Description: CRAN Package 'TemporalGSSA' (Outputs Temporal Profile of Molecules from Stochastic SimulationAlgorithm Generated Datasets) The data that is generated from independent and consecutive 'GillespieSSA' runs for a generic biochemical network is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network. In this way 'TemporalGSSA', is a wrapper for the R-package 'GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. 'TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model is characterized by coefficients such as the slope, arbitrary constant, etc. The user must enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The coefficients of each linear model are averaged (mean slope, mean constant) and are incorporated into a metabolite-specific linear regression model as the dependent variable. The independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations. The generated "technical replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite. For SSA-generated datasets with varying simulation times 'TemporalGSSA' will generate a simulation time-dependent trajectory for each metabolite of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is (2022, Journal of Bioinformatics and Computational Biology) . The algorithm has been deployed in the following publications (2021, Heliyon) and (2016, Journal of Theoretical Biology) . Package: r-cran-temporalhazard Architecture: all Version: 1.1.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5075 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-lintr, r-cran-numderiv, r-cran-pkgdown, r-cran-quarto, r-cran-roxygen2, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-temporalhazard_1.1.0-1.ca2404.2_all.deb Size: 2783620 MD5sum: 4ec4c17835d94469f0baffea3b0db40d SHA1: 6ac1482bae2145fb4b07842b73ec03c3a93824f6 SHA256: 0477e93197f8770b17389e7edaaaf36aad2ff86b139d8adac4239aab44cdd872 SHA512: 5f4d99b1fea530a5e8a37a2b8d27edc8bffbc132505826d7551d378518ebcb6f55dd655be2a1050e4a620c7d368fb758593272440588d00fa46f818052722a4f Homepage: https://cran.r-project.org/package=TemporalHazard Description: CRAN Package 'TemporalHazard' (Temporal Parametric Hazard Modeling) Provides native R implementations of the multiphase parametric hazard model of Blackstone, Naftel, and Turner (1986) with a focus on behavioral parity, transparent numerics, and reproducible validation against reference outputs from the original 'C'/'SAS' HAZARD program, originally developed at the University of Alabama at Birmingham (UAB). The 'SAS'/'C' code and this R package are currently developed and maintained at The Cleveland Clinic Foundation, and the R code was wholly developed at The Cleveland Clinic Foundation. The generalized temporal decomposition family extends to longitudinal mixed-effects settings (Rajeswaran et al. 2018 ). The package is intentionally implemented in pure R first; performance-critical paths may later be accelerated with 'Rcpp' without changing the public interface. Package: r-cran-temporalmodelr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8685 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-deldir, r-cran-sf, r-cran-terra, r-cran-exactextractr Suggests: r-cran-fastcpd, r-cran-ggplot2, r-cran-hypervolume, r-cran-knitr, r-cran-mgcv, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scatterpie Filename: pool/dists/noble/main/r-cran-temporalmodelr_0.3.0-1.ca2404.1_all.deb Size: 5879634 MD5sum: cfa5ad44236fd10ea6e3c9633faa4313 SHA1: 0a76a6134b6fb0cdda5150ba884be816ba1d372e SHA256: a1943cda8121b4b4a832d69da0b4674c8622665940025cf5d6e878f4b92dc31b SHA512: bf62e30b1ce33be60de04fa2b25d2bc102c6f188819646ffa3575c0c529f8d49d11de5266e5038f1ea51f8789353e73b197345efda90d8bb65aab09848f7591a Homepage: https://cran.r-project.org/package=TemporalModelR Description: CRAN Package 'TemporalModelR' (Temporally Explicit Species Distribution Modelling) Increases the ease of implementing a temporally-explicit modeling methodology when building ecological niche and species distribution models. Provides functions to assist with three major steps of temporally-explicit models: (i) preprocessing species and environmental data and generating suitable background or pseudoabsence data, (ii) building a niche model and generating temporally-explicit predictions from that model, and (iii) model postprocessing to explore spatiotemporal trends in model predictions. Methodological and theoretical foundations are described in Ingenloff and Peterson (2021) , Franklin (2010, ISBN:9780521700023), Peterson et al. (2011, ISBN:9780691136882), Blonder (2018) , Senay et al. (2013) , and Li and Zhang (2024) . Package: r-cran-tempr Architecture: all Version: 0.10.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tempr_0.10.1.1-1.ca2404.1_all.deb Size: 253848 MD5sum: cfc66456cc0c2f98cfbc79119529b746 SHA1: ea6e6807266bd7abbd313221f38246974a0c4c1f SHA256: fbe5871fdff01a26919d498c61b60440306d562a4474885dba11b3fa017778f2 SHA512: 9bebb3d2b86b2403e1718a9039bdc9090b379535f9ffec71fbe288106bd6f3a042363d7434118fbc7189297cca44807428ba1ca720d8f01f54bc2672d19ce555 Homepage: https://cran.r-project.org/package=tempR Description: CRAN Package 'tempR' (Temporal Sensory Data Analysis) Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) , Castura, Baker, and Ross (2016) , and Pineau et al. (2009) . Package: r-cran-tempstable Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1063 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-doparallel, r-cran-foreach, r-cran-gsl, r-cran-hypergeo, r-cran-moments, r-cran-numderiv, r-cran-stabledist, r-cran-stableestim, r-cran-rootsolve, r-cran-vgam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-tempstable_0.2.2-1.ca2404.1_all.deb Size: 876152 MD5sum: 2b76eb3514df6a9a8a782d378ad3260c SHA1: 287551ea8d20dbbb928d9ee91b22299ad42402e1 SHA256: 2b86bab12fbb014de40b94af443e493d61667da38301cb5dc9dcde0991cc0b10 SHA512: ab081ec97441f35b2127228e2c371c4ba02ff32c995880760c5436475b88bc44c1d5e4530220c43bf80e3be17b9770256c1947a70df1386a9bb541583b549507 Homepage: https://cran.r-project.org/package=TempStable Description: CRAN Package 'TempStable' (A Collection of Methods to Estimate Parameters of DifferentTempered Stable Distributions) A collection of methods to estimate parameters of different tempered stable distributions (TSD). Currently, there are seven different tempered stable distributions to choose from: Tempered stable subordinator distribution, classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid decreasing TSD, and Kim-Rachev TSD. The package also provides functions to compute density and probability functions and tools to run Monte Carlo simulations. This package has already been used for the estimation of tempered stable distributions (Massing (2023) ). The following references form the theoretical background for various functions in this package. References for each function are explicitly listed in its documentation: Bianchi et al. (2010) Bianchi et al. (2011) Carrasco (2017) Feuerverger (1981) Hansen et al. (1996) Hansen (1982) Hofert (2011) Kawai & Masuda (2011) Kim et al. (2008) Kim et al. (2009) Kim et al. (2010) Kuechler & Tappe (2013) Rachev et al. (2011) . Package: r-cran-tempted Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-tempted_0.1.1-1.ca2404.1_all.deb Size: 3092902 MD5sum: a47f47420598603cd5c89baa7d01411f SHA1: 2f655ec0dd04597c8efa0783e386ca24d58fe6a9 SHA256: c00864ddaec9bf9a709f07b1f0c13b3d59f93742bb2d34c98b9cebe7572530d4 SHA512: c4a217c3dc248c51d0cc2aad8e5466e2cd9fa3b799190c3d2517aa79a8e464bd2357c14c525b0eeff19f0597458baa99b50c8d9b2cd1ac20e7bfc4d8f63c7473 Homepage: https://cran.r-project.org/package=tempted Description: CRAN Package 'tempted' (Temporal Tensor Decomposition, a Dimensionality Reduction Toolfor Longitudinal Multivariate Data) TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed. Package: r-cran-tendril Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1827 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-plyr, r-cran-reshape2, r-cran-magrittr, r-cran-scales, r-cran-plotly Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-tendril_2.0.4-1.ca2404.1_all.deb Size: 1258646 MD5sum: 0387f4b626ed0f148a3bc312045ca2df SHA1: 0254e25c80d080936b8b0fb5d53163780f2e4169 SHA256: 96898c6464d364736d8e9cedf3bcf31f6f2e1b2a2668d00b4786ed6ca959c3dc SHA512: 94a7ab2befb9266bc94dca68f88ea10450102bcfa022853adc34dd9adfb06da7482e22e343d56767c3483655d9b594fd2a25c58dac59b59cdaae0b9feb49b16f Homepage: https://cran.r-project.org/package=Tendril Description: CRAN Package 'Tendril' (Compute and Display Tendril Plots) Compute the coordinates to produce a tendril plot. In the tendril plot, each tendril (branch) represents a type of events, and the direction of the tendril is dictated by on which treatment arm the event is occurring. If an event is occurring on the first of the two specified treatment arms, the tendril bends in a clockwise direction. If an event is occurring on the second of the treatment arms, the tendril bends in an anti-clockwise direction. Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary of the incidence, significance and temporal aspects of adverse events in clinical trials" - JAMIA 2018; 25(8): 1069-1073 . Package: r-cran-tenispolar Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tenispolar_0.1.4-1.ca2404.1_all.deb Size: 21790 MD5sum: a648e5366b0447b3e470016a10709081 SHA1: 2f354bd9dac96adce6a84beef780903d3c0ff04d SHA256: 3a623ed1906e2de48e83efa42768fe8b39f52b9a76becf05549b0d748b7dcc42 SHA512: 9ff8cef7d47fac8bf2a93cd89c43d6388b0941b2c708e6c77a21b86e83a6febd9f34cf0a26f7b21351e26b17631c2466860a9977c08fa392a9b7787123aa7255 Homepage: https://cran.r-project.org/package=tenispolaR Description: CRAN Package 'tenispolaR' (Provides ZENIT-POLAR Substitution Cipher Method of Encryption) Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944). Package: r-cran-tenm Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2698 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-terra, r-cran-sf, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-rgl, r-cran-future, r-cran-tidyr, r-cran-furrr, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tenm_0.5.1-1.ca2404.1_all.deb Size: 1007562 MD5sum: 4d89a8a442d31838fb840c5d44fd2e23 SHA1: 1ce1b93b9323fb18e9fa5cb4b0f40f33056a8660 SHA256: f2249a09160250f908dd4ac494dbee09bba1816c197bf176e4ea20cd8da26743 SHA512: e898d92a984366fb0315ce4025debef22b29e70eaf0ba2dc32dd9f270b74d7d6dd9e9510374be9d20ef7b668a2c5dff14a1eda6b6c7a33f1ddf757587ef99b85 Homepage: https://cran.r-project.org/package=tenm Description: CRAN Package 'tenm' (Temporal Ecological Niche Models) Implements methods and functions to calibrate time-specific niche models (multi-temporal calibration), letting users execute a strict calibration and selection process of niche models based on ellipsoids, as well as functions to project the potential distribution in the present and in global change scenarios.The 'tenm' package has functions to recover information that may be lost or overlooked while applying a data curation protocol. This curation involves preserving occurrences that may appear spatially redundant (occurring in the same pixel) but originate from different time periods. A novel aspect of this package is that it might reconstruct the fundamental niche more accurately than mono-calibrated approaches. The theoretical background of the package can be found in Peterson et al. (2011). Package: r-cran-tensor Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tensor_1.5.1-1.ca2404.1_all.deb Size: 15422 MD5sum: e67bc890928a48f7ca0f1cf57ae1bed5 SHA1: 518224f534ed10d556fa15ef8f209e5e94fa5446 SHA256: dcf7845216f46a2ba4873d403d4fa24da4a30fff4a520f2a871d898dd644ad81 SHA512: 2b4fd9c7ec73a9cbc48423ac36bfc22a1f18c1125cdd74f4f838def017396e56399b09a8f13133e59877eeeb565dfe1106fc84cf2696ed6f3993afd641acdccd Homepage: https://cran.r-project.org/package=tensor Description: CRAN Package 'tensor' (Tensor Product of Arrays) The tensor product of two arrays is notionally an outer product of the arrays collapsed in specific extents by summing along the appropriate diagonals. 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The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling. Package: r-cran-tensorcomplete Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-tensorregress, r-cran-mass Filename: pool/dists/noble/main/r-cran-tensorcomplete_0.2.0-1.ca2404.1_all.deb Size: 87962 MD5sum: 29319f892f453139f808049f36e8fefe SHA1: 82c3e80392a53e33a16cee6c76df171215135ac6 SHA256: 0ec814bc85fde9f071a669241280707c413b5c9feacdbd5fe085e49bee216b26 SHA512: a87c40dcf20b75a16b2208f2e661c3eccf15b2a359972bb9b3e35533e718f9b81bf86a296ef7d14723dbc7fa1696e74ceaef51499e3b410b8ceeaea21c3d11d3 Homepage: https://cran.r-project.org/package=TensorComplete Description: CRAN Package 'TensorComplete' (Tensor Noise Reduction and Completion Methods) Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. 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Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. Package: r-cran-tensorpreave Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtensor, r-cran-mass, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tensorpreave_1.1.0-1.ca2404.1_all.deb Size: 965232 MD5sum: 1a4549d0aa45af2bbed7e99bceb5a47f SHA1: 0eca82dac3ae3597044da87530fffdcd587ec9ef SHA256: 66435601a3fcda7ab8d62e96df58a2832777e59aa50edad685225d51adf42690 SHA512: 092af8f76c4104d70557821dacd838691a0cee64cca2891eb344017a6c3d5da2ffe8d3ec7e601e6e377a77f9030208d8e934145fb5d501d8016fb1f95c6bf910 Homepage: https://cran.r-project.org/package=TensorPreAve Description: CRAN Package 'TensorPreAve' (Rank and Factor Loadings Estimation in Time Series Tensor FactorModels) A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) for more details. Package: r-cran-tensorregress Architecture: all Version: 5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-mass Filename: pool/dists/noble/main/r-cran-tensorregress_5.1-1.ca2404.1_all.deb Size: 539914 MD5sum: c94a2b117265e4445db772de3e5dcc4d SHA1: 1482a28b33a4a4d1ea277cdab5a073063c7e6f22 SHA256: 351d6871ea6e454d65e98cbcee447d82415616303a88ae8f1e91d6d0f23907da SHA512: 833bcba55718319ffe1b54bd624ce4587f21133004c82281df75ad6617c0f19e7a09efb95d339dc5d68543b99774a45982bf26423cc4f32dceedbbe0507feff5 Homepage: https://cran.r-project.org/package=tensorregress Description: CRAN Package 'tensorregress' (Supervised Tensor Decomposition with Side Information) Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 . Package: r-cran-tensortest2d Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-abind, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tensortest2d_1.1.2-1.ca2404.1_all.deb Size: 3417608 MD5sum: 9d3126a65f3b93d9ca797773ae0654ca SHA1: bbb9864414c44ed004a4b7410c7bbb61a4dd4dc5 SHA256: eef08bf52c5690892445415dd8ac7b7aa0aa3bc03e9f4faef74d6dba2c82aa07 SHA512: 9469fb72fc9a2913c7c9cb6a3e2ba8ea86be391c2ba2a18859f70516768fcc9bec9237d297ac2efc29eca5234d16a8db19095453e5022749262bb083d00cfaf2 Homepage: https://cran.r-project.org/package=TensorTest2D Description: CRAN Package 'TensorTest2D' (Fitting Second-Order Tensor Data) An implementation of fitting generalized linear models on second-order tensor type data. 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The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld, E., Kilmer, M., and Aeron, S. (2015) . Package: r-cran-tensorts Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tensor, r-cran-rtensor, r-cran-expm, r-cran-mass, r-cran-abind, r-cran-matrix, r-cran-pracma Filename: pool/dists/noble/main/r-cran-tensorts_1.0.3-1.ca2404.1_all.deb Size: 225962 MD5sum: 99ba15e5fc69e7cf64e9e4301d24ef1c SHA1: 986e43c5e9021a0a505a4a46a41fae083b3a3c56 SHA256: b2313bdff46b5cec6dfe96fe1e168ba938129595902cf91bbabd3d187c1cc09e SHA512: ce05e52a0d852ad9dd5d47542439de7f736cb9e916134612df73cd110b8d6914a6b6cf5a88b7a4adfe846a4de418000559d2f8e47e1c6f6fff58fe92af13c9ce Homepage: https://cran.r-project.org/package=tensorTS Description: CRAN Package 'tensorTS' (Factor and Autoregressive Models for Tensor Time Series) Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) , Chen et al (2020) , Chen et al (2020) , and Xiao et al (2020) . 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For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) and Gerard and Hoff (2016) . Package: r-cran-tepr Architecture: all Version: 1.1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5034 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2, r-cran-pracma, r-cran-ggrepel, r-cran-matrixstats, r-cran-rlang, r-cran-magrittr, r-cran-purrr, r-bioc-rtracklayer, r-bioc-genomicranges, r-bioc-genomeinfodb, r-cran-valr, r-cran-tibble, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tepr_1.1.17-1.ca2404.1_all.deb Size: 3728986 MD5sum: 5e3d47c6179ef4007b419e6fe8e0b76d SHA1: 1622cb85004c818343a3e8cbd7cfdf7632b3750d SHA256: a701a296cdd3e6e0aba93502a60b0be8e77dc9efdf1554550f684dcb6b0d4b9b SHA512: 4de4543c64ac7a4e77b913491185c015db6ec6b6df8bd78ae690cd3f2921da51f6f687112e16197b3363e16ae9f21cbd6e5933e0737710ada1230c0903dae533 Homepage: https://cran.r-project.org/package=tepr Description: CRAN Package 'tepr' (Transcription Elongation Profiling) The general principle relies on calculating the cumulative signal of nascent RNA sequencing over the gene body of any given gene or transcription unit. 'tepr' can identify transcription attenuation sites by comparing profile to a null model which assumes uniform read density over the entirety of the transcription unit. It can also identify increased or diminished transcription attenuation by comparing two conditions. Besides rigorous statistical testing and high sensitivity, a major feature of 'tepr' is its ability to provide the elongation pattern of each individual gene, including the position of the main attenuation point when such a phenomenon occurs. Using 'tepr', users can visualize and refine genome-wide aggregated analyses of elongation patterns to robustly identify effects specific to subsets of genes. These metrics are suitable for internal comparisons (between genes in each condition) and for studying elongation of the same gene in different conditions or comparing it to a perfect theoretical uniform elongation. Package: r-cran-teqr Architecture: all Version: 6.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-teqr_6.0-0-1.ca2404.1_all.deb Size: 62694 MD5sum: 5236ec299d95cc5357b401904f6d6e8f SHA1: a91633e84194d51a66369cd077efb3c4479f2ad4 SHA256: 449ecc826fabd8a1b7784f971457603b948350b9a15ff47297050260c129919e SHA512: 22c32a91fe6f7a14d0823b058edeb066e8a1f354dd80727ce447d60e805ff55caa39e1382a98a7fea3e55863b0f315c6aade29ed3399eafc3597bb3561331e9f Homepage: https://cran.r-project.org/package=TEQR Description: CRAN Package 'TEQR' (Target Equivalence Range Design) The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. 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Package: r-cran-tern.gee Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tern, r-cran-checkmate, r-cran-emmeans, r-cran-formatters, r-cran-geeasy, r-cran-geepack, r-cran-nlme, r-cran-rtables Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rcpp, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-tern.gee_0.1.6-1.ca2404.1_all.deb Size: 389270 MD5sum: fbea74f911bbd2d18da2302402f1b861 SHA1: 0260d83a0d8d5892f9362eb2a5f6eb84422105c3 SHA256: ac6ec623bed75083cf6042c9b0bc65e35d3887a8a03f3ff4c2433e336f0b93b1 SHA512: 9bed225e5c7823bda88088d8ef2eb6deac77e917fd48b2fd3bf0bebc39936d1775cb632055ccba349f5af0134649a1f543309a2755084bdcf26ca8c18ffa7ad2 Homepage: https://cran.r-project.org/package=tern.gee Description: CRAN Package 'tern.gee' (Tables and Graphs for Generalized Estimating Equations (GEE)Model Fits) Generalized estimating equations (GEE) are a popular choice for analyzing longitudinal binary outcomes. This package provides an interface for fitting GEE, currently for logistic regression, within the 'tern' framework (Zhu, Sabanés Bové et al., 2023) and tabulate results easily using 'rtables' (Becker, Waddell et al., 2023). It builds on 'geepack' (Højsgaard, Halekoh and Yan, 2006) for the actual GEE model fitting. Package: r-cran-tern.mmrm Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1302 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tern, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-formatters, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-mmrm, r-cran-parallelly, r-cran-rlang, r-cran-rtables, r-cran-tidyr Suggests: r-cran-broom, r-cran-knitr, r-cran-maditr, r-cran-matrix, r-cran-rcpp, r-cran-rmarkdown, r-cran-testthat, r-cran-tmb, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-tern.mmrm_0.3.4-1.ca2404.1_all.deb Size: 515678 MD5sum: 6e2f5e683f652465a1f8d78bcb9cd625 SHA1: 06e2364f70c17eba147822729c89a19afeec1e14 SHA256: caca623157f9c3bac3e5f894fa308ff76d93179961e7b68239ebcb585c612b66 SHA512: 413745c99c1a4ad212399281a0be347ecb9062dd1d6a2e68fb5f2f865e4ffe9e8fb33e817e110fd801b8b1521a328b8190a9e814358c73bc085d0d8480e94799 Homepage: https://cran.r-project.org/package=tern.mmrm Description: CRAN Package 'tern.mmrm' (Tables and Graphs for Mixed Models for Repeated Measures (MMRM)) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) . This package provides an interface for fitting MMRM within the 'tern' framework by Zhu et al. (2023) and tabulate results easily using 'rtables' by Becker et al. (2023). It builds on 'mmrm' by Sabanés Bové et al. (2023) for the actual MMRM computations. Package: r-cran-tern.rbmi Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbmi, r-cran-tern, r-cran-broom, r-cran-checkmate, r-cran-formatters, r-cran-lifecycle, r-cran-magrittr, r-cran-rtables Suggests: r-cran-bh, r-cran-dplyr, r-cran-knitr, r-cran-matrix, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-tidyr, r-cran-v8 Filename: pool/dists/noble/main/r-cran-tern.rbmi_0.1.6-1.ca2404.1_all.deb Size: 363750 MD5sum: 011ec14d2f9e65b05f50dea06e68f78b SHA1: ba2e99087c1d9ec82ce3b7b6a49a0108f619d308 SHA256: 09f6be22b08417a27d41f672c7a2bf57704c3028d23f4ab1638c9861aec49719 SHA512: 487f7b00a4647bc647552c94a5d35dd960b5f5fa5d22e07cb9a644313ee777a4caf59e019281e87f76f04407412e9bf0d13117f2404417a3156fb81c7152d96c Homepage: https://cran.r-project.org/package=tern.rbmi Description: CRAN Package 'tern.rbmi' (Create Interface for 'RBMI' and 'tern') 'RBMI' implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) ). This package provides an interface for 'RBMI' uses the 'tern' framework by Zhu et al. (2023) and tabulate results easily using 'rtables' by Becker et al. (2023). Package: r-cran-tern Architecture: all Version: 0.9.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11862 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rtables, r-cran-broom, r-cran-car, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-forcats, r-cran-formatters, r-cran-ggplot2, r-cran-gridextra, r-cran-gtable, r-cran-labeling, r-cran-lifecycle, r-cran-mass, r-cran-nestcolor, r-cran-rdpack, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lattice, r-cran-lubridate, r-cran-rmarkdown, r-cran-stringr, r-cran-svglite, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tern_0.9.13-1.ca2404.1_all.deb Size: 4536310 MD5sum: 8a8a9d11ee491b691e4c6360c40f4796 SHA1: 84d590417a3841e316df0a28c213385a8dbe344f SHA256: 0d8e87802b0b702e6dd0c353f26293ef7654199a2fecf53093ebbe8d07f113f5 SHA512: b67ff5b343911e81a3b5d1774aa4b7a8856faef52bdc52d685c93b5f1b144d55a0220aa80593bb00b08fc63b4215990c4bc7283ff383373f5db56ceca7d9a38d Homepage: https://cran.r-project.org/package=tern Description: CRAN Package 'tern' (Create Common TLGs Used in Clinical Trials) Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials. 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The package streamlines the analytical workflow by detecting variable types and applying appropriate statistical tests (Welch t-test, Wilcoxon rank-sum, Welch ANOVA, Kruskal-Wallis, Chi-squared, or Fisher's exact test). Results are formatted as 'tibble' objects and can be exported to 'Word' or 'Excel' using the 'officer', 'flextable', and 'writexl' packages. Optional pairwise post-hoc testing for three-group comparisons (Games-Howell and Dunn's test) is available via the 'rstatix' package. Example data are derived from the landmark adjuvant colon cancer trial described in Moertel et al. (1990) . 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Package: r-cran-terrainr Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2939 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-magick, r-cran-png, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-unifir, r-cran-units Suggests: r-cran-brio, r-cran-covr, r-cran-jpeg, r-cran-knitr, r-cran-progress, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-tiff Filename: pool/dists/noble/main/r-cran-terrainr_0.7.6-1.ca2404.1_all.deb Size: 1988610 MD5sum: 9e1387e22a016e8eeb16974ba4ca7659 SHA1: b4a5d760322357b392a78b84878da4968b0fc02d SHA256: d11a9dcaeb0b7f2cc6ba54405e10a58ac1d5488f1e4713317c9d60e4e9879b32 SHA512: dae7f38c428472bd2160691d106f34d23edd9b0564a3bc5383b6e4f57f100b055a6026f3f45a6c1f573c3121ef586ee510535dafdca731c95977a0341e04e7d8 Homepage: https://cran.r-project.org/package=terrainr Description: CRAN Package 'terrainr' (Landscape Visualizations in R and 'Unity') Functions for the retrieval, manipulation, and visualization of 'geospatial' data, with an aim towards producing '3D' landscape visualizations in the 'Unity' '3D' rendering engine. 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Supports scenario-based planning with budget-constrained optimization, optional impassable areas, packaged parity fixtures, and comparative before-and-after connectivity metrics. The package exposes structural, movement-oriented, and species-oriented strategies in a reproducible workflow aligned with a companion GIS plugin while avoiding a desktop GIS dependency. Package: r-cran-tesiprov Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-digest, r-cran-nloptr, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-evd, r-cran-brms Filename: pool/dists/noble/main/r-cran-tesiprov_0.9.6-1.ca2404.1_all.deb Size: 510332 MD5sum: 67ec6eb136bc6acc27d35dd20c146896 SHA1: d4d63cd6220ccf747005baff9412fed2817c6de5 SHA256: 0050f0f7eca09902ca7a9f41300b41cb8ce832dca03762674279a00dccc1004b SHA512: 6b09ae582187c55c3fa6dd974e173ab7dee5948583e3c609c394969a1573b618d2cc3762a882480ad1ce701a4394d89f339f422f6121db26f42b1e7dbe3ae658 Homepage: https://cran.r-project.org/package=TesiproV Description: CRAN Package 'TesiproV' (Reliability Analysis Methods for Structural Engineering) Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." , Breitung (1989) "Asymptotic approximations for probability integrals." . Package: r-cran-tesouror Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6939 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-stringi, r-cran-stringr, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tesouror_0.3.1-1.ca2404.1_all.deb Size: 4837178 MD5sum: f61b0621a5ba1d2eb8a4d60d702f931c SHA1: 4d680fd5335e1928ff11dd7ca7cb5fbb2bf85965 SHA256: 0009427df6cfb09a195639c40f08c1471880e8c42787a4242021fbb0ec71efb0 SHA512: 1d7fb5e9a8fceb102ee4d1fc928a5041af2076889f0e0d9f0fcbbf14db0b17b44a616e5746ff7865eb915501e9bc1c8309ae8bee7d33f8a30c2265b7e953956e Homepage: https://cran.r-project.org/package=tesouror Description: CRAN Package 'tesouror' (Access Brazilian National Treasury Open Data APIs) Provides a unified interface to access open data from the Brazilian National Treasury ('Tesouro Nacional') and related government APIs. Covers six data sources: 'SICONFI' for fiscal reports ('RREO', 'RGF', 'DCA', 'MSC') and entity information; 'CUSTOS' for federal government cost data; 'SADIPEM' for public debt and credit operations; 'Transferencias Constitucionais' for constitutional transfers to states and municipalities; 'SIORG' for federal organizational structure; and 'SIOPE' ('FNDE'/'MEC') for education spending data. Features automatic pagination, in-memory caching, retry logic, and tidy output. Package: r-cran-tesselle Architecture: all Version: 1.7.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ananke, r-cran-dimensio, r-cran-folio, r-cran-isopleuros, r-cran-kairos, r-cran-khroma, r-cran-nexus, r-cran-tabula Filename: pool/dists/noble/main/r-cran-tesselle_1.7.0-1.ca2404.2_all.deb Size: 104022 MD5sum: 33fb8f689a73bb8042e4d81a3aca731e SHA1: 5d1ed01ed466ddf2ffe564168ca4573be1915e23 SHA256: bf2c149ea2507ef62dad247b9faf983dcba5a89372d44ef4531717f0a697354b SHA512: 18c5f93c45d9e209a594588011ffc53c6344bee72be6dff40a73c798c6a5cb5ad5b4c79fef4844485ab00235d52a5f97e167c3af605d271d5178cf3c633db92d Homepage: https://cran.r-project.org/package=tesselle Description: CRAN Package 'tesselle' (Easily Install and Load 'tesselle' Packages) Easy install and load key packages from the 'tesselle' suite in a single step. The 'tesselle' suite is a collection of packages for research and teaching in archaeology. These packages focus on quantitative analysis methods developed for archaeology. The 'tesselle' packages are designed to work seamlessly together and to complement general-purpose and other specialized statistical packages. These packages can be used to explore and analyze common data types in archaeology: count data, compositional data and chronological data. Learn more about 'tesselle' at . Package: r-cran-test.assessr Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3510 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-checkmate, r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-jsonlite, r-cran-pkgload, r-cran-remotes, r-cran-rlang, r-cran-rmarkdown, r-cran-runit, r-cran-stringr, r-cran-testthat, r-cran-tinytest, r-cran-tidyr, r-cran-withr Suggests: r-cran-curl, r-cran-devtools, r-cran-dt, r-cran-here, r-cran-kableextra, r-cran-knitr, r-cran-r6, r-cran-s7, r-cran-roxygen2, r-cran-testit, r-cran-tidyselect, r-cran-mockery Filename: pool/dists/noble/main/r-cran-test.assessr_2.1.3-1.ca2404.1_all.deb Size: 1710934 MD5sum: 1fcc9da19e398e892475e53ed06435dd SHA1: 91ca9324997f0c659d3f7aa771a4a80c5004461c SHA256: 813b041668a24e08d426479dacf1dcabbd28629a4faf86d5346c21537d8d5c79 SHA512: 6b58328fe40b84454c8a80ebb164be4154dd569d10f61b5a9c767bc8d23b075f0eaa979902be27f17ca9babded56ec511453f7c8d4e5e56cf705d5436742c0dc Homepage: https://cran.r-project.org/package=test.assessr Description: CRAN Package 'test.assessr' (Assessing Package Test Reliability and Quality) A reliable and validated tool that calculates unit test coverage for R packages with standard testing frameworks and non-standard testing frameworks. Package: r-cran-test2norm Architecture: all Version: 0.3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mfp2 Filename: pool/dists/noble/main/r-cran-test2norm_0.3.0.1-1.ca2404.1_all.deb Size: 59062 MD5sum: 4bf733b0513f05527ee9234c7a64acbf SHA1: 32d60caa05104b9d88b212bc27a00da0b29dece8 SHA256: 852e81ae5fc984e8fd1ca8d99571392e8e5fa6e2d9d41eccff303c748dc95c6b SHA512: d9a62168e1db04f4c3a07c04dd280b2a24429583fa497bd2a16dbbacc94d0494fa025fef060492b170485c80e056fcc170070a19bd4b7a3dbbfae15e3b96018d Homepage: https://cran.r-project.org/package=test2norm Description: CRAN Package 'test2norm' (Normative Standards for Cognitive Tests) Package test2norm contains functions to generate formulas for normative standards applied to cognitive tests. It takes raw test scores (e.g., number of correct responses) and converts them to scaled scores and demographically adjusted scores, using methods described in Heaton et al. (2003) & Heaton et al. (2009, ISBN:9780199702800). The scaled scores are calculated as quantiles of the raw test scores, scaled to have the mean of 10 and standard deviation of 3, such that higher values always correspond to better performance on the test. The demographically adjusted scores are calculated from the residuals of a model that regresses scaled scores on demographic predictors (e.g., age). The norming procedure makes use of the mfp2() function from the 'mfp2' package to explore nonlinear associations between cognition and demographic variables. Package: r-cran-testanaapp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mirt, r-cran-shinydashboard, r-cran-estcrm, r-cran-shiny, r-cran-dt, r-cran-golem, r-cran-officedown, r-cran-stringr, r-cran-dplyr, r-cran-brucer, r-cran-officer, r-cran-openxlsx, r-cran-rmarkdown, r-cran-plotrix, r-cran-cowplot, r-cran-flextable, r-cran-shinycssloaders, r-cran-tidyr, r-cran-difr, r-cran-lordif, r-cran-latticeextra, r-cran-semplot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-testanaapp_1.1.2-1.ca2404.1_all.deb Size: 566838 MD5sum: bf9f9aa1c334eaa972859bb710f67267 SHA1: 36d62c1273ed2f65d8623a5a8b5b7eebe815bb23 SHA256: 3e456fd36a07294d102b2c6cf97584e78f2f6061f452da49695c63f6fab38ab3 SHA512: b1b7dc8fe260c088855ab63edf6dce120b23de1ad8579fec48215a8a91bb05e167d79e595dee94ebaa64179ba3caf34fcbca356658a8f78a1ff4960d1b3070dd Homepage: https://cran.r-project.org/package=TestAnaAPP Description: CRAN Package 'TestAnaAPP' (A 'shiny' App for Test Analysis and Visualization) This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition, it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface. Package: r-cran-testarguments Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-testarguments_0.0.1-1.ca2404.1_all.deb Size: 371962 MD5sum: 68843ada2246579ac3a6d05a661be225 SHA1: 2b602497a9f523dbe1b789c693dea0412dae3e5f SHA256: aa0dd321db32a73b2685fea4b721a12b364e0f261da5f0db0e892fd69d7d1823 SHA512: 73d25b03b48f2dbb49e7a6cd5ee0bd7d6eb2da5b55469573ecb0543835862d253a7d1094036c474bbf085df88a8bde3ab398c2967f8f7e61efdc37b9c09d42a3 Homepage: https://cran.r-project.org/package=testarguments Description: CRAN Package 'testarguments' (Test (Multiple) Arguments of a User-Defined Prediction Algorithm) Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic. Package: r-cran-testassay Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-testassay_0.1.3-1.ca2404.1_all.deb Size: 80258 MD5sum: 88afa35638140e9be06fa62f74f986c0 SHA1: 06c4443dc28a7ee944dbe5f44cca0e5779928191 SHA256: 639a164a98e32bcf190fc1c82637b13dff8e1de9e09bbccac7c77c9c83a506dd SHA512: f67ebd61a873ef3e484bb4bd5230468f62b66f786ed02e152911c783c9a373a0c4b49cad4750736e3d4208ad786f43471d0dd040af2a0c2c90a94d42e56b5683 Homepage: https://cran.r-project.org/package=testassay Description: CRAN Package 'testassay' (A Hypothesis Testing Framework for Validating an Assay forPrecision) A common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval'' of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay. This is an implementation of the methods described in Fay, Sachs, and Miura (2018) . Package: r-cran-testcomparer Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-testcomparer_1.1.1-1.ca2404.1_all.deb Size: 132028 MD5sum: 1b2e6ac2257ba20dcfc8c1114a1586d4 SHA1: fb3ec8959afc3f51f9571cbcc644fb6fbfd6d2c6 SHA256: 0a8f1f2824d68cfc9f4c238c1226d3f5e8c19cb750aec53171c3a6e120995001 SHA512: b8ec3af1fcb85e9a9a5ba114f70216da5fa63ee25eaf87c184daf4e623b3efe0506efe2b2da5029eb61b6dd969bed381bdda00085cf0f251439f57f6d25956d0 Homepage: https://cran.r-project.org/package=testCompareR Description: CRAN Package 'testCompareR' (Comparing Two Diagnostic Tests with Dichotomous Results usingPaired Data) Provides a method for comparing the results of two binary diagnostic tests using paired data. Users can rapidly perform descriptive and inferential statistics in a single function call. Options permit users to select which parameters they are interested in comparing and methods for correction for multiple comparisons. Confidence intervals are calculated using the methods with the best coverage. Hypothesis tests use the methods with the best asymptotic performance. A summary of the methods is available in Roldán-Nofuentes (2020) . This package is targeted at clinical researchers who want to rapidly and effectively compare results from binary diagnostic tests. Package: r-cran-testcorr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-reshape2, r-cran-forcats, r-cran-knitr, r-cran-xts, r-cran-zoo Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-testcorr_0.4.0-1.ca2404.1_all.deb Size: 796266 MD5sum: 904d5be9286a1aaa48222c0911ec32cb SHA1: 351e1fbb618d9c912e9343264d4e3cd2a1d4d871 SHA256: 8705e2ad82f24a2107d224b0e652285019d9751ea28303d761a22bf5111bfab6 SHA512: 5bcc2c65aaab622612743d9dea751b9a07443167f9690c54f11b2627700023e8bf15352bd355c8863e9a12b03f55e74b5ee2168a1adea35c05203f84e9088cd6 Homepage: https://cran.r-project.org/package=testcorr Description: CRAN Package 'testcorr' (Testing Zero Correlation) Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2022), , . Package: r-cran-testdat Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-crayon, r-cran-knitr, r-cran-labelled, r-cran-lubridate, r-cran-openxlsx, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-testdat_0.4.4-1.ca2404.1_all.deb Size: 228390 MD5sum: 11676cf6749a6c28234f5faa72832ee3 SHA1: de5f46cacab8d608756106c9caace58cfad9d1c4 SHA256: 643cf956616047dd916e37bd96ffd626e6203be2d7464ed0277e979a496c3e83 SHA512: 7ff90314ee041eacb70af7a429cf36d95febc64a13761ae0464e54ba2f8f2088b1e1b33d9293caf5b6cec63cf2a3de50bcbe8965f007906bce811f4ce7e9b5b4 Homepage: https://cran.r-project.org/package=testdat Description: CRAN Package 'testdat' (Data Unit Testing for R) Test your data! An extension of the 'testthat' unit testing framework with a family of functions and reporting tools for checking and validating data frames. Package: r-cran-testdataimputation Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-amelia Filename: pool/dists/noble/main/r-cran-testdataimputation_2.3-1.ca2404.1_all.deb Size: 65704 MD5sum: 3d66d8d2b8932ed09f0499fc3ab0695f SHA1: c490111fed246205e008b536f445d856216cd7ff SHA256: e6d928ffe7b68d7008160656d6d65f7bb7d9e79b0a03e7589a5060f9269a9e20 SHA512: 86a250a78a7cb10db3cb752eec7143f177212d28197112337a5cb2f6ae29ba4fd312d78d0665f5277be914c952bdff29869aa9e84dd1ff575ae3becb73c37e51 Homepage: https://cran.r-project.org/package=TestDataImputation Description: CRAN Package 'TestDataImputation' (Missing Item Responses Imputation for Test and Assessment Data) Functions for imputing missing item responses for dichotomous and polytomous test and assessment data. This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 ), treating as incorrect (IN, see Lord, 1974 ; Mislevy & Wu, 1996 ; Pohl et al., 2014 ), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation, (see Sijtsma & van der Ark, 2003 ), logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) imputation (see Finch, 2008 ). Package: r-cran-testdimorph Architecture: all Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corrplot, r-cran-dplyr, r-cran-ggplot2, r-cran-morpho, r-cran-multcompview, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-testdimorph_0.5.8-1.ca2404.1_all.deb Size: 320300 MD5sum: 36ab14ed79454626c7010e6e4ef74f9d SHA1: 60bc2422442879b176d7ea21b634ffc36360601b SHA256: 02548956a2816ebd6824f1b35422e858f8c42d5f814e44a4fadcd908c3c0041b SHA512: 28b41151547c230d193d05868aca2e700157730ea19172575e14a6e50a193070d5422374df1301d56e5574f8b1d97ef4d614f8f89e7342a5256ad1ee402b4759 Homepage: https://cran.r-project.org/package=TestDimorph Description: CRAN Package 'TestDimorph' (Analysis of the Interpopulation Difference in Degree of SexualDimorphism Using Summary Statistics) Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) , Greene, D. L. (1989) and Konigsberg, L. W. (1991) . Package: r-cran-testdriver Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-testdriver_0.5.3-1.ca2404.1_all.deb Size: 228294 MD5sum: 407bde329ef7a6db53e0047b2c8bfe48 SHA1: e8418b22a7850b8505e647c169ffb082cf696dc0 SHA256: 00548fa73d1f8adfb5434f62887268ef49dbae84afb62ec20b662c133d2e1f4b SHA512: ddf24296e993da593672affd2bba280c90a9cb14b5f170244da3da5bf9cd91cf0a7c71ccbff27169a43fbf0bb841fd920fd5d0c5fb3261fd5cd9a9797df1b983 Homepage: https://cran.r-project.org/package=testDriveR Description: CRAN Package 'testDriveR' (Teaching Data for Statistics and Data Science) Provides data sets for teaching statistics and data science courses. It includes a sample of data from John Edmund Kerrich's famous coinflip experiment. These are data that I used for statistics. The package also contains an R Markdown template with the required formatting for assignments in my former courses. Package: r-cran-testequavar Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-testequavar_0.1.5-1.ca2404.1_all.deb Size: 39080 MD5sum: 5e6fa2190b9331ca345b34db62d08ba3 SHA1: a856ec24e9b41dc6b448fa0f207bc705dcbf9057 SHA256: 9a5d9203c3d5168913d89421728f2aa60fffa86c64e916814257095e48909f9f SHA512: d439491a3b873f7a313b00e4a6bd33e1cd235901e6a699af74358ef7092d9c6705b939bb94bf42866f4975b755e8e0a2d951d81c8f2549ae01c0490cc3b8af08 Homepage: https://cran.r-project.org/package=testequavar Description: CRAN Package 'testequavar' (Bootstrap Tests for Equality of 2, 3, or 4 Population Variances) Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi{10.1016/j.csda.2010.04.012}. 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The package combines assumption checks, test selection, effect sizes, formatted results, plain-language interpretation, and a sample-size planning module covering continuous, binary, survival, ordinal, bioequivalence, and precision-based designs. Implemented methods follow standard references including Casella and Berger (2002, ISBN:9780534243128), Hollander et al. (2013, ISBN:9781118553299), Agresti (2013, ISBN:9780470463635), Cohen (1988, ISBN:9780805802832), Hosmer, Lemeshow and Sturdivant (2013, ISBN:9780470582473), and Julious (2010, ISBN:9781584887393). 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Three distribution-free variance-component estimators are implemented: Variance Least Squares ('VLS'), Method of Moments ('MM'), and Method of Moments with First-Order Approximation ('MMF'). A permutation procedure is used to obtain finite-sample p-values. Plotting functions support data exploration, model evaluation, and communication of results. Methods are described in Uwimpuhwe, Drikvandi, and Blozis (2026) . Package: r-cran-testscorer Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-testscorer_1.7.2-1.ca2404.1_all.deb Size: 490768 MD5sum: f3d7032a4c7b9e693d328c7f3d8068d9 SHA1: cc65093f5480f2112ab326f49bdc4e692c46b43f SHA256: c82aeb7a98c56d58e10ef24d6fe28976fd62096612a5ce46c9a1c91570606fea SHA512: cb1f177a6798c8188326f416a9444db59f7b3590b65df9aaf3f9c0ce86d26cb9cc53352ea5d90322550a15ad9b617574b773250c9aee84929c18eb6773ed4b88 Homepage: https://cran.r-project.org/package=TestScorer Description: CRAN Package 'TestScorer' (GUI for Entering Test Items and Obtaining Raw and TransformedScores) GUI for entering test items and obtaining raw and transformed scores. The results are shown on the console and can be saved to a tabular text file for further statistical analysis. The user can define his own tests and scoring procedures through a GUI. Package: r-cran-testthatdocs Architecture: all Version: 1.0.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-testthatdocs_1.0.23-1.ca2404.1_all.deb Size: 357274 MD5sum: 03e9498d9dd4f3305fc516d53c2b9242 SHA1: 91bf61fc5b12c74996268acea3fa51a4c7716ead SHA256: e522c5d8865b4b8c8a08180905e3948e9935f60f671d6f5c2a8c5815267dad15 SHA512: d7507170b422adb218bf58581cfa7f42ac4e4161479e80ca395018906af0e25977253ec0d1c29757109700c5566fe7448f6e0873aa3b839a2a4d07271f1ddaea Homepage: https://cran.r-project.org/package=testthatdocs Description: CRAN Package 'testthatdocs' (Automated and Idempotent Unit Tests Documentation forReproducible Quality Assurance) Automates documentation of test_that() calls within R test files. The package scans test sources, extracts human-readable test titles (even when composed with functions like paste() or glue::glue(), ... etc.), and generates reproducible roxygen2-style listings that can be inserted both globally and per-section. It ensures idempotent updates and supports customizable numbering templates with hierarchical indices. Designed for developers, QA teams, and package maintainers seeking consistent, self-documenting test inventories. 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Package: r-cran-text Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4926 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-topics, r-cran-dplyr, r-cran-tibble, r-cran-stringi, r-cran-tidyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-cowplot, r-cran-rlang, r-cran-purrr, r-cran-magrittr, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-reticulate, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-future, r-cran-furrr, r-cran-hardhat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rio, r-cran-glmnet, r-cran-randomforest, r-cran-overlapping, r-cran-covr, r-cran-xml2, r-cran-ranger, r-cran-ggwordcloud, r-cran-reactable, r-cran-osfr, r-cran-vdiffr, r-cran-svglite Filename: pool/dists/noble/main/r-cran-text_1.9-1.ca2404.1_all.deb Size: 2729880 MD5sum: 87a9f57dba03d480863b537b0a00ee3c SHA1: 7b99fd27580fce4a6bd6fe486e5b6c2421f7adae SHA256: 560e73af715d599abf7d7d984bbe7631c5eb5c308084c7aafbd12fa2e23ca355 SHA512: c5ff8f76d9b3a23a242d1d3ae5e40d3f20472c573c98d820b23a161875213022c0c70377e48e08b3f536b7611bb3d5d86f2bc2ac0378af87b76ac16d6c299bee Homepage: https://cran.r-project.org/package=text Description: CRAN Package 'text' (Analyses of Text using Transformers Models from HuggingFace,Natural Language Processing and Machine Learning) Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see . Package: r-cran-textab Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-textab_1.0.1-1.ca2404.1_all.deb Size: 79246 MD5sum: 5787582a82e56b0d17d32fc88c93606f SHA1: f3081762947c3dba263ab48197c5b7de54a13c96 SHA256: 7bb2318a30a048216f453c5d68d9a5eb01045cda94cce1e73c3a76c33437539b SHA512: d28e211cdeda390aebd793cfafa6e3416d11223186642dd55453866c047cf3bcb86824622953a0c889ef618226732aff5caf9cb72dd2a719db72bdabc05d9d96 Homepage: https://cran.r-project.org/package=textab Description: CRAN Package 'textab' (Create Highly-Customized 'LaTeX' Tables) Generate 'LaTeX' tables directly from R. It builds 'LaTeX' tables in blocks in the spirit of 'ggplot2' using the '+' and '/' operators for concatenation in the vertical and horizontal dimensions, respectively. 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Includes preprocessing via 'quanteda', lexical analysis (term frequency-inverse document frequency, log-odds ratios, lexical diversity) via 'tidytext', topic modeling via 'stm' and the 'BERTopic' approach, semantic similarity and document clustering on transformer representations, an interactive 'Shiny' interface with 'ggplot2' visualization, optional 'spaCy' preprocessing, and local 'sentence-transformers' or web-based ('OpenAI', 'Gemini') model providers for retrieval-augmented generation, as described in Shin et al. (2026) . Package: r-cran-textanalyzer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidytext, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-textanalyzer_0.2.0-1.ca2404.1_all.deb Size: 46640 MD5sum: 8bc0a0debf69b295f4048121c9006afb SHA1: e2797ab051f74e357658a06b3f6f3b43a22adda3 SHA256: be3215a796d121ff7f1b6178feb48c817ccf8174c3ed7f298642a87b0149cff4 SHA512: 284dca80255fb3519063430606cb29f2b13e9ac88449f1d9367e27a14935f73f323f54c06f117937ed05e4913ef5d568125ae0624b1fa83c2521795529c4de60 Homepage: https://cran.r-project.org/package=textanalyzer Description: CRAN Package 'textanalyzer' ('textanalyzer', an R Package to Analyze Text) It analyzes text to create a count of top n-grams, including tokens (one-word), bigrams(two-word), and trigrams (three-word), while removing all stopwords. 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Package: r-cran-textboxplacement Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-textboxplacement_1.0-1.ca2404.1_all.deb Size: 164684 MD5sum: e8bc9c2b2bc7a50c7acbafc58ad41114 SHA1: 092a2009d37969f700d22a747fc3e66e290299b5 SHA256: 7241e6f251ebf05b24a1ceb1714e9e79855c233f67010478cd68287308d30d0a SHA512: d4953b92f193a51348d2621851b77e81bd84985357d4ed2634eb4a5981e2cbd999f23f7770abc28f434e427c6738784748b9820c64024dbaecf652e72392b6be Homepage: https://cran.r-project.org/package=textBoxPlacement Description: CRAN Package 'textBoxPlacement' (Compute a Non-Overlapping Layout of Text Boxes to Label MultipleOverlain Plots) Compute a non-overlapping layout of text boxes to label multiple overlain curves. 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Package: r-cran-textclassificationtutorial Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-textclassificationtutorial_0.1.2-1.ca2404.1_all.deb Size: 126170 MD5sum: 53f3f581309d86d16144f10a7a197fe2 SHA1: 23684b7b3b40ee2962df2ae24072e2cd6d7e3fe8 SHA256: 0cd59a8548e0c360562c3f87ce3fbe3130b10c5866dd66c5e49a837c43f994b3 SHA512: edc38d102fc5963b45951a9ad2c8ae194b439cc22b17bba07b3ea03f6844a8712ad6efa8397397f367c07dd12560646f8f2f42b19b3a5f88b80d33325110be76 Homepage: https://cran.r-project.org/package=textclassificationtutorial Description: CRAN Package 'textclassificationtutorial' (Reproducible Text Classification Workflows) Dependency-light tools and tutorials for teaching reproducible text classification. 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Package: r-cran-textclean Architecture: all Version: 0.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1326 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-english, r-cran-glue, r-cran-lexicon, r-cran-mgsub, r-cran-qdapregex, r-cran-stringi, r-cran-textshape Suggests: r-cran-hunspell, r-cran-testthat Filename: pool/dists/noble/main/r-cran-textclean_0.9.7-1.ca2404.1_all.deb Size: 1287570 MD5sum: adbe44fb17b40ee77a5317e962903db7 SHA1: 175a770e0959b366d02b75de5a7579585941d6ea SHA256: 027e1d6fc264737b446e0aa655f60021e5c4dd37ca143f50e036afae7a80b71a SHA512: 0f1a17520d9e9e8a8bfc011ddfc4b02ceaf0f16e15f598505f1a2f1dfc7d94f37a452e0fe07f9832385c6cb495306c4dd39b7b9a0c9e72c998d4db6d366430cf Homepage: https://cran.r-project.org/package=textclean Description: CRAN Package 'textclean' (Text Cleaning Tools) Tools to clean and process text. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards (2001) ) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents. Package: r-cran-textdata Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-rappdirs, r-cran-readr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-textdata_0.4.5-1.ca2404.1_all.deb Size: 500882 MD5sum: 6b3d27281ed1d9ea56d111b7f1fc085a SHA1: 3f7e45a69c7bae2631bc51a7111e8fd1bc8be292 SHA256: fa6b12471baf21f92116cf1871cc844b80be2ab4e9543a164be21e3427d3bdb8 SHA512: 80e8460a76cf2067d50f439bdbe979899f3d3b6ce92d648678575aa200a79a8eb222cff792002a770832b3a8d3523f3310fd225f361fd42a5d2a6a95b8f0f76c Homepage: https://cran.r-project.org/package=textdata Description: CRAN Package 'textdata' (Download and Load Various Text Datasets) Provides a framework to download, parse, and store text datasets on the disk and load them when needed. 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See for additional details. 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A number of wrapper functions are provided for plotting and summarising outputs from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R package and R Shiny application. 2023. Physiological Entomology. . The GUI version of this package is available on the R Shiny online server at: , or it is accessible via GitHub at . We would like to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function. 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Written primarily for research purposes in biological applications of thermal images. v1 included the base calculations for converting thermal image binary values to temperatures. v2 included additional equations for providing heat transfer calculations and an import function for thermal image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous functions for converting thermal image, videos, rewriting and exporting. v3.1. Added new functions to convert files. v3.2. Fixed the various functions related to finding frame times. v4.0. fixed an error in atmospheric attenuation constants, affecting raw2temp and temp2raw functions. Recommend update for use with long distance calculations. v.4.1.3 changed to frameLocates to reflect change to as.character() to format(). 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The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) and in Skaltsa et al (2010, 2012) , . 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Links interims to the summative scale with a latent multivariate normal model that carries each score's measurement error forward and handles missing interims, estimated by the EM algorithm (Dempster, Laird and Rubin, 1977, ) with SQUAREM acceleration (Varadhan and Roland, 2008, ); simulates cold-start versus prior-informed routing in a two-stage multistage test; checks whether priors disadvantage late enrollers, low scorers or fast growers; and evaluates decision accuracy and consistency of through-year scores against a single summative. 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Implements Condition Threshold Sweep (CTS, for one or several conditions), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an explicit analytical dimension and recording the sufficiency solution obtained at each threshold setting. Also provides Fiss (2011) core/peripheral condition classification via compute_fiss_core() and generate_fiss_chart(), enabling four-symbol configuration charts that distinguish core conditions (conditions of the parsimonious term contained in each configuration) from peripheral conditions (intermediate only). Built on top of the 'QCA' package by Dusa (2019) , with function arguments following 'QCA' conventions. Based on set-theoretic methods by Ragin (2008) and established robustness protocols by Oana and Schneider (2024) . The threshold-sweep framework is described in Toyoda (2026) . This package supersedes 'TSQCA'; see the NEWS file for migration guidance. 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This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References: Brown & Maydeu-Olivares (2011) ; Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.; Maydeu-Olivares & Böckenholt (2005) . 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Package: r-cran-tidybayes Architecture: all Version: 3.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggdist, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-coda, r-cran-rlang, r-cran-arrayhelpers, r-cran-tidyselect, r-cran-tibble, r-cran-magrittr, r-cran-posterior, r-cran-withr, r-cran-cli, r-cran-vctrs Suggests: r-cran-knitr, r-cran-testthat, r-cran-purrr, r-cran-forcats, r-cran-vdiffr, r-cran-svglite, r-cran-rstan, r-cran-rstantools, r-cran-runjags, r-cran-rjags, r-cran-jagsui, r-cran-rstanarm, r-cran-emmeans, r-cran-broom, r-cran-mcmcglmm, r-cran-bayesplot, r-cran-modelr, r-cran-brms, r-cran-cowplot, r-cran-covr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-bindrcpp, r-cran-rcolorbrewer, r-cran-gganimate, r-cran-gifski, r-cran-png, r-cran-ragg, r-cran-pkgdown, r-cran-distributional, r-cran-transformr Filename: pool/dists/noble/main/r-cran-tidybayes_3.0.7-1.ca2404.1_all.deb Size: 1660576 MD5sum: 3938f460cedcb4213a0127ed93acfed8 SHA1: a51b1fdab76c7d0a2ba64f3014fadbe29abf1828 SHA256: 0fe640f3e29779b726ca2e5437469ff5b95347d77bc841d82cc7beebd9417aa0 SHA512: cea59538f490a3048b15674eb6cb13829b6ad128617242bbc48af1ae33853c1f11b98d609cc3cd97819d193b7c5e5db1c91c4d5e138cb43b91bcfa2141fc4cac Homepage: https://cran.r-project.org/package=tidybayes Description: CRAN Package 'tidybayes' (Tidy Data and 'Geoms' for Bayesian Models) Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands. Package: r-cran-tidybde Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-lifecycle, r-cran-readr, r-cran-scales, r-cran-tidyr Suggests: r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tidybde_0.7.1-1.ca2404.1_all.deb Size: 313656 MD5sum: dac9480087497f941f91cae5d8274b24 SHA1: 2a86497f2bfd982a27dea00759fdd36c81085b47 SHA256: d615b59742fc4fb097a1df6d1c65c65adbc4c1b195745e9268b9e18ca7daaa64 SHA512: eff7efcf4e4922b755a852a389e8d9fbe533bea5e9b15826438a6ce3a0067a0eaa83d2d187fb09aa90983f6886a6827f098f3e27f0a015ff1ef729bd5292c415 Homepage: https://cran.r-project.org/package=tidyBdE Description: CRAN Package 'tidyBdE' (Retrieve Time Series Data from 'Banco de España') Tools for retrieving 'Banco de España' ('BdE') time series data as 'tibble' objects from bulk CSV files and the 'Statistics web service (API)'. Bulk CSV functions use stable 'BdE' sequential numbers, while API functions use API series codes. Catalog functions support discovery and local caching. Plotting helpers provide 'ggplot2' palettes, scales and themes. 'Banco de España' is the national central bank and, within the framework of the Single Supervisory Mechanism ('SSM'), the supervisor of the Spanish banking system alongside the European Central Bank. This package is not sponsored, endorsed or administered by 'Banco de España'. Package: r-cran-tidybins Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-purrr, r-cran-janitor, r-cran-tibble, r-cran-rlang, r-cran-lubridate, r-cran-scales, r-cran-ggplot2, r-cran-rlist, r-cran-oner, r-cran-strex, r-cran-clusterr, r-cran-framecleaner, r-cran-xgboost, r-cran-badger, r-cran-autostats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-arulescba, r-cran-embed, r-cran-woebinning, r-cran-recipes Filename: pool/dists/noble/main/r-cran-tidybins_0.1.2-1.ca2404.1_all.deb Size: 71284 MD5sum: 1aaf9f072a09ed87087f3edfbfb39f3e SHA1: 63e35a02bd20214fc5c3bca89b021b225c94f116 SHA256: c6b472a4bb5347c251e7ad6542fda2806547e532fe89018719f3725181ca4920 SHA512: 6474e5832b447dcff394215d013f013e707ead1fbcb87c528b94a4be1eada027629381ad32b8411594168645e68856628cdebd09413557a0065007e25dbf2e3b Homepage: https://cran.r-project.org/package=tidybins Description: CRAN Package 'tidybins' (Make Tidy Bins) Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns. Package: r-cran-tidyboot Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-modelr, r-cran-purrr, r-cran-rlang, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tidyboot_0.1.3-1.ca2404.1_all.deb Size: 28512 MD5sum: dab01336ad336ad96a4533050650852e SHA1: 120300a65a00190e0ff4fc06db44d9ba34ad3f7a SHA256: 2fd2919a9e1802995b7512b57f0293a65fe3a661b8c56359a9e461d3105c91c4 SHA512: 74cc97ca65472b6548cbfc11eacb28168471b6c2a5699ca221eb2b996e86149b21975d12b9765eb6df416c7139fc1c75840827cf8ae1e6bb651324ad111f201c Homepage: https://cran.r-project.org/package=tidyboot Description: CRAN Package 'tidyboot' (Tidyverse-Compatible Bootstrapping) Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames. Package: r-cran-tidycat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-stringr, r-cran-forcats Suggests: r-cran-broom, r-cran-ggplot2, r-cran-ggforce, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tidycat_0.1.2-1.ca2404.1_all.deb Size: 476376 MD5sum: 72e8197be40cd8fd56e41633235f167e SHA1: 0bc36f75853f6548ae85d7d08377eeb8b87f8b81 SHA256: 07e50c5e275701b5dc4985fa37ac69e46d3d67a0c147a548e5e7f272eaceda0d SHA512: dcf3913283d128e9a319289f75e4fd04a80ce8f9ad5c39bfe80605159c929e3f2072ff83208b071a5b4a75a84e8bba7da13e25cbd63de6ffdb2b5329d929a529 Homepage: https://cran.r-project.org/package=tidycat Description: CRAN Package 'tidycat' (Expand Tidy Output for Categorical Parameter Estimates) Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates. Package: r-cran-tidycdisc Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-broom.helpers, r-cran-cicerone, r-cran-cli, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggally, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggsurvfit, r-cran-glue, r-cran-golem, r-cran-gt, r-cran-gtsummary, r-cran-haven, r-cran-ideafilter, r-cran-pkgload, r-cran-plotly, r-cran-purrr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sjlabelled, r-cran-stringdist, r-cran-stringr, r-cran-survival, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timevis, r-cran-tippy, r-cran-vroom Suggests: r-cran-devtools, r-cran-knitr, r-cran-shinyalert, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidycdisc_0.2.2-1.ca2404.1_all.deb Size: 2605528 MD5sum: 3421e58886864c4cfb121901f39c9c89 SHA1: ee4f5ac8a8e9fe75370a118e164c721fde93d757 SHA256: ea25dd05be424d728b29affc97e28a0dd51114b019b26e4bdf52d53cae620cf0 SHA512: e9677e5215e999b13046a30af2b5aef28c92ab3638ed9a6ff647f0a42bc2db11b70bff0f421ec24ac5e1f17c5b97044df24c3064d394ffe232f5f6ae55cca98f Homepage: https://cran.r-project.org/package=tidyCDISC Description: CRAN Package 'tidyCDISC' (Quick Table Generation & Exploratory Analyses on ADaM-IshDatasets) Provides users a quick exploratory dive into common visualizations without writing a single line of code given the users data follows the Analysis Data Model (ADaM) standards put forth by the Clinical Data Interchange Standards Consortium (CDISC) . Prominent modules/ features of the application are the Table Generator, Population Explorer, and the Individual Explorer. The Table Generator allows users to drag and drop variables and desired statistics (frequencies, means, ANOVA, t-test, and other summary statistics) into bins that automagically create stunning tables with validated information. The Population Explorer offers various plots to visualize general trends in the population from various vantage points. Plot modules currently include scatter plot, spaghetti plot, box plot, histogram, means plot, and bar plot. Each plot type allows the user to plot uploaded variables against one another, and dissect the population by filtering out certain subjects. Last, the Individual Explorer establishes a cohesive patient narrative, allowing the user to interact with patient metrics (params) by visit or plotting important patient events on a timeline. All modules allow for concise filtering & downloading bulk outputs into html or pdf formats to save for later. Package: r-cran-tidycensus Architecture: all Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3688 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-dplyr, r-cran-tigris, r-cran-stringr, r-cran-jsonlite, r-cran-purrr, r-cran-rvest, r-cran-tidyr, r-cran-readr, r-cran-xml2, r-cran-units, r-cran-rlang, r-cran-crayon, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-survey, r-cran-srvyr, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tidycensus_1.8.1-1.ca2404.1_all.deb Size: 3566196 MD5sum: 4ab6fefcb3d0c90ec72ce6717d9aadd6 SHA1: df29036f395ed1fc033529079de20ebe22781b13 SHA256: 90edabc1e93b397dd6a286e5755af3208b25dfcacf7d40a3d6d1f8e1e1f6c65a SHA512: 4fea9c56c5bb74196fd0faaeef461312583fa1c16e9d68ff8c9638b0effc6778e901f4def47ed38a0e3b57f3e78ce28759265388f36809acb6a572c948cc18e9 Homepage: https://cran.r-project.org/package=tidycensus Description: CRAN Package 'tidycensus' (Load US Census Boundary and Attribute Data as 'tidyverse' and'sf'-Ready Data Frames) An integrated R interface to several United States Census Bureau APIs () and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis. Package: r-cran-tidycensuskr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7901 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-tidyr, r-cran-kosis, r-cran-rlang Suggests: r-cran-knitr, r-cran-withr, r-cran-purrr, r-cran-rmarkdown, r-cran-janitor, r-cran-geofacet, r-cran-ggplot2, r-cran-biscale, r-cran-cowplot, r-cran-tmap, r-cran-geodata, r-cran-testthat, r-cran-ggspatial, r-cran-spdep Filename: pool/dists/noble/main/r-cran-tidycensuskr_0.3.0-1.ca2404.1_all.deb Size: 7068038 MD5sum: 149af72bd85569d592bbd1c5cef68b05 SHA1: ed261c0a18369470336f0057d77ea099ee457526 SHA256: cabc5c55aea3375277140e27e432ad0a12f2983aebf970568d896c612cdd9993 SHA512: 223d2d01cff613e1b1cf280a9dd5098209d05165aed53b63a6b6cb31387ff71bfbe972cacce3b9577fe55403ef3df7bd87077befa7c0a5302d02733cd788b0af Homepage: https://cran.r-project.org/package=tidycensuskr Description: CRAN Package 'tidycensuskr' (Easy Access for South Korea Census Data and Boundaries) Census and administrative data in South Korea are a basic source of quantitative and mixed-methods research for social and urban scientists. This package provides a 'sf' (Pebesma et al., 2024 ) based standardized workflow based on direct open API access to the major census and administrative data sources and pre-generated files in South Korea. Package: r-cran-tidychangepoint Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-changepoint, r-cran-changepointga, r-cran-cli, r-cran-dplyr, r-cran-ga, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-segmented, r-cran-stringr, r-cran-strucchange, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-vctrs, r-cran-wbs, r-cran-xts, r-cran-zoo Suggests: r-cran-bench, r-cran-broom, r-cran-knitr, r-cran-here, r-cran-multitaper, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidychangepoint_1.0.5-1.ca2404.1_all.deb Size: 669608 MD5sum: 2a88011a02aef63e0390678af4f3bac0 SHA1: 716dae4cc4e65320b305a107df83c27e0b7f73b4 SHA256: 02ea801cdfef5e319cfa7a0389c4334af41b21d4488eeefe2bf0abdf0be0e832 SHA512: 3e31e3ae095a7185a680c565107737213748b20e62dc5b25170e1926da7a06f1a0cf7db587c872803e3487b8d70ba3f6731f379f07c89016434995d5ce365f83 Homepage: https://cran.r-project.org/package=tidychangepoint Description: CRAN Package 'tidychangepoint' (A Tidy Framework for Changepoint Detection Analysis) Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages. Package: r-cran-tidycharts Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-knitr, r-cran-magick, r-cran-rsvg, r-cran-rlang, r-cran-testthat, r-cran-htmlwidgets, r-cran-lubridate, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-palmerpenguins, r-cran-tidyverse, r-cran-dplyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-tidycharts_0.1.3-1.ca2404.1_all.deb Size: 467602 MD5sum: 3efbd44927d366cbd4aa9796b41077b8 SHA1: 430d3cff11ce36d415a789780ebd6e13573e34cd SHA256: 1d5d32442acd189d9c0bcfe8443e8b33dbe556b0fc9f7379e50d559ece014216 SHA512: 7dd1a3cbbfd21f1c4e0ec627f6f5276b3f36605496bf778b4686fd4c70d99d378b570d059c6790093c2fc5f6ec591e06abbff88a0d8ef513572a1c08477951b0 Homepage: https://cran.r-project.org/package=tidycharts Description: CRAN Package 'tidycharts' (Generate Tidy Charts Inspired by 'IBCS') There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS'). It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments. We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible. All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'. Package: r-cran-tidycjk Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-stringi, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidycjk_0.1.0-1.ca2404.1_all.deb Size: 115584 MD5sum: 7c836a9980d3f9b9043b4d3efc328815 SHA1: ea1ff2f535ef077d20943f4edd6fdf5138c002aa SHA256: 0b5779f4d0a4fa85d44ccd98147c008438ce9d28321ef01fb3f48b068c0b9a68 SHA512: 288b73640a0d48a8ce8a023ad92d8f66284813a76ceb094176821c6734f8238e0496e4c5f8ad9ed2b76827f4ba7c91088492a3a382e3ba3875c3b9fa4d5715f3 Homepage: https://cran.r-project.org/package=tidycjk Description: CRAN Package 'tidycjk' (Tidy Tools for Chinese, Japanese and Korean Text) A tidy toolkit for text that is written in Chinese, Japanese or Korean. Most text tooling in R assumes that words are separated by whitespace, which CJK writing does not use, so ordinary summaries of a text column either treat a sentence as one undifferentiated blob or split it into isolated characters. Word segmentation is therefore a pluggable engine that the caller names explicitly rather than a bundled dictionary, because where a word ends is a fact about a language and not about Unicode. 'tidycjk' classifies characters by Unicode block, reports which script and which language a text is written in, measures how much of a text is CJK, and turns those measurements into tibbles that slot straight into a 'tidyverse' workflow. It also measures display width in terminal columns, pads and truncates to a width rather than to a character count, and normalises fullwidth and halfwidth forms surgically -- including composing halfwidth katakana voiced marks into single code points -- without the collateral damage of a full 'NFKC' pass. Language detection deliberately returns NA rather than guessing when a text is written in Han characters only, because Japanese written without kana cannot be distinguished from Chinese by script alone. Everything is derived from the Unicode specification; the package makes no network requests and needs no compiled code of its own. Package: r-cran-tidyclust Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2059 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-dials, r-cran-dplyr, r-cran-flexclust, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-mclust, r-cran-modelenv, r-cran-parsnip, r-cran-philentropy, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs Suggests: r-cran-butcher, r-cran-cluster, r-cran-clusterr, r-cran-clustmixtype, r-cran-covr, r-cran-dbscan, r-cran-future, r-cran-future.apply, r-cran-klar, r-cran-knitr, r-cran-lpcm, r-cran-meanshiftr, r-cran-mirai, r-cran-modeldata, r-cran-rcpphungarian, r-cran-recipes, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-workflows Filename: pool/dists/noble/main/r-cran-tidyclust_0.3.2-1.ca2404.1_all.deb Size: 572112 MD5sum: b8a6d95f0bf238edf788c3a44b216b31 SHA1: edda764f886b7a516e30f7b7f208dfb9a7aa99b3 SHA256: 7571ed3547159f3a52a7855d36f6450bc2493a2505f1aa0d1ac5adc86d04c1b6 SHA512: e8f69071c576d793fd59a1f6687829c8a2ed08b812e74a71f257e1b318064d410892352be2ebc77bea52fd3201130080378bfbfbf98d201d37b104d7f1fc39c8 Homepage: https://cran.r-project.org/package=tidyclust Description: CRAN Package 'tidyclust' (A Common API to Clustering) A common interface to specifying clustering models, in the same style as 'parsnip'. Creates unified interface across different functions and computational engines. 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Package: r-cran-tidyild Architecture: all Version: 0.4.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-rlang, r-cran-lme4, r-cran-nlme, r-cran-ggplot2, r-cran-mgcv Suggests: r-cran-bh, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-rcppeigen, r-cran-broom.mixed, r-cran-clubsandwich, r-cran-jsonlite, r-cran-yaml, r-cran-tsibble, r-cran-brms, r-cran-kfas, r-cran-ctsem Filename: pool/dists/noble/main/r-cran-tidyild_0.4.1-1.ca2404.2_all.deb Size: 1271788 MD5sum: 73141df954f26b8488109a09edf1da31 SHA1: c4f717cf2b5bcbf08d8f4f80f4f32e40ad76d524 SHA256: 363eabe600c6892cbc4110d695b428504416bbe1d119b6d28b46a7c889371f2c SHA512: 424019e55c603c77ade91f136445ae80ce8fab0aa58831f86096e14cf11ab0b9901ccbd3108faf3caa123af6d5bad2aa8bf0f3687c7140aaddf26ada60610a29 Homepage: https://cran.r-project.org/package=tidyILD Description: CRAN Package 'tidyILD' (Tidy Intensive Longitudinal Data Analysis) A reproducible, tidyverse-style framework for intensive longitudinal data analysis in R, with built-in methodological safeguards, provenance tracking, and reporting tools. Encodes time structure, enforces within-between decomposition, provides spacing-aware lags, and integrates diagnostics and visualization. Use ild_prepare(), ild_center(), ild_lag(), and related functions for a unified pipeline from raw EMA/diary data to interpretable models. Package: r-cran-tidyindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-tsibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-knitr, r-cran-lmomco, r-cran-lubridate, r-cran-rmarkdown, r-cran-slider, r-cran-spei, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyindex_0.1.0-1.ca2404.1_all.deb Size: 973684 MD5sum: c31fd9b36cfa36dce76e9b650805ee95 SHA1: 00bc16aab7b3859d792022e2627baedc8a0b1710 SHA256: 4e12509025b93ceb1fd46ea930ab3103ec668772fa36c06a9baa9b6570a6a90c SHA512: 9176ab6b5d091573cb88c717043e2fe0794205d48976f06e74f22e0c5e58beba8ddd7abeca037f1b1b1fbde6623e29305bfe21d716ceef75ba5967b22c123dd5 Homepage: https://cran.r-project.org/package=tidyindex Description: CRAN Package 'tidyindex' (A Tidy Data Pipeline to Construct, Compare, and Analyse Indexes) Construct and analyse indexes in a pipeline tidy workflow. 'tidyindex' contains modules for transforming variables, aggregating variables across time, reducing data dimension through weighting, and fitting distributions. A manuscript describing the methodology can be found at . Package: r-cran-tidyjson Architecture: all Version: 0.3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-forcats, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-listviewer, r-cran-lubridate, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rprojroot, r-cran-testthat, r-cran-vctrs, r-cran-viridis, r-cran-wordcloud Filename: pool/dists/noble/main/r-cran-tidyjson_0.3.3.1-1.ca2404.1_all.deb Size: 2198494 MD5sum: 6c83135a2770da47bec9e1141cf81d16 SHA1: 7a13d6f6a849f67f1f95c967575fbdbcf7e41f7d SHA256: d482bdfb6bce775e685f827e31ea9a1f8f29d38f210c53bfed946ab1d1a0b035 SHA512: 37912c60925ecc4eac63bb1a0ecbbf6ea245977dadd49ec40b55b52fee5d51c437a7b415d19d011f66e53b7ea74e2c5e490898514e3a94a57f9ab607cae6a52a Homepage: https://cran.r-project.org/package=tidyjson Description: CRAN Package 'tidyjson' (Tidy Complex 'JSON') Turn complex 'JSON' data into tidy data frames. Package: r-cran-tidyklips Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-readxl, r-cran-haven, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyklips_0.3.0-1.ca2404.1_all.deb Size: 144094 MD5sum: cd3757d1c5a058b255e12e6ccbea502c SHA1: 8f74d95ff8aa0b118c53d6efe235a7a1871dc585 SHA256: b3b384372f57384f7a9e458767d760f604977041441f31259e63de37acfae29b SHA512: f9115b6c235178e7cdbfffddafb977017a6a67577f5cdf1d244dd8f24668f68761ec9cc5bc146364f09e8d8d6cffc53a464f2da3ce730e7b27f257f425fa3d8c Homepage: https://cran.r-project.org/package=tidyklips Description: CRAN Package 'tidyklips' (Load Korea Labor & Income Panel Study (KLIPS) Data as DataFrames) Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package. Package: r-cran-tidylaslog Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidylaslog_0.1.2-1.ca2404.1_all.deb Size: 236604 MD5sum: c1ebc6f013ccb7622962056444175018 SHA1: ecbd25c503f455827733acb5a25cb85b51e12290 SHA256: dda8499eb9fcaeaf914fc1e34235044bf02710c9a31cc7ac1f575c72b5e124a2 SHA512: 02c152ffdd651f987a03941fe42761503526e0d56d4967e2773df873ad88acbeb832e10beb62def4697fe2284412839f73bf45f58a2d8e5ec3a2f35898efae44 Homepage: https://cran.r-project.org/package=tidylaslog Description: CRAN Package 'tidylaslog' (Tidy Import, Indexing, and Export of LAS Well Log Data) Provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII Standard) well log files into tidy, analysis-ready tabular formats. The package separates LAS header information and log data into structured components, builds a searchable index across collections of LAS files, and enables reproducible subsetting of wells based on metadata or curve availability. Output tables can be written to CSV or Parquet formats to support large-scale statistical, machine learning, and earth science workflows. The tidy data structure follows Wickham (2014) . The LAS file structure follows the Canadian Well Logging Society LAS standard . Package: r-cran-tidylearn Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-magrittr, r-cran-e1071, r-cran-gbm, r-cran-glmnet, r-cran-nnet, r-cran-randomforest, r-cran-rpart, r-cran-rsample, r-cran-rocr, r-cran-yardstick, r-cran-cluster, r-cran-dbscan, r-cran-mass, r-cran-smacof Suggests: r-cran-arules, r-cran-arulesviz, r-cran-bigrquery, r-cran-car, r-cran-dbi, r-cran-diagrammer, r-cran-dt, r-cran-ggally, r-cran-ggforce, r-cran-gridextra, r-cran-gt, r-cran-httr2, r-cran-jsonlite, r-cran-keras, r-cran-knitr, r-cran-lmtest, r-cran-moments, r-cran-nanoparquet, r-cran-neuralnettools, r-cran-paws.storage, r-cran-readr, r-cran-rhpcblasctl, r-cran-readxl, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpart.plot, r-cran-rsqlite, r-cran-shiny, r-cran-shinydashboard, r-cran-tensorflow, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-tidylearn_0.6.0-1.ca2404.1_all.deb Size: 2082226 MD5sum: f604c5b38c8998c42f1675f1aad36a4d SHA1: 71706918abbc4a92c693b976157b04d0fb540fb6 SHA256: 236ae72c1f9681fbc6977bfc6661a3c5d2172a740fca9cb6f3a5b16c2241ad3a SHA512: 3d2c84ccecfb27a9a5c9f7934093cf308f0085a2a88b4921b763ab3ef84ba295b4e24501fcc80b811f7b17d8c39887fe81d882e4399152fe5a53b642b2f0c437 Homepage: https://cran.r-project.org/package=tidylearn Description: CRAN Package 'tidylearn' (A Unified Tidy Interface to R's Machine Learning Ecosystem) Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for a supervised method, or $fit$model for an unsupervised one. Methods include random forests Breiman (2001) , LASSO regression Tibshirani (1996) , elastic net Zou and Hastie (2005) , support vector machines Cortes and Vapnik (1995) , and gradient boosting Friedman (2001) . Package: r-cran-tidyllm Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-base64enc, r-cran-glue, r-cran-jsonlite, r-cran-coro, r-cran-curl, r-cran-httr2, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-pdftools, r-cran-tibble, r-cran-cli, r-cran-png, r-cran-lifecycle Suggests: r-cran-later, r-cran-processx, r-cran-shiny, r-cran-wooldridge, r-cran-promises, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-httptest2, r-cran-httpuv, r-cran-withr, r-cran-ellmer Filename: pool/dists/noble/main/r-cran-tidyllm_0.7.0-1.ca2404.1_all.deb Size: 1701382 MD5sum: b9e7221221e581e8cfc3a07908a56167 SHA1: c2644badee2f3d4c1f68c29e1ab822e1653839aa SHA256: 74a44aeb81427051e6f6d036528e9af4372efa24a83db0c9a93adc27392d7771 SHA512: 1223200939af67f28f65309ed374f371d5a89a23c79268a9385f553c88a339d353324a3518365abb5ed02836db987f63e296fc1550ecba0fc5418444007d7b3f Homepage: https://cran.r-project.org/package=tidyllm Description: CRAN Package 'tidyllm' (Tidy Integration of Large Language Models) A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'OpenAI', 'Gemini', 'Mistral', and local models via 'Ollama' into R workflows. The package supports text, image, audio, video, and document interactions; a unified media interface for attaching inline files or uploading to provider file stores; batch request APIs for cost-efficient large-scale processing; and a pipeline-oriented interface for seamless integration into data workflows. Web services are available at , , , and . Package: r-cran-tidylo Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-covr, r-cran-ggplot2, r-cran-janeaustenr, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidytext Filename: pool/dists/noble/main/r-cran-tidylo_0.2.0-1.ca2404.1_all.deb Size: 207422 MD5sum: def47a45c0175997bc7b1bd82a26fe83 SHA1: 322554a92cf8ea36ac165cc89f1a1455470fd127 SHA256: 4e48d7a0dac87dd9da79cd5e10c8ec70bef5e09ce553f926d46110658e50c42e SHA512: e70fee7901f8b8f119243a4dd6bc03e65058f6b82d45e9afa8d1c97b794d1c7e9f515453d60df683a74f864376e94f202e5fe1cf30c8eec71be64d4c2221ed47 Homepage: https://cran.r-project.org/package=tidylo Description: CRAN Package 'tidylo' (Weighted Tidy Log Odds Ratio) How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful? Enter the weighted log odds, which 'tidylo' provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available. Package: r-cran-tidylog Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-clisymbols, r-cran-rlang Suggests: r-cran-testthat, r-cran-units, r-cran-nycflights13, r-cran-covr, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-bench Filename: pool/dists/noble/main/r-cran-tidylog_1.1.0-1.ca2404.1_all.deb Size: 147352 MD5sum: 3eefc59ba5fa1aa4905c46151fcfe635 SHA1: ce9d8f8d96c3e0d6fbcfd6b54c4041d74ea40e41 SHA256: 2fdc61926def4c2eb2b30f133d5f879486c6e4c79b2efacfa02cbfd6f0f39960 SHA512: 60264fae17a266962ceecc0de19fc588fae3531a3901622acdd4a0cd64115345cf67578658798e09bda79ac5217196cac9b20fc103897730d85e0bcd67d13aec Homepage: https://cran.r-project.org/package=tidylog Description: CRAN Package 'tidylog' (Logging for 'dplyr' and 'tidyr' Functions) Provides feedback about 'dplyr' and 'tidyr' operations. Package: r-cran-tidylpa Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 963 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidysem, r-cran-dplyr, r-cran-ggplot2, r-cran-mclust, r-cran-mplusautomation, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-lme4, r-cran-missforest, r-cran-pillar, r-cran-rmarkdown, r-cran-testthat, r-cran-openmx, r-cran-mix Filename: pool/dists/noble/main/r-cran-tidylpa_2.0.2-1.ca2404.1_all.deb Size: 729000 MD5sum: 2b65d347694681ce30ff1c8e62ed843e SHA1: 8d0154d00205d1c81dbd565c8120f4739d3382c8 SHA256: 3965b8f3b08f91b4ad8fc910339d26c01f02ad7b943366d6947e96db3524c593 SHA512: 5bd048a3f55189c5826ac6b10793b1c90b62ca5a832eb90169c78979aadb193050ea4e1edffbaec66f370f8f633f595db2f407ed64f88011d5cf58c4a1f19404 Homepage: https://cran.r-project.org/package=tidyLPA Description: CRAN Package 'tidyLPA' (Easily Carry Out Latent Profile Analysis (LPA) Using Open-Sourceor Commercial Software) Easily carry out latent profile analysis ("LPA"), determine the correct number of classes based on best practices, and tabulate and plot the results. Provides functionality to estimate commonly-specified models with free means, variances, and covariances for each profile. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Models can be estimated using the free open source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program 'MPlus', via the 'MplusAutomation' package. Package: r-cran-tidymatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-pheatmap, r-cran-rmarkdown, r-cran-rtsne, r-cran-umap Filename: pool/dists/noble/main/r-cran-tidymatrix_0.1.0-1.ca2404.1_all.deb Size: 911048 MD5sum: 57a0089ffafe16c462842cac574ebb36 SHA1: 9bf0b434e802566bbb8131279c6ab0b964158335 SHA256: d3bb6d77e56dcb45b67b12de81faaa79b85683694efa84243ba0e1ecfe01cdb5 SHA512: fc8fe4be80a129686c560780af1aeb99b243bc3a43be0230ec333452f69401764e02ed9428b28cdbb3c060ec4897e5b579ad749c3c3ac41d3b76605c760cb3ec Homepage: https://cran.r-project.org/package=tidymatrix Description: CRAN Package 'tidymatrix' (Tidyverse-Style Operations on Matrices with Row and ColumnMetadata) Provides a unified data structure for matrices with associated row and column metadata, enabling 'tidyverse'-style data manipulation. Following the approach of 'tidygraph', users can activate rows, columns, or the matrix itself and operate on it with familiar 'dplyr' verbs, while the matrix and both metadata tables are kept consistent. Package: r-cran-tidymc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-hms, r-cran-kableextra, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidymc_1.0.1-1.ca2404.1_all.deb Size: 189340 MD5sum: 18bd727601680266025e5a9638f88fc6 SHA1: aecf461d71586d13114e310b984e380a62dd430c SHA256: 4c61ccd57b9cc1d72a67544a9b87b63e42613fd0e4a559942bb241bfd99a1641 SHA512: 96db6897aed50e5356f2269f025362ce9f9cdc039fbd477e46472e8384591160ee897fdccdd383160c6a4f320adfcab14c85f945f447eddd84fde2748808a22d Homepage: https://cran.r-project.org/package=tidyMC Description: CRAN Package 'tidyMC' (Monte Carlo Simulations Made Easy and Tidy) Framework to run Monte Carlo simulations over a parameter grid. Allows to parallelize the simulations. Generates plots and 'LaTeX' tables summarizing the results from the simulation. 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To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) . Package: r-cran-tidypredict Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2650 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-jsonlite, r-cran-knitr, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-agua, r-cran-aorsf, r-cran-baguette, r-cran-bonsai, r-cran-c50, r-cran-covr, r-cran-cubist, r-cran-data.table, r-cran-dbarts, r-cran-dbi, r-cran-dbplyr, r-cran-discrim, r-cran-earth, r-cran-glmnet, r-cran-h2o, r-cran-kernlab, r-cran-klar, r-cran-liblinear, r-cran-lightgbm, r-cran-mass, r-cran-mboost, r-cran-mda, r-bioc-mixomics, r-cran-mlbench, r-cran-modeldata, r-cran-naivebayes, r-cran-nnet, r-cran-nycflights13, r-cran-parsnip, r-cran-partykit, r-cran-plsmod, r-cran-quantreg, r-cran-randomforest, r-cran-ranger, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-rpart, r-cran-rsqlite, r-cran-rules, r-cran-sda, r-cran-sparsediscrim, r-cran-survival, r-cran-testthat, r-cran-withr, r-cran-xgboost, r-cran-xrf, r-cran-yaml Filename: pool/dists/noble/main/r-cran-tidypredict_1.2.1-1.ca2404.1_all.deb Size: 880632 MD5sum: 3044b14cdd574819cf864be401245a20 SHA1: 0adab52be1d7854b9fbdfa26977042a69ace00a7 SHA256: 1f341412ab2c9302bc7648400c6a593afc7eadedb395d2962f2e4c682500892a SHA512: 289238a16c1ac80b5da63c3982f04c069a777451505ff3c2ba09306b433f8aaff90a0d5c0004fa3ab7f0ad19102514018ff0136ff3f22dc634c5985a8e165960 Homepage: https://cran.r-project.org/package=tidypredict Description: CRAN Package 'tidypredict' (Run Predictions Inside the Database) It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. Dozens of model classes are supported; see the "Supported models" article at for the current list. Package: r-cran-tidyprf Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tidyprf_0.2.0-1.ca2404.1_all.deb Size: 281824 MD5sum: 67b9a4c601344bcb5323a14fd37da2ba SHA1: 14d5e971bf3c04bb0b5631c958694dcc5965a9d0 SHA256: d0a48fce18fe083de0c4f13f8a2c65ffc9baf6e57f7891c4951888fbbb74f7a2 SHA512: e487a2145203a422ee2f1b70d71735d4a35595c9f9ab36bc64535ba7f0ed366b093d928f4e21d5fb25af3b05fad350eeecc9e9e215a911bd8071081ddcfd28b3 Homepage: https://cran.r-project.org/package=tidyprf Description: CRAN Package 'tidyprf' (Tidy Access to Brazilian Federal Highway Police ('PRF') Data) Download and read Brazilian Federal Highway Police ('PRF') open data on traffic accidents by person, by occurrence, and traffic violations . 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Package: r-cran-tidyprompt Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-stringr, r-cran-cli, r-cran-r6, r-cran-rlang, r-cran-s7 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-here, r-cran-callr, r-cran-skimr, r-cran-jsonvalidate, r-cran-dbi, r-cran-ellmer, r-cran-coro, r-cran-ggplot2, r-cran-mcptools Filename: pool/dists/noble/main/r-cran-tidyprompt_0.4.0-1.ca2404.1_all.deb Size: 784482 MD5sum: 12bfc0f9672871cb5b7894087ffc2db9 SHA1: 5653789a943f8eecf70a8fa5639d9643214598ba SHA256: ef4ee76ad4d72bff09c8ad0dc238fadd2d32231ceaf2937a0ff9100dcd619cae SHA512: 623bf65a8177f12acb10e5cb3c2089878f030882b5f01e2cf5140a8211a799b3b97013770633b91c9da574851fcef1bfa15238814de6b62fb0b69ae674b4d02f Homepage: https://cran.r-project.org/package=tidyprompt Description: CRAN Package 'tidyprompt' (Prompt Large Language Models and Enhance Their Functionality) Easily construct prompts and associated logic for interacting with large language models (LLMs). 'tidyprompt' introduces the concept of prompt wraps, which are building blocks that you can use to quickly turn a simple prompt into a complex one. Prompt wraps do not just modify the prompt text, but also add extraction and validation functions that will be applied to the response of the LLM. This ensures that the user gets the desired output. 'tidyprompt' can add various features to prompts and their evaluation by LLMs, such as structured output, automatic feedback, retries, reasoning modes, autonomous R function calling, and R code generation and evaluation. It is designed to be compatible with any LLM provider that offers chat completion. Package: r-cran-tidyquant Architecture: all Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-httr2, r-cran-curl, r-cran-jsonlite, r-cran-lazyeval, r-cran-lubridate, r-cran-magrittr, r-cran-performanceanalytics, r-cran-robstattm, r-cran-quantmod, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-timetk, r-cran-timedate, r-cran-ttr, r-cran-xts, r-cran-rlang, r-cran-zoo, r-cran-cli Suggests: r-cran-alphavantager, r-cran-riingo, r-cran-tibbletime, r-cran-broom, r-cran-knitr, r-cran-forcats, r-cran-rmarkdown, r-cran-testthat, r-cran-scales, r-cran-rblpapi, r-cran-janitor Filename: pool/dists/noble/main/r-cran-tidyquant_1.0.12-1.ca2404.1_all.deb Size: 1171370 MD5sum: c1a87581355d95c79e9d78dceac4ceea SHA1: f9996a6bc81d52c0b7db71c4c0b6260b992c8a20 SHA256: 251f929b7400888aab322a3f824fff30392d49f84f03366447b6ca68dc74a4e9 SHA512: 64322d832ce67c17d50dff0839a5217111767014eeac0e59a1b30b026a0eb6ed8c07007c57d714d1bc20974780bc25ba09e929ceef4023313e40f2fc6a40b63d Homepage: https://cran.r-project.org/package=tidyquant Description: CRAN Package 'tidyquant' (Tidy Quantitative Financial Analysis) Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples. 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Package: r-cran-tidyqwi Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-xml2, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-labelled, r-cran-furrr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tidyqwi_0.1.2-1.ca2404.1_all.deb Size: 164070 MD5sum: 0eb22328f943bb3def17144567ef652f SHA1: fd2957e4f4dd50ae4fdaf96be8d0ea1f073b717d SHA256: d423db68fb5b717be71b1573f6d1a4a7e406662a8490825fd1cd84956d3a0edd SHA512: 80c5db6a6c076c1a95691b7c1db502ba0924a64c8b37c2ef0553baab9aab4859f0d69ed0bc9713e1f3127350661bd8ab3ac7032b15d7532a07ec8a637defd436 Homepage: https://cran.r-project.org/package=tidyqwi Description: CRAN Package 'tidyqwi' (A Convenient API for Accessing United States Census Bureau'sQuarterly Workforce Indicator) The purpose of this package is to access the United States Census Bureau's Quarterly Workforce Indicator data. 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Package: r-cran-tidyredcap Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-janitor, r-cran-keyring, r-cran-labelvector, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-knitr, r-cran-redcapapi, r-cran-rmarkdown, r-cran-skimr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyredcap_2.1.0-1.ca2404.1_all.deb Size: 849264 MD5sum: d34e6dc194cd16ac7d2c3bd7d42cd233 SHA1: ec68fc5624b2c5581f1e92f02b9f32813e2c8282 SHA256: e5b74594ad3c11e43a25d8520d6fcd2109fa805105606741044d1e27ba750623 SHA512: e543b546b1298c93e68c027e273eeeecb919ca1f54533d6dae10172a0ac03d8f3fcaf217e793b9f33062de2931255f27e748f130c2ec54fd132968e288315fb2 Homepage: https://cran.r-project.org/package=tidyREDCap Description: CRAN Package 'tidyREDCap' (Helper Functions for Working with 'REDCap' Data) Helper functions for processing 'REDCap' data in R. 'REDCap' is a web-enabled application for building and managing surveys and databases developed at Vanderbilt University. 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Package: r-cran-tidyrhrv Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rhrv, r-cran-purrr, r-cran-magrittr, r-cran-pracma, r-cran-tibble Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-vroom, r-cran-readr Filename: pool/dists/noble/main/r-cran-tidyrhrv_1.1.0-1.ca2404.1_all.deb Size: 32550 MD5sum: 1ca6c826a4f8d9d3904715ebf2e6157c SHA1: 34a2db05c5a57a9f2172ada38a5b1cd07657b54a SHA256: d063497a15351a63a33e7105a5eea10d52d12f5ed5d52de28e42fa2f0348b39f SHA512: 8f7f54e31f5adba4ef8553bf9895f81ce6351a6c4dfa784e768b0335155199c842d6497cc9c25b9b10388773a9f0b1714596d8f68f3e6280286353bace0f7278 Homepage: https://cran.r-project.org/package=tidyrhrv Description: CRAN Package 'tidyrhrv' (Read, Iteratively Filter, and Analyze Multiple ECG Datasets) Allows users to quickly load multiple patients' electrocardiographic (ECG) data at once and conduct relevant time analysis of heart rate variability (HRV) without manual edits from a physician or data cleaning specialist. The package provides the unique ability to iteratively filter, plot, and store time analysis results in a data frame while writing plots to a predefined folder. This streamlines the workflow for HRV analysis across multiple datasets. Methods are based on Rodríguez-Liñares et al. (2011) . Examples of applications using this package include Kwon et al. (2022) and Lawrence et al. (2023) . Package: r-cran-tidyrss Architecture: all Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-jsonlite, r-cran-purrr, r-cran-anytime, r-cran-rlang, r-cran-glue, r-cran-vctrs, r-cran-tidyselect Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyrss_2.0.7-1.ca2404.1_all.deb Size: 78894 MD5sum: 390b518669fa05d46507e022e908ad60 SHA1: 85d81a115f1d5b4ba65c93dcfe300b0024687124 SHA256: d6bc2d6f020db6aa5af302353e559d6584792f74bd4ffdbb2189a91480a92ece SHA512: 2959cb683935103703085448dba1916d1a26a16a60afc845902c5f7c1edcc1a74bab6a72b8dc01a97e5ea77f757083399adb091dde282fe4c0c774f45ddc4e8a Homepage: https://cran.r-project.org/package=tidyRSS Description: CRAN Package 'tidyRSS' (Tidy RSS for R) With the objective of including data from RSS feeds into your analysis, 'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame. Package: r-cran-tidyrstats Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-glue, r-cran-purrr, r-cran-dplyr, r-cran-rlang, r-cran-stringr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tidyrstats_0.1.0-1.ca2404.1_all.deb Size: 18344 MD5sum: e30e77023137cae9c0e38d8cf43138c7 SHA1: efad0058eeb7a9af96a12c8e07ca554731ef3b12 SHA256: bc96bef046e11af69e8c35dcad6b8a58f693dcab3d3711b6723d496f64c5d53c SHA512: 4b66dedf82525c1bf12a246f35a165af3f8035c673374b1cca3c1c9332b8cd340e69bddce6ec1c5457f04b0b858fe9ccdc44ba0806856df836f2205d05e9d4ee Homepage: https://cran.r-project.org/package=tidyrstats Description: CRAN Package 'tidyrstats' (Tidy Common R Statistical Functions) Provides functions to scale, log-transform and fit linear models within a 'tidyverse'-style R code framework. Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in 'broom' Robinson (2017) . Package: r-cran-tidyrules Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-magrittr, r-cran-purrr, r-cran-partykit, r-cran-rlang, r-cran-generics, r-cran-checkmate, r-cran-tidytable, r-cran-data.table, r-cran-desctools, r-cran-metricsweighted, r-cran-cli, r-cran-glue, r-cran-pheatmap, r-cran-proxy, r-cran-tibble Suggests: r-cran-ameshousing, r-cran-dplyr, r-cran-c50, r-cran-cubist, r-cran-rpart, r-cran-rpart.plot, r-cran-modeldata, r-cran-testthat, r-cran-mass, r-cran-mlbench, r-cran-rmarkdown, r-cran-palmerpenguins Filename: pool/dists/noble/main/r-cran-tidyrules_0.2.7-1.ca2404.1_all.deb Size: 622948 MD5sum: 7e4db159c2f187c127c6d70b30cb2a35 SHA1: 5a4bc80fcf72cd7d92d3059ec3914d212a1a5ef9 SHA256: 10d284add023df5e706cc2faee610b60382820f9285d23bef117e8c2ab62c570 SHA512: 48f58f53e003d0833c7ab8b6d2687b59c8832a02be5695046323d84fb56c472547a62e797d13a8ee91526d3297957f238e7fd53f26e00a2f27e25492fa7cddca Homepage: https://cran.r-project.org/package=tidyrules Description: CRAN Package 'tidyrules' (Utilities to Retrieve Rulelists from Model Fits, Filter, Prune,Reorder and Predict on Unseen Data) Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe. Package: r-cran-tidysdm Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidymodels, r-cran-spatialsample, r-cran-dalex, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-maxnet, r-cran-parsnip, r-cran-patchwork, r-cran-recipes, r-cran-rsample, r-cran-rlang, r-cran-stars, r-cran-sf, r-cran-terra, r-cran-tibble, r-cran-tune, r-cran-xgboost, r-cran-workflows, r-cran-workflowsets, r-cran-yardstick Suggests: r-cran-blockcv, r-cran-data.table, r-cran-dalextra, r-cran-doparallel, r-cran-earth, r-cran-kernlab, r-cran-knitr, r-cran-mgcv, r-cran-overlapping, r-cran-pastclim, r-cran-ranger, r-cran-rgbif, r-cran-rmarkdown, r-cran-spelling, r-cran-stacks, r-cran-testthat, r-cran-tidyterra, r-cran-vdiffr, r-cran-ggpattern, r-cran-rhpcblasctl Filename: pool/dists/noble/main/r-cran-tidysdm_1.0.4-1.ca2404.1_all.deb Size: 4353898 MD5sum: 4f140f6a9709fd2d1f09008460b98ed3 SHA1: 19717adab339f73df9aaecac11643fe87483a0de SHA256: be8e3f73eb1541eb68cbb974cea8a1fcb19e2ae820f5590ba35bc05e5e3e28c4 SHA512: 45825c6d35a749e12ba46cf81541d82f2386e7b549fa1279050f8681de35bc788208cfe7ca2c9562f5b6b1fb57a953890a9143770e2d49a8dd85ea97020c0132 Homepage: https://cran.r-project.org/package=tidysdm Description: CRAN Package 'tidysdm' (Species Distribution Models with Tidymodels) Fit species distribution models (SDMs) using the 'tidymodels' framework, which provides a standardised interface to define models and process their outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects, models and metrics specific to SDMs, as well as a number of specialised functions to process occurrences for contemporary and palaeo datasets. The full functionalities of the package are described in Leonardi et al. (2024) . Package: r-cran-tidysem Architecture: all Version: 0.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4260 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-lavaan, r-cran-mplusautomation, r-cran-psych, r-cran-gtable, r-cran-dbscan, r-cran-rann, r-cran-matrix, r-cran-car, r-cran-future.apply, r-cran-progressr, r-cran-progress, r-cran-nonnest2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-dplyr, r-cran-stringr, r-cran-covr, r-cran-tidylpa, r-cran-polca, r-cran-umx, r-cran-mclust, r-cran-mass, r-cran-scales, r-cran-yaml, r-cran-formatr, r-cran-dagitty, r-cran-mice, r-cran-ggraph, r-cran-openmx, r-cran-rmarkdown, r-cran-blavaan, r-cran-bain, r-cran-future, r-cran-igraph Filename: pool/dists/noble/main/r-cran-tidysem_0.2.12-1.ca2404.1_all.deb Size: 2692906 MD5sum: bd3ace0875ea8771d4f26409fe4e79b4 SHA1: 4647a527c20683112b95c38c102252b4ce1ee5e3 SHA256: 58005816af4222638c73d49825c624122b176a026936ed7fcaa480201b8030f8 SHA512: 670fb9a0f06d98cc0cb3b147d1333f720570be6ab2c8088376274e833a5912e4e3325bc5d1e3c16720b0c469ce3b32c6872a06949efb7e4fb47fcddf2c1b1f5a Homepage: https://cran.r-project.org/package=tidySEM Description: CRAN Package 'tidySEM' (Tidy Structural Equation Modeling) A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. 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The 'tidyspec' package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" , Sternberg (1983) "Biomedical Image Processing" , Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" , Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" , Wickham et al. (2019) "Welcome to the tidyverse" , and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" . 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We provide tools for ordering a sequential synthesis, feature and target engineering, sampling, hyperparameter tuning, enforcing constraints, and adding extra noise during a synthesis. Package: r-cran-tidytable Architecture: all Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-pillar, r-cran-rlang, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-testthat, r-cran-bit64, r-cran-knitr, r-cran-rmarkdown, r-cran-crayon Filename: pool/dists/noble/main/r-cran-tidytable_0.11.2-1.ca2404.1_all.deb Size: 395390 MD5sum: 420d20a456f4a9aca6e17c2d71521620 SHA1: af023f56aba4543db76f939bd2e7442ab7a5de68 SHA256: 772eddf6927f15af57610cff8426bd273aafe0d273b48c807fe917d812491cff SHA512: 82b48a2ff8f5898c66616f7d39c0ad6d81553a363f0ae7a4ccf34cd0f127ccc7e7edcb8f12f2dd4db2c83587bec19b93cc35dcb062334f2b76cc326a058599b6 Homepage: https://cran.r-project.org/package=tidytable Description: CRAN Package 'tidytable' (Tidy Interface to 'data.table') A tidy interface to 'data.table', giving users the speed of 'data.table' while using tidyverse-like syntax. 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It is designed to let users manipulate spatial data with familiar 'dplyr' and 'tidyr' verbs before visualizing results with 'ggplot2'. Package: r-cran-tidytext Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-janeaustenr, r-cran-lifecycle, r-cran-matrix, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tokenizers, r-cran-vctrs Suggests: r-cran-broom, r-cran-covr, r-cran-data.table, r-cran-ggplot2, r-cran-hunspell, r-cran-knitr, r-cran-mallet, r-cran-nlp, r-cran-quanteda, r-cran-readr, r-cran-reshape2, r-cran-rmarkdown, r-cran-scales, r-cran-stm, r-cran-stopwords, r-cran-testthat, r-cran-textdata, r-cran-tidyr, r-cran-tm, r-cran-topicmodels, r-cran-vdiffr, r-cran-wordcloud Filename: pool/dists/noble/main/r-cran-tidytext_0.4.3-1.ca2404.1_all.deb Size: 2637512 MD5sum: 33bcc9200186caf882345e44a4226ddd SHA1: 9c56ca8ebc8c94b99eecbaf33bbace206fa8f4f6 SHA256: e12ac21a0f64687d557ddcb5d606134dba65ee87afa44640e9569c822f68c238 SHA512: 5c99675a9d302de2883fb814bb96e26228e26d1e90f973da173cc31eca733577fc18207bfaba219456cfdf8ec5bc6a357757a9ccaa8a552ff8d0d2c468adfe35 Homepage: https://cran.r-project.org/package=tidytext Description: CRAN Package 'tidytext' (Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools) Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages. Package: r-cran-tidytidbits Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-forcats, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-extrafont, r-cran-magrittr Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-tidytidbits_0.3.3-1.ca2404.1_all.deb Size: 170466 MD5sum: ef7bad2dee3358f14074f7cc40c3e253 SHA1: 0ca9185df425c6d78013f506c3deca955aa5f6d5 SHA256: 7a1157723d94fdceada581061940460f06968d5f16de0379a9401c0bfe05dbc7 SHA512: 27887d18643a9830b95d644e8f89e2ed4601e6821717e1efab07196c596df1b3520cfcd2cb1ac15a69a0992eb592808b7f16ea943a2bb2f3f53280e9abbb2e10 Homepage: https://cran.r-project.org/package=tidytidbits Description: CRAN Package 'tidytidbits' (A Collection of Tools and Helpers Extending the Tidyverse) A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages. This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of 'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count, various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks, saving to PDF and PNG from a pipe and various small utilities. Package: r-cran-tidytitanic Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tidytitanic_0.0.1-1.ca2404.1_all.deb Size: 389700 MD5sum: 29a9dd8e384c38a6615bf0cfdc78abeb SHA1: 2c1c40cc9ef11f9f17ddc856f75f869cea089a26 SHA256: cc0bef65ad6de577eb8a90ad9208e827840306df78cee4f9e356707cf407ac31 SHA512: 51a5d7d94b3162c343e5362d52685e306e84eae9b79242abdc08352ba614d607d022a458d9b0269988f6be5f0356ba688af52449e24975b4f0e2cf88332cb0bf Homepage: https://cran.r-project.org/package=tidytitanic Description: CRAN Package 'tidytitanic' (Dataframes Based on Titanic Passengers and Crew) A version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, 'titanic', reproduces the Kaggle competition files with minimal preprocessing, 'tidytitanic' combines the train and test datasets into the single dataset, 'passengers', for exploration and summary across all passengers. It also extracts personal identifiers—such as first names, last names, and titles from the raw 'name' field, enabling demographic analysis. The 'passengers' data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets 'tidy_titanic' and 'flat_titanic' based on the 'Titanic' data set from 'datasets' for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases. 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Functionality includes extracting tidy posterior summaries as in 'tidybayes' , estimating (average) treatment effects, common support calculations, and plotting useful summaries of these. 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As the core data manipulation tool in the 'ggtree' ecosystem, 'tidytree' provides tools for converting tree objects to tidy data frames and tidy interfaces for manipulating, analyzing, and visualizing tree data. Package: r-cran-tidyttmoment Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-tidyr, r-cran-fundiversity, r-cran-funrar, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyttmoment_0.0.5-1.ca2404.1_all.deb Size: 43806 MD5sum: a2c22d3c469f747dc8b855427593e5f8 SHA1: a9a1009e67d0a2672bd9ef484dca66806818c645 SHA256: 06789ea6e6f0070829c90029a01a8f11bb88a5536e2e92714a610a810b5d0e0f SHA512: 1c650b17a700809b8a1b313bfd20db9076608b58217f25045018a75236f28776c7b0c7956525b581cf18e6f4ec89804b8bebfae3333b7a6f4d777b688ace28b4 Homepage: https://cran.r-project.org/package=tidyttmoment Description: CRAN Package 'tidyttmoment' (Functional Trait Moment Calculation) Calculates the community four 'moments' (mean, variance, skewness, and kurtosis) of a given trait based on the moments described in Wieczynski et al. (2019) . These functional metrics are extremely useful in characterizing the distribution of traits in a plant community. It also provides tidyverse-friendly wrappers to seamlessly calculate advanced functional diversity indices (e.g., FDis, Rao's Q) using 'fundiversity' (Grenie et al. 2023 ) and functional rarity indices using 'funrar' (Grenie et al. 2017 ). Evaluating these community-weighted moments and diversity metrics allows researchers to evaluate shifts in optimal phenotypes and understand ecological filtering with exactness. 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This package provides the tools to easily download this data and the description of the source. 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Package: r-cran-tidyusmacro Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyusmacro_0.2.0-1.ca2404.1_all.deb Size: 92662 MD5sum: 9dfe71b63357fdecccc9d4046f666c5d SHA1: 319f118328b50948ecf4719e727dab840256c2fd SHA256: 0df1c672061d9e90c80fb8625ea7f206b8eb1a010e757afffe9b792e40654d71 SHA512: b10b974d8d99e01733c20807ef1e41bc9f43743edd91db1b43f6da6b24d46935f778a87874433478d9dd8cf1e3db2e0eac39dcdbaed7f20f99203348881126fb Homepage: https://cran.r-project.org/package=tidyusmacro Description: CRAN Package 'tidyusmacro' (Downloading and Cleaning U.S. Macroeconomic Data) Utilities to retrieve and tidy U.S. macroeconomic data series from public government data providers. Functions streamline access to series from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics flat files, and the Bureau of Economic Analysis National Income and Product Accounts tables, then return consistent, tidy data frames ready for modeling and graphics. The package includes helpers for date alignment, log-linear projections, and common macro diagnostics, along with convenience plot builders for quick publication-quality charts. Package: r-cran-tidyverse Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-conflicted, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-dtplyr, r-cran-forcats, r-cran-ggplot2, r-cran-googledrive, r-cran-googlesheets4, r-cran-haven, r-cran-hms, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-modelr, r-cran-pillar, r-cran-purrr, r-cran-ragg, r-cran-readr, r-cran-readxl, r-cran-reprex, r-cran-rlang, r-cran-rstudioapi, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-covr, r-cran-feather, r-cran-glue, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyverse_2.0.0-1.ca2404.1_all.deb Size: 417218 MD5sum: d2ce99f3c14d309cf102be4a33b25ba3 SHA1: 1b06fb516148833e9ece8ba0b4e424e03ae4d769 SHA256: 2b0d21dbf092ca14b04874e53793da57fa430b0be95a95d5bba1cb90273653a8 SHA512: ed451a646e1cbbfc402dffc01da32da37949327c6a19d081fc585b0b95f68e2edbdbe0a68d82ba701413459996d478f1fa62aa47229be929e597a61620583ed0 Homepage: https://cran.r-project.org/package=tidyverse Description: CRAN Package 'tidyverse' (Easily Install and Load the 'Tidyverse') The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. 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Package: r-cran-tikatuwq Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-tibble, r-cran-rlang, r-cran-ggplot2, r-cran-tidyr, r-cran-lubridate, r-cran-stringr, r-cran-glue, r-cran-scales, r-cran-broom, r-cran-purrr Suggests: r-cran-testthat, r-cran-spelling, r-cran-rmarkdown, r-cran-knitr, r-cran-pkgdown, r-cran-leaflet, r-cran-withr Filename: pool/dists/noble/main/r-cran-tikatuwq_0.10.0-1.ca2404.1_all.deb Size: 447804 MD5sum: da5fe3b28dd77add1d2d49ca3c11bc6c SHA1: 3740d2fcf3ca798f180833be46185b57da4b751a SHA256: d406a40149f589500c5779d11a5dc0fab4cbebd93f1d9309b34f2ea90819bcbe SHA512: 4f558eb3b2ba3ef186d422aab06691882323dd89273950bf18a36fed12c9d1014ec12c827046b07f4d9a7836ad6e8d6a2e47109a8b9e968d6f5ccb73f564eb5d Homepage: https://cran.r-project.org/package=tikatuwq Description: CRAN Package 'tikatuwq' (Water Quality Assessment and Environmental Compliance in Brazil) Tools to import, clean, validate, and analyze freshwater quality data in Brazil. Implements water quality indices including the Water Quality Index ('WQI'/'IQA') using the weighted geometric mean following 'CETESB' methodology, the Trophic State Index ('TSI'/'IET') after Carlson (1977) and Lamparelli (2004) , and the National Sanitation Foundation Water Quality Index ('NSF WQI', Brown (1970)). The package also checks compliance with Brazilian standard 'CONAMA' Resolution 357/2005 including the legal frequency rule (Art. 15, 80% conformity over six or more samples per year), and provides seasonal analysis with regional flow-season calendars, pollutant load computation, exceedance probability estimation, 'IET' visualization, and multivariate 'PCA' tools for routine monitoring workflows. The example dataset ('wq_demo') is a real subset from 'INEMA' monitoring data from a river in Bahia, Brazil (2020-2024). Package: r-cran-tiktokadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-tiktokadsr_0.1.0-1.ca2404.1_all.deb Size: 22738 MD5sum: c7fe2dd5f8e055cf3aacd5d3089f8304 SHA1: ec012ebc4df08376d860327b87561622d1c60379 SHA256: 61c26f56bd378c0d3bbc53d83aa9c11580dc9ba326c21385722ae1af82a2fed5 SHA512: 47d6e77ea7cc27d4e018141fa1dd42b03d61541d4d87989f0faa3fe18ec77506cd4f218dd1f41a791953d5990df6f1b0c3a5c3258a3b52a6440bd5c270e9a1db Homepage: https://cran.r-project.org/package=tiktokadsR Description: CRAN Package 'tiktokadsR' (Access to TikTok Ads via the 'Windsor.ai' API) Collect marketing data from TikTok Ads using the 'Windsor.ai' API . 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Package: r-cran-tilemaps Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-ggplot2, r-cran-igraph, r-cran-sf, r-cran-smoothr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-spdata Filename: pool/dists/noble/main/r-cran-tilemaps_0.2.2-1.ca2404.1_all.deb Size: 1441786 MD5sum: 7b83ddb8b33839bfcd6d74557aea1219 SHA1: e8bb6bc350fb97f09913b72dcf00664731bcd203 SHA256: 57948ade2d3bbe3829519830ecfc0cb23620b9b45a852ed17fa24ea149978202 SHA512: a404786f9caa71da9d63f8450b3ddfdb86784c335e1e9b2c3ea4fa14d7548863bed8e3eefda32806c6176128dbb8904ef74cf3d3539946b2c1fe85b92ddccd8b Homepage: https://cran.r-project.org/package=tilemaps Description: CRAN Package 'tilemaps' (Generate Tile Maps) Implements an algorithm for generating maps, known as tile maps, in which each region is represented by a single tile of the same shape and size. The algorithm was first proposed in "Generating Tile Maps" by Graham McNeill and Scott Hale (2017) . Functions allow users to generate, plot, and compare square or hexagon tile maps. 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This package provides a tile generator function for creating map tile sets for use with packages such as 'leaflet'. In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images. These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with 'leaflet' when applying the simple CRS option. Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images. This package requires 'Python' and the 'gdal' library for 'Python'. 'Windows' users are recommended to install 'OSGeo4W' () as an easy way to obtain the required 'gdal' support for 'Python'. Package: r-cran-tiltdens Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tiltdens_0.2.1-1.ca2404.1_all.deb Size: 226392 MD5sum: c4066c41a80fd097551cecd68cbe6e0b SHA1: 9fad6ee8a88049370797db1a2008271e51cb87c6 SHA256: 9a513bcd8d3e76c987136ab0050e5684d338a8c3286a8ba9e2b46c437115d691 SHA512: 38c1bd6e8c28a7a738557fdcc6fcf3c93c7c635930ed9fa5cc65e0a0aea69f466c5e65ed5427d36fb269c158d4484c131fd1e044e8a2025e165bf400e5849ff6 Homepage: https://cran.r-project.org/package=tiltdens Description: CRAN Package 'tiltdens' (Tilted and Data-Sharpened Nonparametric Density Estimation) High-order nonparametric density estimators built by perturbing a conventional kernel estimator, either by re-weighting the observations ("tilting") or by moving them ("data sharpening"). The perturbation is chosen so that the estimator inherits the fast convergence rate of an infinite-order kernel estimator, such as the sinc or trapezoidal flat-top estimator, while remaining a proper non-negative density without the oscillatory tails those estimators suffer from. Two criteria are provided: minimising the L2 distance to an infinite-order comparator, following Doosti and Hall (2016) , and minimising a cross-validation criterion that needs no comparator and is much faster, following Doosti, Hall and Mateu (2018) . Package: r-cran-tilting Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-tilting_1.1.1-1.ca2404.1_all.deb Size: 34404 MD5sum: 31f07b22144762863e87f4b28e64dae2 SHA1: 497ada5f5649b9c060b87e75a8bf178015cf4d9f SHA256: 3baa9f7353a395893aa9c4d6cb0e917d1bb97bddcba43521d4b9d533a0334e31 SHA512: 85bb394e6887ef9848e568d23bd3ed9d54e3f85ad3fd05f4bf575b8874e24254312be3fddba925a5d495b76e8448c6760a57512b57abc475f39b615d4d7ca20b Homepage: https://cran.r-project.org/package=tilting Description: CRAN Package 'tilting' (Variable Selection via Tilted Correlation Screening Algorithm) Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way. Package: r-cran-timber Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-cowplot, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-miniui, r-cran-shiny Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-minpack.lm, r-cran-purrr Filename: pool/dists/noble/main/r-cran-timber_2.0.1-1.ca2404.1_all.deb Size: 243976 MD5sum: b4b0aa6a53650dbc83dddb529bd75f7f SHA1: c5f435dfddd9279c1c9f0bef65ea8d427e5e9674 SHA256: 7e482e2bb3428634505eddfd56592b035c3c14553d9a3a8153bf8070c13be3b6 SHA512: e28fc3eb9c643f81f4775d7254734960e226a57f520540e5ce379955c22e06996d56c9696f0423c69283fe8c892dd5072b5f0199d90bd6655357bb7a374f783c Homepage: https://cran.r-project.org/package=timbeR Description: CRAN Package 'timbeR' (Calculate Wood Volumes from Taper Functions) Functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). . Package: r-cran-timbr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidygraph, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-timbr_0.3.0-1.ca2404.1_all.deb Size: 68608 MD5sum: 373a74f55fcf0de68662e145db768f07 SHA1: 2d64a16568a919bc3980edab9f456ebac40484db SHA256: 1b6e95f277a84c33d99c7c3ed007ad38d2c9068abafb045cda1f9706809c9ab1 SHA512: de93308d62da1cee0d717f538d05616b76307a13c36bd15ab2db9572595f99598b1fcd46498439d1cfa28ec82f8b43b17dee95b836140e680949f39910b9ceaf Homepage: https://cran.r-project.org/package=timbr Description: CRAN Package 'timbr' (Forest/Tree Data Frames) Provides data frames for forest or tree data structures. You can create forest data structures from data frames and process them based on their hierarchies. Package: r-cran-time.r Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-readxl Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-time.r_1.0.0-1.ca2404.1_all.deb Size: 35176 MD5sum: d10b1eceb03693d7d2dd65eae486bfbb SHA1: a918334b6dd8dd55818a2a0ad7c47b12c923af8e SHA256: 2b5e3ae04f0207c72109337745d8949329eb2965ef071c36f1dc4fd840276304 SHA512: f07cfa63f3595df983ede40a538762c933fb0d6a1e6cbfb9cd637e32ace0a68f0d1b8ef1ec40e73790c68f6de01fa14774c30fc4a8fcc1549819c1163122a82c Homepage: https://cran.r-project.org/package=Time.R Description: CRAN Package 'Time.R' (Estimates Time of Concentration and Lag Time for Watersheds) Estimation of time of concentration and lag times for watersheds based on their morphometric characteristics. It includes various methods for calculation and offers plotting functionalities for comparative analysis. For more details see Bransby-Williams (1922, ISSN 2214-5818), Kirpich (1940) , Kerby (1959, ISBN-13, 979-8355357214), Johnstone & Cross (1949, ISBN:9780823211234), California Division of Highways (1942, ISSN:0012-7353), Clark (1945) , Giandotti (1934) , Passini (1972, ISBN:84-7433-040-8), Témez (1978, ISBN:84-7433-040-8), Pérez (1962, ISSN:0012-7353), Pilgrim (1977) , Bureau of Reclamation (1973, ISBN:9780913232123), Valencia-Zuluaga (1983) , Ventura & Heras (1964) , Soil Conservation Service (1972, ISBN:OL15009517M), Soil Conservation Service (1986) , US Navy - Technical Publication Navdocks (1972) , Federal Aviation Administration (1970, ISBN:9780913236543), Natural Environment Research Council (1975, ISBN:9780114501234), Mimikou (1984) , Watt & Chow (1985) , Haktanir & Sezen (1990) . Package: r-cran-time.slots Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggfittext, r-cran-ggplot2, r-cran-lubridate, r-cran-scales Suggests: r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-time.slots_0.2.0-1.ca2404.1_all.deb Size: 21306 MD5sum: 0f461d74224c921e47b7ecde1e4644eb SHA1: 7edf44eecebefaa9d490815001b8106fb08d1bb2 SHA256: 2822faab070b6f03a7a6a4251144b9730a32959faba2eac75f4f0557e980deb7 SHA512: 2bd24b6134bfe1af0fc3ddbf988a4ad222ffbe24ba9fa9573df5da99dd6b5aaf2d29e059483cca7a842a376dcc1388c8cda841211a285c09323979b055f365d9 Homepage: https://cran.r-project.org/package=time.slots Description: CRAN Package 'time.slots' (Display Data in a Weekly Calendar View) Generate weekly timetables as a ggplot2 layer. Add informative timeslots with elements such as title, key-value pairs, or colour to reveal trends. 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Beyond these standards it provides the "Financial Center" concept which allows to handle data records collected in different time zones and mix them up to have always the proper time stamps with respect to your personal financial center, or alternatively to the GMT reference time. It can thus also handle time stamps from historical data records from the same time zone, even if the financial centers changed day light saving times at different calendar dates. Package: r-cran-timedelay Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-timedelay_1.0.11-1.ca2404.1_all.deb Size: 122388 MD5sum: 046bf0868cba5c73c3a9d17219f17952 SHA1: b70edff56fa892d8ed1e862854208a20d0713b49 SHA256: 27757ca1405aaff1aab66be2a641b1098889a3d889e42708779ecd5efd275049 SHA512: 88b3637e59db3b243ea21ace936bb2d03ca45b0880a216397256ccf6bdb519cf988f6a81e078415f76c59db93b662ddb52ccdf15a62850bc17fd133367178502 Homepage: https://cran.r-project.org/package=timedelay Description: CRAN Package 'timedelay' (Time Delay Estimation for Stochastic Time Series ofGravitationally Lensed Quasars) We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) , Tak et al. (2018) , Hu and Tak (2020) . Package: r-cran-timedepfrail Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-timedepfrail_0.1.0-1.ca2404.1_all.deb Size: 260156 MD5sum: dd766b2b60a608e7170183d8c19f8f20 SHA1: 8c271bea9ef0cd25b75da89a3c4578610fb8befd SHA256: 758ad32ed29bc0ad68e17def210a674787b32b6f5702a7a7fea615f9bc955694 SHA512: e2b13d393642c54f232cb7d357ae4246f7aeae55da318172f67cc0f35f42dc75b457dea41d015fbe8d75ad51cc84478330d315e13a7cf53a74d3ae2045274b68 Homepage: https://cran.r-project.org/package=TimeDepFrail Description: CRAN Package 'TimeDepFrail' (Time Dependent Shared Frailty Cox Model) Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) . Package: r-cran-timedeppar Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-timedeppar_1.0.3-1.ca2404.1_all.deb Size: 187276 MD5sum: 6c5c87b05ceea3b0e27212a16b7b119e SHA1: 0bf730e9c678a4b15279caaede0caa917e698931 SHA256: 668f8dc206f81bdd63ad30fc1469c59cb9d1968396d72f03d1e9178bccd7ae8f SHA512: f054b123d6b07c99878d754ec4dd6dd512f63447b540a401b300fadd3e2b890beded5e73727106d2f252a8374985cfac85c64ed3b6e23c5c7a845f6111e5d6ab Homepage: https://cran.r-project.org/package=timedeppar Description: CRAN Package 'timedeppar' (Infer Constant and Stochastic, Time-Dependent Model Parameters) Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits. The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes. The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes. References: Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation. 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The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test. Package: r-cran-timevarcorr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpridge Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-timevarcorr_0.1.1-1.ca2404.1_all.deb Size: 365080 MD5sum: 9e3fdbadfecd2b9a22c56c3803c8180e SHA1: d8deea6c55e810893b8b9fc0aa9dc0cc00d1bcca SHA256: d16d10cc24af52e54cb4c5d2680d2d029d999e175402b83560fc4399b9e28b52 SHA512: b7c703c2a0e06e5f168c45acafb9f40f2f58ece2879a8ddf83c9ca0de62bc65a272c85942219fa9299c16e223cae49f17b33f986ce0f7285b9e418db57fdfd72 Homepage: https://cran.r-project.org/package=timevarcorr Description: CRAN Package 'timevarcorr' (Time Varying Correlation) Computes how the correlation between 2 time-series changes over time. To do so, the package follows the method from Choi & Shin (2021) . It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy). An automatic selection procedure for the bandwidth parameter is implemented. Alternative kernels can be used (Epanechnikov, box and normal). Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested. 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Timelines can be included in Shiny apps or R markdown documents. 'timevis' includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Based on the 'vis.js' Timeline JavaScript library. 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Designed with simplicity in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays Averages per country presented in tables with accompanying sparklines, providing a quick and attractive overview of the data. The last tab presents A customizable world map colored based on user-defined variables for any chosen number of countries, offering an advanced visual approach to understanding geographical data distributions. This package operates with just a few simple arguments, enabling users to conduct sophisticated analyses without the need for complex programming skills. Transform your time-series data analysis experience with our user-friendly tool. Package: r-cran-timevtree Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-timevtree_0.3.1-1.ca2404.1_all.deb Size: 125320 MD5sum: ce53410f7268b0f631162ece0713f511 SHA1: f21e8834b68b10dc01d1949c9b6c57c4db23e311 SHA256: d60b002fce5f8bdaf15d9672b990545f93106545433ef747828614a3b8bc201d SHA512: c7c96466c561a1e70a4cd6091b31dccfcf5d770ebabb1db44e9f368a4e602565c06052a50532069006fc6ec40dc52178601f7ebb7f57496ae4e09bb2922f5770 Homepage: https://cran.r-project.org/package=TimeVTree Description: CRAN Package 'TimeVTree' (Survival Analysis of Time Varying Coefficients Using aTree-Based Approach) Estimates time varying regression effects under Cox type models in survival data using classification and regression tree. The codes in this package were originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) , Biometrics, 58: 305-315. Development of this package was supported by NIH grants AG053983 and AG057707, and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS). Package: r-cran-tinkr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-commonmark, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-xml2, r-cran-xslt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tinkr_0.3.1-1.ca2404.1_all.deb Size: 211870 MD5sum: 8d9dabbc2a870d0ca3329d1692eecbb3 SHA1: cd97bb038c6ea025fb393216902b8d31a1714498 SHA256: 1f0bb9acb013ab0e7cfb699dd64c3b78054cf3eef84cccc689b779b0166cab86 SHA512: abcd362ae907493e03d7bebf223c517f9c96b24af99975da262c629ec607041be00986a807891872730899d9f568a2acb338da0b32d3a55787ca8a28a4d0f6c6 Homepage: https://cran.r-project.org/package=tinkr Description: CRAN Package 'tinkr' (Cast '(R)Markdown' Files to 'XML' and Back Again) Parsing '(R)Markdown' files with numerous regular expressions can be fraught with peril, but it does not have to be this way. 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Package: r-cran-tinyarray Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1963 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-hmisc, r-cran-dplyr, r-cran-ggplot2, r-bioc-limma, r-cran-patchwork, r-cran-pheatmap, r-cran-stringr, r-cran-curl, r-cran-httr2, r-cran-survival, r-cran-survminer, r-cran-tibble Suggests: r-cran-testthat, r-cran-annoprobe, r-bioc-geoquery, r-bioc-biobase, r-cran-venndiagram, r-cran-factominer, r-cran-factoextra, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-ggpubr, r-cran-ggplotify, r-cran-tidyr, r-cran-labeling, r-cran-rtsne, r-cran-scatterplot3d, r-bioc-complexheatmap, r-cran-circlize, r-bioc-annotationdbi, r-cran-biocmanager, r-bioc-clusterprofiler, r-bioc-org.rn.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-tinyarray_3.0.0-1.ca2404.1_all.deb Size: 1870680 MD5sum: 589fa1a90d1a102a762bdb1240be5575 SHA1: 0f325c98da9024af8a5281f125b198216f23b3aa SHA256: cafae89d626b8e823cc83e1bad75f3ed9ebb8944f29b2337be4a12463f48f489 SHA512: 9272649e7a2aed7f05aa76de00ed79874d79996048732959dcc523d298756917933075b41a92797a1c8168dc8fd279fe298ed94da2504deb427ca71f54c6100d Homepage: https://cran.r-project.org/package=tinyarray Description: CRAN Package 'tinyarray' (Expression Data Analysis and Visualization) A toolkit for microarray and RNA-seq data analysis, including annotation conversion, differential expression, enrichment, survival analysis, and visualization. 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Provides functions for installing, loading, checking, building, and submitting R packages with minimal dependencies (only 'curl' for uploads). Background on R package development is in Wickham and Bryan (2023, ISBN:9781098134945), "Writing R Extensions" , and the 'CRAN' Repository Policy . 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Data frames can be converted to 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', or 'Typst' tables. The user interface is minimalist and easy to learn. The syntax is concise. 'HTML' tables can be customized using the flexible 'Bootstrap' framework, and 'LaTeX' code with the 'tabularray' package. Package: r-cran-tinytest2junit Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-tinytest2junit_1.1.3-1.ca2404.1_all.deb Size: 103200 MD5sum: 80075e9bb08ea72ed09e46985406b989 SHA1: 78dfa4bad8682bee8cd93bf2e6dcb350f8c8b291 SHA256: 4fe13791dc05d2d353c9e16e21cfa86a915b5d59259e726aa9270686789a3d21 SHA512: 316510780991fbdf886c5c9ec35bf14756a53485c91226baeda03d914cbf8f162e8017ed51b2e0ef147ccb99bfa6c0ca883dfecedb6f631338b80ef3d5e04892 Homepage: https://cran.r-project.org/package=tinytest2JUnit Description: CRAN Package 'tinytest2JUnit' (Convert 'tinytest' Output to JUnit XML) Unit testing is a solid component of automated CI/CD pipelines. 'tinytest' - a lightweight, zero-dependency alternative to 'testthat' was developed. To be able to integrate 'tinytests' results into common CI/CD systems the 'tinytests'-object is converted to JUnit XML format. 'tinytest2JUnit' enables this conversion while staying lightweight, having only 'tinytest' as its dependency. Package: r-cran-tinytest Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tinytest_1.4.3-1.ca2404.1_all.deb Size: 678390 MD5sum: 87fbf64e7c22577ff546b19dc8f76265 SHA1: 1e3e5c710b93d0775018d3546b37b419ed1bd802 SHA256: 8a2e4ef5c5a60208d38d08807d9b44d87f9201faf4089b92b8849b1db08be6ed SHA512: 2cf7b47d1e53d9951191d6a99a58ad6e78650af390bfcf791cec24819ca89fbfaf828464d91a44220d4fa816f9bb9fb992a7d7820afd76429a79c76c0035dd96 Homepage: https://cran.r-project.org/package=tinytest Description: CRAN Package 'tinytest' (Lightweight and Feature Complete Unit Testing Framework) Provides a lightweight (zero-dependency) and easy to use unit testing framework. Main features: install tests with the package. Test results are treated as data that can be stored and manipulated. Test files are R scripts interspersed with test commands, that can be programmed over. Fully automated build-install-test sequence for packages. Skip tests when not run locally (e.g. on CRAN). Flexible and configurable output printing. Compare computed output with output stored with the package. Run tests in parallel. Extensible by other packages. Report side effects. Package: r-cran-tinytex Architecture: all Version: 0.61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xfun Suggests: r-cran-testit, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-tinytex_0.61-1.ca2404.1_all.deb Size: 150446 MD5sum: bcfaef9589d97e2e327b96c3a73bb746 SHA1: e19e93759fd2ea0605071a25aa9522d463372992 SHA256: c11519882e0f66ca202a58199a5fbaec65daffc9aa7369cbb539f2670d1855d7 SHA512: a579e4623ca6da8c8df667892085032563faa45d5c1685d6372e538934169007401f18d76d9dbe795fc46b084f4223b833f96676e5f8b851883462e919c52660 Homepage: https://cran.r-project.org/package=tinytex Description: CRAN Package 'tinytex' (Helper Functions to Install and Maintain TeX Live, and CompileLaTeX Documents) Helper functions to install and maintain the 'LaTeX' distribution named 'TinyTeX' (), a lightweight, cross-platform, portable, and easy-to-maintain version of 'TeX Live'. This package also contains helper functions to compile 'LaTeX' documents, and install missing 'LaTeX' packages automatically. Package: r-cran-tinythemes Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-patchwork Filename: pool/dists/noble/main/r-cran-tinythemes_0.0.4-1.ca2404.1_all.deb Size: 26208 MD5sum: 83bef27de2947c2591549342bef4e767 SHA1: c116d4c42caf7a6985dfc8fe22b8fcf4a5d3be72 SHA256: 78366660585fb96928469ebf8d4a3ad9fd9f8eae3f8f50a580363b92cb984926 SHA512: 26f631e4412c7c545ad81015563b2727e23bd35785ba1ed38b33720f447e39f6c05f9ab5ece9150042690c26a6b94861ee6d1b8f69a44dcf8fd52f0bb0a6057e Homepage: https://cran.r-project.org/package=tinythemes Description: CRAN Package 'tinythemes' (Lightweight Repackaging of 'Themes' for 'ggplot2') Themes for 'ggplot2' are a convenient way to style plots. The 'hrbrthemes' package contains a particularly nice one, but brings along a significant tail of dependencies. So this (currently experimental) package brings along just the 'theme_ipsum_rc' theme using the 'Roboto' 'Condensed' font. Should the font not be installed on your system, see the help in the package 'hrbrthemes' on how to install 'Roboto Condensed'. Note that 'hrbrthemes' is now archived at CRAN. Package: r-cran-tinytiger Architecture: all Version: 0.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-curl, r-cran-sf Suggests: r-cran-knitr, r-cran-rappdirs, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tinytiger_0.0.12-1.ca2404.1_all.deb Size: 207384 MD5sum: 0b20e02cbc617c81221bef3e17993676 SHA1: d73270323a3abf9f2ee5722412c70526ff3127b4 SHA256: e2ea79afe38be8e63e15d4843244ce81bc988195334f79152f1c5ca79b0834b7 SHA512: 1f3b23c7b037bc2283444c10dc25e8c1ad93759bc6d5833b547927f6ba197f754714bbc67d52489fd5373fb84b05ef8ad3f17062ab0dac319837d3c3e7c6f488 Homepage: https://cran.r-project.org/package=tinytiger Description: CRAN Package 'tinytiger' (Lightweight Interface to TIGER/Line Shapefiles) Download geographic shapes from the United States Census Bureau TIGER/Line Shapefiles . 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Harrison, Qing He, and Hsin-Hsiung Huang (2022) . TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors. Package: r-cran-tipa Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tipa_1.0.8-1.ca2404.1_all.deb Size: 27760 MD5sum: dfef8675462c748b98b6a90a7613bd5b SHA1: b274888a3fad841eb36f3fdf00992e3e5ae7f07e SHA256: 9401376b57db75276694a0e7f59e71413ff4e1226a6bf271fbbc74db4111f111 SHA512: d1309401e31621ad1386ee022a736c9dc8768f42f849a068efe6f4ac0848683364303ebc8c1a0c6016707c353a6578e172ab4d58035a3805bf400ef04231b170 Homepage: https://cran.r-project.org/package=tipa Description: CRAN Package 'tipa' (Tau-Independent Phase Analysis for Circadian Time-Course Data) Accurately estimates phase shifts by accounting for period changes and for the point in the circadian cycle at which the stimulus occurs. See Tackenberg et al. (2018) . Package: r-cran-tipdatingbeast Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-teachingdemos, r-cran-desctools Filename: pool/dists/noble/main/r-cran-tipdatingbeast_1.1-0-1.ca2404.1_all.deb Size: 1112586 MD5sum: 0b5c13bc0c482a6d2c11c6a6289e5c41 SHA1: 99ffadffcf8abeeee90c63780ea739c136045337 SHA256: fa1fa781ab9bf76fbe28660087b14aade47c2e87f4c7832365a20a4559a7d1b3 SHA512: 43b4f54a4a8c38423da69ed25f3c8413a1be5435b9f08c01757d3285067a70340299cd3fbbba0c0636c1db48d334dd663ea1d3047adadbbeaca3b86e8a369fc2 Homepage: https://cran.r-project.org/package=TipDatingBeast Description: CRAN Package 'TipDatingBeast' (Using Tip Dates with Phylogenetic Trees in BEAST) Assists performing tip-dating of phylogenetic trees with BEAST BEAST is a popular software for phylogenetic analysis. The package assists the implementation of various phylogenetic tip- dating tests using BEAST. It contains two main functions. The first one allows preparing date randomization analyses, which assess the temporal signal of a data set. The second function allows performing leave-one-out analyses, which test for the consistency between independent calibration sequences and allow pinpointing those leading to potential bias. The included tutorial provides detailed step-by-step instructions. An expanded description of the package can be found in article: Rieux, A. and Khatchikian, C.E. (2017), TIPDATINGBEAST: an R package to assist the implementation of phylogenetic tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613. . Package: r-cran-tipmap Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3157 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-coda, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rbest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tipmap_1.0.1-1.ca2404.1_all.deb Size: 1317868 MD5sum: 0ab5ffc48a2f49e15b62a9d727ca6eac SHA1: 5835c458259046f1064c8a1ade9ea797ca98db46 SHA256: f7a20dd3f43b2f61abe0b3334fca460cae094636aac6c395f040eaf930b9ad08 SHA512: bb76983515819c59da99f4cb5f75aedfd24cb7767b96e4167e507cc6dc52e97604d22c6fb7d939b4e7176102be94ad687dbf29d4388b9a5fe303cc1de4be8ffa Homepage: https://cran.r-project.org/package=tipmap Description: CRAN Package 'tipmap' (Tipping Point Analysis for Bayesian Dynamic Borrowing) Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider. Package: r-cran-tippy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-shiny, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-tippy_0.1.0-1.ca2404.1_all.deb Size: 45600 MD5sum: d7a4fe48f3a23ed8a433b14826d6b3b4 SHA1: d76de492662ff5f935cda78bffdd8c4daf20063b SHA256: bc6ddfd9ec9555bffac7310a1aba841caf2441d0f93b6b29ec14b8a36fe8b685 SHA512: 4016bccf5d5191f590740b3579503cda2a938928fcceef4793ac2f74e42666fcddfecf272a55fcc2d54cfc2d91443785580f713795b2e62fe6de263d0ab6d458 Homepage: https://cran.r-project.org/package=tippy Description: CRAN Package 'tippy' (Add Tooltips to 'R markdown' Documents or 'Shiny' Apps) 'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents. Package: r-cran-tipr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-sensemakr, r-cran-tibble Suggests: r-cran-broom, r-cran-dplyr, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tipr_1.0.2-1.ca2404.1_all.deb Size: 307714 MD5sum: fb93b11a8e3ca600b7e6fdd556b1a50c SHA1: b90f41a7770ac5e62254b5d3fb82fe7a0f5f45e7 SHA256: 1f63f6253b58e82d6af7df06db7eec866ce30f6e4b0afd9a3dc05e03e131fa6a SHA512: 46487db943f38e2487db3cd90039b1298047b69a02ab9c9d396774f95bbbc890b23550ff3b708fc44661aeb438b1d760d9b6bf510e55ac67960faa06e9f2925d Homepage: https://cran.r-project.org/package=tipr Description: CRAN Package 'tipr' (Tipping Point Analyses) The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance. Package: r-cran-tipse Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-survival, r-cran-dplyr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tipse_2.1-1.ca2404.1_all.deb Size: 392376 MD5sum: eeeb92ac752b1852720a21e83547f75c SHA1: fe64991271c024899a22038869004e739ce3df59 SHA256: a87542ab751f8a254ee38daa1ccbedb14f0cb4781236596fb3b79b63d30d208b SHA512: 17d0b7240f9cfcad9c2af7613bd0037e448a3ef50a377e15940d529c7f7d030efe120d7d1f033bf44e733a24dde7e0c340f8e4bb48a2108c1dde4a142a6247aa Homepage: https://cran.r-project.org/package=tipse Description: CRAN Package 'tipse' (Tipping Point Analysis for Survival Endpoints) Implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) . Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses. Package: r-cran-tirt Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3223 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tirt_0.4.0-1.ca2404.1_all.deb Size: 3079332 MD5sum: 3d365483a0a7d47b13be1a9b46f03ca5 SHA1: 3d16855b26491a8d4e2813274db7471a7c1cf667 SHA256: 07cfd0892e0eca9c7d19906de3ea6b15f6f36a2087bdeb16022dc53aa46734a9 SHA512: dc1e0df2ab1d914a5e43e74ccabcaec36eb8579f313658052ca370ff4f234f4fa60cd350392d1238d9093fa905796a4203ba77dfd7a3700fdf24a1a9e3f631e4 Homepage: https://cran.r-project.org/package=tirt Description: CRAN Package 'tirt' (Testlet Item Response Theory) Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) ) and Testlet Response Theory (TRT; Wainer et al., (2007) ). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package also offers Bayesian estimation using priors with maximum a posteriori (MAP) method for unidimensional item response theory models. It also provides functions for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations. A suite of post-estimation tools is also provided, including item and test information functions with the conditional standard error of measurement, summed-score to scale-score conversion tables (expected a posteriori, weighted likelihood, and maximum likelihood), person-fit and item-fit statistics, local dependence diagnostics (Yen's Q3), differential item functioning (M-H and logistic regression), reliability coefficients, test characteristic curves, and mixture (latent-class) item response models. Package: r-cran-tissot Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1892 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-proj Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tissot_0.3.0-1.ca2404.1_all.deb Size: 1771034 MD5sum: 6091e044230320d53bacbcbead48e2cb SHA1: 2f6a4f8cf10ad0f3f5d88f8265553f215b2cfae8 SHA256: 00a25af455821009edb512326f21b9345238d126838a8c299c911debcea97350 SHA512: c7af1e9bbbaaf83507d77fe50f9c0e45cd6b87e42c31ea5eed824fa60c8edc1274c85130244d265bfaca85f6abe3768ce88ce9ddd72c83f2c803de7b2fbdecaf Homepage: https://cran.r-project.org/package=tissot Description: CRAN Package 'tissot' (Tissot Indicatrix for Map Projection Distortion) Compute and visualize the 'Tissot Indicatrix' for map projections. The indicatrix characterizes projection distortion by computing scale factors, angular deformation, areal distortion, and convergence at arbitrary points. Based on the calculations shared by Bill Huber on . Uses 'PROJ' for coordinate transformation and distortion factor computation. Developed using the method published in Snyder, JP (1987) . Package: r-cran-titan2 Architecture: all Version: 2.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-cowplot, r-cran-ggridges, r-cran-purrr, r-cran-tibble, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-titan2_2.4.4-1.ca2404.1_all.deb Size: 2225824 MD5sum: ca52b727271d552466386d9100595645 SHA1: 260fd083bb7b284e437e87b5b4dad333a0e36303 SHA256: 3f327418f62dd331fb83685edb2d5386d8c3e6d2a87dee05828e328549a832a4 SHA512: 06f3d4bccbd601593cb6265350531468b68ccae8631d3a6828e20b85c3056a122a7a5475207c6e5555ac17620ee601c608616e57b5c63d0800690a89128407ea Homepage: https://cran.r-project.org/package=TITAN2 Description: CRAN Package 'TITAN2' (Threshold Indicator Taxa Analysis) Uses indicator species scores across binary partitions of a sample set to detect congruence in taxon-specific changes of abundance and occurrence frequency along an environmental gradient as evidence of an ecological community threshold. Relevant references include Baker and King (2010) , King and Baker (2010) , and Baker and King (2013) . Package: r-cran-titanic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-titanic_0.1.0-1.ca2404.1_all.deb Size: 84492 MD5sum: 0751d023d6849c03b31cf49ddfce788a SHA1: 4a66097cdea5831786279abef45f695d7837d2cc SHA256: f9773271e42bd41d62d41959275345f8165e58ede293963cc9b1401fffc2fc59 SHA512: f506ed8571908984a7e5e05a5d67b4e64ac3ca411d254d1513ffbcd9b3645b64f946d8fc82b16939f443e2464a6641e5aef2011a2ab01758b8bd1c144fe42b0c Homepage: https://cran.r-project.org/package=titanic Description: CRAN Package 'titanic' (Titanic Passenger Survival Data Set) This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner "Titanic", summarized according to economic status (class), sex, age and survival. Whereas the base R Titanic data found by calling data("Titanic") is an array resulting from cross-tabulating 2201 observations, these data sets are the individual non-aggregated observations and formatted in a machine learning context with a training sample, a testing sample, and two additional data sets that can be used for deeper machine learning analysis. These data sets are also the data sets downloaded from the Kaggle competition and thus lowers the barrier to entry for users new to R or machine learing. Package: r-cran-titegboin Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-titegboin_0.4.0-1.ca2404.1_all.deb Size: 59326 MD5sum: 90b4fe70d8e2905ed291d0023d50ff2b SHA1: a684f1c725ff8d87468c048ffb37f83ecba48925 SHA256: ca04d4bc84af4a491752a9d40af60fe8a27d06c7d153f889e92555916ed15c2e SHA512: a078b936fd77f7f42a1bb160028aecd577b1dcaa738100ab34bd5575daf113f85d21f0960242e46498ed167faac0a9d564a9d50eecc5a839dd1490c95ea86160 Homepage: https://cran.r-project.org/package=TITEgBOIN Description: CRAN Package 'TITEgBOIN' (Time-to-Event Dose-Finding Design for Multiple Toxicity Grades) In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) ), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) ), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) ) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) ) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules. Package: r-cran-titeir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso Filename: pool/dists/noble/main/r-cran-titeir_0.1.0-1.ca2404.1_all.deb Size: 27200 MD5sum: cb8355f35e9c414867797881647fc5af SHA1: f4a0b6a1ea0ca94bc2cafdbcc84c3536737ecd7d SHA256: 2d287ee68e0e4c56513f14715845d25ca9d14e58285c816af50bf9b906e51af4 SHA512: 0ff4315e88767a55f0673a0a46e1d6c7848c49408fda63fd3c8a6b89ee1eae59765720ab45f2f6f4efcab9eeb6717226f564dcd8722ff0ca28b65e87f345ab91 Homepage: https://cran.r-project.org/package=titeIR Description: CRAN Package 'titeIR' (Isotonic Designs for Phase 1 Trials with Late-Onset Toxicities) Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability. Package: r-cran-titrationcurves Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-titrationcurves_0.1.0-1.ca2404.1_all.deb Size: 461698 MD5sum: 60d08a052a935b95de95777219d7b8bb SHA1: 8bc6d5f125cb83943c2663bbe6d63bbb7d347b43 SHA256: 11a6e82c820cb42695683b2e717b5eb2c4c693fbbc78690c3cfb328e7828b474 SHA512: 7d5cb7f101a1da966200349b56065de68faf56321aedadd9966017f5bea0336e61bce5507999adaf55f083948bccfbf36fbee7ac5cf9de3da30443a540a20b40 Homepage: https://cran.r-project.org/package=titrationCurves Description: CRAN Package 'titrationCurves' (Acid/Base, Complexation, Redox, and Precipitation TitrationCurves) A collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points. Package: r-cran-tivy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-stringr, r-cran-stringi, r-cran-ggplot2, r-cran-leaflet, r-cran-rcolorbrewer, r-cran-rlang, r-cran-httr, r-cran-rvest, r-cran-jsonlite, r-cran-pdftools, r-cran-future, r-cran-future.apply, r-cran-patchwork, r-cran-scales Suggests: r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tivy_0.1.1-1.ca2404.1_all.deb Size: 1625224 MD5sum: abfc6bf70118b66fb79d90a4f8a5b89c SHA1: ead76c1165b7e654dc957fda5326cf073e833476 SHA256: a7fdb4014715662280a7a66fec340b04432ff646f533b75cd363696a9665fa05 SHA512: 019d4405882b13ac68c3e50b4524b1644a78c928547f1773d0830d96392e176ad3a69768cebb3d201ea57ffb8b127a845c6cfc51e431e42aafbba38d8f34b07b Homepage: https://cran.r-project.org/package=Tivy Description: CRAN Package 'Tivy' (Toolkit for Investigation and Visualization of Young Anchovies) Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones. Package: r-cran-tkapprox Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-numderiv, r-cran-maxlik, r-cran-rlang, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-tkapprox_0.1.0-1.ca2404.1_all.deb Size: 360872 MD5sum: 10760dd6f7334cab25d0df292cb57f6e SHA1: 0507effe48bc43b128f8a6b81c65066a3696f1e4 SHA256: a0bea3bf1dcd41f3ee0b5eafa4c1ef22f63d8ddab9755f1b0a2f8cda9cbf9eb7 SHA512: 18a3ee8d6b9fc49553908367aaf04cc05c79b001cd3a30a9a1300f730bc0ec67f33c2ff8779fc2e37b2c38809e91875d304092d647482be034e744a19f0d6040 Homepage: https://cran.r-project.org/package=TKApprox Description: CRAN Package 'TKApprox' (A General Framework for Bayesian Estimation Using the'Tierney'-'Kadane' Approximation) Provides a distribution-independent framework for Bayesian estimation of arbitrary univariate probability models using the 'Tierney'-'Kadane' approximation ('Tierney' & 'Kadane', 1986 ). Users specify the probability distribution, likelihood, prior distributions, and censoring mechanism, while the package automatically constructs the posterior distribution, computes posterior modes and Hessian matrices, approximates posterior expectations under several Bayesian loss functions, and returns Bayesian parameter estimates, posterior covariance matrices, credible intervals, diagnostic plots, and model comparison statistics. Supports complete, right-, left-, interval-, Type-I, Type-II, progressive Type-II, hybrid, and doubly censored data ('Lawless', 2003 ; 'Meeker' & 'Escobar', 1998 ; 'Balakrishnan' & 'Aggarwala', 2000 ; 'Kundu' & 'Pradhan', 2009 ), making it a flexible tool for Bayesian reliability, survival, and lifetime data analysis. Package: r-cran-tkcat Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4053 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-redamor, r-cran-dbi, r-cran-visnetwork, r-cran-dplyr, r-cran-clickhousehttp, r-cran-rlang, r-cran-tidyselect, r-cran-askpass, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-htmltools, r-cran-readr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-markdown, r-cran-promises, r-cran-future, r-cran-xml2, r-cran-matrix, r-cran-uuid, r-cran-crayon, r-cran-roxygen2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rclickhouse, r-cran-stringr, r-cran-data.tree, r-cran-bed Filename: pool/dists/noble/main/r-cran-tkcat_1.2.3-1.ca2404.1_all.deb Size: 1551628 MD5sum: 1b78ece1edec8366f01d5a5f029a9ded SHA1: 2bb09795b46a383a6944257753de9ec662bf78e2 SHA256: 6286cc4a0270e78922e511ab7029eec1748bfd9a96e50846342264534d3accdb SHA512: 4c215e4d8fe06b6e9576f90a4ee355b709a41814887326a2b632ff07f28de9c038d4336dec5c25909f4cc927cee13b367c1708b4189385fcdfd1ca972e0e1e82 Homepage: https://cran.r-project.org/package=TKCat Description: CRAN Package 'TKCat' (Tailored Knowledge Catalog) Facilitate the management of data from knowledge resources that are frequently used alone or together in research environments. In 'TKCat', knowledge resources are manipulated as modeled database (MDB) objects. These objects provide access to the data tables along with a general description of the resource and a detailed data model documenting the tables, their fields and their relationships. These MDBs are then gathered in catalogs that can be easily explored and shared. Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and create new catalogs suited for specific needs. Package: r-cran-tkimgr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tkrplotr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tkimgr_0.0.5-1.ca2404.1_all.deb Size: 78648 MD5sum: 5e2b0903062f7c14fef7dd5ba6f054c4 SHA1: 2e4c5be8734916ee1d6fb831621982622e0e044d SHA256: 1f91b7529c8a15d0383cac07bdd4b3f8f95c6d7d69516fbc5bc4a7dbd55e90f8 SHA512: dd6a1f1e1e7acad5567221a905fd6402eb71bb28abaeda89b68404feb3e75dc459e98287a98abff1e4e2df28a14cd139d55ca4b2ef20e24fc31199d5178ca477 Homepage: https://cran.r-project.org/package=tkImgR Description: CRAN Package 'tkImgR' (Simple Image Viewer for R Using the 'tcltk' Package) A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 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Package: r-cran-tl Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rspdlite Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-tl_0.0.2-1.ca2404.1_all.deb Size: 21968 MD5sum: 4f1f321564b0dd80dc0540a9b48eb776 SHA1: 5c74cfee559433527ddf06facf2d8b5fd21505bd SHA256: 28d5078e2f2b7e9910a87690f67b929031567a44c6508c02e588d4feabe77e73 SHA512: 9f2db0a1d84a8a6e9fd125f402e8cb5a1623568e4475052932880e8c13492a942481564b28c1e38d4df639d33061d4052a06ea099063f43e3e49e1be2cdecbb2 Homepage: https://cran.r-project.org/package=tl Description: CRAN Package 'tl' (Tiny Logging Interface to 'rspdlite' Wrapping 'spdlite' C++20Logging) Just how 'spdl' provides a nice and consistent interface to 'spdlog' (via 'RcppSpdlog'), this package does so for 'spdlite', the lightweight header-only C++-20 logging library that provides a lighter version of 'spdlog'. This package is essentially a thin shim around it for a more compact interface from both R and C++. Package: r-cran-tlaginterim Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-tlaginterim_1.1-1.ca2404.1_all.deb Size: 101538 MD5sum: 08551eb54a7dc11915d49ea95815617d SHA1: 4fe55e71863eff4ef28da6dd472a966e29c5442d SHA256: f9204a5582fbe9e377b07288f66718f3eae1633423f8ce6cc7f28cc975338b6c SHA512: 685cbabc75921a0e6439147ca93a3b4d218d424a328ace8d3291fb80f9cee241e64d97c10ef053cc4fb57f0247dac78dc5e504f891436123d5f149972a8b1995 Homepage: https://cran.r-project.org/package=tLagInterim Description: CRAN Package 'tLagInterim' (Interim Monitoring of Clinical Trials with Time-Lagged Outcome) Implements inverse and augmented inverse probability weighted estimators for common treatment effect parameters at an interim analysis with time-lagged outcome that may not be available for all enrolled subjects. Produces estimators, standard errors, and information that can be used to compute stopping boundaries using software that assumes that the estimators/test statistics have independent increments. Tsiatis, A. A. and Davidian, M., (2022) . 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Tsiatis AA, Davidian M, Holloway ST (2023) . 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Package: r-cran-tlcar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tlcar_0.1.1-1.ca2404.1_all.deb Size: 51376 MD5sum: d3b9e092200da5f0ccf97501aae4a7c0 SHA1: 7d35cdfcf9209e8fc553a5cbe8e892b14e639bb4 SHA256: 6e7c1426d61becc74f8462f3e4dd20ab34f3ca97358279c98341f68cdce1a035 SHA512: f4d1eb01ea729dbf4436b251180bac690c54b84e9908b41496258a3febca31ec25cd2c7702145e78ef745f17006192c4fb14ac5a7072c6080c5c4c0972597661 Homepage: https://cran.r-project.org/package=TLCAR Description: CRAN Package 'TLCAR' (Computation of Topp-Leone Cauchy Rayleigh (TLCAR )distribution's properties) Provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) for further insights. Package: r-cran-tlda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tlda_0.1.0-1.ca2404.1_all.deb Size: 622456 MD5sum: 492cc3bda7a902c966c82087f192401c SHA1: b0e0b8857ba06f7c6a283758917f12e1a51be3c8 SHA256: e4cf547ce1e3cd20be288affd77679858604492837b1ddf9aac1b34141390bb4 SHA512: aca3a8c9372277a259513e2f3f9a18ff0b1473c3d33251b5caf48bf9b022776134550aa8ecf7007e0f9676d2586f8eeb5681c2031ecced03d8a3ca7cb1ddecb6 Homepage: https://cran.r-project.org/package=tlda Description: CRAN Package 'tlda' (Tools for Language Data Analysis) Support functions and datasets to facilitate the analysis of linguistic data. The current focus is on the calculation of corpus-linguistic dispersion measures as described in Gries (2021) and Soenning (2025) . The most commonly used parts-based indices are implemented, including different formulas and modifications that are found in the literature, with the additional option to obtain frequency-adjusted scores. Dispersion scores can be computed based on individual count variables or a term-document matrix. 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Data analysis functions combine multiple base R functions used to describe simple bivariate relationships into a single, easy to use function. Reporting functions will return character strings to report p-values, confidence intervals, and hypothesis test and regression results. Strings will be LaTeX-formatted as necessary and will knit pretty in an 'RMarkdown' document. The package also provides wrappers function in the 'tableone' package to make the results knit-able. 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Package: r-cran-tlsr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbscan, r-cran-fastica, r-cran-fnn, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tlsr_0.3.0-1.ca2404.1_all.deb Size: 1330984 MD5sum: 6b18c611b7afb6e009c7f536e6159db0 SHA1: d6d4240057b81df1c8bb03e95def20ab6c8646f4 SHA256: ddfaba69d15a42e6d0210866ed890ff76d2946b2a14f0355478ee89ec4b8065b SHA512: 15a16babb41f17e219bbbba5952b65e61f398302a28132abe76f2020874d6ffd6576800a2845820e38f3426d67c9304c73510c182b248d72184a46108c229b4a Homepage: https://cran.r-project.org/package=tlsR Description: CRAN Package 'tlsR' (Detection and Spatial Analysis of Tertiary Lymphoid Structures) Fast, reproducible detection and quantitative analysis of tertiary lymphoid structures (TLS) in multiplexed tissue imaging. Implements Independent Component Analysis Trace (ICAT) index, local Ripley's K scanning, automated K Nearest Neighbor (KNN)-based TLS detection, and T-cell clusters identification as described in Amiryousefi et al. (2025) . Package: r-cran-tm.plugin.alceste Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm Suggests: r-cran-stringi Filename: pool/dists/noble/main/r-cran-tm.plugin.alceste_1.1.2-1.ca2404.1_all.deb Size: 19832 MD5sum: 5ab06898745dd46912968190fda96049 SHA1: 64883f6c773f46b46b671b99794e1e264fe29e8e SHA256: 51289737190373cf5c32c3c492ca984e0a520db5e4ff4c164fe46edd40cb145d SHA512: dffb321c5b8bfc50d01fa9274d637735980fb7b355f531c19b7612c4e5bef78c120130e812023631182f91f6d94989301b6f68bb6dfb131e0b414ab31d10ce61 Homepage: https://cran.r-project.org/package=tm.plugin.alceste Description: CRAN Package 'tm.plugin.alceste' (Import Texts from Files in the 'Alceste' Format Using the 'tm'Text Mining Framework) Provides a 'tm' Source to create corpora from a corpus prepared in the format used by the 'Alceste' application (i.e. a single text file with inline meta-data). It is able to import both text contents and meta-data (starred) variables. Package: r-cran-tm.plugin.dc Architecture: all Version: 0.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dsl, r-cran-tm, r-cran-nlp, r-cran-slam Suggests: r-cran-xml Filename: pool/dists/noble/main/r-cran-tm.plugin.dc_0.2-10-1.ca2404.1_all.deb Size: 41234 MD5sum: f422ed118b62852eb4abf59729dc02f9 SHA1: c09ab7651835f0d06cf98ba9ae200934918a510f SHA256: 809a885e61dce64998d304873db53680d55d7351ec119a2abd29e7323a0fdf52 SHA512: 7af341ab5f92dbf4c6b15a4fca9755b822b73ff924a4a2b2c14969dcab4fc2a654d7dedfd8b199eede67d8f4eb7937d6c0dec0ae656f67ea3cd40a7ec88f7452 Homepage: https://cran.r-project.org/package=tm.plugin.dc Description: CRAN Package 'tm.plugin.dc' (Text Mining Distributed Corpus Plug-in) A plug-in for the text mining framework tm to support text mining in a distributed way. The package provides a convenient interface for handling distributed corpus objects based on distributed list objects. Package: r-cran-tm.plugin.europresse Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-xml Filename: pool/dists/noble/main/r-cran-tm.plugin.europresse_1.4.1-1.ca2404.1_all.deb Size: 33966 MD5sum: 41a873e6b10b7419a203ec1a9d425616 SHA1: ca7e24b4e7caf17e627ab0f881d382d394873c11 SHA256: df8fddb662e1ed3b2884259ecaa85848dec19ef4db889e581475cfb25c3ffb26 SHA512: 227f014eb077efa418c58150a6d135ab076976809bdd532a063574bed99ff45472e5ccfb571b1faf252e5f232e1ef5030aadedc0d8e98d59f7efa3bf9be4f85d Homepage: https://cran.r-project.org/package=tm.plugin.europresse Description: CRAN Package 'tm.plugin.europresse' (Import Articles from 'Europresse' Using the 'tm' Text MiningFramework) Provides a 'tm' Source to create corpora from articles exported from the 'Europresse' content provider as HTML files. It is able to read both text content and meta-data information (including source, date, title, author and pages). Package: r-cran-tm.plugin.factiva Architecture: all Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-xml2, r-cran-rvest Filename: pool/dists/noble/main/r-cran-tm.plugin.factiva_1.8.1-1.ca2404.1_all.deb Size: 64552 MD5sum: da03e0173f1738bda7af4bda9a228e98 SHA1: 47ee44ee6e3e05b9c560f9d67b0a485c36068e38 SHA256: da2389055aaa9119acd4b7325e9f5712fc52d0b67acf9c29d7ac42444fca3b2b SHA512: 6782d071fd1419f02f7b145d08c8e5af081aba46ee9b87b8e6e0c2c2b6453f7d10a728e2c452b3f47024e0d2e40584d7f875e339458d99bb95387339f21d86e4 Homepage: https://cran.r-project.org/package=tm.plugin.factiva Description: CRAN Package 'tm.plugin.factiva' (Import Articles from 'Factiva' Using the 'tm' Text MiningFramework) Provides a 'tm' Source to create corpora from articles exported from the Dow Jones 'Factiva' content provider as XML or HTML files. It is able to read both text content and meta-data information (including source, date, title, author, subject, geographical coverage, company, industry, and various provider-specific fields). Package: r-cran-tm.plugin.korpus Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-korpus, r-cran-sylly, r-cran-tm, r-cran-nlp Suggests: r-cran-korpus.lang.en, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tm.plugin.korpus_0.4-2-1.ca2404.1_all.deb Size: 974962 MD5sum: 4ddc3521381a3ce90b27ff0db83220b9 SHA1: e2722870d1f2a90dec4b3da4a45905f1ca679b11 SHA256: be65d8cc3ed489715478b1c4732ff9709572aac095355f50a25352a6c269ce90 SHA512: f3ed985e2bbb33e61c09f2c26c2b76bf1e684104bc630c22b8e7c0a949462c014fbcd5ab95946dee45ee658afad1c328fd295f720191e4ed009ac81767c1a5ba Homepage: https://cran.r-project.org/package=tm.plugin.koRpus Description: CRAN Package 'tm.plugin.koRpus' (Full Corpus Support for the 'koRpus' Package) Enhances 'koRpus' text object classes and methods to also support large corpora. Hierarchical ordering of corpus texts into arbitrary categories will be preserved. Provided classes and methods also improve the ability of using the 'koRpus' package together with the 'tm' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please subscribe to the koRpus-dev mailing list (). Package: r-cran-tm.plugin.lexisnexis Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-xml2, r-cran-isocodes Filename: pool/dists/noble/main/r-cran-tm.plugin.lexisnexis_1.4.2-1.ca2404.1_all.deb Size: 35676 MD5sum: 10c9eac846bd51597c7ce29f21eb2c54 SHA1: 14f36230072fede5310fe782b5b0af1acd38c667 SHA256: 5bb7673356523c3588508657f7572112d57fb486f887ef53adb65c8b35350c7f SHA512: 135f795039d3464422a0264832f041f2636d9cc6a0aadfa42879a60526d39aabed5911ebbd515fc1292f7e6ee9af205a2ec520e6ed469cf0f279c31247c3a876 Homepage: https://cran.r-project.org/package=tm.plugin.lexisnexis Description: CRAN Package 'tm.plugin.lexisnexis' (Import Articles from 'LexisNexis' Using the 'tm' Text MiningFramework) Provides a 'tm' Source to create corpora from articles exported from the 'LexisNexis' content provider as HTML files. It is able to read both text content and meta-data information (including source, date, title, author and pages). Note that the file format is highly unstable: there is no warranty that this package will work for your corpus, and you may have to adjust the code to adapt it to your particular format. Package: r-cran-tm.plugin.mail Architecture: all Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-tm.plugin.mail_0.3-2-1.ca2404.1_all.deb Size: 69862 MD5sum: 23a4dbe9bf55c963170e02203bfaf9c0 SHA1: 07efcaa7340d8be28d9aee5e91ac6be5ae322252 SHA256: 0656e6b1875ccc3782097d20cb79cc8641d207a55a146da00353acc0ebe0a286 SHA512: 4167f1a6b1a3ccd592bc423b31b6c676252c23c2df8c4ce332c74d12f4ad6d62cd7fd100f9973a440c19dbd1ddf4485539489bf7705e21a76019e7fba78e254f Homepage: https://cran.r-project.org/package=tm.plugin.mail Description: CRAN Package 'tm.plugin.mail' (Text Mining E-Mail Plug-in) A plug-in for the tm text mining framework providing mail handling functionality. Package: r-cran-tm1r Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Filename: pool/dists/noble/main/r-cran-tm1r_1.1.8-1.ca2404.1_all.deb Size: 118606 MD5sum: 5736b0659c9d5cea6c614967de8d7817 SHA1: cf35e40f779a7bbb1ec3e5de2dfe4d4de6d8099e SHA256: d841aa021c5009ec3391b92e488d94be74ba99365ed9a898b93f5ceff43e5522 SHA512: 4de05c92ae83b5441d1d3147c8dbcc4feb554110996f2148e4419ebb09d53d0990ab07dc1c788b677164f74c99df5e07f93534394ad11401da2e14cb96fc244e Homepage: https://cran.r-project.org/package=tm1r Description: CRAN Package 'tm1r' (The Integration Between 'IBM COGNOS TM1' and R) Useful functions to connect to 'TM1' instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken. Package: r-cran-tmap.cartogram Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-cartogram Suggests: r-cran-knitr, r-cran-transformr, r-cran-gifski Filename: pool/dists/noble/main/r-cran-tmap.cartogram_0.2-1-1.ca2404.1_all.deb Size: 80130 MD5sum: 9b1ac1d6aed68696e158d0b6bcd68511 SHA1: 64ef185e04c598b729ca0ebea4514daffc6c1ef2 SHA256: 3fda00979c1ec17365ee9eb5fb54d09fa32ff6fe4c96f7f4238163a658774388 SHA512: 5a60995fbe18fd0425d5146aa75c34e1a07ce2b7b154d160a0a2780d3cfee0608e0ddd2d76922692e8c40bec82345700e6ca690a2839fc894197a34e138a36ee Homepage: https://cran.r-project.org/package=tmap.cartogram Description: CRAN Package 'tmap.cartogram' (Extension to 'tmap' for Creating Cartograms) Provides new layer functions to 'tmap' for creating various types of cartograms. A cartogram is a type of thematic map in which geographic areas are resized or distorted based on a quantitative variable, such as population. The goal is to make the area sizes proportional to the selected variable while preserving geographic positions as much as possible. Package: r-cran-tmap.glyphs Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-data.table, r-cran-units Suggests: r-cran-tmap.mapgl Filename: pool/dists/noble/main/r-cran-tmap.glyphs_0.2-1.ca2404.1_all.deb Size: 90136 MD5sum: 1c8deba9b20fee514a3de29accf13309 SHA1: e20629a3f0430af6765662e1a13f924a36ea91ca SHA256: 7d45c1a658a412225150ddf655037a00885c83ff3a0fbc9409262b2433dd1b96 SHA512: 8daf118dced3dfff0be2571be51d0dc11e626e5e586906dd132595ab1edc0a6a576603406379f621d6c6710229aeadf29ed1a403d713202dee4e3916ef693b54 Homepage: https://cran.r-project.org/package=tmap.glyphs Description: CRAN Package 'tmap.glyphs' (Extension to 'tmap' for Creating Glyphs) Provides new layer functions to 'tmap' for drawing glyphs. A glyph is a small chart (e.g., donut chart) shown at specific map locations to visualize multivariate or time-series data. The functions work with the syntax of 'tmap' and allow flexible control over size, layout, and appearance. Package: r-cran-tmap.mapgl Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-mapgl, r-cran-terra, r-cran-rlang, r-cran-cli, r-cran-stars, r-cran-data.table, r-cran-colorspace, r-cran-htmltools, r-cran-htmlwidgets, r-cran-sf, r-cran-tmaptools, r-cran-units Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-tmap.mapgl_0.3-1.ca2404.1_all.deb Size: 420112 MD5sum: d1c1444a50ac8f24cd830de8ef940fb2 SHA1: 940c490536a47980486fd202b7caa5f2c810c78f SHA256: c20286bdb708bd169d0dc96ced5a9767ddbdc315579bfd213f7fdae28045ed16 SHA512: 10e3b574e0236f291c2584557dc28a958e640ea82e620d6ee6daf7076137d3c3dc806453abf7bd51ee2e5a8df5ae1c69e27fbde7e864af8623b5cca8a5bc9228 Homepage: https://cran.r-project.org/package=tmap.mapgl Description: CRAN Package 'tmap.mapgl' (Extensions to 'tmap' with Two New Modes: 'mapbox' and 'maplibre') The 'tmap' package provides two plotting modes for static and interactive thematic maps. This package extends 'tmap' with two additional modes based on 'Mapbox GL JS' and 'MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent 'tmap' interface across all plotting modes. Package: r-cran-tmap.networks Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-sfnetworks, r-cran-lwgeom, r-cran-data.table, r-cran-igraph Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-tmap.networks_0.2-1-1.ca2404.1_all.deb Size: 107426 MD5sum: 0c70ea633f69e12559bf0dd560bc7aec SHA1: 9ee454f467b8c45a8bbb5d0ccdb7352eceeb90a8 SHA256: 12c71f1d52689f53dcd9546674e92a08241d32d02a06b6cb9736ca98a91aa441 SHA512: 155905ca1a3374158a40701b5056512b9f63ff0d3021baa08f583ec8a75a447ce0b87675b0ec7fb438e352ca08213c8ce80874428036552afd8a59f04969a6aa Homepage: https://cran.r-project.org/package=tmap.networks Description: CRAN Package 'tmap.networks' (Extension to 'tmap' for Creating Network Visualizations) Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions. Package: r-cran-tmap.sources Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-httr2, r-cran-jsonlite, r-cran-freestiler, r-cran-data.table, r-cran-servr, r-cran-cli Suggests: r-cran-tmap.mapgl Filename: pool/dists/noble/main/r-cran-tmap.sources_0.1-1-1.ca2404.1_all.deb Size: 72328 MD5sum: a388f96fb6a1486f4d33d3372f77e6f1 SHA1: 9b93f8a62f01d958f5a387134dcda371c884df62 SHA256: bc198100ea9c1d8baa502f3ab0c63eae9599743c3bf9eefbbe695f58415fa61f SHA512: 264e49957286d880b99d05b5978b63eb1c69ab3a594f0b66e37e2ffdb0be70190cc9a17cb6935faf38ff8eb6e5869d99d02508956e16b5720a1f142669c02f2f Homepage: https://cran.r-project.org/package=tmap.sources Description: CRAN Package 'tmap.sources' (Data Sources for 'tmap') Provides support for a variety of spatial data sources in 'tmap', including remote, tiled, and streaming formats. Enables the use of external vector and raster data without requiring full data import, facilitating efficient visualization workflows. Package: r-cran-tmap Architecture: all Version: 4.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4445 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-classint, r-cran-cli, r-cran-cols4all, r-cran-data.table, r-cran-htmltools, r-cran-htmlwidgets, r-cran-base64enc, r-cran-leafem, r-cran-leafgl, r-cran-leaflegend, r-cran-leaflet, r-cran-leafsync, r-cran-maptiles, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-s2, r-cran-tmaptools, r-cran-units, r-cran-servr Suggests: r-cran-av, r-cran-transformr, r-cran-colorspace, r-cran-ggplot2, r-cran-gifski, r-cran-knitr, r-cran-shiny, r-cran-terra, r-cran-testthat, r-cran-widgetframe, r-cran-lobstr, r-cran-rsvg, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-tmap_4.4-1-1.ca2404.1_all.deb Size: 4340438 MD5sum: 0a8c6f614fbcaf14ba0670516e5097ba SHA1: 656eb8cf602a7f0bf364fa3813c5888a234da8d0 SHA256: 31cdfb36604d9fb18da52cb21aa94ec5f4e5b6ec916a4acabfe30a7948615dcc SHA512: 0d21aab6a15e549d95e73baa40e187168e4cd514d15aa8323177600734bcca8964c982e65c192350ba609260579f192c0731fd3e7a2bd192e8814429709a0311 Homepage: https://cran.r-project.org/package=tmap Description: CRAN Package 'tmap' (Thematic Maps) Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps. Package: r-cran-tmaptools Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-lwgeom, r-cran-stars, r-cran-units, r-cran-xml Suggests: r-cran-tmap, r-cran-cols4all, r-cran-rmapshaper, r-cran-osmdata, r-cran-openstreetmap, r-cran-raster Filename: pool/dists/noble/main/r-cran-tmaptools_3.3-1.ca2404.1_all.deb Size: 125328 MD5sum: cd8fe7ea20d30c1e3cd403d4154ac9b0 SHA1: e6b290dd0fb60d83eb1562246e98bf3d0eeb8778 SHA256: ac04a2764f593b28fe7ac833806a9b7a8e90df4bdec667dac6443a89c0cb31fb SHA512: 96a40da7b0a0b2a186ebf4775cf6e5a7f460b034b5cacfa6f6b6735ffff507b4b222a23b1af1ac07d13486366c77aa3c535c1c7980da5d9aae8e06085f04ad20 Homepage: https://cran.r-project.org/package=tmaptools Description: CRAN Package 'tmaptools' (Thematic Map Tools) Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps. Package: r-cran-tmapverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmap, r-cran-tmap.glyphs, r-cran-tmap.networks, r-cran-tmap.cartogram, r-cran-tmap.mapgl, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-cols4all, r-cran-cli, r-cran-crayon Filename: pool/dists/noble/main/r-cran-tmapverse_0.1.0-1.ca2404.1_all.deb Size: 20182 MD5sum: d1cf562e0e9440b1164d93a5bbf14843 SHA1: 86f31a83c0cb566ecb1bb0b96b02db6996f0ad63 SHA256: 8550ed6e1d94d6c7a98899127a298ea28ef9e92c3b8eb699ef786d46633eda58 SHA512: 01a1974c081f84a34399f469bb83d677333bf11da4da60cb3ee7366ed9d5284725d3deee33129a91df2f86be9c3bca5f291c8714f8bf600222fd1f8015b1c760 Homepage: https://cran.r-project.org/package=tmapverse Description: CRAN Package 'tmapverse' (Meta-Package for Thematic Mapping with 'tmap') Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem. Package: r-cran-tmcalculator Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3542 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-bsgenome, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-remotes, r-cran-biocmanager, r-bioc-bsgenomeforge, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-karyoploter, r-cran-ggplot2, r-cran-ggridges, r-cran-ggforce, r-cran-rlang, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-tmcalculator_1.0.9-1.ca2404.1_all.deb Size: 2685204 MD5sum: 48de30e62c32fff3855944fff0e78f0e SHA1: b24c7e3d2b0c3eded40d113f3ec46bf8e05dc337 SHA256: 0f4bfdf7df87f5f3735ea7c3e57814047f076f03c80722be7482621017003e20 SHA512: 536b8a0c1d132b7c9da6a7352c5e2d1a328276cfcbd36b7cecdcd3d6fbf121f2c6dc99c9f645e712246fc1fe9e69afbdd2a3580174ec2bfb6d7da6806c8ec9af Homepage: https://cran.r-project.org/package=TmCalculator Description: CRAN Package 'TmCalculator' (Genome-Wide Nucleic Acid Melting Temperature Profiling andMulti-Omics Integration) Accurate calculation of nucleic acid melting temperature (Tm) is fundamental to many molecular biology applications, and this software scales Tm analysis from individual sequences to genome‑wide thermodynamic profiling. This package extends Tm analysis from simple sequence level computation to comprehensive genome-wide thermodynamic profiling. It takes multiple input formats including sequence strings, FASTA files, genomic coordinates. The implementation provides three Tm calculation methods: the Wallace rule (Thein & Wallace, 1986), empirical GC‑content formulas (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001), and nearest‑neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Allawi, 1997; SantaLucia, 2005). Twenty-seven nearest-neighbor parameter sets are provided, covering DNA, RNA and RNA/DNA hybrid duplexes. These include sets obtained by melting-temperature optimization that are fitted directly at a stated sodium concentration (Weber, 2015; Ferreira, 2019; Basilio Barbosa, 2019), which replace salt correction rather than being corrected; salt correction is skipped automatically when the requested condition matches the one a set was fitted at. Corrections are otherwise supported for salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008) and for chemical conditions such as dimethyl sulfoxide and formamide. This package returns result as a GRanges object for interoperability with Bioconductor workflows and downstream multi-omics analyses. Data-level integration reconciles Tm windows with external multi-omics GRanges objects through overlap, nearest-feature, windowed-count, and binned-average strategies, returning a single unified GRanges object ready for downstream analysis. Visualization-level integration renders multiple feature layers as independent concentric tracks on a shared genomic axis, each retaining its native coordinate resolution. Group comparison supports Wilcoxon rank-sum and Student's t-tests with multiple available correction methods for contrasting Tm and other features across region classes. Package: r-cran-tmdb Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringi Filename: pool/dists/noble/main/r-cran-tmdb_1.1-1.ca2404.1_all.deb Size: 313160 MD5sum: 64f1406d15cfc1f6ee575c7ee48d236a SHA1: d0a3a0b4e02ef362da23617f3bfa6bfd520d2756 SHA256: 2b4ac3ca9d4b44cd921fe657521c1b14755822b64e114a4d4cb031946d367427 SHA512: d4d219d942a417c1fd17143d2d3be0e2454564f07b0cd8384e63dac3ebc2c5c30be962e82ac83d45a33b909e34a6fadc470f14b26ac5a3feb1d5a70aad9c67f9 Homepage: https://cran.r-project.org/package=TMDb Description: CRAN Package 'TMDb' (Access to TMDb API) Provides an R-interface to the TMDb API (see TMDb API on ). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see ). Package: r-cran-tmdbr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tmdbr_0.2.2-1.ca2404.1_all.deb Size: 338908 MD5sum: e8c34c0afaf4c87a071dee523a315b07 SHA1: e036f8eb0723ce67ade091f634a5bf6c3fa9a198 SHA256: 9b6bca44f9c2a96a3bf3ea0a69295ac352e352ad7ac4b265eb625424cf91d1a1 SHA512: 12cfb7fa5ac0e0962c79cb407904f0703e8a5a512e067ee65bcbf52d27bf4826b2c4ad1ca91e2b378d609ba79f665f14ac303c43e61214775594652336a5397c Homepage: https://cran.r-project.org/package=tmdbR Description: CRAN Package 'tmdbR' (Modern 'R' Client for 'The Movie Database' API) A modern, tested client for version 3 of 'The Movie Database' ('TMDB') API. 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Leverages truncated PCA via 'irlba' for sparse matrices, enabling fast model fitting on large corpora. Includes an information-theoretic approach to vocabulary selection, 'broom'-compatible tidiers for extracting word-topic and topic-document matrices into a tidy data workflow, and samplers for constructing simulated corpora for benchmarking and method evaluation. Package: r-cran-tmisc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-rstudioapi, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-tmisc_1.0.1-1.ca2404.1_all.deb Size: 90316 MD5sum: 4634fd95262883779deed1185e3b733a SHA1: 70e4df420aa541f30523f6a9cb469dbd4b98e32a SHA256: c6e631e96b76494c35aa32331fe846a94d8a1a610342cb5f40e7e101c2530032 SHA512: 90148e3b5f0359d268ed892c9da74ed49b9ce91f2ee6fc54eb31d09f260ccdbdf5ffb098f43de991c6ececc41dc78f47e04342f63168e9aac4e9db9dc17abb1f Homepage: https://cran.r-project.org/package=Tmisc Description: CRAN Package 'Tmisc' (Turner Miscellaneous) Miscellaneous utility functions for data manipulation, data tidying, and working with gene expression data. 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These methods may be summarized in the following references: Yoshida, et al. (2022) , Barnhill et al. (2023) , Barnhill and Yoshida (2023) , Aliatimis et al. (2023) , Yoshida et al. (2022) , and Yoshida et al. (2019) . Package: r-cran-tmle Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-superlearner Suggests: r-cran-dbarts, r-cran-gam, r-cran-rocr, r-cran-weightedroc Filename: pool/dists/noble/main/r-cran-tmle_2.1.1-1.ca2404.1_all.deb Size: 212204 MD5sum: 27e2b26385c0483cf63ae87c5c6225fa SHA1: 25aae406c573744ddf34e79fe15dadc868a87b99 SHA256: bd3a08aa2a83f9ee597a36a3f2bfc45f9599d6d38153b4449cd3e86047b3d140 SHA512: ad931150688007da34d1f2541628e4382f62261936d5e99feeab579b63d8e33c4d4d9c1a6fe15af85e7bc2c1d53d2297cfedc74beadbe968067440afe9805075 Homepage: https://cran.r-project.org/package=tmle Description: CRAN Package 'tmle' (Targeted Maximum Likelihood Estimation) Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments. 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Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) . 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Package: r-cran-tmpm Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-tmpm_1.0.3-1.ca2404.1_all.deb Size: 64280 MD5sum: a88672f4b7396a3682d3b572c74c6101 SHA1: 52b3f667212de3afc7cd5dfb2cae4d4e99741cbc SHA256: 4c0df7000823515a8657cc65b83a437c9952cfb33d69bc924b0bcd46f358b136 SHA512: bf2d63bbfa7ff64d45cbcf28f8b39221ae14e217e14c820c09ca0b2eb37198bc40983818c4b0f432e0ec9d57daf95d30fa47deadbf83f5953a7d2e870ded23c4 Homepage: https://cran.r-project.org/package=tmpm Description: CRAN Package 'tmpm' (Trauma Mortality Prediction Model) Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in long or wide format based on Dr. Alan Cook's tmpm mortality model. Package: r-cran-tmsens Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tmsens_0.4.0-1.ca2404.1_all.deb Size: 60376 MD5sum: b59aed5edb9c4d1c92f93eeb0e788679 SHA1: bf58ac4400acfd618b372fe473e130c2f5ffca60 SHA256: 202134706de9d59aa1119d937e89d8ccf0954a87bb60722adce017f8c4e45269 SHA512: f7677296111d4e58f04563fefa03d72c2abf46d1495c1df7f056286bf16e69ab305e4763de8a333569426df550cf34488df015af7333f7a7937348a42113a4ad Homepage: https://cran.r-project.org/package=tmsens Description: CRAN Package 'tmsens' (Sensitivity Analysis Using the Trimmed Means Estimator) Sensitivity analysis using the trimmed means estimator. Package: r-cran-tmvmixnorm Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-tmvmixnorm_1.2.0-1.ca2404.1_all.deb Size: 57352 MD5sum: 27890bfe89f4c08adbd1e18c40461cdc SHA1: d2777adffe643fba5858d284882536e6155bc9ae SHA256: e76bf6e341605db45cbe052d30fee376282e0e4f8557199d95769a3a9c882502 SHA512: 377e7658600e74e3816409c350fc9390d59e08ece316c4140f192825b20972961a87d032cd53cc0b3aad68ce807e043c78826450441e600bda7e236cfa5d92fb Homepage: https://cran.r-project.org/package=tmvmixnorm Description: CRAN Package 'tmvmixnorm' (Sampling from Truncated Multivariate Normal and t Distributions) Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed. Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time). Package: r-cran-tna Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6427 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-cluster, r-cran-cograph, r-cran-colorspace, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-rcolorbrewer, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-gt, r-cran-knitr, r-cran-pracma, r-cran-rmarkdown, r-cran-seqhmm, r-cran-stringdist, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tna_1.3.1-1.ca2404.1_all.deb Size: 3146746 MD5sum: 4a1897f268915b815587ebf8bebb6db4 SHA1: 2caef3a59991492e10ab887219641be87ed83255 SHA256: fd28815741554c3e051b8b4d230fa300edd9588556170bf65e5bd269a3bfe865 SHA512: a6c238ddfbc1dcee6e06034683cecf87e6de22984eae6ab41c29d7c3ed968d3b5ebb83c2910486e0a4823399edd8dededf14dd4c40607663ee5b0e7bee93fe1f Homepage: https://cran.r-project.org/package=tna Description: CRAN Package 'tna' (Transition Network Analysis (TNA)) Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. 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The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. Function "table_stability" offers the summary table of all stability indices in this package. This R package toolStability is part of the main publication: Wang, Casadebaig and Chen (2023) . Analysis pipeline for main publication can be found on github: . Sample dataset in this package is derived from another publication: Casadebaig P, Zheng B, Chapman S et al. (2016) . For detailed documentation of dataset, please see on Zenodo . Indices used in this package are from: Döring TF, Reckling M (2018) . Eberhart SA, Russell WA (1966) . Eskridge KM (1990) . Finlay KW, Wilkinson GN (1963) . Hanson WD (1970) Genotypic stability. . Lin CS, Binns MR (1988). Nassar R, Hühn M (1987). Pinthus MJ (1973) . Römer T (1917). Shukla GK (1972). Wricke G (1962). 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Package: r-cran-topchef Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-magrittr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-topchef_0.2.0-1.ca2404.1_all.deb Size: 193978 MD5sum: 231ac60a634f05e6ad8486bad0ac18c7 SHA1: 0303c7b459843a679bbd8b74e75d5ab519a5e0af SHA256: 9087d9957c4047472f5b3cfbea5c525838a036f71a767d88897108879995f58a SHA512: f1e42c90649052fe6b4e97f215803ca1e694103133f0fa914c8f5275173e81b31ff6721fffdd70b41b2c5d419c15d14834f0c5a5e5e88a8e36caf983cfb7f001 Homepage: https://cran.r-project.org/package=topChef Description: CRAN Package 'topChef' (Top Chef Data) Several datasets which describe the chef contestants in Top Chef, the challenges that they compete in, and the results of those challenges. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Top Chef played out. Package: r-cran-topdom Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4556 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats, r-cran-ggplot2, r-cran-reshape2, r-cran-tibble Suggests: r-cran-diffobj Filename: pool/dists/noble/main/r-cran-topdom_0.10.2-1.ca2404.1_all.deb Size: 4577160 MD5sum: a58bee95909801c1f12bed51827106bd SHA1: 9a7d4d997d1090db7e4cf1cebcdfb06d76f04bd2 SHA256: 418883172247865c2f09984815157027c1c526a5fefa029fd1cf12822f931b35 SHA512: b5e6d93d7fa65fd3a406cc283db61ba632624ffb9ff11c1e8a5c8c74987b3c2a789efc48b039216e7a0be2f7927d1d32c8eea40fa424bb2e043e0e8192bba34e Homepage: https://cran.r-project.org/package=TopDom Description: CRAN Package 'TopDom' (An Efficient and Deterministic Method for IdentifyingTopological Domains in Genomes) The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 ). The authors published an implementation of their method as an R script (two different versions; also available in this package). This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation. It also provides a small number of bug fixes to the original code. Package: r-cran-topdowntimeratio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-geodist, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topdowntimeratio_0.1.0-1.ca2404.1_all.deb Size: 71300 MD5sum: 0f90dce1659c02093f9e46c11c3a4d45 SHA1: 5aa35a11478d2e2039c89ede958633c293510374 SHA256: bc7af135cf36964fda10181b779ca16a14ccf6bc9db89ca09d80e776147a6830 SHA512: 1e2173ac34a5b4af0e44b81523e295a064d8f4f8fd4a2ef1c387ad5b3251eee57bdde32205425295fb21ba3ad144548d047818aa4386282b35fd3cb967682652 Homepage: https://cran.r-project.org/package=topdowntimeratio Description: CRAN Package 'topdowntimeratio' (Top-Down Time Ratio Segmentation for Coordinate Trajectories) Data collected on movement behavior is often in the form of time- stamped latitude/longitude coordinates sampled from the underlying movement behavior. These data can be compressed into a set of segments via the Top- Down Time Ratio Segmentation method described in Meratnia and de By (2004) which, with some loss of information, can both reduce the size of the data as well as provide corrective smoothing mechanisms to help reduce the impact of measurement error. This is an improvement on the well-known Douglas-Peucker algorithm for segmentation that operates not on the basis of perpendicular distances. Top-Down Time Ratio segmentation allows for disparate sampling time intervals by calculating the distance between locations and segments with respect to time. Provided a trajectory with timestamps, tdtr() returns a set of straight- line segments that can represent the full trajectory. McCool, Lugtig, and Schouten (2022) describe this method as implemented here in more detail. Package: r-cran-topicdoc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam, r-cran-topicmodels Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topicdoc_0.1.1-1.ca2404.1_all.deb Size: 121030 MD5sum: cbd0b29b422c50ed9ea0f513e8e4e90e SHA1: 797ccf0d13a71c0a87aa11e4a7310e2e07e10780 SHA256: 1226834d2cad06e85140eb22a2788713cbd206b6a80bf6d8242e2d8ca4be8293 SHA512: 6c969c4334af6dd04260612b9f881ea0ca282b3de0568919713e8aace93cdb1608fdf2d8cb35323097809d23cd133d674443ac1c5deb004477a1611a5b6a060d Homepage: https://cran.r-project.org/package=topicdoc Description: CRAN Package 'topicdoc' (Topic-Specific Diagnostics for LDA and CTM Topic Models) Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package. For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) . Package: r-cran-topiclabels Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-httr, r-cran-progress, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topiclabels_0.4.0-1.ca2404.1_all.deb Size: 179058 MD5sum: 293afd73f891897bfe20c1fef5d2505d SHA1: 5d0d68f0b39a7bbfaa042189104261f2c76dd175 SHA256: 3b76f74cb00d8294b70108835ad7653cdafcaf11ccc3a0da5a427eaab315da24 SHA512: e97ea0debb44658813fd50baa9b9c047417c8b49375ee58b6a3e69d46675f36c7b3a38d49e137764f25a26db94ea3d6d140af1e4569409d5fad2aceeb3b9db93 Homepage: https://cran.r-project.org/package=topiclabels Description: CRAN Package 'topiclabels' (Automated Topic Labeling with Language Models) Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios. Package: r-cran-topicmodels.etm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-torch Suggests: r-cran-udpipe, r-cran-word2vec, r-cran-uwot, r-cran-tinytest, r-cran-textplot, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-topicmodels.etm_0.1.1-1.ca2404.1_all.deb Size: 4112184 MD5sum: 93d0bd636fea5376698547dd599a64ab SHA1: cc98c1baf7d26e99d6d07a258dec4c3bfeca6d01 SHA256: 9c6357a3326d097ba1b70ca69c17ccefc80a7d10909d7d6d805e6bc015ddcfb4 SHA512: c44d0d920d9c65879a2150a79a47f5d70df678dffd4cce70d656c2b58dd6783af5d45e5f2f1497b83514dca451377595253bfe6f46e0abbd9493c1329b4cedbd Homepage: https://cran.r-project.org/package=topicmodels.etm Description: CRAN Package 'topicmodels.etm' (Topic Modelling in Embedding Spaces) Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at . Package: r-cran-topics Architecture: all Version: 1.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5010 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-textminer, r-cran-quanteda, r-cran-ggplot2, r-cran-dplyr, r-cran-ggwordcloud, r-cran-tibble, r-cran-readr, r-cran-stopwords, r-cran-matrix, r-cran-stringr, r-cran-stringi, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-ggforce, r-cran-patchwork Suggests: r-cran-text, r-cran-rjava, r-cran-mallet, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-vdiffr, r-cran-httr, r-cran-arrow Filename: pool/dists/noble/main/r-cran-topics_1.0-1.ca2404.2_all.deb Size: 3061964 MD5sum: 65ae1ab3d522d23a38dd6ccf9e8c2297 SHA1: a0b12e17fa471fbfd2e53030c118df88d7c42ad5 SHA256: c028f3593ac9af29d8725bbd9960a5fe7194e2bc8746254f15b801bfc46bc3ba SHA512: 7d5f4c3c47c41ca2bc13a17ce8cf72e3a79b165f998959f0db5070cc2b98c447edc075be6ba839f7d90247dae1048059f3302513a99c2ece6f5e38c28d170040 Homepage: https://cran.r-project.org/package=topics Description: CRAN Package 'topics' (Creating and Significance Testing Language Features forVisualisation) Implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the 'text' package. For more information, visit and . Package: r-cran-topicscore Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-combinat, r-cran-quadprog, r-cran-matrix, r-cran-slam Filename: pool/dists/noble/main/r-cran-topicscore_0.0.1-1.ca2404.1_all.deb Size: 701366 MD5sum: 081dc9df425c06e33b4233c7438e44e3 SHA1: e1289ec0f21843cd1a773cb768aa0cab35081b4e SHA256: ddfbf32ff917878b0144f244ed6f30ff2089e5ca01898b360d3a951e8ddb48ab SHA512: db43aeca1c6ec318365f84f273bc25a2385e8a43f79e22590ce29d562c09b2b16eb90eee0cefe94881c9774f999561f11bd2d18f92275fc2712bf74023ff7957 Homepage: https://cran.r-project.org/package=TopicScore Description: CRAN Package 'TopicScore' (The Topic SCORE Algorithm to Fit Topic Models) Provides implementation of the "Topic SCORE" algorithm that is proposed by Tracy Ke and Minzhe Wang. The singular value decomposition step is optimized through the usage of svds() function in 'RSpectra' package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise error measure in the word-topic matrix A, and an algorithm for recovering the topic-document matrix W given A and D based on quadratic programming. The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) . Package: r-cran-topictestlet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-topicmodels, r-cran-tm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topictestlet_0.1.0-1.ca2404.1_all.deb Size: 85100 MD5sum: 829ee7651ac2714406c31ab0b0807198 SHA1: eb08f9f0bd09946f8773840feba8f6a94c9e8b08 SHA256: c354577dfbd5b8b10a6badbfb2986d9efb74d0f32c72ede80f70650c6a721d56 SHA512: 47f426a2cee57cfb4e78bf740c84c843b95f51280633c24ddd5e3c9aa9dbed5c627b03204f4fb4f8363e408138c58b82edf8fc57aead133f92dfe40c0da94696 Homepage: https://cran.r-project.org/package=TopicTestlet Description: CRAN Package 'TopicTestlet' (A Topic Testlet Model for Calibrating Testlet ConstructedResponses) Implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) . The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to account for local item dependence in testlets using latent topics from student textual responses. Package: r-cran-topksignal Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-nloptr, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topksignal_1.0-1.ca2404.1_all.deb Size: 139404 MD5sum: d0e0ab394cad71a745e5572a6217de13 SHA1: 153b364c28273587dccfa50ac78f90de60676311 SHA256: c1ba98da1252def0f99400d39f1c672f3242a80d101ff027a407a2caf8543d77 SHA512: e7242f360c6fdb833eb8e21c7313af28af19f670667080412ca0ceaf3a5da6a71fecedd526e5742322c4a02ede6c93ffab74016ed229e3d98acbe0944960aa0f Homepage: https://cran.r-project.org/package=TopKSignal Description: CRAN Package 'TopKSignal' (A Convex Optimization Tool for Signal Reconstruction fromMultiple Ranked Lists) A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from ). Package: r-cran-topodistance Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gdistance, r-cran-plotly, r-cran-raster, r-cran-rcolorbrewer, r-cran-scales, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topodistance_1.0.2-1.ca2404.1_all.deb Size: 886986 MD5sum: 192aa70244a0ab6b3c90f3a2ede04b8d SHA1: 7ba3e562cc199628b345aaacd8a85e49486c40fa SHA256: 4cb40eeef9eceeb68e70da3f819168a745b673e98e72a6e18b3469d9d5169a1a SHA512: 3ba44a4b13154997b25d3a4d9ee18b226ac979f0d4ca6f4ccb1d03e15e522dbd4227c8a86f07bf0ced5803c65f7c4c2637f3eee5fed1bdaf3dfc06c4ba470a8d Homepage: https://cran.r-project.org/package=topoDistance Description: CRAN Package 'topoDistance' (Calculating Topographic Paths and Distances) A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) ), weighted topographic paths (Zhan et al. (1993) ), and topographic least cost paths (Wang and Summers (2010) ). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths. Package: r-cran-topologygsa Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-cran-grbase, r-bioc-qpgraph, r-cran-fields, r-cran-igraph Suggests: r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-topologygsa_1.5.0-1.ca2404.1_all.deb Size: 74780 MD5sum: fcbbb7d00ef053a0c573e0b0e81101c3 SHA1: 728ecfc623a068ad6d67bf2805c92dbbc9a945c6 SHA256: 0d8f9a909365deef2f127049331eb6768e2b7dd8fe4e3adf1eb9f1e3278b297d SHA512: 395d9e09eaaaf3a66745d2471d9815b97e129e9c465c385298fa1cdc37518040e47c09dd77487a436917e03263d2c1190ce2fef53d581fbbd7793c47788cd1bf Homepage: https://cran.r-project.org/package=topologyGSA Description: CRAN Package 'topologyGSA' (Gene Set Analysis Exploiting Pathway Topology) Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) . Package: r-cran-topologyr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topologyr_0.1.2-1.ca2404.1_all.deb Size: 1116868 MD5sum: 371811051b1f41d07d2b842662dc26c7 SHA1: b6b5fc6f9a5683e450915b6a46d6b02b5f1aa854 SHA256: 8e9d03dd2eb2f95d6348e3200584f5351c277ad4a6c80637fc76c77ed04cd219 SHA512: f124623edfd9115924e1a91cb6cd22b7528f96cd7cba403725264cac672a699126e94f52153d61a1b6b21b42c171a44a4a02ed13068a9c0d4719219e346d9ce7 Homepage: https://cran.r-project.org/package=topologyR Description: CRAN Package 'topologyR' (Topological Connectivity Analysis for Numeric Data) Description: Implementation of topological data analysis methods based on graph-theoretic approaches for discovering topological structures in data. The core algorithm constructs topological spaces from graphs following Nada et al. (2018) "New types of topological structures via graphs". Package: r-cran-topolow Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-lifecycle, r-cran-ggplot2, r-cran-dplyr, r-cran-data.table, r-cran-reshape2, r-cran-filelock, r-cran-lhs, r-cran-rlang Suggests: r-cran-coda, r-cran-rtsne, r-cran-ape, r-cran-racmacs, r-cran-vegan, r-cran-umap, r-cran-igraph, r-cran-rgl, r-cran-scales, r-cran-ggrepel, r-cran-plotly, r-cran-gridextra, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topolow_2.0.1-1.ca2404.1_all.deb Size: 1801074 MD5sum: 2a674166cb86996d4b66a3c52d51873b SHA1: 9680b7e0c8b8107aab04c43019228904e275fe9a SHA256: 06c97304b47fd17f89514e3d65165bc109e56a4d3eb45f7905bad80c81e5e80d SHA512: 12676c6052db6aa1adc5d3a2576f723b2bf5a4dae7d5eafe8305776a8ce008d7bfc29c86b5dbc5c6c38ff72572cad955f295a58f32ae14453abd7cc66aed0865 Homepage: https://cran.r-project.org/package=topolow Description: CRAN Package 'topolow' (Force-Directed Euclidean Embedding of Dissimilarity Data) A robust implementation of Topolow algorithm. It embeds objects into a low-dimensional Euclidean space from a matrix of pairwise dissimilarities, even when the data do not satisfy metric or Euclidean axioms. The package is particularly well-suited for sparse, incomplete, and censored (thresholded) datasets such as antigenic relationships. The core is a physics-inspired, gradient-free optimization framework that models objects as particles in a physical system, where observed dissimilarities define spring rest lengths and unobserved pairs exert repulsive forces. The package also provides functions specific to antigenic mapping to transform cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Embedding from Sparse Data: Effectively creates complete and consistent maps (in optimal dimensions) even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models objects (e.g., antigens, landmarks) as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing dissimilarities), and simulates the physical system using laws of mechanics, reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the embedding/map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation (for antigenic data): Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various objects that their dissimilarity may be of interest, ranging from complex biological measurements such as continuous and relational phenotypes, antibody-antigen interactions, and protein folding to abstract concepts, such as customer perception of different brands. Methods are described in the context of bioinformatics applications in Arhami and Rohani (2025a) , and mathematical proofs and Euclidean embedding details are in Arhami and Rohani (2025b) . Package: r-cran-toponym Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geodata, r-cran-ggplot2, r-cran-sf, r-cran-spatstat.geom, r-cran-spatstat.utils, r-cran-terra Filename: pool/dists/noble/main/r-cran-toponym_2.0.1-1.ca2404.1_all.deb Size: 231690 MD5sum: bc83e88a28aafce3104cb2a99f071714 SHA1: fe8374d62fdbc7174ecf3a1f904bb8f7f48f880d SHA256: 4f4fda41edf3e22ab7414a1040b2b7f0d6861fbfd12c99cf2550384511c921c6 SHA512: 1131c754ea6a87d5a6c76fff444a7e60f35f7885aa3fb31e00b14023042d5ade24e082564a52b6d24890be1a21a66e023cf7fbe26b083c37162192f03018b111 Homepage: https://cran.r-project.org/package=toponym Description: CRAN Package 'toponym' (Analyze and Visualize Toponyms) A tool to analyze and visualize toponym distributions. This package is intended as an interface to the GeoNames data. A regular expression filters data and in a second step a map is created displaying all locations in the filtered data set. The functions make data and plots available for further analysis—either within R or in a chosen directory. Users can select regions within countries, provide coordinates to define regions, or specify a region within the package to restrict the data selection to that region or compare regions with the remainder of countries. This package relies on the R packages 'geodata' for map data and 'ggplot2' for plotting purposes. For more information on the study of toponyms, see Wichmann & Chevallier (2025) . 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Package: r-cran-torchdatasets Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-fs, r-cran-zip, r-cran-pins, r-cran-torchvision, r-cran-stringr, r-cran-withr Suggests: r-cran-testthat, r-cran-readr, r-cran-coro, r-cran-tokenizers, r-cran-r.matlab Filename: pool/dists/noble/main/r-cran-torchdatasets_0.3.2-1.ca2404.1_all.deb Size: 185966 MD5sum: 1f1444e4831ab8339f4d257fb01dfc42 SHA1: e5a647a14c0ada3bfd23616090674ed460130ae8 SHA256: ac6c3ab0c0f175568ee4328da722bafa1b60c3730de600e992cce510ba875d3e SHA512: a91e07b774867332808ff03afa494cf7e13a74be2ee6b639f4198492b2c56a82c5652b3a3c12d2e62a4344422835917a95217cc2f7ae75255a6c6d7abda08443 Homepage: https://cran.r-project.org/package=torchdatasets Description: CRAN Package 'torchdatasets' (Ready to Use Extra Datasets for Torch) Provides datasets in a format that can be easily consumed by torch 'dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations. Package: r-cran-torchmaum Architecture: all Version: 2025.7.30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-torchmaum_2025.7.30-1.ca2404.1_all.deb Size: 36606 MD5sum: d77fc1f87a3bf409574a8ac6cf40b54a SHA1: 377e8325d42935bca4ec388e305cbfdd48dcf356 SHA256: e1351c660d23d4fe2f02e3ddc5e0920b1b920fd8f254ef0aa1db347ee6971d2e SHA512: c6d0558db4d163d00e5542408eeda065197ba08419e4c95efb4b8dc806ee2133f8f8c5749b2160ccc7421668370d743252f8309319ae6384945571e7e64f0d82 Homepage: https://cran.r-project.org/package=torchMAUM Description: CRAN Package 'torchMAUM' (Multi-Class Area Under the Minimum in Torch) Torch code for computing multi-class Area Under The Minimum, , Generalization. Useful for optimizing Area under the curve. Package: r-cran-torchopt Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-torch Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-torchopt_0.1.4-1.ca2404.1_all.deb Size: 195822 MD5sum: 25f6f8581a1d2a1ebd596083cfeafdac SHA1: 5298c66b8f4110ff9c3a6835c5eafa0d6d4e0ad0 SHA256: b18c87c34d6072f24d585d941457d310ab904a257a114b592bc143b99831c189 SHA512: 55c324ef5e962cae4e16aa99c20eed0c288e717fe64a13564162933b45832ed5b08806e04978403d43dc9ece099eafbb690fa051afc4dbd4c49cace8429c855e Homepage: https://cran.r-project.org/package=torchopt Description: CRAN Package 'torchopt' (Advanced Optimizers for Torch) Optimizers for 'torch' deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in 'torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) 'adabelief' by Zhuang et al (2020), ; (b) 'adabound' by Luo et al.(2019), ; (c) 'adahessian' by Yao et al.(2021) ; (d) 'adamw' by Loshchilov & Hutter (2019), ; (e) 'madgrad' by Defazio and Jelassi (2021), ; (f) 'nadam' by Dozat (2019), ; (g) 'qhadam' by Ma and Yarats(2019), ; (h) 'radam' by Liu et al. (2019), ; (i) 'swats' by Shekar and Sochee (2018), ; (j) 'yogi' by Zaheer et al.(2019), . Package: r-cran-torchvision Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-fs, r-cran-rlang, r-cran-rappdirs, r-cran-jpeg, r-cran-tiff, r-cran-magrittr, r-cran-png, r-cran-abind, r-cran-jsonlite, r-cran-withr, r-cran-cli, r-cran-glue, r-cran-zeallot Suggests: r-cran-arrow, r-cran-magick, r-cran-prettyunits, r-cran-testthat, r-cran-coro, r-cran-r.matlab, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-torchvisionlib Filename: pool/dists/noble/main/r-cran-torchvision_0.9.0-1.ca2404.1_all.deb Size: 1557328 MD5sum: de9b87dd2903308122712fb821f65f20 SHA1: 433a4c0333b7f098e4eff8f4eb9862041eaa9b44 SHA256: bcc50b257030fc56095007c46e172ef50e3743bea85c80304f2a4527806ef9ed SHA512: 4da858ebf4c36d42b5a75c7ae7efeaf20eb4e3a3df436077076ffa5b305a4557f4439562e7740130e0d850ba96684f4ca727f81da51399e8b58c5a81da72a54e Homepage: https://cran.r-project.org/package=torchvision Description: CRAN Package 'torchvision' (Models, Datasets and Transformations for Images) Provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the 'torch' package and its API borrows heavily from the 'PyTorch' vision package. Package: r-cran-tords Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tords_1.0.0-1.ca2404.1_all.deb Size: 183622 MD5sum: 448fa955ddb842da5d3457963c1da112 SHA1: ad40fa1bf39e376eb6b7114ce3b096faae2cdd3f SHA256: 54a71d625be1c981cfad3c8d48da95e3ed32bffd1f505a83c94502f677fd75f4 SHA512: e7644316617c8059d84f8dcf8def1d4e3d1cbc573883f1c59b1a06828154e0cd228851f34386e48582ade101e78ca7333b4f59bf4c1f3e0bdf10d6001e0c2b35 Homepage: https://cran.r-project.org/package=TORDs Description: CRAN Package 'TORDs' (Third Order Rotatable Designs (TORDs)) Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) ."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). . "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model. Package: r-cran-tornado Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-assertthat, r-cran-ggplot2, r-cran-scales, r-cran-gridextra, r-cran-rlang Suggests: r-cran-testthat, r-cran-caret, r-cran-glmnet, r-cran-randomforest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tornado_0.1.3-1.ca2404.1_all.deb Size: 228308 MD5sum: a74fa16ddeb6c561fc95bf95a70fa9d6 SHA1: 256dc0fe7dd4637c66ab843ee65e70fea7fcffed SHA256: ecb502b34fc2f8462c44e4c3b766a40c18c51f451a24625103e3e9dcbbc94865 SHA512: c6edfe3821ed399434b7645b05a86fdd0f2a73d4f63b95159c23aab6584888394af32810135a6c7f7ffc0e79d3b54d200bfc5bef5c7004c79daf936400f1c12d Homepage: https://cran.r-project.org/package=tornado Description: CRAN Package 'tornado' (Plots for Model Sensitivity and Variable Importance) Draws tornado plots for model sensitivity to univariate changes. 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Package: r-cran-toro Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-sf, r-cran-shiny Suggests: r-cran-devtools, r-cran-dplyr, r-cran-knitr, r-cran-mapview, r-cran-rmarkdown, r-cran-spdata, r-cran-testthat, r-cran-usethis, r-cran-webshot, r-cran-webshot2 Filename: pool/dists/noble/main/r-cran-toro_0.2.0-1.ca2404.1_all.deb Size: 710814 MD5sum: c78855f08b8923a9b1fa21d1b0ef07c1 SHA1: 620ab9f928d7ab6bb9bb1a368a7eba4d353212e0 SHA256: 1c53a0588376b1e9b0b7eb95aa2e0223989eb4439d5f04d2c3da4f35470d793a SHA512: cc9858430c01bb6cee49054391c588b0fe83477c9011f31f499a4fcf59f0130792098d8b5d725f2d2afadeb888f7f2aac1bd418b6b5ccdbe1243007cd77afb0f Homepage: https://cran.r-project.org/package=toro Description: CRAN Package 'toro' (High-Performance Interactive Mapping) Interactive spatial visualisations are a cornerstone for exploring and communicating complexity, and are commonly embedded into reports or interactive dashboards. However, as the amount of data grows, so do the demands on functionality, especially for technical and scientific data. To bridge this gap and create a mapping package that is high performing, a modern approach is needed that draws from best software engineering practices. Toro provides bindings to 'MapLibre GL JS', an open-source 'JavaScript'/'TypeScript' library for rendering interactive maps in the browser, built from the ground up for responsiveness and scale, by the MapLibre Organization (2020) . This connection allows users to create interactive maps that can easily be integrated into both 'Quarto' and the 'R Shiny' dashboard framework. Toro thereby enables spatial visualisation and exploration of data that might otherwise be too limited, too slow, or too hard to scale using more traditional interactive mapping tools such as 'leaflet'. 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In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the 'lda' package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided. Sample data for the vignette is included in the toscaData package, which is available on gitHub: . 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Commands are provided for t tests on means, z tests on proportions, McNemar's test (1947) on proportions and related tests, tests on the regression coefficients from OLS linear regression (not yet implementing all of the current regression options from the 'Stata' 'tostregress' command, e.g., survey regression options, estimation options, etc.), Wilcoxon's (1945) signed rank tests, Wilcoxon-Mann-Whitney (1947) rank sum tests, supporting inference about equivalence for a number of paired and unpaired, parametric and nonparametric study designs and data types. Each command tests a null hypothesis that samples were drawn from populations different by at least plus or minus some researcher-defined level of tolerance, which can be defined in terms of units of the data or rank units (Delta), or in units of the test statistic's distribution (epsilon) except for tost.rrp() and tost.rrpi(). Enough evidence rejects this null hypothesis in favor of equivalence within the tolerance. 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Based on the notion of regression averaging (Matloff (2017, ISBN: 9781498710916)). Package: r-cran-toxcrit Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-toxcrit_1.1-1.ca2404.1_all.deb Size: 14308 MD5sum: 3e4237bd409de7e16d2c9ab818982eb3 SHA1: 7e768997ab51318902463ba0785bf2c9da33605f SHA256: bd6165dede3ad0cbc4479e79d6ff05331dec515bff2c482e78ad25a1fc196e12 SHA512: 36d491ffe4389b236d18dd8fa9677358232d79b391c1312db15b0ff7c75adfb3203d8ba5d2d71bb0b0691bea4c5bce85c5c8b0c5d3622d5c6eeefa4d2b87db17 Homepage: https://cran.r-project.org/package=ToxCrit Description: CRAN Package 'ToxCrit' (Calculates Safety Stopping Boundaries for a Single-Arm Trialusing Bayes) Computation of stopping boundaries for a single-arm trial using a Bayesian criterion. For each m<=n (n=total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety. See also Aamot et al. (2010) "Continuous monitoring of toxicity in clinical Trials - simulating the risk of stopping prematurely" . Package: r-cran-toxdrc Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-outliers, r-cran-purrr, r-cran-rlang, r-cran-drc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-toxdrc_2.0.0-1.ca2404.1_all.deb Size: 170538 MD5sum: 412ae966a7c892dd3bac0078240440cb SHA1: e802ca69681c61ee74ef85ed980d62ee46453a7b SHA256: e4d07af0b1676cc1a7ac40c3986d07eefc0781a79b7f0f9f06d2e665efafc679 SHA512: 6777783895bc36a3ca1c5e458bfb61d6e444e0153d70297a57a1a3097f6b19cb57d516fa059cf088d0a4966a218e753481dc7dd1136c520c80ab7c326eb3bb9a Homepage: https://cran.r-project.org/package=toxdrc Description: CRAN Package 'toxdrc' (Pipeline for Dose-Response Curve Analysis) Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, 'runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work. Package: r-cran-toxeval Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-dt, r-cran-leaflet, r-cran-ggplot2, r-cran-magrittr, r-cran-shiny, r-cran-shinydashboard, r-cran-rcolorbrewer, r-cran-readxl, r-cran-shinyace, r-cran-shinycssloaders Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-here, r-cran-tcpl, r-cran-openxlsx, r-cran-covr Filename: pool/dists/noble/main/r-cran-toxeval_1.4.2-1.ca2404.1_all.deb Size: 3128066 MD5sum: 95837f90ee20c8e03420ad945e5b3405 SHA1: 398feea16f529d104c38e471c53d2fdc1e08cf0b SHA256: 13ff9b8e93b18c9e2185a08f66536c59c140ee8ba8a1dfb51f3cb7f531ae8f3b SHA512: 33f1eb7399566552872441c7d68abc74294cf9a5f439dfc7e78c1f27786521a45ff0f2b433801b79e5304025d9f712d151ac1ca93f85eee47e36b9f2d6df61a4 Homepage: https://cran.r-project.org/package=toxEval Description: CRAN Package 'toxEval' (Exploring Biological Relevance of Environmental ChemistryObservations) Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) . 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Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" . 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Methods includes Mann-Whitney statistic and Jackknife, etc. Package: r-cran-tpc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-bioc-graph Suggests: r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tpc_1.0-1.ca2404.1_all.deb Size: 920758 MD5sum: abd33c625eb4d56561f5aa2e03ddd7c0 SHA1: 546c5ec709dac2d1f42ef1a84df36e335b3fb6b8 SHA256: f36dd29b4bb6cc9bc82a9b07a340273cb1ec618670c0c0567f08186881006a66 SHA512: 18edffa572d0fdb3e8b3e32abbaaf7c15ca77e36ac2b174b4df88be005499b37b77618470251c13e472446b6fbc10c36ac20e746ecdca53c54a2a22ce7b49ec0 Homepage: https://cran.r-project.org/package=tpc Description: CRAN Package 'tpc' (Tiered PC Algorithm) Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. 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Package: r-cran-tpcselect Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-psych, r-cran-mass, r-cran-kernsmooth Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tpcselect_0.8.3-1.ca2404.1_all.deb Size: 43850 MD5sum: eca24026702f612848f3e88ad6b0ecd8 SHA1: 903921e512b0cd84d7e2d4192bfacac11ccd8494 SHA256: 865b0d221ba56277b825881622ab1e8a7b60daeb42a8030e995f95d88f8e20ba SHA512: 55db300d00cbc1a248440bdd9306db0ed5dc938cf250347f85bd246c340a62ec8b4846ce837836effb28713191dfd9a62d082201388b84306c99018b5afec97c Homepage: https://cran.r-project.org/package=TPCselect Description: CRAN Package 'TPCselect' (Variable Selection via Threshold Partial Correlation) A threshold partial correlation approach to selecting important variables in linear models of L. and others (2017) at , and in partial linear models of L. and others (2018) at . 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Measures include trade openness, bilateral export and import intensity, the Herfindahl-Hirschman concentration index, normalized and entropy-based diversification, structural diversification relative to a benchmark, export similarity, trade complementarity, revealed comparative advantage, and intra-industry trade. Functions are vectorized where appropriate, validate economically meaningful inputs, and require no external data service. The definition of trade openness follows the World Bank indicator metadata . Methodological background for several trade indicators is provided by the World Bank's World Integrated Trade Solution and the World Trade Organization (2012, ISBN:9789287038128). 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This package aims at choosing the best model for a particular dataset, regarding its discriminant power and runtime. Package: r-cran-tradepolicy Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-capybara, r-cran-data.table, r-cran-knitr, r-cran-msm, r-cran-tinyplot Filename: pool/dists/noble/main/r-cran-tradepolicy_0.8.0-1.ca2404.1_all.deb Size: 1308658 MD5sum: 61ef76b1cc0d724fc76da653892fa1ac SHA1: 4e03cc5d616d4dab009033e14c4c93ade37fa754 SHA256: f33adae3e9bf614abce5cf519e9b421b8e1137395b7ce663ff582971f2c56e2a SHA512: 498e8f873d63521f570539f8a27f7706eb6505ebfd8c8690b2a9e50a8161bc39b19eaf0546822297c8a1428311b83869bb8801bc7b16e2a4c27fc991c7ab65aa Homepage: https://cran.r-project.org/package=tradepolicy Description: CRAN Package 'tradepolicy' (Replication of 'An Advanced Guide To Trade Policy Analysis') Datasets from Yotov, et al. (2016) (An Advanced Guide to Trade Policy Analysis) and functions to report regression summaries with clustered robust standard errors. Package: r-cran-trader Architecture: all Version: 1.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplr Filename: pool/dists/noble/main/r-cran-trader_1.2-6-1.ca2404.1_all.deb Size: 220034 MD5sum: f600e8053e210b7ae97aaf07fbe82349 SHA1: 692ad45834655cb572f4cebf71cffdef6dc37c0b SHA256: f2e34463bdd29b4669b6b9dc1ebaa8f8d10a8a06a8ce87780fd9b82cf71180d1 SHA512: b18a2b7b074efbd09b496444b254bccfa83f9f38b227ef1a5fa507b41fb3c743b4b20f7a7ca97431f22c256da167930414219e2c757216bf0d21a7c112a7aed2 Homepage: https://cran.r-project.org/package=TRADER Description: CRAN Package 'TRADER' (Tree Ring Analysis of Disturbance Events in R) Tree Ring Analysis of Disturbance Events in R (TRADER) package provides functions for disturbance reconstruction from tree-ring data, e.g. boundary line, absolute increase, growth averaging methods. 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The normalized sample entropy method has been implemented which produces accurate entropy estimation even on smaller datasets. On a separate stream, trades from the five major assets classes and also functionality to use pricing curves, rating tables, Credit Support Annex and add-on tables. The implementation follows an object oriented logic whereby each trade inherits from more abstract classes while also the curves/tables are objects. Furthermore, odds calculators and P&L back-testing functionality has been implemented for the most widely used betting/trading strategies including martingale, 'DAlembert', 'Labouchere' and Fibonacci. Back testing has also been included for the 'EuroMillions', the 'EuroJackpot', the UK Lotto, the Set For Life and the UK 'ThunderBall' lotteries. Furthermore, some basic functionality about climate risk has been included. 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The families of transformations included in the package are the following: Bickel-Doksum (Bickel and Doksum 1981 ), Box-Cox, Dual (Yang 2006 ), Glog (Durbin et al. 2002 ), gpower (Kelmansky et al. 2013 ), Log, Log-shift opt (Feng et al. 2016 ), Manly, modulus (John and Draper 1980 ), Neglog (Whittaker et al. 2005 ), Reciprocal and Yeo-Johnson. The package simplifies to compare linear models with untransformed and transformed dependent variable as well as linear models where the dependent variable is transformed with different transformations. Furthermore, the package employs maximum likelihood approaches, moments optimization and divergence minimization to estimate the optimal transformation parameter. Package: r-cran-trainer Architecture: all Version: 2.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-neuralnet, r-cran-rpart, r-cran-xgboost, r-cran-randomforest, r-cran-e1071, r-cran-kknn, r-cran-dplyr, r-cran-mass, r-cran-nnet, r-cran-stringr, r-cran-rlang, r-cran-adabag, r-cran-glmnet, r-cran-rocr, r-cran-gbm, r-cran-ggplot2 Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-trainer_2.2.12-1.ca2404.1_all.deb Size: 198212 MD5sum: 6ff9776f84cdd12008f3259294b2fee5 SHA1: 83cf5a730854341d1265a057f6709475fd46bd6b SHA256: 5c65d889390355f2399905b00cb79c0841cbca22e82de3d701d4e6cb6729efaa SHA512: 61b3545c4b5a0bbbbef0a112d152e1399749e1db721532903f89302b894a765575d8528ecc084109e9058cb6d30256f64f1bf4ab2599d4ffd972fb9d78604c75 Homepage: https://cran.r-project.org/package=traineR Description: CRAN Package 'traineR' (Predictive (Classification and Regression) Models Homologator) Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004) , Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) , ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) , Extreme Gradient Boosting Chen & Guestrin (2016) , Random Forest Breiman (2001) , Neural Networks Venables, W. N., & Ripley, B. D. (2002) , Support Vector Machines Bennett, K. P. & Campbell, C. (2000) , Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) , Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) . 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(2004), the procedure involves identifying each trajectory to a point in the space of measures. In this context, a measure is a quantity meant to capture a certain characteristic feature of the trajectory. The points in the space of measures are then clustered using a version of the Spectral Clustering algorithm. 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Package: r-cran-trajmsm Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-flexmix, r-cran-ggplot2, r-cran-survival, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-trajmsm_0.1.5-1.ca2404.1_all.deb Size: 131862 MD5sum: 815be2f4526df271276f2a80274bada9 SHA1: d8e3bd1bf1bb88cf5c2c8584e58837fefbb32ab7 SHA256: 70f61e5d1f6d5613124207ea7fec04c6c11f5bbd50a941e97e2bec1991359099 SHA512: 961b2814eb44c0b9ce7fbb0be5e6875d43b639ca709e01f8763255a1deba25395c8d995904f1722ccdaa38f60da771d84e4e8938cce13c91d6ea7d025ec56131 Homepage: https://cran.r-project.org/package=trajmsm Description: CRAN Package 'trajmsm' (Marginal Structural Models with Latent Class Growth Analysis ofTreatment Trajectories) Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) . Package: r-cran-trajr Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal, r-cran-plotrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr, r-cran-sp, r-cran-mass, r-cran-covr Filename: pool/dists/noble/main/r-cran-trajr_1.5.1-1.ca2404.1_all.deb Size: 694090 MD5sum: 06f7170bd7235e1492641b8afe703fcc SHA1: 05622258858543621f9d0db91367a429c40ae416 SHA256: 7cafa36dce15dd78d218ed926a3f7de2c72f4811a0023fae4e4e6bb1653c46dd SHA512: 6a3222f989590c2181f5c7a8ed22eff6523db161c277a7b311bac850cf9f1a61e7871b03a468ac4eb6fbf663e924bb4a5d51be869aca0a12ce2245cca9466925 Homepage: https://cran.r-project.org/package=trajr Description: CRAN Package 'trajr' (Animal Trajectory Analysis) A toolbox to assist with statistical analysis of animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. Some support is provided for 3-dimensional trajectories. McLean & Skowron Volponi (2018) . Package: r-cran-traktok Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-cli, r-cran-cookiemonster, r-cran-curl, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lobstr, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-rvest, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-traktok_0.1.1-1.ca2404.1_all.deb Size: 285488 MD5sum: 03b707eaf1b2d1d93318312f0e6800b8 SHA1: ae15b6d9a828f6dcf0a835832afb939a08e7522a SHA256: 5a449df25a9133ecb7bd9bd7344a160f7b483a2c473f1554c8ad6abf7cf5b99f SHA512: cd78c6c15138194e4e3aab46f69d718f6bccb2a7489f026fb1b0514ae9ea9e9c5027e8ea36b971c8554e007e6f893a782f0492e05c287f7eded27c440e70b4a6 Homepage: https://cran.r-project.org/package=traktok Description: CRAN Package 'traktok' (Collecting 'TikTok' Data) Getting 'TikTok' data () through the official and unofficial APIs—in other words, you can track 'TikTok'. Package: r-cran-tram Architecture: all Version: 1.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3388 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlt, r-cran-mvtnorm, r-cran-formula, r-cran-multcomp, r-cran-variables, r-cran-basefun, r-cran-sandwich, r-cran-survival, r-cran-matrix Suggests: r-cran-mass, r-cran-th.data, r-cran-trtf, r-cran-mlbench, r-cran-knitr, r-cran-quantreg, r-cran-colorspace, r-cran-atr, r-cran-lme4, r-cran-reformulas, r-cran-merderiv, r-cran-sparsegrid, r-cran-alabama, r-cran-numderiv, r-cran-gridextra, r-cran-lattice, r-cran-latticeextra, r-cran-hsaur3, r-cran-ordinalcont, r-cran-coxme, r-cran-mlt.docreg, r-cran-ordinal, r-cran-coin, r-cran-asht, r-cran-gamlss, r-cran-randomforestsrc, r-cran-tramme, r-cran-glmmtmb, r-cran-geepack, r-cran-ranger, r-cran-eha, r-cran-flexsurv, r-cran-frailtyem, r-cran-frailtypack, r-cran-gamlss.cens, r-cran-icenreg, r-cran-mpr, r-cran-rms, r-cran-rstpm2, r-cran-timereg, r-cran-stat2data, r-cran-cotram, r-cran-latex2exp, r-cran-tramvs, r-cran-aer, r-cran-konpsurv, r-cran-gamlss.data, r-cran-xtable, r-cran-english, r-cran-bibtex, r-cran-expm, r-cran-mgcv, r-cran-pracma Filename: pool/dists/noble/main/r-cran-tram_1.4-6-1.ca2404.1_all.deb Size: 2311056 MD5sum: fed5326d39ad2c7793b235247f9222ae SHA1: f0b34756c96d8404eb3fea4865b42c629871eae5 SHA256: ec11b2ed0394ae015f807e0a521de497243e849fecee72d96f7c68b680f4fb01 SHA512: 265cbeac5e5f8f1cb60126572cf0b04ade35dc050434db47837e9db4b7363d6f272e74c8f17f7fdb560d165670525d64ceb295f2b71f22d03ba77585e69a833c Homepage: https://cran.r-project.org/package=tram Description: CRAN Package 'tram' (Transformation Models) Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (, ). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, ). The underlying theory is described in Hothorn et al. (2018) . An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, ) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, ). Multivariate conditional transformation models (Klein et al, 2022, ) and shift-scale transformation models (Siegfried et al, 2023, ) can be fitted as well. The package contains an implementation of a doubly robust score test, described in Kook et al. (2024, ). Package: r-cran-tramicp Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-coin, r-cran-multcomp, r-cran-survival, r-cran-variables, r-cran-basefun, r-cran-mass, r-cran-cotram, r-cran-dhsic, r-cran-ranger, r-cran-sandwich Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tramicp_0.1-0-1.ca2404.1_all.deb Size: 121178 MD5sum: ba6db4f9082db65711bd0e12a1c134f3 SHA1: 8fe1f0fffc44b7852e6d4c330659b1c20cadf289 SHA256: cf98d6fcc6d96cae74ef00582b8c5c2094c949a20357a514f6a93b3c41e549b3 SHA512: f056199c5dcec9ec634c95b5d35bc6a0bf7bd3d583617f6eb10dc5bb2502c128b09dea851ca62b91c81f6b9322a401145598f90d4546c587bffa8dacb7a77f93 Homepage: https://cran.r-project.org/package=tramicp Description: CRAN Package 'tramicp' (Model-Based Causal Feature Selection for General Response Types) Extends invariant causal prediction (Peters et al., 2016, ) to generalized linear and transformation models (Hothorn et al., 2018, ). The methodology is described in Kook et al. (2023, ). Package: r-cran-tramnet Architecture: all Version: 0.0-991-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-basefun, r-cran-sandwich, r-cran-paramhelpers, r-cran-lhs, r-cran-mlr, r-cran-smoof Suggests: r-cran-penalized, r-cran-th.data, r-cran-survival, r-cran-knitr, r-cran-mlbench, r-cran-colorspace, r-cran-mvtnorm, r-cran-glmnet, r-cran-trtf, r-cran-matrix, r-cran-lattice, r-cran-kableextra, r-cran-coin, r-cran-tbm, r-cran-dicekriging Filename: pool/dists/noble/main/r-cran-tramnet_0.0-991-1.ca2404.1_all.deb Size: 879648 MD5sum: d93c7a5a5373d84acfc9933300375df2 SHA1: a522e507e50ac74d096377eb5e394786b0b940e7 SHA256: ae477ecc35c739a53dd06b4531e8cf983ed2e06d845877c49ec499d0f71b1411 SHA512: 478651ac7a519fbd7aac9839bf957c42cd8f7765082e9f9bc2097d933825e195656eb0e7fcca7404a1c3761218650b9afd54551d822e15ee7e10d58d72edeb2c Homepage: https://cran.r-project.org/package=tramnet Description: CRAN Package 'tramnet' (Penalized Transformation Models) Partially penalized versions of specific transformation models implemented in package 'mlt'. Available models include a fully parametric version of the Cox model, other parametric survival models (Weibull, etc.), models for binary and ordered categorical variables, normal and transformed-normal (Box-Cox type) linear models, and continuous outcome logistic regression. Hyperparameter tuning is facilitated through model-based optimization functionalities from package 'mlrMBO'. The accompanying vignette describes the methodology used in 'tramnet' in detail. Transformation models and model-based optimization are described in Hothorn et al. (2019) and Bischl et al. (2016) , respectively. Package: r-cran-trampoline Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coro, r-cran-fastmap, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-bench, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-trampoline_0.1.1-1.ca2404.1_all.deb Size: 45524 MD5sum: f3f70aec1944123c81115601c518afa1 SHA1: 75dffdfc234fb779e7f6a7c7f331f7bbb243270c SHA256: 26e6e58a9a4d5ccec16bf936b80e52cbf516caca145cdb80d0403369c8a80d02 SHA512: 61bf7814e0f440cb02b72d05a46ca7c23c4c899cf89b43c888b98466a779b0f0d76e6587ad89d71bfeb2a0581fd513e15070d3afbf0fcf4c4b53182945282c2c Homepage: https://cran.r-project.org/package=trampoline Description: CRAN Package 'trampoline' (Make Functions that Can Recurse Infinitely) Implements a trampoline algorithm for R that let's users write recursive functions that get around R's stack call limitations, enabling theoretically infinite recursion. 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Package: r-cran-trampr Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trampr_1.0-10-1.ca2404.1_all.deb Size: 488302 MD5sum: dedcf1ac61bdb405642554e49a6858d6 SHA1: 1027124baf73b7efdf805d5e8b70494f36525648 SHA256: 0f189f74dc5a01fac37033460c539777593d1c964a9f873e9a7a783f1598587b SHA512: 98078460268f78fca21c450343d961ab31a7c60f9a2360cab61d41ba5a34ee6f6f61cfccece8d8e3566401db992d2c9b30938296ed8245cf7267cbb60ee1436e Homepage: https://cran.r-project.org/package=TRAMPR Description: CRAN Package 'TRAMPR' ('TRFLP' Analysis and Matching Package for R) Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. 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Package: r-cran-tramvs Architecture: all Version: 0.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-variables, r-cran-cotram, r-cran-future, r-cran-future.apply, r-cran-mvtnorm Suggests: r-cran-abess, r-cran-tramnet, r-cran-colorspace, r-cran-knitr, r-cran-mlt, r-cran-th.data, r-cran-survival, r-cran-ordinal, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tramvs_0.0-9-1.ca2404.1_all.deb Size: 229926 MD5sum: 3fa847ae0cc3e6ed936ce38fde6ada2b SHA1: 39428e6baab66bb718d1c61c5df71683c91ea3f7 SHA256: 616ccac262246b6e8ba8dfa48a918a1d6bf423101469abec4a6bd643a88fa91b SHA512: 3317bac5146f8ec74bd9d014a1455e934ce1e011116b1c1381bbad3d8731692a91048347ff231b5c8432ef265c14ced1977fcc862aaf1d385af45475b5367157 Homepage: https://cran.r-project.org/package=tramvs Description: CRAN Package 'tramvs' (Optimal Subset Selection for Transformation Models) Greedy optimal subset selection for transformation models (Hothorn et al., 2018, ) based on the abess algorithm (Zhu et al., 2020, ). Applicable to models from packages 'tram' and 'cotram'. Application to shift-scale transformation models are described in Siegfried et al. (2024, ). Package: r-cran-transda Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-transda_1.0.3-1.ca2404.1_all.deb Size: 75360 MD5sum: d538104f5ca5addc7d5d4c1dcc06b786 SHA1: ab1f51c90227ac6d27f0c978295de5deacf655e1 SHA256: 6e7b8cf9641c50d83a7362c0c1a335ada3233cd638d99a3514a1739774b497ea SHA512: ad6c78effc08459e47c7ced618b8b219f4de593a81731611faa1b3a1f9abc1132435420c794b44bf34a08f87a6aa4d9f0ecd1291a13403842aeb81e8f2922f1f Homepage: https://cran.r-project.org/package=transDA Description: CRAN Package 'transDA' (Transformation Discriminant Analysis) Performs transformation discrimination analysis and non-transformation discrimination analysis. 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Calibrates the Rasch model in each language group, links the groups robustly through the densest cluster of items rather than assuming DIF cancels out (on anchor selection see Kopf, Zeileis and Strobl, 2015, ), detects small-sample DIF with an empirical-Bayes spike-and-slab model and local false discovery rates (Efron, 2004, ), quantifies whether item-level DIF accumulates into different pass rates, explains DIF by item features to give translators actionable guidance, and drafts a comparability report. Package: r-cran-transferegovr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-transferegovr_0.2.0-1.ca2404.1_all.deb Size: 186874 MD5sum: bcaad3bbee13b4f827d073465107b72b SHA1: d3b54dc6ffdab38f7ec271bd7fc2155cd361011d SHA256: f95cb525dfcbece19d24d75c461ca15d99aeb9c69af0867992e48d15b95390fb SHA512: 3f052c352d25cf7cd972e45bbb8353908394a7bc119b9e9f101c94909e09458e9f1a91a5cde609d14086f1ef0242bd2b2b46ab687a99f9d19fc762003b48bc15 Homepage: https://cran.r-project.org/package=transferegovr Description: CRAN Package 'transferegovr' (Access the 'TransfereGov' Open Data APIs) Provides a modern interface to the open data application programming interfaces of the Brazilian federal government's 'TransfereGov' platform (). Covers the special transfers, fund-to-fund transfers, partnership management, and decentralized credit ('TED') modules, which together publish seventy-four tables on action plans, programs, proposals, partnerships, budget commitments, credit notes, financial execution, management reports, and payment orders. Filters are the services' own typed query parameters, validated against the published schema before a request is made, and results are returned as tidy tibbles with types taken from that schema. Automatic pagination, request throttling, retries with exponential backoff, and an optional response cache are included. Package: r-cran-transforemotion Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3974 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-googledrive, r-cran-lsafun, r-cran-matrix, r-cran-pbapply, r-cran-remotes, r-cran-reticulate, r-cran-textdata, r-cran-jsonlite, r-cran-ggplot2, r-cran-reshape2, r-cran-httr Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transforemotion_0.1.7-1.ca2404.1_all.deb Size: 3871074 MD5sum: 5ed4e18952fb84374cb92a74b88455f3 SHA1: 8a973834ec11bdd53547bb3109c011091bf1c770 SHA256: 0596e08f5822eaf6eab704c8fa4fc0169ce228051c48190b6d808a61e0db36b3 SHA512: 7a1657c3125e5b67a86d4ecb3f8795a1a6599d6645feb9085e3ec7d145dca3d9862d23444f5f2ce080112c87da1648c6b38dd384a4eb3cb6d184e9f33c8692d1 Homepage: https://cran.r-project.org/package=transforEmotion Description: CRAN Package 'transforEmotion' (Sentiment Analysis for Text, Image and Video using TransformerModels) Implements sentiment analysis using huggingface transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa and default image/video pipeline is Open AI's CLIP . All other zero-shot classification model pipelines can be implemented using their model name from . Package: r-cran-transform.hazards Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-timereg, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transform.hazards_0.1.1-1.ca2404.1_all.deb Size: 64380 MD5sum: 6ecf3b3edb79742ad2aaee1dafc45f56 SHA1: cb86c7534ae82b43b335e8787c847c80ead0b10e SHA256: 790941fc48fb450dc1be169b3d3fc8f7bf9816a1e75ef76f1982a3dd5816010c SHA512: f04e040030aa530a952f5830737660513ca315926ca73f88c48c4be781e9d727220c3144c6c8cc987c99bd059af5fefdcd7d7d6b7b8393fc4e32d5f2c81a2327 Homepage: https://cran.r-project.org/package=transform.hazards Description: CRAN Package 'transform.hazards' (Transforms Cumulative Hazards to Parameter Specified by ODESystem) Targets parameters that solve Ordinary Differential Equations (ODEs) driven by a vector of cumulative hazard functions. The package provides a method for estimating these parameters using an estimator defined by a corresponding Stochastic Differential Equation (SDE) system driven by cumulative hazard estimates. By providing cumulative hazard estimates as input, the package gives estimates of the parameter as output, along with pointwise (co)variances derived from an asymptotic expression. Examples of parameters that can be targeted in this way include the survival function, the restricted mean survival function, cumulative incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018) , and further applications in Stensrud, Røysland, and Ryalen (2019) and Ryalen et al. (2021) . Package: r-cran-transform Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-transform_1.1-1.ca2404.1_all.deb Size: 115742 MD5sum: bc9d8848b0ea0c09f08b89edd5f40fec SHA1: 75b4b17453652a10fe590ca465bb9299cb7392f5 SHA256: c259a850bf4095c952d4215448b1ad95447442d56aabc1f94bf2eb500f4250cc SHA512: 1b75305c267aba331b7922b6951aae1acda5dc26ebe596a42dc9a9815a745e30d681c391088a286ad8049ac3dec476424788466f304899c82b0b51030aaa543c Homepage: https://cran.r-project.org/package=Transform Description: CRAN Package 'Transform' (Statistical Transformations) Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) , Glog (Durbin et al., 2002) , Neglog (Whittaker et al., 2005) , Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) , Bickel-Docksum (Bickel and Doksum, 1981) , Yeo-Johnson (Yeo and Johnson, 2000) , Square Root (Medina et al., 2019), Manly (Manly, 1976) , Modulus (John and Draper, 1980) , Dual (Yang, 2006) , Gpower (Kelmansky et al., 2013) . It also performs graphical approaches, assesses the success of the transformation via tests and plots. Package: r-cran-transformer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-attention Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transformer_0.2.0-1.ca2404.1_all.deb Size: 21854 MD5sum: 94165a7e68209ae6a2ffb27b50c557bf SHA1: 76bb2ca3fd20328b98f63317cf6ede8bfe1773af SHA256: 93f1d4eee1733019f2e83f1c60c63e38e245e16d8e3accb94a06a902dc9606a4 SHA512: 86835ca1b79cb5b2b4747ff7b797a579008878b3a801ec8e34be320684e8750937ab8443a98ccd2a373cc55148ecf8d49473eed28c479b9b313d81be97f7701e Homepage: https://cran.r-project.org/package=transformer Description: CRAN Package 'transformer' (Implementation of Transformer Deep Neural Network with Vignettes) Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) ). Package: r-cran-transformerforecasting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-keras, r-cran-tensorflow, r-cran-magrittr, r-cran-reticulate Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lubridate, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transformerforecasting_0.1.0-1.ca2404.1_all.deb Size: 70442 MD5sum: 0cd5128b9b1196396407fd98bde97518 SHA1: 2c893abc4034394473be06b10d54df7f2de5c5cd SHA256: 2cf54dcdbf83c8fbc4584a07138cf830e5f3d1b16c73d27a2ab5c5762a5dbb71 SHA512: 133dadc4704d2830da32e210d54d2df3bb0045f746b9aa6c0a38f9cf8f7fc2b895f776403edfc2871e900e25bee433d8d7a6ca00c8deb96970cab6e50f5d0153 Homepage: https://cran.r-project.org/package=transformerForecasting Description: CRAN Package 'transformerForecasting' (Transformer Deep Learning Model for Time Series Forecasting) Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) and Nayak et al. (2024) . Package: r-cran-transformmos Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-transformmos_0.1.0-1.ca2404.1_all.deb Size: 15786 MD5sum: 303f79fe91272210c9c4acb3b7278129 SHA1: ee5bd03981b02446af70339d841e4ea899bb3276 SHA256: 50d6d95186b1f4d49e7169bab1b8f3e437a980752c6cbbcea540162a860c70d2 SHA512: 1f228fb3dfdbf44f8cf950260491414cd0b3bea247c6b3910275c216ead53fc3ae116cdc0d30cb421d92577d5af3917956bef074bc44ef9dbcf369220a34242e Homepage: https://cran.r-project.org/package=transformmos Description: CRAN Package 'transformmos' (Transform MOS Values to be Robust for using Rank BasedStatistics) Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) . Package: r-cran-transfrgov Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-readr Suggests: r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transfrgov_0.1.1-1.ca2404.1_all.deb Size: 329512 MD5sum: a08e12a2b3b2d99b5cb663957c0988aa SHA1: 1ba24e1766ce9f1885f91b7e4716b12a429dc72d SHA256: 2519a675c3949ef024cc0d61f66e2842c28cbe6b1d7c470d241521f1d21a453b SHA512: 89f50748bff1e2a4d3af7a5b58f009fb88a6f267adf763d8597ed74cc95db5261020ca5857d70b289f868e0084b5dc9e4383254184714c6ee2fb821da28c905e Homepage: https://cran.r-project.org/package=transfRgov Description: CRAN Package 'transfRgov' (Acquisition and Reading of Brazilian Federal Government FundTransfers) Downloads, reads and organizes data on federal fund transfers to Brazilian municipalities. Data are retrieved from the TransfereGov "Fundo a Fundo" API, from the Portal da Transparência open data service and from the Tesouro Transparente CKAN catalogue. The package offers one reader per API endpoint, helpers that map SIAFI municipality codes to IBGE codes, and routines that fetch the monthly transfer archives and the tax waiver records published as open data. Package: r-cran-transgfm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transgfm_1.0.2-1.ca2404.1_all.deb Size: 88494 MD5sum: 30909a5f3da7e98f3a4b9e768598970c SHA1: b623d85823ae2783c6941229c074efaa5b2b5ca5 SHA256: ceb0428eb34fc2d8b9389b4de5bf6f1f84eea01a8c37762fcbc6e1f453fe4dd0 SHA512: 91b49d3bb239ada1dae4d024b77e6c3adc28e255df96dd32a1144d3c407c75388f06de74deec808967073b7bb7c719cf8ba7d5a82dd19bcd34c6b83b61f53cf0 Homepage: https://cran.r-project.org/package=transGFM Description: CRAN Package 'transGFM' (Transfer Learning for Generalized Factor Models) Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain. Package: r-cran-transgraph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glasso, r-cran-clime, r-cran-heteroggm, r-cran-dcov, r-cran-huge, r-cran-evaluationmeasures Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transgraph_1.1.0-1.ca2404.1_all.deb Size: 99358 MD5sum: af4b4924655d3beea724e65ac8a32e0c SHA1: 890ffb0c15de55afbb2789f75daa4a5801ab6b75 SHA256: 059273ff3702f578317b49c2d73edecf44e8678805623481a3871368c9a072f2 SHA512: 95cdae1c9c15f5542f3011882513470189761c0277ca26fdc887db397ad6498a4eb5f681f62ca7c9fff07ba5e26e6c9eb89d3650683e51e6b53a2c2b3bbb3177 Homepage: https://cran.r-project.org/package=TransGraph Description: CRAN Package 'TransGraph' (Transfer Graph Learning) Transfer learning, aiming to use auxiliary domains to help improve learning of the target domain of interest when multiple heterogeneous datasets are available, has been a hot topic in statistical machine learning. The recent transfer learning methods with statistical guarantees mainly focus on the overall parameter transfer for supervised models in the ideal case with the informative auxiliary domains with overall similarity. In contrast, transfer learning for unsupervised graph learning is in its infancy and largely follows the idea of overall parameter transfer as for supervised learning. In this package, the transfer learning for several complex graphical models is implemented, including Tensor Gaussian graphical models, non-Gaussian directed acyclic graph (DAG), and Gaussian graphical mixture models. Notably, this package promotes local transfer at node-level and subgroup-level in DAG structural learning and Gaussian graphical mixture models, respectively, which are more flexible and robust than the existing overall parameter transfer. As by-products, transfer learning for undirected graphical model (precision matrix) via D-trace loss, transfer learning for mean vector estimation, and single non-Gaussian learning via topological layer method are also included in this package. Moreover, the aggregation of auxiliary information is an important issue in transfer learning, and this package provides multiple user-friendly aggregation methods, including sample weighting, similarity weighting, and most informative selection. (Note: the transfer for tensor GGM has been temporarily removed in the current version as its dependent R package Tlasso has been archived. The historical version TransGraph_1.0.0.tar.gz can be downloaded at ) Reference: Ren, M., Zhen Y., and Wang J. (2024) "Transfer learning for tensor graphical models". Ren, M., He X., and Wang J. (2023) "Structural transfer learning of non-Gaussian DAG". Zhao, R., He X., and Wang J. (2022) "Learning linear non-Gaussian directed acyclic graph with diverging number of nodes". 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The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) . 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Package: r-cran-transportr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-generics, r-cran-ife, r-cran-mlr3superlearner, r-cran-origami, r-cran-r6 Suggests: r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transportr_0.1.0-1.ca2404.1_all.deb Size: 164516 MD5sum: b86a8946a6beb8b432b8292e121030fd SHA1: 070bb4145119ad53c73da59df26c80db64218f87 SHA256: e99e0d674e7ab9251e9e2008ce77732e8cd463a36e7330d521233bf30760eda1 SHA512: 376e828a6d7bd274f19af66023a69adfa619c7c8c59b031c87bcb67aa39323f0ea0175b6b968888b649d34ddda7a5099f72e787ae5ed81c365ee7bcd5e2eb87b Homepage: https://cran.r-project.org/package=transportr Description: CRAN Package 'transportr' (Transporting Intervention Effects from One Population to Another) Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) . 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Package: r-cran-transpror Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-bioc-sva, r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-ggdensity, r-cran-ggpubr, r-bioc-ggtree, r-cran-ggraph, r-cran-tidygraph, r-cran-tidyr, r-cran-stringr, r-cran-geomtextpath, r-cran-ggnewscale, r-cran-hmisc, r-cran-circlize, r-cran-spiralize, r-bioc-complexheatmap Suggests: r-cran-prettydoc, r-cran-knitr, r-bioc-ggtreeextra, r-cran-rmarkdown, r-cran-systemfonts Filename: pool/dists/noble/main/r-cran-transpror_1.0.7-1.ca2404.1_all.deb Size: 1117610 MD5sum: e5be6f3a6a18d214b4f51bc58fd40b4a SHA1: 3c7548f15af367e77c4b3317410adfac40486478 SHA256: 6e26d3d3bd135437feb11d60d59d4f4fb36d401c86880958036cedfbababa9bf SHA512: ce0a76b85b68425efeda412cf270b574fe37080cf9218ed7b814cbe0859461cad0146cc9409cde980149b6e563987d1a4eb488e8c189bebfad401efcaae548a5 Homepage: https://cran.r-project.org/package=TransProR Description: CRAN Package 'TransProR' (Analysis and Visualization of Multi-Omics Data) A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. 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For running the vignette (optional), install 'fwelnet' and 'ecpc' from and , respectively. Package: r-cran-transtggm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rtensor, r-cran-tlasso, r-cran-glasso, r-cran-doparallel, r-cran-expm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transtggm_1.0.0-1.ca2404.1_all.deb Size: 4203716 MD5sum: f61a2e4529184edc3bd133ac544491c4 SHA1: 24e8926b8694653a2f7c4889214be2cdeb9dd612 SHA256: b0c8adc98ce2a6a41ba9c44d9e35a0ffea0d73555d215e3799a589922606bf6a SHA512: 1fdb4e3ac56f5b545e731758655447d8baa16ecbbad4f0de55c7921f19bc49ed23e5d89c3541f5100d44b6f98149d837cb3e0044528cba35e98612b9ed69c701 Homepage: https://cran.r-project.org/package=TransTGGM Description: CRAN Package 'TransTGGM' (Transfer Learning for Tensor Graphical Models) Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs. Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. Reference: Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" . Package: r-cran-transx Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-cli, r-cran-testthat, r-cran-knitr, r-cran-desctools, r-cran-outliers, r-cran-rmarkdown, r-cran-mfilter, r-cran-covr Filename: pool/dists/noble/main/r-cran-transx_0.0.1-1.ca2404.1_all.deb Size: 198600 MD5sum: 04e80a7ab86080b563fcc36c4623f613 SHA1: df75aea58b81b9919d10dcda8f5e8f330d3c3010 SHA256: 17084eebd1372278c2cc29f93c5b10794ca8c33d1ff69043fd003b63ffbc4433 SHA512: d829bd3575141ea15e196b786e2112a9430f4b61c0a60dcfa972551e22fdabedae0f521d364684ab35f88c0d20dc0f4215fec6ce6dcb55ec839f4fecc3955d59 Homepage: https://cran.r-project.org/package=transx Description: CRAN Package 'transx' (Transform Univariate Time Series) Univariate time series operations that follow an opinionated design. 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Package: r-cran-tratamentos.ad Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon Filename: pool/dists/noble/main/r-cran-tratamentos.ad_0.2.4-1.ca2404.1_all.deb Size: 251270 MD5sum: c51f685eb10d9a4016420f56d193ef6f SHA1: 81ef89f3dc47f2c9e73f76d3c1619085c6892b55 SHA256: f07a689eb713d5e74323f5d5720d7c0d3b0029f3d5df8d9a80f55240a81010a4 SHA512: 580a6c3e9a48a1a4a7ada9c2bc8d0a6283b7582a06b7726ea6a5f9ac32649bf35de087e9fdf71212a524d209ba172ba243b84fa9c37e01754a199c7d277c04e8 Homepage: https://cran.r-project.org/package=Tratamentos.ad Description: CRAN Package 'Tratamentos.ad' (Pacote Para Analise De Experimentos Com Testemunhas Adicionais) Pacote para a analise de experimentos com um ou dois fatores com testemunhas adicionais conduzidos no delineamento inteiramente casualizado ou em blocos casualizados. "Package for the analysis of one or two-way experiments with additional controls conducted in a completely randomized design or in a randomized block design". Package: r-cran-traudem Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-purrr, r-cran-rlang, r-cran-sys, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra, r-cran-shapefiles, r-cran-sf, r-cran-elevatr, r-cran-fs Filename: pool/dists/noble/main/r-cran-traudem_1.0.3-1.ca2404.1_all.deb Size: 589878 MD5sum: 2b5e9d3e8a6015b8ea64bb71bbba5eed SHA1: cd6affa3d26a381c16262330804603e81e15164d SHA256: dece594893e32831d119d1c717fb976ec6af7144315c90a9998ba052320371eb SHA512: 31bfd384f630a30d8afa0b6f8eafadc574f621148c3f4c92e6616b5435c489df47b5f0e272b2163a477a5492dcbff993336eb35bc7cda81e7b2a6fbabcb3d35f Homepage: https://cran.r-project.org/package=traudem Description: CRAN Package 'traudem' (Use TauDEM) Simple trustworthy utility functions to use TauDEM (Terrain Analysis Using Digital Elevation Models ) command-line interface. This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) and MPI (Message Passing Interface) for different operating systems. Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, and it provides wrapper commands for calling TauDEM methods from R. Package: r-cran-traumar Architecture: all Version: 1.2.7-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1339 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-hms, r-cran-infer, r-cran-lifecycle, r-cran-lubridate, r-cran-nemsqar, r-cran-nortest, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-broom, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-traumar_1.2.7-1.ca2404.2_all.deb Size: 1300092 MD5sum: fe27593f63f88ce534159cdbc9932bb8 SHA1: a436db13f3724f419072f5ff0a8aa23fe72fcae4 SHA256: 056f5cee53d4695335a8ff101a02dc7c756dd3a25c88550c077673223aee7bca SHA512: 690173ab487f9f4544b0d24709fe2f68ae97aa58752d3aa151df8815a394a983b9da41339ff81c74de7be6900d8d48a31dbed10b312096c56f522f047e7221d3 Homepage: https://cran.r-project.org/package=traumar Description: CRAN Package 'traumar' (Calculate Metrics for Trauma System Performance) Hospitals, hospital systems, and even trauma systems that provide care to injured patients may not be aware of robust metrics that can help gauge the efficacy of their programs in saving the lives of injured patients. 'traumar' provides robust functions driven by the academic literature to automate the calculation of relevant metrics to individuals desiring to measure the performance of their trauma center or even a trauma system. 'traumar' also provides some helper functions for the data analysis journey. Users can refer to the following publications for descriptions of the methods used in 'traumar'. TRISS methodology, including probability of survival, and the W, M, and Z Scores - Flora (1978) , Boyd et al. (1987, PMID:3106646), Llullaku et al. (2009) , Singh et al. (2011) , Baker et al. (1974, PMID:4814394), and Champion et al. (1989) . For the Relative Mortality Metric, see Napoli et al. (2017) , Schroeder et al. (2019) , and Kassar et al. (2016) . For more information about methods to calculate over- and under-triage in trauma hospital populations and samples, please see the following publications - Peng & Xiang (2016) , Beam et al. (2022) , Roden-Foreman et al. (2017) . 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Leveraging , this package serves as a wrapper, facilitating the retrieval of daily treasury rates across various categories, including par yield curves, treasury bills, long-term rates, and real yield curves. In addition, it provides access to the monthly published yield curve datasets, including the High Quality Market (HQM) corporate bond yield curve and the Treasury nominal and real coupon-issue (TNC, TRC) and breakeven inflation (TBI) curves. Package: r-cran-treatmentpatterns Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7652 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-stringi, r-cran-jsonlite, r-cran-checkmate, r-cran-dplyr, r-cran-tidyr, r-cran-dbplyr, r-cran-andromeda, r-cran-cdmconnector Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat, r-cran-usethis, r-cran-eunomia, r-cran-databaseconnector, r-cran-sqlrender, r-cran-cohortgenerator, r-cran-resultmodelmanager, r-cran-webshot2, r-cran-circer, r-cran-duckdb, r-cran-dbi, r-cran-withr, r-cran-plotly, r-cran-sunburstr, r-cran-networkd3, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-visomopresults, r-cran-pare Filename: pool/dists/noble/main/r-cran-treatmentpatterns_3.1.2-1.ca2404.1_all.deb Size: 1903660 MD5sum: 26a69e0ad7178a3496483d963dd135aa SHA1: dc992e618c9169a9ccf76bbde0d05891e60d0bb9 SHA256: 8c205a9299b9ba1c5b95f4578c6f87a33c30efc7bdc84655dc3b1a5a1a178b91 SHA512: 45e790eb1d0893ff189527b7797a8d1329cf710d520f590b3e5e539850b0c541bf5d88e8881a97efd65c88cbfb5400a73ddf490d08606895bd3736224f7ced23 Homepage: https://cran.r-project.org/package=TreatmentPatterns Description: CRAN Package 'TreatmentPatterns' (Analyzes Real-World Treatment Patterns of a Study Population ofInterest) Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) . 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Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) for further details. Package: r-cran-tredesigns Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-tredesigns_1.0.1-1.ca2404.1_all.deb Size: 42116 MD5sum: 516ef526cc14610d85e51003ceb5fd11 SHA1: fdbe43c33d6ddff68736bbf7cef3fdefb19f2f92 SHA256: 22ea8f13e07e6a3d946d1be60ecde2924b712b534a1e2c6d3e8b24ba151e7979 SHA512: d281417477966ee356f501a7393208027ba8bab418fde3a5830e27b9d5444e768bc04a260427ac343a96a618a425be0ae0df8989bd6602260855355b09d02edd Homepage: https://cran.r-project.org/package=TREDesigns Description: CRAN Package 'TREDesigns' (Ternary Residual Effect Designs) There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design. Package: r-cran-tree3d Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rayvertex Filename: pool/dists/noble/main/r-cran-tree3d_0.1.2-1.ca2404.1_all.deb Size: 388294 MD5sum: 794e868ce691dd6a5d0f33637123673a SHA1: c4744503be530c2eab858f14c2451748a6410c60 SHA256: a196cb0b889494c012bcfb06f2ee6091d89f20cf1cee04e189550769d346436b SHA512: 15e4f6ce0752934e87640e0acdc21ecbb8f9a90a42381f0266b37e43a3dda37ed532857a1bf7561ea7984fc19a56ffa8710d45e6c7465381b0106a0ebe2fb5ab Homepage: https://cran.r-project.org/package=tree3d Description: CRAN Package 'tree3d' (3D Tree Models) Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk. Package: r-cran-treebalance Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-memoise, r-cran-gmp Filename: pool/dists/noble/main/r-cran-treebalance_1.2.2-1.ca2404.1_all.deb Size: 844084 MD5sum: 8fa921af4fbcb6909eef31db65cff4a3 SHA1: 23d19ad94210ebb191a1f6b19df0342cfbd0655c SHA256: 620dc0ba0b615a20c809f46a44aea9a2142686e69af87e70e752a6052429aac9 SHA512: 423cc8b71868cd9f4c3570ca9a5611a82894be7f62b69f2cb7b753850ce61abd9dab31e76577b7cecb2e7daa1904d63b999c39eb5def76b63080fd7db64229c4 Homepage: https://cran.r-project.org/package=treebalance Description: CRAN Package 'treebalance' (Computation of Tree (Im)Balance Indices) The aim of the 'R' package 'treebalance' is to provide functions for the computation of a large variety of (im)balance indices for rooted trees. The package accompanies the book ''Tree Balance Indices - A Comprehensive Survey'' by M. Fischer, L. Herbst, S. Kersting, L. Kuehn and K. Wicke (2023) , which gives a precise definition for the terms 'balance index' and 'imbalance index' (Chapter 4) and provides an overview of the terminology in this manual (Chapter 2). For further information on (im)balance indices, see also Fischer et al. (2021) . Considering both established and new (im)balance indices, 'treebalance' provides (among others) functions for calculating the following 18 established indices and index families: the average leaf depth, the B1 and B2 index, the Colijn-Plazzotta rank, the normal, corrected, quadratic and equal weights Colless index, the family of Colless-like indices, the family of I-based indices, the Rogers J index, the Furnas rank, the rooted quartet index, the s-shape statistic, the Sackin index, the symmetry nodes index, the total cophenetic index and the variance of leaf depths. Additionally, we include 6 tree shape statistics that satisfy the definition of an (im)balance index but have not been thoroughly analyzed in terms of tree balance in the literature yet. These are: the maximum width, the modified maximum difference in widths, the maximum depth, the maximum width over maximum depth, the stairs1 and the stairs2 index. As input, most functions of 'treebalance' require a rooted (phylogenetic) tree in 'phylo' format (as introduced in 'ape' 1.9 in November 2006). 'phylo' is used to store (phylogenetic) trees with no vertices of out-degree one. For further information on the format we kindly refer the reader to E. Paradis (2012) . 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'TreeBASE' is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them. Package: r-cran-treeclust Architecture: all Version: 1.1-7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart, r-cran-cluster Filename: pool/dists/noble/main/r-cran-treeclust_1.1-7.1-1.ca2404.1_all.deb Size: 76906 MD5sum: 0621494e69c94799e1213a0323b3c340 SHA1: 706161ff654c82ee61c06141a89c8d4c9120af49 SHA256: 85ec38f2dad7b977af5e4c86a127fa8bfafca644d161dca5d40b4204d711eef2 SHA512: 60fae979e72289714f403a074ff4f07dba5361ee871266652496a4b3495d77ecb94c80d7073c3a876897706c1b1d93d4c7ac4ec295cc4b72ddc8a0b2b20deca4 Homepage: https://cran.r-project.org/package=treeClust Description: CRAN Package 'treeClust' (Cluster Distances Through Trees) Create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering. 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(2017) . 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The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a 'data.table' syntax when performing operations on the trait dataset within the 'treedata.table' object. For more details see Roman-Palacios et al. (2021) . Package: r-cran-treedater Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-matrix, r-cran-quadprog Suggests: r-cran-lubridate, r-cran-ggplot2, r-cran-foreach, r-cran-iterators, r-cran-mgcv, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-treedater_2.0.0-1.ca2404.1_all.deb Size: 249940 MD5sum: ad6b54f5cd9b0d8ce5153a58a8d639c2 SHA1: 9266bef2e5aba19a707acfd15f88e600202c39e3 SHA256: 5e4f9c354adb282313c00b8449d35bcf8437e1ca643fd2bba0b1b9966cec7520 SHA512: a69c73b09ecf4b8c3417e192bcfb2cf8197e099cf8024709df2e2a70aee800eecfa77e8f8572f9ac11dc66c03f8d31fec11fc20be2b598ef0ae0112ffae8bc3f Homepage: https://cran.r-project.org/package=treedater Description: CRAN Package 'treedater' (Fast Molecular Clock Dating of Phylogenetic Trees with RateVariation) Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. For more details see Volz and Frost (2017) . Package: r-cran-treedbalance Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rgl, r-cran-r.matlab Filename: pool/dists/noble/main/r-cran-treedbalance_1.2.1-1.ca2404.1_all.deb Size: 237512 MD5sum: a7f5d2822115c432fe5c1fa5ab23606d SHA1: dc180cfdc10c76d3508ea99c347ff3501856579d SHA256: 74dfeb8a514b59f417a9ab5bf939cad20ecce98972141a6dfefe6b7c4f811913 SHA512: 3eb670fc8f51ad6ad7b74a0a27c40891151c4a65097934b4644607c62ea8a50fffec7b955ab40d6c1acc4106bd6a4322b47edc70ba7fdb47474f234ee14dd10f Homepage: https://cran.r-project.org/package=treeDbalance Description: CRAN Package 'treeDbalance' (Computation of 3D Tree Imbalance) The main goal of the R package 'treeDbalance' is to provide functions for the computation of several measurements of 3D node imbalance and their respective 3D tree imbalance indices, as well as to introduce the new 'phylo3D' format for rooted 3D tree objects. Moreover, it encompasses an example dataset of 3D models of 63 beans in 'phylo3D' format. Please note that this R package was developed alongside the project described in the manuscript 'Measuring 3D tree imbalance of plant models using graph-theoretical approaches' by M. Fischer, S. Kersting, and L. Kühn (2023) , which provides precise mathematical definitions of the measurements. Furthermore, the package contains several helpful functions, for example, some auxiliary functions for computing the ancestors, descendants, and depths of the nodes, which ensures that the computations can be done in linear time, or functions that convert existing formats of 3D tree models of other software into the 'phylo3D' format. Moreover, it comprises functions to extract the graph-theoretical topology without vertices of in- and out-degree 1 of rooted 3D trees as well as to adapt node enumerations to the common 'phylo' format. Most functions of 'treeDbalance' require as input a rooted tree in the 'phylo3D' format, an extended 'phylo' format (as introduced in the R package 'ape' 1.9 in November 2006). Such a 'phylo3D' object must have at least two new attributes next to those required by the 'phylo' format: 'node.coord', the coordinates of the nodes, as well as 'edge.weight', the literal weight or volume of the edges. Optional attributes are 'edge.diam', the diameter of the edges, and 'edge.length', the length of the edges. For visualization purposes one can also specify 'edge.type', which ranges from normal cylinder to bud to leaf, as well as 'edge.color' to change the color of the edge depiction. This project was supported by the joint research project DIG-IT! funded by the European Social Fund (ESF), reference: ESF/14-BM-A55-0017/19, and the Ministry of Education, Science and Culture of Mecklenburg-Western Pomerania, Germany, as well as by the project ArtIGROW, which is a part of the WIR!-Alliance 'ArtIFARM – Artificial Intelligence in Farming' funded by the German Federal Ministry of Education and Research (FKZ: 03WIR4805). Package: r-cran-treedep Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-treedep_0.1.3-1.ca2404.1_all.deb Size: 281988 MD5sum: 019c6c1178d69864df43958187df80cc SHA1: 63ca7ac122d45ea359385f3703716b2ee91adc44 SHA256: b1604789a99a34108024d5ce4557a99df675a9a28104134c79d74505d1220d8e SHA512: 42355bbeb8b2d08a881b48abd8052f0fde9a2d61f06c61d3e4fe6b5c284fb07c8556944af940dd0bc3ed4462b2782a7bdd60be9dde0637a4ee100360968dce3d Homepage: https://cran.r-project.org/package=TreeDep Description: CRAN Package 'TreeDep' (Air Pollution Removal by Dry Deposition on Trees) The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) . Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) . US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213. Package: r-cran-treediagram Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ape, r-cran-cowplot, r-cran-tree, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-treediagram_0.1.1-1.ca2404.1_all.deb Size: 51878 MD5sum: 0bf82d29b1995c08ce78e8eccbd80af5 SHA1: 0ea611080ba2d5db8f26fa602b9b65acab686257 SHA256: 68846bd0c5b999406fd9f6e8fe57b4d7311e715e310c715eff46cc67a431aa32 SHA512: 2cf7b51abd53c88ac7b6e59dbcf466a46dda6255079694b7e47964c93b973ed9bf1c3b8681cc698957338c6babfcc2645203f36b660cf5a45e734bded7965c40 Homepage: https://cran.r-project.org/package=TreeDiagram Description: CRAN Package 'TreeDiagram' (Tree Diagram) Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process). Package: r-cran-treediff Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-adjclust, r-bioc-biocgenerics, r-bioc-csaw, r-cran-data.table, r-cran-dplyr, r-bioc-interactionset, r-bioc-limma, r-bioc-summarizedexperiment, r-cran-reshape2, r-cran-testthat, r-cran-rlang, r-bioc-hicparser, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treediff_0.2.3-1.ca2404.1_all.deb Size: 95782 MD5sum: 60a6a7a09c3bfe66307705e841e1583d SHA1: 998e7d27c6956b58cfdef37bf2c3c965e667ef4d SHA256: b797160130808740b9edac226b77268f707723529383251dfbcdc790843ce9eb SHA512: b842a0c6339f5a74fef919af540b0335ffaf198d9f814d8af66d2434e7d889060b63af24cea3871fcdd67722e94d7e240869cf0890e9e0118c6e8aa2e6d5e894 Homepage: https://cran.r-project.org/package=treediff Description: CRAN Package 'treediff' (Testing Differences Between Families of Trees) Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests. Package: r-cran-treefit Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-patchwork, r-cran-pracma Suggests: r-cran-seurat, r-cran-gridextra, r-cran-knitr, r-cran-plotly, r-cran-qpdf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-treefit_1.0.3-1.ca2404.1_all.deb Size: 161198 MD5sum: 3f65828082265e23c908b005a19202c1 SHA1: 1631221608272d4a1b8b1f9c3dcaf68768ff9e11 SHA256: fde05173f4d9f84fed2adb0a655f4631ca20cc4f83117c408c5361e7d3001a11 SHA512: 40c715d79a86be3f83fd6a0fa001c3f9d33939767d4d49106d827d44f52b527e7d1e8353ce258a57ef1b535ba8327fb0d91e7fece75ba00cbc9c88e7e9a13155 Homepage: https://cran.r-project.org/package=treefit Description: CRAN Package 'treefit' (The First Software for Quantitative Trajectory Inference) Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data. Package: r-cran-treeheatr Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dplyr, r-cran-ggnewscale, r-cran-ggparty, r-cran-ggplot2, r-cran-gtable, r-cran-partykit, r-cran-seriation, r-cran-tidyr, r-cran-yardstick Suggests: r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-treeheatr_0.2.3-1.ca2404.1_all.deb Size: 1839304 MD5sum: 22cbd54b53ecfb046a87baed2dcd35be SHA1: 9d6c8a0d790328616fc58ef931adaf487b0ea767 SHA256: 0b69e1dffa6db17bcbf70c1943524431a9b36df99fa15f0f4fc740fb6be0a9fe SHA512: a34ca54b89b6052eb7e99458f7f6da39b754139323b79067498f66d0bba0093abe1bbd7a2c47b4964a7c077473c97303aa6f379b02458b600bdebf1b71e9fd97 Homepage: https://cran.r-project.org/package=treeheatr Description: CRAN Package 'treeheatr' (Heatmap-Integrated Decision Tree Visualizations) Creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. 'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees. Package: r-cran-treemap Architecture: all Version: 2.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-data.table, r-cran-ggplot2, r-cran-gridbase, r-cran-igraph, r-cran-rcolorbrewer, r-cran-shiny Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-treemap_2.4-4-1.ca2404.1_all.deb Size: 323724 MD5sum: 91486df7a0dbf1b6bb275ca92a51070b SHA1: 4ca5532373fc20c3ca78656946c674a0cbc9b95d SHA256: ec5e65052fb6788670f16bbd2520aea80e47510e4f62cf984eee76b3b86a6c29 SHA512: 6b58d278a198ceb8c597c4b75594e17f4de1e8a2e7a5c21a89f2ef22b6e60e4107231901262c555b27c5c06b215b4a52a64f4f89dcc6908668e5a66d448d91c7 Homepage: https://cran.r-project.org/package=treemap Description: CRAN Package 'treemap' (Treemap Visualization) A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps. Package: r-cran-treemapify Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggfittext, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-spelling, r-cran-fontquiver, r-cran-showtext, r-cran-sysfonts, r-cran-svglite Filename: pool/dists/noble/main/r-cran-treemapify_2.6.1-1.ca2404.1_all.deb Size: 558176 MD5sum: 76141ee30e44daa07e6a4e80c1016957 SHA1: 26d5446bb313735c8df3d564b4d60029c974002a SHA256: 3b966a685034ae73b2ec6b2ff362b362200f2eccd02a76ec7b583f7316fee505 SHA512: cb04fa73209e32c098cbf639ab80b080b2514e47120d062e9a816dde0c2833d4cda0101695e0eba06577e815d5f7f2982b34826cf67c45de0f7e46e2505e6e61 Homepage: https://cran.r-project.org/package=treemapify Description: CRAN Package 'treemapify' (Draw Treemaps in 'ggplot2') Provides 'ggplot2' geoms for drawing treemaps. Package: r-cran-treeminer Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-future, r-cran-future.apply, r-cran-cli Suggests: r-cran-testthat, r-cran-tidyr, r-cran-comorbidity, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treeminer_1.1.1-1.ca2404.1_all.deb Size: 685400 MD5sum: 995081cdc29e91d092de225963039401 SHA1: 2b131e462f79331c53f4aa2db613395e2c04371e SHA256: 0a64c0a84059f587011523ce39e3451bd272232b9e4694dfb08ae5e62f122ee8 SHA512: 84d4d90080aa72ef0a87f2ab9cec5f6d36f1cfcf96dab7ecd5ea19cd3bc7864daa9cec83bf7adb879b261025cfd0b2f51a3c88089dfdddc283d283b06433ff84 Homepage: https://cran.r-project.org/package=TreeMineR Description: CRAN Package 'TreeMineR' (Tree-Based Scan Statistics) Implementation of unconditional Bernoulli Scan Statistic developed by Kulldorff et al. (2003) for hierarchical tree structures. Tree-based Scan Statistics are an exploratory method to identify event clusters across the space of a hierarchical tree. Package: r-cran-treeordertests Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-treeordertests_0.1.0-1.ca2404.1_all.deb Size: 27432 MD5sum: 6e0fe52236bd83aada1c2558f0b22ef2 SHA1: cc51c4c8ad2dee60b394b38b1f52ff5edd840ff3 SHA256: 2f36ecd90870592b9daef716dbe0484d442cf30ebe22f372569b1d79a17867ea SHA512: 49f530b5259539fc5b9a78ddc41a00e23eed60d61ef00a34d7c9da21c5cd3360fad2b575badec24120249564f1e56383755fe430f48fd72ad099a48761fbd50b Homepage: https://cran.r-project.org/package=TreeOrderTests Description: CRAN Package 'TreeOrderTests' (Tests for Tree Ordered Alternatives in One-Way ANOVA) Implements a likelihood ratio test and two pairwise standardized mean difference tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" . Package: r-cran-treeplotarea Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fritools, r-cran-sf Suggests: r-cran-checkmate, r-cran-pkgload, r-cran-plotrix, r-cran-rmarkdown, r-cran-rprojroot, r-cran-runit, r-cran-testthat, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-treeplotarea_3.1.0-1.ca2404.1_all.deb Size: 1060880 MD5sum: d743368c77644a3106db41b7eb6888d8 SHA1: d5a1521e107a6439dfd26bdb27119ec556330f06 SHA256: 9aad2cb7c8a6150e8a3c194b602b020677c06a6974376f4c19cd2d5f7a726e9b SHA512: 643e44fa15a2b6999cc84cfcc85576153d70bbb884772ba70a01bd38a7461fa592aafefc3eab989511c6661d6b812937eab866d25631a7eec6840751fca97f86 Homepage: https://cran.r-project.org/package=treePlotArea Description: CRAN Package 'treePlotArea' (Correction Factors for Tree Plot Areas Intersected by StandBoundaries) The German national forest inventory uses angle count sampling, a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung. Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 vom Juni 1947` and extended by Grosenbaugh () as probability proportional to size sampling. When plots are located near stand boundaries, their sizes and hence their probabilities need to be corrected. Package: r-cran-treeringshape Architecture: all Version: 3.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-waldo Filename: pool/dists/noble/main/r-cran-treeringshape_3.0.5-1.ca2404.1_all.deb Size: 909224 MD5sum: 7a70001f65e05dec8f67a271099918a3 SHA1: 042223d6e666e1984c8a7ea6fe70cc27b0f09b5d SHA256: 14422ea4fd973f60ae2c5e79270dfe7665f50e4e92e26ec7ab6f52f5538ee173 SHA512: 6476a32ff7ed3e5c5d2c478dd49fcb28c62fcb2a6bdb70b31d5f5f9c9b8db0dc32865c42a4d35798d5bc9358faf6bf87808ddd107f44b6191efd33caf0bc3fb8 Homepage: https://cran.r-project.org/package=TreeRingShape Description: CRAN Package 'TreeRingShape' (Recording Tree-Ring Shapes of Tree Disks with Manual Digitizingand Interpolating Model) Record all tree-ring Shapefile of tree disk with GIS soft 'Qgis' and interpolating model from high resolution tree disk image. Package: r-cran-treesim Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-geiger Filename: pool/dists/noble/main/r-cran-treesim_2.4-1.ca2404.1_all.deb Size: 172880 MD5sum: a614a59c74f7035db3655ca828af91b8 SHA1: 6855bc68d3a70188c6e4454b72c2c7bc7d34800e SHA256: d5f7b247cfff9a2042aa7cbf65e7bc39fbe02b2e0dd47213b35f58cef77280aa SHA512: 24d40503abb773af57a92f81805db1904a290d867551adf2173e8ac56093a0055a7df9cd2f74e1df2e01c70d4c4166869606669b3af482d1257d485e793f42e0 Homepage: https://cran.r-project.org/package=TreeSim Description: CRAN Package 'TreeSim' (Simulating Phylogenetic Trees) Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees. Package: r-cran-treesimgm Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-treesim, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treesimgm_2.5-1.ca2404.1_all.deb Size: 154798 MD5sum: b85fc55f8f7c9b8a482374fc750c3b1a SHA1: b8c238a1380f6ce29b02016f35bd5dcaffc513b7 SHA256: 5a203aea2015ce38f1570cf75e83145fbd5ac3a98e3208f0a63b165da55c9f17 SHA512: 8be8fcebdfe1963fc65a813e1f7c413fc2e85be593545e20a0742c0a329b86cfeb0c36e925886a00b317abf9e9612bbcb8379886292b044b963245d881d72f5e Homepage: https://cran.r-project.org/package=TreeSimGM Description: CRAN Package 'TreeSimGM' (Simulating Phylogenetic Trees under General Bellman Harris andLineage Shift Model) Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist. Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. . 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Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). . Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp. Package: r-cran-trendeval Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-trending, r-cran-yardstick, r-cran-rsample, r-cran-tibble Suggests: r-cran-testthat, r-cran-dplyr, r-cran-outbreaks Filename: pool/dists/noble/main/r-cran-trendeval_0.1.1-1.ca2404.1_all.deb Size: 101610 MD5sum: b7ee399d2f203eab2b6061f511b64521 SHA1: 75b2614dc0e0703901845843c34ae831e4523384 SHA256: 555ca2d4b1179e18713e8cbfd6a3a57a2a1935d27b8fdf26f9252e0ba6513791 SHA512: 9af7d7cb582849eccbb9019e40ed152927a7ed2f9633c599c4d0bf2d78b2ecdb4087d120a95cd79a59a24368a4af95752a6b2151d1063dbda54727d7364f76d1 Homepage: https://cran.r-project.org/package=trendeval Description: CRAN Package 'trendeval' (Evaluate Trending Models) Provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON () toolkit for outbreak analysis. 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Package: r-cran-trendintrend Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc, r-cran-rms, r-cran-nleqslv, r-cran-pracma Filename: pool/dists/noble/main/r-cran-trendintrend_1.1.3-1.ca2404.1_all.deb Size: 63538 MD5sum: eb9b74e35c9527d8d2bb56ef38bb31cf SHA1: 6e0c64b9771b652cd99920af397e9f679720cec6 SHA256: 82dc49e4b4ca9589a80850d722a307a644542b19d01dd06bd9775f8e7bd847db SHA512: 528d9d90667e2d53d16ecb3763baf0e0b2d19c45ac3d099c44f6c40221c72dd64d7effbd6c23bde8f0aa462c9803917ca3a9f461b6a10fd4f257a8191f754699 Homepage: https://cran.r-project.org/package=TrendInTrend Description: CRAN Package 'TrendInTrend' (Odds Ratio Estimation and Power Calculation for the Trend inTrend Model) Estimation of causal odds ratio and power calculation given trends in exposure prevalence and outcome frequencies of stratified data. 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Package: r-cran-trendseries Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dlm, r-cran-hpfilter, r-cran-lubridate, r-cran-mfilter, r-cran-rcpproll, r-cran-rlang, r-cran-tibble, r-cran-tsbox, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-ekioplot, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-seasonal, r-cran-testthat, r-cran-tidyr, r-cran-tsibble Filename: pool/dists/noble/main/r-cran-trendseries_1.7.0-1.ca2404.1_all.deb Size: 927022 MD5sum: 9e5f2f7aec0e3146f6e006619687efa4 SHA1: f9d9075fc468b74ae2f305b515ff5f7b1df488dd SHA256: 78e8384600988d5d403ca45ade13fe4eeb38172fe3359dfc60b62e5ea44fe443 SHA512: 58dc3d55e3490f24eb083292bebedb1d06359990fc09854375d8d7ef50042865c7601e93eba98684bbd102081972c9da01f894043e2e81575d5e368fa9a2bd49 Homepage: https://cran.r-project.org/package=trendseries Description: CRAN Package 'trendseries' (Extract Trends from Time Series) Provides a unified interface to extract trends, cycles, and seasonal components from monthly and quarterly time series using established filters and smoothers from econometrics and signal extraction, with frequency-aware defaults for common economic frequencies. Rolling and year-to-date aggregations are also available, including the compounded accumulation of rates of change. Package: r-cran-trendslr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-forecast, r-cran-plyr, r-cran-rssa, r-cran-tseries, r-cran-zoo, r-cran-imputets Filename: pool/dists/noble/main/r-cran-trendslr_1.0-1.ca2404.1_all.deb Size: 176946 MD5sum: f5dad02bffc365a3390fbafd3db9dfe2 SHA1: b126f022b20885c8801d2af4be4b1cf9779dd3a4 SHA256: b6e899705ff0a7fb249e9c9e9ffa8d71a25f35d003c3088b3e4dc4888d71b7d8 SHA512: f3f4e5c3c79834c244248d23d5563b7e8747150a1606e60912ac82b83184ed7cc87c3fdf1a16b2f942d96790eb9ce5d4f0a8e8e1791710a2936a4087082024f7 Homepage: https://cran.r-project.org/package=TrendSLR Description: CRAN Package 'TrendSLR' (Estimating Trend, Velocity and Acceleration from Sea LevelRecords) Analysis of annual average ocean water level time series, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on singular spectrum analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also includes a range of diagnostic tools to inspect the components comprising the original time series which enables expert interpretation and selection of likely trend components. A wide range of screen and plot to file options are available in the package. Package: r-cran-trendtestr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-emmeans, r-cran-e1071, r-cran-forecast, r-cran-mass, r-cran-multcomp, r-cran-tidyselect, r-cran-tidyr, r-cran-tseries, r-cran-car, r-cran-fsa, r-cran-ggpubr, r-cran-rlang, r-cran-pscl, r-cran-mgcv Suggests: r-cran-testthat, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-trendtestr_1.0.1-1.ca2404.1_all.deb Size: 254706 MD5sum: 9e8e5fdf2537cf2593f193037d1415b4 SHA1: 2ac4bdd21b6d41d6755e7d9903af7c2548c6cc79 SHA256: 63bf6fff9262fef45199cc86633e56e814f035e3638125663effb1d8a49e8d83 SHA512: f34621c249aed8b5a11b038223134b34052672d0535b5530db2be2c085557c69dd7688dce74744988c35917abc2d21ce13c5d81715f7c9843c78b136d1117870 Homepage: https://cran.r-project.org/package=trendtestR Description: CRAN Package 'trendtestR' (Exploratory Trend Analysis and Visualization for Time-Series andGrouped Data) Provides a set of exploratory data analysis (EDA) tools for visualizing trends, diagnosing data types for beginner-friendly workflows, and automatically routing to suitable statistical tests or trend exploration models. Includes unified plotting functions for trend lines, grouped boxplots, and comparative scatterplots; automated statistical testing (e.g., t-test, Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size calculation; and model-based trend analysis using generalized additive models (GAM) for count data, generalized linear models (GLM) for continuous data, and zero-inflated models (ZIP/ZINB) for count data with potential zero-inflation. Also supports time-window continuity checks, cross-year handling in compare_monthly_cases(), and ARIMA-ready preparation with stationarity diagnostics, ensuring consistent parameter styles for reproducible research and user-friendly workflows.Methods are based on R Core Team (2024) , Wood, S.N.(2017, ISBN:978-1498728331), Hyndman RJ, Khandakar Y (2008) , Simon Jackman (2024) , Achim Zeileis, Christian Kleiber, Simon Jackman (2008) . Package: r-cran-trendtm Architecture: all Version: 2.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-softimpute, r-cran-capushe, r-cran-fda Filename: pool/dists/noble/main/r-cran-trendtm_2.0.21-1.ca2404.1_all.deb Size: 51126 MD5sum: 9e0b372727945d009cd3c27d0719d069 SHA1: 5b34f05d1bbd04b6017db92185388f4d573c02f8 SHA256: d950b126b1442ce746e206ac71cc996729bb8ec8b4e1777425dcd71a4b90fb95 SHA512: fdb857afef7b0671ed5c037d8db9005027ea55d98de25e089edf5be25c992c026ebbe3942036f2333b16ef29267dc24466984227ed72718a0952198cb9ece97d Homepage: https://cran.r-project.org/package=TrendTM Description: CRAN Package 'TrendTM' (Trend of High-Dimensional Time Series Matrix Estimation) Matrix factorization for multivariate time series with both low rank and temporal structures. The procedure is the one proposed by Alquier, P. and Marie, N. "Matrix factorization for multivariate time series analysis." Electronic Journal of Statistics, 13(2), 4346-4366 (2019). Package: r-cran-trendtwosub Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-trendtwosub_0.0.2-1.ca2404.1_all.deb Size: 77432 MD5sum: 66c59f9ab98cc16d3d29f12b0ce1e0e6 SHA1: 27647ce3f1c3164ca04eb2bf7ac109562898a891 SHA256: 58be22eced8c075498e4957d5877c12d2325aeeb4553a374cb16bcd376edd8c5 SHA512: 5c83db78c6d9cb7285a797542b8ac9de509a5e180953c400bd6b9746fffde749a4ba3db49c03a3987853dbc47dae8d6b74ab63a3a0133dede8cae9dc6ad6cc03 Homepage: https://cran.r-project.org/package=Trendtwosub Description: CRAN Package 'Trendtwosub' (Two Sample Order Free Trend Nonparametric Inference) Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) . Package: r-cran-trendyy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtrendsr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-crayon, r-cran-stringr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-trendyy_0.1.1-1.ca2404.1_all.deb Size: 27166 MD5sum: acb66364657d7264a1d0521640b673fb SHA1: c16950e9e3664a9360a6e46c52e4adc42e670878 SHA256: d65690cb1d7ca60ef32f3240760cf008f535a3e4cb6d7d53ae6b163159b51633 SHA512: 8781bc1bcf35898ec6722eb1a8689dd5f17fbace6eb3a50829c37fc3bacf7d0de7edcb58a7eb3219394fa917689b539340e84ed15aedcdc32161161d22e2c834 Homepage: https://cran.r-project.org/package=trendyy Description: CRAN Package 'trendyy' (A Tidy Wrapper Around 'gtrendsR') Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package. Use four spaces when indenting paragraphs within the Description. Package: r-cran-tres Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-manifoldoptim, r-cran-mass, r-cran-pracma, r-cran-rtensor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tres_1.1.5-1.ca2404.1_all.deb Size: 4291634 MD5sum: c0ef2861805daed4a6153982a245abf7 SHA1: 718a7a5642240bfb430b2fa057ffd24d61ed4daf SHA256: a1a92d50d1b62c974c2579e54b0c497b4a2567e56caab42b9a13bca7b35a3627 SHA512: e704408854f154f4d32069fd672e088244127f36317e8fc6d2df338975d2e1ad3dc9e8148b497a51fbce70100aa4f2c9df77683067e9fcc30333988fb46a5f39 Homepage: https://cran.r-project.org/package=TRES Description: CRAN Package 'TRES' (Tensor Regression with Envelope Structure) Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) . For details of TPR, see Zhang X, Li L (2017) . For details of 1D algorithm, see Cook RD, Zhang X (2016) . For details of ECD algorithm, see Cook RD, Zhang X (2018) . For more details of the package, see Zeng J, Wang W, Zhang X (2021) . Package: r-cran-trexselector Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-tlars, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-glmnet, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-wgcna, r-cran-fastcluster, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trexselector_1.0.0-1.ca2404.1_all.deb Size: 861552 MD5sum: 5707966e8616d00fa28a52be174aae9f SHA1: f1e63724133a713f3b3999a60a79552d33ce2d78 SHA256: 6b562af4257c29aa0e9fc4b29a378431b91639cb08282829fdd27970a349a18a SHA512: 0d0d6275a5401609ccac80fd35342c1f9a1fdca8712f84c9e3ed21342a98179fe9947401d523f28ab2986cf6d7e801973a0a0e5731c6bae1f3a0915c82c1eb63 Homepage: https://cran.r-project.org/package=TRexSelector Description: CRAN Package 'TRexSelector' (T-Rex Selector: High-Dimensional Variable Selection & FDRControl) Performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level. The package is based on the paper Machkour, Muma, and Palomar (2022) . Package: r-cran-tri.hierarchical.ibds Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tri.hierarchical.ibds_1.0.0-1.ca2404.1_all.deb Size: 75934 MD5sum: 03df42227674d81e3652fa0b08654a8b SHA1: 99306b922989c767ec34226c5d3651d0cc6640c9 SHA256: 2f145c50d4deec5e1baeea0eaa3aa68bec20ea55692500202f7f5acee58f79b3 SHA512: 9d448814aed6eda61394186b02e1d38e7ef12fd0cc20bb75f1f93929c16c2b09348b2f9dc0984e752b02e593815cd852a9c49ca27c8766797298fa51c3ddf0fb Homepage: https://cran.r-project.org/package=Tri.Hierarchical.IBDs Description: CRAN Package 'Tri.Hierarchical.IBDs' (Tri-Hierarchical IBDs (Tri- Hierarchical Incomplete BlockDesigns)) Tri-hierarchical incomplete block design is defined as an arrangement of v treatments each replicated r times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) . This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions. Package: r-cran-triact Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-checkmate, r-cran-lubridate Suggests: r-cran-signal, r-cran-tibble, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-triact_0.3.1-1.ca2404.1_all.deb Size: 2351220 MD5sum: 25252b3904bb0b152fb67e56d3916328 SHA1: d15bf88cd273a9dcfe07c04690f59b671713d003 SHA256: 5c8e8c1d7c6f91cc8171bc121069e91d889593b3015c9023e6e9ab16f1406f83 SHA512: 57cf2b24acbcbd55f6e898021c0126fd2ac1ccba202ade590ab0976bbe2b58c6c3879b508cd4f706fcc536373895c702d2973d1071904de10ede998f11f948a6 Homepage: https://cran.r-project.org/package=triact Description: CRAN Package 'triact' (Analyzing the Lying Behavior of Cows from Accelerometer Data) Assists in analyzing the lying behavior of cows from raw data recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows the determination of common measures for lying behavior including total lying duration, the number of lying bouts, and the mean duration of lying bouts. Further capabilities are the description of lying laterality and the calculation of proxies for the level of physical activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) . Package: r-cran-triadsim Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-snpstats, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-triadsim_0.3.1-1.ca2404.1_all.deb Size: 687242 MD5sum: 45c74846ffb7e7370939d0ed75cc780e SHA1: ee7d7c738eaf491de51dc87d1882deed74daf222 SHA256: e216bb0ae6c60bf638a1e5469c359c601759c49c227b0b0cbc64eb3f687d2f34 SHA512: 52b8ba76d66371e9cf5888d5cdfbbdf1ac4e340e885b73a04278de885191007712ef6a6e26b69dae8bd396ff91fb19528638ab6d2d10714bfb64cc297e056110 Homepage: https://cran.r-project.org/package=TriadSim Description: CRAN Package 'TriadSim' (Simulating Triad Genomewide Genotypes) Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R. Package: r-cran-triager Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dalex, r-cran-dplyr, r-cran-ellmer, r-cran-ggplot2, r-cran-mice, r-cran-naniar, r-cran-parsnip, r-cran-proc, r-cran-quarto, r-cran-recipes, r-cran-survival, r-cran-tibble, r-cran-tidyr, r-cran-workflows, r-cran-yardstick Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mlbench, r-cran-missforest, r-cran-ranger, r-cran-spelling, r-cran-testthat, r-cran-xgboost, r-cran-mass, r-cran-aorsf, r-cran-censored, r-cran-shiny, r-cran-bslib Filename: pool/dists/noble/main/r-cran-triager_0.2.0-1.ca2404.1_all.deb Size: 284568 MD5sum: 8b77c59af5b4080b5bb239e1687bf1a6 SHA1: 4fe5cf7b33f83e6720b62e925366159d8188f08e SHA256: 915d0f7709ff0e3d7d4905ab7ce547852bf32ba14bc2c0f21a261c9702c33dfb SHA512: 94c424d45657c9b5982570155cb1ef2e05b0877a3c9d1a9d6bec6a7876b3309c866b244d30ecbb2bacad1c3721f600440a19d5cb30f3a1d492a173606d1b8327 Homepage: https://cran.r-project.org/package=triageR Description: CRAN Package 'triageR' (Automated Machine Learning and AI Agent Tools for ClinicalPrediction Modelling) Provides a streamlined workflow for building, validating, and reporting clinical prediction models. Combines standard machine learning tools with an optional AI agent that recommends appropriate statistical methods, runs sensitivity analyses, and flags common pitfalls. Includes automated generation of reports aligned with TRIPOD+AI reporting guidance (Collins et al. (2024 )) for reproducible, guideline-aligned research. Package: r-cran-trialdiff Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-htmltools, r-cran-jsonlite, r-cran-rlang, r-cran-tibble Suggests: r-cran-admiral, r-cran-cards, r-cran-covr, r-cran-diffdf, r-cran-haven, r-cran-igraph, r-cran-knitr, r-cran-lintr, r-cran-metacore, r-cran-pharmaverseadam, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rtables, r-cran-styler, r-cran-tern, r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/noble/main/r-cran-trialdiff_0.2.0-1.ca2404.1_all.deb Size: 262796 MD5sum: bb4350ba8a1ac90c79baf8e972f43e1c SHA1: 6358b5b834e2f0ef1f5aacf74a669dbeb427f0ab SHA256: 8f5a88a9c3e0b7c71d34db343eecbcde6bea9fd78b4b9382d23b1be6b4bc2a0d SHA512: f464b68c86556006e4609c86e16d7009f572eb70f86bec125d6b7240b8ee991296d8f21ad8c95653a4394d213e41ba88ea3e24977f3d9778dfb810d850754412 Homepage: https://cran.r-project.org/package=trialdiff Description: CRAN Package 'trialdiff' (Clinical Trial Data-Cut Change Detection and Impact Assessment) A transparent, rule-based framework for detecting changes between successive data cuts of clinical trial datasets, classifying those changes into clinically meaningful categories, tracing user-defined data lineage, and assessing which downstream analyses and outputs may be affected. The package is designed to complement existing low-level data frame comparison tools by adding clinical-trial-specific classification, lineage and impact-assessment layers on top of deterministic comparison. The rule-based classification is similar in spirit to the data validation infrastructure of van der Loo and de Jonge (2021) . Package: r-cran-trialflowr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-trialflowr_1.0.0-1.ca2404.1_all.deb Size: 27890 MD5sum: ac74ce8159087095232d97767a9a12cf SHA1: 5d543b27c61dca137d0a9257a10f4cc359bbf6f1 SHA256: 9265c1639f4c8f8de5b8a02389fe43c472b94f6260dd65b9e7f5b3808b66e060 SHA512: 16408d44ce4eb0086dcd2bf3f6eb949222aaaf59307091bcfa318d46e8f46ff7d978eda3a2f41316a025805fed18aa343d09e9f841285b9f1547571509a6d5b0 Homepage: https://cran.r-project.org/package=TrialFlowR Description: CRAN Package 'TrialFlowR' (Clinical Trial Flow and Participant Disposition) Summarizes participant flow and disposition in clinical trials, including CONSORT-style randomized controlled trials, parallel-group, crossover, cluster randomized, and multi-arm trials. Provides functions for screening failures, exclusions and reasons, allocation, follow-up, loss to follow-up, withdrawals, intention-to-treat and per-protocol populations, and participant-disposition summaries. The methods are based on established principles for reporting participant flow and disposition in randomized trials; see Schulz et al. (2010) . Package: r-cran-trialsimulator Architecture: all Version: 1.17.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-copula, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-gmcplite, r-cran-htmltools, r-cran-mvtnorm, r-cran-r6, r-cran-rlang, r-cran-rpact, r-cran-rstudioapi, r-cran-survival Suggests: r-cran-data.table, r-cran-dosefinding, r-cran-graphicalmcp, r-cran-kableextra, r-cran-knitr, r-cran-mirai, r-cran-pweall, r-cran-pwexp, r-cran-rmarkdown, r-cran-simdata, r-cran-survminer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trialsimulator_1.17.1-1.ca2404.1_all.deb Size: 2855384 MD5sum: 65545f5999290e59efc26b4c96141e09 SHA1: 58235937469bd6c4e9274283331063c3675d7b3c SHA256: 6987e2dc95a05d9b68c1ff8df8c96b2647b8c174248fcd81324a6540689588c6 SHA512: ab774af3316247d261b460e1f26cf8bbe5454271cd6aa814d88045e661c70a2ba4ed2ed9883bbbb29ac9e6ada6cfbffe9406400b2a7dedcfed091e96500c4ecd Homepage: https://cran.r-project.org/package=TrialSimulator Description: CRAN Package 'TrialSimulator' (Clinical Trial Simulator) Simulate phase II and/or phase III clinical trials. 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Package: r-cran-triangle Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-rmpfr Filename: pool/dists/noble/main/r-cran-triangle_1.1.0-1.ca2404.1_all.deb Size: 130780 MD5sum: 7e5ed6f53d5f8d8d346196596ac8483b SHA1: 5f02093a08f1c89c212cce572ab27698aee86480 SHA256: 6fa9277fe0d8416763db9b65ec37be5b67e4c7fd8da5c5cd6f07fc8755f623ef SHA512: 0272a3f2103e894af095eb807067574b45bac979bc3685031f9903aef9acd839a9fcee3dd17086e09b727ae04be09d53054a8e4b5c613908dd265cc12133eb15 Homepage: https://cran.r-project.org/package=triangle Description: CRAN Package 'triangle' (Distribution Functions and Parameter Estimates for the TriangleDistribution) Provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters. 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Package: r-cran-trip Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3796 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geodist, r-cran-mass, r-cran-raster, r-cran-reproj, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-glue, r-cran-viridis, r-cran-traipse, r-cran-crsmeta, r-cran-dplyr, r-cran-rlang Suggests: r-cran-adehabitatlt, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-lubridate, r-cran-maps, r-cran-spelling, r-cran-lattice Filename: pool/dists/noble/main/r-cran-trip_1.10.0-1.ca2404.1_all.deb Size: 1708032 MD5sum: 9d47990940ec8c50a7ef97af0f886775 SHA1: c96dab66dfeb1604ab28361ae58c02e65ceec3e9 SHA256: 406c00d22273ff755b831bbdcc6b7f69ae2b07cb0a40a662d60e58157746b8ce SHA512: 2dffc0b8562d22f35dc137786a016003036cae3510872b75628bce00c59e8706c77aa6997649cd77e2e74819f95c0fe3789e7c896b0e402b8681255708c166a6 Homepage: https://cran.r-project.org/package=trip Description: CRAN Package 'trip' (Tracking Data) Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, Sumner, MD (2011) . Package: r-cran-tripaccess Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8765 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-tidytree, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-tripaccess_0.2.0-1.ca2404.1_all.deb Size: 8735944 MD5sum: b762fc9095d688ca6422e5d31f00a73a SHA1: a4262551cb44823893725bf81c8fe2d1727426f6 SHA256: ca1b828b653b605ff12d60853603a5c9f5646fdbe748bfa2000ef5fe0c1a111f SHA512: 56346fe8a8956325f0cb0a8a5b6fa6eb4e253ef64692dea1974cb8559d922a5c3f543991540370f67f6f4bc9301d797c1e343023e3e9967429e61dc71084733f Homepage: https://cran.r-project.org/package=tripaccess Description: CRAN Package 'tripaccess' (American Travel Behavior and Access Datasets) Subsets of data from the National Household Travel Survey 2017. It includes personal trips, mobility, demographic, and household information. It is suitable for data visualization, data wrangling, joining datasets, exploratory data analysis, group comparisons, simple linear regression, categorical data analysis, and data ethics discussion in data science and statistics classes. Package: r-cran-tripestimation Architecture: all Version: 0.0-46-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mgcv, r-cran-reproj, r-cran-sp, r-cran-zoo Filename: pool/dists/noble/main/r-cran-tripestimation_0.0-46-1.ca2404.1_all.deb Size: 171126 MD5sum: cdb739cc2e1ab837ff0ed22e295e868d SHA1: c737e89cf023ff6fbaa823188f5254e66dee6798 SHA256: e8f382d5b480d44854562add04f966899560feebdf59e9528aeb0133a3290214 SHA512: 9a3437f3c34415c1c0c0051844bdaf503cac14a4d3b02c10f141d0c32082f5907ef46b619d734efaf222bdc0f7b1fb3800984c72d0111828e7bc692830208b80 Homepage: https://cran.r-project.org/package=tripEstimation Description: CRAN Package 'tripEstimation' (Metropolis Sampler and Supporting Functions for EstimatingAnimal Movement from Archival Tags and Satellite Fixes) Data handling and estimation functions for animal movement estimation from archival or satellite tags. Helper functions are included for making image summaries binned by time interval from Markov Chain Monte Carlo simulations. Package: r-cran-tripler Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr Suggests: r-cran-lme4, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tripler_1.5.5-1.ca2404.1_all.deb Size: 593060 MD5sum: 71ea26d027bcec615bb0253d57f07ad2 SHA1: 09fc86d32e8f4bbf5c4d212bf5174ba73b75abde SHA256: efe9c418506157fb1245c32e2830bca4cdd40acb39f08f43d905db26107dd2d0 SHA512: e9250b63d4b61b9f398d5f5bf9f565284fb8d8480734853033d32dfa49ace82708f760b4a4075e7d6037f526174fa80ea2ec59687d62bc46dd72cf8936265c72 Homepage: https://cran.r-project.org/package=TripleR Description: CRAN Package 'TripleR' (Social Relation Model (SRM) Analyses for Single or MultipleGroups) Social Relation Model (SRM) analyses for single or multiple round-robin groups are performed. These analyses are either based on one manifest variable, one latent construct measured by two manifest variables, two manifest variables and their bivariate relations, or two latent constructs each measured by two manifest variables. Within-group t-tests for variance components and covariances are provided for single groups. For multiple groups two types of significance tests are provided: between-groups t-tests (as in SOREMO) and enhanced standard errors based on Lashley and Bond (1997) . Handling for missing values is provided. Package: r-cran-triplesmatch Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcbalance, r-cran-rlemon, r-cran-mass, r-cran-optmatch, r-cran-rlang Suggests: r-cran-testthat, r-cran-sensitivityfull, r-cran-informedsen, r-cran-matrix Filename: pool/dists/noble/main/r-cran-triplesmatch_1.1.0-1.ca2404.1_all.deb Size: 110390 MD5sum: 6c569b6e550e53bc0b67a543e706d3da SHA1: eb27b46f57ee041e3540cff1f5977956a7ce3de4 SHA256: f36b3423a434aaaf600bfbb42dbaecd54f2d78effec9c6f133fa3b38c50defd8 SHA512: 0763ab47798c44a60bb4687aa129593139abf4b81c2d08082938cfa74752e5eae0b530e36396a167397c3009323385fc15138b225698bb822624eaa9d9c580d6 Homepage: https://cran.r-project.org/package=triplesmatch Description: CRAN Package 'triplesmatch' (Match Triples Consisting of Two Controls and a Treated Unit orVice Versa) Attain excellent covariate balance by matching two treated units and one control unit or vice versa within strata. Using such triples, as opposed to also allowing pairs of treated and control units, allows easier interpretation of the two possible weights of observations and better insensitivity to unmeasured bias in the test statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio allows for the match to be feasible in more situations. The 'rrelaxiv' package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from 'GitHub' at . The 'Gurobi' commercial optimization software is required to use the two functions [infsentrip()] and [triplesIP()]. These functions are not essential to the main purpose of this package. A free academic license can be obtained at . The 'gurobi' R package can then be installed following the instructions at . Package: r-cran-triplot Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dalex, r-cran-glmnet, r-cran-ggdendro, r-cran-patchwork Suggests: r-cran-testthat, r-cran-knitr, r-cran-randomforest, r-cran-mlbench, r-cran-ranger, r-cran-gbm, r-cran-covr Filename: pool/dists/noble/main/r-cran-triplot_1.3.0-1.ca2404.1_all.deb Size: 161326 MD5sum: db9f8a3f5a7380610673bc061e4376a3 SHA1: 7d41956414c63ac378ebb74f1ee3eb9aa7914230 SHA256: 675e6e617e0ba5afada93fb492d1596f6e16b8d330b00aa4e194ed379efdcb7d SHA512: 9bbd0de62e3cd1e6b13ab72c9789087970667c85105658c9f67e153911bcc0431f6b8b011122cc4763632ce22dbca35086ddc427078b05942d8d1cdfce0807e1 Homepage: https://cran.r-project.org/package=triplot Description: CRAN Package 'triplot' (Explaining Correlated Features in Machine Learning Models) Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) . Package: r-cran-tripsanddipr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tripsanddipr_0.1.0-1.ca2404.1_all.deb Size: 17328 MD5sum: eac056cd4686af650641ac77909dfa61 SHA1: ff5c0f80da41300e1f1ca6a5facc1b4f6a46dc7a SHA256: 50aa97104bf902f114a54b36e75246d9956ddca72f3db0e456ff00b6918cc3dd SHA512: 0b5e5719c93c945c0dbfdd80fff18514a5e5bb9b3b3f8d7f8122aa00a75a09638ac168dfa9a378c52879438fcd6a1fc292dcf73c39fa4b1a2fe851534d361e36 Homepage: https://cran.r-project.org/package=tripsAndDipR Description: CRAN Package 'tripsAndDipR' (Identification of 2n and 3n Samples from Amplicon SequencingData) Uses read counts for biallelic single nucleotide polymorphisms (SNPs) to compare the likelihoods for the observed read counts given that a sample is either diploid or triploid. It allows parameters to be specified to account for sequencing error rates and allelic bias. For details of the algorithm, please see Delomas (2019) . Package: r-cran-triversity Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-triversity_1.0-1.ca2404.1_all.deb Size: 65970 MD5sum: a18f6e6efd0eda837f8434fac6872ac3 SHA1: d9d614a9999d114157f24ee79a05260f3d63cf4d SHA256: f19725ae5d9dcd146ef7c34ca3bb9a2de317fad0f6b6f29c9e02578324ffffca SHA512: bd87fac304878fe76a406ceb54efb7ec6a2046a90cdba1d432e40d12886402d6e6aa6be186331d1537257a01494881ff1b2385dc9e3625cae8bd768c239e8936 Homepage: https://cran.r-project.org/package=triversity Description: CRAN Package 'triversity' (Diversity Measures on Tripartite Graphs) Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure. Package: r-cran-trmf Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-limsolve, r-cran-generics Suggests: r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-trmf_0.2.1-1.ca2404.1_all.deb Size: 132020 MD5sum: f6de4128ede4100c243441a0bead20c8 SHA1: d1d8591cb9720bc831240f19ba38b4534e2368b2 SHA256: f558de443518ec305f0cbe42c7ab198b8826078b1fa7a2495500ecd950729530 SHA512: 869f444d3b9d56dcfa4a68d8a15cf1a6b1da36cdd8081d2959183ea6dd8b27ba01c9c316f50cc9dcc3c60c1bcf510f9ab291ebe6ee2a7c91d5f63f12a3981390 Homepage: https://cran.r-project.org/package=TRMF Description: CRAN Package 'TRMF' (Temporally Regularized Matrix Factorization) Functions to estimate temporally regularized matrix factorizations (TRMF) for forecasting and imputing values in short but high-dimensional time series. 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Package: r-cran-troopdata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2076 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-furrr, r-cran-tidyselect, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-viridis, r-cran-ggplot2, r-cran-countrycode, r-cran-tidyr, r-cran-stringr, r-cran-haven, r-cran-maps, r-cran-pdftools Filename: pool/dists/noble/main/r-cran-troopdata_1.1.0-1.ca2404.1_all.deb Size: 2014344 MD5sum: 0778522866e5bffd543912cbc7f43ee1 SHA1: 8c83e7ba995e42e04288a5a2b33c9d54605b1ed9 SHA256: 353ed019b3ff12e400717773997afa81afc8d3e77062d4836dbafbe83c903924 SHA512: 969a114a553a133cc8317f5d580c43d349db7dd51b2865ef1cc9a30485feea200fa7fd817d2d072dfadf1c74ef8d06201996b43a98316514437f72bcf92d359c Homepage: https://cran.r-project.org/package=troopdata Description: CRAN Package 'troopdata' (Tools for Analyzing Cross-National Military Deployment andBasing Data) These functions generate data frames on troop deployments and military basing using U.S. Department of Defense data on overseas military deployments. This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries. Package: r-cran-tropalgebra Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tropalgebra_0.1.1-1.ca2404.1_all.deb Size: 44798 MD5sum: 09d3639da90464f0b6e59e57253a4f41 SHA1: 2c4613f3a674995090790d9b4223abd231e1a4a9 SHA256: e1b6ec7a2787cbf5068904bf75a677fda00958092a03ab9ef481298de0e9528a SHA512: 9c65ad4566178f8282fdf646f0e1ec22e243c0e42cdb1010bd17318c22731f75f1d77380b2e97c06da0014ec9fb99dbad202afafeae53b2686f841ee7c457fd8 Homepage: https://cran.r-project.org/package=tropAlgebra Description: CRAN Package 'tropAlgebra' (Tropical Algebraic Functions) It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 . Package: r-cran-tropfishr Architecture: all Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-msm, r-cran-reshape2, r-cran-mass, r-cran-gensa, r-cran-ga, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plyr Filename: pool/dists/noble/main/r-cran-tropfishr_1.6.6-1.ca2404.1_all.deb Size: 1680186 MD5sum: a5c743a120ca42ed444409f33611415f SHA1: d21ad5af6cc940d888d688808d6b11d5897c72f9 SHA256: 817fa457117ba71f9f3d60d7744ea43cf20712acd584cadc5bee0bcf4ea1c471 SHA512: fedb024d72bbba9f71750fbb8d2ceca6c7d9c0efe955fe3b3525dab539474a1023dcbfac01c8c68986e8b829dd2af98ec93e4b174c6421ae1419f5c2221caf86 Homepage: https://cran.r-project.org/package=TropFishR Description: CRAN Package 'TropFishR' (Tropical Fisheries Analysis) A compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. 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Package: r-cran-trotter Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trotter_0.6-1.ca2404.1_all.deb Size: 161118 MD5sum: e6e59e5a5e90a5fa4eec91efdd626016 SHA1: 496d79ea03db2fe5c36f203496fcf989793c3439 SHA256: 2d5741ef9acd8afdf7f22771fd80219b282f72bd8c603e7d65bde2dcbed12866 SHA512: c92788dcd7322e1788b616f166f85d179e4b180088f0fd5c8843e43a606cc9f004bc15f85faa0321ccc3207fdf47441158c819eeb4eb5655c95e11d4f8a569e0 Homepage: https://cran.r-project.org/package=trotter Description: CRAN Package 'trotter' (Pseudo-Vectors Containing All Permutations, Combinations andSubsets of Objects Taken from a Vector) Class definitions and constructors for pseudo-vectors containing all permutations, combinations and subsets of objects taken from a vector. Simplifies working with structures commonly encountered in combinatorics. 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By automating the process of generating 'AMPL' datasets, this package can help streamline optimization workflows and make it easier to solve complex optimization problems. The methods implemented in this package are described in detail in a publication by Fourer et al. (). Package: r-cran-trps Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-cli, r-cran-dplyr, r-cran-lifecycle Suggests: r-cran-bayesplot, r-cran-ggdist, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidybayes, r-cran-tidyr, r-cran-viridis Filename: pool/dists/noble/main/r-cran-trps_0.1.0-1.ca2404.1_all.deb Size: 302532 MD5sum: 9bb19c96870ac495aaefa75ae20d5758 SHA1: 0aafc971091140eaa2bd2b375736cfeb35a857d7 SHA256: 8fbcd096d82b51cdee3125273772e6c2eddfb124af8ceb635b15377099829f86 SHA512: 4e0848b4ecd7b83a7c445269c9e8e66599b3b1a8e077c47dde2a2d983b8c7efa0d78d0d3af098ed34467461db001ff4df8fcfdb4a147dab6643d86711731c160 Homepage: https://cran.r-project.org/package=trps Description: CRAN Package 'trps' (Bayesian Trophic Position Models using 'stan') Bayesian trophic position models using 'stan' by leveraging 'brms' for stable isotope data. 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Package: r-cran-truelies Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdrcde Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-purrr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-truelies_0.2.0-1.ca2404.1_all.deb Size: 72576 MD5sum: 7ca94f209153920cbad6ec97b4b5b801 SHA1: e4f2c773032728b0b1f6dffa7ceaf3cc31dccfde SHA256: 8faeb97a2cacfd033f2f71c4b3426dd6a70be0fbe71de1af8be55a5685976709 SHA512: 8f1b15df9b213ef001f7738282716055dad998c6b3bfda320ad9a0842fb5580a4155c9cfa161255b971a5990e16601c2dbf88348a0f2fd74c6f2097016d008c4 Homepage: https://cran.r-project.org/package=truelies Description: CRAN Package 'truelies' (Bayesian Methods to Estimate the Proportion of Liars in CoinFlip Experiments) Implements Bayesian methods, described in Hugh-Jones (2019) , for estimating the proportion of liars in coin flip-style experiments, where subjects report a random outcome and are paid for reporting a "good" outcome. 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Package: r-cran-truewap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-ttr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-truewap_0.1.0-1.ca2404.1_all.deb Size: 323160 MD5sum: af4f5b5dc9fa6f31d36b3fcca6fc0886 SHA1: 3ce5b5768dd4453d3eac2f4a3c5743299ccf594b SHA256: 4dba04d7766e48a2dae33780ded1970c3ec562cf8c7e37035819aa1d3c8b1318 SHA512: af7ee9b67a924471547d193b423346bb1f30b0103fe01eea6d8eed141599acb12c36653c766b18408fec188547232f3981b983c6806ec8c9beb4daaf720295ee Homepage: https://cran.r-project.org/package=TrueWAP Description: CRAN Package 'TrueWAP' (True Range-Weighted Average Price ('TrueWAP')) This groundbreaking technical indicator directly integrates volatility into price averaging by weighting median range-bound prices using the True Range. 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Package: r-cran-truh Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast, r-cran-cluster, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-fpc Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-truh_1.0.0-1.ca2404.1_all.deb Size: 56432 MD5sum: 8ddaf8d61f9c32512d93fa502c8d5610 SHA1: 4aef2da6e3b59bc368c67d1ec4d20a8f0d46c81f SHA256: 76b1c4ec64d230b12aee156eeef6fc52f5fcd634502c0acdc468a544acdc097b SHA512: 988ba045d052b1d3285f5a180b34758b38b44fd298bdc9b90472a9c12200825237776612e9b9015cc8a9b6b3709099e5c3a9d8ce1c8371f352474e0c672264ee Homepage: https://cran.r-project.org/package=truh Description: CRAN Package 'truh' (Two-Sample Nonparametric Testing Under Heterogeneity) Implements the TRUH test statistic for two sample testing under heterogeneity. 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Package: r-cran-trumpetplots Architecture: all Version: 0.0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr Filename: pool/dists/noble/main/r-cran-trumpetplots_0.0.1.1-1.ca2404.1_all.deb Size: 191832 MD5sum: 835b403288fe9bc809decb8f121a4c99 SHA1: 4750e0ad46e3e86d85b0f7181088e990c1fcb587 SHA256: 361a71950a8fc14ebaa304e79afe50712b1d97546179902504c01cb53428f946 SHA512: 8e7b77423ca4df3f0898cb70c9ba568e3bb54769c926eb3d737af95f6bf823c19794798e26e0dfbac1b4c7e77a519f2a91042648cbba133a1aa085771cf1ba4f Homepage: https://cran.r-project.org/package=TrumpetPlots Description: CRAN Package 'TrumpetPlots' (Visualization of Genetic Association Studies) Visualizes the relationship between allele frequency and effect size in genetic association studies. The input is a data frame containing association results. 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Package: r-cran-truncaipw Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survpen Filename: pool/dists/noble/main/r-cran-truncaipw_1.0.1-1.ca2404.1_all.deb Size: 112306 MD5sum: cec02ad691e03d2c11a6ff1915433e24 SHA1: fb1a0a20867a91441d35ce4281b05134cc6de80b SHA256: d6b2bc26985eeeee7664953372aa1afce736e8e63a669e7b2b45111ae4efa21d SHA512: 79ee7d66da76fd52242104d80e3646712effe3c96be028aa0e51b0ff41ea0ba5d58dd3523495aac1ae83518594df07f20e4c9edfb89b42de9ab9ab13512ff357 Homepage: https://cran.r-project.org/package=truncAIPW Description: CRAN Package 'truncAIPW' (Doubly Robust Estimation under Covariate-Induced Dependent LeftTruncation) Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. 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Package: r-cran-truncatedpcqm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-truncatedpcqm_0.1.2-1.ca2404.1_all.deb Size: 62300 MD5sum: 9b750136eb02fe960f6061b39cf1954c SHA1: a16299d4531ee172d6f01af5f1fc37d602102a4f SHA256: b55f87b78334d3bd172ed1477440ea2cdba14d23b8af99cc1be6221f6019315c SHA512: 448a3deb93168eaedb89862f30460f4e4dee82cb6c4a587d34302e5aa84d51d18c792558bca7e0b479d839b1fb4d16a9ddf6964c687be475c7054302547607a4 Homepage: https://cran.r-project.org/package=TruncatedPCQM Description: CRAN Package 'TruncatedPCQM' (Density Estimation for Point-Centered Quarter Method withTruncated Sampling) Implements a systematic methodology for estimating population density from point-centered quarter method (PCQM) surveys when distance measurements are truncated by a maximum search radius (right-censored). 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Package: r-cran-truncdist Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd Filename: pool/dists/noble/main/r-cran-truncdist_1.0-2-1.ca2404.1_all.deb Size: 34616 MD5sum: a8f88057fc762e721c5a171a4185cfd1 SHA1: 60702425bfe02d49aec77057dec7e901b5af0f40 SHA256: d9eea46f5d76b0d61ab3859182918c7d899018633f6af8eda4d12177fd4326b9 SHA512: 9c2dc992a85bbe59f95abb287d1a446a23b05ec7d9997758a3054a8f2624e2aacec1a7ddab565f72f818ebb10b16808b51e91d7a09942e129f3aa45c65628f1d Homepage: https://cran.r-project.org/package=truncdist Description: CRAN Package 'truncdist' (Truncated Random Variables) A collection of tools to evaluate probability density functions, cumulative distribution functions, quantile functions and random numbers for truncated random variables. These functions are provided to also compute the expected value and variance. 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The estimators implemented are the Symmetrically Trimmed Least Squares (STLS) estimator introduced by Powell (1986) , the Quadratic Mode (QME) estimator introduced by Lee (1993) , and the Left Truncated (LT) estimator introduced by Karlsson (2006) . 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Package: r-cran-tsclust Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdc, r-cran-cluster, r-cran-locpol, r-cran-kernsmooth, r-cran-dtw, r-cran-longitudinaldata, r-cran-forecast Filename: pool/dists/noble/main/r-cran-tsclust_1.3.2-1.ca2404.1_all.deb Size: 443346 MD5sum: d60a58c873be74351db4531e43093d4c SHA1: 6fc9c5d3d934eb47687d0cc6a630d1c4dda9024b SHA256: fcb976a4d490c72c2310a030f744322667e6c642380aeb2a93a031143d3bd588 SHA512: 3fb5ceb14adaf614bd4e8bf9bee96d5d2cf579dd50671e601c1a622d01ee4de46253e20ff4852a4c73dabb2ea8ae93e1d65de71c94711b552a60721726d75d23 Homepage: https://cran.r-project.org/package=TSclust Description: CRAN Package 'TSclust' (Time Series Clustering Utilities) A set of measures of dissimilarity between time series to perform time series clustering. 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This is mainly a set of wrapper and helper functions as well as some extensions for the packages 'tsibble', 'fable', and 'fabletools'. Package: r-cran-tsdataleaks Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-slider, r-cran-purrr, r-cran-cowplot, r-cran-plyr, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tsdataleaks_2.1.1-1.ca2404.1_all.deb Size: 228138 MD5sum: 63a0dfacedfd8d4e1fb130b15c109adf SHA1: dd613e53775ede2dfb16c0b146cbc1e25a97b6b0 SHA256: 7250eb628cc161d1ead3df94eb3685965166da49fb5977d62d1fba0f556dd044 SHA512: bae7481d76296cc8d96e3f69cada4254b2f3ff07f7df9b88bf60b7464809c805683ebcaab9ca5a7ede90dba3b8c6fe09f343a2ab4b9df22a075da2ea72834240 Homepage: https://cran.r-project.org/package=tsdataleaks Description: CRAN Package 'tsdataleaks' (Exploit Data Leakages in Time Series Forecasting Competitions) Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series, white noise is added in the original time series to form a new time series, etc. 'tsdataleaks' package can be used to detect data leakages in a collection of time series. Package: r-cran-tsdb Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datetimeutils, r-cran-fastmatch, r-cran-zoo Suggests: r-cran-data.table, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-tsdb_1.1-0-1.ca2404.1_all.deb Size: 62852 MD5sum: e6ec26ae27779d68dcf6130b396676da SHA1: 1bb4b3a7a6b0cc1103220b4ff7aea49bc875421d SHA256: 27eed9499c6bfa969dd5ecb24a46fed85bed0cd2926c9d713ed24fa28629e5fb SHA512: 942a42af403d09bc2200748036d83395fb236b0d114eca094a3bc4dc2f6aaf2fdc0574e7bfab877cb31b0bf63e7fee9840e75b0f46263be1311621767797db76 Homepage: https://cran.r-project.org/package=tsdb Description: CRAN Package 'tsdb' (Terribly-Simple Data Base for Time Series) A terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for 'Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as 'zoo' series). Package: r-cran-tsdecomp Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsdecomp_0.2-1.ca2404.1_all.deb Size: 203818 MD5sum: f8a3e45ce3a9d96ff58b791dc37e6100 SHA1: cd11e8bb67ca817d3dadb03dbc0f8621160a4637 SHA256: c253de557b009d858a31eaefe40653542efba91290b92b2199104becfa631345 SHA512: ea0f421971569d2f553a1284d77f6faa577ce555532bb5da6cb6560b05225adb2197e8e8846a9250ec8ae90fbe67ac55c9d4d52a3b9cfc41bdd4e2b9ab66772d Homepage: https://cran.r-project.org/package=tsdecomp Description: CRAN Package 'tsdecomp' (Decomposition of Time Series Data) ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) and Hillmer and Tiao (1982) . 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These models capture temporal dependencies and address vanishing gradient issues in sequential data. The package enables efficient forecasting for univariate time series. For methodological details see Jaiswal and co-authors (2022). . 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The Phase II design is based on Zhong (2012) . Package: r-cran-tsdisagg2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tsdisagg2_0.1.0-1.ca2404.1_all.deb Size: 341606 MD5sum: a3ca4ef98359c9358afe3693e4cb0ad2 SHA1: 842e193ebb8c9676663e86e1ef74b1756e36701a SHA256: 320801e13c430334adefbe4376fffe33cb85ca71e96f23f7a26d8332b56fdbb6 SHA512: cb166749d70fe7d6b4b0395f46a6bc50763c47052a08689372ebf5db5d61cf077b3abc9f0398683f5abe61f92039d082bb55d11c62652008957b2c17be3a1cf1 Homepage: https://cran.r-project.org/package=tsdisagg2 Description: CRAN Package 'tsdisagg2' (Time Series Disaggregation) Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) , Chow and Lin (1971) , Fernandez (1981) and Litterman (1983) . Package: r-cran-tsdisaggregation Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-zoo, r-cran-lars, r-cran-matrix, r-cran-withr Filename: pool/dists/noble/main/r-cran-tsdisaggregation_2.0.0-1.ca2404.1_all.deb Size: 64856 MD5sum: 3f72d84c827f872001da15f7d65e48e2 SHA1: 181252d03054190e7fd0b32deb43b41302c190dc SHA256: 5c48d1b04d346cfb3f7c9ba939e16e1764a2c447c7f6c45cdc4a6ba76e2f96d8 SHA512: 5376577b50c922f37ed625d0baa2b864be1200bb2cee7f13021a2e1240bac21b21e97e8df28b4a58a99b98eaea093243da871cbd623c8af5f7dde0f6d8d0112a Homepage: https://cran.r-project.org/package=TSdisaggregation Description: CRAN Package 'TSdisaggregation' (High-Dimensional Temporal Disaggregation) First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices. Package: r-cran-tsdt Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlbench, r-cran-hash, r-cran-party, r-cran-rpart, r-cran-survival, r-cran-survrm2, r-cran-modeltools Filename: pool/dists/noble/main/r-cran-tsdt_1.0.8-1.ca2404.1_all.deb Size: 456290 MD5sum: 25206d72272559cc21157fba6c1fc836 SHA1: 312f064ed93407e61039dfa524dd3c213f8bd935 SHA256: 90334cb1c20236ce1c06af530ae1d8cc8ad5aa39223d5ad0fd877ea6167bb7af SHA512: 31c1b11f71bd0d9c529648986194e20f574a5ceb35868e57cfe1b7ff8f5f8c387f034c331840b27b6b19ff2fa997cb94cef847ee88977cb2c3bae5d9dfd4ea65 Homepage: https://cran.r-project.org/package=TSDT Description: CRAN Package 'TSDT' (Treatment-Specific Subgroup Detection Tool) Implements a method for identifying subgroups with superior response relative to the overall sample. Package: r-cran-tse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tse_0.1.0-1.ca2404.1_all.deb Size: 62240 MD5sum: 38c08c4e4edac02405b587114251b175 SHA1: e7894f94f7bfa218a536f57d5785db9b29abfd7c SHA256: 182907ce5191aebfea4ff707048182cb36a80372fc77130804c975b82fc9f628 SHA512: 335cdeaeebd942f561d6551ad4e0ba8b9df09b67dc1f24a6d1a41692db761e62be7974a313f614c2be369bc6d7211e6a5aaab12a01fcad01e408d751003d0aec Homepage: https://cran.r-project.org/package=TSE Description: CRAN Package 'TSE' (Total Survey Error) Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics. On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) . Package: r-cran-tseal Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-caret, r-cran-checkmate, r-cran-magrittr, r-cran-mass, r-cran-parallelly, r-cran-statcomp, r-cran-waveslim, r-cran-wdm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tseal_0.1.5-1.ca2404.1_all.deb Size: 4255086 MD5sum: ac75768f249f054f8e708723b787aca4 SHA1: 3d14439733f8d9fc1e1daee49673673a42f8123f SHA256: 9b9cc5e254da38ca5596dec8215d63cc48501123bdc7be3322de209d55922376 SHA512: af91967c7943d69d0f5949ce15639e1a7fb99be172ae5a6f870ff917d064ec565f446b5a140cc6449f95d36dd53de4a2e4bbf72e66f98064c27b9d1f96e7812d Homepage: https://cran.r-project.org/package=TSEAL Description: CRAN Package 'TSEAL' (Time Series Analysis Library) The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) . Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) . Package: r-cran-tseffects Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mpoly, r-cran-car, r-cran-ggplot2, r-cran-sandwich Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-ardl, r-cran-dynamac, r-cran-kardl, r-cran-tidyverse, r-cran-zoo, r-cran-ggplotify, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tseffects_0.4.1-1.ca2404.1_all.deb Size: 635398 MD5sum: b8dbd08fcce837f1f07fec832ebdff0c SHA1: 69ec6a0e17cd7134d2b3f891b83b683effd911a5 SHA256: 3c485572951943d07386acfefc3c8084a953d50840e772b2b458ba7c12e4f4c7 SHA512: 1a603600f692e6f220dc4b4f872fa40d53e4a1e4b5cb1472c574c3d614611c31c136c98aa4dfb5ae0fd1c137c9227cb1825d3e747fea3e37bc8816cb3626e6b1 Homepage: https://cran.r-project.org/package=tseffects Description: CRAN Package 'tseffects' (Dynamic Effects from Single-Equation Time Series Models (withInteractions)) Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse dynamic linear models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest and are built from the Impulse Response Function and Step Response Function (sometimes described as a pulse effect or a cumulative effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applied to (in)dependent variables in differences through the Generalized Dynamic Response Function. These tools are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 ). Package: r-cran-tseind Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tseind_0.1.0-1.ca2404.1_all.deb Size: 57992 MD5sum: cfa440db1ac82bad0bc00d65a84e0022 SHA1: 3fd63785f0d936a9e7c77c444e29d03cee8d7247 SHA256: a44605b62ca411c0f3766868cdfe0f17462ad05d6adc855dc97930fa84a4e170 SHA512: ab37c14a34401ea4b7c2cd68a00bd47d9809a0512f7f37df04e5fa576bd4ab3753114be603305c9ba3540f5c76bef4cdeee455a6fc3df0da30e26911ad1b0d3b Homepage: https://cran.r-project.org/package=TSEind Description: CRAN Package 'TSEind' (Total Survey Error (Independent Samples)) Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) ; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. Package: r-cran-tselca Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-formula, r-cran-multilevlca Suggests: r-cran-nnet, r-cran-polca, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tselca_2.0.0-1.ca2404.1_all.deb Size: 498474 MD5sum: 6eac9be59488b449b0e33501805fd7ef SHA1: 7061bef588ddd5a4d208a51664ab631b43295806 SHA256: 8ffe72058eb3cca519533d25d4b5763434a66bbacd2a06bee72c6feffe3de499 SHA512: 30d200d43b084fd89bbefa65e47304144c8ddd0fb9ee2f20e997c8c0f3b44e7b9036aa3deec717a800724565e507e8ac1e8e81eb013da38bf7f3aefd93afd773 Homepage: https://cran.r-project.org/package=tseLCA Description: CRAN Package 'tseLCA' (Three-Step Estimation for Latent Class Analysis) Bias-adjusted three-step estimation of latent class models with covariates and distal outcomes. The latent class measurement model is estimated first, with 'multilevLCA' (Lyrvall et al., 2025) , and held fixed; observations are then classified; and the classes are related to covariates and distal outcomes with the maximum likelihood correction of Vermunt (2010) and Bakk, Tekle and Vermunt (2013) , or the correction of Bolck, Croon and Hagenaars (2004) . Standard errors account for the uncertainty of the measurement model (Bakk, Oberski and Vermunt, 2014) . Includes class enumeration, modal and proportional class assignment, covariate formulas, Gaussian, Poisson, binomial, and multinomial distal outcomes, the two-step estimator of Bakk and Kuha (2018) , measurement models applied to new samples, and full-information maximum likelihood for missing indicators, standard methods for fitted models, and a data-generating process replicating the simulation design of Bakk and Kuha (2018). Package: r-cran-tsensembler Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpproll, r-cran-ranger, r-cran-glmnet, r-cran-earth, r-cran-kernlab, r-cran-cubist, r-cran-gbm, r-cran-pls, r-cran-monmlp, r-cran-doparallel, r-cran-foreach, r-cran-xgboost, r-cran-softimpute Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsensembler_0.1.0-1.ca2404.1_all.deb Size: 502178 MD5sum: fd10f8667c537a09e4bf916af1f31caa SHA1: eb6790a344242d1fe825ce22bb0fd8830126da8e SHA256: 38745445d0a9da2f56942afe516d0d617aee108734f83faa0623e66b0719fce4 SHA512: d3c860db72e10cdcfd4aab09645d2b914787444023b46c76ce597d1acf8bc41aaa2c99e5b2715d31570e72fc64d2b13e8f6c35f5625019d4a49982f3cb443e9e Homepage: https://cran.r-project.org/package=tsensembler Description: CRAN Package 'tsensembler' (Dynamic Ensembles for Time Series Forecasting) A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 . Package: r-cran-tsentiment Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-wordcloud, r-cran-ggplot2, r-cran-httr, r-cran-syuzhet, r-cran-tidytext, r-cran-dplyr, r-cran-tibble, r-cran-stringi Filename: pool/dists/noble/main/r-cran-tsentiment_1.0.5-1.ca2404.1_all.deb Size: 42312 MD5sum: 2d87737c56b64580175de17a4d0fac9a SHA1: e3c19b7c9274565aa3928b0a9dc22e75fd8b3dfe SHA256: a4c18fde127a7e8555293d2894de6027b7c6ee4a457d396d3f12804f4c3d5189 SHA512: 84f6bb31393f018fd8837affd79d26c4cc977a8fff51e9aa24b553560ec5d3af5440c0eca05a17280790ee05c3c7973eb0d6ec0482a5eaab9d948496bd82b2ea Homepage: https://cran.r-project.org/package=tsentiment Description: CRAN Package 'tsentiment' (Fetching Tweet Data for Sentiment Analysis) Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application. A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information ). In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information ). After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule. 'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at . Package: r-cran-tseriesmma Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tseriesmma_0.1.1-1.ca2404.1_all.deb Size: 16220 MD5sum: d20575a51e3352097788f6f9231637f8 SHA1: 383d526bf10bfebcf0070fc68115b852a9558e24 SHA256: 41fdc8e2e2725f29257318d0592db7d034bd50208aaa4cd3619a6e6b89c4b0c3 SHA512: ef40d3fefc2f0fa3cdef8e939976e97038da415de7ce547b297ff8ca92f6b0fef74e34f5f633615f366a037ec497ea1b6c0922f4bcdfbba5d4c559d9cf7d81a5 Homepage: https://cran.r-project.org/package=TSeriesMMA Description: CRAN Package 'TSeriesMMA' (Multiscale Multifractal Analysis of Time Series Data) Multiscale multifractal analysis (MMA) (Gierałtowski et al., 2012) is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q and the scale of observation s (data window width). Package: r-cran-tsetools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-quantmod, r-cran-quadprog, r-cran-httr, r-cran-rjson Filename: pool/dists/noble/main/r-cran-tsetools_1.0.0-1.ca2404.1_all.deb Size: 83000 MD5sum: 0da51df83b3f0a32fbc5f8b6260e2673 SHA1: 0b0d0259963980bd2faff5fa708520b03441946a SHA256: 9ee75d6c6ab779e8a28b56c39a1e8114bf32eda7ee7c0ece749944afaf0a748e SHA512: f0c8b48f759a41cce6ec98659aa168afa056a9abade3d3a26109f36f477b6c173d9f12b4aa604889c1fdb4a97b8012647a2d573d753ba39b0c41d5e38ce11ffa Homepage: https://cran.r-project.org/package=TSEtools Description: CRAN Package 'TSEtools' (Manage Data from the Finance Markets) A set of tools designed to perform descriptive data analysis on assets, manage asset portfolios and capital allocation, and download, organize, and maintain data from the "Tehran Stock Exchange" and "NOBITEX" platforms. Package: r-cran-tsewgt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tsewgt_0.1.0-1.ca2404.1_all.deb Size: 60432 MD5sum: 0900080313664db82babba768ad587cc SHA1: af2c32ea8acfa1beed5bc6e21403010425741767 SHA256: ffb93a781bb066ac6723562461ecfe487d53ca9802271c8267bb780ba6834060 SHA512: ac3caf58d98e03ecaa7bf76eff0981578176eeb43deef22f77432b50d685ccef4cbd4ec57a3327ae17735c0666f515a25aed1775cd4f6f48f3674e848c5927c9 Homepage: https://cran.r-project.org/package=TSEwgt Description: CRAN Package 'TSEwgt' (Total Survey Error Under Multiple, Different Weighting Schemes) Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) ; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. Package: r-cran-tsf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fracdiff, r-cran-forecast Filename: pool/dists/noble/main/r-cran-tsf_0.1.1-1.ca2404.1_all.deb Size: 21842 MD5sum: 584f36c265a6be2d2150ece775dcb2b2 SHA1: b171f93bf61fe23985c164cd9f8d993d69f39044 SHA256: 445c00fd64455e4d4440aa964fcbacde3375a19a82753f93b4e08a5f4ee5694e SHA512: f3462a4334c5ff4dfd7842e2ff3efbc901f431bd1b9d4778f0b1f49020c7a3ee531269473be91a754e7fc43ccb3529f1cd21b3f1eb64d81cb601661bc9a8b4bb Homepage: https://cran.r-project.org/package=TSF Description: CRAN Package 'TSF' (Two Stage Forecasting (TSF) for Long Memory Time Series inPresence of Structural Break) Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) . 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The features provided are those from Hyndman, Wang and Laptev (2013) , Kang, Hyndman and Smith-Miles (2017) and from Fulcher, Little and Jones (2013) . Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions. Package: r-cran-tsfngm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsfngm_0.1.0-1.ca2404.1_all.deb Size: 16106 MD5sum: 409b9123b4886d3824e1d41b678e9b46 SHA1: bfb1c1e5303009d73553c81695dd0222cbdbda83 SHA256: e56407357265836bc4eb011ec992e022f4efd2ce53cdd7f2d7624373e16d97cd SHA512: 29a95768b2f494fc3492bc671f8f50cdcecb72890f182ab29082097b10d9248601584085372ecf466a3166a42707b51983ffbbe6c4b494b4347ab357367609dc Homepage: https://cran.r-project.org/package=tsfngm Description: CRAN Package 'tsfngm' (Time Series Forecasting using Nonlinear Growth Models) Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models. Package: r-cran-tsforecast Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-forecast, r-cran-tseries, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsforecast_1.3.1-1.ca2404.1_all.deb Size: 291966 MD5sum: 6c4458365d2708184c24c2f1765f1da9 SHA1: 23a1ca58a1176105809e1707b117dee88788d506 SHA256: 03db89f1af88ccf25c10463e076c8c1b2ad3133c83cd1aa56ab3fa0b02fc246a SHA512: f20a67064ec5fe62f263d9d03cb1d5d8fd4d97f17f06da983f6f311b234dec2777e0feaee932c90cdd6c1d6d12ff0a69dd485f668fde666da7933abd4d86858d Homepage: https://cran.r-project.org/package=tsforecast Description: CRAN Package 'tsforecast' (Time Series Forecasting Functions) Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) . 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Includes a reinitialization feature to adapt to new waves, and a leading-indicator extension that uses a related series moving ahead of the variable of interest (e.g. cases ahead of hospitalisations) to improve short-horizon forecasts, with model and lag selection via rolling-origin cross-validation. Applicable to data at daily, monthly, quarterly, or annual frequency, and to non-epidemic trajectories with similar dynamics, such as innovation diffusion and product adoption. Includes functions for data preprocessing, model fitting, forecast visualization, and accuracy evaluation using standard error measures. Methods are described in Harvey and Kattuman (2020) , Harvey and Kattuman (2021) , and Ashby, Harvey, Kattuman, Tang, and Thamotheram (2024) . Package: r-cran-tsgs Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-bioc-edger, r-cran-fastmatch, r-cran-genalg, r-cran-kernlab, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-tsgs_1.0.1-1.ca2404.1_all.deb Size: 62306 MD5sum: df570c21ea9024c37bebd8902b2df229 SHA1: 4b97ae3423ff00f4c3fa9dc4a7577e2e3eab04cd SHA256: 7633a268a5602d0fc1bd9ada710b57321c8a0671a121869e5caf485f3ac20281 SHA512: b06cf86d9e85b5187567395658991d5dec0ff943d58013a5e02f20501a47d80d8780021657c9f40db6c849024178115558659974edaff70d30477b1ad58c5a48 Homepage: https://cran.r-project.org/package=TSGS Description: CRAN Package 'TSGS' (Trait Specific Gene Selection using SVM and GA) Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. 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A shiny module is provided to visually explore periodic/aperiodic temporal patterns. Package: r-cran-tsintermittent Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mapa Filename: pool/dists/noble/main/r-cran-tsintermittent_1.10-1.ca2404.1_all.deb Size: 111736 MD5sum: 90cdfb509885dea0b46ad8187c1f5143 SHA1: 08803c30144dc736b686efb662fd6df83c7b6ad8 SHA256: 98d2ebbba0bc0b1897c04621abd3ac46fe566c2cc9382d2fb6ada2738b383ed6 SHA512: 4edb3f449b63d119246df7c25d27a3f093c836565826f6acfe68807421f0474b61cf4e5d4b60eaf312674fefca6437d3d46f31b383abf6ebebf40a2ce6b48853 Homepage: https://cran.r-project.org/package=tsintermittent Description: CRAN Package 'tsintermittent' (Intermittent Time Series Forecasting) Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. 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The method implemented here is described by Finkenstadt and Grenfell (2000) . Package: r-cran-tslstm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-tsutils Filename: pool/dists/noble/main/r-cran-tslstm_0.1.0-1.ca2404.1_all.deb Size: 23986 MD5sum: 31228ec10de9381353aec2d63992e849 SHA1: b29f09b3208a9e9a78c996a99a726c2c479f7664 SHA256: e85bdc49c4625d5be92b6538c06d48c87ed3864e317cbc324838dec88af8ba84 SHA512: 5c99d2b14c7ed8872587792c87ceee3beb46ca6a2093bf03ebf31dd626ec60c6b060a86a627c22ccdd39cfd4f9847e1aa61a0d4fbf83556ace15d9fee1a7394d Homepage: https://cran.r-project.org/package=TSLSTM Description: CRAN Package 'TSLSTM' (Long Short Term Memory (LSTM) Model for Time Series Forecasting) The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. 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Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) . 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Package: r-cran-tsqca Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-qca Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsqca_1.3.3-1.ca2404.1_all.deb Size: 316178 MD5sum: 049b6e044c822f61af9c92f670d1a979 SHA1: 390254dc7a40af8a9c5ade060a7d580417df442f SHA256: 8f279ed8ae2e341a47b126d30bb53b50d02774fc40e482f0dbc84297e57f394f SHA512: 3de506921c74408cb160706cd24ed02121930856ddb5cad61bd1078a89285a82998867373c928cc361de442b6ebbddc491a75b3061ccd587eec94bb29d4b58ef Homepage: https://cran.r-project.org/package=TSQCA Description: CRAN Package 'TSQCA' (DEPRECATED: Threshold Sweep Extensions for QualitativeComparative Analysis) DEPRECATED: This package has been superseded by 'ThSQCA'. Please use install.packages('ThSQCA') instead. Provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Also provides Fiss (2011) core/peripheral condition classification via compute_fiss_core() and generate_fiss_chart(), enabling four-symbol configuration charts that distinguish core conditions (present in both parsimonious and intermediate solutions) from peripheral conditions (intermediate only). Built on top of the 'QCA' package by Dusa (2019) , with function arguments following 'QCA' conventions. Based on set-theoretic methods by Ragin (2008) and established robustness protocols by Rubinson et al. (2019) . Package: r-cran-tsqlem Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-matrix, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-psych Filename: pool/dists/noble/main/r-cran-tsqlem_0.1.0-1.ca2404.1_all.deb Size: 45294 MD5sum: 885c43af6ebcd067b133d913d0cfc78c SHA1: 5961c65ff0c50fb57a2f0ed3801e628e2333a2b0 SHA256: e35b42d76da50418cc53da3975e6ece1fac93e5b439992b99a00e7fa2cba0ab3 SHA512: bbb71b6233f8ee854221c7f39a802a12bceb7374128b91897c4a6119a0dfaba3205e11563e208d24d99386a1bd38b96fb9173ce9d65edcaaa763dc10c4fca27e Homepage: https://cran.r-project.org/package=TSQLEM Description: CRAN Package 'TSQLEM' (Two Stage Estimation for Generalized Structural Equation Models) Provides a framework to estimate high dimensional generalized structural equation models using two stage quasi-likelihood expectation-maximization. The structural model supports binomial (logit and probit), Poisson, negative binomial, and gamma distributions for the outcome variable. Hattab (2026) "A Two Stage Quasi-Likelihood Estimation Method for High Dimensional Generalized Structural Equation Models" . Package: r-cran-tsqn Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-fracdiff Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsqn_1.2.0-1.ca2404.1_all.deb Size: 159274 MD5sum: 30b22ea791253c1648b7b3f13992a094 SHA1: fced9c921db021798f1495684ab693104876bc99 SHA256: 53b38184827d68045c83368bbb3ddc657c9ce0ffdfef720a325c83e1869a3237 SHA512: 34b2639597aa1c0a15c1d9f0929020fd89784b91377d9dc1f4f10d508baa34469298b725e55374469a1f8332a8b66552307256b851fc91651be23b1733aec3c9 Homepage: https://cran.r-project.org/package=tsqn Description: CRAN Package 'tsqn' (Applications of the Qn Estimator to Time Series (Univariate andMultivariate)) Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) , and Cotta (2017) are provided. The robust pseudo-periodogram of Molinares et. al. (2009) is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) . Package: r-cran-tsqr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-plm, r-cran-spdep, r-cran-spatialreg, r-cran-quantreg, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsqr_0.2.0-1.ca2404.1_all.deb Size: 128310 MD5sum: fa1f33d8bcaf4e4e8e29e9c7409c0eec SHA1: 0d65bbceb96ec2775d442afe633fbcd8e422fdc4 SHA256: 7eefcc5b0b7fddcae4eba0796ccc24cffbec95af8b9819883af6188baa26c3c9 SHA512: 6c209fb3dbe650d769171405b3282b062b12b28a9e4a3a83bad30b3997ff89a58924cb02183f7a7f23528f1b57a0638c29a35f09432659357dae1b194a07c9a4 Homepage: https://cran.r-project.org/package=tsqr Description: CRAN Package 'tsqr' (Sequential Threshold-Spatial-Quantile Panel Estimation) Implements a sequential panel estimation protocol for regional economic panels that combines three estimation layers in a fixed order. 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Package: r-cran-tsriadditive Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-tsriadditive_1.0.0-1.ca2404.1_all.deb Size: 68714 MD5sum: 28b12c49dd0c7f213238906e882615f1 SHA1: c168fddfdc40b379b049da30c80f797a56b051e7 SHA256: f47558b9c1b5af016bd0b3e7817c71c5fa72f868ee2392d1cd4ce12933fd2c33 SHA512: 16e807849e96e87a10b759038489f6f9d2275273534b2ebbf4c879a9f5b7d8a649bca1ec366fb54f8cddb40175c450368b63cb0d44ff500194f1e99d74cf0124 Homepage: https://cran.r-project.org/package=tsriadditive Description: CRAN Package 'tsriadditive' (Two Stage Residual Inclusion Additive Hazards Estimator) Additive hazards models with two stage residual inclusion method are fitted under either survival data or competing risks data. 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A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017). 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Package: r-cran-ttscreening Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-bioc-sva, r-bioc-limma, r-cran-corpcor, r-cran-simsalapar, r-cran-mass, r-cran-brglm2 Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ttscreening_1.8-1.ca2404.1_all.deb Size: 69594 MD5sum: 5295868c34ad7e24d0e610d8654b71d3 SHA1: 48a14745243e890500100cde82e612df45119889 SHA256: 29d2e16da75271d513556498248c7c67f31f894ebb2935d3a48cd9bc196baeac SHA512: 5a5a4f48c8704217650558ee9c184b089d666d8aadc9af437784d1706e1e60842edeb338e8fc1123df17f8f96492bc6dee3c9cb23ac3239e6820b24cc944dfc8 Homepage: https://cran.r-project.org/package=ttScreening Description: CRAN Package 'ttScreening' (Genome-Wide DNA Methylation Sites Screening by Use of Trainingand Testing Samples) A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. 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The user can check properties of games, compute well-known games and calculate several set-valued and single-valued solutions such as the core, the Shapley value, the nucleolus or the core-center. The package also illustrates how the Shapley value flexibly adapts to various cooperative game settings, including weighted players and coalitions, a priori unions, and restricted communication structures. In keeping with the original philosophy of the first versions, special emphasis is placed on the graphical representation of the solution concepts for 3 and 4 players. 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Package: r-cran-tvbounds Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-withr Suggests: r-cran-juliacall, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvbounds_0.1.1-1.ca2404.1_all.deb Size: 412788 MD5sum: 052dc8e7d464388caf639b551afd5a34 SHA1: 20894dbb28900555c87b356d988cf14a21a7d4f8 SHA256: 2363aa239d6264071d137ada387dae7740c1ae0864e2a89c5e19968e32f7cd64 SHA512: 3b71e29f4582276b2ce397cc040f58657a0c433c1854d82b0d848b4a6bb1cdff1e7036a908a2e602145bace0132804be9c4cc706525db1bf7d85547447cb87dd Homepage: https://cran.r-project.org/package=tvbounds Description: CRAN Package 'tvbounds' (Sensitivity Analysis and Bounds under Total VariationNeighborhoods) Implements the sensitivity analysis framework of Palomba (2026) "Sensitivity Analysis in Population Shares" for randomized experiments with attrition, counterfactuals in structural models, and recentered instrumental variables. Computes and plots sensitivity bounds together with their confidence intervals and robustness summary measures. Package: r-cran-tvcure Architecture: all Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubicbsplines, r-cran-rfast, r-cran-mass, r-cran-matrix, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-tvcure_0.6.6-1.ca2404.1_all.deb Size: 532138 MD5sum: 83cae1b13fc3fa4fd9480a39879b4455 SHA1: 06f47cd1b383efb5ca49e2418d80bd80e1e28d9d SHA256: 1e3f4d54f4f452965a5f65074ac7cc551e75d6e3e7c6ed39c6da0bc073ad3e3f SHA512: d1619d7acc84c55587f145287b4e2f2a0beed28c7005f1aff395d4bb5a7824627baeeaeb17a3fe059c73c72b072af39182d40f70a9d4e2e323fe31b2353bd4e8 Homepage: https://cran.r-project.org/package=tvcure Description: CRAN Package 'tvcure' (Additive Cure Survival Model with Time-Varying Covariates) Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) . Package: r-cran-tvem Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvem_1.4.1-1.ca2404.1_all.deb Size: 123684 MD5sum: 71845f2a925594a47b6bea82c18d0c28 SHA1: cfb290c9d61a690f08dbed0cece90f7bbdaedbd2 SHA256: 8595805370524e7d91143731fcee528b183f593bb849a4917cc873b95b89a7d9 SHA512: b376707f942d6efad1864eaef85778b2a8ba454f45f1f113252b1f2155c2f886cbd06faf251862ef24cb1aeae932c9fa8d01d39022427fa01793e29b182d8445 Homepage: https://cran.r-project.org/package=tvem Description: CRAN Package 'tvem' (Time-Varying Effect Models) Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) . We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Package: r-cran-tvgarch Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-garchx, r-cran-zoo, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-tvgarch_2.4.3-1.ca2404.1_all.deb Size: 246898 MD5sum: 10e773000bc61041ee60db6f4eae3de7 SHA1: 5589b36c1fc8e6a605ae4efa1d1d3a84210ee662 SHA256: d25a555a7b8b4b88357aff66bd9108403309c7cbe29f39ad0a1d0f7f720fefcd SHA512: c5bedad2184d59da211111855eda57fec8559c0f812ba9a812f2b270d2c07611b933edd068d1668c7fce72619d31df12ef026d48d8f3c176f95a01a817d5542d Homepage: https://cran.r-project.org/package=tvgarch Description: CRAN Package 'tvgarch' (Time Varying GARCH Modelling) Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) . In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates. Package: r-cran-tvgeom Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggthemes, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-gridextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tvgeom_1.0.1-1.ca2404.1_all.deb Size: 85032 MD5sum: d4d4fb4609abbaab1db40f8c01788b6b SHA1: 9eaa8018a43e3322fa396c583c0ea8c2f9178e81 SHA256: 9869cf998272c265b6a080a254e3aadca835cba14abbe6825747e835f08d378c SHA512: b1844227e7520658437e619622e1078ae6a207a1df013872f299f4b22ba7a3e7c82d7d99d74aecf6fd09e827a9bc5ae0d7e5e3fa2f87ccdd6336da79c7ae623b Homepage: https://cran.r-project.org/package=tvgeom Description: CRAN Package 'tvgeom' (The Time-Varying (Right-Truncated) Geometric Distribution) Probability mass (d), distribution (p), quantile (q), and random number generating (r and rt) functions for the time-varying right-truncated geometric (tvgeom) distribution. Also provided are functions to calculate the first and second central moments of the distribution. The tvgeom distribution is similar to the geometric distribution, but the probability of success is allowed to vary at each time step, and there are a limited number of trials. This distribution is essentially a Markov chain, and it is useful for modeling Markov chain systems with a set number of time steps. Package: r-cran-tvm Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvm_0.5.2-1.ca2404.1_all.deb Size: 80668 MD5sum: c071faa443564b9ae02526eea1675ecf SHA1: d45f3b73004b764385a480415549c77046f16c93 SHA256: 572fb82201943d9b9c5a33a8b0257c97e05033e9b6aaf5070b6d04c44e3e52a3 SHA512: a6d92618710378259bdaca4080dfa1ab8638f472a8232b24c1c33b6c9463feb5693a4b193344706fb7f2f950c040f326187bdc51150b47b1134ff81dd687b37a Homepage: https://cran.r-project.org/package=tvm Description: CRAN Package 'tvm' (Time Value of Money Functions) Functions for managing cashflows and interest rate curves. Package: r-cran-tvmcomp Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-tvmcomp_1.0.2-1.ca2404.1_all.deb Size: 126022 MD5sum: d6157d8f03bf6eee0b1be92be716c217 SHA1: 2841a58a1f0437cfa0734fb19cedbdae4e57fc0d SHA256: bca11d5e217d6ef7aa3f5d67baa0ceb18bf56fdb964278d10a650bf6f672cc30 SHA512: 397a4477f8f6c7d466f06eeb583862c0947b7edf5485027c25bd0f4bf069ac01513975bec3aa4ae7a6779e1bd8a10fe233ac247c94b91f1a7b0922de7d6bee9b Homepage: https://cran.r-project.org/package=tvmComp Description: CRAN Package 'tvmComp' (Discounting and Compounding Calculations for Various Scenarios) Functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (“Financial Management,” 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (“Financial Management: Principles and Applications,” 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (“Fundamentals of Corporate Finance,” 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (“Contemporary Business Mathematics with Canadian Applications,” 2020, ISBN: 9780135285015). Package: r-cran-tvmediation Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-locpol Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvmediation_1.1.1-1.ca2404.1_all.deb Size: 673736 MD5sum: 9f89191fb5b2dc9ec8bf2eba8f7f2997 SHA1: 126a8dc2d1870d9fd7564fac49e8c16ac909caac SHA256: ee857a78b3a6963ec6b56854dde9cb608056a5e94826362ba937a8447e538c89 SHA512: 5844056042c827f55126027ac1f50c90afd4a25f624db6834331874e021ad886232462f57e79e6eaa1332a11537b3a1f154564e565ba47c9a11900bbc9a406f4 Homepage: https://cran.r-project.org/package=tvmediation Description: CRAN Package 'tvmediation' (Time Varying Mediation Analysis) Provides functions for estimating mediation effects that vary over time as described in Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12. Package: r-cran-tvmm Architecture: all Version: 3.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-robustbase, r-cran-tcltk2, r-cran-gridextra, r-cran-mass, r-cran-desctoolsaddins Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-tvmm_3.2.1-1.ca2404.1_all.deb Size: 990078 MD5sum: fd114931baa582c0353c192748005271 SHA1: 2dd7d3a842258509fa76633e4e128ee9ba1c9b53 SHA256: aa316824c14253258b1e70dc7309d96693b37db5fbd2a8e0c9842d5728b7c2da SHA512: b5093e3363ab040975c62f7c4ce9fc0cff3c6ab41c5342ae7f1a52d141b8ac712d09741bc84ebccd706e8003a18f6a5ecf3898cce82f73bf01ae9aab727df2e5 Homepage: https://cran.r-project.org/package=TVMM Description: CRAN Package 'TVMM' (Multivariate Tests for the Vector of Means) This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) ). Package: r-cran-tvmvp Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-prettyunits, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tvmvp_1.0.5-1.ca2404.1_all.deb Size: 728992 MD5sum: 1c07a7740c9f93cdc4558a82449c23a0 SHA1: 1d1995d52287989be0ec4c1af2023bd319cb879c SHA256: b766d6b4dc88a89623ad2166f30de5d7d15046bcb537cc53b8a14aca508dab19 SHA512: d8b0f3117a089af4a156e0dac23a9111ec2ec112a0983cfc8b0bc31b226fb3901e8c9ccdc2c1009a862ba75c10a77d5e179067ed3bb7b9d9aa275280c22f265b Homepage: https://cran.r-project.org/package=TVMVP Description: CRAN Package 'TVMVP' (Time-Varying Minimum Variance Portfolio) Provides the estimation of a time-dependent covariance matrix of returns with the intended use for portfolio optimization. The package offers methods for determining the optimal number of factors to be used in the covariance estimation, a hypothesis test of time-varying covariance, and user-friendly functions for portfolio optimization and rolling window evaluation. The local PCA method, method for determining the number of factors, and associated hypothesis test are based on Su and Wang (2017) . The approach to time-varying portfolio optimization follows Fan et al. (2024) . The regularisation applied to the residual covariance matrix adopts the technique introduced by Chen et al. (2019) . Package: r-cran-tvreg Architecture: all Version: 0.5.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-systemfit, r-cran-mass, r-cran-vars, r-cran-bvarsv, r-cran-plm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvreg_0.5.11-1.ca2404.1_all.deb Size: 1097518 MD5sum: 9b9efc8487ba23335aab45a6f3c90c01 SHA1: 2caea13d4f062de1148a85cf63790cf9a40e9e8d SHA256: dafbd284babb4e2551155dd167e72737d92d253e2b58286a4155c4ae75e08634 SHA512: 443d488098e8db4c06887e498567c783394075e5d5a41941565bec9a0d0cf92c9f6a30a23b8245e918042c9157cf7bc33a55e7569f62f2f5d95dec5c0d16981f Homepage: https://cran.r-project.org/package=tvReg Description: CRAN Package 'tvReg' (Time-Varying Coefficient for Single and Multi-EquationRegressions) Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques. Package: r-cran-tvrmst Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-survival, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-tvrmst_0.0.6-1.ca2404.1_all.deb Size: 59014 MD5sum: 3b68e4e5360cce4f3c41d341979bc759 SHA1: 4715348f989b87c2b8976d571a0cfa3b659306e1 SHA256: e9df2cbcaa048c8e3c60e22cad4ad85a2153a1890636075c6ed1c16c1f8e9620 SHA512: 67ace05c34790246ece9860c00b33743503545da543cc87432741476913ae968b8b4f7cfd4954e0a9672ba800bf85b7dbfeaae3cfa684999f163b7e72abc6f00 Homepage: https://cran.r-project.org/package=tvrmst Description: CRAN Package 'tvrmst' (Time-Varying Restricted Mean Survival Time from SurvivalMatrices) Utilities for restricted mean survival time (RMST) and time-varying restricted mean survival time quantities computed from survival curves provided on a time grid. The package is model-agnostic and accepts only a time vector and survival matrices, returning RMST-based quantities and bootstrap summaries. For restricted mean survival time methodology, see Royston and Parmar (2013) . 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Package: r-cran-tvtools Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dtwrappers Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-covr, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tvtools_0.0.3-1.ca2404.1_all.deb Size: 281816 MD5sum: 5015b71491c54f91a7c12a909a6ac479 SHA1: 04b1e109b3c3e63d7ea47c1b48a9e0e7d657208b SHA256: c5b62117cb871b7c266debbd78d3ec52f0af71e26687610a711c1ff3f37463fb SHA512: 731a95d952b1bb5255bde0926aa8d4f27f6c9be9d19c9109be0bbfd7d938f7980abb9115416071215b9d26ad11bb9642bdd56b05fa7f9f3f0a858ace56dd5e5c Homepage: https://cran.r-project.org/package=tvtools Description: CRAN Package 'tvtools' (Comprehensive Tools for Panel Data Analysis - 'tvtools') Longitudinal data offers insights into population changes over time but often requires a flexible structure, especially with varying follow-up intervals. 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Package: r-cran-twangcontinuous Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lattice, r-cran-gbm, r-cran-survey, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-twangcontinuous_1.0.0-1.ca2404.1_all.deb Size: 373092 MD5sum: 4eabe2b9b011fa4c29f417d1abb87bc2 SHA1: d0182da97de9379fb1555364b6a33001632d6bcf SHA256: 2a86cbfa8352f3ef87519751a64b163792ce5cbcb783f34a55b488a4f0da8432 SHA512: f8c36126999d10a977787a42acfb511e9d507f88a1736f98243d6d98ca93abcb3aafc1fc23b52ede5d42c6ebb4064b5f5d8e2fc60aa6e9173f128b56f7a39482 Homepage: https://cran.r-project.org/package=twangContinuous Description: CRAN Package 'twangContinuous' (Toolkit for Weighting and Analysis of Nonequivalent Groups -Continuous Exposures) Provides functions for propensity score estimation and weighting for continuous exposures as described in Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for estimating generalized propensity scores with continuous treatments. Journal of Causal Inference, 3(1), 25-40. . 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The functionalities carry out automated trading using sentiment indexes computed from 'twitter' and/or 'stockwits'. The sentiment indexes are based on the ph.d. dissertation "Essays on Economic Forecasting Models" (Godeiro,2018) The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage. Package: r-cran-twitterwidget Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-twitterwidget_0.1.1-1.ca2404.1_all.deb Size: 40082 MD5sum: 20f5e46228488a63bdf2408a9847a937 SHA1: 951cb3175fe9a936cc5fc2efd4d2990c0efa485e SHA256: c8e79eb55905a5c995e80a94e0140b70d67b91604d2c54c3e1929a25312ac2ee SHA512: 8a613c267b5658ca0281ea46fe7390d41524476731f0bbde66110cf14043561ac17b0c2c59a8d1ff359fffef54dd73668cad4705374dc47acfa10aee5a11b6b3 Homepage: https://cran.r-project.org/package=twitterwidget Description: CRAN Package 'twitterwidget' (Render a Twitter Status in R Markdown Pages) Include the Twitter status widgets in HTML pages created using R markdown. The package uses the Twitter javascript APIs to embed in your document Twitter cards associated to specific statuses. The main targets are regular HTML pages or dashboards. Package: r-cran-twl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mcmcpack, r-cran-corrplot, r-cran-rfast Filename: pool/dists/noble/main/r-cran-twl_1.0-1.ca2404.1_all.deb Size: 1975644 MD5sum: 41726bde3926a7578e0080363532525a SHA1: 12478c46706bf14d8c916086d6825d8ec42909b2 SHA256: b20dcfef085f8c94f45f3ca7b0573b8d3f3f0c70e84afd1deba1bdfbd54742e8 SHA512: 1594ff5dce8fe2efa41103626bc76e6be536c52ed3f462e7888778fd699f77dcdb94779deef7d926df2163ff3d93130d1580cef414be0a890eb824af608d5250 Homepage: https://cran.r-project.org/package=twl Description: CRAN Package 'twl' (Two-Way Latent Structure Clustering Model) Implementation of a Bayesian two-way latent structure model for integrative genomic clustering. The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation. A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler. The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density. Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources. Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU. See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, , Cold Spring Harbor Laboratory" at for model details. Package: r-cran-twn Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2756 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-crayon, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-readr Filename: pool/dists/noble/main/r-cran-twn_0.2.7-1.ca2404.1_all.deb Size: 2517576 MD5sum: fa6e63d0f8130c1fb1956cbc09310487 SHA1: b40ac8b7604f9d208b3fe991e64dffb54c3aa9fd SHA256: f35bb327ffca5b36c338d61acf81a0a7210667dac268317354e81c5cc53391bd SHA512: 09b741c7d4fa74bde310045dd3e5ac30f05ce1b43a8a33bbca2a34eefdd928b39330281c7d59fa5ce9ebf5be2fd79f05351eb63928c0e9fc90bc1d43d589fa28 Homepage: https://cran.r-project.org/package=twn Description: CRAN Package 'twn' (Taxa Waterbeheer Nederland voor R) The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming taxons in Dutch Watermanagement. This package makes it easier to use the TWN-list for ecological analyses. It consists of two parts. First it makes the TWN-list itself available in R. Second, it has a few functions that make it easy to perform some basic and often recurring tasks for checking and consulting taxonomic data from the TWN-list. Package: r-cran-twoarmsurvsim Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockrand, r-cran-dplyr, r-cran-survival, r-cran-simsurv Filename: pool/dists/noble/main/r-cran-twoarmsurvsim_0.2-1.ca2404.1_all.deb Size: 140852 MD5sum: 96546fb3be045b39450fc644bcc3582e SHA1: add811ef6a813b9e92957677e4b9c0f17c8b1edb SHA256: c4c1a1464692ba2ad84671147102e33ac73e38effffe602952bb28f5581fbc23 SHA512: 22c1b66156ba1722bd03754da63fe6ca47ba7fe613fae111e6b831e0806f5c4d647e81c92d881d83ca5c258fd4a43434b39a068f3e50a25f6b68fb0b1fd0f359 Homepage: https://cran.r-project.org/package=TwoArmSurvSim Description: CRAN Package 'TwoArmSurvSim' (Simulate Survival Data for Randomized Clinical Trials) A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data. Package: r-cran-twocoprimary Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-pbivnorm, r-cran-fpcompare Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-readr Filename: pool/dists/noble/main/r-cran-twocoprimary_1.0.0-1.ca2404.1_all.deb Size: 294846 MD5sum: 78625264085394058024b6d45135e1ca SHA1: 703f73fd10c58400550224121ddb953d2a619c90 SHA256: 5d6c46e02dd02d91a7da356ebd0e4212ffb7789123c33be039f06d005b6d666b SHA512: bc1181680e4b74a78ceebd5d817053b0a1fca77c5fed607c4272ead4ac05e98543f27c07201e48b33a9633ae4215939d17e58fb3d4fad9bbc9d469ec38d62361 Homepage: https://cran.r-project.org/package=twoCoprimary Description: CRAN Package 'twoCoprimary' (Sample Size and Power Calculation for Two Co-Primary Endpoints) Comprehensive functions to calculate sample size and power for clinical trials with two co-primary endpoints. The package supports five endpoint combinations: two continuous endpoints (Sozu et al. 2011 ), two binary endpoints using asymptotic methods (Sozu et al. 2010 ) and exact methods (Homma and Yoshida 2025 ), mixed continuous and binary endpoints (Sozu et al. 2012 ), and mixed count and continuous endpoints (Homma and Yoshida 2024 ). All methods appropriately account for correlation between endpoints and provide both sample size and power calculation capabilities. Package: r-cran-twocutoff Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2437 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-mixtools, r-cran-proc, r-cran-xgboost, r-cran-ggplot2, r-cran-caret, r-cran-patchwork, r-cran-gridextra Suggests: r-cran-rmarkdown, r-cran-diagrammer, r-cran-knitr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-twocutoff_0.1.0-1.ca2404.1_all.deb Size: 671094 MD5sum: 087361ad99ddf297c4602021c8db853e SHA1: d5de56d42ad120957ac0530c0eec64279289b1ee SHA256: 0e19ce2d8e6a8da51022c0a9353356f0152f33645aecb0b6fd612345d09df3f9 SHA512: 602cd1ddf70ead21316f22cf84cb88d6eb977629ccccc86858ad5eed5aea1ad3f443a65d1b02c5030f900f3594b6d7de83818de90db668ad2d513f071b55531d Homepage: https://cran.r-project.org/package=TwoCutoff Description: CRAN Package 'TwoCutoff' (Deriving Clinically Interpretable Cutoffs for Disease Biomarkers) Provides a reproducible pipeline for deriving two clinically meaningful cutoffs for disease biomarkers using a unified two-stage framework. The package integrates finite mixture modeling with risk prediction using biomarker plus clinical features, followed by decision curve analysis to evaluate clinical utility. Outputs include biomarker density plots, risk calibration curves, decision curves, and summary tables of diagnostic performance. Designed for researchers in bio-statistics, neurology, and data science, this package emphasizes reproducibility, transparency, and clear clinical relevance. Package: r-cran-twodcdapsa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-twodcdapsa_0.1.1-1.ca2404.1_all.deb Size: 45356 MD5sum: 74eaa11a68726bf1142c7c0a1815121c SHA1: 2076029752755fae7b03d1691fdeaae7a1a9ad4d SHA256: cec63e3a72e545e0d57202ed2501fc2b1e853bd2148703833c019faa093a260b SHA512: 43871a618067e99509fda08eb4fb459d9f6c0dc9d936700d715f731e563f4883e637aed482fe602f1438e95aac0e74e50350b1dd6c56e8247e46102a28fc603a Homepage: https://cran.r-project.org/package=TwoDcDAPSA Description: CRAN Package 'TwoDcDAPSA' (Calculate TwoDcDAPSA: PROs-Joint Contrast (PJC) andSwollen–Tender Joints Contrast (STC) Scores and Quartiles) Provides a calculator for the two-dimensional clinical Disease Activity index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure that complements the conventional clinical DAPSA score. TwoDcDAPSA captures residual variation in patient-reported outcomes (pain and patient global assessment) and joint counts (swollen and tender) after adjusting for standardized cDAPSA using natural spline coefficients derived from published models. Residuals are standardized and combined with fixed principal component loadings to yield two continuous component scores: the PROs-Joint Contrast (PJC) and the Swollen–Tender joints Contrast (STC), along with quartile-based groupings (including optional combined quartile groupings). The package applies pre-specified coefficients, residual standardization, and loadings to new datasets but does not estimate spline models or principal components itself. Package: r-cran-twodiref Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgam, r-cran-mgcv, r-cran-sp, r-cran-ggplot2, r-cran-scales, r-cran-shiny Suggests: r-cran-refreg Filename: pool/dists/noble/main/r-cran-twodiref_0.1.0-1.ca2404.1_all.deb Size: 79972 MD5sum: 996d02d5201a41f5ae702ec8cbe8d1cc SHA1: 0c1acd65acd7f76119623d8df9e03ad97dd29a02 SHA256: 2d1c29bf47b03e33220bdaf48e7b85924d52679a6cab775c56360c6fb9970692 SHA512: 853b8d28b7b9cda01f717d11d7636c3be220c3e3af5b31c5c0258ee92c064c2caac5ea3c765314632aad6e7c1c89f8ad8756bf860964b8ca924993621074322e Homepage: https://cran.r-project.org/package=TwoDiRef Description: CRAN Package 'TwoDiRef' (Robust Estimation of Conditional 2D Reference Regions) Provides tools for constructing conditional two-dimensional reference regions in continuous data, particularly suited for clinical, biological, or epidemiological studies requiring robust multivariate assessment. The implemented methodology combines directional quantiles with median‑based partial correlation models to produce reliable and interpretable reference regions even in the presence of outliers. Key features include robust conditional modeling for two responses conditioned on covariates, directional quantile regions, cross‑validation of coverage, visualization tools, and flexible formula‑based inputs. Package: r-cran-twomodeclusteringga Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-twomodeclusteringga_1.0.0-1.ca2404.1_all.deb Size: 87244 MD5sum: 8dacb72719043abb861cdf48b3fc633e SHA1: 6fcaed39dc9be7df05ff046ee0aa213d74eecd4e SHA256: 80bfb8d997946566d46c73713aefd33c48f08e0d292723f529dde8dc165aaed7 SHA512: c0aaecc8d97ddbf7f78bbab7a7fcca72892148afc4b775a7513eb7d3aff99cfc701256a70819e107c198caf131ebb8368d2a78e06e71fad65fcd1473e4d56f24 Homepage: https://cran.r-project.org/package=twomodeclusteringGA Description: CRAN Package 'twomodeclusteringGA' (Genetic Algorithm Based Two-Mode Clustering) Implements two-mode clustering (biclustering) using genetic algorithms. The method was first introduced in Hageman et al. (2008) . The package provides tools for fitting, visualization, and validation of two-mode cluster structures in data matrices. Package: r-cran-twopartm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 903 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mass Filename: pool/dists/noble/main/r-cran-twopartm_0.1.0-1.ca2404.1_all.deb Size: 814928 MD5sum: e7248821dd2a48a4412752169b0c2ef3 SHA1: de303c5c88296148976e2750877c920ab75aa860 SHA256: ff418486f1572abe56468865440ce0713275792f740fd3890bb01e150745bf4f SHA512: 74ab0bdd50d04e5c204f2e3e2b8c4a6711ead937515df6e847e3f77cc7a05efe157960353aef7ced84462158de7f55c5416d33a0c6115eb818385b3458d56455 Homepage: https://cran.r-project.org/package=twopartm Description: CRAN Package 'twopartm' (Two-Part Model with Marginal Effects) Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) . Package: r-cran-twopexp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-twopexp_1.0.0-1.ca2404.1_all.deb Size: 49092 MD5sum: 2920d0cea137ae0af47636ef3bbee83b SHA1: e9ff51d2ac9d129d4459c9da01b63e9ee42536c6 SHA256: 66bc7daa870c9c276accf1b7a7731aa19e7a86e5b9fb006ac00c452878ae35b0 SHA512: eace2dc35fb6bd88f7afe4cdef222a26d26a2fa956c9905388610bac900addc50df3403bc66b2af8dc8c0576ad4fbb3129b5c1a983f70d149af7b3ee3424a84c Homepage: https://cran.r-project.org/package=twopexp Description: CRAN Package 'twopexp' (The Two Parameter Exponential Distribution) Density, distribution function, quantile function, and random generation function, maximum likelihood estimation (MLE), penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM), and median rank estimation (MEDRANK) for the two-parameter exponential distribution. MLE and PMLE are based on Mengjie Zheng (2013). QM is based on Entisar Elgmati and Nadia Gregni (2016). MEDRANK is based on Matthew Reid (2022). In addition, the functions for computing the Kolmogorov-Smirnov (D) statistic, the Cramér-von Mises statistic, and the Anderson–Darling statistic are provided. Package: r-cran-twophasecorr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-twophasecorr_1.1.1-1.ca2404.1_all.deb Size: 28988 MD5sum: f2e6923bba94bebacaac8edb8302d4c5 SHA1: e56152a2d293476bd9e23e0820056ea2ef41ee5c SHA256: b11a6692d1f05e5ba6af8d2697dbf9a9253492664807b62d74bc11d42850d04e SHA512: 102103b442f59f7895fc876687c4ac7bc00a5cfaad9e54f79ff778dc561ec02a56854b97edf7239c531cea845432b608ba8ed66d4acf5dcb8e2a16ed92477058 Homepage: https://cran.r-project.org/package=TwoPhaseCorR Description: CRAN Package 'TwoPhaseCorR' (Construct Two-Phase Experimental Designs with Correlated Errors) Tools for constructing and analyzing two-phase experimental designs under correlated error structures. Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) ). Package: r-cran-twophasegas Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dfoptim, r-cran-enrichwith, r-cran-kofnga, r-cran-mass, r-cran-matrix, r-cran-nloptr Suggests: r-cran-gplots, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-twophasegas_1.2.5-1.ca2404.1_all.deb Size: 213298 MD5sum: 3041df65d0f9cb454cabcac04067d44e SHA1: 8a1e820572983a123600ffb4531bda0c35dbab10 SHA256: eb581ef787f6cc23c3d94bdea17220704231473aec854edfda56d76c4a351fcd SHA512: d0c74dab7709592d3337585aa8f9c3ca1249f72112550ca48c417ecf1d73854c6eba43927005b68e03878695effd635027ad2d0c0998dc585fb0885c4c72c6f7 Homepage: https://cran.r-project.org/package=twoPhaseGAS Description: CRAN Package 'twoPhaseGAS' (Two-Phase Genetic Association Study Design and Analysis withMissing Covariates by Design) Provides functionality for designing and analysing two-phase genetic association studies. Phase 1 data usually come from genome-wide association study (GWAS) results and we assume phase 2 data will be part of a targeted genome sequencing or fine-mapping study. At design stage, the package assists in selecting a subset of individuals that will be sequenced for phase 2 via alternative approaches, including a flexible genetic algorithm (GA) for near-optimal designs. Once phase 2 data have been collected, the package implements methods to analyse phase 1 and phase 2 data together using semi-parametric regression models via the expectation-maximization (EM) algorithm. For more details see Espin-Garcia, Craiu and Bull (2018) and Espin-Garcia, Craiu and Bull (2021) . Package: r-cran-tworegression Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lubridate, r-cran-magrittr, r-cran-pautilities, r-cran-proc, r-cran-rcpproll, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tworegression_1.1.1-1.ca2404.1_all.deb Size: 873654 MD5sum: ee7115abdd971143d99b9c70024c8539 SHA1: bcd83278e96f55aa04ffed31690b8ea949ba9757 SHA256: 6fe51260cb8e34bdb5019ede6519f5ab97f780ce368e164748ba163d630cd394 SHA512: e258e571cb09be3bc698822d12abe75c7caec96cb406d10ef68f13ad6009ad965ffbd8d5e443cccea7639307f93cd148bfae48064a36762cbe48caae46711312 Homepage: https://cran.r-project.org/package=TwoRegression Description: CRAN Package 'TwoRegression' (Develop and Apply Two-Regression Algorithms) Facilitates development and application of two-regression algorithms for research-grade wearable devices. It provides an easy way for users to access previously-developed algorithms, and also to develop their own. Initial motivation came from Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2018) . However, other algorithms are now supported. Please see the associated references in the package documentation for full details of the algorithms that are supported. Package: r-cran-twosampletest.hd Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-twosampletest.hd_1.2-1.ca2404.1_all.deb Size: 42120 MD5sum: a7894c0c8628f3181a00d9c3434ac4a1 SHA1: 5bcadd3376c8956618afbf15e841f5d259280002 SHA256: 73276ff7b63da933dda2b901dc1f66f3eb879d3f2244463e14b9cb23e9a5473e SHA512: 8dad5b9e9f2d10a748dfde21600be35876e541ba7093800cb3656918e03b117b4f806756f9e018fc6134adfb3d1dd60eabd130228d560addda8dff484c8ece44 Homepage: https://cran.r-project.org/package=TwoSampleTest.HD Description: CRAN Package 'TwoSampleTest.HD' (A Two-Sample Test for the Equality of Distributions forHigh-Dimensional Data) For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups. Package: r-cran-twosigma Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp, r-cran-glmmtmb, r-cran-pscl, r-cran-pbapply, r-cran-doparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-twosigma_1.0.2-1.ca2404.1_all.deb Size: 591524 MD5sum: 43cfd5851081d7d6aff75b223977b24c SHA1: ab37282ee8bc19eab773720e8b2e911760da7bf3 SHA256: 8201bb8846dade5ab59204b7da58766bd847190a2a8563de42db3a7f4dfb4db6 SHA512: 2d631f18006772576a0e2895d72203f5110a104c639f1874b3d0d331e5ace4e8204cd8a9432fa192135932d5de6335e84a61d9ae7df184d717613227370512ac Homepage: https://cran.r-project.org/package=twosigma Description: CRAN Package 'twosigma' (DE Analysis for Single-Cell RNA-Sequencing Data) Implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) and Van Buren et al. (2021) . Package: r-cran-twostagedesigntmle Architecture: all Version: 1.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmle Suggests: r-cran-dbarts, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-twostagedesigntmle_1.0.1.2-1.ca2404.1_all.deb Size: 55924 MD5sum: c6e191e9767fc9a446738a6dbeea168d SHA1: d0fd005270d0d6761eacc9e5b6ec87cf62c5f73a SHA256: 185ae88dd3d921e74c6ad5d790c4b614240140eabfba234fd424cc6e79714467 SHA512: e9b5a2c8795a78487fb092b5cb2054d881d5c6cf0c416dbc0b7a79f98e970491fd6e0f02a68eeb940a2d38d838c798b5fc5e07fd2377a86e7d092658d2fe53ef Homepage: https://cran.r-project.org/package=twoStageDesignTMLE Description: CRAN Package 'twoStageDesignTMLE' (Targeted Maximum Likelihood Estimation for Two-Stage StudyDesign) An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). 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The semiparametric results were developed in Yang et al. (2022 ), and the nonparametric results were developed in Yang (2025 ). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 ), Pocock et al. 2012 , and Bebu and Lachin 2016 ) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. 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Package: r-cran-uci Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cpprouting, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-pbapply, r-cran-fields, r-cran-sf, r-cran-spdep Suggests: r-cran-covr, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-uci_0.3.1-1.ca2404.1_all.deb Size: 653044 MD5sum: db953ba9beb019af6845ac9fb28a2a79 SHA1: 39317ea0b40308da54b824ba9a577f148a12e76a SHA256: 848f1513f8887adad25e88e25b5410cd63f243cf0c33bf86e0fcd6ad50b2bb80 SHA512: ed1be2795a1c0fa8f9eb93eecd29ad099d88997a9ec9d8b8d0391b83b04aa1abb5270dfc593de238e81a626a65ca3176c5960e9cd8f0a7dceec242730dbfb03c Homepage: https://cran.r-project.org/package=uci Description: CRAN Package 'uci' (Urban Centrality Index) Calculates the Urban Centrality Index (UCI) as in Pereira et al., (2013) . The UCI measures the extent to which the spatial organization of a city or region varies from extreme polycentric to extreme monocentric in a continuous scale from 0 to 1. Values closer to 0 indicate more polycentric patterns and values closer to 1 indicate a more monocentric urban form. Package: r-cran-ucie Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-geometry, r-cran-pracma, r-cran-ptinpoly, r-cran-rgl, r-cran-remotes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ucie_1.0.2-1.ca2404.1_all.deb Size: 33878 MD5sum: 25db65a53cbc9019a7fcc91fba3fff0b SHA1: 624f7ab235037e6a869789484835f05ac943723e SHA256: be8abe1649bc24b8d7fdcf55c0fe0fec19df94aab079b2b847c747cad44d085a SHA512: 5a7ac2ad6d4d4ec85688820511358f3dfe88bceea2fa1ca896f2ec5b1d2fcac35ddb8ef8561cb7eba5b606c2645c608ff35d8abe41fcab01bb5f563a7719c64f Homepage: https://cran.r-project.org/package=ucie Description: CRAN Package 'ucie' (Mapping 3D Data into CIELab Color Space) Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data. Package: r-cran-ucimlrepo Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ucimlrepo_0.0.2-1.ca2404.1_all.deb Size: 660318 MD5sum: 382bd52a349b615d6d16c847525031c0 SHA1: 33013df7c97bf677fa44653414be8d2a4ff305bf SHA256: 518bb5f1f0002d8f3433aeffb6465c7ca1a86c1ea4434ccf6cea590851029001 SHA512: 76b4e673b05b9d520caa70d9de0663dd1e0f6db940fcf69788a6ab1c81d7f06fcd049469070c53baa9edb3dd98231be5fcae1c10e10f1be4d72e60dfc1058cea Homepage: https://cran.r-project.org/package=ucimlrepo Description: CRAN Package 'ucimlrepo' (Explore UCI ML Repository Datasets) Find and import datasets from the University of California Irvine Machine Learning (UCI ML) Repository into R. Supports working with data from UCI ML repository inside of R scripts, notebooks, and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at . Package: r-cran-uclust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dendextend, r-cran-robcor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-uclust_1.0.0-1.ca2404.1_all.deb Size: 160842 MD5sum: 4996de6e7e29e7d4d09364e876265915 SHA1: 2924b7e611e108b36932c1cb08ebca8b53592093 SHA256: 28d51d9e187e247dc469c8e1982ff711630b8d75926aa08a0d7c2fe802b9a36c SHA512: 945c12f06ff36647ff89f2f6ede80c73f2a50348bc7a230fcf2754cd8ad2fbdac3243ed1b5d3f9721699569a1a27efbd3c17526b94766be6f002bf9822067acf Homepage: https://cran.r-project.org/package=uclust Description: CRAN Package 'uclust' (Clustering and Classification Inference with U-Statistics) Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and Sílvia RC Lopes (2018) , Marcio Valk and Gabriela B. Cybis (2020) , Debora Z. Bello, Marcio Valk and Gabriela B. Cybis (2021) . Package: r-cran-ucr.columnnames Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ucr.columnnames_0.1.0-1.ca2404.1_all.deb Size: 19398 MD5sum: c92ea6c11349f5971ff9f59a0d9d8c01 SHA1: fcfea7d7ce7532431ce38e413f625ecd2ce2f0ce SHA256: f316703bce125694f786587ec6a2184aba1f250a2203dc0a7fba961be626d0d8 SHA512: 2eca0e1375172ac55cc86ffb41461d7d507ab9e3144e21369a88a4279c5857655e0a223229dd5776885158bee3c46029edbad9b0ca52505f5d1a9cfc2540891d Homepage: https://cran.r-project.org/package=UCR.ColumnNames Description: CRAN Package 'UCR.ColumnNames' (Fixes Column Names for Uniform Crime Report "Offenses Known andClearance by Arrest" Datasets) Changes the column names of the inputted dataset to the correct names from the Uniform Crime Report codebook for the "Offenses Known and Clearance by Arrest" datasets from 1998-2014. Package: r-cran-ucscxenashiny Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-ezcox, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-httr, r-cran-magrittr, r-cran-ppcor, r-cran-psych, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-ucscxenatools Suggests: r-cran-covr, r-cran-cowplot, r-cran-dt, r-cran-funkyheatmap, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggstatsplot, r-cran-glue, r-cran-knitr, r-cran-pacman, r-cran-plotly, r-cran-plyr, r-cran-rcolorbrewer, r-cran-readr, r-cran-rmarkdown, r-cran-rtsne, r-cran-scales, r-cran-survival, r-cran-survminer, r-cran-testthat, r-cran-umap Filename: pool/dists/noble/main/r-cran-ucscxenashiny_2.2.1-1.ca2404.1_all.deb Size: 3812978 MD5sum: 16509432854de486be264943dc5a7d6a SHA1: 7ae9e6b5344eb4a83114b32f4ea6919d0165e29b SHA256: 2227f8ac689ab16b57c434b4f8749da0137d863c91f70ea25e7be75903fd70de SHA512: 5b41b93ec4c07becd82ca1f79597a8ddfcebfbb50da1ae3ea178721f50de013fb621ce08b9096dfc1aa365fdb21bbc0379ac8a261feab804aa81a868cad932f5 Homepage: https://cran.r-project.org/package=UCSCXenaShiny Description: CRAN Package 'UCSCXenaShiny' (Interactive Analysis of UCSC Xena Data) Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Package: r-cran-ucscxenatools Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ucscxenatools_1.7.0-1.ca2404.1_all.deb Size: 700650 MD5sum: 8d5af6e0b682964dace8c0d345fb44f1 SHA1: 4efda4e4b042016f580cac040deccb0cf6406533 SHA256: 928dc87ecb3d332a18ed6ffd09e90e139929c3ace96b83bdfc67070c963f6e38 SHA512: 1d8afc2a302d597bdea5410e1accda14eadc465727f63c3640db1108786c6ff7aeb1d17ee5496551a64cc3cf5246251a9577d3568c0f0f88e13f3c6a4d774cee Homepage: https://cran.r-project.org/package=UCSCXenaTools Description: CRAN Package 'UCSCXenaTools' (Download and Explore Datasets from UCSC Xena Data Hubs) Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded. Package: r-cran-ucsfindustrydocs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-r6, r-cran-stringr Suggests: r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ucsfindustrydocs_0.1.0-1.ca2404.1_all.deb Size: 65494 MD5sum: 9edc9fc00976a748505373baab1c2e6b SHA1: 5ec65accd8b9a166dd8121aa4c813bf1949cfa47 SHA256: de1532ee09181d7023ffc0b06007dace8f783090a0e27425367506f88c87a05e SHA512: 35b2cdb27ac4a11c311182f5ae5aab0b3c32376a97c3591043596bfcf6f35193f5ca6c9e3140f1f323cf676d884e237ccbe79865acd68c6e65b49b6c1a48714c Homepage: https://cran.r-project.org/package=ucsfindustrydocs Description: CRAN Package 'ucsfindustrydocs' (UCSF Industry Documents Library API Wrapper) Serves as a R wrapper for the University of California San Francisco's [Industry Documents Digital Library] API. The API, and this wrapper, serve to pull metadata about of items within the digital library. For more information the API, see the [API's documentation]. Package: r-cran-uddbart Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bart Filename: pool/dists/noble/main/r-cran-uddbart_0.2.0-1.ca2404.1_all.deb Size: 146588 MD5sum: 4a2bc6e67d07da0cf8bcaff7c86c2994 SHA1: a36813c6850972d10776e9a3b8eb5a55d927d89b SHA256: bd34287dc9dc76a3842db604e1889aa05e887f501db9e880e92442ab2d593c16 SHA512: 03502bdcb044839f176997e3e8b898454213fc6e0ea1436aeb41ae198a343cb00d538bc3ccdde0f55189ead4bc1e8958cae5074da12d49ca3ef0670d4aafc1a4 Homepage: https://cran.r-project.org/package=uddbart Description: CRAN Package 'uddbart' (Unified Dynamic Deep 'BART' for Interval-Censored Survival) Implements U-DDBART-IC, a unified Bayesian workflow for dynamic risk prediction from irregular longitudinal biomarkers when event times are interval-censored between clinical visits. The package turns long-format biomarker histories and patient-level interval endpoints L, R, C and delta into a discrete-time follow-up grid, summarises each landmark history with nine interpretable trajectory features (current, baseline and previous biomarker values, last visit gap, local slope, cumulative decline, best value, elapsed time and visit count), fits discrete-time interval hazards using optional logit-link Bayesian additive regression trees, a generalized linear model fallback, or a lightweight variational approximation, accumulates survival from the discrete-time product, and evaluates the interval-censored likelihood. Fitted models return landmark risk predictions over user-specified horizons with posterior or bootstrap uncertainty by evaluating survival ratios across fitted hazard draws. Utilities are provided for simulation, staged model fitting, plotting and summarising dynamic risk curves, IPCW Brier scores, cumulative/dynamic time-dependent area under the curve, calibration tables, and an anonymised chronic myeloid leukaemia molecular-monitoring example data set. Package: r-cran-udderquarterinfectiondata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-udderquarterinfectiondata_1.0.0-1.ca2404.1_all.deb Size: 39538 MD5sum: 02ad4ed9e9628b9d61996566a361b36b SHA1: 2306e829bc4ba9ffaf40129fe9048696e25f328b SHA256: 61fdf24d270642d622148f5c0d9060ec34ff17fdf8aad407fce803a5bd8597b7 SHA512: ca071adc7b56358cf0909c68b5915a25f39fb767516ac0cf03525474067bab6a040bb8b0a70c36a5a221372a870f13f587353b3b95998d94e75b005a59922afe Homepage: https://cran.r-project.org/package=UdderQuarterInfectionData Description: CRAN Package 'UdderQuarterInfectionData' (Udder Quarter Infection Data) The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 ). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list. Package: r-cran-uei Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factominer, r-cran-factoextra, r-cran-metrics Filename: pool/dists/noble/main/r-cran-uei_0.1.0-1.ca2404.1_all.deb Size: 14714 MD5sum: 9a608c708b0919440428cbe52f209f70 SHA1: 8980cc26a6861edfd86e4036edd9dba819efee38 SHA256: 0b4b152d919b9a1c58aadbdfc1bb0fc62d83e06d834f7ab127f9af8784e0ee44 SHA512: 6ed0da89c79835a108a9c2d39f437c554f15ad5ce8d27e8295740497509836046fd3497e8639b0d0fac44640268ca6bc11998e1c80e1b5c34d7f8fd4de841572 Homepage: https://cran.r-project.org/package=UEI Description: CRAN Package 'UEI' (Compute Uniform Error Index) Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) . Package: r-cran-ufrisk Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-esemifar, r-cran-fracdiff, r-cran-rugarch, r-cran-smoots Filename: pool/dists/noble/main/r-cran-ufrisk_1.0.7-1.ca2404.1_all.deb Size: 427904 MD5sum: 22e5755e09768fa7172ac16190ca8c34 SHA1: 6e5e86e3d2f71dfa41f5922c3a87d634fa452d53 SHA256: af3a8dae89aac751352efacd4d415e984dd6803a661f30991bf51a06c2586630 SHA512: 0e49e7db895bbec6bcb28bad3a5b368ea0f5727e65e407377248dcd9dae5c773805b285bf4474500614d8a03892515960bd6edd5d64455a3ebb75069a83bc83a Homepage: https://cran.r-project.org/package=ufRisk Description: CRAN Package 'ufRisk' (Risk Measure Calculation in Financial TS) Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various parametric and semiparametric GARCH-type models. For the latter the estimation of the nonparametric scale function is carried out by means of a data-driven smoothing approach. Model quality, in terms of forecasting VaR and ES, can be assessed by means of various backtesting methods such as the traffic light test for VaR and a newly developed traffic light test for ES. The approaches implemented in this package are described in e.g. Feng Y., Beran J., Letmathe S. and Ghosh S. (2020) as well as Letmathe S., Feng Y. and Uhde A. (2021) . Package: r-cran-ufs Architecture: all Version: 25.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-diptest, r-cran-dplyr, r-cran-gparotation, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-gridextra, r-cran-gtable, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-pander, r-cran-plyr, r-cran-pwr, r-cran-rmdpartials, r-cran-scales, r-cran-suppdists Suggests: r-cran-bootes, r-cran-car, r-cran-careless, r-cran-ggally, r-cran-jmvcore, r-cran-lavaan, r-cran-mass, r-cran-mbess, r-cran-psych, r-cran-rio, r-cran-remotes, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-viridis Filename: pool/dists/noble/main/r-cran-ufs_25.7.1-1.ca2404.1_all.deb Size: 955128 MD5sum: 57fe47c0da1096c307f67cf4f88a95e6 SHA1: 84d966f5e8d20815538636e5b9ab11a1d9e98c1d SHA256: 20cd2de5d204b3b840bbe94ddffa5d0de3b81e0add6411344768005ffe36226c SHA512: a106f12dfbcbf298cfae9f65ed052da00b025ea214ed8674e97afd9bb1d283bdb4fe08abe8013cd70740688fec6cb98eade9122c99b126249645467be5847d14 Homepage: https://cran.r-project.org/package=ufs Description: CRAN Package 'ufs' (A Collection of Utilities) This is a new version of the 'userfriendlyscience' package, which has grown a bit unwieldy. Therefore, distinct functionalities are being 'consciously uncoupled' into different packages. This package contains the general-purpose tools and utilities (see the 'behaviorchange' package, the 'rosetta' package, and the soon-to-be-released 'scd' package for other functionality), and is the most direct 'successor' of the original 'userfriendlyscience' package. For example, this package contains a number of basic functions to create higher level plots, such as diamond plots, to easily plot sampling distributions, to generate confidence intervals, to plan study sample sizes for confidence intervals, and to do some basic operations such as (dis)attenuate effect size estimates. Package: r-cran-ugarima Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lamw Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ugarima_0.1.0-1.ca2404.1_all.deb Size: 20456 MD5sum: 68a1e4e6d873bd00981080f64c00bba5 SHA1: 9aae4dd30841de2634eec011f98eb63fd93c517f SHA256: 753602adbf1a61af61c08ccbd0efe538a57ae6ce74ae3f14b00036121d567bf3 SHA512: 2d8792482efb3cb735aae28fa0026276e42678d7186c1c4d7a08cdfd2a173c2787d1bd8b65f6ca9da4549449ec043f7ffefc66889c681109525bce306d9029fe Homepage: https://cran.r-project.org/package=UGarima Description: CRAN Package 'UGarima' (The Unit-Garima Distribution) Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution based on Ayuyuen, S., & Bodhisuwan, W. (2024). Package: r-cran-ugatsdb Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-data.table, r-cran-collapse, r-cran-writexl Suggests: r-cran-magrittr, r-cran-xts, r-cran-dygraphs Filename: pool/dists/noble/main/r-cran-ugatsdb_0.2.3-1.ca2404.1_all.deb Size: 71754 MD5sum: 4534a9b94846106288d50c0117450060 SHA1: a8a84ccb6fa09b19930d40aaae5681e379dc2258 SHA256: ec3009406ae67e7760d4f15dc61729d148761f663ab4a37a03151bd60e53ef6e SHA512: 6cdc254ea32b031351ee581af914d658c8854ffe88f5f8fd64ef14bf6eaa31a325a75758f28b1bb7dfa8f622e426547bf14839ee81c4cfc200d024a4c0098d08 Homepage: https://cran.r-project.org/package=ugatsdb Description: CRAN Package 'ugatsdb' (Uganda Time Series Database API) An R API providing easy access to a relational database with macroeconomic, financial and development related time series data for Uganda. Overall more than 5000 series at varying frequency (daily, monthly, quarterly, annual in fiscal or calendar years) can be accessed through the API. The data is provided by the Bank of Uganda, the Ugandan Ministry of Finance, Planning and Economic Development, the IMF and the World Bank. The database is being updated once a month. 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Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) , Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) , Savi Virolainen (2022) . 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Package: r-cran-ukbanalytica Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-ggplot2, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-proc, r-cran-tableone, r-cran-mice, r-cran-mitools, r-cran-sandwich, r-cran-lmtest, r-cran-mass, r-cran-mgcv, r-cran-xml2, r-cran-igraph Suggests: r-cran-testthat, r-cran-rms, r-cran-gbm, r-cran-cobalt, r-cran-survminer, r-cran-matchit, r-cran-regmedint, r-cran-ranger, r-cran-xgboost, r-cran-glmnet, r-cran-e1071, r-cran-nnet, r-cran-rpart, r-cran-boruta, r-cran-rbayesianoptimization, r-cran-randomforestsrc, r-bioc-clusterprofiler, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-cran-qs2 Filename: pool/dists/noble/main/r-cran-ukbanalytica_1.0.0-1.ca2404.1_all.deb Size: 2534882 MD5sum: 67f0ed383ec6783eb3abbb2a84253623 SHA1: f146fd00eb73cbe4250841b7b29f075bb6849234 SHA256: 7628e3e3883e9dd85c800dfc71dfdce74df996afe9d091c53d7673856090c6ae SHA512: 13c645db7f1a94609c4b2c4ad1a325d8766cd7d1c7da268f3c97470b484c666e3b9f68e6063ab4413976cacf199e870e4365c17d3adc4b078c2551d7c92d4f95 Homepage: https://cran.r-project.org/package=UKBAnalytica Description: CRAN Package 'UKBAnalytica' (UK Biobank Data Processing and Survival Analysis Toolkit) Provides an integrated workflow for UK Biobank Research Analysis Platform (RAP) hosted and RAP-generated analysis tables. The package supports RAP phenotype extraction planning, predefined variable sets and disease definitions, standardized baseline preprocessing, multi-source endpoint ascertainment, prevalent and incident case classification, survival-ready cohort construction, regression, multiple imputation, propensity score analysis, mediation analysis, subgroup and sensitivity analyses, machine learning, proteomics enrichment and protein-protein interaction analysis, and publication-oriented visualization. The package workflow is described in He et al. (2026) . 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Package: r-cran-ukc19 Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ukc19_0.0.3-1.ca2404.1_all.deb Size: 3350452 MD5sum: 7a04d5acf809b60573bd7178d372d5a6 SHA1: af6bf808a80e144d251bc52adf0f54da5a63d8ca SHA256: 9e521dd0e552ec00f1dc6ad03ce3277108fba53933266631d987eb3df22951f2 SHA512: 553161d4f9ca61a7f212c5f040266dfe2ad76c7d4aace0ff20c4335f451f625153c6c9399a6d66156266800377641548454d1a09c692459873f54141f0c4b0d1 Homepage: https://cran.r-project.org/package=ukc19 Description: CRAN Package 'ukc19' (Datasets from the UK COVID-19 Outbreak) Provides easy access to a curated selection of pre-processed data sets relevant to the COVID-19 outbreak in the UK for teaching and demonstration purposes. Package: r-cran-ukfe Architecture: all Version: 2.15.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1647 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml2, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ukfe_2.15.1-1.ca2404.1_all.deb Size: 1189560 MD5sum: 0ea6bed231f3fb7bc50b0c2e19150ecb SHA1: 8d874b3172aa808b8fd8c1621e3124659ddcb5d0 SHA256: 5da39ea271e6dee8a8ab7b64ec1e0d4e9feabfb96908af60c4a3c27b9788155c SHA512: 777c36bd69b259f4b192d7915450f36fe0022158a9ba1af91501e21d4c8f76079486124040da526650c486d9ee84fee3a53da2528d45b451874d189b17526596 Homepage: https://cran.r-project.org/package=UKFE Description: CRAN Package 'UKFE' (UK Flood Estimation) Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 15. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, ). "The FEH 2025 statistical method update", UK Centre for Ecology and Hydrology (2025). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Data from the UK National River Flow Archive (, terms and conditions: ). 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Package: r-cran-ukhousing Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ukhousing_0.1.0-1.ca2404.1_all.deb Size: 140026 MD5sum: 57c3f4349a92ad9a90038d4649b6cc85 SHA1: a46af18cedc3c3dd2bcc26e4c507218e8489bfdd SHA256: 91e22479c08e7f7f0d4b7a1fb9a10c06dd1594111f0ee9ef34141eca9d8fd2f0 SHA512: 0b9b209f55a27c973a626e3bb391e14577bf245d25d962550d4456744ba9b5c893195defd7d92f6b1d38ce044608b5e429a2309f77b94d195e6ca34b1dc96db4 Homepage: https://cran.r-project.org/package=ukhousing Description: CRAN Package 'ukhousing' (Access UK Housing Data from Land Registry, EPC, and Planning) Fetch UK housing data from official sources. Access the UK House Price Index and Price Paid Data from 'HM Land Registry' , domestic and non-domestic Energy Performance Certificates from the 'MHCLG' Open Data service , and planning application, brownfield land, and local plan data from 'planning.data.gov.uk' . Data covers all 441 UK local authorities from 1995 to the present. Functions accept flexible filters (postcode, local authority, property type, date range) and return tidy data frames. Downloaded data is cached locally for subsequent calls. Package: r-cran-ukhsadatr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ukhsadatr_0.1.1-1.ca2404.1_all.deb Size: 25636 MD5sum: ad90c9494f95160d9678e3a2e7604050 SHA1: 9bb64015a8a4d3e06575e698a3a876ec7e80650d SHA256: 8e9264ee6303480f80eabe3b1fbebe15491bdbe41a944f397e33b31cce84ff70 SHA512: b44dd0ce9139a3ca36827b7abf4a99a0c1c092c3f7267ff98e491a605d23465a424a7d365406323dee8d3bfc4e072b6845b682fe1f0930e418e381b30fec0132 Homepage: https://cran.r-project.org/package=ukhsadatR Description: CRAN Package 'ukhsadatR' (R Interface to Access UKHSA Dashboard Data) Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. 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There is currently support for plotting means and standard deviations of each category's trace; Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well. The origin of the polar coordinates may be defined manually or automatically determined based on different algorithms. Points for each category can be split into two groups (anterior and posterior) at the point of maximum curvature of each trace. User can specify rays to intersect various parts of the tongue; intersections along these rays serve as input for a pairwise t-test to measure significant contrasts between segments. Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' (). 'UltraTrace' is capable of importing data from Articulate Instruments AAA. 'read_textgrid.R' is required for opening TextGrids to determine category and alignment information of ultrasound traces. 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Offers a library of graphical functions with a highly configurable and simple syntax. Features include HTML tables, time series, bar and pie charts, and maps supported by various JavaScript libraries. Allows seamless transitions between interactive and static graphics without changing the core syntax. Originally developed to standardize the publication of official statistics at the National University of Colombia . Spanish: Proporciona una herramienta de propósito general rápida y consistente para organizar microdatos y generar visualizaciones interactivas y estáticas. Ofrece una biblioteca de funciones gráficas con una sintaxis simple y altamente configurable. Permite transiciones fluidas entre gráficos interactivos y estáticos sin cambiar la sintaxis base. Desarrollado originalmente para estandarizar la publicación de estadísticas oficiales en la Universidad Nacional de Colombia . 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Package: r-cran-unfold Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-purrr, r-cran-imputets, r-cran-lubridate, r-cran-ggplot2, r-cran-scales, r-cran-abind, r-cran-coro Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-unfold_1.0.1-1.ca2404.1_all.deb Size: 150938 MD5sum: d12ca296594012ea84e44a16658712af SHA1: 78dc06a1dd00d2a7023db19270adad52c905ab9e SHA256: 9266f0e7a1321052b7e828c2dd0f07b6b1c42735e9d44cbb61d6ca0ab206c6d7 SHA512: d2f40c885fd2b4c362a38e935ad559a030b657806ac28c37bf64f7f581ef3191c76db1f74bf3df89e5efc65e32b13887c89c0f403016a2477028677295f0f257 Homepage: https://cran.r-project.org/package=unfold Description: CRAN Package 'unfold' (Mapping Hidden Geometry into Future Sequences) A variational mapping approach that reveals and expands future temporal dynamics from folded high-dimensional geometric distance spaces, unfold turns a set of time series into a 4D block of pairwise distances between reframed windows, learns a variational mapper that maps those distances to the next reframed window, and produces horizon-wise predictive functions for each input series. In short: it unfolds the future path of each series from a folded geometric distance representation. Package: r-cran-unglue Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-glue, r-cran-testthat, r-cran-rlang, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-unglue_0.1.0-1.ca2404.1_all.deb Size: 89882 MD5sum: aef3e509d29df23110a43ad06db1d150 SHA1: 0a85e308faf38ad4844714cb8b0746d4e963483e SHA256: d2f666cf755b74ecbff3085f5abd77441e84eecbdf10b2f6e66e0b8fc055c20a SHA512: 686c21ae7d049e3bc27b3b53fdfff89f70771515a3c3fd41ab108d16e493d98796e651a0939282a5b054bdefcb1421cc351395a0c0aca26e4e4462f6e972aaf2 Homepage: https://cran.r-project.org/package=unglue Description: CRAN Package 'unglue' (Extract Matched Substrings Using a Pattern) Use syntax inspired by the package 'glue' to extract matched substrings in a more intuitive and compact way than by using standard regular expressions. 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Package: r-cran-unheadr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-forcats, r-cran-stringr, r-cran-tidyr, r-cran-magrittr, r-cran-tidyxl, r-cran-readxl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-unheadr_0.4.0-1.ca2404.1_all.deb Size: 343672 MD5sum: 0d266762ff007fb8574570db2f510331 SHA1: ded91c89a60933200dd719a83db4c9a16f9abe1a SHA256: 2795399993e0b3a0738e696a7e64f4b34d61c9414d6c62aa0db7dc7e74f61090 SHA512: 0962b89792475e81b36e8d593e0ff8b223f9c39ce2c3757a65bacde106d14e27afccaafe0400445ba981e1335ed195de538f67a325eeed4910100d0cd6672a7f Homepage: https://cran.r-project.org/package=unheadr Description: CRAN Package 'unheadr' (Handle Data with Messy Header Rows and Broken Values) Verb-like functions to work with messy data, often derived from spreadsheets or parsed PDF tables. Includes functions for unwrapping values broken up across rows, relocating embedded grouping values, and to annotate meaningful formatting in spreadsheet files. Package: r-cran-uni.shrinkage Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-uni.shrinkage_1.0.0-1.ca2404.1_all.deb Size: 13472 MD5sum: 4f22e02be85740b28d44a72d7b291e24 SHA1: 7d38618907d7c0e7e359b12e3f13496b6d07d314 SHA256: aa5a96ff690fdb5ab9dcd51fa5cb6591452888085071be5d1a03e4f08c7da826 SHA512: a61f879c9b983f36defdb797108edf05c780020ce746fa0db2e4df287116650ee69a36c65447e7de2e5813039ee1d44ba1f965d164c0710d0f9ad285087124dd Homepage: https://cran.r-project.org/package=uni.shrinkage Description: CRAN Package 'uni.shrinkage' (Shrinkage Estimation for Univariate Normal Mean) Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) and Taketomi et al.(2024-)(under review). Package: r-cran-uni.survival.tree Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-compound.cox Filename: pool/dists/noble/main/r-cran-uni.survival.tree_1.5-1.ca2404.1_all.deb Size: 42794 MD5sum: 414f6a6017036a1e17b1377474c58078 SHA1: b59745fa1503af7b7c38480435e5d87836947025 SHA256: ea0dad88a985944337e93ebfef3e5f14b0a8024d1434e5e6c937cfea8398acf1 SHA512: 4d198238e20636a101bc6b9da27a3a75c02606f91f23714ae8258580418f473f6a4b818542ed279b09da57747b30e923beebe62802806173df14da7bedcede35 Homepage: https://cran.r-project.org/package=uni.survival.tree Description: CRAN Package 'uni.survival.tree' (A Survival Tree Based on Stabilized Score Tests forHigh-dimensional Covariates) A classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review). Package: r-cran-uniah Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-ahaz, r-cran-survival Filename: pool/dists/noble/main/r-cran-uniah_1.2-1.ca2404.1_all.deb Size: 50022 MD5sum: ec60480f94fb26af4bde927338f0c09f SHA1: 7963f77054108cbc77f5fbb5e1e906ffdfe47d4f SHA256: 649d17124188546d6fbd1918dc583a5008d8e258443a570c0fbad6951553f0ae SHA512: 512c2f751257d429b48dd1fdbd7e3efd731dc311d09aacd5141734b3de96da194d4237e33ed0a390d01a83525ab6bff03780884fbcc80a13ec10704e5304ea84 Homepage: https://cran.r-project.org/package=uniah Description: CRAN Package 'uniah' (Unimodal Additive Hazards Model) Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model. Package: r-cran-unicefdata Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1873 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-readr, r-cran-dplyr, r-cran-tibble, r-cran-xml2, r-cran-memoise, r-cran-countrycode, r-cran-yaml, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-digest, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-unicefdata_2.3.0-1.ca2404.1_all.deb Size: 613202 MD5sum: d55eb8403c2ffc0ffd68eae2ee1c3a85 SHA1: 6d82034e8476b8e72b128582df72d48e1344b742 SHA256: d097c98ae8127907bd999c0f7c794f9663f2e36931e11846277302b009d2ee82 SHA512: b9991f1c864d9f9e3e35123378e2ffcaba1a29a6213fd8d49843799ef58ad5e12d1db376f114e7e5f9b0b9a2ee93e93523ee4777c9052f5b202e6959662d6a09 Homepage: https://cran.r-project.org/package=unicefData Description: CRAN Package 'unicefData' (Download Indicators from UNICEF Data Warehouse) An R client to fetch SDMX (Statistical Data and Metadata eXchange) CSV series from the UNICEF Data Warehouse . Part of a trilingual suite also available for 'Python' and 'Stata'. Features include automatic pagination, caching with memoisation, country name lookups, metadata versioning (vintages), and comprehensive indicator support for SDG (Sustainable Development Goals) monitoring. Package: r-cran-unicensor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-unicensor_0.1.0-1.ca2404.1_all.deb Size: 140814 MD5sum: 4763764ef740105f4b8cb6dc508b089a SHA1: c54e4f2f44e522cfd8b1cc5da1baed88b8cdc544 SHA256: 481ab01e9a2e6113474d0f5fbcc35cf0413e3df354f5dcc90eb6db09a61f3ec6 SHA512: 331cae5160316a0b0a045c74edee1d023c32804a0ee151cfe995d435ddd71c5c0ed319757cb65193f1c1845057bd16da7942e98a0847062eb99668de2bcc5748 Homepage: https://cran.r-project.org/package=UniCensor Description: CRAN Package 'UniCensor' (Reproducible Random Samples Under Univariate Censoring Schemes) Generates reproducible random samples from any user-specified univariate distribution under a comprehensive suite of censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, support bounds, and parameters; the same seed and inputs yield identical samples across sessions. Supported schemes include right and left truncation, random, right, left, interval, and middle censoring, block random censoring, balanced joint progressive Type-II (BJPT-II), progressive first failure, joint Type-I, Type-I, Type-II, progressive Type-II, Type-II progressively hybrid, joint Type-II, hybrid, hybrid Type-I, doubly Type-II, Type-I hybrid, and hybrid Type-II censoring. Diagnostic histogram, dot plot, and autocorrelation plots are provided for each scheme to verify distributional behaviour. Methods are described in Nagar, Kumar, and Krishna (2026) , Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) , Goel and Krishna (2026) , Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) , Ding and Gui (2023) , Prajapati, Mitra, and Kundu (2019) , Yadav, Jaiswal, and Yadav (2026) , Iyer, Jammalamadaka, and Kundu (2008) , Banerjee and Kundu (2008) , and Kundu and Joarder (2006) . Package: r-cran-unicensorem Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-testthat, r-cran-maxlik, r-cran-bbmle, r-cran-survival, r-cran-boot, r-cran-future, r-cran-furrr, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-cubature, r-cran-pracma, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-unicensorem_0.1.0-1.ca2404.1_all.deb Size: 271870 MD5sum: 09896a900369e5e0430bfbca525ed088 SHA1: d67d0fe149122b6e4bd98d1c2288af67aebb14a9 SHA256: aced48744a90e6ce4946bc5a90b56abf91cd4d64a7b99fddf71bc90ac1c944cb SHA512: 7bae29d4b018ade91738a6e3536340c0bd23aa4cf107612e2c08aaeec95ed7d66cfccbf029fd8454383a65d8611a2fb716dcf79db4687a18c6ad00a00815e83a Homepage: https://cran.r-project.org/package=UniCensorEM Description: CRAN Package 'UniCensorEM' (EM Algorithm for Parameter Estimation Under Censoring Schemes) Performs parameter estimation using the Expectation-Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) for arbitrary univariate probability distributions under numerous censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, initial parameter vector, support bounds, and observed data; the package automatically estimates parameters using the EM algorithm under the specified censoring scheme. Supported schemes include complete data, right censoring, left censoring, interval censoring, random censoring, block random censoring, Type-I censoring, Type-II censoring, progressive Type-II censoring (Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5)), progressive first failure censoring, joint Type-I censoring, joint Type-II censoring, balanced joint progressive Type-II censoring, hybrid censoring, hybrid Type-I censoring, hybrid Type-II censoring, Type-I hybrid censoring, Type-II progressively hybrid censoring, doubly Type-II censoring, middle censoring, right truncation, and left truncation. Standard errors are computed via the observed information matrix (numerical Hessian), the Louis (1982) method, and the supplemented EM (SEM) algorithm of Meng and Rubin (1991) . The package also provides confidence intervals, model selection criteria (AIC, BIC, AICc, HQIC), goodness-of-fit statistics (Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling), residual analysis (randomized quantile residuals, generalized residuals), parametric bootstrap methods, Aitken acceleration for convergence improvement, and comprehensive visualization tools. References: Wu and Kus (2009) , Kundu and Joarder (2006) , Iyer, Jammalamadaka, and Kundu (2008) , Banerjee and Kundu (2008) , Prajapati, Mitra, and Kundu (2019) , Mondal and Kundu (2020) , Ding and Gui (2023) . Package: r-cran-unico Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compositions, r-cran-config, r-cran-data.table, r-cran-futile.logger, r-cran-mass, r-cran-matrixcalc, r-cran-matrixstats, r-cran-mgcv, r-cran-nloptr, r-cran-pbapply, r-cran-pracma, r-cran-testit Suggests: r-cran-egg, r-cran-hexbin, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-unico_0.1.0-1.ca2404.1_all.deb Size: 1153702 MD5sum: 13256d6b7c597b181b15c13d62c391e3 SHA1: ab1c3c29a505d9230372d206e9e24bd5f62f19b9 SHA256: 8989780cb758cf1974aadccaf5281f1b065715f8518acac2bfb28bfa3ad183a5 SHA512: 96837e73391bfaebb3b2e131a2c163f01730c2ce87d1e963af12e8118ef56e2ff0bdddb754a5ce421b8dffb2d4678024c71b5e33ddf101c159c228b1da7d3bc6 Homepage: https://cran.r-project.org/package=Unico Description: CRAN Package 'Unico' (Unified Cross-Omics Deconvolution) UNIfied Cross-Omics deconvolution (Unico) deconvolves standard 2-dimensional bulk matrices of samples by features into a 3-dimensional tensors representing samples by features by cell types. Unico stands out as the first principled model-based deconvolution method that is theoretically justified for any heterogeneous genomic data. For more details see Chen and Rahmani et al. (2024) . Package: r-cran-unicode Architecture: all Version: 17.0.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-unicode_17.0.0-1-1.ca2404.1_all.deb Size: 439614 MD5sum: 812f24b3386c37b7bd8635c6068750b4 SHA1: c5d600f81432c6c6456880e2fa8d31b744e244ef SHA256: b4ceebfff7abd7cc44e902a12750da9991d5abaf4b91ef0f6207ee86261a929a SHA512: 8c980865c224e694af4d5b5395cad98b4172e5c9c2d88eec19dd5f9d452399c2d212d5392e54338310d6b95f28ae0040007561d955f73c3639b9edcd5c298601 Homepage: https://cran.r-project.org/package=Unicode Description: CRAN Package 'Unicode' (Unicode Data and Utilities) Data from Unicode 17.0.0 and related utilities. 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The 'unicol' package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations. Package: r-cran-unifdag Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2051 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-cran-gmp Suggests: r-bioc-rgraphviz, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-unifdag_1.0.4-1.ca2404.1_all.deb Size: 2058544 MD5sum: b41d817aa5ec58694f7a8392d2795a36 SHA1: d815511a7b4bb66672b2771504079f067cc8da56 SHA256: d0273009f65ce4235aff383c717339de36bab1976269425503948834798e6bf1 SHA512: 4de6c54cba5ad683d6247bc67348e8696b91380b35a59cdb7ab8b24fd40f2b837aff74185ef6d4f28f51af038bba9a0c99a254808bb4337ada6f437184c8d6bb Homepage: https://cran.r-project.org/package=unifDAG Description: CRAN Package 'unifDAG' (Uniform Sampling of Directed Acyclic Graphs) Uniform sampling of Directed Acyclic Graphs (DAG) using exact enumeration by relating each DAG to a sequence of outpoints (nodes with no incoming edges) and then to a composition of integers as suggested by Kuipers, J. and Moffa, G. (2015) . Package: r-cran-unifieddosefinding Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-unifieddosefinding_0.1.10-1.ca2404.1_all.deb Size: 140314 MD5sum: aec23ea13996d6704382e5439e8bdb57 SHA1: 7936499cc9b0d79c5a9ebdb55f1c8e7fbc944cfb SHA256: 1f5da6b4a0de74b532be87ba587504e96693a6379d21fb8c8d19c64efc78e72a SHA512: 10c8107a108ba2c514a81c0d44bca72154a1cf60edc05b1cad60227ee7513c05040c574f3095dca51ef49273842ed8b85269c4ef6a56d0425bf44378f1af2d7a Homepage: https://cran.r-project.org/package=UnifiedDoseFinding Description: CRAN Package 'UnifiedDoseFinding' (Dose-Finding Methods for Non-Binary Outcomes) In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design’s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) ), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) , Pan et al. (2014) ) and generalized BOIN design (Mu et al. (2018) ). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules. Package: r-cran-unifir Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2990 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-proceduralnames, r-cran-r6 Suggests: r-cran-terrainr, r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-styler, r-cran-testthat, r-cran-terra, r-cran-sf Filename: pool/dists/noble/main/r-cran-unifir_0.2.4-1.ca2404.1_all.deb Size: 2248730 MD5sum: 4d917d5c57d9ede1ef2af937a166f5c5 SHA1: ed10b58c4a6ff8b8d19ca0ad099381f1962bf079 SHA256: 81ad8a3709bccd1684ad83eefb06d95ccefc4f63961c2695742c38a92f3ae9ee SHA512: 1cb9592e812efaabeb0a9a864b5893e50ada0e0a53f764d03c7aaff357c68823b05f15e52ed81d8ba365d56ad2d2ca53aaa0b4ab038d8f246fef98c477fec741 Homepage: https://cran.r-project.org/package=unifir Description: CRAN Package 'unifir' (A Unifying API for Calling the 'Unity' '3D' Video Game Engine) Functions for the creation and manipulation of scenes and objects within the 'Unity' '3D' video game engine (). Specific focuses include the creation and import of terrain data and 'GameObjects' as well as scene management. Package: r-cran-uniformly Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-pgnorm, r-cran-rgl Suggests: r-cran-geometry, r-cran-knitr, r-cran-misc3d, r-cran-rmarkdown, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-uniformly_0.5.0-1.ca2404.1_all.deb Size: 208422 MD5sum: 9bb4685d941c5142fe4ddd0e1d6931be SHA1: bfc1d06d5ee79f17de3c38afda88dc40880b9743 SHA256: 523c40ba3a71ef3d31017b9fc03bc5c1007cd80ac3ccec9fdf6f9e91acb134af SHA512: a69bc3b6c22afc47579469e021b9e9a1a886636d8e62fa7dad11f56c141fabadeb88e6cca281ca3989f851862405ac05270c97fa068eb38613116946d11cd0cd Homepage: https://cran.r-project.org/package=uniformly Description: CRAN Package 'uniformly' (Uniform Sampling) Uniform sampling on various geometric shapes, such as spheres, ellipsoids, simplices. Package: r-cran-unifyr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-unifyr_1.0.0-1.ca2404.1_all.deb Size: 22934 MD5sum: ab4ce7a9d8bc060693203ac2ba67aaa1 SHA1: 39e40fe073dca9835956b07321c99b989e727a7e SHA256: e3407c2095fb7148cc55bed0da27ffa0eccb70f4826afdf376700837514d7db4 SHA512: 26218888d1d855668191a52acaaf55151eb53c3334c48a9d964db39078be48f7082cf3477eaf8965785692f156515bd1798212efb761940c596c98155b58c8dc Homepage: https://cran.r-project.org/package=unifyR Description: CRAN Package 'unifyR' (Unified Scores, Reliabilities and Validities from Multiple Tests) In diagnostic contexts, individuals are often assessed using multiple tests that measure the same latent variable (e.g., intelligence). These test scores are typically not exactly identical. Simple averaging neglects the correlation between tests and the reduced variance of their combination. The 'unifyR' package provides functions to compute statistically accurate unified scores, reliabilities and validities of multiple tests. The underlying algorithms build on and extend the method proposed by Evans (1996, ) and have been validated through simulations. Package: r-cran-uniis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-uniis_0.1.0-1.ca2404.1_all.deb Size: 135470 MD5sum: 452612666a4746974e11052ad21ec5f0 SHA1: 0ae029e8d2010e826429c0be4af1a281db8cc549 SHA256: 4bff45b673e37116e0b30e3de4789bc31e35d411a0445b2698b1a57b2752b689 SHA512: b8c2da313cd60608be3e3709e98cc6445ae53fdc26c266be7660145aed5a443fe3e159a99cb3c4a39ed7ab9376006a0128bdb3e3cf18e86f02d472b7b914ba6e Homepage: https://cran.r-project.org/package=UniIS Description: CRAN Package 'UniIS' (Importance Sampling Inference for Censored Univariate Data) Distribution-independent framework for importance-sampling inference with univariate observations subject to censoring or truncation. Users provide probability functions and a proposal over model parameters. Constructs observed-data likelihood contributions, computes numerically stable importance weights, and supplies posterior, likelihood, predictive, diagnostic, and model-comparison summaries. Covers complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, progressive first-failure, doubly Type-II, middle-censored, and left/right-truncated data. Methods for importance sampling and censoring schemes are described in Geweke (1989) , Hesterberg (1995) , Robert and Casella (2004, ISBN:978-0-387-21617-1), Kundu and Joarder (2006) , Banerjee and Kundu (2008) , Iyer, Jammalamadaka, and Kundu (2008) , Wu and Kus (2009) , Prajapati, Mitra, and Kundu (2019) , Mondal and Kundu (2020) , Balakrishnan and Aggarwala (2000, ISBN:980-1-4612-1334-5), Ding and Gui (2023) , Nagar, Kumar, and Krishna (2026) , Goel and Krishna (2026) , Yadav, Jaiswal, and Yadav (2026) , and Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"). Package: r-cran-unikn Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4963 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-unikn_1.0.0-1.ca2404.1_all.deb Size: 3138032 MD5sum: 9dc9a001cd5e7ffdb941d4f8d99d5146 SHA1: 8915d10f75b62262b280c4ee5ebcc8ce7351296f SHA256: 1884fe4a3a03847f25a2390aa02d7bc3e35fe3f0d95d9d7e381508950c0099a2 SHA512: 2e96d4b80d69a1cb9e852ebf58143eaf2249faeff03c6fb812bdd66623095fa7631bd86c887fb5434a70d4fa1584aa9488254d49e3b04196eed6d7ff028e022a Homepage: https://cran.r-project.org/package=unikn Description: CRAN Package 'unikn' (Graphical Elements of the University of Konstanz's CorporateDesign) Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz , but can be adapted and extended for other purposes or institutions. Package: r-cran-unilasso Architecture: all Version: 2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-unilasso_2.11-1.ca2404.1_all.deb Size: 100422 MD5sum: 5450d4cbd61ae1d60bf22bc31c264a84 SHA1: 1ec0f3f63ece02e6352ed7179860c71762be18b3 SHA256: 3e3ad616eed181d2a0a6b27d1b536d81d5f7bc33fa120dba3d2ef27f8dd5a794 SHA512: 1f2e79160f63f0d29ed169854353069b6f661240fd0b04f49ab4606fe97abdac11d1d0a63d32b4687197ee08b340d1d6246ab9c7f8da1caf7ae00fa3521e6961 Homepage: https://cran.r-project.org/package=uniLasso Description: CRAN Package 'uniLasso' (Univariate-Guided Sparse Regression) Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class 'glmnet', so that all of the methods for 'glmnet' are available. See Chatterjee, Hastie and Tibshirani (2025) for details. Package: r-cran-unilindleyapprox Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-mass Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-future, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-unilindleyapprox_0.1.0-1.ca2404.1_all.deb Size: 396030 MD5sum: d4a183036669f1f63078ea7ac488fb08 SHA1: 50440ece193a195c68e16a1e8fea90cf2cf46fb7 SHA256: defa5038d2f09457d622292190c6b659cd2f2829182a0ab486e98493459a6609 SHA512: db8573ea47e0d8f45d95559ee0a92ceaf40e4c6321b3b2ddd6fc2249c003cd12de5ea4b0b98e989171d61802e8de2539859664152764dea504e754c4e1e64b85 Homepage: https://cran.r-project.org/package=UniLindleyApprox Description: CRAN Package 'UniLindleyApprox' (Bayesian Point Estimation Using Lindley's Approximation UnderCensoring Schemes) Performs Bayesian point estimation using Lindley's Approximation (1980) for arbitrary univariate probability distributions under numerous censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, log-prior density, initial parameter vector, support bounds, and observed data; the package automatically computes Bayesian point estimates under various loss functions using Lindley's approximation. Supported schemes include complete data, right censoring, left censoring, interval censoring, random censoring, block random censoring, Type-I censoring, Type-II censoring, progressive Type-II censoring, progressive first failure censoring, joint Type-I censoring, joint Type-II censoring, balanced joint progressive Type-II censoring, hybrid censoring, hybrid Type-I censoring, hybrid Type-II censoring, Type-I hybrid censoring, Type-II progressively hybrid censoring, doubly Type-II censoring, middle censoring, right truncation, and left truncation. The package computes posterior expectations of arbitrary smooth functions, supports multiple loss functions (squared error loss function (SELF), weighted squared error loss function (WSELF), modified quadratic squared error loss function (MQSELF), precautionary loss function (PLF), entropy loss function (ELF), linear-exponential (LINEX), generalized entropy loss function (GELF), Kullback-Leibler loss function (K-Loss), and user-defined), provides model selection criteria (Akaike information criterion (AIC), Bayesian information criterion (BIC), corrected Akaike information criterion (AICc), Hannan-Quinn information criterion (HQIC), consistent Akaike information criterion (CAIC), Kullback information criterion (KIC)), goodness-of-fit statistics (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises, Watson, Chi-square), residual analysis (Cox-Snell, Martingale, Deviance, Pearson, Generalized, Randomized quantile), comprehensive visualization tools, prediction utilities, and simulation functions for benchmarking estimators. Methods are described in Lindley (1980) , Tierney and Kadane (1986) , Tierney, Kass, and Kadane (1989) , Nagar, Kumar, and Krishna (2026) , Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) , Goel and Krishna (2026) , Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) , Ding and Gui (2023) , Prajapati, Mitra, and Kundu (2019) , Yadav, Jaiswal, and Yadav (2026) , Iyer, Jammalamadaka, and Kundu (2008) , Banerjee and Kundu (2008) , and Kundu and Joarder (2006) . Package: r-cran-uniprotr Architecture: all Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-networkd3, r-cran-alakazam, r-cran-gprofiler2, r-cran-progress, r-cran-ggsci Filename: pool/dists/noble/main/r-cran-uniprotr_2.5.1-1.ca2404.1_all.deb Size: 242014 MD5sum: 0cc0caa61e26e5bed55d7ad3e54f2435 SHA1: fdc76c2cd342bc0b55eba7e7612d83ce9b660054 SHA256: db74b351e3b855df9b2440baeae47186dff54280e094c2d87a0042b25c4d79a1 SHA512: 7c1ab98154572d1303c2d294a8db872c5d52e772432790c6399c3c322f222ab282d2b1a22cab124b3465f4442a8d6e6d83624f8f0498059dbbe784ea995fe79c Homepage: https://cran.r-project.org/package=UniprotR Description: CRAN Package 'UniprotR' (Retrieving Information of Proteins from Uniprot) Connect to Uniprot to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication . 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Package: r-cran-uniswapper Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-scales, r-cran-ggplot2, r-cran-ghql, r-cran-jsonlite, r-cran-reticulate, r-cran-patchwork, r-cran-purrr Filename: pool/dists/noble/main/r-cran-uniswapper_0.6.1-1.ca2404.1_all.deb Size: 205938 MD5sum: 1876d992ae37437b5e4eb58033381954 SHA1: 9c7f49b9ca9318a1a38fe4c55c5cd836e9710f2f SHA256: a81eb383cb165237b8ca6a08eb4e762b9ce442192895d94b235ac0d51824b82b SHA512: 3dcce296aa463125ef6920c50bda2196b21a31e3b3845bf3b5ebc6e5db4a9b43777c074aa7c27e4736924f2331f64d42d9499190479359ecac30b0a910bc84e9 Homepage: https://cran.r-project.org/package=uniswappeR Description: CRAN Package 'uniswappeR' (Interact with the Uniswap Platform) Routines to interact with the Uniswap trading platform and its API . The package contains codebase to interact with the uniswap platform directly from R console, Ability to pull and export data related to the platform and analyse some aspects. 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If in interactive mode, tests can be reviewed through the provided interactive environment. Package: r-cran-unitmix Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-unitmix_0.0.1-1.ca2404.1_all.deb Size: 43306 MD5sum: cbbb0a2228dbaf4a9a3fc639d0278e86 SHA1: 9279d8bee03af01efa869e5ef71ed38a05d37f41 SHA256: e6dbea80821086f0cc43bc5a4d200ac63d4248e4de565da3372b76e58b16e09a SHA512: 80e69f54f181f61d877648e009ebaecc7615613dc87266112757fd252bcbc01108089b506c76a6e0ec14e87eb124a9561740c3fe2584987bed5c7126550ad62e Homepage: https://cran.r-project.org/package=UnitMix Description: CRAN Package 'UnitMix' (Detecting Measurement-Unit Errors via Gaussian Mixture Models) Tools to detect and correct measurement-unit errors in multivariate numeric data using model-based clustering. Gaussian mixture models with user-defined translation vectors identify clusters of records that differ in scale or unit. Core functionality includes cluster assignment via the EM algorithm, error correction based on posterior probabilities and pairwise scatterplot visualizations. For more details see Di Zio, Guarnera and Luzi (2005) . Package: r-cran-unitrootests Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg, r-cran-tseries, r-cran-urca, r-cran-strucchange Suggests: r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-cran-unitrootests_1.1.2-1.ca2404.1_all.deb Size: 85272 MD5sum: 1a3def1c42d4c2867d671f5aa52cb068 SHA1: 2467538c6496af78969f69719f645c1ae1b4c950 SHA256: 13707b2e0e0cfda14936482f66e665cbed5b4b2d932623b72f1e7919a39c9172 SHA512: 817485ba9ededbe9e6fb261fc16d536412211573cbd7f191bd346d1eb173d211479b4887ab08fbbd9cdaa0cc5f62ce69736990835ee94e3a0c20ab4de252624e Homepage: https://cran.r-project.org/package=unitrootests Description: CRAN Package 'unitrootests' (Comprehensive Unit Root and Stationarity Tests) A unified framework for unit root and stationarity testing including quantile ADF tests (Koenker and Xiao, 2004) , GARCH-based unit root tests with endogenous structural breaks (Narayan and Liu, 2015) , and comprehensive Dickey-Fuller, Phillips-Perron, KPSS, ERS/DF-GLS, Zivot-Andrews, and Kobayashi-McAleer tests with an Elder-Kennedy decision strategy (Elder and Kennedy, 2001) . Package: r-cran-unitstat Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmtest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-unitstat_1.1.0-1.ca2404.1_all.deb Size: 47974 MD5sum: f8995a64167340495b83fa4b48441b03 SHA1: b89838838dc0ec2a635729b9ff1468185f2ece8e SHA256: 3f162baea47021c2e39ffdc343b3adb8c1dd88585e17aa7f22d5e9854dd5bf7e SHA512: 719c25a87bbf5420d875f73d74d87081ba5595b349393fefa8eb707ca69124d9db0b7b6c7a5751a0e52f03e3a659aaaa5b05fed6425e8bf9fca53c5ce3d649d3 Homepage: https://cran.r-project.org/package=UnitStat Description: CRAN Package 'UnitStat' (Performs Unit Root Test Statistics) A test to understand the stability of the underlying stochastic data. Helps the user’s understand whether the random variable under consideration is stationary or non-stationary without any manual interpretation of the results. It further ensures to check all the prerequisites and assumptions which are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all the 4 lags the function diagnoses the input data and returns with an optimised solution on the same. 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Package: r-cran-unival Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych Filename: pool/dists/noble/main/r-cran-unival_1.1.0-1.ca2404.1_all.deb Size: 54600 MD5sum: 8665e990f1a6a431939df973fef01e0d SHA1: a099aa9dc29f062aa2bd2b87754727db05bbb3d9 SHA256: 66c3eaf31d040209df40135afa6a9384ea294bd7212d535e4883600947ce9f3f SHA512: 1dae126cc0caf84b1463bd7a8bcd8619c068ce126429a36ffb684b2c2085e000c71cdd5717a436c16127b27bc72869dae5438c6132673ed9248668c1223cb37d Homepage: https://cran.r-project.org/package=unival Description: CRAN Package 'unival' (Assessing Essential Unidimensionality Using External ValidityInformation) Assess essential unidimensionality using external validity information using the procedure proposed by Ferrando & Lorenzo-Seva (2019) . Provides two indices for assessing differential and incremental validity, both based on a second-order modelling schema for the general factor. Package: r-cran-univariateml Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-extradistr, r-cran-tibble, r-cran-logitnorm, r-cran-actuar, r-cran-nakagami, r-cran-fgarch, r-cran-rlang, r-cran-intervals, r-cran-rfast, r-cran-pracma, r-cran-sads Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-copula, r-cran-dplyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-univariateml_1.5.0-1.ca2404.1_all.deb Size: 558140 MD5sum: 04e18c78f5d72c0e9d0b1df0de163805 SHA1: b33a85ffa720f19540e949f9d2f0d777f8b5938f SHA256: 71011ea2601bcf625b83385bb394636813c5088ef9a1c727b76c585e31f190d4 SHA512: 4c67da14d2c0ea5a47b7231355110380ab258d70a464fe250ef3b659899f013d102d613073f6a0594060fb40a033c3bcab5b0c14d224735eb0a9f313c30f52bc Homepage: https://cran.r-project.org/package=univariateML Description: CRAN Package 'univariateML' (Maximum Likelihood Estimation for Univariate Densities) User-friendly maximum likelihood estimation (Fisher (1921) ) of univariate densities. Package: r-cran-universalcvi Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mclust Filename: pool/dists/noble/main/r-cran-universalcvi_1.4.0-1.ca2404.1_all.deb Size: 641158 MD5sum: 717350a439839d186a2aac5c87493e75 SHA1: 6af20f72c375e73cff0ff8d4eaf79a6d6231f6e5 SHA256: 09ab1e1274e3e1af262cdee6ae1a8ea9fa14465da05fd22510a9022740546ee8 SHA512: 3ef467465b50af38ddda82a158da224f66619dd7faf1fb1c24acd8b6bbe6788b733ad702598b44827031a7ac981a7ae8786c65c511d0d5e7ee63f0c80482aa07 Homepage: https://cran.r-project.org/package=UniversalCVI Description: CRAN Package 'UniversalCVI' (Hard and Soft Cluster Validity Indices) Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). The details of the indices in this package can be found in: J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) , T. Calinski, J. Harabasz (1974) , C. H. Chou, M. C. Su, E. Lai (2004) , D. L. Davies, D. W. Bouldin (1979) , J. C. Dunn (1973) , F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) , M. Kim, R. S. Ramakrishna (2005) , S. H. Kwon (1998) , S. H. Kwon, J. Kim, S. H. Son (2021) , G. W. Miligan (1980) , M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) , M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) , S. Saitta, B. Raphael, I. Smith (2007) , A. Starczewski (2017) , Y. Tang, F. Sun, Z. Sun (2005) , N. Wiroonsri (2024) , N. Wiroonsri, O. Preedasawakul (2023) , C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) , X. Xie, G. Beni (1991) and P.J. Rousseeuw (1987) and L. Kaufman and P.J. Rousseeuw(2009) and C. Alok. (2010). Package: r-cran-universals Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-nlist, r-cran-testthat Filename: pool/dists/noble/main/r-cran-universals_0.0.5-1.ca2404.1_all.deb Size: 73396 MD5sum: 9f09670b8c82e9cfc701399227732611 SHA1: 55481085ca33eee83165f47c02b6e8f6d9e7c015 SHA256: a947ae38eb0cdacc4679c4091d414b544bc667c30075c96658157de467f25a83 SHA512: a88996d081d66b072da189bf1f8256bdad33c98aa7dadd83ef6967f676082e4c4a53685d1b918255fcfdfef6da9fb9eead97081053826fe977516dcd20254b26 Homepage: https://cran.r-project.org/package=universals Description: CRAN Package 'universals' (S3 Generics for Bayesian Analyses) Provides S3 generic methods and some default implementations for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples. The purpose of 'universals' is to reduce package dependencies and conflicts. 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Package: r-cran-uotm Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-forecast, r-cran-ggplot2, r-cran-hash Filename: pool/dists/noble/main/r-cran-uotm_0.1.6-1.ca2404.1_all.deb Size: 34332 MD5sum: d4623c879ce7313802e13627f460911e SHA1: 2811314137d9eaafc101895d95e098636e10955c SHA256: 62217030e367671fc8e1555b6bdb4399bee8e8677e5da1a5366af27a885450e2 SHA512: 7e918a1e17e1e9c340f9a56a07ff1f0ae3c9ef58e24b375bfb2c4db777ab7cbb85a90944df306153d65af94cabff08cf00c7d43f4c6c78f21bb0c8157d08e7af Homepage: https://cran.r-project.org/package=uotm Description: CRAN Package 'uotm' (Uncertainty of Time Series Model Selection Methods) We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. for more information. 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Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) . Package: r-cran-upg Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-matrixstats, r-cran-mnormt, r-cran-pgdraw, r-cran-reshape2, r-cran-coda, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-upg_0.3.5-1.ca2404.1_all.deb Size: 564716 MD5sum: 85651029ed4bdf36ac617dfe99b10bdc SHA1: e275eb2eca8ca0e2ab23493794092d8ff2c87c67 SHA256: a24350d51f21e7034e7f05c6efd99d54bf735fc0d354cc392a57ead7cf718975 SHA512: 4d7ed8616cb858d8dfd3764d797f789bf85c5fc8614c63dc219b7b5fa26feeea4cd33fb51c2c6f73822005df2bdb82412a65b88ceec21c23050b95350caef49b Homepage: https://cran.r-project.org/package=UPG Description: CRAN Package 'UPG' (Efficient Bayesian Algorithms for Binary and Categorical DataRegression Models) Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association ". Package: r-cran-upmask Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3445 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rsqlite, r-cran-dbi, r-cran-dimred, r-cran-loe Filename: pool/dists/noble/main/r-cran-upmask_1.2-1.ca2404.1_all.deb Size: 2258264 MD5sum: 6df1e2feb8e4430230687bd826f05851 SHA1: 528ae05753ea7b306267d711ceebf6b1459ba63e SHA256: 438363502ca142c696930a04befce3f67bde1a3ac74f27b916da1dde5bbf944c SHA512: 43ac6f82f2aaddb58335e6a0f8a239e15fe19b85568527a6b67845fa0901d24499f77373450ad3676ff3363438ebea15d9ae1bb0b392a26f1a9034a8819812fa Homepage: https://cran.r-project.org/package=UPMASK Description: CRAN Package 'UPMASK' (Unsupervised Photometric Membership Assignment in StellarClusters) An implementation of the UPMASK method for performing membership assignment in stellar clusters in R. It is prepared to use photometry and spatial positions, but it can take into account other types of data. The method is able to take into account arbitrary error models, and it is unsupervised, data-driven, physical-model-free and relies on as few assumptions as possible. The approach followed for membership assessment is based on an iterative process, dimensionality reduction, a clustering algorithm and a kernel density estimation. Package: r-cran-upndown Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cir, r-cran-expm, r-cran-mass, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-upndown_0.3.0-1.ca2404.1_all.deb Size: 241034 MD5sum: dd5fb91e447638f632bc5c7d631981ad SHA1: ff0e79b35bb208b00ddbd73dc316680e95b28926 SHA256: 4a22e29b1c4654511b8d9099dbd5535be8678b9b01621b8dd4fa541d87723832 SHA512: 4c61cf13a594ccbbb2b64c670ff00756db58befd7215566448a6f45292e59fe118f65d9a40ec71995ccf5fe4fc010da8517f2f9ca0bd9c6b9f9fc0185a97ebaa Homepage: https://cran.r-project.org/package=upndown Description: CRAN Package 'upndown' (Utilities and Design Aids for Up-and-Down Dose-Finding Studies) Up-and-Down (UD) is the most popular design approach for dose-finding, but it has been severely under-served by the statistical and computing communities. This is the first package that comprehensively addresses UD's needs. Recent applied UD tutorial: Oron et al., 2022 . Recent methodological overview: Oron and Flournoy, 2024 . Package: r-cran-upset.hp Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mumin, r-cran-vegan, r-cran-glmm.hp, r-cran-ggplot2, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-upset.hp_0.0.6-1.ca2404.1_all.deb Size: 36528 MD5sum: 1d1d2988bd0d7f85cf5c320851003d55 SHA1: 5b11fefa4c8ebe0f7a0d9ed8319795f0931c5584 SHA256: 8d25441f7954154c630130aa8736089d9d1e169b1f42f9600d124ad5c8d59e41 SHA512: eb460ce1e56a20cdfe80b1a1121c834482e9b0745da5c5a8eab0a4ad69a066ee9c0aa1f76cbc737cb459a5080547d406823ebd9107849fbdbe600dbdf2dc442b Homepage: https://cran.r-project.org/package=upset.hp Description: CRAN Package 'upset.hp' (Generate UpSet Plots of VP and HP Based on the ASV Concept) Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on different models. These contributions were derived from variation partitioning (VP) and hierarchical partitioning (HP), applying the algorithm of "Lai et al. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 ". Package: r-cran-upsetjs Architecture: all Version: 1.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2607 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-magrittr Suggests: r-cran-knitr, r-cran-crosstalk, r-cran-rmarkdown, r-cran-formatr, r-cran-tibble, r-cran-testthat, r-cran-styler, r-cran-lintr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-upsetjs_1.11.1-1.ca2404.1_all.deb Size: 313634 MD5sum: f4811822c1b28b2e5a316636af036117 SHA1: d2489b9539ce7c9bb4ac86c782f8b52e845a84d4 SHA256: 7447244936ce5a0086c8cac3f1c851d84b4f29341dae16b67af76a70ca403a57 SHA512: effe7f7ec3f0fe0e593549f28303f2d9406cf51ed9ee1a38f6b3df2bd9703684b8c3e1eacfa9b94e660d8fc5dcb16dde81fc020f8415f2e0ab097e3aa2ebfa45 Homepage: https://cran.r-project.org/package=upsetjs Description: CRAN Package 'upsetjs' ('HTMLWidget' Wrapper of 'UpSet.js' for Exploring Large SetIntersections) 'UpSet.js' is a re-implementation of 'UpSetR' to create interactive set visualizations for more than three sets. This is a 'htmlwidget' wrapper around the 'JavaScript' library 'UpSet.js'. Package: r-cran-upsetly Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-plotly Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-upsetly_0.1.1-1.ca2404.1_all.deb Size: 33110 MD5sum: fc99b8a451feed1a3eec7f7bf570d17c SHA1: 5296387da463707b7b7334e89df94a21772d85b3 SHA256: 09e747bc40def8f8d99cbc80f77ef0c1390d3fe232e1d56ff0fa75d968000e69 SHA512: 5fb728151e8689cf48a2987739fd9ee66417aa4b75d4396bd6917f6d97fe6effb557ab2e5f3988f5f8323743ad7ae552ace9c706645e88850a515d41c00580e9 Homepage: https://cran.r-project.org/package=upsetly Description: CRAN Package 'upsetly' (Interactive UpSet Plots Using 'plotly') Creates interactive UpSet-style visualizations for exploring intersections among multiple sets, following the layout of Lex et al. (2014) . 'plotly' widgets combine set-size bars, intersection-size bars, and membership matrices, and can synchronize complete intersection details with an HTML element for copying in 'Quarto', 'R Markdown', and 'Shiny' documents. Package: r-cran-upsetr Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7828 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-plyr, r-cran-scales Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-upsetr_1.4.1-1.ca2404.1_all.deb Size: 3221358 MD5sum: 7458a13cf3cc69990ab8133ce7e21564 SHA1: 43a82a1bfa3f7024347af8a27d472b2fa6917823 SHA256: 0b93c70940c91141abb9f37c657c453833fb50835364028e8f890aa172e9baac SHA512: fbcd4dd3a639074b2322b85cec9581e9855fc4a5654a3e3d97c7132cb1231d8bd75dba074865813985eafb1d1b8a2535b45e48eeef4b101196dce178966b375e Homepage: https://cran.r-project.org/package=UpSetR Description: CRAN Package 'UpSetR' (A More Scalable Alternative to Venn and Euler Diagrams forVisualizing Intersecting Sets) Creates visualizations of intersecting sets using a novel matrix design, along with visualizations of several common set, element and attribute related tasks (Conway 2017) . 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These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 . Package: r-cran-upstartr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-here, r-cran-stringr, r-cran-readxl, r-cran-magrittr, r-cran-readr, r-cran-purrr, r-cran-ggplot2, r-cran-glue, r-cran-dplyr, r-cran-librarian, r-cran-openxlsx, r-cran-knitr, r-cran-tidytext, r-cran-scales, r-cran-rmarkdown, r-cran-textclean, r-cran-crayon Suggests: r-cran-testthat, r-cran-withr, r-cran-beepr, r-cran-sf, r-cran-tgamtheme Filename: pool/dists/noble/main/r-cran-upstartr_0.2.0-1.ca2404.1_all.deb Size: 149030 MD5sum: 285e1914c4bd8dbb33e143750e70ee62 SHA1: 755014f9d3cb8e51f31b3e71f6a66dc280d06725 SHA256: 25af453a9629293b1e847736fb2c0851c1066b825bbbe41d6088aa61bcced331 SHA512: ed7e383ff345b490c91091ea576ceffe1cdeb7daee19c4d9ac19221bb7cf5b295caa6b62e6073de79ad110d9b0e5288385b8eae504fce9a260890c8a0d6f758c Homepage: https://cran.r-project.org/package=upstartr Description: CRAN Package 'upstartr' (Utilities Powering the Globe and Mail's Data Journalism Template) Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets. 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Package: r-cran-uptasticsearch Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-jsonlite, r-cran-purrr, r-cran-stringr Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-uptasticsearch_1.1.0-1.ca2404.1_all.deb Size: 104576 MD5sum: 034cbe201520541dd924fa7fd1b41550 SHA1: 410301a6940e7120246414f9f276189cf857e8d7 SHA256: 4c09f6070b887fc4ece106a63c608a6f1d6cbdcf86ba67013212b085d862883b SHA512: 6f36d39820b0ceda9dd5ca17bb598b2572792bfa737283bdb6e6cac536e1b1c20cf44767443b60ecda289e3273cc7d590d4b63b3b4d25ed609cfcd1649a5178c Homepage: https://cran.r-project.org/package=uptasticsearch Description: CRAN Package 'uptasticsearch' (Get Data Frame Representations of 'Elasticsearch' Results) 'Elasticsearch' is an open-source, distributed, document-based datastore (). It provides an 'HTTP' 'API' for querying the database and extracting datasets, but that 'API' was not designed for common data science workflows like pulling large batches of records and normalizing those documents into a data frame that can be used as a training dataset for statistical models. 'uptasticsearch' provides an interface for 'Elasticsearch' that is explicitly designed to make these data science workflows easy and fun. Package: r-cran-uptimerobot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjson, r-cran-rcurl, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-uptimerobot_1.0.0-1.ca2404.1_all.deb Size: 78132 MD5sum: d53d4294c8b801818474fec9ba2af4a9 SHA1: cb41d061a8257e586941e66bcad194ef798ea3f4 SHA256: ac58c5a5dbecd58247ffb785929c411b0eac5809119736e438431030918ceb12 SHA512: ff556babb14a3ca9fab6ddb44b30cd1b5466f8fd36b12766c88f6d62e81465b211186a32790f6cc53368f38ab20325e5c374f9d3a7cbac1734daaa06d4b1207c Homepage: https://cran.r-project.org/package=uptimeRobot Description: CRAN Package 'uptimeRobot' (Access the UptimeRobot Ping API) Provide a set of wrappers to call all the endpoints of UptimeRobot API which includes various kind of ping, keep-alive and speed tests. See for more information. Package: r-cran-ura Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-irr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-roxygen2, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ura_1.0.1-1.ca2404.1_all.deb Size: 31724 MD5sum: a8842382eaac38dcacb7f0b2644bed93 SHA1: bced8fabe919f1f24edb8ad62a9ebe78c163aed4 SHA256: e4fb2bf9aa58c1da7fa4d402b4bbd609f99bc7e630f4ccf8d223743982ba6e6e SHA512: 25a5b121292325f92dd1ae31653932af5fc7807de13e316ad5197f74f635fc183f9b5bd5fca97f5705e82e998bf2d5fcfddcf6261619ceb8722eb58fcc5cc939 Homepage: https://cran.r-project.org/package=ura Description: CRAN Package 'ura' (Monitoring Rater Reliability) Provides researchers with a simple set of diagnostic tools for monitoring the progress and reliability of raters conducting content coding tasks. Goehring (2024) argues that supervisors---especially supervisors of small teams---should utilize computational tools to monitor reliability in real time. As such, this package provides easy-to-use functions for calculating inter-rater reliability statistics and measuring the reliability of one coder compared to the rest of the team. Package: r-cran-urbin Architecture: all Version: 0.1-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-sampleselection, r-cran-maxlik, r-cran-mfx, r-cran-mlogit, r-cran-mass, r-cran-mvprobit, r-cran-knitr, r-cran-stargazer Filename: pool/dists/noble/main/r-cran-urbin_0.1-18-1.ca2404.1_all.deb Size: 798608 MD5sum: a70bd3e79d44eba602e8896145c50dfd SHA1: 9a847ea65c9dc93e5a3110f53746c71318530312 SHA256: 1fce15e8a5e98d52456eb1a1c490914955542e75448b9337da039fcb14ddd56c SHA512: 47170759e59da8ca40ab71916dbfbd37ad4a0cd5564c4a6c0eccfe2a8060c7bf18db97e96ec8c8194bdfc279823c172b61ba31b9f32ad25aa64767affd99aea2 Homepage: https://cran.r-project.org/package=urbin Description: CRAN Package 'urbin' (Unifying Estimation Results with Binary Dependent Variables) Calculate unified measures that quantify the effect of a covariate on a binary dependent variable (e.g., for meta-analyses). This can be particularly important if the estimation results are obtained with different models/estimators (e.g., linear probability model, logit, probit, ...) and/or with different transformations of the explanatory variable of interest (e.g., linear, quadratic, interval-coded, ...). The calculated unified measures are: (a) semi-elasticities of linear, quadratic, or interval-coded covariates and (b) effects of linear, quadratic, interval-coded, or categorical covariates when a linear or quadratic covariate changes between distinct intervals, the reference category of a categorical variable or the reference interval of an interval-coded variable needs to be changed, or some categories of a categorical covariate or some intervals of an interval-coded covariate need to be grouped together. Approximate standard errors of the unified measures are also calculated. All methods that are implemented in this package are described in the 'vignette' "Extracting and Unifying Semi-Elasticities and Effect Sizes from Studies with Binary Dependent Variables" that is included in this package. Package: r-cran-urbstatdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-urbstatdata_0.1.0-1.ca2404.1_all.deb Size: 1405846 MD5sum: 33bd1b38b1601a86a5110cb2ec125c7f SHA1: 17a333b995cdbe7844379d91279aeaaa7e878dfe SHA256: 9494e47cc264e9153f95f45584cda4526ac93f6a7e2cffe48b6f5480a9f38a80 SHA512: 1d3ce17068ab0d96a427ebf7a453e14e67c123f23ce20d6eaf9562ed9e4acaea7a70225caa1f0a0aa2218966fd1f87553c9b9b218bddfdb001c95f01bbf98317 Homepage: https://cran.r-project.org/package=urbstatdata Description: CRAN Package 'urbstatdata' (Seven Data Sets for Urban and Built-Environment Statistics) Seven documented data sets from transport, traffic safety, urban planning, construction and architectural engineering. The package provides fixed, redistributable snapshots with consistent variable names. Each help page records the source, licence, unit of observation, transformations and limitations of its data set. Sources include Yeh (2018) , Tsanas and Xifara (2012) , Yeh (1998) , Seoul Bike Sharing Demand (2020) , and Singh and Chaudhari (2018) . 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Specifically developed for 'Bit.ly' (which requires OAuth 2.0) and 'is.gd' (no API key). Package: r-cran-uroot Architecture: all Version: 2.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-uroot_2.1-3-1.ca2404.1_all.deb Size: 1923216 MD5sum: 3f55b12279f344d50237af7ddb0f9a14 SHA1: a50b4d04a6897c6ec616b4343b92d0e14b75692c SHA256: 3b2b0964821852865626d7bd5dc2f3ed905a311be76500bd9faaac8edba923be SHA512: efc0e64b99b2a1bf0c4eb39dee1516bf3187484d2b4b4dc938a95a109298084be2b4a823199606941e5435c96223a473374a2c6b56d9a7ba7c8a85bac5140c02 Homepage: https://cran.r-project.org/package=uroot Description: CRAN Package 'uroot' (Unit Root Tests for Seasonal Time Series) Seasonal unit roots and seasonal stability tests. P-values based on response surface regressions are available for both tests. P-values based on bootstrap are available for seasonal unit root tests. Package: r-cran-urootab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eviewsr, r-cran-knitr, r-cran-magrittr, r-cran-xts, r-cran-zoo Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-urootab_0.1.0-1.ca2404.1_all.deb Size: 34504 MD5sum: b44a498c188a5449d3c6d627b15c51c7 SHA1: fb47fbc43db02d7b5c63f57da870ac2950c7866d SHA256: 673677e4858fe2801cfe345bd024c85289264175ca1f14b10323a9ab95cf3162 SHA512: bd57f2186cd8affe5e2552fb94f8bf5ac1b841619acac82d85ff928fb1b719fadb5966c86a25cb15f38d8489285c78f64c607315b2fafa10c37796c1abe2a5c2 Homepage: https://cran.r-project.org/package=URooTab Description: CRAN Package 'URooTab' (Tabular Reporting of 'EViews' Unit Root Tests) Conduct unit root tests based on 'EViews' () routines and report them in tables. 'EViews' (Econometric Views) is a commercial software for econometrics. Package: r-cran-uroscores Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-uroscores_0.1.0-1.ca2404.1_all.deb Size: 85836 MD5sum: a319c1c6e52b302780b0805606ae4361 SHA1: 28db1f04b684e9457d044102f04256b87cc38a02 SHA256: bdecf8f6ad81b2200694dca8e1bc00566a42a296efeac4765da5477c8daa2c84 SHA512: 31660f58806a1316388015b022eeea8ebc733e24e11654f706d9c31cb982d4f9b3ceb2aada3eefac7fc00497b05c6cf87f53aaedb92044220301e6c819ed1590 Homepage: https://cran.r-project.org/package=uroscores Description: CRAN Package 'uroscores' (Scoring Tools for Urology and Pelvic Health Research Instruments) Scores standardized patient-reported instruments used in urology and pelvic health research, including the International Prostate Symptom Score (Barry et al., 1992), the Overactive Bladder Symptom Score (Homma et al., 2006) , the O'Leary-Sant interstitial cystitis indices, the short forms of the Urogenital Distress Inventory and the Incontinence Impact Questionnaire (Uebersax et al., 1995) , the Sandvik incontinence severity index, and the Benign Prostatic Hyperplasia Impact Index. Instruments are declarative definitions read by a single scoring engine. Responses are checked against the permitted value set of each item, missing items follow the published rule for the instrument or return NA when none was published, severity bands are assigned by membership, and published minimal important difference statistics are included for responder analyses. Package: r-cran-us.census.geoheader Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-us.census.geoheader_1.0.2-1.ca2404.1_all.deb Size: 265622 MD5sum: 6047e1c2127eb5e8aa2cc83e12ba5e92 SHA1: f4f8b1185bb413aa82ef5b43390a53f939281221 SHA256: 82aa4aaf4ef4dab0571c06d2a37f167d551a4037ace0b17b9f5a2537744fcc67 SHA512: 6565c54ef6b224534fb4d8d43bb66fc6540e89b4b0bc7c26040c775674f61a09a194f4f014a462f9cd52b0b63138e537f4ff8168ff24c9b1a61ded218234d5ca Homepage: https://cran.r-project.org/package=us.census.geoheader Description: CRAN Package 'us.census.geoheader' (US 2010 Census SF2 Geographic Header Summary Levels 010-050) A simple interface to the Geographic Header information from the "2010 US Census Summary File 2". The entire Summary File 2 is described at , but note that this package only provides access to parts of the geographic header ('geoheader') of the file. In particular, only the first 101 columns of the geoheader are included and, more importantly, only rows with summary levels (SUMLEVs) 010 through 050 (nation down through county level) are included. In addition to access to (part of) the geoheader, the package also provides a decode function that takes a column name and value and, for certain columns, returns "the meaning" of that column (i.e., a "SUMLEV" value of 40 means "State"); without a value, the decode function attempts to describe the column itself. Package: r-cran-usa.state.boundaries Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6263 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-sf, r-cran-install.load, r-cran-drat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-usa.state.boundaries_1.0.1-1.ca2404.1_all.deb Size: 6374338 MD5sum: 51b5714a01655ef71e6349a7c7fcedfe SHA1: 8e6253bb9588983ddc66fcbd7711830e193c8b81 SHA256: 1c262761d8808431d1c706324d4edddfe5f679c4dec3e16d1b48e9239506f094 SHA512: 7030305754d7a0d8f659da4e1926d31b7bbcf1eba4eeef8e63f858f5fe71616e1b048b4550cd8fe6ea389f9c5a0ff11afd54c32ed3aa07f8c2e24063a42551d7 Homepage: https://cran.r-project.org/package=USA.state.boundaries Description: CRAN Package 'USA.state.boundaries' (WGS84 Datum Map of the USA, Including Puerto Rico and the U.S.Virgin Islands) Contains a WGS84 datum map of the USA, which includes all Commonwealth and State boundaries & also includes Puerto Rico and the U.S. Virgin Islands. This map is a reprojection of the NAD83 datum map from the USGS National Map. This package contains a subset of the data included in the 'USA.state.boundaries.data' package, which is available in a 'drat' repository. To install that data package, please follow the instructions at . Package: r-cran-usa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2027 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble Suggests: r-cran-covr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-usa_1.0.0-1.ca2404.1_all.deb Size: 1918244 MD5sum: 493636a7f9b98c255c448d554e9f2b39 SHA1: c174d1254b36b2ced382517f64990c1957622941 SHA256: e8f8da73c903fe2842aec72a0593fb7df14a8f2b8047ccd2a187b004d980492b SHA512: 1c30bd165b564c9d82043c6e1305e0848c0882a3b78e0b37eb9e59a01757bd11d6e6473e624eecf105d526a93a07a9f1bfdaadf4246005eb79a746d80b809c92 Homepage: https://cran.r-project.org/package=usa Description: CRAN Package 'usa' (Updated US State Facts and Figures) Updated versions of the 1970s "US State Facts and Figures" objects from the 'datasets' package included with R. The new data is compiled from a number of sources, primarily from the United States Census Bureau or the relevant federal agency. Modern tidy tibbles provide richer state-level data including identifiers, geography, capitals, demographics, and socioeconomic statistics. Convenience vectors parallel the base 'datasets' state objects but extend coverage to all 51 jurisdictions: the 50 states and the District of Columbia. Package: r-cran-usaboundaries Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-leaflet, r-cran-pak, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-usaboundaries_0.5.1-1.ca2404.1_all.deb Size: 237400 MD5sum: f0cb6383b81d5939b60f53e4b559bb12 SHA1: 0750d9bf050a5bf2a831ada3c2d07b6b05d732ea SHA256: 3cc0620157393a4974cda863887a16e9369ebdf8922ee70cb4c8e55822ad15e5 SHA512: e583dbd2704be1983df0135f48cd899caa64891bc7cf13c19efe60337209cea93e431b44661c709a7bb0fc9e7f838bce068405d1792ba33fef30851fbfd33082 Homepage: https://cran.r-project.org/package=USAboundaries Description: CRAN Package 'USAboundaries' (Historical and Contemporary Boundaries of the United States ofAmerica) The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Library's Atlas of Historical County Boundaries (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data. Package: r-cran-usaidplot Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extrafont, r-cran-extrafontdb, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-usaidplot_2.0.2-1.ca2404.1_all.deb Size: 128524 MD5sum: b093a295aac2cc6126e3e3fa7b440c48 SHA1: 76dfdb23001f30cc0beb7c470e30bc1a7655770a SHA256: bb7032c5b3c7b88f8b1b442ed8ef26f56a666c03098d34c24ebff9b79cdcbba5 SHA512: f09e539a0d65a8a32c2c0da3326258f841ece9cd1b6fd3aaa686fc6aa7eadbd52dbc93678ec6bf299bc37b4d31637d089d65c997c864ddc10685898345933ef8 Homepage: https://cran.r-project.org/package=usaidplot Description: CRAN Package 'usaidplot' (Make Graphs with US Agency for International Development Colors) Automatically apply the United States Agency for International Development's color palette and fonts for either discrete or continuous variables. Package: r-cran-uscoauditlog Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-openxlsx, r-cran-readxl Filename: pool/dists/noble/main/r-cran-uscoauditlog_1.0.3-1.ca2404.1_all.deb Size: 40984 MD5sum: cfc2a4e0f972f3c110eb26747fca3a40 SHA1: 52766c761064484e07759eaeb54c7eaeab31df3b SHA256: 383ddcde5b614309b8119efb003c84fe8ad54194694fbd3471bc362455a477cb SHA512: 8b3ee9475406af32fb3d9b211844688fe1a59386bae9b0be0aefe7f23205e10af600b5bff5062447cd10b2ae944b96b7f5c2bbe7174fa1d66d658f4fad49cc12 Homepage: https://cran.r-project.org/package=uscoauditlog Description: CRAN Package 'uscoauditlog' (United States Copyright Office Product Management Division SRAudit Data Dataset Cleaning Algorithms) Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database. Package: r-cran-uscongress Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-stringr, r-cran-rvest, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-uscongress_1.0.2-1.ca2404.1_all.deb Size: 21630 MD5sum: cfdf9b805e7dbd66938e599d0c775b63 SHA1: e5e4a8e98d3e9c73c747a7d2aef5c5eb89b0de7c SHA256: c9fc21e34c7866d0afeeea25813a74fa494a96692259849d171c08ab824e412d SHA512: ac797bd82ac16392317b40a1fba06dc2693f5e8c743c0118cb93e450bad21c7fe3db94749408a31951d789f094483d8c963bf6d57f603faf36f45ba12ac9753c Homepage: https://cran.r-project.org/package=uscongress Description: CRAN Package 'uscongress' (Fetch United States Congressional Records (1995-Present)) Fetch United States Congressional Records from their API such as congressional speeches, speaker names, and metadata about congressional sessions, and detailed granule records. 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Package: r-cran-usdampr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-usdampr_1.0.1-1.ca2404.1_all.deb Size: 26768 MD5sum: 4dd4da4acdf8dca70060cff6db62f7da SHA1: 74158e8d85de018c1b00792d76b180ba252185ca SHA256: 4cb409791996131d82d572fc65caa97c214b1b5776fea721fd95ae6bc040f61d SHA512: 8d70ebafa49eac9cf8e1e56a1ddccc99ae043c6b80e0c739bdd167762af9c76a663898674d531788e8f8f63ad2f094dab5a2c34949af09a442e5d2fe76f1ec19 Homepage: https://cran.r-project.org/package=usdampr Description: CRAN Package 'usdampr' (Request USDA MPR Historical Data via the 'LMR' API) Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR') Data API at . The downloaded data can be saved for later off-line use. Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry. 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You must sign up for an API token from the mentioned website in order for this package to work. 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Leveraging the data collected in the Child Health and Mortality Prevention Surveillance (CHAMPS;) project, the package stores misclassification matrix estimates of three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. These estimates are obtained using the framework proposed in Pramanik et al. (2025;) and are analyzed in Pramanik et al. (2026;). Given VA-only data for an age group, CCVA algorithm, and country, the package utilizes the corresponding misclassification matrix estimate in the modular VA-Calibration framework (Pramanik et al.,2025;) and produces calibrated estimates of CSMFs. The package also supports ensemble calibration to accommodate multiple algorithms. More generally, this allows calibration of population-level prevalence derived from single-class predictions of discrete classifiers. For this, users need to provide fixed or uncertainty-quantified misclassification matrices. This work is supported by the Eunice Kennedy Shriver National Institute of Child Health K99 NIH Pathway to Independence Award (1K99HD114884-01A1), the Bill and Melinda Gates Foundation (INV-034842), and the Johns Hopkins Data Science and AI Institute. Package: r-cran-vaccinationimpact Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vaccinationimpact_0.1.0-1.ca2404.1_all.deb Size: 38846 MD5sum: 162f634e22e1c7d65cceec872d432bbc SHA1: 65c03048c3dd194cf804292d4bbc21d0376e3869 SHA256: 77f3ea4023d709a6893ddf1fac2a5510702eb64b6974e837f11476413e9ee902 SHA512: a720e1b428ca6aa7990145603de9eaf8f3734b3414ff0608459cac9902458e373e7d9c4f444f1e6a4190d473ac3778bc5506e577b584f2c9106b15c0f9073cd5 Homepage: https://cran.r-project.org/package=vaccinationimpact Description: CRAN Package 'vaccinationimpact' (Impact Study of Vaccination Campaigns) Tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) and Machado et al. (2019) for influenza vaccination impact. Package: r-cran-vaccine Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-fdrtool, r-cran-ggplot2, r-cran-ggpubr, r-cran-rlang, r-cran-rsolnp, r-cran-superlearner, r-cran-survival, r-cran-survml, r-cran-truncnorm, r-cran-e1071, r-cran-gam, r-cran-ranger, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vaccine_1.4.0-1.ca2404.1_all.deb Size: 770390 MD5sum: 400cfbbb4f7669e27d0c805b224b7713 SHA1: f35bf6ebbfebe56daad068700354aa543d0997ef SHA256: bfe985631ba4341c28bb72c32152bf97b37ae7752253a913434fe8521550518d SHA512: 839da9ed974dab2384a5776099f046721bbfbc2ed9b7c9c699fdb51b2adc0f984cfb776118ae60009d26d80e66348f308b13330b2dd7411d8ed52f1ff039acca Homepage: https://cran.r-project.org/package=vaccine Description: CRAN Package 'vaccine' (Statistical Tools for Immune Correlates Analysis of VaccineClinical Trial Data) Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) and Fay MP and Follmann DA (2023) . Package: r-cran-vaccineff Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1589 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-linelist, r-cran-matchit, r-cran-rlang, r-cran-scales, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vaccineff_1.0.3-1.ca2404.1_all.deb Size: 1169634 MD5sum: 205bb91303ebf098af4ed95ea6f586ea SHA1: cdf396e9a9f96d9fcb375e1edae2575feb7c8f16 SHA256: e7f85035799859189b101e08f5fc9ba5e3954336a8593372b42886500ec03e9e SHA512: d187825797e67a007d24a920f4f3df3f60853d0be9f09b433f5549fa0fc8ffee2dce8e7f9b90d6f65330cfb894e1f41762ab91a54dad0e4816500e2fbc17febb Homepage: https://cran.r-project.org/package=vaccineff Description: CRAN Package 'vaccineff' (Estimate Vaccine Effectiveness Based on Different Study Designs) Provides tools for estimating vaccine effectiveness and related metrics. The 'vaccineff_data' class manages key features for preparing, visualizing, and organizing cohort data, as well as estimating vaccine effectiveness. The results and model performance are assessed using the 'vaccineff' class. Package: r-cran-vachette Architecture: all Version: 0.40.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-prospectr, r-cran-hmisc, r-cran-purrr, r-cran-minpack.lm, r-cran-progress, r-cran-rlang, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mrgsolve, r-cran-tidyvpc, r-cran-testthat, r-cran-learnr Filename: pool/dists/noble/main/r-cran-vachette_0.40.1-1.ca2404.1_all.deb Size: 477102 MD5sum: de17dd35ccae5c7de0f06135c8f1016c SHA1: 2fa6b864ab1244aa95874b7b7b63edb0bf6eae4a SHA256: b2d7bf1c5c537f1b0ed9bf0a7ad9219447aea6ca717ca4a964853d26e169a81c SHA512: e9b7ff038da138a281573d610e116f99a17b0e7ad721088b0008d6329349c1fe239a15bf9dffb783a2e5e93d4dabbf444c4192d272a91025dfd5ce2bd426f5c7 Homepage: https://cran.r-project.org/package=vachette Description: CRAN Package 'vachette' (A Method for Visualization of Pharmacometric Models) A method to visualize pharmacometric analyses which are impacted by covariate effects. Variability-aligned covariate harmonized-effects and time-transformation equivalent ('vachette') facilitates intuitive overlays of data and model predictions, allowing for comprehensive comparison without dilution effects. 'vachette' improves upon previous methods Lommerse et al. (2021) , enabling its application to all pharmacometric models and enhancing Visual Predictive Checks (VPC) by integrating data into cohesive plots that can highlight model misspecification. Package: r-cran-vacuum Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-tidyr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vacuum_0.1.0-1.ca2404.1_all.deb Size: 79546 MD5sum: ae0dd5310b1fe75e72a196b5c3075242 SHA1: b71367205783324b123be6347b61f6d74f408a5c SHA256: 131e0e89e52d6625a428349c416100fd38b73bd03516bb6de4059fdc2f43f7da SHA512: d060c00eeb0050aa886dd2b9b51589994d8d1c57f75e52ab3c5afb770b9b6ccd862afb47d9c25b2a5a20b4233552e51dff0483614a463c78a889708f457649df Homepage: https://cran.r-project.org/package=vacuum Description: CRAN Package 'vacuum' (Tukey's Vacuum Cleaner) An implementation of three procedures developed by John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. Combined, they provide a way to identify, treat, and analyze outliers in two-way (i.e., contingency) tables, as described in his landmark paper "The Future of Data Analysis", Tukey, John W. (1962) . Package: r-cran-vader Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tm Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-vader_0.2.1-1.ca2404.1_all.deb Size: 145264 MD5sum: eeaa0719607e9ef42328803bbf433ebb SHA1: 83b315bcaca3fbb4013d77de0faa0b1423683970 SHA256: 34657a5a3842ce269c624107cc896fce28f8281d99030fdcafd2a18fe0f5f7c6 SHA512: c533b2c2dc86f54fc2fcfa475b02d5a0bb11b3aa9d2ac339f3c8bee60fba29a7a9bd83cff16057f085810c84a8a5d55ac11ad7b6535b34ad6c7785b0467352e9 Homepage: https://cran.r-project.org/package=vader Description: CRAN Package 'vader' (Valence Aware Dictionary and sEntiment Reasoner (VADER)) A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. Hutto & Gilbert (2014) . Package: r-cran-vaersndvax Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vaersvax, r-cran-data.table, r-cran-dplyr, r-cran-rpivottable Filename: pool/dists/noble/main/r-cran-vaersndvax_1.0.4-1.ca2404.1_all.deb Size: 588174 MD5sum: be42519d95292bf72806618620c1778c SHA1: 7d205c081acce3c1ef6b06413aeec3db61cd29f7 SHA256: eed22cd43ed4eb8c66cef254824e9ac8a68a73bbd9f698c7f18c14873e1327ae SHA512: 69d18faacaca304687d12c56bca4a8828d870576122e2ab5bfdda94f48b2151f7ffb081a8ccc96255e3e7835b33729f20a536f5c1c438dd4d182497c97b597a5 Homepage: https://cran.r-project.org/package=vaersNDvax Description: CRAN Package 'vaersNDvax' (Non-Domestic Vaccine Adverse Event Reporting System (VAERS)Vaccine Data for Present) Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaersND' package from the URL below. The URL and BugReports below correspond to the 'vaersND' package, of which 'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("https://gitlab.com/iembry/vaers.git", build_vignettes = TRUE)' and 'devtools::install_git("https://gitlab.com/iembry/vaersND.git", build_vignettes = TRUE)'. "VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a post-marketing safety surveillance program, collecting information about adverse events (possible side effects) that occur after the administration of vaccines licensed for use in the United States." For more information about the data, visit . For information about vaccination/immunization hazards, visit . Package: r-cran-vaersvax Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vaersndvax, r-cran-dplyr, r-cran-data.table, r-cran-pivottabler, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vaersvax_1.0.5-1.ca2404.1_all.deb Size: 148358 MD5sum: 8e39cd5cb6709d6cd4473959c76cc02b SHA1: 201c2a00721af579064bbf28f27a30562f4a88e5 SHA256: 7673e6b00bf2a301a59daec0ffbe856bca69a8e59b9fdc13b6f44d133f206430 SHA512: a3770a41aae4354fa906b70a597654bb52d1c279ef65b79d34f9cd52e9c5e20b6625e38ea1a23f9ce50d3a395f72a4be213042dce68711b28edfccb67e96f7d1 Homepage: https://cran.r-project.org/package=vaersvax Description: CRAN Package 'vaersvax' (US Vaccine Adverse Event Reporting System (VAERS) Vaccine Datafor Present) US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaers' package from the URL below. The URL and BugReports below correspond to the 'vaers' package, of which 'vaersvax' is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("", build_vignettes = TRUE)' and 'devtools::install_git("", build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For more information about the data, visit . For information about vaccination/immunization hazards, visit . Package: r-cran-vagalumer Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-tm Filename: pool/dists/noble/main/r-cran-vagalumer_0.1.6-1.ca2404.1_all.deb Size: 30780 MD5sum: f01804c98d73778b62061f920ab907ad SHA1: 2c69dd1deb368bdcb2454449a1aebc6ce892bb1f SHA256: 70746039e850e5966ad9de6d01a12db40e646a96e75455a0ce9d04c97d8095af SHA512: 9bafba740014a7f4ffef3d517516b733af4b2d833ef353b413607f878c15e57a083de9c8560faf6c8cee6a65845f07ec73b8ebf23336d783c789d166fcc5100d Homepage: https://cran.r-project.org/package=vagalumeR Description: CRAN Package 'vagalumeR' (Access to the 'Vagalume' API) Provides access to the 'Vagalume' API . The data extracted is basically lyrics of songs and information about artists/bands. 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In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) . Package: r-cran-valaddin Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lazyeval Suggests: r-cran-magrittr, r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-valaddin_1.0.2-1.ca2404.1_all.deb Size: 185174 MD5sum: f30e8e978ce74ccaa06f8278fd93824a SHA1: ce0861006db3687987033d59423b7efccae47199 SHA256: d1fab98d69574edcfacffb535bd5e62cfcb8a72236924c4ac5dcd5c86e727056 SHA512: 1a52fa739ab38af9ff0c0138315b3fdcb7487d080da83dd2688d1bcf0465b66a9b98851d36e92c5276e2659eee8f0747c93225d85e2de1de359ebfadea516568 Homepage: https://cran.r-project.org/package=valaddin Description: CRAN Package 'valaddin' (Functional Input Validation) A set of basic tools to transform functions into functions with input validation checks, in a manner suitable for both programmatic and interactive use. 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Designed to streamline integration for analysts and researchers working with 'VALD's external 'APIs'. For further documentation on integrating with 'VALD' 'APIs', see: . For a step-by-step guide to using this package, see: . 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Includes interactive modules for metric exploration, radar charts, longitudinal comparisons, quadrant plots, and athlete reports. Package: r-cran-valection Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat Filename: pool/dists/noble/main/r-cran-valection_1.0.0-1.ca2404.1_all.deb Size: 130216 MD5sum: 5adab1defdd33c51b7dc53f3ea52c1a8 SHA1: d5c75d1f9464e35280eaac717e4dc4dfb8de45fd SHA256: f0ddf81c85620c672f0d19d83daccd8db99bbaf1c094de7ca32f6a11ea837aab SHA512: 572b48fc03a0655127a9b75d95a0993bd558ba6c960a29eb45d404b2fb15b08e39cb8c720e1571cddc359bea2e09460ddbde9b6914262b7ac5359ee93b5a7f9a Homepage: https://cran.r-project.org/package=valection Description: CRAN Package 'valection' (Sampler for Verification Studies) A binding for the 'valection' program which offers various ways to sample the outputs of competing algorithms or parameterizations, and fairly assess their performance against each other. The 'valection' C library is required to use this package and can be downloaded from: . Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; . Package: r-cran-valentine Architecture: all Version: 2025.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ellmer, r-cran-glue, r-cran-rlang Filename: pool/dists/noble/main/r-cran-valentine_2025.2.14-1.ca2404.1_all.deb Size: 539828 MD5sum: d688a3d9f092aa6cf3d9182828a6b07d SHA1: 5c9a03edac647cea80718bb8d37bcf54f9f939c8 SHA256: 280469cb22ff331b9598b20e9460b7c3882f25cb147c418a75804d0555de0fa1 SHA512: ce45c08941ce5bee7ba924a131e6dbbd23379604e12bb0663154d7524aedb8665f0ac1aeccfd1d82d60f582d1b43c56e3902b094b5e40a3cff4270d989c9787d Homepage: https://cran.r-project.org/package=valentine Description: CRAN Package 'valentine' (Spread the Love for R Packages with Poetry) Uses large language models to create poems about R packages. Currently contains the roses() function to make "roses are red, ..." style poems and the prompt() function to only assemble the prompt without submitting it to the model. Package: r-cran-valerie Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicalignments, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-cran-plyr, r-cran-ggplot2, r-cran-pheatmap, r-cran-ggplotify, r-cran-ggpubr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-valerie_1.1.0-1.ca2404.1_all.deb Size: 4448490 MD5sum: 191de99d4b247514fd5af417b4593b27 SHA1: e5414c6e798bfccff316cf089eb14bfd165eeb96 SHA256: ea5861d3feea7e7916c0171db3fa623495dc9a45b0b35574e17a4145568d644d SHA512: 462a059804294eb21cbc3d20d137d87f5a0ef1acb8a5be97f1ae89bce58d6a42737ccf9eb6924c8061a8826d5418e78f78c232eb516df1810259d30e0bef4197 Homepage: https://cran.r-project.org/package=VALERIE Description: CRAN Package 'VALERIE' (Visualising Splicing at Single-Cell Resolution) Alternative splicing produces a variety of different protein products from a given gene. 'VALERIE' enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. 'VALERIE' computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) . PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) . Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the 'pheatmap' package by Kolde (2010) . Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the 'ggplot2' package by Wickham (2007) . Package: r-cran-valet Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-dplyr, r-cran-readr, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-valet_0.9.1-1.ca2404.1_all.deb Size: 26274 MD5sum: c880a6c6d94424fba946cb7b5fff648a SHA1: b756b10c2a704b20d2e1d0c3750e5b3069fee7c5 SHA256: cd6ef20fbe7df75eae5f772cc812f1463f82b1d7582052781c89aa862b968884 SHA512: 085df7d43b5b9a48e5c33d7046644420fe6281d677c8aa2a6e7cc23b337a4e3ac8aba836228d512755d20eeecc71448f99d3565e9a07717530657c06eb9efd1f Homepage: https://cran.r-project.org/package=valet Description: CRAN Package 'valet' (Provide R Client to the Bank of Canada's Valet API) The Bank of Canada updated their Valet API , and no R client currently exists. This provides access to all of Valet's endpoints and serves responses in wide format easy for researchers to handle but also provides tools to access API responses as a list. Package: r-cran-valh Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-googlepolylines, r-cran-curl, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-valh_0.2.0-1.ca2404.1_all.deb Size: 924912 MD5sum: b6a0121bd8b303f9d1625b23749a3f51 SHA1: f1319a519958807753ae8ec9100a07edbcfb9f4d SHA256: 598f101a51d1a27e94793c80fc745e2740e4802433927a6e82da2f372bdffd2c SHA512: b356177d99cf4c8aee9031ed1ef2331315c82242365b7b9ee031c55d805c6d5380b424603a19aab389637be6d46164bfd6cee0b2553bdcf08c05c3cad45ff9ac Homepage: https://cran.r-project.org/package=valh Description: CRAN Package 'valh' (Interface Between R and the OpenStreetMap-Based Routing Service'Valhalla') An interface between R and the 'Valhalla' API. 'Valhalla' is a routing service based on 'OpenStreetMap' data. See for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometer distance). Package: r-cran-valhallr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-purrr, r-cran-jsonlite, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-sf, r-cran-leaflet, r-cran-ggplot2, r-cran-htmltools, r-cran-stringr, r-cran-ggspatial, r-cran-geojsonio, r-cran-rlang, r-cran-cairo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-valhallr_0.1.0-1.ca2404.1_all.deb Size: 320776 MD5sum: 18ad762ceca8c303f7960ae029037ebe SHA1: bbf93cbe57e8035170815a0cdfc81b9bb60c6aca SHA256: c61c9209b965f3e5731b9ba2768abc59e503c9e2a8f4290c0d050c227ac1a83e SHA512: 8404be507d95439d1ce8a010eeb72c6ae62e76490097094fe54cc8815be447791c43a1efeed5cea3100a7719a9992876f47f97170f1973579947ea98072a557e Homepage: https://cran.r-project.org/package=valhallr Description: CRAN Package 'valhallr' (A Tidy Interface to the 'Valhalla' Routing Engine) An interface to the 'Valhalla' routing engine’s application programming interfaces (APIs) for turn-by-turn routing, isochrones, and origin-destination analyses. Also includes several user-friendly functions for plotting outputs, and strives to follow "tidy" design principles. Please note that this package requires access to a running instance of 'Valhalla', which is open source and can be downloaded from . Package: r-cran-validann Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-moments Suggests: r-cran-nnet, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-validann_1.3-1.ca2404.1_all.deb Size: 107026 MD5sum: b3e1804cc4d78429df6268619443e417 SHA1: 18a8058b7ec6d598e8fab8a9aa16e6378af34776 SHA256: b14c5ecb92e8ba0ad523c7915e89655d144db91c1e2276de689bb921e533c829 SHA512: 5e0a7ee774ed036c667faffd8a7c15eb06079de170546046d4737bff81ffb74ab79103fbf942838cbe28554cb5ede700bbd2d138da4edbcdc42ce5bc1402526f Homepage: https://cran.r-project.org/package=validann Description: CRAN Package 'validann' (Validation Tools for Artificial Neural Networks) Methods and tools for analysing and validating the outputs and modelled functions of artificial neural networks (ANNs) in terms of predictive, replicative and structural validity. 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This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) . For more information, please visit . 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Results are returned as a structured 'validation_report' object with 'print' and 'plot' methods for quick inspection. 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Package: r-cran-validatetools Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-validate, r-cran-lpsolveapi Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-validatetools_0.6.1-1.ca2404.1_all.deb Size: 149544 MD5sum: 243661d8f659508fccb2a8b412bb90b2 SHA1: de4c5dd6e9d4c8f614ac21ffbf8977a4954d5ffd SHA256: ee28d53eae6d68c974e18db62f2f67fef15884020deb1ea79cea07d1270fb6b5 SHA512: 1311b6923dc99160ed87665f7343b0d20778b1f22c2d9af780396a1f466b479d973bef6f5ee0220d50b07735c1d8ab31e9aeef7fbf3838aacd3ac9fed7b361d7 Homepage: https://cran.r-project.org/package=validatetools Description: CRAN Package 'validatetools' (Checking and Simplifying Validation Rule Sets) Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'. Package: r-cran-validationexplorer Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rstan, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-validationexplorer_0.1.2-1.ca2404.1_all.deb Size: 401064 MD5sum: 02e651a867fc1f3c4ee5ae1b25ac991c SHA1: 8fd05573f7c8417de168b2acfdf18bff49b1e55b SHA256: d796cc4431d3084b92d019928b4c5e43f5b50358cad13230b7a61840f2e42ac0 SHA512: d48730e8e744dcc822875c92313d80559d484a3874f109dc64f427b6509d3e2e941c4cdfed3a0ed9c7ff53dc8c2c29c50a5c5d00aafd28b97383ffca8310a0ba Homepage: https://cran.r-project.org/package=ValidationExplorer Description: CRAN Package 'ValidationExplorer' (Simulation-Based Tools for Bioacoustic Study Design) Many bioacoustic data workflows rely on manual review (i.e., validation) of a subset of call files to provide information to statistical models that account for misclassification by automated algorithms. Because manual review can be prohibitively expensive, simulation can be a valuable tool to aid the design of studies that use validation. This package provides user-friendly functions to reduce the programming burden of simulation studies that compare validation sampling designs. Simulations assume the count-detection model, which is a realistic model for bioacoustic data, especially for bats. For more information, see Oram et al. (2025) . Package: r-cran-validiclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diptest, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-validiclust_0.1.0-1.ca2404.1_all.deb Size: 28456 MD5sum: 175cc183d6f0e8fe8607a136bdd527b2 SHA1: bec925b0fa5cae0e8743e0957482ac47f6f8f821 SHA256: e705f15da39b0afd4b90c37a760363ef4e6c3e1b6fb0c0ed1c61cb3a2f22fa9f SHA512: f758605f77ea384921d5b661a5c1c3bb99b20d3c8157012402ba5ce8810acbf1e562e1aa2cf735cfab69cf24e097b46dbf8d1d8624b98d81a74031536d525173 Homepage: https://cran.r-project.org/package=VALIDICLUST Description: CRAN Package 'VALIDICLUST' (VALID Inference for Clusters Separation Testing) Given a partition resulting from any clustering algorithm, the implemented tests allow valid post-clustering inference by testing if a given variable significantly separates two of the estimated clusters. Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). "Post-clustering difference testing: valid inference and practical considerations", . 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Based on vectors of observed and predicted values. Method: Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg (2021). . Package: r-cran-valottery Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-valottery_0.0.1-1.ca2404.1_all.deb Size: 228908 MD5sum: 357d9523899051e4a7ef40dfb122a962 SHA1: 905c085e329b0d0c0bc39ab41886a8fa39cff6af SHA256: d779fe2f926ffd2de02e44db801384627c26abb59472367677116348bf0f306e SHA512: a51caf6acddba174c0cb046d8ccfcdc28c27a4b6ed369c9e64c44ac054035e384b8fbae9d18caee5c741a2e335543dfbe105e88b28067bf3ec74f0dbc5773830 Homepage: https://cran.r-project.org/package=valottery Description: CRAN Package 'valottery' (Results from the Virginia Lottery Draw Games) Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/. 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In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected. 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Package: r-cran-var.spec Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-var.spec_1.0-1.ca2404.1_all.deb Size: 122944 MD5sum: f4237bbc1bc3fc0201cfdace4023dee3 SHA1: 9169b1e31811a1e255da08db80d086bb55020f92 SHA256: 266e6ab0583442d72261559fb102f3083a3cdda754db3158e46d9f2c73edd278 SHA512: 3243539df500300f05399b49c4a34e948b3901966fa8004d1040c2b03af21fa4f773fd848dce5455d496b9e32fd05ef7d6287cb10d9a89b1362354886c608ec2 Homepage: https://cran.r-project.org/package=VAR.spec Description: CRAN Package 'VAR.spec' (Allows Specifying a Bivariate VAR (Vector Autoregression) withDesired Spectral Characteristics) The spectral characteristics of a bivariate series (Marginal Spectra, Coherency- and Phase-Spectrum) determine whether there is a strong presence of short-, medium-, or long-term fluctuations (components of certain frequencies in the spectral representation of the series) in each one of them. These are induced by strong peaks of the marginal spectra of each series at the corresponding frequencies. The spectral characteristics also determine how strongly these short-, medium-, or long-term fluctuations of the two series are correlated between the two series. Information on this is provided by the Coherency spectrum at the corresponding frequencies. Finally, certain fluctuations of the two series may be lagged to each other. Information on this is provided by the Phase spectrum at the corresponding frequencies. The idea in this package is to define a VAR (Vector autoregression) model with desired spectral characteristics by specifying a number of polynomials, required to define the VAR. See Ioannidis(2007) . These are specified via their roots, instead of via their coefficients. This is an idea borrowed from the Time Series Library of R. Dahlhaus, where it is used for defining ARMA models for univariate time series. This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage. 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The purpose is to assign a unique Weight-of-Evidence value to each of the calculated binpoints in order to recode the original variable. The package allows users to impose certain restrictions on the functional form on the resulting binning while maximizing the overall information value in the original data. The package is well suited for logistic scoring models where input variables may be subject to restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum information value. 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This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written and crafted since 2014 with years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are supposed to be efficient and easy to use, hence they will be frequently updated to make them more convenient. 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The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079). Package: r-cran-variables Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-variables_1.1-2-1.ca2404.1_all.deb Size: 43302 MD5sum: 6fce5387ec1a028cbc2d328ad9cc6f50 SHA1: 97febafbf7ea5c41a7a92e6e2b2ed339eb82c5c1 SHA256: bb7178bf98509900f7110d330a4b0724b56267b3f946ce1d894bede240aa3479 SHA512: 31f45dae19db1542cf3420099b59c31330b70217cccb0980c1139360053eef13a666989ebe62ff62a8e01d19a7d093bf047de597839f0d7d5f75a7476dff9c57 Homepage: https://cran.r-project.org/package=variables Description: CRAN Package 'variables' (Variable Descriptions) Abstract descriptions of (yet) unobserved variables. Package: r-cran-variablescreening Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-expm, r-cran-mass, r-cran-energy Filename: pool/dists/noble/main/r-cran-variablescreening_0.2.1-1.ca2404.1_all.deb Size: 72964 MD5sum: 4fa82ef52747cbbf2dfb591ac54ceeee SHA1: 9cb17c4211d6430622b877ddfcbdf8bbd742d6c8 SHA256: 36ea55fd977ab5e7e211c087e96f04a9332cb23247cd5a624af1dfe3a5b08299 SHA512: 974ac812579fff04f359ff4a0f4e5210fd439e2cc4192f4d4afb18639f306d139a84c7540ff83d40e961af3911a6947a4ea9fb85bee51fe472db833eb54953c5 Homepage: https://cran.r-project.org/package=VariableScreening Description: CRAN Package 'VariableScreening' (High-Dimensional Screening for Semiparametric LongitudinalRegression) Implements variable screening techniques for ultra-high dimensional regression settings. Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) , Runze LI, Wei ZHONG, & Liping ZHU (2012) , Jingyuan LIU, Runze LI, & Rongling WU (2014) Hengjian CUI, Runze LI, & Wei ZHONG (2015) , and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) . Package: r-cran-variableselection Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-memoise Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-variableselection_1.0.0-1.ca2404.1_all.deb Size: 110788 MD5sum: db8ed9c25b03fe57f1e5cb400d24b295 SHA1: 6f2fe9714d2412e1b4b12cebea5ff9315aa890fc SHA256: 2394d293ee1e5c8f0d99162d5e96de675aa648b9f4c4f7e999c5035603fb339c SHA512: 7bac9a70ff2b0bbd5b8b89d8b0d819335f355d7ff116bac127fecd2dd88b316eff200a85f89f7cd49547fada346336b1c1af62cd981fad8027ecaf2bd59e5929 Homepage: https://cran.r-project.org/package=VariableSelection Description: CRAN Package 'VariableSelection' (Select Variables for Linear Models) Provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) . Package: r-cran-varian Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-mass, r-cran-formula, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-varian_0.2.2-1.ca2404.1_all.deb Size: 85510 MD5sum: 0bb7cf9695c3a8211b8a65e5397155b8 SHA1: 67bdd0643d10a3eda01535f2dc75217340707a0b SHA256: ff1e840855b942d2faa507af02b917114a89e4035dd08d62e228b827529524ca SHA512: 863ca42053fd53803de57793a0963bc5f047b6fc1f315dee8da4b214e1cb932c8083e2f51d88bbfa3416ccba8a12592596ff2ab3b1a36f5c7df8373006d431d2 Homepage: https://cran.r-project.org/package=varian Description: CRAN Package 'varian' (Variability Analysis in R) Uses a Bayesian model to estimate the variability in a repeated measure outcome and use that as an outcome or a predictor in a second stage model. Package: r-cran-variancegamma Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distributionutils, r-cran-generalizedhyperbolic Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-variancegamma_0.4-2-1.ca2404.1_all.deb Size: 145292 MD5sum: bd71cfe2d43b95ee8906be30a01705c9 SHA1: a644a0d12bd2daaa597ed6ccdabc8d0591994141 SHA256: 95d2c5cd73cac60510ad47c5cbd4399bd37a9b9b896888c5a79fe5f1e87d7fd4 SHA512: 0fc5b9a565a61324a65ba4acc2d5a5732a19ae962c76115f0f439bac4408bbf2b8492d2a038cebd87282076ca4cb0b92e6fde77e6af2ba8757c425439ad19539 Homepage: https://cran.r-project.org/package=VarianceGamma Description: CRAN Package 'VarianceGamma' (The Variance Gamma Distribution) Provides functions for the variance gamma distribution. Density, distribution and quantile functions. Functions for random number generation and fitting of the variance gamma to data. Also, functions for computing moments of the variance gamma distribution of any order about any location. In addition, there are functions for checking the validity of parameters and to interchange different sets of parameterizations for the variance gamma distribution. Package: r-cran-variantspark Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1530 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-variantspark_0.1.1-1.ca2404.1_all.deb Size: 1418656 MD5sum: 182f20a054ecae95e6a97b4fe4ff5d50 SHA1: 0e4e5822895cd94c97c1cae73cb5185b9d2b891d SHA256: 187de8f2171ca095055257a57458245897765bd52db3752844b3f5a96490e7e5 SHA512: 66d4f63bfccd6891b8a58e1c45e81feba1858fc4cc355ec70ec9772f7ddd89fe4e2f5aaa7416436cb7cd734f00b8abbdbec3275e49330c1f0547ceae99700450 Homepage: https://cran.r-project.org/package=variantspark Description: CRAN Package 'variantspark' (A 'Sparklyr' Extension for 'VariantSpark') This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is a framework based on 'scala' and 'spark' to analyze genome datasets, see . It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a 'spark' data frame. Package: r-cran-variationaldcm Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-variationaldcm_2.0.1-1.ca2404.1_all.deb Size: 138976 MD5sum: ca7720c0c7beeb59b28ee2d1bfa47d7b SHA1: 33d3a8f164bcf70bd294f9863a0db1417cf3afe8 SHA256: 557a503f249c6a737055af4414df11607d092781e88ffe05d8ad4e4faa75a6f2 SHA512: c7753ad8caafd431cebd450b69d54ff3def92f7670fd9f1820785cebcd7eb5a3dcf87caee203ab3e31fe6cbc1437fd439c21c868cc7250465afcd4e9d3978052 Homepage: https://cran.r-project.org/package=variationalDCM Description: CRAN Package 'variationalDCM' (Variational Bayesian Estimation for Diagnostic ClassificationModels) Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) , Yamaguchi and Okada (2020b) , Yamaguchi (2020) , Oka and Okada (2023) , and Yamaguchi and Martinez (2023) . To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs. Package: r-cran-varimp Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-measures, r-cran-party Suggests: r-cran-testthat, r-cran-ranger Filename: pool/dists/noble/main/r-cran-varimp_0.4-1.ca2404.1_all.deb Size: 42436 MD5sum: 804e46660c5b932f83ee4c6fdb568281 SHA1: 10a8d6b2e3ce15802321a972ae5ce4852adf4caf SHA256: 1be7167a7fa1165b2a6d24c86f22556838a7f5c999f5d599bf8202578ec942be SHA512: 72cab1bf3889307f5f0686c7b98c8e94a9aaa194d869c32a767735b51728e301b539048aa9e0ea118a1d36951c5b0307a8e5e82976f932197a73a3b1da74b7a3 Homepage: https://cran.r-project.org/package=varImp Description: CRAN Package 'varImp' (RF Variable Importance for Arbitrary Measures) Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC). Package: r-cran-variosig Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gstat, r-cran-sp, r-cran-testthat Suggests: r-cran-geor Filename: pool/dists/noble/main/r-cran-variosig_0.3-1-1.ca2404.1_all.deb Size: 39742 MD5sum: 19c6359fef1eb720c95810e919e7580b SHA1: 3b1ddb4373940800c7d204041d46ba26fd379fa3 SHA256: 2a74a2a9dc72b2c543e0332425e167938cdcd99c16e17cd29da35c984d85949e SHA512: b8b5a562985b42ed5268111b97bbbf26a80be3ed399729122974bad93a464b5b8e39faa5b616b8cb243e937058b3d31e41eeec6f31c2b86a71a6afcaf45b4def Homepage: https://cran.r-project.org/package=variosig Description: CRAN Package 'variosig' (Testing Spatial Dependence Using Empirical Variogram) Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test. Package: r-cran-variskscore Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-variskscore_2.0.0-1.ca2404.1_all.deb Size: 22428 MD5sum: fd07681d2db785d3e2e4bb2875a4059d SHA1: cd75f601290e5d33a24b90e06f8a42e9121792d4 SHA256: 0584a2d6bbbbc6d7655fc030cfd091811ad27d51250b367c442bf9a37cf46470 SHA512: ec18f45ddde4725a65b30c7341b5a594b880e56907c847c717feabee7358f7d655953f674afd57b75203ed60012e278c76b6a91c0992268a0ef250824973aa3f Homepage: https://cran.r-project.org/package=vaRiskScore Description: CRAN Package 'vaRiskScore' (VA CVD Risk Score) Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for civilian women, women military service members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the existing American College of Cardiology/American Heart Association CVD risk assessment tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic minority women. See the reference: Jeon‐Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. (2021) . Package: r-cran-varitas Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-yaml, r-cran-openxlsx, r-cran-venndiagram, r-cran-assertthat, r-cran-magrittr, r-cran-tidyr, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-futile.logger Filename: pool/dists/noble/main/r-cran-varitas_0.0.2-1.ca2404.1_all.deb Size: 379054 MD5sum: f1b940e08ba77d5fb875401c732c2657 SHA1: 65580da4e1afdc1f81b84ce9d37ee8e7220b5fe3 SHA256: 0423d8a2fc0c9b556b6e41b16a0ea88c6c8d8ed2e0e4c1d11fdc9ee36a0d44f5 SHA512: 365e5098e24e19acab46c38a8091434b43e79a967871276fb2b2322235973d7f1c5c5ab2096a9c9c88f10c91690d4964f547bc4e022166a4053b0a316a02bc02 Homepage: https://cran.r-project.org/package=varitas Description: CRAN Package 'varitas' (Variant Calling in Targeted Analysis Sequencing Data) Multi-caller variant analysis pipeline for targeted analysis sequencing (TAS) data. Features a modular, automated workflow that can start with raw reads and produces a user-friendly PDF summary and a spreadsheet containing consensus variant information. Package: r-cran-varjmcm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jmcm, r-cran-expm, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-varjmcm_0.1.1-1.ca2404.1_all.deb Size: 42986 MD5sum: 4e72055beafca6b4ec36952587cf30ef SHA1: b856da808904d385e8e51341d863747151db1fbc SHA256: a425bdc8689faa96b63321c1d2c20c46f1a1fb4e27289b8f3b25c1e65e62db12 SHA512: 28072b1f6ccf5914e7dc7a35ee63f82d4a75cadbed977c28f4ed76f07eeeb2f93398e59027e35badc78a65cdf637873eed3fa66154a7e6ba5f62a124cdbe0e7b Homepage: https://cran.r-project.org/package=varjmcm Description: CRAN Package 'varjmcm' (Estimations for the Covariance of Estimated Parameters in JointMean-Covariance Models) The goal of the package is to equip the 'jmcm' package (current version 0.2.1) with estimations of the covariance of estimated parameters. Two methods are provided. The first method is to use the inverse of estimated Fisher's information matrix, see M. Pourahmadi (2000) , M. Maadooliat, M. Pourahmadi and J. Z. Huang (2013) , and W. Zhang, C. Leng, C. Tang (2015) . The second method is bootstrap based, see Liu, R.Y. (1988) for reference. Package: r-cran-varoc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-varoc_1.0.0-1.ca2404.1_all.deb Size: 30156 MD5sum: 200936ef9dea63eee47cda7e4a91ac42 SHA1: db0dce1ba95d02aebd249aa20cdf40dcfec86011 SHA256: a567833a9657212e4fb2e7c9a5bbf43deb59e0dfaac36c61df434bc83db68e2a SHA512: f3c5ecc8872974c5a9f54c681b870c41c5bcf7e10096483ee02e9922f86eeac72534c0dba864f947e615e6e2d074ae754aab92b4fb86d9e4d81f1a21d68c1871 Homepage: https://cran.r-project.org/package=varoc Description: CRAN Package 'varoc' (Value Added Receiver Operating Characteristics Curve) A continuous version of the receiver operating characteristics (ROC) curve to assess both classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models. 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The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) . Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest. 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Antithetic Variates, Inner Control Variates, Outer Control Variates and Importance Sampling algorithms are available in the framework. User can write its own simulation function and use the Variance Reduction techniques in this package to obtain more efficient simulations. An implementation of Asian Option simulation is already available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) . 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Provides position adjustments that arrange over-plotted points so that a statistical model can be shown in data-space, and a helper for graphing extreme value bounds when an experiment encounters attrition. 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Vector-, data frame-, and row-wise operations support blinding for hierarchical and repeated-measures designs. For more details see MacCoun and Perlmutter (2015) and Dutilh, Sarafoglou, and Wagenmakers (2019) . 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More details have been written up in a paper submitted to the journal Statistics in Medicine, and the details of variational Bayesian methods can be found in Ray and Szabo (2021) . It simultaneously performs parameter estimation and variable selection. The algorithm supports two model settings: (1) local models, where variable selection is only applied to homogeneous coefficients, and (2) global models, where variable selection is also performed on heterogeneous coefficients. Two forms of Spike-and-Slab priors are available: the Laplace distribution and the Gaussian distribution as the Slab component. Package: r-cran-vbphenor Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cholwishart, r-cran-pracma, r-cran-knitr, r-cran-dbscan, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vbphenor_1.1.0-1.ca2404.1_all.deb Size: 1099312 MD5sum: 0f3857a1156322ad1c6ec71be37d464f SHA1: 62bea63635c9cb641ceca6e3844f462c5105e8b1 SHA256: 26d236b1e96e96e414e87bc237ae1e20c09f821d6cdf79d28cb8eeb0f66aa9d7 SHA512: 943741fdb7fac7182cbc77900dac67b2392f754482fd0a9c57a96b84075f5114e62b7f00cc4019bbf5ca7d3c9d3cb893700fc5af007bbe03a03d661177c15886 Homepage: https://cran.r-project.org/package=VBphenoR Description: CRAN Package 'VBphenoR' (Variational Bayes for Latent Patient Phenotypes in EHR) Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms. 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Package: r-cran-vbv Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vbv_0.6.2-1.ca2404.1_all.deb Size: 28122 MD5sum: 8503b3b7782e6ab8d379de5d687830f0 SHA1: 4969bbb8e38d0013491207c15d08f5e55500a9f4 SHA256: f11e54f54d5efb529a8d6c0771023f858a99404482746510975fcf6d085f0aa7 SHA512: 32ee52ed1cbd3408801cce11344f22eeb8ea518e99fa610425e134c0205078dad9d1df0585001dfca85bb9346b6320db199c1feeb88420022ead00bd84d3aaec Homepage: https://cran.r-project.org/package=VBV Description: CRAN Package 'VBV' (The Generalized Berlin Method for Time Series Decomposition) Time series decomposition for univariate time series using the "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts- und Finanzwissenschaften', by Hebbel and Steuer, Springer Berlin Heidelberg, 2022 , or 'Decomposition of Time Series using the Generalised Berlin Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut Hebbel (Eds.): Empirical Economic and Financial Research - Theory, Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler. Series: Advanced Studies in Theoretical and Applied Econometrics. Springer 2014, p. 9-40. Package: r-cran-vccp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vinecopula, r-cran-mosum, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vccp_0.1.1-1.ca2404.1_all.deb Size: 139660 MD5sum: 7a0822cb0a229668dcaab6fb7eb9927c SHA1: c1f30591185ac68b6dd9b3f098d3e234b1bbb8b7 SHA256: 38dfb39ae4a5e53948f4613b7f89d98df56afbb6b53e22169b69b35e17d0a2b7 SHA512: 2389ab3c71d852d98811a03f8f42a58159c489127a13077e67c7676e01c3c0b04a4f6ce7c363ceed1aad52ece25d519614d51c462d8b02daab7b10f295fd6bd4 Homepage: https://cran.r-project.org/package=vccp Description: CRAN Package 'vccp' (Vine Copula Change Point Detection in Multivariate Time Series) Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. 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Package: r-cran-vcd2df Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-quarto Filename: pool/dists/noble/main/r-cran-vcd2df_1.0.1-1.ca2404.1_all.deb Size: 17292 MD5sum: d9a8ad9012981eb9a4f154fdf9c19646 SHA1: e7559c94ce9609b57e421da115b48be94f7b5d10 SHA256: 9b426f818ff186ba26ba12ba1d2a894b70fa5b415d7467e4295e0957989cbee7 SHA512: 1616f0e1760c62036becf12056ba71a6d569e229d8330de4048d24269a7fc74ab9ad8990ebdc87c5b5ed7c4f1e9b2b3ae88670d4f9ef759f0a57aa98b27a0bfe Homepage: https://cran.r-project.org/package=vcd2df Description: CRAN Package 'vcd2df' (Value Change Dump to Data Frame) Provides the 'vcd2df' function, which loads a IEEE 1364-1995/2001 VCD (.vcd) file, specified as a parameter of type string containing exactly a file path, and returns an R dataframe containing values over time. 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The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015). Package: r-cran-vcdextra Architecture: all Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4924 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vcd, r-cran-gnm, r-cran-mass, r-cran-dplyr, r-cran-ca, r-cran-igraph, r-cran-colorspace, r-cran-gt, r-cran-scales, r-cran-knitr, r-cran-htmlwidgets, r-cran-webshot2, r-cran-forcats, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-gmodels, r-cran-fahrmeir, r-cran-effects, r-cran-vgam, r-cran-plyr, r-cran-lmtest, r-cran-nnet, r-cran-sleuth2, r-cran-car, r-cran-lattice, r-cran-aer, r-cran-coin, r-cran-hmisc, r-cran-rmarkdown, r-cran-seriation, r-cran-testthat, r-cran-tibble, r-cran-glue, r-cran-here, r-cran-purrr, r-cran-readxl, r-cran-stringr, r-cran-tidyr, r-cran-factoextra, r-cran-rgl Filename: pool/dists/noble/main/r-cran-vcdextra_0.9.8-1.ca2404.1_all.deb Size: 3471998 MD5sum: fb2b0b273ba69004645534956f753bab SHA1: 00a14f2cb9d29507bc1932d578b33fffe934f608 SHA256: 83b3b158ade8e91e2bf1768182ae117a53be71f38f763a65367b9ad4e43723f4 SHA512: 9003b79f7f5c54f4f65f5d9d79f3100fc3f8ec8833b1bc7d9568b85e089e820a0079d2d6896843501677bcddabd829b73e3504290454fe4f6407f81ce0842375 Homepage: https://cran.r-project.org/package=vcdExtra Description: CRAN Package 'vcdExtra' ('vcd' Extensions and Additions) Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. 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For details on the specifications used see Danecek et al. (2021) . 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'vDiveR' allows visualization of the diversity motifs (index and its variants – major, minor and unique) for elucidation of the underlying inherent dynamics. Please refer for more information. 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It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks. Needham, R., Naemi, R., & Chockalingam, N. (2014) . Needham, R., Naemi, R., & Chockalingam, N. (2015) . Tepavac, D., & Field-Fote, E. C. (2001) . Park, J.H., Lee, H., Cho, Js. et al. (2021) . 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Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) , Stewart (2015) , Thomas, Weir and Hass (2018) , Larson and Edwards (2016) , Apostol (1969) , Spivak (1971) , Schey (2005) , Colley (2019) , Lizarazo Osorio (2020) , Sievert (2020) , and Borowko (2013) . 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Functions exist which enable building a valid 'spec' from scratch or importing a previously created 'spec' file. Functions also exist to export 'spec' files and to generate code which will enable plots to be embedded in properly configured web pages. The default behavior is to generate an 'htmlwidget'. 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This package is used to facilitate the rendering. It also provides a means to interact with signals, events, and datasets in a 'Vega' chart using 'JavaScript' or 'Shiny'. Package: r-cran-vegclust Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1065 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vegclust_2.0.3-1.ca2404.1_all.deb Size: 713938 MD5sum: 8e7362485967da3705bcfbbdc8d94ba5 SHA1: d6689007c511e6223c0324e3f488ff05b1f19dda SHA256: 07c2bcc29c55b59bbfa69f51f30bde164cb9111c6c594ea5823ebe9cf723456b SHA512: 1e77908f01ae25f7e8b5cf7f8bd81ccebb1063b5783526be73e177f78fea3b24be946e774ae35fc4649830f18a19c32dae90b4ce906e67c5685680f050377150 Homepage: https://cran.r-project.org/package=vegclust Description: CRAN Package 'vegclust' (Fuzzy Clustering of Vegetation Data) A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) ; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) . 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Taxonomic harmonization (given appropriate taxonomic lists, e.g. GermanSL and EuroSL (Euro+Med extended) see ). Package: r-cran-vegetablessrilanka Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tsibble, r-cran-dplyr, r-cran-naniar, r-cran-visdat, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vegetablessrilanka_1.1.0-1.ca2404.1_all.deb Size: 156390 MD5sum: a2dc0a4c273cfb3e13cc6253f560548a SHA1: 12e9641dee920f2e6d89d617e325aa9235d2778a SHA256: 8fdfb01d1637369b82d967fde8b4eba1ae1866cb57d49adb8da69136cd8fa7bc SHA512: 5ee84637eba7d4c67fe15126db6bcc3b1b168b805ecbb65d4ec66e7aa92d7c7113497ce19b858cdadb103a2a35839faa9d65b14c941b2f991af54540e571e35a Homepage: https://cran.r-project.org/package=vegetablesSriLanka Description: CRAN Package 'vegetablesSriLanka' (Daily Vegetable Prices of Sri Lanka) Provides retail and wholesale vegetable price data from two major market hubs in Sri Lanka, Dambulla and Pettah. Includes tools for analyzing, visualizing, and comparing vegetable prices across markets. Package: r-cran-vegindexcalc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vegindexcalc_0.1.0-1.ca2404.1_all.deb Size: 21444 MD5sum: 5e08d01774f2af184df666577c2825f1 SHA1: 833794e546870869c7863b9324d2ff5701dfb49b SHA256: 66b37cfe27da642a313c60a17f0829b01a6fb23b1a455a2858d0783a3d72881a SHA512: 5219bcdbb1fb32b5b0b52aa4224ea1eefd90f22fcfff80f56b6cc8f99ca6258e7691faf8ca155164349e0725e301d3ec9d44de3bbfd7a0186fa579d6706e5545 Homepage: https://cran.r-project.org/package=vegIndexCalc Description: CRAN Package 'vegIndexCalc' (Vegetation Indices (VIs) Calculation for Remote Sensing Analysis) It provides a comprehensive toolkit for calculating a suite of common vegetation indices (VIs) derived from remote sensing imagery. VIs are essential tools used to quantify vegetation characteristics, such as biomass, leaf area index (LAI) and photosynthetic activity, which are essential parameters in various ecological, agricultural, and environmental studies. Applications of this package include biomass estimation, crop monitoring, forest management, land use and land cover change analysis and climate change studies. For method details see, Deb,D.,Deb,S.,Chakraborty,D.,Singh,J.P.,Singh,A.K.,Dutta,P.and Choudhury,A.(2020). Utilizing this R package, users can effectively extract and analyze critical information from remote sensing imagery, enhancing their comprehension of vegetation dynamics and their importance in global ecosystems. The package includes the function vegetation_indices(). Package: r-cran-vegperiod Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-curl, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-vegperiod_0.4.0-1.ca2404.1_all.deb Size: 126018 MD5sum: 3a6da2beca75ba80cafa528ce1a3b26b SHA1: a2db27446d9b4a4d244cf935008451b3d8d85f8a SHA256: 7e4128d1d9307df6af90d25a878cd5420b6eefb4e4f3f485520ba8c9acf1722d SHA512: 1487c354f3f4a36fbefcf4294ae28595b5d9bed780288da2429b2880b789c1f4797b1021e60b9763d2938f42b82b5406f671b99cc39964dd7f10974d8a3f60f2 Homepage: https://cran.r-project.org/package=vegperiod Description: CRAN Package 'vegperiod' (Determine Thermal Vegetation Periods) Collection of common methods to determine growing season length in a simple manner. Start and end dates of the vegetation periods are calculated solely based on daily mean temperatures and the day of the year. Package: r-cran-vegspecindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vegspecindex_0.1.0-1.ca2404.1_all.deb Size: 43908 MD5sum: c0bf6fe9d585efb36263d7f23db31caa SHA1: fe62cce52be4be7d6dad1956dda4d4517665f8d1 SHA256: c399464669931e3c29726aa77b6b2d0e08f000d371361038d4bd31e02a8f8581 SHA512: 52f66705d5cf74c01c6b507e9731a102713e1a1fb0ef90d9e61d19f83bb49174432660879181e850809978cf480b79c3235765e7fcf825a87f55e7b99417724b Homepage: https://cran.r-project.org/package=VegSpecIndex Description: CRAN Package 'VegSpecIndex' (Vegetation and Spectral Indices for Environmental Assessment) Earth system dynamics, such as plant dynamics, water bodies, and fire regimes, are widely monitored using spectral indicators obtained from multispectral remote sensing products. There is a great need for spectral index catalogues and computing tools as a result of the quick rise of suggested spectral indices. Unfortunately, the majority of these resources lack a standard Application Programming Interface, are out-of-date, closed-source, or are not linked to a catalogue. We now introduce 'VegSpecIndex', a standardised list of spectral indices for studies of the earth system. A thorough inventory of spectral indices is offered by 'VegSpecIndex' and is connected to an R library. For every spectral index, 'VegSpecIndex' provides a comprehensive collection of information, such as names, formulae, and source references. The user community may add more items to the catalogue, which will keep 'VegSpecIndex' up to date and allow for further scientific uses. Additionally, the R library makes it possible to apply the catalogue to actual data, which makes it easier to employ remote sensing resources effectively across a variety of Earth system domains. 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Also import/export routines for exchange of data with 'Juice' () are implemented. Package: r-cran-vek Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-vek_1.0.0-1.ca2404.1_all.deb Size: 37006 MD5sum: 02c36d535ad3b87e1d880324382b49f5 SHA1: 4b5c9839eb383204e6b1f93e5592228f57902bd3 SHA256: 7cb849891426118ced7e75621d4bb029fef0bc8b6fa46b5e870727d58a33269c SHA512: 6c2ba3cb1bb0cac82f40910c82b02fb0dfd3ca75f6203d38f44c9973e6761c9e04e50bc9537adb62d71caf4b655f4eb4f323ebe6c95f48a234725bee904d2ff6 Homepage: https://cran.r-project.org/package=vek Description: CRAN Package 'vek' (Predicate Helper Functions for Testing Simple Atomic Vectors) Predicate helper functions for testing atomic vectors in R. 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Build, render, and statistically analyze Venn / 'UpSet' diagrams from 'CSV' / 'TSV' / 'GMT' / 'GMX' inputs. Provides the same 44 SVG models, intersection / 'Jaccard' / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent 'TSV' exports (parity-tested against the published Python package). Integrates with 'ggplot2', 'tidygraph', and 'broom'. 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These are used to illustrate vector generalized linear and additive models (VGLMs/VGAMs), and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column Interaction Models, and constrained and unconstrained ordination models in ecology). This package now contains some old VGAM family functions which have been replaced by newer ones (often because they are now special cases). Package: r-cran-vhcub Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2039 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-ggplot2, r-cran-seqinr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vhcub_1.0.0-1.ca2404.1_all.deb Size: 2043976 MD5sum: 58c661abc43eb950ab785a8a33f0f3bf SHA1: 61c21ff70d4ebef1fc64b11a7ed2023d7ee5e6b8 SHA256: 3e7c12659dc2f8e89d7787ce05bd8fff2fcbfc05781009c27a1c2a9e1becf3c0 SHA512: 0241f5390efb2ed37683911b43a91f2185d5efba337c3f29bc40a36b1791536ad06a975aa5e60fd557f92d481aab5679ad110922fd267ccd168bed8e7f8401dd Homepage: https://cran.r-project.org/package=vhcub Description: CRAN Package 'vhcub' (Virus-Host Codon Usage Co-Adaptation Analysis) Analyze the co-adaptation of codon usage between a virus and its host, calculate various codon usage bias measurements as: effective number of codons (ENc) Novembre (2002) , codon adaptation index (CAI) Sharp and Li (1987) , relative codon deoptimization index (RCDI) Puigbò et al (2010) , similarity index (SiD) Zhou et al (2013) , synonymous codon usage orderliness (SCUO) Wan et al (2004) and, relative synonymous codon usage (RSCU) Sharp et al (1986) . 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Wallau. et al. (2016) . The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes. 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Package: r-cran-viafoundry Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-askpass, r-cran-stringr, r-cran-mime Filename: pool/dists/noble/main/r-cran-viafoundry_1.0.1-1.ca2404.1_all.deb Size: 147906 MD5sum: d0ac247d8572b2e549babfc21ada353d SHA1: 319e476b134cf0dfd0524a2c7732e7c01c0cbfcb SHA256: a450ae42f3d61f2f90a63c9524ce885b7aecb0127b257add135914d8e26a9286 SHA512: 8b5956f9f013ab0f913fef4e1a65f2cebd7d196cc0e4c18bb90be0ff0214129dbf1e2489238daaf76a9a7f74aba491e9ea5dbd5b5eb0e9d0d620facd5d547070 Homepage: https://cran.r-project.org/package=viafoundry Description: CRAN Package 'viafoundry' (R Client for 'Via Foundry' API) 'Via Foundry' API provides streamlined tools for interacting with and extracting data from structured responses, particularly for use cases involving hierarchical data from Foundry's API. It includes functions to fetch and parse process-level and file-level metadata, allowing users to efficiently query and manipulate nested data structures. Key features include the ability to list all unique process names, retrieve file metadata for specific or all processes, and dynamically load or download files based on their type. With built-in support for handling various file formats (e.g., tabular and non-tabular files) and seamless integration with API through authentication, this package is designed to enhance workflows involving large-scale data management and analysis. Robust error handling and flexible configuration ensure reliable performance across diverse data environments. Please consult the documentation for the API endpoint for your installation. Package: r-cran-viafr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1507 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-utf8, r-cran-crul, r-cran-jsonlite, r-cran-assertthat, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-viafr_0.3.2-1.ca2404.1_all.deb Size: 1504860 MD5sum: 3efbd6ee2c02cdf6532903f9d20994c4 SHA1: f16190ab87ed1e620aa1f58331bb5e4a0d83ed3f SHA256: e4ef872b6b1bd68ee0d5cc418ab525f28560f732b4dd79f28fb0c6f875473eef SHA512: 9278cb61ad17ed146e5f66fbccad6e7a492bead240e84bdb4c6e25a6e87bf80444e479fe61b05bfcb2e8958937cce978e35f9cd417674b2d5e77b50ff157ab1c Homepage: https://cran.r-project.org/package=viafr Description: CRAN Package 'viafr' (Interface to the 'VIAF' ('Virtual International Authority File')API) Provides direct access to linked names for the same entity across the world's major name authority files, including national and regional variations in language, character set, and spelling. For more information go to . Package: r-cran-vibass Architecture: all Version: 1.0.4-1.ca2404.3 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10899 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-extradistr, r-cran-ggplot2, r-cran-golem, r-cran-knitr, r-cran-lme4, r-cran-magrittr, r-cran-r2bayesx, r-cran-rlang, r-cran-rstudioapi, r-cran-shiny, r-cran-tibble, r-cran-tidyr Suggests: r-cran-spelling, r-cran-bayesrules, r-cran-coda, r-cran-colorspace, r-cran-cowplot, r-cran-faraway, r-cran-htmlwidgets, r-cran-islr, r-cran-laplacesdemon, r-cran-magick, r-cran-mass, r-cran-mcmcpack, r-cran-plotly, r-cran-rmarkdown, r-cran-pacman, r-cran-png, r-cran-spdata, r-cran-stringi Filename: pool/dists/noble/main/r-cran-vibass_1.0.4-1.ca2404.3_all.deb Size: 4286066 MD5sum: 92535c95a0e049f1baa08e64118f0cca SHA1: c198e51453b2e84bea2bd6a7c8330465900a1d82 SHA256: 5f4aa60611d56a4e82ecf66bb423702dd58d36d5c4d3184ea118a4f2e9fdac07 SHA512: 4b468eee67838e0303a8f4b599d49a9170a64ebffb621b1ebcf24cc80444a79f0a94b9a4ad840b1e7a7d4c81871d8e208c660aab8786f7f7b4895984229e06a9 Homepage: https://cran.r-project.org/package=vibass Description: CRAN Package 'vibass' (Materials for Introductory Course on Bayesian Learning) Practicals, data sets, helper functions and interactive 'Shiny' apps used in the introductory course on Bayesian inference at the Valencia International Bayesian Summer School. Installing 'vibass' installs all the other packages used during the course and downloads all necessary materials for working off line. Package: r-cran-vicc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-nlme, r-cran-rdpack, r-cran-rjags Filename: pool/dists/noble/main/r-cran-vicc_1.0.0-1.ca2404.1_all.deb Size: 273306 MD5sum: 4a08048c01da92fff76421400071f80c SHA1: 5c092a4f13386cbd3630dc8775668c827901c736 SHA256: 04624c03fca8b21c08b44afee70820e1962371eb5b9e8079d0371c73b334c918 SHA512: 055e4ebc052617d36413f71604e2e9a15273679d43eedff5d918638e729cfb8e2791dd0ef44ee6c5f35a70d7a1dfa751b7e47808ff73cdb6d31e6eb8ffea51b4 Homepage: https://cran.r-project.org/package=vICC Description: CRAN Package 'vICC' (Varying Intraclass Correlation Coefficients) Compute group-specific intraclass correlation coefficients, Bayesian testing of homogenous within-group variance, and spike-and-slab model selection to determine which groups share a common within-group variance in a one-way random effects model <10.31234/osf.io/hpq7w>. Package: r-cran-vici Architecture: all Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dt, r-cran-ggplot2, r-cran-ggpubr, r-cran-nlme, r-cran-shiny, r-cran-tidyr, r-cran-numderiv, r-cran-stringr, r-cran-rcolorbrewer, r-cran-scales, r-cran-shinywidgets Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vici_0.7.3-1.ca2404.1_all.deb Size: 392044 MD5sum: 93aeb7755141a4f3005d71a8922b46a7 SHA1: 149f95706bc2a312b2976c4a84649ca9a73de884 SHA256: a4f6c151bdc5cbff9bf9e7c824710388147004cfb19462b45bd219677fa21cd6 SHA512: c6953f9cf1bd5addedb26913f628bf5ec672d56c800b965418b3814f6fd32d079d8c074f189a0a43704eb33d05c3998c3f3982f490fcbd53424c89ddb3052bfe Homepage: https://cran.r-project.org/package=vici Description: CRAN Package 'vici' (Vaccine Induced Cellular Immunogenicity with Bivariate Modeling) A shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 477:112711. . Package: r-cran-vicmapr Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-sf, r-cran-dplyr, r-cran-purrr, r-cran-cli, r-cran-dbi, r-cran-xml2, r-cran-glue, r-cran-dbplyr, r-cran-rlang, r-cran-curl, r-cran-rvest, r-cran-lubridate, r-cran-knitr, r-cran-kableextra, r-cran-mapview, r-cran-leaflet, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vicmapr_0.2.3-1.ca2404.1_all.deb Size: 276830 MD5sum: 1fa86ab69c91a05505043e41a8982ac3 SHA1: 05be2bbf71b8bc8f12f616f39c68d341e1631bc4 SHA256: b2c67796c2bb6068d0f28af31468f6da3bad5e00a2b1f18ec421d82edac9e9bb SHA512: b9ad5862540c117f1e24ce125c5b7ab9e1584e07e20865e088ffb74e79e996fa72d08f1ae60fdd894b63da4ceb1449149237639b7a45ad8b0af2c2cc7e2fa231 Homepage: https://cran.r-project.org/package=VicmapR Description: CRAN Package 'VicmapR' (Access Victorian Spatial Data Through Web File Services (WFS)) Easily interfaces R to spatial datasets available through the Victorian Government's WFS (Web Feature Service): , which allows users to read in 'sf' data from these sources. VicmapR uses the lazy querying approach and code developed by Teucher et al. (2021) for the 'bcdata' R package . Package: r-cran-vicus Architecture: all Version: 0.99.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-matrix, r-cran-rspectra Suggests: r-cran-scatterplot3d, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vicus_0.99.0-1.ca2404.1_all.deb Size: 122272 MD5sum: b214076391539209c23790ba89f7e094 SHA1: cefa661b7d26b3af3155e7ddffd78ba303620e1f SHA256: 7ddb44efdba6ccb75e7bf3cbf5f5dfc048be026f0467ab3d5a44b18a645619c9 SHA512: ee5a126b0e481719f2a061de9e14c1749daebe5a94065cb970963912e18d3cda4dfa5b7936122b81e49a9add9b9417e460f7d18d5512c4362f4e35c0bf1f3f75 Homepage: https://cran.r-project.org/package=Vicus Description: CRAN Package 'Vicus' (Exploiting Local Structures to Improve Network-Based Analysis ofBiological Data) Compared with the similar graph embedding method such as Laplacian Eigenmaps, 'Vicus' can exploit more local structures of graph data. 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Package: r-cran-vietnamcode Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vietnamcode_0.1.1-1.ca2404.1_all.deb Size: 17016 MD5sum: 212a1b3b3407249464a61bffbed60ad5 SHA1: 185a81550a57fa9fb52c2511b70f6b31aec3d73f SHA256: 89ae83c33f6c4455c655261ad4da1e710590796461b332007f9caf7197b128b7 SHA512: a91950cc574782db68beccaafdf01ffca58ba20d435603daadaf47bab254484ed16d1ba008f04fc162cd0505f68ba61c714d450aa05554f42f82e3d61bfdf9a9 Homepage: https://cran.r-project.org/package=vietnamcode Description: CRAN Package 'vietnamcode' (Convert Vietnam Provincial Codes) Converts Vietnam's provinces' names and ID across different formats. Handles diacritics and different spellings. 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These Vietnamese encodings (TCVN3, VISCII, VPS) are not natively supported in R and lead to printing of wrong characters and garbled text (mojibake). This package fixes that problem and provides readable output with the correct Unicode characters (with or without diacritics). Package: r-cran-viewpipesteps Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rstudioapi, r-cran-tibble Filename: pool/dists/noble/main/r-cran-viewpipesteps_0.1.0-1.ca2404.1_all.deb Size: 1566384 MD5sum: 20ae3ac9b30187689994da75bd22cc08 SHA1: d49fcc8c6944b521b8df0f2cfd4c7ff1bd7b7926 SHA256: 810f2d3bc93f06e9f845adcaaae6f0c81a436e8a771c99dc84db4396c1190f05 SHA512: 14b6b5427601f9befcf99fe44458b6efd0412ad8e93d0c1782f4802e8fa4b8e75727d7b34ec8f409783480d897483ec159e9f1f7750790d57cb481b4e1c91e96 Homepage: https://cran.r-project.org/package=ViewPipeSteps Description: CRAN Package 'ViewPipeSteps' (Create View Tabs of Pipe Chains) Debugging pipe chains often consists of viewing the output after each step. This package adds RStudio addins and two functions that allow outputing each or select steps in a convenient way. 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The app is meant for interactive use and allows users to optionally upload different sources of information, including gene annotation and alignment files, enabling the exploitation and search for candidate genes in a genome browser. In its current version, 'VIEWpoly' supports inputs from 'MAPpoly', 'polymapR', 'diaQTL', 'QTLpoly', 'polyqtlR', 'GWASpoly', and 'HIDECAN' packages. 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ViewR renders 'Kaggle'-style micro-dashboard column headers complete with data-type badges, mini distribution spark-histograms, and data-completeness (missingness) bars. It provides hover metadata cards, a sliding Data Insights drawer with interactive histograms and 'Pareto' category charts, a multi-condition visual query builder (AND/OR), a column visibility picker, and a reproducible code generator that emits 'dplyr', base R, and 'SQL' that matches the active filter and column state. The interface is implemented entirely in dependency-free vanilla 'JavaScript' (no 'React' or build toolchain) and works in the 'RStudio'/'Positron' Viewer, inside 'Shiny' apps, in 'R Markdown'/'Quarto', or as a portable standalone 'HTML' file. A single call to viewr() opens the explorer; the legacy 'Shiny'-gadget ViewR() editor remains available. Package: r-cran-vigicaen Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1625 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-glue, r-cran-gridextra, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tzdb, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-vigicaen_2.1.0-1.ca2404.1_all.deb Size: 1100922 MD5sum: bf777fb9be13ab28c6a98b0f2ffa4fbb SHA1: a4318d3aaf0694ff8ecb4601fd8312e0dd7a7cd5 SHA256: d5ad5c6c4a2f215845f196df9ef3dcbbd56cd74aa29feafcd2000583020dd442 SHA512: 8f8bb69998803d476c6c26768cb2a25231e19e413f1893bdadbcc77d12aff8867f2d0be7b4b95ec43ba65d4c49f5a01219136f6392e8b0a1e3d4a95bce02813f Homepage: https://cran.r-project.org/package=vigicaen Description: CRAN Package 'vigicaen' ('VigiBase' Pharmacovigilance Database Toolbox) Perform the analysis of the World Health Organization (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version), e.g., load data, perform data management, disproportionality analysis, and descriptive statistics. Intended for pharmacovigilance routine use or studies. This package is NOT supported nor reflect the opinion of the WHO, or the Uppsala Monitoring Centre. Disproportionality methods are described by Norén et al (2013) . Package: r-cran-viking Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rcolorbrewer, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-viking_1.0.2-1.ca2404.1_all.deb Size: 81984 MD5sum: be1780e8ee77fdc74fec829879c562d3 SHA1: 0e0eae14b55c29b3d74dae14e018318404229cf0 SHA256: 6889a98214bb882d8a7cbfff960241e5ba1140f41100d59aae100d429fb1e08f SHA512: 6945272b7a76d4d630a34fd92897f9072e5a1859d90a82c25a5ac72b5f9929aef983025bb1309780fdf4c28ae4663a5efcfbd72e83b65a9be812a58ce41106a1 Homepage: https://cran.r-project.org/package=viking Description: CRAN Package 'viking' (State-Space Models Inference by Kalman or Viking) Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) . Package: r-cran-villager Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5293 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-r6, r-cran-uuid Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-leaflet, r-cran-plotly, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-pandoc Filename: pool/dists/noble/main/r-cran-villager_2.0.0-1.ca2404.1_all.deb Size: 1451272 MD5sum: 57cea2cbdecff8cdac86debdce06f10b SHA1: 8305f9b4c0e6d5b4e4b20e5a67c12077310b6488 SHA256: 002005745c0d79ace52a57b9f1c48436f846f1c54af4b0bf5535785b24c6faa4 SHA512: f3328785ba44e479ce37ebb2dcc2c3b3edd15478c50ed36633f3417121844e80da5ec77563f915b88a23a295b98e8eb0eb0b0f10fe7962e4891f811058f70e41 Homepage: https://cran.r-project.org/package=villager Description: CRAN Package 'villager' (A Framework for Designing and Running Agent Based Models) This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs. Package: r-cran-vimean Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vimean_0.1.0-1.ca2404.1_all.deb Size: 10660 MD5sum: eb5783ff9318354c6e6d5ecc2977d696 SHA1: 704d556f9d0c7090f3f65dfd33e0f081ac8d3f24 SHA256: 1adc0b7080fafc6aa8f3aeb7a3cacf1d673f33f1795e71e5c30b9acfdb8032f0 SHA512: 2c2fc98b0d47fc7ae24315197abda6e1de01f754e9c6504437878e345ecd652614bd5274279e2d250c732ff63a44ac88792fc6fa60882a51cba4ebcfbb965410 Homepage: https://cran.r-project.org/package=VIMean Description: CRAN Package 'VIMean' (Variability Independent of Mean) To computed the variability independent of mean (VIM) or variation independent of mean (VIM). The methodology can be found at Peter M Rothwell et al. (2010) . 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For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020). Package: r-cran-vimpclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcamixdata, r-cran-ggplot2, r-cran-polychrome, r-cran-mclust, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vimpclust_0.1.0-1.ca2404.1_all.deb Size: 1494422 MD5sum: e25e82554c80e2b0a9ae7dbca8b94db0 SHA1: 80b55394986658cbc35c7d12511a9d409702f12b SHA256: d6bcd03ae6a4f5942935cbf6bd165aee35ba0f4603cf61f09679bc57c2a23805 SHA512: 0f7ab9b821922926eb78777b57cc5c3241e52e54d2a8dc6eadd16d4b72d1242c385da6b7d6390c130f830b06ac95bc7ff2ce389ed6294ccd3cbc5c3a51cc40b9 Homepage: https://cran.r-project.org/package=vimpclust Description: CRAN Package 'vimpclust' (Variable Importance in Clustering) An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020). Package: r-cran-vimps Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-ggplot2, r-cran-ranger, r-cran-knockoff, r-cran-rocr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vimps_1.0-1.ca2404.1_all.deb Size: 37684 MD5sum: b2db702d0b7ea0271dd128addee51138 SHA1: ead901716cbe5370756133c6cc446b52016df7ae SHA256: ee43e418a525b51539fd659af364a236ba6ac7cafa841634b967e31c533f180f SHA512: 1990c441c086e75c7f6f8bf68f9d80f8afbc3ecf49523d8af94e978bab117a3b2257c6ba9f7875403308551358019811dca0c783dd55f2018e8a9dcef971d96f Homepage: https://cran.r-project.org/package=VIMPS Description: CRAN Package 'VIMPS' (Calculate Variable Importance with Knock Off Variables) The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) . Package: r-cran-vindecodr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Suggests: r-cran-purrr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-vindecodr_0.1.1-1.ca2404.1_all.deb Size: 145772 MD5sum: 77dc4f5f27582c182f9ad779a6f5d230 SHA1: 200e820b5b84f0722f43fd4461159a8ab5c6c4d3 SHA256: 674ca6dd8f22702bbf5892b918fe32fb4695026cd1c218c2e7caa01580a42428 SHA512: 01ef2bec1d855a89da669ada0ba56340f56ee3d4d6605fd2ec354a8dc5388198aaf535ae200a6f3434b4085979a117e36011df5ab4f19922a8aa6fa2daf0a3ae Homepage: https://cran.r-project.org/package=vindecodr Description: CRAN Package 'vindecodr' (Provides an Interface to the Department of Transportation VINDecoder) Provides a programmatic interface in R for the US Department of Transportation (DOT) National Highway Transportation Safety Administration (NHTSA) vehicle identification number (VIN) API, located at . The API can decode up to 50 vehicle identification numbers in one call, and provides manufacturer information about the vehicles, including make, model, model year, and gross vehicle weight rating (GVWR). Package: r-cran-violinplotter Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-violinplotter_3.0.1-1.ca2404.1_all.deb Size: 66756 MD5sum: 2490639ba5d369f8030764cfe22e832e SHA1: b8ac9109689d7fb38be7f29e73f5c77ff0566c26 SHA256: 6e799da311d67c624d4dac3c5296aa0663a98e3cad9d9435ab80de3ef6413702 SHA512: 7efd95b5beff123b70aa68e7ce015b050c8d85630d90caf03651e7b5032c67b2d2547c5eaaeec9014332b4cfee628a4f5c1fcb819c56b421cd8687f3f455cd52 Homepage: https://cran.r-project.org/package=violinplotter Description: CRAN Package 'violinplotter' (Plotting and Comparing Means with Violin Plots) Produces violin plots with optional nonparametric (Mann-Whitney test) and parametric (Tukey's honest significant difference) mean comparison and linear regression. This package aims to be a simple and quick visualization tool for comparing means and assessing trends of categorical factors. Package: r-cran-vioplot Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sm, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vioplot_0.5.1-1.ca2404.1_all.deb Size: 349820 MD5sum: de0b10465dde90d8bff1e6e9edc89505 SHA1: 7d9283192f7b40aef9285ef9fb613efb696011aa SHA256: 8157f8011ba97ffd619363713d4705761d8cd83e69f9624dcd58e55c496dcdfd SHA512: af723f2144ebccce3b8485504811ef126af5b20e9f1accd8aabd92629285974554ff2d9d4d10c4e010b8de3b001a87c72c2594a149a276dcbbbca7c6ac798e73 Homepage: https://cran.r-project.org/package=vioplot Description: CRAN Package 'vioplot' (Violin Plot) A violin plot is a combination of a box plot and a kernel density plot. 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Package: r-cran-vip Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-ggplot2, r-cran-tibble, r-cran-yardstick Suggests: r-cran-bookdown, r-cran-dt, r-cran-covr, r-cran-doparallel, r-cran-dplyr, r-cran-fastshap, r-cran-knitr, r-cran-lattice, r-cran-mlbench, r-cran-modeldata, r-cran-neuralnettools, r-cran-pdp, r-cran-rmarkdown, r-cran-tinytest, r-cran-varimp Filename: pool/dists/noble/main/r-cran-vip_0.4.6-1.ca2404.1_all.deb Size: 2406604 MD5sum: 89e0ca21b0289a8357fe19eefa5ca46b SHA1: 1930d40cd2767182b533382afa0b5d3e7af65781 SHA256: 31e1d548a87ba1ea0147160201f3a8fdc0e43e7b2962b3d11393f2b5f74e830a SHA512: 7d7067f7045e275ae59702405f9d6616b36aab0e9ef435b702a8baf83aa4ff62d686bd7032a2502b932e0e254851f5384af18881709aac6b4cf62b37798a5aec Homepage: https://cran.r-project.org/package=vip Description: CRAN Package 'vip' (Variable Importance Plots) A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) , and 3) the variance-based approach described in Greenwell et al. (2018) . A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details). Package: r-cran-vipor Architecture: all Version: 0.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-beeswarm, r-cran-lattice, r-cran-ggplot2, r-cran-beanplot, r-cran-vioplot, r-cran-ggbeeswarm Filename: pool/dists/noble/main/r-cran-vipor_0.4.7-1.ca2404.1_all.deb Size: 4489944 MD5sum: 9e6663dd347fdd713fbb825b36d4a469 SHA1: ec6adf29e7789a7cb4f616f666b5c9c80658e434 SHA256: 46a7db79654a601301071515014c8f69536bd22d8ad0efd2b5f544f3c8039e02 SHA512: f142fcbe23d7d4bdb32eb6c29e6b3be04b1a77747e27f04bed9cb654ac369b81b82b660cb008355fd44c2de0f66e1624f796e27af74818371ef3140ab3130e52 Homepage: https://cran.r-project.org/package=vipor Description: CRAN Package 'vipor' (Plot Categorical Data Using Quasirandom Noise and DensityEstimates) Generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise. Package: r-cran-viprodesign Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-bioc-decipher, r-cran-cluster, r-cran-pathviewr, r-cran-dbscan, r-cran-ape Suggests: r-cran-optparse Filename: pool/dists/noble/main/r-cran-viprodesign_0.1.0-1.ca2404.1_all.deb Size: 42198 MD5sum: fc7d56917622906ab21d17c60e3264d9 SHA1: cf8e1062e31d7d8625ce1890b69062831a4cd704 SHA256: b9e8df5cefe2bc9e584b4010fd2c1ed39afe303e06f3f1601265aa4affccda22 SHA512: 81f364ae676805566714e649a3aad7c9a85192dc30a08ab7f772835ae795e59ea909dc3b5f9cc22ebdc72bf108f6ec940b4e58d512b811668918c4d6d6a5371e Homepage: https://cran.r-project.org/package=VIProDesign Description: CRAN Package 'VIProDesign' (A Comprehensive Tool for Protein Design) Provides tools for designing virus protein panels through sequence clustering and protein sequence analysis. The package includes functionality for filtering sequences, removing redundancy, identifying outliers, clustering sequences, and calculating entropy to evaluate clustering quality. A publication describing these methods is in preparation and will be added once available. Package: r-cran-viraldomain Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-applicable, r-cran-dplyr, r-cran-earth, r-cran-kknn, r-cran-magrittr, r-cran-parsnip, r-cran-ranger, r-cran-recipes, r-cran-tidyselect, r-cran-workflows Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-viraldomain_0.0.7-1.ca2404.1_all.deb Size: 31218 MD5sum: b05f9e94b22f9d9d8bb329a4d8be1792 SHA1: 3790c205eedccf981c78a4b2d5057971a55e50b8 SHA256: bc5a5fc1c827db7ad0400732b80e8efcc93708064d72dd8e2f04e4810bfde865 SHA512: bf2cbf1eb7336822ee87dbf4f4e6b52c65195d856f8b23bcba95892185f12198bc26f3779797e6b411bf3a403361c4fc0f10520ee19ff0d664e31a8db9ed20cd Homepage: https://cran.r-project.org/package=viraldomain Description: CRAN Package 'viraldomain' (Applicability Domain Methods of Viral Load and CD4 Lymphocytes) Provides methods for assessing the applicability domain of models that predict viral load and CD4 (Cluster of Differentiation 4) lymphocyte counts. 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Package: r-cran-viralentropr Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5250 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-hdcpdetect, r-cran-ecp, r-cran-kableextra, r-cran-lubridate, r-cran-magrittr, r-cran-mclust, r-cran-rlang, r-cran-stringr, r-cran-zoo Suggests: r-bioc-biostrings, r-cran-dt, r-cran-dplyr, r-cran-here, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-viralentropr_0.6.2-1.ca2404.1_all.deb Size: 1033816 MD5sum: 2a6d98bb5c4c8179ca39ad5d15dedd00 SHA1: bc135b79bc485fca9bb8c8ed7788501164b24c1a SHA256: fc18c013f8f7a2a8eb45c03a02688e12f5f6f51f6885bfffc3dbba27eb4ba008 SHA512: a7c06a77c741b8b65b5275e1a978197be33bb6fcc9f6db0d5eac85c30d841875a28196d56b6600b380b4f27a7eabdd3c3457bbf3a2c1d019709ca3bad1c85896 Homepage: https://cran.r-project.org/package=ViralEntropR Description: CRAN Package 'ViralEntropR' (A Computational Pipeline for Entropy-Informed Detection ofEmerging Viral Variants) Implements an entropy-informed pipeline for detecting emerging variants in viral amino acid sequence data, extending prior clustering-based approaches including hemagglutinin clustering methods (Li et al., 2015) . Provides a fully vectorized FASTA preprocessing toolkit covering header parsing, two-pass date and country extraction, ambiguous-residue filtering, and integer encoding under a 25-symbol amino acid alphabet. Computes per-site Shannon entropy across user-defined cumulative, sliding, or disjoint temporal partitions and clusters per-site entropy values using Gaussian mixture models via 'mclust' (Scrucca et al., 2016) . Quantifies temporal distributional shifts between partitions using the Hellinger distance (van der Vaart, 1998) , and detects temporal change points non-parametrically using energy statistics (Matteson and James, 2014) via 'ecp' or wild binary segmentation (Fryzlewicz, 2014) via 'HDcpDetect'. Per-site amino-acid frequency tables and entropy trajectory plots characterize sequence composition and evolutionary dynamics across time. A configurable multi-variant simulation engine generates synthetic sequence time series with known ground truth for benchmarking detection pipelines. A curated dataset of SARS-CoV-2 Variants of Concern and Variants of Interest with associated lineage and surveillance metadata is included, along with a bundled National Center for Biotechnology Information (NCBI) Spike protein sample and vignettes demonstrating the full workflow. Package: r-cran-viralmodels Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-baguette, r-cran-cubist, r-cran-dials, r-cran-dplyr, r-cran-glmnet, r-cran-hardhat, r-cran-kernlab, r-cran-kknn, r-cran-magrittr, r-cran-parsnip, r-cran-purrr, r-cran-ranger, r-cran-recipes, r-cran-rsample, r-cran-rules, r-cran-tidyselect, r-cran-tune, r-cran-viraldomain, r-cran-workflows, r-cran-workflowsets Suggests: r-cran-earth, r-cran-nnet, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-viralmodels_1.3.4-1.ca2404.1_all.deb Size: 29722 MD5sum: b78f37caf5792ffab7c33c2df97f7559 SHA1: 0478d6470ecb26b2c0725a13975e1a55b8beb587 SHA256: 5e17fd45f398b9ac1e8e0af42094bd8f16baae33b38555b95657d7dd15551b81 SHA512: cbd31e0db4a0de796eae4d5d9a0baf6d3f7a9fec7099d74116d2152513c9178136f885916f0baf5cbfec5fb2b3bc9b25da9bb0a38c95f07e9822d82a231e8780 Homepage: https://cran.r-project.org/package=viralmodels Description: CRAN Package 'viralmodels' (Viral Load and CD4 Lymphocytes Regression Models) Provides a comprehensive framework for building, evaluating, and visualizing regression models for analyzing viral load and CD4 (Cluster of Differentiation 4) lymphocytes data. It leverages the principles of the tidymodels ecosystem of Max Kuhn and Hadley Wickham (2020) to offer a user-friendly experience in model development. This package includes functions for data preprocessing, feature engineering, model training, tuning, and evaluation, along with visualization tools to enhance the interpretation of model results. It is specifically designed for researchers in biostatistics, computational biology, and HIV research who aim to perform reproducible and rigorous analyses to gain insights into disease dynamics. The main focus is on improving the understanding of the relationships between viral load, CD4 lymphocytes, and other relevant covariates to contribute to HIV research and the visibility of vulnerable seropositive populations. Package: r-cran-viralx Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-dalextra, r-cran-parsnip, r-cran-recipes, r-cran-workflows Suggests: r-cran-cubist, r-cran-dplyr, r-cran-earth, r-cran-kknn, r-cran-rsample, r-cran-rules, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-viralx_1.3.1-1.ca2404.1_all.deb Size: 71036 MD5sum: 4a9e81c0531bcb91aa7f86744197755b SHA1: 60f363370fcc9a2ff13f1be1d36333b357c59942 SHA256: fe57e881ccaa7136d3b9982c0c793dde34acc953547d8044f9adb01011eef897 SHA512: ed6788f58f8172c7391324c796048164288663d6855c56e0a95b43a21ae8a74dbc252db49eb0cdf2f60fce94049de6ef804246d794ae21c35a77c934b2369a08 Homepage: https://cran.r-project.org/package=viralx Description: CRAN Package 'viralx' (Explainers for Regression Models in HIV Research) A dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the 'tidymodels' framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) , and the 'DALEXtra' tool for explainability by Przemyslaw Biecek (2020) . It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics. Package: r-cran-virf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmgarch, r-cran-mgarchbekk, r-cran-gnm, r-cran-expm, r-cran-bigvar, r-cran-ks, r-cran-matrixcalc, r-cran-matlib Filename: pool/dists/noble/main/r-cran-virf_0.1.1-1.ca2404.1_all.deb Size: 19934 MD5sum: 53b95b76cb2f3a1b62b15c99fda91c03 SHA1: f8969b650bfd2353cf5a1e4f882c61e96f35b711 SHA256: 62aee7e71c7362715446354b12088fd254e90cf83c0db6a1f387fe72499b87ac SHA512: 69e4e17783e311ce48e0c3748b7e53cb93097f8da280c461142793dc4eff6c39fd121b7db42576c066fa1a4ce4bd1229834918d4808db3fba1809521c31dca8d Homepage: https://cran.r-project.org/package=VIRF Description: CRAN Package 'VIRF' (Computation of Volatility Impulse Response Function ofMultivariate Time Series) Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) . Package: r-cran-viridis Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-viridislite, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-hexbin, r-cran-scales, r-cran-mass, r-cran-knitr, r-cran-dichromat, r-cran-colorspace, r-cran-httr, r-cran-mapproj, r-cran-vdiffr, r-cran-svglite, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-maps, r-cran-terra Filename: pool/dists/noble/main/r-cran-viridis_0.6.5-1.ca2404.1_all.deb Size: 2975918 MD5sum: 1743ad17bf4f315f57b1d12d573ecc96 SHA1: ac649efd47ce1e4af8b21dbd11e69c1fa38a4c39 SHA256: 2d5e533842739522a167194c05992f49d1f0bb93a5474673e97c8b9a709a195e SHA512: d09d3bd5be8d62d2bd9f1f93030855fa02072f01935a8d24030a60876a2a061a9fffa9ad9e3edfa2c3300df22a665132b294c0c9435d0cf4f4b60bf0bb834bac Homepage: https://cran.r-project.org/package=viridis Description: CRAN Package 'viridis' (Colorblind-Friendly Color Maps for R) Color maps designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing. This package also contains 'ggplot2' bindings for discrete and continuous color and fill scales. A lean version of the package called 'viridisLite' that does not include the 'ggplot2' bindings can be found at . Package: r-cran-viridislite Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hexbin, r-cran-ggplot2, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-viridislite_0.4.3-1.ca2404.1_all.deb Size: 1296860 MD5sum: acff100ad44f96a1824969288b436d05 SHA1: efa53baece9ba23aa345afabbe50b49738b479d7 SHA256: 06fb2a3b7ff0427edc622fe9a764ab75c4fe8c2bab3ae93a306c74d9a44f975b SHA512: 23a217eaf0d61f92ece87c2e6838dcceffbbf28218415b2ec98fef9fdee9459b918cef4fdbbad63793244cf7f56fcc009e7355c70a6ee056aafd5830dee42587 Homepage: https://cran.r-project.org/package=viridisLite Description: CRAN Package 'viridisLite' (Colorblind-Friendly Color Maps (Lite Version)) Color maps designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing. This is the 'lite' version of the 'viridis' package that also contains 'ggplot2' bindings for discrete and continuous color and fill scales and can be found at . Package: r-cran-viroreportr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-epiestim, r-cran-ggplot2, r-cran-incidence, r-cran-lubridate, r-cran-projections, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-glue, r-cran-mgcv, r-cran-kableextra, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-viroreportr_1.0.4-1.ca2404.1_all.deb Size: 173518 MD5sum: 7349066e2505f4b52ff28e78e0b0f558 SHA1: 2e65e1c3fb9538d4b58ba8a59f9f07031d664522 SHA256: b2330038ec5e2ba3ecb9b4a46e8d423f708290bb2cf3bcefcf0ca221ad073e54 SHA512: f4d8912e7e7288a70ab6fc563b8a0d0ca040cfdb6bc6fed35d6682ff14b98c69d523172fa890976fa703d3ff7244252949c7782d56e5936c1e62f40141b21dbf Homepage: https://cran.r-project.org/package=ViroReportR Description: CRAN Package 'ViroReportR' (Respiratory Viral Infection Forecast Reporting) Tools for reporting and forecasting viral respiratory infections, using case surveillance data. Report generation tools for short-term forecasts, and validation metrics for an arbitrary number of customizable respiratory viruses. Estimation of the effective reproduction number is based on the 'EpiEstim' framework described in work by 'Cori' and colleagues. (2013) . Package: r-cran-virtualpollen Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2470 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-viridis, r-cran-mgcv, r-cran-plyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-virtualpollen_1.0.2-1.ca2404.1_all.deb Size: 2330688 MD5sum: 06e4ab7d8c6574e44b5bae2d41812ea4 SHA1: ca132cf33d3254071ee5277ab07b03c0c3fd84d9 SHA256: e1a061e09043de787e066fa676c364aa33a3873b42761ec4c5828a79eac33440 SHA512: b9856b67a52a8f40a657b590958cc36ef96e919b87e986699de5916b064b2d7c2bcd18c0b3499e7b3a97df899ea945ee51201044d78408e658291fe5634107e4 Homepage: https://cran.r-project.org/package=virtualPollen Description: CRAN Package 'virtualPollen' (Simulating Pollen Curves from Virtual Taxa with Different Lifeand Niche Traits) Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples. Package: r-cran-virtualpop Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msm, r-cran-hmdhfdplus Suggests: r-cran-knitr, r-cran-kableextra, r-cran-ggplot2, r-cran-foreign, r-cran-lubridate, r-cran-xml2, r-cran-eha, r-cran-survival, r-cran-survminer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-virtualpop_2.1.0-1.ca2404.1_all.deb Size: 693046 MD5sum: 31783bc87fc06054f1d58a71e51f4f73 SHA1: c423a494cda8c035dda61b8ac77123f21295b6ca SHA256: 66e2a2c2969ef8282ab15ac6c65d5f54096ae7e8e4cbdf27a28446db1acd0241 SHA512: da43e8f2ca2a40fec91675b5ae4cefc1b6ca8ccf6e68f07d121fcd720f9ae176826e8eb0b203f959f769eba28cdec48b1634b96cf1f6fb60d6c5b239dff0a426 Homepage: https://cran.r-project.org/package=VirtualPop Description: CRAN Package 'VirtualPop' (Simulation of Populations by Sampling Waiting-Time Distributions) Constructs a virtual population from fertility and mortality rates for any country, calendar year and birth cohort in the Human Mortality Database and the Human Fertility Database . Fertility histories are simulated for every individual and their offspring, producing a multi-generation virtual population. Package: r-cran-virtualspecies Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-ade4, r-cran-rnaturalearth, r-cran-raster, r-cran-viridis Filename: pool/dists/noble/main/r-cran-virtualspecies_1.6.1-1.ca2404.1_all.deb Size: 247576 MD5sum: 807225a99e0e6c8c3c24bfc67a2e9dee SHA1: 1a36688f1cee332a812db398dc8b748cbc2237d0 SHA256: 7d4e283ea22bf8b8fb8351ba68b313a394b247f012d8fc3112abf6c342f351b7 SHA512: 25aec0164e61dc477b06e46a24ece39d348eaa5c711569f183e67e157af604189aff76f4304e1ae0c567b6edc00593bed84ead28de45ea33707eaf3236e36cdc Homepage: https://cran.r-project.org/package=virtualspecies Description: CRAN Package 'virtualspecies' (Generation of Virtual Species Distributions) Provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package integrates the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. Package: r-cran-virtuoso Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-odbc, r-cran-processx, r-cran-dbi, r-cran-ini, r-cran-rappdirs, r-cran-curl, r-cran-fs, r-cran-digest, r-cran-ps Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nycflights13, r-cran-testthat, r-cran-covr, r-cran-jsonld, r-cran-dplyr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-virtuoso_0.1.8-1.ca2404.1_all.deb Size: 161410 MD5sum: 2abbf7090225a07fb4e50fb717dcd461 SHA1: eddd66e0ad562717332c57813e8469b82fab0bfd SHA256: ae2ba6e2caa6ab9eb7c3965aa1f49a2875dfd032c22b843705878ca6a56da18a SHA512: db9def1a59d41f869361accca1474be3150e05b4fdebcd1fde420fbeac2150c8cbfb04168520a93f05fe9fee68bdc361f34c1bc90cfbe39694d0b7461b79e451 Homepage: https://cran.r-project.org/package=virtuoso Description: CRAN Package 'virtuoso' (Interface to 'Virtuoso' using 'ODBC') Provides users with a simple and convenient mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface) compatible 'ODBC' (Open Database Connectivity) interface. 'Virtuoso' is a high-performance "universal server," which can act as both a relational database, supporting standard Structured Query Language ('SQL') queries, while also supporting data following the Resource Description Framework ('RDF') model for Linked Data. 'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote 'Virtuoso' servers using popular 'R' packages such as 'DBI' and 'dplyr', while also providing a high-performance solution for working with large 'RDF' 'triplestores' from 'R.' The package also provides helper routines to install, launch, and manage a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using the standard interactive installers from the 'R' command-line. By automatically handling these setup steps, the package can make using 'Virtuoso' considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing 'R' tools, including 'rdflib' and 'redland' packages. Package: r-cran-virusparies Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chromote, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-virusparies_1.1.0-1.ca2404.1_all.deb Size: 448662 MD5sum: 5c91f8f4148f0ac3e6c3ffec9a49cfc7 SHA1: 818723155395b24085f0f269017731cd6d2ebf72 SHA256: d0a4093ce26df311661797ffd3c34d5d3e7da9d51efa17078da5474e7d52daf1 SHA512: 1f9988adebd9bf371cbea9ec2b7f9b56f36a5dd96504f70c01579443840960c2837a3bd8a1973f869f1f786a8023e136181659806106b250843c5846ff7ffd07 Homepage: https://cran.r-project.org/package=Virusparies Description: CRAN Package 'Virusparies' (Visualize and Process Output from 'VirusHunterGatherer') A collection of tools for downstream analysis of 'VirusHunterGatherer' output. Processing of hittables and plotting of results, enabling better interpretation, is made easier with the provided functions. Package: r-cran-virustotal Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-curl, r-cran-openssl, r-cran-jsonlite, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httptest2, r-cran-covr, r-cran-pkgdown, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-virustotal_0.7.0-1.ca2404.1_all.deb Size: 439420 MD5sum: f53d34d3cf4167b0b187633f90765f5e SHA1: 20e0b9df991338c9c89559948dbbf2d4aee5cd87 SHA256: 552f9db35aae34d44e5946c1deb3ab66b40c86ba72bb86421c42473d1f2ce1ce SHA512: a315077f48c2ea5aa8754436cde270e7685bbdb3fca82dd3f62d51c0b515867fe001ef6100caf35ea660c2a010932f4a653245e8e5df3212e2a4d4f2050b33e9 Homepage: https://cran.r-project.org/package=virustotal Description: CRAN Package 'virustotal' (R Client for the 'VirusTotal' API) Provides a comprehensive R interface to the 'VirusTotal' API v3.0 , a Google service that analyzes files and URLs for viruses, worms, trojans and other malware. Features include file/URL scanning, domain categorization, passive DNS information, IP reputation analysis, IoC relationships, sandbox analysis, and comment/voting systems. Implements rate limiting, error handling, and response validation for robust security analysis workflows. Package: r-cran-visa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpmisc, r-cran-magrittr, r-cran-matrix, r-cran-plot3d, r-cran-plotly, r-cran-reshape2, r-cran-rcolorbrewer Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/noble/main/r-cran-visa_1.0.0-1.ca2404.1_all.deb Size: 366276 MD5sum: 4cd8292e0e3265fd7819e79974ef820d SHA1: cbcc42a4ea7a94494ded9538c039e90052c9bbc1 SHA256: 29aeec92188a0c2a49dd0845294c7a7933a87f88fb086639c9185e37a3f01123 SHA512: 09e02c4232feddf6e076cdb7b2536e7ef72a176359b9b5be9f2fc6b5abd698ebd42a4ef4edc197d10282a2f20fb6dc023ff025f9ff03a05ade694969ca274b6d Homepage: https://cran.r-project.org/package=visa Description: CRAN Package 'visa' (Vegetation Imaging Spectroscopy Analyzer) Provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization of spectra and results of analysis in the 'ggplot2' style. Package: r-cran-visachartr Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-visachartr_4.0.1-1.ca2404.1_all.deb Size: 1569848 MD5sum: 7bc96fc5a711af707034df7bf5eb6b7e SHA1: 7f71fa658893346f7cb006df817f5db1184676cd SHA256: 619add40a56cb5a10390e571cc6e59be386e6f121689090192c0abab1173b0e6 SHA512: 452bd77f227d4cd209ac2025de8bd656d916f190f8d5ecba76200cdc914c605da0a0bacc8e713d29384f06ecd748efea38a427ca5d5b94c420ce3e6c74c3bd53 Homepage: https://cran.r-project.org/package=visachartR Description: CRAN Package 'visachartR' (Wrapper for 'Visa Chart Components') Provides a set of wrapper functions for 'Visa Chart Components'. 'Visa Chart Components' is an accessibility focused, framework agnostic set of data experience design systems components for the web. Package: r-cran-visae Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-dplyr, r-cran-ggplot2, r-cran-shinyjs, r-cran-ca, r-cran-tidyr, r-cran-ggrepel, r-cran-rlang, r-cran-dt Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visae_0.2.1-1.ca2404.1_all.deb Size: 451330 MD5sum: 5a938dca410e0d3fff33b6927e88e2cb SHA1: 0708165caa3b7911d6d96b40de5909a628cd7de3 SHA256: 4033498c4a21ca156b8cd8dd8416dd7d1de0144fec353ea6cea98063a1da56eb SHA512: 9fa25478813300e7179736842f926e059c00d7a0fb5f4c58c7bb4165a8bb9d7577de638f9f6b358a2bbfc5b5b66dfcb81377081c4a4bbf206cc6bef4b211f62a Homepage: https://cran.r-project.org/package=visae Description: CRAN Package 'visae' (Visualization of Adverse Events) Implementation of 'shiny' app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021). Package: r-cran-visaotr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rglpk, r-cran-e1071, r-cran-kernlab, r-cran-matrix, r-cran-mboost, r-cran-randomforest, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-visaotr_0.1.0-1.ca2404.1_all.deb Size: 45420 MD5sum: a59a732f39257f7236f788d96a622e4a SHA1: 4a504e0b6293bcc718fcbd8a68d55dbb1fbc4fe2 SHA256: 82e8cb55e1a12c77dadd8abb76b43ada35a0ffcfc90a86414ece1b58f67eebc8 SHA512: 8502e30bbba73ffc5b7a4b1ea7e0511bce462f08886a8cc0c32f6e64ddc8bb6a0c52c43f2343231d29ded1d511133edbe798c25fedf384327253e0857f258647 Homepage: https://cran.r-project.org/package=visaOTR Description: CRAN Package 'visaOTR' (Valid Improved Sparsity A-Learning for Optimal TreatmentDecision) Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) . The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+). Package: r-cran-visatc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-igraph, r-cran-plotly, r-cran-graphlayouts Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-visatc_1.0.0-1.ca2404.1_all.deb Size: 665594 MD5sum: 94bd9369517d23226d6eb3dd5ccaec05 SHA1: 67cc687b7c997e366ff41c180a2d3c34502ba0c5 SHA256: ebe7c71eaf271e2db6f6b015bdd705b0143c8db835faefc72a57c35bc22bc822 SHA512: a8abffc2b836da8882ae01b99239a6f4b9e3a7a8df417d50b83dd5076410f9eb9150fb0348e1537b8df01f6663a242fff37f3d22997673436bc9d7567e9bc851 Homepage: https://cran.r-project.org/package=visATC Description: CRAN Package 'visATC' (Visualise the Anatomical Therapeutic Chemical (ATC) Hierarchy) Visualisation and subsetting of the World Health Organisation Anatomical Therapeutic Chemical (ATC) classification system. Package: r-cran-viscollin Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-car, r-cran-corrgram, r-cran-corrplot, r-cran-dplyr, r-cran-lmtest, r-cran-knitr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-viscollin_0.1.2-1.ca2404.1_all.deb Size: 190064 MD5sum: 537f087455031fc80cc43a0a2bd191f0 SHA1: 5dc5788086de7a6b5cfbc3443adfcb497450731b SHA256: 271279a257ee75786fb78450e1f8793eb2045b4a033b373a1edf41e14a41f3dd SHA512: 5e04b8cd42d022c5abe147798f0f97077b5e71c122b1e965e01edd5f7d9aa3f5d29ecbf4360d9321f577c89c46d32d24362e1c6713c8ada7df05eeabb773bd26 Homepage: https://cran.r-project.org/package=VisCollin Description: CRAN Package 'VisCollin' (Visualizing Collinearity Diagnostics) Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", . These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent. Package: r-cran-viscomp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circlize, r-cran-dplyr, r-cran-ggextra, r-cran-ggnewscale, r-cran-ggplot2, r-cran-hmisc, r-cran-mass, r-cran-netmeta, r-cran-plyr, r-cran-qgraph, r-cran-reshape2, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-viscomp_1.0.0-1.ca2404.1_all.deb Size: 460948 MD5sum: 55ea3590a363fecf8855cc8b504e37ec SHA1: fae6ffec363fe60485d303692feb0f0234ec4d7f SHA256: 412e2be9f6a68c7f328eafe8d42e44ddcb4b48e2c9c0448c8d5a144652bf0f80 SHA512: b6aea4bb25682a57afff4d1ce82e3971eaa2bef1cf31764e2a73a342a64c056548e61302f1dc08a52af66b31f44de94c6de46b5fa22056ff1f56397e3d6b1209 Homepage: https://cran.r-project.org/package=viscomp Description: CRAN Package 'viscomp' (Visualize Multi-Component Interventions in Network Meta-Analysis) A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: - components descriptive analysis - heat plot of the two-by-two component combinations - leaving one component combination out scatter plot - violin plot for specific component combinations' effects - density plot for components' effects - waterfall plot for the interventions' effects that differ by a certain component combination - network graph of components - rank heat plot of components for multiple outcomes. The implemented tools are described by Seitidis et al. (2023) . Package: r-cran-viscov Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesm, r-cran-clustergeneration, r-cran-scatterplot3d, r-cran-kernsmooth, r-cran-trialr Filename: pool/dists/noble/main/r-cran-viscov_1.6.0-1.ca2404.1_all.deb Size: 86692 MD5sum: 1561d891c6cef1c3bc5498f03671cb20 SHA1: 4bfd79ec47c17d60c9996ba0c081c0540cb7ce72 SHA256: 1cc569da5cf6837b6c3ae0d90e8ca8b18b68179e35e33b1b4adeaf7a6d37dd62 SHA512: 686d68b3696b00df25f7b481185d2cfc53f4a45664894cb819c337cc011f5c7753ea803db59f3eab507f5e01297908edc1f72d99e3c71ff53426d9452535f90e Homepage: https://cran.r-project.org/package=VisCov Description: CRAN Package 'VisCov' (Visualizing of Distributions of Covariance Matrices) Visualizing of distributions of covariance matrices. The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) . Package: r-cran-visdat Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tibble, r-cran-glue, r-cran-forcats, r-cran-cli, r-cran-scales, r-cran-rlang Suggests: r-cran-testthat, r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-spelling, r-cran-covr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-visdat_0.6.1-1.ca2404.1_all.deb Size: 1102974 MD5sum: 5c4070c1c24e2bf9ddf51984b395f0b2 SHA1: f0d79eba6f33935cabc471c8876af30d8aab15b5 SHA256: 3605acbc5ac21d3650885774450dd969c7322e7924bae57ad2bc93d45d7564c2 SHA512: b34c61bb4d3c62ef5dc04af8de324127e2088d6473424add194a36528cd7ed82b109ec64732f4ff75779300afc1a55fe780744dba62e9a31c65d357efd7b17dd Homepage: https://cran.r-project.org/package=visdat Description: CRAN Package 'visdat' (Preliminary Visualisation of Data) Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'. Package: r-cran-vise Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-cowplot, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly Filename: pool/dists/noble/main/r-cran-vise_0.1.3-1.ca2404.1_all.deb Size: 604376 MD5sum: e20f6043dc83ec87aa5bbc6f77462906 SHA1: 755c153a48f17c62e5dc34c76b637bfef441776b SHA256: 189f4719bee580c0229c481f5255f7bc8ebccc872a68385d0a8b5db1a11f28e8 SHA512: 0187d163f5d7f4deb41ddfb2eac4f25118448e595843b44b03b3279494a9f15777ea6a032ecd4ab555a53d0d21e5713f9629650526aacb635270a6fbc673adb5 Homepage: https://cran.r-project.org/package=ViSe Description: CRAN Package 'ViSe' (Visualizing Sensitivity) Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) . Package: r-cran-visielse Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-matrix, r-cran-colorspace, r-cran-stringr, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-visielse_1.2.2-1.ca2404.1_all.deb Size: 669654 MD5sum: 08c7c70f283464c23af4d636fb81fedd SHA1: cabb18bad8ab6a498cab2fc531c5035ff6031f1c SHA256: d325aead6df54284ce11abcab122e319fb6aa7b98ffa2172b3a4c1b01dc3bfcd SHA512: c7322a455c888eab0f0d9b08b0edfd46cd030dc5ae88f83c9c4a17e4f236c90be07090f7b295f91b9532fb7de9a50f4c42a47c560274b9d540abf861d2de6adc Homepage: https://cran.r-project.org/package=ViSiElse Description: CRAN Package 'ViSiElse' (A Visual Tool for Behavior Analysis over Time) A graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions. Package: r-cran-visitorcounts Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rssa, r-cran-ggplot2, r-cran-zoo, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visitorcounts_2.0.4-1.ca2404.1_all.deb Size: 822502 MD5sum: 8a0a7a76fb733c2be47cdb13051979bc SHA1: 9bfe8e7ae8f1341f318a8af066d2e107b3589a71 SHA256: 3a3fcdf7c387067ed834e290da8d6660b3bc5a39db30a840d7aaf6db5fc9b795 SHA512: cf77f5766776d549f825cb0902a3febe11d31c6e146318e8c7dde0406fe240d3c39815cc23c33937f4bac2c860d52c71b53a4a32eacd00739b776b09f654b97c Homepage: https://cran.r-project.org/package=VisitorCounts Description: CRAN Package 'VisitorCounts' (Modeling and Forecasting Visitor Counts Using Social Media) Performs modeling and forecasting of park visitor counts using social media data and (partial) on-site visitor counts. Specifically, the model is built based on an automatic decomposition of the trend and seasonal components of the social media-based park visitor counts, from which short-term forecasts of the visitor counts and percent changes in the visitor counts can be made. A reference for the underlying model that 'VisitorCounts' uses can be found at Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) . Package: r-cran-vismeteor Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vismeteor_3.1.0-1.ca2404.1_all.deb Size: 1289228 MD5sum: 333d565141546fa8510261bc6da4f896 SHA1: 25e730b2c4fbbaf32f13c784bdf12176ac526208 SHA256: 91df011975530196e5e44e621bfdbe90985b7b0bb1f7ee3e8432dc413d5daea6 SHA512: e2f3c37e1a09490b9a1ee1827f60bd63ba70bf9fe5c0b6cca53f15015b77bfa1c30d0827ef458e7ee7422a9a747e9df5af66b0ce86fc851e344642b2fc5ec3f4 Homepage: https://cran.r-project.org/package=vismeteor Description: CRAN Package 'vismeteor' (Analysis of Visual Meteor Data) Provides a suite of analytical functionalities to process and analyze visual meteor observations from the Visual Meteor Database of the International Meteor Organization . Package: r-cran-vismi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-ggtext, r-cran-gridextra, r-cran-ggridges, r-cran-patchwork, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-trelliscopejs Suggests: r-cran-mice, r-cran-mixgb, r-cran-ranger Filename: pool/dists/noble/main/r-cran-vismi_1.0.0-1.ca2404.1_all.deb Size: 1939552 MD5sum: f204149be2255b189ca085b646cd7bdc SHA1: 0eb28d3e177a1189e6e4e25d4c5131e5662d219d SHA256: 3fe008d3affea0a32baeff515109891c75e8e168c557884402ccf8579cab9c33 SHA512: b79b21adc60c3a0b3e6114c4607fa971b0f2a3d1347c4054d4fabaddb1709bb4ee537fc64e672e2dd609367da85498098ed2c53d21f3e15e4c0c7e07e00eb1bb Homepage: https://cran.r-project.org/package=vismi Description: CRAN Package 'vismi' (Visual Diagnostics for Multiple Imputation) A comprehensive suite of static and interactive visual diagnostics for assessing the quality of multiply-imputed data obtained from packages such as 'mixgb' and 'mice'. The package supports inspection of distributional characteristics, diagnostics based on masking observed values and comparing them with re-imputed values, and convergence diagnostics. Package: r-cran-visnetwork Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10701 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-igraph, r-cran-rpart, r-cran-shiny, r-cran-shinywidgets, r-cran-colourpicker, r-cran-sparkline, r-cran-ggraph, r-cran-tidygraph, r-cran-flashclust Filename: pool/dists/noble/main/r-cran-visnetwork_2.1.4-1.ca2404.1_all.deb Size: 3766098 MD5sum: 8555c490345ef9bfe9c106b7f1b3e5db SHA1: 9f6be8762183fe802058a939eacea80436fd6e75 SHA256: 5da84773719b58eab4b2ad997d0bb1fcc568f19d88fcedeb7ff6c6b48a6d1138 SHA512: 61503eec8e0be1353e5178baf2ceae064ab2f4f2ae8baeab065fb398780ec7d8977ac9edefb007d8fd5c2e9f1fb003a8e080a6389c3e9628d6f78bb44b5849c6 Homepage: https://cran.r-project.org/package=visNetwork Description: CRAN Package 'visNetwork' (Network Visualization using 'vis.js' Library) Provides an R interface to the 'vis.js' JavaScript charting library. 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Package: r-cran-visomopresults Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5063 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brand.yml, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-systemfonts, r-cran-tidyr Suggests: r-cran-bslib, r-cran-cohortcharacteristics, r-cran-covr, r-cran-dt, r-cran-flextable, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggsankeyfier, r-cran-gt, r-cran-here, r-cran-htmltools, r-cran-incidenceprevalence, r-cran-knitr, r-cran-lifecycle, r-cran-officer, r-cran-palmerpenguins, r-cran-patientprofiles, r-cran-plotly, r-cran-reactable, r-cran-rmarkdown, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-sortable, r-cran-testthat, r-cran-tinytable, r-cran-yaml Filename: pool/dists/noble/main/r-cran-visomopresults_1.5.1-1.ca2404.1_all.deb Size: 2690416 MD5sum: 690e86e43abd43116c2f232815f8de64 SHA1: c0e299333ad80be5603d3d8e25f1ae3df008413d SHA256: 97d828495d02accc5cf7c783a08a618d43a0ea9516fad4979231e4cc85d359c8 SHA512: 2b7cd69444046a174ccf403a45d8084ab672e6e72a1ebb744b4b55613eccf822771ec18f6b2544534a29e598c2c53a1c4c3374b952b31b6593836825bf6f3d9b Homepage: https://cran.r-project.org/package=visOmopResults Description: CRAN Package 'visOmopResults' (Graphs and Tables for OMOP Results) Provides methods to transform omop_result objects into formatted tables and figures, facilitating the visualisation of study results working with the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Package: r-cran-visor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sfheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visor_0.1.1-1.ca2404.1_all.deb Size: 87318 MD5sum: 0ce2d5841e62b59deac2694001153790 SHA1: f93ec86dfc39a8a020018cab933ef034e32dd50b SHA256: 5aa79dd5dfa657052c470dd01b33a1776d8e3fe7751d8bf1b53bfc0f6e7fbbf9 SHA512: 1bcf5b25687f4b4e28f0e7f7f702a22c227f0b9491f17aa76fb583093589bb996f9ad4d345ee4273aae62b0dd20f915548c529a8a47c165cd9e57f2de3cb3484 Homepage: https://cran.r-project.org/package=visor Description: CRAN Package 'visor' (Geospatial Tools for Visibility Analysis) Provides tools for visibility analysis in geospatial data. It offers functionality to perform isovist calculations, using arbitrary geometries as both viewpoints and occluders. Package: r-cran-vispedigree Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-nadiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-vispedigree_0.7.1-1.ca2404.1_all.deb Size: 1244164 MD5sum: 241a27ea105aa95950663119626e0088 SHA1: a14533a371fa9ed495779682d4a091c31e112f54 SHA256: f53f86acf0c881ad946078fb1b50f5ee619f642ce855ae2692c5c927499660e8 SHA512: 12498218b92a9d46c273b575b12d9c3b782bf52bfba556cd39bb87cebe93b391c39495ce898dd59af4d5cc704faa5c16683fefbc74dd302bfa5f7d59eae3fca2 Homepage: https://cran.r-project.org/package=visPedigree Description: CRAN Package 'visPedigree' (Tidying and Visualizing Animal Pedigrees) Built on graph theory and the high-performance 'data.table' framework, this package provides a comprehensive suite of tools for tidying, pruning, and visualizing animal pedigrees. By modeling pedigrees as directed acyclic graphs using 'igraph', it ensures robust loop detection, efficient generation assignment, and sophisticated hierarchical layouts. Key features include standardizing pedigree formats, flexible ancestry tracing, and generating legible vector-based PDF graphs. A unique compaction algorithm enables the visualization of massive pedigrees (e.g., in aquaculture selective breeding population) by grouping full-sib families, maintaining structural clarity without overcrowding. Package: r-cran-visr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2086 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-forcats, r-cran-ggplot2, r-cran-gridextra, r-cran-gt, r-cran-gtable, r-cran-kableextra, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidycmprsk, r-cran-tidyr Suggests: r-cran-data.table, r-cran-ggpubr, r-cran-learnr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-visr_0.4.1-1.ca2404.1_all.deb Size: 1233408 MD5sum: a33f85e3d6e4c011e33f947c8e0d47c5 SHA1: 0aa87bf8c3ce1f809cc833eb3ebb90d4bce286c5 SHA256: c9624cfae9dc18439926b52e07eddb41d41c166b66cb6bc74c5ca3dd07eb7157 SHA512: 3f8b26d00eaf763660f2c0c870d7bf5a8eb40c3f939398bf3a911d35c572491d2f5cdba1f94e9ff57bb90bc6e57b89e033a7a9db63d3df08b0afbe987941fed7 Homepage: https://cran.r-project.org/package=visR Description: CRAN Package 'visR' (Clinical Graphs and Tables Adhering to Graphical Principles) To enable fit-for-purpose, reusable clinical and medical research focused visualizations and tables with sensible defaults and based on graphical principles as described in: "Vandemeulebroecke et al. (2018)" , "Vandemeulebroecke et al. (2019)" , and "Morris et al. (2019)" . Package: r-cran-visreg Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-glmmtmb, r-cran-knitr, r-cran-lattice, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-quarto, r-cran-rgl, r-cran-survival, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-visreg_3.0.0-1.ca2404.1_all.deb Size: 417008 MD5sum: 6ba472c982b30fe66ab0800bfab4270c SHA1: 6e692dad76f69d46e9da70a87b4aab1ece1ea345 SHA256: 83150e70469194e80dc34a36d3043fbe8963a46b3e365f621aafad0cf3bc538d SHA512: 600c6d14d6fac0d5840b67fa063adf117c3303c491dd515f7e398367d4a2ad35896a310c2b86777215ffea264197d62a7667d1dd7ae60bc39471b5f988d39b6f Homepage: https://cran.r-project.org/package=visreg Description: CRAN Package 'visreg' (Visualization of Regression Models) Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.) Package: r-cran-visstatistics Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8655 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cairo, r-cran-multcompview, r-cran-nortest, r-cran-vcd Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visstatistics_0.3.0-1.ca2404.1_all.deb Size: 6340472 MD5sum: 0656778906da3c6505df1e48d1e918a2 SHA1: a0c24d23fe2c3699d2a747e10ac93183374bd41c SHA256: 9780b4165197bc60375fe30c55f5407ffa8fcb069951571fb6c213d136f545aa SHA512: beb3488a99016b589bfc9b4fd3a65e8c75dfae45ed024da9842234d834315200f9824bc779a67146004b0bcd28979516d4d76a0006c765b19a36dc2158b3ab0b Homepage: https://cran.r-project.org/package=visStatistics Description: CRAN Package 'visStatistics' (Automated Selection and Visualisation of Statistical HypothesisTests) Automated test selection, visualised. 'visStatistics' automatically selects and visualises statistical hypothesis tests comparing two vectors, based on their class and distribution. Visual outputs, including box plots, bar charts, regression lines with confidence bands, mosaic plots, residual plots, and Q-Q plots, are annotated with relevant test statistics, assumption checks, and post-hoc analyses where applicable. The algorithmic workflow shifts attention from ad-hoc test selection to visual diagnostic assessment and statistical interpretation. It is particularly suited for server-side R applications, where end users interact solely through a web interface to select data groups and receive a complete visual statistical analysis automatically. The same automation makes it useful in time-constrained contexts such as statistical consulting, where it reduces effort spent on test selection and leaves more room for interpretation. The implemented tests cover the most frequently applied inferential methods in biomedical research (Hayat et al. (2017) ). The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical; those of class ordered are considered ordinal. Assumptions of residual normality and homogeneity of variances are considered met if the corresponding test yields a p-value greater than the significance level alpha = 1 - conf.level. (1) When the response is numerical and the predictor is categorical, a test comparing central tendencies is selected. In the default setting (group_test = NULL), residual normality is assessed at every group size using shapiro.test() applied to the standardised residuals of lm(). If normality is not met, wilcox.test() is used when the predictor has two levels and kruskal.test() followed by pairwise.wilcox.test() otherwise. If normality is met, levene.test() assesses variance homogeneity. For two-level predictors, Student's t.test(var.equal = TRUE) is applied if variances are homogeneous and Welch's t.test() otherwise. For predictors with more than two levels, aov() followed by TukeyHSD() is applied if variances are homogeneous, and oneway.test() followed by games.howell() otherwise. Setting group_test to "welch" or "rank" bypasses these assumption tests and fixes the analysis to Welch-type or to rank-based tests, respectively. (2) When both vectors are numerical, lm() is fitted by default (correlation = FALSE). If correlation = TRUE, Spearman rank correlation is performed. (3) When the response is ordinal, it is converted to numeric ranks and the non-parametric path from (1) is followed (Wilcoxon or Kruskal-Wallis). When both variables are ordinal and correlation = TRUE, Kendall's tau_b is used instead. (4) When both vectors are categorical, Cochran's rule (Cochran (1954) ) is applied to test independence either by chisq.test() or fisher.test(). Package: r-cran-vistime Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-assertthat, r-cran-plotly, r-cran-ggplot2, r-cran-ggrepel, r-cran-rcolorbrewer Suggests: r-cran-prettydoc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-highcharter Filename: pool/dists/noble/main/r-cran-vistime_1.3.0-1.ca2404.1_all.deb Size: 1547742 MD5sum: 611b719a1619bdc2a8caf9c98b86df9f SHA1: 7f6f5261029287de5e5eb345400893cbf5aab5e7 SHA256: 6d80edf30aac9be07972fd9a5bec02ed9a636331561b7cede0000b997f5e30a0 SHA512: a21feafb5c76e8f7f808af64b9debc123f71c47be8f0ce24e2a67cd1255508940f6670fa047fcc2b46d4d92dd08af415c49b481618cc0cf1455336e8bb2d9f9d Homepage: https://cran.r-project.org/package=vistime Description: CRAN Package 'vistime' (Pretty Timelines in R) A library for creating time based charts, like Gantt or timelines. Possible outputs include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom. Package: r-cran-vistree Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-rpart, r-cran-colorspace Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vistree_0.8.1-1.ca2404.1_all.deb Size: 77000 MD5sum: 0e5e20bfd8e1f62cffd4ae8a26b86a41 SHA1: 5d416420c453f088bb0885cabda0fccbb4a9eda3 SHA256: 1275b9395b231e1048bf3b1f464f43952a26c521049a1cfa607915478ac342db SHA512: 47650a196a7d17fab6809eab8c1bd16313c18acb94ce7fd2807f88786aa87ac977a86da2bf53c86373c92a4895f178f9bddf640fe9ef0ec6f2e37e5c554cfd2d Homepage: https://cran.r-project.org/package=visTree Description: CRAN Package 'visTree' (Visualization of Subgroups for Decision Trees) Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes. Package: r-cran-vistributions Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-xplorerr Filename: pool/dists/noble/main/r-cran-vistributions_0.2.0-1.ca2404.1_all.deb Size: 388418 MD5sum: e3096bd329d9e97b48d43d130f1f312d SHA1: da49df54c733f42f0803df46c04f323927286632 SHA256: 8d24b784358de3680fa3d6446122b3327e391de3a17ffa2bc601545cf264b582 SHA512: 27cd820eccfa827f701a1b4a1244b4fe3ddeb171fe07780f4e3dd006166d2cce9706b2f7a8e43e95b6bbcf077104411bc7caff35bc690c843aa8a0dfba45ef5b Homepage: https://cran.r-project.org/package=vistributions Description: CRAN Package 'vistributions' (Visualize Probability Distributions) Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions. Package: r-cran-visual.kaito Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5565 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-htmlwidgets, r-cran-knitr, r-cran-mirt, r-cran-multcomp, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visual.kaito_0.1.0-1.ca2404.1_all.deb Size: 1326744 MD5sum: fffcd3b7346916230241d3a6cb8540a3 SHA1: 0a30cf21fbc9be4ecc3398fad0042b024ed24c7a SHA256: 4427477dd8ded4ea55b9091bee9d8cff6f661ddf047f753fabb358f629e8a8d2 SHA512: 17448c6b88586d798d7e4784c938659ee3fa8a7ba2bbcc17bee3a592f24b8085451c34b46260c5ba3c4e2797acf81e25d5017699c6eec889d394fce341db0c9b Homepage: https://cran.r-project.org/package=visual.kaito Description: CRAN Package 'visual.kaito' (Interactive 3D Visualizations for Group Comparisons) Draws interactive, rotatable statistical visualizations in three dimensions, built on 'plotly'. Two families of plots are provided. Triaxial box plots (boxplot3d(), boxplot3d_interactive()) compare groups on three continuous variables at once, with Tukey, fixed-percentile, mean +/- SD, and letter-value box/whisker conventions, plus parametric and non-parametric significance testing (per-axis and joint 3D via MANOVA / PERMANOVA). Bivariate density plots (ttest_plot3d(), manova_plot3d()) compare two or more groups on two continuous variables as overlapping 3D density surfaces, reporting per-axis t-tests together with a joint Hotelling's T-squared test (two groups), or a one-way MANOVA omnibus test with Bonferroni, Tukey, Fisher's LSD, and Dunnett post-hoc comparisons (more than two groups). All plots include live, pre-computed controls (view, method, scale, transparency) so results can be explored interactively without re-running R code. Package: r-cran-visualdom Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveslim, r-cran-wavemulcor, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-visualdom_0.8.0-1.ca2404.1_all.deb Size: 97778 MD5sum: a2948a937624b06ccbbe8a1f4aaf7d53 SHA1: afbec47cc1ed9cdad4025b7d342ddf7c2897386a SHA256: eb540c4763e82f588073be3dd4063c5334cfdfa4f24a49d22595ad1a26c095a0 SHA512: dd3d1d3c85e3516ae3a9efb11896ca596314e667083bcd011345cd176c109664ade2e9bc71d9ddbe50bcbddd6bb3b20f12632bba42b613e7f97d9b440f47ae23 Homepage: https://cran.r-project.org/package=VisualDom Description: CRAN Package 'VisualDom' (Visualize Dominant Variables in Wavelet Multiple Correlation) Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) , Polanco-Martínez et al. (2020) and Polanco-Martínez (2023, in press). Package: r-cran-visualfields Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-dplyr, r-cran-polyclip, r-cran-deldir, r-cran-plotrix, r-cran-gtools, r-cran-combinat, r-cran-xml, r-cran-oro.dicom, r-cran-rlang, r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-htmltable, r-cran-boot, r-cran-pracma Filename: pool/dists/noble/main/r-cran-visualfields_1.0.7-1.ca2404.1_all.deb Size: 6244508 MD5sum: 2e5b6aeeb9b4fae77e7ec85396954996 SHA1: 7bd4d231698d5e9fdda008b373a53e2ca535fe53 SHA256: ccdaa8b9fa5348e3f929bd762abbca3345aa01fc296226ee8ce846d9fda57c44 SHA512: a984e5ef1eaa038b34dc256720848080560068932f1839370bef4197dd22dcd2f48d4603265d9f0377c7f7a95cc2d9f47717d1ab123af332397404092dac95a8 Homepage: https://cran.r-project.org/package=visualFields Description: CRAN Package 'visualFields' (Statistical Methods for Visual Fields) A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) ]. Package: r-cran-visualize.cran.downloads Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3693 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cranlogs, r-cran-plotly, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-visualize.cran.downloads_1.0.3-1.ca2404.1_all.deb Size: 2947878 MD5sum: ca0ce520064c396011b20ffbb35cdddb SHA1: ce282c5a8e840f92cd95ab416e4db761c3c08e9b SHA256: e332c821bf6eb94c317315cb98de1f72e57542c22303184b56825fa8aca218ba SHA512: 2f5f21f1210afa292020d9a6618e5d48d57fddcb69bffac1d1303259ca42f9332ccb9fc795a74e1f4aec074647f8a8e22fc3908351135309b53375f8cbefc5d6 Homepage: https://cran.r-project.org/package=Visualize.CRAN.Downloads Description: CRAN Package 'Visualize.CRAN.Downloads' (Visualize Downloads from 'CRAN' Packages) Visualize the trends and historical downloads from packages in the 'CRAN' repository. Data is obtained by using the 'API' to query the database from the 'RStudio' 'CRAN' mirror. Package: r-cran-visualize Architecture: all Version: 4.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-visualize_4.5.0-1.ca2404.1_all.deb Size: 348944 MD5sum: 8c6c0f84ca65fbde26350b53a7733aba SHA1: e1ac0fb763e4cc194afcb840dc85e1a18fc76368 SHA256: 09cf9cae7ddd081573b02464bed24749ea1e649a1c1c5c09785acbb788690f70 SHA512: 74602d6e8852ae2619cb3def271eccab982acaad67c0d1fb084ebe5683c2aef6949249853c06b581ec3fc347ce419c1d4720a1da3c662d05f4ab25b082a41127 Homepage: https://cran.r-project.org/package=visualize Description: CRAN Package 'visualize' (Graph Probability Distributions with User Supplied Parametersand Statistics) Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities. Also provided on the graph is the mean and variance of the distribution. Package: r-cran-visualizesimon2stage Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-flextable, r-cran-ggplot2, r-cran-officer, r-cran-geomtextpath, r-cran-ggrepel, r-cran-scales Suggests: r-cran-clinfun, r-cran-knitr, r-cran-quarto, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-visualizesimon2stage_0.2.3-1.ca2404.1_all.deb Size: 467650 MD5sum: c1454d8abd4c43d6489eea2638d5af41 SHA1: 09e0d2e5040a140cada8110047ee4d9381a6eae0 SHA256: c7595a9e6d955cb04d9907289cfd234666cd0c978f93ecff1354d78d0f508b6e SHA512: f69afe112962c153efa47ac2c29b29bcd55d8d94d56bbb7101dc38a11ba890f0cadd357ce8744af8263f5e5b942961219dd1d824e9ac906359258b564da011d9 Homepage: https://cran.r-project.org/package=VisualizeSimon2Stage Description: CRAN Package 'VisualizeSimon2Stage' (Visualize Simon's Two-Stage Design) To visualize the probabilities of early termination, fail and success of Simon's two-stage design. To evaluate and visualize the operating characteristics of Simon's two-stage design. Package: r-cran-visualpred Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1715 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm, r-cran-randomforest, r-cran-nnet, r-cran-e1071, r-cran-mass, r-cran-magrittr, r-cran-factominer, r-cran-ggplot2, r-cran-mltools, r-cran-dplyr, r-cran-data.table, r-cran-mba, r-cran-proc, r-cran-ggrepel Suggests: r-cran-knitr, r-cran-markdown, r-cran-egg Filename: pool/dists/noble/main/r-cran-visualpred_0.1.2-1.ca2404.1_all.deb Size: 1282340 MD5sum: 7b2539917cd93ed54d91c20b11270ab3 SHA1: dadbfbee0c96c42bdcd366c16a728136be6ba680 SHA256: 770715e5bfb84271917e9dd5af92097561d12d338006f0fcb86c147d050266c4 SHA512: 657d15f831f046cfc045a918bb66aaa1d369a6143d928c4cb3252b1ef40344031d6467694d63a16a4e84e08f974a2dc7e7914a90db2977f03ece17320e14ed26 Homepage: https://cran.r-project.org/package=visualpred Description: CRAN Package 'visualpred' (Visualization 2D of Binary Classification Models) Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', 'gbm', 'nnet' and 'svm', presented over two dimensions generated by 'famd' and 'mca' methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses under the 'XAI' paradigm. 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Package: r-cran-vitopack Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-tibble, r-cran-tidyselect, r-cran-rlang, r-cran-lubridate Suggests: r-cran-readxl, r-cran-plotly, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-vitopack_0.1.1-1.ca2404.1_all.deb Size: 119704 MD5sum: 35bb176feb5e650b5cf88b8e329cb450 SHA1: 7548741cd2fe4ba3f2dc5d5a688a34a1a126285c SHA256: 5510890c5ebc986d896146775c88d07513ffc39e434626baab30dfe1d8b10355 SHA512: f136537af0f0a1c14fcd80c511407e5de8f7a13343636fc8d6e3570f2546333576466ceddc1728d71160264ca1051a0e176e0a1ba76a229ee3bbacbabc50dabb Homepage: https://cran.r-project.org/package=vitopack Description: CRAN Package 'vitopack' (Actuarial Helpers for Triangles, Exposures and Czech BirthNumbers) A collection of utilities that grew out of day-to-day non-life actuarial work at Com-PASS Advisory. Provides helpers for building chain-ladder triangles (cumulative, decumulative, run-off, development factors with optional weighting), constructing exposure columns from policy start/end dates, parsing Czech birth numbers ('rodné číslo') into dates, generating smooth RGB color palettes for charts, and loading multi-sheet 'xlsx'/'xlsb' files into a list of data frames. The chain-ladder helpers follow the standard methodology of Mack (1993) . Package: r-cran-vivaglint Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-stringr, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-psych, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-vivaglint_0.1.1-1.ca2404.1_all.deb Size: 276592 MD5sum: 4338a64028992967d9b46f270d37ab16 SHA1: 796b96703b9369cb5ce94e2c1b361f785d58850f SHA256: e066152479982d722b1ba5fc81a95058179a75a075c6fc012059fcc5c9c0e000 SHA512: ea7d3825aac6dfcdc1275845a93af7626a0b2d2f6db9cb06150e95af109c5f0321b07d7d67525a3addda8713b2ea8fd68f718ded14d8144dc8344d37f5a2d57a Homepage: https://cran.r-project.org/package=vivaglint Description: CRAN Package 'vivaglint' (Analysis Tools for 'Viva Glint' Survey Data) Provides functions for importing, validating, and analyzing 'Viva Glint' survey data exports, with optional API-based import via the 'Microsoft Graph' API. Includes tools for data reshaping, question-level analysis, multi-cycle comparisons, organizational hierarchy analysis, factor analysis, and correlation analysis. Harman (1960, ISBN: 0226316513); Husser (2017) . 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The package generates pseudo-samples by interpolating between cluster medoids, enabling the study of gradual changes in feature space. It also computes k-nearest neighbors (KNN)-based statistics to relate pseudo-samples to real data and summarize variable behavior using mean, median, or standard deviation. Finally, the package offers interactive visualizations of variable trajectories along cluster transitions, including both direct trajectory plots and bootstrap-based interactive plots with confidence intervals to assess variability and uncertainty across the transition path. 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Package: r-cran-vlf Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vlf_1.1-3-1.ca2404.1_all.deb Size: 630710 MD5sum: c66517cb1500d27c60cbff0c20ac270b SHA1: 1f60d56cbfd7d0e9601ab9e6d1609603d3d41ee3 SHA256: e1ac01a83e10bb82dbd93b1e531ef657a205ce6ce3caabcefcc5beb6898c588a SHA512: 87226ed972a99a83d05b23c916c65f2b783229722428dfd3eade2ac39aa570a8548c3a72ded7c468a63a842ca432021896a5342b9362a6860c590b0181b40133 Homepage: https://cran.r-project.org/package=VLF Description: CRAN Package 'VLF' (Frequency Matrix Approach for Assessing Very Low FrequencyVariants in Sequence Records) Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences. The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs. The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another. Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) and Phillips et al. (2023) . Package: r-cran-vlmcx Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-berryfunctions Filename: pool/dists/noble/main/r-cran-vlmcx_1.0-1.ca2404.1_all.deb Size: 93760 MD5sum: af3a2f02c0554f9a067d297bf01fa966 SHA1: 3a119a96dce91862b6168365a8e1ae6d2820ba05 SHA256: b2196684ab0852e36000f113bb547928a01b22de2c228a61e73a7a3150f2f9e0 SHA512: 30d630904e32283dde02661a3ccc3f1951656243cff3cc8397150880d8c9e520bc5a70180c6d5a7626380c6e21a04e5bb98114cde1d6475a473371f302a8a9f5 Homepage: https://cran.r-project.org/package=VLMCX Description: CRAN Package 'VLMCX' (Variable Length Markov Chain with Exogenous Covariates) Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. 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Package: r-cran-vltimecausality Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dtw, r-cran-tseries, r-cran-rtransferentropy, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-vltimecausality_0.1.5-1.ca2404.1_all.deb Size: 170090 MD5sum: 30be16383264699a5690bc89da6733ad SHA1: 6845f525e0778f59a0619155580bd64e123f2c6a SHA256: e3c3b2166e8575ef9c95244675e5c060faa62c497c3eb2b9a6df98e38573aac1 SHA512: 0120414da029e203f179a9c12583ec26a1688eae0ef94b088c394d06debee8e79d4aacda5704f4fac6de6e96b8a963e15e5e2feff3a18388694da54936a9f972 Homepage: https://cran.r-project.org/package=VLTimeCausality Description: CRAN Package 'VLTimeCausality' (Variable-Lag Time Series Causality Inference Framework) A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) when referring to this package in publications. 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For method details see (i) K. Dragomiretskiy and D. Zosso (2014) ; (ii) Pankaj Das (2020) . 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This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 . Package: r-cran-voigt Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-invgamma, r-cran-coda, r-cran-pracma Filename: pool/dists/noble/main/r-cran-voigt_2.0-1.ca2404.1_all.deb Size: 50104 MD5sum: 0874e8b8f7fcd2f159363b0ca8f64213 SHA1: 3a7375f8ddde589f938c92cc655fed58d88dca4d SHA256: 441f467b070633d1db046eeaeb7fa60fe2906ad81fb9273fedc93a2a8432645b SHA512: 3b0e073dfd9562ad6a77f093377acbc79ad61525b01a39c0938ee8676de9730600ea344a9570413e244780b543ed4621c54dcbe6c2672bd0bf469a4a9369d953 Homepage: https://cran.r-project.org/package=voigt Description: CRAN Package 'voigt' (The Voigt Distribution) Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Working in three dimensions is useful in an aquatic context when the organisms one wishes to model can be found across a wide range of depths in the water column. The package also contains functions to automatically generate marine training model training regions using machine learning, and interpolate and smooth patchily sampled environmental rasters using thin plate splines. Davis Rabosky AR, Cox CL, Rabosky DL, Title PO, Holmes IA, Feldman A, McGuire JA (2016) . Nychka D, Furrer R, Paige J, Sain S (2021) . Pateiro-Lopez B, Rodriguez-Casal A (2022) . 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Package: r-cran-waffle Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-gtable, r-cran-extrafont, r-cran-curl, r-cran-stringr, r-cran-htmlwidgets, r-cran-dt, r-cran-plyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-waffle_1.0.2-1.ca2404.1_all.deb Size: 1547476 MD5sum: 365ed77c501d40ed8cc34e464963ffbf SHA1: f42f6cb73f47d18e4fdbfaf36779162d695b5cfc SHA256: dca6b80089576fcde17dd347df1dd0196a6f26ee230a936738a72c62095678b4 SHA512: 3e77ab2c325a8900713d57a74e1e420ab2fb06b3d9c508e0e1469abc3876ac611c99d3d768a2129dcdb0101e5d58662447117c6ce8321864bcc48fe3ca905930 Homepage: https://cran.r-project.org/package=waffle Description: CRAN Package 'waffle' (Create Waffle Chart Visualizations) Square pie charts (a.k.a. waffle charts) can be used to communicate parts of a whole for categorical quantities. 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Package: r-cran-waiter Architecture: all Version: 0.2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-shiny, r-cran-htmltools Suggests: r-cran-httr, r-cran-knitr, r-cran-packer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-waiter_0.2.5.1-1.ca2404.1_all.deb Size: 460470 MD5sum: ff57aa0efe090ecbd71ae5c6968672b1 SHA1: 378aef1e73d517f3ac5f6d7cdd9edcc516c18208 SHA256: e3e49c3ad82420317c107bdbd37c0f6ea3a8531189414b2b72d9aa62a2c9393c SHA512: 5246ecb82b78ff3e6e400174e5ef7097c4b3f37ed76bf0a083da9ac537fae28dda0793a8639d06d8cfd9c06dcc1797de94d06f71b57216556a35b952cfd70200 Homepage: https://cran.r-project.org/package=waiter Description: CRAN Package 'waiter' (Loading Screen for 'Shiny') Full screen and partial loading screens for 'Shiny' with spinners, progress bars, and notifications. Package: r-cran-wakefield Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wakefield_0.3.9-1.ca2404.1_all.deb Size: 1259252 MD5sum: 8995488207bd7833b668bc47d93d9cd6 SHA1: 1daeeb96c1efe060252dd655284975106e0894a9 SHA256: b4167cac8ef847d7946ffeb6b60038bff6fd7838fcd20a0f9ac73fb3aa7bfccd SHA512: fac36b5795776eb4776fc80d60459b3a26e3c1b2dba637176de98f3accbd841f0ad860fd277a3fca78c95c1a97762b31aecf76b3cd5b89172aabff42b5a1639e Homepage: https://cran.r-project.org/package=wakefield Description: CRAN Package 'wakefield' (Generate Random Data Sets) Generates random data sets including: data.frames, lists, and vectors. Package: r-cran-wal Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-freesurferformats, r-cran-jpeg, r-cran-png, r-cran-spacesxyz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wal_0.2.1-1.ca2404.1_all.deb Size: 364188 MD5sum: e0c2f000c377935f8cce477004c72cd9 SHA1: ce20a7a5afc63a93cdaff96e6970bde90f9dee56 SHA256: 4d05f6a78a7fc92816c40fdf681716c72b72e430623655d39c0c51dcf4390e1a SHA512: 9c33f8bdbcf0ddc33b8b3fd435855201093bd1da11a28b684acabefb6a717750076240c371bf4eba1f8465306303c48d59dfdd02768a5310dfe8b1dc3953cdfe Homepage: https://cran.r-project.org/package=wal Description: CRAN Package 'wal' (Read and Write 'wal' Bitmap Image Files and Other 'Quake' Assets) Read 'Quake' assets including bitmap images and textures in 'wal' file format. 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Package: r-cran-waldo Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-diffobj, r-cran-glue, r-cran-rlang Suggests: r-cran-bit64, r-cran-r6, r-cran-s7, r-cran-testthat, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-waldo_0.6.2-1.ca2404.1_all.deb Size: 135098 MD5sum: 55f8475cd2d2bcf94df47d74a77012a0 SHA1: 61b8494a9a0febe56cf454e7061d2ed6888a12ef SHA256: 2d2dc694d3a6300c30b0a27b442832670b220612ecc09e7f3fb7dfceed5ab0ce SHA512: 67e6b7e848d7c617e632c45a456a0bafc33150d6ec92fbd50369eb2892004548115857fdd23018baf175688569bc484399424a279d499ab59b39568892096251 Homepage: https://cran.r-project.org/package=waldo Description: CRAN Package 'waldo' (Find Differences Between R Objects) Compare complex R objects and reveal the key differences. Designed particularly for use in testing packages where being able to quickly isolate key differences makes understanding test failures much easier. Package: r-cran-walkboutr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-ggforce, r-cran-ggplot2, r-cran-lubridate, r-cran-lwgeom, r-cran-magrittr, r-cran-measurements, r-cran-sf, r-cran-sp, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-walkboutr_0.6.0-1.ca2404.1_all.deb Size: 531850 MD5sum: 71d67849fe28e763ffe9c10dccad30ab SHA1: aed8e30c91a84b54303bd05ce71964dfe6ecfd21 SHA256: fb32301e052d37e4c220369b66039b71212d029e505817bc533162f8aa152e22 SHA512: b180184117bef55f0515add9fcf786553c90bae2dc837023ff7f3529220f79e2502c2098fdcbe5f76f26b2de7c9ab1ed5a806e99192d16f01c27aad3695f6093 Homepage: https://cran.r-project.org/package=walkboutr Description: CRAN Package 'walkboutr' (Generate Walk Bouts from GPS and Accelerometry Data) Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) . Package: r-cran-walking Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-actibase, r-cran-assertthat, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-matrixstats, r-cran-reticulate, r-cran-signal, r-cran-tidyr Suggests: r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-walking_0.8.2-1.ca2404.1_all.deb Size: 65382 MD5sum: fdac78fbca9cf5f5b2de1170c6e57b96 SHA1: 60579a74ea6e63eb28b703dd35a739798d1f2351 SHA256: 0d66ac39289f271e9b2b67e0d6d102b552c5bf4dbcf7c45cf022869f0deb6803 SHA512: 9f2915d8ed44a28d28d788419145450ab992608b6472edd4dbc1ed3853937fd5067c85607a2d3234db985201d3d6aca38c7a1f3a7f5a33fdf6dfde14ef9c01f8 Homepage: https://cran.r-project.org/package=walking Description: CRAN Package 'walking' (Segments Accelerometry Data into Walking Bouts using Open SourceMethods) Segments walking from accelerometry data using 'forest' python module from Yi (2025) , 'Verisense' original from Rowlands (2022) and 'Verisense' revised from Maylor (2022) , and Step Detection Threshold (SDT) from Ducharme (2021) methods. Package: r-cran-walkscore Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-walkscore_0.1.2-1.ca2404.1_all.deb Size: 17016 MD5sum: e7e6563de52b8471e3c428e6eed459f9 SHA1: 0ed01d7a24200398d00ffdc0923f358cac05b4f6 SHA256: c1142c6d240e023e12d2a8881d154904f0ec63f9af238996f241baa7fcccc4dc SHA512: 153ed3213910b7eccd3e873db50e09322947d425bfb1bef13a762efbfef3e1f15b55d8521bd021e220cef0f474a67d668682ad656abd6d4bfeaf955e8450cdc6 Homepage: https://cran.r-project.org/package=walkscore Description: CRAN Package 'walkscore' (A Tidy Interface to the 'Walk Score' API) Easily collect walk scores, bike scores, and transit scores (where available) from the 'Walk Score' API , a proprietary API that assigns locations a walkability score between 0 and 100. Package: r-cran-walkscoreapi Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-walkscoreapi_1.2-1.ca2404.1_all.deb Size: 63626 MD5sum: a87995a08b26a8e6d121fb870183a1ea SHA1: 267e492d5b208f7d21c0bf3368d6b13e27a6a156 SHA256: d5e94a5681ec0110ba2ddbed01b9976637b835b79e96087bd4c26b5a85ead376 SHA512: b8e5f731dc20f1c768700174abd0ac37b2291aac1925c77dc987f8ab50ed8481f79f13f43f1bd8a163af480f247fc9d29c17848ccf7d0ed45107b2ccd9004a11 Homepage: https://cran.r-project.org/package=walkscoreAPI Description: CRAN Package 'walkscoreAPI' (Walk Score and Transit Score API) A collection of functions to perform the Application Programming Interface (API) calls associated with the Walk Score website (www.walkscore.com) within the R environment. These functions can be used to query the Walk Score and Transit Score database for a wide variety of information using R scripts. This package includes the simple Walk Score and Transit Score API calls, which return the scores associated with an input location, as well as calls which return some data used to calculate the scores. These functions are especially useful for mass data collection and gathering Walk Score and Transit Score values for large lists of locations. Package: r-cran-wallace Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2803 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-ecospat, r-cran-enmeval, r-cran-geodata, r-cran-htmltools, r-cran-knitcitations, r-cran-leafem, r-cran-magrittr, r-cran-markdown, r-cran-rcolorbrewer, r-cran-rjava, r-cran-rlang, r-cran-rmarkdown, r-cran-sf, r-cran-shinyalert, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spocc, r-cran-spthin, r-cran-zip Suggests: r-cran-ade4, r-cran-bien, r-cran-dismo, r-cran-glue, r-cran-jsonlite, r-cran-knitr, r-cran-mapview, r-cran-maxnet, r-cran-predicts, r-cran-rangemodelmetadata, r-cran-raster, r-cran-rgbif, r-cran-sp, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wallace_2.2.1-1.ca2404.1_all.deb Size: 1847182 MD5sum: ba5b4b6e50c98c0cb7218644d3b9365b SHA1: 380efba412baff74378779f4c97c04fd8c92a627 SHA256: 2fe2accdb13479ceb98c4c011c3cf3fa2b8bc7a892cea063f2d82a287c25fe70 SHA512: d57a3cbfb9533620e95c713e3567b6c048fe4df9a6b0bb89e51a8d1dea3dda84b454a443114ddaa9c0122ba90a21e9167bc972574e1f856912a1131e615d35c8 Homepage: https://cran.r-project.org/package=wallace Description: CRAN Package 'wallace' (A Modular Platform for Reproducible Modeling of Species Nichesand Distributions) The 'shiny' application Wallace is a modular platform for reproducible modeling of species niches and distributions. Wallace guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface. An extensive vignette, which guides users through most package functionality can be found on the package's GitHub Pages website: . Package: r-cran-wallomicsdata Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3503 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wallomicsdata_1.0-1.ca2404.1_all.deb Size: 3488660 MD5sum: bcb5ef7cb36dc9152db4f0b36ebad5aa SHA1: 496fd7487ab69f99a4eb5bcea6d0acad8ebb3564 SHA256: 2560f58545fa57e18d7525fc5d121d035111d19e9c3dcdf85dc12d3af745d853 SHA512: 79176bd367a4ff46447096e03aeb1929d449aead110c1bddaaa9d531e3aa9b3c2bcb4d6c5d5c43d0464277f67db7cd8ae376f6e3372634c3a48bc24510889cb5 Homepage: https://cran.r-project.org/package=WallomicsData Description: CRAN Package 'WallomicsData' (Datasets for Multi-Omics Integration in a Plant Abiotic StressContext) Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) and Durufle et al (2020) . Package: r-cran-wally Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prodlim, r-cran-riskregression, r-cran-data.table Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-wally_1.0.10-1.ca2404.1_all.deb Size: 114608 MD5sum: d5604df30069bfc4427103dd6f6599cb SHA1: 03b70283005d8327a1eae1663c14f8fb6dc5be89 SHA256: eea7151d1c09bcdf91d0f61260fe85f4ac94d60ecc359569c0843b9aec3f9e53 SHA512: f991950219eac043f80ef1f2d2420a2b1194d12dd8ac5c4f7731dae6a7aeb76c8d0de2b7eb6379e1889622e1e13d9059362baa9a7f1b32e5fec6fff01455c279 Homepage: https://cran.r-project.org/package=wally Description: CRAN Package 'wally' (The Wally Calibration Plot for Risk Prediction Models) A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events. Package: r-cran-walmartapi Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-purrr, r-cran-httr, r-cran-tibble, r-cran-magrittr, r-cran-stringr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-walmartapi_0.1.5-1.ca2404.1_all.deb Size: 44534 MD5sum: f556e31f03f1525b6c6ab253b7962bc6 SHA1: 244bf33a6f17a09b631e788c34b49a94f3094942 SHA256: 03fd1b235247bc2899c2e7398863100ebbe4f78b45f7f7f8f32c6a6a3d9360f0 SHA512: 1aec64f6dacfc597bbbd0d13ce7d2c28523794ee71dca8590fb9f2881b3b068241279447d4d7a4ca7284c92d31d9239b53ea958b9d3d83e7f39c3931a5ad3e5b Homepage: https://cran.r-project.org/package=walmartAPI Description: CRAN Package 'walmartAPI' (Walmart Open API Wrapper) Provides API access to the Walmart Open API , that contains data about stores, Value of the day and products which includes names, sale prices, shipping rates and taxonomies. Package: r-cran-walrus Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wrs2, r-cran-ggplot2, r-cran-jmvcore, r-cran-r6 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-walrus_1.0.5-1.ca2404.1_all.deb Size: 565528 MD5sum: d97fa2851a00f88beb30fba16d57d00e SHA1: 1b1a99e683eb13b2e9ff4cf4217e873469c43cb4 SHA256: 21acd317c284cae5d8a47832b6affaf2f6d41e51b74de85b16dd070a60a9752e SHA512: e5ebdbe61ca2b269563b58fd9f73074e4696510abf2d908aa43e7afee81eee439e76e653657579a820864c1eb29d214dd7cbab983c0d45b152a1e4d56ff16f2a Homepage: https://cran.r-project.org/package=walrus Description: CRAN Package 'walrus' (Robust Statistical Methods) A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for 'jamovi' (see for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book 'Introduction to Robust Estimation & Hypothesis Testing'. Package: r-cran-wals Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-mass, r-cran-rdpack Suggests: r-cran-aer, r-cran-bayesvarsel, r-cran-bms, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wals_0.2.6-1.ca2404.1_all.deb Size: 347916 MD5sum: 6d0bc45c2c1f96139837d249be68ada7 SHA1: 63702e7fbd82c3c4461a77ed50b590d5e9e156c7 SHA256: 454b73831e6d639d6d2c8674debd21bee56df57a263bab06f5d326e1a1f76f26 SHA512: 5dfeb1183265122a7a501013591fd77aec0e2a70cad3d7ad2ac3d9dda7c19c23171411b17d8ee72585c34a1f2d4706c55c350e0cb67de8610e4927bfb27ed1c5 Homepage: https://cran.r-project.org/package=WALS Description: CRAN Package 'WALS' (Weighted-Average Least Squares Model Averaging) Implements Weighted-Average Least Squares model averaging for negative binomial regression models of Huynh (2024) , generalized linear models of De Luca, Magnus, Peracchi (2018) and linear regression models of Magnus, Powell, Pruefer (2010) , see also Magnus, De Luca (2016) . Weighted-Average Least Squares for the linear regression model is based on the original 'MATLAB' code by Magnus and De Luca , see also Kumar, Magnus (2013) and De Luca, Magnus (2011) . Package: r-cran-wamasim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wamasim_1.0.0-1.ca2404.1_all.deb Size: 69976 MD5sum: d864f5691fb9df893908b6b29e539b59 SHA1: 32db49c8acf6f5bc9b4572f56bc0ebafd595d1f6 SHA256: 067fc2f23493c242c5858fc98510cecc8cd5d717de2b577c7a6f825c3411ca29 SHA512: 5c00de5aad9fe043e6b8e144d4b555dfa153bb05b9576a0a3810fd272e1a4000d2dcf7fc2e095f802327a53abd91e56334f7be1668361d4009abd890d24bd1ee Homepage: https://cran.r-project.org/package=WaMaSim Description: CRAN Package 'WaMaSim' (Simulate Rehabilitation Strategies for Water DistributionSystems) The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland. Package: r-cran-wand Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-wand_0.5.0-1.ca2404.1_all.deb Size: 190334 MD5sum: e2c6d291fc8ac419aecc386385fda637 SHA1: c576d090d93844fc59b5f2a2f0603269c2acdaac SHA256: c36545425aa6478fb864059e7f4cfe9b28ac3d73f5e47d8f37f1f3921e77e9b6 SHA512: d98671795df8a5e7ec2785331d2d12d4cfd6759d04682064dda96249032c7b8f7a4b05df77ab7c0eacc21857ad85bb2f11dd05938c228dae9cd8316a26dd963c Homepage: https://cran.r-project.org/package=wand Description: CRAN Package 'wand' (Retrieve 'Magic' Attributes from Files and Directories) 'MIME' types are shorthand descriptors for file contents and can be determined from "magic" bytes in file headers, file contents or intuited from file extensions. 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Package: r-cran-wanova Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-suppdists Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wanova_0.4.0-1.ca2404.1_all.deb Size: 40466 MD5sum: 9fa2c10fbb9902f3a35e30d5a5ac9ef8 SHA1: 8ec5b7d851af7ffed2db734a7c7359714845373d SHA256: 4c7ccca0bf007a81bcfb5d4322ed486544e15ee3244e1ac8b08e478b381f20e8 SHA512: 3b24f8afa949cc93a2f3ab71329631476aa19d615c979c2cf6379f8b524d707f732d8d56e9170be27e6f8744554e3f7a7c5ed438f4cdf633ee534d68bce0eea8 Homepage: https://cran.r-project.org/package=WAnova Description: CRAN Package 'WAnova' (Welch's Anova from Summary Statistics) Provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) Kirk, R. E. (1996) Carroll, R. M., & Nordholm, L. A. (1975) Albers, C., & Lakens, D. (2018) Games, P. A., & Howell, J. F. (1976) Levy, K. J. (1978a) Show-Li, J., & Gwowen, S. (2014) . Package: r-cran-waou Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4416 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet, r-cran-dplyr, r-cran-stringr, r-cran-glue, r-cran-mice, r-cran-nonprobsvy, r-cran-survey, r-cran-ggplot2, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-waou_0.1.1-1.ca2404.1_all.deb Size: 4461332 MD5sum: 838ac9a32aa25d8557f9ddbbdc2abb8b SHA1: 51fd6d08b6d0a65d4ebc20fcc6ba24a32a7b6378 SHA256: cf4f9f2bbf827b004f4911f13c80a0d1fbbcaa92c2e941521359a0b375d0deb6 SHA512: c37022570007f35a45da7d502cc505a37b6517a8510e5c0c7f1fdd666fa33658629e19db519042821326aeadf440e53024e96804fa7025afc03e158496abfa3c Homepage: https://cran.r-project.org/package=waou Description: CRAN Package 'waou' (Weighting All of Us) Utilities for using a probability sample to reweight prevalence estimates calculated from the All of Us research program. Weighted estimates will still not be representative of the general U.S. population. However, they will provide an early indication for how unweighted estimates may be biased by the sampling bias in the All of Us sample. Package: r-cran-warabandi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-readtext, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-warabandi_0.1.0-1.ca2404.1_all.deb Size: 61854 MD5sum: 33f913dbd2d00f5847ba45cbd54a03a9 SHA1: 676f9bf1466f87b54df170f2ab62102f787c7b0f SHA256: 696552ad076cc4536db6961a3e183298234be38f8ea6a938c24e1e37878e4e27 SHA512: b03a537cae0492065b9910d6e835c7d1ef4e5496d95439783a85b8c67f4652b54f3b5401c5c7adef806869865353754b0c617a109d3b3fc60c503d1fc5d50310 Homepage: https://cran.r-project.org/package=warabandi Description: CRAN Package 'warabandi' (Roster Generation of Turn for Weekdays:'warabandi') It generates the roster of turn for an outlet which is flowing (water) 24X7 or 168 hours towards the area under command or agricutural area (to be irrigated). The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as 'warabandi' and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by 'warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) . Package: r-cran-warden Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-data.table, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-flexsurv, r-cran-mass, r-cran-zoo, r-cran-progressr, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-survminer, r-cran-survival Filename: pool/dists/noble/main/r-cran-warden_1.2.5-1.ca2404.1_all.deb Size: 1071708 MD5sum: c85a14cf87bad54db7e5bdb67d490fc6 SHA1: b328f7069e8b9c243d4cf018ac30725288561f00 SHA256: f4a7ef88840fd64b74a0bdd40265abec24c5dcb6b226931e04a9c44292b539a9 SHA512: 833aa0bb8656307c743dfb9780da7d3d5f76c8ed195a99eefffb1ab53be97a00e8f148f723a1b72e6418fa81c926b4eee593d83b25a8c671895bc9a7ae4b96c3 Homepage: https://cran.r-project.org/package=WARDEN Description: CRAN Package 'WARDEN' (Workflows for Health Technology Assessments in R using DiscreteEveNts) Toolkit to support and perform discrete event simulations without resource constraints in the context of health technology assessments (HTA). 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Package: r-cran-warehousetools Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-clustersim Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-warehousetools_0.1.4-1.ca2404.1_all.deb Size: 106110 MD5sum: a8b912c6a4efd0e392571a6670dc9c7f SHA1: aa7fc7df37f3669928f7f09ef0ad88f8e918c03b SHA256: d0f469bde966581d78570d49da70e174b0cec2586647ca3b0110e9151229861a SHA512: 069154ddaa18d0ddd7adec394667a701192309a106ac405ac9d54de5370fc40f5cab2ae5d2bb9bcab50991ae92a9e4a85baa5eb48f0f2e1fed3184ec843910e4 Homepage: https://cran.r-project.org/package=warehouseTools Description: CRAN Package 'warehouseTools' (Heuristics for Solving the Traveling Salesman Problem inWarehouse Layouts) Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the picker’s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations. see: Bartholdi, J. J., Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1." The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. Georgia Institute of Technology. . Package: r-cran-warmthcompetence Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3176 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-spacyr, r-cran-caret, r-cran-dplyr, r-cran-lexicon, r-cran-ngram, r-cran-qdap, r-cran-politeness, r-cran-qdapdictionaries, r-cran-quanteda, r-cran-sentimentr, r-cran-tidyr, r-cran-tidytext, r-cran-tm, r-cran-quanteda.textstats Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-warmthcompetence_0.1.5-1.ca2404.1_all.deb Size: 3176604 MD5sum: 6dd3ace4bc77d398502d080ae57586ba SHA1: ca3762db3d8ec8ba93cde209cc83b60b1b47bc28 SHA256: 6f336cb358264b8b8316cb12a9434793eeac8a95d85fd62208dfbfcded4ba3bc SHA512: 513209f3a9c3838f27da5f094f69034a9a8801b65ab2aa079163f2394594acf21ff852afb36f96d46508aadff632325e0bea43e9f5ed7b81d266cd50e64380a3 Homepage: https://cran.r-project.org/package=warmthcompetence Description: CRAN Package 'warmthcompetence' (Warmth and Competence Detectors) Detects perceptions of warmth and competence in American English self-presentation language. Using trained elastic net regression models, this package provides a numerical representation of warmth and competence perceptions. Methods are described here:. 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Package: r-cran-washdata Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 879 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-washdata_0.1.5-1.ca2404.1_all.deb Size: 759878 MD5sum: 4350941e06bd04478f6c2ad1b19f1807 SHA1: 7c1774705c16699b970b945e5866044a04d02d61 SHA256: 213b33bddc458ad681413293a0f64ff80cb6fe501a3059719092cd9b24045288 SHA512: 3739396f3a016b4947379756825d0a09cdda64bc55fdb2c04e725a0129aa6a991356ebadc9feaac045f382f2d011391cfb94ceb87a20ef96fd2029c909a3e349 Homepage: https://cran.r-project.org/package=washdata Description: CRAN Package 'washdata' (Urban Water and Sanitation Survey Dataset) Urban water and sanitation survey dataset collected by Water and Sanitation for the Urban Poor (WSUP) with technical support from Valid International. These citywide surveys have been collecting data allowing water and sanitation service levels across the entire city to be characterised, while also allowing more detailed data to be collected in areas of the city of particular interest. These surveys are intended to generate useful information for others working in the water and sanitation sector. Current release version includes datasets collected from a survey conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of a series of surveys to be conducted by WSUP in various cities in which they operate including Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This package will be updated once the surveys in other cities are completed and datasets have been made available. 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This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web. 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Use 'washi' to easily style your 'ggplot2' plots and 'flextable' tables. 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So this package offers functions that analyze and validate the raw data, which must be entered in a determined format; extract specific vectors and matrices from this raw database; normalize the input data; calculate rankings by intermediate methods; apply the lambda parameter for the main method; and a function that does everything at once. The package has an example database called choppers, with which the user can see how the input data should be organized so that everything works as recommended by the decision methods based on multiple criteria that this package solves. Basically, the data are composed of a set of alternatives, which will be ranked, a set of choice criteria, a matrix of values for each Alternative-Criterion relationship, a vector of weights associated with the criteria, since certain criteria are considered more important than others, as well as a vector that defines each criterion as cost or benefit, this determines the calculation formula, as there are those criteria that we want the highest possible value (e.g. durability) and others that we want the lowest possible value (e.g. price). 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Johansen et al. (2019) . Package: r-cran-wateryeartype Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wateryeartype_1.0.1-1.ca2404.1_all.deb Size: 15792 MD5sum: 3c14484959c7d25e7574718ea39ad858 SHA1: 532a2179227a5376a068fad1fe60d8fd6566ab27 SHA256: add34475febc2d20463c3149857066ef2b47e8af32f3f6f3df188a5ae4ba1392 SHA512: 25b42739761938eb4a69d170cbc5b489d22628b84d0ecc33b2f3a410b3f31e0d8a764fccfbb5849ca452289aba3f7bd9115be2857134c804dce07f6da9943a1c Homepage: https://cran.r-project.org/package=waterYearType Description: CRAN Package 'waterYearType' (Sacramento and San Joaquin Valley Water Year Types) Provides Water Year Hydrologic Classification Indices based on measured unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources and subject to revision. 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Package: r-cran-waveletcomp Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-waveletcomp_1.2-1.ca2404.1_all.deb Size: 555900 MD5sum: 25cb0674836f3e3de86f3354dab39df3 SHA1: c557a0b77e493c412e37c2567fd6eeef6d8f6bdd SHA256: dbfcace99a265e78d19e83294e212bf25985f24eca86e99d5ad2f14ebfcbb4f1 SHA512: 70e655d2be7b741ce3f32438cdbe8ac3f92582ce5c78741cb0ac8826d7e27a74e6dffe047627ace99932cc2e20686e5fc00ae22ac26951b771d04657bc97b565 Homepage: https://cran.r-project.org/package=WaveletComp Description: CRAN Package 'WaveletComp' (Computational Wavelet Analysis) Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms. Package: r-cran-waveletets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-metrics, r-cran-tseries, r-cran-wavelets, r-cran-forecast, r-cran-caretforecast Filename: pool/dists/noble/main/r-cran-waveletets_0.1.0-1.ca2404.1_all.deb Size: 19202 MD5sum: d3445de919b7c03a0e9272a29ccef62a SHA1: d482295b7387da61a162352935754a40d5bf055b SHA256: 30d9380710ce6a0ca153e71e2d3d0e0fa4f3c20b7442a893c205d6a2fd9fb494 SHA512: 117260af92c8725d9bf87bd38254fab5aeb03fa0ee7e42c08dc40ddf82bd517ed97fe1b622ab3dc1efdb787551c4a31312eb5adcdaa52708a3d62b8063b5538a Homepage: https://cran.r-project.org/package=WaveletETS Description: CRAN Package 'WaveletETS' (Wavelet Based Error Trend Seasonality Model) ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) . Package: r-cran-waveletgarch Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fints, r-cran-forecast, r-cran-rugarch, r-cran-fracdiff Filename: pool/dists/noble/main/r-cran-waveletgarch_0.1.1-1.ca2404.1_all.deb Size: 37596 MD5sum: 4c7987f37c2e443b1790d2a18a66fa20 SHA1: 24d41a63a5b53f88bb4dbae2f28c354a3ed50122 SHA256: 5655b6b9f2b01cab6dcfe76dfd8393c3e316e9a77063d6265297f830621ce5fd SHA512: 88c0fdc6bafc53e716261bb3a6ccf4994b1229ba74d115b95ad876998781736d7628a374474902ca70001cd656c977b780f3eae049cbe8af3cffe7bbd446e03a Homepage: https://cran.r-project.org/package=WaveletGARCH Description: CRAN Package 'WaveletGARCH' (Fit the Wavelet-GARCH Model to Volatile Time Series Data) Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) . Package: r-cran-waveletgbm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-metrics, r-cran-tseries, r-cran-wavelets, r-cran-gbm Filename: pool/dists/noble/main/r-cran-waveletgbm_0.1.0-1.ca2404.1_all.deb Size: 22974 MD5sum: 781b4f05e6fa239da0d7864b6334cb21 SHA1: 60eea04e89a58a0c9d0f4f8eba27df1e682a18db SHA256: b3bfc2107f11264a82945e7f4ab8b5a3eec31600d8c5cdce8d8fbb19deddb921 SHA512: 4bfddd14c0a042afa1b80c8624a940b6d522cdea7e39976ffae696e9590c0a4e0a20688fe2a8c61c96841017339f981c7eb1284492d912165f7c02004ac9972e Homepage: https://cran.r-project.org/package=WaveletGBM Description: CRAN Package 'WaveletGBM' (Wavelet Based Gradient Boosting Method) Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) . Package: r-cran-waveletknn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-metrics, r-cran-tseries, r-cran-wavelets Filename: pool/dists/noble/main/r-cran-waveletknn_0.1.0-1.ca2404.1_all.deb Size: 22720 MD5sum: 9bfca8c9980b3792c16bd8a998e77fd3 SHA1: be933910bdf846920bf36ebae23173c2365255ff SHA256: 61fb7043d852591229c966043d429887be9748e3ce10435362408a8e7b0e78f6 SHA512: 87d68825e8b44b8617780d1445f2ded0ec3be0a6cc1999544bf124900fdcac8df47e2cdf6ebb2980fefd3e4c6a1112105483f876f77f2bac122cb2367416ac80 Homepage: https://cran.r-project.org/package=WaveletKNN Description: CRAN Package 'WaveletKNN' (Wavelet Based K-Nearest Neighbor Model) The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) . Package: r-cran-waveletlstm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-tseries, r-cran-wavelets, r-cran-tslstm Filename: pool/dists/noble/main/r-cran-waveletlstm_0.1.0-1.ca2404.1_all.deb Size: 20810 MD5sum: fa9b328dcbcc14ad35d6c723dfce7ed8 SHA1: ffeadfbd7cb188aa09590630f113ebafe326df7d SHA256: f5db1e12af8976a0806eae2cec4602de7acb3fdc896711cad68d68d3f5bca9cc SHA512: 6641feed880e8355859fa6cb86d05f72c342c13307e690f5437f376d2771a71f3af280be85a3785505eb35d6f65779da42b2779dc9aab2b3c76bca3e8c581d2c Homepage: https://cran.r-project.org/package=WaveletLSTM Description: CRAN Package 'WaveletLSTM' (Wavelet Based LSTM Model) A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) . Package: r-cran-waveletml Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-fints, r-cran-lsts, r-cran-earth, r-cran-caret, r-cran-neuralnet, r-cran-e1071, r-cran-pso Filename: pool/dists/noble/main/r-cran-waveletml_0.1.0-1.ca2404.1_all.deb Size: 50246 MD5sum: bd20ce997f0bbaf54e40b8755abb8397 SHA1: 414f17ddefcef8e1f8174ccf95a7d47f692d71c6 SHA256: 3d151b5af9b71f5fd201a6f4012beb6d048c4d6df02cd9b3c77aacc5508ff8cd SHA512: c7e8f83ef2e0711093e4a347cc3da7473c3ad84b1715b74f8d15e858132210b9c76301a75805c07252f3933b8582604ef763cbe5136677ff822eb0d19e541b0e Homepage: https://cran.r-project.org/package=WaveletML Description: CRAN Package 'WaveletML' (Wavelet Decomposition Based Hybrid Machine Learning Models) Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) . Package: r-cran-waveletmlbestfl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveletml, r-cran-ceemdanml, r-cran-describedf Filename: pool/dists/noble/main/r-cran-waveletmlbestfl_0.1.0-1.ca2404.1_all.deb Size: 33488 MD5sum: cf3c5f549e66571d6f59324246b2cd9e SHA1: f3e5d6590912ac6f441e1df554e36ffa9381650c SHA256: 1c60368fed0eaa866f375d0f3c887c9069f8abb57ef6c4ee0223008934d620b7 SHA512: 9f1c34af451da2fa995948666887eb81eb1ff227e2c3cb6a86fbe81c774c43bcdee6dda2240d361563e1f16c317e39e82ecc3bbe2373f4f47e712ae33585fe86 Homepage: https://cran.r-project.org/package=WaveletMLbestFL Description: CRAN Package 'WaveletMLbestFL' (The Best Wavelet Filter-Level for Prepared Wavelet-Based Models) Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) . Package: r-cran-waveletrf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-randomforest, r-cran-tsutils Filename: pool/dists/noble/main/r-cran-waveletrf_0.1.0-1.ca2404.1_all.deb Size: 22364 MD5sum: 90dde4f27ef5a2160b98f90effd2f3e7 SHA1: 8a8ec064739c7d37762abcaaee0180f7f336101f SHA256: efac0855a93e6491f39a16d6c5d2b856c061213ed32931d184c02a2c6e40a8a6 SHA512: 5da7bed3a5d683aadd50632f6c91c58c31dfb1c7d2d09635b68947ec0a0e5662bb4bbc75f60ea438916bfc4c9bb7d6b51ef0679276de27eba9d605b7e60c54f5 Homepage: https://cran.r-project.org/package=WaveletRF Description: CRAN Package 'WaveletRF' (Wavelet-RF Hybrid Model for Time Series Forecasting) The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) . Package: r-cran-waveletsvr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-e1071, r-cran-tsutils Filename: pool/dists/noble/main/r-cran-waveletsvr_0.1.0-1.ca2404.1_all.deb Size: 22166 MD5sum: 705246a4f89ccad1d7ff0ce74e00a875 SHA1: 87ee76a3612c20473a963087002f735a9f1428ac SHA256: 2b66976bce46b75b1566a70dd890fc1bc1c7a3a5712d3027b326494012a4fde6 SHA512: 10f67d9e37b6dce949704c44b2cec079f78c8e79832a705b98d936a42ef2155abb4e620954a861845a7ead91574afabc9575c0cc68d06aca0b07e4d527b9df7e Homepage: https://cran.r-project.org/package=WaveletSVR Description: CRAN Package 'WaveletSVR' (Wavelet-SVR Hybrid Model for Time Series Forecasting) The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) . Package: r-cran-wavemulcor Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2647 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveslim, r-cran-plot3d, r-cran-rcolorbrewer Suggests: r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wavemulcor_3.1.2-1.ca2404.1_all.deb Size: 1686872 MD5sum: 32f030c7ce8cb7b5ae984c36752fb699 SHA1: 30f1833b4a084ae9f2674b9c26f573803f4abe0e SHA256: 491fe4254885ca589b13d314a6fecaa662ee4f14d59b5e50eb8be16f44ba9e3b SHA512: f48e74d26505c89757e0a0102cea5786ac31f6294db669476c2b1ed2d5a8cc958f52d68d2a82814947abd7c11f8e85c14bb96f53581d93f2efa75a601f8d43a2 Homepage: https://cran.r-project.org/package=wavemulcor Description: CRAN Package 'wavemulcor' (Wavelet Routines for Global and Local Multiple Regression andCorrelation) Wavelet routines that calculate single sets of wavelet multiple regressions and correlations, and cross-regressions and cross-correlations from a multivariate time series. Dynamic versions of the routines allow the wavelet local multiple (cross-)regressions and (cross-)correlations to evolve over time. Package: r-cran-waver Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-geosphere Suggests: r-cran-lwgeom, r-cran-sp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-waver_0.3.0-1.ca2404.1_all.deb Size: 33910 MD5sum: 35e8d23393486ab7ca4e382ddc2333ae SHA1: 2586ec77abd6272cf02b1037daedcde0cbcc5dff SHA256: 75edd6595d7511a3b66a7c69f148953900e82b97751853d3ab16a69ab07a4145 SHA512: 42cb3bd3dc2a5a99cab6c93afffc3a8743343fa501556615e70e09b6823600008b2a928565edd89ca9b325d8145064713ff349d034b586515fd3f66a06a9a1c2 Homepage: https://cran.r-project.org/package=waver Description: CRAN Package 'waver' (Calculate Fetch and Wave Energy) Functions for calculating the fetch (length of open water distance along given directions) and estimating wave energy from wind and wave monitoring data. Package: r-cran-waverider Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-hmisc, r-cran-matrix, r-cran-colorednoise, r-cran-dosnow, r-cran-foreach, r-cran-matrixstats, r-cran-reshape2, r-cran-truncnorm, r-cran-astrochron, r-cran-rcolorbrewer, r-cran-colorramps, r-cran-viridis, r-cran-magick, r-cran-rlist, r-cran-trapezoid, r-cran-fancova, r-cran-decomposer, r-cran-scico Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-waverider_0.5.1-1.ca2404.1_all.deb Size: 1146734 MD5sum: 977bb136ccbcb7dae5c445ff285828dd SHA1: 0755d2743353ae686f051d6b2db7524ebe29c16b SHA256: a3529474d3f78818186b125f04778db1149c279ab1bb78722b1a9216bf95a8d3 SHA512: 55f4e4710934fafd959ef4ca4ab836238d87ff4a90d75915cfa6ab61d9e4acdae646e214dfba0c4f3c03bdfbee03b3883ff1db4a1ac935388385ece962b003e1 Homepage: https://cran.r-project.org/package=WaverideR Description: CRAN Package 'WaverideR' (Extracting Signals from Wavelet Spectra) Tools for extracting and analyzing cyclic signals from time series. Package: r-cran-waverr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-kimisc Filename: pool/dists/noble/main/r-cran-waverr_1.0-1.ca2404.1_all.deb Size: 18900 MD5sum: 37b2c05599122ecd154f823e656a1184 SHA1: 3ae4e8b08f6ce691774e3bd38a235e4a0f4d4040 SHA256: c0eb06fd69960a88800e2e850f1d552306a6d5126b2aed5183b08fc7f7315280 SHA512: 1181720ad9d91391b2921798bd5bdd022543df92781abd52aa8217466c6136a5f29a42b63c52fab6d03ac4410dc87e6be3007fb9c477a08a2cbc8c17397ab33e Homepage: https://cran.r-project.org/package=WaverR Description: CRAN Package 'WaverR' (Data Estimation using Weighted Averages of Multiple Regressions) For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available. Package: r-cran-waves Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4020 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-pls, r-cran-prospectr, r-cran-randomforest, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-spectacles, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-waves_0.2.7-1.ca2404.1_all.deb Size: 3747106 MD5sum: dc6f7abecebd3128328b3f3f14161415 SHA1: 59115a2cdd2f8b33f18d2add6cd8ec099df3ed7d SHA256: 97eea8c3fa960239a715b639ad88a67b58784c095378aaaad39cbb8e0a4d53be SHA512: b1aaa6da4d6a8e22bf227fb16c3cc22b8afde4b8fda3d803e69c4b0d8537baf69b742ccd23361f030fe03a280ea61bdb06180c7dcb980a1185e9697831543997 Homepage: https://cran.r-project.org/package=waves Description: CRAN Package 'waves' (Vis-NIR Spectral Analysis Wrapper) Originally designed application in the context of resource-limited plant research and breeding programs, 'waves' provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, preprocessing, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data. This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal . Specialized cross-validation schemes are described in detail in Jarquín et al. (2017) . Example data is from Ikeogu et al. (2017) . Package: r-cran-wavest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-neuralnet, r-cran-tsutils, r-cran-wavelets Suggests: r-cran-devtools, r-cran-roxygen2, r-cran-usethis Filename: pool/dists/noble/main/r-cran-wavest_0.1.0-1.ca2404.1_all.deb Size: 41764 MD5sum: 2e1bddf7cd1dfe922f444c7a49810a6d SHA1: aef25f014b604453ed23e73f81bdb38ca22b2261 SHA256: b6ef13dd7a9321e22183c68862dddcaf04423dc3bad7ad643b2b7698187e40af SHA512: 9cd316a0dae4b0925561a6b696ab51bed166342baa2711ae38c9eba0635e9054ab0d071259c2fbbad31d96658adb319b4b953ac451b0b77be329571bb125b7b3 Homepage: https://cran.r-project.org/package=WaveST Description: CRAN Package 'WaveST' (Wavelet-Based Spatial Time Series Models) An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) . 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(2010) ). 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It is known from physics that only gradient fields, also known as conservative, have a well defined potential function. Here we present an algorithm, based on the classical Helmholtz decomposition, to obtain an approximate potential function for non gradient fields. More information in Rodríguez-Sánchez (2020) . Package: r-cran-wayfindr Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-igraph, r-bioc-keggrest, r-cran-desctools, r-bioc-rgraphviz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-polychrome, r-bioc-annotationdbi, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-wayfindr_0.7.1-1.ca2404.1_all.deb Size: 1741904 MD5sum: 3727f394ef5ad36586b7c6df0356f023 SHA1: 01812a90903337ce2f0c40c85ff6e75b356b5965 SHA256: 2f05b736097afe867f88c81411a1d4250b4792748a4934e2c5a939a2be848947 SHA512: 3739eb44e9b745f2604eacd51a94634e5ce8955d323c7ec953e2536af98bb22e7534aa3b83d98432c1c165c9b565c63f839bf14940670443899f1fecb81f1984 Homepage: https://cran.r-project.org/package=WayFindR Description: CRAN Package 'WayFindR' (Computing Graph Structures on WikiPathways) Converts pathways from 'WikiPathways' GPML format or 'KEGG' KGML format into 'igraph' objects. 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Package: r-cran-waywiser Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2653 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fields, r-cran-fnn, r-cran-glue, r-cran-hardhat, r-cran-matrix, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-spdep, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-yardstick Suggests: r-cran-applicable, r-cran-caret, r-cran-cast, r-cran-covr, r-cran-exactextractr, r-cran-ggplot2, r-cran-knitr, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-rsample, r-cran-spatialsample, r-cran-terra, r-cran-testthat, r-cran-tidymodels, r-cran-tidyr, r-cran-tigris, r-cran-units, r-cran-vip, r-cran-whisker, r-cran-withr Filename: pool/dists/noble/main/r-cran-waywiser_0.6.3-1.ca2404.1_all.deb Size: 1795324 MD5sum: c42b2d4b6aaf624d64231bdd76f0a929 SHA1: 0afe11d0ba226c50908921ef61331f56e123bbbb SHA256: 278b02289b4a4076d80203a4e9bfe02867a7e544d1415b45c6977f132bd1bcad SHA512: f3c8e156e36ba459f6360278fb67657cd0f68ebc6ad8596a3a7f0f13d3f85e261954b315783ffc5f4f1b5942318d84533548dcde7f1d48bb9fa2b986da7d0092 Homepage: https://cran.r-project.org/package=waywiser Description: CRAN Package 'waywiser' (Ergonomic Methods for Assessing Spatial Models) Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) ), Geary's C ('Geary' (1954) ), Getis-Ord's G ('Ord' and 'Getis' (1995) ), agreement coefficients from 'Ji' and Gallo (2006) (), agreement metrics from 'Willmott' (1981) () and 'Willmott' 'et' 'al'. (2012) (), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (). 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Cherasia et al., (2022) . Amaratunga et al., (2009) . 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The WCM algorithm attributed to Pervot et al.(1993) . The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work. 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Package: r-cran-wconf Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret Filename: pool/dists/noble/main/r-cran-wconf_1.2.0-1.ca2404.1_all.deb Size: 66648 MD5sum: 5ab744edd7735e855bed52f6e7fa1624 SHA1: 47c07f31af50f0bda5011309c4f6e277d094d532 SHA256: 0a804492374f466d0f1e2697e832ac23822fc224d2386312fc3d69a759b79146 SHA512: aee0462606382c52d2a25885e485785f024000fff87cd7730bf9607cb06fc831ba7aa0d4ef9ff5e3674867fea07db6796d38ab09f1c94d5743c1b9492fb8a101 Homepage: https://cran.r-project.org/package=wconf Description: CRAN Package 'wconf' (Weighted Confusion Matrix) Allows users to create weighted confusion matrices and accuracy metrics that help with the model selection process for classification problems, where distance from the correct category is important. The package includes several weighting schemes which can be parameterized, as well as custom configuration options. Furthermore, users can decide whether they wish to positively or negatively affect the accuracy score as a result of applying weights to the confusion matrix. Functions are included to calculate accuracy metrics for imbalanced data. Finally, 'wconf' integrates well with the 'caret' package, but it can also work standalone when provided data in matrix form. References: Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package" Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization (and the mewto R package)" Monahov, A. (2024) "Improved Accuracy Metrics for Classification with Imbalanced Data and Where Distance from the Truth Matters, with the wconf R Package" Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced Data Classification Results. Pattern Recognition and Image Analysis, 295–302 Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023) "A general framework for implementing distances for categorical variables" . 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Package: r-cran-wdsmatch Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wdsmatch_0.2.1-1.ca2404.1_all.deb Size: 83670 MD5sum: 1935196563a8262c6b84ebbae108cbc2 SHA1: f4925068351910a77b2d44e5a346d38bf5702be8 SHA256: 367f5ea18d38dc64a2fd2c1b96363470c8d1d1bf00d335b9f876e8ab4ce12ef2 SHA512: 05a07396a10c1301e3d88e42a606d952b15f30fd3bcdb72fea8a89491bdb38435593d03d36c77254e931c0670a9dabc240649aa58a3e1829352afc226999704e Homepage: https://cran.r-project.org/package=wdsmatch Description: CRAN Package 'wdsmatch' (Weighted Double Score Matching for Survey-Weighted CausalInference) Implements weighted double score matching (WDSM) for estimating population-level causal effects from complex survey data. Combines propensity scores and prognostic scores with survey design weights for matching, survey-weighted imputation within match sets, and Hajek normalization to target the population average treatment effect (PATE) and the population average treatment effect on the treated (PATT). Supports both retrospective (treatment-dependent) and prospective (treatment-independent) sampling designs. Uses propensity probabilities and arm-specific prognostic scores for matching, with a complete quadratic bias correction in each arm's double score. Provides linearization-based multinomial replication variance estimates and centered normal Wald confidence intervals, retaining the original matching reuse coefficients without re-matching. Supplied scores can be held fixed for inference conditional on those scores. This weight-only interface does not encode survey strata, clusters, or design-specific replicate weights. Package: r-cran-weaana Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1518 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-reshape2, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-weaana_0.3.0-1.ca2404.1_all.deb Size: 1060694 MD5sum: ec78c659e7b329706bb5e1aedccba6d3 SHA1: aeca801ffc7565b1ad621baebf76b7ed31d1e537 SHA256: 87204f1841fa44f0399f9b3ef853c46f6b0655dfb606020e26889c23b0e4b8f7 SHA512: 4cf9e4c2f8f2cfd6357a6409f0c8c8840cd06d15d059016096727d9950654dc1965a1d9bfb775b781b1404abc37378d4915fb9faffaadfd3bfad28e335c95ab3 Homepage: https://cran.r-project.org/package=weaana Description: CRAN Package 'weaana' (Analysis the Weather Data) Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, ). Package: r-cran-weakarma Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-matrixstats, r-cran-vars Suggests: r-cran-timeseries, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-renv Filename: pool/dists/noble/main/r-cran-weakarma_1.0.3-1.ca2404.1_all.deb Size: 275004 MD5sum: f5531f9a0224f2d6aacd522d9fcb6ceb SHA1: 419ff544822908f461479c6a0722e9fde0360787 SHA256: fcb4df70182fbc72a7671cecf67cc1ceeb28ba48a0efe90da74ed6fa7c51e7d1 SHA512: 07a7a6d76aaa174a505057633d63c4c1cbeee838ee375c18b82d6cbe338a3139bb728862582e6a436439f45c3fb3905de75eeb86648a9d41473b12edd0bddcce Homepage: https://cran.r-project.org/package=weakARMA Description: CRAN Package 'weakARMA' (Tools for the Analysis of Weak ARMA Models) Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments 'p', 'q', 'ar' and 'ma'. See Francq, C. and Zakoïan, J. (1998) and Boubacar Maïnassara, Y. and Saussereau, B. (2018) for more details. Package: r-cran-wearables Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-kernlab, r-cran-lubridate, r-cran-magrittr, r-cran-padr, r-cran-r.utils, r-cran-rhrv, r-cran-rlang, r-cran-signal, r-cran-sparklyr, r-cran-varian, r-cran-waveslim, r-cran-xts, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-futile.logger Filename: pool/dists/noble/main/r-cran-wearables_0.11.3-1.ca2404.1_all.deb Size: 1925888 MD5sum: 24496c616836afa5476bf9b637812b19 SHA1: c73a5d7f6a8acb0837b05cff45f3a788efe70420 SHA256: acecc2b8f8a999d0a6285b354a5539681e35b161fa1a7089563728852322412c SHA512: 2eb0732987cd136ef4fb6ba73259efe423dca26c5eeb5051e7759e365023e3e756b137a201829d4cdd090053d1ecf594502238aa37418a4485c8c954416d0a00 Homepage: https://cran.r-project.org/package=wearables Description: CRAN Package 'wearables' (Tools to Read and Convert Wearables Data) Package to read Empatica E4, Embrace Plus, and Nowatch data, perform several transformations, perform signal processing and analyses, including batch analyses. Package: r-cran-weatherindices Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-weatherindices_0.1.0-1.ca2404.1_all.deb Size: 43150 MD5sum: fb7b9eaa418d6d21eb54cc676e495423 SHA1: 285c5c92d4fdaa5346453d5bca8483d7ec02a8e4 SHA256: 64597913e5ed701135e3ef8cdde448bafbd17b9cebc8e5461b66508c3f0b5e87 SHA512: 3445603b99354e3e4909af08361f22c8be8fa4d8f342fdb1120264fbcb00bdcde632964eeed7720346027fe7af2cc5df151557bcfb391b0a5cc74398ccbaa91e Homepage: https://cran.r-project.org/package=weatherindices Description: CRAN Package 'weatherindices' (Calculate Weather Indices) Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, . Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,. Package: r-cran-weatherjoin Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-nasapower, r-cran-digest, r-cran-fst, r-cran-anytime, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-weatherjoin_0.2.3-1.ca2404.1_all.deb Size: 134216 MD5sum: 11f8a63f527eaaaadbe4f89e77df9d47 SHA1: e3ec59317de42fd820063ecfe79ce99bda8bab79 SHA256: dca6213bda14f02ed33fe42ecb7815e71186cf6440076c9ab6ff6d4d8257bddc SHA512: 50e1c9beb8049739ddf6852eb0b598c6d375bd7e14c3b551462bea56644cdfba1ee9e89e406ea4f434ef594942a58871584773cb108d0548ad886d757445909f Homepage: https://cran.r-project.org/package=weatherjoin Description: CRAN Package 'weatherjoin' (Join Gridded Weather Data to Event Tables) High-level tools to attach gridded weather data from the NASA POWER Project to event-based datasets. The package plans efficient spatio-temporal API calls via the 'nasapower' R package, caches downloaded segments locally, and joins weather variables back to the input table using exact or rolling joins. This package is not affiliated with or endorsed by NASA. Package: r-cran-weathermetrics Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-weathermetrics_1.2.2-1.ca2404.1_all.deb Size: 101608 MD5sum: a128300d0b573b94326260c6de273f2c SHA1: 1c94918e8b8c32bc47f6db857e00a507e320d130 SHA256: a967fbd6ab6cc8952466c9363030f70a565c3a79c8cca74f516f99b9ea9783f2 SHA512: 8ec2876a9092496f705a9e223965026d920b3f8d560315e4746e0cfbbf4fb1d1843b825f5d13d10c497e9276941e91fd80e9608b86403810552615b0b4a6ed91 Homepage: https://cran.r-project.org/package=weathermetrics Description: CRAN Package 'weathermetrics' (Functions to Convert Between Weather Metrics) Functions to convert between weather metrics, including conversions for metrics of temperature, air moisture, wind speed, and precipitation. This package also includes functions to calculate the heat index from air temperature and air moisture. Package: r-cran-weathermrjd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-weathermrjd_0.1.1-1.ca2404.1_all.deb Size: 27984 MD5sum: 7df997c6364965bd39ef071523b5aa7e SHA1: a38f45a9eb4a4f7dc4c30d2d19dcf80c7bb89183 SHA256: 1454eb093855148832a9f651dfb6cc7ea30b30a0dfcbd5558f6f1baa1b819604 SHA512: a549da41c1d425b4227120fe57488bb5c17733d3da935f01ca466455f01d6e8d64577f57b06a4528ae53a54ac2c618733601e911850bd00099d2570d227d2fe9 Homepage: https://cran.r-project.org/package=weatherMRJD Description: CRAN Package 'weatherMRJD' (Weather Analysis and Markov Regime Switching Jump DiffusionModels) Provides statistical tools for analyzing weather patterns, temperature anomalies, and climate risk. Implements Markov regime-switching jump diffusion (MRJD) models to capture abrupt shifts, extreme weather events, and structural breaks in environmental time series data. Estimates model parameters using maximum likelihood estimation and offers utility functions for simulating regime-dependent stochastic processes. The regime-switching methodology is based on Hamilton (1989) "Analysis of Time Series Subject to Changes in Regime" . Package: r-cran-weatheroz Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-apsimx, r-cran-clock, r-cran-crayon, r-cran-curl, r-cran-crul, r-cran-data.table, r-cran-foreign, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-xml2 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-mailr, r-cran-mapproj, r-cran-maps, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-vcr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-weatheroz_3.0.1-1.ca2404.1_all.deb Size: 823988 MD5sum: 90199c34db3cc4b03572528b83916190 SHA1: 50978ed7868dd01a427796926ff6db4f86f4f975 SHA256: a5cec5336a29f2efb309b0d2dedfd6ead8a33ebeb36aa4fe5af0038f150077c2 SHA512: 7a283b5df6f9b952395e386d21941920aa1281e0f4c5fda150fe33e69c292a0cb6a30638e739164914702328a44973b6247fee478e42f898bd8fe043c7182096 Homepage: https://cran.r-project.org/package=weatherOz Description: CRAN Package 'weatherOz' (An API Client for Australian Weather and Climate Data Resources) Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science (DES). As well as the Bureau of Meteorology (BOM) of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. DPIRD weather data are accessed through public APIs provided by DPIRD, , providing access to weather station data from the DPIRD weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology (BOM) data and accessed through SILO (Scientific Information for Land Owners) Jeffrey et al. (2001) . DPIRD data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license . SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) . BOM data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate, see for further details. Package: r-cran-weathersentiment Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyverse, r-cran-wordcloud, r-cran-sentimentr, r-cran-tidytext, r-cran-ggplot2, r-cran-stringr, r-cran-data.table, r-cran-rcolorbrewer, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-syuzhet Filename: pool/dists/noble/main/r-cran-weathersentiment_1.0-1.ca2404.1_all.deb Size: 34348 MD5sum: 927294de01246eb6bd263639909a0ef7 SHA1: 42849ec443341d2eea7f4f157a3058806645f908 SHA256: 88f7f960a05e4bd5e5ffb6f1aaeafd53163b6b96b331c52d74763d2ce22062cb SHA512: 2f3a9f41789957b7e51a289b4179d53944848fb99ccc4c37118d4ee0d27e83053ad40c791675c01767f9d93a4f0ee37f3eadf3502cb668e93b97ace2bec63e08 Homepage: https://cran.r-project.org/package=WeatherSentiment Description: CRAN Package 'WeatherSentiment' (Comprehensive Analysis of Tweet Sentiments and Weather Data) A comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret 'Twitter' data for research and commercial purposes. Package: r-cran-weatherstats Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-weatherstats_0.1-1.ca2404.1_all.deb Size: 148796 MD5sum: 67aa073752ff4bcca15c8cd5f91d6393 SHA1: 902958ec930d4d45777d6c7449129f0b15c6ee85 SHA256: e1a250768adf25b01f4c8c8b4dc64e45323054f2ca8f77af5431ca04c9c45814 SHA512: 4cbea7076d92753236bb1a24edb5dccc020258fef70574f4ee2e4f29f46af71325745a6f4672833071261458be4acfc761fcce4a47f73dd79327bb5bc1dc4c21 Homepage: https://cran.r-project.org/package=weatherStats Description: CRAN Package 'weatherStats' (Airport Weather Station Statistics) Download daily weather data recorded at airport weather stations using the National Centers for Environmental Information (NCEI) API . Package: r-cran-weathr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-lutz, r-cran-magrittr, r-cran-purrr, r-cran-sf, r-cran-tibble Suggests: r-cran-gt, r-cran-snakecase, r-cran-tmap Filename: pool/dists/noble/main/r-cran-weathr_0.1.0-1.ca2404.1_all.deb Size: 67004 MD5sum: cb173eccddd7eb6ccf097ca48b25e864 SHA1: bdcfb1717fe1a00bcb49456e176b868854d4c799 SHA256: 45d43eac40d7f9da720af44e6c62ddaaba221323ad70d6b3a08db0ab4ae77507 SHA512: 25ab1e8f906b1ea27de42904c182e5d1bfa45bfbd5456c56dd9af9b5072b47028f97d1090bb41badc32b52ebcfc9bd0c2c4bf6ccfc733a138d8e81a0bf64f0ab Homepage: https://cran.r-project.org/package=weathR Description: CRAN Package 'weathR' (Interact with the U.S. National Weather Service API) Enables interaction with the National Weather Service application programming web-interface for fetching of real-time and forecast meteorological data. Users can provide latitude and longitude, Automated Surface Observing System identifier, or Automated Weather Observing System identifier to fetch recent weather observations and recent forecasts for the given location or station. Additionally, auxiliary functions exist to identify stations nearest to a point, convert wind direction from character to degrees, and fetch active warnings. Results are returned as simple feature objects whenever possible. Package: r-cran-webanalytics Architecture: all Version: 0.9.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable, r-cran-scales, r-cran-brew, r-cran-fs, r-cran-reshape2, r-cran-digest, r-cran-uaparserjs Suggests: r-cran-whoami, r-cran-testthat, r-cran-tinytex, r-cran-data.table Filename: pool/dists/noble/main/r-cran-webanalytics_0.9.15-1.ca2404.1_all.deb Size: 1751328 MD5sum: 0a3584734aa51bf21325780c5f96700d SHA1: cb39cd3b6d6dc2579426c064b315fcae80b4f366 SHA256: ef0d05e81289a18060adcf2fcff4b4c9a748ae38737696a6382a0cf5fb2e9bf3 SHA512: a992fb66527074a8cc70a073bee9d9b2f8d163fa52f912c113017cfe479ef5ef109505a339d8d4b4ace4705f1539faa9c28c321ef8b2c95e39ace4466b66af1c Homepage: https://cran.r-project.org/package=WebAnalytics Description: CRAN Package 'WebAnalytics' (Web Server Log Analysis) Provides Apache and IIS log analytics for transaction performance, client populations and workload definitions. Package: r-cran-webchem Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-rvest, r-cran-jsonlite, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-data.tree, r-cran-tibble, r-cran-base64enc, r-cran-rlang Suggests: r-cran-testthat, r-cran-rcdk, r-cran-covr, r-cran-robotstxt, r-cran-knitr, r-cran-rmarkdown, r-cran-plot.matrix, r-cran-usethis, r-cran-vcr Filename: pool/dists/noble/main/r-cran-webchem_1.3.1-1.ca2404.1_all.deb Size: 361112 MD5sum: a5f2c8e9db9a4feb87f6197a83dc05d3 SHA1: a610bea8b4b1fd4ce9ecb0974851ee7de69e71e4 SHA256: 4e8ae5019ea07eab76cff9b6803f9d8651493efbc6f646bed35a427e732daec8 SHA512: ef16c6364fed9740ed039197b3ecad26ffb8772c47987ff0345677b7aece7cb4d7d727c0d974c4f8d606ca17cdac07c83b94292c374ad41eba29f56751bcb215 Homepage: https://cran.r-project.org/package=webchem Description: CRAN Package 'webchem' (Chemical Information from the Web) Chemical information from around the web. This package interacts with a suite of web services for chemical information. Sources include: Alan Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata. 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Package: r-cran-webdeveloper Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httpuv, r-cran-html5, r-cran-future, r-cran-promises, r-cran-readr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-webdeveloper_1.0.5-1.ca2404.1_all.deb Size: 52210 MD5sum: 9e3a1653d84415d8834207454af3df74 SHA1: c15357b27a3faec401257c9fa190f4477b648ac0 SHA256: 1106f9604ca46de4ec92164a069bb39cf3ee2584bc948fe1c666e44a755fe2bb SHA512: 4df96532e8f3a936a7b0a997b6b01054b95506fcc84402947d556dc524962c3a52c048e2c28f8dfed61c57ade5ded806cb87ce2786e9cc7bf3496652106696f3 Homepage: https://cran.r-project.org/package=webdeveloper Description: CRAN Package 'webdeveloper' (Functions for Web Development) Organizational framework for web development in R including functions to serve static and dynamic content via HTTP methods, includes the html5 package to create HTML pages, and offers other utility functions for common tasks related to web development. 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Package: r-cran-webpower Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lme4, r-cran-lavaan, r-cran-pearsonds, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-webpower_0.9.4-1.ca2404.1_all.deb Size: 415998 MD5sum: 999b163eb5d82e1fa305d0645c540348 SHA1: e7107c38eca727511161e6da5d139de71906d76e SHA256: e4f1030787c7234705295197d19ea0ca9f81cac62b7fcbe8c080ec3984edf488 SHA512: 1d9d0a1b5d505d2135115c8d8745df42b13975b342f9fb7748da306a58ad01733420e47414da29ae773ed9a037ea44e78fe0baea512d99b44682b31f2f27b217 Homepage: https://cran.r-project.org/package=WebPower Description: CRAN Package 'WebPower' (Basic and Advanced Statistical Power Analysis) This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. 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Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) . 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Package: r-cran-webshot2 Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-callr, r-cran-chromote, r-cran-later, r-cran-magrittr, r-cran-promises Suggests: r-cran-httpuv, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-webshot2_0.1.2-1.ca2404.1_all.deb Size: 564108 MD5sum: 6c4a1e2d15c3a1ce5cf12d2d1f890855 SHA1: 9685e0943a604ed79753ad5e0e8c3b5248be2aa9 SHA256: 2d6477b20cd6870668ac7faa7e9556717e02f8805821b595bb9557323455297e SHA512: 165951a073e2148174e744e59ca0439a87f252c8724f23d78be94d2084e0d4c204d728a509856b081da0231bdad6338899fe7f47880c5a1333cbc18903ecbfbf Homepage: https://cran.r-project.org/package=webshot2 Description: CRAN Package 'webshot2' (Take Screenshots of Web Pages) Takes screenshots of web pages, including Shiny applications and R Markdown documents. 'webshot2' uses headless Chrome or Chromium as the browser back-end. 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Package: r-cran-webtrackr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatch, r-cran-adar, r-cran-httr, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-webtrackr_0.3.2-1.ca2404.1_all.deb Size: 1375382 MD5sum: 3cbc3b4ab4d92e2c55ea0244fd16fcb4 SHA1: a523b35187f05bf2d50c92fef20a04ab5f325add SHA256: e346525a4a2f78bf0e484b2e5fbe19efabda9f0ffb8a02606fcc41d3d7f0edb8 SHA512: 9b4455a598b16ea3c559dbb56c4fab4443cb5bec96e4b4432b7275cad25ec94555af528daeed54468524d00a2820ffc842cda8bf737ccadf9c7752d900b4876d Homepage: https://cran.r-project.org/package=webtrackR Description: CRAN Package 'webtrackR' (Preprocessing and Analyzing Web Tracking Data) Data structures and methods to work with web tracking data. 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Package: r-cran-wec Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-wec_0.4-1-1.ca2404.1_all.deb Size: 118500 MD5sum: ad92eea4e0df154fe377dfe2af5b42e2 SHA1: f2b7f38af56eb9c977b72fac42b661cd9cfaf3a0 SHA256: 7c928ae0299195d6bde15c4d6b9e980d8de52747d16733588bd8dabf63677df9 SHA512: d126836e318ced323b2ae96a3180dd89f932a870a447c0e355e7e7a74b279109bb82ffa133cffabe148a987e2b311a10cb62f325c51ac23e6f93e54770c44e95 Homepage: https://cran.r-project.org/package=wec Description: CRAN Package 'wec' (Weighted Effect Coding) Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different. Package: r-cran-weed Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-magrittr, r-cran-tidytext, r-cran-stringr, r-cran-tibble, r-cran-geonames, r-cran-countrycode, r-cran-purrr, r-cran-tidyr, r-cran-forcats, r-cran-ggplot2, r-cran-sf, r-cran-here Filename: pool/dists/noble/main/r-cran-weed_1.1.2-1.ca2404.1_all.deb Size: 109326 MD5sum: 52f81535ef656c4d6f191a793430dfd1 SHA1: ba5c2c6f34c2ca5f0d4fa305ae7a71a884908089 SHA256: 06a363cbedf061edf31955429fdb6e2b7dd3c4fd491648e6523cafaae7f49172 SHA512: 5f3c3c325b31dde4016f8a488747452db3c699eebb49a2511dd606fe54716c1e02f3d8fc6a6ad33b70c7d9ccfa4ce18a46f0cd6b2133fdb86a0dcccd2f9e0ba2 Homepage: https://cran.r-project.org/package=weed Description: CRAN Package 'weed' (Wrangler for Emergency Events Database) Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions. Package: r-cran-wefnexus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wefnexus_1.0.0-1.ca2404.1_all.deb Size: 339070 MD5sum: 339be048594b64db368bfe91dfd76913 SHA1: cbabdd2ec4f76266fe4d8448c9ca2c8859a83c1c SHA256: a0d72c383f9072fe5078596dc59b864f61322025d6a3f93e2ac0eaa7b41d1f8f SHA512: 04b7edb321381253d1c2ce949b0747620d797158e8f3af3c148d0f21e8aeba5a39cb78a5bf9decf541526a5c1c61216fe490277764b1d8442ff4cef3ba3cefa7 Homepage: https://cran.r-project.org/package=wefnexus Description: CRAN Package 'wefnexus' (Water-Energy-Food-Nutrient-Carbon Nexus Analysis for AgronomicSystems) Provides functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. Includes functions for computing water use efficiency (WUE), water productivity (WP), and water footprint (WF) including green, blue, and grey components following the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy budgeting tools for energy use efficiency (EUE), energy return on investment (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), and partial factor productivity (PFP) as defined by Dobermann (2007) and Congreves et al. (2021) . Estimates carbon footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC) stocks, and global warming potential (GWP) using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) default values (CH4 = 27, N2O = 273) as reported in Forster et al. (2021) . Computes composite Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices, trade-off correlation matrices, and generates radar and heatmap visualizations for comparing agricultural treatments. Supports conservation agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid production environments. Methods follow Lal (2004) for carbon emissions from farm operations, and Hoover et al. (2023) for water use efficiency indicators. Package: r-cran-wege Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-raster Filename: pool/dists/noble/main/r-cran-wege_0.1.0-1.ca2404.1_all.deb Size: 52040 MD5sum: b27d02b86d843835484313814b5ff007 SHA1: d3eb6863fe0456a30c2b9c2e19254a163e0d2694 SHA256: a766b479be1f47ded70a0a886aac5df6b885e877f79f553797eda1569fc3917f SHA512: 44c7923126e79e6750e0f65d2a8f9c544a178757987c688e22d46db4c99b2ea1396ed685b4098ae0973b74c251a3ce91bd430ca95363a80a20d75f6a7fdbc661 Homepage: https://cran.r-project.org/package=WEGE Description: CRAN Package 'WEGE' (A Metric to Rank Locations for Biodiversity Conservation) Calculates the WEGE (Weighted Endemism including Global Endangerment index) index for a particular area. Additionally it also calculates rasters of KBA's (Key Biodiversity Area) criteria (A1a, A1b, A1e, and B1), Weighted endemism (WE), the EDGE (Evolutionarily Distinct and Globally Endangered) score, Evolutionary Distinctiveness (ED) and Extinction risk (ER). Farooq, H., Azevedo, J., Belluardo F., Nanvonamuquitxo, C., Bennett, D., Moat, J., Soares, A., Faurby, S. & Antonelli, A. (2020) . Package: r-cran-wehoop Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3569 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-glmnet, r-cran-magrittr, r-cran-matrix, r-cran-progressr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringdist, r-cran-stringi, r-cran-stringr, r-cran-tidyr Suggests: r-cran-arrow, r-cran-cachem, r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-dt, r-cran-ggplot2, r-cran-ggrepel, r-cran-gh, r-cran-glue, r-cran-knitr, r-cran-memoise, r-cran-piggyback, r-cran-rmarkdown, r-cran-rsqlite, r-cran-testthat, r-cran-tibble, r-cran-tictoc, r-cran-usethis, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-wehoop_3.0.0-1.ca2404.1_all.deb Size: 3299756 MD5sum: e004df19bd6ec150cc6a91e7d2887d03 SHA1: bad5cc05f65763f5d73df06e63538b10e3aab552 SHA256: af69299a989077009378e4d4078d9516704afb9b03aeb7705bbdc0597e783ad5 SHA512: 0122dd1dbcd860b283407267f9cc8960a73a6476e1f52d8d2f0ebd975d413dd2775c7310bead4fe302eb100addb2a1064b613021f8b5c831986093fa50729b36 Homepage: https://cran.r-project.org/package=wehoop Description: CRAN Package 'wehoop' (Access Women's Basketball Play by Play Data) A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API and ESPN's women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves. Package: r-cran-weibullfit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-xtable, r-cran-sqldf, r-cran-r.oo, r-cran-fadist, r-cran-mixdist, r-cran-optimx, r-cran-ksamples, r-cran-e1071, r-cran-r.methodss3 Filename: pool/dists/noble/main/r-cran-weibullfit_0.1.0-1.ca2404.1_all.deb Size: 201138 MD5sum: 51a4d50754d483d109f9f01a1c608b11 SHA1: 961cc46c2bfd82aae285c2094c73cc1933da5434 SHA256: 238c11b6b786e805fe7038e48f6146e44fa323555d69e8de0c70a422e90c8dc0 SHA512: e6239a7ee837328e5c13264376ba0306f8c14b319e61b8e8c8bd5ce5d71f7041d2ec36b2c7818749e76395d60e8080abd055eef48b5b81892e672ce573e0e0e6 Homepage: https://cran.r-project.org/package=WeibullFit Description: CRAN Package 'WeibullFit' (Fits and Plots a Dataset to the Weibull Probability DistributionFunction) Provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the 'folder' parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" . Package: r-cran-weibullmodiamr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-weibullmodiamr_0.1.0-1.ca2404.1_all.deb Size: 35366 MD5sum: 8805f6d385180ed2d7a04d13aed11294 SHA1: 6f11d6e47224fcb2d7c40ad6f760ee00228bfe1a SHA256: b3e1f117f5aa2a3cd4ba02f27106002406f6d6f964b5e83cf4133203283b9e73 SHA512: ff7d231a7827282bfa2a3fcef8a98845a4916e5a5ef4b9ced214b88654cb72c795616e2375fe832a4f9805a3f04149550df5b15737da1583bf723f6467565abe Homepage: https://cran.r-project.org/package=WeibullModiAMR Description: CRAN Package 'WeibullModiAMR' (Fit Modified Weibull-Type Distributions) Provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009. Package: r-cran-weibullness Architecture: all Version: 2.26.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4429 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bsgof Filename: pool/dists/noble/main/r-cran-weibullness_2.26.9-1.ca2404.1_all.deb Size: 3580112 MD5sum: 1398d9ea53ed833c794c9938398b24c4 SHA1: 35f43cab6ccaaed1ad889c1d940203af4ec262f9 SHA256: 23e6ea2b5b974aa4bad9601f91c161b2f6f8e9d1c117b5d9339ff718e24d5bf6 SHA512: 561fc1f96e56d9ed0de929ac6e85dfc0ad29349ab704cbebc3df26cb5ab41284a6fd1c07bcf168d293bfb939db0d43073720d07391230190c0fea17fa112fbed Homepage: https://cran.r-project.org/package=weibullness Description: CRAN Package 'weibullness' (Goodness-of-Fit Test for Weibull Distribution (Weibullness)) Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) , Park (2018) , and Park (2023) . This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (No. 2022R1A2C1091319). Package: r-cran-weibullr.alt Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-weibullr Filename: pool/dists/noble/main/r-cran-weibullr.alt_0.7.2-1.ca2404.1_all.deb Size: 107302 MD5sum: 3cebc85ea96823b5fe74de93e487d2bc SHA1: d962b3715fc0a348c18b74d44960def40b986a1d SHA256: 59647ac1cf7a2f83c8ab033575315dae6a2d4f4180905620cdb4aa4f9c8e9a6b SHA512: 498cfd0537764f0cb8314c36ad76e505244381460de148c50d314eb47ab1aee3ea4bc0f64a53da7039bd828f57013c4393a42f7295c4f19f86329555097a26ed Homepage: https://cran.r-project.org/package=WeibullR.ALT Description: CRAN Package 'WeibullR.ALT' (Accelerated Life Testing Using 'WeibullR') Graphical data analysis of accelerated life tests. Methods derived from Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6). 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(2023) , a framework for building interactive learning modules in R. 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Package: r-cran-weightederm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-weightederm_0.1.0-1.ca2404.1_all.deb Size: 65674 MD5sum: 0e24ebb41a7f4b3ff0713da2e9a71644 SHA1: 3edf36c2b12012ea8e8f02395a4fd1b8380aabc0 SHA256: cc0f88ed9924739c0fd60f5a38b0b5f0c57700cea92b0e7353ddbe1dc683ad30 SHA512: d0e35174e1aaf688b792d0b3b3c196866e8769e49e9dbd24c307cfb11c0e4ef9118c62e311ceab4a747f8d406aa457e66224dcfe54d1f89dda72cfafc7ddb853 Homepage: https://cran.r-project.org/package=weightederm Description: CRAN Package 'weightederm' (Weighted Empirical Risk Minimization for Changepoint Regression) R interface to the 'weightederm' package for 'Python', which provides 'scikit-learn'-style estimators for offline change point regression (data segmentation) via weighted empirical risk minimization. Supports least-squares, Huber, and logistic losses with fixed or cross-validated numbers of change points. Wraps 'Python' via 'reticulate'. Arpino and Venkataramanan (2026) . 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The test is based on a weighted form of the sample covariance of the residuals after a nonlinear regression on the conditioning variables. Details are described in Scheidegger, Hoerrmann and Buehlmann (2022) "The Weighted Generalised Covariance Measure" . The test is a generalisation of the Generalised Covariance Measure (GCM) implemented in the R package 'GeneralisedCovarianceMeasure' by Jonas Peters and Rajen D. Shah based on Shah and Peters (2020) "The Hardness of Conditional Independence Testing and the Generalised Covariance Measure" . Package: r-cran-weightedporttest Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-weightedporttest_1.1-1.ca2404.1_all.deb Size: 28056 MD5sum: 33f2ca0bab03d88bb3b82505da072082 SHA1: 71550969baa1ea1986dd61b1c1d77fcbd6159614 SHA256: 65c9348ad47323571257961d3300e7d3d62efab6529e96280689a8f53132ad82 SHA512: 1dce3e34a3b1e4517987f21127e43b31dd818eca019f7c8171e64cd092a3a4b09deac97ec00ebd7d4e15f302aac771e58889fbb01df14a411389d9f5ce326120 Homepage: https://cran.r-project.org/package=WeightedPortTest Description: CRAN Package 'WeightedPortTest' (Weighted Portmanteau Tests for Time Series Goodness-of-Fit) An implementation of the Weighted Portmanteau Tests described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing" published by the Journal of the American Statistical Association, Volume 107, Issue 498, pages 777-787, 2012. 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Steps cover within-cluster selection, subsampling for two-phase designs, nonresponse by weighting classes or response-propensity models (optionally machine-learning, with cross-fitting), calibration to known totals following Deville and Sarndal (1992) , optionally model-assisted, non-probability samples by pseudo-weighting, mass imputation and doubly robust estimators, and range-restricted trimming. Rotating and pure panels add panel-selection probabilities, attrition, longitudinal weights, gross flows and composite estimation. Variances come from a recipe-aware bootstrap and jackknife that resample or delete primary sampling units and re-apply the entire cascade on each replicate, following Rao and Wu (1988) ; panel replicates are coordinated across waves, so the sample overlap enters the variance of a net change as covariance, and two-phase variances split into first- and second-phase components (V = V1 + V2). 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Package: r-cran-welo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xts, r-cran-rdpack, r-cran-boot, r-cran-rio, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-welo_0.1.5-1.ca2404.1_all.deb Size: 264402 MD5sum: e9d1bb7aedfdf8590a6e1303bbd495c4 SHA1: 734190c587045752841342675f8232146c4f9cb6 SHA256: d823ccade4934725e473269261816b1797e410f957630612c7a4791d216653a4 SHA512: 3e33e5e63c2a8182649de9c337e94fe65d7facf351f93473d9f411e735718f6dcc1b7c78b119713a47740af48a256f25fe3102e990dc50b7d3dfc037c31436f4 Homepage: https://cran.r-project.org/package=welo Description: CRAN Package 'welo' (Weighted and Standard Elo Rates) Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 ) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet. Package: r-cran-wemix Architecture: all Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-numderiv, r-cran-matrix, r-cran-minqa, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-tidyr, r-cran-edsurvey, r-cran-glmmtmb Filename: pool/dists/noble/main/r-cran-wemix_4.0.3-1.ca2404.1_all.deb Size: 592658 MD5sum: 50d9a769119fb96917508d2154a03750 SHA1: ba67195d3a0f07352ee09ae9a0df8665ac5ac3b3 SHA256: 6f13ff84adabedf2d44919091095d382c42b79dcec6907860f62ce2794c1b225 SHA512: ba58a9148bc7055f3f3863801e68cf6d06487b411796273f54dc5f69f9897f8220976a21df6c02ff41a9331566f075b1bb7746ab5e19d8aadaab181ba4839998 Homepage: https://cran.r-project.org/package=WeMix Description: CRAN Package 'WeMix' (Weighted Mixed-Effects Models Using Multilevel Pseudo MaximumLikelihood Estimation) Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) ). Package: r-cran-wes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-foreach, r-cran-data.table, r-cran-dplyr, r-cran-hdinterval, r-cran-zoo, r-cran-openmeteo, r-cran-chirps, r-cran-whitebox, r-cran-exactextractr, r-cran-elevatr, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-rworldmap, r-cran-stars, r-cran-rcurl, r-cran-xml, r-cran-httr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-rworldxtra, r-cran-progress Filename: pool/dists/noble/main/r-cran-wes_1.0.0-1.ca2404.1_all.deb Size: 153772 MD5sum: 9d10372a61c71c380de3ce07253de441 SHA1: 07de9d10ce3e19e86f7b46b3f61947342daba841 SHA256: 2fdd7e400a5c1a189b2fe6f58ecdc3bfe14d90fb703b1c26cc56e7b6011c7900 SHA512: d3f38facf9d786ba3111573605bc0d2276b250a4c26963fe408dd7c09d8789bb76a0623ec06df81fce5148c2cf0861c6b29598d80d15edd2f69321da84868953 Homepage: https://cran.r-project.org/package=WES Description: CRAN Package 'WES' (Tools for Analyzing Wastewater and Environmental Sampling Data) Provides reproducible functions for collating and analyzing data from environmental sampling studies. Environmental Sampling (ES) of infectious diseases involves collecting samples from various sources (such as sewage, water, air, soil, or surfaces) to monitor the presence of pathogens in the environment. Analysis of ES data often requires the calculation of Real-Time Quantitative PCR (qPCR) variables, normalizing ES observations, and analyzing sampling site characteristics. To help reduce the complexity of these analyses we have implemented tools that assist with establishing standardized ES data formats, absolute and relative quantification of qPCR data, estimation of qPCR amplification efficiency, and collating open-source spatial data for sampling sites. Package: r-cran-wesanderson Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-wesanderson_0.3.7-1.ca2404.1_all.deb Size: 20004 MD5sum: 68f81a1b0dc6cc90879ec34bf6223768 SHA1: 1ae70600887f468f08fabe3833cb85b2634f8f9b SHA256: b1896c1a857c520a450117599b24d7329dd40d97a7f89039f7c1e45c4a775dca SHA512: 1bfaa175d94b6b9d095422991b532d012dca693f2981c3d6e81d35da2365cecf9dbaa5fee52c88994de60be0f0afdd50763f967f3d21aa66920039cf545c085a Homepage: https://cran.r-project.org/package=wesanderson Description: CRAN Package 'wesanderson' (A Wes Anderson Palette Generator) Palettes generated mostly from 'Wes Anderson' movies. Package: r-cran-westerlund Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-scales, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-westerlund_0.1.4-1.ca2404.1_all.deb Size: 209718 MD5sum: f64fbb43939ff1ae320eea5449798b55 SHA1: e7855665897dc0fd2161122f9b2919565b5a98a2 SHA256: 84900575765eeea6cc053853c0fdb5ad18231d919f8cceb3b9e695967e584fce SHA512: 9992a828d77234f9b8d9f40326f2ef3d6479089ee0471f376251bdb4ec1fe73b70b75c1323e84387ab03215be57686197acf52212c366b18710cd43df10a6b81 Homepage: https://cran.r-project.org/package=Westerlund Description: CRAN Package 'Westerlund' (Panel Cointegration Tests Based on Westerlund (2007)) Implements a functional approximation of the four panel cointegration tests developed by Westerlund (2007) . 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Package: r-cran-wevid Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-reshape2, r-cran-zoo Filename: pool/dists/noble/main/r-cran-wevid_0.7.0-1.ca2404.1_all.deb Size: 258760 MD5sum: 8d91ca4134de7006b9934cda2357723f SHA1: f5b96e1dfd0b0c75fd61c7b1ba22f0b4d82a470a SHA256: 0e832c5014a54361475e73407cf19fde0b7ff59674ed47c6fc06a2051ff5fadd SHA512: fa48980f05027bccdf967ad3e4ae90b5407aa05f9b791d16a51c9e463bfe554b4341f97aaa997ae7a1b64f82170a4b1dd93ae70d7aa4da755f99a3f06dc9a746 Homepage: https://cran.r-project.org/package=wevid Description: CRAN Package 'wevid' (Weight of Evidence for Quantifying Performance of a BinaryClassifier) The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status. In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted. Package: r-cran-wex Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fkf, r-cran-kfas Filename: pool/dists/noble/main/r-cran-wex_0.1.1-1.ca2404.1_all.deb Size: 66906 MD5sum: c7a112ee3b074a515fca28d11a26f810 SHA1: c39662911861c71626a4a8031cd6512e135e336b SHA256: 3c7a13c543eced66cf34d3d8009c95fa358bacce0117b453da15a2c66a0eef70 SHA512: fd76f817e9547a25a6fe2f61413e501b2d9050ad72d3a258099383a1809c4c1b4b00717cd6761c4c3b152e9386d6909adf3caaf3364fa7cc2bc40f7d398a938e Homepage: https://cran.r-project.org/package=wex Description: CRAN Package 'wex' (Exact Observation Weights for the Kalman Filter and Smoother) Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) . 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To cite the package in publications, use Hankin 2022 . Package: r-cran-wf Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-gh, r-cran-jsonlite, r-cran-rlang, r-cran-yaml Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-wf_0.0.1-1.ca2404.1_all.deb Size: 76818 MD5sum: b3b57ca8dbbb13683b2a682c678c6d71 SHA1: 43c90006445ba25a168e04dc270285d3ea68d317 SHA256: 7ee0f2add3d237293ca0931566091dcb2f9c8ffc773e1c1b9ae91520c5defd95 SHA512: ed3b1eb845289cf4eebb9eb26d6de5c1f6a756efd99f5152a19a8274e9b47c133e064052a81539b24cfeebdc772725af05ab10533fad6f6603f13bea27716abc Homepage: https://cran.r-project.org/package=wf Description: CRAN Package 'wf' (Artificial Intelligence Workflow Tools) Manage skills for large language model coding agents. 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Weight construction requires design-only data, a verified external target, outcome-blind planning, and human approval before weight locking, with bilingual reports for decision and statistical audiences. Converts calibrated and replicate weights into standard survey designs, provides optional broom-style result projections, and records serializable production pipeline provenance. Supports fixed, predeclared soft calibration tolerances and categorical entropy balancing from verified margins, plus panel attrition weighting, high-influence unit diagnostics, Fay's balanced repeated replication, and opt-in parallel execution for long runs. Calibration methods follow Deville and Saerndal (1992) , and entropy balancing follows Hainmueller (2012) . 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Package: r-cran-wgscan Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat, r-cran-matrix, r-cran-mass, r-cran-seqminer, r-cran-data.table Filename: pool/dists/noble/main/r-cran-wgscan_0.1-1.ca2404.1_all.deb Size: 111844 MD5sum: d0c4ba03931d1b2f9c90abfea966934d SHA1: 33553e4955deb63f141d028f3c6cd3608441002e SHA256: f94c17aa6508571e58147d4c305713f36eb7c9de1e3ec8d9d88e77420d95bf5c SHA512: 2cc5a55fbba0f0c81c6899af52550cf0a357a7097f4bc294f4897d4c8e07837e295e2b93d7864dba2cd5c1d0bb2f6ceba9bfe0f682fd9fe25d25e2a8c905745d Homepage: https://cran.r-project.org/package=WGScan Description: CRAN Package 'WGScan' (A Genome-Wide Scan Statistic Framework for Whole-Genome SequenceData Analysis) Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test. Package: r-cran-wgteff Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wgteff_0.1.2-1.ca2404.1_all.deb Size: 30190 MD5sum: 04883df70cb75fce4166947f90741e1e SHA1: d68da155625953f40452603166b4c8975b4ff422 SHA256: 44956753247e64f62b5adde98e216554dc6ba5244eaed2aa9263039407c24fae SHA512: 19afe3bf332015b154f50daeee768bab6945c3eb0a6c7bd2f9ef4c15383277afa47c8b99be25bfac8c5cee335d6a8879ce9f71eb3691118ba42eca901e8ee06e Homepage: https://cran.r-project.org/package=WgtEff Description: CRAN Package 'WgtEff' (Functions for Weighting Effects) Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error. Package: r-cran-wh Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wh_1.1.2-1.ca2404.1_all.deb Size: 283868 MD5sum: 37a3864ecabe3fb9e101b3357a486efd SHA1: 33652fe3bfe1b9b63b6e8a512e110dbb0db9c149 SHA256: 8bc1e08ad9b24f05cf936f15b298a09ea69c5452acfa06cbe780437b7a169755 SHA512: d32b94915e81d1f03155bdb69097c3e6c7de28a1031981dcfce8cb67dfb3190ca51ba13f9b7c15e339869c9b2a2cec71f98a7dd418de47bcfec088759f1e8fad Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the gradation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2023) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whalestrike Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1096 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-desolve, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whalestrike_0.6.2-1.ca2404.1_all.deb Size: 712388 MD5sum: 8f238e1de837faaeea705c032e112452 SHA1: 32df413281d67bfb20f595b9b8272d3e3495dbe1 SHA256: 58304242ff06188c550964f2d0679687aa659664069bb18db2fa13deaaae4aee SHA512: f870de6c86c0fdf6332eccd3b1b1b59c6f3442ba0ec88caaeb89a5433f6dc60801bf465d1139fe8e23b98bf2ee755ad0c8174ef0e41ebf34a20a036da7c10765 Homepage: https://cran.r-project.org/package=whalestrike Description: CRAN Package 'whalestrike' (Simulate Whale Ship Strikes) Provides tools for simulating the biophysical effects of vessel-strikes on whales. The aim is to support the evaluation of marine policies limiting ship speeds through regions in which whales reside. This is important because ship strikes are a major source of lethality for several whale species, including the critically endangered North Atlantic right whale. In this analysis, whales are modelled with a four-layer system comprising skin, blubber, sub-layer (muscle or organ) and bone. Reasonable values for the material properties of these layers, along with other factors such as whale surface area and mass, are provided for a variety of whale species. Similarly, key values are provided for several ship types. The collision is modelled according to Newtonian dynamics, with stresses and strains within the whale layers being simulated over time. The simulation results are analyzed in the context of whale-strike data, to develop a Lethality Index for the whale in the modelled collision. For the underlying science, see Kelley and other "Assessing the Lethality of Ship Strikes on Whales Using Simple Biophysical Models." (2021) . For more on the R code, see Kelley "`whalestrike`: An R package for simulating ship strikes on whales" (2024) . Package: r-cran-whapi Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-readr, r-cran-lubridate, r-cran-jsonlite, r-cran-mime, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-whapi_0.0.2-1.ca2404.1_all.deb Size: 245452 MD5sum: 05a3cc85b68284c4768e53f56d9c1d0e SHA1: 16bef81b0c1f61e203bcf4eb5567880310e78167 SHA256: da72053eef338c9466b87f5d3fd3ecc25862aca48e019902db6355ffadd163b6 SHA512: a1974e9a2aac74ab16052fe5355126ce0102043b569e5f7b0ad9a5289b0420a1022847021d0122ac61296e7870d7e50a25470d8368e1a3c578dcc9aac5449b5b Homepage: https://cran.r-project.org/package=whapi Description: CRAN Package 'whapi' (R Client for 'whapi.cloud') Provides an 'R' interface to the 'Whapi' 'API' , enabling sending and receiving 'WhatsApp' messages directly from 'R'. 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However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. 'WhatIf' offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. 'WhatIf' implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) ; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) . 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Package: r-cran-whippr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-stringr, r-cran-lubridate, r-cran-magrittr, r-cran-tibble, r-cran-zoo, r-cran-purrr, r-cran-tidyr, r-cran-broom, r-cran-cli, r-cran-ggplot2, r-cran-glue, r-cran-minpack.lm, r-cran-patchwork, r-cran-rlang, r-cran-nlstools, r-cran-pillar Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-collapsibletree, r-cran-testthat, r-cran-shiny, r-cran-miniui, r-cran-datapasta, r-cran-rstudioapi, r-cran-htmltools, r-cran-readr, r-cran-anomalize, r-cran-ggforce, r-cran-ggtext, r-cran-forcats Filename: pool/dists/noble/main/r-cran-whippr_0.1.4-1.ca2404.1_all.deb Size: 1364606 MD5sum: 449aa1ffaebc5a4100a6fdb77e8dd763 SHA1: fed542be64b16d52424d3085356aae3253e48f40 SHA256: 7e183dfcd10b754df1e0374f081108626af4e8b15b8b69a27b482368eb2530c7 SHA512: 09da9e4a58f0890c41782a64fd7c9116b28185c4b0f8ff7b39260634d211c64103a1a2fb43362226959beda3c71b099ede9105ad8abf87529548b25c272dd159 Homepage: https://cran.r-project.org/package=whippr Description: CRAN Package 'whippr' (Tools for Manipulating Gas Exchange Data) Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing. Package: r-cran-whirl Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-purrr, r-cran-quarto, r-cran-r6, r-cran-renv, r-cran-reticulate, r-cran-rlang, r-cran-sessioninfo, r-cran-stringr, r-cran-tibble, r-cran-unglue, r-cran-withr, r-cran-yaml, r-cran-zephyr Suggests: r-cran-ggplot2, r-cran-rstudioapi, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-whirl_0.3.2-1.ca2404.1_all.deb Size: 1022040 MD5sum: b73e8349783dfaf30da11362d5b0623f SHA1: 84594d6b5fac7d1bc71827b8381bf62e60acbc01 SHA256: 356379102e9ecec252449c38bbe928d3bcaa756c2651c0fd6d860803e559baa2 SHA512: e8ad84f581b4d98d50a77d920741277b03b4d1d5c8aa87c93db171ff62638df9d403a9201d8797fb28664bc89bc8dd204dd29405557c99562318953b4b4098b4 Homepage: https://cran.r-project.org/package=whirl Description: CRAN Package 'whirl' (Log Execution of Scripts) Logging of scripts suitable for clinical trials using 'Quarto' to create nice human readable logs. 'whirl' enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution. Package: r-cran-whisker Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-markdown Filename: pool/dists/noble/main/r-cran-whisker_0.4.1-1.ca2404.1_all.deb Size: 63006 MD5sum: d4b04023243f45e35ccd4b31eff53931 SHA1: c09135cd8c6d8b9135cc6e12ff3feaf85c531950 SHA256: ebcf8e3e7511078c4eb101a35f199aadf696707263a2984552ffd782663dce35 SHA512: f9c6883db1c7d5c05619bef3a0a1dc07ce8963290bba3b0ae44660e4a81a81933f5d86489dc443bd72cf6f643ffcc8fe24dee9802e13bce7e1bc762f0fc77d58 Homepage: https://cran.r-project.org/package=whisker Description: CRAN Package 'whisker' ({{mustache}} for R, Logicless Templating) Implements 'Mustache' logicless templating. Package: r-cran-whisper Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1811 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-torch, r-cran-av, r-cran-jsonlite, r-cran-hfhub, r-cran-safetensors Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-whisper_0.5.1-1.ca2404.1_all.deb Size: 696606 MD5sum: aafa34fe420fc1b69ab2553db9c5f753 SHA1: 263c65b76f28f72143725b955ad5a629e360823f SHA256: e8843fd7833e4d064f2ce6cb29b841cbd2c8eb033796f7debddfa4422f41bd99 SHA512: b3be786a972f5c17f529e9b523d5c9eb61a2ebc2606a94a1a0e24a83d8c9a32f9d9cf47fff52209c199fc7d8e4fe358baaeca40e489cd1d7f891e6e023baba24 Homepage: https://cran.r-project.org/package=whisper Description: CRAN Package 'whisper' (Native R 'torch' Implementation of 'OpenAI' 'Whisper') Speech-to-text transcription using a native R 'torch' implementation of 'OpenAI' 'Whisper' model . Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from 'HuggingFace' via the 'hfhub' package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the 'torch' package. Package: r-cran-whitebox Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3635 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra, r-cran-sf, r-cran-raster Filename: pool/dists/noble/main/r-cran-whitebox_2.4.3-1.ca2404.1_all.deb Size: 2734790 MD5sum: 6f069f6e138c8d7cff603e810f9074ef SHA1: eeb409d700f0ba4931431082e81410d388e4723d SHA256: fc0a412923b6fb9c044a4116fa973f6681ffbf095761a9dcdb3047fb401ffbbf SHA512: 1071ade7b96dd00ef565a8919ee82217d00d934cfe11ed7cd25a8fd9bf9d00557a430d4946c8f09e0b5cd27f82e34437f073140eec9a4f80333372dfa451461d Homepage: https://cran.r-project.org/package=whitebox Description: CRAN Package 'whitebox' ('WhiteboxTools' R Frontend) An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) . Package: r-cran-whitechapelr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-igraph Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whitechapelr_0.3.0-1.ca2404.1_all.deb Size: 37558 MD5sum: d643ee467f6e9a20610d209c8fe815a7 SHA1: 7f2e2ae86b4ff5edcda0d1b9ee6e0d246686fad3 SHA256: d3e9a0d4ecd07911b6bfff395d472475bc7f80c84aeb85ee5d29491ef4d4bbcf SHA512: 409f1461df6d90215e2dc7848ca6b75607d73c65e201258441cafdf8328690a980317c3759d725e81af2d162e1dea29bf693682e5c652d50d36ffb20d10a53bd Homepage: https://cran.r-project.org/package=whitechapelR Description: CRAN Package 'whitechapelR' (Advanced Policing Techniques for the Board Game "Letters fromWhitechapel") Provides a set of functions to make tracking the hidden movements of the 'Jack' player easier. By tracking every possible path Jack might have traveled from the point of the initial murder including special movement such as through alleyways and via carriages, the police can more accurately narrow the field of their search. Additionally, by tracking all possible hideouts from round to round, rounds 3 and 4 should have a vastly reduced field of search. Package: r-cran-whitening Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-whitening_1.4.0-1.ca2404.1_all.deb Size: 527162 MD5sum: e60e4531a4c5ca66c03b7ef70b95b60d SHA1: d49aa3952ff057148f61d7c6de376928e1c70bd5 SHA256: cf2c2dc75ff2528fb655e56a8533d43d6ebd97ea0f4572f17d954b2f10478cd6 SHA512: 17b0ecba886b1ac76d803c06983b2c38bc4700d1bcd4ad2ae2e5009d950aac41852541574556cbab6969e521189167e6249d3307c5304848e0c940239ea5ca2b Homepage: https://cran.r-project.org/package=whitening Description: CRAN Package 'whitening' (Whitening and High-Dimensional Canonical Correlation Analysis) Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", , as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) "A whitening approach to probabilistic canonical correlation analysis for omics data integration", . The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data). Package: r-cran-whitestrap Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-whitestrap_0.0.1-1.ca2404.1_all.deb Size: 202880 MD5sum: 1fe3cb73618d88829d78fe68e1baf9c4 SHA1: 6ebc0df8055ea3f0ffd5b7088331a7a7c9e661ce SHA256: e2a5f22f5e867b5b17f43c3cba67e909755bdc51758e12ddcd20b03356b36583 SHA512: c467c2f50ff97bc8fafc32e84e704962ea6c23c422e587b20fab813d82ed9bd7e8c8f85fcd8572cfe142cb9bc49735883f4028adc217b9af5a6d8907cee2767b Homepage: https://cran.r-project.org/package=whitestrap Description: CRAN Package 'whitestrap' (White Test and Bootstrapped White Test for Heteroskedasticity) Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) . Package: r-cran-whitestripe Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1729 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.nifti, r-cran-mgcv, r-cran-neurobase Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-whitestripe_2.5.0-1.ca2404.1_all.deb Size: 1667956 MD5sum: 0cea3f3bd0da6829fd424786461c63e0 SHA1: 9a3137011eb9e6fae603def3bd84fded6bc3d01d SHA256: c6f1b376c126b6329bd8cd046c3414f35d3d80a48b86989d3b5e5bf9bb61cf76 SHA512: 866fd5260f86a31ebdeaa74812dfbe0acdc0d0a090ecf3630a7c8cea76c00efebfb3d8c8075024837e574b0329cc6c4f4ef604c1d8e41cba1ea72d41ea44ec79 Homepage: https://cran.r-project.org/package=WhiteStripe Description: CRAN Package 'WhiteStripe' (White Matter Normalization for Magnetic Resonance Images) Shinohara (2014) introduced 'WhiteStripe', an intensity-based normalization of T1 and T2 images, where normal appearing white matter performs well, but requires segmentation. This method performs white matter mean and standard deviation estimates on data that has been rigidly-registered to the 'MNI' template and uses histogram-based methods. Package: r-cran-whitewater Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dataretrieval, r-cran-dplyr, r-cran-cli, r-cran-crayon, r-cran-furrr, r-cran-httr, r-cran-plyr, r-cran-purrr, r-cran-stringr, r-cran-usethis, r-cran-lubridate, r-cran-readr, r-cran-tidyr, r-cran-future Suggests: r-cran-ggplot2, r-cran-covr, r-cran-rmarkdown, r-cran-knitr, r-cran-ggfx, r-cran-broom, r-cran-patchwork, r-cran-jsonlite, r-cran-kendall, r-cran-parallelly, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whitewater_0.1.4-1.ca2404.1_all.deb Size: 2311750 MD5sum: 3b6640c246b4bda9155b014b6b671420 SHA1: 7170eb301c85c102f620e66e6314f6cbe0e07753 SHA256: f22bd82ddf38534ca060f233aa60e067dc2ade1665573e6d56b6fb16d661e63e SHA512: ee85c976a7ee1a566d1e081bdfa05956474275951d258d0d36eb260346a8beaa96857a039885c38e119e9a22e37c766ea72f57e13a8bf47238fe0ab2eb56219c Homepage: https://cran.r-project.org/package=whitewater Description: CRAN Package 'whitewater' (Parallel Processing Options for Package 'dataRetrieval') Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 ). In addition to parallel methods, data wrangling and additional statistical attributes are provided. Package: r-cran-whoami Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-covr, r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-whoami_1.3.0-1.ca2404.1_all.deb Size: 30858 MD5sum: cf7f9cdfd796a16174cc5611c0aec36a SHA1: fbc6937160d84e4e5466fcdb22828c67e5ac1b07 SHA256: 03478037e3ae500a306b9372d731d91867a98fe2de50234dc9feee75d55d1d59 SHA512: ef02e431446350611a10c8a9259aac87fb3aebcda389bc38227acbf2c37c2c70761ac36e18f972706e9691016f3b68f6f4e8e2583538a836c2c67055cce5097c Homepage: https://cran.r-project.org/package=whoami Description: CRAN Package 'whoami' (Username, Full Name, Email Address, 'GitHub' Username of theCurrent User) Look up the username and full name of the current user, the current user's email address and 'GitHub' username, using various sources of system and configuration information. Package: r-cran-whomds Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4008 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-colorspace, r-cran-dplyr, r-cran-erm, r-cran-ggraph, r-cran-ggplot2, r-cran-gparotation, r-cran-igraph, r-cran-nfactors, r-cran-plyr, r-cran-polycor, r-cran-purrr, r-cran-rcolorbrewer, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-srvyr, r-cran-stringr, r-cran-tam, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-wrightmap Suggests: r-cran-covr, r-cran-httr, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whomds_1.1.1-1.ca2404.1_all.deb Size: 1659344 MD5sum: cc0cb11157045c2bf2ed5cf8f6444666 SHA1: e6bc7b0d4448fd81924a4b1fe72ba3e3cbf9a5cc SHA256: 9bff184ba1098e9773b1a7fd7fa8fc053c45564a1973929b4f6418b45e354dce SHA512: acf27ee471a76398da63b0bae4f39316cd4f3d78363fa1a0f1f299a32770c0474ab288d9172e7d33e776b7ded399d6a2c854f4df7d3e189a7c65aca8e337aeb0 Homepage: https://cran.r-project.org/package=whomds Description: CRAN Package 'whomds' (Calculate Results from WHO Model Disability Survey Data) The Model Disability Survey (MDS) is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health . This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) ) to calculate a metric scale for disability. Package: r-cran-whoriskcalculator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-renv, r-cran-spelling Filename: pool/dists/noble/main/r-cran-whoriskcalculator_1.0.0-1.ca2404.1_all.deb Size: 50614 MD5sum: 7296ce0d1d97e6abbedeb6e7bf291163 SHA1: c1dd49af1c3f50a8e7ff4a43ddfd15244a4da910 SHA256: 3f7095c406047f279a00b9a8b84702667b244beff68535ce7080fbd84183366c SHA512: 4a80de55d7a0a53431d6eb0f989722a040b02359cc556a4911dbdf2cb9bccbd30d99b738f4a9a56122d3a16e628fafd7b2a46d139a36c2a4ecd5393aed88b79e Homepage: https://cran.r-project.org/package=WHORiskCalculator Description: CRAN Package 'WHORiskCalculator' (WHO Cardiovascular Disease Risk Calculator) Implements the 2019 World Health Organization (WHO) cardiovascular disease (CVD) risk prediction models, as described in Kaptoge et al. (2019) . Provides two validated models for estimating 10-year risk of fatal and non-fatal cardiovascular events (myocardial infarction and stroke): a laboratory-based model using age, sex, systolic blood pressure, total cholesterol, smoking status, and diabetes history; and a non-laboratory-based model substituting body mass index (BMI) for cholesterol and diabetes, suitable for resource-limited settings. Risk estimates are recalibrated to 21 Global Burden of Disease regions using region-specific incidence rates and risk factor distributions derived from the Emerging Risk Factors Collaboration. Functions are fully vectorized for efficient batch calculations and support automatic country-to-region mapping via ISO 3166-1 alpha-3 country codes. Package: r-cran-whsample Architecture: all Version: 0.9.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-openxlsx, r-cran-dplyr, r-cran-purrr, r-cran-bit64 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whsample_0.9.6.2-1.ca2404.1_all.deb Size: 49246 MD5sum: f0cebe9b0816279239ca077e98af66fb SHA1: a790526f34dd6801a88ecca95dce1537629b1cfc SHA256: 8f863dec07f378dff9d84b20b548b8c7fcccb8adbbdd608badd3c6258f41e293 SHA512: aa80e4c255062398bd4e24bcd31651db0d46491fc8dfd19b54d89475f47f86d3c73da2634adc56a8ac532a54ab53a4f84001b7101e44988b7aca9e6fd9922a0d Homepage: https://cran.r-project.org/package=whSample Description: CRAN Package 'whSample' (Utilities for Sampling) Interactive tools for generating random samples. Users select an .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the sample type (Simple Random Sample or Stratified), the number of backups desired, and a "stratify_on" value (if desired). The sample size is determined using a normal approximation to the hypergeometric distribution based on Nicholson (1956) . An .xlsx file is created with the sample and key metadata for reference. It is menu-driven and lets users pick an output directory. See vignettes for a detailed walk-through. 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These files are in a format suitable for direct use in the 'WINFAP' software, hence the name of the package. 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(2023) . Package: r-cran-winn Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lmtest, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-sva, r-cran-testthat Filename: pool/dists/noble/main/r-cran-winn_0.1.5-1.ca2404.1_all.deb Size: 301526 MD5sum: 6e8eaff40f78057d0ebb0e959c298805 SHA1: 0ba9fe2ff2a9a8d0275c44a6310b93302f15e7b0 SHA256: 836a676341903e5ac0708c6ed328f23337dc6d87726f6f9bc44182ab9506956b SHA512: aa7838bf73fe4463e33632699acc5bff95155dcf1bb798b8a639b3060e5124e6887a70fb6a1473e8b8d3b7b2b9e88df1b78541a2c1cb843ea0fdbbc9df625bbc Homepage: https://cran.r-project.org/package=winn Description: CRAN Package 'winn' (White Noise Normalization for Mass Spectrometry Profiling Data) Provides a decision-guided workflow for correcting technical variability in chemical profiling data. Tests for white noise identify measured features that need correction while preserving those that already pass. The workflow combines robust outlier adjustment, adaptive drift detection, change-point segmentation, batch correction, and probabilistic quotient normalization in a single pipeline or as modular steps. It supports parameter tuning using pooled quality-control samples as well as operation for studies without pooled controls. Package: r-cran-winning Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mvtnorm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-winning_0.3.0-1.ca2404.1_all.deb Size: 142970 MD5sum: c71d41365b7cb79bc570f3b6e9eb7efa SHA1: 2d86564d2c3483713d192b0bd96ba62534ed3d2f SHA256: f6d7283e9d1a4fdd6c494d98754a6d4df8add8f9bfc0f94a531cda93d3a4bb69 SHA512: fc8472e9f4cbb99b7efb4fb0bdf39f8bdc5faa734bce961e734cd1a2f96167b9bd272d11ff494bac6533d729921eebd69b4ed889fd95f06f5f86a173ae1d9370 Homepage: https://cran.r-project.org/package=winning Description: CRAN Package 'winning' (Contest Win Probabilities and Ability Calibration) Solves the horse race problem in both directions. 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Package: r-cran-winr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-winr_1.0.0-1.ca2404.1_all.deb Size: 87590 MD5sum: 28f8170b882f460997b907541d958788 SHA1: 23937595afefb4ba88367577e3fe04fab20aa981 SHA256: 43c22199f42d6bb8a9d9d66dcab7a66ba980095a9e162b6c96f7d645c5d059ba SHA512: 88906e8141a7a09bfe8763fec0623cbbe40e7048a9d5345aab1ceb2ede71ae151f5ff33c99e0e387641453c56b2e85ad0e43ad3216cb623e2ebc412803728910 Homepage: https://cran.r-project.org/package=winr Description: CRAN Package 'winr' (Randomization-Based Covariance Adjustment of Win Statistics) A multi-visit clinical trial may collect participant responses on an ordinal scale and may utilize a stratified design, such as randomization within centers, to assess treatment efficacy across multiple visits. Baseline characteristics may be strongly associated with the outcome, and adjustment for them can improve power. The win ratio (ignores ties) and the win odds (accounts for ties) can be useful when analyzing these types of data from randomized controlled trials. This package provides straightforward functions for adjustment of the win ratio and win odds for stratification and baseline covariates, facilitating the comparison of test and control treatments in multi-visit clinical trials. For additional information concerning the methodologies and applied examples within this package, please refer to the following publications: 1. Weideman, A.M.K., Kowalewski, E.K., & Koch, G.G. (2024). “Randomization-based covariance adjustment of win ratios and win odds for randomized multi-visit studies with ordinal outcomes.” Journal of Statistical Research, 58(1), 33–48. . 2. Kowalewski, E.K., Weideman, A.M.K., & Koch, G.G. (2023). “SAS macro for randomization-based methods for covariance and stratified adjustment of win ratios and win odds for ordinal outcomes.” SESUG 2023 Proceedings, Paper 139-2023. Package: r-cran-winratiosim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-winratiosim_1.0.0-1.ca2404.1_all.deb Size: 63390 MD5sum: 08ff3b79f9eb1d763a4bd924e1cdce5b SHA1: 7bbf99384417d613d1dbe2fe578662e6acf5a5a7 SHA256: 8d192ecc0556998e6edc9fff87a9fc03821eed0880c8de1b845bff3107c0b4e5 SHA512: f4fafeb82a0251a6b7d717f14b0518ae16cc831b2ed5052696eafe5d92cbae7f7564aae57c22292a1e113f50c32339fda9c91cc752f9b64a472a2049bdaa608e Homepage: https://cran.r-project.org/package=winratiosim Description: CRAN Package 'winratiosim' (Simulate Power for Hierarchical Win Ratio Endpoints) Provides simulation tools for power analysis in two-arm clinical trials with hierarchical win ratio endpoints. 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These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024). For more information, see the package documentation or the vignette titled "Introduction to wintime." Package: r-cran-wipf Architecture: all Version: 0.1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wipf_0.1.0-4-1.ca2404.1_all.deb Size: 86506 MD5sum: c6bd9655e3686ba03e3f9ba03ab26caa SHA1: 67bc96d2f2ec3179c96fc309e9a8c78e4fa98543 SHA256: c6c62a5caef78fb53e8dbd9ba60e30ac55ad92af7bd18f506364ea7f0f5ca22e SHA512: b08bc3e0e432e732e2c38f703af4025e08cb8eab205b1996cca953bfa26038b02a4249a75e8f6edb14be680e160b8e5f1dd62b8a54f9c25d0ec9be3673a7d5ed Homepage: https://cran.r-project.org/package=WIPF Description: CRAN Package 'WIPF' (Weighted Iterative Proportional Fitting) Implementation of the weighted iterative proportional fitting (WIPF) procedure for updating/adjusting a N-dimensional array given a weight structure and some target marginals. Acknowledgements: The author wish to thank Conselleria de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031), Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research. 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Package: r-cran-wordbankr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-quantreggrowth, r-cran-rlang, r-cran-robustbase, r-cran-stringr, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wordbankr_2.0.0-1.ca2404.1_all.deb Size: 89268 MD5sum: c0f1fe6cef79637736036b6e97ecfab5 SHA1: 2c883322534a5843108ecfe605e5bc7a21886c33 SHA256: 9a69788dfe7c0006e07727f75a40be08ea70a2cc8e25645fd0d7e79e929621eb SHA512: 1a2afbf577bb7e4dfbb4b2ac466be53d3fb095806879f514924054ffcc05d60a226539b248a419b55b5d589bff131af69bad251556354a7aad7bad4115c8c621 Homepage: https://cran.r-project.org/package=wordbankr Description: CRAN Package 'wordbankr' (Accessing the 'Wordbank' Database) Connecting to 'Wordbank' , an open repository for developmental vocabulary data from the MacArthur-Bates Communicative Development Inventories (Frank et al. 2017 ). 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'wordcloud2.js' is a JavaScript library to create wordle presentation on 2D canvas or HTML . Package: r-cran-wordler Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wordler_0.3.1-1.ca2404.1_all.deb Size: 114336 MD5sum: 4bab1bcdc7385c380f30c05b2da6b322 SHA1: 871e46134083bb9ede595420cdbf89c4240811ac SHA256: 29c3115090c28f8d7399b8c21ad43945d3fbea801603b74f68843aaebdc12716 SHA512: 2cd0eb495ab617f40af133460b8fc173fbb912d82048c5b2b813392275bfc8256c20d2c4881f93fe0eecb46f1aa9b50aa20b916d2eaedf51ca98786bb61517c4 Homepage: https://cran.r-project.org/package=wordler Description: CRAN Package 'wordler' (The 'WORDLE' Game) The 'Wordle' game. Players have six attempts to guess a five-letter word. After each guess, the player is informed which letters in their guess are either: anywhere in the word; in the right position in the word. This can be used to inform the next guess. Can be played interactively in the console, or programmatically. Based on Josh Wardle's game . 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It estimates the underlying distribution to generate new descriptive words Canessa et al. (2023) , applies a new clustering model, and uses simulations to estimate the probability that two persons describe the same words based on their descriptions Canessa et al. (2022) . 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Package: r-cran-wordofmouth Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambertw Filename: pool/dists/noble/main/r-cran-wordofmouth_1.2.0-1.ca2404.1_all.deb Size: 161696 MD5sum: 82cfa388626f42804e4ecd1f18679ecb SHA1: 977b7aa7ada842cd1eb6a91900dfbce671176c45 SHA256: 344b6c135e8b4477af35f14d7003ba757588cc8e40189755e09ea30922f9bfc4 SHA512: e94e4d9f94e53ce3a71eb305a6376192a7ea001ec373ce0082378d270a72ae6809ccf9c45b374308db3bc9261a4b0ebc10fd51aefdc8309a3a435dfb94c07d20 Homepage: https://cran.r-project.org/package=WordOfMouth Description: CRAN Package 'WordOfMouth' (Estimates Economic Variables for Word-of-Mouth-Campaigns) Methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks. 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Package: r-cran-wordpiece.data Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wordpiece.data_2.0.0-1.ca2404.1_all.deb Size: 296754 MD5sum: 34c54612aa5e680c0719accb7f455adb SHA1: 2cc693a523f130e4040b44ee024b34f8d58c198c SHA256: 38c13a87002bc3f064d17a846486679bff4f8fcfa1bff573fd2616dbaf26cba0 SHA512: 549d76bd0eab8ea5ebd0e825da9b65b12a9b4518ef6cc87fdad3a9d0abccdd115e5916716db6d0907b6093a3fdc742f16e77febb0a4969346a2d1cc59bdd460a Homepage: https://cran.r-project.org/package=wordpiece.data Description: CRAN Package 'wordpiece.data' (Data for Wordpiece-Style Tokenization) Provides data to be used by the wordpiece algorithm in order to tokenize text into somewhat meaningful chunks. Included vocabularies were retrieved from and and parsed into an R-friendly format. 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Package: r-cran-wordsalad Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-text2vec, r-cran-word2vec, r-cran-fasttextr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wordsalad_0.2.0-1.ca2404.1_all.deb Size: 53852 MD5sum: ea9cf2b46e02b951110a57f4ecefcd72 SHA1: 96825301fb37668d6f14f251ef758554760403a7 SHA256: d1836db55714858f83e1714a05f65daeff7e0b38654cfbc05dd115c7e4bd39d9 SHA512: 7a3ae73260d160bfcf3faac64d6561c6458a094f11df4c95666ea98dcd2a9ca2045808efc8ca9d197561285e9782570262ed7adbfd2a21f423bca2bbfa0a0567 Homepage: https://cran.r-project.org/package=wordsalad Description: CRAN Package 'wordsalad' (Provide Tools to Extract and Analyze Word Vectors) Provides access to various word embedding methods (GloVe, fasttext and word2vec) to extract word vectors using a unified framework to increase reproducibility and correctness. 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Package: r-cran-workflows Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-modelenv, r-cran-parsnip, r-cran-recipes, r-cran-rlang, r-cran-sparsevctrs, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-butcher, r-cran-covr, r-cran-dials, r-cran-glmnet, r-cran-knitr, r-cran-magrittr, r-cran-matrix, r-cran-modeldata, r-cran-probably, r-cran-rmarkdown, r-cran-tailor, r-cran-testthat Filename: pool/dists/noble/main/r-cran-workflows_1.3.0-1.ca2404.1_all.deb Size: 282204 MD5sum: 4618c91ba11387f197ffadd686ebdda9 SHA1: 2cbf378d1a535e0ccbdfe82d7023132ebeb34966 SHA256: f0fb7bd9ec4ac53afe7591a752c607817536b4bd07cd796509ae7b1930285a06 SHA512: ee475a5db8531bece4c1cc56facb5ac989fb75071b987ba74f97791d36a2e6240b0495eae8e45d479aa1a6f8f2c0c6a5ef52204bb931bcce861090bcf0090dce Homepage: https://cran.r-project.org/package=workflows Description: CRAN Package 'workflows' (Modeling Workflows) Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object. Package: r-cran-workflowsets Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-hardhat, r-cran-lifecycle, r-cran-parsnip, r-cran-pillar, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs, r-cran-withr, r-cran-workflows Suggests: r-cran-covr, r-cran-dials, r-cran-finetune, r-cran-kknn, r-cran-knitr, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyclust, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-workflowsets_1.1.1-1.ca2404.1_all.deb Size: 1857678 MD5sum: 7abb59b4509d4d9c05154877e0761108 SHA1: 7798bb757a4473cfa723e748b1b08447760623cb SHA256: 985ae6f19c3af2f2db88b86ff13547b479912e696967d5121463f0421e213568 SHA512: 7741ada32f4dd2d83bf7f3227e9048fcd7824be807a0104415c1012a99b493fd3bcdfd29db0a223f9b64e51b9b1633c611a757257891f4c994aa66b439b41244 Homepage: https://cran.r-project.org/package=workflowsets Description: CRAN Package 'workflowsets' (Create a Collection of 'tidymodels' Workflows) A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) ). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results. Package: r-cran-workloopr Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-signal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-workloopr_1.1.4-1.ca2404.1_all.deb Size: 773616 MD5sum: 1faeadce75d5c5cb0f369114dc272410 SHA1: f151fefb7faeb874200fe811f24f591026f8adf8 SHA256: 74b1f7590750a579c6a3c5858e02f755e91d0d4dcc5b7efb5ca29b7d277d2639 SHA512: 1197baeb0b3a077b11c3ed30c9507a967ba4fbeb76eb6b375006456ac5dcad1e2da430139f6d3ab6faf8d2c48985b2e979066d500fe399cf07ac9edd97548ee2 Homepage: https://cran.r-project.org/package=workloopR Description: CRAN Package 'workloopR' (Analysis of Work Loops and Other Data from Muscle PhysiologyExperiments) Functions for the import, transformation, and analysis of data from muscle physiology experiments. The work loop technique is used to evaluate the mechanical work and power output of muscle. Josephson (1985) modernized the technique for application in comparative biomechanics. Although our initial motivation was to provide functions to analyze work loop experiment data, as we developed the package we incorporated the ability to analyze data from experiments that are often complementary to work loops. There are currently three supported experiment types: work loops, simple twitches, and tetanus trials. Data can be imported directly from .ddf files or via an object constructor function. Through either method, data can then be cleaned or transformed via methods typically used in studies of muscle physiology. Data can then be analyzed to determine the timing and magnitude of force development and relaxation (for isometric trials) or the magnitude of work, net power, and instantaneous power among other things (for work loops). Although we do not provide plotting functions, all resultant objects are designed to be friendly to visualization via either base-R plotting or 'tidyverse' functions. This package has been peer-reviewed by rOpenSci (v. 1.1.0). Package: r-cran-workspace Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-yaml, r-cran-zip Suggests: r-cran-sf, r-cran-terra, r-cran-testthat, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-workspace_0.1.6-1.ca2404.1_all.deb Size: 104860 MD5sum: 8bfccb7a38aa2c8961c1273e2ba83c33 SHA1: d6f113bffcf1e6340563ff907b63bd7731508272 SHA256: 90a9e3e372c4d97fcffb014f94e34e471a1c2721dea75d9333e3cc68bb0f7ef1 SHA512: ed86566483313a746345f46dd8ec1eb66aecec31fc7febf4b8fd46f8ab50a6b97ad1d2714c7f9b9a2af83308cd45ccd1dd678dc2d21cabd3f7b3c5bff1afc9e3 Homepage: https://cran.r-project.org/package=workspace Description: CRAN Package 'workspace' (Manage Collections of Datasets and Objects) Create, store, read and manage structured collections of datasets and other objects using a 'workspace', then bundle it into a compressed archive. Using open and interoperable formats makes it possible to exchange bundled data from 'R' to other languages such as 'Python' or 'Julia'. Multiple formats are supported 'Parquet', 'JSON', 'yaml', spatial data and raster data are supported. Package: r-cran-worldbank Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-ggplot2, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-worldbank_0.11.0-1.ca2404.1_all.deb Size: 433294 MD5sum: f77d3f2e4ec7910e15467b4b8b8ca987 SHA1: 466ec2386728ea61fd45213c028c056e7b01c5ec SHA256: 84fcf1acac6b340b1ec79657d4e87ce2aab4595b7a57e3bb60464794f0ead4b1 SHA512: b23fff95b88caa0f78fe121b8964c3c1e25d9655e6264c96c73a6edff252f573ae16c6645c46e1a8d810bdfd5cd78f1b8ea0daad551c4fa0f2d0b114251ceff1 Homepage: https://cran.r-project.org/package=worldbank Description: CRAN Package 'worldbank' (Client for the 'World Bank' APIs) Download and search data from the 'World Bank' APIs, including the 'Indicators' API, the 'Poverty and Inequality Platform (PIP)' API, the 'Finances One' API, the 'Projects' API, and the 'Documents & Reports' API. See for further details. Package: r-cran-worldflora Architecture: all Version: 1.14-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-stringr, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-stringdist Filename: pool/dists/noble/main/r-cran-worldflora_1.14-5-1.ca2404.1_all.deb Size: 156948 MD5sum: bedd216bf96e98ea8f837e2e972a85d7 SHA1: 04fdecf9452190c3b16cddbed0cae057a180cda3 SHA256: c86065b930f4625311a951dac4d337651fae8f765fe70df869a3d2b7b11d062a SHA512: 79d40d7f61c3dbe2bb4fdd2eda6a0c9f3b32001b1a5e656ade075e36ee23e416278e522ce1c4b8270a30d76573dcc14d97699c0a95443a68fdc86415e11866e4 Homepage: https://cran.r-project.org/package=WorldFlora Description: CRAN Package 'WorldFlora' (Standardize Plant Names According to World Flora OnlineTaxonomic Backbone) World Flora Online is an online flora of all known plants, available from . Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (), a working list of plant names that has become static since 2013. 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Data is collected from a number of popular sites, including 'FBref', transfer and valuations data from 'Transfermarkt' and shooting location and other match stats data from 'Understat' and 'fotmob'. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis. Package: r-cran-worldmapr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-cran-countrycode, r-cran-ggfx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-worldmapr_1.3.0-1.ca2404.1_all.deb Size: 1690324 MD5sum: b772bb00a9c039fea0058d8eb7ef9f81 SHA1: f257511f37b9dc74d66443126f1d03356f442356 SHA256: e52848c52f55ff8d3720908884b0bbef5d3d67e7b21ae193efc49f9c4cf1b2a6 SHA512: 11a179e2d103e25991d543135b65b330a2b83443d66f52144a0a142af3b33f6817ad22ec854e623af65432b2aed682bfeb25ce2fbcee7c3bbd46f5c9aee687c7 Homepage: https://cran.r-project.org/package=WorldMapR Description: CRAN Package 'WorldMapR' (Worldwide or Coordinates-Based Heat Maps) Easily plot heat maps of the world, based on continuous or categorical data. Country labels can also be added to the map. Package: r-cran-worldmet Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carrier, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-mirai, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-arrow, r-cran-knitr, r-cran-leaflet, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-worldmet_1.1.0-1.ca2404.1_all.deb Size: 363500 MD5sum: 344f74eace01a9c485348a922c64943b SHA1: 73da42afb727d9557fb4dbfe7c968383594581ba SHA256: b68882d6951353f02efbaf6ac633015aa8bf2fbea1d088589bd624312061edf5 SHA512: d68c79a571c85231d617b04143f1c29e1164eb9937414ad41ffb2d35f51e598d8bca19d6a796c385655cdd0a0907a9d673a415607668b7bc61e0d18f3598738b Homepage: https://cran.r-project.org/package=worldmet Description: CRAN Package 'worldmet' (Import Surface Meteorological Data from NOAA) Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) and Integrated Surface Database (ISD). 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The distance matrices are assumed to be calculated between the cells of multiple animals ('Caenorhabditis elegans') from input time-series matrices. Some functions for generating distance matrices, performing clustering, evaluating the clustering, and visualizing the results of clustering and evaluation are available. We're also providing the download function to retrieve the calculated distance matrices from 'figshare' . Package: r-cran-worrms Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-tibble, r-cran-jsonlite, r-cran-data.table Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-worrms_0.4.3-1.ca2404.1_all.deb Size: 100988 MD5sum: 7bf2205fb8196ff4d3ffc6ac33c54af6 SHA1: 41c56fcf0be206a4b2fd34d3706ce53e9801bd3a SHA256: fbff48587af678a2d2109619fe972e8cafc288b029ff3b4955bc668e616459de SHA512: 00c87c977b5747a212ac854d2b9e6f814f532524e6757c4763f18eafd256b7ea0501f8f5bc84a818d2d008e8c6769df48b34dafd41d546e31972c8a83a2d17be Homepage: https://cran.r-project.org/package=worrms Description: CRAN Package 'worrms' (World Register of Marine Species (WoRMS) Client) Client for World Register of Marine Species (). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification. Package: r-cran-worrrd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-yaml, r-cran-glue, r-cran-ggtext, r-cran-ggfittext, r-cran-cowplot Suggests: r-cran-magick, r-cran-emoji, r-cran-rvest, r-cran-english, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-worrrd_0.1.0-1.ca2404.1_all.deb Size: 409808 MD5sum: 1e0ac0ca82bde6fa2f64963d03cd3abd SHA1: 73069c8803d12beec2d3170d1642297f9fb00164 SHA256: bacea6a840c1786bee6d152a8e95828fcb1a9d27e40ad2d53711349e591b8ed7 SHA512: 1ccdd8e444fdb28f6e41fd74deb8c1ce1297eefb0a42b5a61b5ee243fb3503e102e89efc78f8233f35d4b458f38075105e3accc788da7d4c553343fa526dd856 Homepage: https://cran.r-project.org/package=worrrd Description: CRAN Package 'worrrd' (Generate Wordsearch and Crossword Puzzles) Generate wordsearch and crossword puzzles using custom lists of words (and clues). Make them easy or hard, and print them to solve offline with paper and pencil! 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The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) . The package relies on two packages for continuous wavelet transforms: 'WaveletComp', which can be installed automatically, and 'wmtsa', which is optional and available from the CRAN archive . Users need to manually install 'wmtsa' from this archive if they prefer to use 'wmtsa' based decomposition. 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Provides Wavelet Quantile Regression and Multivariate Wavelet Quantile Regression after Adebayo and Ozkan (2024) , Wavelet Quantile-on-Quantile regression with bootstrap p-values extending Sim and Zhou (2015) , the nonparametric Causality-in-Quantiles test of Balcilar, Gupta and Pierdzioch (2016) together with its wavelet variant, Wavelet Quantile Mediation and Moderation, Wavelet Quantile Correlation, and a wavelet-based nonparametric Quantile Density estimator. The Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition is performed via 'waveslim' and Short / Medium / Long band aggregation is supported throughout. For plain Quantile-on-Quantile regression see the companion CRAN package 'QuantileOnQuantile'. All interactive 3D surfaces, heatmaps and contour plots default to the 'MATLAB' 'Parula' colour map. Package: r-cran-wqs Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-glm2 Filename: pool/dists/noble/main/r-cran-wqs_0.0.1-1.ca2404.1_all.deb Size: 107956 MD5sum: ca12a580c6c91ace3fd260b289d7ffa4 SHA1: e473b01252c8f8d6c4b85abed7868e1467afa916 SHA256: 396b1bbea4c9fb11df18f0f987efc8c10394742f19e0e0177fb20d30097cdfd8 SHA512: eb89cdcf7ffd1d5798412530d22ed5c39613395c59d276bc508f185391b3822cfc61d7d7858c3e89134972a1257af242d9a598b1cd826f9b83d65af4b01c1627 Homepage: https://cran.r-project.org/package=wqs Description: CRAN Package 'wqs' (Weighted Quantile Sum Regression) Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights. 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Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. 2015 ). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. 2022 ). 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(2019) . Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and visualization of results. 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The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations. 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Package: r-cran-wrds Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-keyring, r-cran-rlang, r-cran-rpostgres, r-cran-tidylog Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-wrds_0.1.1-1.ca2404.1_all.deb Size: 87404 MD5sum: 97bf67c169819e8038b805318622ba65 SHA1: bff568f4aa16a011970dfdc40712c272f633e2a9 SHA256: 0c08a70351fd87e3bef16786c71ef6518dbaafb962444d8ce049fa0309e77bb9 SHA512: fab113244639ca4a6f3b753ba5468a0e9e7ca898fa6ac2ae323ecd000ffe5797d0f80f2367e87963c876f678a9579e005ccecab1b692b9baf6fbb4fb0e757ba8 Homepage: https://cran.r-project.org/package=wrds Description: CRAN Package 'wrds' (Access 'Wharton Research Data Services' ('WRDS')) Provides simple functions for accessing data from 'Wharton Research Data Services' ('WRDS'), a widely used financial database in academic research. 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Package: r-cran-wrictools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1396 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcurl, r-cran-readr, r-cran-rlang, r-cran-tidyr, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wrictools_1.0.1-1.ca2404.1_all.deb Size: 543806 MD5sum: 12781de1228770d2f2734f2870397dcd SHA1: 41fa353fec622fef46aecd334e83ba8423751d8c SHA256: 404ce4fb927e8028ab41b90ab2073d208e2b6f3680778000875e5c7218ac980a SHA512: 7cc8af83fa994ed171aa1c60c42768bdf168f355664de6dfc801fcec01b6b05a53b2ec03f886037a3e1a4f66e707ca9947f7ff41727d8ad9b8f3128e9f11a5e0 Homepage: https://cran.r-project.org/package=wrictools Description: CRAN Package 'wrictools' (Analyze Whole Room Indirect Calorimetry (WRIC) Data) Provides functions, tutorials, and examples to preprocess, analyze, and visualize data from whole room indirect calorimeters (WRIC) by Maastricht Instruments, using the 'OmniCal' software. 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Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.), 'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly. The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters. 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Package: r-cran-writer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-glue, r-cran-cli, r-cran-rlang Suggests: r-cran-rsqlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-writer_0.1.0-1.ca2404.1_all.deb Size: 345618 MD5sum: 59740000402860dd70ae5cd8e3a6a6f6 SHA1: 2e1372077d8e14ea95a087f4d20678379e7f64aa SHA256: bb0f24418f464a7b97af7813312acd193ea660ace980fe1670d29b81439c5b2d SHA512: f841318696ff8745e2b82c39d619c0d024f0015799df5d252c66794c1209621f5b3e40fd184a60c0b226dc162d3bde61c2912a48aabc4332f0bfe4bb2d14206a Homepage: https://cran.r-project.org/package=writer Description: CRAN Package 'writer' (Write from Multiple Sources to a Database Table) Provides unified syntax to write data from lazy 'dplyr' 'tbl' or 'dplyr' 'sql' query or a dataframe to a database table with modes such as create, append, insert, update, upsert, patch, delete, overwrite, overwrite_schema. 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The worksheet name will be the name of the data frame it contains or can be specified by the user. 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Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too. Package: r-cran-wrproteo Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-knitr, r-bioc-limma, r-cran-wrmisc Suggests: r-cran-data.table, r-cran-fdrtool, r-cran-kableextra, r-cran-mass, r-cran-rcolorbrewer, r-cran-readxl, r-bioc-rots, r-cran-rmarkdown, r-cran-r.utils, r-cran-sm, r-cran-wrgraph Filename: pool/dists/noble/main/r-cran-wrproteo_2.1.0-1.ca2404.1_all.deb Size: 5412370 MD5sum: 00b9bf91646b91082335939b719a9a31 SHA1: 0cf1e3db9f010886a7c3cbcb606c6994e334b6de SHA256: 7d0554f2e921cd56591929379f18323ee1be7cc2166796808da10b3684b1bad7 SHA512: f4448cfaec5e85151e11b024cf3d5e9dd3d5290b83dde891c5b97ad2785102a0c71f6f01c26fcadc616a6fbaeaa340a29850d82280040962ec074d4b5c78d0ab Homepage: https://cran.r-project.org/package=wrProteo Description: CRAN Package 'wrProteo' (Proteomics Data Analysis Functions) Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium ) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 ), Dia-NN (Demichev et al 2020 ), Fragpipe (da Veiga et al 2020 ), ionbot (Degroeve et al 2021 ), MassChroq (Valot et al 2011 ), OpenMS (Strauss et al 2021 ), ProteomeDiscoverer (Orsburn 2021 ), Proline (Bouyssie et al 2020 ), AlphaPept (preprint Strauss et al ) and Wombat-P (Bouyssie et al 2023 . Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 ) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package 'limma' () can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 , Ramus et al 2016 ), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 ) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 () quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins. 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It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures. 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Package: r-cran-wwntests Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sde, r-cran-ftsa, r-cran-rainbow, r-cran-mass, r-cran-fda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-compquadform, r-cran-tensora Filename: pool/dists/noble/main/r-cran-wwntests_1.1.0-1.ca2404.1_all.deb Size: 148874 MD5sum: e334b031800e13e5f16f24878593c5b0 SHA1: 23f3676d0c861ed01fec3c7dad6064b5e874e258 SHA256: abd803401e1be7c786d632bfd00dfc29a8262de7c881dcf288a568aaf48628fe SHA512: 932e7f78759c4d4549cbb5d434bd549ff9b1ab0b5be01d76d3fb021c35b910c9d0acd9bd69e9b06aa9df67bf97aadee1a9e530b213b6c1c6c5862de829da6d2f Homepage: https://cran.r-project.org/package=wwntests Description: CRAN Package 'wwntests' (Hypothesis Tests for Functional Time Series) Provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) , Characiejus and Rice (2019) , Gabrys and Kokoszka (2007) , and Kim et al. (2023) respectively. Package: r-cran-wxgenr Architecture: all Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2627 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-msm, r-cran-sm, r-cran-dplyr, r-cran-plyr, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-dorng, r-cran-mc2d, r-cran-qmap, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggpubr, r-cran-moments, r-cran-seas, r-cran-padr, r-cran-hydrogof, r-cran-specsverification, r-cran-ggridges, r-cran-tigris, r-cran-terra, r-cran-stringr, r-cran-mapview, r-cran-xts, r-cran-sf, r-cran-lfstat, r-cran-feddata, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-wxgenr_1.4.5-1.ca2404.1_all.deb Size: 2062952 MD5sum: 078fd3baf2ded1963e4426e18a9c8372 SHA1: 709ea55665e61de1e70fe25723ac9fb93e28c0bb SHA256: a5d6a244def4c2ea6182adf1754f380504794b7b1128c847ec7cc1915d649d16 SHA512: 19375fb917d5ba0204caded12e9066a8a5c937d4a95a58a54dfae93ece9609e14cd856881c15951988c3a28291a2ea4bb9af65d3a396bcd22a72d2f1529c2b01 Homepage: https://cran.r-project.org/package=wxgenR Description: CRAN Package 'wxgenR' (A Stochastic Weather Generator with Seasonality) A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. . Package: r-cran-wyz.code.metatesting Architecture: all Version: 1.1.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-wyz.code.offensiveprogramming, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wyz.code.metatesting_1.1.22-1.ca2404.1_all.deb Size: 222908 MD5sum: e0035cf5d21c10f630a7388966c94a92 SHA1: f03b70fa932dca6e52cff8597193a4ed8a443a61 SHA256: 1e2721f6a1ee1d5238bea22ed9e74ddcc8889be6fdd9dc0bc7daf99fb06616cc SHA512: 0de98c0a101aa1a236dc38b9c945601dd42c2261a95286ae6ca29382987a865db9502b662ffc06102cc97173c56fb22351d5c746d8d57d99eb99a69ff8d46884 Homepage: https://cran.r-project.org/package=wyz.code.metaTesting Description: CRAN Package 'wyz.code.metaTesting' (Wizardry Code Meta Testing) Meta testing is the ability to test a function without having to provide its parameter values. Those values will be generated, based on semantic naming of parameters, as introduced by package 'wyz.code.offensiveProgramming'. Value generation logic can be completed with your own data types and generation schemes. This to meet your most specific requirements and to answer to a wide variety of usages, from general use case to very specific ones. While using meta testing, it becomes easier to generate stress test campaigns, non-regression test campaigns and robustness test campaigns, as generated tests can be saved and reused from session to session. Main benefits of using 'wyz.code.metaTesting' is ability to discover valid and invalid function parameter combinations, ability to infer valid parameter values, and to provide smart summaries that allows you to focus on dysfunctional cases. Package: r-cran-wyz.code.offensiveprogramming Architecture: all Version: 1.1.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-stringr, r-cran-r6, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wyz.code.offensiveprogramming_1.1.24-1.ca2404.1_all.deb Size: 296874 MD5sum: 3d0b415247239319f76f94942b37e6f4 SHA1: ad0342ecc153a1d15f66ed7f36fede61175a978d SHA256: 890ee0f40e04a20a6e6ccbecffb93d6345c43d28bb0f536a130d2aa8cf07fe82 SHA512: c9a8a4249b8efdc5ab5a21284fb37f303c92efe8ad3fafb16d3ab27e96852a54686550ddc02778c32c6a2f1a2a83c7e1659b97582a8926b8bb875ee2e8136d6d Homepage: https://cran.r-project.org/package=wyz.code.offensiveProgramming Description: CRAN Package 'wyz.code.offensiveProgramming' (Wizardry Code Offensive Programming) Allows to turn standard R code into offensive programming code. Provides code instrumentation to ease this change and tools to assist and accelerate code production and tuning while using offensive programming code technics. Should improve code robustness and quality. Function calls can be easily verified on-demand or in batch mode to assess parameter types and length conformities. Should improve coders productivity as offensive programming reduces the code size due to reduced number of controls all along the call chain. Should speed up processing as many checks will be reduced to one single check. Package: r-cran-wyz.code.rdoc Architecture: all Version: 1.1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-wyz.code.offensiveprogramming, r-cran-stringr, r-cran-r6, r-cran-crayon, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wyz.code.rdoc_1.1.19-1.ca2404.1_all.deb Size: 1525398 MD5sum: 64e45487380f4b17eb66c9740d83452a SHA1: bea5a3ea58f70f8747c59d634be5f9c9b174b753 SHA256: d955e2fa2d83ab4375fd1578b1c9e486230a0db3e97b0b1dfd26046d408b88ea SHA512: e429be80e48ef1be9f1535ad2b52813308d9b173c67577cac2bd9f5022d6477b0c89b3cb2c89ebc634fa3b673634a589dd3ab46519b95cba0f446b9ceb0f0728 Homepage: https://cran.r-project.org/package=wyz.code.rdoc Description: CRAN Package 'wyz.code.rdoc' (Wizardry Code Offensive Programming R Documentation) Allows to generate on-demand or by batch, any R documentation file, whatever is kind, data, function, class or package. It populates documentation sections, either automatically or by considering your input. Input code could be standard R code or offensive programming code. Documentation content completeness depends on the type of code you use. With offensive programming code, expect generated documentation to be fully completed, from a format and content point of view. With some standard R code, you will have to activate post processing to fill-in any section that requires complements. Produced manual page validity is automatically tested against R documentation compliance rules. Documentation language proficiency, wording style, and phrasal adjustments remains your job. 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Generated test files are complete and ready to run. Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of errors in test case production, be able to test immediately generated files without any need to view or modify them, and enter a zero time latency between code implementation and industrial testing. As with 'testthat', you may complete provided test cases according to your needs to push testing further, but this need is nearly void when using 'wyz.code.offensiveProgramming'. Package: r-cran-x.ent Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4508 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-xtable, r-cran-jsonlite, r-cran-ggplot2, r-cran-statmod Filename: pool/dists/noble/main/r-cran-x.ent_1.1.7-1.ca2404.1_all.deb Size: 1366490 MD5sum: caff1958764b2cba77e21f0ba742a06d SHA1: 211ebf37f5ab9f914a2d450622465a350646053c SHA256: b7aeb162b6adbd53770a36856d1cec0afd710f62c13252fbc254550757ae3548 SHA512: 4b8b30e7a747e42858590791d0e489fd7fbc17d49e6dcaadfe967130b43d8eed94657c7a5a748fd2f02912453004ae066e5dd8d3965b92f776173bcb50895daa Homepage: https://cran.r-project.org/package=x.ent Description: CRAN Package 'x.ent' (eXtraction of ENTity) Provides a tool for extracting information (entities and relations between them) in text datasets. It also emphasizes the results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. 'x.ent' uses parsing with Perl functions and JavaScript to define user preferences through a browser and R to display and support analysis of the results extracted. Local grammars are defined and compiled with the tool Unitex, a tool developed by University Paris Est that supports multiple languages. See ?xconfig for an introduction. Package: r-cran-x12 Architecture: all Version: 1.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-x13binary, r-cran-stringr Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-x12_1.11.0-1.ca2404.1_all.deb Size: 587202 MD5sum: 46848416aec532a816a0a990f4994f15 SHA1: 2d2d810da2488423983f9a2835aa63b1ee31318c SHA256: a1c7eed0113885ea2eed822a080f40a092d73b05ab39ea01212befb81e745170 SHA512: 64107ea47c6726579deb02dd6412c8178a615cd48efa0c7f977ab200189236d625da9b21bdc469349c64283ff9962916738c91b8ae1033b2501cf81b9eda6644 Homepage: https://cran.r-project.org/package=x12 Description: CRAN Package 'x12' (Interface to 'X12-ARIMA'/'X13-ARIMA-SEATS' and Structure forBatch Processing of Seasonal Adjustment) The 'X13-ARIMA-SEATS' methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'. 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Implements the methodology described in Garrido, Milhaud & Olympio (2025) for a French/European actuarial climate index, building on the American Academy of Actuaries framework, to any country in the world. Package: r-cran-xadmix Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-xadmix_1.0.0-1.ca2404.1_all.deb Size: 201112 MD5sum: 7eda6a221baa0596963e91626dc70bfe SHA1: 7b9c7c8580cfc87ac173e4be070307bf75b5dacf SHA256: c89c60207c636d5f395b1bc6806013c1dbb3ee00db227f29c2031b298f1ded06 SHA512: 1531c934c5710a68f844e95ce7e15bd19e95149eb6cf5ab7b0c9f951193cae1d7232f51096a82cc98b75d2f5094ee4a3712a44c96e005fc789726d47ae6d648e Homepage: https://cran.r-project.org/package=xadmix Description: CRAN Package 'xadmix' (Subsetting and Plotting Optimized for Admixture Data) A few functions which provide a quick way of subsetting genomic admixture data and generating customizable stacked barplots. Package: r-cran-xaihydro Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dalex, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-cli, r-cran-rlang Suggests: r-cran-randomforest, r-cran-xgboost, r-cran-ranger, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-xaihydro_0.1.0-1.ca2404.1_all.deb Size: 149934 MD5sum: 3468820b35bad7f52af49f7a170c7e05 SHA1: 238f58dec23cdf3ec56b25db92e3479867748365 SHA256: f57797d8438b9c6cbb93e4b146ed0f444eaffff63a5e18ac055fd7d2920291b8 SHA512: 2e80005f059bb08bcdd48aad96191867b48ca0c03d298e6ba6967ae744c47c68919d3f87971221908a0c59e1785f5999ddd0f030e3a0f0fcf8592e904c47683c Homepage: https://cran.r-project.org/package=xaiHydro Description: CRAN Package 'xaiHydro' (Explainable AI Tools for Hydro-Climate Modelling) Provides a unified workflow for applying Explainable Artificial Intelligence (XAI) methods to hydro-climate predictive models. Functions implement a permutation-based Monte Carlo SHAP estimator (Strumbelj and Kononenko (2014) ; Lundberg and Lee (2017) ), a self-contained locally weighted linear surrogate LIME (Ribeiro et al. (2016) ), and Partial Dependence Plots with Accumulated Local Effects (Friedman (2001) ; Apley and Zhu (2020) ) with hydrology-specific visualisations and interpretation utilities. Supports any model object compatible with the 'DALEX' explainer interface (Biecek (2018) ), including random forests, gradient boosting, and neural networks trained on streamflow, drought indices, flood risk, or evapotranspiration data. Hydrology-standard performance metrics Nash-Sutcliffe Efficiency (NSE, Nash and Sutcliffe (1970) ) and Kling-Gupta Efficiency (KGE, Gupta et al. (2009) ) are computed alongside standard regression metrics. Designed to accompany the book chapter: Islam, S., Dheeraj, A., Ali, S., Kaushal, R. and Venkatesh, G. (2026). Explainable Artificial Intelligence for Hydro-Climatic Modelling: Methods, Applications, and Implementation Using the xaiHydro R Package. In Chandniha, S. K. et al. (Eds.), Hydro-Climate Analytics: Remote Sensing, AI and Geospatial Modelling. Springer. 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Complete your slide theme with 'ggplot2' themes that match the font and colors used in your slides. Customized styles can be created directly in your slides' 'R Markdown' source file or in a separate external script. Package: r-cran-xcertainty Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-tidyr, r-cran-dplyr, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-ggdist, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-xcertainty_1.0.1-1.ca2404.1_all.deb Size: 1309942 MD5sum: ef5e4e8cd1d71048600b0f94d5458028 SHA1: 33579d8288dd64a3f5855213f5c6adcdef406478 SHA256: 454db5884672348ed39c5cf32b98804544f992b5ec027f6c0a72e54e43489e21 SHA512: b28ac0aa49e14859e5254e92e537e2e66209feb64d1f02895b49728a6d2759edd553a14df166a4ea48af10e1962ed3ef174d5f33d3d95725ca0f7056088fcdda Homepage: https://cran.r-project.org/package=Xcertainty Description: CRAN Package 'Xcertainty' (Estimating Lengths and Uncertainty from Photogrammetric Imagery) Implementation of Bayesian models for estimating object lengths and morphological relationships between object lengths using photographic data collected from drones. The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, ). Package: r-cran-xefun Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-xefun_0.1.5-1.ca2404.1_all.deb Size: 47672 MD5sum: 255d01efb07c82750119e0662197c2d6 SHA1: fde680109dc51e1bb9360ac84f4d2f9875b82d55 SHA256: 3c6d5b1503070f420243253cafae15ec06ebdf8638c3531b81a88c867ef1bffa SHA512: ef0c8e1b16db16cb024ff7d4836d37a08ea62f76ba507fb109de607c5786d16bbaf3e7546cd97f0ea5da7e935648ada480cf456c542cd517c24dc565797a7159 Homepage: https://cran.r-project.org/package=xefun Description: CRAN Package 'xefun' (X-Engineering or Supporting Functions) Miscellaneous functions used for x-engineering (feature engineering) or for supporting in other packages maintained by 'Shichen Xie'. 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Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) ), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) ), and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in Arabinia, H. (2006, ISBN:978-193-241596-3). All algorithms reuse basic adaptive mechanisms for performance optimization. For the architecture, see Geyer-Schulz, A. (2025) . Sequential or parallel execution with master-slave pattern (on multi-core machines, local clusters, and high-performance computing environments) is available for all algorithms. See . Homogeneous and heterogeneous island models with asynchronous and synchronous communication and configurable communication topology as well as migration strategy are supported. See . Package: r-cran-xegabnf Architecture: all Version: 1.0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegabnf_1.0.0.5-1.ca2404.1_all.deb Size: 120886 MD5sum: 37fbd3f261cca7ad85c2504ba3830bc9 SHA1: 3584270e1dbb34093cbe588343f35ac624805694 SHA256: b280c4e1944f5004ea0f093880d91075d3cc0ebc932f6e1a16349401d3cd1bab SHA512: 70db44519bd7bab242e14ac48b0355f87cb9e4555ae9be3130421fe25e2ec363395c1ad003a141ddbc4ddd7b415f0c9b7c032e0448ec6995f0b497e12ab9c7c4 Homepage: https://cran.r-project.org/package=xegaBNF Description: CRAN Package 'xegaBNF' (Compile a Backus-Naur Form Specification into an R GrammarObject) Translates a BNF (Backus-Naur Form) specification of a context-free language into an R grammar object which consists of the start symbol, the symbol table, the production table, and a short production table. 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Package: r-cran-xegaderivationtrees Architecture: all Version: 1.0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xegabnf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegaderivationtrees_1.0.0.6-1.ca2404.1_all.deb Size: 131140 MD5sum: a0626f4b5b87969bbe86bc7e10c67304 SHA1: c10b703aabc835ffabbe1db38d891cd59cfa011e SHA256: 093e8b34f357d3cd4868c823afbbda82b590c01d49f6424363a1fa71a8db7c72 SHA512: a96608308a6955c99490f28c8d1fbcbd217f8656b4759b46235a385d18d066088829eec643af7a668193824f534160ac9c4806122496bf226fe6438ca8f0daaf Homepage: https://cran.r-project.org/package=xegaDerivationTrees Description: CRAN Package 'xegaDerivationTrees' (Generating and Manipulating Derivation Trees) Derivation tree operations are needed for implementing grammar-based genetic programming and grammatical evolution: Generating a random derivation trees of a context-free grammar of bounded depth, decoding a derivation tree, choosing a random node in a derivation tree, extracting a tree whose root is a specified node, and inserting a subtree into a derivation tree at a specified node. These operations are necessary for the initialization and for decoders of a random population of programs, as well as for implementing crossover and mutation operators. Depth-bounds are guaranteed by switching to a grammar without recursive production rules. For executing the examples, the package 'BNF' is needed. The basic tree operations for generating, extracting, and inserting derivation trees as well as the conditions for guaranteeing complete derivation trees have been presented in Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). The use of random integer vectors for the generation of derivation trees has been introduced in Ryan, C., Collins, J. J., and O'Neill, M. (1998) for grammatical evolution. Package: r-cran-xegadfgene Architecture: all Version: 1.0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegadfgene_1.0.0.10-1.ca2404.1_all.deb Size: 109234 MD5sum: 3d7f232510f431cdfd00dee8a3e56d40 SHA1: 934c129ecb44e39cdae00627e61b0985be6e0212 SHA256: 869b7f5a5f1c86dedfc0689ab770427f6dac5a1627f16ed90ad81d713f9601f3 SHA512: 757996965a62890bb0722bb71c3b213ddb7cb42630b0ce1b1391b44001715f68d117b0e88c57667009d6637b66d6c29ac1f0cf2d4a0012e9ffc22dbaf7eb48c8 Homepage: https://cran.r-project.org/package=xegaDfGene Description: CRAN Package 'xegaDfGene' (Gene Operations for Real-Coded Genes) Representation-dependent gene-level operations for genetic and evolutionary algorithms with real-coded genes used in the R-package 'xega' are collected in this package. The common feature of the gene operations is that all of them are useful for derivation-free optimization algorithms. At the moment the package implements initialization, mutation, crossover, and replication operations for differential evolution as described in Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) . In addition, several (more recent) methods for determining the scale factor are provided. For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegagagene Architecture: all Version: 1.0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegagagene_1.0.0.7-1.ca2404.1_all.deb Size: 181220 MD5sum: be6a8cae4c74480fd86374f50801562f SHA1: 46d010d9981306c7c953dfc0c2559b8f4ea9495f SHA256: 6951b467c1742e5ce8db04596b6d9cb924ced487a73d19f67d55347f1bddf5b4 SHA512: e590eb1a53afc592981bebe7136edff816106d29b3463b71776212c6dad328ee678bed15e88a7605e944ec64951e496008aae0d3053b1a5d82acfc62f480fd13 Homepage: https://cran.r-project.org/package=xegaGaGene Description: CRAN Package 'xegaGaGene' (Binary Gene Operations for Genetic Algorithms) Representation-dependent gene level operations of a genetic algorithm with binary coded genes for the R-package 'xega' : Initialization of random binary genes, several gene maps for binary genes, several mutation operators, several crossover operators with 1 and 2 kids, replication pipelines for 1 and 2 kids, and, last but not least, function factories for configuration. See Goldberg, D. E. (1989, ISBN:0-201-15767-5). For crossover operators, see Syswerda, G. (1989, ISBN:1-55860-066-3), Spears, W. and De Jong, K. (1991, ISBN:1-55860-208-9). For mutation operators, see Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegagegene Architecture: all Version: 1.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers, r-cran-xegaselectgene, r-cran-xegabnf, r-cran-xegaderivationtrees Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegagegene_1.0.0.3-1.ca2404.1_all.deb Size: 81048 MD5sum: 364266b416b0512c3d932da72d19378d SHA1: 14fee5f20cc04de7358aff33dc27d6770e721e81 SHA256: ce833309e47ef03511273174599685d24d561fe51c747caf2cae9a5a89a1b313 SHA512: edce1ac3200aef02101454d9a3f292551e5d1cbe6d8e25d6a86920d35bc402fa681f9d8c8c3b22fd15e8f4cfe5eec67adfd3d6a7e3bf8ec302e558cc14cd2a44 Homepage: https://cran.r-project.org/package=xegaGeGene Description: CRAN Package 'xegaGeGene' (Grammatical Evolution) Grammatical evolution (see O'Neil, M. and Ryan, C. (2003,ISBN:1-4020-7444-1)) uses decoders to convert linear (binary or integer genes) into programs. In addition, automatic determination of codon precision with a limited rule choice bias is provided. For a recent survey of grammatical evolution, see Ryan, C., O'Neill, M., and Collins, J. J. (2018) . Package: r-cran-xegagpgene Architecture: all Version: 1.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xegabnf, r-cran-xegaderivationtrees, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegagpgene_1.0.0.3-1.ca2404.1_all.deb Size: 77000 MD5sum: aed74a39c81ee5c968f24bd1c465e6e6 SHA1: 69c5a7358d60b5d8e4ba6dc1616ac91993f5dd60 SHA256: dbdb64d9d4bc8799ad593531aecda1c443eb64e598f908dbe883a1eca51ef82b SHA512: 41e9f27d41b735b8c747b956d8e0e16906b15ba3ee1d278af6ceaf56c50c1217ed6ce5c9fcc3073540905dfeaeb009c3df1cdd7495a27bec25038f31e6f8f1f0 Homepage: https://cran.r-project.org/package=xegaGpGene Description: CRAN Package 'xegaGpGene' (Genetic Operations for Grammar-Based Genetic Programming) An implementation of the representation-dependent gene level operations of grammar-based genetic programming with genes which are derivation trees of a context-free grammar: Initialization of a gene with a complete random derivation tree, decoding of a derivation tree. Crossover is implemented by exchanging subtrees. Depth-bounds for the minimal and the maximal depth of the roots of the subtrees exchanged by crossover can be set. Mutation is implemented by replacing a subtree by a random subtree. The depth of the random subtree and the insertion node are configurable. For details, see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). Package: r-cran-xegamigration Architecture: all Version: 0.5.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xegaselectgene, r-cran-xegapopulation Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegamigration_0.5.0.4-1.ca2404.1_all.deb Size: 580378 MD5sum: 58b825f8a4beef5c125b6e93c5d2d50d SHA1: 92509463ad5eb57b30cbd9e49f2201ab7f46a905 SHA256: 91613a454b068c3e5672c40e0f577c348fe9006d939736e8abed6f481feff1c3 SHA512: 2e73fa2e39c2b33f22dfc468454388d4d5a4e312fa39f41afb774a9d26dc0b7a259573149916948322b00cb81bd396b8ad699f8ff79ee47bca61e0ba51d07393 Homepage: https://cran.r-project.org/package=xegaMigration Description: CRAN Package 'xegaMigration' ('Xega' Island Models) Implements asynchronous message-passing communication protocols for island models of extended and evolutionary algorithms (see Tomassini, Marco (2005, ISBN:978-3-540-24193-5)) for the R-package 'xega' . Basic asynchronous as well as synchronized communication primitives are supplied based on file I/O operations ('rds') on a shared file system or by 'openMPI' (MPI) messages. The gene selection and replacement strategies, the migration policy as well as the communication topology between islands are configurable. Homogeneous and heterogeneous island algorithms are supported. For examples (R and shell-scripts), see . Package: r-cran-xegapermgene Architecture: all Version: 1.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegapermgene_1.0.0.2-1.ca2404.1_all.deb Size: 85656 MD5sum: f0536ad16672c4bc7639152963b59887 SHA1: 642d63b2c73b36bf65380a2503a8f060dd4f2869 SHA256: 8af33f045c38c7e37dc171567d53ab371a8822ed2e8f2f281ea3b3a8b18f6f1d SHA512: f4ebaf4d3343fbf008e32dbd013b2ab24fa76fb8d21e50a128c2f00b822c98b7394f350d2bb27921b2ee6f415ac5669b0052318a570961d398b411a9ba123e8a Homepage: https://cran.r-project.org/package=xegaPermGene Description: CRAN Package 'xegaPermGene' (Operations on Permutation Genes) An implementation of representation-dependent gene level operations for genetic algorithms with genes representing permutations: Initialization of genes, mutation, and crossover. The crossover operation provided is position-based crossover (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). For mutation, several variants are included: Order-based mutation (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), randomized Lin-Kernighan heuristics (Croes, G. A. (1958) and Lin, S. and Kernighan. B. W. (1973) ), and randomized greedy operators. A random mix operator for mutation selects a mutation variant randomly. Package: r-cran-xegapopulation Architecture: all Version: 1.0.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future.apply, r-cran-xegagagene, r-cran-xegaselectgene Suggests: r-cran-testthat, r-cran-future, r-cran-parallelly Filename: pool/dists/noble/main/r-cran-xegapopulation_1.0.0.16-1.ca2404.1_all.deb Size: 227428 MD5sum: 3f256ad593d4e5a1094b2cb535d06eb9 SHA1: b7e08c7e7925760c5e14e373b401622dc8a6c14e SHA256: 24394e07a1399c3b4004709c91733b7d24a4eb697f4ce8ca45802dfdb2f0b04d SHA512: d54aaa990741529a9a33c5b1c3870824df1561b265260147e0d1d9be29092ee0f15d6997dd9a46ef3c5d22effcb424de16156018b52a93caf9906acd22e3f716 Homepage: https://cran.r-project.org/package=xegaPopulation Description: CRAN Package 'xegaPopulation' (Genetic Population Level Functions) This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support for the R-package 'xega' . The population layer consists of functions for initializing, logging, observing, evaluating a population of genes, as well as of computing the next population. For parallel evaluation of a population of genes 4 execution models - named Sequential, MultiCore, FutureApply, and Cluster - are provided. They are implemented by configuring the lapply() function. The execution model FutureApply can be externally configured as recommended by Bengtsson (2021) . Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) , and Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized approximate search procedure elements. Adaptive crossover and mutation rates depending on population statistics generalize the approach of Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegaselectgene Architecture: all Version: 1.0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegaselectgene_1.0.0.4-1.ca2404.1_all.deb Size: 253614 MD5sum: a64df6f6c2a3559769cf16ca24117f20 SHA1: 5fa54bd0b4d7aba9f362d5525fe00df0e8069bd4 SHA256: 96c134d39aa9272f13bd6ad82f55bc13c5095ff49b1728dfd81c59ba15772e9a SHA512: 8d4157172bf0a27748945c828bfc7135b6536e24e1301c36d1b1f28bd45081da1b53b1cadca5e69f44c991735da39f0c589a90759258811a653d5c01ddbb4129 Homepage: https://cran.r-project.org/package=xegaSelectGene Description: CRAN Package 'xegaSelectGene' (Selection of Genes and Gene Representation Independent Functions) This collection of gene representation-independent mechanisms for evolutionary and genetic algorithms for the R-package xega contains four groups of functions: First, functions for selecting a gene in a population of genes according to its fitness value and for adaptive scaling of the fitness values as well as for performance optimization and measurement offer several variants for implementing the survival of the fittest. Second, evaluation functions for deterministic functions avoid recomputation. Evaluation of stochastic functions incrementally improve the estimation of the mean and variance of fitness values at almost no additional cost. Evaluation functions for gene repair handle error-correcting decoders. Third, timing and counting functions for profiling the algorithm pipeline are provided to assess bottlenecks in the algorithms. Fourth, a small collection of problem environments for function optimization, combinatorial optimization, and grammar-based genetic programming and grammatical evolution is provided for tutorial examples. For xega's architecture, see Geyer-Schulz, A. (2025) . The methods in the package are described by the following references: Baker, James E. (1987, ISBN:978-08058-0158-8), De Jong, Kenneth A. (1975) , Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X), Grefenstette, John J. (1987, ISBN:978-08058-0158-8), Grefenstette, John J. and Baker, James E. (1989, ISBN:1-55860-066-3), Holland, John (1975, ISBN:0-472-08460-7), Lau, H. T. (1986) , Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) , Reynolds, J. C. (1993) , Schaffer, J. David (1989, ISBN:1-55860-066-3), Wenstop, Fred (1980) , Whitley, Darrell (1989, ISBN:1-55860-066-3), Wickham, Hadley (2019, ISBN:978-815384571). 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Package: r-cran-yarr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-yarr_0.1.2-1.ca2404.1_all.deb Size: 36552 MD5sum: 2d35bc3a2c05473e61af72a76218d8a1 SHA1: 453889c9701481d9a21441c089b0a4db9225b2de SHA256: c9adbde97c0c40a7bb7265856fd5ec23e0eb3701c730a341abc0c5d295108469 SHA512: cbd045f677a52e6e627902bcd577059dacb2ae4dde7703bb2d8803d79205a883c2280ca3e9f9afcc8f6cf6e82c97f7da530571d783a88148ce22a9515006566d Homepage: https://cran.r-project.org/package=yarr Description: CRAN Package 'yarr' (Yet Another 'ARFF' Reader) A parser and a writer for 'WEKA' Attribute-Relation File Format in pure R, with no dependencies. As opposed to other R implementations, this package can read standard (dense) as well as sparse files, i.e. those where each row does only contain nonzero components. 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The core functionality of the 'yfhist' package abstracts the complexities of interacting with Yahoo Finance APIs, such as session management, crumb and cookie handling, query construction, date validation, and interval management. This abstraction allows users to focus on retrieving data rather than managing API details. Use cases include historical data across a range of security types including equities & ETFs, indices, and other tickers. The package supports flexible query capabilities, including customizable date ranges, multiple time intervals, and automatic data validation. It automatically manages interval-specific limitations, such as lookback periods for intraday data and maximum date ranges for minute-level intervals. The implementation leverages standard HTTP libraries to handle API interactions efficiently and provides support for both R and 'Python' to ensure accessibility for a broad audience. 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Package: r-cran-ypmodel Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ypmodel_1.4-1.ca2404.1_all.deb Size: 378280 MD5sum: 61270264bd83359ca346cb29b95d2fd2 SHA1: fee4bf6a85ba1a885bf1d7cc6f5b1884f06f7991 SHA256: 2ffb28f8de73a095e2d0b83d0cb165bcdefc411567ca144b2e776ccb512db3d1 SHA512: 4e8fc36e66b83cfcb9bffc2e1720e8dd40493b4ba97c9a0c86e040a17f6ca31f557a6e1884f28339b8f2c2408717a5e3568acf6e98b6ca7489fe5163dc32572d Homepage: https://cran.r-project.org/package=YPmodel Description: CRAN Package 'YPmodel' (The Short-Term and Long-Term Hazard Ratio Model for SurvivalData) Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. 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It relies on options on Short Term Interest Rate futures or options on government bond futures. It models the futures price at options' maturity as a mixture of lognormal densities. Leveraging on this, the package provides with the RNDs and cumulative densities of the money market rate or the government bond yield inferred from the futures price, using the RND of the futures price. The package also extracts from options prices on bond futures in one go the RND of the cheapest-to-deliver (ctd) bond repo rate from options' to futures' maturity and the RND of the ctd bond yield at options' maturity. The package also provides with the probability attached to each bond in the delivery basket of a government bond futures to be the cheapest at options' maturity from an examination of either the implied repo rate or the net basis of bonds in the delivery basket. 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Package: r-cran-zenstats Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-polite, r-cran-purrr, r-cran-rcurl, r-cran-readr, r-cran-rlang, r-cran-rvest, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zenstats_0.1.2-1.ca2404.1_all.deb Size: 14762 MD5sum: 32c8e08ca90e59260ea78105daed694e SHA1: a2f2c4f44a13c10184598e67047f6046e2cccadd SHA256: 78d63424d32f254a6495d6e12edd86ec352b573eb4f843cd29229eb36d844a05 SHA512: 978222d2a94ca60278ad759362bf3e383ed4ac6de33a8ffedd798c215380b8e39c22babb0bf9c17b5d5e4a9a9f45947814671f254c9db12d170888e3f731d65b Homepage: https://cran.r-project.org/package=zenstats Description: CRAN Package 'zenstats' (Statistics from 'Zenodo' Deposits) Fetch statistics about views, downloads and data volume from 'Zenodo' deposits. The package collects a 'Zenodo' () deposit file information, respecting the website scrapping policies. Package: r-cran-zentrar Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-zentrar_0.1.3-1.ca2404.1_all.deb Size: 130320 MD5sum: e69db4438f670cbb52cb455e41b1d67c SHA1: 57e9932ce82cf496b11110e1bcb5c67354da34f5 SHA256: 61d52e18ad304ea57c083187218f9715e66bf152b621286ba4a83e904d286400 SHA512: 73d4b3b2fb1a12466df6a221586f7d825a33e13de9f91ff0431672ea83538e01294c8ba709b3bf077a35eb3460313694c0ce228a9b6f49932bee4362c9be8069 Homepage: https://cran.r-project.org/package=zentraR Description: CRAN Package 'zentraR' (R Client for the ZENTRA Cloud V5 API) Downloads environmental sensor data from the ZENTRA Cloud V5 API () into tidy data frames. Provides device discovery, reading retrieval with automatic pagination and rate-limit handling, tidy long output with a wide-format helper, and an incremental 'sync' engine with pluggable local storage (RDS files, CSV files, or return-only) so that new readings can be fetched on a schedule and appended to a growing local record. Package: r-cran-zep Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-animation Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zep_0.3.1-1.ca2404.1_all.deb Size: 59644 MD5sum: 335ed58492494c1e4c4c365d0550c221 SHA1: 6c2a8102cc2b5e8902436695976a0a535ad6d16e SHA256: c9f02b3469628ee2be88c76ed2a4f6d8ac1c1eb4f6d1eeb7500dc596cbd455fa SHA512: f4c0131113469f11b7dffbd3350c99d18cb385f947508e28b98b88ad12f77d8f84b008b4e8b70c1c3a06eebb600fdbdac56822ea8ea10d7acc959720c3553eaf Homepage: https://cran.r-project.org/package=ZEP Description: CRAN Package 'ZEP' (Procedures Related to the Zadeh's Extension Principle for FuzzyData) Procedures for calculation, plotting, animation, and approximation of the outputs for fuzzy numbers (see A.I. Ban, L. Coroianu, P. Grzegorzewski "Fuzzy Numbers: Approximations, Ranking and Applications" (2015)) based on the Zadeh's Extension Principle (see de Barros, L.C., Bassanezi, R.C., Lodwick, W.A. (2017) ). Package: r-cran-zephyr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang Suggests: r-cran-callr, r-cran-checkmate, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-zephyr_0.1.3-1.ca2404.1_all.deb Size: 110496 MD5sum: 080193d5ae19c4f227d940bab689b4a1 SHA1: cb15ceed7341ff2d9a581bb1ceb0c40a051a646f SHA256: 20b336cceb7e87b683b39916dad0d58c0a36e3b4c54ee7020e633fd5ee285b96 SHA512: 8c3f382cbdd990dfe1f5f04f9b02cdb30dde26f516d4dcd8f4bbc6a374b40696745c670f1c1d936b177f0281b7cf631d47959da9c27a5019ea2e9f7d9b45af49 Homepage: https://cran.r-project.org/package=zephyr Description: CRAN Package 'zephyr' (Structured Messages and Options) Provides a structured framework for consistent user communication and configuration management for package developers. Package: r-cran-zeroeqpart Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ppcor, r-cran-mass Filename: pool/dists/noble/main/r-cran-zeroeqpart_0.1.0-1.ca2404.1_all.deb Size: 37496 MD5sum: 0d066620220344e2b8d30945361bc302 SHA1: 08b7d422c1fda83e90997f1cd7e9a2e91d16298e SHA256: 0202068d13e688cea000ce245fa2aaba12b8cacb9fcb4cdab952f33e644daaaf SHA512: d5aa828a19c8e68019ed17a7ae850e67ca312a11dd5b8140c6f851fbd2eedddf47632c6eedb5cf66f86806b125b1bb6eb8d4c9114e028d664a86075a79ae07c6 Homepage: https://cran.r-project.org/package=zeroEQpart Description: CRAN Package 'zeroEQpart' (Zero Order vs (Semi) Partial Correlation Test and CI) Uses bootstrap to test zero order correlation being equal to a partial or semi-partial correlation (one or two tailed). Confidence intervals for the parameter (zero order minus partial) can also be determined. Implements the bias-corrected and accelerated bootstrap method as described in "An Introduction to the Bootstrap" Efron (1983) <0-412-04231-2>. Package: r-cran-zeroonedists Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gamlss Filename: pool/dists/noble/main/r-cran-zeroonedists_1.0.1-1.ca2404.1_all.deb Size: 181678 MD5sum: e921db5dda77a86a23a7f09650b7d1cf SHA1: df4452372a1c71a9e3ef7bb31841673189cc2b9a SHA256: cbcd214210e40a87fc8584b948ce629cdfe7ec74af454500fcaaabcc596217aa SHA512: 8ef5dee269f26a2256b4510ce83e209c0b2b8bad7d7bfacb7353a32f0cab83742ca53f8ed218f5e99d447357a3711b2f0c4d57d88a85b1c03ead4b4bbd620b8a Homepage: https://cran.r-project.org/package=ZeroOneDists Description: CRAN Package 'ZeroOneDists' (One Zero Statistical Distributions) Implementation of new statistical distributions in (0, 1) interval. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters using Generalized Additive Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos (2005) . Package: r-cran-zerotradeflow Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tidyverse, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zerotradeflow_0.1.0-1.ca2404.1_all.deb Size: 2512110 MD5sum: 612d46e3bf9854aaade1f61819a2c9ea SHA1: 18d70f0bef42a18a4821f570f240d42271f08d8d SHA256: e8b9977e17a6870013763bb466e26dcc554a86976b97d57a1c6dcf5e5e116952 SHA512: 2b70ea81a86f0aa7e52a272945ae7c35f05b590415bf87dbbd3422ac956bd837aa52fec06e9f12dcf4bfb032b624e9ef5cbae9d666da3088907f97a7509b82c9 Homepage: https://cran.r-project.org/package=zerotradeflow Description: CRAN Package 'zerotradeflow' (An Implementation for the Gravitational Models of Trade) A system for creating the bilateral trade flow between a country pair equal to zero. You provide the data, tell get_zerotradeflow() which variables are of interest and it expands the base by creating the bilateral zero trade flow. The bases on the flow of trade between countries only report positive trade (greater than zero), however, for some analyzes of gravitacional models, data on zero flow is also necessary. Some examples for Gravity Model: Figueiredo and Loures (2016) and Yotov, Piermartini, Monteiro and Larch . Package: r-cran-zetadiv Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scam, r-cran-car, r-cran-mgcv, r-cran-vegan, r-cran-geodist, r-cran-nnls, r-cran-glm2 Filename: pool/dists/noble/main/r-cran-zetadiv_1.3.0-1.ca2404.1_all.deb Size: 413268 MD5sum: 47fde7e44c8c8b81724db6bf8d95c53f SHA1: a798dd0abf29d26700d5d3fd4bf32b44865e87d7 SHA256: e921059709ad0fc0c758d06758d78e69121c9c7ca95d4c8ce5264cb84a8ecc12 SHA512: 4191a2f25690af0e0a55d7ea8ef287b14aee043fbd17061ba55b9356b5d978f033050d16488328ab749b7b293d1a10ab22f8a55e0e03a9c1e641566d35e6b3a3 Homepage: https://cran.r-project.org/package=zetadiv Description: CRAN Package 'zetadiv' (Functions to Compute Compositional Turnover Using Zeta Diversity) Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines. Package: r-cran-zetasuite Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4809 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-e1071, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-mixtools, r-cran-shinyjs, r-cran-shinydashboard, r-cran-shiny, r-cran-plotly, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-zetasuite_1.0.3-1.ca2404.1_all.deb Size: 4492072 MD5sum: c197e87fec64a1a07e9f0e1b985823e7 SHA1: fc882fcdd33a9f1d1538d0387ac8117bd78e6142 SHA256: 3a4ac9ae2b0c8342e743afe258ab1dedd10a73b96c86ff95530e78c73bc56101 SHA512: 3cb340034305c113b990943cbb3863ed75dc2d07f5f5f3370abc19ff8893f37dba621fbda033d52864e96a06808bc63750cb564771282855e3f375f735b7b752 Homepage: https://cran.r-project.org/package=ZetaSuite Description: CRAN Package 'ZetaSuite' (Analyze High-Dimensional High-Throughput Dataset and QualityControl Single-Cell RNA-Seq) The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) and Tsherniak A (2017) ) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) ). In 'ZetaSuite', we have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength. Package: r-cran-zfit Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-estimatr, r-cran-mass, r-cran-pls, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zfit_0.4.0-1.ca2404.1_all.deb Size: 285850 MD5sum: f0e4291dd8c17eda33fb91bcdb280b35 SHA1: a23ffa40c33662d36f819d7b463cac6665bdc5c4 SHA256: d851e900f362e723f5b3d2d71059378f004ea3f378d0d3413d9ebe44aa2b4355 SHA512: 1e43280d1d3d2781511f5c7500853ded289897b4342e8a907c6ef7c96c7023ddc7526903dc0de1aaab3851575f1f62181139f95321c90ad1c44700d11c18d1b9 Homepage: https://cran.r-project.org/package=zfit Description: CRAN Package 'zfit' (Fit Models in a Pipe) Improve the usage of model fitting functions within a piped work flow. Package: r-cran-zibr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod Suggests: r-cran-betareg, r-cran-dplyr, r-cran-lme4, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zibr_1.0.3-1.ca2404.1_all.deb Size: 362524 MD5sum: baa5c766c8f6023d123522ddd1bd709c SHA1: a8b5caacf73ff8e0dde29b71a74abb13928a7bd1 SHA256: 9bc53ecdbd0807ffda0fd0dc59de81f64839c8af9d5d745ba1faad87f5008f44 SHA512: bbbad7abcc09265dc1e87a15b37756764518ba056ad8d1ae44c9d786fb8869f0231bbb5224dc98fe60d4077615959216eee2a7cc3d1c25d102e146dcf272cb95 Homepage: https://cran.r-project.org/package=ZIBR Description: CRAN Package 'ZIBR' (A Zero-Inflated Beta Random Effect Model) A two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. Eric Z. Chen and Hongzhe Li (2016) . Package: r-cran-zidw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dwreg, r-cran-actuar, r-cran-maxlik, r-cran-count, r-cran-gtools, r-cran-matrixcalc, r-cran-discreteweibull, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zidw_0.1.0-1.ca2404.1_all.deb Size: 137228 MD5sum: a73062f4415279306b3466abb2d17d86 SHA1: de7dc09955ddc9d04c2d4989068e8742e874ad5a SHA256: 649c7504c734afbed3fa2598e9b9efd0eac2865ac512884e15b929dba50e782b SHA512: b98e9a3bff84fd8ff9b69041968f45b20b3359654d98c575402c1777fe5f0d386ecb699151f8a330ca6612c3bedbb6a414a1987b1b9186b2601b01fc6735b151 Homepage: https://cran.r-project.org/package=ZIDW Description: CRAN Package 'ZIDW' (Zero-Inflated Discrete Weibull Models) Parameter estimation for zero-inflated discrete Weibull (ZIDW) regression models, the univariate setting, distribution functions, functions to generate randomized quantile residuals a pseudo R2, and plotting of rootograms. For more details, see Kalktawi (2017) , Taconeli and Rodrigues de Lara (2022) , and Yeh and Young (2025) . Package: r-cran-zihinar1 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-vgam, r-cran-actuar, r-cran-matrixstats, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-hmmpa Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-zihinar1_0.2.1-1.ca2404.1_all.deb Size: 73620 MD5sum: 5ebcc9da4f0d08233c8ed91073638a73 SHA1: 2e4787f1294fa3280bc546361c989315372b3874 SHA256: 3ad7afcf1a8ba2acdf6755df3b6efdf110bf1df63c4b0d954f3771d859d39c60 SHA512: 7499fc48c560a2c0710362703d592172661c21d9440d0f3939063822a12e39776bc55df76d6edbc88ea7ac383ebea979a23bcab1cf0bfc7ceee68625a0a89091 Homepage: https://cran.r-project.org/package=ZIHINAR1 Description: CRAN Package 'ZIHINAR1' (Zero-Inflated and Hurdle INAR(1) Models) Provides tools for estimating Zero-Inflated INAR(1) (ZI-INAR(1)) and Hurdle INAR(1) (H-INAR(1)) models using 'Stan'. It allows users to simulate time series data for these models, estimate parameters, and evaluate model fit using various criteria. Functions include model estimation, simulation, and likelihood-based metrics. Package: r-cran-zillowr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-xml Suggests: r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-zillowr_1.0.0-1.ca2404.1_all.deb Size: 63894 MD5sum: 070ff4fbff435835e0bf52ba75f8dc4a SHA1: cc449b17efa73ad54ab19d7503936d0d80460eb6 SHA256: 9fa97a6c1d289b264ffb246b444d356966851b56ea030f5f68406a21465c7a84 SHA512: 4fb4b1d6e89d2d0706971f4c979fac2f118fe163376cf445c39de94221ff478ceb476eace7f85775a9bf6e2bc8e97c2afb82509144373b1cca85ccd8e2ee92b0 Homepage: https://cran.r-project.org/package=ZillowR Description: CRAN Package 'ZillowR' (R Interface to Zillow Real Estate and Mortgage Data API) Zillow, an online real estate company, provides real estate and mortgage data for the United States through a REST API. The ZillowR package provides an R function for each API service, making it easy to make API calls and process the response into convenient, R-friendly data structures. See for the Zillow API Documentation. NOTE: Zillow deprecated their API on 2021-09-30, and this package is now deprecated as a result. Package: r-cran-zim4rv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-pscl, r-cran-compquadform, r-cran-skat, r-cran-rnomni Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zim4rv_0.1.1-1.ca2404.1_all.deb Size: 261672 MD5sum: d9d9afc30b4b512aa28c98c06556a67d SHA1: 354559f1588c48125b5eb18f95959a281d34e8d7 SHA256: c05a37d40033cfb29b8aa27da947f45b4d72b34e3fedfe3890ff55d0268d0630 SHA512: b8e9b2087f75991691ac7ee24ecb7e52ae4c42ee27b25ece0dbc2fece1f735bb518256240fa2ef2a5e17632691f347a9a65c6ec3318cb66cc35ba3765e42f57d Homepage: https://cran.r-project.org/package=ZIM4rv Description: CRAN Package 'ZIM4rv' (Gene‐based Association Tests of Zero‐inflated Count Phenotypefor Rare Variants) Gene‐based association tests to model count data with excessive zeros and rare variants using zero-inflated Poisson/zero-inflated negative Binomial regression framework. This method was originally described by Fan, Sun, and Li in Genetic Epidemiology 46(1):73-86 . Package: r-cran-zim Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-dplyr, r-cran-pscl, r-cran-tsa, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-zim_1.1.2-1.ca2404.1_all.deb Size: 153296 MD5sum: 438de93e70d870dfa8239bbbd9b3d982 SHA1: d4fc4a36e3fc2f8d93dadac12a4d6259e6cc0d9e SHA256: a3e229379b8bd8b5e1ae82a4acd6806465b9ceb668288f8bd9df5f762a48316f SHA512: e216841a9e6b0f78c0afb1fb4d401af1b180ce9ec76b80388c10af460dc80f10ae581a3ba52af79266e7880b4be7cad9b74ece2f2f72eeeff2aee75b609a75bf Homepage: https://cran.r-project.org/package=ZIM Description: CRAN Package 'ZIM' (Zero-Inflated Models for Count Time Series with Excess Zeros) Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) and state-space models by Yang et al. (2015) . They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions. Package: r-cran-zinar1 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss.dist, r-cran-vgam, r-cran-mass, r-cran-statmod, r-cran-gtools, r-cran-scales Suggests: r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-zinar1_0.1.0-1.ca2404.1_all.deb Size: 44102 MD5sum: 17beb950f0f3c987f815b9d1f146df69 SHA1: b048bdade54c153886d7d6e8052305b5ef01f006 SHA256: 257e24ab5400bf478f585ca6341d57e828b94bcc5d7eca72975253b37340dde1 SHA512: 667cfe5c318bb9d03075b7deba023fd235d64a6fdd662bad76f15fe29d8c35c761943dbdeffd4e550198ac2330da344e59b0e779538f1216b7e7173952e08a43 Homepage: https://cran.r-project.org/package=ZINAR1 Description: CRAN Package 'ZINAR1' (Simulates ZINAR(1) Model and Estimates Its Parameters UnderFrequentist Approach) Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) . Package: r-cran-zinarp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-progress Filename: pool/dists/noble/main/r-cran-zinarp_0.1.0-1.ca2404.1_all.deb Size: 40172 MD5sum: 2238f3d96c121531bee00f8517896d7b SHA1: b532c4b81c918e9ca2c808f5c774f2eb66881d8e SHA256: 98fc1fbcb2085a3e59aadf574ba06c318e5c85c91fbb12d430a5170b7dca6b93 SHA512: fe77967ad33dcfcedadae8df5331121fcc797f468af1d4c378c3ea8792c54fbfbbce9ecbbe9cb6b1cfdfc9747c2ba1aa6ee92305d149cc3e32f5250e8a50d4bb Homepage: https://cran.r-project.org/package=ZINARp Description: CRAN Package 'ZINARp' (Simulate INAR/ZINAR(p) Models and Estimate Its Parameters) Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) . Package: r-cran-zinb.gp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2036 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayeslogit, r-cran-laplacesdemon, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-mvtnorm Suggests: r-cran-coda, r-cran-knitr, r-cran-posterior, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-zinb.gp_1.0.0-1.ca2404.1_all.deb Size: 1833394 MD5sum: e5a751aaaffd1b75882b184cddfd7164 SHA1: ddb39c05f2f811df0e364df68e2a18c4f2ae225c SHA256: 31e6069ea5213edb702c8cc965f0b4f3c9ecd37973799e25bf08e7d0c5b4d7ab SHA512: 2cf0a9d8a684b525727bea427769ff0922cdd104c977cf392a910c1d0f707c32bdf65342e1ca31ce40aaafa105e79fb7813d189be68f6891f4bc7cb35ffc7432 Homepage: https://cran.r-project.org/package=ZINB.GP Description: CRAN Package 'ZINB.GP' (Bayesian Zero-Inflated Negative Binomial Gaussian Process Models) Fits Bayesian zero-inflated negative binomial regression models with Gaussian process random effects for spatial, temporal, or spatiotemporal count data. Provides Markov chain Monte Carlo sampling, configurable random effects in the zero-inflation and count components, and posterior predictive draws. Implements a full GP version of the methods described by He and Huang (2024) . Package: r-cran-zipangu Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-memoise, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-arabic2kansuji Suggests: r-cran-curl, r-cran-testthat, r-cran-scales Filename: pool/dists/noble/main/r-cran-zipangu_0.3.3-1.ca2404.1_all.deb Size: 248748 MD5sum: 22461d4d2ec80b8811baa1d8e584ff11 SHA1: 368db2b1489c51dabcd987ef301a8e46a90b62e5 SHA256: 2ade9f0288d9eb6ba33ddb39d619d0e2141e83cfa2b1fe2db9ec8a9264d00364 SHA512: caa6d1a43aa92c21daf002f2c9611c1c09ddf0ae0c1486961b0d1936d1b807bc6ce1a6a1f7c695d174583109611a7dafa4864a8fb6b35fb417c958fb20b95d4b Homepage: https://cran.r-project.org/package=zipangu Description: CRAN Package 'zipangu' (Japanese Utility Functions and Data) Some data treated by the Japanese R user require unique operations and processing. These are caused by address, Kanji, and traditional year representations. 'zipangu' transforms specific to Japan into something more general one. Package: r-cran-zipcoder Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3812 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-stringr, r-cran-raster, r-cran-tidycensus, r-cran-tidyr, r-cran-dplyr, r-cran-jsonlite, r-cran-httr, r-cran-curl, r-cran-rsqlite, r-cran-dbi Suggests: r-cran-openssl, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-readr, r-cran-testthat, r-cran-covr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zipcoder_0.4.1-1.ca2404.1_all.deb Size: 3702132 MD5sum: ea08d64cb69303922dc339220c30dfa6 SHA1: 2e6595a9437329ac69feae78b49dac6134fb37e2 SHA256: ba06ae0237ec1fe8bb62548eda2ead34b4527d6909fbc13a5063f2d9defa6450 SHA512: 10151941d48fb69f22cd1764d36e3a1e8d9ea20fd98b8719e6e333f6d87df70de83f2b1622fac1c74ef500c2f512e5406d59995bdaeb8440539144d5fa341475 Homepage: https://cran.r-project.org/package=zipcodeR Description: CRAN Package 'zipcodeR' (Data & Functions for Working with US ZIP Codes) Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating ZIP codes to Census data, geocoding & distance calculations. New analyses can select an immutable modern data bundle through the next-generation API, while the historical interface remains compatible with version 0.3.5 for reproducible research. Package: r-cran-zipfa Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-optimx, r-cran-trustoptim Filename: pool/dists/noble/main/r-cran-zipfa_0.8.1-1.ca2404.1_all.deb Size: 72090 MD5sum: 9df8880af3976c6353288e104832698f SHA1: f4807632d153f31a9e6f0f5e8fd0d4de81e0f05d SHA256: 65775ab5957e7d30567001fc69ba103f9f4b7f0437c2c9d9073722a6943f4051 SHA512: 020452b4d22dc85c3de2d61684aa8803831b3ef50ec18638c78d460b4a8cd7a64b48ccc29b502e914c5f6fdee56642d826ae5e9a4387541abb3be8704b3d5979 Homepage: https://cran.r-project.org/package=ZIPFA Description: CRAN Package 'ZIPFA' (Zero Inflated Poisson Factor Analysis) Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. It provides estimates of coefficients in a new type of zero-inflated regression. It provides a cross-validation method to determine the potential rank of the data in the ZIPFA and conducts zero-inflated Poisson factor analysis based on the determined rank. Package: r-cran-zipfextr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-tolerance, r-cran-copula Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zipfextr_1.0.2-1.ca2404.1_all.deb Size: 170428 MD5sum: d906287aed92cac8c0049fc54f5e8b70 SHA1: 80f80f1e1c14737aa91bc18d9f95427fe622aa43 SHA256: 2439ae1d2299531c7c91f680abad4b6ca6191f0bdd6ccd9372a749d326f424ec SHA512: 82290272d621408eee8af901b29294df066de22ff64f85d2bbc895b40ebc118e708d4e994a72feec9de016a7814d00a5cc564997ba32dd4e7f5aba837ae95282 Homepage: https://cran.r-project.org/package=zipfextR Description: CRAN Package 'zipfextR' (Zipf Extended Distributions) Implementation of four extensions of the Zipf distribution: the Marshall-Olkin Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) , the Zipf-Poisson Extreme (Zipf-PE), the Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. In log-log scale, the two first extensions allow for top-concavity and top-convexity while the third one only allows for top-concavity. All the extensions maintain the linearity associated with the Zipf model in the tail. Package: r-cran-zipfr Architecture: all Version: 0.6-70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2667 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-zipfr_0.6-70-1.ca2404.1_all.deb Size: 2477978 MD5sum: cb65c1dcad91653c2ead666486f9b53f SHA1: ca538e000f4beef14369d019ca0aef22a0e21093 SHA256: 80765276db0336f3902c6b479f3bee2181fac4fdeefa5acf1f7e93670cfdda7f SHA512: 8cf6e98c7647a8e83e7af9033c74a44233d3b0d3e2b6131addf226b99529facb3e9f6e2ce13382414a1485d5603062ccd2b6cb4c2b000788ed2fc38fc590cc59 Homepage: https://cran.r-project.org/package=zipfR Description: CRAN Package 'zipfR' (Statistical Models for Word Frequency Distributions) Statistical models and utilities for the analysis of word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. The package also implements several statistical models for the distribution of word frequencies in a population. (The name of this package derives from the most famous word frequency distribution, Zipf's law.) Package: r-cran-zipg Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pscl, r-cran-mass Filename: pool/dists/noble/main/r-cran-zipg_1.1-1.ca2404.1_all.deb Size: 309382 MD5sum: d62a2dc8b19389b6e6086b111e09bc2d SHA1: 4938fcf0ee4b8c3fb3af997c415328cd2fb63b37 SHA256: b7e22d734dea94fdeaa8a06fc37a6db9766aaf394643f84e21f39cfb2af4ef1c SHA512: f5119dcf33897d5ab12b255d75da0d4dc43897c9b5f3503e28dc9d1ca2148612c5ae1aa3b131afa5b4928d64270ab1c3fc05cb7043344f6a20569316af3b6374 Homepage: https://cran.r-project.org/package=ZIPG Description: CRAN Package 'ZIPG' (Zero-Inflated Poisson-Gamma Regression) We provide a flexible Zero-inflated Poisson-Gamma Model (ZIPG) by connecting both the mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) . Package: r-cran-zipper Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2900 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-sf, r-cran-tibble, r-cran-tidycensus, r-cran-tigris Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-zipper_0.2.0-1.ca2404.1_all.deb Size: 2617666 MD5sum: 4f44a61a7867f6fed2cdf352da10aee4 SHA1: b3492a282823dae6969737f0ddb5f9b284fd44d8 SHA256: 6cf2acb42a005321e90564708f66dc2e2c352c0ae645f7ceb48199de822a6eba SHA512: 8073727c36e630580e3849d07e23f76faa0d72a6c75fe923470806826c079aaee3513de437e3644ef5a4da4e29f23832fad55bb8da15e0d6c86d9b0443588a60 Homepage: https://cran.r-project.org/package=zippeR Description: CRAN Package 'zippeR' (Working with United States ZIP Code and ZIP Code Tabulation AreaData) Provides a set of functions for working with American postal codes, which are known as ZIP Codes. These include accessing ZIP Code to ZIP Code Tabulation Area (ZCTA) crosswalks, retrieving demographic data for ZCTAs, and tabulating demographic data for three-digit ZCTAs. Package: r-cran-zipr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-zipr_0.1.1-1.ca2404.1_all.deb Size: 20160 MD5sum: dfad663d8df58abc2a2297ba2381323f SHA1: 26a16b3375fb966285c6e90fa6b790fba3f340dc SHA256: 2dd8653e539d365f0409e303d09ed6a3880c4e7b7189d35c10c0654d24bf8c2f SHA512: f9de401634738e7bc5672eeac42dcf018a5685442a5759a7508c853892b9770a47d02c0ef6f5b3ab82e28e94209dd4b7ee58cafc8fa0f000ec32194cb88e4bf9 Homepage: https://cran.r-project.org/package=zipR Description: CRAN Package 'zipR' (Pythonic Zip() for R) Implements Python-style zip for R. Is a more flexible version of cbind. Package: r-cran-ziprop Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgenoud, r-cran-purrr, r-cran-data.table Suggests: r-cran-markdown, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggthemes, r-cran-kableextra, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ziprop_0.1.1-1.ca2404.1_all.deb Size: 202898 MD5sum: ae99bdbd8d67de6642f3e0d23fe8ec31 SHA1: 39c0766dce0b36bf6ee5b51801f4982399cdad41 SHA256: 04bc4ddfc5acb1fe65263a57e224a7669527337746414938c59be8fdd8b3e0ec SHA512: dce546731e1f830ee47cdabcce54805b33c4e97dd6e56578cbe089764b05fa9c9fbebc720d7b78b4924ab8b01da7e79c294ebee1d34007e9f47ea81a5e78d2cd Homepage: https://cran.r-project.org/package=ZIprop Description: CRAN Package 'ZIprop' (Permutations Tests and Performance Indicator for Zero-InflatedProportions Response) Permutations tests to identify factor correlated to zero-inflated proportions response. Provide a performance indicator based on Spearman correlation to quantify the part of correlation explained by the selected set of factors. See details for the method at the following preprint e.g.: . Package: r-cran-zipsae Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-zipsae_1.0.2-1.ca2404.1_all.deb Size: 25240 MD5sum: cccb294ec1b1b7b3ee742fd848cee85f SHA1: 36bd18c34ac8d253ed05adec74f3689318886688 SHA256: bfc946d9f38aaafab0435a4bb4b8d49e29f0e9035106dfd80f4425abbfc5ab20 SHA512: fa3d22f0c55feca0205a3f99e03d5e5f30493b92052b31d06ab6f3329d3592c5dafa5a49c710635d3362b0902136a733929c8b11e9a85fa8eec21795b7e4f7f1 Homepage: https://cran.r-project.org/package=zipsae Description: CRAN Package 'zipsae' (Small Area Estimation with Zero-Inflated Model) This function produces empirical best linier unbiased predictions (EBLUPs) for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018). For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015) and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016). 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